layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts co-morbidity factors associated with influenza in nigeria aishatu b. gubio*1, saka j. muhammad2, aisha mamman3, ado zakari4 and oladayo biya1 1nigeria field epidemiology and laboratory training program (nfeltp), federal capital territory, nigeria; 2department of epidemiology and community health university of ilorin, faculty of clinical science, college of medicine, ibadan, nigeria; 3ahmadu bello university zaria, zaria shika, nigeria; 4kaduna state ministry of health kaduna, kaduna, nigeria objective to analyze influenza surveillance data from 2009 to 2010 the northern, southern, and western zones in nigeria and determined co-morbidity factors associated with influenza in nigeria. introduction influenza is viral illness that affects mainly the nose, throat, bronchi and occasionally, the lungs. influenza viruses have been an under-appreciated contributor to morbidity and mortality in nigeria. they are a substantial contributor to respiratory disease burden in nigeria and other developing countries. nigeria started influenza sentinel surveillance in 2008 to inform disease control and prevention efforts. methods we conducted a cross sectional study on secondary data analysis of influenza surveillance data from january 2009 to december 2010 obtained from nigeria’s federal ministry of health. epidemiological data were obtained for suspected ili and sari cases defined in accordance with who regional office for africa’s guidelines. laboratory confirmation for presence of influenza viruses was done using real time pcr assays. standardized case investigation forms used for sample collection were analyzed using epi-info software to generate frequency and proportions. results of the 5,860 suspected influenza cases reported between 2007 and 2011 from all the influenza sites in nigeria, 1104 (18.8%) and 2,510 (42.8%) of the total cases were recorded in 2009 and 2010 respectively. a total of 296 (7.3%) were positive for flu a, while 147 (2.9%) for flu b. the northern zone recorded a total of 1908(ar: 2.6/100,000) suspected cases while the southern zone recorded 554(ar: 1.48/100,000) and the western zone reported 549(2/100,000) suspected cases. of the 443 that were positive 43 (1.5%) were health workers, 446 (8.0%) had co infection of chronic respiratory tract disease, 50(3.7%) had co infection with heart disease. exposure to poultry was 2797(98.2%). conclusions co-morbidity factors associated with influenza viruses are an important contribution to the burden of respiratory illnesses in nigeria predominantly affecting children less than 5years and adults 25years and above. additional years of data are needed to better understand the co-morbidity factors associated epidemiology of influenza viruses in nigeria. influenza and chronic obstructive pulmonary disease (copd) only 585 (10.9%) had chest indrawing, with majority of the influenza subtype pdm a/h1n1 cases 14 (9.3%) had chest indrawing. influenza and chronic chest disease less than 5% of the respondents with influenza cases had chronic shortness of breath keywords surveillance; influenza; nigeria; co-morbidity acknowledgments we would like to acknowledge and express our sincere gratitude to the personnel of the national influenza reference laboratory, and the sentinel surveillance sites for assisting us technically during this write up. we would also like to thank the federal ministry of health specifically epidemiology division for all the support to the sentinel surveillance site. references oshin o. enzyme immunoassay of antibodies to influenza a virus in nigerian children. trop geogr med. 1979;31:509-17 njoku-obi an oo. viral respiratory diseases in nigeria: a serological survey. ii complement fixing antibody levels of influenza a, b and c and para-influenza 1. j trop med hyg 1966;69:147-9 mccoy l et al. a multiple cause-of-death analysis of asthma mortality in the united states, 1990-2001.journal of asthma, 2005, 42:757–763. olaleye od, omilabu sa, olabode ao and fagbami ah. serological evidence for influenza virus activity in nigeria (1985-1987). virologie 1989;40:11-7 adams a et al. the influence of patient and doctor gender on diagnosing coronary heart disease.sociology of health and illness, 2008, 30:1– 18. *aishatu b. gubio e-mail: yabintu@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e110, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts monitoring the impact of heat waves with emergency service utilization data in los angeles county emily kajita*, patricia araki, monica luarca and bessie hwang los angeles county dept. of public health, los angeles, ca, usa objective to assess current indicators for situational awareness during heat waves derived from electronic emergency department (ed) and 911 emergency dispatch call (edc) center data. introduction los angeles county’s (lac) early event detection system captures over 60% of total ed visits, as well as 800 to 1,000 emergency dispatch calls from los angeles city fire (lacf) daily. both ed visits and edc calls are classified into syndrome categories, and then analyzed for aberrations in count and spatial distribution. during periods of high temperatures, a heat report is generated and sent to stakeholders upon request. we describe how syndromic surveillance serves as an important near real-time, population-based instrument for measuring the impact of heat waves on emergency service utilization in lac. methods daily electronic ed registration data, edc calls, and high temperatures from palmdale, california were queried from january 1, 2010 to august 26, 2012 and aggregated into centers for disease control (cdc) weeks. a custom “heat exposure” category was created by searching ed chief complaints for key terms such as “heat stroke,” “hyperthermia,” “overheat,” and relevant icd9 diagnosis codes. similarly, edc calls were classified if related to “heat exposure.” pearson correlation tests were used to determine correlation between total ed visits, heat-related ed visits, heat-related edc calls, and daily maximum temperatures. results thus far 2012 has exceeded counts cumulative to august 26th for the past two years in the number of heat-related ed visits, heat-related edc calls, and hot days (table 1). in particular, the number of 105 degree-and-up days this year has already doubled what was observed all year during 2010 and 2011. age groups were similarly distributed in total ed visits, heat-related ed visits and edc calls, with a 18 to 44 year old majority (37%, 37%, and 42% respectively), followed by 45 to 64 year olds (23%, 21%, 23%). total ed visits did not increase during summer months, and were therefore not found to be correlated to temperature (!=0.06, p=0.46) or heat-related edc calls (!=0.07, p=0.4). heat-related ed visits however, were positively correlated with both edc calls (!=0.85, p< 0.001) and temperatures (!=0.59, p<0.001). heat-related edc calls were also correlated with temperature (!=0.56, p<0.001). conclusions due to small numbers of heat-related visits relative to total ed visits, any effects that increased temperatures may have on total ed visits are undetectable. total ed volume should therefore not be used as an indicator for measuring the impact of heat on lac’s population. filtering chief complaints to obtain heat-specific ed visits, however, enables patterns of increase to emerge which correlate with higher temperatures and heat-related emergency dispatch calls. about 35% of the week to week variation in heat-related ed visits, and 32% of the week to week variation in heat-related edc calls can be explained by week to week variations in temperature. that heat-related visits were similarly distributed in age as all visitors suggests that heat does not disproportionately affect children and the elderly any more than the other acute health conditions that bring visitors to the ed. syndromic analysis of ed data and edc can provide baselines for health conditions such as hyperthermia that are otherwise difficult to obtain. table 1. number of heat-related ed visits, edc calls, and days exceeding temperatures to 8/26 and to the year’s end. figure 1. weekly heat-related ed visits and heat-related edc calls (left axis); and 7-day averaged maximum daily temperatures in palmdale, california (right axis). heat-related ed visits are stratified by age group. keywords emergency department; dispatch; heat; situational awareness references weather underground. 2012. 27 aug. 2012. the weather channel companies. <http://www.wunderground.com/> *emily kajita e-mail: ekajita@ph.lacounty.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e153, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts defining public health situation awareness – outcomes and metrics for evaluation don olson*, rob mathes, marc paladini and kevin konty nyc dohmh, long island city, ny, usa objective review concept of situation awareness (sa) as it relates to public health surveillance, epidemiology and preparedness [1]. outline hierarchical levels and organizational criteria for sa [2]. initiate consensus building process aimed at developing a working definition and measurable outcomes and metrics for sa as they relate to syndromic surveillance practice and evaluation. introduction a decade ago, the primary objective of syndromic surveillance was bioterrorism and outbreak early event detection (eed) [3]. syndromic systems for eed focused on rapid, automated data collection, processing and statistical anomaly detection of indicators of potential bioterrorism or outbreak events. the paradigm presented a clear and testable surveillance objective: the early detection of outbreaks or events of public health concern. limited success in practice and limited rigorous evaluation, however, led to the conclusion that syndromic surveillance could not reliably or accurately achieve eed objectives. at the federal level, the primary rationale for syndromic surveillance shifted away from bioterrorism eed, and towards allhazards biosurveillance and sa [4-6]. the shift from eed to sa occurred without a clear evaluation of eed objectives, and without a clear definition of the scope or meaning of sa in practice. since public health sa has not been clearly defined in terms of operational surveillance objectives, statistical or epidemiological methods, or measurable outcomes and metrics, the use of syndromic surveillance to achieve sa cannot be evaluated. methods this session is intended to provide a forum to discuss sa in the context of public health disease surveillance practice. the roundtable will focus on defining sa in the context of public health syndromic and epidemiologic surveillance. while sa is often noted in federal level documents as a primary rationale for biosurveillance [1, 4-6], it is rarely defined or described in operational detail. one working definition presents sa as “real-time analysis and display of health data to monitor the location, magnitude, and spread of an outbreak”, yet it does not elaborate on the methods, systems or evaluation requirements for sa in public health or biosurveillance [3]. in terms of translating sa into public health surveillance practice [1], we will discuss and define the requirements of public health sa based on its development and practice in other areas [2]. the proposed theoretical framework and evaluation criteria adapted and applied to public health sa [2] follow: level 1: perceive relevant surveillance data and epidemiological information. level 2: integrate surveillance and non-surveillance data in conjunction with operator goals to provide understanding of the meaning of the information. level 3: through perceiving (level 1) and integrating and understanding (level 2) provide prediction of future events and system states to allow for timely and effective public health decision making. results sample questions for discussion: what is the relevance of syndromic surveillance and biosurveillance in the sa framework? where does it fit within the current public health surveillance environment? to achieve the roundtable discussion objectives, the participants will work towards a consensus definition of sa for public health, and will outline measureable outcomes and metrics for evaluation of syndromic surveillance for public health sa. keywords evaluation; biosurveillance; situational awareness; syndromic surveillance; local public health acknowledgments this work was carried out in conjunction with a grant from the alfred p. sloan foundation (#2010-12-14). we thank the members of the new york city department of health and mental hygiene syndromic surveillance unit. references 1. thacker sb, qualters jr, lee lm. public health surveillance in the united states: evolution and challenges. mmwr 2012;61:3-9. 2. endsley mr. towards a theory of situation awareness. human factors 1995;37:32-64. 3. fricker rd. some methodological issues in biosurveillance. stat med 2011;30:403-15. 4. 109th congress of the united states, amendment to the public service act. pandemic and all-hazards preparedness act (2006). pub l no. 109-417, 101 et seq. 5. homeland security presidential directive 21 (hspd-21), “public health and medical preparedness,” 18 oct 2007. 6. white house, national strategy for biosurveillance, july 2012. *don olson e-mail: drolson@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e196, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts selecting targeted symptoms/syndromes for syndromic surveillance in rural china li tan1, jie zhang1, liwei cheng1, weirong yan1, 2, vinod k. diwan2, lu long1 and shaofa nie*1 1tongji medical college, wuhan city, china; 2karolinska institutet, stockholm, sweden objective to select the potential targeted symptoms/syndromes as early warning indicators for epidemics or outbreaks detection in rural china. introduction patients’ chief complaints (ccs) as a common data source, has been widely used in syndromic surveillance due to its timeliness, accuracy and availability (1). for automated syndromic surveillance, ccs always classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. however, in rural china, most outpatient doctors recorded the information of patients (e.g. ccs) into clinic logs manually rather than computers. thus, more convenient surveillance method is needed in the syndromic surveillance project (issc). and the first and important thing is to select the targeted symptoms/syndromes. methods epidemiological analysis was conducted on data from case report system in jingmen city (one study site in issc) from 2004 to 2009. initial symptoms/syndromes were selected by literature reviews. and finally expert consultation meetings, workshops and field investigation were held to confirm the targeted symptoms/syndromes. results 10 kinds of infectious diseases, 6 categories of emergencies, and 4 bioterrorism events (i.e. plague, anthrax, botulism and hemorrhagic fever) were chose as specific diseases/events for monitoring (table 1). two surveillance schemes were developed by reviewing on 565 literatures about clinical conditions of specific diseases/events and 14 literatures about ccs based syndromic surveillance. the former one was to monitor symptoms (19 initial symptoms), and then aggregation or analysis on single or combined symptom(s); and the other one was to monitor syndromes (9 initial syndromes) directly (table 2). the consultation meeting and field investigation identified three issues which should be considered: 1) the abilities of doctors especially village doctors to understand the definitions of symptoms/syndromes; 2) the workload of data collection; 3) the sensitive and specific of each symptom/syndrome. finally, scheme 1 was used and 10 targeted symptoms were determined (table 2). conclusions we should take the simple, stability and feasibility of operation, and also the local conditions into account before establishing a surveillance system. symptoms were more suitable for monitoring compared to syndromes in resource-poor settings. further evaluated and validated would be conducted during implementation. our study might provide methods and evidences for other developing countries with limited conditions in using automated syndromic surveillance system, to construct similar early warning system. table 1. epidemiological analysis on cases and emergencies data * chronic infectious diseases (excluded). † selected specific diseases (top 5) or events (non-infectious excluded). table 2 list of symptoms/syndromes * the incidence of symptom was >= 20% of specific disease(s)/event(s). ** the number of times of syndromes monitored was >= 4 times. asthma (4 times) and diarrhea (5 times) were excluded due to study objectives. † final targeted symptoms. keywords syndromic surveillance; chief complaint; early warning acknowledgments this study was funded by [european union’s] [european atomic energy community’s] seventh framework programme ([fp7/2007-2013] [fp7/2007-2011]) under grant agreement no. [241900]. references 1.chapman ww, dowling jn, wagner mm. generating a reliable reference standard set for syndromic case classification. j am med inform assoc. 2005;12:618-29. *shaofa nie e-mail: sf_nie@mails.tjmu.edu.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e140, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the organizational structures and human resources allocation of infectious disease surveillance system in rural china biao xu*1, qi zhao1, huijian cheng2, tao tao1, yipin zhu1, miao yu1 and hui yuan2 1school of public health, fudan university, shanghai, china; 2jiangxi provincial center for disease control and prevention, nanchang, china objective to understand the structure and capacity of current infection disease surveillance system, and to provide baseline information for developing syndromic surveillance system in rural china. introduction to meet the long-term needs of public health and social development of china, it is in urgency to establish a comprehensive response system and crisis management mechanism for public health emergencies. syndromic surveillance system has great advantages in promoting early detection of epidemics and reducing the burden of disease outbreak confirmation (1). the effective method to set up the syndromic surveillance system is to modify existing case report system, improve the organizational structures and integrate new function with the traditional system. methods since august 2011, an integrated syndromic surveillance project (issc) has been implemented in china. before the launching of the project, a cross-sectional study was carried out in fengxin county and yongxiu county of jiangxi province during october 11 to 18, 2010. institution information were investigated in the county hospital, township hospital and county center for disease control and prevention (cdc) to understand the performance of existing case report system for notifiable infectious diseases with regard to its structure, capacity and data collection procedure. health care workers from each township hospital and village health station were questionnaire interviewed for information on qualification of human resources, basic healthcare delivery condition, hardware and software needs for issc. results an internet-based real-time (quasi real-time) case report system for notifiable infectious diseases, based on the three-tier public health service system, had been established in these two counties since 2004. the farthest end of net user in case report system was township hospital. blood routine test, urine routine test, b ultrasound and electrocardiogram were available in all township hospitals. there was no laboratory equipment in village health stations in these two counties. all the township hospitals in these two counties were equipped with land-line telephones and desktop computers. the internet covers all township hospitals in both counties. most clinical doctors in township hospital(th) and village health station(vhs) were male. the age of doctors ranged from 21 to 72 years old, with the average at 42 and median at 40 years. the village health workers were significantly older, less educated and served in health care longer than the township hospital doctors. in yongxiu county, 95.6% of the village health stations were equipped with computers, including private-owned computers, and 80.7% of them had access to the internet; while in fengxin county, 66.5% of the village health stations possessed computers, among which most were private property of village doctors, and only 44.2% of them had access to the internet. conclusions the current case report system, with full coverage and stable human resource, has established a solid basis for developing syndromic surveillance system in rural china. the syndromic surveillance system could play its role in early detection of infectious disease outbreaks in rural area where laboratory service for infectious disease diagnosis are not available. however, the lack of computerized patient registration in village and township health care facilities and incomplete internet coverage in rural area and relatively low quality of human resource in village level should be taken into consideration seriously before establishing the syndromic surveillance system in rural china. figure1 flowchart of case report system for notifiable infectious disease in different level of health facilities in fengxin and yongxiu county keywords syndromic surveillance; rural area; human resources; case report system acknowledgments this study was funded by [european union’s] [european atomic energy community’s] seventh framework programme ([fp7/2007-2013] [fp7/2007-2011]) under grant agreement no. [241900]. references 1. heffernan r, mostashari f, das d, et al.syndromic surveillance in public health practice, new york city. emerging infectious diseases2004;10(5):858864. *biao xu e-mail: bxu@shmu.edu.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e87, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts statistical models for biosurveillance of multiple organisms doyo g. enki*1, angela noufaily1, c. p. farrington1, paul h. garthwaite1, nick andrews2, andré charlett2 and chris lane2 1mathematics and statistics, the open university, milton keynes, united kingdom; 2health protection agency, london, united kingdom objective to look at the diversity of the patterns displayed by a range of organisms, and to seek a simple family of models that adequately describes all organisms, rather than a well-fitting model for any particular organism. introduction there has been much research on statistical methods of prospective outbreak detection that are aimed at identifying unusual clusters of one syndrome or disease, and some work on multivariate surveillance methods (1). in england and wales, automated laboratory surveillance of infectious diseases has been undertaken since the early 1990’s. the statistical methodology of this automated system is described in (2). however, there has been little research on outbreak detection methods that are suited to large, multiple surveillance systems involving thousands of different organisms. methods we obtained twenty years’ data on weekly counts of all infectious disease organisms reported to the uk’s health protection agency. we summarized the mean frequencies, trends and seasonality of each organism using log-linear models. to identify a simple family of models which adequately represents all organisms, the poisson model, the quasi-poisson model and the negative binomial model were investigated (3,4). formal goodness-of-fit tests were not used as they can be unreliable with sparse data. adequacy of the models was empirically studied using the relationships between the mean, variance and skewness. for this purpose, each data series was first subdivided into 41 half-years and de-seasonalized. results trends and seasonality were summarized by plotting the distribution of estimated linear trend parameters for 2250 organisms, and modal seasonal period for 2254 organisms, including those organisms for which the seasonal effect is statistically significant. relationships between mean and variance were summarized as given in figure 1. similar plots were used to summarize the relationships between mean and skewness. conclusions statistical outbreak detection models must be able to cope with seasonality and trends. the data analyses suggest that the great majority of organisms can adequately – though far from perfectly – be represented by a statistical model in which the variance is proportional to the mean, such as the quasi-poisson or negative binomial models. figure 1. relationships between mean and variance. (top) histogram of the slopes of the best fit lines for 1001 organisms; the value 1 corresponds to the quasi-poisson model; (bottom) log of variance plotted against log of mean for one organism. the full line is the best fit to the points; the dashed line corresponds to the quasi-poisson model; the dotted line corresponds to the poisson model. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e107, 2013 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts keywords biosurveillance; public health surveillance; data analysis; infectious disease outbreaks; statistical model acknowledgments this research was supported by a project grant from the uk medical research council, and by a royal society wolfson research merit award. references 1. unkel s, farrington cp, garthwaite ph, robertson c, andrews n. statistical methods for the prospective detection of infectious disease outbreaks: a review. j. r. statist. soc. a 2012; 175:49-82. 2. farrington cp, andrews nj, beale ad, catchpole ma. a statistical algorithm for the early detection of outbreaks of infectious disease. j. r. statist. soc. a 1996; 159: 547-563. 3. mccullagh p, nelder ja. generalized linear models. 2nd ed. london: chapman & hall; 1989. 4. hastie tj, tibshirani rj. generalized additive models. london: chapman & hall; 1990. *doyo g. enki e-mail: d.gragn@open.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e107, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts modeling baseline shifts in multivariate disease outbreak detection jialan que* and fu-chiang tsui university of pittsburgh, pittsburgh, pa, usa objective outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. in this paper, we propose a multinomial-generalized-dirichlet (mgd) model to adjust for baseline shifts. introduction population surges or large events may cause shift of data collected by biosurveillance systems [1]. for example, the cherry blossom festival brings hundreds of thousands of people to dc every year, which results in simultaneous elevations in multiple data streams (fig. 1). in this paper, we propose an mgd model to accommodate the needs of dealing with baseline shifts. methods existing multivariate algorithms only model disease-relevant data streams (e.g., anti-fever medication sales or patient visits with constitutional syndrome for detection of flu outbreak). on the contrary, we also incorporate a non-disease-relevant data stream as a control factor. we assume that the counts from all data streams follow a multinomial distribution. given this distribution, the expected value of the distribution parameter is not subject to change during a baseline shift; however, it has to change in order to model an outbreak. therefore, this distribution inherently adjusts for the baseline shifts. in addition, we use the generalized dirichlet (gd) distribution to model the parameter, since gd distribution is one of the conjugate prior of multinomial [2]. we call this model the multinomial-generalized-dirichlet (mgd) model. results we applied mgd model in our previous proposed rank-based spatial clustering (mrsc) algorithm [3]. we simulated both outbreak cases and baseline shift phenomena. the experiment includes two groups of data sets. the first includes the data sets only injected with outbreak cases, and the second includes the ones with both outbreak cases and baseline shifts. we apply mrsc algorithm and a reference method, the multivariate bayesian scan statistic (mbss) algorithm (which only analyzes the disease-relevant data streams) [4], to both data sets. fig. 2 shows the performance of outbreak detection: the roc curves and amoc curves of analyzing the data sets with baseline shifts (solid lines) and without (dashed lines). we can see from fig. 2 that the performance of mbss dropped much more significantly than mrsc when analyzing the data sets with baseline shifts. conclusions the mgd model can be a good supplement model used to detect disease outbreaks in order to achieve both better sensitivity and better specificity especially when baseline shifts are present in the data. fig. 1 eight data streams of nrdm categories collected by rods system (anti-diarrhea, anti-fever adult, chest rubs, cough/cold, baby/child electrolytes, nasal products, rash and thermometers) between apr. 3, 2011 and apr. 8, 2011 in washington dc. fig. 2 roc and amoc curves of mrsc (red) and mbss (blue). the solid lines represent the two algorithms applied on the data sets injected with both outbreak cases and baseline shift phenomena. the dashed lines represent the two algorithms applied on the data sets injected with outbreak cases only. keywords biosurveillance; disease outbreak detection; algorithm acknowledgments this research was funded by pa department of health syndromic surveillance grant and cdc center of excellence grant p01-hk000086. references [1] reis, by, kohane, is and mandl, kd, an epidemiological network model for disease outbreak detection, plos medicine, vol. 4, p. 210, 2007. [2] wong, tt, generalized dirichlet distribution in bayesian analysis, applied mathematics and computation 97, pp. 165-181, 1998. [3] que, j and tsui, fc, rank-based spatial clustering: an algorithm for rapid outbreak detection, j am med inform assoc, vol. 18, pp. 218224, 2011. [4] neill, db and cooper, gf, a multivariate bayesian scan statistic for early event detection and characterization, machine learning, vol. 29, pp. 261-282, 2010. *jialan que e-mail: jialan.que@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e9, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts utilization of various data sources to locate west nile clusters in tulsa county nicole schlaefli*2, kiran duggirala2 and scott meador1 1tulsa city-county health department, enviromental health program, tulsa, ok, usa; 2tulsa city-county health department, health, data, and evaluation divison, tulsa, ok, usa objective identify, analyze, and summarize wnv in tulsa county, oklahoma introduction as the summer temperatures soared to their highest ever recorded, oklahoma experienced its highest disease count ever since the disease had been discovered in new york in 1999. tulsa county is the second most populous county in oklahoma and accounted for over onefourth of the west nile cases in oklahoma. tulsa city county health department is also the only funded mosquito control program in the state that regularly reports to cdc’s abornet. methods as part of the mosquito surveillance program run by tcchd’s environmental health program, 75 mosquito traps are placed around the county. the traps are tested once a week during the season which runs may to november. the areas that the traps are located in are then sprayed with mosquito repellent. the ehp also gathers addresses of the west nile positive persons that are reported to the epidemiology department. the positive trap locations and the human case addresses are then mapped onto a geographic representation map of tulsa county using arcgis arcmap 10.0 software.historical trend analysis data of past west nile cases by year, age, location and climate are then compared to the current year. results from interviews conducted with west nile positive human cases, the majority of cases reported the likelihood of being bitten on their property. once the human case locations were overlaid with positive trap locations and a map of the storm drain system in tulsa county, clusters formed and areas that needed to be sprayed were identified. conclusions recommendations are made throughout the season to community officials based on analysis and results found. lessons learned from the outbreak response conducted included: addition of larvacidal treatment of tulsa county storm drainage system aggressive marketing campaign in regards to prevention methods purpose and role of long term acute care centers in regards to human recovery proposed creation of a west nile survivors group west nile virus disease keywords surveillance; west nile; mosquito acknowledgments christie mcdonald-hamm, mph; surveillance officer, department of informatics; oklahoma state department of health references tulsa county, oklahoma public works department public health investigation of disease database in oklahoma (phiddo) mosquito database, environmental health program, tulsa city county health department http://www.cdc.gov/ncdod/dvbid/westnile/index.htm centers for disease control and prevention, u.s. environmental protection agency, national oceanic and atmospheric agency, and american water works association. 2010. when every drop counts: protecting public health during drought conditions— a guide for public health professionals. atlanta: u.s. department of health and human services. *nicole schlaefli e-mail: nschlaefli@tulsa-health.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e143, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts public health surveillance in pilot drinking water contamination warning systems chrissy dangel*1, steven c. allgeier1, darcy gibbons2 and adam haas2 1us epa, cincinnati, oh, usa; 2csc science & engineering, alexandria, va, usa objective this paper describes the lessons learned from operation and maintenance of the public health surveillance (phs) component of five pilot city drinking water contamination warning systems (cws) including: cincinnati, new york, san francisco, philadelphia, and dallas. introduction the u.s. environmental protection agency (epa) designed a program to pilot multi-component contamination warning systems (cwss), known as the “water security initiative (wsi).” the cincinnati pilot has been fully operational since january 2008, and an additional four pilot utilities will have their own, custom cwss by the end of 2012. a workshop amongst the pilot cities was conducted in may 2012 to discuss lessons learned from the design, implementation, operation, maintenance, and evaluation of each city’s phs component. methods when evaluating potential surveillance tools to integrate into a drinking water contamination warning system, it is important to consider design decisions, dual use applications/considerations, and the unique capabilities of each tool. the pilot cities integrated unique surveillance tools, which included a combination of automated event detection tools and communication and coordination procedures into their respective phs components. the five pilots performed a thorough, technical evaluation of each component of their cws, including phs. results four key lessons learned were identified from implementation of the phs component in the five pilot cities. first, improved communication and coordination between public health and water utilities was emphasized as an essential goal even if it were not feasible to implement automated surveillance systems. the wsi pilot project has helped to strengthen this communication pathway through the process of collaborating to develop the component, and through the need to investigate phs alerts. second, the approximate location of specific cases associated with phs alerts was found to be an essential feature that allowed a crosscomparison to water pressure zones when attempting to locate the source of possible contamination. more specific location data (e.g., latitude and longitude) leads to a more efficient investigation, however, just narrowing the case location down to a specific hydraulic region within the water distribution system is extremely useful. third, the ability to quickly visualize spatial distribution of cases via a visual interface was reported to be valuable to investigators during alert investigations. most pilots implemented a cws dashboard, in the form of a central graphical display, which presents the alerts and was used by the water utility and public health to obtain an understanding of geospatial relationships between cases, alerts and water pressure zones. finally, public health and water utility representatives from several of the wsi pilots acknowledged that their automated surveillance tools currently have limited capabilities for detection of chemical contaminants (which may result in a sudden onset of symptoms), with the main deficiency being the timeliness of the alerts relative to the window of opportunity to respond in a meaningful and effective manner. while they currently focus on detection of traditional waterborne diseases, these tools could potentially be adapted to also detect chemical contaminants. conclusions the results of the pilots have demonstrated that it is important to construct and formalize standard operating procedures, so that public health personnel and water utilities have a standard communication protocol. as a basic step to a phs component, it is important to establish a relationship between utilities and public health. in addition to the efforts of the wsi pilots, research is currently being conducted by the u.s. epa to analyze health seeking behavior of symptomatic individuals, because all phs tools rely on data generated from behavior pursued by the affected population during a public health incident. results from analysis of both emergency department data and poison control center follow-up phone data are currently underway. keywords evaluation; public health surveillance; lessons learned; contamination warning system; drinking water references [1] us epa. 2008. water security initiative: cincinnati pilot post-implementation system status report. epa 817-r-08-004. *chrissy dangel e-mail: dangel.chrissy@epa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e93, 2013 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts current assessment of risks of anthrax outbreaks in ukraine nataliya vydayko* and yuriy novohatniy state institution ukrainian center of diseases control and monitoring ministry of health of ukraine (si ucdcm moh), kyiv, ukraine introduction anthrax is an acute especially dangerous infectious disease of animals and humans. bacillus anthracis is a potential bioterrorism tool. in ukraine, there are favorable natural conditions for the spread of anthrax. there are 13.5 thousand of constantly anthrax-troubled points. anthrax epidemic situation in ukraine could be characterized as unstable. because of the continuing reform of ukrainian human health entities, the state sanitary epidemiological service (sses) has lost its control functions and is remaining in an uncertain state, which increases possible risks. methods epidemiological analysis of official data has been performed using information from the following sources: state sanitary epidemiological service of ukraine (sses), state veterinary and phytosanitary service of ukraine, and analytical materials from si ucdcm. collected papers distribution and epidemiological characteristics of major human infectious diseases in ukraine (kyiv research institute of epidemiology, microbiology and parasitology, 1976) were also used during the study. materials were compiled for the period from 1945 to 2015. results in the early xx century, more than 10,000 cases of anthrax in humans were annually registered in tsarist russia. in 1913, 1,473 cases of anthrax in animals were recorded only in kherson province (currently, kherson oblast of ukraine). the morbidity among humans increased again during the wwii. in the late 40s, massive epizootic anthrax among animals was eliminated and morbidity among people significantly reduced because of planned government measures, strengthened veterinary, sanitary, and epidemiological surveillance. since 1950, significant reduction of incidence of human anthrax has been being recorded in ukraine. since 1964, certification and mapping of persistent anthraxtroubled points in ukraine have been being performed. compulsory vaccination of people against anthrax was cancelled and compulsory vaccination of all livestock was introduced in 1990. the period from 1976 to 1993 is characterized as epidemically safe. single cases of the disease in human were registered with intensity rates of 0.01 – 0.002 per 100,000 population (excluding 1985). no human cases were registered during the certain years: 1978, 1982, 1987, 1988. the epidemic situation complicated during the period 19942001. the following outbreaks were registered: table 1. total number of disease cases/ including the number of cases during outbreaks within regions the main reason for the complication of the epidemiological situation was weakening of epidemiological and veterinary surveillance during the economic crisis characterizing this period. epizootiological outbreaks arose from incomplete anti-anthrax vaccination of agricultural animals and from violation of veterinarysanitary rules for their keeping as well. more than 80% of human infection cases happened resulting compelled cattle slaughtering, while the rest 20% resulted from meat product distribution and consumption without corresponding sanitary-veterinary expertise. six human cases of anthrax were registered during 2002-2015. fig. 1. dynamics of anthrax cases in humans in ukraine during 1945 – 2015 (absolute numbers) table 2. chronology of anthrax epidemiological surveillance milestones in ukraine conclusions relative wellbeing regarding anthrax in ukraine persists owing to the implementation of ruled veterinary-sanitary activities and state sanitary epidemiological surveillance in meatand leather-processing industries as well as because of active food control. the main risks, which could trigger complication in the current epidemiological situation with anthrax, are the following: 1) uncertainty in the system of sanitary-epidemiological and veterinary surveillance, which resulted from the reformation of the state sanitary-epidemiological and state veterinary services. 2) existence of favorable conditions for anthrax agent circulation (considerable number of persistent anthrax-troubled points in all regions). 3) economic instability in the country. 4) uncontrolled epidemic situation in the zone of the anti-terrorist operation (donetsk and luhansk oblasts). table 1. table 2. keywords anthrax; outbreaks; risk assessment; epidemiological surveillance *nataliya vydayko e-mail: vydaykon@ukr.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e151, 2017 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a piece of the public health surveillance puzzle: social contacts among school-aged children molly leecaster*1, warren pettey1, damon toth1, jeanette rainey2, amra uzicanin2 and matthew samore1 1internal medicine, university of utah, salt lake city, ut, usa; 2centers for disease control and prevention, atlanta, ga, usa objective to enhance public health surveillance and response for acute respiratory infectious diseases by understanding social contacts among school-aged children introduction timely and effective public health decision-making for control and prevention of acute respiratory infectious diseases relies on early disease detection, pathogen properties, and information on contact behavior affecting transmission. however, data on contact behavior are currently limited, and when available are commonly obtained from traditional self-reported contact surveys [1, 2]. information for contacts among school-aged children is especially limited, even though children frequently have higher attack rates than adults, and schoolrelated transmission is commonly predictive of subsequent community-wide outbreaks, especially for pandemic influenza. within this context, high-quality data are needed about social contacts. precise contact estimates can be used in mathematical models to understand infectious disease transmission [3] and better target surveillance efforts. here we report preliminary data from an ongoing 2year study to collect social contact data on school-aged children and examine the transmission dynamics of an influenza pandemic. methods our aim is to capture mixing patterns and contact rates of schoolaged children in 24 schools and other non-school-related venues. we used a stratified design to ensure coverage of urban, suburban, and rural school districts, as well as climatically different areas (mountains and desert) in utah. elementary, middle, and high schools were chosen in each stratum. we defined a self-reported contact as anyone with whom the participant talked to face-to-face, played with, or touched. contact logs collected subjective information (age, location, and duration) on self-reported contacts during a 2-day period. objective contact data were collected by using proximity sensors [4] that recorded signals from other sensors within approximately 3-4 feet. mixing patterns during school and non-school-related activities were summarized for participating school-aged children. we developed contact networks using proximity sensor data, providing visualizations of contact patterns as well as numeric contact measures. contact networks were characterized with respect to degree distribution, and density. the degree for each person was calculated as the number of unique contacts. the density for a network was calculated as the number of observed contacts divided by the number of possible contacts. results two elementary schools, four summer camps, and one club participated in the study between may and august, 2012. data were processed for the two schools and one camp. the mean degrees for the two schools were 28 and 29, with network sizes 109 and 129, respectively. the mean degree from camp was 43, whose network size was 141. the density of contacts was 0.26 and 0.22 for the schools and 0.31 for the camp. the density within classrooms at the two schools ranged from 0.78 to 0.98. school-aged children typically underreported contacts using the contact log compared with objective proximity sensor data; this difference was statistically significant. conclusions the variability in these and other contact network characteristics represent factors that could impact influenza transmission. quantifying these factors improves our understanding of influenza transmission dynamics, which in turn can be used to adapt surveillance methods and control and prevention strategies. almost all contact among students in our two elementary schools occurs within the classroom and the contact patterns differ by classroom, due to desk arrangement or other characteristics. thus, during an elementary school outbreak it may be beneficial to focus on classroom-specific surveillance and control strategies. the study is ongoing and we expect the variability in contact rates and mixing patterns will be even greater for middle and high schools where students switch classrooms and classmates each period. these schools could benefit from alternative surveillance and control strategies that account for the heightened overall mixing of the student body. keywords children; respiratory infectious disease; social network; transmission model; proximity sensor acknowledgments this study is funded by the centers for disease control and prevention 5u01ck000177. references 1. mossong, j, et al 2008 plos medicine 5(3): 381–391. 2. glass l and r glass 2008 bmc public health 8(61). 3. keeling m and k eames 2005 j.r.soc.interface 2:295-307. 4. salathé m, et al 2010 pnas 1009094108. *molly leecaster e-mail: molly.leecaster@hsc.utah.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e25, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts casefinder: a flexible real-time online surveillance registry for infectious disease physicians to report cases of carbapenem-resistant enterobacteriaceae (cre) donald curtis*1, scott weissman2, dimitri drekonja3, susan beekmann4, benjamin buckmaster1, john lynch5, april abbott6, ferric fang6 and philip polgreen4 1coe college, cedar rapids, ia, usa; 2seattle children’s hospital, seattle, wa, usa; 3minneapolis veterans affairs medical center, minneapolis, mn, usa; 4university of iowa, iowa city, ia, usa; 5harborview medical center, seattle, wa, usa; 6university of washington, seattle, wa, usa objective to create a flexible online surveillance system for infectious disease experts to report cases of emerging infectious diseases. introduction the infectious disease society of america’s emerging infections network (ein) is a sentinel network of over 1,200 practicing infectious disease physicians, supported by the centers for disease control and prevention (cdc). in january 2012, the ein listserv fielded a member inquiry about treatment recommendations for a complicated polymicrobial wound infection in a traveler returning to the united states from india. the posting led to a member-to-member communication that resulted in shipment of clinical microbiology isolates from one member’s hospital to another’s research laboratory. molecular evaluation of the clinical isolates uncovered previously undetected carriage of the emerging ndm-1 enzyme in 2 of the enterobacteriaceae species. based on this interaction, we built a flexible online surveillance registry (casefinder) for infectious disease physicians to report cases of cre. methods to ascertain the frequency and nature of cre infections treated by ein members, a survey was sent to ein members in july 2012 that elicited risk factors and clinical features associated with cre. survey opt-out items also allowed respondents to specify that they had not treated any cre infections. concurrently, we developed a formal relational data model for cre infection survey data, allowing for analysis and visualization. the data model was implemented in python using the object-relational mapping provided by the django web framework, which we used to implement the backend server component to the online registry. an interactive front-end web application, written in javascript using the jquery library, retrieves data via the ajax web protocol. geolocated data is visualized using the openlayers library to render map tiles and provide interactive controls such as panning and zooming. results the crowd-sourced online registry for infectious disease experts to report cre infections, called casefinder (http://casefinder.org/), was developed, released, and seeded with data from the ein survey. to date, a total of 69 cases have been submitted, describing 53 infections with klebsiella pneumoniae, 7 with escherichia coli and 9 with other enterobacteriaceae, representing 7 of 9 us census divisions. another 214 members have indicated that they have not seen any cases to date. casefinder includes: an online data entry component (to supplant the original ein listserv survey); real-time filtering of data; and interactive maps that geolocate survey responses using the first 2 digits of the treating facility’s zip code. users can filter data based on species, clinical features (age, gender), resistance profile, or 2-digit zip code. casefinder can also display clinical case data in an exportable line-item format. conclusions we have created a web-based data registry for cre infections in the us. populated by ein survey responses, the registry already has a collection 283 data points—69 cases of cre and 214 reports indicating the absence of cases—and is open for ongoing submission of data represented in real time. this system can serve as a de facto national surveillance system for cre infections an important but not yet universally reportable condition. our platform can be expanded to map and track other emerging infections seen by infectious diseases physicians. we are currently working to incorporate molecular fingerprinting and typing information into the data model. the site will also provide incentives for infectious disease experts to submit cases in underrepresented geographic areas. in future efforts we will incorporate “machine learning” techniques to leverage knowledge from infectious disease experts on existing cases and provide features such as an intelligent automated alert system. keywords surveillance; carbapenemases; antibiotic resistance; enterobacteriaceae; klebsiella pneumoniae *donald curtis e-mail: dcurtis@coe.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e28, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts traditional and mobile public health alert communications with health care providers debra revere*, ian painter and janet baseman university of washington, seattle, wa, usa objective to systematically compare mobile (sms) and traditional (email, fax) communication strategies to identify which modality is most effective for communication of health alerts and advisories between public health agencies and health care providers in order to improve emergency preparedness and response. introduction the effectiveness of emergency preparedness and response systems depends, in part, on the effectiveness of communication between agencies and individuals involved in emergency response, including health care providers who play a significant role in planning, event detection, response and communication with the public. although much attention has been paid to the importance of communicating clinical data from health care providers to public health agencies for purposes of early event detection and situational awareness (e.g., biosense) and to the need for alerting health care providers of public health events (e.g., health alert networks), no studies to date have systematically identified the most effective methods of communication between public health agencies and community health care providers for purposes of public health emergency preparedness and response. the reach (rapid emergency alert communication in health) study is a 4-year randomized controlled trial to evaluate and compare the effectiveness of mobile (sms) and traditional (email, fax) communication strategies for sending public health messages to health care providers—physicians, pharmacists, nurse practitioners, physician’s assistants and veterinarians. methods providers were recruited from three sites (king county, wa; spokane county, wa; and across the state of montana; n=845) and randomized to receive time-sensitive public health messages via email, fax, short message service (sms) or to a control group that did not receive messages. for one year, alerts based on real events of public health interest were sent quarterly with follow-up telephone interviews conducted 5-10 days after the delivery date. interviews consisted of approximately six questions that elicited information about message receipt, recall of its content and perceived credibility and trustworthiness of the message and source. in addition, provider access to online alert information and delivery success or failure was collected. results frequency of receipt, timeliness, content awareness, perceived credibility and perceived trustworthiness were measured and compared across communication delivery systems. on average 84.0% of participants were contacted in each follow-up survey following all alerts and across all three sites. primary data analysis was designed to measure differences between the three communication groups using intent-to-treat methods. a set of secondary analyses examined the outcomes excluding providers who could not have received messages (due to incorrect contact information, known technical failures, or because providers could not receive messages by the assigned delivery message—for example, a provider without a fax number randomized to the fax group). we will discuss preliminary results of intent-to-treat analyses regarding rate of recall of study alert message content between traditional and mobile communications and perceived trustworthiness and credibility of message and message source by providers. in addition, we will report on frequency of accessing online alert information between traditional and mobile delivery groups. conclusions there is currently no evidence-based research to guide or improve the practice of public health communication between public health agencies and health care providers before, during and after a public health emergency. improving this communication via the use of effective media can enhance disease surveillance, which will aid in early detection and enhance case finding and situational awareness for public health emergencies. by systematically evaluating the relative effectiveness of mobile and traditional message delivery systems for emergency preparedness and response communications, the reach study contributes to building the evidence base for novel and effective approaches to emergency communications. keywords emergency preparedness and response; public health communication; surveillance and alerting acknowledgments this work was made possible by the cdc office of public health preparedness and response extramural research program preparedness and emergency response research centers award, grant no. 5p01tp000297, to the university of washington. the contents are solely the responsibility of the authors and do not necessarily represent the official views of the cdc. *debra revere e-mail: drevere@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e124, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts towards estimation of electronic laboratory reporting volumes in a meaningful use world brian e. dixon*1, 2, 4, roland e. gamache1, 2 and shaun j. grannis3, 2 1school of informatics, indiana university, indianapolis, in, usa; 2regenstrief institute, indianapolis, in, usa; 3indiana university school of medicine, indianapolis, in, usa; 4center of excellence on implementing evidence-based practice, department of veterans affairs, veterans health administration, health services research and development service, indianapolis, in, usa objective to support health department estimation of future electronic laboratory report volumes from hospitals that achieve stage 2 meaningful use. introduction the american recovery and reinvestment act of 2009 authorized the centers for medicare and medicaid services (cms) to incentivize hospitals and physicians to become meaningful users of electronic health record (ehr) systems. in a final rule issued august 2012, cms outlined the requirements for stage 2 meaningful use to be effective in 2014 (1). the stage 2 criteria require eligible hospitals to submit electronic laboratory reports to health departments. while many state health departments receive some portion of notifiable disease reports electronically, the final stage 2 rule is likely to increase the volume of incoming electronic reports. the centers for disease control and prevention are urging health departments to prepare for the sharp increase in electronic laboratory reporting (elr). crucial to preparedness is estimation of how many elr reports can be expected. however, few health departments have experience with high volume elr, making estimation difficult. the indiana network for patient care (inpc), a regional health information exchange, has been processing high volumes of elr for over a decade (2). to support health departments estimate potential elr increases, the inpc examined its current volumes from hospitals with advanced ehr capabilities. methods the inpc uses an automated case-detection system called the notifiable condition detector (ncd) developed by investigators at the regenstrief institute (3). the ncd uses a standards-based messaging and vocabulary infrastructure to process more than 350,000 clinical transactions daily, including laboratory studies, diagnoses, and transcriptions from more than 40 hospitals, national labs and local ancillary service organizations. data processed between january 1, 2010 and december 15, 2011 were extracted from the ncd. validated cases of notifiable conditions of interest to the indiana state department of health were filtered out for use in this analysis. we further eliminated duplicate cases of the same reportable record for the same individual. unique notifiable disease cases were divided by the population of the indianapolis metropolitan statistical area (msa) to obtain a ratio for estimation of future volume. results we identified a total of 77,199 unique notifiable disease cases. according to 2010 census data, the population of the indianapolis msa is 1,834,672. this produces a ratio of 2,104 elr cases per 100,000 population per year. conclusions roughly 2% of the population had an unique notifiable disease case reported, more than double current rates (4). actual rates could be higher given this analysis eliminated duplicate reports for chronic diseases, such as tuberculosis, hepatitis b and c, and sickle cell disease. the impact on local and state health departments is likely to be significant given scarce resources. although the calculated ratio may stimulate conversations within health departments, it represents an approximate estimator. future work will seek to refine estimation techniques by accounting for acute versus chronic notifiable disease as well as additional factors, such as the notifiable condition and/or the relative size of the hospital sending lab data to the health department. these refined estimators will enable improved planning efforts within state and local health departments. keywords electronic laboratory reporting; public health surveillance; public health informatics; electronic health records acknowledgments this work was funded in part by a grant (5r01hs020209) from the agency for healthcare research and quality. references 1. centers for medicare & medicaid services. medicare and medicaid programs; electronic health record incentive program—stage 2. federal register [internet]. 2012 [cited 2012 august 24]. available from: http://www.ofr.gov/ofrupload/ofrdata/2012-21050_pi.pdf. 2. dixon be, mcgowan jj, grannis sj. electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. amia annu symp proc. 2011;2011:32230. 3. fidahussein m, friedlin j, grannis s. practical challenges in the secondary use of real-world data: the notifiable condition detector. amia annu symp proc. 2011:402-8. 4. centers for disease control and prevention. summary of notifiable diseases: united states, 2009. mmwr morb mortal wkly rep. 2011 may 13;58(53):1-100. *brian e. dixon e-mail: bdixon@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e52, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts data quality: a systematic review of the biosurveillance literature tera reynolds*1, ian painter2 and laura streichert1 1international society for disease surveillance, brighton, ma, usa; 2university of washington, seattle, wa, usa objective to highlight how data quality has been discussed in the biosurveillance literature in order to identify current gaps in knowledge and areas for future research. introduction data quality monitoring is necessary for accurate disease surveillance. however it can be challenging, especially when “real-time” data are required. data quality has been broadly defined as the degree to which data are suitable for use by data consumers [1]. when compromised at any point in a health information system, data of low quality can impair the detection of data anomalies, delay the response to emerging health threats [2], and result in inefficient use of staff and financial resources. while the impacts of poor data quality on biosurveillance are largely unknown, and vary depending on field and business processes, the information management literature includes estimates for increased costs amounting to 8-12% of organizational revenue and, in general, poorer decisions that take longer to make [3]. methods to fill an unmet need, a literature review was conducted using a structured matrix based on the following predetermined questions: -how has data quality been defined and/or discussed? -what measurements of data quality have been utilized? -what methods for monitoring data quality have been utilized? -what methods have been used to mitigate data quality issues? -what steps have been taken to improve data quality? the search included pubmed, isds and amia conference proceedings, and reference lists. pubmed was searched using the terms “data quality,” “biosurveillance,” “information visualization,” “quality control,” “health data,” and “missing data.” the titles and abstracts of all search results were assessed for relevance and relevant articles were reviewed using the structured matrix. results the completeness of data capture is the most commonly measured dimension of data quality discussed in the literature (other variables include timeliness and accuracy). the methods for detecting data quality issues fall into two broad categories: (1) methods for regular monitoring to identify data quality issues and (2) methods that are utilized for ad hoc assessments of data quality. methods for regular monitoring of data quality are more likely to be automated and focused on visualization, compared with the methods described as part of special evaluations or studies, which tend to include more manual validation. improving data quality involves the identification and correction of data errors that already exist in the system using either manual or automated data cleansing techniques [4]. several methods of improving data quality were discussed in the public health surveillance literature, including development of an address verification algorithm that identifies an alternative, valid address [5], and manual correction of the contents of databases [6]. communication with the data entry personnel or data providers, either on a regular basis (e.g., annual report) or when systematic data entry errors are identified, was mentioned in the literature as the most common step to prevent data quality issues. conclusions in reviewing the biosurveillance literature in the context of the data quality field, the largest gap appears to be that the data quality methods discussed in literature are often ad hoc and not consistently implemented. developing a data quality program to identify the causes of lower quality health data, address data quality problems, and prevent issues would allow public health departments to more efficiently and effectively conduct biosurveillance and to apply results to improving public health practice. keywords biosurveillance; data quality; literature review acknowledgments we thank the isds data quality workgroup for initiating this project, which was supported by cdc through contract with the task force for global health. references 1. wang ry, strong dm. beyond accuracy: what data quality means to data consumers. jmis. 1996:5–33. 2. dixon be, mcgowan jj, grannis sj. electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. proc amia symp. 2011;2011:322. 3. redman tc. the impact of poor data quality on the typical enterprise. commun acm. 1998;41(2):79–82. 4. maydanchik a. data quality assessment. technics publications, llc; 2007. 5. zinszer k, charland k, jauvin c, et al. the influence of address errors on detecting outbreaks of campylobacteriosis. emerg health threats j. 2011;4(s59):68–69. 6. chen l, dubrawski a, waidyanatha n, weerasinghe c. automated detection of data entry errors in a real time surveillance system. emerg health threats j. 2011;4(s69):9–10. *tera reynolds e-mail: treynolds@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e20, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts extracting surveillance data from templated sections of an electronic medical note: challenges and opportunities adi gundlapalli*1, 2, guy divita1, 2, marjorie carter1, 2, shuying shen1, 2, miland palmer1, tyler forbush1, 2, brett south1, 2, andrew redd1, 2, brian sauer1, 2 and matthew samore1, 2 1va salt lake city health care system, salt lake city, ut, usa; 2internal medicine, university of utah school of medicine, salt lake city, ut, usa objective to highlight the importance of templates in extracting surveillance data from the free text of electronic medical records using natural language processing (nlp) techniques. introduction the main stay of recording patient data is the free text of electronic medical records (emr). while stating the chief complaint and history of presenting illness in the patients ‘own words’, the rest of the electronic note is written by the provider in their words. providers often use boiler-plate templates from emr pull-downs to document information on the patient in the form of checklists, check boxes, yes/no and free text responses to questions. when these templates are used for recording symptoms, demographic information or medical, social or travel history, they represent an important source of surveillance data [1]. there is a dearth of literature on the use of natural language processing in extracting data from templates in the emr. methods a corpus of 1000 free text medical notes from the va integrated electronic medical record (cprs) was reviewed to identify commonly used templates. of these, 500 were enriched for the surveillance domain of interest for this project (homelessness). the other 500 were randomly sampled from a large corpus of electronic notes. an nlp algorithm was developed to extract concepts related to our target surveillance domain. a manual review of the notes was performed by three human reviewers to generate a document-level reference standard that classified this set of documents as either demonstrating evidence of homelessness (h) or not (nh). a rulebased nlp algorithm was developed that used a combination of key word searches and negation based on an extensive lexicon of terms developed for this purpose. a random sample of 50 documents each of h and nh documents were reviewed after each iteration of the nlp algorithm to determine the false positive rate of the extracted concepts. results the corpus consisted of 48% h and 52% nh documents as determined by human review. the nlp algorithm successfully extracted concepts from these documents. the h set had an average of 8 concepts related to homelessness per document (median 8, range 1 to 34). the nh set had an average 2 concepts (median 1, range 1 to 13)”. thirteen template patterns were identified in this set of documents. the three most common were check boxes with square brackets, yes/no and free text answer after a question. several positively and negatively asserted concepts were noted to be in the responses to templated questions such as “are you currently homeless: yes or no”; “how many times have you been homeless in the past 3 years: (free text response)”; “have you ever been in jail? [y] or [n]”; are you in need of substance abuse services? yes or no”. human review of a random sample of documents at the concept level indicated that the nlp algorithm generated 28% false positives in extracting concepts related to homelessness when templates were ignored among the h documents. when the algorithm was refined to include templates, the false positive rate declined to 22%. for the nh documents, the corresponding false positive rates were 56% and 21%. conclusions to our knowledge, this is one of the first attempts to address the problem of information extraction from templates or templated sections of the emr. a key challenge of templates is that they will most likely lead to poor performance of nlp algorithms and cause bottlenecks in processing if they are not considered. acknowledging the presence of templates and refining nlp algorithms to handle them improves information extraction from free text medical notes, thus creating an opportunity for improved surveillance using the emr. algorithms will likely need to be customized to the electronic medical record and the surveillance domain of interest. a more detailed analysis of the templated sections is underway. keywords natural language processing; surveillance; templates; va acknowledgments funding from the us department of veterans affairs (hsr&d); resources from veterans informatics computing infrastructure and va salt lake city health care system and all our research team members who have worked on this project. references 1. delisle, s., et al., combining free text and structured electronic medical record entries to detect acute respiratory infections. plos one, 2010. 5(10): p. e13377. *adi gundlapalli e-mail: adi.gundlapalli@hsc.utah.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e75, 2013 ojphi-06-e91.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 98 (page number not for citation purposes) isds 2013 conference abstracts automated real-time surveillance using health indicator data received at different time intervals joseph lombardo*1, julie pavlin2, christopher cuellar1, yevgeniy elbert1 and jean-paul chretien2 1johns hopkins university applied physics laboratory, laurel, md, usa; 2armed forces health surveillance center, silver spring, md, usa � �� �� �� � � �� �� �� � objective ��������� � ��� �� ������������ ����� ���� �� � � ���������������� ��� �������������� ������������������� ������������ ���������� �� ��������� ������ � ������������� � ������� ���� � introduction ������������ ������ ����� �������! ��� ���� ��� ��������� "�������� � ��#������� ����$�� ���� ��������� ��� �������� ������� ����������� � � 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crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 140 (page number not for citation purposes) isds 2013 conference abstracts adverse childhood experiences and smoking among urban youths in oyo state, south western nigeria mobolaji m. salawu*1 and eme owoaje1, 2 1community medicine department, university college hospital, ibadan, nigeria; 2college of medicine, university of ibadan, ibadan, nigeria � �� �� �� � � �� �� �� � objective ����������� �� ������ � ��������� ����� ���� ��� �������� �� ���� �������� ������� ���� ���� ����� ��� �� ����� ��� � ��������!� ���� �"���� �#��� ��$ introduction ���� ���� ��� �������� �� ����� ��� ��� ����� ������� �� �� % &��� ���� ���������� ��� 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�7���7+������ �� ��$ ��$� 2$ �,�$��� � ���&� �,��������� � ���� ����� ��� $�#���� ���/��� � )����0� ���� ��� ���$�(*''5� ���677���$���$���7 ���� ����� 7� ��7 +������ �� �i ���� ���$ ��$ *mobolaji m. salawu e-mail: sannibolaji@yahoo.com� � � � 140 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e9, 2014 public health and health information exchange: the indiana experience leveraging health information exchange to support public health situational awareness: the indiana experience 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 leveraging health information exchange to support public health situational awareness: the indiana experience shaun j. grannis 1,2 , kevin c. stevens 3 , ricardo merriwether 2 1 the regenstrief institute 2 indiana university school of medicine 3 marion county health department, indiana abstract public health situational awareness is contingent upon timely, comprehensive and accurate information from clinical systems. ad-hoc models for sending non-standard clinical information directly to public health are inefficient and increasingly unsustainable. information sharing models that leverage health information exchanges (hies) are emerging. hies standardize, aggregate and streamline information sharing among data partners, including public health stakeholders, and hie has supported public health practice in indiana for more than 10 years. to accelerate nationwide adoption of hie-supported situational awareness processes, the cdc awarded three hies across the nation, including indiana, new york and washington/idaho. the indiana partners included indiana university school of medicine, regenstrief institute, indiana health information exchange, indiana state department of health, health & hospital corporation of marion county, and children’s hospital boston. activities included augmenting biosurveillance processes, enabling bi-directional communication, enhancing automated detection of notifiable conditions, and demonstrating technological advances at national forums. hie transactions destined for public health were enhanced with standardized clinical vocabulary and more complete physician contact information. during the 2009 h1n1 flu outbreak, the hie delivered targeted public health broadcast messages to providers in marion county, indiana. we will review the partnership characteristics, activities, accomplishments and future directions for our health information exchange. keywords: health information exchange, situational awareness, biosurveillance, syndromic surveillance, influenza. introduction under an initiative entitled “accelerating situational awareness through health information exchange” the centers for disease control and prevention (cdc) partnered with an indiana coalition including the indiana state department of health (isdh), the marion county health department (mchd) in indianapolis, the regenstrief institute (ri), indiana university (iu), and children’s hospital boston. coalitions from new york and washington/idaho also partnered with the cdc, and the combined groups are collectively referred to as the “cdc hie leveraging health information exchange to support public health situational awareness: the indiana experience 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 collaborative.” the cdc charged the collaborative with exploring methods for sharing information between public health and clinical entities to support public health situational awareness and case reporting in the context of the emerging nationwide health information network (nhin). this paper summarizes the activities of the indiana coalition. partners the organizational characteristics of the contract participants are described below. regenstrief institute, inc. – in 1969 philanthropist samuel n. regenstrief established the regenstrief institute to conduct and facilitate medical research, medical education and clinical care. as a supporting organization of indiana university, regenstrief is an internationally recognized informatics and healthcare research organization dedicated to improving health through research that enhances the quality and cost-effectiveness of health care. the institute employs approximately 150 full-time staff in addition to 35 investigators and affiliated researchers who are faculty members of indiana university. indiana university school of medicine – indiana university’s medical school was established in 1903 and is one of the nation's largest medical centers. the indianapolis campus includes indiana's only medical and dental schools and the nation's largest nursing school. iu medical school supports three adult hospitals (university, wishard, and roudebush veteran's), a pediatric hospital (james whitcomb riley hospital for children), a health center (regenstrief institute for health care) and a number of unique teaching and research facilities. indiana health information exchange (ihie) – in 2004 ihie was incorporated in the state of indiana as a non-profit company. ihie is extending and scaling the principles and infrastructure devised, demonstrated, and built by the regenstrief institute. ihie works hand-in-hand with regenstrief to create sustainable business models and provide commercial support for the institute’s technologies in the marketplace. ihie has grown more than 50 employees and continues to add new data sources and new customers each month. ihie provides a clinical results delivery service called docs4docs® that transmits more than 1.4 million electronic clinical results per month to over 19,000 physicians. because clinical workflow and health information technologies are highly varied, the transmission of results is tailored to accommodate specific workflows by delivering results directly to emr’s, web portals, and other receipt mechanisms. additional services that leverage the hie infrastructure are being launched. indiana state department of health (isdh) – the indiana state department of health, founded as the state board of health in 1881, supports indiana's economic prosperity and quality of life by promoting, protecting, and providing for the health of hoosiers in their communities. in addition to providing epidemiological support to most of the local health departments in the state, isdh offers a full complement of skills including epidemiology, information technology and program management. the department’s headquarters are located in downtown indianapolis. health & hospital corporation of marion county (hhc) -for over 50 years hhc of marion county has served as the public health and hospital system for marion county, indiana. leveraging health information exchange to support public health situational awareness: the indiana experience 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 hhc operates the marion county health department (mchd) and wishard health services (whs). mchd is indiana’s largest local health department and provides a variety of services to improve population and environmental health. whs is the public hospital and healthcare system for uninsured and underinsured residents of marion county. hhc is mandated by the indiana general assembly to provide medical services to all residents of marion county, regardless of ability to pay. a seven-member board of trustees, appointed by the mayor of indianapolis, the city-county council, and the county commissioners, governs hhc. the director and most of the medical staff at the mchd are faculty at the indiana university school of medicine. children’s hospital boston (chb) – founded in 1869, chb is home to the world's largest research enterprise based at a pediatric medical center. the children’s hospital informatics program at the harvard-mit division of health sciences and technology (chip) is a core program of the center for biomedical informatics at harvard medical school, and a core program of the nih funded national center for biocomputing, a cornerstone of the nih roadmap initiative. chip investigators lead several regional and national efforts in public health surveillance, data integration across multiple hospitals, and personally controlled health records. activities summary activities supported by the cdc hie collaborative were grouped according to major tasks and included 1.) biosurveillance, 2.) technical demonstrations, 3.) aggregate summary data exchange, 4.) delivering public health alerts, 5.) pre-populated reporting forms, 6.) enhancing automated notifiable condition detection, and 7.) improving data quality for public health. the following sections provide an overview of activities in each of these areas. 1. biosurveillance a number of successful activities helped to augment traditional public health surveillance. extracting public health concepts from free-text data: many conditions of interest to public health are recorded in widely varying free-text formats. we developed natural language processing and other information extraction methods to identify concepts in non-standard freetext reports. concepts that suggest the presence of a condition of interest to public health (e.g., the term “miliary” in a chest x-ray report suggests tuberculosis) can augment syndromic surveillance data streams. resource utilization monitoring: hospital bed utilization data is often gathered by manual data entry, which further encumbers already overburdened personnel. we created a prototype process to evaluate the feasibility of characterizing hospital bed utilization using existing admission, discharge and transfer (adt) messages received by the hie. we confirmed the ability to gage bed utilization trends at participating hospitals in a predictable fashion using these transactions. assessing the minimum biosurveillance data set (mbds): from 2006 to 2008 the american health information community (ahic), a national subject matter expert panel, established a minimum biosurveillance data set (mbds) containing a representative collection of data elements to support public health surveillance processes. to assess the utility and feasibility of leveraging health information exchange to support public health situational awareness: the indiana experience 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 using this data set, we completed a technical review of the data elements by first mapping data fields from the existing hie transactions to specific mbds elements and second, we determined the percent completeness for each data element in the current operational data flow. ems and inpatient surveillance: while the hie infrastructure continues to support the indiana public health emergency surveillance system (phess) by delivering minimum biosurveillance data set (mbds) elements from 83 indiana hospitals, we also seek other useful surveillance sources. in addition to traditional surveillance we developed a strategy to exchange hie data with emergency medical services (ems) mobile units, supporting surveillance in the prehospital phase. developing additional ems interfaces and incorporating additional data feeds from ambulatory care facilities are ongoing. to enhance indiana’s public health surveillance activities, in 2010 we began leveraging the hie data network to collect inpatient chief complaint data and currently transmit this data to isdh every 3-hours. because we could leverage existing infrastructure, we were able to develop data feeds from more than 50 hospitals in less than 3 months. evaluating open source syndromic surveillance tools: in partnership with children’s hospital boston, we locally deployed and evaluated an open source surveillance tool called automated epidemiologic geotemporal integrated surveillance system (aegis). this work resulted in documents describing surveillance systems and a technical characterization of aegis. 2. technical demonstrations in the face of newly created interoperability specifications there is a need to demonstrate the feasibility of deploying new technology. the cdc hie collaborative demonstrated the federal biosurveillance interoperability use-case at the 5 th nhin forum on december 15, 2008 in washington, d.c. where indiana representatives from regenstrief, mchd and isdh demonstrated syndromic surveillance data exchange using the newly designed nhin gateway. in february 2009 regenstrief demonstrated the ihe personnel white pages (pwp) profile, which provides access to basic human workforce user directory information. also demonstrated at the integrating the healthcare enterprise (ihe) connect-a-thon in chicago were the hitsp constructs t63 (emergency message distribution element transaction), t64 (identify communication recipients transaction), c84 (consult and history & physical note component), c82 (emergency common alerting protocol component), and t81 (retrieval of medical knowledge transaction). in august 2009, the cdc hie collaborative and the cdc demonstrated the biosurveillance situational awareness use case at the cdc-sponsored public health information network (phin) conference in atlanta. 3. aggregate summary data exchange we reviewed the workflow and existing technical systems for identifying and transmitting public health reportable case data including influenza, influenza-like-illness (ili) and pneumonia. the cdc hie collaborative worked closely with cdc stakeholders to create an aggregated data collection format that is used to send data through the nhin connect gateway. this aggregated data collection format is named the geocoded interoperable population summary exchange (gipse). this format was deployed in september 2009 to send data to the cdc. its first use was to provide local, state and cdc stakeholders with h1n1 surveillance data stratified across leveraging health information exchange to support public health situational awareness: the indiana experience 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 covariates including age, gender, and geography using a variety of case definitions for ili and influenza as defined by hie and cdc stakeholders. 4. delivering electronic public health alerts to physicians public health organizations must communicate with clinical care systems for a variety of purposes including case reporting and management, biosurveillance and situational awareness data sharing, and public health alerting. traditional methods for communicating between public health and clinical entities using telephone, fax, and u.s. postal service often are cumbersome, delayed and inefficient. and although e-mail is a potential option for delivering public-health communications to clinicians, it remains unclear whether e-mails are received in a timely fashion due to several factors including unintentional e-mail filtering, invalid or outdated addresses, and a lack of a reliable mechanism for tracking receipt of such information. to deliver public-health alerts to directly to clinicians in a manner that seamlessly integrates into their workflow, we developed technology that appends public health messages to the existing clinical results delivery service that currently delivers 1.4 million clinical results per month to over 19,000 physicians. we designed, implemented, tested and deployed a public-health alerting system that was first used during the h1n1 influenza outbreak to quickly reach over 3,000 physicians in marion county. in addition to broadcasting to all physicians, the system can target customized combinations of physician specialties and geographic regions. 5. delivering pre-populated public health reporting forms to physicians to address physician under-reporting of public health notifiable conditions we developed a prototype system that generates and delivers electronic pre-populated reportable forms to providers using the docs4docs®clinical messaging system. the system the clinical messaging sends all manner of clinical results to providers who receive them either as a fax, through a designated web-portal or directly into an ehr. the reportable forms system prepopulates the required indiana reporting forms for reportable diseases and sends the form to physicians at the same time they receive the positive lab result. we hypothesize that because pre-populated forms will reduce the information gathering burden associated with reporting, physician reporting rates will improve when presented with such a form. future work will evaluate this intervention. 6. enhancing automated notifiable condition detection building on standards for message structure and content (hl7 and loinc®), the regenstrief institute has implemented and maintained an automated notifiable condition reporting system for more than 10 years. the system receives real-time hl7 clinical results from a variety of hie stakeholders, and automatically translates disparate proprietary codes into standard loinc codes. it then determines whether the results carried by the message indicate a notifiable condition by checking the abnormal flag sometimes contained in the message, or by comparing the test results with criteria in the phin notifiable condition mapping table. we evolved our existing infrastructure to create a modular notifiable condition processor re-usable by other health information exchanges and public health stakeholders. in may, 2009 we were pleased to deliver the notifiable condition detector (ncd) version 1.0 and condition detectors for shigella, salmonella and mrsa as an openmrs module in both binary and source code formats. the newly enhanced system currently processes up to 300,000 transactions daily and automatically leveraging health information exchange to support public health situational awareness: the indiana experience 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 detects and transmits up to 450 public health notifiable conditions daily to local and state health departments. 7. improving data quality for public health clinical transactions often lack all fields necessary to fully support public health practice. we built and deployed methods within the hie to enhance the quality of data sent to public health. two approaches are described below. automated loinc mapping: we worked with the hl7 standards development process to advocate for a new coded element data type (cwe) that permits up to three synonymous vocabulary terms in the hl7 observation identifier field (obx-3). this enhancement provides greater capacity for sending multiple synonymous code sets, and was necessary to incorporate logical observation identifiers names and codes (loinc ® ) into hie transactions. to standardize hie data for public health uses, we deployed an ongoing process to automatically transform non-standard inbound codes into loinc ® codes. enhancing provider data: because identifying physician information helps speed public health case management processes, we leveraged a master provider file to enhance incomplete provider information for incoming message streams. this process increases completeness of the provider telephone number by more that 40%. providing mchd with this information allows epidemiologists to perform case management functions more efficiently. conclusions as this work continues, we will advance the quality and quantity of clinical data and will seek methods for providing public health personnel with increasingly seamless and direct access to the hie data repository for approved purposes. after completing provider enhancement, we will focus on enhancing patient data using the hie global patient registry. this will allow public health to have complete patient information for case reporting. these and other technological advancement will be the benchmark for a hie to send clinical data to public health and the cdc through the nhin. the longstanding, successful indiana coalition has developed leading-edge technologies that clearly demonstrate the feasibility and value of leveraging hie to support a variety of public health use cases. by re-using hie for public health purposes we have made substantive meaningful improvements in the quality and quantity of clinical data that is currently being exchanged with public health. specifically, provider information that is often missing or incomplete has been improved so public health doesn’t have to search for the information. further, by participating in demonstrations at the ihe, himss and phin annual conferences, we were able to highlight the biosurveillance detection and monitoring scenarios to various audiences. moreover, using public health alerting and pre-populated forms as prototypes, we have enabled rapid and seamless bidirectional communication between public health and clinical care systems. this framework presents a host of new opportunities to increasingly support public health practice. by connecting stakeholders from clinical care and public health organizations through comprehensive, sustained activity, we will advance the digital channels leveraging health information exchange to support public health situational awareness: the indiana experience 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 and trust relationships that are necessary to establish the next generation of infrastructure that will meaningfully support increasingly complex public health processes. conflicts of interest the authors have no conflicts of interest to report at this time. acknowledgments this project was funded by the centers for disease control & prevention under contract 2002008-24368. the content of this publication does not necessarily reflect the views or policies of the department of health and human services, nor does mention of trade names, commercial products, or organizations imply endorsement by the u.s. government. correspondence shaun j. grannis, md, ms, faafp sgrannis@regenstrief.org overcoming data challenges examining oral health disparities in appalachia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 overcoming data challenges examining oral health disparities in appalachia denise d. krause 1 , warren l. may 1 , jeralynne s. cossman 2 1 university of mississippi medical center 2 mississippi state university abstract objective: the objective of our study of oral health disparities in appalachia was to use existing data sources to geographically analyze suspected disparities in oral health status in the 420 counties of appalachia, and to make sub-state comparisons within appalachia and to the rest of the nation. the purpose of this manuscript is to describe the methods used to overcome challenges associated with using limited oral health data to make inferences about oral health status. methods: oral health data were obtained from the behavioral risk factor surveillance system (brfss). because the brfss was designed for state-level analysis, there were inadequate numbers of responses to study appalachia by county. we set out to determine the smallest possible unit we could use, aggregating data to satisfy cdc minimum requirements for spatially identified responses. for sub-state comparisons, data were first aggregated to appalachian and non-appalachian regions within appalachian states. next, urban versus rural areas within appalachian and non-appalachian regions were examined. beale codes were used to define metropolitan and non-metropolitan statistical regions for the united states. results: aggregating the data as described proved useful for smoothing the data used to analyze oral health disparities, while still revealing important sub-state differences. using geographic information systems to map data throughout the process was very useful for determining an effective approach for our analysis. discussion: studying oral health disparities on a regional or national level is difficult given a lack of appropriate data. the brfss can be adapted for this purpose; however, there is a limited number of oral health questions and because they are also optional, they are not routinely asked by all states. expanding the brfss to include a larger sampling frame would be very helpful for studying oral health disparities. conclusions: novel techniques were introduced to use brfss data to study oral health disparities in appalachia, which provided informative sub-state results, useful to health planners for targeting intervention strategies. keywords: behavioral risk factor surveillance system (brfss), beale codes, oral health, disparities, geographic information systems (gis) http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 introduction the surgeon general’s report “oral health in america” declared that no less than a “silent epidemic of oral disease is affecting our most vulnerable citizens poor children, the elderly, and many members of racial and ethnic minority groups.” 1 there are many factors which affect equitable access to oral health care. understanding those inequities is a prerequisite to eliminating them. for example, socioeconomic status is a key factor contributing to oral health disparities among population subgroups in the u.s. people living in rural areas also experience oral health disparities disproportionately. despite improved care over the last 20 years 2 , dental care is still identified as the primary health need of u.s. children. 3 due to lack of care and inadequate preventive measures, childhood caries (also known as tooth decay) are the most common chronic disease among children—in fact, it is five to eight times more common than asthma. 4 even more alarming is the concentration of childhood caries: 80% of dental caries are found in 25% of children, most of whom are from lower socioeconomic households. 5 appalachia is a rural region known to be economically disadvantaged. as with other health indicators, oral health care exhibits disparities within the region. funding was provided by the appalachian regional commission to use existing data sources to geographically analyze suspected disparities in oral health status in the 420 counties that make up appalachia, and to make sub-state comparisons within appalachia, and to the rest of the nation. as a part of the overall study, we also examined relationships between oral health disparities, socioeconomic status indicators, and other indicators in that area. details of the comprehensive analysis are reported elsewhere. 6 unfortunately, there is a paucity of available data relating to oral health status, in appalachia, or the nation as a whole. we describe the methodology used to overcome challenges associated with limited availability of oral health data in an effort to make informed inferences about oral health status in the appalachian region. geographic visualization techniques were used to assess the usefulness of the oral health data throughout the project and to define a strategy for producing useful results. http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 1. percentage of respondents to the behavioral risk factor surveillance system survey reporting ‘no’ to having had a dental visit within the past year. brfss data are presented at the state level. sources: centers for disease control, u.s. department of health and human services, 2008. methods the only known publicly available and readily accessible data source we could obtain to test sub-state oral health disparities was the behavioral risk factor surveillance system (brfss). the brfss survey is a continuous telephone health survey system which is used for monitoring health conditions and health-risk behaviors across the united states. the brfss provides highquality state-level data that informs policymakers of regional disparities in both health conditions and positive health practices (figure 1). this survey, however, was not intended to be used for small area, or sub-state, estimates. recently, there has been attention placed on developing statistical methodology using brfss data for small area estimation, such as health district or county-level analyses. 7-12 the topic of oral health presents a special challenge. unfortunately, there are only a few common questions pertaining to oral health included in the brfss survey 13 . oral health questions are: 1. how long has it been since you last visited a dentist or dental clinic for any reason? 2. how many of your permanent teeth have been removed because of tooth decay or gum disease? do not include teeth lost for other reasons, such as injury or orthodontics. 3. how long has it been since you had your teeth cleaned by a dentist or dental hygienist? additionally, these oral health questions have been included only as an optional module of the http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 survey and have not been asked every year in every state, due to the overall length and expense of the survey. it is left to the discretion of individual states to decide whether to use the optional modules in any given year, and then they must have a budget to cover the additional expense. this can be especially problematic for poorer states, such as some of those in appalachia. ultimately, this leads to difficulty obtaining large enough sample sizes for annual county-level analysis. the centers for disease control (cdc) requires at least 50 responses per county to use that question’s responses for any given survey year. that means that very high response rates may be necessary in some rural counties for their data to be usable. to examine the oral health data at the county level, we merged county identifiers obtained from the cdc with brfss responses. to aid in addressing the small sample limitation, we combined eight years (1999-2006) of data from the brfss to increase sample sizes at the county level. we used the survey question inquiring as to whether a person had a dental visit in the last 12 months. additionally, we used the brfss coding scheme for the oral health indicator reporting the number of teeth that have been removed and imputed age, to recode variables as "any teeth removed for ages 35-44", "six or more teeth removed for ages 35-44", and "all teeth removed for age 65 and over". all three of these indicators refer to tooth loss attributed to decay or gum disease, not to injury or orthopedic treatment. this provided information on adult oral health status (any tooth loss or significant tooth loss), and senior oral health status (complete tooth loss). participants with missing data (coded "9" or system missing) were eliminated. raw proportions were examined for trends that would preclude using the combined estimates. sas v 9.1.3 was used to combine the data, and the survey procedures in sas were used to find countylevel estimates by including the cdc final weights 14 and pre-defined strata in the estimation process. a geographic information system (gis) was built with sociodemographic and oral health data, and these data were mapped using esri arcgis 9.3 software. for the purposes of this paper, we illustrate the methodology by presenting maps pertaining to only one of the three oral health variables used for the overall study (dental visit in the past year). the dental visit variable has far more responses than the other oral health variables. even using the variable with the greatest number of responses, we faced sample size limitations for county-level analysis. these challenges were greatly exacerbated for the other oral oral health variables on tooth loss. our original intention was to attain adequate sample sizes to estimate prevalence at the county level after combining multiple years of brfss data. by mapping oral health indicators at the county level, it became evident that geographic areas larger than counties would have to be considered to obtain sufficient sample sizes. county-level estimates were not feasible according to the cdc guidelines, even after combining several years of survey data, as many counties still had fewer than 50 respondents (figure 2). as shown in figure 2, many of the more rural counties and those states that did not participate in collecting oral health data in optional years proved to be most difficult in obtaining a clear picture of regional oral health. at this point, the only sub-state comparison we could make using oral health data with more than 50 respondents was a comparison of appalachian regions to non-appalachian regions, within appalachian and non-appalachian states. the 420 counties of appalachia were identified using http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 designations established by the appalachian regional commission. again, estimates of prevalence for counties with 50 or more responses were mapped. this time counties with fewer than 50 were classified with the rest of the appalachian or non-appalachian region of the state (figure 3) in an effort to provide at least some information for those counties that could not be shown due to limitations of sample size. this provided a more local view of some areas where data were available, but proved to be somewhat difficult to interpret. figure 2. brfss data (1999-2006) mapped showing respondents of all ages who had a dental visit within the past year, for all u.s. counties. even after combining eight years of data, many counties had inadequate number of responses to be included in the analysis. the appalachian region is outlined in blue. http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 3. brfss oral health indicator for having had a dental visit within the past year, aggregated to obtain 50 or more responses. some counties have an adequate number of responses. others are aggregated to the appalachian / non-appalachian portions of the states. the appalachian region is outlined in blue. to more clearly represent these data and further expand our analysis, we set out to determine the smallest possible unit we could use, aggregating brfss data to satisfy cdc minimum http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 requirements. because we suspected that dental services vary depending on urban or rural settings, we decided to examine metropolitan versus non-metropolitan areas within appalachian and non-appalachian regions. beale codes, developed by the u.s. department of agriculture 15 , were used to define the metropolitan and non-metropolitan statistical regions for sub-state comparisons within the appalachian region and across the united states. beale codes form a classification scheme that distinguishes metropolitan counties by size and non-metropolitan counties by degree of urbanization and proximity to metropolitan areas. instead of typical ruralurban classifications based on population density, beale codes account for proximity to metropolitan areas and, therefore, to potentially greater access to care. 15 each survey response that reported county of residence was assigned a beale code and an appalachian code. again, due to sample size issues, we combined the nine categories of the original beale classifications so that codes 1-3 represent metropolitan areas and codes 4-9 represent non-metropolitan areas. these designations are shown in figure 4. the prevalence for each oral health indicator was then calculated for each of the following groups: (1) appalachian/ metropolitan, (2) appalachian/non-metropolitan, (3) non-appalachian/metropolitan, and (4) nonappalachian/non-metropolitan. figure 4. metropolitan and non-metropolitan areas defined using beale codes. http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 all counties in west virginia are within the appalachian region. since west virginia data did not meet county-level minimum requirements, west virginia has prevalence estimates only for metropolitan and non-metropolitan regions. other states have both appalachian and non-appalachian counties. after aggregating and weighting the data to the larger geographic areas just described, we were able to obtain valid (n > 50) contiguous data, and oral health indicators could be mapped and compared to national averages (figure 5). figure 5. brfss data aggregated to metropolitan and non-metropolitan areas defined by beale codes, showing the proportion of respondents who had a dental visit within the last year for all u.s. counties, with the appalachian region outlined in blue. results figure 1 shows the more typical state-level presentation of brfss data, a graphical view of the percentage of persons who had a dental visit within the past year for all states. of the appalachian states, mississippi had the lowest percentage of dental visits, followed by west virginia, and kentucky. however, the presentation of brfss data at the state-level provides no meaningful differences on sub-state differences. after combining multiple years of brfss data, we mapped the oral health indicators at the county level. figure 2 illustrates the geographic distribution of dental visits as an oral health indicator for only those counties with sample sizes large enough to meet the cdc guidelines. there was a tremendous amount of missing data and it was clearly not possible to conduct this http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 study at the county level, with such sparse data. we did not have enough data for about half of the counties of appalachia. we then aggregated those counties that did not have the needed 50 or more responses into larger areas of appalachia/non-appalachian portions of the states. figure 3 provides a more detailed picture of the appalachian region. at this point, again we see that mississippi, eastern kentucky, and west virginia were areas of greatest concern, with one large county in north mississippi and two counties in west virginia especially standing out. to make the analysis easier to interpret and more informative, we decided to make rural vs. urban distinctions, further refining the county-level maps by aggregating the counties into metropolitan and non-metropolitan areas, using beale codes. thus, we created four groups for analysis: (1) appalachian/metropolitan, (2) appalachian/non-metropolitan, (3) nonappalachian/metropolitan, and (4) non-appalachian/non-metropolitan. states that surround the appalachian region were also mapped for comparisons (figure 4). figure 5 shows the utility of mapping metropolitan/non-metropolitan areas in helping to smooth the oral health data. regions of mississippi, eastern kentucky and west virginia, had low rates of dental visits, but, now, using beale codes, we can also see that most of the areas with poorer oral health status (as we measure it), were non-metropolitan areas. furthermore, other areas within the appalachian region showed rates similar to the rest of the 48 contiguous united states. the map also reveals apparent differences between the appalachian region of mississippi in the northeast corner of the state, with slightly higher rates of dental visits, and the central and delta regions of the state. note that mississippi, arkansas and okalahoma had large nonmetropolitan areas where dental visits were at a lower rate than metropolitan areas, a potential indicator of lack of access to care. discussion and limitations oral health data are difficult to obtain for small area estimation. while a county-level analysis of oral health indicators would have been ideal, it proved impossible due to limitations with the availability and accessibility of oral health data. the brfss is an extensive and large national dataset that includes oral health questions. however, we found that there were not enough responses to perform county-level analysis for oral health indicators even after combining multiple years of brfss data. unfortunately, the oral health questions are optional and, therefore, are not asked every year in every state. ultimately, to perform sub-state analyses, we aggregated appalachian vs. non-appalachian regions within appalachian states. then, using beale codes, we also examined metropolitan and non-metropolitan differences, while adjusting for poverty. using brfss data, we were able to make estimations smaller than the state level, but not as small as the county or local level, which would have been preferable. there is wide variation in health status throughout the appalachian region and we were not able to detect local differences using brfss data. however, by using the method described, we were able to conduct informative analyses about oral health in the appalachian region at this sub-state level, examining metropolitan/non-metropolitan differences, and to make further comparisons between appalachia and the rest of the nation. 6 http://ojphi.org/ overcoming data challenges examining oral health disparities in appalachia 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 there is a pressing need for better oral health data to study oral health status and to measure the effectiveness of funded initiatives, nationally, and specifically in the appalachian region. the brfss is a valuable source of data, but it has its limitations for small area estimation as we have shown here. discussions should begin with the centers for disease control (cdc) to modify the sampling procedures used for the brfss to collect consistent responses to oral health questions each year in all states. a larger sampling frame would provide the data needed to better inform public health planners and politicians of what areas or population groups should be targeted to improve oral health conditions in appalachia. conclusion aggregating the data as described proved useful for analyzing oral health disparities in appalachia, while still revealing important sub-state differences. using gis to map data provided very useful “views” of the data that helped determine the best methodological approach for conducting this study and provided interesting visual results of the comparisons of oral health indicators in appalachia. mapping the data also helped visualize that low socioeconomic status and rurality contribute to oral health disparities in appalachia. figures 1-5 illustrate the process we used to glean meaningful information from limited oral health data. maps are presented of only one of the oral health indicators obtained from the brfss and used for our analysis of oral health disparities in appalachia. we began the process with brfss data intended for state use (figure 1) and ended up with four groups for analysis. the oral health variable, dental visit within the past year, is presented in figure 5, showing differences in metropolitan/non-metropolitan areas. the brfss can be a useful data source for studying a number of health topics, but may require some small area estimation techniques such as those described here, to overcome data challenges, especially on health topics not included as core questions in the brfss survey. using brfss health data and these methods to examine oral health disparities in appalachia, we were able to to make some interesting observations about oral health disparities in appalachia for policymakers and health planners, identifying areas of concern for targeted intervention strategies. corresponding author denise d. krause associate professor university of mississippi medical center email: dkrause@umc.edu http://ojphi.org/ mailto:dkrause@umc.edu overcoming data challenges examining oral health disparities in appalachia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 references 1. satcher d. oral health in america: a report of the surgeon general. in: u.s. department of health and human services, editor. rockville, md: national institute of dental and craniofacial research, national institutes of health; 2000. 2. brown lj, wall tp, lazar v. trends in untreated caries in permanent teeth of children 6 to 18 years old. j am dent assoc. 1999;130(11):1637-44 contd. epub 1999/11/26. pubmed pmid: 10573947. 3. newacheck pw, hughes dc, hung yy, wong s, stoddard jj. 2000. the unmet health needs of america's children. pediatrics. 105(4 pt 2), 989-97. epub 04 2000. 4. mouradian we, wehr e, crall jj. 2000. disparities in children's oral health and access to dental care. jama. 284(20), 2625-31. epub 11 2000. http://dx.doi.org/10.1001/jama.284.20.2625 5. edelstein bl. disparities in oral health and access to care: findings of national surveys. ambulatory pediatrics : the official journal of the ambulatory pediatric association. 2002;2(2 suppl):141-7. epub 2002/04/13. pubmed pmid: 11950385. 6. krause dd, may wl, lane nm, cossman js, konrad tr. an analysis of oral health disparities and access to services in the appalachian region. in: appalachian regional commission, editor. washington, d.c. 2012. 7. jia h, muennig p, borawski e. 2004. comparison of small-area analysis techniques for estimating county-level outcomes. am j prev med. 26(5), 453-60. epub 05 2004. doi:http:// dx.doi.org/10.1016/j.amepre.2004.02.004. 8. jia h, link m, holt j, mokdad ah, li l, et al. 2006. monitoring county-level vaccination coverage during the 2004-2005 influenza season. am j prev med. 31(4), 275-80. epub 09 2006. doi:http://dx.doi.org/10.1016/j.amepre.2006.06.005. 9. congdon p. 2009. a multilevel model for cardiovascular disease prevalence in the us and its application to micro area prevalence estimates. int j health geogr. 8, 6. epub 02 2009. doi:http:// dx.doi.org/10.1186/1476-072x-8-6. 10. li w, land t, zhang z, keithly l, kelsey jl. 2009. small-area estimation and prioritizing communities for tobacco control efforts in massachusetts. am j public health. 99(3), 470-79. epub 01 2009. doi:http://dx.doi.org/10.2105/ajph.2007.130112. 11. schneider kl, lapane kl, clark ma, rakowski w. 2009. using small-area estimation to describe county-level disparities in mammography. prev chronic dis. 6(4), a125. epub 09 2009. 12. zhang z, zhang l, penman a, may w. 2011. using small-area estimation method to calculate county-level prevalence of obesity in mississippi, 2007-2009. prev chronic dis. 8(4), a85. epub 06 2011. 13. office of surveillance, epidemiology, and laboratory services. questionnaires: centers for disease control and prevention; 2011 [cited 2012 10/10/2012]. available from: http:// www.cdc.gov/brfss/questionnaires/index.htm. 14. office of surveillance, epidemiology, and laboratory services. brfss annual survey data: centers for disease control and prevention; 2012 [11/27/2012]. available from: http:// www.cdc.gov/brfss/technical_infodata/weighting.htm. 15. economic research service. rural-urban continuum codes: united states department of agriculture; 2004 [updated november 3, 2004april 18, 2012]. available from: http:// www.ers.usda.gov/data/ruralurbancontinuumcodes/. http://ojphi.org/ http://dx.doi.org/10.1001/jama.284.20.2625 http://dx.doi.org/10.1016/j.amepre.2004.02.004 http://dx.doi.org/10.1016/j.amepre.2004.02.004 http://dx.doi.org/10.1016/j.amepre.2006.06.005 http://dx.doi.org/10.1186/1476-072x-8-6 http://dx.doi.org/10.1186/1476-072x-8-6 http://dx.doi.org/10.2105/ajph.2007.130112 http://www.cdc.gov/brfss/questionnaires/index.htm http://www.cdc.gov/brfss/questionnaires/index.htm http://www.cdc.gov/brfss/technical_infodata/weighting.htm http://www.cdc.gov/brfss/technical_infodata/weighting.htm http://www.ers.usda.gov/data/ruralurbancontinuumcodes/ http://www.ers.usda.gov/data/ruralurbancontinuumcodes/ http://ojphi.org/ http://www.cdc.gov/brfss/questionnaires/index.htm http://www.cdc.gov/brfss/technical_infodata/weighting.htm http://www.ers.usda.gov/data/ruralurbancontinuumcodes/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the activity of rabies vaccines against genetic clusters of rabies virus circulating at the territory of ukraine mykola ivanov*, ivan polupan and oleg deryabin institute of veterinary medcine, kyiv, ukraine objective to identify the presence of genetic clusters of rabies virus at the territory of ukraine and to determine the degree of activity of rabies vaccines against these genetic clusters. introduction to develop and implement an effective program of rabies eradication in ukraine in 2008 was founded the unique collection of samples of pathological materials confirmed as positive in rabies at the regional veterinary laboratories of ukraine. the collection is constantly updated and to present moment it includes 1389 samples from all regions of ukraine, selected from 17 animal species and humans. methods identification of the rabies virus in samples of pathological material for their further selection was carried out using the test developed by us which based on rt-pcr with primers complementary to the conservative fragments of the 5’-end of nucleoprotein gene of rabies virus. for the study of the street rabies virus isolates from the collection we use rt-pcr with the primers pair (509, 304) flanking the variable 3’-end part of nucleoprotein gene of the reference strain of rabies virus cvs (fragment in 377 bp). studies of rabies vaccines activity were carried out with modified method of u.s. national institutes of health using rabies virus street isolates of both genetic clusters instead of the challenge virus standard (cvs). all isolates of street rabies virus were inoculated in a dose of 5–50 ld50. the criteria for evaluation of protective activity of rabies vaccine was effective dose (lg ed50). results in molecular genetic studies with variant-specific primers we established the presence in ukraine of two clusters of rabies virus. clusters i circulates on the right bank of the dnipro river (the largest water barrier that divides the country into eastern and western side), and cluster ii – on the left bank of the dnieper. the relationship of these variants with the epizootic situation was researched. for this purpose epizootological zoning of ukraine according to the intensity of the epizootic situation in 2005-2009 was carried out. as a result of this analysis all the regions of ukraine belong to three categories: high, medium and low epizootic situation intensity of rabies. the projection of differentiated genetic clusters on the epizootic situation showed that cluster ii circulating at left bank of the dnieper in areas with high and medium intensity of the epizootic situation, and the cluster i – at the right bank of the dnieper, mainly in the areas with low intensity of the epizootic situation. that’s why our interest was in the degree of protection of rabies vaccines against street rabies virus isolates belonging to these two genetic clusters. the commercial vaccines made with rabies virus vaccine strains sad (street-alabama-dufferin) and wistar pm/wi were chosen to evaluate this parameter. after the mathematical calculations of effective dose and the analysis of the data the less effective protection of rabies vaccines (at 29–30 %) against street rabies virus isolates belonging to cluster ii in comparison with isolates belonging to cluster i irrespective to the strain vaccine is made was shown. conclusions the data will be used for the effective planning of specific prophylaxis of rabies in ukraine based on differentiated approach to distribution of rabies vaccines in according to region and their activity. keywords rabies vaccine; vaccine activity; street rabies virus isolates; genetic variants of rabies virus *mykola ivanov e-mail: ivanovny@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e154, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the black canyon forecast station: experiences and lessons learned james m. wilson*1, 2, bonnie koehler3, kathleen sramek2 and treve henwood2 1black canyon forecast station, delta, co, usa; 2delta county memorial hospital, delta, co, usa; 3delta county health and human services, delta, co, usa objective to evaluate the sociological effect on indigenous biological event signature recognition and community resilience due to the operational activities of an infectious disease forecast station. introduction the nation’s first operational infectious disease forecast station, modeled after warning protocols developed in the meteorology community, was activated in 2011. the approach was originally pioneered in haiti following the 2010 earthquake. methods we assembled global event signature and forecast libraries that reflected locally diagnosed infectious disease activity and infrastructure impact in a rural community from a public health, veterinary, and human clinical medicine perspective. the deployment site is home to a variety of infectious disease including hantavirus, plague, tularemia, and west nile in the context of high wildlife-livestockhuman interfacing. information derived from the issuance of forecasts coupled to situational awareness was shared with the public, local officials, public health officers, veterinarians, healthcare providers, and patients through various social media methods. results provision of 30-60-90 day forecasts for routine and non-routine endemic infectious disease activity and impact facilitated better coordination of public health messaging and daily conversation with patients in the inpatient and outpatient settings. the signature of an unusual, infrastructure-disruptive outbreak of metapneumovirus and respiratory syncytial virus was recognized and communicated with enough time to activate effective clinical mitigation protocols. cost estimates demonstrated financial benefit at a local level to anticipating surges of infectious disease activity with enough time to mitigate patient demand. community-wide engagement with infectious disease forecasts and live event advisories included the promotion of proactive infection control and public health surveillance and response, healthcare provider recognition of non-routine infectious disease, clinical sampling and diagnostic testing protocols, clinician and patient education, and synchronization of proactive disease reporting both in the routine daily clinical setting and in times of crisis. collateral benefit of consistent messaging delivered to the public by the participating entities was noted. community awareness of the repertoire of indigenous infectious disease activity was expanded beyond the official public health notification list. neither issuance of infectious disease forecasts nor advisories issued during crises triggered an influx of anxious well phone calls or visits to the medical system that was deemed operationally relevant. conclusions activation of a local infectious disease forecast station modeled after a local weather station promotes routine communication of a broader array of infectious disease activity than that monitored by public health; facilitates proactive, cost effective healthcare; and enabled recognition of unusual, disruptive infectious activity with enough time to enable mitigation of clinical, infrastructure, and financial impact to the community. routine communication of comprehensive infectious disease forecast and situational awareness information promotes community adaptive fitness to a wide variety of infectious hazards. the results suggest it is possible to transform the traditional public health model of data collection and analysis to one of transparent and open data availability to support innovative reduction in morbidity and mortality. keywords biosurveillance; forecast; meteorology acknowledgments the authors would like to acknowledge the tremendous contributions of the staff of ascel bio llc for the design and management of the black canyon forecast station; the personal contributions of mr. michael smith regarding meteorological warning operations; delta county memorial hospital: betty kahrs, janet moore, jason cleckler, bev carlson, randall koehn, johanna roeber, and tom mingen; david van metre of colorado state university and delta county colorado state university agriculture extension agent robbie b. lavalley; and colorado department of agriculture field veterinarian, dan love dvm. *james m. wilson e-mail: jwilson@ascelbio.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e27, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a system for surveillance directly from the emr richard f. davies*1, jason morin1, ramanjot s. bhatia1 and lambertus de bruijn2 1university of ottawa heart institute, ottawa, on, canada; 2national research council canada, ottawa, on, canada objective our objective was to conduct surveillance of nosocomial infections directly from multiple emr data streams in a large multi-location canadian health care facility. the system developed automatically triggers bed-day-level-location-aware reports and detects and tracks the incidents of nosocomial infections in hospital by ward. introduction hospital acquired infections are a major cause of morbidity, mortality and increased resource utilization. cdc estimates that in the us alone, over 2 million patients are affected by nosocomial infections costing approximately $34.7 billion to $45 billion annually (1). the existing process of detection and reporting relies on time consuming manual processing of records and generation of alerts based on disparate definitions that are not comparable across institutions or even physicians. methods a multi-stakeholder team consisting of experts from medicine, infection control, epidemiology, privacy, computing, artificial intelligence, data fusion and public health conducted a proof of concept from four complete years of admission records of all patients at the university of ottawa heart institute . figure 1 lists the data elements investigated. our system uses an open source enterprise bus ‘mirth connect’ to receive and store data in hl7 format. the processing of information is handled by individual components and alerts are pushed back to respective locations.the free text components were classified using natural language processing. negation detection was performed using negex (2). data-fusion algorithms were used to merge information to make it meaningful and allow complex syndrome definitions to be mapped onto the data. results the system monitors: ventilator associated pneumonia (vap), central line infections (cli), methicillin resistant staph aureus (mrsa), clostridium difficile (c. diff) and vancomycin resistant enterococcus (vre). 21452 hospital admissions occurred in 17670 unique patients over four years. there were 41720 cxrs performed in total, of which 10546 were classified as having an infiltrate. 4575 admissions were associated with at least one cxr showing an infiltrate, 2266 of which were hospital-acquired. hospital acquired infiltrates were associated with an increased hospital mortality (6.3% vs 2.6%)* and length of stay (19.5 days vs 6.5 days)*. 253 patients had at least one positive blood culture. this was also associated with an increased hospital mortality (23,3% vs. 2.8%)* and length of stay (10.8 vs 40.9 days)*. (* all p values < 0.00001) conclusions this proof of concept system demonstrates the capability of monitoring and analyzing multiple available data streams to automatically detect and track infections without the need for manual data capture and entry. it acquires directly from the emr data to identify and classify health care events, which can be used to improve health outcomes and costs. the standardization of definitions used for detection will allow for generalization across institutions. keywords electronic health records; surveillance; pneumonia; hospital acquired infections acknowledgments this work was supported by defence research and development canada centre for security science and the chemical, biological, radiological/nuclear, and explosives research and technology initiative (crti) under project crti 06-0234ta and the following participatory and advisory partners. references 1. report on cdc website (http://www.cdc.gov/hai/pdfs/hai/scott_costpaper.pdf) accessed: 10th september, 2012. 2. chapman, w. et. al. 2001. evaluation of negation phrases in narrative clinical reports. proc amia symposium, 105-114. *richard f. davies e-mail: rfdavies@ottawaheart.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e29, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the surveillance window – contextualizing data streams kirsten mccabe*, lauren castro, mac brown, william daniel, eric nick generous, kristen margevicius and alina deshpande los alamos national laboratory, los alamos, nm, usa objective the goal of this project is the evaluation of data stream utility in integrated, global disease surveillance. this effort is part of a larger project with the goal of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease. introduction los alamos national laboratory has been funded by the defense threat reduction agency to determine the relevance of data streams for an integrated global biosurveillance system. we used a novel method of evaluating the effectiveness of data streams called the “surveillance window”. the concept of the surveillance window is defined as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. we used a stepwise approach to defining disease specific surveillance windows; 1. timeline generation through historical perspectives and epidemiological simulations. 2. identifying the surveillance windows between changes in “epidemiological state” of an outbreak. 3. data streams that are used or could have been used due to their availability during the generated timeline are identified. if these data streams fall within a surveillance window, and provide both actionable and non-actionable information, they are deemed to have utility. methods figure 1 shows the overall approach to using this method for evaluating data stream types. our first step was identifying a list of priority diseases to build surveillance windows for and our primary sources were our sme panel, cdc priorities, as well as dod priorities. we also conducted a literature review to support our selection of diseases. we ensured that there was representation of human, animal and plant diseases and there was enough data available for selected outbreaks to facilitate evaluation of all data stream types identified. we then selected representative outbreaks for diseases to generate a timeline for defining surveillance windows. surveillance windows were then defined (based on four specific biosurveillance goals developed by lanl) and information for applicable data streams was collected for the duration of the outbreak. a data stream was deemed useful if it was determined to be available within the defined surveillance window. in addition, evaluation of the ideal use case of the data streams was performed. in essence, if used more effectively could this data stream provide greater support to understanding, detection, warning or management of disease outbreaks or event situations? results results presented in this abstract are from retrospective analyses of historical outbreaks selected as being representative of fmd, ebola, influenza and e.coli. graphs indicating case counts and geographical spread were combined and a timeline was created to determine the length of time between changes in “epidemiological state” that defined various surveillance windows. this timeline was then populated with durations when data streams were used during the outbreak. results showed varying surveillance windows times are dependent on disease characteristics. in turn, epidemiology of the disease affected the occurrence of data streams on the timeline. conclusions surveillance window based evaluation of data streams during disease outbreaks helped identify data streams that are of significance for developing an effective biosurveillance system. some data streams were identified to have high utility for early detection and early warning regardless of disease, while others were more disease and operations specific. this work also identified data streams currently not in use that could be exploited for faster outbreak detection. key useful data streams that are underlying to all disease categories and thus important for integration into global biosurveillance programs will be presented here. figure 1: overall approach to surveillance window based evaluation keywords surveilliance windows; data streams; biosurveilliance acknowledgments this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra). *kirsten mccabe e-mail: kjmccab@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e113, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts objective to document the current evidence base for the use of electronic health record (ehr) data for syndromic surveillance using emergency department, urgent care clinic, hospital inpatient, and ambulatory clinical care data. introduction historically, syndromic surveillance has primarily involved the use of near real-time data sent from hospital emergency department (eds) and urgent care (uc) clinics to public health agencies. the use of data from inpatient and ambulatory settings is now gaining interest and support throughout the united states, largely as a result of the stage 2 and 3 meaningful use regulations [1]. questions regarding the feasibility and utility of applying a syndromic approach to these data sources are hampering the development of systems to collect, analyze, and share this potentially valuable information. solidifying the evidence base and communicating the results to the public health surveillance community may help to initiate and build support for using these data to advance surveillance functions. methods we conducted a literature search in the published and grey literature that scanned for relevant articles in the google scholar, pub med, and ebsco information services databases. search terms included: “inpatient/ambulatory electronic health record”; “ambulatory/inpatient/hospital/outpatient/chronic disease syndromic surveillance”; and “ehr syndromic surveillance”. information gleaned from each article included data use, data elements extracted, and data quality indicators. in addition, several stakeholders who provided input on the september 2012 isds recommendations [2] also provided articles that were incorporated into the literature review. isds also invited speakers from existing inpatient and ambulatory syndromic surveillance systems to give webinar presentations on how they are using data from these novel sources. results the number of public health agencies (phas) routinely receiving ambulatory and inpatient syndromic surveillance data is substantially smaller than the number receiving ed and uc data. some health departments, private medical organizations (including hmos), and researchers are conducting syndromic surveillance and related research with health data captured in these clinical settings [2]. in inpatient settings, many of the necessary infrastructure and analytic tools are already in place. syndromic surveillance with inpatient data has been used for a range of innovative uses, from monitoring trends in myocardial infarction in association with risk factors for cardiovascular disease [3] to tracking changes in incident-related hospitalizations following the 2011 joplin, missouri tornado [3]. in contrast, ambulatory systems face a need for new infrastructure, as well as pose a data volume challenge. the existing systems vary in how they address data volume and what types of encounters they capture. ambulatory data has been used for a variety of uses, from monitoring gastrointestinal infectious disease [3], to monitoring behavioral health trends in a population, while protecting personal identities [4]. conclusions the existing syndromic surveillance systems and substantial research in the area indicate an interest in the public health community in using hospital inpatient and ambulatory clinical care data in new and innovative ways. however, before inpatient and ambulatory syndromic surveillance systems can be effectively utilized on a large scale, the gaps in knowledge and the barriers to system development must be addressed. though the potential use cases are well documented, the generalizability to other settings requires additional research, workforce development, and investment. keywords syndromic surveillance; ehr; meaningful use acknowledgments we thank the isds meaningful use workgroup for their assistance with the literature review, and all the presenters in the isds meaningful use webinar series (http://www.syndromic.org/webinars/meaningfuluse). work supported by cdc through isds contract with task force for global health. references 1. health information technology for economic and clinical health (hitech) act. title xiii of division a and title iv of division b of the american recovery and reinvestment act of 2009 2009; pub. l. no. 111-5. 2. isds. electronic syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record data: recommendations from the isds meaningful use workgroup. 2012. www.syndromic.org 3. various presenters. isds meaningful use webinar series: 3/13/21/2012. http://www.syndromic.org/webinars/meaningfuluse 4. pavlin ja, murdock p, elbert e, milliken c, hakre s. conducting population behavioral health surveillance by using automated diagnostic and pharmacy data systems. mmwr 2004;53 (supp.):166-172. *rebecca zwickl e-mail: bzwickl@syndromic.org utility of syndromic surveillance using novel clinical data sources rebecca zwickl*, charles ishikawa and laura c. streichert isds, brighton, ma, usa online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e33, 2013 electronic health in ghana: current status and future prospects 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi electronic health in ghana: current status and future prospects ebenezer afarikumah1 1. open university malaysia/accra institute of technology, accra abstract the health-care system in ghana is similar to those in other developing countries and access to health services for remote communities is extremely limited. in july, 2010, the government of ghana launched the national e health strategy. a number of international organizations have initiated various pilot projects, including disseminating and collecting data, education initiatives and telemedicine. in addition, several institutions and organizations are dedicated to the promotion of e-health and a range of web-based health consultancy services have begun. the main objective of this study is to provide an overview of ehealth activities in ghana. it was a daunting task, not least because of the need to gather information on ehealth projects and initiatives in ghana, as there is no existing repository of such information. through literature search in africa journals online, hinari, medline, google.com, journal of telemedicine and ehealth, journal of telemedicine and telecare, journal of medical internet research and interaction with ehealth experts, followed up with some of the authors' for directions to other projects, and following the references in some articles. a total of twenty-two (22) pilot projects have been identified in ghana. mobile devices in use range from pdas to simple phones and smart phones. the key findings of this research are that there are about 22 ehealth project at various stages of implementation in ghana. some of these projects have wind up and others are still being implemented. mobile devices in use range from pdas to simple mobile phones and smart phones. most of the projects have been donor initiated. data collection started in march 2010 to june 2013. although ehealth seems to have a limited role in ghana at present, there is growing interest in the opportunities it may offer in terms of improving the delivery and access to services, especially in remote locations. recommendations for further research are provided. keywords: ehealth, health, ghana, developing countries, information and communications technology corresponding author. afari.telemedicine@yahool.com doi: 10.5210/ojphi.v5i3.4847 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health info rmatics. readers may copy articles without permission of the copyright owner(s), as long a s the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction reliable information and effective communication are crucial elements in individual health institutions, disease monitoring and prevention, public health systems, and health care generally. icts, therefore, can in many ways be vital tools in combating disease, promoting individual health and making health systems more effective and efficient. they can be particularly powerful in monitoring the outbreak and spread of disease, disseminating health information (including information about health-promoting and diseasepreventing individual behaviour), and providing training, information and long-distance http://ojphi.org/ electronic health in ghana: current status and future prospects 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi support to health care practitioners. a particular challenge for developing countries is ensuring that icts are effectively mobilized to improve health outcomes and combat disease among the poorest and most remote populations. this is an area where the potential for effective use of the full range of icts (including radio and television) is particularly great. in july, 2010, the government of ghana launched the national e health strategy. the key strategies under the national e-health strategy; streamlining the regulatory framework for health data and information management, building sector capacity for wider application of ehealth solutions in the health sector, increasing access and bridging equity gap in the health sector through the use of information and communication technology, and towards a paperless records and reporting system. ehealth is the term more commonly used in relation to ict deployments in health care. there have been several attempts to define ehealth [1-3] there is still no universal agreement on the precise meaning of this term. according to [4], ehealth is widely used by many individuals, academic institutions, professional bodies and funding organisations. it has become an accepted neologism despite the lack of an agreed-upon clear or precise definition. communication among the many individuals and organisations that use the term could be improved by comprehensive data about the range of meanings encompassed by the term (ibid). fifty –one (51) unique definitions that we retrieved showed a wide range of themes, but no clear consensus about the meaning of the term ehealth. in addition, two universal themes (health and technology) and six less general (commerce, activities, stakeholders, outcomes, place, and perspectives) were identified. the widespread use of the term ehealth suggests that it is an important concept, and that there is a tacit understanding of its meaning (ibid). however, thinks that any definition of ehealth should encompass the full spectrum of icts whilst appreciating the context of use and the value they bring to society [5]. one definition which they identified as taking into consideration the various facets is the one proposed by [6], who defined ehealth as: ‘’...an emerging field of medical informatics, referring to the organisation and delivery of health services and information using the internet and related technologies. in a broader sense, the term characterises not only a technical development, but also a new way of working, an attitude, and a commitment for networked, global thinking, to improve healthcare locally, regionally and worldwide by using information and communications technology. [6]’’ ehealth programs according to [7] offer the potential for enhanced reach, including traditionally underserved populations, at relatively low cost; scalability; time efficiency; and the capacity to provide tailoring and customisation for individual patients and consumers. despite these potential benefits, there are barriers to the full implementation of ehealth solutions, and the limitations of access, health and technology literacy, and quality measures must be addressed [8,9]. it was concluded by [5] saying that “ehealth interventions have considerable potential to transform the health sector, hopefully better. as with many intervention, however, the risk of harm exists, so policy makers, commissioners, clinicians, and patients alike need to remain aware of this possibility”. it was suggested earlier by [10] that if we are to maximise the benefits associated with ehealth interventions whilst minimising risks, we must be able simultaneously to evaluate ehealth interventions while they are being designed, developed, and deployed. solutions which are provided through http://ojphi.org/ electronic health in ghana: current status and future prospects 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi ehealth initiatives within hospitals has been identified by [11] as hospital information systems (his), telemedicine services, electronic health records and internet services. methods the following is not a systematic review of ehealth in ghana, but rather an attempt to gather a diversity of perspectives on the topic from a variety of sources. various papers were identified on these topic by searching through medline, google scholar, journal of health informatics for instances of “ehealth”, “telehealth”, “telemedicine”, “store and forward”, and “teleconsultation + ghana” to name a few. moreover, references were solicited from a variety of experts in the field, many of whom were responsible for building and deploying their own systems. interactions with ehealth experts [12-14]. in addition, follow-up with some of the authors' for directions to other projects. data collection started in march 2010 to june 2011. papers were selected once they discuss an ehealth project in ghana. results using the above criteria and personal contacts of researcher, 22 projects were identified. these projects are: i. sene pda the aim of the project is to use information technology to improve service delivery at the lowest level of service delivery – community-based health planning and services (chps) zones. it is one of the pioneer mobile health projects in ghana. objecti ves of the project are: to use appropriate technology to generate more accurate reports that can be used to make decisions by the community health officers (cho) and the district health managers; to use current technology to reduce the time cho’s spent to generate monthly report on services; improve the follow up of children/mothers registered for services and reduce the dropout rate for immunization and safe motherhood services ii. mobile technology for community health (motech) in ghana the project aims to determine how to use mobile phones to increase the quantity and quality of prenatal and neonatal care in rural ghana, with a goal of improving health outcomes for mothers and their newborns. iii. millennium villages and mobile telemedicine millennium village project is a new approach for fighting extreme poverty. the concept is to target the "poorest of the poor, village by village throughout africa, in partnership with government and other committed stakeholders, providing affordable and science-based solutions to help people lift themselves out of extreme poverty.” iv. pan african enetwork the basic objective of the project is to assist africa in capacity building by way of imparting quality education to 10,000 students in africa over a 5 -year period in various disciplines from some of the best indian universities/educational institutions. besides, this would provide tele-medicine services by way of on line medical consultation to the medical practitioners at the patient end location in africa by indian medical specialists in various disciplines/specialties selected by african union for its member states http://ojphi.org/ electronic health in ghana: current status and future prospects 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi v. onetouch medicareline (ml) • ml phase 1 calls for free phone calls and text messages; • the planned ml phase 2 calls for mms and data reports over sms, and ml • phase 3 calls for free smartphones, reference tools, and custom applications. • 1700 of 2000 enrolled • 2 million calls made • ghana medical association & onetouch telecom vi. ghana consultation network this is a web‐based interface consultation network. it has a network of asynchronous servers hosted at each hospital and integrated with the referral system. it has on board 125 doctors in 6 countries. the project was supported by ghana physicians foundation & ministry of health. other projects associated with this project are the technology infrastructure for emerging regions (tier) and remote asynchronous communication for health care (reach) project vii. moorfields /korle bu eye centre moorfields eye hospital is using the internet to share the specialist knowledge and advice of its consultant eye specialists with hospitals in the developing world. as part of a new project, run in partnership with international telecommunications group, cable & wireless, eye specialists in south africa, tanzania, gambia and ghana will be able to access a dedicated internet site set up by moorfields eye hospital nhs trust viii. mobile teledermatology in ghana mobile teledermatology ‘involves the use of mobile telecommunication technologies allowing easy submission of dermatologic cases without the use of physical internet connectivity’. patients were randomly selected from three outpatient clinics in accra and kumasi ghana. patients underwent physical consultation by an onsite dermatologist. these patients also went through clickdoc data collection and image capture using a samsung u900 soul mobile phone. remote ghanaian dermatologists connected to the patient database using a web-based interface (africa.telederm.org) from the phone in a remote location and viewed cases. for each case, the remote specialists made their own diagnosis on the basis of the patient data and images. ix. pdas in africasatellife‟s experience the goal of the satellife pda project was to demonstrate the viability of handheld computers -also called personal digital assistants or pdas -for addressing the digital divide among health professionals working in africa. in december 2001 satellife’s worked with the american red cross to conduct a pilot that tested the efficacy of pdas for measles field surveys in ghana. thirty ghanaian red cross volunteers, trained over a two -day period, had no trouble with the technology, though some of them had never before used a computer. they were able to complete over 2,400 surveys in just three days, where the traditional paper and pen survey method generally yielded about 200 finished surveys. survey data was turned in at noon on the last day of the pilot; analysis was completed promptly after the data was hot synched into a computer; and a complete report wa s delivered to the ghanaian ministry of health by 5pm. the entire pilot was completed in less than a week, and the speed and ease of gathering this epidemiological data was unprecedented. community volunteers using pdas http://ojphi.org/ electronic health in ghana: current status and future prospects 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi to collect data as part of measles vaccination program in ghana. the project yielded compelling evidence of the value of pdas for data collection and reporting x. disease control/who the who–ghana supported the disease control unit of ghs to develop and pilot a facilitative tool using epi-surveyor. epi-surveyor is a free tool to enable data collection on pdas. there are plans to use the lessons learnt from this pilot and institutionalize the use of pdas for integrated facilitative supervision within the service xi. usaid-deliver project the usaid deliver project in collaboration with the president’s malaria initiative and the national malaria control program has since july 2009 promoted the use of epi -surveyor, mobile phone survey software for collecting data on malaria logistics at the service points every quarter. xii. early warning systemsfocus region project the usaid sponsored project which is working in gar, wr and cr is piloting a logistics management system using mobile phones in six districts in the target regions. the system will facilitate data collection from sdps over sms through facility workers‟ personal mobile phones. the smss will then be sent to a toll-free short code registered with each mobile network in ghana. xiii. sms for life logistic management this project that is yet to be implemented. it is similar to usaid deliver project. it will be implemented as a pilot project in six districts in three regions (ba, ue, gar). xiv. mimcom.net project (http://www.nlm.nih.gov/mimcom/background.html) the national library of medicine chairs the communications working group of the multilateral initiative on malaria (mim), which began in 1997. the objective is to support african scientists and malaria researchers in their ability to connect with one another and sources of information through full access to the internet and the resources of the world wide web, as well as create new collaborations and partnerships. the initial meeting of the mim cwg was held in january 1998 at the nlm/nih in bethesda, maryland. in attendance were malaria research scientists, health information professionals, telecommunications experts and representatives of the major mim funding agencies. in keeping with the underlying goal of supporting a broad spectrum of basic and operational malaria research needs, the researchers requested communications and connectivity capabilities sufficient to provide, at a minimum: robust and reliable e-mail, links to other research sites, access to full text journal articles, database searching, exchange of large files and mapping data, and timely access to electronic information resources worldwide. in july 1999, redwing satellite solutions ltd. (based in the uk) and nlm’s technical consultant mark bennett successfully installed very small aperture terminal (vsat) ground stations at two malaria research sites in kenya, at kisian (cdc funded) and kilifi (wellcome trust funded). the 64kbs dedicated bandwidth purchased was shared by the two sites. these two sites join the malaria research and training center in mali which has full http://ojphi.org/ electronic health in ghana: current status and future prospects 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi internet access via microwave technology, funded by niaid and made operational in june 1998. the nlm team brought on two further sites in december 1999, in ghana in legon near accra (the noguchi memorial institute for medical research) and in navrongo (navrongo health research centre). the ghanaian sites, engaged in malaria vaccine testing, are funded jointly by niaid/nih, the naval institute of medical research, and usaid. when these sites came on line, the overall bandwidth increased to 128kbs and monthly charges were reduced since more sites were sharing the bandwidth. xv. trinet project (http://www.sysmod.com/trinet.htm) this is a project initiated by the informatics development institute (idi). their mission is to provide cost-effective communications for remote regions of developing countries. in 1999, the idi secured european commission backing for a communications network project, entitled trinet, targeted at developing countries in africa. partners were located in ghana, uganda, zambia and zimbabwe, and use a low-earth-orbiting satellite (leosat) for storeand-forward email communications suing amateur packet radio technology, and internet email gateways in developed countries. xvi. mpedigree (http://en.wikipedia.org/wiki/mpedigree) mpedigree refers both to a mobile telephony shortcode platform that interconnects gsm mobile networks in the west african republic of ghana to a central registry wherein pedigree information of product brands belonging to participant manufacturers are stored, as well as the organisation that has emerged in the country to manage and promote this registry to organisations and firms in the health sector of ghana and africa. the latter is named the mpedigree network. in november 2008, the nigerian national agency for drug administration & control (nafdac) reported to an industry publication that its technical committee was evaluating the security credentials of the mpedigree system for a possible roll-out in that country. nafdac and the nigerian pharmaceutical companies formed a consortium in june 2009 to roll the service out for all medicines in nigeria, though this has not happened as at end of 2010. xvii. ehealth initiative this is an electronic health delivery system, launched to enable doctors reach their patients online and bring health care to the door steps of the citizenry. it has a remote doctor/patient interface, which allows a patient to see a doctor without leaving his home or office. this does not seek to prevent patients from visiting hospital but to augment existing health care delivery services. in order to assess the product one has to go online to book an appointment with a doctor on www.ehealthghana.com after which an appointment coordinator will assign doctors to patient depending on the ailment. xviii. vodaphone healthline project (http://www.ghanaweb.com/ghanahomepage/health/artikel.php?id=274377) telecommunications giant, vodafone ghana, launched a health oriented initiative dubbed “healthline”, which aims at educating and informing millions of ghanaians about pertinent health issues. the project, which takes the form of a television and radio show, embarked on a research to solicit basic health questions from ghanaians which are to be answered by medical doctors. according to vodafone ghana, the project will ultimately educate the public and demystify health related issues and practices. it also has the healthline 255, the first medical phone service in ghana powered by vodafone. healthline 255 guarantees accurate medical advice and provides expert medical advice and information to people in need of http://ojphi.org/ electronic health in ghana: current status and future prospects 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi quality health care from the convenience of their phones and has succeeded in revolutionalising access to health information and advice for ghanaians as it provides important information that would help ghanaians make the best health decisions. the healthline call centre is an extension of vodafone ghana’s award winning television programme, healthline which recently won the chartered institute of marketing ghana award for the second year in a row as the ‘best television programme of the year. xix. mahiri mobile (http://www.telmedx.com/ghana-rural-medicine.html) mahiri mobile services of accra has outfitted nurses in rural villages with wireless tablets that deliver high quality, live medical-grade video™ back to the doctors in tamale and nsawam for medical advice. a wide variety of illnesses and medical conditions are being treated that would otherwise not be cared for, ranging from skin disorders and infections to neurological conditions to maternity and pre-natal care. patients are seen at home, in remote clinics, in schools or in community gatherings by traveling nurses trained to use the new technology. this mobile video platform was developed by telmedx of san diego, california, and it allows a doctor on a web browser to examine patients over the high-resolution cameras of mobile phones and tablets for live, realtime consultations. doctors can also take high-resolution photos of patient conditions from a web browser by remotely controlling the back cameras of wireless phones and tablets. the live video and still photos appear side-by-side on a computer screen, and the photos can easily be saved into medical records by the doctors. systems were also identified and these are: 1. ghs ihost http://directbusinesssolution.org/ihost 2. health administration management system (hams) www.infotechsystemsonline.com 3. district health information system (dhis) 4. health information management system (hims) 5. hospital administration management systems discussions and conclusion this paper sought to present an overview of ehealth projects in ghana. ict offer huge opportunities that should be shared with the neediest persons. however, the temptation is to transfer technology without any considerations for local needs and obstacles specific to the place concerned. this will be a great mistake, leading to a waste of money, whatever it comes from private fund or, maybe worse, from cooperation budget. a critical mass of professional and community users of icts in health has not yet been reached in developing countries. many of the approaches being used are still at a relatively new stage of implementation, with insufficient studies to establish their relevance, applicability or cost effectiveness [15]. this makes it difficult for governments in developing countries to determine their investment priorities [15]. however, there are a number of pilot projects that have demonstrated improvement, such as a 50 percent reduction in mortality or 25 -50 percent increases in productivity within the healthcare system [16]. the key findings of this research are that there are about 22 ehealth project at various stages of implementation in ghana. some of these projects have wind up and others are still being implemented. mobile devices in use range range from pdas to simple mobile phones and smart phones. most of the projects have been donor initiated. further studies should investigate factors responsible for the success or failure of some of the projects. with the passage of the ehealth strategy document by the http://ojphi.org/ electronic health in ghana: current status and future prospects 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi ministry of health, the ehealth terrain will surely be regulated and investors will be protected. acknowledgements i thank all those who provided input for this research. conflicts of interest none references 1. oh h, rizo c, enkin m, jadad a. (2005). what is ehealth (3): a systematic review of published definitions. j med internet res 2005;7(1):e1<url: http://www.jmir.org/2005/1/e1/. accessed 10 august 2011 2. judi, hm, aa razak, n. sha‟ari and h. mohamed. 2009. feasibility and critical success factors in implementing telemedicine. inform. technol. j. 8, 326-32. http://dx.doi.org/10.3923/itj.2009.326.332 3. straub d, loch k, hill c. 2001. transfer of information technology to developing countries: a test of cultural influence modelling in the arab world. j glob inf manage. 9, 6-28. http://dx.doi.org/10.4018/jgim.2001100101 4. walji m, sagaram s, sagaram d, meric-bernstam f, johnson c, mirza nq, et al. efficacy of quality criteria to identify potentially harmful information: a cross-sectional survey of complementary and alternative medicine web sites. j med internet res 2004 jun; 29;6(2):e21. 5. kushniruk aw, patel vl. 2004. cognitive and usability engineering methods for the evaluation of clinical information systems. j biomed inform. 37, 56-76. pubmed http://dx.doi.org/10.1016/j.jbi.2004.01.003 6. stella ouma, and m. e. herselman, 2008; e-health in rural areas: case of developing countries. international journal of biological and life sciences 4:4 2 7. reichertz, p. l 2006; “hospital information systems, past, present, future,” international journal of informatics, vol.75, pp 282-299. in: stella ouma, and m. e. herselman, 2008; e-health in rural areas: case of developing countries. international journal of biological and life sciences 4:4 2 8. tan, j, 2005; e-healthcare information systems; an introduction for students and professionals. jossey bass. in: stella ouma, and m. e. herselman, 2008; e-health in rural areas: case of developing countries. international journal of biological and life sciences 4:4 2 9. mbananga, n 2006; introduction to health informatics. notoro publishers, sa. in: stella ouma, and m. e. herselman, 2008; e-health in rural areas: case of developing countries. international journal of biological and life sciences 4:4 2 10. hanson,c.w 2006; healthcare informatics. mc graw hill. in: stella ouma, and m. e. herselman, 2008; e-health in rural areas: case of developing countries. international journal of biological and life sciences 4:4 2 11. riva,g 2000; from tele-health to e-health: internet and distributed virtual reality in health care”, cyber psychology & behaviour, vol.3, no.6,2000. mary ann liebert, inc. in: stella ouma, and m. e. herselman, 2008; e-health in rural areas: case of developing countries. international journal of biological and life sciences 4:4 2 12. kwankam (2007). making ict work for health: who priority ehealth programs. med-etel 2007. luxembourg, april 18-20, 2007 http://ojphi.org/ http://dx.doi.org/10.3923/itj.2009.326.332 http://dx.doi.org/10.4018/jgim.2001100101 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15016386&dopt=abstract http://dx.doi.org/10.1016/j.jbi.2004.01.003 electronic health in ghana: current status and future prospects 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e230, 2014 ojphi 13. luk r, ho m, aoki pm. (2008). asynchronous remote medical consultation for ghana. chi 2008 proceedings · healthcare in the developing world april 5 -10, 2008 florence, italy. 14. luk r, ho m, aoki pm. “asynchronous remote medical consultation for ghana,” proc. chi 2008, acm (2008), 743-752. 15. omotosho a, emuoyibofarhe oj, adegboola o. (2006). ict in health care delivery system: a framework for developing nations. http://www.wikehealth.org/sites/default/files/whitepapers/139/ictforhealthpaper.pdf retrieved on 12/1/2011 16. chetley a. with contribution by jackie davies, bernard trude, harry mcconnell, roberto ramirez, t shields, peter drury, j kumekawa, j louw, g fereday, caroline nyamai kisia. (ed.) (2006)improving health, connecting people: the role of icts in the health sector of developing countries. http://www.infodev.org/infodevfiles/resource/infodevdocuments_84.pdf retrieved on 12/1/12 http://ojphi.org/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts novel emergency department high utilizer surveillance in new hampshire david j. swenson*1, cole zanetti2, elizabeth r. daly1 and jose t. montero1 1state of nh, concord, nh, usa; 2nh dartmouth family medicine residency, concord, nh, usa objective to develop a manageable surveillance methodology to detect emergency department (ed) patients with the highest healthcare utilization, and monitor their targeted treatment improvement and medical health cost reductions over time for overall improvements in statewide health. introduction researchers have demonstrated benefits to identifying and developing interventions for patients that frequently seek healthcare services in the ed. the new yorker magazine, recently published an article titled the hot spotters, summarizing work being done in the united states to lower medical costs by giving the neediest patients better healthcare (1). in camden, nj, physician jeffrey brenner closed his regular practice to focus on hot spotter patients (directing resources and brainpower to help their improvement) and measured a 40% reduction in hospital inpatient and ed visits and a 56% medical cost reduction for the first 36 hot spotters. a 2008 nh office of medicaid business and policy (ombp) outpatient medicaid ed frequency visit study was conducted, which cited that frequent ed users were more likely to have higher costs and rates of illness or disease than all medicaid members (2). it was noted that increased prevention and wellness could reduce frequent ed use and increase cost savings (5% of the nh medicaid population contributed to approximately 38% of ed costs). the nh division of public health services initiated a pilot project to examine nh emergency department (ed) surveillance data to identify high utilizer patients and realize improved health benefits and medical cost reductions. methods this pilot focused first on identifying 2010 ed high utilizers in one hospital. a high utilizer was defined as a patient with 9 or more ed visits (top 10%) and were identified using medical record number followed by sorting number of visits in descending order. visit diagnosis codes were identified for the top 10% of high utilizers, along with the date of admission, resident town, age, and gender. finally, the top 10% over utilizers were mapped, identifying the total number of utilzers in each town. we overlaid this information on a map of nh’s social vulnerability index to provide comparative data. plans for future expansion include: creating a gis system that trends data over 10 years; identifying ed high utilizers by location (visits by town, number of diagnosis codes per visit, date of visits, and summative data); and creating hospital and total state-wide breakdown of highest utilizers, most common state-wide diagnosis for ed high utilizers, and specific data from each hospital in relation to state-wide social vulnerability index data. results over 56,000 ed visits at one pilot hospital were analyzed targeting 2,005 high utilizer visits for 158 patients. identification by location, age, gender and icd-9-cm diagnosis coding was determined for each ed over utilizer. mapping of the high utilizers showed that 142 of the 158 patients resided in the city where the hospital was located. the most frequent diagnosis codes for the top 10% ed high utilizers included mental health, substance abuse, and chronic disease conditions (such as diabetes mellitus). conclusions this pilot project provides a manageable approach for identifying ed high utilizers for both health improvement and cost reduction surveillance efficiencies, and is a model for other surveillance management monitoring applications. potentially, 2.1 million dollars per year could be saved, if each over utilizer used the ed one less time (2). keywords surveillance; informatics; chronic disease references 1. the hot spotters, can we lower medical costs by giving the neediest patients better care? gawande a, the new yorker, medical report, january 24, 2011. 2. office of medicaid business and policy, nh comprehensive health care information system (chis). frequent outpatient emergency department use by new hampshire medicaid members (an evaluation of prevalence, diagnoses, utilization, and payments). concord; october 2008. *david j. swenson e-mail: dswenson@dhhs.state.nh.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e191, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts enhanced surveillance during the democratic national convention, charlotte, nc lana deyneka*1, amy ising2 and meichun li2 1north carolina division of public health, raleigh, nc, usa; 2university of north carolina, chapel hill, nc, usa objective to describe how the existing state syndromic surveillance system (nc detect) was enhanced to facilitate surveillance conducted at the democratic national convention in charlotte, north carolina from august 31, 2012 to september 10, 2012. introduction north carolina hosted the 2012 democratic national convention, september 3-6, 2012. the nc epidemiology and surveillance team was created to facilitate enhanced surveillance for injuries and illnesses, early detection of outbreaks during the dnc, assist local public health with epidemiologic investigations and response, and produce daily surveillance reports for internal and external stakeholders. surveillane data were collected from several data sources, including north carolina electronic disease surveillance system (nc edss), triage stations, and the north carolina disease event tracking and epidemiologic collection tool (nc detect). nc detect was created by the north carolina division of public health (nc dph) in 2004 in collaboration with the carolina center for health informatics (cchi) in the unc department of emergency medicine to address the need for early event detection and timely public health surveillance in north carolina using a variety of secondary data sources. the data from emergency departments, the carolinas poison center, the pre-hospital medical information system (premis) and selected urgent care centers were available for monitoring by authorized users during the dnc. methods within nc detect, new dashboards were created that allowed epidemiologists to monitor ed visits and calls to the poison center in the charlotte area, the greater cities readiness initiative region and the entire state for infectious disease signs and symptoms, injuries and any mention of bioterrorism agents. the dashboards also included a section to view user comments on the information presented in nc detect. data processing changes were also made to improve the timeliness of the ems data received from premis. results the dnc dashboards added to nc detect streamlined the workflow by placing all syndromes and annotations of interest in one place, with the date ranges and locations already pre-selected. graphs in the dashboards could be easily copied and pasted into situation reports. the prompt development of these user-friendly tools provided effective surveillance for this mass gathering and ensured timely control measures, if necessary. conclusions syndromic surveillance systems can be enhanced to provide detailed, specific surveillance during mass gathering events. elements that facilitate this enhancement include strong communication between skilled users and the informatics team, in order to minimize the burden placed on the surveillance team system users, data sources and system developers during the event. the visualizations developed as part of these new dashboards will be leveraged to provide additional tools to other nc detect user groups, including hospital-based public health epidemiologists and local health department users. keywords dashboards; enhanced surveillance; democratic national convention acknowledgments 1.stephanie griese, md, mph lieutenant commander, us public health service eis officer, cdc, communicable disease branch nc dph 2. aaron fleischauer, phd, msph,commander, us public health service,career epidemiology field officer (cefo),centers for disease control and prevention (cdc),communicable disease branch nc dph 3. jennifer macfarquhar, rn, mph, cic,career epidemiology field officer,centers for disease control and prevention,communicable disease branch nc dph 4. grace oguntebi, mph cdc/cste applied public health informatics fellow, communicable disease branch nc dph references 1.joseph s. lombardo, carol a. sniegoski, wayne a. loschen, matthew westercamp, michael wade, shandy dearth, and guoyan zhang public health surveillance for mass gatherings johns hopkins apl technical digest, volume 27, number 4 (2008) 2.kaiser r, coulombier d. epidemic intelligence during mass gatherings. euro surveill. 2006;11 3.ising a, li m, deyneka l, vaughan-batten h, waller a. improving syndromic surveillance for nonpower users: nc detect dashboards. emerging health threats journal 2011, 4: 11702 doi: 10.3402/ehtj.v4i0.11702 *lana deyneka e-mail: lana.deyneka@dhhs.nc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e190, 2013 current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi current approaches and trends in graduate public health informatics education in the united states: four case studies from the field diane g. schwartz1, mls, scott p. mcgrath, phd 2, karen a. monsen, phd, rn, famia, faan3,4, brian e. dixon, phd, mpa 5,6 1department of biomedical informatics, jacobs school of medicine and biomedical sciences, university at buffalo, suny*, 2providence health & services, missoula, mt, 3university of minnesota school of nursing, minneapolis, mn; institute for health informatics, university of minnesota, minneapolis, mn, 4richard m. fairbanks school of public health, indiana university, indianapolis, in, 5regenstrief institute, inc., center for biomedical informatics, indianapolis, in, 6department of veterans affairs, veterans health administration, health research and development service, indianapolis, in abstract background: public health informatics (phi) has taken on new importance in recent years as health and well-being face a number of challenges, including environmental disasters, emerging infectious diseases, such as zika, ebola and sars-cov-2, the growing impact of the influenza virus, the opioid epidemic, and social determinants of health. understanding the relationship between climate change and the health of populations adds further complexity to global health issues. objectives: to describe four examples of curricula that exist in u.s. based graduate-level public and population health informatics training programs. methods: biomedical informatics educators are challenged to provide learners with relevant, interesting, and meaningful educational experiences in working with and learning from the many data sources that comprise the domain of phi. programs at four institutions were reviewed to examine common teaching practices that stimulate learners to explore the field of public health informatics. results: four case studies represent a range of pedagogical approaches to meeting the requirements of three established accreditation/certification agencies relevant to phi education. despite their differences, each program achieved the established learning objectives along with a substantive record of student learning achievements. conclusion: the overarching goal of empowering learners to serve an active and dynamic role in enhancing preventive measures, informing policy, improving personal health behaviors, and clarifying issues such as quality, cost of care, and the social determinants of health, are essential components of phi education and training, and must receive additional consideration now and in the future by educators, policy makers, administrators, and government officials. keywords: public health informatics; health education; curriculum; population health abbreviations: phi: public health informatics current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi introduction public health informatics (phi) is defined as the application of informatics to public health research, education, and practice [1-3]. as public health informaticians we study how data and information are captured, managed, used, and shared by people and organizations in the context of population health. this includes management of information by governmental public health agencies as well as engagement of consumers in their personal health and well-being through preventative medicine. phi is increasingly important as health and wellness are challenged by environmental disasters, infectious diseases (including the sars-cov-2 pandemic), the opioid crisis, and social determinants of health. understanding the connection between climate change and the health of populations adds further complexity to global health issues. phi is increasingly relevant given changes in both the health care marketplace and the techniques used to combat the rising number of global health threats. health systems increasingly seek to automate processes by which clinical teams can manage the health of populations and motivate individuals to pursue healthy behaviors. informatics serves a critical role in identifying and reporting emerging pathogens that cause infectious diseases across state, national, and international borders [4]. several graduate-level institutions provide specializations in phi [5], while a broad range of biomedical informatics programs vary in the number of phi courses offered. the courses and focus areas seek to motivate instructors, students and future researchers to develop new tools and techniques that will facilitate creative approaches to working with and analyzing large population health data sets, and to inform public health decision-making [6]. instructors are stimulated to employ creative methods to ensure that students learn about informatics tools and techniques from a variety of perspectives so that the goals, and objectives are accomplished. it also is essential to incorporate a solid curriculum framework for instructional programs to ensure quality, creativity and a substantive scientific focus [7, 8]. ceph: council on education for public health cahiim: commission on accreditation for health informatics and information management education cib: clinical informatics board amia: american medical informatics association aco: accountable care organization *correspondence: diane g. schwartz digs@buffalo.edu doi: 10.5210/ojphi.v12i1.10703 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. mailto:digs@buffalo.edu current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi in this paper, we highlight the approaches and techniques for educating students in the field of phi at four different institutions. the review of these varied approaches identifies current trends in phi education and highlights challenges for those interested in biomedical informatics education. the paper expands on presentations delivered at the 2019 amia informatics educator’s forum. the review provides an overview of current curricula offerings at each institution, while placing the didactic programs in the context of u.s. accreditation and certification. methods the project reviewed graduate-level phi education at the institutions of the authors, four academic informaticians with a special interest in phi instruction, who teach in programs that represent the breadth of phi training. two authors teach phi in public health programs; one teaches phi in a nursing informatics program; and one teaches phi in a biomedical informatics program. together they reviewed the current state of, and best practices associated with, instruction in the domain of phi at their u.s. based training programs. the goal of the review was to identify common themes among the four examples of curricula that exist in u.s. based graduate-level public and population health informatics training programs, and discuss how instructional programs evolve as population health becomes increasingly important. we defined the desired outcomes of phi instruction at each institution, explaineded how the outcomes related to accreditation/certification (a/c) issues, and presented each institution’s training program objectives, along with the curricula goals and learning objectives for the domain of phi. goal of public health informatics the discipline of phi applies information and computer science to enhance public health processes and decision-making [9]. applications of phi seek to improve the health and well-being of the populace by enhancing preventative measures, informing policy, and personal health behavior, and clarifying issues such as quality, cost of care, and the social determinants of health [3]. the training programs offered by the four institutions represented in this study, while varying in scope, duration, and orientation, all are committed to achieving these fundamental goals [7]. current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi figure 1. goals of public health informatics accreditation guidelines and unifying themes educational training programs should be built upon mission, vision, goals and objectives that can be measured and validated to ensure that quality learning outcomes are achieved [8]. from the perspective of educational objectives this project traced its roots to three influential accrediting bodies for informatics instruction, and certification located in the u.s.: the council on education for public health (ceph) [10], the commission on accreditation for health informatics and information management education (cahiim) [11], and the clinical informatics board (cib) certification for physicians in the united states [12]. an essential component of the project was to explore both the unifying themes among these organizations, and the distinctive educational and professional goals promulgated by each association as a means of testing the validity and reliability of their specific mission, vision, goals, and objectives. the guidelines established by the three accrediting organizations serve as the backbone for the methods section: • the council on education for public health (ceph), a nationally recognized accrediting body, accredits schools of public health and stand-alone programs. ceph offers the following key benefits to students: comprehensiveness, rigor, flexibility, qualification, eligibility, to sit for the certified in public health (cph) exam, opportunities for internships and fellowships, and recognition. current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi • the commission on accreditation for health informatics and information management education (cahiim) is the globally recognized and trusted accreditation organization for training in these specialties. cahiim’s mission is to serve the public interest in advancing the value of health informatics and health information management through quality education by establishing and enforcing accreditation standards, recognizing programs that meet the standards, assessing student achievement, respecting educational innovation and diversity, recognizing academic institutions' autonomy, emphasizing the principle of volunteerism and peer-review, and embracing a culture of continuous quality improvement. • the clinical informatics board (cib) certification for physicians in the united states is based within the american medical informatics association (amia) because the organization serves as the core institution for practicing informaticians from across the spectrum of clinical informatics backgrounds and professional clinical performance. amia created a code of ethics for the discipline in 2007 to serve as the framework for the development of the core content that defines the knowledge, skills and competencies required for certification to practice clinical informatics. amia requested and received administrative board sponsorship for the board from the american board of medical specialties (abms) in 2009. key curricula goals and learning objectives ceph, cahiim, and cib provide cohesion, authority, and validity to phi programs and courses, while ensuring that students understand the role that public health informaticians serve in applying methodologies from the fields of information science, computer science, statistics, and information technology to public health [6]. population health also has become an important component of phi instruction due to the increased importance of the social determinants of health [13]. a review of the curricula being offered by four training programs revealed that the critical issues that inform contemporary public health informatics instruction are: standardization and interoperability of population data, improving the health and well-being of populations, enhancing preventive measures, informing policy and personal health behavior, and clarifying basic issues such as quality, and the cost of care. each of these issues align with the curricula goals and learning objectives disseminated by ceph, cahiim, and cib. in order to evaluate the current presence of introductory public health informatics courses beyond the four targeted phi programs, we conducted a non-exhaustive search of biomedical informatics focused programs in the united states. the intent was to find a sufficient number of programs to present a well-rounded sample of schools offering at least a master’s degree in either biomedical, health, clinical, or public health informatics. initially, the search investigated institutions that were registered with amia to advertise their informatics programs. additional programs were added through internet search queries. the approach yielded 98 different accredited schools, with 21.4% (n = 21) offering an introductory course in public or population health informatics. inclusion criteria required that the class was focused principally on phi, and not a survey course of general health or biomedical informatics. current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi the search focused on identifying only introductory phi classes, while excluding advanced public health informatics courses. the objective was to provide a better picture of the prevalence of introductory phi classes, and not skew the results due to one program’s investment in additional advanced phi courses. once the courses were identified, additional details were captured to examine whether the classes were offered as elective or required for the degree. we noted if the class was offered in an online only format (online), in both an on campus and online offering (campus/online), or only as an on-campus option (in-person). figure 2 depicts the findings from this query. of the 22 programs, the majority (86%, n = 19) offered their phi class as a degree requirement; only 3 (14%) listed their class as an elective. distribution of class offerings was even with 7 courses offered in the oncampus and online option, 6 courses were offered on-campus only, and 9 courses were listed as online only which implies a trend toward offering more phi classes online. figure 2. mapping the identified phi classes according to their assignment in each school’s curriculum (as a core course, or an elective) and how the class was offered (in-person, online, or a combination of the two). results here we summarize common themes among phi curricula at four u.s. institutions of higher education. the diversity of available training programs is described, and a commitment to current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi achieving the same overall learning objectives, that is, to train individuals who are capable of improving the health and well-being of populations, was observed [3]. descriptions of the curricula the university at buffalo’s, suny, department of biomedical informatics is a basic science department in the jacobs school of medicine and biomedical sciences. one phi course is offered clinical population research and public health informatics. the course’s learning objectives seek to create a distinctive educational experience by working with domain experts from diverse professional backgrounds who possess broad, in depth knowledge of the field, and a willingness to share their expertise. the opioid crisis served as the backdrop for guest lecturers including the county executive, the county commissioner of health, and staff of a regional substance use disorder, rehabilitation, and mental health service provider. each presenter shared their intimate knowledge of the crisis by providing detailed data regarding the shifting dynamics of the crisis, and how their agency collected, analyzed, and interpreted the data being amassed. other speakers addressed the role of epidemiology, local environmental disasters, climate change, infectious diseases, such as the ebola, and as of 2020 the sars-cov-2 virus pandemic, consumer health informatics, injury, violence, bioterrorism, population health data sources and the contributions made by clinical laboratory medicine/pathology in using large-scale data to manage information about diseases. students also received literature search training using medline, and endnote. the assignment for the course was to prepare a systematic review on a public or population health informatics topic. the paper prepared by two teams of students was subsequently published. the university of minnesota’s school of nursing doctor of nursing practice (dnp) nursing informatics specialty offers a population health informatics course with a simulated accountable care organization (aco) exercise which was incorporated into the curriculum to teach students the use of informatics tools to achieve standardization, interoperability, and data-based decision making in the context of population health. included in the instructional platform are two working pophrs which uses heterogeneous data from a geographic public health perspective to connect health determinants with the health of populations. pophrs support population health management and public health principles to enable learners to understand whole-person health in the context of all available data for individuals and populations. the pophr automates extraction, harmonization, linking, and integration of all available data for persons and populations to support population health measurement and monitoring with the goal of improving decision making [14]. hands-on data experiences accelerate the student’s appreciation of major challenges in standardization and interoperability that are essential for the successful advancement of phi. because nursing programs require clinical practicum hours for each specialty, students must complete 1,000 practicum hours to graduate. to achieve this requirement a population health informatics practicum course was created to meet 240 of the required hours. the course, which is offered completely on-line in a real practice site, enables students to work together to address a current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi critical population health informatics problem. the sites have included local and state public health departments and non-governmental entities, including a health services innovation company. as a result of these experiences students demonstrated improved knowledge and skill in population health data, interoperability, and measurement across settings and populations. the over-arching learning objective focused on teaching data-based decision making to support a broad understanding of the role and importance of population health in the context of nursing education and practice. the project established the importance of information technology’s (it) role in enabling improved decision making and reducing costs [15]. indiana university’s richard m. fairbanks school of public health offers a foundations of public health informatics course to graduate mph (masters of public health) students as well as a specialized phi concentration in the mph program. the foundational course provides core instructional modules that focus on epidemiology, healthcare policy and management, informatics, and data science. the course uses a combination of didactic instruction on core informatics theory with practical, real-world exercises that require students to practice informatics skills. one assignment asks students to use data from a public data source (e.g., census data on education levels) with aggregate disease counts from electronic health records to analyze risk factors, including social determinants. students gain hands-on experience with a geographic information system as well as an epidemiology information system in the process of capturing, managing, and analyzing their data. the mph degree with a concentration in phi provides the groundwork for engineering data and information systems within health systems, as well as governmental and non-profit public health organizations to support the collection, storage, management, analysis, application and sharing of information to improve population health outcomes. students learn to think critically about population level data and apply informatics approaches to address significant public health issues [16]. five phi courses are offered: fundamentals of data management, foundations in public health informatics, healthcare information systems, information exchange for population health, and population and public health. students also take courses from the school of informatics and computing on the university of indiana campus, enabling them to further their understanding of methods and techniques from the broader field of biomedical informatics as a component of their training program. informatics courses are complemented by core public health courses as well as an internship in a public health organization or health system. capstone projects challenge students to develop, implement, or analyze an information system for a public health organization or health system. the university of omaha’s school of health and kinesiology offers a public health undergraduate degree, which has a graduation requirement of completing a phi course. this brought the discussion full-circle by introducing the importance of standards and accreditation. thereby ensuring that educational programs are based on quality indicators that will develop students who are capable of delivering excellence in practice, research and service. from a methodological perspective quality education for public health professionals is based on the competencies established by ceph. eight competencies define the essential expectations of a public health informatics professional: (1) evidence-based approaches; (2) knowledge and understanding of public health and health care systems; (3) planning and management to promote health; (4) policy; (5) leadership; (6) communication; (7) inter-professional practice, and (8) current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi systems thinking [10]. a phi professional is expected to be capable of selecting, analyzing and interpreting, quantitative and qualitative data for conducting public health research, developing policies, evaluating outcomes, and engaging in sound professional practice. a variety of ideas, strategies and techniques are provided for conducting dynamic, interesting and educationally sound online courses, such as, 5-10-minute mini-lectures, embedding questions or recaps between the lectures, using the learning system’s discussion board to help make the content relevant, and incorporating ted talks, youtube videos, case studies, and system demonstrations. discussion we examined trends in phi education across u.s. institutions of higher education offering instruction in biomedical informatics. one-fifth (21%) of institutions with a biomedical informatics program provide at least one course in phi. the majority of these courses are required for degree completion, even if the specialty or concentration is not focused on phi. in addition, we conducted an in-depth review of four representative programs that summarize the breadth of educational offerings in biomedical informatics training. all four programs train their students on how informatics can be leveraged by health systems to enhance preventive measures, inform policy, improve personal health behaviors, and clarify issues such as quality, cost of care, and the social determinants of health, which we conclude are essential components of phi training [3]. our review also reinforced the principle that phi is evolving towards population health across governmental public health agencies and health delivery systems, highlighting that instructional programs must keep pace with the changing dynamics in the discipline while maintaining a commitment to meet accreditation standards and offer students instruction that meets market expectations and needs, all while stimulating learners to pursue creative, new scholarly challenges [6,7]. educational strategies to support and enhance instruction in phi benefit from the advancement of instructional technology, which led to a variety of new modalities and contributed to a reengineered perception of the field. incorporating a case-based curriculum exposed students to a diverse set of experts, resulting in an improved understanding of the potential contributions to the health of the public that informatics is capable of making. the use of simulation technology fostered a new appreciation of the value of informatics tools in achieving standardization, and interoperability, while hands-on data experiences for students accelerated their perception of the major challenges that exist in attempting to achieve standardization and interoperability. despite the difficulties, these learning modules reinforced how essential both elements are for the successful advancement of phi beyond the classroom. the incorporation of inter-professional hands-on population health informatics simulation training in which students generate, share, and analyze clinical data within a fictitious aco using a shared data model, further reinforced the value of incorporating state of the art practices into the curriculum. whether phi training is offered within an informatics program or as part of a public health program, the key goal is to use systems and methods from information science, statistics and computer science, and apply these techniques to the practice of public health informatics. the objective is to provide a foundation for engineering data and information at the population level, and emphasizing within health systems, whether academic, governmental, or commercial, the need to learn how to collect, store, manage, analyze, apply, and share information to improve population health outcomes [17]. this is one of the most important skill sets to be achieved by learners. public current approaches and trends in graduate public health informatics education in the united states: four case studies from the field 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e7, 2020 ojphi health programs remain challenged to instill an interest in developing informatics skills among their students at all levels of training. of preeminent importance to this discussion is the need to achieve standards that will meet the accreditation requirements and competencies established by ceph, cahiim, and cib for various public health and informatics programs, and to cultivate students who are capable of delivering excellence in practice, research, education, and service. conclusions while challenges continue to face public health informatics educators, we remain vigilant in our goal to overcome the obstacles to train the next generation of phi professionals to access, use, analyze, apply and share data to improve the health of the public [17]. we are further committed to educating the public on the value and contributions of informatics methods and strategies to strengthen public 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2the altarum institute, atlanta, ga, usa; 3centers for disease control and prevention, atlanta, ga, usa objective to assess the utility of inpatient and ambulatory clinical data compiled by public and commercial sources to enhance the centers for disease control and prevention’s surveillance activities. introduction medical claims and ehr data sources offer the potential to ascertain disease and health risk behavior prevalence and incidence, evaluate the use of clinical services, and monitor changes related to public health interventions. passage of the hitech act of 2009 supports the availability of standardized ehr data for use by public health officials to obtain actionable information. while full adoption of ehrs is still years away, there are presently publiclyand commerciallyavailable ehr and medical claims data sets that could enhance public health surveillance at a national, regional and state level. the purposes of this evaluation were to i.) demonstrate the feasibility of gaining access to such data, ii.) evaluate their ability to augment current surveillance activities by developing measures for twenty separate healthcare indicators (e.g., hiv screening), iii.) evaluate each data source across a set of criteria needed for an effective surveillance system, and iv.) assess the ability of the data sources to evaluate changes in healthcare utilization and preventive services that may be a result of the 2009 health reform legislation. methods ten separate data sources were selected for inclusion in the study based on a number of criteria, including availability, representativeness, population, data structure and content, cost, and longitudinality. in collaboration with staff from seven divisions across the cdc, detailed specifications were developed for twenty separate indicators of healthcare utilization or preventive services using best practices in healthcare quality measurement. specifications were developed separately for ehr and medical claims data due to their differing structure, content and use of medical code sets and terminologies. specifications for ehr data sources relied on the national quality forum (nqf) meaningful use (muse) clinical quality measure specifications. the use of nqf muse specification guidelines allowed us to gauge the current ability of each data source to measure healthcare utilization and preventive services as recommended by nqf, the national leader in healthcare measurement. each of the data sources was also evaluated across established public health surveillance criteria, including data quality, representativeness, and flexibility, among others. data analysis was performed using sas 9.3 (sas institute, cary, nc). results all twenty of the healthcare indicators were developed for at least one data source; however, many of the indicator specifications had to be modified due to the low frequency of certain code sets (e.g., cpt4 ii, loinc). the observed strengths of medical claims data were the relatively low cost, ability to track patients longitudinally, and the standardized representation of procedures and diagnoses through use of medical codes, such as icd-9-cm, cpt-4 and hcpcs. the observed strengths of ehr data sources were the availability of information related to health behavior (e.g., current smoker), health assessment (e.g., bmi), prognostic indicators (e.g., vital signs, laboratory result), diagnostic testing, and functional status. while ehr data also capture diagnoses using icd-9-cm, procedures such as medical and laboratory procedures remain documented through use of free text or semi-structured text fields, making it difficult to process. conclusions currently available healthcare data can improve the timeliness of health outcome monitoring and add complementary information on healthcare utilization to improve our interpretation of traditional public health surveillance data. medical claims data support measurement of health outcomes and healthcare services provided to patient populations; however, without clinical encounter information, they cannot develop measures estimating the impact of services received on quality of care. ehr data have richer clinical information; however, the continued use of non-standards-based medical codes and free and semi-structured text fields make it difficult to analyze data at scale. meaningful use and other hitech initiatives are changing this by incentivizing the standardization and aggregation of electronic healthcare data. in time, these data may yield timely, accurate and actionable information for public health surveillance. keywords surveillance; evaluation; healthcare; electronic health record *joseph r. egger e-mail: joseph.egger@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e30, 2013 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts epidemiology of visceral leishmaniasis in georgia nora kokaia*1, nino iashvili1, manana murusidze1 and giorgi babuadze2 1s. virsaladze research institute of medical parasitology and tropical medicine in georgia, tbilisi, georgia; 2national center for disease control and public health, tbilisi, georgia objective the purpose of this study was to describe the epidemiology of visceral leishmaniasis in georgia and to define new control measures. introduction visceral leishmaniasis (vl) is a zoonotic, protozoal infection that is endemic in georgia, which commonly affects young children. in recent years, the incidence of vl has increased sharply and the geographic distribution has increased. recently, vl moved to highly populated areas as new foci appeared from 2010-2015, during which, 610 laboratory confirmed cases of vl were registered in georgia. the majority of cases were found in east georgia (94.2%) and 5.8% of cases in west georgia (representing new foci of vl in georgia). methods blood samples from 2,100 individuals suspected to have vl were tested using the rk39 based vl rapid diagnostic test, an enzyme-linked immunosorbent assay (elisa). also, 1,575 randomly selected dogs (stray and pet) and 77 wild canids were tested for vl using the same elisa. confirmed human cases were followed up for 9-12 months. results the most affected age group was 0-5 years (72.2%). of the patients, 13.9% were hiv positive and lethal outcomes were observed in 2.1%of patients. mortality was associated with delayed diagnosis and hiv co-infection. relapse developed in 6.4% of cases. among hiv positive patients, secondary prophylaxis was conducted with liposomal amphotericin b, which decreased the number of relapses by 76% in 12-24 month follow-ups. a high incidence of vl in humans was associated with a high prevalence of leishmaniasis in stray and domestic dogs. leishmania antibodies were found in 23.7% of stray and domestic dogs and 2.6% of wild animals screened in tbilisi. conclusions overall, the vl situation in georgia is concerning and new control measures are needed. our study revealed a high prevalence of vl in humans and dogs in east georgia. early and accurate diagnosis/ treatment and effective control measures should be conducted regularly to prevent the spread of vl in georgia. in addition, secondary prophylaxis in hiv infected patients is also recommended. keywords epidemiology; zoonotic; visceral leishmaniasis; one health acknowledgments participation in this conference was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed. *nora kokaia e-mail: irma.kokaia@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e152, 2017 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts comparing prescription sales, google trends and cdc data as flu activity indicators avinash patwardhan* and david lorber clinical affairs, walgreen co., deerfield, il, usa objective to examine if the prescription sales data from a large retail pharmacy chain in the us were comparable to google flu trends and cdc’s us ili network data as flu activity indicator. introduction in a 2007 survey of public health officials in the united states, international society for disease surveillance found that only 7% used pharmacy prescription sales data for surveillance (1). there have been many reports suggesting effective use of prescription sales data in syndromic surveillance (2, 3, 4, 5). community pharmacies can provide a valuable supplementary tool for syndromic surveillance of infectious diseases. methods we extracted five years of de-identified prescription sales data from the proprietary pharmacy computer system of a large retail pharmacy chain in the united states. the prescriptions were written for the common drugs for the treatment of influenza: amantadine, oseltamivir, rimantadine, and zanamivir. we acquired google flu trends national aggregate counts data that represented the estimates of the ili cases per 100,000 physician visits. we acquired cdc ilinet data for 2007. we calculated pearson ‘r’ between our data and google and cdc data. we also created comparable trends graphs after converting the counts of the influenza scripts and the counts of the google estimated ili cases to logarithmic scale. results the pearson ‘r’ between the aggregate counts of scripts for all the four drugs and the google estimates of the ili cases for years 2007, 2008, 2009, 2010, and 2011 were 0.85 (95% ci, 0.75-0.91), 0.92 (95% ci, 0.86-0.95), 0.91(95% ci, 0.85-0.95), 0.88 (95% ci, 0.800.93), and 0.87 (95% ci, 0.78-0.92) and 0.92 (95% ci, 0.90-0.94) for years 2007 through 2011 together. the pearson ‘r’ between the aggregate counts of scripts and the cdc % unweighted ili (2007) was 0.97 (95% ci, 0.95-0.98). conclusions a strong to very strong correlation between prescription sales data and google flu trends and cdc’s ili activities data for influenza suggests that the former can serve as a good and valid influenza activity indicator. community pharmacies with large footprints might want to proactively build automated syndromic surveillance systems with near real time reporting capabilities and explore the possibility of building predictive models (3) and of forming partnerships with public health agencies for syndromic surveillance. pearson ’r’ between prescription sales data and google flu trends and cdc ili data keywords public health; syndromic surveillance; flu; prescription sales; activity or early warning indicator acknowledgments we wish to acknowledge criselda valencia (lead analyst support & deployment data warehouse at walgreen co.), erma villarreal (it manager support & deployment data warehouse at walgreen co.), and zhongwen huang (senior analyst outcomes & analytics at walgreen co.) for pulling data for us. references 1. the international society for disease surveillance (isds) available: http://www.syndromic.org/ accessed may 2, 2012. 2. chadwick d (2009) the rhode island department of health. the first statewide system for tracking disease using prescription data. press release archives. state of rhode island. department of health. available: http://www.health.ri.gov/news/archives/2009/ published october 27, 2009. accessed may 2, 2012. 3. sugawara t, ohkusa y, ibuka y, kawanohara h, taniguchi k, okabe n (2012) real-time prescription surveillance and its application to monitoring seasonal influenza activity in japan j med internet res. 14(1):e14. 4. vergu e, grais rf, sarter h, et al. (2006) medication sales and syndromic surveillance, france. emerging infectious diseases. 12(3):416. 5. van den wijngaard c, van pelt w, nagelkerke n, kretzschmar m, koopmans m (2011) evaluation of syndromic surveillance in the netherlands: its added value and recommendations for implementation. euro surveill. 16(9):19806. *avinash patwardhan e-mail: avinash.patwardhan@walgreens.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e43, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts improvement control system of rabies in ukraine serhiy nychyk*1, ostap zhukorskiy2, ivan polupan1, mykola ivanov1 and alina nikitova1 1institute of veterinary medicine, kyiv, ukraine; 2national academy of agrarian sciences of ukraine, kyiv, ukraine objective the purpose of the research was to find out the reasons of rabies antropurgisation in ukraine. introduction in ukraine in spite of considerable financial expenses on oral immunization of foxes and parenteral immunization of dogs and cats, it is not succeeded to reach considerable results in the fight with rabies. unfortunately there was a negative tendency to increasing a part of dogs and cats in the structure of rabies disease which are the main source of rabies in people. methods analysis of 228 anamnesis data of rabies infected dogs during 2008-2012. research of 234 samples of the blood serum from dogs on existence of antibodies to the rabies virus by the elisa method. results analysis of animal morbidity on rabies in ukraine in period of 2006-2011 found out the changes of structure of morbidity in animal species that means decreasing a part of wild animals (from 49,0 % in 2006 to 38,7 % in 2011) and increasing a part of dogs (from 18,3 % in 2006 to 23,2 % in 2011) and cats (from 19,8 % in 2006 to 25,0 % in 2011) in the general amount of animals which perished from rabies (fig. 1). a lot of ukrainian scientists and doctors of veterinary medicine consider that the main reason of spreading the rabies is a great number of homeless animals which factually are the reservoirs of infection in towns and villages. however, in our opinion spreading of rabies shows the insufficient level of measures of control of rabies among home animals. it was confirmed with conducted analysis that only 26 (12,9 %) dogs were stray, others 202 (87,1 %) had owners, but didn’t get necessary protective rabies vaccination. according to ukrainian instruction “preventive measures against rabies of animals”, all the dogs must be vaccinated against rabies, but it actually appears it is quite not so. at research of 234 samples of the blood serum of dogs on existence of antibodies to the rabies virus it was determined that the level of population immunity in dogs is 36,6 % in ukrainian towns, but the protective level of antibodies was found in 9,1 % of village dogs. we consider that the main reason of this is imperfection of the ukrainian legislation in the questions of responsibility of proprietors of animals. for breaking of epizootic chain it is necessary the percent of vaccinated animals (in this case dogs) to be on a high level – more than 50%. nowadays for reaching epidemic welfare in rabies it is important within the framework of the registered epidemic incidents dogs to be brought over to the ecocycles of infection sporadically without taking part in circulation of exciter, stay their biological deadlock and have low epidemic potential. conclusions the conducted analysis expressly demonstrates that at present problems the eradication of rabies in ukraine is impossible considering the low level of dogs’ protection from rabies. obtained results were sent to the state committee of veterinary medicine of ukraine and will be the argument for enhancement of control after conducting rabies vaccination of dogs. rabies cases in the years 2006-2011. keywords population rabies immunity; rabies vaccination; stray dogs *serhiy nychyk e-mail: vetmedic@ukr.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e155, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts processing of novel electronic health data to support public health surveillance peter hicks*, henry rolka, mark wooster and lynette brammer cdc, atlanta, ga, usa objective to describe data management and analytic processes undertaken to rapidly acquire and use previously unavailable data during a public health emergency response. introduction accurately gauging the health status of a population during an event of public health significance (e.g. hurricanes, h1n1 2009 pandemic) in support of emergency response and situation awareness efforts can be a challenge for established public health surveillance systems in terms of geographic and population coverage as well as the appropriateness of health indicators. the demand for timely, accurate, and event-specific data can require the rapid development of new data assets to “fill-in” existing information gaps to better characterize the scope, scale, magnitude, and population health impact of a given event within a very narrow time-window. such new data assets may be concurrently under development and evaluation while being used to support response efforts. recent examples include the “drop-in” surveillance processes deployed at evacuation centers following hurricane katrina1 and the illness and injury surveillance systems established for response workers during the deepwater horizon oil spill response. during the 2009 h1n1 pandemic response, cdc acquired access to data from several national-level health information systems that previously had been un-vetted as public health information sources. these sources provided data extracts from massive administrative or electronic medical records (emr) based in hospital and primary care settings. it was hoped that such data could supplement existing influenza surveillance systems and aid in the characterization of the pandemic. few of these new data sources had formal documentation or concise information on the underlying populations and geographies represented. methods throughout cdc’s h1n1 response; epidemiologists, data managers, and it specialists collaborated to develop standardized methods to rapidly characterize, process, store, and provision these new data for analysis and reporting by subject matter experts.these new data were not part of a formally designed sample so each data source needed to undergo extensive empirical review to understand, representativeness, unique nuances, and facilitate the interpretation of analytic results and accurate reporting to public health decision makers. results such work requires a multi-disciplinary approach that cyclically reviews incoming data iteratively while concurrently documenting findings, modifying initial business rules (e.g. extraction, binning, or coding logic), and analytic techniques to produce the most interpretable and informative results. to elucidate the underlying complexity for these sequential and contingent activities occurring across information technology, informatics, and epidemiology domains, we retrospectively described the intersection of the discrete tangible tasks and workforce roles via a taskflow diagram (figure 1). vertical “swim lanes” represent discrete tasks: on-boarding/documentation, analysis/visualization, and visualization/reporting. workforce roles such as data management, epidemiological analysis, and communications are broken into three horizontal “swim lanes” as each requires dramatically different skillsets and are accomplished by different individuals. each of the steps (1-9) in the diagram were leveraged to produce supplemental artifacts (e.g. code books, extraction guides, defined analytic methods, etc.) to support ongoing analysis, interpretation, reporting, and over process improvement. the totality of all of these interrelated activities have an a priori purpose of characterizing population health during an event of public health significance to support disease prevention and control efforts in a timely fashion. conclusions this presentation describes the underlying business processes, activities, and roles used in transforming novel data sources, during the h1n1 response, into informative assets to support public health surveillance. by formally articulating and describing each of these steps, in a structured manner, we hope to contribute to the dialogue of developing useful practices for leveraging electronic health data to meet public health surveillance challenges. keywords informatics; surveillance; emergency response; h1n1; data management *peter hicks e-mail: phicks@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e31, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts increasing mild enterovirus cases provides an important signal of up-coming trends in elevating severe enterovirus cases ta-chien chan*1, rung-hung chen2 and chwan-chuen king3 1center for gis, research center for humanities and social sciences, academia sinica, taipei city, taiwan; 2department of medicine, college of medicine, national taiwan university, taipei city, taiwan; 3institute of epidemiology and preventive medicine, college of public health, national taiwan university, taipei city, taiwan objective this study was to elucidate the spatio-temporal correlations between the mild and severe enterovirus cases through integrating enterovirus-related three surveillance systems in taiwan. with these fully understanding epidemiological characteristics, hopefully, we can develop better measures and indicators from mild cases to provide early warning signals and thus minimizing subsequent numbers of severe cases. introduction in july 2012, the 54 children infected with enterovirus-71(ev71) were died in cambodia [1]. the media called it as mystery illness and made asian parents worried. in fact, the severe epidemics of enterovirus occurred frequently in asia, including malaysia, singapore, taiwan and china [2]. the clinical severity varied from asymptomatic to mild (hand-foot-mouth disease and herpangina) and severe pulmonary edema/hemorrhage and encephalitis [3]. up to now, the development of vaccine for ev-71 and the more effective antiviral drug was still ongoing [4]. therefore, surveillance for monitoring the enterovirus activity and understanding the epidemiological characteristics between mild and severe enterovirus cases was crucial. methods three main databases including national notifiable diseases surveillance, sentinel physician surveillance and laboratory surveillance from july 1, 1999 to december 31, 2008 were analyzed. the pearson’s correlation coefficient was applied for measuring the consistency of the trend. the poisson space-time scan statistic [5] was used for identifying the most likely clusters. we used gis (arcmap, version9.0; esri inc.,redlands, ca, usa) for visualization of detected clusters. results temporal analysis found that the pearson’s correlation between mild ev cases and severe ev cases occurring in the same week was 0.553 (p<0.01) in figure 1. such a correlation became moderate (data) when mild ev cases happened in 1~4 weeks before the current severe ev cases. among the 1,517 severe ev cases notified to taiwan cdc during the study period, the mean age was 27 months, 61.4% was male and 12% were fatal. these severe ev cases were significantly associated with the positive isolation rate of ev-71, with much higher correlation than the mild cases [ 0.498 p<0.01 vs. 0.278, p<0.01] . using the space-time cluster method, we identified three possible clusters in june 2008 in six cities/counties (figure 2). conclusions taiwan’s surveillance data indicate that local public health professionals can monitor the trends in the numbers of mild ev cases in community to provide early warning signals for local residents to prevent the severity of future waves. figure 1. the temporal trend between mild and severe ev cases. figure 2. the spatio-temporal clusters of ev severe cases. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e108, 2013 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts keywords enterovirus; suveillance; space time clusters references 1. who, severe complications of hand, foot and mouth disease (hfmd) caused by ev-71 in cambodia – conclusion of the joint investigation[http://www.who.int/csr/don/2012_07_13/en/index.html] 2. lee ms, chiang ps, luo st, huang ml, liou gy, tsao kc, lin ty: incidence rates of enterovirus 71 infections in young children during a nationwide epidemic in taiwan, 2008-09. plos negl trop dis 2012, 6(2):e1476. 3. chen sc, chang hl, yan tr, cheng yt, chen kt: an eight-year study of epidemiologic features of enterovirus 71 infection in taiwan. am j trop med hyg 2007, 77(1):188-191. 4. chang ly: enterovirus 71 in taiwan. pediatr neonatol 2008, 49(4):103-112. 5. kulldorff m, athas wf, feurer ej, miller ba, key cr: evaluating cluster alarms: a space-time scan statistic and brain cancer in los alamos, new mexico. am j public health 1998, 88(9):1377-1380. *ta-chien chan e-mail: dachianpig@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e108, 2013 a focus area maturity model for a statewide master person index 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi a focus area maturity model for a statewide master person index jeffrey duncan 1,3 , wu xu 3 , scott p. narus 1,4 , stephen clyde 2 , barry nangle 3 , sid thornton 4 , julio facelli 1,5 1 department of biomedical informatics, university of utah, salt lake city, ut usa, 2 department of computer science, utah state university, logan, ut usa, 3 utah department of health, salt lake city, ut usa, 4 intermountain healthcare, salt lake city, ut usa, 5 center for high performance computing, university of utah, salt lake city, ut usa objective this paper describes efforts in utah to create a statewide master person index (smpi) to uniquely identify each individual in the state receiving healthcare or public health services to support healthcare and public health operations and research. the smpi is a collaborative effort abstract objective: the sharing of personally identifiable information across organizational boundaries to facilitate patient identification in utah presents significant policy challenges. our objective was to create a focus area maturity model to describe and evaluate our progress in developing a policy framework to support a statewide master person index (smpi) for healthcare and public health operations and research in utah. materials and methods: we used various artifacts, including minutes from policy guidance committee meetings over a span of 18 months, a report from utah’s digital health services commission, and a draft technical requirements document to retrospectively analyze our work and create a focus area maturity model describing the domain of policy needed to support the smpi. we then used our model to assess our progress and future goals. conclusions: the focus area maturity model provides an orderly path that can guide the complex process of developing a functional statewide master person index among diverse, autonomous partners. while this paper focuses on our experience in utah, we believe that the arguments for using a focus area maturity model to guide the development of state or regional mpis is of general interest. keywords (mesh): medical record linkage; systems interoperability; organizational policy; confidentiality correspondence: jeff.duncan@utah.edu copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. a focus area maturity model for a statewide master person index 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi between diverse partners with different missions, goals, and stakeholders, including the university of utah (uu), utah department of health (udoh), intermountain healthcare and the utah health information network (uhin). a description of these organizations is given below. the sharing of personally identifiable information required to create a smpi across organizational boundaries presents significant policy challenges and requires new models for data sharing. (1, 2) we present a focus area maturity model(3), developed retrospectively, as a means of documenting, organizing, and understanding our work on the smpi. our maturity model can serve to guide discussions, identify challenges, establish priorities, and assess progress in this and other cross-enterprise information sharing initiatives. background and significance the need to exchange patient-specific clinical data among autonomous healthcare entities to improve care has been the driving force behind health information exchange (hie).(4) patientspecific data exchanges are also important for treatment (5) and public health purposes(6) and are increasingly vital for biomedical research. notable examples include comparative effectiveness and translational research (7) , the shared health research information network (shrine)(8), the cancer biomedical informatics grid (cabig)(9), and the federated utah research and translational health erepository (further).(10) emerging infrastructures like these, critically depend on the ability to correctly link patients and their records across heterogeneous electronic systems operated under different administrative domains. the goal of the smpi project, initially funded by an award from the nih national library of medicine, is to create a cross-enterprise resource to uniquely identify all individuals receiving health care or public health services in utah to support clinical care, clinical and translational research, and public health practice. subsequent to the nih grant award a multi-disciplinary policy governance committee (pgc) was created to begin identifying and documenting high level policy and governance issues inherent in this effort. the pgc met 24 times in a span of 18 months between november, 2009 and april, 2011. meeting agendas focused on a broad spectrum of topics including legal authority, trust, consent, governance, security, confidentiality, and sustainability. these meetings produced several artifacts, including meeting minutes and a draft technical requirements document. at the same time, to secure public policy support, the smpi project was presented to utah’s digital health service commission (dhsc), a statutory advisory board that was created by the utah legislature in 2000 to advise and make recommendations to udoh and state executive and legislative leaders on matters related to digital health. its members, appointed by the governor and confirmed by the state senate, consist of stakeholders in digital health, including physicians, non-physician providers, health insurance agencies, healthcare institutions and members of the public. a subcommittee of the dhsc conducted public meetings for 15 months and developed a comprehensive set of policy recommendations regarding the smpi. in august 2011, the dhsc submitted a document titled, “recommendations for establishing a statewide secure patient directory”, to udoh. (this document is included in supplementary materials.) the pgc included diverse professional perspectives including attorneys, healthcare operations managers, privacy and security risk managers, health system representatives, institutional review board representatives, researchers, public policy experts, consumers and record linkage experts. attendees were not compensated for their participation in this effort. they came from a focus area maturity model for a statewide master person index 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the university of utah, udoh, uhin, intermountain healthcare, the utah population database, utah telehealth network, a third-party provider organization, and consumer advocates, but they did not officially represent their institutions. following is a brief description of the main partner organizations involved in the smpi project. udoh, as utah’s public health agency, has numerous systems that receive identified patientspecific data from healthcare entities. these systems, which are not integrated, include vital records (births and deaths), hospital inpatient, outpatient and emergency department encounters, newborn hearing screening, metabolic screening, and the statewide immunization registry. intermountain healthcare is a not-for-profit, integrated healthcare delivery system based in salt lake city, utah. intermountain provides healthcare throughout the state and covers over half of the healthcare market in utah, with 22 inpatient facilities, including 12 rural hospitals, and over 200 ambulatory clinics. intermountain has significant experience maintaining an electronic medical record (emr) and enterprise mpi (empi). university of utah health care (uuhc) is an academic, integrated healthcare delivery system. uuhc, associated with the medical school of the university of utah, has four hospitals, 10 community clinics, and several specialty clinics throughout the salt lake valley and adjoining metropolitan area. uuhc has a mature emr system. the utah health information network (uhin) is a coalition of utah healthcare insurers, providers, the utah department of health, and other interested parties that have collaborated to build a statewide network for electronic data interchange. uhin is a not-for-profit network that currently serves all the hospitals, ambulatory surgery centers, national laboratories, and approximately 90% of the medical providers in utah. uhin is also a state-designated health information exchange (hie) entity to implement the clinical health information exchange (chie) for the state of utah. as the name of the pgc indicates, its purpose and charter was not to authorize or approve policy, but rather to explore and formalize governance and policy issues as they related to the smpi. figure 1 is an illustration of the smpi. a focus area maturity model for a statewide master person index 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 1. utah statewide master person index methods we created a focus area maturity model for the smpi using the following artifacts to inform our work:  meeting minutes from 24 pgc meetings between november, 2009 and april, 2011  smpi draft technical requirements document  dhsc recommendations (included in supplementary materials) maturity models are conceptual frameworks that have been used to describe levels of progress in many different domains.(11, 12) these models provide a framework with which to understand, assess and improve processes or products. a typical model describes maturity as a series of plateaus, each of which represents a significant advancement in system or product capability. such models have been criticized as being too simple to represent the complexity of modern systems,(13) but focus area maturity models (famm) overcome this limitation by describing levels of capability in multiple distinct, interrelated focus areas. therefore the resulting model can be used to assess incremental advancements in system maturity. (3) here we used a modified version of the method identified by van steenbergen et al., to create our famm, which proceeds as follows: 1) identify the scope and functional domain; 2) identify focus areas; 3) determine capabilities; 4) determine dependencies; 5) create matrix. (3) once the maturity model was created we used it to assess the progress of our efforts toward building the smpi. a focus area maturity model for a statewide master person index 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi step 1—domain and scope of model our model’s functional domain is the policy framework needed to support information exchange across organizational boundaries to support the smpi for both healthcare operations and research. the field of information policy defines the policy domain to include laws, regulations, and practices involving information creation, processing, flows, access, and use. (14) thus, the functional domain and scope of our maturity model is the set of laws, regulations and practices involving the creation, processing, flow, access and use of information in the smpi. step 2—identify focus areas within a functional domain, focus areas may be thought of as groups of activities, events, deliverables or other products that are closely related by a single purpose in support of the overall domain. policy issues involved with real-time information exchange in support of biomedical research were studied by researchers associated with the cancer biomedical informatics grid (cabig). in an effort to understand the regulatory challenges posed by the realtime federation of clinical data for cabig, researchers interviewed decision-makers from participating research centers and identified several policy concerns grouped around a number of broad themes, including governance, legal and financial, technical infrastructure, trust, auditing, risk management, and patient consent.(15) we have chosen these themes as initial focus areas for our model. we then analyzed pgc meeting minutes and the dhsc report to determine the completeness of our focus areas. we reviewed the minutes from the pgc meetings and documented answers to the following questions: 1. which of our proposed focus areas were addressed in the meeting described by the minutes? 2. were there any topics in the meeting that did not fall into our focus areas? 3. for each focus area mentioned, what capabilities, if any, were discussed? our review indicated 11 significant discussions of new legislation and existing legal constraints, 11 discussions of patient consent issues, 8 discussions of governance issues, 5 of financial sustainability, 5 of technical infrastructure, and 1 of auditing. the ability of users to audit system use, a key issue in trust management, was considered to be a capability of the technical infrastructure and was not included as a focus area. the issue of consent was not as straightforward. while the capability to enforce consent could be considered a technical capability, core issues of patient consent underlie the very ability to exchange data. therefore consent was retained as a focus area. our final list of focus areas included: governance, legal, financial, technical infrastructure, and patient consent. step 3—identify capabilities for each of the focus areas established in step 2 we identified specific capabilities, or evolutionary levels of maturity, based on expert discussions, experience, and where possible relevant literature. governance the term governance describes the existence of a governing board or committee that develops, implements, and enforces policies and procedures.(16) these functions include adding new a focus area maturity model for a statewide master person index 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi member organizations or dropping existing ones, oversight, auditing and other operational aspects of the exchange. governance maturity defines the level to which the governance policies and procedures are formalized, authorized, and implemented. following are the levels of governance maturity: a. informal—governance by an ad hoc committee, formed for a specific purpose and consisting of very little guidance (e.g. pgc) b. formal—a standing committee with a charter giving it authority, powers, establishing membership, procedures. derives authority from trust agreement between partners. (e.g. institutional review board) legal authority the legal focus area encompasses both existing legislation that authorizes and limits the exchange of protected health information as well as enabling legislation for a new resource. we identified three capabilities for the legal focus area: a. existing legislation—information exchange and disclosure is authorized under existing federal and state laws. b. delegated legislation—regulations created by an agency of the executive branch of government to enforce legislation. for example, udoh has authority to implement and administer laws passed by the utah legislature through administrative rulemaking. c. administrative rulemaking—delegated legislation enables udoh to use its administrative rulemaking authority to establish a governance structure under the broad authority of the law. financial development of the smpi, like many similar projects, was initiated with grant funding that was limited in time, amount, and scope. its ongoing operation will depend on the development of a viable funding mechanism, most likely from payments from the participating entities that benefit from it. we identified the following capabilities for the financial focus area: a. startup funding—development and operation are funded through funding that is limited in size and time, such as government or private grants. b. sustainable funding—the resource has a sustainability model that provides value to participating programs and ensures its ongoing operation. c. value-added funding—more mature resources may develop new funding streams that reduce the financial burden on participating programs and increase revenues for system enhancement. for example, the smpi could provide identity resolution for approved research projects or other purposes as allowed by law as a fee for service technical infrastructure limited by the scope of our model, technical infrastructure refers only to that infrastructure needed to protect the privacy and security of sensitive information in an information exchange. in a cross-enterprise exchange such as the smpi it includes privacy and security infrastructure already in place at participating programs, and that developed specifically for the exchange. the a focus area maturity model for a statewide master person index 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi smpi is complicated by the need to provide provisioning, access control, auditing and other security-related functions related to trust. capabilities for technical infrastructure include: a. existing infrastructure—participating programs use existing infrastructure to enforce privacy and security. b. dedicated infrastructure—dedicated hardware and /or software components are put into place solely to meet the basic needs of the exchange, such as user authentication, authorization and auditing. c. enhanced infrastructure—while a dedicated infrastructure meets the basic needs of the exchange, an enhanced infrastructure provides additional functionality such as automated policy enforcement and consent management to streamline the real-time flow of information. patient consent exchanges of patient data for operational or research purposes, though authorized by law, often require patient consent. the multi-institution nature of the smpi complicates the issue of managing consent by creating the need to manage multiple and possibly conflicting consents from a single individual received from multiple participating programs. levels of capabilities include: a. no consent –the system does not enforce patient consent. b. simple consent—the system supports enforcement of simple consent decisions, such as opt-in, opt-out. c. complex consent—the system supports enforcement of complex consent decisions (opt in or out with exceptions), with conflict resolution or finer granularity of consent decisions enforced. figure 2 illustrates the final focus areas and associated capabilities. a focus area maturity model for a statewide master person index 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 2. focus areas and capabilities for policy development step four—determine dependencies for each capability in each focus area we determined those that fully depend on completing capabilities in other focus areas. the starting point for this analysis was sustainability. the model of using public funding to build collaborative exchanges and private funding to sustain them is common in endeavors such as the this, and underscores the need to develop a sustainability plan early to avoid the risk of failure. a survey of five not-for-profit hie organizations conducted in 2010 found that all five relied on state and federal grant programs for the majority (65%-70%) of costs associated with implementing and operating the hie.[23] only one of the five indicated that it was operating under a successful organizational business plan. a review of 11 successful hies, both for-profit and not-for-profit, by the national ehealth collaborative identifies critical success factors and barriers to sustainability.(17) not surprisingly, building trust by providing accurate and reliable data was critical for engaging stakeholders and attracting new subscribers and payers, thus ensuring the long-term sustainability of the exchange. as such, financial sustainability and a successful business model depend upon adequate technical infrastructure to build trust between participants. sustainability also depends on the governance model selected, as operational costs can vary significantly by governance model selected. a report issued by the national ehealth collaborative examining sustainability issues in 12 fully operational hies, including three public-private partnerships, three commercial and one governmental exchange, identified a wide variety of business models and value-add services employed by different governance models. (18) a focus area maturity model for a statewide master person index 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3 shows a matrix illustrating dependencies for our model. note that the letters a-c denote progressively mature capabilities for each focus area in which a specific capability can only be implemented when all prerequisite capabilities for other focus areas have been previously implemented. figure 3. focus area maturity model for a statewide master person index for example the matrix illustrates that capability b of the governance focus area, formal governance, cannot be achieved without final legal authority, level c. furthermore, financial sustainability cannot be planned until the issue of consent is resolved, and consent cannot be resolved until a governance model is chosen. through documenting dependencies, the matrix provides the opportunity to visualize what work has been done, and what remains. results and discussion in march 2012, with the passage of utah house bill 25: patient identity validation, the utah legislature amended the utah health code (title 26, chapter 1, utah code) to authorize udoh to “establish methods for health care providers, public health entities, and healthcare insurers to coordinate among themselves to verify the identity of the individuals they serve.” under the statute, udoh can use its administrative rulemaking authority to implement policies for governance and data ownership as consensus is reached among stakeholders. in our model, level c of the legal focus area, will be reached when udoh finalizes administrative rules for governance. with the delegated legislation in place, udoh must convene stakeholders and develop a governance structure and associated trust agreements to lead the smpi to the next level. the issue of governance generated significant discussion in both the pgc and dhsc deliberations. the dhsc report identified the need for a unique governance structure for such a unique resource as the smpi. the report further recommended governance with a balance of representation between government, public and private sectors. specific governance committee member organizations and interest groups recommended in the report include, in addition to the partners in this project, the state department of insurance, the utah medical association, utah hospital and health system association, and advocates for patient privacy, consumer issues, behavioral health, and people with special healthcare needs. while the report outlined the general makeup of a governance committee it stopped short of proposing or endorsing a specific governance model. the pgc meeting deliberations focused on the reasonable and intuitive approach that ongoing operation of the smpi should be funded from users who pay for the value of services received for a focus area maturity model for a statewide master person index 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi participating in the project. this raises two important questions: how much will ongoing operation of the smpi cost? what is the value of services received by participants? in answer to the first question, as our model illustrates, accurate estimation of the ongoing operational cost of the smpi is not possible without first identifying a governance structure as funding needs vary considerably by the governance model selected. furthermore, estimating the value of services received for participants is equally challenging. each of the healthcare systems currently devotes significant resources to manually resolving mpi records that fail automatic linkage. one utah health system’s internal study estimated a cost of $60 per patient who required manual resolution and approximately $2 million annual cost for identity resolution for their system. in udoh, staff in the immunization registry and child health programs also expends significant resources conducting manual linkage of patient records. opportunities are lost for udoh to provide services to providers’ quality improvement or the state hie’s simplification of administration due to lack of a statewide master person index. for example, udoh could use the smpi to identify avoidable hospital readmissions to reduce rehospitalizations across multiple health care systems; the smpi could be used by uhin to coordinate health plan benefits for its members. with these potential value-added services, the pgc and dhsc agreed on the principle that who benefits is who should pay for the smpi. as the nation moves toward healthcare reimbursement models based on fee-for-value, organizations will become increasingly dependent on patient correlations across provider organizations. prospective de-identification strategies derived from patient-centered qualitative analytics and reporting may offset some of the current costs of retrospective correlation for research purposes. one of the most intractable issues the dhsc and pgc dealt with was the issue of consent management. hie’s have grappled with consent issues for operational sharing of data, which is a variation of the debate over who owns health data. a white paper published by the office of the national coordinator for health information exchange (onc) identifies four typical consent models: no consent, opt-out, opt-in, and opt-in with restrictions.(19) in our model we characterize opt-in and –out as simple, and opt-in with restrictions as complex. the exchange of data for identity resolution presents similar consent issues as data exchange for hies, with one important difference. in order to enforce a patient’s consent or denial thereof, the patient must be identifiable. a quote extracted from pgc meeting minutes reads, “people do not have the right to remain ambiguous.” in other words, a centralized record is needed to track the consent or non-consent of individuals. the central consent question for the smpi was that, with the vastly different data sources contributing information, what would the system do if consent status differed across sources? in addition, the dual purposes of the smpi as both an operational and research resource led to the question of whether patients could consent or decline consent for one purpose but not the other. to develop a model for consent, the pgc examined the role of consent in other operational and research domains. institutions and integrated healthcare systems have the legal authority to link their own patient records to establish identity, and patients have no ability to opt out of that process. other operational systems such as cancer registries do not allow patients to opt out of being in the registry, but patients can opt out of sharing their data for research.(20) in the course of discussions it became apparent that issues of consent are intertwined with the issue of governance. the final resting place of the smpi, be it under state government, the state a focus area maturity model for a statewide master person index 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi hie, or a non-profit administrator, could have legal ramifications for the ability of the smpi to compile and share data with potential consumers. the governance structure makes an impact on policy decisions, and without the question of governance resolved, the issue of consent will remain unclear. to compile information on the effect of governance on consent requirements, it was suggested to look at the cooperative agreements of other state hies. the final consent policy, which is included in supplementary materials, made a distinction between consent to access and consent to disclose. when consenting to access, a patient agrees to allow smpi consumers to access his or her demographic information. with consent to disclose, a patient is authorizing his or her provider to disclose demographic information to the smpi. the policy further specifies that consent must be obtained by smpi data sources per transaction, or visit. for research purposes, it was agreed to use the consent requirements of the approving irb. implementing these consent requirements in an operational exchange requires a sophisticated technical infrastructure. by shading in areas of the maturity matrix up to each capability that has not been reached we can see a path toward implementing smpi. (figure 4) while the technical infrastructure is under development udoh must convene stakeholders to develop first a governance model and then a sustainability plan. the final consent model will depend upon governance; however the technical infrastructure requirements dictate support for simple opt-in or out consent. as use of the smpi grows, enhanced infrastructure to support more granular consent or for automated policy enforcement can be used to support value-add services. figure 4. current level of maturity of the smpi conclusions exchanging personally identifiable information across enterprises for healthcare identity resolution requires new models for data sharing and a complex policy framework to mitigate risks to participants and ensure cooperative success. a focus area maturity model can be used to assess work and identify a path forward in developing such systems. using the framework proposed here provides an orderly path to address interdependences that can guide the complex process of developing a functional smpi, avoiding conflicts between policy and technology that may lead to non functional implementations. while this paper focuses on our experience in utah, we believe that the arguments for using a focus area maturity model to guide the development of state or regional mpis is of general interest. a focus area maturity model for a statewide master person index 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi acknowledgments this project was funded under nih grant no. 1rc2lm010798-01, development of a statewide master person index. we would like to thank members of the pgc and dhsc for their time, dedication and expertise. corresponding author jeffrey duncan, ms 1,3 department of biomedical informatics university of utah 26 south 2000 east room 5775 hseb salt lake city, ut 84112-5775 daytime phone: 801-538-7023 email: jeff.duncan@utah.edu references 1. mcdonald cj, overhage jm, barnes m, schadow g, blevins l, dexter pr, et al. the indiana network for patient care: a working local health information infrastructure. an example of a working infrastructure collaboration that links data from five health systems and hundreds of millions of entries. health aff (millwood). 2005;24(5):1214-20. epub 2005/09/16. 2. boyd ad, hosner c, hunscher da, athey bd, clauw dj, green la. an 'honest broker' mechanism to maintain privacy for patient care and academic medical research. international journal of medical informatics. 2007;76(5-6):407-11. epub 2006/11/04. 3. steenbergen m, bos r, brinkkemper s, weerd i, bekkers w. the design of focus area maturity models. in: winter r, zhao jl, aier s, editors. global perspectives on design science research: springer berlin heidelberg; 2010. p. 317-32. 4. menachemi n, collum th. benefits and drawbacks of electronic health record systems. risk management and healthcare policy. 2011;4:47-55. epub 2012/02/09. 5. center bp. challenges and strategies for accurately matching patients to their health data. bipartisan policy center; [cited 2013 03/28/2013]; available from: http://bipartisanpolicy.org/library/staff-paper/challenges-and-strategies-accurately-matchingpatients-their-health-data. 6. nangle b, xu w, sundwall dn. mission-driven priorities: public health in health information exchange. amia annual symposium proceedings / amia symposium amia symposium. 2009;2009:468-72. epub 2009/01/01. 7. brixner di, watkins jb. can cer be an effective tool for change in the development and assessment of new drugs and technologies? j manag care pharm. 2012;18(5 supp a):s0611. epub 2012/06/08. 8. weber gm, murphy sn, mcmurry aj, macfadden d, nigrin dj, churchill s, et al. the shared health research information network (shrine): a prototype federated query tool for clinical data repositories. j am med inform assoc. 2009;16(5):624-30. epub 2009/07/02. mailto:jeff.duncan@utah.edu http://bipartisanpolicy.org/library/staff-paper/challenges-and-strategies-accurately-matching-patients-their-health-data http://bipartisanpolicy.org/library/staff-paper/challenges-and-strategies-accurately-matching-patients-their-health-data a focus area maturity model for a statewide master person index 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi 9. wang h, yatawara m, huang sc, dudley k, szekely c, holden s, et al. the integrated proactive surveillance system for prostate cancer. open med inform j. 2012;6:1-8. epub 2012/04/17. 10. livne oe, schultz nd, narus sp. federated querying architecture with clinical & translational health it application. j med syst. 2011;35(5):1211-24. epub 2011/05/04. 11.afshari. information resource management maturity model. proceedings of the world academy of science, engineering and technology. 2009;37. 12.farah b. a maturity model for the management of information technology risk. international journal of technology, knowledge & society. 2011;7(1):13-25. 13.mccormack k, willems j, van den bergh j, deschoolmeester d, willaert p, štemberger mi, et al. a global investigation of key turning points in business process maturity. business process management journal. 2009;15(5):792-815. 14.rowlands i. understanding information policy: concepts, frameworks and research tools. journal of information science. 1996;22(1):13-25. 15.manion fj, robbins rj, weems wa, crowley rs. security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study. bmc medical informatics and decision making. 2009;9:31. epub 2009/06/17. 16.goedert j. governance: the hie differentiator. health data management. 2009;17(8):26, 8, 30 passim. epub 2009/08/25. 17.hie business and technical profiles. available from: http://www.himss.org/content/files/201009himsshiebusinessandtechnicalprofiles.pdf. 18.prestigiacomo j. hie sustainability secrets. nehc report shares hie success stories of alternate revenue streams and payer buy-in. healthcare informatics : the business magazine for information and communication systems. 2011;28(11):24, 6, 8. epub 2011/11/30. 19.consumer consent models for hie: policy considerations and analysis. [03/25/2013]; available from: http://www.himss.org/content/files/201009himsshiebusinessandtechnicalprofiles.pdf. 20.beskow lm, sandler rs, weinberger m. research recruitment through us central cancer registries: balancing privacy and scientific issues. american journal of public health. 2006;96(11):1920. http://www.himss.org/content/files/201009himsshiebusinessandtechnicalprofiles.pdf http://www.himss.org/content/files/201009himsshiebusinessandtechnicalprofiles.pdf layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts automated surveillance of outpatients with pneumonia: a performance evaluation hongzhang zheng1, 2, tariq siddiqui1, 2 and sylvain delisle*1, 2 1va maryland health care system, baltimore, md, usa; 2medicine, university of maryland, baltimore, md, usa objective to determine if influenza surveillance should target all patients with acute respiratory infections (ari) or only track pneumonia cases. introduction effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. in recent work, we combined previously developed ari case-detection algorithms (cda) [1] with text analyses of chest imaging reports to identify ari patients whose providers thought had pneumonia. in this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ari. methods time series of daily casecounts (backgrounds) were created by applying either an ari cda (ari icd-9 codeset [1]) or a pneumonia cda (ari icd-9 codes and chest imaging obtained and positive results from automated text analysis that identify those chest imaging reports that support the diagnosis of pneumonia) to electronic medical record (emr) entries related to outpatient encounters at the va maryland health care system. we used an age-structured metapopulation influenza epidemic model for baltimore to inject factitious influenza cases into backgrounds. injections were discounted by the known sensitivity of the ari cda [1]. for injections into the pneumonia backgrounds time series, factitious ari cases were further discounted by the expected pneumonia rate in the modeled influenza epidemic (10%). from the time of injection, ears or cusum statistics [2,3] were applied on each successive day on paired background+injection vs. background-only time series. each injection-prospective-surveillance cycle was repeated 52 times, each time with the injection shifted to a different week of the one-year study period (2010-11). we computed: 1) the “detection delay”, the average time from injection to the first alarm present in the background+injection dataset but absent from the background-only dataset; 2) the “false-alarm rate” (far), defined as the number of unique false-alarms originating in the background-only dataset during the study year, divided by 365 days. to create activity monitoring operating characteristic (amoc) curves, we empirically determined the corresponding delay-far pairs over a wide range of alarm thresholds. results the figure compares amoc curves for otherwise identical surveillance systems that included either any ari outpatient visits (red circles, using the ears w2c statistic [2] and blue triangles, using the cusum statistic) or pneumonia (blue triangles, using the cusum statistic modified for sparse data [3]). note that detection delay (y-axis) is lower at any given far when surveillance aims at patients with pneumonia. sensitivity analysis suggests that this advantage remains true when pneumonia complicates influenza ! 5% of the time. conclusions our results suggest that emr-based influenza surveillance that targets patients with pneumonia can outperform systems that monitor all ari patients. keywords influenza; surveillance; modeling; simulations references [1] delisle s, south b, anthony ja, kalp e, gundlapalli a, et al. combining free text and structured electronic medical record entries to detect acute respiratory infections. plos one (2010) 5(10): e13377 [2] hutwagner l, thompson w, seeman gm, treadwell t. the bioterrorism preparedness and response early aberration reporting system (ears). journal of urban health: bulletin of the new york academy of medicine 2003; 80(2) (suppl. 1) i89-i96. [3] he, s., huang, w. and woodall, w. h. cusum charts for monitoring a zero-inflated poisson process. qual. reliab. engng. int. (2012) 28: 181–192. *sylvain delisle e-mail: sdelisle@umaryland.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e38, 2013 the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks michael l. popovich, msse1*, todd watkins1, bsie1, ousswa kudia, mph1 1. scientific technologies corporation, 411 s. 1st phoenix, az 85004. abstract public health immunization registries and the immunization ecosystem have evolved over the past two decades to become significant population health data assets. clinical providers and pharmacists are reporting the immunizations given to their patients to public health registries in 49 states and all territories, creating consolidated immunization event patient records. most of these immunization events are reported through the provider’s electronic health record system (ehr), pharmacy management system (pms), online, or through data uploads. meaningful use and health data standards (hl7) became the drivers that accelerated reporting to immunization registries and significantly improved the quantity and quality of the data. the infrastructure supporting the immunization ecosystem (ie) has enabled real-time compliance reporting and, more importantly, realtime patient queries. the provider community now has online access to a patient’s immunization history in over three quarters of the states, and growing. this access includes a forecast of the patient’s immunization gaps provided by public health decision support tools based upon the most recent acip recommendations. this is creating an opportunity for the provider and the patient to work together to reduce their risk of suffering a vaccine-preventable disease. this ie and the data in an immunization information system (iis) are especially useful as pharmacies expand their immunization practices and create opportunities to reduce the adolescent and adult immunization gaps. in a few states, this provider-public health ecosystem has begun to extend to individuals by allowing them to access the iis online through the use of myir. myir provides them with the electronic version of their immunization "yellow cards," recommendations for immunizations due, and the ability to print official certificates. this emerging consumer engagement creates opportunities to empower individuals to be more proactive in their family’s health care. this paper builds upon early experiments to empower individuals in this ecosystem by leveraging the value of these public health data assets and trusted communications, illustrating the possibilities for engaging consumers to support reducing the impact of emerging diseases, outbreaks and the next pandemic. this paper will suggest the value of the ie and the role individuals can play within their own social networks to advance public health efforts to manage disease events. in turn, this social mission would encourage consumers to be more proactive in managing their own healthcare. keywords: immunization information system, pandemic, disease x, consumer engagement, consumer empowerment, public health registries, preparedness, immunization ecosystem, health information technologies, vaccinations, vaccines the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi abbreviations: world health organization (who), crimean-congo haemorrhagic fever (cchf), middle east respiratory syndrome coronavirus (mers-cov), rift valley fever (rvf), health information technology (hit), electronic health record (ehr), health level 7 (hl7), severe acute respiratory syndrome (sars), scientific technologies corporation (stc), my immunization record (myir), immunization information system (iis), centers for disease control and prevention (cdc), centers for medicare & medicaid services (cms), meaningful use (mu), office of the national coordinator for health information technology (onc), pharmacy management system (pms), immunization ecosystem (ie), advisory committee on immunization practices (acip). *correspondence: michael_popovich@stchome.com doi: 10.5210/ojphi.v10i2.9147 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction when asked what scared him the most and kept him up at night, tom frieden, the former director of the cdc, replied: "the biggest concern is always for an influenza pandemic." [1] a recent cnn article [2] outlined the world health organization’s (who) review of potential public health emergency diseases that included the top 10 global concerns: crimean-congo haemorrhagic fever (cchf), ebola virus disease and marburg virus disease, lassa fever, middle east respiratory syndrome coronavirus (mers-cov) and severe acute respiratory syndrome (sars), nipah and henipaviral diseases, rift valley fever (rvf), zika, and finally disease x. currently there are no vaccines to prevent these. the who’s disease x was included in this “list of blueprint priority diseases” [3] because the world does not know what pathogen can cause the next epidemic. globally, as well as in the u.s., epidemiologists and public health professionals work endlessly to ensure the risk and impact of existing and emerging diseases are minimized, and that neither turns into a pandemic. as the world increasingly becomes interconnected through travel and technology, timely information and accurate data become more imperative. early warning of disease occurrence and assessing the resulting impact on the public are paramount. early warning systems referred to as "syndromic surveillance," along with mandated notifiable disease reporting, capture data and electronically process the information to present public health with a view into future potential impacts. over the past two decades, new information systems have played a key role in improving public health’s early warning and case management for disease outbreaks. as improved analytics are used to predict risk in populations, researchers and epidemiologists open new doors to disease cures, clinical research develops new medicines, and providers develop new care models. the role of technology and public health to support these efforts therefore becomes more valuable. health information systems are traditionally considered to be used for electronic medical record or payer billing systems. they are not paired with technology advancements that exist in the mailto:michael_popovich@stchome.com the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi hands of consumers which could encourage patients to be more proactive with their healthcare. efforts today to link information and technology to engage consumers are championed by health plans and healthcare providers. engaging and empowering individuals to be proactive when presented with their medical records is not a simple problem to solve. it is not just a matter of making data available but making it actionable. actionable information may not achieve the desired success. but what if… this was augmented with another social mission? what if… the health community engaged consumers to help them fight disease outbreaks; what if… consumers become frontline activists to report occurrences and outcomes, and become “intelligent connections” to extend the right information in their social networks? this would encourage today’s consumer technology to be better integrated with the clinical health information technology (hit). it would encourage continued investment in evolving and sustaining critical public health ecosystems. it would create opportunity to engage consumers, empowering each to be more proactive in supporting population health and their own healthcare. another large part of the health information system are immunization information systems (iis) where individuals who have received vaccines are documented in a confidential computer based system in a specific geographic area [4]. the iis can be used for disease surveillance purposes and provide valuable information to public health authorities [5]. as an extension of one of the existing iiss, myir was created where any state iis, pharmacy or provider can provide patients direct access to family state immunization records – regardless of the type of immunization information system used. providers can communicate to patients using myir to increase patient engagement. automated vaccine reminders can be sent using this system as well. purpose during a pandemic, the need for accurate and timely information is vital. we propose that if there were direct public health agency communication channels to individuals by building on existing immunization networks, the public would receive correct information quicker. furthermore, there is value that can be leveraged from social networks to advance public health efforts to manage disease events and in turn encourage consumers being more proactive in managing their own health care. method one could envision a public-health-engagement-approach to empower consumers begins by offering individuals a challenge and a mission they care about. a mission that allows them to contribute to the social good with the added benefit of making them more attentive to their own healthcare. placing this mission in the palms of their hands through every cell phone is the first objective. the second is to create value for this phone’s owner to become active in supporting the mission. the third objective is to provide a commonly understood health event that is a cornerstone component to this phone owner's health and welfare that helps launch them toward utilizing their complete health information profiles. universally the most significant public health event in the 20th century was the power of vaccines and applied immunizations to individuals and large populations [6]. the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi the most significant action an individual can take to reduce their risk of a vaccine-preventable disease is to become vaccinated and stay up-to-date on their immunizations. however, there is a significant gap between believing in the value of an immunization and in ensuring one’s own (and one's family's) immunizations are current. how many individuals really understand what the immunization schedule recommends or are proactive in ensuring they have no immunization gaps? how many of these same individuals in an outbreak or with news of a new disease ask the question "is there a vaccine and what are my risks?" the challenge is how to engage individuals directly, empower them to be advocates for their own health and in an outbreak become sources of trusted public health messages as they communicate within their social network, effectively supporting the higher-level mission. the lesson learned from every outbreak? that the public demands accurate, timely, and transparent communication from the government. if this public health information is communicated directly to this new evangelistic network, there is the potential to expand to larger consumer networks. a few separate experiments were conducted using myir. increase sustainment the first experiment aimed to increase sustainment. the engagement project was to reach out in an effort to increase sustainment by contacting users who had not used myir and accessed their data from the iis in over 30 days. the baseline looked at number of users that were logging into myir more than once a month. the target was non-engaged users. in this category there were nearly 8,000 accounts. flu shot customer engagement the second experiment aimed to engage the consumer for the flu shot. for this campaign, on november 21st, 2017, an email was sent to 7,772 users that asked them a simple question, "did you get your flu shot?" if they selected yes, they saw a funny meme and received positive affirmation. if they clicked no, the message was an encouragement to get their flu shot before thanksgiving 2017. customer engagement-healthy lifestyle in the third experiment, consumer engagement effort was initiated january 19, 2017. capitalizing on the popularity of new year’s resolutions around wellness this engagement experiment created a healthy lifestyle page within myir. it featured an arizona food blogger, simple, sassy and scrumptious, who offers readers nutritious easy meal ideas. outreach efforts the fourth experiment focused on outreach efforts where louisiana targeted users who had failed to complete the two step process to fully enroll for access to their immunization histories. the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi efficacy of flu the last experiment was in april 2018, where a myir outreach initiative in washington and louisiana asked two questions to a sample of 212 consumers who access their records from the state iis: did you get a flu shot this year? do you feel like you got the flu this year? trusted digital communication consider the limitation of traditional media coverage of disease events; whether it is the efficacy of this year’s flu vaccine, reporting on the occurrence of a previously eliminated vaccine preventable disease or coverage of an emerging disease from zika to disease x. common communication through media and healthcare-related web sources rely on consumer education campaigns that include uncontrolled dissemination and information exchange after the initial message. a more optimal approach is to extend this message from trusted public health sources to individuals. trusted communication links exist. an example is retail pharmacies. these trusted digital communication channels are used every day by pharmacists to notify their patients of prescriptions, immunizations due, and results of point-of-care testing. if these channels were leveraged to support major public health events, a national scalable digital trusted social network would extend across the country to achieve the desired result. building this communication network on an existing national framework would leverage prior investments and day-to-day operational processes. the question is, does such a network currently exist? the answer is yes and it is one that is growing every day. one that was created by the need to reduce the impact of disease. it is the national immunization ecosystem. forty-nine states have established immunization information systems (iis) to capture and consolidate patient immunization events as reported by their clinicians or pharmacists [7]. hl7 is a set of standards for the transfer of clinical and administrative data between applications among the healthcare system’s stakeholders. in nearly all these states, and soon all, secure hl7 data exchanges exist, allowing electronic health records (ehrs) and pharmacy systems to report a patient immunization event to the state iis in real time. furthermore, this infrastructure allows a clinician or pharmacist to review a patient’s vaccine history and actionable immunization intelligence provided by public health decision support tools that using the latest advisory committee on immunization practices (acip) recommendations to identify immunization gaps and establish opportunities for closing these through communication at the patient point-of-care. the immunization ecosystem is based on what public health has spent the past quarter century developing. this is based on statewide population health environments consisting of key data assets and technical infrastructure for reporting, accessing and assessing immunization activity. additionally, these systems layer decision support, analytics, and communications across the entire population health environment. the value that the immunization ecosystem creates is the ability for all key “players” to integrate or connect. thus, a national immunization ecosystem exists today and, as it continues to evolve, the framework and platform are primed to impact the cost of health care and improve patient outcomes. the key is to fully extend the framework to the consumer, creating end–to-end communication channels between trusted health authorities and individuals. the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi pandemic preparedness planning that takes advantage of iiss, their infrastructure and their players while engaging individuals to be consumer activists in an emergency response network, is potentially a new approach to accelerating individual health accountability while offering public health the ability to communicate valuable outbreak or pandemic information. by doing so, the outcome should influence day-to-day consumer behavior to mitigate the outbreak’s impact on the population. delivering the right message the 2003 sars outbreak (with a 40-billion-dollar economic impact) [8], the 2009 h1n1 pandemic, and the 2015 zika outbreak confirmed that the questions asked by the public in each of these events were: • is there a vaccine for this [9]? • am i or is my family at risk [10] [11]? • should i travel [10] [11]? the ability for continued government and health authority responses to these questions is an important step toward managing the welfare of concerned populations while outbreaks are studied, and public health mitigation plans are put into place. the velocity of information, both accurate and inaccurate, cannot be controllable through current communication channels available to disseminate information to individuals, especially those most at risk. public health has an expectation and relies on their disease specific health education reaches at risk individuals, and is clearly understood and accepted. this reliability can be impacted by the social media and online consumer world today. much of the public’s information and behavior during a pandemic will be influenced through these social media and online networks [12]. individuals will search for data they choose to believe [13]. once found, they will push this information to their family and friends and their social media sphere of influence [14]. information from social media can pose as a challenge to stability for the trusted information that public health wishes to deliver in order to inform and encourage those at risk to take action [15]. when a pandemic occurs, the need for accurate and timely information accelerates. if the odds of receiving accurate information during a pandemic are against you in the social media world, consider the opportunity if there were direct public health agency communication channels to individuals by building on existing immunization networks. accurate communications would be the goal during a public health event. in washington and louisiana currently, over 35,000 consumers access their immunization records through real-time connections to the state iis through myir. the "players" in these states encourage individuals to enroll for access. once enrolled, an individual is connected to one of their health data attributes through the immunization platforms, which also establishes information access to trusted health agencies. it creates the opportunity to accelerate consumer engagement and activism in a pandemic or outbreak by layering digital communication to individuals to include those most at risk, and supporting the extension of this trusted communication through the individual’s social network. the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi communication attributes to empower based on the experiences gathered in working with immunization data and health pandemics, the following steps are proposed to increase communication to the public during a health outbreak: 1. using existing infrastructure for immunization data exchange, establish communication links to trusted public health authorities supporting the following actions: a. inform and engage consumers by proactively alerting and notifying each individual of their immunization coverage gaps. b. identify the nearest locations capable of providing specific immunizations and antivirals to high risk populations. c. capture feedback and monitor outcomes or concerns of individual immunization events at their time of occurrence as well as over an extended period of time. d. capture surveillance of personal (family) health events, such as influenza or influenza-like illness. 2. integrate provider, pharmacy and laboratory patient influenza tests, public health influenza reporting, and overall tracking of the most recent outbreak. 3. integrate outbreak occurrences through alerts and visual displays (risk and outbreak maps). 4. provide social media exchanges to link immunization ambassadors and general social networks that can support the concept of “layering” to reduce the impact of an outbreak and to ensure a controlled, consistent and accurate dissemination of information. illustrations: how the immunization ecosystem supports consumer engagement since 2016, a number of immunization consumer engagement campaigns have been undertaken by stc to explore the power of direct links to individuals who have enrolled and have access to their immunization records. in a 90-day period from mid-august 2016 to mid-november, the stc consumer tool myir had 15,000 total user visits to the state immunization registry. of these 23% were returning users. in that same period 3,200 users visited myir more than once a month. they spent an average of 5 minutes on the site each time and viewed seven different pages. typically, this included the initial login, requests for their immunization records, and reviewing the forecast or immunizations due or past due for each family member. in reviewing the individuals that enroll for user access to the immunization registry, they currently are from a specific demographic. the majority are within the ages of 25-44, with 72% being female. this suggests the people who see the most value in myir are likely to be women with a family. the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi results increase sustainment 27% of people opened the initial email and 3.6% of these individuals used myir within 30 days to access their immunization records. flu shot customer engagement 9% answered the question with 80% of these saying “yes” they got their flu shot. as a result, 25 new immunizations were administered to these individuals within 60 days. one nine-month-old family member received a full series of age appropriate immunizations. customer engagement-healthy lifestyle a 7.1% increase in returning users were tracked and a 9.1% increase in engaged users, again defined as logging in more than once a month. there was also a 6.7% increase in average session duration. outreach effort five hundred fifty-six emails were sent and a 30% open rate was achieved. the email contained a single step to finalize the enrollment and thus access to their immunization medical records. fifty of those who opened the email followed through and completed the process. efficacy of flu respondents were asked: "did you get a flu shot this year? do you feel like you got the flu this year?" 78% responded they did receive the flu shot this year. of these 61.5% felt they got the flu this year which equates in this group to a 38.5% efficacy rate for this year’s influenza vaccine. in february, cdc had determined the interim estimates for the effectiveness of the influenza were 36% [16]. these consumer engagements were early experiments. they initially targeted to increase consumer access and utilization of the information contained in a public health immunization registry. they have moved toward soliciting input from active users to test the concept of empowering advocates to support a larger public health mission and engage with trusted communications from government. they created the thought processes that lead to the concept of extending the ie to proactively engage individuals to support public health missions, notably outbreaks and the next pandemic. the data, technical frameworks and infrastructure of the iiss form the environment to engage and empower consumers to be field assets to support outbreak mitigation efforts and be instrumental within the social networks if a pandemic were to occur. discussion our aim was to illustrate examples where public health agencies using direct communication channels to individuals by building on existing immunization networks, could increase the efficacy of reaching the public with correct information. the illustrations used to demonstrate the the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi consumer engagement potential were not designed to test the hypothesis that consumer activism and the value of public health immunization registries in a pandemic would prove effective. they do demonstrate the potential of engaging individuals that have enrolled to access their immunization records from public health registries. they demonstrate that a subset of these individuals will provide information requested from public health authorities. it was through these early experiments and the growing data assets in state immunization systems that create a framework and technical platform to accelerate the potential value of engaging individuals in response plans for pandemic preparedness planning and support of today’s outbreak. the next step is to begin to engage individuals to establish those that would be willing to provide ongoing information to public health specific to immunizations and disease occurrences. this would include developing experiments that test a social campaign to engage these individuals over the course of a year to ensure they remain activists in this network and then initiate efforts to encourage them to be proactive in their health care. the impact would be monitored in order to begin to establish a model with key parameters that would allow scaling across states and different demographics of users. the experiments illustrated were not statistically tested or compared to other sources.they were not designed to collect specific and more detailed information in support of an outbreak to determine if it is possible to enroll advocates and ensure consumer activism. however, sufficient assets and a growing community of individuals with access to their online immunization histories suggest a specific demonstration project to test this hypothesis is warranted. limitations the paper presents a concept of empowerment and a few example consumer engagement tests that indicate the possible opportunity for public health. the results of the consumer engagements were not measured against a specific research goal to determine their effectiveness. there were no comparisons to other public outreach and education methods that would established the potential. the consumer outreach efforts were not regional, culturally, or demographic specific and as such there was no intent to determine the true public health education to populations approach. these are all recommendations for next steps although there is some justification to simply begin to enhance and expand the current process and develop a stronger strategy to be tested. conclusion this is the start. the immunization ecosystem is a robust environment to build upon. consolidated immunization information systems and technology supported by public-private partnerships have reached the dynamic where data and information is available across wide networks of users, stakeholders, and consumers. while health plans, providers and pharmacists struggle to engage their networks, by encouraging patients to be proactive in their healthcare, public health immunization assets may be the tipping point to accelerate this movement since the single most common health event is an immunization, required from birth to death. furthermore, pandemic preparedness planning is primed to step beyond just response actions and traditional communication plans to reach directly into areas where outbreaks are occurring, the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi soliciting consumer activism through this ecosystem to report disease occurrences and disseminate accurate information as public health works to mitigate the outbreak. the hypothesis can extend one step further: if you are active in supporting a higher cause and technology is the facilitator, the belief is this would not only reduce the risk of outbreaks and help mitigate the economic impact and costs, but also create momentum for more people to become proactive with all their healthcare. continued investment through government immunization programs, centers of medicare and medicaid services (cms) 90/10 match programs, office of national coordinator (onc) efforts supporting innovation, and consumer empowerment are essential to continue to evolve and sustain the immunization ecosystem and data assets. as these assets create added value to each stakeholder, the government investment begins to create a positive return. the private sector benefits also grow. their investment in this same ecosystem is necessary. today, opportunities to use this ecosystem to drive down healthcare costs and improve patient outcomes are unlimited. the value of this virtual ecosystem to the nation is untapped. being ready for the next pandemic through everyday practice is unique. we are in unique times. 1new hampshire is the only state that has not implemented an immunization registry, although they have one in test and expect to begin to implement this system in 2018. 2players in this sense represent all key stakeholders that utilize immunization information and include public health, providers, pharmacists, payers, employers, school nurses, foster care, consumers, and more. 3for example, feedback such as: did you receive this year’s influenza immunization? do you feel like you have the flu? (the dollar cost of the flu on the u.s. economy, according to cdc health affairs 2016-17, was $5.8 billion.) in a pandemic with a new vaccine, the question might be: do you feel you had a reaction to the vaccine? 4federal funding provided by onc has partially supported the expansion and testing of stc’s myir since 2014. 5sustainment of public health immunization registries and technical infrastructure is the subject of a separate stc paper, “sustaining the public health immunization ecosystem through public private partnerships.” 6the content and concepts of this paper are based upon stc's 25 years of working in the immunization registry sector, which includes implementing state public health iis, implementing electronic hl7 connections to every state iis from over 30,000 pharmacies and 10,000 providers, and using data and analytics to tell the story of the power of the ecosystem. acknowledgements we would like to thank theresa munanga for her editorial assistance. we would also like to thank all stc employees, especially those who assisted with the myir studies. the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi references 1. sun lh, outgoing cdc. chief talks about agency’s successes—and his greatest fear. the washington post [internet]. 2017 nov [accessed 2018 jun 01]. available from: https://www.washingtonpost.com/news/to-your-health/wp/2017/01/16/outgoing-cdc-chieftalks-about-the-agencys-successes-and-his-greatestfear/?noredirect=on&utm_term=.e89c73526485. 2. scutti s. world health organization gets ready for 'disease x'. cnn [internet]. 2018 mar [accessed 2018 mar 12]. available from: https://www.cnn.com/2018/03/12/health/diseasex-blueprint-who/index.html. 3. r&d blueprint: list of blueprint priority diseases. world health organization [internet]. 2018 feb [accessed 2018 jun 01]. available from: http://www.who.int/emergencies/diseases/2018prioritization-report.pdf?ua=1 4. centers for disease control prevention (cdc). about immunization information systems. atlanta: cdc; 2012 may 15 [accessed 06 jun 2018]. available from: http://www.cdc.gov/vaccines/programs/iis/about.html 5. derrough t, olsson k, gianfredi v, et al. 2017. immunisation information systems – useful tools for monitoring vaccination programmes in eu/eea countries, 2016. euro surveill. 22(17), 30519. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc5434883/#r1. pubmed https://doi.org/10.2807/1560-7917.es.2017.22.17.30519 6. centers for disease control and prevention (cdc). 1999. ten great public health achievements--united states, 1900-1999 [internet]. mmwr morb mortal wkly rep. 48(12), 241-43. https://www.cdc.gov/mmwr/preview/mmwrhtml/00056796.htm. accessed jun 06, 2018. pubmed 7. williams w. immunization information systems (iis) fundamentals:overview and development. centers of disease control and prevention [internet]. 2017 june 6 [accessed 06 jun 2018]. available from: https://www.hhs.gov/sites/default/files/williams_iis%20fundamentals%20remediated.pdf 8. lee jw, mckibbin wj. estimating the global economic costs of sars. in: institute of medicine (us) forum on microbial threats; knobler s, mahmoud a, lemon s, et al., editors. learning from sars: preparing for the next disease outbreak: workshop summary. washington (dc): national academies press (us); 2004. available from: https://www.ncbi.nlm.nih.gov/books/nbk92473/ 9. world health organization (who). pandemic (h1n) 2009: frequently asked questions. who [internet]. 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https://www.ncbi.nlm.nih.gov/pmc/articles/pmc3636326/ 16. morbidity and mortality weekly report (mmwr): interim estimates of 2017-18 seasonal influenza vaccine effectiveness – united states, february 2018. centers for disease control and prevention [internet]. 2018 feb [accessed 2018 apr 24]. available from: https://www.cdc.gov/mmwr/volumes/67/wr/mm6706a2.htm https://doi.org/10.1016/j.chb.2011.10.002 the power of consumer activism and the value of public health immunization registries in a pandemic: preparedness for emerging diseases and today’s outbreaks abstract introduction purpose method increase sustainment flu shot customer engagement customer engagement-healthy lifestyle outreach efforts efficacy of flu trusted digital communication delivering the right message communication attributes to empower illustrations: how the immunization ecosystem supports consumer engagement results increase sustainment flu shot customer engagement customer engagement-healthy lifestyle outreach effort efficacy of flu discussion limitations conclusion acknowledgements references layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts differentiating zip codes in syndromic data; what can they tell us? marc paladini, romona lall and stephen e. schachterle* new york city department of health and mental hygiene, queens, ny, usa objective to classify visits to nyc emergency departments (ed) into nyc residential, nyc po box or commercial building, commuters to nyc, and out-of-town visitors. to describe patterns in each group, to evaluate how they differ, and to consider how the differences can affect syndromic surveillance analyses and results. introduction the nyc department of health and mental hygiene (dohmh) ed syndromic surveillance system receives data from 95% of all ed visits in nyc totaling 4 million visits each year. the data include residential zip code as reported by the patient. zip code information has been used by the dohmh to separate visits into nyc and nonnyc for analysis; and, a closer examination of non-nyc visits may further inform disease surveillance. methods visits were initially differentiated into six home zip code types. nyc residential zip codes, po boxes and commercial buildings were identified with 2010 us census and data from the sas institute (sas institute inc., cary, nc, usa). commuter visits to the eds were classified as any zip codes from the nyc core based statistical area (cbsa; united states office of management and budget). out of town visits were identified using with the 2010 us census. unknown zip codes included all of those zip codes that were not identified by any of the previous methods, and missing zip codes were those that were blank. zip codes were verified with the united states postal service website (www.usps.com). once zip codes were categorized, spatial and temporal trends in total ed visits by home zip code type were analyzed. results of the approximately 4 million ed visits in nyc during 2011, the number of visits by commuters and out-of-town visitors were 125,236 (3.1%) and 45,158 (1.1%) respectively (figure 1). there were 4,676 (0.1%) visits with a nyc po box or building zip codes and 48,077 (1.2%) visits with a missing or non-interpretable zip codes. the majority of commuter and out-of-town ed visits occur at a smaller set of hospitals. out-of town visitors mostly visited hospitals in manhattan rather than hospitals in the outer boroughs. while the seasonal trends and day-of-week patterns for the nyc residents and the commuters appear to be fairly similar, this is not the case for out-of-town visitors. for example, total ed visit trends correlated well for nyc residents and commuters (r=0.77), but there was no correlation between nyc residents and out-of-town visitors (r=-0.18) over time. the number of ed visits among out-of-town visitors was higher during summer months and the winter holiday season, and this trend may have reflected the larger number of visitors during these periods. dayof-week patterns were similar for nyc residents and commuters with weekdays associated with larger numbers of visits compared to weekends. however, the opposite was found true for out-of-town visitors with larger number of visits occurring over the weekends compared to weekdays. conclusions considerable differences in temporal trends were found among out-of-town visitors, nyc residents, and commuters to nyc. out-oftown visitors also tend to visit eds located in manhattan rather than in the outer boroughs. these results suggest that out-of-town visitors represent a unique population ed visitors. analyzing nyc residents, commuters, and out-of-town visitors separately may provide additional information that could prove useful to daily syndromic surveillance activities. figure 1: total ed visits from the nyc core based statistical area (cbsa) and the surrounding region. keywords gis; new york; spatial analysis acknowledgments this work was carried out in conjunction with a grant from the alfred p. sloan foundation (#2010-12-14). we thank the members of the new york city department of health and mental hygiene syndromic surveillance unit. *stephen e. schachterle e-mail: steve.schachterle@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e127, 2013 editorial manager(tm) for sexually transmitted diseases manuscript draft development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois wiley d. jenkins department of family and community medicine southern illinois school of medicine abstract: chlamydia trachomatis is the most prevalent infectious disease in the united states. complications include pelvic inflammatory disease (pid), ectopic pregnancy, and infertility. the cost of pid in 1998 was estimated at greater than $1.9 billion. screening intervention strategies are often consumed by those at low risk. the objective of this study is the development of a more costeffective intervention strategy by employing geographic information systems and census bureau demographic data in selected local health departments in illinois. case studies of intervention activities at the state and local level were performed. an evaluability assessment model of current inputs, processes and outcomes was created. a proposed model utilizing additional state inputs was developed and tested. interventions were evaluated for effectiveness in reducing the incidence of chlamydia trachomatis. societal cost effectiveness analysis was also performed. the proposed model was tested in 2006. results indicate that only minimal changes in annual incidence are required for gis-augmented interventions to be cost-effective. 1. introduction chlamydia trachomatis (ct) is the most prevalent infectious disease in the united states, and the most prevalent sexually transmitted bacterial infection worldwide with an estimated 50 million cases in 1999.(1) there were 877,478 reported cases in the united states in 2003.(2) state case rates range from 78.7 to 462.3 per 100,000. there were 48,294 reported cases in illinois in 2003.(3) while greater than 70% of ct cases are asymptomatic,(4) long-term morbidity is observed in increased pelvic inflammatory disease (pid) which can lead to ectopic pregnancies, infertility and other morbidity. the national financial cost of pid in 1998 was estimated at greater than $1.9 billion.(5) public sources paid 30% of these direct costs while managed care groups, private insurance, and individuals paid the remainder.(6) while interventions at both the state and federal levels include screening tests, these are often consumed by those at low risk (approximately 30% of all tests in illinois). there is a need to define a process and policy by which resources may be more directly applied to the population at greatest need. the utility of geographic information systems (gis) to identify sexually transmitted disease (std) concentrations and assist intervention and control has been shown in multiple studies.(7-10) this is consistent with the core theory of std transmission and corroborates recent work in the field of core group and bridge population descriptions.(11) in the study of sexual behavior patterns, researchers have seen that std endemic levels depend upon a “core” group of people who are frequently infected. this core is usually a small proportion of the population and their level of mixing with “bridge populations” influences the spread of an std in the general population (12). while the impact of bridge populations on overall morbidity varies according to local population and disease characteristics, identifying the high-prevalence disease clusters may be beneficial in designing an effective intervention. it has also been show that directing resources to a geographic area can be an effective complement to traditional intervention methods (13, 14). development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 the objective of this study is the development of a more cost-effective ct intervention strategy. we describe the existing policy and its limitations and compare it to a new policy and its potential benefits. an additional state input, consisting of gisbased information and census bureau demographic data (gis/cb), is utilized as a tool for targeting resources to populations at increased risk of becoming infected with ct. we also construct an evaluability assessment (logic) model of the current and existing policies, test the proposed model, and perform a cost-effectiveness analysis (cea). a well-described logic model will help us identify program resources, associated activities and outcomes, and the relationships involved.(15) the study seeks to show that the introduction of gis technology and census bureau demographic data ( gis/cb) into ct intervention policy is effective in reducing the incidence of ct and also costeffective (ce) from society’s perspective. 2. materials and methods the current policy concerning ct interventions entails the local health department (lhd) devising and implementing their own, often unique, intervention. this is in addition to the standard screening and testing performed by each lhd. the illinois department of public health’s sexually transmitted diseases section (istd) staff provided testing resources, medications, materials and literature, and advice to support the local efforts. the extent and effectiveness of these interventions are largely dependent upon the locally available staff and resources, and vary widely by department. the majority of lhd std sections are managed by nurses, many of whom do not have any formal training in public health. in addition, an association of state and territorial health officials (astho) survey reported that the nursing occupational class is the area in public health with the greatest shortage of workers.(16) in many instances, the result is an understaffed std section attempting activities for which they have had no formal training. a case study approach was used to describe current intervention policy elements in illinois. the use of other qualitative and quantitative data sources added insight to structure and gave more depth to program descriptions.(15) for a state-level description of policy, the istd section chief and the surveillance and evaluation coordinator were interviewed on multiple occasions between june 2005 and january 2006. they were able to describe state policy and istd activities, provide copies of the istd budget, organization charts and section outbreak response plan. they were also able to speak at length concerning their interactions with lhds, and the relative strengths and weaknesses of the current system. the istd section is involved in a variety of activities ranging from data collection to active participation in local intervention efforts. such activities can be grouped into three broad categories: 1. local, state and national data collection, monitoring, reporting and evaluation. 2. indirect services such as technical assistance to health care providers; policy development; legislative and administrative rule work; training. 3. direct services such as data reporting and interpretation, the provision of std testing and screening, the supply of medication, provision of materials and literature, intervention design and implementation advice, and direct disease intervention. for a local-level description of policy, std staffs in thirteen lhds were interviewed in november and december 2005. there are 95 certified health departments in illinois serving 99 counties. three counties are serviced by the istd directly. lhds are funded through a local health protection grant, local taxes, and additional specific grants. the departments selected for study were development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 chosen to be representative of the range of circumstances and resources encountered throughout illinois. std staff in each lhd were asked to discuss their structure and activities in six areas: std staffing (those involved with ct); intervention activities; collaborations (with community organizations); educational and promotional materials; interactions (with the istd); actions taken (in response to a std outbreak). one reported area, interactions, produced uniform responses from all lhds. all reported that such interactions involved the provision of case data to and from the istd, advice, training and materials by the istd, and a high degree of assistance and customer service from the istd to any kind of requested help. table 1 compares and contrasts lhd responses to the remaining five areas. the close intertwining of hiv and other std work, even though they are often funded separately, makes it difficult to clearly separate many duties and staff. thus, the numbers of staff assigned to non-hiv std work are only close approximations. table 1. summary of case study responses (data as of 12/05)* area criteria measure high value/yes low value/no std staffing ftes number 153 0 ptes number 14 0 md present yes/no 8 5 intervention activities on-site clinic number 12 1 off-site clinic number 3 10 internet outreach yes/no 3 10 paid advertising yes/no 3 10 health fairs yes/no 8 5 unique** yes/no 8 5 collaborations family planning yes/no 7 6 local clinics, hospitals yes/no 12 1 drug/alcohol rehab yes/no 7 6 comm. service agencies yes/no 7 6 faith based yes/no 5 8 detention centers yes/no 9 4 school/university yes/no 10 3 unique yes/no 7 6 educational and promotional materials free from istd/cdc yes/no 13 0 purchased yes/no 7 6 created in-house yes/no 9 4 actions taken increased clinic/lab yes/no 9 4 local provider notification yes/no 12 1 developing additional education/outreach yes/no 11 2 more aggressive partner notification yes/no 12 1 unique yes/no 4 9 * the following county health departments were surveyed for this study: adams, alexander, champaign, jackson, kankakee, mclean, macon, peoria, vermilion, winnebago. also included were the cities of chicago and springfield (now incorporated into sangamon county), and the east side health district. ** unique criteria represents an activity in an area that only one health department performs. development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 2.1 logic (evaluability assessment) model of the ct intervention strategy a key component to the successful integration of additional state inputs into intervention development is the determination of a program theory and creation of an evaluability assessment model. the program theory represents actual activities and was obtained from case studies, site visits and various documents. it identifies program resources and activities and the causal relationships between them and outcomes. to assess the effectiveness of the current theory, a model is created with input from identified stakeholders, managers and data users. figure 1 presents the logic model for the current and proposed policies. the proposed policy indicates where gis/cb might be effectively added, where processes may be evaluated, and where outcomes are monitored to influence future activities. these models are intended to clarify the assumed relationships among the resources, activities and outcomes. the models can be used to identify opportunities to change program components or utilize information to improve performance and outcomes.(15) the evaluability assessment models of the current and proposed ct screening policies. 2.1.1 program input state inputs (current policy) the istd section has several inputs including ct case data summaries and other reports, advice, condoms and treatment medications and educational materials. these inputs are directed to the lhd, which then utilizes them in a locally-derived intervention. state inputs (proposed policy) the proposed additional state input consists of the provision of gis-based targeting information and census bureau demographic data to the lhds. this information is derived from county-level case and demographic data and is used to target local intervention resources to a specific geographic area, aid intervention development and enlist partners. identified areas may harbor core transmitters, and their identification and treatment may significantly decrease local incidence and prevalence. the proposed policy enables istd to have directly contributed to the intervention development policy. development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 state inputs (proposed policy) the proposed additional state input consists of the provision of gis-based targeting information and census bureau demographic data to the lhds. this information is derived from county-level case and demographic data and is used to target local intervention resources to a specific geographic area, aid intervention development and enlist partners. identified areas may harbor core development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 transmitters, and their identification and treatment may significantly decrease local incidence and prevalence. the proposed policy enables istd to directly contribute to the intervention development policy. lhd inputs (current policy) each lhd implements intervention strategies in its jurisdiction. the types and degree of interventions depend greatly upon lhd inputs that include the following: ct case data; staffing levels; budget; equipment; and the knowledge, skills and abilities of individual staff members. lhd inputs and the local incidence directly determine the nature and level of local intervention development. interventions are developed and implemented based upon the population demographics, the level of current local capabilities and the available budget. 2.1.2 program process the process in this model consists of new and continuous intervention activities. associated with this process are intervention and evaluation activities. of the thirteen lhds involved in case studies, twelve of them indicated intervention the desire for state intervention activities in addition to the standard screening. intervention activities (current): these were unique to each lhd studied. strategies for additional outreach and community engagement included: increased screening beyond the recommended guidelines; increased clinic hours; additional methods of literature distribution; on-site outreach through health fairs or mobile clinics; public service announcements and other regular interactions with print and video media; community outreach through collaborations with local groups. it was found that no two lhds had the same strategy, just as no two lhds had the same skills and resources. intervention activities (proposed): the proposed intervention activity would consist of the use of additional state inputs to devise and implement an intervention targeted towards a geographic area. evaluation activities (current and proposed policies): most lhds infrequently perform only informal evaluations, if any. no lhd interviewed indicated that they had any formal evaluation process in place. the proposed policy utilizes cea as an integral portion of the intervention. once an intervention based upon the gis/cb has been implemented, an evaluation will be conducted. there have been many studies of the cost-effectiveness of ct interventions, and most of them have concerned the utility of various screening strategies.(18,19) work has been done to show which type of modeling (stochastic versus deterministic) is most accurate for this situation.(20) for our purposes, the simpler deterministic model is more useful.(21) the cea utilizes the following three types of data: 1. direct costs – includes: pro-rated time and salary of involved personnel, costs of printing and advertising, automobile mileage; 2. indirect costs – includes: pro-rated personnel retirement and administrative costs, time denied other projects; 3. intervention results – resultant incidence rates post-intervention. 2.1.3 program outcomes the primary outcome is a decreased level of disease incidence in the lhd’s jurisdiction. this will be measured directly by case reporting to the istd. the goal is to determine effective interventions utilizing gis/cb technology. the outcome is measured in decreased local incidence. the cost-effectiveness of these interventions will also be done to determine if the net societal benefit in development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 averted costs is greater than the intervention costs. 3. methodology five illinois counties agreed to test this model. to illustrate how gis and maps may be used, figure 2 shows example maps of peoria county. in each figure, chlamydia cases for all of 2005 have been geocoded and mapped (due to confidentiality rules, maps of actual geocoded cases are not shown). the census block groups (cbg) for each county were then stratified by total case count and case concentration to locate the areas of highest case concentrations. the istd provided the lhd gis maps accompanied by census bureau demographic data specific to the identified cbgs. this data was then be used to devise a cbg-specific intervention that was implemented in one or more of the identified cbgs. five control counties were chosen and assigned to cases based on annual incidence rate similarity. figure 2. example maps of peoria county once the intervention was completed, the incidence rate for that county, and its assigned control, would be observed. if the model is effective, the incident rate for the targeted county should have a resultant incidence lower than that of the control. the model would then be evaluated by cea. the istd would assist the lhd in collecting information concerning all costs incurred during the intervention and measure them against the expected savings due to averted diseases. success of the model would be its iterative use to reduce the incidence of the entire county to an acceptable level. since chlamydia is unlikely to be eradicated, it is proposed that lowering the county incidence to the level of the state average be considered acceptable. in this approach, the lhd and istd perform iterative models, mapping and interventions targeting cbgs with decreasing case counts and concentrations until the desired incidence is reached. development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 4. results participating lhds received their maps and data in september 2006. they devised and implemented their interventions by november 14, 2006. the first evaluation of the process is qualitative as each lhd performed a limited, and unique, intervention. intervention consumption was directly observed through personal interactions, receipt of educational materials, and increased clinic visits and partner referrals. statistical analysis of available data (through february, 2007; estimated at >95% ci) indicates that vermilion county experienced a significant decrease in incidence not matched by its control (see table 2). two other control counties also experienced incidence decreases. the decline in adams county may be explained by the excessive time observed for reporting results. of the 14 cases reported for january, six of them required more than 42 days to be reported to the istd and another 4 required more than 74 days. the decline in sangamon county may be related to the consolidation of the city and county health departments during 2006. costs involved with the project involving both the author and lhds staff, have been collected (data not shown). chlamydia complication rate estimations and their associated costs have been collected from the literature.(5, 22-37) from these data we estimate the number of averted cases needed for a county’s intervention to be cost-effective. estimations are made utilizing a range of progression rates (untreated chlamydia to pid) and lifetime costs of pid taken from the literature (table 3). ce for most counties, and most progression rates and costs, may be attained with an incidence decrease of <2%. the specific analysis for vermilion county (table 4) indicates that the intervention produced a net societal benefit in the range of $2,002-$56,061. development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 table 2. comparison of post-intervention and pre-intervention data county/status data cases t-test for equality of means independent samples mean range t df sig. champaign/case macon/control post-intervention 88.3 79-100 -0.233 6 0.823 pre-intervention 90.3 72-105 post-intervention 53.0 51-55 -1.021 6 0.347 pre-intervention 58.0 45-68 mclean/case adams/control post-intervention 40.3 36-46 -1.820 6 0.119 pre-intervention 50.8 3 6-64 post-intervention 9.5 4-14 -3.130 6 0.020 pre-intervention 18.3 15-22 peoria/case jackson/control post-intervention 124.8 115-138 -1.602 6 0.160 pre-intervention 138.5 119-152 post-intervention 35.5 30-39 -0.253 6 0.809 pre-intervention 36.5 32-46 vermilion/case kankakee/control post-intervention 31.5 26-3 9 -2.63 5 6 0.039 pre-intervention 40.5 39-45 post-intervention 43.0 38-45 0.23 1 6 0.825 pre-intervention 42.3 34-46 winnebago/case sangamon/control post-intervention 119.5 92-155 -0.147 6 0.888 pre-intervention 121.5 115-130 post-intervention 67.3 61-82 -5.468 6 0.002 pre-intervention 94.8 92-96 table 3. minimum change required for cost-effectiveness utilizing low-to-high values of disease progression and lifetime cost* county champaign mclean peoria vermilion winnebago intervention costs $1,680 $1,892 $10,564 $1,179 $7,448 2005 total cases 1,133 493 1,292 368 1,522 number of averted cases required for ce 10%; $1,060 20 20 100 20 80 25%; $2,150 4 4 20 4 16 50%; $3,180 2 2 8 2 6 percent decrease from 2005 for ce 10%; $1,060 1.8% 4.1% 7.7% 5.4% 5.3% 25%; $2,150 <1.0% <1.0% 1.5% 1.1% 1.1% 50%; $3,180 <1.0% <1.0% <1.0% <1.0% <1.0% * minimum averted cases required for ce was determined using the range of values found in the literature for both disease progression in untreated chlamydia infection (10-50%; 25% median) and lifetime cost ($1,060-$3,180; $2,150 median). table 3 shows the minimum change required for cost-effectiveness utilizing low-to-high values of disease progression and lifetime cost, while table 4 presents the cost-effectiveness of vermilion county interventions table 4. cost-effectiveness of vermilion county intervention development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 cases ct averted progresion to pid cases pid averted average cost of pid savings intervention cost net societal benefit/loss 36 10% 3 $1,060 $3,180 $1,179 +$2,001 50% 18 $3,180 $57,240 $1,179 +$56,061 5. discussion the study has found that systematic modeling and evaluation can assist in the development of a more cost-effective chlamydia intervention strategy. as seen in the logic model of the current policy, intervention development is largely dependent on the local std staff who often lack training and resources to develop interventions and evaluate them for effectiveness. the proposed policy showed where additional state inputs (gis/cb) could assist in intervention development. process evaluation is done to assess intervention implementation. finally, the entire intervention is evaluated for effectiveness and cost-effectiveness. the process of developing the model, testing it in five counties, and performing the evaluation shows that there was a significant effect in at least one county, and that it was cost-effective. most interventions and estimations show ce being achieved with a reduction <2%. utilizing the lowest estimation for progression to pid and highest average lifetime cost has all counties achieving ce at a 7.7% reduction. evaluability models have been in use for more than twenty years. they have been used for such purposes as evaluating work flow in large organizations and describing outpatient care alternatives. the utility of gis to assist in std intervention development has been documented for both syphilis and gonorrhea. gis has also been used to describe the extent of chlamydia in areas ranging in size from military bases to a canadian province. cost-effectiveness analysis is quite well documented in a wide variety of fields. most of the ceas done for chlamydia focus on alternative screening strategies and subsequent costs for different treatments and sequalae. literature searches returned no studies applying program theory development and evaluability assessment to std intervention policies. this study describes the partners, roles, inputs, processes and outcomes for a std policy at the state and local level. we have been able to determine an area where an additional input may be incorporated into a process which results in a more favorable outcome. this systematic approach allows for evaluation at different stages, engages partners who have the authority to act of results and outcomes, and has identified a new policy (state input) which may be both more effective (at reducing local chlamydia incidence) and cost-effective (societal benefit exceeds costs). there are three main limitations of this study. the first was the lack of supplemental funding available to the participants to more aggressively act on the additional state input. all agreed to participate in this project while utilizing existing budgets and resources. as a result, the interventions were generally small in scale, utilized only existing staff with other duties, and lacked community partners and widescale activities. in spite of these limitations, one county had a significant incidence decrease. it is unknown if other counties would have had a decrease with additional resources. a second limitation is its scale. it was tested in only five counties during one short portion of the year. the five test counties were chosen based upon a single large city within their jurisdiction and a high chlamydia rate. it is unknown if performing the study during a different time (e.g. summer), or if measuring incidence for a longer time period post-intervention, would have had different results. it is also not known if providing the same state inputs into more, and more diverse counties (in terms of size, population and location) would have returned similar results. development and evaluation of gis-based chlamydia trachomatis intervention policy in illinois 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 finally, the assignment of control counties to participants (cases) is imperfect at best. while vermilion’s decrease was not matched by its assigned control, two other control counties did experience significant decreases. more work will have to be done in examining vermilion county’s intervention, and its subsequent incidence, before a better determination can be made as to its effectiveness. references 1) quinn tc. recent advances in diagnosis of sexually transmitted diseases. sex transm dis. 1994 mar-apr;21(2 suppl):s19-27. 2) centers for disease control and prevention. sexually transmitted disease supplement, chlamydia prevalence monitoring project. atlanta, ga: u.s. department of health and human services, centers for disease control and prevention, october 2004. 3) sexually 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strengthening ehealth systems to support universal health coverage in sub-saharan africa 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi strengthening ehealth systems to support universal health coverage in sub-saharan africa adebowale ojo1,2*, herman tolentino1, steven s. yoon1 1 division of global hiv and tb, centers for disease control and prevention, atlanta, ga 2 public health informatics fellowship program, division of scientific education and professional development, center for surveillance, epidemiology, and laboratory services, centers for disease control and prevention, atlanta, ga abstract the aim of universal health coverage (uhc) is to ensure that all individuals in a country have access to quality healthcare services and do not suffer financial hardship in using these services. however, progress toward attaining uhc has been slow, particularly in sub-saharan africa. the use of information and communication technologies for healthcare, known as ehealth, can facilitate access to quality healthcare at minimal cost. ehealth systems also provide the information needed to monitor progress toward uhc. however, in most countries, ehealth systems are sometimes non-functional and do not serve programmatic purposes. therefore, it is crucial to implement strategies to strengthen ehealth systems to support uhc. this perspective piece proposes a conceptual framework for strengthening ehealth systems to attain uhc goals and to help guide uhc and ehealth strategy development. keywords: ehealth, universal health coverage, global health, sub-saharan africa abbreviations: dream disease relief through excellent and advanced means ehealth – electronic health emr electronic medical record (emr) his health information system hiv human immunodeficiency virus icts information and communications technologies iom institute of medicine (iom) lmics lowand middle-income countries sdgs sustainable development goals uhc universal health coverage strengthening ehealth systems to support universal health coverage in sub-saharan africa 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi un united nations who world health organization correspondence: aojo@cdc.gov* doi: 10.5210/ojphi.v13i3.11550 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction at the 2015 united nations (un) general assembly, member countries agreed upon 17 sustainable development goals (sdgs), including sdg 3.8, with the target of achieving universal health coverage (uhc) by 2030. uhc includes financial risk protection, access to quality essential healthcare services, and access to safe, effective, high-quality, and affordable essential medicines and vaccines for all. since the alma-ata declaration of 1978, the world health organization (who) has promoted health for all, meaning that individuals should have an acceptable level of health that allows for social and economic productivity [1]. who has continued to encourage national governments to achieve uhc [2,3], defined as ensuring “all people receive the quality health services they need without suffering financial hardship” [4]. according to a 2017 who and world bank report on uhc global monitoring [5], progress toward uhc has been considerably slow, particularly in sub-saharan africa. as noted in the report, globally, 50% of people do not have access to essential healthcare, and each year, out-of-pocket healthcare expenses lead to extreme poverty for approximately 100 million people. similarly, the who computes a uhc service coverage index to indicate a country’s progress towards coverage for essential health services and financial protection for their population [6]. the index score is derived using the average of 14 indicators and reported on a scale of 0 to 100, indicating a low to a high level of uhc service coverage. as shown in figure 1, the map of african countries shows that most of the countries have an index score of less than 50. strengthening national healthcare systems is a critical step toward uhc. this includes robust financing structures and processes that spread financial risk across a population and that measures risk protection gaps and outcomes; integrated, people-centered healthcare services; adequate numbers of healthcare workers with the expertise to deliver these services; investments in programs that develop this workforce; good governance; supply chain systems for procuring, tracking, distributing, and delivering quality medicines and other interventions; and wellfunctioning, scalable, and interoperable health and related information systems. aside from improving these services, uhc also focuses on how these components are funded, managed, and delivered. mailto:aojo@cdc.gov* strengthening ehealth systems to support universal health coverage in sub-saharan africa 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi who notes that achieving uhc is difficult without the support of ehealth [7], defined as the “cost-effective and secure use of information and communications technologies (icts) for health and health-related fields, including healthcare services, health surveillance, health literature, and health education, knowledge and research [8].” ehealth can solve or prevent health problems, improve access to healthcare systems and services, and improve health outcomes [9]. in line with uhc objectives, ehealth has the potential to improve access and quality of care, expand coverage, minimize the cost of accessing healthcare, enable connectivity in healthcare systems, and build healthcare capacity [10–12]. via interoperable health information systems (his), ehealth can provide the information needed to ensure accountability and to monitor progress toward uhc goals [13,14]. as such, who member countries have agreed on the importance of ehealth for strengthening healthcare systems and attaining uhc [15]. however, the design, development, and implementation of ehealth systems are not without challenges. for instance, most ehealth systems in lowand middle-income countries (lmics) are not yet scalable and sustainable [16,17]. challenges include policy and governance, financing, the use of standards, and workforce capacity. similarly, the who [7] conducted a global survey on ehealth development and their role in achieving uhc. the report, published in 2016, showed the state of development on different components of ehealth across who member countries. a variable of interest is the availability of a national electronic health record (ehr) system, a possible indicator of the extent of a country’s population covered by ehealth. findings from the survey, as depicted in figure 2, show that only five out of the 33 africa countries surveyed have a national electronic health record (ehr) system. figure 1: uhc service coverage index in africa (data source: who [6]) strengthening ehealth systems to support universal health coverage in sub-saharan africa 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi figure 2: availability of a national electronic health record (ehr) system in africa (data source: who [7]) we argue that strengthening ehealth systems can accelerate progress toward uhc goals. therefore, we propose a conceptual model for strategically strengthening ehealth systems to improve uhc outcomes. we describe potential ehealth interventions that could help attain uhc outcomes; important foundational elements necessary for strengthening ehealth systems; and the proposed conceptual model. ehealth interventions and uhc outcomes in measuring progress toward achieving uhc, it is important to capture healthcare system coverage, including the number of people receiving care and the availability and quality of essential healthcare services. data on household healthcare expenditure and the proportion of household income used for healthcare are also important. who has recognized the role of ehealth in supporting the uhc outcome of ensuring quality healthcare services that cover all persons [9,18]. similarly, in the united states, buntin et al. [19] highlighted the potential contributions of the provisions of the health information technology for economic and clinical health act to achieving the objectives of the affordable care act. for instance, it was noted that the use of health information technology by service providers for individual-level information management could contribute to efforts aimed at quality improvement, cost reduction, and increased access and coverage. in line with the who digital health interventions classification [20], we examined the potential of stakeholder-driven ehealth interventions as a means to attaining uhc outcomes. strengthening ehealth systems to support universal health coverage in sub-saharan africa 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi quality of healthcare services access to quality healthcare services is one of the fundamental tenets of uhc. a publication of the us institute of medicine (iom) noted that although the number of people with access to healthcare services worldwide is increasing, these services are often of poor quality [21]. according to iom, the six key dimensions to measuring the quality of healthcare services are safety, efficiency, person-centeredness, timeliness/accessibility, effectiveness, and equity [21]. interestingly, some of these dimensions are also embedded in the access and coverage and financial risk protection dimensions of uhc, which are discussed below. accurate and timely information is needed to measure quality and contribute to quality improvement efforts. ehealth interventions such as health management information systems and health information repositories provide both individual-level and aggregate data that could facilitate quality improvement at different levels of healthcare [19]. specifically, for measures such as patient safety and efficiency, electronic medical record (emr) systems are used for managing patients’ clinical and laboratory information over time and space. thus, emrs have the potential to improve patient care and safety and also reduce waste associated with duplicate diagnostic tests, leading to efficiency [19]. similarly, studies have shown the potential of telemedicine in improving safety, especially by reducing medication errors and malpractice claims and costs [22,23]. access and coverage tanahashi model and uhc. another key dimension of uhc is that all persons can access necessary healthcare services. drawing from the widely cited tanahashi model of health service coverage [24], five key measures of healthcare access and coverage are availability coverage, accessibility coverage, acceptability coverage, contact coverage, and effective coverage. these measures are represented as cascades of successive levels in which challenges at one level affect the next, thus creating healthcare system performance gaps in quality, coverage, and affordability. mehl and labrique [25] adapted the tanahashi model to uhc and updated these measures to include accountability coverage, availability of commodities and equipment, availability of human resources, continuous coverage, and financial coverage. the updated model also served as the basis of the who recommendations for digital health interventions for healthcare systems [18]. accountability coverage. a fundamental tenet of uhc is the ability to account for all individuals that need access to healthcare services. accountability coverage means being able to quantify the population enrolled in the healthcare system. to achieve this and promote person-centered care, ehealth systems enable countries to monitor an individual’s health across time and place, that is, including birth, key health events, and death. ehealth systems such as emrs, identification registries, civil registration and vital statistics, and health information repositories, can capture data needed to determine accountability coverage. for example, biometric data via fingerprint identification have been used to link community data and hospitals to uniquely identify individuals seeking care in two district hospitals in ghana [26]. accessibility of healthcare facilities, availability of commodities and equipment, and availability of human resources. to achieve the uhc objective of quality healthcare services, healthcare facilities need sufficient qualified and motivated workers and accessible essential commodities strengthening ehealth systems to support universal health coverage in sub-saharan africa 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi and equipment. ehealth systems, such as telemedicine and client communication systems, can be used to remove barriers that may prevent individuals from accessing healthcare services. telemedicine has been used to bring healthcare services to hard-to-reach populations and enhance learning in instances of insufficient expertise [27,28]. for example, the disease relief through excellent and advanced means (dream) program operating in sub-saharan africa [29] has used telemedicine to improve the knowledge of local healthcare workers in treating neurological disorders in patients with hiv. furthermore, human resource information systems have been used to deliver interventions addressing healthcare worker shortages, training, and regulatory concerns in kenya and zambia [30]. logistics management information systems, such as cstock in malawi, have been used for reporting stock data, thereby enhancing supply chain management [31]. contact coverage and continuous coverage. in the context of equity in healthcare service access, contact coverage is the number of individuals who are in contact with the needed service. continuous coverage, on the other hand, is the extent to which those in contact with a needed service can complete the full course of intervention required. barriers such as affordability, negative experiences with a care provider, and social discrimination may affect individuals’ demand for healthcare services, thus affecting coverage. to improve the demand for healthcare services, client communication systems can enhance interaction between clients and providers. emrs and shared health records or health data repositories can ensure continuity of care and allow for longitudinal tracking of patients. for example, studies have shown that the use of text messages for patient education, appointment and medication reminders, and integration of laboratory results with medical records improved maternal retention and antiretroviral therapy adherence among hiv-positive pregnant women [32]. effective coverage. effective coverage refers to the number of individuals in need of healthcare services who receive quality and satisfactory services. sometimes, healthcare providers’ inefficiencies may contribute to patients’ dissatisfaction with healthcare services, thus leading to reduced effective coverage. as noted earlier, telemedicine interventions could serve as a learning and collaboration platform for healthcare providers. furthermore, the use of decision support systems can improve providers’ knowledge of evidence-based best practices and increase their efficiency and effectiveness. for example, in south africa, primary care clinicians use a checklistbased decision support tool for common health conditions [33]. similarly, a study assessing a decision support system for disease surveillance in sierra leone reported that the system aided decision making for operational tasks while also reducing the time spent on data analysis [34]. financial coverage. a key objective of uhc is to ensure that the cost of using healthcare services does not put people at risk for financial hardship. one of the performance measures is the number of individuals protected from poverty due to receiving healthcare services. this implies that there are healthcare financing mechanisms in place to ensure effective and efficient service delivery while minimizing costs to patients. healthcare finance and insurance information systems can deliver interventions that encourage clients to seek healthcare services and providers to perform them. similarly, such systems also facilitate managing health insurance issues such as membership enrollment and verification, and claims management. for example, in kenya, studies have shown how m-tiba (http://m-tiba.co.ke), a mobile health application, helps families save money for future healthcare costs [35]. strengthening ehealth systems to support universal health coverage in sub-saharan africa 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi ehealth foundations most lmics struggling toward uhc also face challenges related to ehealth policy implementation, leadership, funding, infrastructure, and workforce capacity [16,17]. for example, investments and funding required for complex interoperable his design, development, implementation, and use are not readily available and sustainable in the long term. simultaneously, the benefits of large investments in his are often not readily visible in the short term. similarly, gaps in economic evaluation can hinder continued financial investments in ehealth projects [36]. furthermore, countries often lack the informatics workforce capacity needed to design, develop, implement, and effectively use ehealth [10]. finally, most countries lack a national-scale ehealth infrastructure, which hinders interoperability needed for linking disparate his [11]. we adapt the informatics-savvy health department framework [37] to conceptualize what is needed to optimize ehealth systems to help achieve uhc. the informatics-savvy health department framework was developed as a call for health departments to implement measures needed for evolving information needs. an informatics-savvy health department or ministry has “a clear vision, strategy, and governance for information management and use; a workforce skilled in using information and information technologies; and well-designed and effectively used information systems” [37]. thus, the framework is comprised of three main elements: vision and strategy, competent workforce, and well-designed information systems. we add ehealth financing as a distinct element to be considered as part of the ehealth foundations. while one may argue that ehealth financing is already a component of vision and strategy, we posit that financing is a crucial factor to be considered in strengthening ehealth systems, particularly in sub-saharan africa, where lack of funds has often been cited as a major sustainability challenge [38,39]. vision and strategy a vision statement and strategy can help position ehealth to support uhc. this addresses a fundamental and pervasive challenge of ehealth projects that have been unable to reach scale in most countries. key issues such as an existing national ehealth strategy, knowledgeable leadership, sustained funding, and strong information partnerships are important indicators of vision and strategy. a national ehealth strategy helps coordinate ehealth activities in a country and should reflect the country’s ehealth vision, action plan, and monitoring and evaluation plan [40]. ehealth strategies facilitate the development and adoption of standards for interoperability, and the required regulations needed for the ehealth ecosystem to thrive. although some countries have developed an ehealth strategy, there is still scarce evidence of implementation and outcomes. similarly, leadership is fundamental to driving strategy and implementation actions. knowledgeable, decisive, and supportive leaders are crucial to implementing change, establishing and sustaining partnerships, and motivating the workforce toward the realization of a country’s ehealth goals [16,41,42]. considering the diverse stakeholders involved in planning and implementing ehealth projects, it is important to develop strong information partnerships to align the country’s ehealth goals. strengthening ehealth systems to support universal health coverage in sub-saharan africa 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi ehealth financing integral to the successful implementation and sustainability of ehealth interventions is balanced ehealth financing approaches, including short-term and long-term funding, enterprise funding, and programmatic funding from internal and external sources. the implementation of ehealth systems requires significant capital investment at the outset. also, maintenance and sustainability of ehealth systems require continuous funding, which could be justified by quantitative and nonquantitative articulation of benefits—capacities usually scarce in low-resource settings. abu taher et al. [43] revised the tsuji-akematsu model of quantifying ehealth benefits to propose that if the cost of using ehealth system could be quantified for each user, these calculations could be used to indicate a revenue source for financing ehealth systems. for instance, in a system where health is financed through users’ contribution, the cost of using ehealth systems, when known, could also be added to the cost of using healthcare. given potential, systematic, and ehealth-enabled percapita quantification of costs and benefits of ehealth interventions, we posit that healthcare financing models adopted by countries to support uhc could also be applied to financing ehealth. competent workforce studies have shown that the ehealth workforce in sub-saharan africa is insufficient, with some countries even lacking the skills needed to drive ehealth initiatives [41,44]. the ehealth workforce is expected to be able to “discriminate vast amounts of information and extract and synthesize knowledge that is necessary for clinical and population-based decision making” [45]. strategies are needed to motivate the existing workforce to improve their knowledge and skills and to teach these healthcare workers. similarly, strategies that align with ehealth goals are needed to recruit and retain healthcare workers. lastly, training institutions can develop and strengthen academic programs in health information and communications technologies to ensure the continuous availability of a well-trained workforce. well-designed ehealth architecture the importance of a well-designed ehealth architecture cannot be overemphasized. linking disparate information systems is critical for the accountability objective of uhc. a key indicator is an ehealth enterprise architecture that establishes the various data sources required by the country’s healthcare sector and addresses interoperability, data standards, security and confidentiality, and information systems (software, hardware, and infrastructure) issues. this, in addition to plans documented in a national ehealth strategy, can guide a country toward attaining ehealth goals [46]. conceptual model we propose a framework for strengthening ehealth systems to support uhc outcomes and to help guide joint uhc and ehealth strategic planning (figure 3). our conceptual model is a logic model with three main components: ehealth foundations, stakeholder-driven ehealth interventions, and uhc outcomes. ehealth foundations, as adapted from the informatics-savvy health department framework, consist of vision and strategy, competent workforce, well-designed ehealth architecture, and financing. strengthening ehealth systems to support universal health coverage in sub-saharan africa 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi these interrelated foundational elements, as shown in the model, enable the effective design, development, and implementation of ehealth interventions. the second component of the model, stakeholder-driven ehealth interventions, is derived from who’s classification of digital health interventions, which is an effort to create a taxonomy of the digital and mobile technologies used to address healthcare system challenges. these interventions, which we believe should be driven by relevant stakeholders, are broadly classified based on the primary users: clients, healthcare service providers, healthcare system managers, and cross-cutting data services. the model shows the various ehealth interventions that could support the measurement and improvement of uhc indicators. the third component of the model is the uhc outcomes: quality healthcare services, access and coverage, and financial risk protection. the quality dimension of uhc, as shown in the model, is derived from iom’s definition of healthcare quality [22]. the dimensions of access, coverage, and financial risk protection are adapted from the tanahashi model of healthcare service coverage [25] and mehl’s and labrique’s cascading model [26], which prioritizes mhealth strategies for attaining uhc. figure 3: a conceptual strategic framework for leveraging ehealth to support universal health coverage however, for ehealth interventions to deliver expected benefits, especially in sub-saharan africa, vision and strategy, adequate financing, a competent workforce, and a well-designed ehealth architecture are important components that should be considered. although this model was conceptualized for supporting uhc outcomes, it could apply to various disease domains (figure 4). for instance, in the hiv domain, programmatic efforts are aimed at improving the quality of services, access, coverage, and financial risk protection for patients. strengthening ehealth systems to support universal health coverage in sub-saharan africa 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi strengthening ehealth systems could enable the design, development, and implementation of ehealth interventions that support quality hiv care and services, thus reducing new infections and aids-related deaths. similarly, ehealth interventions could lead to better coverage in terms of increased uptake in hiv testing and increased antiretroviral therapy coverage. for example, studies have shown that a common ehealth intervention in the hiv domain is using mobile technologies like text messaging and mobile applications to improve communication between patients and care providers, enhance appointment and medication adherence, improve uptake of testing, and support remote monitoring of patients [47–49]. in the case of financial risk protection, m-tiba helps users save funds for their healthcare expenses. the application provides health insurance functionalities that allow users to receive funds from government, donor agencies, or family members; save funds; and spend saved funds only on medical treatment [35]. strengthening ehealth systems to support universal health coverage in sub-saharan africa 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e17, 2021 ojphi figure 4: the conceptual strategic framework for leveraging ehealth applied to the hiv disease domain strengthening ehealth systems to support universal health coverage in sub-saharan africa 12 ojphi limitations the conceptual framework proposed in this paper is an adaptation of existing models. as such, it should be subjected to further refinements and empirical tests. conclusion we propose a framework for strengthening ehealth systems to support the attainment of a country’s uhc goals and to inform joint uhc and ehealth strategy development. although the role of ehealth in supporting uhc objectives has been recognized, few publications have suggested appropriate strategies that can be used to implement effective ehealth systems. we posit that countries can strategically strengthen their ehealth system for supporting the uhc outcomes of quality healthcare, access, and financial risk protection via ehealth systems based on vision and strategy, adequate financing, a competent workforce, and a well-designed architecture. the proposed framework could aid countries in planning for supporting uhc with ehealth or in evaluating and identifying gaps in ehealth to achieve uhc. the framework can also be applied to other disease domains as part of ongoing efforts to strengthen healthcare systems. further research exploring the direct contribution of ehealth interventions on uhc outcomes is recommended. acknowledgments this publication has been supported by the president’s emergency plan for aids relief (pepfar) through the centers for disease control and prevention (cdc). disclaimer the findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the centers for disease control and prevention (cdc). conflicts of interest: the authors have no conflicts of 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https://doi.org/10.1186/s12879-018-2972-5 https://doi.org/10.1186/s12879-018-2972-5 https://pubmed.ncbi.nlm.nih.gov/28983684 https://doi.org/10.1007/s10461-017-1923-2 paper details improving agent based models and validation through data fusion 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 improving agent based models and validation through data fusion marek laskowski, bryan c.p. demianyk, marcia r. friesen, robert d. mcleod and shamir n. mukhi 1 internet innovation centre, university of manitoba 1 the canadian network for public health intelligence (cnphi) 1015 arlington street, winnipeg, mb abstract this work is contextualized in research in modeling and simulation of infection spread within a community or population, with the objective to provide a public health and policy tool in assessing the dynamics of infection spread and the qualitative impacts of public health interventions. this work uses the integration of real data sources into an agent based model (abm) to simulate respiratory infection spread within a small municipality. novelty is derived in that the data sources are not necessarily obvious within abm infection spread models. the abm is a spatial-temporal model inclusive of behavioral and interaction patterns between individual agents on a real topography. the agent behaviours (movements and interactions) are fed by census / demographic data, integrated with real data from a telecommunication service provider (cellular records) and person-person contact data obtained via a custom 3g smartphone application that logs bluetooth connectivity between devices. each source provides data of varying type and granularity, thereby enhancing the robustness of the model. the work demonstrates opportunities in data mining and fusion that can be used by policy and decision makers. the data become real-world inputs into individual sir disease spread models and variants, thereby building credible and non-intrusive models to qualitatively simulate and assess public health interventions at the population level. keywords: agent based modeling; personal contact patterns. introduction complex networks underlie the transmission dynamics of many epidemiological models of disease spread, in particular agent based models (abm). network-based epidemiological models use a percolation-like principle to simulate disease spread through the population [1]. agent based models are being increasingly employed due to their potential to capture complex emergent behaviours during the course of an simulated epidemic, where these behaviours arise from the non‐linearities of human-human contacts. abms may employ an explicit or implicit social contact network defined by structured agent interactions. in the explicit case, a disease model (e.g., susceptible ‐ exposed ‐ infected ‐ recovered, seir type) can be implemented directly on the network, although in the case of abm, these resemble simulation models rather than the steady state analysis of network based models mentioned in [1]. in all cases, though, the fidelity of an agent-based model relies in part on the credibility of the social contact network data that feeds it. potential data sources include census and improving agent based models and validation through data fusion 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 demographic data (coarse) and finer-grained data made availability by various means of polling personal electronics such as cell phones. in related work, it was demonstrated that data to model a social contact network can be collected through web services or wireless sensory devices or “motes” worn by individuals in the target population and subsequently used in an infectious disease spread model [2]. such an approach has been previously undertaken to gather data, for example in an organization (workplace or school). the resulting estimated social contact network was used to model an influenza‐like illness (ili) within the setting [3], based on a standard seir type model. in this time‐stepped model, infection spreads between two vertices (individuals) along the weighted edges of the network which represent the amount of social contact between the two individuals. estimating social contact networks in larger populations, (metropolitan scale or larger), is an area of research still in its relative infancy. in cases where precise contact network data is unavailable, an alternative is to mine data as done by episims [4] which uses united states department of transportation information to estimate the schedules of the agents in several metropolitan areas. this presumes that the choices of places for agents to interact is constrained by the transportation network (model), which itself is a complex network. schedules for the agents are synthesized from census and usdot data. a simulation is then run during which a synthetic contact network is constructed from the interactions of the agents and their locations. the resulting dynamic bipartite graph [4] is used to simulate disease spread in the manner stated earlier, except on a much larger scale. both episims and another well‐vetted infectious disease simulator, biowar [5], initially perform validation on model components separately. this is an important component of plausibly reasoned argument, supporting the statement that the model as a whole functions as specified. the objective of the present work is to investigate methods to begin validating abms in varying stages of development by comparing extracted contact networks to known theoretical social contact network models. ideally, networks which embed some notion of space or time will be essential drivers of disease spread in the real world. thus, extracted networks may need to be weighted, for example, to associate weight with the time period during which two agents were in contact. the first such model is of a rural community in the province of manitoba, canada. the emphasis in this work is in integrating data from emerging sources that can be used within discrete time and space disease spread abms. the contagions of interest are influenza like illnesses (ili) or other respiratory infections that are primarily contracted through direct or proximal contact. methods in the first part of the study, we discuss a small scale abm of two adjacent communities in the rural municipality of stanley, manitoba with a combined population of approximately 16,500 residents: winkler, manitoba at 10,000 residents and morden, manitoba at 6500 residents. this is a spatial temporal model with demographic data coming from statistics canada [6]. from this perspective, agents are provided with schedules, and a model of disease spread is run. figure 1 illustrates the topography of the region of interest. improving agent based models and validation through data fusion 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 1: the topography of the rural region of interest the towns of morden and winker are roughly seven miles apart in southwest manitoba. one of the reasons for selecting this area is that it is representative of many north american rural municipalities. figure 1 also illustrates the location of three cellular service towers with mts allstream as the service provider. the abm is discussed in terms of model validation using data that is mined from anonymized cell phone use records. in addition to cell phone usage, the model is also improved using a smartphone application that provides greater fidelity of proximity contacts using bluetooth enabled devices as proxies for people. there are two primary obstacles to fusing data to a model. the first is the collection of the data, with assurances that the data collected is meaningful and accurate, and mining or interpreting the data for parameters or characteristics useful to the model. the second difficulty is integrating the data into the model itself, running simulations and attempting to qualify (and ideally, quantify) the outputs. in many instances, the results of the simulations may be self fulfilling, as in, overcrowding in isolated and impoverished communities leads to increased infection spread. the interventions that one could model may provide guidance for policies that may then be considered. for example, an intervention associated with reducing infection spread may be a recommendation to stay home while ill; in overcrowded residential communities a more effective intervention may be quarantine or a modified quarantine policy whereby an infected person may be advised to seek temporary housing in a facility set up specifically for that purpose. while somewhat self-evident, modeling with real data may help to elucidate these types of options or interventions. the model and abm simulator the model described here is a milestone in the process of designing and implementing an abm simulation framework geared towards high fidelity modeling of human institutions of varying scales. the broad design goals of this framework, called simstitution, are based on the collective experience of the authors gained while developing agent based models of human institutions. originally, models of hospital emergency departments [7] and cities [8] were implemented upon “one-shot” simulators, that is, a simulator strongly coupled to the specific modeling application [9]. a one-shot simulator is comparatively easy to implement, and gives the modeler fine control over the simulator processes, enabling them to fulfill their requirements. typically, in order to minimize development effort, the designer will make assumptions which ease the implementation of the model at hand, without consideration for how these assumptions will constrain or complicate re-purposing the simulator to implement a different model. from a software engineering perspective, part of the reason that one-shot models are so easy to produce is that little or no effort go into making the software reusable or improving agent based models and validation through data fusion 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 extendible. the large number of one-shot simulators observed in the literature [9] is problematic because by their nature they are difficult to re-use. the reusability of the simulator in turn affects the reliability of the simulator; the more researchers that (re)use a particular simulator the more chances that bugs will be identified and fixed. furthermore, when a number of models produce reasonable results using a common simulator, confidence in the credibility of the simulator is increased. publishing results from a series of models built upon a common simulator framework, combined with verification of model components (or submodels), is a common path for building confidence in simulator frameworks for epidemiological modeling [4][5]. simstitution design goals although there are several frameworks [10]-[15] which can be used to develop agent based models, these are dwarfed by the number of one-shot or otherwise domain-specific simulators, suggesting that no framework has yet hit upon a “sweet-spot” between flexibility, extendibility, and specific support classes for human-centric domains [9]. human-centrism includes the notion that agents are spatially oriented and situated since humans are physical entities that occupy and traverse space, rather than existing in some abstract information domain. simulator support for a range of human time steps on the order of seconds to hours or days is also desirable. other design features include adherence to software engineering principles to improve re-use and maintainability of the framework, as well as extendibility especially where machine learning can be leveraged for automated generation of agent policy [16][17]. for rapid model construction, a next generation abm framework should facilitate the incorporation of real-time data such as from database leading to increasingly data-driven simulation. a tool for visualization and interacting with the model in a graphical manner (gui) also facilitates model development, validation, and debugging. visualization is also key for communicating results with subject matter experts and stakeholders [18]. such a visualization tool can also be extended to serve as a tool for model construction or editing model parameters imported from real data. the accessibility of agent behavior development to persons with a non-programming background can be improved by first providing a scripting layer on top of the compiled code, and then perhaps adding a visual or block (e.g. openblocks [19]) programming (drag and drop) on top of that. over time a library of useful scripted behaviors can be built up. the increasing availability of parallel or distributed computing systems also suggests that contemporary or future agent based simulator frameworks have support for distributed, parallel, or cluster computing. the increasing availability of cluster-based compute resources (a consequence of moore’s law), sensitivity to real-time computational constraints, and medical data privacy issues augur well for cluster-based computing. as a result, the simstitution design emphasizes scalability with respect to multiple processors and discrete memory spaces over efficiency in executing one particular type of model. currently, there is an emergence of general-purpose computing on graphics processing units (gpgpu) as excellent accelerators for data parallel applications with regular data access patterns. this leads to opportunities for accelerating agent based simulation as well. however, optimization is still challenging, as the data access patterns are still somewhat irregular for improving agent based models and validation through data fusion 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 most abms. currently, gpus are very well suited to abms that resemble cellular automata, percolation, game-of-life, or particle swarm models. without doubt, higher level abm (social autonomous interacting agents) simulations will also benefit from the compute resources of gpus as the technology evolves (optimizing compilers, etc.). naturally limiting the degree of accessibility of the environment limits what agents can perceive and interact with in the environment (including other agents). localizing agent perception, not only fits in well with the agent paradigm, it also limits to what extent information needs to be shared between processes in a distributed model, which should facilitate using spatial decomposition as a guide for distributing computational load. these disparate goals require balance in feature choice and design. simstitution design details simulated entities within simstitution fall into either of two major categories; agents (simagent), which are the autonomous entities that make decisions and interact with the environment; and instances of the simregion class, which represent spatially partitioned subdivisions of the environment. note from figure 2 that the simobject is abstract, and exists because simagent and simregion have much of their interfaces in common. figure 2: class diagram for core simstitution class hierarchy one of the core design tenets of simstitution is that the spatial division is closely intertwined with the division of computational work across processors and discrete memory boundaries. therefore, simregion is unit of spatial decomposition as well as a convenient unit of computation. in the latter role, it can be considered as a container for agents that need to have their next state computed. figure 3 illustrates the details of this relationship. a particular instance of simregion can be the parent container of simagents or simregions but not both types at the same time. this restriction will in practice result in tree hierarchies of simregions, with simagents contained in the leaf simregions, and the “top region” at the root of the tree. the simregion spatial decomposition granularity becomes increasingly fine away from the root and towards the “leaf regions” of the tree. time advances in the simulation when the simulator advances the time of the top region (root of the tree) by some discrete time step. the top region will then advance the time of its children by the same time step in a recursive fashion such that the tree is traversed in a depth first manner until all the simagents in the leaf regions have been simulated for that time step. the simulator will restart this process again, until a certain number of time steps have elapsed. improving agent based models and validation through data fusion 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 3: relationships between core class instances, forming a tree individualpolicy is a modular unit that affects the behavior of the subscribed simagent, which may also require the individualpolicy to store encapsulated simagent state data specific to that individualpolicy. examples are a schedule policy which causes the simagent to observe a particular day/night work/home schedule, or in the case of a hospital being modeled, a doctor policy which causes the simagent to treat patients within a hospital. within a simregion, each possible concrete derived individualpolicy class has a corresponding grouppolicy for that simregion. the grouppolicy acts as a factory for the corresponding individualpolicy and, if required, facilitates coordination between one or more derived individualpolicy classes (ex. healthcare worker policy in a hospital that coordinates interaction between nurse and doctor individualpolicies). implicit here is the assumption that the properties of the local environment constrain the behavior of agents (ex. airport security lineup, swimming pool, hospital, bank, etc.). the associations between simregion, simagent, grouppolicy, and individualpolicy are shown in figure 4. figure 4: relationships involving modular agent policies communication or interaction between simagents exclusively uses messages passed between simagents. messages received by a simagent are relayed to its individualpolicies which can lead to an internal change of state, or an action to be taken which could lead to additional messages being sent to other individualpolicies on the same subscribed simagent, or messages sent to other simagents. message passing fits well with the agent paradigm, since the alternative implies a direct mapping between external events and internal agent state which violates the principle of agent autonomy [20]. improving agent based models and validation through data fusion 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 details of small town model morden the current work incorporates the framework features mentioned in the previous section, and includes visualization capabilities to observe emergent model behavior during execution. the model is fairly basic so the simregion tree only consists of two layers; the root or top simregion (morden) and the leaf simregions which represent the home, school, and work locations that agents occupy. the leaf simregions are arranged in a grid with empty spaces between structures to allow for simagent travel. agents are assigned work, school, and home locations based on demographic data [6]. figure 5: screenshot of running simulation. morden (left), close-up of 6 classrooms (right). figure 5 shows a screenshot of the morden simulation at a particular time step. on the left side the entire city is shown. on the right is a detailed view of six classrooms in the center of town in which individual simagent details can be seen. details include the gender and age of the simagent, as well as disease status. disease status is the most interesting, and is indicated by the color of the simagent icon. the icon changes color, with green indicating a susceptible state. once the agent is infected it turns yellow, orange, and red depending on how long they have spent in the infected state. finally, recovered simagents turn blue. the leaf simregions are depicted as colored squares where the color of the square shows the aggregated disease state of the simagents within that region. simregions with no simagents contained inside are white. those with one or more simagents display a blended color tile based on the aggregated disease state of the simagents inside. four concrete individualpolicy subclasses were used to generate the simagent behavior in the morden model. the schedulepolicy determines whether a particular agent wants to be at its assigned work, school, or home, depending on the demographic profile of the particular simagent, and the current time which advances in increments of one hour. the schedulepolicy sends messages containing the desired destination to the simagent’s movementpolicy which handles the actual movement. the influenzapolicy maintains the particular simagent’s disease state, and if in the infected state, sends “infection” messages to other simagents in the same simregion, which is how disease spreads between simagents. finally, the bluetoothtrackingpolicy emulates the bluetooth smartphone contact app, and is the source of the synthetic contact data. currently the corresponding grouppolicies were used to facilitate aggregation of data in a spatially explicit manner to achieve the tiling effect in figure 5. improving agent based models and validation through data fusion 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 framework roadmap – next steps the next step or milestone will be to extend the framework by developing modules to simulate finer granularity time and space, namely facilities for agents finding paths and steer through complex environments at time steps on the order of seconds. one such prototypical institutional environment would be improved hospital models [7]. following that, we intend to scale up the number of agents, leveraging parallelism where possible to determine whether spatial partitioning will facilitate execution speedup and if so, under what conditions. in a concurrent development process (possible due to the modularity of the design) tools are also being created to facilitate the integration of increasingly detailed data such as street maps and demographics of places such as morden. in order to promote the ideals of software re-use, once the core simstitution simulator has reached a reasonable level of functional maturity, the code will be made available to other researchers under a general public license. results augmenting data sources in addition to demographic data, the two sources of augmenting data here are associated with coarse grained data from anonymized cellular records and a finer grained smartphone application programmed to log close-proximity bluetooth devices. data from cellular records typically provide service providers with input for network planning, investments, and management of evolving needs. this type of data also has considerable application to public health interests, although at this time it is difficult to derive its direct benefit in contrast to more explicit inputs such as those associated with census and demographic data, due to both technology and policy issues. cellular data data from four consecutive weekdays in november 2010 was extracted from the data provided by the cellular service provider. the data includes the cell tower gps and antenna sector (if applicable) that the mobile device is associated with, the aaa record (every time the phone accesses the network excluding voice and sms), and time stamp of the access. even at four days, this represented just over 14 gb of data. once processed for the connections with the towers of interest (figure 1), this amounted to just under 500,000 records. although statistical in nature, the data can be further processed to estimate flux of persons between the two neighboring towns. within an infection spread model, this type of information helps in estimating patterns of movement that contribute to infection spread. once stored in a database, queries allowed for extracting anonymized device activities. figure 6 illustrates the breakdown of mobile devices accessing the towers in morden and/or winkler. for an individual, a duty cycle can be estimated, illustrating the percentage of time a person is likely to be in one region or another. the timestamp can also be used to infer primary community of residence. users counts here indicate that approximately 2650 users remained in morden, approximately 485 users remained in winkler, while 2285 users spent time in both morden as well as winkler over the four day data collection period. improving agent based models and validation through data fusion 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 6: morden and/or winkler mobile user aggregates this data can be refined further based upon those with access records in both morden and winkler. figure 7 illustrates the breakdown of users who access cell towers in both communities over the duration of a single connection of their cellular device to the network. the actual device accesses between the two communities break down as approximately 65/35, reflecting durations more accurately. figure 7: breakdown of users with records in both communities bluetooth smartphone data the second source of data was a smartphone application designed to poll its local environment on regular intervals for close-proximity bluetooth enabled devices. the application is representative of automated and non-intrusive proximity data collection methods where it is tacitly assumed that consumer electronics serve as proxies for their users. this assumption has limitations, including the disproportionate distribution of cellular devices within a given population to certain demographic subsets; yet, arguably these techniques have increasing credibility as more and more people carry electronic devices. to date, a pilot test has been undertaken with four smartphones collecting data on close-proximity bluetoothenabled devices for just over a three month period. during this time approximately 500,000 records were collected. platforms to date include blackberry storm and htc hero devices. data includes the mac and any assigned meta-identity of both the probe device and the polled (probed) device, the timestamp, and a location if the probe device is gps-enabled. improving agent based models and validation through data fusion 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 8 illustrates samples of the data collected and residing on the database. some records provide more information than others and as such, several records are perhaps more interesting than others. the second highlighted row indicates a device called “general motors”, scanned while the agent 2 probe was on a local highway. many other devices are much more easily identified and more easily associated with actual persons. culling of bluetooth devices that are not obviously a person is possible but not undertaken here at this time. figure 8: a sample of data collected the bluetooth contact data is conjectured to be a type of data that can be described by empirical laws. the distribution used follows the pareto law. pareto's law is given in terms of the cumulative distribution function (cdf), i.e. in this case the number of contacts (nc) with duration larger than or equal to a duration is an inverse power of the duration as expressed below: from the pareto distribution, a power law exponent was calculated and varied from 1.4 to 1.75 for the four probe devices used (r 2 values were consistently above 0.95). a power law exponent less than 2 implies that there is no first moment or mean associated with the distribution. as the data obtained from the probe devices is finite, a mean can be calculated, though. an interesting but not surprising parameter that can be extracted from the pareto principle is the 80/20 rule. from the data collected, the 80/20 rule was applied to indicate the number of contacts that comprised 80% of the total contact duration. from this, it was estimated that 80% of a person’s time is spent with a number of personal contacts that varied between 7 and 20, for the four probe devices. this was extracted from the number and duration of contacts with approximately 5,000 unique bluetooth devices probed. this is consistent with intuition that although the total number of daily contacts may be large, the majority of one’s time is spent with only a small number of people. evolving the abm this section discusses how models, in this case the abm can be improved and validated to some degree through inclusion of as many data sources as practical. the first and most obvious would be using as accurate demographic data as possible. the abm developed here is based on data obtained through the federal census by statistics canada. in addition, models of schools have been refined to provide for reasonable class sizes, data which are estimated here but would benefit from using real data of this type. with this model, a disease spread simulation was run and provided a baseline for modeling the spread of a respiratory improving agent based models and validation through data fusion 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 infection or ili. figure 9 illustrates the spread of a disease among a urban community, represented by morden, in isolation. figure 9: sir disease spread simulation in the first effort to improve the basic abm, it was instrumented in terms of agent contacts and durations which should reflect the patterns in data extracted from the bluetooth probe devices. the objective was to see how well the model reflected real person-person networks. for the baseline simulations of the single town abm typical contact patterns for all agents were instrumented. the results of this analysis are summarized as follows: figure 10: rank ordering of all agents (aggregated) figure 10 illustrates the rank ordering aggregated over all agents. the rank order exponent (zipf’s law) is approximately 1.9. this yields a estimated power law exponent of approximately 1.53. the implication is that an agent’s contact pattern would follow a power law distribution (heavy tail) without finite moments. this result is expected from both the bluetooth proximity pilot as well as well intuitive perceptions of real face-to-face contact patterns. this instrumentation of the abm helps validate it as approximating real world contact patterns. from these abm simulations and the aggregated rank orderings, an 80/20 rule can also be estimated. in this case, 80% of the contact durations are spent with approximately 4% of a person’s contacts (25/670). this again is consistent with data extracted from the bluetooth data collection pilot. figure 11 illustrates the rank ordering of contact parameterized by demographic. intuitively these profiles appear reasonable. school age children spend considerable time with three groups, household members, school classmates, and friends. the knee in the curve of school age children is between 20 and 32. for samples of age groups the exponents associated with zipf’s law are presented in table i. perhaps it is also intuitive that a 2 year old and a 70 year old have similar contact patterns, presumably though improving agent based models and validation through data fusion 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 the 2 year old eats more dirt. also the distribution of the adults perhaps reflects the famous quote by american philosopher and naturalist henry david thoreau who said, “the mass of men lead lives of quiet desperation”. this type of parameter extraction is also consistent with actual survey results reported in [21]. the consequence of the rank ordering implies that the coefficient associated with the corresponding pareto distribution would be between 0 and 1. the lack of a finite mean in the corresponding contact pdf approximation would imply that a few long duration contacts are a significant vector of infection spread. in these cases the (heavy) tail wags the dog. figure 11: rank ordering of agents of different demographics table i. zipf exponents for various demographics age zipf exponent r 2 2 -1.86 0.76 6 -1.51 0.80 12 -1.85 0.78 16 -1.66 0.80 20 -2.0 0.94 30 -1.87 0.96 40 -1.95 0.95 50 -1.95 0.97 70 -1.50 0.85 discussion other means of validating the data from a simulation like this abm includes its relation to other types of published data. for example, in [21] contact patterns are analyzed as derived from a large population survey that indicated that for their preliminary modeling “5to 19year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible”. these expectations are consistent with the contact patterns generated by our abm. improving agent based models and validation through data fusion 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 in the second instance of enhancing the abm, it was recognized that morden does not exist in isolation and as such, flux of persons into and out of the area is required. this is not unlike large scale efforts where simulations are based upon data extracted from airline travel, for example. in this case the data albeit voluminous is reasonably extractable. it is more difficult to obtain inter-community travel in rural settings. in this environment, there are few if any directly available data sets but rather opportunities for inferencing from more disparate sources. although an abm running a bounded topography may be applicable to geographically isolated communities, in semi-rural settings there is considerable interaction with surrounding towns that need be accounted for. from figure 6, an indication of interactions between morden and winkler can potentially be inferred from cellular tower access. the data suggests that of the cell phone carrying persons (approximately 4000) with primary residence in morden, approximately 34% are seen to have records in both winkler and morden, with that person spending on average 65% of their time in morden and 35% in winkler. similarly of the approximately 1400 phone carrying persons with primary residence in winker, approximately 65% are seen to have records in both winkler and morden, with that person spending on average 65% of their time in winkler and 35% in morden. these very coarse estimates nonetheless allow one to begin modeling multiple communities and their interactions. one can burrow deeper into the data and determine periods of time a representative individual would spend in each community. further simulations will include representative agent movement trajectories extracted from the cell records integrated into the simulator. figure 12 illustrates a typical duty cycle associated with randomly selected users and their access to cellular towers in morden and winkler. the first two user data duty cycle plots reinforces routine activity theory as users are primarily seen in morden during the night with intertown tower records primarily during the day. the third user’s behavior is considerably more erratic. in either case these types of trajectories are required in improving interacting abms. figure 12: temporal sequence diagram of a user spending accessing towers in morden and winkler model evolution is depicted in figure 13 where external sources are integrated as they become available. at present, these are done in a manual fashion but are amenable to automation improving agent based models and validation through data fusion 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 and/or machine learning further adapting the model to the real world. in general the abm for winkler would follow similar process of development. a benefit to developing abm in this fashion is that they provide opportunities for increasing levels of computational efficiencies by exploiting parallel compute paradigms. figure 13: seir disease spread simulation conclusion this work has demonstrated the potential of incorporating disparate data sources within an infection spread abm with the objective to improve the credibility and validity of the model. the data sources included a smartphone application that estimated proximate contacts and durations to similar devices, serving as proxies for collection of face to face data. the second source of data that is underexploited is associated with cellular phone logs in helping to estimate a person’s trajectory. there are a number of limitations in attempting to incorporate real data from somewhat disparate sources. ideally one would like to compare the output of a disease spread model with major outbreaks. for a number of reasons this is not always possible. the purposes of models are to aid in understanding how effective planned interventions will be in the event of future outbreaks. as such, when using abms, an objective is to make the models as accurate as possible using real data to the greatest degree possible. this is one of the major advantages of using abm, in that they lend themselves to inclusion of real data which is correspondingly becoming increasingly available. although not modeled here, there is also a significant medical facility intermediate between morden and winkler providing an effective vector for infection spread as both patients and health care workers largely come from both morden and winkler corresponding author robert mcloed mcleod@ee.umanitoba.ca mailto:mcleod@ee.umanitoba.ca improving agent based models and validation through data fusion 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 references [1] newman m, spread of epidemic disease on networks. physical review. 2002 jul;66(1):016128. 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[21] mossong j, hens n, jit m, beutels p, auranen k, mikolajczyk et al. social contacts and mixing patterns relevant to the spread of infectious diseases. plos med. 2008;5(3):e74. doi:10.1371/journal.pmed.0050074 ojphi-06-e123.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 57 (page number not for citation purposes) isds 2013 conference abstracts automating biosense 2.0 locker processing for local program-specific surveillance harold gil*, jeffrey johnson, brit colanter and jessica yen county of san diego, health and human services agency, public health services, san diego, ca, usa � �� �� �� � � �� �� �� � objective �������� �� ���� ��� ����� �� ������ ��������� ���� ���� � ����� 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� � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e123, 2014 safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention adeola bamgboje-ayodele 1*, leonie ellis1, paul turner1 1. school of technology, environment & design, college of sciences and engineering, university of tasmania, hobart, tasmania, australia abstract objectives: diffusion of smartphones has normalised consumers’ use of mobile applications (apps). but how do app designs and contexts of use interact with differential consumer attributes to impact on their effectiveness, usability and value over time? for consumer food safety, answering these questions is of importance as numerous food choices increase challenges in safe food management (sfm). this research reports on results of a randomised field experiment with australian consumers using an sfm mobile app developed by the researchers. method: the sfm app development employed insights from the health literacy online heuristics framework and the experiment involved evaluation of information and/or knowledge acquisition from the app versus from a paper-based version. the experiment spanned four weeks and involved eight participants (experimental group n=4; control group n=4). results: the results highlight differentials in cognitive burden between paper and the app; beneficial affordances from the app for refreshing consumer knowledge; and longer knowledge retention on safe food management from app use over-time. discussion: we identified two key impacts of the app on consumer knowledge acquisition and knowledge retention. first, the sfm app takes longer to achieve knowledge acquisition but results in longer knowledge retention than the control. second, the sfm app induces some level of cognitive load in adoption however; the affordance of its reuse for quick but infrequent revisitations facilitates knowledge retention. although the study is limited by the small sample size, it however highlights the need for a large scale and purely quantitative investigation that are generalisable to the australian population. conclusion: it is anticipated that the insights gained from this study can be used to develop nationwide interventions for addressing consumer sfm knowledge gaps in the home; thus, moving a step closer towards addressing sfm behaviours of australian consumers. keywords: safe food management, smartphone applications (apps), usability, information modalities, knowledge retention. *correspondence: adeola.bamgboje@utas.edu.au doi: 10.5210/ojphi.v10i3.9542 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi 1 introduction alleviating food safety risks is a major source of concern for government authorities, the food industry and increasingly many consumers. diverse mechanisms focused on monitoring and controlling production processes across supply chains, such as iso22000 [1], haccp control systems [2], harpc control systems [3], traceability systems [4], have been employed to address many of these risks. however, most of these food safety mechanisms are largely focused on supply chain activities from ‘paddock-to-purchase’ (pre-purchase) as the legal obligations of supply chain partners on food safety tends to be completed once consumers purchase the products [5]. thus, mitigating food safety risks during the process of consumption (post-purchase), that entails domestic food management from the point of purchase (purchase) to the point of actual consumption (plate), is largely the responsibility of consumers. although the unsettling level of food poisoning outbreaks through domestic food mismanagement from ‘purchase-to-plate’ is not a new phenomenon, the complexity and dynamism of the characteristics of available foods and diversity of consumers has made it very difficult to address. for example; the varying degree of food safety knowledge has facilitated many public food safety information campaigns, education and awareness programs [6]. despite these efforts, many consumers remain inadequately informed about food safety and continue to engage in unsafe food handling practices. there are a range of approaches to support consumers in safe food management including using information and communication technologies (icts), insights from consumer behaviour theory, knowledge management practices and food safety management guidelines. the widespread diffusion of smartphones has now normalised the adoption and use of mobile applications [7]. the highly personalised nature of smartphones embody a potential userempowering characteristic [8], thus providing users with an array of capabilities and experiences that can be tailored to their interests. downloading apps onto their smartphones [9] affords consumers the opportunity to inform themselves about specific areas of interests [10] including safe food management (sfm). consumers can inform themselves about food in terms of tasks such as personalized grocery shopping apps [11], food cooking apps [12] and food storage or wastage apps [13]. this stated, a key question that arises is how do app designs and contexts of use interact with differential consumer attributes to impact on their effectiveness, usability and value over time? these issues can be examined in three ways. first, in terms of the context of use, there is evidence that existing apps provide siloed information about the various aspects (safe shopping, transportation, storage and preparation of perishable food items and appropriate kitchen hygiene practices) of domestic sfm for australian consumers [14]. second, in terms of user experiences, there is insufficient evidence that existing apps have drawn upon information modality studies that highlight differences arising from use of textual [15], visual [16], verbal [17] or integrated information modalities on consumer behaviours pre-purchase. aligned to these studies is the principle of modality effect [18], which argues that materials presented in a format that simultaneously uses the auditory and the visual sensory modality is better than by a format that uses only the visual modality [19]. however, available evidence suggests the use of this principle only within pedagogical frameworks [20] thus, it is unclear if this principle is applicable to adult consumers and whether it will improve user experience during the use of sfm apps. third, there is insufficient evidence to suggest that existing apps in sfm have been comprehensively evaluated [21] or that they were developed based on frameworks guiding mobile health safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi consumer apps. this lack of evidence raises questions about whether best practice guidelines were adhered to. significantly, there is a dearth of research that assesses how well the content of the app has been designed for consumers with considerations for both usability and health literacy. the few evaluations that have been conducted have been restricted to usability assessments and marginalised contexts of use and consumer attributes and behaviours. this research reports on results of a randomised field experiment with australian consumers using a safe food management (sfm) mobile app developed by the researchers to explore these issues more comprehensively. the next section describes the method used in the conduct of this research. 2 method this research adopted an interpretive research philosophy and deployed a mixed-method design structured in three overlapping phases. phase 1 (consumer understanding) involved the conduct of a nationwide survey to identify problems with the current food handling practices of australian consumers and their information and communication preferences (both prepurchase and post-purchase). the findings of this survey have been previously published [5]. this led to the selection of three existing apps (text-based, graphics/picture-based and integrated) that most clearly address the sfm practice being targeted to provide insight into consumers preferred styles of design. phase 2 (design) involved the heuristic evaluation of the three existing apps based on monkman and kushniruk [22] health literacy online heuristics (hloh) framework to identify problems with the apps from an expert’s perspective. following this, a second usability evaluation from the consumers’ perspective, using the apps as a high-fidelity prototype in scenario-based focus group sessions, was conducted. this research activity aimed to identify the impact of the three information modalities on consumer understanding and to generate user requirements for a new app. the outcome of this phase, which has been accepted for publication elsewhere, provided rich insights into consumer requirements for a safe food management app. this led to the design of a single smartphone application (shown in figure 1) for educating and assisting consumers on the sfm practices. safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi figure 1: an enhanced user-centred design approach source: authors phase 3 (implementation and evaluation) involved the actual implementation and evaluation of the app designed in phase 2. phase 3 is the focus of this research paper. after the sfm app was developed, it was evaluated by conducting a randomised field experiment, within a 4-week period. the aim of this research activity was to evaluate the impact of the design on the retention of knowledge on sfm practices over time. 2.1 research design randomised field experiments “allow researchers to scientifically measure the impact of an intervention on a particular outcome of interest through random assignment of study subjects” [23]. it has been argued that randomised field experiments are the ‘gold standard’ as they yield the most accurate analysis of the effect of an intervention [23]. of these techniques, stratified randomization was deemed most appropriate for this research because it addressed the need to balance and control the influence of co-variates in order to avoid any risk to the conclusions of the study [24]. whilst this method is difficult to implement for larger studies, it is deemed more appropriate and simple for smaller studies with limited sample sizes [25]. moreover, it is also appropriate for this study because all the participants were identified through the recruitment process before group assignment [24]. therefore, like skarphedinsson, weidle [26], the authors chose to incorporate stratified randomization. the two key co-variates that might influence the research are gender and age group. in this context an inclusion criterion for each potential participant to fulfil is their ability to purchase and cook meat in their own homes. there is evidence to support the argument that food preparation is still a strongly gendered household task [27]. in agreement, worsley, wang [28] have argued that cooking remains a female responsibility in australia, thus portraying the importance of gender as a co-variate in this study. second, the other criterion is the ownership safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi and use of smartphones. there is also evidence to support the argument that electronic channel preferences through the use of smartphones is a higher preference amongst younger australians [28], thus portraying the importance of age group as a co-variate in this study. to randomize participants, a stratified randomization procedure was applied using gender and age group as stratification variables, to provide a total number of strata of six as much as it was possible based on the available participants. following this, each participant was selected through simple randomization. to ensure that randomization could not be predicted in advance, the randomization procedure utilised tags only. 2.2 participant recruitment participants met the study inclusion criteria if they purchase and cook red meat; if they have a smartphone (specifically an android phone 4.0.3 or an iphone 4 and newer versions) and if they are australian adults (18 years and above). the recruitment efforts spanned across three months and delivered a final group of 12 participants out of which 4 dropped out due to family related issues. 2.3 procedure and research instrument knowledge optimisation involves ensuring that knowledge is acquired, retained and can be applied. drawing upon the revised version of bloom’s taxonomy of meaningful learning [29], achieving knowledge optimisation requires three of the six cognitive processes; remember, understand and apply. in this study, ‘remembrance’ demonstrated the level of knowledge acquired, ‘understanding’ demonstrated the level of knowledge retained and ‘application’ demonstrated the level of knowledge applied. these were assessed using multiple choice questions for knowledge acquisition; a problem based learning approach using scenario-based questions for the knowledge retention; and knowledge application process. following on from similar studies [30], ‘remembering’ has been evaluated after the use of a mobile app for knowledge acquisition [31]. in a study by ahmed and parsons [31], their method involved quantitative assessment through a post-test that was delayed for two-months after the instructional period. they also used questionnaires for the pre and post-tests. furthermore, ‘understanding’ has been evaluated after the use of a mobile app for knowledge acquisition in many studies [32]. what these studies have in common is their use of preand post-test format and multiple choice or short answer questions to assess conceptual understanding [30]. their questions are typically derived from a curriculum, a standardized test, or created by experienced teachers or researchers. in addition, ‘applying’, which is also known as ‘knowledge application’ has been evaluated after the use of a mobile app for knowledge acquisition [33]. in a study by hwang, tsai [33] their method also involved the use of questionnaires for pre and post-tests. it is however worthy to note here that the aforementioned studies on ‘remembering’, ‘understanding’ and ‘applying’ have been conducted based on pedagogical frameworks, as none of those studies have been conducted based on adult learning frameworks situated within the sfm space focused on consumers. on day 1 (pre-test), the 2-hour session started with briefing the participants, providing them with the information sheet and consent form. next, they were provided a 20-item baseline questionnaire which was collected from them after it was answered. following this, those in the experimental group were separately asked to download and install the sfm app on their phone while those in the control group were given a paper-based document. they were asked safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi to use the learning material (app or document) to answer a copy of the unanswered baseline questionnaire given to them. after completing this activity, they were asked to brainstorm on the facts learnt from the learning material within their group. at the end of this session the baseline questionnaire was collected from them, they were allowed to take the learning material home and they were de-briefed. care was taken to ensure the participants in the control group did not have access to the app while those in the experimental group did not have access to the paper-based tool. on day 8 (post-test 1), the same baseline was presented to the participants but with re-ordered questions and answer options. they were not allowed to refer to any learning material. on day 16 (post-test 2), open-ended scenario-based questions that are directly related to the base-line questionnaire were presented to the participants and they were asked to provide short answers to each of the 4 questions. on day 24 (post-test 3), participants were presented with open-ended scenario-based questions that are directly related to the baseline questionnaire but based on raw food products in a kitchen environment. they were asked to provide short answers to each of the 4 questions. this ended with a de-brief. details of the questions are not provided due to space constraints. after the data collection, the data was exported to microsoft excel 2010 for initial formatting and then imported into ibm spss software version 22.0 for better analysis. the data for weeks 1 and 2 were mainly analysed using descriptive statistics while the data for weeks 3 and 4 were first analysed manually based on the correctness of the answers before importing the scores to spss for descriptive analysis. 3 results 3.1 demography of the participants all the respondents live in hobart, tasmania, australia and they are above 18 years of age. the eight participants (4 males and 4 females) were divided into two groups of 4 persons each for the experimental group (app users) and the control group (paper-based tool users). in the experimental group, the highest educational qualification of three of the participants is bachelor or higher, while the fourth participant has a diploma or advanced diploma. all candidates within the control group have a bachelor or higher educational qualification. this is important as it suggests that the participants are learned, and they can easily access, read and understand text presented to them in the experiment. 3.1.1 smartphone usage for the experimental group, all participants within the experiment group own and use a smartphone. 50% of the participants are android phone users while the others use ios-based phones. 50% of the participants have been using a smartphone for more than 4 years while the others have been using smartphones for more than 2 years but less than 4 years. 50% of the participants consider themselves medium smartphone users, 25% regard themselves as light users while 25% believe they are heavy users. for the control group, all participants within the control group own and use a smartphone. 75% of them are ios-based phone users while the others use android phones. 75% of them have been using a smart phone for more than 4 years while the others have been using smartphones for more than 2 years but less than 4 years. 50% of the participants consider themselves medium smartphone users, 25% regard themselves as light users while 25% believe they are very heavy users. safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi therefore, it is reasonable to state that these participants are familiar with the use of smartphones and mobile phone apps. thus, suggesting that they will be able to easily access an app presented to them in the course of the experiment. 3.1.2 food handling for the experimental group, all participants have mixed diet which includes red meat and white meat, which shows that none of them are vegetarians or vegans. 50% of them purchase their meat products from supermarkets, 25% from fresh food markets and 25% from delicatessens. all participants within this group cook raw meat products at least once a week. therefore, this shows that the participants within this group are food handlers in their homes. for the control group, 75% of the participants have mixed diet which includes red meat and white meat, but 25% have mixed diet which includes only red meat. this shows that none of them are vegetarians or vegans. 75% of them purchase their meat products from supermarkets and 25% from fresh food markets. 75% of the participants within this group cook raw meat products at least once a week while others cook raw meat products at least once a fortnight. therefore, this shows that the participants within this group are food handlers in their homes. 3.2 experiment findings this section presents the findings for each week of the field experiment which was conducted to evaluate participants’ level of knowledge acquisition, knowledge retention and knowledge application. 3.2.1 pre-test – week one for all 20 questions all participants were told to select the correct answer based on their current knowledge. each question represents one point. the mean score of the experimental group was 13.25, while the mean score for the control group was 14.75. this reveals that participants in the control group had a better pre-existing knowledge of safe food handling in the home when compared to the experimental group. 3.2.2 post-test 1 – week two the follow up questionnaire (post-test 1) is the same as the baseline questionnaire but the only difference is that the questions and answer options are re-ordered. therefore, there were 20 questions and each participant in both groups was told to select the correct answer based on their current knowledge. each question represents one point. the mean score of the experimental group was 17.5, while the mean score for the control group was 19.75. this reveals that participants in the control group were able to remember what was learnt in the previous week better than the experimental group. 3.2.3 post test 2 – week three in week three, the participants were presented with scenario-based questions that were drawn from, and strongly aligned to, the baseline questionnaire in week one. the focus of this week was for the participants to demonstrate their understanding of the acquired information in the previous weeks. the format of the scenarios would appear familiar to them. there are 4 scenarios, with one scenario for each question. each question is assigned 5 points and points safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi are allocated to each participant based on the correctness of their response. the answers for each question are drawn from the smartphone app or paper-based tool which was provided to the participants in the previous weeks. the mean score of the experimental group was 16.875, while the mean score for the control group was 16.25. this reveals that participants in the experimental group were able to demonstrate a slightly better understanding of what was learnt in the previous weeks better than the control group. 3.2.4 post-test 3 – week four in week four, the participants were presented with open-ended questions based on real scenarios presented to the participants in a kitchen environment. the questions were drawn from, and strongly aligned to, the baseline questionnaire in week one. the focus of this week was for the participants to apply the knowledge they had acquired in the previous weeks. there were four scenarios, with one scenario for each question. for each scenario, a table is presented to each participant with a certain arrangement of food products to support the question being posed. the mean score of the experimental group was 16.375, while the mean score for the control group was 14.875. this reveals that participants in the experimental group were better in applying the knowledge gained within the previous weeks than the control group. 4 discussion as the overarching aim of the research is to provide insights into how best to share information to facilitate knowledge retention through the use of technology in an attempt to improve the food management behaviour of australian consumers, it was imperative to investigate the impact of the sfm app on consumers. whilst two tools (paper-based and app) were involved in the study, the focus was on the app as the paper-based tool was used as a baseline, which contained only textual information modality but the app contained multiple information modalities (text, pictures and videos). therefore, there was a need to understand the impact of the sfm app on consumer knowledge acquisition and knowledge retention on sfm. impact 1: the safe food management (sfm) app requires more time to be spent to achieve knowledge acquisition which resulted in retaining the knowledge for a longer period of time than the traditional information delivery techniques. the authors draw on the cognitive load theory as the tasks and learning activities in the study required simultaneous integration of multiple and various sets of knowledge, skills and behaviours at a specific time and place [34]. the cognitive load theory (clt) integrates three key components of the cognitive architecture: memory systems (sensory, working and longterm memory (ltm)), learning processes and types of cognitive load (intrinsic, extraneous and germane) imposed on working memory (wm) [35]. extraneous cognitive load refers to the burden imposed on the working memory of the learner which is not essential to the task [34]. this load tends to arise when learners use an app at first sight which leads to a distraction that is not related to the knowledge acquisition task. as the initial use of a smartphone app induces a higher level of extraneous cognitive load, this places a level of demand on the working memory and reduces the rate at which knowledge acquisition occurs. according to brunken, plass [18], extraneous cognitive load occurs due to the format and manner of information presentation and the requirements of the instructional activities on the working memory. however, this type and level of cognitive load does not occur when a traditional information delivery technique is used, as evidenced by this study. safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi it was however discovered that the app users demonstrated a higher level of knowledge retention over time when compared to the document users. this could be explained by the splitattention effect in relation to the cognitive load theory. this effect involves the phenomenon whereby the physical integration, rather than physical separation, of verbal and pictorial information sources enhances learning [36]. however, when split attention occurs, it increases demands on the learner’s working memory (wm) and has the tendency to impact learning negatively [37]. one way to avoid the split attention effect is by externally integrating the different sources of information together into a single integrated source of information [37] as was achieved with the sfm app. it is believed that this strategy was instrumental to the successful outcome of the level of knowledge retention emanated by the participants. the app contained videos of sfm practices that incorporated the modality effect as the visual figures are linked with auditory (spoken) rather than visual (written) elements [38]. mayer [38] has argued that the modality effect can only occur under the condition in which the multiple sources of information are unintelligible in isolation and rely on each other for intelligibility to avoid the redundancy effect. this condition was met by the videos included in several pages of the app as they comprised of picture frames (visual elements) and spoken elements that rely on each other for intelligibility; thus complementing the features portrayed by one another [39]. initially, more time was spent on the app used in this study but the rate at which information and/or knowledge was acquired was lower than that of document users. however, more indepth details revealed that the app users acquired the knowledge slowly but retained it longer in contrast to the document users. these findings are in line with the study conducted by herrlinger, höffler [40] and leahy and sweller [41] who have argued that pictures and spoken text enhanced learning better than written text. similar to this finding is the study conducted by wang, tsai [42] which revealed that when more attention was paid to the video and less attention paid to the text there was better retention of the learning outcomes. however, the findings in this study differ from those of chandler and sweller [43] who found that students viewing integrated instruction spent less time processing the materials as the app users in this study spent more time acquiring the knowledge due to the extraneous cognitive load which occurred as a result of the additional learning that was required for the initial use of an app. nonetheless, chandler and sweller [43] also agreed that students viewing integrated instruction outperformed those with split attention condition. on the other hand, the findings are in line with the study conducted by schmidt‐weigand, kohnert [44] who also revealed that participants showed a better learning performance the more time they spent looking at visualizations when text was spoken and integrated. therefore, in consonance with schmidt‐weigand, kohnert [44], it can be argued that the time devoted to process visualizations with spoken and integrated text such as videos may be an indicator of the quality of processing this information. from this perspective, this study suggests that the time a learner spends in using an app containing visualizations with spoken and integrated text such as it is featured in the safe food management (sfm) app, during the information and/or knowledge acquisition phase, may be advantageous in facilitating knowledge retention for a longer period of time than traditional information delivery techniques. impact 2: the sfm app induces some level of cognitive load in adoption however; the affordance of its reuse for quick but infrequent revisitations facilitates knowledge retention. safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi this research has found that the initial use of the smartphone app which was developed for information and/or knowledge acquisition purposes induces a higher level of extraneous cognitive load; thus reducing the rate at which knowledge is acquired during the first use. according to brunken, plass [18], extraneous cognitive load occurs due to the format and manner of information presentation and the requirements of the instructional activities on the working memory. cognitive load was discovered in this study as evidence suggests that participants using the app experienced a level of demand on the working memory. based on arguments from moreno and mayer [45], that the principle of modality effect can indeed reduce extraneous cognitive load for knowledge acquisition tools developed on mobile devices, this study incorporated the principle. yet, the results indicate that some level of cognitive load was induced. although the evidence is lacking, it appears to the authors, that the hloh framework seem to have minimized the cognitive burden. thus, there was a better demonstration of knowledge retention after the app has been reused over a short period of time. when participants spent less time on the smartphone app after the initial use, they demonstrated better retention of knowledge whereas spending more time on the paper-based tool resulted in poorer retention of information and/or knowledge. this finding is in line with the temporal patterns that have been identified in the usage of smartphones and their applications which suggests short bursts of smartphone interactions [46]. for instance, yan, chu [47] found that mobile phone usage is brief as half of mobile phone engagement (time between unlocking and relocking) lasts less than 30 seconds. similarly, ferreira, goncalves [48] found that some apps are used in short bursts of less than 15 seconds. also, a large scale study by böhmer, hecht [49] revealed that smartphone devices are used for an average of 59 minutes daily while an average application session lasts 72 seconds. with a focus on overall smartphone users’ habits, oulasvirta, rattenbury [50] suggest that smartphones are “habit-forming” devices as users emanate the “checking habit” through brief inspection of content quickly accessible on their smartphones. a follow up study by ferreira, goncalves [48] revealed that this habit is one of the behavioural characteristics that leads to short bursts of interactions with applications. in addition, this habit has largely been focused on users making quick revisits to applications that contain fast changing content [48,50]. however, jones, ferreira [46] has argued that apps that relate to personal activities such as food handling and food management follow a slow revisitation pattern. as such, this explains the slow revisitation pattern and the little time spent on the sfm app during its subsequent use in this study. thus, as this facilitated a better demonstration of knowledge retention on safe food management, it suggests that the affordance of re-use for quick but infrequent revisitations facilitates knowledge retention. therefore, as it has been earlier argued that multiple information channels enhance food safety information dissemination [51], it can be further argued that other information channels such as tv adverts, brochures, pamphlets and other media can be useful in drawing attention to the reuse or revisitation of such smartphone apps to reinforce and support the retention of consumer knowledge. this indicates that optimising consumers’ safe food management knowledge cannot be a one-off activity as they require cues that prompt them into revising the app so as to maintain adequate knowledge level from time to time. 5 limitations due to the difficulty in recruiting a sample that was representative of the australian population, participants were limited to consumers in hobart, tasmania; thus, the outcome of the research safe food management and smartphone technology: investigating the impact of an app on consumer knowledge retention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e222, 2018 ojphi may be skewed. based on this small number of participants, the findings of this study cannot be generalised to the australian population and it may lead to a possibility of potential alternative explanations for the findings which favoured the use of the app rather than the document for knowledge retention. as such further large-scale studies would need to be conducted based on a sample that is representative of the australian population. 6 conclusion this research was focused on investigating how the affordances of smartphone technology can be leveraged to enhance the provision of information and facilitate knowledge retention as a step towards improving the sfm behaviour of australian consumers. this paper has presented findings from a randomised field experiment using a developed sfm app for information and/or knowledge acquisition as the intervention and a paper-based document as control with assessments conducted at baseline, week 2, 3 and 4. we identified 2 key impacts of the app on consumer knowledge acquisition and knowledge retention. first, we discovered that the safe food management (sfm) app requires more time to be spent to achieve knowledge acquisition which resulted in retaining the knowledge for a longer time than the traditional information delivery techniques. second, we found that the sfm app induces some level of cognitive load in adoption however; the affordance of its reuse for quick but infrequent 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coverage and timeliness of combined military and veteran surveillance systems howard s. burkom1, yevgeniy elbert1, carla winston2, julie pavlin*3, cynthia luceroobusan2 and mark holodniy2 1johns hopkins university applied physics laboratory, laurel, md, usa; 2veterans health administration, palo alto, ca, usa; 3armed forces health surveillance center, silver spring, md, usa objective we determined the utility and effective methodology for combining patient record information from the departments of veterans affairs (va) and defense (dod) health surveillance systems. introduction an objective of the joint va/dod biosurveillance system for emerging biological threats project is to improve situational awareness of the health of combined va and dod populations. dod and va both use versions of the electronic surveillance system for the early notification of community-based epidemics (essence). with a retrospective outpatient data collection available, we analyzed relative coverage and timeliness of the two systems to understand potential benefits of a joint system. methods we used the us office of management and budget’s core-based statistical area (cbsa) to group data from the respective systems by megapolitan (>1 million), metropolitan (50,000-1 million) and micropolitan (10,000-50,000) areas. we performed frequency analyses and mapped coverage of the va and dod medical systems in these cbsas. to determine comparability, we compared international classification of diseases, 9th revision (icd-9) code usage from 2007-2010 by age group in the respective systems and then formulated a working definition of influenza-like illness (ili). we then compared cbsa-level temporal detection timeliness in the two systems for the h3n2 epidemic of 2007-9 and the h1n1 pandemic in 2009. results we identified a total of 939 cbsas, with generally diffuse geographic coverage by va facilities and higher concentration in larger metro and mega areas for dod facilities. of the 51 mega cbsas, all have at least one va facility and 63% have a dod facility. coverage is sparser for the metro cbsas and lighter still for the micro cbsas (table 1). although the va coverage is greater, in many cbsas with dual coverage, the dod visit volume is comparable or greater. patient age distribution differs sharply, with >85% of the va patients over 45 years of age compared to 22% of dod patients. for all cbsas, the overall va/dod visit ratio is 1.92, but the ratios for 0-17 years is 0.004, 18-44 years 0.33, 45-64 years 5.20 and >65 years 11.63. based on an analysis of icd-9 codes used in the two systems, the dod uses symptom-based ili codes far more frequently than the va, especially codes for diseases often seen in children (e.g., otitis media). analysis of ili-related codes assigned in both systems led to a common code set for comparative analysis. from applying alerting algorithms to visit counts based on this code set, detection was better in dod data for 57% and 77% of cbsas for seasonal and pandemic influenza, respectively, and better in va data for 37% and 14% of cbsas (table 2). the va system performed better during the typical h3n2 seasonal flu compared to the h1n1 outbreak. the dod system performed better during the h1n1 pandemic, although outperformed the va for both. conclusions the coverage analysis demonstrates two complementary surveillance systems with evident benefits to a fused national health picture. the va system patient volume roughly doubles the dod system, and provides better geographic coverage in smaller cbsas; however, the dod includes younger populations, better coverage in strategic metro areas, and more pre-diagnostic ili coding. from analysis of both outbreaks, relative timeliness could be improved in 92% of cbsas with access to both systems, with more information provided in cbsas where only one type of facility exists. table 1. counts of cbsas containing va and dod facilities table 2. relative timeliness by cbsas with both dod and va facilities with a minimum average of 2 ili visits/week keywords syndromic surveillance; merged systems; government *julie pavlin e-mail: julie.pavlin@us.army.mil online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e39, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a syndromic approach to emergency department surveillance for skin and soft tissue infections larissa may*1, marcus rennick2, leah gustafson1 and julia gunn2 1emergency medicine, the george washington university, washington, dc, usa; 2boston public health commission, boston, ma, usa objective we sought to describe the epidemiology of emergency department (ed) visits for skin and soft tissue infections (ssti) in an urban area with diverse neighborhood populations using syndromic surveillance system data for the time period from 2007-2011. our aims were threefold: to demonstrate a proof of concept using syndromic surveillance for ssti surveillance in the absence of laboratory data, to estimate the burden of ed visits associated with ssti, and to determine potential geographic “hotspots” for these infections. introduction the incidence of and hospitalizations for ssti have steadily increased over the last decade in the united states, primarily due to the emergence and spread of community acquired methicillin resistant staphylococcus aureus (ca-mrsa). the ed is a common site for ssti treatment and serves populations not captured by traditional surveillance, including the homeless and uninsured. the use of near real-time syndromic surveillance within the ed to detect unusual activity for further public health investigation has been used to augment traditional infectious disease surveillance. however, the use of this approach for monitoring local epidemiologic trends in ssti presentation where laboratory data are not available, has not previously been described. methods we sought to describe the epidemiology of ed visits for ssti in an urban area with diverse neighborhood populations using the boston public health commission’s (bphc) syndromic surveillance system (bsynss) data for a five year time period (2007 through 2011). ssti related visits were defined by either chief complaints with ssti associated words (abscess, cellulitis) or final diagnosis international classification of diseases (icd-9 cm) codes for sstis. ssti related visits were de-duplicated using demographics and visit identifiers and then stratified by age group, gender, race, and neighborhood of residence defined by zip code. each of boston’s 15 neighborhoods has a unique demographic profile with distinct differences in race, socioeconomic status, and age. finally, we examined trends in characteristics of potential “hotspots” of neighborhood clustering for sstis in eds. results using our ssti syndrome definition, we estimated unique ssti visits represented 3.29 % (n= 45,252) of all visits within boston’s ten eds during the study period with a seasonal pattern peaking during the summer months (july through september). the majority of ssti visits (54%) were among patients 18 to 44 years old, which is consistent with the age distribution of the boston population. however, a disproportionate number of ssti visits (43%) were among black patients when compared to both the overall boston population (22% black) and to the racial distribution of all ed visits (39% black). the five-year average rate of ssti visits for black patients (281.2 per 10,000 population) was significantly greater at 2.8 times [ci 2.7, 3.0] than the rate for white patients (99.0 per 10,000 population). geographic neighborhood distribution of ssti visits ranged from a low of 2.69% to a high of 4.11% of all neighborhood-specific ed visits. disposition data are available for 2010 and 2011 only and show that 24% and 23% of patients in 2010 and 2011, respectively, were admitted for their ssti. conclusions our study results suggest that syndromic surveillance data can be used to track the burden and patterns of ssti in an urban population, including disease severity through the use of disposition data. furthermore, syndromic surveillance can provide information on the local epidemiology of ssti, including data related to health inequalities. the burden of sstis should be compared to overall ed use for a specific population to control for biases in health care seeking behaviors and choice of provider type. a local syndromic surveillance system has the potential to provide public health authorities and ed clinicians near real-time monitoring of trends in severity and demographic risk factors, and may provide an alternative to tracking the severity of illness where no laboratory data are readily available. keywords syndromic surveillance; epidemiology; skin and soft tissue infections; racial disparities *larissa may e-mail: larissa.may@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e61, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts tweeting fever: are tweet extracts a valid surrogate data source for dengue fever? jacqueline s. coberly*1, clayton r. fink1, eugene elbert1, in-kyu yoon2, john m. velasco2, agnes tomayo2, v. roque3, s. ygano4, durinda macasoco4 and sheri lewis3 1the johns hopkins university applied physics laboratory, laurel, md, usa; 2armed forces research institute for medical research, bangkok, thailand; 3national epidemiology center, manila, philippines; 4cebu city health office, cebu city, philippines objective to determine whether twitter data contains information on dengue-like illness and whether the temporal trend of such data correlates with the incidence dengue or dengue-like illness as identified by city and national health authorities. introduction dengue fever is a major cause of morbidity and mortality in the republic of the philippines (rp) and across the world. early identification of geographic outbreaks can help target intervention campaigns and mitigate the severity of outbreaks. electronic disease surveillance can improve early identification but, in most dengue endemic areas data pre-existing digital data are not available for such systems. data must be collected and digitized specifically for electronic disease surveillance. twitter, however, is heavily used in these areas; for example, the rp is among the top 20 producers of tweets in the world. if social media could be used as a surrogate data source for electronic disease surveillance, it would provide an inexpensive pre-digitized data source for resource-limited countries. this study investigates whether twitter extracts can be used effectively as a surrogate data source to monitor changes in the temporal trend of dengue fever in cebu city and the national capitol region surrounding manila (ncr) in the rp. methods we obtained two sources of ground truth incidence for dengue. the first was daily dengue fever incidence for cebu city and the ncr taken from the philippines integrated disease surveillance and response system (pidsr). the second ground truth source was fever incidence from cebu city for 2011. the cebu city health office (ccho) has monitored fever incidence as a surrogate for dengue fever since the 1980s. tweets from cebu city, and the ncr were collected prospectively thru twitter’s public application program interface. the cebu city fever ground truth data set was smoothed with a seven day moving average to facilitate comparison to the pidsr and twitter data. a vocabulary of words and phrases describing fever and dengue fever in the tweets collected were identified and used to mark relevant tweets. a subset of these ‘fever’ tweets that mentioned fever related to a medical situation were identified. the incidence and the temporal pattern of these medically-relevant tweets were compared with the incidence and pattern of fever and dengue fever in the two ground truth data sets. pearson correlation coefficient was used to compare the correlation among the different data sets. noted lag periods were adjusted by moving the data in time and re-computing the correlation coefficient. results 26,023,103 tweets were collected from the two geographic regions: 10,303,366 from cebu city and 15,719,767 tweets from the ncr. 8,814 (0.02%) tweets contained the word fever and 4099 (0.01% of total) mentioned fever in a medically-relevant context, for example. “…i have a fever…” vs. “…football fever….” the medically-relevant tweets were compared with both ground truth data sets. the correlation between the tweets and each of the incidence data sets is shown below. conclusions tweets containing medically-relevant fever references were correlated (p<0.0001) with both fever and dengue fever incidence in the ground truth data sets. the signal indicating fever in the medicallyrelated tweets led the incidence data significantly: by 6 days for the cebu city fever incidence; and by 12 days for the pidsr dengue fever incidence. temporal adjustment to account for observed lag periods increased the correlation coefficient by about one-third in both cases. this was a limited pilot study, but it suggests that twitter extracts may provide a valid and timely surrogate data source to monitor dengue fever in this population. further study of the correlation of twitter and dengue in other areas, and of twitter with other illnesses is warranted. table 1: correlation between twitter extracts and fever & dengue fever incidence data sets * p<0.0001 † twitter shifted right by 6 days ‡ twitter shifted right by 12 days keywords dengue; social media; twitter *jacqueline s. coberly e-mail: jacqueline.coberly@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e64, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts bayesian contact tracing for communicable respiratory disease ayman shalaby* and daniel lizotte university of waterloo, waterloo, on, canada objective the purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. control of the spread of respiratory pathogens (e.g. novel influenza viruses) in the population using vaccination is a challenging problem that requires quick identification of the infectious agent followed by large-scale production and administration of a vaccine. this takes a significant amount of time. a complementary approach to control transmission is contact tracing and quarantining, which are currently applied to sexually transmitted diseases (stds). for stds, identifying the contacts that might have led to disease transmission is relatively easy; however, for respiratory pathogens, the contacts that can lead to transmission include a huge number of face-to-face daily social interactions that are impossible to trace manually. introduction the evolution of novel influenza viruses in humans is a biological phenomenon that can not be stopped. all existing data suggest that vaccination against the morbidity and mortality of the novel influenza viruses is our best line of defence. unfortunately, vaccination requires that the infectious agent to be quickly identified and a safe vaccine in large quantities is produced and administered. as was witnessed with the 2009 h1n1 influenza pandemic, these steps took a frustratingly long period during which the novel influenza virus continued its unstoppable and rapid global spreading. in addition to the different vaccination strategies ( e.g. random mass vaccination, age structured vaccination), isolation and quarantining of infected individuals is another effective method used by the public health agencies to control the spreading of infectious diseases. isolation is effective against any infectious disease, however it can be very hard to detect infectious individuals in the population when: 1. symptoms are ambiguous or easily misdiagnosed ( e.g. 2009 h1n1 influenza outbreak shared many symptoms with many other influenza like illnesses) 2. when the symptoms emerge after the individual become infectious. methods we developed a dynamic bayesian network model to process sensor information from users’ cellphones together with (possibly incomplete) diagnosis information to track the spread of disease in a population. our model tracks real-time proximity contacts and can provide public health agencies with the probability of infection for each individual in the model. for testing our algorithm, we used a real-world mobile sensor dataset with 120 individuals collected over a period of 9 months, and we simulated an outbreak. results we ran several experiments where different sub-populations were “infected” and “diagnosed.” by using the contact information, our model was able to automatically identify individuals in the population who were likely to be infected even though they were not directly “diagnosed” with an illness. conclusions automatic contact tracing for respiratory pathogens is a powerful idea, however we have identified several implementation challenges. the first challenge is scalability: we note that a contact tracing system with a hundred thousand individuals requires a bayesian model with a billion nodes. bayesian inference on models of this scale is an open problem and an active area of research. the second challenge is privacy protection: although the test data were collected in an academic setting, deploying any system will require appropriate safeguards for user privacy. nonetheless, our work llustrates the potential for broader use of contact tracing for modeling and controlling disease transmission. keywords outbreak detection; syndromic surveillance; mobile; contact tracing; bayesian algorithms *ayman shalaby e-mail: aymanshalaby11@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e208, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts an integrated syndromic surveillance system for monitoring scarlet fever in taiwan wan-jen wu*, yu-lun liu, hung-wei kuo, wan-ting huang, shiang-lin yang and jenhsiang chuang epidemic intelligence center, taiwan centers for disease control, taipei city, taiwan objective to develop an integrated syndromic surveillance system for timely monitoring and early detection of unusual situations of scarlet fever in taiwan, since hong kong, being so close geographically to taiwan, had an outbreak of scarlet fever in june 2011. introduction scarlet fever is a bacterial infection caused by group a streptococcus (gas). the clinical symptoms are usually mild. before october, 2007, case-based surveillance of scarlet fever was conducted through notifiable infectious diseases in taiwan, but was removed later from the list of notifiable disease because of improved medical care capacities. in 2011, hong kong had encountered an outbreak of scarlet fever (1,2). in response, taiwan developed an integrated syndromic surveillance system using multiple data sources since july 2011. methods more than 99% of the taiwan population is covered by national health insurance. we first retrospectively evaluated claims data from the bureau of national health insurance (bnhi) by comparing with notifiable diseases reporting data from taiwan centers for disease control (tcdc). the claims data included information on scarlet fever diagnosis (icd-9-cm code 034.1), date of visits, location of hospitals and age of patients from outpatient (opd), emergency room (er) and hospital admissions. daily aggregate data of scarlet fever visits or hospitalizations were prospectively collected from bnhi since july 2011. over 70% of the deaths in taiwan are reported to the office of statistics of department of health electronically. we obtained daily data on electronic death certification data and used sas enterprise guide 4.3 (sas institute inc., cary, nc, usa) for data management and analysis. deaths associated with scarlet fever or other gas infections were identified by text mining from causes of death with keywords of traditional chinese ‘scarlet fever’, ‘group a streptococcus’ or ‘toxic shock syndrome’ (3). results from january 2006 to september 2007, the monthly opd data with icd-9-cm code 034.1 from bnhi showed strong correlation with tcdc’s notifiable disease data (r=0.89, p<0.0001). from july 6, 2008 (week 28) through july 28, 2012 (week 30), the average weekly numbers of scarlet fever visits to the opd, er and hospital admissions were 37 (range 11–70), 7 (range 0–20) and 3 (range 0–9). eighty-five percent of the scarlet fever patients were less than 10 years old. in taiwan, scarlet fever occurred year-round with seasonal peaks between may and july (fig. 1). from january 2008 to july 2012, we identified 12 potential patients (9 males, age range 0–82 years) who died of gas infections. no report had listed ‘scarlet fever’ as cause of death during the study period. conclusions taiwan has established an integrated syndromic surveillance system to timely monitor scarlet fever and gas infection associated mortalities since july 2011. syndromic surveillance of scarlet fever through bnhi correlated with number of scarlet fever cases through notifiable disease reporting system. text mining from cause of death with the used keywords may have low sensitivities to identify patients who died of gas infection. in taiwan, syndromic surveillance has also been applied to other diseases such as enterovirus, influenzalike illness, and acute diarrhea. interagency collaborations add values to existing health data in the government and have strengthened tcdc’s capacity of disease surveillance. fig. 1. weekly numbers of nationwide scarlet fever opd and er visits, and hospital admissions, with baseline opd visits and 95% confidence interval calculated by a serfling’s model, week 28 of 2008 to week 30 of 2012. keywords syndromic surveillance; taiwan; scarlet fever; claims data acknowledgments we thank the bureau of national health insurance and office of statistics of department of health for providing data required for this study. references 1.hsieh yc, huang yc. scarlet fever outbreak in hong kong, 2011. j microbiol immunol infect. 2011;44:409-11. 2.tse h, bao jy, davies mr, et al. molecular characterization of the 2011 hong kong scarlet fever outbreak. j infect dis. 2012;206:341-51. 3.chen pj, wu wj, huang wt, chuang jh. an early warning system for pneumonia and influenza mortality in taiwan. emerging health threats journal 2011;4: 11024. *wan-jen wu e-mail: teatea@cdc.gov.tw online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e160, 2013 improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya aliza monroe-wise1, john kinuthia2, sherrilynne fuller3, matthew dunbar4, david masuda5, elisha opiyo6, betty muchai1, christopher chepken6, elijah omwenga6, robert oboko6, alfred osoti2, daniel masys5, michael h. chung 1 1departments of global health and medicine, university of washington 2department of obstetrics and gynaecology, university of nairobi, nairobi, kenya 3department of biomedical informatics and information school, university of washington 4center for studies in demography and ecology, university of washington 5department of biomedical informatics and medical education, university of washington 6school of computing and informatics, university of nairobi abstract objectives information and communication technology (ict) tools are increasingly important for clinical care and international research. many technologies would be particularly useful for healthcare workers in resource-limited settings; however, these individuals are the least likely to utilize ict tools due to lack of knowledge and skills necessary to use them. our program aimed to train researchers in low-resource settings on using ict tools and to understand how different didactic modalities build knowledge and skills in this area. methods we conducted a tiered, blended learning program for researchers in kenya on three areas of ict: geographic information systems, data management, and communication tools. each course included three tiers: online courses, skills workshops, and mentored projects. concurrently, a training of trainers course was taught to ensure sustainable ongoing training. a mixed qualitative and quantitative survey was conducted at the end of each training to assess knowledge and skill acquisition. results course elements that incorporated local examples and hands-on skill building activities were most valuable. discussion boards were sometimes distracting, depending on multiple factors. mentored projects were most useful when there were clear expectations, pre-existing projects, and clear timelines. discussion training in the use of ict tools is highly valued among researchers in low-income settings, particularly when it includes hands-on skill-building and local examples. improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi 1. introduction information and communication technology (ict) tools have transformed the ways in which research and healthcare are conducted and data is managed, becoming integral components of both biomedical research and healthcare delivery throughout the world [1-3]. these technologies can change the ways in which medical data is collected and managed for clinical trials by using mobile devices [4], providers communicate with patients for monitoring health and disease [5], and the creation of large interoperable data management systems for analysis across multiple platforms and studies [6]. many of these ict tools are accessible and freely available on the internet or as open source software [3,7]. while innovative ict practices would have the highest impact on health research and patient care in resource-limited settings [8], these areas often have the lowest ict uptake and utilization rates of anywhere in the world [9,10]. lack of knowledge and skills have been identified as primary barriers to the use of ict tools among health researchers in resource-limited settings [10-12], and there is an increasingly recognized need for improved access to ict training opportunities for health researchers worldwide. it is clear that international collaboration is vitally important for building research capacity in resource limited academic centers [13], and also clear that ict tools can improve the efficiency of multinational research projects that span cultures, languages and time zones [14,15]. as such, international collaborations may both facilitate training surrounding the use and integration of ict tools and also support the research resulting from increased utilization of the tools [16,17]. in kenya, while certain research projects have successfully been implemented in recent years using select ict tools [18,19], a great need exists to increase knowledge and understanding of new technologies in order to expand utilization [20,21], similar to other resource-limited settings. in response to the identified need for improved and expanded training programs in ict utilization for our students demonstrated acquisition of new skills and felt these skills to be valuable in their workplaces. conclusions further training in ict skills for researchers should be considered in other low-resource settings using our program as a foundational model.key words: information and communication technology (ict), blended learning, kenya, e-learning corresponding author: aliza monroe-wise, md, msc. email: alizamw@uw.edu university of washington, 325 ninth avenue, box 359909, seattle, wa 98104-2499 doi: 10.5210/ojphi.v11i3.10323 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi health research in kenya, the university of washington (uw) partnered with the university of nairobi (uon) to develop a training program to meet the needs of local researchers in nairobi. we also aimed to gain a deeper understanding of which modalities of ict training were most and least valuable to the facilitation of applied learning in this setting through post-training survey data collection. this paper describes the design and content of our training program, and the evaluation of post-training survey data describing trainees’ views on how various training components fostered the development of ict knowledge and skills. 2. materials and methods leading faculty from uw and uon in the areas of computing, biomedical informatics, geographic information systems, and e-learning convened in january 2015 to collaboratively identify target areas for training and appropriate and effective teaching methods to deliver content. they drew on existing research on the use of ict in biomedical research, experience with effective delivery platforms, and adult learning theory to inform the development of both course content and delivery, and they took several factors into consideration. first, given the importance of international collaboration, the inclusion of international faculty as lecturers in the program was prioritized. given increasing evidence supporting the efficacy of e-learning modalities for distance training [22,23], an e-learning component was included in order to deliver content from international experts in a financially sustainable manner. finally, faculty identified a need to include skills transfer and mastery, in addition to knowledge delivery. studies have shown that blended learning, in which online content is combined with in-person training, is most effective for educational programs that focus on the development of skills [24]; therefore a blended learning approach was taken with an online course featuring international experts followed by mentored, in-person learning. the faculty designed a three-tiered, blended learning program (figure 1). three separate tracks of training were chosen to correspond to areas of highest need and interest: geographic information systems (gis), principles and practice of research data management (ppr), and research management and communication tools (rct). for each of these training tracks, three tiers of training were offered. the first tier was an online course consisting of a series of 4-6 lectures (in voice over powerpoint format), in conjunction with other educational materials, homework and assignments. tier 1 target enrollment was 100 students per course. tier 2 consisted of hands-on workshops for each topic that took place over 5 days after the completion of online courses. participants were selected from those who had completed and passed the online course for each topic. tier 2 workshop target enrollment was 30 students per workshop. finally, the top 15 students from each workshop were identified based on scores from final presentations. these top 15 were then invited to participate in tier 3 for each topic, intensive mentored projects. mentors were chosen from university of nairobi ict experts in each topic, and the mentorship experience lasted 3 months. mentors met with students regularly and guided students through the application of an ict tool to a research project. students were tasked with presenting their projects via either an oral presentation online or at the university of nairobi std/aids collaborative group conference held in nairobi annually. the entire 3-tier training took place over a 6-month timeframe (figure 2). improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi figure 1. overall training structure figure 2. training timeframe a training of trainers (tot) course was simultaneously offered to all faculty and staff at uon who provide ict support and training. this course was led by ict specialists from both uw and uon, and comprised an initial introductory online course that also served as an introduction for the other courses followed by a 5-day hands-on workshop. participants learned how to: help researchers improve their research design and analysis using ict; mentor researchers constructively in their projects; and teach researchers to give effective oral presentations. post-course surveys were offered to participants in each element of this training program. surveys were conducted electronically for online courses, and on paper for in-person workshops and mentoring. surveys comprised both quantitative and qualitative questions covering topics about length of time participants took for the training program, which elements of the course or program were most valuable and least valuable, and whether and how the participants’ knowledge and skills were improved from what they learned in the course. surveys also assessed whether participants felt they could use the knowledge and skills acquired in their workplaces. programmatic data was also collected in the evaluation, including data on course enrollments, course completion, pass rates for each course, and deliverables expected and produced for each course as supporting information to describe skill acquisition. improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi 2.1 course content 2.1.1 introduction to ict in health research (intro) and training of trainers (tot) the goal of the intro course was twofold: first, to provide foundational knowledge to a cadre of faculty and staff who could ensure sustainability and extensibility of the training program and as a resource for ict applications in health research; second, to provide a common introduction to concepts for students in the other three courses. detailed information about objectives and activities for all four courses can be found in appendix a. the core online intro course considered the state of the art data management tools for ensuring reliable collection, aggregation, back-up, analysis, reporting and archiving of data in useful ways. a unique focus of the course was assessing the ability of tools to work together to successfully ensure smooth movement of data and findings from one tool to the next. another focus of the course was evaluation of health research studies to identify those which successfully (or unsuccessfully – thus covering the research pitfalls) incorporate ict in resource-limited settings. because ict changes so rapidly, the course also covered how to remain informed of emerging ict approaches and also how to successfully plan for adaptation to future technology changes. for faculty and staff already familiar with ict, the course provided an introduction to the needs of health researchers and the issues that they face in conducting clinical trials or medical investigations research. the use and research applications of social technologies including crowdsourcing and social media was a particular focus of the course. the online course was followed by a five-day in-person workshop for faculty in the training the trainers (tot) course. the workshop allowed participants to apply their learning from the online course in a hands-on laboratory setting, and provided an opportunity for ict experts and health researchers to meet each other and teach each other the reciprocal skills of ict and health research as well as to collaborate to design new solutions to research challenges. teaching assistants for the other 3 courses were chosen from this tot course. 2.1.2 geographic information systems (gis) the foundation of this course introduced participants to key features of the discipline of geography, and explored how geospatial technologies, such as gis, are commonly used to incorporate spatial theory, analysis, and visualization into health research. the remaining majority of the course was focused on the following objectives: learning how to assess the use of gis in research, performing basic mapping and analysis using open-source gis software, and developing and implementing spatial research questions using gis. this course attempted to teach both the theory to understand how gis is used in research (weekly lectures), and the technical software skills necessary to implement a subset of those concepts (weekly assignments). the workshop component of the gis course focused on expanding the practical technical skills of the participants, while concurrently deepening their understand of geographic science. four workshop modules (mapping, analysis, data manipulation, and rasters) provided practical training to advance the participants competence in qgis from the introductory level in the online course to an intermediate skill level. improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi 2.1.3 principles and practice of research data management (ppr) the ppr course was anchored by three conceptual areas that are converging worldwide in research that involves human volunteer participants. the first of these are the international good clinical practice (gcp) standards developed by the international council for harmonisation of technical requirements for pharmaceuticals for human use [25]. gcp standards have become the accepted global guidelines for ensuring ethical treatment of research participants and creation of research records that are of high quality and amenable to audit for scientific integrity. the second theme of the ppr course was knowledge of data modeling, database design, and data management technologies capable of supporting research ranging from simple single investigator studies to international multisite clinical trials. the third theme was understanding the strengths and hazards of using the internet to acquire and manage research data, with a focus on principles and practice of information security. the independent study assignment for the course required students to bring these related sets of knowledge and skills together to create a research data management plan, including budget, that could become part of a research study proposal to a sponsoring organization. 2.1.4 research management and communication tools (rct) the course was informed by the centrality of the internet as a tool in the research process enabling researchers to access funding, identify collaborative partners, keep abreast with advances in research, and disseminate their research findings quickly and effectively. skills-based learning objectives are outlined in table 1. the course focused on open access ict tools that researchers can creatively leverage in tandem to better manage the different stages of the research process, from proposal writing through to research dissemination and uptake. a key focus of the course was hands-on practice on various research indexing tools, bibliography management tools, research networking tools and platforms, online collaborative tools, social media platforms, data sharing tools and platforms, data back-up and archival tools and platforms, presentation tools, and low-cost communication tools. effective use of a variety of search engines to enhance retrieval of relevant research findings was threaded throughout the course. students benefitted greatly from demonstrations of tools and technologies by their peers. finally, the course equipped learners with strategies for creating an effective, online personal brand to enhance their global visibility, extend their research dissemination audience, and increase the uptake potential of their research. 3.course evaluation results 3.1 overall participation rates & general feedback a total of 978 individuals applied to take the four online courses (table 2). acceptance into courses was based on educational level (high school certificate or higher) and country of residence. pass rates ranged from 42% for the rct course to 100% for the tot course and workshops. the low pass rate for the rct course was due to the required discussion board participation and difficulty level of the material. finally, the 27 students who participated in three-month mentored projects were tasked with producing a deliverable at the end of the training period. overall, 23 of the 27 improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi enrolled successfully submitted deliverables, producing a pass rate of 85% for the mentored projects. online courses and workshops were generally well-received in terms of the timing and pace, organization, and overall effectiveness of the courses. the majority of learners spent between 2 and 6 hours per week on online courses. students occasionally encountered technical challenges, particularly during the online courses. about 15% of respondents in both the gis and rct online courses reported having some technical difficulties accessing materials, the most common of which were due to slow internet limiting access to lectures or difficulty logging in to course content. the most commonly cited elements that posed a distraction to learning in the online courses were discussion boards and technical difficulties. other occasionally cited distractions included problems with learning the content, format of the content (i.e the way in which content was presented), and having too much ancillary content presented in the form of links or additional readings. table 2. acceptance, completion and pass rates for each course training of trainers (tot) geographic information systems (gis) principles & practice of research data management (ppr) research management & communication tools (rct) totals online course total applicants 47 150 397 384 978 participants1 34 (72%) 132 (88%) 241 (61%) 319 (83%) 726 (74%) completed2 32 (94%) 107 (81%) 188 (78%) 232 (73%) 559 (77%) passed3 32 (100%) 84 (78%) 155 (82%) 98 (42%) 369 (66%) workshops total applicants 32 61 87 41 221 participants1 32 (100%) 28 (46%) 30 (34%) 29 (71%) 119 (54%) completed2 32 (100%) 28 (100%) 30 (100%) 29 (100%) 119 (100%) mentored projects participants n/a 10 7 10 27 completed 6 (60%) 7 (100%) 10 (100%) 23 (85%) 1percentage accepted for each course out of total applicants improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi 2percentage who completed course assignments out of total number registered 3percentage who passed the course out of total who completed figure 4. valuable components of online courses figure 5. valuable components of workshops 3.3 online courses while online courses generally received high scores in course evaluations, the most valuable components of these courses varied greatly between the different tracks (figure 4). lectures were considered greatly valuable for the training of trainers (tot) course, whereas assignments were considered the most valuable component of the geographic information systems (gis) course. in general, online courses with active learning components (gis and ppr) were considered especially improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi valuable. gis included weekly activity assignments in which learners were tasked with locating and utilizing mapping resources available on the internet to design, interpret or modify maps. these exercises were widely lauded as being extremely valuable to the learning process. similarly, the ppr course included an independent project which was one of the highlights of the course. conversely, the rct course did not include any practical component, and this was noted by several respondents in the course evaluations with comments such as “increase practical application,” and include “guided practice of the tools.” with the lowest pass rate of the online courses, the rct course was notable in its use of mandatory discussion board assignments rather than active learning components, which may have hindered learning efficacy. one of the well-received assignments in the ppr course utilized published clinical research studies from kenyan researchers to understand the research methods and review the ict methods and tools that were used in the study as well as to identify what additional ict tools and methods might have been used. linking actual kenyan research studies to use of ict tools and technologies proved very effective as a practical teaching aid. similarly, throughout the ppr course, the students successfully partnered with others who had complementary skills, pairing those who had experience in research methods and ict with those who did not. those with clinical research experience could give examples of their own research experience and challenges as it related to the use of specific ict tools they were learning about. discussion boards received mixed reviews from students in the different courses. some felt that the discussion boards were quite a valuable addition to the other components of the course, while others ranked discussion boards as the most distracting feature of the course. supervision and structure in discussion board interactions added more value to this component of the different courses. 3.4 workshops workshops were very well received, earning higher average overall scores than either the online courses or the mentored projects. similar to online courses, the most valuable elements of each workshop differed greatly (figure 5). while practical skills acquired during workshops were among the most highly valued element by participants in the gis and rct workshops, the interactive nature of the tot workshop was its most valued component and the active learning assignments were the highlight of the ppr workshop. lectures and theory were the lowest rated component of all four workshops. specific skills learned that were mentioned frequently were working with shape and raster files, accessing online data and utilizing data for map creation, using plug-ins for spatial analysis, and vector analysis in gis; how to collect high quality data, maintain data accuracy, set up and configure servers, ensure data security using encryption, and programming odk and redcap in ppr; and mind maps, data sharing and management tools, project management and collaboration tools, conceptual framework formulation and referencing tools in rct. workshops also increased exposure to tools the students had never before heard of, broadening their understanding of the vast array of ict applications available. students largely felt that the workshops should have been longer, as there were additional skills and tools they had hoped to master. one limitation of the improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi workshops mentioned by several participants was that android devices were not provided, but were necessary for some of the exercises. 3.5 mentored projects overall, mentored projects received mixed reviews with lower average scores than the other two components of the training. while the value of the mentorship experience was felt to be immense when mentorship was carried out in an ideal manner, there were many obstacles to successful engagement in mentored projects. a common pitfall described in all three courses was lack of clarity in expectations for the program and for completion of the project, with around 40-50% of students stating that expectations were clear in both ppr and gis. each mentee was required to produce a deliverable by the end of the mentorship period. in the gis course, students submitted abstracts with maps that showcased their use of gis concepts and technology. for ppr, mentees sent evidence of forms they had designed to collect data, in the form of either screenshots or downloadable odk forms. in the rct course mentees demonstrated the use of three different tools that were discussed in the workshop by submitting screenshots of engagement with the tools. despite these goals, mentees did not feel well informed regarding mentored project expectations. in the gis mentored projects, the lack of clarity on project deliverables may have contributed to the low pass rate of 60%. having timelines for project completion was mentioned as one way to improve expectations in several of the courses. another frequently cited obstacle was difficulty engaging with mentors, due to lack of mentor’s available time or failure to establish a clear schedule for meetings. one participant recommended that mentor-mentee meetings be mandatory at regular intervals to ensure adequate meeting frequency. another suggestion was to include meetings with other trainees to add a peer mentoring component. finally, similarly to the workshops, some participants mentioned that it was problematic when students were required to produce their own project ideas with supporting data, as several of the students did not have data readily available and suggested that the mentors have data for use in the mentorship projects. overall when the mentored projects went well, they were considered extremely useful. one participant in the rct mentorship project raved “this was a wonderful opportunity that changed my academic life, research work, and approaches for the better of my future career in research.” 4. discussion while information and communication technology (ict) has become integral to the successful implementation of research and health delivery worldwide, there remains a gap in the utilization of these tools among researchers in resource-limited settings, largely due to the lack of education and training programs available. drawing on an international collaboration to expand expertise and reinforce the importance of global collaborations in research, we designed and conducted a group of tiered, blended learning courses focused on training kenyan researchers in the use of ict tools. we collected data from our post-course evaluation surveys to better understand how different didactic modalities functioned to deliver both content and skills in ict. improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi previous programs aimed at providing ict training to audiences in low-resource settings are limited, but highlight the importance of relevance of the concepts taught to the participants’ work [26]. our program affirmed the need for relevance, with highly rated activities being those that incorporated local examples or skills that could be easily applied to everyday work. many of the most highly valued content elements taught in all of the courses were those that focused on how to use specific tools, such as redcap, odk, mendeley, and google tools. we found the tiered training program to be useful, although both the online course and the mentorship components were subject to pitfalls. technical and infrastructure challenges are common barriers to implementing e-learning programs [27,28] but might be overcome through stakeholder and institutional support [27]. although several studies have highlighted the utility of discussion boards [29,30] and they are generally considered to be productive components of online learning [31], they have been noted to be problematic before [30,32], largely due to lack of perceived need and unfamiliarity with the technology. our program data demonstrated that discussion boards were most useful when provided in a structured manner, with instructors regularly logging in and monitoring the interactions taking place, echoing previous research showing that the type and frequency of instructor interaction in online discussion boards is vital to their didactic success [31]. while mentorship has been shown to positively affect mentees’ career choices and development [33], pitfalls can be common [34,35]. mentored projects in our training were subject to many of these obstacles, and were most useful when projects and data were available at the start of the project, mentors had sufficient time to provide considerable effort and oversight, and when expectations, timelines and deadlines were made clear at the beginning of the project period. finally, previous studies have emphasized the workshop format as an ideal modality for delivering skills-based training in low-resource settings, particularly in the realm of ict [36]. our evaluation results underscore this finding, with the workshops succeeding in delivering both knowledge and skills without the obstacles that are commonly encountered in e-learning formats and during mentorship. the interpretation of data from this study is subject to several potential limitations. first, our surveys all relied on self-reported measures, which can be influenced by desirability and recall biases, among others. we mitigated these issues by conducting surveys immediately following trainings, and by using normalizing language where possible. additionally, we have triangulated self-reported data with more objective measures, such as pass rates and deliverables produced for each course. second, outcomes from our training programs may not be applicable to other lowresource settings in africa or elsewhere. while trainings must be tailored to their specific audience and setting, we believe that the core concepts and structure of our ict courses can be applied broadly. as training in ict becomes more important for researchers worldwide, the need for effective educational modalities to provide knowledge and skills to researchers in resource-limited settings is increasingly apparent. our training program may serve as an excellent foundation on which future courses in ict may be based. additionally, the inclusion of the tot course contributes significantly to the didactic sustainability of the course content, and may become the basis of similar, locally-offered courses in the future. improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi authors’ contributions mc, jk and eo conceived of the project. mc, jk, eo, bm, sf, md, dm, cc, eo, ro, ao and dm implemented the training or designed and taught courses in the training program. amw, mc, bm and jk analyzed the data. amw, dm, sf and mc wrote the manuscript and all authors reviewed the manuscript for content. acknowledgements the authors wish to thank the students and faculty at the university of nairobi for their participation in this training activity. funding this training was funded by nih grant 1r25tw009692. the funding agency did not play a role in the research design, data collection, data analysis or interpretation. ethical approval the collection of data for this study was approved the kenyatta national hospital 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https://www.ncbi.nlm.nih.gov/pmc/articles/pmc4587084/ appendix a. learning objectives, target skills acquired, and topics covered course title learning objectives target skills topics & activities covered creative integration of ict tools and technologies for enhancing research design, management and implementation (train the trainers module as well as introductory module for the other courses) • understand the role of ict tools in responding to health research challenges • use ict tools to finding and managing the scientific literature; • discuss and use social media • use data visualization tools • apply ict tools in scientific communications • apply research productivity tools • use various mobile technologies and tools • use ict tools for project management. • retrieve relevant research articles from appropriate research databases to support the development of research hypotheses and research proposals. • use a bibliographic management tool (mendeley or similar) to download and management research citations and full-text articles • identify and select relevant mobile technology tools to design and implement a simple research data collection instrument. • use a variety of desktop productivity tools in interactive ways to enhance data analysis and reporting. • use tools and technologies to improve research • the role of ict tools in responding to health research challenges • finding and managing the scientific literature • social media • data visualization • scientific communications • research productivity • mobile technologies and tools • project management https://www.ncbi.nlm.nih.gov/pmc/articles/pmc4587084/ improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi communications including written and oral presentations. • demonstrate creative integration of disparate ict tools to support all phases of the research process. geographic information systems • recognizing the growing significance of spatial analysis and geography for advancing health research • describe how gis can be used to bring together mapping, data management, and geospatial analysis techniques in the context of health research • understand how to use gis as a tool to perform basic quantitative geographic analysis and spatial data visualization (mapping) • recognize the unique challenges of performing quantitative analysis with spatial data • assess the use of gis in the research literature of any knowledge domain/discipline • understand the components of a geographic information system (gis) and how it can be used effectively throughout scientific research • competency using quantum gis (qgis), an open source gis software package, to merge, manipulate, analyze and map spatial data • ability to locate spatial data of the correct type and quality, in order to address a research question • comprehension of basic cartographic design (mapping) skills in order to clearly communicate results to your audience • understand how gis brings together mapping, data management, and • what is geography and gis?: exploring and interpreting web mapping systems • learning about gis software: installing and navigating qgis • gis data types: importing data layers and basic mapping • gis data sources: key resources for spatial data and quality considerations • spatial analysis: working with analysis functions in qgis • cartography: design and formal map layout in qgis • gis research workflows: evaluate the use of basic gis techniques in other’s research improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi • develop and implement spatial research questions using gis geospatial analysis technologies for health research principles & practice of research data management • understand the unique ethical and technical requirements of research involving human participants • know the historical evolution of methods for managing clinical research data • know types of data management software that are appropriate for single site and multicenter research, for studies of varying size and complexity • understand the characteristics of successful research data management operations • know how research data audits are conducted and how to prepare for one • understand methods for ensuring the security and confidentiality of physical and electronic research data • understand the strengths and • managing research data in compliance with international good clinical practice standards • data modeling for design of computerized databases that accommodate sparse data and repeated measures • ability to create processes that result in research data that is accurate, complete, timely, verifiable, secure, and available for analysis • ability to write a complete research data management plan for a clinical research project, including process, people, technology, and budget components • ability to design high quality paper and electronic data capture forms • ability to use two contemporary online data management systems (redcap • what is biomedical informatics, and how does it contribute to effective data management? • principles of observational and interventional research involving human volunteers • characteristics of ‘sensitive’ data, it’s acquisition, storage • what are international good clinical practice standards and why are they important? • planning sequence for designing and implementing a research study and its data management infrastructure • strengths and weaknesses of hierarchical, objectoriented, xml and relational database technologies • data coding standards to maximize utility and re-usability of data • designing data capture forms to improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi hazards of using the internet for acquisition and management of sensitive data and open data kit odk) to design and implement a simple research study maximize usability and data completeness • specialized contemporary electronic data capture technologies, including barcoding, scantron and teleforms. • design and use of smartphone apps for research data management research management & communication tools • describe the skills used in the research process and link with a highlight of the research process steps already covered in previous courses such as data management. • find and use a data set to do basic data analysis focusing on descriptive statistics, and basic parametric tests. • perform data analysis using descriptive statistics and basic parametric tests using an open source data analysis software such as pspp. • describe results dissemination • understand the research process. • demonstrate how to identify data sets. • demonstrate knowledge of descriptive statistics, and basic parametric tests • understand how to use a statistical tool to analyse data to generate results for descriptive statistics, and basic parametric tests • understand how to make oral presentations and write research papers • understand how indexing and web searching and • ict skills used in the research process and link with a highlight of the research process steps already covered in previous courses i.e. introduction to the research process, data management and data visualization • data analysis. identify a data set and guide the trainees on how to do basic data analysis, perhaps focusing on descriptive statistics, and basic parametric tests. this will be done as theory. • data analysis using descriptive statistics improving information and communications technology (ict) knowledge and skills to develop health research capacity in kenya online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e22, 2019 ojphi techniques such as presentations, policy briefs. • describe how research collaboration can occur using linkedin as a tool, and one other tool for collaboration. • demonstrate how indexing and web searching and bibliography is done. bibliography are done. and basic parametric tests using an open source data analysis software such as pspp. the learners should run some designed tests to see some sample results screens displayed, and then they run tests as assignments • results dissemination techniques: presentations, policy briefs, etc • research collaboration, using linkedin as a tool, and one other tool for collaboration • handling of indexing and web searching and bibliography layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the biosurveillance resource directory a one-stop shop for systems, sources, and tools kristen margevicius*, mac brown, lauren castro, william b. daniel, eric n. generous, kirsten taylor-mccabe and alina deshpande los alamos national laboratory, los alamos, nm, usa objective the goal of this project is to identify systems and data streams relevant for infectious disease biosurveillance. this effort is part of a larger project evaluating existing and potential data streams for use in local, national, and international infectious disease surveillance systems with the intent of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease. introduction local, national, and global infectious disease surveillance systems have been implemented to meet the demands of monitoring, detecting, and reporting disease outbreaks and prevalence. varying surveillance goals and geographic reach have led to multiple and disparate systems, each using unique combinations of data streams to meet surveillance criteria. in order to assess the utility and effectiveness of different data streams for global disease surveillance, a comprehensive survey of current human, animal, plant, and marine surveillance systems and data streams was undertaken. information regarding surveillance systems and data streams has been (and continues to be) systematically culled from websites, peer-reviewed literature, government documents, and subject-matter expert consultations. methods a relational database has been developed and refined to allow for detailed analyses of data streams and surveillance systems. to maximize the utility of the database and facilitate one-stop-shopping for biosurveillance system information, we have expanded our scope to include not only biosurveillance systems, but also data sources, tools, and biosurveillance collectives. captured in the information collected about the resource (if available) is the name and acronym of the resource, the date the resource became available, the accessibility of the resource (is it open to all, or are there limitations to access), the primary sponsors, if the resource is associated with gis functionality, and if the focus is health. also collected is contact information, information regarding the scope and domain of the resource, the pertinent diseases or disease categories, and the geographic and population coverage of the resource. websites associated with the resource are directly accessible from the database. data stream information is also captured based on our developed data stream framework. if the resource uses other specified systems/sources/tools for data gathering or analysis, then that is also captured and directly linked within the database. results the biosurveillance resource directory (brd) is in the process of being tested by multiple potential end users in the public health, biosecurity, and biosurveillance communities. feedback from these testers is being used to refine the database to maximize functionality and utility. additionally, methods for dynamically updating and maintaining the database are being evaluated. automated and semi-automated queriable reports have been developed and are integral to demonstrating specific use-case scenarios in which the brd would be beneficial for end-users. conclusions a need for a biosurveillance one-stop shop has been increasingly called for to help in evaluating what data streams and systems are available and relevant for many different biosurveillance needs and goals. the prototype biosurveillance resource directory is a searchable, dynamic database for biosurveillance systems, sources, and tools information. keywords infectious disease; biosurveillance; database acknowledgments this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra). *kristen margevicius e-mail: kmargevicius@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e138, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using the flow of people in cluster detection and inference sabino j. ferreira*1, francisco s. oliveira1, ricardo tavares2 and flavio r. moura2 1federal university of minas gerais, belo horizonte, brazil; 2federal university of ouro preto, ouro preto, brazil objective we present a new approach to the circular scan method [1] that uses the flow of people to detect and infer clusters of regions with high incidence of some event randomly distributed in a map. we use a real database of homicides cases in minas gerais state, in southeast brazil to compare our proposed method with the original circular scan method in a study of simulated clusters and the real situation. introduction the traditional satscan algorithm[1],[2] uses the euclidean distance between centroids of the regions in a map to assemble a connected (in the sense that two connected regions share a physical border) sets of regions. according to the value of the respective logarithm of the likelihood ratio (llr) a connected set of regions can be classified as a statistically significant detected cluster. considering the study of events like contagious diseases or homicides we consider using the flow of people between two regions in order to build up a set of regions (zone) with high incidence of cases of the event. in this sense the regions will be closer as the greater the flow of people between them. in a cluster of regions formed according to the criterion of proximity due to the flow of people, the regions will be not necessarily connected to each other. methods we consider a study map with a number of observed cases and risk population for each region. the original circular scan algorithm randomly chooses one region as the first zone and calculates its respective llr. in the next step a new zone is created including the first region and the region closest to it according the euclidean distance between their centroids and the respective llr is calculated. this process is repeated until the zone population exceeds a certain percentage of the total population of the map. in our spatial flow scan algorithm everything works in the same manner except that the degree of proximity of two regions is given by the flow of people between them, the higher the flow between the regions closest one is the other. instead of considering an order of increasing distances to add a region and create a new zone our algorithm uses a decreasing flow of people. in this way we can obtain a zone/cluster candidate composed of a number of non necessarily connected regions. results minas gerais state is located in brazil south-eastern region composed of 853 municipalities or regions with an estimated population of 19,150,344 in 2005. all data were obtained from the brazilian ministry of health (www.datasus.gov.br ) and brazilian institute of geography and statistics (www.ibge.gov.br). in the period of 2003 to 2008 were recorded 20,912 homicides at a rate of 22 cases per 100,000. to measure the flow of people between the cities we obtain the data of bus round trips between all the 853 minas gerais municipalities from state department of highways (www.der.mg.gov.br ). as a large number of pairs of cities have zero bus trips between them we use a gravity model [3] to estimate the flow of people. we use 30% as upper percentage for a zone population. with the real data of homicides cases the original circular scan found a significant cluster containing the city of belo horizonte which is the minas gerais state capital and large urban area that include belo horizonte and 22 more cities totalizing a population of about 3.5 milion people. our adapted spatial scan algorithm also found a similar cluster including the capital belo horizonte but with two small cities less. conclusions in simulation studies where the real cluster is known we observe that our spatial flow scan algorithm has a performance similar to the circular scan concerning detection power and slightly worse in relation to the positive predicted value (ppv) and the sensitivity when the real cluster is regular. however, the performance of our algorithm is clearly better with regard to the sensitivity and the ppv when the real cluster is irregular and or non-connected. keywords spatial scan statistics; flow of people; spatial flow scan algorithm; gravity models acknowledgments sjf acknowledges the support by fapemig, mg, brazil. references [1] kulldorff m. a spatial scan statistic, comm. statist. theory meth., 1997, 26(6), 1481-1496. [2] kulldorff m. satscan: software for the spatial, tem-poral and spacetime scan statistics. [www.satscan.org]. [3] signorino g.; pasetto r.; gatto e.; mucciardi m.; rocca m. la; muso p. gravity models to classify commuting vs. resident workers. an application to the analysis of residential risk in a contaminated area. . int. j. of health geographics, 2011. 10:11, pp. 1-10. *sabino j. ferreira e-mail: sabjfn@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e12, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts implementation of a mobile-based surveillance system in saudi arabia for the 2009 hajj wei li* centers for disease control and prevention, atlanta, ga, usa objective to develop and implement a mobile-based disease surveillance system in the kingdom of saudi arabia (ksa) for the 2009 hajj; to strengthen public health preparedness for the h1n1 influenza a pandemic. introduction the hajj is considered to be the largest mass gathering to date, attracting an estimated 2.5 million muslims from more than 160 countries annually (1). the h1n1 influenza a pandemic of 2009 generated a global wave of concern among public health departments that resulted in the institution of preventive measures to limit transmission of the disease. meanwhile, the pandemic amplified an urgent need for more innovative disease surveillance tools to combat disease outbreaks. a collaborative effort between the ksa ministry of health (moh) and the u.s. centers for disease control and prevention (cdc) was initiated to implement and deploy an informatics-based mobile solution to provide early detection and reporting of disease outbreaks during the 2009 hajj. the mobile-based tool aimed to improve the efficiency of disease case reporting, recognize potential outbreaks, and enhance the moh’s operational effectiveness in deploying resources (2). methods we designed a case-based system consisting of a mobile-based data collection toolkit and interactive map-based user interface to perform geospatial analysis and visualization. a train-the-trainer approach was adapted to provide training to the ksa moh. results more than 200 public health and information and communication technology (ict) professionals were trained, and 100 mobile devices were deployed during the 2009 hajj. nine diseases and conditions that were considered as highest priority during the hajj were under surveillance, including h1n1 influenza a and influenza-like illness. pilot testing of the system was conducted during the first week of ramadan and a modified system was fully operational during the hajj. data collected on smartphones were sent to the system via a secured network. the data were processed immediately and visualized on highly interactive maps with local and global views. conclusions effective public health decision-making requires timely and accurate information from a variety of sources. mobile-based systems (e.g., personal digital assistants and smartphones) for data collection, transmission, reporting, and analyses provide a faster, easier, and cheaper means to communicate standardized and shareable public health data for decision-making (3). mobile-based systems have been recognized as a quick and effective response solution to mass gatherings and recommended as data gathering and communication systems with geographical information system (gis) capability (2). this paper explored the development and implementation of the global positioning system/ geographic information system (gps/gis) enabled mobile-based disease surveillance system as a feasible and effective way to support and strengthen preparedness for h1n1 influenza a during the 2009 hajj. mobile computing technology can be utilized to provide rapid and accurate data collection for public health decision-making during mass gatherings. the gis-based interactive mapping tool provided a pioneering example of the power of a geographically based internetaccessible surveillance system with real-time data visualization. the technical challenges in the process of implementation and in the field were also identified. a need now exists for a comprehensive and comparative review of parameters such as handheld device cost, training required, and system evaluations because selecting the appropriate software/hardware and system remains a challenge not only to public health professionals, but to the development and application of informatics technology as well. keywords mobile technology; gis/gps; mass gatherings; surveillance system; public health preparedness acknowledgments many people contributed to the mobile-based surveillance system’s development and implementation for the 2009 hajj. the author would like to acknowledge dr. z. memish and his colleagues at ksa moh, mr. c. gillespie and his colleagues at the u.s. embassy in ksa, the cdc foundation, and colleagues across cdc. references 1. memish za, ahmed ga. mecca bound: the challenges ahead. j. travel med 2002; 9:202-10 2. memish za, mcnabb s, mahoney f, et al. establishment of public health security in saudi arabia for the 2009 hajj in response to pandemic influenza a h1n1. the lancet, published online november 14, 2009; doi:10.1016/s0140-6736(09)61927-9 3. yu, p. et al. the development and evaluation of a pda-based method for public health surveillance data collection in developing countries, int. j. med. inform. (2009; 78(8):532-42 *wei li e-mail: for5@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e137, 2013 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 137 (page number not for citation purposes) isds 2013 conference abstracts an early warning surveillance platform for developing countries nsaibirni robert fondze jr*1, 2, gaëtan texier2, 3, patrice tchendjou2, george edouard kouamou1, richard njouom2, maurice demanou2 and maurice tchuente1 1department of computer science, faculty of science, university of yaoundé i, yaoundé, cameroon; 2centre pasteur du cameroun, yaoundé, cameroon; 3umr 912 sesstim inserm/ird/aix-marseille université, 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cited. isds 2012 conference abstracts usefulness of syndromic surveillance for early outbreak detection in small islands: the case of mayotte pascal vilain*1, olivier maillard3, julien raslan-loubatie2, mohamed ahmed abdou3, tinne lernout2 and laurent filleul1 1regional office of the french institute for public health surveillance of indian ocean, saint-denis, reunion; 2regional office of the french institute for public health surveillance of indian ocean, mamoudzou, mayotte; 3hospital center of mayotte, mamoudzou, mayotte objective to present the usefulness of syndromic surveillance for the detection of infectious diseases outbreak in small islands, based on the experience of mayotte. introduction mayotte island, a french overseas department of around 374 km2 and 200 000 inhabitants is located in the north of mozambique channel in the indian ocean (figure1). in response to the threat of the pandemic influenza a(h1n1)2009 virus emergence, a syndromic surveillance system has been implemented in order to monitor its spread and its impact on public health (1). this surveillance system which proved to be useful during the influenza pandemic, has been maintained in order to detect infection diseases outbreaks. methods data are collected daily directly from patients’ computerized medical files that are filled in during medical consultations at the emergency department (ed) of the hospital center of mayotte (2). among the collected variables, the diagnosis coded according to icd-10 is used to categorize the syndromes. several syndromes are monitored including the syndromic grouping for conjunctivitis and unexplained fever. for early outbreak detection, a control chart is used based on an adaptation of the cusum methods developed by the cdc within the framework of the ears program (3). results each week, about 700 patients attend the ed of the hospital. the syndromic surveillance system allowed to detect an outbreak of conjunctivitis from week 10 (figure2). during the epidemic peak on week 12, conjunctivitis consultations represented 5% of all consultations. the data of the sentinel practitioner network confirmed this epidemic and the laboratory isolated enterovirus (4). at the same time, an unusual increase of unexplained fever was detected. conclusions due to its geographical and socio-demographical situation, the population of mayotte is widely exposed to infectious diseases. even on a small island, syndromic surveillance can be useful to detect outbreak early leading to alerts and to mobilize a rapid response in addition to others systems. figure1. map of the western indian ocean featuring mayotte island figure2. weekly number of conjonctivitis and unexplained fever consultations and statistical alarms detected keywords syndromic surveillance; early outbreak detection; mayotte island acknowledgments we are thankful to all the sentinel network practitioners and the practitioners of the emergency department. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e149, 2013 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts references 1. lernout t, durquety e, chollet p, helleisen f, javaudin g, lajoinie g, filleul l. [influenza a (h1n1) 2009 surveillance on mayotte island: the challenge of setting up a new system facing the pandemic]. bull soc pathol exot. 2011 may;104(2):114-8. 2. filleul l, durquety e, baroux n, chollet p, cadivel a, lernout t. [the development of non-specific surveillance in mayotte and reunion island in the context of the epidemic influenza a(h1n1)2009] [article in french]. bull epidémiol hebd. 2010;(24-26):283-5. 3. hutwagner l, browne t, seeman gm, fleischauer at. comparing aberration detection methods with simulated data. emerg infect dis. 2005 feb;11(2):314-6. 4. lernout t, maillard o, boireaux s, collet l, filleul l. a large outbreak of conjunctivitis on mayotte island, france, february to may 2012. euro surveill. 2012 jun 7;17(23). pii: 20192. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e149, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts extraction of disease occurrence patterns using mistic: salmonellosis in florida vipul raheja* and k. s. rajan international institute of information technology hyderabad (iiit-h), hyderabad, india objective this work leverages spatio-temporal data mining (st-dm), the mistic (mining spatio-temporally invariant cores)[1,6] method for infectious disease surveillance, by identifying a) extent of spatial spread of disease core regions across populations-scale of disease prevalence b) possible causes of the observed patterns-for better prediction, detection & management of infectious disease & its outbreaks introduction infectious diseases, though initially tend to be limited geographically to a reservoir; a subsequent spatial variation in disease prevalence (including spread & intensity) arises from the underlying differences in physical-biological conditions that support pathogen, its vectors & reservoirs. different factors like spatial proximity, physical & social connectivity, & local environmental conditions which add to its susceptibility influence the occurrence[2]. in disease management, analysis of historical data over various aspects of geography, epidemiology, social structures & network dynamics need to be accounted for. large amounts of data raise issues of data processing, storage, pattern identification, etc. in addition, identifying the source of disease occurrence & its pattern can be of immense value. st-dm of disease data can be an effective tool for endemic preparedness[3], as it extracts implicit knowledge, spatial & temporal relationships, or other patterns inherent in such databases. here, core region is defined as a set of spatial entities(eg.counties) aggregated over time, which occur frequently at places having high values in a defined region (considering areas of influence around them)[1]. methods here, mistic algorithm detects spatio-temporally invariant cores with respect to disease occurrence. it involves both a spatial analysis step to detect focal points & a spatio-temporal analysis over the time period of study to identify core regions, which are then classified as – chd, cld & cnd. they refer to cores with high, low and no (mostly random) dominating points respectively based on frequency of occurrences of disease. the predominantly occurring focal points capture the localized behavior of the disease whereas the neighborhood constraints capture the nature (dynamic or non-dynamic) of the event. results county-level annual data of salmonellosis incidence from florida department of health [3] covering a period of 50 years (1961-2010) is used. two types of cores were identified based on type of neighborhood contiguous (cc) & within a defined radius (cr). table1 shows the analysis of counties according to valid frequency criteria for both cc & cr (r=2) & their sub-classification. salmonellosis etiology shows that it is caused by tainted food, hygiene, local environment etc. which are largely sanitation-related [4]. taking the level of urbanization [5] as a proxy for sanitation, it can be seen from fig.1, 12 of 19 cores occur in rural counties. conclusions it is observed that cc is better indicator of cores than cr, implying that salmonellosis manifests itself in a highly localized manner. thus, use of mistic is promising & provides a way for identifying disease “hot-spots”. it also provides valuable insight into the understanding of disease prevalence in different regions based on their history over space and time. classification of core polygons map showing overlay of metropolitan areas and cores keywords disease cores; salmonellosis; spatio-temporal data mining; patterns references 1. k sravanthi, k s rajan: spatio-temporal mining of core regions study of rainfall patterns in monsoonal india. 11th ieee international conference on data mining workshops (icdmw) 2011, pp.30-37 2. chris bailey-kellogg et al.: spatial data mining to support pandemic preparedness. acm sigkdd explorations newsletter. 2006; 8(1):80-82 3. http://www.floridacharts.com [20/5/2012] 4. http://www.cdc.gov/healthypets/diseases/salmonellosis.htm [2/7/2012] 5. http://www.census.gov [25/5/2012] 6. k sravanthi; mistic; ms thesis. iiit hyderabad *vipul raheja e-mail: vipul.raheja@research.iiit.ac.in online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e19, 2013 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts evaluation of syndromic surveillance data streams in animal health morgan hennessey*, julianna b. lenoch, cynthia zepeda, leah estberg and judy akkina usda-aphis-vs, fort collins, co, usa objective to implement a systematic and uniform approach to evaluating data sources for syndromic surveillance within the united states department of agriculture (usda) animal and plant health inspection services (aphis) veterinary services (vs) group. introduction usda-aphis-vs utilizes several continuous data streams to increase our knowledge of animal health and provide situational awareness of emerging animal health issues. in addition, usdaaphis-vs often conducts pilot projects to see if regular data access and analysis are feasible, and if so, if the information generated is useful. syndromic surveillance was developed for three goals: a syndromic monitoring system to identify new diseases, as an emerging disease early warning system, and to provide situational awareness of animal health status. current efforts focus on monitoring diverse data, such as laboratory accessions or poison center calls, grouped into syndromic or other health indicator categories, and are not intended to identify specific pre-determined diseases or pathogens. it is essential to regularly evaluate and re-evaluate the effectiveness of our surveillance program. however, there are difficulties when using traditional surveillance evaluation methods, since the objectives and outcomes of monitoring novel data streams from pilot projects are not easily measurable. an additional challenge in the evaluation of these data streams is the identification of a method that can adapt to various context and inputs to make objective decisions. until recently, assessment efforts have looked at the feasibility of regular analysis and reporting, but not at the utility of the information generated, nor the plausibility and sustainability of longer term or expanded efforts. methods methods for surveillance evaluation, syndromic surveillance evaluation, and specifically for animal health syndromic surveillance evaluation were researched via a literature review, exploration of methods used in-house on traditional surveillance systems, and through development over time of criteria that were seen as key to the development of functioning, sustainable systems focusing on animal health syndromic surveillance. several methods were adapted to create an approach that could organize information in a logical manner, clarify objectives, and make qualitative value assessments in situations where the quantitative aspects of costs and benefits were not always straight forward. more than 25 articles were reviewed to determine the best method of evaluation. results the risksur evaluation support tool (eva) provided the majority of the methodology for the evaluations of our data sources. the eva tool allows for an integrated approach for evaluation, and flexible methods to measure effectiveness and benefits of various data streams. the most useful and common factors found to evaluate pilot data sources of interest were how well the information generated by the data streams could provide early detection of animal health events, and how well and how often situational awareness information on animal health was generated. the eva tool also helps identify and organize criteria that are used to assess the objectives, and assign value. conclusions the regular evaluation of syndromic surveillance data streams in animal health is necessary to make best use of resources and maximize benefits of data stream use. it is also useful to conduct regular interim assessments on data streams in pilot phase to be certain key information for a final evaluation will be generated during the project. the risksur eva tool was found to be very flexible and useful for allowing estimates of value to be made, even when evaluating systems that do not have very specific, quantitatively measurable objectives. this tool provides flexibility in the selection of attributes for evaluation, making it particularly useful when examining pilot project data streams. in combination with additional review methodologies from the literature review, a systematic and uniform approach to data stream evaluation was identified for future use. keywords syndromic surveillance; animal health; emerging disease *morgan hennessey e-mail: morgan.j.hennessey@aphis.usda.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e153, 2017 a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes angella musiimenta 1 1 mbarara university of science and technology, and bishop stuart university kakoba abstract unlike traditional approaches to sexuality and hiv education which can be constrained by the sensitive nature of the subject, information technology (it) can be an innovative teaching tool that can be used to educate people about hiv. this is especially relevant to interventions targeting young people; the population group fond of using it, and the same group that is more vulnerable to hiv/aids. yet, there are significantly few empirical studies that rigorously evaluated computer-assisted school-based hiv/aids interventions in developing countries. the modest studies conducted in this area have largely been conducted in developed countries, leaving little known about the effectiveness of such interventions in low resource settings, which moreover host the majority of hiv/aids infections. this research addresses this gap by conducting a controlled pre-post intervention evaluation of the impacts of the world starts with me (wswm), a computer-assisted hiv/aids intervention implemented in schools in uganda. the research question was: did the wswm intervention significantly influence students’ sexual behaviors, hiv/aids knowledge, attitudes and self-efficacy? to address this question, questionnaires were simultaneously administering to 146 students in an intervention group (the group receiving the wswm intervention) and 146 students in a comparison group (the group who did not receive the wswm intervention), before (february 2009) and after the intervention (december 2009). findings indicate that the intervention significantly improved students’ hiv/aids knowledge, attitudes self-efficacy, sex abstinence and fidelity, but had no significant impact on condom use. the major reason for non-use of condoms was lack of knowledge about condom use which can be attributed to teachers’ failure and inabilities to demonstrate condom use in class. to address this challenge, intervention teachers should be continuously trained in skills-based and interactive sexuality education. this training will equip them with self-confidence and interactive teaching skills, including tactics for emphasizing building students’ skills through role plays and interactive assignments. in addition, the hiv interventions themselves should include interactive virtual condom use demonstrations that can be accessed by students themselves. a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 key words: ict for hiv/aids; wswm; sexual behaviors, knowledge and attitudes; school. 1. introduction 1.1 computer-assisted behavioral change interventions the literature on the effectiveness of computer-assisted behavioral change interventions reports some success in increasing knowledge (coumba et al 2005; campbell et al 2004), changing attitudes, and coping self-efficacy (wikgren 2003; stout et al 2001; gustafson et al 2001; richie et al 2000). however, computer-assisted behavioral change interventions are reported to have limited success in changing behavior and yielding quantifiable health benefits (littlejohns et al 2003; hersh et al 2001; campbell 2004; eysenbach et al 2004; howells et al 2002). although there are significantly few empirical studies focusing on computer-assisted hiv/aids interventions, the modest studies conducted in this area show some positive benefits. there is evidence that computer-assisted hiv/aids innovations increase participants’ knowledge of sexual health and hiv/aids (bailey et al 2010; young and rice 2011; noar et al 2009; tian et al 2007; ito et al 2008; lou et al 2006; halpern et al 2008), attitudes and self-efficacy (gustafson et al 2001; ashton et al 2005; coursaris et al 2009). the efficacy of such interventions on changing sexual behavior such as condom use remains inconclusive (bailey et al 2010; bull et al 2009; wantland et al 2004). however some studies report positive impacts such as reduction of risky sexual behaviors (young and rice 2011), condom use intentions (ito et al 2008); significant reduction on number of partners, and increased condom use (noar et al 2009). despite the potential benefits, most of these studies have been conducted in developed countries, leaving little known about the effectiveness of computer-assisted hiv/aids interventions in developing countries. moreover, the loss of life, and the social and economic burdens created by hiv/aids (e.g. carer burdens on affected families, lost output in productivity and burdens on healthcare facilities are sufficiently severe to justify the need for targeting research and intervention to the prevention of hiv/aids in developing countries (muller 2005). 1.2 the evaluation of school-based sexuality and hiv/aids interventions although there is some evidence from previous studies of increased hiv/aids knowledge and attitudes from school-based hiv/aids intervention (cheng et al 2008; jahanfar et al 2009), impacts on sexual behavior remain inconclusive as the same studies report no differences in students’ behavioral scores. out of 49 interventions to prevent hiv/aids and pregnancy in the united states, only four interventions increased the use of condoms or other contraceptives (kirby et al 1995). another review of 26 pregnancy prevention interventions (including 10 school-based ones) reported no effect on sex abstinence, condom use or unplanned pregnancy (dicenso et al 2002). it can be argued that the absence of significant effects on behavioral change is a result of limited behavioral-gap due to the shortness of follow-up assessments that are normally allowed by school-based evaluation. however, results from walker et al (2006) defeat this argument, since even the increase in condom use a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 reported immediately after the intervention could not be maintained one year after the intervention. in addition, in a long follow-up, wight et al (2002) report no statistically significant difference on students’ rate of contraception uptake. noteworthy, however, is that some reviews indicate that school-based interventions can decrease students’ hiv/aids risky behavior (lonczak et al 2002). overall, whether or not school-based hiv/aids interventions influence students’ sexual behavior remains highly controversial. in addition, school-based hiv/aids studies have largely been conducted in developed countries, leaving little known about the effectiveness of such interventions in low resource settings, which moreover host the majority of hiv/aids infections. recent meta-analyses persistently caution of a lack of rigorous evaluation of school-based hiv/aids interventions in africa (magnussen et al 2004; paul-ebhohimhen et al 2008), while a recent literature review of 87 studies, 70% of which were school-based, affirms a lack of evaluated sexuality and hiv interventions in developing countries (unesco 2009). uganda is among the sub-saharan african countries that has been severely infected and affected by hiv/aids since 1982. although the country had recorded a decline in hiv/aids rates in 1990’s, the rates started increasing again especially among the youth (ministry of health uganda 2005; biraro et al 2009). this increase has attracted some local and international organisations to implement hiv interventions targeting young people, of which the world starts with me (wswm) the intervention evaluated in this study is one of such interventions. this investigation aimed to assess the level of significance of the impacts of the wswm on students’ sexual behaviors, knowledge of hiv/aids, attitudes and perceived self-efficacy. 2. methodology 2.1 the world starts with me (wswm) intervention developed by butterfly works, schoolnet uganda and uganda local experts, the wswm is a fourteen-lesson computer-assisted sexuality and hiv/aids intervention implemented in over 200 secondary schools in uganda since 2003. this intervention has also been implemented in kenya, india, thailand, indonesia, and vietnam under the same sponsorship of world population foundation (wpf). in uganda, it is delivered in classrooms with the help of oriented intervention teachers using the web-based version (http://www.theworldstarts.org), the cd-rom and/or the hard copy version. by 2008, over 8,000 young people had accessed the intervention website, 2000 young people had accessed the intervention print-outs. other it-related features of this intervention include the online counselling and support centre (http://schoolnetuganda.sc.ug/wswmonlinesupport/) that enables the exchange of sexual health and hiv/aids-related information between sexual reproductive health counsellors and young people. included also is the use of virtual peer educators, interactive safer sex quizzes, story boards, and role plays. 2.2 the intervention and control groups the major objective of this investigation was to assess the level of statistical significance of the impacts of the wswm intervention on students. to achieve this objective, this investigation involved the intervention group and the comparison group (the group that never had the intervention). investigating the intervention and comparison groups before and after http://www.theworldstarts.org/ http://schoolnetuganda.sc.ug/wswmonlinesupport/ a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 the intervention improves the reliability with which the identified intervention impacts can be attributed to the intervention, rather than to external factors (wyatt 2000; remenyi et al 2002; wyatt and wyatt 2003). the controlled pre-post intervention study is based on the second cycle of the intervention that ran from february 2009-december 2009. the comparison school did not implement the intervention at any time prior to or during the period of this research. permission to investigate the intervention school was obtained from the intervention leader (the executive and training director-etd) between may and september 2008. this school was selected due to the researcher’s initial contact with the intervention teachers, the anticipated high hiv vulnerability of students since it was a school in a military barracks with many war-orphaned students, and the school’s close proximity to the researcher’s residence. the intervention teachers used three computers connected on the internet, one television set that used an intervention cd, and computer print-outs to deliver the intervention. authorisation to investigate the comparison school was obtained from the head of school in january 2009. this comparison school was selected due to the anticipated similarities with the intervention school since it was also located in a military barracks with many war-orphaned students. as shown in table 1 below, there was no statistically significant difference in the demographic characteristics of the intervention and comparison groups at pre-test. 2.3 participant selection the researcher had no control regarding the implementation procedures of the intervention including the enrollment of students into the intervention. thus, this investigation relied on groups that pre-existed in schools. the school’s fixed implementation procedures and timetables of the intervention dictated the choice and the number of participants. the intervention school had a total of 180 students in senior one, 83 of whom were in stream a, while 77 were in stream b. in february 2009 when this study started, 152 students had registered their names to attend the intervention, all of whom were involved in the pre-test assessment of this study. the comparison group had a total of 218 students in senior one in three streams i.e. 72 students in stream a, 76 in stream b and 70 in stream c. in order to get a relatively equal number of participants in the intervention and comparison groups, stream a and stream b (148 participants) were involved in the present study. in both schools, participants were requested by intervention teachers to enroll for this study and were also informed that their participation had no impact on their academic assessment. 2.4 outcome measures 2.4.1 sexual behaviors there were three main measures for sexual behaviors: abstinence, number of sexual partners and condom use, which were measured using 5 statements as shown in table 2. participants were also asked to state reasons for use or non-use of condoms at last sex. 2.4.2 hiv/aids awareness and perception of vulnerability seven statements were employed to measure these variables (table 4). the questions were extracted from a questionnaire that had been specifically previously developed by the centre for aids prevention studies, california and pilot-tested on junior high school students in california (population council 2008). using previously developed and tested questions a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 ensures reliability of findings (wyatt and wyatt 2003). some questions on aids-related knowledge that the present study rendered culturally inappropriate were excluded. each statement was assessed using a four-point likert scale measuring the degree of agreement or disagreement with the statement, ranging from “strongly agree” to “strongly disagree” including an option for “no answer”. 2.4.3 attitudes towards girls’ condom initiation and negotiation this assessment used eight statements adapted from a questionnaire which was developed, validated and used in a programme for hiv prevention in thai schools (population council 2008). only statements related to attitudes towards young women’s condom use were adapted (table 5). minor adjustments were made to statements in order to emphasise girls’ initiation and negotiation of condom use. this was done by introducing the word “girl” in many of the statements. for example, a statement such as: “if i carry a condom, my partner will think that i am planning to have sex”, was adjusted to “if a girl carries a condom, her partner will think that she is planning to have sex.” each statement was assessed using a four point likert scale measuring the degree of agreement or disagreement with the statement, ranging from “strongly agree” to “strongly disagree” including the option for “no answer”. 2.4.4 adherence to men’s infidelity-related norms participants were asked the extent to which they believe in the statement: “whereas it is ok for boys/men to have more than one sexual partner at the same time, girls/women should only have one sexual partner at the same time”. a four-point likert scale was used to measure the degree of agreement or disagreement with the above statement, ranging from “strongly agree” to “strongly disagree”, including the option for “no answer”. 2.4.5 girls’ perceptions of condom assertiveness self-efficacy girls’ perceptions of condom assertiveness self-efficacy were assessed by the sexual assertiveness scale (sas) (population council 2008), which has a proven reliability among diverse female populations. sas was developed and validated from four studies that sampled 513 young women of at least 18 years of age. in line with aim of the present study, sas’s subscale for pregnancy-std prevention was adapted. no major modifications were made to the statements apart from replacing the term “latex barrier” with the term “condom”. this is was after realising from pilot tests of the questionnaires that the majority of respondents had never heard about latex barriers. five statements (table 7) were assessed using a four-point likert scale measuring the degree of agreement with each statement, ranging from “strongly agree” to “strongly disagree”, including the option for “no answer.” 2.5 the pre-test and post-test data collection the pre-test and post-test studies consisted of the simultaneous administering of the same questionnaire to both the intervention and comparison groups at pre-test in february 2009, one week before the intervention group was exposed to the intervention, and at post-test in december 2009 one week after the intervention group was exposed to the intervention. the pre-test questionnaire was aimed at exploring the initial status of students’ sexual behaviors, hiv/aids knowledge, hiv-related self-efficacy and attitudes. these results were later used as a basis for comparison when explaining the impacts of the intervention. a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 the post-test questionnaire was aimed at exploring the immediate impact of the intervention on students’ sexual behaviors, hiv/aids knowledge, self-efficacy and attitudes. both the pre-test and post-test questionnaires were the same in content, except that demographic questions were only included in the pre-test questionnaires. the pre-test involved 300 participants, of whom 152 were in the intervention group and 148 in the comparison group. however, the post-test involved 292 participants, of whom 146 belonged to the intervention group, while 146 were in the comparison group. during the posttest assessment, eight participants were absent from their respective schools and were therefore excluded from the study. for both the pre-test and post-test of the intervention and comparison groups, participants were gathered in one hall where questionnaires were distributed and collected immediately after completing them. questionnaires were administered by peers (who were class leaders) rather than the researcher as it was anticipated that participants were more likely to open up while filling in the questionnaires distributed by peers rather than the researcher. prior to administering the questionnaires, the researcher introduced the objectives of the questionnaire to the participants. this introduction involved assuring participants of the confidentiality of the information being collected, and giving them codes that were to be used as reference points during the post-study follow-up. peers were briefed about the contents of the questionnaire as well as about questionnaire administering. the researcher remained within the school environment while peers administered the questionnaires in order to attend to any queries that peers would not be able to answer. the total duration taken by participants to fill in the questionnaire ranged between 15-30 minutes. 2.6 the pre-test and post-test data analysis the pre-test and post-test data collected from the groups was coded, entered and entered into spss version 16.0 for analysis. responses to likert-scaled statements were treated as ordinal/ranked variables and assigned codes; code 1 for “strongly agree” to code 4 for “strongly disagree”, and 5 for “no answer”. none of the respondents selected the “no answer” option, thus, this option was ignored. wilcoxon signed rank test was employed to assess the level of significance of impacts of the intervention on sexual behaviors. wilcoxon signed rank test requires two nominal variables and one measurement variable (pallant 2007). in this study, pre-test and post-test scores represent the first nominal variable, assessment statements represent the second nominal variable, while percentages represent measurement variable (e.g. see mcdonald 2008). response for the assessments of sexual behaviors were given numerical codes, treated as nominal variables and summarised using descriptive statistics. the statistics were then treated as continuous variables and analysed using wilcoxon signed rank test. this approach is recommended by (mcdonald 2008) for “ambiguous variables” which seem not to perfectly qualify for continuous, nominal or ordinal/ranked variables, as is the case in the variables used for assessment of sexual behaviors. paired-sample t-tests were applied to assess the impacts of the intervention by comparing the, hiv/aids knowledge, attitudes and self-efficacy at the pre-test and post-test assessments of the intervention group. the paired samples t-test (also referred to as repeated measures) is a statistical measure used to assess the level of significance in mean scores of a group, or more than one group, investigated at pre-test and at post-test (pallant 2007). wilcoxon signed rank test and paired sample t-tests calculate the probability (p) values of responses in each statement for pre and post assessments to determine the level of significance. p<=0.05 a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 implies a statistical significance difference, while p>= 0.05 denotes an insignificant difference (pallant 2007). paired sample t-tests have also been effectively used by other researchers (jahanfar et al 2009) evaluating hiv interventions that involved the pre-test and post-test assessments. 2.7 social-demographic details of participants table 1: descriptive statistics for the social-demographic variables for the intervention and the comparison groups at pre-test variable intervention group (max n = 146) comparison group (max n = 146) sex male =61(42%) female =85(58%) total =146(100%) male =46(32%) female =100(68%) total =146(100%) age (years) 11-13 14-16 24(16%) 37(25%) 17(12%) 68(47%) 41(28%) 105(72%) n=146(100%) 14(10%) 32(22%) 23(15%) 77(53%) 37(25%) 109(75%) n=146(100%) religion christian muslim 47(32%) 14(10%) 80(55%) 5(3%) 127(87%) 19(13%) n=146(100%) 44(30%) 2(1%) 89(61%) 11(8%) 133(91%) 13(9%) n=146(100%) parental status one or both parent dead both parents alive 49(34%) 12(8%) 57(39%) 28(19%) 106(73%) 40(27%) n=146(100%) 34(23%) 12(8%) 63(43%) 37(25%) 97(66%) 49(34%) n=146(100%) parent occupation 1 non-professionals professionals 50(34%) 11(8%) 67(46%) 18(12%) 117(80%) 29(20%) n=146(100%) 43(30%) 3(2%) 85(58%) 15(10%) 128(88%) 18(12%) n=146(100%) max n = maximum number of participants. 1 professional occupation includes engineers, lawyers, and teachers. non-professional occupations include soldiers, shop keepers, and farmers. the majority of students were within the age range of 14-16 years, were christians, orphans, and had parents with non-professional jobs. family background e.g. a lack of parents or poverty may explain the wide age range in senior one. a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 3. results 3.1 impact on sexual behaviors table 2: wilcoxon signed rank test demonstrating the differences in sexual behaviors between pre-test and post-test for the intervention and the comparison groups sexual behaviors assessment statements intervention group (n = 145) comparison group (n = 145) paired intervention & comparison (n=145) pre(%) post(%) p pre(%) post(%) p pre(p) post(p) number of partners/abstinence not had sex in the last three months 61 73 0.00 59 58 0.16 0 16 0.00 had sex with 1 partner in the last 3 months. 22 12 0.00 23 21 0.16 0.32 0.00 had sex with 2 or more partners in the last 3 month 17 14 0.05 15 17 0.08 0.83 0.05 condom use ever used condom 45 46 0.32 43 44 0.60 0.08 0.57 used condom at last sex 23 25 0.64 21 23 0.26 0.08 0.64 compared to comparison group, the intervention had a statistically significant influence on students’ sex abstinence; (from 61% at pre-intervention to 73% at post-intervention; p=0.00) reporting not to have had sex in the last three months. having sex with one partner in the last three months significantly reduced from 22% to 12%; p=0.00, while having sex with two or more partners significantly reduced from 17% to 14; p=0.00. however, the intervention had no significant impact on students’ condom use. three factors strongly contributed to students’ non-use of condoms at last sex: lack of knowledge of using condoms (25%); feelings of embarrassment associated with buying and suggesting condom use (21) %; and perceptions that one can still get hiv/aids even if condoms are used (3%). a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 3.2 impact on hiv/aids awareness and perception of vulnerability table 4: paired sample t-tests demonstrating the hiv/aids knowledge scores for the intervention and comparison groups at pre-test and post-test knowledge assessment statements intervention group (n = 146) comparison group (n = 146) paired intervention & comparison (n=146) m1 m2 sd p m1 m2 sd p p1 p2 showering, or washing one's private parts after sex keeps a person from getting hiv/aids 3.33 3.51 0.38 0.00 3.25 3.30 0.47 0.22 0.07 0.00 eating healthy foods can keep a person from getting hiv/aids 3.42 3.58 0.36 0.00 3.37 3.38 0.83 0.32 0.25 0.00 taking the birth control pill keeps a woman from getting hiv/aids 3.24 3.61 0.56 0.00 3.18 3.23 0.32 0.07 0.06 0.00 a person with hiv/aids can look and feel healthy 2.62 2.34 1.41 0.02 2.55 2.56 0.33 0.62 0.06 0.05 there is a vaccine that can cure people from hiv/aids 3.17 3.50 0.51 0.00 3.16 3.21 0.57 0.39 0.32 0.00 a person can get hiv/aids even if she or he has sex with another person only one time 2.22 1.69 0.88 0.00 2.22 2.19 0.42 0.44 0.32 0.00 people are likely to get hiv/aids by deep kissing, putting their tongue in their partner's mouth, if their partner has hiv/aids 2.60 2.24 0.76 0.00 2.64 2.56 0.57 0.66 0.20 0.00 m1= mean at pre-test; m2= mean at post-test; sd=standard deviation; p= within-group p-value, p1= between-group p-value at pre-test; p2= between-group p-value at post-test; n=number of responses unlike the comparison group, the intervention group experienced statistically significant mean increases in the likelihood of participants to disagree with misconceptions of associating hiv/aids cure with washing ones private parts (m1=3.33 to m2=3.51; sd=0.38; p=0.00), eating healthy foods (m1=3.42 to m2=3.58; sd=0.36; p=0.00), taking birth control pills (m1=3.24 to m2=3.61; sd=0.56; p=0.00), and existence of vaccination for hiv/aids cure (m1=3.17 to m2=3.50; sd=; p=0.00). the group was also significantly likely to disagree that a person can get aids from having sex only once (m1=2.22 to m2=1.69; sd=0.88; p=0.00), can get aids from deep kissing (m1=2.60 to m2=2.24; sd=0.76; p=0.00), and that a person with hiv/aids can feel and look healthy (m1=2.62 to m2=2.34; sd=1.41; p=0.02). a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 3.3 attitudes towards girls’ condom initiation and negotiation table 5: paired sample t-tests demonstrating the attitudes towards gender equity in condom use initiation and negotiation scores for the intervention and comparison groups at pre-test and post-test statement for assessing gender equity in condom negotiation intervention group (n = 146) comparison group ( n=146) paired intervention & comparison (n=146) m1 m2 sd p m1 m2 sd p p1 p2 if a girl carries a condom, her partner will think that she is planning to have sex. 2.13 2.26 0.67 0.02 2.13 2.12 0.19 0.66 0.32 0.02 a girl loses a man’s respect if she asks him to use a condom 2.74 2.83 0.44 0.02 2.72 2.74 0.14 0.08 0.08 0.02 it is embarrassing for a girl to buy or ask for condoms 2.14 2.09 0. 33 0.05 2.16 2.14 0.50 0.61 0.25 0.05 using a condom is a sign of girls not trusting their partner 2.33 2.21 0.53 0.00 2.35 2.36 0.17 0.32 0.32 0.03 condom use initiation should only be done by boys 3.13 3.18 0.30 0.05 3.12 3.11 0.08 0.32 0.32 0.02 if a girl carries a condom it means they are experienced in sexual matters 2.37 2.44 .0.42 0.05 2.35 2.36 0.08 0.32 0.18 0.02 girls who carry condoms and insist on using them are prostitutes and such girls are not respected 2.39 2.47 0.42 0.02 2.40 2.42 0.17 0.32 0.32 0.05 it is okay for a girl to suggest condom use 1.72 1.62 0.43 0.00 1.74 1.75 0.08 0.32 0.18 0.00 unlike the comparison group, the intervention group experienced a significant mean decrease in the like hood of students to associate girl’s condom carrying and negotiation with planning to have sex (m1=2.13, m=2.26; sd=0.67; p=0.02), loss of respect (m1=2.74, m2=2.83; sd=0.44; p=0.02), embarrassment (m1=2.14, m2=2.09; sd=0.33; p=0.05), lack of trust (m1=2.33, sd=0.53; m2=2.21; p=0.00), sexual experience (m1=2.37, m2=2.44; sd=0.42; p=0.05), and prostitution (m1=2.39, m2=2.47; sd=0.42; p=0.02). unlike the comparison group, the intervention group significantly disagree that condom use initiation should only be done by boys (m1=3.13, m2=3.18; sd=0.30; p=0.05), and significantly agree that girls can suggest condom use (m1=1.72, m2=1.62; sd=0.43; p=0.00). a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 3.4 attitudes towards men’s infidelity-related norms table 6: paired sample t-tests demonstrating the attitudes towards norms that condone multiple sexual partners for men for the intervention and comparison groups at pre-test and post-test statement for assessing attitude towards norms condoning men’s multiple sexual partners intervention group ( n=145) comparison group (n=145) paired intervention & comparison (n=145) m1 m2 sd p m1 m2 sd p p1 p2 whereas it is ok for boys/men to have more than one sexual partner at the same time, girls/women should only have one sexual partner at one time 2.56 2.75 0.81 0.00 2.55 2.57 0.60 0.68 0.78 0.03 unlike the comparison group (m1=2.45, m2=2.57; sd=0.60; p=0.68), the intervention group experienced statistically significant mean increases (m1=2.56, m2=2.75; sd=0.81; p=0.00) in the likelihood of participants to disagree that men should have multiple sexual partners while at the same time women should have only one partner. 3.5 impact on girls’ sexual and condom assertiveness self-efficacy table 7: paired sample t-tests demonstrating girls’ perceived condom assertiveness selfefficacy scores for the intervention and comparison at both pre-test and post-test statements used to assess girls’ perceived condom assertiveness self-efficacy intervention group (n =84) comparison group (n =84) paired intervention & comparison (n=84) m1 m2 sd p m1 m2 sd p p1 p2 i could have sex without a condom if my partner doesn’t like them, even if i want to use one. 3.05 3.39 1.05 0.00 3.03 3.01 0.22 0.32 0.32 0.00 i could make sure my partner and i use a condom when we have sex. 2.09 1.48 1.26 0.00 2.11 2.09 0.15 0.16 0.16 0.00 i could have sex without using a condom if my partner wants 3.00 3.43 1.11 0.00 2.98 2.97 0.11 0.32 0.32 0.00 i could insist on using a condom even if my partner doesn't want them 2.76 2.45 0.73 0.00 2.77 2.75 0.15 0.16 0.32 0.00 i could refuse to have sex if my partner refuses to use a condom 2.16 1.55 0.96 0.00 2.18 2.20 0.15 0.16 0.32 0.00 note: this question was only meant for girls. a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 before the intervention, there were no statistically significant differences between the intervention and comparison groups regarding their perceived condom assertiveness selfefficacy. however, after the intervention, there were statistically significant differences between the two groups. this implies that the intervention had a significant effect on girls’ perceived condom assertiveness self-efficacy. for example, unlike the comparison group, participants in the intervention group significantly agreed that they would make sure that they use condoms (m1=2.09, m2=1.48; sd=1.26; p=0.00), insist on using condoms (m1=2.76, m2=2.45; sd=0.73; p=0.05), and refuse to have unprotected sex (m1=2.16, m2=1.55; sd=0.96; p=0.03). 4. discussion 4.1 impact on sexual behaviors results indicate that the intervention had significant impact on students’ sex abstinence and partner faithfulness. this is similar to the contentions of previous studies investigating computer-assisted hiv interventions as they reported reduction in the number of sexual partners (noar et al 2009), reduction in risky sexual behaviors (young and rice 2011). despite the significant influence on sex abstinence and partner faithfulness, the impact on condom use was not significant. an insignificant impact of computer-assisted interventions on condom use is consistent with those of previous researchers (bailey et al 2010; bull et al 2009; wantland et al 2004). although the present study reports results of a short follow-up, a longer follow-up of 18 years olds still showed no significant impacts on unprotected sex (stephenson et al 2004). walker et al (2006) report students inability to maintain the behavioral impacts (condom use in particular) reported during short follow-up. three reasons (i.e. lack of knowledge of using condoms, feelings of embarrassment associated with buying and suggesting condom use, and perceptions that one can still get hiv/aids even if condoms are used) largely contributed to non-use of condoms. other mediators that constrained condom use include: perceptions that condoms cause cancer and contain germs, lack of money to buy condoms, desire to prove manhood by making girls pregnant, perception of partner trust, condoms’ interference in sexual pleasure, perceptions that students don’t fit in condoms resulting in condoms slipping off and remaining in a girl’s body, and religious constraints. other studies also report constraints in condom use e.g. challenges in initiation and negotiation (sionean et al 2002), religious constraints (mosley 2003), and partner trust (kelly & parker 2000). the condom use constraint of lack of skills may have been attributed by intervention teachers’ failure to practically demonstrate condom use in class as it was reported in the results of implementation evaluation. the intervention teachers’ reservations on teaching about condom use ultimately influenced their level of condom emphasis and details revealed to students. teachers’ failure and inabilities to demonstrate condom use is also reported in a recent systematic review (shepherd et al 2010). 4.2 hiv/aids knowledge consistent with those other researchers (bailey et al 2010; young and rice 2011; noar et al 2010; tain et al 2007; ito et al 2008; lou et al 2006; halpern et al 2008), the present study indicates that the computer-assisted hiv/aids intervention significantly influenced students’ knowledge of hiv/aids transmission and prevention. compared to the comparison group, students in the intervention group were significantly more likely to disagree that hiv/aids can be prevented by: washing private parts after sex, eating healthy foods, taking birth control a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 pills, and to disagree that there is a vaccination that cures hiv/aids. also, compared to the comparison group, the intervention group were significantly more likely to agree that a person can get aids from having sex only once and deep kissing and to agree that a person with hiv/aids can feel and look healthy. although increase in knowledge does not always guarantee changes in behavior, reliable information especially about the involved risks can be vital in motivating health behavioral change (prochaska and diclemente 1983). 4.3 attitudes towards girls’ condom initiation and negotiation. cultural expectations of women’s passiveness and ignorance in sexuality constrain their sexual negotiating power, including negotiating for safer practices (pearson 2006). computer-assisted hiv interventions can significantly influence people’s attitudes towards hiv prevention (coursaris et al 2009). the present study reports statistically significant improvement in students’ perceptions of girls’ condom negotiation, and in attitudes towards men’s infidelity practices. the positive attitudes towards females’ condom negotiation present an important step forward in tackling the gender-related vulnerability of hiv/aids and pregnancy. 4.4 attitudes towards and men’s infidelity-related norms compared to the comparison group, the intervention group experienced significant reduction in participants’ adherence to the socially defined gender-biased ideologies that condone men’s practice of having concurrent multiple sexual partners while constraining women’s sexuality to single partners. studies evaluating hiv school-based interventions rarely incorporate gender-related constructs in their assessments. noteworthy, thought not schoolbased, studies such as coursaris et al (2009) report significant influence in attitudes resulting from computer-assisted hiv interventions. 4.5 girls’ sexual and condom assertiveness self-efficacy results indicate a significant increase in girls’ sexual and condom assertiveness self-efficacy. given the persistently reported students’ difficulties in practical translation of hiv knowledge to hiv prevention practices (bazargan et al 2000), the reported improvements in hiv/aids knowledge and attitudes may not make significant impact on behaviors without appropriate self-efficacy to adopt hiv preventive measures. self-efficacy plays an important role in closing the awareness-behavior gap by equipping individuals with positive capability beliefs and abilities to adopt healthy behaviors (rimal 2000). previous studies also report improved young women’s refusal self-efficacy (karnell et al 2006) and condom negotiation self-efficacy (roberto et al 2007) after exposure to the sexuality intervention. while computer-assisted studies (e.g. gustafson et al 2001; ashton et al 2005; coursaris et al 2009) affirm positive influence on participant’s self-efficacy. girl’s condom negotiation and sexual assertiveness self-efficacy has a direct relationship with condom use (sionean et al 2002). the reported distinctive condom negotiation self-efficacy and sexual assertiveness can be instrumental in combating hiv/aids and its uneven burdensome consequences among young women. a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 5. limitation of the study if the experimental conditions permitted, rather than using pre-existing groups in two schools, randomly allocating participants to the intervention and control groups within the same school would have improved the reliability of the results. however, given the free interaction of students in the school environment, drawing groups within the same school would have been reliable to a challenge of ensuring that participants in the control group are completely uninfluenced by the intervention. stephenson et al (2008) also warns about this potential methodological bias resulting from the spreading of the school-based sexuality interventions to control groups. in addition, this approach was not feasible due to the researcher’s limited control on the implementation procedures of the intervention. nearly all the students in senior one of the intervention group had registered to attend the intervention. this implies that there were not enough participants to be allocated to the comparison group. the only option was to draw the comparison group from another school, separate from the intervention school. thus, rather than composing and randomly allocating groups, this study relied on groups that pre-existed in separate schools. the fixed schools’ implementation procedures and timetables of the intervention dictated the choice and the number of participants. nevertheless, the pre-test analysis indicated no significant differences between the intervention and the comparison groups. 6. conclusion this paper conducted a quantitative pre-post intervention study aimed at investigating the impacts of the computer-assisted sexuality and hiv/aids intervention implemented in schools in uganda. to achieve this aim, questionnaire was administered to both the intervention group (n=146) and the comparison group (n=146) at pre-test (february 2009) and at post-test (december 2009). the results indicate that the intervention significantly improved students’ sex abstinence and reduction in number of sexual partners, improved knowledge and perception of vulnerability to hiv/aids, improved their attitudes towards gender equity in hiv/aids and pregnancy prevention, reduced adherence to men’s infidelity-related norms and improved girls’ perception of condom assertiveness self-efficacy. however, condom use appeared to be unaffected by the intervention. three reasons significantly contributed to non-use of condoms: lack of skills in using condoms which was mainly attributed to lack of practical condom use demonstrations in classes, feelings of embarrassment associated with buying and suggesting condom use and perceptions that one can still get hiv/aids even if condoms are used. teachers training in skills-based and interactive sexuality education, inclusion of interactive virtual condom use demonstrations in the web-based version of the intervention, and community sensitisations about the role of condoms in hiv prevention can help address these condom use constraints. a controlled pre-post evaluation of a computer-based hiv/aids education on students’ sexual behaviors, knowledge and attitudes 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 corresponding author angella musiimenta senior lecturer mbarara university of science and technology bishop stuart university email: angellamusiimenta@yahoo.com references 1. ashton e, vosvick m, chesney m, gore-felton c, koopman c, et al. 2005. social support and maladaptive coping as predictors of the change 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2health monitoring systems, inc, pittsburgh, pa, usa objective to describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system. introduction on july 11, 2012, new jersey department of health (doh) communicable disease service (cds) surveillance staff received email notification of a statewide anomaly in epicenter for paralysis. two additional anomalies followed within three hours. since paralysis anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant. methods in new jersey, 68 of 81 total acute care and satellite emergency departments (eds) are connected to epicenter, an online syndromic surveillance system developed by health monitoring systems, inc (hms) that incorporates statistical management and analytical techniques to process health-related data in real time. chief complaint text is classified, using text recognition methods, into various public health-related and other categories. anomalies occur when any of several statistical methods detect increases in incoming data that are outside of established thresholds. after receiving three anomaly notifications related to paralysis in a 4-hour time period, njdoh surveillance data staff enlisted cds and local epidemiologist colleagues to review the data and determine if there was an infectious cause. results the first epicenter anomaly notification was received on july 11, 2012 at 1:22 pm as a result of increased ed visits classified as paralysis based on facility location for the period beginning at noon on july 10, 2012. using cusum ema analysis, 76 reported interactions exceeded the predicted value of 50.49 and the threshold of 70.72. the second anomaly, also based on facility location, was received at 3:20 pm and the third anomaly notification, based on home location, was received at 4:32 pm. cusum ema and exponential moving average analysis methods detected these anomalies. table 1 describes the anomalies in more detail. compiled data from all anomalies were reviewed by cds epidemiology and surveillance staff to determine whether there was a public health event taking place. a total of 89 patients were seen in 39 (57%) of the 68 nj facilities reporting to epicenter with no geographic centralization. age and gender of patients were reviewed with no clear pattern discerned. figure 1 shows the time distribution of these visits. upon further investigation, it was determined that a moderate increase in paralysis visits over a relatively short time span was sufficient to create an anomaly under the default threshold for those visits. multiple analysis methods created multiple anomalies which gave an impression the event was of greater significance compared to a single anomaly. to follow up, njdoh requested that local epidemiologists investigate within their jurisdictions by contacting hospitals directly where epicenter data proved inconclusive. their reports confirmed njdoh’s findings that the anomalies did not signal an event of public health concern. conclusions this investigation of three paralysis anomalies is an important introduction to the newly implemented system’s capabilities in anomaly detection, and also to anomaly investigation procedures developed by njdoh for local surveillance staff. as a result of this experience, these anomaly investigation procedures are being fine-tuned. the fact that these sequential anomalies resulted in an investigation being undertaken highlights the importance in setting investigationgenerating alert thresholds within epicenter at a level that will minimize “false” positives without risking the missing of “true” positives. table 1: anomaly details keywords syndromic; surveillance; investigation acknowledgments nj lincs epidemiologists, nj doh epideimologists, nj doh surveillance staff *teresa hamby e-mail: teresa.hamby@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e126, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts computerized text analysis to enhance automated pneumonia detection sylvain delisle*1, 2, tariq siddiqui1, 2, adi gundlapalli3, 4, matthew samore3, 4 and leonard d’avolio5, 6 1va maryland health care system, baltimore, md, usa; 2medicine, university of maryland, baltimore, md, usa; 3va salt lake city health care system, salt lake city, ut, usa; 4university of utah, salt lake city, ut, usa; 5va boston health care system, boston, ma, usa; 6harvard medical school, boston, ma, usa objective to improve the surveillance for pneumonia using the free-text of electronic medical records (emr). introduction information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ari). in this work, we use data from the emr to identify patients with pneumonia, a key landmark of ari severity. we asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured emr data to uncover pneumonia cases. methods a previously validated ari case-detection algorithm (cda) (sensitivity, 99%; ppv, 14%) [1] flagged vamhcs outpatient visits with associated chest imaging (n = 2737). manually categorized imaging reports (non-negative if they could support the diagnosis of pneumonia, negative otherwise; kappa = 0.88), served as a reference for the development of an automated report classifier through machinelearning [2]. emr entries related to visits with non-negative chest imaging were manually reviewed to identify cases with possible pneumonia (new symptom(s) of cough, sputum, fever/chills/night sweats, dyspnea, pleuritic chest pain) or with pneumonia-in-plan (pneumonia listed as one of two most likely diagnoses in a physician’s note). these cases were used as reference for the development of the emr-based cdas. cda components included icd-9 codes for the full spectrum of ari [1] or for the pneumonia subset, text analysis aimed at non-negated ari symptoms in the clinical note [1] and the above-mentioned imaging report text classifier. results the manual review identified 370 reference cases with possible pneumonia and 250 with pneumonia-in-plan. statistical performance for illustrative cdas that combined structured emr parameters with or without text analyses are shown in the table. addition of the “text of imaging report” analyses increased ppv by 38-70% in absolute terms. despite attendant losses in sensitivity, this classifier increased the f-measure of all cdas based on a broad ari icd-9 codeset. with the possible exception is cda 6, whose f-measure was the highest achieved in this study, the text analysis seeking ari symptoms in the clinical note did not add further value to those cdas that also included analyses of the chest imaging reports. conclusions automated text analysis of chest imaging reports can improve our ability to separate outpatients with pneumonia from those with a milder form of ari. keywords situational awareness; influenza; surveillance; electronic medical record; pneumonia references [1] delisle s, south b, anthony ja, kalp e, gundlapalli a, et al. combining free text and structured electronic medical record entries to detect acute respiratory infections. plos one (2010) 5(10): e13377. [2] d’avolio l, nguyen t, goryachev s, fiore l. automated conceptlevel information extraction to reduce the need for custom software and rules development. journal of the american medical informatics association 2011 18(5): 607. *sylvain delisle e-mail: sdelisle@umaryland.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e74, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts eidss application for cchf foci activity epi-analysis and prediction in kazakhstan stanislav v. kazakov1, alexey v. burdakov*2, kenes s. ospanov3, aizhan s. esmagambetova4, andrey o. ukharov2, veronika p. sadovskaya1 and umirbek b. usenov3 1kazakh scientific center of quarantine and zoonotic diseases, almaty, kazakhstan; 2black & veatch, overland park, ks, usa; 3scientific center of sanitary and epidemiological expertise and monitoring, almaty, kazakhstan; 4committee of state sanitary and epidemiological surveillance of the ministry of health of kazakhstan, astana, kazakhstan objective test of the electronic integrated disease surveillance system (eidss) for epi-analysis and prediction of situation in cchf foci in kazakhstan. introduction cchf foci are reported in 3 southern regions of kazakhstan with population of 1 million. the ixodic ticks in the area are cchf carriers. human infections (3 to 12 cases per year) occur through tick bites and contact with cchf patient blood. cchf epidemiological process in kazakhstan has prominent seasonality (spring-summer period) and the rhythm of epidemic appearances (5-6-years). the rhythmical population incidence rate is associated with natural and climate factors, which govern the increase in the number of ixodic ticks, their infection rate (virus carrier state), and directly correlates with the population density and the livestock number that are the principal tick feeders in nature. methods eidss version 4 provides capability to collect, share and process epidemiological, clinical and laboratory data on infectious diseases in medicine, veterinary and environment sectors. it is currently deployed in kazakhstan at 150 sites of the ministry of agriculture (planned up to 271), and at 8 sites of the ministry of health (planned up to 23). three available indicators (for 2007-2011) were used for analysis: population; tick infection rate (relative density of cchf seropositive tick samples per total number of tested laboratory samples); cchf human case rate by districts per 10’000. the following procedure was conducted: 1) demographic information, diagnosis and location data entry into eidss 2) tick collection location data, total number of tested samples (pools), and number of seropositive data entry into eidss 3) correlation joint analysis of data on vectors and epidemiological surveillance in analysis, visualization and reporting (avr) module results eidss generated 12 different maps filtered according to the selected regions, ticks, demographics and cchf human incidence, aggregated by region, correlated by 3 indicators entered into the database. this allowed visualizing information to support epi-analysis. as a result, for each of the 3 regions specific districts with the highest risk of the cchf epidemic outbreaks were identified. the resulting information was grouped into 3 clusters of risk with the following criteria: population density, tick infection rate and human cases for each of 25 cchf-disadvantaged districts (see map). these results predict the epidemic situation in a particular area and support management decisions for planning and correction of preventive antitick and anti-epidemic measures and funding requirements. conclusions eidss with natural vectors and the avr modules has capabilities for analysis and prediction of epizootic and epidemic processes in vector-borne virus infections foci. it is an easy to use and free-ofcharge tool that can become the basic instrument for especially dangerous diseases field epidemiologists as well as for the ministries and local governments for cchf prophylaxis decision support. keywords cchf; one health; electronic disease surveillance; eidss; multi-factor analysis references 1. kazakov s.v., et.al. cchf causation model in the roitman’s ring graphs // hygiene, epidemiology and immunobiology, 2000, nos.34, pp.88-90 2. durumbetov e.e., et.al. practical application of epidemiological triad model for managerial decision-making within the system of episurveillance in moyinkum foci of cchf // proceedings of new technologies in medicine and pharmacy, astana 2001, pp.34-35 3. kazakov s.v., et.al. methodological approaches to study epidemic process of cchf in moyinkum foci of zhambyl region. // hygiene, epidemiology and immunobiology, 2001. nos.1-2, pp.75-84 4. onishchenko g.g., et.al. using ifa and rt-pcr in study of ixodic tick infection rate collected in cchf foci in kazakhstan and tajikistan in 2001-02. // virology matters. m., 2005. no.1 pp.23-27 5. ospanov k.s., kazakov s.v., et.al. on prospects of further study of cchf foci in kazakhstan // science and disease surveillance review, btrp, atlanta, 2009, p.126 6. burdakov a., ukharov a. transforming national human and veterinary disease surveillance systems from paper into integrated electronic form in the fsu countries // 15th international congress on infectious diseases (icid), bangkok 2012 *alexey v. burdakov e-mail: burdakovav@bv.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e109, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts new strategy to monitor and assess laboratory biosafety programs heather n. meeks1, betiel h. haile2, ngozi a. erondu2, lisa ferland2, meeyoung park2 and scott j. mcnabb*2, 3 1defense threat reduction agency, basic & applied sciences, fort belvoir, va, usa; 2public health practice, llc, atlanta, ga, usa; 3hubert department of global health, emory university, rollins school of public health, atlanta, ga, usa objective to develop a toolset to monitor and assess laboratory biosafety program performance and cost introduction laboratory biosafety – a component of biosecurity – has specific elements that together, comprise a facility’s capability to both protect employees and the surrounding public and environment. measuring these elements permits assessment and the costing of program-specific safety interventions. in the absence of a strategy and toolset, we developed a conceptual framework and toolset that monitors and assesses laboratory biosafety programs (lbps) and provides useful information (e.g., return on investment [roi]) for decision makers. methods we conducted academic and open source literature reviews of lbps and affiliated organizations laboratory manuals to identify objectives, goals, and indicators. these findings were aligned to laboratory biosafety-specific inputs, activities, outputs, and outcomes to create a strategic, conceptual framework (logic models) used to assess performance and measure the cost and roi. indicators were identified in existing literature or developed and mapped to the logic model elements. results six logic models were created: laboratory biosafety, biosurety, procedural, biocontainment, information security, and training. the laboratory biosafety logic model served as the overall framework for the remaining five sub-logic models. we also established a database containing 161 indicators mapped to each of the logic model elements. conclusions we developed a strategic framework that monitors and evaluates lbps. while evaluation of cost-impacts in lbps provides business intelligence for resource planning, this integrated approach also provides information about gaps. we plan to pilot this toolset and refine indicators using principal component analysis. keywords laboratory biosafety; evaluate laboratory; program performance acknowledgments defense threat reduction agency, basic & applied sciences references 1. bakanidze l, imnadze p, perkins d. biosafety and biosecurity as essential pillars of international health security and cross-cutting elements of biological nonproliferation. bmc public health. 2010;10 suppl 1:s12. 2. organization wh. international health regulations (2005) ihr monitoring framework: checklist and indicators for monitoring progress in the development of ihr core capacities in states parties. geneva; 2010. 3. carr k, henchal ea, wilhelmsen c, carr b. implementation of biosurety systems in a department of defense medical research laboratory. biosecurity and bioterrorism : biodefense strategy, practice, and science. 2004;2(1):7-16. epub 2004/04/08. 4. garaigordobil m. evaluation of a program to prevent political violence in the basque conflict: effects on the capacity of empathy, anger management and the definition of peace. gac sanit. 2012. epub 2012/01/31. 5. jahrling p, rodak c, bray m, davey rt. triage and management of accidental laboratory exposures to biosafety level-3 and -4 agents. biosecurity and bioterrorism : biodefense strategy, practice, and science. 2009;7(2):135-43. epub 2009/07/29. 6. le duc jw, anderson k, bloom me, estep je, feldmann h, geisbert jb, et al. framework for leadership and training of biosafety level 4 laboratory workers. emerging infectious diseases. 2008;14(11):1685. 7. lewis m, development cfg. governance and corruption in public health care systems: center for global development; 2006. 8. losinger wc, bush ej, hill gw, smith ma, garber lp, rodriguez jm, et al. design and implementation of the united states national animal health monitoring system 1995 national swine study. preventive veterinary medicine. 1998;34(2-3):147-59. 9. miller sr, bergmann d. biocontainment design considerations for biopharmaceutical facilities. journal of industrial microbiology & biotechnology. 1993;11(4):223-34. 10. murray cjl, evans db. health systems performance assessment: world health organization; 2003. *scott j. mcnabb e-mail: scottjnmcnabb@emory.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e165, 2013 improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project brian e. dixon 1 , kellie kaneshiro 2 1 regenstrief institute, indiana, indiana university school of informatics, dept. of veteran affairs, veterans health administration 2 indiana university school of medicine abstract background: human immunodeficiency virus and acquired immunodeficiency syndrome (hiv/aids) remains a significant international public health challenge. the statewide hiv/aids information network (shine) project was created to improve hiv/aids health information use and access for health care professionals, patients, and affected communities in indiana. objective: our objective was to assess the information-seeking behaviors of health care professionals and consumers who seek information on the testing, treatment, and management of hiv/aids and the usability of the shine project’s resources in meeting end user needs. the feedback was designed to help shine project members improve and expand the shine project’s online resources. methods: a convenience sample of health care professionals and consumers participated in a usability study. participants were asked to complete typical hiv/aids information-seeking tasks using the shine project website. feedback was provided in the form of standardized questionnaire and usability “think-aloud” responses. results: thirteen participants took part in the usability study. clinicians generally reported the site to be “very good,” while consumers generally found it to be “good.” health care professionals commented that they lack access to comprehensive resources for treating patients with hiv/aids. they requested new electronic resources that could be integrated in clinical practice and existing information technology infrastructures. consumers found the shine website and its collected information resources overwhelming and difficult to navigate. they requested simpler, multimedia-content rich resources to deliver information on hiv/aids testing, treatment, and disease management. conclusions: accessibility, usability, and user education remain important challenges that public health and information specialists must address when developing and deploying interventions intended to empower consumers and support coordinated, patient-centric care. keywords: hiv, acquired immunodeficiency syndrome, information seeking behavior, internet, public health informatics http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 introduction human immunodeficiency virus and acquired immunodeficiency syndrome (hiv/aids), a progressive disease that dismantles the human immune system and has no cure, remains one of the greatest international public health and health care challenges. the united nations estimates that 33 million people worldwide are living with hiv/aids (1). in the united states, while the number of new diagnoses of and deaths from aids are decreasing, the u.s. centers for disease control and prevention (cdc) reports that the number of people living with hiv/aids (plwha) is increasing (2). hiv/aids disproportionally affects minorities and men who have sex with men (1, 2). furthermore, plwha are more likely to be unemployed and/or poor and much more likely to be uninsured or dependent on public insurance programs such as medicaid (3). diagnosing, treating, and managing hiv/aids in the u.s. occurs in an increasingly fragmented health care ecosystem, presenting challenges for both health care providers and plwha. diagnosing patients remains problematic as our nation’s testing strategy often fails to reach those at most risk. hiv testing occurs primarily (44 percent) in private settings (e.g., physician practice), yet hospitals and emergency rooms (22 percent) and community clinics (9 percent) are the most likely places to find positive results (3). only five percent of hiv tests are performed in public health clinics, correctional settings, sexually transmitted diseases clinics, and drug treatment clinics. thirty nine us states offer anonymous testing, so even if testing occurs, it does not guarantee that a patient’s status will be known to his or her treating provider (4). even if a patient’s status is known, strict federal and state laws regarding disclosure of hiv/aids status (5) and the lack of a sophisticated electronic infrastructure for sharing test results (6, 7) make it difficult to share that knowledge with a treating physician in another practice. the result of a poor testing and restricted knowledge sharing is an estimated 500,000 patients who are not receiving care for their disease, of which approximately 250,000 do not know they are hiv positive (8). additionally, many individuals who receive a diagnosis of hiv infection late during the course of the disease are less likely to receive standard-of-care antiretroviral therapy (3). this challenge is compounded by a shortage of infectious disease specialists and primary care doctors. broad consensus exists that the care of plwha should be guided by an hiv expert. expert care has been associated with reduced morbidity, mortality, and cost of care (9-12). however, in a recent survey of hiv specialty clinics, only 15% indicated they could absorb a significant increase in patient load (13). these clinics reported a 30-70 percent increase in patient volume, yet only a handful had financial resources to hire more staff. while adept at providing a patchwork of services, state and local health departments often struggle to provide hiv prevention and care services due to inadequate funding, fragmented systems, and a host of federal and state regulations (14). these factors, combined with a general aging of the hivinfected population, have resulted in increased “mainstreaming” of plwha into primary care and generalist care practices, which places a greater knowledge burden on clinicians in primary and general care (15). the goal of the statewide hiv/aids information network (shine) project is to improve hiv/aids health information use and access for hiv/aids health care professionals, patients, http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 and affected communities in indiana. a national study of hiv care continuing education and consultation needs of health care professionals revealed that 64% of respondents cited quality of care or difficulties staying up-to-date as their main reason for their “unwillingness” to accept new patients with hiv or aids (16). the shine project attempts to make information resources available to clinicians (e.g., hiv experts, primary care, and generalists) to support increasingly complex clinical decision-making, including but not limited to the diagnosis of acute hiv infection, initiation of antiretroviral therapy (art), antiretroviral side effect awareness and monitoring, toxicity of antiretroviral agents, as well as routine primary care screening for diseases such as breast and prostate cancers for plwha. the shine project further attempts to make resources available directly to healthcare consumers, including plwha, and caregivers of plwha (e.g., family members, social workers, case managers). previous studies have documented the various methods by which plwha and their caregivers access information related hiv infection and aids. the previously identified methods include healthcare providers, print media, the internet, other plwha, education programs, and social networks (17-20). personal contacts, including healthcare providers, professional caregivers (e.g., social workers, case managers), and peers, have been cited as a preferred source of information for plwha. the internet is often a secondary source of information for plwha, yet professional caregivers are twice as likely to provide information from the internet or print something from the internet and give it to their patients (20). furthermore, plwha have reported that the myriad of sources provide an overwhelming, often confusing, and sometimes unscrupulous array of information that can be just as frustrating as it might be helpful (17, 18). finally, it has been documented that the information needs of plwha changes over time (21). plwha, often when first diagnosed, begin as sponges and seek to absorb as much information as possible about the disease, coping with life as someone infected, and available treatments and management of hiv/aids. as the disease progresses and/or the plwha advance in age, information needs shift away from absorption towards experiential knowledge exchange (21). to meet the needs of providers and consumers in indiana, the shine project created at website (http://library.medicine.iu.edu/shine) to serve as a statewide information and training resource. the site provides access to electronic information resources (e.g., journal articles, up-to-date guidelines, medlineplus®) that providers and consumers need to make informed hiv/aids testing, treatment, and quality of life decisions. the site further links users to inhealthconnect, a statewide database of educational and social services available to providers and consumers who desire to either learn more about hiv/aids treatment and care or gain access to counseling, shelter, and food. the shine project involved a public-private collaborative consisting of core team members from the indiana university school of medicine and regenstrief institute and an informal advisory board composed of individuals from hiv care providers, public health agencies, and hiv community organizations. to ensure that the site is both user-friendly and meets users’ needs, the shine team conducts periodic site evaluation. this article focuses on the results of recent usability testing, a common and recommended strategy for assessing a web site (22). from december 2009 through february 2010, the shine team held a series of usability testing sessions to gather detailed knowledge on the information needs of key, prioritized audiences: clinicians (e.g., those who treat patients with hiv/aids) and consumers (e.g., those at risk or living with hiv/aids). in http://library.medicine.iu.edu/shine http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 this paper, we present the lessons from the usability testing sessions as they have implications for the shine project and other public health initiatives that develop and deploy information resources for use by clinicians and consumers. methods participants an equal number of participants from each prioritized population were invited to participate. for usability testing, a population of 6-8 in each audience segment is a recommended best practice. the shine team attempted to recruit 8 clinicians and 8 consumers. clinicians were recruited primarily by email invitations sent through professional organizations, including the midwest aids training and education center (matec) indiana, a provider of hiv/aids education to physicians and nurses throughout the state, and the indiana rural health association (irha), a federation of nurses, physicians, social workers, and administrators working in indiana-based rural clinical settings. physicians practicing at sites near the indiana university school of medicine were also targeted. however, the shine team initially focused efforts on recruiting clinicians that were based outside of the indianapolis metropolitan area to ensure a representative mix of clinicians from around indiana. clinicians were not incentivized to participate in the usability testing. consumers were primarily recruited on site at hiv/aids service providers in indianapolis. one consumer approached the shine team staff via email in response to the call for clinician participation described above. consumer participants were selected through a convenience approach, and randomization was not utilized. these participants were offered a $10 grocery store gift certificate for participation. participants ranged between 23-62 years of age, and the mean participant age was 40.5 (n=13). nearly all of the participants felt they were both experienced web users and knowledgeable about the latest trends and research in hiv/aids. half of the clinicians who participated (n=3) were physicians. the remaining clinicians included nurses (n=2) and a licensed clinical social worker (lcsw). one of the physicians was an infectious disease specialist while the other two physicians were internists. settings participants completed the testing from a comfortable setting where they normally access the computer and internet. health care providers participated from their office workstation. the physician participants were in the same room with the facilitator at their normal workstation. the nurses and social worker were remotely tested using methods described previously by dixon (23). consumers were tested on a workstation at the service provider location. the consumer who approached us also participated remotely, but in this instance the facilitator followed the procedures as described by dixon (23) and the user simply interacted with the facilitator via speakerphone instead of an online conferencing tool. http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 study design this study was approved by the iupui-clarian institutional review board (irb), study #ex0911-15. each participant was provided with a one-page overview of the study that explained the methods and process used to capture their feedback regarding the shine website. upon reviewing the instructions, a study facilitator answered any participant questions then proceeded to assign the participants a series of tasks to complete using the shine website. participants were given tasks until either they completed all available tasks or 15 minutes had elapsed, whichever came first. the tasks were considered “normal” use cases for why an individual might come to the shine website. upon completing the assigned tasks, users were asked to complete a questionnaire about their experience interacting with the site. the primary instrument for measuring the user experience was the system usability scale (sus). the sus is a widely used and scientifically validated usability instrument (24). the sus produces a usability score between 0-100, based on respondents’ component scores. users were also asked about their general impressions of the site, recommended changes they had for the site developers, and other open-ended questions intended to capture qualitative feedback. the study facilitator took detailed notes while the users interacted with the site to complete their assigned tasks. users were encouraged to “think-aloud” while completing each task. the facilitator recorded participant’s comments, and the facilitator asked and recorded responses to follow-up questions. if a participant appeared to be stymied, facilitators were allowed to assist users, but facilitators were not allowed to “give away” the answer to the task at-hand or suggest where the user should click on the site to reach a destination. data analysis the quantitative sus scores were averaged to derive statistical mean scores which describe the general usability of the shine site. participants’ qualitative feedback included notes from the facilitator and open-ended responses from a post-test questionnaire. analysis involved a comparative method of continuously comparing important concepts within the collected data through a process where the authors collated, annotated, and discussed the data and their meaning. the authors looked for salient and recurring topics related to users’ information needs as well as satisfaction with the shine site. results usability scores the results from the usability testing provide data on how well the site supports a positive user experience. the sus values from the usability tests ranged from 40 95 with a mean sus score of 75.77 (n=13) – a “very good” score – and a standard deviation of 15.86. a comparison of providers to consumers reveals a clear distinction in sus scores. providers favored the site more than consumers. the mean sus score for providers (n=6) was 85.42 – an “excellent” score – with a standard deviation of 10.54. the mean sus score for consumers (n=7) was 67.50 – a “good” score – with a standard deviation of 15.41. http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 information seeking behaviors all of the participants reported spending a significant amount of time on the internet. the top named site of choice was google. the majority of participants were frequent visitors to online news sites, such as cnn, new york times, msn, and the indianapolis star. slightly more than half of the participants (n=8) were frequent visitors to social networking sites such as facebook and youtube. the clinicians varied widely in their readership of peer-reviewed journals and trade publications. journals specifically named by participants included the new england journal of medicine, journal of the american medical informatics association, annals of internal medicine, clinical infectious diseases, emergency nurses association journal, journal of forensic nursing, forensic examiner, and nurse management. however, clinicians reported reading journals on a sporadic basis due to time constraints. fewer than half of clinicians were regular readers of trade publications. for those clinicians who did read trade publications, e-newsletters were mentioned more frequently than paper-based periodicals. participants were asked to identify current sources they regularly consult when looking for information on hiv/aids. providers were evenly split between those who preferred to learn from a human being (e.g., ask a peer, attend an educational session) and those who were willing to search for information using an electronic resource such as medline. consumers were similarly divided. slightly more than half (n=4) of the consumers reported they turned to a human being (e.g., care coordinator, their doctor), while others said they learned by reading (e.g., brochures in their doctor’s office, the public health department’s website, email discussion lists for those living with hiv/aids). user satisfaction in addition to differences in the sus scores assigned by providers and consumers, there were differences in opinions between these groups when asked about their satisfaction with the site. clinicians the qualitative responses from clinicians complemented their quantitative scores. clinicians generally liked the site. most commented verbally that they would visit the site in the future. one of the clinicians said she was excited to share the site’s url with a colleague. none of the clinicians had used or knew about the site prior to the usability test. this made them a novice site user even though all but one labeled himself or herself an experienced internet user. clinicians quickly navigated to the “for clinicians” section, or clinician toolkit, when completing their assigned tasks. physicians in particular preferred this section of the site. the clinicians found the section easy to navigate. one clinician had trouble with the anchors/bookmarks, and the other clinicians tended to use the back button to move from a given section of the toolkit to the main list of categories. one clinician said verbally she felt that the toolkit allowed her to get close to what she needed in one-to-two clicks, which was ideal in her opinion. http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 several clinicians commented that the site, and the toolkit in particular, contained a “comprehensive” list of resources. one clinicians said she often receives emails from other sites about their resources but found this site to have several resources of which she was unaware. clinicians said they were likely to visit the site in the future. however, they cautioned that their return would be to perform a select list of actions. first, several clinicians expressed a desire for resources to pass along to their patients. physicians and nurses indicated the resources they would want for their patients include information on where they could go for testing, treatment, and support groups. one resource of particular interest was a site where the physician could find information on who to refer their patient when the patient moves out of the area. this included resources inside and outside the state. the social worker said she also desired resources for her patients that included follow-up services, including financial assistance, medical assistance, and help with nutrition. clinicians further indicated a desire for up-to-date access to information on post-exposure prophylaxis (pep). pep for exposure in the workplace has been a cdc recommended practice since the 1990s (25-27), and in 2005 the cdc expanded its recommendations to include nonwork related exposures (28). while the cdc recommendations are available online through google, pubmed, and other public websites, the guidelines are dense and complex which does not lend itself to quick decision-making in clinical practice. furthermore, the guidelines do not reflect the latest in research and development of antiviral medications, side effects, or the challenges and interactions with co-morbidities and other medications. clinicians who participated in this study requested an easy, quick, and up-to-date reference online for making decisions for their patients. finally, clinicians said they desired information on prophylaxis, including what clinicians should do when treating other sexually transmitted infections (stis) and common reactions between stis and retroviral medications. one physician mentioned that he currently uses a printed resource regarding the appropriate time to initiate prophylaxis treatment. he suggested that this resource would be more useful if electronic and available “on the web” so he could access from wherever he might be when making clinical decisions. beyond these specific actions, clinicians indicated they generally discuss hiv/aids prevention, treatment, and post-exposure “best practices” with their colleagues and hospital administrators. they were unsure that the site would be used by clinicians to get a lot of information about guidelines because these are often provided by resources integrated into electronic health record (ehr) systems. consumers overall, consumers found useful resources on the site. however, the majority of consumers criticized the site for lacking organization and having a boring appearance. like the clinicians, consumers seemed to find the “for consumers” section, or consumer toolkit, quickly. two consumers, however, suggested that the shine team could improve the site to more easily direct consumers to the toolkit. once at the toolkit, consumers found a lot of information – sometimes an overwhelming amount of “technical” information (two users said the http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 site was too technical). consumers felt the site had credibility and useful information, even if it was difficult to read through pages of text. several consumers requested the site add more graphics or pictures to help draw users into the various parts of the consumer toolkit. consumers overwhelmingly indicated they would come back to the site to access the “latest information” on hiv/aids. most of the consumers specifically mentioned “medications” or “drugs” as the type of information they felt would be provided on the site. results of new studies and research were a close second choice. only one consumer pointed out that the site featured testing location information. this indicates that several of the consumers were likely hiv positive and would use the site to access information they could use for treatment and management of the disease. terminology was an issue for some consumers. one consumer didn’t understand the term “clinician.” another consumer didn’t understand the term “consumer.” two users said “medical encyclopedia” sounded like it was for physicians and not patients. two of the consumers felt uncomfortable using a computer. one of these consumers stated he only accesses the internet at the library and does not own a personal computer. in general, consumers felt the site was often too busy and required “too much reading.” they strongly preferred sites that used a lot of graphics. this is evident in their preferred websites. most of them simply used the web to access news and general information or engage in social networking. they expected a clean user interface similar to those of sites like cnn and facebook. discussion usability testing was performed with a convenience sample of providers and plwha to assess user information needs and satisfaction with a particular online resource. the resource was generally perceived as a valuable source of information, but participants’ information seeking needs and behaviors varied widely. the feedback from participants provided useful feedback that the shine project will use to improve its web site in the future. the feedback further suggests several lessons that may be useful for public health, informatics, and computing professionals who desire to create, enhance, or support the use of similar information systems and resources. below we discuss the implications of our findings for the public health informatics community, and we outline planned changes to the shine site. analysis of participants’ feedback validates previous work that has demonstrated the following aspects of information resource development are critical to meet the information needs of users: 1) accessibility; 2) usability; and 3) user education. accessibility clinicians and consumers reported that they often lack access to useful information resources when making decisions about the testing for, treatment of, and maintenance of hiv/aids. in one case, a consumer indicated he or she lacked physical access to a computer except through the http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 public library. in another instance, a clinician reported having access to the internet but his or her institution did not have access to a specific information resource. accessibility challenges, however, are not a new phenomenon. it is well known that plwha are more likely to be socio-economically disadvantaged, and there exists a significant digital divide with respect to personal computers and internet access (29, 30). furthermore, it is known that many small and rural hospitals as well as physician practices often lack access to journals and other medical information resources (31-33). participant feedback in this study demonstrates that these kinds of accessibility issues were present in our sample, and they continue to be a challenge for clinicians and plwha who are most at-risk. in addition to general accessibility issues, clinicians in this study reported a lack of access to a specific resource they felt would be useful for treating plwha. these clinicians expressed a desire for an electronic referral tool to support coordinated care for plwha. the infectious disease specialist indicated an electronic referral tool would help her refer patients to primary care physicians who could support hiv/aids management. the internists indicated they desired a referral tool for help in finding the limited number of specialists who could best serve their patients. given the general trend towards mainstreaming plwha to primary care and general practice clinicians, the feedback suggests that electronic tools for supporting bi-directional referrals and communications for plwha may warrant further informatics investigation. the comments confirm earlier research that shows general and specialty practices are limited in the number of hiv/aids patients they can handle, and mechanisms for coordinating care across a fragmented health system are necessary to support caring for hiv/aids patients that are living longer and managing a complex disease (13, 15). given that the participating physicians in this study practice in advanced, urban health systems that utilize highly integrated information systems suggests that additional research and development may be necessary to support the national aims of better care coordination and patient-centered outcomes. electronic referral tools, for example, have been shown to improve the transfer of administrative and clinical information (34), reduce duplicate test-ordering (35), and improve both the referring and subspecialty physician’s ability to make treatment decisions (36). this is true not only in urban areas but also in safety net providers (37). beyond reporting a lack of accessibility to certain resources, participant feedback demonstrates that clinicians and plwha generally have access to a wide range of information sources that might contain knowledge about hiv/aids testing, treatment, or management. clinicians indicated using human and technology resources to find answers to their questions about hiv/aids. larger studies have suggested that clinicians use a wide range of sources but generally prefer to rely on colleagues to answer most of their questions about patient care (38, 39). this is due, in part, to access their peers’ tacit knowledge, although there is evidence that the most likely reason is that of practicality. asking a colleague in the office is quicker than searching for an answer in a book, journal, or information system. this is true even when clinicians are provided access to resources with higher quality information, suggesting that accessibility trumps quality in real-world, busy clinical practice settings (40). http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 consumers in this study also identified a wide range of sources used to access hiv/aids information, something demonstrated in several other studies on consumer access to hiv/aids information (41, 42). many of the sources indicated by consumers were part of the mass media, the predominant source of hiv/aids information for consumers in other studies (43, 44). these data suggest that consumers, like clinicians, generally choose sources based on their accessibility. mass media and human beings are more available than specialized, targeted online resources focused on hiv/aids testing, treatment, and disease management. therefore information resources need to be made available when and where clinicians and consumers are asking questions and making decisions. resources for clinicians integrated in applications already in use by clinicians, such as electronic health record (ehr) systems, might make more sense than a standalone resource available only through a google search. resources for consumers available as web sites, mobile phone applications, and accessible through personal health record (phr) systems may be a better approach than a traditional standalone internet site. usability common feedback from patients and caregivers in this study included statements like the shine site is “overwhelming,” “intimidating,” and “boring.” furthermore, consumers, on average, scored the site a 67.5 on the sus. the comments and ratings suggest that the shine site should be enhanced to better meet users’ information needs. the feedback is also in line with previous studies that have examined end user information needs. these studies found that patients and caregivers generally express feeling overwhelmed by the volume of information available on the web (45, 46). the studies’ authors suggest creating a resource that identifies the important web links or “quick tips” for end users, reducing their need to scan pages of search results. this is what the shine site attempts to do, index available, high quality information on hiv/aids for quick reference by providers, consumers, and caregivers. therefore similar feedback obtained in this study suggests that a usable web site is much more than just a resource that can distill thousands of search results into a convenient index. usable web sites are those that can connect users to the information they seek in efficient ways that support user workflows, preferences, and cognitive understanding of the information. the feedback from clinicians in this study suggests that the site supports clinician’s cognitive models of hiv and care delivery, but the site fails to meet clinician workflow or the reality of a busy practice setting. consumer and caregiver, on the other hand, workflows were supported by the site, but the site did not present information in a way that met their cognitive understanding of hiv/aids or resources for plwha. nor did the site support consumers’ preferences for splashy landing pages and highly graphical interfaces often found on mass media sites. specifically consumers complained that the site contains “too much text” and requires “a lot of reading.” when asked how to improve the site, consumers responded that it needed more “color,” “graphics,” “multimedia” content, “web 2.0” applications, and words that “pop.” these comments suggest that users with limited health literacy had difficulty approaching the site’s content and interpreting the resources indexed on the site. approximately one-third of patients in the united states have limited health literacy.(47) these patients have difficulties correctly reading basic items commonly encountered in the health care setting, such as prescription drug warning labels, appointment slips, and health education http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 materials. despite readability concerns, materials created for patients continue to be written at too high a level for the average american, and they are incomprehensible to those with limited health literacy (48, 49). this includes patient handouts, materials provided through an ehr system, and material generally available on the internet (49-52). information resources for consumers and caregivers need to be accurate, accessible, and actionable. currently the shine team includes a medical librarian and a graduate student in public health who evaluate and select information links on the consumer portion of the site. the team further performs periodic examinations, like the one detailed in this report, of the site to ensure accessibility and ease of use. the results suggest that the team also needs to screen the site and its contents for readability. otherwise, consumers and caregivers may be able to access the site and find resources that will support their information needs, but they may not be able to fully understand the content or act upon it (e.g., make informed decisions about their health). currently there are no widely accepted methods for determining whether health information resources are accurate, accessible, and actionable. such methods should be developed by the research community, so information system designers and implementers can put them to use for consumer materials on web sites and other health information systems. meanwhile, system developers and implementers can apply best practices from the printed material world to their products (53). user education in addition to strengthening accessibility and usabilty, user feedback demonstrates that the shine team needs to provide better education with respect to the availability of the site and how the site can be used to improve health care decision-making and quality of life for plwha. the clinicians and consumers participating in this study had never heard of the shine site prior to the study despite their recruitment through key partner organizations that have helped inform the development of the site and its contents. the shine team therefore needs to do more outreach with its partners and the community to make plwha, clinicians, and the general public more aware of resources to support the prevention, testing, treatment, and maintenance of hiv/aids. educating clinicians about the existence of shine is especiall important, because clinicians are generally unaware or unfamiliar with health information tools available for use by their patients (33). by educating clinicians about resources available for use by plwha, we may be able to increase clinician recommendation for their hiv/aids patients to access the site and benefit from the resources contained therein. several clinicians, especially nurses and social workers, in this study commented that they would indeed be willing to share the shine site information with their patients. in combination with presentations at hiv/aids shelters and treatment facilities, educating the wide specture of care providers for plwha might improve adoption and use of the shine site by those who have a need for the information on the site. outreach and education, however, should not just be limited to an awareness of the website and its contents. a recent literature review that examined clinician search behavior concluded that providers may not be equipped with the skills for effective use of information resources that are available to them (39). outreach librarians, as well as others invested in seeing clinicians utilize http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 information resources to improve patient outcomes, need to do more to educate health professionals on locating, accessing, and utilizing health information resources. established through the medical library assistance act of 1965, the national network of libraries of medicine member libraries and information centers provide health professionals and the general public with health information resources and services (54). services include education on effective search techniques and applying health information to decision-making processes. some of these medical libraries have outreach librarians who not only work directly with health professionals and consumers but also librarians at non-medical libraries to enhance resources available to the general public. studies have shown that professionally led library services have an impact on health outcomes for patients and may lead to time savings for healthcare professionals (55). yet it remains unclear how often these services are utilized. furthermore, health professionals and consumers cannot be forced to use library resources or services. additional interventions within libraries, as well as interventions from other groups such as professional societies, ehr implementation teams, consumer advocacy groups, and informatics programs, might improve providers and consumers’ access and comprehension of health information resources available to them. in addition to outreach and education about shine and how to use the site’s contents to make informed health care decisions, the shine team should explore ways to integrate consumerfocused resources into the ehr systems in use by clinicians throughout central indiana. currently there is little knowledge about ehr systems’ support for such “patient education materials.” however, requirements of the u.s. centers for medicare and medicaid services (cms) ehr incentive program for hospitals and physician practices include making consumerfocused materials available to patients during a clinical encounter (56). in support of this requirement, the u.s. national library of medicine (nlm) launched an initiative to integrate medlineplus resources into ehr systems and patient portals (57). additional research and development work will likely be necessary to fully integrate patient materials available from public health information resources like shine into ehr systems and patient portals, but doing so will support greater clinician access to (and possibly prescription for) materials that can meet the information needs of plwha. limitations participants were selected using convenience and non-randomized methods. therefore the sample populations represented in the usability testing may not reflect the general population of clinicians and plwha. the sample sizes were small, which is appropriate for usability testing but makes generalization of the findings to larger populations difficult. preferences for a website that disseminates information resources about hiv/aids in indiana may not reflect the desires of clinicians and patients in other u.s. states or other nations. conclusion a number of factors are moving health care delivery processes to be more patient-centric and consumer-driven in which the patient plays a stronger decision-making role in his or her care. in such a world, higher quality care and outcomes requires clinicians to understand disease and medicine as well as strategies for engaging consumers in self-management. sites like shine http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 aim to provide information resources to consumers and clinicians to support and enhance communication and collaboration between patient and providers. however, access to information is just one part of the equation. shine must also meet the needs of plwha, over time, as they age with hiv/aids and their health status changes. shine must further consider the health literacy needs of plwha and others who access the site in support of caring for plwha. this includes clinicians who require access to patient-focused educational materials as well as information on social, nutritional, and other community resources that support care for the whole person. this is not easy, and the usability testing reveals that shine can do more to meet its aims. the testing does show, however, that progress is being made and reveals insights for other public health, clinical, and informatics professionals also striving to make patientcentric and consumer-driven health care a reality. we hope the lessons from shine support the development of a wide range of informatics applications that better support plwha, caregivers, clinicians, and others living with a chronic illness. acknowledgements the authors would like to thank michael wilkinson, mls, and john d. patton for their dedication to the shine project and its mission. they further thank mr. patton for his role in assisting in the collection of data for the study described here. the authors also thank julie j. mcgowan, phd, for her feedback on early drafts of this manuscript. the shine project is a project of the indiana university school of medicine library. this work was funded in part by the national library of medicine, national institutes of health, u.s. department of health and human services under purchase order no. hhsn276200800492p. the views expressed in this article are those of the authors and do not necessarily represent the views of the national library of medicine, national institutes of health, department of health and human services, or department of veterans affairs. conflicts of interest the authors declare that they have no conflicts of interests. correspondence brian e. dixon, mpa, phd research scientist regenstrief institute, inc. 410 west 10th street, suite 2000 indianapolis, in 46202-3012 317-423-5582 (phone) 317-423-5695 (fax) email: bdixon@regenstrief.org mailto:bdixon@regenstrief.org http://ojphi.org improving access to hiv and aids information resources for patients, caregivers, and clinicians: results from the shine project 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 references 1. joint united nations programme on hiv/aids (unaids). 2008. report on the global aids epidemic. mexico city. 2008, jc1510e. 2. centers for disease control and prevention. hiv/aids surveillance report, 2007; 2009 [cited 2010 may 8]; 19. available from: 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aids information resources for patients, caregivers, and clinicians: results from the shine project 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 irha: indiana rural health association iupui: indiana university-purdue university indianapolis matec: midwest aids training and education center msn: microsoft network pep: post-exposure prophylaxis plwha: people living with hiv and aids shine: statewide hiv/aids information network sti: sexually transmitted infection sus: system usability scale http://ojphi.org public health informatics and the h1n1 pandemic public health informatics and the h1n1 pandemic 1 online journal of public health informatics * issn 1947-2579 * http://journals.uic.edu public health informatics and the h1n1 pandemic joseph lombardo, johns hopkins university applied physics laboratory on june 11th 2009, dr. margaret chan, director-general of the world health organization (who), announced that the influenza alert level was being raised from a phase 5 to phase 6 indicating the start of the 2009 pandemic. the pandemic resulted from a previously uncirculated strain of h1n1 influenza which spread rapidly in the spring of 2009. the second wave of the pandemic began as schools reopened in the late summer. at the time of the writing of this commentary, h1n1 was widely spread in 48 states and canada. due to the intensive surveillance and preparedness activities surrounding a rapidly spreading avian h5n1 influenza, the who and the centers for disease control and prevention, were able to closely monitor the progression of the h1n1 strain in the human population and initiate an accelerated vaccination program. this heightened activity comes at a time of reduced budgets for state and local health departments who have primary responsibility for monitoring and containment within their jurisdictions. as resources become more limited, public health workers are forced to focus their attention on the most urgent priorities. in order to effectively multi-task, public health must leverage new technologies to get more done with existing resources. public health informatics is an emerging field that has the potential to immediately support the ten essential public health services. automated case specific disease monitoring applications can now collect, analyze and present to the user information that would have taken days to assemble only a few years ago. applications such as essence, rods, and biosense have been able to monitor spread of h1n1 at state, local, and national levels. the internet has become an educational tool for informing the population on the latest research to maintain and improve health. the websites of local health departments, cdc, who, webmd, google and many others have all seen increased activity as primary caregivers seek information on how to protect their family members from h1n1. emerging knowledge repositories showcased at national conferences have demonstrated the ability to transparently provide public health alerts to clinicians during patient encounters. information is provided to support disease management while enhancing real-time monitoring. the internet provides a secure collaborative environment to support public health monitoring across jurisdictional boundaries. the distribute project creates a national view of h1n1 trends from data provided by public health agencies across the country that have the desire to collaborate with their counterparts in other jurisdictions. these are just a few public health informatics applications that support h1n1 containment. as the field of public health informatics expands, its researchers and developers must keep in mind the goal of translating their achievements into open environment so that their informatics products can be made available to public health practitioners when they are needed. traditionally, informatics tools are presented to the community at conferences and through journal articles. local public health funding limitations may restrict travel at conferences or membership in societies that publish articles in their journals. the online journal of public health informatics (ojphi) provides an open access vehicle that supports the presentation of the latest open source informatics tools to the public health community to support their translation to practice. as an open access communications vehicle to public health, articles published in ojphi could provide knowledge of open source informatics tools to help public health manage the next emerging health risk. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) sunghee h. boté1* 1university of illinois at chicago, school of public health abstract objectives: in recent years, the devastating effects of u.s. opioid epidemic has been making news headlines. this report explores background information and trends on opioid misuse, overdose fatalities and its impact on public health. in addition, various efforts to improve surveillance, timeliness of data and prescription drug monitoring program (pdmp) integration and interoperability are reviewed. method: pubmed and internet searches were performed to find information on the u.s. opioid epidemic. in addition, searches were performed to retrieve information about pdmps and state-specific mandates along with presentation slides and learnings from the 2018 national rx drug abuse & heroin summit in atlanta, ga. results: it is clear that the u.s. opioid epidemic has a tremendous impact on public health including the next generation of children. various data, surveillance & technology-driven efforts including cdcfunded enhanced state opioid overdose surveillance program (esoos) and use of telemedicine for opioid use disorder treatment aim to improve prevention, treatment and targeted interventions. in addition, pdmp integration and interoperability efforts are advancing to provide prescribers meaningful decision support tools. discussion: the opioid epidemic has a complex impact on public health intertwined with variable factors such as mental health and social determinants of health. given the statistics and studies that suggest many of the illicit opioid users start with prescription opioids, continued advancement in the area of pdmp integration and interoperability is necessary. the pdmp integrated clinical decision support systems need to supply to healthcare providers access to complete, timely and evidence-based information that can meaningfully inform prescribing decisions and communication with patients that affect measurable outcomes. conclusion: while prescription drug monitoring programs (pdmps) are valuable tools for providers in making informed prescribing decisions, the variable state mandates and varying degrees of integration and interoperability across states may limit their potential as meaningful decision support tools. sharing best practices, challenges and lessons learned among states and organizations may inform strategic and systematic use of pdmps to improve public health outcomes. key words: opioid epidemic, prescription drug monitoring programs (pdmps), prescription monitoring programs (pmps) u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi * corresponding author: email: sungheebote@gmail.com doi: 10.5210/ojphi.v11i2.10113 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. 1 introduction the opioid epidemic in the united states was declared a national public health emergency on october 26, 2017 [1]. according to the centers for disease control and prevention (cdc), approximately 130 americans die from an opioid overdose each day [2], and 46 overdose deaths involve prescription opioids [3]. the death toll from legal and illegal opioid use increased 6-fold between 1999 and 2017 [2]. opioids accounted for approximately 68% of all drug overdose deaths in 2017 [2], and 35% of all opioid deaths involved prescription opioids such as methadone, oxycodone and hydrocodone [3]. drug overdose death rate increased 137% between 2000 and 2014 which includes 200% increase in overdose deaths involving heroin and prescription opioids indicated for treatment of pain [4]. in a study analyzing emergency department syndromic and billing data, there was a 29.5% increase in opioid overdoses in 52 areas in 45 states from july 2016 to september 2017 and a 69.7% increase in opioid overdoses in the midwestern region during the same time period [5]. additionally, large cities in 16 states experienced an opioid overdose increase of 54.1% [5]. prescription opioids are generally used to treat moderate to severe pain, and some opioids can be used for treatment of diarrhea or cough [6]. common prescription opioids are codeine, morphine, hydrocodone, oxycodone, oxymorphone and fentanyl [6]. opioids are highly addictive, and overdose can lead to respiratory depression that can result in permanent brain damage, coma or death [6]. naloxone is a medication that can reverse opioid overdose when administered right away [6]. over 191 million opioid prescriptions indicated for the treatment of moderate to severe pain were dispensed in the u.s. in 2017 with wide variability among states [7]. this variability is not reflective of health status of the state [7]. additionally, the increase in the amount of opioids dispensed in the u.s. since the 1990s does not reflect the amount of pain americans report [7]. some risk factors identified for prescription opioid abuse and overdose include: receiving overlapping prescriptions using multiple prescribers and pharmacies, high daily doses of opioids, mental health disorder or history of substance abuse, and low income in rural settings [7]. heroin is an illegal semi-synthetic opioid drug. it is estimated that approximately two out of 1,000 people in the u.s. used heroin in 2017, and the most notable increases of heroin use are in women, privately insured and higher income population [8]. unintentional heroin-related poisoning accounted for 81,326 of emergency department visits in 2015, and drug overdoses involving heroin claimed 15,000 lives in the u.s. in 2017 [8]. the most prominent risk factor for heroin use is u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi history of prescription opioid misuse [9]. according to the cdc, 75% of new heroin users between 2000 and 2013 reported prior misuse of prescription opioids [8]. fentanyl is a synthetic opioid which is 50 to 100 times more potent than morphine and is available as a prescription drug for the treatment of severe pain such as cancer pain [10]. while it is possible prescription fentanyl can be misused and abused, there has been a growing concern about illicit fentanyl and its analogs distributed through illegal drug markets [10,11]. the increase in fentanylrelated overdose deaths are thought to be driven by illicit fentanyl and its analogs [11]. carfentanil, which is a fentanyl analog is used as a tranquilizer for elephants and not meant for human use [12], and it is estimated to be 10,000 times more potent than morphine and often sold mixed with heroin, cocaine or counterfeit pills [11]. 2 method pubmed and internet searches were performed to find information on the u.s. opioid epidemic. in addition, searches were performed to retrieve information about pdmps and state-specific mandates along with presentation slides and learnings from the 2018 national rx drug abuse & heroin summit in atlanta, ga. 3 results 3.1 background on the u.s. opioid epidemic figure 1 [13] shows the three waves attributed to the rise in opioid overdose deaths. the 1990s mark the first wave with increase in opioid prescriptions with notable increase in opioid overdose deaths around 1999 [2]. there was an increased awareness and focus on treatment of chronic pain around this time, and an institute of medicine report attributed the increase in prevalence of chronic pain during the 1990s to “greater patient expectations for pain relief, musculoskeletal disorders of an aging population, obesity, increased survivorship after injury and cancer, and increasing frequency and complexity of surgery. [14]” pharmaceutical companies continued to proliferate in manufacturing opioids including sublingual, transdermal, extended-release and nasal spray formulation products, and some pharmaceutical manufacturers marketed opioids by minimizing addiction potential and promoting off-label uses of opioid indicated for acute breakthrough pain in cancer patients [14]. the second wave began around 2010, and it is characterized by a significant rise in heroin-related overdose deaths [2,14]. between 2010 and 2015, heroin overdose deaths tripled [14]. the last wave which began in 2013 and continues today is characterized by a pronounced increase in synthetic opioid-related overdose deaths, predominantly related to the rise in illicit fentanyl [2]. overdose deaths related to fentanyl analogs increased by 540% between 2013 and 2016 [14]. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi figure 1: overdose death rates involving opioids, by type, united states, 2000-2017 source: cdc [13] 3.2 impact on public health cdc announced that u.s. life expectancy declined in 2017 for the third year in a row, driven by drug overdose deaths and suicides [15]. the average life expectancy decreased from 78.9 in 2014 to 78.6 in 2017, and it is the longest sustained life expectancy decline since 1915 through 1918 during world war 1 and a flu pandemic that killed estimated 50 million worldwide [15,16]. there has been a 19% increase of overdose deaths among teenagers from 2014 to 2015, and blood-born infection transmission from drug injection equipment sharing continues to be a problem [17]. according to cdc, estimated one in 23 women and one in 36 men using drugs via injections will have hiv diagnosis in their lifetime, and opioid use is thought to have contributed to hepatitis c infection transmission which is estimated to have tripled between 2000 and 2015 [17]. furthermore, infants exposed to opioids in utero are at a risk of neonatal abstinence syndrome (nas), and prenatal exposure to opioids have been associated with poor fetal growth, preterm birth or still birth [18]. the incidence of nas is reported to have increased 433% between 2004 and 2014, and this translates to increase from 1.5 per 1,000 hospital births to 8.0 per 1,000 hospital births [18]. feder, letourneau & brook [19] address at least five ways the opioid epidemic may affect and harm health and safety of children and adolescents: intentional or accidental ingestion of prescription drugs, misuse of opioids in pregnancy resulting in problems such as nas, inadequate prenatal care and low birth weight, impaired parenting and attachment due to parental opioid misuse, material deprivation due to family finances being spent on drugs, and extended separation such as foster care due to parent’s incarceration, drug treatment or death. the opioid epidemic has a complex impact on public health intertwined with variable factors such as mental health and social determinants of health. studies suggest links between childhood trauma and substance use u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi disorder [20]. a study of urban primary care patient sample by khoury, tang, bradley, cubells, & ressler [20] concluded “data show strong links between childhood traumatization and suds, and their joint associations with ptsd outcome.” studies also suggest there is a history of major depressive disorder in greater than 50% of patients with opioid use disorder, and opioid use disorder was associated with risk of suicidal ideation and suicide [21]. dasgupta, beletsky & ciccarone [14] “posit that the crisis is fundamentally fueled by economic and social upheaval, its etiology closely linked to the role of opioids as a refuge from physical and psychological trauma, concentrated disadvantage, isolation, and hopelessness. overreliance on opioid medications is emblematic of a health care system that incentivizes quick, simplistic answers to complex physical and mental health needs.” the authors state the increase in opioid prescription is not solely to blame for the current opioid epidemic, but various structural factors contributed to it [14]. for example, individuals may somaticize social disasters and economic hardships into physical pain, and childhood trauma is associated with increased opioid use in later years [14]. the counties with highest overdose rates have lowest levels of social capital, and individuals living in low socioeconomic areas have increased likelihood of developing chronic pain after automobile accidents which is mediated by stress response genes [14]. the authors concluded there is an urgency to integrate clinical care and addressing individuals’ structural environments [14]. park, lin, hosanagar, kogowski, paige & bohnert [22] state “opioid dosage was the factor most consistently analyzed and also associated with increased risk of overdose. other risk factors include concurrent use of sedative hypnotics, use of extended-release/longacting opioids, and the presence of substance use and other mental health disorder comorbidities.” the opioid epidemic also has a significant impact on the safety of first responders when they come into contact with highly potent and fast-acting fentanyl and fentanyl analogs during their routine emergency responses [23]. thirty-three pounds of fentanyl seized in boston is estimated to be enough to wipe out the entire state of massachusetts [23]. first responders must be trained to recognize signs of exposure such as disorientation, respiratory distress, coughing, sedation, and cardiac arrest; they also need to receive adequate training, wear gloves, masks, eye protection and be prepared to administer opioid overdose reversal drug naloxone when necessary [23]. cdc estimates the annual “economic burden” of prescription misuse in the u.s. including healthcare cost, productivity loss, addiction treatment and cost of criminal justice is $78.5 billion [24]. the public broadcasting service [25] news hour segment titled “how the opioid crisis decimated the american workforce” explored through interviews, the devastating impact of opioid crisis on the northeastern ohio families and workforce. the segment revealed that employers are having difficulty finding skilled employees that can pass drug tests, and alan krueger from princeton university stated in the interview, “for both prime-age men and prime-age women, the increase in prescriptions over the last 15 years can account for perhaps 20 percent of the drop in labor force participation that we have seen [25].” according to burke, goplerud & hartley [26], entertainment, recreation, food and construction industry have higher than average relative prevalence of substance use disorder; costs of missed work per employee range from $187 to $3,941 annually depending on the industry sector [26]. additionally, the job turnover rate is 36% vs. 25% in general workforce when employees have u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi untreated or active substance use disorder (sud) with annual per capita cost from $512 to greater than $4,000 [26]. in 2014, the cost of healthcare for an employee with sud was $2,198, and cost of healthcare for an employee with pain medication use disorder was approximately double at $5,586, primarily driven by emergency department use [26]. employers’ investment in treatment and workers in recovery for one year show improvement and return to baseline [26]. 3.3 prevention, treatment, reversal of opioid overdose the centers for disease control and prevention (cdc) promotes the following approach in opioid overdose prevention [27]: • improve opioid prescribing: in 2016, cdc published the “cdc guidelines for prescribing opioids for chronic pain” which covers three conceptual areas: when to initiate or continue opioid therapy for treatment of chronic pain, selection of opioid, dosage, duration, follow-up and discontinuation of opioid therapy, and assessment of risk and addressing harm from opioid use [28,29]. this guideline aims to reduce the gap in opioid prescribing training for primary care providers and address safer and more effective treatment of chronic pain outside of active cancer treatment, palliative and end-of-life care [29]. • prevent opioid use disorder: promote various ways to help reduce opioid exposure and opioid use disorder (oud) including the use of prescription drug monitoring program (pdmp), patient education on safe storage and disposal of prescription opioid medications, and prescription insurance prior authorization/quantity limit strategies [30]. additionally, funding of “prevention for states” program for 29 states provides resources and support for interventions to prevent prescription drug overdoses, and cdc’s rxawareness campaign shares resources to educate patients and families about the risk of prescription opioids and the impact and cost of overdose [30]. • treat opioid use disorder: expand access to evidence-based treatments such as medication-assisted treatment (mat) in addition to behavioral therapy and counseling [31]. substance abuse and mental health services administration (samhsa) makes “behavioral health treatment services locator” available for patients to confidentially and anonymously find treatment facilities [32]. • reverse overdose to prevent death: expand access to life-saving opioid overdose reversal medication naloxone through training of and use by law enforcement officials and emergency medical staff, local organizations, and standing orders for dispending at the pharmacies [33]. in addition, the u.s. department of health and human services (hhs) announced its 5-point strategy in response to the u.s. opioid epidemic [34]: • better prevention, treatment and recovery services: grants in support of access to treatment, prevention, recovery and facilitate treatment coverage through state medicaid programs [35]. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi • better data: support of more timely and accelerated data and reporting including cdc drug overdose data and the centers for medicare and medicaid services (cms) medicare opioid prescription mapping tool [36]. • better pain management: promote evidence-based pain management methods including identification and recommendation proposals to address gaps/inconsistencies and establishing healthy people 2020 objectives [37]. • better availability of overdose-reversing drugs: improve access to overdosereversing naloxone drugs to individuals such as those receiving prescription opioid pain medications, illicit drugs such as heroin or fentanyl, and family and friends of individuals with opioid use disorder [38]. • better research: support pain and addiction research to ensure evidence-based policies, inform clinical practitioners and reduce prescription opioid use [39]. in addition to the approaches and strategies outlined by cdc and hhs, there is an increased emphasis on harm reduction such as syringe service programs to prevent transmission of hepatitis c infection and human immunodeficiency virus infections [4]. furthermore, education and access to treatment need to be expanded to address and prevent a baby being born with nas every 15 minutes [40]. public health and law enforcement agencies, medical examiners and coroners working collaboratively can improve detection of drug overdose outbreaks related to illicit opioids allowing for expedited response and targeted intervention [4]. 3.4 data, surveillance & technology-driven initiatives there are various data, surveillance and technology-driven initiatives in response to the opioid epidemic. the initiatives aim to research and develop targeted interventions, strengthen access to opioid use disorder treatment, and develop strategies and tools for “faster data” to track and response to opioid overdoses. examples are cdc-funded enhanced state opioid overdose surveillance program (esoos) and the use of telemedicine technology for substance use disorder/behavioral health treatments [41]. 3.4.1 cdc-funded enhanced state opioid overdose surveillance program (esoos) the esoos program funded 12 states in september 2016 with 20 states and district of columbia added in september 2017 [41]. the first strategy for esoos is syndromic surveillance using emergency department data for timely non-fatal opioid overdose reporting, and this is achieved by detecting sharp increases or decreases [41]. the second strategy is reporting on fatal opioid overdose through state unintentional drug overdose reporting system (sudors) that captures detailed death scene investigation and toxicology information; other risk factors related to the fatal overdoses are also captured [41]. this strategy can detect and inform newly emerging substances, common drug combinations, and helps identify risk factors and circumstances that may be associated with fatal overdoses [41]. lastly, the third strategy is widespread dissemination of findings to key stakeholders to improve local and state public health prevention and response efforts, and trends are tracked to inform national policy [41]. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi singleton, saavedra & broad [42] presented “states with fast data: lessons learned from kentucky, new mexico and wisconsin” at the 2018 national rx drug abuse & heroin summit. kentucky, new mexico and wisconsin are three of the states receiving the esoos grant [42]. kentucky made efforts to improve drug overdose surveillance reporting including better drug specificity on death certificates, and while the process is slow, kentucky is making progress in reducing lag time between death events and reporting [42]. this is done by making improvements to drug overdose fatality surveillance system (dofss) such as adding a full-time abstractor to decrease case initiation to completion time, automation of toxicology reporting, database platform change and emergency medication services integration [42]. for example, the mean time from death to case initiation decreased from 15 months in 2014 to 4 months in 2017, and the mean time from receiving coroner report to entry in dofss decreased from 87 days in 2016 to 27 days in 2017 [42]. emergency departments (ed) voluntarily participate in syndromic surveillance of nonfatal overdoses, and providers who elect to participate in kentucky’s ed sys forward messages to ky health information exchange (khie) where messages are de-identified and batches transmitted to cdc biosense platform [42]. median days between admit to first message, chief complaint, diagnosis in electronic surveillance system for the early notification of communitybased epidemics (essence) decreased from days to hours between may 2016 to march 2018 [42]. the emergency department data has its challenges; participation is voluntary with the primary incentive being meaningful use and dropped feeds and system lags are not resolved quickly [42]. the new mexico department of health learned through the esoos program that buy-in of emergency medical system (ems) data managers, providing feedback to emergency medical technicians (emt), and discussion with other states utilizing ems data for surveillance is vital in establishing standard definitions for suspected overdose cases and reducing missing information from the field [42]. new mexico also faced challenges due to lack of uniformity in ed data and found communicating to the ed staff about how the data collected is used can help bridge the gap [42]. in wisconsin, the wisconsin ambulance run data systems (wards) which has been in use since 2010 captures 90% of ems data [42]. while the data submission is mandatory within seven days of incident, it is challenging to work with the data due to decentralization and free text data entry [42]. the ambulance data is linked with multiple data systems for hospital discharge and death certificate information, and the future goal is integration of pdmp and national violent death reporting system (nvdrs) [42]. additionally, while 84% of hospital ed submit syndromic surveillance data through biosense platform funded through meaningful use, the large volume of data reduces system performance, and historic data upload and data quality validation is crucial and a challenge [42]. despite the challenges, local health departments were educated on using the syndromic data, and a pilot alert system was developed [42]. wisconsin department of public health also worked through challenges related to decentralization of toxicology testing and data collection from corners and medical examiners, and as of the presentation in april 2018, death investigation reports were available for 95% of opioid fatalities [42]. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi 3.4.2 telehealth it is estimated that less than 20% of individuals who need treatment for substance use disorder receive it, and access to behavioral health care and opioid use disorder treatment is particularly challenging in rural areas and correctional facilities [43]. telehealth may bridge the gap in underresourced areas by delivering virtual behavioral health and education services most frequently using live video but also using emails, text messages and telephony; it can also support rural clinicians in need of specialty consultation [43]. according to yellowlees [44], virtual care may have advantages for the patient given s/he can build relationships with providers outside of the residing community and may provider better environment to discuss awkward, embarrassing or stigmatizing topics. additionally, transcription and translation services can be integrated [44]. in a 2 year-retrospective data analysis study of individuals diagnosed with opioid use disorder by zheng, et al. [45], there was no significant statistical difference in additional substance use, average time to 30 and 90 days of abstinence and treatment retention rates between telepsychiatry buprenorphine mat interventions compared with face-to-face mat treatments. 4 discussion 4.1 prescription opioid utilization in the u.s. & changing opioid prescribing practices the washington post published an article on march 15, 2017 written by humphreys [46] titled “americans use far more opioids than anyone else in the world.” in this article, humphreys notes that according to the united nations data of top 25 opioid consuming countries in the world, the united states is the heaviest consumer of opioids in the world [46]. according to the u.n. report, 99% of world’s supply of hydrocodone is consumed by americans; this disparity of heavy opioid consumption does not appear to be related to the aging population, because other countries such as australia and italy have higher proportion of population 65 years and older [46]. in another washington post article, humphreys [47] explores if the high consumption of opioids in the u.s. is due to unusually high levels of pain. according to a 2008 research, the age standardized prevalence of chronic pain in the u.s. was similar to italy and france; however, the opioid consumption per capita was approximately 6 to 8 times higher in the u.s. compared to italy and france [47]. humphreys [46,47] points out in both articles that a notable difference in the u.s. compared to other countries is the lack of constraints placed on the pharmaceutical manufacturers to market and promote their products to patients and prescribers, as well as to lobby and donate to political causes and regulatory bodies. in the 2017 report of office of inspector general (oig), opioid prescription records from 2016 were analyzed as part of a strategy to protect medicare part d beneficiaries from prescription drug abuse and harm [48]. the analysis found that in 2016, one in three medicare part d recipients received at least one prescription opioid, and approximately 500,000 beneficiaries received high doses of opioid defined as greater than 120mg morphine equivalent dose (med) per day for at least 3 months [48]. additionally, approximately 400 prescribers had prescribing habits considered outside the norm warranting further scrutiny [48]. furthermore, the study found that approximately 70,000 beneficiaries were at serious risk by receiving very high doses of opioid [48]. approximately 22,000 beneficiaries appeared to be doctor shopping based on the number of u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi prescribers and pharmacies used to obtain these medications and indicated potential drug seeking behavior for nonmedical use or diversion; they may also be instances of stolen beneficiary number or that the beneficiary’s care is not closely monitored or coordinated by the providers [48]. the overall opioid prescribing rate in the u.s. declined from 2012 to 2017 as illustrated in figure 2 [49]. in 2017, the rate declined to the lowest in more than 10 years; however, opioid prescribing rate is still at 58.7 prescriptions per 100 persons totaling more than 191 million prescription in 2017 [49]. furthermore, prescribing rates remain high in certain areas of the country. according to cdc, 16% of counties dispensed enough opioid prescriptions for every person with some counties at rates seven times higher than the overall national rate [50]. figure 2: trends in annual opioid prescribing rates by overall and high-dose prescriptions source: cdc [49] nelson, juurlink, & perrone [51] stated “the chronic, relapsing nature of opioid addiction means most patients are never ‘cured,’ and the best outcome is long-term recovery. the lifelong implications of this disease far outweigh the limited benefits of opioids in the treatment of chronic pain, and in many cases the risks inherent in the treatment of acute pain with opioids. the encouraging finding of declining opioid initiation rates should be tempered by the increasing rates of nonmedical opioid use disorders and the limited utilization of treatment programs. although multifaceted approaches are needed to successfully address the opioid epidemic, an important step is to start at the beginning and keep opioid-naive patients opioid naive.” 4.2 prescription drug monitoring program (pdmp) prescription drug monitoring programs (pdmps), also referred to as pmps are state-level electronic databases used to track controlled substance prescription data [52]. pdmps are tools for u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi providers to make informed prescribing and dispensing decisions based on the patient’s utilization, and pdmps have the potential to help improve patient safety and public health [52]. despite the mixed findings, pdmp evaluations demonstrated prescribing behavior changes, reduction in utilization of multiple providers, and decrease in substance abuse treatment admissions [52]. the benefit of pdmp is only realized when the provider actively utilizes the information in the pdmp. each state implements pdmp in ways it sees fit; some states mandate the use of pdmps in established circumstances such as prior to prescribing or dispensing controlled substances [52]. in addition, while pharmacists enter prescription data into the state’s pdmp, the data submission intervals may range from minutes, days, weeks or month [52]. data collected may also be used by state health departments, state insurance programs, healthcare licensure boards and law enforcement agencies as shown in figure 3 below [52]. figure 3: prescription drug monitoring program (pdmp) source: cdc [52] when opioid medications are prescribed, there needs to be a mutual understanding between the provider and the patient that long term use of opioids can result in physiological dependence, particularly when opioid are prescribed for non-cancer diagnoses [53]. in addition to pain agreement to ensure mutual understanding and expectation of risks, benefit and responsible medication use, pdmp is a tool providers can use to make informed prescribing decisions [53]. for example, pdmp can provide information to confirm lost prescriptions are in fact lost and not being diverted [53]. in addition, pdmp can inform the prescriber about the patient’s controlled substance utilization including those from other prescribers filled at multiple pharmacies and resolve concerns regarding early fills by checking the previous fill dates submitted by the pharmacy [53]. the following is an example of an institutional protocol implementation including a pdmp consult. according to kolodny [54], dentistry accounted for 28.9% of opioid prescriptions. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi additionally, more opioids are prescribed for teenagers by dentists compared with all other specialties [55]. furthermore, miech, johnston, o’malley, keyes & heard [56] concluded that the “use of prescribed opioids before the 12th grade is independently associated with future opioid misuse among patients with little drug experience and who disapprove of illegal drug use.” given these concerns, the university of minnesota, school of dentistry has established a protocol for teaching and clinical practice to use non-opioid analgesics as first-line agent and use opioid at the lowest effective dose when required [54]. as part of the protocol, clinicians must consult the pdmp prior to issuing an opioid prescription, and reasons for deviation from the protocol are documented [54]. 4.2.1 prescription drug monitoring program (pdmp) mandates by state as previously mentioned, pdmps are state-level electronic databases implemented and regulated by each state [52]. as of 2018, 41 states mandate providers to use the pdmp; however, the mandated circumstances vary including drugs it tracks, frequency, and exemption parameters [57]. for example, arizona statute a.r.s. § 36-2606 mandates medical practitioners who possess dea license to review patient’s record in the pdmp for preceding 12 months prior to prescribing schedule ii controlled substances or a benzodiazepine [58]. additionally, effective april 26, 2018, dispensing pharmacists working in an outpatient setting and whose employer has a u.s. dea registration must review patient records in the pdmp for preceding 12-month prior to dispensing schedule ii controlled substances [58]. the state of michigan recognizes gabapentin as schedule v controlled substance and requires gabapentin prescription data submission to its pdmp, michigan automated prescription system effective january 4, 2019 [59]. according to pew [57], comprehensive mandates that apply to all prescribers and to all initial opioid prescriptions at minimum are associated with reduction in utilization of multiple prescribers, pharmacies and number of opioid prescriptions. a study by rasubala, pernapati, velasquez, burk, & ren [60] found that since the mandate to consult pdmp prior to opioid prescribing in new york state, there has been a 78% reduction in absolute quantity of opioids prescribed by dentists over 3 months from 5096 pills to 1120. paulozzi, kilbourne, desai [61] concluded pdmps appear to have minimal effect on the overall opioid consumption and overdose mortality rate, and improvement in the use of pdmp data to positively impact overdose rate is necessary. patrick, fry, jones & buntin [62] determined that states with prescription drug monitoring programs reduced opioid-related overdose deaths by 1.12 per 100,000 population and the states with more robust program characteristics such as tracking larger number of drugs of abuse potential and updating the pdmp data at least weekly had a higher reduction in mortality. 4.2.2 prescription drug monitoring program (pdmp) challenges, integration, interoperability and improvement efforts while pdmps can be useful tools for providers, the limited data sharing across states limit their usefulness particularly for providers working near state borders. in addition, lack of pdmp data integration with health information systems such as health information exchange (hie), electronic health record (ehr) and pharmacy dispensing software (pds) systems present workflow challenges and barriers [63]. some specific deterrents for providers include the need for clinicians u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi to log in for pdmp search, time it takes for the search to return the records requested, and limited understanding on using data that is returned from the search [64]. the substance abuse and mental health services administration (samhsa) funded the pdmp electronic health records integration and interoperability expansion (pehriie) program in nine states during fiscal years 2012 to 2016 [63]. this effort aimed to address pdmp data integration challenges, better inform clinical decisions through interstate data sharing and promote point of care interventions thus improvement in outcomes [63,64]. figure 4 depicts general ehr, pharmacy and pdmp integration established in the pehriie program [63]. figure 4. ehr, pharmacy, and pdmp integration source: cdc [63] five of eight states were able to integrate pmdp reports with pds systems (local or statewide), hies or ehrs [63]. three states, kansas, washington and illinois had data to examine possible impact of the program; kansas completed integration of pdmp with the via christi health network [63]. solicited reports by via christi health network providers increased greater than sevenfold from 31,156 in 2013 to 223,000 in 2015 [63]. as a comparison, statewide pdmp reports solicited excluding via christi prescribers increased 183% during the same time period from 23,171 in 2013 to 65,242 in 2015 [63]. the state of washington completed pdmp integration with its hie, one health port and the emergency department information exchange (edie) late 2014 [63]. during the 2014 calendar year, 26,546 reports were provided via edie; it increased 80fold in 2015 calendar year to 2,222,446 [63]. lastly, data from illinois’ pdmp integration with ehrs at anderson hospital suggest that increase in pdmp report resulting from the integration u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi was associated with decreased opioid prescribing [63]. it is worth noting there was a 145-fold increase in reports solicited by registered prescribers at the anderson hospital, from 6.9 reports per provider in 2013 to 998.2 per provider in 2015 [63]. in comparison, statewide number during the same period was 7.26 per provider in 2013 to 9.27 in 2015 [63]. furthermore, there was a 22% reduction in number of opioid prescriptions provided by anderson hospital provider compared with a 13% increase statewide during the same period [63]. there was also a 41% reduction in patients receiving at least one opioid prescription from anderson hospital providers compared with a 1% statewide increase [63]. in addition to the pdmp report integration with ehrs, pds and hies, six of nine states were able to initiate interstate data sharing [63]. illinois, kansas and west virginia achieved two-way interstate data exchange with three-quarters of their border states; the interstate data exchange expanded to include average of 90% of their border states for indiana and ohio [63]. there was a notable number of pdmp requests by in-state providers for out-of-state data [63]. various other states are also making efforts to improve their pdmps with goal of making the data content more meaningful to providers, easier to use and affect prescribing and utilization. according to mcdonald [65], state of rhode island implemented pdmp “dispenser threshold alerts” in 2017 that include alerts on use of more than 3 prescribers and pharmacies in the previous 6 months, morphine milligram equivalent greater than 90mg per day, overlapping opioid and benzodiazepine prescriptions and continuous days of opioid. these clinical alerts are important in knowledge, risk and time management, public health interest and connects clinical alerts to existing regulations such as the food and drug administration’s black box warning regarding the risk of concurrent administration of opioid and a benzodiazepine [65,66]. there has been a gradual decrease in total number of clinical alerts from may 2017 through january 2018, and the rhode island department of health will continue to evaluate how the clinical alerts affect pdmp targeted medication utilization, prescribing and ease of use for providers [65]. the state of new mexico like many other states utilizes the pdmp data to generate prescriber reports. the “new mexico prescription monitoring program prescriber feedback report” compares a particular provider’s 6 month controlled substance prescribing data to the average of other prescribers in the same specialty [42]. this report is generated in partnership with the state board of pharmacy and sent to prescribers who have 20 or more controlled substance patients in the review period [42]. the state of new mexico cited pdmp challenges including individual patient identification, linking the pdmp report request and patients, linking prescribers who may have multiple dea numbers and noted the importance of data validation [42]. carolinas healthcare system is a health system that consists of over 40 hospitals, over 900 care locations, over 15,000 clinicians and greater than 10 million annual encounters [67]. in order to address the prescription opioid abuse and overdoses, clinical decision support, prescription reporting with immediate medication utilization mapping (primum) was implemented within the electronic medical record (emr) and tested to determine how it affects prescribing behaviors [67,68]. upon selection of a controlled substance, the emr searches patient’s chart for risk factors defined within the system; if a risk factor is identified, the emr provides an alert and gives the provider an option to continue to prescribe or discontinue the prescription [67]. the emr also has a direct hyperlink to the north carolina and south carolina pdmp [67]. risk factor triggers such u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi as early refills, previous history of opiate or benzodiazepine overdose, and a positive screening for cocaine and marijuana were selected based on literature review [67]. this study determined that the alerts had moderate effect resulting in 13-25% of prescriptions cancelled and the effect on the behavior varied by specialty [67]. the carolinas healthcare system has plans to further analyze the data and collaborate with the north carolina and south carolina pdmps for integration [67]. 5 limitations due to the substantial number of literatures available on the subject of opioid epidemic and pdmps, only a small sample was selected for review and does not represent complete spectrum of available views and findings. in addition, due to the variability in pdmp mandates by state, only a small sample was selected as examples. 6 conclusion the world health organization (who) defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity [69].” recent tragic death of the legendary musician prince at the age of 57 brought to light the indiscriminate effect of opioid addiction and overdose deaths; it was determined that prince died of fentanyl overdose, taking what he thought was vicodin but actually laced with fentanyl [70]. it is clear that the u.s. opioid epidemic has a tremendous impact on public health that not only affects the adults in this country but also the next generation. given the statistics and studies that suggest many of the illicit opioid users start with prescription opioids, continued advancement in the area of pdmp integration and interoperability is necessary. pdmps are valuable tools for providers in making informed prescribing decisions. however, variability in state mandates and varying degrees of integration and interoperability across states may limit their usefulness as decision support tools. for example, a provider in a state that requires pharmacies to submit the controlled substance dispensing information within 24 hours may not see the prescriptions filled on the same day from other prescribers at different pharmacies on the pdmp report. moreover, the prescriber may need to log in each time to obtain a pdmp report which may disrupt his/her workflow and become a deterrent for consistent use. for pdmps to be meaningful clinical decision support tool for prescribing decisions and data source for public health departments to inform targeted interventions, complete and consistent real time data is necessary. furthermore, they need to be simple, efficient and integrated into the healthcare providers’ workflow, interoperable with other systems such as ehrs, emrs, and hie and scalable. the pdmp integrated clinical decision support systems need to supply to healthcare providers access to complete, timely and evidence-based information that can meaningfully inform prescribing decisions and communication with patients that affect measurable outcomes. sharing best practices, challenges and lessons learned among states and organizations may inform strategic and systematic use of pdmps to improve public health outcomes. as with other system development or enhancement efforts, stakeholder engagement, particularly end-user engagement and participation are crucial; privacy, security, and hipaa considerations must also be a priority. u.s. opioid epidemic: impact on public health and review of prescription drug monitoring programs (pdmps) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e18, 2019 ojphi 7 references 1. u.s. department of health & human services [internet]. washington, d.c.: hhs; 2017 oct 26 [cited 2019 apr 5]. hhs acting secretary declares public health emergency to address national opioid crisis; [about 1 screen]. available from: https://www.hhs.gov/about/news/2017/10/26/hhs-acting-secretary-declares-public-healthemergency-address-national-opioid-crisis.html 2. centers for disease control and prevention [internet]. atlanta: the center; 2018 dec 19 [cited 2019 apr 5]. opioid overdose: understanding the epidemic; [about 2 screens]. available from: https://www.cdc.gov/drugoverdose/epidemic/index.html 3. centers for disease control and prevention [internet]. atlanta: cdc; 2018 dec 19 [cited 2019 apr 5]. opioid overdose: prescription opioid data; 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http://ojphi.org * vol.4, no. 3, 2012 bridging the communication gap: successes and challenges of mobile phone technology in a health and demographic surveillance system in northern nigeria henry v. doctor, phd 1,2* , alabi olatunji, msc 3 , and abdul’azeez jumare, pgd comp sc 4,5 1 columbia university, mailman school of public health, department of population & family health, new york, usa 2 prrinn-mnch program, operations research unit, abuja, nigeria 3 prrinn-mnch program, operations research unit, gusau, zamfara state, nigeria 4 prrinn-mnch program, operations research unit, gusau, zamfara state, nigeria 5 ahmadu bello university, department of community medicine, zaria, nigeria abstract maternal and child health indicators are generally poor in nigeria with the northern part of the country having the worst indicators than the southern part. efforts to address maternal and health challenges in nigeria include, among others, improvement in health and management information systems. we report on the experience of mobile phone technology in supporting the activities of a health and demographic surveillance system in northern nigeria. our experience calls for the need for the nigerian government, the mobile network companies, and the international community at large to consolidate their efforts in addressing the mobile network coverage and power supply challenges in order to create an enabling environment for socio-economic development particularly in rural and disadvantaged areas. unless power and mobile network challenges are addressed, health interventions that rely on mobile phone technology will not have a significant impact in improving maternal and child health. keywords: public health surveillance systems; public health informatics; nigeria introduction the reported maternal and child health (mch) indicators in nigeria are generally poor. in particular, mch indicators from northern nigeria are worse. for example, the maternal mortality ratio (mmr) in the north is much higher than the national average, exceeding 1,000 per 100,000 live births compared to fewer than 300 per 100,000 live births for the southern region [1]. recent studies have revealed that the mmr in nahuche area of zamfara state in north west (nw) nigeria is 1,049 deaths per 100,000 live births [2]. child mortality estimates are also high as evidenced by the under-five mortality in nigeria which was estimated at 143 per 1,000 live births in 2010 [3]. efforts to address these challenges range from interventions aimed at improving the quality and access to maternal, newborn, and child health services by strengthening planning and http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 training of human resource for health, improving the state of health infrastructure, provision of supplies and commodities (including drugs), and community engagement to promote appropriate mch behavior and increase demand for maternal health services in general and in particular emergency obstetrics care. the partnership for reviving routine immunization in northern nigeria (prrinn); maternal, newborn, and child health (mnch) program (hereafter ‘prrinn-mnch program’), received funding from the united kingdom department for international development (dfid) and the norwegian government between 2007 and 2008 to revitalize immunization, improve the quality, access, and utilization of maternal, newborn, and child health services in northern nigeria. the program was initiated by a consortium consisting of health partners international (uk), save the children (uk), and grid consulting (nigeria) and operates in four states in northern nigeria: jigawa, katsina, yobe, and zamfara. part of the comprehensive activities of the prrinn-mnch program includes setting up a health and demographic surveillance system (hdss) in nahuche emirate of bungudu local government in zamfara state of north west nigeria. in collaboration with the zamfara state government, the nahuche hdss (nhdss) was established to provide a platform for measuring the impact of the program’s interventions and also as a platform for future surveys and trials. a detailed description of the nhdss set up, design, data collection, and processing procedures has been described elsewhere [4]. the set up activities of the nhdss included a pilot census in may/june 2009 followed by a baseline census (sept-dec 2010) and bi-annual cycles of data collection beginning in january 2011. trained interviewers collect routine data in rural communities under surveillance on pregnancies, births, deaths, migration, marriages, and vaccination coverage. these data are recorded in registers and reported to the nhdss computer centre for processing. trained community key informants (ckis volunteers) support the nhdss data collection activities by routinely reporting key events such as births and deaths as they occur in their communities. as of june 2012, nhdss had a surveillance population of about 138,000 located in 20,194 households. in order to determine the probable cause of deaths occurring at the community level, the nhdss initiated the verbal autopsy data collection system in october 2012. the data collection and processing activities of nhdss follow the guidelines of the international network for the demographic evaluation of populations and their health (indepth network). the indepth network is an umbrella body which provides an international platform of sentinel demographic sites that provides health and demographic data and research to enable developing countries to set health priorities and policies based on longitudinal evidence. as of november 2012, the network consisted of 44 hdss sites in selected countries in africa, asia, and oceania [5]. mobile communication infrastructure and surveillance operations while the hdss sites provide data aimed at measuring the impact of interventions and systems to monitor progress towards achieving health-related national goals, the hdss sites are set up in countries with varying degrees of infrastructure. except in a few cases, such as nairobi hdss in kenya (focusing on urban slums), virtually all hdss sites are in rural and under-resourced settings. while a summary of the cross-country variations in the infrastructure of communities http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 with hdss sites is beyond the scope of this paper, we focus on the infrastructure of nhdss in northern nigeria with respect to its mobile communication capacity in supporting data collection, field management, and data processing activities. in brief, nahuche study area consists of 306 villages under the leadership of six district heads of bella, gada, karakkai, nahuche keku, nahuche ubandawaki, and rawayya. literacy levels are very low and infrastructure such as road network, power generation and supply, is inadequate. the general sanitation in the area is poor and the area has a warm climate with temperatures rising to 38 degrees celcius from march to may. farming is the most common economic activity and unemployment is rampant with associated temporary labor migration of men [4]. nahuche is benefiting from the substantial growth in mobile telephone subscriptions that has occurred since the 1980s in both developing and developed countries [6]. for example, the nhdss baseline census of 2010 showed that 40.3% of 19,193 households within the surveillance area had access to a mobile phone. of interest is the fact that in much of sub saharan africa there are more mobile phones than fixed lines [6] and nigeria is no exception, mobile phone subscriptions have grown. since the liberalization of nigeria’s telecommunication sector in 2000, the industry has become the key source of new jobs in the economy, employing about 6,000 professionals, and overseeing, indirectly close to half a million jobs [7]. the increases in mobile coverage has many more advantages such as improving what people already do in terms of faster and cheaper communication. for example, in india, fishermen can reduce fish wastage by phoning in advance coastal markets to find out the need for supply. mobile banking also offers the flexibility and convenience for many customers [7]. the increase in mobile telephones has also led to a growing attraction for mobile telephones as health interventions. this attraction has been influenced by, among other things, the fact that mobile phones are functionally easier to use for people with lower levels of skills than those needed for computers or the internet [6]. irrespective of whether mobile phones are functionally easier to use for those with lower level or higher level skills, mobile phones have become useful in data collection of health-related information. in the northern nigeria hdss at nahuche, and just as in many other hdss sites, fieldworkers are expected to communicate to a large extent with the field office-based team and to a minor extent with the data processing team on a daily basis to resolve any data collection problems that may arise in order to expedite data processing activities. however, many times the field-based and office-based teams have problems in communicating with each other due to poor mobile network coverage but also due to limited power supply when the mobile phone batteries gets discharged. the nhdss study area has virtually no electricity supply from the national grid and majority of households rely on electricity from rechargeable lamps for lighting. for example, the baseline census results on household characteristics conducted in 2010 in nhdss revealed that 2.5% of the 19,193 households had access to electricity from the national grid and another 0.5% from electric generators [8]. the nhdss fieldworkers come from households or communities which struggle to access electricity. while field supervisors are expected to visit the fieldworkers on a daily basis in the surveillance areas, field problems that emerge after the supervisors’ visits can only be addressed during the subsequent visits by the supervisors or whenever the fieldworkers have enough power on their mobile phones. a more proactive way would be for fieldworkers to fully charge their mobile phones and ensure that they have enough power each and every morning before they start http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 their work. however, at least within the nhdss operational setting, fieldworkers are not provided with official mobile phones. as a result, even with a fully charged mobile phone in the morning, they can make and receive calls from their friends and family members. by the time they start making or receiving official phone calls, the mobile phones are on average discharged. even if they had access to an official mobile phone, recharging the phones would still be a major challenge. the ckis also experience similar challenges in mobile communication. while the nhdss office-based team is able to recharge their mobile phones using power from a generator, they are often unable to communicate with their field staff. we are aware of alternative options to charge mobile phone batteries such as mini solar chargers but their efficacy and duration of charging varies. some of the available solar panels do not provide optimal solar power during the rainy season. what is needed is a more permanent solution: regular supply of power for community members in nahuche and nigeria at large. this is inevitable since the application of mobile health technology or intervention relies, among other things, on the ability of users to have constant power supply for recharging. while the nhdss efforts to expedite data collection and processing are compromised by the virtually non-existent power supply, future efforts to link the hdss activities with those of the adjacent nahuche health research centre in tracking immunization defaulters through mobile phone technology (i.e., alerts on service uptake) will be challenging. in addition to the very limited power supply is the poor mobile network coverage across many parts of nigeria. intermittent mobile network from the providers contributes to the high discharge rate of mobile phones since subscribers often have to keep trying a line for several times before they get connected. eventually, some of the mobile phone subscribers in the nhdss study area end up forgetting (except those who are able to save their numbers elsewhere) their mobile numbers since the network coverage is virtually non-existent. our team has experienced this during data collection of migration events. a respondent who reports that some of the household’s members have migrated to another area is asked for the mobile phone number of the migrant. in many instances, fieldworkers are not able to get the mobile numbers because the respondents do not know the numbers. when asked reasons for not knowing the numbers, majority of them sarcastically state that there is no need to know the number since there is no mobile network coverage in their area unless they go to the zamfara state capital, gusau. related to the poor mobile network coverage are the high tariff charges on communication. our field workers often complain about high tariff on airtime recharge cards. they are often unable to call the field office at the nahuche research centre due to insufficient airtime on their mobile phones. while high tariff charges can be managed through budgetary allocation for airtime purchases, the power supply and mobile network coverage remain an enormous challenge for field operations. future outlook while we are very optimistic with the effectiveness of mobile technology in the future, health intervention packages, which take advantage of mobile phone technology, are currently a nonhttp://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 starter in majority of rural communities in nigeria. how can this problem be addressed? the answer is simple but doing it is an enormous task. mobile health technologies would require constant power supply and strong network coverage in all communities. nigeria’s power supply is currently erratic and insufficient. generally, there is no difference between the rural and the urban areas except for the fact that the urban areas by virtue of their status as ‘business hubs’ tend to push consumers to use electric generators more than often the rural areas. as reported earlier, only 0.4% of the households in the nhdss baseline census in 2010 had access to electricity from generators. while this is likely to be representative of most rural communities in northern nigeria, efforts to seek alternative solutions for power generation are inevitable. the government of nigeria through various media has acknowledged the need to find alternative solutions to generate more power for the populace. for example, according to a newspaper report on october 3, 2011, the director general of the energy commission of nigeria, prof. abubakar sambo, stated that 1% of nigeria’s land area could be used to generate 600,000 megawatts of electricity using solar energy [9]. solar energy, described as the best form of renewable energy, has a very high investment cost but the life span of the solar panel could be as long as 25 years if they are properly installed. nevertheless, some anecdotal evidence shows that in some states in nigeria solar panels have failed to deliver the expected results due to corrupt practices associated with procurement of substandard panels. irrespective of this, solar energy is never exhausted unlike the conventional energy of oil, coal, and gas. while some newspapers in september 2012 reported that some petrol (or gas) stations are reporting declines in their sales due to improved power supply [10], the saturation effects of the increased power generation will take a considerable time to be felt by all nigerians. the reported increase in power generation, estimated at slightly over 4,000 megawatts as of october 2012, is welcome and will support a lot of business and service delivery activities that rely on power. while the backbone of the nhdss field operations relies on mobile technology, there is a need for organizations and stakeholders involved in improving mch outcomes in northern nigeria and similar settings to find alternative solutions to address power problems in the intervention areas. the role of power supply in improving mch cannot be overemphasized in the contemporary world. drugs or vaccines need to be stored in a cool place (considering the warm weather in nigeria for a greater part of a year), surgery and other treatment rooms need power, lower level health facilities need to communicate with referral facilities on the need for an ambulance, and many others. to overcome this challenge, the prrinn-mnch program has rehabilitated a number of health facilities in its program states and installed solar panels to ensure constant power supply in all critical areas of health facility operations. from an operations point of view, mobile technology is critical for nhdss activities. field staff, field management, and data processing teams are expected to be in constant communication to report and resolve problems instantly and ensure rapid processing and dissemination of data to policy makers and other stakeholders. to-date, the success of mobile phones in aiding nhdss fieldwork operations has been dismal. as we get closer to the deadline for achieving the millennium development goals in 2015, the most realistic priority in ensuring the effectiveness of mobile technology in field operations as well as any mobile phone-based health care interventions is to ensure that communities have regular access to power. the nhdss has set up a system of routine monitoring of health and population dynamics in nahuche area in northern http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 nigeria. however, electricity power supply remains an enormous challenge particularly with recent developments within the indepth network in which some hdss sites are piloting activities to migrate hdss data collection from the traditional and expensive paper-based method to the less expensive mobile-based data collection using devices such as mobile phones or tablets. these devices need constant power supply to charge the batteries since the speed of data collection and processing will depend, among other things, on sufficient power supply for the mobile devices. conclusion what is the future of power generation in nigeria? from the local media, we hear of increased power generation although the actual reported megawatts vary from one source to the other. nevertheless, nigeria’s power sector reform initiative which was launched in 2005, recognizes the need to improve power sector performance as a critical step in its efforts to address development challenges. through the 2010 roadmap, nigeria revitalized the challenging process of implementing reforms by outlining the government’s strategy and actions to undertake comprehensive power sector reform to expand supply, open the door to private investment, and address some of the chronic sector issues hampering improvement of service delivery [11]. we know that power supply is one of the many challenges nigeria is expected to address to ensure that the country is on course to meet the mdgs, particularly those related to health. increased power supply will strengthen, among other things, health management information systems of which the nhdss is part. we hope that the nigerian government, the mobile network companies, and the international community at large will consolidate their efforts in addressing the mobile network coverage and power supply challenges in order to create an enabling environment for socio-economic development particularly in rural and disadvantaged areas. acknowledgments we are most grateful to the people of nahuche emirate in bungudu local government, zamfara state for their committment to the nahuche hdss activities since 2009. we also acknowledge the support of traditional and political authorities. the nahuche hdss has been set up with technical support from consultants from the indepth network (through navrongo and kintampo hdss sites in ghana) and columbia university through the operations research technical assistance unit of prrinn-mnch programme. the department for international development (uk), the norwegian government, zamfara state and bungudu local governments are acknowledged for their financial assistance. the entire prrinn-mnch management, nahuche field, and data management team are acknowledged for their continued cooperation and support. corresponding author henry victor doctor associate research scientist columbia university, mailman school of public health e-mail: hvd2105@columbia.edu http://ojphi.org/ mailto:hvd2105@columbia.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 references [1] centre for reproductive rights and women advocates research and documentation centre. (2008). broken promises: human rights, accountability, and maternal death in nigeria. new york, lagos: crr, wardc. [2] doctor, h.v., olatunji, a., findley, s.e., afenyadu, g.y., abdulwahab, a. and jumare, a. (2012). maternal mortality in northern nigeria: findings of a health and demographic surveillance system in zamfara state, nigeria. tropical doctor, 42, 140-143. [3] unicef. (2011). levels and trends in child mortality – 2011 report. new york: unicef (on behalf of the united nations inter-agency group for child mortality estimation. [4] doctor, h.v., findley, s.e. and jumare, a. (2011). evidence-based health programme planning in northern nigeria: results from the nahuche health and demographic surveillance system pilot census. journal of rural and tropical public health, 10, 21-28. [5] the indepth network. (2012). brief profiles of member centres. accessed on 1 november 2012 at www.indepth-network.org [6] kaplan, w. (2006). can the ubiquitous power of mobile phones be used to improve health outcomes in developing countries? globalization and health 2:9; doi:10.1186/1744-8603-29 [7] singh, r. (2009). mobile phones for development and profit: a win-win scenario. overseas development institute. united kingdom: overseas development institute. [8] olatunji, a., doctor, h.v., idowu, o., and jumare, a. 2011. a report on the health and demographic surveillance system baseline census. gusau, zamfara state, nigeria: nahuche health research centre. [9] this day online news. 2011. nigeria can generate 600,000mw from solar energy. accessed on 1 november 2012 at http://www.thisdaylive.com/articles/-nigeria-can-generate-600000mw-from-solar-energy-/99738/ [10] sule, t. (2012). petrol station managers see drop in sales as power generation steadies. accessed on 1 november 2012 at http://www.businessdayonline.com/ng/index.php/news/76-hot-topic/44404-petrol-stationmanagers-see-drop-in-sales-as-power-generation-steadies [11] world bank. 2012. nigeria overview: economic overview and performance. accessed on 1 november 2012 http://www.worldbank.org/en/country/nigeria/overview http://ojphi.org/ http://www.indepth-network.org/ http://www.thisdaylive.com/articles/-nigeria-can-generate-600-000mw-from-solar-energy-/99738/ http://www.thisdaylive.com/articles/-nigeria-can-generate-600-000mw-from-solar-energy-/99738/ http://www.businessdayonline.com/ng/index.php/news/76-hot-topic/44404-petrol-station-managers-see-drop-in-sales-as-power-generation-steadies http://www.businessdayonline.com/ng/index.php/news/76-hot-topic/44404-petrol-station-managers-see-drop-in-sales-as-power-generation-steadies http://www.worldbank.org/en/country/nigeria/overview layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using medications sales from retail pharmacies for syndromic surveillance in rural china weirong yan*1, 2, liwei cheng2, li tan2, miao yu3, shaofa nie2, biao xu3, lars palm4 and vinod diwan1 1karolinska institutet, stockholm, sweden; 2huazhong university of science and technology, wuhan, china; 3fudan university, shanghai, china; 4future position x, gavle, sweden objective to use an unconventional data pharmaceutical sales surveillance for the early detection of respiratory and gastrointestinal epidemics in rural china. introduction drug sales data as an early indicator in syndromic surveillance has attracted particular interest in recent years (1, 2), however previous studies were mostly conducted in developed countries or areas. in china, many people (around 60%) choose self-medication as their first option when they encounter a health problem (3), and electronic sales information system is gradually used by retail pharmacies, which makes drug sales data become a promising data source for syndromic surveillance in china. methods this experimental study was conducted in four rural counties in central china. from apr. 1st 2012, there are 56 retail pharmacies joined the study, including 21 county pharmacies and 35 township pharmacies. 123 drugs were selected under surveillance based on the analysis of local historical sales volume and consultation with local pharmacists, including 19 antibiotics, 15 antidiarrheal medications, 9 antipyretics, 41 compound cold medicine, and 39 cough suppressants. daily sales volume of the selected drugs was recorded into the database by pharmacy staff at each participating unit via electronic file importing or manual entering. figure 1 showed the user interface for data viewing, query and export. field training and supervision were regularly conducted to ensure the data quality. results from apr. 1st to jun. 30th 2012, there were 103814 sales records reported in the system, including 44464 (42.83%) records from county pharmacies and 59350 (57.17%) from township pharmacies. among all surveillance drugs, the sales of compound cold medicine accounted for the largest proportion (43.42%), followed by antibiotics (22.52 %), cough suppressants (18.50%), antidiarrheal drugs (9.49%) and antipyretics (6.06 %). more than 80% data were reported into the system within 24 hours after the sales date, and the reporting timeliness of county pharmacies improved with time (table 1). missing report rate was less than 5% for all surveillance units. several reporting mistakes were found during the first three-month implementation, which might be due to system bugs, data provider unfamiliar with the system especially when manual reporting, data providers’ carelessness, and some pharmacies reluctant to share sales data amongst others. conclusions although the current reporting timeliness and completeness are satisfying, it is noteworthy the quality of data is not stable during the beginning phase of the implementation. further validation of the data will be required. to ensure the accuracy of data and the effective and sustainable deployment of the system, it is imperative to establish a data sharing policy between pharmacies and public health agencies, and achieve automated data collection to avoid additional human labor involvement. table 1: timeliness of reporting records from various pharmacies, apr. 1st jun. 30th, 2012 figure 1 user interface in the system for data viewing, query and export keywords syndromic surveillance; medication sales; developing settings acknowledgments the study is financially supported by a grant under the european union framework program 7 (project no: 241900). references 1. magruder s. evaluation of over the counter pharmaceutical sales as a possible early warning indicator of human disease. johns hopkins apl technical digest 2003;24(4):349-53. 2.das d, metzger k, heffernan r, balter s, weiss d, mostashari f. monitoring over-the-counter medication sales for early detection of disease outbreaks—new york city. mmwr 2005;54 suppl:41-6. 3. wen y, lieber e, wan d, hong y; nimh collaborative hiv/std prevention trial group. a qualitative study about self-medication in the community among market vendors in fuzhou, china. health soc care community. 2011;19(5):504-13. *weirong yan e-mail: weirongy@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e145, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts national collaborative for bio-preparedness meredith arasaratnam*1, 2, david potenziani1, 2, marc hoit3, 1, colleen jenkins4, 1 and charles cairns2, 1 1national collaborative for bio-preparedness, chapel hill, nc, usa; 2university of north carolina, chapel hill, nc, usa; 3north carolina state university, raleigh, nc, usa; 4sas institute, cary, nc, usa objective demonstrate the functionality of the national collaborative for bio-preparedness system. introduction the national collaborative for bio-preparedness (ncb-prepared) was established in 2010 to create a biosurveillance resource to enhance situational awareness and emergency preparedness. this jointinstitutional effort has drawn on expertise from the university of north carolinachapel hill, north carolina state university, and sas institute, leveraging north carolina’s role as a leader in syndromic surveillance, technology development and health data standards. as an unprecedented public/private alliance, they bring the flexibility of the private sector to support the public sector. the project has developed a functioning prototype system for multiple states that will be scaled and made more robust for national adoption. methods ncb-prepared recognizes that the capability of any biosurveillance system is a function of the data is analyzes. ncb-prepared is designed to provide information services that analyze and integrate national data across a variety of domains, such as human clinical, veterinary and physical data. in addition to this one-health approach to surveillance, a primary objective of ncb-prepared is to gather data that is closer in time to the event of interest. ncb-prepared has validated the usefulness of north carolina emergency medical services data for the purposes of biosurveillance of both acute outbreaks and seasonal epidemics (1). a unique model of user-driven valuing of data-providing value back to the provider in their terms-motivates collaboration from potential data providers, along with timely and complete data. ncbprepared approaches potential data providers, partners and users with the proposition that enhanced data quality and analysis is valuable to them individually and that an integrated information system can be valuable to all. with the onboarding of new data sources, ncb-prepared implements a formal process of data discovery and integration. the goal of this process is three-fold: 1) to develop recommendations to enhance data quality going forward, 2) to integrate information across data sources, and 3) to develop novel analytic techniques for detecting health threats. ncb-prepared is committed to both utilizing standard methods for event detection and to developing innovative analytics for biosurveillance such as the text analytics and proportional charts method (tap). the sophisticated analytic functionality of the system, including improved time to detection, query reporting, alert detection, forecasting and predictive modeling, can be attributed to collaboration between analysts from private industry, academia and public health. ncb-prepared followed the formal software development process known as agile development to create the user interface of the system. this method is based on iterative cycles wherein requirements evolve from regular sessions between user groups and developers. the result of agile development and collaborative relationships is a system which visualizes signals and diverse data sources across time and place while providing information services across all levels of users and stakeholders. conclusions lessons learned: 1. understand the functionality of new biosurveillance system, ncb-prepared 2. identify the benefit of creating collaborative relationships with data providers and users 3. appreciate the value of a public/private partnership for biosurveillance and bio-preparedness keywords biosurveillance; analytics; preparedness; emergency references cairns c., potenziani d., hoit m., jenkins c., edgemon s. novel approach to statewide biosurveillance using emergency medical services (ems) information. emerg health threats j 2011; 4, doi: 4: 10.3402/ehtj.v4i0.11183 (abstract). *meredith arasaratnam e-mail: arasarat@med.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e198, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts effectiveness of the 2011-12 influenza vaccine: data from us military dependents and us-mexico border civilians damaris padin*, anthony hawksworth, peter kammerer, erin mcdonough and gary brice operational infectious diseases, naval health research center, san diego, ca, usa objective to assess effectiveness of the influenza vaccine among us military dependents and us-mexico border populations during the 2011-12 influenza season. introduction as a result of antigenic drift of the influenza viruses, the composition of the influenza vaccine is updated yearly to match circulating strains. consequently, there is need to assess the effectiveness of the influenza vaccine (ve) on a yearly basis. ongoing febrile respiratory illness (fri) surveillance captures data and specimens that are leveraged to estimate influenza ve on an annual basis. methods data from ongoing fri surveillance at us military and us-mexico border clinics were used to estimate ve. we conducted a case– control study between weeks 3 and 17 of the 2011-12 influenza season. specimens were collected from individuals meeting fri case definition (fever ! 100.0 f with either cough or sore throat). cases were laboratory confirmed influenza infection and controls were negative for influenza. interviewer-administered questionnaires collected information on patient demographics and clinical factors and vaccination status. logistic regression was used to calculate the crude and adjusted odds ratios (or) and ve was computed as (1-or) x 100%. vaccine protection was assumed to begin 14 days post-vaccination. results a total of 155 influenza positive cases and 429 influenza negative controls were included in the analysis 72 cases were influenza a(h3n2), 38 cases were influenza a(h1n1), and 45 cases were influenza b. overall adjusted ve against laboratory-confirmed influenza was 46% (95% ci, 19–64%); unadjusted was 39% (95% ci, 11–58%). influenza subtype analyses revealed moderate protection against a/h3 and a/h1 and lower protection against b. lowest estimated ve was seen in older individuals, age 65 and older. conclusions influenza vaccination was moderately protective against laboratory confirmed influenza in this population. continued surveillance is important in monitoring the effectiveness of the influenza vaccine. keywords influenza; vaccine efficacy; influenza-like illness surveillance acknowledgments the authors gratefully acknowledge the work of on-site research assistants and nhrc laboratory staff who produced the data used in this analysis. we also thank global emerging infections system (geis) division of the armed forces health surveillance center (afhsc) for their support of fri surveillance. *damaris padin e-mail: damaris.padin@med.navy.mil online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e5, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluating a social network analytic tool to support outbreak management and contact tracing in an outbreak of pertussis esther munene*, s. mottice and j. reid utah department of health, slc, ut, usa objective to determine the feasibility and value of a social network analysis tool to support pertussis outbreak management and contact tracing in the state of utah. introduction pertussis (i.e., whooping cough) is on the rise in the us. to implement effective prevention and treatment strategies, it is critical to conduct timely contact tracing and evaluate people who may have come into contact with an infected person. we describe a collaborative effort between epidemiologists and public health informaticists at the utah department of health (udoh) to determine the feasibility and value of a network-analytic approach to pertussis outbreak management and contact tracing. methods the partnership: in early 2012, epidemiologists from udoh’s vaccine preventable disease program and udoh’s public health informaticists formed a partnership to determine the feasibility and value of the organizational risk analyzer (ora) in pertussis outbreak management and contact tracing (1). both entities have a longstanding partnership. a characteristic that has made the collaboration particularly strong and mutually beneficial is that both partners have expertise in disease surveillance and outbreak management. in addition, the informaticists have expertise in devising systems that help frontline healthcare providers. the organizational risk analyzer (ora): ora is a computational tool that extends network analysis by using a meta-matrix model. a meta-matrix is defined as a network of connecting entities. the tool uses one or more matrices in an organization’s meta-matrix as input. from this input the tool calculates measures that describe the relationships and ties among the entities. ora contains over 50 network and node level measures which are categorized by the type of risk they detect (1). procedures: following approval from udoh’s institutional review board, we analyzed records from 629 deidentified pertussis patients from the ut-nedss database from january 2011 to december 2011. the test data included demographics and epidemiological information. we used excel to create .csv data files, uploaded the data into ora, and displayed the data in meta-matrices consisting of nodes (cases/contacts) and edges (relationships). we used ora’s visualizer to check for data-entry errors before performing the network analysis. data analysis: ora’s centrality measures (degree, closeness, betweenness, hub, and eigenvector) were used to identify geographic locations with high infection rates and the patients who were central to sustaining the outbreak. next, we applied a concor algorithm to find groups in the meta-network that might be hard to spot visually. visualizations were used to supplement the metrics. results the ora analysis identified 5 individuals who were central to perpetuating the outbreak in that their centrality measures were higher than other patients in the network. the index patient (fig 1) was traced back to utah county and was linked to 6 direct contacts in the same county and several indirect ties in adjacent counties. the individual was highly connected to others within the network (hub centrality = 1.41 and eigenvector centrality = 1.00). salt lake county had the highest number of cases, followed by utah county and weber county. the concor analysis revealed hidden networks, including a cluster of patients grouped by age group and case status (fig 2). conclusions the ora was found to be a valuable tool for supporting pertussis outbreak management and contact tracing. although network analysis is relatively new to public health, it can increase public health’s understanding of how patterns of social relationships can aid or inhibit the spread of communicable diseases and provide the information needed to target intervention efforts effectively. fig. 1. the index case. fig. 2. concor cluster of patients, by age group and case status. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e72, 2013 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts keywords surveillance; informatics; social network analysis acknowledgments this study was supported in part by the cste applied public health informatics fellowship program and funded by the cdc. references 1. organizational risk analyzer: center for analysis of social and organizational systems 2008. *esther munene e-mail: emunene@utah.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e72, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts synergies between human and animal health syndromic surveillance: triple-s outputs céline dupuy*1, jean-baptiste perrin1, anne bronner1, didier calavas1, pascal hendrikx2 and anne fouillet3 1french agency for food, environmental and occupational health safety (anses), lyon, france; 2french agency for food, environmental and occupational health safety (anses), maisons-alfort, france; 3french institute for public health surveillance (invs), saint-maurice, france objective the objective of this study, based on the triple-s project outputs, was to present the existing synergies between human and animal health syndromic surveillance (sys) systems in europe and a proposal to enhance this kind of collaboration. introduction the triple-s project (syndromic surveillance systems in europe, www.syndromicsurveillance.eu), co-financed by the european commission and involving twenty four organizations from fourteen countries was launched in september 2010 with the following objectives 1) performing an inventory of existing or planned sys systems in europe both in animal and public health, 2) building a network of experts involved in sys 3) producing guidelines to implement sys systems, 4) developing synergies between human and animal health sys systems. the project is based on a cooperation between human and animal health experts, as supported by the one health initiative [1]. methods a network of european experts involved in sys was identified through the triple-s inventory of sys systems. a meeting of human health experts was organized back to back with a similar meeting with animal health experts in paris, september 12-14, 2011. a joint session human/animal health allowed experts to discuss the interest of synergies between both sides. the objectives were to 1) encourage experience and knowledge transfer, 2) discuss what and how information should be shared between both sides to improve respective performances. results the results of the inventory of veterinary sys systems showed that 40% of identified systems already shared or had planned to share information with human health sector. for these systems the collaboration between human and animal health sectors consisted in meetings on a regular basis to discuss the surveillance results. discussions during the triple-s meeting highlighted two reasons for enhancing synergies between both sides. first human health and animal health epidemiologists face common statistical and epidemiological issues when dealing with sys, i.e. use of data collected for other purpose than surveillance; standardization of clinical observations; syndrome definition; anomaly detection; interpretation of unspecific signals; response to alerts. both sides have thus interest in sharing their experiences and knowledge to improve their respective systems. second, systems on both sides have similar objectives and target health events potentially threatening both animal and human populations: zoonoses, extreme weather events, environmental / food contamination, bioterrorist attack... for those events, animal population can play the role of sentinel for human population. regular information flow between human and animal sys could thus enhance the timeliness and sensitivity of sys systems for detecting unexpected health events. moreover, sharing information could help animal and human health experts to interpret and confirm unspecific signals, and confirm the impact of common health threats. all participants of the meeting agreed on the idea to routinely share outputs of the systems but were sceptical about sharing raw data to perform global analysis. conclusions each aspect of the triple-s project includes both human and animal health and will thus contribute to build natural collaboration between both sides. such a project has demonstrated that scientific community is more and more willing to collaborate beyond the boundaries of these two health fields. synergies between human and animal health seem as necessary for syndromic surveillance as it is for traditional surveillance, if not more. they seem especially important for the detection of emerging zoonotic threats but not only. sharing surveillance outputs from both sides would be the first step of collaboration but deeper synergy, e.g. sharing data and analyse them globally, could also be considered. triple-s guidelines for implementation of sys systems in europe will take into account and promote synergies between human and animal health. keywords syndromic surveillance; synergy; early warning acknowledgments the authors thank all participants to the triple-s project activities. references 1.zinsstag, j., et al., potential of cooperation between human and animal health to strengthen health systems. the lancet, 2005. 366(9503): p. 2142-2145. *céline dupuy e-mail: celine.dupuy@anses.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e158, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of integrated disease surveillance and response (idsr) using bacillary dysentery as a priority disease, tanzania, 2012 alfred g. mwanyika*1, senga sembuche1 and agricola joachim2 1tanzania field epidemiology and laboratory training program (tfeltp), dar es salaam, united republic of tanzania; 2muhimbili university of health and allied sciences (muhas), dar es salaam, united republic of tanzania objective to determine whether the idsr system meets its purpose and objectives, to evaluate the system attributes, and provide recommendations to improve the idsr system, using the example of bacillary dysentery, a priority disease in tanzania. introduction each year ministry of health and social welfare of tanzania under epidemiology section has been reporting many suspected cases of shigella throughout the country. however only fewer laboratories have been reporting the confirmed cases. methods the study was conducted between november 2011and february 2012.hospital staff including nurses, physicians and laboratory personnel and other stakeholders from the ministry of health and social welfare (mohsw) were enrolled in the study. data was collected from a review of documents, questionnaires and interview of stakeholders. surveillance system attributes were evaluated using updated guidelines for evaluating public health surveillance system (2007) from morbidity and mortality weekly report (mmwr). results questionnaires were administered to fifteen health personnel from four regional hospitals. four health staff from epidemiology and laboratory section of mohsw were interviewed. only one regional hospital laboratory was conducting laboratory diagnosis for bacillary dysentery and sending reports to mohsw. data from this laboratory was reviewed. out of 641 records from bacillary dysentery testing, 271 (42.3%) did not include age data, 5 (0.78%) missed sex, 624 (97.3%) missed the district where the patient came from, 26 (4.4%) did not include information on specimen quality, 1(0.2%) had no report of pathogens found and 636 (99.2%) did not include antimicrobial sensitivity testing (ast). the predictive value positive (pvp) of the system was 0.62%. one (6.7%) of the health workers was trained in idsr. conclusions idsr in tanzania generally is not performing well as only one (25%) of the four visited hospitals conducts and reports laboratory diagnosis of bacillary dysentery. however the system is representative as it covers all regions of the united republic of tanzania and all ages of people. the system is flexible since national idsr guideline (2001) was revised in 2011. more emphasis should be placed on strengthening laboratory capacity in disease diagnosis and reporting at all levels. keywords surveillance; evaluation; idsr; bacillary dysentery acknowledgments we acknowledge tfeltp and regional hospitals for their corporations to achieve this study. references monthly diseases reports. ministry of health and social welfare of tanzania. www.moh.go.tz *alfred g. mwanyika e-mail: geofalfred@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e132, 2013 immunization registries in the emr era 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi immunization registries in the emr era lindsay a. stevens 1,2 , jonathan p. palma 1,2 , kiran k. pandher 3 , christopher a. longhurst 1,2 1 department of pediatrics, stanford university school of medicine, stanford, california, 2 department of clinical informatics, lucile packard children's hospital, palo alto, california, 3 department of information services, lucile packard children's hospital, menlo park, california abstract background: the cdc established a national objective to create population-based tracking of immunizations through regional and statewide registries nearly 2 decades ago, and these registries have increased coverage rates and reduced duplicate immunizations. with increased adoption of commercial electronic medical records (emr), some institutions have used unidirectional links to send immunization data to designated registries. however, access to these registries within a vendor emr has not been previously reported. purpose: to develop a visually integrated interface between an emr and a statewide immunization registry at a previously non-reporting hospital, and to assess subsequent changes in provider use and satisfaction. methods: a group of healthcare providers were surveyed before and after implementation of the new interface. the surveys addressed access of the california immunization registry (cair), and satisfaction with the availability of immunization information. information technology (it) teams developed a “smart-link” within the electronic patient chart that provides a single-click interface for visual integration of data within the cair database. results: use of the tool has increased in the months since its initiation, and over 20,000 new immunizations have been exported successfully to cair since the hospital began sharing data with the registry. survey data suggest that providers find this tool improves workflow and overall satisfaction with availability of immunization data. (p=0.009). conclusions: visual integration of external registries into a vendor emr system is feasible and improves provider satisfaction and registry reporting. key words: electronic medical records, immunization registries, emr integration, hitech, meaningful use abbreviations: california immunization registry (cair), computerized physician order entry (cpoe), electronic medical record (emr), health information technology for economic and clinical health act (hitech), health level 7 (hl7), immunization information system (iis), information technology (it), lucile packard children's hospital (lpch) immunization registries in the emr era 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi introduction since their inception nearly 20 years ago, immunization registries have been shown to increase vaccine coverage rates and decrease duplicate immunizations. 1-3 the cdc provides funding for immunization information system (iis) programs in all 50 states. 4 as part of the health information technology for economic and clinical health act (hitech) of 2009, providers with electronic medical records (emrs) are encouraged to submit electronic immunization data to iiss to achieve meaningful use. 4-6 many institutions have established unidirectional interoperability by which their emr immunization records are uploaded to a designated registry—thereby preventing the need for staff to manually update each patient chart in the registry. 5-1 bidirectional interoperability is preferable as it would allow accurate immunization data from the iis to be sent directly to the hospital’s or practice’s emr, however integration is expensive and difficult to implement. 11 visual integration into the emr with context-sensitive access can meet clinician needs without the expense of bidirectional data integration. lucile packard children's hospital (lpch) has a strong history of developing informaticsenabled innovations. 12-16 lpch had not, however, previously participated in the statewide immunization registry, the california immunization registry (cair), largely due to concerns for duplication of efforts by clinical staff who would need to manually enter the same data in both the vendor emr and the iis. the previous system was paper-based and involved scanning copies of the patient records in to the emr. no immunization information was tracked directly in our emr. this information was not complete and only contained data for those immunizations recorded at lpch. the few published reports on bidirectional registries involved health level 7 (hl7) web service, which have not been widely replicated given that the technical work involved is far greater and difficult to sustain. 17, 18 decision support services in the emr are much more limited as the compared to those native in the registries. our team’s goal was to develop a visually integrated interface by which clinical staff could quickly and easily access the registry to evaluate a patient’s immunization history from within our emr. it was hypothesized that initiating the association between lpch and cair would add to the number of patients included in the iis, increase provider utilization of this tool, and improve provider satisfaction and perceived efficiency. correspondence: lindsay.stevens@stanford.edu copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. immunization registries in the emr era 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi methods integration creation of the visually integrated registry interface within the vendor emr required two different efforts. the first was to upload hospital immunization data to the iis, transferring nightly any new immunizations recorded at lpch using hl7 code. historical data, including all immunizations previously ordered using computerized physician order entry (cpoe) from, were also uploaded. the second aspect was to create a “smart link” – a web-based icon in the “patient summary” area of the chart that directs providers to the cair registry site. (figure 1). clicking this link sends patient identifiers to the cair database, using an institutional login to access the patient’s cair chart. the cair interface includes both immunizations from lpch data uploads and any others added to the database by outside institutions, as well as its native decision support tool. (figure 2). the new interface was implemented at the end of the 2011 calendar year. figure 1: screen shot of cair link in patient chart immunization registries in the emr era 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 2: screen shot of the patient’s immunization record that is accessed when the link is clicked. survey healthcare providers at lpch were electronically surveyed several months prior to the rollout of the tool with regards to their satisfaction with the prior paper system and their use of the statewide registry (n=41). all lpch residents, general pediatrics attending physicians, primary care clinic nurses and nursing assistants were invited to participate. the providers were again asked to participate in a follow-up survey 4 months after the rollout (n=41). a brief, 7-item survey, including multiple-choice and free-response questions, was developed to assess the provider’s use of cair, perceived impact on their workflow, and satisfaction with the hospital’s immunization recording systems (appendix). r for mac (the r foundation for statistical computing, vienna, austria) was used to perform fisher’s exact test for categorical variables, and the wilcoxon rank sum test to compare the distribution of satisfaction ratings. this project and study were done as part of quality improvement measures and thus irb approval was not required. results figure 3 exemplifies that use of this tool has increased incrementally during the first 4 months following implementation. data from lpch was uploaded to the cair database starting in december 2011—sharing all previously ordered immunizations and any immunizations ordered immunization registries in the emr era 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi using cpoe after that date. over 20,000 new immunization records—both historic and recent— were successfully inserted into the cair database since initiation. forty-one independent providers responded to each of the preand post-surveys as seen in table 1. there was a slight statistical difference of the demographic distribution, as more attending physicians participated in the post-survey. responders indicated increased usage of the registry following interface implementation (p = < 0.001). of the providers surveyed, the majority had never accessed cair prior to the availability of the link. afterwards, the number of providers who had accessed cair significantly increased (p = < 0.001). although, the majority of our respondents were physicians, the primary care clinic nursing staff reported the most frequent use of the tool. overall provider satisfaction increased slightly (p = 0.009), however, the majority of providers (56%) perceived that the smart link improved their efficiency (figure 4). 68% of the surveyed providers felt that the smart link increased the likelihood that their patients’ immunization record was up to date (figure 5). table 1. respondents of preand post-intervention surveys immunization registries in the emr era 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3: usage of cair link since introduction figures 4: post-intervention survey responses 0 50 100 150 200 250 300 350 400 jan '12 feb '12 mar '12 apr '12 usage of cair link since introduction # click throughs the new emr interface makes it more likely that my patients' immunization information is up to date. agree or strongly agree neutral disagree or strongly disagree immunization registries in the emr era 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figures 5: post-intervention survey responses. discussion few published examples of bidirectional interfaces between emrs and immunization registries exist, and to the authors’ knowledge, none about visual integration within a vendor emr. 17, 18 the hitech act lays out objectives in order to promote emrs and their meaningful use in 3 successive stages. in order to receive economic incentives, each stage of criteria must be met, including those related to electronic data sharing with iis. 5, 6, 11, 19 while unidirectional interoperability is all that is required under hitech, visual integration of the registry is a significant enhancement because institutional immunization data is not only shared with the registry, individual patient data is easily accessible by providers within their workflow. 11 these data suggest that use of cair among providers at lpch has increased since the introduction of the interface to the emr and a significant increase in the link utilization in the months since implementation is shown. an iis is only as robust as the information it receives and the amount it is utilized. 20 the cdc hopes to have >95% of children under 6 included in an iis by the year 2020, 21 a goal that will require participation of many additional institutions. given the parallel federal incentives to implement emr systems, many institutions are similarly hesitant to participate in an iis due to concerns about extra work for staff. 22, 23 the interface implemented at lpch is a web-based link, which preserves the sanctity of the registry database while improving provider workflow. system maintenance is relatively minimal once the algorithms are established. this link was established in a vendor-based system, which could be utilized by many other institutions. the only previously reported of bidirectional interface in nyc was with two smaller emrs, both internally developed. 17, 18 providers at lpch indicated that having the link increased the likelihood that their patients’ immunization information was up to date, as well as their overall efficiency. having my patients' immunization information accessible through the smart link has improved my efficiency agree or strongly agree neutral disagree or strongly disagree immunization registries in the emr era 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi although this study has a relatively small sample size, the authors feel that the responses reflect the general consensus of primary care providers at lpch. despite the slight difference in the demographics of the preand postsurvey groups, it is unlikely that these differences bias the main outcome measures of this study. another limitation is that the outpatient clinics at lpch have not yet started utilizing cpoe to order immunizations, so the data shared with the iis is currently limited to hospitalized inpatients, including newborns in the well baby nursery and obstetric patients. however, given that the link allows access to the entire registry’s data set and not just patients who have had immunizations documented at lpch, its integrated nature makes it useful for providers (both inpatient and outpatient), who are able to examine records from patients’ seen at other area clinics. conclusions the successful implementation of a unidirectional interface between a commercial emr and the california state immunization registry with visually integrated universal access has resulted in significant enhancement in the comprehensive nature of patient immunization records at this hospital. it is the authors’ hope that this example will serve to inspire other institutions with vendor-based emr systems to implement similar interfaces for the good of all communities and patients. acknowledgements the authors would like to acknowledge joshua faulkenberry, pragati kamath, and nestor llerena at lpch, as well as eric dansby, sarah kang, and jagadesh talluri at cair, for their technical expertise and assistance with this project. christopher stave’s assistance with the literature search is also greatly appreciated, as is lisa chamberlain’s inspiration and advocacy leadership. funding: none financial disclosure: the authors have no financial relationships relevant to this article to disclose. conflict of interest: the authors have no conflicts of interest relevant to this article to disclose. author contribution statements: dr. stevens helped conceptualize the project, administered the surveys, analyzed the data, drafted the manuscript, and approved the final version for submission. dr. palma contributed to the statistical analysis, reviewed the manuscript, and approved the submission version. ms. pandher helped design and worked with the technical development of the system, as well as aided in data acquisition and final approval of the manuscript. immunization registries in the emr era 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi dr. longhurst contributed substantially to the conception, design, acquisition of data, analysis, and interpretation of data; revising the article for important intellectual content; and final approval of the published version. corresponding author lindsay stevens, md department of pediatrics 770 welch road, suite 100 palo alto, ca 94304 lindsay.stevens@stanford.edu 650-497-8000 references [1] abramson j. s., o'shea t. m., ratledge d. l., lawless m. r., givner l. b. development of a vaccine tracking system to improve the rate of age-appropriate primary immunization in children of lower socioeconomic status. j pediatr. 1995;126(4):583-586. [2] linkins r. w. immunization registries: progress and challenges in reaching the 2010 national objective. j public health manag pract. 2001;7(6):67-74. [3] placzek h., madoff l. c. the use of immunization registry-based data in vaccine effectiveness studies. vaccine. 2011;29(3):399-411. [4] weinberg st. immunization registries: where we’ve been and where we’re headed. aap news. 2010;31(12):28. [5] meaningful use and immunization information systems. in. [6] meaningful use of electronic health records ehrs. in: american academy of pediatrics. [7] mahon b. e., shea k. m., dougherty n. n., loughlin a. m. implications for registry-based vaccine effectiveness studies from an evaluation of an immunization registry: a crosssectional study. bmc public health. 2008;8(160):160. [8] hinman a. r., ross d. a. immunization registries can be building blocks for national health information systems. health aff (millwood).29(4):676-682. [9] centers for disease control and prevention immunization registry support branch, national immunization program, centers for disease control and prevention. national immunization program. implementation guide for immunization data transactions using version 2.3.1 of the health level seven (hl7) standard protocol, version 2.2. in; 2006. [10] spooner s. a. special requirements of electronic health record systems in pediatrics. pediatrics. 2007;119(3):631-637. [11] dombkowski k. j., clark s. j. redefining meaningful use: achieving interoperability with immunization registries. am j prev med. 2012;42(4):e33-35. [12] palma jp vaneaton eg, longhurst ca. neonatal informatics: information technology to support handoffs in neonatal care. neoreviews. 2011;12(10):e560-e563. [13] bernstein j imler d, sharek p, longhurst c. improved physician workflow and satisfaction after integration of sign-out notes into the emr. joint commission journal of quality and patient safety. 2010. [14] frankovich j longhurst ca, sutherland sm. evidence-based medicine in the emr era. n engl j med. 2011;365(19):1758-1759. immunization registries in the emr era 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [15] longhurst ca parast l, sandborg ci, widen e, sullivan j, hahn js, dawes cg, sharek pj. decrease in hospital-wide mortality associated with implementation of a comprehensive electronic medical record. pediatrics. 2010;126(1):14-21. [16] adams es longhurst ca, pageler n, widen e, franzon d, cornfield dn. computerized physician order entry with decision support decreases blood transfusions in hospitalized children. pediatrics. 2011;127(5):1112-1119. [17] arzt nh. service-oriented architecture in public health: interoperability case studies. journal of healthcare information management. 2010;24(2):45-52. [18] arzt nh forney k, chi a, suralik m, schaeffer p, aponte a. meaningful use and public health: an immunization information system case study. journal of healthcare information management. 2011;25(4):37-44. [19] blumenthal d. launching hitech. n engl j med. 2009;362(5):382-385. [20] davidson a. j., melinkovich p., beaty b. l., chandramouli v., hambidge s. j., phibbs s. l., et al. immunization registry accuracy: improvement with progressive clinical application. am j prev med. 2003;24(3):276-280. [21] progress in immunization information systems united states, 2010. mmwr morb mortal wkly rep. 2012;61(25):464-467. [22] kairys s. w., gubernick r. s., millican a., adams w. g. using a registry to improve immunization delivery. pediatr ann. 2006;35(7):500-506. [23] saville a. w., albright k., nowels c., barnard j., daley m. f., stokley s., et al. getting under the hood: exploring issues that affect provider-based recall using an immunization information system. acad pediatr. 2011;11(1):44-49. immunization registries in the emr era 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi appendix – survey questions pre-survey 1. what is your current position? 2. how often do you need to access immunization information about a patient? 3. have you ever accessed the california immunization registry? the questions below used the likert scale strongly disagree to strongly agree (1-5): 4. i am satisfied with the current system of recording immunizations. 5. i feel that integrating the centralized immunization record into our electronic medical record will improve my workflow. 6. using a centralized resource for immunization records will improve patient care. 7. do you have any additional comments? post-survey 1. what is your current position? 2. how often do you need to access immunization information about a patient? 3. did you ever access the california immunization registry before the link in cerner was available? 4. have you ever accessed the california immunization registry using the link in cerner? 5. if so, how many times have you accessed it in the last 2 weeks? 6. how strongly do you agree or disagree with the following statements?  i am satisfied with the current system of recording immunizations.  having my patients' immunization information accessible through the smart link has improved my efficiency.  the new emr interface makes it more likely that my patients' immunization information is up to date. 7. do you have any additional comments? layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts tau-leaped particle learning jarad niemi*1 and michael ludkovski2 1iowa state university, ames, ia, usa; 2university of california, santa barbara, santa barbara, ca, usa objective develop fast sequential bayesian inference for disease outbreak counts. introduction development of effective policy interventions to stem disease outbreaks requires knowledge of the current state of affairs, e.g. how many individuals are currently infected, a strain’s virulence, etc, as well as our uncertainty of these values. a bayesian inferential approach provides this information, but at a computational expense. we develop a sequential bayesian approach based on an epidemiological compartment model and noisy count observations of the transitions between compartments. methods for simplicity, consider an sir epidemiological compartment model where compartments exist for susceptible, infected, and recovered individuals. transitions between compartments occur in discrete time with transitions numbers given by poisson random variables, the tau-leaping approximation, whose means depend on the current compartment occupancy and some unknown fixed parameters, e.g. virulence. binomial observations, with possible unknown sampling proportion, are made on these transitions. the standard sequential bayesian updating methodology is sequential monte carlo (smc), a.k.a. particle filtering. the original bootstrap filter is effective when the system has no fixed parameters, but exhibits marked degeneracy otherwise [1]. an approach based on resampling the fixed parameters from a kernel density estimate provides a generic approach with less degeneracy [2]. we build methodology based on a particle learning framework [3]. in this framework, each particle carries a set of parameter-specific sufficient statistics and samples parameter values whenever necessary. in addition, the methodology promotes a resample-move approach based on the predictive likelihood that reduces degeneracy in the first place. an improvement on the particle learning framework in this model is that some fixed parameters can be integrated out of the predictive likelihood. this rao-blackwellization provides an smc methodology with reduced monte carlo variance. results for a fixed number of particles or computational expense, we show improvements in accuracy relative to the kernel density approach and an alternative approach based on sufficient statistics [4] where compared with a gold-standard markov chain monte carlo analysis. conclusions many surveillance systems collect counts of adverse events related to some disease. these counts are expected to be a fraction of the true underlying disease extent. the methodology developed here allows a fully bayesian analysis that uncovers the true number of infected individuals as well as disease virulence based on these count data. this statistical approach can be combined with an optimal policy map to help public health officials react effectively to initial disease reports. keywords surveillance; bayesian; sequential monte carlo; particle learning references [1] gordon, salmond, and smith. novel approach to nonlinear/nongaussian bayesian state estimation. iee proceedings part f: communications, radar and signal processing. 140(2): 107-113 (1993). [2] liu and west. combined parameter and state estimation in simulation-based filtering. doucet, de freitas, and gordon, ed. sequential monte carlo methods in practice. springer-verlag, new york. 197— 217 (2001). [3] carvalho, johannes, lopes, and polson. particle learning and smoothing. statistical science. 25(1): 88—106 (2010). [4] storvik. particle filters in state space models with the presence of unknown static parameters. ieee transactions on signal processing. 50(2): 281—289 (2002). *jarad niemi e-mail: niemi@iastate.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e8, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts malaria trends in six outpatient sites in uganda, 2008— 2011 ruth k. nassali*1, arthur mpimbaza1, stella kakeeto1, asadu sserwanga1, fred kizito1, denis rubahika2, melody miles3, michelle chang3, grant dorsey4 and moses kamya1 1infectious diseases research collaboration, kampala, uganda; 2national malaria control program, kampala, uganda; 3centers for disease control, atlanta, ga, usa; 4univeristy of california, san francisco, san francisco, ca, usa objective to estimate trends in malaria morbidity at six sentinel sites in uganda. introduction over the past five years, efforts to control malaria have been intensified in uganda (1). with the intensification of these efforts, accurate and timely data are needed to monitor impact of the interventions and guide malaria control program planning (2, 3). we present data on trends in malaria burden over four years from six outpatient health facilities located in regions of varying malaria endemicity in uganda. methods the study utilized data from the on-going malaria sentinel surveillance program involving six level iv outpatient health facilities: aduku, nagongera, walukuba, kasambya, kihihi and kamwezi. major malaria control interventions between 2008 and 2010 in subcounties where these sites are located included indoor residual spraying (irs) conducted in aduku; insecticide-treated nets (itns) distributed in nagongera and kamwezi. there has been no major control intervention(s) in sub-counties where walukuba, kasambya and kihihi are located. treatment with artemisinin-combination therapies have however been deployed nationally. patient information; demographics, malaria test results and diagnosis are recorded on a standardized patient record. the test positivity rate (tpr) defined as the number of persons testing positive for malaria divided by the total number of persons tested was calculated by year from 2008 to 2011 for two age categories (< 5 years and > 5 years ). results a total of 560,586 patients were seen, of which 25% were <5 years. over 325,500 patients were suspected to have malaria, with the proportion of these having a confirmatory test done increasing from 62% in 2008 to 98% in 2011. between 2008 and 2011, the proportion of the <5 years testing positive for malaria significantly decreased from 66% to 34% in aduku, from 61% to 41% in nagongera, and from 54% to 24% in kamwezi. however, significant increases were seen in kasambya and kihihi from 41% to 51% and from 28% to 44% respectively. the tpr at walukuba remained stable (41% to 45%). similar trends were seen in the > 5 years. conclusions sentinel site surveillance has been a reliable and timely method/tool for monitoring trends in malaria morbidity thereby informing and guiding the uganda malaria control program. keywords surveillance; malaria; trends acknowledgments acknowledgements to nmcp team/moh, cdc/pmi, umsp team, health facility staff. references 1) yeka a, gasasira a, mpimbaza a, achan j, et al. malaria in uganda: challenges to control on the long road to elimination: i. epidemiology and current control efforts. acta trop. 2012 mar;121(3):184-95. epub 2011 mar 21 2) breman jg, holloway cn. malaria surveillance counts. am j trop med hyg 2007;77:36-47 3) bryce j, roungou jb, nguyen-dinh p, naimoli jf and breman jg. evaluation of national malaria control programmes in africa. bull world health organ 1994;72:371-81 *ruth k. nassali e-mail: ruth.nassali@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e81, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts disease mapping with spatially uncertain data justin manjourides*1, ted cohen2, 3, caroline jeffery4 and marcello pagano5 1dept of health sciences, northeastern university, boston, ma, usa; 2div of global health equity, brigham & women’s hospital, boston, ma, usa; 3dept of epidemiology, harvard school of public health, boston, ma, usa; 4intl health group, liverpool school of tropical medicine, liverpool, united kingdom; 5dept of biostatistics, harvard school of public health, boston, ma, usa objective uncertainty regarding the location of disease acquisition, as well as selective identification of cases, may bias maps of risk. we propose an extension to a distance-based mapping method (dbm) that incorporates weighted locations to adjust for these biases. we demonstrate this method by mapping potential drug-resistant tuberculosis (drtb) transmission hotspots using programmatic data collected in lima, peru. introduction uncertainty introduced by the selective identification of cases must be recognized and corrected for in order to accurately map the distribution of risk. consider the problem of identifying geographic areas with increased risk of drtb. most countries with a high tb burden only offer drug sensitivity testing (dst) to those cases at highest risk for drug-resistance. as a result, the spatial distribution of confirmed drtb cases under-represents the actual number of drug-resistant cases[1]. also, using the locations of confirmed drtb cases to identify regions of increased risk of drug-resistance may bias results towards areas of increased testing. since testing is neither done on all incident cases nor on a representative sample of cases, current mapping methods do not allow standard inference from programmatic data about potential locations of drtb transmission. methods we extend a dbm method [2] to adjust for this uncertainty. to map the spatial variation of the risk of a disease, such as drtb, in a setting where the available data consist of a non-random sample of cases and controls, we weight each address in our study by the probability that the individual at that address is a case (or would test positive for drtb in this setting). once all locations are assigned weights, a prespecified number of these locations (from previously published country-wide surveillance estimates) will be sampled, based on these weights, defining our cases. we assign these sampled cases to drtb status, calculate our dbm, repeat this random selection and create a consensus map[3]. results following [2], we select reassignment weights by the inverse probability of each untested case receiving dst at their given location. these weights preferentially reassign untested cases located in regions of reduced testing, reflecting an assumption that in areas where testing is common, individuals most at risk are tested. fig. 1 shows two risk maps created by this weighted dbm, one on the unadjusted data (fig.1, l) and one using the informative weights (fig. 1, r). this figure shows the difference, and potentially the improvement, made when information related to the missingness mechanism, which introduces spatial uncertainty, is incorporated into the analysis. conclusions the weighted dbm has the potential to analyze spatial data more accurately, when there is uncertainty regarding the locations of cases. using a weighted dbm in combination with programmatic data from a high tb incidence community, we are able to make use of routine data in which a non-random sample of drug resistant cases are detected to estimate the true underlying burden of disease. (l) unweighted dbm of risk of a new tb case that received dst being positive for drtb, compared to all new tb cases that received dst. (r) weighted dbm of the risk of a new tb case that received dst being positive for drtb, based on lab-confirmed drtb cases and ipw selected nondst tb cases, compared to all new tb cases. keywords surveillance; multiple addresses; distance based references [1] h lin, et al. assessing spatiotemporal patterns of multidrug-resistant and drug-sensitive tuberculosis in a south american setting. epi infect, 2010. [2] c jeffery. disease mapping and statistical issues in public health surveillance. phd thesis, harvard university, 2010. [3] j manjourides, et al. identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. tuberculosis, 92(3), 2012. *justin manjourides e-mail: justin.manjourides@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e18, 2013 ojphi-06-e18.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 180 (page number not for citation purposes) isds 2013 conference abstracts autoregressive integrated moving average (arima) modeling of time series of local telephone triage data for syndromic surveillance micael widerström*1, 2, maria omberg1, martin ferm3, ann-katrine pettersson4, malin rundvik eriksson1, ingela eckerdal4 and johan wiström5, 6 1department of communicable disease control and prevention, jämtland county council, östersund, sweden; 2department of clinical microbiology, unit of clinical research centeröstersund, umeå university, umeå, sweden; 3centre of registers in northern sweden, umeå university, umeå, sweden; 4swedish health care direct 1177, jämtland county council, östersund, sweden; 5department of clinical microbiology, infectious diseases, umeå university, umeå, sweden; 6department of communicable disease control and prevention, västerbotten county council, umeå, sweden � �� �� �� � � �� �� �� � objective �������� � ����� �� � ���� ������ ���������� � ��������� ������ ����������� � �� ��������� ��� ������ ����������� � ������������� �� �� �������� ��� ��� �� ���� ���������������� � ����� ����� ��!"!�� �������� ����� � ���������� � ��������������������# introduction $��������� � ���� �� ���������� � ���� ��������� �� ����� ��� ��� ��� ��� ���� ����� ��������� ��� ������ ��������� � ����� ����� � ������ ��� �� ���� ��%�&�#�'����������������� � �������� ��� (��� � � � � �������� �� �� ���� ���������� ���� � ����������#�) ���� ��� ��� �� ��������������� � ���� ������ � ���� ������������� ��� ��� �� ���� � � � �� �������� ��*� �� �� � ���� ��� ���� ������ ����������� �� ��������������� ��� ������������� � ������� � �� ��������������� � ����� �� ������ ������ ���������� ����+ � ����������� �� �� �,--./ ,-%%��0�#�1� � �(�������������� ������ ��� ���������� �� ���� (� ������� �� ��� (����� ���������� ��� ����������� � ���� ������ ���������� ����������������� �������������� �� ������ ������������ � �# methods ) �������� ��� ��������� � ���� ������ �������+ �� ��1 ����� 2�� �3�� ���%%..������ �� �������$�� �� ��,--.����3 � �� ��,-%,� ��� ����������������� ����45�������2�����(�+ � �(� � ������ ����� � ���������� �� �� ���� ���� ����������� ��� ���6!7������ �� ���������� � �� ������ �� ��������� ������ ������8������ �9���� 8��� �������������9������!""��8� � �9�����8�����9�#�3��������� � � � ���� �� ������ ��� � ��� �� �6����������� ����� ������� ������� ���� ����������������������� ���� �� �������� �� � ����&#-#,-%&�-:� %,������;<< #�����= ��#����#�+ � ������������ ������� � � � � � � ��� ��������� > �!�����������2��� �������!2�������� ���� �� � �������� � � � ������ ��� ����� ��� *��� �� ������7+��#�?�� �� ����� ������� ����������� ���� � � � �����@-a�����@:a(�� � ���� ��(� ���� � �� ������ � ��� � ����� #�?�� ������� ������� ����������� �� ��� � � � �����@-a�����@:a(�� � ���� ��(����� � �� ������ � ��� � ����� # results b� � � ������6!7����� �� �� �������� �������� ������ ����� ��������� ����� ������������!"!����� � ���� ����� � �� �� ������� ������� � ��� ������ �������������!"!����.����c���������� �������� �� �,-%,/,-%&������ � ���������������� � � ��# conclusions ���� � ������6!7����� ��� ����������� � ���� ������ ������� � � �� ����������!"!������ ��� ����� ������ ���=����������� ���� ��� ��� ����������� #�b�� ���� �� ����� ���������� ����� �������������� ����� ���� ����������� ������ � � � � ��� ���� ���� !"!� ��� ���� � ����� � � �������������� �� #�b�� ������������������� ���������� � �������� ����� � ���� �� � �� ����� ������� ����� ����� ������� �������# keywords +��������� ��� ������ d������ ���� � �����d�3��������� � d�3� � � ����� ��d�'������1 �����+��� ������ acknowledgments b�� � ���� � � ���� �� ��� �� � + �� �� 2����� 2������ ��� � �� ���� �7+e�# references %#�3��� � �����(�2��� ��3(�+������(�� ������(�2��� �����f(�g ����� �� $(� �� ��#� ����������� ��� ��������� ��� ������ � �� �� ��� $�������� 1 �����+ ���� �3�� ���� ��� ������h������������)�� #�77)6� 7����7������)����6 �#�,--:�����,id:0�+����;%%.�,,# ,#�+�����+(���������4(�7���������2(�7�����3(�1���� � ��2�j�4(�"��� � +(� ����#�g��� ���� ��������� ��� ������ ����������������������� �� � ����� ������ ����� ���������� ����2����� ���������� ����� ���� $���������� ��� (� ?��� �� k������(� 4�� � �� 4���� ,--c#� ����� +��� � ���#%:�&&�;%@i0&# &#�l���)k(�b �� �k+(������ ��(�k� ������k(�k���������7(�'��� ��6(� �� ��#�b � ���� ������ � ���� ����������� � �������������� � ����� ����� #� '"�+��� #�,--@d0�0�; :,i-# 0#���� � ���b(�e= ������'(�1������(�"�����4(�+� ������+(�)�� � ����� 7#�+��������� ��� ������ ���������������� ���� � ����������� �� � � ;� ��������������� ��� ����� �������� ��� ��� �� ���� ����� � ���� � ����� (� ���� ��*� �� ������� ���� ������ ����������� �� #����� � �����!�� ��#�,-%&�7���%:;%�%%# *micael widerström e-mail: micael.widerstrom@jll.se� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e18, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts open source health intelligence (oshint) for foodborne illness event characterization catherine ordun, jane w. blake*, nathanael rosidi, vahan grigoryan, christopher reffett, sadia aslam, anastasia gentilcore, marek cyran, matthew shelton and juergen klenk booz allen hamilton, mclean, va, usa objective we propose a cloud-based open source health intelligence (oshint) system that uses open source media outlets, such as twitter and rss feeds, to automatically characterize foodborne illness events in real-time. oshint also forecasts response requirements, through predictive models, to allow more efficient use of resources, personnel, and countermeasures in biological event response. introduction an increasing amount of global discourse reporting has migrated to the online space, in the form of publicly accessible social media outlets, blogs, wikis, and news feeds. social media also presents publicly available and highly accessible information about individual, real-time activity that can be leveraged to detect, monitor, and more efficiently respond to biological events. methods salmonella and escherichia coli (e. coli) events were selected based on the magnitude and number of reported outbreaks to the centers for disease control (cdc) in the last ten years (1). these events affect multiple states and were large enough to ensure appropriate confidence levels when developing response metrics obtained from our prediction models. we collected social media data between 2006 – 2012 due to the emergence of twitter, facebook, and other social media utilization during this time period. characterization is defined as the process of identifying specific event features that inform overall situational awareness. the number hospitalized, dead, or injured, in addition to patient demographics and symptoms were determined to be useful for our characterization and forecast event metrics. analytical methods, such as term-frequency-inverse document frequency (tf-idf), natural language processing (nlp), and information extraction, were used to characterize events according to our metrics. lexicon development, during nlp implementation, was generated from online news articles used to describe the events. lastly, forecasting algorithms were developed to predict the potential response based on similar historical events that were initially characterized by our information extraction algorithms. results the oshint system was developed in amazon web services and includes real-time social media collection for event characterization (see figure 1). oshint currently characterizes number of victims ill, hospitalized, and dead due to foodborne illness events. oshint was used to characterize the recent national 2012 salmonella event related to cantaloupes, during which oshint characterized social media posts related to the event, as news articles and twitter tweets streamed into the system (figure 2). on august 17, 2012 the oshint system identified a large increase in twitter tweets mentioning salmonella. social media data found absent (victims missing work or school day), death, hospital, and sick events to involve 2, 4, 17, 283 media mentions, respectively. our tf-idf algorithm characterized the salmonella event impact as two dead and 150 sickened by salmonella-tainted cantaloupe. retrospective analysis of cdc reported data on august 30, 2012 indicated the salmonella event involved two deaths in 204 cases (2). conclusions the oshint team is continually developing and refining characterization and forecasting algorithms used in the system. upon completion, oshint will characterize symptoms, geography, and demographics for e. coli and salmonella events. the system will also forecast number sick, dead, and hospitalized for an effective and quick response. we will refine our algorithms and evaluate the system against past and future events to provide confidence in our results. figure 1. oshint system in amazon web services. figure 2: 2012 salmonella outbreak in cantaloupe keywords open source; forecasting; social media; response; food safety acknowledgments frederika conrey, kenneth decker, willam lei, dania shor, misha zhurkin references (1) cdc. retrieved september 7, 2012 from http/www.cdc.gov/outbreaknet/investigations. (2) cdc. retrieved august 30, 2012, from http://www.cdc.gov/salmonella/typhimurium-cantaloupe-08-12/index.html. *jane w. blake e-mail: blake_jane@bah.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e128, 2013 development and assessment of a development and assessment of a public health alert delivered through a community health information exchange 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 development and assessment of a public health alert delivered through a community health information exchange roland gamache 1, 3 , kevin c. stevens 2 , rico merriwether 3 , brian e. dixon 3 , shaun grannis 1, 3 1 indiana university school of medicine 2 marion county health department, indiana 3 regenstrief institute abstract timely communication of information to health care providers during a public health event can improve overall response to such events. however, current methods for sending information to providers are inefficient and costly. local health departments have traditionally used labor-intensive, mail-based processes to send public health alerts to the provider community. this article describes a novel approach for delivering public health alerts to providers by leveraging an electronic clinical messaging system within the context of a health information exchange. alerts included notifications related to the 2009 h1n1 flu epidemic, a syphilis outbreak, and local rabies exposure. we describe the process for sending electronic public health alerts and the estimated impact on efficiency and cost effectiveness. keywords: public health alerts, health information exchange, syndromic surveillance, clinical messaging, h1n1 flu, broadcast alert introduction a major challenge for public health is facilitating timely communication of information between public health agencies and health care providers. for example, the underreporting of many public health conditions can make accurate surveillance difficult. [1] conversely, public health professionals find it difficult to let physicians, nurses, and other front line health care workers know that an outbreak may be occurring in their region. a high-priority public health message in the form of an alert is commonly used by public health agencies to share important information with providers.[2] for local health departments, these alerts are typically paper-based, delivered through postal mail, and they target a broad range of health care providers in a specific geographic region (e.g., zip code). although health alert networks (hans) have utilized electronic alerts for several years, hans are often state-run networks that use workflows optimized for non-clinical providers and may reach a limited set of clinical recipients, typically focusing on sentinel physicians. [3, 4] current paper-based alerting processes have inherent constraints that limit their effectiveness. first, it may take 1 to 4 days for a message sent by the united states postal service to reach the development and assessment of a public health alert delivered through a community health information exchange 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 physician and additional time to open the mail. second, the delivery of the mail item is not verified, so it is uncertain if the physician actually receives the message. finally, traditional sources for physician contact information such as state licensing boards may be incomplete or out-of-date. these issues occur in part because physicians may provide a home or other nonclinical address, or may change practice location after license registration. consequently, the physician may never receive the alert, receive a delayed alert, or receive the alert in a location where the information may not impact the clinician’s decision-making (e.g., the following day when he or she is at the clinical practice site). the use of a health information exchange (hie) to deliver public health alerts may help to mitigate these challenges. hie organizations facilitate the sharing of clinical and administrative health care data among health care institutions, providers, and data repositories.[5] an hie is an organized entity, often a legal corporation, that specializes in facilitating electronic exchange of health information among a diverse group of often competing health care system stakeholders, including hospitals, laboratories, and physician practices.[6, 7] a recent survey reported that as many as one-third of community hies may involve data exchange with public health agencies.[8] we hypothesize that public health agencies may more efficiently communicate with health care providers if hie infrastructure is leveraged to deliver public health provider alerts. this hypothesis is supported because a core competency of hies is to assure that clinical information reaches the appropriate provider at the appropriate place and time, and they often ensure the information is received and utilized. transmitting public health alert messages can be managed in a fashion nearly identical to clinical information messages, and their receipt can be verified. finally, electronic public health alerts sent to a provider at their place of practice and incorporated into their routine work processes may increase the likelihood that the information will impact clinical decisions. with 40 years of experience as a medical informatics research organization regenstrief researchers have demonstrated that an hie can be leveraged to support and improve core functions of public health, such as surveillance. [9-11] we have also shown that alerts and reminders incorporated into electronic health record systems can affect clinical decision-making. [12-14] thus, it should be feasible to leverage an hie in a similar fashion to deliver alerts and reminders to broad physician cohorts, beyond the scope of one organization and one electronic health record (ehr) system. this paper describes our initial experiences using an hie to deliver electronic public health alerts to clinicians. we present a novel framework for alerting community physicians about emerging public health threats while incurring minimal changes to clinical workflow, and we illustrate the framework with real-world scenarios in which the local health department utilized the hie. additionally, we discuss draft measures to support evaluating the effectiveness of this framework for public health alert delivery as compared to delivery by current methods. development and assessment of a public health alert delivered through a community health information exchange 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 methods clinical message overview in our context, the term ”clinical messaging” describes the delivery of clinical results to physician offices using one of three transmission methods: a) direct import into an ehr system, b) electronic push to a secure, web-based portal, or c) facsimile (fax) transmission. designed and developed at the regenstrief institute, the docs4docs® (d4d) clinical messaging service is provided by the indiana health information exchange (ihie). the service receives laboratory, transcription, and radiology information from participating data sources (e.g., hospitals, laboratories) via real-time, secure health level 7 (hl7) feeds.[15] d4d converts the computable clinical results into a standardized reporting format that includes a header with the sending organization’s logo and contact information. once converted, the report is delivered to the intended provider. to ensure that all messages are successfully delivered, ihie personnel follow-up on any non-fax message left unopened after 72 hours. the docs4docs® service also delivers electronic copies of discharge summaries, operative notes, and electrocardiograms (ekgs). as of february 18, 2009, there were 3,636 practices with just over 10,000 physicians utilizing the docs4docs® service throughout indiana. of those practices, 2,335 (64 %) received messages via fax, 1,337 (37 %) viewed messages via the webbased inbox, and 52 (one %) accessed their messages from within their ehr system. public health alerting in addition to delivering clinical data, a clinical messaging service can act as an adjunct to existing public health alert network functions.[16] in 2008, the regenstrief institute, indiana university school of medicine, ihie, and the marion county health department (mchd) collaborated to develop a public health alerting interface within the docs4docs® service. the interface allows public health alerts to be delivered in a manner consistent with the physicians’ existing message delivery preferences. messages can be sent to all physicians within a jurisdiction, or messages can be targeted to specific practices based on clinical specialty (e.g., family physician, pediatrician) or geography (e.g., zip code). figure 1 shows a screenshot of an actual alert delivered to a fictitious user’s inbox. development and assessment of a public health alert delivered through a community health information exchange 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 figure 1. physician's screen showing a public health alert sent to a docs4docs® web-based inbox. this alert describes action to be taken for a shigella outbreak. alerting workflow after creating the alert message, the public health agency emails it to the regenstrief institute where personnel, with access to the public health alerting interface, submit the message. the clinical messaging system delivers the public health alert to physicians specified in the alert. the early alerting activity has focused on the indianapolis metropolitan statistical area including marion county. in 2009, the united states census estimated marion county's population at 891,000. as the state capital and indiana's largest city, indianapolis is the 14th largest city in the united states. mchd is one of only two local health departments in indiana to conduct syndromic surveillance[17] with the state and provides a variety of public health services related to population and environmental health. syndromic surveillance to conduct syndromic surveillance, mchd utilizes two systems: the public health emergency surveillance system (phess)[18] and the electronic surveillance system for the early notification of community-based epidemics (essence). both systems are supplied data from 74 indiana hospital emergency departments in real-time and managed by ihie. in monitoring the magnitude of the h1n1 flu outbreak, state and county epidemiologists monitored the count of influenza-like illness (ili) cases. results the public health alert interface within docs4docs® was completed in early 2009. since then, nine public health alerts have been disseminated to physicians in marion county. table 1 development and assessment of a public health alert delivered through a community health information exchange 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 provides a timeline of the various alerts distributed via the docs4docs® service. three of the alerts provided updates regarding the h1n1 outbreak of 2009. the remaining alerts distributed information regarding local outbreaks and updates concerning public health policies. table 1. summary of alerts sent by the health information exchange for the local public health agency date alert sent description of the alert 4/29/09 h1n1 flu alert 5/13/09 h1n1 flu alert (follow-up) 8/26/09 syphilis outbreak alert 9/15/09 h1n1 flu vaccination information 2/17/10 rabies information and treatment update 4/1/10 new vaccination requirements for school 4/15/10 syphilis outbreak reminder on april 29, 2009, mchd sent a h1n1 flu alert to marion county physicians who utilize the docs4docs® service. using their syndromic surveillance system, mchd identified a steep rise in h1n1 chief complaints and influenza testing (figure 2) and wanted to provide physicians with information on how to handle suspected cases. a subsequent h1n1 flu alert was sent on may 13, 2009, when a school closed due to a dramatic rise in h1n1 cases within the county, and a h1n1 flu vaccination information alert was sent on september 15, 2009, informing physicians on how to ensure their practice had vaccines on hand for the impending flu season. figure 2. laboratory testing for h1n1 during the flu season by date development and assessment of a public health alert delivered through a community health information exchange 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 r a ti o o f p o si ti v e t e st s b y t o ta l t e st s date ratio of positive influenza tests ratio of positive tests figure 3. ratio of positive influenza tests by date since january 2009, mchd has reported an outbreak of syphilis in marion county, indiana. a syphilis public health alert was sent on august 26, 2009, to inform physicians of the ongoing outbreak. since the syphilis outbreak was unresolved in early 2010, another public health alert was sent on april 15, 2010. information about treating individuals who may have rabies was sent on february 17, 2010. in addition to public health alerts related to diseases, mchd sent an alert on april 1, 2010 notifying physicians about new school vaccination requirements. there were 3,085 physicians eligible to receive an alert through docs4docs® as of april 29, 2009. of those, docs4docs® successfully processed messages for 3,021 (97.9%) providers. messages to 64 providers were "lost" during the message generation process. this bug was fixed prior to the sending of the second broadcast message. of the 3,021 providers, 158 (5%) of them were returned as undeliverable. in an effort to reduce the laboratory testing burden, on april 30, 2009 the indiana state department of health transmitted an alert (through the state’s han system) providing new guidelines for sample testing of the ”worried well” during the early phases of the h1n1 flu outbreak.[19] data from the hie-based influenza surveillance system points to a decrease in the number of samples tested after the alert was sent (figure 3). this suggests the potential impact of the electronic alerts in changing provider behavior in a short time frame for an issue of large impact on the health of the community. development and assessment of a public health alert delivered through a community health information exchange 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 figure 4. number of requested syphilis tests per week during 2010. on april 15, 2010, mchd sent an alert describing a syphilis outbreak in the community. a review of the hie-based data for the number of syphilis tests ordered during this period did not show a significant increase in the number of tests ordered after the april 15 date. in fact, after the first week the number of tests requested for syphilis showed signs of a decrease. a graph of this data is presented in figure 4. (the horizontal line on the graph represents the average number (1,787) of syphilis tests ordered per week based on the first 23 weeks of 2010.) discussion we developed and implemented an integrated alerting service for public health agencies to communicate more effectively and efficiently with clinicians. our experience suggests that electronic public health alerts, when integrated into existing clinical workflows, have the potential to improve public health practice and clinical decision-making. although assessment of the new service was limited, there are many potential benefits of this kind of service to public health, providers, and hies. value of alerts there are several areas of added value through the process of sending public health alerts through the health information exchange in the community. one such value is improved timeliness: the alert is delivered to the physician the same day that it is sent. with more timely information the clinical delivery system may be more likely to respond to the inciting event and potentially reduce some of the resource burden to the community.[20] if only one organization is maintaining a list of electronic addresses for providers and the list is used by multiple organizations, the total cost for this list maintenance is reduced. there should 0 500 1000 1500 2000 2500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 number of tests week number number of syphilis tests per week 2010 average number of tests per week follow-up syphilis alert sent development and assessment of a public health alert delivered through a community health information exchange 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 also be an additional added value for the hie in identifying bad addresses earlier in the process and prevent the delay of sending urgent clinical data to the provider in a timely fashion. with enhanced information about the provider, such as practice location and practice specialty, alerts from public health could be targeted to those providers that would most benefit from a community alert. examples of targeted messages could be for outbreaks in a particular geographic location or information targeted to providers that deal in primary care or pediatrics. public health agencies stand to receive several benefits from an automated alerting system through a community hie. first, agencies can improve the speed in which alerts reach clinicians. whereas the united states postal mail can take several days, electronic messages through an hie can be transmitted to clinicians within minutes. second, the messages are received by providers through established work patterns. third, the delivery and receipt of the messages can be monitored to confirm delivery and more quickly identify communication errors. finally, there is a direct cost savings to the public health agency when using an hie compared to a paper-based system. the estimated costs for a public health alert mailing are based on previous public health mailings that occurred in the state over the last several years. the costs for envelops, letterhead, and labels were obtained from our purchasing department. the costs listed are per 100 items to make the amounts easier to read (i.e., no fractional cents are listed). the total amount of labor time to order these materials is based on placing and validating the order. the total time required for placing the labels on the envelops, printing the labels, stuffing envelopes and adding postage is based on prior mailings that averaged approximately twenty hours of total staff time for 2,000 mailed items. the distribution among the categories listed is an estimate, however; the total time is reliable and based on several mailings that occurred over the last several years. the postage cost is a savings based on the united states postal service listing and is a generally used business practice for larger mailings. finally, the indirect rate is an estimate of actual indirect costs (electric, rent, etc.) and not the indirect rate that is negotiated for grants and contracts. all of these numbers are presented so that public health departments can use their own estimates if they wish to calculate an estimated savings from their own experience. the total cost savings for the public health agency in this study was estimated to be $3,638 for each set of alerts sent. this dollar amount was calculated based on sending 3,085 alerts to providers and community. development and assessment of a public health alert delivered through a community health information exchange 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 table 2. potential cost and labor benefits for public health by using electronic alerts a description estimated cost (per 100 letters) cost of letterhead b $16.67 cost of envelopes (pre-printed return address) c $16.13 cost of labels d $2.00 labor cost of supply ordering e, f $6.25 labor cost of printing and affixing labels e, g $8.33 labor cost of stuffing envelopes and adding postage e, h $16.67 postage cost i $41.40 indirect cost j $10.47 estimated savings $117.92 notes a. the cost of writing the alert is not included since the alert would be written and approved for either process b. 1500 sheets/box at $250 per box c. 1500/box at $242 per box d. 1500 labels package at $30 per package e. assume labor and fringe at $25/hr f. estimated at 15 minutes per 100 g. estimated at 20 minutes for printing one hundred labels and affixing labels to envelopes h. estimated labor of 40 minutes for 100 letters i. commercial presorted rate from the united state postal service at $0.414 per letter j. the indirect rate is estimated to be 33.5% on labor costs only improving the speed of delivery of public health alerts and ensuring their integration into clinical workflow increases the likelihood that public health agencies can influence clinical decisionmaking. if providers are able to access timely, actionable information through existing work practices, there is a better chance they will modify standard care in response to the new information. previous research has shown that providers do respond to public health alerts.[2123] for example, a community alert was sent out to providers, which established refined protocols for testing for the h1n1 virus. these testing criteria included specific information about patient symptoms in order to be tested for this virus. the provider community, adjusted the testing criteria, based on these protocols. we observed this same change in behavior when mchd used the docs4docs® system to send an h1n1testing alert to providers. in comparison, surveillance data following the broadcast alert describing an increase in syphilis in the community did not reveal any evidence of change in provider behavior based on a change in the number of tests requested for syphilis following the last broadcast alert. there are several reasonable explanations to be explored in order to fully understand why no effect was seen in this instance. first, the broadcast alert sent out in 2010 was a follow-up reminder from a previous alert sent out in 2009. also, information about the outbreak was provided to the physician community through many professional channels, including other public health agency announcements and communications through supplementary professional channels. therefore, providers may have already changed their behavior and consequently no significant increase in syphilis testing rate would be detected in the physician practice. development and assessment of a public health alert delivered through a community health information exchange 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 additionally, many of the symptoms for syphilis remain undisclosed by the patient or undetectable on physical exam. if a potential syphilis patient presents at a clinic or office with chief complaints for a condition unrelated to a potential syphilis case, the encounter may not trigger a consideration by the physician to order a syphilis test for this individual. in order for providers to better respond to this type of public health alert, a more specific epidemiological profile may be needed to notify the provider community about patients that present at the encounter with chief complaints unrelated to underlying syphilis symptoms. another possible explanation would be if the patient demographics shifted over the period between the two public health alerts, particularly in consideration of the change in the economic climate or other social factors. many of these cases initiate from the county hospital. if there was a change based on the downturn in the economic conditions in the community, there would be an expected increase in patient volume at the county hospital. therefore, there would also be an expected increase in the number of patients screened for syphilis testing. we considered looking at a change in rates for the analysis of this parameter; however, determining the denominator for this analysis was difficult. there is only an average of 1,787 syphilis tests ordered per week and approximately 2.7 million messages over this same period. therefore, the calculated rates would be small and any change hard to measure. once again, a more specific epidemiological profile would help in this analysis as well. it is clear that further study is necessary in order to fully assess and understand the ability of public health broadcast alerts to impact physician behavior.[24] providers also stand to benefit from electronic public health alerts delivered through a community hie. first, providers will see a reduction in the postal mail they receive from public health. second, the information sent from public health will be timelier, potentially improving clinicians’ perception of public health agencies. finally, the information arriving in the alerts will be actionable and provide tailored guidance that clinicians can use to enhance the quality and safety of care they deliver to patients. preliminary conversations with providers in our region reveal that many clinicians welcome this kind of change in how public health communicates with the provider community. the community hie also benefits from this partnership. this activity provides an additional service to a common platform and provides another mechanism for validating and verifying provider addresses in the community. this updated provider information is important when sending out clinical information regarding patients in the provider’s practice. since the infrastructure for delivering clinical health information is being reused for the delivery of broadcast alert messages, total costs for the community are potentially reduced. finally, information concerning the contact lists for physicians is necessary for both public health and the community health information exchange. since this information is collected only once and maintained by one organization, total costs are reduced. the overall community also benefits from this partnership. since information about community outbreaks are provided in an manner that are easily maintained and delivered through a timely mechanism, the disease burden experienced by the community should be reduced. this change development and assessment of a public health alert delivered through a community health information exchange 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 should result in a more effective response to community acquired infections, and therefore a more effective use of healthcare resources. future directions end-user acceptance of this new data sharing mechanism (broadcast messages) will be strongly influenced by end-user perception of the process. consequently, the attitude of physician and public health groups to how these new data sharing mechanisms are perceived will be evaluated. we will also examine if there is a difference of perception or practice by physicians that are primarily paper-based in the office versus those physicians are using a clinical messaging system like docs4docs. each of these outcome measures may be evaluated for a survey tool to gain the perspective of the position and may be measured empirically through information gained in the clinical data repository. we plan to evaluate the perceived utility of these interventions with stakeholders from both public health and clinical health care settings to gage unforeseen complexities and advantages. finally, because general alerts seem to be less effective in producing behavioral changes in the provider practice, future work to identify what types of detailed information included in the public of alert are most likely to produce provider changes is warranted. conclusions the process of providing public health alerts through the community health information exchange provides a cost savings to public health over the traditional system of a mail-based public health alert. more analyses are needed to fully assess the other impacts of this type of delivery system to include provider groups and the health information exchanges. delivering public health alerts to physicians using an existing clinical messaging system represents a well-scoped and incremental step forward that has the potential to improve process efficiency, including reduced costs and improved timeliness. by engaging both clinical stakeholders and public health stakeholders in such a pervasive and ongoing activity, we can continue to build the digital bridge and trust relationships between the two domains that are necessary to establish the infrastructure for the next stage of more complex public health decision support processes. acknowledgements this work was funded by the centers for disease control & prevention under contract 2002008-24368. the content of this publication does not necessarily reflect the views or policies of the department of health and human services, nor does mention of trade names, commercial products, or organizations imply endorsement by the u.s. government. the views expressed in written conference materials or publications and by speakers and moderators at hhs-sponsored conferences, do not necessarily reflect the official policies of the department of health and human services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the u.s. government. development and assessment of a public health alert delivered through a community health information exchange 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 references [1] thacker, s.b. and r.l. berkelman, “public health surveillance in the united states. epidemiology review”, 1988. 10: p. 164– 190. [2] cdc. health alert network. 2010; available from:http://www2a.cdc.gov/han/index.asp. [3] daniel, j.b., et al., connecting health departments and providers: syndromic surveillance's last mile. mmwr morb mortal wkly rep, 2005. 54 suppl: p. 147-50. [4] baker, e.l. and j. porter, the health alert network: partnerships, politics, and preparedness. j public health manag pract, 2005. 11(6): p. 574-6. [5] ahrq. health information exchange. key topics 2009 february [cited 2010 february 7]; available from: http://healthit.ahrq.gov/hie. [6] dixon, b.e., a. zafar, and j.m. overhage, a framework for evaluating the costs, effort, and value of nationwide health information exchange. j am med inform assoc, 2010. 17(3): p. 295301. [7] dixon, b.e. and s. scamurra. is there such a thing as healthy competition? in annual himss conference & exhibition. 2007. new orleans, la: himss. [8] hessler, b.j., et al., assessing the relationship between health information exchanges and public health agencies. j public health manag pract, 2009. 15(5): p. 416-24. [9] overhage, j.m., s. grannis, and c.j. mcdonald, a comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. am j public health, 2008. 98(2): p. 344-50. [10] grannis, s., et al., the indiana public health emergency surveillance system: ongoing progress, early findings, and future directions. amia annu symp proc, 2006: p. 304-8. [11] grannis, s.j., et al., how disease surveillance systems can serve as practical building blocks for a health information infrastructure: the indiana experience. amia annu symp proc, 2005: p. 286-90. [12] rosenman, m., et al., computerized reminders for syphilis screening in an urban emergency department. amia annu symp proc, 2003: p. 987. [13] dexter, p.r., et al., a computerized reminder system to increase the use of preventive care for hospitalized patients. n engl j med, 2001. 345(13): p. 965-70. [14] dexter, p.r., et al., effectiveness of computer-generated reminders for increasing discussions about advance directives and completion of advance directive forms. a randomized, controlled trial. ann intern med, 1998. 128(2): p. 102-10. [15] barnes, m., lessons learned from the implementation of clinical messaging systems. amia annu symp proc, 2007: p. 36-40. [16] grannis, s., p.g. biondich, and b.w. mamlin, how disease surveillance systems can serve as practical building blocks for a health information infrastructure: the indiana experience. amia annu symp proc, 2005. [17] grannis, s., et al., the indiana public health emergency surveillance system: ongoing progress early findings and future directions. amia annu symp proc, 2006. [18] gamache, r.e. and m. wade, the indiana public health emergency surveillance system (phess), in ehealth initiative, blueprint award for improving population health. 2007: washington, dc. [19] health, i.s.d.o., indiana epidemiology newsletter, 2009. 17(4). http://www2a.cdc.gov/han/index.asp http://healthit.ahrq.gov/hie development and assessment of a public health alert delivered through a community health information exchange 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 [20] elson, r.b. and d.p. connelly, “computerized patient records in primary care: their role in mediating guidelinedriven physician behavior change. archives of family medicine, 1995. 4: p. 698–705. [21] marc rosenman, m., et al., computerized reminders for syphilis screening in an urban emergency department. amia annu symp proc., 2003. 2003. [22] gamache, r.e., the indiana medical error reporting system, in indiana healthcare executives network. 2006: indianapolis, in. [23] staes, c.j., et al., computerized alerts improve outpatient laboratory monitoring of transplant patients. journal of the american medical informatics association, 2008. 15(3): p. 324-332. [24] the purpose of first syphilis broadcast alerts that were sent through the community hie were to determine the feasibility of the hie to send these types of messages. the measurement of the impact of these messages was clearly a greater priority after the feasibility was demonstrated. the associated attributes for messages sent after the initial messages are clearly more aligned to measure outcome in the provider community. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a binational model of collaboration for enhancing cross-border id surveillance kristine ortwine*2, 1, karen ferran1 and esmeralda iniguez-stevens1 1early warning infectious disease surveillance, california department of public health, san diego, ca, usa; 2san diego state graduate school of public health, san diego, ca, usa objective the purpose of this demonstration is to describe the cross-border collaborative processes used for the development of a transparent methodology to identify and prioritize zoonotic infectious disease agents in the california-baja california border region. introduction international borders present unique challenges for the surveillance of infectious disease. border communities represent locations with vast differences in cultures and languages, governing institutions, healthcare access, and priorities for the collection and surveillance of disease data. pathogens and the health and security risks they create do not respect geographical and political boundaries. however, the organizations responsible for the surveillance and control of these agents must function within the borders of their respective governments. one border one health (oboh) is a binational, multidisciplinary initiative aimed at engaging partners in the us and mexico to identify and implement methods for successful communication and collaboration to enhance health capacity and disease surveillance within the border region. the advancements of international initiatives such as oboh will help to develop the types of multi-country networks necessary for the effective monitoring of disease patterns and risks. methods one border one health surveillance committee participants represent multi-disciplinary professionals working together for the advancement of one health principles in the california/baja california border region. this showcase documents the identification and prioritization of zoonotic infectious disease agents along the us-mexico border, by use of a transparent methodology which engaged public and private partners from both countries. preliminary research and input from collaborators in government, academic, and private sectors in the us and mexico allowed for review and discussion of current methodologies available for prioritizing infectious agents. the discontools work package 2 prioritization scoring model was selected as the basis for scoring and weighting various zoonotic diseases of concern within border region. subject matter experts were then asked to review and score an initial list of diseases, in order to produce a final ranked list of pathogens. the intent is that these prioritized pathogens will be used by government agencies to make informed decisions, integrating priorities from both nations with regards to infectious disease surveillance. this collaboration provides insight into the binational cooperation needed for the selection of diseases to be considered in a regional, integrated disease surveillance system. to the authors’ knowledge this is the first transparent scientific-based approach to pathogen prioritization in the us-mexico border region. conclusions oboh is the first binational regional network of its kind along the us-mexico border recognizing the interconnectivity between human, animal, and environmental health. given the limited resources in the current economic climate, the use of regional integrated surveillance systems provide an opportunity to protect and improve border health and security by moving away from species-specific surveillance programs. the process showcased here for the transparent review and prioritization of pathogens along the california-baja california border can be used as a model along the entire us-mexico border. the ultimate aim is to protect border communities through the creation of a binational, early warning surveillance system which would allow for actionable and timely interventions to limit emergence, mitigate spread, provide gap analysis, and enhance prevention and control for several emergent and re-emergent diseases. ultimately, this will decrease negative health and environmental impacts while improving agricultural and economic outcomes in both nations. however, obstacles such as continued sustainability, identification of new multidisciplinary collaborators, cooperation between government agencies, and identifying funding for advancement of integrated regional surveillance systems remain challenges. keywords one health; disease surveillance; cross-border collaborative *kristine ortwine e-mail: kristine.ortwine@cdph.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e206, 2013 development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi development of a web gis application for visualizing and analyzing community out of hospital cardiac arrest patterns hugh semple 1 , han qin 1 , comilla sasson 2 1 department of geography and geology, eastern michigan university, ypsilanti, mi, 2 department of emergency medicine, university of colorado at denver, denver, co background each year, almost 300,000 individuals die from out-of-hospital cardiac arrest (ohca) in the united states (roger et al., 2011). sasson et al. (2010) pointed out that the survival rate for ohca varied between 6.7% and 8.4%, a statistic that had largely remained relatively stagnant for over thirty years. geographically, nichol et al. (2008) have shown that survival rates from out-of-hospital cardiac arrest vary tremendously by city in the us. for example, the survival to discharge rate in seattle in 2008 was estimated at 8.1% while in dallas, it was only 2.4% (nichol et al., 2008). given the wide geographic variability in ohca survival rates, a major task is identifying communities that have high risk for ohca and targeting them for cpr education abstract improving survival rates at the neighborhood level is increasingly seen as a priority for reducing overall rates of out-of-hospital cardiac arrest (ohca) in the united states. since wide disparities exist in ohca rates at the neighborhood level, it is important for public health officials and residents to be able to quickly locate neighborhoods where people are at elevated risk for cardiac arrest and to target these areas for educational outreach and other mitigation strategies. this paper describes an ohca web mapping application that was developed to provide users with interactive maps and data for them to quickly visualize and analyze the geographic pattern of cardiac arrest rates, bystander cpr rates, and survival rates at the neighborhood level in different u.s. cities. the data comes from the cares registry and is provided over a period spanning several years so users can visualize trends in neighborhood out-of-hospital cardiac arrest patterns. users can also visualize areas that are statistical hot and cold spots for cardiac arrest and compare ohca and bystander cpr rates in the hot and cold spots. although not designed as a public participation gis (ppgis), this application seeks to provide a forum around which data and maps about local patterns of ohca can be shared, analyzed and discussed with a view of empowering local communities to take action to address the high rates of ohca in their vicinity. correspondence: hsemple@emich.edu copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi outreach, priority placement of automated external defibrillators (aeds), and other intervention activities, as a means to help to improve overall survival rates in these communities. the purpose of this paper is to report on an ongoing project to develop a user-friendly, interactive web mapping application that allows epidemiologists, policy makers, program managers, and the general public to quickly understand the geographic pattern of cardiac arrest rates, bystander cpr rates, and survival rates at the neighborhood level in selected u.s. cities. the selected cities are all cities in which the cardiac arrest registry to enhance survival (cares) has operations. cares is a registry established in 2004 by the centers for disease control (cdc) and the department of emergency medicine at the emory university school of medicine to monitor ohca events in the us (mcnally et al., 2009). the goal of the web mapping application is to provide map-based information and raw data to health care professionals, policy makers, and members of the public to enable them to visualize and analyze the ohca situation at the neighborhood level, and, where necessary, to take actions to address the high rates of ohca in certain neighborhoods. in the following sections of this paper, we outline the application development process and the usability design considerations employed in building the web mapping application. we also describe the application itself as well as user evaluations of the website. method the application development process to develop the ohca web application, we followed, to a large extent, the five-stage usercentered design model put forward by kinzie et al., (2002). the kinzie et al., model was used to design a web application to assist patients in recording and maintaining their family health histories. these histories can be used by both patients and physicians to identify potential health problems, and by physicians for preventive or treatment recommendations (kinzie et al., 2002). the five stages of application development set forth in the model are: (1) identification and assessment of client and users’ needs, (2) goal/task analysis (3) initial prototype design, (4) evaluations and refining of prototypes and final stage application, and (5) project implementation and maintenance. the client for our project was a researcher working with the cares group (https://mycares.net/). in stage one of application development, several meetings were held with the client to develop a clear picture of the conceptual requirements of the application from the client’s perspective. within these meetings, the goal of the application, a stage two requirement of the model, was clarified. the main goal of the application was stated as: to provide interactive web maps and data to public health professionals, policy makers, and members of the public to help them to visualize and analyze the geographic pattern of out-of-hospital cardiac arrest rates, bystander cpr rates, and survival rates at the neighborhood level in different u.s. cities. stage three of the application development process called for the development of an initial prototype. we used the documented goal of the research, the client’s requirements of the development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi application’s functionalities, and our own background research about similar type applications to design the initial prototype. in stage four of the application development, we used several rounds of semi-formal evaluations to incorporate user perspective into the unpolished prototype designs. participants in these evaluations were graduate students with knowledge of cartography, programming and gis, and members of the general public. convenience sampling was used to select the evaluators. although the average sample size was 10 persons, the small sample size was considered more than sufficient as the usability evaluation literature points out that after the first five or six interviews, about 75 percent of the fatal design errors and problems will be identified (nielsen 1994, krug 2006, shneiderman and plaisant 2006). open-ended questions via email were the main technique used to solicit feedback from evaluators. users were directed to the project’s website and asked to carry out various tasks using the application. they were then requested to comment on the degree of success carrying out the tasks, their satisfaction level, userfriendliness, and quality of output. feedbacks from these semi-formal evaluations were used to update the prototype leading to a more improved product. indeed, user feedback in stage four of the application development process helped us to considerably improve the design of the application each time the survey was completed. for example, users suggested that hyperlinks should be created to provide access to technical discussions on the topics portrayed on the maps. comments were made about legend design, color choice, the quality of dynamic labeling, interface layout, and the functioning of various tools. after each survey, we addressed users’ concerns by incorporating their suggestions into the application. a more expanded user evaluation was done at the beta release of application development, when all the software functionalities were completed. this final beta evaluation will be described later in the paper. incorporating usability design considerations into the ohca web application given that many of the users of the application were not expected to be experts in gis, we were particularly interested in incorporating state-of-the art usability ideas in the design of the ohca web application. in web mapping application development, usability issues surfaced as important considerations since the early part of the last decade as researchers sought to create interactive web mapping applications that met user needs and expectations (maceachren and kraak 2001, maceachren 1995). usability problems associated with interactive web mapping applications include:  poor user interface design (e.g., too small map area; legend too large, incompatible colors information overload, and poor layout (arleth 1999).  users unable to understand map tools or map tools being too difficult to use (harrower et al., 2000).  motivated users not interpreting the published maps in intended ways (ishikawa et al., 2005).  unreadable or badly placed text; poor visualization of search results; lack of useful help or guidance to use the software (nivala et al., 2008). development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi user-centered design is recognized by many researchers as the best means of avoiding the design problems mentioned above. user-centered design is essentially a product development methodology that incorporates the views of users at all stages of the product development cycle in order to create a product that meets users' needs. the methodology forces designers to consider both the objectives of the design and the needs and preferences of users within the context of existing technology (norman 2002). while many cartographers have discussed usability issues in web mapping design, a few have concentrated on the usefulness of web mapping applications, arguing that applications should not only be easy to use, but that they should also serve useful ends (fuhrmann et al., 2005). norman (2005), the well-known usability researcher, has argued that focusing too much on usability often leads to the development of “cool” applications that fail to help people accomplish needed tasks. he called for designers to place emphasis on functionalities that meet the goal of the web application because these activity-centered designs are better placed than usability designs to deliver tools that effectively support users in real-world contexts. in this project, we sought to achieve both ‘usability’ and ‘usefulness’ in web map design by working closely with both the client and the intended users of the application to incorporate their feedback into the project at every stage in the design process. we relied on the agile approach to software development to guide this process. this approach to software development is one that emphasizes consultations with the client at each of several iterations in the project cycle (abrahamsson et al., 2010, ambler 2002). feedbacks from these consultations are immediately incorporated into the design process, thus the final product greatly reflects user concerns. in addition to utilizing ideas from the agile approach to software development, we incorporated the traditional geovisualization concept of presenting multiple views of the same data into the application design (maceachren and kraak 2001). our application provides downloadable cartographic representations of the data based on user-constructed queries. raw data in the form of shapefiles and attribute data are also provided. advanced user of the application can use these raw datasets to analyze and create their own representations of the ohca problem in the various neighborhoods. in later versions of the application, we intend to include capabilities that would allow users with meaningful local knowledge of the ohca problem to upload data to the web application and share the details of their differing perspectives with other users of the application. recently, the web gis design literature, taking its cue from developments in e-commerce, has begun discussing issues related to how the general public and expert gis users develop trust in interactive mapping websites sufficient to enable them to confidently interact with the application’s data and analytical output (skarlatidou et al., 2011). to improve the trustworthiness of web gis applications, skarlatidou and her colleagues point out that the responsibility is on the person or organization supplying the web gis application, the trustee, to establish the necessary trust attributes. according to the authors, there are two main types of trustee attributes which must be developed to foster increased trusts in web applications, perceptual attributes and functional attributes. perceptual trustee attributes deal with the trustee’s honesty, integrity and reliability. functional attributes, on the other hand, deal with the application features, e.g. aesthetics and usability features that increase the trustworthiness of the application. development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi in designing our web application, we implemented both perceptual and functional trustee attributes. for example, we used trust cues such as the logo of the cardiac arrest agency that promotes strategies to reduce the rates of ohca at the neighborhood level. we also included feedback mechanisms in the form of easy access to social media pages on discus, facebook and twitter to enable users to comment on the validity of the official data that was presented. two other trust cues we provided were information to contact the designers of the application, and hyperlinks to published online reports that supported the cardiac arrest patterns displayed on our maps. with respect to functional trustee attributes, we relied on addressing the usability concerns mentioned earlier, e.g., improved interface design, attention to color use, dynamic labeling, reliance on multiple views of the data, ensuring correctness of data, and correctness of algorithms and queries used to produce end-user maps, etc. results the ohca web mapping application the ohca web mapping application developed for the project can be viewed at http://geodata.acad.emich.edu/ohca. figure 1 shows the graphical user interface (gui) of the application with a census tract level ohca rates map for the city of columbus, ohio being displayed. in this project, census tracts were used as proxies for neighborhoods. the title of the application, “cares out of hospital cardiac arrest (ohca) web mapping application”, a perceptual trust attribute, is prominently displayed at the top left of web page. towards the top right of the application, an additional trust cue, the cares logo, is prominently displayed and hyperlinked to the cares website. links to the project home page is also highly visible. a link to information to contact the designers of the application, which is another perceptual trust attribute, is placed at the top right of the page. towards the top left of the user interface, a brief note informs the user about the purpose of the application and provides a general idea of how to start using it. below this note, drop down boxes allow users to select parameters to build queries. users select a city of interest, the period for which they want to display data, and the type of map desired, e.g., ohca rates map, bystander cpr map, survival rates map, etc. once the submit button is clicked, users can generate maps that show ohca rates, bystander cpr rates, etc., at the census tract level for the city. http://geodata.acad.emich.edu/ohca development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 1: the ohca web mapping application showing ohca neighborhood rates for columbus, ohio, 2004 whenever the map for a particular city is displayed, the application automatically produces a histogram below the map showing the rates distribution for whatever is being mapped. this is part of the multi-view approach to presenting the data. in addition to the histogram, summary statistics are displayed to the lower left of the screen. the attribute table associated with the map layer is also displayed allowing the user to peruse the actual data used to create the map. if desired, users can download the attribute table in excel, txt, or pdf formats. each row in the attribute table is hyperlinked to the related feature on the map allowing users to explore locational aspects of the neighborhood. users can explore the geography of census tracts by accessing an aerial imagery layer supplied by the national agriculture imagery program (naip). the application also allows for each location on a thematic layer to be viewed directly in google map. a set of navigation tools are provided at the top of the screen. access to social-media software to foster discussion about local patterns of ohca is placed right alongside the navigation tools for high visibility. to the lower right of the screen are the legend and an overview map to help users locate themselves in the us if they are zoomed into to a particular neighborhood. the application was built using mapserver, a popular open source platform for creating interactive web mapping applications (http://mapserver.org/). the default user interface for a basic mapserver application is created using only html and css. this means that development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi functionalities such as panning, zooming, querying, etc., are implemented in simple, often inelegant ways. we used the p.mapper framework to add dynamism to the static html user interface. for example, the p.mapper framework allowed us to add a customizable navigation toolbar for panning and zooming the map, returning to previous or full extent, etc. the toolbar also contains functions for identifying map features and their attributes through point and click, and for selecting features through the use of select boxes. p.mapper also facilitated the use of a slider to alternatively zoom the map. user-specified attribute queries of the map were implemented by writing several php and javascript functions to perform the queries. the ohca application displays the following types of maps at the neighborhood level: ohca rates, bystander cpr rates, percent in-home cardiac arrest rates, and hot and cold spots. the rates were calculated outside of the application using arcgis, and geoda, an open source spatial statistics software. rates were smoothed using geoda’s routines for spatial empirical bayesian rates. for the hot and cold spot maps, hot spots (i.e., high-risk census tracts) were defined as census tracts having a higher than expected ohca incidence risks and lower than expected incidence risks of bystander cpr over a period of two consecutive years. alternatively, cold spots (i.e., low risk census tracts) were defined as those having a lower than expected ohca incidence risks and higher than expected incidence risks of bystander cpr over a period of two consecutive years. the actual spatial analysis to identify the ohca hot and cold spots was done outside the web application using geoda and arcgis software. the technical details of this process have been described in (semple et al., 2012). essentially, for any given year, local moran’s i was used to separately identify clusters of ohca rates and bystander cpr rates. the two sets of hotspots were then overlaid on each other to identify the tracts that had both high rates of ohca and low rates of bystander cpr rates for that year. because hotspots can be temporally unstable, we overlaid hotspots of one year on top of hotspots for the preceding year to identify what we called “persistent” clusters of high-risk communities. utilizing trustworthiness interface design ideas, a hyperlink to the online paper describing how the maps were created was supplied. also, to aid user interpretation of the maps, a note is provided on the screen when the maps display to indicate how to interpret the maps. final user-evaluation in this section, we describe and present results of the user evaluation that was conducted on the beta application. at this stage, we wanted to make a final determination as to whether users were able to use the application to carry out specific tasks with ease. we also wanted to carry out a usability assessment of the application, as measured on the system usability scale (sus)(brooke 1996). although the sus is a quick and easy way to conduct a usability assessment, its reliability has been confirmed by many writers (bangor et al., 2008). using the work of brooke (1996) as a guide, the goals of the final evaluation were defined as follows: (1) to determine the ability of users to easily complete tasks for which the application was designed; (2) test the level of difficulty required to complete tasks; and (3) to determine whether the output of tasks were satisfactory to users. eleven persons were purposefully selected to participate in the evaluation. the sample reflected potential users of the application and consisted of both technical gis users as well as members of development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the general public without previous gis experience. the url of the application was again emailed to the evaluators along with login information to survey monkey, an online questionnaire survey site that hosted the questionnaire. a formal questionnaire was developed for this evaluation (appendix 1). evaluators were asked to perform specific tasks using the web application and then respond to a set of close and open-ended questions about the layout of the application, ease of use of tools, problems encountered, and the severity of the problems. responses to the close-ended questions were measured on a 5-point likert scale ranging from “strongly disagree (1)” to “strongly agree” (5). evaluators were also requested to fill out the questions on the systems usability scale (appendix 1). following bangor et al., (2008), we used a slightly modified version of brooke (1996) system usability scale. for the modified version, bangor et al. (2008) suggested that the word “cumbersome” in question 8 be changed to “awkward” to read “i found the system very awkward to use”. also, on three occasions, the word “system” was changed to “product” for greater clarity. the response rate to the survey was 70%. this was considered to be good since the response rate for many web surveys is around 30-40% even with populations that have easy access to the web (archer 2008). in terms of the overall ability of users to easily complete the seven tasks they were given, 67% agreed that they were able to complete all of them (table 1). the two tasks that gave users the most difficulties were task 3 – “ determine the ohca rates for census tracts in worthington, a community in columbus, ohio” and task 4 – “determine the three communities in columbus, ohio that had the highest rates of ohca”. only 44% of respondents strongly agreed that these tasks were easy to complete (table 1). evidently, the steps for performing these tasks were not intuitive. we subsequently addressed these issues by providing explanations on the help page. as to the questions about whether users were pleased with the output of the various tasks, only 36% strongly agreed, while 36% agreed (table 2). users did not like the quality of the output maps, particularly the quality of feature labeling and the placement of certain map elements. also, while users were able to easily generate hot and cold spots from the application, they could not easily interpret the meaning of hot and cold spots. users also complained that the fields in the attribute table should be formatted at each query to reflect only fields of information pertinent to the query and not display the entire set of fields in the table. these issues have since been addressed. responses to questions about user interface are shown in table 3. most users agreed that the application had a user-friendly interface and that the navigation tools and other control were easy to use. in terms of response to the individual sus questions, the average sus score from the respondents was 72.2, with a standard deviation of 6.23. following bangor et al. (2009), this score converts to an overall b when translated to a letter score. according to bangor and his colleagues, the average sus score is 68, so our sus score of 72.2 indicates that most users had a fairly positive experience using our web application. altogether, the values indicated to us that the participants felt that the web mapping application was a useful, user-friendly geovisualization tool, but additional work was needed to enhance output quality. development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table 1. results for ease of accomplishing tasks on the web application was task easy to accomplish? strongly agree agree neutral disagree strongly disagree task 1. create a neighborhood rates map of ohca rates in columbus, ohio for 2004. 83% 17% task 2. create a map showing percent "in-home" ohca arrests by neighborhoods for columbus, ohio for the range of years, 2004 2006. 83% 17% task 3. determine the ohca rates for census tracts in worthington, a neighborhood in columbus, ohio. 33% 33% 33% task 4. determine the three neighborhoods in columbus, ohio that had the highest rates of ohca. 33% 33% 16.7% 17% task 5. display maps of high-risk and low-risk areas for ohca in columbus for 2008. 83% 17% task 6. print a map of the neighborhood ohca rates for columbus, ohio for 2006. 67% 33% task 7. download a csv file of ohca rates for different communities in columbus and open the file in excel. 83% 17% development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table 2. results for “i was pleased with the map output of the ohca rates map” was task easy to accomplish? strongly agree agree neutral disagree strongly disagree task 1. create a neighborhood map of ohca rates for columbus, ohio 2004. 33% 67% task 2. create a map showing percent "in-home" ohca arrests by neighborhoods for columbus, ohio for the range of years, 2004 2006. 50% 50% task 3. determine the ohca rates for census tracts in worthington, a neighborhood in columbus, ohio. 17% 33% 50% task 4. determine the three neighborhoods in columbus, ohio that had the highest rates of ohca. 33% 33% 17% 17% task 5. display maps of high-risk and low-risk areas for ohca in columbus for 2008. 20% 40% 20% 20% task 6. print a map of the neighborhood ohca rates for columbus, ohio for 2006. 33% 17% 33% 17% task 7. download a csv file of ohca rates for different communities in columbus and open the file in excel. 67% 17% 17% development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table 3. results for user interface evaluation responses strongly agree agree neutral disagree strongly disagree the ohca mapping application has a user-friendly user interface. 16.70% 83% i was able to easily use the map navigation tools. 66% 33% i was able to easily use the zoomin/zoom-out tools. 83% 17% i was able to easily use the "select tool". 67% 33% i am comfortable with the color schemes used for the maps and legends. 33% 67% i was able to easily query the map using the "sql" tool. 67% 33% kindly click on the “go to google” button and investigate how this tool works. this tool was helpful in excel. 33% 33% 33% for people who know how to use gis software, we are allowing them to download our data to create their own maps of neighborhood out of hospital cardiac arrest patterns. this was a good idea. yes: 83% no: 17% discussion in creating the ohca web mapping application, we sought to depart from an application development model in which designers believe that they knew exactly what the users needed or wanted. we wanted to incorporate ideas from the web mapping usability literature to create an application that would allow users to effectively interact with the map information and quickly come to an understanding of the geographic distribution of ohca events in their city and the risk of the event in their own neighborhood. while it is still too early to assess whether the goal of the web mapping application was achieved, a number of issues emerged from this project that may be of interest to others engaged in the development of similar type applications. first, it was remarkable the amount of feedback information obtained from the intermediate evaluations as well as from the final beta evaluation. members of the general public and experts in the field of cartography, programming and gis contributed significant insights into the design development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi of the application and the usefulness of certain functionalities. for example, some users wanted to be able to click on the points that showed the location of cares cities around the country and be taken to the main query window. there were also requests for the histogram that appears as part of map queries to be made interactive. users wanted the bars in the histogram to be linked to the specific map features, so that selection of a bar would result in the selection of the map feature associated with the bar, as well as the record in the attribute table. several noted that the names of fields in the attribute tables were unclear and that the application needed to provide more information about cardiac arrest victims, e.g. average age, race, gender, etc. from a cartography perspective, comments were made about the need for greater clarity in the legend text and better coordination in color choice. some users pointed out that the dynamic text placement of mapserver was not properly managed and resulted in clutter at certain zoom levels. these users called for greater finesse in labeling map features at all zoom levels. although we were not able to incorporate all users requests into our design, we are strongly convinced that designing from a user-centered perspective is a superior way of designing web mapping software compared to one in which the major design elements are left entirely to the software developer or to a team of developers. secondly, we are convinced that displaying data across multiple views (map, tabular, chart, summary statistics, and raw data) is a superior way of presenting cartographic data compared to a style of presenting only one view. we believe that this presentation style addresses the needs of different types of users. for example, those with only a need to view available maps and summary statistics can simply view these products online. others with the need to do their own analysis on the data can download the data and analyze it using their own techniques and thus create their own view of the geography of ohca events. thirdly, despite the fact that web maps allow us to easily understand geographic disease patterns, a review of web sites dedicated to discussing public health issues reveal that these sites are generally not designed as a forum around which local health issues can be discussed. most use a tabular or interactive map presentation paradigm and do not provide tools at the website to foster discussions about the tabular or geographic patterns inherent in the data and their health significance to local communities. such discussions often occur elsewhere in social media websites. during the course of this project, we began exploring the idea of building a web application that links tabular data provision and interactive geovisualization with social networking and social bookmarking tools to allow users to discuss, at the particular website, the significance of different disease patterns from a local community perspective. our attempts to do so in this project has been simply to create twitter, discuss and facebook pages and link these to buttons from within the web application so that users can easily access the online forums where the local ohca patterns are being discussed. in future versions of this application, we hope to add a volunteered geographic data component to allow users to supplement the official data presented on the website with local, user-generated data. this is an idea already present in the public participation literature, but combining these capabilities within a container that facilitates discussion using social media software can yield significant benefits. development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi conclusion the application development process described in this paper allowed us to focus specifically on what ohca researchers and potential users of the web mapping application felt were important to them in a web mapping application created to disseminate ohca information and data. the application was built based on detailed analysis of client and user needs and careful selection of available technology based on cost considerations and software capabilities. incorporating stateof-the art usability concepts into the application design was also an important consideration. although this first version of the application is effective in supplying a wide range of maps and datasets to aid in the identifying of high-risk areas for ohca events at the neighborhood level, we feel that there is scope for enhancement of the application in many areas. in later versions of the software, we will increase the interactivity between histograms and the maps, allow for greater integration of multimedia in the design and generally expand the paradigm of building a public health web mapping application that is centered around interactive maps, summary statistics, use of social media technology to discuss patterns inherent on the map, and the ability of users to add local information to the map to supplement and add richness to the official data used to create the basemaps. corresponding author hugh semple professor, department of geography and geology eastern michigan university ypsilanti, mi email: hsemple@emich.edu references [1] abrahamsson p, oza n, siponen mt. agile software development methods: a comparative review. in: dingsøyr t, dybå t, moe nb, eds. agile software development. berlin, heidelberg: springer berlin heidelberg; 2010:31–59. available at: http://link.springer.com/chapter/10.1007/978-3-642-12575-1_3?null. accessed september 11, 2012. 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[25] shneiderman b, plaisant c. strategies for evaluating information visualization tools: multidimensional in-depth long-term case studies. in: proceedings of the 2006 avi workshop on beyond time and errors: novel evaluation methods for information visualization. beliv ’06. http://circ.ahajournals.org/content/early/2010/12/15/cir.0b013e3182009701 http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s10900-012-9611-7#_blank http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s10900-012-9611-7#_blank development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi new york, ny, usa: acm; 2006:1–7. available at: http://doi.acm.org/10.1145/1168149.1168158. accessed september 11, 2012. appendix 1 please use the ohca web mapping application (http://geodata.acad.emich.edu/ohca) to carry out the tasks below and then report your satisfaction level for each task. task 1 create a map showing neighborhood ohca rates for columbus, ohio for 2004. 1. the above task was easy to accomplish. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 2. i was pleased with the map output of the ohca rates map. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 3. if you were displeased with the ohca rates map, what were you displeased with? _________________________________________________________________ task 2 create a map showing percent "in-home" ohca arrests by neighborhoods for columbus, ohio for the range of years, 2004 2006. 4. the task describe above was easy to accomplish. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 5. i was pleased with the "in-home" ohca arrests map by neighborhoods for columbus, ohio, 2004-2006. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 6. if you were displeased with the "in-home" ohca rates map, what were you displeased with? ______________________________________________________________________ task 3 determine the ohca rates for census tracts in worthington, a neighborhood in columbus, ohio. 7. the task described above was easy to accomplish. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 8. i was pleased with the map and table output of the above task. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 9. if you were displeased with the worthington rates map, what were you displeased with? http://doi.acm.org/10.1145/1168149.1168158 development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi ___________________________________________________________________ task 4 determine the three neighborhoods in columbus, ohio that had the highest rates of ohca. 10. the task described above was easy to accomplish. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 11. i was pleased with the rates information i obtained from the above task. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 12. if you were displeased with the rates information you obtained, what were you displeased with? _____________________________________________________________________ task 5 display maps of high-risk and low-risk areas for ohca in columbus for 2008. 13. the task described above was easy to accomplish. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 14. i was pleased with the map output of the above task. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 15. if you were displeased with the hot or cold spot maps what were you displeased with? ______________________________________________________________________ task 6. print a map of the neighborhood ohca rates for columbus, ohio for 2006. 16. the task described above was easy to accomplish. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 17. i was pleased with the appearance of the map. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 18. if you were displeased with the appearance of the printed map, what specifically were you displeased with? ____________________________________________________________________ task 7 download a csv file of ohca rates for different communities in columbus and open the file in excel. 19. the task described above was easy to accomplish. development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 20. i was pleased with the format of the data i downloaded. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 21. if you were displeased with the csv file you downloaded, what specifically were you displeased with? ___________________________________________________ user-interface evaluation 22. the ohca mapping application has a user friendly user interface. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 23. i was able to easily use the map navigation tools. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 24. i was able to easily use the zoom-in/zoom-out tools. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 25 i was able to easily use the "select tool". (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 26. i am comfortable with the color schemes used for the maps and legends. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 27. i was able to easily query the map using the "sql" tool. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 28. kindly click on the “go to google” button and investigate how this tool works. this tool was helpful (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 29. for people who know how to use gis software, we are allowing them to download our data to create their own maps of neighborhood out of hospital cardiac arrest patterns. this was a good ideas. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree development of a web gis application for visualizing and analyzing community out-of-hospital cardiac arrest patterns 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi overall system usability evaluation 1. i think that i would like to use this system frequently. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 2. i found the system unnecessarily complex. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 3. i thought the system was easy to use. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 4. i think that i would need the support of a technical person to be able to use this system. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 5. i found the various functions in this system were well integrated. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 6. i thought there was too much inconsistency in this system. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 7. i would imagine that most people would learn to use this system very quickly (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 8. i found the system very cumbersome to use. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 9. i felt very confident using the system. (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree 10. i needed to learn a lot of things before i could get going with this system (1) strongly disagree (2) disagree (3) neutral (4) agree (5) strongly agree isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts first detection of salmonella spp. in backyard production farms from central chile raul alegria-moran*1, 2, 3, andres lazo1, dacil rivera4, viviana toledo4, andrea moreno-switt5 and christopher hamilton-west1, 3 1department of preventive veterinary medicine, faculty of veterinary and animal science, universidad de chile, santiago, chile; 2phd program in agriculture, forestry and veterinary science, universidad de chile, santiago, chile; 3emerging and reemerging zoonoses research network, santiago, chile; 4universidad nacional andres bello, santiago, chile; 5center of veterinary medicine, universidad nacional andres bello, santiago, chile objective the purpose of this study was to detect the presence of circulating salmonella spp. on backyard production systems (bps) with poultry or swine breeding in central chile introduction characteristics and conditions of backyard production systems (bps) transform them into potential maintainers of priority zoonotic agents, like salmonella spp., highly important agent because of its impact in animal and public health (1). methods a stratified and proportional random sampling approach was performed (2), based on 15 provinces from the study area (regions of valparaiso, metropolitana and lgb o’higgins). 329 bps sampled (equivalent to 1,744 samples). stool content inoculated in test tubes with peptone water (apt, difco®) supplemented with novobiocin (sigma®), incubated for 18 to 24 hours at 37° c. subcultured on modify semisolid rappaport vassiliadis (msrv, oxoid®) agar supplemented with novobiocin, incubated for 24 to 48 hours at 41.5° c. samples compatible with growth and/or diffusion were sub-cultured by exhaustion on xylose lysine deoxychocolate (xld, difco®) agar and then incubated for 24 hours at 37° c (3). confirmation made by conventional pcr for inva genes (4). serotypes were predicted using a combination of pcr and sequencing, aimed directly at genes coding for o, h1 and h2 antigens (5). results 1,744 samples were collected belonging to the 329 bps. 15 positive bps (4.6%) detected. serotypes detected correspond to salmonella typhimurium (21.7%), followed by salmonella enteritidis (13.0%) and salmonella infantis (13.0%), salmonella hadar or istanbul (8.7%), salmonella [z42] or tenessee (4.4%), salmonella kentucky (4.4) and unknown (34.8%) (table 1). conclusions this is the first evidence of serotypes of salmonella spp. circulating at a regional level in bps from central chile. a relevant pathogen for public health. table 1. characterization of salmonella spp. circulating in bps from central chile ?? = unkonwn keywords salmonella spp.; backyard production systems; one health; backyard surveillance acknowledgments founded by fondecyt 11121389 to chw and conicyt 21130159 to ra-m. references 1. iqbal, m. 2009. controlling avian influenza infections: the challenge of the backyard poultry. journal of molecular and genetic medicine, 3, 119–120. 2. dohoo, r., martin, w. & stryhn, h. 2010. veterinary epidemiologic research, second edition. ver inc., prince edward island, canada. 3. marier, e. a., snow, l. c., floyd, t., mclaren, i. m., bianchini, j., cook, a. j. c., davies, r. h. 2014. abattoir based survey of salmonella in finishing pigs in the united kingdom 2006–2007. preventive veterinary medicine, 117, 542-553. 4. malorny, b., hoofar, j., bunge, c., helmuth, r. 2003. multicenter validation of the analytical accuracy of salmonella pcr: towards an international standard. applied and environmental microbiology, 69, 290-296. 5. ranieri, m. l., shi, c., moreno-switt, a. i., den bakker, h. c., wiedmann, m. 2013. comparison of typing methods with a new procedure based on sequence characterization for salmonella serovar prediction. journal of clinical microbiology, 51, 1786-1797. *raul alegria-moran e-mail: ralegria@veterinaria.uchile.cl online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e154, 2017 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts content analysis of tobacco-related twitter posts mark myslín*, shu-hong zhu and michael conway uc san diego, san diego, ca, usa objective we present results of a content analysis of tobacco-related twitter posts (tweets), focusing on tweets referencing e-cigarettes and hookah. introduction vast amounts of free, real-time, localizable twitter data offer new possibilities for public health workers to identify trends and attitudes that more traditional surveillance methods may not capture, particularly in emerging areas of public health concern where reliable statistical evidence is not readily accessible. existing applications include tracking public informedness during disease outbreaks [1]. twitter-based surveillance is particularly suited to new challenges in tobacco control. hookah and e-cigarettes have surged in popularity, yet regulation and public information remain sparse, despite controversial health effects [2,3]. ubiquitous online marketing of these products and their popularity among new and younger users make twitter a key resource for tobacco surveillance. methods we collected 7,300 tobacco-related twitter posts at 15-day intervals from december 2011 to july 2012, using ten general keywords such as cig* and hookah. each tweet was manually classified using a tri-axial scheme, capturing genre (firsthand experience, joke, news, …), theme (underage usage, health, social image, …), and sentiment (positive, negative, neutral). machine-learning classifiers were trained to detect tobacco-related vs. irrelevant tweets as well as each of the above categories, using naïve bayes, k-nearest neighbors, and support vector machine algorithms. finally, phi correlation coefficients were computed between each of the categories to discover emergent patterns. results the most prevalent genre of tweets was personal experience, followed by categories such as opinion, marketing, and news. the most common themes were hookah, cessation, and social image, and sentiment toward tobacco was more positive (26%) than negative (20%). the most highly correlated categories were social image–underage, marketing–e-cigs, and personal experience–positive sentiment. e-cigarettes were also correlated with positive sentiment and new users (even excluding marketing posts), while hookah was highly correlated with positive sentiment, pleasure, and social relationships. further, tweets matching the term “hookah” reflected the most positive sentiment, and “tobacco” the most negative (figure 1). finally, negative sentiment correlated most highly with social image, disgust, and non-experiential categories such as opinion and information. the best machine classification performance for tobacco vs. nontobacco tweets was achieved by an svm classifier with 82% accuracy (baseline 57%). individual categories showed similar improvements over baseline. conclusions several novel findings speak to the unique insights of twitter surveillance. sentiment toward tobacco among twitter users is more positive than negative, affirming twitter’s value in understanding positive sentiment. negative sentiment is equally useful: for example, observed high correlations between negative sentiment and social image, but not health, may usefully inform outreach strategies. twitter surveillance further reveals opportunities for education: positive sentiment toward the term “hookah” but negative sentiment toward “tobacco” suggests a disconnect in users’ perceptions of hookah’s health effects. finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, allowing for automated tobacco surveillance applications. sentiment in “hookah” tweets is disproportionately more positive than in “cig” and especially “tobacco” tweets. keywords social media; surveillance; twitter; tobacco; nlp references [1] chew, c. & eysenbach, g. pandemics in the age of twitter: content analysis of tweets during the 2009 h1n1 outbreak. plos one. 2010; 5(11):e14118. [2] ayers, j. w., ribisl, k. m., & brownstein, j. s. tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. am j prev med. 2011; 40(4):448–453. [3] grekin, e. r. & ayna, d. waterpipe smoking among college students in the united states: a review of the literature. j am coll health. 2012; 60(3):244–249. *mark myslín e-mail: mmyslin@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e66, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts use of control bar matrix for outbreak detection in syndromic surveillance system tao tao1, qi zhao1, huijian cheng2, lars palm3, xin lu4, 5, hui yuan2, xiaoxiao song1 and biao xu*1 1school of public health, fudan university, shanghai, china; 2jiangxi provincial center for disease control and prevention, nanchang, china; 3future position x, gävle, sweden; 4division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden; 5college of information systems and management, national university of defense technology, changsha, china objective to develop and test the method of incorporating different control bars for outbreak detection in syndromic surveillance system. introduction aberration detection methods are essential for analyzing and interpreting large quantity of nonspecific real-time data collected in syndromic surveillance system. however, the challenge lies in distinguishing true outbreak signals from a large amount of false alarm (1). the joint use of surveillance algorithms might be helpful to guide the decision making towards uncertain warning signals. methods a syndromic surveillance project (issc) has been implemented in rural jiangxi province of china since august 2011. doctors in the healthcare surveillance units of issc used an internet-based electronic system to collect information of daily outpatients, which included 10 infectious related symptoms. from issc database, we extracted data of fever patients reported from one township hospital in gz town between august 1st and december 31st, 2011 to conduct an exploratory study. six different control bar algorithms, which included shewart, moving average (ma), exponentially weighted moving average (ewma) and ears’ c1, c2, c3, were prospectively run among historical time series of daily fever count to simulate a real-time outbreak detection. each control bar used 7 days’ moving baseline with a lag of 2 days [the baseline for predicting day(t) starts from day(t-9) to day(t-3), c1 method used a lag of zero day]. we set the threshold of µ+2! for shewart and ma, and 2.1 for ewma c1, c2 and c3. an alarm was triggered when the observed data exceeded threshold, and the detailed information of each patient were checked for signal verification. microsoft excel 2007 was used to calculate the simulation results. results during the 5 months, gz township hospital reported 514 outpatients with fever symptom, with an average of 3.4 per day. all control bars were simultaneously operated among daily counts of fever cases. of the 153 days on surveillance, 29 triggered alarms by at least one of the control bars. nine days triggered alarms from >= 3 control bars while on one day (12/30) all 6 algorithms raised alarms. figure 1 shows the date, fever count, algorithm and warning level (color) of each alarm, which we called a control bar matrix. it can be seen that c3 and ewma present a higher sensitiveness towards tiny data change whereas c1, c2 and ma focus on large increase of data. c3 also had a memory effect on recent alarms. no infectious disease epidemic or outbreak event was confirmed within the signals. most fever patients on the nine high-warning days (red and purple) were diagnosed as upper level respiratory infection. however, we discovered that the sharp increase of fever cases on 12/30 was attributed to 5 duplicate records mistakenly input by the staff in gz hospital. conclusions by combining control bars with different characteristics, the matrix has potential ability to improve the specificity of detection while maintaining a certain degree of sensitivity. with alarms categorized into hierarchical warning levels, public health staffs can decide which alarm to investigate according to the required sensitivity of surveillance system and their own capacity of signal verification. though we did not find any outbreak event in the study, the possibility of localized influenza epidemic on high-warning days cannot be wiped out, and the matrix’s ability to detect abnormal data change was apparent. the proper combination, baseline and threshold of control bars will be further explored in the real-time surveillance situation of issc. figure 1: detailed information of alarm signals generated by control bar matrix (no-alarm days were omitted). keywords syndromic surveillance; matrix; control bar; signal acknowledgments this study was funded by [european union’s] [european atomic energy community’s] seventh framework programme ([fp7/2007-2013] [fp7/2007-2011]) under grant agreement no. [241900]. references 1. fearnley l: signals come and go: syndromic surveillance and styles of biosecurity. environment and planning a 2008, 40(7):1615-1632. *biao xu e-mail: bxu@shmu.edu.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e111, 2013 editorial: ojphi vol 3, no 2 (2011) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 editorial: ojphi vol 3, no 2 (2011) the current issue of ojphi contains eight refereed articles and a working paper. while these articles came from independent sources, they represent a common theme—the use of information science and communication technologies to advance the fields of public health practice and healthcare in general. they cover articles that use xforms standards to demonstrate bidirectional communication between provider and public health systems, explore the migration of hand-held health records from paper-based systems to electronic formats to track health risks in developing countries, utilize agent based models to study the spread of infectious diseases within communities, explore crowdinforming as a process control strategy to balance patient loads among emergency departments, analyze the use of food safety informatics as a technological tool to protect consumers in real time against foodborne illnesses, demonstrate the efficacy of the use of telemedicine to remotely supervise newly graduated general dentists in rural india, identify the factors that facilitate the adoption web-based health portals for health statistics dissemination in indonesia, and explore patient-centric modifications to the electronic medical records architecture. early detection of risks to the community such as outbreaks of infectious or foodborne diseases depend on the timely reporting of notifiable conditions to public health agencies by health care providers, laboratories, and others mandated to carry our such notifications. notifiable condition reporting and alerting are two important public health functions. the recent hitech act of 2009 emphasizes interoperability between provider systems and public health systems. in a paper entitled “applying the xforms standard to public health case reporting and alerting,” rebecca a hills et al. used xforms standards and nationally recognized technical profiles to demonstrate bi-directional communication in a health information exchange environment. the authors suggest that health departments explore the use of xforms or similar technologies to use xml documents for notifiable condition reporting and patient-specific public health alerting. patient-held health records have been used over the years to track health risks, vaccinations and other preventative health measures performed. there is evidence that mothers who have timely access to their health records and the records of their children have greater ability to track their own health and engage in prevention activities. their families tend to have better healthcare outcomes. in many developing countries, patient-held maternal and/or child health records are mostly paper-based. there is the urge in most countries to transfer these paper-based records into electronic formats. however, not enough is known about the health information seeking and utilization behavior in developing countries. in a paper entitled “patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries?” kathleen e. turner et al. explore, among other issues, maternal information needs regarding pregnancy, post-natal and infant healthcare. the study shows that that pregnant women and mothers from all different societies prefer to receive health information from a person, whether a healthcare provider, a friend, or family member. the authors recommend that, before investing significant resources in migrating current paper-based records into digital formats in developing countries, it is necessary study the information seeking behaviors of mothers and pregnant women. editorial: ojphi vol 3, no 2 (2011) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 in modelling the spread of diseases within communities or populations, researchers are increasingly using agent based models (abm) due to their potential to capture complex emergent behaviours that arise from non-linearities of human contacts during the course of an epidemic. in a paper entitled “improving agent based models and validation through data fusion” marek laskowski et al. integrate data from emerging sources within discrete time and space disease spread abms, with application to respiratory infections that are primarily contracted through direct or proximal contact. the data sources include anonymized cell phone use records that help to estimate a person’s trajectory and a smartphone application using bluetooth enabled devices as proxies for people. a major advantage of using abms is that they lend themselves to inclusion of real data which is becoming increasingly available to researchers. this work demonstrates that incorporating data from disparate sources within an abm of the spread of infectious diseases has the potential to improve the credibility and validity of the model. overcrowding in emergency departments and longer waiting times are important problems facing healthcare administrators, especially in urban clinics. in a paper entitled “load balancing at emergency departments using ‘crowdinforming’,” friesen et al. utilize simulation models to explore crowdinforming as a process control strategy to balance patient loads among emergency departments in an urban setup. results suggest that emergency department performance could benefit from load balancing efforts. this model can be incorporated into disaster preparedness strategies aimed at optimizing the performance of urban clinics during major public health emergencies. while the outbreak of foodborne diseases has become a major public health problem very little research has been carried out on the implementation of food safety informatics as a technological tool to protect consumers in real time against foodborne illnesses. government inspectors, working through local health departments, depend primarily on paper-based documentation provided by businesses to verify that the foods we consume are free of contamination. recurring violations are handled through re-inspections or, in some cases, fines, suspension of permits, or closures of food facilities. to usher food safety surveillance into the information age cynthia tucker et al, in a paper entitled “food safety informatics: a public health imperative,” developed and piloted a wireless food safety informatics tool in a university student foodservice setting. the results of the study demonstrate the use of information technology in the detection of food safety abnormalities in real-time. it is not difficult at all to forecast the use of cloud computing to scale up the adoption of food safety informatics technologies by small businesses that cannot afford their own in-house technical personnel. loss of teeth is a major oral health problem in developing countries, resulting in nutritional deficiencies that affect the quality of life. this problem is more acute in the rural areas since dental specialists prefer to locate in urban areas where they can enjoy modern amenities. in a paper entitled “effectiveness of tele-guided interceptive prosthodontic treatment in rural india,” arun keeppanasserril et.al demonstrate that remotely supervised newly graduated general dentist can use telemedicine to provide over-dentures of sufficient quality to rural populations. this strategy has the potential to improve access to care and elevate the level of dentistry available to rural populations in developing countries. the results of the study indicate that dental public health policy makers in developing countries could leverage information and communication technology infrastructure to enhance access to dental care in rural areas. editorial: ojphi vol 3, no 2 (2011) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 governments in developing and emerging economies have realized that the timely provision of accurate and updated health information is a pre-requisite for the achievement of a healthy society. most developing countries, however, are yet to transition to web-based health portals. in a paper entitled “internet-based public health information and statistics dissemination efforts for indonesia,” febiana hanani et al. describe health statistics dissemination efforts in indonesia, identify the factors that will facilitate the adoption web-based health portals, and develop a website for health statistics dissemination for indonesia. usability tests demonstrated promising results compared to the status quo. a major emphasis of the health information technology for economic and clinical health (hitech) act is the transition to accountable care organizations that use electronic medical records (emr). the goal of emr development must be to facilitate a patient-centered clinical encounter. neil nusbaum, in an article entitled “the electronic medical record and patientcentered care,” employs qualitative analysis to suggest that, patient-centric modifications to the emr architecture may also facilitate quality improvement and research activities. the working paper by wongyu lewis kim et al. describes development and implementation of a surveillance network system for emerging infectious diseases across three islands: martinique, st. lucia and dominica. the major objective of the “network of networks” surveillance system is to improve the responsiveness and representativeness of existing health systems through automated data collection, processing, and transmission of information from various sources. edward mensah, phd editor-in-chief online journal of public health informatics 1603 w taylor st, rm 757 chicago. il. 60612 email: dehasnem@uic.edu office: (312) 996-3001 ojphi-06-e88.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 124 (page number not for citation purposes) isds 2013 conference abstracts an evaluation of heat-related emergency department visits based on differences in heat syndrome definitions in northern illinois megan t. patel2 and stacey hoferka*1 1illinois deptartment of public health, chicago, il, usa; 2cook county department of public health, oak forest, il, usa � �� �� �� � � �� �� �� � objective ��� ����� ��� � ������ ��� �� ���� ���� � ��� 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keywords c���d�'����� d��� ��� ��� *stacey hoferka e-mail: stacey.hoferka@illinois.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e88, 2014 websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease ronak hamzehei1, masoumeh ansari2, shahabedin rahmatizadeh3, saeideh valizadeh-haghi4* 1. clinical research development unit of shahid beheshti hospital, hamadan university of medical sciences, hamadan, iran 2. medical educational and therapeutic center of kowsar, kurdistan university of medical sciences, sanandaj, iran 3. department of health information technology and management, school of allied medical sciences, shahid beheshti university of medical sciences, tehran, iran 4. department of medical library and information sciences, school of allied medical sciences, shahid beheshti university of medical sciences, tehran, iran abstract objectives: health service providers use internet as a tool for the spreading of health information and people often go on the web to acquire information about a disease. a wide range of information with varying qualities and by authors with varying degrees of credibility has thus become accessible by the public. most people believe that the health information available on the internet is reliable. this issue reveals the need for having a critical view of the health information available online that is directly related to people's life. the ebola epidemic is an emergency situation in the international public health domain and the internet is regarded as an important source for obtaining information on this disease. given the absence of studies on the trustworthiness of health websites on ebola, the present study was conducted to assess the trustworthiness of websites which are focused on this disease. methods: the term "ebola" was searched in google, yahoo and bing search engines. google chrome browser was used for this purpose with the settings fixed on yielding 10 results per page. the first 30 english language websites in each of the three search engines were evaluated manually by using the honcode of conduct tool. moreover, the official honcode toolbar was used to identify websites that had been officially certified by hon foundation. results: almost the half of the retrieved websites were commercial (49%). complementarity was the least-observed criterion (37%) in all the websites retrieved from all three-search engines. justifiability, transparency and financial disclosure had been completely observed (100%). discussion: the present study showed that only three criteria (justifiability, transparency and financial disclosure) out of the eight hon criteria were observed in the examined websites. like other health websites, the websites concerned with ebola are not reliable and should be used with caution. conclusion: considering the lack of a specific policy about the publication of health information on the web, it is necessary for healthcare providers to advise their patients to use only credible websites. websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi introduction nowadays the internet has become one of the dominant ways of obtaining health information and ranks first among the sources of health information. the internet is regarded as a reliable and accessible source of information for patients and other individuals [1], and one out of every three people uses the internet to obtain their health information [2]. the internet is used as a tool for the spreading of health information by health service providers and as a source for obtaining health information by patients. people often go on the web as the first step in acquiring some rudimentary data about a disease [1]. the health information available on the web allows for an interactive communication between the producers and consumers of the information. the positive features of the internet as the leading source of health information does not mean that we can overlook its negative attributes, because not all internet users know the proper method of searching for information, and the information stored on the web also lacks a proper classification, and these issues can make the search for health information difficult [3]. moreover, the internet is uncontrollable, and there is no authority for controlling the credibility and accuracy of the information available through it. furthermore, putting information on the web is easy, inexpensive or free, and anyone with any level of expertise can easily post information on this medium. a wide range of information with varying qualities and by authors with varying degrees of credibility has thus become accessible by the general public [4]. in addition, more than 80% of people believe that the health information available on the internet is reliable [5], and many of these consumers of health information do not consult with health specialists about the health information retrieved on the web[6]. this issue reveals the need for having a critical assessment of the health information available online that is directly related to people's life and health as well as for the evaluation of health websites by organizations and individuals. the health information published on the internet affects people's perceived health and the patients’ decisions about treatment choices [7]. the ebola epidemic is an emergency situation in the international public health domain [8]. the first outbreak of this disease started in 1976 in democratic republic of congo, and the other in south sudan in west africa. the 2014–2016 outbreak in west africa was the largest and most complex ebola outbreak since the virus was first discovered in 1976[9]. ebola virus disease is a seriously fatal. there is currently no standard treatment for this disease [10], and no vaccines have yet been developed to prevent it. since prevention is always better than cure, it is highly important furthermore, teaching them the criteria for assessing the trustworthiness of health websites would be helpful. keywords: patient portals, internet, online health information, ebola, self-care, patient education *correspondence: saeideh valizadeh-haghi, email: saeideh.valizadeh@gmail.com doi: 10.5210/ojphi.v10i3.9544 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:saeideh.valizadeh@gmail.com websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi for people to know about this disease, its development as well as prevention methods. in this regard, the internet is considered as an important source for obtaining this information. nonetheless, not all health websites are trustable, and some of them contain incorrect and unreliable information [3]. given the absence of studies on the trustworthiness of health websites in relation to ebola, the present study was conducted to assess the credibility of health websites that are focused on this disease. methods study samples and setting search engines are the first and main tools used to search for information on the web [11] and have a major role in obtaining medical and health information by non-specialists and specialists in medicine [12]. google, yahoo and bing are the three most popular search engines used by people around the globe [13,14]. for the present study, the term "ebola" was searched in these three search engines. google chrome browser was used for this purpose with the settings fixed on yielding 10 results per page. considering that most of search engine users only view the results appearing on the first three pages of their search [15,16], the first 30 websites in each of the three search engines were reviewed, making for a total of 90 results. the non-english websites, repetitive websites, articles in medical journals, non-relevant websites and inaccessible links were excluded from the assessment, and 43 out of the 90 retrieved websites were thus assessed (figure 1). data were collected through direct observation on dec. 5, 2017. figure 1: internet search flow diagram data collection tools there are various instructions and guidelines available for assessing the trustworthiness of health websites, and the health on the net foundation code of conduct (honcode) was selected for this research. this code is used in 102 countries for more than 7300 websites and 10 million pages as a reference for publishing health information [17]. the research tool consisted of a checklist websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi developed according to the honcode (figure 2). honcode has eight criteria: authoritative, complementarity, privacy, attribution, justifiability, transparency, financial disclosure and advertising policy[18]. this tool has been used in many studies for assessing the credibility of health websites[19-22]. the websites to be evaluated were divided into four categories: university, governmental, commercial and organizational. they were then manually assessed by ma and rh, and the validity of the resulting data was reassessed by sv and sr. the official honcode toolbar was used to identify websites that had been officially assessed. the data obtained were analyzed in spss-17. figure 2: honcode principles. the figure information is adopted from the hon website[18] results: of the 90 retrieved websites, 43 were unique and were assessed in this study. table 1 shows the frequency of the range of websites retrieved from google, bing and yahoo search engines compared to each other. with 20 results, google had the most unique pages retrieved. websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi table 1: the frequency of retrieved websites range in three search engines of bing, yahoo and google search engine number of retrieved sites duplicate websites no. of unique websites hon verified bing 30 3 8 2 yahoo 30 6 15 4 google 30 3 20 2 total 90 12 43 8 the reviewed websites were divided into four categories by domain: university, commercial, organizational and governmental (figure 3). many of the retrieved websites were commercial (49%). 49% 28% 16% 7% commercial organizational governmental educ ational figure 3: distribution of websites by domain address table 2 presents the consistency of the websites assessed with the hon criteria. complementarity was the least-observed criterion in all the websites retrieved from all three search engines. justifiability, transparency and financial disclosure had been completely observed (100%). in addition, of the 43 websites assessed, only eight had been officially approved by the honcode, and none of the other websites had fully observed the eight criteria of the hon. websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi table 2: evaluation results based on the components of hon criteria categorized by search engines search engine quality criterion google (n = 20) yahoo (n = 15) bing (n = 8) no. of websites (n = 43) authority 11 (55%) 8 (53%) 5 (62%) 24 (56%) complementarity 6 (30%) 7 (47%) 3 (37%) 16 (37%) privacy 18 (90%) 15 (100%) 8 (100%) 41 (95%) attribution 18 (90%) 15 (100%) 7 (87%) 40 (93%) justifiability 20 (100%) 15 (100%) 8 (100%) 43 (100%) transparency 20 (100%) 15 (100%) 8 (100%) 43 (100%) financial disclosure 20 (100%) 15 (100%) 8 (100%) 43 (100%) advertising policy 11 (55%) 12 (80%) 4 (50%) 27 (63%) discussion the present findings showed that the websites providing information about ebola have a poor degree of credibility, which agrees with the results of other studies conducted on health websites focusing on different issues [21,23-26]. governmental and university websites focused on health generally seek to provide educational information [27], but only 16% of the websites retrieved in the present study belonged to governmental organizations and 7% to universities. as in line with the results of previous studies [28,29], the majority of websites retrieved at the present study (43%) were commercial. compared to other websites, commercial websites have poor quality and credibility [30,31]. therefore, while searching information on ebola, people come across websites that are less valid than other websites and that may obtain incorrect information that could put their health at risk. it should be noted that merely being a university website does not ensure the higher quality of the information contained [32], and the accuracy of the information available on these websites should also be assessed. in the present study, of the 43 websites assessed, only eight had been officially assessed by the hon foundation, which agrees with the results of other studies conducted on similar subjects [33,34]. these websites are examples of websites that users will come across when searching information on ebola. to help empower patients for facing various diseases, including ebola, access to valid websites that contain high-quality information will be beneficial. non-compliance with the hon criteria in the examined websites shows that users will come across less credible websites that may contain poor-quality information, which affects their proper decision-making about the prevention and treatment of ebola. websites as a tool for public health education: determining the trustworthiness of health websites on ebola disease online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e221, 2018 ojphi health information written by specialists are more reliable [35]. nevertheless, in the present study, just 56% of the websites had specified the name and specialization of the author(s). also in a similar research that investigated testicular websites, only 32% of the examined websites had specified the author's name [33] while observing this criterion is indicative of the validity and trustworthiness of the information source [35]. the medical information provided on health websites should not replace the direct doctor-patient relationship. in fact, the information provided on websites is for the purpose of support and education and cannot replace consultation with a doctor who is directly in contact with the patient. this point should be clearly stated on health websites. in the present study, however, only 37% of the examined websites had declared this point. given that only a small percentage of people consult with their doctor regarding the health information retrieved online [36], it is imperative for health websites to pay greater attention to this criterion. so that people can be well informed and refrain from replacing their doctor with the medical information obtained from health websites and will use online information after further consultation with the doctors. the present study showed that only three criteria (justifiability, transparency and financial disclosure) out of the eight hon criteria were observed in the examined websites (table 2). same as other health websites [24,25,37,38], the websites concerned with ebola are not reliable and should be used with caution. nevertheless, it should be noted that the hon criteria do not necessarily show the quality of the information published on a website and merely indicate the credibility of the website itself. patients and other users of online health information should carefully assess the quality of the information retrieved through websites, even if this information has been obtained from credible websites. given the importance of the internet in spreading health information and its extensive use for obtaining health information and given that only a small percentage of people consult with their doctor about the medical information obtained on the web, thus, clinicians have a key role in guiding patients to using trustable websites, so that they can make informed decisions about diseases and their health. conclusions considering the lack of a specific law or policy about the publication of health information on the web, it is necessary for healthcare providers to advise their patients to use only credible websites that contain quality information. furthermore, it is necessary to teach them the criteria for assessing the trustworthiness of health websites. people's knowledge of health website evaluation tools such as the honcode for identifying and using websites with a higher credibility will help them use better and higher-quality information. people will thus be able to have a better understanding of their health and can make more informed decisions about their health and illness. also, since people use the internet to obtain health information and in the absence of a unique tool used globally for the assessment of health 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orthop relat res. 472(5), 1597-604. pubmed https://doi.org/10.1007/s11999-013-3425-5 satscan on the cloud satscan on a cloud: on-demand large scale spatial analysis of epidemics 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 satscan on a cloud: on-demand large scale spatial analysis of epidemics ronald c price 1 , warren pettey 2 , tim freeman 3 , kate keahey 3 , molly leecaster 2 , matthew samore 2 , james tobias 4 , julio c facelli 1,5 1 center for high performance computing, departments of internal medicine 2 and biomedical informatics 5 , the university of utah 3 mathematics and computer science division of argonne national laboratory 4 national center for public health informatics, centers for disease control abstract: by using cloud computing it is possible to provide ondemand resources for epidemic analysis using computer intensive applications like satscan. using 15 virtual machines (vm) on the nimbus cloud we were able to reduce the total execution time for the same ensemble run from 8896 seconds in a single machine to 842 seconds in the cloud. using the cabig tools and our iterative software development methodology the time required to complete the implementation of the satscan cloud system took approximately 200 man-hours, which represents an effort that can be secured within the resources available at state health departments. the approach proposed here is technically advantageous and practically possible. introduction satscan [1] is a computer intensive application that is commonly used to detect cluster characteristics of epidemics that provide decision support to epidemiologists. in practical applications long ensemble runs of satscan provide public health analysts with insight into the epidemics’ progression that result in higher confidence policy decisions. satscan ensemble runs test the alternative hypothesis that there is elevated disease risk within a defined cluster. the estimated p-value for these tests is based on the rank of the likelihood from the real data compared to that from the random data sets generated during the monte carlo randomizations. this rank is conditional on the random data sets generated and if the random seed were not set to a constant would vary for each replication of the software run. although only one random set is realized, it is part of a distribution of possible ranks if the random seed were allowed to vary. the variance in this distribution depends on the number of monte carlo realizations. the more monte carlo realizations that are run, the variance in the p-value will be smaller and the estimate will be closer to the true p-value. for decisions in epidemiology that involve possible implementation of contact tracing or other satscan on a cloud: on-demand large scale spatial analysis of epidemics 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 expensive and invasive processes where the statistical significance is close to the decision threshold, an estimated p-value close to the truth is especially important. an estimated p-value from a small number of monte carlo realizations has a greater chance of under or over estimating the truth and leading to an incorrect decision. an estimated p-value from a large number of monte carlo realizations is closer to the true value and is more likely to lead to a correct decision. unfortunately, ensemble runs long enough to provide adequate confidence in decisions require computational resources that are usually beyond those available at typical health department analytical facilities. cloud computing provides such resources without deploying extensive computational resources for very limited and sporadic use. moreover, cloud resources could be implemented on top of existing infrastructures dedicated to routine office tasks in public health departments or similar organizations. similar work reported in the literature includes the visual statistical data analyzer (visda), a grid-based analytical tool [2,3] that includes spatial analyses, and work done using the open-source grid-computing technology to improve processing time for geospatial syndromic surveillance [4]. both projects illustrated the value of grid computing in spatial analysis. our work leverages the cloud which has the ability to be flexible in the amount of nodes involved and is not limited by hardware constraints in terms of amount of computer resources available. moreover, the cloud provides resources at a much lower level of abstraction than grids and eliminates many of the cumbersome infrastructural and sharing agreements needed to deploy computational grids [5]. this paper reports our successful implementation of a satscan cloud system using the nimbus tp2.x software [6]. to demonstrate its use we present the analysis of epidemic data from high-fidelity, agent-based simulation of pertussis epidemics. the model was built by the virginia bioinformatics institute using their episimdemics simulation platform [7] and consists of the space-time details of 2.2 million in silico individuals modeled after utah population and physical geography [8]. this model maintains a disease profile for each individual that simulates both the presence and severity of symptoms, infectivity, and likelihood for seeking the help of a doctor. individuals who were treated became less infectious or non-infectious once treated. the disease transmission model was based on the van rie and hethcote compartmental model for pertussis [9]. methods design decisions & software implementation while the work presented here could be implemented using soap, the wsrf implementation is a better approach because it allows the integration of the cloud version of satscan into emerging public health grid infrastructures [10, 11]. at the time of this implementation the only wsrf (grid) solution for cloud computing accessible to the authors was the nimbus cloud deployed at argonne national lab. nimbus is an open source toolkit satscan on a cloud: on-demand large scale spatial analysis of epidemics 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 that allows developers to turn a cluster into an infrastructure-as-a-service (iaas) cloud (http:// workspace.globus.org). we accomplished our implementation using an iterative development approach with short iterations: iteration 1 involved installing and configuring satscan on a linux based computer at the university of utah and then wrapping satscan into a grid service; iteration 2 consisted of the deployment of this service on the argonne nimbus cloud; and, the final stage consisted of testing the performance and scalability of the cloud version of satscan. a major design decision that we faced was where to implement the cloud client logic that would provide the on-demand functionality of the satscan cloud system. the choice was either to create a grid service that had the ability to stand-up satscan grid services and another grid service that could be invoked to run satscan jobs or to create a single satscan grid service that could perform both functions. because the nimbus server provides a general interface that allows users to stand-up various virtual machines, such as the satscan grid node virtual machine, there was no need to duplicate the nimbus server-side capability to stand up a virtual machine (vm), greatly simplifying our deployment efforts. satscan is a legacy application and in order to rapidly create a satscan grid service we used introduce, gravi and the cagrid portal [12]. these tools and others developed by the cagrid project (http://cagrid.org/display/introduce/home) provide a set of tools and a layer of abstraction around globus ws-core that significantly reduce the amount of effort required to deploy grid services. introduce is an extensible toolkit to support easy development and deployment of ws/wsrf compliant grid services by hiding low level details of the globus toolkit and enabling the semi automatic implementation of stronglytyped grid services. introduce has many useful plug-ins that are also available for further assistance. we used the grid rapid application virtualization interface (gravi), a plug-in that allowed us to quickly wrap and deploy legacy application as globus compliant grid services (http://dev.globus.org/wiki/incubator/gravi). our development started by determining the parameter set required to execute satscan from the command line, then we used gravi and introduce to wrap the satscan command line interface into a grid service. the cagrid portal provided an efficient and effective way to verify that the satscan grid service was deployed correctly. the cagrid portal leverages google maps to depict grid services from the particular grid for which it has been configured. to test the deployment of the satscan grid service we used the cagrid training grid. as depicted in figure 1, the satscan grid service appears correctly on the cagrid portal implying that the deployment has been successful. to verify that the satscan grid service was functional, we invoked the service using the satscan grid service client that was also created automatically by introduce and gravi. http://cagrid.org/display/introduce/home http://dev.globus.org/wiki/incubator/gravi satscan on a cloud: on-demand large scale spatial analysis of epidemics 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 1: the cabig portal for the test grid showing that the satscan grid service has been deployed successfully in salt lake city. ut to demonstrate the dynamic scalability of our grid service with the goal to provide the on-demand satscan computer resources, we used the argonne national lab nimbus cloud. to accomplish this we used the satscan grid service implemented on a linux vm. the vm editing features available on the nimbus client-side allowed us to use an existing linux vm and edit it as needed. as part of the customization we added the cagrid software stack, the satscan grid service and configured a minimum of necessary services to initialize at boot time. at this point we were able to successfully stand-up a satscan grid node on-demand on the cloud and invoke it from a remote client. in order to automatically manage the satscan clients we created a handler using the bash programming language. this handler, satscan handler, manages all aspects of each satscan grid client including stage-in, job status progress and stage-out. the architecture of the handler is similar to the one used in our previous reported work on digital sherpa [13]. satscan on a cloud: on-demand large scale spatial analysis of epidemics 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 scaling tests for the scaling experiments we were able to stand-up up to 15 vms in the argonne nimbus cloud. the first step in this process is to dynamically acquire the resources (vms) needed for the desired run by invoking the nimbus workspace service using the nimbus workspace client. fig. 2 depicts the different systems involved in this process. figure 2: initializing dynamic allocation of vms using the nimbus workspace service once the nimbus work space service has been secured it is possible to start booting the linux vm with the satscan grid services. the system obtained after the boot process completes is depicted in fig. 3 satscan on a cloud: on-demand large scale spatial analysis of epidemics 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 3: satscan vms have been dynamically acquired and deployed in the cloud. they are ready to execute ensemble runs as they are sent by the satscan clients managed by the satscan handler. the satscan handler manages the ensemble runs, which for these tests included up to 15 satscan grid clients. as depicted in fig. 3, each satscan grid client in the satscan handler submits a single satscan run to be processed by a satscan grid service, which delivers the task to the satscan executable. upon completion the client moves the output files to the local host and the satscan handler assembles the complete output of the ensemble run. we prepared satscan’s instruction files (.prm) to run a total of 9,990 monte carlo simulations using the same data files on 5, 8, 12, 13 and 15 vms running the satscan grid services, but using different seeds for the satscan’s random number generator. the data files satscan on a cloud: on-demand large scale spatial analysis of epidemics 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 included all 2.2 million individuals divided into the 292 utah zip codes (population & coordinates files) for the full 210 simulated days (the full duration of the simulation) with resolution at the “day” level. a total of 4,521 cases were reported in the “case” file. for analysis type, we set satscan to run a retrospective space-time analysis using a poisson distribution that assumes rare events. aggregated and packaged data files for satscan, including case, coordinates and population, were approximately 1 mb and each of the satscan instruction files was approximately 8 kb. these are relatively small files and their transfer across the network does not increase the execution times significantly. each vm in the cloud received approximately equal numbers of monte carlo simulations that are inversely proportional to the total number of vms involved. for example, if we have ten vms each one received 999 monte carlo replicates to compute. to establish a base line performance we also instructed one single node to run all 9990 monte carlo replicates using the same data files and analysis instructions. we verified that satscan ensemble runs performed in parallel on the cloud produce the same results as the sequential runs. results and discussion to evaluate the potential usefulness of the satscan cloud service for prospective users, we addressed the following user-oriented questions:  when a user requests a cloud vm from the grid service, how long will it take before the vm is available for use?  when using the cloud, what is the overhead incurred by the calculations?  what is the overall speed up of the calculations and how does it reflect on the perceived turnaround? the turnaround time of the simulations, which these questions address, is paramount for epidemiologists. depending on the results of each simulation they must decide on either performing new simulations or taking preventive action through normal public health communication channels. the first question was addressed under the assumption that there is no contention for the requested resources, i.e. we measured the time required to stand up a cloud node as the cumulative time of transferring the os image that represents the vm and booting the guest os on the nimbus cloud. further delays may be observed if the cloud available to the runs is oversubscribed. in order to make boot-up faster we created an image that initializes as few services as possible. using this strategy we were able to reduce the time needed for one node to boot from 283 seconds to 207 seconds (all times plus or minus 10-15 seconds). we also tried compressing the image to improve transfer time but the overhead due to the time required to uncompress the image far outweighed the benefits. while this overhead is significant, it is only a small fraction of the total execution time of a typical ensemble run, for comparison our 9,990 monte carlo ensemble run required approximately 8,996 seconds in a single processor. satscan on a cloud: on-demand large scale spatial analysis of epidemics 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 to address the second question we ran 999 and 9,990 monte carlo replicas. the execution times without the vm and the grid services overhead were 901 seconds and 8,996 seconds, respectively. when running the satscan grid service on a vm these times increased to 1,078 seconds and 10,700 seconds, respectively. this represents an increase of 10.87 % and 18.99 % of the execution time; the slow down for a larger job can be attributed to the deeper software stack and vm cpu overhead. table 1: scalability results of the satscan grid services provided in the nimbus cloud (execution times in seconds). vms execution time speedup replicates per node 1 10700 1 9990 5 2144 4.99 1998 8 1289 8.30 1249 10 986 10.85 999 13 725 14.75 769 15 635 16.85 666 to address the third question we compared the run of a satscan job of 9,990 monte carlo replicates on a single vm using the satscan grid service with the execution of the same number of monte carlo replicas in different number of nodes using also the vms and the satscan grid services. the results are entered in table 1 and fig. 4. the excellent scaling (for a constant size problem) depicted in the table is due to the nature of ensemble runs with embarrassing parallel characteristics. in parallel computing, an embarrassingly parallel workload is one for which little or no effort is required to separate the problem into a number of parallel tasks. this is often the case when no dependency (or communication) exists between the parallel tasks. http://en.wikipedia.org/wiki/parallel_computing satscan on a cloud: on-demand large scale spatial analysis of epidemics 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 4: total execution time of satscan on the nimbus cloud as function of the number of vms used. execution times are in seconds. moreover, the overhead incurred by using a vm increases with the number of monte carlo replicates, and by running multiple copies of smaller number of replicates we are able to reduce this overhead, leading to the super-linear scaling depicted in table 1. the scaling depicted in table 1 is excellent, but for large number of vms the start up cost may become a potential bottle neck. for the levels of parallelism explored here we believe that our results demonstrate that using a cloud approach provides ondemand computational resources for epidemiology surveillance. it is remarkable that when using 15 vms the total execution time of 842 seconds, which includes 635 seconds of execution and the 207 seconds needed to stand up the vms, is one order of magnitude smaller than the 8,896 seconds required to run the complete ensemble in one machine. conclusions by using cloud computing and a computer intensive application like satscan, it is possible to provide on-demand resources for epidemic analysis. therefore, implementing a cloud across the existing internal infrastructure of a health department may be a viable approach for large-scale epidemiology surveillance on demand. we have demonstrated that when using satscan we achieved an order of magnitude improvement in the turnaround, making possible a detailed analysis that may not be possible with the typical resources existing in public health departments. the techniques used for satscan on the cloud could be generalized to any application that exhibits substantial parallel content. using the cabig tools and our software development methodology the time required to complete implementation took approximately 200 man-hours, an effort that could be secured with typical state health department resources. the approach proposed here is technically advantageous and can be practically implemented. satscan on a cloud: on-demand large scale spatial analysis of epidemics 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 acknowledgements this work was partially funded by the centers for disease control and prevention through the rocky mountain center of excellence in public health informatics # 1p01hk000069-10, national library of medicine training grant # lm007124 and ncrr clinical and translational science award 1kl2rr025763-01. conflicts of interest: the authors do not declare any conflict of interest. references [1] kulldorff m, nagarwalla n. spatial disease clusters: detection and inference. stat med 14(8):799-810. [2] wang j, li h, zhu y, yousef m, nebozhyn m, showe m, et al. visda: an open-source cabig analytical tool for data clustering and beyond. bioinformatics 2007; 23(15):2024-7. [3] zhu y, li h, miller dj, wang z, xuan j, clarke r, et al. cabig visda: modeling, visualization, and discovery for cluster analysis of genomic data. bmc bioinformatics 2008; 9:383. [4] grannis s, olson k, egg j, overhage jm. using open-source grid-computing technology to improve processing time for geospatial syndromic surveillance data. advances in disease surveillance 2006. [5] rings t, caryer g, gallop j, grabowski j, kovacikova t, schulz s, et al. grid and cloud computing: opportunities for integration with the next generation network. journal of grid computing 2009; 7(3):375-93. [6] keahey k, freeman t, editors. science clouds: early experiences in cloud computing for scientific applications,. cloud computing and its applications (cca-08); 2008; chicago, il. [7] barrett c, bisset k, eubank sg, feng x, m m, editors. episimdemics: an efficient and scalable framework for simulating the spread of infectious disease on large social networks. international conference for high performance computing, networking, storage and analysis (sc08); 2008; austin, texas. [8] pettey w, benuzillo j, walker b, parks a, kramer h, gesteland p, rubin m, drews f, livnat y, samore m. using agent-based simulations of infectious disease spread to enhance public health decision support tools. american public health association 173th annual meeting; 2009; philadelphia. [9]van rie a, hw. h. adolescent and adult pertussis vaccination: computer simulations of five new strategies. vaccine 2004; 22:3154-65. [10] staes c, xu w, lefevre s, price r, narus s, gundlapalli a, et al. a case for using grid architecture for state public health informatics: the utah perspective. bmc medical informatics and decision making 2009; 9(1):32. [11] staes cj, xu w, lefevre sd, narus sp, gundlapalli a, samore m, et al. a case for using grid architecture in state public health informatics: the utah perspective. helthgrid 2008; chicago 2008. [12] oster s, langella s, hastings s, ervin d, madduri r, phillips j, et al. cagrid 1.0: an enterprise grid infrastructure for biomedical research. j am med inform assoc 2008 marapr;15(2):138-49. satscan on a cloud: on-demand large scale spatial analysis of epidemics 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 [13] price rc, wayne bb, victor eb, julio cf. digital sherpa: a set of high level tools to manage scientific applications in a computational grid. proceedings of the 15th acm mardi gras conference: from lightweight mash-ups to lambda grids: understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the incremental adoption of key capabilities; baton rouge, louisiana: acm; 2008. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts fusion analytics: a data integration system for public health and medical disaster response decision support dina b. passman* aspr/opeo, hhs, washington, dc, usa objective the objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (ebi) solution to support decisionmaking in disasters. introduction fusion analytics is the data integration system developed by the fusion cell at the u.s. department of health and human services (hhs), office of the assistant secretary for preparedness and response (aspr). fusion analytics meaningfully augments traditional public and population health surveillance reporting by providing webbased data analysis and visualization tools. methods fusion analytics serves as a one-stop-shop for the web-based data visualizations of multiple real-time data sources within aspr. the 24-7 web availability makes it an ideal analytic tool for situational awareness and response allowing stakeholders to access the portal from any internet-enabled device without installing any software. the fusion analytics data integration system was built using off-the-shelf ebi software. fusion analytics leverages the full power of statistical analysis software and delivers reports to users in a secure web-based environment. fusion analytics provides an example of how public health staff can develop and deploy a robust public health informatics solution using an off-the shelf product and with limited development funding. it also provides the unique example of a public health information system that combines patient data for traditional disease surveillance with manpower and resource data to provide overall decision support for federal public health and medical disaster response operations. conclusions we are currently in a unique position within public health. one the one hand, we have been gaining greater and greater access to electronic data of all kinds over the last few years. on the other, we are working in a time of reduced government spending to support leveraging this data for decision support with robust analytics and visualizations. fusion analytics provides an opportunity for attendees to see how various types of data are integrated into a single application for population health decision support. it also can provide them with ideas of how they can use their own staff to create analyses and reports that support their public health activities. keywords situational awareness; public health informatics; disaster response *dina b. passman e-mail: dina.passman@hhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e207, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts implications of icd-9/10 cm transition for public health surveillance: challenges, opportunities, and lessons learned from multiple sectors of public health peter hicks*1 and atar baer2 1cdc, atlanta, ga, usa; 2public health seattle and king county, seattle, wa, usa objective to provide a forum for local, state, federal, and international public health/ health care sectors to share promising practices and lessons learned in transitioning their organizations in the use of icd-9 to icd-10 codes for their respective surveillance activities. introduction this roundtable will provide forum for a diverse set of representatives from the local, state, federal and international public health care sectors to share tools, resources, experiences, and promising practices regarding the potential impact of the transition on their surveillance activities. this forum will promote the sharing of lessons learned, foster collaborations, and facilitate the reuse of existing resources without having to “reinvent the wheel”. it is hope that this roundtable will lay the ground-work for a more formal, collaborative, and sustainable venue within isds to aid in preparing the public health surveillance community for the coming icd-9/10 cm transition. methods the moderators will engage the participants in the discussion through dialogue in how their programs are currently using icd-9 cm codes for surveillance and how the transition will impact their respective programs. keywords icd-9; icd-10; transition *peter hicks e-mail: phicks@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e194, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts biosense 2.0 kelley g. chester* rti international, milton, ga, usa objective to familiarize public health practitioners with the biosense 2.0 application and its use in all hazard surveillance. introduction biosense 2.0 protects the health of the american people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. biosense 2.0 is the result of a partnership between the centers for disease control and prevention (cdc) and the public health community to track the health and well-being of communities across the country. in 2010, the biosense program began a redesign effort to improve features such as centralized data mining and addressing concerns that the system could not meet its original objective to provide early warning or detect local outbreaks. methods using the latest technology, biosense 2.0 integrates current health data shared by health departments from a variety of sources to provide insight on the health of communities and the country. by getting more information faster, local, state, and federal public health partners can detect and respond to more outbreaks and health events more quickly. from flu outbreaks to car accidents, biosense 2.0 provides the critical data, information, and tools that public health officials need to better understand and address health problems at the local, state, regional, and national levels. also, by knowing what is happening across local borders, public health professionals can anticipate potential health problems and respond effectively to protect the health of all people. the demonstration will include a basic overview of the biosense 2.0 application and the functionality available to public health departments and their data providers. the presenter will also show an example of how biosense 2.0 can be used in a real-world public health example. conclusions over the past two years much has been accomplished during the redesign effort. biosense 2.0 was launched in november of 2011 and the collaboration between the biosense program and the public health community has yielded an application based on a user-centered design approach and built on a platform that allows for flexible data sharing across jurisdictions and with partners. the public health community has played a critical role in designing and improving the biosense 2.0 application and through continued collaboration the system will continue to improve. innovative features of the biosense 2.0 application include the use of cloud technology, a novel and flexible data sharing feature, a community driven approach, enhanced algorithms, and no cost statistical analysis tools available in the cloud. each of these features will be discussed during the presentation. keywords syndromic surveillance; informatics; situation awareness *kelley g. chester e-mail: kchester@rti.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e200, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using syndromic emergency department data to augment oral health surveillance john p. jasek*1, nicole hosseinipour1, talia rubin1 and ramona lall2 1nyc department of health and mental hygiene, bureau of health care access and planning, long island city, ny, usa; 2nyc department of health and mental hygiene, bureau of communicable diseases, long island city, ny, usa objective to utilize an established syndromic reporting system for surveillance of potentially preventable emergency department (ed) oral health visits (ohv) in new york city (nyc). introduction nyc department of health and mental hygiene recently reoriented its oral health care strategy to focus on health promotion and expanded surveillance. one surveillance challenge is the lack of timely ohv data; few dental providers are in our electronic health record project, and statewide utilization data are subject to delays. prior research has examined ohv using icd-9-cm from ed records, and has suggested that diagnostic specificity may be limited by ed providers’ lack of training in dental diagnoses (1-3). we considered our existing ed syndromic system as a complement to periodic population-based surveys. this system captures approximately 95% of all ed visits citywide; 98% of records have a completed chief complaint text field whereas only 52% contain an icd-9-cm diagnosis. methods we used chief complaint text to define ohv in two ways: (1) a basic definition comprised of ‘tooth’ or ‘gum’ in combination with a pain term (e.g., ‘ache’); (2) a more inclusive definition of either specific oral health diagnoses (e.g., ‘pulpitis’) or definition (1). for both definitions, we excluded visits likely to have stemmed from trauma (e.g., ‘accident’). data from 2009-2011 were analyzed by facility, patient age and residential zip code, and day/time using sas v9.2 (sas institute; cary, nc). results ohv in 2009-2011 totaled 72,410 (def. 1) and 103,594 (def. 2), or 0.6% and 0.9% of all ed visits, respectively. ohv (def. 2) spiked at age 18 and were highest among 18 to 29 year olds (fig. 1). neighborhood ohv rates (def. 2) ranged from 74 to 965 per 100,000 persons. 59% of ohv occurred between 8am and 6pm (fig. 2). highly specific dental conditions were rare; terms such as “tooth ache” were most common. conclusions findings suggest that ohv are a particular problem among ages 18 to 29. this pattern may reflect lower insurance coverage among young adults. the proportion of daytime visits suggests that eds are substituting for regular dental treatment and there may be opportunities to promote daytime linkages to office-based dental providers. a well-established syndromic reporting system holds promise as a method of ohv surveillance. strengths include near complete chief complaint reporting, rapid availability, and the potential to identify populations and facilities that could benefit from expanded access and preventive education. limitations include the need to gather site-specific facility information (e.g., presence of dental residents, coding practices) to better understand patterns. also, the absence of some important fields in the syndromic system (e.g., insurance coverage, income) limit assessment of the degree to which cost barriers may be driving ohv. fig 1. ohv (def.2) by age, 2009-2011 fig 2. ohv (def.2) by day/time, 2009-2011 keywords chief complaint; surveillance; syndrome definition; oral health acknowledgments the authors would like to thank the bureau of communicable diseases’ data analysis and syndromic surveillance unit for data collection and analytic guidance. references 1. pew center on the states, “a costly dental destination,” accessed august 22, 2012, http://www.pewstates.org/research/reports/a-costlydental-destination-85899379755 2. hong l, ahmed a, mccunniff m, et al. secular trends in hospital emergency department visits for dental care in kansas city, missouri, 2001-2006. public health reports; (march-april 2011) vol 126, no. 2, 210-219. 3. california health care foundation, “emergency department visits for preventable dental conditions in california,” accessed august 22, 2012, http://www.chcf.org/~/media/media%20library%20files/ pdf/e/pdf%20edusedentalconditions.pdf *john p. jasek e-mail: jjasek@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e112, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts international collaboration for improved public health emergency preparedness and response in india obaghe edeghere*1, giri shankar1, alastair bartholomew1, srikrishna ramachandra2, vivek singh2, pradeep banandur3, linda parr1, kenny yap1, babatunde olowokure1 and sue ibbotson1 1the health protection agency, birmingham, united kingdom; 2indian institute of public health, hyderabad, india; 3rajarajeswari medical college and hospital, bangalore, india objective this project aimed to contribute to ongoing efforts to improve the capability and capacity to undertake disease surveillance and emergency preparedness and response (epr) activities in india. the main outcome measure was to empower a cadre of trainers through the inter-related streams of training & education to enhance knowledge and skills and the development of collaborative networks in the regions. introduction the international health regulations (ihr) 2005, provides a framework that supports efforts to improve global health security and requires that, member states develop and strengthen systems and capacity for disease surveillance and detection and response to public health threats. to contribute to this global agenda, an international collaborative comprising of personnel from the health protection agency, west midlands, united kingdom (hpa); the indian institute of public health (iiph), hyderabad, andhra pradesh (ap) state, india and the department of community medicine, rajarajeswari medical college and hospital (rrmch), bangalore, karnataka state, india was established with funding from the hpa global health fund to deliver the objectives stated above. methods in 2010, the project partners jointly developed training materials on applied epidemiology & disease surveillance and epr using existing hpa material as the foundation. over a 2 year period, a total of two training courses per year were planned for each of the two locations in india. courses were designed to be delivered through didactic lectures, simulation exercises, workshops and group discussions at the two locations, namely bangalore and hyderabad. the target audience included senior state level programme officers, district medical and health officers, postgraduate students, academic and research staff from community medicine departments and staff from the collaborating institutions. course modules were formally evaluated by participants using structured questionnaires and an external evaluator. debrief sessions were also arranged after each course to review the key lessons and identify areas for improvement. in addition, staff exchanges of up to six weeks duration were planned during which public health specialists from both countries would spend time observing health protection systems/processes in their host country. results during january 2010 to december 2011, a total of seven (n=7) training courses were delivered in bangalore and hyderabad with approximately 231 public health personnel in attendance over the period. participants comprised of 128 personnel representing 74 organisations in 41 districts (22 districts from ap) at the hyderabad location and 103 personnel from 14 organisations (30 districts) at the bangalore location. course participants evaluated the content of the courses favourably with the majority (92%) rating the course modules as excellent or good. external evaluation of the courses was also favourable with several aspects of the course rated as good or excellent. iiph and rrmc continue to deliver the courses and in the state of karnataka, some participants at the epr course were chosen by the health ministry to be part of rapid response teams at district levels. two public health specialists from each of the indian organisations spent six (6) weeks in the united kingdom as part of the planned staff exchanges. the exchanges were assessed to have been successful with important areas for future collaboration identified including proposals to jointly develop an emergency preparedness and response manual for the indian public health audience. conclusions the implementation and maintenance of effective and sustainable systems to ensure global health security relies on a well-trained public health workforce in member states. this innovative collaborative project has gone some way towards meeting its objective of establishing and supporting a cadre of trainers to ensure sustainable improvement in public health capacity and capability in india. after the initial (training) phase of the project that was completely funded by the hpa, the partner organisations in india have worked to sustain and further develop the core objectives of this project. as a further step, course materials developed as part of this project will be used to provide a framework upon which e-learning material and postgraduate modules will be developed in each of these institutions in india. keywords surveillance; training; epr; ihr acknowledgments leanne baker – hpa, uk krishna gayathri iiph, hyderabad *obaghe edeghere e-mail: obaghe.edeghere@hpa.org.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e152, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts early detection of influenza activity using syndromic surveillance in missouri fei wu* and amy kelsey missouri department of health and senior services, jefferson city, mo, usa objective to assess how weekly percent of influenza-like illness (ili) reported via early notification of community-based epidemics (essence) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ili in essence and improve ongoing influenza surveillance efforts in missouri. introduction syndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. missouri has used emergency department (ed) visits to monitor and track seasonal influenza activity since 2006. methods the missouri essence system utilizes data from 84 hospitals, which represents up to 90 percent of all ed visits occurring in missouri statewide each day. the influenza season is defined as starting during centers for disease control and prevention (cdc) week number 40 (around the first of october) and ending on cdc week 20 of the following year, which is usually at the end of may. a confirmed influenza case is laboratory confirmed by viral culture, rapid diagnostic tests, or a four-fold rise in antibody titer between acute and convalescent serum samples. laboratory results are reported on a weekly basis. to assess the severity of influenza activity, all flu seasons were compared with the 2008-09 season, which experienced the lowest influenza activity based on laboratory data. analysis of variance (anova) was applied for this analysis using statistical analysis software (sas) (version 9.2). the standard essence ili subsyndrome includes ed chief complaints that contain keywords such as “flu”, “flulike”, “influenza” or “fever plus cough” or “fever plus sore throat”. the essence ili weekly percent is the number of ili visits divided by total ed visits. time series of weekly percent of ili in essence were compared to weekly counts of laboratory confirmed influenza cases. spearman correlation coefficients were calculated using sas. the baseline refers to the mean of three flu seasons with low influenza activity (2006-07, 2008-09 and 2010-11 seasons). the threshold was calculated as this baseline plus three standard deviations. the early warning potential of the essence weekly ili percent was evaluated for five consecutive influenza seasons, beginning in 2006. this was accomplished by calculating the time lag between the first essence ili warning versus the first lab confirmed influenza warning. a warning was identified if either lab confirmed case counts or weekly percent of ili crossed over their respective baselines. results for each influenza season evaluated, weekly ili rates reported via essence were significantly correlated with weekly counts of laboratory-confirmed influenza cases (table 1). the baseline of ili activity in essence was 1.8 ili /100 ed visits/week and the threshold was set at 4.1 ili visits per 100 ed visits/week. the essence ili baseline provided, on average, two weeks of advanced warning for seasonal influenza activity. figure 1 shows that two influenza seasons (2007-08 and 2009-10) were more severe than others examined based on the essence percent ili threshold analysis, this result is consistent with the examination of severity of influenza activity based on lab confirmed influenza data (p<0.05). conclusions the significant correlation between ili surveillance in essence and laboratory confirmed influenza cases justifies the use of weekly ili percent in essence to describe seasonal influenza activity. the essence ili baseline and threshold provided advanced warning of influenza and allowed for the classification of influenza severity in the community. table 1. correlation between laboratory confrmed influenza cases and essence ili weekly percent in five influenza seasons, 2006-2011 keywords essence; syndromic surveillance; influenza-like illness (ili); baseline; threshold acknowledgments we thank lesha peterson, rong he and peggy hartman at the missouri department of health and senior services for providing influenza case data. *fei wu e-mail: fei.wu@health.mo.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e37, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts recommendations for syndromic surveillance using inpatient and ambulatory ehr data geraldine johnson1, charles ishikawa2, rebecca zwickl2, maiko minami4, taha kass-hout3 and laura streichert*2 1ny state health dept., albany, ny, usa; 2isds, brighton, ma, usa; 3division of informatics solutions and operations, cdc, atlanta, ga, usa; 4hln consulting, san diego, ca, usa objective to develop national stage 2 meaningful use (muse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (ehr) data. introduction muse will make ehr data increasingly available for public health surveillance. for stage 2, the centers for medicare & medicaid services (cms) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. together, these data can strengthen public health surveillance capabilities and population health outcomes (figure 1). to facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines. methods isds, in collaboration with the division of informatics solutions and operations at the centers for disease control and prevention (cdc), and hln consulting, convened a multi-stakeholder workgroup of clinicians, technologists, epidemiologists, and public health officials with expertise in syndromic surveillance. recommended muse guidelines were developed by performing an environmental scan of current practice and by using an iterative, expert and community input-driven process. the workgroup developed initial guidelines and then solicited and received feedback from the stakeholder community via interview, e-mail, and structured surveys. stakeholder feedback was analyzed using quantitative and qualitative methods and used to revise the recommendations. results the muse workgroup defined electronic syndromic surveillance (ess) characteristics. specifically, data are characterized by their timeliness, sensitivity rather than specificity, population focus, limited personally identifiable information, and inclusion of all patient encounters within a specific healthcare setting (e.g., emergency department, inpatient, outpatient). based on stakeholder input (n=125) and workgroup expertise, the guidelines identify priority syndromic surveillance uses that can assist with: 1. monitoring population health; 2. informing public health services; and 3. informing interventions, health education, and policy by characterizing the burden of chronic disease and health disparities. similarly, the workgroup identified data elements to support these uses in the hospital inpatient setting and possibly in the ambulatory care setting. they were aligned to previously identified emergency department and urgent care center data elements and stage 1-2 clinical muse objectives. core data elements (required for certification) cover treating facility; patient demographics; subjective and objective clinical findings, including chief complaint, body mass index, smoking history, diagnoses; and outcomes. other data elements were designated as extended (not required for certification) or future (for future consideration). the data elements and their specifications are subject to change based on applicable state and local laws and practices. based on their findings and recommended guidelines detailed in the report, the workgroup also identified community activities and additional investments that would best support public health agencies in using ehr technology with syndromic surveillance methodologies. conclusions the widespread adoption of ehrs, catalyzed by muse, has the potential to improve population health. by identifying and describing potential ess uses of new sources of ehr data and associated data elements with the greatest utility for public health, the recommendations set forth by the isds muse workgroup will serve to facilitate the adoption of muse policy by both healthcare and public health agencies. figure 1: syndromic surveillance data can inform public health functions. keywords ehr; syndromic surveillance; meaningful use; inpatient; ambulatory acknowledgments we thank the isds muse workgroup. this work supported by cdc contract #200-2011-41831. *laura streichert e-mail: lstreichert@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e53, 2013 successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 1 ojphi successful public health information system database integration projects: a qualitative study matthew roberts, mph, drph school of public health, university of illinois, chicago, il, usa abstract objective: to explore the most important public health information system database integration project success factors to include: technological, organizational, project-specific, or external. methods: this study involved a cross-case design. cases were identified through literal replication logic and screened through a survey and review of available literature. study participants were interviewed through hour-long sessions steered by a semi-structured guide. survey responses, interview transcripts and available documents were coded and analyzed deductively, and matrices were developed to illustrate relationships. results: leadership among the project’s participants is the most important integration project success factor. this leadership factor manifests in the following ways. executive sponsors champion the initiative. informaticians facilitate communication and system requirement collection. program directors contribute substantive energy to the project and remove obstacles. some other factors also contribute to project success. for example, strong financial management and support promotes project initiation. technological aspects impact the final product’s quality. utilizing formal project management techniques, particularly the agile software development methodology, contributes to successful project resolution by ensuring daily operational effectiveness. discussion: the principal finding illustrates important contributions by project leaders, transcending those of the executive sponsor. other participants, notably informaticians and program directors, substantially contribute to the project’s success. additionally, the agile software development methodology is emerging as a successful approach to project management for these and related projects. conclusion: investing in the leadership and project management skills of database integration project participants could improve the success of future projects. state health department staff considering these projects should carefully select project participants and train them accordingly. keywords: public health information systems, leadership, systems integration, agile, agile methodology abbreviations: public health information systems (phis), child health information system (chis), association of state and territorial health officials (astho), informatics directors peer network (idpn), information technology (it), national electronic disease surveillance system (nedss) correspondence: matthew roberts, university of illinois at chicago, 217-415-7931, matthew.wesley.roberts@gmail.com doi: 10.5210/ojphi.v10i2.9221 mailto:matthew.wesley.roberts@gmail.com successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 2 ojphi copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction public health information systems (phis) are foundational components of public health infrastructure, providing how health departments collect and maintain data for public health practice [1]. these data support population health services such as controlling outbreaks or designing health promotion programs targeting teen smokers. state governments often establish phis through the state health department, and the systems primarily serve state and local health department data needs [2]. the use of information technology to develop databases is a critical aspect of phis. these databases store public health data, and advances in information technology have improved the ability to develop databases that suit specific program requirements [3]. specialized state public health databases have propagated because of this technical development ease and categorical funding incentives. computing advancements have also readily allowed for the integration of separate databases [4]. database integration often entails the development of a common database for the organization that consolidates operational data from multiple sources [5]. when individual databases are integrated, they collectively create more complete records by piecing together different data elements from different sources. access to complete records can improve coordination of public health activities and reduce costs [6]. the joint council of governmental public health agencies suggest that 1) health departments must integrate databases and 2) these integrated databases must meet information needs at the service delivery level [7]. previous child health information system (chis) database integration activities illustrate the following: the development of a specific phis database integration business case, actions required to successfully execute the project, and prior integration project evaluation efforts [8]. a large measles outbreak in the late 1980s prompted public health and healthcare leaders to evaluate data collection and usage techniques, which led to the initiation of chis database integration efforts. a workgroup identified programs such as immunizations and vital registration as a suitable starting point for the integration projects [9]. evaluation activities included documenting and studying the critical success factors for these integration projects. findings from chis integration studies informed research in related areas [10]. customized program-specific databases have proliferated but often they have not integrated with other databases throughout the health department. many public health program managers have established databases without considering broader database integration. these databases meet the individual program’s data needs, but do not address enterprise information management needs across the organization [11]. silo public health databases result in inefficiencies, such as poor disease control and outbreak response coordination; incomplete service delivery at the local level; and underperforming population health protection measures during public health emergencies [12]. while leaders integrated and evaluated some chis databases, few other successful phis successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 3 ojphi database integration initiatives have been studied. it is not known which phis database integration project factors are most important and how they impact successful public health database integration projects. this study explored factors that most contribute to successful intra-state public health information system database integration projects. technological factors, such as the quality of data within the originating data systems, impact the functionality of the integrated database. organizational factors, such as meaningfully engaged executive leadership and strategic plans, impact the agency’s readiness and commitment to the initiative. project-specific factors, such as effective governance and formal project management techniques, impact the day-to-day administration of the initiative. other factors outside the agency’s direct control, such as data privacy laws or the actions of external stakeholders can also influence the project’s success. the health resources & services administration’s sourcebook lists the nine non-technical integration project elements (factors) [13]. table 1 includes this list in addition to the technical factors. all factors have been grouped into logical domains for this study based upon prior research [14-21]. this manuscript will also describe how agile software development facilitated the daily project management for many of the phis database integration initiatives illustrated in this study. the use of the agile software development methodology in public health practice is poorly understood. agile software development, or simply “agile”, is emerging as a popular software development project management alternative to more traditional approaches such as the widely-used waterfall methodology. the waterfall model entails a prescriptive stage-oriented software development process characterized by exhaustive initial requirement collection and design phases [22]. agile is considered a “lightweight” method for developing software, with principles that focus on intensive collaboration and rapid software iteration versus extensive up-front system requirement documentation and highly-regimented planning [23]. many technology companies utilize agile to rapidly iterate software products and gain a competitive advantage. organizations have utilized agile to create software for healthcare applications [24], and others have modified aspects of the organizational culture by adopting agile practices for managing other types of projects [25]. researchers have studied their experiences in utilizing agile to create and maintain biomedical software, and found the agile approach to be a good fit for these projects [26]. following the failed rollout of healthcare.gov, some departments of the united states federal government immersed themselves in agile methodology with some success [27]. implementing the agile methodology does not come without its risks for failure, but its success factors have been studied [28]. the role of agile in the phis database integration projects identified in this study will be illustrated further. table 1. integration project success factors, grouped organizational domain leadership the project has an executive sponsor, a high level official who advocates for the project, and a champion, someone who is willing to devote a significant effort to see the project succeed. organizational and technical strategy successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 4 ojphi the project has a strategy that takes into consideration local issues such as funding, the political environment, organizational structure, the strengths of the organization, and stakeholder beliefs and values. the selected technical integration approach accounts for internal data governance and data sharing needs, which must conform to state and federal laws and agreements made with stakeholders. project oriented domain project governance the project is guided by a steering committee representing all key stakeholders. the steering committee develops the integration strategy, based on clearly defined business processes. project management the project has formalized management strategies and project management methodologies designed to assure consistent communications, accountability, and resource constraints. technical support and coordination technical information systems support and coordination is organized centrally to assure consistent support and a robust infrastructure capable of maintaining and complying with standards. a business analyst supports implementation. financial support and management the project is adequately funded and has multiple funding sources. evaluation the project has some form of qualitative and/or quantitative monitoring or evaluation that is performed regularly. external domain stakeholder involvement frequent communication with stakeholders and involvement of stakeholders in the integration project throughout the life cycle of the project contributes to its success and credibility. policy support rules, regulations, legislation, and policy advisory or policymaking bodies are supportive or at least neutral to the integration of health information systems. executive sponsors educate policymakers about sensitive issues to garner their support. technical domain source systems databases contain quality program-specific data to be contributed to the database integration project. development technology project managers select a particular technology to be utilized for the integration project including architecture, hardware, database software, data integration engines, user interface, etc. successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 5 ojphi methods a cross-case study design was used for this research. the state health department is the unit of observation, and the database integration projects are the unit of study. case selection the researcher identified cases through literal replication logic, seeking successful state health department intra-state public health information system database integration projects [29]. a threephase screening procedure was utilized, entailing 1) deploying a survey; 2) reviewing successful phis database integration projects; and 3) reputational case selection. the survey targeted state health department informatics staff as members of the association of state and territorial health officials (astho) informatics directors peer network (idpn). the survey was administered to idpn members, and the first phase of screening reduced the candidate list to cases that most closely fit the literal replication design. seven participants responded to the survey, and three met the criteria for additional follow-up. next, the researcher identified and screened cases based on criteria specified through a literature review of frequently documented, successfully completed phis database integration projects. peer reviewed literature, books, and federal agencies have documented these projects, and this literature illustrates best practices and exemplars. in particular, chis database integration research and studies evaluating the environmental public health tracking activities provide substantive insight into successful phis database integration initiatives [30]. the researcher contacted representatives from state health departments presented in these research bodies for inclusion as participants in this study. three cases were selected through this literature review process. finally, one case was identified through reputational case selection referral by the public health informatics institute. through these processes, the researcher retained seven cases that best fit the literal replication design of successful phis database integration projects. the study protocol was reviewed and approved by the university of illinois – chicago institutional review board. survey and interview questions the researcher adapted questions from a previous chis database integration study [31]. the survey questionnaire addressed case demographics and questions that pertain to the technical and project planning domains. the interview guide was piloted with four informaticians from state health departments, and the final version was organized into the domain groupings from table 1. the interview guide asked participants about the agency’s informatics projects and the impact of each domain’s factors on the integration project’s success. procedure the researcher conducted and recorded approximately one-hour semi-structured interviews with state health department informatics directors along with referral follow-up interviews of program directors, bureau chiefs, system administrators, and technical staff. the researcher interviewed twenty-five participants through nineteen interviews (some interviews included two participants) from april to september of 2016. the survey responses were then paired with associated interview transcripts. finally, the researcher obtained from participants and websites copies of pertinent successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 6 ojphi project documentation for review: strategic information technology plans and operational plans for the database integration projects; agency budgets; project meeting minutes; project charters; progress reports; policy documents; and protocols. analyses the analysis included within-case analysis followed by cross-case analysis. the within-case analysis entailed comparing and summarizing survey and interview responses, then contrasting these summaries with the document content analyses. once this was performed for each case, the cases were compared. data were analyzed using a priori theory-based codes with deductive coding: assigning labels to data to summarize the basic topic of a qualitative data passage. the coding began with an initial list of codes based upon pre-existing theory, largely from the chis database integration studies [32]. additional codes emerged inductively. all data analyses were performed using atlas.ti to code and compare thematic survey responses, interview transcripts and the document contents [33]. a common codebook was established serving as the base for all analyses. data display matrices were created to illustrate systemic relationships and the within-case and cross-case synthesis. results study participants discussed state phis database integration projects and the factors that contributed to the projects’ success. supporting documentation substantiated participants’ claims. chart 1 illustrates quote frequencies from the survey responses, interview transcripts, and codes from the document review, and it provides a basis for drawing initial conclusions. chart 1 integration factor quote frequency these counts principally illustrate how much the participants spoke about any of the integration project factors, as specified through the coding process. the technological aspects of the successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 7 ojphi integration projects are discussed most frequently, followed by leadership aspects or cross-cutting departmental projects more generally. leadership emerges as the most important factor after obtaining information about how the factors contribute to the project’s success, participants were asked which factors were the most important and why. these factors are illustrated in table 2, which organizes each interview grouped by case, with columns illustrating the most important project integration factors as reported by the interview participants and an overall conclusion from the researcher’s perspective. leadership aspects of the projects predominate in importance. in particular, participants highlighted the leadership roles of executive sponsors, program directors, and informaticians. financial support and management; project management; and the project’s technology are three other factors that regularly surfaced as important project success factors. table 2. reported most important project success factor and explanation participant by state most important reported factor(s) researcher's explanation state b participant 4 -financial support -organizational strategy -technical support & coordination organizational alignment and accreditation set the stage. executive staff serve as project champions. informatics business analysts make a difference. demonstrating value secures flexible funding. participant 5 -financial support -informatics leader dedicated funding is crucial. informaticists bridge communication gaps. participants 6 & 7 -financial support -technical support & coordination project completely stalled when the funding temporarily vanished. well-defined system requirements propel the project. participant 8 -leadership, executive* -financial support executive champions play a critical role. funding is crucial, and can be frustrating. participant 9 -leadership, executive* -financial support executive champions and project funding are crucial. state f participants 19 & 20 -leadership, program* -informatician leader team dynamics and personalities make or break the project. program-level leadership, not executive support, makes the most difference. participants 21 & 22 -leadership, program* -informatician leader -technology interaction between the tech team and business analyst/informatician is critical. division-level (not executive) leadership facilitates success. a competent and capable information technology team is key. successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 8 ojphi -technical support & coordination state a participant 1 -organizational strategy -informatician leader -policy development organizational changes linked to strategic planning can have a big impact. informatician leaders have an enterprise approach. effective policy facilitates technical decisions. participants 2 & 3 -technology -project management source data matters. dedicate a tech person to the project. strong project management includes subject matter experts. state e participant 16 -leadership, executive* -informatician leader engaged executive leadership provide vision and support, and can facilitate practical changes, such as the shift to agile project management. informatics staff lead the projects. participant 17 -project management -leadership, program* moving to agile from waterfall profoundly impacted the project's success and augmented team synergy. program directors provide substantive leadership. participant 18 -leadership, program* -financial support sustained program director leadership was crucial. agency timing was right--the will emerged. federal grants were critical. state g participant 23 -leadership, program* relationships are important. program director leadership remove obstacles and ensure team synergy. participant 24 -leadership, program* -informatician leader teamwork is most critical. the involved programs have the same program director and they frequently collaborate. lead informatician is instrumental in making it a success. state d participants 14 & 15 -financial support -leadership, executive and program* federal funding has been critical. the first phase of the projects directly involves senior leadership, whereas latter phases require program leaders to step up. state c participant 10 -financial support -informatics leader -leadership, executive* federal funding for a related initiative was leveraged for this project. informatician and it tenacity are critical. senior-level support and interest are required. participant 11 -informatics leader team dynamics achieve the outcomes. informatics capacity must be carefully maintained or it can erode. successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 9 ojphi participants 12 & 13 -technology -leadership, program* standards makes much of the work possible. program directors facilitate project success. funding plays an important prioritization role. data sharing agreements are necessary. *illustrates the distinction between executive-level and program-level leadership. further exploration into the leadership dimension illustrated important nuances. participants agreed upon the contributions of the informatics staff involved in the initiatives. informaticians seemed to facilitate the collection of system requirements, translated and communicated needs across project participants and to project sponsors, and developed collaborative team dynamics. however, the contributions of executive leadership and that of program directors were less universally-acknowledged. some participants attributed project success to the involvement of meaningfully-engaged executive sponsors, whereas others suggested success was due to the regular involvement of program directors directly impacted by the project. agile emerges as a promising project management technique project management was indicated by many participants as an important success factor for these initiatives. table 3 illustrates the project management technique used for each case’s project and a summary of the technique’s impact on the project’s success. all but one case referenced agile project management in some way, and the participants appeared to express favorable opinions about the role the agile project management methodology played in the project’s success. table 3. project management techniques by case case project management methodology project management impact on project’s success state a agile with scrum specifically scrum techniques facilitate project management. agency recently moved to agile model. “in terms of the meetings and stuff go, we are using the scrum process here, an agile scrum process for development, which was also a big change. we used to use waterfall… but it’s proven that it’s working pretty well, since we switched a couple of years ago.” state b none, although vendor possibly utilized agile minimal impact from participants’ perspective. “i think they used the agile method with short sprints.” state c agile agile methodology referenced by one participant but not by others. “we do agile development. so pretty standardized as far as project management, planning and the reporting is concerned.” state d agile regular, sustained activities move the project forward. successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 10 ojphi “[the nedss] uses the agile development approach. all the local users’ representatives really committed a lot of time to do it.” state e agile moved to agile from waterfall methodology and this change has had a substantive impact, including leading other areas of the agency to adopt the methodology. “we also have moved from a waterfall method for project management to an agile methodology. it has made all of the difference in the world; i cannot tell you what a difference it has made. it’s been incredible.” “i think that if we hadn’t had agile, we still wouldn't have a system up. we would have trashed the build and still wouldn’t have something.” state f waterfall for most projects but agile for one consider agile to be the better method but not used consistently across the organization. “and agile to me was superior and definitely what we should implement here.” state g none minimal impact from participants’ perspective. the participants described that by utilizing agile, the program staff, informatics personnel, technological developers, and others involved in the project closely collaborated in the development of the integrated database, producing a better product and overall experience than that through traditional software development techniques. one case’s participants in particular, state e, stated that the shift to agile from the waterfall methodology introduced a profound cultural shift within the agency that transcended phis database integration and general information technology project management. other areas of the agency began applying agile methodology to other projects based upon the success identified in its use with the phis database integration projects. participant 16 described this profound shift: “since that time we don’t do anything but agile. what’s really cool is some of our business side—our service areas—want to start using agile with their staff, because it holds people accountable. you have to stand up and say ‘this is what i did yesterday, and this is what i’m going to do today.’ everybody gets to hear it; everybody has to be accountable. it builds that team that you just don’t always see with things like that. it was a profound difference, i’ll tell ya.” this shift to agile methodology had substantively changed the project management experience for some of the study’s participants. participant 17 from state e suggested that the utilization of the agile methodology was the most important success factor for the project: “i definitely think it’s the agile process in and of itself. it helped the project move forward. even when we had a roadblock it’s not like everything just stopped…it created this wonderful team atmosphere where everybody knows we’re working for this same end goal.“ successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 11 ojphi “switching to agile made a huge difference, and i would recommend it for any process.” “so it’s a very interactive, engaged process. it’s incredible, i’ll tell ya. i’ve been amazed at the differences—-the speed at which a project gets done. all of that front-end time is lost.” agile clearly changed the way the health department conducted business, and positively impacted many of the integration projects described in this study. consistency across cases the case summary table (table 4) illustrates cross-case comparisons and distinct features of each. the participants’ remarks from each case seem to consistently reflect across the spectrum of cases. funding is cited as a project catalyst, and leadership involvement across multiple levels of the organization ensures project success in various ways. technological factors such as the quality and structure of source data, ensuring standards are employed, and maintaining data warehousing expertise impact the development of the integrated database. effective project management facilitates project success, and agile is regularly referenced as a useful method. however, important differences surface when participants describe the contributions of the executive leaders compared with those of program directors. as alluded to in table 2, some cases evenly highlight the contributions of both groups, whereas other cases are characterized by substantive involvement of either executive leaders or program directors, but not both. table 4. case summary table state summary state a strategic planning and policy development set the project context. the informatician plays a critical role by fully engaging team members. the quality of the source data impacts development progress. state b executive leaders align resources and seed funding. informaticians collect thorough business requirements. prior strategic planning that addressed information management principles seems to have had a lasting positive effect. state c leadership by the program staff and informatician ensure functional team dynamics. technological standards facilitate other programs’ integration efforts. executive support and interest bolster project activities. funding is critical. state d executive leadership set the project vision and initial activities, and strong program and bureau leaders are required for project sustainment. funding is essential. state e program and executive-level leadership both impact the project. an agency-wide shift to agile project management changed the organizational culture and facilitated success. informatics staff lead these initiatives. funding was crucial. successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 12 ojphi state f program-level leadership and informatics leaders promote healthy relationships and team dynamics. information technology team member permanence ensures continuity. state g program directors and informaticians ensure success by fostering functional team dynamics and relationships. discussion principal findings the study’s principal findings illustrate the complex involvement in phis database integration project leadership beyond the role of the executive leaders. much is known about how executive leaders contribute to project success through their sponsorship and support. this study suggests that other project participants, notably informaticians and program directors, substantially contribute to the project’s success. while executive involvement might be critical for initiating the project, program directors ensure project staff remain engaged, and informaticians provide a crucial role in facilitating project conversations across diverse participants. additionally, the agile software development methodology is emerging as a successful approach to project management for these and related projects. some participants claim adopting this approach introduced a dramatic shift in how the integration projects progressed, and one suggested this was the main reason that site’s project succeeded. agile improves project accountability and team member involvement and interaction, while speeding the deployment of useful software. implications this study has three primary implications. first, developing the leadership skills of informaticians, relevant program directors, and executive leaders may promote the success of these and related initiatives. since these projects require informatics savviness, these individuals may benefit from informatics training more generally, and phis database integration training specifically. secondly, project financing challenges are not new to public health departments, and this aspect seems to impact phis database integration project success, especially the launching of these initiatives. federal programs have funded these efforts in the past, and future funding could facilitate their initiation. finally, employing formal project management techniques might ensure the project runs smoothly. investing in agile methodology training and enabling its use could be an effective approach to ensuring the project is properly managed. limitations this study has three principal limitations. first, cases purposefully recruited represent an exemplary and small subset of all state public health departments. therefore, the study’s results should not be interpreted as representative of all state health departments. secondly, the data are based on survey responses, interviews, and a document review. participant responses may be affected by subjectivity, and undiscovered documentation may suggest alternative conclusions. the data had not been triangulated with onsite visits and additional observations to corroborate findings. thirdly, a single researcher performed the data collection, coding, and analysis. inclusion successful public health information system database integration projects: a qualitative study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e207, 2018 13 ojphi of another researcher could confirm codes and findings. despite these limitations, the study’s findings provide useful insight into integration project success. more research in this area is needed to further understand this topic. conclusion this study improves the understanding of the most important public health information system database integration project success factors. public health database integration needs persist, and stakeholders may use these findings to improve the likelihood of future project success. acknowledgments i would like to acknowledge the following individuals: my committee chair and advisor, dr. patrick lenihan, and committee members dr. eve pinsker, dr. seth foldy, dr. edward mensah, and dr. kee chan. i also thank the study’s participants for their insight and wisdom. financial disclosure no financial disclosures competing interests no competing 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https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19823148&dopt=abstract https://doi.org/10.1097/phh.0b013e3181abbec8 successful public health information system database integration projects: a qualitative study abstract introduction methods survey and interview questions procedure analyses results leadership emerges as the most important factor agile emerges as a promising project management technique consistency across cases discussion principal findings implications limitations conclusion acknowledgments financial disclosure competing interests references evaluating multi-purpose syndromic surveillance systems – a complex problem 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi evaluating multi-purpose syndromic surveillance systems – a complex problem roger morbey1*, gillian smith1, isabel oliver2, obaghe edeghere3, iain lake4, richard pebody5, dan todkill3, noel mccarthy6, and alex j. elliot1 1 real-time syndromic surveillance team, field service, national infection service, public health england, birmingham b2 4bh, united kingdom; 2 field service, national infection service, public health england, bristol bs1 6eh, united kingdom; 3 field epidemiology west midlands, field service, national infection service, public health england, birmingham b2 4bh, united kingdom; 4 school of environmental sciences, university of east anglia, norwich, nr4 7tj, united kingdom; 5 influenza and other respiratory virus section, immunisation and countermeasures division, national infection service, public health england, london nw9 5eq, united kingdom; 6 warwick medical school, division of health sciences, university of warwick, cv4 7al, united kingdom abstract surveillance systems need to be evaluated to understand what the system can or cannot detect. the measures commonly used to quantify detection capabilities are sensitivity, positive predictive value and timeliness. however, the practical application of these measures to multi-purpose syndromic surveillance services is complex. specifically, it is very difficult to link definitive lists of what the service is intended to detect and what was detected. first, we discuss issues arising from a multi-purpose system, which is designed to detect a wide range of health threats, and where individual indicators, e.g. ‘fever’, are also multi-purpose. secondly, we discuss different methods of defining what can be detected, including historical events and simulations. finally, we consider the additional complexity of evaluating a service which incorporates human decision-making alongside an automated detection algorithm. understanding the complexities involved in evaluating multi-purpose systems helps design appropriate methods to describe their detection capabilities. keywords: public health, epidemiology, surveillance, outbreaks abbreviations: positive predictive value (ppv) * correspondence: roger.morbey@phe.gov.uk doi: 10.5210/ojphi.v13i3.10818 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:roger.morbey@phe.gov.uk evaluating multi-purpose syndromic surveillance systems – a complex problem 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi introduction syndromic surveillance syndromic surveillance involves monitoring health care data on symptoms, signs and diagnoses to provide information for public health action [1]. syndromic surveillance is often multi-purpose, using many different syndromes or clinical indicators to monitor different conditions and events of public health interest. public health organisations may operate a syndromic surveillance ‘service’ that includes several ‘systems’, with each ‘system’ using data from one source, e.g. emergency departments, family doctors or ambulances. an on-going syndromic surveillance service is more than a series of data processing steps, it involves analysis, interpretation, reporting and enabling decision-making for appropriate action. it also requires a cycle of continuous improvement, with development of novel approaches and their subsequent application into the service. when interpreting information from syndromic surveillance systems, public health practitioners, e.g. epidemiologists or incident directors, need to understand the capabilities of those systems to support decision making and choice of actions. incident directors and other users want answers to apparently simple questions such as: “how many cases of cryptosporidiosis need to occur before your system detects an outbreak in this area?”; or “how much early warning can you provide of increases in seasonal influenza?” evaluating syndromic surveillance existing evidence base the centre for disease control and prevention (cdc) in the united states of america created a framework for evaluating a syndromic surveillance service [2]. this framework has been widely adopted and used to evaluate both syndromic and traditional non-syndromic surveillance. the framework has been applied to evaluate services both quantitatively and qualitatively [3,4]. furthermore, a wide range of statistical aberration detection algorithms have been applied to syndromic surveillance, to identify unusual exceedances that might indicate a threat to public health [5-7]. consequently, much of the published research on quantifying the public health benefit of syndromic surveillance focuses on the use of the statistical algorithms. however, retrospectively identifying that an algorithm can detect outbreaks does not inform whether appropriate public health action was taken by the syndromic surveillance service or the impact on public health [8]. it is also important to evaluate the service’s decision-making and operational processes [9]. surveillance does not end with the generation of a statistical alarm. following an alarm there will be decisions about the importance of the alarm, possibly further epidemiological investigations and analysis to summarise findings in key messages, and finally there will decisions about appropriate public health action. therefore, further work is also needed to evaluate these later stages of syndromic surveillance as well as the detection algorithms. similarly, published evaluations of syndromic systems often focus on just one disease or syndrome [10], whereas syndromic surveillance services are often multi-purpose [5]. importantly, syndromic surveillance has the potential to detect future unknown hazards, for instance symptoms resulting from a newly emerging disease, such as covid-19, for which laboratory tests may not yet be available [11]. therefore, there is a gap in our understanding of the detection capabilities of multi-purpose syndromic surveillance services because services are usually only evaluated as if they have a single purpose and only in terms of the ability to generate statistical alarms. evaluating multi-purpose syndromic surveillance systems – a complex problem 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi quantifying the detection capabilities of a multi-purpose service a complex problem ideally, simple clear quantitative measures should be provided to describe a multi-purpose service’s detection capabilities. however, published quantitative estimates for detection capabilities have usually been restricted to single diseases or to the automated part of a service. for example, it is much easier to deliver estimates structured as “the algorithm had a sensitivity of 98% and a specificity of 84% for simulated influenza outbreaks” rather than “this syndromic service resulted in appropriate action 85% of the time, with 20% of actions subsequently found to be unnecessary”. this research focus may be because quantifying the detection capabilities of a multi-purpose syndromic service is not as straightforward as it might initially appear. in fact, this is not just a complicated problem but a complex one. a complicated problem might be large and require considerable resources but can be answered by a single rule-based process, whereas a complex problem requires a range of context-specific methods to obtain answers. similar issues of complexity have been found in evaluating public health interventions [12]. here, we provide a perspective paper on the complexities involved in providing meaningful answers for what can and cannot be detected by a multi-purpose syndromic surveillance service. thus, we aim to suggest a way forward in tackling this complex problem, which can be adopted by other organisations and countries coordinating a multi-purpose syndromic surveillance service. measures for quantifying detection – laboratory tests analogy syndromic surveillance systems are often used alongside and complement traditional surveillance systems such as those based on laboratory testing. therefore, we use laboratory tests as an example to describe how detection capabilities can be quantified. then, by analogy we discuss what is required to quantify the detection capabilities of syndromic systems. quantifying laboratory tests – a ‘simple’ example a laboratory test needs to be able to identify disease rapidly with few ‘false alarms’ [13]. therefore, evaluation measures must include: a measure for how likely the test is to detect disease; a measure for how likely it is to create false alarms; and for how quickly it will detect disease. firstly, sensitivity (also called recall) can be defined as the proportion of patients with disease correctly identified by a positive test. secondly, false alarms can be quantified using, specificity or positive predictive value (ppv; also, called precision). specificity can be defined as the proportion of tested patients without a disease with a negative test result, and ppv by the proportion of positive tests that come from patients with the disease. finally, timeliness can be defined as the time between a sample being taken and the laboratory report being available. calculating these quantitative measures for a laboratory test requires a list of patients, with a variable for whether the disease or condition is present, and a linked list of samples, with a variable for whether the laboratory test was positive for the disease or condition (figure 1). evaluating multi-purpose syndromic surveillance systems – a complex problem 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi sensitivity = 𝑪𝒐𝒓𝒓𝒆𝒄𝒕 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏𝒔 𝑷𝒂𝒕𝒊𝒆𝒏𝒕𝒔 𝒘𝒊𝒕𝒉 𝒅𝒊𝒔𝒆𝒂𝒔𝒆 specificity = 𝑪𝒐𝒓𝒓𝒆𝒄𝒕 𝒓𝒆𝒂𝒔𝒔𝒖𝒓𝒂𝒏𝒄𝒆𝒔 𝑷𝒂𝒕𝒊𝒆𝒏𝒕𝒔 𝒘𝒊𝒕𝒉 𝒏𝒐 𝒅𝒊𝒔𝒆𝒂𝒔𝒆 ppv = 𝑪𝒐𝒓𝒓𝒆𝒄𝒕 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏𝒔 𝑷𝒐𝒔𝒊𝒕𝒊𝒗𝒆 𝒕𝒆𝒔𝒕 𝒓𝒆𝒔𝒖𝒍𝒕𝒔 did the patient have the disease? yes no was the laboratory test positive? yes correct detection (true positive) false warning (false positive) no fail to detect (false negative) correct reassurance (true negative) figure 1. results matrix for evaluating the sensitivity and specificity of a single laboratory test quantifying syndromic surveillance – a ‘complex’ example by analogy, it should be possible to create the same quantitative measures i.e. the sensitivity, specificity, ppv and timeliness of a syndromic surveillance service (figure 2). however, instead of comparing a list of patients and test results, we need a list of events we want to detect and a linked list of detections made by the service (throughout this paper, we will use the term ‘event’ to cover all the different public health threats a service aims to detect, including outbreaks with different aetiologies, public health incidents and the impact of environmental exposures etc., figure 3). sensitivity = 𝑪𝒐𝒓𝒓𝒆𝒄𝒕 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏𝒔 𝑬𝒗𝒆𝒏𝒕𝒔 𝒐𝒄𝒄𝒖𝒓𝒊𝒏𝒈 specificity = 𝑪𝒐𝒓𝒓𝒆𝒄𝒕 𝒓𝒆𝒂𝒔𝒔𝒖𝒓𝒂𝒏𝒄𝒆𝒔 𝑵𝒐 𝒆𝒗𝒆𝒏𝒕𝒔 𝒐𝒄𝒄𝒖𝒓𝒓𝒊𝒏𝒈 ppv = 𝑪𝒐𝒓𝒓𝒆𝒄𝒕 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏𝒔 𝑨𝒍𝒍 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏𝒔 𝒓𝒆𝒑𝒐𝒓𝒕𝒆𝒅 did an event occur? yes no did the syndromic service report detection? yes correct detection (true positive) false warning (false positive) no fail to detect (false negative) correct reassurance (true negative) figure 2. results matrix for evaluating a multi-purpose syndromic surveillance service. in theory, given a linked list of events to be detected and a list of detections reported by a syndromic service, we can quantify the detection capabilities of the service. however, in practice, creating definitive linked lists of events and detections is complex. evaluating multi-purpose syndromic surveillance systems – a complex problem 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi what do we want to detect with syndromic surveillance? multi-purpose surveillance syndromic surveillance was originated to provide population-level surveillance for early warning for bioterrorism threats but it has subsequently been used for early warning of other events and is increasingly used for reassurance of the lack of adverse health impact in a specific context, or for situational awareness after a known exposure [1,14]. a multi-purpose syndromic surveillance service may have multiple objectives [2, 8, 10]: • early warning of unexpected events, e.g. bioterrorism, emerging new diseases, outbreaks; • early warning of aberrant trends by monitoring endemic or seasonal diseases, e.g. scarlet fever or seasonal influenza; • reassurance and monitoring during mass gatherings e.g. olympic and paralympic games; • situational awareness during pre-identified outbreaks or environmental incidents, e.g. covid-19, an influenza pandemic or heat wave; therefore, a multi-purpose syndromic surveillance service will need to detect a wide range of events, reflecting potential threats to public health, including infectious disease, environmental impacts and mass gatherings (figure 3). purpose objective event type c o m p re h e n siv e p o p u la tio n su rv e illa n c e provide early-warning of unexpected threats to public health epidemic of severe respiratory illness, e.g. sars, covid-19 cryptosporidium outbreaks norovirus outbreaks food poisoning outbreaks bioterrorism monitor trends to give early warning of atypical activity seasonal influenza seasonal respiratory syncytial virus scarlet fever “back to school” asthma [1] measles mumps rubella evaluating multi-purpose syndromic surveillance systems – a complex problem 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi pertussis hay fever insect bites t a r g e te d su b -g r o u p su rv e illa n c e monitoring of specific context to provide reassurance or early warning of impact on health vaccine impact volcanic ash cloud floods large industrial fires s itu a tio n a l a w a re n e ss measuring impact of known exposure out of season pandemic influenza extreme cold weather heat waves “thunderstorm asthma” [2] impact of air pollution impact of water contamination figure 3. types of events that a multi-purpose syndromic surveillance service aims to detect. compiling a list of events to be detected through multi-purpose surveillance is complex because different types of events are defined in different ways. for example, point-source outbreaks might have a clear start and end date, whilst propagated or seasonal epidemics cannot be clearly defined in this way [8]. similarly, how suspected events are validated will vary by type. for infectious diseases, laboratory reports provide a ‘gold-standard’ for incidence, however, independent data may not be available for other types of events, e.g. increase in hay fever reports. for some types of events, e.g. extreme weather or mass-gatherings, it may be easy to validate exposure but less obvious how to independently validate impact on the population’s health. consequently, we may be able to create a list of events which have been detected by other surveillance systems (but not those which haven’t), but not be certain about the timing and size of any public health impacts that the syndromic service needs to detect. obtaining historical examples it is important that syndromic services are evaluated across the full range of event types and different sizes of event [17, 18]. however, for some types of event there may be no historical data available or only a limited range of outbreak sizes, locations etc. [8]. therefore, synthetic simulated data are often used to evaluate syndromic systems [19]. there are advantages and disadvantages for using real historical events or using synthetic events, historical events may be rare whilst synthetic events may be unrealistic [20]. the main disadvantage of using synthetic events is that they require modelling assumptions, for example, healthcare seeking behaviours for a range of diseases need to be estimated from other research, which is not straightforward [21]. a commonly used approach is to ‘inject’ synthetic simulations of events into ‘real’ historic syndromic data [5]. furthermore, real scaled events can be injected to reduce modelling assumptions about the relationship between outbreak size and syndromic indicators [17, 22-24]. however, results will still depend upon assumptions about the lag between exposure, symptom onset and whether a person presents to health care. evaluating multi-purpose syndromic surveillance systems – a complex problem 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi completeness of event lists to evaluate a syndromic service, the list of events to be detected must be comprehensive and exclusive (figure 3). furthermore, to estimate specificity or ppv, an identified period without such events is also needed. however, even for event types where numerous independently verifiable outbreaks are available, it may be impossible to guarantee that all events have been identified. it is perfectly plausible that syndromic data contain unverified events, for example, increases in respiratory illness have been observed in autumn that cannot be explained by comparison with laboratory data [20]. these unverified outbreaks within baseline syndromic data can result in lower specificity and ppv estimates [8, 14]. figure 4 summarizes the complexities around defining what needs to be detected by syndromic surveillance, as discussed above. reason definition is complex example little or no historical data may be available bioterrorism, newly emerging diseases simulated data is sensitive to modelling assumptions patients’ health-seeking behaviour is difficult to predict event may not be routinely monitored by nonsyndromic systems seasonal hay fever exposure may be clearly defined but impact on public health is still uncertain heat waves laboratory ‘gold-standard’ for independent verification may not exist newly emerging pathogen precise start and end date of exposure might be uncertain seasonal influenza events causing similar symptoms may occur at the same time air pollution and seasonal respiratory illness control period without events may be unavailable syndromic baseline data is rarely zero figure 4. reasons why defining ‘events’ to be detected by syndromic surveillance are complex. defining detection with syndromic surveillance whilst it is relatively straightforward to define the detection parameters for statistical algorithms [25], it becomes more complex when we consider the whole syndromic surveillance service. firstly, we need to consider how the service reports detection, which may depend on its ‘surveillance objective’. secondly, we need to decide how to link detection to events in the context of multi-purpose syndromic surveillance. objectives for a syndromic surveillance service the objective that a syndromic service is fulfilling will affect both the definition of detection and its ability to detect events. for example, when acting as an early warning system a evaluating multi-purpose syndromic surveillance systems – a complex problem ojphi syndromic service may define detection as alerting the appropriate authorities prior to any other surveillance system. successful early warning depends on a service’s routine surveillance practices and reporting arrangements. by contrast, when providing situational awareness during a known event, the multi-purpose service can focus on a geographical area and subset of syndromic indicators, which will increase the probability of detecting an impact. also, when providing situational awareness, the service may define detection as identifying small changes in trends, which would not have triggered an early warning response to a hitherto unknown event. similarly, a service that routinely monitors seasonal diseases (e.g. influenza) may have specifically developed thresholds that are more sensitive than those that warn of undefined new threats [26]. finally, the objective of a syndromic service may change when an event becomes publicly known through media reports, e.g. covid-19. moreover, syndromic indicators may be affected by changes in patient health-seeking behaviour because of increased awareness after an event [8, 10, 27], or changes in government advice e.g. during a lock-down. in summary, creating a list of detections requires consideration of whether the event was expected and the service’s objective at the time of detection. multi-purpose syndromic indicators the ability to link what is detected by syndromic surveillance to specific events is further complicated because many syndromic indicators are multi-purpose. whilst some syndromic indicators are very specific (e.g. bloody diarrhoea) others (e.g. gastrointestinal) are designed to have a high sensitivity but low specificity to maximise the chance of detecting events or to ensure that new emerging threats, such as covid-19, are captured [3, 4, 28]. these broad syndromic indicators may detect a range of different types of events. for example, generic respiratory indicators (e.g. cough or difficulty breathing) have been found to be associated with changing trends in laboratory reports for several different respiratory pathogens [20, 29-30] as well as seasonal allergies [31]. consequently, a syndromic service will often detect an increasing trend but not be able to link it to a specific event or individual organism, without further context. however, the ability to link detection to events may also depend on the objective of the surveillance system. for example, during a known laboratory-confirmed measles outbreak, a syndromic service may use a general indicator, e.g. rash, for situational awareness, which would not be considered as an effective early warning indicator for unknown measles outbreaks [10]. furthermore, when laboratory data are not available to verify causal pathogens, syndromic indicators or combinations of symptoms may be used to suggest probable causes of outbreaks [3, 32], particularly for multi-system surveillance [20]. finally, during a pandemic of an emerging disease like covid-19, new processes or diagnostic codes may be introduced which have an impact on existing syndromic indicators. discussion much of the published research evaluating syndromic surveillance focuses either on just one type of event or on the detection capabilities of statistical algorithms. we have reflected on and highlighted the complexities of evaluating and quantifying the detection capability of a multipurpose syndromic service, which may explain the lack of published evidence on this subject. however, to address questions from users of syndromic surveillance about detection capabilities, we need to avoid over-simplifications and provide descriptions which directly address the complexities and wide-ranging utility of these services. therefore, we argue that syndromic surveillance service evaluations need to measure separately different types of event that the service aims to detect and to consider all surveillance 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 evaluating multi-purpose syndromic surveillance systems – a complex problem 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi stages. whilst the authors support the use of the cdcs framework for evaluation of surveillance systems [2], we also believe the complexity of multi-purpose systems needs to be considered in such frameworks. firstly, separate answers are needed for different types of event both to address users’ specific questions and because different types of events will require different methods for evaluation. crucially, these separate evaluations should be done in the context of a multi-purpose service where other types of events can affect detection capabilities and the ability to identify causes is also addressed. secondly, syndromic services should be evaluated beyond the generation of statistical alarms to provide results that inform public health action. service evaluations should include consideration of the routine surveillance messages and the impact of public health actions for different event types. to quantify the detection capabilities of syndromic surveillance it is important to compare events that the system aims to detect with what was detected. however, in this commentary we have shown that for a multi-purpose service, defining and linking these events is complex. the complexities arise from the wide range of events covered by a multi-purpose service and the need to assess not just the performance of statistical algorithms but the whole service process. measure each event type separately when considering a multi-purpose syndromic surveillance service, no single measure can helpfully describe its detection capabilities across all the different types of events it aims to detect. therefore, it is important to consider all the different type of events to be detected and measure detection capabilities separately for each. measuring each type of event separately means that a different approach can be used for different event types, for instance how events are defined or the user questions to be addressed. involving key internal and external stakeholders (including users of the service) in the evaluation is very important to ensure relevance [17]. for example, stakeholders can steer how narrowly the event types are defined and to address issues such as whether it is sufficient to estimate detection for all gastrointestinal outbreaks or do users require separate estimates for specific pathogens e.g. cryptosporidium or rotavirus. when measuring each event type separately there is still a need to consider how other types might affect detection capabilities. for example, does the ability to detect the health impact of air pollution change during an influenza epidemic? also, where there are multi-purpose indicators, correct detection of one type of event could be considered as a false alarm for detecting another type of event. importantly, evaluating a multi-purpose service by measuring different event types separately is not the same as performing a series of parallel evaluations in each of which the service is treated as if it had only one purpose. clearly, it requires much more work to tackle each event type separately, particularly if a range of different approaches are needed. however, this will provide a much richer understanding of the service’s capabilities and enhance users’ interpretation and confidence in the service outputs. evaluating each stage in the surveillance process the automated statistical detection algorithm is just one stage in a syndromic service’s many processes [33]. the stages can be characterized as: data collection, storage and extraction; aggregation to syndromic indicators; application of detection algorithms; and interpretation, evaluating multi-purpose syndromic surveillance systems – a complex problem 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi reporting and taking action. it is important to evaluate the service as a whole, so detection involves not just automated alarms but their interpretation, prioritization, reporting and public health impact [34]. however, evaluating each stage in the process separately can provide useful insights into which factors affect the service’s ability to detect events [35]. firstly, evaluating data collection will reveal what proportion of the target population is covered by the service and whether there are any delays in receiving information. for example, a sentinel service will be unable to detect local outbreaks in locations not covered by the system [36]. secondly, the underlying codes, diagnoses or free text included in syndromic indicators will determine their sensitivity and specificity [28], for example, a multi-purpose indicator may be able to detect different diseases with varying success due to different disease characteristics [7]. evaluating detection algorithms enables users to choose the most appropriate method for their service, which may vary by event type. finally, evaluating the interpretation and reporting stage usually involves assessing which automated statistical alarms require further action, therefore this stage should improve ppv and specificity but with a cost for timeliness and possibly sensitivity [6]. considering each stage separately should enable service users to identify areas where a system can be improved, for example, what are the main causes of delays? or is more data being collected than can be analyzed? figure 5 summarizes how each stage can impact on sensitivity, ppv and timeliness as discussed above. each additional stage may introduce delays to timeliness and a drop in sensitivity but should increase the ppv. surveillance stage potential problems causing… failure to detect false alarms delays data collection, storage and extraction sentinel system does not cover location of ‘event’ data quality, duplicates, test data etc. delay between exposure and presenting to health care aggregation to syndromic indicators symptoms not covered by existing indicators similar symptoms caused by other reasons data processing application of detection algorithms alarm thresholds set too high (no alarm) or too low (more alarms than can be analysed) alarm thresholds set too low computational complexity also alarm volume impacts on next stage interpretation, reporting and taking action failure to take appropriate public health action following alarm failure to distinguish between false alarm and potential health threat staff time, waiting for ‘repeat’ alarms to provide confirmation, decision-making processes figure 5. impact on detection capabilities of different stages in syndromic surveillance. future work we have focused on the complexities surrounding evaluation of a multi-purpose syndromic service, therefore we have not considered other important issues such as cost-effectiveness or evaluating multi-purpose syndromic surveillance systems – a complex problem 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e15, 2021 ojphi the added value of additional data sources. however, understanding evaluation complexities will be useful for future studies into cost-effectiveness etc. evaluation of a multi-purpose syndromic surveillance service should not be a one-off process, it should be periodic creating a positive feedback loop. information about a service’s detection capabilities should be updated as new evidence comes to light, or in response to major incidents such as the current covid19 pandemic. also, the most valuable information for assessing a service will come from its on-going performance. therefore, a syndromic service should have clear objectives and maintain a database of past events of different types and detections to enable on-going validation [37]. the process of identifying the different types of event that the users want a multi-purpose syndromic service to detect should help identify gaps in our knowledge about service detection capabilities, and in turn, this should help guide research priorities. acknowledgements the authors would like to thank the members of phe’s real-time syndromic surveillance team who helped develop england’s complex multi-purpose syndromic surveillance system, including: amardeep bains, sally harcourt, helen hughes, paul loveridge, sue smith and ana soriano. rm, gs, il and aje are affiliated to the national institute for health research health protection research unit (nihr hpru) in emergency preparedness and response. gs, oe, il, nm and aje are affiliated to the nihr hpru in gastrointestinal infections. io is affiliated to the nihr hpru in behavioral science 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in england. epidemiol infect. 144, 2241-50. pubmed https://doi.org/10.1017/s0950268816000480 19. madouasse a, marceau a, lehebel a, brouwer-middelesch h, van schaik g, et al. 2013. evaluation of a continuous indicator for syndromic surveillance through simulation. application to vector borne disease emergence detection in cattle using milk yield. plos one. 8, e73726. pubmed https://doi.org/10.1371/journal.pone.0073726 20. van den wijngaard c, van asten l, van pelt w, nagelkerke nj, verheij r, et al. 2008. validation of syndromic surveillance for respiratory pathogen activity. emerg infect dis. 14, 917-25. pubmed https://doi.org/10.3201/eid1406.071467 21. fan y, wang y, jiang h, yang w, yu m, et al. 2014. evaluation of outbreak detection performance using multi-stream syndromic surveillance for influenza-like illness in rural hubei province, china: a temporal simulation model based on healthcare-seeking behaviors. plos one. 9, e112255. pubmed https://doi.org/10.1371/journal.pone.0112255 22. noufaily a, enki dg, farrington p, garthwaite p, andrews n, et al. 2013. an improved algorithm for outbreak detection in multiple surveillance systems. stat med. 32, 1206-22. pubmed https://doi.org/10.1002/sim.5595 23. morbey ra, elliot aj, charlett a, ibbotson s, verlander nq, et al. 2014. using public health scenarios to predict the utility of a national syndromic surveillance programme during the 2012 london olympic and paralympic games. epidemiol infect. 142, 984-93. pubmed https://doi.org/10.1017/s095026881300188x 24. colon-gonzalez fj, lake ir, morbey ra, elliot aj, pebody r, et al. 2018. a methodological framework for the evaluation of syndromic surveillance systems: a case study of england. bmc public health. 18, 544. pubmed https://doi.org/10.1186/s12889018-5422-9 25. noufaily a, morbey ra, colon-gonzalez fj, elliot aj, smith ge, et al. 2019. comparison of statistical algorithms for daily syndromic surveillance aberration detection. bioinformatics. 35, 3110-18. pubmed https://doi.org/10.1093/bioinformatics/bty997 26. vega t, lozano je, meerhoff t, snacken r, mott j, et al. 2013. influenza surveillance in europe: establishing epidemic thresholds by the moving epidemic method. influenza other respir viruses. 7, 546-58. pubmed https://doi.org/10.1111/j.17502659.2012.00422.x 27. elliot aj, hughes he, astbury j, nixon g, brierley k, et al. 2016. the potential impact of media reporting in syndromic surveillance: an example using a possible https://pubmed.ncbi.nlm.nih.gov/16177698 https://pubmed.ncbi.nlm.nih.gov/27033409 https://doi.org/10.1017/s0950268816000480 https://pubmed.ncbi.nlm.nih.gov/24069227 https://doi.org/10.1371/journal.pone.0073726 https://pubmed.ncbi.nlm.nih.gov/18507902 https://doi.org/10.3201/eid1406.071467 https://pubmed.ncbi.nlm.nih.gov/25409025 https://doi.org/10.1371/journal.pone.0112255 https://pubmed.ncbi.nlm.nih.gov/22941770 https://pubmed.ncbi.nlm.nih.gov/22941770 https://doi.org/10.1002/sim.5595 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community-based epidemics. mil med. 172, 346-52. pubmed https://doi.org/10.7205/milmed.172.4.346 29. bourgeois ft, olson kl, brownstein js, mcadam aj, mandl kd. validation of syndromic surveillance for respiratory infections. ann emerg med. 2006;47:265 e1. 30. morbey ra, elliot aj, harcourt s, smith s, de lusignan s, et al. 2018. estimating the burden on general practitioner services in england from increases in respiratory disease associated with seasonal respiratory pathogen activity. epidemiol infect. 146, 1389-96. pubmed https://doi.org/10.1017/s0950268818000262 31. wallstrom gl, hogan wr. 2007. unsupervised clustering of over-the-counter healthcare products into product categories. j biomed inform. 40, 642-48. pubmed https://doi.org/10.1016/j.jbi.2007.03.008 32. paterson bj, kool jl, durrheim dn, pavlin b. 2012. sustaining surveillance: evaluating syndromic surveillance in the pacific. glob public health. 7, 682-94. pubmed https://doi.org/10.1080/17441692.2012.699713 33. morbey ra, elliot aj, charlett a, verlander nq, andrews n, et al. 2015. the application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (rammie) method for syndromic surveillance in england. bioinformatics. 31, 3660-65. pubmed https://doi.org/10.1093/bioinformatics/btv418 34. smith ge, elliot aj, ibbotson s, morbey r, edeghere o, et al. 2017. novel public health risk assessment process developed to support syndromic surveillance for the 2012 olympic and paralympic games. j public health (oxf). 39, e111-7. pubmed 35. buckeridge dl. 2007. outbreak detection through automated surveillance: a review of the determinants of detection. j biomed inform. 40, 370-79. pubmed https://doi.org/10.1016/j.jbi.2006.09.003 36. morbey r, hughes h, smith g, challen k, hughes tc, et al. 2019. potential added value of the new emergency care dataset to ed-based public health surveillance in england: an initial concept analysis. emerg med j. 36, 459-64. pubmed 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https://pubmed.ncbi.nlm.nih.gov/31253597 https://doi.org/10.1136/emermed-2018-208323 https://pubmed.ncbi.nlm.nih.gov/27118150 https://doi.org/10.1111/tmi.12711 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts comparing syndromic surveillance and poison center data for snake bites in missouri karen h. pugh*1, amy kelsey2 and rebecca tominack3, 1 1st. louis university school of medicine and school of public health, saint louis, mo, usa; 2missouri department of health and senior services, jefferson city, mo, usa; 3missouri poison center, saint louis, mo, usa objective this study intends to use two different surveillance systems available in missouri to explore snake bite frequency and geographic distribution. introduction in 2010, there were 4,796 snake bite exposures reported to poison centers nationwide (1). health care providers frequently request help from poison centers regarding snake envenomations due to the unpredictability and complexity of prognosis and treatment. the missouri poison center (mopc) maintains a surveillance database keeping track of every phone call received. essence, a syndromic surveillance system used in missouri, enables surveillance by chief complaint of 84 different emergency departments (ed) in missouri (accounting for approximately 90% of all ed visits statewide). since calling a poison center is voluntary for health care providers, poison center data is most likely an underestimation of the true frequency of snake envenomations. comparing mopc and essence data for snake envenomations would enable the mopc to have a more accurate depiction of snake bite frequency in missouri and to see where future outreach of poison center awareness should be focused. methods archived data from toxicall®, the mopc surveillance system, was used to query the total number of snake bite cases from 01/01/2007 until 12/31/2011 called into the mopc center by hospitals that also participate essence. next, essence data was used to estimate the total number of snake envenomations presenting to eds in missouri. this was accomplished using the same date range as well as searching for key terms in the chief complaints that would signify a snake bite. the results of each datasearch were compared and contrasted by missouri region. results the toxicall® search showed a total of 324 snake bite cases. the initial essence data query showed a total of 1983 snake bite cases. after certain data exclusions, there was a total of 1763 essence snake bite visits. this suggests that approximately 18% of all snake bite visits reported in missouri essence were called into the mopc. the results are demonstrated by missouri region in figure 1. this figure also shows that the greatest number of essence visits for snake bites were reported by southwest region hospitals whereas the eastern region hospitals placed the greatest number of calls to mopc regarding snake bites. conclusions the total number of snake bite cases from missouri essence ed visits is much greater than the number of snake bites cases called into the mopc by essence participating hospitals. this underutilization of the poison center demonstrates the increased need for awareness of the mopc’s free services. in missouri, the mopc should target hospitals in the southwest region for outreach in particular based on these findings. poison centers are staffed by individuals trained in all types of poisonings and maintain a list of consulting physicians throughout the united states experienced in management and treatment of venomous snake bites (2). any heathcare facility would benefit from mopc assistance. finally, syndromic surveillance allows for quick and easy data compilation, however there are some difficulties when attempting to search for a particular condition. communication and partnership between two different public health organizations will be beneficial toward future public health studies. keywords essence; surveillance; missouri; poison center; snake references 1. bronstein ac, spyker da, cantilena lr, green jl, rumack bh, dart rc. 2010 annual report of the american association of poison control centers’ national poison data system (npds): 28th annual report. clinical toxicology. 2011;49:910-41. 2. gold bs, barish ra, dart rc. north american snake envenomation: diagnosis, treatment, and management. emerg med clin n am. 2004;22:423-43. *karen h. pugh e-mail: khill9@slu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e102, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts adaptation of guardian for syndromic surveillance during the nato summit julio c. silva1, dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, michael waddell2 and shon doseck2 1rush university, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to develop and implement a framework for special event surveillance using guardian, as well as document lessons learned postevent regarding design challenges and usability. introduction special event driven syndromic surveillance is often initiated by public health departments with limited time for development of an automated surveillance framework, which can result in heavy reliance on frontline care providers and potentially miss early signs of emerging trends. to address timelines and reliability issues, automated surveillance system are required. methods the north atlantic treaty organization (nato) summit was held in chicago, il, may 19-21, 2012. during the nato summit, the chicago department of public health (cdph) was charged with collecting and analyzing syndromic surveillance data from emergency department (ed) visits that may indicate a man-made or naturally occurring infectious disease threat. ten days prior to the nato summit surveillance period, rush university medical center (rumc) received a guidance document from cdph outlining the syndromes for systematic surveillance, specifically febrile rash illness, localized cutaneous lesion, acute febrile respiratory illness, gastrointestinal illness, botulism-like illness, hemorrhagic illness, along with unexplained deaths or severe illness potentially due to infectious disease and cases due to toxins or suspected poisoning. rumc researchers collected relevant icd-9 codes for each syndrome category. guardian (1), an automated surveillance system, was programmed to scan patient charts and match free text using national library of medicine free-text term to unique medical concept, which were further mapped to relevant icd-9 codes. the baselines were developed using ed patient data from 1/1/2010 to 12/31/2011. statistical references were established for unsmoothed, 24 hour counts (baseline = average; threshold = +2 standard deviations). during the nato surveillance timeframe (may 1326, 2012) automated results with prior reporting period’s counts, reference statistics, and charts were electronically sent to cdph. in addition, ed charge nurses made manual surveillance reports by telephone at least daily. open lines of communication were maintained between rumc and cdph during the event to discuss potential positive cases. in addition, a post-event debriefing was conducted to document lessons learned. results the automated guardian surveillance reports not only provided timely counts of potentially positive cases for each syndrome but also provided trend analysis with baseline measures. the guardian user interface was used to explain what data points could trigger positive cases. the epic system was used to review patient charts, if further explanation was necessary. the observed counts never exceeded +2 standard deviations during the nato surveillance period for any of the syndromes. based on the debriefing meeting between rumc and cdph, the top three achievements and lessons learned were as follows: 1. quick turnaround time (~ 10 days) from surveillance concept development to automated implementation using guardian 2. surveillance data was timely and reliable 3. additional statistical information was beneficial to put trends in context 4. system may be too sensitive resulting in false alarms and additional investigative burden on public health departments 5. need for development of user-interfaces with drill down capabilities to patient level data 6. clinicians don’t necessarily utilize exact terminology used in icd-9 codes which could result in undetected cases. conclusions this exercise successfully highlights rapid development and implementation of special event driven automated surveillance as well as collaborative approach between front-line entities such as emergency departments, surveillance researchers, and the department of public health. in addition, valuable lessons learned with potential solutions are documented for further refinements of such surveillance activities. keywords emergency department; nato summit; automated surveillance acknowledgments guardian is funded by us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-091-0662 and w81xwh-11-1-0711. references j. silva, d. rumoro, m. hallock, s. shah, g. gibbs, m. waddell, k. thomas, disease profile development methodology for syndromic surveillance of biological threat agents, emerging health threats journal, 2011, 4:11129. *gillian s. gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e192, 2013 design thinking to create a remote patient monitoring platform for older adults' homes 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi design thinking to create a remote patient monitoring platform for older adults' homes h. a. kolnick, jennifer e. miller, olivia dupree, lisa gualtieri, phd, scm department of public health and community medicine, tufts university school of medicine abstract how might clinicians collect the vitals needed for effective scheduled video visits for older adults? this challenge was presented by aarp to graduate students in a digital health course at tufts university school of medicine. the design thinking process was used to create a product that would meet this need, keeping the needs and constraints of older adults, especially those with chronic conditions or other barriers to health, central to the solution. the initial steps involved understanding and empathizing with the target audience through interviews and by developing personas and scenarios that identified barriers and opportunities. the later steps were to ideate potential solutions, design a prototype, and define product success. the design thinking process led to the design of home health hub, a remote patient monitoring (rpm) platform designed to meet the unique needs of older adults. additionally, home health hub can conceivably benefit all users of telehealth, regardless of health status—an important need during the covid-19 pandemic, and in general due to increased use of virtual visits. home health hub is one example of what can be achieved with the dedicated use of design thinking. the design thinking process can benefit public health practice as a whole by encouraging practitioners to delve into a problem to find the root causes and empathize with the needs and constraints of stakeholders to design innovative, human-centered solutions. *correspondence: lisa.gualtieri@tufts.edu doi: 10.5210/ojphi.v13i1.11582 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. 1. background a growing population of older adults (people 50 years of age and older) in the us and increased adoption of telehealth have given rise to a need for accessible and effective methods to gather patient vitals remotely. telehealth visits are increasing, and there is a market for and widespread interest in remote patient monitoring (rpm) devices. there are already rpm devices and systems on the market which meet the needs of select populations. the authors sought to design an rpm platform that would combine the best features of existing rpm solutions and designate a space in the home for health care to meet the needs of older adults regardless of level of mobility, digital literacy, access to transportation, and wireless internet [1]. design thinking to create a remote patient monitoring platform for older adults' homes 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi 1.1. telehealth adoption telehealth is the use of electronic information and telecommunications technologies to provide clinical health care, health education, public health and health administration over a long-distance [2]. many different technologies can be used to provide health care when a patient and provider aren’t in the same location at the same time including videoconferencing, imaging, streaming media, the internet, and wireless communication. telemedicine, on the other hand, refers specifically to remote clinical health care, while telehealth refers to a broader range of healthrelated services. in 2017, the american hospital association (aha) found that 76 percent of u.s. hospitals connect with patients and consulting practitioners using telehealth [3]. in march of 2020 early in the pandemic, telehealth visits increased by 154 percent compared to 2019. providers were forced to perfect a complicated system of health information technology virtually overnight. while use of telehealth has been steadily increasing for decades, it has recently been the focus of health providers and public health departments nationwide as covid-19 has forced people into their homes and away from hospitals to avoid infection [1]. to reduce the spread of the coronavirus, telehealth options for needed care have proliferated. it is now possible to use telehealth for an annual wellness visit, prescription consultation, dermatology, eye exams, nutrition counseling, mental health counseling, and even urgent care conditions like sinusitis, urinary tract infections, rash, or pain [4]. in addition to reduction of exposure to covid-19, there are many other benefits to the use of telehealth. telehealth can reduce the need to commute and make care more accessible for people by reducing transportation-related barriers for those with low mobility, those who live in rural areas, those who don’t drive and those who don’t have access to a vehicle or public transportation. people without access to child care, those who can’t get the time off of work, or those who live in an area with extreme heat or weather conditions can also benefit. telehealth is also often offered with little to no co-pay and allows for overall reduced spending among all stakeholders, patient, provider, and insurer alike. telehealth is particularly beneficial for older adults, the focus of this design thinking project, because it allows them to meet with their providers and own their care, regardless of level of mobility, often without help from a loved one. in more ways than one, telehealth helps to close the equity gap caused by many of the social determinants of health. 1.2. remote patient monitoring one major concern shared by patients and providers alike regards the collection of patient vitals. the need for providers to know their patients’ vital signs is particularly crucial among older adults and those with chronic conditions such as hypertension and diabetes. many solutions to this need exist in various capacities. remote vital sign collection for telehealth visits, or remote patient monitoring, can be done in a variety of ways including kiosks and home kits. kiosks refer to stations in pharmacies, grocery stores, or other public locations where patients can have their vitals checked in a self-service fashion. kits, or “home kits” refers to a suite of rpm devices that can be kept and used at home: a scale, thermometer, blood pressure cuff/monitor, blood glucose monitor, pulse oximetry, and even a smartwatch—many of which now possess electrocardiogram (ecg) capabilities, fall detection, and respiratory rate tracking. use of and design thinking to create a remote patient monitoring platform for older adults' homes 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi interest in rpm devices is at an all-time high. according to a 2019 survey conducted by a healthcare solutions provider, 64 percent of participants over the age of 40 said they would use a health monitoring device if it would reduce their number of physical trips to the doctor or hospital [5]. an analysis of the strengths, weaknesses, opportunities, and threats (swot) of kiosks and home kits was used to determine where to focus the ideation stage of the design thinking process and refine the space of possible solutions (complete swot analyses available in appendix a). in this analysis, we found that kiosks had the advantages of being cost-effective and not raising issues of storage or maintenance for patients. yet they were not accessible for patients living in rural areas or those with limited mobility. further, cleanliness and disinfection were issues, especially during a pandemic. existing home kits, while convenient and hygienic, were still found to possess several weaknesses related to digital literacy, wi-fi access, and the potential for error with non-medicalgrade products. 1.3. remote patient monitoring platforms we shifted our research from kiosks and home kits to rpm platforms providing devices for inhome use available on the market. the convenience and benefits of rpm could not be denied. studies have shown that home rpm can increase medication compliance, improve patient satisfaction, and decrease emergency room (er) visits, hospital admissions, and medical spending by all stakeholders [6]. rpm adoption by providers is high; 88 percent of healthcare providers have already invested in or are shopping rpm technologies, specifically to support chronically ill patients with increased risk for hospitalization [7]. we considered two popular platforms: livongo and 100plus. livongo, a prototype of sorts, goes above and beyond what many other platforms are willing or able to do. 100plus is simpler and similar to the majority of other platforms that exist for medicare patients only. livongo intends to empower its members with chronic conditions to live fuller and healthier lives. they have specialists available to help support the patient to navigate their treatment plan, out-ofrange readings, and any other concerns that may arise. they claim to reduce deaths from diabetes by 21 percent, reduce heart attacks by 14 percent, and reduce peripheral heart disease by 43 percent. livongo focuses on care for adults with specific health conditions and a high level of digital literacy [8]. 100plus offers a simple payment model and straightforward configuration of their devices. all of their devices are fully cellular and ready to use right out of the box—no wi-fi, bluetooth, or sim cards are needed. we believe this will be a key feature to support patients without internet or wifi access. they work exclusively with providers to meet the needs of older adults with medicare. providers bill the center for medicare and medicaid services (cms) for all cpt codes related to rpm and/or chronic care management. 100plus profits from a percentage of the provider’s reimbursement from one cpt code and a small fee charged to the patient. providers are often able to increase revenue with this model due to cms’ 2020 increase of available reimbursement potential. 100plus works specifically with medicare patients and their products do not provide individualized feedback nor do they offer patients a portal to review and store results [9]. design thinking to create a remote patient monitoring platform for older adults' homes 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi 2. target audience a majority of older adults are open to using rpm devices when the practice translates to less time spent traveling to meet with their health care providers [5]. the high level of interest, combined with the increased benefit to the age group, led the authors to identify older adults as the primary user population in the design thinking project. in consideration of family members and health care professionals who assist with and provide care to older adults, we also identified a secondary user population: family and professional caregivers. we identified which specific target populations of our overall user population would benefit most from an accessible and effective solution to collecting vitals remotely. the target populations identified were as follows: immunocompromised individuals and those with chronic conditions, those with concerns about visiting the doctor during the global covid-19 pandemic, those living in rural locations, individuals with mobility issues or transportation barriers, and finally, any older adult with a preference for telehealth. the process of defining and segmenting our audience also led the authors to identify individuals and groups who were perceived to be stakeholders in a potential solution. we determined that the main stakeholders would be patients aged 50 and older, health care providers, and insurance providers—namely, cms. cms is the largest payer of healthcare services in the country, and millions of older adults in our primary user population rely on medicare to help cover the cost of their medical bills [10]. other stakeholders we identified include producers and manufacturers of rpm devices. patients, and caregivers of patients, in long-term or skilled-care facilities would be least likely to benefit from our design-thinking solution. this is primarily due to the assumption that, in most cases, these facilities would already be in possession of devices to collect patient vitals and have health care providers on staff to conduct visits with patients. 2.1. interviews designing and conducting interviews was an opportunity to learn more about stakeholder populations who are affected by the need for accessible and effective methods to gather vitals for telehealth visits. (the complete list of interview questions available in appendix b). four interviews were completed using a convenience sample, with a focus on collecting data related to internet access, smartphone ownership, comfort and perceived skill using digital technology, history of chronic conditions, and self-collecting vitals. seventy-five percent of interviewees stated they had wi-fi, a smart phone, were comfortable using digital technology, and had a chronic condition. for patients who had taken vitals at home before, we asked several questions to understand perception and experience, including the level of perceived difficulty associated with taking their own vitals from home. certain themes arose in the interviews that influenced the design process. one interviewee in her 70s mentioned the struggle to find her international normalized ratio (inr) kit whenever she needed to test the thickness of her blood. another interviewee stated, “my whole bedroom is like a pharmacy now… i’ve got an entire cabinet of supplies here,” in talking about storing supplies as a caregiver [11]. these responses highlighted our research and reinforced the need for a centralized place in the home dedicated to health. additionally, we assessed whether interviewees believed taking vitals regularly to be important design thinking to create a remote patient monitoring platform for older adults' homes 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi and to score level of difficulty. finally, the interviews uncovered whether self-taken vitals were shared with doctors and if the measurements had any influence on the conversation that took place with their care provider. 2.2. personas and scenarios the process of designing and conducting interviews led to the development of four unique personas who could potentially benefit from rpm solutions to support their telehealth use. (a complete list of personas and scenarios is in appendix c). jim is a 78-year-old cisgender male veteran who lives in rural minnesota. he lives with multiple chronic conditions, including cardiovascular disease, ptsd, and declining mobility. his social supports include a small circle of friends and his children—however, jim values and wishes to protect his independence more as he ages. jim learned from an advertisement in the mail that his health care facility is beginning to offer telehealth visits. the telehealth option would save him a considerable amount of time, as he is accustomed to frequent travel into the city to meet with his care team. billy is a 63-year-old african american cisgender male who has had high blood pressure for 10 years. he has been managing his high blood pressure with in-person visits to the doctors yearly. he has internet access at home and a smartphone but low digital literacy. billy was recently diagnosed with type 2 diabetes and would love to learn how to safely manage his care from home. this new diabetes diagnosis will require him to visit his doctor every three months. in early march, billy visited his doctor to have his vitals checked and a1c tested. shortly after this visit, covid19 infections increased, and he was advised to stay home as much as possible. in june, he was due for more testing but was very uneasy going into the doctor's office due to the pandemic and his new diagnosis. billy would love to learn how to safely manage his care from home, especially during a global pandemic. carmen is a 56-year-old cisgender female who is recently retired and loving her new free time. she had a mild heart attack in her late forties and has since taken charge of her health in a big way. she still has bouts of hypertension and works with her doctor to keep it at bay. she has wi-fi access at home and moderate digital literacy. she has an annual appointment coming up but doesn’t want to travel all the way to see her doctor because it will mean missing the beginning of her beloved salsa class. she wants to be able to check her blood pressure and meet with her provider from home. caveh is a 33-year-old nonbinary/genderqueer physical therapist with wi-fi access and high digital literacy skills. they help take care of their father rashad who has diabetes. caveh prefers to accompany rashad to his appointments but often has to miss one due to work restraints. they recently learned about the availability of telehealth visits from rashad’s primary care provider, but caveh is worried that vital health information will be missed without access to the same tools used in the doctor’s office. design thinking to create a remote patient monitoring platform for older adults' homes 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi 3. ideation the ideation process began by revisiting all of the existing solutions discovered while doing initial research. a swot analysis showed that kiosks are not ideal for rural patients nor those with limited mobility. wearable solutions will not allow all needed health metrics to be collected like weight and blood pressure. the notion of an all-in-one wearable device was appealing but the technology does not yet exist. the evaluated platforms offered considerable products and features, but none that fully supported the four personas. after evaluating existing solutions, the authors considered how the global pandemic is forcing people to spend more time at home—and how important it is to have dedicated areas in the home for specific activities, such as a home office. a source of inspiration was products which are designed to consolidate their function and save space in the home, such as murphy beds and folding desks. initially, a pull-down/murphy bed-style compartment was considered an option for consolidating rpm and other health-related devices. it could be stored vertically and hinged to the wall for easy access when needed. this idea infiltrated the ideation process. the ideation process also focused on the rpm tools the personas would need and how to organize them. beyond the concept of consolidation and having a dedicated space in the home for all rpm devices, the product needed to be usable, cellular-enabled, and have a modular design. 4. introducing: home health hub home health hub is the culmination of research, interviews, persona and scenario development, and ideation. rpm-focused companies such as livongo and 100plus have already demonstrated success bringing rpm tools to patients in need of vital collection. home health hub, goes a step further by offering a place in the home where all rpm devices can be stored and conveniently accessed. home health hub is designed to reduce the likelihood for devices to get lost by consolidating multiple devices into one solution. it also opens opportunities to solve other problems, such as where patients can safely store medications. home health hub creates one space in the home dedicated to health—offering a suite of rpm devices consolidated and housed in a modular, space-saving location which can also serve as a desk for patients to complete their telehealth visits. 4.1. prototype our design thinking process led to the development of a low-fidelity prototype for home health hub. when not in use, home health hub folds into a size slightly larger than a standard briefcase (see figure 1). the system features retractable handles which allow the user to easily carry it from storage to a surface for use. alternatively, home health hub can be securely attached to a wall using the included mounting hardware. the latter option allows home health hub to act as a designated space in the home for health-monitoring activities. home health hub plugs into a standard electrical outlet to supply power to the included devices. design thinking to create a remote patient monitoring platform for older adults' homes 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi figure #1 exterior view of home health hub note: a view of the exterior of home health hub from the front and side, displaying dimensions and key features. when home health hub is in use, it unfolds to reveal its function as a folding desk (figure 2). the upright side of the system includes shelves for the storage of medications and supplies. devices are stored in a modular format. there is a channel at the top of the desk to allow users to place tablets or phones upright for telehealth visits. home health hub offers five rpm devices, in addition to a cellular-enabled tablet for those patients who do not have a device at home. the five devices include a scale, thermometer, blood pressure monitor, blood glucose monitor, and smartwatch. the watch includes several features such as fitness tracking, fall detection, pulse oximetry, heart rate, ecg, as well as respiratory rate. design thinking to create a remote patient monitoring platform for older adults' homes 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi figure #2 home health hub in situ and bird’s eye view note: on the left, a view of home health hub in situ. the system is mounted to the wall and reveals the shelves allowing for the storage of medical supplies. on the right, a bird’s eye view of home health hub including rpm devices. the process of creating the low-fidelity prototype for home health hub allowed the authors to merge and cement design ideas. it further helped to generate additional plans for the design of the system and the component devices. 4.2. features all home health hub devices are fully cellular, rechargeable, and ready to use out of the box. they don’t require internet access or batteries. in the research stage of our process, digital literacy was identified as a key barrier to successful implementation of rpm devices for older adult telehealth users. cimperman and authors write that, “the level at which hts (home telehealth services) are perceived as easy to use and manage is the leading acceptance predictor in older users’ hts acceptance” [12]. this barrier presents an opportunity to design a product that is accessible to all users regardless of digital literacy. the simple patient portal provides personalized feedback on easy-to-read health metrics. it also allows its users to access community networking, and nutrition and fitness guides. technical and clinical support are standing by to guide our users through enrollment and can be reached over the phone, via text, or through our app 24/7. instant guidance is provided on out-of-range readings from expert health coaches. for interested users, the home health hub app patient portal is convenient and informative. however, use of the patient portal is completely optional and not required to get full use of the devices. varying levels of digital literacy and digital interest mean that some users may never care for the app regardless of how much customer support they receive. regardless of patient portal usage, the patient’s health metrics are sent seamlessly to their provider. design thinking to create a remote patient monitoring platform for older adults' homes 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi home health hub was designed to remove some of the most critical obstacles for providers. the provider dashboard consists of simple graphs and tables of health metrics providing a comprehensive understanding of each patient in a glance and seamlessly syncing with their ehr. home health hub strives to pay attention to compliance, so providers can focus on their patients. it is fully hipaa compliant for app and video care coordination. 4.3. payment model the authors developed a proposed payment model that utilizes partnerships with insurance providers to pay a monthly capitated rate for each user. additionally, cms has expanded reimbursements for rpm and chronic care management for the 2020 year increasing revenue potential for providers, ease of insurance coverage, and ability to offer the platform free to patients. this payment model is viable based on success in other industries. car insurance companies are lowering premiums for customers that drive safely [13]. a similar model could be applied to incentivize insurers to cover the cost of home health hub for their patients, knowing it will translate to savings [12]. additionally, adherence requirements ensure use and positive outcomes. if a patient is found to not be using their device according to treatment recommendations, they will be contacted by home health hub health coaches to troubleshoot and receive support with using their devices as directed. 5. limitations the authors designed multiple iterations of home health hub, beginning with low fidelity models and increasing the level of fidelity with each prototype; however, some limitations should be noted stemming from the context of this being work completed in a graduate digital health course. these limitations include the need to identify barriers to market entry, increase interview sample size and selection, and conduct further market research. the design thinking process established a creative framework for the authors to design a product which would meet the increasing need for older adults to connect with health care providers, monitor their health remotely, and assist with the organization of health artifacts. the proposed product should be further refined with a focus on producibility and market entry in order to increase the odds that it would be successful. the older adults and caregivers selected to be interviewed for this project were identified via a convenience sample, and a total of four interviews were conducted. the product may be better refined if informed by a larger interview pool or survey effort. a few rpm platforms were studied in the design thinking process and their best features were considered and improved upon in the creation of home health hub. a more robust comparative research analysis of existing rpm platforms and home health tools could improve the constitution of the product. design thinking to create a remote patient monitoring platform for older adults' homes 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi 6. next steps successful implementation and introduction of home health hub would be demonstrated by an overall decrease in medical spending, emergency room visits, hospital admissions, and hospital readmissions. likewise, an increase in provider revenue and medication compliance for home health hub users would be indicators of success. finally, the opportunity to partner with major healthcare providers would allow a wider audience beyond the initial target population to use and benefit from home health hub for preventative care regardless of age or health status. 7. conclusion home health hub, designed through the design thinking process, is proposed as a solution to telehealth and rpm needs. it is a place in the home designed specifically for users to be able to conveniently access their health care information, rpm devices, and providers. the authors designed a product aiming to address patient vital collection for telehealth appointments in the target population of older adults and beyond. the design thinking process carried the authors further in the development of home health hub than research would have alone. it was tempting to begin designing a product on day one—the very moment we were given a problem to solve. in retrospect, it is easy to see how you don’t know what you don’t know. the authors began the process with interviews that opened our eyes to our own unrealized judgments and biases. initially, we might not have included a patient portal in our design. but after interviewing older friends and loved ones we found that while some are less than interested in digital literacy, others are very tech-savvy and passionate about patient and provider engagement, and ownership of health metrics and records. we took the interviews and applied them to the design of our personas. the personas forced us to see the world from multiple perspectives and begin to envision a product that served them all equally and efficiently. once we had the personas in mind, we were able to clearly identify what kinds of barriers would affect each one and come up with solutions and opportunities to get beyond them. to develop the scenarios that led each persona to take an interest in home health hub, we imagined what a day in the life would be like for jim, billy, carmen, and caveh. when we began to ideate, we embodied a “yes, and” communication style and embraced each other’s ideas no matter how wacky they seemed at the onset. the “murphy bed of health,” which began as a silly injection, turned into one of the core features of our product. by the time we began prototyping home health hub, we had done enough research and ideation that we could see clearly what our target population needed and what we wanted to design. if there was a feature that seemed advantageous to include in home health hub, we tried to validate it. the authors were able to design the unicorn of all rpm platforms that addresses the limitations of existing platforms yet is feasible to implement. design thinking empowered us to work together to understand our problem and think critically and imaginatively about our solution in a way that wouldn’t otherwise have been possible. home health hub is the proud result of this process. design thinking provides a process that has the potential to solve other key public health challenges design thinking to create a remote patient monitoring platform for older adults' homes 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi by supporting a process that systematically focuses a group on creative solutions that address complex needs and constraints of stakeholder populations. references 1. covid-19 pandemic drives telehealth boom, but older adults can't connect. (2021, march 25). retrieved april 02, 2021, from https://www.ucsf.edu/news/2020/08/418201/covid-19pandemic-drives-telehealth-boom-older-adults-cant-connect 2. what is telehealth? how is telehealth different from telemedicine? (2019, october 17). retrieved december 18, 2020, from https://www.healthit.gov/faq/what-telehealth-howtelehealth-different-telemedicine 3. fact sheet. telehealth. (n.d.). retrieved december 18, 2020, from aha.org website: https://www.aha.org/factsheet/telehealth 4. understanding telehealth. (n.d.). retrieved december 18, 2020, from https://telehealth.hhs.gov/patients/understanding-telehealth/ 5. vivalnk. (2019, june 25). survey shows reducing doctor visits will drive remote patient monitoring adoption. retrieved december 18, 2020, from https://www.prnewswire.com/news-releases/survey-shows-reducing-doctor-visits-willdrive-remote-patient-monitoring-adoption-300874238.html 6. hennick c. (2020, april 27). how remote patient monitoring programs are beneficial. retrieved december 18, 2020, from publisher website: https://healthtechmagazine.net/article/2020/04/how-remote-patient-monitoring-programsare-beneficial 7. malkary g. (2019, october). trends in remote patient monitoring. retrieved december 18, 2020, from https://www.spyglassconsulting.com/abstracts/spyglass_rpm2019_abstract.pdf 8.livongo: an easy way to fit health into your life. hello.livongo.com. (n.d.). retrieved november 20, 2020, from https://hello.livongo.com/gen/tld?experiment_id=vwo168_gen 9. remote patient monitoring devices. 100plus. (n.d.). retrieved may 27, 2021 from https://www.100plus.com/ 10. quality initiatives general information. (2019, november). retrieved december 18, 2020, from https://www.cms.gov/medicare/quality-initiatives-patient-assessmentinstruments/qualityinitiativesgeninfo 11. dupree, o., kolnik, h., miller, j. (2019, november 23). personal interview https://www.healthit.gov/faq/what-telehealth-how-telehealth-different-telemedicine https://www.healthit.gov/faq/what-telehealth-how-telehealth-different-telemedicine https://hello.livongo.com/gen/tld?experiment_id=vwo168_gen https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/qualityinitiativesgeninfo https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/qualityinitiativesgeninfo design thinking to create a remote patient monitoring platform for older adults' homes 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi 12. cimperman m, brenčič mm, trkman p. 2016. analyzing older users’ home telehealth services acceptance behavior—applying an extended utaut model. int j med inform. 90, 22-31. doi:https://doi.org/10.1016/j.ijmedinf.2016.03.002. pubmed 13. metz j. (2020, december 10). usage-based insurance rewards good drivers. retrieved december 18, 2020, from https://www.forbes.com/advisor/car-insurance/usage-basedinsurance/ acknowledgments the authors express gratitude to alison bryant, phd, senior vice president, aarp research & enterprise lead, tech & digital equity, for providing the original challenge that led to this work. we also appreciate her guidance throughout the design thinking process and feedback on home health hub throughout the process. the authors also thank avi patel, md candidate. tufts school of medicine '23, for his thoughtful review of the document. appendix a swot analyses swot analysis: home kits strengths ● convenient ● no transportation required ● some have personalized messaging/coaching feature ● some are cellular weaknesses ● potential for error/not medical grade ● some require wi-fi ● require some degree of digital literacy opportunities ● emerging/unmet need due to covid19 ● wide range of design possibilities ● can be configured to meet various combinations of needs threats ● sanitation/cleanliness ● misuse due to lack of digital literacy swot analysis: kiosks strengths ● cost-effective ● doesn’t require individual purchase or storage of products weaknesses ● requires transportation/not ideal for rural health care https://doi.org/10.1016/j.ijmedinf.2016.03.002 https://pubmed.ncbi.nlm.nih.gov/27103194 design thinking to create a remote patient monitoring platform for older adults' homes 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi ● not ideal for patients with mobility concerns ● not available 24/7 ● not available for all vitals collection ● may become expensive if used often opportunities ● emerging/unmet need due to covid19 ● wide range of design possibilities ● can be configured to meet various combinations of needs threats ● pay out-of-pocket/not reimbursed by insurance ● misuse due to lack of digital literacy ● interpretation of results appendix b complete interview question list 1. do you have internet access at home? 2. do you own a smartphone? 3. are you comfortable using digital technology such as cellphones, computers, tablets? 4. do you have any chronic conditions that require regular monitoring? a. how many? 5. have you ever taken your vitals at home? a. if yes: i. are there health indicators that you monitor regularly? ii. how hard is it to take your vitals at home, on a scale of 1 (easy) to 10 (hard) 1. if score 1-4, why is it easy/relatively easy? 2. if score 5+, why is it relatively hard/hard? 6. how did you share your results with your doctor? 7. how much did your self-taken vitals influence your conversation with your doctor? a. if no: i. why not? (lack of resources, etc.) 8. do you think it’s important to check your vitals regularly? why or why not? appendix c complete list of personas and scenarios name jim age 78 design thinking to create a remote patient monitoring platform for older adults' homes 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi sex/gender cisgender male race/ethnicit y white (non-hispanic/latinx) marital status widower education level high school graduate location rural minnesota brief description jim is a veteran living in a rural setting, with mobility issues and chronic conditions (cardiovascular disease and ptsd). jim does not have wifi or a smartphone. he has an independent attitude and is somewhat introverted. his children and small circle of friends are his social supports. scenario jim has learned through a mailing advertisement that his psychiatrist and primary care provider, both associated with the same rural hospital, have begun offering virtual visits. jim would like to learn how to access care remotely due to his rural setting and declining mobility, but he does not feel confident that he can learn to use rpm technology on his own. jim’s children, who visit him regularly, agree it would be a good option for him and are ready to support him with setup. quote “it would be great if i could cut down on the amount i need to travel in order to see my doctors.” name billy age 63 sex/gender cisgender male race/ethnicit y black/african american marital status married education level high school graduate design thinking to create a remote patient monitoring platform for older adults' homes 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi location suburban massachusetts brief description billy lives with high blood pressure. he has internet access at home and a smartphone but has low digital literacy skills. billy was recently diagnosed with type 2 diabetes. scenario billy has been managing his high blood pressure with in-person visits to the doctors yearly. in december 2019, billy went into the doctor's office for a routine check-up and was diagnosed with type 2 diabetes. this new diabetes diagnosis will require him to visit his doctor every 3 months. in early march, billy visited his doctor to have his vitals checked and a1c tested. shortly after this visit, covid-19 hit and everyone was advised to stay in their homes. in june he was due for more testing but was very uneasy going into the doctor's office due to the current pandemic and his new diagnosis. billy would love to learn how to safely manage his care from home. quote “i want to do what’s best for my health, but i am worried about going into the doctor's office during a pandemic.” name carmen age 56 sex/gender cisgender female race/ethnicit y latina columbian marital status married education level bachelor’s degree location long island, ny brief description carmen recently retired after more than 30 years in hospital administration and is loving retired life! she can’t sit still. her weekly activities include salsa dance classes, singing in her church choir, a walking group with other women in her neighborhood, and swimming lessons. design thinking to create a remote patient monitoring platform for older adults' homes 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi scenario carmen had a mild heart attack in her late 40s and has taken charge of her health ever since. she eats well, exercises every day and is the healthiest she’s ever been. she still has bouts of high blood pressure and works with her doctor to keep it at bay. she has an annual visit coming up and laments having to go to the doctor. she’d rather be having fun! quote “i would love to have my annual visit from home. it takes too long to get to and from my doctor’s office and if i have to go in for my next appointment i’ll be late for salsa class!” name caveh age 33 sex/gender genderqueer/non-binary race/ethnicit y asian persian marital status single education level doctorate location san francisco, ca brief description caveh is a physical therapist and part-time caregiver for their father, rashad. they are tech-savvy and enthusiastic about helping rashad receive the best care possible. scenario caveh is very engaged in rashad’s health and prefers to accompany him to his doctor’s visits. unfortunately, this is not always possible due to their busy work and commute schedule. rashad has type 2 diabetes, so caveh helps rashad manage his care from home with the help of an insulin pump, regular exercise, healthy eating, stress management and regular observation of his blood pressure and cholesterol levels. caveh learns that his father’s provider is now offering virtual visits but is concerned that vital health information may be missed without access to the same tools used in the doctor’s office. design thinking to create a remote patient monitoring platform for older adults' homes 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e9, 2021 ojphi quote “if we could monitor my dad’s vitals and meet with his providers from home it would be easier for me to make sure he’s getting all the information and support he needs.” layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts geographical information systems: a tool to map and analyze disease spread indumathi srinath*1, barbara szonyi1, maria esteve-gassent1, blanca lupiani1, raju gautam1, alfonso clavijo2, sang-shin park1 and renata ivanek-miojevic1 1texas a &m, college station, tx, usa; 2texas veterinary medical diagnostic laboratories, college station, tx, usa objective use gis to illustrate and understand the association between environmental factors and spread of infectious diseases. introduction spatial methods are an important component of epidemiological research motivated by a strong correlation between disease spread and ecological factors (1). our case studies examine the relationship between environmental conditions, such as climate and location, and vector distribution and abundance. therefore, gis can be used as a platform for integrating local environmental and meteorological variables into the analysis of disease spread, which would help in surveillance and decision making. methods case study 1lyme disease -lyme disease is a tickborne infection caused by the bacterium borrelia burgdorferi. the goal of this study was to analyze the association between meteorological factors and lyme disease risk in humans in texas. a total of 1,212 cases reported from 138 counties in texas from jan 2000 to dec 2010 were analyzed. we used temperature and precipitation raster grids to generate humidity maps for texas region. our results indicated that there is a strong positive association between lyme disease incidence and humidity, with western cross timbers region having a higher risk then the low plains. case study 2spinach – motivated by the recent increase in foodborne outbreaks related to fresh produce, one of the objectives for this study was to use the geospatial analysis to elucidate factors that contribute to contamination of produce at preharvest. we collected 955 spinach samples from 13 produce farms in colorado and texas during 2010-2011 and tested the samples for listeria monocytogenes, escherichia coli o157:h7 and salmonella contamination. the spinach contamination results were then used in conjunction with the national resource information (nri) databases along with the ssurgo database to predict environmental and meteorological factors contributing to spinach contamination. our findings would help to reduce frequency of human foodborne illnesses related to fresh produce. case study 3valley fever coccidioidomycosis or valley fever (vf) is a fungal zoonosis affecting humans and a variety of animal species. in this study, we used texas veterinary medical diagnostic laboratories (tvmdl) data of all dog sera tested for coccidioidomycosis from july 1999 december 2009. census data on human population density for texas were used to determine the dog population density and identify disease clusters for the 5,871 submitted dog sera over a period of 10.5 years. both the isopleth map of the vf seropositive rates in dogs across texas and the identified spatial and spatio-temporal clusters of the disease suggested that vf occurs in the western and southwestern part of texas at a much higher rate than in other areas of the state (2). since vf is not a reportable disease in tx, dogs could be used as a sentinel for human infection. results the above studies illustrate the utility of gis as a tool in integrating different ecological factors to understand disease occurrence and spread. the geographical and temporal patterns found in these studies provide benchmark to support disease control activities in texas. additionally, the identification of high-risk areas may be useful for decision makers to improve and prevent future disease spread. conclusions spatial epidemiological research has challenges, such as dealing with coarse level and aspatial datasets. testing laboratories provide limited spatial information up to the zip code level due to the confidentiality concerns. spatial analysis of such dataset prevents research at finer resolutions (census block or block group). despite these limitations, spatial epidemiology continues to be an invaluable field in the research and surveillance of infectious disease. keywords geospatial analysis; disease mapping; environmental variables references 1. auchincloss ah, gebreab sy, mair c and diez roux av. a review of spatial methods in epidemiology, 2000–2010. annu. rev. public health. 2012; 33:107-22. 2. gautam r, srinath i, clavijo a, szonyi b, bani-yaghoub b, et al. identifying areas of high risk of human exposure to coccidioidomycosis in texas using serology data from dogs. zoonoses and public health. article first published online: 1aug,2012 *indumathi srinath e-mail: isrinath@webmail.cvm.tamu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e129, 2013 public health/health information exchange (hie) collaborative public health/ health information exchange collaborative: a model for advancing public health practice 1 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 public health/ health information exchange collaborative: a model for advancing public health practice charles magruder, centers for disease control and prevention (cdc) in early 2007, senior leadership in cdc’s new office of surveillance, epidemiology and laboratory services (osels) examined ways to improve biosurveillance capabilities in collaboration with public and private partners. after careful consideration, it was determined that many key aspects of surveillance could be enhanced through health information exchange (hie) interactions. approximately one year later in february 2008, cdc awarded three 1-year contracts (with options for 4 additional years) to address this area through an initial focus on current public health priorities, such as pandemic influenza. although only three sites were selected to become public health/hie collaboratives, the high number of qualified applicants indicated tremendous potential for future growth. the initial awardees were diverse in many ways. for example, one collaborative proposed development of an hie system in a community of about 500,000 people in washington state, while another focused its efforts on a large, metropolitan area in indiana. the third collaborative proposed development of a statewide hie in new york that would include several communities of varying sizes. in addition, the lead partner for each is different—representing the private sector, a not-for-profit research institute, and a state health department, respectively. from its inception, osels has worked collaboratively with various programs at health and human services (hhs), including the office of the national coordinator for health information technology (onc). in addition, osels has been committed to promoting the appropriate use of health it standards and providing the support needed to launch critical demonstration projects. at the same time, osels is focusing on issues that are unique to cdc and working with the biosurveillance coordination unit and other internal cdc departments. further, osels coordinates its activities with state and local health authorities in the areas supported by the public health/hie collaborative and encourages other partners, such as the association of state and territorial health officials, the council of state and territorial epidemiologists, and the national association of city and county health officials, to participate in these efforts. in a relatively short time, the collaborative have made great strides in supporting a variety of hhs and cdc initiatives and objectives. for example, an assessment of minimum biosurveillance data set acquisition capability has been completed, and plans to address shortcomings have been developed. in addition, an implementation guide for managing and transporting these data was created in accordance with mandated health information technology standards panel (hitsp). some unanticipated deliverables have been produced through this process as well. for example, the regenstrief institute, the coordinator of the indiana public health/hie collaborative, demonstrated a natural language processor for electronic laboratory reports, a breakthrough that could reduce costs and increase the amount of useable data received. public health/ health information exchange collaborative: a model for advancing public health practice 2 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 in addition, osels has strengthened its collaborative efforts with other federal agencies, working closely with hhs’s office of science and data policy to develop a more comprehensive evaluation process. osels also continues to make progress in demonstrating various aspects of the biosurveillance use case, showcasing advances in public health practice that are possible with hie collaboration. in december 2008, osels successfully demonstrated its capabilities in this area at an hhs nationwide health information network conference in washington, dc. a few months later at a healthcare information and management systems society meeting, osels demonstrated its ability to use appropriate standards to transmit deidentified, aggregated data from individual health care sources through the hie to state and local health departments. once the data were analyzed at the local and state levels, these were sent to cdc for further analysis from a national perspective. subsequently, cdc used the resulting national trend data to quickly send out public health alerts to appropriate areas of the country. thus, osels demonstrated a functional, bidirectional communication process; one in which population data can be assessed quickly and important information can be released quickly to public health and health care practitioners. in august 2009, osels completed another demonstration at the phin conference interoperability showcase. in this scenario, osels emphasized new capabilities that were developed to support h1n1 influenza surveillance activities, including a new, standardized format for h1n1 surveillance called the geocoded interoperable population summary exchange (gipse). in september 2009, the public health/hie collaborative began using the gipse format to send h1n1 influenza data to cdc. currently, the members of the collaborative are working together to modify the gipse format to address new h1n1 influenza challenges identified by cdc and epidemiologists working in state and local health departments. foremost among these challenges is the need to develop a severity index. at the same time, each member is working on new initiatives that can be supported by hie collaborations, including enhancements in case reporting, development of a quality use case to address chronic diseases, enhanced alerting capabilities, and development of new biosurveillance formats for other infectious diseases. even at this early point in the process, there is sound evidence to substantiate this investment. osels will continue to work to improve its preparedness capabilities at local, state, and federal levels and to strengthen its basic infrastructure to support other medical and public health activities. charles magruder, md, mph medical epidemiologist center for disease control (cdc) national center for public health informatics 1600 clifton road ms e-68 atlanta, ga usa 30047 email: zgu4@cdc.gov mailto:zgu4@cdc.gov layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts risk and protective factors for arthritis status and severity masaru teramoto*1, fred breukelman2, ferdinando a. gatto2 and sheniz moonie3 1health sciences, drexel university, philadelphia, pa, usa; 2delaware health and social services, dover, de, usa; 3university of nevada, las vegas, las vegas, nv, usa objective to examine how cigarette smoking, alcohol consumption, obesity, and physical activity are associated with the risk and severity of arthritis among adults living in delaware. introduction research has indicated several risk and protective factors for arthritis, including cigarette smoking, alcohol consumption, obesity, and physical activity (1–4). however, it is not well understood how all these factors interact to increase/decrease the risk of arthritis. methods data from the 2009 delaware behavioral risk factor surveillance system (brfss) were analyzed in the current study. potential risk and protective factors for arthritis status and severity examined in this study included: smoking status, alcohol consumption, weight status, and physical activity level. weighted percentages were calculated for the risk and protective factors by arthritis status and activity limitation due to arthritis/joint symptoms, and were analyzed using a raoscott !2 test. a logistic regression analysis was performed to determine an odds ratio (or) while adjusting for gender, age, race/ethnicity, and education. results adults living in delaware self-reporting arthritis were more likely to be former and current smokers than those without self-reported arthritis (p < 0.001, or = 1.64–1.70). moderate and heavy alcohol drinking was associated with lower prevalence and severity of arthritis (p < 0.001, or = 0.45–0.74). there was a significant relationship between obesity and arthritis status or activity limitation due to arthritis/joint symptoms (p < 0.01, or = 1.62–2.14). furthermore, people with arthritis having activity limitation due to arthritis/joint symptoms were more likely to not meet the current physical activity recommendations (5) (p = 0.013, or = 1.49). conclusions cigarette smoking, alcohol consumption, obesity, and physical activity are all associated with the prevalence and severity of arthritis. it is possible that smoking and obesity have a negative impact on the risk and severity of arthritis, whereas alcohol consumption and physical activity may reduce its risk and severity. further research, including prospective cohort studies, is necessary to determine the true absolute risk of developing arthritis, so that we can design the effective prevention strategies. table 1. risk and protective factors by arthritis status and severity notes: values given as % (se). anot overweight/obese: < 25.0 kg/m2, overweight: 25.0–29.9 kg/m2, obese: " 30 kg/m2. bmoderate physical activity for " 30 minutes/day on " 5 days/week, or vigorous physical activity for " 20 minutes/day on " 3 days/week (5). *rao-scott !2 test. keywords alcohol; smoking; arthritis; behavioral risk factor surveillance references 1. albano sa, santana-sahagun e, weisman mh. cigarette smoking and rheumatoid arthritis. semin arthritis rheum 2001; 31:146–159. 2. maxwell jr, gowers ir, moore dj, et al. alcohol consumption is inversely associated with risk and severity of rheumatoid arthritis. rheumatology 2010; 49:2140–2146. 3. anandacoomarasamy a, caterson i, sambrook p, et al. the impact of obesity on the musculoskeletal system. int j obes 2008; 32:211–222. 4. manninen p, riihimaki h, heliovaara m, et al. physical exercise and risk of severe knee osteoarthritis requiring arthroplasty. rheumatology 2001; 40:432–437. 5. haskell wl, lee im, pate rr, et al. physical activity and public health: updated recommendation for adults from the american college of sports medicine and the american heart association. circulation 2007; 116:1081–1093. *masaru teramoto e-mail: masaru.teramoto@drexel.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e179, 2013 layout 1 the international society for disease surveillance held its eleventh annual conference in san diego on december 4th and 5th, 2012, under the theme expanding collaborations to chart a new course in public health surveillance. during these two days, practitioners and researchers across many disciplines gathered to share best practices, lessons learned and cutting edge approaches to timely disease surveillance. a record number of abstracts were received, reviewed and presented – the schedule included 99 orals, 4 panels, 94 posters, 5 roundtables and 12 system demonstrations. presenters represented 24 different countries from africa, north and south america, europe, and asia . topics covered included, but were not limited to, statistical methods for outbreak detection, border health, data quality, evaluation of novel data streams, influenza surveillance, best practices and policies for information sharing, social network analysis, data mining techniques, surveillance during weather events and mass gatherings, syndrome development, and novel uses of syndromic surveillance data. there were also discussions on the impact of regulations and standards development on disease surveillance, including meaningful use and the international health regulations. the 2012 conference was also host to several exciting keynote and plenary talks, including those given by james fowler, professor of medical genetics and political science at the university of california, san diego and bill davenhall, global manager of esri's health and human service solutions group. plenary speakers steve waterman, centers for disease control and prevention (cdc); simon hay, university of oxford; and brian mccloskey, health protection agency in london, reflected on the importance of effective collaborations in their respective topics of migrant and border health, malaria disease epidemiology and mass gathering health. national and international representatives from the cdc, the world health organization and the department of homeland security also discussed their respective strategic plans for disease surveillance. in addition, the 2012 data visualization event enabled conference attendees to collaborate and gain knowledge of visualization tools and techniques applied to a rich, de-identified set of ambulatory electronic health record (ehr) data. participants developed visualizations of chronic disease events using this common data set and presented their work during the evening poster session. the goals for this event were to demonstrate and share visualization tools and techniques that attendees could learn to apply to their own data and also to provide exposure to data elements available in ambulatory ehr systems and highlight their potential for surveillance and research. my hope is that attendees of the 2012 isds conference strengthened existing collaborations and fostered new ones, and returned to their places of work or study energized with new ideas and approaches to disease surveillance. the challenge for all of us is to sustain this new energy throughout the coming year and to leverage the tools available to us to share best practices and reach out for assistance when needed. we all want to improve the health of our populations, and collaborations will enable us to achieve that goal. a. ising carolina center for health informatics, department of emergency medicine, school of medicine, university of north carolina at chapel hill, chapel hill, nc, usa; 2012 isds scientific program committee chair isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts 2012 international society for disease surveillance conference expanding collaborations to chart a new course in public health surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e1, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts detecting the determinants of health in social media caitlin rivers*1, bryan lewis1 and sean young2 1network dynamics and simulation science laboratory, virginia bioinformatics institute, blacksburg, va, usa; 2ucla david geffen school of medicine, berkley, ca, usa objective create an analysis pipeline that can detect the behavioral determinants of disease in the population using social media data. introduction the explosive use of social media sites presents a unique opportunity for developing alternative methods for understanding the health of the public. the near ubiquity of smartphones has further increased the volume and resolution of data that is shared through these sites. the emerging field of digital epidemiology[1] has focused on methods to analyze and use this “digital exhaust” to augment traditional epidemiologic methods. when applied to the task of disease detection they often detect outbreaks 1-2 weeks earlier than their traditional counterpart [1]. many of these approaches successfully employ data mining techniques to detect symptoms associated with influenza-like illness [2]. others can identify the appearance of novel symptom patterns, allowing the ability to detect the emergence of a new illness in a population [3]. however, behaviors that lead to increased risk for disease have not yet received this treatment. methods we have created a methodology that can detect the behavioral determinants of disease in the population. initially we have focused on risky behaviors that can contribute to hiv transmission in a population, however, the methodology is generalizable. we collected 15 million tweets based on 32 broad keywords relating to three types of risky behaviors associated with the transmission of hiv: drug use (e.g. meth), risky sexual behaviors (e.g. bareback), and other stis (e.g. herpes). we then hand coded a subset of 2,537 unique tweets using a crowd-sourceable “game” that can be distributed online. this hand-coded set was used to train a simple n-gram classifier, which resulted in an algorithm to select relevant tweets from the larger database. we then generated geocodes from text locations provided by the tweet author, supplemented by the ~1% of tweets that are already geolocated. we scaled these geocodes to the state and county levels, which allowed us to compare hiv prevalence in our collected data with public health data. results we present the correlation between behaviors identified in social media and the corresponding impacts on disease incidence across a large population. hand coding revealed that 34% of tweets with one or more of the 32 initial keywords was relevant to behaviors associated with hiv transmission. among the three categories of initial search terms, the drug category yielded 21% true positives, compared to 9% for risky behaviors, and 2% for other stis. the n-gram classifier measured 66% sensitivity and 44% specificity on a test set. in addition, our geolocation algorithm found coordinates for 88% of text locations. of those, a test sample of 59 text locations showed that 83% of geolocations are correctly identified. these components combine to form an analysis pipeline for detecting risky behaviors across the united states. conclusions we present a surveillance methodology to help sift through the vast volumes of these data to detect behaviors and determinants of health contributing to both disease transmission and chronic illness. this effort allows for identification of at-risk communities and populations, which will facilitate targeted, primary and secondary-prevention efforts to improve public health. keywords social media; hiv/aids; digital epidemiology acknowledgments we thank our external collaborators and members of the network dynamics and simulation science laboratory (ndssl) for their suggestions and comments. this work has been partially supported by: nih midas grant 2u01gm070694-09 and dtra cnims contract hdtra1-11-d-0016-0001 references [1] salathé, m., bengtsson, l., bodnar, t. j., brewer, d. d., brownstein, j. s., buckee, c., campbell, e. m., et al. (2012). digital epidemiology. (p. e. bourne, ed.)plos computational biology, 8(7), e1002616. doi:10.1371/journal.pcbi.1002616 [2] achrekar, h., lazarus, r., & park, w. c. (2011). predicting flu trends using twitter data. the first international workshop on cyber-physical networking systems (pp. 713–718). [3] neill db. fast bayesian scan statistics for multivariate event detection and visualization. stat med. 2011feb.28;30(5):455–69. *caitlin rivers e-mail: cmrivers@vbi.vt.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e161, 2013 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts influenza study of backyard animals in georgia anna machablishvili*1, lela ursuhadze1 and ivane daraselia2 1national center for disease control and public health, tbilisi, georgia; 2tbilisi zoological park, tbilisi, georgia objective the purpose of this study was to identify zoonotic influenza viruses in swine and poultry populations in georgia and to define their pandemic potential. introduction aquatic birds are the main reservoirs of influenza viruses, however pigs represent an essential host in virus ecology as they are susceptible to both avian and human influenza viruses. circulating zoonotic influenza (a/h7n9, a/h5n1, and a/h3n2v) viruses could mutate into forms easily transmissible from human-to-human and become a public health concern. georgia is located along routes used by migrating birds where different species of aquatic birds are found. in 2006, highly pathogenic influenza virus a/h5n1 was detected in two wild swans in adjara (western georgia). moreover, in the frame of wild bird surveillance, various subtypes of influenza a viruses were detected in mallard and gulls in georgia (lewis, 2013). thus domestic animals in georgia have a potential chance to contract influenza viruses from wild birds. methods the kakheti region, the leading region in cattle breeding and poultry production in georgia, was selected for study. villages were selected for door-to-door visits to search for ill backyard animals showing influenza-like symptoms. in case of identification of a sick animal, samples were obtained for laboratory investigations; sample collection forms were filled out to generate epidemiological data. cloacal and tracheal swabs were taken from poultry; and pharyngeal and nasal swabs were collected from pigs. each specimen was screened for influenza a matrix gene by real-time rt-pcr using a protocol from the centers for disease control prevention. results eighty four villages in the kakheti region were surveyed for domestic animals with influenza-like illness symptoms. in total, 164 specimens were collected from 112 backyard animals in 55 households (107 samples were from 55 poultry and 57 samples were from 57 pigs). all samples tested negative for influenza a virus by real time rt-pcr. the questionnaire data revealed that the age range of both pigs and poultry varied from one month to two years; median and mode were both 1 year. chickens and ducks primarily freely ranged in backyards (67%), while half the number of pigs were kept in closed premises. equally, 61% of pigs and poultry had contact with other pigs or poultry within the premises. conclusions in spite of the negative findings, we cannot exclude the circulation of influenza viruses in domestic animals in georgia. especially, considering the fact that a domestic duck with influenza a/h10 virus was identified during veterinarian training in 2010 in grigoleti (black sea cost of georgia) manifesting no clinical symptoms. therefore, larger scale studies, including swabbing more backyard animals without any clinical symptoms are necessary to identify interspecies virus transmission in the country. keywords zoonotic influenza; pigs; poultry; georgia acknowledgments funding to conduct this study was provided by crdf georgia mini grant #60211. participation in this conference was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed. references lewis ns, et al. (2013). avian influenza virus surveillance in wild birds in georgia: 2009–2011. plos one 8(3): e58534. doi:10.1371/ journal.pone.0058534 *anna machablishvili e-mail: a_machablishvili@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e155, 2017 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts determinants of outbreak detection performance nastaran jafarpour*1, doina precup2 and david buckeridge2 1department of computer engineering, ecole polytechnique de montreal, montreal, qc, canada; 2mcgill university, montreal, qc, canada objective to predict the performance of outbreak detection algorithms under different circumstances which will guide the method selection and algorithm configuration in surveillance systems, to characterize the dependence of the performance of detection algorithms on the type and severity of outbreak, to develop quantitative evidence about determinants of detection performance. introduction the choice of outbreak detection algorithm and its configuration can result in important variations in the performance of public health surveillance systems. our work aims to characterize the performance of detectors based on outbreak types. we are using bayesian networks (bn) to model the relationships between determinants of outbreak detection and the detection performance based on a significant study on simulated data. methods the simulated surveillance data that we used was generated by surveillance lab of mcgill university using simulation analysis platform [1] considering surveillance in an urban area to detect waterborne outbreaks due to the failure of a water treatment plant. we focus on predicting the performance of the c-family of algorithms, because they are widely used, state-of-art outbreak detection algorithms [2]. we investigate the influence of algorithm characteristics and outbreak characteristics in determining outbreak detection performance. the c1, c2, and c3 are distinguished by the configuration of 2 parameters,the guardband and memory. generally, gradually increasing outbreaks can bias the test statistic upward, so the detection algorithm will fail to flag the outbreak. to avoid this situation, the c2 and c3 use a 2-day gap, guardband, between the baseline interval and the test interval. the c3 includes 2 recent observations, called memory, in the computation of the test statistic. the w2 algorithm is a modified version of the c2 which takes weekly patterns of surveillance time series into account [3]. in the w2, the baseline data is stratified to 2 distinct baselines: one for weekdays, the other for weekends. the w3 includes 2 recent observations of each baseline while calculating the test statistic in the corresponding baseline. we ran the c1, c2, c3, w2, and w3 on 18k simulated time series and measured the sensitivity and specificity of detection. then we created the training data set of 5400000 instances. each instance was the result of performance evaluation of an outbreak detection algorithm with a specific setting of parameters. in order to investigate the determinants of detection performance and reveal their effects quantitatively, we used bn to predict the performance based on algorithm characteristics and outbreak characteristics. results we developed 2 bn models in the weka machine learning software [4] using 5-fold cross-validation. the first bn determines the effect of the guardband, memory, alerting threshold, and the weekly pattern indicator (0 for c-algorithms, 1 for w-algorithms) and outbreak characteristics (contamination level and duration) on the sensitivity of detection. the value of sensitivity was mapped to 4 classes: (0, 0.3], (0.3, 0.6], (0.6, 0.9], (0.9, 1]. the developed bn correctly classified 67.74% of instances. the misclassification error was 0.9407. the second bn for predicting the specificity of detection correctly classified 95.895% of instances in 10 classes and the misclassification error was 0.2975. conclusions the contamination level and duration of outbreaks, alerting threshold, memory, guardband, and whether the weekly pattern was considered or not influence the sensitivity and specificity of outbreak detection and given the c-algorithm parameter settings, we can predict outbreak detection performance quantitatively. in future work, we plan to investigate other predictors of performance and study how these predictions can be used in algorithm and policy choices. keywords outbreak detection; public health surveillance; machine learning; bayesian networks; detection performance references 1.buckeridge, d.l., et al. simulation analysis platform (snap): a tool for evaluation of public health surveillance and disease control strategies. 2011. american medical informatics association. 2.hutwagner, l., et al., the bioterrorism preparedness and response early aberration reporting system (ears). journal of urban health: bulletin of the new york academy of medicine, 2003. 80(supplement 1): p. i89-i96. 3.tokars, j.i., et al., enhancing time-series detection algorithms for automated biosurveillance. emerging infectious diseases, 2009. 15(4): p. 533. 4.hall, m., et al., the weka data mining software: an update. acm sigkdd explorations newsletter, 2009. 11(1): p. 10-18. *nastaran jafarpour e-mail: nastaran.jafarpour@polymtl.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e90, 2013 design principles in the development of (public) health information infrastructures 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 design principles in the development of (public) health information infrastructures roderick neame 1 1 university of queensland, qld 4072, australia abstract in this article the author outlines the key issues in the development of a regional health information infrastructure suitable for public health data collections. a set of 10 basic design and development principles as used and validated in the development of the successful new zealand national health information infrastructure in 1993 are put forward as a basis for future developments. the article emphasises the importance of securing clinical input into any health data that is collected, and suggests strategies whereby this may be achieved, including creating an information economy alongside the care economy. it is suggested that the role of government in such developments is to demonstrate leadership, to work with the sector to develop data, messaging and security standards, to establish key online indexes, to develop data warehouses and to create financial incentives for adoption of the infrastructure and the services it delivers to users. however experience suggests that government should refrain from getting involved in local care services data infrastructure, technology and management issues. key words: regional information management infrastructure design principles introduction public health strategies aim to improve population health and quality of life by reducing the incidence of avoidable illness, unnecessary morbidity and premature mortality: this can be achieved by analysis and identification of threats and hazards to health, as well as by early identification and containment of new syndromes and epidemics. in order to achieve these goals, it is necessary to monitor patterns of disease and of care in order to identify health priorities, to research causes of clusters of diseases and to accumulate evidence about which interventions are effective in different clinical situations. obtaining the data to support these vital functions can be difficult, especially where there is a need for near real time data to identify health hazards (such as failing implants) and monitor the spread/patterns of epidemics – all in the context of a budget that typically demands more to be delivered using less resources. data on which to make such judgements may be difficult to obtain: quality and timely data even harder. even the most basic data on what services are purchased/ provided with public funding can be difficult to obtain, so making quality, timely and cost-effective health business decisions almost impossible. even more elusive is data on the reasons for care decisions, and the outcomes of treatments. data about care services provided is generally design principles in the development of (public) health information infrastructures 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 abstracted by clerks (not by those directly involved in the care encounter) and compiled into summaries and mandatory data returns, but the quality of these data often leaves much to be desired: the degree of separation between the clinical encounter and the coder reporting on it leaves room for extensive misunderstanding and misinterpretation – not to mention simple errors of abstraction and coding. data abstracted from records by clerks, even in the best environments, is often of insufficient quality to meet the demands placed upon it 1 . in addition the inherent delay in reporting may be inconsistent with the needs for real time surveillance of risks: many public health reports are more than 1 year old when released, and real time data is scarce. there is a volume published by the world health organisation on improving health data quality 2 which contains much useful material on the topic. however it starts from a palpably false assumption, which is that everyone, including clinical staff, is dedicated to the production of high quality coded data about each and every care event and encounter. few clinicians are even remotely interested in servicing the needs of public health information: their priorities are with the care of their patients, the enhancement of their personal diagnostic and therapeutic acumen, their research interests (if any) and their professional standing and, last but not least, their remuneration. even so, clinical input is essential in providing the high quality data required for public health purposes and this presents a real challenge. there is plenty of analysis as to why data quality may be poor, and prominent amongst the factors is the lack of clinician involvement as well as poor working arrangements between clinical, ward, records and coding staff. poor quality data is reassuring, but falsely so, since it tells a story that is materially different from what exists and is happening in the field. the absence of data may be ‘better’ than poor data, simply because it does not falsely reassure, and does not divert attention from issues that are actually priorities. the us institute of medicine reports that many care errors and adverse incidents occur as a result of poor data and information 3 ; but more than that, poor quality data increases costs as well as preventing measurement of performance, impeding research and analysis, and obstructing quality assurance 4 . ways forwards there are four significant considerations that provide ways of taking things forwards. each is briefly outlined below. information economy: quality information suffers from being seen as an ‘add-on’ to the main activity and services for which the provider is paid. as such, it appears to lack importance and status, and this is reflected in its management at every point: the minimum possible effort is invested in reporting the data, since it is not ‘worth’ enough for anyone to pay for it. it is self-evident that quality data has a value: the logic is to separate the information about care services provided into a separate ‘economy’ which recognises the value of quality information as an entity separate from the care services themselves, and rewards those who provide it. therefore consider splitting the payments due to those providing care services into 2 parts: one part would be for providing the service(s) to the patient; the other part would be for the provision of timely, accurate and auditable data on the reasons for and clinical data associated with the provision of the care service(s). these fees can be adjusted relative to each other in order to secure the required result. where providers fail to furnish the required information within the allowed time period about the service(s) provided, they will receive only one part of the total potential fee. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_047417.hcsp?ddocname=bok1_047417#notes design principles in the development of (public) health information infrastructures 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 as soon as there is a financial incentive associated with the provision of quality clinical information, one of the main obstacles is overcome: the development of an information economy where there are profits to be made will rapidly spawn new services designed to supply that need, and will assure the interest of clinicians. the financial incentives for information will drive the adoption of information management systems which can provide the required data automatically and quickly. this investment in information systems will create a marketplace where value added services provided by private enterprise will become very attractive. feeding back information derived from data collections is often an invaluable aid to establishing the value of data and information: such information can help drive efficiencies, promote effectiveness, reveal poor performance, identify areas of risk and generally improve competitiveness and services delivery. local valued resources: there are information resources that are carefully maintained at a local level because they support the needs of staff working with those patients: where information resources are valued by local staff, the information they contain will be accurate and validated. the key, therefore, is to access these data in order to generate the data required for public health purposes. for the most part the ‘key’ resource is the medical record, and it is from this document that clerks attempt to abstract data for required returns. increasingly medical records are moving towards being held electronically in point-of-care systems, and this makes abstracting public health data automatically relatively easy. so supporting the development of electronic medical records is a priority for public health: and ensuring that any public health data developments that take place are entirely consistent with the implementation of electronic records systems is a priority. developing a ‘public health information system’ that are incompatible with electronic medical records, whether these are introduced earlier or later, would be absurd. data aggregation: the crucial issue is to ensure that data collected from different sources is able to be aggregated. for that to happen, there must be agreed data definitions (what each term means), agreed classifications and codings and agreed data sets to be provided in respect of each reportable situation so that the data from one encounter can be combined with the same data from many other encounters and locations. whilst the temptation to develop ‘new’ classification and coding systems is ever present, the disadvantages of this are so extensive that the idea should be rejected out of hand. the critical element in planning a health information infrastructure lies here. each healthcare enterprise that implements information management technology that suits its needs can be seen as an ‘island of technology’. those islands can be structured in many different ways using open or proprietary applications, classifications, codings, interfaces, messages and data definitions – and it has often been to the advantage of vendors to make their systems proprietary and base them on their own ‘in-house standards’. joining up these islands is the critical challenge, and for this there has to be a clear specification of how they will be joined at technical connectivity as well as data exchange levels. the technical connectivity has been effectively by-passed since the industry has found ways of linking all types of systems because of the commercial pressure to enable linking into the internet. the remaining issues relate to data: definitions, classifications, coding, sets, messages etc; these are where the effort has to be invested to create an infrastructure that can join the islands together, irrespective of how they function internally. design principles in the development of (public) health information infrastructures 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 public health data warehouse: the value in any data collection will only emerge when large quantities of quality data are aggregated into a warehouse that supports sophisticated analysis. much of the current analysis of the data is based on hypothetico-deductive research: an hypothesis is developed and the data is used to support or refute that hypothesis – which is the way research has been carried out for decades. the problem is in the nature of the hypotheses that are developed and tested: because of human cognitive limitations, the hypotheses tend to be relatively simplistic, deterministic and boolean, of the form “if sign a positive, and test b positive, and medication c negative, then diagnosis d”. however medicine is increasingly revealing itself to be based on relationships and associations which are more multi-factorial, fuzzy and probabilistic – none of which humans find comfortable to work or hypothesise with, although they present no problems for computers. developments in data warehousing and mining, driven mainly by commercial and retail interests, provide the technology for intelligent systems to analyse large data collections to identify patterns and associations that were previously unsuspected and/or unrecognised. the capacity to ‘drill down’ into the data warehouse allows these associations to be explored in greater detail. even where there is no apparent scientific reason or explanation for a cluster of data with various common factors and data associations, the fact that it exists, and is statistically significant, is important in its own right and may suggest new avenues for study and research, and ultimately for prevention and treatment. health information infrastructure implementations in 1993 the first national health information infrastructure went live in new zealand (nzhis). some 18 years later a far bigger health information infrastructure for the uks national health service, was formally abandoned in 2011. the similarities and differences between these are useful as a basis for deriving some design and development issues and principles. 1. new zealand the nzhis 5 was the first such national health information infrastructure: the author was chief government consultant for design, development and implementation. the system cost less than usd$5million, which was recouped in less than 1 year from retirement of legacy systems and services; there was a 2 year development period. the stated aims of the system were to support financial accountability in the context of the separation of funder and provider roles (previously funds were disbursed to providers without knowing what was being purchased), to facilitate and promote information integration between primary and secondary care, to support the national public health agenda and to allow non-government service providers and health care plans/insurers to compete for public funds and offer alternative services to the community. a prime focus of the system was to support the public health agenda. the system was designed to gather the data required to identify community health needs, evaluate health policy, allocate resources equitably, monitor service quality and performance, and meet reporting obligations. strong emphasis was placed on an open and contestable architecture, where a key parameter was the specification of standards for data and connectivity which were developed to act as a guide to service providers in their information systems procurements. the system created an online national healthcare user index 6 , a personal care summary (conditions, treatments, warnings and immunisations etc) accessible to authorised users from any location, and a minimum data set defined and to be collected for each design principles in the development of (public) health information infrastructures 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 secondary care event. the event data was copied to the funding agency for payment management. a major emphasis was placed on data privacy, and on explaining to all parties how their data was protected: this involved legally binding agreements with users and telcos, encryption, and robust data pseudonymisation. a detailed review of the goals and focus of the system was published contemporaneously 7 . since that time there have been many enhancements of that system, some initiated by government, and others by the private sector. the emphasis on data and communication standards promoted the implementation of electronic records systems and services. the nonproprietary nature of the infrastructure, together with its emphasis on standards, created a viable marketplace and encouraged many third party technology providers to offer enhanced services compatible with and leveraging off that system. ultimately the nzhis was disestablished in 2008 having fulfilled its developmental purpose: its functions and services were distributed amongst other government departments who took on responsibility for their operation and maintenance (eg user and practitioner indices, data warehouses, classification, terminology and data dictionary services etc). 2. uk the uk nhs national program for it (‘connecting for health’ cfh)was initiated in 2005, and was formally terminated in 2011 following a formal audit 8 which revealed an unacceptable pattern of delays, performance problems, and extensive professional concerns as to whether the plan was deliverable. the system cost somewhere in excess of £10billion. the aims of the system were to provide patients with more choice and control, to provide better information for patients and clinicians and thereby to deliver better care, to reduce the risks associated with care, and to provide quality information for secondary uses, especially public health. the core planned services included delivery of electronic records (ehr) systems with detailed care records held locally and summary care records held centrally/nationally on ‘the spine’, applications for online booking of referrals (‘choose and book’ c&b) and electronic prescribing (eps), picture communications (pacs), as well as some improvements to connectivity with greater security (virtual private network vpn) and an nhs email directory service. the spine system was intended to act as the records repository and therefore as the main resource for individual identification and those services depending upon it, as well as being the data warehouse for encounter/event reports and payments management. the plan was divided up into two parts: national services (eg the ‘spine’, the vpn and email services etc) and the local services. for the local services, a small number of local implementation service providers (lisps) were identified, each of whom was contracted to create a system and deliver it to institutions within an allocated geographic region, so giving the end users no choice in the systems available to them – other than to decline to accept them. the cfh data privacy plan was seen as flawed from its inception and was brought into question by several experts 9,10 . in broad terms it can be seen that the core goals and the national services of this system were congruent with those of the nz system outlined above – that is to create a central data repository, with online patient and provider indices, and online access to key personal health information, as well as a set of standards for data and communications. however the uk plan extended into additional areas, such as ehr, pacs, eps, c&b, and what amounts to an nhs vpn: these were areas that the nz system deliberately left blank to enable institutions to choose those services they valued (in the light of the nationally defined data and design principles in the development of (public) health information infrastructures 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 communications standards), and to permit private enterprise to devise, develop and market such services. phii design and development principles the crucial requirement at a functional level is that the system should make possible the aggregation of data within a common data structure and format – in other words that the same terms mean the same thing to all those connected to the system, and that there is a common format for aggregation of data, including data classification and coding and the sets of data to be collected. at the same time this infrastructure enables the exchange of data ‘sideways’ between care providers and enterprises – the only difference being that there must be common standards for a wider range of data elements as well as a wider range of sets of data to be exchanged (eg tests and investigation requests and reports, administrative data on admissions, separations, transfers, pharmacy prescriptions, discharge summaries, entire electronic records exchange etc). there is no fundamental difference between the infrastructure required for data collections for public health purposes, and for data exchanges between providers: and it is vital to ensure that ‘public health’ data is not seen as different in any way, nor is it developed separately from ‘general’ health data. government, as the coordinator and principal source of funds, has a vital role to play in making this happen. government must show initiative and leadership in setting standards (with the relevant professionals) in respect of the data sets to be exchanged, the message structures and formats by which they will be exchanged, the data classification and codings, and the data definitions. almost all of this already exists in various repositories: however there are often several alternatives that could be used, and the sector as a whole needs to decide which to select for their purposes, and where there may be alternatives, options or deficiencies that need to be managed. this creates the vital piece which enables the various parts of the health sector to communicate, but it does not impose on them any requirement as to how they manage their own data internally within their ‘island’: that said it soon becomes clear that in order to make best use of the infrastructure, there are some internal data infrastructures that will align better with the external infrastructure than others. it is here that the core information systems development principles become most relevant. these are based on the guiding principles formulated at the inception of the nzhis project 7 and followed throughout its implementation. 1. the system should facilitate integration of personal health records horizontally between service providers as well as aggregation vertically to ‘higher levels’ in the system, including summaries of care and preventive records as well as current personal clinical alerts and warnings (eg significant conditions and risks, important current treatments and medications) 2. the system should be based as far as possible on an open and contestable architecture and messaging infrastructure, with standards for data and communications clearly specified: proprietary systems and services should be used only where there is no practicable alternative, and even then the proprietary restrictions should be negotiated away as far as possible. 3. the communications environment should be specifically selected to facilitate and encourage third party providers to develop value-added services on top of the basic design principles in the development of (public) health information infrastructures 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 and national services developed by government. 4. local information systems are the province of local management and should be selected by local management and clinicians to meet and support their needs: the ‘centre’ (health department, government etc) should be at all times aware that stepping over the threshold and becoming involved in the choice and operation of local systems greatly enhances the risk of failure where all problems can be laid at the feet of government interference, irrespective of their cause. 5. government must take on the key role of managing the online healthcare users and providers indexes, and of promulgating (with proper consultation) data definitions, data sets and messaging standards so facilitating information aggregation and exchange; but government must refrain from developing clinical or administrative systems or imposing choices on institutions as to what systems to select and how to manage them. 6. information privacy and systems security are not only an ethical imperative but a legal obligation, and an issue of the highest sensitivity: it must therefore be planned as an integral element of all systems and services ensuring the highest level of ethical acceptability, and these plans opened to public scrutiny. in particular the use of robust identification of staff/users and patients is essential; and robust pseudonymisation (see below) of all personalised data used for purposes other than clinical (inclusive of payment and audit). 7. all users must be enabled to connect with the system at minimal cost and with the minimum of barriers to entry, irrespective of the brand, size and platform of the internal systems they have chosen, and using the services of their own it systems providers/support: this generally means development of a free basic api (applications program interface) that can be run on any platform, but can be fully integrated into systems as and when users decide to do so. 8. honest and open explanations of the needs, purposes and solutions being adopted, and especially the approach to privacy and security, should be disseminated widely in formats designed for the different categories of individuals (health professionals, administrators, lay public etc). bridges of common understanding need to be built between government and health professionals, as well as with strategic community groups. 9. incentives for using the systems need to be incorporated. where government funds care services, payments can be linked to provision of data, and speed of payment can be linked to the speed with which data is provided. the unique national patient identifier can be required to substantiate all basic claims for payment; the prescribed minimal data set relevant to the clinical situation can be required to support claims lodged electronically for the full payment; and electronic reimbursement can be made the same day as claims are received and validated. 10. updates to data definitions, sets, classifications and coding systems, message definitions etc must be negotiated with the sector and published some considerable time ahead of their mandatory introduction, so that institutions, their it services and systems developers have sufficient time to incorporate these into local systems. design principles in the development of (public) health information infrastructures 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 as a brief observation, it would seem that the uk nhss cfh project definitely adhered only to principle 1 above: it seems likely that it breached principles 8 and 10 above, and it is clear that it breached principles 2 – 7. principle 9 is probably irrelevant in the context of the operational management of the nhs. phii benefits and risks the benefits from a phii development are twofold. one benefit is that information can be exchanged between providers caring for the same patient, improving continuity and integrity of care, and allowing patients to choose where they go for care services, rather than being ‘locked-in’ to an institution which holds their medical records. the other main benefit is the aggregation of data into warehouses that permits all types of cross-sectional and longitudinal studies to be undertaken to analyse incidence of diseases/syndromes, immunisation and prevention status, best care practices, previously unknown associations between entities, etc. all of this will become invaluable as the progressive move is made into greater use of artificially intelligent decision support and alerting systems, which rely heavily on a comprehensive and up-to-date knowledgebase derived from the evidence that is abstracted from the data warehouses. there is a potential risk to patient information privacy. all data passing across public networks can be protected from eavesdropping by strong encryption, using a technology appropriate to the risk, but migrating progressively towards a secure public key infrastructure (pki) encryption environment. for the most part it is quite unnecessary for the identity of the patient to be attached to data used for research purposes: the personal identifying elements can be replaced with a cipher, a process sometimes known as ‘pseudonymisation’. this is effective only where it is robust, and there is no ready access to enable users to re-establish the identity of the individual – although as in the nzhis a ‘key in escrow’ arrangement can be made so that in the event of, for example, a serious problem being identified that could threaten the well-being of individuals (eg a faulty implant), a decision can be made at top level to apply the key solely to re-identify those affected and advise their care provider(s) of the potential risks. information feedback the value of health information and evidence lies in making use of it to improve community health status, to inform and educate both clinicians and patients, and to get the best possible value for every health dollar that is spent. generating data is all well and good: but using it effectively is vital. the research shows that those providing data do so more willingly and conscientiously if they get something back from their efforts, so feeding back useful information to the workface is all important. tables of statistics for many people have little impact: graphic representations of the data (pie-charts, histograms etc) often mean much more to the recipients of the information, and it is only if they understand the data that they will look to modify their behaviour appropriately. timely data is the most useful, so providing updates on current outbreaks of disease and on newly identified syndromes is vital. most competitive services welcome comparative feedback identifying strengths, weaknesses and opportunities for improvement. the use of charts which place the performance of each service provider/unit in the context of the performance on the same parameters of all similar service units (all being anonymised), gives design principles in the development of (public) health information infrastructures 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 a clear idea of where there is cause for concern as well as for self-congratulation. crucially as cost-effectiveness becomes the new driving force in health service delivery, it will be vital to compare unit performance based on their adherence to best practice guidelines and on overall costs for each clinical entity. timely feedback is essential. where feedback is delayed, bad results can and usually are dismissed as out-of-date and ‘changes have already been made’ to improve performance. the goal must be real time feedback preferably whilst the patient is still in care identifying those individuals where care costs are out of control, and clinical parameters/outcomes are sub-optimal so that lessons can be learned before it is too late. but it is just as important to engage the community in this feedback process, alerting them individually and as a community to risks and hazards, to better and worse performing care service units, to epidemics, to the need for appropriate preventive care and much more. patients have to make informed decisions about their own health and the way in which they can make best use of the available services: they can only do this if they are well informed about risks and options. where technology is less advanced the impact of this sort of approach on care service providers depends on the level of technology they have access to. those with no automation, not even an office computer, will be able to provide their data for an initial period on paper forms – but this should be phased out with incentives to move to a higher level of technology. where there is basic office automation – just a computer connected to the internet – providers will be able to use the free api to submit the required data in support of their claims for payment. those with more advanced systems will be able to use the infrastructure specifications to have their it staff develop an interface between their systems and the api to enable fully automatic submission of data and claims. systems developers and providers will have a clear information infrastructure definition to guide their development of next generation systems. it is vital that the full set of required data elements for each clinical situation are collected within the software and coded using the agreed classification and coding system in order for the link between the systems and api to be easy to engineer. once the infrastructure has been clearly specified, and there is a clear marketplace, it does not take long for entrepreneurs to identify a range of value-added commercially viable services that can be developed for health sector users, compatible with the infrastructure and offering further performance enhancements and benefits to users, so effectively further embedding the use of these systems in the sector. in this way the relatively small investment of the government in infrastructure leads to a much larger investment by the private sector in an expansion of the environment. conclusion public health information management must be developed as part of a general health information management strategic plan: they need to be developed side-by-side to ensure complete consistency and compatibility. strategies need to be implemented that can engage the interests of clinicians in the provision of quality, timely information: associating the provision of information with financial incentives is suggested. design principles in the development of (public) health information infrastructures 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 privacy concerns always emerge as a key issue in such information infrastructures and the data repositories associated with them. both longitudinal and cross-sectional research studies can be conducted on pseudonymised data without any breach of personal privacy, although maintaining a decryption key-in-escrow may be a wise precaution. data warehouses and their tools for data mining will bring considerable added value to the data collections, and analysis using neural networks will quickly identify patterns and association in the data that human analysis cannot readily discern. these data collections will be invaluable in determining best quality practices and providing the knowledgebase for artificially intelligent systems in healthcare. feeding back information abstracted from such analysis to those providing the information, as well as to the public, will be important in ensuring the continuing cooperation of clinicians and patients alike, and in ensuring practitioner adherence to best quality care protocols. distilling down the 10 principles outlined above, the big issues, based on a wealth of practical experience, appear to be:  that the issue of personal information privacy protection, both relating to patients and to care providers, is addressed thoroughly and planned for meticulously in the context of both the law and highest ethical principles, and laid open to public scrutiny  that government takes a leadership role and defines the required standards for data interchange (data and messages), as well as creating the requisite ‘back-end’ services to support the system (eg online identifiers, data collections/warehouses etc) – all based on open and non-proprietary standards, and with minimal barriers to adoption and use  that government does not impose systems or services on clinical service providers and enterprises, thereby infringing their autonomy, but having defined the infrastructure and created incentives for its use, then leaves commercial vendors to develop and market value-added services that leverage off that infrastructure. limitations the 10 principles outlined above have been derived empirically: there may be others that are equally relevant, but have not yet been identified; and the 10 that have been outlined will likely benefit from further refinement and modification. because of the size, complexity and expense of such major projects, however, it is difficult to envisage that there will be many experiments conducted specifically to test the principles. however it may be that where such infrastructures are being planned and developed, those involved may reflect on the principles, decide in advance which to adopt and which to dismiss, and subsequently review their progress, and difficulties, in the light of these principles. some of these same principles might be applicable to the many smaller (eg enterprise wide) systems integration projects that arise as enterprises acquire new facilities and seek to integrate them into their existing care and billing infrastructure. however for the most part these projects tend to revolve around pragmatic decisions as to how to extend existing systems (good or bad) to embrace new members, rather than exploring how best to link together multiple islands of technology each of which has as much merit as the next, and at the same time to develop the resources required for the ‘public good’ that support better management of public health. design principles in the development of (public) health information infrastructures 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 conflicts of interest: none correspondence roderick neame, ba,ma,phd,mb,bchir,fachi health information consulting ltd, 16 glen eden court, flaxton, qld 4560, australia email: roddyneame@hic-ltd.com references 1. bruce s. audit commission criticises data quality. ehealth insider, 16 april 2009 http:// www.ehi.co.uk/news/ehi/4756 2. improving data quality: a guide for developing countries. world health organisation, geneva isbn 92 9061 0506 http://www.wpro.who.int/nr/rdonlyres/73a68297b5be-42d3-83ca-d5a00468b2b4/0/improving_data_quality.pdf 3. institute of medicine. to err is human: building a safer health system. washington, dc: national academy press, 2000 4. 2006. ahima e-him workgroup on ehr data content. "data standard time: data content standardization and the him role. j ahima. 77(1), 26-32. 5. new zealand health information service. new zealand ministry of health, wellington http:// www.nzhis.govt.nz/moh.nsf/indexns/about 6. johnston j, neame rlb. (1994) a national on-line population-based index of healthcare consumers: issues and insights from the new zealand experience. proceedings of medical informatics europe (mie 94) (may 22-6) lisbon 320-327 7. neame r, johnston j. developing a national health information network: insights from experiences in new zealand. proceedings of hc94 (march 1994), harrogate, 503-509; and international journal of bio-medical computing, volume 40, issue 2, pages 95-100, october 1995 http://www.journals.elsevierhealth.com/periodicals/ijbold/article/0020-7101%2895% 2901131-w/pdf 8. the national programme for it in the nhs. an update on the delivery of detailed care records systems. national audit office, london may 2011 http://www.nao.org.uk/publications/1012/ npfit.aspx 9. sturcke j, campbell d. nhs database raises privacy fears, say doctors. the guardian sunday 7 march. http://www.guardian.co.uk/society/2010/mar/07/nhs-database-doctors-warning 10. neame r. 2008. privacy and health information: health cards offer a workable solution. inform prim care. 16(4), 263-70. http://www.ehi.co.uk/news/ehi/4756 http://www.wpro.who.int/nr/rdonlyres/73a68297-b5be-42d3-83ca-d5a00468b2b4/0/improving_data_quality.pdf http://www.wpro.who.int/nr/rdonlyres/73a68297-b5be-42d3-83ca-d5a00468b2b4/0/improving_data_quality.pdf http://www.nzhis.govt.nz/moh.nsf/indexns/about http://www.journals.elsevierhealth.com/periodicals/ijbold/issues?issue_key=s0020-7101%2800%29x0002-5 http://www.journals.elsevierhealth.com/periodicals/ijbold/article/0020-7101%2895%2901131-w/pdf http://www.journals.elsevierhealth.com/periodicals/ijbold/article/0020-7101%2895%2901131-w/pdf http://www.nao.org.uk/publications/1012/npfit.aspx http://www.guardian.co.uk/society/2010/mar/07/nhs-database-doctors-warning http://www.guardian.co.uk/society/2010/mar/07/nhs-database-doctors-warning http://www.ncbi.nlm.nih.gov/pubmed/19192327 the geographic distribution of mammography resources in mississippi 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi the geographic distribution of mammography resources in mississippi elizabeth n. nichols1, denae l. bradley1, xu zhang2, fazlay faruque3 and roy j. duhé4 1. murrah high school, jackson, ms 39216, usa. 2. center of biostatistics & bioinformatics, university of mississippi medical center, jackson, ms 392164505, usa. 3. gis & remote sensing program, university of mississippi medical center, jackson, ms 39216-4505, usa. 4. department of pharmacology and toxicology & department of radiation oncology, university of mississippi medical center, jackson, ms 39216-4505, usa. abstract objective: to determine whether the availability of mammography resources affected breast cancer incidence rates, stage of disease at initial diagnosis, mortality rates and/or mortality-to-incidence ratios throughout mississippi. methods: mammography facilities were geocoded and the numbers of residents residing within a thirty minute drive of a mammography facility were calculated. other data were extracted from the mississippi cancer registry, the u.s. census, and the mississippi behavioral risk factor surveillance survey (brfss). results & discussion: there were no statistically-significant differences between breast cancer incidence rates in black versus white females in mississippi; however, there were significant differences in the use of mammography, percentages of advanced stage initial diagnoses, mortality rates, and mortality-to-incidence ratios, where black females fared worse in each category. no statistically-significant correlations were observed between breast cancer outcomes and the availability of mammography facilities. the use of mammography was negatively correlated with advanced stage of disease at initial diagnosis. by combining black and white subsets, a correlation between mammography use and improved survival was detected; this was not apparent in either subset alone. there was also a correlation between breast cancer mortality -to-incidence ratios and the percentage of the population living below the poverty level. conclusions: the accessibility and use of mammography resources has a greater impact on breast cancer in mississippi than does the geographic resource distribution per se. therefore, intensified mammography campaigns to reduce the percentage of advanced-stage breast cancers initially diagnosed in black women, especially in communities with high levels of poverty, are warranted in mississippi. key words: breast cancer; mammography; health disparities; geographic information system (gis) correspondence: rduhe@umc.edu doi: 10.5210/ojphi.v5i3.4982 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ the geographic distribution of mammography resources in mississippi 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi introduction breast cancer is the most frequently-occurring cancer in women; fortunately, breast cancer mortality rates have steadily declined in the united states since 1990 [1,2]. this decline appears to have resulted from the combined benefits of the increased use of screening mammography a nd adjuvant therapy. however, improvements in breast cancer survival have not been uniform in all populations and in all geographic regions throughout the nation. most notably, despite their lower incidence rates for breast cancers, black women die from these diseases at higher rates than do white women, and this trend has persisted for many years [3]. given that approximately 37% of mississippians are of african ancestry, the disparate outcomes affecting african american women may have a significant impact on mississippi's cancer burden. an important aim of this study is to compare cancer outcomes between black and white female mississippi residents and to explore the possible reasons for the differential outcomes. however, there are other demographic characteristics of particular relevance to mississippi, and these too may result in a worsened overall impact due to breast cancer mortality rates. relative to the other states in america, mississippi has one of the lowest levels of educational attainment [4] and lowest rates of high school graduation [5]. mississippi has chronically ranked as one of the poorest states in the usa, whether measured by childhood poverty rates [6], median household income 1 or by percentage of the population living below poverty level [7]. it has been well-documented that low socio-economic status (ses) parameters such as literacy and income [8,9] are generally associated with higher cancer mortality rates, and this is also true for breast cancer mortality rates [10]. to better understand how ses parameters can affect breast cancer mortality rates, much research has been invested in understanding the relationships between ses and specific aspects of health care consumption and delivery. this is a complicated issue, and these relationships may not be preserved in all geographic locations, because health care systems are subject to regional variations. in fact, some studies suggest that, although early-stage cancers are less likely to be detected in poor areas due to decreased mammography rates, this has no overall bearing on breast cancer survivorship [11]. some aspects of racial disparity are difficult to understand. for example, maly and co -workers described a pronounced disparity in the diagnostic delay between black women and white women with breast cancer, regardless of whether their breast abnormalities were initially selfdetected or detected by health care providers [12]. a study conducted with a south carolina cohort also found racial disparities in the interval time between the first abnormal clinical breast examination and determination of final status in economically-disadvantaged black versus white women, although there was no significant disparity associated with overall completion of mammographic work-up [13]. it is unclear why such delays should be greater for black women with breast cancer than for comparable white women. in addition to the socio-economic factors that might contribute to racial disparities in breast cancer outcomes, one must also consider the influence of biological factors contributing to these disparities. there are a variety of breast cancer subtypes which can be distinguished by their gene expression profiles, and these subtypes correlate with differing clinical outcomes [14,15]. for example, patients with the luminal a breast cancer subtype have very good survival prospects, and approximately 90% of such patients can expect to be long-term survivors. in contrast, patients with the basal-like breast cancer subtype have the least favorable survival odds, and may not survive longer than four years post-diagnosis. the immunohistochemical tools http://ojphi.org/ the geographic distribution of mammography resources in mississippi 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi routinely used by clinical pathology laboratories to verify the presence of estrogen receptor α, progestin receptor b and her2/neu (human epidermal growth factor receptor 2) have been useful in identifying major breast cancer subtypes in patients. the luminal a and b subtypes both express estrogen receptor α, for example, which indicates that these tumors may respond to endocrine therapy with aromatase inhibitors or selective estrogen receptor modulators (serms). her2 amplification and overexpression is a significant diagnostic feature which indicates that a tumor may respond to trastuzumab, lapatinib or pertuzumab therapy. the basal-like breast cancer subtype, in contrast, often presents with an immunohistochemically "triple-negative" phenotype, i.e., these breast cancers lack immunoreactivity for estrogen receptor α, progestin receptor b and do not contain genomic amplification of her2/neu. basal-like tumors can be treated with chemotherapy, but a substantial fraction of these tumors respond poorly to therapy and have a poor prognosis. over a decade has passed since five breast cancer subtypes (luminal a, luminal b, normal -like, basal-like and her2-enriched) were first identified by distinctive gene expression profiles, and during that interval much has been learned about how these subtypes may contribute to the population-based disparities in breast cancer mortality. it is now well-established that not all populations possess an equal distribution of these subtypes. recently, genome-wide association studies identified a common risk variant for er-negative breast cancer on chromosome 5p15, and the allele frequency of this variant was nearly twice as high in women of african ancestry as it was for women of european ancestry [16]. “triple-negative” breast cancers [tnbc] themselves are a highly heterogeneous group that may include up to 6 different subtypes (basal like 1, basal-like 2, mesenchymal, mesenchymal stem-like, immunomodulatory and androgenreceptor enriched) that respond differently to treatment [17,18]. it is still unknown whether these subtypes are differently distributed among different ethnic groups. tnbcs are disproportionately observed in women of african ancestry. this observation has been corroborated in numerous u.s. studies, including those based on the california cancer registry [19,20], the carolina breast cancer study [21,22], a thomas jefferson university hospital cohort [23] and the seer database [23]. the high prevalence of these aggressive cancers in women of african ancestry is a worldwide phenomenon and is clearly rooted in biological diversity. in one study the prevalence of "triple-negative" breast cancers in ghanian women was reported to be a s high as 82% versus a 16% prevalence in american women of european ancestry [24]. while the prevalence of triplenegative/basal-like breast cancer is clearly higher in women of african versus european ancestry, it appears that the prognosis for survival may be equally grim for women of all races and ethnicities afflicted by this aggressive cancer subtype, but only when patient cohorts are properly matched for other factors that affect survival [25,26]. in addition to research that focuses on tnbc breast cancer subtypes, other research has investigated other possible biological explanations of population-based breast cancer mortality disparities. one group has observed more extensive cpg island methylation in the promoters of the rassf1a, rarβ2 and cdh13 loci in tumors taken from women of african versus european ancestry [27]. methylational silencing of tumor suppressor genes commonly occurs through such mechanisms. since these authors also observed an association between worse overall survival and higher methylation in these loci, they suggested that their discovery would be consistent with a biological explanation of disparity [27]. http://ojphi.org/ the geographic distribution of mammography resources in mississippi 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi as stated earlier, a major factor in the national decline in breast cancer mortality has been the increasing use of screening mammography. based on this evidence [28], the u.s. preventive services task force (uspstf) recommended mammography screening every 1 to 2 years for women age 40 years and older in 2002 [29]. in 2009, however, the uspstf recommended against routine mammography screening for women age 40 – 49, and they also recommended against clinicians teaching women how to perform breast self-exams [30]. these changes in uspstf recommendations were not without controversy, and several responses and rebuttals to the 2009 uspstf recommendations have been noted. despite the new uspstf recommendations, the majority of primary care physicians favor aggressive mammography screening of women from ages 40 to 79 [31]. the american cancer society continues to advise average-risk women to begin mammography screening at age 40, and they also recommend clinical breast exams every three years for women between the ages of 20 to 39 [32]. however, mammography is a capital-intensive diagnostic procedure, and in a poor (relative to u.s. standards) rural state such as mississippi, it may be difficult for all women to have easy access to mammography facilities. one should bear in mind that there are two aspects to be considered: availability and accessibility. resource availability refers to the physical presence of that resource; because all mammography facilities must be certified by the u.s. f.d.a, one can literally map mammography availability throughout the state. resource accessibility refers to the ability of a given individual to use that resource; this can be a much more complex aspect to quantify because it encompasses socioeconomic barriers to use, such as transportation to the resource, ability to pay for resource use, etc. in this manuscript we examine publicly-accessible data concerning breast cancer in mississippi to determine whether the geographic distribution of mammography facilities has a discernible effect on breast cancer outcomes in the state. the hypothesis to be tested is that limited availability of mammography facilities limits the accessibility of these resources, which results in an increased advanced stage at initial diagnosis and increased breast cancer mortality. methods spatial analysis spatial analysis in this project involved delineating areas within 30-minute drive time distance from the mammography facilities and then identifying demographic characteristics within and outside the drive time areas by apportioning data from census block groups. a spreadsheet for mammography facilities with data from the u.s. food and drug administration's mammography facility database (http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmqsa/mqsa.cfm) was prepared to geocode using arcgis 10.1 (environmental systems research institu te, inc., redlands, california). a map was created showing the location of the geocoded facilities along with the counties and major transportation network in mississippi (figure 1). using the same gis software areas within and outside the drive time distance in respect to all mammography facilities were identified based on optimum driving route. since facilities from neighboring states that are within the desired drive time distance (30 minute) can serve mississippians, facilities from all neighboring states were taken into account for this analysis (figure 2). this drive time distance area was also used to calculate the % female outside the desired distance from the mammography facility per public health district in mississippi (table 2). the delineated areas http://ojphi.org/ the geographic distribution of mammography resources in mississippi 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi were superimposed on census geography (block group) to proportionately apportion the demographic data from census geography to drive time distance area geography. demographic attributes were obtained from the 2010 u.s. census data. mcr breast cancer data breast cancer incidence and mortality data were obtained from the mississippi cancer registry (http://mcr.umc.edu/) over the years 2005 through 2009 to provide a more reliable "snapshot" of recent cancer statistics in a state dominated by low population density areas. mississippi has a fairly stable population, as evidenced by u.s. census data showing that the 0.3% change in population in mississippi from 2010 to 2011 was below the national average of 0.9%, and population changes for individual mississippi counties ranged from -3.0% to 3.2%. breast cancer data were derived from only the female cohort because mammography is not a recommended screening modality for males. mississippi's behavioral risk factor surveillance system (brfss) public health district survey report (http://msdh.ms.gov/brfss/index.htm) was the source of data on the use of mammography in women aged 40 and above were obtained from the district reports for the years 2005, 2006 and 2008 (this question is not annually included in the brfss). data analysis histograms were constructed for the cancer outcome, mammography usage and socio -economic variables to examine the normality of the distributions. correlation analysis was performed between cancer outcome variables and mammography usage or socio-economic variables to assess the degree of association. the pearson correlation coefficient was evaluated when both variables were normally distributed. if one variable or both variables had skewed distribution, the spearman rank correlation coefficient was utilized. comparison of cancer outcome variables between black and white women residents was performed using the two-sample t test. all p values were two-sided and p values less than 0.05 were considered significant. statistical analysis was performed using the software sas (version 9.3, sas institute inc.). results the gis mapping of fda certified mammography facilities in mississippi is shown in figure 1. the densest clustering of mammography facilities was located along major interstate highways. for example, thirteen mammography facilities were on the i-10 corridor that passes through hancock, harrison and jackson counties along the gulf coast, whereas there was only one mammography facility not adjacent to an interstate highway in the three count ies (pearl river, stone and george counties) immediately to the north. interstate-55 and interstate-20 intersect in jackson, mississippi, and there were sixteen mammography facilities in the tri -county region (hinds, madison and rankin counties) which defines the greater jackson metropolitan area. in addition to these sixteen facilities, there were seven on i-55 north, four along i-55 south, two on i-20 west and five on i-20 east. five additional mammography facilities were found along the i 59 corridor through jones, forrest, lamar and pearl river counties. thus, of the ninety fdacertified mammography facilities in mississippi, 52 (57.8%) were located along the interstate highway system. http://ojphi.org/ the geographic distribution of mammography resources in mississippi 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi figure 1. map of fda-certified mammography facilities in mississippi. the u.s. food and drug administration mammography facilities database was searched to identify the geographic address of all 90 mammography facilities in mississippi. these are shown as red crosses on the map, which contains references to the 82 counties of mississippi and the interstate highway system. (http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmqsa/mqsa.cfm) we then sought to estimate how many mississippi women had convenient access to these facilities, assuming that all women had equal access to automobile transportation. using the arcgis 10.1 software package (environmental systems research institute, inc., redlands, california), we calculated the drive time area in which a driver could travel to a mammography facility within thirty minutes for each facility. these 30-minute drive time areas for mississippi and its neighboring states are shown in green and purple in figure 2. because women who live http://ojphi.org/ the geographic distribution of mammography resources in mississippi 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi near the state border might choose to travel to mammography facilities outside of mississippi, we also calculated these 30-minute driving buffer areas for mammography facilities in the neighboring states louisiana, arkansas, tennessee and alabama which are shown in purple. by integrating the land areas covered within these buffer areas, one can see that 54% of mississippi was within a thirty minute drive to a mammography facility. in comparison, 58% of louisiana, 43% of arkansas, 76% of tennessee and 71% of alabama were situated within a thirty minutes' drive time to a mammography facility. figure 2. geographic availability of mammography facilities within a thirty-minute driving radius. this map displays the area that can be reached within a thirty-minute automobile drive from each mammography facility. the green buffer zone indicates areas surroundi ng mississippi-based facilities; purple buffers indicate areas surrounding facilities based in the surrounding states of louisiana, arkansas, alabama and tennessee. populations are not uniformly distributed, therefore the above percentages of land area within a thirty minute drive were not equivalent to the percentage of women living within those buffer areas. to determine the percentage of women who live within a thirty-minute drive to a mammography facility, we used 2011 estimated population data based on the 2010 u.s. census data. using these data, one can estimate that 84.10% of mississippi females of age 40 and above lived within a thirty minute drive to a mammography facility. in comparison, 94.36% of the females of age 40 and above in louisiana, 83.21% of those in arkansas, 94.50% of those in tennessee and 93.04% of the females in alabama resided within a thirty minutes' drive time to a mammography facility. thus, based on both the percentage of land area and the percentage of the female population within a thirty-minute driving area, mammography facilities in arkansas were slightly less available than they were in mississippi, and within this five-state territory, mammography facilities were most available in tennessee. these data are summarized in table 1. http://ojphi.org/ the geographic distribution of mammography resources in mississippi 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi table 1. geographic distribution of mammography facilities in mississippi and surrounding states. region alabama arkansas louisiana mississippi tennessee number of fda-certified mammography facilities 130 83 150 90 192 percentage of state territory within 30 minute drive to mammography facility 71% 43% 58% 54% 76% percentage of female residents within 30 minute drive to mammography facility 93.04% 83.21% 94.36% 84.10% 94.50% note: the data depicted in figure 2 are tabulated in table 1, along with the calculated territorial area and percentage of residents within a thirty-minute driving distance to a mammography facility. using such gis maps, one can assess whether breast cancer outcomes were correlated to the geographic distribution of mammography facilities throughout mississippi. considering that mississippi is a sparsely-populated, predominantly rural state with low population densities, there may be a problem of low statistical power if one uses too small of a geocoded area as the basis for comparison. thus, the first analysis was conducted at the level of mississippi's nine public health districts (phds). table 2 contains data derived from various sources which described certain aspects of the public health districts. the data of most interest are the percentages of females (age 40 and above) who reside outside of a thirty-minute drive to a mammography facility. using public data from the mississippi cancer registry, we examined the age-adjusted breast cancer incidence rates and the age-adjusted breast cancer mortality rates over the years from 2005 to 2009 for each of the state's phds. we were particularly interested in knowing whether the availability of mammography resources had a bearing on the breast cancer mortality-to-incidence ratio of the entire female population within these districts. we used this ratio to estimate the likelihood of surviving a diagnosis of breast cancer. the percentage of breast cancers initially diagnosed at an advanced stage (either regional or distant disease) was also considered as a potentially relevant outcome of limited availability of mammography resources. we applied pearson correlation analysis to the data in table 2 in pairwise fashion, but we were unable to detect any statistically meaningful correlations between any of these variables and http://ojphi.org/ the geographic distribution of mammography resources in mississippi 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi breast cancer mortality rates or mortality-to-incidence ratios when applied to the entire female population. however, the analysis revealed a significant negative correlation between the percentage of women who reported having mammography and clinical breast exams and the percentage of women who initially presented with advanced disease at the time of their initial diagnosis. the pearson sample correlation coefficient is -0.671 with a p value of 0.047 (95% ci 0.917 to 0.030). both the brfss data concerning mammography usage and the mississippi cancer registry data concerning stage of disease at initial diagnosis are available as black and white subsets, and we therefore re-examined these data accordingly and plotted them in figure 3 (upper panel). it was immediately apparent that the data describing the black population (blue dots) was distinctly different from the data describing the white population (red crosses) for all nine public health districts (table 2). this was confirmed by applying the two-sample t test to phd-level data, which showed that four characteristics were significantly different between the black and white female subpopulations: 1) the percentage of advanced-stage breast cancers detected at initial diagnosis (40.8% in black women vs. 31.5% in white women; p <0.0001); 2) the percentage of females age 40 and older who had mammography and clinical breast exams (73.13% in black women vs. 82.57% in white women; p = 0.0001); 3) the age-adjusted breast cancer mortality rate (33.38 per 100,000 in black women vs. 20.16 per 100,000 in white women; p = 0.0003); and 4) the breast cancer mortality-to-incidence ratio (0.2442 in black women vs. 0.1509 in white women; p = 0.0005). unfortunately, in all four of these characteri stics, black females fared worse than white females in mississippi, confirming that the extent of population -based breast cancer is pervasive throughout all nine phds in the state. only one characteristic was not statistically different, and that is the age-adjusted breast cancer incidence rate (p = 0.735). table 2. data characteristics of female breast cancer and breast cancer screening resources in mississippi's public health districts. region mammogr aphy facilities women per mammograph y facility ageadjusted breast cancer incidence rate (all female per 100,000; 2005-2009) ageadjusted breast cancer mortality rate (all female per 100,000; 20052009) breast cancer mortality -toincidenc e ratio % highstage (regional + distant) at diagnosis % females (age 40+) who had mammography & clinical breast exam % females (age 40+) beyond 30 minute drive to mammography facility mississippi 90 16900 134.96 24.27 0.1798 0.342 79.43% 15.88% northwest public health district 1 8 20384 130.70 24.73 0.1892 0.373 74.43% 15.44% northeast public health district 2 13 13927 130.97 24.83 0.1896 0.341 78.20% 11.65% http://ojphi.org/ the geographic distribution of mammography resources in mississippi 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi delta/hills public health district 3 7 16817 123.51 31.30 0.2534 0.368 74.87% 14.83% tombigbee public health district 4 8 15869 127.44 21.10 0.1656 0.338 79.27% 21.17% west central public health district 5 21 15696 155.31 24.82 0.1598 0.332 83.53% 11.68% east central public health district 6 8 15754 125.48 19.53 0.1556 0.362 78.63% 21.34% southwest public health district 7 5 18180 125.60 32.42 0.2581 0.367 80.93% 28.32% southeast public health district 8 5 31196 130.66 22.04 0.1687 0.335 81.23% 26.12% coastal plains public health district 9 15 15308 135.15 21.73 0.1608 0.310 81.03% 8.40% note: the characteristics listed in this table include the number of mammography facilities, the number of women per mammography facility, the age-adjusted breast cancer incidence and mortality rates, the mortality-to-incidence ratios, the percentage breast cancers initially diagnosed at advanced stage (regional + distant disease), the percentage of women (age 40 and older) who reported ever receiving a mammogram and clinical breast exam, and the percentage of women residing beyond a 30 minute driving distance from a mammography facilities in each of mississippi's public health districts. given the magnitude of these disparities, we performed correlation analyses on black and white subsets to determine whether the use of mammography in either black or white women was correlated to advanced stage at initial diagnosis for either group. the pearson correlation coefficient was -0.861 (p = 0.001, 95% ci -0.967 to -0.416) for black women, but did not reach statistical significance for white women, which reflects the more homogeneous use patterns amongst white women throughout the state. interestingly, when the black and white subsets were recombined, the common pearson correlation coefficient was -0.920 (p < 0.0001, 95% ci 0.968 to -0.783), indicating a strong correlation. only when the black and white subsets were recombined was it possible to observe a significant pearson correlation between the mortality-toincidence ratio and the percentage of women with advanced stage breast cancer at initial diagnosis (pearson coefficient = 0.771, p =0.0001, 95% ci 0.457 to 0.906). while no si gnificant correlation existed between mortality-to-incidence ratio and mammography use in either the black or the white subsets, a significant correlation (pearson correlation coefficient = -0.728, p < 0.0001, 95% ci -0.887 to -0.376) was observed upon recombination of these disparate subsets (fig 3, lower panel). http://ojphi.org/ the geographic distribution of mammography resources in mississippi 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi figure 3. public health district patterns of mammography use are inversely correlated with adverse breast cancer outcomes. mississippi brfss public health district survey report data (http://msdh.ms.gov/brfss/index.htm) from black female (blue dots) and white female (red crosses) respondents were obtained from the district reports for the years 2005, 2006 and 2008, then averaged and plotted along the abscissa. upper panel: the percentag e of women initially diagnosed with advanced stage breast cancer (defined as the sum of the percentages of regional and distant disease initial diagnoses over the years 2005 through 2009) calculated from mississippi cancer registry data for each of the nine public health districts http://ojphi.org/ the geographic distribution of mammography resources in mississippi 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi (http://mcr.umc.edu/documents/femalebreastphddatacombinedyears20052009new.pdf), then plotted against the ordinate. these black and white subsets were subjected to a common pearson analysis to obtain a correlation coefficient of -0.920 (p < 0.0001). lower panel: ageadjusted breast cancer incidence and mortality rates were obtained from the mississippi cancer registry (http://www.cancer-rates.info/ms/index.php) for black and white women over the years 2005 through 2009 for each of the nine public health districts, and the ratio of mortality-toincidence was calculated and plotted along the ordinate. the pearson correlation coefficient between these two variables was -0.728 (p < 0.0001). the observation shown in figure 3 is consistent with other published evidence that because ageappropriate mammography is capable of detecting early-stage breast cancer, broad usage of screening mammography can reduce the proportion of advanced-stage breast cancers at initial diagnosis [33]. the problem appeared to be most severe in the northwest public health district 1, where only 62% of black women age 40 and older reported ever having received mammography and clinical breast exam, and where 48% of the initial breast cancer diagnoses reveal advanced stage disease in black women. it is interesting to note that at the phd level, there was no statistically significant negative correlation between the percentage of women reporting mammography usage and the percentage of women who must drive more than thirty minutes to reach a mammography facility (pearson correlation coefficient = 0.119, p = 0.769). nor was there a statistically meaningful negative correlation at the phd level between the percentage of advanced-stage disease at diagnosis and the percentage of women who must drive more than thirty minutes to reach a mammography facility (pearson correlation coefficient = 0.413, p = 0.281). however, one must note that we were unable to distinguish between the geographic availability of mammography resources for black versus white women within any given geographic region, so we were unable to perform subset correlation analyses for this variable. thus, if there was any effect of the availability of mammography facilities on breast cancer outcomes, then our analysis was inadequate to detect this effect. it is reasonable to assume that by examining data at the public health district level, important demographic details may be hidden due to the homogenization of small geographic tracts with distinctive characteristics. using public data from the mississippi cancer registry, we further examined age-adjusted breast cancer incidence rates and age-adjusted breast cancer mortality rates over the years from 2005 to 2009 for all of the state's counties. even with this long time interval, data from issaquena county must be censored because that county's population is less than 1700; therefore issaquena county data will be excluded from further consideration. we initially applied spearman rank correlation analysis to determine whether, at the county level, either breast cancer incidence, breast cancer mortality, breast cancer mortality-to-incidence ratios or the percentage of advanced-stage breast cancers detected at initial diagnosis were associated to the percentage of females age 40 and above residing outside of a thirty-minute drive to a mammography facility. because this is the only variable with skewed distribution, the spearman rank correlation coefficient is a suitable criterion for association assessment. the spearman rank correlation coefficient for the association between the percentage of advanced-stage breast cancers detected at initial diagnosis and the percentage of females age 40 and above residing outside of a thirty-minute drive to a mammography facility was 0.208, but this association did not reach statistical significance (p = 0.063). thus, neither county-level data nor phd-level data can detect a statistically-significant association between the availability of mammography resources and stage of disease at initial diagnosis. further analysis of county level data reveals no http://ojphi.org/ the geographic distribution of mammography resources in mississippi 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi statistically meaningful correlation between the geographic availability of mammography facilities (as measured by the percentage of females residing outside of a thirty-minute drive to a mammography facility) and either breast cancer mortality rates (p = 0.681) or the mortality -toincidence ratios (p = 0.984). unfortunately, at the county level we lacked sufficient data (such as the brfss data) to measure the accessibility of mammography resources. when we looked for correlations between other potentially relevant parameters (% of population below poverty level, median household income, % high school graduates aged 25 and above, % black, and % white), two statistically-meaningful correlations were observed. there was a moderate correlation between breast cancer mortality-to-incidence ratios and the percentage of the population living below the poverty level (figure 4, lower panel). the pearson correlation coefficient for these two parameters was 0.504 (95% ci 0.318 to 0.649), with a p value of <0.0001. there was also a moderate correlation between breast cancer mortality rates and the percentage of the population who are black (figure 4, upper panel). the pearson correlation coefficient for these two parameters was 0.422 (95% ci 0.222 to 0.585), with a p value of <0.0001. it is very important to note that, in mississippi, the two parameters were strongly correlated. the pearson correlation coefficient between the percentage of black residents in a county and the percentage of the population in that county who live below the poverty level was 0.802 (95% ci 0.706 to 0.867), with a p value of = 0.0001. based on the breast cancer disparities between black and white women in mississippi observed in the preceding public health district data, cancer outcome comparison between black and white women using the county-level data may prove insightful. due to the rural nature of mississippi, data from several counties could not be considered in this subset analysis because the black or white population in those counties was less than 1700. therefore benton, choctaw, franklin, george, greene, hancock, issaquena, itawamba, perry, stone and tishomingo county data were excluded from the black female subset, and claiborne, jefferson, humphreys, issaquena, noxubee, quitman, sharkey, tunica and wilkinson county data were excluded from the white female subset. the two-sample t test was applied to county-level data, which confirmed that two characteristics were significantly different between the black and white female subpopulations: 1) the age-adjusted breast cancer mortality rate (p <0.0001) and 2) the breast cancer mortality-to-incidence ratio (p<0.0001). again, black females fared worse than white females in mississippi, confirming that the existence of significant population-based breast cancer mortality disparities in mississippi. as observed at the public health district level, the age-adjusted breast cancer incidence rate was not statistically different between black females and white females in mississippi (p = 0.735). http://ojphi.org/ the geographic distribution of mammography resources in mississippi 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi figure 4. county-associated parameters correlated with breast cancer mortality outcomes. breast cancer incidence and survival data were obtained from the mississippi cancer registry; demographic data were obtained from the u.s. census (http://quickfacts.census.gov/qfd/states/28000.html); all mississippi counties are represented, with the exception of issaquena county. upper panel: age-adjusted breast cancer mortality rates correlated with the percentage of the population who are black; the pearson sample correlation between these two variables was 0.422 (p <0.0001). lower panel: the ratios of bc mortality-to-incidence were correlated with the percentages of population living below poverty level; the pearson sample correlation between these two variables was 0.504 (p <0.0001). http://ojphi.org/ the geographic distribution of mammography resources in mississippi 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi county-level subset correlation analyses confirmed several of the observations at the phd level: there was no statistically-significant correlation between the availability of mammography resources (as measured by the % of women residing within a 30 minute drive to a mammography facility) and either the breast cancer incidence rate, the breast cancer mortality rate, or the mortality-to-incidence ratios for either the black female or the white female subsets. perhaps more importantly, the correlation between the percentage of population living below poverty level and the mortality-to-incidence ratio was observed for black females (pearson correlation coefficient = 0.346, p = 0.003, 95% ci 0.120 to 0.534), but surprisingly, not for white female subsets (p = 0.274). one must guard against over-interpretation of the latter observation because the "percentage of population living below poverty level" data were unadjusted for race and were determined at the county level. due to the low populations at risk, some of the counties with the highest percentage of residents living below the poverty level in mississippi (claiborne, jefferson, humphreys, noxubee, quitman, and sharkey counties) were excluded from the white female subset, which may have introduced bias against detecting the effect of poverty in this subset. conversely, some of the counties with the lowest percentage of residents living below the poverty level (george, stone and itawamba counties) were excluded from the black female subset, which may have introduced bias towards detecting the effect of poverty in this subset. this reflected of the strong correlation between race and poverty in mississippi; a pearson correlation coefficient of 0.802 (p<0.0001) exists between the percentage of population who are black and the percentage of population living below the poverty level. discussion one cannot properly assess the burden of breast cancer in mississippi without recognizing the problem of population-based disparities. this study initially sought to assess whether geographic disparities existed based on the statewide distribution of mammography facilities, but data analysis quickly revealed that poverty and race were the dominant characteristics affecting geographic disparities in breast cancer outcomes in mississippi. at the county level, the percentage of population below poverty level and the percentage of population who are black are strongly correlated. thus, it is apparent that breast cancer places a disparate burden on poor african-american communities in mississippi. this study was not designed to include direct measures of the impact of aggressive "triple-negative" breast cancer subtypes, so one can only assume that it contributes to the disproportionate mortality rates observed in black mississippians, as discussed in the introduction section. recent analysis of national breast cancer data confirm that poverty and race remain as dominant risk factors in breast cancer mortality and that screening rates are lower in poorer women than in wealthier women [33]. the data shown in figure 3 indicate that the proportion of advanced-stage breast cancers at initial diagnosis in african-american women could be significantly reduced throu gh a concerted campaign to increase the rate of participation in mammographic breast screening in african-american women in mississippi. a decrease in the proportion of breast cancers diagnosed at an advanced stage may affect not only mortality rates (the probability of long-term survival is strongly negatively correlated with stage), but also additional parameters. these include among others the cost and morbidity associated with treatment (advanced stage breast cancers that recur require additional chemotherapy, imaging and often salvage procedures such as spine stabilization, brain irradiation etc.). it is not entirely clear why there was no apparent correlation between the availability of mammography facilities and the use of mammography. further studies designed for the county level of geographic resolution, or in selected communities, may identify the community -specific http://ojphi.org/ the geographic distribution of mammography resources in mississippi 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi barriers to mammographic use, and hopefully these barriers can be overcome through community-targeted educational outreach campaigns. it has been estimated that 84 women between the ages of 40 and 84 need to be screened annually in order to save one life from breast cancer [34]. care must be taken to note that screening alone is inadequate in mississippi if the true objective is to increase breast cancer survivorship; women who receive positive screening results must also receive appropriate follow-up care. a proposal to increase breast cancer screening participation should address whether such an effort in mississippi would result in problems associated with overdiagnosis of breast cancer. by definition, overdiagnosis occurs through the detection of cancers that would not otherwise cause symptoms or death to occur within the lifetime of an individual [35]. overdiagnosis, by itself, can create needless emotional stress and anxiety considered harmful to a person's wellbeing, but the major harm associated with the overdiagnosis of breast cancer arises from overtreatment that often accompanies overdiagnosis. estimates for the overdiagnosis of breast cancer vary tremendously. a study of norwegian women estimated that approximately 15% to 25% of breast cancer cases were overdiagnosed [36], and estimates as high as 42% overdiagnosed breast cancers were obtained from an australian cohort [37]. studies reporting overdiagnosis predominantly involve european populations or populations of european ancestry, and are focused on ductal carcinoma in situ (dcis), a non-invasive form of breast cancer which ranges from low-grade to high-grade. although dcis is considered non-invasive, clinicians are currently unable to predict which specific cases of dcis are highly likely to progress, and which cases are likely to remain indolent and can be safely left untreated. the increasing frequency of routine mammography screening is considered to be the cause of the increasing diagnosis of dcis, which had an incidence of less than 2 per 100,000 women in the early 1970s and thirty years later, had an incidence of approximately 32 per 100,000 women [38]. approximately 1 in 1300 mammograms will result in a diagnosis of dcis [39], which accounts for approximately 20% to 25% of breast cancer diagnoses in the u.s.a. today [38]. however, there are no indications that overdiagnosis and overtreatment of breast cancer is currently a significant problem in mississippi, where the percentage of dcis at initial diagnosis is lower than the national average [33] and where the underuse of mammography appears to be linked to increased probability of mortality after diagnosis. conclusion as previously discussed, one cannot properly assess the burden of breast cancer in mississippi without recognizing the problem of population-based disparities. this study initially sought to determine whether the geographic availability of mammography resources influenced breast cancer outcomes, but data analysis quickly revealed that poverty and race were the dominant characteristics affecting geographic disparities in breast cancer outcomes in mississippi. the data presented in this manuscript supports two general observations pertaining to mississippi’s cancer control efforts. first, intensified mammography campaigns to reduce the percentage of advanced-stage breast cancers initially diagnosed in black women are justified and warranted. second, efforts are needed to ensure that once breast cancers are diagnosed, effective medical treatment will occur, especially for all women living in poverty. http://ojphi.org/ the geographic distribution of mammography resources in mississippi 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e226, 2014 ojphi limitations before discussing the above results, the following limitations of the study design should be noted. these studies were based on publicly available data, and as such, all health and economic data were analyzed in aggregate form. data were not obtained at the individual level, so the interpretations and implications of the evidence are limited to broadly defined localities and communities. out of respect for individual medical privacy, public cancer registry data are censored for statistically sparse counties, so some women with breast cancer were excluded from this study based on their place of residence (e.g., issaquena county). finally, to compensate for such problems associated with the low population density in many parts of mississippi, a five year data collection window was used based on the assumption that the recent trends in breast cancer outcomes have remained reasonably stable. acknowledgements the authors would like to acknowledge dr. robin rockhold and the base pair program for sponsoring science mentorship in central mississippi for over twenty years, and for providing numerous high school students with opportunities to design and conduct scientific research in a professional environment. the authors also acknowledge dr. donna sullivan, ms. gail howell and mr. jeffrey stokes for their essential roles in the base pair program, and they gratefully acknowledge the financial support of the base pair program (funded by the howard hughes medical institute). the authors gratefully acknowledge the advice and assistance of ms. deirdre rogers and data provided by the mississippi cancer registry. the helpful comments of dr. lucio miele and dr. marinelle payton are greatly appreciated. conflicts of interest rjd is a member of the advisory board of the mississippi cancer registry and the 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(page number not for citation purposes) isds 2013 conference abstracts best practices for implementing electronic disease surveillance systems in resources-constrained settings carmen c. mundaca*1, vivek singh2, kayumba kizito3 and julie pavlin4 1uniformed services university, bethesda, md, usa; 2indian institute of public health, hyderabed, india; 3voxiva sarl, kigali, rwanda; 4armed forces health surveillance center, silver spring, md, usa � �� �� �� � � �� �� �� � objective �������� � � ���� ���� ���� �� ������� ���� �� ��������� ����� � ���� ����� � �� � ������� ������� ���������� ��������� ����� ����� ��� ������������ ������ �� ����������� ���������� ����� ��������� ���� �������������� ��������� ������ ������ � � �������������� � ��� �� � ������ ������������ ����� ����������������������� ��������� � �������� ��� ������� ���� introduction ���� ������ �������� � ����� ���� ����� �� ������� ��� ��������� � �� � �� � ����������������� ����������� ��������������������������������� � �� 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�������������'�������������� ����������� � � �� �� ��������� ������������ ��������� � �������� � ���� ���� ������� ���������� ���������������������� keywords � ������������ 9���� ���������������� 9����� �������������� references !�� )�����������:�;���3��3����������:�)��<�� � ������������ �#������� ����� �/�� ���4��� ����� 7���� ��������� ������ ��� ���� ������ ��� ��� ��� �������� � ����-������ ���/�� ���������&66=��!,&07����!!=�&!� &�� *� ���������5����������� �� ����������������� �� 7���������������� ���� � ��� ���� ������ ��� ����� ������/�� ����� ����� ����&66=��&&,!07���� !+�&6� +�� *�*��(�������>���� ��� �����?�� ���������� ���/�� ���)����� ����� )� �����&66!��*����� ������� �� ��*����� ���������������� *carmen c. mundaca e-mail: mundaca.cecilia@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e2, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of essence in the cloud using meaningful use syndromic surveillance data wayne loschen*1, william stephens2, taha kass-hout3, miles stewart1, dave heinbaugh2 and joseph lombardo1 1johns hopkins university applied physics laboratory, laurel, md, usa; 2tarrant county public health, fort worth, tx, usa; 3public health surveillance and informatics program office, office of surveillance, epidemiology, & laboratory services, centers for disease control and prevention, atlanta, ga, usa objective this project represents collaboration among cdc’s biosense program, tarrant county public health and the essence team at the johns hopkins university apl. for over six months the tarrant county public health department has been sending data through the biosense 2.0 application to a pilot version of essence on the amazon govcloud. this project has demonstrated the ability for local hospitals to send meaningful use syndromic surveillance data to the internet cloud and provide public health officials tools to analyze the data both using biosense 2.0 and essence. the presentation will describe the tools and techniques used to accomplish this, an evaluation of how the system has performed, and lessons learned for future health departments attempting similar projects. introduction in november of 2011 biosense 2.0 went live to provide tools for public health departments to process, store, and analyze meaningful use syndromic surveillance data. in february of 2012 essence was adapted to support meaningful use syndromic surveillance data and was installed on the amazon govcloud. tarrant county public health department agreed to pilot the essence system and evaluate its performance compared to a local version essence they currently used. the project determined the technical feasibility of utilizing the internet cloud to perform detailed public health analysis, necessary changes needed to support meaningful use syndromic surveillance data, and any public health benefits that could be gained from the technology or data. methods this project investigated database and visualization changes necessary to support meaningful use syndromic surveillance data in essence. it evaluated the internet cloud environment and determined the benefits and disadvantages to using this technology as a platform for essence. this included scalability, performance, and cost analysis of the internet cloud platform. after using the system for a period of time, the tarrant county users evaluated the internet cloud version of the system. results many technical adaptations to the essence system were made to support the new meaningful use syndromic surveillance elements. several optimizations, including a new database schema and cube table structures, were developed to improve performance of essence in the internet cloud and incorporating the meaningful use requirements. the internet cloud platform offered many levels of performance that could alter the essence user experience. smaller configurations allowed for 100 concurrent users to experience 16 second response times, whereas larger configurations supported experiences of 2 second response times. conclusions public health departments are dealing with new meaningful use syndromic surveillance data elements and the cost of maintaining local systems. this collaborative team have researched and evaluated tools, technologies, and solutions that can be used throughout the country. keywords electronic medical records for public health; interoperability; meaningful use; syndromic surveillance; internet cloud acknowledgments the essence in the cloud initiative is supported by the cdc’s division of notifiable diseases and healthcare information (dndhi) biosense program. *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e54, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts can we use syndromic surveillance data to identify primary care visits to nyc eds? jessica athens* new york city department of health and mental hygiene, new york city, ny, usa objective to develop a syndrome classification based on patient chief complaint to (1) estimate the proportion of primary care-related emergency department (ed) visits in new york city (nyc) hospitals and (2) explore predictors of such visits. introduction nyc eds saw nearly 4 million visits in 2011. studies have demonstrated that non-urgent visits can account for more than 50% of visits to eds [1,2]. designed to provide rapid diagnosis and first-line treatment of serious illness, eds often function as a primary care site due to their accessibility. unfortunately, use of eds for primary care may affect their ability to meet the needs of severely ill patients. methods we examined syndromic surveillance data from 45 hospitals in nyc for 2011 and classified visits into a primary care syndrome based on the chief complaint field. data from 4 hospitals were omitted due to data quality issues, as were records from non-nyc residents. primary care (pc) syndrome visits included visits recorded as referrals, screenings, suture removal/dressing changes, or medication refills; records with a blank or non-informative (e.g. “x”) chief complaint field were omitted from analysis. using unique patient ids, we identified patients who visited the same ed multiple times in the previous 12 months. a hierarchical generalized linear mixed effects model with hospital-level random effects was used to explore patient characteristics associated with pc syndrome visits. the model included a random intercept for hospital and the following covariates: duplicate visit, patient gender and age group (ages 0-4, 5-17, 18-64, and 65+), and time of visit (midnight to 8 am, 8 am to midnight). covariates for month and day of week were included to control for temporal trends in ed visits. model parameters were estimated by maximum likelihood. estimation was performed in sas version 9.2 [3] using the glimmix procedure. results citywide, 7.5% (n=190,431) of visits to eds during 2011 were classified as pc syndrome visits, but varied by hospital with a median of 4.6% (iqr: 3% to 9%) across hospitals.the average proportion of pc syndrome visits varied by hospital. of the 45 hospitals included in the analysis, 18 had a lower baseline, 13 were the same, and 14 had a higher baseline than the city mean. hospitals with a larger census had a larger proportion of pc syndrome visits. age had a significant effect on the odds of a pc syndrome visit; ages 0–4 had the greatest odds of a pc syndrome visit relative to the 65+ age group. visits from patients ages 5–17 and 18–64 were also more likely to be primary care visits. patients with repeat visits were more likely to have pc syndrome visits. female gender and early morning visits (12a–8a) were associated with lower odds of a pc syndrome visit. conclusions with limited detail on patient visits, our syndrome likely undercounts primary care visits to eds. however, the relationships between our explanatory variables—age, time of day, and duplicate visits— and pc syndrome visits are consistent with the literature on ed usage for primary care. gender is an exception [1], but earlier findings may be confounded by the fact that females seek health care more frequently in general. the variation in pc syndrome visits among nyc eds is significant and may be explained by hospital or community measures not captured in our model, such as clinic wait times, ed capacity, or insurance coverage. in fact, disparities in such predictors of pc syndrome visits could be targets for interventions. our ability to replicate previous findings on the use of eds for primary care visits suggests that syndromic data may be a near real-time data source for following trends in such visits. predictors of pc syndrome visits all covariates significant at p < .001. keywords misuse of emergency medical services; primary care; variation in emergency department use references 1. carret m, gastal fass a, rodrigues domingues a. inappropriate use of emergency services: a systematic review of prevalence and associated factors. cad. saúde pública. 2009;25(1): 7–28. 2. tang n, stein j, hsia ry, masselli jh, gonzales r. trends and characteristics of us emergency department visits, 1997–2007. jama. 2010;304(6):664–70. 3. sas institute, inc., cary, nc. *jessica athens e-mail: jathens@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e56, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts estimation of influenza incidence by age in the 2011/12 seasons in japan using sassy yasushi ohkusa*1, yoshinori yasui1, tamie sugawara1, nobuhiko okabe2, 1, kiyosu taniguchi1 and kazunori oishi1 1idsc,niid, shinjuku, japan; 2kawasaki city institute for public health, kawasaki, japan objective so far, it is difficult to show the incidence rate of influenza in the official sentinel surveillance in japan. hence we construct the system which record infectious diseases at schools, kindergartens, and nursery schools, and then can show the accurate incidence rate of influenza in children by age/grade. introduction so as to develop more effective countermeasures against influenza, timely and precise information about influenza activity at schools, kindergartens, and nursery schools may be helpful. at the infectious diseases surveillance center of the national institute of infectious diseases, a school absenteeism surveillance system (sassy) has been in operation since 2009. sassy monitors the activity of varicella, mumps, mycoplasma pneumonia, pharyngoconjunctival fever, hand-foot-mouth disease, influenza, and many other infectious diseases in schools. in 2010, sassy was extended to the nursery school absenteeism surveillance system (nsassy). these systems record the number of absentees due to infectious diseases in each class of all grades of schools every day. as a powerful countermeasure to the pandemic flu of 2009, sassy was activated in 9 prefectures, in which included more than 6000 schools, and it is gradually being adopted in other prefectures. as of february 2012, 18 prefectures and 4 big cities, which together comprised 15,700 schools (about 35% of all schools in japan), utilized sassy. nsassy is used in more than 4100 nursery schools, which is about 18% of all nursery schools in japan. some studies of similar systems were performed in the uk (1), hong kong (2), and the usa (3,4), examined surveillance systems for monitoring infectious disease incidence, but the systems to construct in those studies do not operate nationwide like sassy or nsassy, and they cannot provide influenza incidence rates in children. methods all schools, kindergartens, and nursery schools in the community, enter data of the absentees due to infectious diseases into the system every day, thereby providing real-time data regarding infectious diseases prevalent in schools, to the schools around, school boards, public health centers, local governments, and medical professionals. it analyzed data for the 2011/2012 season (from september 1, 2011 to march 31, 2012) mainly, but also two seasons (2010/2011 and 2011/2012) were compared in some prefectures. in total, 12 prefectures, which comprised 2,352,839 children, were participated in 2011/2012 season. in the 2010/2011 season, 1,795,766 children of 9 prefectures were analyzed. results the incidence rate in the first grade of elementary schools is the highest both in the two seasons. the highest incidence rate in this grade distributes from 17.8% to 40.3% in 2011/2012 season, and from 11.0% to 30.7% in 2010/2011 season. conclusions this study proved sassy and nsassy are quite useful for monitoring of influenza outbreak in schools and it will be gold standard of surveillance for school children in japan. the present study also showed incidence rate of influenza in children at schools, kindergartens, and nursery schools, and proved the highest incidence was in the first grade of the elementary school. this is the first finding using such the huge number of subjects, which is more than 2 million. the intervention targeting to the weak age/grade is necessary for effective countermeasure and control of influenza and other infectious diseases. keywords surveillance; influenza; school absenteeism acknowledgments this paper is financial supported by ministry health, labour and welfare, japan. references 1) schmidt wp, pebody r, mangtani p: school absence data for influenza surveillance: a pilot study in the united kingdom, eurosurveillance, volume 15, issue 3, 21 january 2010 2) calvin k.y. cheng, benjamin j. cowling, eric h.y. lau, lai ming ho, gabriel m. leung, and dennis k.m. ip, electronic school absenteeism monitoring and influenza surveillance, hong kong, emerg infect dis. 2012 may 3) buehler jw, berkelman rl, hartley dm, peters cj: syndromic surveillance and bioterrorism-related epidemics. emerg infect dis. 2003; 9:1197-204. 4) besculides m, heffernan r, mostashari f, weiss d.:evaluation of school absenteeism data for early outbreak detection, new york city. bmc public health. 2005 oct 7;5:105 *yasushi ohkusa e-mail: ohkusa@nih.go.jp online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e142, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 111 (page number not for citation purposes) isds 2012 conference abstracts evaluating the variation on public health’s perceived field need of communicable disease reports uzay kirbiyik*1, 3, roland gamache2, 3, brian e. dixon2, 3 and shaun grannis1, 3 1indiana university, school of medicine, indianapolis, in, usa; 2indiana university, school of informatics, indianapolis, in, usa; 3health informatics, regenstrief institute, inc., indianapolis, in, usa objective to assess communicable disease report fields required by public health practitioners and evaluate the variation in the perceived utility of these fields. introduction communicable disease surveillance is a core public health function. many diseases must be reported to state and federal agencies (1). to manage and adjudicate such cases, public health stakeholders gather various data elements. since cases are identified in various healthcare settings, not all information sought by public health is available (2) resulting in varied field completeness, which affects the measured and perceived data quality. to better understand this variation, we evaluated public health practitioners’ perceived value of these fields to initiate or complete communicable disease reports. methods we chose four diseases: histoplasmosis, acute hepatitis b, hepatitis c and salmonella. we asked public health practitioners from marion county health department (mchd) of indianapolis to list the fields they felt were necessary when submitting a communicable disease report. we then asked them to evaluate those fields using the following criteria: required – a critical case attribute, when missing or unknown, would make the task of initiating and/or closing a case impossible or exceedingly difficult. desired – a case attribute allowing more complete epidemiologic profiles to be developed but, if missing, would not prohibit initiating and/or closing a case. not applicable – a case attribute that is not usually collected to initiate and/or close a case for the particular condition. to quantify the need for the fields, we assigned a number to each response as follows: 0 not applicable 1 desired 2required we summed the numbers for each field for each disease and created a table for the perceived need of that field (table 1). results the perceived needs table showed a difference between the fields needed to initiate or close a case. moreover the perceived need for fields varied by disease as well. to assess the difference in perceived needs, we calculated the standard deviation of the fields (table 2). conclusions data quality is essential, not only for research but to support routine public health practice as well. many factors affect data quality; one of them is perceived need of the information by public health practitioners. despite working with public health stakeholders from the same organization we observed variation in their perceived needs for these fields to initiate or close a communicable case. these results highlight another source of the problem regarding health information quality and its goodness of fit issues. table 1. perceived need for the selected communicable disease reports fields. higher numbers (darker color) reflect greater perceived need. table 2. standard deviation of perceived need values for each field. higher numbers reflect more disagreement among responses. isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 112 (page number not for citation purposes) isds 2012 conference abstracts keywords completeness; data quality; communicable disease reports acknowledgments we thank the mchd of indianapolis for their help with this research. references 1. morbidity and mortality weekly report (mmwr) june 1, 2012 / 59(53);1-111 http://www.cdc.gov/mmwr/preview/mmwrhtml/ mm59 53a1.htm 2. indiana confidential report of communicable diseases state form 43823 (r2/11-96) https://forms.in.gov/download.aspx?id=5082 3. wang r y, strong d m, guarascio l m. beyond accuracy: what data quality means to data consumers. journal of management information systems 1996; 12 (4); 5–33. *uzay kirbiyik e-mail: ukirbiyi@iupui.edu layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts state foodborne illness surveillance and response laws: compilation and analysis stephanie david, jenna burton, chris chadwick and rebecca katz* george washington university, washington, dc, usa objective to document and assess the variation in state legislation relating to foodborne disease surveillance and outbreak response for all 50 states and the district of columbia by creating a database and appendix of laws and regulations that will be made available to researchers and policymakers. introduction foodborne illnesses sicken 48 million and kill 3,000 americans every year, presenting an enduring threat to the public’s health. in just the past three years alone, the united states has experienced at least four major multistate outbreaks in food. despite this growing problem, efforts to prevent foodborne illness pose a particular public health challenge due in part to the widely variable laws governing foodborne illness surveillance and outbreak response. the recent passage of the food safety modernization act (fsma) presents an opportunity for researchers, program managers, and policy makers to assess and correct the legal barriers that may hinder states in effectively implementing the fsma’s vision with regard to increased state and local capacity for surveillance and outbreak response. methods we conducted a systematic review and analysis of laws and regulations relating to foodborne illness surveillance and outbreak response in all 50 states and the district of columbia, using the following methods: (1) we created a database to record state laws and regulations relating to foodborne illness surveillance and outbreak response in all 50 states and the district of columbia; (2) we conducted a basic gap analysis of state foodborne illness surveillance and outbreak response laws and policies collected in the database; and (3) we conducted case study analyses of previous multistate outbreaks from 2008-2011. results through compilation of the state foodborne illness surveillance and outbreak response laws and regulations and analysis of previous multistate outbreaks, we are able to present trends, variations, and gaps in the legislation that directly impacts the ability of public health officials to conduct foodborne outbreak investigations. we also present policy recommendations for strengthening state laws and regulations. keywords surveillance; foodborne disease; outbreak response; public health law; legal preparedness acknowledgments this research was funded by a grant from the rwj foundation. *rebecca katz e-mail: rlkatz@gwu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e70, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a survey of data recording procedures at new york city emergency departments jessica sell*1 and alyssa wong2 1nyc department of health and mental hygiene, long island city, ny, usa; 2columbia university, mailman school of public health, new york, ny, usa objective to describe the development, implementation, and analysis of a hospital based emergency department (ed) survey and site visit project conducted by the new york city (nyc) department of health and mental hygiene (dohmh). introduction data is collected daily by the dohmh from 49 of the 52 nyc eds, representing approximately 95% of all ed visits in nyc. variability in data fields between and within eds has been noticed for some time. differences in chief complaint (cc) characteristics and inconsistent availability of data elements, such as disposition and diagnosis, suggest that procedures, coding practices and health information systems (his) are not standardized across all nyc eds, and may change within eds. these differences may have an unapparent effect on the dohmh’s ability to consistently categorize ed visits into syndrome groupings, which may alter how syndromic trends are analyzed. prior to this project, the dohmh had no method in place to regularly capture, evaluate or utilize this level of ed-specific information. methods a member of the dohmh contacted all 49 eds to request a brief interview with the ed director, administrator and/or appropriate staff. a questionnaire was designed to collect the following information about each ed: the clinical and administrative his used to collect patient information and report it to the dohmh (including any recent system changes); cc coding practices (i.e. who records the cc, and into which his, and in what format); disposition and diagnosis recording practices and availability. questions regarding hospital specific trends and characteristics were also included. interviews were conducted in person by two members of the dohmh. information from the survey was compiled into a microsoft excel spreadsheet by the interviewers. a descriptive analysis was performed comparing and detailing his used, cc coding practices, and recording procedures for disposition and diagnosis. a member of the dohmh followed up with ed staff and it personnel to resolve any outstanding data quality issues. results all 49 eds were contacted and interviewed. a median of 43 days (ranging from 7 to 167) elapsed between the initial attempt to contact the ed director, and the completion of the interview. all interviews lasted approximately 40 minutes. according to the results of the survey, the dohmh receives information from the clinical his from approximately 20% of eds, from the administrative his from approximately 70% of the eds, and approximately 10% of the eds did not know which system was used to generate the daily reports sent to the dohmh. nearly 100% of the eds reported that the chief complaint was entered into the clinical his by a triage nurse. however, it is not known who records the cc into the administrative system. four eds reported that a dropdown menu is used to record cc into the clinical his, 23 eds cc is in free-text format, and 22 eds cc is a combination of free-text and drop-down format. diagnosis was recorded by the physician at 45% of the eds, and by other staff, including nurses and clerks, at 55% of the eds. two thirds of the eds reported a lag time of less than one week between the visit and assignment of diagnosis codes. disposition is recorded by the physician at 80% of eds. discharge disposition is often required for a patients chart to be considered complete. as a result of the visits the dohmh was able to better understand problems that cause routine data quality problems (e.g. missing data or unusable data) by hospital and identify methods to improve those problems. missing and up to date disposition codebooks were obtained from hospitals. current hospital contacts were identified for follow up. discussions with hospital personnel regarding specific trends, characteristics and interests helped to strengthen the relationship the dohmh has with the hospital ed staff. conclusions differences in practices, procedures, and his used can lead to variability in data quality and characteristics which may affect the ability to categorize visits into effective syndrome groupings and understand trends. further research is needed to develop an improved method for analyzing ed data that takes ed-specific characteristics into consideration. additionally, it is important to establish good working relationships with key members of each ed’s staff in the event of a possible outbreak, and in keeping up to date on any changes within each ed that may affect data quality. keywords emergency department; syndromic surveillance; coding practices acknowledgments nyc dohmh alfred p. sloan foundation *jessica sell e-mail: jsell@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e114, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts ili and sari surveillance along the california & arizona borders with mexico, 2011-12 pete kammerer*, gary brice, anthony hawksworth and chris myers operational infectious diseases, nhrc, san diego, ca, usa objective to identify the pathogens responsible for influenza-like illness (ili) and severe acute respiratory illness (sari) along the u.s.-mexico border region in san diego and imperial counties, ca and pima county, az. introduction national borders do not prevent the transmission of pathogens and associated vectors among border populations. the naval health research center (nhrc) has collaborated with the mexican secretariat of health, the u. s. department of state’s biosecurity engagement program (bep) and the u. s. centers for disease control and prevention (cdc) in concert with local health officials to conduct ili surveillance (since 2004) and sari surveillance (since 2009) in the border region. methods respiratory swabs were collected from patients with ili (fever ! 100f, and sore throat or cough) or sari (! 5 y.o.: ili with hospital admission; < 5 y.o.: clinical suspicion of pneumonia with hospital admission) and stored at -70c. specimens were tested with molecular techniques, viral and bacterial culture. results nhrc received and tested 295 ili specimens collected from four surveillance sites in 2011-12. demographics: 53% female, 47% male; 36% 0-4 yrs old, 50% 5-24 yrs old, 8% 25-49 yrs old, 4% 50-64 yrs old, 2% >64 yrs old. pathogens identified included influenza a (15%); rhinovirus (8%); respiratory syncytial virus (rsv) (7%); adenovirus (6%); influenza b (4%) and parainfluenza virus (piv) 1; (4%). 335 sari specimens were collected from 6 sites. demographics: 52% female, 48% male; 41% 0-4 yrs old; 9% 5-24 yrs old, 12% 25-49 yrs old, 11% 50-64 yrs old, 28% >64 yrs old. pathogens identified included rsv (17%); rhinovirus (10%); influenza a (9%); adenovirus (6%); influenza b (2%) and piv 1 (1%). conclusions in 2011-12, our surveillance identified a difference in the proportion of respiratory pathogens affecting outpatients and inpatients. influenza a was isolated more frequently in outpatients, whereas rsv was more frequent in hospitalized patients. we also noted an increased proportion of specimens from the 50-64 yr old and the >64 yr old age groups in the sari surveillance, whereas 86% of the ili specimens are from patients 24 yrs old or less. additional benefits of this collaborative surveillance have been the cooperation, joint training and communication between the participating entities. these preestablished lines of communication are invaluable during a public health emergency, which was demonstrated during the recent influenza pandemic. keywords influenza; ili; respiratory syncytial virus; us-mexico border; sari *pete kammerer e-mail: peter.kammerer@med.navy.mil online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e134, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts an isds-based initiative for conventions for biosurveillance data analysis methods michael coletta4, howard burkom*1, jeffrey johnson2 and wendy chapman3 1johns hopkins applied physics laboratory, laurel, md, usa; 2san diego county health and human services agency, san diego, ca, usa; 3university of california san diego, san diego, ca, usa; 4national association of county and city health officials, washington, dc, usa objective the panel will present the problem of standardizing analytic methods for public health disease surveillance, enumerate goals and constraints of various stakeholders, and present a straw-man framework for a conventions group. introduction twelve years into the 21st century, after publication of hundreds of articles and establishment of numerous biosurveillance systems worldwide, there is no agreement among the disease surveillance community on most effective technical methods for public health data monitoring. potential utility of such methods includes timely anomaly detection, threat corroboration and characterization, follow-up analysis such as case linkage and contact tracing, and alternative uses such as providing supplementary information to clinicians and policy makers. several factors have impeded establishment of analytical conventions. as immediate owners of the surveillance problem, public health practitioners are overwhelmed and understaffed. goals and resources differ widely among monitoring institutions, and they do not speak with a single voice. limited funding opportunities have not been sufficient for cross-disciplinary collaboration driven by these practitioners. most academics with the expertise and luxury of method development cannot access surveillance data. lack of data access is a formidable obstacle to developers and has caused talented statisticians, data miners, and other analysts to abandon the field. the result is that older research is neglected and repeated, literature is flooded with papers of varying utility, and the decision-maker seeking realistic solutions without detailed technical knowledge faces a difficult task. regarding conventions, the disease surveillance community can learn from older, more established disciplines, but it also poses some unique challenges. the general problem is that disease surveillance lies on the fringe of disparate fields (biostatistics, statistical process control, data mining, and others), and poses problems that do not adequately fit conventional approaches in these disciplines. in its eighth year, the international society of disease surveillance is well positioned to address the standardization problem because its membership represents the involved stakeholders including progressive programs worldwide as well as resource-limited settings, and also because best practices in disease surveillance is fundamental to its mission. the proposed panel is intended to discuss how an effective, sustainable technical conventions group might be maintained and how it could support stakeholder institutions. methods members of a technical conventions group would have experience and dedication to advancing the science of disease surveillance. primary functions would include: 1. specify and communicate technical problems facing professionals involved in routine monitoring of population health. alternative use applications would also be considered, such as the use of epidemiological findings to inform clinical diagnoses. 2. independently evaluate the utility of proposed analytical solutions to well-defined problems in public health surveillance and confer approval or certification, perhaps on several levels, such as whether results can be replicated with shareable data. approved solutions might be restricted to commonly available software such as the r language or microsoft excel. 3. facilitate sharing of tools and methodologies to evaluate methods and to visualize their results the framework to be discussed in the proposed panel would be a means of keeping open lines of collaboration and idea-sharing. overcoming obstacles toward this goal is worthy of a conference panel discussion whether or not it concludes that a conventions group is a viable approach. results three 15-minute panelist talks are proposed: 1. background: in-depth description of the dimensions of the problem above 2. constraints facing public health practitioners and requirements for practical analytic tools 3. strawman conventions group: role, logistics, inclusiveness, methods of communicating with stakeholders and related organizations and producing/disseminating output. for the 45 minutes of discussion, the first 15-20 will invite reactions to the first two talks to sharpen the scope of the effort. the remainder of the session will cover the advisability, feasibility, and logistics of an isds-based conventions group, and modify the strawman group concept. keywords standards; data analysis; statistical algorithms; certification *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e99, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts poison center data for public health surveillance: poison center and public health perspectives royal k. law*1, josh schier1, jay schauben2, katherine wheeler3 and prakash mulay4 1centers for disease control and prevention, chamblee, ga, usa; 2florida poison information center jacksonville, jacksonville, fl, usa; 3new york city department of health and mental hygiene, new york city, ny, usa; 4florida department of health, tallahassee, fl, usa objective to describe the use of poison center data for public health surveillance from the poison center, local, state, and federal public health perspectives and to generate meaningful discussion on how to address the challenges to collaboration. introduction since 2008, poisoning has become the leading cause of injury-related death in the united states (us); since 1980, the poisoning-related fatality rate in the us has almost tripled.1 many poison-related injuries and deaths are reported to regional poison centers (pcs) which receive about 2.4 million reports of human chemical and poison exposures annually.2 federal, state, and local public health (ph) agencies often collaborate with poison centers and use pc data for public health surveillance of poisoning-related health issues. many state and local ph agencies have partnerships with regional pcs for direct access to local pc data which help them perform this function. at the national level, cdc conducts public health surveillance for exposures and illnesses of public health significance using the national poison data system (npds), the national pc reporting database. though most pc and ph officials agree that pc data play an important role in ph practice and surveillance, collaboration between ph agencies and pcs has been hindered by numerous challenges. to address these challenges and bolster collaboration, the poison center and public health collaborations community of practice (cop) was created in 2010 by cdc as a means to share experiences, identify best practices, and facilitate relationships among federal, state and local public health agencies and pcs. to date, the poison center and public health collaborations cop includes over 200 members from state and local public health, regional pcs, cdc, the american association of poison control centers (aapcc), and the environmental protection agency (epa). a leadership team was created with representatives of the many stakeholders of the community to drive its direction and oversee activities. methods the panel will consist of 4 presenters and 1 moderator, who are members of the poison center and public health collaborations cop leadership team. each presenter will bring a unique perspective of the use of pc data for ph practice and surveillance: cdc, state department of health, a local department of health, and a pc. royal law from the cdc national center for environmental health will present on using pc data for identification of exposures and illnesses of public health significance identified from npds data collected from all 57 pcs. dr. jay schauben from the florida/usvi poison information center jacksonville will discuss pc participation in surveillance and use of pc data for tracking and mitigation of ph events in florida. dr. prakash mulay from the florida department of health will discuss utilization of pc data to enhance essence-based chemical-associated exposure and illness surveillance in florida. katherine wheeler from the new york city (nyc) department of health and mental hygiene will discuss nyc’s use of pc data in surveillance of potential emerging issues, from energy drinks to synthetic marijuana. each presenter will discuss the use of pc data for ph practice and surveillance in his or her organization and jurisdiction, the successes of using pc data, and their challenges. results the moderator will engage the audience by facilitating discussion of the successes and challenges to using pc data for ph practice and surveillance with the audience. sample questions: what are your current capacities and collaborative activities between your state/local health department and your poison center? what non-funding related barriers hinder the collaboration between your state/local health department and poison center? if more funding were available, how would you use this funding to increase the level of interactivity with the poison center and state/local health department? keywords surveillance; poison center; community of practice references 1. warner m, chen lh, makuc dm, anderson rn, and minino am. drug poisoning deaths in the united states, 1980–2008. national center for health statistics data brief, december 2011. accessed 8/29/2012. 2. bronstein ac, spyker da, cantilena lr, green jl, rumak bh, dart rc. 2010 annual report of the american association of poison control centers’ national poison data system (npds): 28th annual report. clin toxicol 2011; 49: 910-941. *royal k. law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e101, 2013 ojphi-06-e121.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 149 (page number not for citation purposes) isds 2013 conference abstracts an evaluation of the key indicator based surveillance system for international health regulations (ihr) -2005 core capacity requirements in india 1public health foundation of india (phfi), indian institute of public health hyderabad, hyderabad, india; 2directorate of health, department of health, medical & family welfare, government of andhra pradesh, hyderabad, india; 3public health foundation of india, new delhi, india; 4institute for health metrics and evaluation, university of washington, seattle, wa, usa; 5london school of hygiene and tropical medicine, london, united kingdom � �� �� �� � � �� �� �� � objective ������� �� ���� � ������� ����� � ���������������� ����� ������� ���� ��� ��������� ����� ����� � ������������� ����� 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�������������������� �&�� ���'��� ����� #�?7/���� ������ ��#�)*2*>2*� ���� � 2a�)#� ���7��� �7��a� )22;-c);#� ������� /����� � �7/��a� �7/-**++9;#�,����)*2*i2)i))#����# +#�4�� ��&�� ���5�����6�����a�/��������� ���� �� �� ����� ��������� �� ��� �� � ��������������������������������� �������4�� ��&�� ��� 5�����6�����"�)**0# 0#�7�8�����d"�/���������"�'����7"�4������"�8 �������"�! ����4"���� � #�/�������� � �������$�� ���� ������ ��� ����� �������������������� �� ���� �������������� ��� �������� ���#�?7/���� ������ ��#�)**)>)a)#� ���7����7��a�22c;0cce#��������/����� ��7/��a�0++ec# *vivek singh e-mail: vivek.singh@iiphh.org� � � � vivek singh*1, jagan mohan2, u prasada rao2, lalit dandona3, 4 and david heymann5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e121, 2014 sustainable surveillance paper 10.01.2012nm.docx.docx steps to a sustainable public health surveillance enterprise 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi steps to a sustainable public health surveillance enterprise a commentary from the international society for disease surveillance nabila mirza, 1 tera reynolds, 1 michael coletta, 2 katie suda, 3 ireneous soyiri, 4 ariana markle, 5 henry leopold, 6 leslie lenert, 7 erika samoff, 8 alan siniscalchi, 9 laura streichert 1 1 international society for disease surveillance; 2 national association of county and city health officials; 3 university of tennessee; 4 monash university; 5 university of california, los angeles; 6 healthwizer; 7 university of utah health care; 8 university of north carolina – chapel hill; 9 connecticut department of public health introduction at a time when populations are changing and disease outbreaks and other events of public health significance pose increasing risks to global health, economic stability, and national security, it is essential that, as a nation, we invest in the systems needed to promote and protect the public’s health. abstract more than a decade into the 21 st century, the ability to effectively monitor community health status, as well as forecast, detect, and respond to disease outbreaks and other events of public health significance, remains a major challenge. as an issue that affects population health, economic stability, and global security, the public health surveillance enterprise warrants the attention of decision makers at all levels. public health practitioners responsible for surveillance functions are best positioned to identify the key elements needed for creating and maintaining effective and sustainable surveillance systems. this paper presents the recommendations of the sustainable surveillance workgroup convened by the international society for disease surveillance (isds) to identify strategies for building, strengthening, and maintaining surveillance systems that are equipped to provide data continuity and to handle both established and new data sources and public health surveillance practices. keywords: disease surveillance, enterprise, sustainable, policy, information technology, epidemiology correspondence: lstreichert@syndromic.org copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:lstreichert@syndromic.org steps to a sustainable public health surveillance enterprise 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi in 2002, the institute of medicine (iom) reported that the public health system in the united states had a multitude of deficiencies that impact the ability to effectively conduct public health surveillance. these included outdated and vulnerable technologies; a public health workforce lacking training and reinforcements; lack of real-time surveillance and epidemiological systems; and ineffective and fragmented communications networks. 1 while considerable headway has been made since the iom report was published, there is still evidence of a need for further improvements. a recent report by trust for america’s health, for example, found that there are persistent gaps in the ability of state and local public health agencies to respond to events ranging from bioterrorist threats to natural disasters and disease outbreaks. 2 the question is—how can we reduce these gaps? nationwide and globally, rapid changes in health information systems, cloud computing technologies, communications, and global connections are catalyzing a re-examination of disease surveillance as an enterprise that needs coordinated and integrated system elements. sustainable surveillance, which we define as ongoing data collection, analysis, and application, coupled with a capability to respond to novel demands, is needed to ensure that public health agencies can perform reliably regardless of shifts in public health funding and priorities. the isds sustainable surveillance workgroup identified the following steps to maintain and advance the public health surveillance enterprise: 1. recognize systematic and ongoing public health surveillance as a core public health function that is essential for population health, economic stability, and national security. 2. create and support funding mechanisms that reinforce enterprise (i.e., integrated systems), rather than categorical (i.e., disease or program specific) surveillance infrastructures and activities in order to reduce inefficient silos, leverage resources, and foster synergies. 3. oppose further cuts to spending for surveillance activities. 4. invest in surveillance workforce development to build competencies and improve organizational capacity to utilize technological advances in surveillance practice. 5. advance a rigorous surveillance research and evaluation agenda that will deepen the understanding of community health, identify best practices, and provide evidence for decision-making. steps to a sustainable public health surveillance enterprise 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 1: recommended steps to a sustainable surveillance enterprise background public health surveillance is defined as, “the systematic and ongoing collection, management, analysis, interpretation, and dissemination of information for the purpose of informing the actions of public health decision makers.” 3 in addition to providing information about the health status of our communities, surveillance is a foundation of emergency preparedness, food safety, infectious disease outbreak prevention and control, chronic disease assessments, and other key areas that protect the health, economy, and security of the public. while public health surveillance policy and practice have been indicated as priorities for policymakers at the national steps to a sustainable public health surveillance enterprise 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi and global levels, 4–7 questions remain about how to move forward from planning to implementation, especially in a time of critical cuts to federal funding. progress in health information technology (it) and the increased use of electronic data and new data streams offer great potential for innovation in surveillance science and practice. for instance, the self-reporting of health information through social media (e.g., twitter), as well as crowdsourcing projects such as flu near you (www.flunearyou.org) offer new options for collecting timely data. in addition, the health information technology for economic and clinical health (hitech) act, 8 legislated as part of the american recovery and reinvestment act (arra) of 2009, is fueling the adoption of electronic health record (ehr) systems in the u.s. 9 in return for financial subsidies to implement ehr systems, hospitals and doctors are required to share data for public health purposes 9 with the intent to improve both population health outcomes and the quality of clinical practice. sustainable surveillance systems have the potential to advance both of these goals. 10 the value of public health surveillance 1. recognize systematic and ongoing public health surveillance as a core public health function that is essential for population health, economic stability, and national security. public health surveillance data is the foundation of public health programs and is required for a number of purposes, including: to demonstrate the size and impact of the public health problem being addressed by a program; to identify the population groups to which additional prevention efforts should be directed; to determine whether the problem is growing in size or abating; to provide feedback to data providers; and as part of an overall program evaluation strategy. the significant health impacts and economic costs of disease outbreaks illustrate the critical importance of effective public health surveillance and rapid response, as well as the cost of inaction. 11 table 1 provides examples of the health and financial burdens posed by some naturally occurring and intentional infectious disease outbreaks. http://www.flunearyou.org/ steps to a sustainable public health surveillance enterprise 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table 1: examples of health impacts and economic costs associated with disease outbreaks and epidemics disease transmission health impact financial cost severe acute respiratory syndrome (sars), global, 2002 and 2003 droplet (direct) 8,096 infected, including almost 800 deaths 12 $40-$54 billion 13 anthrax attack, united states, 2001 bioterrorism (indirect) 22 cases, including 5 deaths 14 about $320 million 15 pandemic flu, united states droplet (direct] projected death of millions of people 16 projected cost of $800 billion over a whole year 17 pertussis, washington state, 2012 droplet (direct) over 3000 cases through early july 18 over $2,000 per case 19 west nile virus, sacramento county, 2005 vector (indirect) 163 people infected 20,21 $2.98 million [treatment cost and productivity loss] 21 salmonella, north dakota, 2009 foodborne (indirect) 180 people infected 22 $38,000 in investigation cost [travel, laboratory and staff time) 22 cholera, latin america, 1991 waterborne (indirect) 400,000 cases including over 4000 deaths 23 $770 million loss in food trade embargoes and adverse effects on tourism 23 tuberculosis, global, 2011 droplet (direct) 8.7 million cases, 1.4 million deaths 24 projected economic cost of up to $8 billion per year between 2013 and 2015 for low and middle income countries 24 the values reported in table 1 do not fully reflect additional indirect costs of diseases and their potentially crippling effects on a community, nor do they address costs that are underreported/ unreported due to lack of data. higher rates of illness, for example, can lead to lower worker productivity, 11 while premature mortality can reduce the size of the labor force, both of which have economic ramifications. there is growing evidence that these economic and societal costs can be mitigated by surveillance systems that are stable; a stable system provides the best foundation for identifying whether the problem being addressed is getting bigger or smaller or disproportionately affecting a section of the population, etc., while still allowing flexibility to provide useful information quickly about emerging issues. the optimum mix of stability and flexibility will depend on the purpose(s) of surveillance and the particular health condition under surveillance. for example, in steps to a sustainable public health surveillance enterprise 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the case of sars, an effective surveillance system has the potential to decrease the size of an epidemic by one-third and the duration by 4 weeks, with significant cost savings. 25 another study found that the early detection of an outbreak of highly infectious bacterial meningitis saved approximately $2 for every dollar invested in infectious disease surveillance. 26 yet another evaluation of surveillance practice found that technological improvements in a sentinel influenza-like illness (ili) surveillance system in virginia saved over $9,500 (1,992 hours) in staff-time during the 2007-2008 influenza seasons. 27 ongoing surveillance can also inform the design and evaluation of prevention and intervention programs in order to control the escalating costs associated with chronic diseases in the u.s. and abroad. 28 some experts forecast that chronic disease prevention programs could save up to $48.9 billion per year by 2030, 29 while others predict applying electronic medical record implementation and networking to the prevention and management of chronic disease will exceed the currently projected $81 billion in annual savings. 30 enterprise models for surveillance practice and funding 2. create and support funding mechanisms that reinforce enterprise (i.e., integrated systems) rather than categorical (i.e., disease or program-specific) surveillance infrastructure and activities in order to reduce inefficient silos, leverage resources, and foster synergies. siloed surveillance systems are outdated, inefficient, and incapable of meeting today’s demands for electronic data exchange and for the informatics capabilities needed to use the information for maximum benefit. integrated programs and collaboration, on the other hand, facilitate the efficient management of the complex, varied, and proliferating issues and information sources that exist today. the nature of public health surveillance also lends itself to multiple-purpose approaches in that strategies for preventing and controlling diseases, such as west nile virus, are to a great extent the same as for an influenza epidemic, a foodborne disease outbreak, or a bioterrorist attack. 31 technology that enhances communication and data sharing across disease programs, surveillance systems, and even across jurisdictions increases the ease of obtaining and disseminating useful information to a broad audience, including public health agencies, healthcare providers, policymakers, and the general public. 6,32 this rapid information exchange not only facilitates timely response, but can also reduce emergency room visits, hospital admissions, and even costs of care. 33 however, many health departments currently have systems that are not flexible enough to respond to changing health it needs, which makes it difficult to deliver information when and where it is needed. 4 disease or program-specific funding also exacerbates program vulnerability to funding and budgetary cuts. for example, when funding is earmarked for specific purposes (e.g., emergency preparedness and associated surveillance systems), and then is reduced, such as has occurred for public health emergency preparedness cooperative agreement funding through cdc in the past seven years, 34 it can undermine and reverse efforts to establish sustainable systems that serve multiple crosscutting purposes throughout public health. steps to a sustainable public health surveillance enterprise 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi by contrast, an enterprise approach provides a cohesive framework that will better equip public health practitioners to address the challenges of processing large volumes of electronic data, and the concomitant analytical and visualization requirements. specifically, enterprise funding supports a reliable, flexible infrastructure that can adapt to technological and information requirement changes, and allows for ongoing data collection and the integration of new data sources to advance all-hazard preparedness. a 2004 white house memo acknowledged how programmatic funding can lead to inefficiencies and redundancies in system acquisitions and usage and called for applying technological and human resources across programs. 35 by encouraging collaboration within and between departments, surveillance professionals can take advantage of shared platforms and resources to optimize data collection, analysis, storage, and dissemination, thus helping to reduce operational costs and improve efficiency. for example, collaboration could create opportunities for the effective integration of syndromic and reportable disease data for public health use. 36 stable funding and sustainable surveillance 3. oppose further cuts to spending for surveillance activities. a lack of consistent and sustainable funding is hampering the necessary expansion and improvement of public health surveillance systems at local, state, and national public health agencies. a 2010 survey of local health departments conducted by the national association of city and county health officials (naccho) found that 72% of local health departments reported insufficient funding as one of their major barriers to modernizing their it systems. 37 health data collection systems that take advantage of recent technological advances have proven to be more cost effective and sustainable in the long-term. 38 stable funding is essential to supporting the adoption of hardware and software systems as they become available, leading to a robust and sustainable public health surveillance infrastructure able to integrate, manage, and communicate the plethora of data necessary to generate actionable results. 39 build the base for success 4. invest in surveillance workforce development to build competencies and improve organizational capacity to utilize technological advances in surveillance practice. the new age of disease surveillance requires a skilled public health workforce able to manage large volumes of increasingly complex electronic information, to understand the data flows, and to extract meaning from them. this calls for sophisticated and integrated competencies in public health informatics, epidemiology, statistics, and other areas, and the ability to present findings, draw conclusions, and make recommendations based on surveillance data. furthermore, in addition to needing people who can effectively operate existing surveillance systems and carry out tasks (such as the onboarding process for collecting newly available ehr data) there is also demand for people who can identify and assess new opportunities for surveillance and design new systems that take advantage of these opportunities. 6 steps to a sustainable public health surveillance enterprise 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi attracting and retaining experts in these fields is especially challenging in light of the comparatively low base salaries allotted to public health workers compared to the salaries of technology-intensive positions in other sectors. 40 to align the surveillance workforce with new demands, the isds sustainable surveillance workgroup suggests the following approaches: ● provide training programs for existing and prospective public health workers to equip themselves with the necessary expertise and skills to work in rapidly evolving it systems. ● promote public health careers at the primary, secondary, undergraduate, and graduate levels across disciplines. ● provide competitive salaries to recruit and retain a workforce skilled in public health surveillance and informatics. toward informed decision-making 5. advance a rigorous research and evaluation agenda that will deepen the understanding of community health, identify best practices, and provide evidence to inform decision-making. research and evaluation play an important role in connecting the processes of information collection, information use for decision-making, and translation of decisions to actions and measurable outcomes. research-based evidence and evaluation results can help to identify the limitations and benefits of different surveillance procedures for better decision-making and more effective resource allocation. investing in research and applying the rigors of science to public health surveillance questions leads to informed decisions on how best to direct efforts and resources. in addition, periodic evaluations of surveillance infrastructures – the systems and people—are needed to assess return on investment and opportunities for quality improvement. conclusion effective and efficient surveillance systems are proven to save money and lives. the ability to detect and respond to known and emerging pathogens is central to protecting and maintaining population health. 41 the breakdown or absence of a stable public health surveillance infrastructure, on the other hand, can undermine efforts to mitigate disease outbreaks and other public health events. 31 public health surveillance systems built on a strong infrastructure of core workforce competencies, information systems, and organizational capacity, 42 and supported by consistent and enterprise-based funding, are essential if we are to understand and respond to the real and growing threats to population health. by providing political commitment and financial support to this issue, decision makers can play an active role in advancing the health of individuals, communities, and nations. steps to a sustainable public health surveillance enterprise 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi acknowledgements isds thanks the following members of the isds 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[42] the department of health and human services. public health’s infrastructure, a status report to the u.s. senate appropriations committee, 2001. centers for disease control and prevention; 2001. a confidence-based aberration interpretation framework for outbreak conciliation a confidence-based aberration interpretation framework for outbreak conciliation 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 a confidence-based aberration interpretation framework for outbreak conciliation shamir nizar mukhi, phd 1 1 university of manitoba, public health agency of canada abstract: health surveillance can be viewed as an ongoing systematic collection, analysis, and interpretation of data for use in planning, implementation, and evaluation of a given health system, in potentially multiple spheres (ex: animal, human, environment). as we move into a sophisticated technologically advanced era, there is a need for cost-effective and efficient health surveillance methods and systems that will rapidly identify potential bioterrorism attacks and infectious disease outbreaks. the main objective of such methods and systems would be to reduce the impact of an outbreak by enabling appropriate officials to detect it quickly and implement timely and appropriate interventions. identifying an outbreak and/or potential bioterrorism attack days to weeks earlier than traditional surveillance methods would potentially result in a reduction in morbidity, mortality, and outbreak associated economic consequences. proposed here is a novel framework that takes into account the relationships between aberration detection algorithms and produces an unbiased confidence measure for identification of start of an outbreak. such a framework would enable a user and/or a system to interpret the anomaly detection results generated via multiple algorithms with some indication of confidence. keywords: health, surveillance, outbreak, bioterrorism, anomaly, syndromic, confidence, infectious disease 1. introduction recent advances in technology have made it possible to gather, integrate, and analyze large amounts of data in real-time or near real-time. these new technologies have touched off a renaissance in public health surveillance. for the most part, the traditional purposes of health surveillance have been to monitor long-term trends in disease ecology and to guide policy decisions. with the introduction of real-time capabilities, data exchange now holds the promise of facilitating early event detection and to assist in day-to-day disease management. with the availability of dozens of different aberration detection algorithms, it is possible, if not probable, to get different results from different algorithms when executed on the same dataset. the results of the study in [1] suggest that commonly-used algorithms for disease surveillance often do not perform well in detecting aberrations other than large and rapid increases in daily counts relative to baseline levels. a new approach, denoted here as confidence-based aberration interpretation framework (caif), may help address this issue in disease surveillance by using a collective approach rather than algorithm specific approach. 2. the problem statement consider a system with multiple anomaly detection algorithms as illustrated in figure 1. due to differences in the implementation of the algorithms and parameters used (ex: thresholds, training periods and averaging windows), the outbreak decisions may vary significantly from a confidence-based aberration interpretation framework for outbreak conciliation 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 one algorithm to another. on the other hand, there is also a possibility that these decisions are very similar for some set of algorithms. these two extremes create a dilemma for decision makers in that there could be a situation where most of the algorithms in a system suggest an outbreak, however, not knowing the relationships between these algorithms can result in a biased decision. figure 1. the outbreak detection problem as illustrated, there are three main points of concern: • false negative: depending on the algorithm employed, there is a possibility of missing a real outbreak indicated as 1 in figure 1. obviously, this can be very damaging if the system were to make a decision based on that specific algorithm. false negatives can lead to potentially exponential damage within the general public due to delayed response to an outbreak. • false positive: some algorithms are susceptible to reporting false positives, that is, detect an anomaly during peace time (indicated as 2 in figure 1). most systems set their anomaly detection thresholds to be as sensitive as possible to minimize the risk of missing important events, producing frequent false alarms, which may be determined to be false positives by subsequent investigation. these systems face inherent trade-offs among sensitivity, timeliness and number of false positives. false positives have a negative impact on public health surveillance because they can lead to expensive resource utilization for further investigation and can cause undue concern among the general public. • delayed identification: during initial stages of an outbreak, the number of cases are on the rise and hence detecting an outbreak at this point could be very effective and potentially aid in minimizing the impact of a potential bioterrorist attack. however, depending on the algorithm(s) employed, a system may end up with some algorithms detecting outbreaks well beyond the actual start day (indicated as 3 in figure 1). this, once again, can be very costly to public health community and impact it negatively for obvious reasons. these three concerns result in a trade-off situations between false positives, false negatives and detection time which are typically addressed by looking at sensitivity, specificity and time to detect parameters. a confidence-based aberration interpretation framework for outbreak conciliation 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 in summary, a framework needs to be implemented that would enable a user/system to interpret the anomaly detection results with some indication of confidence. that is, is there a potential start of an outbreak with twenty percent confidence or is it ninety percent confidence? a framework that takes into account the relationships between algorithms and produces an unbiased confidence measure for identification of start of an outbreak is presented. 3. the proposed solution the proposed anomaly interpretation framework aims to enhance surveillance decisionmaking by combining results of multiple aberration detection algorithms through the use of key result metrics. figure 2 depicts the four steps of the proposed framework and the associated linkages between them. figure 2. the confidence-based aberration interpretation framework a confidence-based aberration interpretation framework for outbreak conciliation 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 step 1: specificity, sensitivity and time to detect evaluator traditionally, specificity and sensitivity have been used for comparing various algorithms and their performances. in this study, these two parameters are key in helping identify a subset of algorithms (referred to as minimal set) that would be sufficient to deduce an overall decision to detect start of an outbreak. the hypothesis is that the system may not require all candidate algorithms to come up with a good decision as some of them may provide redundant information. sensitivity of an algorithm for a given dataset is defined as the total number of outbreaks during which the algorithm flagged (at least once per outbreak) divided by the total number of outbreak periods in the dataset 1 . specificity of an algorithm for a given dataset, on the other hand, is defined as the total number of non-outbreak days on which the method did not flag divided by the total number of non-outbreak days in that dataset [2]: ))/((= outbreaksofnumbertotalcountpositivetrueysensitivit ))/((= daysoutbreaknoofnumbertotalcountnegativetrueyspecificit in addition to specificity and sensitivity, a third parameter called time to detection (ttd) defined as the average number of days from the first day of an outbreak until it was flagged by the algorithm, plays a vital role in the forthcoming analysis. this is a very important parameter as it aids in segregating a set of algorithms into various groups (or classes) and provides a very clear differentiation between set of algorithms based on its interpretation. figure 3 illustrates, in time, a progression of a sample outbreak over multiple days. periods with no outbreaks are referred to as peace-time, while outbreak-mode refers to a time period with outbreak days. figure 3. a sample outbreak the three parameters discussed in this section provide a wealth of insight into the goal of 1 a single outbreak usually lasts more than one day a confidence-based aberration interpretation framework for outbreak conciliation 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 identifying a minimal sub set of algorithms sufficient for generating an overall confidence value for an anomaly indicator. step 2: agreement analyzer agreement analyzer deals with quantifying the degree of agreement or relationship between any given two algorithms executed on the same data set. that is, are all candidate algorithms producing unique results? or, is it that some algorithms yield similar results and thus provide no added value to the overall decision? this step of the framework exploits such relationship and/or agreement between any two algorithms using two quite different approaches: correlation and kappa coefficient. correlation correlation is one of the most common and most useful statistics. a correlation, r, is a single number that describes the degree of linear relationship between two variables (also referred to as bivariate relationship). a positive relationship, in general terms, means that higher scores on one variable tend to be paired with higher scores on the other and that lower scores on one variable tend to be paired with lower scores on the other. the correlation between two variables, in this case the two algorithm values or decisions, can be obtained using [3]: ])(][)([ ))(( = 2222 yynxxn yxxyn r     where x and y are the time series for daily counts, n is the total number of days in the time series, xy is the sum of products of paired counts, x is the sum of counts from first algorithm in the pair, y is the sum of counts from second algorithm in the pair, 2 x is the sum of squared x counts and 2 y is the sum of squared y counts. ncorrelatio , the agreement matrix based on correlation, is obtained using the above formula as follows:               nnnn n n ncorrelatio rrr rrr rrr    21 22221 11211 = where xy r is the correlation value for algorithm x against algorithm y and n is the number of algorithms in the candidate set. a minimum agreement threshold based on correlation ncorrelatio a t needs to be defined that can be used in the next step of the framework to identify nearest neighbors for each algorithm based on the strength of the relationships. a confidence-based aberration interpretation framework for outbreak conciliation 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 kappa coefficient an alternative approach to correlation matrix is the computation of kappa coefficient, which is an index that compares the agreement against that which might be expected by chance. kappa can be thought of as the chance-corrected proportional agreement, where possible values range from +1 (perfect agreement) via 0 (no agreement above that expected by chance) to -1 (complete disagreement). cohen's kappa coefficient approach [4] can be used to generate kappa coefficient matrix. consider a 2x2 table capturing decision outcomes by two different algorithms being compared as shown in figure 4. figure 4. kappa coefficient: 2 by 2 table the following formula was used to compute the kappa coefficient between any two algorithms: )(1 )( = c co p pp    ,= t yynn p o  t yyyn t yyny t ynnn t nynn p c    **= where o p is the relative observed agreement and c p is the probability that the agreement is due to chance. kappa  , the agreement matrix based on kappa coefficients, is obtained using the above formulas as follows: a confidence-based aberration interpretation framework for outbreak conciliation 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010               nnnn n n kappa        21 22221 11211 = where xy  is the kappa coefficient for algorithm x against algorithm y and n is the number of algorithms in the candidate set. once the kappa matrix has been computed, it is necessary to consider the significance of obtained agreement values between any pair of algorithms. landis and koch [5] give the following table for interpreting the significance of the  value. although inexact, this table provides a useful benchmark on the significance of the above matrix.  interpretation negative poor agreement 0.0  0.20 slight agreement 0.21  0.40 fair agreement 0.41  0.60 moderate agreement 0.61  0.80 substantial agreement 0.81  1.00 almost perfect agreement based on the results and table above, the minimum agreement threshold based on kappa kappa a t can be deduced, which can be set to 0.5 based on the above table. this is the value that will be used in the next step of the framework to identify nearest neighbors for each algorithm based on the strength of the relationships. step 3: minimal set identifier once the sensitivity, specificity and time to detect parameters are well established for each algorithm and the agreement levels between every possible algorithm pair is known, a minimal set of algorithms can be identified that would be sufficient to produce quantifiable confidence value for the overall decision. figure 5 illustrates a five-step process developed to identify this minimal set based on results from the previous two steps of the proposed framework. a confidence-based aberration interpretation framework for outbreak conciliation 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 5. minimal set identification process • task 1: this task is basically setting up the agreement matrix  generated from step 2 of the framework. that is, initialize  with computed ncorrelatio  or kappa  values. note that only the upper triangle of the matrix needs to be analyzed to avoid any recursive relationships between two algorithms. that is, if a1 highly correlated to a2, then a2 is highly correlated with a1. • task 2: the next task deals with setting up the closest relative matrix. a closest relative to a specific algorithm x is algorithm y that has an agreement value of at least some minimum agreement threshold ( a t ) and has the highest agreement value with respect to x against all other algorithms within the set. the idea is that for each algorithm in the set, a corresponding algorithm with highest agreement value must be identified. it is entirely possible that a specific algorithm will not have a closest relative. in that case, the algorithm would be considered as an independent and thus needs to be included for next filtration task. for example, in the illustrated figure, a2 is closest relative to a1 as an is to a3. however, algorithms a2 and an are independent. • task 3: this task simply formalizes the algorithms that were selected in the previous task by removing all the algorithms from the closest relative matrix that have relatives identified, that is, the non-independent algorithms. this produces a working set of algorithms identified as 1 in the 1xn matrix. • task 4: the next task is to categorize the algorithms from the working set into three a confidence-based aberration interpretation framework for outbreak conciliation 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 groups based on ttd value. the ttd was divided into three sets: close to zero days (ttd  0.1), less than one day (0.1  ttd  1.0) and greater than one day (ttd  1.0). this categorization makes intuitive sense because typically one would be interested in ttd value of less than a day. optimally, ttd should be as close to zero as possible, but realistically, public health individuals typically identify an outbreak more than a day later. • task 5: once the groups have been identified, the final task deals with identifying the minimal set of algorithms through one more stage of filtration using specificity and sensitivity values obtained from step one of the framework. this task scans through each of the groups and attempts to flag algorithms that have both highest sensitivity and highest specificity when compared to other algorithms in the same group. if one algorithm has higher sensitivity but some other algorithm has higher specificity, then both the algorithms need to be considered. this step of the framework yields a minimal subset of candidate algorithms that have minimal relation with each other and thus, form close to an independent minimal set that would be sufficient to deduce a confidence measure for an outbreak decision for a given day. step 4: point-based confidence evaluator the final step of the proposed framework deals with pulling together the findings from the first three steps and working out a scheme that produces a value that corresponds to overall confidence. there are three main parameters that need to be investigated. parameters of interest: rise rate the first parameter is the rate of change (referred to as rise rate) of actual daily count values over a specific time period, which provides some basic knowledge of the positive or negative trend over the last few days and also yields the speed with which the change is occurring. figure 6. rise rate analysis figure 6 illustrates a typical snapshot from daily counts data where the y-axis represents daily raw count and the x-axis represents the day with )(d representing the current day. the rate of change (  ) is computed using basic linear regression method [6] to define a line that fits the daily count values in best possible manner: a confidence-based aberration interpretation framework for outbreak conciliation 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 22 )( = xxn yxxyn      where n is the number of points being considered, x is the day and y is the count. to be effective, the computation of rate is limited to a specific time frame referred to as an epidemiologically significant window,  , which is defined in number of days. parameters of interest: count delta next parameter of interest is analyzing the importance of the current day's count with respect to  . that is, does today's count follow a typical trend identified by the linear regression or is it drastically different and thus deserves special attention. as shown in figure 7, there could be a scenario where past (  1) values yield a negative direction, however current day's value ( h ) is very high but cannot influence the linear regression formula to produce a positive slope which is more accurate in this case. figure 7. count delta for such cases, the framework takes into account a second parameter of interest called count delta ( ). this value is simply the ratio between current day value, h , and the average value over  . i ii ii x h   = 1= 1 = where i is the current day and i x is the time series for daily counts. parameters of interest: outbreak decisions based on the output of step three of the framework, the individual outbreak decision flags need to be considered. these provide the third parameter of interest, i  , where i refers to the algorithms in the minimal set. each i  can have one of two values: true representing an outbreak has been detected by algorithm i and false representing no outbreak decision by algorithm i . a confidence-based aberration interpretation framework for outbreak conciliation 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 point system: rules the overall objective of the framework is to produce a set of algorithms, that is as minimal as possible, to evaluate an aberration decision for any given day with some confidence value. due to availability of multiple algorithms, a system that facilitates incremental confidence building based on contributions from various algorithms needs to be developed. a bimodal approach to confidence evaluation is proposed to address this issue as shown in figure 8. this bimodal approach is based on the concept of contribution to positive and negative confidence of a decision. the fundamental premise of the proposed scheme is a rule set, which is defined as the set of rules that collectively contribute to either positive or negative confidence. positive confidence is a measure of collective strength of rules that contribute to a decision that supports identification of start of an outbreak. on the other hand, negative confidence is a measure of collective strength of rules that contribute to a decision that is against the decision of start of an outbreak. rule sets are made of weighted combination of identified parameters of interest. further discussion on details of rule sets will follow shortly. once the rule set has been identified, appropriate weights (or points) are assigned to the members of the rule set contributing to either side. a set of rules that contribute to positive confidence by collective summation of all of their respective points ( p ) are referred to as the r set. on the contrary, a set of rules that contribute to negative confidence by collective summation of all of their respective points ( n ) are referred to as the l set. that is, each side adds its collective contribution followed by )( np  to come up with overall confidence with 0 as the no decision point. figure 8. point assignment scheme the following rules contribute to incremental positive confidence (r side rules):                1 1 *> *> = dud dud i t t kitrue    where d is the current day and k is the number of algorithms in the minimal set. that is, there are 2k rules that contribute to positive confidence with each rule having a point magnitude of k p , where k  (k+2). the following rules contribute to incremental negative confidence (l side rules): a confidence-based aberration interpretation framework for outbreak conciliation 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010                1 1 *< *< = ddd ddd i t t kifalse    where d is the current day and k is the number of algorithms in the minimal set. that is, there are 2k rules that contribute to positive confidence with each rule having a point magnitude of k p , where k  (k+2). the use of  and  requires introduction of some threshold value that defines the decision points in both the upward and downward directions. thus, the scheme makes use of u t parameter for the positive (or upside) threshold value and d t for the negative (or downside) threshold value. both of these values can be computed using sophisticated approaches like neural networks, however, a simple intuitive approach using hysteresis (figure 9) was adopted. that is,  and  would contribute to positive confidence if the current day values were at least u t times bigger than the previous day values. however, they would only contribute to negative confidence if the current day values were less than d t times previous day values. this approach assists in identifying abrupt rises and falls in the count values with respect to immediate history. the proposed rule of thumb is to use du tt *3 . figure 9. threshold hysteresis to summarize, there are total of 2)2(= kz rules that define a specific rule set i  for a given point assignment i . in an attempt to simply the representation of rules and associated point assignments for l and r rules, a concise convention was designed as follows: pv pv l p p l p p l p p l p p l p p li r v rrrrr ,,,5,4,3,21= 5 5 4 4 3 3 2 2 1 1  where numbers 1 to v represent the v 2)(= k rules, pv l is the point assignment for the a confidence-based aberration interpretation framework for outbreak conciliation 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 vth left rule and pv r is the point assignment for the vth right rule. with 2 z possible rules on each side, the most obvious choice is a balanced system with the maximum number of points for negative confidence and the maximum number of points for positive confidence to equal multiple of 2 z . that is, if both sides matched in their outcomes, then the overall confidence value would equate to 0, an indecisive line. to facilitate wider base of different points and associated effects on overall decision, a system that exercises the point assignment with an unbiased (random) allocation of points is necessary. however, before such a system can be developed, the value of maximum points for each side ( m ) needs to be established. this can be achieved as follows: mp i z i = 1=  where i p represents point allocation for th i rule. figure 10. maximum number of points in figure 10, x-axis represents m and y-axis represents the total number of point assignment possibilities for z = 12 (that is, k = 6). in this specific case, m = 12 seems reasonable as it is located at the knee of the rising curve and provides 6188 assignment possibilities, a number that is quite reasonable for simulation purposes. now that the rules and point assignment method have been designed, there is a need for devising a system that interprets outcomes of the application of identified rules and associated points and yields an optimal point assignment that produces desired outcome. the proposed approach is to group sensitivty and specificity values obatined using numerous random point assignments into clusters of interest as shown in figure 11. the idea is to identify specific areas of interest (aoi) on this scatter plot that produce outcome that is superior when compared to any single algorithm. that is, three aois are identified as follows: high specificity (left top); high sensitivity (bottom right) and maximum sensitivity/specificity (knee). a confidence-based aberration interpretation framework for outbreak conciliation 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 11. clusters any of the commonly used clustering techniques may be used to identify aois. the proposed approach utilizes k-means clustering [7] technique as it allows identification of initial centroids of desired clusters, which is attractive since, as discussed above, typically one would like to look at very specific clusters that provide, for instance, high specificity and high sensitivity that is, aoi(3). the objective of k-means approach is to minimize total intra-cluster variance, or, the squared error function: 2 1= ||= ij ij x k i x s v    where there are k clusters kis i ( ), j x is the sensitivity/specificity pair on the scatter plot corresponding to i  and i  is the centroid or mean point of all the points within cluster i . application of clustering methodology yields a multitude of rule sets i  each of which produce a sensitivty/specificity pair i  yielding:   ki i k  ,=  once k  has been figured out, the idea is to then pick an appropriate rule set in a given cluster k that falls in the desired aoi and use it for computing the overall confidence value. note that one could develop an algorithm to identify an optimal point assignment within a cluster. 4. nomenclature the proposed caif framework utilizes a number of variables as follows: • n is the number of algorithms in the candidate set. •  is the agreement matrix between all pairs of algorithms within the candidate set. • a t is the minimum agreement threshold used to identify nearest neighbors. • k is the number of algorithms in the minimal set. a confidence-based aberration interpretation framework for outbreak conciliation 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 • z is the total number of positive and negative rules. • m is the maximum number of points typically a multiple of 2 z . • u t is the positive (or upside) threshold value for point assignment scheme. • d t is the negative (or downside) threshold value for point assignment scheme. •  is the epidemiologically significant window in days. •  is the rate of change of actual daily count values over a specific time period  . •  is the relation of the current day's count with respect to  . • j  is the individual algorithm's outbreak decision flag based for a specific algorithm j within the minimal set. • i  is the rule set based on minimal set and specific point assignment i . • i  is the sensitivity/specificity pair computed for a specific rule set i . • k  is a set of sensitivity/specificity pairs computed for all point assignments within a cluster k . based on this list, the following set, referred to as caif parameters, needs to be populated using various steps of the framework:  ,,,,,,,= dua ttzktnvariablescaif  with following parameters:   j parameterscaif  ,,= and following output values:   k i i outputscaif   ,,= using the above nomenclature, the proposed four-step framework can be outlined as follows: step 1: (a) identify outbreak data set(s) (b) initialize candidate algorithm set define n (c) compute sensitivity, specificity and time-to-detect for each algorithm step 2: compute agreement analyzer   ( ncorrelatio  or kappa  ) define  and a t step 3: execute minimal set identification process define k , z and m step 4: (a) setup inputs to point assignment scheme: define u t , d t ,  a confidence-based aberration interpretation framework for outbreak conciliation 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 (b) compute  ,  and j  (c) execute randomized strategy to obtain i compute specificity/sensitivity pairs i  (d) apply clustering technique(s) to generate k  (e) compute overall confidence value utilizing one of the rules sets in k  5. simulation results a simulation environment was setup that comprised of custom simulator for some aspects of the proposed approach as well as an open source package (r [8]) to compute various statistical and epidemiological parameters used in the proposed approach. the data for simulation were obtained from cdc [2]. nine candidate algorithms were selected based on literature review of most commonly used aberration detection algorithms: 3-day (ma3), 5-day (ma5) and 7-day (ma7) moving average, weighted moving average (wma), exponentially weighted moving average (ewma), cumulative sum (cusum) and early aberration reporting system c1-c3 [9]. the epidemiological parameters (sensitivity, specificity and time to detect) were computed using the simulation environment. a minimal set using step 3 of the proposed framework was identified as [wma, cusum, c1, c3]. the caif variable list was found to be:  7=12,=0.5,=1.15,=6,=4,=0.5,=,=9,= mttzktn duakappa  the caif simulator was setup to perform numerous iterations to produce a large variety of point assignment using randomized point assignment strategy where only unique combinations of points for each set were allowed. this produced a scatter plot of specificity against sensitivity, over which k-means clustering was applied to identify points that lie within the desired aois (figure 12). figure 12. identified areas of interest a confidence-based aberration interpretation framework for outbreak conciliation 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 from table 1, the three clusters of interest representing the aois were 2, 5 and 10 with the following centroids (98.35, 53.42), (66.50, 94.63) and (86.89, 94.41). for aoi(1), none of the point assignments provided a better result than simply running wma algorithm which yielded (99.17, 52.12) as the specificity and sensitivity values. thus, the conclusion was that the proposed framework does not provide any benefit in cases when highest possible specificity is desired. on the other hand, for aoi(2), the identified centroid of (66.50, 94.63) provided a cluster with about 125 point assignments some of which provided better results than any single algorithm. table 1. cluster centres cluster specificity (%) sensitivity (%) 1 92.94 88.15 2 98.35 53.42 3 84.93 92.50 4 90.15 87.38 5 66.50 94.63 6 88.28 90.78 7 94.52 54.74 8 89.10 54.39 9 81.46 95.92 10 86.89 94.41 for aoi(3), the identified centroid of (86.89, 94.41) is quite close to the result produced by ears c3 algorithm. however, this cluster has over 200 point assignments some of which yield higher sensitivity and specificity values than ears c3 which provides the best pair from all algorithms in the candidate set. for example, the following rule set yields (86.39, 95.50): 2 1 5 3 3 0 2 1 0 6 0 1 ,6,5,4,3,21 which translates to positive confidence associated with the following rules,                               pointst pointst pointstrue pointstrue pointsna pointsna ddd r dud r c r wma r r r 2*>:6 5*>:5 3=:4 2=:3 0:2 0:1 1 1 1     negative confidence points associated with the following rules, a confidence-based aberration interpretation framework for outbreak conciliation 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010                               pointt pointst pointsna pointfalse pointsfalse pointfalse dddl dudl l wmal cl cusuml 1*<:6 3*<:5 0:4 1=:3 6=:2 1=:1 1 1 3      note that each side of the rule set contributes a maximum of 12=m points providing an overall confidence measure ranging from -12 (100% negative confidence) to +12 (100% positive confidence). next, one of the rule sets from the aoi(3) cluster were applied to a sample outbreak within the simulated data sets and confirm its effectiveness. (figure 13) illustrates a snapshot that superimposes daily counts during outbreak mode along with computed confidence measure using the above rule set. figure 13. simulated outbreak analysis as shown, the framework suggests an outbreak day with confidence measure of +1 ( 12 1 or 8.33% positive confidence) on day 6, a day before an outbreak is going to start (point a). although a false positive decision, it is a weak false positive that aids in planning for the following day which will have a strong positive confidence measure of +7 translating to 12 7 or 58.3% positive confidence (point b). this is exactly what the aim of this framework was set to be, that is, identify start of an outbreak with some level of confidence measure at an early stage. further to note, as the outbreak progresses, the confidence seems to drop to negative values. this is because the framework is intended to monitor initial start of an outbreak. as the values stabilize during an outbreak, the confidence measure of start of an a confidence-based aberration interpretation framework for outbreak conciliation 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 outbreak will diminish as expected. a detailed step by step simulation results for the proposed framework have been provided in [10]. 6. real scenario the rule set for aoi (3) from previous section was applied to a subset of real emergency room visit data from the canadian early warning system (cews). as shown in figure 14, one of the key observations is that the indication that an outbreak is going to occur in the next few days was identified by a higher confidence value on day 8, which was most likely the first day of an outbreak curve with peak on day 11. further, the confidence measure was computed based on a minimal set identified by the proposed framework and not the entire set of nine algorithms. that is, the minimal set identified by the proposed framework was sufficient to detect the start of an event a few days earlier than it was actually detected. the following is some analysis of some of the days with interesting observations.  day 8: three of nine algorithms suggest an outbreak out of which two are from the identified minimal set. looking at this at face value would produce a biased decision that we had no signs of start of an outbreak on day 8. however, considering only the minimal set, there is a split decision, and using the proposed point assignment system a confidence measure of +5 translating to 5/12 or 41.7% positive confidence is produced. thus, there were clear signs for start of an outbreak on that day as suggested by a strong confidence value.  day 9: the confidence value drops drastically to just above the 0 or no decision line. this is due to the actual count staying at similar level as the count for previous day thus the λ and ω values did not change much and did not contribute to the overall confidence value as strongly as they did on the previous day. however, the confidence value still stayed above zero point indicating some level of activity.  day 11: this is the day when the counts of cases during an outbreak are the highest. all four algorithms of the minimal set declare an outbreak, however, the framework produces confidence measure of only +5. this is because the framework is monitoring start of an outbreak and not necessarily the peak. at the peak, both λ and ω do not contribute their portion to the overall confidence measure since neither the recent most count nor the count delta satisfy the rules as defined in the positive set. using the proposed framework, the identification with significant confidence would have been detected on day 8 and initial start of some activity instead of delayed identification which most likely occurred on day 11. a confidence-based aberration interpretation framework for outbreak conciliation 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 14. application of ciaf to real scenario 7. limitations the following list highlights some limitations of the proposed framework and thus potential areas for future research: 1. identification of optimal rule: the proposed framework employs basic techniques for clustering and point identification. use of more sophisticated clustering techniques as well as optimal point identification systems to come up with best rule to use within a given area of interest. 2. further generalization: it would be useful to implement of other versions of exponential smoothing schemes which include seasonality corrected approach and apply to the overall framework. a confidence-based aberration interpretation framework for outbreak conciliation 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 3. time effect: taking into account time of day, day of week, week of month and month of year within the framework and use it to deduce further redundancy between various algorithms. 4. data labeling: a feedback mechanism for public health specialists to close the loop for labeling outbreaks and no-outbreak decisions. this will extend the framework to allow for other techniques for evaluation purposes. 5. invariant minimal set: there is no question that some algorithms are better than others when looking at different disease outbreaks. applying a variety of outbreak types to the data (beyond log normal, daily spikes, etc) will help in figuring out if the minimal set produced by the framework is invariant. 8. conclusion a novel aberration interpretation framework has been proposed for producing a confidence based system decision focusing on high confidence values at the start of an outbreak. the framework comprises of multiple steps to allow identification of a subset of algorithms as well as a dynamic point assignment scheme for computing a balanced decision. the proposed framework provides a multitude of benefits: • savings in the computation effort by identifying only a smaller subset of algorithms that are necessary and sufficient for a sound system decision. • provides a mechanism to derive confidence value based on dynamic point assignment system. • produces a superior overall system decision within desired aoi when compared to any single algorithm. • provides a framework for future research to investigate optimal point allocation systems as well as analysis of new algorithms and their effects on the overall decision. the proposed framework is also adaptable or extensible. it captures the essential elements of a confidence based decision process. acknowledgement many thanks to dr robert mcleod of electrical and computer engineering department at the university of manitoba for his support and guidance throughout the course of this research. contact shamir nizar mukhi, phd email: shamir.nizar.mukhi@phac-aspc.gc.ca references [1] jackson m, baer a, painter i and duchin j. a simulation study comparing aberration detection algorithms for syndromic surveillance. bmc medical informatics and decision making. vol. 7. 2007. [2] centre for disease control (cdc). data sets. available at: http://www.bt.cdc.gov/surveillance/ears/datasets.asp. [3] trochim w. correlation. available at: mailto:shamir.nizar.mukhi@phac-aspc.gc.ca http://www.bt.cdc.gov/surveillance/ears/datasets.asp a confidence-based aberration interpretation framework for outbreak conciliation 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 http://www.socialresearchmethods.net/kb/statcorr.htm [4] jacob c. a coefficient of agreement for nominal scales. educational and psychological measurement. vol. 20, pp. 37–46. 1960. [5] landis j and koch g. the measurement of observer agreement for categorical data. biometrics. vol. 33, no. 1, pp. 159-174. 1977. [6] waner s and costenoble s. linear regression. available at: http://people.hofstra.edu/faculty /stefan_waner/realworld/tutorialsf0/ frames1_5.html. [7] wikipedia. k-means algorithm. available at: http://en.wikipedia.org/wiki/kmeans_algorithm [8] the r package for statistical computing. available at: http://www.r-project.org/ [9] hutwagner l, thompson w, seeman g and treadwell t. the bioterrorism preparedness and response early aberration reporting system (ears). journal of urban health: bulletin of the new york academy of medicine. vol. 80 no. 2, supplement 1. pp. i89-i96. 2003. [10] mukhi s. an integrated approach to real-time biosurveillance in a federated data source environment. phd thesis, university of manitoba. june 2007. http://www.socialresearchmethods.net/kb/statcorr.htm http://people.hofstra.edu/faculty http://en.wikipedia.org/wiki/k-means_algorithm http://en.wikipedia.org/wiki/k-means_algorithm http://www.r-project.org/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a review of evaluations of electronic event-based biosurveillance systems kimberly gajewski*1, jean-paul chretien2, amy peterson2, julie pavlin3 and rohit chitale2 1emory university, atlanta, ga, usa; 2division of integrated biosurveillance, silver spring, md, usa; 3headquarters, armed forces health surveillance center, silver spring, md, usa objective to assess evaluations of electronic event-based biosurveillance systems (eebs’s) and define priorities for eebs evaluations. introduction eebs’s that use near real-time information from the internet are an increasingly important source of intelligence for public health organizations (1, 2). however, there has not been a systematic assessment of eebs evaluations, which could identify uncertainties about current systems and guide eebs development to effectively exploit digital information for surveillance. methods we searched pubmed and consulted eebs experts to identify eebs’s that met the following criteria: uses publicly-available internet info sources, includes events that impact humans, and has global scope. we constructed a list of 17 key evaluation variables using guidelines for evaluating health surveillance systems, and identified the key variables included in evaluations per eebs, as well as the number of eebs’s evaluated for each key variable (3,4). results we identified 10 eebs’s and 17 evaluations (table 1). the number of evaluations per eebs ranged from 1 (gen-db, godsn) to 7 (gphin, healthmap). the median number of variables assessed per eebs was 6 (range, 3-12), with 5 (25%) evaluations assessing 7+ variables. nine (53%) published evaluations contained quantitative assessments of at least 1 variable. the least-frequently studied variable was cost. no papers examined usefulness as specific public health decisions or outcomes resulting from early event detection, though 8 evaluations assessed usefulness by citing instances where the eebs detected an outbreak earlier, or by eliciting user feedback. conclusions while eebs’s have demonstrated their usefulness and accuracy for early outbreak detection, no evaluations have cited specific examples of public health decisions or outcomes resulting from the eebs. future evaluations should discuss these critical indicators of public health utility. they also should assess the novel aspects of eebs and include variables such as policy readiness, system redundancy, input/output geography (5); and test the effects of combining eebs’s into a “super system”. table 1. number of published evaluations and variables on identified eebs’s table 2. key variables used in evaluations of eebs keywords evaluation; biosurveillance; event-based surveillance references heymann dl, et al. hot spots in a wired world: who surveillance of emerging and re-emerging infectious diseases. lancet infect dis. 2001;1:345–53. keller m, et al. use of unstructured event-based reports for global infectious disease surveillance. emerg infect dis. 2009;15:689–95. german rr, et al. guidelines working group centers for disease control and prevention (cdc).updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr recomm rep. 2001;50(rr-13):1–35. buehler jw, et al. framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the cdc working group. mmwr recomm rep. 2004;53(rr-5):1–11. corley cd, et al. assessing the continuum of event-based biosurveillance through an operational lens. biosecur bioterror 2012;10:131-141. *kimberly gajewski e-mail: kimberly.gajewski@emory.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e131, 2013 editorial: vol 2, no 2 (2010) 1 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 editorial: vol 2, no 2 (2010) the major challenges facing the u.s. healthcare systems are escalating costs, lack of access to almost 40 million residents, and inconsistent quality. it is hypothesized that the use of electronic health records systems and the implementation of interoperable health information exchanges (hie) will result in significant cost savings, outcomes and quality improvement, reduction in medical errors and redundancies, and the enhancement of public health and disease surveillance. hies are organizations that facilitate secure health data exchanges among consenting and authorized stakeholders. the ultimate result of the current unprecedented increase in the investment in health information technology is the transformation of the u.s. healthcare system. in order to achieve such a transformation and the attendant benefits the health information technology for economic and clinical health act (hitech) of the american recovery and reinvestment act of 2009 allocated over 30 billion dollars to the states to encourage the use of electronic health records and develop interoperable health information exchanges. what is the public health case for participating in the implementation of the health information exchanges? the occurrences of major public health threats in recent years have highlighted the need for integrating public health data sources and surveillance systems into the emerging health information exchanges. such an integrated system will facilitate the timely distribution and sharing of relevant health data across the various stakeholders such as public health practitioners, clinicians, and policy makers, and payers. data sharing will improve the situational awareness operations of the stakeholders and lead to improved decision making regarding the control of emergencies, treatment of individual cases, and efficient resource allocation. if public health practitioners are aware of the critical events in a region but the information is not available to clinicians at a place and time when it could be utilized, then the information is of limited value. to encourage the adoption of hie-supported situational awareness among healthcare stakeholders the centers for disease control and prevention (cdc) awarded three hie grants to recipients from indiana, new york, and washington state-idaho. the awardees were charged with investigating and developing methods for sharing information between public health and clinical practitioners to support situational awareness and case reporting. this special issue of the journal is dedicated to disseminating the achievements of the awardees in terms of articles published, conferences attended, technologies implemented, and lessons learned. the indiana coalition (made up of university of indiana, marion county health department ( mchd), and regenstrief institute), among other achievements, demonstrated a novel approach for sending public health alerts to providers by leveraging an electronic clinical messaging system within a health information exchange. hies, in their current formats, assure that clinical information is sent to the intended providers in a timely manner at the appropriate location, with the capability to provide feedbacks to the senders. this later feedback capability is quite important because receipt and utilization of the health data can be verified. by delivering public health alerts using the existing health information exchanges the process of introducing the alerts editorial: vol 2, no 2 (2010) 2 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 into the workflows of the clinicians is optimized, thereby improving the chances that the information will be utilized for clinical decision making. the washington-idaho partnership (the northwest public health information exchange: nwphie), developed an algorithm for sending syndromic surveillance feeds from hospitals to public health in washington state. the partnership also developed an automated process for performing electronic laboratory reporting (elr) for notifiable disease conditions in washington state. the automated elr system and the syndromic surveillance data feeds will be very essential in the management of disease outbreaks in the future. the emergency preparedness of public health officials in indiana and washington state will be greatly improved with the implementation of the technologies developed from the grants. by facilitating communication between public health and clinical stakeholders the projects have the potential to improve process efficiency, reduce costs, and provide quality data on notifiable disease conditions to public health for the development of surveillance systems. the bidirectional communication has the added benefit of creating trust among the stakeholders, a very important factor for successful adoption of health information systems. the projects presented in this issue demonstrate the value to public health agencies, clinicians, individuals, and the general community, of using health information exchanges to deliver targeted health messages to stakeholders. as more states receive funding from hhs and prepare to develop hies, it is important for the public health stakeholders to study the novel technologies developed by the awardees of the situational awareness grant in order to improve the value of their participation in the hie development process. a major issue unaddressed in the situational awareness projects is how the integrated public health-hies will be sustained after the start-up money runs out. to address the sustainability question it is important to estimate the value of hies to the different stakeholders and institute cost-recovery charges in proportion to the accrued benefits. while this will be a difficult exercise for the public health sector, due to the externalities involved, the private benefits to the clinicians and hospitals could be estimated. for example, the ability to electronically access important test results at the point of care without relying on the postal services or faxes results in time savings, reduced errors rates, and improvements in quality of care. these benefits are quantifiable. to develop a sustainable and integrated system these and other benefits should be estimated, at least for the clinical health sector, and cost-recovery charges implemented. rebecca roberts, md ojphi special issue editor research director, department of emergency medicine john stroger hospital of cook county chicago. il. 60612 email: rroberts@ccbh.org phone: (312) 864-0095 edward mensah, phd editor-in-chief online journal of public health informatics 1603 w taylor st, rm 757 chicago. il. 60612 email: dehasnem@uic.edu office: (312) 996-3001 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts one world, one health, one medicine: an assessment of intersectoral collaboration in avian influenza control in lagos state abimbola aman-oloniyo*1, olalekan allison2 and musbau a. razaq3 1lagos state ministry of health, nigeria; 2lagos state ministry of agriculture and cooperatives, nigeria; 3lagos state ministry of information, nigeria objective to assess the collaborative efforts in avian influenza control that could be harnessed for the control of other zoonotic diseases. introduction the livestock sector is vital to the socio-economic development of nigeria; it contributes about 9-10% of agricultural gdp. livestock represents an important source of high quality animal protein providing about 36.5 % of total protein intake of nigerians (1). lagos state, located in the south-western part of nigeria, has the smallest landmass (3577 sq. km) and the highest human population density (2519.75 per sq. km) in the country (2). the state has a poultry population of 2.5 million birds and the largest outlet for poultry products with 207 live bird markets, 375 poultry farms and a large number of poultry products consumers (3). avian influenza (“bird flu”) is an infectious disease of birds caused by type a strains of the influenza virus. the infection is known to cross species barrier to infect humans (4). between march 2006 and september 2007 avian influenza (ai) outbreaks occurred in 99 poultry farms in lagos state (3). the only human case of ai in nigeria was detected at a health facility in lagos in january 2007. methods following the ai outbreaks in nigeria, a lot of human and material resources were devoted to the control of ai in the health, information and veterinary sectors at all levels of administration. a desk review of the ai response activities and collaborative efforts at the state level was conducted. results the inter-ministerial state steering committee and state technical committee on avian influenza (stcai) were established comprising of stakeholders in the health, information and veterinary sectors drawn from public and private institutions. a number of interventions were carried out including formation of public enlightenment, health and veterinary sub-committees to deal with sector-specific issues. also, reconstitution and training of state and local council epidemic response teams (ert), training/retraining of state and local council health, information and veterinary officers on epidemic preparedness and response to avian influenza, establishment and equipping of desk offices in the state departments of health, veterinary and information, and appointment of ai desk officers the local council level. these interventions facilitated the conduct of joint surveillance of poultry and live bird markets by stakeholders, joint outbreak investigation and response, joint sensitization of human and animal health workers on ai, joint public enlightenment and community/grassroots mobilization activities (including advocacy visits to local government council administrators, traditional leaders and trade union leaders) on avian influenza control. all of these were instrumental in the quick containment of ai in lagos state. conclusions the collaboration between health, information and veterinary departments following the ai epidemic helped to curb the disease within the state. a good structure has been put in place for control of ai that can be harnessed for the control of other zoonotic diseases in the spirit of one world, one health, one medicine (owohom). figure 1. clinico-epidemic curve of the investigated human case of avian influenza. keywords collaborate; avian influenza; zoonotic references 1. the integrated national avian and pandemic influenza response plan, 2007. 3, 19 23 2. reducing health disparities in lagos statean investment case, 2012. 7-8 3. lagos state ministry of agriculture and cooperatives, 2009. 4. world health organization. avian influenza fact sheet. <www.who. int/mediacentre/factsheets/avian_influenza/en/>. accessed july 30, 2007. 5. world health organization. cumulative number of confirmed human cases of avian influenza a/(h5n1) reported to who. <http://www. who.int/csr/disease/ avian_influenza/country /cases_table2007_07_25/ en/index.html>. accessed july 30, 2007. *abimbola aman-oloniyo e-mail: bimskoms@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e188, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts disease models for event prediction courtney d. corley*1 and laura pullum2 1pacific northwest national laboratory, richland, wa, usa; 2oak ridge national laboratory, oak ridge, tn, usa objective the objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. introduction one of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information to decision makers, in order to identify areas for future research. two critical characteristics differentiate this work from other infectious disease modeling reviews [1,2]. first, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. second, we considered models involving pathogens of concern as determined by the us national select agent registry. background: a rich and diverse field of infectious disease modeling has emerged over the past 60 years and has advanced our understanding of populationand individual-level disease transmission dynamics, including risk factors, virulence and spatio-temporal patterns of disease spread. recent modeling advances include biostatistical methods, and massive agent-based population, biophysical, ordinary differential equation, and ecological-niche models. diverse data sources are being integrated into these models as well, such as demographics, remotely-sensed measurements and imaging, environmental measurements, and surrogate data such as news alerts and social media. yet, there remains a gap in the sensitivity and specificity of these models not only in tracking infectious disease events but also predicting their occurrence. methods we searched dozens of commercial and government databases and harvested google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, this returned 13,767 webpages and 12,152 citations. after de-duplication and removal of extraneous material, a core collection of 6,503 items was established, these publications and their abstracts are presented in a semantic wiki at http://biocat.pnnl.gov. next, pnnl’s in-spire visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model. as a result, we systematically reviewed 44 papers, and the results are presented in this analysis. results the models were classified as one or more of the following types: event forecast (9%), spatial (59%), ecological niche (64%), diagnostic or clinical (14%), spread or response (20%), and reviews (7%). the distribution of transmission modes in the models was: direct contact (55%), vector-borne (34%), wateror soil-borne (16%), and nonspecific (7%). the parameters (e.g., etiology, cultural) and data sources (e.g., remote sensing, ngo, epidemiological) for each model were recorded. a highlight of this review is the analysis of verification and validation procedures employed by (and reported for) each model, if any. all models were classified as either a) verified or validated (89%), or b) not verified or validated (11%; which for the purposes of this review was considered a standalone category). conclusions the verification and validation (v&v) of these models is discussed in detail. the vast majority of models studied were verified or validated in some form or another, which was a surprising observation made from this portion of the study. we subsequently focused on those models which were not verified or validated in an attempt to identify why this information was missing. one reason may be that the v&v was simply not reported upon within the paper reviewed for those models. a positive observation was the significant use of real epidemiological data to validate the models. even though ‘validation using spatially and temporally independent data’ was one of the smallest classification groups, validation through the use of actual data versus predicted data represented approximately 33% of these models. we close with initial recommended operational readiness level guidelines, based on established technology readiness level definitions. keywords disease models; event prediction; operational readiness references [1] lloyd-smith jo, george db, pepin kp, pitzer ve, pulliam jrc, dobson a, hudson pj, and grenfell bt. epidemic dynamics at the human-animal interface. science 4 december 2009: 326 (5958), 1362-1367. [doi:10.1126/science.1177345] [2] bravata dm, sundaram v, mcdonald km, smith wm, szeto h, schleinitz md, et al. detection and diagnostic decision support systems for bioterrorism response. emerg infect dis [serial online] 2004 jan [9-sept 2012]. http://wwwnc.cdc.gov/eid/article/10/1/030243.htm *courtney d. corley e-mail: court@pnnl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e180, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts identification and assessment of public health surveillance gaps under the ihr (2005) ngozi erondu*1, betiel hadgu haile1, lisa ferland1, meeyoung park1, affan shaikh1, heather meeks2 and scott jn mcnabb1 1public health practice, atlanta, ga, usa; 2defense threat reduction agency, washington, dc, usa objective to conceive and develop a model to identify gaps in public health surveillance performance and provide a toolset to assess interventions, cost, and return on investment (roi). introduction under the revised international health regulations (ihr [2005]) one of the eight core capacities is public health surveillance. in may 2012, despite a concerted effort by the global community, the world health organization (who) reported out that a significant number of member states would not achieve targeted capacity in the ihr (2005) surveillance core capacity. currently, there is no model to identify and measure these gaps in surveillance performance. likewise, there is no toolset to assess interventions by cost and estimate the roi. we developed a new conceptual framework that: (1) described the work practices to achieve effective and efficient public health surveillance; (2) could identify impediments or gaps in performance; and (3) will assist program managers in decision making. methods published articles and grey-literature reports, manuals and logic model examples were gathered through a literature review of pubmed, web of science, google scholar, and other databases. logic models were conceived by categorizing discrete surveillance inputs, activities, outputs, and outcomes. indicators were selected from authoritative sources or developed and then mapped to the logic model elements. these indicators will be weighted using the principle component analysis (pca), a method for enhanced precision of statistical analysis. finally, on the front end of the tool, indicators will graphically measure the surveillance gap expressed through the tool’s architecture and provide information using an integrated cost-impact analysis. results we developed five public health surveillance logic models: for ihr (2005) compliance; event-based; indicator-based; syndromic; and predictive surveillance domains. the ihr (2005) domain focused on national-level functionality, and the others described the complexities of their specific surveillance work practices. indicators were then mapped and linked to all logic model elements. conclusions this new framework, intended for self-administration at the national and subnational levels, measured public health surveillance gaps in performance and provided cost and roi information by intervention. the logic model framework and pca methodology are tools that both describe work processes and define appropriate variables used for evaluation. however, both require real-world data. we recommend pilot testing and validation of this new framework. once piloted, the framework could be adapted for the other ihr (2005) core capacities. keywords public health surveillance; evaluation; ihr (2005); gaps assessment; cost-impact analysis acknowledgments defense threat reduction agency and the world health organization references 1. implementation of the international health regulations., stat. resolution wha65.23 ( 26 may 2012 ). 2. may l, chretien jp, pavlin ja. beyond traditional surveillance: applying syndromic surveillance to developing settings—opportunities and challenges. bmc public health. 2009;9:242. epub 2009/07/18. 3. wilson k, mcdougall c, fidler dp, lazar h. strategies for implementing the new international health regulations in federal countries. bulletin of the world health organization. 2008;86(3):215-20. epub 2008/03/28. 4. organization wh. international health regulations (2005) second edition. 2008. 5. sturtevant jl, anema a, brownstein js. the new international health regulations: considerations for global public health surveillance. disaster med public health prep. 2007;1(2):117-21. epub 2008/04/05. 6. lyons s, zidouh a, ali bejaoui m, ben abdallah m, amine s, garbouj m, et al. implications of the international health regulations (2005) for communicable disease surveillance systems: tunisia’s experience. public health. 2007;121(9):690-5. epub 2007/06/05. 7. calain p. exploring the international arena of global public health surveillance. health policy plan. 2007;22(1):2-12. epub 2007/01/24. 8. baker mg, forsyth am. the new international health regulations: a revolutionary change in global health security. n z med j. 2007;120(1267):u2872. epub 2007/12/25. 9. cash ra, narasimhan v. impediments to global surveillance of infectious diseases: consequences of open reporting in a global economy. bulletin of the world health organization. 2000;78(11): 1358-67. epub 2001/01/06. 10. fidler dp. globalization, international law, and emerging infectious diseases. emerg infect dis. 1996;2(2):77-84. epub 1996/04/01. 11. organization wh. world health organization: disease surveillance. weekly epidemiological record [internet].1998. *ngozi erondu e-mail: ngozierondu@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e80, 2013 title: using secure web services to visualize poison center data for nationwide biosurveillance: a case study using secure web services to visualize poison center data for nationwide biosurveillance: a case study 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 using secure web services to visualize poison center data for nationwide biosurveillance: a case study thomas g savel 1 , alvin bronstein 2 , william duck 1 , m. barry rhodes 1 , brian lee 3 , john stinn 3 , katherine worthen 4 1 centers for disease control and prevention, atlanta, ga 2 rocky mountain poison and drug center, denver, co 3 deloitte consulting llp, atlanta, ga 4 ciber, inc. greenwood village, co abstract objectives: real-time surveillance systems are valuable for timely response to public health emergencies. it has been challenging to leverage existing surveillance systems in state and local communities, and, using a centralized architecture, add new data sources and analytical capacity. because this centralized model has proven to be difficult to maintain and enhance, the us centers for disease control and prevention (cdc) has been examining the ability to use a federated model based on secure web services architecture, with data stewardship remaining with the data provider. methods: as a case study for this approach, the american association of poison control centers and the cdc extended an existing data warehouse via a secure web service, and shared aggregate clinical effects and case counts data by geographic region and time period. to visualize these data, cdc developed a web browser-based interface, quicksilver, which leveraged the google maps api and flot, a javascript plotting library. results: two iterations of the npds web service were completed in 12 weeks. the visualization client, quicksilver, was developed in four months. discussion: this implementation of web services combined with a visualization client represents incremental positive progress in transitioning national data sources like biosense and npds to a federated data exchange model. conclusion: quicksilver effectively demonstrates how the use of secure web services in conjunction with a lightweight, rapidly deployed visualization client can easily integrate isolated data sources for biosurveillance. keywords: public health, surveillance, architecture, web services, soa using secure web services to visualize poison center data for nationwide biosurveillance: a case study 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 introduction public health surveillance is the ongoing, systematic collection, analysis, interpretation, and dissemination of data about a health-related event for use in public health action to reduce morbidity and mortality and to improve health.[1] surveillance provides the critical information necessary to support several public health functions, including outbreak detection, estimating the impact of a disease or injury, portraying the natural history of a health condition, determining the distribution and spread of illness, facilitating public health interventions, evaluating the effectiveness of those interventions, and facilitating planning.[2] historically, public health information systems supporting surveillance activities have been constructed to support specific program areas within health departments. these systems were often populated with data reported from healthcare providers, often via paper based forms, not surprisingly, leading to numerous “siloed” surveillance systems. having multiple related but disparate surveillance systems, creates many inefficiencies in the areas of analysis and communications, which become most apparent during public health emergencies. with over 3,000 state and local public health agencies, tens of thousands of other public health stakeholders (including clinical providers, laboratories, first responders, and researchers) the goal of creating an efficient, secure, and flexible centralized, nationwide biosurveillance system which meets the needs and expectations of all users becomes essentially insurmountable. today, this lack of integration presents major vulnerabilities in the healthcare system’s ability to rapidly detect and mitigate health emergencies. the goal in creating cdc’s biosense system has been to mitigate this existing vulnerability. within the biosense system, patient level data (specifically, chief complaint and diagnosis data) are transmitted securely, and subsequently stored and maintained in a centralized data warehouse. various analyses are then performed, with results being shared with biosense users as well as the original data providers. this model has proven difficult to scale, costly to maintain, and challenging for end users. it is the authors’ perspective that enhancement to the current functionality in existing biosurveillance applications (e.g., essence, rods, or biosense) with secure web services, distributed computing, grid architectures, and an open federated model for data access and exchange is one approach that could potentially correct many of their current limitations. one of the principal challenges facing existing biosurveillance implementations is their limitation in sharing data across multiple jurisdictions to support real-time surveillance activities at a national level. the ability to generate actionable health intelligence by increasing access to data and fusing multiple data streams to yield effective situational awareness is a key priority to the national biosurveillance strategy for human health.[3] efforts to move legacy biosurveillance application models from centralized and historically siloed environments to federated and distributed models of data exchange are under way in order to effectively provide timely population-wide biosurveillance coverage. federation of data enables local jurisdictional control over data procurement and stewardship while permitting secure access to approved external entities (e.g., state and federal agencies). finding new methods and technologies to augment existing biosurveillance capabilities to bridge this data exchange gap has been a focus of ongoing research at cdc in collaboration with its national and international partners. using secure web services to visualize poison center data for nationwide biosurveillance: a case study 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 methods in 2008 cdc’s national center of public health informatics (ncphi) embarked on a collaborative initiative with biosense and the american association of poison control centers (aapcc) to visualize aggregate clinical effect data based on telephone calls to the 61 poison control centers across the nation.[4,5] aapcc poison centers maintain a 24-hour poison help hotline staffed by pharmacists, physicians, and toxicology specialists that provide exposure management instructions and general poison information to patients. as part of their current workflow, all poison case information (clinical effect, demographics, medical outcomes, therapeutic interventions, etc.) is stored in the national poison data base (npds) poison surveillance database and then summarized into an annual report. prior to this initiative, poison control data was unavailable for biosurveillance visualization and analytics purposes at a national level. through the creation of a secure web service in conjunction with a lightweight, java-based visualization client known as quicksilver, cdc users were provided the tools to visualize aggregated clinical effect data both geospatially and via a time series chart. the project was able to successfully demonstrate how a federated data approach could enhance existing biosurveillance capacity by providing information access between the biosense program and the npds. creation of the quicksilver tool was a two-fold effort that involved close collaboration between the aapcc’s npds development team and the ncphi public health grid (phgrid) team. the first effort involved creation of a web service that could provide aggregate clinical effect counts from multiple geographic regions (state, zip-3, zip-5) within a single query from the npds. once this was completed, development then began on a simple object access protocol (soap) service oriented architecture (soa) client that could accept and visualize the query results returned from the npds web service. the visualization client was created in java, and is deployable to any j2ee java servlet container (i.e., is java servlet 2.0 compliant). the client was also specifically tested using the apache tomcat application server (v5.5 and v6.0). the user interface is browser-based, leveraging html and javascript, and was tested with the internet explorer, firefox, chrome, and safari web browsers. quicksilver has been successfully deployed within the cdc network, with user access controlled directly by the quicksilver business steward. a diagram outlining the relationship between the npds web service and visualization client can be seen in figure 1. using secure web services to visualize poison center data for nationwide biosurveillance: a case study 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 1. quicksilver logical deployment diagram the visualization client has a small database that stores static data such as geographic information consisting specifically of spatial polygons, centroids, and list relationships for state, zip-3, and zip-5 locations. this database is quite minimal (<100mb) and is only used for assistance with drawing shapes. no poison center data is stored at the cdc. the client uses the google maps javascript api [6] to visualize the aggregate clinical effect counts within any selected geographic region. the google maps javascript api is used, rather than the google maps kml api to prevent any poison center data from being sent to the google maps internet server. to enhance the performance of the client as a user zooms in and out of different geographic entities, use of the douglas-peucker algorithm was applied to reduce the number of onscreen points drawn, thus reducing the rendering time of map polygons. [7] because the visualization client was built upon asynchronous javascript and xml (ajax) apis that support google maps, performance under internet explorer had to be enhanced to achieve timely rendering of a selected geographic region. visualization of aggregate clinical effects data was also achieved in a time series chart using flot [8], an open source ajax api used to produce graphical plots of arbitrary datasets in real-time. a modified version of the early aberration reporting system (ears) c2 algorithm was used to detect aggregate clinical effects counts ≥ 4 standard deviations (sd) from the mean call volume using a rolling 28-day average. [9] data points which were 4 sd units above or below the mean were automatically highlighted (in red) to facilitate rapid examination. all statistical calculations were performed using java’s standard math libraries. using secure web services to visualize poison center data for nationwide biosurveillance: a case study 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 to optimize performance, the npds web service results from frequent user queries were cached in ram. this transient, in-memory cache is used to reduce the number of transactions sent to the remote web service. the cache only exists temporarily and is cleared and reset each time the application server is stopped or started. results: two iterations of the npds web service were completed in 12 weeks. the visualization client, quicksilver, was developed in four months, with client releases being reviewed by cdc and aapcc representatives on a weekly basis. when a user first enters quicksilver, they select a geographic location (state, zip-3, zip-5), date range, and clinical effect. the npds web service then returns the query results within several seconds and shows a choropleth map shaded according to clinical effects thresholds established by the user (figure 2). figure 2. choropleth map of georgia in quicksilver using secure web services to visualize poison center data for nationwide biosurveillance: a case study 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 each shaded area of the map represents a unique zip-3 or zip-5 region within the selected state. to show a time series of each respective zip-3 region within the state of georgia, the user clicks anywhere within the zip-3 or zip-5 area and a pop-up time series graph is then displayed using the flot open source charting api previously described (figure 3). figure 3. time series graph of zip-310 in georgia flot allows the user to also zoom into any date range within the time series to examine clinical effects outliers which fall outside of the empirically set statistical threshold (figure 4). using secure web services to visualize poison center data for nationwide biosurveillance: a case study 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 4. magnified time series chart showing clinical effects outlier when we used quicksilver to visualize all nausea data collected by npds in 2008 by state, over 13 significant ( >10 sd units above 28-day moving average) increases were found. discussion: the use of secure web services to enable access to previously isolated data sources for biosurveillance consumption is an emerging and appealing concept. this model aligns itself with ongoing, existing efforts within healthcare it domain such as the nationwide health information network (nhin). nhin’s vision of providing healthcare information exchanges among hospitals, laboratories, and independent healthcare providers is based upon federated data access and the use of secure web services in accordance with web services interoperability organization (ws-i) standards. the same protocols that were used to create the nhin gateway soa infrastructure were used to create the web service described in this document. it is vital using secure web services to visualize poison center data for nationwide biosurveillance: a case study 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 that biosurveillance and healthcare it infrastructures remain synchronized moving forward to ensure access and interoperability amongst web services deployed through the nhin gateway or other related entities (e.g., phgrid nodes). although this document focuses on the development of the npds web service, this model can be applied to other data sources which have the potential of providing valuable biosurveillance information. other web services are currently under development, and can be accessed through the phgrid service registry. [10] although still at an early stage of development, service registries such as this, provide a method to easily survey what data sources are available for integration into applications for detection, analysis, and visualization of public health threats. these services adhere to a common set of ws-i and w3c standards (e.g. soap, xml, schema, wsdl, ws-security) enabling heterogeneous technology platforms to communicate without developing intricate adapters to bridge different implementations. because of this adherence to standards, small teams were able to extend the functionality of a .net based web service using java based analysis and visualization techniques. as the national biosurveillance strategy continues to move forward, all public health stakeholders should continue to support enabling technologies as they provide the means to address the priority areas of the national biosurveillance strategy. ongoing informatics research should accelerate exploration of new methods to enhance how the healthcare community transforms, manipulate, and analyzes unstructured data sources. these efforts will yield actionable biosurveillance intelligence that can be used to achieve optimal situational awareness capability. aggressive financial and intellectual investments need to be made on the behalf of the healthcare community to apply these technology tools to current biosurveillance infrastructures. without these investments, the social and economic costs associated with recent disease outbreaks will likely only continue to escalate. this implementation of web services combined with a visualization client represents incremental positive progress in transitioning national data sources like biosense and npds to a federated data exchange model. it is the authors’ hope that next steps will include extending existing web services into grid-enabled web services to be then used over a secure and robust grid infrastructure. legacy biosurveillance architectures should consider this approach as it establishes a foundation for an integrated information sharing environment with minimal financial and development resource requirements. although data access services are extremely valuable, this approach also opens the door to the development of many other types of web services. viable candidates for web service development and distribution over a grid infrastructure include, but are not limited to, aberration detection, epidemiological analysis, standardized vocabularies, and visualization toolsets. future biosurveillance applications will likely require on-demand access and query capability to multiple data and analysis streams. implementing dynamic web services which can allow consumers to combine those streams however they see fit for their own biosurveillance purposes is the ultimate goal of the approach described in this document. using secure web services to visualize poison center data for nationwide biosurveillance: a case study 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 conclusion: quicksilver effectively demonstrates how the use of secure web services in conjunction with a lightweight, rapidly deployed visualization client can easily integrate isolated data sources for biosurveillance. the technologies deployed to build quicksilver provided a model to visualize npds data in a way that was not previously available to cdc nor the aapcc. [11] gridenabled web services can allow agencies like the aapcc to remain focused on their primary goals of data stewardship and the provisioning of clinical effects data through npds. agencies like cdc which depend on this data for biosurveillance and analysis purposes can then easily and quickly obtain access to this information without the responsibility of securing third party information, and thus dramatically reducing data warehousing and processing costs. overall, this project demonstrates the use of low-cost, open source, and federated technologies to create a valuable public health application that provides analysis and visualization of relevant data sources for biosurveillance consumption. acknowledgments the authors wish to thank the many collaborators on this initiative including members of the ncphi grid team. this project was supported in part by the cdc contract # 200-2006f016934. references [1] cdc. updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr 2001;50(no. rr-13). [2] teutsch sm, churchill re. principles and practice of public health surveillance. 2 nd ed. oxford, new york: oxford university press, 2000. [3] arthur r., bailey s., bell, b., bernhardt j., besser r., et al. national biosurveillance strategy for human health 2008-2013, version 1.0 / december 2008. available at http://sites.google.com/site/nbshh10/ accessed november 5, 2009. [4] national poison data system. available at: http://www.aapcc.org/dnn/npds/nationalpoisondatasysteminformation/tabid/311/default.aspx. accessed april 15, 2009. [5] biosense. available at: http://www.cdc.gov/biosense/. accessed april 15, 2009. [6] google maps api. available at: http://code.google.com/apis/maps/. accessed april 24, 2009 [7] douglas-peucker algorithm. available at: http://en.wikipedia.org/wiki/ramer-douglaspeucker_algorithm. accessed april 15, 2009. [8] flot. available at: http://code.google.com/p/flot/. accessed april 15, 2009. [9] yiliang z., wang w., artrubin d., wu y. initial evaluation of the early aberration reporting system --florida. mmwr supplement 2005; 54(suppl);123-130. [10] public health informatics research grid wiki, service registry. available at http://sites.google.com/site/phgrid/home/service-registry. accessed april 15, 2009. [11] american association of poison control centers. available at http://www.aapcc.org/dnn/. accessed april 15, 2009. http://sites.google.com/site/nbshh10/ http://www.aapcc.org/dnn/npds/nationalpoisondatasysteminformation/tabid/311/default.aspx http://www.cdc.gov/biosense/ http://code.google.com/apis/maps/ http://en.wikipedia.org/wiki/ramer-douglas-peucker_algorithm http://en.wikipedia.org/wiki/ramer-douglas-peucker_algorithm http://code.google.com/p/flot/ http://sites.google.com/site/phgrid/home/service-registry http://www.aapcc.org/dnn/ a three-step approach for creating successful electronic immunization record exchanges a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health janet balog 1 1 scientific technologies corporation; 4400 e. broadway blvd. ste. 705; tucson, az 85711 abstract population health and individual health are strengthened through proactive immunization programs. clinicians refer to immunization records at the point of care about to decide which vaccinations their patients and families need to reduce the risk of contracting (and spreading) vaccine preventable disease (vpd). understanding the earliest possible age intervals that are safe to administer vaccinations provides the youngest children with as much immunity as possible as early as possible. this is especially useful for children at highest risk as their visits to a medical provider may be sporadic. this, coupled with the continuous development of new and combined vaccines and complex vaccination schedules, challenges the provider to understand the appropriate vaccinations to order for their patients. under-vaccinating increases patients’ vpd risk; over-vaccinating increases provider and consumer health care costs. clinicians want to make the best clinical and economically responsible decisions — this is the challenge. the solution lies in providing clinicians timely and accurate vaccination data with decision support tools at the point of care. the use of electronic health records (ehrs) alone cannot achieve this goal. it will take an accountable team made up of the clinician organization, their ehr vendor, and a public health agency to effectively manage immunization coverage for a patient population. this paper provides a three-step approach to establish and maintain ehr data exchanges, demonstrates the value of both clinical and technical testing prior to data exchange implementation, and discusses lessons learned. it illustrates the value of federal meaningful use criteria and considers how its objective to advance data exchange with public health systems increases providers’ access to timely, accurate immunization histories and achieves desired mutual health outcomes for providers and public health programs. key words meaningful use, immunization information systems (iis), public health informatics, electronic health records (ehrs), consumer engagement, vaccine preventable disease (vpd), population health outcomes, health information technology, health information exchange, vaccines for children program (vfc), vaccine accountability, advisory committee on immunization practices (acip) http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 background in the early 1990’s the centers for disease control and prevention (cdc) established an objective for public health agencies to implement population-based data systems to capture immunization events. the goal of the data systems was to increase the vaccine coverage levels of school-aged children. the vision was straightforward: an immunization record should be available to a provider during a patient visit regardless of where that patient had received prior immunizations. in 1998, the results of a nationwide survey which assessed the development of state immunization information systems (iis) were published. the findings indicated that fifteen (15) state iiss were considered advanced for the time while the remainder of the states had little to no formal iis development efforts underway. 1 by 2009, the cdc’s immunization information system annual report (iisar) showed that nearly 85% of the sixty-four (64) state, city, and territorial iiss were receiving birth data from vital record systems. 2 in some cases these data also include the date the infant received a birth dose of hepatitis b vaccine. iis messaging, transport and security standards were established to enable data exchange between clinical systems and the iis. private providers’ early data exchanges were implemented through their practice management systems, their electronic medical record system or through 3 rd party billing applications. presently, iiss serve as a model for other electronic health record systems, given their ability to maintain secure systems and utilize practices that ensure exceedingly high data quality. because they receive data from many different sources, iiss must be able to resolve duplicate patients and vaccinations, and they do so exceedingly well. the public health expertise supporting iiss ensures their clinical credibility and their use as a clinical decision support tool. a perfect storm for immunization data exchange as of 2009, incremental steps over the previous fifteen years had slowly advanced iis development without the full commitment of the provider community. on december 30, 2009, incentives and direction for health information exchange was established through the health information technology for economic and clinical health (hitech) act. this act authorized the department of health and human services to establish programs to improve health care quality, safety, and efficiency through the promotion of health information technology (hit). funding from the american recovery and reinvestment act covered payments, commonly known as meaningful use incentives, for providers to purchase ehrs and utilize them to improve patient care outcomes. the hitech act included a provision for how providers and 1 state immunization information systems and public opinion: a case for georgia; state and local government review: vol. 30, no. 3 (fall 1998): 194-204; http://www2.gsu.edu/~padgds/streib%20immunization%20and%20public%20opinion.pdf 2 centers for disease control and prevention, 2009 immunization information system annual report; http://www2a.cdc.gov/nip/registry/iisar/iisar_query.asp http://ojphi.org/ http://www2.gsu.edu/~padgds/streib%20immunization%20and%20public%20opinion.pdf http://www2a.cdc.gov/nip/registry/iisar/iisar_query.asp a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 ehrs would engage public health agencies and systems. 3 one of the identified public health objectives involved working with the iis community. among the meaningful use stage 1 standards is a requirement to demonstrate that the provider or hospital ehr can send immunization data to an iis. the recently released meaningful use stage 2 standards include the requirement to send clinically correct and complete immunization records from the provider’s ehr to an iis. 4 future stages will expand these requirements to include bi-directional data exchange (sending data to the iis and receiving data from the iis). the cdc healthy people 2020 objectives, 5 which have been called “the nation’s roadmap for a 21 st century vaccine and immunization enterprise,” directs iiss to provide informed decisionmaking support to both consumers and health care providers. 6 national health policies that motivate communities of care and supporting technology vendors to rapidly adhere to these policies create the perfect storm for the immunization provider and public health community. after decades of collecting immunization records and supporting immunization programs, state iiss are repositories of high quality health data which can be used to significantly reduce the incidence of vaccine-preventable diseases. the challenge (for whom?) is to harness these initiatives so that data exchange between provider ehrs and iiss removes barriers to participation in the state iis, ensures that high quality immunization data is collected and exchanged between the systems, and that the data is actively used to ensure that the population is fully and appropriately immunized against vpd. the purpose of this paper is to identify steps that programs can take that will lead them toward a successful electronic immunization record exchanges between the state’s iis and the her vendor. we identify leading vendors that have a record of successful implementations and describe specific actions within a three-step implementation plan based on over 10years of observing and participating in these exchange initiatives. finally, we provide a discussion on additional considerations for state iis programs when beginning an electronic exchange initiative with a vendor or vendors. methods and approach efficient electronic data exchanges in day-to-day clinical practice are presently implemented by thousands of healthcare providers and will be common in the next few years. the accelerated pace by which states implement exchanges and support the incentive programs is illustrated by a recent unpublished survey 7 conducted by scientific technologies corporation (stc). ninety percent (90%) of states currently are currently or will soon be capable of receiving electronic 3 the office of the national coordinator for health information technology, http://healthit.hhs.gov/portal/server.pt?open=512&objid=2996&mode=2 4 medicare and medicaid programs; electronic health record incentive program – stage 2. http://www.ofr.gov/ofrupload/ofrdata/2012-21050_pi.pdf 5 healthy people 2020: immunization and infectious disease. usdhhs, washington, d.c. 2012. http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/hp2020objectives.pdf http://www.hhs.gov/nvpo/vacc_plan/ 6 healthy people 2020: immunization and infectious disease; iid-18. usdhhs, washington, d.c. 2012. http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/hp2020objectives.pdf 7 scientific technologies corporation, mollen immunization registries retail health collaboration project, state data exchanges, unpublished survey, 2012. http://ojphi.org/ http://healthit.hhs.gov/portal/server.pt?open=512&objid=2996&mode=2 http://www.ofr.gov/ofrupload/ofrdata/2012-21050_pi.pdf http://www.hhs.gov/nvpo/vacc_plan/ http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/hp2020objectives.pdf http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/hp2020objectives.pdf a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 immunization records through standard health level 7 (hl7) data exchanges. stc facilitated electronic record exchange projects with ten state iiss between 2002 and 2012. we gathered information during each of these projects on various technical, programmatic, clinical and business processes and workflow issues that can predict the likelihood of a successful data exchange implementation. we identified ehr product functionalities such as data validation processes impacting the ability to collect data meeting state iis requirements. state iis field-level requirements do vary, but a new focus on dose-level dose accountability for vaccines provided through the federal vaccine for children (vfc) program are moving them towards standardization. the steps and tasks recommended in this document involve successfully implementing data exchange between ehr vendors and an iis. we identified the steps and tasks necessary to ensure information shared between the ehr and the iis is clinically correct and complete. we also describe the steps to overcome potential barriers to a successful data exchange, lastly, we identify ways to identify ehr functional deficiencies and ways to assist the provider staff to identify changes in their business practices and workflow to compensate for those deficiencies. findings and recommendations we identified three steps to implement and operate a successful electronic information exchange system. the process begins with investigation and discovery, continues with test and evaluation, and finally concludes with the implementation itself. within each of these general steps are specific activities that must be and should be accomplished prior to moving to the next step. we describe each step and describe specific activities below. step 1: investigation and discovery the purpose of this phase is to learn as much as possible about the provider, their ehr, their ehr vendor, and their business processes before testing patient data with the iis. it is also crucial to identify primary contacts for each project stakeholder at this time. table 1 below lists ehr vendors that are market leaders and have history of successful data exchange implementations. table 1 also lists each vendor’s supported type of hl7 data exchange that meets or exceeds current requirements. http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 table 1: electronic record exchange between state immunization registries and ehr vendors type of hl7 data exchange supported ehr vendor real time export batch export supports iis queries connexin, office practicum x x nextgen ehr x mie webchart (medical informatics engineering) x x rpms (indian health service) x x (testing) netsmart technologies (insight) x x greenway medical / prime suite x allscripts professional x cerner power chart x cerner millenium x sage software (intergy) x emds x epicare (hospital and provider practices) x mccormick and mitchell x eclinical works x ehs (now success ehs) x practice partner (mckesson) x compugroup medical x ge centricity (logician) x cpsi x meditech (hospital solution) x provider activities  hold an official kick-off meeting with the provider and iis team to outline the process.  describe the data quality testing processes.  estimate the anticipated length of time until the data exchange will go live.  have the provider commit to a minimal turnaround time when responses or investigations need to be completed by the provider staff.  clarify how patient confidentiality will be maintained throughout the testing process.  ensure that the provider’s practice will commit leadership staff to the project through to completion.  identify the technical and clinical staff accountable for testing and implementation.  identify the provider’s current, past, or anticipated future participation in the vfc program.  establish if there are multiple provider locations in the provider organization.  identify if the practice is independent or owned by or affiliated with another entity. http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012  identify where the computer server resides from which the data will be uploaded to the iis.  validate the current quantity of the immunization data in the ehr:  identify the length of time the practice has used the ehr.  identify where the immunization legacy data resides and how or if it will be migrated to the ehr.  identify where immunization data has been recorded since the ehr was launched. (ehr versus continued entry in the iis).  identify provider staff who will be accountable for completing manual file uploads and how often these will be completed.  identify if the provider staff is capable of generating test data from their ehr and if not who will be completing that task.  explain how the data they enter into their ehr impacts their ability to track vaccine usage in the iis and their ability to order and get approval for vaccines from the state vfc program. ehr vendor activities  provide the iis data specifications to the ehr vendor:  identify the fields that are mandatory and recommended for the specific state iis to accept the data.  establish how or if the ehr validates these data fields.  identify which data fields are included in the interface.  meet with the ehr vendor’s technical team to respond to their questions about the iis application, the specifications, and the data exchange testing and implementation processes.  identify an ehr vendor project leader and lead technical staff assigned to the project.  review the current and planned export data exchange formats, e.g., hl7 v2.3.1, hl7 v2.5.1, csv.  establish the data exchange capabilities to securely send and receive data:  if the ehr can receive data from the iis, determine where the data will be stored, how it will be displayed in the patient record, and the processes the ehr will use to de-duplicate both patient and vaccine records.  establish what triggers data to be exported from the ehr to the iis. examples include: any change in patient data; updated demographic data only; new vaccination entry, administered or historical or the patient record has been “closed” in the ehr. understand if these triggers happen automatically or if a manual process must also occur.  identify how the ehr manages iis opt out and opt in indicators in their ehr. states that require consent to allow data to be sent to the iis are opt in states. in these states, the ehr will need to indicate that consent has been signed and parse the patients included in the export by those who have consented.  identify how and if the ehr supports vfc status information at the patient level (how the patient qualifies for the vfc program) and the vaccination level (the vaccine funding source).  determine if the ehr documentation is fully integrated with the provider’s billing (i.e., what is documented in the ehr that determines what is generated on the patient bill).  review the cvx/ cpt and mvx codes that are available in the ehr to ensure they are complete, accurate and active: http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012  inactive codes may be used to document vaccinations that were given in the past (historical).  ensure that the iis and the vendor have a common definition for historical and administered vaccinations. demonstration  have the provider staff demonstrate how they use their ehr to input the following:  patient demographic information including next of kin or legal guardian and race information. note: patient demographics may be collected in a separate module that then populates the ehr with limited demographics.  historical and administered vaccinations. be sure to have the provider define what historical vaccinations mean to them.  note what fields are entered as free text.  review the choices available in all drop down lists and if the provider can change those values.  determine if the ehr has a vaccine inventory function and if they use it.  note if a vaccine funding source can be identified for administered vaccines, e.g., vfc versus private.  determine if there is a vaccine forecast in the ehr. if it exists, ask how or if history of disease or contraindications impact the vaccine forecast. review what the forecast displays.  identify the mandatory fields and review the data integrity checks. warning: users may be able to “trick” the ehr to bypass mandatory fields using generic placeholder information.  identify where the provider documents history of disease and other contraindications to vaccination.  ask if the ehr has a way to document vaccination refusals, and if so, where and how.  ask if the provider knows how to generate a sample of data from their live production system. if not, who would do it for them and how. step 2: test and evaluate there are two important testing steps. the first validates the technical capability to send appropriately formatted and fully populated hl7 messages to the iis. the second ensures that patient and immunization event data is clinically accurate, correct and complete. the state iis will not implement a data exchange until both criteria have been met. technical testing this process requires an hl7 test data set to be exported from the ehr to the iis. the test file should contain at least 250 patient records to identify possible random technical transfer issues. this is usually done with mock patient records first and then live patient data. live patient data must ultimately be tested to know what the iis will ultimately get from the provider’s system.  messages are reviewed to ensure the expected data set is received in the iis test environment without error. they are also reviewed for compliance with hl7 message standards and the frequency the data is populated. adherence to the hl7 message version used is also reviewed, e.g., v2.3.1 or v2.5.1. meaningful use stage 2 requires that ehrs send data in http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 v2.5.1 by 2014. 8  when progressing to testing live patient data, at least 1,000 patients with immunization messages should be reviewed. fewer messages are insufficient to identify random data quality issues that may occur. multiple iterations of test messages are usually needed to ensure that clean, accurate, and complete data will be sent to the iis when the exchange is operational. an average of three to six test exchanges is generally required. when an ehr application does not support entering valid and complete immunization and demographic data, changing the provider’s workflow and business processes may compensate for the ehr deficiencies (e.g., making a field mandatory). frequently, it is the iis rather than the ehr vendor who discovers the issues and makes recommendations to the providers for these changes. the testing process is more than a validation; it is also provider and ehr education which is ideally provided by a public health specialist as opposed to a technology expert. the public health specialist has the depth of understanding across all systems and clinical programs to make these recommendations. data quality testing some data quality testing is accomplished in the connectivity testing process described above. however, it is recommended that a more detailed process be conducted to validate the quality and quantity of immunization data captured in the provider’s ehr. the tasks include:  review the accuracy of current cpt/cvx (vaccine) and mvx (manufacturer) codes in their ehr:  ensure combination vaccines can be documented as some ehrs support only the entry of single vaccine antigens. combination vaccines impact vaccine forecasting so vaccines should be able to be documented in the formulation they are given.  review how and where contraindications to vaccines and history of disease are documented. if they are captured, determine what codes will be sent for each value and where will they be sent in the hl7 message. ensure that discontinued codes are not being used for administered vaccines.  ensure that the ehr vaccination list with the corresponding codes which will be sent in the import file is complete and correct. providers should have vaccine descriptors in their system that reflect vaccines they have available and administer as well as those that they need to document as historical vaccinations.  ensure the ehr has vaccine descriptors that correspond to the correct cpt or cvx code. it is not unusual to find that the ehr upgrade has been distributed with these errors versus the provider entering them incorrectly.  review the vaccination related fields to ensure that they are being consistently populated. the fields include vaccinator name, vaccine manufacturer, vaccine lot number, and vaccine expiration date.  for an opt-in iis, a field to indicate that consent has been obtained must be available and populated and only consented records should import into the iis.  for those providers who participate in the vfc program, an indicator must be available in the ehr to mark how the patient qualifies for vfc. this must be updated with each 8 the office of the national coordinator for health information technology, http://healthit.hhs.gov/portal/server.pt?open=512&objid=2996&mode=2 http://ojphi.org/ http://healthit.hhs.gov/portal/server.pt?open=512&objid=2996&mode=2 a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 vaccination visit. many states are now requiring this information in the hl7 message. note: vfc information is only expected in records for patients who are under age 19 years. if the vfc indicator is sent for all patients, then patients at 19 years or older should default to “ineligible.”  prior to implementation, compare the hl7 messages from the provider’s ehr against what the clinician sees in their ehr:  randomly select 50 or more live patient immunization messages from the group that has passed testing.  have the provider pull those patient records up in their ehr and compare them; they should match exactly.  have the provider identify discrepancies and review them with the vendor. one or both of the following could occur:  the ehr vendor needs to correct technical errors or change the application to support staff workflow/needs.  the ehr vendor needs to retrain clinical staff in the appropriate use of the ehr application so that documentation practices support the interface design.  after the corrective actions have taken place, a clean test file is imported to the iis test system to demonstrate that all issues have been addressed.  after the iis team accepts the test file, the provider’s ehr is ready for live data exchange. step 3: implementation phase after steps 1 and 2 are complete the provider is ready to implement data exchange between their ehr and the state iis. after the initial implementation, the data exchange processes require ongoing monitoring and evaluation. many things may change causing the import process that was originally established to fail or be interrupted. for example, with meaningful use incentive payments available, many providers are switching from one ehr to another. provider staff members change. ehr upgrades may interrupt the connection to the iis. finally, the provider’s failure to upgrade the ehr in a timely manner may cause incorrect data to be sent to the iis. the following items should be reviewed with the provider and the ehr vendor at the iis data exchange implementation:  ensure the provider understands their ongoing responsibilities for the data exchange:  ensure data files are sent to the iis on a regular basis:  if data files are manually uploaded, determine the frequency of the data uploads as daily (preferred) or weekly (acceptable). best practice calls for two individuals within the provider’s office to understand the upload task.  if data files are automatically uploaded, assign staff who will confirm that successful data file transmission occurs on a regular basis. replace assigned staff as needed when turnover occurs.  establish how the cpt/cvx codes in an ehr application will be kept current. this includes adding codes as new codes are established and inactivating those that are discontinued. persons assigned this responsibility should link to the cdc listserve that automatically sends notices about changes to codes (additions, inactivation, changes). 9 vaccine codes should be 9 cdc website: mvx code list; http://www2a.cdc.gov/vaccines/iis/iisstandards/vaccines.asp?rpt=mvx cdc http://ojphi.org/ http://www2a.cdc.gov/vaccines/iis/iisstandards/vaccines.asp?rpt=mvx a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 reviewed when system upgrades occur to ensure accuracy. some providers are responsible to update their own applications and may or may not be alerted to do so by their ehr vendor. identify who will be responsible to keep ehr upgrades current. some providers will choose to lag behind when new major releases occur because they feel those releases will have more system bugs.  intermittent data quality checks should be a formal process for both the iis and the provider. discuss an established schedule for these reviews and how issues will be communicated between the parties. some iiss can give the provider permissions to check the hl7 message error log themselves with appropriate iis system permission.  if the ehr receiving data from the iis supports iis queries, ensure that the ehr has a process to manage duplicate patients and vaccinations. ehrs typically handle patient duplicates well because of their experience with receiving data from other systems such as labs and radiology facilities. ehrs are not usually equipped with the logic to de-duplicate vaccinations like an iis. while this is the ehr vendor’s responsibility, they will require support from the iis as they resolve their issues. discussion and recommendations meaningful use has added a level of motivation for clinical providers and hospitals to exchange data with an iis. rigorous processes to implement these links will pay dividends to public health programs and clinical providers. the state immunization program must retain the expertise and be the “source of truth” for all things immunization. they must support both ehr and provider education. this partnership must recognize that sending data to a state iis is only the first step. the power in the data will come from the ability to query the iis for patient vaccination records and accept iis generated patient age-appropriate forecasts. this will maximize the health data assets of the immunization provider community and empower clinical care physicians, nurses, and pharmacists with the complete and accurate information needed to support their vaccine administration decisions. we have identified best practices for implementing data exchange between the iis and ehr and describe these activities in detail within three main steps: identification and discovery, testing and validation, and implementation. regardless of specific circumstances, there are several important considerations regarding the implementation steps we’ve outlined in this paper. these include assumptions and preconceptions regarding the process itself, aspects of the ehr and coding, expectations on the process time and effort required, need for on-going monitoring, how to handle ehr upgrades, and others we describe in detail below. future research that would identify under what circumstances these considerations come into play will be extremely useful to reduce potential for a state’s initiative to be unsuccessful, and/or to reduce costs of implementation. first, each data exchange interface is unique. each provider or their representative has purchased and/or modified an ehr to suit their needs. each provider has been trained to use their ehr by different people and what they retain about that training varies. each provider has developed workflow processes and business practices around their ehr implementation. many providers website: cpt/cvx code list; http://www2a.cdc.gov/vaccines/iis/iisstandards/vaccines.asp?rpt=cpt http://ojphi.org/ http://www2a.cdc.gov/vaccines/iis/iisstandards/vaccines.asp?rpt=cpt a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 choose to implement their ehr in phases. experience has shown the value of using the following practices and processes:  make no assumptions during any phase of the process. take the time to verify all ehr information with the vendor, the provider, and the provider’s clinical staff. the quality and quantity of data entered into the ehr is impacted by the business practices used, the workflow, staff implementation and ongoing training on the ehr application, and individual staff compliance/errors.  ehrs are not a turn-key data exchange solution for an iis. because clinicians are entering immunization and other data into the ehr, providers and the iis may assume that the record of the immunization event can and will be accurate and complete. this is not the case unless the ehr has the field level validation and other functionality to require it from the user.  expect the testing process to be long and challenging. less vendor attention has been paid to the ehr’s immunization module compared to other core ehr functionality mandated under meaningful use stage 1. providers may think this process will be easier than it is. explaining this fully before the process begins can help mitigate this expectation and help diffuse the perception that the iis community is the barrier to implementation.  ongoing data transmission between the ehr and iis must be monitored and nurtured. routine software upgrades and hardware changes may cause an established exchange to fail. this is another common reason that continuous monitoring at the iis is important in order to spot aberrations in information flow.  ehr upgrades impact immunization data quality. how and when ehr vaccination codes are updated varies by the vendor’s releases and how soon the provider chooses to implement them. after implementing the data exchange, it is important that the provider understand that their vaccine choices need to match what is being administered in their offices. keeping their systems updated in a timely manner impacts their ability to do this.  ehr users are creative. they discover ways to record data in their ehr when the application does not offer them the correct vaccine choice. this applies to both doses they administer in their practice and doses that have been given elsewhere (historical doses). here are some scenarios observed during the testing phase:  if the correct vaccine is not available in the ehr database, users simply select the closest choice, e.g., td, when they are actually giving tdap; or pcv7 when they are giving pcv13. this is typically due to the vendor not providing the upgrade in a timely manner or the provider opts to delay system upgrades.  users documented they administered a varicella vaccine when they did not because their ehr did not offer a way to document chicken pox history in the vaccination record.  ehr vaccine descriptions are frequently linked to the wrong vaccine code. iiss read the vaccine code in the hl7 message, not the vaccine description. this is discovered when reviewing the ehr vaccine and manufacturer code table review for accuracy and completeness and during the provider’s demonstration of their ehr. most ehr applications allow providers to change vaccine codes themselves by selecting from a hard-coded list. some allow an administrative user to originate new vaccine codes and descriptors in their system. http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012  data fields required by the state iis for data exchange may not be mandatory or exist within the ehr. state iiss need their data to support dose level vaccine accountability for vfc vaccinations. the concept of vfc is relatively new to the ehr community. many ehrs either do not offer the fields in the ehr for the provider to complete or the data validation does not exist to make sure the fields are complete and correct. fields such as patient vfc status, vaccine funding source, vis publication date, and the date the vis was given to the patient or the parent may not exist.  ehr data field validation and functionality to support complete and correct documentation of the vaccination event are sporadic. many ehrs allow free text in data fields that are required in the iis, making it highly susceptible to error. still others offer drop down lists from which to choose vaccine lot numbers but the user may be able to overwrite the field and enter free text.  next of kin information is frequently not collected in the ehr. ehrs most often have responsible party, emergency contact, or guarantor (person responsible for the bill) fields which may not be the legal guardian. parent and legal guardian information is important to support de-duplication of patients in the iis:  the iis may not accept these alternate fields in lieu on next of kin information.  guarantor information now frequently asks for how the patient is related to the insured (natural child, step child) versus how the insured is related to the child (parent, step-parent). this makes using guarantor information in lieu of next of kin impossible.  for children whose insurance is medicaid, the child’s name must be listed as the guarantor as they are the insurance holder. as such, there will never be parent or legal guardian in this field for children with medicaid coverage.  clinical documentation may drive the provider’s bill. ehrs may be fully integrated with the provider’s billing. if the iis recommends changes to the ehr vaccine codes, ensure that the charges linked to that code are also reviewed before they are finalized. disrupted billing or erroneous billing puts the provider at legal and financial risk.  vaccination and manufacturer codes of hospital ehr systems are generally set by the pharmacy. many hospital ehrs require extensive vaccine code mapping or “aliasing” to send data correctly. requests for additional data fields or changes to the ehr workflow are expensive and usually take many months to be completed. historical vaccinations are rarely recorded in a hospital ehr because a bona fide record to verify the information is usually not available.  hospital ehr systems are not prepared to document patient or vaccination vfc information. this issue is largely unanswered in hospital ehrs. most hospitals that have worked with an iis have entered the required data directly into the iis. clinicians receive medications from the pharmacy. they are the only ones that would know if the vaccine was supplied by the vfc program. since hospitals gather the information needed to determine if a patient qualifies for vfc in other areas of the ehr, the patient vfc status could be derived.  understand the provider’s organizational structure. the provider may have no direct control over the ehr in use. it is important to understand who supports the ehr in use, who has the ability to make changes in it, and where the data imported to the iis is stored. many providers are employees of larger healthcare networks and they contract for it services controlled by another entity. a single hl7 import may include data that is coming from http://ojphi.org/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 different ehr products. the data may also go through middleware that could change it before it arrives at the iis.  clinical training issues will be discovered in this process. clinical vaccination errors will become evident when hl7 messages are reviewed. most provider offices are now staffed with a number of unlicensed healthcare workers who administer vaccines. these issues should be discussed with the provider’s clinical manager so that appropriate retraining can occur. this highlights the value of a robust ehr immunization module with logic to mark invalid vaccinations and forecast vaccinations based on advisory committee on immunization practices (acip) recommendations. many clinicians become dependent on this functionality available in the iis. limitations a limitation of our findings is that our recommendations are not based on a scientific approach, but rather we leveraged observations and experience gained over the course of many different projects. no project was exactly the same and we had no control over programs’ choices in many cases. another limitation is that some of our recommendations will not apply for all situations. there may be cases when a program’s implementation process may not be successful due to not be able to complete all needed activities when some do not apply. finally, we acknowledge that even if all recommendations are followed in their entirety, a program’s implementation may still not be successful due to unforeseen factors. conclusion in summary, using proven practices is key to implementing and supporting ongoing meaningful use data exchanges. immunization information system processes must be in place to monitor, alert and support the ehr and clinical care community. implementation can be achieved through following specific activities within three major steps, while keeping certain considerations regarding the process in mind. ultimately, public health-supported decisions will mark a milestone in improving healthcare outcomes, increasing individual and population protection against disease, and reducing the significant economic and health impacts — saving both dollars and lives. corresponding author janet balog, bs, rn tel: +1.520.202.3333 fax: +1.520.202.3340 email: info@stchome.com references 1. state immunization information systems and public opinion: a case for georgia; state and local government review: vol. 30, no. 3 (fall 1998): 194-204; http://www2.gsu.edu/~padgds/ streib%20immunization%20and%20public%20opinion.pdf http://ojphi.org/ mailto:info@stchome.com http://www2.gsu.edu/~padgds/ a three-step approach for creating successful electronic immunization record exchanges between clinical practice and public health online journal 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collaboration project, state data exchanges, unpublished survey, 2012. 8. the office of the national coordinator for health information technology. http:// healthit.hhs.gov/portal/server.pt?open=512&objid=2996&mode=2 9. website cdc. mvx code list; http://www2a.cdc.gov/vaccines/iis/iisstandards/ vaccines.asp?rpt=mvx cdc 10. website: cpt/cvx code list; http://www2a.cdc.gov/vaccines/iis/iisstandards/ vaccines.asp?rpt=cpt http://ojphi.org/ http://www2a.cdc.gov/nip/registry/iisar/iisar_query.asp http://healthit.hhs.gov/portal/server.pt%ed%af%80%ed%b0%a2open%ed%af%80%ed%b0%a0%ed%af%80%ed%b0%9812%ed%af%80%ed%b0%89ob%ed%af%80%ed%b1%8di%ed%af%80%ed%b0%a7%ed%af%80%ed%b0%a0299%ed%af%80%ed%b0%99%ed%af%80%ed%b0%89mode%ed%af%80%ed%b0%a02 http://healthit.hhs.gov/portal/server.pt%ed%af%80%ed%b0%a2open%ed%af%80%ed%b0%a0%ed%af%80%ed%b0%9812%ed%af%80%ed%b0%89ob%ed%af%80%ed%b1%8di%ed%af%80%ed%b0%a7%ed%af%80%ed%b0%a0299%ed%af%80%ed%b0%99%ed%af%80%ed%b0%89mode%ed%af%80%ed%b0%a02 http://www.ofr.gov/ofr%ed%af%80%ed%b0%b8pload/ofr%ed%af%80%ed%b0%a7ata/2012-210%ed%af%80%ed%b0%980%ed%af%80%ed%b1%82pi.pdf http://www.healthypeople.gov/2020/topicsob%ed%af%80%ed%b1%8dectives2020/pdfs/%ed%af%80%ed%b0%abp2020ob%ed%af%80%ed%b1%8dectives.pdf http://www.hhs.gov/nvpo/vacc%ed%af%80%ed%b1%82plan/ http://www.hhs.gov/nvpo/vacc%ed%af%80%ed%b1%82plan/ http://www.healthypeople.gov/2020/topicsob%ed%af%80%ed%b1%8dectives2020/pdfs/%ed%af%80%ed%b0%abp2020ob%ed%af%80%ed%b1%8dectives.pdf http://healthit.hhs.gov/portal/server.pt%ed%af%80%ed%b0%a2open%ed%af%80%ed%b0%a0%ed%af%80%ed%b0%9812%ed%af%80%ed%b0%89ob%ed%af%80%ed%b1%8di%ed%af%80%ed%b0%a7%ed%af%80%ed%b0%a0299%ed%af%80%ed%b0%99%ed%af%80%ed%b0%89mode%ed%af%80%ed%b0%a02 http://healthit.hhs.gov/portal/server.pt%ed%af%80%ed%b0%a2open%ed%af%80%ed%b0%a0%ed%af%80%ed%b0%9812%ed%af%80%ed%b0%89ob%ed%af%80%ed%b1%8di%ed%af%80%ed%b0%a7%ed%af%80%ed%b0%a0299%ed%af%80%ed%b0%99%ed%af%80%ed%b0%89mode%ed%af%80%ed%b0%a02 http://www2a.cdc.gov/vaccines/iis/iisstandards/ http://www2a.cdc.gov/vaccines/iis/iisstandards/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a systematic evaluation of data streams for global disease surveillance alina deshpande*, mac brown, lauren castro, william b. daniel, eric n. generous, andrea hengartner, kristen margevicius and kirsten taylor-mccabe defense systems analysis division, los alamos national laboratory, los alamos, nm, usa objective the overall objective of this project is to provide a robust evaluation of data streams that can be leveraged from existing and developing national and international disease surveillance systems, to create a global disease monitoring system and provide decision makers with timely information to prepare for and mitigate the spread of disease. introduction living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. in addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. los alamos national laboratory (lanl) has been funded by the defense threat reduction agency to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. specifically, this project entails the evaluation of data streams either currently in use in surveillance systems or new data streams having the potential to enable early disease detection. an overview of this project will be presented, together with results of data stream evaluation. this project will help gain an understanding of data streams relevant to early warning/monitoring of disease outbreaks. methods three specific aims were identified to address the overall goal of determining the relevance of data streams for global disease surveillance. first, identify data streams as well as define metrics for the evaluation. second, evaluate data streams using two different methodologies, decision analysis modeling using a support tool called logical decisions® that assigns utility scores to data streams based on weighted metrics and assigned values specific to data stream categories; and a surveillance window concept developed at lanl that assigns a window or windows of time specific to a disease within which information coming from various data streams can be determined to have utility. this would obtain a ranked list of useful data streams. additionally, evaluate data integration algorithms useful for a global disease surveillance system through a review of scientific literature. finally, validate the top-ranked data streams by application of specific historical outbreaks to determine whether the data streams are capable of providing early warning or detection of the particular disease before it became a large outbreak. results seventeen categories of data streams were identified that ranged from traditional ones such as clinic/healthcare provider and laboratory records to newly emerging sources of information such as social media and internet search queries. the logical decisions® based evaluation of data streams identified 5 data streams that consistently showed utility regardless of the goal of biosurveillance. however, different data streams varied in rank, given different biosurveillance goals, and there is no one top ranked data stream. surveillance window based evaluation of data streams during disease outbreaks identified data streams that had high utility for early detection and early warning regardless of disease, while others were more disease and operations specific. additionally, we have built a searchable biosurveillance resource directory that houses information on global disease surveillance systems. conclusions lanl has developed a robust evaluation framework to determine the relevance of various traditional and non-traditional data streams in integrated global disease surveillance. through the use of defined surveillance goals, metrics and data stream categories, not only have we identified data streams currently in use that have high utility, but also new data streams that could be exploited for the early warning/monitoring of disease outbreaks. our robust evaluation framework facilitates the identification of a defensible set of options for decision makers to use to prepare for and mitigate the spread of disease. keywords evaluation; disease surveillance; data streams acknowledgments we would like to acknowledge the defense threat reduction agency (dtra)-joint science and technology office (jsto) for their support and guidance on this project. references dr. alina deshpande, dr. mac brown, ms. lauren castro, dr. william brent daniel, mr. eric nicholas generous, ms andrea hengartner, dr. kristen margevicius, dr. kirsten taylor-mccabe, los alamos national laboratory, los alamos, nm 87545. *alina deshpande e-mail: deshpande_a@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e2, 2013 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts monitoring of brucellosis in agricultural animals in ukraine during 2013-2015 oleg nevolko* state scientific research institute of laboratory diagnostics and veterinary and sanitary expertise, kyiv, ukraine objective analysis of brucellosis monitoring in agricultural animals in ukraine to control epizootic situation and prevent possible brucellosis in humans. introduction brucellosis is one of the most widespread zoonosis in the world. only 17 countries informed who that their territory is free from brucellosis. about 500 thousand cases of brucellosis in humans are registered in the world each year. the problem of brucellosis has remained actual to agriculture and health care for many years. almost all agricultural animals are highly susceptible to brucellosis. socio-economic significance of brucellosis problem is determined by peculiarities of the course of the disease and the main contingent that can be infected, namely the working population that is connected with both professional factors and social reasons. brucellosis is a chronic infectious disease. the disease in animals has the following signs: abortions and retention of secundines, orchitis, unviable litter and sterility. brucellosis is included to the list of quarantine diseases due to its social threat. methods studies of blood sera of cattle, small ruminants, horses and pigs from different ukrainian regions that were selected during the annual spring clinical examination in 2013-2015. the following serological methods were used for the studies: complement-fixation test (cft), agglutination reaction (ar), rose bengal test (rbt), prolonged complement fixation test (pcft). results currently, ukraine is free from brucellosis of animals. the last brucellosis case in pigs was registered in 2008 in odesa oblast. the last case of brucellosis in cattle in ukraine was registered in 1992. according to the ministry of health, a case of brucellosis in humans is registered in ukraine almost every year. annual serological brucellosis studies of servicing bulls, cows, heifers older than one year, horses, stud rams, ewes, boars and sows are held once a year in ukraine. during 2013-2015, the monitoring serological brucellosis studies of blood sera from cattle, small ruminants, horses and pigs from different farms in 25 oblasts of ukraine were conducted at state laboratories of veterinary medicine and state scientific and research institute of laboratory diagnostics and veterinary and sanitary expertise. table 1. serological research results in 2013, seropositive results were obtained in ar crimea – six cases in cattle, dnipropetrovsk oblast 12, kyiv oblast – 31, sumy oblast – 118, and luhansk oblast – 25 using ar and rbt techniques. in small ruminants, seropositive results were determined in luhansk oblast – 26 animals (ar). testing pigs by rbt showed the following positive results: 82 animals in dnipropetrovsk oblast, 16 in luhansk, and 1 in sumy oblast. twenty seven horses were detected positive by rbt in luhansk oblast. fig. 1. brucellosis monitoring results, 2013 in 2014, seropositive results in cattle were received in kyiv (20), dnipropetrovsk (28), sumy (66), chernihiv (37) and zhytomyr (2) oblasts using ar, rbt, and cft. ar tests were positive for one small ruminant in dnipropetrovsk and for three in sumy oblasts. five seropositive pigs were found in sumy oblast using rbt. fig. 2. brucellosis monitoring results, 2014 in 2015, seropositive results (ar, rbt, and cft) in cattle were obtained in sumy (8 animals), dnipropetrovsk (34), and chernihiv (10) oblasts. for small ruminants, one seropositive animal was found in dnipropetrovsk and three in sumy oblasts using ar. employing rbt, one pig was diagnosed in dnipropetrovsk oblast. two horses were found positive using rbt and ar in sumy oblast. fig. 3. brucellosis monitoring results, 2015 the seropositive animals were destroyed. bacteriological studies were not conducted. conclusions 1. during the studies of blood sera of agricultural animals from different ukrainian regions, positive results were obtained in 7 oblasts of ukraine indicating a possible circulation of the causative agent of brucellosis. 2. studies need the in-depth analysis that must include bacteriological testing of seropositive animals. table 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e156, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords brucellosis; monitoring; serology; ukraine *oleg nevolko e-mail: olegnevolko2010@ukr.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e156, 2017 isds16_abstracts-final 136 isds16_abstracts-final 137 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts enabling essence to process and export meaningful use syndromic surveillance data miles stewart1, wayne loschen*1 and taha kass-hout2 1johns hopkins university applied physics laboratory, laurel, md, usa; 2public health surveillance and informatics program office, office of surveillance, epidemiology, & laboratory services, centers for disease control and prevention, atlanta, ga, usa objective the objective of this project is to enable the essence system to read in, utilize, and export out meaningful use syndromic surveillance data using the health level 7 (hl7) v2.5 standard. this presentation will detail the technical hurdles with reading a meaningful use syndromic surveillance data feed containing multiple sources, deriving a common meaning from the varying uses of the standard and writing data out to a meaningful use hl7 2.5 format that can be exported to other tools, such as biosense 2.0 (2). the presentation will also describe the technologies employed for facilitating this, such as mirth, and will discuss how other systems could utilize these tools to also support processing meaningful use syndromic surveillance data. introduction in order to utilize the new meaningful use syndromic surveillance data sets (3) that many public health departments are now receiving, modifications to their systems must be made. typically this involves enabling the storage and processing of the extra fields the new standard contains. open source software exists, such as mirth connect, to help with reading and interpreting the standard. however, issues with reliably reading data from one source to another arise when the standard itself is misunderstood. systems that process this data must understand that while the data they receive is in the hl7 v2.5 standard format, the meaning of the data fields might be different from provider to provider. additional work is necessary to sift through the similar yet disjoint fields to achieve a consistent meaning. methods this project utilized 3 separate instances of essence and biosense 2.0. for both importing and exporting hl7 v2.x standard files, the project used the open source tool mirth connect. for importing data the project adapted versions of tarrant county and cook county essence systems in the amazon govcloud to receive meaningful use syndromic surveillance data files sent from biosense 2.0. for exporting data to biosense 2.0, the project used mirth connect to poll the local version of cook county’s essence database and export the data into an hl7 v2.5 file. the resultant file was sent over secure file transfer protocol (sftp) to biosense 2.0. the team then evaluated the process by comparing the data in the local instances of essence and the corresponding instances hosted on the internet cloud. results many issues were encountered during the reading of the hl7. while the standard suggests that hospitals and hospital systems would all send data in the same fields for the same data, the reality was far different. although hl7 v2.5 is a standard and there is a defined use for each field, it can be interpreted in many ways. a large portion of time was spent communicating with the local health department to determine exactly what each field meant for a particular hospital. comparing the internet cloud and local versions did have some difficulties due to local filtration rules that eliminated non-er related records from the local tarrant county system. the project was able to utilize new query features in essence to filter down to only er related records on the internet cloud version to support the comparisons. the project was able to re-use much of the configuration that was created when moving from one jurisdiction to the other. this will help when describing how others may use the same technology in their own systems. conclusions reading and interpreting the data consistently from a data feed containing multiple sources can be challenging. confusion with the hl7 v2.3 or 2.5 standards causes many health organizations to transmit data in inconsistent ways that betrays the notion of a messaging standard. however, with the tools this project have created and the lessons we have learned, the pain of implementing meaningful use syndromic surveillance data into a system can be reduced. keywords analytics; electronic medical records for public health; interoperability; meaningful use; internet cloud acknowledgments the essence in the cloud initiative is supported by the cdc’s division of notifiable diseases and healthcare information (dndhi) biosense program. references 1) kass-hout, et al, cdc’s biosense 2.0: bringing together the science and practice of public health surveillance, ajpm prevention in practice, november 15, 2011. 2) phin messaging guide for syndromic surveillance: emergency department and urgent care data. accessed august 30, 2012: http://www.cdc.gov/ehrmeaningfuluse/syndromic.html. *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e55, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the epidemiologic vocabulary of the west and the former soviet union: different sides of the same science anna grigoryan*1, carmen clarke2, lyudmila zueva3, tetyana chumachenko4, edmond f. maes1 and bonnie smoak2 1cdc, atlanta, ga, usa; 2wrair, silver spring, md, usa; 3north-western state medical university after i.i. mechnikov, st.petersburg, russian federation; 4kharkiv national medical university, kharkiv, ukraine objective the purpose of this project was to develop an english-russian epidemiology dictionary, which is needed for improved international collaboration in public health surveillance. introduction as part of the us department of defense strategy to counter biological threats, the defense threat reduction agency’s cooperative biological engagement program is enhancing the capabilities of countries in the former soviet union (fsu) to detect, diagnose, and report endemic and epidemic, man-made or natural cases of especially dangerous pathogens. during these engagements, it was noted that western-trained and soviet-trained epidemiologists have difficulty, beyond that of simple translation, in exchanging ideas. the soviet public health system and epidemiology developed independently of that of other nations. whereas epidemiology in the west is thought of in terms of disease determinants in populations and relies on statistics to make inferences, classical soviet epidemiology is founded on a more ecological view with the main focus on infectious diseases’ spread theory. consequently many fundamental soviet terms and concepts lack simple correlates in english and other languages outside the soviet sphere; the same is true when attempting to translate from english to russian and other languages of the fsu. systematic review of the differences in fsu and western epidemiologic concepts and terminology is therefore needed for strengthening understanding and collaboration in disease surveillance, pandemic preparedness, response to biological terrorism, etc. methods following an extensive search of the russian and english literature by a working group of western and fsu epidemiologists, we created a matrix containing english and russian definitions of key epidemiologic terms found in fsu and western epidemiology manuals and dictionaries, such as a dictionary of epidemiology (1), epidemiology manual (2) and many other sources. particular emphasis was placed on terms relating to infectious disease surveillance, analysis of surveillance data, and outbreak investigation. in order to compare the definitions of each term and to elucidate differences in usage and existing gaps, all definitions were translated into english and russian so that the definitions could be compared side by side and discussed by the working group. results six hundred and thirty one terms from 27 english and 51 russian sources were chosen for inclusion based on their importance in applied epidemiology in either the west or the fsu. review of the definitions showed that many terms within biosurveillance and infectious disease public health practice are used differently, and some concepts are lacking altogether in the russian or english literature. significant gaps in fsu epidemiology are in the areas of biostatistics and epidemiologic study designs. there are distinctive differences in fsu and western epidemiology in the conceptualization and classification of disease transmission, surveillance practices, and control measures. conclusions epidemiologic concepts and definitions significantly differed in the fsu and western literature. to improve biosurveillance and international collaboration, recognition of these differences must occur. detailed analysis of epidemiology terminology differences will be discussed in the presentation and paper. major limitations of the work were scarcity of prior research on the subject and lack of bilingual epidemiologists with the good understanding of fsu and western approaches. a bilingual reference in the form of a dictionary will greatly improve mutual comprehension and collaboration in the areas of biosurveillance and public health practice. keywords surveillance; dictionary; collaboration references 1. porta miquel. a dictionary of epidemiology, fifth edition, oxford university press, usa, 2008 2. zueva l.p.yafaev p.kh. epidemiology: manual. “foliant publishers”, russia, 2005. *anna grigoryan e-mail: ffg7@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e45, 2013 public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi public health quality improvement exchange: a tool to support advancements in public health practice stephen l. brown1*, barbara l. massoudi1, jamie m. pina1, kusuma madamala2 1. public health informatics program, rti international, research triangle park, nc, united states 2. public health system consultant, portland, or, united states abstract objectives: the public health quality improvement exchange (phqix) is a free, openly available online community that supports public health practitioners in the rapidly evolving landscape of public health quality improvement (qi). this article’s objective is to describe the user-centered development of phqix and its current content and examine how elements of a qi initiative may vary by an organization’s characteristics or qi experience. methods: phqix was developed by taking a user-centered iterative design approach, seeking early and continued input from users to gather requirements for the website. we performed an exploratory analysis of the published qi initiative descriptions, reviewing all qi projects that phqix users shared as of january 1, 2018. results: phqix features 193 qi initiatives from a variety of health departments and public health institutes using a wide range of qi methods and tools. discussion: submitted qi initiatives focus on many public health domains and favor the pdca/pdsa cycle; kaizen; and fishbone diagrams, flowcharts, process maps, and survey methods. limitations include data coming only from users who represent health departments with sufficient time to complete the phqix submission template. additionally, many initiatives were submitted in part to fulfill a grant requirement, which could skew results. conclusion: as the field of qi in public health practice evolves, resources targeted to qi practitioners should build on and advance the available resources. findings from this study will provide insight into qi initiatives being performed and the types of projects that can be expected as organizational experience and collaboration grow. keywords: informatics, public health, public health accreditation, quality improvement, science of improvement abbreviations: plan, do, check/study, act (pdca/pdsa); public health quality improvement exchange (phqix); quality improvement (qi); expert panel (ep); user group (ug) correspondence: stephenbrown@rti.org* doi: 10.5210/ojphi.v10i3.9566 copyright ©2018 the author(s) mailto:stephenbrown@rti.org* public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi introduction quality improvement (qi) has become an increasingly important activity for health departments as they seek to efficiently improve the health of populations they serve. public health departments are implementing qi initiatives rapidly, given these initiatives’ potential to streamline processes, reduce costs, improve health outcomes for populations, and implement a culture of quality in organizations [1-3]. health departments are also moving toward qi to document capacity and performance standards as part of the process of accrediting their organizations [4]. the public health quality improvement exchange (phqix) is a free, openly available online community that supports public health practitioners in the rapidly evolving landscape of qi in public health [5]. supported by the robert wood johnson foundation, this effort has published 193 in-depth descriptions of real-world qi initiatives on its website since it launched in october 2012. phqix is unique among resources for public health practitioners because of its exclusive focus on qi in public health, qi project documentation, its community features, and its integrated search capacity that allows users to find examples of recent qi work from other health departments around the country [6]. the primary resource on the phqix website is the set of initiative descriptions that present detailed data in a structured format. the descriptions may also include attachments such as diagrams, qi tools and process templates, surveys, policies, and storyboards. users can browse and search these descriptions through free-text search or by using a set of faceted terms based on a public health qi taxonomy developed specifically for the website. for example, users can search for projects in a variety of topical categories such as immunizations, laboratory services, maternal and child health, environmental health, and administrative areas. although the qi initiative descriptions are not intended to represent best practices, they help practitioners learn from previous work performed at other health departments and allow them to adopt or adapt elements of the initiatives for their own purposes. users frequently seek out initiatives that would be practical to implement at their own health departments and are interested in using products of these initiatives (e.g., storyboards, surveys) to increase qi capacity and accreditation readiness at their institutions [7]. the aim of this study is to describe the user-centered development of the tool and its current content and to examine how elements of a qi initiative may vary by an organization’s characteristics or qi experience. the content descriptions include an initiative’s methods (e.g., lean/six sigma, kaizen), tools (e.g., surveys, process maps), and focus activities (e.g., immunizations, data collection, administrative activities). this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi methods phqix was developed by taking a user-centered iterative design approach [8-11], which sought early and continued input from users to gather requirements for the website, test early prototypes, and participate in an evaluation of the site [7]. early in the project, we formed two sets of experts to guide the design and development of the system. those working in qi in public health became members of our user group (ug), and national-level experts in qi became members of the expert panel (ep). the ug and the ep participated in focus groups; from these groups, we developed storyboards to reflect requirements and design elements for the system. the storyboards were vetted through the ep and were used to guide the development of the initial prototypes, which were tested by stakeholders at public health conferences. these stakeholders were selected because they represented the intended users of the system, and they were identified through the ep. feedback about the usability of the prototypes was sought using the think-aloud process while stakeholders interacted with the prototype by working through real-life scenarios. this feedback was then incorporated into the next prototype version, which was then tested with users again. a final iteration of the prototype was produced and launched in october 2012 at the american public health association annual meeting and expo. after the rollout of the first operational system in 2012, limited development occurred over the next 2 years, and in 2014, the system went into operations and maintenance mode. community supporting features that were added during those 2 years included a map of submitters’ locations; qi spotlight articles, featuring aspects of qi work in public health; video highlights of exemplary projects; ask an expert q&a; the community forum threaded discussion list and associated incentives for participation; the monthly newsletter detailing information about the community and the project; and the weekly digest, providing quick snapshots of information about qi happenings. our exploration of the published qi initiative descriptions consisted of a review of all 193 public health qi projects and the organizational characteristics that registered phqix users shared, as of january 1, 2018, approximately 5 years after the website’s initial launch. the users who submitted the qi initiative data were employees of state, local, and tribal health departments who had participated in a public health qi activity. submissions are subject to a review process by the ep, composed of qi subject matter experts. upon receiving new submissions, the site’s submission coordinator confirms that all appropriate data fields are properly completed, then distributes the initiative to an expert panel member for review. the panel member then assesses the relevance of the submission as a true qi initiative and determines whether sufficient documentation is provided. finally, the panel member works directly with the initiative’s submitter to make any necessary clarifications or additions. all accepted submissions are then published on the website and have been included in this study population. the unit of analysis for this study is the qi initiative or project. we conducted an exploratory analysis of the data fields across the qi initiatives, examining what implications an organization’s characteristics have for the type of initiative it will conduct. we sought to determine whether health departments of a particular size, type, or experience with qi would affect the likelihood of them using specific tools or methods or selecting a specific area of services for improvement. the exploration of the initiatives included information about the health department and the population it serves, tools and methods used, project duration, types of partner public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi organizations with which the health department collaborated, and the health department’s level of qi activity. for any qi initiative, it is possible to have used multiple methods, tools, or focus areas. we used the chi-squared test to confirm all statistically significant associations among variables. this paper reports descriptive statistics of these self-assigned attributes and the results of multivariate analyses among qi methods, tools, and health department qi activity level. results the phqix website features 193 qi initiatives from 38 states (figure 1), which included 159 submissions from local health departments, 28 from state health departments, 2 from tribal health departments, and 4 from public health institutes. washington state and north carolina have produced the most published submissions, with 16 initiatives each. oregon (13), wisconsin (13), illinois (12), and michigan (11) all also published more than 10 submissions. the midwest and pacific northwest regions have had more phqix submission activity than other regions, whereas 12 states have not published qi initiatives on phqix. the self-reported organizational qi activity level at the submitting organization varies in an ordinal range including formal qi in specific areas (39.9%), informal qi (21.2%), formal agency-wide qi (20.7%), qi culture (11.9%), and qi community (3.6%) [12]. the organizations serve populations ranging from fewer than 24,499 people (5.2%) to more than 1 million (17.1%). the most common population groups are 100,000 to 249,999 (23.3%), more than 1 million (17.1%), and 250,000 to 499,999 (16.2%). the most common submitting organization type is county health department (45.6%), followed by state health department (14.5%), city-county health department (7.8%), and multi-county health department (6.7%). public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi figure 1: published phqix qi initiatives, by state: 2012–2017 the data show that submissions come from a wide variety of public health departments, with smaller health departments submitting fewer initiatives (26.9%), and health departments serving more than 100,000 people submitting more initiatives (73.1%). health departments with a qi activity level of at least “formal qi in specific areas” submitted the majority (76.2%) of published initiatives, whereas health departments with “informal qi” submitted only 23.8% of the total number. table 1 shows that although the plan, do, check/study, act (pdca/pdsa) cycle is the most commonly used qi method or approach (88.1%), health departments also use kaizen (19.2%), rapid-cycle improvement (17.1%), lean/six sigma (13.5%), and model for improvement (13.0%). the most frequently used qi tools reported in table 2 were brainstorming (67.9%), fishbone diagrams (54.4%), process maps (47.2%), flowcharts (45.6%), surveys (42.5%), root cause analyses (39.4%), and the 5-whys (32.6%). the most commonly reported partner organizations of the leading organization conducting the qi initiatives were local health departments (20.7%), state health departments (16.1%), community-based organizations (9.9%), and universities (6.2%). qi methods and tools do not vary widely by population served, type of health department, or level of qi activity. pdca/pdsa methods are used widely across all organization characteristics, but health departments serving larger populations use the kaizen method more (30.9% for populations greater than 500,000) compared with those serving smaller populations (12.7% for populations fewer than 500,000) (p=.005). health departments for larger populations are also slightly more public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi likely to use lean/six sigma (17.6% for populations greater than 250,000) than those for smaller populations (8.3% for populations fewer than 250,000). the same is true for rapid-cycle improvement (21.2% for populations greater than 250,000 and 14.6% for populations fewer than 250,000), although these observations did not achieve statistical significance. table 1: qi methods/approaches for published qi initiatives (n=193)* qi method/approach number of initiatives percentage plan, do, check/study, act cycle 170 88.1% kaizen 37 19.2% rapid-cycle improvement 33 17.1% lean/six sigma 26 13.5% model for improvement 25 13.0% nominal group technique 5 2.6% business process analysis 4 2.1% adaptive promising practice 1 0.5% standardize, do, check, act cycle 1 0.5% total quality management 1 0.5% *the methods/approaches listed in this table are not mutually exclusive; therefore, the sum of percentages exceeds 100.0%. public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi table 2: qi tools for published qi initiatives (n=193)* qi tool number of initiatives percentage brainstorming 131 67.9% fishbone diagram 105 54.4% process map 91 47.2% flowchart 88 45.6% survey 82 42.5% root cause analysis 76 39.4% 5-whys 63 32.6% cause-and-effect diagram 56 29.0% prioritization matrix 49 25.4% affinity diagram 45 23.3% pareto chart 22 11.4% run chart 21 10.9% multi-voting technique 17 8.8% check sheet 14 7.3% force-field analysis 13 6.7% histogram 8 4.1% strengths-weaknesses-opportunities-threats analysis 8 4.1% control chart 7 3.6% interrelationship digraph 6 3.1% radar chart 6 3.1% control and influence plot 5 3.6% tree diagram 4 2.1% process decision program chart 3 1.6% specific, measurable, achievable, realistic, timely (smart) chart 3 1.6% *the tools listed in this table are not mutually exclusive; therefore, the sum of percentages exceeds 100.0%. health departments submitting to phqix also indicate the types of organizations they partnered with during their qi initiative. although the most common partner organization types are not public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi significantly affected by submitting organization type or organizational qi activity level, state health departments are more likely to have a partner organization for their qi initiative. state health departments partner with local health departments (p=.02), other state health departments (p=.002), and community-based organizations (p=.001) in 36%, 36%, and 39% of initiatives, respectively. meanwhile, local health departments partner with the same organization types at rates of 16%, 12%, and 5%, respectively. table 3 shows the most common focus areas for published initiatives: policies/internal procedures and processes, qi and accreditation readiness, organizational effectiveness, and customer service/satisfaction, all in the administration category. in addition, the data indicate that organizations with a higher organizational qi activity level reported with higher frequency that they perform initiatives focused on policies/internal procedures and processes and qi and accreditation readiness. table 3: most common focus activities for qi initiatives submitted by health departments (n=193) focus activity number of initiatives percentage policies/internal procedures and processes 40 20.7% organizational effectiveness 37 19.2% qi and accreditation readiness 34 17.6% customer service/satisfaction 32 16.6% access to care 25 13.0% data collection and management/information technology 22 11.4% environmental health 22 11.4% communications 20 10.4% women, infants, and children programs 16 8.3% workforce development 16 8.3% communicable/infectious diseases 15 7.8% prenatal care 14 7.3% capacity development 14 7.3% performance management 13 6.7% childhood immunizations: administration of vaccine to population 12 6.2% maternal and child health (data collection, epidemiology, and surveillance) 12 6.2% reportable diseases 10 5.2% public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi collaboration/resource sharing 10 5.2% maternal and child health home visits 9 4.7% food safety education 9 4.7% financial management 8 4.1% tobacco 8 4.1% as shown in figure 2, the number of qi initiatives an organization reports performing per year appears to be associated with that organization’s self-reported organizational qi activity level. figure 2 illustrates that of the organizations reporting informal qi, 83% perform only one to three initiatives per year, whereas only 29% of those reporting a qi community perform the same number of initiatives. additionally, 0% of organizations reporting informal qi perform 7 to 10 initiatives per year, whereas 43% of those reporting a qi culture do so. similarly, the percentage of organizations that perform 11 to 20 initiatives annually rises from 0% to 13% for the same groups. the informal qi organization set is also the only one with health departments that perform no qi initiatives per year (10%). organizations with less formal qi undertook fewer qi initiatives in a given year. the most common duration of qi initiatives submitted to phqix, measured from start to finish, is 6–12 months (52.8%), followed by less than 6 months (21.2%) and 12–18 months (7.8%). figure 2: annual number of qi initiatives, by organizational qi level: 2012–2017 public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi discussion the phqix website and community were designed and developed with engagement of a broad group of public health and qi stakeholders. this ensured that the website would meet the needs of the intended user audience, which is largely composed of local and state public health departments and institutes. in examining those that submitted qi projects to phqix, we found that local health departments are much more likely to submit initiatives than state health departments. this is probably because of the significantly higher proportionate number of existing local health departments and because of increased interaction with phqix from local health departments that received qi grants from the robert wood johnson foundation. a higher percentage of submissions were received from staff at health departments working to formalize qi at their health department, which may be the result of increased efforts to perform and document qi work as they strive for accreditation. conversely, health departments with informal qi perform fewer qi initiatives and may be less inclined to share their work, feeling that it may not meet the standards of qi performed by health departments with more formalized qi. state health departments showed a higher likelihood of collaboration with a partner, which may be caused by many factors, including assisting local health departments, a larger project scope, or more initiative stakeholders. the submitted qi initiatives focus on many different public health domains and favor the pdca/pdsa cycle; kaizen; brainstorming; and using fishbone diagrams, flowcharts, process maps, and survey methods. supporting similar national findings of local and state health departments, the pdca/pdsa cycle remains the predominant choice for qi method [13,14]. (insert citations of astho and naccho profile data) more recent submissions indicate growing use of the kaizen method (particularly for organizations that serve larger populations), and future research should monitor this trend. health departments serving larger populations may be more likely to use such methods as kaizen and lean/six sigma, perhaps because they have funds budgeted for trainings or seek grants featuring such methods. although no significant relationships are evident among health departments’ type, capacity, or the methods and tools employed, an organization’s reported qi activity level may have an association with the number of qi initiatives it performs annually and the duration and focus of those initiatives. health departments seeking to formalize qi will continue to increase the number of initiatives performed annually. . conclusion as the field of qi in public health practice evolves, resources targeted to qi practitioners should build on and advance the available resources. the increasing number of health departments across the country seeking accreditation will continue to fuel interest in qi information and trainings. as more health departments are likely to increase the formalization of qi at their organizations, future research should continue to monitor the trends of initiatives from organizations with a growing qi culture. understanding the trajectory of the field of qi in public health is important for practitioners and researchers alike. findings from this study will provide insight into qi initiatives being performed and the types of projects that can be expected as organizational experience and collaboration grow. as previous studies used qi initiative data to establish a framework to define and assess the impact of qi [15] and to determine which characteristics of qi projects affect whether a given project will achieve its stated goals [16], collecting measures of efficiency or effectiveness could help to expand the usefulness of the database. public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi phqix is now being transitioned to the public health accreditation board [17], which will become the host of the resource. limitations this study had several limitations: the first is that the data come only from users who represent health departments that could dedicate the time to complete the phqix submission template, along with making any necessary revisions. as a result, the phqix database represents a snapshot of qi activities in public health, and it is unknown whether that snapshot is representative of the universe of public health qi projects. many of the initiatives described were submitted in part to fulfill a grant requirement, which could skew the data if only specific types of health departments were eligible for this grant funding or if such grants focused on implementing a specific qi method or tool. additionally, although a panel of qi experts review the submissions, the accuracy of some of the collected data fields is reliant on the submitter’s understanding of the various options (e.g., health department’s organizational qi activity level) and are therefore subject to errors of selfreport. finally, although the database of qi initiatives featured on phqix is substantial, it may not be large enough to infer statistical significance in all observed trends and associations where true differences exist. acknowledgements we would like to acknowledge the support of the phqix user group, expert panel, and all website users for their contributions to phqix since its inception in 2012. financial disclosure this work was supported by a contract from the robert wood johnson foundation to rti international (#716472). competing interests no competing interests references 1. riley wj, moran jw, corso lc, beitsch lm, bailek r, et al. 2010. defining quality improvement in public health. j public health manag pract. 16(1), 5-7. https://doi.org/10.1097/phh.0b013e3181bedb49 pubmed 2. livingood wc, sabbagh r, spitzfaden s, hicks a, wells l, et al. 2013. a quality improvement evaluation case study: impact on public health outcomes and agency culture. am j prev med. 44(5), 445-52. https://doi.org/10.1016/j.amepre.2013.01.011 pubmed 3. livingood wc, peden ah, shah gh, marshall na, gonzalez km, et al. 2015. comparison of practice based research network based quality improvement technical assistance and https://doi.org/10.1097/phh.0b013e3181bedb49 https://doi.org/10.1097/phh.0b013e3181bedb49 https://doi.org/10.1016/j.amepre.2013.01.011 https://doi.org/10.1016/j.amepre.2013.01.011 public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi evaluation to other ongoing quality improvement efforts for changes in agency culture. bmc health serv res. 15, 300. https://doi.org/10.1186/s12913-015-0956-3 pubmed 4. riley wj, bender k, lownik e. 2012. public health department accreditation implementation: transforming public health department performance. am j public health. 102(2), 237-42. https://doi.org/10.2105/ajph.2011.300375 pubmed 5. the public health quality improvement practice exchange. 2018 [cited 2018 nov 16]; available at: http://www.phqix.org/ 6. pina j, massoudi bl, chester k, koyanagi m. 2018. synonym-based word frequency analysis to support the development and presentation of a public health quality improvement taxonomy. j public health mgmt pract. epub ahead of print. doi: 10.1097/phh.0000000000000805. 7. porterfield ds, marcial lh, brown s, throop c, pina j. 2017. evaluation of a quality improvement resource for public health practitioners: the public health quality improvement practice exchange. pub hlth reports. 132(2), 140-48. https://doi.org/10.1177/0033354916689609 8. bernard hr. handbook of methods in cultural anthropology. lanham, md: altamira press; 1998. 9. muller mj. participatory design: the third space in hci. in: jacko ja, sears a, editors. the human-computer interaction handbook: fundamentals, evolving technologies and emerging applications. mahwah, nj: lawrence erlbaum associates; 2003. 10. pilemalm s, timpka t. 2008. third generation participatory design in health informatics— making user participation applicable to large-scale information system projects. j biomed inform. 41(2), 327-39. https://doi.org/10.1016/j.jbi.2007.09.004 pubmed 11. stanton na, salmon pm, rafferty la, walker gh, baber c, et al. human factors methods: a practical guide for engineering and design. 2nd ed. burlington, vt: ashgate publishing company; 2013. 12. the phases of a culture of quality. 2018 [cited 2018 nov 16]; available at: http://qiroadmap.org/the-phases-of-a-culture-of-quality/ 13. association of state and territorial health officials. astho profile of state and territorial public health: volume 4. 2017 [cited 2018 nov 20]; available at: http://www.astho.org/profile/volume-four/2016-astho-profile-of-state-and-territorialpublic-health/ 14. national association of county & city health officials. 2016 national profile of local health departments. 2017 [cited 2018 nov 20]; available at: http://nacchoprofilestudy.org/wpcontent/uploads/2017/10/profilereport_aug2017_final.pdf https://doi.org/10.1186/s12913-015-0956-3 https://doi.org/10.1186/s12913-015-0956-3 https://doi.org/10.2105/ajph.2011.300375 https://doi.org/10.2105/ajph.2011.300375 https://doi.org/10.1177/0033354916689609 https://doi.org/10.1016/j.jbi.2007.09.004 https://doi.org/10.1016/j.jbi.2007.09.004 public health quality improvement exchange: a tool to support advancements in public health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e223, 2018 ojphi 15. mclees aw, nawaz s, thomas c, young a. 2015. defining and assessing quality improvement outcomes: a framework for public health. am j public health. 105(suppl 2), s167-73. https://doi.org/10.2105/ajph.2014.302533 pubmed 16. beitsch lm, carretta h, mckeever j, pattnaik a, gillen s. 2013. the quantitative story behind the quality improvement storyboards: a synthesis of quality improvement projects conducted by the multi-state learning collaborative. j public health manag pract. 19(4), 330-40. https://doi.org/10.1097/phh.0b013e3182629054 pubmed 17. the public health accreditation board (phab). 2018 [cited 2018 nov 16]; available at: http://www.phaboard.org/ https://doi.org/10.2105/ajph.2014.302533 https://doi.org/10.2105/ajph.2014.302533 https://doi.org/10.1097/phh.0b013e3182629054 https://doi.org/10.1097/phh.0b013e3182629054 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts surveillance of heat-related morbidity: relation to heatrelated excess mortality robert mathes*1, kristina b. metzger2, kazuhiko ito1 and thomas matte1 1new york city department of health and mental hygiene, queens, ny, usa; 2city of austin/travis county health and human services department, austin, tx, usa objective to describe the extent to which heat-illness indicators increase with extreme heat and to evaluate the association among daily weather, heat-related illness and natural cause mortality. introduction the impact of heat on mortality is well documented [1-3] but deaths tend to lag extreme heat and mortality data is generally not available for timely surveillance during heat waves. recently, systems for near-real time surveillance of heat illness have been reported [4] but have not been validated as predictors of heat related mortality. in this study, we examined the associations among weather, indicators of heat-related ambulance calls and emergency department visits and excess natural cause mortality in new york city (nyc). methods we analyzed daily weather conditions, emergency medical system (ems) calls flagged as heat-related by ems dispatchers, emergency department (ed) visits classified as heat-related based on chief complaint text, and natural cause deaths. ems and ed data were obtained from data reported daily to the city health department for syndromic surveillance. we fit generalized linear models to assess the relationships of daily counts of heat related ems and ed visits to natural cause deaths after adjustment for weather conditions during the months of may-september between 1999 and 2008. results we observed an increase in mean total calls to ems and a decrease in mean total visits to eds during 10 observed heat waves (maximum heat index ! 90° f (fahrenheit) for four or more consecutive days with the first three days ! 95° f and at least one day !100°f) in nyc between 1999 and 2008. both ems and eds experienced an increase in heat-related incidents during heat waves though the increase in heat-related ems calls was much greater. a modest increase in mean natural cause deaths was also observed. controlling for temporal trends, an 11% (95% confidence interval (ci): 5-18) and 7% (95% ci: 4-9) increase in natural cause mortality was associated with an increase from the 50th percentile to 99th percentile of same-day and one-day lagged heat-related ems calls and ed visits, respectively. after controlling for both temporal trends and weather, we observed a 10% (95% ci: 4-16) increase in natural cause mortality associated with one-day lagged heat-related ems calls and a 5% mortality increase with one-day lagged ed visits (95% ci: 2-8). conclusions heat-related ems calls and ed visits lagged one day predicted natural cause mortality in our temporal and weather-adjusted model. in particular, risk of mortality rapidly increased as the number of heatrelated ems calls approached high levels (>100 heat-related calls/day). heat-related illness can be tracked during heat waves using ems and ed data which are indicators of heat associated excess natural cause mortality during the warm weather season. keywords surveillance; heat; morbidity; mortality acknowledgments this research was funded by the environmental protection agency, star grant r833623010, and in conjunction with the alfred p. sloan foundation, grant 2010-12-14. we thank the members of the new york city department of health and mental hygiene syndromic surveillance unit. references 1. curriero, f.c., et al., temperature and mortality in 11 cities of the eastern united states. am j epidemiol, 2002. 155(1): p. 80-7. 2. dolney, t.j. and s.c. sheridan, the relationship between extreme heat and ambulance response calls for the city of toronto, ontario, canada. environ res, 2006. 101(1): p. 94-103. 3. knowlton, k., et al., the 2006 california heat wave: impacts on hospitalizations and emergency department visits. environ health perspect, 2009. 117(1): p. 61-7. 4. chau, p.h., k.c. chan, and j. woo, hot weather warning might help to reduce elderly mortality in hong kong. int j biometeorol, 2009. 53(5): p. 461-8. *robert mathes e-mail: rmathes@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e156, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts hiv surveillance in india: an overview & implications for future divya persai* public health fiooundation of india, delhi, india objective to study and analyze the surveillance activities in hiv prevention and control in india. introduction surveillance of risky behaviors of hiv infection and its manifest diseases has provided a better understanding of the complex nature of the hiv epidemic in india. however, little attempt is made to analyze progress of these surveillance activities. methods a review & analysis of surveillance activities undertaken in india were done. pub-med, cohrane library and peer-reviewed journals were referred for relevant literature. results initially, medical officers from multiple types of government hospitals in india were expected to report aids cases, including deaths. however, this reporting mechanism was inadequate, complicated by many disparate types of reporting units with incomplete and delayed reports. therefore aids case reporting has been replaced by hiv case reporting from the 4532 integrated counseling and testing centers. newer surveillance strategies like behavior sentinel surveillance measure behaviors that affect risk for acquiring hiv. however, behavioral and biological data are resource-intensive and time-consuming. facility-based sero-surveillance (also called hiv sentinel surveillance or hss) has emerged as the key surveillance strategy for hiv/aids in india. starting with 55 urban sentinel sites hiv sentinel surveillance expanded to 1215 in 1994. most of these pre-selected sites were antenatal clinics but also included sexually transmitted infection clinics and special facilities. subsequent expansion of high-risk group sites has improved the representation of all sub-populations in hss. while stigma against most high-risk populations and hiv-positive people continues, it has lessened as shown by the behavioral surveys. also, accessibility to testing sites has increased with increased availability of care and treatment options for infected individuals. conclusions while acknowledging the vastness and diversity of india, the key limitations remain suboptimal coverage and lack of representativeness surveillance data. moreover, due to selection bias, the populations selected for hss at targeted intervention sites may not represent everyone in that community. there is lack of national information system to collect hiv testing information from the private sector. further efforts are needed to improve hiv surveillance data and usage of this data to predict the epidemic. keywords surveillance; hiv; india references 1. department of aids control, ministry of health and family welfare, national aids control organization (naco) annual report, 20092010. www.nacoonline.org/ 2. national family health survey, india: http://www.nfhsindia.org/ 3. who case definitions of hiv for surveillance and revised clinical staging and immunological classification of hiv-related disease in adults and children; isbn: 978 92 4 159562 9. august 2006. 4. family health international (fhi) website: fhi-conducted bss and ibbss reports 1989-2009. www.fhi.org/en/hivaids/pub/survreports/index.htm 5. bachani d, sogarwal r, rao ks. a population bases survey on hiv prevalence in nagaland, india. saarc j tuber lung dis hiv/aids 2009 (1)1-11. 6. the world health organization’s global strategy for prevention and assessment of hiv drug resistance; diane e bennett, s bertagnolio, d sutherland, c f gilks: antiviral therapy 13 suppl 2:1-13; 2008 international medical press:1359-6535. 7. technical consultation to review hiv surveillance in india, 23-25 april 2008, new delhi, india. who/sear and naco. sea/aids/182. available at www.searo.int/hiv-aids publications. 8. hiv/aids epidemic in india: risk factors, risk behavior & strategies for prevention & control; godbole s, mehendale s: indian j med res 121, april 2005(356-368). 9. stigma in the hiv/aids epidemic: a review of the literature and recommendations for the way forward; mahajan a p, sayles jn, patel va, remien rh, ortiz d, szekeres g, coates tj. aids 2008 august; 22(suppl 2): s67-s79. 10. epidemiological analysis of the quality of hiv sero-surveillance in the world: how well do we track the epidemic?: walker n, garciacalleja jm, asamoah-odei e, poumerol g, lazzari s, ghys pd, scwartlander b, stanecki ka. aids 2001 aug 17; 15(12):1545-54. 11. advances and future directions in hiv surveillance in lowand middle-income countries. diaz t, garcia-calleja jm, ghys pd, sabin k. curr opin hiv aids. 2009 jul;4 (4):253-9. *divya persai e-mail: dpersai@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e173, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts enhanced disease surveillance during the 2012 republican national convention, tampa, fl david atrubin*, michael wiese, rebecca snider, kiley workman and warren mcdougle hillsborough county health department, tampa, fl, usa objective to describe disease and illness surveillance utilized during the 2012 republican national convention (rnc) held august 26-30, 2012 in tampa, fl. introduction while the tampa bay area has previously hosted other high profile events that required heightened disease surveillance (e.g., two super bowls), the 2012 rnc marked the first national special security event (nsse) held in florida. the hillsborough county health department (hchd), in conjunction with the pinellas county health department (pinchd) coordinated disease surveillance activities during this time frame. this presentation will focus of the disease surveillance efforts of the hillsborough county health department during the 2012 rnc. in addition to the surveillance systems that are used routinely, the hchd epidemiology program implemented additional systems designed to rapidly detect individual cases and outbreaks of public health importance. the short duration of rnc, coupled with the large number of visitors to our area, provided additional surveillance challenges. tropical storm isaac, which threatened tampa in the days leading up to rnc, and an overwhelming law enforcement presence likely dissuaded many protestors from coming to tampa. as a result, a tiny fraction of the number of protestors that were expected actually showed up. methods our normal daily analysis of the emergency department (ed) data using the electronic surveillance system for the early notification of community-based epidemics (essence) was expanded to look in detail at ed volumes and chief complaints of those patients who live outside of a 5-county tampa bay area. this analysis used patient zip code to determine place of residence. additionally, essence queries were utilized to look for heat, tear gas, and rnc related exposures. the essence system also receives poison control data every 15 minutes. expanded analyses of the poison control data were conducted as well. two disaster medical assistance teams (dmats) were deployed in tampa during the rnc. data was collected electronically and transmitted through essence as well. the hchd also asked infection preventionists, health care providers, hotels, labs, and mosquito control to lower their reporting threshold to us during the rnc period. we provided updates to all our partners with respect to diseases and outbreaks of public health importance occurring in our county. results no epidemiologic events linked to the rnc were detected through the hchd’s enhanced surveillance that was conducted. decreased patient volumes were seen during the rnc at our eds closest to the convention site. no significant increases in ed visits from outside of our 5-county area were noted during the rnc. urgent care centers reported seeing patients associated with the rnc for a variety of reasons including respiratory and gastrointestinal illness. dmat surveillance showed mainly routine visits but four secret service agents did seek care for respiratory illness during the convention. conclusions substantial time and resources were devoted to disease surveillance in the 6 months leading up to the rnc and during the event. while no epidemiologic events were detected, the public health surveillance infrastructure has clearly been strengthened in our county. we are receiving our ed syndromic data, from many of our hospitals, every two hours as opposed to every day. we have established relationships with our urgent case centers and hope to begin receiving urgent care center data on a daily basis in the near future. receiving dmat data through essence could prove very useful in the future, especially in florida where hurricanes are always a threat. lastly the improved relationships with our health care providers should be beneficial as we move forward. keywords mass gathering; national special security event; convention acknowledgments janet hamilton, aaron kite-powell, and aaron chern, florida department of health, bureau of epidemiology cynthia lewis-younger and joann chambers-emerson, florida poison information center –tampa dina passman, health and human services references hick j, frascone r, grimm k, hillman m, griffith j, hogan m, trotskysirr r, branu. health and medical preparedness and response to the 2008 republican national convention. disaster medicine and public health preparedness 3(4);224-232. kade k, brinsfield k, serino r, savoia e, koh h. emergency medical consequence planning and management for national special security events after september 11: boston 2004., disaster medicine and public health preparedness 2(3);166-173. *david atrubin e-mail: david_atrubin@doh.state.fl.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e68, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts framing the use of social media tools in public health jennifer stoll*, richard quartarone and miguel torres-urquidy centers for disease control and prevention, atlanta, ga, usa objective recent scholarship has focused on using social media (e.g., twitter, facebook) as a secondary data stream for disease event detection. however, reported implementations such as (4) underscore where the real value may lie in using social media for surveillance. we provide a framework to illuminate uses of social media beyond passive observation, and towards improving active responses to public health threats. introduction user-generated content enabled by social media tools provide a stream of data that augment surveillance data. current use of social media data focuses on identification of disease events. however, once identification occurs, the leveraging of social media in monitoring disease events remains unclear (2, 3). to clarify this, we constructed a framework mapped to the surveillance cycle, to understand how social media can improve public health actions. methods this framework builds on extant literature on surveillance and social media found in pubmed, science direct, and web of science, using keywords: “public health”, “surveillance”, “outbreak”, and “social media”. we excluded articles on online tools that were not interactive e.g., aggregated web-search results. of 2,064 articles, 23 articles were specifically on the use of social media in surveillance work. our review yielded five categories of social media use within the surveillance cycle (table 1). this framing within surveillance illuminates a range of roles for social media tools beyond disease event detection. [insert image #1 here] finally, we used the 1918 influenza pandemic to illustrate an application of this framework (fig 1), if it were part of the public health toolkit. in 1918, america was already becoming a “mass media” society. yet a key difference in mass communications today is the enabling of public health to be more adaptive through the interactivity of social media. results we used this “pre-social media” disease event to underscore where the real value of social media may lie in the surveillance cycle. thus for 1918, early detection of disease could have occurred with many, e.g., sailors aboard ships in new york city’s port sharing their “status updates” with the world. [insert image #2 here] after detection, social media use could have shifted to help connect and inform. in 1918, this could include identifying and advising the infected on current hygiene practices and how to protect themselves. social media would have enabled the rapid sharing of this information to friends and family, allowing public health officials to monitor the response. then, to support multiple intervention efforts, public health officials could have rapidly messaged on local school closures; they could also have encouraged peer behavior by posting via twitter or by “pinning” handkerchiefs on pinterest to encourage respiratory etiquette, and then monitored responses to these interventions, adjusting messaging accordingly. conclusions the interactivity of social media moves us beyond using these tools solely as uni-directional, mass-broadcast channels. beyond messaging about disease events, these tools can simultaneously help inform, connect, and intervene because of the user-generated feedback. these tools enable richer use beyond a noisy data stream for detection. table 1. social media use in supporting information for action fig. 1. social media mapping to 1918 epi curves for ny state (1). keywords surveillance; public health; social media references 1. goldstein e. et al. reconstructing influenza incidence by deconvolution of daily mortality time series. natl acad sci u s a. 2009 dec 22;106(51):21825-9 2. heaivilin n. et al. public health surveillance of dental pain via twitter. j dent res. 2011 sep;90(9):1047-51 3. luck, j. et al. using local health information to promote public health. health affairs 2006;25(4): 979-991 4. napolitano ma. et al. using facebook and text messaging to deliver a weight loss program to college students. obesity 2012 *jennifer stoll e-mail: jstoll@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e67, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts moh+: a global, integrated, and automated view of official outbreak reporting chi bahk*1,2, david scales1, sumiko mekaru1,3, john s. brownstein1,4,5 and clark freifeld1,6 1children’s hospital informatics program, division of emergency medicine, children’s hospital boston, boston, ma, usa; 2dept of global health and population, harvard school of public health, boston, ma, usa; 3dept of epidemiology, boston university school of public health, boston, ma, usa; 4dept of pediatrics, harvard medical school, boston, ma, usa; 5dept of epidemiology, biostatistics and occupational health, mcgill university, montreal, qc, canada; 6dept of biomedical engineering, boston university, boston, ma, usa objective to introduce moh+, healthmap’s (hm) real-time feed of official government sources, and demonstrate its utility in comparing the timeliness of outbreak reporting between official and unofficial sources. introduction previous studies have documented significant lags in official reporting of outbreaks compared to unofficial reporting (1,2). moh+ provides an additional tool to analyze this issue, with the unique advantage of actively gathering a wide range of streamlined official communication, including formal publications, online press releases, and social media updates. methods outbreaks reported by official sources were identified through moh+ (healthmap.org/mohplus), which collects surveillance data published globally by ministries of health (moh), other related ministries, government portals, government-affiliated organizations, and international governing bodies (fig. 1). reporting of these outbreaks was also identified in unofficial sources using various hm feeds including google news, promed, and participatory surveillance feeds. of the 109 outbreaks identified since may 2012, 65 were excluded as they started before data collection, 7 were excluded as they were not reported by unofficial sources, and 1 was excluded as it was a non-natural outbreak. for the remaining 36 outbreaks, the median difference in first date of report between official and unofficial sources was analyzed using a wilcoxon sign rank test. results outbreak reporting in official sources lagged by a statistically significant median of 2 days (p=0.003). among unofficial sources, online news most often (75%) was the fastest to report an outbreak, followed by promed (22%) and participatory surveillance (3%). among official sources, national government affiliated institutes were most often (41%) the fastest, and repeatedly providing prompt outbreak reports were the us centers for disease control and prevention (cdc), public health agency of canada, finnish food safety authority, health protection scotland, uk health protection agency, and french institute of public health surveillance (fiphs). following such institutes were the european cdc (ecdc) with 22% of first reports of outbreaks; moh’s (17%); and who (10%). there were 4 instances in which official sources reported before unofficial sources—3 by the ecdc and 1 by fiphs. conclusions compared to the chan study reporting a 16 day lag between first public communication and who outbreak news (1) and the mondor study reporting a 10 day lag between non-government and government sources (2), the present study shows a much condensed lag of 2 days between unofficial and official sources. because the two earlier studies cover a much broader historical time frame, one explanation for the reduced lag time is increased adoption of online communication by official government agencies. however, despite such improvements in communication, the lag persists, pointing to the importance of using informal sources for outbreak surveillance. the present study was limited by small sample size, as the study is in its early stages. we will continue to gather data and all numbers will be updated in time for the presentation to reflect the larger database. future directions of this study include characterization of official and unofficial reporting by region, language, disease, and source. fig. 1. interactive visualization of healthmap moh+, at healthmap.org/mohplus keywords disease surveillance; outbreak reporting; timeliness; moh; official sources references 1. chan et al. global capacity for emerging infectious disease detection. pnas 2010. 2. mondor et al. timeliness of nongovernmental versus governmental global outbreak communications. eid 2012. *chi bahk e-mail: cbahk@hsph.harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e62, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts syndromic surveillance at the new york state veterinary diagnostic laboratory kylius wilkins*1, bruce akey2, 3, belinda thompson2, 3 and daryl nydam3 1veterinary services, usda aphis, ames, ia, usa; 2nysvdl, ithaca, ny, usa; 3cornell university, ithaca, ny, usa objective to assess the use and utility of a syndrome check list on the general submission form of a high volume veterinary diagnostic laboratory, and compare to the results of a 2009 pilot study. introduction the new york state veterinary diagnostic laboratory (nysvdl) receives more than 100,000 diagnostic submissions a year that are not currently used in any formal syndromic surveillance system. in 2009, a pilot study of syndrome classification schemes was undertaken and in 2011 a new general submission form was adopted, which includes a check list of syndromes, as part of the clinical history. monitoring submissions to a veterinary diagnostic laboratory for increases in certain test requests is an established method of syndromic surveillance (1, 2). the new general submission form allows for clinician selected syndromes to be monitored in addition to test request. methods we selected 420 “contract cases” from all submissions for bovines since the implementation of the new form, may 2011, though february 2012. submissions were reviewed for use of the new form, use of the syndrome check list and tests requested. test requests were assigned syndromes, if possible, to allow for comparison with the clinician selected syndromes. the selection of cases was weighted towards the end of the period as use of the new form increased with time and to a lesser extent toward the beginning of the period in an attempt to find any early use of the form. “contract case” refers to new york state department of agriculture and markets subsidization of agricultural animal and herd health testing done under specific guidelines. the benefit of “contract cases” is the guidelines require a complete clinical history, which should include selection of syndromes. finally, selection was limited bovine submissions as was done in the pilot study. results 16% (69/420) of submissions used the new form and of these 23 selected syndromes. as was seen in the pilot study the most commonly occurring syndrome in the clinical history was “gastrointestinal/diarrhea” 56% (13/23). the next most common syndromes were “respiratory” (7/23), “sudden death” (6/23) and “fever” (4/23). syndromes assigned based on test request followed a similar pattern with “gastrointestinal/diarrhea” (166/254) and “respiratory” (52/254) best represented. an important difference was the syndromes “sudden death” and “fever”, which were never assigned to a test request. these syndromes represent a new source of information for surveillance. these results fit well with the pilot study which found the clinical history was typically incomplete but contained additional information for syndromic surveillance that was not available from monitoring the test request alone and that monitoring syndromes or test requests alone would provide incomplete information nearly a third of the time. conclusions we found monitoring syndromes, in addition to test requests, to be useful and necessary for completeness. monitoring clinical history provided additional information not available from test requests alone. we recommend the syndromes “sudden death” and “fever” be monitored as these syndromes always provided additional information not available in test requests. other syndromes that provide new information should be investigated across species and in various clinical scenarios. accumulating baseline data for all syndromes is recommended to create more accurate models for syndromic surveillance and improve data retrieval for retrospective studies. despite poor use of the new general submission form and the syndrome check boxes, future compliance is likely to improve significantly with the implementation of online submission and thanks to the continuous training and consultation provided by the nysvdl staff. keywords syndromic surveillance; veterinary diagnostic laboratory; veterinary surveillance acknowledgments we would like to thank dr. robert gilmour and the cornell university veterinary investigator program for supporting this research. references 1. shaffer, l., funk, j., rajala-schultz, p., wallstrom, g., wittum, t., wagner, m., saville, w. (2007) early outbreak detection using an automated data feed of test orders from a veterinary diagnostic laboratory. in: d. zeng et al. (ed), biosurveillance 2007, pp. 1–10 2. glickman, l.t., moore, g.e., glickman, n.e., caldanaro, r.j., aucoin, d., lewis, h.b. (2006) purdue university–banfield national companion animal surveillance program for emerging and zoonotic diseases vector-borne and zoonotic diseases. 6(1), 14-23. 3. dorea, f.c., sanchez, j., crawford, w.r. (2011) veterinary syndromic surveillance: current initiatives and potential for development. preventive veterinary medicine 101, 1-17 *kylius wilkins e-mail: kmw97@cornell.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e104, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluating biosurveillance system components using multi-criteria decision analysis eric nicholas generous*1, alina deshpande1, mac brown1, lauren castro1, kristen margevicius1, william brent daniel1 and kirsten taylor-mccabe2 1defense systems analysis division, los alamos national laboratory, los alamos, nm, usa; 2bioscience division, los alamos national laboratory, los alamos, nm, usa objective the use of multi-criteria decision analysis (mcda) has traditionally been limited to the field of operations research, however many of the tools and methods developed for mcda can also be applied to biosurveillance. our project demonstrates the utility of mcda for this purpose by applying it to the evaluation of data streams for use in an integrated, global biosurveillance system. introduction the evaluation of biosurveillance system components is a complex, multi-objective decision that requires consideration of a variety of factors. multi-criteria decision analysis provides a methodology to assist in the objective analysis of these types of evaluation by creating a mathematical model that can simulate decisions. this model can utilize many types of data, both quantitative and qualitative, that can accurately describe components. the decision-maker can use this model to determine which of the system components best accomplish the goals being evaluated. before mcda can be utilized effectively, an evaluation framework needs to be developed. we built a robust framework that identified unique metrics, surveillance goals, and priorities for metrics. using this framework, we were able to use mcda to assist in the evaluation of data streams and to determine which types would be of most use within a global biosurveillance system. methods mcda was implemented using the logical decisions® software. the construction of the evaluation framework was carried out in several steps: identification and definition of data streams, metrics and surveillance goals, and the determination of the relative importance of each metric to the respective surveillance goal being evaluated. sixteen data streams types were defined and identified for evaluation from a survey we conducted that collected over 200 surveillance products. a subject matter expert (sme) panel was assembled to help identify the biosurveillance goals and metrics in which to evaluate the data streams. to assign values for the metrics, we referenced properties of data streams used in currently operational systems. results our survey identified sixteen different classes of data streams: ambulance records, clinic/ health care provider records, ed/ hospital records, employment/school records, established databases, financial records, help lines, internet search queries, laboraotry records, news aggregators, official reports, police/fire department records, personal communication, prediction markets, sales, and social media. four biosurveillance goals were identified: early warning of health threats, early detection of health events, situational awareness, and consequence management. eleven metrics were identified: accessibility, cost, credibility, flexibility, integrability, geographic/population coverage, granularity, specificity of detection, sustainability, time to indication, and timeliness. using the framework, it was possible to use mcda to rank the utility of each data stream for each goal. conclusions the results suggest that a “one size fits all” approach does not work and that there is no ideal data stream that is most useful for each goal. data streams that scored more highly for speed tended to rank more highly when the biosurveillance goal is early warning or early detection, whereas data streams that scored more highly for data credibility and geographic/population coverage ranked highly when the goal was situational awareness or consequence management. however, there are several data streams that rank consistently within the top 5 for each goal: internet search queries, news aggregators, clinic/ health care provider records, ed/ hospital records, and laboratory records and may be considered useful for integrated, global biosurveillance for infectious disease. keywords evaluation; biosurveillance; multi-criteria decision analysis; data stream; evaluation framework acknowledgments this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra) *eric nicholas generous e-mail: generous@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e157, 2013 development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) wongyu lewis kim 1,2 , chelsea anne ducharme 2 , bernard jean-marie philippe bucher 3,4 1 university of illinois at chicago, 2 school of public health and health service, the george washington university, 3 centre international pour la coopération médicale et sanitaire dans la caribbean et les amériques, 4 university college london abstract dengue fever, including dengue hemorrhagic fever, has become a re-emerging public health threat in the caribbean in the absence of a comprehensive regional surveillance system. in this deficiency, a project entitled aricaba, strives to implement a pilot surveillance system across three islands: martinique, st. lucia, and dominica. the aim of this project is to establish a network for epidemiological surveillance of infectious diseases, utilizing information and communication technology. this paper describes the system design and development strategies of a “network of networks” surveillance system for infectious diseases in the caribbean. also described are benefits, challenges, and limitations of this approach across the three island nations identified through direct observation, open-ended interviews, and email communications with an on-site it consultant, key informants, and the project director. identified core systems design of the aricaba data warehouse include a disease monitoring system and a syndromic surveillance system. three components comprise the development strategy: the data warehouse server, the geographical information system, and forecasting algorithms; these are recognized technical priorities of the surveillance system. a main benefit of the aricaba surveillance system is improving responsiveness and representativeness of existing health systems through automated data collection, process, and transmission of information from various sources. challenges include overcoming technology gaps between countries; real-time data collection points; multiple language support; and “componentoriented” development approaches. keywords: outbreak, surveillance, syndromic surveillance, forecasting, emerging infectious diseases, caribbean, development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 introduction in 1999, the association of caribbean states (acs) recognized emerging infectious diseases as a major challenge after a large dengue fever epidemic in cuba and made a call for proposals for a comprehensive regional surveillance system. in 2005, a preliminary study entitled vigilia, was conducted from martinique to test strategies, models, and hypotheses for integrated surveillance in the caribbean. the pilot system demonstrated early warning capacity based on inference from collected data by correctly predicting an upcoming outbreak. martinique’s regional council and the centre international pour la coopération médicale et sanitaire dans la caribbean et les amériques (cicomsca) initiated a project entitled aricaba, meaning “to look” in the carib language. aricaba incorporates lessons learned from vigilia to implement a surveillance system across three islands: martinique, st. lucia, and dominica. this project has been funded by the european union with an approximately €3 million total budget (6). about aricaba aricaba, with cooperation of the regional council of martinique, st. lucia, and dominica governments, envisions a comprehensive emerging infectious disease surveillance system to detect and forecast threats and ultimately protect the citizens and visitors to the three participating islands. the mission of aricaba is to heighten protection of caribbean residents and visitors by closing the gap in existing infectious disease surveillance systems, especially emerging threats such as dengue fever and influenza. the combination of global and local partnerships; systems strengthening and integration; and the development of mathematical modeling will permit improved detection and forecasting of selected diseases. to accomplish this mission, the aim is to establish a network for epidemiological surveillance of infectious diseases, based on information collected through various sources of data. country background information it is essential to contextualize the countries in which the system will function prior to describing surveillance system design. understanding martinique, dominica and st. lucia is imperative to designing a surveillance system suitable for their social, economic, and political environments. table 1 illustrates background country information including health indicators and health systems (6). martinique martinique, colonized by france in 1635, remains one of several french overseas departments in the caribbean (4). it measures a total of 1,060 square km; the island is home to a mountainous terrain, tropical climate, and a five-month rainy season (4). the population is approximately 403,688 and inhabitants speak mainly creole and french (4). according to recent data, the gdp (ppp) was $4.5 billion. martinique is relatively more “westernized” and developed as compared to the other pilot countries (5). dominica dominica, located directly north of martinique, has a population of 72, 969 persons (4). presently an independent country, this 751 square km island is known as the “nature island” due to its lush, varied flora and rugged volcanic terrain. english is the official language though french patois, a form of creole, is widely spoken (4). dominica has the lowest income among the three pilot countries. politically, the dominican ministry of health is very well respected , including their development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 epidemiology unit. their health information technology unit is housed under the epidemiology unit which eases coordination among the two teams. st. lucia though possession transferred between britain and france fourteen times, st. lucia was granted independence in 1979 by england (4). bordering martinique to the south, approximately 161, 557 persons inhabit this tropical 606 square km island (4). english is the official language of this island (4). as an independent country, there exists a ministry of health, an epidemiology unit and a health information unit. st. lucia is currently developing its own surveillance system: sluhis. table 1. country information dominica martinique st. lucia basic information size 751 square km 1,060 square km 616 square km population 72,660 436,131 173,765 political status parliamentary republic, independent since 1978 overseas region of france, part of the european union parliamentary democracy, independent since 1979 gdp (purchasing power parity) $380 million $4.5 billion $1.75 billion health indicators life expectancy male: 72 years female: 76 years male: 79 years female: 78 years male: 72 years female: 78 years fertility rate 2.08 children born/woman 1.79 children born/woman 1.82 children born/woman probability of dying under five (per 1,000 live births) 15 7.44 14 % years lost due to communicable diseases 20% (not available) 17% existing health surveillance health information unit, carec, national public health surveillance and response team, national health information system in development health unit in general council of martinique, regional council of martinique (health planning) epidemiology unit, carec, national health information system in development development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 research methods aricaba project data was mainly collected through three three-month fellowships through the global health service fellow scholarship program at the center for global health at the george washington university from september 2010 to august 2011. data collection occurred in martinique, st. lucia and dominica, where the aricaba pilot project functions. data collection techniques include meetings, direct observation, open ended interviews and email communications with an in-site it consultant, key informants and project director, and direct observation; this data was shared openly among all fellows. information was also obtained through review of aricaba technical documents, literature reviews and web searches. key search words included surveillance system, syndromic, and outbreak detection. web search engines utilized include pubmed, md consult, science, and google. before surveillance system design, target infectious diseases were identified; dengue fever was chosen as a model for a vector-borne disease while influenza-like illnesses were chosen to represent man-to-man transmission (2). dengue fever and influenza-like illnesses were chosen for surveillance in this project based on prevalence, modes of transmission, and complexity (2). as a network of networks, the aricaba surveillance system ideally consists of linked databases of the three countries that connect to all public and private hospitals, public and private labs, community health center, pharmacies, airports and seaports. figure 1 shows that each country’s health system will link to its data collection points (dcps) and the dcps will link to a data warehouse where the automated early detection analyses will operate. in this paper, the design of the surveillance system will focus on the data warehouse rather than dcps since each dcp will be unique to the country’s it infrastructures and political structure (ex: health units). nonetheless, all stakeholders will provide necessary data that allows data analyses within the warehouse based on an agreed upon aricaba charter. the design of system of the data warehouse as well as data collection should ideally:  monitor confirmed disease cases;  forecast upcoming outbreaks;  capture necessary data from dcps such as symptoms, meteorological conditions, overthe-counter (otc) sales, human migration, mosquito distribution, etc; and  effectively disseminate data and information. likewise, the development strategies should meet the needs of:  size and the operation system (os) of the aricaba data warehouse;  information dissemination tools; and  forecasting algorithms. development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 results design there are two components in the data warehouse. the first is a conventional disease monitoring system that collects suspected, probable, and confirmed cases; type of disease; and date of event and report. the other component is a syndromic surveillance system that collects syndrome of diseases from data contributors that allows automated epidemic detection for early warning systems. the rationale of having two components in one system is to maximize the system’s “software ergonomics” to match with the public health professional’s work flow (9). since public health professionals tend to spend more time with disease monitoring and intervention implementation rather than detecting the outbreak itself, the system should be able to detect the outbreak and also enable the ability to monitor the disease and progress of interventions spatially and temporally. therefore, the data warehouse will receive all data from the dcps. then, the syndromic system will function to detect the outbreak and the disease monitoring system will demonstrate the location and time of the outbreaks and allow evaluation of interventions. figure 1. network of networks overview development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 1) disease monitoring system as previously mentioned, the disease monitoring system will collect suspected, probable, and confirmed cases based on a case definition from health providers and laboratories to track the diseases retrospectively. this section will discuss case definition of dengue fever, dengue hemorrhagic fever, and influenza-like illness as well as data sources, contents, and dissemination method of the disease monitoring system. i) develop case definition a. dengue fever (2): i. suspected defined by the association of at least:  sudden-onset high-grade fever (≥38.5°c) of less than 10 days duration,  pain: headache ± joint pain ± muscle pain ± back pain, and lack of any infectious focus. ii. probable either a case of suspected dengue with at least two of the following clinical and biological criteria:  skin rash  minor signs of bleeding  thrombocytopenia (platelets < 100,000/mm3)  crp < 30 mg/l or a suspected case of dengue occurring during an outbreak. iii. confirmed a suspected or probable case of dengue confirmed by at least one of the following laboratory tests:  mac-elisa of a single serum sample evidencing specific igm,  serum culture or pcr identifying the dengue virus, significant rise in specific b. dengue haemorrhagic fever (2): fever with at least one haemorrhagic (bleeding) manifestations – i.e. purpura, epistaxis, hemoptysis, and melena -with or without jaundice. c. influenza like illness (2): i. suspected  undifferentiated fever  fever and respiratory symptoms ii) determine data source (type of system) the scope of data sources should focus on where, who, and when: geographic area, populations and time period to be covered. additionally, the scope must define how much data must be collected. in martinique, data sources are public and private hospitals, including the university hospital in fort de france, the largest hospital in martinique. private and public laboratories are also key development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 data sources. data should be collected on a weekly basis or a request basis, dependent on data. the amount of data should fulfill the list of data contents. in st. lucia and dominica, the main data source will be epidemiology unit databases in the department of health for the reported cases. laboratories, public, and private hospitals are also data sources dependent on it infrastructure. data should be collected on a weekly basis or a request basis, dependent on data. the amount of data should fulfill the list of data contents. iii) determine data contents from the data source, specific information about each individual, health care provider, or event should be collected. data contents for the disease monitoring system should describe the context of an outbreak including where, when, and to whom it occurred. the contents should be able to show types, sources, and treatment of diseases to assist in determining effective interventions. the data contents to be collected from data sources are date reported; disease; subtype; county; location name; date of event; latitude; longitude; diagnosis source; disease description; diagnosis date; dates of treatment; and date of death (see appendix i). ideally, data should be collected in real-time. however, every eight hours or daily reporting is acceptable. in addition, the data should be collected on a request basis. iv) develop data-collection instruments the resource to gather the data contents from the data sources should be identified. in martinique, the main instruments to collect the data will be computerized information systems such as email and web sites since it systems are available in health facilities. however, phone and fax can also be used. due to challenges in information systems and technology in low-resource settings such as st. lucia and dominica, phone and fax are currently important data collection instruments for hospitals, laboratories, and community health centers. computerized information systems should be implemented to enhance data collection speed and accuracy. v) develop and test analytic approach the collected data should be processed and analyzed. the data entry process will be separate from data coding in order to minimize data transcription or coding errors (9). during data entry, the staff member must check and edit data before entering into the data warehouse. routine analyses will be used for conditions of target diseases. baseline data will include five years of backlog reporting history in martinique, st. lucia and dominica. national data will be utilized for the unit of analysis, presenting time, person, and place. it must show whether or not the diseases have decreased or increased as a ratio in a weekly period (9). updated or corrected data will be used for later reports and analyses. a sensitivity and predictive value positive of the method must be presented. development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 vi) develop dissemination mechanism a data dissemination mechanism will focus on ensuring that those who need the information will receive it (9). after analyzing the data, the findings should be disseminated to public health officials and the public, if applicable. the findings from the disease monitoring system should be written as a report and must contain the number of hospitalized, number of deaths, and number of confirmed cases, including serotype. the dissemination mechanism for the data will be mainly web-based. public health officials or professionals will log into a secure web site to update the analyzed data and post a report. email is also suitable to send a report for key informants. important findings also should be available to the public through a website and computerized social networks such as facebook and twitter. vii) ensure use of analysis and interpretation it is important to evaluate the usefulness of data that is collected to those who use it (9). the evaluation will be conducted by a monthly survey to public health officials and professionals. also, web-statistics will be used to see how many people access which information and for how long. the survey will also include what other information would be useful in future analyses. 2) syndromic surveillance system the syndromic surveillance component will enable outbreak detection at early stages. this section will describe data sources and collection strategies; data analysis and outbreak detection methods; and data visualization, information dissemination and reporting of the aricaba syndromic surveillance system. the design of the syndromic surveillance system focuses on providing flexible and scalable infectious disease information, sharing (across species and jurisdictions), alerting, analyses, and visualization platforms (7,10). also, the system needs to support interactive, dynamic, spatial-temporal analysis of epidemiological textual and sequential data. i) data sources and collection strategies for the syndromic surveillance system, timely data should be provided with electronic prediagnosis health indicators (10). data sources for this system will include healthcare providers, schools, pharmacies, laboratories, and military medical facilities. from the source, data such as chief complaints from emergency department visits; ambulatory visit records; hospital admissions; otc drug sales from pharmacy stores; triage nurse calls; emergency calls; work or school absenteeism data; veterinary health records; laboratory test orders; and health department requests for influenza testing will be collected to monitor syndromes. it will also collect demographic data such as gender, age, area of residence and data relevant to patient visits. the syndromes to be monitored in this system include rash, fever, respiratory distress, hemorrhagic illness, severe illness, and death. development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 2. data collection and sources for the data warehouse disease monitoring database syndromic surveillance database collect data date reported disease subtype county location name date of event latitude longitude diagnosis source disease description diagnosis date dates of treatment date of death emergency department visit chief complaints, ambulatory visit records, hospital admissions, over the counter (otc) drug sales from pharmacy stores, triage nurse calls, emergency calls, work or school absenteeism data, veterinary health records, laboratory test orders and results, health department request for influenza testing data sources healthcare providers laboratories healthcare providers schools pharmacies laboratories military medical facilities. monitoring suspected cases confirmed cases (including types) number of hospitalized number of death rash fever respiratory hemorrhagic illness severe illness and death based on the data collection and source identified, figure 3 shows main entities and attributes of the aricaba syndromic surveillance system in the database. development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 2. aricaba erd (entity relationship diagram) for the collection strategy, these data will be collected in real-time through hl7 messages from other computer systems such as registration systems and laboratory information systems, over a secure shell-protected internet connection in an automated mode. for effectiveness and validity of data usage for illness pattern detection, it is important to consider a possible time lead compared with diagnosis (10). for example, it should compare chief complaints and discharge diagnosis to cross check from multiple sources to ensure similarity. development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 ii) data analysis and outbreak detection in order to analyze collected data for outbreaks, the first step is to define syndrome classifications (3). it will apply a keyword classifier and icd-10 classifier to chief compliant data for a syndrome classification. as a syndrome classifier, the chief complaint coder (coco) module, which is based on bayesian classifiers and has been used in the real-time outbreak and disease surveillance (rods) system in the university of pittsburgh, will be utilized. the co-co module has been proven effective through the rods system. since the size of the geographical tested area of the rods system is similar to the aricaba project, the co-co module may also be an effective syndrome classifier for the aricaba project. therefore, data analysis and outbreak detection algorithms of rods can be modified and applied to aricaba’s data analysis and outbreak detection algorithms. iii) data visualization, information dissemination, and reporting like the disease monitoring system, it is important to make available a sophisticated spatialtemporal visualization environment to help visualize public health case reports and analyze results in both retrospective and prospective spatial-temporal clustering approaches (zeng 2008). the aricaba syndromic surveillance system will provide multiple graphing techniques with both time-series and geographical displays available via password-protected web interface. it must be able to provide the following (10):  time-series plots updated on each syndrome daily. the user will be able to view these graphs by county or for the three countries.  an interactive view categorized by the syndrome, region, start dates, and end dates, to generate customized time-series plots.  location mapping by use of geographic information system (gis), which can display disease cases’ spatial distribution using patients’ zip code information. development strategies this paper will focus on development of the software and exclude hardware requirements. the hardware requirements will be determined based on the volume of data from the dcps and the internet connection capacity. thus, we will discuss the software for the server such as operating system (os), database, web interface, programs for data analysis, and the geographical information system (gis) for data visualization. data warehouse data warehouses will be implemented in martinique because it has the most advanced it infrastructure among the three countries. the data warehouse will be linked to dcps to receive the collected data (figure 3). the collected data will go through an extract, transform and load (etl) process for data cleaning (figure 3). development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 microsoft windows server 2008 will be used as the os. the rationale for selecting a windows server system instead of a linux or sun system is due to ease of it technician recruitment for windows system in martinique; it is important to consider local it human resources for creation of it systems in developing countries. er win 4.0 is used to develop the entity relationship diagram (erd), which allows development of a logical and a physical model of data modeling for the data warehouse. the erd is developed based on a platform of microsoft sql server. the data warehouse will also have a data mart for data analysis through mathematical algorithms. the server will provide internet information service (iis) web service for a web site. the web interface will be developed with asp.net language in order to display data, provide interactive user-interface via the website. the data warehouse will also provide a gis application server component with arcims (figure 3). these web and gis servers will be implemented in a separate server machine in order to lessen the burden of the data warehouse. a. disease monitoring system figure 3. data warehouse architecture development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 b. syndromic surveillance system geographical information system gis will be developed to demonstrate mappings of the incidence and prevalence of diseases and health facilities for interventions. the arcgis 9.3 version will be used as the gis system. the mapping of incidence and prevalence of diseases will be developed for all three countries: martinique, st. lucia, and dominica. therefore, incidence and prevalence of disease data must include location, including longitude and latitude. health facility mapping must also be developed among the three countries. health facilities include public and private hospitals, clinics, and laboratories. for more interactive maps, arcgis api for flex will be used to create a flash file for the web. forecasting algorithm for the outbreak detection algorithms, multiple forecasters will be used for outbreak detection algorithms to minimize the expected squared error of the forecast. outbreak detection algorithms include cumulative sums (cusum), smart, scan statistics, recursive least squares (rls), wavelet-detection algorithms, and what is strange about recent event (wsare). several of these algorithms will be used in order to compare the results. first, a combination of cusum and ewma will predict next-day counts and monitor the differences between the cumulated average and predictions. the second monitoring tool will be a recursive least squares (rls) algorithm, which fits “an autoregressive model to the counts and updates estimates continuously by minimizing prediction error” (8). the third tool will include a wavelet approach, which “decomposes the time series using haar wavelets, and uses the lowest resolution to remove longterm trends from the raw series” (8). the residuals are then monitored using an ordinary development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 shewhart i-chart with a threshold of 4 standard deviations (8). by comparing these algorithms, it will monitor whether all the monitoring tools predict similar patterns, rather than relying on one forecasting algorithm. in developing a forecasting algorithm, data clustering will be performed to organize the pattern of data, based on similarity. afterward, regression analysis will be conducted to find a relationship (coefficients) between the data. pasw (spss modeler 13) will be used for the analysis. the pattern of data and associated factors will be plugged into a mathematical algorithm that incorporates environmental characteristics such as weather conditions and mosquito distribution to develop an outbreak forecasting algorithm for dengue fever. for the mathematical algorithm, holt winter’s multiplicative seasonality model will be tested in the pilot. discussion benefit of the aricaba surveillance system and design one of the main benefits of the aricaba surveillance system is that it will improve responsiveness and representativeness of the existing health systems in the three countries through an automated approach of the collection and processing of data and transmission of information from various sources. by doing so, the network system will homogenize and organize the data that are collected from relevant stakeholders in dominica, st. lucia and martinique, utilizing information and communication technology. by incorporating a disease monitoring system for confirmed cases and a syndromic surveillance system for forecasting, it will empower public health professional to prepare and intervene effectively when outbreaks occur. detection algorithm in a syndromic surveillance system, one of the keys for accurate outbreak detection is well designed and tested mathematical algorithms. although the aricaba syndromic surveillance system uses the basic idea of the rods’s mathematical model, it is important to develop its algorithms from feedback in order to capture and predict the outbreak in the caribbean. it is crucial to continuously question what other algorithm combinations are available and investigate lesson learned from other syndromic surveillance systems. data collection in order for the data warehouse to function properly, a strategic plan for collecting data is critical. necessary data for the data analysis must be collected in sufficient amounts. in addition, data collection should occur in a timely and complete manner while following agreed upon data standards. hospitals, labs, and clinics have different data systems in each country. when each dcp collects data from the data sources, each dcp must be able to convert the data into data warehouse system. however, the absence of medical informatics and electronic medical record systems in martinique and dominica remains a challenge. system requirements system requirement must take the volume and size of data into account. it must consider the data such as the list of syndromes, weather conditions, and migration to be collected from the three islands. in addition, it is important to design system requirements in a flexible manner so that the development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 system can expand in case of increased data volume in the future. the system requirement for the data warehouse should also consider the performance capacity of cpu and ram for calculating the outbreak detection algorithms. sensitivity vs. specificity a critical component regarding the algorithms for outbreak detection is balancing the level of specificity. for example, if the system has specificity set to low, there will be a lot of "falsepositive" epidemic alerts, which will tire the system. on the other hand, if it is too specific, the system may miss true epidemics, which does not meet the purpose of the system either (2). technology gaps since the level of economic development in all three countries is different, it is important to consider the technology gaps among the islands. not only internet capacity, but more importantly, the gap in medical informatics is an immense challenge to implement automated data collection from hospitals and laboratory. the automated, real-time chief complaint collection from emergency departments or clinics is especially crucial for a syndromic surveillance system; implementing adequate medical informatics system in hospitals among the countries must be completed first in order for the syndromic system to remain fully functional. another way to overcome the technology gap is to utilize a “component-oriented” development approach, which develops each component of system separately so that it can be a “plug-in” to other systems when needed. languages as mentioned, the three countries speak different languages. among a mixed language setting, it is important to establish multiple language systems for the entire surveillance system. for example, all data should be displayed in french, english and creole. also, data translation features are necessary to communicate between the three countries. for instance, data or information entered in english in st. lucia should be able to be read as french in martinique; this highly interactive language feature remains a challenge. limitations this study involved several limitations. a holistic, contextual view of this project was not analyzed; only certain aspects of the parts of the project have been analyzed. therefore, causal conclusions were difficult to obtain. for example, time in the fellowship was a limiting factor in accessing data; budget and cost-effective analyses could have been valuable quantitative information but were not completed. additionally, a possibility of observer and interviewer bias in handling the qualitative data exists. there may be interviewer bias in handling the data because a majority of technical concepts originate from martinique. observer bias in collected data is reduced due to three fellows working on the project with different backgrounds and expertise. in addition, the areas in which fellows worked vary with some overlap. however, the information from different perspectives enriches the perspective and qualitative analysis of this study. finally, a lack of technical documentation limited this study to rely mainly on interviews and discussion. conclusion the aricaba surveillance project was initiated to protect caribbean citizens and tourists from development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 emerging infectious diseases by using network systems for early warning and forecasting. the aim is to implement a comprehensive surveillance system that enhances health systems by connecting current structures and maximizes its capacity through information technology in the caribbean, where resources are limited. this paper recommends a system architecture and database system for data warehousing. it proposes two systems: a disease monitoring system and a syndromic surveillance system within the data warehouse to predict the outbreak and monitor/control the disease. data contents, sources, collection and visualizations strategies have also been suggested. however, collecting data, especially chief complaints from ers in real-time, remains a challenge where medical informatics system are limited. also, full language support for the entire system is complex. considering these challenges, recommendations include reducing technology gaps, in terms of medical informatics, between three countries; improving real-time data collection for the syndromic surveillance system; and establishing multi-language support system. for development strategies, “component-oriented” development approaches are recommended for the sustainability of aricaba. the caribbean region should continue to move forward in their efforts to improve surveillance systems and protect the health of their citizens. acknowledgements we would like to acknowledge cicomsca and the regional council martinique for their support of this project. additionally, many thanks to the center for global health at the george washington university for the opportunity to participate with aricaba through their global health service fellowship. references [1] bliss k. health in latin america and the caribbean: challenges and opportunities for u.s engagement, center for strategic and international studies global health policy center. 2009; washington, dc. [2] bucher b. aricaba: an emerging infectious disease surveillance system, implementation & evaluation plan. 2010; martinique. [3] chen h. ai for global disease surveillance, ieee computer society. 2009: 118. [4] central intelligence agency (cia). the world factbook [internet]; [cited october 15, 2011]. available from: https://www.cia.gov/library/publications/the-world-factbook/ [5] dieye m. cancer incidence in martinique: a model of epidemiological transition. european journal of cancer prevention 2007apr; 16(2):95-101. [6] ducharme c. understanding implementation barriers to an electronic emerging infectious disease surveillance system in the caribbean, aricaba: a case study. 2011; the department of global health, the george washington university, washington d.c. [7] heymann d, rodier g. global surveillance of communicable diseases. emerging infectious diseases. 1998; 4(3):362-65. https://www.cia.gov/library/publications/the-world-factbook/ development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba) 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 [8] shmueli g, burkom s. statistical challenges facing early outbreak detection in biosurveillance. technometrics (special issue on anomaly detection). 2010; 52(1): 39-51 [9] teutsch s. principles and practices of public health surveillance, 2nd. ed. new york: oxford university press; 2000. 406p. [10] zeng d. syndromic surveillance systems: public health and biodefense. review of information science and technology (arist) [internet]. 2008 [cited 2011 july 15]; 42. available from: http://iasec.eller.arizona.edu/docs/zeng-survey-manuscript-revised.pdf [11] zeng d. infectious disease informatics and outbreak detection. medical informatics. new york: springer science; 2005. 647p. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the use of the international classification of diseases, ninth revision (icd-9) coding in identifying chronic hepatitis b virus infection in health system data: implications for surveillance reena mahajan*1, anne c. moorman1, stephen j. liu1, loralee rupp2, monina klevens1 and (checs) for the chronic hepatitis cohort investigators3, 2, 1 1centers for disease control and prevention, atlanta, ga, usa; 2henry ford health system, detroit, mi, usa; 3kaiser permanentehawaii; geisinger health system, pennsylvania; kaiser permanente-northwest, oregon, atlanta, ga, usa objective to evaluate the sensitivity, specificity, positive and negative predictive values of the icd-9 coding system for surveillance of chronic hepatitis b virus infection (hbv) using data from an observational cohort study in which icd-9-coded hbv cases were validated by chart review. introduction in the united states, 800,0001.4 million people are chronically infected with hepatitis b virus (hbv); these persons are at increased risk for chronic liver disease and its sequelae (cdc, 2010; wasley, 2010). current national viral hepatitis surveillance is a passive laboratory-initiated reporting system to state or local health departments with only 39 health departments reporting chronic hbv infection in the national notifiable disease surveillance system (nndss). since active hbv surveillance can be expensive and labor-intensive, the icd-9 coding system has been proposed for surveillance of chronic hepatitis b. methods we examined the electronic health records (ehrs) available as part of an existing cohort study of persons with chronic viral hepatitis. records from 1.6 million adult patients who had one or more services from 2006-2008 in four integrated health care systems were reviewed. complex algorithms using laboratory data and/or use of qualifying hepatitis b icd-9 codes were applied to ehr patient data to create the chronic hbv cohort. disease status was manually validated by abstractor review of the medical record. sensitivity, specificity, positive and negative predictive values were calculated based upon presence of either one hepatitis b-specific icd-9 code or two such icd-9 codes separated by at least six months. results of 1,652,055 adult patients, 2,202 (0.1%) met criteria for inclusion into the chronic hbv cohort. of the 2,202 confirmed cases, the sensitivity of use of one icd-9 code was 83.9%, positive predictive value was 61.0%, specificity was 99.9% and the negative predictive value was over 99.9% (table 1). in comparison, use of two hepatitis b-specific icd 9 codes separated by six months, resulted in a sensitivity of 58.4%, a positive predictive value of 89.9%, and specificity and negative predictive value similar to use of one icd 9 code. conclusions our findings suggest that use of one or two hepatitis b specific icd 9 codes can identify cases with chronic hbv infection. for health departments with access to electronic medical records, collection of icd-9 data may be useful for surveillance and potentially improve reporting of chronic hbv infection. measurement of sensitivity, specificity, and predictive values of using one hepatitis b-specific icd-9 code among persons receiving services from four health care systems from 2006-2008 sensitivity= 1,847/2,202= 83.9% specificity= 1,648,671/1,649,853= 99.9% positive predictive value= 1,847/3,029= 61.0% negative predictive value= 1,648,671/1,649,026= >99.9% keywords surveillance; hepatitis b virus; icd-9 acknowledgments the authors thank dr. fujie xu, division of viral hepatitis, centers for disease control and prevention for her helpful suggestions with this study. references 1. cdc, 2010. viral hepatitis statistics and surveillance. http://www.cdc.gov/hepatitis/statistics/index.htm. accessed july 20, 2012 2. wasley a, kruszon-moran d, kuhnert w, simard ep, finelli l, mcquillan g, et al. the prevalence of hepatitis b virus infection in the united states in the era of vaccination. j infect dis 2010;202:192201. *reena mahajan e-mail: rmahajan1120@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e26, 2013 subjective well-being and personality traits: towards personalized persuasive interventions for health and well-being. personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi personality and subjective well-being: towards personalized persuasive interventions for health and well-being. aisha muhammad abdullahi 1, rita orji 2, abbas muhammad rabiu, 1 abdullahi abubakar kawu 3 1faculty of computing, federal university dutse, nigeria, 2faculty of computer science, dalhousie university, halifax canada, 3ibrahim badamasi babangida university, lapai 740005, nigeria abstract subjective well-being (swb) is an individual’s judgment about their overall well-being. research has shown that activities that elevate people’s sense of swb have a significant effect on their overall health. there are two dimensions of swb: affective and cognitive dimensions. however, studies on swb usually focus more on one dimension, ignoring the other dimension. also, most existing studies on swb focused on individuals from western cultures. research has shown that the influence of personality on subjective well-being is moderated by culture. thus, to advance research in personalizing persuasive health interventions, this study focuses on africans (n=732). specifically, we investigate the relationship between the big-five personality traits and both dimensions of swb using the constructs: happiness, satisfaction with life, social, psychological and emotional well-being. our results reveal that health informatics designers who design persuasive technologies to promote swb would need to tailor designs along personality traits and swb constructs. accordingly, for users high in agreeableness, the design should be focus on promoting their feelings of happiness and social wellbeing. for users who exhibit neuroticism, designers should focus on designing to promote psychological wellbeing and emotional well-being. based on our findings, we offer guidelines for tailoring persuasive health interventions to promote individuals’ swb based on their personality. we thus highlight areas that personal health informatics design can benefit. ccs concepts • human-centered computing → personalization → hci design and evaluation methods → user models keywords subjective well-being, big five personality traits, persuasive health applications, personalization. correspondence: am.abdullahi@fud.edu.ng, rita.orji@dal.ca, mambas86@fud.edu.ng, abdullahikawu@ibbu.edu.ng doi: 10.5210/ojphi.v12i1.10335 copyright ©2020 the author(s) this is an open access article. authors own the copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without the permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi 1. introduction according to the world health organization, “health is a state of physical, mental and social wellbeing and not merely the absence of disease or infirmity” [1]. in 2012, the united nations emphasized the importance of individual and societal wellbeing in achieving the millennium development goals [2]. as a result, it has been advocated that health interventions focus on promoting health and well-being by targeting the individual components that contribute to them. subjective well-being is an individual’s judgment about their overall well-being, which includes a cognitive dimension (satisfaction with life and happiness) and an affective dimension (social well-being, emotional well-being, and psychological well-being) [3]. research has shown that there is a relationship between people’s subjective well-being and their physical health [4,5]. for example, skaff et al. [6] showed in their study that negative emotions predicted rising blood glucose levels the next day and black et al. [7] explains how stress leads to inflammation, which can harm health when it is chronic. it has also been found that surgical patients healed more quickly if they are high in life satisfaction [8]. this suggests that interventions that raise people’s sense of well-being may contribute to improving physical health. thus, theories on how to promote people’s subjective well-being have been established [9,10]. some existing personal health informatics (self-tracking) tools provide some level of personalization, but the focus is largely on the aesthetics of the tool. most consumer products have aesthetic ways to customize the tools—both software (e.g., color, user information, a wide variety of user interface designs to choose from) and hardware (e.g., medium, form factor, and types), thereby reducing the devices prospective benefits to the user [11]. for example, during setup, fitbit asks people to enter details for their profile, such as gender and height, from which they estimate a number of health information such as bmi (used for managing body weight). although important, these personalization supports fall short of realizing the full potential of personalized tracking because they are applied to the secondary side of the tracking tool, not towards the subjective circumstance and lived experience of the user (such as their mood or mental state at the time of use) especially in technologies that intend to persuade or change behavior. when self-tracking tools do not completely satisfy personalization and by implications tracking needs, people give up tracking entirely [12]. to accommodate a wide range of tracking needs, designers should identify ways to incorporate the subjective situations of users while using the health tracking tools as attempted in omnitrack [13]. this realization has motivated a shift of pt design from the traditional one-size-fits-all approach to a personalized approach that adapts to the preferences of individuals. the personalized approach treats each user as a different entity, it assumes that a persuasive strategy that works for one user may not work for another. therefore, persuasive health interventions need to be tailored to users to be effective [14]. as a result, research into personalizing health interventions to individual preferences has gained some attention among pt designers. specifically, research into investigating personality traits as personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi a factor that can influence individual differences attracted the attention of researchers [15,16]. this is because what constitutes well-being for one may not for another. however, most existing literature focuses on individuals from western culture. there is limited literature on the generalization of their findings to individuals from developing countries. research has shown that the influence of personality on the subjective well-being components is moderated by culture [17]. also, most existing literature focuses on one dimension of subjective well-being ignoring its other dimension. therefore, in this paper, we investigate the relationship between personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and the two dimensions of subjective well-being (affective dimension and cognitive dimension) using distinct subjective well-being components (satisfaction with life, happiness, psychological, social and emotional well-being) in people from africa (nigeria specifically) to outline ways that persuasive health interventions can be personalized to be more effective for people from non-western cultures based on their personalities. to achieve this, we conducted an empirical study (n=732), using structural equational model (sem) analysis to develop a model showing how people of different personalities relate to various subjective well-being components. interestingly, our results reveal that personality traits play significant roles in their various subjective well-being components. for example, to design pts to promote swb for people high in agreeableness, designers should focus on designing to promote their feeling of happiness and social well-being, while for neuroticism, designers should focus on designing to promote psychological well-being and emotional well-being. our work offers four main contributions to the field of persuasive technology and health intervention design. first, we reinforce the need to personalize persuasive health systems by revealing that individuals of different personality traits relate differently to distinct subjective wellbeing components. second, we establish that personality trait is an important characteristic for personalizing persuasive health interventions targeting african audience. so far, none of the existing works investigated the relationship between personality and subjective well-being among africans. third, we examine the relationship between individual personality traits and the different subjective well-being components and develop design guidelines for personalizing persuasive health applications to individuals based on their personality traits. finally, we suggest some persuasive strategies to promote individual components of subjective well-being. this is an essential step toward developing personalized health applications that will effectively engage users and promote desired behavior change. 2. background and related work in this section, we provide an overview of personality traits, subjective well-being, and related work. 2.1 personality traits personality traits are the combination of habitual behaviors, cognitions and emotional patterns that make up an individual's distinctive character [18]. psychologists argue that personality is unique to everyone [19]. understanding your personality and what makes you different from others, can personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi lead to better life choices. personality traits have been shown to play important roles in people’s well-being and overall success [20]. this may be because personality traits are significant predictors of our behaviors and attitudes in life. over the years, several tools for identifying personality traits have been developed. among these tools are the myers-briggs type indicator (mbti) [21], pen model [22] and big five [23]. the big-five personality traits– openness, conscientiousness, extraversion, agreeableness, neuroticism is the most widely used personality type. the big five personality traits have been shown to influence subjective wellbeing among other populations, for example, south koreans [15], taiwanese [16] and spaniards [24]. these five components are: 1. openness personality trait describes how open someone is to a variety of experiences or how concretely or abstractly someone thinks about things. those high in this trait tend to hold unconventional values and are often creative thinkers. 2. conscientiousness personality trait describes how self-disciplined, organized and goal-oriented a person is. those high in this trait tend to be good at planning rather than being spontaneous. 3. extraversion is a personality trait characterized by how sociable, energetic and warm a person is. those high in this trait tend to be chatty and associate a lot with others. 4. agreeableness personality trait describes how kind, sympathetic and cooperative a person is. those high in this trait tend to be helpful, less competitive and friendly to others. 5. neurocism describes how emotionally unstable, nervous, distressful and fearful a person is. those high in this trait tend to worry or be temperamental. 2.2 subjective well-being the term subjective well-being refers to people’s perception and evaluations of their lives, and well-being, including cognitive evaluation, such as satisfaction with life and affective evaluation such as emotional, social and psychological well-being [3]. people’s subjective well-being has been widely acknowledged to play an important role in their overall physical and mental health. as a result, the past four decades have witnessed an explosion of research on the design for wellbeing [25-27]. the most widely accepted definition of subjective well-being distinguishes the cognitive and an affective dimension of subjective well-being [3,15,28]. the cognitive dimension is based on an overall assessment of one's life. peoples’ happiness and satisfaction with life are considered a cognitive component of subjective well-being [28]. researchers have used the happiness scale and satisfaction with life scale to assess the cognitive dimension of swb [15]. the affective dimension reflects the number of pleasant feelings (positive affect) and unpleasant feelings (negative affect) that people experience in their lives [28]. the affective dimension of swb brings measurement very close to assessing mental health [28]. some researchers have used the positive affect scale and negative affect scale to assess the affective dimension of subjective well-being [24]. however, to have broader and richer information about the affective dimension of subjective well-being, several researchers have used the psychological well-being, social wellpersonality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi being, and emotional well-being scales to assess the affective dimension of subjective well-being [29,30]. in this study, the satisfaction with life, happiness, psychological, social and emotional well-being scales is used to assess participants' overall subjective well-being. we discuss these five components briefly in this section. 1. satisfaction with life is defined as one's evaluation of life and how they feel about their directions and options for the future [31], or people’s judgment that at least on balance, their life measures up favorably against their standards or expectations [32]. research has shown that satisfaction with life is a predictor of health-related quality of life (hrqol) [33]. for example, strine et al. [33] in their study revealed that as the perceived life satisfaction of people decreased, the prevalence of unhealthy behaviors that contributes to general ill-health increases. this includes smoking, obesity, physical inactivity, heavy drinking, sleep deprivation, and chronic illnesses. thus, persuasive technological interventions aimed at increasing an individual’s well-being can target promoting their overall feeling of satisfaction with life as a way of fostering well-being. 2. happiness is defined as the momentary feeling of intense joy [34]. it has been shown that happy people are healthier [35]. therefore, a persuasive intervention designed to increase an individual’s overall happiness will likely impact on their health and well-being. 3.psychological well-being is defined as the general perception experienced by individuals that there will be positive outcomes to events or circumstances (p. 497) [36]. ryff described six key-elements of psychological well; self-acceptance, personal growth, purpose in life, environmental mastery, autonomy and positive relations with others [37]. these six elements are key to positive psychological well-being. positive psychological well-being makes people better able to deal with life’s challenges which in turn promotes other desirable qualities like creativity, productivity, and vitality. a frequent experience and expression of positive psychological well-being make people more optimistic, resilient, and resourceful. also, research has shown that people who have positive psychological well-being are healthier generally [38]. therefore, a persuasive intervention designed to increase an individual’s psychological well-being may have a positive effect on their overall health and well-being. 4. social well-being refers to an individual’s interaction and relationship with others. “it involves using good communication skills, having meaningful relationships, respecting yourself and others, and creating a support system that includes family members and friends” [39]. high social well-being makes it easy for people to build and maintain positive relationships with others and their community. it has been shown that people who experience a high sense of belonging in various cultural activities and within their communities are generally healthier [40]. for example, barton and grant’s [41] showed that people who belong to socially excluded groups have poorer health than their counterparts. therefore, a persuasive intervention designed to increase an individual’s social well-being may have a positive influence on their overall health and well-being. personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi 5. emotional well-being is defined as a feeling of relaxation and stress freeness [42]. emotional well-being reflects how well individuals manage their thoughts, feelings, and actions to function in their everyday lives. it has been shown that people’s emotional well-being influences their mental health [28]. positive emotional well-being is key to experiencing balanced mental health and overall well-being. research has shown that people who have positive emotional health are better able to cope with everyday stresses and problems and therefore have more stable mental health and overall well-being [43]. more specifically, the studies of burnner [44] and wilkinson [45] revealed that emotional distress creates susceptibility to physical illness by affecting the immune response, thus leading to poor health conditions. therefore, a persuasive intervention designed to increase an individual’s emotional well-being will likely impact positively on their overall health. 2.3 related work investigating the relationship between personality traits and swb has received some attention and has been studied extensively by previous literature [15,16,24]. for example, ha et al [15] in their study showed that there is a statistically significant relationship between personality traits and subjective well-being. in their study of south koreans, they explored the direct influence of personality on subjective well-being. however, the study focused on one dimension of subjective well-being (the cognitive dimension) ignoring its other dimension (the affective dimension). the cognitive dimension was measured using happiness and life satisfaction scales. ha et al [15] found that personality traits, particularly emotional stability and extraversion, are positively associated with happiness and life satisfaction. similarly, gutiérrez et al. [24] revealed that personality is an important correlate of subjective well-being. they conducted a study of spaniards to examine the association between, personality traits and subjective well-being. still, the study focused on one dimension of subjective well-being (the affective dimension) ignoring its other dimension (the cognitive dimension). they used the positive affect and negative affect scales. gutiérrez et al. [24] revealed that neuroticism and extraversion correlate with the two components used to measure subjective well-being (positive and negative affect) while openness and agreeableness correlate with only one of the two components (positive affect). a recent study by chen [16] also showed a significant and substantively important relationship between personality traits and subjective well-being. chan [16] investigated the relationship between personality traits and the subjective well-being of online game playing teenagers in taiwan. the study also assessed one dimension of subjective well-being (the cognitive dimension), using the satisfaction with life scale. the study concluded that, neuroticism ana d agreeableness have significant negative influence on people’s satisfaction with life while and openness has significant positive influence on satisfaction with life. also, soto [46] showed a significant relationship between personality traits and components of subjective well-being. in his study, with australians, he explored the relationship between personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi personality traits and subjective well-being using satisfaction with life, positive affect, and negative affect scales. he found that individuals with more-extraverted, agreeable, conscientious, and emotionally stable personalities tend to experience higher life satisfaction, more frequent positive affect, and less frequent negative affect. furthermore, costa et al. [47] carried out a study of participants from boston, usa, focusing on one dimension of subjective well-being (the affective dimension). they found that extraversion is positively associated with positive affect and neuroticism is positively associated with negative affect. another study by deneve and cooper [48] used four components: life satisfaction, happiness, positive affect, and negative affect, to assess subjective well-being. they found that neuroticism is strongly associated with life satisfaction, happiness, and negative affect, while extraversion and agreeableness are strongly associated with positive affect. in a similar study, libran [49] used life satisfaction, positive affect, and negative affect scales to assess the subjective well-being of university students in catalan, span. as regards personality traits, he considered only the extraversion and neuroticism traits. results from his work show neuroticism as one of the most important correlates of the components of subjective well-being. specifically, he found that neuroticism is strongly negatively associated with life satisfaction and positive affect, but strongly positively associated with negative affect. on the other hand, extraversion correlated positively with satisfaction with life and positive affect, but not with negative affect. his study concluded that the correlations of neuroticism with the components of subjective well-being are higher than those obtained between these same components and extraversion. that is, extraversion seems to be less significant than neuroticism as a predictor of the components of subjective well-being. this present study differs from existing studies in three major ways: one, we investigate a developing african nation (nigeria) which is often neglected by researchers. two, we investigate both dimensions of subjective well-being and their relations with personality. happiness and satisfaction with life are used to assess the cognitive dimension while the three components of psychological well-being, emotional well-being, and social well-being are used to assess the affective dimension. this provides a richer insight into subjective well-being. three, we offer design guidelines and design considerations to inform persuasive health intervention design especially those targeted at african audiences. 2.3 health informatics and subjective well-being. health informatics (hi) is an area with wide applications to encompass public and personal health informatics [50]. while public health informatics refers to the systematic application of information and computer science and technology to public health practice, research, and learning [51], personal health informatics focus on the collection and use of personal data, often from trackers and life-loggers for achieving specific health goals for individuals [52]. in both realms, some sentiments infer that the current state of technologies can benefits from other dimensions of improvement, beyond software and hardware. tracking circumstance and subjective situations could mean support towards measuring other dimensions of ‘improvements’ personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi [52] that go beyond activities, soft or hardware. this may thus include subtle dimensions like the account of swb and personality traits. although, this is somewhat difficult in practice [11] and can be challenging from user experience and privacy perspectives [53], we believe it may fit mccarthy and wright’s discussion of “technology as experience” [54] and their call for design to engage with the felt life. 3. method this study was designed to investigate how people of different personalities relate to the two subjective well-being dimensions (cognitive and affective) using five components satisfaction with life, happiness, psychological, social and emotional well-being. this will inform the tailoring of persuasive health interventions to the personalities. to achieve this, we collected data about participant’s personality traits and their subjective well-being components and conducted structural equational model (sem) analysis, specifically, path analysis using amos 2.0. 3.1. sample and sampling technique the sample was drawn from north-west nigeria in 2018. seven states were selected: kano, kaduna, katsina, kebbi, sokoto, jigawa, and zamfara. 100 participants were randomly selected in all the states except for kano were 132 participants were selected. universities, colleges, government/private offices from these states were randomly enlisted and personally visited by the research team. after a short introduction of the study to the head of each organization, participants were then randomly selected and approached. the purpose of the study was explained to them and their verbal consents were sought. a paper-pencil questionnaire was given to each respondent, the majority of the respondents completed the survey immediately (took approximately 15 minutes), a small number of the respondents were left with the questionnaire booklet and was collected after a mutually agreed period (at most after 24 hours). random sampling was used for convenience in the selection of organizations and respondents. in keeping with the research aim, the research team deliberately selected respondents from both genders and various age groups. a total of 732 people participated in this study. participants were drawn from several works of life in nigeria. participants were well distributed in terms of gender and age. as regards age, 21% (16-24), 19% (25-34), 17% (35-44), 13% (45-54), 13% (55-64), 11% (65-74), and 6%(above75). with regards to gender, 52% are males and 48% are females. 3.2. measurement instruments to determine participant’s personality traits we employed the 10 item personality traits (tipi) [55]. the tipi scale has been widely validated and used by many researchers including [56,57] for measuring the big five personality traits. the tipi scale consists of 10 items, two items measure each trait using a 7-point likert scale, ranging from 1= strongly disagree to 7=strongly agree. to determine participants’ subjective well-being, five prior validated scales nations, satisfaction with life, happiness, psychological, social and emotional well-being scales. the happiness scale developed by lyubomirsky and lepper [34] which consists of 4 items is a 5-point likert scale, personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi ranging from 1= very unhappy to 5 = very happy is used to elicits participants happiness. a sample item includes: “if you were to consider your life, in general, these days, how happy would you say you are?” the social well-being scale developed by huppert et al. [39] which consists of 14 items is a 5-point likert scale, ranging from 1= strongly disagree to 5 = strongly agree is used to measures participants social well-being. a sample item includes: “i gladly have contact with other people via social media (facebook, e-mail).”.” the satisfaction-with-life scale developed by diener et al. [31] which consists of 5 items is a 5-point likert scale, ranging from 1= strongly disagree to 5 = strongly agree is used to measured participants’ life satisfaction. a sample item includes: “if i could live my life over, i would change almost nothing.” the psychological wellbeing scale developed by diener et al. [42] which consists of 12 items is a 5-point likert scale, ranging from 1= strongly disagree to 5 = strongly agree is used to elicits participants psychological well-being. a sample item includes: “i am competent and capable in the activities that are important to me.” lastly, the emotional well-being scale developed by diener et al. [42] which consists of 16 items is a 5-point likert scale, ranging from 1= strongly disagree to 5 = strongly agree is used to measure participants emotional well-being. a sample item includes: “i have been dealing with problems well.” 3.3 procedure participants willingly volunteered to participate in this study, so no incentives were awarded to them. paper-pencil questionnaires were handed out to participants in their workplaces, which took approximately 15 minutes to complete. no identifying information was collected. the data collection was overseen by the federal university of dutse’s research ethics committee. 3.4. data analysis to analyze the data, we conducted structural equational modelling sem analysis using amos 20. specifically, we employed sem to develop a model showing how people of different personalities relate to various subjective well-being components. 3.5. validation of study instrument we conducted a confirmatory factor analysis (cfa) to test the validity of our study instruments and tested for the model fitness. we established the internal consistency of our constructs through their cronbach’s alpha (α) values. the results from the cfa show the cronbach alpha (α) for all constructs use to measure the personality traits to be between 0.75 and 0.83, all above the recommended threshold of 0.70. similarly, we established convergent validity from the values of the average variance extracted (ave). the results show the ave to be above 0.5 for all the scales of subjective well-being components. 4. results we conducted structural equation modelling (sem) to establish the relationship between the five personality traits and the individual components of the subjective well-being. in this section, we report results from our model. the goodness-of-fit indices shows that the hypothesized model was a good fit to the data; χ2 (10) = 10.334, the degree of freedom (df) = 4, p < .001, comparative fit personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi indices (cfi) = 0.987 (cfi > .90 is the recommended value) and root mean square error approximation (rmsea) = 0.033 (rmsea < .08 is the recommended value). χ2/ df = 2.583 (χ2/ df < 3 is the recommended value). the structural model the structural models determine the relations between the people’s personality traits and the individual components of the subjective well-being, figure 1. an important criterion to measure the strength of relationships between variables in structural models is to calculate the level of the path coefficient (β) and the significance of the path coefficient (p). path coefficients measure the influence of a variable on another. the individual path coefficients (β) and their corresponding level of significance (p) obtained from our models are summarized in table 1. figure 1: sem model structure table 1: path coefficient results personality traits subjective well-being components path coefficient (β) pwb ewb sowb swl h openness 0.14 0.17 0.21 0.32 conscientiousness 0.15 0.12 0.52 0.54 extraversion 0.18 0.27 0.53 0.43 0.47 agreeableness 0.21 0.16 0.11 0.15 0.13 neuroticism -0.44 -0.35 -0.23 -0.15 psychological well-being=pwb, emotional well-being=ewb, social well-being=sowb, satisfaction with life=swl, happiness=h. relationship between personality traits and subjective well-being. bolded coefficients are p<.001, non-bolded coefficients are p<.01, and ‘-’ represents non-significant coefficients. relationship between personality traits and subjective well-being. our results show personality traits to be strong predictors of subjective well-being components. we report how each component of the subjective well-being relates to the five personality traits. swb personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi happiness: our results show that happiness is significantly positively associated with all the personality types except for emotionally unstable people (neuroticism) who show no significant relationship: openness (β= 0.32, p<0.01), conscientiousness (β= 0.54, p<0.001), extraversion (β= 0.47, p<0.01), agreeableness (β= 0.13, p<0.01). this means that people who are high in openness, conscientiousness, extraversion, and agreeableness are more likely to harbor a higher level of happiness as a contributor to swb compared to those high in neuroticism. interestingly, conscientiousness has the strongest relationship with happiness. this finding is supported by barrick et al. [58] who found that conscientious employees achieved a higher volume of sales than their unconscientious co-workers. the feeling attached to achieving their goals makes them feel happier than their counterparts. thus, persuasive technological interventions aimed at increasing an individual’s well-being can target promoting their overall feeling of happiness as a way of fostering well-being. a plausible explanation of why happiness is not significantly associated with neuroticism is that people high in neuroticism due to their inherent characteristics of being emotionally unstable, are often stressed out and nervous. this makes them incapable of appreciating beauty and driving pleasure from simple things of life that make other people happy. satisfaction with life: our results show that satisfaction with life is positively associated with conscientiousness (β= 0.52, p<0.001), extraversion (β= 0.43, p<0.01), and agreeableness (β= 0.15, p<0.01). however, it is negatively associated with neuroticism (β= -0.15, p<0.01) and does not have a significant association with openness. these results mean that people who are high in conscientious, extraversion, agreeable personality traits are more likely to be more satisfied with life in general than people who are high in openness and neurotic personality. as expected, conscientiousness is most strongly positively associated with satisfaction with life. our findings are consistent with those of soto [46] who found that individuals high in conscientiousness tend to experience higher life satisfaction. this is expected because, conscientious people like to be very organized, often avoid making impulsive decisions and abide by rules. as a result, their lives often go as planned without hitches because they try to avoid doing things spontaneously which may consequently make them feel unsatisfied with their everyday affairs and lives generally. this is followed by extraversion, which is in line with ha et al [15] who also found that extraversion is positively associated with satisfaction with life among south koreans. on the other hand, satisfaction with life is negatively associated with people who are high in neuroticism. this finding is also consistent with those of soto [46] and chen [16] who revealed in their work that emotionally unstable individuals tend to experience lower life satisfaction. the association between satisfaction with life and openness is not significant while the positive association between satisfaction with life and agreeableness is weak. this implies that designing persuasive interventions to increase the satisfaction with life of people who are open to experience (openness), emotionally unstable (neuroticism) and cooperative (agreeableness) will increase their overall swb and hence impact positively on their overall health and well-being. emotional well-being: our results show that emotional well-being is positively associated with all the personality types except neuroticism: openness (β= 0.17, p<0.01), conscientiousness (β= 0.12, p<0.01), extraversion (β= 0.27, p<0.01), and agreeableness (β= 0.16, p<0.01). however, the positive association between emotional well-being and conscientiousness, openness and agreeableness is weak. emotional well-being is negatively associated with neuroticism (β= -0.35, p<0.001). this means that people who are high in extraversion are more likely to experience a personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi higher level of emotional well-being as a contributor to subjective well-being compared to other personality types. this means that designing persuasive interventions to increase the feeling of relaxation and stress freeness can greatly improve the swb and hence the overall health and wellbeing of people who are open to experience (openness), cooperative (agreeableness), goaloriented (conscientiousness) and neurotic people. social well-being: our results show that social well-being is positively associated with openness (β= 0.21, p<0.01), extraversion (β= 0.53, p<0.001), and agreeableness (β= 0.11, p<0.01). on the other hand, social well-being is negatively associated with neuroticism (β= -0.23, p<0.01) and is not significantly associated with people who are high in conscientiousness. extraversion emerged as the personality with the strongest positive associated with social well-being. this may be due to their inherent nature, extraversion tends to attach so much importance to having strong social networks, connecting and interacting with people. this is further supported by kendra [59] who describes extroverts as people who tend to feel isolated when they spend much time alone, hence, they tend to prefer to spend most of their time being around people. this is also supported by wido et al. [60] who found that extraverts participate in greater amounts of social activity compared to other people since they tend to enjoy it. the positive association between social well-being and agreeableness is weak. this means that people who are high in extraversion and openness are more likely to harbor a higher level of social well-being as a contributor to subjective well-being compared to those high in conscientiousness, neuroticism, and agreeableness. thus, designing persuasive interventions to increase the sense of belonging and social connectedness can greatly impact the sense of swb and hence overall health and well-being of goal-oriented people (conscientiousness), emotionally unstable (neuroticism) and cooperative (agreeableness). phycological well-being: our results show that psychological well-being is positively associated with all personality traits except neuroticism: openness (β= 0.14, p<0.01), conscientiousness (β= 0.15, p<0.01), and extraversion (β= 0.18, p<0.01), agreeableness (β= 0.21, p<0.01), and neuroticism (β= -0.44, p<0.01). the negative association of neuroticism with psychological wellbeing is expected since people high in neuroticism tend to experience strong negative affect more often than other personalities. the positive association between psychological well-being and openness, conscientiousness, and extraversion is quite weak. this means that people who are high in agreeableness are more likely to maintain a higher level of psychological well-being as a contributor to subjective well-being compared to those high in openness, conscientiousness, extraversion, and neuroticism. this implies that designing persuasive interventions to increase psychological well-being will greatly impact the sense of swb and hence overall health and wellbeing of people who are open to experience, agreeable, conscientiousness, and emotionally unstable (neuroticism). 5. discussion this study presents the results from investigating the relationship between personality traits and distinct components of subjective well-being in an african country where such a relationship has not been empirically confirmed. in this section, we discuss the results in relation to personality traits. personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi extraversion is a personality trait characterized by the tendency to associate with others and seek excitement. our findings show that extraversion is weakly positively associated with psychological well-being. this means that people high in extraversion do not involve themselves in activities that give them a high sense of psychological well-being. this implies that persuasive interventions designers targeted at promoting the overall health and well-being of people who are outgoing and enthusiastic can achieve that by designing their interventions to promote the psychological well-being component. this finding suggests that the overall health and well-being of people from african nations who are extroverted can be significantly improved if their psychological well-being is enhanced. therefore, we recommend that persuasive intervention designers targeted at promoting health and well-being among people who are outgoing and enthusiastic (high in extraversion) could focus on designing to enhance their psychological well-being to boost their swb and hence overall health. several techniques can be used in pt design to promote the psychological well-being of individuals. for example, feeling secure about the future, being hopeful, being positive, being enthusiastic have been shown to promote the sense of psychological well-being [36]. therefore, persuasive strategies such as reward and praise for small achievements have the power to evoke some feel-good emotions while self-monitoring and simulation that track and project the impact of an individual’s micro efforts towards achieving the desired behavioral change can raise the anticipation of positive results hence promote psychological well-being. conscientiousness is a personality trait that describes an individual’s tendency to be selfdisciplined, result-oriented, and goal-oriented. our findings show that conscientiousness is weakly positively associated with emotional well-being and psychological well-being and does not have a significant association with social well-being. this means that people high in conscientious tendencies harbor low emotional well-being, psychological well-being, and social well-being. one possible explanation of why social well-being is not significantly associated with conscientious people is that their goal-oriented and result-driven nature may make them too focused and unable to spare time to socialize with people around them. they are more likely to set strict goals and targets that make them conscious of how they spend their time, hence, they may not involve in social activities that are not an explicit part of their goals. this implies that persuasive intervention designers targeted at promoting overall health and well-being of people from african nations who are result-oriented and strict on following norms and rules to achieve their goals can achieve that by designing their interventions to promote these three components of subjective well-being. therefore, we suggest that persuasive technology designers aimed at promoting health and well-being among people high in conscious tendencies could focus on designing to promote their emotional well-being, psychological well-being and most especially the social well-being. several techniques can be used in pt design to create opportunities for an individual to interact and relate with others (social well-being). for example, persuasive techniques from the social support category of the persuasive system design (psd) framework [61] such as the social comparison, cooperation, and competition which provides opportunities for people to share and compare information about their behavior, interact and work together with other people, and personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi compete with others could be employed by designers to promote an individual’s sense of social well-being. similarly, some techniques can be used in pt design to promote emotional well-being. for example, activities that make people experience serenity, love, support, the company have been shown to promote people’s sense of emotional well-being [42]. consequently, persuasive strategies such as social facilitation, cooperation, and social learning could be implemented to provide opportunities for users to discern that other people are performing the behavior (along with them) and offer some social support could be employed by designers to promote emotional well-being and hence overall health and well-being of individuals. the strategies also give them the motivation and boost to continue the behavior change task, neuroticism is a personality trait characterized by the tendency to often be nervous, fearful, anxious or emotionally unstable. our findings show that people high in neuroticism tendency are negatively associated with emotional well-being, psychological well-being, satisfaction with life, and social well-being and do not have a significant association with happiness. this means that people high in neuroticism are usually not satisfaction with their lives, and experience negative social well-being, emotional well-being, and psychological well-being with very low happiness. a possible explanation of why satisfaction with life is negatively associated with people's high neuroticism is that due to their distrustful and pessimistic nature, they may find it hard to see the positives in most life situations and hence tend to be unsatisfied with life. another possible explanation is that people high in neuroticism may be too fearful to explore a variety of experiences that add meaning to life and therefore tend to limit themselves to a certain lifestyle that they may not be satisfied with. similarly, a possible explanation of why psychological well-being is negatively associated with people high in neuroticism is that they tend to be pessimistic and hence may find it hard to cope with anticipated negative results or outcomes. this feeling of insecurity or negativity may result in low psychological well-being. an explanation for this finding is well captured in the statement of ankrom [62] “that anxiety is a response to an unknown threat.” these findings are also in line with the study of chamberlain [28] who shows that neuroticism is negatively associated with mental health. a plausible explanation of why emotional well-being is negatively associated with people high in neuroticism is that due to their nervous and sensitive nature they are often vulnerable to anxiety [63]. again, social well-being is negatively associated with people high in neuroticism because due to their distressful and fearful nature, they often avoid or decline any opportunities to socialize and integrate with other community members. finally, a possible reason why happiness is not significantly associated with neuroticism is that due to their inherent characteristics of being emotionally unstable, they are often stressed out and nervous. this makes them incapable of appreciating beauty and driving pleasure from simple things of life that make other people happy. this means that persuasive interventions designers targeted at promoting overall health and well-being of people from african nations who are high in neuroticism can achieve that by designing their interventions to promote all the five components of subjective wellbeing. however, neuroticism is most strongly associated with emotional well-being and psychological well-being. this implies that the overall health and well-being of people from african nations who are high in neuroticism can be more promoted if activities that give them personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi more sense of emotional well-being and psychological well-being are enhanced. therefore, although designers aimed at designing persuasive intervention to promote health and wellbeing among people who are high in neuroticism could focus on designing to promote their satisfaction with life, happiness, and social well-being, they should focus more on designing to promote their emotional and psychological well-being which are the strongest determinants of their swb and hence their overall health and well-being. some techniques can be used in pt design to promote happiness. for example, activities and strategies such as expressing gratitude, acts of kindness, savoring, optimism, committing to one’s goals have been shown to promote people’s feeling of happiness [10] [64]. therefore, persuasive strategies such as rewards which give individuals credit for performing the target behavior and praise, in recognition of good behaviors can be employed by persuasive intervention designers to promote health and well-being. self-monitoring and simulation can also be used to enable the user to see the projected and accumulated benefits of their tiny efforts towards achieving the desired behaviors as a way of promoting happiness and hence overall health and well-being. likewise, some techniques can be used in pt design to promote satisfaction with life. for example, activities such as setting and achieving goals, attaining status, gaining respect, have been shown to promote people’s satisfaction with life [33] [65]. consequently, persuasive strategies such as goal setting which provides people with opportunities to set their goals and feedback which evaluates peoples’ performance and provides them with information about their progress and achievements could be employed to promote a sense of achievement and fulfillment for people. similarly, the recognition strategy which provides opportunities for people’s achievements to be publicly recognized could be employed by designers to make people experience feelings of pride and satisfaction with life and hence promote their swb and overall health and well-being. agreeableness is a personality trait characterized by the tendency to be kind, sympathetic and cooperative. our findings show that agreeableness is weakly positively associated with social well-being, emotional well-being, satisfaction with life, and happiness. surprisingly, this finding contradicts chen’s [16] work among taiwanese, in which he found that agreeableness is negatively associated with satisfaction with life. one possible explanation for this contradiction is the influence of cultural differences in the target audience, as explained by schimmack et al. [17] who found that the influence of personality on the cognitive component of subjective well-being is moderated by culture. these findings suggest that people high in agreeableness do not naturally engage in activities that give them a high sense of social well-being, emotional well-being, satisfaction with life and happiness and hence harbor less of these components of swb. this implies that persuasive intervention designers targeted at promoting the overall health and wellbeing of people from african nations, who are helpful, less competitive and friendly can target promoting these four components of subjective well-being. thus, we recommend that persuasive intervention designers aimed at promoting health and well-being among people who are high in agreeableness should focus on designing to promote their social well-being, emotional well-being, satisfaction with life, and happiness as a way of promoting their swb and hence overall health and well-being. personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi openness is a personality trait characterized by the tendency to be open to a variety of experiences. our findings show that openness is weakly positively associated with emotional well-being and psychological well-being and does not have a significant associated with satisfaction with life. a possible reason why the association between satisfaction with life and openness is not significant is that people who are high in openness tend to explore a variety of life experiences, therefore, they may be overwhelmed if their life does not measure up favorably against their standard. they are more interested in exploring different life experiences. interestingly, these findings contradict chen [16] who found a significant correlation between openness and satisfaction with life. as explained earlier, this contradiction could be due to cultural differences in the target audience. this means that persuasive intervention designers aimed at promoting the overall health and well-being of people from african nations who are open to experience can do so by targeting these three components of subjective well-being. therefore, although designers aimed at promoting health and well-being among people who are high in openness could focus on designing to promote their emotional well-being and psychological well-being, they should emphasize more on their satisfaction with life. persuasive strategies such as reward, praise, and recognition for micro-behaviors could be employed in persuasive interventions for promoting health and well-being to improve individuals’ satisfaction with life in line with positive reinforcement. in summary, our findings show that people high in extraversion are most strongly positively associated with all the five swb components. this means that in general, they experience a higher sense of swb compared to other personality types. on the other hand, people high in neuroticism are most strongly negatively associated with the swb components. this suggests that they harbor a low sense of swb when compared to other personality traits. hence pt designers should pay special attention to how to design to promote swb among people high in neuroticism. 6. limitations one limitation of this study is that we used self-report measurements to assess people’s personality traits and subjective well-being. although this is still the standard practice, we acknowledge that it may be biased 7. conclusion this paper presented the results of a large-scale study of 732 participants from a developing african country investigating the relationship between personality traits and distinct subjective well-being components. interestingly, our findings show that the relationship between personality traits and subjective well-being of africans (predominantly nigerians) are to some extent similar to those of other nations. consistent with other studies, our results show that there are statistically significant relationships between the big five personality traits (openness, conscientiousness, entravision, agreeableness, and neuroticism) and the distinct components of subjective wellbeing (happiness, satisfaction with life, psychological well-being, emotional well-being, and social well-being). specifically, our results show that people high in extraversion are weakly associated with psychological well-being. we also uncovered that people high in personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi conscientiousness are weakly positively associated with emotional well-being and psychological well-being and have no significant association with social well-being. our study also uncovered that people low in neuroticism are strongly negatively associated with emotional and psychological well-being. furthermore, our study revealed that openness is weakly positively associated with emotional well-being, psychological well-being, and does not have a significant associated with satisfaction with life. finally, we found that agreeableness is weakly positively associated with social well-being, emotional well-being, satisfaction with life and happiness. findings from this study imply that the interplay between personality traits and subjective wellbeing could play an important role in health informatics design. this study suggests that health informatics designers who seek to promote the health and well-being of individuals of different personality traits could target promoting specific components of the subjective well-being that an individual is weak on or negatively associated with. we suggest some design guidelines and persuasive strategies for promoting different subjective well-being 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[accessed: 16-jun-2018]. appendix: personality traits and subjective well-being measurement instrument personality traits on a scale of 1 to 5(1= strongly disagree to 5 = strongly agree), to what extent do you agree with the following statements. i see myself as someone who: 1. is reserved. 2. is generally trusting. 3. tends to be lazy. 4. is relaxed, handles stress well. 5. has few artistic interests. 6. is outgoing, sociable. 7. tends to find fault with others. 8. does a thorough job. 9. gets nervous easily. 10. has an active imagination. happiness scale on a scale of 1 to 5 (1= very unhappy to 5 = very happy), please circle one number that corresponds to your response to each question. 1. if you were to consider your life, in general, these days, how happy or unhappy would you say you are? 2. compared to most of your peers, you consider yourself? 3. some people are generally happy. they enjoy life regardless of what is going on, getting the most out of everything. to what extent does this characteristic describe you? personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi 4. some people are generally not happy, although they are not depressed, they never seem as happy as they might be. to what extent does this characteristic describe you? 5. please, list things that make you happy (you can list up to 10) 6. please, list things that make you unhappy (you can list up to 10) satisfaction with life scale on a scale of 1 to 5 (1= strongly disagree to 5 = strongly agree), to what extent do you agree with the following statements. 1. in most ways, my life is close to my ideal 2. the conditions of my life are excellent. 3. i am satisfied with my life 4. so far, i have gotten the important things i want in life. 5. if i could live my life over, i would change almost nothing. 6. all things considered, i am satisfied with my life these days. 7. please, list things that give you satisfaction in life (you can list up to 10 things): 8. please, list things that make you unsatisfied with life (you can list up to 10 things): social well-being scale 1. i have close contact with my direct neighbors 2. i think it's important to be a member of an association 3. i'm content with my social position 4. i'm content with the relation to my neighbours 5. people in my neighbourhood handle each other in a positive manner. 6. i see myself as a part of society 7. i gladly have contact with other people via social media (facebook, e-mail) 8. there are enough people with who i feel strongly connected 9. i gladly help other people if they need my help 10. i'm content with the composition of the population in my neighbourhood. 11. i feel accepted in my neighbourhood 12. i trust in the people in my surrounding 13. i gladly participate in activities in my neighborhood 14. my work situation contributes to my well-being. 15. i gladly spent time with online gaming with other people 16. i'm content with my surroundings. psychological well-being scale 1. i lead a purposeful and meaningful life. 2. i am engaged and interested in my daily activities. 3. i am competent and capable in the activities that are important to me. 4. i am a good person and live a good life. 5. my material life (income, housing, etc.) is sufficient for my need 6. i am satisfied with my religious or spiritual life. 7. i am optimistic about the future. personality and subjective well-being: towards personalized persuasive interventions for health and well-being. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e1, 2020 ojphi 8. i have no addictions, such as to alcohol, illicit drugs, or gambling 9. people respect me. 10. i have been feeling optimistic about the future. 11. i actively contribute to the happiness and well-being of others. 12. i generally trust others and feel part of my community emotional well-being scale 1. i have been feeling useful. 2. i have been dealing with problems well. 3. i have been thinking clearly. 4. i have been feeling close to other people. 5. i have been feeling confident. 6. my social relationships are supportive and rewarding 7. i have been interested in new things. 8. i have not been feeling depressed. 9. i have not been feeling sad. 10. i have not been feeling afraid. 11. i have been feeling contented. 12. i have been feeling positive. 13. i have been feeling joyful. 14. i have been feeling cheerful. 15. i have been able to make up my mind about things. 16. i have been feeling loved vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi vincent: a visual analytics system for investigating the online vaccine debate anton ninkov1*, kamran sedig1 1. insight lab, western university, canada abstract this paper reports and describes vincent, a visual analytics system that is designed to help public health stakeholders (i.e., users) make sense of data from websites involved in the online debate about vaccines. vincent allows users to explore visualizations of data from a group of 37 vaccine-focused websites. these websites differ in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. by integrating webometrics, natural language processing of website text, data visualization, and human-data interaction, vincent helps users explore complex data that would be difficult to understand, and, if at all possible, to analyze without the aid of computational tools. the objectives of this paper are to explore a) the feasibility of developing a visual analytics system that integrates webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner; b) how a visual analytics system can help with the investigation of the online vaccine debate; and c) what needs to be taken into consideration when developing such a system. this paper demonstrates that visual analytics systems can integrate different computational techniques; that such systems can help with the exploration of online public health debates that are distributed across a set of websites; and that care should go into the design of the different components of such systems. keywords: visual analytics, public health, vaccine debate, webometrics, natural language processing, data visualization, human-data interaction abbreviations: visual analytics system (vas), multi-dimensional scaling (mds), natural language processing (nlp), natural language understanding (nlu), vincent (visual analytics system for investigating the online vaccine debate) *correspondence: anton ninkovaninkov@uwo.ca doi: 10.5210/ojphi.v11i2.10114 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. 1. introduction as the use of the internet expands, people engage in social discourse and debate in different areas of interest, generating a great deal of online data. one broad area of interest generating vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi such online information is public health. public health data is often large, complex, and difficult, if at all possible, to analyze without the aid of computational tools. public health informatics is a research area that focuses on “the systematic application of information, computer science, and technology to public health practice, research, and learning” [1]. visual analytics systems (vases) can be of great utility in public health informatics [2]. vases are computational tools that combine data visualization, human-data interaction, and data analytics. they allow users to interactively control data visualizations to change how data is analyzed and presented to them. vases make it possible for users to quickly make sense of online data that would otherwise be impossible or take more time and effort to accomplish. in this paper, we report and describe a vas designed to help public health stakeholders (users) make sense of data from websites involved in the online debate about vaccines. the vas, vincent (visual analytics system for investigating the online vaccine debate), allows users to explore visualizations of data from a group of 37 vaccine-focused websites (listed in appendix 1). these websites range in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. while numerous vases have been developed and studied previously, vincent is novel in that it integrates webometrics (i.e., co-link analysis), natural language processing (i.e., text-based emotion analysis), data visualization, and human-data interaction. the research questions this paper examines are as follows: 1. is it feasible to integrate webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner to develop a vas? 2. can such a vas help with the investigation of the online vaccine debate? 3. what are some of the considerations that need to go into developing such a system? the remainder of this paper is organized as follows. section 2 provides a conceptual and terminological background--i.e. vaccine debate, visual analytics systems, webometrics, and natural language processing. section 3 describes the development of vincent and includes an in-depth discussion of the various components of the vas. section 4 provides a summary and conclusions. 2. background this section provides a conceptual and terminological background for this paper. we will first describe the issue that vincent aims to clarify--i.e. the vaccine debate. next, we will review visual analytics. finally, we will discuss the data analytics methods (webometrics and natural language processing) that are used in this research. 2.1 vaccine debate in light of increased recent news coverage of outbreaks of diseases such as measles and whooping cough, the anti-vaccination movement appears to be a new and emerging phenomenon vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi [3-5]. the world health organization has listed the rise of the anti-vaccination campaign as a top ten health emergency in 2019 [6]. however, anti-vaccination views and sentiments are not a recent development. since edward jenner’s discovery of the smallpox vaccine, vaccination has garnered much attention both positive and negative. from the beginning, some have felt that the practice of vaccination is ineffective, violates personal freedoms, and is “unchristian” [7]. however, the centers for disease control reports that vaccines have had a positive impact on global health and are “one of the greatest achievements of biomedical science and public health” [8]. despite the medical community’s unified support of immunization, there are many reasons for the persistence of anti-vaccine views. there is some suggestion that increasingly polarized political views (especially in the united states) have generated an environment in which the rejection of scientific facts has become more prevalent and accepted [9]. this erosion of trust in scientific findings among segments of the population may also contribute to this increased polarization. additionally, the rise in accessibility to, and widespread use of, the internet has played a role in amplifying the voice of the anti-vaccination movement [10,11]. [11] states, “the connective power of the internet brings together those previously considered on the fringe. members of marginalized groups (e.g. holocaust deniers, 9/11 'truthers', aids deniers) can easily and uncritically interact with like-minded individuals online… anti vaccine groups have harnessed postmodern ideologies and by combining them with web 2.0 and social media, are able to effectively spread their messages”. hence, the internet plays an important role in the antivaccination movement, helping spread their message and promoting their views on vaccination dangers. the polarity of the vaccine debate is creating a clear divide and this has been revealed through both qualitative classification of inlinks [12] and quantitative co-link analysis [13]. the divide is having harmful effects on the health of the general population. “providers and policymakers must begin to recognize the jagged, context-dependent, equifinal nature of how parents sort through vaccination-related information or account for their vaccination decisions in order to reverse declining vaccination rates” [14]. some of the themes of the discussion that have developed in this polarized debate include those related to autism and vaccines, evil government conspiracies, and technological developments [15]. a more automated approach that would allow an analysis of such online discussions and information could help illuminate this public health problem. 2.2 visual analytics systems (vases) in today's environment of big data, people are often victims of information overload. they can get lost in and overwhelmed by the voluminous data and its meaning that they encounter [16]. by combining human insight with powerful data analytics and integrated data visualizations and human-data interaction, vases can help alleviate this problem. vases can enable potential stakeholders to make sense of data. “just like the microscope, invented many centuries ago, allowed people to view and measure matter like never before, (visual) analytics is the modern equivalent to the microscope” [17]. vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi vases are composed of three integrated components: an analytics engine, data visualizations, and human-data interactions [18,19]. the analytics engine pre-processes and stores data (e.g., data cleaning & fusion), transforms it (e.g., normalization), and analyzes it (e.g., multidimensional scaling, emotion analysis) [20]. examples of data analytics techniques that can be integrated into the analytics engine are webometrics and natural language processing (nlp). data visualizations in a vas can be visual representations of the information derived from the analytics engine. visualizations extend the capabilities of individuals to complete tasks by allowing them to analyze data in ways that would be difficult or impossible to do otherwise [19,21]. for instance, a scatterplot can be used to visually represent coordinates of entities, and this, in turn, helps the user determine quickly the proximity between data points. human-data interaction is used in vases to allow the user to control the data they see and the way the data is processed. interaction in vases supports users through distributing the workload between the user and the system during their exploration and analysis of the data [18,22,23]. some examples of the numerous human-data interactions that can be incorporated into vases include filtering, scoping, and drilling of data [24], with each interaction supporting different epistemic actions on information by the user. one of the theories that can help with the conceptualization of vases is general systems theory. systems theory views a system as composed of entities, properties, and relationships [25]. vases are complex, multi-level systems, consisting of systems within systems [18]. these multi-level systems consist of super-systems, systems made up of other systems, and subsystems, together making up a super-system [25]. with this understanding of systems theory, we can see how vases work. when building and examining vases, the interactions of the user with the system can have an impact on any of these levels. at the highest level, super-system interactions will change the overall display of the vas. at lower levels, the interaction subsystem will change specific components of the system. these interactions, regardless of level, are important to the functioning of the vas and necessary for making sense of the data being presented. there are several resources available to assist in developing vases. two of the most widely used vas resources include the open source d3.js javascript library [26] and tableau software [27]. the advantage of d3.js is the almost limitless customization capabilities it offers, as it is bound only by programming constraints, and the fact that it is open source. however, the time, effort, and programming skills required by developers to create systems is greater for d3.js than other solutions, as there are fewer templates and starting points to work with. tableau, on the other hand, is a proprietary data visualization software that provides users with the ability to develop interactive data visualizations with only minimal coding effort. one feature that makes tableau particularly appealing is that there are several templates available to users to build their own interactive visualizations. as well, tableau allows users to create dashboards easily, which place multiple interactive visualizations together in one system that automatically connects data together. while both d3.js and tableau can be useful solutions for developing visual analytics, tableau has been used in this research because of its ability to create a functioning and useful visual analytics system while at the same time reducing the programming workload. vases incorporate one or more data analysis techniques including (but not limited to) supervised learning (i.e. decision trees or svm), or cluster analysis [16]. previous vas research vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi has incorporated similar data analysis techniques used in vincent. for example, researchers have investigated how incorporating multi-dimensional scaling of co-occurrence data (discussed in section 2.3) in vases help users investigate entities and identify clusters in a variety of data sets [28,29]. as well, researchers have utilized emotion analysis (discussed in section 2.4) in vases that help users investigate online text from both social media and the general web regarding a variety of topics [30-32]. both these data analysis techniques have been implemented in vas research independently of each other, however there have been no published studies examining the integration of the two techniques in a single vas, as proposed in vincent. 2.3 webometrics webometrics is the “quantitative study of web-related phenomena” [33]. with the everincreasing adoption of the internet, the various metrics used for analyzing its data, such as hyperlinks, become important to investigate. two types of webometrics research methods exist: evaluative and relational [34,35]. evaluative webometrics can include examining webpages for properties such as (but not limited to) the number of external inlinks they receive (links directed to a website from another website) and the website location [12,35,36]. examining the number of inlinks a website receives has been shown to be an indicator of performance in a variety of measures for organizations [33,3739]. additionally, geographic location has demonstrated to be a valuable resource in conducting evaluative webometrics research [40,41]. relational webometrics focuses on “providing an overview of the relationships between different actors” [35]. co-occurrence measurements to indicate similarity are important for relational analysis in webometrics [35,36,42]. the concept behind this method is that the more entities share occurrences, the more likely they are to be similar in some way [34]. this method can apply to webometrics in the study of co-links to help analyze similarity in terms of shared online presence between websites [41,43-45]. to represent and examine co-link data, numerous studies have been conducted with multi-dimensional scaling (mds)--studies using mds to analyze business [45], university [46], government [47], and political domains [48,49]. all these studies found that using mds to analyze co-links generated worthwhile insights into the data. 2.4 natural language processing (nlp) nlp is a vast area of research that focuses on using computational methods to understand and produce human language content [50]. nlp encompasses a wide range of research topics, two of which are text-based emotion detection and word frequency [51]. text-based emotion detection has been examined previously in nlp research [51-54]. one resource, in particular, that has been developed that makes it possible for researchers to automatically conduct this type of analysis is ibm’s natural language understanding (nlu) api [55]. the nlu api (formerly referred to as the alchemyapi) has been widely used by many researchers to study topics, sentiments, and emotions in text [56-59]. the nlu api allows researchers to either input text directly or pull text from urls of webpages and return a number of different nlp analyses, one of which is emotion analysis. furthermore, the nlu api can not vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi only detect emotion on the entirety of a text/webpage, but can also return emotion scores for specified target words/phrases [55]. the study of word frequency in text has been examined and used in nlp research [60-62]. one of the main concerns for word frequency analysis is how to manage meaningless or unimportant words. in english, like any language, there are many words that are repeated frequently that are not necessarily the key point of interest to a reader. some of the more obvious examples of these words are “the”, “and”, and “of”. other types of undesirable words can exist depending on the domain of interest (e.g., dates or numbers). to deal with this issue, the technique of filtering for a list of stop words has been used, and preliminary lists of these words have been created that allow researchers to automatically exclude words that are not of interest [63]. to display word frequency data, word clouds have been used successfully [60-62]. word clouds display identified words in varying sizes, with larger words being the more frequent. word clouds are useful because they allow users to quickly see the most prevalent words of a text document and enable them to make quick assessments about what the overall text of a document/website may be discussing. 3. system design the design of vincent, displayed in figure 1, consists of three primary components: the analytics engine, data visualizations, and human-data interactions. in this section, we will discuss these components of the system and explain how the data was collected and managed. vincent was developed in tableau, version 10.5. figure 1: vincent: a visual analytic system vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi 3.1 analytics engine the analytics engine of vincent utilizes webometrics and nlp as its data analysis methods. in this section, we will discuss how, using these methods, data was collected, transformed, and processed. for webometrics, this included leveraging inlink data and geographic location data. for nlp, this included leveraging word frequencies and emotion detection analysis. the list of 37 vaccine websites (appendix 1) in vincent was created based on a list produced for a study on co-link analysis of vaccine websites which included a total of 62 websites [13]. websites from that study could be included in vincent if they had a central focus on the vaccine debate and a minimum of 200 inlinking domains. the reduction from the original list was primarily due to the elimination of website that were more minor, websites that had increased their scope beyond just vaccination, and websites that had gone obsolete or merged with another website to form a new website. this list should not be viewed as comprehensive of all vaccine websites, but rather as a sample of some of the more major english-based ones from both sides of the polarized debate. 3.1.2 webometrics inlink data was collected from each website using moz’s link explorer tool (https://www.moz.com/link-explorer). diverging from some of the previous webometrics research using inlink data, which mostly investigated inlinks coming from pages (41,43–45) and sites (13), vincent uses inlink data about the inlinking domains. changes in september 2018 to the data provided by moz required us to adapt and examine the feasibility of using domainlevel inlink data. after comparing domain-level inlink data to data collected for a previous study (13), we determined that the domain-level inlink data was a suitable replacement and would be used in the analytics engine of vincent. the shared online presence between the set of websites (appendix 1) was analyzed using mds. following similar data analysis techniques to that of previous mds research [13], the inlink data collected on each website was used to create a similarity matrix, which is based on the number of co-links each website shared with one another. using a computer program originally developed for a previous study [13], this co-link data was generated from the collected raw data. using the output co-link matrix, the data was input into spss version 25 and an mds analysis was conducted. the results of this analysis provided a scatter plot in which each data point was plotted according to the number of co-links they shared, or in other words their shared online presence. websites that shared more inlinks (and therefore more online presence) were more similar and plotted closer together, while those with fewer inlinks were plotted further away from each other. the goodness of fit between the output scatter plot and the co-link matrix had a stress value of less than 0.05, which suggests a good fit between the two. data was also collected regarding the geographic location of the websites. this data was collected through two primary means. the first way of collecting location data was through the sites themselves. many of the websites had identifying information about the managing owner or organization. the data usually came from an “about us” or “contact us” page and required manual labor to find. for those that did not indicate on their website a location, icann whois vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi registration data was collected. for each of the various collected locations, latitude and longitude coordinates were generated to plot each website on the map of website locations. 3.1.2 nlp word frequency data was collected using the following process. first, each website was analyzed and crawled using insite5, a software package developed by inspyder (https://www.inspyder.com/products/insite). with this software, we were able to obtain a csv export file containing a list of all the words contained on each website, along with the frequency of those word occurrences. after collecting all the raw data about each website, the word frequency lists were filtered to meet the requirements of our analysis. in other words, we wanted only unique words related to the vaccine debate to be displayed. in this effort, we manually created a stop words list to remove irrelevant or common words. the list was built, first, using the natural language toolkit list of stop words for english [63]. this list of stop words contains some of the most common english words (e.g., “i”, “you”, “too”). from this starting point, the list was expanded to include words that needed to be removed including, but not limited to, letters (e.g., “a”, “b”), dates (e.g., “january”, “wednesday”), self-reference names (e.g., “nvic”, “voices for vaccines”), people’s names (e.g., “tom”, “katie”), internet words (e.g., “blog”, “post”), and common vaccine debate words (e.g., “vaccines”, “vaccination”). in total, the stop words list, used to refine the word frequency data, consists of 1231 words. after finalizing each of the website’s individual word frequency list, combined word frequency lists were created for 3 sets of websites: all websites, anti-vaccine websites, and pro-vaccine websites. for each word, the sum of the word frequency was normalized by sum of the total number of words in that set. this generated a proportional count of each word’s presence on the website for each website’s top 25 words. this was a more accurate reflection of the presence of the word on the site rather than simply counting the word frequency totals as some sites had more total words than others. with these proportional word frequencies generated, a list of top 25 words for the 3 sets of websites was also created: all websites, anti-vaccine websites, and provaccine websites. text-based emotion detection in the website was conducted with the use of ibm’s nlu tool. this tool provides nlp automation through the use of their api and, specifically, can do targeted phrase emotion detection. a user can input text or a url of a webpage of interest and specify target phrases of interest. the nlu api will return scores for the level of emotion detected for those phrases. five different emotions (joy, fear, anger, sadness, and disgust) are provided for analysis, which is an overrepresentation of negative emotions [64]. for this system, we did not want to bias our data by over-representing negative emotions. consequently, the data was cleaned up by merging the 4 negative emotions into one and the labels were changed to reflect a binary of positive emotion (joy) and negative emotion (fear, anger, sadness, and disgust). the vaccines of interest that were examined included: flu, mmr, measles, chicken pox, whooping cough, hpv, polio, hepatitis b, and meningitis. the text was processed using the nlu api’s targeted emotion analysis tool. for each of the vaccines, we manually sampled 2 webpages that contained meaningful discussion about the specified vaccine. several alternate ways of referencing the vaccines were all targeted. for example, with the mmr, targeted phrases included “mmr”, “mmr vaccines”, and “mmr vaccination”, among others. the data from vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi each of these different phrases for a vaccine were then merged to reflect the total emotion detected about the specified vaccine. 3.2 data visualizations vincent is comprised of four main visualization components: an online presence map, a word cloud, a map of website locations, and an emotion bar chart. each of these visualizations represents an important aspect of the websites' information and involves some type of webometrics or nlp data analytics. in this section, each of these visualizations will be discussed, looking at the decisions that were made to represent the data. 3.2.1 online presence map the online presence map, displayed in figure 2, is a representation of the hyperlink data analyzed from each website. the generated mds scatter plot map of the websites displays each website in proximity to each other based on their shared online presence. websites that are plotted closer together share more online presence, while those plotted further away share fewer. based on this map, polarity between the antiand pro-vaccine websites was evident, similar to findings in previous related research [13]. all anti-vaccine websites ended up on the left side of the map, while all pro-vaccine websites are located on the right side with a space in the middle dividing the two. to display the existence of this polarity, a line dividing the two groups of websites was added to the map with labels for the anti-vaccine and pro-vaccine sides. online presence for each website was encoded as a circle representing each of the websites. in this representation, each of the circles was sized based on their total number of inlinking domains. the larger a circle, the more inlinks and, therefore, the larger online presence it has. for reference, the site with the most inlinking domains (9,986) is immunize.org, while the site with the fewest inlinking domains (248) is vaccine injury help center. vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi figure 2: mds similarity map 3.2.2 word cloud the word cloud, displayed in figure 3, is a representation of the 25 most common unique words that are related to the vaccine debate from each website or group of websites. words are sized based on the frequency with which they appeared on the website or group of websites. the bigger a word is on the word cloud, the more frequently it is used on the website, while the smaller a word is, the less frequently it is used. each word was colored differently to assist with differentiating words from each other. vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi figure 3: word cloud for all websites 3.2.3 map of website locations the map of website locations, displayed in figure 4, shows a representation of the locations of each website on a world map. website location is an important piece of data as it allows users of the system to explore the geographic diversity of the websites and identify where clusters of websites may exist. similar to the online presence map, the website locations use circles to encode each website. different from the online presence map, the circles were all sized equally to help the user see the location of each website, and to avoid confusion with excessive overlapping and occlusion of the circles. vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi figure 4: map of website locations 3.2.4 emotion bar chart the emotion bar chart, displayed in figure 5, represents positive and negative emotions for a selection of each website's text about a set of vaccines. the two bar charts represent the negative (red) and positive (green) emotions detected by the api. each bar is composed of individual rectangles that refer to individual websites in the set studied. the width of each of these individual rectangles represents the degree of detected emotion on that specific website. the wider the rectangle, the more that emotion is detected. the entire bar is made up of all the smaller rectangles (websites). this bar then represents the overall detected emotion in the text of the complete website set. the negative and positive bar charts will change in response to the data set that is selected. this will be discussed in more detail in section 3.3.2. figure 5: emotion bar chart vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi 3.3 human-data interactions to support users to gain insight into the data and explore the online vaccine debate, many interactions are built into vincent. these interactions take place on a global level as well as in the sub-systems of vincent. in this section we will explore these interactions and discuss how they will assist users to explore the data. 3.3.1 global system interactions there are several interactions that users can perform on vincent that occur at the global system level. these interactions not only affect displayed data at individual, sub-system levels of vincent, but also change displayed data at the level of the whole system. global system interactions in vincent include website selection and filtering of websites. the website selection interaction allows users to focus on a single website. using this interaction (see figure 6), users can highlight a single website’s data throughout the system in order to determine quickly the website’s position on vaccination, online presence, location in the world, and emotion about specific vaccines. consider the following use case. a user is interested in learning more about the website “sanevax”. they would select this website (figure 6) from the existing options. vincent would then highlight the data points associated with this website, as displayed in figure 7. for this selected website, the user can immediately find that the website's position is anti-vaccine, that it has strong online presence, that it is located in north western part of north america, that it has more negative emotions regarding vaccines than positive, and that it discusses many issues related to hpv (i.e., cervarix, gardasil, cancer, silgard, hpv). figure 6: website selection interaction vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi figure 7: vincent after website selection interaction in addition to the website selection interaction, users have the ability to filter the data to focus on a selected group of websites. users can highlight and select websites using any of the 3 visualizations, thereby filtering and isolating the data points of a subset of websites. this can be done using the online presence map, map of website locations, or emotion bar chart. consider a sample use case. a user is curious to learn more about the websites located in north eastern part of north america. the user goes to the map of website locations and picks websites located in that geographic region. in reaction, the data points on the online presence map and the data of the emotion bar charts are filtered to show only these data points, as displayed in figure 8. simultaneously, the stat tracker on the bottom right changes to give the user a numeric count of how many websites they are utilizing now, and how many of each vaccine position is included. the user will quickly see that they have selected 15 websites (10 pro-vaccine and 5 anti-vaccine websites), that the websites are wide ranging in shared online presence, and that they have approximately equal degree of positive and negative emotions associated with the vaccines. vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi figure 8: global filtered selection (north eastern north america) 3.3.2 sub-system interactions there are a number of interactions that can be performed at the sub-systems level of vincent. these interactions are focused on isolated elements of the system. they include such interactions as filtering the emotion bar chart to display selected vaccines, hovering display elements to expand an information box, and navigating the map of website locations. the vaccine selection interaction allows users to filter the displayed data on the emotion bar chart. upon opening vincent, the emotion bar chart displays the overall vaccine emotion data. when a user selects a specific vaccine, the bar chart changes to display only the emotion data that is collected about that specific vaccine. consider a sample use case. a user is curious about the emotions of the entire set of websites regarding the mmr vaccine. the user would select this vaccine (see right-hand panel in figure 9), and the bar charts change to display the data. the user can immediately see that there is a greater level of negative emotion on the set of websites than positive emotion regarding the mmr vaccine. figure 9: filtered vaccine selection (mmr) vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi users also have the option to hover over the online presence map, map of website locations, or emotion bar charts to expand an information box (this is referred to in tableau as a tooltip) about each specific data point. when a user hovers off the data point, the information box disappears. again, a sample use case is illustrative of this. a user is interested in identifying which of the pro-vaccine websites have the greatest online presence. to do this, the user would examine the online presence map, determine which token on the pro-vaccine side of the map is the largest, and hover the mouse icon over the token to reveal the information (see figure 10). in this case, it would be “immunization action coalition”. similarly, if the user were interested in knowing more about a website at a specific location or emotion score, they would hover over those data points to reveal that information. figure 10: hover to expand information box finally, on the map of website locations, users have the ability to navigate through the set of websites. on the map of website locations, users can zoom in and out of the map to focus on specific areas. as well, users can click on and drag over the map to move the area of focus. consider a sample use case. a user is interested in looking at websites in europe to get a better sense of where exactly they are located. by zooming in on the map and going to europe (as seen figure 11), they can clearly identify four websites located there in england, germany, belgium, and switzerland. vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi figure 11: navigate map of website locations 4. summary and conclusions in this paper, we have reported the development of vincent, a vas to help with the investigation of data from websites involved in the online debate on vaccination. vincent was created using tableau, version 10.5. vincent incorporates three main sub-systems, each comprised of other sub-systems. an analytics engine made up of webometric (co-link analysis) and nlp (text-based emotion detection) data analysis components; visualization, made up of several different data visualizations; and interaction, made up of a set of different human-data interactions. the development of vincent demonstrates that it is feasible to integrate webometrics, natural language processing of website text, data visualization, and human-data interaction into a vas. vincent is novel in its incorporation and integration of the data analysis techniques used (i.e. co-link analysis and text-based emotion analysis) with data visualization and human-data interaction, which had never been previously attempted. vincent supports user exploration of data derived from a set of 37 vaccine websites and enables the user to investigate and develop an overall perspective on the vaccine debate. by looking at data from individual websites and groups of websites, a user can identify the breakdown of proand anti-vaccine websites, the emotions contained within these websites about specific vaccines, the locations of these websites, and the frequency of vaccine words that appear in these websites. furthermore, by integrating the data from these different websites, users can associate the various types of data and uncover patterns that would be otherwise difficult to identify. several considerations should go into creating vases such as vincent. first, deciding which tool to use to create the vas is important. there are advantages and disadvantages to using more programming intensive solutions (such as d3.js) versus more rigid, yet easier to use, toolkitbased solutions (such as tableau). as well, identifying the appropriate data sources is a challenge that is unique to each project. online data sources are constantly changing; therefore, it is important for researchers to keep abreast of the current available data. depending on the resources available to the developer, alternate methods and sources for acquiring proprietary data could improve the value of the system. next, determining which visualizations are most vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi appropriate for each type of studied dataset is important. for example, the emotion bar charts, presented here, went through several iterations. at first, tree maps were tested but were found to be inadequate at representing certain aspects of the data. researchers who develop similar vases need to consider all facets of their data and desired interactions and test various iterations of their system. finally, incorporating meaningful interactions into the vas is important. it is necessary to analyze the tasks that users would need to perform, and then determine what combinations of interactions would facilitate the performance of these tasks. in the case of vincent, such tasks included comparing websites, identifying groups of websites, and identifying trends in the entire set of websites. vincent was developed to help users make sense of the data from vaccine websites and, ultimately, the online vaccine debate. however, there are many other areas, both within and outside of public health, for which a system such as this could also prove useful. in public health, a similar vas would be useful for surveillance of other online health debates, such as debates on the efficacy of alternate health claims or debates regarding different medications and drugs. outside of public health a system similar to vincent could prove beneficial in the areas of business, academia, or politics. one example of such an area that would be well served by a similar vas is the online discussion about cannabis use. there are diverging positions regarding the risks and benefits of cannabis, and a system similar to vincent could enable users to further investigate the debate and make sense of the data from existing websites. with such a system, users would be able to quickly identify the positions of different websites (i.e., proor anti-cannabis, medical or recreational focus on cannabis, and so on), obtain a geographic breakdown of website locations, determine the focus of each website, and identify the detected emotions about various concepts related to cannabis (i.e., “essential oils” or “epilepsy”). performing tasks such as these could help researchers acquire valuable insight into the online debate on cannabis and determine what (if any) actions could be taken (or policies adopted) to improve public health in this area. 4.1 limitations there were two key limitations to the development of vincent. the first set of limitations was related to the data and analysis tools that we used. social media data could have generated very rich and revealing data for investigation, but these types of data are proprietary and not freely accessible to conduct research of this scale. moz link explorer provided only enough data on inlinks for an adequate co-link analysis at the domain inlink level; getting data for the pageor site-level analysis was not feasible due to the associated cost. as the trend in the area of webometrics is towards collected data becoming increasingly proprietary, researchers need to consider alternative ways of making do with the limited data availability. additionally, resources like the nlu api are limited in their ability to analyze the websites emotions. tools like nlu api are essentially only in the infancy of their development. in the future, tools for emotion detection and nlp will certainly improve and be able to achieve a broader range of analysis and better results than are currently possible. the second set of limitations was related to the interaction capabilities afforded by tableau as a toolkit. for example, it was not possible in tableau to allow the filtering interaction to also filter vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi the word cloud selection. ideally, a user would want to be able to see word clouds of the top 25 words of any subset of websites selected in the other visualizations. however, given the manner by which tableau allows for the structure of data, and the data management solutions it works with, this was not possible to achieve. the work-around we used for this was to create the website selection interaction that allowed individuals to filter for a specific website throughout vincent. 4.2 future research in a follow up paper, we plan to conduct user testing of vincent to evaluate whether there is observable benefit to using vincent, and, if so, to what extent and in what ways. the findings of this research will lead to the development of best practices for creating similar vases. they will also help with the identification of potential 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canada http://immunize.ca institute for vaccine safety http://www.vaccinesafety.edu/ national vaccine information center http://www.nvic.org/ parents requesting open vaccine education http://vaccineinfo.net/ prevent childhood influenza http://www.preventchildhoodinfluenza.org/ sabin vaccine institute http://www.sabin.org/ safe minds http://www.safeminds.org/ sanevax http://sanevax.org/ vincent: a visual analytics system for investigating the online vaccine debate online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e5, 2019 ojphi shots of prevention http://shotofprevention.com/ the immunization partnership http://www.immunizeusa.org/ the informed parent http://www.informedparent.co.uk/ the thinking moms revolution http://thinkingmomsrevolution.com/ think twice global vaccine institute http://thinktwice.com/ vaccinate your family https://www.vaccinateyourfamily.org/ vaccination information network http://www.vaccinationinformationnetwork.com/ vaccination liberation http://vaclib.org/ vaccination news http://www.vaccinationnews.org/ vaccine choice canada http://vaccinechoicecanada.com vaccine injury help center http://www.vaccineinjuryhelpcenter.com/ vaccine injury info http://www.vaccineinjury.info/ vaccine liberation army http://vaccineliberationarmy.com/ vaccine resistance movement http://vaccineresistancemovement.org/ vaccine truth http://vaccinetruth.org/ vaccines today http://www.vaccinestoday.eu/ vaccines.gov http://www.vaccines.gov/ vaxxter http://vaxxter.com voices for vaccines http://www.voicesforvaccines.org/ world association for vaccine education http://novaccine.com/ http://vaccines.gov/ http://vaxxter.com/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts biosurveillance adaptable framework for teaming, exploration and reuse (bioafter) timothy dasey* and lars fiedler chemical and biological defense systems, mit lincoln laboratory, lexington, ma, usa introduction next-generation software environments for disease surveillance will need to have several important characteristics, among which are collaboration and search and discovery features, access to various data sets, and a variety of analytic methods. however, perhaps the most important feature is the least often mentioned – the ability to have the system adapt over time without high reengineering cost. the public health community cannot afford software redesigns every few years as data sets expand, analysis needs evolve, and software deficiencies are exposed. in addition to the need to adapt an environment over longer time periods, epidemiologists have high variability in their day-to-day needs that require adaptability over short time periods as well. each outbreak or health situation has unique aspects, and analysts need to be able to bring in data and methods unique to that situation that may not be easily anticipated a priori. the most common approach to increasing reusability and decreasing upgrade costs are open architecture software frameworks such as service-oriented architectures (soas). if well implemented, soas can significantly reduce software upgrade costs by allowing services (a software module) to be easily swapped out for improvements or supplemented with additional services. soas can help with long-term adaptability, but are not useful in short-term adaptability, since the software development team must be engaged in each cycle. another approach is to include an app store. unfortunately, app stores for government use have often been disappointing. apps can tend to be quite simple, and even slight changes from what is programmed – a predictable situation with the variability seen in disease surveillance realm will result in an epidemiologist having to get a software developer to make them a new app. methods instead of the power for adaptability remaining solely in the control of software developers, that power needs to also be in the hands of the users themselves. the bioafter project builds upon soa and app store concepts by allowing apps to be strung together in unique combinations, according to the problem of the day. as examples, these apps can be data access programs, data quality editing, algorithms of various complexity, or reporting and visualization modules. the app store feature allows software developers, including the public health academic community, to add new methods to the environment, while the composability feature allows ad-hoc combinations of apps to suit particular situations. the user composability feature would be of limited value without collaboration features in the environment. we expect that only a subset of users will make the effort to do composition. the rest will want to learn from those “super users”. the bioafter environment allows data, apps (including new apps created by compositions of other apps), and analysis results to be shared with the entire epidemiological community, or with a set of “friends” or people with common interests. analysts who use various apps can rate their value. the environment is intended to allow search and discovery of apps, expertise, or even people who may have needed expertise or experience with similar situations (these features are in development). results the motivations and features of the bioafter environment will be described at the isds conference, and a brief demonstration of the capabilities on an example use case will be given. keywords biosurveillance; collaboration; discovery; composition; adaptability acknowledgments this work is sponsored by the office of the assistant secretary of defense for research and engineering (asd(r&e)) under air force contract #fa8721-05-c-0002. opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the united states government. *timothy dasey e-mail: timd@ll.mit.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e201, 2013 ojphi-06-e19.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 56 (page number not for citation purposes) isds 2013 conference abstracts joe gibson1, bryant thomas karras2 and gideon s. gordon*3 1marion county public health department, indianapolis, in, usa; 2washington state department of health, shoreline, wa, usa; 3ehealth, association of state and territorial health officials, 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���������%�������������� ��� �!���%����� �!������� ����������"����������� ������ � ��� ����� "���"���"������ �����������,������ 0 ����� �"������������������&������ ��!���1�����"�������������� ��� �!����������������� ��!�� ������� �"������ �� ����+� ���������� �� /��&���������� ������ ���������������"� ���� ��""�,��������"���!����� ���������� �� �� ��=���""���� &���� �����"�������� ��� ������+��� ���� ������""��������"�������"������������������ ���� (�!�������� (����� � ������ � � � ���������� ������ ���� ���� �� �� �� ���� ���������� ��""� ��������� ��� ������� � � �� �� �� ���� �!�"!� ������(�!��������(�������""��������������!�"����������� ���� �� ���������� �����������"����� � ������ ��������� ������� ��� �� ����� ��%"������"������������� ��� � ������!�"������� keywords �� �� �>� �� �� �� ���>� ��������� ��!��""����>� ��!�������>� �"�� ��� *gideon s. gordon e-mail: sgordon@astho.org� � � � biosense 2.0 governance: surveying users and stakeholders for continued development online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e19, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using information entropy to monitor chief complaint characteristics and quality shaun grannis*1, 2, brian dixon4, 2, yuni xia3 and jianmin wu4, 2 1indiana university school of medicine, indianapolis, in, usa; 2regenstrief institute, indianapolis, in, usa; 3indiana university purdue university indianapolis, indianapolis, in, usa; 4indiana university school of informatics, indianapolis, in, usa objective we describe how entropy, a key information measure, can be used to monitor the characteristics of chief complaints in an operational surveillance system. introduction health care processes consume increasing volumes of digital data. however, creating and leveraging high quality integrated health data is challenging because large-scale health data derives from systems where data is captured from varying workflows, yielding varying data quality, potentially limiting its utility for various uses, including population health. to ensure accurate results, it’s important to assess the data quality for the particular use. examples of sub-optimal health data quality abound: accuracy varies for medication and diagnostic data in hospital discharge and claims data; electronic laboratory data used to identify notifiable public-health cases shows varying levels of completeness across data sources; data timeliness has been found to vary across different data sources. given that there is clear increasing focus on large health data sources; there are known data quality issues that hinder the utility of such data; and there is a paucity of medical literature describing approaches for evaluating these issues across integrated health data sources, we hypothesize that novel methods for ongoing monitoring of data quality in rapidly growing large health data sets, including surveillance data, will improve the accuracy and overall utility of these data. methods our analysis used chief complaint data derived from the original real-time hl7 registration transactions for ed encounters over a 3year study period between january 1, 2008 and december 30, 2010 from over 100 institutions participating in the indiana public health emergency surveillance system (phess) [1]. we used the following syndrome categories based on various definitions: respiratory, influenza like illness, gastrointestinal, neurological, undifferentiated infection, skin, and lymphatic. we calculated entropy for chief complaint data [2]. entropy measures uncertainty and characterizes the density of the information contained in a message, commonly measured in bits. we analyzed entropy stratified a) by syndrome category, b) by syndrome category and time, and c) by syndrome category, time, and source institution. results analysis of more than 7.4 million records revealed the following: first, overall information content varied by syndrome, with “neurological” showing greatest entropy (figure 1). second, entropy measures followed consistent intraorganizational trends: information content varied less within an organization than across organizations (figure 2). third, information entropy enables detection of otherwise unannounced changes in system behavior. figure 3 illustrates the monthly entropy measures for the respiratory syndrome from 5 sources over 36 months. one source changed registration software. their visit volume didn’t change, but the information content of the chief complaint changed, as indicated by a substantial shift in entropy. conclusions as we face greater data volumes, methods assessing the data quality for particular uses, including syndrome surveillance, are needed. this analysis shows the value of entropy as a metric to support monitoring of surveillance systems. future work will refine these measures and further assess the inter-organizational variations of entropy. keywords analytics; data quality; surveillance; system monitoring; information theory acknowledgments this work was performed at the regenstrief institute in indianapolis, indiana. this study is supported in part by the cdc through the indiana center of excellence in public health informatics (1p01hk000077-01). online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e16, 2013 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts references 1. finnell jt, overhage jm, grannis s. all health care is not local: an evaluation of the distribution of emergency department care delivered in indiana. amia annu symp proc. 2011; 409-16. 2. shannon, claude e. (july/october 1948). “a mathematical theory of communication”. bell system technical journal 27 (3): 379–423. *shaun grannis e-mail: sgrannis@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e16, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts an experimental study using opt-in internet panel surveys for behavioral health surveillance carol a. gotway crawford*1, catherine a. okoro1, haci m. akcin1 and satvinder dhingra2, 1 1centers for disease control, atlanta, ga, usa; 2northrop grumman, atlanta, ga, usa objective to present the design and preliminary results of a pilot study to investigate the use of opt-in internet panel surveys for behavioral health surveillance. introduction today, surveyors in both the private and public sectors are facing considerable challenges with random digit dialed (rdd) landline telephone samples. the population coverage rates for landline telephone surveys are being eroded by wireless-only households, portable telephone numbers, telecommunication barriers (e.g., call forwarding, call blocking and pager connections), technological barriers (call-blocking, busy circuits) and increased refusal rates and privacy concerns. addressing these issues increasingly drives up the costs associated with dual-frame telephone surveys designed to be representative of the target population as well as hinders their ability to be fully representative of the adult population of each state and territory in the united states. in an effort to continue to meet these challenges head on and assist state and territorial public health professionals in the continued collection of data that are representative of their respective populations, novel approaches to behavioral health surveillance need continued examination. both private and public sector researchers are evaluating the use of internet opt-in panels to augment dual-frame rdd survey methods. compared to dual-frame rdd, opt-in internet panels offer lower costs, quick data collection and dissemination, and the ability to gather additional data on panelists over time. however, as with dual-frame rdd, this mode has similar challenges with coverage error and non-response. nevertheless, survey methodologists are moving forward and exploring ways to reduce or eliminate biases between the sample and the target population. methods a collaborative pilot project was designed to assess the feasibility and accuracy of opt-in internet panel surveys for behavioral health surveillance. this pilot project is a collaboration between the cdc, four state departments of health, opt-in internet panel providers and the leads of several large surveys and systems such as the patientreported outcome measures information system (promis) and the cooperative congressional election study (cces). pilot projects were conducted in four states (ga, il, ny, and tx) and four metropolitan statistical areas (atlanta, chicago, new york city, and houston). data were collected using three different opt-in internet panels and sampling methods that differ with respect to recruitment strategy, sample selection and sample matching to the adult population of each geography. a question bank consisting of 80 questions was developed to benchmark with other existing surveys used to assess various public health surveillance measures (e.g., the behavioral risk factor surveillance system, the promis, national survey on drug use and health, and the cces). results we present comparative analyses that assess the advantages and disadvantages of different opt-in internet panels sampling methodologies across a range of parameters including cost, geography, timeliness, usability, and ease of use for technology transfer to states and local communities. recommendations for future efforts in behavioral health surveillance are given based on these results. keywords random digit dialing; brfss; survey methods; sample matching; representativeness references ansolabehere s, schaffner bf. 2010. re-examining the validity of different survey modes for measuring public opinion in the u.s.: findings from a 2010 multi-mode comparison. http://projects.iq.harvard. edu/cces/files/ansolabehere_schaffner_mode.pdf. curtin r, presser s, singer e. 2005. changes in telephone survey nonresponse over the past quarter century. public opinion quarterly;69(1):87-98. liu h, cella d, gershon r, shen j, morales ls, riley w, hays rd. 2010. representativeness of the patient-reported outcomes measurement information system internet panel. j clin epidemiol 63(11):1169-78. rivers, d., 2007. sampling for web surveys, paper presented at the joint statistical meetings. http://www.laits.utexas.edu/txp_media/html/ poll/files/rivers_matching.pdf *carol a. gotway crawford e-mail: cdg7@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e24, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts serum zinc concentration and acute diarrhea in children from different regions of uzbekistan gulnara a. ibadova*, t. a. merkushina, e. s. abdumutalova and aybek v. khodiev infectious and parasitic diseases of children, tashkent institute of postgraduate medical education, tashkent city, uzbekistan objective to study the blood serum zinc concentration in children with acute diarrhea (ad) in in-patient facilities before and after therapy. introduction there are several reports of zinc deficiency in pathogenesis of acute and chronic diarrhea. the literature review showed children with diarrhea and chronic gastroduodenitis performed zinc deficiency in majority of cases (1). the normal values of zinc in blood serum are 12.8-27.8 µmol/l (2). there is a threshold of 13µmol/l zinc concentration for zinc deficiency diagnosis. the zinc level 8.2 µmol/l and below is poor prognostic criteria (3). methods totally 102 children (1-14 years old) with ad in in-patient facility from different regions were studied for serum zinc concentration before and after treatment. termez and saraosie cities are located in south of uzbekistan, in the region with high negative impact from the nearly tajikistan located aluminum producing plant. the serum zinc level measured by neutron-activation method in the institute of nuclear research (inr). results the zinc concentration in serum significantly varied by the region (table 1). the level of zinc in children from tashkent estimated at lower normal limit with reduction below normal values after treatment. children from termez during admission to the in-patient facilities were zinc deficient with further reduction to the poor prognostic level. children in saraosie admitted to the in-patient with significant zinc deficiency that remained on poor prognostic level after treatment. conclusions the study results may indicate the treatment of ad in children do not replenish the zinc to the appropriate level. though some confounding factors may contribute the observed zinc disorders the results may indicate environmental factors, such as pollution by aluminum producing plant emission to contribute the difference in zinc concentration and should be considered for the correction and treatment of ad in children. table 1.the serum zinc concentration in children with acute diarrhea from different regions of uzbekistan before and after treatment. keywords acute diarrhea; zinc; zinc deficiency acknowledgments authors express their gratitude to the staff of institute of nuclear research. references 1. brooks wa, santosham m, roy sk, faruque asg, wahed ma, nahar k, khan ai, khan a f, fuchs gj, black re. efficacy of zinc in young infants with acute watery diarrhea. [internet]. the american journal of clinical nutrition 2005 sep;82(3):605–10.available from: http://www.ncbi.nlm.nih.gov/pubmed/16155274 2. ackland ml, michalczyk a. zinc deficiency and its inherited disorders -a review. [internet]. genes & nutrition 2006 mar;1(1):41– 9.available from: http://www.ncbi.nlm.nih.gov/pubmed/18850219 3. karlinskiy vm. zinc deficiency syndrome. nutrition issues 1980;1:10– 18. *gulnara a. ibadova e-mail: prof.ibadova@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e181, 2013 editorial: volume 2, number 1 (2010) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 editorial: volume 2, number 1 (2010) welcome to the first issue of the 2nd volume of the online journal of public health informatics, the first journal dedicated to the dissemination of information about the best public health informatics practices among practitioners, researchers, and educators. by all accounts, interest in the journal has been overwhelming, as evidenced by the growth in submission of original articles. this issue contains five original articles and one review article. topics covered in the original articles range from the construction of a flexible query interface for web-based disease surveillance systems to the use of cloud computing to provide on-demand resources for epidemic analysis. in the first article, titled advanced querying features for disease surveillance systems, the authors build a flexible interface for web-based surveillance disease systems. the interface allows users from different health departments and jurisdictions to build, save, and share queries, thereby improving the efficiency of operations and, under certain circumstances, eliminate the need for application developers and database administrators to make modifications to the surveillance systems. the second article presents a method for integrating aberration detection models into disease surveillance systems in order to identify deviations from expected patterns. it is quite possible that different aberration algorithms will generate different results when applied to the same datasets. this will present problems to public health experts responsible for making resource allocating decisions for controlling disease outbreaks. the methodology developed in this paper accounts for the relationships between multiple algorithms and enables public health professionals to interpret aberration detection results with some degree of confidence. successful intervention and containment of an epidemic depends on the early detection and timely response to outbreaks by epidemiologists and other health professionals. the analytical processes involved in accurately identifying outbreaks of epidemics can be very resource-intensive and are usually beyond the human and financial resources available in an average state health department. the third article, titled on-demand large scale spatial analysis of epidemics, uses cloud computing to provide on-demand resources for epidemic analysis by using satscan, a software application for identification of disease clusters at the initial stages of an outbreak. an advantage of cloud computing is that it provides the required computational resources within the budgetary constraints of the typical health department. the healthcare system’s ability to rapidly detect and respond to emerging threats is compromised by the lack of integration and interoperability between the disparate surveillance systems in the nation. this creates inefficiencies in analysis and communication, resulting in increased morbidity, mortality, and costs. the fourth article, titled using secure web services to visualize poison center data for nationwide biosurveillance, demonstrates the use of a federated data exchange model and secures web services to enhance existing biosurveillance capacity. editorial: volume 2, number 1 (2010) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 colorectal cancer is a major cause of mortality among american men and women. in the fifth article the authors used geographic information systems and asset mapping technologies to explore the availability and accessibility of colorectal cancer screening resources in medically underserved communities. the paper yielded asset maps that helped in the development of targeted strategies for addressing the barriers to colorectal cancer screening. experts in the field of disaster management have long recognized that an up-to-date continuity of operations plan (coop) is a core component of any disaster preparedness and response strategy. recent experiences in disaster management, involving sars, hurricane katrina, and the h1n1 influenza threat, demonstrate that public health departments lack access to decision support technologies for coop planning. in the final article, titled use of technology to support information needs for continuity of operations planning, the authors review published studies of information systems and technology projects that are applicable to public health continuity of operations planning. the findings from this review article will assist public health informaticians in the development of information systems to support public health operational continuity. edward mensah, phd editor-in-chief online journal of public health informatics 1603 w taylor st, rm 757 chicago. il. 60612 email: dehasnem@uic.edu office: (312) 996-3001 mailto:dehasnem@uic.edu ojphi-06-e89.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 28 (page number not for citation purposes) isds 2013 conference abstracts analytic biosurveillance methods for resource-limited settings howard burkom*, yevgeniy elbert, erhan guven and jacqueline coberly johns hopkins applied physics laboratory, laurel, md, usa � �� �� �� � � �� �� �� � objective �������� ���� �� �� ����� � ������� ������ �������� ������� � ������� � ���������� ���� ������ �� �� � ������������� � ���������� ���������� �� ��������� � ����������� ������� introduction �� �� ������������ �� � ����������������������������������������� ��� ������������ ���� ����������� � ���������������� ���������� �� � ������������������ ������������ ���������������� �� � !��������� � �� ��������� �� ������������������� �����������������"������������� � ���!������������������� ������ ������ ������ ���������#$%��&���� �� �������������� ����������������������������� �������������������� ���� ����������������� ����� �������� � ������&������ �'�� ������ (� ����)���� ������ &� ����������&'()&�������� ������ �� ��� �� ��� !�� ������ ��������� ���� ��� ���� ������������� ������ ��������� ������������������#*%��������� ��� �"�� ����������������� ��������� ��� ��� � ������ �&'()&�����!������� �� ������ ����������� ���������� ���+���� ���������� �� ,��� ����������� ��������� ���� ��� ����������-���������� ���� ������� ����� ���� ��� ���������������������� � ��������������!����� ���������� ���������� ������������� ��� ��� �������� ��� ���� ���� ������������� ��� ������������������ �������� ����������������� ������� �� ����������� ���������������� ���������� �������� �������!�������������� �� �� �� ��&�������������� ���������������� � �� � ������ methods . �� ��� �� ��������������������!�������������������&'()&� ������� ����������������� ������ ����������� ��� ������ � ����$/� ���� ���� �0�� �������.��� �&'()&�������� ��� �������� ���������� �����!�� �������� ������ �������������� ������������� ���������� �� ������ ��� �� ��� ��������������!���� � ����� �������!��������� �� ���������� ����. �������� ���� � �������������!��������� �� ��������� ��� �������������� ����+��������� ��������� ��������� ��� ��� �� � ����� ��������������������� ������� ��� �������������*������� ����� ���� ����� �������1������� ����!��������+������� �������+������� � �������� ������������ ����������� ���� ��� ����� ��� �������� ��� �������� ��23&34�����),4'���� ������������ �&'()&!������� ������ �� ������252�)'6&�2*���� ����!�����(&7&'()&!������� �� ������ ������ ��������1� ���������� ���������������� ���+����� � � � ��������������� � ����� ������������ �"�� ������� �� �� ���� �� � ������8����� � ���� ���� ����������� ������� �����������&'()&�� ) ������ ��� ��� ��� ��������� ���� ��� � �� ��+������� �������������� �� ����������� ����!������������ ��������� �� ����+���� ��� ���� ����� ������������!����� ��� � ����+������� �������� �������+�������������� � ���������� ���!� �� �� �����!����� ��� � ����+������� �������� �������� �� ������99:�!����� ������������������ ������� �� ������������������� 6�� �������� ����� ���� � ���� ������������� �������� ����� ������� ������������������������ ���������� ������� ��� ������������� ���� ������ ��� ���� �� ����������� ������;�/0<�!�� �� ������;*0<�!� 99:��;=><�!������������������;/?<��� results 6������-� '�� ����� �� �������� ��� � � &'()&� ��� �� �� �� ���������� ����� �"�������������� ������� ����������������9� ����� �� ���������������� � ��������� ������ ��� ��� ��� � ��� ������� �� ����������������������� ��� conclusions 8����� � ������� ������ ��������� ����� ��������� �� ��������� �� �������� ���� ��� �����������������1������� �� � ����������� ������������ ��������������� ��� ��� � ��� ������� �������� � ���� �� � ��������� ������������� ��� �!��������� ������ �� ������ ��� ��� �� ���� � ���� �����������,������������� � ������������ ��!����� ����� ��������������99:������ �� ����!������������!������ � ������� �� ������������� ���+�������� ������� ��� ��!� ��� ���������� ���������� ����� ��� �������� ������ ��� ���� '�� �����6�� �������� ������5����2 ����� � 2 ���-�$@� ��� ��!�*@����������!�=@� �� �� ������� keywords ��� ����a�� �� ����� �a��1� ���������� �����a�&'()&a� �� � ��� ������� acknowledgments �������� ������ ������������������ ������3�&��' ����. ���� ������ &� ���������2���� �(� ����)�� �����b������ ��&�����!����������������� ��� ��� ������� references $�� 2� ������c9!��� � �� &!�d�����& !���������*>>/��&��� ����&� ���� �����-�'��������b�� ��� ���� �5� �� �����&���������9d &�4�������� 0�=�-��?* *�� d�����&d!�.������ �� !�d ������,'!���������*>$$��&'()&-�'�&����� ��. �����' ��������& ���� ��� ���� �)���� ����5�������&� ���� ���������6�� � ���d�������&���������9d &�8e)�f�0�-��$/?0> *howard burkom e-mail: howard.burkom@jhuapl.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e89, 2014 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts rabies vaccination coverage and antibody profile of owned dogs in abuja, nigeria grace o. olayemi*1, jarlath u. umoh1, grace s. kia1 and asabe a. dzikwi2 1veterinary public health and preventive medicine, ahmadu bello university zaria, zaria, nigeria; 2university of jos, department of vet public health, jos, nigeria objective to determine the vaccination status of owned dogs, assess the rabies antibody titre of vaccinated dogs and risk factors associated with vaccination of dogs in abuja, nigeria. introduction rabies is a zoonotic disease of high public health importance1. there have been documented reports of rabies in vaccinated dogs2. rabies is enzootic in domestic dogs in nigeria. hence, annual vaccination campaigns of dogs are advocated with the aim of rabies elimination. vaccination status, type of vaccination and the immunogenicity of the various rabies vaccines used in abuja nigeria has not been studied. to date, no effective medical therapy has been established for rabies3. most human rabies deaths occur in the developing countries and though effective and economical control measures are available their application in developing countries is hampered by a range of economic, social and political factors. it is widely recognized that the number of deaths officially reported in most developing countries greatly underestimates the true incidence of disease, with several factors contributing to widespread underreporting3. preventive vaccination against rabies virus is a highly effective method for preventing rabies in humans and animals3 but do people vaccinate and how long does the immunity conferred by the vaccine remain protective in the dogs in abuja?. rabies has high financial expenditure burden on any country where it is endemic mainly associated with costs incurred on post-exposure prophylaxis (determined by the type of vaccine, vaccine regimen and route of administration as well as the type of immunoglobulin used). methods dog serum samples (n=276) were collected from abuja the federal capital territory (fct) nigeria, from 5 locations (phase 1, 2, 3, gwagwalada and kubwa) based on availability and owners consent. rabies antibody serum titer was determined using an indirect enzyme linked immuno-sorbent assay. face to face structured questionnaires were used to obtain demographic and zoographic information from the dog owners. associations between the demographic variables, vaccination status and rabies antibody titer of each dog were assessed using χ2 analysis. results of the dogs sampled, 229 (83%) had certified antirabies vaccination record. the dogs sampled, which were vaccinated from phase i, ii, iii and the satellite towns were; 109/118 (92.37%), 32/33 (96.97%), 48/49 (97.96%) and 40/76 (52.63%), respectively. a total of 276 serum samples were collected, processed and analyzed during this study. out of the 276 dogs sampled, 239 (86.6%) had rabies antibody titre ≥ 0.6eu/ml whilst 37 (13.4%) had less than 0.6eu/ml. there was a marked decline in rabies antibody titre with increase in time. out of the 228 exotic breeds of dogs sampled, 218 (95.6%) were vaccinated whilst 11 (22.9%) of the 48indigenous breed of dogs sampled were vaccinated. all the exotic breed of dogs had rabies antibody titre ≥ 0.6eu/ml whilst 37 (77.1%) of the indigenous breed of dogs had less than 0.6 eu/ml levels of rabies antibody titre. all dogs within 6 months to 1 year and greater than 10 years of age had ≥ 0.6eu/ml rabies antibody titre whilst dogs within 1-5 years had 1 (0.5%) and 36(69.2%) dogs of age 6-10 years had rabies antibody titre < 0.6eu/ml. twelve (7.6%) of the males and 25 (21.2%) of the females had less than 0.6eu/ml rabies antibody titre. all the dogs acquired by importation and from breeders had rabies antibody titre ≥ 0.6eu/ml whilst 37 (27.2%) of the dogs acquired from friends had less than 0.6eu/ml rabies antibody titre. significant associations were observed between breed (χ2 = 203, df = 1, p-value < 0.05), age (χ2 = 172, df = 3, p-value < 0.05), sex (χ2 = 10.75, df = 1, p-value < 0.05), source (χ2 = 43.99, df = 2, p-value < 0.05), rabies vaccination status (χ2 = 276.00, df = 2, p-value < 0.05) and the rabies antibody prevalence of sampled dogs. conclusions this cross-sectional study shows that not all dog owners vaccinate their dogs and that the vaccines conferred protection beyond 12 months. the preventive vaccination against rabies virus is a highly effective method for preventing rabies in humans and animals. policies to enhance mass mandatory annual vaccination to achieve 70% coverage should be implemented in order to eradicate rabies. keywords rabies; antibody; antirabies vaccine; abuja nigeria; dog owners acknowledgments we acknowledge the macarthur foundation msc. epidemiology, department of veterinary public health and preventive medicine ahmadu bello university zaria the sponsorship for o.g. oladiran/vet. med./53282/abu2012-2013. references 1. adedeji, a.o., eyarefe, o.d., okonko, i.o., ojezele, m.o., amusan, t.a. and abubakar, m.j. (2010). why is there still rabies in nigeria? a review of the current and future trends in the epidemiology, prevention, treatment, control and possible elimination of rabies., 1(1), 10-25. 2. rupprecht, c. e., smith, j. s., fekadu, m. and childs j. e. (1995). the ascension of wildlife rabies: a cause for public health concern or intervention? emerging infectious diseases, 1:107–114. 3. health organization: who expert consultation on rabies. who technical report series 981. geneva, switzerland, who. 2013. *grace o. olayemi e-mail: olamigracie@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e157, 2017 and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi and ghana was scared: media representations of the risk of an ebola outbreak in ghana iddrisu seidu head of research and programs, center for development and policy advocacy, box tl 1233, tamale, ghana abstract introduction: the 2014 ebola virus outbreak in parts of west africa marked the 25th occurrence of the disease since its discovery in 1976. while earlier outbreaks in central and eastern africa had limited geographical extension and little media coverage, news media interest in the 2014 epidemic was remarkably high. in countries like ghana, where the risk of imported infection was estimated to be among the highest, news coverage for the epidemic proliferated. this study aimed to describe and analyze the central themes which characterized media representations of the risk of ebola outbreak in ghana. method: a quantitative content analysis (ca) was employed to study news media reportage of the risk of ebola outbreak in ghana. two daily newspapers, the daily graphic and today were sampled. an online search for ebola news stories in the selected newspapers was conducted, and all hits with ebola downloaded and screened. a total of 332 articles were retrieved and 156 articles met the inclusion criteria. three independent coders carried out the coding using identical story analysis form. results: in the course of the 2014 ebola epidemic in parts of west africa, the daily graphic and today newspapers in ghana published 332 stories about the epidemic. of this number, the study analyzed 156 news articles which met the inclusion criteria. the analysis found that, media coverage for the risk of ebola outbreak in ghana reflected nine salient themes: concerns about the ghana’s preparedness, support for ghana’s preparation, public education on ebola virus, assurances on ghana’s readiness, suspected cases of ebola, effects of ebola, critique of ebola risk handling, misinformation and other. conclusion: analysis of news media coverage for the threat of ebola outbreak in ghana revealed nine important themes. these themes, contributed to an understanding of the broad impact of the recent ebola outbreak on various sectors of the population. key words: media coverage, ebola threat in ghana, epidemic preparedness, 2014 west africa ebola outbreak correspondence: saha.seidu1@gmail.com doi: 10.5210/ojphi.v10i2.9229 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the 2014 ebola virus disease (evd) outbreak in parts of west africa marked the 25th occurrence of the disease since its discovery in 1976 [1]. compared to all previous outbreaks mailto:saha.seidu1@gmail.com and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi combined, the recent evd epidemic was by far, the longest and largest [2]. from december 2013 when the first case was identified in the gueckedou district of guinea up till december 2014, over 9,800 estimated deaths were reported in guinea, liberia and sierra leon [3]. occurring in countries with fragile health structures, the epidemic epitomized a major catastrophe in the hardest-hit areas. while earlier outbreaks of evd in central and eastern parts of africa had limited geographical extension and little media coverage, news media interest in the 2014 ebola epidemic was remarkably high. the globalizing style of the virus, the extraordinary high cases and transmissions to cities with major international airports (i.e. lagos, freetown, conakry and monrovia) seemed plausible reasons. in ghana, where the risk of imported infection was estimated to be among the highest [4], news coverage for the epidemic heightened. news media perform a critical role in public health emergencies, when demand for accurate and timely information increases. they are also among the first-line receivers of risk information, and therefore occupy an important place in the social amplifications of risk [5]. even with the rise of social media, enabled by increased use of internet, computers and smart phones, the news media continue to be essential because many people have limited access to information from medical literature especially on diseases about which the public is unfamiliar [6]. when properly monitored and studied, statistics from news media can serve as feedback mechanism on emergency response intervention. data from either news or social media can also provide us with quantitative indicators of negative emotions (e.g. fear, anger, and anxiety) and positive sentiments (e.g. happiness and calm), which in turn could be useful in community engagement strategies [7]. equally, media misrepresentations of public health risk can lead to misinformation and public confusion. this, in relation to ebola, is significant because misinformation about the disease and its containment protocols has in times of outbreak, led to mistrust, resistance and hostilities from local people [8]. the record size of the 2014 epidemic seems to have renewed varied research interest on ebola virus. in a recent study, lee-kwan and colleagues [9] examined the cultural and community factors to safe burials of ebola victims. earlier in october, 2014, the international spreading risk of the outbreak to other parts of africa and beyond was similarly investigated [4]. a few others [10,11], have also studied the use of digital and culturally-centered health communication efforts in the context of the outbreak. despite these investigations, important gaps, nonetheless, exist in current knowledge. studies of public sentiments on ebola and perceptions of risk exposure expressed through the news media are lacking. the few attempts in this regard have focused largely on developed countries particularly, the us [12,13], whereas vast amount of data in west african countries remain understudied. this paper is the first to study the content of ghanaian newspapers’ coverage for the 2014 ebola outbreak. the study aims to describe and analyze the central themes which characterized media representations of the risk of ebola outbreak in ghana. method and sample: a quantitative content analysis (ca) was employed to study news media reportage of the risk of ebola outbreak in ghana following the 2014 ebola epidemic in parts of west africa. two daily newspapers, the daily graphic and today were sampled for the study. the daily graphic newspaper is the oldest in ghana and was purposively selected because it is widely read and has the highest daily circulation across the country. the selection of today in contrast was done by means of simple random sampling. an online search was conducted in the months of may and june, 2017 using key words including ‘ebola virus in 2014, ‘ghana’, ‘daily graphic’ and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi and ‘today newspaper’. all hits with ebola in the headline or in the main story were retrieved and screened to ensure that only articles from the selected newspapers were retained. the search in total retrieved 332 articles consisting of news, editorials, columns, and opinion articles. for inclusion, news articles needed to directly report on the threat of ebola in ghana; thus, news articles on different subjects about ghana which contained passing references to ebola were not included. the second criterion was that news articles needed to contain information about the risk of ebola in ghana which had been published after 31st december, 2013 and before january 31st, 2015. by these criteria, 172 articles were eliminated, leaving a final sample of 156 news articles for the study. this is illustrated in figure 1. figure 1: study flow chart coding an initial review of 50 randomly selected news articles by the author informed a categorization of the data into ‘general’ and ‘thematic’ categories. a complete coding manual was then developed to guide the process. coding for the general category included story type (news, editorial, column, and opinion), newspaper (daily graphic, today), news source (daily graphic, today, city fm, joy news, other) story length, month (january – december 2014). codes for the thematic category in contrast, comprised the following: ebola education, suspected cases of ebola in ghana, misinformation about ebola, concerns about ghana’s and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi preparedness, support for ebola prevention, critique of ebola risk handling, effects of ebola, assurances on ebola preparation and other. to guarantee inter-coder reliability, a detail story analysis form, which contained coders’ initials, the general and thematic categories was used to guide coders. three independent coders (the author and two graduates, who were given training prior to the exercise) carried out the coding, with codes by the author serving as control. inter-coder reliability test was performed by randomly selecting and double-coding 20% of the sampled news articles. cohen’s kappa for the thematic categories (article theme) was 0.71, while for the general categories, news source and news story type yielded inter-coder reliability scores of 0.91 and 0.85 respectively. data analysis data analysis was performed using spss version 20. descriptive statistics involving frequencies and percentages were run to describe the sampled article characteristics, and to allow for meaningful understanding of the data. results during the 2014 ebola virus outbreak in parts of west africa, the daily graphic and today newspapers in ghana published 332 stories about the epidemic. of the 156 news articles which met the inclusion criteria and were thus analyzed, the daily graphic newspaper, accounted for 40% of both newspaper type and primary source of newspaper stories on evd. today newspaper in contrast, contributed 60% of newspaper type, and 22% of primary source of newspaper story. the analysis in table 1 shows that today newspaper, compared to the daily graphic newspaper, published more secondary news on the epidemic. joy news (25%) and city fm (8%) were the common sources of ebola news for today newspaper besides itself (22%). the majority (83%) of ebola information in the news media was presented as news stories. a modest number of opinion articles (11.5%) also constituted a common source of ebola information while editorial and column news articles were the least forms of news media information on the epidemic. put together, media coverage for the threat of ebola virus outbreak in ghana reflected nine salient themes, defined in table 2. the analysis also found that, four of the nine identified themes accounted for over half (58%) of media reporting on ebola risk in ghana. these included concerns about the country’s preparedness (17%), support for ghana’s preparation for the outbreak (16%), public education on the virus (14%) and assurances on ghana’s readiness (11%). less dominant themes such as suspected cases of ebola in ghana and effects of the threat of ebola also had a regular reportage of 9.5% and 9% respectively. the results further showed that two themes: misinformation about ebola and critique of ebola risk handling had the smallest percentage coverage of 3.5% and 5.5% respectively. and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi table 1 news article characteristics n(156) % newspaper type daily graphic 62 39.7 today 94 60.3 news story type news 130 83.3 editorial 6 3.8 column 2 1.3 opinion articles 18 11.5 news original source daily graphic 62 39.7 today 34 21.8 joy news 39 25 city fm 13 8.3 other 8 5.1 table 2 definition of ebola news themes based on coverage theme definition 1. concerns about ghana’s preparedness messages in this theme included fears expressed by healthcare workers about their readiness to handle ebola patients; complaints about porous border control systems including but not restricted to lack of screening at entry points; worries about inadequate isolation and treatment centers; and reservations about some hospitals’ capacity to conduct ebola test 2. support for ebola preparation in ghana this comprised news stories on trainings for frontline health workers, material and logistical contributions to enhancing ghana’s preparation for the outbreak by the government, the corporate world, diplomatic community and development partners 3. ebola education this included news articles on sensitizations and public education on the ebola virus disease mode of spread, signs, symptoms, risk factors, prevention techniques, where and when to report for medical care 4. assurances on ebola preparation these were messages from political leaders and leadership of ghana’s health sector guaranteeing readiness for any eventuality 5. suspected cases of ebola in ghana this included news stories on suspected ebola infections in the country and laboratory test on alleged cases and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi 6. effects of ebola comprised news messages about the impact of the threat of ebola particularly on education, social life, entertainment, and business activities 7. critique of ebola risk handling news articles that criticize or commend the efficacy of response mechanisms vis-à-vis the threat of ebola 8. misinformation about ebola news stories about ebola virus diseases that contain medically incorrect statements about the disease, including traditional and religious claims on ebola treatment and prevention 9. other messages in this category included miscellaneous news stories about the virus including but not limited to conspiracy theories about ebola, travel warnings, calls for specific interventions etc. figure 2: monthly coverage of ebola of news, january december 2014 the regularity of the nine identified themes is illustrated in both figure 2 and table 3. the analysis showed that, by march, 2014, only 1 (0.6%) news story had been reported on support for ebola prevention in ghana. this coverage unsurprisingly, followed the formal recognition of the then evolving infection in parts of guinea as ebola virus. the month of april saw an increase in news coverage for the epidemic from just 1 in march to 10 (3.2%). while news coverage for the risk of the epidemic in ghana partly reflected crucial happenings in the worst affected countries, it is interesting to observe that the months of may and june had no coverage on any of the identified themes even though both cases and fatalities continued, particularly in june. from july, however, ghanaian media coverage for the epidemic witnessed marked increase to about 9%, peaked in august with 31.4% and declined significantly in september to about 19.9%. the decreasing coverage continued in october to about 19.2% 0 5 10 15 20 25 30 35 jan feb mar apr may jun jul aug sep oct nov dec 0 0 0.6 3.2 0 0 9 31.4 19.9 19.2 10.9 5.8 pe rc en ta ge month of coverage news coverage and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi through november (10.9%) to december (5.8%). compared to other months, august and september had news coverage on all the 9 themes while october and november had coverage on 8 themes each. coverage for concerns about ghana’s preparedness increased in july (4), peaked in september (12) and decreased considerably in october (3) and december (1). this perhaps explains a similar trend on coverage in respect of support for ebola readiness in ghana, which started in july (1) and increased consistently up to november (7) before diminishing to 3 in december. the results equally showed that news coverage for suspected imported cases of ebola virus into ghana was highest in july (7) and august (6), and almost disappeared in november (1) and december (1). this high reportage on suspected ebola cases in july and august, might have contributed to a corresponding surge in coverage for education on the contagion in the months of july (6) and august (6). other themes such as effects of the outbreak (9) and critique of ebola risk handling in ghana (5) were reported prominently in august, followed by significant declines in subsequent months. discussion this study is the first to analyze ghanaian news media’s coverage for the 2014 ebola epidemic in west africa. from obscurity in the first half of 2014, media coverage for the risk of ebola outbreak in ghana proliferated in july, and peaked in august. this watershed seems to have been heavily shaped by four external factors the swift increases in july/august caseload, the declaration of ebola as public health emergency of international concern, the exportation of ebola to nigeria on july 20th and the infection of two american workers in liberia, kent brantly and nancy writebol on july 27th. the results showed that, as media reporting of ebola risk in the country increased, nine major themes became salient and characteristic of the coverage. these included concerns about the ghana’s preparedness, support for ebola preparation, education on ebola, assurances on ghana’s readiness, suspected cases of ebola, effects of ebola threat, critique of ebola risk handling, misinformation about ebola and other. away from the usual research focus on ebola treatment and vaccine trials, the findings of this study contribute to an understanding of the broad impact of the recent ebola outbreak on various sectors of the population. while each of the identified themes may offer useful lessons for future occurrences, a few perhaps deserve further discussion. the finding that concerns on ghana’s preparation for the ebola virus was the leading theme in media coverage, requires policy attention. widespread concerns in an emergency situation can undermine public health surveillance. infected persons can abscond from isolation centers out of fear, and others may become hostile to emergency workers. a closer examination of this theme showed high prevalence of fear among the public and healthcare workers. in some instances, medical staff actually panicked in handling suspected ebola patients (today, 19/11), while in others, suspicious patients in dire need of medical attention were simply abandoned (today, 19/11). this result is similar to earlier findings in ethiopia, where abebe and colleagues [14] found ebola caused fears among more than 58% of healthcare professionals. it also concurs with previously reported episodes of fear, avoidance and flight from hospitals by medical staff in earlier outbreaks of ebola [8]. key among the drivers of ebola triggered concerns was general perception of weak epidemic preparedness in ghana. limited availability of personal protective equipment, porous borders and ghana’s struggle to contain an outbreak of cholera at the time [15], lent credence to the perceptions of weak preparedness. in one article for instance, the writer was alarmed that: ‘‘in 2014, cholera still remains an epidemic ghana has been unable to effectively combat and now, we have to deal with almighty ebola disease which we are told is the deadliest virus the world has ever seen’(today, 08/12). similar coverage by the daily graphic observed that ‘both the frontiers at aflao and northern ghana and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi are leaking badly so far as illegal entries are concerned. besides, those who enter are not screened before mingling with our indigenes; this is ... happening at a time the ebola scare is being magnified’ (daily graphic, 06/09). another interesting finding which perhaps involves positive lessons for ghana’s health system is the result that, news coverage of ebola threat in ghana had fewer instances of misinformation. the few misrepresentations about the epidemic were commonly centered on traditional and religious claims on the causes of ebola and treatment opportunities. while this tendency was helpful, it may have also been the result of a relatively high public education on the virus which started as early as april (5), stopped in may/june, and continued in july (6) up to december (3). since this analysis was limited to print media sources, it is also likely that public education on the virus was much higher than the present estimates. this finding on instances of misinformation on ebola, however, differs from analysis of ebola related misinformation on social media in guinea, liberia and nigeria in the first week of september 2014, which found that the majority of tweets (55.5%) and retweets (58.9%) on ebola contained medical misinformation [16]. this variation understandably, could be due to the gate keeping structures in mainstream media, which inter alia, can verify information prior to publication. while it is difficult to ascertain the true impact of the 2014 ebola crisis in non-infected countries such as ghana, media coverage on the effect of the ebola threat points to a wider impact on different sectors of the ghanaian society. the analysis showed that, the effect was positive for some sectors and negative for others. in the health sector for instance, the threat stimulated both short and long term investments in epidemic preparedness. these included but were not limited to emergency constructions of three new isolation centers and procurement of new medical equipment. other sectors such as education, was negatively impacted in respect of rescheduling of school periods, learning time lost to screening for ebola etc. the threat also lowered commodity prices for some goods e.g. game meat, and thus adversely affected the livelihoods of many ghanaians (daily graphic, 05/08; today, 15/08). game meat, known popularly as ‘bush meat’ is a delicacy in ghana, providing employment to many in the value chain (e.g. hunters, wholesalers, retailers, game meat kebab sellers and bar operators). although the game meat etiology of the recent outbreak has been contested [17], this effect seems logical given the risk of animal to human transmission of the deadly contagion. and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi table 3 frequency and percent of ebola news theme by month, january – december, 2014 recurring theme month of publication theme total percent percent of cases jan feb mar apr may jun jul aug sep oct nov dec concerns about preparedness 2 4 9 12 3 3 1 34 17% 21.8% support for ebola preparation 1 0 7 6 8 7 3 32 16% 20.5% education on ebola 5 6 6 2 4 2 3 28 14% 17.9% assurances on preparation 1 2 6 4 7 1 1 22 11% 14.1% suspected case of ebola in ghana 1 7 6 3 1 1 19 9.5% 12.2% effects of ebola 9 2 4 2 1 18 9% 11.5% critique of ebola risk handling 1 5 2 1 2 11 5.5% 7.1% misinformation about ebola 1 1 1 3 1 7 3.5% 4.5% other 11 8 7 3 29 14.5% 18.6% monthly total (%) 0.6% 3.2% 9% 31.4% 19.9% 19.2% 10.9% 5.8% and ghana was scared: media representations of the risk of an ebola outbreak in ghana online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e203, 2018 ojphi limitations this study notwithstanding its significance has several limitations. the study used data from only two print media outlets in ghana. this implies that ebola related discussions and perspectives shared through radio and television stations in ghana were not included. another important shortcoming of the study is the likelihood that the sampled news stories might not be representative due to the relatively high illiteracy rate in ghana; it is conceivable that the data for this study may reflect the sentiments of the well-educated, who unlike their illiterate counterparts, can express and share their experiences through the print media. finally, mainstream media publications compared with social media, have several gatekeeping structures which might modify or exaggerate individuals’ experiences and sensitivities to public health epidemics. conclusion media coverage for the risk of an ebola outbreak in ghana during the 2014 ebola epidemic was studied using quantitative content analysis. the study identified nine important themes which characterized media reporting of the epidemic: concerns about the ghana’s preparedness, support for ebola preparation, education on ebola, assurances on ghana’s readiness, suspected cases of ebola, effects of ebola threat, critique of ebola risk handling, misinformation about ebola and other. these themes provided valuable understanding on public responses to the threat and its widespread impact on the ghanaian society. it is significant to recognize that while vaccinations and medical treatments are helpful ways of controlling infectious disease outbreaks, they probably would not be the most effective way to approach unfamiliar infections. the biggest resources for containment of deadly epidemics of ebola’s kind are robust surveillance and containment capacity, including well-equipped medical staff, with reasonable protection against the perils of working with diseases of highcase fatality. the extensive impact of ict in this day and age will be helpful in building robust response mechanisms in ghana. these could be ehealth tools designed to facilitate community mobilization, logistics management, contact tracing and timely data collection. with a high smartphone penetration rate in ghana, the use of ehealth tools will effectively situate the country to move beyond facility-based disease surveillance, to digitally driven participatory engagement of populations at risk. further research on how ict based surveillance strategies involving media and mobile data platforms can strengthen ghana’s surveillance capacity will be useful. conflict of interest i declare that there is no conflict of interest. references 1. 2014. the lancet infectious diseases. ebola in west africa [editorial]. lancet. 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https://doi.org/10.4081/jphia.2016.570 https://doi.org/10.1136/bmj.g6178 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25315514&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25315514&dopt=abstract https://doi.org/10.1126/science.1259657 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25214632&dopt=abstract and ghana was scared: media representations of the risk of an ebola outbreak in ghana introduction method and sample: coding data analysis results discussion limitations conclusion conflict of interest references layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts development of automated data quality indicators and visualizations using florida’s essence system wayne loschen*1 and aaron kite-powell2 1johns hopkins university applied physics laboratory, laurel, md, usa; 2florida department of health, jacksonville, fl, usa objective the objective of this project was to develop visualizations and tools for public health users to determine the quality of their surveillance data. users should be able to determine or be warned when significant changes have occurred to their data streams, such as a hospital converting from a free-text chief complaint to a pick list. other data quality factors, such as individual variable completeness and consistency in how values are mapped to standard system selections should be available to users. once built, these new visualizations should also be evaluated to determine their usefulness in a production disease surveillance system. introduction understanding your data is a fundamental pillar of disease surveillance success. with the increase in automated, electronic surveillance tools many public health users have begun to rely on those tools to produce reports that contain processed results to perform their daily jobs. these tools can focus on the algorithm or visualizations needed to produce the report, and can easily overlook the quality of the incoming data. the phrase “garbage in, garbage out” is often used to describe the value of reports when the incoming data is not of high quality. there is a need then, for systems and tools that help users determine the quality of incoming data. methods a series of data quality visualizations were developed and implemented in the florida department of health’s version of essence. users were given numerous pages that showed different aspects of data quality, such as variable-level percent completeness measurements shown by hospital or county. other items included the percent of time a value that should have been a part of a specific reference list was actually present and matched to known values, the number of input files received by a hospital, and the time each data source was processed. finally, an algorithm and visualization was developed to alert users when data quality factors had changed significantly. with access to all these new screens, users of the system were given the opportunity to use the system and their usage and opinions were collected. results the data quality portal has been active the florida essence system since march 31st, 2012. between that time and august the portal has been accessed over 1300 times. the presentation will include additional statistics about which specific features were most used and those features that users have found the most useful. in addition, data quality issues that have been discovered using the new tool will be discussed. conclusions with the ever increasing amount of data that public health must analyze due to meaningful use, it is imperative that tools and visualizations that can decipher data quality issues be made available in an easily accessible format and without the need for tools external to the system. if systems continue to ignore changes in the data they receive automatically, it can easily produce degraded or incorrect analyses and interpretation of events, leading to wasted resources. these results can negatively impact the decisions and responses that public health users make, especially in light of the increasing reliance on these types of systems for up to the minute information. this project has developed tools and visualizations that can help determine the data quality issues as they are occurring. this presentation will outline the lessons learned for using and creating these tools so they can be shared for everyone to benefit. keywords visualization; data quality; meaningful use *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e17, 2013 a virtual data repository stimulates data sharing in a consortium 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi a virtual data repository stimulates data sharing in a consortium suzanne siminski1*, soyeon kim1, adel ahmed1, jake currie1, alex benns1, amy ragsdale2, marjan javanbakht2, pamina m. gorbach2, and the c3pno cohort investigators. 1frontier science foundation, amherst, ny and brookline, ma 2university of california los angeles, los angeles, ca abstract research data may have substantial impact beyond the original study objectives. the collaborating consortium of cohorts producing nida opportunities (c3pno) facilitates the combination of data and access to specimens from nine nida-funded cohorts in a virtual data repository (vdr). unique challenges were addressed to create the vdr. an initial set of common data elements was agreed upon, selected based on their importance for a wide range of research proposals. data were mapped to a common set of values. bioethics consultations resulted in the development of various controls and procedures to protect against inadvertent disclosure of personally identifiable information. standard operating procedures govern the evaluation of proposed concepts, and specimen and data use agreements ensure proper data handling and storage. data from eight cohorts have been loaded into a relational database with tables capturing substance use, available specimens, and other participant data. a total of 6,177 participants were seen at a study visit within the past six months and are considered under active follow-up for c3pno cohort participation as of the third data transfer, which occurred in january 2020. a total of 70,391 biospecimens of various types are available for these participants to test approved scientific hypotheses. sociodemographic and clinical data accompany these samples. the vdr is a web-based interactive, searchable database available in the public domain, accessed at www.c3pno.org. the vdr are available to inform both consortium and external investigators interested in submitting concept sheets to address novel scientific questions to address high priority research on hiv/aids in the context of substance use. keywords: common data elements, data repository abbreviations: national institute on drug abuse (nida), collaborating consortium of cohorts producing nida opportunities (c3pno), human immunodeficiency virus (hiv), acquired immunodeficiency syndrome (aids), injecting drug users (idu), virtual data repository (vdr) correspondence: siminski@frontierscience.org* doi: 10.5210/ojphi.v13i3.10878 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged i n the copy and the copy is used for educational, not-for-profit purposes. mailto:siminski@frontierscience.org* a virtual data repository stimulates data sharing in a consortium 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi introduction research data are undeniably a valuable resource, requiring considerable effort, time, and funding to produce. by increasing access to research data, the “impact, efficiency, and effectiveness of scientific activities and funding opportunities” are also increased [1]. affirming its commitment to access to data generated through support of public funds, the national institutes of health (nih) issued a statement requiring a plan for data sharing for investigator-initiated applications [2]. the rationale for the requirement is that [s]haring data reinforces open scientific inquiry, encourages diversity of analysis and opinion, promotes new research, makes possible the testing of new or alternative hypotheses and methods of analysis, supports studies on data collection methods and measurement, facilitates the education of new researchers, enables the exploration of topics not envisioned by the initial investigators, and permits the creation of new datasets when data from multiple sources are combined.3 in order to leverage investments in publically-funded research such as ongoing cohorts that address high priority research on hiv in the context of substance use, the national institute on drug abuse (nida) issued rfa-da-17-019 to solicit cooperative agreement applications “to establish a virtual repository, and facilitate the leadership of the cohorts steering committee (sc), consisting of representatives from the nida-funded cohorts and nida staff” in order to “provide a strong resource platform for current and future collaborative efforts with other investigators to address emerging questions related to hiv pathogenesis, prevention, and treatment in the context of substance abuse, as well as to foster the creativity and efficiency of investigator–initiated research projects.” [3] the goal is not only to optimize collaborations among cohort investigators but also to provide better access to data and specimens for researchers external to the cohorts. the collaborating consortium of cohorts producing nida opportunities (c3pno) [4] was funded to facilitate broader access to rich data and biological specimens from the nine nidafunded cohorts described in table 1. an important activity of the consortium has been the creation of a virtual data repository (vdr). a vdr is an online repository of data. a vdr facilitates information sharing within and beyond narrowly defined research communities. by access through an online interactive platform, a vdr can allow a user to define search criteria and obtain a summary of the number of participants meeting the criteria. a vdr may include information such as concise data descriptions displayed in the form of a master data catalog, provide details on the populations who contributed the data, and facilitate disseminating information to the interested users of the data and users of well-characterized banked specimens. by making a vdr available in the public domain, users are able to triage the appropriateness of the data for their purposes without having to make a formal request to each of the study teams for information. by coordinating efforts and providing access to data and specimens across cohorts, data may have substantial value beyond that of addressing the original research studies’ objectives. the most obvious value of combining data across cohorts is the greater sample size it affords, thereby increasing power to test hypotheses, which is particularly important in smaller populations or when events of interest are rare, for example, hiv seroconversion. in addition, research consistent with a virtual data repository stimulates data sharing in a consortium 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi nida’s mission may be outside the expertise of the cohorts’ investigators and providing broad access to data and specimens to researchers from other disciplines, including new investigators, may facilitate the establishment of new knowledge bases. because the specimens come from wellcharacterized individuals, they are particularly valuable for assessing biomarkers. it is worthwhile to note that the nine cohorts above were established independently, span nearly 20 years in dates of inception, and are funded separately. each cohort has its own leadership structure and addresses different nida priorities. each cohort has distinct study aims, research objectives, study populations, specimens, and data, and each stores data in separate formats. to take advantage of c3pno cohorts’ unique opportunity to study the intersection of substance use and hiv to answer questions in key populations, otherwise not possible with a single cohort’s data, requires that certain challenges be overcome. we address these challenges by marshalling a multidisciplinary team with expertise in data curation and mapping, epidemiology, bioinformatics, data standardization, and data linking. table 1: overview of cohorts cohort population year started no. of study participants access hiv-positive pwid 2005 1100 alive pwid 1988 1500 heart study hiv-positive african americans 2004 1400 hym young men of color who have sex with men 2015 450 jhhcc hiv-positive persons in receiving care through johns hopkins hiv/aids services ambulatory clinics 1989 1100 mash hispanic persons in south florida 2002 1400 mstudy latino and african-american/black men msm at ucla vine street clinic 2013 500 radar young msm 2014 1100 v-dus hiv-negative pwid 1996 3500 *abbreviations: no., number; hiv, human immunodeficiency virus; pwid, persons who inject drugs; aids, acquired immunodeficiency syndrome; msm, men who have sex with men; ucla, university of california los angeles. the cohorts and coordinating center the consortium of cohorts are described elsewhere [4]. briefly, the cohorts geographically span the united states and canada and include: the aids care cohort to evaluate access to survival services (access) study (vancouver, canada); aids linked to the intravenous experience (alive) study (baltimore, md); the heart study (baltimore, md); the healthy young men’s (hym) study (los angeles, ca); the johns hopkins hiv clinical care cohort (jhhcc) (baltimore, md); the miami adult hiv (mash) study (miami, fl); mstudy (los angeles, a virtual data repository stimulates data sharing in a consortium 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi ca); radar (chicago, il); and the vancouver drug users study (v-dus) (vancouver, canada). the cohorts follow hiv-positive and high risk hiv-negative persons, people who use drugs including persons who inject drugs (pwid), and men who have sex with men (msm) in either community or clinical settings. they include participants in and out of hiv care and substance use treatment. some cohorts focus specifically on adolescents and young adults. all follow participants longitudinally. the cohorts collect extensive demographic, behavioral, clinical, and laboratory assay-based information on study participants. the differing study objectives of the cohorts result in heterogeneous populations and thus various types of data collected, ultimately leading to a lack of common standards across cohorts. even when data domains align, instrument selection may be tailored to the population or to best serve the original studies’ aims. c3pno, among other functions, has developed and maintains a vdr which is described herein. methods common data elements (cdes) the c3pno steering committee, composed of the cohorts’ principal investigators, nida scientific staff, and a scientific advisory board, developed an initial list of high-priority cdes, which were selected based on their perceived importance for defining study populations, risk factors, potential confounders, or outcomes for assessing the feasibility of a wide range of research proposals addressing high priority research domains. because some cohorts have been in existence for decades while others were established in the last five years and because some data elements are captured repeatedly and can vary with time (e.g., participant age, cd4+ counts, hiv/hcv viral loads, substance use patterns, sexual behaviors), the initial focus of this effort was on the most recent full visit for a participant. as such, not all participants currently or ever enrolled in the cohorts are represented in the available repository data, but it does ensure that all the data represent an up-to-date snapshot of cohort participants. the first data submission transfer in may 2018 included general demographic, socioeconomic, substance use, behavioral, and hiv related data from cohort participants. the first submission allowed the consortium’s data staff to learn about cohorts’ data management systems, obtain data dictionaries and/or data catalogues, establish data transfer agreements, and comply with any institutional regulatory requirements. with the first transfer, we also established processes for data transfer, data mapping, and conversion to a common format. this process allowed us to establish rapport between the coordinating center staff and cohort pis and data managers. multiple discussions, including an initial on-site, face-to-face meeting, followed by emails and teleconferences assisted in resolving any pending issues. not all cdes are collected by all nine cohorts, and data elements collected by multiple cohorts were not standardized across the cohorts. some cohorts collected a data element with finer granularity than others. initially data mapping to a common format was performed centrally by the c3pno coordinating center, and mapping and final tabulations of cdes were reviewed with data managers and cohort pis for accuracy. in the case that additional data were submitted by the cohort data center, only data that are part of the cde were mapped. data transfer, transformation, updates, and retention in order to minimize burden, data were submitted by cohorts in a format convenient to the cohort data team and in single or multiple files, although future data transfers are expected to be submitted in the format used for the initial transfer. a secure web-based file sharing system is used to submit a virtual data repository stimulates data sharing in a consortium 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi data. each user has a password protected account and access limited to that user’s cohort directory. datasets are transformed using a commercial extract-transform-load (etl) system. each cohort has its own transformation which can be run independently, and is tested and validated to ensure data integrity. all cde records are retained per regulatory requirements, but when new data are submitted, previous submissions are flagged as inactive. this allows a clear delineation of what data was available at any point in time, and, if needed, the data can be rolled back to a previous version. in the most recent data transfer, data was expanded to include longitudinal data. data mapping was performed by the data team of each individual cohorts based on mapping guide provided by the coordinating center. the cohort data team performs the mappings and submits the data to the coordinating center with documentation of the mappings. the database data are stored in a commercial relational database and, key fields are linked across tables. database constraints are defined to enforce data integrity. it is expected that future data requests will expand the data included in the cde set. the structure of the database is flexible so that new data elements can be easily incorporated. participant data are stored in three normalized database tables. the core participant database table (cohortparticipantdataitem) holds demographic, socioeconomic, clinical, and sexual behavior data, e.g., sex at birth, age, income, weight, hemoglobin, number of male sex partners. there is one record per participant data item (multiple per participant), which allows for the inclusion of additional data items as the cde is expanded with subsequent data transmissions without changing the original database structure. while substance use and biospecimen data could have been included in a single table, this would have required additional complexity not needed for both types of data. for both substance use and biospecimen data, it was determined that data were specialized to such a degree that it would be more efficient to maintain a separate database table for each. the substance use database table (cohortparticipantsubstanceuse) has fields for specific drug, reporting method, and administration route. data on available biospecimens are stored in another database table (cohortparticipantbiospecimen) which likewise contains fields for number of aliquots, specimen type, additive, and derivative type. vdr investigators can use the interactive platform to determine the number of participants and specimens that are available for their potential research study. filters are utilized to allow the user to specify inclusion criteria for their study population. for example, the user can determine how many male hiv-positive people reporting use of heroin are in each c3pno cohort and the number and type of specimens available for cohort participants meeting the inclusion criteria. importantly, no individual participant-level data are displayed; only summary counts of the number of total participants and of each biospecimen type meeting the specified criteria are shown. in addition to counts, the results page displays the specified search criteria used in generating the tabulations for the benefit of the end user. more complex searches can be obtained by working with the c3pno coordinating center. a virtual data repository stimulates data sharing in a consortium 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi bioethics experts were consulted in the development of procedures to protect against inadvertent disclosure of personally identifiable information by allowing researchers to perform queries on the database on the interactive platform. for example, query results are not displayed if fewer than ten participants are included in a given category. if a search results in less than ten participants for a specific cohort, data are only displayed collapsed across cohorts and only when the minimum required numbers are displayed in the query result. results table 2 shows a sampling of key types of demographic and clinical data and table 3 shows a sampling of substance use data available at the www.c3pno.org website. data from 6,177 participants represent cohort participants who have been seen at the last full visit. additional data for approximately 3,000 more participants in the canadian cohorts, (access and v-dus) will be available once participants are re-consented to allow their data to be shared with the consortium. currently, a small fraction of participants from the v-dus cohort have had an opportunity to provide their consent at a study visit. a virtual data repository stimulates data sharing in a consortium 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi table 2: number of participants with a recent full visit contributing specific data elements in c3pno virtual data repository (c3pno.org january 27, 2020) cohort: alive heart hym jhhc c mas h mstudy rada r vdus total total participants 1328 580 400 948 1016 560 1030 315 6177 demographics sex at birth 1328 580 400 948 1016 560 1030 315 6177 transgender status * * 400 948 1016 * 1030 315 3709 race 1328 580 397 948 1016 547 1030 302 6148 ethnicity (hispanic or non-hispanic) 1328 580 400 948 1016 547 1030 302 6151 homelessness 1328 * 400 * 49 10 19 314 2120 incarceration * * 398 * 1012 545 1030 315 3300 health care health insurance 1324 * 400 * 975 525 913 * 4137 accessed health care past 6 month status 1328 † 298 † 1015 546 666 315 4168 hiv-related hiv status 1328 580 400 948 992 560 1030 315 6153 antiretroviral treatment 372 406 66 942 480 * 209 * 1995 cd4 count (cell/mm3) 372 368 65 948 475 281 95 * 2604 hiv-1 viral load (copies/ml) 374 371 65 948 474 284 79 * 2595 other testing/ diagnoses hepatitis b status 1284 * 313 * 1015 81 * 315 3008 hepatitis c status 1327 * 313 942 1016 * * 313 3911 tuberculosis * * 948 * * * * 948 chlamydia * * 386 * * 544 1021 315 2266 gonorrhea * * 386 * * 544 1023 315 2268 syphilis * * 384 * * 143 * 315 842 abbreviations: hiv, human immunodeficiency virus; cohort. *no data of this specific type were reported by the cohort as available for use. †heart and jhhcc studies are clinical cohorts so all participants are in care. a virtual data repository stimulates data sharing in a consortium 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 ojphi table 3: number of participants with a recent full visit with data on drug usage, either by self-report within the last 6 months or by urinalysis (c3pno.org january 27, 2020)* cohort: alive heart hym jhhcc mash mstudy radar v-dus total total participants 1328 580 400 948 1016 560 1030 315 6177 cocaine self-report 1321 580 400 428 1014 545 1030 315 5633 urinalysis 0 0 356 440 1012 559 1027 254 3648 heroin self-report 1321 580 400 63 1015 545 1030 315 5269 urinalysis 0 0 356 47 1008 559 1027 0 2997 methamphetamines self-report 0 0 400 74 1016 545 1030 315 2364 urinalysis 0 0 356 47 1011 559 1027 0 3000 prescription pain killers self-report 1321 0 399 0 1012 196 1030 315 4273 urinalysis 0 0 0 404 0 0 0 254 658 fentanyl self-report 0 539 0 8 1014 349 0 315 2225 urinalysis 0 0 0 56 997 398 0 254 1705 cannabis self-report 1321 580 400 486 1015 545 1030 315 5692 urinalysis 0 0 356 439 1011 559 1027 254 3646 alcohol (self-report) 0 553 400 942 1016 0 1030 315 3736 nicotine (self-report) 1319 580 114 614 1014 0 0 315 3956 speedball (self-report) 1321 577 0 0 0 0 0 289 2187 hallucinogen (self-report) 1321 0 400 70 1014 0 1030 0 3835 stimulants (self-report) 1321 0 400 22 1015 0 1030 293 4081 * when a cohort did not collect drug usage data by a method they are reported in the table as 0. a virtual data repository stimulates data sharing in a consortium 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 data elements include race, sex, substance use, clinical history, and sexual behavior, and additional elements can be added with each update – refer to the c3pno.org website for data currently available. as some of the cohorts follow men who have sex with men (msm) only, there are more participants who report male sex at birth than female overall (72% and 28%, respectively). at last visit, approximately 61% are black/african american, 18% are hispanic, and nearly half are hivpositive. all cohorts include assessments of self-reported recent substance use at each study visit, but the substances assessed varied across the cohorts (see table 3). in the last data transfer, all cohorts assessed cocaine use; eight cohorts assessed heroin use; six cohorts assessed methamphetamine use; six cohorts assessed prescription pain medication use; and eight cohorts assessed cannabis use. urinalysis results for toxicological screens for substance use are also available in some cohorts for some substances. table 4 highlights the numbers of participants with a recent full visit who have biospecimens available by each cohort by type. plasma is available on 68%, serum on 47%, and pbmcs on 37% of participants. for some cohorts, additional biospecimens are available, including whole blood, oral rinse, passive drool, rectal swabs and sponges, nail, hair, buffy coat, and pellet specimens. investigators can propose research using these biospecimens for consideration by the c3pno steering committee. table 4: number of participants with a recent full visit for whom biospecimens are available (c3pno.org january 27, 2020). cohor t alive hear t hym jhhcc mas h mstud y rada r vdus tota l total participa nts 1328 580 400 948 1016 560 1030 315 6177 pbmc 386 * * 690 1016 184 17 * 2293 plasma 925 566 * 730 1016 184 755 * 4176 serum 695 * * 678 1016 490 * * 2879 buffy coat * * * 568 * * * * 568 whole blood * * * * 1016 * * * 1016 hair * * * * * 187 * * 187 nail * * * * * 412 * * 412 oral rinse * * * * * 452 * * 452 passive drool * * * * * 490 * * 490 pellets * * * * * 183 * * 183 rectal sponge * * * * * 123 * * 123 rectal swab * * * * * 489 778 * 1267 abbreviations: pbmc, peripheral blood mononuclear cells. * no biospecimens of the specific type were reported by cohort as available for use. subject to minimum threshold requirements (to guard against any potential unintentional disclosure of identifying information), the number of biospecimens from individuals meeting specific criteria can be obtained by adding filters. for example, the vdr can provide a tally of the number of plasma specimens from hiv-positive persons who have cd4+ cell counts less than 200 cells/mm [5]. a virtual data repository stimulates data sharing in a consortium 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 we previewed the c3pno vdr at a preconference for the 22nd international aids conference (aids 2018), and have disseminated availability of data and specimens through nida, international workshop on hiv and hepatitis observational databases (iwhod), 25th scientific conference of the society on neuroimmune pharmacology (snip), and additionally through links on a growing number of websites. limitations questionnaires were designed by each cohort team to best suit their population and study objectives. this results in heterogeneity in measures available for the cohorts. for example, substance use recall periods and assessment of frequency of use are not uniform across cohorts. the c3pno consortium is currently conducting projects to allow data linking of drug use and other key data measures to facilitate cross-cohort analyses. conclusions the benefits of data sharing are readily acknowledged by researchers, including those participating in the c3pno consortium. data linking work to address the use of different data instruments and to address differing definitions across cohorts for analytic purposes is ongoing. cross-cohort analyses are in various stages of planning and execution. a number of other nih-funded consortia have also created vdrs to improve access to their data and specimens. the vdrs differ in the types of data and populations that are housed. the nih is taking a leadership role in funding and requiring participation in vdr efforts. in the context of hiv treatment and prevention, the actg (aids clinical trials group), impaact (international maternal pediatric adolescent aids clinical trials network), and hvtn (hiv vaccine trials network) have developed a combined vdr that allows an investigator to perform an interactive search to learn about the specimens available (http://www.specimenrepository.org). the vdr allows filtering on the types of study and participant characteristics. concept proposals are sent to specific network for their review. while conceptually the hiv vdr is similar to the c3pno vdr, the populations and key data elements differ. the i2b2 (informatics for integrating biology & the bedside) now n2c2 (national nlp clinical challenges) clinical research platform for precision medicine is a nih-funded research platform that makes clinical data in electronic health records into analyzable data by using natural language processing to make unstructured text into data sets (i2b2.org). software is available to run queries and transmart tools are available for use in data exploration, display and analysis (https://i2b2transmart.org). these and other vdrs share the benefit of reducing the effort and time required by a researcher to a minimum for determining the feasibility of many concepts. the vdrs differ in the provenance of and type of data available which are tailored to the specific populations that are of interest. they may also have associated specimens that can be used for running assays. it is necessary for external researchers to be aware that such cohorts exist in order to enhance utilization of the data. furthermore, external researchers and those across the c3pno consortium need access to the characteristics of the study population, data collection methods, and a virtual data repository stimulates data sharing in a consortium 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 storage/types of specimen in order to develop research proposals and plan analyses. for some research questions, data from multiple cohorts must be combined to have sufficient power. a vdr can facilitate the process. the advantages of a vdr in the public domain are numerous, perhaps the greatest being the enhanced ability to promote collaborative science through curated data sharing. data sharing and transformation is a critical step in ensuring that existing data can be used in additional research studies. the barriers to data sharing are being overcome by transforming information collected into a set of cdes for the purpose of vdr display. the c3pno vdr is available to inform consortium and external investigators interested in submitting concept sheets for research proposing to use consortium data for consideration by c3pno. a system is in place to streamline the submission of concept sheets, and to track the review, data use agreement, data and/or specimen transmission, and publication process. these data can be further utilized by other investigators for scientific inquiry. acknowledgements first and foremost, we are most grateful for the involvement of our cohort participants for making this research possible. it is their time, commitment and involvement that allows us to collect the necessary data to draw meaningful scientific conclusions and advance our collective research via the consortium. we thank the c3pno cohort principal investigators (pi) and data managers (dm) for their leadership, expertise, and technical contributions to the consortium. in alphabetical order by cohort: access: pi, m-j milloy, dms, wing yin (janet) mok and ekaterina nosova; alive: pis, greg kirk and shruti mehta, dms, jacquie astemborski and todd noletto; hym: pi, michele kipke, dms, julia moore, ji hoon ryoo, and su wu; heart study: pi, shenghan lai, dm, shaoguang chen; jhhcc: pi, richard moore, dms, jeanne keruly, steven xu, li ming zhou, and charles collins; mash: pi, marianna baum, dm, qingyun liu; mstudy: pis, pamina gorbach and steven shoptaw; dms, india richter, fiona whelan, shahrzad divsalar, alexander moran, allison rosen, and stone shih; radar: pi, brian mustanski, dms, antonia clifford, daniel ryan and kitty buehler; and v-dus: pis, kora debeck and kanna hayashi, dms, wing yin (janet) mok, and ekaterina nosova. lastly, we thank the additional members of the frontier science technical team; david goss, astrid fuentes, lynn strusa and kris ricusso. this project is supported by the national institute of drug abuse (nida) of the national institutes of health under award numbers: u24da044554, u01da0251525, u01da036297, u01da036926, u01da040325, u01da036935, u01da040381, u01da036267, u01da036939, 2u01da038886. additional information about each c3pno cohort can be found at www.c3pno.org including objectives, current research, contact information, and links to cohort specific websites. financial disclosure no financial disclosures. competing interests no competing interests. http://www.c3pno.org/ a virtual data repository stimulates data sharing in a consortium 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e19, 2021 references 1. lee dj, stvilia b. 2017. practices of research data curation in institutional repositories: a qualitative view from repository staff. plos one. 12, e0173987. pubmed https://doi.org/10.1371/journal.pone.0173987 2. final nih statement on sharing research data. 2003. (accessed august 27, 2019, at https://grants.nih.gov/grants/guide/notice-files/not-od-03-032.html.) 3. rfa-da-17-019 coordinating center for the hiv/aids and substance use cohorts program (u24). at https://grants.nih.gov/grants/guide/rfa-files/rfa-da-17-019.html.) 4. gorbach p, siminski s, ragsdale a, javanbakht m, kim s, chandler r. cohort consortium profile: the collaborating consortium of cohorts producing nida opportunities (c3pno). 2019. 5. nih announces draft statement on sharing research data. 2002. (accessed august 27, 2019, at https://grants.nih.gov/grants/guide/notice-files/not-od-02-035.html.) https://pubmed.ncbi.nlm.nih.gov/28301533 https://doi.org/10.1371/journal.pone.0173987 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts potential use of multiple surveillance data in the forecast of hospital admissions objective this paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases. introduction a sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower planning. in hong kong, the timing of influenza peak seasons are variable and early short-term indication of possible surge may facilitate preparedness which could be translated into strategies such as early discharge or reallocation of extra hospital beds. in this study we explore the potential use of multiple routinely collected syndromic data in the forecast of hospital admissions. methods a multivariate dynamic linear time series model was fitted to multiple syndromic data including influenza-like illness (ili) rates among networks of public and private general practitioners (gp), and school absenteeism rates, plus drop-in fever count data from designated flu clinics (dfc) that were created during the pandemic. the latent process derived from the model has been used as a measure of the influenza activity [1]. we compare the cross-correlations between estimated influenza level based on multiple surveillance data and gp ili data, versus accident and emergency hospital admissions with principal diagnoses of respiratory diseases and pneumonia & influenza (p&i). results the estimated influenza activity has higher cross-correlation with respiratory and p&i admissions (!=0.66 and 0.73 respectively) compared to that of gp ili rates (table 1). cross correlations drop distinctly after lag 2 for both estimated influenza activity and gp ili rates. conclusions the use of a multivariate method to integrate information from multiple sources of influenza surveillance data may have the potential to improve forecasting of admission surge of respiratory diseases. table 1. cross correlations between the estimated influenza activity based on the multivariate dynamic linear model, gp ili rate versus a&e respiratory diseases and p&i admissions *negative lags refer to correlations between lagged surveillance data and hospital admissions keywords influenza; surveillance; admission; respiratory references lau eh, cheng ck, ip dk, cowling bj. situational awareness of influenza activity based on multiple streams of surveillance data using multivariate dynamic linear model. plos one 7(5): e38346. doi:10.1371/journal.pone.0038346 *eric h. lau e-mail: ehylau@hku.hk eric h.y. lau*¹, dennis k.m. ip¹ and benjamin j. cowling¹ ¹school of public health, the university of hong kong, hong kong online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e168, 2013 lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi lyme disease in maine: a comparison of nedss surveillance data and maine health data organization hospital discharge data sara robinson1 1. maine center for disease control and prevention and university of illinois at chicago abstract background: lyme disease is the most commonly reported vector borne disease in the united states and is a major public health concern in maine. maine center for disease control and prevention (maine cdc) monitors lyme disease through a passive surveillance system. in order to validate the lyme disease surveillance system, maine cdc was interested in comparing trends with a secondary data source. specifically, maine cdc was interested in comparing trends by age group, gender, geography, and timelines. also, because hospitalization due to lyme disease is rare, this analysis provided an opportunity to look at the diagnosis codes used for lyme disease visits. the purpose of this paper is to compare the data acquired through surveillance to a secondary data source in order to evaluate the completeness of the data and verify trends. methods: surveillance data was extracted from maine’s nedss base system for the years 2008 – 2011. only confirmed and probable cases were included in data analysis. the maine health data organization (mhdo) collects information on all hospital inpatient and outpatient data visits and was used for this comparison. mhdo inpatient and outpatient hospital encounters with a diagnosis of 08881 in any diagnosis field were extracted from the full dataset from 2008 – 2011. results: surveillance data showed the 5-14 year old age group had the highest rates of lyme disease while outpatient data showed adults over the age of 45 to have the highest rates. outpatient data showed a higher percentage of females with lyme disease visits. geographic trends did not match well between surveillance data and mhdo data which may be due to the hospital being used as proxy for the patient address. timeliness trends we re consistent between all sources, with the majority of lyme disease occurring in the summer months of june, july and august. the majority of outpatient visits had lyme disease listed as their primary diagnosis while the majority of inpatient visits had lyme disease as a secondary or lower diagnosis. conclusions: there were several limitations to this study including incomplete data, and the inability to differentiate between new and old lyme diagnoses. there is reasonably good similarity in the trends of these two systems helping validate the usefulness of maine’s lyme disease surveillance system. many of the discrepancies warrant further investigation, and may lead to future opportunities for education or improvement in lyme disease surveillance. correspondence: sara.robinson@maine.gov doi: 10.5210/ojphi.v5i3.4990 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health info rmatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi background lyme disease is the most commonly reported vectorborne illness in the united states, and 95% of cases are reported from thirteen states, of which maine is one [1]. lyme disease is caused by the borrelia burgdorferi spirochete bacteria and is transmitted through the bite of an infected ixodes scapularis or deer tick. the most common early symptom of lyme disease is the formation of a characteristic expanding rash called erythema migrans or em. other early symptoms include fever, headache, joint and muscle pains, and fatigue. in maine, lyme disease is the third most commonly reported infectious disease behind chlamydia and hepatitis c ii . cases of lyme disease have increased exponentially over the last decade following the expansion of the ixodes scapularis tick [2]. lyme disease was first detected in the southern counties within the state, but has spread up the coast and into western maine. lyme is now considered endemic in all sixteen maine counties. maine center for disease control and prevention’s infectious disease epidemiology program is responsible for monitoring disease incidence within the state. lyme disease is a reportable condition, and lyme disease surveillance in maine is a passive system. maine cdc receives reports of clinically diagnosed lyme disease (em rash), as well as positive laboratory results. each suspect case is entered into maine’s surveillance system which is a nedss base system (nbs). a case report form is sent to the provider to collect information on demographics, symptoms, and risk factors. returned case report forms are then classified by an epidemiologi st using the standard council of state and territorial epidemiologists case definition [3]. the updated information is entered into maine’s nbs. an em rash alone is confirmatory in maine, because lyme disease is endemic in all of our counties. the maine health data organization (mhdo) was created by legislature in 1996 to “collect clinical and financial health care information and to exercise responsible stewardship in making this information accessible to the public” [4]. mhdo collects information on inpatient and outpatient hospital encounters which are available annually. this reporting is required in maine rules and the definitions of who must submit data and what data must be submitted are clearly spelled out. because maine’s lyme disease surveillance system is a passive system it would be useful to compare the data acquired through surveillance to a secondary source in order to evaluate the completeness of the data and verify trends. specifically we are looking for similarity in trends of age groups, gender, geography, and timelines. reports of hospitalizations due to lyme disease are rare, but we have not reviewed hospitalization records to determine the validity of the rates. little is known about the individuals hospitalized for lyme disease in maine, including if lyme disease is the primary diagnosis, or a secondary diagnosis. methods surveillance data was extracted from maine’s nbs for the years 2008 – 2011. only confirmed and probable cases were included in data analysis. this data source includes patients who were seen by a provider for lyme disease and met the federal case definition. http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi mhdo inpatient and outpatient hospital encounters with a diagnosis of 08881 in any diagnosis field were extracted from the full dataset from 2008 – 2011. data were de-duplicated using hospital id, medical record number, date of service, and sequential visit number. data for inpatient visits and outpatient visits were analyzed separately. this data includes provider visits for lyme disease, but no case classification is applied. all data analysis and manipulation was performed using sas 9.3 statistical software. all geographic mapping and analysis was performed using arc gis arc info 10. rates were calculated using census data for each year, and are per 100,000 persons. data and analysis overall results from 2008 -2011 data was available for outpatient visits, inpatient visits, surveillance cases, and surveillance cases that were hospitalized. overall, outpatient visits decreased significantly in 2010 and 2011. inpatient visits, and surveillance cases that were hospitalized remained relatively stable during all four years. surveillance cases increased yearly with the exception of 2010 (table 1). table 1: number of visits for lyme disease and surveillance cases of lyme disease – maine, 2008-2011 mhdo data surveillance data outpatient visits inpatient visits cases hospitalizations 2008 3048 109 909 38 2009 3544 118 976 46 2010 1173 107 751 25 2011 1278 127 1012 51 age groups for the purpose of looking at trends by age group, outpatient visit data was used to compare to surveillance data. we assume that inpatient data is likely skewed based on other underlying conditions, so outpatient visit data would be more comparable to sur veillance data. the data was stratified into six standardized age groups (<5 years, 5 – 14 years, 15 24 years, 25 – 44 years, 45 – 64 years and over 65 years). counts were converted to rates and are displayed for both outpatient data and surveillance data in table 2. surveillance data consistently shows the highest rate to be in the 5 -14 year age group, with the second highest rate in the 45-64 year age group, and the third highest rates in the over 65 years age group. the age group with the lowest rate varies by year (figure 1). http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi table 2: rates per 100,000 persons of lyme disease by age group, surveillance and outpatient data – maine, 2008-2011 2008 2009 2010 2011 age groups surveillance outpt surveillance outpt surveillance outpt surveillance outpt <5 43.6 63.3 58.6 88.6 36.1 21.7 68 20.7 5 14 104 130.4 117.2 175.4 77.2 34.0 111 46.9 15 24 50 118.6 58.2 137.6 40.6 52.5 64.4 57.8 25 44 51.6 261.2 50.8 250.0 46.2 98.8 58 93.2 45 64 84 304.7 85.4 352.6 65.2 113.0 85.8 130.9 65+ 61 255.2 69.7 356.9 59.6 114.5 71.2 121.6 figure 1: rate per 100,000 persons by age group, surveillance data – maine, 2008-2011 the outpatient data shows the highest rates to be in the 45 – 64 years and over 65 years age groups. children less than 5 years old consistently have the lowest rate in the outpatient data (figure 2). http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi figure 2: rate per 100,000 persons by age group, mhdo outpatient data – maine, 2008-2011 absolute counts were used to look at hospitalization data because the denominator for hospitalizations was unknown. mhdo inpatient visits were used to due to higher numbers than the surveillance hospitalizations. the majority of inpatient visits for lyme disease occurred in adults 25 years and older. the age group with the highest count varied by year, but shifted from the 45 – 64 to the over 65 years age group over time (figure 3). figure 3: hospitalization by age group, mdho data – maine, 2008-2011 http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi gender surveillance data and inpatient data showed no significant gender differences for lyme disease. outpatient visits had a higher percentage of female visits than male visits; this was consistent for all four years (table 3). table 3: gender of lyme disease cases; mdho and surveillance data – maine 2008-2011 2008 2009 2010 2011 mhdo surv mhdo surv mhdo surv mhdo surv gende r inpt outp t case s inpt outp t case s inpt outp t case s inpt outp t case s f 53 1805 418 51 1977 439 50 691 353 55 744 433 m 56 1243 493 67 1567 531 57 482 398 72 534 579 % f 48. 6 59.2 45.9 43. 2 55.8 45.3 46. 7 58.9 47 43. 3 58.2 42.8 geography surveillance trends in maine show that lyme disease was first endemic in the southern counties of the state, and then moved up the mid-coast region. over half of the lyme disease cases have occurred in the southern two counties in the state (cumberland and york). however, these two counties are the most populated counties in the state, so to look at the true burden of disease we converted counts into rates. accounting for population, the southern two counties (cumberland and york), and the four mid-coast counties (knox, lincoln, sagadahoc, and waldo) had the highest rates of lyme disease (appendix 1). mhdo outpatient visit data rates varied by year. franklin county in western maine had consistently high rates, and the mid-coast area had relatively high rates. sagadahoc county had a rate of zero, but this is likely due to the fact that there is no hospital in sagadahoc county, not that there are no cases there (appendix 2). timelines ticks can be active any time the temperature is above 40 degrees fahrenheit, so cases of lyme disease are acquired year round in the state. however, the prevalence of ticks is much higher during the summer months, and therefore we expect to see the majority of the cases to be acquired during these months. surveillance data uses the onset date to classify the case into months. we used the date of the visit for the mhdo dataset to classify cases into months. the mhdo inpatient and outpatient data and the surveillance data all showed june, july, and august to be the months with the most lyme disease visits and cases. (figures 4, 5 and 6) http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi figure 4: inpatient visits for lyme disease by month – maine, 2008-2011 0 5 10 15 20 25 30 jan feb mar apr may jun jul aug sep oct nov dec # l y m e v is it s 2008 ip 2009 ip 2010 ip 2011 ip figure 5: outpatient visits for lyme disease by month – maine, 2008-2011 0 100 200 300 400 500 600 jan feb mar apr may jun jul aug sep oct nov dec # l y m e v is it s 2008 op 2009 op 2010 op 2011 op figure 6: surveillance cases of lyme disease by month – maine, 2008-2011 0 50 100 150 200 250 300 jan feb mar apr may jun jul aug sep oct nov dec # l y m e c a se s 2008 2009 2010 2011 http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi diagnosis codes the first ten diagnosis codes were available for all mhdo inpatient and outpatient visits. this data was analyzed to determine the percentage of visits with a lyme disease code within the first five diagnoses. the last five diagnoses were collapsed into an ”other diagnosis”category due to the small numbers. it is assumed that diagnosis 1 is the primary diagnosis; diagnosis 2 is the secondary diagnosis and so on. lyme disease was the primary diagnosis in only 27 -40% of inpatient visits (table 4). more than half of all outpatient visits had lyme disease as the primary diagnosis (table 4). table 4: percentage of inpatient and outpatient visits for lyme disease by diagnosis code – maine, 2008-2011 inpatien t diagnosis 1 diagnosis 2 diagnosis 3 diagnosis 4 diagnosis 5 other diagnoses 2008 33.0 31.2 17.4 5.5 4.6 8.3 2009 40.7 34.7 8.5 7.6 1.7 6.8 2010 27.1 36.4 17.8 7.5 3.7 7.5 2011 28.3 33.1 20.5 10.2 6.3 1.6 outpatient 2008 71.2 15.9 7.3 2.7 1.7 1.2 2009 67.9 17.4 8.8 3.5 1.3 1.1 2010 52.8 26.3 11.7 4.4 3.5 1.3 2011 52.1 22.6 11.4 6.0 4.9 3.0 discussion and conclusion lyme disease is a major public health concern in maine. the number of surveillance cases reported each year continues to rise, with no plateau in sight. because maine has a passive system for lyme disease surveillance, it was important to compare surveillance results with a secondary source in order to ascertain the effectiveness of our current surveillance system. there are several interesting findings through this comparison. this was our first attempt to use mhdo data to look for trends and the mhdo data is not directly comparable to surveillance case data for many reasons. first, individuals may be counted in the mhdo set more than once. each time the person sought care counted as a new visit, which may dramatically inflate the numbers as one patient may be seen multiple times and at multiple locations. the surveillance data is patient centric, and each individual can only be counted once within a year. it is also recognized that the surveillance case definition is more specific than a clinical diagnosis. in other words, not all clinical cases of lyme disease will be counted as cases using the surveillance definitions. however, it seems reasonable that although the scale is inflated for the mhdo data, the trends should still exist. the first concern identified in the data is the dramatic decrease in outpatient visits in 2010 and 2011. the surveillance case numbers continued to rise, but the http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi outpatient visits fell. this could be due to a change in how the data was collected, or in how the data was pulled for this analysis. this difference warrants further investigation. in looking at the age group trends, it was surprising to see that the 5-14 year old age group did not have the highest rates in the outpatient data as expected. this could be due to the fact that children may be more likely to see a pediatrician rather than going to a hospital related outpatient clinic, but warrants further investigation. adults over 45 years had high rates in both the surveillance data and the mhdo outpatient data which was expected. adults over 45 years also had the highest number of hospitalizations, but this is not surprising because as individuals age they tend to develop other co-morbidities or underlying conditions that may make them more prone to hospitalization in general. the gender analysis showed a much higher percentage of females with lyme disease related visits in the mhdo outpatient data than either the inpatient data or the surveillance case data. the literature shows that women tend to access health care more than men, so this may not be an unusual finding [5]. the geographic analysis was perhaps the most interesting finding of this comparison. the rates of lyme disease cases by county did not match the rates of lyme disease visits by county. the distribution of surveillance cases matches the distribution of the ixodes scapularis tick, and is well recognized as how the disease is moving through the state [6]. we expect the mhdo data to be skewed towards population centers because that is where the major hospitals are, and where there are more hospital related clinics. the data are also affected where there is no hospital (sagadahoc county with a consistent rate of 0). however cumberland county has a consistently lower rate than its surrounding counties which does not make sense. perhaps there is a difference in how people in different counties utilize the health care system. maybe some of the larger counties have more options for health care and so they are less likely to use a hospital system provider. it is unclear as to why the geographic trends do not match, but this is definit ely something to investigate further. the timeliness trends showed no surprises. the majority of visits and onset dates for lyme disease were in the summer months which are when the tick density is highest, but cases are seen year round. analysis of the diagnosis data provided some interesting information. the majority of outpatient visits had lyme as the primary diagnosis, which can be used to infer that lyme is the reason the patient went to the provider. however, only a small number of inpatient visits had lyme disease as the primary diagnosis. this suggests that lyme is either a secondary finding after hospitalization, or a contributing factor in most cases. we expect low hospitalization rates due to lyme disease itself, so this finding confirms that theory. it would be interesting to see what the common primary diagnoses are for patients for whom lyme is not the primary diagnosis. overall, this analysis showed that there are strengths and opportunities for maine’s current surveillance system. the majority of the trends investigated were similar in both systems which supports the usefulness of the surveillance system. however, the mhdo data has much higher numbers than the surveillance data. it is difficult to tell if this is due to duplication in the mhdo dataset, or missed cases in the surveillance data – it is probably a combination of both. we are http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi well aware that providers have low compliance in reporting em rash alone, and the mhdo data supports the idea that we might be missing clinically diagnosed ca ses. further investigation is needed to look at the differences identified through this analysis. limitations there are many limitations to this analysis. the mhdo data is not as clean as surveillance data, and may be misleading due to multiple visits by a single patient. the data set used for this analysis may be incomplete as suggested by the drop in numbers for 2010 and 2011. using the facility for the geographic analysis is not as accurate as using patient demographic data, but patient level data was unavailable for this analysis. another limitation is that the mhdo only contains data for hospital systems and not all outpatient providers. this skews the analysis to those who use emergency rooms and urgent cares as opposed to individuals who may use an independent health care provider. a major limitation is the use of icd -9 codes for analysis. there is only a single icd-9 code for lyme disease, and because of this we cannot tell if the patient is being newly diagnosed with lyme disease, or if the diagnosis is old. icd-10 coding will improve this, as it will have multiple coding options for lyme disease. future directions this analysis created some questions that should be investigated further. an investigation into why the outpatient lyme disease visits dropped so dramatically in 2010 and 2011 is warranted. the geographic analysis should be repeated with patient level demographics as opposed to facility locations to see if that improves the degree of matching between the two systems. further investigation into the primary diagnoses of inpatient lyme disease visits may reveal useful information. as hospital systems begin to change to icd-10 codes this presents an opportunity to use the maine health information exchange (hin) to identify cases that may be missed during surveillance. icd-10 has a code specifically for erythema migrans, which is confirmatory in maine. as hospitals begin to switch to icd-10 codes maine cdc can collaborate with maine hin to transmit cases with this diagnosis directly to our nbs. this will improve provider compliance with reporting and will make our surveillance system more robust. references: 1. centers for disease control and prevention. lyme disease. updated december 6, 2013. available at http://www.cdc.gov/lyme/stats/index.html. accessed 12/10/2013. 2. robbins a, mallis h. reportable infectious disease in maine: 2012 summary. available at http://www.maine.gov/dhhs/mecdc/infectious-disease/epi/publications/2012-annual-report.pdf. accessed 12/10/2013. 3. centers for disease control and prevention. national notifiable diseases surveillance system (nndss): lyme. updated november 14, 2013. available at http://wwwn.cdc.gov/nndss/script/conditionsummary.aspx?condid=100. accessed 12/10/2013. 4. maine health data organization. available at https://mhdo.maine.gov/index.aspx. accessed 11/10/2013. 5. bertakis kd, azari r, helms lj, callahan ej, robbins ja. 2000. gender differences in the utilization of health care services. j fam pract. 49(2), 147-52. pubmed http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10718692&dopt=abstract lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi 6. maine medical center research institute. lyme and other vector-borne disease information: map of deer tick distribution in maine updated 2011. available at http://www.mmcri.org/home/websubcontent.php?list=websubcontentlive&id=197&catid=4&subcatid=19. accessed 12/10/2013. http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi appendix 1: surveillance lyme disease rates by county, maine 2008-2011 http://ojphi.org/ lyme disease in maine: a comparison of nedss surveillance data and maine online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e231, 2014 ojphi appendix 2: mhdo outpatient lyme disease rates by county, maine 2008-2011 http://ojphi.org/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts category-specific comparison of univariate alerting methods for biosurveillance decision support yevgeniy elbert*, vivian hung and howard burkom jhuapl, laurel, md, usa objective for a multi-source decision support application, we sought to match univariate alerting algorithms to surveillance data types to optimize detection performance. introduction temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. in a project for the armed forces health surveillance center to enable multivariate decision support, we obtained 4.5 years of outpatient, prescription and laboratory test records from all us military treatment facilities. a proof-of-concept project phase produced 16 events with multiple evidence corroboration for comparison of alerting algorithms for detection performance. we used the representative streams from each data source to compare sensitivity of 6 algorithms to injected spikes, and we used all data streams from 16 known events to compare them for detection timeliness. methods the six methods compared were: 1) holt-winters generalized exponential smoothing method (1) 2) automated choice between daily methods, regression and an exponential weighted moving average (2) 3) adaptive daily shewhart-type chart 4) adaptive one-sided daily cusum 5) ewma applied to 7-day means with a trend correction; and 6) 7-day temporal scan statistic sensitivity testing: we conducted comparative sensitivity testing for categories of time series with similar scales and seasonal behavior. we added multiples of the standard deviation of each time series as single-day injects in separate algorithm runs. for each candidate method, we then used as a sensitivity measure the proportion of these runs for which the output of each algorithm was below alerting thresholds estimated empirically for each algorithm using simulated data streams. we identified the algorithm(s) whose sensitivity was most consistently high for each data category. for each syndromic query applied to each data source (outpatient, lab test orders, and prescriptions), 502 authentic time series were derived, one for each reporting treatment facility. data categories were selected in order to group time series with similar expected algorithm performance: 1) median > 10 2) 0 < median ! 10 3) median = 0 4) lag 7 autocorrelation coefficient " 0.2 5) lag 7 autocorrelation coefficient < 0.2 timeliness testing: for the timeliness testing, we avoided artificiality of simulated signals by measuring alerting detection delays in the 16 corroborated outbreaks. the multiple time series from these events gave a total of 141 time series with outbreak intervals for timeliness testing. the following measures were computed to quantify timeliness of detection: 1. median detection delay – median number of days to detect the outbreak. 2. penalized mean detection delay –mean number of days to detect the outbreak with outbreak misses penalized as 1 day plus the maximum detection time. results based on the injection results, the holt-winters algorithm was most sensitive among time series with positive medians. the adaptive cusum and the shewhart methods were most sensitive for data streams with median zero. table 1 provides timeliness results using the 141 outbreak-associated streams on sparse (median=0) and nonsparse data categories. [insert table #1 here] the gray shading in the table 1 indicates methods with shortest detection delays for sparse and non-sparse data streams. the holt-winters method was again superior for non-sparse data. for data with median=0, the adaptive cusum was superior for a daily false alarm probability of 0.01, but the shewhart method was timelier for more liberal thresholds. conclusions both kinds of detection performance analysis showed the method based on holt-winters exponential smoothing superior on non-sparse time series with day-of-week effects. the adaptive cusum and shewhart methods proved optimal on sparse data and data without weekly patterns. keywords biosurveillance; timeliness; detection; alerting methods; sensitivity references 1. elbert y, burkom h, shmueli g, development and evaluation of a dataadaptive alerting algorithm for univariate temporal biosurveillance data stat. med. 2009; 28:3226-3248 2. burkom h, elbert y, thompson m, et al, development, adaptation, and assessment of alerting algorithms for biosurveillance jhuapl technical digest, volume 24, number 4 (2003) *yevgeniy elbert e-mail: yevgeniy.elbert@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e89, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts advancing surveillance of chronic and noncommunicable disease—a path forward elissa r. weitzman1, 3 and nadia waheed*2, 4 1harvard medical school, boston, ma, usa; 2tufts university school of medicine, boston, ma, usa; 3boston children’s hospital, boston, ma, usa; 4new england eye center, boston, ma, usa objective to characterize current and future approaches to surveillance of chronic and non-communicable diseases and establish the agenda for both methodological and condition-specific progress. introduction major global stakeholder groups including the united nations, world health organization and institute of medicine seek to raise awareness of the threat to global health and security of chronic and non-communicable diseases. these conditions comprise 50-85% of the global annual morbidity burden and constitute a major drain on national economies. to move from awareness of this problem to action and amelioration of issues, we need effective means for monitoring and intervening with populations using approaches that span primary, secondary and tertiary prevention. methods this session will begin with a discussion of key concepts and terms and their implications for defining target problems, populations and surveillance strategies. we will also begin by reviewing the epidemiologic and economic arguments for advancing surveillance in this area. the discussion will center on a critical assessment of issues related to surveillance of chronic and non-communicable diseases: how do approaches differ from established and evolving approaches to surveillance of infectious disease? are there opportunities for synergy with current surveillance efforts and assets? where are new methods needed? how might surveillance approaches be advanced in different regions (e.g., developing and industrialized settings)? might new approaches predicated on “citizen science” and engaged patient and public health cohorts provide platforms for advancing surveillance of chronic and non-communicable diseases and what is required to ensure their success? results points of discussion: 1) participants are encouraged to come prepared to share their experiences engaging patient and public health cohorts in this area, including sharing experiences engaging cohorts using online social networks, participatory research and surveys. 2) brainstorm ideas for development of a workshop in non-communicable disease surveillance. sample questions: 1) what are the issues related to surveillance in the context of resource rich and poor contexts? 2) what are the special needs for establishing cost-effective and sustainable methods for longitudinal tracking? 3) how can technological advances and engaged patient and public health cohorts be used in the advancement of surveillance? what are methods to maximize engagement in both the developed and developing world? conclusions non-communicable diseases are a major and growing morbidity and mortality burden globally. this round table discussion will focus on the importance of non-communicable disease surveillance, attempt to elicit participant’s experiences in the surveillance of these conditions, and outline special needs for establishing cost-effective and sustainable methods for longitudinal tracking of non-communicable diseases. keywords surveillance; non communicable diseases; chronic diseases acknowledgments dr weitzman’s work is supported by po1hk000088-01 from the centers for disease control and prevention (cdc), national library of medicine grant 5r01lm007677 and 1u54rr025224-01 from ncrr/nih. *nadia waheed e-mail: nwaheed@tuftsmedicalcenter.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e197, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts case based surveillance for measles in lagos, south western nigeria, september 2011 olawunmi o. adeoye*1, abimbola aman-oloniyo2, patrick nguku1, abiola oduneye2 and modupe dawodu2 1nigeria field epidemiology and laboratory training programme, abuja, nigeria; 2lagos state ministry of health, alausa, ikeja, lagos, nigeria objective the objective of this study was to describe the performance of the measles surveillance in lagos, characterize the epidemiologic pattern of measles infection and determine the measles vaccine efficacy. introduction measles is a vaccine preventable disease that has been successfully eliminated in some parts of the world. it causes high morbidity and mortality with the potential of large outbreaks. about a third of reported measles cases involve one or more complications including diarrhea, pneumonia, otitis media, blindness, post infections encephalitis and subacute sclerosing panencephalitis. it is however, one of the leading causes of childhood morbidity and mortality in nigeria despite availability of safe and effective vaccines methods we obtained the measles surveillance data for all the 20 local governments areas (lgas) in lagos and reviewed all the measles case based investigation forms between the period 1st january to 31st december 2010.the who recommended surveillance standards for measles was used. data was analyzed using epi info version 3.5.3. results of the 615 suspected measles cases, 63(10.2%) were laboratory confirmed (measles igm+) and 3(0.5%) clinically confirmed. cases investigated within 48 hours was 222 (36%) (target ! 80%), 510 (83%) had adequate blood sample collected (target ! 80%) and 595 (97%) of sample results were received from the lab within 7 days (target ! 80%). the surveillance system sensitivity was 6.5/100,000 (target >2/100,000) with a predictive value positive of 10.73%. the overall attack rate was 0.73/100,000 population with 1 mortality (case fatality rate 1.5%). the under 1 year attack rate (8.33/100,000) was higher than the 14 years attack rate (3.48/100,000) (p= 0.01). those vaccinated with at least 1 dose of measles vaccine had a 3 times lower risk of measles infection than the unvaccinated. the proportion of unvaccinated cases was 36%. the measles vaccine efficacy was 60%. conclusions the quality of surveillance need to be strengthened by improving the time lapse between notification and investigation of suspected cases. measles is still a significant cause of morbidity particularly among the under 1 year age group.the proportion of unvaccinated cases is also high, suggesting a low vaccine coverage among susceptibles. prompt investigation of cases, good vaccine coverage and high vaccine efficacy are all vital in eliminating measles from nigeria. morbidity and mortality rates no of reported measles cases in lagos, south western nigeria by lga with onset date from 1st january 31st december 2010 legend keywords surveillance; measles; case based; lagos acknowledgments lagos state ministry of health, alausa, ikeja, lagos. central public health laboratory, yaba, lagos. world health organisation, lagos office, ikoyi, lagos. references kohlhagen jk et al. 2011. meeting measles elimination indicators: surveillance performance in a regional area of australia. western pacific surveillance and response journal,2011,2(3). who.2001. modules on best practisesfor measles surveillance. available at www.who.int/vaccines-document. (accessed september 2011). who.2003.recommended standards for surveillance of selected vaccine preventable diseases.available at www.who.int/vaccines-document. (accessed september 2011). *olawunmi o. adeoye e-mail: wunmiolat@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e151, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts risk of cardiovascular morbidity and mortality in relation to temperature robert mathes*, kazuhiko ito and thomas matte new york city department of health and mental hygiene, queens, ny, usa objective to examine the effects of temperature on cardiovascular-related (cvd) morbidity and mortality among new york city (nyc) residents. introduction extreme temperatures are consistently shown to have an effect on cvd-related mortality [1, 2]. a large multi-city study of mortality demonstrated a cold-day and hot-day weather effect on cvd-related deaths, with the larger impact occurring on the coldest days [3]. in contrast, the association between weather and cvd-related morbidity is less clear [4, 5]. the purpose of this study is to characterize the effect of temperature on cvd-related emergency department (ed) visits, hospitalizations, and mortality on a large, heterogeneous population. additionally, we conducted a sensitivity analysis to determine the impact of air pollutants, specifically fine particulates (pm2.5) and ozone (o3), along with temperature, on cvd outcomes. methods we analyzed daily weather conditions, ed visits classified as cvd-related based on chief complaint text, hospitalizations, and natural cause deaths that occurred in nyc between 2002 and 2006. ed visits were obtained from data reported daily to the city health department for syndromic surveillance. inpatient admissions were obtained from the statewide planning and research cooperative system, a data reporting system developed by new york state. mortality data were obtained from the nyc office of vital statistics. data for pm2.5 and o3 were obtained from all available air quality monitors within the five boroughs of nyc. to estimate risk of cvd morbidity and mortality, we used generalized linear models using a poisson distribution to calculate relative risks (rr) and 95% confidence intervals (ci). a non-linear distributed lag was used to model mean temperature in order to allow for its effect on the same day and on subsequent days. models were fit separately for cold season (october through march) and warm season (april through september) given season may modify the effect on cvd outcomes. for our sensitivity analysis, we included pm2.5 and o3 in our model. results during the cold season, cvd-related ed visits and hospitalizations increased, while mortality decreased, with increasing mean temperature on the same day and lagged days. extremely cold temperature was associated with a small increase of same day in-hospital mortality though generally cold temperatures did not appear to be associated with higher mortality. the opposite was observed in the warm season as ed visits and hospitalizations decreased, and mortality increased, with increasing mean temperature on the same day and on lagged days. our sensitivity analysis, in which we controlled for pm2.5 and o3, demonstrated little effect of these air pollutants on the relationship between temperature and cvd outcomes. conclusions our results suggest a decline in risk of a cvd-related ed visit and hospitalization during extreme temperatures on the same day and on recent day lags for both cold and warm seasons. in contrast, our findings for mortality indicate an increase in risk of cvd-related deaths during hot temperatures. no mortality effect was observed during cold temperatures. the effects of extreme temperatures on cvd-related morbidity may be explained by behavioral patterns, as people are more likely to stay indoors on the coldest and hottest days. keywords morbidity; mortality; cardiovascular; temperature acknowledgments this research was funded by the environmental protection agency, star grant r833623010, and in conjunction with the alfred p. sloan foundation, grant 2010-12-14. we thank the members of the new york city department of health and mental hygiene syndromic surveillance unit. references 1. basu, r. and b.d. ostro, a multicounty analysis identifying the populations vulnerable to mortality associated with high ambient temperature in california. am j epidemiol, 2008. 168(6): p. 632-7. 2. braga, a.l., a. zanobetti, and j. schwartz, the effect of weather on respiratory and cardiovascular deaths in 12 u.s. cities. environ health perspect, 2002. 110(9): p. 859-63. 3. medina-ramon, m. and j. schwartz, temperature, temperature extremes, and mortality: a study of acclimatisation and effect modification in 50 us cities. occup environ med, 2007. 64(12): p. 827-33. 4. josseran, l., et al., syndromic surveillance and heat wave morbidity: a pilot study based on emergency departments in france. bmc med inform decis mak, 2009. 9: p. 14. 5. knowlton, k., et al., the 2006 california heat wave: impacts on hospitalizations and emergency department visits. environ health perspect, 2009. 117(1): p. 61-7. *robert mathes e-mail: rmathes@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e144, 2013 covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina gregory d. kearney, drph1*, katherine jones, phd1, yoo min park, phd2, rob howard, ms2, ray hylock, phd3, bennett wall, mba4, maria clay, phd5, peter schmidt, phd6, john silvernail, md, mph7 1department of public health, brody school of medicine at east carolina university, 2department of geography, planning and environment, east carolina university, 3department of health services & information management, east carolina university, 4vidant health, integrated care, 5department of bioethics & interdisciplinary studies, brody school of medicine at east carolina university, 6department of neurology, grossman school of medicine, new york university, 7pitt county health department abstract background: the initial limited supply of covid-19 vaccine in the u.s. presented significant allocation, distribution, and delivery challenges. information that can assist health officials, hospital administrators and other decision makers with readily identifying who and where to target vaccine resources and efforts can improve public health response. objective: the objective of this project was to develop a publicly available geographical information system (gis) web mapping tool that would assist north carolina health officials readily identify highrisk, high priority population groups and facilities in the immunization decision making process. methods: publicly available data were used to identify 14 key health and socio-demographic variables and 5 differing themes (social and economic status; minority status and language; housing situation; at risk population; and health status). vaccine priority population index (vpi) scores were created by calculating a percentile rank for each variable over each n.c. census tract. all census tracts (n = 2,195) values were ranked from lowest to highest (0.0 to 1.0) with a non-zero population and mapped using arcgis. results: the vpi tool was made publicly available (https://enchealth.org/) during the pandemic to readily assist with identifying high risk population priority areas in n.c. for the planning, distribution, and delivery of covid-19 vaccine. discussion: while health officials may have benefitted by using the vpi tool during the pandemic, a more formal evaluation process is needed to fully assess its usefulness, functionality, and limitations. conclusion: when considering covid-19 immunization efforts, the vpi tool can serve as an added component in the decision-making process. keywords: informatics, covid-19, public health, spatial, vaccine abbreviations: vaccine priority index (vpi) correspondence: kearneyg@ecu.edu* mailto:kearneyg@ecu.edu* covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi introduction on february 9, 2021, the centers for disease control and prevention reported nearly 27 million cases and 464,000 deaths related to covid-19 in the u.s.[1] as vaccine supply became increasingly more available, the initial deployment of phased vaccine roll-out plans across the u.s. brought about significant challenges. according to one report, the vaccine shortage saw many states diverging from cdc guidance, operating on different timelines and prioritizing different groups, and were increasingly dependent on where a person lived.[2] other news reports described the slow roll-out and allocation of covid-19 vaccine as chaotic and marred by logistical inconsistencies, with varying strategies and disproportionate socioeconomic power structures.[35] without question, the covid-19 pandemic crisis created new and unprecedented challenges. amid the rush to mass immunize the public with limited vaccine supplies, critical strategic planning and evaluation efforts were needed a priori to ensure efficient and equitable distribution of vaccine to high-risk, priority populations. for example, older adults living in isolated rural areas struggled with low vaccine allocation and transportation barriers. [6] the ability for health officials to identify high risk populations and facilities in advance, could assist in planning efforts, including improving supply chain delivery, providing accurate estimates of doses to safeguard communities. this paper describes the north carolina, vaccine priority index (vpi) mapping tool developed by researchers and partners at east carolina university and vidant medical center. this product was developed during the pandemic as an attempt to assist busy health officials and hospitals in the vaccine decision making process. prioritizing vaccine distribution in september 2020, the national academies of sciences and medicine released the preliminary framework for equitable allocation of covid-19 vaccine. [7] as described in the report, the committee recognized that when available, the allocation of initial supply of covid-19 vaccine would be tightly constrained. furthermore, setting priorities for the equitable allocation of vaccine were admittingly challenging given the differing risk and exposure factors by varying population and occupational groups. it was further noted that assigning priority at an individual level posed considerable constraints and were impractical for delivering vaccine. [7] doi: 10.5210/ojphi.v13i3.11617 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi figure 1. national academies of science and medicine phased approach to vaccine access. [7] considering the many factors associated with the allocation of vaccine, the committee operationalized risk criteria by characterizing populations and occupational groups based largely on risk and ability of vaccine to mitigate those risks. using the best available evidence, the committee recommended a four “phased,” successive approach for covid-19 vaccine allocation (figure 1). in phase 1 (highest priority), high risk workers in healthcare facilities, first responders, people with significant comorbid conditions and older adults in congregate or overcrowded conditions/settings would receive the initial doses of vaccine. as noted, “equity” was included as a “cross-cutting consideration” as part of the phased allocation process for vaccine access. to assist with identifying geographical areas for prioritization, the centers for disease control and prevention (cdc), social vulnerability index (svi) was suggested. in brief, the svi is a standardized, data driven system, vulnerability mapping application tool developed primarily to assist emergency disaster management personnel with identifying geographic areas of economic loss, providing social services and public assistance following natural disasters. [8] covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi approach to vulnerability mapping while the decision to allocate vaccine based solely on population density is one approach for helping communities reach herd immunity, it is flawed by taking into account inherent differences in risks and vulnerability experienced by different individuals and population groups. [9] by identifying where high-risk, vulnerable populations are located, health officials, hospital administrators and other decision-makers can better plan where to target vaccine delivery and intervention strategies. the social, economic, and health geographics of north carolina varies considerably across the state; regions, counties, and census tracts differ in population density, urban and rural status, income, race, ethnicity, and health status. likewise, covid-19 morbidity and mortality rates differ widely. some geographic regions have more vulnerable populations including the medically fragile and elderly. other high-risk groups including frontline health workers, first responders, and workers in occupations deemed “essential” tend to congregate near facilities where they work. while some individuals have resources to help them cope with the virus, others may not. for example, low-wage workers often lack health insurance, do not have transportation to get tested, or go to work when they are ill because they cannot afford to miss work. such challenges pose considerable exposure risk to covid-19 for individuals and their co-workers, families, and others in their communities. these low socio-economic factors are key drivers of a person's susceptibility to contracting the virus and managing their sickness once they become ill. given these limitations, a model of prioritization, allocation, and distribution model for covid-19, based solely on population density has inherent limitations in reducing the spread. the aforementioned cdc, svi method is one approach to consider when prioritizing population vulnerability primarily attributed to the community where they reside.[8] the svi was designed to standardize a measure of socio-economic status and vulnerabilities at the geographical census tract level. the index has been used extensively in the application for identifying population vulnerability with natural disasters. [10-13] while the svi continues to prove useful for mapping vulnerability, it was not designed with considerations of risk factors associated with an infectious agent. as covid-19 spreads, health decision-makers need to have the ability to quickly identify geographic areas that include high-risk facilities and populations so that resources can be allocated and deployed to protect public. a vaccine priority index for n.c. on october 16, 2020, the state of north carolina released its covid-19 vaccination plan and prioritized critical population groups that would receive the vaccine.[14] using guidance from the nam recommendations, it identified a “phased approach” that included high risk health workers, staff in long term care, people over 65 and staff of congregate living settings (i.e., migrant farm camps, jails, prisons, homeless shelters, and anyone with two or more chronic conditions identified by cdc to be high risk for covid-19 complications). with the covid-19 pandemic in full swing, researchers from east carolina university and vidant medical center worked rapidly to create a website and develop an online, publicly available geospatial web mapping tool. our goal was to assist health officials, hospital administrators and other decision makers identify and target priority populations and high-risk facilities. phases 1a and covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi phase 1b of the n.c., vaccine prioritization index (vpi) is similar to the nam priority phased, framework. for this initial approach we focused our efforts on phases 1a and 1b. the purpose of this project was to develop a publicly available, online mapping tool that could assist decision makers quickly identify geographical areas of “high-risk, high priority” populations in phases 1a and 1b in the vaccination roll out plan in n.c. in pandemic situations where the need to identify locations and population characteristics are critical for mass vaccinations, this type of applied spatial method offers critical insight for planning, distribution, and delivery of targeted mitigation strategies. methods data, themes and variables the vaccine priority index is composed of 14 variables that describe risk and vulnerability relative to the covid-19 virus; the 14 variables are grouped into 5 themes (table 1). the themes are, 1) socioeconomic status, 2) minority status and language, 3) at-risk populations, 4) housing, and 5) health status. the variables within each of the themes were selected based on a combination of factors including population risk and vulnerability relative to the covid-19 virus, other vulnerability mapping tools, and a review of the literature. this project was reviewed and approved by east carolina university, institutional review board (umcirb# 20-001299). the vpi is modeled on the social vulnerability index. the vpi borrows some variables from the svi, but also adjusts for incorporated risk and vulnerability data pertinent to the covid-19 pandemic. the vpi also uses a similar ranking methodology to the svi to assign sub-index values to each census tract based on individual variables. sub-index values are then combined to create “themes” and the themes are combined to create an overall priority index. the data for four of the themes and eleven of the variables were derived from u.s. census data (american community survey 5-year data, 2014-2018). data for the remaining theme (health status) and three variables were obtained from the cdc, diabetes atlas and the center for medicare and medicaid services chronic conditions data warehouse. [15,16] covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi table 1. themes and associated variables for constructing the vaccine priority index themes socioeconomic status minority status & language special-at-risk population housing health status selected variables % below poverty % unemployed average per capita income % no high school diploma % minority % that speak english “less than well” % age 65 or older % of population who list occupation in high-risk group % of households crowded % of people who live in group quarters % of housing that is multi-unit % of adults who have diabetes % of adults who are obese % of medicare beneficiaries age 65 or older who have 2 or more chronic health conditions rationale for themes and variables theme 1, social and economic status. theme 1 combines percent below poverty, percent unemployed, percent without a high school diploma, and average per capita income in a census tract to create a theme to assess socio-economic status. it has been well established that individuals with fewer economic resources are less resilient when responding to disasters, and it is intuitive that this would also be true in a pandemic. this theme contains the same variables as the svi and is calculated in a similar fashion, except the census tracts are ranked only for the state of north carolina instead of the entire u.s. theme 2, minority status and language. theme 2 combines percent of the tract that is minority (non-white, or hispanic) and the percent that speak english “less than well.” in general, minority populations are at high risk of infectious diseases, particularly when living in multi-generational households and spatially concentrated in neighborhoods. [17,18] immigrants for whom english is not their native language are also disadvantaged when considering access to health care, testing, and vaccines. [19,20,21] this theme contains the same variables as the svi and is calculated similarly, but only includes north carolina and not the entire u.s. census tracts. theme 3: special-at-risk populations (also are those prioritized in phase 1). theme 3 combines the percent of the population that is age 65 and over and the percent of the population whose occupations are healthcare or first responder. increased age is a risk factor for becoming ill from covid19, and, for those who do become ill, for getting seriously ill or dying. covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi therefore, census tracts with a higher percent of individuals over age 65 are ranked higher priority for vaccine prioritization. certain occupations (e.g., healthcare) are also at higher risk for contracting covid-19 because of close contact with covid-positive individuals or because the worker is in an occupation where social distancing may not be possible (e.g., first responders). using census data, we calculated a percent of the total population in each census tract for occupational groups corresponding with healthcare workers and first responders. for healthcare workers, we combined healthcare practitioners, healthcare technical occupations, and healthcare support occupations. healthcare practitioners included physicians of all specialties, physician assistants, nurses, and dentists. healthcare technical occupations includes licensed practical nurses, emts, and health equipment technicians, including respiratory technicians. healthcare support occupations includes home health aides, nursing assistants, and cleaners and orderlies in a health care setting. the protective service occupation category was used for first responders and includes law enforcement, firefighters, and correctional service workers. the numbers for each of these groups were summed to create a total number of frontline workers for each census tract, and then the percent of the population was calculated. a census tract with a higher percent is deemed to be at a higher risk, and thus requires a higher priority for the vaccine. theme 4: housing. as a highly contagious virus, covid-19 transmission can occur in social settings, such as in congregate living, close work environments, and/or social occasions such as church services, weddings, funerals, restaurants, and bars. the home is an important transmission environment and one where many people are not able to socially distance from one another. the housing theme combines three census variables that describes features of the home and living situation that may increase risk for covid-19, primarily due to the closeness of the setting, proximity to others, and the inability of individuals to socially-distance. the first variable in the housing theme is multi-unit housing. this variable assesses settings such as apartment buildings, where people are sharing hallways, elevators, and mail stations. the variable measures the percent of housing units in a census tract that are part of a structure that contains 10 or more units. the second variable in the housing theme is crowded housing. housing units (rental or owner occupied, house or apartment) with more than 1 person per room are considered crowded. the variable assesses the number of housing units in a census tract that are crowded, as a percent of all housing units in the census tract. note, this measures persons per all rooms in the housing unit, not bedrooms. the third variable for the housing theme is the percent of individuals in the census tract who live in group quarters (institutional settings) such as, nursing homes, assisted living, college dorms, psychiatric hospitals or other long-term healthcare institutional settings, and correctional facilities. such congregate living arrangements have emerged as one of the riskiest settings for the spread of covid-19, and numerous outbreaks have been tied to these settings. the group quarters variable measures the number of persons in the census tract that live in such a setting, as a percent of all people in the tract. where the percent is higher, there is a higher level of vulnerability. covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi theme 5: health status. it is widely recognized that individuals with certain underlying health conditions are at significantly higher risk of severe covid-19 illness, or death. while the exact reasons and extent of this are not fully understood, the course of the pandemic thus far has demonstrated that individuals with diabetes, heart disease, kidney disease, copd, obesity, and immune compromised individuals are all at risk for more severe illness and death if they contract covid19. individuals with 2 or more of these or other serious health conditions are at an especially elevated risk. the vpi health status theme combines county-level data on the prevalence of diabetes, obesity, and chronic illness. we use county-level data because census tract level health status data is not readily available; the county value is assigned to each census tract within the county. three variables are combined for this theme. the first two variables for health status are the percent of adults in the county with diabetes and the percent who are obese. this data was obtained from the cdc, online diabetes atlas, which is a subset of the cdc, behavioral risk factor surveillance system (brfss).[15,22] the brfss is an annual health telephone survey conducted by states but standardized at the national level. these two health conditions (diabetes and obesity) are both important co-morbidity risk factors for covid-19, and this data provides a uniform, reliable estimate for health status at the county level. the third variable in the health status theme is the percent of medicare beneficiaries in the county over age 65 who have two or more chronic health conditions. the data was obtained from the center for medicare and medicaid services chronic conditions data warehouse [16] and defines an individual as having a chronic condition if persons had a claim for a service or treatment related to that condition within the previous year. it was selected to be included as a measure of population health as it provides a good, general estimate for health status at the county level. good quality health status data is scarce at the county level, so this data set was chosen for its availability, reliability, and consistency. vp index method to construct the vp index score for phase 1, source data from table 1 were compiled and scores were calculated by ranking all census tracts in north carolina with a non-zero population (n=2193) from low to high based on the individual variable. a census tract with a low percent in poverty is presumed to have lower vulnerability (and lower vaccine priority), while a tract with a high percent in poverty has a higher vulnerability (and a higher vaccine priority). one exception was income, which was inversely ranked high to low, since tracts with a higher income are presumed to have lower vulnerability, and lower income is presumed to have higher vulnerability. once ordered by low to high, each census tract was assigned a rank. the rank number was then used to create an index value. the index value is the rank number divided by the total number of tracts for that variable. the vpi was calculated using the formula, vaccine priority index = (rank -1)/ n-1 covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi census tracts with no population, or a zero value for the variable being ranked (no individuals in poverty live in that tract, for instance) were not assigned a rank, and received a zero value for that index. to calculate themes, indices for individual variables were summed together, and then the summed values re-ordered, low to high. a new rank was assigned for the summed value, and a new, combined index value calculated, using the same method. to calculate the overall vp index, the 5 theme indices were summed and re-ranked, and their rank number used to calculate the final index. the vp index ranges from 0 to 1. lower values indicate lower vulnerability, and lower priority for the vaccine, while higher values indicate higher vulnerability, and higher priority for the vaccine. application features word-press was used as the operating platform to build a host website to display the vpi (https://enchealth.org/). the website included covid-19 related dashboard visualizations and arcgis online (esri) mapping tool (figure 2). users that access the vpi through the website can navigate the mapping visualizations and view the numeric index values and estimated number and percent of high priority vaccine groups identified in phase 1a and phase 1b counties and census tracts. as shown in figure 3, a mouse-over a geographic area over the map will display a pop-up menu that provides an estimated count of high priority individuals in each county and census tract. the user can also turn the map layers “on or off” on the right-hand panel to display the vpi or the cdc’s social vulnerability index (svi). by zooming in, key point locations, such as nursing homes, pharmacies, or health clinics are displayed. using the filter panel on the left-hand side of the map, the vaccine priority index category filter and pick the “high” category. the map filters to show only those census tracts in the highest priority group. displays of the svi and vpi are both available at state, county, and census tract levels. the filters can also be used to filter geographic areas based on vpi categories or by the number of priority phases 1a and 1b groups including percent of healthcare workers, first responders, residents in nursing homes, assisted living facilities, and individuals with chronic health conditions. other features including map layers (e.g., imagery, streets, community maps), and individual facilities including the locations of health departments, hospitals nursing homes, meat processing facilities, correctional institutions and pharmacies) with address and information related to each facility are also viewable. https://enchealth.org/ covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi figure 2. eastern north carolina (enc health) vaccine priority index (https://enchealth.org/) covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi figure 3. vaccine priority index mapping tool (https://enchealth.org/) discussion the vpi was created in rapid response to the covid-19 pandemic and the need to provide health officials decision makers with added information for quickly identifying high risk populations and facilities for vaccine delivery. the advantages and challenges of the vpi are listed below. • efficient the vpi translates priority groups (e.g., nam recommended phase 1a and phase 1b priority groups) into a ranked index value to quickly determine where vulnerable groups are located. • rapid visualization key point locations of high-risk, key facilities including hospitals, nursing and assisted living homes, meat packing facilities, fqhc’s, pharmacies, correctional institutions and health departments can readily be found. additional base map layer features including roads and other imagery can be added to maps. as new location centers emerge (e.g., large vaccination sites or drive through sites) can easily be incorporated into the model. covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi • vaccine delivery while the data is derived from the us census and other publicly derived, national datasets, it is only a metric or a guide to assist with the vaccine delivery and distribution process. strengths and limitations the timing of the release of the vpi tool to n.c. health departments was critically important to it’s success and came at a time when vaccine plans for nc were initially being rolled out. our efforts to promote the tool’s usefulness and abilities to health officials and hospital administrators gained positive attention. however, given the overwhelming duties and responsibilities of healthcare workers and providers to the pandemic, our ability to gain feedback was somewhat limited. nevertheless, future plans include distributing formal surveys to health departments and others to gain additional insight so that modifications can be made accordingly. also, at the onset of this project, our vpi tool was unique, and there were few other mapping tools available. however, as time progressed, an increase in commercial and non-commercial mapping tools have become publicly available. many of the newer products display features with varying features, and capable of conducting advanced statistical analysis. nevertheless, the vpi remains unique to n.c. the data for the vpi were derived primarily from the u.s. census and health surveys. while we used the most current data sets for this project, changing population composition and people not necessarily living where they work, or play presents inherent limitations. [8] recommendations and conclusions covid-19 remains a highly communicable, infectious agent that threatens the health and safety of society. at the time of this writing, mass vaccination campaigns have occurred throughout the u.s. as covid-19 case and death rates show an overall downward trend, new variant strains continue to emerge, questioning the return to normal operations. other challenges such as hesitancy, access to care, conspiracy theories and other reasons contribute to an average of only 59.4% of adult americans having been fully vaccinated. [23] despite these challenges, public health and healthcare workers continue to remain vigilant in its prevention, intervention, and treatment efforts. the vpi tool can be considered a viable option for assisting decision makers with identifying high risk populations and areas to target to protect north carolina communities. acknowledgements thanks to support from people that assisted with the development of this project including the dartmouth atlas project, esri, dr. keith keene and cheryl walters denny at east carolina university. financial disclosure funding for this project was provided by north carolina house bill 1043, 2020, covid-19 recovery act. competing interests the authors have no competing interests. covid-19: a vaccine priority index mapping tool for rapidly assessing priority populations in north carolina 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e13, 2021 ojphi references 1. covid data tracker. centers for disease control and prevention website. accessed february 10, https://covid.cdc.gov/covid-data-tracker/#datatracker-home 2. mukherjee s. states are “diverging from cdc guidance,” resulting in unequal vaccine rollout, experts say. fortune. february 17, 2021. accessed february 18, 2021. https://fortune.com/2021/02/17/covid-vaccine-cdc-guidelines-us-states-unequal-rolloutcoronavirus-vaccines-pfizer-biontech-moderna/ 3. appleby j. the state of vaccine supply: “opaque.’ unpredictable. “hard to pin down.” kaiser health news. february 5, 2021. accessed february 18, 2021. https://khn.org/news/article/thestate-of-vaccine-supply-opaque-unpredictable-hard-to-pin-down/ 4. johnson a. lack of health services and transportation impede access to vaccine in communities of color. washington post. feb. 13, 2021. accessed february 17, 2020. https://www.washingtonpost.com/health/2021/02/13/covid-racial-ethnic-disparities/ 5. khidir h, molina m. opinion: moral tragedy looms in early chaos of u.s. covid-19 vaccine 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non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts when it rains it pours: real-time situational awareness for two weather emergencies in connecticut kristen soto*, jaime krasnitski, therese rabatsky-ehr and matthew cartter epidemiology, connecticut department of public health, hartford, ct, usa objective to characterize the utility of the connecticut hospital emergency department syndromic surveillance (hedss) system for real-time situational awareness during two weather-related emergencies. introduction on august 28, 2011 tropical storm irene made landfall in connecticut. on october 29, 2011 connecticut was impacted by winter storm alfred. both of these storms included high winds and heavy precipitation which resulted in prolonged power outages, disruption of public drinking water systems, property damage, and widespread debris throughout the state. the hedss system was utilized to provide real-time situational awareness during the response and recovery phases of both storm events. methods the hedss system receives electronic patient abstract data from 21 of 32 emergency departments on a daily basis. free-text chief complaint data are characterized into syndrome categories. ed visits for carbon monoxide exposure (co), gastrointestinal illness (gi), injury, hypothermia, motor vehicle accidents (mva), and asthma syndromes were monitored throughout the response and recovery periods of both storm. odds ratios were calculated using the two weeks preand post-storm as reference dates. co visits were further assessed for geographic and demographic trends to target public health messages. the hedss system was evaluated to make recommendations for event monitoring during future public health emergencies. results following both storms there was a high completeness of daily hedss reporting despite extensive power outages (96% post-tropical storm, 91% post-winter storm). increased emergency department utilization for co (or: 26.20, 95% ci: 3.57-192.64) was observed post-tropical storm. increased emergency department utilization for co (or: 14.61, 95% ci: 7.43-28.72), hypothermia (or: 17.02, 95% ci: 3.01-359.30), and asthma (or: 1.17, 95% ci: 1.05-1.30) were observed following the winter storm. regional increases in ed utilization for injuries and mva were observed following both storm events; no increase in gi was associated with either storm event. during the 2 weeks post-tropical storm 28 cases of co exposure were reported through hedss and 5 cases through laboratory surveillance; during the winter storm 131 cases were reported through hedss and 162 through reportable disease surveillance. of the 167 cases reported through laboratory surveillance, 111 (66%) were from hospitals that sent data to hedss hospitals and 94(56%) were able to be matched to a specific ed record; of these 22(20%) were characterized as visiting the ed for co syndrome, 13(14%) had symptoms consistent with co as their chief complaint, 11(12%) had smoke/gasoline inhalation exposures as their chief complaint and 2(2%) had unrelated chief complaints. during the post-storm period the hedss systems detected 137 potential co exposures that were not reported through laboratory surveillance. conclusions tropical storm irene and winter storm alfred both had significant health impacts, particularly increased ed utilization for co due to prolonged power outages. the hedss system is the only all-hazards surveillance system that was able to provide near-real time information during the storm response phase. in addition to the current co syndrome definition, where the chief complaint must specifically mention the term co, a broader definition should also be used in the future to better assess the magnitude of co-related exposures. the broad definition should include symptomology and related exposures, such as smoke inhalation, to improve case detection. the hedss system should continue to be used in conjunction with reportable disease surveillance for situational monitoring during public health emergencies. keywords emergency department; public health practice; syndromic surveillance; carbon monoxide; weather acknowledgments patricia przysiecki, mph; brian toal, msph references 1.witt associates. connecticut october 2011 snowstorm power restoration report. december 21, 2011.http://www.wittassociates.com/assets/860/ctpowerrestorationreport20111201_final_1_.pdf *kristen soto e-mail: kristen.soto@ct.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e97, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts one health surveillance with electronic integrated disease surveillance system alexey v. burdakov*, andrey o. ukharov and thomas g. wahl black & veatch, overland park, ks, usa objective the objective of this demonstration is to show conference attendees how one-health surveillance in medical, veterinary and environmental sectors can be improved with electronic integrated disease surveillance system (eidss) using cchf as an example from kazakhstan. introduction eidss supports collection and analysis of epidemiological, clinical and laboratory information on infectious diseases in medical, veterinary and environmental sectors. at this moment the system is deployed in kazakhstan at 150 sites (planned 271) in the veterinary surveillance and at 8 sites (planned 23) in human surveillance. the system enforces the one-health concept and provides capacity to improve surveillance and response to infectious disease including especially dangerous like cchf. eidss has been in development since 2005 and is a free-of-charge tool with plans for open-source development. the system development is based on expertise of a number of us and international experts including cdc, wrair, usamriid, et al. methods effective monitoring and control of zoonotic diseases requires integrated approach to surveillance in medical, veterinary and environmental sectors. capability to rapidly collect and analyze information from these sectors is challenging due to diversity of different systems often used in these areas. eidss presented a unique integrated solution which allows collecting, sharing and analyzing data across these sectors. in those countries where this system is implemented both in human and veterinary surveillance (georgia, azerbaijan and kazakhstan), it provides a unique opportunity to improve monitoring and control capability. in kazakhstan and other countries experts are working on creating and improving effective analysis methods. in particular a method of real-time control of cchf situation was developed in kazakhstan. it allows the assembly of raw data gathered at the lower level in, surveillance system throughout the country on cchf cases in humans, assemble ticks vector surveillance campaigns and laboratory diagnostic results and analyze these data against population density. this gives a one-step tool to an epidemiologist to understand the situation and plan response at the national and regional level (see sample map). a quick link with the veterinary response teams allow to rapidly act with domestic animals prophylaxis measures. demonstration of the tool encouraging the one health approach to the surveillance which is already in place in a number of countries provides an exclusive opportunity to review different aspects of its utilization in practice as well as discuss challenges and benefits of this method in resource limited environments. conclusions eidss provides a capacity to improve one-health disease surveillance in human, veterinary and vector sectors by rapidly collecting, disseminating and analyzing data on infectious diseases. particular methods which are being developed in kazakhstan and other participating countries provide an instrument to epidemiologists to make decisions and more effectively plan response measures. currently particular methods were tested for cchf infection. it is planned to introduce methods for brucellosis and other infectious diseases of special interest in central asia and caucasus region. keywords eidss; electronic surveillance; one-health references 1. burdakov a., ukharov a. transforming national human and veterinary disease surveillance systems from paper into integrated electronic form in the fsu countries // 15th international congress on infectious diseases (icid), bangkok 2012 2. burdakov a., ukharov a. electronic integrated disease surveillance (eidss) // world congress on information and communication technologies for development (wcid’09) congress in beijing, china 2009 3. burdakov a. implementation of information-telecommunication part of the threat agent detection and response (tadr) program in republic of kazakhstan // official bulletin of state sanitary-epidemiological service of republic of kazakhstan, 1/37. 69-70. *alexey v. burdakov e-mail: burdakovav@bv.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e199, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts syndromic surveillance for outbreak detection and investigation tom andersson*1, 2, 4, pär bjelkmar3, anette hulth1, johan lindh1, stephan stenmark5 and mikael widerström6 1swedish institute for communicable disease control (smi), solna, sweden; 2national food agency, uppsala, sweden; 3inera ab, stockholm, sweden; 4stockholm university section for mathematical statistics, stockholm, sweden; 5västerbotten county medical officer, umeå, sweden; 6jämtland county medical officer, östersund, sweden objective for the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data – telephone triage of acute gastroenteritis (swedish health care direct 1177), web queries about symptoms of gastrointestinal illness (stockholm county’s website for healthcare information), and otc pharmacy sales of anti-diarrhea medication. introduction a large part of the applied research on syndromic surveillance targets seasonal epidemics, e.g. influenza, winter vomiting disease, rotavirus and rsv, in particular when dealing with preclinical indicators, e.g. web traffic (hulth et al, 2009). the research on local outbreak surveillance is more limited. two studies of teletriage data (nhs direct) have shown positive and negative results respectively (cooper et al, 2006; smith et al, 2008). studies of otc pharmacy sales have reported similar equivocal performance (edge et al, 2004; kirian and weintraub, 2010). as far as we know, no systematic comparison of data sources with respect to multiple point-source outbreaks has so far been published (cf. buckeridge, 2007). in the current study, we evaluated the potential of three data sources for syndromic surveillance by analyzing the correspondence between signal properties and point-source outbreak characteristics. methods the extracted data streams were compared with respect to nine waterborne and foodborne outbreaks in sweden in 2007-2011. the analysis consisted of three parts: (1) the validation of outbreak signals by comparing signal counts during outbreak and baseline periods, (2) the estimation of detection limits by modeling signal rates (signalto-case ratios), and (3) the evaluation of early warning potential by means of signal detection analysis. results the four largest outbreaks generated strong and clear outbreak signals in the 1177 triage data. the two largest outbreaks produced signals in otc sales of anti-diarrhea. no signals could be identified in the web query data. the outbreak detection limit based on triage data was about 100-1000 cases. for two outbreaks, triage data on diarrhea provided outbreak signals early on, weeks and months respectively, potentially serving the purpose of early warning. conclusions the sensitivity and specificity were highest for telephone triage data on patient symptoms. it provided the most promising source of syndromic data for surveillance of point-source outbreaks. currently, a project has been initialized to develop and implement a national system in sweden for daily syndromic surveillance based on 1177 health care direct, supporting regional and local outbreak detection and investigation. keywords syndromic surveillance; outbreak detection; point-source outbreak; outbreak investigation; data analysis acknowledgments the study is part of an ongoing research and development project on syndromic surveillance (sumo) funded by the swedish agency for contingency planning (msb). references buckeridge dl. outbreak detection through automated surveillance: a review of the determinants of detection. j biomed inform. 2007 aug;40(4):370-9. cooper dl, verlander nq, smith ge, charlett a, gerard e, willocks l, o’brien s. can syndromic surveillance data detect local outbreaks of communicable disease? a model using a historical cryptosporidiosis outbreak. epidemiol infect. 2006 feb;134(1):13-20. edge vl, pollari f, lim g, aramini j, sockett p, martin sw, wilson j, ellis a. syndromic surveillance of gastrointestinal illness using pharmacy over-the-counter sales. a retrospective study of waterborne outbreaks in saskatchewan and ontario. can j public health. 2004 nov-dec;95(6):446-50. hulth a, rydevik g, linde a. web queries as a source for syndromic surveillance. plos one. 2009;4(2). kirian ml, weintraub jm. prediction of gastrointestinal disease with over-the-counter diarrheal remedy sales records in the san francisco bay area. bmc med inform decis mak. 2010 jul 20;10:39. smith s, elliot aj, mallaghan c, modha d, hippisley-cox j, large s, regan m, smith ge. value of syndromic surveillance in monitoring a focal waterborne outbreak due to an unusual cryptosporidium genotype in northamptonshire, united kingdom, june july 2008. euro surveill. 2010 aug 19;15(33):19643. *tom andersson e-mail: tom.andersson@smi.se online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e78, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts detection of patients with influenza syndrome using machine-learning models learned from emergency department reports arturo lópez pineda*, fu-chiang tsui, shyam visweswaran and gregory f. cooper university of pittsburgh. department of biomedical informatics, pittsburgh, pa, usa objective compare 7 machine learning algorithms with an expert constructed bayesian network on detection of patients with influenza syndrome. introduction early detection of influenza outbreaks is critical to public health officials. case detection is the foundation for outbreak detection. previous study by elkin el al. demonstrated that using individual emergency department (ed) reports can better detect influenza cases than using chief complaints [1]. our recent study using ed reports processed by bayesian networks (using expert constructed network structure) showed high detection accuracy on detection of influenza cases [2]. methods the dataset used in this study includes 182 ed reports with confirmed pcr influenza tests (jan 1, 2007-dec 31, 2009) and 40853 ed reports as control cases from 8 eds in upmc (jul 1, 2010-aug 31, 2010). all ed reports were deidentified by de-id software with irb approval. an nlp system, topaz, was used to extract relevant findings and symptoms from the reports and encoded them with the umls concept unique identifier codes [2]. two subsets were created: ds1-train (67% of cases) and ds1-test (remaining 33%). the algorithms used for training the models are: naïve bayes classifier, efficient bayesian multivariate classification (ebmc) [3], bayesian network with k2 algorithm, logistic regression (lr), support vector machine (svm), artificial neural networks (ann) and random forest (rf). the predictive performance of each method was evaluated using the area under the receiver operator characteristic (auroc) and the hosmer-lemeshow (hl) statistical significance testing, that describes the lack-of-fit of the model to the dataset. results the evaluation results of all the models using ds1-test, including the auroc, its confidence interval, p-value (between each algorithm and the expert) and the calibration with hl are shown in table 1. conclusions all models achieved high auroc values. the pairwise comparison of p-values in table 1 demonstrates that the aurocs of all the machine-learning models and the expert model were not significantly different. nevertheless, ebmc is the best fitted. the model created by ebmc is shown in figure 1. one limitation of the study is that the test dataset has low influenza prevalence, which may bias the detection algorithm performance. we are in the process of testing the algorithms using higher prevalence rate. the same process could also be applied to other diseases to further research the generalizability of our method. predictive performance and calibration area under the roc curve (auroc) with 95% confidence interval; pvalue relative to the expert model; and hosmer-lemeshow calibration statistic influenza syndrome model created using the ebmc algorithm keywords influenza; machine-learning; ed reports acknowledgments this research was funded by grant p01-hk000086 from the cdc in support of the university of pittsburgh center for advanced study of public health in informatics. the international fulbright s&t award and conacyt-mexico support alp. references [1] elkin, p. l., froehling, d. a., wahner-roedler, d. l., brown, s. h., & bailey, k. r. (2012). comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes. annals of internal medicine, 156(1 pt 1), 11–18. [2] tsui, f.-c., wagner, m., cooper, g. f., que, j., harkema, h., dowling, j., sriburadej, t., et al. (2011). probabilistic case detection for disease surveillance using data in electronic medicalrecords. online journal of public health informatics, 1–17. [3] cooper, g. f., hennings-yeomans, p. p., & barmada, m. m. (2010). an efficient bayesian method for predicting clinical outcomes from genomewide data. amia 2010 symposium proceedings, 2010, 127–131. *arturo lópez pineda e-mail: arl68@pitt.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e41, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts collaborative automation reliably remediating erroneous conclusion threats (carrect) jonathan c. lansey*1, paul picciano1, ian yohai1, fred grant2 and robert gern2 1aptima inc., woburn, ma, usa; 2northrop grumman corporation, falls church, va, usa objective the objective of the carrect software is to make cutting edge statistical methods for reducing bias in epidemiological studies easy to use and useful for both novice and expert users. introduction analyses produced by epidemiologists and public health practitioners are susceptible to bias from a number of sources including missing data, confounding variables, and statistical model selection. it often requires a great deal of expertise to understand and apply the multitude of tests, corrections, and selection rules, and these tasks can be time-consuming and burdensome. to address this challenge, aptima began development of carrect, the collaborative automation reliably remediating erroneous conclusion threats system. when complete, carrect will provide an expert system that can be embedded in an analyst’s workflow. carrect will support statistical bias reduction and improved analyses and decision making by engaging the user in a collaborative process in which the technology is transparent to the analyst. methods older approaches to imputing missing data, including mean imputation and single imputation regression methods, have steadily given way to a class of methods known as “multiple imputation” (hereafter “mi”; rubin 1987). rather than making the restrictive assumption that the data are missing completely at random (mcar), mi typically assumes the data are missing at random (mar). there are two key innovations behind mi. first, the observed values can be useful in predicting the missing cells, and thus specifying a joint distribution of the data is the first step in implementing the models. second, single imputation methods will likely fail not only because of the inherent uncertainty in the missing values but also because of the estimation uncertainty associated with generating the parameters in the imputation procedure itself. by contrast, drawing the missing values multiple times, thereby generating m complete datasets along with the estimated parameters of the model properly accounts for both types of uncertainty (rubin 1987; king et al. 2001). as a result, mi will lead to valid standard errors and confidence intervals along with unbiased point estimates. in order to compute the joint distribution, carrect uses a bootstrapping-based algorithm that gives essentially the same answers as the standard bayesian markov chain monte carlo (mcmc) or expectation maximization (em) approaches, is usually considerably faster than existing approaches and can handle many more variables. results tests were conducted on one of the proposed methods with an epidemiological dataset from the integrated health interview series (ihis) producing verifiably unbiased results despite high missingness rates. in addition, mockups (figure 1) were created of an intuitive data wizard that guides the user through the analysis processes by analyzing key features of a given dataset. the mockups also show prompts for the user to provide additional substantive knowledge to improve the handling of imperfect datasets, as well as the selection of the most appropriate algorithms and models. conclusions our approach and program were designed to make bias mitigation much more accessible to much more than only the statistical elite. we hope that it will have a wide impact on reducing bias in epidemiological studies and provide more accurate information to policymakers. figure 1 screenshot of user selecting imputation parameters. keywords bias reduction; missing data; statistical model selection acknowledgments this material is based upon work supported by the walter reed army institute of research (wrair) under contract no. w81xwh-11-c-0505. any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the wrair. references james honaker and gary king, “what to do about missing values in time series cross-section data” american journal of political science vol. 54, no. 2 (april, 2010): pp. 561-581. gary king, james honaker, anne joseph, and kenneth scheve. “analyzing incomplete political science data: an alternative algorithm for multiple imputation”, american political science review, vol. 95, no. 1 (march, 2001): pp. 49-69. *jonathan c. lansey e-mail: jlansey@aptima.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e189, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluating syndromic data for surveillance of noninfectious disease ramona lall* and marc paladini new york city department of health and mental hygiene, queens, ny, usa objective to evaluate several non-infectious disease related syndromes that are based on chief complaint (cc) emergency department (ed) syndromic surveillance (ss) data by comparing these with the new york statewide planning and research cooperative system (sparcs) clinical diagnosis data. in particular, this work compares ss and sparcs data for total ed visits and visits associated with three noninfectious disease syndromes, namely asthma, oral health and hypothermia. introduction syndromic surveillance data has predominantly been used for surveillance of infectious disease and for broad symptom types that could be associated with bioterrorism. there has been a growing interest to expand the uses of syndromic data beyond infectious disease. because many of these conditions are specific and can be swiftly diagnosed (as opposed to infectious agents that require a lab test for confirmation) there could be added value in using the icd9 ed discharge diagnosis field collected by ss. however, ss discharge diagnosis data is not complete or as timely as chief complaint data. therefore, for the time being ss chief complaint data is relied on for non-infectious disease surveillance. sparcs data are based on clinical diagnoses and include information on final diagnosis, providing a means for comparing the chief complaint (from ss) to a diagnosis code (from sparcs), for evaluating how well the syndrome is captured by ss and for assessing if it would be advantageous to get ss ed diagnosis codes in a more timely and complete manner. methods syndromes previously developed by the dohmh were used for this work. syndrome definitions are based on querying the cc field in ss data for terms associated with asthma, oral health and hypothermia. the asthma syndrome consists of search terms for ‘asthma’, ‘wheezing’ and ‘copd’. the oral health syndrome uses (‘tooth’ or ‘gum’) and (‘ache’, ‘hurt’) and excludes visits resulting from trauma (e.g., ‘injury’, ‘accident’). the hypothermia syndrome is limited to search for the word ‘hypothermia’. for the purpose of comparison of the ss data with sparcs data for the three syndromes, the following icd9 diagnosis codes were considered in sparcs: 493 for asthma, 521-523, 525, 528-529 for oral health and 991 for hypothermia. ss and sparcs data for 2007 were used for this work as this was the most recent and complete sparcs ed dataset that was available. overall city-wide daily counts and hospital-level annual counts for total ed, asthma-, oral healthand hypothermia-related visits were computed for ss ed data and sparcs ed data. a comparison of daily and hospital trends for ss and sparcs for total and syndrome-related counts were conducted using correlation coefficients. results there is a high correlation between total ed ss and sparcs daily counts (r=0.98, p-value<0.001). on average, sparcs daily counts are higher by approximately 75 visits (range: -674, 591) per day. correlations between ss and sparcs daily counts for asthma, oral health and hypothermia were 0.96 (p-value<0.001), 0.66 (pvalue<0.001) and 0.45 (p-value<0.001), respectively. correlations between ss and sparcs hospital-level annual counts for asthma, oral health and hypothermia were 0.89 (p<0.001), 0.87 (p<0.001) and 0.07 (p=0.61). in 2007, less than 8% of individual ss records had a discharge diagnosis, and this was found to vary between hospitals (069%); therefore, a comparison between ss discharge diagnosis and sparcs diagnosis data was not possible. conclusions overall, syndromic surveillance data was found to be a useful data source for public health surveillance of non-infectious disease. total ed visits were found to be comparable between ss and sparcs. while direct comparison of counts for syndromes is not possible, the daily syndrome counts between ss and sparcs correlated well. however, the strength of correlation varied depending on the syndrome, with a better correlation for syndromes with larger volume of visits to the ed (e.g., asthma) and with more commonly used terms in the cc search (e.g., ‘tooth ache’) compared to syndromes with very specific search terms (e.g., ‘hypothermia’). in certain instances, it is hypothesized that ss discharge diagnosis would provide more reliable and representative estimates than cc for tracking non-infectious disease. future work will consider a period with more complete ss ed discharge diagnosis data for further comparisons and to test the hypothesis that more complete and timely ss ed discharge diagnosis data could improve surveillance efforts. keywords chief complaint; syndromic surveillance; new york city; non-infectious disease; discharge diagnosis acknowledgments data analysis and syndromic surveillance unit, bureau of communicable diseases *ramona lall e-mail: rlall@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e163, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts #wheezing: a content analysis of asthma-related tweets gwendolyn gillingham*1, michael a. conway2, wendy w. chapman2, michael b. casale3 and kathryn b. pettigrew3 1linguistics, ucsd, la jolla, ca, usa; 2ucsd division of biomedical informatics, la jolla, ca, usa; 3west health institute, la jolla, ca, usa objective we present a content analysis project using natural language processing to aid in twitter-based syndromic surveillance of asthma. introduction recently, a growing number of studies have made use of twitter to track the spread of infectious disease. these investigations show that there are reliable spikes in traffic related to keywords associated with the spread of infectious diseases like influenza [1], as well as other syndromes [2]. however, little research has been done using social media to monitor chronic conditions like asthma, which do not spread from sufferer to sufferer. we therefore test the feasibility of using twitter for asthma surveillance, using techniques from nlp and machine learning to achieve a deeper understanding of what users tweet about asthma, rather than relying only on keyword search. methods we retrieved a large volume of tweets from the twitter api. search terms included “asthma,” and several misspellings of that word; terms for common medical devices associated with asthma such as “inhaler” and “nebulizer”; and names of prescription drugs used to treat the condition, including “albuterol” and “singulair.” a randomly sampled subset of these tweets (n=3511) was annotated for content, based on an annotation scheme that coded for the following elements: the experiencer of asthma symptoms (self, family, friend, named other, unidentified, and all-non-self, which was the union of these last four categories); aspects of the type of information being conveyed by each tweet (medication, triggers, physical activity, contacting of a medical practitioner, allergies, questions, suggestions, information, news, spam); as well as negative sentiment, future temporality, and non-english content. further details on the annotation scheme used can be found at http://idiom.ucsd.edu/!ggilling/annotation.pdf. inter-annotator agreement on a subset of the tweets (n=403) fell in an acceptable range for all categories (cohen’s kappa >0.6). once annotation was complete, the tweets’ texts were stemmed and converted into vectors of unigram and bigram counts. these were then stripped of sparse terms (all those words appearing in fewer than 1 in 200 tweets), which left multi-dimensional vectors consisting of the counts of the remaining words in all tweets. statistical machine-learning classifiers including k-nearest neighbors, naive bayes and support vector machines were then trained on the unigram and bigram models. results svm with 10-fold cross-validation achieved greatest prediction accuracy with the unigram model, as shown in table 1. categories that showed the greatest reduction in classification error using the unigram model were non-english, self, all-non-self, medication, symptoms and spam. the majority of these categories showed very high precision, as well as fairly high recall for the unigram model. unexpectedly, the bigram model faired far worse than the unigram model, which suggests that individual words in these tweets were more reliably predictive of content than pairs of words, which occurred less frequently. conclusions text-classification increases the utility of twitter as a data-source for studying chronic conditions such as asthma. using these methods, we can automatically reject tweets that are non-english or spam. we can also determine who is experiencing symptoms: the twitter user or another individual. fairly simple models are able to predict with good certainty whether a user is talking about their symptoms, their medication, or triggers for their asthma, as well as whether they are expressing negative sentiment about their condition. we demonstrate that social media such as twitter is a promising means by which to conduct surveillance for chronic conditions such as asthma. table 1: performance of classifiers on unigram and bigram models keywords social media; natural language processing; asthma; content analysis acknowledgments this work was financially supported by the west wireless health institute and idash summer internship program (nih u54hl108460). references 1. chew, c. & eysenbach, g. 2010. pandemics in the age of twitter: content analysis of tweets in the h1n1 outbreak. plos one 5(11): e14118. 2. collier, n. & doan, s. 2011. syndromic classification of twitter messages. proc. ehealth 2011, malaga, spain. november 21-23. *gwendolyn gillingham e-mail: gwen.gillingham@ling.ucsd.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e65, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts establishing a federal and state data exchange pilot for public health situational awareness dina b. passman*1, aaron kite-powell2, dara spector1, wayne loschen3, barry harp1, aaron chern2, janet hamilton2, cary eggers2 and joseph lombardo3 1u.s. department of health and human services, office of the assistant secretary for preparedness and response, washington, dc, usa; 2florida department of health, bureau of epidemiology, jacksonville, fl, usa; 3johns hopkins university applied physics laboratory, laurel, md, usa objective u.s. department of health and human services (hhs) office of the assistant secretary for preparedness and response (aspr) partnered with the florida department of health (fdoh), bureau of epidemiology, to implement a new process for the unidirectional exchange of electronic medical record (emr) data when aspr clinical assets are operational in the state following a disaster or other response event. the purpose of the current work was to automate the exchange of data from the aspr electronic medical record system emr-s into the fdoh electronic surveillance system for the early notification of community-based epidemics (essence-fl) system during the 2012 republican national convention (rnc). introduction aspr deploys clinical assets, including an emr system, to the ground per state requests during planned and no-notice events. the analysis of patient data collected by deployed federal personnel is an integral part of aspr and fdoh’s surveillance efforts. however, this surveillance can be hampered by the logistical issues of field work in a post-disaster environment leading to delayed analysis and interpretation of these data to inform decision makers at the federal, state, and local levels. fdoh operates essence-fl, a multi-tiered, automated, and secure web-based application for analysis and visualization of clinical data. the system is accessible statewide by fdoh staff as well as by hospitals that participate in the system. to improve surveillance aspr and fdoh engaged in a pilot project whereby emr data from aspr would be sent to fdoh in near realtime during the 2012 hurricane season and the 2012 rnc. this project is in direct support of healthcare preparedness capability 6, information sharing, and public health preparedness capability 13, public health surveillance and epidemiological investigation. methods in 2011, fdoh approached aspr about securely transmitting raw emr data that could be ingested by essence-fl during aspr deployments in the state. upon conclusion of an agreement for a date exchange pilot, data elements of interest from the aspr emr were identified. due to the modular design essence-fl microsoft sql databases were easily adapted by the johns hopkins university applied physics laboratory (jhu/apl) to add a new module to handle receipt of aspr emr data including code to process the files, remove duplicates and create associations with existing reference information, such as system-defined geographic regions and age groups. scripts were developed to run on the aspr server to create and send updated files via secure file transfer protocol (sftp) every 15 minutes to essence-fl. prior aspr event deployment data was scrubbed and sent to essence-fl as a test dataset to ensure appropriate receipt and ingestion of the new data source. results emr data was transmitted through a central server at aspr to essence-fl every 15 minutes during each day of the 2012 rnc (august 26-31). in essence-fl, configuration allowed the data to be queried, analyzed, and visualized similar to existing essencefl data sources. in all, data from 11 patient encounters were successfully exchanged between the partners. the data were used by aspr and fdoh to simultaneously monitor in near real-time onsite medical response activities during the convention. conclusions timely access to patient data can enhance situational awareness and disease surveillance efforts and provide decision makers with key information in an expedient manner during disaster response and mass gatherings such as the rnc. however, data are siloed within organizations. the collaboration between fdoh, aspr and jhu/apl made emr data sharing and analysis more expeditious and efficient and increased timely access to these data by local, state, and federal epidemiologists. the integration of these data into the essence-fl system created one location where users could go to access data and create epidemiologic reports for a given region in florida, including the rnc. to achieve these successes with partners in the future, it will be necessary to develop partnerships well in advance of intended data exchange. future recommendations include robust pre-event testing of the data exchange process and planning for a greater amount of lead-time between enacting the official agreement and beginning data exchange. keywords syndromic surveillance; public health informatics; data exchange; federal and state collaboration *dina b. passman e-mail: dina.passman@hhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e48, 2013  advanced querying features for disease surveillance systems 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 advanced querying features for disease surveillance systems mohammad r. hashemian 1 1 the johns hopkins university applied physics laboratory abstract: most automated disease surveillance systems notify users of increases in the prevalence of reports in syndrome categories and allow users to view patient level data related to those increases. occasionally, a more dynamic level of control is required to properly detect an emerging disease in a community. dynamic querying features are invaluable when using existing surveillance systems to investigate outbreaks of newly emergent diseases or to identify cases of reportable diseases within data being captured for surveillance. the objective of the advance querying tool (aqt) is to build a more flexible query interface for most web-based disease surveillance systems. this interface allows users to define and build their query as if they were writing a logical expression for a mathematical computation. the aqt allows users to develop, investigate, save, and share complex case definitions. it provides a flexible interface that accommodates both advanced and novice users, checks the validity of the expression as it is built, and marks errors for users. keywords— public health informatics, population surveillance, disease outbreaks, software tools introduction in its 2007 annual report, the world health organization warned of the increased rate at which diseases spread in a world where 2 billion people travel by air [1]. the early detection of known and emerging illnesses is becoming more important. automated disease surveillance systems have been in existence for over 10 years [2-4]. most of these systems analyze data by syndrome and search for disease outbreaks. a syndrome in this context is defined as a group of diseases related in some fashion, such as respiratory diseases. this level of investigation is often sufficient, but a more dynamic level of control may be required to understand an emerging illness in a community. for example, during the 2002–2003 severe acute respiratory syndrome (sars) disease epidemic [5], the respiratory syndrome definition used by most automated disease surveillance systems was too broad to track sars [6]. in this case, the users needed to create queries that looked for specific keywords in the patient chief complaint or specific combinations of icd-9 codes [7]. a chief complaint is text entered by a triage professional in an emergency room or a advanced querying features for disease surveillance systems 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 clinic, based on a patient‟s description of their primary symptoms. today‟s public health departments must deal with a multitude of data coming from a variety of sources. for example, electronic medical record (emr) data include sources such as radiology, laboratory, and pharmacy data. a more sophisticated querying tool is needed to assist investigators with creating inquiries across multiple data sources [8-10]. currently, there are surveillance systems, such as the electronic surveillance system for the early notification of community-based epidemics (essence) [11], which provide limited dynamic querying capability. however, we wanted to design a flexible and simple graphical user interface (gui) for this and other types of surveillance systems. our prototype system, the advanced querying tool (aqt), allows the investigators to handle complex cases where one can incorporate any data elements available in a disease surveillance system, then mix and match these data elements in order to define valid queries. hence, this system removes the need for database administrators and application developers to define pre-packaged database queries and user interfaces every time a new and innovative query is written. as an example, investigating a potential influenza outbreak in an adult population may require respiratory syndrome queries only, while investigating a similar outbreak in children under 4 years old may involve queries in both gastrointestinal and respiratory syndromes (figure 1). figure 1. running multiple inquires as one query table 1 provides examples of how a dynamic query tool exploits combinations of data elements available to disease surveillance systems. most automated disease surveillance systems have a fixed number of predefined syndromes. these applications severely limit the surveillance system value for diseases that fall outside of its broad syndrome categories. the background noise level rises when all the chief complaints that potentially fall into a syndrome category are included, which in turn requires many more positive cases to identify an abnormal condition. merely adding sub-syndrome categories, that are more granular than syndromes and cover a broader range of conditions than typical syndromic surveillance like injures and chronic disease [12], provides the users with a more comprehensive means to filter the analysis window. if a disease surveillance system has 400 sub-syndromes, then taken singly the user has 400 additional choices; by combining two or three sub-syndromes, the analysis options are magnified to over ten million choices. of course not all of these options are sensible, so the actual number of advanced querying features for disease surveillance systems 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 options is somewhat less. even greater analytic flexibility is provided through the use of data elements contained within electronic medical records. the capability to select a combination of a microbiology laboratory result, radiology result, and icd-9 code provides for a powerful tool that enables the public health community to rapidly identify specific high risk patients. table 1. potential analysis combinations using multiple data sources in combinations data type single items double combinations triple combinations total analysis choices syndromes 10 na na 10 sub-syndromes 400 79,800 10,586,800 10,667,000 advanced query tool: icd-9 codes 1,000 499,500 166,167,000 166,667,500 laboratory tests 300 44,850 4,455,100 4,500,250 radiology 100 4,950 161,700 166,750 prescription drugs 10,000 49,995,000 1.67e+11 1.66667e+11 icd-9 +lab+rad+prescr 11,400 50544300 1.66787e+11 1.66838e+11 objectives the following objectives summarize the design features of the aqt: the tool‟s interface will help generate queries that can process any kind of data regardless of its source (e.g., emergency room visit, office visit, pharmacy, and laboratory). unlike fixed-form query interfaces, aqt will not restrict users in what they can query. instead, the user will be able to formulate ad-hoc queries across assorted data sources without the need to understand the underlying data models and the query languages associated with different systems. in addition, using this tool should save investigators‟ valuable time in obtaining the query results. currently, if the surveillance system cannot generate the desired queries, the application developers and/or database administrators may have to create new interfaces or functionalities. the aqt, however, empowers the users to move forward with their research without waiting for developer or administrator modifications to the surveillance systems. the interface will accommodate users with different levels of experience in creating complex and valid queries. the process will be natural and follow the same patterns that one uses to express a mathematical equation. at the same time, it will give the more experienced users, who are familiar with the data elements, the freedom to define complex queries by advanced querying features for disease surveillance systems 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 sidestepping the guiding tools. the advanced users will have the ability to type in their queries and the tool will validate them and provide feedback on possible syntax errors. the interface will allow users to save and share queries with other public health professionals, even in different jurisdictions. after defining a complex query the user has the ability to store the query for future investigations. one should be able to execute the stored query repeatedly in the future, include it as a segment of a bigger query, or customize and execute it. these saved queries can then be shared as part of collaborative efforts among users in different departments and jurisdictions. aqt will provide an interface for disease surveillance systems to store, retrieve, and share queries. these capabilities are especially valuable for users employing a case definition for following a disease outbreak. a case definition is a set of symptoms, signs, etc., by which public health professionals define those patients considered to be directly a part of the disease outbreak. finally, the tool should be self-contained and generic. this allows most web-based disease surveillance systems to incorporate the aqt into their systems. methods interface the entire functionality of the tool is placed within a single web page (figure 2). figure 2. advanced querying tool interface advanced querying features for disease surveillance systems 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 the screen in figure 2 is divided into 5 major sections. starting at the top, the user can filter the data by picking the data source from a dropdown list, start and end date. the surveillance system should supply this list of data sources to the aqt. the next area below is the message area where the gui communicates with the user. any information, warnings, or error messages are displayed in this section. the next area, the query section, contains the query expression. the users can either directly type the query expression or use the tool to generate the query expression and paste it in this area. alternatively, they can use a combination of the two methods by typing part of the expression and pasting the rest using the query builder. the query section is followed by the query builder section where the tool provides list boxes, buttons, etc., to direct the user through the process of generating the query expression. the bottom section is where an action on the query is performed. users can validate the expression‟s syntax, save the query for their own future use, save it to be shared with others in the user community, clear the query expression and start over, or simply execute the query and get the results. data source as mentioned earlier, the capability to generate queries on data from a variety of sources is one of the objectives of the aqt. each data source has its own distinctive set of data elements. the interface has to provide a list of data elements pertaining to the chosen data source. for example, the data might represent different geographic regions from one data source to the other. that is, one source might have data identified by zip codes while another source uses some other type of defined region such as hospitals, pharmacies, and schools. another area where data sources can be different is in medical groupings. for example, office visits often use icd-9 codes [7], while emergency departments use patient chief complaints. the interface is designed to distinguish valid data elements for each data source and populate the data element list box accordingly. after selecting a data source the tool populates a list box with a set of associated data elements for the data source. the list box is divided into three major areas:  the geography system  the medical grouping system  others such as age, sex, saved and shared queries. figure 3 shows how the medical grouping systems differ for emergency room (right) and over the counter (left) data sources. advanced querying features for disease surveillance systems 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 3. different data elements for each data source flexibility as mentioned earlier, a main objective of the aqt is to provide an interface that caters to both novice and experienced users. the experienced users simply type the query, while beginners and those who are more comfortable with a guided interface can use list boxes and buttons to generate the queries. in fact, one can type part of the query and use the tool to generate the rest of the query (figure 4). when a user types a query directly, it is assumed that the user knows the syntax and valid data elements pertaining to the data source, though the tool does check the syntax and provide feedback. advanced querying features for disease surveillance systems 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 4. generate query expression because we want the users to define and build their query as if they were writing a logical expression for a mathematical computation, the syntax is simple and close to the “where” clause of a structure query language (sql) statement. however, one does not need to know sql to write the expressions. a query consists of one or more simple expressions joined by “and” and/or “or,” negated by “not,” and grouped by parentheses. a simple expression is enclosed within square brackets ([]) and defined by a variable, a logical operator, and a value. for example, if an investigator is searching for reported fever cases within a specified zip code, the query then consists of two simple expressions; one which searches for the specified zip code and the other which checks the fever syndrome. the final query may look like the expression below: [zipcode=“21043”] and [syndrome=“fever”] if the investigators want to narrow the search into a certain age group they can type or use the tool to add and [age = “0-4”] to the above expression. hence, the users can add more conditions without worrying about the underlying data model. the most complex part of the syntax occurs when searching for values that contain, start with, or end with a set of characters (figure 5). in this case, the syntax uses “*” as the wildcard character. for example, a user would type [chief-complaints = “*head*”] in the query box if he/she is looking for all the records of chief-complaints that include the word “head.” similarly, if a user types [chief-complaints = “head*”] or generates it using the tool (selects the starts with from the operator list box and types head in the text field), the resulting query would search for all the records where the chief-complaints field begins with the word “head.” figure 5. wildcard in expressions advanced querying features for disease surveillance systems 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 natural flow the procedure for generating expressions follows the same pattern a person would use to create a logical expression. the interface will provide a natural flow to help the users to create an expression as if they are typing it. they may start with selecting a data element or variable such as „sex‟, then a logical operator like „=‟, and finally a value like „male‟ or „female‟. the user can add „and‟ or „or‟ and create the next expression using this same process. the user can interject expressions in the middle of a query, remove parts of the query, or undo the last change made to the query. as changes are being made, the tool validates the entire query in the background and provides instant feedback. this method of constructing queries is more intuitive to the users than that of creating all the individual expressions first and then joining them together. once the data source is selected, a list of core data elements is provided in a list box. from the list box the user can select a data element. based on the type of the data element, a list of valid logical operators for that data element is placed in another list box. figure 6 shows the list of valid operators for text fields. figure 6. valid operators for long text fields in cases such as zip code and syndrome, „=‟ and „<>‟ operators are also valid. for age the operators „>‟, „<‟, „<=‟, and „>=‟ are added to the list. once the user selects a data element, a list of valid values pertaining to the data element is listed in yet another list box. the user can select one or more of these values, and if more than one value is selected the user can choose to group these values using „and‟ or „or‟. note that the aqt generates the expression in a left to right progression in the same manner as one typing the expression (figure 7). advanced querying features for disease surveillance systems 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 7. select multiple values the next step is to add this expression to the query. by clicking on the “add expression” button, the expression is pasted at the cursor location in the query area. one can add more expressions to this query by clicking and or or buttons and following the same process (figure 8). figure 8. add expressions to the query the aqt helps users quickly identify limits for variables with large sets of values. because data elements such as zip codes and icd-9 codes have a lot of values for dropdown lists, finding a particular value in these list boxes is very cumbersome. the tool provides an intermediate step for filtering these options into a more manageable list (figure 9). for example, if the investigators are interested in data from certain zip codes in a state, they can reduce the options by typing the first two digits of the zip code and thereby filtering the list. advanced querying features for disease surveillance systems 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 9. filter value list validation the tool will generate valid expressions and provide a mechanism to check the query expressions when a user types parts or all of them. every time an expression is generated by the tool and the add expression button is clicked, the tool examines the entire query expression, checking it against the syntax rules. before saving or executing the expression the aqt automatically checks the syntax and if it detects any syntax errors it will provide meaningful error messages in the message area (figure 10). additionally, at any point the user can click on the validate button and check the syntax. figure 10. validate the query expression save and share queries frequently, investigators want to execute a query over time, run the same query with different values, or use the query inside more complex queries. similarly as all the other data elements (zip code, syndrome, region, etc.), the permanent storage and retrieval of queries (file system, database, or any other mechanism) are the responsibility of the disease surveillance system. the aqt is merely an interface to assist the investigators with their research by hiding the complexity and inner workings of the underlying data model. advanced querying features for disease surveillance systems 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 once the users define the desired query they can click on [save public expression] or [save private expression] buttons. if the query is valid, the screen provides an area to enter a unique name for the query (figure 11). figure 11. saved and shared queries if the query is successfully validated the aqt passes the name and query expression to the surveillance system. it is the surveillance system‟s responsibility to confirm that the query‟s name is unique and provide feedback to the aqt the success or failure of the save operation. based on the feedback received the aqt provides an appropriate message in the message area. in a collaborative environment users would like to share their findings and queries with others. providing the capability to save and share the queries for collaborative use enables others in the user community to run these queries as they are or to make the modifications necessary to help with their own investigations. the aqt facilitates saving public queries by providing an interface similar to saving private queries (figure 11). the surveillance system should implement the inner workings of the permanent storage and retrieval of public queries. the next step is retrieving these saved queries. there are two options in the data element list box in the query builder section of the aqt: one option is for retrieving the private saved queries, and the other option is for retrieving public saved queries (figure 12). upon selection of either one, a list of corresponding queries will be presented to the users. this list includes the text of the query and the unique name given to that query. by clicking on the query name the saved query will be added to the expression in the query area. advanced querying features for disease surveillance systems 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 figure 12. retrieve saved public and private queries at this point users can add more conditions to the same query, such as specifying a zip code, changing the value for age, etc. portability the final objective of this project is for the aqt to have the capability to be used with most web-based surveillance systems. one can think of the aqt as a widget, or an add-on with some defined interfaces. the back end can be implemented in a variety of popular technologies such as .net, java servlet, or any other server technology as long as it can communicate via an http protocol. the surveillance system has to provide the interfaces that supply values for the different parts of the screen, and the functionality to parse the final query text and run it against the underlying database. making the tool adaptable to many web-based systems requires the aqt to contain all the processing dynamically, including validating the query syntax and changing the contents of the list boxes. in a web-based environment, this means using browser components such as html, cascading style sheets (css) [13], javascript, and the document object model (dom) [14] to implement application logic. in developing aqt, we utilized html, javascript, and ajax (asynchronous javascript and xml) and placed all the processing on the local machine to avoid any server dependency. we used javascript to apply validation, data handling, and screen processing on the browser side, and ajax for communicating with server applications. ajax is used for creating interactive web applications and is a cross-platform technique usable on many different operating systems, computer architectures, and web browsers, because it is based on open standards such as javascript and xml. the intent of this technique is to make web pages more responsive by exchanging small amounts of data with the server behind the scenes, so that the entire web page does not have to be reloaded each time the user requests a change. this feature increases the web page‟s interactivity, speed, functionality, and usability. ajax is asynchronous in that loading does not interfere with normal page loading. advanced querying features for disease surveillance systems 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 the aqt uses ajax calls to obtain required data for populating the different list boxes on the screen. for example, when the user selects a data source the tool calls the surveillance system, passes the selected data source, gets a list of data elements from the server (the surveillance system), and then populates the data element list box. the communication to the server is done by an ajax call, and the javascript processes the returned data and populates the list. implementation essence has been one of the early adaptors of aqt. although the capability to create efficient custom queries for emergency room chief complaints data existed prior to the aqt, the query building process was cumbersome and not user-friendly. it was easy to make syntax errors while typing a query, and there was no mechanism to validate the logic of the query statement. furthermore, while “and” and “or” and “andnot” expressions were possible, there was no method to construct complex boolean operations with parentheses to clarify the order of operations. the previous capability allowed the user to base the custom query on data source, geography system, or medical grouping system, however, since the selections were not part of the query statement they could not be modified without returning to the pre-selection screens and re-starting the query process. additionally, the original capability did not allow for querying of data beyond the fundamental chief complaints-level. the following screen shot shows the query options that were available with the original feature. a sample chief complaints query designed to capture influenza-like-illness is shown in figure13. figure 13. influenza-like-illness query the aqt not only contains several capabilities that were not previously available, but also provides an intuitive user-friendly interface that allows the user to build simple or highly ^cough^,and,^fever^ ,or,^sorethroat^,and, ^fever^,andnot, ^asthma^ advanced querying features for disease surveillance systems 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 complex queries more easily. two new features in the aqt are parentheses, which allow the user to clarify the order of operations, and the ability to select variables such as region, zipcode, hospital, syndrome, sub-syndrome, chief complaint, age, and sex, as part of the query statement. this allows for easy query modifications. additionally, the aqt lets the user query data beyond the fundamental chief complaints level into a more sensitive sub-syndrome or syndrome level. it also allows users to develop queries that contain combinations of chief complaints, syndromes, and sub-syndromes into one query. the query can also contain combinations of different geographies such as zipcodes and regions. this capability is not available without aqt. during the query building process the aqt automatically validates the logic of query expression as it is created, and the user has the option to conduct a final validation prior to executing the query. this feature allows the user to quickly identify syntax errors and correct them before adding further complexity or executing the query. the following screen shot (figure 14) shows the query options available within the aqt feature. a sample chief complaints query designed to capture influenza-like-illness in region_a is shown. figure 14. influenza-like-illness query for region a advanced querying features for disease surveillance systems 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 conclusions we believe that the aqt will provide an interface that can assist public health investigators in generating complex and detailed case definitions. the interface supports saving queries for future use and sharing queries with others in the user community. the interface is intuitive and accommodates both novice and experienced users. finally, the aqt is a selfcontained tool that can be plugged into most web-based disease surveillance systems with relative ease. acknowledgements the author would like to express his appreciation to colleen martin and jerome tokars of the u.s. centers for disease control and prevention, to sanjeev thomas of science applications international corporation, and to wayne loschen, joseph lombardo, jacqueline coberly, rekha holtry, and steven babin of the johns hopkins university applied physics laboratory. conflict of interest statement the author declares that he has no competing interests. summary table what was already known on the topic  early detection of known and emerging illnesses is becoming vital with the increased rate at which diseases spread world-wide.  most automated disease surveillance systems analyze data by syndrome and look for disease outbreaks within a community, hence overlooking the diseases that fall outside of the broad syndrome categories. what this study added to our knowledge  electronic disease surveillance systems need a more sophisticated querying tool to assist public health investigators in conducting inquires across multiple data sources.  superior analytic flexibility through the use of data elements contained within electronic medical records enables the public health community to rapidly identify specific high risk patients.  the advanced querying tool (aqt) was designed as a flexible and simple graphical user interface (gui) that allows users to develop, investigate, and share complex case definitions. advanced querying features for disease surveillance systems 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 references [1] world health organization. the world health report 2007 a safer future: global public health security in the 21st century. geneva, switzerland, world health organization press, 2007, p. 38. [2] vacalis t, bartlett c, shapiro c. electronic communication and the future of international public health surveillance. emerg. infect. dis. 1995; 1(1):34-35. http://dx.doi.org/10.3201/eid0101.950108 [3] teutsch s, thacker s. planning a public health surveillance system. epidemiol. bull. 1995; 16(1):1-6. [4] farrington c, andrews n, beale a, catchpole m. a statistical algorithm for the early detection of outbreaks of infectious disease. j. r. statist. soc. a. 1996;159(3):547-563. http://dx.doi.org/10.2307/2983331 [5] zhong n, zheng b, li y, poon l, xie z, chan k, et al. epidemiology and cause of severe acute respiratory syndrome (sars) in guangdong, people‟s republic of china, in february, 2003. lancet. 2003; 362:1353-1358. http://dx.doi.org/10.1016/s0140-6736(03)14630-2 [6] shih f-y, yen m-y, wu j-s, chang f-k, lin l-w, ho m-s, et al. challenges faced by hospital healthcare workers in using a syndrome-based surveillance system during the 2003 outbreak of severe acute respiratory syndrome in taiwan. infect. control and hosp. epidemiol. 2007; 28(3):354-357. http://dx.doi.org/10.1086/508835 [7] hart a, hopkins c, editors. 2003 icd9cm expert for hospitals, 6th ed. salt lake city (ut): st. anthony publishing; 2003. [8] bates d, gawande a. improving safety with information technology. new england journal of medicine. 2003; 348: 2526-2534. http://dx.doi.org/10.1056/nejmsa020847 [9] hillestad r, bigelow j, bower a, girosi f, meili r, scoville r, et al. can electronic medical record systems transform health care? potential health benefits, savings, and costs. health affairs. 2005; 24(5):1103-1117. http://dx.doi.org/10.1377/hlthaff.24.5.1103 [10] miller r, sim i. physicians‟ use of electronic medical records: barriers and solutions. health affairs. 2004; 23(2):116-126. http://dx.doi.org/10.1377/hlthaff.23.2.116 [11] lombardo j, burkom h, pavlin p. essence ii and the framework for evaluating syndromic surveillance systems. mmwr morb mortal wkly rep. 2004 sep 24; 53(suppl.):159-165. [12] standardizing clinical condition classifiers for biosurveillance. available at http://www.cdc.gov/biosense/files/phin2007_subsyndromespresentation-08.22.2007.ppt [13] mcfarland ds. css: the missing manual. sebastopol (ca): o‟reilly; 2006. [14] flanagan d. javascript: the definitive guide, 5th ed. sebastopol (ca): o‟reilly; 2006. author mohammad r. hashemian, ms (computer science) the johns hopkins university applied physics laboratory 11100 johns hopkins road laurel, md 20723 usa mohammad.hashemian@jhuapl.edu fax number: 443-778-3686 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts regional study of anthrax foci marina nikolaishvili*, marina zakareishvili, irma beradze, m donduashvili, nino vepkhvadze, lela kerdzevadze and maka kokhreidze laboratory for the ministry of agriculture, tbilisi, georgia objective the purpose of this study was to describe anthrax foci along the georgia-azerbaijan border and to describe control measures in identified areas. introduction anthrax is endemic in the south caucasus region. there is a lack of understanding of the regional epidemiology of the causative pathogen, bacillus anthracis, and the trans-boundary factors related to its persistence. methods to increase the local and regional understanding of anthrax ecology, ecological risk factors, and the genetic relationships and distribution among georgian and azerbaijani b. anthracis strains, a regional study of the ecology of anthrax foci was conducted in georgia and azerbaijan. six regions in georgia (that border azerbaijan) were selected for environmental sampling based on historical data. soil samples were collected in lagodekhi and sagarejo and tested at the laboratory of the ministry of agriculture using standard bacteriological and molecular biology methods. results a total of 185 soil samples were collected. bacteriological tests revealed four positive samples from kakheti (two from lagodekhi, gelati; two from dedoplistskaro), from which, cultures were isolated and confirmed by pcr. georgian scientists continue collecting and testing soil samples. after sample collection and bacteriological testing is completed, the molecular characteristics of the pathogen will be examined. conclusions this study will assist in the formulation of targeted public health interventions aimed at increasing knowledge of the disease within specific demographics. public health interventions can focus on livestock surveillance and control in identified areas. keywords anthrax; one health; endemic; trans-boundary; public health acknowledgments the research study described in this presentation was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed. *marina nikolaishvili e-mail: marina.nikolaishvili@lma.gov.ge online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e158, 2017 generation of analysis of personal contact graphs models for use in infection control agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 agent based modeling of “crowdinforming” as a means of load balancing at emergency departments ryan neighbour 1 , luis oppenheimer 1,2 , shamir n. mukhi 1,3 , marcia r. friesen 1 , robert d. mcleod 1 1 university of manitoba 2 winnipeg regional health authority 3 public health agency of canada abstract this work extends ongoing development of a framework for modeling the spread of contacttransmission infectious diseases. the framework is built upon agent based modeling (abm), with emphasis on urban scale modelling integrated with institutional models of hospital emergency departments. the method presented here includes abm modeling an outbreak of influenza-like illness (ili) with concomitant surges at hospital emergency departments, and illustrates the preliminary modeling of ‘crowdinforming’ as an intervention. ‘crowdinforming’, a component of ‘crowdsourcing’, is characterized as the dissemination of collected and processed information back to the ‘crowd’ via public access. the objective of the simulation is to allow for effective policy evaluation to better inform the public of expected wait times as part of their decision making process in attending an emergency department or clinic. in effect, this is a means of providing additional decision support garnered from a simulation, prior to real world implementation. the conjecture is that more optimal service delivery can be achieved under balanced patient loads, compared to situations where some emergency departments are overextended while others are underutilized. load balancing optimization is a common notion in many operations, and the simulation illustrates that ‘crowdinforming’ is a potential tool when used as a process control parameter to balance the load at emergency departments as well as serving as an effective means to direct patients during an ili outbreak with temporary clinics deployed. the information provided in the ‘crowdinforming’ model is readily available in a local context, although it requires thoughtful consideration in its interpretation. the extension to a wider dissemination of information via a web service is readily achievable and presents no technical obstacles, although political obstacles may be present. the ‘crowdinforming’ simulation is not limited to arrivals of patients at emergency departments due to ili; it applies equally to any scenarios where patients arrive in any arrival pattern that may cause disparity in the waiting times at multiple facilities. keywords—contact graphs, agent based models, infection spread models agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 introduction the worldwide h1n1 influenza pandemic (ph1n1) in 2009 and 2010 has mobilized and renewed research attention to the many facets of infection control and impact, ranging from the epidemiology of the illness, development and deployment of vaccines and other pharmaceutical interventions, and public health and emergency management measures. predicting how an infection may spread within a population and the consequent impact that it may have includes forecasting with intelligent models and real data, as well as back-casting based on available data as one validation process. the focus of this paper is to present a computer simulation framework (model) of an urban community to model the spread of ph1n1 in the community, in which the model carries the capacity to model the impact of various intervention strategies, e.g. temporary clinics, vaccination, chemoprophylaxis, hygienic and social distancing measures. the intervention of direct interest in the present study is to provide the public with additional information related to expected waiting times at regional hospital emergency departments (eds), and its potential impact on patient loads and consequent service delivery. a true metric associated with estimating expected wait times at a particular hospital is a random variable of many factors and difficult to estimate even with extensive ed data. the work here demonstrates the role that patient redirection can play if this metric were available. in the interim, we have associated the number of patients with a ‘busyness’ metric that would be amenable for presentation to the community. as the social dynamics and agent behaviours coupled with real data (economic, cultural, and social) become better defined, the computer simulation naturally allows one to focus on the population subsets and apply the framework to other jurisdictions. the work is located within the larger context of healthcare informatics. the role of informatics in healthcare re-engineering and optimization has become a new reality in efforts to improve service delivery and efficiencies in healthcare. both well vetted engineering approaches as well as emerging methods are being applied to generate solutions for health policy and decision makers. new service delivery paradigms are developed and justified from a variety of domains, including statistics, operations research and lean manufacturing concepts from industrial engineering, and business models [1]-[5]. in healthcare, the challenge is exacerbated by the inherent unpredictability of social behaviour; this contributes to the computational irreducibility of the problems encountered within many healthcare environments. as a consequence, modeling and simulation are playing a larger role as a design aid or tool in support of decision making [6][9]. background the computer simulation framework is built, in part, on an agent-based model/modeling (abm) approach. a central premise of this work is that abms – combined with real data and as high a resolution (fidelity) as the computer system affords – will create a new paradigm for a better understanding of epidemiology within a social system dynamics, and thereby lead to more effective tools for policy makers guiding the future. abm is a relatively new approach to disease modeling (10+ yrs), an area historically addressed by well-vetted mathematical modeling techniques (70+ yrs). however, the use of abm for simulating infection spread within an urban area and built upon the incorporation of real data is only now emerging (2+ yrs). abm is based on simulating a collection of agents – i.e. the people in the model – in terms of their agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 characteristics, behaviours, and interactions with other agents. agents (people) are purposeful and autonomous entities able to assess their situations, make decisions and compete with one another. abm’s conceptual depth is derived from its ability to model emergent behaviour that may be counterintuitive, and to discern a complex behavioural whole that is greater than the sum of its parts. counterintuitive or counter intent behaviour may arise from non-obvious non-linear feedback loops that may exacerbate system dynamics, such as financial incentives to remain working while infected, or non-obvious pressures within the healthcare system that strain the system to its breaking point. complex behaviour that is greater than the sum of its parts may be associated with an agent’s ability to learn or make heuristic-based adaptations to their behaviours. abm provides a natural description of a system that can be calibrated and validated by representative expert agents (healthcare specialists), and is flexible enough to be tuned to high degrees of specificity (sensitivity) in agents’ behaviours and interactions. abms are particularly well suited to system modeling in which agent behaviour is complex, non-linear, stochastic, and may exhibit memory or path-dependence [10][11][12]. early application areas of abm include logistics, economics, and transportation systems. abm also provides one of the most useful tools available in terms of knowledge transfer and requirements capture, independent of whatever other techniques may also be employed. the model construction forces ideas to be clarified; unclear and hidden assumptions are exposed and debated in a common and familiar lexicon, leading to the abm having a direct correspondence to the problem as understood by the practitioner and the developer. the resulting model closely resembles the system description, which could come from business rules or some other description by stakeholders who need not be overly familiar with abm itself. many abms are developed to gain a better understanding of operations through the use of what-if scenarios, and in doing so will provide a decision support tool to public health decision-makers. a more recent and considerable area of application for abms has been country scale (community-level) disease spread modeling in human populations [13][14]. the focus generally constitutes large scale community-level epidemics of respiratory infections, as this is an important public health and policy issue with far-ranging health and economic impacts. our own work has included the development of one of the first urban-level epidemic proof-of-concept abms, based on a paradigm of a ‘discrete space scheduled walker’ (dssw) [15]. the urban scale abm is one of the most appropriate modeling levels in terms of incorporating high resolution data (individual based), as well as for simulating social dynamics reinforced by patterns of behaviour and readily available topographical data. the proof-of-concept is built on synthesized data and a very limited range of agent parameters. it can model a medium-sized north american city using approximately 650,000 discrete agents (people), each of whom are assigned a demographic profile and a weekly schedule on the topography of the city of winnipeg, canada. running the simulation with these types of numbers however is computationally tasking for a reasonable desktop environment. the proof-of-concept was built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias, meaning that the system dynamics emerge directly out of the agents’ characteristics and behaviours, rather than by the inclusion of artificial small-world networks. the proof-of-concept addresses where, who, when, and what elements. where: underlying topographical (network / graph) data is extracted from map and search engine utilities such as google earth, in order to build a network of objects, denoted institutions. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 institutions are existing geographic locations such as homes, businesses, schools, leisure sites, hospitals, airport, and transport vehicles. who: agents are people that make up a community, and between whom an infection would be spread. when: a central premise of the dssw-abm is that agents are primarily creatures of habit, operating on routine schedules with slight random perturbations [16]. what: the factor of interest is the spread of the ph1n1 virus or other viruses that cause respiratory infections among the population of agents, through agent-to-agent contact associated with routine daily activities. once infected, the agents probabilistically choose to stay home for the duration of the infection or attend a hospital ed. as an intervention, we have considered modeling the potential role of ‘crowdinforming’ in directing or governing an agent’s behaviour. the notion of ‘crowdinforming’ is a natural extension of ‘crowdsourcing’ [16], whereby data collected by crowdsourcing is again fed back to the ‘crowd’ via public access. low-tech examples of the role of ‘crowdinforming’ in modifying human behaviour are readily available, such as line-ups at vaccination clinics as vaccine shortages are announced in the mass media. while a fairly obvious intervention, ‘crowdinforming’ is a novel inclusion into the abm and simulation. methods the abm engine is coded in c++, an object oriented language. the object oriented approach has natural extensions to the spatial modeling inherent in the spatial nature of the system under study. developing an abm within an object oriented framework from the ground up provides an additional degree of understanding the problem in contrast to using a more commercial platform. the simulated world is a two-dimensional (x, y) discrete cartesian world of extremely high resolution. building upon the proof-of-concept, the abm is general purpose and at present is a spatially directed abm reflective of a specific topography – in this case, the city of winnipeg, canada. agents can access a limited number of features of other agents and objects, which can be set by the programmer, depending on the model. they can also pass messages to other agents in order to achieve interaction. the abm houses institutions such as hospitals, homes, malls, leisure facilities, schools, businesses, and transportation institutions such as cars and public transport. graphical inputs serve as defining location derived from maps and community planning documents. the abm framework is illustrated in fig. 1. the publicly accessible information ‘dashboard’ is illustrated on the left-hand side, providing near real time information on waiting times at various eds at hospitals within a region. currently, this information is available in individual facilities’ waiting rooms; yet, for the present simulation it is assumed to be web accessible to the general public via readily available web services. the web services can include traditional web 2.0 applications supporting dynamic updating of web pages based on new information from various facility waiting time estimates, as well as access via traditional wired services to those offered over 3g cellular networks or ‘smart phones’. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 fig. 1. the abm framework encompassing various data sources. a. modeling surges at hospitals this section outlines the urban based abm under conditions associated with an ili outbreak in winnipeg, canada. in the model, a parameterized distribution of households is associated with a colour as a graphical input. for this scenario, various colours represent a number of multiple person households uniformly distributed across winnipeg, as shown in fig. 2. at this scale, the colours tend to blend although roads, rivers, and other infrastructure and features are clearly evident. fig. 2. a screenshot of the abm input. a number of institutions such as schools, large businesses, restaurants, leisure institutes as well as hospitals are modelled. the spread of infection is a stochastic process with the probability of infection being directly related to social contact. social contact can take place at home, work, school, or on public transport, with institutions also having probabilities of contraction associated with them. a baseline simulation involves scheduled agents being modelled, and as they become ill, they probabilistically go to the closest ed. once at the ed, another individual-based state or phase model is introduced as illustrated in fig. 3. at the ed, the agent may be discharged, agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 treated, or admitted. once admitted, the patient may again undergo treatment and recover, or alternatively the agent may not recover. the current institutional ed model is quite simple but could be extended and refined with a more detailed model as required, an example of which has been developed by the authors [18][19]. fig. 3. the emergency department individual based model. a statistic instrumented during the simulation was the number of agents arriving at individual hospital eds. these surges are shown in fig. 4 for the seven hospitals in the winnipeg area. fig. 4. simulated surges at winnipeg hospitals during an outbreak agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 fig. 5. day of week variations at winnipeg hospitals during an outbreak fig. 4 illustrates a disparity between various hospitals, corresponding to population density variations with the city. fig. 5 illustrates the variation within weekly routines or schedules. these data are averaged over five runs. this averaging raises an interesting consideration from a policy perspective: as the data is averaged, it tends to become smoother and may tends to mask shortterm variations that may otherwise be present and important to note in the physical environment as well as the model. results: self redirection b. ‘crowdinfoming’ diversions although expected, reduced surges at hospital eds can reasonably be argued to facilitate improved treatment and service efficiencies during an outbreak. these outcomes illuminated a potential indirect intervention. the modelling initially planned for reactive hospital diversions, i.e. setting demarcation levels related to capacity to support diversion policy and redirection [20]. with recent interests in ‘crowdsourcing’ [16] and as a means to aid in biosurveillance of potential infection spread, the model was adapted to model the counterpoint of ‘crowdsourcing’, that being ‘crowdinforming.’ one of the fundamental tenets of ‘crowdsourcing’ is that the feedback loop needs to be closed, as information mined through crowdsourcing flows back to the crowd that generated it, presumably to accrue benefit. at present, one trial hospital in the winnipeg regional health authority (wrha) jurisdiction has an ed ‘dashboard’ in place, updating the number of patients waiting and their waiting times in terms of maximum wait at various triage levels. the dashboard is intended to inform those already in the waiting room of the anticipated wait before being seen. these dashboards are derived from the wrha electronic data information system (edis) which was recently rolled out across the wrha hospitals. our model develops this notion further and conjectures that in a reasonably short period of time, this data could be made publicly available through a web service, such that an individual would be able to query the hospital ‘dashboards’ from the wrha site over the internet with any browser, be it mobile cellular or wired, or made available agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 via a community call centre such as a 311 service whereby a patient would call and enquire about expected wait times. for our modeling purposes, in this ‘crowdinforming’ scenario, a proactive decision is then made by an agent seeking treatment at an ed. in addition to the stochastic process of deciding upon going to an ed, each agent is also provided with estimates of wait times and the number of patients waiting. this data is available and easily instrumented within the abm. as an illustration of our weighted fair redirecting, we assumed four hospitals with waits of 1, 2, 3 and 4 hours with travel times of 60 minutes, 45 minutes, 30 minutes, and 15 minutes, coarsely extracted from topographical distances. an agent would create a roulette wheel to guide their decision once they have elected to go to an ed. the associated normalized probabilities associated with informed self redirection are shown in table 1. table 1. informed emergency department self redirect probabilities ed 1 ed 2 ed3 ed4 tot al wait time (hr) 1 2 3 4 travel time (hr) 1 0.75 0.5 0.25 total time (hr) 2 2.75 3.5 4.25 12.5 1/over ½ 1/2. 75 1/3. 5 1/4. 25 1.38 probabili ty 0.36 0.26 4 0.20 7 0.17 1.0 table 1 combines estimates of travel time and wait times in an additive manner. alternatively, it may be more appropriate to generate a total time as a linear combination weighted appropriately as in (1). (1) using such a method, the agents make a probabilistic decision weighted by the least anticipated wait. as a consequence, the overall surge seen at hospitals is dampened by the behaviour of informed individuals. modeling the ‘crowdinformed’ load-balancing results in surges at hospital as shown in fig. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 fig. 6. surges at winnipeg hospitals with ‘crowdinforming’. crowdinforming appears to filter the simulated patient surges. a simple statistical measure associated with mean and variance indicates that the load balancing is statistically significant given our behavioural assumptions, where a known estimated wait time probabilistically influences a person’s decision in attending a particular ed. the overall effect was expected to be that of a low pass filter smoothing out the peaks and valleys in both space and time. fig. 6 clearly illustrates filtering in space as each ed roughly serves the same number of patients within a few percent. filtering in time is expected to result from incorporation of an estimate of travel time as indicated in equation 1. this simulation illustrates load balancing at the cost of greater variance in the patient load over time. this type of emergent behaviour inherently has greater information content than anticipated or expected results. while the simulation results are presently a coarse approximation of a policy implementation, they serve as an indication of possible trends and side effects. as a further extension, the additions of temporary clinics were also modeled. the temporary clinics were modeled as being available provided sufficient staff resources existed to off-load eds during a serious influenza outbreak. in this case, hospitals were augmented with temporary clinics and the public was informed of their location and services. temporary clinics are modelled as being staffed with and providing a level of service similar to a hospital ed. in the following simulation, six temporary facilities were instantiated in highly populated postal code areas. given the model of the information provided to the public, the consequent balancing of loads at hospital eds and temporary clinics are seen to be balanced. fig. 7 also demonstrates the effect of prioritizing travel in the decision-making process (fig. , with 80% of infected agents seeking treatment. in addition, acute patients are modeled as not presenting themselves at a clinic but rather deciding to directly attend an ed. an additional modification is that the temporary clinics are more heavily recommended once hospitals near capacity, or alternately that hospitals are preferred destinations until they are near capacity. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 fig. 7. surges at winnipeg hospitals and clinics with ‘crowdinforming’. an additional parameter of interest was the location of temporary clinics. for the simulation above, the clinics were located in central and highly populated postal code areas. the simulation allows for one to vary the location of clinics, such that they may better serve a community or region. fig. 8 illustrates a simulation run without averaging over multiple runs (which carries the effect of averaging over multiple days). without averaging, the day to day variations are clearly apparent. if this variability were to be an actual consequence of the patient self redirection policy, it may be that the increase in day to day variation would not be a reasonable trade-off for a balanced load. it is these types of insights that abm and what-if scenarios provide for policy makers a-priori to policy implementation. fig. 8. hospital loads without averaging. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 discussion although preliminary, this work represents one of the first modelled instances of the potential of ‘crowdinforming’ in providing a policymaker with a simulation to assist in public health decision support. in doing so, this work illustrated the role that an abm can play in developing policy decision support systems. the model presented here illustrates how one specific intervention – that of proactive information dissemination or ‘crowdinforming’ – can provide a degree of patient load balancing at eds. a similar model was constructed corresponding to the scenario where sufficient public health resources existed for the deployment of temporary clinics. in the event of the temporary clinics, the model also allows for an investigation into their utilization where the public would be informed of their location and services. the findings are somewhat self-evident in that the model for ‘crowdinforming’ contributed to load balancing at individual hospital eds. the findings also indicate that the location of the clinics is reasonably important in off-loading eds. the most significant limitations associated with research of this nature is that stochastic models of behaviour have to be estimated. other shortcomings are associated with the access and usability of real data. these latter barriers are technological, while the former are theoretical. the validity of the underlying abm framework is enhanced as the characterization of agents and their behaviours is improved and refined with additional real data. consequently, barriers to access and usability of real data – whether technological or political – are limitations to the work. a more significant deficit in the model is that the deployment of temporary clinics assumes that the resources exist for staffing and treatment. summary the work presented here allows a policy for patient directed redirection to be simulated, adding a qualitative assessment to a model that may otherwise be experiential or best-intent. this work is one of the first demonstrations of the ‘crowdsourcing’ intervention, and it demonstrates the role an abm and similar technologies will continue to play in the future. . acknowledgments the authors acknowledge maciej borkowski, marek laskowski, and bryan demianyk for early developments of the abm framework. the authors also acknowledge manitoba hydro for financial support. correspondence: bob mcleod university of manitoba mcleod@ee.umanitoba.ca agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 references [1] marshall a, burns l. a bayesian network hybrid model for representing accident and emergency waiting times. proceedings of the ieee symposium on computer-based medical systems. june 2007; 91-96. [2] komashie a, mousavi a. modeling emergency departments using discrete event simulation techniques. proceedings of the winter simulation conference. dec. 2005; 2681-2685. [3] patrick j, puterman m. reducing wait times through operations research: optimizing the use of surge capacity. healthcare quarterly, 2008;11(3): 77-83. [4] khurma n, bacioiu g, pasek z. simulation-based verification of lean improvement for emergency room process. proceedings of the winter simulation conference. dec 2008;14901499. [5] mcnulty t, re-engineering health care: the complexities of organizational transformation, new york, oxford university press, 2002. [6] kanagarajah a, lindsay p, miller a, parker d. an exploration into the uses of agent-based modeling to improve quality of health care. international conference on complex systems. june 2006; 1-10. [7] gunal m, pidd m. simulation modelling for performance measurement in healthcare. proceedings of the winter simulation conference, dec. 2005; 2663 2668. [8] blachowicz d, christiansen j, ranginani a, simunich k. how to determine future her roi: agent-based modeling and simulation offers a new alternative to traditional techniques, j. healthcare information management. winter 2008;22(1):39-45. [9] gunal m, pidd m. understanding accident and emergency department performance using simulation. proceedings of the 38 th conference on winter simulation, 2006;446-452. [10] bonabeau e. agent-based modeling: methods and techniques for simulating human systems. proceedings of the national academy of science. may 2002 [online]. 99(suppl 3), pp. 7280-7287. available: http://www.pnas.org/content/99/suppl.3/7280.full#xref-ref-31 [11] epstein j. modelling to contain pandemics. nature. 2009:460;687. [12] hupert n, xiong w, mushlin a. the virtue of virtuality: the promise of agent-based epidemic modeling,” translational research. 2008:151(6):273-274. [13] epstein j. artificial society: getting clues on how a pandemic might happen by creating a huge model of the united states, the brookings institution. [online]. available: www.brookings.edu/interviews/2008/0402_agent_based_epstein.aspx. [14] merler s, ajelli m, jurman g, furlanello c, rizzo c., bella a, massari m, ciofi degli atti m. modeling influenza pandemic in italy: an individual-based approach. the 2007 intermediate conference of the italian statistical society. june 2007. available http://www.sis-statistica.it/files/pdf/atti/sis%202007%20venezia%20intermedio_121131.pdf [15] m. borkowski m, podaima b, mcleod r., epidemic modeling with discrete space scheduled walkers: possible extensions to hiv/aids,” bmc public health, vol. 9 (suppl 1): s14, 2009. [online}. available: doi:10.1186/1471-2458-9-s1-s14. [16] song, c, qu z, blumm n, barabási a. limits of predictability in human mobility, science, 2010 327(5968);1018-1021. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 [17] howe j, the rise of crowdsourcing. wired. june 2006, http://www.wired.com/wired/archive/14.06/crowds.html. [18] laskowski m, borkowski m, demianyk b, friesen m, mcleod r. the utility of agentbased models for healthcare applications,” the second iasted international conference on telehealth and assistive technology, cambridge, ma, nov. 2009. [19] laskowski m, mcleod r,friesen m, podaima b, alfa a., models of emergency departments for reducing patient waiting times. plos one,. 2009;4(70: e6127. [online]. available: doi:10.1371/journal.pone.0006127, 2009. [20] mukhi s, laskowski m, agent-based simulation of emergency departments with patient diversion. electronic healthcare, d. weerasinghe, ed. berlin: springer. 2009;25-37. paper details an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 an agent based model for simulating the spread of sexually transmitted infections grant rutherford 1 , marcia r friesen 1 , robert d mcleod 1 1 electrical & computer engineering, university of manitoba, canada abstract this work uses agent-based modelling (abm) to simulate sexually transmitted infection (stis) spread within a population of 1000 agents over a 10-year period, as a preliminary investigation of the suitability of abm methodology to simulate sti spread. the work contrasts compartmentalized mathematical models that fail to account for individual agents, and abms commonly applied to simulate the spread of respiratory infections. the model was developed in c++ using the boost 1.47.0 libraries for the normal distribution and opengl for visualization. sixteen agent parameters interact individually and in combination to govern agent profiles and behaviours relative to infection probabilities. the simulation results provide qualitative comparisons of sti mitigation strategies, including the impact of condom use, promiscuity, the form of the friend network, and mandatory sti testing. individual and population-wide impacts were explored, with individual risk being impacted much more dramatically by population-level behaviour changes as compared to individual behaviour changes. keywords: agent based modelling, modelling and simulation, sexually transmitted diseases, social networks introduction the objective of this work was to develop an agent-based model (abm) to simulate the spread of sexually transmitted infections (stis) within a population of interacting agents. as a preliminary application of the abm methodology to sti spread, the focus of this work was to explore the inherent suitability and potential of the abm method to this particular context. agent based modelling is becoming an effective tool in understanding infection spread and is particularly well suited to environments where the agents themselves and their interaction with one another are the principal vectors of infection spread. agent-based models have emerged in the past decades as a complementary approach to the long history of differential equation-based models that require a macroscopic perspective of the population of interest [ 1]. agent based modelling is ‘bottom-up’ systems modelling from the perspective of constituent parts. systems are modelled as a collection of agents (in this case, people) imbued with properties: characteristics, behaviours (actions), and interactions that attempt to capture actual properties of individuals. in the most general context, agents are both adaptive as well as autonomous decision-making entities who are able to assess their situation, make decisions, compete with one another on the basis of a set of rules, and adapt future behaviours on the basis of past interactions. agent properties may be conceived by the modeller or may be derived from actual data that reasonably describe agents’ behaviours – i.e. their http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 movements and their interactions with other agents. the modeller’s task is to determine which data sources best govern agent profiles in a given abm simulation [ 2] ,[ 3]. the foundational premise and conceptual depth of abm is that simple rules of individual behaviour will aggregate to illuminate or exhibit complex and emergent group-level phenomena that is not specifically encoded by the modeller [ 3] ,[ 3]. this emergent behaviour may be counterintuitive or a complex behavioural whole that is greater than the sum of its parts. furthermore, abm provides a natural description of a system that can be calibrated and validated by subject matter experts, and is flexible enough to be tuned to high degrees of sensitivity in agent behaviours and interactions. abms are considered particularly applicable to situations where interactions are local and potentially complex, where agents are heterogeneous, where the phenomenon has inherent temporal aspects, and where agents are adaptive [ 2][ 5]. much of the work in abm to date has focussed on the simulation of contact-based infection spread associated with influenza-like illnesses and other respiratory infections, including coarse-scale community, country, and global models [ 6][ 13] to finer-scale community and institutional models [ 14] ,[ 15]. increasingly, there are considerable data that can be used to improve the fidelity of an abm in representing real social networks in ways that are highly amenable to understanding the type of contacts (casual and behaviour-agnostic) that facilitate infection spread. these data may be generated for other purposes but are increasingly available toward secondary and tertiary applications within an emerging ‘data culture’. these data that can be mined and analysed to generate agent contact and movement patterns include, for example, intelligent transportation systems (vehicular, public, air travel, etc), cellular service provider data, and a range of location-based services and technologies that may leverage smartphones and other bluetooth-enabled mobile devices. additionally, there is a large body of literature on modelling sexually transmitted infection spread based on equation-based monolithic approaches or compartmental mathematical models [ 15] ,[ 16] ,[ 17]. monolithic analytical models use one governing equation to model the population, while compartmental tools reduce the population to a few key characteristics which are relevant to the infection under consideration. these models are limited to treating simplified scenarios and are not amenable to including data directly pertaining to each individual agent. in contrast, the strength of abm lies in its detailed and naturalistic representation of agents and scenarios and the ability to directly integrate real data. however, modelling of sexually transmitted infections presents a more significant challenge to abms than modelling of contact-based respiratory infections. proximate contact and geographical location (important factors in respiratory infection spread) are a minor factor in sti transmission, while agent behaviour becomes a defining parameter, including the formation of relationships, and an agent’s network of contacts through which relationships may be established. in addition the topic of sexually transmitted infections and sexual behaviour is a sensitive topic for most people, and accurate and full disclosure of activities in order to accurately characterize agents in the model is difficult to achieve. this work derives novelty in applying the abm methodology to the context of sti spread, which has traditionally been modelled by equation-based and compartmental methods. at the early stage of applying abm methods to the simulation of sti spread, qualitative comparison to general known outcomes is a meaningful objective. overall, the usefulness of abm validation by qualitative comparison to known outcomes has been established by others [ 1]. http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 there are known limitations stated at the end of the paper, and these limitations would need to be addressed thoroughly before reliable results could be extracted from the simulation. at this stage of the work, the focus is on exploring the potential and suitability of the abm method to this context. drawing on these findings, future work will refine the abm and then emphasize and analyse the results of the simulations themselves in detail. methods the agent based model this work developed an agent-based model to simulate the spread of sexually transmitted infections, or stis, which are difficult to study directly. the study created an abm of a population of sexually active people. a disease is introduced to this population from the external world, and its progress can be traced through the model. this approach allows the effects of various mitigative and control policies and behaviours to be easily analyzed. the use of a computer model allows thousands of populations to be tested in a short amount of time, so the possible effects of new policies and behaviours can be evaluated quickly and easily. within the model, each agent has both predetermined and fixed parameters which regulate their behaviour. each agent can also be in a number of different states which affect the expression of their predetermined parameters. the movement of agents from a healthy state to an infected state simulates the spread of a sexually transmitted infection through the population. each simulation was run with a population of 1000 people for a period of 10 simulated years (3652 days) after the initial infection. these output included the proportion of the population which was infected, and records of the individuals along with their final infection state and the number of people to whom they passed the infection. in all cases, the original source of the infection is the “outside world”, and the infection enters the population through sexual contact between the population and the outside world. the process of simulating a population involves two major steps. the population is generated and a period of one year is simulated without any chance of infection in order for the proportion of monogamous relationships to become stable. following this initial calibration step, the simulation runs for exactly 10 years after the first agent in the population is infected. each simulation is repeated approximately 5000 times with a new population generated each time. results are accumulated and averaged to show the trends in the data. the abm framework was coded in c++ using the boost 1.47.0 libraries for the normal distribution and opengl for visualization. model setup the first step of simulation is the generation of the agent population. each individual agent in the simulation is randomly assigned unique values which will govern their behaviour. these values are assigned from a distribution which is characterized by global mean and standard deviation parameters. these global distribution parameters define the mean and variance of a parameter in a population, but each individual agent is assigned a specific and fixed value for each parameter upon generation, so that their profile is unique in relation to other agents in the simulation. standard parameter values for the distributions are given in table 1. http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 for both the baseline simulation scenario and to explore various intervention scenarios, the simulation is run multiple times from same initial model conditions in order to average the results. the population of agents is regenerated for each simulation run, so that the agents are different each time, but the distributions from which their parameter values are drawn are constant for a given scenario. more specifically, each agent is randomly assigned to be male or female. following this, each person is assigned a unique value for each of 15 unique parameters, which vary with a restricted normal distribution between 0 and 1 (table 1). each of these 15 parameters represents a probability, or an offset to a probability. a 16 th parameter, “desiredfriends” is an integer and is determined by taking the floor of a normally distributed value with a mean of 2 and a standard deviation of 2, and re-rolling the value if it falls below 1. in this manner everyone has at least one friend. where possible, parameter values were informed by the body of literature on the topic [ 18] ,[ 19]. friendship model the “friendship” model simulates connections between people. these connections may be interpreted as frequent contact: a close friend, a co-worker, etc. during the simulation, these links and networks will often be used to find partners for relationships and for sex. realistically, a person would be expected to have many such connections, but this model only included the most relevant. for the sake of clarity, from now on these connections will be referred to as friends. the “cliquefactor” parameter has the ability to control the separation of people into tight groups of friends (high cliquefactor) or a more connected but randomly shaped network (low cliquefactor). a cliquefactor of 0.7 was chosen as the standard value for the model, which gives a well-connected network with tight clusters of friends. the network evolves over time as relationships instantiated and later dissolve, while maintaining a small world flavour. http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 table 1: population parameters with standard values parameter type mean std. dev. description ismale 50/50 na na male or female basesexavailability (bsa) re-roll m = 0.5 f = 0.03 m = 0.2 f = 0.05 base chance of availability for sex outside of monogamy basesexseeking (bss) restricted m = 0.1 f = 0.002 m = 0.1 f = 0.002 base chance to be seeking sex if also available, outside of monogamy basecondomuse (bcu) restricted 0.9 0.5 base chance of desiring a condom availablemonogamy re-roll 0.08 0.04 chance of being available for a new mono. relationship seekingmonogamy re-roll 0 0.01 chance of seeking a new monogamous relationship exitmonogamy re-roll 0 0.001 chance of terminating an existing mono. relationship monogamoussexrate re-roll 0.15 0.1 chance of desiring sex during monogamy monosexavailabilitydecrease dependent 1 x bsa 0.4 x bsa offset to bsa while monogamous monosexseekingdecrease dependent 1 x bss 0.3 x bss offset to bss while monogamous monocondomdecrease restricted 0.5 0.5 offset to bcu while monogamous diseaseavailabilitydecrease dependent 1.2 x bsa 0.8 x bsa offset to bsa while diseased diseaseseekingdecrease dependent 1.5 x bss 0.5 x bss offset to bss while diseased diseasecondomincrease restricted 0.8 0.5 offset to bcu while diseased testinghealthy restricted -0.1 0.05 daily chance of getting tested while healthy testingsymptoms restricted 0.1 0.1 daily chance of getting tested while symptomatic desiredfriends special 2 2 desired number of friends simulation the simulation of the infection spread through the population happens in discrete units of time, chosen to be one day per step. during each simulated day, the following steps are done in order. the simulation continues for 3652 days (10 years) after the first agent is infected. while the model is running there are a number of relationship changes that can occur. these include: step 1 monogamy changes: in this step, a list of people in monogamous relationships is generated, along with probabilities of relationships ending, new relationships desired, and new monogamous relationships formed within the population. when new relationships form, there is a 95% chance that a person will choose someone from within their friend network, and a 5% chance that a person will choose someone at random from the list of available people. finding someone through the friend network uses a breadth-first search of up to 5 hops, with the closest available person of the opposite gender selected. if more than http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 one person is found at the same distance, then one is selected at random. if no suitable person is found using the friend network, then the seeker will default to selecting someone at random from the list of available people of the opposite gender. the above process is repeated until either the seeking list is depleted, or the list of available people is depleted of either gender. at this point, monogamous relationship matching is completed. step 2 – determining sexual encounters:  monogamous sexual encounters: each person in a monogamous relationship uses their monogamoussexrate variable in order to determine if they desire monogamous sex. this value is first modified by subtracting their diseaseavailabilitydecrease parameter if they are confirmed to have the infection. if they are symptomatic for the infection, but have not been tested, then they subtract half of the value of this parameter. if the resulting value is less than zero, then a zero probability is used.  non-monogamous sexual encounters: this is a complex parameter impacted by sex availability and modulated by the agent’s non or symptomatic state, but following the general process of matching people into monogamous relationships. “cheating” within monogamous relationships is also accounted for. step 3 spreading the infection: while the model is running there are a number of factors that impact the spread of infection, including the basecondomuse parameter modified by their monogamy status and their infection status (including varying probabilities for symptomatic but unconfirmed, vs. tested and confirmed). once condom use has been determined, each encounter between a person who is uninfected and a person with the infection has a probability of infection transmission (modified by condom use) and a subsequent probability of becoming symptomatic if infected. becoming symptomatic further impacts probability of testing and probability of behaviour modification. step 4 testing for infection: the final step in the simulated day is to test members of the population for the disease, accounting for persons (asymptomatic) who may submit to a spontaneous test, and the probability of symptomatic persons seeking testing. results and discussion when evaluating the simulation, the metric used was the infected proportion of the population after 10 years. ten years was chosen in order to capture an average infection prevalence of around 50%, which allows ample room to both evaluate changes in outcomes through parameter changes. with the standard parameter values, the average infection prevalence after 10 years was 48.71% over 4732 trials. two types of analysis were performed on the model. the first set involved 4732 trials (simulations) using the standard parameters, and collecting data on the 4,732,000 simulated agents (1000 agents x 4732 trials) generated for these trials. by measuring correlations between agent parameters and infection outcomes, some trends regarding individual risk can be identified. the second type of analysis involved using the standard parameter values for all parameters but one, and varying a single parameter through a range of values. these trials http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 can be used to investigate the results of general policy or behaviour changes applied to the entire population. individual risk: condom use: the most pronounced impacts related to condom use. condom use while healthy and single was strongly related to an individual's risk of acquiring an infection (fig. 1). condom use while single and aware of being infected is strongly correlated to the likelihood of passing the infection to others (fig. 2). figure 1: individual risk vs. condom use (healthy & single) figure 2: infection spread to others vs. condom use (infected & single) http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 sexual availability was correlated with individual risk of acquiring an infection. however, the risk of infection remains near 25% even for persons who are never available for sex, due to the possibility of acquiring infection through partners in monogamous relationships (accounting for a degree of ‘cheating’). the risk of infection was only moderately affected by an agent’s average number of friends, with nine friends corresponding to an infection risk of 55.2% and having one friend corresponding to an infection risk of 46.5%. since a large proportion of seeking occurs through the friend network, the proximity of a person to potential partners through the friend network will impact frequency of interaction. population data: in comparison with the individual data in the previous section, the population data was obtained by changing the standard model parameters, one by one, thereby of changing the properties and behaviour of the entire population. four parameters were varied: condom use, promiscuity, time between mandatory testing, and cliquey-ness of the friend network. in total, 60 discrete values of each of the four noted parameters parameter were tested, and the simulation results were averaged over 50 repeated simulation runs at each value. condom use: global changes to condom use have a very significant effect on the disease prevalence (fig. 3), with an infection prevalence of 97.4% and 13.9% with mean condom use of 0 and 1, respectively. dramatic changes to condom have the potential to reduce the infection prevalence by more than a factor of 7. figure 3 shows a steep change in infection prevalence at a mean condom use between 60% and 80%, suggesting a threshold over which the ubiquitous use of condoms severely slows the spread of infection. beyond this level, the benefits from higher condom attenuate. figure 3: infection prevalence vs. population condom use http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 promiscuity: the impact of promiscuity was simulated by controlling availability for sex and sex-seeking characteristics. population infection prevalence ranged from 48.7%, 13.6% and 88.2% with promiscuity parameters at standard, halved, and doubled values, respectively. mandatory infection testing: this parameter varied the length between mandatory infection testing of all individuals (including healthy, asymptomatic) from daily testing to testing every 14.7 years, in increments of 91 days. the infection prevalence decreased steadily as the test interval decreased. annual testing resulted in population infection prevalence of 15.3%. with testing at 14.7 year intervals, the population infection prevalence dropped from 48.7% (the standard model) to 43.0%. even in the impractical scenario of daily mandatory testing, an endemic level of infection remains in the population. friend network: the impact of the form of the friend network was examined by altering the connectedness of the network. the results showed no meaningful correlation between the form of the friend network and infection prevalence within the population. conclusion there are some known limitations to this agent-based model for sti spread. while the 16 parameters act both independently and in combination and represent numerous complex combinations and many unique agent profiles, they nonetheless do not capture the full range of agent profiles, agent behaviours, and infection transmission dynamics within a population. for example, this model only considers opposite-sex sexual encounters. second, in this simulation, agents never leave the population and new agents do not enter the population. agents remain in state and are immortal, representing a simplification of real movement of people into and out of networks. furthermore, in this model, the agent behavioural probabilities (seeking, condom use, etc.) remain the same, regardless of whether the overall infection prevalence within the population is low or high. in reality, some behaviour modification is initiated when infection prevalence rises due to personal knowledge of risk and public health messages. population norming becomes a feedback mechanism (e.g. public health messages toward hand hygiene and cough etiquette during pandemic influenza). in this model, a sexual encounter is equated with an infection transmission with some probability (determined by interacting agent parameters). in reality, the mapping of the sexual encounter to infection transmission is more complex, as disease epidemiology needs to be taken into account. it is known, for example, that the rate of transmission may be dependent on the stage of a disease, which is not accounted for in the current model. notwithstanding the limitations, the qualitative results of the model correspond well with known priorities in sti mitigation strategies. this finding supports the objective of determining the potential and suitability of the abm methodology to the context of sti spread, and an sti-abm’s future potential as a tool in public health decision-making and policy. a person can significantly control their individual infection risk by using a condom and avoiding non-monogamous sex. this result is intuitive and supported by simulation. however, individual risk is not reduced as easily or dramatically through individual behaviour modification as it is through collective behavioural changes in the population which reduces collective risk. this mirrors the impact of vaccination for childhood diseases, where protection is only achieved through population-wide compliance. this result is also intuitive, considering the cascading effects within a network, where collective behaviour changes are amplified because individuals benefit from the changes to their own behaviour and also from the reduction in risk due to the change in behaviour of their peers. this makes http://ojphi.org/ an agent based model for simulating the spread of sexually transmitted infections online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 a strong case for public health policy promoting sexual health, such as the promotion of condom use and responsible sexual behaviour. mandatory testing for stis can have a very strong effect in limiting their spread. challenges including the lack of incentive for testing particularly if asymptomatic, the personal disincentives (e.g. potential embarrassment), and the stigma attached to stis. this work has presented an agent-based modelling framework for the simulation of sexually transmitted infection spread within a population of 1000 people, over a 10-year period, in order to explore the applicability of the abm approach to sti modelling. while these results provide some preliminary support for the suitability of the abm methodology to an sti application, all of these results need to be more robustly explored and verified by developing the abm further to address the stated limitations. in general, the methodology is well suited to practitioners and educators, and lends itself to qualitative assessments of various mitigation and control measures related to sti spread. there is considerable refinement that may be easily undertaken as the abm methodology explicitly facilitates dialog within an easily communicated lexicon. an accompanying youtube video illustrating the model and evolving network can be found at http://www.youtube.com/watch?v=aql8mbgns8u funding this research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. conflicts of interests none. corresponding author marcia friesen assistant professor, design engineering university of manitoba, canada email: marcia.friesen@ad.umanitoba.ca references 1. emrich s, suslov v, judex f. fully agent based modelling of epidemic spread using anylogic. proc. eurosim 2007, 9-13 sept. 2007, ljubljana, slovenia. 2. bonabeau e. 2002. agent-based modelling: methods and techniques for simulating human systems. proc natl acad sci usa. 99(suppl 3), 7280-87. http://www.pnas.org/content/99/ suppl.3/7280.full#xref-ref-3-1. http://dx.doi.org/10.1073/pnas.082080899 3. rand w, rust rt. agent-based modelling in marketing: guidelines for rigor. international journal of research in marketing 2011, http://ijrm.feb.uvt.nl/uploads/ forthcoming_d-10-00071_randrust.pdf http://ojphi.org/ http://www.pnas.org/content/99/suppl.3/7280.full#xref-ref-3-1 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http://www.sis-statistica.it/files/pdf/atti/sis%202007%20venezia%20intermedio_121-131.pdf http://www.nyc.gov/html/doh/html/survey/survey.shtml http://dx.doi.org/10.101%ed%af%80%ed%b0%99/j.tics.200%ed%af%80%ed%b0%98.07.009 http://dx.doi.org/10.101%ed%af%80%ed%b0%99/j.trsl.2008.0%ed%af%80%ed%b0%97.002 http://dx.doi.org/10.1038/%ed%af%80%ed%b0%97%ed%af%80%ed%b0%990%ed%af%80%ed%b0%9987a http://dx.doi.org/10.1038/%ed%af%80%ed%b0%97%ed%af%80%ed%b0%990%ed%af%80%ed%b0%9987a http://dx.doi.org/10.1073/pnas.0%ed%af%80%ed%b0%99012%ed%af%80%ed%b0%99%ed%af%80%ed%b0%99103 http://dx.doi.org/10.1073/pnas.0%ed%af%80%ed%b0%99012%ed%af%80%ed%b0%99%ed%af%80%ed%b0%99103 http://dx.doi.org/10.112%ed%af%80%ed%b0%99/science.111%ed%af%80%ed%b0%98717 http://dx.doi.org/10.1038/nature0%ed%af%80%ed%b0%9779%ed%af%80%ed%b0%98 http://www.sisstatistica http://dx.doi.org/10.118%ed%af%80%ed%b0%99/1%ed%af%80%ed%b0%9771-2%ed%af%80%ed%b0%97%ed%af%80%ed%b0%988-9-s1-s1%ed%af%80%ed%b0%97 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and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? kathleen e. turner 1 , sherrilynne fuller 1 1 university of washington, information school and university of washington, center for public health informatics abstract though improvements in infant and maternal mortality rates have occurred over time, women and children still die every hour from preventable causes. various regional, social and economic factors are involved in the ability of women and children to receive adequate care and prevention services. patient-held maternal and/or child health records have been used for a number of years in many countries to help track health risks, vaccinations and other preventative health measures performed. though these records are primarily designed to record patient histories and healthcare information and guide healthcare workers providing care, because the records are patient-held, they also allow families a greater ability to track their own health and prevention strategies. a literature search was performed to answer these questions: (1) what are maternal information needs regarding pregnancy, post-natal and infant healthcare, especially in developing countries? (2) what is known about maternal information seeking behavior in developing countries? (3) what is the history and current state of maternal and/or child patient-held healthcare records, do they provide for the information needs of the healthcare provider and what are the effects and outcomes of patient-held records in general and for maternal and/or child health in particular? specific information needs of pregnant women and mothers are rarely studied. the small numbers of maternal information behavior results available indicate that mothers, in general, prefer to receive health information directly from their healthcare provider as opposed to from other sources (written, etc.) overall, in developing countries, patient-held maternal and/or child healthcare records have a mostly positive effect for both patient and care provider. mothers and children with records tend to have better outcomes in healthcare and preventative measures. further research into the information behaviors of pregnant women and mothers to determine the extent of reliance on interpersonal information seeking is recommended before expending significant resources on enhanced patient-held maternal and/or child healthcare records including storage on mobile devices. in particular, research is needed to explore the utility of providing targeted health messages to mothers regarding their own health and that of their children; this might best be accomplished through mobile technologies. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 keywords: child health services, developing countries, information seeking behavior, maternal health services, medical records introduction around the world in developing nations, maternal and child healthcare has been on the forefront of consciousness for improving the lives of global citizens [1-3]. though improvements in infant and maternal mortality rates have occurred over time, women and children still die every hour from preventable causes [4-6]. in addition, each country has its own policies and challenges with delivering healthcare to its citizens [7-9]. various regional, social and economic factors are involved in the ability of women and children to receive adequate care and prevention services [10, 11]. most importantly, though, is making sure the improvements in maternal and child healthcare and preventative measures in developing countries lead to decreased morbidity and mortality in these vulnerable populations [12-14]. the united nations (un) millennium development goals for 2015 include several goals defined by the world health organization (who) as pertaining to health, particularly in developing countries. these health related goals include: worldwide reduction in maternal mortality by three-fourths and in mortality of children under the age of five by two-thirds from year 2000 levels [15]; forty percent of these childhood deaths are in newborns [16]. only 19 of the 68 priority countries are on track to reach the healthrelated goals for child mortality and maternal health [15]. though many of these struggling countries have been severely impacted by the hiv/aids epidemic [17], the major causes of neonatal death continue to be sepsis and pneumonia, birth asphyxia, complications of pre-term birth, tetanus and diarrhea [18, 19]. the majority of these conditions could be prevented or treated with proper pre-natal, childbirth and neonatal healthcare, maternal and child nutrition and maternal education [18]. given the lack of access to healthcare in developing countries, there have been various measures proposed and enacted to enable patients to become greater participants in their own healthcare [20]. in developing countries, self-care measures are important for empowering people and communities who have limited access to a formal healthcare system to make a difference in their own well-being [19-22]. medical personnel have worked to improve systems for accurately determining higher risk patients, in particular pregnant women who are most likely to need referral for delivery of their babies [4, 19, 21]. other healthcare interventions include timely vaccination, treatment for infectious and parasitic diseases and malaria, prevention of nutritional deficiencies, smoking cessation education and prophylactic therapy for hiv/aids [16, 18, 21]. patient-held maternal and/or child health records (phmr or phcr) have been used for a number of years in many countries to help track health risks, vaccinations and other preventative health measures performed [23-27]. though these records are designed to record patient histories and healthcare information and to guide healthcare workers providing care, because the records are patient-held, they also allow families a greater ability to track their own health and prevention strategies [7, 27]. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 objectives in preparation for a pilot project to transfer a patient-held mother and child health record from paper to a web-enabled cell phone platform, a literature review was needed to help answer these general questions: 1) what are maternal information needs regarding pregnancy, post-natal and infant healthcare, especially in developing countries? 2) what is known about maternal information seeking behavior in developing countries? 3) what is the history and current state of maternal and/or child patient-held healthcare records (especially in developing countries,) do they provide for the information needs of the healthcare provider and what are the effects and outcomes of patient-held records in general and for maternal and/or child health in particular? the revised (2010) kenyan maternal & child health booklet provides a good example of a paper record used currently in a developing country [28-31]. this 17-page booklet is larger than many of the other maternal-child records [27], and has room for recording information regarding one pregnancy and child. most of the seven pages of “maternal profile” seem designed for use by the healthcare provider; it includes the medical, surgical and obstetrical history. there are spaces for recording examination findings from first encounter to delivery. a graph for tracking the mother’s weight gain, preventive therapy schedule, family planning chart, and notes section seem to be the main areas for providing for maternal information needs regarding the pregnancy. the “child health card” section of the booklet seems more designed to provide information to the child’s family. on almost every page, there are notes for parents such as immunization and vitamin reminders, developmental milestones, appropriate weight to height chart, retroviral prophylaxis chart and follow-up, notes, and infant feeding recommendations. methods this study included two related literature searches performed concurrently. databases searched include: cinahl plus, dissertation abstracts, embase, global health library, global health archive, pubmed, science direct, social science research network, web of science, who library database (wholis) and who statistical information system (whosis). for the first query topic the library, information science & technology abstracts (lista) database was also included. searches took place in january and february 2011; articles retrieved were limited to the english language literature. the searches were conceptual in nature. approaching the two questions regarding maternal information seeking and information needs, the first search included the concepts of <maternal or pregnant women’s healthcare/care-giving information behavior (information needs, information seeking)> and <developing countries>. the second search centered on answering the third question regarding patient-held records and their usefulness. this search utilized the idea of <patient-held maternal and/or child healthcare record>, then added in the notion of <outcomes>. the search was expanded by the use of pearlgrowing techniques [32]; applying database-specific subject headings or descriptors from a known article to search for related articles [33]. investigating database-identified related articles, article citations and article reference lists further expanded the search. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 some general search terms were used either combined or separately for each topic. the search terms listed are in a single format, but the format was altered depending on the search criteria and preferential use by each individual database. search strings were also expanded and contracted depending on the number of results obtained in each individual database, and search terms might include: (“mother” or “maternal”), (“child”), (“health” or “medical”), (“information” or “data”), and (“developing” or “undeveloped” or “third world”). to further define the searches, the following terms were added: (“information need*” or “information seek*” or “information behavior”), (“record” or “card” or "booklet"), ("health information system"), (“patient held” or “hand held”) and (“outcomes”). articles focusing on behavior of information seeking in specific places, such as the internet or libraries, were not included. pearlgrowing techniques were especially important for the maternal and child record search. in particular, use of the subject headings and reference list for the 1993, multi-site study, evaluation of the home-based maternal record: a who collaborative [24], proved particularly helpful for locating literature on this topic. results a. maternal information needs and information seeking behavior the literature covering maternal information behavior specifically for medical or health related information needs in developing countries seems rather limited. only eight published papers from six separate studies of information behavior, including health information needs, of women and mothers in developing countries were retrieved using the literature search criteria (table 1). health information needs for family, prenatal and infant care are ranked high in the studies of overall women’s information needs in developing countries [34, 35, 38, 39], and a few studies look specifically at health information seeking behavior in these populations [36, 37, 40, 41]. a few common themes emerge from the available research literature; mothers in the developing countries studied tend to seek medical information and advice for their children and families more commonly than searching for other information needs, and the first source or most common source for information comes from other people. basic infant and child developmental and care information are mentioned as important to mothers in the studies from tanzania and turkey [36, 37, 40]. in order to get a broader view of maternal information behavior, some studies of disadvantaged mothers from developed countries were also included (table 2). while these studies come from different countries and regions of the world, they show some interesting similarities as well as trends in the direction of information behavior. unfortunately, due to the small number of studies and the small number of participants, true generalities cannot be drawn, though comparisons may be possible. the earlier studies from the 1990s in developing countries [34, 35] show women using personal information sources first when seeking information for many reasons including health related. the later studies, and studies from developed countries [36-40, 42-47], indicate that women, both in developed and developing countries, seek a majority of health-related information from their healthcare providers. the one study of adolescents, girls and boys, shows a majority of these young people from sub-saharan africa use mass media sources in addition to school and personal sources to meet their reproductive and sexual health needs [41]. the theme that comes through all of these studies is the idea that pregnant women and mothers from all http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 different societies, both developing and developed, show a preference for receiving health information from a person, whether a healthcare provider or not. mothers in the population of adolescents in sub-saharan africa [41] appear to be the main exception to that finding. b. maternal and/or child healthcare record 1) healthcare providers information needs the articles listed in table 3 are, for the most part, descriptions of various forms of the maternal and/or child healthcare record, and describe the specific information needs of maternal and/or child healthcare providers as they offer suggestions for the data set and format important to collect to provide appropriate prenatal and early childhood care. it seems clear that development proceeded over a number of years to arrive at the most current versions of the maternal and child record in developing countries [27, 31]. currently these records are individualized for each country or region, but include information such as: the names of the mother, father and child; the child’s date of birth; antenatal examination findings; recommended vaccination and prophylactic therapy schedule for the mother and child; growth charts for both the child and pregnant woman; varying levels of advice for care during pregnancy and young childhood; as well as location specific physical parameters and findings such as maternal blood pressure, maternal hemoglobin and child’s developmental and nutritional status. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 1. health information needs studies (concerning mothers, women/families and/or reproductive health) in developing countries study type of study (number of participants) research question* results * [34] fairer-wessels fa. 1990. qualitative survey and interview (#80) what are the daily information needs of urban black south african women, are they generally able to fulfill those needs, how and where do they search and would development of community information centers help? generally these women use interpersonal sources for seeking information needs, and the most commonly sought information is regarding health issues. a community information center sounds like a good idea (no reasons really offered). [35] ngimwa, p, et al. 1997. qualitative survey and interview (#312) what is the media accessibility and use of rural women in kenya? additionally what are their main information needs and information sources? the women in this study tend to use interpersonal sources of information most frequently (60% use friends and relatives, and 34% use professionals as a first information source, with 74.1% expressing satisfaction with source) and the researchers recommend alternative methods for providing information to these women rather than media like radio. women tend to have most questions about healthcare needs (43.3%) and farming/agricultural issues (29.8%). [36] lugina hi, et al. 2001. [37] lugina hi, et al. 2004. qualitative survey interview (#110) qualitative interview + card sorting activity (#110) what are the concerns of first time mothers in dar es salaam, tanzania immediately and six weeks post-partum? what are mothers concerns regarding the post-partum period, and are there better methods for getting at the in this population, some maternal worries change over six weeks, some stay the same. worries were mainly around the baby's general condition (with lesser concern about care and behavior) and the mothers' feelings (with lesser concerns regarding appearance, family reactions, and sexuality), switching to more interests and confidences in these areas after 6 weeks. questions are raised about how to provide timely http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 information in developing countries? information. overall between 16 weeks post partum, worries decrease from 29%-15% (about baby from 31%-14% & self from 30%-20%) and interests (overall 38%-42%, baby 41%50%, self 38%-41%), and confidences (overall 32%-43%, baby 29%-36%, self 32%-39%) increase. this study will help healthcare providers to understand the types of information these women are looking for post-partum. additionally, using card sorting seems to get better response than just interview alone for concerns, interests, etc. of first time mothers. [38] mooko, n. p. 2005. [39] mooko, n. p. 2002. qualitative interview and focus groups (#60) what are the information needs and information seeking behaviors of rural botswanan women? the most common information need of women in the study related to health information for the women and their families, and the most common and helpful information source was a healthcare provider. [40] ertem io, et al. 2007. random survey and interviews (#1200) what do mothers in a developing country (turkey) know about young child development? in general, mothers felt that developmental milestones occur later than actual for normal childrenthe majority of women did not know that sight (52%), vocalization (79%), social smiling (59%), and overall brain development (68%) begin in the early months of life. women with more education and fewer children had a better idea of actual childhood development. this study suggests that healthcare providers need to educate mothers in child development for optimum provision of pediatric healthcare. [41] bankole a, et al. 2007. national household survey what is the knowledge level of young teens in four sub-saharan countries (burkina faso, malawi, uganda, and ghana), these kids use multiple information sources, most commonly mass media (45.6%78.9% depending on gender and country), but also teacher/school (17.7%-69.8%, depending on gender and country) and friends http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 and how do they fill their information needs regarding sexual behavior, stis and pregnancy? (18.2%-59.7%, depending on gender and country). the researchers suggest that inschool education programs might be most effective. * see notes regarding research question(s) and results table 2. health information needs studies of mothers (particularly disadvantaged mothers) in developed countries study type of study (number of participants) research question* results* [42] green jm, et al. 1990. prospective survey (#825) how do expectations of childbirth coincide with satisfaction, especially in the realm of feelings of control and adequate information reception on the part of the mother (southeastern england)? in this study, high expectations did not seem to lead to poor outcomes, and lower expectations seemed to lead to less satisfaction. women wanted to retain control as much as possible and many reported that greater information given to them by their healthcare providers about what to expect led to a greater feeling of control. [43] baker lm, et al. 2007. qualitative interviews (#30) what are the health literacy levels, and information seeking behaviors toward the vaccines given to their children of this group of mothers? in this very small sample, most of the women were unaware of the purpose of the vaccines their children were receiving (26 of 30). health literacy levels of this group of detroit mothers were relatively low, and they tended to receive their information regarding their children's vaccines from the healthcare provider (22% from doctors, 18% from clinic nurses, the rest from 1-9% from 10 other sources). [44] smith sk, et al. 2009. qualitative interviews (#73) how do education levels and health literacy affect people's information needs and expectations for health decision-making? in this population from sydney, australia, more highly educated/health literate patients seem to take a higher responsibility for making their own decisions regarding health care, whereas less educated patients relied more on health care providers to make decisions to which they would either agree http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 or disagree. [45] shieh c, et al. 2009. standardized test of health literacy and interview (#143) how do health literacy levels relate to the use of health information sources and barriers to information seeking in low-income pregnant women in urban midwestern u.s.? higher levels of health literacy were related to a greater ability to use multiple information sources with lower barriers to information seeking. results suggest that information seeking skills should be taught to patients with lower health literacy. both the high (85.3%) and low health literacy (14.7%) group used healthcare professionals most frequently (low 90.5%, high 74.6%), with books/brochures (low 57.1%, high 58.2%) and family and friends next most frequently (low 57.1%, high 51.5%). [46] shieh c, et al. 2009. qualitative interviews (#84) what are the information seeking behaviors (information needs and barriers) in this population of lowincome pregnant women? in this urban midwestern u.s. population it was shown that information seeking was highest in those women with the highest needs (asthma and first pregnancy) and the lowest barriers to obtaining information. also showed that healthcare providers were the highest source of information. [47] shieh c, et al. 2010. survey and standardized testing (#143) do health literacy, positive measures of mother's fetal locus of control and maternal self-efficacy correlate positively with health information seeking in this midwestern u.s. population of lowincome pregnant women? feelings of maternal control toward fetal wellbeing (r=0.27, p=0.003) and self-efficacy (r=0.33, p=0.0004) were positively correlated with maternal information seeking. health literacy was not (r=-0.05, p =0.63). in this study, low health literacy was correlated with a feeling of lowering self-fetus control, in other words, these pregnant women tended to rely on information from healthcare providers more than women with higher health literacy. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 3. maternal and/or child healthcare record information needs of providers study type of study/document research question* results* studies of patient-held records [48] hartfield vj. 1973. descriptive is there a better method of record keeping for mothers in developing countries? this is an early proposal for use of card to improve record keeping. positive outcomes for phmr suggested. [49] dissevelt ag, et al. 1976. descriptive what are features of record to facilitate detection of highrisk pregnancy in rural kenya? earlier kenyan maternal card, positive benefits suggested. [50] sims p. 1978. descriptive what are features of record to facilitate detection of highrisk pregnancy? provider information, dense information, not for illiterates, ph card prototype. positive value felt by author, especially since patient generally has information availableimportant especially in case of emergency [51] shah kp, et al. 1981. descriptive what are features of indian record to facilitate detection of high-risk pregnancy? description of card, apparently useful to help detect risk factors. [52] chabot ht, et al. 1986. descriptive what are features of record to facilitate detection of highrisk pregnancy? prototype for pictorial card, describing the need for testing and use in guinea bissau where most pre-natal care done by illiterate tbas. results unknown. felt to be necessary and helpful for helping tbas, but difficult to get right. suggestions for single card usable for http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 both lit and ill to allow mother to carry only one card. area determines different procedures done by each. [23] kumar v, et al. 1988. descriptive what are features of record to facilitate detection of highrisk pregnancy/improve quality of care in india? description of card, apparently useful to help detect risk factors. benefits of using for illiterate traditional birth attendant (tba) in order to aid in earlier detection of risks and improved maternal self-care. [25] world health organization. 1992. instructional booklet what are guidelines for implementing home-based child health records? in depth instructions for implementing phcr card or booklet. specifications for how to implement and how to alter to fit the particular circumstances for each area of implementation. [26] world health organization. 1994. instructional book what are guidelines for implementing home-based maternal records? in depth instructions for implementing phmr card or booklet. specifications for how to implement and how to alter to fit the particular circumstances for each area of implementation. studies of clinic-held records [53] poulton em. 1966. descriptive reasons for record keeping for maternal child health care in developing countries. basic outline of the purpose of records. [54] essex bj, et al. 1977. descriptive what are features of record to facilitate detection of highrisk pregnancy? early card for providers’ use, not for illiterates, card prototype reminder of need to test http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 against existing. the new card demonstrated a high rate of agreement between providers, and was felt to be useful in tanzania [55] alisjahbana a, et al. 1984. observation (#20) how can we improve traditional birth attendants’ (tba) reporting of high-risk births in indonesia? this study showed that tbas able to report, assess, and respond accurately if trained and risk indicators defined in a way they understood. [56] kennedy i, et al. 1984. descriptive what are the reasons for restructuring record in botswana? ability to follow pregnancy by use of an obvious graph to compare between visits seems helpful to catch problems. not necessarily designed for developing countries studies of electronic records [57] moidu k, et al. 1992. expert consensus what is the essential data set of an electronic maternal health record? examines feasibility of creating and using the data set, data set listed. importance is that data set might be different for each location. data sets being tested in sweden and india. [58] phelan st. 2008. descriptive what are the comparisons between the current well-organized and useful paper record to an electronic record (u.s.)? the authors clearly don't want to lose the positive aspects of the pre-natal record that has been working well for a number of years, but recognize the portability and potential for back-up and legibility of the electronic record, while recognizing the inherent difficulties of setting up a new system. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 2) patient held records for a number of years, a variety of developed and developing countries have used patient-held maternal health records and/or parent-held child health records [25, 26, 48-52, 59]. more recently, as described above for kenya [31], countries have started adopting patient-held combined maternal and child health records. these records are frequently designed with guidance from the who, though each jurisdiction is encouraged to develop the record best suited to its culture and populace [25, 26]. the literature review results fall into a few categories based on whether utility of the record to the patient/parent (tables 4, 5 and 6) or the healthcare provider (tables 7, 8 and 9) was the main focus of the study; also whether the record was specific for maternal and/or child healthcare or for other types of healthcare. additionally, findings tended to vary for studies carried out in developed versus developing countries. a) utility to patients the majority of results are neutral for the effects of the patient-held maternal and/or child record in the studies conducted in developing countries (table 4). increasing patient education was felt to be one way to improve the card’s utility in all four of the studies with neutral results [59, 61-63], and use and understanding of the card is felt to be key in the two positive outcomes [59, 60]. where noted, loss of the record was not felt to be a significant issue [59, 63]. in the 13 studies showing a positive outcome for the patient-held maternal and/or child record studies in developed countries [64-76], words like confidence, control, access (better informed), satisfaction, and communication (interaction) were repeated (table 5). in addition, in eight of the nine studies where recorded, there were few or no missing or lost records, and some families retained the records for many years [65-70, 72, 75, 77]. the two studies showing inconclusive or neutral results were focused on the health outcomes of the record [76, 77]. all eleven studies of the patient-held (not maternal and/or child) records were carried out in developed countries (table 6). results in these studies were variable. the six positive outcomes were qualitative assessments of patient benefit [78-83]. one of the two studies with negative results reports less satisfied patients, and the other reports a potential imbalance of power relationship [81, 82]. the seven studies including neutral results, were just that, the results were inconclusive [82-88]. where noted, patients are generally willing and able to carry the card [80]. b) utility to care providers the care provider is most likely to be influenced by the results in the 15 studies of the patient-held maternal and/or child records in developing countries (table 7). the ten positive results demonstrated here are, for the most part, improved outcomes in healthcare results or http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 preventative measures such as detection of risk, quality of care, higher rates of care, as well as increased educational opportunities [24, 63, 89-96]. the six studies with neutral results can show no conclusive positive results, but provide a positive overall feeling toward the record [61, 96-100]. in studies where noted, the majority of women were able to keep track of the record even if they weren’t always brought to healthcare provider visits [63, 92, 94, 96]. the seven studies of patient-held maternal and/or child records from developed countries (table 8) show results that most likely to influence care providers. these outcomes offer a more mixed view of the effects of the records. definite positive benefits were shown with children’s immunizations [69, 101], return of record following education about its importance [103], and impressions of improvement in communication, access and care [68, 102]. neutral results center on management of the record [69, 103] and inconclusive health results [76]. negative results arise from confidentiality concerns, increase in burden of work, size of the record, and increased surgical intervention [68, 76, 104]. this final concern noting increased surgical interventions with possession of the patient-held record might be considered positive in developing countries where detection of risk factors and elucidating the need for referral are crucial to the records’ function [89-92]. the majority of patients were able to produce records when requested in studies reporting this factor [68, 69, 103]. the three final studies (table 9) of the influence on care providers of the patient-held (not maternal and/or child) records show some positive benefits in compliance in patients with possession of the record [81], though the other two studies demonstrate the patients just not using or carrying the record [105, 106]. the studies listed in tables 4-9 delve into the usefulness of and outcomes for the patient-held record. in total, 48 studies were listed in the six categories of type of patient-held record: (maternal and/or child or not,) care provider or patient most influenced/effected, and research done in developed or developing country. nine (one study in two categories) of the studies are felt to have mixed results [59, 68, 69, 76 (twice), 81-83, 96, 103], and six of the studies are felt to concern both patient and care provider [61, 63, 68, 69, 76, 81]. of these results, 37 show positive effects or influences, 24 show neutral effects or influences, while only five studies show negative effects or influences produced with use of a patient-held health care record. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 4. who studied: what studied – where patient: patient held maternal and/or child records developing countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [60] kusumayati, a, 2007. repeated cross-sectional survey (#611, #621, #630) what are the effects of the mch in western sumatra on using maternal health services? the mothers using (not simply owning) the mch had 2.5 times better knowledge of the benefits of some pre-natal care measures, and were 3 times more likely to seek out needed care. [59] nakamura y 2010. descriptive what is the history of the mch handbook in japan? this study included here, as the mch handbook was first distributed in japan in 1947. the positive benefits of the mch handbook include ease of the main concerns are the costs (though less than multiple separate cards), the fear of loss (not found to be a significant problem), and the uneven use of the cards http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 understanding, access to child and maternal health information, and having records available when needed. depending on the quality/amount of care available. [61] harrison d, et al. 1998. descriptive / interview (#185) what are the opinions of mothers/caregivers (#150) and health care providers (#35) regarding accuracy and completeness of the road to health card in cape town, south africa? health care providers like the concept, but would like information to be in a more useful format. points out need to determine what information is important to family and healthcare providers in order for them to actually fill out all information. [62] mahomed k, et al. 2000. descriptive /interview (#51) how feasible is having a phmr in rural zimbabwe, and do mothers understand the reasons for the record? the introduction of the record seems feasible, but much more education of mothers is http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 needed for them to understand value as only 49.1% returned at end of study. [63] tarwa, c., et al. 2007. survey (#300) is the south african road-to-health card brought to consultations and used by health care providers? the rth card is not brought to 48% of consultations. adults mostly (72%) thought they were only to bring the card to wellbaby clinics. care providers are missing an opportunity to educate and provide health monitoring. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 5. who studied: what studied where patient: patient held maternal and/or child records developed countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [64] draper j, et al. 1986. case controlled survey (#171) what are cambridge (uk) women's views on carrying mhr? generally positive view (71/88 liked carrying record; 83/88 thought there were advantages), women appreciate access to information. [65] elbourne d, et al. 1987. randomized controlled trial (#290) what are women's preferences for carrying own mhr (oxford)? positive effects of carrying more complete record as opposed to notes are: possibly decreased clerical time, a greater sense of feeling of control, confidence talking with medical personnel. no increase in lost notes over system. [66] lovell a, et al. 1987. randomized controlled trial (#246) what are women's preferences for carrying own mhr (london, uk), and does carrying own increase satisfaction with care? positive effects of carrying more complete record as opposed to notes are: possibly decreased clerical time, increased feeling of control. decrease in lost/mislaid notes (0 for phmr) over system (25%). [67] saffin k, et al. 1991. case controlled survey (#452) how well are children's records kept by parents, and do parents who have phr (#284) and those who don’t (#168) prefer to keep their children's records (oxfordshire)? parents who kept their children's records had more positive view of practice (75% phr vs. 26% nonphr. appreciated access, 90% phcr available for audit. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 [68] charles r. 1994. survey and case control comparison of physical records (#155) is the parent held record an effective means of communication, does it derive any benefit if yes, and is the north staffordshire phr a good quality source of patient information for parents (#100) and professionals (#55)? the vast majority of parents (8799%), nurses (67-100%) and health visitors (70-100%) agreed with a smaller majority of doctors (53-78%) that the child's individual record plus the information on child healthcare helped improve communication and care in at least 3 areas. audits compared to clinic held records revealed significantly more information recorded on the parent held record. [69] jeffs d, et al. 1994. random sample interview (#622) are phr retained and used to appropriately to record immunizations, and are parents and providers satisfied with their use (new south wales)? the majority (93%) of parents retained their records, with the majority having at least one (91%), and a smaller majority (68%) having all immunizations recorded in the record by. the majority of providers are (80-90%) satisfied with the use of the record. [70] webster j, et al. 1996. descriptive /survey (#200) what are women's preferences for carrying own mhr in brisbane, australia, and does carrying own increase satisfaction with care? greater satisfaction with care in phr group, though 36% forgot record at least once in at least 5 visits. women felt increased control with phr. [71] homer cs, et al. 1999. randomized controlled trial (#150) what are women's preferences for carrying own mhr (as opposed to a care card,) and does carrying full record increase satisfaction with care (new south wales)? women tended to feel more confident carrying full record, and reported a significantly greater feeling of control and access to information about their pregnancy; 89% would choose to do so again. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 5 (continued) who studied: what studied where patient: patient held maternal and/or child records developed countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [72] phipps h. 2001. qualitative interview (#21) what is impact of carrying own record during pregnancy (sydney, australia)? great majority of women favored carrying their own record in this and subsequent pregnancy, felt themselves and family to be better-informed, minimal worry about losing record. [73] usha kiran ts, et al. 2002. prospective survey (#72) what are women's preferences for carrying own mhr and is it an increased burden (south wales, uk)? the majority (90.2%) of mothers preferred to carry own notes; feeling it improves access to their case notes. [74] shaw e, et al. 2008. randomized controlled trial (#193) does secure access to pre-natal records lead to higher access to online information and greater satisfaction with care (hamilton, ontario)? study group accessed prenatal information much more frequently, and average of 8.6 more logins (including own record: 84.2% of time) both groups satisfied with information provided. [75] clendon j, et al. 2010. qualitative – interview (#35) what is the impact of the phcr in new zealand this is a good tool for improving interaction between mother and nurse. mothers keep the record for years; sometimes pass http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 them on to child when grown. [76] brown hc, et al. 2004. systematic review (3 trials) what are the effects of having women carry their own case notes during pregnancy? positive patient view of more control of care, and an increased sense of satisfaction. inconclusive health outcomes [77] bjerkeli grøvdal l, et al. 2006. randomized controlled trial (#309) do phr have positive effect on parents' knowledge, collaboration with or utilization of healthcare in norway? no health effect or improvement in other measures noted by parents. majority of parents carried record. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 6. who studied: what studied where patient: patient held records in general developed countries type of study research question * results (positive outcome of having record) results (neutral) results (negative outcome of having record) [78] giglio r, et al. 1978. descriptive /survey (#30) are people interested in carrying their own phr (amherst, ma)? study shows that patients are willing to make the effort to carry own card, further study needed to determine if makes a difference in outcomes [79] liaw st, et al. 1998. randomized controlled trial (#72) what is the impact of a phr on responsibility, information sharing and preventative health care of patients holding a phr in adelaide, australia? statistically significant improvement noted in responsibility and information sharing of patient, and may help patient awareness/participation in own care. [80] jerdén l, et al. 2004. descriptive /survey (#418) to what extent do patients report a lifestyle change when they have a phr? swedish study indicates positive lifestyle changes in 25% of those patients who received an informative health booklet (and record) [81] dickey ll. 1993. literature review (#7 trials) have studies shown any benefit to phr for preventative care? some positive benefits for patient involvement in their own care in the majority of studies. immunization records for children seem to show the highest positive value. future possibility of electronic mini-records. potential barriers include disruption of traditional power barrier, and perception of increased time required. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 23 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 [82] lecouturier j, et al. 2002. randomized controlled trial (#189) does holding own record increase cancer patient satisfaction and positive feelings about communication with care provider (newcastle-upontyne, uk)? healthcare staff had positive impression. 53% with phr found it helpful. patients with phr less satisfied (58% vs. 86% very satisfied) with information given, perhaps due to higher expectations. [83] williams jg, et al. 2001. randomized controlled trial (#501) do patients feel phr improves quality of life (wales, uk)? improved sense of control of cancer management for some patients. no demonstrated improvement in quality of life for cancer management. 52% of patients would have preferred not to have phr. [84] drury m, et al. 2000. randomized controlled trial (#650) does holding own record increase patient satisfaction (oxford)? no demonstrated improvement in satisfaction for cancer management. [85] cornbleet ma, et al. 2002. randomized controlled trial (#244) does holding own record increase cancer patient satisfaction in urban scotland? patients like it, but no difference noted on patient satisfaction and imposing on the providers on top of other records may be too much on workload. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 24 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 6 (continued) who studied: what studied where patient: patient held records in general developed countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [86] lester, h, et al. 2003. randomized controlled trial (#201) do patients in birmingham, uk feel that phr improves outcomes? no good evidence that phr helped schizophrenics, but not apparently harmful, and a higher symptom score was associated with not having record. [87] gysels m, et al. 2007. systematic review (#12 studies) do phr improve patient satisfaction with communication and information exchange? extensive literature review into efficacy of phr to improve patient satisfaction for specific cancer patients. random controlled trials show different outcomes (negative/neutral) than qualitative studies that show a more positive outcome. provider attitude and use of phr seems important in outcome and efficacy. [88] ko h, et al. 2010. systematic review (#14 trials) is there any improvement in outcomes or patient satisfaction with phr in chronic disease management? no demonstrated improvement in patient satisfaction measures and communication or care outcomes with holding phrs in chronic disease management in developed countries. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 25 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 7. who studied: what studied where care provider: patient held maternal and/or child records developing countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [89] kumar v, et al. 1981. descriptive (tbas from 15 villages) what are features of record to facilitate detection of high-risk pregnancy in india? description of card, apparently useful to help detect risk factors. benefits of using for illiterate tba in order to aid in earlier detection of risks. [90] watson ds. 1984. descriptive survey (#53 notes in 198081 and #60 in 198283) what are features of record to facilitate detection of high-risk pregnancy? early record for in-clinic use by australian aboriginal health workers. equivalent results to normal records, results are positive. [91] abraham s, et al. 1985. house-to house survey (#400) what features of record are needed to improve quality of care and improve record keeping (vellore, india)? mchcc evaluation reveals positive effects on quality of care, detecting risks. improvement needed in stressing importance for educating mothers/families, as 7% of mothers lost record and 18% discarded it following sterilization. [92] abraham s, et al. 1991. non-randomized control (#2446) does provision of phmr card improve outcomes in pregnancies in rural india? some positive outcomes for referral and knowledge of people involved knowledge higher for most measures in women with phmr. good acceptance by families, but suggestions for greater acceptance. [24] shah pm, et al. 1993. large, multi-center collaborative comparative pre/post intervention study (#13 in #8 evaluate the function of the phmr following set of who guidelines. substantial improvement in maternal and neonatal care, and continuity of care in areas using phmr (examples: philippines 91-100% vs. 36.6-51.9%; zambia 93.5% vs. 49.8%). records adapted to local situation. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 26 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 countries) improvement noted in maternal knowledge for self care. [93] daly ad, et al. 2003. interview survey (for #177 children and #220 women) do opportunities for vaccination get missed in swaziland? in this study, children and adults with health card present less likely to be a missed opportunity for vaccination. [63] tarwa, c., et al. 2007. survey (#300) is the south african road-to-health card brought to consultations and used by health care providers? the rth card is not brought to 48% of consultations. adults mostly (72%) thought they were only to bring the card to well-baby clinics. care providers are missing an opportunity to educate and provide health monitoring. [94] corrigall j, et al. 2008. household survey (#3705) what is level of routine immunization coverage in the western cape? in this study, possession of road to health card is highest predictor for vaccination coverage, and children possessing the card were 39.5 times more likely to be vaccinated. [95] osaki k, et al. 2009. 1997 and 2002/3 indonesian demographic and health survey what is level of routine immunization coverage? ownership of mch booklet positively associated with young children's full vaccine coverage (70.9% vs. 42.9%) in indonesia. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 27 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 7 (continued) who studied: what studied where care provider: patient held maternal and/or child records developing countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [96] mukanga do, et al. 2006. random household interview survey (#260) what factors contribute to family having and retaining phcr in uganda? there is a positive relation to improved health with card retention. children with a card were 10 times as likely to be fully immunized. mothers don't receive card as frequently if they don't use a health care center. children delivered at a healthcare facility were 4 times as likely to have card; children who had been to a facility in the past 3 months were 2 times as likely to have card. [97] chabot ht, et al. 1990. literature review and descriptive survey would including pictorial and written risk indicators make a single phmr more useable for all prenatal caregivers? includes literature review of current mhr in use and suggestions for single card usable for both literate and illiterate health care providers to allow mother to carry only one card. area determines different procedures done by each. example from mali. [98] kumar r. 1993. descriptive/ interview (#14) does the simplified mhr improve workload and improve statistical reporting in rural india? the simplification decreased the workload for healthcare workers, but no or minimal improvement in http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 28 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 reporting of vital statistics. [99] goldman n, et al. 1994. data from the 1987 encuesta nacional de salud materno infantil (national survey) how does the official government record of immunization in guatemala compare with phr and maternal recall for obtaining a more accurate view of immunization levels? compares (with great limitation) data obtained from the card as opposed to maternal recall-is likely to be at least as/or more accurate than the government (potentially overestimated record). [61] harrison d, et al. 1998. descriptive / interview (#185) what are the opinions of mothers/caregivers (#150) and health care providers (#35) regarding accuracy and completeness of the road to health card in cape town, south africa? most health care providers (80%) support the concept, but most (80%) would like information to be in a more useful format. points out need to determine what information is important to family and healthcare providers in order for them to actually fill out all information. [100] nuwaha f, et al. 2000. retrospective comparison of national survey did immunization levels improve after introduction of vaccination cards and vitamin a supplementation in uganda? immunization cards may have been seen as proof of vaccination and caring parent. people with cards seemingly get better care. vaccine levels increased after introduction of cards and vitamin a supplementation, though causality could not be determined. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 29 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 8. who studied: what studied where care provider: patient held maternal and/or child records developed countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [101] mcelligott jt, et al. 2010. governmentprovided data analysis are phr for childhood immunizations positively correlated with being up-to-date on vaccines? in us, especially with more disadvantaged families, holding vaccination record associated with higher rates of immunization; odds for child being up-to-date determined as 62% greater for children with phr. [102] macfarlane a, et al. 1990. retrospective study (#239) what are the reactions of general practitioners and health visitors of phcr? in oxfordshire, the majority of providers (over 90%) with experience with phr have positive response to phcr due to ability to access information, minimal experience of loss. providers without experience much more uncertain, only http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 30 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 59% view phr positively. [103] toohill j, et al. 2006. audit /survey (#1256) are phmr returned with mother at time of delivery, and does education improve return rate (australia)? the majority of mothers returned their records. compliance numbers increased over time with education on importance of recordkeeping (82 to 88.5% increase in compliance). there were some issues for healthcare providers for maintaining record completeness if record not available. [69] jeffs d, et al. 1994. random sample interview (#622) are phr retained and used to appropriately to record immunizations, and are parents and providers satisfied with their use (new south wales)? the majority (93%) of parents retained their records, with the majority having at least one (91%), and a smaller majority (68%) having all immunizations recorded in the record by. the majority of providers are (8090%) satisfied with the use of the record. a smaller than hoped for number of providers (2979%) had the purpose of the phr explained to them, and a wide range in the professionals who used the records (3096%). http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 31 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 [68] charles r. 1994. survey and case control comparison of physical records (#155) is the parent held record an effective means of communication, does it derive any benefit if yes, and is the north staffordshire phr a good quality source of patient information for parents (#100) and professionals (#55)? the vast majority of parents (87-99%), nurses (67-100%) and health visitors (70-100%) agreed with a smaller majority of doctors (53-78%) that the child's individual record plus the information on child healthcare helped improve communication and care in at least 3 areas. audits compared to clinic held records revealed significantly more information recorded on the parent held record. doctors expressed concerns about maintaining confidentiality, extra burden of work in maintaining the records, the size of the record and fears that patients wouldn't bring the record to clinic visits (this final concern may be dispelled by the increased amount of information recorded in the phr). http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 32 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 8 (continued) who studied: what studied where care provider: patient held maternal and/or child records developed countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) [76] brown hc, et al. 2004. systematic review (3 studies) what are the effects of having women carry their own case notes during pregnancy? inconclusive health outcomes. providers report an increase in the number of surgical interventions with women carrying their phr. this might be a positive finding in developing countries where the problem is lack of intervention in highrisk cases. providers report an increase in the number of surgical interventions with women carrying their phr. this might be a positive finding in developing countries where the problem is lack of intervention in highrisk cases. [104] wilkinson sa, et al. 2007. descriptive survey (#7) /review discussion (#25+) what are the effects of having women carry a new enhanced record during pregnancy (queensland)? care providers felt that the new record was too large for the patient to carry, and contained too much information to be useful to mother. suggested a smaller patient-centered document for mother, and full record to be kept in clinic. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 33 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 9. who studied: what studied where care provider: patient held records in general developed countries type of study research question* results (positive outcome of having record) results (neutral) results (negative outcome of having record) i. [81] dickey ll. 1993. quasi experimental comparison (#25) is patient compliance with preventive care guidelines improved with phr (san francisco, ca)? some positive benefits noted by 54-82% of careproviders for 7 separate parameters, with increased compliance providing preventative care in study groups (9.3-11.6% higher compliance than control). [105] atkin pa, et al. 1995. prospective survey (#187) are medication phr cards used (sydney, australia)? for older population in sydney, australia, medication cards don't seem to be used (presentation of card dropped from 61% to 23% over 12 months) or improve compliance in research population (21% of users said card helpful). http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 34 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 [106] dijkstra rf, et al. 2005. randomized controlled trial (#769) does phr improve quality of care for diabetes patients in the netherlands? modest improvements in patient health parameters. disappointing results on maintaining card, 36% using card at end of study. * see notes regarding research question(s) and results http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 35 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 discussion a. maternal information behavior though the number of information behavior studies located in this literature review is quite limited [34-47], the sense one gets from them is that families, in particular mothers in developing countries, are interested in information about healthcare issues for their family. while these studies come from different countries and regions of the world, they show some interesting similarities. generalization of mothers’ information behavior is not possible due to the small number of studies and participants. in the studies that measure all types of information seeking behavior, health information needs rank high in the list of overall women’s information needs in developing countries [34, 35, 38, 39]. the few studies that look specifically at health information seeking behavior in these populations [36, 37, 40], show that mothers studied in the developing countries tend to seek medical information and advice for their children and families more commonly than searching for other information needs, and the first or most common source for information comes from other people. there is some indication that these mothers are interested in information regarding child development and care. another common theme in the studies from both developed and developing countries is that mothers from diverse backgrounds prefer to receive health information directly from their healthcare provider. the one exception to this might be mothers in the population of adolescents in sub-saharan africa [41], though it is also possible that the nature of the information or the population involved lends itself to a different preferred mode of delivery. these findings lead one to consider very carefully how mothers might use a home-based healthcare record as a source of information regarding their own and their children’s care. the literature retrieved in this review puts forward the idea that pregnant women and mothers from all different societies, both developing and developed, show a preference for receiving health information from a person, whether a healthcare provider or not. it seems likely, that unless there is a demographic shift in information behavior, mothers may not choose to use information provided in any format of healthcare record. instead they may continue to seek out interpersonal sources. b. maternal and/or child healthcare record the earliest studies retrieved regard maternal and child healthcare records in developing countries, and mainly consist of how-to diagrams with the care provider/designer demonstrating their ideas about creation of these records. due to the descriptive nature of most of the articles listed in table 3, the assumption was made that they reflected the information needs of their healthcare provider and agency creators. the information needs of healthcare providers and other healthcare agencies must be inferred from the proposals and guidelines developed for the production of maternal and/or child healthcare records. the progression shows some measure of the evolution of these records over time [24-26, 47-52]. the other studies retrieved from the search in all categories (tables 4-9), delve into the usefulness and outcomes of the patient-held record. very few negative results noted for either healthcare provider or patient in the patient-held record. in the patient-effected categories http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 36 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 (tables 4-6), the most patient-noted positive effect in developing countries was the increased knowledge of the benefits of healthcare, as well as having the records available when needed [59, 60]. a lack of understanding of the record’s use pointed to a need for greater education in studies where the patient effect was neutral [61-63]. the effect of the patient-held record seems most positive on patients holding maternal and/or child records in developed countries. these mothers, for the most part, tend to relate positive feelings of confidence, control, access (feeling better informed), satisfaction, and improved communication and interaction during the healthcare process (table 5). as mothers in developing countries become better informed and want to play a greater role in their own care, perhaps carrying their own maternal/child records can engender these same feelings. in contrast to mother and child records, most other patient held records have not shown to be of significant benefit to either the patient or the healthcare provider (tables 6 and 9). the results for the influence of patient-held records on care providers was more mixed (tables 7-9). care providers in developing countries seemed to recognize the most positive outcomes in terms of improving health and prevention practices with patients carrying the maternal and/or child record (table 7). though follow-up study needs to continue, the improvements noted for patients are encouraging. in addition, the majority of studies, where this was measured [59, 63, 65-70, 72, 75, 77, 80, 91, 94, 96, 103], showed that patients tend not to lose patient-held maternal and/or child records, though some of the general patient-held records were more readily lost to follow-up [105, 106]. this finding seems significant and may be worth continued study in determining the importance of these records, especially to families in developing countries. in general, these studies show some positive outcomes related to the use of the patient-held maternal and/or child record. the most positive effects relate to the patient’s (mother) emotional state and feelings of control and access to information, particularly in developed countries, and results of improved health outcomes with the patient-held maternal and/or child record in developing countries. the fact still remains that 49 of the 68 priority countries are not on track to reach the un millennium development goals for 2015 [15], and these positive results need to be further leveraged to help developing countries meet their goals for decreasing mortality and improving health. study limitations the results obtained in the literature review may have suffered, both from the inability to find all applicable research in the field, as well from a limited time frame for study. in particular, it was impossible to pursue all potential sources for research in information behavior and patient-held records. the research questions addressed in the studies on patient-held records retrieved from the literature search were quite varied, and therefore difficult to compare aside from impressions of the effect or influence of the record on the patient and/or care provider. in addition, several of the studies produced mixed results, further confusing the comparison. finally, reviewer bias, access to articles and limitations to the english language inevitably factored into which search avenues were pursued and which articles were included in the study. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 37 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 recommendations the information behavior of women, particularly in developing countries, needs further investigation. it is unclear whether the childcare and healthcare information provided in existing patient held, maternal-child healthcare records, such as the kenyan maternal & child health booklet [31] and others [27], meets the needs of mothers and families. the literature suggests that pregnant women and mothers (tables 1 and 2) prefer to seek information from human sources. in particular, mothers appear to prefer to receive information from healthcare providers. healthcare providers must also be included in any discussion of maternal-child healthcare records; providers’ input on needed data is crucial to the success of any healthcare record (table 3). several studies have demonstrated the use of mobile technology, such as cell phones and personal digital assistants (pda), in healthcare in both the developed [105-111] and developing [112-115] world. protocols have been developed for creating healthcare forms and questionnaires for small mobile devices [114, 116-118]. the technology currently exists for enhancing patient-held records for storage on web-enabled mobile devices [113, 119]. healthcare providers currently use short message services (sms) to send targeted health-related messages to their patients [109, 115, 120]. in addition, electronic devices allow for communication beyond just text and the pictorial representation allowed by paper records; cell phones allow for photographic and graphic visual display, as well as voice and text messaging, electronic storage, and two-way capabilities [121, 122]. the next step in evaluating the appropriateness of web-enabled cell technology for a patient-held maternal-child healthcare record in developing countries is to determine whether a mobile platform can meet the information needs of women and families, as well as the healthcare providers in the region. currently a pilot study is underway in peru to “[d]evelop an interactive computer-based system and a common mobile phone-based platform to support maternal and child care among pregnant women” [123]. this project, a public-private partnership, also hopes to improve health services to pregnant women by increasing access to timely information, allowing greater monitoring capability by the health system, and finding empirical evidence of the social and economic impacts of mobile technologies. going forward, further research is needed to explore the utility of providing targeted health messages to mothers regarding their own health and that of their children. additionally, an assessment of the infrastructure and current practices must be complete to determine if this might best be accomplished through mobile technologies [124, 125]. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 38 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 conclusions information behavior of women, in particular disadvantaged pregnant women and mothers in developed and developing countries and other caregivers in developing countries, seems to rely most commonly on seeking information from interpersonal sources. for health-related information, most of these women look to healthcare providers. more study is necessary to determine if delivering health information in an alternative format would be acceptable or well received. the development of maternal-child healthcare records in developing countries over time offers the best insight into the basic information needs of maternal-child healthcare providers. the presence of a maternal and/or child healthcare record appears to have a positive effect, for the most part, on both care providers and patients in developing countries. in addition, the presence of a maternal and/or child healthcare record appears to have a positive effect, for the most part, on patients’ sense of control and feelings of satisfaction in developed countries. other types of patient-held records, in developed countries in particular, have not been as positively received. notes due to space limitations, it was necessary to restate and/or paraphrase research questions and study results listed in the tables above. the first author is responsible for interpreting research questions gleaned from the abstract, introduction and/or problem sections of the articles reviewed. the first author is also responsible for the interpretation and inclusion (or exclusion) of results obtained from the abstract and/or results sections of the articles reviewed. acknowledgements i would like to thank sherrilynne fuller and grace john stewart, md, phd, mph and other members of the center for integrated health of women, children, and adolescents at the university of washington for suggesting i pursue this literature review as the culminating project of my mlis degree. in addition i’d like to thank my family for enduring the many weeks of my “absence” while completing this project. http://ojphi.org/ patient-held maternal and/or child health records: meeting the information needs of patients and healthcare providers in developing countries? 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identifies coronary and heart failure events in the electronic health record jawali jaranilla*1, thomas e. kottke1 and courtney j. baechler2 1health partners institute for education and research, minneapolis, mn, usa; 2university of minnesota, minneapolis, mn, usa objective the objective of this project was to identify criteria that accurately categorize acute coronary and heart failure events exclusively with electronic health record data so that the medical record can be used for surveillance without manual record review. introduction surveillance to track the incidence, prevalence and treatment of disease is a fundamental task of public health. the advent of universal health care coverage in the united states and electronic health records could make the medical record a valuable disease surveillance tool. this can only happen, however, if the necessary data can be extracted from the medical record without manual review. methods we serially compared 3 different computer algorithms to manual record review. the first two algorithms relied on icd9cm codes, troponin levels, ecg data and echocardiographic data. the 3rd algorithm relied on a very detailed coding system, imo statements, troponin levels and echocardiographic data. results cohen’s kappa for the initial algorithm was 0.47 (95%ci 0.410.54). cohen’s kappa was 0.61 (95%ci 0.55-0.68) for the second algorithm. cohen’s kappa for the third algorithm was 0.99 (95%ci 0.98-1.00). conclusions we conclude that electronic medical record data are sufficient to categorize coronary heart disease and heart failure events without manual record review. however, only moderate agreement with medical record review can be achieved when the classification is based on 4-digit icd9cm codes because icd9cm 410.9 includes myocardial infarction with st elevation (stemi) and myocardial infarction without st elevation (nstemi). nearly perfect agreement can be achieved using imo statements, a more detailed coding system that tracks to icd9, icd10 and snomed-ct. imo statements are available in many electronic medical record systems. keywords validity; surveillance; coronary artery disease; heart failure; electronic medical record acknowledgments funding provided by the following: the healthpartners research foundation (a partnership grant to tek); the heart disease and stroke prevention unit at the minnesota department of health from a capacity building cooperative agreement grant from the centers for disease control and prevention cdc) 5u50dp000721-04; and, nih training grant t32 hl69764 (supporting cjb). *jawali jaranilla e-mail: jjaranil@jhsph.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e159, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of the pyrrolizidine alkaloid induced liver disease (paild) active surveillance system in tigray, ethiopia cindy chiu*1, danielle buttke1, girmay welde2, richard luce3, asfaw debela4, amsalu bitew5, tesfaye bayleyegn1, sara vagi1, matthew murphy1, daniel woldemichael2, teshale seboxa6, gidey g. libanos7, zeyeda beyene7, yohannes g. hawaria7, daddi jimma4, israel tareke8, danielle rentz1 and colleen martin1 1centers for disease control and prevention, atlanta, ga, usa; 2tigray agriculture bureau, tigray, ethiopia; 3centers for disease control and prevention, addis ababa, ethiopia; 4ethiopian health and nutrition research institute, addis ababa, ethiopia; 5suhul hospital, tigray, ethiopia; 6addis ababa university, addis ababa, ethiopia; 7tigray regional health bureau, tigray, ethiopia; 8world health organization, addis ababa, ethiopia objective to describe the results of the evaluation of the paild active surveillance system and lessons learned for similar surveillance efforts in a resource-limited setting. introduction a liver disease of unknown etiology, called unknown liver disease (uld) by the community, was first identified in 2002 in tigray; a rugged, semi-arid, mountainous region that is considered one of the most drought-prone and food insecure regions of ethiopia. uld is a chronic condition characterized by epigastric pain, abdominal distention, ascites, emaciation, and hepato/splenomegaly. in 2005, the ethiopian health and nutritional research institute was assigned by the ethiopia ministry of health to assist the tigray regional health bureau and oversee the disease investigation. in 2008, centers for disease control and prevention (cdc) assisted the ethiopian team and jointly developed the surveillance tools. the surveillance system was implemented in 2009 with the objectives to determine the magnitude and distribution of the disease; identify disease trends; detect cases to provide them with clinical care; and inform health officials and funding bodies for resource allocation. after several investigations, a local plant containing a particular type of pyrrolizidine alkaloid (pa) toxin that contaminated local foodstuffs was identified as the etiologic agent, and uld was renamed paild in 2011. methods from 20 september to 1 october 2011, we conducted site visits, held semi-structured interviews with 20 staff members, reviewed reporting materials, and summarized the information flow including data collection, reporting, analysis, and dissemination. results this surveillance system was implemented in 13 rural, resourcelimited districts in the nw, central and western zones. the system identified a total of 1033 cases, including 314 deaths, as of september 2011; guided medication distribution to the health facilities; served as a registry for patient follow up; and provided decision-makers with information needed to allocate resources. a large-scale training was conducted in 2010; however, high staff turnover and a lack of backup surveillance staff at each site suggested that additional training may be needed. due to the absence of a diagnostic test, the case definition was very simple to enable frontline staff in the communities and at the health posts/centers to identify disease cases. these individuals travelled long distances by foot to deliver paper surveillance forms to the district health offices. a surveillance team placed in the nw zonal office collected missing reports from the health facilities given limited transportation; however they have left since this evaluation. information from the surveillance system was shared with partner agencies at the national level every 3 to 6 months; however, this information was not shared with frontline staff. conclusions the paild active surveillance system met its objectives as originally defined. evaluation of this unique surveillance system for a chronic disease with unknown cause in a resource-limited setting provides several lessons that can inform similar surveillance efforts. ongoing logistical challenges (e.g., shortage of paper forms, lack of transportation, and long distances between locations) complicated data collection and reporting. while electronic reporting may have helped overcome some of these difficulties, it was not feasible in this setting. frontline staff identified cases in the community so that they could receive treatment; these key staff can be further incentivized by receiving regular training and surveillance reports. ongoing support will be critical to overcome these unique challenges to ensure continual disease monitoring as interventions to disrupt pa exposure are implemented in the community. keywords evaluation; pyrrolizidine alkaloid induced liver disease; active surveillance system; ethiopia *cindy chiu e-mail: vic2@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e167, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts use of syndromic data to determine oral health visit burden on emergency departments howard burkom*1, sherry burrer1, laurie barker3, valerie robison3, peter hicks1 and amy ising2 1cdc, osels, public health surveillance program office, atlanta, ga, usa; 2university of north carolina department of emergency medicine, chapel hill, nc, usa; 3cdc, nccdphp, div. of oral health, atlanta, ga, usa objective the objective was to use syndromic surveillance data from the north carolina disease event tracking and epidemiologic collection tool ncdetect and from biosense to quantify the burden on north carolina (nc) emergency departments of oral health-related visits more appropriate for care in a dental office (ed). calculations were sought in terms of the medicaid-covered visit rate relative to the medicaid-eligible population by age group and by county. introduction concern over oral health-related ed visits stems from the increasing number of unemployed and uninsured, the cost burden of these visits, and the unavailability of indicated dental care in eds [1]. of particular interest to nc state public health planners are medicaid-covered visits. syndromic data in biosurveillance systems offer a means to quantify these visits overall and by county and age group. methods using biosense data received from ncdetect, 60.8 million records from 12.9 million ed visits were collected, covering all nc visits for state fiscal years (sfy) 2008-2010. roughly 4% of visits were dropped because of patient residence zip codes missing or outside nc. a careful multi-step procedure involving both dentist consultants and data analysis was used to derive classification criteria for visits whose main reason was a nontraumatic oral health problem [2]. this procedure yielded 243,970 visits by ~174,600 patients based on hospital-specific patient identifiers. nontraumatic oral health-related visits were collected in a study set with added fields for method of payment, patient residence county, and age group. based on previous studies, consultant preferences, and nc medicaid eligibility guidelines, selected age groups were 0-14, 15-19, 20-29, 30-49, 50+ years. stratified counts of medicaid-eligibles were obtained from the nc dental director by study year. using these tables and the ed visit study set, rates of nontraumatic oral health-related medicaid visits per 10,000 eligibles were tabulated by county and age group for each study year. demographics of multiple-visit patients were also studied. results rates of ed oral health-related visits were substantially higher for young adults than for other age groups. from statewide rates in table 1, this age factor was consistent across study years. county-level rates showed the same age pattern to varying degrees. detailed analysis showed problem areas, with rates in 21 of 100 counties exceeding 60 per 10,000 eligibles for the 20-29 year age group. plots and tables complemented understanding of the ed oral health visit burden by age and county. the state total ed burden for oral health problems was ~2% (0.2% 9.7% by county). conclusions judicious use of syndromic data with external information, such as the detailed medicaid denominators and the method of payment codes for each visit above, can give quantified estimates for policyrelated public health issues. in the current study, the derived oral health visit rates gave numerical detail to concerns about the use of nc eds for nontraumatic oral health problems by low-income persons affected by the economic recession. results also show rate variation by county and can be combined with access-to-care data to inform planning of effective local measures to improve access to dental services and thus reduce the ed visit burden. table 1. nc statewide oral health medicaid visits to emergency departments per 10,000 eligibles keywords emergency department; chief complaint; biosense; oral health; ncdetect acknowledgments mark casey, north carolina dental director for medicaid eligibility and care provider data; chris okunseri, marquette university for case definition advice; lana deyneka of the north carolina department of health and human services for consultation; dennis falls of ncdetect for method of payment information. references 1. shortridge, e. and moore, j. 2010. “use of emergency departments for conditions related to poor oral health care.” final report. washington, dc: office of rural health policy. august 2010. 2. burrer s, burkom h, okunseri c, barker l, robison v, nontraumatic oral health classification developed from syndromic data, abstract, international society of disease surveillance conference 2012. *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e57, 2013 ojphi-06-e20.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 104 (page number not for citation purposes) isds 2013 conference abstracts biosurveillance data stream framework: a novel approach to characterization and evaluation kristen margevicius*, eric generous, kirsten taylor-mccabe, mac brown, w. brent daniel, lauren castro, andrea hengartner and alina deshpande los alamos national laboratory, los alamos, nm, usa � �� �� �� � � �� �� �� � objective 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� ����� ������������������ ����� ������ ����� � ����� ��������������� ��� ����� ���� ���������� ������� ����� ��� � ������ keywords � � ��� � �8������ ����� ���8���������������� �� acknowledgments ����� �&��������� � �����������9����� �� ���.������� ��������������� "� ���� ���:�����(������� ��������������;������$:(�;%��"��������� � �� /���������+������$"�/+%� *kristen margevicius e-mail: kmargevicius@lanl.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e20, 2014 building the foundations of an informatics agenda for global health—2011 workshop report building the foundations of an informatics agenda for global health 2011 workshop report 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 building the foundations of an informatics agenda for global health 2011 workshop report muzna mirza 1 , mary kratz 2 , donna medeiros 3a , jamie pina 3b , janise richards 1 , xiaohui zhang 4 , hamish fraser 5 , christopher bailey 6 , ramesh krishnamurthy 6 1 centers for disease control and prevention, atlanta, ga, usa 2 university of michigan, ann arbor, mi, usa 3a (former) rti international, research triangle park, nc, usa; (current) futures group, washington, dc, usa 3b rti international, research triangle park, nc, usa 4 scientific technologies corporation, tucson, az, usa 5 harvard university, cambridge, ma, usa 6 world health organization, geneva, switzerland abstract strengthening the capacity of public health systems to protect and promote the health of the global population continues to be essential in an increasingly connected world. informatics practices and principles can play an important role for improving global health response capacity. a critical step is to develop an informatics agenda for global health so that efforts can be prioritized and important global health issues addressed. with the aim of building a foundation for this agenda, the authors developed a workshop to examine the evidence in this domain, recognize the gaps, and document evidence-based recommendations. on 21 august 2011, at the 2011 public health informatics conference in atlanta, ga, usa, a four-hour interactive workshop was conducted with 85 participants from 15 countries representing governmental organizations, private sector companies, academia, and non-governmental organizations. the workshop discussion followed an agenda of a plenary session planning and agenda setting and four tracks: policy and governance; knowledge management, collaborative networks and global partnerships; capacity building; and globally reusable resources: metrics, tools, processes, templates, and digital assets. track discussions examined the evidence base and the participants’ experience to gather information about the current status, compelling and potential benefits, challenges, barriers, and gaps for global health informatics as well as document opportunities and recommendations. this report provides a summary of the discussions and key recommendations as a first step towards building an informatics agenda for global health. attention to the identified topics http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 and issues is expected to lead to measurable improvements in health equity, health outcomes, and impacts on population health. we propose the workshop report be used as a foundation for the development of the full agenda and a detailed roadmap for global health informatics activities based on further contribution from key stakeholders. the global health informatics agenda and roadmap can provide guidance to countries for developing and enhancing their individual and regional agendas. key words: global public health informatics, agenda, policy, capacity development, collaboration introduction in today’s globalized world, where diseases, conditions or events affecting health transcend national boundaries,(1) the need to strengthen the capacity (2) to protect and promote the health of the global population continues to be important. the application of information and communication technology (ict) for health i.e. ehealth (3) is an important aspect of the delivery of global health.(4) leveraging informatics best practices and principles is essential for improving global health response capacity through implementation of ehealth.(5) a critical step towards strengthening this capacity is developing an informatics agenda for global health which can provide guidance to countries for developing and enhancing their national and regional country agendas. the 2005 58th world health assembly ehealth resolution (wha58.28) urges member states “to consider drawing up a long-term strategic plan for developing and implementing ehealth services.”(6) since then, two significant collaborative activities have addressed wha58.28: a. initial high-level efforts at bellagio 2008 led to an “ehealth call to action”(7); and b. mahidol global health information forum 2010 convened informatics experts,(2) resulting in a “call to action” agreeing to general ehealth principles. in an effort to continue to address wha58.28, the authors identified the need for an activity to examine the evidence for use of informatics for global health, recognize the gaps in evidence, and provide evidence-based recommendations as the next logical steps toward building a comprehensive informatics agenda for global health. to ensure perspective diversity and stakeholder involvement, a group of 85 invited experts representing a diverse stakeholder community gathered for the global health workshop at the 2011 public health informatics conference on 21 august 2011 in atlanta, ga, usa. the workshop goals were to begin building an informatics agenda addressing current and future global health challenges,(8) and to support the implementation of ehealth initiatives using informatics principles and practices to improve global health. workshop objectives included: 1. engage health informatics and global health experts to discuss the foundational elements of an informatics agenda for global health. 2. identify informatics evidence-base, best practices and principles to draft key elements of the agenda for global health. http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 3. define global health challenges and priorities, and formulate next steps to move towards the full agenda. the full scope of topics from the 2008 bellagio call for action (7) was reorganized into tracks according to topic affiliation to structure expert dialog on key areas described below. plenary session: planning and agenda setting: the plenary session engaged all participants together to discuss global health challenges that implementation of informatics scientific principles and practice can address, thus setting the stage for the subsequent track discussions. track 1 policy and governance: supportive policy and governance development and implementation are essential to successful implementation of ehealth and thus are important to include in informatics agendas.(9) political authorities, policymakers and stakeholders must take collective action for consensus-based use of institutional resources. formation of councils, creation of ehealth policy frameworks, toolkits and a trained workforce are key elements for policy and governance development and implementation. track 2 knowledge management, collaboration, and global partnerships: collaboration is a fundamental success factor for global health—it supports the use of “the resources, knowledge, and experience of diverse societies to address health challenges throughout the world.”(1) the implementation of relevant informatics best practices support collaboration and knowledge management among the ehealth stakeholders. multisectoral collaboration has been recommended by wha58.28 for addressing global health needs.(6) track 3 capacity building: capacity building efforts occur at the individual, institutional, and societal levels. these efforts support the development of in-country expertise to build infrastructure and harness resources, bolstering capabilities and economic value. the scope of this track encompassed informatics training and education in diverse domains including leadership and management. participants also discussed mechanisms to support country-level initiatives and foster country ownership to ensure sustainability of ehealth efforts, and capacity building in data management. track 4 globally reusable resources: metrics, tools, processes, templates and digital assets: ehealth resources that can be shared and reused via various models facilitate the development of scalable and sustainable infrastructures and institutes essential to the effectiveness and productivity of a health enterprise. examples of successful models include: metrics – health metrics network (hmn) framework; tools – free and open source software, virtual data toolkit http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 (vdt); processes – monitoring and evaluation processes; templates – for standard operating procedures; and digital assets – digital libraries.(10) methods invitations to participate in the workshop were sent to public health informatics and global health experts identified through purposive sampling (i.e., snowball sampling). workshop registration link was also provided on the conference website for open enrollment. registrants were asked to indicate their track of interest during online registration. the registration lists were reviewed and shortlisted because of limited seating capacity; best efforts were made to include stakeholders from all represented groups in each track. each track was supported by the track lead and invited experts. to facilitate discussion some of the larger tracks broke into smaller discussion groups. each track used the following predefined discussion format: 1. set the stage: why is this area of informatics important? 2. current status: where are we today? 3. compelling benefits: what are the established and potential outcomes? 4. challenges, barriers and gaps: what are the hurdles on the way towards our goals? 5. opportunities and recommendations: what are the opportunities and where do we aim to be tomorrow? results the workshop was organized as an ancillary meeting in the pre-conference session of the 2011 public health informatics conference, atlanta, ga, usa. there were 85 participants from 15 countries representing governmental organizations, private sector companies, academia, and nongovernmental organizations. the following summaries—organized by track and following the dialog format methodology—represent the key outcomes of the workshop discussions. additional information is provided in exhibits. plenary session: planning and agenda setting the workshop co-chairs led the plenary track and recognized that regular reassessment of the rapidly changing state of ict, ehealth initiatives, and informatics practices and principles can provide evidence to develop and continually strengthen a robust informatics agenda, implementation of which could move the ehealth activities forward more systematically. however, to set an agenda, a clear definition of global health is essential. global health as a term is found in literature but there is no consensus on definition. (1, 11-13) the scope of global health as defined by koplan et al (1) was used as a reference to define the scope of this agendasetting workshop. health information systems development to provide quality data and information to make better health-related decisions is crucial for supporting global health goals. enterprise architecture is a methodology that assists in systematically organizing the multiple elements in the design and development of health information systems and ict infrastructure to meet global health equity and health impact goals. global health equity requires focusing on both domestic health disparities and cross-border concerns. (1) health impact assessment requires well-defined http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 measures to assess population health improvement due to the implementation of ict through ehealth initiatives using informatics practices and principles to guide implementation. a fuller discussion of how global health enterprise architecture could be developed, implemented, and evaluated was identified to be beyond the scope of this workshop and recommended as the topic of a future workshop. track 1: policy and governance set the stage many nations have some form of ehealth policy, but commonality remains limited. policymakers face numerous challenges in thinking beyond local representation needs in a global context.(3) the potential of ehealth to meet both national and global health objectives has not been fully developed and requires strong leadership.(14) current status global ehealth policy and governance development and implementation is an iterative and ongoing process. enormous advances have been made in medical knowledge, technology, and training of health care professionals, but ehealth application based on standard methodologies is significantly lagging because of policy gaps. policymakers generally react to specific problems and crises leading to gaps in the overall national and global polices. nations around the world struggle to effect ehealth policy models that can build on evidence-based practices and measures. some sporadic, focused efforts have occurred before and since the meeting in bellagio 2008 (exhibit 1). however, to date, no global, sustained collaborative effort has been established towards developing global ehealth policy and governance guidelines. collaborative investments in global policy and governance development are needed to usher in the promise of ehealth.(15) exhibit 1. key recent ehealth policy and agenda setting activities  the 2005 58th world health assembly ehealth resolution (wha58.28) urged member states to consider developing long-term strategic plans for developing and implementing ehealth services. the world health organization (who) global observatory for ehealth (goe) supports these goals by providing member states with strategic information and guidance on effective practices, policies, and standards in ehealth. a  leadership series forums on health information systems (his) have convened in several who regions. b  the health metrics network (hmn), established in 2005, is the first global partnership dedicated to strengthening national health information systems. hmn operates as a network of global, regional, and country partners. among hmn’s initiatives, a target of having accurate, real-time data from the vital registration systems in all countries by 2020 was proposed in september 2011 by thomas frieden, director of the u.s. centers for disease control and prevention. c  the “making the ehealth connection” effort, led by the rockefeller foundation, in coordination with internationally recognized conveners in the fields of global health, international development, and information and communications technology (ict), resulted in the 2008 bellagio meeting. d http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012  the mhealth alliance (mha) was formed in 2009 to advance mobile health through policy, research, advocacy, and support for the development of interoperable solutions and sustainable deployment models. e  the international organization for standardization (iso) ehealth standards (in draft) contains ehealth architecture and capacity-building standards. public health task force, a working group hosted by the iso/technical committee (tc) 215 “joint initiative on standards development organization (sdo) global health informatics standardization”, drafts the standards. f  european countries have made substantial progress toward modern ehealth infrastructures and implementations, thereby leading the rest of the world. the european union (eu), comprising 27 countries of differing economic levels that share a vision of health care developed in the 2004 ehealth action plan, called on member states to develop an ehealth roadmap to 2010. annually, the eu convenes ehealth conferences, meetings of ministers of health, and holds initiatives on interoperability, quality criteria, and lead markets. g ________________________________ a fifty-eighth world health assembly, resolution twenty eight, 16-24 may 2005, geneva, switzerland. wha58-28 on ehealth. [accessed 9 october 2011]; available from: http://apps.who.int/gb/ebwha/pdf_files/wha58/wha58_28-en.pdf and http://www.docstoc.com/docs/101599959/resolution-wha5828-ehealth---who--world-healthorganization b his forum. webpage on the internet. [accessed 21 february 2012] available from: http://hisforum.org/ c health metrics network (hmn). webpage on the internet. [accessed 21 february 2012] available from: http://www.who.int/healthmetrics/en/ d khoja s, durrani h, fahim a. scope of policy issues for e-health: results from a structured review [internet]. new york: rockefeller foundation [accessed 15 jan 2010]; available from: http://www.ehealth-connection.org/files/confmaterials/scope%20of%20policy%20issues%20for%20ehealth_0.pdf e mobilizing innovation for global health, un foundation. mhealth alliance. [accessed 20 october 2011]; available from http://www.mhealthalliance.org/ f the joint initiative on sdo global health informatics standardization. global health informatics standards. [accessed 20 october 2011]; available from: http://www.global-e-health-standards.org/ g mars m, scott r. global e-health policy: a work in progress. health aff (millwood) 2010;29(2):23945. compelling benefits health in all countries could benefit from the development of a coordinated global ehealth policy to support national legislation development for effective use of ehealth resources. challenges, barriers and gaps a unified voice for ehealth policy development is absent.(16) often, ministries of health do not fully understand the benefits that ehealth can bring to national public health programs. numerous fragmented pilot efforts continue to reinvent the wheel and consume resources. http://apps.who.int/gb/ebwha/pdf_files/wha58/wha58_28-en.pdf http://www.docstoc.com/docs/101599959/resolution-wha5828-ehealth---who--world-health-organization http://www.docstoc.com/docs/101599959/resolution-wha5828-ehealth---who--world-health-organization http://hisforum.org/ http://www.who.int/healthmetrics/en/ http://www.ehealth-connection.org/files/conf-materials/scope%20of%20policy%20issues%20for%20ehealth_0.pdf http://www.ehealth-connection.org/files/conf-materials/scope%20of%20policy%20issues%20for%20ehealth_0.pdf http://www.mhealthalliance.org/ http://www.global-e-health-standards.org/ http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 informatics tools to guide on-the-ground baseline assessment, implementation, scaling, and evaluation of information systems are needed, using substantial resources already in existence such as the hmn tools.(9) exhibit 2 summarizes the track 1 discussion. (exhibit 2) exhibit 2. summary of policy and governance track compelling benefits  informed, globally coordinated decision making and thus enhanced global health response capacity.  improved availability of quality information to national policy developers.  improved health care service quality through ehealth implementation based on informatics standards and best practices. challenges, barriers and gaps  lack of a globally coordinated effort for ehealth strategic planning.  lack of evidence-based practices for ehealth policy and governance guidelines.  limited and inadequate funding for development of global ehealth policy and governance strategic roadmap.  limited experience in technology and resistance to change.  changing national priorities and cultural challenges are not generally addressed in existing ehealth policy and governance material. recommendations  engage stakeholders, including ministry of health (moh) officials, and provide information and resources for assessments and evaluations.  develop national and global research programs to identify information needs, recognize barriers to access, and translate and use information to evaluate the impact of information and knowledge-sharing interventions on health outcomes.  target donor funding and involvement, encourage adoption of open source platforms and of technologies focused on sustainability.  align donor funding to reduce global health fragmentation, harmonize reporting requirements, and consolidate reporting structures.  train local public health leadership in informatics, thus building capacity at country level.  provide tools and resources to encourage governments to adopt a culture of ehealth interoperability based on open standards.  encourage moh-appointed leadership for health information technology to establish countrylevel agendas, and obtain stakeholder buy-in and funding.  encourage governments to adopt a culture of ehealth interoperability in health system strengthening programs, and strengthen the capacity to access and use health information as evidence to improve decision making.  encourage adoption of regional and cross-border information sharing, knowledge management and collaboration, and creation of virtual communities of practice and centers of excellence that lead to expanded knowledge in the use of new and existing technologies. http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 opportunities and recommendations bringing organizations together, building consensus, and developing a roadmap are complex propositions that present great opportunities and challenges and require momentum.(17) a logical next step would be to build a collaborative environment for convening sustainable effort. track participants developed a 5-year national ehealth framework approach that could be used to lay the foundations for national policy development efforts (exhibit 3). exhibit 3. recommendation: country-level 5-year ehealth policy framework 1–3 year country-specific action items a. engage and inform stakeholders at all levels about the need and value for timely and reliable health information. b. assess country health profile, and document donor health information activity profile. c. conduct inventory of legislation to identify policy gaps at all levels. d. establish policy-based coordination mechanism to institutionalize and operationalize health information use for evidence-based decision making. e. throughout, hold meetings and disseminate information gathered through a collaborative community. 3–5 year country-specific action items a. finance health information systems capacity development at all levels. b. establish governing mechanism to implement appropriate standards-based information systems at all levels through standards development organizations (sdos). c. harmonize and align legislation to comply with national obligations for reporting of health data to support global public health needs. track 2: knowledge management, collaboration and global partnerships setting the stage collaboration among diverse stakeholders to share knowledge and expertise is a fundamental factor for public health practice. knowledge management systems are critical tools to bring people, processes, information, and technology together. a 2005 association of state and territorial health officials (astho) report provided perspectives for public health knowledge management,(18) and liebowitz has summarized selected international efforts in this domain.(19) current status overall, there is an increasing trend in the application of knowledge management and collaboration principles and methods to the health enterprise to develop sustainable global partnerships. however, there have been few implementations that are inclusive of low-resource settings. some ehealth specialties, such as mobile health (mhealth),(20) extensively leverage collaborative methodologies and knowledge management practices; however, ehealth in general http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 has not yet widely adopted these informatics best practices. additionally, training and practice in these domains are still quite limited in the global health setting. compelling benefits the participants highlighted existing knowledge management systems and collaborative methodologies, and recognized that web 2.0 applications and mobile technologies (21) have provided tremendous opportunities for knowledge sharing and collaboration. experience has demonstrated that collaborating to share data, information and knowledge empowers us to increase transparency, improve research and education, support better decision making, help deliver care, and save lives.(22) challenges, barriers and gaps many public health information systems are functionally isolated from other systems because they are not based on informatics standards nor on sustainment models. planning generally lacks collaboration and systems thinking; therefore, systems are not interoperable. additionally, collaborative projects across multiple governments and organizations face policy and sociocultural barriers, in addition to lack of resources and skills, that are often bigger than technical challenges. opportunities and recommendations resources need to be better coordinated at all levels to optimize efficiency and efficacy. collaborative projects should be encouraged to use international informatics standards and promote free access to international standards, thus enabling interoperability, while sharing innovation and best practices. formation of a working group to facilitate the development of the domain of global public health knowledge management and collaboration is necessary to identify strategic goals, align priorities among stakeholders, create an inventory of standards, and promote sharing of best practices. (exhibit 4) http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 exhibit 4: summary of knowledge management, collaboration and global partnerships track compelling benefits  increased transparency  improved health research, education and care delivery  enhanced public health essential practice challenges, barriers and gaps  information systems planning efforts lack collaboration and use of standards.  information system implementation lacks use of standards and best practices.  data collection systems generally do not collect metadata and spatial information and thus collaboration for using the data is challenging.  insufficient funding for education and workforce training in the domain.  policy, sociocultural, and technical barriers in collaboration agreements.  disease specific funding streams are generally not conducive to collaboration across diseases and funding streams. recommendations  better coordinate the funding sources: include knowledge management and collaboration in the planning phases of projects.  leverage international standards: promote free access to international standards to improve interoperability and thus promote collaboration around using data.  form a working group to build a collaborative community: the goals of the working group could be to identify strategic goals and priorities for global health informatics, and create an inventory of informatics standards, knowledge and best practices. track 3: capacity building set the stage strengthening informatics workforce capacity positively affects public health by promoting quality of care and increasing the ability to monitor health trends. sustainable changes require long-term planning and investment. global health spending currently exhibits a trend toward long-term programmatic commitment, which suggests that a stable level of funding for global health initiatives will exist in the long-term. (23) sustainable national informatics capacity will ensure continuity of informatics applications, which will promote continued improved outcomes when external funding eventually expires. current status externally funded development initiatives provide support to resource-constrained countries, offering immediate relief for pressing health concerns. however, such efforts rarely include a strategic plan for creating long-term change in local information management infrastructure and workforce capacity. http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 compelling benefits a robust informatics workforce supports local care delivery and public health practice. a strong local workforce provides independence from external funding and supports expertise for information management activities over the long term. challenges, barriers and gaps local informatics capacity building is limited in several ways. stakeholders with varying priorities provide funding to health-promoting programs.(24) however, strategies to develop incountry information management capacity are not typically a focus of these programs. when informatics is included as a fundable activity in such programs, the goal is to provide local systems for the life of a project instead of developing capacity that will serve future efforts as well. lack of informatics training is a major barrier to ict implementation in resourceconstrained environments.(25) training programs, when present as part of an externally-funded program, are often focused on the use of a specific system for a finite purpose instead of basic computer literacy and information management principles. as a result, learners are rushed into information system usage without acquiring foundational skills. as there are few professional paths for health informaticians in these settings, there is little incentive for local technology talent to seek work in public health programs. opportunities and recommendations the participants advocated in-country health programs should create actionable plans to bolster local informatics capacity. externally funded programs should contribute to local capacity while concurrently fulfilling their health intervention missions. a neutral entity must develop a framework for local informatics capacity building. the framework should build off of existing efforts to standardize capacity development,(15) and focus on sharing core training materials and curricula. adherence to standards for capacity development should be an integral part of every public health intervention and include building off of local innovations to support viable economic development, fostering government ownership to enable ehealth benefits and knowledge transfer through portable accreditation programs. (exhibit 5) http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 exhibit 5: summary of capacity building track compelling benefits  improved quality of care  improved communication  sustainability challenges, barriers and gaps  training deficits are not well studied and documented  strategic plans are dedicated to health issues, and often do not have informatics components  commercial markets do not exist for global health informatics products recommendations to develop a framework for capacity building that includes the following components:  building off of local innovation: capacity building initiatives should support the creation of viable commercial markets for health informatics products, to support sustainability.  foster local government ownership and buy-in for informatics training initiatives: ministries of health should be provided with tangible examples of the value of informatics for implementation of ehealth when advocating for ehealth.  portable accreditation: to allow workers to easily apply their knowledge to new environments. track 4: globally reusable resources: metrics, tools, processes, templates, and digital assets set the stage in today’s resource-constrained global economy, globally reusable resources are useful for developing sustainable ehealth infrastructures and systems essential to a health enterprise. their use allows the development of community solutions, professional networks for data sharing, and open access to health information.(26, 27) existing evidence indicates that dedication to developing globally reusable resources can lead to production of agile, efficient health systems, which can result in timely and more accurate responses to public health events.(28-30) current status many standards-based free and open source software (foss) products, especially m-health applications, are available online.(31) a number of these open source applications have achieved worldwide impact—notably, the linux operating system and the apache web server, which dominate the market. however, foss business models vary and may not always work across a global context. additionally, total cost of ownership involving foss components must account for implementation and operational support as well as software update costs. the incentive model for private-sector companies must be modified to support reuse of ict resources. capacity building of the workforce should not be underestimated; standardized tools find success primarily through user adoption. most mainstream public health information systems are not yet http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 developed with reusable resources. in the health care domain, vista — based on open source components — has the largest market share for comprehensive hospital information systems in the us,(14) and has been adopted in many international settings as well. compelling benefits the primary benefits are cost reduction and interoperability by avoiding duplication of effort and following standards. quality of generated information can improve if organizations use welltested and validated components for systems development. cloud computing (software as a service – saas model) provides significant cost savings.(32) the main savings are in the resources required to support ict infrastructure; shared services have cost and workflow efficiencies when resources are reused across a virtual organization.(28) challenges, barriers and gaps challenges to building the global health infrastructure using standards-based reusable resources such as tools and services are threefold. first, basic computer science skills required to shift from the current model of siloed or isolated databases into distributed systems are limited. second, there is a lack of policy and legal regulations in support of open access to information across the global context.(33) finally, standardizing medical concept definitions is time consuming, making data inconsistent between systems and slowing the process of developing systems. (34) translating the benefits of reusable resources to resource-poor environments can be challenging. reliable internet access is rare in rural areas, though mobile phone networks are improving. many projects need to manage local databases, though some are able to synchronize the data to a central server when connectivity is available. electricity availability represents a challenge in many rural and even urban locations, but solar power, low-power devices and better batteries, and generators offer options to address outages. opportunities and recommendations policy and governance development for the oversight of shared reusable resources is a global responsibility.(35) certification for quality assurance may ensure high system quality and interoperability, leading to improved user satisfaction while easing data management burdens. readiness assessments that measure economic and human capacity realities should be required. assistance could be provided by a global community of practice to low-resource countries as required, to ensure that response and remediation to global health challenges are addressed quickly and efficiently. the next logical step is to make health informatics standards, especially those developed using public funds, available in the public domain. (exhibit 6) http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 exhibit 6: summary of globally reusable resources track compelling benefits  productivity is optimized and increased (reduced labor burden and cost efficiency).  implementing standards-based systems facilitates sharing of information, knowledge and infrastructure.  standardization and use of shared methods reduce duplication of effort.  open systems can be more effectively evaluated over time.  better decision making for sustainable health system operations and infrastructure.  standards-based design of tools provides greater flexibility while reducing costs.  equal proactivity is the notion that equity in tools and infrastructure increases global workforce capacity and efficiency.  horizontalization of health systems expands reach of health services. a challenges, barriers and gaps  data from legacy systems is generally not machine-readable and thus is not shareable seamlessly electronically among systems.  open access/public access: confusion (or ambiguity) exists about standards and legality of open access to data.  proprietary issues surround the customized code built out of collaborative efforts.  lack of skilled workforce required to build shared tools and reusable components.  user interfaces are not standardized for medical information access, thus collaborative use requires considerable training, making it challenging for users to accept a new system. recommendations  ensure better policy development and governance for oversight of quality assurance and policy implementations.  clearly define the most important information gaps in global health.  support evidence-based evaluations of tools, services, and other reusable resources.  invest in robust infrastructures with scalable resources; build on sustainable solutions.  make health informatics standards available in the public domain.  encourage open source software development and implementation and suitable business models to ensure high quality standards, acceptable development time, and long-term support for maintenance.  promote sharing of medical content, including data dictionaries, coding standards and indicators among global stakeholders.  establish a worldwide mechanism for certification and testing of globally reusable services.  provide better methods for findability of shared products.  evaluate total cost of operation versus initial purchase price for foss projects and attempt to improve cost efficiency. a bacharach, s., “technology convergence, market horizontalization and, voila: information fusion”, directions magazine, http://www.directionsmag.com/articles/technology-convergence-markethorizontalization-and-ivoilai-information-fus/122770, 24 january 2008 http://www.directionsmag.com/articles/technology-convergence-market-horizontalization-and-ivoilai-information-fus/122770 http://www.directionsmag.com/articles/technology-convergence-market-horizontalization-and-ivoilai-information-fus/122770 http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 limitations due to limited funding for this activity, only participants of the 2011 public health informatics conference were able to attend the workshop. thus, there was under-representation from many low-resource countries. an attempt was made to overcome this limitation by ensuring that the participants selected from the online registrants list brought a diverse experience of supporting the under-represented areas. discussion following the 2008 meeting in bellagio, gerber et al identified the need for “an international public-private sector framework for understanding and moving forward on ehealth”(15). the 2011 global health workshop was an important step towards addressing this need and bridging a gap in the global health informatics domain. the workshop discussions created a rich environment to examine the evidence base and the participants’ experience for the current status; compelling and potential benefits; challenges, barriers, and gaps; and recommendations for the key domains of informatics as applied to global health. the strength of the workshop lay in the unique blend of participants who represented 15 countries and a diverse stakeholder landscape of governmental organizations, private sector companies, academia, and non-governmental organizations. in addition to their regular jobs, the participants provide voluntary support to various informatics and ehealth technical workgroups around the world and thus also represented those collaborative virtual organizations. the participants recommended that effective policies and governance mechanisms must be in place prior to global crises response so that global health events do not overwhelm public health response capacity. the development of an infrastructure to support global health information exchange must address legal, technical, political and economic barriers. establishment of priorities to guide economic investments in informatics research and innovation should support patient care, global health goals, public access to data and knowledge sharing among countries across both international and regional boundaries. the health informatics community needs to collaboratively focus on addressing global health problems and challenges by leveraging existing infrastructure, harmonizing global policy and governance efforts, and advocating effectively.(36) for such a diverse community to coalesce and collaborate, appropriate resources must be acquired and allocated optimally to provide sustainable virtual and physical organizational support. such a global community can drive rapid development of knowledge management mechanisms and systems that facilitate the identification of global health priorities; create, manage, share, and disseminate information; and facilitate decision making for evolving global health challenges, policies and events. in resource-constrained environments, building informatics capacity allows for clinical delivery organizations and national ministries of health to continue to benefit from standards-based information management practices beyond the duration of externally-funded interventions or programs. when building informatics capacity, stakeholders must examine methods to train, http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 maintain and retain a workforce at the local level with skills to design, develop, implement and sustain the deployment of health information systems for ehealth initiatives. reusable ict resources play a critical role in support of global health.(37) primary drivers of adoption are economic because of reducing duplicate efforts; interoperability by building solutions on common technology standards;(38) developing partnerships with mutual benefits; pooling of experience and knowledge; and instituting a two-way information and knowledge flow between developed and developing countries. low-threshold access to more resources than any single health system could afford individually can facilitate country participation in the global health information exchange projects while concurrently strengthening national infrastructure.(39) many countries have developed national ehealth strategies and initiatives to enhance the implementation of ict in health. however, few have realized that developing ehealth national strategies while focusing only on the domestic needs has the potential to technically isolate their country from the rest of the world at times when there is need for relevant information sharing across borders. examples of such use cases include reporting cases of public health emergencies of international concern by complying with the 2005 international health regulations (ihrs) (40), or when their residents visit other countries and health providers in the international location need to access the visitors’ previous health information to provide care.(41) following the informatics recommendations of standards-based implementation of ehealth will ensure that countries get prepared for participation in regional and global networks in support of use cases such as global health surveillance and global access to health care, with minimal additional effort required for each emerging event. the 2011 global health workshop recommendations pave the way for establishing an overarching global informatics strategy, following which will help countries stay technically synchronized with countries outside their borders. because of the myriad of ongoing activities in the ehealth domain, individual countries would benefit from the informatics agenda for global health by acquiring guidance for developing and enhancing their individual and regional agendas. roadmap development activities on a global scale require commitment and funding support from global health funding organizations. the 2011 workshop was supported by limited funds from a few organizations; travel funds were not available for more key stakeholders to be included and thus the workshop lacked more global representation among the participants. additionally, more funding would have enabled us to develop a longer and more comprehensive plan for the workshop. the large number of participants representing a diverse stakeholder group who acquired their organizations’ support to attend the workshop clearly highlights the value of this activity. this report provides evidence that more investment in this domain is required. we anticipate that major global health donors will consider supporting the next phase of this activity. in summary, the participants highlighted the importance of developing and implementing good informatics policy and governance guidelines in support of global health. they recognized the importance of global health stakeholders to focus on transparency, equity and technological interoperability as local needs change in response to domestic and global health events. to improve knowledge management and collaboration, stakeholders highlighted the need to explore methods for identifying, collating and sharing best practices, and for the adoption of social http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 networking technologies to foster rich collaborations. local informatics workforce capacity development was recognized as a key foundation-stone for long term sustainability of ehealth initiatives. in the use and creation of health information resources, stakeholders must ensure that the products are adaptable, adoptable, and reusable by the global health community. attention to these issues can lead to measurable improvements in health equity, individual health outcomes, and positive impact on global public health. conclusion global health informatics best-practices and principles continue to support global health systems via implementation of ehealth. the workshop provided a venue to examine the evidence base and the participants’ experience to provide outcomes as summaries of the current status; compelling benefits; challenges, barriers, and gaps; and recommendations for the application of informatics scientific principles and best practices to support ehealth implementation for global health. we propose that these workshop findings be used as a foundation for the development of the full agenda and a detailed long-term roadmap for global health informatics activities. the global health informatics agenda and roadmap can potentially provide valuable guidance to countries for developing and enhancing their individual and regional agendas. corresponding author muzna mirza centers for disease control and prevention 1600 clifton road ne, atlanta, 30333, ga. email: mmirza@cdc.gov acknowledgments we applied the percent-contribution-indicated (pci) approach for the sequence of authors. we are grateful for the stimulating discussions and comments by dave ross, beatriz de faria leao, laura raney, sherrilynne fuller, lincoln moura, raymond ransom, brian robie, linda carr, seth foldy, brian agbiriogu, andrew grant, james kauivihi, patrick nguku, sam malamba, and many others who participated in the workshop. thanks indeed to august gering for excellent editorial support and to rita sembajwe and prachi mehta for logistical support for the workshop. funding the workshop was organized and sponsored by the centers for disease control and prevention, rti international, and scientific technologies corporation. mailto:mmirza@cdc.gov http://ojphi.org building the foundations of an informatics agenda for global health 2011 workshop report 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 competing interests the authors have declared that no competing interests exist. disclaimer the findings and conclusions in this report are those of the authors and do not necessarily represent the official positions of the centers for disease control and prevention (cdc), department of health and human services, usa, and the world health organization (who), switzerland. references 1. koplan jp, bond tc, merson mh, et al. 2009. towards a common definition of global health. lancet. 373(9679), 1993-95. http://dx.doi.org/10.1016/s0140-6736(09)60332-9 2. mahidol conference. call for action. 2010 [cited 2011 october 9 ]; 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prediction model 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model shahid a. choudhry 1 , jing li 1 , darcy davis 2 , cole erdmann 1 , rishi sikka 2 , bharat sutariya 1 1 cerner corporation, kansas city, 2 advocate health care, chicago abstract introduction: preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. advocate health care in chicago and cerner (acc) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. however, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average c-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. methods: the acc team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. the prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. results: the acc admission and discharge models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (c-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. conclusions: the acc admission and discharge models embody the design qualities of ideal prediction models. the acc plans to continue its partnership to further improve and develop valuable clinical models. key words: 30-day all-cause hospital readmission, readmission risk stratification tool, predictive analytics, prediction model, derivation and external validation of a prediction model, clinical decision prediction model correspondence: ali.choudhry@cerner.com copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi introduction curbing the frequency and costs associated with hospital readmissions within 30 days of inpatient discharge is needed to improve the quality of health care services (1-3). hospitals are held accountable for care delivered through new payment models, with incentives for improving discharge planning and transitions of care to mitigate preventable readmissions (4, 5). consequently, hospitals must reduce readmissions to evade financial penalties by the centers for medicare & medicaid services (cms) under the hospital readmissions reduction program (hrrp) (6). in 2010, hospital referral regions (hrrs) in the chicago metropolitan area had higher readmission rates for medical and surgical discharges when compared with the national average (7), and were among the top five hrrs in illinois facing higher penalties (8). although penalizing high readmission rates has been debated since the introduction of the policy (9), there has been consensus on the need for coordinated and efficient care for patients beyond the hospital walls to prevent unnecessary readmissions. augmenting transitions of care during the discharge process and proper coordination between providers across care settings are key drivers needed to reduce preventable readmissions (10-12). preventing readmissions must be followed up with post-discharge and community-based care interventions that can improve, as well as sustain, the health of the population to decrease hospital returns. while several interventions have been developed that aim to reduce unnecessary readmissions by improving the transition of care process during and post-discharge (13-17), there is a lack of evidence on what interventions are most effective with readmission reductions on a broad scale (18). one approach to curtailing readmissions is to identify high risk patients needing effective transition of care interventions using prediction models (19). ideally, the design of prediction models should offer clinically meaningful discrimination ability (measured using the c-statistic); use reliable data that can be easily obtained; utilize variables that are clinically related; be validated in the populations in which use is intended; and be deployable in large populations (20). for a clinical prediction model, a c-statistic of less than 0.6 has no clinical value, 0.6 to 0.7 has limited value, 0.7 to 0.8 has modest value, and greater than 0.8 has discrimination adequate for genuine clinical utility (21). however, prediction models should not rely exclusively on the c-statistic to evaluate utility of risk factors (22). they should also consider bootstrapping methods (23) and incorporate additional performance measures to assess prediction models (24). research also suggests that prediction models should maintain a balance between including too many variables and model parsimony (25, 26). a systematic review of 26 hospital readmission risk prediction models found that most tools performed poorly with limited clinical value (average c-statistic of 0.66), about half relied on retrospective administrative data, a few used external validation methods, and efforts were needed to improve their performance as usage becomes more widespread (27). in addition, a few parsimonious prediction models were developed after this review. one was created outside the u.s. and yielded a c-statistic of 0.70 (28). the other did not perform external validation for geographic scalability and had a c-statistic of 0.71 (29). one of the major limitations of most prediction models is that they are mostly developed using administrative claims data. given the myriad of factors that can contribute to readmission risk, models should also consider including variables obtained in the electronic health record (ehr). a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi fostering collaborative relationships and care coordination with providers across care settings is needed to reduce preventable readmissions (18). care collaboration and coordination is central to the health information technology for economic and clinical health (hitech) act in promoting the adoption and meaningful use of health information (30). therefore, health care providers should also consider collaborating with information technology organizations to develop holistic solutions that improve health care delivery and the health of communities. advocate health care, located in the chicago metropolitan area, and cerner partnered to create optimal predictive models that leveraged advocate health care’s population risk and clinical integration expertise with cerner's health care technology and data management proficiency. the advocate cerner collaboration (acc) was charged with developing a robust readmission prevention solution by improving the predictive power of advocate health care’s current manual readmission risk stratification tool (c-statistic of 0.69), and building an automated algorithm embedded in the ehr that stratifies patients at high risk of readmission needing care transition interventions. the acc developed their prediction models taking into consideration recommendations documented in the literature to create and assess their models’ performance, and performed an external validation for generalizability using a heterogeneous population. while previous work relied solely on claims data, the acc prediction models incorporated patient data from the ehr. in addition, the acc team used a mixed-method approach to evaluate risk factors to include in the prediction models. objectives the objectives of this research project were to: 1) develop all-cause hospital readmission risk prediction models for utility at admission and prior to discharge to identify adult patients likely to return within 30-days; 2) assess the prediction models’ performance using key metrics; and 3) externally validate the prediction models’ generalizability across multiple hospital systems. methods a retrospective cohort study was conducted among adult inpatients discharged between march 1, 2011 and july 31, 2012 from 8 advocate health care hospitals located in the chicago metropolitan area (figure 1). a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 1. geographic location of 8 advocate health care hospitals an additional year of data prior to march 1, 2011, was extracted to analyze historical patient information and prior hospital utilization. inpatient visits thru august 31, 2012, were also extracted to account for any readmissions occurring within 30 days of discharge after july 31, 2012. encounters were excluded from the cohort if they were observation, inpatient admissions for psychiatry, skilled nursing, hospice, rehabilitation, maternal and newborn visits, or if the patient expired during the index admission. clinical data was extracted from cerner’s millennium® ehr software system and advocate health care’s enterprise data warehouse (edw). data from both sources was then loaded into cerner’s powerinsight® (piedw) for analysis. the primary dependent variable for the prediction models was hospital readmissions within 30days from the initial discharge. independent variables were segmented into 8 primary categories: demographics and social characteristics, hospital utilization, history & physical examination (h&p), medications, laboratory tests, conditions and procedures (using international classification of diseases, ninth revision, clinical modification codes icd-9 cm), and an exploratory group (figure 2). a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 2. acc readmission risk prediction conceptual model risk factors considered for analysis were based on literature reviews and a mixed-method approach using qualitative data collected from clinical input. qualitative data were collected from site visits at each advocate health care hospital through in-depth interviews and focus groups with clinicians and care mangers, respectively. clinicians and care mangers were asked to identify potential risk factors that caused a patient to return to hospital. field notes were taken during the site visits. information gleaned was used to identify emerging themes that helped inform the quantitative analyses. all quantitative statistical analyses were conducted using sas® version 9.2 (sas institute). descriptive and inferential statistics were performed on the primary variable categories to identify main features of the data and any causal relationships, respectively. the overall readmission rate was computed using the entire cohort. for modeling, one consecutive encounter pair (index admission and readmission encounter) was randomly sampled from each patient to control for bias due to multiple admissions. index encounters were restricted to a month prior to the study period’s end date to capture any readmissions that occurred within 30 days (figure 3). a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3. multiple readmission sampling methodology to develop and internally validate the prediction models, the cohort was then split into a derivation dataset (75%) and a validation dataset (25%). model fitting was calculated using bootstrapping method by randomly sampling two-thirds of the data in the derivation dataset. the procedure was repeated 500 times and the averaged coefficients were applied to the validation dataset. stepwise logistic regression was performed and predictors that were statistically significant using a p-value ≤ 0.05 were included in the model. two predictive models were developed: one at admission and one prior to discharge using readily available data for the patient. the admission prediction included baseline data available for a patient once admitted to the hospital. the discharge prediction model was more comprehensive, including additional data that became available prior to discharge. the performance of each prediction model was assessed by 3 measures. first, discrimination ability was quantified by sensitivity, specificity, and the area under the receiver operating characteristic (roc) curve, or c-statistic that measures how well the model can separate those who do and do not have the outcome. second, calibration was performed using the hosmerlemeshow (h&l) goodness-of-fit test, which measures how well the model fits the data or how well predicted probabilities agree with actual observed risk, where a p-value > 0.05 indicates a good fit. third, overall performance was quantified using brier’s score, which measures how close predictions are to the actual outcome. external validation of the admission and discharge prediction models were also performed using cerner’s healthfacts® data. healthfacts® is a de-identified patient database that includes over 480 providers across the u.s. with a majority from the northeast (44%), having more than 500 beds (27%), and are teaching facilities (63%). healthfacts® encompasses encounter level demographic information, conditions, procedures, laboratory tests, and medication data. a sample was selected from healthfacts® data consistent with the derivation dataset. the fit of both prediction models was assessed by applying the derivation coefficients, then recalibrating the coefficients with the same set of predictors and the coefficients by using the healthfacts® sample. the performance between models was then compared. a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi results a total of 126,479 patients comprising 178,293 encounters met the cohort eligibility criteria, of which 18,652 (10.46%) encounters resulted in readmission to the same advocate health care hospital within 30 days. after sampling, 9,151 (7.25%) encounter pairs were defined as 30-day readmissions. demographic characteristics of the sample cohort are characterized in table 1. table 1. demographic characteristics of the sample cohort demographic characteristics 30 day readmission no readmission n=9,151 (7.25%) n=117,328 (92.75%) age µ = 66.01 µ = 57.65 gender female 5,045 (55.13) 70,917 (60.44) male 4,106 (44.87) 46,411 (39.56) race caucasian 5,737 (62.69) 71,796 (61.19) african american 2,357 (25.76) 26,446 (22.54) hispanic 648 (7.08) 10,867 (9.26) other 409 (4.47) 8,219 (7.01) language english 6,851 (94.26) 141,624 (93.29) no english 417 (5.74) 10,187 (6.71) marital status married 3,771 (41.21) 58,159 (49.57) not married 5,380 (58.79) 59,169 (50.43) employment status employed 991 (10.83) 23,073 (19.67) not employed 4,930 (53.87) 46,973 (40.04) unknown 3,230 (35.30) 47,282 (40.30) insurance type commercial 2,828 (30.90) 56,286 (47.97) medicare 5,118 (55.93) 44,187 (37.66) medicaid 778 (8.50) 8,352 (7.12) self-pay 378 (4.13) 6,751 (5.75) other 49 (0.54) 1,752 (1.49) the acc admission model included 49 independent predictors: demographic, utilization, medications, labs, h&p, and exploratory variables. the acc discharge model included 58 independent predictors comprising all the aforementioned variables plus conditions, procedures, length of stay (los), and discharge disposition. the variables included in the acc admission and discharge prediction models are presented in table 2. a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table 2. acc admission and discharge prediction models’ variables variables admission model (n=49) discharge model (n=58) demographics   utilization   lab tests   exploratory   h&p   medications   conditions  procedures  length of stay  discharge disposition  assessment of the acc admission model’s performance yielded c-statistics of 0.76 and 0.75, h&l goodness-of-fit tests of 36.0 (p<0.001) and 23.5 (p=0.0027), and brier scores of 0.062 (7.6% improvement from random prediction) and 0.063 (6.6% improvement from random prediction) from the derivation and internal validation datasets, respectively. assessment of the acc discharge model’s performance yielded c-statistics of 0.78 and 0.77, h&l goodness-of-fit tests of 31.1 (p<0.001) and 19.9 (p=0.01), and brier scores of 0.060 and 0.061 (9.1% improvement from random prediction) from the derivation and internal validation datasets, respectively. the average c-statistic for the acc admission model was 0.76 and for the discharge model it was 0.78 after the 500 simulations in derivation dataset, resulting in a small range of deviation between individual runs. external validation of the acc admission and discharge model resulted in c-statistics of 0.76 and 0.78, h&l goodness-of-fit tests of 6.1 (p=0.641) and 14.3 (p=0.074), and brier scores of 0.061 (8.9% improvement from random prediction) and 0.060 (9.1% improvement from random prediction) after recalibrating and re-estimating the coefficient using healthfacts® data, respectively. the acc admission and discharge models’ performance measures are represented in table 3. a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table 3. acc admission and discharge prediction model’s performance measures dataset performance measures admission model discharge model derivation (n=94,859) discrimination c-statistic 0.76 0.78 calibration hosmer-lemeshow goodness-offit test (p-value) 36.0 (p<0.001) 31.1 (p<0.001) overall performance brier score (% improvement) 0.062 (7.6%) 0.060 (9.1%) bootstrapping 500 simulations average (min. to max.) 0.76 (0.75 to 0.76) 0.78 (0.77 to 0.78) internal validation (n=31,619) discrimination c-statistic 0.75 0.77 calibration hosmer-lemeshow goodness-offit test (p-value) 23.5 (p=0.003) 19.9 (p=0.01) overall performance brier score (% improvement) 0.063 (6.6%) 0.061 (9.1%) external validation (without recalibration) (n=6,357) discrimination c-statistic 0.69 0.71 calibration hosmer-lemeshow goodness-offit test (p-value) 216.9 (p<0.001) 156.3 (p<0.001) overall performance brier score (% improvement) 0.065 (2.5%) 0.064 (4.0%) external validation (with recalibration) (n=6,357) discrimination c-statistic 0.76 0.78 calibration hosmer-lemeshow goodness-offit test (p-value) 6.1 (p=0.641) 14.3 (p=0.074) overall performance brier score (% improvement) 0.061 (8.9%) 0.060 (9.1%) the probability thresholds for identifying high risk patients (11%), was determined by balancing the tradeoff between sensitivity (70%) and specificity (71%) by maximizing the area under roc curves for the prediction models (figure 4). a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 4. roc curves for acc admission & discharge model discussion we observed several key findings during the development and validation of our acc admission and discharge models. both our all-cause models performed reasonably better than most predictive models reviewed in the literature used to identify patients at risk of readmission (2729). both our models yielded a c-statistic between 0.7 and 0.8 during derivation, internal validation, and external validation after recalibration—a modest value for a clinical predictive rule. when comparing c-statistics between the admission (c-statistic of 0.76) and discharge models (c-statistic 0.78), the discharge model’s discrimination ability improved because conditions and procedures, los, and discharge disposition variables were included; which helped further explain a patient’s readmission risk since medical conditions and surgical procedures accounts for immediate health needs, los represents severity of illness, and discharge disposition to a post-acute setting that doesn’t meet their discharge needs could result in a return to the hospital. we also observed the same c-statistic for our acc discharge model on the development and external validation sample, suggesting that it performs well both in the intended population and when using a heterogeneous dataset. our acc discharge model also had a somewhat higher c-statistic during derivation when compared to the c-statistic observed during internal validation (c-statistic of 0.77), which is typically higher when assessing predictive accuracy using the derivation dataset to develop the model (21). our acc admission and discharge models also demonstrated reasonable model fit during external validation after recalibrating the coefficient estimates. a non-significant h&l p-value indicates the model adequately fits the data. however, caution must be used when interpreting h&l statistics because they are influenced by sample size (31). our models did not demonstrate adequate model fit during derivation and internal validation due to a large sample. yet during a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi external validation with a smaller sample, the h&l statistics for both the acc admission and discharge model improved to a non-significant level. since the h&l statistic is influenced by sample size, the brier score should also be taken into account to assess prediction models because it captures calibration and discrimination features. the closer the brier score is to zero, the better the predictive performance (24). both our prediction models had low brier scores, with the acc discharge model’s 0.06 representing consistent percent (9.1%) improvement over random prediction during derivation, internal validation, and external validation after recalibration. there was concern that too many independent variables would increase the possibility of building an over-specified model that only performs well on the derivation dataset. thus, validating a comprehensive model using an external dataset to replicate the derivation results would be challenging. our findings indicate that our model’s performance slightly diminished during the acc admission model’s external validation when compared with the more comprehensive acc discharge model. when we externally validated our acc admission model, the c-statistic was 0.74 on the development dataset, but reduced to 0.66 when using the initial derivation coefficients on the external dataset, and then increased to 0.70 after recalibrating the coefficients. the c-statistic for our acc discharge model decreased from 0.78 on the development dataset, to 0.71 using the unchanged derivation coefficients, and then increased back to 0.78 after recalibration using the external validation sample dataset. it is expected to see performance decrease from derivation to validation, but our models had no more than 10% shrinkage from derivation to the validation results (32). we further tested our acc admission model using only baseline data available for a patient (e.g., demographic and utilization variables). the c-statistic for a more parsimonious admission model was 0.74 on the development dataset, decreased to 0.66 when using the derivation coefficients on the external dataset, but then increased to 0.70 after recalibrating the coefficients. our findings suggest that including additional variables in the model is more likely to generalize better in comparison with a parsimonious model during external validation post-recalibration. overall, our admission and discharge models’ performance indicates modest discrimination ability. while other studies relied on retrospective administrative data, our models incorporated data elements from the ehr. we utilized a mixed-method approach to evaluate clinically-related variables. our models were internally validated in the intended population and externally validated for utility in heterogeneous populations. our admission model offers a practical solution with data available during hospitalization. our discharge model has a higher level of predictability according to the c-statistic and improved performance according to the brier score once more data is accessible during discharge. creating a highly accurate predictive model is multifaceted and contingent on copious factors, including, but not limited to, the quality and accessibility of data, the ability to replicate the findings beyond the derivation dataset, and the balance between a parsimonious and comprehensive prediction model. to facilitate external validation, we discovered that a compromise between a parsimonious and comprehensive model was needed when developing logistic regression prediction models. we also found that utilizing a mixed-method approach was valuable and additional efforts are needed when selecting risk factors that are of high-quality data, easily accessible, and generalizable across multiple populations. we also believe that a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi bridging statistical acumen and clinical knowledge is needed to further develop decision support tools of genuine clinical utility, by soliciting support from clinicians when the statistics does not align with clinical intuition. limitations our findings should be considered under the purview of several limitations. there might be additional research conducted on readmission risk tools developed after the systematic review performed by kansagara. additional readmission risk prediction models were developed (33, 34), but they did not publish their performance statistics to help us compare our prediction models. our readmission rate was limited to visits occurring at the same hospital. readmission rates based on same-hospital visits can be unreliable and dilute the true hospital readmission rate (35). one promising approach is using a master patient index (mpi) to track patients across hospitals. data is being linked across hospitals and to outside facilities through mpi and claims data. using our own method to create a mpi match, we performed some preliminary analysis and were able to identify 5% more readmissions across the other advocate health care hospitals, increasing the readmission rate by approximately 1%. we also assessed the utility of claims data to match the other hospitals with millennium® encounters to gauge a more representative readmission rate. the claims data allowed us to track approximately 8% more readmissions, increasing the readmission rate by approximately 1%. overall, using both approaches we were able to identify a more representative readmission rate that increased from 10.46% to 12.5%. we are currently working to see how this impacts our models’ performance. data captured through ehrs is growing, but are incomplete with respect to data relevant to hospital readmission prediction and the lack of standard data representations limits generalizability of predictive models (36). as a result, we could not include certain data elements into our models due to data quality issues, a large percentage of missing data, and since some of the information is difficult to glean. therefore, we could not include social determinants identified by clinicians and care managers during qualitative interviews such as social isolation (i.e., living alone) and living situation (e.g., homelessness) known to be salient factors and tied to hospital readmissions (37, 38). initially, we only mined a single source for this information in the ehr. however, new data sources have been identified in the ehr and the utility of these risk factors are currently being assessed in our prediction models. additional factors are also being considered in our models such, as functional status (37, 39), medication adherence and availability of transportation for follow-up visits post-discharge (40). our prediction models do not distinguish between potentially preventable readmissions (ppr) (41, 42). we did perform some preliminary analysis and found that the overall ppr readmission rate for advocate health care in 2012 was about 6% of all admissions. we estimated around 60% of all readmissions were deemed avoidable. this is higher than the median proportion of avoidable readmissions (27.1%), but falls within the range of 5% to 79% (43). we plan to further assess ppr methodology and test our models’ ability to recognize potentially avoidable readmissions to help intervene where clinical impact is most effective. a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi our initial analysis plan proposed to include observation patients (n=51,517) in the entire inpatient cohort. we performed some preliminary analysis and found the overall readmission rate increased to 10.72%, but the c-statistics for our admission and discharge models reduced to 0.75 and 0.77, respectively. our models’ discrimination ability probably diminished due to improved logic needed in making a distinction in situations where observation status changes to inpatient and vice-versa. further assessment of observation patients is needed to better understand their importance in an accountable care environment. steps are underway to mitigate limitations and continue to improve the clinical utility of our readmission risk prediction models. data is being linked across hospitals and to outside facilities through mpi and claims data. additional data sources in the ehr that encompass social determinants and other risk factors were identified are being assessed for use in our models. also, we are researching potentially preventable readmissions so that the models can focus on cases where clinical impact is most needed. conclusions the acc admission and discharge models exemplify design qualities of ideal prediction models. both our models demonstrated modest predictive power for identifying high-risk patients early during hospitalization and at hospital discharge, respectively. performance assessment of both our models during external validation post-recalibration indicates reasonable model fit and can be deployed in other population settings. our admission model offers a practical and feasible solution with limited data available on admission. our discharge model offers improved performance and predictability once more data is presented during discharge. the acc partnership offers an opportunity to leverage proficiency from both organizations to improve and continue in the development of valuable clinical prediction models, building a framework for future prediction model development that achieves scalable outcomes. corresponding author shahid a. choudhry, phd 1 clinical intelligence researcher, advocate cerner collaboration (acc) ali.choudhry@cerner.com acknowledgements mitali barman, ms 2 , technical architect, acc sam bozzette, md, phd 1 , physician executive, cerner research darcy davis, phd 2 , data scientist, acc cole erdmann, mba 1 , project manager, acc tina esposito, mba 2 vp, center for health information services (chis) zachary fainman, md 2 , director medical care management mary gagen, mba 2 , manager, business analytics, chis harlen d. hays, mph 1 , manager, quantitative research and biostatistics stephen himes, jd, phd, copy editor qingjiang hou, ms 1 , scientist, life science research a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi andrew a. kramer, phd 1 , senior research manager, critical care abu jing li, phd 1 , data analyst, acc douglas s. mcnair md, phd 1 , engineering fellow & president, cerner math bryan nyary, mba, medical editor samir rishi, bsrt, mha, lean 2 , clinical process designer, acc lou ann schraffenberger, manager, clinical data, chis rishi sikka, md 2 , vp, clinical transformation rajbir singh, ms 1 , engineer, acc bharat sutariya, md 1 , vp & cmo, population health xinyong tian, phd 2 , data analyst, acc luke utting, bs 1 , senior engineer, acc fran wilk, rn, bsn, ma 2 , clinical process designer, acc center for health information services team 2 advocate health care hospital site visit participants 2 references [1] jencks sf, williams mv, coleman ea. rehospitalizations among patients in the medicare fee-for-service program. new engl j med. 2009 apr 2;360(14):1418-28. 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[41] goldfield ni, mccullough ec, hughes js, ang am, eastman b, rawlins lk, averill rf. identifying potentially preventable readmissions. health care financ rev. 2008 fall;30(1):75–91. http://www.hhs.gov/ocr/privacy/hipaa/administrative/enforcementrule/hitechenforcementifr.html http://www.hhs.gov/ocr/privacy/hipaa/administrative/enforcementrule/hitechenforcementifr.html http://www.advisory.com/technology/crimson-real-time-readmissions/about-crimson-real-time-readmissions http://www.advisory.com/technology/crimson-real-time-readmissions/about-crimson-real-time-readmissions http://www-03.ibm.com/press/us/en/pressrelease/35597.wss http://www.uptodate.com/contents/hospital-discharge a public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [42] vest jr, gamm ld, oxford ba, gonzalez mi, slawson km. determinants of preventable readmissions in the united states: a systematic review. implement sci. 2010 nov 17;5:88. [43] van walraven c, bennett c, jennings a, austin pc, forster aj. proportion of hospital readmissions deemed avoidable: a systematic review. cmaj. 2011;183(7):391-402. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts syndromic surveillance based on emergency visits: a reactive tool for unusual events detection pascal vilain*1, arnaud bourdé2, pierre-jean marianne dit cassou3, yves jacquesantoine4, philippe morbidelli5 and laurent filleul1 1regional office of the french institute for public health surveillance of indian ocean, saint-denis, reunion; 2university hospital, saint-denis, reunion; 3university hospital, saint-pierre, reunion; 4hopital center, saint-benoît, reunion; 5hopital center, saint-paul, reunion objective to show with examples that syndromic surveillance system can be a reactive tool for public health surveillance. introduction the late health events such as the heat wave of 2003 showed the need to make public health surveillance evolve in france. thus, the french institute for public health surveillance has developed syndromic surveillance systems based on several information sources such as emergency departments (1). in reunion island, the chikungunya outbreak of 2005-2006, then the influenza pandemic of 2009 contributed to the implementation and the development of this surveillance system (2-3). in the past years, this tool allowed to follow and measure the impact of seasonal epidemics. nevertheless, its usefulness for the detection of minor unusual events had yet to be demonstrated. methods in reunion island, the syndromic surveillance system is based on the activity of six emergency departments. two types of indicators are constructed from collected data: qualitative indicators for the alert (every visit whose diagnostic relates to a notifiable disease or potential epidemic disease); quantitative indicators for the epidemic/cluster detection (number of visits based on syndromic grouping). daily and weekly analyses are carried out. a decision algorithm allows to validate the signal and to organize an epidemiological investigation if necessary. results each year, about 150 000 visits are registered in the six emergency departments that is 415 consultations per day on average. several unusual health events on small-scale were detected early. in august 2011, the surveillance system allowed to detect the first autochthonous cases of measles, a few days before this notifiable disease was reported to health authorities (figure 1). in january 2012, the data of emergency departments allowed to validate the signal of viral meningitis as well as to detect a cluster in the west of the island and to follow its trend. in june 2012, a family foodborne illness was detected from a spatio-temporal cluster for abdominal pain by the surveillance system and was confirmed by epidemiological investigation (figure 2). conclusions despite the improvement of exchanges with health practitioners and the development of specific surveillance systems, health surveillance remains fragile for the detection of clusters or unusual health events on small scale. the syndromic surveillance system based on emergency visits has proved to be relevant for the identification of signals leading to health alerts and requiring immediate control measures. in the future, it will be necessary to develop these systems (private practitioners, sentinel schools) in order to have several indicators depending on the degree of severity. figure 1. epidemic curve of measles cases figure 2. line-list of patient characteristics in an abdominal pain cluster. keywords syndromic surveillance; unusual event detection; reunion island acknowledgments we are thankful to all the practitioners of emergency departments. references 1. josseran l, nicolau j, caillère n, astagneau p, brücker g. syndromic surveillance based on emergency department activity and crude mortality: two examples. euro surveill. 2006;11(12):225-9. 2. d’ortenzio e, do c, renault p, weber f, filleul l. enhanced influenza surveillance on réunion island (southern hemisphere) in the context of the emergence of influenza a(h1n1)v. euro surveill. 2009;14(26). pii: 19257. 3. filleul l, durquety e, baroux n, chollet p, cadivel a, lernout t. the development of non-specific surveillance in mayotte and reunion island in the contexte of the epidemic influenza a(h1n1) 2009 [article in french]. bull epidemiolhebd. 2010:283. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e150, 2013 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluating usefulness of maine’s syndromic surveillance system for hospitals, 2012 stefanie devita* and amy robbins infectious disease epidemiology, maine cdc, augusta, me, usa objective to assess the usefulness and acceptability of maine’s syndromic surveillance system among hospitals who currently participate. introduction maine has been conducting syndromic surveillance since 2007 using the early aberration reporting system (ears). an evaluation of the syndromic surveillance system was conducted to determine if system objectives are being met, assess the system’s usefulness, and identify areas for improvement. according to cdc’s guidelines for evaluating public health surveillance systems, a surveillance system is useful if it contributes to the timely prevention and control of adverse health events. acceptability includes the willingness of participants to report surveillance data; participation or reporting rate; and completeness of data. methods results a weekly basis, including a statewide data summary, as useful. respondents also recommended that data be shared back with participants using 30-day line graphs for each syndrome (4 respondents). the three syndromes respondents found most useful were influenzalike illness (7 respondents), gastrointestinal (5 respondents), and respiratory (5 respondents). the three syndromes respondents found least useful were the broad heat syndrome (4 respondents), the narrow heat syndrome (4 respondents), and the other syndrome that captures all visits not classified into any syndrome (4 respondents). chief complaint data, which is used to classify emergency department visits into syndromes, is most often recorded by a drop-menu (4 respondents). conclusions with a low survey completion rate, it is difficult to generalize responses to all hospitals who participate in syndromic surveillance. hospitals that did not respond to or complete the survey will be followed up with to determine their reasons for not doing so, as this may be useful information. in general, those who responded have more factors that influence them to contribute to syndromic surveillance than factors that hinder them. most hospitals find the current method of sharing data back with the hospitals useful. also, it is advantageous to know which syndromes the hospitals find most useful, as they are the entities that collect and report the data. opinions differ among system users, which is why it is important to evaluate a system throughout all points of interaction. keywords syndromic surveillance; evaluation; acceptability acknowledgments the authors would like to acknowledge those hospital partners who completed the survey. references updated guidelines for evaluating public health surveillance systems. (2001) morbidity and mortality weekly report, 50(rr13);1-35. retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5013a1.htm *stefanie devita e-mail: stefanie.devita@maine.gov a survey was created in 2012 to measure usefulness and acceptability among hospital partners who submit emergency department data to maine cdc for syndromic surveillance. currently, 24 of maine's 37 emergency departments collect syndromic surveillance data and 20 of those receive a weekly syndromic surveillance report from maine cdc. the survey was included with the report on august 14, 2012, and hospitals were given two weeks for completion. the survey included questions about how useful hospitals find syndromic surveillance and how data is shared back with the hospitals; which syndromes are most and least useful; and chief complaint data collection at individual hospitals. the survey was completed by 13 out of 22 emergency departments (59% participation rate), and six out of 13 respondents (46%) completed the entire survey. the factors reported as having an influence on a hospital's decision to submit data for syndromic surveillance were: public health importance of events (6 respondents) and assurance of privacy/confidentiality (5 respondents). the majority of respondents (5 respondents) reported that there are no factors that limit their ability to send emergency department data. among hospitals that did report factors that limit their ability to send data, lack of information technology support in the hospital (2 respondents) and manually entering data/lack of electronic health records (1 respondent) were the most frequently reported. six out of seven hospitals who answered (86%) reported the current method of sharing syndromic surveillance data on online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e174, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of cholera and other diarrheal disease surveillance system, niger state, nigeria-2012 adebobola t. bashorun*1, anthony ahumibe1, saliman olugbon1, patrick nguku1 and kabir sabitu2 1nigeria field epidemiology and laboratory training program, fct, nigeria; 2ahmadu bello university, zaria, nigeria objective to determine how the cholera and other diarrheal disease surveillance system in niger state is meeting its surveillance objectives, to evaluate its performance and attributes and to describe its operation to make recommendations for improvement. introduction cholera causes frequent outbreaks in nigeria, resulting in mortality. in 2010 and 2011, 41,936 cases (case fatality rate [cfr]-4.1%) and 23,366 cases (cfr-3.2%) were reported (1). reported cases in nigeria by week 26, 2012 was 309 (cfr-1.29%) involving 20 local government areas in 6 states. in nigeria, there are currently eleven (11) states including niger state at high risk for cholera/bloodless diarrhea outbreaks. in 2011, niger state had 2472 cholera cases (cfr-2%) and 45,111 other diarrhea diseases cases, recorded in more than half of state purpose of surveillance system is to ensure early detection of cholera and other diarrheal cases and to monitor trends towards evidencebased decision for management, prevention and control. methods we conducted evaluation in july, 2012. we used cdc guideline on surveillance system evaluation (2001) as guide to assess operation, performance and attributes (2). we conducted key informant/in-depth interviews with stakeholders. we examined cholera action plans for preparedness and response, conducted laboratory assessment, extracted and analyzed cholera surveillance (2005-2012) for frequencies/proportions using microsoft excel. thematic analysis was done for qualitative data. we shared findings with stakeholders at all levels. results surveillance system was setup for early detection and monitoring towards evidence-based decision. state government funds system. case definition used is highly sensitive and is any patient aged 5 years or more who develops acute watery diarrhea, with/without vomiting. though simple case definition, laboratory confirmation makes surveillance complex. a passive system, active during outbreaks; has formal and informal sources of information and part of integrated disease surveillance and response (idsr) system and flow(fig.1). it takes 24-48 hours between outbreaks onset, confirmation and response. line list showed undefined/poorly labeled outcomes. of 2472 cases in 2011 1320 (49%) were found in line list. 2011 monthly data completeness was 75%. so far in 2012, 5(0.02%) of all diarrhea cases were cholera. system captures only age as sociodemographics. of 11 suspected cholera cases tested during 2011 epidemic, 7 confirmed as cholera (ppv-63%). of 3 rumours of cholera outbreaks (january 2011-july 2012), one (ppv-33%) was true. acceptability of system is high among all stakeholders interviewed. timeliness of monthly reporting was 68.7% (table 1). laboratory can isolate vibro cholerae isolation but has no cary blair transport medium and cholera rapid test kits. conclusions evaluation revealed that surveillance system is meeting its objectives by early detection and response to cholera outbreaks. system is simple, stable, flexible, sensitive with poor data quality, low ppv, fair laboratory capacity and moderate timeliness. we recommended electronic and internet-based reporting for timeliness and data quality improvement; and provision of laboratory consumables. table 1. summary of performance attributes of cholera surveillance system, niger state, 2012. keywords surveillance; evaluation; cholera; nigeria acknowledgments niger state ministry of health references 1. federal ministry of health, nigeria weekly epidemiology reports, ncdc/federal ministry of health – nigeria volume 1 no 1-52. 2011 2. centers for disease control and prevention. updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr 2001;50 (no. rr-13) *adebobola t. bashorun e-mail: bashogee@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e146, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts fsws typology and condoms use among hiv high risk groups in sindh, pakistan: a developing country perspective suleman m. otho*1, shazia perveen2 and qamar abbas1 1aga khan university hospital, karachi, pakistan; 2global funds for aids, tb and malaria, round 9, sindh aids control program, karachi, pakistan objective we aimed to determine the association of fsws typology with condom use among hiv high risk groups in sindh, pakistan introduction hiv is growing rapidly worldwide resulting in estimated 34 million population [1]. recently, its epidemic has spread in africa, latin america, and the caribbean, and most parts of asia [2]. according to antenatal sero surveillance study conducted in 2011 by agriteam canada, it’s prevalence in pakistan is <0.1 [3].focusing narrowly, its prevalence in sindh, (one of the provinces of pakistan) is similar in general population, but it is in the phase of concentrated epidemic (having more than 5% of prevalence in high risk groups)in vulnerable groups like idus and male sex workers and transgender [4]. sexual intercourse has been identified as major route especially in hiv high risk groups including male sex workers, female sex workers (fsws), transgender (hijras) and iv drug users. among them, fsws are at high risk because of unprotected sex and illicit drug use. their prevalence is found to be 30.7% in low and middle income countries [5]. south asia contributed with 12.63 lakh fsw in india only [6]. on the basis of their station of work, they are categorized into facility based (kothikhana, brothel or home) and mobile (street, mobile or beggars). they use different preventive measures including condom for their protection from hiv [7]. it varies with availability and access [8] . fsws typology have different cliental and mode of action, therefore, it important to explore the preventive methods. methods data was extracted from second generation surveillance, integrated behavioral and biological survey, round iv for hiv infection conducted by agriteam canada in partnership with national aids control program, pakistan in 2011. it was a cross sectional survey for high risk groups including fsws from pakistan. it was ethically approved by review board of the public health agency of canada and hope international’s ethical review board, pakistan. from sindh province, fsws based in karachi, sukkur and larkana were recruited. considering typology, they were categorized as mobile or facility based. after informed consent, socio-demographic and risk behavior were inquired. hiv was tested by elisa/eia and confirmed by western blot. data was analyzed on spss 19. continuous variables were expressed as mean±sd while categorical as frequency(%). logistic regression assessed the association of fsws typology with condoms use among hiv high risk groups. results out of 4567 high risk population, 1127 were identified as fsws. mean age was 26.9 years. most of them were facility based (72.8%) and 81.3% used condoms. typology, age, education, duration of involvement, number of client per day, number of paid oral sex per month, knowledge about sti and knowledge about drop in center were significantly associated with condom use among hiv high risk groups. conclusions majority of facility based fsws use condoms to prevent hiv infection. awareness and access to home based fsws should be increased. it may help in targeting and designing preventive strategies for them at government and mass level. keywords fsw; typology; condoms; hiv high risk groups; pakistan references world health organization. global summary of hiv/aids epidemic december 2010. accessed on june 19, 2012. url: http://www.who.int/ hiv/data/en/. 2. unaids. 2010. unaids report on the global hiv epidemic: 2010. accessed on june 19, 2012. url: http://www.unaids.org/globalreport/global_report.htm. 3. usaid/ pakistan: hiv/ aids health profile. accessed on june 19, 2012. url: http://www.usaid.gov/our_work/global_health/aids/ countries/asia/pakistan_profile.pdf. 4. enhanced hiv/aids control program sindh. data. accessed on june 19 2012. url: http://www.sacp.org.pk/data.php. 5. baral s, beyrer c, muessig k, poteat t, wirtz al, decker mr, et al. burden of hiv among female sex workers in low-income and middleincome countries: a systematic review and meta-analysis. lancet infect dis. 2012:doi:10.1016/s473-3099(12)70066-x. 6. annual report 2010-11. national aids control organization: department of aids control. accessed on june 19, 2012. url: http://www.nacoonline.org/upload/reports/naco%20annual%20report%202010-11.pdf. 7. joseph tfl, amy syt, tsui hs. the relationship between condom use, sexually transmitted diseases, and location of commercial sex transaction among male hong kong clients. aids: epidemiology & social. 2003;17(1):105-12. 8. morris cn, morris sr, ferguson ag. sexual behavior of female sex workers and access to condoms in kenya and uganda on the transafrica highway. aids behav. 2009 oct;13(5):860-5. *suleman m. otho e-mail: drotho@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e141, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts mental illness and co-morbid conditions: biosense 2008 2011 achintya n. dey*, anna grigoryan, soyoun park, stephen benoit and taha kass-hout dndhi, cdc, atlanta, ga, usa objective the purpose of this paper was to analyze the associated burden of mental illness and medical comorbidity using biosense data 20082011. introduction understanding the relationship between mental illness and medical comorbidity is an important aspect of public health surveillance. in 2004, an estimated one fourth of the us adults reported having a mental illness in the previous year (1). studies showed that mental illness exacerbates multiple chronic diseases like cardiovascular diseases, diabetes and asthma (2). biosense is a national electronic public health surveillance system developed by the centers for disease control and prevention (cdc) that receives, analyzes and visualizes electronic health data from civilian hospital emergency departments (eds), outpatient and inpatient facilities, veteran administration (va) and department of defense (dod) healthcare facilities. although the system is designed for early detection and rapid assessment of all-hazards health events, biosense can also be used to examine patterns of healthcare utilization. methods we used 4 years (2008 – 2011) of biosense civilian hospitals’ eds visit data to perform the analysis. we searched final diagnoses for icd-9 cm codes related to mental illness (290 – 312), schizophrenia (295), major depressive disorder (296.2 – 296.3), mood disorder (296, 300.4 and 311) and anxiety, stress & adjustment disorders (300.0, 300.2, 300.3, 308, and 309). we used biosense syndromes/sub-syndromes based on chief complaints and final diagnoses for comorbidity. for the purpose of this study, comorbidity was defined broadly as the co-occurrence of mental and physical illness in the same person regardless of the chronological order. the proportion was calculated as the number of mental health visits associated with comorbidity divided by the total number of mental illness relevant visits. we ranked the top 10 proportions of comorbidity for adult mental illness by year. results from 2008-2011, there were 4.6 million visits where mental illness was reported in the eds visits. average age of those reported mental illness was 44 years, 55% were women and 45% were men. more women were reported with anxiety (67%), mood (66%), and major depressive disorders (59%) than men; while men were reported more with schizophrenia (56%) than women (44%). the most common comorbid condition was hypertension, followed by chest pain, abdominal pain, diabetes, nausea & vomiting and dyspnea (table 1). ranks of injury, falls, headache and asthma were slightly variant by year. conclusions this study supports prior findings that adult mental illness is associated with substantial medical burden. we identified 10 most common comorbid condition associated with mental illness. the major limitation of this work was that electronic data does not allow determination of the causal pathway between mental illness and some medical comorbidity. in addition, data represents only those who have access to healthcare or those with health seeking behaviors. familiarity with comorbid conditions affecting persons with adult mental illness may assist programs aimed at providing medical care for the mentally ill. table1. rankings of comorbidity conditions reported in adults wiht episodes of mental illness in eds keywords ed visits; adult mental illness; medical comorbidity references [1] kessler rc, heeringa s, lacoma md et al individual and societal effects of mental disorders on earnings in the united states: results from the national comorbidity survey replication. am.j.psychiatry 2008; 165:703-11 [2] chapman dp, perry gs, strine tw. the vital link between chronic disease and depressive disorders. prev.chronic dis 2005;2;a14 *achintya n. dey e-mail: adey@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e59, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts loinc and snomed ct code use in electronic laboratory reporting—us, 2011 sanjaya dhakal*1, sherry l. burrer1, carla a. winston2, mathew miller3 and samuel l. groseclose4 1cdc/osels/phsipo, atlanta, ga, usa; 2department of veterans affairs, washington, dc, usa; 3mcking consulting, atlanta, ga, usa; 4cdc/ophpr/sphp, atlanta, ga, usa objective to examine the use of loinc and snomed ct codes for coding laboratory orders and results in laboratory reports sent from 63 non-federal hospitals to the biosense program in calendar year 2011. introduction monitoring laboratory test reports could aid disease surveillance by adding diagnostic specificity to early warning signals and thus improving the efficiency of public health investigation of detected signals. laboratory data could also be employed to direct and evaluate interventions and countermeasures, while monitoring outbreak trends and progress; this would ultimately result in better outbreak response and management, and enhanced situation awareness. since electronic laboratory reporting (elr) has the potential to be more accurate, timely, and cost-effective than reporting by other means of communication (e.g., mail, fax, etc.), elr adoption has been systematically promoted as a public health priority. however, the continuing use of non-standard, local codes or text to represent laboratory test type and results complicates the use of elr data in public health practice. use of structured, unique, and widely available coding system(s) to support the concepts represented by locally assigned laboratory test order and result information improves the computational characteristics of elr data. out of several coding strategies available, the office of the u.s. national coordinator for health information technology has recently suggested incorporating logical observation identifiers names and codes (loinc) for laboratory orders and systemized nomenclature of medicineclinical terms (snomed ct) codes for laboratory results to standardize elr. methods we assessed the use of loinc and snomed ct codes in laboratory data reported to biosense, a near real-time national-level, electronic syndromic surveillance system, managed by the centers for disease control and prevention. elr data reported by 63 non-federal hospitals to biosense in 2011 were analyzed to examine loinc and snomed ct use in coding laboratory orders and results. we used relma software, developed and distributed by regenstrief institute inc for identifying loinc codes. results in 2011, a total of 14,028,774 laboratory test order or result reports from 821,108 individual patients were reported from the 63 hospitals in 14 states. since, by design the biosense program monitors a select set of syndromes mainly representing infectious conditions, 94% of the total reports were microbiology test orders or results. seventy-seven percent of all test orders (n = 10,776,494) used loinc codes. of all test results with at least one value either in observation identifier (obx3) or observation value (obx5) segments of their health level 7 (hl7) elr message (n = 12,313,952), 81% had only loinc codes, 0.1% had only snomed codes, 7% had both loinc and snomed codes, and 12% used no codes. in total, 1,428 unique loinc and 608 unique snomed codes were used to describe the results, and 805 unique loinc codes were used to describe the orders. of the 608 unique snomed codes, 111 (18.3%) did not have corresponding loinc codes. fifty-one (46%) of these 111 snomed codes could have been matched to corresponding loinc codes based on the concept. however, our search for matching loinc codes in relma for certain snomed concepts indicated that loinc does not have codes for select types of laboratory test results, particularly qualifier (such as reactive, negative, and resistant) or structural (labia, urethra, and vagina) concepts. conclusions our analysis showed that the use of snomed ct codes for laboratory test results by non-federal hospitals reporting laboratory data to biosense was extremely limited. these hospitals more frequently used loinc codes than snomed ct in reporting test results. we found that a large percentage of test results with snomed ct codes could be represented by loinc codes that exactly or closely match snomed ct codes. using loinc codes to report both test order and results in these databases could increase the availability and use of laboratory data in public health and surveillance activities. however, to increase the sensitivity of the coding further, a small number of tests could benefit by using loinc along with snomed ct codes. evaluation of use of syndromic surveillance case definitions that incorporate laboratory result information is required to determine if it improves syndromic surveillance performance for enhanced outbreak detection or improved situation awareness. keywords loinc; snomed; laboratory reporting; elr *sanjaya dhakal e-mail: hgj2@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e130, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a grid based approach to share public health surveillance applications the r example kailah davis*1 and julio facelli1, 2 1department of biomedical informatics, university of utah, salt lake city, ut, usa; 2center of high performance computing, university of utah, salt lake city, ut, usa objective this poster describes an approach which leverages grid technology for the epidemiological analysis of public health data. through a virtual environment, users, particularly epidemiologists, and others unfamiliar with the application, can perform on-demand powerful statistical analyses. introduction currently, there’s little effective communication and collaboration among public health departments. the lack of collaboration has resulted in more than 300 separate biosurveillance systems (1), which are disease specific, not integrated or interoperable, and may be duplicative (1). grid architecture is a promising methodology to aid in building a decentralized health surveillance infrastructure because it encourages an ecosystem development culture (2), which has the potential to increase collaboration and decrease duplications. methods this project had two major steps: creation and validation of the grid service. for the first step [creation of the service], we first determined the parameter set required to execute r from the command line. we then used the cagrid introduce toolkit (3) and grid rapid application virtualization interface (gravi) (4) to wrap the r command line interface into a grid service. the service was then deployed to the cagrid training grid. after deployment, the service was invoked using the r grid service client which was automatically created by introduce and gravi. our second step was aimed at validating the service by using using the grid service client to illustrate the working principles of r in a grid environment. for this illustration, we selected the article by hohle et al (5). in this article, the ‘surveillance’ package was developed to provide different algorithms for the detection of aberrations in routinely collected surveillance data. for validation purposes, only a subset of the analyses presented in the article, namely the farrington and cusum algorithms, were reproduced. using the grid web client, we uploaded the necessary data files for processing, as well as the rscript which was used to replicate the results of (5). the application then ran the r script on the execution machine; this machine had all the necessary r packages needed for the specific scenario. results the implementation of was validated by showing that the results of the original paper can be reproduced using gird based version of r. figure 1 shows the plots related to the steps described above; the plots illustrating the farrington and cusum algorithms are seen to be identical to that in (5). conclusions we demonstrated that it is possible to easily deploy applications for public health surveillance uses. we conclude that the techniques we used could be generalized to any application that has a command line interface. future work will be aimed creating a workflow to access data services and grid-enabled text processing and analytic tools. we believe that by providing a set of examples to demonstrate the benefit of this technology to public health surveillance infrastructure may provide insight that may lead to a better, more collaborative system of tools that will become the future of public health surveillance. fig 1. recreated plots keywords grid computing; public health grid; analytical service acknowledgments this work was supported by nlm training grant #t15lm007124 and cdc center of excellence for public health informatics # 1p01hk000069-10. references 1. subcommittee nba. improving the nation’s ability to detect and respond to 21st century urgent health threats: first report of the national biosurveillance advisory subcommittee. 2009. 2. facelli jc. an agenda for ultra-large-scale system research for global health informatics. acm sighit record. 2012;2(1):12-. 3. hastings s, oster s, langella s, ervin d, kurc t, saltz j. introduce: an open source toolkit for rapid development of strongly typed grid services. journal of grid computing. 2007;5(4):407-27. 4. chard k, tan w, boverhof j, madduri r, foster i, editors. wrap scientific applications as wsrf grid services using gravi. 2009: ieee. 5.höhle m, mazick a. aberration detection in r illustrated by danish mortality monitoring. biosurveillance: methods and case studies. 2010:215-37. *kailah davis e-mail: kailah.davis@utah.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e135, 2013 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 1public health england, birmingham, united kingdom; 2warwick university, coventry, united kingdom objective to develop smoothing techniques for daily syndromic surveillance data that allow for the easier identification of trends and unusual activity independent of day of the week and holiday effects. introduction real-time syndromic surveillance requires daily surveillance of a range of health data sources. most real-time data sources from health care systems exhibit large day of the week fluctuations as service provision and patient behaviour varies by day of the week. regular day of the week effects are further complicated by the occurrence of public holidays (usually 8 per year in england), which can limit the availability of certain services and affect patient behaviour. simple seven day moving averages fail to provide a smoothed trend around public holidays and can lead to false alarms or potentially delays in detection of outbreaks. methods data were used from four national syndromic surveillance systems (a non-emergency medical number, emergency department records, and information from family doctor in hours and unscheduled care consultations) coordinated by public health england. day of the week effects were modelled in the absence of public holidays by calculating the percentage of a week’s activity that occurs on each day of the week for a range of different syndromic indicators and syndromic surveillance systems. simple statistical t-tests were used to check for the significance of differences between days of the week. syndromic data were examined to test how public holidays impacted on day of the week effects. days immediately preceding and following holidays were also examined to identify any significant changes. differences between public holidays based on the time of year and the number of holidays within a single year were also examined. smoothing techniques for different syndromic systems were developed to remove artificial spikes around public holidays in simple seven day moving averages. results the impact of day of the week effects were found across all syndromic surveillance systems. unsurprisingly gp in hours data reported very little activity at weekends and on holidays, whilst gp unscheduled care and telephone help line data activity was roughly double at weekends and holidays. day of the week effects were much less clear in emergency department attendances but significant differences were still demonstrable. public holiday activity was similar to weekend activity in most cases, although activity on 25th december, christmas day was considerably less than other holidays. evidence was seen both of increased activity immediately prior to public holidays and on the first working day after a public holiday. seven day moving averages that are adjusted for bank holidays were shown to be effective in smoothing out artificial spikes in data. conclusions improved understanding of day of the week and public holiday effects enables improved modelling of baselines used in statistical detection algorithms, for instance 25th december should not be treated as having the same impact as other public holidays. future work will also consider condition-specific differences where the case mix varies by day of the week and during holidays. these improved smoothing techniques have enabled improved data visualization tools, enabling investigators to easily identify unusual activity during daily surveillance. example of improved smoothing for daily rates of herpes zoster consultations. keywords surveillance; visualisation; smoothing acknowledgments we acknowledge support from: royal college of emergency medicine, eds participating in the emergency department system (edsss), ascribe ltd and l2s2 ltd; ooh providers submitting data to the gpoohss and advanced heath & care; tpp and participating systmone practices and university of nottingham, clinrisk, emis and emis practices submitting data to the qsurveillance database; and nhs 111 and hscic for assistance and support in providing the anonymised call data that underpin the remote health advice syndromic surveillance system. we thank the phe real-time syndromic surveillance team for technical expertise. the authors received support from the national institute for health research health protection research unit in emergency preparedness and response. the views expressed are those of the authors and not necessarily those of the nhs, the nihr, the department of health or public health england. *roger morbey e-mail: roger.morbey@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e69, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts challenges and opportunities in routine time series analysis of surveillance data isabelle devaux*1, esther kissling2, gilles desve2, frantiska hruba1, francisco luquero2, chantal quinten1, joana gomes-diaz1, marta valenciano2 and denis coulombier1 1ecdc, solna, sweden; 2epiconcept, paris, france objective to discuss challenges and opportunities in the introduction of an automated approach for time series analysis (tsa) regarding epidemiological methodology for generation of hypotheses, steps to be performed and interpretation of outputs. introduction ecdc long term strategies for surveillance include analysis of trends of communicable disease of public health importance for european union countries to guide public health actions. the european surveillance system (tessy) holds data on 49 communicable diseases reported by 30 countries for at least the past five years. to simplify time related analysis using surveillance data, ecdc launched a project to enable descriptive and routine tsa without the need for complex programming. methods protocols for tessy data were developed specifying hypotheses to be tested, types and format of variables needed for tsa for several diseases, including vtec, and legionellosis. stata scripts were developed to comply with the basic steps of tsa, including data aggregation, data checking, data description, analysis of trends and seasonality, residual analysis, simple modelling and long-term forecasting. tsa steps were presented as successive tabs in a tsa dialogue box in stata. before using the stata tsa dialogue box, experts were offered a two-day training, and provided with an in-depth manual supporting use and interpretation of tsa outputs using the stata tsa dialogue box. results for vtec, it was possible to identify a small increase in the trend and a seasonal pattern in surveillance data with an estimate of the start of the increased risk for infection in the beginning of the summer season [1]. for legionellosis, an increasing trend in the number of reported cases was observed in 2010 [2]. feedback from the training showed that using the stata tsa dialogue box enables a quick exploratory analysis even by non-stata users who could focus on interpretation of results, rather than the programme writing. however, we emphasise that statistical knowledge of tsa as well as rigorous preparation of the datasets (including data quality checks) and generation of hypotheses, are essential to ensure appropriate analysis and meaningful interpretation of the results. conclusions using the stata tsa dialogue box saves time when performing rapid exploratory tsa of epidemiological data, avoiding the need for complex programming which is still needed for sophisticated tsa. results of exploratory tsa analysis can trigger new hypothesis, for more advanced and sophisticated tsa. the introduction of a new technology (stata tsa dialogue box) does not replace multi-disciplinary approach, knowledge and application of a methodological approach to tsa to produce meaningful results that can inform public health decision making. further testing and training will be performed to enhance simplicity before appropriate dissemination of the stata tsa dialogue box for a wider use. keywords surveillance; epidemiology; statistical model; data analysis; software tool acknowledgments ecdc experts in food and water-borne diseases (angela lahuerta-marin, taina niskanen, johanna takkinen, therese westrell), and legionnaires’ disease (julien beaute). references [1] joana gomes dias, franti!ka hrubá, chantal quinten, bruno ciancio, isabelle devaux, taina niskanen, therese westrell, angela lahuertamarin, johanna takkinen. time-series analysis of vtec/stec surveillance data, 2008–2010. poster to be presented in escaide (www.escaide.org) 24-26 october 2012. [2] julien beaute, birgita de jong. time series analysis of communityacquired legionnaires’ disease in europe. poster to be presented in escaide (www.escaide.org) 24-26 october 2012. *isabelle devaux e-mail: isabelle.devaux@ecdc.europa.eu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e182, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts surveillance for radiation-related exposures reported to the national poison data system royal k. law*1, colleen martin1, alvin bronstein2, arthur chang1 and joshua schier1 1centers for disease control and prevention, chamblee, ga, usa; 2american association of poison control centers, alexandria, va, usa objective to describe radiation-related exposures of potential public health significance reported to the national poison data system (npds). introduction for radiological incidents, collecting surveillance data can identify radiation-related public health significant incidents quickly and enable public health officials to describe the characteristics of the affected population and the magnitude of the health impact which in turn can inform public health decision-making. a survey administered by the council of state and territorial epidemiologists (cste) to state health departments in 2010 assessed the extent of state-level planning for surveillance of radiation-related exposures and incidents: 70%–84% of states reported minimal or no planning completed. one data source for surveillance of radiological exposures and illnesses is regional poison centers (pcs), who receive information requests and reported exposures from healthcare providers and the public. since 2010, the centers for disease control and prevention (cdc) and the american association of poison control centers (aapcc) have conducted ongoing surveillance for exposures to radiation and radioactive materials reported from all 57 united states (us) pcs to npds, a web-based, national pc reporting database and surveillance system. methods we collaborated with the american association of poison control centers (aapcc), poisindex® and thomson reuters healthcare to develop an improved coding system for tracking radiation-related exposures reported to us pcs during 2011 and trained pc staff on its usage. we reviewed npds data from 1 september 2010 – 30 june 2012 for reported exposures to pharmaceutical or nonpharmaceutical radionuclides; ionizing radiation; radiological or nuclear weapons; or x-ray, alpha, beta, gamma, or neutron radiation. cdc medical toxicology and epidemiology staff reviewed each reported exposure to determine whether it was of potential public health concern (e.g. exposures associated with an ongoing public health emergency, several reported exposures clustered in space and time). when further information was needed to classify the potential public health importance of a call, cdc and aapcc staff contacted the regional pc where each call originated. when exposures were spatially and temporally clustered, we reviewed news stories in the public media for evidence of an associated radiation incident. results of 419 exposures reported during the study period, 25 were associated with a radiation-related incident. of these, 4 were related to an exposure to x-ray radiation from an industrial radiography incident, 11 were related to a transportation accident involving potential contamination with radioactive material, and 10 were related to the fukushima daiichi japan nuclear reactor disaster. public health, hazardous materials, or hospital radiation safety staff were involved in responding to each of these events. we also identified 26 reported exposures associated with a regional radiation anti-terrorism exercise. the reported exposures were followed-up and removed from analysis once we determined they were part of the exercise. the remaining (n=368; 88%) were either requests for information, confirmed non-exposures, or exposures deemed unrelated or non-significant. conclusions the capability to monitor selfor clinician-reported exposures to radiation and radioactive materials is available in npds for state and local public health use in collaboration with their regional pc and may improve public health capacity to identify and respond to radiological emergencies. next steps include testing the system’s capability to accurately classify and rapidly respond to a cluster of calls to pcs reporting radiation exposures associated with a “dirty bomb” exercise during july, 2012. keywords surveillance; poison center; radiation references council of state and territorial epidemiologists. the status of state-level radiation emergency preparedness and response capabilities, 2010. available at: http://www.cste.org/webpdfs/2010raditionreport.pdf. accessed july 19, 2012. *royal k. law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e96, 2013 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 92 (page number not for citation purposes) isds 2013 conference abstracts antibiotic sensitivity and clinical outcome for methicillin resistant staphylococcus aureus dennis o. laryea*1, yaw a. amoako2 and joyce asamoah1 1public health unit, komfo anokye teaching hospital, kumasi, ghana; 2department of medicine, komfo anokye teaching hospital, kumasi, ghana � �� �� �� � � 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��������� ����������� �� ����� ��"������ � ���������������� ��� ������������� ��������� � � �!�1�������� ������� �������"����� � � �� �� ������"����������������������������*�����!�� ���"������"���� ���� ������������������������� ����������������*��)������"� ��� ������&������ ����� ����� ������� � � ������ ��������������� ��� ����� "���� ��������� ������������!� ��������� �������� ���� �� ������������ �������� ���������������� ���� ���"���� �������������"��"������������� �� �� ��� ����+�!������� ���� ��� �������� ����� ��������� �"� �� �����"���������������������� ����������������� ���3���� � �>�� !�(��� ������������� ������� ����������� �������������������������"��������������"���� ����� ����! keywords ����b�%����b������ ��� �b������ ���� acknowledgments (�� ���� ����+�� ��"�� ���� ������� ���*��������+����������"�)��� �� � �d�� ���)�� ��>�� ����� ����������� �"�� ����� ��� references 0!� (����� �+�&� @b� )������&� eb� %��"���&� -@b� � ���&� �b� ��� ��&� �b� @���+��� ��"���-�� �� 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public health 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 the last mile: using fax machines to exchange data between clinicians and public health stephen m. downs, md, ms, 1,2 vibha anand, phd, 1.2 , meena sheley, 1 bs, shaun j. grannis, md, ms 2 1 children’s health services research, indiana university 2 regenstrief institute, inc. indianapolis, in abstract there is overlap in a wide range of activities to support both public health and clinical care. examples include immunization registries (ir), newborn screening (nbs), disease reporting, lead screening programs, and more. health information exchanges create an opportunity to share data between the clinical and public health environments, providing decision support to clinicians and surveillance and tracking information to public health. we developed mechanisms to support two-way communication between clinicians in the indiana health information exchange (ihie) and the indiana state department of health (isdh). this paper describes challenges we faced and design decisions made to overcome them. we developed systems to help clinicians communicate with the isdh ir and with the nbs program. challenges included (1) a minority of clinicians who use electronic health records (ehr), (2) lack of universal patient identifiers, (3) identifying physicians responsible for newborns, and (4) designing around complex security policies and firewalls. to communicate electronically with clinicians without ehrs, we utilize their fax machines. our rule-based decision support system generates tailored forms that are automatically faxed to clinicians. the forms include coded input fields that capture data for automatic transfer into the ihie when they are faxed back. because the same individuals have different identifiers, and newborns’ names change, it is challenging to match patients across systems. we use a stochastic matching algorithm to link records. we scan electronic clinical messages (hl7 format) coming into ihie to find clinicians responsible for newborns. we have designed an architecture to link ihie, isdh, and our systems. key words: newborn, screening, informatics, public health, immunization, registry health information exchange introduction a major challenge for public health informatics is facilitating the exchange of information between public health and clinical care. data in public health information systems often come the last mile: using fax machines to exchange data between clinicians and public health 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 from forms filled out by hand, which are later computer-coded. even when reporting is electronic, initial data entry is typically still manual. as a result reportable diseases and conditions may be underreported. 1 data need to flow automatically to public health from clinical environments. when these data are appropriately compiled by public health information systems, they can allow more rapid and accurate assessments and disease control responses, as well as the formulation of improved clinical guidelines and interventions. conversely, automated presentation to clinicians of prevention guidelines has been shown to improve clinical care. 2 there are numerous ways in which the skills and activities of the public health community could benefit clinical care. electronic information sharing is a means by which we believe public health and clinical care activities can be integrated. 3 this is especially important in children, who undergo a series of preventive and therapeutic health interventions and activities that are relevant to public health, including newborn screening, immunizations, and lead screening. typically, each activity includes collection and submission of data to a dedicated public health system. 4 programs that can integrate decision support and data capture into the clinical workflow are likely to improve these shared functions. for example, newborn screening programs improve outcomes and are among the most costeffective (even cost saving) strategies in the healthcare system. 5 effective newborn screening programs consist of not only the screening tests, but also confirmation and assurance of appropriate treatment and follow-up of identified conditions. primary care physicians play an important role in the success of the newborn screening system and should have appropriate ongoing involvement in follow-up and tracking efforts. most pediatricians believe that primary care physicians should be responsible for informing families about a positive newborn screen, arranging confirmatory testing, and coordinating subspecialty referral. however, many do not feel competent to discuss conditions included in newborn screening panels, 6 which emphasize the need for decision support. likewise, immunizations are one of the most successful and effective public health tools for preventing disease, disability, and death from preventable disease. 7 the american academy of pediatrics (aap) and the advisory committee on immunization practices (acip) of the centers for disease control and prevention (cdc) have provided national standard guidelines for immunization and have constantly updated them since 1997. 8 to maximize vaccination rates, immunization registries have been developed in most states to track the complex series of vaccines that may be delivered by a variety of providers. however, registries never really reach their potential unless they are utilized by the vast majority of clinicians who provide immunizations. while 75% of children have vaccine data in registries, and registries are widespread and capable of sharing data, only a minority of providers routinely access them, 9 largely because only 3% of pediatricians use electronic medical records (emr) that are both fully functional and pediatric-appropriate. 10 this may be because so few systems are specifically designed with pediatric needs in mind. even among those who have an emr, most of their systems do not provide clinical decision support. one survey of 1000 primary care pediatricians 11 found only 21% used electronic health records. among those that had emr, a paltry 49% have preventive services prompts, and only 33% provided alerts for abnormal results. pediatricians who did not use emrs cited two the last mile: using fax machines to exchange data between clinicians and public health 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 significant barriers to emr implementation. the first, cost, was identified by 94% and was especially problematic for small practices. interestingly, 58% perceived no potential improvement in care from an emr. clearly there is a need for electronic communications between public health systems and clinicians. moreover, clinicians would benefit from decision support in the areas of immunization forecasting and management of conditions detected by newborn screening. these needs are reflected in the “meaningful use” rules set forth by the office of the national coordinator for health information technology. 12 however, until there is more widespread adoption of fully functional emr technology in pediatrics practices, other strategies of connecting public health to pediatric practitioners will be needed. in indiana, we are leveraging a statewide health information exchange 13 to explore methods for connecting public health systems and decision support to clinicians who may have no technology more elaborate that a fax machine. methods our approach builds on two initiatives – the indiana health information exchange (ihie) and the child health improvement through computer automation system (chica). by monitoring messages entering ihie from the state health department and from the health systems in the exchange, we are able to link clinicians with public health. using rule based logic, our system can deliver decision support to both clinicians and the health department. finally, using a tailored scannable paper as an interface with optical character recognition (ocr) and optical mark recognition (omr), we can capture coded data from clinicians with nothing more than a fax machine. ihie and the indiana network for patient care the indiana health information exchange operates the nation’s largest health information exchange, partnering with communities throughout indiana. 14 ihie connects hospitals, rehabilitation centers, long term care facilities, laboratories, imaging centers, clinics, community health centers and other healthcare organizations. data are transmitted from these sources by hl7 messaging and stored in separate files within one data repository known as the indiana network for patient care (inpc). 13 the inpc receives data from 70 hospitals and 18,000 physicians. it contains data from over 11 million patients with almost 24 million patient registrations and 3.8 billion clinical results. 14 ihie operates a results delivery system, docs4docs, 15 which has delivered over 77 million clinical results to participating clinicians. these results can be delivered to an emr, to a secure electronic mailbox or to a fax machine. chica and adaptive turnaround documents to leverage the health information exchange and the results delivery system, we needed a decision support engine that could interpret inbound hl7 messages from different sources and generate appropriate information to deliver to the health department and the appropriate clinician. we started with the framework of the child health improvement through computer automation (chica) system. 16 the last mile: using fax machines to exchange data between clinicians and public health 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 chica is a clinical decision support system for pediatric primary care that has been running in pediatric clinics in indiana for seven years. the system has an hl7 message processor that allows it to communicate with its underlying emr, the regenstrief medical record system (rmrs). it is built on an open source medical record framework known as openmrs. 17, 18 chica has a logic module that interprets rules encoded in arden syntax. 19 these rules (or medical logic modules, mlms) can extract and interpret data in the underlying medical record and write new data into the record. one of chica’s most unusual features is its user interface, which consist of tailored, scannable forms printed on paper. we refer to these forms as adaptive turn around documents (atad). atad forms are printed and scanned using the teleform desktop suite version 10.x (www.verity.com) that includes an automerge publisher, an ocr/omr reader and a verifier. the automerge publisher populates a form template (to be completed by the patient or physician) with patient specific questions or prompts that have been generated by the system with arden mlms. the forms are then printed by a standard laser printer. data entry is achieved by scanning a patient questionnaire form (completed by the family in the waiting room) or the physician worksheet using a standard document scanner. the system also stores a tiff image of the scanned forms. the teleform reader interprets the handwritten numbers and checkbox responses. enhancing the state newborn screening program the atad model allows data to be captured from facsimile (fax) documents as well as scanned documents. with this capability, we wanted to test the feasibility of using atads to link newborn screening (nbs) programs, subspecialists and the medical home. the system is intended to enhance the nbs process in three ways. first, it provides just-in-time information to the medical home (physicians and families). second, it can prevent missed opportunities to screen by identifying children with medical encounters anywhere in the state for whom no nbs result is known. finally, it would facilitate long term tracking of children with identified conditions. the approach utilizes ihie, docs4docs and chica in the following steps. first, we expanded the basic chica architecture with a fax server (faxpress 2500, opentext, bellevue, wa, faxsolutions.opentext.com), a device that enables us to send and receive faxes from any device on the network. thus, the fax server can receive a completed atad from a fax machine anywhere and render it as a tiff image that the teleform software can interpret just as it does the scanned images in the basic chica implementation. the newborn screening laboratory, under contract with the isdh, conducts metabolic screening on dried blood spots, compiles the results, combines them with newborn hearing screening results, and sends them to isdh. we have worked with isdh to establish an hl7 version 2.x standard for packaging the results in messages that can be sent to the inpc. these hl7 messages are captured by our new born screening system (nbss), a software instantiation similar to chica. the hl7 message is parsed, and a stochastic matching algorithm (see below) matches the message against records in the openmrs database. if the result matches an existing patient, the http://www.verity.com/ the last mile: using fax machines to exchange data between clinicians and public health 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 record is appended. if it is a new patient, a new record is created. early manual testing of this stochastic algorithm show it to be highly reliable, and the production system does not require manual checking. often, duplicate hl7 messages are sent. our system recognizes and eliminates these in two steps. first, a numeric hash function is applied to each hl7 message. then the resulting hash value is compared among messages, and exact duplicates are eliminated. when an hl7 message arrives, the system invokes the arden syntax rules to interpret the nbs results contained in the message. if the results are abnormal and require further diagnosis and treatment or if the specimen is inadequate and the screen needs to be repeated, chica generates an atad. (figure 1) these atads incorporate the american college of medical genetics action sheets. 20 these sheets provide information about the condition, confirmatory diagnosis, and emergency management. among the biggest challenges nbs programs face is the identification of the physician who has responsibility for an identified child. another challenge is finding children in the community who have not been screened. to meet these challenges, the system parses all incoming hl7 messages related to any child under the age of one month anywhere in the inpc. these may include admission or discharge messages, laboratory result messages, etc. each of these messages is parsed and matched against our database of nbs results. if the child is identified in the system, the arden rules determine if there was a normal newborn screen result in the database. if so, no further action is needed. if there is an abnormal or missing result, an appropriate atad is generated. matching records presented a challenge because data identifying newborns is highly variable among systems and across time. for example, at birth a child’s name may be recorded as “baby boy smith” because his mother’s name is smith. however, in a week’s time, the name may be “matthew jones, jr.,” after his father. to match such variable records across systems requires a matching algorithm that is more sophisticated than most. we utilize a probabilistic matching algorithm developed in our group. 21 the matching algorithm is based on the information theory concept of entropy and utilizes patient identifying data in the patient identification (pid) and next of kin (nk1) segments of the hl7 messages. the pid segment includes information about the patient such as name, date of birth, sex, race, address and phone number. the nk1 segment contains similar information about the baby’s mother. our work shows that comparing these using a statistical algorithm produces highly accurate matches even when some data are missing or changed. 21 the last mile: using fax machines to exchange data between clinicians and public health 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 1: an example of an adaptive turnaround document about a child with an abnormal newborn screen delivered to a primary care clinician using the docs4docs system jennifer d. patient dob: 14 may 2007 newborn screening alert: elevated c8 with lesser elevations of c6 and c10 acylcarnitine suggestive of medium-chain acyl-coa dehydrogenase (mcad) deficiency condition description: mcad deficiency is a fatty acid oxidation (fao) disorder. fao occurs during prolonged fasting and/or periods of increased energy demands (fever, stress) when energy production relies increasingly on fat metabolism. in an fao disorder, fatty acids and potentially toxic derivatives accumulate because of a deficiency in one of the mitochondrial fao enzymes. medical emergency take the following immediate actions:  contact family to inform them of the newborn screening result and ascertain clinical status (poor feeding, vomiting, lethargy).  consult with pediatric metabolic specialist.  evaluate the newborn (poor feeding, lethargy, hypotonia, hepatomegaly). if signs are present or infant is ill, initiate emergency treatment with iv glucose. transport to hospital for further treatment in consultation with metabolic specialist. if infant is normal initiate timely confirmatory/diagnostic testing, as recommended by specialist.  educate family about need for infant to avoid fasting. even if mildly ill, immediate treatment with iv glucose is needed.  report findings to newborn screening program. diagnostic evaluation: plasma acylcarnitine analysis will show elevated octanoylcarnitine (c8). urine acylglycine will show elevated hexanoylglycine. diagnosis is confirmed by mutation analysis of the mcad gene. please check all of the following that apply: [ ] family contacted [ ] newborn clinical status assessed [ ] problems (poor feeding, vomiting, lethargy, hypotonia, hepatomegaly)  [ ] treated with iv glucose [ ] infant stable [ ] family provided attached educational materials diagnostic evaluation [ ] plasma acylcarnitine sent [ ] referral made to metabolic center [ ] family could not be contacted [ ] this is not my patient the last mile: using fax machines to exchange data between clinicians and public health 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 linking practices to the immunization registry a second application of the chica model to link clinicians to public health systems is the chica immunization assistant (chia). chia helps facilitate participation in an immunization information system (iis or registry) by clinicians who lack emr systems or access to the internet (because of technical or time constraints). as with the nbs module, the concept is to provide an interface that requires minimal technology – a fax machine – and will fit into the clinic workflow. the isdh has a well developed statewide iis known as the children and hoosier immunization registry project (chirp). chirp offers a web application that allows enrolled users to search for patients, view vaccination records, and add or edit patient records and vaccination records. (https://chirp.in.gov) chirp captures immunization data from all the indiana health departments and from medicaid claims. unfortunately, use of chirp by pediatric practitioners has not been high. chirp provides an hl7 interface for ehr systems. 22 however, because of the low penetration of comprehensive ehrs among pediatricians, 10 a new solution was needed. chia serves as a “low tech” front end to chirp. equipment required by the practice to use chia will be only a fax machine and scannable forms. in order for a physician to enroll a patient through chia, a form is completed and faxed. this form has spaces for the patient’s name, date of birth, mother’s name, address, phone number, social security number, and membership number in the us department of agriculture women, infants and children (wic) program. the clinic completes whatever fields on the form are available and faxes it to the chia toll-free number. the fax is received by the chia fax server, and the coded fields from the faxed form are extracted and read using ocr by teleform into the system’s data tables. ocr accuracy is heavily dependent on handwriting, fax quality, and whether the field is numeric or alpha numeric. the system is in an early pilot phase, and several prototypes and revisions will be required to achieve appropriate settings for the software which has sensitivity and specificity settings that can be changed. once the data are extracted from the form, chia generates an hl7 vaccination record query (vxq) message that is sent to chirp. in our implementation, hl7 messages are passed through the indiana network for patient care (inpc), which maintains a virtual private network (vpn) connection to chirp. the vxq message contains patient identifying information. chirp matches this information against records in the registry and returns the vaccination record via an hl7 vaccine query response (vxr) or returns an hl7 response to vaccination query with multiple patient matches (vxx) message containing possible patient matches. in the case that no match is found, chia will create a record in chirp, using an unsolicited vaccination record (vxu) message. when more than one match is found, the clinician will be required to disambiguate. when a patient match is achieved, chirp responds by faxing a form back to the clinician (figure 2). this form contains a unique identifier, generated by chia, which is encoded in a bar code. this allows chia to recognize the form and the patient to whom it refers when it is faxed back. the form provides the vaccine data available in chirp and a recommendation for what the last mile: using fax machines to exchange data between clinicians and public health 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 vaccines should be given on that date. in addition, the form allows the clinician to record past vaccination dates that are missing from chirp and to indicate, by checking boxes, which vaccines were given. once these forms are completed, they are faxed to the chia toll-free number. the data are transferred to the chirp database. the form remains for the clinician to use as part of the paper medical record. this obviates the need for duplicate data entry. page 1 of fax the last mile: using fax machines to exchange data between clinicians and public health 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 2: the chia immunization form used to transfer immunization data to and from the immunization registry by machine readable fax. pages 1 and 2 shown at subsequent encounters, the clinician can fax any of the existing paper forms completed for this patient because they all have the bar code that identifies the patient. faxing a form will trigger the chia system to query chirp with another vxq and capture the data from the registry. chia will fax back a new immunization form with any new vaccines and new recommendations. results the project is in its preliminary, technical development stage. described below are important milestones achieved to date as well as specific technical challenges and their solutions. the last mile: using fax machines to exchange data between clinicians and public health 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 hl7 transmission an early achievement was the establishment of a standard hl7 stream carrying newborn screening lab results. the output of the newborn screening laboratory consists of a set of proprietary results codes in a “pipe-delimited” file format. these are converted at the isdh into standard hl7 version 2.x messages that are captured by our system. the results are still in a proprietary coding system but will, in time, be converted to a loinc standard. 23, 24 an additional step was creating a filter that captures all hl7 messages entering the inpc for those whose patient identification (pid) segments show an age of 1 month or younger. these messages are packaged along with provider information (pv1 segments) and next of kin (nk1 segments) into an hl7 message that is passed on to the newborn screening system. we have demonstrated the process of parsing newborn screening lab messages, matching them against messages from the inpc, generating atads. in an initial pilot test of the matching algorithm, we parsed 100,785 messages from the newborn screening laboratory. in the same timeframe, there were 2,561 hl7 messages entering the inpc for children of aged 1 month or younger. thus, about 2.5% of newborns in indiana will have a subsequent healthcare visit that appears in the inpc. of the 2,561 inpc messages, 2,243 (88%) could be linked to the messages from the newborn screening lab. this left 318 (12%) of messages from the inpc that could not be tied to a newborn screening result. on manual review of these, most were “junk” messages that did not contain real patient data. many were test messages or other information from external sources that have nothing to do with patients. however a small number (99) were patients who had not been screened. all but one of these were in neonatal intensive care units, a setting in which newborn screening can be missed. 25 crossing domains and security policies in order to deliver the messages to clinician participating in ihie, we had to produce the atads and send them through the results delivery system, docs4docs. to do this the atads had to be converted to portable document format (pdf), the only image format the system handles. this required additional software to make the translation. the forms were then placed in the obx segment of an hl7 message and sent back to the inpc to be picked up by the docs4docs server and delivered. establishing servers in secure environment and establishing the interfaces with external systems (hl7 input and output feeds, fax server, teleform) represented a significant technical hurdle. figure 3 shows a schematic of the processes involved in the bringing the teleform functionality, the chica rules engine, ihie and the docs4docs services into one working system. although the technical steps were facilitated by using health it industry standards whenever feasible, negotiating each entities security policies required substantial effort. handling paper in an electronic environment we also found that the docs4docs results delivery system adds a header and footer to the images it delivers. the result is shrinkage of the image. because the teleforms software has to recognize bar codes and other markers on the forms, this posed a challenge. with experimentation and adoption of specific barcode formats, we found that the software could tolerate an image reduced by 15% or less and still scan reliably. the last mile: using fax machines to exchange data between clinicians and public health 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 when atads are completed and faxed back, the quality of the fax machine comes into play. low quality machines can skew and distort the imagines, making machine recognition of the form and interpretation of the coded fields impossible. knowing quality of forms we get back from the clinicians in the inpc will have to wait until full deployment of the system. however, manual review of critical forms may still be necessary. in our pilot program, data managers in the children’s health services research section at indiana university manually check faxes that are not interpretable by the ocr software. fax server ihie domain chica production service teleforms server hl7 messages from inpc & isdh rta application software create atd: -parse hl7 -create xml atd using arden rules database teleform auto merge publisher convert atd: tiff to pdf t iff hl7 exporter -construct hl7 with encoded atd attached pdf teleform reader hl7 to docs4docs 2 1 3 4 5 t iff x m l physician completes form and faxes back 6 7 archive and send to teleform reader 8 9 parse xml and store observations 10 figure 3: complexities of connecting multiple systems across domains, firewalls and security policies the last mile: using fax machines to exchange data between clinicians and public health 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 discussion the potential benefits of linking public health functions to clinical practice seem obvious. the link between newborn screening programs and clinical follow-up is tenuous and highly variable from state to state, 26 and information systems offer a way to improve communication. likewise, immunization rates are well below those needed to establish herd immunity, 27 and immunization registries can have a significant effect, but only if there is adequate participation by clinicians in the community. the health information technology for economic and clinical health (hitech) act includes “meaningful use” criteria and incentives for the meaningful adoption of health information technology, including linking ehrs to immunization registries. 12 however, these incentives will have relatively less impact on pediatric care because they only apply to clinicians whose patient panels include minimal levels of medicare or medicaid enrollees. because many pediatricians won’t meet these levels, and the majority don’t have comprehensive emrs, the atad approach we are developing may be important for some time. the advantage of our approach is that the clinician can share data with the public health community with only a fax machine and a phone line. however, this approach pushes the technical complexity upstream to entities running the health information exchange and the health department. the complexity of supporting a paper interface in an otherwise electronic health information exchange is substantial. for example, although most faxes can be interpreted by the software without human intervention, personnel with the skills to review faxes and manually enter data are needed if all of the data are to be captured. software systems for generating tailored paper forms and recognizing and interpreting those forms when returned by fax are complex, expensive and prone to errors. moreover, our approach depends on the existence of the health information exchange and the results delivery system that can handle images such as our atads. even when this infrastructure is in place, our programs had to link myriad systems across domains. hit standards help this effort, but security policies to link the domains must also be addressed. however, we see this project’s effort as another illustration of what can be achieved when a functional health information exchange has been established. we believe this approach will bridge the last mile, at least until emrs are in widespread use. admittedly, the bridge is not ideal. communication is asynchronous because a paper must be completed and faxed for each step in the communication. for use cases in which the data exchange must be faster, synchronous communication such as a real-time ehr connection, a web interface, or even a phone call will be necessary. in the future, the approach we have taken can be adapted to the delivery of electronic data directly to and from an ehr. in that case, the errors inherent in scanning paper, the additional manual effort to check the scans, and the need for fax machines will be eliminated. however, for a busy clinician without a linked ehr, logging into a web interface or making a phone call represents a diversion from the clinic workflow. only a medium that fits into busy clinic workflow will be acceptable. this is why a tailored, faxable, machine interpretable paper interface (i.e., an atad) offers a viable option. the last mile: using fax machines to exchange data between clinicians and public health 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 acknowledgements this work was funded by grant number 1p01hk000077 from the centers for disease control and prevention. corresponding author stephen m. downs, md, ms 460 west 10 th street, hs1000 indianapolis, in 46202 office: 317-278-0552 fax: 317-278-0456 e-mail: stmdowns@iupui.edu) references 1. thacker sb, berkelman rl. 1988. public health surveillance in the united states. epidemiol rev. 10, 164-90. 2. elson rb, connelly dp. 1995. computerized patient records in primary care: their role in mediating guidelinedriven physician behavior change. arch fam med. 4, 698-705. http://dx.doi.org/10.1001/archfami.4.8.698 3. yasnoff w, o’carroll p, koo d, linkins r, kilbourne e. 2000. public health informatics: improving and transforming public health in the information age. j public health manag pract. 6(6), 67-75. http://dx.doi.org/10.1097/00124784-200006060-00010 4. hinman a, atkinson d, diehn t, et al. principles and core functions of integrated child health information systems journal of public health management and practice. 2004 10 s52s56. 5. carroll ae, downs sm. 2006. comprehensive cost-utility analysis of newborn screening strategies. pediatrics. 117(5 pt 2), s287-95. 6. kemper ar, uren rl, moseley kl, clark sj. 2006. primary care physicians' attitudes regarding follow-up care for children with positive newborn screening results. pediatrics. 118(5), 1836-41. http://dx.doi.org/10.1542/peds.2006-1639 7. u.s. department of health and human services. 2000. office of disease prevention and health promotion--healthy people 2010. nasnewsletter. 15(3), 3. 8. zimmerman r. aafp, aap and acip release 1998 recommended childhood immunization schedule. 1998 9. progress in immunization information systems --united states, 2009. mmwr morb mortal wkly rep. jan 14;60(1):10-12. 10. leu mg, o'connor k, marshall r, klein jd. pediatricians' use of health information technology: a national survey. paper presented at: pediatric academic societies annual meeting2010. 11. kemper ar, uren rl, clark sj. 2006. adoption of electronic health records in primary care pediatric practices. pediatrics. 118(1), e20-24. http://dx.doi.org/10.1542/peds.2005-3000 mailto:stmdowns@iupui.edu the last mile: using fax machines to exchange data between clinicians and public health 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 12. health information technology: initial set of standards, implementation specifications, and certification criteria for electronic health record technology. final rule. fed regist. jul 28;75(144):44589-44654. 13. biondich pg, grannis sj. 2004. the indiana network for patient care: an integrated clinical information system informed by over thirty years of experience. j public health manag pract. suppl, s81-86. http://dx.doi.org/10.1097/00124784-200411001-00013 14.2010 annual report: indiana health information exchange;2011. 15. barnes m. 2007. lessons learned from the implementation of clinical messaging systems. amia annu symp proc. •••, 36-40. 16. anand v, biondich p, liu g, rosenman m. sm d. child health improvement through computer automation: the chica system. paper presented at: medinfo 20042004 san francisco. 17. seebregts cj, mamlin bw, biondich pg, et al. 2009. the openmrs implementers network. int j med inform. 78(11), 711-20. http://dx.doi.org/10.1016/j.ijmedinf.2008.09.005 18. wolfe ba, mamlin bw, biondich pg, et al. 2006. the openmrs system: collaborating toward an open source emr for developing countries. amia annu symp proc. •••, 1146. 19. jenders ra, hripcsak g, sideli rv, et al. 1995. medical decision support: experience with implementing the arden syntax at the columbia-presbyterian medical center. proc annu symp comput appl med care. •••, 169-73. 20. watson ms, mann my, lloyd-puryear ma, rinaldo p, howell rr. 2006. american college of medical genetics newborn screening expert group newborn screening: toward a uniform screening panel and system-executive summary. pediatrics. 117(5), s296-307. 21. zhu vj, overhage mj, egg j, downs sm, grannis sj. 2009. an empiric modification to the probabilistic record linkage algorithm using frequency-based weight scaling. j am med inform assoc. 16(5), 738-45. http://dx.doi.org/10.1197/jamia.m3186 22. davidson k. adapting vendor clinical systems for real-time registry participation using hl7. national immunization registry conference. vol atlanta, ga2003. 23.downs sm, van dyck pc, rinaldo p, et al. improving newborn screening laboratory test ordering and result reporting using health information exchange. j am med inform assoc. janfeb;17(1):13-18. 24. newborn screening ahic detailed use casewashington. dc: u.s. department of health and human services office of the national coordinator for health information technology; 2008 25. spivak l, dalzell l, berg a, et al. 2000. new york state universal newborn hearing screening demonstration project: inpatient outcome measures. ear hear. 21(2), 92-103. http:// dx.doi.org/10.1097/00003446-200004000-00004 26. kim s, lloyd-puryear ma, tonniges tf. 2003. examination of the communication practices between state newborn screening programs and the medical home. pediatrics. 111(2), e120-26. http://dx.doi.org/10.1542/peds.111.2.e120 27. centers for disease control and prevention, national immunization survey. 2011; http:// www.cdc.gov/nchs/nis/data_files.htm. accessed august 14, 2011. http://www.cdc.gov/nchs/nis/data_files.htm layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts syndromic surveillance from a local perspective – a review of the literature don olson*, kevin konty, rob mathes and marc paladini new york city department of health and mental hygiene, long island city, ny, usa objective review of the origins and evolution of the field of syndromic surveillance. compare the goals and objectives of public health surveillance and syndromic surveillance in particular. assess the science and practice of syndromic surveillance in the context of public health and national security priorities. evaluate syndromic surveillance in practice, using case studies from the perspective of a local public health department. introduction public health disease surveillance is defined as the ongoing systematic collection, analysis and interpretation of health data for use in the planning, implementation and evaluation of public health, with the overarching goal of providing information to government and the public to improve public health actions and guidance [1,2]. since the 1950s, the goals and objectives of disease surveillance have remained consistent [1]. however, the systems and processes have changed dramatically due to advances in information and communication technology, and the availability of electronic health data [2,3]. at the intersection of public health, national security and health information technology emerged the practice of syndromic surveillance [3]. methods to better understand the current state of the field, a review of the literature on syndromic surveillance was conducted: topics and keywords searched through pubmed and google scholar included biosurveillance, bioterrorism detection, computerized surveillance, electronic disease surveillance, situational awareness and syndromic surveillance, covering the areas of practice, research, preparedness and policy. this literature was compared with literature on traditional epidemiologic and public health surveillance. definitions, objectives, methods and evaluation findings presented in the literature were assessed with a focus on their relevance from a local perspective, particularly as related to syndromic surveillance systems and methods used by the new york city department of health and mental hygiene in the areas of development, implementation, evaluation, public health practice and epidemiological research. results a decade ago, the objective of syndromic surveillance was focused on outbreak and bioterrorism early-event detection (eed). while there have been clear recommendations for evaluation of syndromic surveillance systems and methods, the original detection paradigm for syndromic surveillance has not been adequately evaluated in practice, nor tested by real world events (ie, the systems have largely not ‘detected’ events of public health concern). in the absence of rigorous evaluation, the rationale and objectives for syndromic surveillance have broadened from outbreak and bioterrorism eed, to include all causes and hazards, and to encompass all data and analyses needed to achieve “situational awareness”, not simply detection. to evaluate current practices and provide meaningful guidance for local syndromic surveillance efforts, it is important to understand the emergence of the field in the broader context of public health disease surveillance. and it is important to recognize how the original stated objectives of eed have shifted in relation to actual evaluation, recommendation, standardization and implementation of syndromic systems at the local level. conclusions since 2001, the field of syndromic surveillance has rapidly expanded, following the dual requirements of national security and public health practice. the original objective of early outbreak or bioterrorism event detection remains a core objective of syndromic surveillance, and systems need to be rigorously evaluated through comparison of consistent methods and metrics, and public health outcomes. the broadened mandate for all-cause situation awareness needs to be focused into measureable public health surveillance outcomes and objectives that are consistent with established public health surveillance objectives and relevant to the local practice of public health [2]. keywords evaluation; biosurveillance; situational awareness; syndromic surveillance; local public health acknowledgments this work was carried out in conjunction with a grant from the alfred p. sloan foundation (#2010-12-14). we thank the members of the new york city department of health and mental hygiene syndromic surveillance unit. references 1. langmuir ad. evolution of the concept of surveillance in the united states. proc roy soc med. 1971;64:681-4. 2. smith pf, hadler jl, stanbury m, rolfs rt, hopkins rs; cste surveillance strategy group. “blueprint version 2.0”: updating public health surveillance for the 21st century. j public health manag pract. 2012; july 2 [epub ahead of print]. 3. mostashari f, hartman j. syndromic surveillance: a local perspective. j urban health 2003;80(2 suppl 1):i1-7. *don olson e-mail: drolson@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e82, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts tracking drug overdose trends in ohio using ed chief complaints alise l. brown*, william e. storm and brian e. fowler ohio department of health, columbus, oh, usa objective preliminary analysis was completed to define, identify, and track the trends of drug overdoses (od), both intentional and unintentional, from emergency department (ed) and urgent care (uc) chief complaint data. introduction the state of ohio, as well as the country, has experienced an increasing incidence of drug ods over the last three decades [3]. of the increased number of unintended drug od deaths in 2008, 9 out of 10 were caused by medications or illicit drugs [1]. in ohio, drug ods surpassed mvcs as the leading cause of injury death in 2007. this trend has continued through the most current available data [3]. using chief complaint data to quickly track changes in the geographical distribution, demographics, and volume of drug ods may aid public health efforts to decrease the number of associated deaths. methods chief complaint data from ed/uc visits were collected and analyzed from ohio’s syndromic surveillance application for 2010-2012. ninety-six percent of all ohio ed visits were captured during this timeframe. due to the nonspecific nature of chief complaints as well as the lack of detail given upon registration at the ed/uc, attempting to separate visits into intentional vs. unintentional was not feasible. therefore, a fairly specific classifier was created to define all potential ed/uc visits related to drug ods. the data were analyzed, using sas v 9.3, via time series analyses, and stratified by age, gender, and geographic region. although these data types are pre-diagnostic in nature, they are more readily accessible than discharge data. results on average, ohio observed approx 66 ed/uc visits per day related to drug ods from 2010-2012. the data show an increasing trend from 2010 through 2012 as well as a slight seasonal trend with higher visits observed in the spring/summer months as opposed to the autumn/winter months (figure 1). the data showed that females attributed to a higher frequency of the drug ods than males by approximately 4 ed/uc visits per day. other data sources show a higher incidence in males than females related to unintentional drug ods [3]. the highest age category attributing to the increase was the 18-39 years of age for both males and females, as shown in figure 2. population rates were calculated to identify those counties most affected by drug ods. the data showed the highest rates of ed/uc visits related to drug ods to be found in mostly rural areas of ohio. conclusions the annual death rate from unintentional drug poisonings by ohio residents has increased from 3.6 in 2000 to 13.4 per 100,000 population in 2010[3]. as a result, the ohio governor created a drug abuse task force in 2009[4]. ohio legislation (hb 93) implemented a prohibition on the operation of pain management clinics without a license on june 19, 2011[3]. according to this preliminary analysis, ed/uc visits related to drug ods 1 year post-implementation of hb 93 continue to increase. it is unclear if hb 93 has slowed the rate of increase. additionally, pre-diagnostic data has significant limitations including the significant possibility of misclassifying non-od patient encounters as ods. further study of post-diagnostic data to confirm these trends is warranted. keywords drug overdose; chief complaints; prescription drug abuse references 1. bm kuehn. poisonings top crashes for injury-related deaths: jama. 2012;307(3):242-242. doi:10.1001/jama.2011.1998. 2. ohio department of health. (july, 2012). ohio’s opioid epidemic: an overview of the problem. http://www.odh.ohio.gov/sitecore/content/healthyohio/default/vipp/data/rxdata.aspx 3. ohio injury prevention partnership, prescription drug abuse action group. hb 93 summary. http://www.ccbh.net/storage/prescriptionmeds/hb93summary.pdf 4. ohio prescription drug abuse task force. final report. http://www.odh.ohio.gov/~/media/odh/assets/files/web%20team /features/drug%20overdose/opdatffinalreport.ashx *alise l. brown e-mail: alise.brown@odh.ohio.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e121, 2013 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts seroprevalence of zoonotic diseases among farm animals in kvemo kartli (georgia) k goginashvili*1, m donduashvili1, gaga osiashvili1, ryan arner2 and lile malania3 1laboratory of the ministry of agriculture, tbilisi, georgia; 2metabiota inc, san fransisco, ca, usa; 3national center for disease control and public health, tbilisi, georgia objective the purpose of this research was to study the seroprevalence of zoonotic diseases among farm animals in the kvemo kartli region of georgia. introduction zoonotic diseases are an important cause of human morbidity and mortality; around 75% of recently emerging human infectious diseases are zoonoses. herein we report the first seroprevalence study to include a range of emerging or re-emerging zoonotic pathogens of economic concern (including: bacillus anthracis, coxiella burnetii, francisella spp., brucella spp., and crimean-congo hemorrhagic fever virus (cchfv)) affecting domestic animals (e.g., cattle, sheep, goat, and dog) in georgia. methods cattle (n=177) from gardabani, marneuli, and tsalka (kvemo kartli region) were sampled for the study as were small ruminants and dogs (n=30). bacillus anthracis, brucella spp., cchfv, and c. burnetii (phase i) were detected using elisa methods. francisella tularensis was detected using a microscopic agglutination test (mat). results of the cattle sampled, 11 were positive for f. tularensis, 39 were positive for brucella spp., and seven were positive for c. burnetii. all samples were negative for cchfv. three goat samples were positive for c. burnetii, one goat sample and one dog sample were positive for f. tularensis. conclusions domestic animals serve as a source of disease that can spread to humans through vectors or direct contact. in georgia, domestic animals were not previously studied for exposure to zoonotic diseases, with the exception of cattle, which were surveyed for brucellosis. in particular, the finding of f. tularensis seropositive animals is novel in georgia, as this region was considered free of the pathogen. screening studies of domestic/farm animals for zoonotic pathogens such as this can serve as a source of baseline data for regional risk assessments and to better inform one health measures. keywords emerging diseases; re-emerging diseases; zoonotic diseases; crimean-congo hemorrhagic fever acknowledgments the research study described in this presentation was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed *k goginashvili e-mail: goginashvili@lma.ge online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e159, 2017 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts sharing public health information with non-public health partners wayne loschen*, rekha holtry, kalman hazins and sheryl happel lewis johns hopkins university applied physics laboratory, laurel, md, usa objective the objective of this project is to provide a technical mechanism for information to be easily and securely shared between public health essence users and non-public health partners; specifically, emergency management, law enforcement, and the first responder community. this capability allows public health officials to analyze incoming data and create interpreted information to be shared with others. these interpretations are stored securely and can be viewed by approved users and captured by authorized software systems. this project provides tools that can enhance emergency management situational awareness of public health events. it also allows external partners a mechanism for providing feedback to support public health investigations. introduction automated electronic disease surveillance has become a common tool for most public health practitioners. users of these systems can analyze and visualize data coming from hospitals, schools, and a variety of sources to determine the health of their communities. the insights that users gain from these systems would be valuable information for emergency managers, law enforcement, and other nonpublic health officials. disseminating this information, however, can be difficult due to lack of secure tools and guidance policies. this abstract describes the development of tools necessary to support information sharing between public health and partner organizations. methods the project initially brought together public health and emergency management officials to determine current gaps in technology and policy that prevent sharing of information on a consistent basis. officials from across the national capital region (ncr) in maryland, virginia, and the district of columbia determined that a web portal in which public health information could be securely posted on and captured by non-public health users (humans and computer systems) would be best. the development team then found open source tools, such as the pebble blogging system, that would allow information to be posted, tagged, and searched in an easily navigable site. the system also provided rss feeds both on the site as whole and specific tags to support notification. the team made modifications to the system to incorporate spring security features to allow the site to be securely hosted requiring usernames and passwords for access. once the pebble system was completed and deployed, the ncr’s aggregated essence system was adapted to allow users to submit daily reports and post time series images to the new site. an additional feature was created to post visualizations every evening to the site summarizing the day’s reports. results the system has been in testing since march of 2012 and users of the system have provided valuable feedback. based on the success of the tests, public health users in the ncr have begun working on the policy component of the project to determine when and how it should be used. modifications to the system since deployment have included a single sign on capability for essence users and the desire to allow other features of essence to be posted beyond time series graphs, such as gis maps and statistical reports. conclusions having tools that can promote exchange of information between public health and non-public health partners such as emergency management, law enforcement, and first responders can greatly enhance the situational awareness and impact overall preparedness and response. by having tools embedded in essence, users are able to integrate the information sharing aspects into their daily routines with a small amount of effort. with the use of open source tools, the same type of capability can be easily replicated in other jurisdictions. this presentation will describe the lessons learned and potential improvements the project will incorporate in the future. keywords open source; emergency management; information sharing *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e47, 2013 a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 1 ojphi a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states greg arling1, matthew blaser2, michael d. cailas3*, john r. canar4, brian cooper4, joel flaxhatch3, peter j. geraci5, kristin m. osiecki6, and apostolis sambanis5 1 purdue university, school of nursing, college of health and human sciences 2 united states environmental protection agency, research associate under an inter-agency agreement with oak ridge institute for science and education 3 environmental and occupational health sciences, school of public health, university of illinois chicago 4 united states environmental protection agency region v; and health policy and administration, school of public health, university of illinois chicago 5 health policy and administration, school of public health, university of illinois chicago 6 university of minnesota, rochester, center for learning innovation abstract considering the potential for widespread adoption of social vulnerability indices (svi) to prioritize covid-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the covid-19 pandemic. the university of illinois at chicago school of public health public health gis team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final analysis, classifiers. application of this methodology to several midwestern states with a commonly used svi indicates that by using only the svi rankings there is a risk of assigning a high priority to locations with the lowest mortality rates and low priority to locations with the highest mortality rates. based on the findings, we propose using a two-dimensional approach to rationalize the distribution of vaccinations. this approach has the potential to account for areas with high vulnerability characteristics as well as to incorporate the areas that were hard hit by the pandemic. *corresponding author: mihalis@uic.edu doi: 10.5210/ojphi.v13i1.11621 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged i n the copy and the copy is used for educational, not-for-profit purposes. introduction this research brief summarizes the findings of an in-progress study conducted by the sph-phgis research team, which aims to identify the limitations and potentials of svis for prioritizing mailto:mihalis@uic.edu a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 2 ojphi vaccination plans. due to this issue's urgency and importance, the cdc's social vulnerability index (cdc.svi) will be used as a case study. here we present preliminary findings, with more to be reported in the coming weeks. the objectives of our research are to: assess the performance of the cdc.svi in classifying counties according to their covid-19 mortality rates; and propose an alternative approach to prioritization that incorporates both social vulnerability and actual experience of losses, i.e., covid-19 mortality. our goal is to provide better information on a community's vulnerability to a pandemic, as well as to the impact of vaccinations or other mitigation efforts in reducing mortality from the pandemic. background in october 2020, the national academies of sciences, engineering, and medicine (nasem) released a consensus study recommending a four-phase framework for equitable covid-19 vaccine allocation [1]. in december 2020, the advisory committee on immunization practices (acip) recommended a detailed phased implementation plan for vaccination, starting with health care personnel and residents of long-term care facilities [2]. given the limited supply of vaccines, the acip recommends for the next phase, 1b, to vaccinate "persons aged ≥75 years and frontline essential workers." [2] both of these public health institutions raise the issue of promoting justice and mitigating health inequalities, especially for the particular racial and ethnic minority groups that were disproportionally affected by covid-19 [1,2]. on this issue the nasem study made specific recommendations that "vaccine access should be prioritized for geographic areas identified through cdc's social vulnerability index or another more specific index." [1] the cdc's social vulnerability index (cdc.svi) is one of the many indices in use aiming to "help local officials identify communities that may need support before, during, or after disasters." [3] this svi, which is constructed from census data at various scales of aggregation, seeks to classify the relative social vulnerability of a location to a hazard based on a combination of factors. the cdc.svi has a separate set of rankings for census tracts and counties according to 15 social attributes, including unemployment, minority status, and disability obtained from the american community survey. the cdc.svi further groups these attributes into four related themes: socioeconomic status; household composition & disability; minority status & language; and housing type & transportation. census tract or county rankings can be obtained by state or nationally based on the individual indicator rankings, either summed across all 15 indicators or within each of the four themes. although each of the indices (i.e., themes) attempts to represent the underlying construct of social vulnerability, tapsell et al. have pointed out that "there is still no consensus on a) the primary factors that influence social vulnerability, b) the methodology to assess social vulnerability, or c) an equation that incorporates quantitative estimates of social vulnerability into either overall vulnerability assessment or risk." [4] the supporting documents for the acip recommendations raise a few of the issues that are likely to be exacerbated with the use of svis (see acip's evidence table for covid-19 vaccines allocation in phases 1b and 1c of the vaccination program). a notable strength of the cdc.svi is the ease with which public health (ph) agencies can obtain the cdc.svi ranking of their location from the well-organized cdc portal [3]. from the perspective of planning and resource allocation, social vulnerability is presumed to be an indicator of a community's risk for covid-19, along with the need for additional resources to mount mitigation efforts against the pandemic. the popularity of this rank-based svi has spawned other a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 3 ojphi rank-based indicators taking the same approach but directly addressing the covid-19 pandemic. for example, the covid-19 community vulnerability index (ccvi) “incorporates the latest evidence on covid-19 risk factors, fine-tuned with data collected over the course of the pandemic." [5] considering the potential for widespread adoption of svis to prioritize covid-19 vaccinations, we feel that this approach should be carefully assessed, particularly for correspondence with outcomes (such as loss of life) in the context of the covid-19 pandemic. most applications of the rank-based cdc.svi have been to single events such as natural or environmental disasters [6]. the covid-19 pandemic, in contrast, involves a series of events characterized by waves and extended now over more than a year. for the midwest, we know that the two waves have different characteristics [7] that are likely to challenge the applicability of svis. although cases have been distributed across different age groups, loss of life has been concentrated among older adults and disproportionately in nursing homes and other long-term care settings [8]. the modeling of risk and social vulnerability must take these unique circumstances into account. our earlier work in cook county, illinois, revealed differences in spatial patterns of covid-19 deaths in private households compared to those in long-term care facilities [8]. neighborhood characteristics were predictive of household deaths but not deaths in long-term care facilities. also, vaccination rollouts have been rapid and comprehensive for residents of long-term care facilities [9]. although people of advanced age and those from racial and ethnic groups living in the community have been assigned high priority for vaccinations, these groups have had substantially lower vaccination rates than whites [10]. nearly all major national covid-19 reporting portals disclose the total number of covid-19 deaths, with some offering separate reports on deaths in long-term care facilities. yet, none of them report deaths occurring in households only (i.e., persons not in long-term care or other group settings). also, much of the modeling of covid-19 incidence and mortality has failed to distinguish between these residential settings [11]. a strength of our research is the separation of mortality figures for the two settings [7,8,11]. in this analysis we examine deaths among individuals in private households, under the assumption that this is where the challenge of vaccine prioritization lies. assessment methodology the university of illinois at chicago school of public health public health gis (uic-sphphgis) team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final analysis, classifiers. within this context, a svi represents categories of a societal state (i.e., defined by socioeconomic and environmental factors) that unrealized events such as a pandemic have the potential to harm and thereby cause losses. in essence, the index derivation approach (e.g., the ranking of the original variables and the additive model for the themes in the cdc.svi) becomes a classifier for each one of the n locations in terms of a potential for loss. the potential for loss is a common construct for many social vulnerability definitions, for example, "social vulnerability to natural hazards is the potential for loss and is complex interaction among risk, mitigation, and the social fabric of a place." [12] to validate the index as a classifier, a realized disaster loss (dl) event must be used corresponding to an actual disaster loss for each one of the n locations (e.g., number of deaths for a census tract or county). consequently, performance assessment of the svi classifier is easily accomplished by comparing the svi (i.e., potential for loss) and the dl severity (i.e., actual losses or harm) rankings of each a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 4 ojphi location. to simplify the assessment, a confusion or error matrix is used with m classes for each dimension; m << n and usually contains 3 to 5 classes to correspond to the common color visualization schemes seen in the plethora of svi maps. these classes are derived with the application of a binning or discretization methodology which transforms the n numerical values of a variable into m categorical counterparts. for our study, the m×m phgis performance assessment (pa) matrix has the structure in figure 1. figure 1. schematic of the performance assessment (pa) matrix proposed by the phgis team for evaluating the svis. the rank-based cdc.svi as a prioritization tool for covid-19 vaccination programs needs to identify high-risk areas, which are likely to be those with the greatest losses. the implicit assumption is that socioeconomic and environmental conditions, as well as the health condition of the residents, are the underlying causes of this elevated risk that leads to losses. this is aptly expressed in a rank-based ccvi report focusing on loss data that "can help us understand where and how the disease is impacting vulnerable populations, in order to prioritize resources and rapid response accordingly." [5] the matching areas in terms of the two classifiers are contained in the diagonal elements of the phgis pa matrix, cii, providing an overall classification performance measure (figure 1). for example, the first element of the pa matrix, c11, contains the areas (e.g., counties) classified as having the lowest vulnerability and realization of losses. the sum of the matching areas divided by the total number of areas, n, yields an overall classification performance (ocp) rate. similarly, the off-diagonal elements, cij, of the pa matrix identify the misclassified areas. these are areas recording a discrepancy between the class of their vulnerability status and the severity of the losses from past or on-going events. for example, the last element of the 1st column, cm1, indicates the number of areas with the highest actual disaster loss that were classified to be the lowest vulnerable areas by the svi. the sum of these below off-diagonal elements divided by the total number of areas, n, yields an overall underestimation rate (our). on the other hand, the element on the top right-hand corner indicates the areas that were predicted to have the highest vulnerability but a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 5 ojphi experienced the lowest level of dl. the elements above the diagonal indicate overestimation error (oe). the sum of these upper off-diagonal elements divided by the total number of areas, n, yields an overall overestimation rate (oer). ideally, a well-designed covid-19 vaccination plan will have a minimum of both our and oer. a high our implies high-risk areas that are not accounted for by the svi, whereas a high oer implies allocation of valuable vaccine resources in areas with low risk. case studies the study areas for our assessment of the cdc.svi are counties in illinois(n=102) and wisconsin (n=72). the index rankings for all of the counties were derived from the cdc.svi portal. we focus on the second covid-19 wave, because of its recency and because it has different patterns from the first wave. illinois and wisconsin were selected due to their differences in spatial mortality patterns between waves. the assessment variable (target) was the household mortality rate (per 100,000). details about the wave dates and characteristics are provided by the authors at the midwest comprehensive visualization dashboards: covid-19 mcvd [13]. the vulnerability status to the pandemic is accounted for with the use of the summary classification (i.e., sum of the four cdc.svi themes). the realization of the pandemic is represented by the dl (i.e., covid-19 related deaths as of january 17, 2021). for this study, dl is the mortality rate per 100,000 of the household (not nursing home or long-term care) population recorded in the counties during the second wave of the pandemic [11]. for this application four classes of severity were used (1 is the lowest, 4 the most severe). findings for counties in illinois and wisconsin are reported separately for the sum of the ranks across all four themes and for theme 3, minority status and language (figures 2 to 5). if vaccine prioritization were to be based solely on vulnerability according to the cdc.svi, it would channel vaccinations and other resources first into svi category 4, followed by svi category 3, svi 2, svi 1. unfortunately, the cdc.svi categories for the counties do not match well with covid-19 mortality rates. the mismatch patterns are consistent across all four pa matrices. a good match between vulnerability status and dl severity, by county, is seen in the diagonal elements of the pa matrix. counties in the lower left cells would have high dl combined with low vulnerability. this mismatch could be of special ph concern in the cells furthest from the diagonal (i.e., close to c41), as they would have the lowest priority for vaccination even though they were in the top quartile for dl severity. conversely, counties in the upper right cells have low dl combined with a high vulnerability index. the cells furthest from the diagonal (i.e., close to c14) would be given high priority for vaccination despite being in the bottom quartiles for dls. a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 6 ojphi figures 2 and 3. illinois counties phgis performance assessment (pa) matrix with four categories applied for the summary cdc.svi and the theme 3 ranking of counties and their mortality rates. figures 4 and 5. wisconsin counties phgis pa matrix with four categories applied for the summary cdc.svi and the theme 3 ranking of counties and their mortality rates from a statistical perspective, the measures of (mis)classification, or match/mismatch between vaccine prioritization (vulnerability index) and covid-19 mortality rate in the counties were consistent across the four matrices, two for each state. the overall classification performance (ocp) was relatively low (ranging from 25.0% to 31.4%), while the overestimation rate (oer) and underestimation rates (uer) were in the same consistently high range (33.3% to 38.2%). when we constructed similar matrices for three other midwestern states (figure 6), we found generally a lower matching classification (i.e., ocp) and higher overand underestimation rates a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 7 ojphi (oer and uer). for example, the ocps ranged from 14.9% to 28.3%, the uers ranged from 31.5% to 41.0%, and the oers ranged from 35.6% to 44.8%. figure 6. classification performance (ocp) and over and under estimation rates (uer and oer) for the cdv.svi ranking of counties in three midwestern states by dl categories use of the phgis pa matrix as a tool for setting and monitoring vaccine priorities our assessment suggests that the cdc.svi may not be tapping into key factors contributing to losses from the covid-19 pandemic. therefore, this tool will need to be augmented to improve its value in planning for an initial vaccination rollout and for monitoring the effectiveness of vaccinations as they are deployed in a community. an effective prioritization tool will need to predict initial vulnerability to a loss as well as being able to track a community's success in mitigating against risk through vaccinations or other interventions. the phgis pa matrix, which we applied in assessing the performance of the cdc.svi, could also serve as a prioritization tool. the phgis-pa approach overcomes the one-dimensional aspect of svis. it takes into account (albeit incompletely) factors contributing to vulnerability, as well as a community's actual experience of losses. • the matrix could be applied for vaccination planning and priority setting by determining which counties fit into each of the cells based on their combinations of svi vulnerability and realized losses, i.e., from the beginning of the current wave up to the time of initial vaccination priority setting. • in addition, the matrix could be used for tracking the effects of vaccinations by updating the loss categories periodically, i.e., updated weekly based on a four-week rolling average. by tracking transitions from one loss category to another, it would be possible to assess vaccination roll-out effectiveness over time. • the matrix structure could be expanded to include additional dimensions, such as rates of vaccinations and priority populations, in order to obtain a clearer picture of effectiveness. as an example of how the matrix could be employed for priority settings, we use the cdc.svi, in lieu of a more-refined svi that we have under development. we refer again to figures 2 to 5. the a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 8 ojphi ordering of initial priorities would be straightforward for counties in cells along the diagonal, where vulnerability and losses line up well. the difficult cases are the counties in cells off the diagonal. counties in the lower left cells would have high dl combined with a low vulnerability index. this mismatch could be of special concern in the cells furthest from the diagonal (i.e., close to c41) because they would have a lower priority for vaccination if vulnerability were the main criterion. yet, they were the counties with the worst losses. the vulnerability of these counties – as well as any factors that would suggest high losses continuing into the future, absent of mitigation efforts – need to be further investigated. conversely, counties in the upper right cells would have low dl yet a high vulnerability index. the cells furthest from the diagonal (i.e., close to c14) would be given high priority for vaccines from the standpoint of vulnerability, despite being in the bottom quartiles for dls. again, further investigation would be advised to determine if the counties should remain a high priority (because, for example, of the potential for increased losses). application of the phgis-pa matrix approach for tracking and continuous priority setting would involve an updating of the matrix as new data arrived about losses, and perhaps vaccine rollouts. the focus would be on changes in a county's cell membership between periods, which could indicate the need for re-ordering of priorities to counties experiencing increases in losses despite mitigation efforts. conclusions given the vaccine limitations and the need to ration the doses, the use of a social vulnerability index such as cdc's svi alone as a planning tool for prioritizing vaccinations will not suffice to satisfy the multifaceted mitigation needs of a rational vaccination strategy. assessment of this index with the phgis pa matrix approach found that the cdc.svi risks assigning high priority to locations with the lowest mortality rates, and low priority to locations with the highest mortality rates. the uic sph phgis team is proposing to use a two-dimensional approach for rationalizing the distribution of vaccinations. this approach has the potential to account for areas with high vulnerability characteristics as well as to incorporate the areas that were hard hit by covid-19. further research is under way to develop a planning tool with improved predictive performance that is trained on the covid-19 experience and that incorporates the social vulnerability factors that contribute most to a community's vulnerability to the covid-19 pandemic. these findings could be further explored at a state and county level with the use of the midwest comprehensive visualization dashboard (mcvd), designed specifically for visualizing the spatial distribution of vulnerability and mortality at a county level throughout the midwest. this dashboard is available at: https://univofillinois.maps.arcgis.com/apps/mapseries/index.html?appid=8bd3f5653abb41619b5 0d8c974e8a72b references 1. national academies of sciences, engineering, and medicine. 2020. framework for equitable allocation of covid-19 vaccine. washington, dc: the national academies press. https://doi.org/10.17226/25917. https://univofillinois.maps.arcgis.com/apps/mapseries/index.html?appid=8bd3f5653abb41619b50d8c974e8a72b https://univofillinois.maps.arcgis.com/apps/mapseries/index.html?appid=8bd3f5653abb41619b50d8c974e8a72b a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 9 ojphi 2. dooling, k, marin, m., wallace, m., et al. the advisory committee on immunization practices’ updated interim recommendation for allocation of covid-19 vaccine — united states, december 2020. mmwr morb mortal wkly report 2021; 69:1657-1660. doi: http://dx.doi.org/10.15585/mmwr.mm695152e2external icon. 3. social vulnerability index cdc. centers for disease control and prevention, geospatial research analysis, and services program (grasp), division of toxicology and human health sciences. available at: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html. accessed february 15, 2021. 4. tapsell s, mccarthy s, faulkner h, alexander m. (2010). social vulnerability and natural hazards. caphaz-net wp4 report, flood hazard research centre – fhrc, middlesex university, london available at: http://caphaz-net.org/outcomes-results/caphaznet_wp4_social-vulnerability.pdf. accessed may 21, 2018. 5. surgo ventures. vulnerable communities and covid-19: the damage done, and the way forward. version 1, published january 27, 2021. available at: https://precisionforcovid.org/ccvi. accessed february 10, 2021. 6. flanagan be, gregory ew, hallisey ej, heitgerd jl, lewis b. 2011. a social vulnerability index for disaster management. j homel secur emerg manage. 8(1), 3. doi:https://doi.org/10.2202/1547-7355.1792. 7. arling gw, blaser m, cailas md, canar j, cooper b, et al. 2020. a second wave of covid-19 in cook county: what lessons can be applied? online j public health inform. 12(2), e15. doi:https://doi.org/10.5210/ojphi.v12i2.11506. pubmed 8. blaser m, cailas md, canar j, cooper b, geraci p, et al. analyzing covid-19 mortality within the chicagoland area: data limitations and solutions. research brief no. 117. policy, practice and prevention research center, university of illinois chicago. chicago, il. july 2020. doi: https://doi.org/10.25417/uic.13470324.v19. cdc covid data tracker. federal pharmacy partnership for long-term care (ltc) program. centers for disease control and prevention. available at: https://covid.cdc.gov/covid-datatracker/#vaccinations-ltc. accessed february 18, 2021. 10. recht h, weber l. covid-19: as vaccine rollout expands, black americans still left behind. kaiser health news. january 29, 2021. available at: https://khn.org/news/article/asvaccine-rollout-expands-black-americans-still-left-behind. accessed february 18, 2021. 11. canar j, osiecki k, sambanis a, arling g, cooper b, et al. a comprehensive analytic framework for covid-19 mortality applicable to major metropolitan centers. bmc public health. (submitted to). 12. schimidlin tw, hammer bo, ono y, king ps. 2009. tornado shelter-seeking behavior and tornado shelter options among mobile home residents in the united states. nat hazards. 48, 191-201. https://doi.org/10.1007/s11069-008-9257-z https://doi.org/10.2202/1547-7355.1792 https://doi.org/10.5210/ojphi.v12i2.11506 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=33381281&dopt=abstract https://doi.org/10.1007/s11069-008-9257-z a data driven approach for prioritizing covid-19 vaccinations in the midwestern united states online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e5, 2021 10 ojphi 13. blaser, m, canar, j., arling, g., cailas, m. (2021): midwest comprehensive visualization dashboards: covid-19 mcvd. university of illinois at chicago. phgis program report. phgis-wp-2.2020.12.28. https://doi.org/10.25417/uic.13650440.v1 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a biosurveillance-driven home score to guide strep pharyngitis treatment andrew fine*1, 2, victor nizet3 and kenneth mandl1, 2 1boston children’s hospital, boston, ma, usa; 2harvard medical school, boston, ma, usa; 3ucsd, la jolla, ca, usa objective 1. to derive and validate an accurate clinical prediction model (“home score”) to estimate a patient’s risk of group a streptococcal (gas) pharyngitis before a health care visit based only on history and real-time local biosurveillance, and to compare its accuracy to traditional clinical prediction models composed of history and physical exam features. 2. to examine the impact of a home score on patient and public health outcomes. introduction gas pharyngitis affects hundreds of millions of individuals globally each year, and over 12 million seek care in the united states annually for sore throat. clinicians cannot differentiate gas from other causes of acute pharyngitis based on the oropharynx exam, so consensus guidelines recommend use of clinical scores to classify gas risk and guide management of adults with acute pharyngitis. when the clinical score is low, consensus guidelines agree patients should neither be tested nor treated for gas. a prediction model that could identify very-low risk patients prior to an ambulatory visit could reduce low-yield, unnecessary visits for a most common outpatient condition. we recently showed that real-time biosurveillance can further identify patients at low-risk of gas. with increasing emphasis on patient-centric health care and the well-documented barriers impeding clinicians’ incorporation of prediction models into medical practice, this presents an opportunity to create a patient-centric model for gas pharyngitis based on history and recent local epidemiology. we refer to this model as the “home score,” because it is designed for use prior to a physical exam. methods analysis of data collected from 110,208 patients 3 years and older who presented with pharyngitis to a national retail health chain, from 2006-08. practitioners collected standardized historical and physical exam information based on algorithm-driven care, and all patients with pharyngitis were tested for gas. we used a previously validated biosurveillance variable reflecting disease incidence called recent local proportion positive (rlpp), which represents the proportion of patients who tested gas positive in a local market in the previous 14 days. to derive the “home score,” candidate variables were restricted to demographic factors, historical items and the rlpp, while physical exam variables (such as exudate), were excluded. multivariate analytic techniques were used to identify predictors of gas. for each home score (0-100), we calculated the percent of patients who tested positive, and we examined the relationship between the home score and gas positivity. standard metrics (sensitivity, specificity, positive and negative predictive value, and auc) were used to compare the performance of the home score to standard scores. we computed the number of patients aged >= 15 years who, according to the home score, were at low risk for gas, and therefore might avoid or delay a trip to a medical provider. outcomes included the numbers of reduced visits and the number of additional missed gas cases compared to the standard centor score approach (do not test/do not treat if centor score is 0-1). to facilitate comparison across different risk thresholds, we calculated outcomes for hypothetical cohorts of 1000 patients, and extrapolated these findings to provide the impact on 12 million annual national pharyngitis visits. results the 3 best predictors were fever (or 2.43, 95%ci 2.33-2.54), absence of cough (1.71,1.63-1.80) and rlpp (1.04,1.04-1.04 per unit change in rlpp). using a home score cutoff of 0.10 to identify adults at low risk would save 230,000 ambulatory visits annually while missing only 8500 additional gas cases. at a 0.20 cutoff, 2.9 million visits would be saved, and 320,000 more cases missed each year. there was a strong correlation between the percent testing positive and the home score (r-square=0.98). as the home score increases, there is a linear increase in the risk of gas (slope=1.02). the home score auc was 0.66, approaching the centor score (0.69) even without any physical exam information. conclusions a biosurveillance-driven home score to guide treatment of strep pharyngitis could save millions of visits annually by identifying patients in the pre-visit setting who would be unlikely to be tested or treated. keywords biosurveillance; pharyngitis; retail health *andrew fine e-mail: andrew.fine@childrens.harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e23, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts refinement of a population-based bayesian network for fusion of health surveillance data howard burkom*, yevgeniy elbert, liane ramac-thomas, christopher cuellar and vivian hung johns hopkins applied physics laboratory, laurel, md, usa objective the project involves analytic combination of multiple evidence sources to monitor health at hundreds of care facilities. a demonstration module featuring a population-based bayes network [1] was refined and expanded for application in the department of defense electronic surveillance system for community-based epidemics (essence). introduction the essence demonstration module was built to help dod health monitors make routine decisions based on disparate evidence sources such as daily counts of ili-related chief complaints, ratios of positive lab tests for influenza, patient age distribution, and counts of antiviral prescriptions [1]. the module was a population-based (rather than individual-based) bayesian network (pbn) in that inputs were algorithmic results from these multiple aggregate data streams, and output was the degree of belief that the combined evidence required investigation. the module reduced total alerts substantially and retained sensitivity to the majority of documented outbreaks while clarifying underlying sources of evidence. the current effort was to advance the prototype to production by refining components of the fusion methodology to improve sensitivity while retaining the reduced alert rate. methods the multi-level approach to sensitivity improvement included expanded syndromic queries, more data-sensitive algorithm selection, improved transformation of algorithm outputs to alert states, and hierarchical training of bayesian networks. components were tested individually, and the net result was iteratively refined with performance using documented outbreaks. we examined time series of classes of prescribed drugs and laboratory tests during known events and discussed outbreak-associated elements with domain experts to liberalize data queries. algorithms were matched to data streams with injection testing applied to 4.5 years of data from 502 outpatient clinics. a hierarchical approach was applied for improved training and verification of pbns for events related to categories of influenza-like illness, gastrointestinal, fever, neurological, and rash, chosen both for public health importance and for availability of multiple supporting data types. hierarchical, modular training was applied to common subnetworks, such as a severity indicator pbn depending on case disposition, acute case indicators, complex evaluation/management codes, and patient bounce-backs, depicted in figure 1. conversion of individual algorithm outputs to belief states (e.g. “at least two red alerts/past 7 days”) was broadened using analysis of lags between data sources. with data from the known events, we calculated decision support thresholds for the parent-level pbn decision nodes with a stochastic optimization technique maximizing the ratio of alert rates during outbreak to non-outbreak periods. results the expanded data queries, more stream-specific algorithm selection, generalized state transformation, and hierarchical pbn training detected 22 of an expanded collection of 24 documented outbreaks, with incremental improvement ongoing. the mean alert rate drop achieved by the bayes net was 87% (minimum of 85%) compared to the combined alerts of all component algorithms across syndromes and facilities. conclusions expansion and further technical validation upheld the pbn approach as a user-friendly means of analytic decision support given multiple, variably weighted evidence sources. the pbn affords not only sharply reduced alerting, but also transparent indication of evidence underlying each alert. the older algorithm approach remains available as backup. beta testing of the resulting production system will drive further modification. figure 1: pbn subnetwork for event severity, based on outpatient data fields keywords fusion; bayesian network; multivariate; decision support acknowledgments drs. julie pavlin and rhonda lizewski of the armed forces health surveillance center for dataand event-related consultation and joe lombardo and wayne loschen of johns hopkins apl for consultation on production enhancement. references burkom h, elbert y, ramac-thomas l et al., analytic fusion of essence clinical evidence sources for routine decision support, emerging health threats journal supplements, eissn 1752-8550, issn 2001 1350 (print). *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e6, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts sages update: electronic disease surveillance in resource-limited settings lewis l. sheri, timothy c. campbell, jacqueline s. coberly, richard a. wojcik, shraddha v. patel and brian feighner* johns hopkins university applied physics laboratory, laurel, md, usa objective the suite for automated global electronic biosurveillance (sages) is a collection of modular, flexible, open-source software tools for electronic disease surveillance in resource-limited settings. this demonstration will illustrate several new innovations and update attendees on new users in africa and asia. introduction the new 2005 international health regulations (ihr), a legally binding instrument for all 194 who member countries, significantly expanded the scope of reportable conditions and are intended to help prevent and respond to global public health threats. sages aims to improve local public health surveillance and ihr compliance with particular emphasis on resource-limited settings. more than a decade ago, in collaboration with the us department of defense (dod), the johns hopkins university applied physics laboratory (jhu/apl) developed the electronic surveillance system for the early notification of community-based epidemics (essence). essence collects, processes, and analyzes non-traditional data sources (i.e. chief complaints from hospital emergency departments, school absentee data, poison control center calls, over-the-counter pharmaceutical sales, etc.) to identify anomalous disease activity in a community. the data can be queried, analyzed, and visualized both temporally and spatially by the end user. the current sages initiative leverages the experience gained in the development of essence, and the analysis and visualization components of sages are built with the same features in mind. methods sages tools are organized into four categories: 1) data collection, 2) analysis & visualization, 3) communications, and 4) modeling / simulation / evaluation. within each category, sages offers a variety of tools compatible with surveillance needs and different types or levels of information technology infrastructure. sages tools are built in a modular nature, which allows for the user to select one or more tools to enhance an existing surveillance system or use the tools en masse for an end-to-end electronic disease surveillance capability. thus, each locality can select tools from sages based upon their needs, capabilities, and existing systems to create a customized electronic disease surveillance system. new openessence developments include improved data query ability, improved mapping functionality, and enhanced training materials. new cellular phone developments include the ability to concatenate single sms messages sent by simple or smart android cell phones. this ‘multiple-sms’ message ability allows use of sms technology to send and receive health information exceeding normal sms message length in a manner transparent to the users. conclusions the sages project is intended to enhance electronic disease surveillance capacity in resource-limited settings around the world. we have combined electronic disease surveillance tools developed at jhu/apl with other freely-available, interoperable software tools to create sages. we believe this suite of tools will facilitate local and regional electronic disease surveillance, regional public health collaborations, and international disease reporting. sages development, funded by the us armed forces health surveillance center, continues as we add new international collaborators. sages tools are currently deployed in locations in africa, asia and south america, and are offered to other interested countries around the world. keywords software; surveillance; electronic; open-source *brian feighner e-mail: brian.feighner@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e203, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts adapting syndromic surveillance systems to increase value to local health departments erika samoff*1, mary t. fangman1, amy ising2, lana deyneka3 and anna e. waller2 1ncperrc, university of north carolina, chapel hill, nc, usa; 2carolina center for health informatics, university of north carolina school of medicine, chapel hill, nc, usa; 3north carolina division of public health, chapel hill, nc, usa objective our objective was to describe changes in use following syndromic surveillance system modifications and assess the effectiveness of these modifications. introduction syndromic surveillance systems offer richer understanding of population health. however, because of their complexity, they are less used at small public health agencies, such as many local health departments (lhds). the evolution of these systems has included modifying user interfaces for more efficient and effective use at the local level. the north carolina preparedness and emergency response research center previously evaluated use of syndromic surveillance information at lhds in north carolina. since this time, both the nc detect system and distribution of syndromic surveillance information by the state public health agency have changed. this work describes use following these changes. methods data from nc detect were used to assess the number of users and usage time. staff from 14 nc lhds in 2009 and from 39 lhds in 2012 were surveyed (may-august of 2009 and june of 2012) to gather information on the mode of access to syndromic surveillance information and how this information was used. data were analyzed to assess the link between the mode of access and use of syndromic surveillance data. results system changes made between 2009 and 2012 included the creation of “dashboards” (figure 1) which present users with lhd-specific charts and graphs upon login and increases in the distribution of syndromic surveillance information by the state public health agency. the number of lhd-based nc detect system users increased from 99 in 2009 to 175 in 2012. sixty-two of 72 respondents completed the 2012 survey (86%). syndromic surveillance information was used in 28/40 lhds (70%) for key public health tasks. among 20 nc edss leads reporting an outbreak in the past year, 25% reported using data from nc detect for outbreak response, compared to 23% in 2009 (figure 2). among 30 responding nc edss leads, 57% reported using data from nc detect to respond to seasonal events such as heat-related illness or influenza, compared to 46% in 2009. nc detect data were reported to have been used for program management by 30% (compared to 25% in 2009), and to have been used in reports by 33% (compared to 23% in 2009). conclusions changes in how syndromic surveillance information was distributed supported modest increases in use in lhds. because use of syndromic surveillance data at smaller lhds is rare, these modest increases are important indicators of effective modification of the nc syndromic surveillance system. keywords evaluation; public health practice; syndromic surveillance; surveillance; local health department acknowledgments we thank aaron fleischauer, anne hakenewerth, and north carolina local health department staff members for their time and insights. this research was carried out by the north carolina preparedness and emergency response research center (ncperrc) which is part of the unc center for public health preparedness at the university of north carolina at chapel hill’s gillings school of global public health and was supported by the centers for disease control and prevention (cdc) grant 1po1 tp 000296. the contents are solely the responsibility of the authors and do not necessarily represent the official views of cdc. additional information can be found at http://cphp.sph.unc.edu/ncperrc/. *erika samoff e-mail: erika.samoff@unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e83, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts surveillance-based program planning rossi sanusi* center for health service management, gadjah mada university school of medicine, yogyakarta, indonesia objective to analyze the integrated behavioral & biological surveillance (ibbs) 2011 data for designing a condom utilization program. introduction the ibbs is part of the indonesian moh hiv surveillance system, which include serological surveillance, behavioral surveillance, reproductive tract infection survey, and monthly hiv/aids facilitybased (hospitals, hcs, vct sites) monthly reports. the ibbs 2011 was conducted in 11 provinces (22 districts/municipalities) encompassing eight most at risk populations (marps) – injection drug users, transsexuals, men who have sex with men, youths, inmates, mobile men, direct female sex workers (fsws), and indirect fsws. data of 442 direct fsws of the jayapura municipality and jayawijaya district (papua province) showed that 406 (91.85%) have sex with partners who did not use condoms. of these 406 fsws 60 (14.78%) were hiv positive and 231 (56.89%) were std positive. methods items of the direct fsw questionnaire, ibbs 2011, were examined and items that would yield information regarding content and method of hiv prevention interventions by means of condoms were identified. the stata12 software was used to inspect/codebook the variables related to the selected items, to recode numeric data into categorical data, to generate one-way and two-way tables, and to produce pairwise correlations (and their significance levels). results the direct fsws ibbs 2011 data of the jayapura municipality and jayawijaya district showed that there are significant positive correlations between condom use behavior variables of fsws (i.e., to know, to possess, to buy, and to offer male condoms) and variables of last-sex encounter condom use by customers, and between the latter and hiv and std lab results. the correlations were low, however, of the condom use behavior variables and variables that are related to comprehensive knowledge of hiv prevention, condom use by more steady sex partners (e.g., husbands, boyfriends, other males) and female condom utilization, and during last-week and last-month sex transactions. the data analyses also indicated details of the distribution of the fsws, with their condom use behaviors, according to individual characteristics, cie (communication, information & education) intervention utilization, condom acquirement, and sexual behavior. conclusions the condom utilization campaigns ought to focus on continuous reminders (instead of education programs) about how to persuade customers and other sex partners to use condoms, or to allow fsws to use female condoms, and about where to go for hiv/std testing and treatment. the condom promotion drives should use posters, tv ads, and field/health workers, the program should also make certain that good quality condoms be made avaible by local managers (of brothels, hotels, bars, etc.) and local vendors (drugists, stands, mobile carts). keywords ibbs; condom use; hiv; fsws; papua acknowledgments the author thanks the directorate general of disease control & environmental health, moh, republic of indonesia (for allowing to use the ibbs 2011 data) and family health indonesia (for funding the ibbs 2011 data analysis training program). references ghimire, l., smith, w., & van teijlingen, e.r. (2011). utilisation of sexual health services by female sex workers in nepal. bmc health services research 2011, 11:79. longfield, k., panyanouvong, x., chen, j., & kays, m.b.. (2011). increasing safer sexual behavior among lao kathoy through an integrated social marketing approach. bmc public health 2011, 11:872. moh, republic of indonesia. (2011). report on the development of hiv & aids in indonesia until june 2011. *rossi sanusi e-mail: rossi_sanusi@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e175, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts advancing surveillance outside the usa: the canadian policy, practice, and research context effie gournis*1, 2 and david buckeridge3, 4 1toronto public health, toronto, on, canada; 2dalla lana school of public health university of toronto, toronto, on, canada; 3mcgill university, montreal, qc, canada; 4direction de sante publique de montreal, montreal, qc, canada objective 1) to explore how isds can better support researchers and public health practitioners working in the field of disease surveillance outside the united states; and 2) to identify current surveillance issues in the canadian public health system where isds can support dialogue and action. introduction the international society for disease surveillance has successfully brought together practitioners and researchers to share tools, ideas, and strategies to strengthen health surveillance systems. the society has evolved from an initial focus on syndromic surveillance to a broader consideration of innovation in health surveillance. more recently, isds has also worked to support surveillance research and practice in international resource-constrained settings. individuals who work in surveillance in developed countries outside the usa, however, have received little direct attention from isds. the policy and practice contexts in these countries are often quite different than the usa, so there is a need to support surveillance innovation in these countries in a manner that fits the context. canadian surveillance practitioners and researchers comprise the largest international group of isds members, and these members have expressed an interest in working with isds to translate surveillance innovations into practice in canada, where a national surveillance network and forum is lacking. this round table will consider how isds can help to support members in countries like canada and will identify next steps for promoting the science and practice of disease surveillance in the canadian context. methods individuals attending the isds 2012 conference with an interest in public health surveillance in canada or other similar countries outside the usa will be invited to discuss how isds can better support their activities. the discussion will be structured around questions and results received for a survey circulated to canadian isds members. the goal will be to discuss whether there is a specific formal role isds can play in helping members in canada and other similar countries working in public health surveillance. results discussion will be prompted through sharing results of a recent survey distributed to all canadian isds members and affiliates aimed at gauging their interest in developing a canadian focused group within isds, whether they believe there is a need, and how we might accomplish this. the survey questions, range of answers, and implications to future actions suggested in survey responses would drive the discussion. keywords international surveillance; canada; surveillance network *effie gournis e-mail: egourni@toronto.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e193, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts enhanced influenza surveillance using telephone triage data in the va essence biosurveillance system cynthia a. lucero-obusan*1, carla a. winston1, patricia l. schirmer1, gina oda1, anoshiravan mostaghimi1 and mark holodniy1, 2 1department of veterans affairs, office of public health, washington, dc, usa; 2division of infectious diseases & geographic medicine, stanford university, stanford, ca, usa objective to evaluate the utility and timeliness of telephone triage (tt) for influenza surveillance in the department of veterans affairs (va). introduction telephone triage is a relatively new data source available to biosurveillance systems.1-2 because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, overthe-counter drug sales, electronic laboratory reporting, internet searches (e.g. google flu trends) and tt]. these sources may provide disease activity alerts earlier than conventional sources. little is known about whether va telephone program influenza data correlates with established influenza biosurveillance. methods veterans phoning va’s tt system, and those admitted or seen at a va facility with influenza or influenza-like-illness (ili) diagnosis were included in this analysis. influenza-specific icd-9-cm coded emergency department (ed) and urgent care (uc) visits, hospitalizations, tt calls, and ili outpatient visits were analyzed covering 2010-2011 and 2011-2012 influenza seasons (july 11, 2010-april 14, 2012). data came from 80 va medical centers and over 500 outpatient clinics with complete reporting data for the time period of interest. we calculated spearman rank-order coefficients, 95% confidence intervals and p-values using fisher’s z transformation to describe correlation between tt data and other influenza healthcare measures. for comparison of time trends, we plotted data for hospitalizations, ed/uc visits and outpatient ili syndrome visits against tt encounters. we applied essence detection algorithms to identify high-level alerts for influenza activity. essence aberration detection was restricted to the 2011-2012 season because limited historical tt and outpatient data from 2009-2010 was available to accurately predict aberrancy in the 2010-2011 season. we then calculated the peak measure of healthcare utilization during both influenza seasons (2010-2011 and 2011-2012) for each data source and compared timing of peaks and alerts between tt and other healthcare encounters to assess maximum healthcare system usage and timeliness of surveillance. results there were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ili-coded outpatient visits. spearman rank correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); ed/uc visits (0.85); and ili-outpatient visits (0.88), respectively (p< 0.0001 for all correlations). peak influenza activity occurred on the same week or within 1 week across all settings for both seasons. for the 2011-2012 season, tt alerted with increased influenza activity before all other settings. conclusions data from va telephone care correlates well with other va data sources for influenza activity. tt may serve to augment these existing clinical data sources and provide earlier alerts of influenza activity. as a national health care system with a large patient population, va could provide a robust early-warning system for influenza if ongoing biosurveillance activities are combined with tt data. additional analyses are needed to understand and correlate tt with healthcare utilization and severity of illness. keywords surveillance; influenza; telephone triage; veterans references 1. yih wk, teates ks, abrams a, kleinman k, kulldorff m, pinner r, harmon r, wang s, platt r: telephone triage service data for detection of influenza-like illness. plos one 2009, 4(4):e5260. 2. van dijk a, mcguinness d, rolland e, moore km: can telehealth ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health? cjem 2008, 10(1):18-24. *cynthia a. lucero-obusan e-mail: cynthia.lucero@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e103, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts update from cdc’s public health surveillance & informatics program office (phsipo) james buehler*, laura conn, carol crawford and kathleen gallagher centers for disease control & prevention, atlanta, ga, usa objective to provide updates on current activities and future directions for the national notifiable diseases surveillance system (nndss), biosense 2.0, and the behavioral risk factor surveillance system (brfss) and on the role of phsipo as the “home” at cdc for addressing cross-cutting issues in surveillance and informatics practice. introduction the practice of public health surveillance is evolving as electronic health records (ehrs) and automated laboratory information systems are increasing adopted, as new approaches for health information exchange are employed, and as new health information standards affect the entire cascade of surveillance information flow. these trends have been accelerated by the federal program to promote the meaningful use of electronic health records, which includes explicit population health objectives. the growing use of internet “cloud” technology provides new opportunities for improving information sharing and for reducing surveillance costs. potential benefits include not only faster and more complete surveillance but also new opportunities for providing population health information back to clinicians. for public health surveys, new internet-based sampling and survey methods hold the promise of complementing existing telephonebased surveys, which have been plagued by declining response rates despite the addition of cell-phone sampling. while new technologies hold promise for improving surveillance practice, there are multiple challenges, including constraints on public health budgets and the workforce. this panel will explore how phsipo is addressing these opportunities and challenges. methods panelists will provide updates on 1) phsipo’s role in engaging health departments, the organizations that represent them, and cdc programs in shaping national policies for implementing the meaningful use program, 2) how the biosense 2.0 program is supporting growth in syndromic surveillance capacity, including its partnership with isds in developing standards for syndromic surveillance as part of meaningful use, 3) improvements that are underway in strengthening the nndss, including efforts to improve cdc’s support for health department disease reporting systems and to develop a “shared services” approach that could provide a platform for streamlining the exchange of information between health departments and cdc, 4) pilot development of internet-based panels of survey volunteers to supplement existing telephone-based sampling in the brfss and of approaches to extend brfss survey information through consentbased linkage of survey responses to selected measures recorded in respondents’ ehrs. results potential questions or discussion points that might arise include: what can or should be done to assure that the population health objectives of meaningful use are fulfilled? what are the lessons learned to date in leveraging investments in the meaningful use of ehrs to improve disease reporting and syndromic surveillance systems? what are the next steps in developing biosense 2.0 to assure that it leads to strengthened surveillance capacity at both state/local and regional/national levels? how can insights from the biosense redesign be applied to improve case reporting and other surveillance capacities? what is cdc doing to address states’ concerns about the growing number of cdc surveillance systems? how will national discussions about the future of public health affect the future surveillance practice? what can be done to assure the ongoing representativeness of population health surveys? is it feasible to link brfss responses to information obtained from ehrs? how can data from surveillance become part of the real-time evidence base for clinical decision making? conclusions the intended outcome of the panel is to foster a conversation between the panelists and the audience, to inform the audience about recent developments in phsipo, to obtain insights from the audience about innovations and ideas arising from their experience, and to generate new ideas for approaches to meeting the needs of public health for surveillance information. keywords surveillance; biosense 2.0; notifiable diseases; brfss—behavioral risk factor surveillance system acknowledgments the authors wish to acknowledge the many individuals from health departments, academia, and other agencies who have contributed to the ongoing operation and improvement of the nndss, biosense 2.0, and the brfss. references for more information about phsipo, see: http://www.cdc.gov/osels/phsipo *james buehler e-mail: jwb2@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e98, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts disease surveillance and achieving synergy in public health quality improvement peggy a. honoré*1 and laura c. streichert2 1u.s. department of health and human services, washington, dc, usa; 2international society for disease surveillance, brighton, ma, usa objective to examine disease surveillance in the context of a new national framework for public health quality and to solicit input from practitioners, researchers, and other stakeholders to identify potential metrics, pivotal research questions, and actions for achieving synergy between surveillance practice and public health quality. introduction national efforts to improve quality in public health are closely tied to advancing capabilities in disease surveillance. measures of public health quality provide data to demonstrate how public health programs, services, policies, and research achieve desired health outcomes and impact population health. they also reveal opportunities for innovations and improvements. similar quality improvement efforts in the health care system are beginning to bear fruit. there has been a need, however, for a framework for assessing public health quality that provides a standard, yet is flexible and relevant to agencies at all levels. the u.s. health and human services (hhs) office of the assistant secretary for health, working with stakeholders, recently developed and released a consensus statement on quality in the public health system that introduces a novel evaluation framework. they identified nine aims that are fundamental to public health quality improvement efforts and six cross-cutting priority areas for improvement, including population health metrics and information technology; workforce development; and evidence-based practices (1). applying the hhs framework to surveillance expands measures for surveillance quality beyond typical variables (e.g., data quality and analytic capabilities) to desired characteristics of a quality public health system. the question becomes: how can disease surveillance help public health services to be more population centered, equitable, proactive, health-promoting, risk-reducing, vigilant, transparent, effective, and efficient—the desired features of a quality public health system? any agency with a public health mission, or even a partial public health mission (e.g., tax-exempt hospitals), can use these measures to develop strategies that improve both the quality of the surveillance enterprise and public health systems, overall. at this time, input from stakeholders is needed to identify valid and feasible ways to measure how surveillance systems and practices advance public health quality. what exists now and where are the gaps? methods improving public health by applying quality measures to disease surveillance will require innovation and collaboration among stakeholders. this roundtable will begin a community dialogue to spark this process. the first goal will be to achieve a common focus by defining the nine quality aims identified in the hhs consensus statement. attendees will draw from their experience to discuss how surveillance practice advances the public health aims and improves public health. we will also identify key research questions needed to provide evidence to inform decision-making. results the roundtable will discuss how the current state of surveillance practice addresses each of the aims described in the consensus statement to create a snapshot of how surveillance contributes to public health quality and begin to articulate practical measures for assessing quality improvements. sample questions to catalyze discussion include: —how is surveillance used to identify and address health disparities and, thereby, make public health more equitable? what are the data sources? are there targets? how can research and evaluation help to enhance this surveillance capability and direct action? —how do we identify and address factors that inhibit quality improvement in surveillance? what are the gaps in knowledge, skills, systems, and resources? —where can standardization play a positive role in the evaluation of quality in public health surveillance? —how can we leverage resources by aligning national, state, and local goals? —what are the key research questions and the quality improvement projects that can be implemented using recognized models for improvement? —how can syndromic surveillance, specifically, advance the priority aims? the roundtable will conclude with a list of next steps to develop metrics that resonate with the business practices of public health at all levels. keywords public health quality; metrics; framework references 1. honoré pa, wright d, berwick dm, clancy cm, lee p, nowinski j, koh hk. creating a framework for getting quality into the public health system. health aff (millwood). 2011 apr;30(4):737-45. *peggy a. honoré e-mail: peggy.honore@hhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e195, 2013 implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology stephen swanik* division of health policy and administration, public health informatics program, school of public health, university of illinois at chicago and departments of medical education and dentistry at advocate illinois masonic medical center, chicago, il. abstract objectives: the development of new information technology has significant effects on the health care system, and its implementation and the associated change management process can bring some positive changes and gains in understanding, but there are challenges with making the transition. these benefits and challenges are explored in the context of a hospital based dental department. additionally, the concept of the integration of oral health to overall systemic health is explored in context with an electronic medical records system implementation, and the american dental association’s recent recognition of dental anesthesiology as a clinical subspecialty. method: qualitative survey of attending dental faculty members of the department, who represent a broad range of dental specialties and experience in private practice, hospital based practice, teaching, and public health practice. results: the faculty survey yielded some consistent themes, ranging from enhanced information to make better diagnoses, to challenges in transitioning to emr, as well as concerns about data security and too much time and effort in front of a computer screen. discussion: a brief summary of the history of the stand-alone development of dentistry is given, which contributed to the separate development of dental emrs from hospital emrs. the various modalities of clinical care provided by the department of dentistry at advocate illinois masonic medical center, chicago, il are presented to give a scope of the areas of need a successful emr solution must meet in a hospital based dental setting. public health aspects are included in the discussion. conclusion: macro level health data sets (ie nhanes, state level datasets) have the potential to be expanded to include more thorough data, combining medical health data and oral health data in the same datasets. *correspondence: sswani3@uic.edu, steve.swanik@advocatehealth.com, sswanik15@yahoo.com mailto:sswanik15@yahoo.com implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi 1. introduction this writing will take a look at the considerations that go along with implementation of an electronic dental record system in a hospital based environment, with a discussion on the implications for public health that can come from a higher quality data yield that can result from a model consistent with integration of oral health and overall medical/systemic health. the department featured is the department of dentistry at advocate illinois masonic medical center in chicago, il, usa, a teaching hospital in the non-profit advocate-aurora health system. this is a large regional hospital network consisting of 27 hospitals and 500 sites of care across the states of illinois and wisconsin. the dental department at illinois masonic is the only full clinical dental department in this entire health system, and it is anticipating a transition to the epic wisdom dental emr platform in 2020. the department currently uses paper charts for clinical records and an electronic system for scheduling and billing. 2. department background and brief history: teaching/education and public health components the dental department was originally started in 1970 as a means to provide dental care for special needs children, benefiting from a close relationship with masonic charities through its affiliation with illinois masonic hospital. it developed a postgraduate general practice residency dental training program (gpr), and a dental practice. presently, the dental department is on the same standing in the hospital as the other clinical departments. it provides emergency on call services for the hospital, provides dental consults to inpatients, and sees patients with other medical comorbidities that make it medically risky for them to receive dental care in stand-alone dental practices. in 2000, it launched a mobile dental van program, providing dental care to a diverse set of sites including chicago public schools, thresholds psychiatric rehabilitation centers, and others. in 2016, the department aligned with the neighboring howard brown health center, an fqhc with a focus on providing healthcare to the lgbt community, to provide routine and complex dental care for its patient population. the department is launching a residency training program in dental anesthesiology, the newest recognized dental specialty [1] in july, 2019. when advocate health care and aurora care merged in 2018, system leadership decided to implement epic throughout the entire system. as of this writing, the dental department is slated to convert from paper based records to epic wisdom in june 2020. doi: 10.5210/ojphi.v11i2.10131 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi the department has had significant historical challenges with respect to its conversion to emr, yet it enjoys very broad success with its teaching programs and its public health programs. the department houses a longstanding postgraduate dental general practice residency (gpr) training program and will be launching the training program in dental anesthesiology in july 2019. at any given time, there are approximately 35 attending members of the department, most of whom have part time involvement ranging from activity such as teaching the residents a half a day a month, or as clinicians who perform dental cases in the hospital’s operating room. the gpr houses 9 residents and is a one year program in length. the dental anesthesiology program will house two residents per year and is a 3 year long training program. the structure of the department, considering its public health practice, its relationship with its hospital, and its upcoming conversion to an emr system lends to it being a good example with respect to strategically planning for public health utility of an emr system. it can additionally serve as a good example of dynamic change management in process, with positive takeaways and errors to learn from. the department has programs to provide care to different population modalities. it has a longstanding special patient dental care program, providing dental care for special needs patients, often times under iv sedation provided by a dental anesthesiologist and the dental residents. it has a mobile dental van which serves a variety of sites, including schools, assisted living facilities, psychiatric centers, and others on a monthly rotating basis. it also has a partnership with howard brown health center, a local fqhc which provides care primarily to chicago’s large lgbt community, to provide oral health care to its patient population. the launch of the dental anesthesiology program will additionally provide an avenue to increase departmental capacity to provide care for special needs patients. 2.1 major population groups served/sociodemographic analysis the department serves a diverse patient population, and has many programs in place to provide services to a diverse variety of underserved patients. the major population groups that are served by the department, in addition to a cohort of general ppo patients seeking routine dental care and hospital employees are as follows: the special patient dental care program --this program, as its name implies, provides dental care to special needs patients. these conditions include cerebral palsy, autism spectrum disorder, and others. many of these patients are treated under iv sedation guided by a dental anesthesiologist, dr. ken kromash. additionally, this is a significant component of training for the dental residents. with the upcoming addition of a dental anesthesiology training program, the department is anticipating increasing its capacity to provide care to special needs patients, particularly special needs children. hbhc and lgbt population --in 2016, howard brown partnered with the dental department to provide dental care for its patients. some of these patients are hiv +, some are transgender, implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi and many of them are lgbt. other literature exists [2,3] on the considerations of providing culturally competent care for the lgbt community, and this partnership aims to meet those ends. the hbhc partnership provides approximately 25% of the patient population of the clinic. medically compromised patients --the department, being hospital based, assumes oral health responsibility for the hospital inpatients. it sees medically compromised patients who require dental care that may be unsafe to provide in a private practice dental setting due to the medical condition of the patient. an example of this modality would be an inpatient requiring a complicated anesthetic intubation who receives a preoperative dental clearance or dental treatment from the dental department before the other medical treatment. mobile van populations --the department’s mobile van populations include a variety of patients from a variety of sites. some of the partnerships exist merely to meet a socioeconomic need, but some sites, such as psychiatric and geriatric sites, may present a coexisting medical need as well. each of these populations, for one reason or another, presents a potential need for clinical care that is a union of both medical care and dental care. 2.2 history and current status of emr conversion there have been two previous unsuccessful attempts to convert the department to emr in the last ten years. the most recent was an attempt at a conversion to eaglesoft, a major dental emr program in the market. the vendor and hospital technical teams were in the process of resolving security compatibility of a dental software program that was not developed with a primary focus of being implemented in a large hospital system, and the business contracts were in the process of being developed, signed, and executed. this project was aborted when advocate health care merged with aurora health care, and the decision was made to convert all advocate emr systems to epic systems, inc. – meaning of course that the dental program became contractually obliged to utilize epic wisdom, the dental module for the epic software platform. this transition is now scheduled to occur in 2020. the challenges associated with security capability can be traced at least somewhat back to the concept of the historical division of oral health from overall systemic health. as dental emr software systems were initially designed, the concept of implementing them into hospital-based environments was not the first priority, and conversely when hospital emrs were developed, the dental/oral health components were not the first priority. a previous attempt in around 2014 – 2015 also failed, but this failure was specifically at the departmental level, as clinicians and administrative management were unable to successfully collaborate and agree on an emr system. what was particularly telling about this is that the postgraduate residents in the educational training program, though highly wanting to utilize emr, strongly objected to the chosen product, in part because “it is apparent that this system was chosen based on convenience and the long-term relationship built with a current vendor. we feel that even implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi though the institution wants to respect a long-term relationship with the vendor, the software offered does not satisfy the needs of the organization.” 3. intersection between emr system and physician there already exists significant literature on the topic of the relationship between the physician, the patient and the emr system. challenges that still exist include the fact that many physicians feel burdened by certain aspects of the emr system, as examples they spend much more time performing administrative tasks, and many of them feel that during patient encounters they are spending too much time looking at a computer screen and not interacting with patients. schulte and fry recently published a summary [4] of this conversation in medscape. some of these challenges were reported in a survey conducted of the department teaching faculty. 4. brief summary of the history of the divide between oral health and systemic health in the us another factor to look at is the relationship between oral health and systemic health in the u.s. health system. this has been a longstanding “fait accompli” in the us. however there are current initiatives to move towards a more full integration of oral health and systemic health, as biologically these two systems are fully intact and interact with each other – a dental abscess leading to an infection serving as a routine example. mertz wrote a summary [5] in 2016 of how we got to where we are today in health affairs. some of the highlights of this include the historical routes of “barber-surgeons,” “distinct from physicians, nurses, and pharmacists.” as such, dental schools were independently established after the development of medical schools. dental insurance was not originally packaged with health or medical insurance and was developed as a separate product line from medical insurance. the clinical delivery system was designed separately and distinctly from medical systems; dental practice and hygiene teams have their own separate training apparatuses. and though the affordable care act took a stab towards pediatric dental coverage, it did not significantly address dental coverage for adults. mertz also touches a key point for this writing, that “electronic dental records are rarely interoperable with medical records, limiting oral and systemic health research, and dentistry lacks a fully deployed set of diagnostic codes, stymieing comparative effectiveness research.” simon advocates in the ama journal of ethics [6] for integration and increased collaboration as a way to promote health equity, mentioning areas in which physician training can incorporate areas of oral health education, and vice versa, but does not cover the role of an emr. the american dental association additionally has invested some resources exploring the integration of oral health with overall systemic health and has produced literature on this topic. its chief economist, dr. marko vujicic, wrote an editorial [7] in the journal of the american dental association about the topic. some key topics he addressed include defining and systematically measuring oral health, and reforming the care delivery model. implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi an area of high significance with respect to both oral and medical care is the presence and slated expansion of dental anesthesiology in the department. in 2016, giovannitti et al wrote a detailed summary of the specialty [8] and its history. this article covers a brief history of the discovery of anesthesia, and the clinical relationship between dentistry and anesthesia. it also gives a history of the development of dental anesthesiology training programs, and a detailed and thorough explanation of the educational standards. it then goes into a discussion of need, growth, and challenges of the discipline. as the topic of this article was about the specialty of dental anesthesia, and highly focused on clinical training standards, it did not address emr systems. an analogy can be made however between the clinical integration of oral and medical health brought about by dental anesthesiology and the digital integration of digital oral health and digital medical health brought about by an emr system encapsulating both. a key takeaway from this writing is that an integrated emr can be used as a tool to drive the integration, and that this integrated knowledge is beneficial for patient care and ultimately public health. 5. survey of dental faculty there are approximately 35 faculty members on the medical staff at aimmc, and they were all asked to participate in a voluntary survey about their experiences and thoughts with emr. the survey focused on the public health aspects of an emr and the clinicians’ thoughts about data interoperability. the goal was to obtain a minimum of 10 quality responses, and this was achieved. the faculty provided many high quality comments, and these responses converged over some major themes and reinforced the necessity to further address those themes – particularly themes such as user friendliness, data security. the survey questionnaire was developed with the help of drew gripentrog, dmd, who is an alum of the gpr program and a current attending faculty member. there were 12 responses, and these responses yielded some consistent themes. these were the questions asked in the questionnaire: 1. what in your opinion are the most important features/characteristics of an emr system itself? 2. do you currently use an emr in private practice? is it cloud based? 3. have you ever been involved in the history of an implementation/conversion in either a private practice or a hospital/school-based environment? what are some key takeaways from that experience? 4. what do you find to be the most important oral health considerations that emr can help to identify or address? 5. do you see any key public health insights that can be derived from emr? 6. do you notice any trends among population groups with respect to any specific oral health concerns – whether those population groups are age, income, ethnicity, geographic locations, or others? how does emr facilitate identifying trends? implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi 7. what characteristics do you most hope to see in the future dental emrs? 8. how significant do you find data transmission between sites or other colleagues to be with respect to care? things such as patient referrals, transfer of x-rays from one sight to another sight? what type of information transfer do you find the most important to be for effective care? 9. if you could integrate your private practice’s emr and its respective data with any hospital systems you are affiliated with, would you? what do you see as potential strengths of this? what concerns would you have? 10. has the opioid crisis affected your clinical practice and if so how? here are some selected responses with some comments: “i converted my private pediatric dental practice in the chicago suburbs from a paper based charting system and computerized billing (on an antiquated dental software program) with digital radiography to a completely paperless integrated system (emr). it took 6 months, i had to purchase a new $10,000 server, and lots of extra manpower to scan all that paper and convert the accounts from one computer system to another. choose the new software system wisely (do your research for ease of use and conversion support), be sure your it people are aware of your storage needs first, and be sure your office staff have proper training, motivation, and take ownership of the project. set goals and achieve them.” this is a response to question 6, aligning with the concept of utilization of emr to pull reports for public health purposes. “yes perio in the hispanic population. a lot of enamel hypoplasia in our geographic location. yes much easier to pull reports. this is also good for financials and seeing public health trends” this comment was in response to question 7, regarding future potentials of emrs. an interesting catch with respect to the medical history and how further development can still come for more accurate diagnostic information in medical histories. “compatibility with their physicians would be amazing so that the medical history wouldn't be self reported instead we could see what is really going on and what medications they are taking, how well they are controlled, etc.” another survey: “identification of oral habits can be correlated to race and culture, caries risk assessment and anticipatory guidance can be quite related to social economical status.” implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi “it will be very beneficial if any oral health provider could somehow communicate within the emr system regardless of the hospital network they belong to.” this comment ties into the large historical conversation of it development and interoperability as it relates to the proprietary features of computer systems: historically apple products and microsoft pcs and products couldn’t talk to one another – and this change has started to become more commonplace in large part due to consumer demand and expectation. this perhaps may be area where health care can learn from history in other segments. if the system develops so that emrs establish a de factor oligopoly, then the question of proprietary rights and effective transmission of data across competitors will need to be addressed. another survey: “yes. data collection. being able to monitor disease incidence, prevalence, and treatment outcomes. being able to determine quality measures for value. being able to look at trends over time. being able to share information with non-medical folks who can help with things like nutritional counseling, smoking cessation programs, etc.” “i hope to see a way to integrate with medical emr systems. i hope to be able to refer directly to other colleagues like behavioral therapists, occupational therapists, physical therapists, nutritionists, drug and smoking cessation counselors, pharmacists. i'd want those colleagues to see what dental treatments we were providing and why. i'd want my dental treatment to need a diagnostic code so other providers can know why i'm performing certain treatments. i'd want my dental diagnostic code to have to link to a medical code when necessary. for example, if i've diagnosed periodontal disease, i'd want a medical diagnostic code that connects when relevant. for example, if a patient has periodontal disease and diabetes, those therapies should link together somehow in the emr so that we can track the outcomes of treatment.” this comment is a very broad comment, touching on many important aspects, with an emphasis on coordination of care. this respondent also comments on data transferability, from the perspective of providing complete care to patients: “it's necessary but not a modern system. you'd think that as easy as it is to take a photo on my phone and share it with the world automatically, i should be able to transfer data seamlessly without having to email attachments, etc. if i'm treating a dental patient, everything implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi that i do, any images, etc, should automatically be in the patient's medical record and accessible. the physician should get a notification when patients are visiting the dentist and vice versa.” another response re-emphasized the importance of transferability of data: “the most important feature of any emr system is the ease of portability of the records. i think that someday in the future, patients will be the owners and maintainers of their own health records and they can take them with them wherever they go. it is theoretically easier for doctors/hospitals to transfer records digitally if the information is available. if patients are the keepers of their own health records, and it is stored digitally on a chip, then in an emergency, the records can be available even if the patient is not able to communicate. that can save lives.” “i think it will be easier to identify population health statistics from emr.” “among my patient population, across all the categories you cited, people want to save their teeth and have them looking good. nobody likes flossing! emr can facilitate identifying the trends because all the parameters are readily available within a software program.” the theme of the difficulty of making the emr systems compatible with physician workflow was also mentioned: “i want them more user friendly so i don't have to click as many boxes or open as many windows to make an entry.” “it is easier and often faster to get patient records. that can impact patient care, especially when you have the patient in the office and you need a record from another office immediately. the most important information i have found to be useful for patient care has been images, either radiographic or otherwise.” – conversation about which information is most important for clinicians “i don't know if i would integrate my emr data with a hospital system. there would be security concerns. also, i think the only time it would be truly useful is when there is a crossover between medicine and dentistry, such as cancer of the head and neck. potential strengths are better care of the patient.” “the biggest surprise that i have from emr is that i thought it would make my life easier and i would be spending less time on records. it is completely the opposite. i am spending more time on them and it implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi is making the practice of health care much less enjoyable. and now i have to worry about data security a lot more. there a many more ways phi can be obtained with an emr than with a paper record.” there were some key responses regarding conversion to emr. “yes, was involved in transitioning from paper charts to emr in dental school. the experience was pretty bumpysome faculty even chose to retire over it instead of learning the new system. they tried to implement "superusers" who were trained ahead of time, but there were either not enough of these, or these users didn't really know how to work the software either. i think a soft release would've helped, but we all went live on the day they picked and no one really did their homework of knowing how to work it. a lot of money was lost with not walking out the correct procedures or knowing how to bill. it was a big hassle.” “in my private practice, i converted from paper charts to emr. the key takeaways for me were: 1) you just have to make the change. no looking back--just walk to the edge and jump! 2) it will be stressful, but there are advantages. 3) it won't make your life that much easier. 4) i am spending more time on records now than i was before. 5) it costs a lot 6) there are more threats to security with emr than with paper.” “if emr can help to shorten appointment lengths, or make sure to remind patients of appointments and keep them on a recall schedule, then that is very useful. it also makes charting and keeping an accurate history of visits and services much easier and clearer.” this comment addressed the theme of the utility of emrs and digital systems for simplifying and reducing error in basic routines. “emr over time should be able to pick up trends in services provided or reasons for visits. good emr should be able to run reports looking at almost any variable, be is by service, or zip code, or utilization, etc. the possibilities are endless, and this is something you just couldn't do with paper charting.” this respondent was aware of a common theme in information technology circles, “garbage in, garbage out,” or “gigo.” “people tend to be wanting implants more, but this also varies greatly with ses. people tend to be wanting full mouth makeovers i.e. big veneer cases less (due to economic conditions/uncertainty) implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi again, a good emr should be able to run almost any report over a defined period of time with respect to procedure codes and zip code. unfortunately you don't usually have income data, but you do have age, race, ethnicity, zip code, etc. however, emr reports are only as good as the data and accuracy of data put in.” “i would love to be able to transfer a prescription electronically to any pharmacy. i would love to be able to send an xray or cbct scan electronically and securely through emr. getting records from another office is usually a sadly difficult thing to do. i would love to be able to click and send over a digital referral to a specialist and know they got it.” this is an important comment, particularly as the clinical discipline of dentistry is heavily dependent on the general dentist’s ability to develop a specialty referral network, and to efficiently communicate patient need between specialists. “this is very difficult usually. either the patient forgets, the e-mail gets lost, the other office forgets, sometimes the requests are denied, etc. it's almost always easier to just take your own new xrays. even if you get previous xrays, sometimes the quality is so poor due to the format, they are essentially useless.” question 9, the question about integrating a private practice’s dental records with a hospital system, yielded some interesting responses as well. “i don't think integrating my private dental practice with a hospital system in particular would be that useful, but maybe if there was a generic third party portal to transfer data on an as needed basis that could be useful.” “i understand that epic, the hospital's computer system, has a dental component but no one uses it. typically i use epic to write operative reports for patients treated under general anesthesia in the or or emergency patients in the er. if we need to transfer information to a physician's office, we send it via encrypted email. if we need to access something from epic, the practice owner has remote access in his office. i don't think mds would understand our charting systems and dental procedures (in our dental software or in the dental portion of epic) enough for it to be beneficial without our interpretation.” “yes! absolutely. the only concerns would be that we would end up seeing too much but really everything is applicable since we take care of the whole person.” implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi 6. public health considerations there already are existing sources for oral health data. examples of these include the dental quality alliance [9] (dqa), affiliated with the ada, and the center for disease control’s national health and nutrition examination survey (nhanes). the cdc additionally has a website [10] set up to explore oral health data by location, https://www.cdc.gov/oralhealthdata/. the state of illinois department of public health has oral health resources as well [11]. however, most of these are stand-alone data sets focusing essentially exclusively on oral health, and as such they do not completely capture the corresponding medical status of the populations in the same way as a fully-integrated medical record consisting of both medical data and oral health data could. the opportunity for the future is the upcoming potential to capture and use the population level data and metadata generated from hospital based dental emr systems and other to be developed systems to elucidate further understanding. oral health information can now be directly linked to and keyed with corresponding medical data, allowing for more opportunities clinical research, and to inform policy makers, advocates, and other stakeholders with strategies to improve overall health. examples such as the link between periodontitis and cardiovascular health can now be much further explored, as can demographic, and even geographic based data be generated. 7. concluding remarks dental emr conversions have happened before, and literature [12] already exists about the challenges associated with this. now, we take the next steps by looking at the integration of a dental emr in a hospital based environment. this distinction opens up the discussion to other important areas beyond just the technical specifics of an emr system. these areas include the applications and broader utility that can come from the use of the implemented system as a tool to provide insight into the systemic integration of oral health into total health. an integrated emr is posited as a tool to better increase population health. as hospital based dental practices are equipped to provide oral health to a variety of dentally underserved populations the subsequent aggregated metadata resulting from this treatment can in the future be used to broaden the public health discourse. many of the survey comments show that faculty are already inherently aware of many of the strengths and current challenges with emr. key concepts repeated multiple times include the benefit of facilitated aggregation and utility of population level data, and the subsequent generation of metadata enabled by deployment of emr, yet the challenges of security, user friendliness, and the gigo moniker are mentioned as well. a key related takeaway is both the need and the potential for the development of a standardized dataset that contains both oral and medical aspects, as the currently existing oral health datasets are severely lacking commensurate medical data. dental anesthesia can serve as a clinical example of oral and systemic health integration – the utilization of anesthesia specifically for the dental context is a clinical example of this integration. implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi with the recent specialty status recognition of dental anesthesia by the american dental association, this specialty is in a position to grow, and this development will also contribute to the conversation. this will be another area in which emr systems can play a role. that the dental department at illinois masonic is still using paper charts in its dental clinic is a clear indication of the importance of successful change management strategies, and the influence of multiple factors and needs affecting multiple stakeholders. from a certain perspective, it can be deemed problematic that a clinical department significantly involved with medical care does not have an emr system in 2019. nevertheless, this example shows the often surprising challenges that come with the development and implementation of new technology. 8. limitations the limitations in this writing stem from the fact that it explores only one department in one hospital system. additionally, that department has yet to fully launch some key features -the dental anesthesiology training program has a begin date of july 1, 2019, and the full emr conversion is scheduled for june, 2020. there is an anticipated ramp up process in the future, however as of right now there has yet to be a critical mass of numbers developed for extended datasets discussed. secondly, the investigation for this was very focused on human experiences and interactions. though valuable, they should be framed and taken into context as such. individual experiences and perspectives are not empirically tested; they are only recorded and reported. another important consideration is that only dentists were surveyed for this project, so the perspectives of computer engineers, software developers, and so forth are not covered. a next step 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(n.d.). retrieved from http://www.dph.illinois.gov/topics-services/preventionwellness/oral-health https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29659418&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27920303&dopt=abstract https://doi.org/10.1377/hlthaff.2016.0886 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27669140&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27669140&dopt=abstract https://doi.org/10.1001/journalofethics.2016.18.9.pfor1-1609 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29478442&dopt=abstract https://doi.org/10.1016/j.adaj.2018.01.006 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27480705&dopt=abstract implementation of an emr system for a comprehensive dental service within a large regional hospital network: challenges and opportunities presented by the introduction of new technology online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e19, 2019 ojphi 12. sidek yh, martins jt. 2017. perceived critical success factors of electronic health record system implementation in a dental clinic context: an organisational management perspective. int j med inform. 107, 88-100. pubmed https://doi.org/10.1016/j.ijmedinf.2017.08.007 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29029696&dopt=abstract https://doi.org/10.1016/j.ijmedinf.2017.08.007 citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi citizen science models in health research: an australian commentary ann borda1*, kathleen gray1, laura downie2 1health and biomedical informatics centre faculty of medicine, dentistry and health sciences the university of melbourne melbourne, vic 3010, australia 2department of optometry and vision sciences faculty of medicine, dentistry and health sciences the university of melbourne melbourne, vic 3010, australia abstract this qualitative review explores how established citizen science models can inform and support meaningful engagement of public in health research in australia. in particular, with the growth in participatory health research approaches and increasing consumer participation in contributing to this research through digital technologies, there are gaps in our understanding of best practice in health and biomedical citizen science research to address these paradigm shifts. notable gaps are how we might more clearly define the parameters of such research and which citizen science models might best support digitally-enabled participation falling within these. further work in this area is expected to lead to how established citizen science methods may help improve the quality of and the translation of public engagement in health research. keywords: citizen science, community based participatory research, crowdsourcing, public health corresponding author: ann borda, health and biomedical informatics centre, faculty of medicine, dentistry and health sciences, the university of melbourne, parkville, victoria 3010, australia; aborda@unimelb.edu.au* doi: 10.5210/ojphi.v11i3.10358 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction global growth in participatory research approaches to address complex health challenges is being progressively supported by online platforms, tools and digital sensing devices. the mainstreaming of this form of public engagement is at the forefront of national research and health policy programmes [1,2]. citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi within australia, “consumer and community participation” in research is part of a paradigm shift leading to the increasing active involvement of the public (i.e. consumers and community members) and researchers working together to make decisions about health research priorities, policy and practice [3]. in the findings of a 2018 australian-wide survey (= 868 respondents) on the perceptions of such engagement, the majority of survey respondents (97%) reported that public involvement has meaningful value to all phases of health and medical research. the survey findings also acknowledged that public involvement is complex and differs across the research spectrum, thus requiring models that are flexible and have applicability in diverse research situations [4]. in this context, the authors provide an overview of the applicability of potential models which might be considered by participatory health researchers in australia. specifically, public engagement in health and biomedical research is increasingly being influenced by the paradigm of “citizen science”, that is, active participation in research teams by members of the general public with no formal training in the field of research concerned [5-7]. citizen science as an approach for public engagement in research dates back well-over a century in some fields of research, for example, natural history, where the us audubon society’s christmas bird count began in 1900 [5]. citizen science activity has dramatically increased in the 21st century, influenced by societal and technological changes and participatory democracy. critically, it has enabled the large-scale collection and processing of scientific data and widespread dissemination of scientific knowledge and discoveries, notably in environmental sciences, ecology, and astronomy [8-10]. citizen science traditions in australia australia has long-standing traditions in citizen science and public participation in scientific enquiry. although the term “citizen science” came into prominence in the mid-1990s [5,6], citizen science in australia has been established by grassroots activities and bottom-up approaches. over a century ago, ferdinand von mueller (1825-1896), the reputed nineteenth century australian botanist advertised for the assistance of “lady plant collectors” across the country in one of the most notable citizen science projects. von mueller inspired an estimated 1300 volunteers to participate in the project in submitting specimens, which culminated into informing the seven volume flora australiensis published between 1863 and 1878 by george bentham [11]. australia is also a country holding unique local indigenous traditional knowledge that spans thousands of years of ecological monitoring and empirical mapping of phenomena that precedes defined citizen science and participatory research approaches [12,13]. indigenous knowledge and citizen science represent parallel discourses which have emerged in both southern and northern hemispheres [14]. the australian citizen science association (acsa) was established in 2014 following national consultations in recognition of the need for a community of practice to support the expanding field of citizen science in australia, and aligned with international advancements [15]. around this time, citizen science practices were formalised through communities of practice organisations in europe with the establishment of the european citizen science association (ecsa) and in the usa, citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi through the citizen science association (csa). the launch of acsa in 2015 was particularly bolstered by a white paper occasional paper on citizen science by the then chief scientist of australia [16]. acsa has adapted the ecsa principles of citizen science as its working principles that highlight the common good aspects of involvement of citizens in scientific endeavours which, for example, can generate new knowledge or understanding, provide benefits to science and society, support reciprocity, ethical approaches and publicly available results [17]. australia has fostered an increasing number of citizen science developments, most visibly in the field of biodiversity as exemplified by one of the largest nation-wide platforms of its kind, the atlas of living australia (ala). launched in 2010, the ala is a collaborative, national project that aggregates biodiversity data from multiple sources and currently holds data on over 120,000 individual species in the ala database [18]. the ala also hosts the australian citizen science project finder online database, with over 600 projects across australia [19]. opportunities for large-scale projects in australia have been made possible as citizen science is associated with increasing funding, infrastructure and support at different levels of government [20]. citizen science models of participatory health research health and biomedical citizen science is not readily defined as a separate practice or associated with a specific framework or schema, although the field is increasingly identified with a number of engagement models [21-24]. projects involving human health and participants can be viewed as citizen science among various forms of community-based participatory research, action research, patient and public involvement (ppi), self-quantification, crowdsourced health research, among other practices [15,23-26]. the commonalities are associated with an enabled and widening participation, most often supported by ict and digital and social media which have rapidly increased the range of people who can participate in health and biomedical data collection and, where appropriate, data analysis and other tasks [25,27]. these technological developments, combined with policy initiatives and consumer empowerment, have also given rise to new ways of activation, for example, in health advocacy, health self-management and reporting, and research design. generally, citizen science projects have been differentiated according to the extent of responsibilities that participants undertake for research activities, such as collecting and analysing data (contributory) and interpreting and disseminating results (collaborative) [6,8]. in most studies, citizen projects are typically instigated by professionally trained researchers in which participants are supporting tasks in a research process; however, community scaled participatory research projects, for example, can be a cooperative activity. in the latter example, a higher level of ownership may be associated with co-creation in which researchers and members of the public work together across research processes [6,8,28]. a broadening of this framework is citizen-led or extreme citizen science approaches, which aim to provide tools and methods to enable communities to develop participatory research projects to address issues that concern them [28,29]. meaningful participation, according to kelty et al [30], engages participants along seven dimensions: for example, receiving an educative dividend; involvement in decision-making and goal-setting; and having control or ownership of the resources produced by participation. there citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi should be a “collective, affective” experience of participating in order for participants to feel they are part of something greater than themselves [30]. in community based participatory research (cbpr), for instance, participants can provide researchers with advice concerning the design of research projects, potentially including the research goals, design of survey instruments, recruitment, and data interpretation and dissemination, among other activities [31-33]. the need to ensure ownership and control over local knowledge is highly relevant in situations of cbpr that can inform wider citizen science practices [32-35]. such multidimensionality of participatory activities in health research is necessarily expressed in different schemas. engaging large numbers of the public is often undertaken through crowdsourcing [36] which has been the most visibly applied citizen science method to the fields of health and biomedical research [25,27]. crowdsourced health research studies have been defined as the nexus of three contemporary trends: “citizen science, crowdsourcing, and medicine 2.0” [37]. crowdsourced tasks can be quite wide-ranging but mainly rely on the use of digital tools and platforms for use by the public in undertaking specific tasks [27]. for instance, volunteers are classifying images of the m. tuberculosis bacteria as part of the bash the bug project hosted on the zooniverse citizen science web portal, and have reached over one million classifications [38]. crowdsourced data processing can further involve both lay-people and those knowledgeable in a discipline, particularly where complex tasks, e.g. forms of annotation, relational tasks or problemsolving are applied. cochrane crowd, the citizen science platform of cochrane, a global network promoting evidence-informed health decision-making by producing high-quality accessible systematic reviews, is comprised of a crowdsourced community of volunteers who undertake supporting tasks such as randomised controlled trial (rct) identification in research papers [39]. crowdsourced health research projects can further target health conditions in which participants undertake self-reporting using mobile apps and wearable sensors [40]. health promotion is increasingly another context in which the engagement of the public through self-reporting and data collection is contributing to forms of public health research and policy [34,35]. critically, in a public health landscape, citizen science can support localised participatory action research and “participatory epidemiology” leading to the capture of qualitative and quantitative epidemiological information contained within community observations, including traditional oral history [35]. the use of gamification – often termed as “serious games” is a digitally-enabled method to support participatory action research. for example, spotlab is an example of a disease surveillance platform that uses gaming and mobile phones turned into low-cost microscopes to provide collective diagnosis of global health diseases, such as malaria diagnosis [40]. serious games developed by professional scientists, such as the multiple sequence alignment online game, phylo developed at mcgill university (montreal, canada), are further instances of contribution by the public in problem-solving tasks as in this example of finding ways of sequence arrangement of dna and rna to identify regions of similarity [41,42]. there is a concurrent rise of digitally-engaged and activated citizens, alternatively called “biocitizens” by some in the health research literature and to distinguish from other types of nonhealth related participation, especially at the individual level [37,43]. the notion of biocitzenship firstly emerged through the concept of biosociality [44] – that is the making of connections citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi between biotechnology and citizenship. recently the concept of “participatory biocitizen” coined by melanie swan [37] refers to an activated individual and as a means to realise personalized medicine by sharing life-logging and self-quantification data through social media platforms. selfquantifiers, in particular, represent high levels of activation that may motivate these individuals to independently mobilise citizen scientists and/or approaches [43,45,46]. these approaches are often typically outside of the instigation of organised health professionals or scientific organisations, as in the case of biohackers [43]. in the us, “people powered research networks” (pprn) are leading on the sharing of quantifiable data, exchanging experiences on treatments, and searching for clinical trials on online platforms [45,47]. examples include the personalized health and research network, patientslikeme [48], and advocacy network iconquerms which is focused on the multiple sclerosis (ms) community [49]. commercial and government-led research partnerships provide another means by which public engagement can be supported. for instance, sage bionetworks and the university of rochester partnered with apple researchkit to study the lifestyle of 17,000 parkinson’s disease patients in the mpower initiative [50]. the nation-wide $215 million precision medicine initiative all of us in the u.s. aims to build a large-scale research platform between public and private sectors, calling for one million volunteers to contribute their health data [51]. other opportunities are provided through joint collaborations between the public and healthcare organisations in co-creating knowledge. one example is the sarroch bioteca foundation established in 2012 in pursuit of a “citizenveillance on health” project in italy [52]. among the national agencies at the forefront of the development and promotion of public and community involvement in health research include involve in the uk [53]. involve is a government funded programme established in 1996 and is part of, and funded by, the national institute for health research, to support active public involvement in the national health service (nhs), public health and social care research. related initiatives are the patient centred outcomes research institute (pcori) of the national institutes of health in the u.s. and the strategy for patient oriented research (spor) of the canadian institutes of health research [1,4]. the australian context aligning with international developments, australia is implementing various approaches to strengthen public involvement in health research. the national health and medical research council (nhmrc), for instance, provides national guidelines for responsible research practices and advocates for appropriate public involvement in research [3]. the consumer and community health research network based in western australia is an example of a practical implementation of a service which is leading programs and resource development to promote awareness and support [54]. the network is among several agencies comprising the australian health research alliance (ahra) which was involved in undertaking a major review project in 2017-2018, including the deployment of an australia-wide survey to capture the extent and nature of consumer and community involvement across ahra member organisations. recommended priorities include the development of guidance on incorporating public involvement across the research life cycle, and associated tools and resources to enable and support partnerships, as well as other capacity building initiatives, such as training [4]. citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi as a complementary direction to these priorities, a review of the application of existing citizen science models to participatory health research has identified several examples for the purposes of this commentary. the selected projects are based or led from within australia and focus on forms of digitally-enabled public participation in health research which use publicly accessible ‘citizen science’ tools and platforms. examples draw on the models of contributory (e.g. participation via data collection and processing), collaborative (e.g. public involvement in refining research questions, analysing data, and/or disseminating findings), co-creation (e.g. researchers and members of the public working together across key research processes), and extreme citizen science in which researchers provide tools and methods to enable communities to develop their own participatory research projects [6,8,28]. projects are thematically clustered using a public health research lens as follows: indigenous science and environmental health in the context of indigenous participation and environmental health, the aboriginal concept of health appears as part of a health impact assessment approach by the australian indigenous doctors’ association [55] and mentioned as an exemplar in a prominent position paper on citizen science and public health [56]. several indigenous partnership projects are hosted in the atlas of living australia database (ala), and discoverable in the acsa project finder. for instance, the tracks app of the indigenous desert alliance (ida) is an ecological science mobile app co-created with stakeholders to support the involvement of aboriginal people and indigenous ranger groups in remote areas [57]. the tracks app supports the collection of information on native animal tracks and other signs, such as scats, diggings, burrows, bones and feathers. the commonwealth scientific and industrial research organisation (csiro), an independent australian federal government agency responsible for scientific research, is supporting different citizen science solutions in a number of environmental health projects. the eye on water project is an example of a digitally-enabled participation project using a water quality monitoring app to collect data about water changes around australia due to climate change phenomena [58]. the project routinely involves school students who assist in making physical and chemical measurements, as well as providing digitally captured data, alongside members of the volunteer public. researchers are using the datasets to compare water quality data with satellite-derived ocean colour or coastal/inland water feature data [58]. an example of a serious game supporting environmental health education and contributory approaches, questagame was launched in 2014 in canberra through a successful crowdfunding campaign. the game has players around the world mapping biodiversity sightings and sending photographs through the questagame mobile app for identification. the data feeds are downloaded for research, of which 1.6 million verifications have been identified at the time of this article [59]. health promotion contributory models of approach have largely been applied to public participation in health promotion and public health research in several large-scale campaigns in australia. for example, the anti-cancer council of the australian state of victoria has been running sun protection citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi programmes for several decades: slip! slop! slap! from 1980 to 1988 and sunsmart from 1988 to the present [60]. sunday morning is an australian social media health promotion movement that asks participants to publicly set a personal goal to stop drinking or reduce their alcohol consumption for a set period of time, and to record their reflections and progress on blogs and social networks [61]. the hello sunday platform was created in 2010 and, thus far, participants have shared over 2 million stories according to the website [62]. foodswitch is a mobile phone app that allows users to access easy-to-understand information about the nutritional characteristics of packaged foods and, where available, to suggest healthier alternative products [63]. a particular innovation in the app was the incorporation of a crowdsourcing function whereby users are able to contribute information on missing products. if a barcode is scanned but the corresponding universal product code (upc) is not identified in the database, then the user is prompted to photograph the front of the package, the nutrition information panel (nip), and the ingredients list. the data are uploaded to the data management center site, and the information is added to a national database. periodic updates to the database are then made to ensure that the app is supported by complete and up to date product information [64]. epidemiology and exposure science australian researchers have also employed citizen science contributory models to engage the public in specific areas of health condition enquiry. the big sleep survey undertaken in 2010 solicited the contribution of participants who monitored their sleeping habits for one week for the woolcock institute and sydney university [65]. more than 12,000 participants reported on their personal sleep habits in the questionnaire, while over 3,500 people completed the week-long sleep diaries recording the time they went to bed, fell asleep and woke up, for example [66]. dustsafe is a citizen-science initiative based in the department of environmental sciences at macquarie university in sydney a local chapter of the global program: 360 dust analysis [67], which encompasses research groups in australia, asia, the united kingdom, and the u.s. together, these programs are focused on characterising household dust in an effort to understand the potential health exposure hazards residing in that dust. greater urbanisation means people are spending up to 90% of their time indoors. consequently, environmental health risks are dominated by fine dust particles from indoor air aerosols and harmful agents that can penetrate deep into lungs and migrate into the blood stream. knowledge gaps are addressed by engaging the public to submit vacuum dust samples for chemical and biological analysis [68]. an example of a participatory sensing initiative, the citizen science project is a collaboration between rmit university in melbourne and the university of new south wales in sydney begun in 2018 [69]. the project aims to involve the public in measuring urban heat island effects and local climate change. public participants perform outdoor microclimatic measurements of temperature, humidity, wind speed, among other measures, using portable handheld sensing devices provided by the project on a selected day at a selected location. health data systems the garvan institute of medical research in sydney and the vodafone foundation have recently initiated a computing platform that uses the collective processing capacity of smartphones to citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi analyse numbers and compare genetic profiles of tumours through the dreamlab mobile app [70]. the public can contribute their mobile phone’s processing power when a phone is fully charged or charging overnight, for instance, whilst the dreamlab app analyses research data for breast, ovarian, prostate and pancreatic cancers. an example of a collaborative platform, crowdsourcing critical appraisal of research evidence or crowdcare is a freely available online tool developed by university of melbourne researchers that aims to teach critical appraisal and facilitate the sharing of appraisals amongst a global community of clinicians [71]. after completing compulsory training modules, individuals can contribute to, and benefit from, an evolving collection of appraised research evidence generated from an interdisciplinary group, committed to practicing evidence-based practice (ebp). this platform is unique in that it goes beyond the use of crowdsourced judgments by article type (e.g. rcts), as in cochranecrowd to an in-depth assessment of the methodological rigour of the articles [72]. on the level of individual self-tracking, which can be a means of contributing personal physiological data to larger research studies, there are numerous communities using self-tracking tools and technologies, many of whom identify with the quantified self (qs) movement. qs is defined as “embodying self-knowledge through self-tracking” [73], and one of the early quantified-self group setups outside of the u.s. was based in sydney, australia [46]. strava, the international social fitness network, has an australian membership of self-quantifiers who measure physical performance, primarily tracking cycling and running activities using gps data [74]. australian researchers have partnered with strava members in a range of studies, such as understanding the personal data practices of commuting cyclists, for example [75]. challenges and limitations public participation in health research in australia potentially faces several shared challenges to those experienced world-wide in the citizen science community. the lack of standard definitional boundaries of what constitutes digitally-enabled public participation in health research relates to a diversity of approaches, e.g. citizen science, community-based participatory research (cbpr), action research, patient and public involvement (ppi), and crowdsourcing, among other approaches [15,23,76]. critically, within this context, there is a clear gap in readily identifying tools and platforms, particularly those which are supporting digitally-enabled participation of the public, their efficacy and support of different levels of participation. for instance, tools supporting local participatory engagement, e.g. cbpr, may differ from those in use by self-quantifying participants or by a broader public in larger crowdsourced projects, for instance. generally, in public participation in health research, the more closely-associated a project is with the participant (e.g. in the home or the individual person), the greater the potential for legal, ethical, privacy, biosafety and data management and ownership complications to be raised [77,78]. it is, therefore, necessary to consider what shared standards, methodologies and practices might be applicable [2,79]. a related challenge is that stronger public involvement in health research requires improved understanding of research processes [1]. for instance, public involvement in research data citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi processes and ensuring data quality are pervasive issues; primarily due to the fact that a non-expert public is generally considered to be untrained in research data management or research integrity, or may be prone to systematic errors which can impact data quality [80,81]. data quality can also be highly context dependent (i.e. “fitness for use”) [81-83]. a higher level of quality assurance is often associated with the use of crowdsourcing in which many people carry out the same work or task, such as contributing to peer review [39,84], or replication of an analysis, such as image identification [27]. such an approach is desirable across the sciences for validation, accuracy, and in reducing bias. the critical research appraisal tool crowdcare, for instance, has shown that novices can be trained to appraise the rigor of published systematic reviews and, on average, achieve a high degree of accuracy relative to the experts [72]. additionally, there are identified gaps in the literature on reporting methods and the extent of public involvement in clinical trials which constitute a critical share of health and biomedical research design. a study of public research involvement in online trials concerning health selfmanagement, for example, found that detailed reporting of such involvement was hindered by role confusion between research volunteers and trial participants [22]. other related study findings contribute to the literature by documenting researchers’ perspectives and experiences about sharing results with research participants. one such study surveyed health researchers in which the majority of respondents indicated that health research results should always be shared with participants [85]; although the described barriers to results sharing and various reported reasons not to share results suggest difficulties with a “one-size-fits-all” approach to improving results sharing. legal jurisdictional areas may differ in terms of the extent of sharing of results with participants. in the u.s., for instance, it is not normative for health research ethics review boards to encourage health researchers to share aggregate study findings in contrast to practices in australia, for example [85]. a poll conducted by research australia in 2019 indicated that a majority of australians were willing to share their personal health information for research purposes, in order to advance health and medical research (78%), support healthcare providers in improving patient care (68%), and to assist public health officials in tracking diseases, disabilities and their causes (61%) [86]. however, the proportion of consumers in support of sharing health data with government organisations can be significantly lower [86,87]. indigenous communities are particularly concerned with issues pertaining to handling, treatment, and ownership of tissue as well as knowledge gained from specimen analysis. this stems from a strong and integrated sense of cultural connection to ancestors and traditional lands and indigenous communities view biological specimens as inseparable from these connections [88]. conclusion australia has long-standing traditions in aspects of citizen science and participatory approaches to scientific enquiry, as can be gleaned from major projects and historical activities outlined in the present paper. however, there are gaps in our understanding of the extent of public participation in health research as part of this paradigm shift, particularly as it may relate to digitally-enabled processes. progress in this area depends on identifying partnerships across the shared challenges, citizen science models in health research: an australian commentary online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e23, 2019 ojphi and extending work on how established citizen science models may help support the quality of and the translation of public engagement in health research in australia. acknowledgements ann borda received an expert visit grant in 2019 as part of the epic project funded under the eu horizon 2020 programme (ict) to explore advancing approaches to citizen science methods, platforms and capabilities in healthcare and medical research. references 1. todd al, nutbeam d. 2018. involving consumers in health research: what do 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public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi leveraging informatics and technology to support public health response: framework and illustrations using covid-19 jane l. snowdon phd1*, william kassler md mph1, hema karunakaram mph1, brian e. dixon mpa phd2,3, kyu rhee md mpp1 1ibm watson health, cambridge, ma, usa 2center for biomedical informatics, regenstrief institute, indianapolis, in, usa 3department of epidemiology, richard m. fairbanks school of public health, indiana university, indianapolis, in, usa abstract objective: to develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic. method: the conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response. results: mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to covid-19 in areas such as applied science, epidemiology, communications, and business continuity. two examples of technologies used in covid-19 illustrate novel applications of informatics encompassed by the framework. first, we examine a hub from the weather channel, which provides covid-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. second, we examine ibm watson assistant for citizens, an ai-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about covid-19 via digital and telephone-based interaction. discussion: early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders. conclusion: the framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. effective solutions are critical to success in recovery from covid-19 and future pandemics. keywords: coronavirus, pandemics, public health informatics, clinical informatics, artificial intelligence, information technology correspondence: snowdonj@us.ibm.com* doi: 10.5210/ojphi.v13i1.11072 mailto:snowdonj@us.ibm.com* leveraging informatics and technology to support public health response: framework and illustrations using covid-19 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi introduction the united states has faced numerous pandemics including the hiv/aids pandemic; the zika, mers, and ebola outbreaks; and the current novel coronavirus disease 2019 (covid-19) pandemic. each experience has revealed opportunities for improvement by highlighting unmet data and information needs [1] among clinicians, public health agencies, policymakers, and researchers during the three stages of a disease outbreak: preparedness, response, and recovery. some of these prior lessons are summarized in table 1, adapted from an article by paules et al. [2]. table 1. optimal response to emerging infectious disease outbreaks: lessons learned global surveillance to detect outbreaks easily transparency and communication in response to outbreaks incorporation of infrastructure and capacity building domestically and internationally in outbreak responses conduct of basic and clinical research associated with outbreaks in a coordinated and collaborative manner involvement of the afflicted communities in policy decisions pursuit and perfection of adaptable platform technologies for vaccines, diagnostics, and therapeutics importance of flexible funding mechanisms benefits of data standards and standardization to facilitate the interoperability of solutions critical need for investing in skills, training, and skills in science, technology, engineering, and mathematics (stem) to grow national competencies in just 6 months, covid-19 spread rapidly from the first reported cases of the sars-cov-2 virus in wuhan, china in december 2019 across the globe causing 9,843,073 cases and 495,760 deaths in over 185 countries as of june 28, 2020 [3,4]. unlike prior pandemics, covid-19 response and recovery will rely more heavily on informatics and technology given their extensive diffusion into business, health care and everyday life. already the us has leveraged cloud computing, video conferencing, collaboration tools, and digital security solutions to support remote working by employees, as well as artificial intelligence (ai) to diagnose covid symptoms [1,5-7]. copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. leveraging informatics and technology to support public health response: framework and illustrations using covid-19 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi prior lessons and frameworks for public health response do not focus on the roles of informatics or technology, and they tend to be heavily focused on vaccine and therapeutic development [2]. the informatics community needs a method for framing covid-19 response and lessons learned to highlight the diverse ways that computing, algorithms, and informaticians contribute to addressing the pandemic. such a framework could be used by researchers, policymakers, and organizational leaders to measure response activities to covid-19, monitor recovery, and help to prepare and plan for future health threats. the objective of this paper is to describe and illustrate a novel, comprehensive framework that encompasses the myriad ways informatics and technology can support public health response to a pandemic like covid-19. in this context, public health response spans government, industry, academia, and citizens, and is not solely limited to government. the pandemic framework supports and expands the broader concept of public health 3.0 [8,9]. two case examples showing early outcomes of how informatics and technology are supporting the u.s. response to covid-19 are given. consideration of health informatics and technology from an outbreak’s local onset to a global pandemic relies on trusted sources of public health protocols, plans, and data from governmental organizations such as the world health organization (who), centers for disease control and prevention (cdc), centers for medicare and medicaid services (cms), and from the private sector such as the weather company (twc). having good data is critical for developing predictions, assessing risks, allocating resources, and measuring the effectiveness of interventions, such as wearing masks and social distancing. incorrect or incomplete data may cause harm by omitting important socioeconomic factors, obscuring trends or correlations, and may lead to illinformed or incorrect actions by decision-makers. methods the covid-19 pandemic highlights the complexity and challenges in responding to a significant, widespread man-made or natural disaster including emerging infectious diseases. saving lives, maintaining critical infrastructure, and restoring essential services require coordinated response across various governmental and private sector organizations to mobilize resources and capabilities to provide support and services [10]. these tasks involve a diverse set of functions that are data-rich and heavily quantitative. informatics and technology can play significant supportive roles before, during, and after the public health emergency. figure 1 illustrates a conceptual model of public health preparedness for major health threats. the model is adapted from the traditional public health stages of disease outbreak [11] and expanded to incorporate stakeholders and sectors beyond governmental public health. note that there is overlap among the set of stakeholders who are not mutually exclusive. leveraging informatics and technology to support public health response: framework and illustrations using covid-19 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi figure 1. a conceptual model of public health preparedness encompassing the phases, economic sectors, and cross-sector organizations that engage in preparing, responding, and recovering to major health threats. prepare preparing for a crisis requires an understanding of the potential challenge, having the foundation in place to assure continuity of operations, developing the plans and policies to enable a rapid response, and building the infrastructure to support those in need. taking steps to reduce vulnerability to disaster before the emergency is critical for success. unlike some natural disasters that occur regularly and with predictable periodicity (e.g., hurricanes, fires), pandemics are spontaneous and unpredictable. because infectious disease outbreaks can rapidly spread across the globe, having plans, policies and resources in place allows government officials to take early steps to limit disease from spreading. while it is impossible to plan for every contingency, building capacity to respond can significantly mitigate the impact on a community. respond responding is the period when stakeholders implement plans, translate science into practice, and course-correct based on new data to improve practices. as exhibited during covid-19, the impact of a rapid and widespread, novel infectious disease can cause massive disruption across many sectors of the economy. thus, pandemic response is a coordinated effort across multiple sectors. leveraging informatics and technology to support public health response: framework and illustrations using covid-19 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi in the area of basic science, response is mobilizing to characterize the etiologic agent and its genetics, and to develop diagnostic tests, drugs and vaccines. the life sciences industry plays a critical role in executing the applied science and manufacturing needed to develop vaccines, tests and treatments, and to successfully bring them to market. in the healthcare sector, payers, health plans and providers work in concert to assure those in need get services in the face of an unprecedented surge in demand resulting from local outbreaks. government has a unique role in helping to monitor the situation, coordinate across numerous public and private organizations, mobilize supportive assets and provide financial resources. epidemiology plays a key role in understanding patterns of transmission, describing the clinical course, individual and population risk factors, quantifying the burden of illness, and creating mathematical models to predict demographic and geographic spread of disease. public health departments focus on interventions to mitigate the spread, including case finding, contact tracing, quarantine, and isolation. communication and dissemination of information is critical to response, particularly in the face of rapidly evolving events. government is the natural leader for overall coordination and communication efforts. government also plays a key role in translation of newly emerging evidence into best practices and policy. disasters often stress the economy and, as in the case of covid-19, expose structural weaknesses revealing significant gaps in organizational capacity, staff and resources. private industries across economic sectors work to assure continuity of operations, repairing broken supply chains and creating and adapting to new ways of working to mitigate transmission. recover pandemic recovery takes time. even before the pandemic recedes, many recovery actions can begin, starting with measured re-opening of business, schools and other public spaces, as well as providing short-term relief for people most impacted. intermediate-term recovery involves dealing with the secondary impact. as the epidemic curve wanes and healthcare surges decline, additional impact from secondary surges will be felt. new curves will arise resulting from the increased demand following disruptions in routine healthcare. delayed primary and secondary prevention will result in surges of pent up demand for elective surgical procedures, in routine care for chronic disease, and in increased admissions for ambulatory sensitive and prevention sensitive conditions (e.g. increased infectious disease outbreaks for vaccine preventable diseases, increased admissions for poorly controlled diabetes and asthma, and more presentation of severe and later-stage illness such as cancer). given the stress, anxiety, social isolation, economic hardships, and collective trauma, another surge in demand for mental health services is also expected. long-term recovery means building individual and community resiliency. this involves an afteraction review of the responses carried out during the crisis, a formal evaluation of what interventions did and did not work in order to understand best practices and lessons learned, and feedback of insights into the preparation stage to improve future emergency responses. leveraging informatics and technology to support public health response: framework and illustrations using covid-19 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi a framework for pandemic preparedness, response, and recovery building upon this conceptual model, we developed a framework for characterizing informatics and technology support of the diverse tasks involved in public health preparedness, response and recovery as depicted in figure 2. each category consists of multiple informatics and technology solutions that can be leveraged during one or more phases of a significant health threat. furthermore, each category is primarily driven by a specific stakeholder group. it should be noted that multiple stakeholders may play a role in funding, implementing, or using the informatics and technology solutions that correspond to a given category. figure 2. a framework for leveraging informatics and technology to support public health preparedness, response, and recovery. benchside benchside solutions focus on applied science and span the “prepare” and “respond” phases of the pandemic. life sciences companies who have invested in informatics solutions for drug and vaccine development can quickly ramp up to address a pandemic. automated digital tools, including ai, can support drug development with clinical trial study protocol design and development, and electronic data capture and management, to shorten the timeline to release new drugs and vaccines to the market [12-15]. bedside bedside solutions support clinical care and decision-making for provider organizations in the “respond” phase. these solutions may help automate triage of patients seeking care based on symptoms or may reallocate clinical staff across an organization based on demand for services. leveraging informatics and technology to support public health response: framework and illustrations using covid-19 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi reeves et al. [16] describe the design and rapid creation of a covid-19 operational dashboard by a multidisciplinary team of providers and administrators. the dashboard provided real-time data and analytics on the number of patients tested, test results, intensive care unit bed availability, ventilator unit availability, and ambulatory visit volume. disease investigation, modeling and monitoring epidemiologists use a variety of tools that span the “respond” and “recover” phases of the pandemic to model disease outbreak and monitor infectious disease cases. surveillance helps provide situational awareness and enhances early detection and response. public and private scientific organizations may use these tools to map the spread of disease, track infection rates and outcomes across populations, and predict where more resources need to be allocated. as the understanding of the disease evolves, solutions can also assist contact tracing, quarantine and isolation efforts to contain the disease. one example is spatiotemporal epidemiologic modeler (stem), an open source software project supported by the eclipse foundation, which is used by a global community of researchers and public health officials for disease modeling and tracking [17]. another example is the regenstrief covid-19 dashboard, deployed to support public health and health systems monitor the evolving situation in the state of indiana [18]. community coordination and programs once a pandemic is declared, public health agencies activate a number of “respond” and “recover” activities. these efforts seek to mobilize community-based organizations and public health programs to help individuals, families, and at-risk populations. examples include establishing additional screening or testing locations to identify impacted populations, targeted testing efforts in specific locations (e.g., nursing homes, food processing plants), or erecting field hospitals that might serve as triage sites or post-acute recovery centers. public health agencies also initiate programs that address social needs such as housing for individuals without a safe place to isolate, or feeding individuals who are food insecure during quarantine. these programs are typically coordinated with governmental social services agencies and community-based organizations that may manage the programs or contribute volunteers to ensure sufficient operation. communications & automated assistance communications and automated assistance solutions are critical across all phases of a pandemic but are primarily used during the “respond” phase. government agencies, providers, and payers may all experience an influx of questions from individuals wanting to understand new policies, check symptoms, or find assistance. solutions such as virtual assistants, conversational agents and chatbots may be employed to broadly communicate changes in local guidelines, automatically answer common questions about the pandemic, and triage more complex questions to the appropriate sources of information. return to work as the pandemic wanes and social distancing measures gradually lift, focus shifts to restoring businesses and services. businesses that have been impacted by the pandemic will be eager to leveraging informatics and technology to support public health response: framework and illustrations using covid-19 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi open, yet returning to the workplace before a therapeutic drug or vaccine are available poses risks to front-line employees and organizations. employers need to plan for a safe way to resume operations. these solutions may guide the transition of employees back into work environments and inform critical business processes to ameliorate negative economic impacts from the pandemic. decision-makers are faced with a sense of urgency: when to reopen and how to do so in a responsible manner. such decisions require analyzing and balancing numerous factors to support tailoring evidence-based return to workplace policies for different sites and job roles. factors include monitoring local infection rates and population health trends; gathering and monitoring employee symptoms, assessing their status and referring employees to appropriate testing and medical care; weighing employee vulnerability; understanding state and local regulations; and responding to a range of employee questions in a scalable and comprehensive way. business continuity and resiliency business continuity and resiliency solutions are focused on anticipating and addressing disruptions to business operations and span the “prepare” and “respond” phases of the pandemic. business continuity solutions may help organizations more easily transition to remote working, keep essential systems running for users, and bolster cloud environments. supply chain disruption to ensure smooth operations across an organization’s supply chain, solutions addressing potential disruptions to supply chains span all three phases of the pandemic response. proactive investment in these solutions can ensure that key materials and parts have backup suppliers and customers can continue to access essential products. new ways of working solutions that enable new ways of working look to the future across communication and business continuity and span the “respond” and “recover” phases of the pandemic. employers may invest in solutions that enhance productivity, collaboration, and learning opportunities for employees who are working from home, learning from home, or transitioning to new business processes. mapping we mapped ibm’s existing commercial solutions and technology assets to the pandemic framework, which identified some gaps in capabilities and opportunities for building new products where we could make the largest impact based on our resources and the needs of our stakeholders. the company responded quickly, embraced a change in culture by breaking down silos, and united behind common goals to either integrate and bundle existing products and services to address urgent customer requirements or to develop new solutions with speed and agility. a few of the new solutions that were developed based on this mapping are described in the results section. leveraging informatics and technology to support public health response: framework and illustrations using covid-19 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi results to illustrate the framework, we present two case examples that detail how informatics and technology are supporting the u.s. response to covid-19. these examples represent early findings from technologies actively in use, which may evolve further as the pandemic continues. both examples are from the communications and automated assistance category in figure 2. the weather channel covid-19 information hub the weather channel app and weather.com website provide weather forecasts, current conditions, interactive radar, satellite maps, real-time severe weather alerts, and offer tools that predict local risk of flu or seasonal allergy symptoms up to 15 days in advance at a city level [19,20]. combined, the website and app serve more than 300 million monthly users globally. as covid-19 spread, the weather channel team developed and deployed in less than 1 month a covid-19 information hub (see weather.com/coronavirus) [21]. the hub provides interactive maps, data on confirmed cases, trend graphs, a q&a chatbot, and news about covid-19. the hub, which runs on a hybrid cloud infrastructure, uses watson ai to integrate relevant covid19 data from the who, cdc, and many state as well as local governments every 30 minutes. where available, data is provided at the county level in the united states [22]. for those seeking additional data, ibm created a separate interactive dashboard, built using ibm cognos analytics on ibm’s public cloud, available at https://accelerator.weather.com. the dashboard provides users, such as researchers, data scientists, and media, with analysis and filtering of regional covid-19 data. users can drill down to the region, country, state, and even county and leverage different visualizations (charts, maps, graphs) to examine case as well as mortality trends. since launching on march 26, the weather channel covid-19 hub has seen an average of 2.9 million visitors daily and more than 299 million visits (as of june 28, 2020). the ‘covid-19 q&a with watson’ chatbot has answered more than 2.2 million questions, with “signs and symptoms” being the most popular topic. in a survey of users in april 2020 to gather reaction to the covid19 hub, participants (n=5,422) expressed a high degree of satisfaction (mean 4.38 using a 5-point likert scale). the most desired information focused on areas where cases are concentrated (63%), severity of cases in their local area (62%), predictions for cases in their area (60%), predictions for when the covid-19 outbreak would end (58%), and for confirmed cases (54%). watson assistant for citizens healthcare providers and payers, government agencies, and businesses faced staggering increases in telephone call volume as a result of the covid-19 pandemic. patients, employees, and citizens have questions requiring timely and accurate responses about covid-19 symptoms and case counts, how and where to get tested, what to do if they recently traveled, whether schools and facilities are closed, what a facility’s emergency plan is, whether events are cancelled or postponed, and more. the rapid pace of change in the evolution of the pandemic is further challenging communications. from tracking the disease and local response, to keeping up with the leveraging informatics and technology to support public health response: framework and illustrations using covid-19 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi emerging clinical and public health science, organizations must ensure that their communication is accurate, timely and up-to-date. vetting and curating that content is no small task. institutions are establishing call centers and deploying virtual agents to assist in providing accurate, reliable and timely answers to common questions related to covid-19. watson assistant, a conversational ai platform that supports both multi-channel text and speech-based interfaces, combined with natural language processing technology and watson discovery, an enterprise ai search technology for unstructured data, enables virtual agents to dynamically crawl the cdc website daily for the most current covid-19 guidance as well as state websites and local sources about school closings and news. in table 2 we highlight three institutions that used watson assistant to manage inquiries from the public when staff resources were limited. the time required to design, develop and deploy the conversational, ai-enabled virtual agents ranged from 2 to 14 days. the benefits of watson assistant are that it is able to use conversational ai through many channels, and it effectively handles voice in multiple languages. table 2. various applications of watson assistant in support of covid-19 response during which users interacted via voice, text or the internet to ask questions about getting tested or accessing services during the pandemic. *choa data represents interactions from march 26 through june 3, 2020. **city of austin, texas and university of arkansas for medicine sciences data represents interactions during april 2020. organization children’s hospital of atlanta* city of austin, texas** university of arkansas for medicine sciences** description of technology deployed in response to covid-19 pediatric covid-19 assessment tool [23] city information chatbot [24] covid-19 screening survey agent [25] # unique conversations 10,414 15,998 3,514 # questions asked during conversation -24,378 7,480 # conversations where advice given or information resources provided 7,729 23,159 6,956 % conversations with a successful outcome 74% 95% 93% design and implementation time 2 days 7 days in english; 14 days in spanish 9 days leveraging informatics and technology to support public health response: framework and illustrations using covid-19 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi the first is the children’s hospital of atlanta (choa), which implemented a covid-19 pediatric assessment tool to help parents answer questions pertaining to their child, such as what should be done if a child has a fever and/or cough?; and what should be done if a child may have been around someone with covid-19 (exposed) but has no symptoms? the tool used decision tree analysis to evaluate a child’s symptoms (e.g. fever, marked lethargy, coughing, cyanosis). importantly, the tool presented content at an appropriate reading level for a diverse lay audience. the virtual agent suggested next steps for a parent to take (e.g. advice to stay home, see a doctor, call 911) according to choa’s established protocols. the bilingual tool handled multiple channel mediums (e.g., english and spanish text, english voice). the second is the city of austin, texas, the 11th largest city in the united states with more than 990,000 residents, which deployed a bilingual (english and spanish) chatbot to respond to citizens. the 24x7 virtual chatbot facilitated questions and answers about the cdc’s covid-19 recommendations, stay home orders, local screening availability, operating guidance and resources for businesses, job loss and unemployment services, homeless benefits including shelter and financial assistance, senior citizens, and building permits and inspections for developers. the third represents online and telephone screening for covid-19 at the university of arkansas for medicine sciences (uams) for adults aged 18 years and older residing in arkansas who have no covid-19 symptoms. ibm collaborated with two uams physicians to implement a pre-visit covid-19 screening survey. pregnancy screening questions and educational materials were also added. when the survey is completed by the user, the virtual assistant sends an email containing the answers to uams’ covid-19 test center so that the patient information is waiting for them when they arrive to be tested. a mobile covid-19 triage clinic helps to speed response. depending on responses, an interactive video consult may be conducted with referral to appropriate response teams. discussion we describe a framework designed to characterize how informatics and technology can support public health preparedness, recovery, and response during a pandemic. the framework maps the roles that informatics and technology play during a pandemic onto the major phases of a pandemic and the stakeholders involved. the framework further encapsulates the complexity of multiple sectors working together to respond and recover from a pandemic. underlying this framework are three enabling forces. leading businesses are investing in ai and cloud to predict future outcomes, create intelligent workflows that automate decisions and experiences, and empower people to do higher value work. data and security are fueling digital transformation. ai and advanced analytics are unlocking the value of trusted data through insights. the convergence of these forces is catalyzing innovation in the creation of scalable cognitive solutions with intelligent interfaces to rapidly address disruptive change such as a pandemic. the covid-19 pandemic highlights the importance of informatics to public health in an interconnected world and at the local level. the concept of public health 3.0 [8] requires a robust, interoperable infrastructure that supports data management and exchange between public health and other sectors. to fully achieve public health 3.0, patients, citizens, healthcare professionals, leveraging informatics and technology to support public health response: framework and illustrations using covid-19 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e1, 2021 ojphi public health officials, academics, and businesses all need to play a role in the development and use of health information technology and applications for pandemic preparedness, response and recovery. investment in solutions for disease modeling for public health agencies are needed in the same way as solutions for working from home, selling goods and services from the cloud, and returning to the workplace. the examples given in this paper to illustrate framework components are works-in-progress, and they are just a few of the many technologies used for public health preparedness, response, and recovery against covid-19. evaluation studies of these and other approaches and technologies are required to fully understand their contributions to preparedness, recovery, and response for specific populations. conclusion the framework illustrates how broad stakeholder groups across multiple sectors leverage technology and informatics during a pandemic. while this framework shows how each informatics methodology or tool fits into the landscape of public health preparedness, response, and recovery, studies are required to examine each tool and its contribution to a given phase of public health response or outcomes on specific populations. understanding how these technologies performed, what worked, what was not effective and what could have worked better is critical to success in the next pandemic. acknowledgments we would like to gratefully acknowledge the contributions and support of sheri bachstein, carol bales, and melissa medori from the weather channel and edward cadow, judy kelly, preeth muthusamy, thomas nisbet, steve payment, paul roma, elizabeth transier, michael volpe, and leigh williamson from watson health. no 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app. 2020. https://newsroom.ibm.com/flu-season-is-here-stay-informed-by-usingflu-insights-with-watson-in-the-weather-channel-app. 20. chukura r, sulpovar m. how ai and weather data can help you plan for allergy season, 2020. https://www.ibm.com/blogs/think/2020/04/the-question-this-spring-is-it-covid-orallergies/. 21. buscemi j. the self-assembled ibm team behind two widely used covid-19 data tracking tools. 2020. https://newsroom.ibm.com/index.php?s=34178&item=31953. 22. ibm offers free tools based on trusted data to track covid-19 cases on your phone and online. 2020. https://newsroom.ibm.com/2020-03-25-ibm-offers-free-tools-basedon-trusted-data-to-track-covid-19-cases-on-your-phone-and-online. 23. choa covid-19 pediatric assessment tool. https://covid-choa.mybluemix.net/dashboard?view=chat. 24. city of austin covid-19 free assessment & testing. https://covid19.austintexas.gov/s/?language=en_us. 25. novel coronavirus uams. (covid-19) screening. https://uams.virtriage.com/#/uamscovid19. https://doi.org/10.1177/2168479018769292 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29714600&dopt=abstract https://doi.org/10.1093/jamia/ocaa037 https://doi.org/10.1089/hs.2019.0018 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=31433284&dopt=abstract https://doi.org/10.1093/jamia/ocab004 https://pubmed.ncbi.nlm.nih.gov/33480419/ leveraging informatics and technology to support public health response: framework and illustrations using covid-19 abstract introduction methods prepare respond recover a framework for pandemic preparedness, response, and recovery benchside bedside disease investigation, modeling and monitoring community coordination and programs communications & automated assistance return to work business continuity and resiliency supply chain disruption new ways of working mapping results the weather channel covid-19 information hub watson assistant for citizens discussion conclusion acknowledgments competing interests references crowdinforming during public health emergencies: a commentary crowdinforming during public health emergencies: a commentary 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 crowdinforming during public health emergencies: a commentary rebecca roberts, md 1 , edward mensah, phd 2 1 john stroger hospital of cook county, chicago 2 university of illinois at chicago, school of public health during the recent 2009 novel h1n1 influenza pandemic, public health safety efforts included prevention and mitigation actions such as mass vaccination programs, community education focused on infection control, social distancing and how to avoid contracting and spreading influenza.[1-3] there were also programs to rapidly deploy caches of ventilators, antivirals and personal protective equipment to treat and reduce transmission of influenza infection.[1,3,4] despite these efforts, many became ill.[12] where and when to seek medical care was part of the public health education message. the problem becomes continuing to meet concurrent public health prevention goals, plus ongoing medical obligations with existing staff and space.[4,6,7] the same medical staff members delivering antiviral medications to those exposed and running mass vaccination programs were also treating the ill. in addition, aggressive viral culture acquisition and special processing was instituted.[1,9] screening for febrile employees and exposed personnel in high risk facilities was started so that antiviral prophylaxis could be rapidly administered. alternate care sites were initiated to address the increased volumes and to sequester possibly infective patients. [1] hospitals often make plans to delay routine care and redeploy the staff and treatment space if the influenza surge required this step.[6,7] in addition to all that new activity, some jurisdictions instituted new influenza-like-illness (ili) reporting requirements for hospitals.[2] even normal staffing levels may be insufficient to meet these new responsibilities and existing staff numbers may be further reduced due to illness during this pandemic.[10] emergency departments (eds) are a good place to begin addressing load distribution during patient surge events such as the 2009 novel h1n1 pandemic. they are open 24/7, serve all who present for treatment, and do not incur the scheduling delays associated with primary care or other office-based appointments. they are prepared to address the most severe acuity of illness and are in hospitals which are often centrally located and highly familiar to the local community. indeed, unprecedented patient surges were reported during the 2009 influenza season. [1, 8] in ojphi, vol 2, no. 1, bob mcleod introduced a novel combination of agent based modeling (abm), electronic medical record dashboards to predict ed waiting room times, and crowdinforming as a method to redistribute patients seeking ed care.[11] the purpose is to balance area hospital waiting room loads during pandemics surges. this is a very innovative idea with important applications in medicine and public health. in short, they propose using emr dashboards to estimate real-time ed patient waiting times for area hospitals.[11,12,13,14] this information is widely broadcast using the internet. the aim is crowdinforming during public health emergencies: a commentary 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 to let potential ed patients add waiting room time to their decision-making on when and which ed to visit for current symptoms. this would help both patients seeking care and hospitals that often become overburdened during influenza season. we postulate that the model and the dissemination of the data have further important purposes. the primary goal for any new medical innovation is better health in the community. with this in mind, we propose additional considerations to better inform decision making. we also recommend that the abm and waiting room data are first communicated to key users such as the eds, hospitals, clinics and local public health departments; enabling them to better collaborate in serving the community and to organize their staff and facilities to best address anticipated surge changes. the abm model and crowdinforming might be inferred to refer only to ili patients seeking emergency care and the waiting times might be construed as first-come, first-serve. even during a pandemic, the majority of ed patients are there for other emergencies. hospitals in santiago, chile, reported that 78% of all ed visits were for influenza.[8] however, during the epidemic peak in chicago, slightly more than 14% of all ed visitors had ili.[2] among all u.s. ambulatory visits, approximately 16% were ili and australia’s flutracking netted approximately 9% ili visits during the peak.[5,15] another key feature of ed waiting time is the triage procedure. universally, systems are used to ensure that patients of high acuity are treated more rapidly than those with chronic, self-limited illness, or conditions where treatment success is not time-dependent. [16] in short, based on complex triage rules, severity of illness defines who waits the longest.[17] in the crowdinforming model, this same concept would ideally apply to those who are deciding to seek care or considering long-distance travel to go to an ed with shorter waiting times. the problem becomes the degree of medical knowledge required to make these decisions wisely. a patient with a potential myocardial infarction (heart attack) or limb-threatening injury would wisely take an ambulance to the closest emergency department and expect to be seen immediately even in an ed with long average waiting times. a patient with very mild ili symptoms could safely wait several hours to see a clinician. at the same time, one would not want the mildly ill but contagious, coughing influenza patient to ride public transportation for an hour then wait in an ed infecting others. more importantly, antivirals are most effective in disease treatment and transmission prevention when started very early in the course of illness while patients may not seek care for days or wait until they become quite severe. [4,8,17] these issues are difficult for medical professionals to solve and may be even more difficult for potential patients or those who must develop public health messages to inform the community.[3,10,18,19] another resource that may inform this new modality is the literature on ed waiting room patients who left without being seen (lwbs). the literature indicates that longer waiting times, younger age and less severe disease are associated with lwbs.[20-23] however, this obscures the fact that even though age and acuity are statistically associated with waiting room behavior, some who leave do have high acuity problems.[17,21] the statistical tests gain strength from very large sample sizes. an important proportion of lwbs patients are hospitalized within a week.[21] the unintended consequence of crowdinforming might be to influence a critically ill crowdinforming during public health emergencies: a commentary 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 person to defer care or travel inordinate distances when they would best be seen at the closest hospital and triaged for immediate care. current staffing and medical supplies in departments critical to patient care must be taken into consideration when releasing information to the public that will cause rapid increases at the slower hospitals. staff configuration at individual eds and associated hospital wards, intensive care units, laboratories, pharmacies and radiology departments may need to be adjusted before crowdinforming induces dramatic change.[7,12] eds that are currently slow and might expect to remain slow could become swamped in the short-term. at the same time, overloaded hospitals may have instituted back-up staff and supply procedures for currently anticipated surge volumes, only to have the problems evaporate. for these reasons, we recommend that local public health departments, ems, hospitals, and public education professionals should be closely involved in the formulation and response to crowdinforming before messages are delivered to the public. this could foster cooperation and collaboration in the deployment of space, staff and supply resources throughout an area to best serve the public health.[3,6,7] references 1. fagbuyi db, brown km, mathison dj, kingsnorth j, morrison s, saidinejad m, greenberg j, knapp m, chamberlain jm. a rapid medical screening process improves emergency department patient flow during surge associated with novel h1n1 influenza virus. ann emerg med. 2011; 57:52-9. 2. city of chicago department of public health. pandemic influenza a (h1n1) in chicago, 2009. communicable disease information. february 2010. available at: http://www.cityofchicago.org/content/dam/city/depts/cdph/infectious_disease/communicable _disease/ip_cdinfo_feb2010_pandemicflu.pdf. assessed december, 2010. 3. bishop jf, murnane mp, owen r. australia’s winter with the 2009 pandemic influenza a (h1n1) virus. nejm. 2009; 361:2591-2594. 4. bradt da, drummond cm. avian influenza pandemic threat and health systems response. emerg med australas. 2006; 18:430-43. 5. centers for disease control and prevention. 2010-2011 influenza season week 50 ending december 18, 2010. dec 23, 2010. available at: http://www.cdc.gov/flu/weekly/ accessed december, 2010. 6. u.s. department of homeland security. target capabilities list – a companion to the national preparedness guidelines. september 2007. available at: http://www.fema.gov/pdf/government/training/tcl.pdf. accessed may 2010. 7. hospital incident command system. external scenario 3: biological disease outbreak – pandemic influenza. available at: http://www.emsa.ca.gov/hics/files/ext_03.pdf. accessed april 2010. 8. torres jp, o’ryan m, herve b, espinoza r, acuna g, manalich j, chomali m. impact of the novel influenza a (h1n1) during the 2009 autumn-winter season in a large hospital setting in santiago, chile. clin infect dis. 2010; 50:869-70. http://www.cityofchicago.org/content/dam/city/depts/cdph/infectious_disease/communicable_disease/ip_cdinfo_feb2010_pandemicflu.pdf http://www.cityofchicago.org/content/dam/city/depts/cdph/infectious_disease/communicable_disease/ip_cdinfo_feb2010_pandemicflu.pdf crowdinforming during public health emergencies: a commentary 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 9. cheng pk, wong kk, mak gc, wong ah, mg ay, chow sy, lam rk, lau cs, ng kc, lim w. performance of laboratory diagnostics for the detection of influenza a (h1n1) virus as correlated with the time after symptom onset and viral load. j clin virol. 2010; 47:182-5. 10. lipsitch m, phil d, riley s, phil d, cauchemez s, ghani ac, ferguson nm. managing and reducing uncertainty in an emerging influenza pandemic. nejm. 2009; 361;2:112-15. 11. mcleod b. agent based modeling of “crowdinforming” as a means of load balancing at emergency departments. online journal of public health informatics. 2010 vol 2, no. 1. 12. gunal mm, pidd m. understanding accident and emergency department performance using simulation. proceedings of the 2006 winter simulation conference. 2006, pages 44652. 13. bonabeau e. agent-based modeling: modeling and techniques for simulating human systems. pnas. 2002; 99:7280-87. 14. laskowski m, mcleod rd, friesen mr, podaima bw, alfa as. models of emergency departments for reducing patient waiting times. plos one. 2009; 4:e6127. 15. carlson sj, dalton cb, durrheim dn, fesja j. online flutracking survey of influenza-like illness during pandemic (h1n1) 2009, australia. emerg inf dis. 2010; 16,12:1960-1962. 16. bernstein sl, argonsky d, duseja r, epstein s, handel d, hwang u, mccarthy m, mcconnell kj, pines jm, rathlev n, schafermeyer r, zwemer f, schull m, asplin br. the effect of emergency department crowding on clinically oriented outcomes. acad emerg med. 2009; 16:1-10. 17. zarychanski r, stuart tl, kumar a, doucette s, elliot l, kettner j, plummer f. correlates of severe disease in patients with 2009 pandemic influenza (h1n1) virus infection. cmaj. 2010; 182:257-64. 18. osterholm mt. preparing for the next pandemic. nejm. 2005; 352:1839-42. 19. kendal ap, macdonald ne. influenza pandemic planning and performance in canada, 2009. can j public health. 2010; 101:447-53. 20. kennedy m, macbean ce, brand c, sundararajan v, mcd taylor d. review article: leaving the emergency department without being seen. emerg med australas. 2008; 20:30613. 21. baker dw, stevens cd, brook rh. patients who leave a public hospital emergency department without being seen by a physician. jama. 1991; 266:1085-90. 22. kulstad eb, hart km, waghchoure s. occupancy rates and emergency department work index scores correlate with leaving without being seen. westjem. 2010; 11:324-28. 23. baibergenova a, leeb k, jokovic a, gushue s. missed opportunity: patients who leave emergency departments without being seen. healthcare policy. 2006 1;35-41. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts mining surveillance data: does radiation treatment of prostate cancer cause rectal cancer? john w. morgan*1, 2, brice jabo1, mark e. ghamsary1 and kevork kazanjian3 1dept epidemiology, biostatistics & population medicine, loma linda university school of public health, loma linda, ca, usa; 2desert sierra cancer surveillance program, region 5 of california cancer registry, loma linda, ca, usa; 3dept of surgery, division of surgical oncology, loma linda university school of medicine, loma linda, ca, usa objective we sought to assess whether external beam radiation (rad) treatment of prostate cancer, that exposes the rectum to ionizing radiation, was followed by increased hazards for rectal cancer, relative to prostatectomy (surg). introduction prostate cancer (pc) is the most common invasive cancer diagnosed among us men. the majority of pcs are organ-confined at diagnosis making them candidates for treatment using rad, surg, or other protocols. several studies have provided preliminary evidence that radiation treatment of prostate cancer may increase subsequent rectal cancer risk (1-2). data specifying type of rad treatment of pc was not available for the study period. methods we conducted record linkage for all 322,327 organ confined new prostate cancers and 53,204 new rectum and rectosigmoid junction (rectal) cancers among california males from 1988-2009, identifying men diagnosed with rectal cancer more than five years following treatment of organ-confined prostate cancer with rad or surg. among the men treated with rad vs surg, the cox proportional hazards ratio (hr) for subsequent rectal cancer was assessed. demographic covariates included: race/ethnicity as asian/other (a-o), non-hispanic black (nhb), hispanic (hisp), and non-hispanic white (nhw), and socioeconomic status quintiles (1-5 highest). other covariates included age, as a continuous variable, and year of pc diagnosis. results among the 43,130 men having organ-confined prostate cancer that had been treated with rad only, 166 were diagnosed with rectal cancer more than five years following pc treatment. likewise, 69,104 men treated with surg only, yielded 242 rectal cancer cases more than 5 years later. following is the demographic factor adjusted hazards ratio (hr) for rectal cancer with 95% confidence intervals (ci) contrasting findings for the two pc treatment cohorts: rectal cancer hrrad/surg=1.39; 95% ci=1.12-1.74. hr contrasts for demographic factors included age(hrage= 1.02; 95% ci=1.01-1.04), race/ethnicity(hra-o/nhw= 1.10; 95% ci=0.72-1.67, hrnhb/nhw= 1.19; 95% ci=0.82-1.74 and hrhisp/nhw= 1.01; 95% ci=0.72-1.43), and sescontrasts (hrses1/ses5= 0.95; 95% ci=0.65-1.39), hrses2/ses5=1.20; 95% ci=0.89-1.62, hrses3/ses5= 1.17; 95% ci=0.88-1.55, and hrses4/ses5= 1.14; 95% ci=0.87-1.49). the hr for pc year of diagnosis (hryear= 0.91; 95% ci=0.89-0.94) a protective effect for more recent years. conclusions these findings reveal increased hazards for rectal cancer among organ-confined prostate cancer patients treated with rad, relative to patients treated with surg, that are substantially independent of demographic covariates. treatment of rectal cancer among these patients is further complicated because they are ineligible for radiation treatment of rectal cancer due to the high-dose pelvic radiation received during prostate cancer treatment. further analyses that seek to distinguish roles of different dose and delivery methods for rad are ongoing. keywords radiation; cancer; rectal; prostate; surveilance references 1. e rapiti, g fioretta, hm verkooijen, r zanetti, f schmidlin, h shubert, a merglen, r miralbell, c bouchardy. increased risk of colon cancer after external radiation therapy for prostate cancer. int. j. cancer: 123, 1141–1145 (2008). 2. nn baxter, je tepper, sb durham, da. rothenberger,ba virnig. increased risk of rectal cancer after prostate radiation: a populationbased study. gastroenterology 2005;128:819–824. *john w. morgan e-mail: john.w.morgan@att.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e183, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts searching for complex patterns using disjunctive anomaly detection maheshkumar sabhnani*, artur dubrawski and jeff schneider carnegie mellon university, pittsburgh, pa, usa objective disjunctive anomaly detection (dad) algorithm [1] can efficiently search across multidimensional biosurveillance data to find multiple simultaneously occurring (in time) and overlapping (across different data dimensions) anomalous clusters. we introduce extensions of dad to handle rich cluster interactions and diverse data distributions. introduction modern biosurveillance data contains thousands of unique time series defined across various categorical dimensions (zipcode, age groups, hospitals). many algorithms are overly specific (tracking each time series independently would often miss early signs of outbreaks), or too general (detections at state level may lack specificity reflective of the actual process at hand). disease outbreaks often impact multiple values (disjunctive sets of zipcodes, hospitals, multiple age groups) along subsets of multiple dimensions of data. it is not uncommon to see outbreaks of different diseases occurring simultaneously (e.g. food poisoning and flu) making it hard to detect and characterize the individual events. we proposed disjunctive anomaly detection (dad) algorithm [1] to efficiently search across millions of potential clusters defined as conjunctions over dimensions and disjunctions over values along each dimension. an example anomalous cluster detectable by dad may identify zipcode = {z1 or z2 or z3 or z5} and age_group = {child or senior} to show unusual activity in the aggregate. such conjunctive-disjunctive language of cluster definitions enables finding realworld outbreaks that are often missed by other state-of-art algorithms like what’s strange about recent events (wsare) [3] or large average submatrix (las) [2]. dad is able to identify multiple interesting clusters simultaneously and better explain complex anomalies in data than those alternatives. methods we define the observed counts of patients reporting on a given day as a random variable for each unique combination of values along all dimensions. dad iteratively identifies k subsets of these variables along with corresponding ranges of their values and time intervals that show increased activity that cannot be explained by random fluctuations (k is generally unknown and could be 0). the resulting set of clusters maximizes data likelihood while controlling for overall complexity. we have successfully derived a versatile set of scoring functions that allow normal, poisson, exponential or non-parametric assumptions about the underlying data distributions, and accommodate additive-scaled, additive-unscaled or multiplicative-scaled models for the clusters. results we present results of testing dad on two real-world datasets. one of them contains daily outpatient visit counts from 26 regions in sri lanka involving 9 common diseases. the other data contains semisynthetically generated terrorist activities throughout regions of afghanistan (sigacts). both span multiple years and are representative of data seen in biosurveillance applications. figure 1 shows dad systematically outperforming wsare and las. each algorithm’s parameters were tuned to generate one false positive per month in baseline data. the graphs represent average days-to-detect performance of 100 sets with synthetically injected clusters using additive-scaled (as), additive-unscaled (au), and multiplicative-scaled (ms) models of cluster interactions. conclusions we extend applicability of dad algorithm to handle wide variety of input data distributions and various outbreak models. dad efficiently scans over millions of potential outbreak patterns and accurately and timely reports complex outbreak interactions with speed that meets requirements of practical applications. keywords outbreak detection; anomalous clusters; disjunctive anomaly detection; prospective surveillance acknowledgments this material is based upon work supported by the national science foundation under grant no. iis-0911032. references 1. sabhnani m., dubrawski a., schneider j. detection of multiple overlapping anomalous clusters in categorical data. advances in disease surveillance, 2010. 2. shabalin a., weigman v., perou c., nobel a. finding large average submatrices in high dimensional data. annals of statistics 3(3):9851012, 2009. 3. wong w., moore a., cooper g., wagner m. what’s strange about recent events (wsare). j. of machine learning research, 6:19611998, 2005. *robin sabhnani e-mail: sabhnani@cs.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e14, 2013 food trends and popular nutrition advice online – implications for public health 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi food trends and popular nutrition advice online – implications for public health divya ramachandran*1,2, james kite1, amy jo vassallo1, josephine y chau1, stephanie partridge1,3, becky freeman1, timothy gill1,2 1. prevention research collaboration, sydney school of public health and charles perkins centre, faculty of medicine and health, university of sydney nsw, australia 2. the boden institute of obesity, nutrition, exercise & eating disorders, sydney school of public health and charles perkins centre, faculty of medicine and health, university of sydney nsw, australia 3. westmead applied research centre, faculty of medicine and health, university of sydney nsw, australia abstract objectives: consumers routinely seek health and nutrition-related information from online sources, including social media platforms. this study identified popular online nutrition content to examine the advice and assess alignment with the australian guideline to healthy eating (aghe). methods: we used facebook page “likes” as an indicator of popularity to identify online nutrition and diet content. websites and blogs associated with pages that had more than 100,000 australian likes on 7th september 2017 were included. the dietary advice promoted was collected and compared with the aghe across nine categories (vegetables, fruits, legumes, grains, lean meat, dairy/alternative, fat, sugar, salt) results: nine facebook pages met the inclusion criteria. the four most-liked pages were hosted by celebrities. only two pages and their associated websites had advice consistent with aghe recommendations across all nine categories reviewed. the concept of “real food” was a popular theme online. while most sources advocated increasing vegetable consumption and reducing processed food, other advice was not evidence-based and frequently deviated from the aghe. discussion: health information seekers are exposed to a variety of online dietary information and lifestyle advice. while few public health goals are promoted, there are many contradictions, as well as deviations from the aghe, which can create confusion among health information seekers. public health organisations promoting aghe on facebook are few and not as popular. conclusion: public health organisations need to be more engaged on popular internet platforms such as facebook. the prevailing popular nutrition advice online may increase consumer confusion, scepticism and even avoidance of dietary advice. proactive efforts are needed by public health organisations, in partnership social marketing experts, to create and share engaging and accurate nutrition content. partnership with celebrities should be explored to improve reach and impact of evidence-based diet recommendations online. food trends and popular nutrition advice online – implications for public health 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi keywords: public health, health communication, diet fads, nutrition guidelines, social media, internet correspondence: divya ramachandran, mph; the boden institute, level 2, charles perkins centre, d 17 johns hopkins drive, camperdown nsw 2006 australia; email: divya.ramachandran@sydney.edu.au doi: 10.5210/ojphi.v10i2.9306 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction optimal nutrition is important for improved health and wellbeing and reducing the risk of dietrelated health conditions including chronic disease [1]. in australia, the national health and medical research council (nhmrc) publishes the australian dietary guidelines (adg) and the australian guidelines to healthy eating (aghe) for healthy eating based on the best available scientific evidence [2]. however, most australians do not follow recommended guidelines for healthy eating [3]. consumers have reportedly found dietary guidelines confusing [4]. this confusion is aggravated by exposure to conflicting and changing nutrition information [5]. the continuously evolving body of nutrition evidence and inaccurate news media reporting contributes to the public perception that “the science keeps changing” [6]. exposure to these contradictions provokes negative responses ranging from consumer scepticism to anger and anxiety [7]. in some, it induces a sense of inaction and avoidance of all guidelines [5,6] or a backlash which can potentially deter intentions to adopt healthy lifestyle behaviours [5]. in others, it promotes an active search for ‘truth’ [7] or looking for information from sources perceived to be neutral and free from hidden agendas [8]. online health and nutrition information seeking is a common phenomenon [9]. a national survey in the us reported that nearly 60% of all adults accessed health information online with over a quarter accessing it through social media [9]. nearly 32% of us adults shared their perceptions and knowledge of health online, and 9% of social media users had started or joined a health-related group. information on diet, nutrition, vitamins and supplement information has been reported as one of the more common reasons why people use the internet [10]. a similar phenomenon is evident in australia, with a study in western australia showing a dramatic increase in online nutrition information seeking, from less than 1% in 1995-2001 to 33.7% of all adults in 2012 [11]. in order for public health organizations to address nutrition misinformation, it is essential to first understand the current online nutrition information landscape. this study aimed to identify popular online nutrition content in australia and examine the dietary advice promoted and its alignment with the aghe. food trends and popular nutrition advice online – implications for public health 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi methods study design and approach facebook is the most popular social media platform [12] and searching for health information on social media is a growing phenomenon [13]. we therefore, used facebook “likes” as an indicator of popularity to identify most popular diet and nutrition content producers in australia. the “about” section of facebook pages provide a description of the page including links to associated websites and blogs. these websites or blogs contained the page hosts’ food philosophy and diet advice. in order to do a contained study, we excluded analysing individual facebook posts, but extracted relevant content from the “about section” of the facebook page, and from the publicly available content on associated websites or blogs. sample selection socialbakers [14], a social media analytics company, lists pages with most likes on their website. we used data available on socialbakers on 7th september 2017 and identified the most “liked” facebook pages in australia that made recommendations on healthy eating. all categories of pages were examined, however only the categories “celebrities”, “brands” and “lifestyle” under “communities” contained the pages of interest. pages that had 100,000 australian “likes” or more under these three categories were extracted (n =1304). we then excluded pages that were not related to food (n= 1120), food and beverage brands, industry groups, and food retailers (n = 136), recipe pages (n = 28), and news service pages that simply channelled health and nutrition news articles from various sources but did not develop original content (n=7), and thirteen pages remained. three of these pages (clean eating recipes, just eat real food, fitness recipes) catered to paleo, vegan, gluten-free, dairy-free lifestyles; and one page (skinnytastes) promoted low-calorie eating. however, the content in these pages and associated websites did not contain explicit statements comparable with aghe food groups, and so were subsequently excluded from the sample, leaving a final sample of nine pages for analysis. data collection we used a two-step approach to first, describe the popular facebook pages, and second, to examine the dietary advice made by the authors of pages on their websites or blogs. data recorded included facebook page name, associated website/blog link, and the number of global and australian page ‘likes’. all websites had either main or sub-pages or blog posts that indicated author's food preferences and advice on what to eat or not. step 1 – description of popular facebook pages in order to describe the pages, we developed a unique coding scheme to categorise type of author, diet pattern or theme, references to dietary guidelines, using the definitions below. author type the type of page host, including ‘celebrities’, ‘weight loss programs’, ‘dietitians/nutritionists’, or ‘other’ food trends and popular nutrition advice online – implications for public health 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi diet pattern or theme whether the page promoted a particular theme or pattern of diet, including: • ‘real food’ (a diet consisting of organic whole foods that are as close to their natural state as possible, with an avoidance of processed foods); • ‘paleo’ (consumption of foods presumed to have been the foods available to or consumed by humans during the paleolithic era. therefore, grains, dairy, oil, sugar, processed foods are all excluded) • ‘calorie-count’ (diets that recommend tracking calories consumed in a day) • ‘raw’ (diets that emphasize mostly raw food, rather than cooked) • ‘vegan’ (diet based on plant-based foods, avoiding animalbased foods including dairy, eggs and honey) • ‘sugar free’ (a diet that emphasizes elimination of almost all sugar from the diet) • dairy free (a diet devoid of dairy products) • gluten free (a diet devoid of wheat, wheat products and barley) • ‘other’ (other diet themes – e.g. fruit and vegetables for children, protein powders, gut and psychology syndrome or gaps diet) reference to dietary guidelines whether there was any reference or mention of alignment with any government-backed dietary guidelines. step 2 – assessment of the dietary advice and alignment with aghe data extraction for this step was guided by three questions: 1. do the authors recommend eating, limiting, and/or avoiding food groups? 2. do authors specify items to eat, limit, and/or avoid within the food groups? 3. do authors prescribe the selection of food in any manner (for example: organic, grass-fed, pesticide-free, non-gmo, canned, frozen), or cooking technique (for example: soaking, fermenting). the content extracted was recorded verbatim along with a link to the content. two reviewers (dr and av) independently coded the content, and summarised diet advice using the three questions listed above as a guide. where coding differences could not be reconciled between the two primary reviewers, they were referred to a third reviewer (jk). examples of coding are available in the appendix. we then assessed the coded summaries for alignment with food trends and popular nutrition advice online – implications for public health 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi the aghe recommendations for each of the five food groups vegetables, fruit, lean meat, grain, dairy/alternative; we coded legumes separately as they are included under vegetables as well as lean meat food groups in aghe; and for fat, sugar and salt. results step 1 – description of popular facebook pages as described above, nine pages were found to meet the eligibility criteria for inclusion: michelle bridges 12 week body transformation (12wbt), jamie oliver, chef pete evans (pete evans), i quit sugar (iqs), the healthy mummy (healthy mummy), super healthy kids (shk), quirky cooking, weight watchers aunz (weight watchers), and rebel dietitian. as shown in table 1, these nine pages had nearly 16 million ‘likes’, with 2,967,788 ‘likes’ from australia. 12wbt had the highest number of australian likes at 778,066, whereas rebel had the least of those sites in our sample at 104,132 likes. the four most popular pages (12wbt, jamie oliver, pete evans and iqs) were hosted by celebrities. two pages (shk and rebel dietitian) were hosted by registered dietitians, two pages (weight watchers and healthy mummy) were commercial weight loss programs. all pages except three (healthy mummy, shk and weight watchers) promoted “real food”or the shift to consuming organic whole foods that are as close to their natural state as possible, with an avoidance of processed foods. in addition, a variety of dietary patterns and themes such as paleo, gluten-free, sugar-free, raw, vegan and their variants were promoted. these niche diets were promoted as healthy ways of eating for everybody and not limited only to specific patient groups such as coeliac patients or those with allergies and intolerances. six of the pages (12wbt, jamie oliver, iqs, healthy mummy, weight watchers, and shk) quoted or referenced governmentrecommended dietary guidelines including those of australia, uk and us. two pages (12wbt and weight watchers) recommended tracking calories consumed. table 1. top nine facebook pages in australia that provide nutrition advice (as on 7th september 2017) facebook page url australia n likes total likes author diet type / theme reference to dietary guidelines michelle bridges 12 week body transform ation (12wbt) https://w ww.12wb t.com/ 728 214 778 066 celebrity real food, calorie-count yes australian dietary guidelines [2] jamie oliver http://ww w.jamieol iver.com/ 450 198 6 525 310 celebrity real food yes uk guidelines [26] food trends and popular nutrition advice online – implications for public health 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi chef pete evans (pete evans) http://ww w.thepale oway.co m 440 339 1 528 167 celebrity paleo, real food no i quit sugar (iqs)* https://iq uitsugar.c om/,http:/ /www.sar ahwilson. com 402 756 980 875 celebrity sugar free, real food yes – australian dietary guidelines [2] the healthy mummy (healthy mummy) https://w ww.healt hymumm y.com/ 312 367 361 663 other other –includes healthy mummy protein shakes yes – australian dietary guidelines [2] super healthy kids (shk) http://ww w.superh ealthykid s.com 204 120 3 274 660 dietitian other – focus on fruit and vegetable intake for children yes – us dietary guidelines [27] quirky cooking https://w ww.quirk ycooking. com.au/ 198 340 267 268 other real food; paleo, dairy-free, gluten-free, other -gaps diet no weight watchers aunz (weight watchers) https://w ww.weig htwatcher s.com/au/ 127 322 160 867 weight loss program calorie-count yes – australian dietary guidelines [2] rebel dietitian (rebel) https://re beldietitia n.us 104 132 1 948 694 dietitian real food, vegan, raw. no *note: the iqs website was taken down may 31, 2018, however the sarah wilson website and blog as well as ebooks are still available online. step 2 – assessment of the dietary advice and alignment with aghe table 2 indicates alignment or deviation/ contradiction between advice of popular pages and the aghe on what to eat or limit. of the nine pages and associated websites reviewed, two (12 wbt, food trends and popular nutrition advice online – implications for public health 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi weight watchers) aligned with all nine aghe categories. three (rebel dietitian, healthy mummy and shk) aligned with 8 aghe categories; and one aligned with the aghe on 5 (quirky cooking), 4 (jamie oliver), 3 (pete evans), and 2 (iqs) categories. two (jamie oliver, shk) deviated from the guidelines only due to insistence on organic versions. iqs deviated from the guidelines by an inappropriate focus on fructose elimination. table 2 – alignment of popular online dietary advice with the australian guide to healthy eating page vegetables fruit legumes grains lean meat dairy/ altern ative fat sugar salt michelle bridges 12 week body transformation (12wbt) √ √ √ √ √ √ √ √ √ jamie oliver x* x* √ √ x* x* x* √ √ chef pete evans (pete evans) √ x x x √ x x √ x i quit sugar (iqs) √ x x x x √ x x** x the healthy mummy (healthy mummy) √ √ √ √ √ √ x √ √ super healthy kids (shk) √ √ √ √ x √ √ √ √ quirky cooking √ √ √ √ x √ x x x weight watchers ausnz (weight watchers) √ √ √ √ √ √ √ √ √ rebel dietitian √ √ √ √ √ √ x √ √ √ -aligned with aghe.x-conflicting / contradictory to aghe * consumption advice aligns with aghe but stipulates organic versions as healthier. ** consumption advice aligns with aghe, but advice to eliminate fructose is not supported by evidence. table 3 provides the advice of popular online authors summarised by food groups. italics have been used where: 1. the advice is directly contradictory to aghe such as limiting fruit, dropping food groups, eating saturated fat; and 2. non-evidence-based advice that overstate the health benefits or harms of categories and sub-categories of food that deviates from government guidelines – for example food trends and popular nutrition advice online – implications for public health 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi eating organic food, choosing himalayan salt, replacing sugar with natural sugar or eliminating fructose. only two websites,12wbtand weight watchers, were fully consistent with recommendations in the aghe, across all food groups, fat, sugar and salt. jamie oliver was consistent on all nine aghe recommendations; however, the advice on fruits, vegetables, lean meat and dairy goes beyond the guidelines by stipulating organic versions of are healthier. similarly, the website of shk too aligned on all nine categories, but promotes organic meat. the aghe does not recommend organic varieties over conventionally grown foods, as there is no consistently proven nutritional advantage [15]. further, food standards australia and new zealand (fsanz) specify a maximum residual limit (mrl) for agricultural or veterinary chemical residue that is legally allowed for all food sold in australia [16] ensuring conventionally grown food is safe for consumption. thus, insisting on only organic versions as the healthier option may compromise attempts to increase fresh food consumption among all australians due to the additional costs and lesser availability of organic produce. the healthy mummy and rebel dietitian were aligned with aghe on all food groups, salt and sugar except in the promotion of saturated fats. pete evans, quirky cooking, and iqs presented the most contradictions with aghe, with advice including limiting fruit (pete evans, iqs) to going dairy-free (pete evans and quirky cooking) or gluten-free or dropping grains completely. all three promoted “real food” versions such as grassfed meat, pastured and free-range poultry and eggs, wild caught fish and espoused consuming fullfat dairy, and saturated fats, including coconut oil. although these websites limited sugar, iqs advice claimed only fructose elimination (component of fruits) was more important than addressing total added sugars. these websites also promoted himalayan, pink or celtic varieties of salt. discussion and conclusion our assessment revealed that the most popular nutrition information pages on facebook are often hosted by celebrities, followed by dietitians, weight loss programs or other persons. only two were fully aligned with government guidelines, while the rest deviated from aghe in some way – either through direct contradiction on one or more categories, misinformation, or through overlyrestrictive recommendations, exposing health information seekers to conflicting nutrition information. while some public health goals such as consumption of vegetables and avoiding ‘junk’ foods are prominent, the balance of the advice does not align closely with aghe. the “real food” trend characterized by organic food choices is very popular online within our study sample. public health organisations promoting aghe on facebook are few and have negligible likes compared with popular pages. proactive efforts are needed by public health organizations in partnership with social media and social marketing experts to leverage facebook to promote dietary guidelines. partnering with celebrities may be a vehicle to boost reach of evidence-based nutrition information and countering misinformation, by improving quality and consistency of nutrition messaging. food trends and popular nutrition advice online – implications for public health 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi table 3 – diet and nutrition advice of popular facebook pages / websites page vegetables fruit legumes grains lean meat, poultry, fish, eggs dairy/alternat ive fats sugar salt michelle bridges 12 week body transformation (12wbt) eat all vegetables. choose nonstarchy vegetables. 5 serves a day. eat fruit. choose seasonal, variety. avoid dried fruit and juice eat legumes eat wholemeal or whole grain. eat meat, poultry, fish, eggs. eat lean, grilled. 1 serve a day. eat dairy or alternatives. choose low-fat option. two serves a day limit butter/margarin e use unsaturated fat options. avoid deepfrying eliminate soft drinks. eliminate salt. chose lowsodium foods jamie oliver eat vegetables. choose variety of colours, seasonal, organic. eat fruit. choose variety of colours, seasonal, organic. eat beans. regularly. eat wholemeal or whole grain. chose complex carbohydrates. eat. quality over quantity. choose organic, freerange or higher-welfare, responsibly sourced. eat dairy. choose low fat, reduced saturated fat and reduced sugar. choose organic. eat unsaturated fat. coconut oil (saturated fat) may be exception. avoid added sugar. reduce salt. chef pete evans (pete evans) eat vegetables. choose fibrous (non-starchy) vegetables and greens. choose cultured and fermented vegetables. limit fruit avoid legumes avoid grains eat meat, poultry, fish and eggs. choose grass-fed meat, pastured and free-range poultry and eggs, wild caught fish avoid dairy eliminate vegetable oils. use olive or nut oil unheated. use natural fats such as duck fat, tallow, pastured lard. use coconut oil. eliminate refined sugar. choose himalayan and celtic sea salt i quit sugar (iqs) eat vegetables. choose variety of colours. plenty. maximise green vegetables. limit fruit. avoid dried fruit and juice avoid legumes. if eaten then soak / activate. limit carbohydrates. choose glutenfree. use fermented, sprouted, wholegrain. eat meat, poultry, eggs and fish. choose sustainable, organic, grassfed, grain-fed (organic grain), free range eat full fat dairy. eliminate low-fat products. eat saturated fats. avoid polyunsaturated fatty acids. avoid omega 3 supplements. get omegas from food sources. eliminate transfats. eliminate fructose. eat glucose, maltose and lactose in moderation. eliminate refined tablesalt. choose pink salt, whole food sources of salt. food trends and popular nutrition advice online – implications for public health 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi the healthy mummy (healthy mummy) eat vegetables eat fruit eat legumes and beans. use dried or canned varieties. eat wholegrains. try quinoa a gluten-free grain. eat. choose less calorie, lean, low-fat, high protein like turkey. avoid high sugar dairy. eat saturated fats, eat coconut oil avoid processed fats eliminate soft drinks reduced salt or no salt super healthy kids (shk) eat a variety of fresh, frozen, canned, dried, raw or cooked vegetables eat fresh, frozen, canned. limit fruit juice. or dried fruit. eat legumes eat wholegrain eat organic or hormone free eat low fat/fat free dairy avoid trans fat salt – not too much quirky cooking eat vegetables. eat organic. eat fruits. avoid fruits affected by pesticides. eat organic. eat legumes. soaked. eat grain free and/or gluten free/ low gluten pesticide free, soaked, sprouted, fermented grains eat grass fed, free-range, organic meat avoid dairy, except for butter/ghee eat saturated fats. avoid polyunsaturated vegetable oils. choose macadamia oil, tallow, duck fat, or ghee. chose fats with high smoke point. avoid refined sugar. replace with more natural sugars. choose himalayan or celtic salt weight watchers ausnz (weight watchers) eat vegetables. choose variety and in season eat fruit. variety and in season eat pulses as a meat replacement eat wholegrains eat lean meats eat low fat dairy eat vegetable, nut and seed oils limit sugar limit sugar rebel dietitian eat a variety of vegetables eat a variety of fruit. naturally dried fruit is ok eat legumes. minimally processed. soaked. eat wholegrains recommend soaking. avoid processed grains limit meat and processed meat. if eaten, choose organic. avoid fish and shellfish products toxic contaminants. limit dairy avoid animal sources of saturated fat. use unrefined and coldpressed oils at room temperature sparingly use. saturated plantbased fat for cooking avoid added sugars, avoid processed sugars iodised salt note: italicised text indicate non-evidence-based advice or those that deviate from aghe. food trends and popular nutrition advice online – implications for public health 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi although the sources we reviewed were consistent with aghe on increasing vegetable consumption and limiting sugar and ‘junk foods,’ food fads and misinformation were otherwise common. promotion of ‘niche’ dietary patterns such as gluten-free and diary-free are concerning because they are promoted to everybody, and not limited only to special patient groups for whom they may be necessary. equally, although some health benefits have been reported in small samples and for specific health conditions for the paleo diet [17] there is no evidence around its long-term safety and efficacy within the general population. paleo pages’ advice to the general population to eat saturated fat, exclude grains and legumes, and exclude dairy not only directly contradict official dietary guidelines, but can potentially aggravate the problem of australians not eating minimum recommended serves of several food groups [3]. while the government guidelines are evidence-based and promote balanced diets drawing on all food groups, diet trends such as promotion of coconut oil and pink salt, or arousing public fear of fructose, deviate from guidelines [18]. such emphasis to consume or eliminate particular foods or food components, and the trend of dropping entire food groups, create fertile ground for contradictory nutrition messaging and may lead people to doubt dietary guidelines and health recommendations in general [5]. the “real food” trend is predominant online and promoted across popular facebook pages. while there are no formal definitions for “real food,” the pages in our study broadly refer to “real food” as organic and responsibly sourced whole foods, and exclusion of highly processed foods. sustainability and sources of food appear to be important to facebook followers of popular nutrition pages. this is consistent with findings from an earlier study on food beliefs and perceptions of australians [18]. public health organizations can learn much from popular pages on fostering public engagement by linking discussions on healthy eating with other values important to australians, such as environmental sustainability and animal welfare. the lack of facebook pages dedicated to the promotion of government dietary guidelines amidst various popular pages was particularly striking. for example, pages of nutrition australia [19] and daa [20] had less than 25,000 likes and seemed to be followed by professional nutritionists and dietitians, rather than by the general public. we also found a single post on australian dietary guidelines on the page of department of health [21] which had less than 75,000 likes. an earlier study looking for facebook presence of public health organizations also found only one nutritionrelated page that of nutrition australia [22]. it appears that current online dissemination of evidence-based dietary guidelines does not have a large reach in the general population and lacks a strong enough presence on facebook to counter misinformation propagated by popular pages. our study finds a clear opportunity for public health organizations and health communicators to leverage facebook to promote healthy eating guidelines. for example, public health organizations can create facebook pages dedicated to promoting healthy eating, by disseminating evidencebased guidelines, and countering misinformation. content should be tailored in light of popular online nutrition themes and broader food choice issues identified in this study and leveraged along with effective facebook strategies identified in existing research [22]. building a network and reaching audiences on facebook is not easy. celebrity-power, on the other hand, allows their pages to have vast following and social media influence. positive influences that celebrities can have on public health has been highlighted before [23] and this is exemplified by popular and government response to jamie oliver’s ministry of food [24] and the more recent sugar smart uk [25] campaigns. we recommend public health organizations explore partnerships with celebrities in food trends and popular nutrition advice online – implications for public health 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi promoting accurate healthy eating guidelines. we believe this can vastly improve reach and impact of nutrition and diet communication. study limitations as facebook is the single largest social media platform, we used number of “likes” to extrapolate nutrition websites that are popular in australia. it is possible that some popular dietary trends not promoted on facebook or did not have more than 100,000 facebook likes were not included in our study (for example intermittent fasting and ketogenic diets). we did not analyse data across all online and social platforms or quantify repetitions of themes within these platforms. nonetheless, while not definitive, the approach taken may be a reasonable indicator of the predominant nutrition and food choice related themes trending, to inform public health agencies in approaching nutrition communication efforts. as a next step, research examining effectiveness of a dedicated evidencebased nutrition facebook page, and countering misinformation is recommended. celebrity partnership may be explored for such a page along with assessment of reach and impact. conclusion our study shows that that the popular diet and nutrition information websites are not fully aligned with evidence-based guidelines. even those popular pages that reference government guidelines do so with their own interpretation and perceptions, which can create confusion among online health information seekers. a concentrated effort is required to promote healthy eating guidelines to the general public and counter the misinformation easily accessible online. such online efforts may be well served by beginning with facebook given its near universal popularity and reach. references 1. nutrition | national health and medical research council [internet]. nhmrc.gov.au. 2018 [internet]. 2017 [cited 7 september 2017]. available from: https://www.nhmrc.gov.au/healthtopics/nutrition. 2. nhmrc.gov.au. australian dietary guidelines (2013) | national health and medical research council [internet]. 2018 [cited 15 june 2015]. available from: https://www.nhmrc.gov.au/guidelines-publications/n55. 3. 4364.0.55.012 australian health survey: consumption of food groups from the australian dietary guidelines, 2011-12 [internet]. abs.gov.au. 2018 [cited 12 june 2018]. available from: http://www.abs.gov.au/ausstats/abs@.nsf/mf/4364.0.55.012. 4. boylan s, louie jcy, gill tp. 2012. consumer response to healthy eating, physical activity and weight‐related recommendations: a systematic review. obes rev. 13(7), 606-17. pubmed https://doi.org/10.1111/j.1467-789x.2012.00989.x 5. nagler rh. 2014. adverse outcomes associated with media exposure to contradictory nutrition messages. j health commun. 19(1), 24-40. pubmed https://doi.org/10.1080/10810730.2013.798384 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22404752&dopt=abstract https://doi.org/10.1111/j.1467-789x.2012.00989.x https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24117281&dopt=abstract 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https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21208498&dopt=abstract https://doi.org/10.1017/s0029665110004714 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18569057&dopt=abstract https://doi.org/10.1080/10410230802056396 https://doi.org/10.1177/1440783311407947 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26310192&dopt=abstract https://doi.org/10.2196/jmir.4548 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23569602&dopt=abstract https://doi.org/10.5210/ojphi.v3i1.3561 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26269362&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26269362&dopt=abstract https://doi.org/10.3945/ajcn.115.113613 food trends and popular nutrition advice online – implications for public health 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi 19. nutrition australia [internet]. facebook.com. 2018 [cited 25 june 2017]. available from: https://www.facebook.com/nutritionaustralia/. 20. dietitians association of australia [internet]. facebook.com. 2018 [cited 25 june 2017]. available from: https://www.facebook.com/dietitiansassociation/?ref=br_rs. 21. australian department of health [internet]. facebook.com. 2018 [cited 25 june 2017]. available from: https://www.facebook.com/healthgovau/. 22. kite j, foley bc, grunseit ac, freeman b. 2016. please like me: facebook and public health communication. plos one. 11(9), e0162765. pubmed https://doi.org/10.1371/journal.pone.0162765 23. chapman s. 2012. does celebrity involvement in public health campaigns deliver long term benefit? yes. bmj: british medical journal. 345, e6364. pubmed https://doi.org/10.1136/bmj.e6364 24. the good foundation [internet]. jamie's ministry of food. 2018 [cited 13 may 2018]. available from: https://www.jamiesministryoffood.com.au/the-good-foundation. 25. welcome | sugar smart uk [internet]. sugarsmartuk.org. 2018 [cited 13 may 2018]. available from: https://www.sugarsmartuk.org/ 26. the eatwell guide [internet]. gov.uk. 2018 [cited 19 april 2018]. available from: https://www.gov.uk/government/publications/the-eatwell-guide. 27. 2015–2020 dietary guidelines for americans health.gov [internet]. health.gov. 2018 [cited 19 april 2018]. available from: https://health.gov/dietaryguidelines/2015/. appendix supplementary material content analysis examples of coding of popular diet advice the examples below show how popular diet advice summarised in table 3 were derived. of each piece of content, three questions were asked: 1. do the authors recommend eating, limiting, and/or avoiding food groups? 2. do authors specify items to eat, limit, and/or avoid within the food groups? 3. do authors prescribe the selection of food in any manner (for example: organic, grassfed, pesticide-free, non-gmo, canned, frozen), or cooking technique (for example soaking, fermenting). example 1: fruit recommendation url: https://www.12wbt.com/blog/nutrition/can-really-much-fruit/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27632172&dopt=abstract https://doi.org/10.1371/journal.pone.0162765 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23015036&dopt=abstract https://doi.org/10.1136/bmj.e6364 food trends and popular nutrition advice online – implications for public health 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e213, 2018 ojphi content: "how much fruit is enough? finding the ‘juicy’ sweet spot is important for overall nutrition balance, but we need to keep in mind our energy requirements and our food intake for a whole day. aim to choose fruit in season and mix up your variety (berries, citrus, tropical, etc.) to not only get fabulous flavour and nutrient hits, but to keep costs down and support the local produce! it is important to note that it is very easy to overeat dried fruit and fruit juices – both of which can increase the risk of tooth cavities due to their acidity (juice) and ability to stick to teeth (dried fruit). so keep these in check!" described as: eat fruit. avoid dried fruit and fruit juice, chose seasonal and variety. example 2: fat recommendation url: https://www.quirkycooking.com.au/substitutes-recipe-conversions/dairy/ content: “in addition to the specific benefits of omega 3s found in natural foods, there is a massive benefit to gut health that is to be gained by switching from polyunsaturated vegetable oils to animal fats.” “i now mostly use macadamia oil, tallow, duck fat, or ghee for shallow frying, as they have high smoke points. i used to use coconut oil, but you need to be very careful with frying with coconut oil as the smoke point is only 170c.” described as: eat saturated fats; avoid polyunsaturated vegetable oils; choose macadamia oil, tallow, duck fat, or ghee; choose fats with high smoke point. food trends and popular nutrition advice online – implications for public health abstract introduction methods study design and approach sample selection data collection step 1 – description of popular facebook pages step 2 – assessment of the dietary advice and alignment with aghe results step 1 – description of popular facebook pages step 2 – assessment of the dietary advice and alignment with aghe discussion and conclusion study limitations conclusion references appendix supplementary material content analysis examples of coding of popular diet advice editorial: ojphi vol 5, no 2 (2013) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi editorial: ojphi vol 5, no 2 (2013) welcome to the current issue of the online journal of public health informatics. at this stage i will like to take the opportunity to thank the reviewers who have been doing a wonderful job in providing prompt reviews of the articles assigned to them. we have achieved our status as a pubmed indexed, peer-reviewed journal mainly because you have taken this ‘labor of love’ assignment quite seriously. we plan to give graduate students an opportunity to submit summaries of their capstone reports and dissertations for publication as working papers in future issues of the journal. authors will also be invited to deliver webinars on their articles to the public health informatics community. these webinars and their accompanying discussions will be recorded and archived. students will be encouraged to attend these webinars. it is fair to say that, through the support of the reviewers and readers we have succeeded in positioning this journal as the sole portal for disseminating public health informatics research findings. the capability to exchange patient-specific health data among autonomous healthcare entities is at the core of successful implementation of health information exchanges. this capability is also important for patient treatment, public health services delivery, and research. wu xu et al. describe efforts to create a statewide master person index in utah to uniquely identify each individual receiving healthcare or public health services. exchanging personally identifiable information across enterprises for healthcare identity resolution requires new models for data sharing and a complex policy framework to mitigate risks to participants and ensure cooperative success. the authors developed a focus area maturity model to guide the complex process of developing a functional statewide master person index (smpi) among diverse, autonomous partners. the proposed framework provides an orderly path to address interdependences that can guide the complex process of developing a functional smpi, avoiding conflicts between policy and technology that may lead to nonfunctional implementations. immunization registries have been shown to increase vaccine coverage rates and reduce duplicate immunizations. in order to achieve meaningful use the health information technology for economic and clinical health (hitech) act of 2009 encourages providers to submit electronic immunization data to regional or statewide immunization information systems. at present, many providers have established unidirectional interoperability for uploading emr immunization data to designated registries. it is recognized that bidirectional interoperability is preferable because it allows immunization data to be transmitted directly to vendor emrs. integration is, however, expensive and difficult to achieve. lindsay a. stevens, jonathan p. palma, et.al develop and test the feasibility of visually integrating external registries into vendor emr systems. the study shows that this procedure meets providers’ need for relevant data, increases reporting of immunizations, improves provider satisfaction, and avoids the increased costs of bidirectional data integration. approximately 300,000 individuals die from out-of-hospital cardiac arrest (ohca) annually in the united states. it has been observed that there is a wide disparity in the ohca survival rates between cities in usa. hugh semple et al. present results from an on-going project to develop user-friendly, interactive web mapping application that allows public health professionals and the general public to visualize the geographic patterns of cardiac arrest rates, bystander cpr rates, and target specific locations for public health services delivery. participants in a preliminary editorial: ojphi vol 5, no 2 (2013) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi evaluation felt that the web mapping application was a useful, user-friendly geovisualization tool. it is expected that this project will encourage the development of public health web mapping applications that are centered on interactive maps, summary statistics, and the use of social media technology. various social and institutional issues present challenges in the implementation of school-based sexual health education in developing countries. angella musiimenta of mbarara university of science and technology, uganda, presents the results of a study aimed at identifying the factors responsible for successful implementation of technology-aided sexual health education in two ugandan schools. the results indicate that, rather than focusing exclusively on technology, program administrators should create social, institutional, and religious climate that is more supportive of school-based computer-assisted education. health services and public health researchers increasingly rely on search engines to identify relevant articles. the reliability of these search engines is very necessary if one is to avoid costly mistakes. google scholar (gs) is widely-recognized as an excellent source of grey literature in biomedicine. it is a useful tool to help researchers quickly locate relevant papers from billions of pages across the web. research by dean guistini et al. demonstrates that gs is not flexible, precise or indexed enough to be used alone for systematic reviews. the authors show that google search’s ‘keyword search' capability, allied to google’s pagerank, is a poor replacement for controlled vocabulary searching and its interface does not provide enough flexibility to accommodate search filters by discipline, such as ‘health and medicine’. the authors recommend that google search developers should provide full details about its database coverage and improve its interface search capabilities (e.g., indexing, semantic search filters, stored searching, etc.) in order to satisfy the demands of thorough, replicable searches as required by systematic reviews for health services and public health research. thomas g. savel et al. describe the development of partial thromboplastin time (ptt) advisor, a cdc-supported initiative to develop a mobile clinical laboratory decision support application. this is among the first of a handful of ios-based applications funded by cdc. the application offers clinicians a resource to quickly select the appropriate follow-up tests to evaluate patients with prolonged ptt and a normal prothrombin laboratory result. the authors address some of the challenges involved in the development and deployment of the application. the free mobile ptt advisor app was approved by apple and published in their itunes app store. lessons learned from this project will assist other mobile health/public health application developers understand and overcome some of the challenges involved in such projects. the determination of priorities is an essential component of community health status assessment. priority setting enables the rational allocation of limited resources among competing programs. james studnicki et al. utilized the analytical capabilities of online analytic processing( olap) interface to create a community health status prioritizing system which, among other attributes, is capable of ranking different types of health status outcomes and also provides flexibility in the weighting of the evaluation criteria. the authors demonstrate that rankings of community health outcomes based on olap provide sufficient information for priority setting compared to previous methods based on a static set of criteria with fixed weighting factors. editorial: ojphi vol 5, no 2 (2013) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the adoption of certified electronic health records and the implementation of health information exchanges are expected to facilitate the sharing of patients’ health records by authorized providers. records can thus follow patients resulting in reduced delays, duplications, errors, quality improvements and lower costs. ultimately, the idea is to enable the patient to control access to their own data. access to care records is currently often difficult or impossible in cases where records contain personal identifiers; they have to be secured behind services that greatly impede ready access. existing access control infrastructures are proprietary, further making it impossible to retrieve patient records electronically on demand from a workstation that is not part of the record keeper’s network. existing solutions to the data portability problem have raised issues as to whether the public would have confidence that their personal records were safe, secure and private, especially when cloud-based or controlled by third party commercial service providers such as microsoftvault, googlehealth, etc. roderick neame outlines a platform-independent method that avoids most of the issues raised by existing record portability solutions and ensures continuum of care for patients such that their care records follow them wherever they go. in order for such a methodology to be implemented successfully the author acknowledges that it is necessary to have an agreement on the record data types and sub-types, their associated xml tags, as well as develop a browser add-on that can import and display flexibly the xml marked-up records. these conditions are not difficult to achieve with existing technologies. research shows that the incidence of healthcareassociated infections (hais) has increased significantly in the past 20 years in usa. the number of patients who suffer from hais annually in usa is estimated to be approximately 2 million, with about 100,000 deaths annually, ranking hai among the leading causes of death in us acute-care hospitals. the federal government has mandated hospitals to publically report hai rates in order to increase transparency and trust between hospitals and consumers, and to disseminate best practices. however, there is limited guidance in the medical and public health literature related to public reporting of health careassociated infections data. yair rajwan et al. demonstrate that visual communication can provide effective evidence-based information to consumers for decision making and to practitioners for providing patient safety outcomes and processes. the prevention of hospital readmissions improves the quality of individual care as well as population health status. under the hospital readmissions reduction program hospitals must reduce readmissions in order to avoid being penalized financially. accurately predicting the risks of readmissions is a requirement for improving the transition of care process during and postdischarge. the use of administrative claims data is a major limitation of most risk prediction models. shahid choudhry, jing li et al. utilized electronic health records data and a mixedmethod risk prediction model to evaluate post-discharge risk factors. the model demonstrated reasonable fit in heterogeneous populations. given the range of variables that contribute to readmission risks it is necessary to include variables from electronic health records in developing hospital readmission risks. noncomunicable diseases ( ncd) currently constitute the leading causes of deaths in all regions of the world except africa. the largest increase in ncd deaths by 2020 is projected for africa, eastern mediterranean and south-east asian countries. stephan kohler reviews web portals that provide information for reducing preventable lifestyle-related risk factors associated with ncds. editorial: ojphi vol 5, no 2 (2013) 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the author also discusses an open access web portal initiated by two german states for ncd prevention and health promotion activities. this issue includes two commentaries. the first commentary addresses the sustainability of public health surveillance systems. in recent years the world has witnessed disease outbreaks and epidemics resulting in loss of lives and significant economic costs. for example, the global severe acute respiratory syndrome of 2002-2003 resulted in a financial cost of $40 billion to $54 billion dollars while the anthrax attack in us in 2001 resulted in financial cost of 320 million dollars, 22 cases, including 5 deaths. the significant health impacts and economic costs of disease outbreaks illustrate the critical importance of effective public health surveillance and rapid response. in order to respond effectively to the growing threats to population health, public health surveillance systems must be built on a stable infrastructure of core workforce competencies, information systems, and organizational capacity, and must be supported by enterprise-based funding. nabila mirza, terra reynolds et al. present the recommendations of the sustainable surveillance workgroup convened by the international society for disease surveillance to identify strategies for building, strengthening, and maintaining surveillance systems. a disparity currently exists between the accuracy of icd-9 admission coding and discharge coding with some error rates as high as seventy percent. it is envisioned that the transition to icd-10 coding could increase this disparity. in the second commentary, christopher bell, arash jalali, et.al. propose a decision support technology, the icd-10 anatomographer, which could assist emergency department physicians working in busy trauma units in finding accurate icd10 codes efficiently, thereby improving quality of care. best regards edward mensah, phd editor-in-chief online journal of public health informatics 1603 west taylor street, room 759 chicago, illinois, 60612 email:dehasnem@uic.edu office: 312-996-3001 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts towards linking anonymous authorship in casual sexual encounter ads jason a. fries*1, alberto m. segre1 and philip m. polgreen2 1computer science, the university of iowa, iowa city, ia, usa; 2the university of iowa department of internal medicine, iowa city, ia, usa objective this paper constructs an authorship-linked collection or corpus of anonymous, sex-seeking ads found on the classifieds website craigslist. this corpus is then used to validate an authorship attribution approach based on identifying near duplicate text in ad clusters, providing insight into how often anonymous individuals post sexseeking ads and where they meet for encounters. introduction the increasing use of the internet to arrange sexual encounters presents challenges to public health agencies formulating std interventions, particularly in the context of anonymous encounters. these encounters complicate or break traditional interventions. in previous work [1], we examined a corpus of anonymous personal ads seeking sexual encounters from the classifieds website craigslist and presented a way of linking multiple ads posted across time to a single author. the key observation of our approach is that some ads are simply reposts of older ads, often updated with only minor textual changes. under the presumption that these ads, when not spam, originate from the same author, we can use efficient near-duplicate detection techniques to cluster ads within some threshold similarity. linking ads in this way allows us to preserve the anonymity of authors while still extracting useful information on the frequency with which authors post ads, as well as the geographic regions in which they seek encounters. while this process detects many clusters, the lack of a true corpus of authorship-linked ads makes it difficult to validate and tune the parameters of our system. fortunately, many ad authors provide an obfuscated telephone number in ad text (e.g., 867-5309 becomes 8sixseven5three oh nine) to bypass craigslist filters, which prohibit including phone numbers in personal ads. by matching phone numbers of this type across all ads, we can create a corpus of ad clusters known to be written by a single author. this authorship corpus can then be used to evaluate and tune our existing near-duplicate detection system, and in the future identify features for more robust authorship attribution techniques. methods from 7-1-2009 until 7-1-2011, rss feeds were collected daily for 8 personal ad categories from 414 sites across the united states, for a total of 67 million ads. to create an anonymous, author-linked corpus, we used a regular expression to identify obfuscated phone numbers in ad text. we measure the ability of near-duplicate detection to link clusters in two ways: 1) detecting all ads in a cluster; and 2) correctly detecting a subset of ads within a single cluster. ads incorrectly assigned to more than 1 cluster are considered false positives. all results are reported in terms of precision, recall, and f-scores (common information retrieval metrics) across cluster size, expressed as number of ads. results 652,014 ads contained phone numbers, producing a total of 46,079 authorship-linked ad clusters. for detecting all ads within a cluster, precision ranged from 0.05 to 0.0 and recall from 0.02 to 0.0 for all cluster sizes. for detecting partial clusters, see figure 1. conclusions we find that near-duplicate detection alone is insufficient to detect all ads within a cluster. however, we do find that the process can, with high precision and low recall, detect a subset of ads associated with a single author. this follows the intuition that an author’s total set of ads is itself comprised of multiple self-similar subsets. while a near-duplicate detection approach can correctly identify subsets of ads linked to a single author, this process alone cannot attribute multiple clusters to a single author. future work will explore leveraging additional linguistic features to improve author attribution. (top) evaluations for partial cluster detection using the near-duplicate identification approach to linking anonymous authorship in craigslist ads and (bottom) the distribution of ad cluster sizes. keywords surveillance; public health; stds; authorship attribution; computer science references [1] ja fries, am segre, pm polgreen .using online classified ads to identify the geographic footprints of anonymous, casual sex-seeking individuals. ase/ieee international conference on social computing 2012. *jason a. fries e-mail: jason-fries@uiowa.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e164, 2013 ojphi-06-e21.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 159 (page number not for citation purposes) isds 2013 conference abstracts capacity and needs assessment for establishing a syndromic surveillance system in rural china: a mixed study tao tao*1, qi zhao1, huijian cheng2, weirong yan3, 4, hengjin dong5, 6 and biao xu1 1school of public health, fudan university, shanghai, china; 2jiangxi provincial center for disease control and prevention, nanchang, china; 3division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden; 4department of epidemiology and biostatistics, school of public health, tongji medical college, huazhong university of science and technology, wuhan, china; 5institute of public health, heidelberg university, heidelberg, germany; 6center for health policy studies, school of public health, zhejiang university school of medicine, hangzhou, china � �� �� �� � � �� �� �� � objective �������� �� ��������� ����������������������� ����������� ��������� 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�����������;���������;��������������� acknowledgments 0 � � ������� ��������"��'��������(����) � ����� �&������� �+��� ��������<&+*=6>>*�6>95?�<&+*=6>>*�6>99?��������������������������#� <6@9a>>?# *tao tao e-mail: ttsuper2000@hotmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e21, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a health department’s collaborative model for disease surveillance capacity building ikechi konkwo*1, 2, robert g. harmon1, william c. livingood2, 1, thomas bryantiii1 and saad zaheer1 1institute for public health informatics and research, duval county health dept, jacksonville, fl, usa; 2center for health equity and research university of florida/shands medical center, jacksonville, fl, usa objective highlight one academic health department’s unique approach to optimizing collaborative opportunities for capacity development and document the implications for chronic disease surveillance and population health. introduction public health departments are increasingly called upon to be innovative in quality service delivery under a dwindling resource climate as highlighted in several publications of the institute of medicine. collaboration with other entities in the delivery of core public health services has emerged as a recurring theme. one model of this collaboration is an academic health department: a formal affiliation between a health professions school and a local health department. initially targeted at workforce development, this model of collaboration has since yielded dividends in other core public health service areas including community assessment, program evaluation, community-based participatory research and data analysis. the duval county health department (dchd), florida, presents a unique community-centered model of the academic health department. prominence in local informatics infrastructure capacity building and hosting a cdc-cste applied public health informatics fellowship (aphif) in the institute for public health informatics and research (iphir) in partnership with the center for health equity research, university of florida & shands medical center are direct dividends of this collaborative model. methods we examined the collaborative efforts of the dchd and present the unique advantages these have brought in the areas of entrenched data-driven public health service culture, community assessments, program evaluation, community-based participatory research and health informatics projects. results advantages of the model include a data-driven culture with the balanced scorecard model in leadership and sub-departmental emphases on quality assurance in public health services. activities in iphir include data-driven approaches to program planning and grant developments, program evaluations, data analyses and impact assessments for the dchd and other community health stakeholders. reports developed by iphir have impacted policy formulation by highlighting the need for sub county level data differentiation to address health disparities. unique community-based mapping of duval county into health zones based on health risk factors correlating with health outcome measures have been published. other reports highlight chronic disease surveillance data and health scorecards in special populations. partnerships with regional higher institutions (university of florida, university of north florida and florida a&m university) increased public health service delivery and yielded rich communitybased participatory research opportunities. cutting edge participation in health it policy implementation led to the hosting of the fledgling community hie, the jacksonville health information network, as well as leadership in shaping the landscape of the state hie. this has immense implications for public health surveillance activities as chronic disease surveillance and public health service research take center stage under new healthcare payment models amidst increasing calls for quality assurance in public health services. dchd is currently hosting a cdc-funded fellowship in applied public health informatics. some of the projects materializing from the fellowship are the mapping of the current public health informatics profile of the dchd, a community based diabetes disease registry to aid population-based management and surveillance of diabetes, development of a proposal for a combined primary care/general preventive medicine residency in uf-shands medical center, jacksonville and mobilization of dchd healthcare providers for the roll-out of the state-built electronic medical records system (florida hms-ehr). conclusions academic health centers provide a model of collaboration that directly impacts on their success in delivering core public health services. disease surveillance is positively affected by the diverse community affiliations of an academic health department. the academic health department, as epitomized by dchd, is also better positioned to seize up-coming opportunities for local public health capacity building. keywords academic health departments; collaborative model; health informatics projects acknowledgments this study was supported in part by an appointment to the aphif program administered by cste and funded by the cdc cooperative agreement 3u38hm000414-04w1. *ikechi konkwo e-mail: ikechi_konkwo@doh.state.fl.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e46, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts can novel flu surveillance be conducted with limited resources? alan siniscalchi*1 and amanda schulte2 1state of ct dept of public health, hartford, ct, usa; 2international society for disease surveillance, boston, ma, usa objective this project was organized to facilitate discussions on whether successful novel flu surveillance can be conducted by jurisdictions with limited resources. the discussions will focus on gathering opinions regarding the best combination of surveillance systems to quickly and efficiently identify the presence of influenza a (h3n2)v and other novel influenza viruses in circulation. introduction the past decade has witnessed rapid development and implementation of numerous syndromic and other advanced surveillance systems to supplement traditional laboratory testing to identify the presence of novel influenza strains and track the impact on local populations. while much of the development and widespread implementation of these systems had been supported by public health preparedness funding, the loss of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. the periodic appearance of novel flu viruses, such as h3n2v, requires agencies to carefully choose which systems will provide the most cost-effective data to support their public health practice. methods this project will be facilitated by an experienced public health practitioner who has conducted surveillance for a variety of disease agents. additional public health practitioners are being recruited among members of the international society for disease surveillance (isds) public health practice committee (phpc) to contribute information on comparative approaches to cost effective surveillance. questions were selected for discussion and responses will be collected from influenza surveillance coordinators using a web-based survey tool managed by isds staff on behalf of the phpc. survey responses and subsequent recommendations will be presented at a phpc meeting. results initial questions selected for the survey tool and subsequent discussions include: what surveillance systems does your agency use for conducting influenza surveillance? which surveillance systems require trained and experienced public health and informatics staff to maintain? is your agency having difficulties in recruiting and retaining trained surveillance staff? has influenza a (h3n2)v been identified in your state or jurisdiction? does your agency have sufficient staff and other resources to be able to conduct targeted surveillance of novel influenza strains, such as identifying h3n2v cases associated with agricultural fairs or school surveillance for ili cases? which surveillance systems provide useful data for monitoring health impact during seasons with highly pathological influenza strains? which surveillance systems provide useful data for identifying the presence of novel influenza strains and conducting situational awareness? keywords situational awareness; influenza surveillance; h3n2v; resource limitations *alan siniscalchi e-mail: alan.siniscalchi@ct.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e169, 2013 editorial: ojphi vol 3, no 3 (2011) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 editorial: ojphi vol 3, no 3 (2011) the centers of excellence in public health informatics: improving public health through innovation, collaboration, dissemination, and translation e. lee husting phd, mph 1 , kim gadsden-knowles ms, mph 1 1 division of informatics practice, policy, and coordination, public health informatics and technology program office, office of surveillance, epidemiology, and laboratory services, centers for disease control and prevention mesh key words: public health informatics, translational research the centers for disease control and prevention (cdc) centers of excellence in public health informatics (coe) program was established to advance the research and practice of public health informatics through several collaborative efforts (1). the coe program supports the development, translation, and dissemination of informatics knowledge; and provides expertise to public health professionals to maximize the potential of information systems to improve the health of the nation. the office of surveillance, epidemiology, and laboratory services (osels) funds the centers using a program project grant (p01) with each center leading two major research projects in collaboration with local public health partners (2). five original centers were funded in 2005 to serve as innovative incubators for transformational public health informatics research. a new competitive announcement was released in 2009 which resulted in awards to the four current centers: harvard pilgrim children’s hospital boston center of excellence, rocky mountain center for translational research in public health informatics, indiana university center of excellence in public health informatics, and university of pittsburgh center for advanced studies of informatics in public health. the current centers conduct research that leverages and builds upon the developmental accomplishments achieved by the original centers and supports cdc’s goals and strategic priorities. the centers’ research strengthens surveillance and epidemiology at cdc while supporting state, tribal, and local health departments to improve the public’s health (3). the centers activities address many of the critical priorities that cdc has determined to have large scale impact on health with known and effective strategies to include obesity, healthcareassociated infections and foodborne diseases (4). this special issue includes two articles from each of the four centers that highlight their recent contributions to the field of public health informatics. harvard pilgrim children’s hospital boston center of excellence harvard’s research focuses on uses of personally controlled electronic health records in the prevention, control, and reporting of chronic disease. klompas et al. describe the utility of an electronic medical record system to augment the behavioral risk factor surveillance system editorial: ojphi vol 3, no 3 (2011) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 (brfss) and other traditional methods for diabetes surveillance. weitzman et al. present a novel approach to monitoring preventive and self-care practices for diabetes using an online social network. rocky mountain center for translational research in public health informatics utah’s research activities include implementing a visual analytic and decision support system to enhance community health assessment and public health surveillance and deploying new methods and models from computer science, social and behavioral sciences and other disciplines to represent knowledge and exchange information relevant to public health practice. staes et al. present an evaluation and analysis of reportable condition mapping tables relative to nationally defined reporting logic and recommend “using knowledge management tools to author, verify, improve, and authenticate logic, and continually incorporate improved logic that has been validated in clinical systems” (5). xu et al. describe how their framework for collaboration leads to successful translation of their research into public health practice. indiana university center of excellence in public health informatics indiana’s research includes developing adaptive turnaround document systems (computerinterpreted paper forms) to support newborn screening and immunization tracking and enhancing basic infrastructure capabilities to support public health. downs et al. review technical challenges in exchanging data between clinicians in the indiana health information exchange and the indiana state department of health. dixon et al. describe a process for developing a framework to continuously analyze public health data to improve data quality. university of pittsburgh center for advanced studies of informatics in public health pittsburgh’s research focuses on developing bayesian disease surveillance methods including case detection and outbreak detection and characterization and translating them into technologies for public health practice. wagner et al. present a probabilistic, decision-theoretic system for disease surveillance and control and use the example of influenza surveillance to describe how the software components transform data collected by the healthcare system into useful data for public health practice. tsui et al. demonstrate how a probabilistic case detection system uses emergency department dictated notes and laboratory results to compute the posterior probability of influenza and influenza-like illness. references 1. coe [internet].[cited 2011 dec 16]. available from: http://www.cdc.gov/osels/ph_informatics_technology/coe.html 2. coe p01 grant funding announcement [internet].[cited 2011 dec 16]. available from: http://www07.grants.gov/search/search.do?oppid=45247&mode=view 3. cdc fact sheet [internet].[cited 2011 dec 16]. available from: http://www.cdc.gov/about/resources/facts.htm 4. cdc winnable battles [internet].[cited 2011 dec 16]. available from: http://www.cdc.gov/winnablebattles/ 5. staes c, altamore r, han e, mottice s, rajeev d, bradshaw r. evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management. online j public health inform. forthcoming 2011. http://www.cdc.gov/osels/ph_informatics_technology/coe.html http://www07.grants.gov/search/search.do?oppid=45247&mode=view http://www.cdc.gov/about/resources/facts.htm http://www.cdc.gov/winnablebattles/ editorial: ojphi vol 3, no 3 (2011) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 conflict of interest the authors declare that they have no real or apparent conflicts of interest. disclaimer the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the centers for disease control and prevention. corresponding author e. lee husting phd, mph scientific program officer (extramural) division of informatics practice, policy, and coordination public health informatics and technology program office office of surveillance, epidemiology, and laboratory services centers for disease control and prevention 1600 clifton road, ms-76 atlanta, ga 30333 email: ehusting@cdc.gov mailto:ehusting@cdc.gov online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 1 online public health preparedness training programs: an evaluation of user experience with the technological environment priya nambisan, phd 1 1 university at albany, suny abstract objectives: several public health education programs and government agencies across the country have started offering virtual or online training programs in emergency preparedness for people who are likely to be involved in managing or responding to different types of emergency situations such as natural disasters, epidemics, bioterrorism, etc. while such online training programs are more convenient and cost-effective than traditional classroom-based programs, their success depends to a great extent on the underlying technological environment. specifically, in an online technological environment, different types of user experiences come in to play—users’ utilitarian or pragmatic experience, their fun or hedonic experience, their social experience, and most importantly, their usability experience—and these different user experiences critically shape the program outcomes, including course completion rates. this study adopts a multi-disciplinary approach and draws on theories in human computer interaction, distance learning theories, usability research, and online consumer behavior to evaluate users’ experience with the technological environment of an online emergency preparedness training program and discusses its implications for the design of effective online training programs. . methods: data was collected using a questionnaire from 377 subjects who had registered for and participated in online public health preparedness training courses offered by a large public university in the northeast. results: analysis of the data indicates that as predicted, participants had higher levels of pragmatic and usability experiences compared to their hedonic and sociability experiences. results also indicate that people who experienced higher levels of pragmatic, hedonic, sociability and usability experiences were more likely to complete the course(s) they registered for compared to those who reported lower levels. discussion: the study findings hold important implications for the design of effective online emergency preparedness training targeted at diverse audiences including the general public, health care and public health professionals, and emergency responders. strategies for improving participants’ pragmatic, hedonic, sociability and usability experiences are outlined. conclusion: there are ample opportunities to improve the pragmatic, hedonic, sociability and usability experiences of the target audience. this is critical to improve the participants’ learning and retention as well as the completion rates for the courses offered. online emergency preparedness online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 2 programs are likely to play a crucial role in preparing emergency responders at all levels in the future and their success has critical implications for public health informatics. introduction preparing the public health workforce to mitigate, respond to, and recover from natural and man made disasters is not a minor undertaking. both governmental and non governmental organizations have called on universities and other educational institutions to develop programs to efficiently and effectively train our public health workforce [1,2,3,4]. many educational institutions across the country have responded to this by developing and offering virtual or online programs that incorporate ‗canned courses‘—i.e. courses that do not require an instructor and instead allow students to download the materials and self-learn at their time of convenience [5]. the effectiveness of such online courses depends on delivering rich learning experiences for the students. however, unlike traditional classroom-based education, the online environment is not under the control of an instructor. students‘ learning experience in such online situations could be affected by not only the structure and content of the course but also the student interactions facilitated by the technology-based infrastructure and the usability of such infrastructure. thus, to measure the effectiveness of online courses, we need to go beyond the evaluation tools that are currently used to evaluate offline or classroom-based courses and use tools that provide a more holistic view of users‘ online learning experience. specifically, to understand and evaluate the learning experience in an online program, we need to draw on our understanding of people‘s behavior in online environments. prior studies in consumer psychology and human computer interaction offer an appropriate foundation for this. research in consumer psychology indicate that experience has two primary dimensions—a utilitarian (or cognitive) dimension and a hedonic (or affective) dimension [6,7,8,9,10]. however, in an online environment, factors that are either related to the technology itself or to the interactions of the people with the technology could also shape such experience. prior studies in the area of human-computer interaction and computer-mediated communication [11, 12, 13] indicate the relevance of two other dimensions—sociability experience and usability experience. in this study, the online offerings of an emergency preparedness program offered by a public university in the northeast was evaluated on the above four dimensions of online user experience. in addition, in this study we also examine whether online user experience had any impact on course completion. prior research in this area has shown that online distance education courses often have higher non-completion rates than traditional in-class courses [14, 15]. the reasons cited for this include student isolation and technological barriers which in turn de-motivates students and lead to course drop out [16, 17, 18]. the current study will provide insights into how the technological environments can be developed so that users (i.e. students) would not only learn but also have a positive experience that in turn enhances the probability of course completion. further, we empirically show that higher levels of student self-motivation do not translate into course completion, which in turn emphasizes the need to focus on student‘s experience during the course to enhance program success. online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 3 the remainder of this paper is organized as follows: the next section reviews the background literature and theories for this study: 1) workforce development for public health emergency preparedness, 2) online learning environment and online consumer experience, 3) cognitive affective learning, 4) social learning theory, 5) usability in distance-learning environments, and 6) motivation and course completion. following that we formally define our study research questions. this is followed by the methodology section which includes details on data collection and data analysis. next, we discuss the study results and their implications. the report ends with a brief conclusion and key recommendations for improving the users‘ online experience and thereby enhancing program effectiveness. . background workforce development for public health emergency preparedness in 1997, the u.s. department of health and human services issued a report titled ‗the public health workforce: an agenda for the 21 st century‘, which highlighted the gap in training and preparation for public health professionals for emergency preparedness [2]. it is estimated that there are around 500,000 people in the public health workforce at the federal, state and local levels. in addition, there are around 3 million people working in the healthcare system (private and non profit) who play a key role in public health emergencies [1]. in case of an emergency situation, be it an epidemic, terrorist attack or a natural disaster, these are the people who will be deployed to the front lines and the report raised concerns regarding their training and readiness. according to their assessment this ―compelling and urgent programmatic forces are making enhanced training and education opportunities for public health professionals a necessity.‖ [1]. as a result, in september 2000, the center for disease control (cdc) and the association of schools of public health together brought out a plan to develop a national network of public health preparedness centers. as part of the plan, they funded several university programs to start centers of public health preparedness (cphp) in around 10 regions across the country. cphps in all these regions have been offering relevant courses to train the public health workforce for emergency preparedness [4]. the institute of medicine later released a report in 2003 titled ―who will keep the public healthy?” that not only reiterated the need for education and training for the public health workforce, but also stated that online distance learning was the best solution to train this large number of diverse public health workers in a cost-effective manner. this has led several state universities and local governmental agencies to start their own online education programs [19, 20] for training the public health workforce in emergency preparedness. despite the growing numbers of such programs, there have been very few initiatives focused on evaluating the online learning environments of these programs, especially for the cphp offerings, other than the evaluations done by cdc itself. online (or distance) education is definitely a cost effective and efficient way of training such large numbers of public health workforce. however, in order to evaluate such programs, one needs to adopt an interdisciplinary perspective as diverse aspects (technology, social, etc.) assume importance. this study offers a theory based framework drawn from multiple disciplines to evaluate the online environments of such distance education programs. online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 4 online learning environment and online experience online classrooms and learning environments are inevitable to meet the demands of training requirements for public health emergency preparedness. it provides the economies of scale and convenience that will not be available in traditional classroom settings. there are innumerable benefits for students from online distance learning, flexibility and convenience perhaps being the most important [16]. however, this is not without many disadvantages and problems [21,22,23]. student isolation [17] and student frustrations [18] have been found to be two of the major disadvantages with online distance education. a recent study on an online medical self-paced course noted that the major challenges were technological problems and the fact that opportunities for social interaction was much lower [16]. another study found that there were eight main factors that impede online distance education: administrative issues, lack of social interaction, academic skills, technical skills, learner motivation, time and support for studies, cost and access to the internet, and technical problems [24]. sustained frustrations and isolation can impede learning, especially the cognitive and affective dimensions of the learning experience [25]. studies in this area show that these would also decrease the storage and processing capacity of working memory [26, 27]. in addition, frustration and anxiety are major factors that lead to de-motivation among students [25]. motivation is critical for this kind of online learning environments [28]. it becomes even more critical when training public health emergency preparedness workers as many students are much older, have full time jobs and other work and family commitments, as compared to young college students [20]. cognitive affective learning similar to the research in consumer psychology, where pragmatic and hedonic component of experience received much attention, in the education and learning literature, the cognitive (pragmatic) and affective (or hedonic) dimensions of learning has been the focus of many researchers. the cognitive dimension was considered most critical for learning in many of the earlier studies. while the cognitive dimension is critical, researchers also began discovering that there is an affective dimension that impacts learning, memory, retention and inference making. more recently this component received even more focus in the context of online learning which led researchers in the mit media lab to work on affective agents where a robotic computer aims to improve user‘s motivation to learn. the robotic computer is capable of expressing affect by rewarding or showing pleasure when the learner does something right, and when the learner gets distracted, it would try to entertain the learner and so on. there has also been significant work done in developing affective interface agents that are capable of working as teaching assistants in monitoring and managing online distance learning [29, 30, 31]. the objective of this line of research is to detect the affective or emotional state of the learner and provide appropriate affective or hedonic support to keep the learner engaged in the content and also motivate them to complete the tasks before them. research in consumer online behavior shows that when users are engrossed in the online activity, they do not keep track of time and get into a state of ―flow‖ [32, 33, 34]. this stream of research suggests that when people are provided with activities that they get engrossed in and start deriving fun from, they reach a state of flow [34]. in the online learning environment, if students are provided with activities that they could get immersed in and achieve a state of flow, it would not only improve learning but also online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 5 enhance the course completion rates and the student retention rate. hence, while the cognitive dimension of learning leads the student to evaluate the pragmatic value or the usefulness of the course content, it is the affective component that enables the user to have a better hedonic experience. social learning theory another relevant stream of research adopts the social learning perspective in which the conjecture is that knowledge is socially constructed and occurs when individuals engage in discourse about a subject matter [35, 36, 37]. knowledge is embedded in individuals, and by providing effective communication channels and opportunities to interact with one another—either socially or in a classroom setting—it would lead to more knowledge transfer and creation, and in turn offer a richer learning environment [36, 37]. this perspective has been widely accepted in the context of online distance education and it is often emphasized that student interactions are central and critical for a successful learning experience and consequently the success of online courses [35, 38]. these interactions could be with other students or with the instructor. in the context of online public health emergency preparedness courses, especially cphp courses, almost all the courses are ‗canned courses‘ without an instructor or fellow students. this could potentially affect the sociability experience and thereby impact learning and course completion rates. usability in distance-learning environments as mentioned previously, technological barriers and usability issues are the two most often cited reasons for student frustrations and poor completion rates. several studies have considered the usability issues of different online courses [39, 40, 41] and have broadly concluded that usability is a critical factor in determining the success of any online course. usability is the extent to which a user can successfully accomplish the tasks with effectiveness and efficiency [42]. in the distance education context, usability would be the effective and efficient accomplishment of learning related tasks or goals in the online environment (with or without using specified tools for that system). in the context of emergency preparedness training courses, it is a critical evaluation component as users‘ interaction with the system is more than users‘ interaction with the instructor. usability issues are more widely accepted by course providers as a potential problem and many understand the need to rectify them. however, usability issues are much more difficult to evaluate as users often attribute usability issues to their own lack of skills or a problem at their end (for example, their problematic home computer or internet connection). in addition, many specific usability questions such as ―is navigation through the website easy or difficult?‖ can be answered in two different ways – navigation through the website is easy or difficult for ‗everybody else who is skilled in computing technology‘; or navigation through the website is easy or difficult for ‗me‘ specifically. analysis of the results also becomes difficult as users may hold different technology standards, different levels of skills, and access to different levels of technological assistance. online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 6 to overcome these measurements issues, in the current study, we used a simple pre-validated scale to evaluate whether the overall technological environment was easy/difficult; confusing/not confusing; consistent/inconsistent; stressful/not stressful; simple/complicated and tiring/not tiring. this usability tool has been found to be effective in understanding whether the overall usability experience was satisfactory to the user [43, 44]. motivation and course completion lack of motivation has been cited as one of the major impediments to online learning [15, 24, 45, 46]. motivation to enroll for courses could come from both internal forces and external forces [47]. intrinsic (or internal) motivation has been indicated as one of the key factors that drive people to register for courses as it reflects a person‘s need to enhance their skill set, their market value, self-esteem, etc. extrinsic (or external) motivation relates to one‘s profession including mandatory job requirements, cpe credits, suggestion from one‘s boss and colleagues, etc. extrinsic motivation would also include motivation from educational institutions or the course providers (e.g. instructors, universities, and program administrators). however, the cphp is not organized to provide this kind of motivation. hence, the main sources of extrinsic motivation seemed to be from their own professional life. while both intrinsic and extrinsic motivation could certainly lead students to register for courses, there is no evidence yet that this would lead to course completion. in this study we empirically examine whether there is any significant difference in the intrinsic and extrinsic motivation levels of students who completed the courses and that of students who did not complete the courses. research questions: the above literature review suggests that poor online course experience (that in turn may arise from a lack of instructors, lack of social interactions, technological problems in the online courses, etc) could de-motivate students and lead them to drop the courses that they had registered for. the discussion also suggests that motivation to enroll for a course, while an important factor, may not be enough to ensure that the student completes the course. thus, in our empirical study, we address two research questions that reflect the above two issues. first, are there any significant differences in students‘ online course experiences (pragmatic, hedonic, sociability and usability) based on their course completion status? second, are there any significant differences in students‘ intrinsic and extrinsic motivation levels based on their course completion status? based on the theories and concepts outlined previously, we define student‘s online course experience— i.e. the overall experience a student derives from his or her interaction in the online course environment—along four dimensions: pragmatic, hedonic, sociability, and usability. pragmatic experience is the pragmatic or utilitarian value the student experiences in the online learning environment. this dimension is related to goal-oriented behavior [33] of the student and reflects whether the student found the experience in the online learning environment useful, valuable, and/or worthwhile [43, 44]. the hedonic dimension is the intrinsic value the customer derives from the interactions in the online learning environment. it reflects the enjoyment and excitement students derive during the online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 7 learning process as well as during their interactions in the online learning environment. the sociability dimension is the social experience students derive from the interactions in the online learning environment. it captures students‘ perceptions regarding the overall openness, friendliness and politeness of the community in the learning environment [11,48]. even though there weren‘t much human-human interactions in this study context, there were human-computer interactions and such interactions can also lead to sociability experience [48]. the usability dimension is defined as the students‘ experience in navigating and using the online materials. as such, this dimension captures the ease of use and clarity of the technological features of the online learning environment. higher levels of usability experience reflect the ability of the student to navigate and participate in the online learning environment smoothly and effortlessly and without any obstructions or annoyances that might distract them from their goals or interests [11]. next, we describe our empirical study.. method data collection and data analysis data was collected using a web-based questionnaire from students who had registered for the courses offered by a cphp based in a large public university in upstate ny. emails were sent to approximately 2700 students who had enrolled in one or more of the courses during the past one year. each email briefly described the study and invited the student to respond to a survey—the link to the survey was included in the email (the survey was available from the cphp‘s web site). there were 415 responses to the email invite. 38 responses had to be excluded from the analysis due to high amount of missing data. thus, there were a total of 377 usable responses. data was collected on different aspects of the online program, including, student motivations, student profile, and their overall experience with the cphp web site and with the courses (specifically, the 4 dimensions of user experience—pragmatic, hedonic, sociability and usability). the questionnaire was built using existing scales for measuring each of the variables. student‘s online course experience was measured using an existing validated scale designed to measure online experience [43, 44]. a tool to measure student motivation to enroll was developed by the cphp staff for an earlier study and was adapted and used in this survey. course completion data was collected using a simple yes/no question as to whether they completed all the courses they had registered for. a factor analysis of the data related to student motivation yielded two distinct factors— intrinsic motivation and extrinsic motivation. see table 1 for items and their factor loadings. the intrinsic motivation factor included 4 items and extrinsic motivation factor also had 4 items. online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 8 table 1: factor scores for ‘motivation’ intrinsic motivation i registered to gain more knowledge .922 i registered myself for personal development .922 i registered myself for professional development .887 i registered to do something useful/constructive .810 extrinsic motivation it was required for other educational programs .839 it was recommended by someone outside my workplace .703 it provided continuing education credit .620 it was required/highly recommended for my job .588 similarly factor analysis for each of the online experience dimensions were done separately. items and factor loadings are provided in table 2. as can be seen in table 2, pragmatic experience was measured using a 7 item scale (reliability α = 0.96), hedonic experience by using a 9 item scale (α = 0.95), sociability experience by using a 5 item scale (α = 0.87), and usability experience by using 6 item scale (α = 0.91). table 2: factor scores for online experience pragmatic scores valuable/not valuable .938 practical/impractical .918 relevant/irrelevant .915 informative/not informative .905 worthwhile/worthless .904 productive/not productive .903 useful/not useful .893 hedonic stimulating/boring .914 exciting/not exciting .892 captivating/not captivating .872 fun/not fun .856 satisfying/unsatisfying .846 enjoyable/not enjoyable .831 entertaining/not entertaining .809 deeply engrossing/not deeply engrossing .803 pleasant/unpleasant .802 online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 9 sociability inviting/uninviting .851 friendly/unfriendly .840 polite/impolite .808 personal/impersonal .799 social/unsocial .748 usability simple/complicated .866 easy/difficult .858 confusing/not confusing .827 not tiring/tiring .827 consistent/inconsistent .826 stressful/not stressful .807 an independent sample t test was used to compare the means of the four experience dimensions (pragmatic, hedonic, sociability and usability) between students who completed all the courses they registered for and students who did not complete one or more of the courses they registered for. the data was analyzed using spss, all the experience dimensions were entered as test variables and the item ‗did you complete all the courses you registered for‘ was entered as the grouping variable. similarly, an independent sample t test was used to compare the means of intrinsic and extrinsic motivation between students who completed all the courses they registered for and students who did not complete all the courses they registered for. results and discussion majority of students had registered for just one course. specifically, 157 people (41.6%) registered for 1 course; 73 people (19.4%) registered for 2 courses, and 24 people (6.4%) had enrolled for a course that was not listed in the survey. the study sample also included students from 31 countries although the large majority was from the united states. the number of female students was much higher (61%). this represents the actual student population ratio at this cphp. racial distribution was as follows: 73.7% white non hispanic, 7.6% black non-hispanic, 5.4% hispanic or latino and 5% south east asian. this distribution also mirrors the student population distribution at this cphp. the mean and standard deviation for all the 4 dimensions of online experience and the two factors of motivation are provided in table 3. table 3 –means and standard deviation of study variables variables mean s.d 1. pragmatic experience 6.1 1.12 2. hedonic experience 5.0 1.26 3. sociability experience 4.9 1.22 4. usability experience 5.6 1.15 online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 10 5. intrinsic motivation 5.4 1.96 6. extrinsic motivation 3.8 1.52 online experience & course completion analysis of the data indicates that, overall, participants had higher levels of pragmatic and usability experience compared to hedonic and sociability experience (see mean values in table 3). the results from the independent sample t-test showed that there was significant difference in the scores for all the 4 dimensions of experience between students who completed the course and students who did not. results are shown in table 4. table 4 t test results for online experience & course completion experience means n std. deviation dof t value pragmatic yes 6.2 no 5.5 275 67 .96 1.48 79.9 3.89*** hedonic yes 5.1 no 4.4 274 67 1.15 1.53 84.9 3.67*** sociability yes 5.1 no 4.3 275 67 1.07 1.57 81.6 3.68*** usability yes 5.8 no 4.9 275 67 1.04 1.32 87.3 4.92*** dof – degrees of freedom yes – completed all the courses they enrolled no – did not complete all the courses they enrolled *** p<.001; ** p<.01; *p<.05 the mean scores for pragmatic experience for students who completed the courses (m =6.2, sd =.96) was significantly higher than for those who did not complete the courses (m=5.5, sd=1.48); t(79.9)=3.89, p<.001. the mean scores for hedonic experience for students who completed the courses (m =5.1, sd =1.15) was significantly higher than for those who did not complete the courses (m=4.4, sd=1.53); t(84.9)=3.67, p<.001. similarly, the mean scores for sociability experience for students who completed the courses (m =5.1, sd = 1.07) was significantly higher than for those who did not complete the courses (m=4.3, sd=1.57); t(81.6)=3.68, p<.001. finally, the mean scores for usability experience for students who completed the courses (m =5.8, sd =1.04) was significantly higher than for those who did not complete the courses (m=4.9, sd=1.32); t(87.3)=4.92, p<.001. overall, the results support the broader study thesis that students who experience higher levels of pragmatic, hedonic, sociability and usability experiences are more likely to complete the course(s) they registered for compared to those who report lower levels. in other words, these results indicate that people who dropped out had less positive online experience on all the four dimensions—pragmatic, hedonic, sociability and usability. the four-dimensional online experience questionnaire is useful in such situations where one can capture the underlying experience and derive insights on what aspect of the user experience really leads to non completion. online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 11 our analysis also shows that students rated ‗hedonic‘ experience and ‗sociability‘ experience lower than ‗pragmatic‘ and usability experience. for sociability experience, a sizeable number of the students gave a rating of 4 (neutral) on a scale of 1 to 7, which indicates that they did not perceive sociability to be either negative or positive. it could also indicate the lack of sociability experience in this cphp online program. motivation and course completion there was no statistically significant difference in students‘ extrinsic motivation levels between those who completed all the courses they registered for and those who didn‘t (see table 5). there was mild statistically significant difference in students‘ intrinsic levels between those who completed all the courses they registered for and those who did not (m=5.2, sd =2.01); t(106.7) = -3.2, p<.05. however, the results from the independent sample t test indicate a negative effect. in other words, students who completed all the courses they registered for had lower intrinsic motivation levels compared to those who did not complete all the courses they registered for. this indicates that lower levels of intrinsic motivation do not imply that they would drop out from the course. on the same lines, higher levels of intrinsic motivation do not imply that they would complete the course. in short, the results from this study indicate that student motivation (both intrinsic and extrinsic) is not a good predictor of course completion. table 5 t test results for motivation and course completion motivation means n std. deviation dof t value intrinsic yes 5.2 no 6.0 238 59 2.01 1.62 106.7 -3.2* extrinsic yes 3.8 no 3.9 213 51 1.45 1.78 66.7 -.259 dof – degrees of freedom yes – completed all the courses they enrolled no – did not complete all the courses they enrolled *** p<.001; ** p<.01; *p<.05 this finding combined with the earlier finding further indicates the importance of students‘ online experience (all the four dimensions) for maintaining student interest and ensuring that they complete the courses. in other words, while motivation may play a key role in bringing the student to the program (i.e. enrolling for the course), it is their perceived experience during the online course that critically determines whether or not they would complete the course. study implications the results from this study have several implications for cphps, and more generally, for similar online training programs. first, this study indicates the need to focus on the four key dimensions of user‘s online experience (i.e. their underlying feelings and perceptions) rather than on ad-hoc issues. prior studies and evaluations have mainly considered specific problems perceived by the course provider rather than the actual user experience. the evaluation tool described here brings out users‘ sentiments online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 12 about different aspects of the program and gives a much more fundamental and holistic understanding of the program‘s potential weaknesses and areas for improvement. the study also highlights the importance of hedonic and sociability experience for students in such online training environments. many online courses focus mainly on the pragmatic value of a particular course for the students and neglect the potential hedonic experience. while pragmatic experience is important and should be the primary focus, boredom and lack of fun can make students weary and demotivated. including elements that enhance fun and entertainment as part of the learning experience would be invaluable. even in face-to-face classroom situations, instructors constantly try to include fun activities such as field trips, role-playing, including videos and movie clips etc that are relevant for the content of the course. the type of fun activities would be different in an online course (a few suggestions are provided in table 6), but necessary especially for training programs that use ‗canned courses‘. as discussed previously, social learning theory suggests the importance of, sociability experience in learning; the current study findings indicate that sociability experience is equally important to ensure higher course completion rates. good sociability experience prevents students from ―feeling lonely‖, and more importantly, enables them to engage in ―active learning‖. indeed, student interactions have been found to be critical for the success of many online distance education courses [49, 50, 51]. such interactions allow students to feel that they are part of a community of learners and share experiential knowledge that enhance the overall quality of learning. finally, this study found that majority of the students who enrolled in these programs were self-driven or self-motivated. intrinsic factors such as professional and personal development seem to drive these public health professionals to enroll in such training courses. at the same time, such motivation did not translate into ensuring course completion. this implies that rather than depend on student motivation, course providers would need to provide such self-motivated individuals with a positive and engaging online learning experience to ensure high levels of course completion. conclusions and key recommendations key recommendations that follow from the study findings are given below (also summarized in table 6). 1) improving pragmatic experience: in this study, the majority of the students found the courses to be useful and valuable (the mean score for pragmatic experience was higher than those for the other three experience dimensions). however, this is still relative to the very low hedonic and sociability experiences and indicates the potential for improvement. an important means to enhance pragmatic experience is effective student expectations management. students should be able understand upfront what they will be getting out of each course. this can be done by bringing more clarity to course descriptions and also detailing as to what specific goals students will be able to accomplish by taking each course. it will also help to indicate who would benefit by taking a particular course. 2) improving hedonic experience: hedonic or fun and entertainment from these courses were rated quite low. it is true that fun and entertainment is not one of the primary objectives of these courses. however, as mentioned previously, when people get engrossed in the learning material, their learning online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 13 and retention of the material are typically much higher [52]. in addition, they would try to finish the courses, instead of procrastinating and/or getting distracted with other things. an effective way to improve hedonic experience is to create more interactive and fun user interface. for example, one could incorporate video clips made in ‗second life‘ that will give the user a personal view of a disaster and how things could be as he/she approaches a disaster area in addition to being a fun experience. play2train http://play2train.us/wordpress/ developed by idaho bioterrorism awareness and preparedness program using ‗second life‘ is a good example of this. 3) improving sociability experience: sociability experience was another weak factor in cphp courses... as noted previously, positive sociability experience would enable students to feel that they are part of the overall community of students who are enrolled in the program. one solution would be to provide students with access to an online community/forum within the cphp that will enable interactions with fellow students as well as with the cphp staff. this would not only improve students‘ sociability experience, but also enhance their learning and networking potential, and in turn, improve student retention. developing such forums is a very cost-effective solution with proven benefits given the low cost of associated information technologies. 4) improving usability experience: it is important to ensure that the design of the online environment provides seamless and enjoyable navigation experience for the user. best practices in usability include offering simple and clutter less user interface, intuitive navigational features, and avoiding technological jargons in user guidance. . in addition, usability can be significantly improved by offering online programs on mobile platforms and thereby catering to today‘s public health worker who is likely to be very mobile. if courses can be accessed through smart phones (this would require redesigning the interface to fit the mobile device) it would improve the convenience factor significantly. in conclusion, there are ample opportunities to improve the pragmatic, hedonic, sociability and usability experiences of the target audience. this is critical to improve the participants‘ learning and retention as well as the completion rates for the courses offered. online emergency preparedness programs are likely to play a crucial role in preparing emergency responders at all levels in the future and their success has critical implications for public health informatics. however, we need more studies in the future to understand the factors that affect students‘ overall experience in the online learning environment of cphp courses. future research could focus on understanding how the experience (pragmatic, hedonic, sociability and usability) would impact student performance in the courses, student learning and retention of the subject matter, and more importantly, their real life job performance. in addition, conducting qualitative studies with a cohort group of students could help us better understand the factors that shape the overall experience specific to this set of population as well as whether such online training is an effective long term solution for training our public health workers. http://play2train.us/wordpress/ online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 14 table 6 key strategies for improving course completion rates and overall program success key strategies how to benefits for students benefits for cphp 1) improving pragmatic experience expectations management: make clear what the content of the course is make it clear upfront who would benefit from the course and who should be taking it. collect feedback from students at the end of each course on how useful and valuable the course was. will not unnecessarily register for a course that they do not need. will be able to target courses better at the right individuals will be able to improve the content of the course 2) improving hedonic experience improve fun and entertainment: add more interactive elements in the courses include video clips made with ‗second life‘. e.g. play2train http://play2train.us/wordpress/ include pictures and graphics (pictures speak a thousand words) will capture the attention of students will improve learning and retention of the material. will see courses as more fun than as a chore. will keep student engrossed (time flies when you are deeply engrossed). will keep them from getting distracted. will improve the success of the overall program. will improve student ratings will be able to attract more students (such online programs don‘t have any boundaries, so the potential is immense). will be able to retain students and get them to come back for more courses. 3) improving sociability experience improve possibilities for social interaction. provide an online community/forum for students to interact allow students as well as cphp staff to interact in the community will improve networking potential will improve their social experience will improve learning and retention (collective learning seems to improve information processing) will improve cphp‘s relationship with students (strong ties). will be able to attract more students through ‗word-of-mouth‘ marketing (which is a potential outcome http://play2train.us/wordpress/ online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 15 offer some courses in ‗blended format‘ – i.e. part online and part in-class. will feel part of the cphp community will not feel that they are on their own blended format offers the convenience of online courses but will provide some f2f time that will enhance sociability experience. however, this will be limited to local students. of such online communities). will be able to understand student needs by keeping abreast of the ongoing discussions in the community (instant feedback loop). -online communities have been found to improve motivation as well (huett et al, 2007) blended format will allow cphps to improve the variety of courses offered. it will allow cphps to get to know their students better. will improve student retention in the local region. 4) improving usability experience 1) improve usability experience by using some of the standard usability practices (nielson, 2000). update the websites regularly (at least every 2 years or so if not more frequently) using the latest technologies use simple designs (nielson, 2000) -remove unnecessary content and avoid clutter. 3) make cphp courses accessible through mobile phones improved usability would make it easier for students to access the course materials and reduce the learning curve related to the course technologies -convenience would be the biggest benefit for students. beneficial for public health improved usability can improve 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[52] malone tw, lepper mr. making learning fun: a taxonomy of intrinsic motivations for learning in r.e. snow and m.j. farr (eds.), aptitude, learning and instruction, hillsdale, nj: ebraum, 1987, 223-253. http://gabrielleconsulting.com/docs/gabrielleaect.pdf online public health preparedness training programs: an evaluation of user experience with the technological environment online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 19 correspondence: priya nambisan, phd assistant professor dept. of health policy, management, & behavior school of public health, rm. 185 affiliated faculty: dept. of informatics college of computing and information university at albany, suny 1 university place, rensselaer, ny 12144 ph: (518) 402-0332; fax: (518) 402-0414 email: pnambisan@albany.edu mailto:pnambisan@albany.edu crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 167 (page number not for citation purposes) isds 2013 conference abstracts antibiotic susceptibility of salmonellosis pathogens laziz tuychiev*, amir m. bektemirov and gulnara abdukhalilova research institute of epidemiology, microbiology and infectious diseases, tashkent, uzbekistan � �� �� �� � � �� �� �� � objective ����������� ����� �� ������ ������ ������� ��� ������� �� ������ ����� � ������� �� ������ ����� �� ��� ����� ��� ��� ��� introduction �������� ��������� ��� ���� ������������ �� �� ��� ����� �� ������ ������ ����� ����� ����� ������ ����� ������� �� ���� �� ��� ������� ���������������� ��������� �� �������� ��� ����� ������������������� ���������������� ������ �������� ����� ������� �� �� ��� ������� ��� �������!"��#�������� ����$%%&��� ������ ��� ��� �������'((&� methods ) ��� ��������� ������������ ������� �� ��������� �������� �� ���� ������� ��� �������� ����������� �� �� ������)����� �� �������� *)���" �����)�� ��������)����"����������)��� ��� ��+�����)�� �� �� � ������� � ���� ����������,������ � ��!"��#������ ���� ����� ��� ���� �� ����$%%&�*-.��� ����+���������� �'((&�*'.��� ����+��/����� �� ����� ����������� ������������������������������������ �� ���� ���� ������� �� ���� �� ��������� � �������� ��� ������)���� �������0�� �� �� ����� �������������*)0 ���'($'+�� ����� ���������������������� �� � ��� ��� ��*1�)+����� �� ������1�)%(��2��� � ��� ��� �� ����� ������������ �� �������������3$'�4�(�(5�� �6��� results 2���1�)��������� �-.��� ����� �� ��������� �������$%%&��� ����� � �� ��7�)���" ������.�(�4�$5�(�� �6���*1�)%(�&�(�� �6��+��)�� � ���������(�3�� �6����)����"��������(�'3�� �6����)��� ��� �����(�(5� '�(�� �6���*1�)%(�$�(�� �6��+������)�� �� �� ��8�(�(5�� �6���� 2���1�)��������� �-.��� ����� �� ��������� �������'((&�� �������� ������� ���)����� �� ������������������� ����� ��(�'3���93$'�� �6 ���*1�)%(�-'�(�� �6��+������� �)�� �� �� �������4�(�3�4�$�(�� �6 ���*1�)%(�$�(�� �6��+� 2���1�)��������� �'&��� ����� �� ������ ����������$%%&��� ����� � �� ��7�)���" ������$�(�4�'�(�� �6���*1�)%(�'�(�� �6��+��)�� � �������4�(�(5�(�'3�� �6���*1�)%(�(�('3�� �6��+��)����"������ 4�(�$'3�� �6����)��� ��� ���4�(�(5�'�(�� �6���*1�)%(�$�(�� �6 ��+��)�� �� �� �����8�(�(5� �6����2���1�)��������� �'.��� ����� �� ������ ����������'((&��� ������ �� ��7�)���" ������'�(�4�&�(�� �6 ���*1�)%(�.�(�� �6��+��)�� �������4�$�(�4�'�(�� �6���*1�)%(� '�(�� �6��+��)����"������4�(�'3�$'&�� �6���*1�)%(�-'�(�� �6��+�� )��� ��� ���4�(�(5�'�(�� �6���*1�)%(�'�(�� �6��+��)�� �� �� ��� ��8�(�(5�� �6��� conclusions 2����� ��� ��� ��� �� ���*�� ����$%%&+����������� ���������� *���� �'((&+��� ����� ���������� ����� � ���������������,������ � �� !"��#��������� ��� ���������������� ������ ������ �� �� ��� ���� �������� ����� ������� ���������������� � �� �� �� ����������� ������ 2�� ��� ��� ��� �� ������ ������������� ����������� ������� ������� ������ ������������ �������� ��� ���� ����� ������ �������� ���� $%%&� keywords /����� ����� ������� �:� �� ������:�!"��#����� references $�� ,�������)���� ������;�����;������ ���/����� �����,������� �� <�������� ��� ���������� � ���� ������� ��; ��� ������ ��������2���� ������=� �� � � ����>��?����1���� ���@$*$(+7�$-.%�$-33��'((%� '�� 1� ��<��a ��������>�������1� ����������������� �����������1����� � ���,������� �������<� ����) ��� �� �� ��b ����� ����� ��� ������ ���� � ���� �������c�� ��������� �� ���d������������� � �� �6����� ? ��$3��b ��-�1� ��'((%� -�� a;�; �#�����c������ � �������/����� �����,������� �� ��c��� � � ;��� ��������d���� ��,��� ���e������ ������>�;����f�������? ���-@�� b �-��'((&�����5(�5% *laziz tuychiev e-mail: l_tuychiev@mail.ru� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e12, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts detecting changes in chief complaint word count: effects on syndromic surveillance jessica sell*, robert mathes and marc paladini nyc department of health and mental hygiene, long island city, ny, usa objective to identify changes in emergency department (ed) syndromic surveillance data by analyzing trends in chief complaint (cc) word count; to compare these changes to coding changes reported by eds; and to examine how these changes might affect the ability of syndromic surveillance systems to identify syndromes in a consistent manner. introduction the new york city (nyc) department of health and mental hygiene (dohmh) receives daily ed data from 49 of nyc’s 52 hospitals, representing approximately 95% of ed visits citywide. chief complaint (cc) is categorized into syndrome groupings using text recognition of symptom key-words and phrases. hospitals are not required to notify the dohmh of any changes to procedures or health information systems (his). previous work noticed that cc word count varied over time within and among eds. the variations seen in cc word count may affect the quality and type of data received by the dohmh, thereby affecting the ability to detect syndrome visits consistently. methods the daily mean number of words in the chief complaint field were examined by hospital from 2008-2011. spectral analyses were performed on daily cc word count by hospital to explore temporal trends. change point analysis (cpa) using taylor’s method with a maximum change level of four was conducted on the cc field by hospital using 1,000 bootstrap samples. according to taylor, a level one change is the most important change detected on the program’s first pass through the data. for this analysis, a change point was considered significant if it was level 1, detected an average change of more than 0.50 words per day, and was sustained for at least 6 months before a level 2 change of at least 0.50 words occurred. results of the cpa were compared to reported changes identified by a survey conducted by dohmh staff of 49 hospitals that collected information about their his and coding practices, including any recent system changes. when a significant level one change was identified, time series graphs for six months before and after the change were created for five syndromes (cold, diarrhea, fever-flu, influenza-like-illness, and respiratory) and the syndrome’s constituent symptom categories (e.g. cough fever, etc.). changes in syndrome count and composition at the level one change in word count were noted. results the mean chief complaint word count across all hospitals from 2008 – 2011 in nyc was 3.14, with a range of 0 to 18 words. cpa detected a significant level 1 change in 21 hospitals, with a mean change of 0.60 words, with 9 increases (mean= 0.71 words) and 12 decreases (mean= 0.53 words). according to the results of a survey of 49 nyc eds, 19 have changed coding practices or health information systems since 2008. cpa identified a coincident and significant shift in word count for 8 of these hospitals. cpa also detected significant shifts in word count for 13 hospitals that did not report any changes. figure 1 shows the results of cpa from one ed in nyc we observed immediate changes in daily syndrome count after the detected change in cc word count. for example, respiratory syndrome count increased with increased word count and decreased with decreased word count for 10 of the 21 eds with a significant change in word count. only 2 eds saw an opposite effect on respiratory syndrome count. meanwhile, 9 eds saw no obvious change in respiratory syndrome count. furthermore, these changes in daily cc word count coincided with subsequent changes in syndrome composition, the breakdown of syndromes into constituent symptoms. conclusions change point analysis may be a useful method for prospectively detecting shifts in cc word count, which might represent a change in ed practices. in some instances changes to cc word count had an apparent effect on both syndrome capture and syndrome composition. further studies are required to determine how often these changes happen and how they may affect the quality of syndromic surveillance. figure 1: mean daily cc word count with cpa results marked. keywords chief complaint; word count; change point analysis references taylor, w. a. (2000).”change-point analysis: a powerful tool for detecting changes”. retrieved july 5, 2012, from http://www.variation.com/cpa/tech/changepoint.html *jessica sell e-mail: jsell@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e21, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts spatial scan statistics for models with excess zeros and overdispersion max sousa de lima2, luiz h. duczmal*1 and letícia p. pinto1 1universidade federal de minas gerais, belo horizonte, brazil; 2universidade federal do amazonas, manaus, brazil objective to propose a more realistic model for disease cluster detection, through a modification of the spatial scan statistic to account simultaneously for inflated zeros and overdispersion. introduction spatial scan statistics [1] usually assume poisson or binomial distributed data, which is not adequate in many disease surveillance scenarios. for example, small areas distant from hospitals may exhibit a smaller number of cases than expected in those simple models. also, underreporting may occur in underdeveloped regions, due to inefficient data collection or the difficulty to access remote sites. those factors generate excess zero case counts or overdispersion, inducing a violation of the statistical model and also increasing the type i error (false alarms). overdispersion occurs when data variance is greater than the predicted by the used model. to accommodate it, an extra parameter must be included; in the poisson model, one makes the variance equal to the mean. methods tools like the generalized poisson (gp) and the double poisson [2] may be a better option for this kind of problem, modeling separately the mean and variance, which could be easily adjusted by covariates. when excess zeros occur, the zero inflated poisson (zip) model is used, although zip’s estimated parameters may be severely biased if nonzero counts are too dispersed, compared to the poisson distribution. in this case the inflated zero models for the generalized poisson (zigp), double poisson (zidp) and negative binomial (zinb) could be good alternatives to the joint modeling of excess zeros and overdispersion. by one hand, zero inflated poisson (zip) models were proposed using the spatial scan statistic to deal with the excess zeros [3]. by the other hand, another spatial scan statistic was based on a poisson-gamma mixture model for overdispersion [4]. in this work we present a model which includes inflated zeros and overdispersion simultaneously, based on the zidp model. let the parameter p indicate the zero inflation. as the the remaining parameters of the observed cases map and the parameter p are not independent, the likelihood maximization process is not straightforward; it becomes even more complicated when we include covariates in the analysis. to solve this problem we introduce a vector of latent variables in order to factorize the likelihood, and obtain a facilitator for the maximization process using the e-m (expectation-maximization) algorithm. we derive the formulas to maximize iteratively the likelihood, and implement a computer program using the e-m algorithm to estimate the parameters under null and alternative hypothesis. the p-value is obtained via the fast double bootstrap test [5]. results numerical simulations are conducted to assess the effectiveness of the method. we present results for hanseniasis surveillance in the brazilian amazon in 2010 using this technique. we obtain the most likely spatial clusters for the poisson, zip, poisson-gamma mixture and zidp models and compare the results. conclusions the zero inflated double poisson spatial scan statistic for disease cluster detection incorporates the flexibility of previous models, accounting for inflated zeros and overdispersion simultaneously. the hanseniasis study case map, due to excess of zero cases counts in many municipalities of the brazilian amazon and the presence of overdispersion, was a good benchmark to test the zidp model. the results obtained are easier to understand compared to each of the previous spatial scan statistic models, the zero inflated poisson (zip) model and the poisson-gamma mixture model for overdispersion, taken separetely. the e-m algorithm and the fast double bootstrap test are computationally efficient for this type of problem. keywords scan statistics; zero inflated; overdispersion; expectation-maximization algorithm acknowledgments the authors acknowledge the grants provided by fapeam and cnpq. references [1] kulldorff, m. (1999). spatial scan statistics: models, calculations and applications, in j. glaz & n. balakrishnan (eds), scan statistics and applications, springer netherlands, pp. 303–322. [2] efron, b. (1986) double exponential families and their use in generalized linear regression, journal of the american statistical association, 81, pp. 709-721. [3] cançado a., da silva c. and da silva m.(2011) a zero-inflated poisson-based spatial scan statistic. emerging health threats journal, 2011;4: [4] zhang t., zhang z.; lin g.(2012) spatial scan statistics with overdispersion. statistics in medicine,31(8):762-774. [5] davidson, r. and j. g. mackinnon (2001). “improving the reliability of bootstrap tests”, queen’s university institute for economic research discussion paper no. 995, revised. *luiz h. duczmal e-mail: duczmal@ufmg.br online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e88, 2013 nc catch: advancing public health analytics 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 nc catch: advancing public health analytics james studnicki 1 , john w. fisher 1 , christopher eichelberger 2 , colleen bridger 3 , kim angelon-gaetz 4 , debi nelson 4 1 university of north carolina, charlotte college of health and human services department of public health sciences 2 college of computing and informatics software solutions laboratory, charlotte, north carolina 3 gaston county health department 3 gastonia, north carolina 4 north carolina office of healthy carolinians/health education 4 raleigh, north carolina abstract the north carolina comprehensive assessment for tracking community health (nc catch) is a web-based analytical system deployed to local public health units and their community partners. the system has the following characteristics: flexible, powerful online analytic processing (olap) interface; multiple sources of multidimensional, event-level data fully conformed to common definitions in a data warehouse structure; enabled utilization of available decision support software tools; analytic capabilities distributed and optimized locally with centralized technical infrastructure; two levels of access differentiated by the user (anonymous versus registered) and by the analytical flexibility (community profile versus design phase); and, an emphasis on user training and feedback. the ability of local public health units to engage in outcomes-based performance measurement will be influenced by continuing access to event-level data, developments in evidence-based practice for improving population health, and the application of information technology-based analytic tools and methods. nc catch: advancing public health analytics 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 introduction the 1988 institute of medicine (iom) report titled “the future of public health”, and other iom reports since then, have advanced the idea that community health status could be improved by a data-driven continuous iterative cycle of assessment, program implementation, reassessment of results, and further implementation of newly focused programs. 1 these reports emphasized the need for a regular and systematic collection, assemblage, and analysis of information on the health status of communities which would support priority setting and evaluation of the impacts of programs and policies, and stimulate the collaboration and actions necessary to improve community health outcomes. 2,3 in response to this measurement mandate, there has been a continuing production of frameworks, models, and community health status report cards. 4,5,6,7 each of these efforts presents a rendition of community health status accompanied by a set of indicators or measures linked to determinants of health (e.g. poverty, race), root causes of adverse variations on health (e.g. smoking, obesity), or key intervention points related to selected health issues (e.g. immunizations, screening). in some cases, these community measures are weighted and mathematically manipulated in order to derive a community score or ranking. 8,9 static models using a fixed selection of indicators and a similarly static scoring algorithm provide the basis for coarse comparisons, but are not alone sufficient to enable communities to discover their own unique determinant-outcome relationships and practice priorities for subpopulations defined by race, ethnicity, age, poverty, geography, outcomes and other factors. 10 brief catch history the catch methodology evolved from a series of comprehensive community health status assessments conducted in florida in the 1990s. these extensive hardcopy reports were manually cobbled together from multiple data sources using a comparative framework which enabled each community (usually a county or group of counties) to compare itself against sociodemographically similar peer communities. 11 funding from the u.s. department of commerce, telecommunications and information infrastructure assistance program (tiiap) in 1998 enabled the automation of many of the analytic steps and resulted in larger and more complex reports, as well as a vibrant research agenda with studies in racial and ethnic disparities, the impact of special taxing districts on health outcomes, warehouse applications to bioterrorism alert algorithms, and improved methods for community health status assessment. 12,13,14,15 with the realization that the same data and analytical capability required to support these research endeavors was necessary to understanding variations in the health status of defined populations, the catch effort in north carolina evolved away from simply providing data and reports to deploying an operating analytical environment composed of a rich repository of data harnessed to a powerful analytic capability. nc catch: advancing public health analytics 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 nc catch: system elements in north carolina, the state center for health statistics (schs) maintains an inventory of databases to support the mandated community health status assessment process and works closely with the office of healthy carolinians and health education (ohc) and local community partnerships in performing assessments and mobilizing community action. with assistance from a health services research and technical development team from the university of north carolina at charlotte (uncc), the nc division of public health initiated the development and deployment of a system that would address many of the weaknesses of current systems, thus bringing the benefits of modern web-enabled software technology to public health. key components of the system include: data from multiple sources. extant data from multiple sources with conformed definitions are organized into the warehouse: demographic/population data at the census tract level; mortality; pregnancies; births; hospital discharges; emergency room visits; behavioral risk factor survey data (regional and county level only); cancer incidence and treatment data; and other miscellaneous social, economic, and health related data available at various levels of granularity. data are geocoded to the census tract where possible. an important future source of data is the electronic health record (ehr), since the analytical capabilities of the system are congruent with the goal of at least one category of “meaningful use” of ehrs as specified by the office of the national coordinator (onc) for information technology 16,17 ; i.e. to improve population and public health. the ability to move clinical practice data from health information exchanges (hies) into a catch data warehouse in a timely manner will enable broader use of that data for management, evaluation and policy purposes. on-line analytical processing (olap). the most prevalent electronic storage system is the relational database, in which data elements are organized into two-dimensional tables of columns (that remain fixed) and rows (that can be added to, deleted from, and modified in place). the following (table 1) illustrates one such simplified data table. table 1. simplified death record death record i.d. age race cause of death 2185 65 01 icd-10-cm codes 7364 85 01 icd-10-cm codes 1122 21 02 icd-10-cm codes 7419 53 03 icd-10-cm codes this structure facilitates storing transactions which are single (row-based) assertions about each death: patient identity, cause of death, age and race of the deceased, etc. each different type of data, however, requires a separate data table. these individual tables can be logically joined through common data elements such as the death record id or cause of death. though efficient for storing individual facts, this structure is not particularly conducive to open-ended data exploration tasks because the user has to traverse all of the tables to assemble a coherent view of nc catch: advancing public health analytics 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 the data that are spread across the entire transactional database. olap-based data warehouses address this shortcoming by providing pre-assembled collections of system-wide data into hypercubes (or just "cubes" for brevity). the following (figure 1) illustrates one such simplified cube: figure 1. multidimensional “hypercube” even though this example cube contains only some of the columns from the preceding data table, it can contain an arbitrary number of dimensions, typically including geography as well as time. every intersection of these dimensions represents a cell that can contain one or more precomputed, aggregate measures such as the total number of deaths, mean mortality rates, total cost of services, etc. the following (table 2) contrasts relational databases, pre-computed aggregate indicators, and olap cubes: nc catch: advancing public health analytics 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 table 2. comparison of database structures relational database pre-computed aggregate (indicator) olap cube identity? all records are identified. no records are identified. no records are identified. aggregation? these are event-level (fully disaggregated) data with specific values, such as mrn, dob, or cause of death. data are binned into ranges, but a single indicator typically allows only one column to vary, e.g., death rate by age-band for a fixed location, time period, race(s), cause(s) of death. data are binned into ranges (that can be organized into hierarchies), but all dimensions can be explored in any combination, even mixing and matching hierarchy levels. big picture? must join multiple tables into a single, sparse matrix, but making sense of this is difficult. even simple domains require thousands of indicators to express the full nature of the problem. each cube is the big picture. a crucial advantage of this cube-like structure is the ability to extract arbitrary subsets very quickly. asking for everything related to any death record yields a subset (or "slice") that contains all of the pre-computed measures relating to this single death across all other characteristics such as age, race, and cause of death. asking for the intersection of all deaths belonging to 65-year-old whites produces the aggregates relating to this one specific age-ofdeath by race (the shaded area in figure 1). the principal advantage of having loaded the base transactional data into a data warehouse is that it allows the local health departments to sift-andsort through their data in a much more interactive -and much more natural -way than would have been possible through a traditional transaction-oriented data store. olap cubes can produce an answer for complex queries much faster than the same query on an online transaction processing (oltp) system. 18 multidimensional, event-level data. for simple, shallow, pre-computed reports, summary data aggregated at the county, region, or state level may suffice. to take full advantage of the exploratory capabilities that are provided by nc catch, however, requires having event-level data wherever possible, because the system cannot anticipate what level of analysis the end users wish to conduct. a mature platform for data exploration should allow its users to query data by geography, time, demographics, and data-set-specific properties such as disease, cause of death, birth weight, procedure performed, etc. this is what nc catch does, and it works best with data that are fully described; that is, entirely disaggregated. nc catch: advancing public health analytics 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 consider, for example, the various dimensions and measures which are available for inclusion in the typical hospital discharge (fact) data set: reporting year, reporting quarter, hospital number, type of admission, source of admission, discharge status, patient race, patient sex, patient zip code, principal diagnosis, secondary diagnoses, principal procedure, secondary procedures, principal payer, charges by revenue groups, drg code, patient age at admission, length of stay, day of week admitted, days from admission to procedure, patient county, facility county, and (in some states) attending and operating physician identification numbers. each dimension will have a set of hierarchical elements which themselves can be relatively coarse such as patient sex (i.e. male, female, unknown) or fine grained such as diagnosis (i.e. thousands of possibilities based upon the icd-9-cm coding system). the analytical potential of this extensive information is only available to the user who can access all of the detail and has the infrastructure to enable the analyses, as well as the knowledge and experience to exploit this potential for maximum insight. access to fine grained, event-level data, such as hospital discharge datasets, also makes it possible to utilize analytical software which has been developed by third parties (including government agencies) specifically to analyze this available information. nc catch, for example, utilizes a series of software tools that are available without cost from the agency for healthcare research and quality (ahrq). the prevention quality indicators (pqis) are a set of measures to be used with inpatient discharge data to identify ambulatory sensitive conditions (asc) in discharges; i.e. conditions for which good outpatient care can potentially prevent hospitalization or for which early intervention could prevent complications or more severe disease. although these indicators are based on hospital inpatient data, they are often used to provide insight into the community health care system or services outside the hospital setting. other ahrq indicator sets available in nc catch are the inpatient quality indicators (iqis) and the patient safety indicators (psis). the iqis are a set of measures that reflect quality of care inside hospitals including inpatient mortality for certain procedures and medical conditions; the utilization of procedures for which there are questions of overuse or underuse; and the volume of procedures for which there is evidence that higher volume is associated with lower mortality. a subset of the indicators is recommended for area-level utilization rate analysis. the psis are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures and childbirth. six of the indicators also have area level analogs and can be used to detect patient safety problems on a regional level, or for subpopulations defined in other ways. although commonly used in many static report card systems, summarized data that are aggregated from event level data have no analytical flexibility and are, therefore, of limited usefulness in interpreting the various relationships which influence population health status. an example of such an indicator is the hospitalization rate for ambulatory sensitive conditions (asc) per 1,000 medicare enrollees. this indicator aggregates all causes for an asc admission and provides data only for medicare, thus providing a very restricted view of preventable hospitalizations within any community. by contrast, with access to multiple years of event level hospital discharge data and the ahrq suite of analytical software, nc catch is able to derive the full analytical benefit from the asc construct – to understand avoidable hospitalizations for subgroups defined in multiple ways, e.g. by diagnosis, age, race, payer source, geographical nc catch: advancing public health analytics 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 location, pathway of hospitalization (scheduled or through the er), trends in the variables over time, and many other factors. the following screenshot (figure 2) shows a query which displays the distribution of four specific diabetes related types of avoidable hospitalizations within a single county, by gender and type of admission. with the ability to provide flexible alternative views of preventable hospitalizations, nc catch is able to model across dimensions, through hierarchies, and across members inside any population of interest. this flexibility enables the public health analyst to understand the nature of preventable hospitalizations as manifested uniquely in each community. figure 2. screenshot: diabetes related asc admissions by type and gender nc catch: advancing public health analytics 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 two levels of access. nc catch supports both anonymous public users and registered users (figure 3). figure 3. access architecture for nc catch anonymous users have access to the community profiles that summarize, by category, public health indicators relating to any county of their choice. these indicator groupings were composed by a committee of system users in order to enable the local analyst to select the category or categories of particular interest; e.g. overall mortality (shown), injury and violence, reproductive health and others. each selected group of indicators opens to a series of gauges which place the subject county value in reference to the state average and highest and lowest county values for each indicator (figure 4). these indicator values are contrasted with both the values of the county's peers -chosen specifically for each county on the basis of selected sociodemographic characteristics -and with the state values. there is some additional detail available to the users of this level of the system, such as thematic mapping for geographical granularity (census tract, community, county). the flexible customized views of the underlying data cubes (i.e. design phase) within the warehouse are restricted to registered users, giving them the ability to explore the data for deeper relationships and greater understanding. the process of becoming a registered nc catch user requires approval by the local health officer and the county administrator. nc catch: advancing public health analytics 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 figure 4. nc catch public access county profiles and indicator groupings operational governance and structure. all aspects of the nc catch system are directed by the schs working with an advisory committee composed of representatives from the schs, the office of healthy carolinians and health education (ohc), local public health directors and staff, and the uncc development team. the advisory committee sets the strategy for new development and incorporates modifications, as appropriate, based on user feedback on various aspects such as the look and feel of the interface, the grouping of various health measures into meaningful categories, and the content and conduct of training sessions. the advisory committee is responsible for maintaining a coherent vision of the nc catch system as it changes over time, and for determining that the maintenance and enhancement of the system is consistent with that strategic vision. the technical infrastructure is centralized to minimize development and maintenance costs, but the analytic capabilities are distributed and optimized locally. this enables even the smallest, resource poor local public health unit to have access to this powerful, flexible system. use of the hypercube aggregation model (olap) also addresses privacy concerns by allowing full analysis of event-level data while protecting the data itself. no event-level data is actually deployed; only the precomputed aggregates are populated for every combination of dimension cross sections. nc catch: advancing public health analytics 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 training. after the launch of phase 1 (county health profiles) in october 2008, the program was introduced to target users through a series of webinars. the webinars exposed the need for instruction and training particularly for the olap design phase. the ohc staff was tasked with the planning, designing and evaluation of the on-site trainings. health department staff and their healthy carolinians partners from all counties in north carolina were invited to one of 25 training sessions conveniently located throughout the state. training groups were limited to 15 or fewer participants. the five-hour trainings were composed of four modules: introduction to nc catch, understanding statistics, using the county health profile, and tailoring county reports. the “understanding statistics” section reviewed the basic statistics featured in nc catch and familiarized users with vocabulary and notations specific to the system. the last two modules focused on learning how to gather and interpret data through the system to meet cha needs and accreditation standards. during the training, participants completed both instructor-guided and independent exercises to practice creating useful data queries. for example, one exercise asked participants to examine and graph their county’s pregnancies by maternal age, allowing them to practice selecting and filtering many fields to find the answer to a relevant question in the design phase. ohc developed a training manual as a reference for the new user trainees that reviewed basic statistical concepts, the documentation available in the system (metadata) to aid data interpretation, and highlights of additional features available to the advanced user. pre and post training evaluations were administered to determine whether participants learned the basic concepts presented. in addition, each participant evaluated both the on-site and webinar trainings, so that the effectiveness of each training method could be compared. results from the tests and evaluations were reviewed weekly. trainings were modified when necessary, based on feedback from the training participants. nc catch training sessions were typically held at a computer lab or conference room in the local county health department or community college. between may and october 2009, over 200 health professionals from 83 out of 100 counties were trained on nc catch. most participants were health educators, although health directors, epidemiologists, program evaluators, and health policy staff also regularly attended the trainings. participants worked in priority areas including youth tobacco prevention, nutrition, childhood obesity, environmental health, hiv/std prevention, cancer prevention, women’s health, and substance abuse prevention. most participants had formal education in public health and qualitative data analysis; however, most had not had recent training in statistics and quantitative data interpretation. anonymous evaluations were used to determine the participants’ satisfaction with the training sessions and their reactions to the system itself. improvement and expansion of training opportunities for nc catch users continues to be a system priority. in person and online (webinar) training is now available. a hypertext help file is available online. video answers (screen video to frequently asked questions) are in the process of development. a formal user group has been established with regular feedback to the schs regarding system enhancements and training needs. nc catch: advancing public health analytics 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 future development issues the evolution of a distributed analytical environment for population health measurement and improvement is particularly dependent upon three major issues: data availability. a frequent complaint from public health decision makers is the paucity of hard data about the health status and behaviors of vulnerable subpopulations. however, the trend in most states is toward more, not fewer, restrictions on access to health outcome data. driven primarily by patient privacy concerns and in response to ever-more powerful data aggregation technologies, access to event-level data is becoming increasingly difficult. even pre-aggregated data is often suppressed. for instance, the cdc wonder data warehouse suppresses all mortality data where the total death count is less than six in counties of under 100,000 population and the time span is less than three years 19 . in north carolina, over 75% of the counties are under 100,000 population (2007 estimates). the desire to use patient encounter data for wider purposes undergirds such efforts as the agency for health care research and quality’s provider based population health initiative and the onc-it beacon communities program. the allure of gaining greater understanding of patient behaviors and the “meaningful use” mandate will require some accommodation of privacy concerns if data are to be utilized in anything approaching their true potential. the current default strategy is selective masking and total suppression. a more useful strategy is the practice of forcing aggregation until sufficient numbers of events and/or populations are covered. for instance, if a particular cause of death for a small geographical area for a single year, specific gender and particular race results in too few events to satisfy data identification concerns, aggregation across either time, race, gender, or years can be forced until sufficient numbers are achieved. for this approach to satisfy the needs of researchers and decision makers, however, the end user must be in control of the aggregation. evidence based practice. current thinking regarding population health status is oriented to the measurement model best typified by the national quality forum (nqf) measurement endorsement process, most successfully applied to healthcare structural, process, and outcome measures. 20 major limitations in this approach are apparent when attempting to apply this process to health status outcomes for geographically defined populations. evidence for community level interventions (in the form of programs and services) that will produce reliable and valid results across communities of varying sizes, sociodemographic composition, and other characteristics (measured and unmeasured) is sparse. the science of measuring healthcare performance has made progress in the last decade largely through rigorous evidence-based review, the development of risk-adjustment techniques and methods, and access to event-level clinical data. deployment of electronic health record technology is expected to accelerate this ability to measure healthcare services and outcomes. by contrast, public health practice has been largely bypassed by the advances in modern information technology: event-level data is difficult to access; no model of comprehensive community risk adjustment has been validated; and the local public health unit, with rare big city exceptions, has limited analytical infrastructure with which to determine local priorities or evaluate the impact of programs and services. nc catch: advancing public health analytics 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 information technology. the existence of the catch infrastructure opens up the possible utilization of many methodologies and technologies which can enhance the system, among them data mining and non-linear pattern recognition. one area of particular promise is visual analytics which is the science of analytical reasoning facilitated by visual interactive interfaces. visual analytics is most useful in situations which are complex and where the need for closely coupled human and computer analytics may make them otherwise infeasible; for example, where one is trying to determine the varying contribution of community racial composition on a large number of multiple outcomes such as many specific causes of mortality. these techniques hold the promise of providing the ability to analyze large and complex datasets rapidly either independently or as a screening precursor to traditional computational analysis. conclusions the shortcomings of the system of local public health units in the united states have been well documented: lack of modern information technology, an aging workforce in need of training, declining public financial support, and the lack of a clear vision about its role. the performance measurement initiative taking place in the healthcare system has not been replicated with similar urgency in public health; program evaluation is rare, the evidence base for public health practices is growing but still sparse, and population outcomes are neglected. 21 advances in information technology and software development have made it cost-effective to provide powerful and flexible analytic capability to local public health units. this important infrastructure for evolving an analytical culture for public health is also a necessary component for measuring and improving population health. acknowledgements the nc catch system has been supported by development and maintenance contracts from the nc division of public health. a grant from the kate b. reynolds charitable trust supported the original system deployment. nc catch: advancing public health analytics 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 references 1) institute of medicine: summary of recommendations, in waterfall w (ed): the future of public health. washington, dc, national academy press, 1988; 7-9. 2) institute of medicine: measurement tools for a community health improvement process, in: durch j. bailey l, stoto m (eds): improving health in the community: a role for performance monitoring. washington, dc, national academy press, 1997; 126. 3) institute of medicine: healthy communities: new partnerships for the future of public health. washington, dc, national academy press, 1996. 4) green lw. patch: cdc’s planned approach to community health, an application of preceed and an inspiration for proceed. j health educ. 1992; 23(3): 140-147. 5) us department of health and human services. healthy people 2000: national health promotion and disease prevention objectives. washington, dc: us government printing office; 1991. 6) perrin eb, koshel jj, eds, panel on performance measures and data for public health performance partnership grants, national research council. assessment of performance measures for public health, substances abuse and mental health. washington, dc, national academy press; 1997. 7) national association of county and city health officials. assessment protocol for excellence in public health. washington, dc, 1991. 8) fielding je, sutherland ce, halfon n. community report cards: results of a national survey. am j prev med. 1999; 17(1): 79-86. 9) www.countyhealthrankings.org. 10) wolfson, michael c., notes on measurement and accountability, presentation to iom committee on public health strategies to improve health meeting two, january 2010, http://iom.edu/~/media/files/activity%20files/publichealth/phstrategies/meeting%202/wo lfson2.pdf 11) studnicki j, steverson b, meyers b, et. al. a community health report card: comprehensive assessment for tracking community health (catch). best pract benchmarking healthc. 1997; 2(5): 196-207. 12) studnicki j, berndt d, luther s. hispanic health status in orange county, florida. j public health manag pract. 2005; 11(4): 326-332. 13) studnicki j, gipson l, fisher j, et. al. special healthcare taxing districts: association with population health status. am j prev med. 2007; 32(2): 116-123. 14) berndt d, fisher jw, craighead jg, et. al. the role of data warehousing in bioterrorism surveillance. decision support systems. 2007; 43: 1383-1403. 15) studnicki j, luther sl, kromrey j, et. al. a minimum data set and empirical model for population health status assessment. am j prev med. 2001; 20(1): 40-49. 16) ehealthinitiative.org. “national progress report”. 7 dec 2010 <http://www.ehealthinitiative.org/national-progress-report-improving-population-health leveraging-electronic-data.html> 17) office of the national coordinator for health information technology. “hit_strategic_framework_2010-05-10.pdf”. 7 dec 2010. <http://healthit.hhs.gov/portal/server.pt/document/911844/hit_strategic_framework051010_p df> ../../../../jstudnic/local%20settings/temporary%20internet%20files/content.outlook/uxitgqsa/www.countyhealthrankings.org http://iom.edu/~/media/files/activity%20files/publichealth/phstrategies/meeting%202/wolfson2.pdf http://iom.edu/~/media/files/activity%20files/publichealth/phstrategies/meeting%202/wolfson2.pdf nc catch: advancing public health analytics 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 18) chaudhuri s, umeshwar d. an overview of data warehousing and olap technology. sigmod rec. 1997. (acm)26: 65. doi: 10.1145/248603.248616.http://doi.acm.org/10.1145/248603.248616. 19) center for disease control. “compressed mortality file 1979-1998 and 1999-2007”. 7 dec 2010. <http://wonder.cdc.gov/wonder/help/cmf.html> 20) national quality forum. the abcs of measurement. nqf, washington, dc 20005. www.qualityforum.org. 21) jacobson pd, gostin lo. restoring health to health reform. jama. 2010; 304(1): 85-86. correspondence: james studnicki, sc.d. irwin belk endowed chair and professor 1 jstudnic@uncc.edu phone: 704-687-8981 fax: 704-687-6122 http://www.qualityforum.org/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts localized cluster detection applied to joint and separate military and veteran subpopulations howard burkom*1, yevgeniy elbert1, carla winston2, julie pavlin3, cynthia lucero-obusan2 and mark holodniy2 1johns hopkins applied physics laboratory, laurel, md, usa; 2office of public health surveillance research, veterans health administration, palo alto, ca, usa; 3armed forces health surveillance center, silver spring, md, usa objective we examined the utility of combining surveillance data from the departments of defense (dod) and veterans affairs (va) for spatial cluster detection. introduction the joint va/dod biosurveillance system for emerging biological threats project seeks to improve situational awareness of the health of va/dod populations by combining their respective data. each system uses a version of the electronic surveillance system for early notification of community-based epidemics (essence); a combined version is being tested. the current effort investigated combining the datasets for disease cluster detection. we compared results of retrospective cluster detection studies using both separate and joined data. — does combining datasets worsen the rate of background cluster determination? — does combining mask clusters detected on the separate datasets? — does combining find clusters that the separate datasets alone would miss? methods cluster determination runs were done with a spatial scan statistics implementation previously verified [1] by comparison with satscan software [2] using dod data from the biosense system. input data files were extracted from a repository of outpatient records from both dod and va facilities covering 4 years beginning jan. 1, 2007. this repository includes over 37 million dod records and over 86 million va records. input files were matrices of daily influenza-like-illness (ili) or gastrointestinal (gi) visit counts. matrix rows were consecutive days, columns were patient residence zip codes, and entry (i, j) was the number of visits on day i from with zip code j. these files were made for dod data, va data, and combined data. for assessing the alerting burden from combining datasets, three sets of runs were executed using data from three regions, baltimore/washington d.c. (dominated by dod data), los angeles (mainly va data), and tampa (representation of both). for each region, sets of 1672 daily runs were executed for ili and gi syndrome data. lastly, focused runs were done to investigate known outbreaks in new york (gi, jan-mar 2010), san diego (ili, dec 2007-apr 2008 and fall 2009), and new jersey (gi, jan-mar 2010). results combining the data sources increased the rate of significant cluster alerting by a manageable 1-10% across run sets. some clusters found only when the data were combined persisted over several days and may have indicated small events not reported in either system; however, we were unable to validate minor events that may have occurred in past years. retrospective looks at known outbreaks were successful in that clustering evidence found in separate dod and va runs persisted when data sets were combined. for the new york run, a west point outbreak was seen in repeated clusters of combined data, beginning days before the event report. however, clustering did not consistently produce alerts before outbreak report dates. in the new jersey dod runs, repeated clusters indicated a 10-week gi outbreak at fort dix; adding va data that dominated the record counts gave the same clusters with no added cases, so the dod event was probably self-contained. the san diego runs were aimed at detecting unusually severe influenza epidemics in february 2008 and in the fall of 2009, and numerous clusters were found but did not enhance regional disease tracking. conclusions from the analysis, combining dod and va data enhances cluster detection capability without loss of sensitivity to events isolated in either population and with manageable effect on the customary alert rate. for cluster detection, there may be many geographic regions where a health monitor in one of the systems would benefit from combined data. more detailed outbreak information is needed to quantify the timeliness/sensitivity advantages of combining datasets. in events examined, clustering itself yielded an occasional but not consistent timeliness advantage. keywords essence; department of defense; scan statistics; cluster detection; veterans administration references [1] xing j, burkom h, moniz l, edgerton j, leuze m, and tokars j. evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system, international journal of health geographics 2009, 8:45 [2] satscan: software for the spatial, temporal, and space-time scan statistics. www.satscan.org (last accessed 20aug2012) *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e10, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts s&i public health reporting initiative: improving standardization of surveillance michael coletta*1, nikolay lipskiy2, david birnbaum3 and john abellera2 1naccho, washington, dc, usa; 2cdc, atlanta, ga, usa; 3washington state department of health, olympia, wa, usa objective the objective of this panel is to inform the isds community of the progress made in the standards & interoperability (s&i) framework public health reporting initiative (phri). also, it will provide some context of how the initiative will likely affect biosurveillance reporting in meaningful use stage 3 and future harmonization of data standards requirements for public health reporting. introduction the s&i framework is an office of national coordinator (onc) initiative designed to support individual working groups who focus on a specific interoperability challenge. one of these working groups within the s&i framework is the phri, which is using the s&i framework as a platform for a community-led project focused on simplifying public health reporting and ensuring ehr interoperability with public health information systems. phri hopes to create a new public health reporting objective for meaningful use stage 3 that is broader than the current program-specific objectives and will lay the ground work for all public health reporting in the future. to date, the initiative received over 30 descriptions of different types of public health reporting that were then grouped into 5 domain categories. each domain category was decomposed into component elements and commonalities were identified. the phri is now working to reconstruct a single model of public health reporting through a consensus process that will soon lead to a pilot demonstration of the most ready reporting types. this panel will outline progress, challenges, and next steps of the initiative as well as describe how the initiative may affect a standard language for biosurveillance reporting. methods michael coletta will provide an introduction and background of the s&i phri. he will describe how the phri intends to impact reporting in a way that is universal and helpful to both hit vendors and public health programs. nikolay lipskiy will provide an understanding of the ground breaking nature of collaboration and harmonization that is occurring across public health programs. he will describe the data harmonization process, outcomes, and hopes for the future of this work. david birnbaum has been a very active member of phri and has consistently advocated for the inclusion of healthcare associated infections (hai) reporting in meaningful use as a model. david has been representing one of the largest user communities among those farthest along toward automated uploading of data to public health agencies. he will describe the opportunities and challenges of this initiative from the perspective of a participant representing an already highly evolved reporting system (cdc’s national healthcare safety network system). john abellera has been the steward of the communicable disease reporting user story for the phri. he will describe the current challenges to reporting and how the phri proposed changes could improve communicable disease reporting efforts. this will be followed by an open discussion with the audience intended to elicit reactions regarding an eventual consolidation from individual report specific specification documents to one core report specification across public health reporting programs which is then supplemented with both program specific specifications and a limited number of implementation specific specifications. results plan to engage audience: have a prepared list of questions to pose to the audience for reactions and discussion (to be supplied if participation is low). keywords standards; interoperability; meaningful use; reporting; stage 3 *michael coletta e-mail: mcoletta@naccho.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e100, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts time of arrival analysis in nc detect to find clusters of interest from unclassified patient visit records meichun li*1, wayne loschen2, lana deyneka3, howard burkom2, amy ising1 and anna waller1 1emergency medicine, unc chapel hill, chapel hill, nc, usa; 2johns hopkins university applied physics laboratory, laurel, md, usa; 3north carolina division of public health, raleigh, nc, usa objective to describe a collaboration with the johns hopkins applied physics laboratory (jhu apl), the north carolina division of public health (nc dph), and the unc department of emergency medicine carolina center for health informatics (cchi) to implement time-of-arrival analysis (toa) for hospital emergency department (ed) data in nc detect to identify clusters of ed visits for which there is no pre-defined syndrome or sub-syndrome. introduction toa identifies clusters of patients arriving to a hospital ed within a short temporal interval. past implementations have been restricted to records of patients with a specific type of complaint. the florida department of health uses toa at the county level for multiple subsyndromes (1). in 2011, nc dph, cchi and cdc collaborated to enhance and evaluate this capability for nc detect, using nc detect data in biosense 1.0 (2). after this successful evaluation based on exposure complaints, discussions were held to determine the best approach to implement this new algorithm into the production environment for nc detect. nc dph was particularly interested in determining if toa could be used for identifying clusters of ed visits not filtered by any syndrome or sub-syndrome. in other words, can toa detect a cluster of ed visits relating to a public health event, even if symptoms from that event are not characterized by a predefined syndrome grouping? syndromes are continuously added to nc detect but a syndrome cannot be created for every potential event of public health concern. this toa approach is the first attempt to address this issue in nc detect. the initial goal is to identify clusters of related ed visits whose keywords, signs and/or symptoms are not all expressed by a traditional syndrome, e.g. rash, gastrointestinal, and flu-like illnesses. the goal instead is to identify clusters resulting from specific events or exposures regardless of how patients present – event concepts that are too numerous to pre-classify. methods in late 2011, nc dph and jhu apl signed a software license agreement and soon thereafter cchi received the toa software package. in may 2012, the toa controller was adapted and set up to run against ed visit data for all nc detect hospitals. the toa looks for clusters in all ed visits by hospital based solely on arrival time in both 30-minute and 60-minute intervals. there is no pre-classification of the chief complaints or triage notes into syndromes. toa alerts are viewable on the nc detect web application and, as of august 2012, users are able to document any actions taken on these alerts. results from april 15, 2012 to july 31, 2012, toa generated 173 alerts across all 115 hospitals reporting to nc detect. the toa identified a group of scabies-related ed visits that was not captured in another syndrome. the toa also identified clusters identified by hospitals as disaster-related which included misspellings that had not been previously identified, e.g. “diaster” and “disater,” as well as events involving out-of-town groups that will not be identified spatially (table 1). this preliminary review of toa alerts did not evaluate toa for false negatives. conclusions our preliminary review of toa shows that this algorithm approach can be helpful for identifying clusters of ed visits that are not captured by existing syndromes and can be used to identify hospital coding schemes for disaster events. the toa will continue to be monitored in our production environment and evaluated for additional effectiveness. we will also explore tools that will display counts of terms within a toa alert to assist in signal investigation. table 1: sample clusters detected with toa analysis keywords cluster detection; time-of-arrival analysis; syndrome classification references 1. burkom h, loschen w, kite-powell a et al. a collaboration to enhance detection of disease outbreaks clustered by time of patient arrival, presented at the international society for disease surveillance, 2010 annual conference, park city, utah, dec 2, 2010 2. deyneka l, xu z, burkom h, hicks p, benoit s, vaughan-batten h, ising a. finding time-of-arrival clusters of exposure-related visits to emergency departments in contiguous hospital groups. emerging health threats journal 2011, 4: 11702 doi: 10.3402/ehtj.v4i0.11702 *meichun li e-mail: mcli@email.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e13, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts an improved algorithm for outbreak detection in multiple surveillance systems angela noufaily*1, doyo enki1, paddy farrington1, paul garthwaite1, nick andrews2 and andre charlett2 1the open university, milton keynes, united kingdom; 2health protection agency, london, united kingdom objective to improve the performance of the england and wales large scale multiple statistical surveillance system for infectious disease outbreaks with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks. introduction there has been much interest in the use of statistical surveillance systems over the last decade, prompted by concerns over bio-terrorism, the emergence of new pathogens such as sars and swine flu, and the persistent public health problems of infectious disease outbreaks. in the united kingdom (uk), statistical surveillance methods have been in routine use at the health protection agency (hpa) since the early 1990s and at health protection scotland (hps) since the early 2000s (1,2). these are based on a simple yet robust quasi-poisson regression method (1). we revisit the algorithm with a view to improving its performance. methods we fit a quasi-poisson regression model to baseline data. one of the limitations of the current algorithm is the small number of baseline weeks used. we propose a simple seasonal adjustment using factors. we extend the model to include a 10-level factor. we fit the trend component always irrespective of its statistical significance. we are concerned that the existing weighting procedure is too drastic. the baseline at a certain week is down-weighted if the standardized anscombe residual for that week is greater than 1. this condition was chosen empirically to avoid reducing the sensitivity of the system in the presence of large outbreaks in the baselines, but may be increasing the fpr unduly when there are no or only small outbreaks in the baselines. we investigate several other options, including reducing the down-weighting to cases where the anscombe residuals are greater than 2 or 3. we evaluate a new re-weighting scheme informed by past decisions. using this adaptive scheme, baseline data where an alarm was flagged are down-weighted to reduce their effect on current predictions. the criterion we use for re-weighting, here, is the value of the exceedance score. finally, we investigate the validity of the upper threshold values based on the quasi-poisson model when the data are generated using known negative binomial distributions. results our evaluation of the existing algorithm showed that the false positive rate (fpr) is too high. a novel feature of our new models is that they make use of much more baseline data. this resulted in a better estimation of the trend and variance and decreased the fpr. in addition, we found that the trend should always be fitted even when non-significant (or extreme). this decreases the discrepancies in the results when moving from one week to another. the adaptive reweighting scheme was found to give broadly equivalent results to the reweighting method based on scaled anscombe residuals. using the latter as in the original hpa method, but with much higher threshold for reweighting decreased the fpr further. our investigations also suggest that the negative binomial model is a reasonable one, though not ideal in all circumstances. thus, there is a good case for replacing the quasi-poisson model with the negative binomial. one of the unusual features of the hpa system is that it is run every week on a database of more than 3300 distinct organisms, which is likely to produce a large number of aberrances. we found that retaining the exceedance score approach based on the 0.995 quantile is perfectly reasonable. this involves ranking aberrant organisms in order of exceedance. conclusions we have undertaken a thorough evaluation of the hpa’s outbreak detection system based on simulated and real data. the main conclusion from this evaluation is that the fpr is too high, owing to a combination of factors notably excessive down-weighting of high baselines and reliance on too few baseline weeks. keywords outbreak; negative binomial regression; quasi-poisson acknowledgments this research was supported by a project grant from the medical research council, and by a royal society wolfson research merit award. references 1. farrington cp, andrews nj, beale aj, catchpole ma. a statistical algorithm for the early detection of outbreaks of infectious disease. journal of the royal statistical society series a. 1996; 159: 547-563. 2. mccabe gj, greenhalgh d, gettingby g, holmes e, cowden j. prediction of infectious diseases: an exception reporting system. journal of medical informatics and technologies. 2003;5: 67-74. *angela noufaily e-mail: a.noufaily@open.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e148, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts state and local health agency engagement in hie: a cross-sectional survey brian e. dixon*1, 2, 3, roland e. gamache1, 2 and shaun j. grannis4, 2 1school of informatics, indiana university, indianapolis, in, usa; 2regenstrief institute, indianapolis, in, usa; 3center of excellence on implementing evidence-based practice, department of veterans affairs, veterans health administration, health services research and development service, indianapolis, in, usa; 4indiana university school of medicine, indianapolis, in, usa objective to characterize state and local health agency relationships with health information exchange organizations. introduction there is growing interest in leveraging available health information exchange (hie) infrastructures to improve public health surveillance (1). the health information technology for clinical and economic health act and meaningful use criteria for electronic health record (ehr) systems are among the factors driving the development, adoption and use of hies. hies deliver or make accessible clinical and administrative data as patients are admitted, discharged, and transferred across hospitals, clinics, medical centers, counties, states and regions (2). while several hie infrastructures exist (3), there is little evidence on the engagement in hie initiatives by state and local health agencies. methods an online survey of state and local health officials was conducted in six states where hies were known to be present. half of the states were funded by the centers for disease control and prevention (cdc) to engage public health agencies in hie activities; the other half received no such funding. a total of 143 officials were invited to participate; 73 (51%) responded. the survey asked respondents about their agencies awareness, engagement, and data exchange with hies. the survey further asked agencies about their perceptions of barriers and challenges to public health engagement with hie organizations. results just 25% of agencies had a formal relationship, typically created through a memorandum of understanding or data usage agreement, with at least one nearby hie. the majority (54%) of agencies either had no relationship (20%) or only an informal relationship (34%) with an hie. the remaining agencies (18%) reported that no hie existed in their jurisdiction. agencies in states that had received cdc funding for hie engagement were more likely (14 versus 2) to be formally partnered with an hie. conclusions few public health agencies are formally engaged in hie. financial costs, human resources, and concerns regarding privacy/security were the top cited barriers to broader engagement in hie. for public health to be an active participant in and reap the benefits of hie, greater investment in state and local public health informatics capacity, including human resources, and education regarding hie privacy and security practices are needed. keywords health information exchange; electronic laboratory reporting; public health surveillance; public health informatics acknowledgments this work was supported, in part, by the indiana center of excellence in public health informatics through a grant from the cdc (501hk000077). references 1. savel tg, foldy s. the role of public health informatics in enhancing public health surveillance. mmwr surveill summ. 2012;61:204. 2. dixon be, zafar a, overhage jm. a framework for evaluating the costs, effort, and value of nationwide health information exchange. j am med inform assoc. 2010;17(3):295-301. 3. ehealth initiative. the state of health information exchange in 2010: connecting the nation to achieve meaningful use. washington, dc2010 [cited 2010 september 29]; available from: http://ehealthinitiative.org/uploads/file/final%20report.pdf. *brian e. dixon e-mail: bdixon@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e105, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts incorporation of school absenteeism data into the maryland electronic surveillance system for the early notification of community-based epidemics (essence) zachary faigen*, anikah salim and isaac ajit office of preparedness & response, maryland department of health and mental hygiene, baltimore, md, usa objective the state of maryland has incorporated 100% of its public school systems into a statewide disease surveillance system. this session will discuss the process, challenges, and best practices for expanding the essence system to include school absenteeism data as part of disease surveillance. it will also discuss the plans that maryland has for using this new data source, as well as the potential for further expansion. introduction syndromic surveillance offers the potential for earlier detection of bioterrorism, outbreaks, and other public health emergencies than traditional disease surveillance. the maryland department of health and mental hygiene (dhmh) office of preparedness and response (op&r) conducts syndromic surveillance using the electronic surveillance system for the early notification of community-based epidemics (essence). since its inception, essence has been a vital tool for dhmh, providing continuous situational awareness for public health policy decision makers. it has been established in the public health community that syndromic surveillance data, including school absenteeism data, has efficacy in monitoring disease, and specifically, influenza activity. schools have the potential to play a major role in the spread of disease during an epidemic. therefore, having school absenteeism data in essence would provide the opportunity to monitor schools throughout the school year and take appropriate actions to mitigate infections and the spread of disease. methods dhmh partnered with the maryland state department of education (msde), local health departments, and local school systems to incorporate school absenteeism data into the syndromic surveillance program. there are 24 local public school systems and 24 local health departments in the state of maryland. op&r contacted each local school superintendent and each local health officer to arrange a joint meeting to discuss the expansion of the essence program to include school absenteeism data. once the meetings were arranged, op&r epidemiologists traveled to each local jurisdiction and presented their plan for the essence expansion. at each meeting were representatives from the local health department, as well as school health, school attendance, and school it staff. this allowed all questions and concerns to be addressed from both sides. in addition to the targeted meetings and presentations, the secretary of health issued an executive order which required all local school systems to sign a memorandum of understanding (mou) with dhmh. this mou detailed the data elements to be shared with the essence program and the process by which this would be shared. while this order made data contribution mandatory, the site visits by the op&r staff created a working relationship and partnership with the local jurisdictions. data was collected from all public schools in the state including elementary, middle, and high schools. results as of june 30, 2012, maryland became the first state in the united states to incorporate 100% of its public school systems (1,424 schools) into essence. each school system reports absenteeism data daily via an automated secure ftp (sftp) transfer to dhmh. due to its unique properties, johns hopkins applied physics laboratory (jhuapl) designed a new detection algorithm in essence specifically for this data source. op&r epidemiologist review and analyze this data for disease surveillance purposes in conjunction with other data sources in essence (emergency department chief complaints, poison control center data, thermometer sales data, and over-the-counter medication sales data). integrating school absenteeism data will provide a more complete analysis of potential public health threats. the process by which maryland incorporated their public school systems’ data could potentially be used as a best practice for other jurisdictions. not only was dhmh able to obtain data from all public schools in the state, but the process also enhanced collaboration between local health departments and public school systems. keywords essence; surveillance; absenteeism acknowledgments the office of preparedness & response at the maryland department of health and mental hygiene would like to acknowledge and thank the maryland state department of education and the 24 local health departments and public school systems for their support and collaboration to successfully implement this project. *zachary faigen e-mail: zfaigen@dhmh.state.md.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e176, 2013 a web-based system for mapping laboratory networks: analysis of gladmap application 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 a web-based system for mapping laboratory networks: analysis of gladmap application shamir n mukhi 1 , kashmeera meghnath 2 , theodore i kuschak 1 , may chu 3 , lai king ng 1 1 national microbiology laboratory, public health agency of canada 2 university of saskatchewan, canada 3 centres for disease control and prevention, usa abstract public health emergencies such as h1n1 and sars pandemics have demonstrated and validated the necessity of a strong and cohesive laboratory response system that is able to respond to threats in an efficient and timely manner. individual laboratories, through connection with other laboratories or networks, are able to enhance their capacity for preparedness and response to emergencies. efficient networks often establish standards and maintain best practices within member laboratories. the global laboratory directory mapping tool (gladmap) supports the efforts of laboratory networks to improve their connectivity by providing a simple and efficient tool to profile laboratories by geographic location, function or expertise. the purpose of this paper is to evaluate the effectiveness of the gladmap search tool and the completeness of the descriptive content of networks and laboratories that are currently contained within the gladmap database. we determined the extent of information volunteered and how the system is being used. although the system aims to attract an array of users from around the globe, our analysis reveals minimal participation and information sharing and that the low profile participation rate limits the tool’s functionality. the global laboratory directory platform has addressed barriers to participation by adding optional functionality such as restricted access to laboratory profiles to protect private information and by implementing additional functional applications complementary to gladmap. keywords: laboratory, informatics, web-based, mapping, networks background one essential function of public health laboratories is to identify etiologic agents of disease in an accurate and timely manner. international health regulations 2005 [1] were established to facilitate the reporting and dissemination of public health emergencies of global concerns through the world health organization (who). however, the practicality and potential of these laboratories in the detection and monitoring of threats over a wide geographic range is limited by unclear case definitions, communication barriers, inadequate laboratory capacity, economic and political challenges as well as differing priorities of local authorities in meeting ihr recommendations. establishing and sustaining global, regional, and local laboratory networks serves to alleviate numerous technical challenges by sharing resources to complement individual laboratories’ capabilities and capacities. http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 laboratory networking is the key to establishing and maintaining operational standards, and for advancing technologies for disease detection and confirmation. networks do this by facilitating member laboratories’ standardization of testing and reporting procedures, and through provision of reagents, equipment, training, reference materials, quality control indicators, and other forms of support [2,3]. networking or collaboration between and among laboratories over time builds trust and affords rapid and accurate information sharing during an outbreak. networking enables the sharing of information about the magnitude of outbreaks and the responsible strains that are circulating in particular regions, leading to faster response and more targeted and effective control of the threat, while still respecting inter-jurisdictional policies. currently 10% of research funding goes towards challenges faced by 90% of the population [4] and is referred to as the 10/90 gap. health security in developing countries is impeded by the poor capacity for locally or nationally available research and by limited access to the relevant research occurring abroad [4,5]. developing countries also face “digital” and “knowledge” divides due to inadequate access to the same resources and knowledge available to developed countries. networking and international collaborations could bridge this gap through long-term partnerships between local and global institutions to provide comparable expertise to local institutions and access to up-to-date information. the global laboratory directory (glad) concept was developed in response to the international health regulations (ihr) 2005 revision, which called for the strengthening of core capacities and enhanced international collaboration to mitigate the spread of diseases. glad is a collaboration among the world health organization, the national microbiology laboratory (public health agency of canada), and the centers for disease control and prevention (usa). glad strives to connect laboratory networks and their member laboratories to a global peer network. glad acts as a social networking support system for laboratories world wide to facilitate enhanced communication, capabilities, and capacities in order to increase emergency preparedness and response. the global laboratory directory concept is comprised of three components; gladresource, gladsupport, and gladmap [6]. in this paper, we focus on gladmap, developed by the public agency of canada. its database currently holds laboratory profiles of some of the networks and national reference laboratories. gladmap overview the gladmap component provides an intuitive, multi-faceted search engine and visual display of the interactive relationships among networks and their member laboratories. gladmap uses a visualization tool that displays information provided by the network and its member laboratories. it enables users to: (1) find laboratories or networks that are dedicated to a specific objective or function, (2) connect with those located in geographical locations of interest, and (3) search for providers of specific type(s) of services and expertise/experts. gladmap consists of three fundamental hierarchical information units: 1) laboratory, 2) institution and 3) network. a laboratory is defined as a place (room, building or facility) set http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 apart for a group of scientists to conduct applied investigations in science (e.g. bacteriology, virology, toxicology, parasitology) or production of reagents for such investigations (animal facility, media preparation) and analysis and interpretation of results (e.g. bioinformatics, biostatistics, mathematical modeling) from undertaking the investigations. a laboratory can be complex and multi-faceted or very simple (e.g, set up for basic sample collection as its sole function). a laboratory is the most basic information unit within the gladmap system. users can define a laboratory’s relationship with their networks and/or institutions. the second information unit within gladmap is an institution, which is defined as an organization established to provide public services. it houses one or more laboratory units that are the official workspaces of scientists and principal investigator(s) where they perform scientific work. an institution is a legal entity that has an official designation that gives them the mandate or authority to house the laboratory units. any number of laboratory units may or may not be co-located within the same institution. the third information unit within the gladmap system is a network, which is defined as an interconnected entity (usually championed by a "leader" or "manager") and linked by common interest (a community of practice). members develop a working relationship for professional benefit and visibility. networks are often established to achieve common goals that are accomplished more easily together than by one or two entities on their own. networks may have formal or informal organizational and administrative structures and may also collaborate together to form networking alliances through formal or informal agreements. for example, the canadian public health laboratory network (cphln) and the association for public health laboratories (aphl) in the united states share a memorandum of understanding. methodology to determine the status of network-related information available on gladmap, each network’s public website was accessed in order to locate a listing of member laboratories [8-20]. the public listing was then compared to the laboratories listed in gladmap to determine if information gaps existed. in a number of cases, the website provided a total number of laboratories but did not list them individually. in these cases, if the number of laboratories on the website exceeded the number in gladmap, it was concluded that gaps existed; and if there were fewer laboratories publicly listed than available on gladmap, it was deemed inconclusive since there was no way to compare the individual laboratories. all network websites were accessed between may 23, 2011 and may 31, 2011. the remainder of the analysis was completed using data collected on june 7, 2011. this included data regarding profile completion of each laboratory, search function data for each laboratory, search keywords and the originating ip address of each search. laboratory profile completion was analyzed to determine how many of the laboratories listed in glad had completed their profiles. this was done by organizing laboratory profiles by network and defining all the profiles that had more than half of the optional fields filled in as being considered completed. to determine the correlation between profile completion and profile search function access, data regarding the number of times each laboratory appeared in the http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 search results (“found”) and the number of times each profile was accessed from the search results (“clicked”) was used. the profile completion rates were determined for the most commonly “found” and “clicked” profiles and were compared to the overall “found” and “clicked” rates in order to determine if any relationship existed. the gladmap search function allows a user to perform searches by network, country, who region, laboratory name, contact name, affiliated institution, or test. to determine the most commonly used search field, the search keywords from all queries run between october 2009 and april 2011 were sorted into each of the previous categories. if the context of the search keyword was ambiguous, it was categorized as “other”. the number of searches for each category was summed to determine the most commonly used search fields. it was also important to determine the origin of the search terms in order to understand who is using the system. to determine the geographic location of gladmap users, ip address information for each search query was used. the location of the ip address associated with each individual search term was identified using the website ip-lookup.net, and the number of searches per country was organized by who region. to determine whether different geographic regions used gladmap differently, search keywords from each who region were again categorized by field (network, country, who region, laboratory name, contact name, affiliated institution, or test) and the number of searches by each field was summed for each who region. the african and eastern mediterranean regions were omitted from the results due to insufficient data. results and discussion gladmap, at the time of analysis, contained 33 networks and 1,075 laboratories. the networks varied greatly in size, with each network containing between 0 and 200 member laboratories and affiliated institutions (figure 1) entered into the current database. the networks with 0 members are the result of networks that registered, but did not provide a membership list. http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 figure 1: affiliated laboratories and institutions per network when comparing the laboratory listings available in gladmap to the publicly available laboratory listings for each network, significant gaps were identified. of the 33 networks in gladmap, public laboratory listings or information about the total number of member laboratories were available for 14 networks. of these 14 networks, 11 contained laboratories not listed in gladmap, 2 contained no missing listings, and 1 was inconclusive (figure 2). figure 2: laboratory gaps in gladmap networks http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 laboratory profile completion is an extremely important aspect of gladmap since it increases the number of search terms that will include the laboratory in the search results. one of the most useful functions of gladmap is the ability for users to efficiently locate laboratories based on a specific function, but this ability hinges on the completeness of their profiles. overall, the completion rate of all the laboratories in gladmap was 9.8%. furthermore, only 10% of all laboratory profiles have ever been accessed through the search results. within these 10%, the profile completion rate is 39.7% indicating that completed profiles are more likely to be found and accessed using the search function. figure 3 shows laboratory profile submission rates by network. this figure shows that of the 33 networks, only 5 are 100% complete, and 3 more are at least 50% complete. the majority of network profiles are incomplete, and therefore are not searchable by function. figure 3: laboratory profiles by networkcomplete and total notably, thailand has two regional networks listed in gladmap with complete or nearly complete laboratory profiles. similarly, the largest network with a complete set of profiles is red nacional de laboratorios de salud pública (rnlsp), a network of 29 local public health laboratories in mexico. national public health networks may have higher profile completion rates because they are smaller in size with a more central governance structure compared to some http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 of the larger international networks. the 10-member who human papilloma virus (hpv) network profile is also complete. search function usage statistics were analyzed to gather demographic information about who is using the system and how they are using it. search function data including keywords and ip addresses were analyzed to determine the origin of gladmap searches and users’ preferred fields and search terms. there have been 1463 searches originating from 42 countries. the top 3 countries using gladmap are switzerland, canada and the united states. it is apparent that the european and american who regions are by far the greatest users of the system, mostly due to the heavy use from canada, switzerland and the united states (figure 4). figure 4: gladmap searches by who region analysis of search field usage was also conducted in order to determine how the search function was being employed (figure 5). using 2565 search terms it was determined that the most common search was by network name, followed by test type, country, who region, laboratory name, and institution. the large number of test type searches is significant, because, as mentioned previously, only those labs with completed profiles will appear in the search results for this field. http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 figure 5: global search field usage (n=2565) search field usage was also examined for each who region (figure 6). for all the regions, network was the most commonly used search term. use of the other fields varied across the regions. sample sizes for the southeast asian and western pacific regions were significantly smaller than the american and european regions, thus results from these regions may be less accurate. figure 6: search field usage by who region http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 limitations one of the limitations currently facing gladmap is the large number of incomplete profiles. the ability to search for labs by the test that they perform is a key function expected of the system. for gladmap to be a useful tool for networking and surveillance a greater degree of participation is required from the individual laboratories; they will need to take the time to fill out their profiles. low profile completion rate may be due to many factors, such as: (i) different laboratories have varying level of comfort in offering information to a new social network; (ii) some fields are not applicable to them; (iii) members need further clarification of utility of information; (iv) it takes time to buy in, but they expect to volunteer more in the future; (v) potential duplication of data entry; (vi) institutions may have policies on posting information on a public site, so individual laboratory units may have difficulty in completing profiles. until the recent past, there were no security settings to protect profile information within the application. gladmap now includes a feature that allows profiles to be either publicly available or made secure. some laboratories had expressed privacy concerns; securing the profiles may foster an increase in the completed profiles. another limitation is incomplete networks within the system. that is, networks with missing laboratories. reasons for these may include: (i) some networks are complex and large so it will take time for a network manager to contact members who may have different priorities; (ii) uncertainty by individual laboratories on the objectives and intent of the gladmap project; (iii) individual laboratories require approval from authorities; (iv) language barriers; and (v) unclear of ownership of the web database and its long term support. furthermore, more than half of the national public health laboratories in the world are not associated with networks. in order to achieve this, a catergory “does not belong to network” was created under networks for a quick search. it is important to capture them into the application so that they become visible and to have an opportunity to be invited for collaboration or networking. conclusion the global laboratory directory provides a platform for laboratory networks to unite experts and exchange knowledge in order to collectively work towards global health security. providing support for laboratory networks plays an important and direct role in the eradication of disease, as increased regional or global collaboration facilitates faster response and more effective control of global health threats. from the search usage statistics, it is apparent that the system is primarily being used by switzerland, canada, and the us, countries that are home to glad’s partners at the who, phac, and cdc, respectively, indicating that the creators of the system are the ones who are using it the most. gladmap would be of particular use to laboratories situated in middle to lowincome countries, where it can be used to interact with laboratories around the globe in order to build capacity and strengthen response. http://ojphi.org/ a web-based system for mapping laboratory networks: analysis of gladmap application 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 additional support tools that provide users with the capability to share resources and interact with other users around the globe could be useful. we hope that the opportunity to partake in the global scientific community will attract users to contribute and participate in the global laboratory directory. acknowledgements the authors like to thank all who have participated in the pilots and provided information for the profiles on gladmap application. corresponding author shamir mukhi shamir.nizar.mukhi@phac-aspc.gc.ca references 1. baker, m.g. and fidler, d.p. global public health surveillance under new international health regulations. emerging infectious diseases. 2006;12:1058-65. 2. hull, b.p. and dowdle, w.r. poliovirus surveillance: building the poliovirus laboratory network. j infect dis. 1997;175(suppl):s113-6 3. featherstone, d., brown, d., and sanders, r. development of the global measles laboratory network. j infect dis. 2003;187(suppl 1):s264–9 4. al-tuwaijri, s., currat, louis j., davey, s., de francisco, a., ghaffar, a., jupp, s., mauroux, c. the 10/90 report for health research 20032004. switzerland: global forum for health research. 2004. 5. harris, e. building scientific capacity in developing countries. embo reports. 2004;5:7-11. 6. mukhi, s.n., ng, l.k., kuschak, t.i. and chu, m. knowledge integration to support networking for laboratory preparedness and response to emerging pathogens, new research on knowledge management technology, huei-tse hou (ed.), isbn: 978-953-510074-4, intech, available from: http://www.intechopen.com/articles/show/title/knowledge-integration-to-supportnetworking-for-laboratory-preparedness-and-response-to-emerging-pat 7. wertheim, h.f.l., puthavathana, p., nghiem, n.m., van doorn, h.r., nguyen, t.v., et al. laboratory capacity building in asia for infectious disease research: experiences from the south east asia infectious disease clinical research network (seaicrn). plos medicine. 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(2011). japanese encephalitis laboratory network [online]. available: http://www.wpro.who.int/health_topics/laboratory/jelab.htm 11. thai national influenza center (2011). thai nic, dmsc laboratory listing [online]. available: http://www.thainihnic.org/labnetwork.asp. 12. the global polio eradication initiative (2010). the global polio laboratory network. 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national influenza centers [online]. available: http://www.who.int/csr/disease/influenza/centres/en/index.html 19. whonet (2011). whonet community [online]. available: http://www.whonet.org/dnn/whonetcommunity/tabid/63/language/en-us/default.aspx 20. whonet (2011). argentina [online]. available: http://www.whonet.org/dnn/whonetcommunity/argentina/tabid/62/language/enus/default.aspx. http://ojphi.org/ http://www.wpro.who.int/health_topics/laboratory/jelab.htm http://www.polioeradication.org/dataandmonitoring/surveillance/globalpoliolaboratorynetwork.aspx http://www.polioeradication.org/dataandmonitoring/surveillance/globalpoliolaboratorynetwork.aspx http://www.pulsenetinternational.org/networks/pages/asiapacific.aspx http://www.pulsenetinternational.org/networks/pages/canada.aspx http://www.pulsenetinternational.org/networks/pages/latinamerica.aspx http://www.aphl.org/aboutaphl/memberlabs/pages/default.aspx http://www.pasteur-international.org/ip/easysite/pasteur-international-en/institut-pasteur-international-network/the-network http://www.pasteur-international.org/ip/easysite/pasteur-international-en/institut-pasteur-international-network/the-network http://www.who.int/csr/disease/influenza/centres/en/index.html http://www.whonet.org/dnn/whonetcommunity/tabid/63/language/en-us/default.aspx http://www.whonet.org/dnn/whonetcommunity/argentina/tabid/62/language/en-us/default.aspx http://www.whonet.org/dnn/whonetcommunity/argentina/tabid/62/language/en-us/default.aspx layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts analysis of heat illness using michigan emergency department syndromic surveillance fatema mamou* and tiffany henderson michigan department of community health, lansing, mi, usa objective the purpose of this work was to conduct an enhanced analysis of heat illness during a heat wave using michigan’s emergency department syndromic surveillance system (msss) that could be provided to public health and preparedness stakeholders for situational awareness. introduction the msss, described elsewhere (1), has been in use since 2003 and records emergency department (ed) chief complaint data along with the patient’s age, gender and zip code in real time. there were 85/139 hospital eds enrolled in msss as of june 2012, capturing 77% of the annual hospital ed visits in michigan. the msss is used routinely during the influenza season for situational awareness and is monitored throughout the year for aberrations that may indicate an outbreak, emerging disease or act of bioterrorism. the system has also been used to identify heat-related illnesses during periods of extreme heat. very young children, the elderly, and people with mental illness and chronic diseases are at the highest risk of preventable heatrelated illnesses including sunburn, heat exhaustion, heat stroke and/or death (2). during a heat wave in the summer of 2012, data was reviewed on an ad hoc basis to monitor potential increases in heat-related ed visits. methods msss ed visits were queried to identify those with the primary complaints of: “heat”, “sun”, or “dehydration” including word derivatives and misspellings. the query excluded terms and misspellings such as “sunday”, “heater”, and “heatlh”. daily maximum temperatures for four major cities in michigan were tracked using measures from the national oceanic and atmospheric administration’s national weather service (3). multiple analyses were performed. for this abstract, ed data from a 10-day period of sustained above normal temperatures are presented with data from the prior 10day period used as reference. visits were categorized into 1 of 3 syndromes based on the chief complaint: sun-associated, heat-associated, and dehydration. gender, age group, and syndrome for the period of interest were compared to the reference period. heat-related visits during the period of extreme heat were also analyzed by michigan public health preparedness region. results during the period of june 28–july 7, 2012 the south and central regions of michigan sustained maximum daily temperatures surpassing 90°f with maximum temperatures at or above 100°f on at least 2 days. among the cities reviewed, a total of 9 high temperature records were set or tied during that period. the number of heat-related ed visits reported into msss increased compared to the previous period of june 18–june 27, 2012. heat-associated ed visits such as heat exhaustion and heat stroke were more frequent than the reference period, 30.0% vs. 13.7% (p<0.0001). sun-associated ed visits such as sunburn were lower compared to the reference period, 17.3% vs. 23.8% (p=0.01). dehydration complaints were elevated among those 20-29 years of age, 17.7% vs. 10.0% (p=0.01). while the proportion of ed visits due to heat-related complaints was highest in the central and northwestern areas of the state, increases were observed in all regions of michigan. on july 6, 2012 an initial analysis summary was issued via the michigan health alert network (mihan) to provide situational awareness related to a concurrent heat advisory for much of the state. by july 23, 2012 mdch issued a media release reporting this increase in heat-related ed visits. conclusions although cases used in the analysis may not represent all potential cases of heat-related illness and also may represent non-heatrelated illnesses, ed data are useful in describing trends in illness presentations over time. as the msss covers a large proportion of michigan’s population, the data from the msss can be stratified by type of heat-related injury, age group, and region, providing detailed situational awareness to public health stakeholders. this type of indepth analysis further contributes to our knowledge of heat events and allows public health to relay important information regarding the severity of the situation and information about groups at risk for illness. keywords syndromic surveillance; heat illness; extreme heat references 1. sheline kd. evaluation of the michigan emergency department syndromic surveillance system. advances in disease surveillance. 2007; 4: 265 2. cdc. extreme heat prevention guide. 2012. available from: http://emergency.cdc.gov/disasters/extremeheat/heat_guide.asp 3. national oceanic and atmospheric administration’s national weather service. available from: http://www.nws.noaa.gov/climate/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e139, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts smart platforms: building the app store for biosurveillance kenneth d. mandl* 1boston children’s hospital, boston, ma, usa; 2harvard medical school, boston, ma, usa objective to enable public health departments to develop “apps” to run on electronic health records (ehrs) for (1) biosurveillance and case reporting and (2) delivering alerts to the point of care. we describe a novel health information technology platform with substitutable apps constructed around core services enabling ehrs to function as iphone-like platforms. introduction health care information is a fundamental source of data for biosurveillance, yet configuring ehrs to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations. despite a $48b investment in hit, and meaningful use criteria requiring reporting to biosurveillance systems, most vendor electronic health records are architected monolithically, making modification difficult for hospitals and physician practices. an alternative approach is to reimagine ehrs as iphone-like platforms supporting substitutable apps-based functionality. substitutability is the capability inherent in a system of replacing one application with another of similar functionality. methods substitutability requires that the purchaser of an app can replace one application with another without being technically expert, without requiring re-engineering other applications that they are using, and without having to consult or require assistance of any of the vendors of previously installed or currently installed applications. apps necessarily compete with each other promoting progress and adaptability. the substitutable medical applications, reusable technologies (smart) platforms project is funded by a $15m grant from office of the national coordinator of health information technology’s strategic health it advanced research projects (sharp) program. all smart standards are open and the core software is open source. the smart project promotes substitutability through an application programming interface (api) that can be adopted as part of a “container” built around by a wide variety of hit, providing readonly access to the underlying data model and a software development toolkit to readily create apps. smart containers are hit systems, that have implemented the smart api or a portion of it. containers marshal data sources and present them consistently across the smart api. smart applications consume the api and are substitutable. results smart provides a common platform supporting an “app store for biosurveillance” as an approach to enabling one stop shopping for public health departments—to create an app once, and distribute it everywhere. further, such apps can be readily updated or created—for example, in the case of an emerging infection, an app may be designed to collect additional data at emergency department triage. or a public health department may widely distribute an app, interoperable with any smart-enabled emr, that delivers contextualized alerts when patient electronic records are opened, or through background processes. smart has sparked an ecosystem of apps developers and attracted existing health information technology platforms to adopt the smart api—including, traditional, open source, and next generation ehrs, patient-facing platforms and health information exchanges. smart-enabled platforms to date include the cerner emr, the worldvista ehr, the openmrs ehr, the i2b2 analytic platform, and the indivo x personal health record. the smart team is working with the mirth corporation, to smart-enable the healthbridge and redwood mednet health information exchanges. we have demonstrated that a single smart app can run, unmodified, in all of these environments, as long as the underlying platform collects the required data types. major ehr vendors are currently adapting the smart api for their products. conclusions the smart system enables nimble customization of any electronic health record system to create either a reporting function (outgoing communication) or an alerting function (incoming communication) establishing a technology for a robust linkage between public health and clinical environments. keywords electronic health records; biosurveillance; informatics; application programming interfaces acknowledgments this work was funded by the strategic health it advanced research projects award 90tr000101 from the office of the national coordinator of health information technology. *kenneth d. mandl e-mail: kenneth_mandl@harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e32, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a binational influenza surveillance network – california/baja california esmeralda iniguez-stevens*, sarah marikos and karen ferran ewids, california department of public health, san diego, ca, usa objective to enhance cross-border surveillance for influenza-like-illness (ili) in the california/baja california (ca/bc) border region through the formation of a border binational surveillance network. introduction in response to the 2009 h1n1 pandemic, the early warning infectious disease surveillance program (ewids) office of binational border health in the california department of public health sought to strengthen outpatient ili surveillance along the ca/bc border by creating the first binational influenza surveillance network in the region. the establishment of this network was crucial for enhancing cross-border situational awareness of influenza activity, especially in a region characterized by high levels of population mobility. methods during summer of 2009, an assessment of current ili surveillance activities in the ca/bc border region was conducted. findings were utilized to guide recruitment efforts and build a cross-border surveillance network. in ca the assessment revealed that sentinel sites in the border region participating in cdc’s ilinet surveillance program were primarily pediatric or school-based clinics and that family practice patients were not equally represented. in bc the need to enhance surveillance among the private sector was identified, particularly among patients belonging to binational healthcare service plans. these plans offer care to us workforce individuals who seek medical care in bc. other needs identified included the need to enhance surveillance among underserved populations such as farm workers and tribal communities which were not currently being represented. working together with partners from both sides of the border ewids initiated efforts to address identified gaps. over a three-year period ewids recruited private and public sector clinics to participate in the network. results as a result of the assessment recruitment efforts were focused on inviting family practice clinics, private clinics, tribal health centers and clinics that provide care to underserved populations to participate in the network. these efforts led to the establishment of the california/baja california border outpatient provider ili surveillance network, which monitors syndromic and virologic influenza activity. in total ewids recruited 22 (13 in ca, 9 in bc) sentinel sites to participate; of these, 17 are family practice sites and 5 are pediatric sites. additionally, prior to the ewids enhancement local tribal health clinics were not represented in the surveillance system. ewids efforts resulted in the inclusion of 8 tribal sites in ca and 1 in bc. figure 1 shows the geographical location of network sites, which includes sites recruited by ewids post-assessment as well as preexisting sites. over the past three influenza seasons (2009-2012) ewids recruited sites have constituted 47% of all network sites. since the 2009-2010 influenza season 483,772 individuals have been screened for ili by participating sites; of these, 65.8% (n=318,295) were screened by ewids recruited sites. since the establishment of the network ewids has focused on sentinel site retention, logistical support, data collection, and dissemination of surveillance results. a weekly report summarizing syndromic and virologic activity is distributed to public health officials throughout the influenza season. conclusions the network serves as an example of a successful binational coordinated effort to establish an early warning system for enhancing situational awareness of influenza activity in a cross-border setting. next steps include conducting a formal evaluation of the existing surveillance system, enhancing specimen collection for virologic testing, and continuing to foster and build public/private partnerships. figure 1. surveillance network keywords influenza; surveillance; syndromic; virologic; binational acknowledgments we gratefully acknowledge all our border partners for their contributions and support including: border infectious disease surveillance program, imperial county public health department, county of san diego public health services epidemiology & immunization services branch, naval health research center laboratory, california department of public health center for infectious diseases, laboratorio estatal de salud publica de baja california, departamento de epidemiologia estatal baja california, and our medical partners. *esmeralda iniguez-stevens e-mail: einiguez@cdph.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e3, 2013 android and odk based data collection framework to aid in epidemiological analysis 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi android and odk based data collection framework to aid in epidemiological analysis a. raja1, a. tridane2, a. gaffar1, t. lindquist 1and k. pribadi3 1. department of engineering, arizona state university, mesa, arizona, usa, 2. department of mathematical science, united arab emirates university, al ain, uae 3. renaissance sciences corporation, chandler, arizona, usa. abstract periodic collection of field data, analysis and interpretation of data are key to a good healthcare service. this data is used by the subsequent decision makers to recognize preventive measures, provide timely support to the affected and to help measure the effects of their interventions. while the resources required for good disease surveillance and proactive healthcare are available more readily in developed countries, the lack of these in developing countries may compromise the quality of service provided. this combined with the critical nature of some diseases makes this an essential issue to be addressed. taking advantage of the rapid growth of cell phone usage and related infrastructure in developed as well as developing countries, several systems have been established to address the gaps in data collection. android, being an open sourced platform, has gained considerable popularity in this aspect. open data kit is one such tool developed to aid in data collection. the aim of this paper is to present a prototype framework built using few such existing tools and technologies to address data collection for seasonal influenza, commonly referred to as the flu. keywords: android; open data kit; influenza; data collection and surveillance correspondence: a-tridane@uaeu.ac.ae copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction public healthcare services rely on accurate and efficient surveillance of public’s health for providing proactive and timely measures to prevent and control a disease. this information is not only used to target interventions and start investigations of a disease, but also for alerting the public on possible outbreaks and guide them through essential preventive measures. the bidirectional communication allows for higher chances to control a disease [1]. the means of data collection and analysis have undergone several changes in the last few years. paper based modes of information gathering are slowly being replaced with the use of emerging technologies for better, faster and more error-free processes. health care business analytics are also growing towards the use of cloud computing due to the lower financial risks involved and the flexibility which it offers. using traditional means such as paper forms or personal digital assistants for information gathering is not only time consuming but also adds an additional cost android and odk based data collection framework to aid in epidemiological analysis 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi to the organization collecting the data. the widespread nature of these tasks makes them economically taxing as well. the distribution, maintenance, ease of use etc. are other factors which need to be handled. many health departments recognize the need for adapting the emerging technology for improving notifiable condition reporting (also known as case reporting – case reporting from healthcare providers to public health agencies) and public health alerting. in recent years, the center for disease control and prevention (cdc) in collaboration with the council for state and territorial epidemiologists (cste) have proposed a model for the exchange, sharing and retrieval of notifiable condition reporting from an electronic health record [1]. this suggests that electronic notifiable condition reporting may soon be feasible at larger scales. the use of mobile phones for data collection has also seen a considerable growth. adapting the mobile data collection to work with standard used in health care domain for data reporting could very well be a futuristic way of case reporting. mobile phones, including smartphones, are becoming very popular in most parts of the world. quoting an ihs isuppli wireless communications market tracker report from information and analytics provider ihs (nyse: ihs), ‘the smartphone shipments in 2013 are forecast to account for 54 percent of the total cellphone market, up from 46 percent in 2012 and 35 percent in 2011 [2]. by 2016, smartphones are expected to represent 67.4 percent of the total cellphone market’, as shown in the fig 1 below. figure 1 smartphone market share forecast by isuppli [2] with the rapid growth in the mobile industry, the capability of phones has also tremendously increased. mobile phones now have built in features to capture media, gps, share information seamlessly and have enhanced display. technologies such as bluetooth, sms, wi-fi and web are well integrated together. there has also been significant increase in the data holding capacity in these devices and their processing powers. another significant advantage is that competition and the huge market for smart mobiles has rendered these devices more affordable than laptops, computers and tablets. android, being an open source project has inspired various developers to use the api for designing need based applications. in the field of data surveillance and analysis, various applications such as pendragon forms [3], open data kit (odk) [4], epicollect [5], ecaalyx android and odk based data collection framework to aid in epidemiological analysis 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi [6] have been talked about and explored. open data kit in particular has exposed a few open source generic tools which can be used either individually or together [7]. this is mentioned more in detail in the framework section. the advantage of open data kit is that the tools are open source and are based on open standard interfaces, which allows us to leverage them for our needs. in this report we talk about reacap. reacap is a part of a framework designed to use the computational capability of smart phones for the process of information collection, modeling and analysis for infectious diseases. providing means for easier form design, form retrieval and storage, analysis and modeling, all using the capability and features of smartphones is demonstrated. in this report we focus on reacap – the surveillance application. in order to build a prototype application showing the advantages of this framework we chose influenza or seasonal flu as the disease to demonstrate the application. influenza, also known as flu is a contagious respiratory illness spread through the air. the severity of the disease usually varies with one season to the other depending on the type of flu virus, availability of the vaccines and how well the flu vaccine is matched to the virus. it is often confused with common cold due to the similarity between their symptoms. the disease affects thousands of individuals every year. in arizona usa alone, where the project was developed, the number of lab reported cases of influenza for the 2012-2013 season was 10304 [8]. the first section in this report covers the framework technologies used in the development of this project. the second section is the application description. it talks about the design of the application and the various processes followed in its implementation. the third section talks about the advantages and disadvantages seen in this approach. section 4 presents the summary and conclusion. framework technologies this section describes the core technologies used in the development of reacap. reacap as mentioned before is the data collection interface for a framework comprising of surveillance, analysis, forecast and collaboration, developed for monitoring and analysis of seasonal influenza. the technologies describe here provide the foundations on which reacap was built. they are described in detail below. figure 2: transformation code xml is an established standard that has been around for several years, and has reached a level of maturity that greatly enhances software applications compatibility. if fact, the power of xml is android and odk based data collection framework to aid in epidemiological analysis 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi that it can enhance interoperability among disparate software applications even without xml being an end goal to any of them; it can work as a middle ground [9]. without xml, any application can still communicate with another application by writing a special “transformation code” that reads the output format of the source application, and generate the equivalent input format of the “destination” application. for bi-directional communication, another set of transformation code will be needed to support the reverse direction of communication (from destination to source). with a large number of software application standards around, the number of transformation classes needed would be prohibitively large. assuming that we have n software applications written with different standards, the number of transformation classes needed for bi-directional communication would be 2 *(n (n-1)) as per figure 2 [10]. xml is the only standard format that is used as a middle ground between other software standards (see figure 3). if any software application is capable of using xml as a communication middle ground (that would still require one transformation code to xml and one from xml back to the application), we can reduce the number of transformation codes to 2n. this can be a significant reduction in complexity looking at the large number of software standards (n) we have today. furthermore, any new software standard will only need to use xml as a common ground rather than having to write transformation code to all existing standards. figure 3: xml as a common ground for this reason, we focus on using xml as a base for our approach. with the widespread versatility in mobile standards, xml will allow us to be compatible with any one of them. on the back end, additional savings exist since the data collected will be written as xml data, which is platform independent. the xml space xml itself is text based, allowing it to be human readable, without the need of any specialized software. we often refer to it as “document oriented xml”. however, greater advantages can be attained beyond that. being fully structured, xml could also be processed by software tools as a “formal document” following well-defined models [11], [12]. unlike html, xml is written using a formal structure using several document descriptions (like the xml schema, the data object model) to describe a strict document structure needed for software tools. the xml document itself is validated against these rules to ensure that the document can be parsed by software tools to correctly extract the semantics [13]. this allows developers to write several android and odk based data collection framework to aid in epidemiological analysis 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi xml-based tools to automatically parse and process an xml document for different needs. therefore, in the realm of xml, numerous applications have been written to transform xml document in many different ways, making it suitable to compile and run xml-based application in the same way we do any other software. xml document can work as a base, carring all necessary information and structure. it can then be transformed by parsing it using xsl transformation, and then presented in any of the known formats (presentation) on the right. marshalling and unmarshalling allows for structural compatibility between linear (serial) formats (like html, pdf, and xml itself), and parallel formats of high-level programming languages (like java, c/c++).sub-heading 1 xforms xforms is a model view controller based xml format. it was developed by the world wide web consortium (w3c) to overcome the limitations of older html forms [14]. traditional html web forms do not distinguish between the content and the presentation of a form. xforms on the other hand, is comprised of two parts, the xforms model (describing the form's purpose, logic and initial data) and the xforms user interface (describing the forms presentation). the connection between the xforms model and the xforms user interface is called the binding, and it uses a common w3c technology called xpath. xpath uses path expressions to identify nodes in an xml document. in xforms this is done by using the 'ref' or 'bind' attribute. fig 4 below describes the main components of an xform. figure 4 components of xform xforms allow for flexible presentation options. the xforms model is capable of working with variety of standard or proprietary user interfaces in order to render the form. the presentation of an xform can be interpreted differently by different interfaces. for instance, based on the style sheet used by a browser, the interpretation of xforms by a mobile browser and web browser would be different. controls in a cell phone are easier when described by lists and menus as opposed to traditional pop-up choice boxes. xforms also allow for form validation and data restraints. a particular field could be defined read only or the restraints in data entered would android and odk based data collection framework to aid in epidemiological analysis 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi prevent erroneous inputs. this acts a light validation mechanism before the data is sent to the data manager/database [14]. xforms have been demonstrated for usage in many different settings. their use has been explored in web services, xforms processors and even for linking data models to forms in insurance industry. in the healthcare domain, the specification is still underutilized but is starting to be recognized for its advantages [1]. researchers in australia used xforms for developments of decision system support. in germany, developers implemented an information system to maintain details of prescription drug formulary [1]. openmrs is an example of the usage of xforms in clinical and public health systems. to read the xforms and present the form document to the user, xform clients are used. in this project, the xform client we are using is javarosa [15]. javarosa is a mobile based xform client. due to the limited capability of cell phones as compared to desktops/laptops, javarosa only supports a subset of xforms. additional customizations, specific to mobile use, have also been introduced. this will be covered in more detail in the section below. javarosa javarosa is an xforms client developed for mobile phones. it is written in j2me. it was developed as a product of the openrosa consortium. it is basically a mobile application platform which can be tailored by developers to suit their needs. it contains an xform engine at its core. the xform engine is responsible for reading the form elements, use the binding specified in xform to interpret the elements and present the element to the user [15]. the logic behind the nature of the element and how it is presented to the user is determined by the xform engine. mobile devices differ greatly from conventional desktops in their computational power and their user interface. they are limited in power and have enhanced ui. this prompted the adaptation of javarosa to tailor their xform support to the mobile market. they support only a subset of xform specification and in some cases support a feature only in a particular way. javarosa has also introduced some additional form features which enhances the xform experience on mobiles. this includes additional features as well as redefining preexisting xform features. one of the core components on which reacap was built, open data kit collect, utilizes javarosa for form logic and form processing. designing a form for reacap requires a good understanding of the underlying javarosa specifications. forms can be developed by writing raw xml or by using a form designer such as odk build [7], purcforms [11], xls2xform [16]. in the current implementation of reacap, forms were developed using xls2xform. these will be talked about in more detail below open data kit (odk) open data kit is an open source suite of tools which was designed to help users build information services for developing nations. odk started as a google.org sponsored sabbatical project and was continued back at the university of washington seattle. it currently supports various tools, most notable of which are odk build, odk collect and odk aggregate. the tools are designed android and odk based data collection framework to aid in epidemiological analysis 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi to be used independently or together. being built on existing open standards, they enable users to build services to collect and distribute information in places where user limitations or limitations on infrastructure has long posed problems [4]. for the design of reacap, we evaluated the a few of the odk tools for compatibility. odk build [4] is a drag and drop web based form designer. even though odk build is a developing application, it was best suited for designing simple forms. in order to allow flexibility in our influenza survey form, we decided to use odk build only as a starting point in the design of our form and allow for design of more complex forms. odk collect is an android based mobile client which acts as the interface between the user and the underlying form. collect takes the xform logic of the form and displays it to the user in a one prompt at a time format. javarosa provides the form processing and form logic which odk uses. in disconnected mode, odk collect stores the application logic and the form data on the phone in a xml format and as binary files for media. the user can choose to synchronize with a server as required. files are sent using standard http post to any open rosa compatible server [4]. since reacap is a prototype application developed to aid in data collection for influenza, using odk collect to design the android client seemed appropriate. simple, ease of navigate, ease of comprehension and thoroughness were some of the characteristics which we were looking at for reacap. odk collect was a good match. the form processing logic, its display and offline storage were some of the features of odk collect which was used in reacap. odk is also supported by an open source community that has contributed training documents, localization support as well as additional tools. these advantages made odk collect a suitable choice for this project. epicollect epicollect is a free, open source, data collection tool developed by researchers in the imperial college at london and the university of bath in uk. it allows an mobile user to submit geotagged forms with or without images to central server located within www.spatialepidemiology.net. the server, allows the mapping and visualization (to google maps or earth) and analysis of the data. the data can also be downloaded or viewed on the phone using google maps [5]. fig 5 below captures one of the use cases of epicollect. data collected by registered users is sent to a central database. data can then be viewed on google maps/earth or on phones. epicollect also allows the user to filter the data as shown in the figure. android and odk based data collection framework to aid in epidemiological analysis 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 5 data collection, collation and visualisation framework using epicollect and www.spatialepidemiology.net [5] epicollect provides a generic framework for point data collection and analysis, both for android and ios. the analysis is sorely done on the server/visualization platform and has to be communicated back to the users. while this application can be widely adapted for simple data collection and viewing, it lacks the engine to process complex forms which was seen in odk. the form interface presented to the users is made to suit the needs of informed data collection agents. odk presents a more intuitive form suitable for easier use. these factors led to the preference of odk over epicollect. dropbox a central server or database as shown in fig 6, having the ability to store information securely and reliably is of prime importance in a multi-client application. this allows the clients to synchronize information more easily and provide a storage space which is limited in mobiles. availability of data from all clients in one place also aids in better analysis and decision making. the restriction of access based on privilege is also a good feature to have. one of the disadvantages of having a standalone private server is the cost of maintaining the server. this usually plays a huge role in the operational costs. the question of reliability and security also exist. the need to keep with emerging technologies, constant updates, security checks, maintaining synchronization between users are other factors due to which the popularity of cloud computing is on the rise [17]. cloud computing allows the enabling of convenient access to a shared pool of computing resources, available on demand. android and odk based data collection framework to aid in epidemiological analysis 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 6 depiction of a centralized global server for a variety of devices they require minimum management effort or service provider information. in huge organizations, the use of cloud computing leads to much lower financial risks as well. the following table i illustrates some of the pros and cons of cloud computing. table i. pros and cons of cloud computing pros cons scalabiliy and cost lock-in encapsulated change management reliability next generation architectures lack of control choice and agility security dropbox is a cloud storage service provided to store and share your data among many applications. it provides inbuilt encryption security and access restrictions. the api allows the user to build the features of dropbox directly into a mobile application or a desktop application. it is compatible with windows, mac, linux, iphone, ipad, blackberry, and android devices. the api provides methods to read and write from dropbox securely, any changes made can be synchronized back to shared devices. other notable features include simple sharing, search, and restoring files to past revisions [18]. in the scope of this project, dropbox serves two purposes. it acts as the storage medium for the information collected by users using reacap. all the information is stored in a format which would make the retrieval and analysis of data more efficient. it is also used to store information which is the output of the reaview engine, to enable viewing the data gathered from reacap across multiple users. the synchronization feature of dropbox ensures that with the availability of network, all users will be notified of the availability of new information. android and odk based data collection framework to aid in epidemiological analysis 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi epiml epiml is a new xml based document interchange protocol which was developed on the area project. epiml is being designed to allow data exchange between area apps and mobile devices. epiml is also the protocol to upload/download data to reacloud. data gateways can be developed to translate epiml data to health level 7 (hl7) protocol allowing data interchange with mhs such as the navy marine corps epidata center and other health databases. epiml is the core enabler of the area systems to interchange input data, configurations, and output data allowing for full collaborations between area users and data/analysis reporting to area cloud servers. application description reacap, an information capture and survey application, is a part of overall project named area which is aimed to research the use of mobile applications for health surveillance, data analysis and forecasting. applications for rapid epidemiological analysis, also know an area, were targeted to study the feasibility of the using mobile devices for this purpose. mobile devices, smartphones in particular, have gained huge popularity in the last few years. they have become more user programmable, allowing users to mould mobile devices to their taste. they are now capable of capturing media, have inbuilt gps capabilities, and provide numerous means of sharing information. communication modes amongst devices include wi-fi, bluetooth, sms, 3g, touch and so on. these make smartphones a sought after choice for use as field instruments. area apps consist of 5 main components. reacap, the focus of this paper, is the surveillance app for collecting field information of the patients. it is built for android and uses open data kit's collect as its base. reacap allows the user to download pre built forms from the server and display them to the user. users can collect geospatial coordinates, photographs, video, audio, and any number of structured data types as their inputs. the forms can be saved at any stage and on completion, allow the user to submit the form to the server (reacloud). the other components of area include reaview, reamodel, reacould and reaconfig. reacap was developed to work in collaboration with these components. reaview is the platform to view the analysis and forecast done on the information collected. reamodel is the forecast engine available as an application on the smartphone. reaconfig is the mobile and web based configuration tool which allows the user to design forms which will then be used in reacap. the final component of area, reacloud, is the cloud based server which acts as the core to all the other applications. epiml is the data interchange format designed to talk between all these components. the output from reacap is in the epiml format. this data exchange format has the ability to contain all information required for the forms and also the modeling and viewing of the information collected. this information is stored in reacloud. reacloud also contains the forms developed by the users which are sent to reacap on request. as of the current implementation, the other components of area use the information collected from reacap through the reacloud. fig 7 shows the current peripherals of reacap. android and odk based data collection framework to aid in epidemiological analysis 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 7 current peripherals of reacap the intent of reacap is to enable the collection of information for any canonical disease. a prototype using influenza (seasonal flu) was built to demonstrate this. the scope of this project involved studying influenza to determine the information to be collected. this information was then translated into a format which was suitable to be read by reacap. javarosa complaint xforms were used for this purpose. the forms were stored in the server (dropbox). on request by a reacap user, the forms were downloaded to reacap. the user could then fill the forms offline and send the forms back in epiml format to the server. the other area components use this information for their functions. fig 8 depicts the process followed. the sections below describe each of the components required for this project in detail. figure 8 processes involved in the development of reacap gathering information for form definition influenza was chosen to demonstrate the need for simple data surveillance and analysis in our case. influenza or seasonal flu is an airborne disease whose most common symptoms consist of chills, fever, sore throat, muscle pains, severe headache, coughing and fatigue [19]. it is often confused with other influenza like diseases such as common cold but influenza is more severe and is caused by a different kind of virus. the numbers of people affected vary depending on season and the severity depends upon the circulating influenza types and subtypes and existing immunity in the community. the survey below from the arizona department of health services show the number of influenza cases by season and age group in arizona alone. android and odk based data collection framework to aid in epidemiological analysis 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi table ii. age group of reported influenza cases, 2010-2011 through 2012-2013 seasons [8] in order to design a form to gather information about influenza, the following parameters were considered. • what are the most common symptoms? • how does the disease spread? what kind of environmental conditional facilitate the survival of the virus? • what makes a person more susceptible for influenza related complications? • what kind of information do we need in order for us to do preventive analysis i.e. analyze the information available and use it for precautionary measures. e.g. the influenza has an average of 2 days incubation period and virus shedding, depending on the age of person, vary from one day before symptoms through 6-11 days after the symptoms for an adult to several days before symptoms for children and can be infectious up to two weeks. • what clinical conditions are available for necessary tests in the vicinity? two types of users were considered. first a health care professional. these people are trained medical professionals well versed with the disease and are capable of making note of additional information which can help provide a better analysis (fig 9). information such as the requirement for the patient to seek medical assistance or the clinical tests which would aid the diagnosis can be suggested by them. the second use case is for anyone to use the form for everyday analysis (fig 8). an example of the two use cases are shown below figure 9 use case individual android and odk based data collection framework to aid in epidemiological analysis 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 10 use case health care professional table iii below depicts the form contents which were determined based on our analysis. it includes information on how the form is presented to the user as well as information on conditional forwarding. depending on the type of user and the previous inputs the next set of information to be displayed is determined. table iii. influenza form flow logic information screen number screen information summary screen contents next screen patient professional 1 information to determine who is using the reacap application 3 2 2 login screen for the health care professional na if login was successful – 10 android and odk based data collection framework to aid in epidemiological analysis 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi 3 basic details about the patient. note: location can be recorded using gps. in case of no network, it will have to be entered manually 4 (a patient id is generated at this point ) na 4 to capture fever if yes 6 no 5 i don't know 7 na 5 cough/sore throat if yes 8 no 9 na 6 cough/sore throat 8 na 7 sweating, shiver, chills if yes 6 no 9 na android and odk based data collection framework to aid in epidemiological analysis 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi 8 to make a note of how long the patient has been having the symptoms 9 na 9 final screen na na 10 locality information na 11 11 patient information na 12 android and odk based data collection framework to aid in epidemiological analysis 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi 12 patient symptoms na if fever is yes and any of the rest are yes 13 if fever is a 'no' 9 13 epidemiologic al inputs na 14 android and odk based data collection framework to aid in epidemiological analysis 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi 14 determine tests na 9 (if from screen 14, we determine that the patient requires diagnostic tests, refer to the nearest health care facility for the required/ appropriate influenza diagnostic tests) the next section describes the conversion of this information into a suitable format which can be read by reacap. defining the influenza form using xforms xforms as mentioned earlier is a model to represent form data using xml. but the xforms specification is long and complex. to have an engine running to read and translate xforms would require a lot of memory and cpu resources. this is usually not available and not sought after when designing mobile applications. javarosa is the mobile client used in this project for xforms. it is tailored to run on devices with limited computational capability. it performs the task of rendering the form to the user. there are numerous tools such as purcforms designer, odk build, vellum, kobo etc which are available which help create xforms that work on javarosa platform. in this project, we have used xls2xform to help create our xform. xls2xform is a tool which simplifies the creation of xforms by letting us design the form with microsoft excel and then converting this to a javarosa compatible xform on their web based tool [16]. the working of xls2xform tool is as follows the excel workbook contains two worksheets survey and choices. the survey describes the contents and the structure of the xform. control structures such as groups or loops are specifies in this sheet as well. the questions which need to be posed to the user, related media, and how it is resented is defined in the survey sheet. android and odk based data collection framework to aid in epidemiological analysis 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi the choices worksheet is used to define the choices for the multiple choice questions. a row represents an entry for a multiple choice. choices are grouped be ‘list name’ column in table vii and the corresponding entries in the ‘name’ column are displayed to the user. this design allows the user to reuse the same set of multiple choice options (specified by list name). the elements (columns) of the worksheets need to be maintained in order for the validity of the xform. certain columns are mandatory. in addition, the worksheet contains optional columns which allow the user to specify constraints for each row. for example, type, name and label entries are mandatory columns in the worksheet. but other columns entries such as image, constraint etc need not be specified at all times. the order of these columns is irrelevant. optional columns could be left out if not required. blank rows are not processed. another useful feature is that the xls formatting is ignored while processing the sheet. user could highlight the entries to make it more readable but this would not affect the creation of xforms. some examples from the designing of influenza form are shown below metadata at the beginning of the form, we collect information in the background. this metadata includes the start time of the survey, the device id and the day of the survey (table iv). table iv. example of metadata type name label start start today day deviceid id branching the user type selected by the mobile user is stored in the <profession> tag as either • patient • professional based on the value in the tag, the locality information is displayed. this is done by using the relevant column in the xls form as shown below. note the highlighted section. the locality information is grouped and the constraint is applied on the entire group. repeating a particular set of questions based on previous answer at many instances, the need to repeat a particular set of questions arises. if a person has taken many prior tests and we need to record details of all of them, we can use the repeat feature of xlsform. this is shown in table v. table v. example of branching based on condition type name label hint constra int constraint_m essage relevant begin group locality_i nfo locality information ${professi on}='profe ssional' android and odk based data collection framework to aid in epidemiological analysis 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi select one from yes_no detected has infection been detected? begin group infection _info infection data ${detected }='yes' decimal rate infection rate enter percentage of people infected select one from inf_types type type detected choose the influenza type select one from infection_t ype infection prevelent infections end group begin group env_info environment information text food food enter comments on the food (dietary habits meat?/vegetari an?, livestock condition) text water water enter comments on the water in the locality (hygiene, supply source etc) end group end group android and odk based data collection framework to aid in epidemiological analysis 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi table vi. example of repetition based on condition type name label hint constraint constraint_ message relevant begin group prior_tests prior tests ${fever} ='yes' select one from yes_no test_taken any prior influenza tests taken in the last 3-4 days? begin repeat test test ${test_ta ken}='ye s' begin group test_details test details text test_name name of the test text test_result results end group end repeat end group image names can be provided in the excel to specify which images to associate with a particular screen. these images must be stored in the corresponding <form name>-media folder in the forms folder of reacap (odk collect) the below table (table vii) shows images associated with choices in the choices sheet of our excel. table vii. association of images with screens list name name label image user_type patient patient user_type professio nal professio nal gender male male gender female female android and odk based data collection framework to aid in epidemiological analysis 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi fever_opts yes yes fever_opts no no fever_opts idk i don’t know fever.pn g yes_no yes yes yes_no no no cough_yes _no yes yes cough_yes _no no no cough.pn g shiver_yes _no yes yes shiver_yes _no no no sweating .png once the form has been created in the excel format, we can convert it to xforms by uploading it formhub.com and publishing it. the web based tool lets you download the xform when done. all the media mentioned in the form needs to be in a folder named <form name>-media, located at the same level as the xform. this is necessary for the reading of the form in the current implementation. the next section describes the main features of the reacap application. reacap reacap is a data surveillance application designed for android. as mentioned earlier, reacap is built on top of open data kit’s collect application. it uses javarosa engine to process its form logic and display it to the user. reacap allows the user to download the forms from a centralized server. the forms are stored in the server along with the form media. since these forms are to be used in mobile applications, they need to be javarosa compatible. availability of network is necessary for downloading of the forms. once downloaded, the user can fill the form in an offline mode. the following fig 11 displays the capabilities of reacap. android and odk based data collection framework to aid in epidemiological analysis 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 11 screen depicting the capabilities of reacap connect to server establishes an authorization check for the user. each healthcare worker will have a separate login. this will allow us to track the locations which have been surveyed and organize surveillance teams better. health care workers will also have certain privileged access to the server contents. general users will be provided a common logging to gain access to the storage server. get blank form will allow the user to download forms from the server once they are authorized to access it (i.e. once they are able to login to the server). the forms are stored as xml documents and their corresponding media need to be stored in a folder with the same name with a '-media' appended to the end. this enables the application to download the corresponding media along with the form. fill blank form and edit saved forms allow the user to fill the downloaded forms and edit forms respectively. user can save the form at completion or in the middle. this will be stored on the device till the user decides to send it to the server. send finalized form button allows the user to do that. there is a slight difference between saved forms and finalized forms. finalized forms are indicated by the user while filling the form. they tell the application that the forms are ready to be sent. saving forms while filling lets the user get back to them when they want but these forms cannot be sent to the server unless the user specifically marks them finalized. finalized forms can be edited as well. this distinction is necessary to avoid confusion while dealing with multiple forms and avoid flooding the servers with incomplete forms by error. the last button, delete saved forms, helps clear the devices memory of the unnecessary forms and media. the javarosa engine and odk collect displays the prompts to the user in a one prompt at a time format. users can navigate from one prompt by a simple swipe motion. the form logic determines the sequence of prompts shown and the input variables. the fig 12 below illustrate a few screens. android and odk based data collection framework to aid in epidemiological analysis 23 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 12: few screens from the influenza form on reacap the form is saved in an xml format on the device. the output of reacap is a simple tag based xml output. a sample output is shown in fig 13. as seen in the below figure, the value filled in the prompts are stored within their corresponding name tags. this is enough to enable us to parse the information and analyze it. but in order to enable the reuse of forms, we want to package the form structure as well as the form output. for this purpose, we used epiml to convey the information to the server. this is covered in the section below. android and odk based data collection framework to aid in epidemiological analysis 24 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi figure 13 xml output from odk collect/reacap uploading the form to the server epiml is the proposed data interchange format between area devices. access to other health, environmental, and geographic information sources by the area systems will be accomplished through a centralized data server (reacloud) for uniform and timely data distribution to area equipped mobile devices. more information on epiml is out of scope of this report. network connection is required to download or send forms to the server. once uploaded to the server, the information can be analyzed and stored back in the epiml format. this information can then be used to view the finished form, analyze the information in the form of charts and graphs, model the information to predict the spread etc. analysis many factors played a part in the design of this approach. first and foremost was the need for efficient surveillance and analysis in the public health domain. lack of awareness and adequate medical response has a huge impact on the spread of the illness. this can be controlled to an extent by taking proactive measures such as creating awareness and using analytics to model the illness. with the growing popularity of smartphones, their role in this aspect has seen considerable growth. it has been observed that the use of smartphones as opposed to traditional modes of surveillance and analysis is not only quicker but also less expensive [20,21]. the decision for use of xforms for data transfer was based on the flexibility and portability that the specification provides. in addition to this, though it is not a defined standard used in healthcare right now, there is an increasing adaptation of xforms in the industry [1]. the application in itself has two audiences health care organizations and the general public. while the intent of the application is to enable faster data collection and analysis, it also serves an a awareness tool among the general population. when working with bigger groups, the design of the application allows the ability to track and coordinate information. information can be shared using epiml and reacloud. android and odk based data collection framework to aid in epidemiological analysis 25 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e228, 2014 ojphi the simple layout of the forms ensures that the application is usable by a wider audience. another advantage is that the network connection is only required while downloading or uploading the forms. android and xforms allow metadata collection as well. this eliminates the need for the user to enter information such as device identity, time etc. taking advantages of android features, we can use the gps to record the location coordinates instead of manually entering such information. using xforms as the format to read the data also gives more freedom to the users. since xml is used as the mode of storage, the application does not impose memory issues on the device. at this stage, the form is created using the xlstool. with the availability of reaconfig, this process will also be simplified. this design in itself imposes certain restrictions as well. although reacap can be used as a standalone tool, the complete potential can only be realized when used in conjunction with other area apps. the use of epiml makes it simpler to design the interface targeting epidemiological data and hence makes it more efficient when parsing and analyzing the data. cloud storage allows the ability to store large quantities of data which can then be used for analysis. using mobile devices to interact this information, allows updated information to be available faster to the users. summary and conclusions the area framework on the whole tries to use the capability of mobile applications to create a framework for efficient data collection and analysis. existing data collection frameworks tend be of a very generic nature. we want to add to this and create a framework suitable for the surveillance, analysis and modeling of infectious diseases. we have implemented a prototype capable of handling complex form logic, displaying data and provide analysis. we have tried to be generic and at the same time not compromise on the final product. in this project, we have created the prototype for one of the area apps – reacap. reacap as mentioned before is an android based data collection application. it allows the user to gather information based on the form presented and uploads the data to the cloud based server once submitted. in this project, we have depicted the use of reacap for monitoring influenza. as mentioned before, the application has its strengths in its simplicity in the way the data is presented and shown to the user. it caches the information on the device when there is no data connectivity, thereby removing the complete dependency on the network. another key advantage is the use of xforms and javarosa engine for rendering complex form logic. the computing power of smartphones is constantly increasing. using this to our advantage, the project is designed to allow to interaction between each of the area components. in the absence of data connectivity, partial analysis can still be done using the mobile phone’s processing capability. this can be very advantageous in regions with little or no network. in conclusion we have tried to show that mobile devices can be used to a large extent in the healthcare industry. they are not only already prevalent but have untapped potential which can help make the 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"rest api," . 19. department of health and human services. center for disease control and prevention (cdc). available: http://www.cdc.gov/flu/about/disease/index.htm. 20. schmoldt d, rösch a, hasenkamp u, mondorf w, pollmann h. 2012, improved surveillance of haemophilia home treatment using mobile phones, in etelemed 2012, the fourth international conference on ehealth, telemedicine, and social medicine, pp. 143-146. 21. bredican j, mills aj and plangger k,, 2013, imedical: integrating smartphones into medical practice design, journal of medical marketing: device, diagnostic and pharmaceutical marketing, 2013. 22. purcforms. broswer based xforms form designer and runtime engine, available: https://code.google.com/p/purcforms/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts utility of system generated syndromic surveillance alerts to detect reportable disease outbreaks carrie eggers*, aaron kite-powell and janet hamilton bureau of epidemiology, florida department of health, tallahassee, fl, usa objective in light of recent outbreaks of pertussis, the ability of florida department of health’s (fdoh) electronic surveillance system for the early notification of community-based epidemics (essence-fl) to detect emergent disease outbreaks was examined. through a partnership with the johns hopkins university applied physics laboratory (jhu/apl), fdoh developed a syndromic surveillance system, essence-fl, with the capacity to monitor reportable disease case data from merlin, the fdoh bureau of epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. the purpose of this evaluation is to determine the utility and application of essence-fl system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures. introduction reportable disease case data are entered into merlin by all 67 county health departments in florida and assigned confirmed, probable, or suspect case status. de-identified reportable disease data from merlin are sent to essence-fl once an hour for further analysis and visualization using tools in the surveillance system. these data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. these statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information. methods within the essence-fl query portal, the merlin reportable diseases data source was selected with a weekly time resolution by event date. case classification included all confirmed, probable and suspect cases, reported and not yet reported, during the time period of week 35, 2011, to week 35, 2012. the essence weighted moving average (ewma 1.2) detector was used to classify weekly counts as either of normal, warning or alert status based on previous weeks’ counts, indicating the possibility of an emerging outbreak. these weekly statuses were then compared with outbreaks reported in merlin’s fully integrated outbreak reporting system and with outbreak reports submitted to epicom, florida’s epix or health alert network. an essence-fl generated warning or alert was considered valid if a corresponding outbreak of 2 or more epi-linked pertussis cases were reported in either merlin’s outbreak module or in epicom. for the sake of brevity in this abstract, the analysis of pertussis is presented, while other reportable disease conditions of immediate interest will be presented at the conference. results examination of 494 pertussis cases reported from september 2011 to september 2012 showed that of 53 weeks, 38 weeks contained normal case counts, 11 weeks generated warnings, and 4 weeks produced alerts. the number of warnings that corresponded to actual outbreaks was 6 of 11, whereas 2 of the 4 alerts matched reported outbreaks. of the remaining 38 weeks, 12 had outbreaks reported with no warning or alert generated by essence-fl. when comparing confirmed outbreak status with essence-fl weekly data count status, warning/alert versus normal, it was found that the sensitivity of essence-fl to detect a true outbreak was 40.0% while the specificity was 78.8%. this comparison generated a positive-predictive value of 53.3% and a negative predictive value of 68.4%. conclusions the ability of essence-fl to act as a first alert system for emerging disease events using merlin reportable disease data should be considered with constraint. while warnings or alerts about potential pertussis outbreaks were generated correctly about half the time, the nearly one-third of reported outbreaks with no warning or alert makes the utility of the alerts questionable as far as initiating immediate action without prior verification of the alert. florida does not currently have a requirement for centrally documenting all outbreaks, so it is likely that outbreaks occurred but were not recorded, precluding verification of all outbreaks. keywords syndromic; surveillance; outbreaks acknowledgments applied public health informatics fellowship cdc in collaboration with cste, astho and phii. *carrie eggers e-mail: carrie_eggers@doh.state.fl.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e22, 2013 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz bureau of epidemiology and public health informatics, kansas department of health and environment, topeka, ks, usa objective measure the correlation between influenza-like illness (ili) data collected by the u.s. outpatient influenza-like illness surveillance network (ilinet) and the national syndromic surveillance program (nssp) in kansas for the 2014-2015 influenza surveillance period. introduction influenza is not a notifiable disease in kansas; patient-level influenza data is not reported to the kansas department of health and environment (kdhe). kansas’ primary method of influenza surveillance is the u.s. outpatient influenza-like illness surveillance network (ilinet), a collaboration between the centers for disease control and prevention (cdc) and state health departments. during the 2014-2015 influenza surveillance period (september 28, 2014 through may 16, 2015), 35 health care providers (20 family practice clinics, nine hospital emergency departments, four university student health centers, and two pediatric clinics) served as ilinet sites. providers were instructed to report the previous week’s influenzalike illness (ili) data, including the number of patients who met the ili case definition and the total number of patients seen, by 11:00 am each tuesday. an average of 16 providers (45%) met the deadline each week. kansas’ method of syndromic surveillance is the national syndromic surveillance program (nssp). during the 2014-2015 surveillance period, 98 of 129 kansas hospital emergency departments (eds) automated submission of electronic health record data to nssp. twenty-three eds submitted data at least once per day throughout the season; the remaining eds were still undergoing testing and validation to meet minimum data quality standards, and therefore were prone to erratic data submission and indeterminate data quality. methods the weekly proportion of ilinet provider patients who met the ilinet ili case definition (fever ≥100°f with cough or sore throat) were compared to the weekly proportion of nssp-enrolled emergency department patients who met the nssp syndrome definitions work group ili syndrome definition (ili-s). patients were included in ili-s if influenza-related symptoms or diagnosis codes were present in the chief complaint, diagnosis code, or diagnosis text portions of their nssp record. ilinet providers that also submitted data to nssp were removed from the ilinet data set to ensure those seven emergency departments did not influence correlation. ilinet and nssp data submitted before the weekly ilinet deadline were compared to evaluate if nssp could be used as a proxy for accurate situational awareness in kansas, given nssp’s automatic, daily data submission and the reporting delays seen with ilinet. pearson correlation coefficients (rho) were calculated using sas 9.3 (sas institute inc., cary, nc, usa) results ilinet and nssp ili data were highly correlated, both when comparing all data submitted during the surveillance period (rho=0.91, p<0.001; figure 1) and data submitted prior to the weekly ilinet deadline (rho=0.69, p<0.001; figure 2). conclusions despite the differences in ili definitions applied to each surveillance system, variation in the number of sites submitting data each week to each system, and differences in the type, geographic distribution, and total number of reporting sites, the weekly proportions of ili patients reported to ilinet and nssp were highly correlated. the correlation was higher when comparing all data collected during the surveillance period than when comparing only data submitted before the weekly ilinet deadline. applying the ili-s syndrome definition to nssp data may provide useful situational awareness for states whose ilinet providers do not routinely meet the weekly data submission deadline. keywords influenza like illness; syndromic surveillance; nssp; ilinet; influenza acknowledgments the authors wish to thank cdc’s influenza division and the nssp syndrome definitions workgroup for their assistance. *daniel j. neises e-mail: dneises@kdheks.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e70, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the 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into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts refactoring indicator into an advanced information system for one health monitoring ian brooks*1, 2, mario felarca1 and bernie a’cs1 1ncsa, urbana, il, usa; 2university of saskatchewan, saskatoon, sk, canada objective to redesign indicator for one health, establish a common data format, and provide for long term scalability. introduction indicator is a multi-stream open source platform for biosurveillance and outbreak detection, currently focused on champaign county in illinois[1]. it has been in production since 2008 and is currently receiving data from emergency departments, patient advisory nurse call center, outpatient convenient care clinic, school absenteeism, animal control, and weather sources. long term scalability was however compromised during the 2009 h1n1 influenza pandemic as immediate public health needs took priority over our systematic development plan. with the impending addition of veterinary clinic data and recognizing that the health of a community also depends on animal and environmental factors, we decided to revisit the indicator architecture and redesign it to be a more holistic and scalable system. we also decided to revisit the data submission format, keeping in line with the philosophy of making opportunistic secondary use of as much data about the health of a community that we can obtain. methods following a formal evaluation of the existing production version of indicator we established the systems architecture shown in figure 1 to leverage work in other cyberinfrastructure projects at ncsa. results we have now implemented the back end changes, including unifying the multiple physical mysql database systems and multiple apache tomcat application engines into a single system. a web application, using service oriented principles and the gwt library, has been developed that can query and display the newly unified data and provide new options for input of data to the system. in order to streamline and simplify the data format we decided to define a single format that can be used by different kinds of healthcare providers, both human and veterinary. although we recognize the limitations in this approach we define a reported event to be a simple what, when, and where containing the following seven fields or the relevant subset based roughly on the isds meaningful use recommendations [2] 1. date of incident 2. icd-9 code for the primary diagnosis 3. free text of the diagnosis (not the text definition of the icd-9 code) 4. text chief complaint at triage 5. location 6. count 7. species in this way we can handle, in a single format, data from emergency departments, convenient care clinics, patient advisory nurse call centers, veterinary clinics, veterinary labs, and veterinary poison control centers. conclusions indicator has been significantly redesigned and is now more integrated, scalable, and secure. it is now placed to become a one health integrated monitoring system. figure 1. indicator system architecture keywords open source; surveillance; informatics; one health acknowledgments we would like to thank carle foundation, christie clinic, champaign county animal control, champaign county humane society, the veterinary medicine data base, and schools in champaign county for sharing their data. we would also like to thank julie pryde and awais vaid, from champaign-urbana public health district for their ongoing support and assistance. references [1] brooks i, edwards w, indicator: a cyberenvironment for biosurveillance and response. syndromic 2009. [2] isds meaningful use recommendations. *ian brooks e-mail: ian@ncsa.illinois.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e120, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a bayesian approach to characterize hong kong influenza surveillance systems ying zhang*, ali arab and michael a. stoto georgetown university, washington, dc, usa objective our goal is to develop a statistical model for characterizing influenza surveillance systems that will be helpful in interpreting multiple streams of influenza surveillance data in future outbreaks. introduction syndromic surveillance has been widely used in influenza surveillance worldwide. however, despite the potential benefits created by the large volume of data, biases due to the changes in healthcare seeking behavior and physicians’ reporting behavior, as well as the background noise caused by seasonal flu epidemics, contribute to the complexity of the surveillance system and may limit its utility as a tool for early detection [1,2]. since most current analysis methods are developed for outbreak detection, there are few tools to characterize influenza surveillance data for situational awareness purposes in a quantitative manner. hong kong centre for health protection (chp) has a comprehensive influenza surveillance system based on healthcare providers, laboratories, schools, daycare centers and residential care homes for the elderly. hong kong usually experiences a summer peak in july and august [3], which potentially doubles the data volume and constitutes a natural experiment to assess the effect of school-age children in the influenza transmission dynamics. the richness of the available data and the unique epidemiological characteristics make hong kong an ideal study object to develop and evaluate our model. methods we have constructed a bayesian statistical model for influenza surveillance data by parameterizing factors that describe disease transmission, behavior patterns in health care seeking and provision, and biases and errors embedded in the reporting process (figure 1). the prior distributions are selected for each of the parameters to reflect knowledge of influenza epidemiology and the likely biases in each data system. using the markov chain monte-carlo (mcmc) method in openbugs, a posterior distribution can be generated for every parameter to characterize each data stream. the ratios of specific pairs of data streams are assessed in order to identify patterns in the change of ratios at different stage of the flu season. results preliminary results, as shown in figure 2, incorporate confirmed influenza infection (solid line), influenza-like illness (double solid line), fever cases (dashed line), and google search index (round dashed line). although most of these data series track together, differences among them suggest reporting bias related to public awareness, which will be addressed in the statistical modeling. conclusions the posterior distribution for parameters and ratios between individual data streams can be used to characterize influenza surveillance systems in terms of tendency in peak early or late, or to over or under represent actual influenza cases. to better interpret syndromic surveillance data for situational awareness purposes, behavioral data related to healthcare resource utilization, such as the percentage of intended gp visit among people with ili, need to be collected together with the flu activity surveillance. conceptual model for influenza surveillance statistical model blue circles: unobservable true value; white boxes: observation; orange boxes: factors hong kong flu activity in 2009 ph1n1 outbreak keywords situational awareness; modeling; epidemiology; influenza surveillance; bayesian acknowledgments cdc, hong kong university center for health protection, hong kong sar references 1.zhang, y., may, l., & stoto, m. a. (2011). evaluating syndromic surveillance systems at institutions of higher education (ihes): a retrospective analysis of the 2009 h1n1 influenza pandemic at two universities. bmc public health, 11, 591. 2. stoto ma (2012) the effectiveness of u.s. public health surveillance systems for situational awareness during the 2009 h1n1 pandemic: a retrospective analysis. plos one 7(8): e40984. 3. chan, p. k. s., mok, h. y., lee, t. c., chu, i. m. t., lam, w., & sung, j. j. y. (2009). seasonal influenza activity in hong kong and its association with meteorological variations. journal of medical virology, 81(10), 1797-1806. *ying zhang e-mail: yz62@georgetown.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e172, 2013 5040-38639-1-ce.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 94 (page number not for citation purposes) isds 2013 conference abstracts application of a bayesian spatiotemporal surveillance method to nyc syndromic data alison levin-rector*1, ana corberán-vallet3, andrew b. lawson2, ramona lall1 and robert mathes1 1nyc dept of health and mental hygiene, queens, ny, usa; 2medical university of south carolina, charleston, sc, usa; 3university of valencia, valencia, spain objective introduction methods results conclusions keywords references *alison levin-rector e-mail: alevinrector@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e13, 2014 gis and icd-10.docx a decision support tool for using an icd-10 anatomographer 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi a decision support tool for using an icd-10 anatomographer to address admission coding inaccuracies: a commentary christopher m. bell 1, 2 , arash jalali 2, 3 , edward mensah 3 1 university of illinois hospital & health sciences system, 2 university of illinois at chicago, biomedical and health information sciences, 3 university of illinois at chicago, school of public health the proposed icd-10 anatomographer much focus has been given to the pending adoption of icd-10 coding standards in the us. the transition has been delayed for many years and many organizations are spending millions of dollars in preparing their workforces for the adoption date on october 1, 2014.[5] coding productivity is expected to decrease ten through thirty percent for the first six months after the transition and lower levels will probably persist after that.[6] as europe and the rest of the world await the release of icd-11 in 2015, the us is at risk of further falling behind the international community by delaying icd-10 adoption.[7] there is the potential for a decline in accuracy in coding when the us adopts icd-10, but the degree to which that severity is realized will depend on the education and tools available to medical professionals. abstract in the chaotic environment of an emergency department trauma unit, accuracy and timeliness in decision making are required to save a patient’s life. in a large urban city, where gun violence is high, emergency department physicians must have a wide array of tools in order to effectively and efficiently treat victims of gun violence and ensure that their diagnoses are properly coded. a disparity currently exists between the accuracy of icd-9 admission coding and discharge coding with some error rates as much as seventy percent. [1,2,3,4] the elevated error rate is poised to increase even more, as the us transitions from icd-9 to icd-10 coding standard. the proposed decision support tool, the icd-10 anatomographer, will have many advantages to medical professionals working in high-intensity settings. emergency department physicians in busy trauma care units in large urban hospitals will be able to utilize this technology to find the accurate icd-10 code in an efficient manner, thereby improving quality of care and saving lives. keywords: decision support, icd-9 to icd-10 transition, anatomography correspondence: chrisbel@uic.edu copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. a decision support tool for using an icd-10 anatomographer 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi icd-10 provides greater specificity and more clinical information than icd-9, which allows for the possibility of a more accurate clinical diagnosis.[8] physicians and coders need a tool to assist in finding the appropriate icd-10 code in a timely manner. we propose a new visual clinical decision support tool that incorporates an anatomography software package with icd-10 codes. there are many anatomography software packages that render human anatomy in 3d, but currently no package incorporates icd-10 codes into their models. many academic institutions have been integral in developing these software packages; and as a learning tool, anatomography software packages have helped to visualize the science of anatomy. the following three datasets would be used to create the icd-10 anatomographer: icd-10-cm release (2013), made available by the centers for medicare and medicaid services; the fma code descriptions, made available from the university of washington structural informatics group; and the anatomography.obj files, made available from bodyparts3d. the use agreementmaker, “a flexible ontology and schema matching system,” [9] would allow for the icd-10-cm dataset to be linked to the fma code descriptions. the linked datasets would then be linked to the anatomography .obj files using esri arcgis. in order to update the existing bodyparts3d files, esri city engine would be used to link the .obj files from esri arcgis. the updated anatomography .obj files would then be able to be saved to a server. table 1: icd-10 anatomographer datasets description dataset source icd-10-cm release (2013) fma code descriptions anatomography .obj files centers for medicare and medicare services university of washington structural informatics group bodyparts3d a decision support tool for using an icd-10 anatomographer 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 1. icd-10 anatomographer entity relationship diagram as a decision support tool, the proposed icd-10 anatomographer would have many advantages to medical professionals. for emergency department physicians, the proposed icd-10 anatomographer would help to find the accurate icd-10 code in an efficient manner. the icd10 anatomographer could be integrated into an existing electronic medical record as an application or as a standalone application accessible to users via the internet. given the current error rate for icd-9 admission coding [1,2,3,4], the proposed icd-10 anatomographer could be used to address many inpatient coding errors. as a visual tool the user interface of a 3d application would make finding the correct code intuitive, as codes are selected by clicking on the specific body part within the icd-10 anatomographer application. a preliminary proof of concept exercise performed by the authors confirms the proposed concept would be feasible. further research and testing is needed to confirm the proposed icd-10 anatomographer would be a viable solution to addressing the current coding inaccuracy problem. corresponding author christopher m. bell, capm project coordinator university of illinois hospital and health sciences system chicago, il email: chrisbel@uic.edu references [1] o'malley kj, cook kf, price md, wildes kr, hurdle jf, ashton cm. measuring diagnoses: icd code accuracy. health services research. 2005; 40:5p2, 1620-1639. mailto:chrisbel@uic.edu a decision support tool for using an icd-10 anatomographer 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [2] benesch c, witter dm, wilder al, duncan pw, samsa gp, matchar db. inaccuracy of the international classification of diseases (icd-9-cm) in identifying the diagnosis of ischemic cerebrovascular disease. neurology. 1997; 49:3, 660-664. [3] faciszewski t, broste sk, fardon d. quality of data regarding diagnoses of spinal disorders in administrative databases. a multicenter study. the journal of bone & joint surgery. 1997; 79:10, 1481-8. [4] goldstein lb. accuracy of icd-9-cm coding for the identification of patients with acute ischemic stroke effect of modifier codes. stroke. 1998; 29:8, 1602-1604. [5] centers for medicare & medicaid services. icd-10. april, 2013. available at: http://www.cms.gov/medicare/coding/icd10/index.html?redirect=/icd10. accessed june, 2013. [6] carmichael a. icd-10 and its impact on coder productivity. september, 2011. available at: http://www.icd10monitor.com/index.php?option=com_content&id=208:icd-10-and-itsimpact-on-coder-productivity-&itemid=113. accessed june, 2013. [7] world health organization. the international classification of diseases 11th revision is due by 2015. 2013. available at: http://www.who.int/classifications/ icd/revision/en/. accessed june, 2013. [8] hazelwood ac, venable ca. icd-10-cm and icd-10-pcs preview. ahima. 2004. [9] advances in information system laboratory at the university of illinois at chicago. agreementmaker. may, 2012. available at: http://agreementmaker.org/wiki/index.php/agreementmaker. accessed june, 2013. http://www.cms.gov/medicare/coding/icd10/index.html?redirect=/icd10 http://www.icd10monitor.com/index.php?option=com_content&id=208:icd-10-and-its-impact-on-coder-productivity-&itemid=113 http://www.icd10monitor.com/index.php?option=com_content&id=208:icd-10-and-its-impact-on-coder-productivity-&itemid=113 http://www.who.int/classifications/%20icd/revision/en/ http://agreementmaker.org/wiki/ http://agreementmaker.org/wiki/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts copd-related ed visits in north carolina: hospitalizations and return visits steven j. lippmann*, karin b. yeatts, anna waller, kristen hassmiller lich, debbie travers, morris weinberger and james f. donohue university of north carolina, chapel hill, nc, usa objective to investigate hospital admissions and short-term return visits resulting from chronic obstructive pulmonary disease (copd)-related emergency department (ed) visits. introduction copd is a prevalent chronic disease among older adults; exacerbations often result in ed visits and subsequent hospital admissions.[1,2] a portion of such patients return to the ed within a few days or weeks.[3] in this study, we investigated patterns of hospital admissions and short-term return visits resulting from copd-related ed visits. methods we performed a population-based study of ed visits for copd using state-wide surveillance data from nc detect[4], including all ed visits made by nc residents aged !45 years in 2008-2009. visits were considered copd-related if the firstor second-listed discharge diagnoses contained one of the following icd-9-cm codes: 491.*, 492.*, 493.2*, 494.*, or 496.*. hospital admissions were captured by ed disposition codes. if a patient had made another copdrelated ed visit within the prior 3 or 30 days, we defined the current visit as a 3-day or 30-day return visit. we compared the prevalence of hospitalization and 3and 30-day return visits by age, sex, and payment method. we also described the disposition patterns for return visit pairs. results there were 97,511 copd-related ed visits made by adults age 45 and older in nc in 2008-2009, made by 64,568 individuals. hospital admissions: nearly half (46.3%) of all copd-related ed visits resulted in hospital admission. hospitalization prevalence increased with age, but there were no differences by gender. ed visits that were non-insured (self-pay) or paid by medicare or medicaid were less likely to lead to hospitalization than those with private insurance. return visits: 1.6% (1607) of the copd-related ed visits were categorized as 3-day return visits and 11.2% (10922) were considered 30-day return visits. there were no statistical differences by gender for 3-day returns, while 30-day returns were more likely to be made by men. prevalence of return visits for both intervals initially increased with age compared to the 45-49 years age group, then decreased steadily after age 65. visits that were non-insured or paid by medicare or medicaid were statistically more likely to be 3-day or 30-day returns than those paid by private insurance. disposition patterns: we also examined the permutations of 1st and 2nd ed visit dispositions that make up these return visit pairs. while many return visits were discharged at both visits in the return visit pair, a substantial proportion were admitted at one or both visits. surprisingly, in 8% of the 3-day return visit pairs, the patient was hospitalized at the 1st ed visit but yet still returned to the ed within 3 days; for the 30-day visit pairs, 37% returned despite the patient being admitted at the 1st visit. conclusions this population-based study describes the short-term outcomes of a large number of copd-related ed visits using a unique state-wide surveillance system. we found a high prevalence of hospital admissions and return ed visits, including many repeat hospitalizations. this study also demonstrates how surveillance data can be used for research on “acute on chronic” disease epidemiology. keywords chronic obstructive pulmonary disease; chronic disease surveillance; emergency department data; hospitalizations; return visits acknowledgments the statistical analysis for this research was supported by a university of north carolina, gillings school of public health gillings innovation laboratory grant. data were obtained from the nc dhhs/dph nc detect system under a data use agreement. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. references 1. rowe bh, voaklander dc, marrie tj, senthilselvan a, klassen tp, rosychuk rj. outcomes following chronic obstructive pulmonary disease presentations to emergency departments in alberta: a population-based study. can resp j 2010;17(6):295. 2. tsai c-l, clark s, cydulka rk, rowe bh, camargo ca. factors associated with hospital admission among emergency department patients with chronic obstructive pulmonary disease exacerbation. acad emerg med 2007;14(1):6–14. 3. kim s. prospective multicenter study of relapse following emergency department treatment of copd exacerbation. chest 2004;125(2):473–81. 4. carolina center for health informatics, university of north carolina at chapel hill. the unc department of emergency medicine carolina center for health informatics report, nc detect emergency department data: 2008 [internet]. 2010 [cited 2012 sep 2]. available from: http://www.ncdetect.org/pubs.html. *steven j. lippmann e-mail: slippmann@unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e60, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts new strategy and innovative projects at the national biosurveillance integration center steven bennett and teresa quitugua* homeland security, washington, dc, usa objective enhance knowledge of the vision, mission, strategic goals, and objectives of the national biosurveillance integration center (nbic). learn about innovative biosurveillance projects ongoing in nbic. introduction for a number of years, the federal government has provided biosurveillance in various domains within different departments and agencies. congress recognized the need for a means of integrating these separate information sources into a more useable resource by chartering nbic within the department of homeland security. methods nbic engaged the biosurveillance community within and beyond the federal government through a series of extensive discussions, workshops, and symposia to define a strategy for future development of integrated biosurveillance activities grounded in legislative and presidential direction. the nbic strategic plan was extensively reviewed by the twelve federal departments that comprise the national biosurveillance integration system (nbis) as well as the white house office of management and budget. the nbic strategic plan is currently being revised for release of a public version. the nbic also engaged partners in the development of projects designed to develop and test new approaches to biosurveillance. results the nbic strategic plan was delivered to congress in august, 2012. the plan explains the center’s approach, why it is needed, and how it seeks to execute the mission of integrating national biosurveillance information to provide relevant and timely information that effectively supports decision making. projects are underway involving text analyses of emergency medical system data, changes to poison control center data collection and analysis, and the application of machine learning to social media analyses. a sub-working group of the nbis has been established to guide selection of future pilot project areas to address prioritized requirements for integrated biosurveillance. conclusions nbic has increased flexibility in its commitment to collaboration and coordination, engaged in bold new approaches, and is defining requirements that will encourage buy-in and support of the users across the levels of government and the private sector. with success in its mission, nbic will support its partners’ missions and provide relevant and timely information that effectively supports decision making. keywords nbic; nbis; strategy references (1) public law 110-53, “implementing recommendations of the 9/11 commission act of 2007,” august 2007. accessed online at http://intelligence.senate.gov/laws/pl11053.pdf. (2) the white house, national strategy for biosurveillance, july 2012. accessed online at http://www.whitehouse.gov/sites/default/files/national_strategy_for_biosurveillance_july_2012.pdf. (3) institute of medicine, “information sharing and collaboration: applications to integrated biosurveillance workshop summary,” november 2011. (4) government accountability office, report on biosurveillance: developing a collaboration strategy is essential to fostering interagency data and resource sharing, gao 10-171 (washington, dc: gao, 2009). (5) u.s. department of health and human services. national biosurveillance strategy for human health. february 2010. accessed online at http://www.cdc.gov/osels/pdf/nbshh_v2_final.pdf. (6) u.s. department of health and human services. national health security strategy, december 2009. accessed online at http://www.phe.gov/preparedness/planning/authority/nhss/strategy/documents/nhss-final.pdf. (7) u.s. department of health and human services. implementation plan for the national health security strategy of the united states of america, may 2012. accessed online at http://www.phe.gov/preparedness/planning/authority/nhss/ip/documents/nhss-ip.pdf. (8) the white house, “biodefense for the 21st century,” homeland security presidential directive 10, april 28, 2004. accessed online at http://www.fas.org/irp/offdocs/nspd/hspd-10.html. (9) the white house, “defense of united states agriculture and food,” homeland security presidential directive 9, january 30, 2004. accessed online at http://www.aphis.usda.gov/animal_health/emergency_management/downloads/hspd-9.pdf. *teresa quitugua e-mail: teresa.quitugua@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e79, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts detection of a swine erysipelas outbreak using enhanced passive surveillance judy akkina*1, wolf weber1 and lisa becton2 1usda, aphis, veterinary services, fort collins, co, usa; 2national pork board, des moines, ia, usa objective to describe detection and response for an erysipelas outbreak in market swine in the united states (u.s.) using food safety and inspection service (fsis) slaughter condemnation data, and coordination with the swine industry in an enhanced passive surveillance (eps) pilot project. introduction eps is a comprehensive effort to complement other types of surveillance and provide early detection and situational awareness of significant endemic, zoonotic, and emerging diseases of livestock. the concept for eps involves gathering syndromic and observational data from multiple animal health surveillance sources, including private practitioners, livestock markets, livestock harvest facilities, and veterinary diagnostic laboratories. a signal indicating a potential animal health event in one data stream can be corroborated in the other streams. for swine surveillance in the u.s., usda-aphis monitors the number of swine condemned for specific reasons. likewise, industry practitioners share front-line clinical information within their practitioner network to detect anomalies. this case summary demonstrates the successful outcome of implementing an eps pilot program through federal and industry partnership. methods fsis animal disposition reporting system swine condemnation data are monitored by usda-aphis veterinary services (vs) for several condemn conditions, including erysipelas, a bacterial disease of swine. typically, slaughter condemnations for erysipelas are rare. the monitored data represent 83 market swine harvest facilities throughout the u.s. a modification of the ‘c3’cusum aberration detection method from the early aberration reporting system (ears) is applied to the data at both the slaughter plant level and at a larger multi-plant swine catchment basin level which represents separate swine production areas. the national pork board (npb), a u.s. swine producer association, hosts a quarterly conference call with a sentinel network of swine veterinarians to exchange information about anomalies in swine health observed by practitioners. during mid-february 2012, several practitioners suspected a local increase in erysipelas in finishing swine. absent baseline data on erysipelas occurrence nationally, the scope of the problem was uncertain. following the call, the npb in collaboration with vs attempted to validate the information reported by swine practitioners. results beginning the week of january 8, 2012, vs analysts noted a slight increase in erysipelas cusum signaling activity in the greater iowa catchment basin slaughter plants. during the seven-week period between january 8 and february 25, eight weekly plant-level cusum signals were observed, while the previous 36-week period yielded only fourteen plant-level signals. on average, 0.39 signals per week were noted in the weeks prior to the outbreak period while the corresponding average for the seven-week outbreak period was 1.14 plant signals per week. seven of the eight plants that signaled during the outbreak period did not report large weekly spikes; however, the weekly accumulation of condemns were sufficient to trigger concern. since the erysipelas signals were not large compared to the background noise, there was uncertainty whether the increased signaling activity truly represented a disease event. after cross validating the slaughter surveillance data with front line practitioner information, a swine health alert regarding the increase in erysipelas cases was issued by the american association of swine veterinarians. intervention measures were initiated as deemed appropriate by each private veterinarian. conclusions this example of an enhanced passive surveillance program demonstrates use of independent streams of information from government and private industry to detect an outbreak of erysipelas in market swine. the communication process was facilitated by the npb and the american association of swine veterinarians, and coordinated with the industry resulting in an appropriate response to prevent swine losses at very early stages of the outbreak. corroboration and validation between the two data streams (slaughter and practitioner) provided confidence that an outbreak was beginning and assisted the swine industry in decision making to enhance disease prevention activities. this type of early warning and response can reduce the cost of disease outbreaks to swine producers as well as provide confidence in the national disease status for swine in the united states. keywords animal health surveillance; federal and industry partnership; enhanced passive surveillance; swine erysipelas *judy akkina e-mail: judy.e.akkina@aphis.usda.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e44, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts improving ili surveillance using hospital staff influenzalike absence (ila) lydia drumright*1, simon d. frost2, mike catchpole3, john harrison5, mark atkins5, penny parker5, alex j. elliot4, douglas m. fleming6 and alison h. holmes1, 5 1medicine, imperial college london, london, united kingdom; 2university of cambridge, cambridge, united kingdom; 3centre for infections, health protection agency, london, united kingdom; 4health protection agency syndromic surveillance team, birmingham, united kingdom; 5imperial college healthcare nhs trust, london, united kingdom; 6royal college of general practitioners research and surveillance centre, birmingham, united kingdom objective to address the feasibility and efficiency of a novel syndromic surveillance method, monitoring influenza-like absence (ila) among hospital staff, to improve national ili surveillance and inform local hospital preparedness. introduction surveillance of influenza in the us, uk and other countries is based primarily on measures of influenza-like illness (ili), through a combination of syndromic surveillance systems, however, this method may not capture the full spectrum of illness or the total burden of disease. care seeking behaviour may change due to public beliefs, for example more people in the uk sought care for ph1n1 in the summer of 2009 than the winters of 2009/2010 and 2010/2011, resulting in potential inaccurate estimates from ili (1). there may also be underreporting of or delays in reporting ili in the community, for example in the uk those with mild illness are less likely to see a gp (2), and visits generally occur two or more days after onset of symptoms (3). work absences, if the reason is known, could fill these gaps in detection. methods weekly counts and rates of hospital staff ila (attributed to colds or influenza) were compared to gp ili consultation rates (royal college of general practitioners weekly returns service)(4) for 15-64 year olds, and positive influenza a test results (pitr) for all inpatients hospitalised in the three london hospitals for which staff data were collected using both retrospective time series and prospective outbreak detection methods implemented in the surveillance package in r (5) results rates of ila were about six times higher than rates of ili. data on hospital staff ila demonstrated seasonal trends as defined by ili. compared to the ili rates, ila demonstrated a more realistic estimate of the relative burden of pandemic h1n1 during july 2009 (1) (figure). ila provides potentially earlier warnings than gp ili as indicated by its ability to predict ili data for the local region (p < 0.001), as well as its potential for daily ‘real time’ updates. using outbreak detection methods and examining peak weeks, alarms and thresholds, ila alarmed, reached threshold rates and peaked consistently earlier or in the same week as ili and pitr, with the exception of the july 2009, suggesting that it may be predictive of both community and patient cases of influenza (table). conclusions this study has demonstrated the potential to further explore the usefulness of using ila data to complement existing national influenza surveillance systems. this work could improve our accuracy in monitoring of influenza and has the potential to improve emergency response to influenza for individual hospitals. table: week of the year that alarms commenced and peaks were reached for each of the four official influenza events from march 2008 to april 2011. ila = influenza like absences among hospital staff; ili = influenza like illness from rcgp data in london, ages 15-64; pitr = positive influenza a test results among patients from the same hospital as staff contributing ila data; threshold for ili data was set at 30/100,000 as defined by the health protection agency. the ila threshold set at 60/100,000, such that all ili above a threshold of 30/100000 were also above a threshold for ila. figure: weekly counts of ila among hospital staff (blue), pitr among hospital patients (orange), and ili in the community (red) from april 2008 to march 2011 and prospective alarms for elevated counts (circles) using a bayesian subsystem algorithm, using the previous six weeks as the reference for prediction. data plotted by counts rather than rates for clarity. keywords influenza; syndromic surveillance; hospital staff; emergency prepardness references 1) birrell pj, et al. bayesian modeling to unmask and predict influenza a/h1n1pdm dynamics in london. proceedings of the national academy of sciences 2011;108(45):18238-18243. 2) evans b, et al. has estimation of numbers of cases of pandemic influenza h1n1 in england in 2009 provided a useful measure of the occurrence of disease? influenza and other respiratory viruses 2011. 3) ross am, et al. presentation with influenza-like illness in general practice: implications for use of neuraminidase inhibitors. communicable disease and public health 2000;3:256-260. 4) fleming dm. weekly returns service of the royal college of general practitioners. communicable disease and public health 1999;2(2):96-100. 5) hoehle m. an r package for the monitoring of infectious diseases. computational statistics 2007;22(4):571-582. *lydia drumright e-mail: l.drumright@imperial.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e116, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts parametric uncertainty in intra-herd foot-and-mouth disease epidemiological models eric nicholas generous* defense systems analysis division, los alamos national laboratory, los alamos, nm, usa objective the objective of this project is to understand how parametric uncertainty within intra-herd foot-and-mouth disease epidemiological models affects the outbreak simulations and what implications this has on surveillance and control strategy and policy. introduction the rapid transmission and poor control policy response during recent foot-and-mouth disease (fmd) outbreaks have underscored the need for better decision support tools. at the foundation of these decision support tools are the epidemiological models that are parameterized with the data generated from pathogenesis studies of the fmd virus that contain contact transmission data. these values being used to parameterize the model, contrary to assumption, contain a significant amount of uncertainty, which propagates throughout the model affecting output. to understand how parametric uncertainty might affect output, a variety of disease transmission parameters were generated from contact transmission data and parameterized to an intra-herd model. methods data was initially collected and analyzed for papers that could meet several criteria: they must be contact transmission studies, they must measure viremia (the level of virus in the blood), and they must observe clinical signs. for the studies that met the criteria, tables were constructed and the following information from each paper was collected: serotype, strain, animal species, unique animal identifier, unit of measurement utilized by virus quantification, duration and quantity of viremia, and the time to first report of clinical signs. three different durations of disease states for the latent, sub-clinically infectious, and clinically infectious periods were generated from the viremia data for each individual animal and grouped in three ways: by strain of virus, by similar experimental design, and all together. gamma, weibull, and normal distributions were fitted to the data in each group. the distributions for each group were then used to parameterize a stochastic, state transition intra-herd model. output from the model was analyzed by examining the uncertainty and variance in time to 50% herd infected, time to 2% herd clinically infected, and percentage of herd infected at 2% herd clinically infected for each distribution and group. results there is a lack of a standardized definition for disease state durations of the foot-and-mouth disease virus in the literature. as a result, many different models utilize slightly differing values generated from the same data. this project discovered that depending on the definitions used to determine the disease state durations, the model output varied significantly. additionally, durations of the disease state periods do not follow a normal distribution as may be assumed by many modelers, and are more accurately described by distributions that allow for non-zero skewness. conclusions the data being used to parameterize intra-herd foot-and-mouth disease models contains a significant amount of uncertainty that can cause the model output to vary significantly. this uncertainty needs to be clearly communicated to decision makers who use results generated from fmd intra-herd models and illustrates the need for more resources to be put into addressing the issue of basic parameters such as contact rate and disease state duration. currently no studies have been conducted on the contact rate of animals on farms and the current values used for disease state durations vary drastically depending on the data and methods used. without a better understanding of the basic parameters, even the most advanced models will not be accurate. keywords control; foot and mouth disease; epidemiological model; uncertainty; parameters *eric nicholas generous e-mail: generous@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e147, 2013 molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand areeya disratthakit, penpitcha thawong, pundharika piboonsiri, surakameth mahasirimongkol* division of genomic medicine and innovation support, department of medical sciences, ministry of public health, nonthaburi, thailand abstract objective: to support the end tb strategy with an informatics system that integrates genomic data and the geographic information system (gis) of mycobacterium tuberculosis (mtb) clinical isolates. we aim to develop a system prototype for implementing genomic data to support multiple drug-resistant tuberculosis (mdr-tb) control. methods: a 12-step data value chain was applied to describe the information flow within the system. a prototyping-oriented system development method was utilized to test the feasibility of certain technical aspects of a system, and as specification tools to determine user requirements. a simulated dataset was entered as input for initial system testing. results: system prototype, namely integrated mol outbreak detection and joint investigation (imoji), was established. the data entry modules consisted of (1) patient registration, (2) sample registration, (3) laboratory data entry and data analysis, and (4) verification and approval of the analyzed data. the initial system test demonstrated connectivity among modules without error. the system was able to report integrated genomic data and gis information of mdr-tb for clustering analysis. conclusion: imoji provides an interactive model for determining molecular epidemiological links of mdr-tb and corresponding spatial information to guide public health interventions for tuberculosis control. keywords: molecular epidemiology, tuberculosis, gis information, genomic, imoji abbreviations: geographic information system (gis), mycobacterium tuberculosis (mtb), multidrugresistant tuberculosis (mdr-tb), integrated mol outbreak detection and joint investigation (imoji) correspondence: surakameth mahasirimongkol* division of genomic medicine and innovation support department of medical sciences, ministry of public health 88/7 tiwanon rd. muang nonthaburi 11000 thailand molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi introduction spatiotemporal information and genomics data have been shown to have high utility for guiding the public health interventions to control tuberculosis (tb) outbreaks [1]. a transmission network for an epidemic strain of multidrug-resistant tuberculosis (mdr-tb) is difficult to identify in areas of thailand with a high mdr-tb burden [2,3]. molecular epidemiology data of mdr-tb are of public health importance as they can be used to track the geographic distribution and spread of drug-resistant mycobacterium tuberculosis (mtb) clones. it is vital to identify the mdr-tb transmission events so that appropriate interventions can be carried out for its control. this project focuses on the rapid detection of mdr-tb and on outbreak investigation utilizing spatiotemporal epidemiology coupled with classification of transmission events by molecular methods. mtb genotyping is routinely performed in countries with a low incidence of tb. genotyping is usually an extension of the national tb control program. in 2010, the us centers for disease control and prevention (cdc) established a tb genotyping information management system (tb-gims), which links surveillance, mtb genetics, and informatics data to control tuberculosis. the tb-gims enables the early detection of tb outbreaks in the united states [4]. the health protection agency at public health england demonstrated that whole genome sequencing (wgs) combined with epidemiological information is a useful approach to prioritize public health resources towards tracking and responding to tb outbreaks [5]. outbreak identification and investigation along with genotyping of m. tuberculosis are considered an important means of understanding the tb transmission network [6]. despite the readiness of lab infrastructure and the availability of human resources for genotyping services, m. tuberculosis is not routinely genotyped in thailand. furthermore, currently there is no information system in place to collect spatiotemporal data for analyzing the tb molecular cluster in thailand. in order to develop an information system, a data value chain has been reviewed for processing data into a usable and desired format. the data value chain consists of four major stages: collection, publication, uptake, and impact [7]. to develop such an information system, a prototype is first used to collect user requirements for refined output. the prototyping aims to establish a link between user needs, designer’s ideas, and the final system requirements [8]. there are three main prototyping approaches: exploratory, experimental, and evolutionary [8-11]. tel. (66)2-951-0000 ext. 98095, 98096 e-mail: surakameth.m@dmsc.mail.go.th doi: 10.5210/ojphi.v12i1.10416 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. mailto:surakameth.m@dmsc.mail.go.th molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi in this study, we aim to develop system for integrating genomic data and the gis of mdr-tb using a prototyping-oriented system development method. the system development involved in three major steps: system requirements analysis, system design and system testing [12]. methods data value chain analysis system planning was performed to define the proposal and design concepts. the proposal established explicit terms for the objectives of the system. the design defined representations of the system [13]. data value chain was applied for system planning. the data value chain has four main stages: collection, publication, uptake, and impact. these main stages are further divided into the following twelve steps: identify, collect, process, analyze, release, disseminate, connect, incentivize, influence, use, change, and reuse [7]. system modules were built on the basis of information derived from the value chain analysis. role-based access control (rbac) was adopted for module access control. prototyping-oriented system development the prototyping-oriented methodology described an iterative paradigm of system development. the first step in system development was requirements analysis. an exploratory approach was used to clarify system requirements and potential solutions, which can be supported by information technology. system requirements included input, output, process, and data handled. architecture and component design were the next step to define system architecture and module specifications. system prototype was produced to meet the system architecture [14]. finally, system testing was performed to ensure that the simulated data entered exactly matches data source and the data processed successfully without error. seventy mdr-tb cases occurring between 2013 and 2017 were simple randomly selected from tuberculosis case management database (tbcm) [15] for generating a simulated dataset including patient information, location of tb cases, drug resistance profile and genomic data. the system prototype was developed under a collaborative project between the department of medical sciences, ministry of public health, thailand, the cdc and the thailand moph–us cdc collaboration (tuc). initial system testing the simulated data were entered manually into the system protype for testing module connectivity. patient addresses were added for processing data to provide gis information. genomic information inputs were sent to linux system for clustering analysis. molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi results system requirements analysis system planning through literature analysis showed that currently, there is no information system in place that combines epidemiological data and mtb genetic information to support the detection of mdr-tb outbreaks and the investigation of drug-resistant tuberculosis in thailand. therefore, we developed the imoji system to efficiently collect both tb case information and mtb genotyping data in order to provide evidence of mdr-tb clustering. a design concept with a webbased system was used to collect essential data, including case-centric gis obtained from patient registration in health facilities and mtb genotypes from laboratories. the system prototyping scheme was built on a 12-step data value chain (figure 1). as described above, cases of mdr-tb and genetic information of mtb were the essential inputs provided by health facilities, tb control program, and the mycobacterium genetic laboratory. considering data privacy, a tb registration number (tb no.) was used as the primary key for processing data related to mdr-tb cases, and a laboratory number (laboratory no.) was generated as an identifier for processing mtb genetics information. the first three steps of the collection phase provided inputs into the publication phase. analysis of mtb sequencing data and the mdr-tb case location (xy coordinates) released mtb genotype and geospatial data. data were disseminated in an easy-tounderstand format as an mtb genotyping profile and a cluster map of mdr-tb cases. the uptake phase was the next step in system prototyping. a web-based system was used to connect four data sources to imoji. role-based access control (rbac) was used to ensure data security. to increase the data value, geospatial and mtb genotype data were integrated to identify potential hotspots of mdr-tb transmission. it is expected that the imoji system would facilitate case-area targeted interventions in response to mdr-tb outbreaks. however, the impact depends on how effectively the system could deliver this hotspot and mtb molecular linkage information to authorities in the tb control program. molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi figure 1: imoji system prototyping scheme aligned with the 12-step data value chain. system architecture and user flow diagram users were categorized on the basis of their usage of the four user interface modules: • patient registration module – user: health facilities staff, e.g., tb clinic and the office of disease prevention and control (odpc) • sample registration module – user: sample inventory staff • laboratory data entry and data analysis module – user: mtb genetics laboratory analyst • verify and approve analysis data – user: data reviewer concerning data privacy and security, rbac was used to manage permissions. each user group had its own set of access rights to a particular module and could view and edit only data accessible in that module (figure 2). visualization of the summary results from the data analysis is the only information shared across modules. in this design, to protect patient privacy, the patient address was automatically transformed into xy coordinates at the sub-district level. this coordinate data was then integrated with mtb genotype data for clustering analysis. molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi figure 2: imoji process model mapped to four application modules (patient registration, sample registration, laboratory data entry and analysis, verification and approval of analyzed data). the imoji system was designed according to the above prototyping scheme. four distinguished information sources were linked to imoji via the web-based system as show in figure 3. initial system testing simulated tb cases information were entered into patient registration module as shown in figure 4. the inputs were applied for generating xy coordinates and new sample record correctly as shown in figure 5. these results demonstrated that an intramodular connectivity functioned properly without error. molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi figure 3: high-level imoji system architecture. figure 4: patient registration module molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi figure 5: data table with generated data from patient information module. the sample records were sent to a sample registration module which facilitated communication between modules using sample id and dmsc sample id (figure 6). genomic data were entered into a laboratory data entry and data analysis module to integrate with geographic information system (gis) data from the sample module for clustering analysis. these results indicated that intermodular connectivity was established successfully without error. figure 6: data table with information links between modules. molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi discussion establishing information systems to support the control of mdr-tb outbreaks is the cornerstone of public health practice. the imoji system prototype was developed to meet the needs of diverse users related to molecular epidemiology and surveillance program in thailand. due to social stigma, person with tb disease always conscious about their privacy [16]. the system requirements analysis suggested that the use of tb no. and laboratory no. was applicable to personal data protection. in addition, rbac was applied to restricting module access to authorized users. the initial system testing results demonstrated that the imoji prototype actually performed without error. however, the prototype system has limitations of data querying and visualization of the data in an easy-to-understand format for communication with the tb control program. visualization of the information is an important issue that should be addressed to improve usability of the system [17]. the imoji system prototype accomplished the first seven steps of 12-step data value chain. to achieve the last five steps, further system development is needed. for example, online and mobile devices for data collection are important tools for scale-up phase in tb outbreak control, involving forecasting using patient-level data scenario simulations for response planning. furthermore, mandatory features such as response monitoring dashboards, statistical modeling of the intervention strategies, and risk assessment of the surrounding areas are required for tb surveillance [18]. understanding and interpreting molecular cluster information of mdr-tb cases are crucial to leverage usability of the imoji system prototype. finally, tb control program’s recognition of usefulness is a significant measure of effective application of the developed system. conclusion in this study, we described “imoji”, an information system, that was capable of integrating genomic data and gis data to detect mdr-tb cluster on molecular level. testing the system on the simulated dataset provided a proof of concept for determining molecular epidemiological links of mdr-tb and corresponding spatial information to guide public health interventions for tuberculosis control. acknowledgements we are grateful to the growing expertise in e-health knowledge and skills (geeks) training program for critical perspective on the important concepts such as the information value cycle, problem solving for data storage and retrieval processes (informatics) and the boundary spanning nature of informatics work. the program is launched and funded by the u.s. government’s center for disease control and prevention (cdc) in collaboration with mahidol university and the thailand ministry of public health. the study was oversighted by the institutional review board of the institute for the development of human research protections. molecular epidemiological information system to support management of multidrug-resistant tuberculosis in thailand 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e5, 2020 ojphi financial disclosure this work has been co-funded by centers of disease control and prevention (cdrf agreement no. tbnx-17-63098-1) and department of medical sciences, ministry of public health, thailand. competing interests no competing interests supplementary supplementary 1 user manual: integrated mol outbreak detection and joint investigation (imoji) (prototype) references 1. walker tm, ip clc, harrell rh, evans jt, kapatai g, et al. 2013. whole-genome sequencing to delineate mycobacterium tuberculosis outbreaks: a retrospective observational study. lancet infect dis. 13(2), 137-46 pubmed https://doi.org/10.1016/s1473-3099(12)70277-3. 2. jiraphongsa c, wangteeraprasert t, henpraserttae n, sanguanwongse n, 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decision makers can use quantitative approaches to guide outbreak responses. philos trans r soc lond b biol sci. 374, 20180365 pubmed https://doi.org/10.1098/rstb.2018.0365. 19. jie z, ho m, bin c, chee e, ong rt, et al. 2018. international journal of infectious diseases investigation of a cluster of multi-drug resistant tuberculosis in a high-rise apartment block in singapore. int j infect dis. 67, 46-51 pubmed https://doi.org/10.1016/j.ijid.2017.12.010. 20. chee cbe, gan s-h, ong rt, sng l-h, wong cw, et al. 2015. multidrug-resistant tuberculosis outbreak in gaming centers, singapore, 2012. emerg infect dis. 21(1), 179-80 pubmed https://doi.org/10.3201/eid2101.141159. https://doi.org/10.1016/0005-1098(69)90008-9 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26998830&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26998830&dopt=abstract https://doi.org/10.1371/journal.pone.0151321 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=31104605&dopt=abstract https://doi.org/10.1098/rstb.2018.0365 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29253709&dopt=abstract https://doi.org/10.1016/j.ijid.2017.12.010 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25531726&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25531726&dopt=abstract https://doi.org/10.3201/eid2101.141159 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts use of syndromic surveillance information for expanded assessment of wildfire disaster jeffrey johnson*1, michele ginsberg1, nancy french2, brian thelen2 and benjamin koziol2 1san diego county public health services, san diego, ca, usa; 2michigan tech research institute, ann arbor, mi, usa objective this presentation describes how syndromic surveillance information was combined with fire emission information and spatio-temporal fire occurrence data to evaluate, model and forecast climate change impacts on future fire scenarios. introduction syndromic surveillance information can be a useful for the early recognition of outbreaks, acute public health events and in response to natural disasters. inhalation of particulate matter from wildland fire smoke has been linked to various acute respiratory and cardiovascular health effects. historically, wildfire disasters occur across southern california on a recurring basis. during 2003 and 2007, wildfires ravaged san diego county and resulted in historic levels of population evacuation, significant impact on air quality and loss of lives and infrastructure. in 2011, the national institutes of healthnational institute of environmental health sciences awarded michigan tech research institute a grant to address the impact of fire emissions on human health, within the context of a changing climate. san diego county public health services assisted on this project through assessment of population health impacts and provisioning of syndromic surveillance data for advanced modeling. methods various historical data sets were used during this study. these included: emergency department syndromic surveillance from 17 hospitals, as well as air quality and particulate matter, meteorological, wildland burn fuel, and atmospheric dispersion data. the study area was san diego county. these data were linked temporally and spatially to create statistical models based on selected modeling approaches including generalized additive modeling. future fire frequency was modeled for the entire region to determine the impacts of climate change on future fire and health outcomes. modeling based upon previous fire occurrence was used to develop models for future fire indexes, risk of ignition, potential burning, and fire weather. results several models were developed to produce expected respiratory health impacts under future climate conditions for the san diego county region. model results showed that at peak fire par-ticulate concentrations, the odds of a person seeking emergency care increased by approxi-mately 50% compared to non-fire conditions. also developed was a model to forecast future fire occurrence based on regional climate model predictions. this forecast covering the next the next three decades reveals that san diego county will experience approximately two extreme fire seasons each decade by 2040. conclusions syndromic surveillance data are useful during disasters for situational awareness. these data may also provide value for post-disaster analytic work and predictive modeling for future disas-ters. this study demonstrates utility of syndromic data for collaborative work resulting in better understanding of environmental interactions on human health. we do know that wildfire oc-curs with some degree of historic regularity and these results may be useful for preparedness planning. we also know the conditions which must be present for medium to high wildland fire impact upon the population. this study further supports the notion that agencies should be able to strategically deploy resources and messaging immediately preceding the fire period with the goal of reducing human health risk factors and encouraging changes in community behaviors before, during and after a fire. future fire model shows san diego county will experience approximately two extreme fire seasons each decade by 2040. this project also promoted collaboration between public health and environmental health entities to better understand de-terminants of health during a disaster. in addition to developing a better understanding of the consequences of climate change on fire-induced respiratory illness, the project funding has provided support for san diego county to improve their syndromic surveillance capacity and infrastructure. keywords syndromic; disaster; forecasting; wildfire; san diego acknowledgments this work has been supported by the nih-niehs climate change grant #1rc1es018612-01. we would also like to acknowledge the research teams at michigan tech research institute, michigan technology university, and the university of maryland department of geography. *jeffrey johnson e-mail: jeffrey.johnson@sdcounty.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e95, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts on the implementation of the biological threat reduction program in the republic of uzbekistan laziz tuychiev*1 and marifjon madaminov2 1sanitary-epidemiological department of ministry of health, tashkent, uzbekistan; 2center for prophilaxis and quarantine of most hazardous infections of uzbekistan, tashkent, uzbekistan objective to review the implementation of the biological threat reduction program (btrp) of the u.s. defense threat reduction agency in the republic of uzbekistan since 2004. introduction the biological threat reduction program (btrp) has been being implemented in the republic of uzbekistan since 2004 within the framework of the agreement between the government of the republic of uzbekistan and the government of the united states of america concerning cooperation in the area of the promotion of defense relations and the prevention of proliferation of weapons of mass destruction of 06.05.2001. threat agent detection and response activities that target a list of especially dangerous pathogens are being carried out under the btrp within the health care system of uzbekistan. this presentation reviews some of the achievements of the program to date. results btrp, in partnership with the government of uzbekistan, has funded the establishment of five regional diagnostic laboratories (rdl) and ten epidemiological support units (esu), operated by the ministry of health of uzbekistan, which are intended to improve the diagnosis of quarantine and especially dangerous infections, and to ensure timely preventive and anti-epidemic measures. rdls provide a high level of biosafety and biosecurity to conduct rapid laboratory diagnostics (pcr, elisa) of especially dangerous infections. rdls are equipped with up-to-date diagnostic laboratory equipment that conforms to internationals standards, as well as with all necessary consumables. personnel of rdls have been appropriately trained in epidemiology, clinical and diagnostic techniques for especially dangerous infections, including such state-of-the-art techniques as rapid pcr and elisa diagnostics, as well as in work and equipment operation safety regulations. epidemiological support units (esu) have been established on the basis of the especially dangerous infections divisions of oblast, city and rayon centers for state sanitary and epidemiological surveillance (ses) of the ministry of health. the btrp esu efforts include renovation activities, supply and installation of appropriate equipment for rapid laboratory diagnostics, and vehicles. esus are meant to ensure emergency notification in cases of suspected occurrence of quarantine and especially dangerous infections, for timely implementation of anti-epidemic and preventive measures. three cooperative biological research projects on quarantine and especially dangerous infections have been implemented within btrp with the financial support of the us civilian research and development foundation (crdf). to ensure the sustainability of training and availability of a pool of skilled personnel, a training laboratory is to be established at the tashkent institute for post-graduate medical education (tipme) to train personnel of rdls and esus. the training laboratory will fully replicate the setup of a bsl-2 regional diagnostic laboratory, but will maintain no operations with live pathogens. conclusions the implementation of btrp within the health care system of the republic of uzbekistan contributes to the stable and sustainable wellbeing in the population of the country. keywords biological threat reduction program; especially dangerous pathogens; resource-poor setting *laziz tuychiev e-mail: laziz.tyuchiev@minzdrav.uz online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e177, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts nontraumatic oral health classification for alternative use of syndromic data sherry burrer*1, howard burkom1, christopher okunseri2, laurie barker1 and valerie robison1 1centers for disease control and prevention, atlanta, ga, usa; 2marquette university, milwaukee, wi, usa objective to develop a nontraumatic oral health classification that could estimate the burden of oral health-related visits in north carolina (nc) emergency departments (eds) using syndromic surveillance system data. introduction lack of access to regular dental care often results in costly, oral health visits to eds that could otherwise have been prevented or managed by a dentist (1). most studies on oral health-related visits to eds have used a wide range of classifications from different databases, but none have used syndromic surveillance data. the volume, frequency, and included details of syndromic data enabled timely burden estimates of nontraumatic oral health visits for nc eds. methods literature review, input by subject matter experts (smes), and analysis of syndromic data was used to create the nontraumatic oral health classification. biosense, a near real-time, national-level, electronic health surveillance system was the source of the nc ed syndromic data. visits with at least one oral health-related icd-9-cm code were extracted for nc fiscal years 2008–2010. univariate analyses of chief complaint (cc) and final diagnosis data along with sme consultation were used to determine the cc substrings and ‘white list’ of icd-9-cm codes used as inclusion criteria to classify visits as oral health-related. these analyses and consultations also determined the trauma-related codes and substrings used to exclude visits. results table 1 shows all nontraumatic oral health-related icd-9-cm codes used for the characterization. codes likely related to the types of dental emergencies that routine dental care could not have prevented were excluded. approximately 275,000 patient records were evaluated to determine the cc substrings. the final cc substrings chosen (table 1) represented over 56% of visits in the candidate record dataset. over 334,000 biosense patient records were evaluated, and smes reviewed the 32 icd-9-cm codes that co-occurred most commonly in visits containing oral health-related icd-9-cm codes to determine which co-occurring icd-9-cm codes (white list, table 1) could be present and still maintain the main reason for the visit as an oral health-related problem. trauma-related visit criteria used for exclusion were derived from a subset of biosense sub-syndromes (falls; fractures and dislocation; injury, nos; sprains and strains; and motor vehicle traffic accidents) and from selected cc substrings (‘assault’, ‘fight’, and ‘brawl’). in summary, an ed visit had a nontraumatic oral health classification if it contained 1) an oral health-related cc substring with no trauma-related icd-9-cm codes or cc substrings or 2) an oral health-related icd-9 code accompanied by no oral health-related or trauma-related cc substrings and with no other diagnosis codes except for those on the whitelist. conclusions there is increasing demand to determine ways to use syndromic surveillance data in an alternative way for population health surveillance. this use of biosense data provided a practical classification of patient records for the tracking of nontraumatic oral health-related visits to nc eds. visit estimates created using this classification in combination with other pertinent information could prove useful to policymakers when deciding upon resource allocation aimed at reducing this unnecessary burden on the nc ed system. the large volume of records in syndromic surveillance systems offers substantial weight of evidence for alternative use in epidemiological studies; however, accurate classification of records is required to select cases of interest. while data volume precludes validation of every included record, a combination of human expertise and data analysis can provide credible classification criteria. table 1. inclusion criteria for nontraumatic oral health classification x = includes all numbers under this icd-9-cm subheading *except 528.3 and 528.5 **most common misspelling of abscess keywords syndromic surveillance; oral health; emergency departments acknowledgments amy ising, nc detect; lana deyneka, nc dhhs; and rebecca king, nc dhhs references 1. davis ee, deinard as, maïga ew. doctor, my tooth hurts: the costs of incomplete dental care in the emergency room. j public health dent. 2010 summer;70(3):205-10. *sherry burrer e-mail: sburrer@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e58, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts fast multidimensional subset scan for outbreak detection and characterization daniel b. neill* and tarun kumar event and pattern detection laboratory, carnegie mellon university, pittsburgh, pa, usa objective we present multidimensional subset scan (md-scan), a new method for early outbreak detection and characterization using multivariate case data from individuals in a population. md-scan extends previous work on multivariate event detection by identifying the characteristics of the affected subpopulation, and enables more timely and accurate detection while maintaining computational tractability. introduction the multivariate linear-time subset scan (mltss) [1] extends previous spatial and subset scanning methods [2-3] to achieve timely and accurate event detection in massive multivariate datasets, efficiently optimizing a likelihood ratio statistic over proximity-constrained subsets of locations and all subsets of the monitored data streams. however, some disease outbreaks may only affect a subpopulation of the monitored population (age group, gender, individuals engaging in a specific high-risk behavior, etc.), and mltss is unable to use this additional information to enhance detection ability. methods rather than using the aggregate counts for each monitored location and data stream, we assume a set of multivariate data records representing each affected individual, with attributes such as date, home zip code, prodrome, gender, and age decile. md-scan jointly optimizes the likelihood ratio statistic over subsets of the values for each monitored attribute, identifying a space-time region (subset of locations and time steps) and subpopulation (including gender(s) and age groups) where the number of recent cases for a subset of the monitored prodromes is significantly higher than expected. to do so, the linear-time subset scanning property [3] is used to efficiently and exactly optimize over subsets of a given attribute, conditioned on the current subsets of all other attributes. md-scan then iterates over all attributes until convergence to a local optimum, and performs multiple random restarts to approach the global optimum. additional constraints can be incorporated into each conditional optimization step, including spatial proximity, temporal contiguity, and connectedness. more details are provided in [4]. results we evaluated md-scan using simulated disease outbreaks injected into real-world emergency department data from allegheny county, pa. each outbreak was assumed to differentially affect a specific subpopulation (e.g. “adult females” or “children and the elderly”). mdscan achieved significantly earlier detection than mltss when the distribution of injected cases for the monitored attributes was sufficiently different from the background data, particularly when multiple attributes were affected or the inject was biased toward a less common attribute value. for simulated gender-specific and age-biased injects which affected only children and the elderly, md-scan detected over one day faster than mltss, and achieved 10% higher spatial accuracy. md-scan was also able to accurately identify the affected age and gender groups (figure 1), while mltss does not characterize the affected subpopulation. runtime of md-scan, while 9x slower than mltss, was still extremely fast, requiring an average of 4.15 seconds per day of data. conclusions our results demonstrate that md-scan is able to accurately identify the subpopulation affected by an outbreak, as represented by a subset of values for each monitored attribute. additionally, md-scan substantially improves timeliness and accuracy of detection for outbreaks which differentially affect a subset of the monitored population. detection performance was further enhanced by incorporating additional constraints such as spatial proximity and graph connectivity into the iterative md-scan procedure. keywords event detection; disease surveillance; scan statistics acknowledgments this work was partially supported by nsf grants iis-0916345, iis0911032, and iis-0953330, and a upmc healthcare technology innovation grant. references [1] neill db, mcfowland e, zheng h, fast subset scan for multivariate spatial biosurveillance. emerging health threats journal, 2011, 4: s42. [2] kulldorff m, a spatial scan statistic. communications in statistics: theory and methods, 1997, 26: 1481-1496. [3] neill db, fast subset scan for spatial pattern detection. j. royal statistical society b, 2012, 74: 337-360. [4] kumar t, neill db, fast tensor scan for event detection and characterization. submitted. *daniel b. neill e-mail: neill@cs.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e91, 2013 ojphi-06-e22.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 (page number not for citation purposes) isds 2013 conference abstracts cervical cancer knowledge,screening service utilization and predictors of precancerous cervical changes: a population based survey of sexually active women in lagos, south western nigeria olawunmi o. adeoye*1, 2, olufunmi fawole3, 2, ike ajayi3 and patrick nguku2 1university college hospital, ibadan, nigeria; 2nigeria field epidemiology and laboratory training programme, abuja, nigeria; 3department of epidmiology and medical statistics, uch, ibadan, nigeria � �� �� �� � � �� �� �� � objective �������� ������������ ��� ��������������� �������� ����������� �� ����������������� ����������� ��� ��������� �������� �� � ���� ������ �� ����� ������ ��������������������������� ������������������������ ���� ��� introduction � ����������� ����������������������������� !� �� ��� ��� ����� � ��������������� ��������������� ������ ���������� ������������� ����� ����������������� ����"������� ������������� �������� ������ ����� ���� ����������� ��� ��������� �������� �� ������ ������ ������ ��������������� ���� ��������� �!��� ��������������� ��� �������������� !��� ���� methods ������� ����������������!��� ��� ����������������� �������#������ ���������������������$���������� �����������%%&�������� ����� ��� �������������'��� ������# ����(�'#�)����������*������*�� ��� ���� ��������� ���������!����+,� �� ��������������� 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��������������������� keywords � ����������� <������� ��<������������<������<�3 ������ ������ ������ ������� references +��#e� ��/��# ������/��f����� ����� �#����� �����'��� ���4���������� #���� ����� ������� ��� ��� � ������ ���� �� ����#� � g�"� �+88.� h�!<--(&)c-+�%� &��/#5 �'�4i4 #���/#5 �>�� !�������� ���� ��3 ���������� � ���� ���� �*� ��������� ����h ����c�/��� ���������#���� �5���� ������ ���� ��&77.� %���������g��4���4e������ ��f��4���#e� ��/��4���4���!� ���#��4���h������ 4��/��j�4�� ����4���3����������i��� �3 ������������#!�� ���� � � ������ ����� �h�� ������� �������5����h���� �����/!� �������� ��c� /�������������� � � ������ ���� � ��� ���3 �� ������ �>�����3����� �����0� ���/�������������#���� �����&7+&��,=��&,+�.� *olawunmi o. adeoye e-mail: wunmiolat@yahoo.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e22, 2014 abstract editorial: vol 2, no 3 (2010) 1 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 editorial: vol 2, no 3 (2010) the online journal of public health informatics (ojphi) was developed in recognition of the fact that, up until december, 2009, there was no journal dedicated to the dissemination of information about best informatics practices among public health practitioners, researchers, and educators. the ojphi has attracted over 200 registered researchers, educators, policy makers, and practitioners from major universities, research institutes, and health care and public health agencies. our current record indicates that the journal has a bright future as exemplified by the quality of the papers submitted for publication. the current issue contains interesting papers on topics ranging from the infoshare, crowdsourcing, to the application of twitter to crisis management. in the introductory article, entitled, “infoshare: an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak” the authors develop real-time electronic disease surveillance system for monitoring the health of a population during mass gatherings or special events covering multiple jurisdictions. major public events with attendees from multiple regions, such as the super bowl, the olympics, and presidential inaugurations, are examples of events that can benefit from cross-jurisdictional electronic surveillance to assess the health risks for the gathered and returning populations. in the next paper, entitled” utility of the essence surveillance system in monitoring the h1n1 outbreak” the authors compare emergency department visits for influenza-like illness from 2008 to 2009 in the national capital region. information generated from the monitoring process enabled local public health practitioners to have an enhanced understanding of the magnitude of different ili outbreaks in their jurisdictions, leading to the development of improved response and control measures for the novel 2009 h1n1 influenza outbreak. the next paper entitled “agent based modeling of „crowdinforming‟ as a means of load balancing at emergency departments” develops a framework for modeling the outbreak of influenza-like illness (ili) with accompanying surges in hospital emergency departments. the method presented in this paper employs “crowdinforming”, a component of “crowdsourcing” technology, to disseminate collected and processed information to the “crowd” via public access. the aim of this technology is to provide the public with reliable information for improved decision making about their visits to emergency departments during disease outbreaks. advances in information technology and software development have made it cost-effective to provide powerful and flexible analytic capability to local public health units. in the paper entitled “nc catch: advancing public health analytics” the authors develop a web-based analytical system for local public health units and their community partners in north carolina. the system provides the public health units the ability to engage in outcomes-based performance editorial: vol 2, no 3 (2010) 2 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 measurements at the local level and the information required to develop appropriate programs to improve community health. in recent years several public health educational institutions and government agencies have developed web-based training programs for first responders and others likely to be involved in the management emergency situations. in general, the methodologies for evaluating the success of these online programs have been based on the traditional face-to-face models. in the final peer-reviewed paper of this issue, dr.priya nambisan develops a methodology for evaluating online emergency preparedness courses that draws on theories of human computer interaction, distance learning, usability research, and online consumer behavior. the study outlines strategies for improving participants‟ pragmatic, hedonic, sociability, and usability experiences in an online technological environment. in the final paper of this issue, published in the working papers section of the journal, dr. lisa gualtieri of tufts university school of medicine describes the use communication channels ranging from twitter, text messages to megaphones, to provide information to the citizens of greater boston when a major water pipe break interrupted water services. the emergency planning processes, the response strategies, and the lessons learned in this crisis are presented in this paper. this is the final issue (volume 2, issue 3) of the online journal of public health informatics in 2010. by all indications, this has been a challenging but worthwhile exercise. many thanks to the editors, the journal manager, and all the volunteers who have been working so tirelessly to make this project a success. through your hard work, the field of public health informatics now has a journal solely dedicated to the dissemination of information about best practices and latest research to our stakeholders. happy holidays!! edward mensah, phd editor-in-chief online journal of public health informatics 1603 w taylor st, rm 757 chicago. il. 60612 email: dehasnem@uic.edu office: (312) 996-3001 mailto:dehasnem@uic.edu population segmentation using a novel socio-demographic dataset ojphi population segmentation using a novel socio-demographic dataset elisabeth l. scheufele, md, ms1; brandi hodor, bs2; george popa, jr., ms, mhsa3; suwei wang, phd3; william j. kassler, md, mph4 1 boston children's hospital, boston, ma 2 merative, ann arbor, mi 3 ibm watson health, cambridge, ma 4 palantir technologies, denver, co abstract appending market segmentation data to a national healthcare knowledge, attitude and behavior survey and medical claims by geocode can provide valuable insight for providers, payers and public health entities to better understand populations at a hyperlocal level and develop cohort-specific strategies for health improvement. a prolonged use case investigates population factors, including social determinants of health, in depression and develops cohort-level management strategies, utilizing market segmentation and survey data. survey response scores for each segment were normalized against the average national score and appended to claims data to identify at-risk segment whose scores were compared with three socio-demographically comparable but not at-risk segments via nonparametric mann-whitney u test to identify specific risk factors for intervention. the marketing segment, new melting point (nmp), was identified as at-risk. the median scores of three comparable segments differed from nmp in “inability to pay for basic needs” (121% vs 123%), “lack of transportation” (112% vs 153%), “utilities threatened” (103% vs 239%), “delay visiting md” (67% vs 181%), “delay/not fill prescription” (117% vs 182%), “depressed: all/most time” (127% vs 150%), and “internet: virtual visit” (55% vs 130%) (all with p<0.001). the appended dataset illustrates nmp as having many stressors (e.g., difficult social situations, delaying seeking medical care). strategies to improve depression management in nmp could employ virtual visits, or pharmacy incentives. insights gleaned from appending market segmentation and healthcare utilization survey data can fill in knowledge gaps from claims-based data and provide practical and actionable insights for use by providers, payers and public health entities. keywords: public health, social marketing, health care survey, marketing segmentation, social determinants of health. abbreviation: designated marketing area (dma), random digit dialing (rdd), index of concentration (ioc) doi: 10.5210/ojphi.v14i1.11651 correspondence: brandi hodor, merative, 100 phoenix drive, ann arbor, mi, 48108, 517-281-9336 bhodor@merative.com copyright ©2022 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:bhodor@merative.com population segmentation using a novel socio-demographic dataset ojphi introduction for decades, public health and healthcare leaders have advocated for the use of population segmentation and other techniques borrowed from the practice of marketing to improve delivery of services through more targeted approaches.1,2 in the commercial sector, marketing uses segmentation to design and target products and services to meet the specific needs and desires of a particular group of consumers. however, to improve the effectiveness of programs and messages, social marketing in the health sector would involve the use of segmentation to more precisely tailor interventions and outreach to specific sub-populations. marketing has achieved success in changing consumer behavior by using segmentation models that include: knowledge, attitudes and beliefs; past behaviors; social norms and culture; and psychological characteristics such as needs, wants, values and lifestyle, readiness to change and future intentions. while targeting interventions based on traditional demographic characteristics (e.g., age, race, gender, educational level, income) reflects the basic tenants of epidemiology, social marketing proposes to refine that practice using a number of more personalized characteristics related to health in general to the specific outcome or behavior being sought. however, in spite of its potential, population segmentation remains underutilized in public health and healthcare, in part because of the lack of pertinent data and analytic approaches.3 this paper describes a novel approach for population segmentation, based on appending the geocoded responses from a national survey on health-related knowledge, attitudes and beliefs, with commercial market segmentation data containing sociodemographic data at the block group level. we further illustrate the additional benefits of linking these survey and sociodemographic data to administrative claims containing individual-level information on healthcare process and outcomes with a prolonged use case on the public health concern of depression. in the us, people report suffering from depression at about a rate of 7-8%.4,5 depression also affects the workplace, as 27% of those affected report trouble with work or home.4 the world health organization reports that in the us, every dollar used to treat depression results in 4 dollars of positive impact in productivity and health.6 businesses should invest in supporting the mental health of their employees as good mental health is also associated with optimizing employee workrelated output and their overall well-being. the use case will address the issue of depression, both a major public health and workplace concern, by using the insights from pulse® and prizm® to support better characterization of the population of concern, uncover barriers and opportunities for care, and organize outreach and management strategies to bridge gaps and overcome barriers to better mental health management. methods the survey the pulse® healthcare survey is a nationwide household survey reflective of health-related behaviors, attitudes and health care utilization habits of people in the united states, conducted annually since 1988. the survey has two components: core topics and survey topics. core topics remain standard from year to year and include health status (e.g., self-perceived health status), insurance coverage, and demographics (e.g., respondent age, respondent gender, population segmentation using a novel socio-demographic dataset ojphi household composition). survey topics and related questions are changed annually to reflect current topics of interest to a variety of stakeholders. in total, there are about 100 survey questions spread across the numerous topics. for instance, the 2019 pulse survey topics included: primary care utilization and access, social determinants of health, telemedicine, attitudes and factors influencing physician selection, mental health status, and avoiding healthcare. the pulse survey starts each year in january with the pretest period and then is followed by 11 subsequent monthly waves. during each wave, a subset of the topics is surveyed. the pretest period serves to determine: the survey length, the response rate, and the comprehension of the question by the respondent. all the questions are tested on 2000 participants during the pretest. the information gleaned from the pretest informs the selection of a subset of survey questions, how they are distributed over the 11 waves, and organized so the survey can be executed in 11 minutes on average. auditing is performed to ensure a threshold of 2000 (affirmative) responses has been achieved for each question to provide an appropriate level of reliability in survey output. to attain the primary goal of 80,000 respondents, 10 waves (march through december) have a threshold of 7250 participants and february has a threshold of 7500 which includes the january responses. the survey is multi-modal, and performed via landline telephone, with the addition of cell phones and internet modalities in 2013. the distribution of communication methods is 50% via phone (with 90% via landline, and 10% via cell), and 50% via the internet with sample matching, which is an internet survey method to allow incorporation of survey answers from engaged rather than inattentive on-line participants. to ensure that distribution across the country is proportional to the percentage of households by the designated marketing area (dma), random digit dialing (rdd) is employed which makes sure that the area code and the exchange portion of the number are tracked to ensure coverage, but the last four digits are randomized to obtain a location-controlled randomized sample. the rdd process results in about 60% of the survey respondents, and participants from previous pulse surveys make up the remaining 40%. the fielding is performed by a third-party vendor, who performs the interview portion of the survey exercise and delivers the survey dataset. the market segmentation system the claritas prizm® premier segmentation system is a market and consumer segmentation model that has been used by commercial entities to enhance their marketing and messaging strategies since the mid-1970s. prizm® premier provides geo-demographic data by combining insights from data on consumerism behaviors with geographic data, down to the household level. the market segmentation system is developed from several data sources that include the us census and the supplementary census data, provided via “the american community survey.” the data undergoes proprietary regression modeling called “multivariate divisive partitioning,” which is a modified classification and regression tree method that allows partitioning to go across numerous elements to result in distinct behaviorally similar clusters.7 the resultant 68 distinct nodes become the market segments, which are given descriptive names for more facile reference (e.g., “new melting pot”, “generation web”). claritas also employs a modeling process to establish urbanicity classes by defining the concept of urbanicity to improve the distinction population segmentation using a novel socio-demographic dataset ojphi among geographic regions that may have similar population density but differentiate by urban vs rural type of experience. the current version of 68 prizm premier market segments are divided into 11 lifestage groups and 14 social groups, of which then roll up into 3 lifestage classes and 4 urbanicity classes, respectively (see figure 1). the social groups are based on urbanicity and affluence, where the latter is defined by income, education and home value, with examples including u1 urban uptown or u2 midtown mix. these social groups roll up into 4 urbanicity classes, including urban, suburban, second city, and town & rural. two examples of market segments that roll up into social groups are generation web in city centers and new melting pot in micro-city mix, all of which roll up to the second city urbanicity class.7 (these social groups and market segments will be revisited in the use case below). figure 1: claritas market segments distributed by urbanicity and affluence. reprinted with permission from claritas®, cincinnati, ohio, united states. population segmentation using a novel socio-demographic dataset ojphi appending marketing segmentation to survey results appending the segmentation to the survey involves post-survey processing that includes weighting, appending by block group, and tabulating response rates. a weighting protocol is applied by the third-party vendor to the demographic data and to the responses by household and by individuals, to represent national demographics. as the survey responses include the respondent address, the segmentation data are then appended to the survey responses by geocode. an age-cluster matrix of rates of the geocoded responses for each question are then tabulated for each of the more than 217,000 us block groups. these calculations are further broken down by age of head of household who answered the survey questions, to align with the census data [age ranges of < 35, 35-64, >=65] to further calculate the response score by age group and block group. the index of concentration (ioc) is calculated once the market segments are appended to the survey responses. ioc is the ratio of the proportional responses by market segment clusters against the proportional responses of the national average. for example, consider that the national affirmative response rate to a question on whether the participant smokes is 20%. if the response rate for cluster a aggregates to 15%, and the response rate to cluster b aggregates to 25%, then the ioc for cluster a would be 15%/20%, or 75, and for cluster b would be 25%/20%, or 125. this would be interpreted as cluster a is 25% less likely to smoke than the national average, and cluster b is 25% more likely to smoke than the national average. the ioc scores are centered to zero instead of 100%. thus, if the ioc score is 125, the discussed score will be reported as “25% above the national average.” analysis claims data from an employer dataset with 19,616 members from a midwest location were appended to the pulse and prizm data at the block group level. an at-risk market segment was identified for study as the one with the highest affirmative score for pulse response for depression and low claims utilization for depression. then three segments in the same urbanicity class were identified, which would indicate some similarity in social demographic details, who have high utilization by claims for the diagnosis of depression. the at-risk segment was compared to the three comparable segments to identify potential reasons for the higher number of medical encounters on depression by the three comparable segments, implying better medical management for chronic depression. the mann-whitney-u test was applied to the original pulse survey ioc scores of the at-risk segment against the median scores of comparable segments to identify pulse responses that are statistically significant in their differences. then the appended survey and market segmentation data was reviewed to better characterize the circumstances of each segment and identify specific barriers to healthcare which can be used to direct the design and targeting of interventions. human participant compliance this study was reviewed and granted an exemption determination by the western institutional review board. population segmentation using a novel socio-demographic dataset ojphi results use case: characterizing an at-risk population, and developing public health strategy from appending claims data to the pulse/prizm dataset second city, one of the urbanicity groups, was initially explored and one of its market segments, new melting pot (with 334 members), was found to have the highest affirmative survey response for “being depression most of the time” (49% higher than the national average). this market segment was compared with three others who had the highest health care utilizations by encounters for the diagnosis of depression in the social group. selected data on behaviors and affluence from the prizm market and selected responses from the pulse survey as they apply to the use case of addressing depression were further investigated. the claims for all members demonstrated an average of 4.9 members per 100 with a diagnosis of depression in a 12-month period (july 1, 2018 to june 30, 2019). rates of depression were over 30% higher than the average for american classics (6.7 members per 100 with encounters for depression) and bright lights, li’l city (6.5 members per 100). the extremes were observed with generation web, who had a rate of 8.3 members per 100 with encounters for depression, which was 68% higher than the average, and new melting pot, who had a rate of 4.8 which was 2% less than the average, indicating that new melting pot had the least members per 100 with claims for the diagnosis of depression of these four segments. the contrast in having the highest survey response in reporting depression but with the lowest utilization rate via claims data could indicate that the new melting pot segment may have some unmet needs when it comes to mental health management. low healthcare utilization can also suggest barriers to access. when the same populations were analyzed with the pulse and prizm data, additional information can provide context to the situation in further insight into the subpopulations. in particular, the pulse responses collected for this use case included those that provide value to characterizing the cohort at risk. these responses were evaluated via nonparametric mann-whitney u test to detect significant differences between new melting pot vs the other three segments which provide insight into why they had higher reports of depression but less utilization than comparable segments and how to bridge those gaps (see table 1). appending market segments to survey responses provided additional location-based perspectives (see figure 2). in the analysis for depression, a focused subset of information from the pulse survey brought to light some of the possible reasons why new melting pot would have a higher score for being “depressed most of the time” when compared to the other 3 segments. new melting pot was associated with reports of numerous concerns, including “worrying about food” (123% higher than the national average) and “utilities being threated to be turned off” (138% higher), but also the “safety of their neighborhood” (93% higher), all of which could impact or exacerbate their symptoms of depression while at the same time could make it difficult to focus on their healthcare. understanding the types of drivers of stress may also explain disparities at the neighborhood level, and can allow public health, employer or health plan organizations to better understand and characterize these targeted populations and the households they were living in. population segmentation using a novel socio-demographic dataset ojphi table 1: nonparametric mann-whitney u test comparing median of three comparable segments against segment at risk. the test performed on the original ioc values for the pulse responses (p-value compares the new melting pot segment with the median value of the other three segments bright lights, li'l city, generation web, and american classics). cluster median new melting pot p-value ability to pay for basic needs somewhat hard 121 125 < 0.001 lack of transportation affected daily living 112 153 < 0.001 utilities threatened to turn off yes 103 239 < 0.001 delay visiting md next 3 months highly likely 67 181 < 0.001 delay/not fill a prescription next 3 months highly likely 117 182 < 0.001 depressed: all/most time 127 150 < 0.001 internet: using app/software to manage health 106 123 < 0.001 internet: had virtual visit past 12 months 55 130 < 0.001 neighborhood safe for family strongly disagree/disagree 61 193 < 0.001 struggle put food on table strongly agree/agree 81 102 < 0.001 continuing with the issue of depression, the survey data could help identify barriers to accessing healthcare while the socio-demographic data and health perspective of the market segmentation data provided further insight into the gaps in care. new melting pot reported higher levels of “delay seeing the doctor” (79% higher than the national average), reported more frequently “delaying filling their prescriptions” (81% higher), and reported higher concerns with “lacking transportation” (52% higher) (see figure 2). from the segmentation data, people in new melting pot were associated more often with a high school reading level and with working primarily in the services industry. these insights indicated lack of basic resources for this market segment, such as consistent access to transportation, work schedules that may not be flexible enough to accommodate weekday visits to the doctor, poor access to affordable medications or prescription plans, and potential challenges in health literacy. the survey data can help identify specific factors and drivers not typically available in claims data and can provide additional perspective to help focus on the needs of a cohort at risk. the pulse/prizm dataset can be used to develop strategies for addressing depression management for the at-risk population. as discussed above, new melting pot was a resource low market segment, struggling to manage basic needs including food and utilities, and lacking in transportation options. they also had trouble filling their prescriptions and making it to their doctor’s appointment. however, information from the survey data suggested there may be some options to fill the gap in care. new melting pot reported above average “use of virtual visits in the last 12 months” (28% higher than average), and higher reports of “having used/using an app for health care” (20% higher) (see figure 2). strategies to enable people in new melting pot population segmentation using a novel socio-demographic dataset ojphi might include providing travel vouchers, such as for ride shares, to help with transportation. another option can be to support and provide telehealth access, as this group had reported higher use than average and also was technically inclined. education should be provided at the appropriate level to facilitate comprehension. prescription vouchers programs could be directed to these patients for any medication needs to help with their mental healthcare. organizations can use these insights to better strategize for campaigns to target members that will resonate with their needs and capabilities, moving beyond a blanket approach for healthcare engagement and applying more personalization at the community level. figure 2: four market segments identified to evaluate depression in cohorts. the claims dataset has been appended to prizm market segmentation and to pulse survey responses, with use case pertinent information from each dataset detailed by market segment, and also pulse responses centered to zero for ease of discussion. population segmentation using a novel socio-demographic dataset ojphi discussion this paper presents a unique approach to aggregating information that is often not available in clinical or claims datasets. data from a us annual national healthcare survey appended to a market segmentation model can be further combined with patient-level data by geocode to generate very precise population insights at a hyperlocal level. just as commercial marketing aims to use population segmentation techniques to impact consumer behavior, healthcare and public health can use these techniques to impact health-related behavior. segmentation into precision cohorts can identify groups with specific needs and risks, and furthermore helps to target specific communication messages and outreach strategies that match the targeted community. social marketing applied to health concerns is hard to implement, and these data and analytic methods may provide practical resources to operationalize this approach. information from the pulse/prizm appended dataset can bring sociodemographic details and attitudes and behavior on health and health care of patients at the community level that can help better elucidate the perspectives and barriers of a group of patients. for the illustrated use case of depression, the pulse survey data provided possible reasons for why the group may be depressed, demonstrating that the cohort may lack resources, socioeconomic status and access to care. the prizm marketing data demonstrated that the group is under-resourced with specific barriers to accessing care. knowing that this cohort is amenable to virtual health visits and understanding that this group tends to delay or not see a doctor, suggested the opportunity to overcome these barriers through telehealth visits. applying the pulse/prizm dataset to better understand a patient population is, at its core, an ecological analysis of a cohort of people. consider that the strength of such an analysis is strongest at the population level, where it provides broad knowledge localized to a cohort of people. however, a limitation includes when insights are considered at the individual level. because people tend to self-assort with those who are similar, survey and marketing characteristics are highly representative of the predominant group at the block level. although peers and neighbors may generally have similar or overlapping tendencies and behaviors, any specific individual may not be representative of those generalizations. one of the means by which to address this limitation is the addition of appended patient level claims, which can provide the specificity that is not available from the pulse/prizm dataset alone. the appended super dataset speaks probabilistically to a block group, but there may be individuals in the block group for which this does not apply. thus, caution should be used when generalizing to the individual in the group, which can be mitigated with individual level claims data. conclusion enriching claims data with hyperlocal insight from a market segmentation model and a national health care survey can fill gaps in socio-demographic, behavioral, and attitudinal information not normally found in clinical datasets. public health agencies and healthcare organizations that seek to optimize the health of a population under their purview (e.g., insurers, public health entities, employers) require more information than what is present in commonly available datasets to better understand their patients’ social needs and develop community-level focused strategies on issues of concern. insights from pulse and prizm can be applied to claims data to better characterize population segmentation using a novel socio-demographic dataset ojphi a neighborhood or population group at risk to better understand their vulnerability, identify barriers to management or care, and more precisely target outreach. acknowledgements not applicable financial disclosure no funds, grants, or other support was received. competing interests all authors were employees of ibm watson health at the time this study was conducted. references 1. slater md, flora ja. 1991. health lifestyles: audience segmentation analysis for public health interventions. health educ q. 18(2), 221-33. doi:https://doi.org/10.1177/109019819101800207. pubmed 2. lynn j, straube bm, bell km, jencks sf, kambic rt. 2007. using population segmentation to provide better health care for all: the “bridges to health” model. milbank q. 85(2), 185-208. doi:https://doi.org/10.1111/j.1468-0009.2007.00483.x. pubmed 3. vuik si, mayer ek, darzi a. 2016. patient segmentation analysis offers significant benefits for integrated care and support. health aff (millwood). 35(5), 769-75. doi:https://doi.org/10.1377/hlthaff.2015.1311. pubmed 4. prevalence of depression among adults aged 20 and over: united states, 2013–2016. 2018 [accessed january 19, 2021]; available from: https://www.cdc.gov/nchs/products/databriefs/db303.htm 5. major depression. 2019 [accessed january 19, 2021]; available from: https://www.nimh.nih.gov/health/statistics/major-depression.shtml 6. mental health in the workplace. 2020 [accessed january 19, 2021]; available from: https://www.who.int/mental_health/in_the_workplace/en/ 7. claritas prizm premier methodology 2019, copyright 2018 https://doi.org/10.1177/109019819101800207 https://pubmed.ncbi.nlm.nih.gov/2055779 https://doi.org/10.1111/j.1468-0009.2007.00483.x https://pubmed.ncbi.nlm.nih.gov/17517112 https://doi.org/10.1377/hlthaff.2015.1311 https://pubmed.ncbi.nlm.nih.gov/27140981 https://www.cdc.gov/nchs/products/databriefs/db303.htm https://www.nimh.nih.gov/health/statistics/major-depression.shtml population segmentation using a novel socio-demographic dataset abstract introduction methods the survey the market segmentation system appending marketing segmentation to survey results analysis human participant compliance results use case: characterizing an at-risk population, and developing public health strategy from appending claims data to the pulse/prizm dataset discussion conclusion acknowledgements financial disclosure competing interests references layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts influenza forecasting with google flu trends andrea f. dugas*1, mehdi jalalpour1, yulia gel1, 2, scott levin1, fred torcaso1, takeru igusa1 and richard rothman1 1johns hopkins university, baltimore, md, usa; 2university of waterloo, waterloo, on, canada objective we sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. secondly, we evaluated how the addition of a real-time influenza surveillance system, google flu trends, would impact the forecasting capabilities of this model. introduction each year, influenza results in increased emergency department crowding which can be mitigated through early detection linked to an appropriate response. although current surveillance systems, such as google flu trends, yield near real-time influenza surveillance, few demonstrate ability to forecast impending influenza cases. methods forecasting models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004 – 2011) divided into training and out-ofsample verification sets. forecasting procedures using classical boxjenkins, generalized linear, and autoregressive methods were employed to develop the final model and assess the relative contribution of external variables such as, google flu trends, meteorological data, and temporal information. models were developed and evaluated through statistical measures of global deviance and loglikelihood ratio tests. an additional measure of forecast confidence, defined as the percentage of forecast values, during an influenza peak, that are within 7 influenza cases of the actual data, was examined to demonstrate practical utility of the model. results a generalized autoregressive poisson (garma) forecast model integrating previous influenza cases with google flu trends information provided the most accurate influenza case predictions. google flu trend data was the only source of external information providing significant forecast improvements (p = 0.00002). the final model, a garma intercept model with the addition of google flu trends, predicted weekly influenza cases during 4 out-of-sample outbreaks within 7 cases for 80% of estimates (figure 1). conclusions integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of google flu trends confirming the predictive capabilities of search query based syndromic surveillance. this accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases. figure 1: number of weekly confirmed influenza cases during the verification period (2008-2011) comparing actual data (circles) and values forecasted by the final model [3rd order generalized autoregressive poisson intercept model with google flu trends] (solid line). keywords google flu trends; crowding; surveillance; influenza; forecasting acknowledgments this work was supported by the department of homeland security (pacer: national center for study of preparedness and response [grant number: 2010-st-061-pa0001]); the national science foundation systems engineering and design program [grant number: nsf cmmi 0927207]; and the natural sciences and engineering research council of canada. *andrea f. dugas e-mail: adugas1@jhmi.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e40, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts use of a real-time syndromic surveillance system to improve influenza like illness screening and documentation in emergency departments during the h1n1 pandemic david meurer*1, 3 and james talbot2, 1 1university of alberta, edmonton, ab, canada; 2government of alberta, edmonton, ab, canada; 3alberta health services, edmonton, ab, canada objective screening for influenza like illness (ili) is an important infection control activity within emergency departments (ed). when ili screening is routinely completed in the ed it becomes clinically useful in isolating potentially infectious persons and protecting others from exposure to disease. when routinely collected, ili screening in an electronic clinical application, with real time reporting, can be useful in public health surveillance activities and can support resource allocation decisions e.g. increasing decontamination cleaning. however, the reliability of documentation is unproven. efforts to support the adoption of ili screening documentation in a computer application, without mandatory field support, can lead to long term success and increased adherence. methods we evaluated the impact of efforts to improve ili screening documentation adherence in an electronic ed information system (edis) during wave 2 of the september-november 2009 h1n1 pandemic. ili screening documentation rates were calculated across the 8 sites in edmonton zone of alberta health services and subsequently correlated to interventions. five interventions were evaluated: real-time verbal reminders (one-to-one nurse reminders), delayed email reminders (with the ili screening documentation rates), meetings (strategize to improve documentation rate), media (visual media broadcasts) and clinic awareness (opening and operation of the influenza assessment clinic). a logistic regression model was used to derive odds ratios (or) and 95% confidence intervals (ci) for correlation between the interventions and the screening rate change. results the ili screening not-documented (n/d) rate on september 27, 2009, was 75% (n/d = 781; ed visits = 1039). by november 25, the n/d rate had fallen to 11% and remained below 20% into july 2010. october 18, 2009 marked the first day that the daily positive (pos) ili screen rate was at or above 10% of patient visits with a rate of 12% (pos = 139; ed visits = 1164). the pos rate sustained values >10% until november 25(peaking at 40% on october 28, 2009) reflecting influenza activity and informing public health and other decision makers. when all site screening rates were aggregated and compared to the intervention variables – e-mail reminders (or = 2.176; 95% ci: 2.078-2.279), meetings (or = 2.286; 95% ci: 2.0892.501), media (or = 4.894; 95% ci: 4.219-5.677), clinic awareness (or = 1.145; 95% ci: 0.998-1.313) were positively associated with increased adherence. where one-to-one reminders to document ili screening were provided at one site, the ili documentation increased (or = 2.663; 95% ci: 2.260-3.138). e-mail reminders (or = 0.852; 95% ci: 0.732-0.992) and meetings (or = 0.696; 95% ci: 0.5050.960) had less influence on ili documentation when the single site was analyzed. conclusions a variety of interventions successfully improved ili screening documentation. the greatest impact was associated with e-mail reminders for recording ili screening results, meetings on how to improve adherence and media broadcasts associated with the circulating pandemic influenza. the strongest reported effect size was seen in one site following one-to-one nurse reminders to record the ili screening results. these results suggest that ili documentation adherence can be successfully increased using a variety of interventions. implementing and monitoring the effect of the interventions was made possible by the syndromic surveillance system, which at the same time, contributed to improved data used for infection prevention and control and public health purposes. keywords decision support; influenza like illness; screening; documentation; adherence acknowledgments alberta health and wellness, government of alberta. alberta health services. *david meurer e-mail: david.meurer@ualberta.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e34, 2013 use of technology to support information needs for continuity of operations planning in public health: a systematic review use of technology to support information needs for continuity of operations planning in public health: a systematic review 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 use of technology to support information needs for continuity of operations planning in public health: a systematic review blaine reeder 1 , anne turner 1 , george demiris 1 1 department of medical education and biomedical informatics, university of washington, seattle, wa, usa 98195 abstract objectives: continuity of operations planning focuses on an organization’s ability to deliver essential services before, during and after an emergency. public health leaders must make decisions based on information from many sources and their information needs are often facilitated or hindered by technology. the aim of this study is to provide a systematic review of studies of technology projects that address public health continuity of operations planning information needs and to discuss patterns, themes, and challenges to inform the design of public health continuity of operations information systems. methods: to return a comprehensive results set in an under-explored area, we searched broadly in the medline and ebscohost bibliographic databases using terms from prior work in public health emergency management and continuity of operations planning in other domains. in addition, we manually searched the citation lists of publications included for review. results: a total of 320 publications were reviewed. twenty studies were identified for inclusion (twelve risk assessment decision support tools, six network and communications-enabled decision support tools, one training tool and one dedicated video-conferencing tool). levels of implementation for information systems in the included studies range from proposed frameworks to operational systems. conclusion: there is a general lack of documented efforts in the scientific literature for technology projects about public health continuity of operations planning. available information about operational information systems suggest inclusion of public health practitioners in the design process as a factor in system success. keywords: public health; continuity of operations; business continuity, emergency management; information systems; technology use of technology to support information needs for continuity of operations planning in public health: a systematic review 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 introduction disasters such as hurricane katrina and hurricane rita of 2005, the terrorist attacks on september 11, 2001 and the recent h1n1 influenza threat have raised awareness of the role of public health in emergency management in the united states in the last decade. an in-place and up-to-date continuity of operations plan (coop) is an important part of any disaster preparedness and response strategy as recognized by emergency management experts [1-3] . a coop -also known as a business continuity plan (bcp) -provides guidelines for an organization to sustain delivery of essential services before, during and after a crisis. however, given that “state and local public health agencies lack sufficient staff and resources to manage their multiple preparedness responsibilities simultaneously” [4] , technology that supports coop decision-making information needs during a crisis may be lacking within the overall emergency management strategies of many local public health departments. a further complication is that coop efforts and emergency response efforts compete for the same organizational resources with the potential risk that emergency response may deplete resources necessary to maintain continuity of operations in public health. rozek and groth, in their discussion of coop for health care organizations, state that it “is no longer just a phase or project to be implemented when time and resources allow. it must be an ongoing program implemented to protect data, and ensure the integrity and security of the total organization”. [1] this need is also reflected in the work of dawes et al. who interviewed 29 people directly involved in the world trade center (wtc) response and found that "[m]ost interviewees urged much greater attention to continuity of operations and disaster recovery"; however, in spite of that need they observe that "most small businesses, nonprofit organizations, and local governments remain have-nots in terms of resources, technology, preparedness, and response and recovery capability." [2] our experiences since january 2008, while documenting public health leadership decisionmaking information needs at a large municipal public health agency, confirm a lack of decision support technology for continuity of operations planning within the public health domain. [5] in addition, somers finds that “remarkably little attention has been paid to the documentation of local government coop planning efforts” and cites a fema [federal emergency management agency] report that states the “ability of an agency to execute its essential functions . . . is dependent upon the identification, availability, and redundancy of critical communications and information technology systems to support key government leadership, internal elements, other agencies, critical customers, and the public”. [3] fraser remarks on recent public health budget cuts and observes a lack of standard preparedness measures and assessment criteria. he concludes that the united states may not be ready for a pandemic or disaster. [6] taken together, these findings indicate a need for information technology to support public health coop activities but also show that this critical need may be unaddressed or not fully recognized in practice. technology must address the information needs of public health coop planners and practitioners to successfully support their work processes and decision-making activities. in order to assess technology support of public health coop, we compiled a list of information needs [7] suggested by prior work in public health knowledge management [8] , coop planning [6, 9use of technology to support information needs for continuity of operations planning in public health: a systematic review 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 11] , post-disaster studies of health care worker information needs [12, 13] , public health preparedness [14-18] and disaster planning [19-21] . the information needs on this list are included based on author familiarity with a broad corpus of public health and continuity of operations literature gained through searches of the literature over time and experiences of the authors working with public health continuity of operations planners since january 2008. this list represents broad categories of information needs for public health coop and may not represent every information need available from the published literature. in addition to these information needs, there is a basic requirement that decision-making information be available and up-to-date at all times before, during and after a crisis. these information needs are:  synthesized information [8]  centralized data access [8]  coordination/incident command support [6, 9-21]  staff training/education [6, 9-21] , planning  plan testing/exercise support [6, 9-17, 20, 21]  interoperable external communication/alerting [6, 9, 11-21]  internal communication/alerting [6, 9, 11-17, 19, 21]  staff attendance/contact list management [6, 9, 11, 13-17, 19, 21]  resource tracking/capacity management [6, 11, 13-17, 19-21]  collaboration [8, 11-14, 16, 17, 19-21]  remote work/portable data [6, 11, 17-19, 21]  geographic data [11, 13] objectives previous technology efforts in the area of public health can inform the identification of system features that support public health continuity of operations planning and emergency decision support. furthermore, it is important to explore technologies, approaches and challenges to advance the design of future public health coop information systems. the aim of this paper is to identify and review published studies of information systems and technology projects that address information needs in the context of public health continuity operations planning and review public health emergency management information systems that are applicable to public health coop. methods we conducted a systematic literature review of studies of public health continuity of operations planning technology projects. however, as public health coop has not been studied extensively, we searched broadly using terms from our categories of information needs compiled from publications about public emergency management and private continuity of operations planning. these terms were used in combination with the terms “public health”, “disaster”, “emergency”, “technology”, “information system”, “informatics” and “decision support”. we conducted our initial search in the medline bibliographic database as well as in ebscohost from january 1990 to january 2009, using the same term combinations, in january of 2009. based on this strategy, results from a search of both bibliographic databases yielded a total of 469 articles (medline: use of technology to support information needs for continuity of operations planning in public health: a systematic review 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 213, ebscohost: 256). in addition, we reviewed the table of contents for all issues of the journal of business continuity and emergency planning, because of its specialized focus that is of high relevance to our study. all citations were imported into a commercial reference manager. three reviewers rated ten percent of the article abstracts (thirty-two) to validate inclusion/exclusion criteria. after a high degree of agreement between reviewers was determined at a face-to-face meeting, the remaining articles were rated by the first author. reference lists for each included article were reviewed for other studies that might be eligible for inclusion. in addition, information systems discovered during the full-text review of each article were noted and searches for studies of these systems were conducted. the process followed during the literature review is illustrated in figure 1. figure 1. process flow of literature survey methods from search strategy to data analysis inclusion criteria included studies of technologies that were developed specifically to support continuity of operations planning information needs. in addition, inclusion criteria included studies of technologies developed for general emergency management purposes that implement features or approaches that could support continuity of operations planning information needs. our inclusion criteria were defined to encompass both specific continuity of operations planning systems and general emergency management systems to broaden the number of potential systems and technologies included for analysis. exclusion criteria included articles that focus mainly on public health data stores, registries and general technology infrastructure as well as those that discuss general public health policy, emergency management policy or continuity of operations planning but do not reference or evaluate a specific system or systems. in addition, exclusion criteria included articles that discuss studies, reports and evaluations of public health interventions, live training exercises, general use of technology to support information needs for continuity of operations planning in public health: a systematic review 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 computer training and efforts to improve quality of service lines. lastly, exclusion criteria included studies of safety monitoring, disease surveillance, clinical or laboratory information systems and work flow in specific settings not related to public health. as our search strategy is necessarily comprised of terms that make it somewhat broad in order to return a result set that includes the systems and technologies we are targeting, our list of exclusion criteria was very detailed and specific. results after duplicates were removed in the commercial reference manager, a total of 320 articles remained and were further reviewed to determine inclusion eligibility. twenty studies of information systems and technology projects met our public health continuity of operations planning information needs inclusion/exclusion criteria. included studies were published between 1995 to 2008. most included studies originated from the united states (13) but some included studies originated from greece (2), canada (1), mexico (1), spain (1), turkey (1) and the united kingdom (1). this list of systems and projects is by no means an exhaustive list of such systems worldwide but is a list of systems and projects that have reported details of design, implementation or findings published in scientific indexed literature and met our criteria for inclusion. table i presents the details and a short description of each system or project. discussion the concept of technology specifically designed to serve public health continuity of operations planning information needs is relatively novel and, indeed, only one system in the included studies (access grid [22] ) explicitly mentions continuity of operations (as “business continuity”) in the context of public health. the inclusion of only one such study is not surprising in that, as we have previously observed, continuity of operations planning and information technology to support it, are underexplored in the public health domain. this apparent lack of studies of public health continuity of operations information systems and technology projects presents many opportunities for informatics research, including the design of integrated systems that support routine public health processes and remote access to data. however, awareness of continuity of operations as a component that competes for public emergency management resources must be raised and addressed at an organizational level before such informatics efforts can be successful. use of technology to support information needs for continuity of operations planning in public health: a systematic review 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 table 1. selected system features and details system name & study authors pub. year system features and details level of implementation method or technique funding source emergency computer communicat ions network (cdc wonder/pc) o'carroll, et al. [23] 1995  possibly the first electronic public health system of its kind in a disaster relief operation  local data entry with secure e-mail transmission of database files  demonstrated the effectiveness of digital communication in reducing the impact of a disaster operational system cdc conducted consultations with iowa health department officials about implementation. 11 two person teams installed systems and trained staff for 99 county installations. stafford act funds authorized by the federal emergency manageme nt agency epedat eguchi, et al. [24] 1997  integrates real-time and historical earthquake data  provides lossestimation methods for earthquake damage  applicable to a variety of regions and users partially functional system iteratively links earthquake data source parameters and damage algorithms. displays output using gis technology. california governor’s office of emergency services icem-se project bedard, et al. [25] 2003  integrates gis with olap (on-line analytical processing) for decision support  developed for environmental health interventions and outcomes  could be applied to any planning data source for coop planning prototype integrates commercially available software to simplify geographic visualization of environmental health data and then analyzing prototypes by comparing task steps several canadian governme nt funding agencies gepimi ptochos, et al. [26] 2004  developed to establish a public health network in greece, bulgaria, albania, fyrom and turkey  integrates medical, pilot system integrates multiple data sources using many different technologies and greek ministry of health and welfare and the use of technology to support information needs for continuity of operations planning in public health: a systematic review 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 epidemiological and environmental data with gis technology  accesses and standardizes information from numerous systems provides data display via the web and gis. european commissio n maxi-vac washington, et al. [27] 2005  developed for mass vaccination planning support through simulations  can be used in a variety of settings  facilitates staff management based on service demand operational system for a small-pox vaccination scenario in a hypothetical clinic developed with user input integrates several commercially available modeling tools, data sources and expert opinion to determine staff utilization centers for disease control arcview 3.2 with 2000 census data waring, et al. [28] 2005  gis technology applied to rapid needs assessment for disasters in the houston, tx  a method of using existing gis software tools and data sources  first study of its kind in the houston area pilot system tested during the aftermath of a tropical storm employs an integrated gis and data methodology that combines commercial software with available year 2000 census data university of texas center for public health and policy studies (champs ) dss-dm aleskerov, et al. [30] 2005  flexible simulation of various earthquake disaster scenarios based on different inputs  developed for planning and mitigation purposes  provides micro-level estimates of human losses and damage for better decision support proposed system developed and tested with user involvement uses pc-based commercial software (ms access and delphi) and data from district and sub-district administrators to produce microlevel loss estimations for building clusters bogaziçi university, istanbul, turkey use of technology to support information needs for continuity of operations planning in public health: a systematic review 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 table 1. selected system features and details system name & study authors pub. year system features and details level of implementation method or technique funding source realopt lee, et al. [31] 2006  rapid simulation system for infectious disease disaster scenario effects on populations for real-time decision support about resource allocation  allows for flexible "what-if" configuration  allows users to create floor plans and specify inputs operational system implemented with staffing information from an anthrax field exercise. tests simulations of staffing for medication dispensing in small pox scenarios and compares the results against a commercial system that uses the same model institute of technolog y, atlanta, ga/ centers for disease control access grid hauenstein, et al. [22] 2006  demonstrates access grid videoconferencing as an approach to transnational communications  directly addresses public health business continuity collaboration information need  requires additional participant installations and testing to be a full solution operational system evaluated during a 210 minute test of a videoconference platform through participation of ten asia pacific economic cooperation emerging infections network (apec einet) on january 19, 2006. apec, ibm and the global health and securities initiative, nti toxmap hauenstein, et al. [32] 2006  nlm system that integrates epa hazardous chemical data with gis data  creates dynamic maps of chemical releases, trends, facility locations and superfund sites  provides links to relevant information operational system avalaible online at http://toxmap. nlm.nih.gov/t oxmap/main/i ndex.jsp integrates toxic release inventory (tri) data and web-based gis technology national library of medicine http://toxmap.nlm.nih.gov/toxmap/main/index.jsp http://toxmap.nlm.nih.gov/toxmap/main/index.jsp http://toxmap.nlm.nih.gov/toxmap/main/index.jsp http://toxmap.nlm.nih.gov/toxmap/main/index.jsp use of technology to support information needs for continuity of operations planning in public health: a systematic review 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 aid-n hauenstein, et al. [33] 2006  allows data access through web services  implements data standards for disaster information exchange  designed with realtime communication in mind test bed shared data model creates federated data models housed in a central server. exposes data through web services architecture. national library of medicine herds gotham, et al. [34] 2007  state-wide all-hazards preparedness system in new york state  archetypal public health preparedness system  provides flexible functionality during planned drills and real events operational system used in actual health events including a blackout and a hurricane readiness activation. uses a collaborative development across local health jurisdictions. employs an allhazards approach. establishes a single data reporting stream built upon existing infrastructure. new york state departmen t of health safe chronaki, et al. [35] 2007  integrates multiple modes of communication including satellites with a mobile coordination center  uses satellite video as one means of collaboration  models work flow of user groups during a typhoid outbreak proposed architecture integrates satellite communications with existing health information data sources, videoconferencin g and gis technology safe consortium and the european space agency (esa) use of technology to support information needs for continuity of operations planning in public health: a systematic review 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 table 1. selected system features and details system name & study authors pub. year system features and details level of implementation method or technique funding source exploris marti, et al. [36] 2008  prototype developed to to estimate earthquake damage on tenerife (canary islands)  uses a deterministic simulation model of a volcanic eruption  creates a vulnerability map for casualties and fatalities using spatial analysis and gis prototype tested for a number of specific sites in europe (all sites not disclosed in study) integrates volcanic hazard, building typology and population data to estimate casualties and fatalities during eruptions. european union ddss lozanofuentes, et al. [37] 2008  inexpensive gis solution to increase public health capacity  developed for decision-support in combating vector-borne disease  incorporates high resolution satellite imagery with disease data to display city infrastructure information pilot demonstration uses satellite imagery and google-earth feature-making tools to develop city infrastructure layers and visualize vectorborne disease data innovative vector control consortium brfss + gis technology holt, et al.[ 38] 2008  developed with needs assessment of vulnerable populations in mind  integrates population and gis data available from the cdc  flexible for use in different disaster scenarios like hurricanes and influenza outbreaks sample assessment integrates brfss data and gis technology to visually display a derived risk assessment for a hurricane landfall along the east and gulf coasts centers for disease control use of technology to support information needs for continuity of operations planning in public health: a systematic review 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 hazus-mh ding, et al. [39] 2008  models flooding, hurricanes and earthquakes  provides for standard and customized estimations  developed to answer needs of government officials to estimate disaster damage operational system that has been in use since the late 1990s validates the flood model in hazus-mh during the harris county risk assessment program (hcrap) in harris county, texas by comparing outputs against local analyses federal emergency managemen t agency epims senior and copley [40] 2008  developed to manage, assess and monitor information during emergencies  allows for internal and external communication  visualizes incident response and allows access to planning documents operational system used during actual emergencies integrates disparate data sources to allow data and communication access to all users through web, gis and database technologies rotherham metropolita n borough council bcms sheth, et al. [41] 2008  strictly focuses on continuity of operations planning  provides an integrated view of data from multiple sources  industry independent and flexible for use during different scenarios proposed system based on user needs and involvement technology independent model based on software maturity/capabili ty models state of maryland judiciary use of technology to support information needs for continuity of operations planning in public health: a systematic review 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 patterns in information need support looking at table ii, we can make some observations about the information needs supported by a given system or technology project. all systems satisfied the need for synthesized information. this is not surprising in that information systems are often designed to integrate data while decision-making is often dependent upon data that provides better situational awareness through synthesis. in addition, all systems that satisfied the need for coordination/incident command support also satisfied the need for collaboration. again, this is not surprising in that coordination and collaboration activities both require communication and information flow but coordination often applies a more directed hierarchical approach. all systems that satisfied the need for centralized data access for a variety of practitioner roles also satisfied the need for remote work capability and/or portable data. this situation is likely due to the relatively recent advent of networked data stores that are accessed through web technologies; both centralized data access and data access are inherent features of these widely adopted technologies. of the twenty included studies, only two (the emergency computer communications network [23] and epedat [24] ), were published before september 11, 2001 (1995 and 1997, respectively). included studies after that begin in 2003. these results coincide with the substantial federal funding available in the united states for the research and design of technology to support emergency management and combat terrorism after the 9/11 terrorist attacks. the two year lag in publications from 2001 to 2003 likely indicates the start up time to design, develop and conduct research on a given information system or technology project. with the exception of two systems (nccphp training web site [29] and aid-n framework [33] ), nearly all systems satisfied the need for planning/plan testing/exercise support. this observation can also be explained by the push for preparedness in a post-9/11 world. in addition, with the exception of one system (nccphp training web site [29] ), nearly all systems satisfied the need for resource tracking/capacity management. this can be attributed to the fact that resource and capacity information are necessary to support decision-making in an emergency. use of technology to support information needs for continuity of operations planning in public health: a systematic review 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 table 2. public health continuity of operations information needs supported by included systems author system name (location) year of study pub. s y n th e si z e d i n fo r m a ti o n c e n tr a li z e d d a ta a c c e ss c o o r d in a ti o n a n d i n c id e n t c o m m a n d s u p p o r t s ta ff t r a in in g a n d e d u c a ti o n p la n n in g , p la n t e st in g a n d e x in te r o p e r a b le e x te r n a l c o m m u n ic a ti o n a n d a le r ti n g in te r n a l c o m m u n ic a ti o n a n d a le r ti n g s ta ff a tt e n d a n c e a n d c o n ta c t l is t m a n a g e m e n t r e so u r c e t r a c k in g a n d c a p a c it y m a n a g e m e n t c o ll a b o r a ti o n r e m o te w o r k a n d p o r ta b le d a ta g e o g r a p h ic d a ta sheth, et al. [41] bcms (uk) 2008 x x x x x x x holt, et al. [38] brfss (us) 2008 x x x x lozanofuentes, et al. [37] ddss (mexico) 2008 x x x senior and copley [40] epims (uk) 2008 x x x x x x x x x x marti, et al. [36] exploris (spain) 2008 x x x ding, et al. [39] hazus-mh (us) 2008 x x x x gotham, et al. [34] herds (us) 2007 x x x x x x x x x x x chronaki, et al. [35] safe (greece) 2007 x x x x x x x x x kimball, et al. [22] access grid (us) 2006 x x x x x hauenstein, et al. [33] aid-n (us) 2006 x x x x x x x lee, et al. [31] realopt (us) 2006 x x x x hochstein, et al. [32] toxmap (us) 2006 x x waring, et al. [28] arcview 3.2 with 2000 census data (us) 2005 x x x x x aleskerov, et al. [30] dss-dm (turkey) 2005 x x x washington, et al. [27] maxi-vac (us) 2005 x x x use of technology to support information needs for continuity of operations planning in public health: a systematic review 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 horney, et al. [29] nccphp training web site (us) 2005 x x x ptochos, et al. [26] gepimi (greece) 2004 x x x x x x x bedard, et al. [25] icem-se project (canada) 2003 x x x x x eguchi, et al. [24] epedat (us) 1997 x x o'carroll, et al. [23] emergency computer communications network (us) 1995 x x x x x x while all the public health continuity of operations information needs described in our list of categories were identified in the entire set of published studies, only two of the twenty included studies (herds [34] and safe [35] ) supported all of the needs included in our list. of these, safe is a proposed framework and herds a fully operational system. as such, herds represents an archetypal working system that other public health continuity of operations planning informatics projects should look to for successful system design. that we start to see information about systems like herds and safe available in the published literature only as recently as 2007 may indicate a growing level of maturity in public health emergency management development efforts and knowledge. interestingly, the emergency computer communications network [23] , successfully deployed in 1993 and published about in 1995, satisfied many of the information needs that both herds and safe support using pre-web technology. as such, it can be judged as an innovative and sophisticated front-runner of current, more “modern” systems. systems and technology projects span the range of implementation levels from proposed frameworks based on existing technology to fully operational systems in current use. all are described at some measure of detail that can help to inform the future design of information systems to support public health continuity of operations. sixteen of twenty projects explicitly note sponsoring agencies or resource contributors. where not noted, university affiliation has been listed as the sponsoring agency in table i. in all cases where noted, project sponsoring agencies are government agencies or a combination of government agencies. categories of systems based on their descriptions and common patterns of supported information needs, we were able to group the information systems and technology projects from the included studies into four categories of our own creation. these categories are:  risk assessment decision support tools  networked and communications-enabled decision support tools  dedicated training tools use of technology to support information needs for continuity of operations planning in public health: a systematic review 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010  dedicated video-conferencing tools information systems and technology projects are shown grouped by category and the information needs each category supports in table iii. table 3. categories of systems and projects grouped by information needs they support category supported information needs system name risk assessment decision support tools (n = 12) risk assessment decision support tools typically support the following set of information needs: synthesized information, planning/plan testing/exercise support, resource tracking/capacity management and geographic data epedat [24] icem-se project [25] maxi-vac [27] arcview 3.2 with 2000 census data [28] dss-dm[28] realopt [31] toxmap [32] exploris [36] ddss [37] brfss + gis technology [37] hazus-mh [39] bcms [41] networked and communicationsenabled decision support tools (n = 6) networked and communications-enabled decision support tools typically support the following set of information needs: synthesized information, centralized data access, coordination/incident command support, planning/plan testing/exercise support, interoperable external communication/alerting, resource tracking/capacity management, collaboration, remote work/portable data emergency computer communications network (cdc wonder/pc) [23] gepimi [26] aid-n [33] herds [34] safe [35] epims [40] dedicated training tools (n = 1) these tools are dedicated specifically to training nccphp training web site [29] dedicated videoconferencing tools (n = 1) these tools are dedicated specifically to maintaining a video-conference platform access grid [22] use of technology to support information needs for continuity of operations planning in public health: a systematic review 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 follow-up to initial search we repeated our search using the same methodology and terms in february of 2010 for the period between january 2009 to february 2010 in order to provide coverage for time that passed during data analysis and the writing of this manuscript. thirty-nine publications were returned in the results set for this search. of these, one article [42] was a follow-up to an included business continuity videoconferencing article already included in the review [22] . in addition, three studies [43-45] demonstrated methods that combined gis technology and public health data but did not meet our inclusion/exclusion criteria. all three studies were published in the international journal of health geographics, a newer journal dedicated to the application of health-related gis. this journal and these articles may indicate a trend that gis technology has an expanded role to play in the design of future public health coop information systems. this finding suggests a full review of current use of gis technologies in public health may be appropriate. however, such an undertaking is beyond the scope of this project. we include references to the following articles [46-51] and books [52-54] as sources for those interested in the application of gis to public health. for those interested in the application of gis to disaster management we offer references to the following books. [55-57] limitations one limitation of this systematic review is that it relies on a list of information needs based on author familiarity with a corpus of literature gained through searches over time coupled with author experience gained through information design research with public health continuity of operations planners rather than an exhaustive list of information needs compiled through a formal literature review. in addition, this review does not undertake a formal analysis of the differential use of the same or similar terms across disciplines although we believe that this limitation is addressed by the divergent approach in the search term selection and broad inclusion criteria applied to the set of documents returned as a result of the search process. implications for future research this review will raise awareness of the importance of continuity of operations planning in public health as well as the attendant need for integrated systems to support public health coop. in addition, these findings can help public health informaticians in defining the types of systems that need to be designed and developed to support public health operational continuity. conclusion while there have been a number of recent efforts to develop information systems that support public health emergency management, overall, we lack documented efforts of information systems that support public health continuity of operations planning. consequently, we lack much evidence of what does and does not work in this area of research. one quick and simple way to raise awareness of the coop topic for researchers might be to index emergency use of technology to support information needs for continuity of operations planning in public health: a systematic review 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 1, 2010 management technology studies with coop and bc key words. however, while public health practitioners involved in continuity of operations planning and emergency management activities may have similar information needs before, during and after a crisis, the goals of these activities are different and, indeed, they compete with each other for organizational resources. as such, determining design specifications for information technology-based coop systems should involve public health practitioners who engage in coop activities as part of their work flow to ensure support of their information needs. though there are common themes (as highlighted in this paper) between public health coop and emergency response, some coop activities in the public health domain are unique and technology designed to support these activities must be context-specific to address the uniqueness of the environment in which they are introduced. conflicts of interest the authors have no conflicts of interest to report at this time. acknowledgments this study was supported in part by the national library of medicine medical informatics training grant t15 lm007442-07. correspondence blaine reeder: breeder@u.washington.edu references [1]. rozek p, groth d. business continuity planning. it's a critical element of disaster preparedness. can you afford to keep it off your radar? health manag technol 2008 mar;29(3):10-2. 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[57]. kataoka m. gis for homeland security. redlands, calif.: esri press; 2007. google scholar is not enough to be used alone for systematic reviews 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi google scholar is not enough to be used alone for systematic reviews dean giustini 1 , maged n. kamel boulos 2 1 ubc library, ubc ischool, canada, 2 plymouth university, uk abstract background: google scholar (gs) has been noted for its ability to search broadly for important references in the literature. gehanno et al. recently examined gs in their study: ‘is google scholar enough to be used alone for systematic reviews?’ in this paper, we revisit this important question, and some of gehanno et al.’s other findings in evaluating the academic search engine. methods: the authors searched for a recent systematic review (sr) of comparable size to run search tests similar to those in gehanno et al. we selected chou et al. (2013) contacting the authors for a list of publications they found in their sr on social media in health. we queried gs for each of those 506 titles (in quotes ""), one by one. when gs failed to retrieve a paper, or produced too many results, we used the allintitle: command to find papers with the same title. results: google scholar produced records for ~95% of the papers cited by chou et al. (n=476/506). a few of the 30 papers that were not in gs were later retrieved via pubmed and even regular google search. but due to its different structure, we could not run searches in gs that were originally performed by chou et al. in pubmed, web of science, scopus and psycinfo®. identifying 506 papers in gs was an inefficient process, especially for papers using similar search terms. conclusions: has google scholar improved enough to be used alone in searching for systematic reviews? no. gs’ constantly-changing content, algorithms and database structure make it a poor choice for systematic reviews. looking for papers when you know their titles is a far different issue from discovering them initially. further research is needed to determine when and how (and for what purposes) gs can be used alone. google should provide details about gs’ database coverage and improve its interface (e.g., with semantic search filters, stored searching, etc.). perhaps then it will be an appropriate choice for systematic reviews. mesh keywords: google scholar, information retrieval, pubmed, searching, systematic reviews correspondence: mnkamelboulos@plymouth.ac.uk copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:mnkamelboulos@plymouth.ac.uk google scholar is not enough to be used alone for systematic reviews 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi introduction since its debut in 2004, google scholar (gs) has been viewed in the field of biomedical research as a flawed but useful tool in searching the scientific literature [1,2]. gs is widely-recognized as an excellent source of grey literature in biomedicine [3-5]. despite its broad coverage, gs is considered ill-designed for expert searching [6]. one librarian said that “… plug-in-the-keywordand-hope-for-the-best tools like google scholar are poor choices for serious search questions such as clinical queries, bibliographic reviews, comprehensive literature searches, or other questions that require a more sophisticated approach” [7]. expert searchers were admonished to use trusted databases such as the cochrane library, pubmed and embase when literature reviews were required (i.e., for grants, clinical trials and systematic reviews) [8]. the early buzz of gs eventually ebbed and was replaced by detailed comparisons against other tools such as pubmed and scirus [9]. a consensus seemed to emerge that gs was not as current as pubmed and some expert searchers placed it a year behind or more [7]. searchers also noticed that pubmed and google scholar fulfilled different purposes [10]. in head-to-head comparisons with curated databases, gs was deemed inadequate for subject searching and did not offer what expert searchers wanted to see in a literature database. medline, produced by the us national of medicine in bethesda, maryland, has been the gold standard for structured searching (especially via ovid’s interface) for decades. while its place in biomedical searching seems secure, some researchers have argued that gs is a better choice for some retrieval queries, especially in browsing for articles and locating highly-cited papers [11]. in recognition of its speed and familiar interface, one editorial asked google to think about creating a subset of gs for evidence-based medicine. but that would require transparency from google about gs, and they were not about to produce a list of journal suppliers and grey literature that were crawled to create the database. searchers were left to surmise its scope and make guesses as to what was in it [12]. google scholar is a useful tool to help researchers locate in seconds relevant papers from billions of pages across the web [13] (and in many cases directly retrieve the full text of those papers). for that, it is highly-valued and useful, and every expert searcher should use it for that purpose. allied to its easy-to-use interface, gs is a time-saver for quick searches especially compared to similar searches on pubmed, which can be unwieldy. in any case, knowing the strengths and weaknesses of gs will help researchers decide when and how to use it. google has created a useful tool with links to articles and grey literature. but gs was already deemed unsuitable for literature reviews due to its limited search (filtering and qualifiers) functionality; its inability to draw on the power of the mesh vocabulary (used in medline/pubmed) was cited as a critical flaw [14,15]. in 2013, french researchers, gehanno et al., published a study that asked a simple question to which most expert searchers thought they knew the answer: ‘is google scholar enough to be used alone for systematic reviews?’ [16] the authors state that gs’ coverage has improved and ask whether its “coverage is high enough to be used alone in systematic reviews”. in other words, the authors ask whether gs might replace medline and other bibliographic databases to perform costly, time-intensive searches for systematic reviews. the clearly-stated question and google scholar is not enough to be used alone for systematic reviews 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi conclusions of gehanno et al. are examined in this paper; we ask whether google scholar has improved enough over the years to be used alone in systematic reviews. methods the authors searched for a systematic review that was comparable in size to gehanno et al. we selected a recent study in our area of expertise (health/public health informatics), chou et al. (2013), and contacted the authors for a list of the 506 publications they found in their sr on social media in health 1 . to test google scholar’s ability to locate articles from an existing systematic review, we searched for all of the publications found by chou et al. [17]. we tested whether the 500+ articles that formed the basis of chou et al.’s sr were indexed by gs. since we knew what we were looking for, and were not testing gs’ ability to produce relevant documents, our searches were straightforward title searches. chou et al. provided us with an excel spreadsheet of the titles of papers (n=514) that comprised their systematic review. after correcting for minor errors, we looked for 506 unique items occurring either as simple citations or full-text links to papers within gs. we checked for the presence of these 506 publications by querying gs for the title of each study (in quotes ""), one by one. when a search failed to retrieve the required article, or produced too many results to browse, we opted to use google’s allintitle: command to increase our precision and search accuracy by limiting our search to the titles of articles. some papers that were not found in gs were later searched and found in regular google search. our results were double-checked title-by-title for completeness and accuracy against those listed by chou et al. secondly, we tried to replicate chou et al.’s search strategy and keywords (as detailed in [17]) in gs. we queried gs for: health* and ("social media" or "new media" or "participatory media" or "user-generated content" or facebook or myspace or twitter or youtube or "second life" or linkedin or wiki* or blog* or "web 2.0" or "online social network" or "social networking"). we set query conditions as follows: year range as 2004-2011; include citations. it should be noted that google uses stemming technology instead of asterisks, so those asterisks in the above query are ignored/not needed in gs. due to the different database structure and search syntax used in gs, our searches for these 506 papers using chou et al.’s original search strategy and keywords yielded unmanageable results of approximately >750,000 items (as at 5 february 2013). using google’s allintitle: command reduced our search results considerably to a collection of <450 items, but this was not a full subset of chou et al.’s 506 items. multiple attempts and combinations of keywords (and syntax) are needed if one were to find (discover) in gs the 476 (out of 506) papers cited in chou et al.’s systematic review without already knowing their full titles. 1 other reasons chou et al (2013) was chosen: we were able to contact the authors and obtain a full list of publications they found in their review. the list was a good representative size (about 500), and made a good test case and head-to-head challenge. most importantly, it was comparable (in size) to the set of 700+ papers used in the gehanno et al.’s study, which they pooled from 29 systematic reviews. we expect other researchers to try and replicate gehanno et al.’s approach in their own fields, since gs coverage may vary by discipline. google scholar is not enough to be used alone for systematic reviews 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi results even though gs produced records for ~95% of those papers as cited by chou et al. (n=476/506), numerous iterative searches were required to find all of them. in gs, we could not build search sets effectively or transfer results to a spreadsheet or reference manager. gs made our work more difficult as citations had to be managed one at a time. due to its rudimentary structure, we could not run the search strings as used by chou et al. in pubmed, web of science, scopus and psycinfo®. gs did not understand these expert search strings and was unable to translate them in any coherent way using its auto-correct feature. a few of the 30 papers that we could not find in gs (see table i) were found in pubmed and even regular google search. identifying each paper was inefficient, especially where two papers used similar keywords or metadata. 2 gs’ ability to search into the full-text of papers combined with pagerank’s algorithm is useful, and helps with browsing. these features on their own do not compensate for gs’ obvious problems with searchability (discoverability) and database quality. pubmed is clear that its database is built on a foundation of medical subject headings or mesh terms, and each field in its 23 million citations is searchable. gs builds its structure on a simple interface design, vast interdisciplinary content and link popularity (which papers are cited most often). on the positive side, gs achieved a high percentage (95%) of “known-items” from chou et al. but not all. papers not found in gs were unique items from the four curated databases mentioned by chou et al., pubmed, web of science, scopus and psycinfo® (table i). gs is not flexible, precise or indexed (enough) to be used alone for systematic reviews. its ‘keyword search' capability, allied to google’s pagerank, is a poor replacement for controlled vocabulary searching and its interface does not provide enough flexibility to accommodate search filters, wildcards and expert search hedges, all of which are required for systematic reviews. we particularly noted the lack of a gs search filtering option to limit the scope of search results ‘by discipline’ such as ‘health and medicine’, since gs is catering for, and indexing articles from a very wide range of disciplines, and the same keywords can sometimes retrieve irrelevant, nonhealth-and-medicine-related articles. in this modest study, identifying 506 papers among results and multiple screens was akin to searching for a needle in a haystack – painful, prickly and a time waster. gehanno et al.’s search for 738 papers from 29 systematic reviews was similarly onerous but they, like us, knew what they were looking for [16]. this is a critical point in both studies: searching for known items is a much simpler exercise than trying to locate (or discover) those papers in the first place. gs’ broad, undocumented corpus produces a lot of noise (and irrelevant hits) in its results, making it an unsuitable exclusive choice for systematic review searching. 2 searching for papers in google scholar was not an easy task. after about 200 queries from the same machine, google scholar decided that our searches indicated we were bots and blocked our ip address. clearing cookies only partially solved the problem: gs used captcha to solve each submitted query, so another ip address was needed to continue checking the remaining publications. google scholar is not enough to be used alone for systematic reviews 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi table i. articles missing in google scholar (as at 5 february 2013) and their original database sources where chou et al. found them. the last two articles in the table were only (indirectly) retrievable when we dug deep in search results, but not as direct/first search result hits or via gs allintitle: command. authors title source title year database where chou et al. found article white, j. everything you always wanted to know about stress (but were afraid to ask) or trying to reach the 'hard to reach' clinical psychology forum 2011 scopus gannon, ke; moreno, ma display of risk and protective health behaviors on incoming freshmen's facebook profiles pediatric research 2009 web of science horrigan, bj nih and wikimedia foundation collaborate to improve online health information explore-the journal of science and healing 2009 web of science hwang, k; etchegaray, j; bernstam, e; thomas, e predictors of intention to share educational health information via online social network ties journal of general internal medicine 2010 web of science pemu, pe; quarshie, aq; josiah-willock, r; ojutalayo, fo; alemamensah, e; ofili, eo socio-demographic psychosocial and clinical characteristics of participants in e-healthystrides (c): an interactive ehealth program to improve diabetes selfmanagement skills journal of health care for the poor and underserved 2011 web of science [no authors listed] why blog on about mental health? mental health today 2006 medline gronstedt, a. second life produces real training results t+d (training + development) 2007 scopus hawn, c. report from the field: take two aspirin and tweet me in the morning: how twitter, facebook, and other social media are reshaping health care health affairs 2009 scopus malvey, d., alderman, b., todd, a.d. blogging and the health care manager health care manager 2009 scopus russell, j. web 2.0 technology: how is it impacting your employer brand? nursing economics 2009 scopus google scholar is not enough to be used alone for systematic reviews 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi strongin, r. health reform in 140 characters medical device and diagnostic industry 2010 scopus tan, l. psychotherapy 2.0: myspace® blogging as self-therapy american journal of psychotherapy 2008 scopus [anonymous] web 2.0, health and informatics methods of information in medicine 2009 web of science arikan, y; benker, t internet and social media impacts on turkish healthcare professionals' reaching health and drug side effectrelated information drug safety 2011 web of science benker, t; arikan, y turkish patients' use of internet and social media for healthcare and drug side effect information drug safety 2011 web of science botelho, r motivate healthy habits (part ii): using web 2.0 & 3.0 technologies to generate social movements swiss medical weekly 2009 web of science evans, wd; mcleod, c; thomas, sl social media marketing and health behaviours: industry strategies, consumer behaviours, and public health responses annals of behavioral medicine 2011 web of science grinfeld, mj; hensel, bk; cassidy, jt; walker, se; parker, jc a new media solution to coordination of care for juvenile arthritis: the jahelp.org advocacy-oriented health care access project arthritis and rheumatism 2006 web of science hamm, km; simeonov, im; heard, se using technology to harness and organize expertise in the development of health education materials: how a wiki can help you collaborate clinical toxicology 2009 web of science hartland, d; duffton, r; home, j; d'aguilar, c; berktay, l; tomkinson, a; et al. health promotion (hp) and health outcomes: impacts of old and new media campaigns on referral patterns for hiv testing: implications for the national hiv saving lives campaign hiv medicine 2011 web of science hartoonian, n; ormseth, s; bantum, eo; owen, j process and outcome evaluation of a social-networking website for health promotion annals of behavioral medicine 2008 web of science google scholar is not enough to be used alone for systematic reviews 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi kane, i; walkosz, b; giese, b dosomethingonthe.net: health marketing for new media annals of behavioral medicine 2010 web of science kondro, w health and environment blog canadian medical association journal 2011 web of science nocker, g; schachinger, a trends of future health communication and promotion via-web 2.0 /social media european journal of public health 2010 web of science ojcius, d tracking public health via twitter nature reviews microbiology 2011 web of science paek, hj; hove, t; jeong, hj; kim, m peer or expert? the persuasive impact of youtube public service announcement producers international journal of advertising 2011 web of science toth-cohen, s the garden of healthy aging: collaborative project development in the virtual world of second life gerontologist 2009 web of science wapner, j the healthy type the therapeutic value of blogging becomes a focus of study scientific american 2008 web of science truccolo, i.; bufalino, r.; annunziata, m.a.; caruso, a.; costantini, a.; cognetti, g.; et al. national cancer information service in italy: an information points network as a new model for providing information for cancer patients tumori 2011 scopus bastida, r use of collaborative web-based technology in mental health wiki use in practice international journal of mental health nursing 2008 web of science google scholar is not enough to be used alone for systematic reviews 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi conclusions is google scholar enough to be used for systematic review searching? no. contrary to gehanno et al.’s conclusions that gs “could even be used alone” [16], we found that gs was not up to the required search standard for systematic reviews. despite its high sensitivity and vast coverage, gs was unable to locate all known-items cited in a previously-completed systematic review. we were able to retrieve most (but not all) of the papers used by chou et al. [17] in their systematic review, because we already knew their titles and were searching for them one by one. but would we have been able to discover them as easily if we did not already know their exact titles? based on our results, the answer was ‘no’ (when we tried to replicate chou et al.’s search strategy in gs and queried gs for the topics of those papers [instead of their titles]). gs can sometimes be less precise than pubmed and similar bibliographic databases, returning hundreds or thousands of results, many of them irrelevant, thus requiring extensive human filtering of the results [5, 18]. furthermore, gs’ changing content, unknown updating practices and poor reliability make it an inappropriate sole choice for systematic reviewers. as searchers, we were often uncertain that results found one day in gs had not changed a day later and trying to replicate searches with date delimiters in gs did not help. papers found today in gs did not mean they would be there tomorrow. in summary, gs could not be viewed on par with tools such as medline, embase and the web of science. gray et al. said it best that "google scholar's value to the sciences" may be that it can be used “for initial & supplemental information gathering" [11]. google scholar’s shortcomings, while not insignificant, should not exclude it from being used in systematic reviews [18]. on the contrary, we argue that further investigation is needed to determine when and how (and for what subjects, or disciplines) gs can be used for systematic review searching. until then, its engineers should provide full details about its database coverage and aim to improve its interface search capabilities (e.g., indexing, semantic search filters, stored searching, etc.). only then will it be equal to the demands of thorough, replicable searches as required by systematic reviews. authors’ contribution: mnkb conceived the study idea. dg provided his unique professional medical librarian insight on the subject. both authors contributed equally to the study execution, data collection and paper writing. acknowledgements: chou, wen-ying (sylvia) (nih/nci, usa) and her colleagues for providing a list of 500+ publications which they used in their systematic review. corresponding author maged n. kamel boulos associate professor of health informatics plymouth university, united kingdom email: mnkamelboulos@plymouth.ac.uk google scholar is not enough to be used alone for systematic reviews 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi references [1] henderson j. google scholar: a source for clinicians? cmaj. 2005;172(12): 1549–50. [2] giustini d. how google is changing medicine. bmj. 2005 dec 24;331(7531): 1487-8. [3] banks ma. the excitement of google scholar, the worry of google print. biomed digit libr. 2005;2(1):2. [4] kousha k, thelwall m. sources of google scholar citations outside the science citation index: a comparison between four science disciplines. scientometrics. 2008;74(2):273-294. [5] anders me, evans dp. comparison of pubmed and google scholar literature searches. respir care. 2010 may;55(5):578-83. [6] shultz m. comparing test searches in pubmed and google scholar. j med libr assoc. 2007;95(4):442-453. [7] vine r. google scholar. j med libr assoc. 2006;94(1):97–9. [8] giustini d, barsky e. a look at google scholar, pubmed, and scirus: comparisons and recommendations. jchla / jabsc. 2005;26(3):85–9. [9] vine r. google scholar. j med libr assoc. 2006;94(1):97–9. [10] nourbakhsh e, nugent r, wang h. medical literature searches: a comparison of pubmed and google scholar. health info libr j. 2012;29:214–222. [11] gray je, hamilton mc, hauser a. scholarish: google scholar and its value to the sciences. iss sci tech librarianship. 2012;70. [12] walters wh. comparative recall and precision of simple and expert searches in google scholar and eight other databases. portal: libraries & the academy. 2011;11(4):971-1006. [13] hightower c. shifting sands: science researchers on google scholar, web of science, and pubmed, with implications for library collections budgets. iss sci tech librarianship. 2010;63:76-94. [14] neuhaus e, asher a. the depth and breadth of google scholar: an empirical study. libraries and the academy. 2006;(2):127–41. [15] jacsó p. google scholar: the pros and the cons. online info rev 2005;29(2):208–14. [16] gehanno jf, rollin l, darmoni s. is the coverage of google scholar enough to be used alone for systematic reviews. bmc med inform decis mak. 2013 jan 9;13(1):7. [17] chou wy, prestin a, lyons c, wen ky. web 2.0 for health promotion: reviewing the current evidence. am j public health. 2013 jan;103(1):e9-18. [18] mastrangelo g, fadda e, rossi cr, zamprogno e, buja a, cegolon l. literature search on risk factors for sarcoma: pubmed and google scholar may be complementary sources. bmc res notes. 2010 may 10;3:131. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts content analysis of syndromic twitter data bethany keffala*1, mike conway2, son doan2 and nigel collier3 1linguistics, university of california, san diego, la jolla, ca, usa; 2university of california, san diego division of biomedical informatics, la jolla, ca, usa; 3national institute of informatics, tokyo, japan objective we present an annotation scheme developed to analyze syndromic twitter data, and the results of its application to a set of respiratory syndrome-related tweets [1]. the scheme was designed to differentiate true positive tweets (where an individual is experiencing respiratory symptoms) from false positive tweets (where an individual is not experiencing respiratory symptoms), and to quantify more finegrained information within the data. introduction the popularity of twitter, a social-networking service, creates the opportunity for researchers to collect large amounts of free, localizable data in real-time. data takes the form of short, user-written messages, and has been employed for general syndromic surveillance [2] and surveillance of public attitudes toward the h1n1 flu outbreak [3]. accessibility of tweets in real-time makes them particularly appropriate for use in early warning systems. data collected through keyword search contains a significant amount of noise, however, annotation can help boost the signal for true positive tweets. methods the annotation scheme was developed based on information relevant for early warning systems (e.g. who is experiencing symptoms, and when) as well as other information present in the tweets (e.g. aspirations regarding symptoms, or abuse of substances such as cough syrup). categories included experiencer: self/other, temporality: current/non-current, sentiment: positive/negative, information: providing/seeking, language: non-english, aspiration, hyperbole, and substance abuse. all categories with the exception of language and substance abuse were defined in reference to diseases or symptoms. the scheme was applied to 1,100 respiratory syndrome-related tweets (544 false positive, 556 true positive) from a previously collected corpus of syndromic twitter data [2]. inter-annotator agreement was calculated for 9% of the data (100 tweets). results inter-annotator agreement was generally good, however certain categories had lower scores. categories for experiencer, temporality, sentiment: negative, information: providing, and language all had kappa values above .9, sentiment: positive, aspiration, and substance abuse had kappa values above .7, and information: seeking and hyperbole had kappas above .6. there was good separation between true positive tweets and false positive tweets, especially for the experiencer: self, temporality: current, sentiment: negative, aspiration, hyperbole, and substance abuse categories (see table). true positive data were more likely to belong to any category except information: providing, and substance abuse, in which cases false positive tweets had greater likelihood of category inclusion. within the true positive data, we found that users were more likely to reference symptoms that they themselves were currently experiencing than they were to reference another person’s symptoms or non-current symptoms. sentiment was largely negative, and there was significant use of aspiration and hyperbole. conclusions future work will apply the scheme to other syndromes, including constitutional, gastrointestinal, neurological, rash, and hemorrhagic. table 1. percentages of tweets included in each category. keywords social media; surveillance; respiratory syndrome references 1. n. collier, r. matsuda goodwin, j. mccrae, s. doan, a. kawazoe, m. conway, a. kawtrakul, k. takeuchi, d. dien. (2010). “an ontology-driven system for detecting global health events”, proc. 23rd international conference on computational linguistics (coling), beijing, china, august 23-27, pp. 215-222, available from http://aclweb.org/anthology/c/c10/c10-1025.pdf. 2. collier, n. & doan, s. (2011). “syndromic classification of twitter messages”, proc. ehealth 2011, malaga, spain. november 21-23. 3. chew, c. & eysenbach, g. (2010). pandemics in the age of twitter: content analysis of tweets during the 2009 h1n1 outbreak. plos one 5(11): e14118. *bethany keffala e-mail: bkeffala@ucsd.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e162, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts who’s not coming to dinner? evaluating trends in online restaurant reservations for outbreak surveillance elaine o. nsoesie*1, 2, 4, david l. buckeridge3 and john s. brownstein1, 2, 3 1children’s hospital informatics program, boston children’s hospital, boston, ma, usa; 2department of pediatrics, harvard medical school, boston, ma, usa; 3department of epidemiology, biostatistics and occupational health, mcgill university, montreal, qc, canada; 4network dynamics and simulation science laboratory, virginia bioinformatics institute, virginia tech, blacksburg, va, usa objective the objective of this study is to evaluate whether trends in online restaurant table reservations can be used as an early indicator for a disease outbreak. introduction epidemiologists, public health agencies and scientists increasingly augment traditional surveillance systems with alternative data sources such as, digital surveillance systems utilizing news reports and social media, over-the-counter medication sales, and school absenteeism. similar to school absenteeism, an increase in reservation cancellations could serve as an early indicator of social disruption including a major public health event. in this study, we evaluated whether a rise in restaurant table availabilities could be associated with an increase in disease incidence. methods we monitored table availability using opentable; an online restaurant table reservation site for cities in the usa and mexico. our analysis can be summarized as follows. first, using the opentable site, we searched for the number of restaurants with available tables for two persons at lunch and dinner. since different regions and individuals have different eating habits, we defined the lunch period between 12-3:30pm and dinner between 6-10:30pm. we searched for available tables every hour and half past the hour for every day of the week. next, we investigated any occurrences of social unrest and natural disasters, which might have affected the trend in the time series. lastly, using moving averages, cross-correlations and regression models, we elucidated and compared the time-trend in the data of table availabilities to data collected for various disease outbreaks. in the usa, we examined table availability for restaurants in boston, atlanta, baltimore and miami. for mexico, we studied table availabilities in cancun, mexico city, puebla, monterrey, and guadalajara. results preliminary results indicated differences in mean table availabilities observed during weekdays and weekends. however, these differences were statistically significant only for boston and miami (p < 0.01). statistical significant differences were also observed for mean table availabilities at lunch and dinner for all the cities (p < 0.001). conclusions the unavailability of reasons for cancellations introduces limitations to this data source. however, monitoring increases in cancellation of restaurant table reservations may be moderately useful for detecting epidemics especially in developing countries with limited public health infrastructures and resources. we therefore present a framework for future surveillance efforts. keywords developing countries; infectious diseases; alternative data sources; reservation sites *elaine o. nsoesie e-mail: elaine.nsoesie@childrens.harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e170, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts long-term asthma trend monitoring in new york city: a mixed model approach stephen e. schachterle*, robert w. mathes, marc paladini and don weiss new york city department of health and mental hygiene, queens, ny, usa objective show the benefits of using a generalized linear mixed model (glmm) to examine long-term trends in asthma syndrome data. introduction over the last decade, the application of syndromic surveillance systems has expanded beyond early event detection to include longterm disease trend monitoring. however, statistical methods employed for analyzing syndromic data tend to focus on early event detection. generalized linear mixed models (glmms) may be a useful statistical framework for examining long-term disease trends because, unlike other models, glmms account for clustering common in syndromic data, and glmms can assess disease rates at multiple spatial and temporal levels (1). we show the benefits of the glmm by using a glmm to estimate asthma syndrome rates in new york city from 2007 to 2012, and to compare high and low asthma rates in harlem and the upper east side (ues) of manhattan. methods asthma related emergency department (ed) visits, and patient age and zip code were obtained from data reported daily to the nyc department of health and mental hygiene. demographic data were obtained from 2010 us census. zip codes that represented high and low asthma rates in harlem and the ues of manhattan were chosen for closer inspection. the ratio of weekly asthma syndrome visits to total ed visits was modeled with a poisson glmm with week and zip code random intercepts (2). age and ethnicity were adjusted for because of their association with asthma rates (3). results the glmm showed citywide asthma rates remained stable from 2007 to 2012, but seasonal differences and significant inter-zip code variation were present. the harlem zip code asthma rate that was estimated with the glmm was significantly higher (5.83%, 95% ci: 3.65%, 9.49%) than the asthma rate in ues zip code (0.78%, 95% ci: 0.50%, 1.21%). a linear time component to the glmm showed no appreciable change over time despite the seasonal fluctuations in asthma rate. glmm based asthma rates are shown over time (figure 1). conclusions glmms have several strengths as statistical frameworks for monitoring trends including: 1. disease rates can be estimated at multiple spatial and temporal levels, 2. standard error adjustment for clustering in syndromic data allows for accurate, statistical assessment of changes over time and differences between subgroups, 3. “strength borrowed” (4) from the aggregated data informs small subgroups and smooths trends, 4. integration of covariate data reduces bias in estimated rates. glmms have previously been suggested for early event detection with syndromic surveillance data (5), but the versatility of glmm makes them useful for monitoring long-term disease trends as well. in comparison to glmms, standard errors from single level glms do not account for clustering and can lead to inaccurate statistical hypothesis testing. bayesian hierarchical models (6), share many of the strengths of glmms, but are more complicated to fit. in the future, glmms could provide a framework for grouping similar zip codes based on their model estimates (e.g. seasonal trends and influence on overall trend), and analyzing long-term disease trends with syndromic data. figure 1: harlem zip code (red), the upper east side zip code (blue), and citywide (black) estimates are shown as dotted lines surrounded by 30% credibility bands in solid lines. keywords asthma; long term trends; generalized mixed models acknowledgments this work was carried out in conjunction with a grant from the alfred p. sloan foundation (#2010-12-14). we thank the members of the new york city department of health and mental hygiene syndromic surveillance unit. references 1. diggle, p.j., liang k.y., & zeger, s. l. (1994). analysis of longitudinal data. oxford: oxford university press. 2. gelman, a., hill, j., (2007). data analysis using regression and multilevel/hierarchical models. new york: cambridge university press. 3. claudio, l. et al. (1999) socioeconomic factors and asthma hospitalization rates in new york city. journal of asthma. 36(4): pp. 343350. 4. jones, k., johnston r. j., and pattie c. j. (1992). people, places and regions: exploring the use of multi-level modelling in the analysis of electoral data. british journal of political science, 22, pp 343-380. 5. kleinman, k., lazarus, r., platt, r., (2004). a generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. aje, 159, pp 217-224. 6. chan, t.c., et al. probabilistic daily ili syndromic surveillance with a spatio-temporal bayesian hierarchical model. plos one. 5(7): e11626. *stephen e. schachterle e-mail: steve.schachterle@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e123, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts user experience of the u.s. department of defense (dod) respiratory disease dashboard jennifer a. cockrill*1, alice y. tsai1, timothy c. campbell2, jean-paul chretien3, julie a. pavlin1 and ronald l. burke1 1division of geis operations, armed forces health surveillance center, silver spring, md, usa; 2applied physics laboratory, johns hopkins university, laurel, md, usa; 3division of integrated biosurveillance, armed forces health surveillance center, silver spring, md, usa objective evaluate the user experience of a novel electronic disease reporting and analysis system deployed across the dod global laboratory surveillance network introduction lessons learned from the 2009 influenza pandemic have driven many changes in the standards and practices of respiratory disease surveillance worldwide. in response to the needs for timely information sharing of emerging respiratory pathogens (1), the dod armed forces health surveillance center (afhsc) collaborated with the johns hopkins university applied physics laboratory (jhu/apl) to develop an internet-based data management system known as the respiratory disease dashboard (rdd). the goal of the rdd is to provide the afhsc global respiratory disease surveillance network a centralized system for the monitoring and tracking of lab-confirmed respiratory pathogens, thereby streamlining the data reporting process and enhancing the timeliness for detection of potential pandemic threats. this system consists of a password-protected internet portal that allows users to directly input respiratory specimen data and visualize data on an interactive, global map. currently, eight dod partner laboratories are actively entering respiratory pathogen data into the rdd, encompassing specimens from sentinel sites in eleven countries: cambodia, colombia, kenya, ecuador, egypt, honduras, nicaragua, paraguay, peru, uganda, and the united states. a user satisfaction survey was conducted to guide further development of the rdd and to support other disease surveillance efforts at the afhsc. methods user training was provided to partner laboratories during a transition of data submission from excel spreadsheet to rdd electronic data entry between november 2011 and may 2012. a user experience survey was distributed to the participating laboratories in august 2012 and based on the experience of 139 entries. the survey adopted elements of the swot (strength-weaknesses-opportunities-threats) analysis to determine the system’s strengths and weaknesses as well as to solicit users’ perspectives on the efficiency of the system in assisting with disease surveillance data entry and visualization. questionnaires in an open-ended (free-text response) format were distributed to all eight participating laboratories. common themes were identified based on the solicited responses. results although only four of eight participating laboratory partners replied to the survey (50% survey response rate), all survey were completed without any omission of questions (100% completion rate). 2/25 (8%) total responses were neutral comments and therefore omitted in the thematic analysis (table 1). in general, there was a distinct dichotomy in opinion between overseas laboratories and domestic laboratories with regard to the usefulness of the rdd, with overseas laboratories viewing the rdd as more useful than domestic laboratories. a review of the comparison between weekly specimens submitted to the afhsc via excel spreadsheet and data entered directly into the rdd revealed misunderstandings about the meaning of the data entry labels in the rdd interface. it was noted by four laboratories that a “quick start” user manual would be useful to clarify the definitions of some data labels. conclusions overall, this user experience evaluation has identified the needs for additional training on rdd data entry procedures and a “quick start” user manual to support the standardization of surveillance definitions. in general, users appreciate the visualization of the global dod laboratory network data. this evaluation demonstrated the importance of active participation from data contributors and the invaluable organizational support in the development of the rdd as an electronic disease reporting and analysis system. keywords outbreak detection; disease surveillance; user experience evaluation; data management acknowledgments sheri lewis, mph, jhu/apl program manager & jose l. sanchez, md, mph, afhsc/global emerging infections surveillance and response systems (geis) operations, sti pillar head. this work was supported in part by an appointment to the postgraduate research participation program at the u.s. army public health command (usaphc) administered by the oak ridge institute for science and education through an interagency agreement between the u.s. department of energy and usaphc. references 1. who. pandemic influenza preparedness framework for the sharing of influenza viruses and access to vaccines and other benefits. geneva:who press, 2011. http://whqlibdoc.who.int/publications/ 2011/9789241503082_eng.pdf *jennifer a. cockrill e-mail: jennifer.cockrill@us.army.mil online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e184, 2013 factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. richmond d. nangsangna1, 2, frances da-costa vroom3* 1school of public health, college of health sciences, university of ghana 2district health directorate, kwahu afram plains south, ghana health service, eastern region, ghana 3biostatistics department, school of public health, college of health sciences, university of ghana abstract over the years, health care delivery and ways of accessing health information have transformed rapidly through the use of technology. the internet has played a key role in this advancement by serving as an important source of health information to people regardless of their location, language or condition. this cross sectional study was conducted in the kwahu west municipal to determine factors influencing online health information seeking behaviours among patients. three hospitals in the municipality were purposively selected for the study. outpatients attending these facilities were systematically selected and data was collected using structured interviewer administered questionnaire. the study findings revealed that internet usage rate among patients was 85.8%. however, only 35.7% of patients ever used the internet to access health information. sex, education and average monthly income were significant factors associated with online health information seeking. the study also showed that, computer and internet experience factors increased the probability of using internet for health information. after adjusting for confounding factors; being employed, earning higher income and owning a computer were positive predictors of online health information seeking. it is important to explore other means of reducing the disparity in information access by improving skill and health literacy among the low social class who cannot afford internet ready devices. health care providers should recognize that patients are seeking health information from the internet and should be prepared to assist and promote internet user skills among their patients. keywords: internet, online health information seeking behaviour * corresponding author: frances da-costa vroom: email: fbvroom@ug.edu.gh doi: 10.5210/ojphi.v11i2.10141 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi introduction the internet is defined as a global network of computers and other communication gadgets that enables communication directly and transparently. it is commonly said to be a network of networks or network of computers. it is also said to be a system for information sharing and a means for interaction between individuals and communication devices regardless of their location. lately, internet use has been said to be increasing rapidly and the vast increase, can be attributed to rapid evolvement in information and communication technology (ict), availability of information and affordability to access [1]. in the past, people obtained health information by visiting the library, consulting a health care professional or speaking to trusted associates. the phenomenon has since changed due to the rapid evolvement of information technology [2]. the internet serves as a major source of health information for both professional and private reasons. according to the world health organization, many individuals especially in the developed world, access online health information, participate in online communities and buy products and services for health and wellbeing. on the other side, records of patients are also networked by health institutions [3,4]. this has led to new forms of risk such as sale and use of health data of individuals and groups. individuals also are at risk of poor health due to counterfeit, adulterated and unapproved drugs trade, and other products that are illegally promoted by some internet sources [5]. research has indicated that, there has been a 20% increase in internet users, with 80% of adult internet users in the us (93 million) using the internet to obtain health-related information [6]. online health information seeking is on the increase and information obtained has varied impacts on the health outcomes of individuals. apart from the development in ict, individuals desire to contribute to their health and to reduce cost of care is known to be a contributing factor [7]. it has the potential of bridging the gap of inadequate access to health care especially in rural areas with limited health resources. the search for health information via the internet is fast growing among all health stakeholders. for example, studies show that the use of internet by elderly people in china is increasing rapidly as it is considered convenient and meets their needs [6]. in the usa and europe, about 72% and 71% of internet users conduct health related searches. availability and access to health information online is known to empower patients and improve physician and patient relationship to be more participatory. the development is known to have positive impact as patients who are adequately informed of their condition, tend to have better adherence to treatment. it also has the potential of reducing health care cost [2]. a study conducted among patients visiting the surgical care department of a tertiary hospital in ghana, revealed that among 35.7% of patients who had access to the internet, only 30.4% of them were aware of the availability of health information related to their disease condition on the internet. subsequently, only 15.7% had visited the internet to access health information concerning their condition [8]. characteristics of online health information seekers several studies conducted in developed countries have found a lot of characteristics of online health information seekers, which include socio-demographic and health related characteristics. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi age is one of such characteristics. patients who use the internet for health related purposes are middle-aged and older people, females, more educated individuals and people with internet usage skills [9]. little is known about the behaviours and characteristics of online health information seekers in developing countries especially in africa. these could help understand the needs of online health seekers as well as the factors that affect their search and use of information [9]. also, in spite of the large health information that exists on the internet, there is little consistency in terms of how many people actually use this information, if it meets their expectations, and the implications of this usage [9]. there are many reasons why people should seek health information from the internet however, they may not utilize this resource as much as they should [10]. several factors have been identified as influencing the search for health information online. for example, access to internet and search for health information in ghana is said to be affected by unreliable and slow internet connection, high cost of internet service, and unreliable power supply [1]. a study among nigerian university students also revealed that, 42% of the respondents perceived the cost of internet access as a major constraint to accessibility of information via the internet as well as erratic power supply [10]. in ghana, doctor to population ratio as at 2017 was 1:7374 [12]. this is above the who recommendation of 1:1000. this is especially, worsened by the unequal distribution of few health care facilities in the country. it is therefore imperative that, individuals will resort to the internet for health information in order to avoid the pressures of accessing care through the few facilities and staff available. few studies however, have investigated individuals’ online health information seeking behaviour. the few identified have focused on students and urban dwellers [1,11,13]. access to internet in ghana has greatly increased especially through widespread telecommunication networks and use of smart phones across all areas; urban, semi-urban and rural [14]. it presupposes that, rural dwellers may be having similar online health seeking behaviours as their urban counterparts. however, little is known about online health seeking behaviours among rural or semi-urban dwellers in ghana. this study therefore seeks to identify factors influencing online health information seeking behaviours among patients in a semi-urban and rural district in ghana. methods study area the study was conducted in nkawkaw, the administrative municipal capital of the kwahu west municipality. kwahu west municipal forms part of the twenty six (26) municipalities and districts in the eastern region of ghana, with a total land size of 401km2. the municipality is commercial and cosmopolitan with its capital, nkawkaw, located about 241 kilometres northwest of accra. the municipality had a total projected population of about 114,409 in 2018. of the population 11 years and older, 87.0 percent are literate. the proportion of literate males is higher (92.0%) than that of females (82.5%). according to the 2010 national population and housing census report, fifty-five percent (55.0%) of individuals 12 years and older in the municipality, own mobile phones of which males constitute 59.2%. about seven percent (6.6%) of the population who are 12 years factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi and older use internet facilities in the municipality. only six percent of the total households in the municipality have desktop/laptop computers. the municipality is divided into eleven (11) sub-municipals with fifty (50) health facilities. the health facilities comprise of 36 government owned community-based health planning and services (chps) and 9 health centres, 2 private maternity homes, two private hospitals (kenop care hospital and agyakwa hospital) and one faith-based hospital (holy family hospital). according to the district health annual performance report 2017, the annual institutional opd attendance rate saw the three hospitals with major coverage of 78% (holy family hospital = 57%, kenop care hospital = 31/% and agyakwa = 12%). study design and population this was a descriptive cross sectional quantitative study conducted among outpatients attending the three hospitals in the municipality. the study involved all outpatients seeking health care at hospitals in the kwahu west municipality. outpatients visiting the three health facilities (kenop care hospital, agyakwa hospital and holy family hospital), on days of data collection qualified to be part of the study. patients attending health facility for the first time, emergency cases, and patients who were too ill were excluded from the study. sample size determination using the formula adopted from cochran [14] for descriptive cross sectional studies, i.e. 𝑛 = 𝑍2𝑃(1−𝑃) 𝑑2 (where n= estimated sample size, z= standard value for 95% confidence level; 1.96, p = estimated proportion of patients who access health information online from a previous study; 15.7% (8) and d = margin of error; (0.05), a total of 204 patients were recruited for the study. sampling procedure three hospitals which are holy family hospital, agyakwa hospital and kenop care hospital, all in the municipality were purposively selected for the study. proportionate sampling method was used to determine number of outpatients to be interviewed from each hospital based on the opd institutional attendance rate (57% from holy family hospital, 31% from kenop care hospital and 12% from agyakwa hospital). at the hospitals, systematic sampling method was used to select outpatients. during the visit, the day’s records of outpatients containing serial numbers/outpatient record numbers which are documented in the outpatient register, was used. using the total sample size divided by each facility sample size, an interval of 2, 3 and 8 was obtained for holy family hospital, kenop care hospital and agyakwa hospital respectively. randomly selecting a record number from the day’s outpatient register as the 1st patient, other outpatients were selected using the obtained intervals. selected patients who agreed to be interviewed were recruited and questionnaires administered by the interviewer. this was done in each hospital until the desired sample size for the study was achieved. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi data collection procedure data was collected from respondents using a structured questionnaire administered by the researcher. to avoid interruptions, interviews were conducted by researcher after patients had completed their visit or were not being engaged by health care providers. a private place was identified in each hospital, to conduct interviews. this was to ensure confidentiality and genuine responses. data collection tools a structured questionnaire was used to collect data from patients. the questionnaire contained close-ended questions and a few likert type questions. the questionnaire included questions on patient background and clinical characteristics as well as patients’ computer and internet experience. quality control questionnaires for data collection were reviewed by the research supervisor after they were designed by the researcher. pre-testing of the questionnaire was conducted at kwahu government hospital, located at the capital of kwahu south district; atibie, a neighbouring district. all errors identified in the questionnaires were corrected before a final copy was printed for use. one participant was entitled to answer only one questionnaire. questionnaires were checked daily to ensure accuracy and completeness. data processing and analysis to ensure easy data entry, each question was assigned a code. completed questionnaires were entered into the ibm statistical package for social sciences (spss) version 21 software. after data entry, the data was verified and cleaned to eliminate errors and ensure completeness. the cleaned data was then exported to stata se v.13 for analysis. quantitative variables were summarized using measures of central tendency, specifically mean and standard deviation. categorical variables were summarized using frequencies and percentages. for comparison, bivariate analysis was conducted using chi-square test to determine patient characteristics that influence online health information seeking. logistic regression model was used to determine patient factors that affect use of online health information. a significance level of 95% was used with α = 0.05. results were then presented in tables. ethical considerations approval to conduct the study was sought from administrators of the various hospitals. informed consent was obtained from study participants. participants signed or thump-printed the consent forms prior to completing the questionnaire. participants were adequately informed about their right to refuse to participate in the study and opt out at any time if they wished to do so. participants’ privacy, confidentiality and anonymity were protected. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi results general characteristics of study population recruitment efforts resulted in a total of 204 participants. the ages of study participants ranged from 16 to 94 years (mean = 37.7 years, sd = 13.5). the highest proportion was in the age category below 30 years (34.8%). more than half of the participants (58.3%) were married, with majority (37.3%) having 1 to 2 children. almost three quarters (72.5%) of participants were christians, 24.5% belonged to the islamic religion whilst 1.5% were traditionalists or do not believe or practice any religion. majority of participants (48%) had secondary education, 34.3% had tertiary education whilst 7.4% had no formal education. over half of the participants (54.4%) were employed, 18.6% were unemployed with 2.5% having retired from active service. over one quarter (28.4%) had monthly income between 500 and 1000ghs, 26% below 500ghs, and 17.6% had income >1000ghs. the proportion of participants who were registered on the national health insurance scheme were 94.6% and 20.1% were on other health insurance policies. clinical history revealed that 74.0% of participants did not have any chronic illness, 21.1% had one chronic illness and only 5.4% had two or more chronic illnesses. twenty-four percent of participants reported they had no friend or relative with chronic illness. on frequency of visits made by participants to clinician in the past six months, 32.8% reported visiting 1 to 2 times, 19.1% visited more than 5 times and 43% perceived their personal health status as very good. health information seeking and the internet out of 175 (85.78%) participants who have ever used the internet, sixty-four of the participants (31.37%) reported that they have ever used the internet for seeking health information. details are provided in table 1. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi socio-demographic factors and health information seeking using the internet at a significance level <0.05, sex (p=0.037), level of education (p<0.0001), employment status (p<0.0001), average monthly income (p=0.001) and membership on other insurance policy (p=0.002) were found to be significantly associated with the use of internet for health information purposes (table 2). factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi use of internet for health information and clinical history results in table 3 revealed that none of the studied clinical factors has a significant effect on seeking health information using the internet. however, proportion of participants who used the internet for health information was higher among those who have a known chronic disease, those who indicated they have only one chronic disease, participants who have a friend or a relative with a known chronic disease as well as participants who perceived their health status as very good. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi use of internet for health information and computer/internet experience binary logistic regression was also performed in order to identify computer and internet related factors that influence patients search for health information through the internet. all studied computer and/or internet experience factors were found to be significant to the use of the internet for seeking health information. this is shown in table 4. factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi factors that affect patients online health information seeking factors that affect online health information seeking among patients were identified in this study. in controlling for other factors, the multiple logistic regression output shown in table 5 revealed that, employment, average monthly income above ghs1000 and computer ownership at home were significant factors that affect the use of internet for health information purposes. as compared to individuals who were employed, individuals who were students and employed at the same time, had lesser odds of using the internet for health information purposes (or = 0.05, ci = 0.01-0.40, p=0.004). participants whose average monthly income is above ghs 1000 and those who owned personal computers at home had higher odds of using internet for seeking health information (or=12.34, ci=1.87-81.55, p=0.009) and (or=5.23, ci=1.47-18.50, p=0.011) respectively (table 5). factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi discussion studies have shown that there is an increasing interest in health information among the general population and the internet has become an important source in recent times [16]. out of 204 participants interviewed in this study, 175 of them representing 85.8% reported ever using the internet for one or more purpose. all participants who reported ever using the internet were also aware of the availability of health information online. the high coverage of internet use is evident of the increasing demand and use of internet in developing countries [17]. this finding is not surprising, considering the rising popularity of the internet and the ease of access by android, ios, and microsoft powered smartphones and other hand-held devices. despite the high internet use and awareness of the availability of health information on the internet among participants, only 64 of them (31.37%) indicated ever using the internet for health related purposes. this finding is higher compared to a study conducted among patients attending korlebu teaching hospital where internet access was 35.7% and internet use for health information purposes was 15.7% [8]. the difference in rates in these studies can be attributed to the increased rate of technology penetration and internet usage among the population over time. also, compared to our findings, internet usage rate of 67.7% for health related purposes was found among students in a ghanaian [1]. the authors indicated the high rate among students could be due to high access to wi-fi on the university campuses. a similar study conducted in kuwait among patients also revealed that, 62.9% of patients used the internet for obtaining health related information [16]. there is discrepancy in the low internet use for health information purposes in this study as compared to the high internet access and awareness of the availability of health information on the internet. just as in many parts of developing countries, awareness and availability of information does not often imply access and use [18]. this is particularly so since the possession and access to internet-ready devices such as computers at work or home and particularly smart phones is as high as 81.86%. the internet has the potential to critically change the doctor-patient relationship, in that it offers an opportunity for patients to increase their knowledge, become more informed and increase their involvement in the health care decision-making process [19]. it is unclear if health workers in the study site also access health information using the internet. health workers with such skills could play an essential role in assisting, educating and motivating their clients about health information on the internet. many factors have been determined to influence online health information seeking [16]. just as revealed in other research findings, women in this study were more likely than men to search for health information using the internet. also, online health information seekers were mostly educated, employed, earn more income and have access to smart phone and computer at home and at work [2,8,16,17]. the likely reason for females being more likely to search online for health information than males can be attributed to the fact that, males often use the internet for entertainment purposes and females are much concerned about their health and that of their children or relatives [20]. also, being employed and earning more income ensures that individuals are capable of acquiring internet-ready devices such as smart phones and laptops. educated people may be more aware of information sources other than health care professionals and may not hesitate to use them especially when they are not satisfied with the information given by health factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi care professionals. they are also likely to be able to interpret and understand information they read from the internet [21]. similar to other study findings [16], age, marital status, parental status and religion in this study, were found not to be significantly associated with the use of internet for health information purpose. this finding however does not agree with other studies [2,21]. for age, marital and parental status, the difference in results between the two studies could be attributed to the fact that, majority of participants in this study were young people who are not likely to search for health information online but would rather use the internet for entertainment and other purposes and also because they are mostly free from chronic illnesses compared to the aged [22]. it is also important to note that, most of the young persons in this study were males who are not likely to use internet for health information compared to the females. having greater number of children is said to influence online health information search in order to meet their health needs [2]. in this study, only a few study participants reported having more than 3 children which could explain the nonsignificant association with internet use for health information. findings on the influence of health insurance on health information seeking behaviour on the internet are contradictory. a higher probability of using the internet for health information purposes was found among state insured individuals [23]. in contrast, a related study revealed a higher probability among individuals with private health insurance [24]. in this study, there was no significant association between national health insurance status and health information access using the internet. the association was however significant for private health insurance and the use of internet for health information purposes. the relationship between health information seeking through the internet and health insurance status is attributed to the amount spent on seeking health care. where out of pocket payment and private insurance are costly, there are financial constraints to accessing health care services and hence individuals may seek health information through other means including the internet [2]. ghana has a nation-wide national health insurance coverage. this could partly explain why there was an association between use of internet for health information and subscribing to private health insurance. the health status of some people may require more care than the general population. with increased use of technology in the general population, it is expected that more patients will begin to accept e-health in order to save time, reduce costs and avoid inconvenience [4,24]. a finding in this study that is inconsistent with earlier studies [2,21] indicates that, patients’ clinical history such as having a chronic disease or a relative/friend having a chronic disease, number of chronic diseases, health status of patients and number of visits to a clinician were statistically insignificant with the use of internet for health information purposes. this finding in our study however conforms to a study conducted among patients in kuwait where none of the variables relating to patients’ clinical history was proved to affect online health information seeking [16]. it is worth noting that majority of participants were young with mean age of 37.7 years. the insignificant association in our study may partly be explained by the association between age and these clinical characteristics as older individuals tend to have poor health, chronic illnesses and access health services more [17,20]. people who have access to computer and internet at home or work will generally be more knowledgeable and experienced in using the computer in different daily activities including factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi searching for information. findings from studies conducted by [16,19,21] showed that, computer and internet experience are significantly associated with health information access from the internet. consumers having access to computers and internet from home or office, were more likely than others to use the internet for health information purposes. the study also revealed that, internet usage skills and time spent on the internet increases chances of health information seeking on the internet. the authors established that these variables were linked to each other as having access to a computer and internet and being experienced in using the internet, provides the freedom to use this resource anytime and hence the likelihood of searching for other information including health information. this is also evident in a study conducted among university students in ghana where students who used the internet on a daily bases also used the internet for health purposes [1]. our findings on computer & internet experience and the search for health information using the internet, strongly conform with these earlier studies, as all computer and internet experience variables showed a significant association with searching for health information online. having good internet usage skills also enables refined searches to be conducted in order to find accurate results to the questions. this in turn serves as a motivation for more internet search. in the present study after adjusting for confounding effects, employment status, average monthly income and ownership of computer at home were proven to be significant factors that affect patients search for health information using the internet. several studies among others have reported similar findings, this seems to represent a global public health problem [2,8,21]. this shows a health information seeking behaviour only among the middle to higher class of society compared to persons of lower social class who are unemployed, earn less income and do not own a computer. despite having less access to health care, persons of lower social standing tend to have limited access to internet resources that could lessen their health care burden. thus, to reduce inequalities in health and achieve universal access to health, the differences of health information seeking behaviour using the internet must be minimized through public health actions. limitations firstly, this cross sectional study provides a snapshot during the study period, on patients’ online health information seeking behaviour. further longitudinal studies are needed to unravel changes in patterns of internet use among patients. secondly, responses provided were that of individual patients based on their understanding of questions posed by the researcher. even though questions were posed in simple terms and in language patient understands, their understanding may influence responses especially patients who are not formally educated. thirdly, the study did not point out the type of information and websites information is obtained from in order to determine its accuracy. conclusions and recommendations this study was conducted in order to identify factors influencing online health information seeking behaviours among patients in kwahu west municipal. findings revealed that, internet usage rate among patients was 85.8%. however, only 35.7% of patients ever used the internet to access health information. sex, education and use of private health insurance were significant factors that influence health information seeking behaviour (hisb) using the internet. the study also showed that, computer and internet experience factors also increased the probability of using internet for factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi health information. an important finding of this study is that being employed, earning higher income and owning a computer at home are positive predictors of health information seeking behaviour using the internet. this simply implies that, one’s ability to afford internet-ready gadgets affects the use of internet for health information purposes. it is important to explore other means of reducing this disparity in information access by improving skill and health literacy, especially among the low social class who cannot afford internet ready devices. based on the findings of the study, the following recommendations were made; 1. primary health care providers in the municipality should recognize that patients are using the internet for health information and should be prepared to assist, encourage, motivate and promote internet user skills among their patients. this will increase patient involvement in care and improve treatment outcomes. 2. further research is needed to ascertain the impact of internet use for health information purposes among patients. studies to determine if health workers in the municipality use internet for health information purposes and if they possess the necessary skills to guide patients in the search for health information is also necessary. conflict of interest the authors declare no potential conflict of interests with respect to the study, authorship and/or publication of this article. references 1. osei asibey b, agyemang s, boakye dankwah a. 2017. the internet use for health information seeking among ghanaian university students: a cross-sectional study. int j telemed appl. 2017, 1756473. doi:https://doi.org/10.1155/2017/1756473. pubmed 2. nölke l, mensing m, krämer a, hornberg c. 2015. sociodemographic and health(care-)related characteristics of online health information seekers: a cross-sectional german study. bmc public health. 15(1). doi:https://doi.org/10.1186/s12889-015-1423-0. pubmed 3. johnston ac, worrell jl, di gangi pm, wasko m. 2013. online health communities. inf technol people. 26(2), 213-35. doi:https://doi.org/10.1108/itp-02-2013-0040. 4. world health organization. global diffusion of ehealth making universal health coverage achievable. report of the third global survey on ehealth. geneva: 2016. 5. world health organization. safety and security on the internet challenges and advances in member states: based on the findings of the second global survey on ehealth. geneva: world health organization, 2011. 6. wu d, li y. 2016. online health information seeking behaviors among chinese elderly. libr inf sci res. 38(3), 272-79. doi:https://doi.org/10.1016/j.lisr.2016.08.011. https://doi.org/10.1155/2017/1756473 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29225620&dopt=abstract https://doi.org/10.1186/s12889-015-1423-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25631456&dopt=abstract https://doi.org/10.1108/itp-02-2013-0040 https://doi.org/10.1016/j.lisr.2016.08.011 factors influencing online health information seeking behaviour among patients in kwahu west municipal, nkawkaw, ghana. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e13, 2019 ojphi 7. xiao n, sharman r, rao hr, upadhyaya s. 2014. factors influencing online health information search: an empirical analysis of a national cancer-related survey. decis support syst. 57, 417-27. doi:https://doi.org/10.1016/j.dss.2012.10.047. <jrn>8. kyei my, clegg-lamptey jn, ampaw-asiedu d, kyei jm. health information seeking using the internet by patients attending a surgical care department in a tertiary hospital in sub-sahara africa. 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450-458. pubmed 14. zurek k. over 10 million ghanaians use the internet report. graphic online. feb 19, 2018. 15. cochran wg. sampling techniques. 3rd ed. new york, ny: wiley; 1977. 16. alkhatlan hm, rahman kf, aljazzaf bh. 2018. factors affecting seeking health-related information through the internet among patients in kuwait. alexandria journal of medicine. 54(4), 331-36. doi:https://doi.org/10.1016/j.ajme.2017.05.008. 17. dickerson s, reinhart am, feeley th, bidani r, rich e, garg vk, hershey co. 2004. patient internet use for health information at three urban primary care clinics. j am med inform assoc. 11(6). pubmed 18. odutola ab. 2003. developing countries must invest in access to information for health improvements. j med internet res. 5(1), e5. doi:https://doi.org/10.2196/jmir.5.1.e5. pubmed 19. yusuf aa, alhaji aa. 2015. knowledge, attitude, and use of internet for medical information by patients attending specialist clinics in abuth zaria-nigeria. sub-saharan african journal 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res. 4(2), e7. https://doi.org/10.2196/jmir.4.2.e7 pubmed https://doi.org/10.4172/2167-7182.1000367 https://doi.org/10.1177/1363459307077547 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17606698&dopt=abstract https://doi.org/10.2196/jmir.4.2.e7 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12554554&dopt=abstract utility of the essence surveillance system in monitoring the h1n1 outbreak 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 utility of the essence surveillance system in monitoring the h1n1 outbreak rekha s. holtry 1 , lang m. hung 1 , sheri h. lewis 1 1 johns hopkins university, applied physics laboratory abstract the electronic surveillance system for the early notification of community-based epidemics (essence) enables health care practitioners to detect and monitor health indicators of public health importance. essence is used by public health departments in the national capital region (ncr); a cross-jurisdictional data sharing agreement has allowed cooperative health information sharing in the region since 2004. emergency department visits for influenza-like illness (ili) in the ncr from 2008 are compared to those of 2009. important differences in the rates, timing, and demographic composition of ili visits were found. by monitoring a regional surveillance system, public health practitioners had an increased ability to understand the magnitude and character of different ili outbreaks. this increased ability provided crucial community-level information on which to base response and control measures for the novel 2009 h1n1 influenza outbreak. this report underscores the utility of automated surveillance systems in monitoring community-based outbreaks. there are several limitations in this study that are inherent with syndrome-based surveillance, including utilizing chief complaints versus confirmed laboratory data, discerning real disease versus those healthcare-seeking behaviors driven by panic, and reliance on visit counts versus visit rates. key words: h1n1, swine flu, surveillance background the electronic surveillance system for the early notification of community-based epidemics (essence) enables public health practitioners to observe abnormal behavior of health indicators across jurisdictions and view geographical spread of outbreaks that span across regions [1]. the washington, dc metropolitan area of the united states (u.s.), also referred to as the national capital region (ncr), encompasses two counties in maryland, five counties in virginia, and the district of columbia. with its large population, high-profile establishments and events, the ncr draws visitors from all over the world, thereby leading to increased chances of introduction of emerging infectious diseases [2]. utility of the essence surveillance system in monitoring the h1n1 outbreak 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 the ncr disease surveillance network employs the essence system to maintain a regional surveillance capability. this network utilizes a variety of data sources collected both at a regional and jurisdictional level. locally collected sources include emergency department (ed) chief complaints while regionally collected sources include over-the-counter (otc) pharmaceutical sales and poison control call center data [3]. a cross-jurisdictional data sharing agreement established among the jurisdictions in 2004 allows cooperative sharing of health information across state/district boundaries for syndrome-based disease surveillance. per the agreement, de-identified data from the secure local databases are transmitted to a central node for aggregation with data from other regional sources. these data are then made available to epidemiologists in the participating jurisdictions. advanced visualization tools are used to organize the resulting wealth of information into a coherent view of population health status. within the ncr, sharing of aggregated data via this distinctive architecture provides useful disease surveillance information to authorized public health users while complying with hipaa privacy requirements. in the spring of 2009, a novel influenza a (h1n1) virus resulted in increased influenza-like illness (ili) activity in the u.s. throughout the summer and fall [4]. h1n1 was declared a pandemic, widespread human infection, by the world health organization (who) in early june 2009 [5]. as a result, public health departments across the u.s. were continually monitoring the health of their communities. methods ed chief complaints, the primary source of clinical data in essence, were the data used for the analyses of this study. these data are typically provided as a free text field, and as a result, the data must go through a 13-step natural language parsing process to sanitize the text and determine syndrome grouping [6]. during the syndrome grouping step, essence bins chief complaints into two levels of progressively more sensitive groups. the first level groupings are called subsyndromes and the second, syndromes. for example, a chief complaint record containing “bad food” or “food” or “food poison” is assigned to the “food poisoning” subsyndrome, and the “food poisoning” subsyndrome, along with several other subsyndromes such as “diarrhea,” “vomiting,” and “gastroenteritis,” make up the gastrointestinal (gi) syndrome. furthermore, in addition to being defined by the baseline chief complaint terms, some subsyndromes can be defined by other subsyndromes. the hierarchical binning process of chief complaints to syndromes allows maximum sensitivity for capturing particular health conditions that can present in a variety of different ways while retaining the ability to narrow down health presentations by subsyndrome. important to note is that the individual syndrome or subsyndrome definitions are determined by expert consensus and may be influenced by several factors such as data source characteristics, surveillance focus area, public health practitioner/agency priorities, etc [7]. in the ncr, the syndrome groups are: botulism-like, fever, gastrointestinal, hemorrhagic illness, localized lesion, lymphadenopathy, neurological, other, rash, utility of the essence surveillance system in monitoring the h1n1 outbreak 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 respiratory, and sudden-illness/death. the otc data source contains non-prescription medication and medical supply sales. for this data, essence bins applicable medications and supplies to the fever, gastrointestinal and the respiratory syndrome only. additionally, the user can query this data source by specific otc category or otc type. for this study, ed chief complaint and otc sales data in the ncr from 01 january 2008 through 31 december 2009 were used to compare the trends in ili during the typical flu season and then after the discovery of the novel h1n1 strain. for ed data, to maximize specificity while maintaining sensitivity, time series were generated for the ili subsyndrome made up of the fever, cough, and sore throat subsyndromes [8]. for the otc data source, thermometer sales within otc type were queried because thermometer sales were known to track closely with ili trends [9]. using the essence query portal, time series were generated for the ili subsyndrome for all ages for 2008 and 2009. then, additional time series were generated by age groups for the two years. otc thermometer time series were also generated for each of the years. thereafter, a time series for both years and a time series for each year by age group were plotted on a single graph using a specialized feature in essence. lastly, ili time series for 2008 and 2009 were plotted against otc thermometer sales for those years. results figure 1 comparing the trends from january through late march for 2008 and 2009 show gradual seasonal elevation and subsequent decline in ili counts for both years. however, for 2009 only, markedly elevated counts for ili are seen around may, june, and october. ed visits related to the ili subsyndrome in 2008 and 2009. figure 1. time series of ili-related chief complaints for 2008 and 2009. utility of the essence surveillance system in monitoring the h1n1 outbreak 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 in figure 2, the time series graph by age group for 2008 shows seasonal elevation from the prior year into january and a gradual decline in counts for all age groups, with most counts in the 1844 group. however, it is important to take into consideration that the 18-44 age group, having the largest age span, also encompasses the largest volume of counts and therefore may not necessarily reflect a true disproportion of disease. for 2009, remarkable findings include markedly high counts for all age groups with notably high counts for the 5-17 age group for each of the spikes around may, june, and october. additionally in 2009, the 0-4 age group also closely follows this pattern; however, a relatively flat time series with unremarkable counts is seen for the 65+ age group. ed visits related to the ili subsyndrome by age group in 2008 and 2009. figure 2. time series of ili chief complaints for 2008 and 2009 by age group. 2008 2009 0-4 5-17 18-44 45-64 64+ jan feb mar apr may jun jul aug sep oct nov dec jan feb mar apr may jun jul aug sep oct nov dec e d il i v is it s 100 75 50 25 0 e d il i v is it s 400 300 200 100 0 0-4 5-17 18-44 45-64 64+ utility of the essence surveillance system in monitoring the h1n1 outbreak 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 table 1 shows that age group 5-17 presented with the most dramatic increase in counts at 345% in 2009 as compared to 2008, followed by the 0-4 and 18-44 age groups (table 1). table 1. ed visit counts of ili-related chief complaints by age group for 2008 and 2009 with percent increase in cases for 2009. percent increase in ed visits related to ili subsyndrome by age group in 2009 compared to 2008. age group 2008 ili count 2009 ili count percent increase in cases for 2009 0-4 6200 13574 218% 5-17 4065 14054 345% 18-44 7518 16398 218% 45-64 2944 5092 172% 65+ 1005 1275 126% total 21,732 50,393 232% figure 3 below shows a noticeable correlation between otc thermometer sales and ed ili visits for 2008 and 2009. elevations in otc thermometer sales can also be observed in may, june, and october time frames in 2009. utility of the essence surveillance system in monitoring the h1n1 outbreak 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 ed visits related to the ili subsyndrome and corresponding otc thermometer sales for all ages in 2008 and 2009. figure 3. time series of ili-related chief complaints and otc themometer sales for 2008 and 2009. 2008 2009 time unit jan feb mar apr may jun jul aug sep oct nov dec jan feb mar apr may jun jul aug sep oct nov dec e d il i v is it s e d il i v is it s 200 150 100 50 ili subsyndrome otc thermometer ili subsyndrome otc thermometer 800 600 400 200 0 0 utility of the essence surveillance system in monitoring the h1n1 outbreak 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 discussion the retrospective syndromic data show the now confirmed h1n1 influenza epidemics of 2009 (figure 1) [10] with the first outbreak occurring in late spring of 2009 [11] and the second occurring in the fall of 2009 [12]. the two-year time frame highlights substantial differences in ili activity after the introduction of h1n1 into the community. the frequency of ed visits more than doubled in 2009, thereby showing the effect of h1n1 in 2009 as compared to seasonal ili in 2008. the 2009 h1n1 strain also displays an uncharacteristic early seasonal onset. this is starkly different from that of the seasonal flu, which typically peaks between late november and early march of a given year [13]. furthermore, it appears that h1n1-related chief complaints tapered off in early november, which is historically considered the beginning of the seasonal flu [14]. when broken down by age groups, it is evident that h1n1 had a greater impact on the very young to middle-aged groups for the period of study (figure 2, table 1). while all the age groups had increased ili presentations in 2009 as compared to 2008, ages 0-4, 5-17, and 18-44 appear to be most at risk (figure 2, table 1). this is in keeping with the clinical evidence that approximately 90% of hospitalizations and 88% of deaths from 2009 h1n1 occurred in people younger than 65 years old [15]. as ili-related chief complaints increased, otc purchases to remedy ili symptoms also increased (figure3). in 2008, both ed visits and otc sales followed the typical seasonal flu trend. for 2009, while otc thermometer sales for 2009 were again visibly correlated, atypical spikes were seen around may, june, and october periods with the onset, escalation, and decline of h1n1 outbreaks. these trends observed in the ncr are consistent with literature described by the centers for disease control and prevention (cdc) [911, 13]. the cdc noted that h1n1 appears to primarily affect the younger age groups, an important departure from seasonal flu, which impacts the vulnerable populations at both ends of the age spectrum [15]. these findings seen in essence in the ncr are important because they corroborate findings in other regions and trends seen nationally [16]. these findings support efforts by the cdc, as well as the state and local health department, whose efforts are focused on providing health messages and vaccinations to pre-school and school-aged children to slow down the spread of h1n1 [17]. surveillance and monitoring will need to continue to determine whether the emergence of h1n1 has lasting effects on seasonal influenza patterns. study limitations: there are several limitations that are inherent with syndrome-based surveillance because of its reliance on health indicator data sources. perhaps the most important among these to note is that ed patient visit counts, used in this study as the clinical data source, are based on patient chief complaints and triage entries rather than confirmed laboratory results. hence, an implicit assumption in this study is that patients categorized into the ili subsyndrome are those (at least the majority) infected with h1n1. this, however, is not an unreasonable assumption given that utility of the essence surveillance system in monitoring the h1n1 outbreak 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 in august of 2009, the cdc, attested credibility to reporting unconfirmed cases based on icd-9 syndromic diagnosies [18]. the cdc requested states to report both laboratory confirmed or “syndromic” cases of hospitaliztions and deaths. because of the sheer enormity of outbreak, the cdc reasoned that testing could not be conducted on every individual seeking care and so “laboratory-confirmed data is a vast underestimate of the true number of cases and this bias would be exacerbated over the course of the pandemic as more and more people become ill” [18]. an associated limitation of using unconfirmed data is, discerning real disease from healthcareseeking behavior driven by panic. in late april and early may of 2009, in the weeks after the recognition of cases in mexico and the who reporting confirmed cases in california and other us states [19], fueled by media headlines, eds across the us became inundated with record number of worried individuals seeking care. the vast majority presented with mild or no illness and did not require in-hospital care; furthermore, the increase in visits did not correspond to an increase in mortality [20]. however, due to the sensitive nature of syndrome-based surveillance, the “worried well” were captured within electronic disease surveillance systems in the ncr and in other states across the country [20]. in response, to increase specificity and enable extraction of cases warranting further scrutiny, ncr health departments searched incoming ed data for patients presenting with a fever, those who met specific chief complaints-based case definitions, and those who reported travel to affected areas. while these custom querying efforts partially aided in limiting the number of records for further investigation, it was still a laborious task for public health practitioners to isolate potential h1n1 cases within the large influx. there are other limitations; systems such as essence rely on visit counts, and for several reasons do not typically divide by a denominator and report by rates of illness. therefore, unreported (or unobserved) pre-processing data flow interruptions or delays can cause artifacts in time series that may be indistinguishable from real health patterns. furthermore, by reporting counts and not rates, trends seen in time series may at first glance appear elevated simply because of the of nature of the data or the type of comparisons being made. an example within this study is the 18-44 age group presenting with the most counts on the time series. as discussed previously, this group represents a large span and proportion of the general population; high counts within this group may not necessarily mean disproportionate disease distributions. yet, it is possible to overcome most of these limitations by remaining vigilant to incoming data streams, developing a good understanding of baseline counts typical for a particular region, understanding characteristics of the data and patient healthcare seeking behavior during large public health events, and employing common sense and judgment when interpreting the outputs from the system. utility of the essence surveillance system in monitoring the h1n1 outbreak 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 conclusion these findings document the emergence and spread of the 2009 h1n1 epidemics within specific health indicator data as seen in the ncr disease surveillance network. the ncr has a unique network set up for health-indicator data collection and sharing that optimizes regional and interjurisdictional disease surveillance. in 2009, regular examination of the near real-time data sources through essence allowed public health practitioners in the ncr to better understand the full extent of illness in the community that may not have been captured by traditional sources. while there are important applications of electronic disease surveillance systems, there are known limitations that must be recognized prior to interpreting findings and drawing conclusions. applied appropriately, supplementary information from systems such as essence can be invaluable and serve as a critical tool for public health decision-making. in this study, the trends observed in representative ili time series within ed and otc data sources in the ncr provided important information on outbreak characteristics and corroborated with trends seen nationally within syndromic and traditional sources [21]. specifically, comparing the health indicator data across 2008 and 2009 afforded the following observations in 2009: 1) h1n1 sustained through the spring and summer months unlike seasonal flu; 2) h1n1 spread rapidly through the community; and 3) h1n1 disproportionately affected the younger age groups. as was seen during the 2009 outbreaks of h1n1 in the ncr, information garnered from systems such as essence supported by a strong data-sharing architecture, serves a critical role in comprehensive disease surveillance. such information can assist with narrowing focus to regions most in need of vaccines, triage clinics, disease prevention/management education, and other resources. the full potential of these systems may be realized by continuous monitoring and proper application by the end users. acknowledgements this work was made possible through grant number 7uasi137-01 from the department of homeland security’s (dhs) urban area security initiative. its contents are solely the responsibility of the authors and do not necessarily represent the official views of dhs. the authors would like to acknowledge the exceptional cooperation of the maryland department of health and mental hygiene (dhmh), the virginia department of health, division of surveillance and investigation (vdh/dsi), district of columbia department of health, bureau of epidemiology and health risk assessment (dcdoh), the eight local health departments represented in the ncr enhanced surveillance operating group (esog), and the metropolitan washington council of governments’ (mwcog) health officials 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[cited 2010 feb 9]. http://www.cdc.gov/flu/weekly/. [15] centers for disease control and prevention. 2009 h1n1 flu (“swine flu”) and you. [cited 2010 feb 12]. http://www.cdc.gov/h1n1flu/qa.htm. [16] centers for disease control and prevention. cdc estimates of 2009 h1n1 influenza cases, hospitalizations and deaths in the united states, april 2009-january 16, 2010. [cited 2010 feb 12]. http://www.cdc.gov/h1n1flu/estimates_2009_h1n1.htm. [17] centers for disease control and prevention. vaccine against 2009 h1n1 influenza virus. [cited 2010 feb 12]. http://www.cdc.gov/h1n1flu/vaccination/public/vaccination_qa_pub.htm. [18] centers for disease control and prevention. surveillance for influenza and pneumoniaassociated hospitalizations and deaths for the 2009-2010 season. [cited 2010 feb 12]. http://www.cdc.gov/h1n1flu/reportingqa.htm. [19] world health organization. influenza-like illness in the united states and mexico. 2009 apr 24 [cited 2010 feb 12]. http://www.who.int/csr/don/2009_04_24/en/index.html. [20] 2009 new york city department of health and mental hygiene health alert #15: h1n1 (swine origin) update. 2009 may 1 [cited 2010 feb 15]. www.nyc.gov/html/doh/downloads/pdf/cd/2009/09md15.pdf. [21] centers for disease control and prevention. 2009 h1n1 flu u.s. situation update. [cited 2010 feb 15]. http://www.cdc.gov/h1n1flu/updates/us/. http://reuters.com/article/idustre59j58h20091012 http://www.cdc.gov/flu/weekly/ http://www.cdc.gov/h1n1flu/qa.htm http://www.cdc.gov/h1n1flu/estimates_2009_h1n1.htm http://www.cdc.gov/h1n1flu/vaccination/public/vaccination_qa_pub.htm http://www.cdc.gov/h1n1flu/reportingqa.htm http://www.who.int/csr/don/2009_04_24/en/index.html http://www.nyc.gov/html/doh/downloads/pdf/cd/2009/09md15.pdf http://www.cdc.gov/h1n1flu/updates/us/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts use of biosense for rapid assessment of the safety of medical countermeasures ralph j. coates* and kathleen gallagher division of notifiable diseases and healthcare information, centers for disease control and prevention, atlanta, ga, usa objective to conduct an initial examination of the potential use of biosense data to monitor and rapidly assess the safety of medical countermeasures (mcm) used for prevention or treatment of adverse health effects of biological, chemical, and radiation exposures during a public health emergency. introduction biosense is a national human health surveillance system for disease detection, monitoring, and situation awareness through near realtime access to existing electronic healthcare encounter information, including information from hospital emergency departments (eds). mcm include antibiotics, antivirals, antidotes, antitoxins, vaccinations, nuclide-binding agents, and other medications. although some mcm have been extensively evaluated and have fda approval, many do not (1). current fda and cdc systems that monitor drug and vaccine safety have limited ability to monitor mcm safety, and in particular to conduct rapid assessments during an emergency (1). methods to provide a preliminary assessment of the use of biosense for this purpose, we reviewed selected publications evaluating the use of electronic health records (ehrs) to monitor safety of drugs and vaccinations (medications), focusing particularly on systematic reviews, reviewed biosense data elements, and consulted with a number of subject matter experts. results more than 40 studies have examined use of ehr data to monitor adverse effects (aes) of medications using administrative, laboratory, and pharmacy records from inpatientand out-patient settings, including eds (2-4). to identify aes, investigators have used diagnostic codes; administration of antidotes, laboratory measures of drug levels and of biologic response, text searches of unstructured clinical notes, and combinations of those data elements. biosense ed data include chief complaint text, triage notes, text diagnosis, as well as diagnostic and medical procedure codes. investigations used a variety of study designs in various populations and settings; examined a wide range of medications, vaccinations, and aes; and developed a diverse set of analytic algorithms to search ehr data to detect and signal aes (2-4). most research has been done on fda-approved medications. most studies used ehr data to identify individuals using specific medications and then searched for potential aes identified from previous research. none of the studies investigated use of ehr data to monitor safety when records of an individual’s medication use could not be linked to that individual’s records of aes. biosense data could be used for ae detection, but linking aes to mcm use would require follow-back investigation. since there is limited research on aes of some mcm, there would be limited information to guide identification of potential aes. performance characteristics of the ae monitoring systems have been mixed with reported sensitivities ranging from 40-90%; specificities from 1% to 90%, and positive predictive values from < 1% to 64%, depending on the medication, ae and other characteristics of the study (2, 4). however, the small numbers of studies with common characteristics has limited the ability of reviewers to determine which types of systems have better performance for different medications and aes. some experts suggest that data in biosense, might contribute to safety surveillance of mcm. they also caution that poor predictive values and high rates of false positives reported in the literature raise concerns about burden to those conducting investigations in response to ae alerts, particularly in the context of a public health emergency. conclusions these findings suggest that biosense data could potentially contribute to rapid identification of safety issues for mcm and that some methods from published research could be applicable to the use of biosense for this purpose. however, such use would require careful development and evaluation. keywords biosense; countermeasures; safety; monitoring; medical references 1. national biodefense science board. where are the countermeasures? protecting america’s health from cbrn threats. u. s. department of health and human services, march, 2010. 2. forster aj, et al. a systematic review to evaluate the accuracy of electronic adverse drug event detection. jamia 2012;29:31-38. 3. lucado j, et al. medication-related adverse outcomes in u.s. hospitals and emergency departments, 2008. hcup statistical brief # 109. ahrq, 2011. 4. handler sm, et al. a systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. jamia 2007;14:451-8. *ralph j. coates e-mail: rjc5@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e85, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts development of a vietnamese language outbreak mapping and surveillance system david bui*1, 2, sumiko mekaru1, 3, clark freifeld1, 6 and john s. brownstein1, 5, 4 1children’s hospital informatics program, division of emergency medicine, children’s hospital boston, boston, ma, usa; 2dept of epidemiology, mel enid zuckerman college of public health, tucson, az, usa; 3dept of epidemiology, boston university school of public health, boston, ma, usa; 4dept of pediatrics, harvard medical school, boston, ma, usa; 5dept of epidemiology, biostatistics and occupational health, mcgill university, montreal, qc, canada; 6dept of biomedical engineering, boston university, boston, ma, usa objective to present the development of a surveillance system utilizing online vietnamese language media sources to detect disease events in vietnam and the south east asian region. introduction in the south east asia region (sear), infectious disease continues to be a leading cause of death. sear countries, like vietnam, are also at risk for outbreaks of emerging diseases due to high population density, proximity to animals and deforestation.(1) given vietnam’s location in sear and its recurrent outbreaks of zoonotic diseases— timely surveillance in vietnam is critical to global public health.(1) online news sources have been recognized as potential sources for early detection of emerging disease outbreaks, as was the case with sars. (2) healthmap, an innovative disease surveillance system developed at boston children’s hospital, leverages the expediency of online news media by using text-mining technology to monitor and map global disease outbreaks reported by news sources. methods healthmap currently monitors disease related news in 15 languages. building on healthmap’s existing infrastructure, we translated the system’s existing disease and location name dictionaries to corresponding vietnamese terms to train the system to detect disease and locations cited in vietnamese news sources. to ensure comprehensive capture of disease terms, both formal disease names and colloquial synonyms were used. vietnamese locations were sourced through the official vietnamese government website. search queries were developed using a variety of outbreak related terms like “bùng phát” (outbreak) or “bênh” (disease), and specific disease names like “cúm gia câm” (avian influenza). automated searches are performed in the vietnamese version of google news. results as of august 18, 2012, after 2 months in operation, the system has mapped 433 alerts in 27 diseases reported in the vietnamese media compared to 7 diseases in english in the same time period. the collected alerts were mapped to 699 province level and district level (or lower) locations compared to only 16 in the english feed. to date, the system collected 38 alerts of avian influenza from vietnamese sources compared to only 2 in english sources; 30 alerts of dengue compared to 7 in english; and 25 alerts of hand foot and mouth compared to 6. the system also collects outbreak case counts in vietnam. for example, counts of human dengue cases in 97 locations in vietnam were collected, providing a rich dataset for monitoring epidemic spread and progression. the surveillance feed also received 2 reports of outbreaks in crops. zoonotic disease outbreaks in vietnam were more comprehensively covered in the vietnamese feed compared to english. conclusions leveraging the expediency of freely available online news media, the developed surveillance system is able to detect and map outbreaks occurring in vietnam in near real-time, providing health organizations and researchers with timely and comprehensive coverage of disease events to assess pandemic risk and mobilize aid as necessary. screenshot of healthmap vietnamese alerts keywords surveillance; infectious disease; vietnam; healthmap; mapping acknowledgments sumiko mekaru, john brownstein, clark freifeld, susan aman, and the whole healthmap gang. references 1. world health organization. combating emerging infectious diseases. searo.who.int. available at: http://www.searo.who.int/linkfiles/ avian_flu_combating_emerging_diseases.pdf. accessed august 28, 2012. 2. j.s. brownstein. digital disease detection: harnessing the web for public health surveillance. n engl j med 2009; 360:2153-2157 available at: http://www.nejm.org/doi/full/10.1056/nejmp0900702. accessed april 16, 2012. *david bui e-mail: davidbui@email.arizona.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e63, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using google dengue trends to estimate climate effects in mexico rebecca t. gluskin*1, mauricio santillana2 and john s. brownstein1, 3 1boston children’s hospital, boston, ma, usa; 2harvard school of engineering and applied sciences, cambridge, ma, usa; 3department of pediatrics, harvard medical school, boston, ma, usa objective to evaluate the association between dengue fever (df) and climate in mexico with real-time data from google dengue trends (gdt) and climate data from nasa earth observing systems. introduction the incidence of dengue fever (df) has increased 30 fold between 1960 and 2010 [1]. the literature suggests that temperature plays a major role in the life cycle of the mosquito vector and in turn, the timing of df outbreaks [2]. we use real-time data from gdt and real-time temperature estimates from nasa earth observing systems to examine the relationship between dengue and climate in 17 mexican states from 2003-2011. for the majority of states, we predict that a warming climate will increase the number of days the minimum temperature is within the risk range for dengue. methods the gdt estimates are derived from internet search queries and use similar methods as those developed for google flu trends [3]. to validate gdt data, we ran a correlation between gdt and dengue data from the mexican secretariat of health (2003-2010). to analyze the relationship between gdt and varying lags of temperature, we constructed a time series meta-analysis. the mean, max and min of temperature were tested at lags 0 -12 weeks using data from the modern era retrospective-analysis for research and applications. finally, we built a binomial model to identify the minimum 5° c temperature range associated with a 50% or higher dengue activity threshold as predicted by gdt. results the time series plot of gdt data and the mexican secretariat of health data (20032010) (figure 1) produced a correlation coefficient of 0.87. the time series meta-analysis results for 17 states showed an increase in minimum temperature at lag week 8 had the greatest odds of dengue incidence, 1.12 odds ratio (1.09-1.16, 95% confidence interval). the comparison of dengue activity above 50% in each state to the minimum temperature at lag week 8 showed 14/17 states had an association with warmest 5 degrees of the minimum temperature range. the state of sonora was the only state to show an association between dengue and the coldest 5 degrees of the minimum temperature range. conclusions overall, the incidence data from the mexican secretariat of health showed a close correlation with the gdt data. the meta-analysis indicates that an increase in the minimum temperature at lag week 8 is associated with an increased dengue risk. this is consistent with the colon-gonzales et al. mexico study which also found a strong association with the 8 week lag of increasing minimum temperature [4]. the results from this binomial regression show, for the majority of states, the warmest 5 degree range for the minimum temperature had the greatest association with dengue activity 8 weeks later. inevitably, several other factors contribute to dengue risk which we are unable to include in this model [5]. ipcc climate change predictions suggest a 4° c increase in mexico. under such scenario, we predict an increase in the number of days the minimum temperature falls within the range associated with df risk. figure 1. time series correlation: google dengue trends vs. secretariat of health, mexico 2003-2010 keywords time series; mexico; google dengue trends; climate change; meta-analysis acknowledgments funded by the nih grant # 1r01lm010812-01 and the applied public health informatics fellowship program administered by cste and funded by the centers for disease control and prevention (cdc) cooperative agreement 3u38hm000414-04w1. references 1.(who), w.h.o., dengue. 2010. 2.yang, h.m., et al., assessing the effects of temperature on the population of aedes aegypti, the vector of dengue. epidemiol infect, 2009. 137(8): p. 1188-202. 3.chan, e.h., et al., using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. plos negl trop dis, 2011. 5(5): p. e1206. 4.colon-gonzalez, f.j., i.r. lake, and g. bentham, climate variability and dengue fever in warm and humid mexico. am j trop med hyg, 2011. 84(5): p. 757-63. 5.thai, k.t. and k.l. anders, the role of climate variability and change in the transmission dynamics and geographic distribution of dengue. exp biol med (maywood), 2011. 236(8): p. 944-54. *rebecca t. gluskin e-mail: rgluskin@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e94, 2013 ojphi-06-e117.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 93 (page number not for citation purposes) isds 2013 conference abstracts a conceptual strategy for stengthening esurveillance in the african region dennis d. lenaway*, helen perry and robert fagan division of global health protection, center for global health, centers for disease control and prevention, atlanta, ga, usa � �� �� �� � � �� �� �� � objective ��� ������� � � � 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������������" ��� �� � � (��"�������� ���������� �!�� � ������ �������������$������ � ��$�� �� ����� � ������ � ������������� ��������� ��� $� ��� $��� ��� $�� �� �� ���� ��� ���������� ���� ��� �� ��� �������� � ��� ��������� �� ��0���-� ���;��������� ����������� ��� �� ���� ���������������#��� ���#� � �������� ����������� ���� ��� ��������� ������ ������ ���� � ������� � ������� �� �� � �"� ��������� ������� � ���� � keywords ������ � ��<������ � ��<�� ��������� references 5 ���� ��%�� � �&��� �3���� ������ � �&���������������� �(���� ��� ���� ���� ������� � ���� ��������������� � ����������������$�2�������$� ���$�������"���=$�*+5> *dennis d. lenaway e-mail: drl7@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e117, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts enabling syndromic surveillance in pakistan ross maciejewski*1, shehzad afzal2, adam j. fairfield1, arif ghafoor2, david s. ebert2, naeem ayyaz4 and maaz ahmed3 1computer science, arizona state university, tempe, az, usa; 2purdue university, west lafayette, in, usa; 3king edward medical university, lahore, pakistan; 4university of engineering and technology, lahore, pakistan objective this work presents our first steps in developing a global real-time infectious disease surveillance system (gridds) employing robust and novel in-fectious disease epidemiology models with real-time inference and pre/exercise planning capabilities for lahore, pakistan. the objective of this work is to address the infectious disease surveillance challenges (specific to developing countries such as pakistan) and develop a collaborative capability for monitoring and managing outbreaks of natural or manmade infectious diseases in pakistan. methods utilizing our partner hospitals in the lahore, punjab area, we have begun developing a theoretical model of patient hospital visits with respect to diseases and syndromes within pakistan. our first thrust has focused on the collection, categorization and cleansing of data based on expert knowledge from our partnering institutions in pakistan. data consists of a patient’s home address and chief complaint which is then categorized into syndromes. home addresses are geocoded utilizing the google api with a resultant 72% accuracy. unknown geolocations are aggregated only at the hospital level. using this cleaned data, we employ methods similar to our previous work [1] on syndromic surveillance for early disease detection. currently, we have collected over 600,000 patient records over 1.5 years. we employ the use of choropleth maps, isopleth maps utilizing kernel density estimation of patient addresses, traditional control chart methods such as exponentially weighted moving averages (ewma), and a non-parametric time series analysis approach (seasonal trend decomposition using loess smoothing (stl) [2]) which requires only 90 days of historical data to be put into operation. the time series models are deployed as part of a real-time surveillance system in which temporal anomalies over regions can be analyzed and disease outbreaks reported. results figure 1 illustrates our visual analytics toolkit in operation. here we see the location of our partner hospital in the lahore region. the hospital coverage is in the most populous location of the city, providing data as a sentinel site for the overall health of the city. currently, our system employs the use of interactive filters and linked isopleth or choropleth maps with time series analysis on mouse over. conclusions currently our research has focused on one partner location within the city of lahore. our ongoing work is focusing on the adoption of such a system to other regions of the country and the development of disease spread simulations (particularly dengue fever) utilizing baseline data collected by our partners. we plan to integrate these models into our visual analytics system for real-time planning and simulation. keywords syndromic surveillance; visual analytics; pakistan acknowledgments this work is supported by the defense threat reduction agency award number hdtra1-10-1-0083. references [1] maciejewski, r., et al.., “forecasting hotspot – a predictive analytics approach,” ieee transactions on visualization and computer graphics, 17(4): 440-453, 2011. [2] hafen, r. p., et al., “syndromic surveillance: stl modeling, visualizing and monitoring disease counts,” bmc medical informatics and decision making, 2009. *ross maciejewski e-mail: rmacieje@asu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e106, 2013 a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle melanie j. firestone1*, sripriya rajamani2, craig w. hedberg1 1 university of minnesota, school of public health, division of environmental health sciences, minneapolis, mn, usa 2 university of minnesota, institute for health informatics and school of nursing, minneapolis, mn, usa; project consultant, minnesota department of health, st. paul, mn, usa abstract foodborne illnesses remain an important public health challenge in the united states causing an estimated 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths per year. restaurants are frequent settings for foodborne illness transmission. public health surveillance – the continual, systematic collection, analysis, and interpretation of reports of health data to prevent and control illness – is a prerequisite for an effective food control system. while restaurant inspection data are routinely collected, these data are not regularly aggregated like traditional surveillance data. however, there is evidence that these data are a valuable tool for understanding foodborne illness outbreaks and threats to food safety. this article discusses the challenges and opportunities for incorporating routine restaurant inspection data as a surveillance tool for monitoring and improving foodborne illness prevention activities. the three main challenges are: 1) lack of a national framework; 2) lack of data standards and interoperability; and 3) limited access to restaurant inspection data. tapping into the power of public health informatics represents an opportunity to address these challenges. advancing the food safety system by improving restaurant inspection information systems and making restaurant inspection data available to support decision-making represents an opportunity to practice smarter food safety. keywords: restaurant inspections, public health informatics, surveillance data, environmental health, information systems *correspondence: melanie j. firestone, fire0018@umn.edu doi: 10.5210/ojphi.v13i1.11087 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi background foodborne illnesses remain an important public health challenge in the united states. each year, an estimated 48 million people get sick, 128,000 are hospitalized, and 3,000 die from foodborne illness in the united states [1,2] resulting in an annual estimated cost of $51 billion [3]. reducing the occurrence of foodborne illness infections is a healthy people 2030 objective [4]. achieving this objective requires targeted strategies across the food system. restaurants are linked to outbreaks more often than other places of food preparation, accounting for two-thirds of outbreaks in 2017 [5]. more than 3,000 state, local, and tribal agencies are responsible for regulating the more than 1 million food establishments in the united states [6]. routine public health inspections of restaurants are conducted to identify and correct food handling errors within individual restaurants. public health surveillance – the ongoing, systematic collection, analysis, and interpretation of reports of health data to prevent and control disease and illness – is a prerequisite for an effective food control system [7]. traditionally, restaurant inspection data have not been aggregated for use as a hazard surveillance tool to identify food safety gaps and to inform foodborne illness prevention activities because these data are often not stored in ways that allow for real-time analysis. in addition, inspection conditions at a particular point of time that are either corrected immediately or shortly after the inspection are not collected. this treatment of violations as discrete events rather than as indicators of underlying trends leads to a lack of understanding in how these data relate to foodborne illness risk [8]. however, access to data on restaurant inspections could provide added value to existing food surveillance schemes and enhance food safety. the current covid19 pandemic highlights the need for better management of public health data that links the occurrence of illness to resources needed to control transmission [9]. as the united states recognizes and works toward improving public health data systems, there is an opportunity to include advancements that support enhanced food safety prevention activities. public health informatics (phi) is defined as the systematic application of information, computer science and technology to public health practice, research and learning [10]. a recent update defines phi by the effective use of information and information technology to improve population health outcomes [11]. the practice of public health is increasingly recognized as being data and information intensive [12,13]. there is a need for “informatics-savvy” health departments [14,15] where informatics is a strategic priority [16]. in recent decades, the use of electronic health records (ehrs) has increased. although the primary goal of ehrs is to improve clinical practice, ehrs provide a low-cost and timely means of accessing rich data sources for population health surveillance [17]. investment in ehrs, syndromic surveillance, and electronic case reporting (ecr), and electronic laboratory reporting (elr) have greatly advanced population health surveillance for both chronic and infectious diseases [18]. in a similar manner, creating a framework to connect restaurant inspection data to surveillance for individual cases and outbreaks of foodborne illness could directly enhance public health efforts to reduce transmission of illness in restaurant settings. the framework for an evolved public health system, referred to as public health 3.0 emphasizes leveraging cross-sector collaboration and environmental, policy, and system-level approaches to directly impact social determinants of health [19,20]. while public health 3.0 includes a focus on a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi timely and locally relevant health information systems, challenges remain in terms of people, policies and politics to realize the full potential. in this paper, we present challenges to incorporating restaurant inspection data in the overall schema of food safety and surveillance: 1) lack of a national framework to integrate restaurant inspection data into other foodborne illness surveillance systems, 2) lack of data standards and interoperability between information systems operated by public health and food regulatory agencies at local, state and federal levels, and 3) limited access to restaurant inspection data by regulators, public health researchers and consumers. these hurdles can be addressed by tapping into the power of phi. we advocate for advancing the field of phi to bring in missing pieces of data to connect the dots for betterment of food surveillance and safety. lessons learned from covid-19 are applicable to foodborne illness surveillance. as upgrades to the public health infrastructure are considered, food safety systems and surveillance should be part of those considerations. landscape: restaurant inspection data and challenges the food and drug administration (fda) publishes the food code, an evidence-based, voluntarily-adopted model that provides jurisdictions with a technical and legal basis for regulating retail food service [21]. while the food code provides uniform national standards for retail food safety, it has not been adopted nationally. local, state, and tribal regulators use this as a model to develop or adopt their own food safety regulations. since 2014, more than half of money spent on food each year in the us is spent on food prepared away from home [22]. given that food prepared away from home is an integral part of the diet in the us, there is a need to monitor food safety risks associated with it. under current covid-19 pandemic circumstances, restaurants have been a key social setting to be monitored. restaurants were ordered to comply with various directives imposed by their local jurisdictions and states to reduce transmission of sars-cov-2, the virus that causes covid-19. restaurants have gone through various phases of adapting to this new landscape. while some restaurants have closed completely others have shifted to operating on a take-out or delivery only model and some have opened with adherence to socially distanced seating, mask mandates and relevant public health guidance, or a combination of these strategies depending on local covid19 prevention measures. as public health measures are adapted to local transmission patterns of covid-19, there may be critical challenges associated with restoring supply chains and inspection practices. most inspections serve primarily an operational or administrative function. recently inspectors have had an increased role in enforcement in many areas for compliance with covid19 prevention measures. the diverse array of inspection data systems in use do not support the needs of public health practice research. the lack of interoperability between these restaurant inspection data systems means that we do not have the ability to assess critical threats to restaurant food safety in this unprecedented time. a. lack of a national framework surveillance for foodborne illnesses is a complex, multi-faceted endeavor coordinated by multiple agencies across federal, state and local levels in the united states. individual cases of reportable foodborne illness and suspected outbreaks of foodborne illness are reported to local and state health departments under state-specific reportable disease rules. many foodborne illnesses, a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi including, salmonella and shiga toxin-producing escherichia coli (stec) infections are nationally notifiable conditions that are monitored through the national notifiable diseases surveillance system (nndss) at the centers for disease control and prevention (cdc) [23]. foodborne illness outbreaks are reported to the national outbreak reporting system (nors). while authority for disease investigation resides with state and local agencies, cdc plays a critical coordinating and capacity building role. cdc’s principal method for helping to build this public health capacity is the epidemiological and laboratory capacity (elc) cooperative agreement program, which provides funding to 64 participating jurisdictions, including all 50 states, three freely associated states, five territories, and six local governments. this funding supports the staff, supplies, training, and equipment needed for public health departments to participate in nationwide surveillance networks. pulsenet, a national network of public health and food regulatory laboratories that provides molecular characterization of important foodborne pathogens demonstrates the value of a national framework for foodborne illness prevention. by working together to rapidly detect foodborne illness outbreaks across the country, pulsenet has led to the prevention of an estimated 270,000 illnesses every year from salmonella, e. coli o157 and listeria monocytogenes. this was estimated to save $507 million in medical costs and lost productivity, an economic benefit at least 70 times greater than the cost [24]. although restaurants are a frequent setting for foodborne illnesses and outbreaks, there is no coordinated surveillance system for food safety hazards identified by routine restaurant inspections, like pulsenet. the centers for disease control and prevention maintains the national environmental assessment reporting system (nears), which captures environmental assessment data from foodborne illness outbreak investigations [25]. environmental assessments differ from routine inspections because they are targeted inspections that seek to identify how and why a foodborne illness outbreak occurred with the goal of identifying opportunities for prevention. review of routine inspection reports may provide additional useful information for identifying the causes of outbreaks and prevention opportunities. the fda retail food risk factor study is a 10-year study to measure the occurrence of foodborne illness risk factors, food safety practices and behaviors, and interventions in food service facilities. the goal of this study is to provide consistent monitoring of food safety trends and efforts over time. trained data collectors observe and record food safety practices in restaurants using a standardized tool. however, the survey is episodic, only a subset of restaurants in the us are studied and the generalizability is limited to the extent that facilities in the sampling zones are generalizable to the overall industry [26]. a nears review of 9,788 restaurant-associated outbreaks showed that most commonly reported contributing factors were associated with food handling and preparation practices [27]. the center for science in the public interest examined more than 500 restaurant inspection reports in 20 cities and found that over 66% of restaurants had at least one high-risk violation [28]. data from 821 restaurants in the 2013-14 fda retail food risk factor study, showed that 86% of restaurants had violations for improper food holding/time and temperature, and 75% had violations for poor personal hygiene, both of which are major risk factors for foodborne illness transmission in a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi restaurants [26]. while these data show that major risk factors for foodborne illness transmission are commonly cited, there is no national standard for tracking, monitoring, or maintaining these vital data that could be used to monitor food safety risks more broadly or to evaluate the effectiveness of food safety interventions. b. lack of data standards and interoperability b.1. lack of data standards and interoperability apart from the issue of lack of a robust, national restaurant inspection data information system, another hurdle is a lack of standardization of both inspection practices, the inspection reports, and the relevant data. most health departments divide inspection violations into two or three categories based on the risk that the violation could lead to foodborne illness transmission. currently, the food code categorizes violations into three types based on importance – priority, priority foundation, and core [29]. prior to the 2009 food code, violations were categorized as either being “critical” or “non-critical” [30]. as of the end of 2018, 10 states had at least one state regulatory agency that was using a version of the food code older than 2009 [31]. jurisdictions may also choose to use other language to categorize violations. for example, minnesota uses 3 categories priority 1, priority 2, or priority 3 [32]. this shows that even among states that have adopted the voluntary food code, there is considerable variation in language used across jurisdictions. another issue to address is the lag in development and adoption of standards related to data in an inspection report. the fda recognized the lack of national uniformity among retail food safety programs and started the voluntary national retail food regulatory program standards initiative with the goal of defining what constitutes highly effective and responsive retail food programs. as of january 2020, there were 865 enrollees, however, there is considerable variability in levels of conformance with the nine program standards [33]. since this program is voluntary, there are still broad practical challenges for analyzing inspection data across programs. we recently conducted a study that evaluated restaurant inspection data in the context of an outbreak [8] in minnesota. this study affirmed the issue of lack of standardization in inspection reports. violations were abstracted from inspection reports from 13 jurisdictions and were mapped to the conference for food protection’s inspection report structure due to the variation in the inspection forms used across jurisdictions, even though all jurisdictions were within the same state. overall, there is a lack of standards for restaurant inspections that limits the ability to compare violations across jurisdictions. in addition, there is a lack of national standards to support the exchange of data across systems. b.2. lack of scalable information systems inspection data are rarely organized like other surveillance systems. while nears maintains national data from environmental assessments, there is no national surveillance structure for routine restaurant inspections. there is considerable variation in how routine inspection data are maintained across jurisdictions. some maintain analyzable databases, others have scanned copies of pdfs, and some maintain only paper records. among jurisdictions that maintain analyzable databases, a lack of interoperability across these systems limits the ability to monitor food safety hazards across jurisdictions. jurisdictions without analyzable restaurant inspection databases lack a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi the ability to compare and monitor food safety hazards both within their jurisdictions and across other jurisdictions. c. limited access to restaurant inspection data c.1. access for consumers restaurant inspection reports are public data, but there is considerable variation in public disclosure practices. some jurisdictions choose to not actively release restaurant inspection results to the public, while others share them online, in the news, or at restaurants themselves. in recent years, numerous jurisdictions have adopted or considered publicly posting results. los angeles county began to publicly post inspection results in restaurant windows so that they are visible prior to entry in 1998 and new york city followed suit in 2010. however, the format of disclosure can vary greatly even among jurisdictions that publicly post results. for example, north carolina publicly posts a numerical score in addition to letter grades. rather than being posted so that they are visible before entry, they are posted within restaurants so that they are visible upon entry. furthermore, an a grade in one jurisdiction may not have the same meaning as an a grade in another jurisdiction, which poses a challenge for consumer understanding. c.2. access for regulators/public health researchers restaurant inspection results can provide valuable information to understand the root causes of foodborne illness, to monitor food safety hazards and to evaluate the effectiveness of inspection programs. in new york city, inspection data are collected on handheld computers using standardized forms and the data is transmitted nightly. analysis of these data suggested that existing program incentives were not leading to improvements in sanitary conditions. the existence of a data system that enabled data analysis allowed for the detection of a problem and was used to inform the design of a new inspection system (letter grading) and later the evaluation of the new system [35]. furthermore, our research suggests that valuable information can be gleaned from routine inspection reports as surveillance data to help understand why outbreaks occur [8]. if analyzed during an outbreak investigation, routine restaurant inspection data can supplement traditional surveillance data, which could be useful for identifying why and how an outbreak occurred in addition to what caused the outbreak. access to data for research and regulatory purposes can yield valuable insights and we see this action with the ongoing covid-19 pandemic. case data is being linked with travel history, exposures, pre-existing conditions, health insurance and other socio-economic indicators to present various perspectives on risk factors and outcomes. research enables the use of novel methods like data mining to discover patterns in the data, machine learning to predict, and applications of models to simulate transmission and project public health impact and disease spread. a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi advancing the food safety system i. support robust public health data infrastructure a lack of coordination, a general lack of integrated data, and the inability to analyze data rapidly were recognized as some of the major challenges for a coordinated u.s. response to the covid19 pandemic [9]. prior to the pandemic, funding was requested to support the cdc’s data modernization initiative which aims to update the agency’s core data reporting, analysis and surveillance capabilities to better track emerging health threats. a recent report by the council of the state and territorial epidemiologists (cste) advocated for a “public health data superhighway” that is based on a robust core public health data infrastructure that supports automatic and interoperable data exchange [36]. the success of pulsenet in preventing foodborne illnesses demonstrates the value of a coordinated national framework for disease prevention. there is an opportunity to include restaurant inspection data to the foodborne illness surveillance that adds the missing piece of the puzzle (figure 1). figure 1: food-borne illness surveillance systems and the missing piece in the puzzle there is a need to examine the role of informatics across the spectrum – information and communication technologies can support public health surveillance in a multitude of ways ranging from prevention, detection, and response, laboratory reporting, push notification, analytics and predictive surveillance [37]. the overwhelming covid-19 news coverage highlights the need for public health officials to have reliable tools built on an interoperable information infrastructure. this pandemic must be a “wake-up call” for investing in the needed resources [38]. an unprecedented opportunity is presented to public health through passage of cares act [39] and a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi much needed funding to embark on this informatics journey. these investments can have spillover effects on food safety and there is an opportunity to advance the food safety surveillance systems along with other public health information systems. an overarching strong data and information systems backbone will help food safety surveillance by making it feasible for restaurant inspection data to be available in timely fashion. ii. promote standards and interoperability imagine data coming in electronically (not over phone/fax/paper) not just for covid-19, but also for foodborne illness cases and outbreaks. this is feasible only with the standards for transmission of data which support transactions such as electronic case reporting [36]. now let us take it further by integrating restaurant inspection data into disease surveillance systems in real-time to support epidemiological decision making. these efficient and interoperable data flows are not just fiction but made feasible through informatics. the adoption of standards for representation of data will ensure the “apples-to-apples” comparison across jurisdictions. there is currently a lack of standards across the board: lack of uniformity in data collection, lack of standardized data representation and a lack of standards in transmission of data across systems. while the voluntary national retail regulatory program standards is an important step for promoting standards, there is still a need for a widespread adoption, interoperability of data systems and a national framework for tracking and monitoring hazards in restaurants. iii. advocate open data movement there is an increasing trend to make the government data accessible for public utility. many agencies from the u.s. department of health and human services (centers for medicare and medicaid services, cdc, fda, agency for health care research and quality) and other state partners are making data available at healthdata.gov [40]. of the 4,280 datasets posted as of april 1, 2020, only 10 pertain to restaurant inspection data. these datasets represent only 7 jurisdictions (2 states, 1 county, 1 multi-county, and 3 cities). some jurisdictions do also provide restaurant inspection data on their own respective websites with the goal of increasing transparency. however, this practice is not a national standard and there is a need to advocate for making data available more broadly. iv. empower practitioners, researchers and consumers covid-19 has also highlighted the power of data and making it accessible for variety of interested parties in a timely manner. integration of data related to foodborne illness surveillance – data from traditional disease reporting methods bolstered by restaurant inspection data along with consumer data social media reviews of restaurants [e.g. yelp [41,42], feeds on a particular restaurant [e.g. twitter [43,44] and novel data [45] immensely enable the ability of practitioners to understand the many facets of an outbreak. researchers can utilize this to determine causalities, role of risk factors and varying outcome trajectories across population. consumers are empowered as their data is used for decision-making and in turn can influence the behaviors of other consumers and the restaurants. a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e6, 2021 ojphi conclusions public health 3.0 emphasizes cross-sector collaboration and environmental, policy and systemslevel actions and underscores the role of technology, tools and data [19,20]. this holistic view should serve as impetus for better food safety surveillance. a broad emphasis on informatics is needed to empower public health decisions through state-of-the-art information systems, high quality data and better workforce capacity. in april 2019, the fda announced steps to usher in a new era of smarter food safety that leverages technology and other tools to create a more digital, traceable food safety system. cdc’s partnership with other federal agencies, health information technology (hit) vendors and associations is essential to cover the scope of food safety and surveillance. the covid-19 pandemic underscores the need for a framework of data, information, knowledge, wisdom and practice, and it is more important than ever to appreciate the power of data. now is the time to push for an overarching food safety surveillance framework which incorporates restaurant inspection data as an essential ingredient and put this missing piece in the puzzle. financial disclosure the authors do not report any financial disclosures and declare that the research was conducted in the absence of any commercial or financial relationships. competing interests the 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jk, et al. 2017. using twitter to identify and respond to food poisoning: the food safety stl project. j public health manag pract. 23(6), 577-80. pubmed https://doi.org/10.1097/phh.0000000000000516 44. harris jk, et al. 2018. evaluating the implementation of a twitter-based foodborne illness reporting tool in the city of st. louis department of health. int j environ res public health. 15(5). pubmed https://doi.org/10.3390/ijerph15050833 45. tucker ca, larkin sn, akers ta. 2011. food safety informatics: a public health imperative. online j public health inform. 3(2). pubmed https://doi.org/10.5210/ojphi.v3i2.3832 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28166175&dopt=abstract https://doi.org/10.1097/phh.0000000000000516 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29695038&dopt=abstract https://doi.org/10.3390/ijerph15050833 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23569605&dopt=abstract https://doi.org/10.5210/ojphi.v3i2.3832 a public health informatics solution to improving food safety in restaurants: putting the missing piece in the puzzle abstract background landscape: restaurant inspection data and challenges a. lack of a national framework b. lack of data standards and interoperability b.1. lack of data standards and interoperability b.2. lack of scalable information systems c. limited access to restaurant inspection data c.1. access for consumers c.2. access for regulators/public health researchers advancing the food safety system i. support robust public health data infrastructure ii. promote standards and interoperability iii. advocate open data movement iv. empower practitioners, researchers and consumers conclusions financial disclosure competing interests references layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a novel syndrome definition validation approach for rarely occurring diseases julio c. silva1, shital c. shah1, dino p. rumoro1, marilyn m. hallock1, gillian s. gibbs*1 and michael j. waddell2 1rush university, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to develop and test a novel syndrome definition validation approach for rarely occurring diseases. introduction early detection of rarely occurring but potentially harmful diseases such as bio-threat agents (e.g., anthrax), chemical agents (e.g., sarin), and naturally occurring diseases (e.g., meningitis) is critical for rapid initiation of treatment, infection control measures, and emergency response plans. to facilitate clinicians’ ability to detect these diseases, various syndrome definitions have been developed. due to the rarity of these diseases, standard statistical methodologies for validating syndrome definitions are not applicable. methods syndrome definitions were developed by researchers for the geographic utilization of artificial intelligence in real-time for disease identification and alert notification (guardian) surveillance system (1). the main steps for validation of the syndrome definitions were: 1) partition of literature articles: literature articles that described positive cases were randomly divided to generate detection (75% of articles) and testing (25% of articles) syndrome definitions. 2) synthetic case generation: syndrome definitions and associated statistical measures were reverse engineered using probability of occurrence and inverse gaussian function to generate potentially infinite positive artificial cases. 3) clinical filter application: to avoid clinically incompatible combinations of newly generated symptoms, rules based on clinically guided knowledge from emergency department (ed) physicians were applied. steps 2 and 3 were repeated for both detection and testing syndrome definitions. 4) detection phase validation: a. ed negative case sample: detection syndrome definitions were tested using a random sample of negative ed cases. knowledge gained through false positive cases was utilized to modify the surveillance algorithms and system thresholds. b. 10-fold cross-validation: standard 10-fold cross-validation on detection articles of positive cases and ed negative cases was utilized to generate performance metrics. suspected cases were reviewed by ed clinicians for threshold enhancement. c. literature articles (n=1): the ability of syndrome definitions to correctly flag literature articles with n=1 case was documented. 5) testing phase validation: a. testing sample: synthetic positive cases generated from the testing articles along with another set of ed negative cases were evaluated by the respective syndrome definition. suspected cases were clinically evaluated. b. literature articles (n=1): similar to detection step 4c, articles with n=1 were tested using syndrome definitions. c. true positive samples: when available, true positive cases from an ed were identified and sent through the guardian system. 6) multi-syndrome validation: a combined sample of positive cases of multiple syndromes and ed negative cases were evaluated for detection of individual syndromes among other similar syndromes. results to demonstrate the validation approach, the anthrax syndrome definition was utilized. this syndrome definition was developed with 25 articles containing positive anthrax cases used for detection, and the remaining 11 articles used for testing. with a 10-fold cross validation of the detection phase, the initial results showed accuracy was 99.4% (false positive rate of 0.65% and false negative rate of 0.00%). the testing phase initial validation revealed 99.2% accuracy for the anthrax syndrome definition. conclusions syndrome specific synthetic samples that are validated through clinical filters allowed the generation of an unlimited number of positive cases. correct identification by guardian of these cases indicates robust and reliable syndrome definitions. utilization of these cases, in conjunction with adherence to a methodological process, was the cornerstone of the guardian syndrome definition validation approach. the validation approach was successfully demonstrated on anthrax and can be applied to other bio-threat agents, chemical agents, and naturally occurring diseases. keywords syndromic surveillance; bioterrorism; infectious diseases acknowledgments guardian is funded by us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-091-0662 and w81xwh-11-1-0711. references 1. j. silva, d. rumoro, m. hallock, s. shah, g. gibbs, m. waddell, k. thomas, disease profile development methodology for syndromic surveillance of biological threat agents, emerging health threats journal, 2011, 4:11129. *gillian s. gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e76, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using surveillance data to identify risk factors for severe h1n1 in first nations kathryn morrison*1, yanyu xiao2, seyed moghadas2 and david buckeridge1 1epidemiology & biostatistics, mcgill university, montreal, qc, canada; 2york university, toronto, on, canada objective we sought to measure from surveillance data the effect of proximity to an urban centre (rurality) and other risk factors, (e.g., age, residency on a fn reservation, and pandemic wave) on hospitalization and intensive care unit admission for severe influenza. introduction research has shown that canadian first nation (fn) populations were disproportionately affected by the 2009 h1n1 influenza pandemic. however, the mechanisms for the disproportionate outcomes are not well understood. possibilities such as healthcare access, infrastructure and housing issues, and pre-existing comorbidities have been suggested. we estimated the odds of hospitalization and intensive care unit admission for cases of h1n1 influenza among fn living in manitoba, canada, to determine the effect of location of residency and other factors on disease outcomes during the 2009 h1n1 pandemic. methods we obtained surveillance data on laboratory confirmed cases of pandemic h1n1 influenza from the province of manitoba. these data described demographic characteristics, residence location, and dates of hospital and icu admission. we measured the rurality of each case using a pre-exiting scale (rambeau & todd, 2000). we tabulated the number of hospitalizations (and icu admissions) stratified first by reservation residency and second by rurality and calculated unadjusted odds ratios. we then used logistic regression to calculate the odds of hospitalization given infection (and the odds of icu admission given hospitalization), adjusting for age, reservation residency, rurality, and pandemic wave. we also investigated the effect of rurality and reserve residency on time to hospitalization from infection. results fn individuals diagnosed with influenza and living on-reserve were more likely to be hospitalized than those living off-reserve, even after controlling for the effects of rurality (or: 2.16, 95% ci: 1.15, 4.05) . fn living in rural areas were hospitalized more frequently and experienced longer delays between infection and hospitalization than fn residing in more urban areas. rurality and reserve residency had less effect on icu admissions once an individual was hospitalized. conclusions while it is established that fn individuals had disproportionately high rates of severe outcomes from h1n1, the causal mechanisms at work are not well understood. reasonable possibilities include barriers to healthcare access, lack of proper housing and infrastructure, and pre-existing comorbidities. this research using surveillance data suggests that geographic location has an effect on healthcare access, including both on vs. off reserve residency as well as rurality. keywords influenza; first nations; severe outcomes references rambeau, sheila, & kathleen todd. january 2000. census metropolitan area and census agglomeration influenced zone (miz) with census data. ottawa: statistics canada, geography division. *kathryn morrison e-mail: kt.morrison@mail.mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e42, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of electronic ambulatory care data for use in the influenza-like illness surveillance network (ilinet) kathleen stigi*1, atar baer2 and kathy lofy1 1communicable disease epidemiology, washington state department of health, shoreline, wa, usa; 2public health seattle king county, seattle, wa, usa objective to determine if a syndromic influenza-like illness (ili) definition previously validated for emergency department (ed) data accurately identified ili visits in electronic ambulatory care data. introduction during summer 2012, washington state department of health (wa doh) surveyed ilinet providers and found that more than half either utilize their electronic medical record system (emrs) to gather and report weekly ilinet data, or intend to implement queries to do so in the future. there are a variety of emrs being used state-wide, and providers that currently utilize these systems to report ilinet data apply a wide range of methods to query their data. there exists great interest in the evaluation of ambulatory care data within the context of meaningful use and little research is published in this area. wa doh sought to evaluate electronic data from wa outpatient clinic networks in order to determine if a syndromic ili definition previously validated for emergency department (ed) data accurately identified ili visits in electronic ambulatory care data. methods public health seattle king county (phskc) receives electronic health data from the university of washington physicians network (uwpn), comprised of ten outpatient clinics, on an automated basis. data are sent daily for all outpatient visits that occurred the previous day and include clinic name, visit date and time, patient age, sex, zip code, chief complaint and diagnoses, and both a visit and patient key. outpatient data from august 2007 through august 2012 were queried for ili visits using the syndromic category for ili previously validated for ed syndromic surveillance data: (1) icd codes for influenza or mention of “flu” in chief complaint or diagnosis, or (2) a chief complaint or diagnosis of fever plus cough, or (3) a chief complaint or diagnosis of fever plus sore throat. using this definition, we assessed the correlation between the proportion of visits for ili in the uwpn data and number and percentage of positive influenza laboratory tests reported by the university of washington (uw) virology laboratory. we plan to apply this methodology to evaluate outpatient data from an additional clinic network, with statewide locations, and present these findings. results the median number of weekly visits captured in the data was 6,622. three clinics were excluded from further analyses due to insufficient data, leaving seven clinics remaining in the dataset (median number of weekly visits: 6,167). overall, the proportion of ili visits in the uwpn data strongly correlated with the number and percentage of positive influenza tests reported by the uw laboratory during august 2007 through august 2012 (correlation coefficients 0.85 and 0.77, respectively). the correlation between proportion of ili visits and number positive influenza tests among individual clinics ranged from 0.62 — 0.83. overall, the proportion of ili visits among the age category 5 to 24 years most strongly correlated with number positive influenza tests (correlation coefficient: 0.86). conclusions during august 2007 through august 2012, the percentage of ili visits detected in uwpn data using a previously validated definition for ili in ed syndromic surveillance data strongly correlated with influenza activity in the community. based on these findings, data from the uwpn network will be incorporated into ilinet during the 2012-2013 influenza season. findings from our analysis support the validity of using syndromic ambulatory data for ili surveillance. furthermore, we plan to use these results to formulate guidance for ilinet providers who want to utilize emrs for weekly ilinet reporting. proportion of ili visits within electronic clinic network data and number positive influenza tests, august 2007 august 2012, washington state keywords influenza; ilinet; influenza-like illness (ili); electronic medical records (emr); ambulatory data acknowledgments university of washington physicians network, university of washington virology laboratory, wa state ilinet providers, natasha close (wa doh) *kathleen stigi e-mail: katie.stigi@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e36, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts characteristics of veterans accessing the veterans affairs telephone triage who have depression or suicidal ideation: opportunities for intervention alison ludwig*1, 2, cynthia lucero-obusan1, patricia schirmer1 and mark holodniy1, 3 1va office of public health surveillance and research, palo alto, ca, usa; 2epidemic intelligence service, centers for disease control and prevention, atlanta, ga, usa; 3stanford university school of medicine, palo alto, ca, usa objective to characterize veterans who call telephone triage because of suicidal ideation (si) or depression and to identify opportunities for suicide prevention efforts among these telephone triage users using a biosurveillance application. introduction veterans accessing veterans affairs (va) health care have higher suicide rates and more characteristics associated with suicide risk, including being male, having multiple medical and psychiatric comorbidities, and being an older age, compared with the general u.s. population. the veterans crisis line is a telephone hotline available to veterans with urgent mental health concerns; however, not all veterans are aware of this resource. by contrast, telephone triage is a national telephone-based triage system used by the va to assess and triage all veterans with acute medical or mental health complaints. methods the va electronic surveillance system for early notification of community-based epidemics (essence) was queried for telephone triage calls during january 1–june 30, 2012. calls were classified as si or depression when the triage nurse selected si or depression as the veteran’s chief complaint from a set of fixed options. demographic and recommended follow-up time and location information was reviewed. a random sample of 20 si calls and 50 depression calls were selected for chart review to determine whether veterans were examined in a clinic or followed up by a clinician by telephone within 2 weeks of the veteran’s call. results during january 1–june 30, 2012, 253,573 total calls were placed to telephone triage. among these calls, 2,460 unique veterans placed 417 calls for si and 2,290 calls for depression. this represents 1% (2,707/253,573) of all calls placed during the period. all encounter information is available in the surveillance application within 24 hours of the call being placed. median age of callers was 55 years (range: 19–94); 86% were male; and 6% placed repeat calls. the median number of repeat calls was 2 (range: 2–10). among the 2,707 calls for si or depression, 1,286 (48%) were made after routine business hours (5:00 p.m.–8:00 a.m.), and 646 (24%) were made on weekends. the greatest proportion of calls were from wisconsin and northern illinois (17%) and the southeastern united states (14%). among the 2,290 calls for depression, 1,401 callers (61%) were recommended for urgent follow up or within 24 hours. 771 (34%) were assigned a follow up location of an emergency department; 117 (5%) an urgent care; 1,332 (58%) a physician’s office or clinic; 52 (2%) self-care at home; and 18 (1%) were unspecified. among the 417 calls for si, callers 410 (98%) were recommended for urgent follow-up or within 24 hours. 330 (79%) were assigned a follow-up location of an emergency department; 38 (9%) an urgent care; 43 (10%) a physician’s office or clinic; 3 (1%) self-care at home; and 3 (1%) unspecified. among the 20 si and 50 depression calls for which the charts were reviewed, 1 (5%) si call and 6 (12%) depression calls had no documented follow-up by telephone or in person with a clinician within 2 weeks of initial call. conclusions telephone triage represents an additional data source available to surveillance applications. although telephone triage is not the traditional method provided by the va for triage of urgent mental health concerns, >2,000 veterans called it with acute symptoms of si or depression during january–june 2012. training for suicide prevention should be prioritized for operators working during the high-volume periods of off-hours and weekends when approximately half and onequarter of calls were received, respectively. we recommend standard notification of suicide prevention coordinators regarding calls to telephone triage for si or depression to prevent loss to follow-up among veterans at risk for suicide. further investigation into reasons for increased call burden in identified geographic areas also is recommended. keywords surveillance; veterans; suicide risk *alison ludwig e-mail: alison.ludwig@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e136, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a web-based electronic health record system for national surveillance jake r. marcus* practice fusion, san francisco, ca, usa objective this showcase aims to demonstrate the viability of practice fusion’s web-based electronic health record system for national surveillance. practice fusion also wishes to provide aggregate data to public health departments for surveillance for free. this showcase also hopes to engage those potential partners around uses of the company’s research database. introduction practice fusion is a web-based electronic health record system with over 150,000 medical professional users treating over 50 million patients. the company focuses on small, ambulatory practices and is predominately comprised of practices in the field of primary care. the user base makes it an ideal system for public health surveillance. the research division has undertaken pilot projects to demonstrate the viability of using the data for surveillance for acute diseases, like influenza-like illness, chronic diseases, like diabetes, and risk factors, like hypertension. methods surveillance systems based on electronic health records have clear advantages over case based reporting, but the majority of those systems are limited to the small geographical area affiliated with the hospital or health plan associated with the project. practice fusion has coverage in all 50 states and runs on a single, multi-tenant database making comparisons across those states and the localities within them immediately feasible. the company wishes to engage the organizations represented at isds in order to advance public health surveillance using the research database. it is very difficult to obtain electronic health record data currently, but with the appropriate data use agreement practice fusion believes that it is a moral imperative to use its aggregate data for surveillance. the research division has developed methods for the surveillance of influenza-like illness with its system and comparisons to the cdc have proven its viability. conclusions by comparing trends and levels of influenza-like illness generated from practice fusion’s research database to google flu trends and the gold standard estimates produced by the cdc, web-based electronic health record systems have proved to be a viable foundation for syndromic surveillance. the implementation of the system also shows that case definitions for surveillance need not be overly simplistic if they do not require cases to be submitted from physicians, but rather can be programmed to be identified through automated algorithms. keywords public health surveillance; electronic health record; practice fusion acknowledgments the data team at practice fusion references www.practicefusion.com research.practicefusion.com *jake r. marcus e-mail: jmarcus@practicefusion.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e204, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of emergency department data quality following phin syndromic surveillance messaging guide hwa-gan chang*1, charlene weng1, charlie didonato2, dave dicesare1, jian-hua chen1 and debra blog1 1nysdoh, albany, ny, usa; 2ntt data, albany, ny, usa objective to evaluate the readiness and timeliness of ed data submitted by hospitals following phin syndromic surveillance messaging guide and to evaluate the availability of minimum data elements. to validate the accuracy and completeness of data from adt messages compared with data currently reported to the ny syndromic surveillance system. introduction the final rules released by the centers for medicare and medicaid services specified the initial criteria for eligible hospitals to qualify for an incentive payment by demonstrating meaningful use of certified electronic health record (ehr) technology. syndromic surveillance reporting is one of three public health objectives that eligible hospitals can choose for stage 1. the phin messaging guide for syndromic surveillance was published for hospitals to construct emergency department data using admit discharge transfer (adt) messages, with the minimum dataset that is standard among hospitals and public health agencies. currently new york hospitals are reporting emergency department (ed) visit data to the ny syndromic surveillance (ss) system. patient chief complaint data are monitored for trends of illness at the community level in order to detect possible outbreaks and situational awareness. methods 12 hospitals using three ehr certified vendors pilot tested syndromic surveillance data for mu. hospitals started to transmit ed data in hl7 v 2.5.1 to the ny pre-certification server beginning october 2011. the month of data from july 2012 was evaluated for availability by data elements listed in the implementation guide. the adt message types were analyzed and the timeliness of reporting was calculated from visit date to report date of the first message type. the data from the pre-certification server was matched against data from the production ss system by medical record number and visit date to evaluate the data content. results there were 5 hospitals from vendor a, 3 from vendor b and 4 from vendor c participating in the pilot testing; 5854, 9882, and 13316 ed visits were reported from the three vendors respectively for the month of july. the type of first message by vendor is shown in table 1. the availability of data elements is listed in table 2. there were 79%, 82%, and 87% ed visit records received within 24 hours for vendor a, b, and c respectively. one hospital from vendor a, 3 hospitals from vendor b and 4 hospitals from vendor c also reported ed data to the production system, and their comparison with pilot testing data is shown in table 3. conclusions the types of adt messages first reported varied by vendor and hospital. not all data elements specified in the implementation guide were available or complete, and varied by vendor. an average 83% of first messages were received within 24 hours and the chief complaint from adt messages did not match well with the current ed system in production. it is a very time consuming and resource demanding process to move a hospital from successful attestation stage to production and requires public health, ehr vendor, and hospital it to work together. the learning experience from these three vendors in implementing syndromic surveillance for mu will help public health and ehr vendors to prepare for stage 2. table 1: type of first adt message by vendor table 3: percent matched data content by vendor table 2: data element availability by vendor keywords syndromic surveillance; meaningful use; phin messaging guide *hwa-gan chang e-mail: hgc04@health.state.ny.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e50, 2013 5041-37947-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 129 (page number not for citation purposes) isds 2013 conference abstracts application of ewma and cusum models to school absenteeism surveillance for detecting infectious disease outbreaks in rural china qin qin1, jing wu1, jie zhang1, li tan1, yunzhou fan1, li liu1, lihong tian1, ying wang1, hongbo jiang1, sheng wei1, vinod k. diwan2, weirong yan1, 2 and shaofa nie*1 1tongji medical college, huazhong university of science and technology, wuhan, china; 2division of global health (ihcar), department of public health science,karolinska institutet, stockholm, sweden � �� �� �� � � �� �� �� � objective �������� �� ����� � ��� ���������� �� ��� ����� � � � ����������� ����������������������� ���������� ������!��� ���� ��" ������� �� #� ��� �$��� � �����#���#� ��"�����������" ! ��%����� " ��������& introduction �!��� ���� �����' �� ������ �'������� � � ��'� ����� �$��� � ( �����#������ � �����&�� ������!��� ���� ��" ������� �� �"��� � ��$� �� � � ����'' �'� ������#��!��� ������� � ��������� 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�� � �� � � � � ��!����&�����0�"���1���������#���##� �� � ������)� �� ����� � ����� � � � � ��!��� �&� �� ���'� ��'���� # �3"�� $� "��� � ������ ����� )� �� ��##� �� � �� � � � � keywords � ������!��� ���� ;��" ������� �;�����;������ acknowledgments ����� "�$����#���� ����$��"��� ���!$���' �� �"��� � ����" ������������ < � �)� %��' � �9��� �=� ���4��>�8**� references �&�?��� �?��@� ���.��/�������/��� $���a�&��!��� ���� &�+�4���'�� � ������� ������� $���a������ � �&�,���!��%��#�:���" ������� �� :" ���' ��4�� ��� � �������**b4�b�(b7& *shaofa nie e-mail: sf_nie@mails.tjmu.edu.cn� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e14, 2014 introduction applying the xforms standard to public health case reporting and alerting 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 applying the xforms standard to public health case reporting and alerting rebecca a hills 1 , janet g baseman 2 , debra revere 3 , craig l k boge 2 , mark w oberle 3 , jason n doctor 4 , william b lober 5 1 dept. of medical education and biomedical informatics, university of washington,seattle 2 dept. of epidemiology, school of public health, university of washington, seattle, wa 3 dept. of health services, school of public health, university of washington, seattle, wa 4 school of pharmacy, university of southern california, los angeles, ca 5 school of nursing, school of medicine, school of public health, university of washington abstract notifiable condition reporting and alerting are two important public health functions. today, a variety of methods are used to transfer these types of information. the increasing use of electronic health record systems by healthcare providers makes new types of electronic communication possible. we used the xforms standard and nationally recognized technical profiles to demonstrate the communication of both notifiable condition reports and patienttailored public health alerts. this demonstration of bi-directional communication took place in a prototypical health information exchange environment. we successfully transferred information between provider electronic health record systems and public health systems for notifiable condition reporting. patient-specific alerts were successfully sent from public health to provider systems. in this paper we discuss the benefits of xforms, including the use of xml, advanced form controls, form initialization and reduction in scripting. we also review implementation challenges, the maturity of the technology and its suitability for use in public health. keywords alerting, bi-directional communication, notifiable condition reporting, public health informatics, public health practice acronyms ccd – continuity of care document cda – clinical document architecture cdc – centers for disease control and prevention ehr – electronic health record hcp – health care provider hl7 – health level 7 ihe – integrating the healthcare enterprise rfd – retrieve form for data-capture http://ojphi.org applying the xforms standard to public health case reporting and alerting 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 introduction surveillance of the public's health depends on the collection, investigation and distribution of data and information about illness and health. timely reporting of notifiable conditions (e.g., tuberculosis, vibriosis, or chlamydia, among numerous other conditions) to public health agencies by health care providers (hcps) , health care facilities, laboratories, veterinarians, food service establishments, child day care facilities, and schools supports early detection of risks to the community such as outbreaks of infectious or foodborne diseases. public health and other government organizations use the information collected in these case reports to prevent and control diseases. also important to the protection of the population’s health is the communication of health information from public health agencies to the community. one specific type of communication is public health alerting, e.g., public health warnings about outbreaks, preventive measures, and recommendations sent to hcps. case reporting, also referred to as notifiable condition reporting, and public health alerting are part of a bi-directional transfer of information in which information in the form of case reports are transferred from hcps to public health agencies and information in the form of public health alerts is transferred from public health agencies to hcps. in the us, this bi-directional communication is being carried out with varying levels of sophistication and success: approaches to public health alerting cover a broad range of communication types, including print, fax, email, and text message (1). notifiable condition reporting methods are similarly varied, ranging from faxed case-report forms to sophisticated electronic laboratory reporting systems. public health informaticians recognize the importance of data and information exchange standards which define the structure and syntax for sending and receiving information (2-5). strengthening the connection between public health and provider systems requires interoperability and the use of standards in both public health and clinical care settings. although public health organizations in the us have been slow to adopt these standards, public-private partnerships (6-8) have been working to ensure that bi-directional communication between public health and hcps is incorporated into national health information infrastructure standards. public health use cases describe the interactions between the various components of an information exchange based on a real-life scenario, thus providing a common focus for the different activities to inform development of specific requirements, architecture, and standards. this paper describes our experience using technical profiles and implementing xforms in a notifiable condition reporting and patient-tailored alerting public health use case. xforms were implemented in a prototypical health information exchange (hie) demonstration and testing environment. we also explore the feasibility and possible implications of the use of these profiles and standards in public health. background notifiable condition reporting (case reporting) the timeliness and completeness of notifiable conditions data which public health agencies rely on to track diseases, target interventions, mitigate harmful exposures, initiate investigations, and http://ojphi.org applying the xforms standard to public health case reporting and alerting 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 develop program activities and policies vary widely. in an analytic literature review, doyle et al. (2002) found that reporting completeness varied from 9% to 99% and was strongly associated with the specific disease reported. for example, active surveillance systems for certain diseases, like sexually transmitted infections, had higher completeness rates (9). and while timeliness requirements are often specified by health jurisdictions or state law, measures of timeliness do not always meet the specified standards. (10) timely and complete case reports are essential to public health surveillance work. technological developments such as the adoption of electronic laboratory reporting systems have improved the timeliness and completeness of reported notifiable conditions data (11-13). for example, national electronic disease surveillance system (nedss) was introduced in 2000 as a new method for us notifiable condition reporting and surveillance. nedss was designed to facilitate electronic surveillance of infectious diseases outbreaks, emerging or reemerging pathogens and to identify possible bioterrorist attacks. this system further evolved to become a reporting system which would allow rapid communication among public health authorities of varying size and technical capacity (14). nedss also prompted some state and municipal health departments to begin researching and building electronic surveillance systems for their regions, resulting in significant improvements in reporting rates and data quality (15). many health departments in the us share similar work practices (16); however, nationally recognized standards for content, collection and delivery of notifiable condition data have not been widely adopted. to accelerate adoption, in 2007 the centers for disease control and prevention (cdc), in cooperation with the council for state and territorial epidemiologists (cste), began work on an implementation guide for health level 7 (hl7) version 3 clinical document architecture (cda). the implementation guide provides a framework and related standards for the exchange, integration, sharing, and retrieval of notifiable condition reporting from an electronic health record (ehr) to public health (17). in 2010 a more general, nondisease-specific model for automated public health case reporting using hl7 version 2.5 was proposed (18). rapid development in the areas of messaging and data standards in health care, as well as the ever increasing technical capability of both public health and provider organizations, suggest that electronic notifiable condition reporting may soon be feasible on a large scale. these same advancements in technology also provide the infrastructure to support changes in the way public health communicates information to providers, such as context-specific public health alerts. public health alerting alerting systems that facilitate the delivery of public health information to hcps rely on the interactive contribution of hcps both prior to and during a public health event. bi-directional communication between hcps and public health has been well-documented, particularly since 2001. a systematic review of us disease outbreak detection reported that the coordination necessary for aggregating, analyzing, and sharing data between the clinical health care system and local and state public health agencies was a key component in prompt detection of infectious disease outbreaks (19). additionally, many infectious disease agents are initially difficult to identify: signs may be nonspecific and illnesses may be scattered geographically (20). increasing numbers of individuals presenting to hcps, pharmacists, hospital emergency rooms, http://ojphi.org applying the xforms standard to public health case reporting and alerting 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 and others can serve as sentinel events for disease outbreaks in the community (21-23). in the event of a bioterrorist attack, in which there may be a delay between exposure and symptom onset, public health relies on hcps and laboratories to report cases of unexplained or unusual illness to public health officials who, in turn, may be able to identify specific epidemiologic patterns or characteristics indicative of a bioterrorist act (24). several programs are designed to facilitate alert communications between public health agencies and hcps, however, few specify the appropriate timing of communications or contain details regarding which specific organizations or providers should be contacted in a particular type of emergency. while the 2001 anthrax attacks identified electronic communications systems as high priority in facilitating effective infectious disease surveillance and investigation, it is not known the extent to which systems such as the epidemic information exchange and the cdc’s health alert network have improved surveillance or communications. these public health alerting systems use electronic communication methods such as e-mail and broadcast fax to link public health agencies with hcps and other community groups (25). however, coverage of messages relayed via these methods is unknown or lower than it could be as the system relies on hcp registries that may contain incomplete, missing or out-of-date e-mail and/or fax contact information. the receipt and assimilation of messages by providers is a prerequisite to any related subsequent action, including enhanced event reporting and responsible communication of information to patients (26). though alerts can be communicated using various methods, using ehrs as the communication resource offers the potential to provide both timely and context specific information to hcps (27). in indiana, public health alerts have been added to the current clinical results delivery service in order to integrate the communication into the physician’s workflow (28). unfortunately, overwhelmed providers often suffer from ‘alert fatigue,’ dismissing even context-specific alerts from clinical decision support or computerized provider order entry systems (29-31). more research is needed to measure the impact of different types of public health alerts as technological developments offer the chance to augment public health-provider communication. one such development is the xforms standard. xforms web forms are a common tool used on websites to accept user input for activities such as searches, surveys, file uploads, and purchases. they are also common in other areas where structured communication is important, including public health. some simple web forms can be built using html. the data collected using an html form, as a set of name-value pairs, can be submitted to a server or sent using email. however, html forms have several limitations: reliance on scripting for managing form behavior and dynamic content; difficulty initializing a form with existing data; and constraints of the formats for encoding html form data (32). html forms are appropriate for some simple tasks, but the complex needs of users and the limitations of html forms have led to the development of web form alternatives. one alternative is xforms. in 2000, recognizing the limitations of html forms, a world wide web consortium forms working group (fwg) was created to help guide the development of new web forms technology (33). the fwg leveraged existing xml recommendations (34) in meeting the following http://ojphi.org applying the xforms standard to public health case reporting and alerting 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 development goals: support for structured data; improvement in accessibility; support for interrupted form completion; and decoupling of the data, logic, and presentation of a form (33). while the fwg goals are compelling, it is important to note that xforms does not represent a document type that can stand alone, but is meant to be integrated into other markup languages such as xhtml. however, xforms was touted as the “next generation of web forms” because of its flexibility, portability, and unique separation of data and presentation (35). additional advantages include: the ability to incorporate metadata to describe the history and attributes of a particular form instance; the capability to include validation information for data elements on the form which reduces the amount of script needed for data validation; and the availability of components that allow the user to interact with xforms using either stand-alone programs or a web browser. xforms has been demonstrated, used, and explored in a variety of settings; xforms specification has proved useful in dynamic query development and enabling exploration of data for those with no knowledge of the structure of the data to be queried (36). researchers have described successful implementations of xforms processors across diverse environments using different layout models (37) and in support of dynamic and adaptable document types (38). other work has explored the use of xforms with web services (39), for enhancing accessibility of web interfaces (40), and for linking data models to commonly used forms in the insurance industry (41). xforms is still an underutilized specification with an uncertain future and has been slow to gain acceptance within the healthcare industry. however, some early adopters in public health and provider settings have realized the advantages of using xforms to standardize the presentation of, and data collected by, web forms. in germany, developers successfully implemented an information system to manage details of a prescription drug formulary using xml and xforms (42). researchers in australia used xforms for decision support system development (43) and scientists in south korea proposed a radiology information system using xforms for a report management module (44). xforms has also been used in clinical and public health systems. one example is its successful implementation within openmrs, an open source medical record system, as an alternative to microsoft’s infopath web forms solution (45). xforms has also been considered for use in public health surveillance. a group from the cdc proposed use of xforms as a part of the framework for public health form creation and management (46). although the technologies in use today range from primitive to sophisticated, it is clear that improved efficiency, timeliness, and completeness could be gained by improving connections between public health and provider systems. we saw a potential match between xforms’ capabilities and the need to facilitate bi-directional electronic communication between public health and provider systems. in this paper we present our experience using the xforms standard in the public health context for provider-initiated notifiable condition reporting and patientspecific public health alerting. http://ojphi.org applying the xforms standard to public health case reporting and alerting 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 methods/experience beginning in 2005, we participated in a series of large national development and demonstration projects as a part of the integrating the healthcare enterprise’s connectathon and interoperability showcase. we played several parts in the demonstrations, including roles that required the use interoperability profiles such as retrieve forms for data capture (rfd) and use of the xforms standard for notifiable condition reporting and patient-specific public health alerting. below we describe our experiences in bi-directional communication demonstrations at two large health informatics conferences. integrating the healthcare enterprise integrating the healthcare enterprise (ihe) was formed in 1998 by a group of healthcare and industry professionals with the goal of improving interoperability in healthcare information systems (7). the organization encourages the adoption of standards by developing, promoting and demonstrating interoperability profiles which are implementation guides for incorporating standards and that describe the business rules, specific transactions and standards which can be used in a structured way to address specific clinical and population health use cases. in the public health domain, the standards are typically those identified by the health information technology standards panel (hitsp) (6). annually, vendors and other participants gather to test interoperability and implementation of profiles during the ihe connectathon; this testing is followed by the interoperability showcase which takes place at the healthcare information management systems society annual conference and demonstrates the implementation of standards and ihe profiles. in addition, the cdc’s public health information network conference has also hosted a smaller-scale, public health focused showcase. the demonstrations use scenarios to tell a story, usually about a patient’s experience in the healthcare system. we participated in several population health scenarios and implemented interoperability profiles, including those to support notifiable condition reporting and patient-specific public health alerting (47). retrieve forms for data capture and xforms for public health the primary profile we used for the demonstration of the reporting and alerting public health use cases was rfd, which uses xforms and enables viewing, pre-population, completion, and submission of forms and form data. the rfd profile specifies how different roles will function during the transaction: forms manager; forms filler; forms receiver; and forms archiver. these roles can be filled by any organization but are fundamentally descriptions of computer systems that are equipped to satisfy the needs of each role. the general exchange of data that takes place in an rfd transaction can be broken down into three steps as illustrated in figure 1: retrieve form request; form delivery; and submission of a completed form to the forms receiver and the forms archiver. http://ojphi.org applying the xforms standard to public health case reporting and alerting 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 1. roles and steps for retrieve forms for data capture transactions to further illustrate the details of how xforms functions within the rfd transaction, we describe our role in two public health use cases: notifiable condition reporting and patientspecific public health alerting. 1. notifiable condition reporting in the notifiable condition reporting scenario we used rfd and xforms to enable the capture of provider initiated notifiable condition case-report data from within an ehr. in this scenario, a patient has tested positive for salmonella, a notifiable condition. the scenario includes several steps: 1) the patient’s medical record on the local ehr system is open while the hcp is explaining the test results to the patient. 2) knowing salmonella is a notifiable condition, the hcp clicks a “retrieve case-report form” button within the ehr. 3) the ehr system (form filler) sends a message to a local public health system (form manager) requesting a salmonella case-report form. the form request includes a structured document called a ccd (continuity of care document). 4) the form manager finds the requested form and pre-populates it with data from the ccd. most of the patient demographic information is pre-populated on the salmonella case-report form (figure 2), but some fields about exposure are left blank because they were not included in the ccd. 5) the pre-populated http://ojphi.org applying the xforms standard to public health case reporting and alerting 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 form is returned to the provider, who is able to view the form, complete the empty fields, and click “submit.” 6) the form is submitted as an xml document to public health, the form receiver, and to a backup location, the form archiver. figure 2. salmonella case-report form using xforms this part of the case reporting scenario demonstrates public health as a form manager, i.e., serving as a repository of available case-report forms, as well as a form receiver, i.e., accepting completed case-report forms from providers. in this demonstration, public health used a casereport management system to import, access, and edit the submitted xml instance data. 2. patient-specific public health alerting we also used the rfd profile for a demonstration of patient-specific public health alerting. in this scenario, a patient visits her hcp complaining of diarrhea, vomiting, fever, and headache. the scenario includes several steps: 1) after examining the patient, the hcp enters the patient's data into the electronic patient record. because the symptoms sound like a possible food borne pathogen, the provider clicks a button within the patient record to “check for public health alerts.” 2) this mouse-click initiates a retrieve form request to a public health system serving as the form manager. as in the first scenario, a ccd is attached to the request, providing some of the patient’s basic demographic and symptom information. the public health system accepts the request for a form and examines several fields within the ccd: patient age, patient zip code, and conditions and dates from the problems section. using this information, public health’s form http://ojphi.org applying the xforms standard to public health case reporting and alerting 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 manager determines the appropriate form to return to the provider. in this case, based on the zip code and symptoms, the “salmonella outbreak alert” form is delivered because public health officials have been made aware of an ongoing outbreak of salmonella in the area. 3) this form appears within the ehr and provides all relevant details about the current outbreak with recommendations for laboratory testing and treatment as well as contact information for the local public health department. (note that if no matching alerts were found, an unobtrusive “no current alerts” message would have been sent). 3. combining notifiable condition reporting and patient-specific public health alerts because the alert “form” is not asking for any input, the alerting scenario could end after the hcp views and acknowledges the context-specific alert. in the case of an infectious disease outbreak when the disease is also a notifiable condition, public health may want to not only provide alert information, but also collect case information from the provider. to demonstrate this, we included a button on the alert form to “retrieve a case-report form”. clicking this button initiated another “retrieve form request” to public health, this time for a specific form, the salmonella case-report form. again, the ccd data were used to pre-populate the case-report form and the provider needed only to complete the empty fields and click “submit,” sending the instance data as an xml file back to public health. results through this work we have demonstrated that notifiable condition reporting and patient-specific public health alerting can be accomplished with a set of technical profiles that use nationally identified standards. the flexibility of the rfd profile was essential in implementing these two use cases. we found that the versatility of both rfd and xforms were beneficial, but significant challenges arose with use of xforms technology in rfd. retrieve forms for data capture the rfd interoperability profile provides a method for collecting data from within one system while meeting the requirements of an external system and enables interoperability with other systems that have implemented rfd. we provided the url of our form manager to participating vendors and this url served as the endpoint for the vendor form requests. although ccd is an optional component of rfd, the ability of the form manager to use the ccd was an important part of the success of these demonstrations. including ccd data in the form request allows for both the pre-population of case-report forms and tailoring public health alerts to a particular patient. most ehr vendors participating in these demonstrations have the ability to create cda documents, including ccds, but without this capability, much of the benefit of using rfd is lost. xforms at the time of this publication, xforms are specified within the rfd profile. xforms were included in this profile because of their ability to negotiate issues such as partial completion of forms, series of forms, and forms filled out across different user sessions. we benefited from the ability of xforms to support series of forms when we combined the alerting and case reporting http://ojphi.org applying the xforms standard to public health case reporting and alerting 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 use cases. we also found some of xforms’ fundamental traits to be useful in our implementation. for our project the most important feature of xforms was the use of xml to define form data. the use of xml documents to not only build a form, but also to store and transport form instance data, combined with the near universality of xml, made this one of the key benefits to using xforms. another major benefit is the ease of use of advanced controls available in xforms. one control available in xforms is the range-selection control, adding a volumecontrol like slider to a form for ease of user data input. range selection only recently became available for html forms. the reduction in the need for scripting to add logic to form controls also reduced development time for some components of the work, but the barriers we encountered were significant as was the time spent on xforms-related problems. challenges of xforms implementation we experienced significant challenges related to the development and implementation of xforms. first, two of the most common browsers, internet explorer and mozilla firefox, do not include native support for xforms. they require plug-ins in order to display the forms, and, when the same form is displayed in different browsers, different issues arise. in some cases, an xforms document displays properly in one browser but is not recognized as a form in the other enabled browser. this issue necessitates scripting within the forms to identify the browser and specify conditional code. although it was not an issue for this demonstration, form developers need to be mindful of the complexities of plug-in installation when browsers are used by forms fillers. second, although several xforms editors were tested, none provided adequate support and hand-coding was necessary for all of our form development. without the help of an editor, and with little online support, the coding of forms was significantly more time-consuming than developing html forms. third, though xml is ubiquitous in computing today, xforms is not and vendors are often unwilling to integrate support for xforms. this limited the number of partners for our demonstration, and could have more significant implications for use in practice. overall, our demonstrations were successful. the rfd profile, including the xforms standard, was implemented by our team and by participating vendors. use of xforms had benefits, including its use of xml, availability of advanced form controls and reduction in necessary scripting for form behavior. however, our experience developing and using xforms, and the challenges we encountered, such as compatibility issues and time-consuming development, indicate that this technology may not yet be mature enough for widespread use for public health form development. discussion we have identified and demonstrated technology that enables two public health functions, casereporting and patient-specific public health alerting, where communication between public health and provider is essential. using electronic information exchange from provider to public health for case-reporting, and from public health to provider for alerting, the rfd profile and the xforms standard were sufficient to meet the simplified set of user needs represented in our demonstration scenarios. http://ojphi.org applying the xforms standard to public health case reporting and alerting 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 the use of rfd and xforms not only helped integrate case-reporting into the provider’s workflow, but it also leveraged a standard xml representation of patient data to initialize the case-report form, thus demonstrating the potential to reduce the burden of reporting for the provider. the use of this technology also has the potential to positively influence timeliness and completeness of reported data. implementations in practice settings are called for in order to quantify these effects. implementing a system such as this for case-reporting would be a significant change to the way many providers currently go about notifiable condition reporting activities. if ehr-integrated, provider-initiated case-reporting is to be successful, provider and public health practitioner workflow should be further studied, and used to inform system design. our demonstration of patient-specific public health alerting is, in practice, more similar to the way clinical decision support is currently implemented than the way most public health alerts are distributed. today, public health alerting is rarely context or patient-specific; the alerts we demonstrated represent a significant change to current practice. before patient-specific public health alerts are implemented in the field, it will be important to assess the information needs, preferences, resources and capacity of both public health and provider organizations. both of our scenarios used provider initiated events, i.e., public health’s action was triggered by an event within the provider’s system, in both cases a mouse-click. this trigger could be any type of event; instead of the provider asking for the case-repot form, the form’s appearance might be triggered by code running in the background to check patient characteristics. similarly, instead of the provider asking if there are any relevant public health alerts, these alerts could appear before a patient’s record is closed or after a problem list is updated. though the ideal format for this type of information exchange has not yet been established, enabling bi-directional information flow between providers and public health is becoming increasingly important. interoperability between provider systems and public health systems is emphasized in parts of the us health information technology for economic and clinical health act of 2009. under this act, medicare and medicaid will provide financial incentives for the “meaningful use” of ehrs. published rules indicate that communication of public health information from providers to public health, as well as patient-specific decision support services will be among the criteria used to certify and assess ehr systems (48). standards are one important part of achieving interoperable public health and clinical systems. determining which of the existing standards will be adopted is a challenging task. by participating in ihe’s interoperability showcase we’ve engaged with one of the largest groups of vendors actively testing and demonstrating specific use cases and standards. we believe that our participation has helped encourage a more prominent role for public health in the scenario development process, and has increased our awareness of some of the benefits and drawbacks of use of rfd and xforms. xforms technology is still immature and its trajectory is uncertain. recently, the development of html5 has led to questions about the future of web forms. though html5, the most recent update to html, offers simpler support for more complex multimedia elements, it’s most important overlap with xforms is in the area of more advanced validation and controls. html5 does not replace xforms, in fact some xforms implementations use html and will be able to take advantage of some of the new html5 features. http://ojphi.org applying the xforms standard to public health case reporting and alerting 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 we believe that as xforms or a similar standard is used and tested, as support becomes more widely available, and as we gain a better understanding of provider and public health preferences related to these functions, it is likely that tools using forms standards to facilitate bi-directional communication between ehr systems and public health information systems will become more practical. limitations our findings are limited in several ways. first, the environment in which we implemented xforms was unique in that the collaborating ehr vendors are, as evidenced by their participation in the interoperability showcase, early-adopters, and therefore this sample may not be representative of all vendors. future work should engage with vendors who do not participate in the interoperability showcase or similar venues. despite engaging with the public health practice community regarding the impact of implementation of xforms on work practice, we may have encountered different challenges if this technology was implemented in a state or local health department. future work needs to explore the barriers and facilitators to xforms implementation and the impact of this technology on current hcp and public health practices. it is well-known that health departments across the us have heterogeneous work practices, thus potentially limiting the generalizability of our conclusions. because this work took place as part of a demonstration project, we were not able to fully explore user information needs related to bi-directional communication, workflow in the hcp office and public health departments, or the suitability of the technology for other use cases, for instance other notifiable conditions. we suggest that future work regarding xforms and other data capture technology for public health reporting and alerting continue to explore the use of standards and nationally recognized profiles, but also explores the information needs and workflow of the users in public health practice and the healthcare providers targeted. lastly, our report is limited by the fast pace of technology development and adoption. new tools, e.g., html5, became available after our initial demonstration. a side-by-side comparison of xforms and some of the newer tools would be a useful artifact. and a comparison between these tools and the xforms technology described here would have been useful. conclusion although xforms present significant development and implementation challenges, we believe the benefits of using a standardized method for representing form content, presentation, and logic is an important step for public health. we suggest that health departments with some system development capacity consider exploring the use of xforms or similar technologies to use and re-use xml documents for notifiable condition reporting and patient-specific public health alerting. early projects making use of xforms should, if possible, measure the impact of the technology on timeliness and completeness of reporting and on effectiveness of context-specific alerting. most resource-constrained organizations will benefit from continued migration of data toward an xml model. however, we suggest these organizations wait to implement xforms http://ojphi.org applying the xforms standard to public health case reporting and alerting 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 until the technology is more mature, tools for development have been proven, and the capacity within local ehrs has reached a level that will make the investment worthwhile. acknowledgements the authors would like to thank jim sibley, eric webster and qian yi for their work on this project. we also wish to acknowledge integrating the healthcare enterprise (ihe) as well as lori reed-fourquet and the participants in the interoperability showcase demonstrations. conflicts of interest the authors have no conflicts of interest to disclose. funding partial support for this work was provided by the centers for disease control and prevention through the center of excellence in public health informatics (project # cdc 1p01cd00026101), the national library of medicine (training grant # t15 lm007442-07) and saic. corresponding author rebecca a hills, msph department of medical education and biomedical informatics school of medicine, box 357240 university of washington, seattle, wa 98195 email: hillsr@uw.edu phone: 206.972.3998 fax: 206.221.2671 references 1. revere d, nelson k, thiede h, duchin j, stergachis a, et al. 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http://dx.doi.org/10.1016/j.ijmedinf.2008.09.00546 understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 understanding hmis implementation in a developing country ministry of health context an institutional logics perspective ime asangansi 1 1 department of informatics, university of oslo, norway abstract globally, health management information systems (hmis) have been hailed as important tools for health reform (1). however, their implementation has become a major challenge for researchers and practitioners because of the significant proportion of failure of implementation efforts (2; 3). researchers have attributed this significant failure of hmis implementation, in part, to the complexity of meeting with and satisfying multiple (poorly understood) logics in the implementation process. this paper focuses on exploring the multiple logics, including how they may conflict and affect the hmis implementation process. particularly, i draw on an institutional logics perspective to analyze empirical findings from an action research project, which involved hmis implementation in a state government ministry of health in (northern) nigeria. the analysis highlights the important hmis institutional logics, where they conflict and how they are resolved. i argue for an expanded understanding of hmis implementation that recognizes various institutional logics that participants bring to the implementation process, and how these are inscribed in the decision making process in ways that may be conflicting, and increasing the risk of failure. furthermore, i propose that the resolution of conflicting logics can be conceptualized as involving deinstitutionalization, changeover resolution or dialectical resolution mechanisms. i conclude by suggesting that hmis implementation can be improved by implementation strategies that are made based on an understanding of these conflicting logics. keywords: legal and social issues in public health informatics; developing countries; health management information systems; institutional logics; institutional aspects of information systems; action research; nigeria; ministry of health; change management introduction health management information systems (hmis) refer to information systems for health management at district, state, regional and/or national level(s). by implication, they are often government-led and assumedly the foundation for decision-making within health ministries. globally, they have been hailed as important tools for health reform (1). however, they are yet to http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 live up to expectations because of the significant proportion of failure of implementation efforts (2; 3). researchers have attributed this significant failure of hmis implementation, in part, to the complexity of meeting with and satisfying multiple interests and logics in the implementation process (2; 4; 5). particularly, an emerging body of literature characterizes this situation as that of competing or conflicting institutional logics: a situation where decisions and actions in the implementation process are contested by the different (and sometimes divergent) rationalities or belief systems of the different actors involved (5–7). accordingly, researchers have indicated the importance of understanding these institutional logics, including how they shape and are embedded in the implementation process, and how they may increase the risk of failure. for example, chilundo and aanestad (8) opine that discerning the multiple rationalities that shape implementations is a prerequisite for understanding the implementation process, and a major step towards developing change management strategies to improve it and reduce failures. this is particularly relevant in the context of developing countries where investments in hmis implementation draw on already stretched resources. in nigeria, the empirical context for this paper, the implementation of hmis is a major concern for the government and its partners, and has been a central component of its public health reform (9). yet, since its first hmis implementations in the 1990s, the nigerian government has continued to struggle with the deployment of information systems to support its public health strategies with limited success (10). however, the recent renewed vigor for hmis deployments fueled by substantial commitments from international donors and the government’s avowed pursuit of the millennium development goals (mdgs) is creating a situation where an in-depth understanding of hmis implementation and the institutional logics that shape them are desperately useful if not crucial. this creates an imperative for hands-on implementation research that can derive lessons that are practically pertinent, contextually relevant, yet theoretically sound such that they can be reused in similar settings (11; 12). furthermore, the paucity of research in this regard as well as the potential to contribute to the broader sphere of research into institutional logics in information systems implementation add to the strong research motivation for this work. empirically, this paper discusses an action research undertaken in katsina state, northern nigeria, with the participation of multiple actors in an hmis implementation. the implementation provided an opportunity to unravel how conflicting institutional logics may shape an hmis implementation process. this paper aims to capture and conceptualize these logics and conflicts as well as how they were resolved, and then to suggest implications for practice and research. the composition of the rest of the paper is as follows: the next section explains the conceptual notion of institutional logics, on which the analytical objectives of the paper lie. thereafter, the methodology utilized is explained, followed by a description of the context and the findings, interspersed with analytical insights. the paper then presents an analytical summary, after which a discussion on the logics, the conflicts, and the approaches to resolving the conflicts are presented. http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 conceptual framework institutional logics the theoretical aspects of this paper are founded on an organizational analytic approach based on the concept of institutional logics. institutional logics refer to belief systems that are carried by a collection of individuals, guiding their actions and giving “meaning to their activities” (13; 20). they “provide the formal and informal rules of action, interaction, and interpretation that guide and constrain decision makers in accomplishing the organization’s tasks” (14). these logics inscribe the “organizing principles” that shape participants’ thinking, influencing both the means and ends of their behavior (15). thus, institutional actors reproduce logics dominant within their institutional setting (13). examples of pervasive institutional logics include religious inclinations, marriage preferences, cultures, political ideologies, professional tendencies and ethnically influenced behavior (15; 16). more specifically, and related to our concern, the concept has been applied to understanding information systems implementation in organizations and in a wide variety of domains (6; 17) including the health domain (18–23). these authors describe how institutional logics are embedded within health information systems implementations. particularly, focus has been on the situation where these logics conflict or compete. for example, gutierrez and friedman (23) explain that hmis project goals and expectations often expose contradictions in the different institutional logics. they argue that hmis implementation design and planning efforts represent a natural source of contradiction and often involve incompatible perspectives and logics. similarly, currie and guah (20), on analyzing the hmis in the united kingdom, describe healthcare as: “infused with institutional logics emanating from various sectors across the field. healthcare is politically contentious where societal level logics created by government are embodied in policies and procedures that cascade down from the environment to organizations. various stakeholders including clinicians, managers, administrators and patients interpret and re-interpret these logics according to the degree to which they affect changes to the perceived or real material resource environment of the institutional actors”. currie and guah (ibid) further argue that “one of the significant challenges facing hmis implementation is to reconcile competing institutional logics.” similarly, avgerou (21) articulates, in her analysis of an hmis implementation in jordan, that the hmis project had to satisfy two lines of authority with divergent logics the local bureaucratic structures of the health services, and the usaid (united states agency for international development) mission whose fundamental values and principles about development and organizing were in conflict. she describes: “these clashed on several issues. initially, the usaid mission, consistent with its general policy of promoting administrative decentralization, favored a system to address the planning requirements of the 12 governorates (regions) of the country, excluding the central decision makers from the system’s reporting flows. this created friction with the ministry of health (moh), in effect attempting to circumvent technically the current power structures (at the ministry). second, from the initial conception of the project, the http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 usaid mission wished to focus exclusively on improving the quality of reproductive health services, which is another area of concern and policy for this development agency, (to which the ministry was strongly in disagreement). the aid recipient negotiators of the ministry of health shifted the emphasis of the project to primary health care (phc) instead. nevertheless, after analysis specifications were drawn and the first prototypes built, a new usaid mission director raised the family planning issue again and asked for the specifications to be changed” (21). avgerou, thus, highlighted two conflicts: one between the logics of decentralization and centralized control; and another involving the scope of intervention between a ‘vertical’ focus on reproductive health (by usaid) and a horizontal broad focus on primary health care (by the ministry). recognizing the central role that conflicting logics play in the implementation process, researchers have emphasized the need to understand how to resolve such conflicts. the current understanding is that the resolution of such conflicts usually takes place through a change management process involving deinstitutionalization of one logic and the institutionalization of another (19; 24–27). deinstitutionalization refers to the process by which institutional logics erode and disappear (25). sahay et al (19) apply this to describing their work in deinstitutionalizing the logic of paper-based data collection and the institutionalization of a computer-based (electronic processing) logic. however, i argue that conflict resolution in change management processes can also occur without deinstitutionalization or the extermination of one logic. i demonstrate in this paper that the resolution of conflicting logics can occur in an implementation through processes other than deinstitutionalization. i identify two such processes, which i have termed changeover resolution and dialectical resolution. i use changeover resolution to refer to the situation where participants reach a compromise, and then move the project from one dominant logic to another. in other words, changing over dominant logics, yet acknowledging the weakened logic, which becomes recessive but can later be operationalized. on the other hand, i use dialectical resolution to refer to the situation where conflicting logics are resolved through an approach that synthesizes the competing logics into one solution, rather than exterminating one for the other. here, i adapt the concept of hegelian dialectics as applied to organizational change i.e. that organizational change occurs through tensions and contradictions, resolved through a synthesis of competing logics (28–33). in sum, conflicting logics can be resolved through deinstitutionalization, changeover resolution or dialectical resolution. the choice of one depends on the logics involved. thus, it is important that conflicting logics are identified and understood within projects. however, within hmis research, there remains a large gap in understanding and identifying institutional logics and the conflicts that may occur between them. this study contributes to filling this gap. methodology – the action research the study in this paper focuses on a case of hmis implementation involving an attempt at introducing and institutionalizing an hmis software within a ministry of health (moh) with the involvement of many actors. these actors included an international donor funded nongovernmental organization (anonymously referred to as ngo in this paper), a (technical) http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 implementation team, the national and state ministries of health, district health information officers, and health facility workers. the empirical context of this study, as will be described later, is thus a rich one, providing a good setting for studying institutional logics, including how they may shape hmis implementation. however, studying institutional logics demands an approach that involves being grounded in the context and interacting directly with the logics through active participation with the associated actors. the action research (ar) methodology provides this avenue as it ensures active participation in an organizational change situation while undertaking rigorous theoretically informed research (34; 35). the author was involved as an implementer-researcher within the health information systems programme (hisp) team, a long-term action research project implementing hmis in developing countries. ar was applied to this research using the phases described by susman and evered (34) problem diagnosis, action planning, action taking (intervention), evaluation and reflection (see figure 1 below). in this case though, the phases were not determined beforehand; rather, they evolved and emerged phase after phase, as it was uncertain what the next issues were going to be, or if there would be continued funding for the work. each phase was decided based on problems and opportunities in the preceding phase, but the implementation and decision-making hinged on the availability of funding, which was intermittent and weak. thus, planning was piecemeal, rather than following a carefully thoughtout ‘grand scheme’. overall, there were four overlapping implementation 1 phases. these phases (also in table 1 below) include: the establishment of a sentinel system and introducing a computer-based hmis; strengthening the state hmis and piloting the mhealth technology; stabilizing/institutionalizing the mhealth technology by hosting internally at the moh; and finally, migration to internet-based dhis2 with remote access through mobile modems. diagnosis evaluation action taking action planning reflection figure 1. components of an action research phase (34) table 1. four action research phases carried out in this study. phase 1 establishment of sentinels; introducing computer-based hmis phase 2 strengthening the state hmis and introducing mobile technology phase 3 stabilizing/institutionalizing the mobile technology by hosting internally at the moh phase 4 migration to the internet with remote access through mobile modems data collection 1 implementation in this paper refers to the processes of action planning, action taking (intervention) and evaluation as described in action research (34, 35) http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 data collection was ethnographically informed (36; 37) and was done through participant observation (38; 39). the author was involved in all stages of the study, with more involvement in the first three phases. seven field trips were made between 2008 and 2012, each trip lasting between one and three weeks. besides the field trips, the implementation work involved numerous phone calls, skype calls, emails and use of remote login software. before the study, the author had worked within the moh in a neighboring state with similar socio-organizational characteristics (language, culture and moh organizational setup), and had some understanding of the context. data collection (into field notes) was conducted through activities such as implementation planning meetings with the ngo, discussions with state moh and district officials; collaborative software customization and configuration activities; field visits to implementation sites; and training and evaluation trips to districts and health facilities. data analysis and interpretation the focus of analysis was on the implementation project, taking it as an organizational field where the actors and their institutional logics play. the collected field notes were analyzed to capture the dominant logics of the different actors in the different phases. thus data analysis — moving from raw data to interpretation was based on reiteratively reading and identifying recurring themes from the field data. the data was then transferred to and coded in nvivo software (40), where case dynamics matrix displays (41) were generated from the institutional logics identified during the reading and rereading process. the notions of institutional logics and its relations were used as a sensitizing device (42), for interpreting meanings and for understanding rationalities behind the dynamics of the implementation process. nvivo’s support for the framework analysis method (43; 44) was used to generate these displays (see figure 2) and help visualize and identify ‘what was really happening’ in the collected data. in the next section, i present the action research narrative phase by phase, combined with analytical insights into the dominant logics. before this, the organizational context is presented. case and findings organizational context the nigerian government has long considered the application of ict as vital to improving the monitoring and evaluation of its public health system via strengthening the system of data collection from health facilities (9). the availability of timely and accurate data is fundamental to improving decision-making within the public health administration, and would help move the country away from its poor health indices. nigeria, with a population at 162 million (45), is africa's most populous country and among ten countries with the world's worst public health indices: maternal mortality is as high as 1200 per 100,000 live births in some states, i.e. approximately three hundred times more than the average in, for example, italy (with 3.9 per 100,000 live births). similarly, infant mortality is among the world’s highest; for example, the last published figures (2003) had infant mortality as high as 100 deaths per 1000 births, compared to 64 in neighboring ghana and approximately 4 in developed countries such as norway and italy in the same year (46). http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 since nigeria's return to democratic rule in 1999, after three decades of military government and systemic neglect of the health care system, there have been concerted efforts at meeting the millennium development goals (including reducing maternal and child mortality). consequently, public health care reform has been prioritized. these recent reform efforts have been met with increased international vigor and foreign aid to boost the fight against maternal and child mortality, and have involved a focus on strengthening the public health management information system. this paper is centered on a case where foreign aid channeled through an international donor-funded ngo was applied to strengthen the health data collection system in katsina state, northern nigeria. katsina state is one of nigeria's 36 states, and lies at its northern border with niger republic. with a population of approximately 6 million, it is nigeria's fourth most populous state – and sixty percent of the population is rural (47). it has a low gdp per capita; over 70% of the population subsists on under 1usd per day and unemployment is over 25% (48). katsina state is divided geopolitically into 34 local government areas, hereafter referred to as districts. the state has one of the worst indices for maternal and child health in nigeria, and is considered by the federal government as an educationally less developed and disadvantaged state (49). successive governments in katsina state have continued to invest in primary health care (phc): recent efforts have been aimed at building and rehabilitating phc facilities, provision of equipment, and the implementation of mobile ambulance services particularly to the state's difficult-to-reach and rural geographical areas (50). however, management systems such as hmis have not received much focus, as priority is given to 'tangible' goods like drugs, health personnel, and buildings. this is the case in much of nigeria, where investing in public health management resources is in tension with providing physical deliverables in a sociopolitical system where the masses are desperate for tangible results from the polity. the current project was initiated by the ngo whose focus was on working with the katsina state government and districts to improve the availability of maternal and child health services, as well as strengthen the structures and systems that underlie these services. the goal was to establish an information system that could track, monitor, and evaluate progress as well as help strengthen the capacity of state and local government health departments to plan, make decisions and act, based on health data. in addition, the ngo was interested in quickly establishing a monitoring and evaluation (m&e) system to collect data that could be used to demonstrate progress to stakeholders and funders. thus, there was a project-based quick-fix rationality in the foreground, although there was an implicit understanding of the rationale of strengthening the routine system of data collection in the state. wary of establishing a new parallel information system, and dedicated to adapting to the development ethos of strengthening the state information infrastructure, the project considered the existing (weak) state hmis as its point of departure. the hmis the hmis is responsible for data management and statistics within the health ministry at national, state, and district levels. it was established as a "management tool for informed decision-making at all levels" (10), functioning to assess the state of health of the population, identifying major health problems, and monitoring progress towards stated goals. data flow was http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 designed to be hierarchical, and in a command-and-control structure that reached from health facilities to districts, to state, and then to the federal level. however, although administrative positions were set up and staff employed, the hmis remained dysfunctional. hmis forms were usually unavailable at health facilities, health workers were not trained on how to fill the forms, and filled forms were not submitted. according to the moh: “as a result of neglect and underfunding over the years, the national health management information system suffered a lot of setbacks and could not meet the objectives for which it was set up. it has been defective and hence it is not possible to calculate even the simplest indicators” (10). the hisp implementation team was invited by an ngo working in katsina state to help with hmis reform and implementation based on the existing national hmis policy and established guidelines. phase 1: establishing sentinels and strengthening the state hmis at the beginning of the project, an initial assessment was done which revealed a weak state hmis system. the hmis unit was poorly staffed and existing staff were not computer literate. in addition, there were no dedicated computers for hmis activities and the paper forms/registers recommended by the federal hmis were not budgeted for. altogether, there was no culture of information collection, processing, and use. much thought was put into how to approach the task of improving the hmis from the state level through 34 districts to over a thousand health facilities. with limited resources, a prioritization approach was adopted by the ngo to allow focus on a few selected 'representative' facilities while beginning the process of strengthening the routine state hmis team. an improvised sentinel site monitoring system (ssms) that could swiftly provide some of the initial information required for the monitoring and evaluation of activities was setup. 2 the initial setup phase of the sentinel monitoring system lasted five weeks in april/may 2008 and involved planning meetings with the senior management of the moh, selection of sites, training sessions, and installation of the hmis software system at the state moh. health facilities with the highest level of utilization by the populace (using estimates of maternal and childcare visits) from each of the state's five zones were chosen to satisfy both geographical and political representation. the officers-in-charge of the sentinel sites and their respective district health officers were trained on the indicators, sentinel system forms, and the data flow for the sentinel site system. after a series of training, monthly data collection by participating health facilities commenced with the forms, which had six indicators focused on immunization. these forms were submitted to the state hmis office where they were aggregated and analyzed. analysis was done using the district health information software version 1.4 (dhis 1.4). the dhis 1.4 is a microsoft access-based software system for the collection, collation, analysis, and 2 a sentinel site is a health facility that can provide information considered to be representative of the population's health indices serving as a proxy for assessing the health system. http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 presentation of aggregate statistical data designed for the hmis. it is a generic tool with a flexible configuration system that allows for easy definition of data collection forms. the choice of the dhis was based on its adoption by the federal moh as the de facto standard for public health information management in nigeria (51). the introduction and configuration of the system was used to train the state hmis team on basic hmis functions as well as dhis installation and data entry. subsequently, follow-up field visits were made to assess the system while providing on-the-job training for health information officers and technical support. monitoring visits were made to follow-up on the progress that had been made in strengthening the hmis and establishing the ssms. during the visits, on-the-job training was facilitated and technical support was given. however, during follow-up visits, based on feedback from the state government that the project should scale up, 31 new sites were added to the initial 14. conflicting logics sentinel project focus vs. statewide focus the focus on sentinels introduced minor tensions. it appeared that while hmis was systemically weak, the priorities of the ngo required a quick solution. the ngo’s logic of implementing a quick-result-seeking system focused on time-bound results was in slight conflict with the state ministry’s focus on the entire routine hmis. the state ministry’s logic was to strengthen the hmis statewide and across all aspects of phc rather than what they considered a quick fix sentinel system focused on one program (immunization). as one of the state ministry’s health managers argued, “the implementation needs to involve all the districts, health facilities and all aspects of the hmis, and not just collecting immunization data in a few selected sites.” on the other hand, the ngo had concerns about spreading too thin across the state. yet, it was generally acknowledged that the sentinel system needed to be gradually adapted into a broader statewide routine data system, so that it could be adapted into the state's broader agenda of universal and equitable coverage. a district officer involved at this stage also noted: “while it is appreciated that the sentinel system is important to monitor ngo interventions, the associated data collection, flow, and analysis should be part of an overall strengthening of the monitoring capacity of the district and state, rather than as a vertical ngo immunization-focused activity”. with further discussions, the focus began to shift towards the state and district. a more extensive evaluation of the hmis at all levels and in all districts was thought necessary. thus, the conflict in this phase was an enabler for the next phase. phase 2: expanding focus from sentinels to strengthening the routine state hmis, and introducing mobile technology following recommendations from the previous phase, an extensive situational analysis was undertaken in the first quarter of 2009 involving the state, district, and health facility levels. it revealed the very low base from which the hmis had to be developed. the evaluation showed http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 that there was difficulty with distribution of data collection materials to district facilities across the state moreover, district hmis staff had a very poor understanding of the required data collection, collation, and analysis techniques. furthermore, it was difficult to maintain computer systems within the local governments, with their characteristic poor power supply and sometimes-nonexistent budget for hmis activities. in addition, transportation between the state and the districts was difficult because of bad roads and a challenging terrain, especially to and from the rural areas. paper forms were largely unavailable as the state had failed to provide them; reports from the facilities were submitted late to the state/district and data quality was untimely and often incomplete; and communication between district officers and the state hmis office was poor. sentinel sites did some reporting but using immunization-focused forms. with improved mobile network coverage in katsina (as in the rest of nigeria), an opportunity to use mobile technology to circumvent some of the mentioned data collection challenges was identified. in addition, the ngo decided to expand the work on the ssms into the statewide routine hmis. the routine hmis became the focus of the intervention. a large-scale program of training of trainers for the hmis was designed and implemented. hmis officers from key data-producing organizations in the state such as the state phc board, the state’s main hospital, and the districts were included. the focus was on attaining great effect first at the level and then at lower levels. at the same time, a hmis mobile pilot was designed with the goal of exploring the possibilities of using mobile phones for the collection of data from the districts and health facilities. the hisp team developed a simple form-based mobile application for collection and transmission of data into the state level dhis 1.4 instance. in designing the application, mobile network coverage fluctuations typical of such settings were considered. data were stored on the phone using a basic java-based record management store (rms) functionality, allowing data to be forwarded when mobile network reception returned. it allowed for the retrieval of previously filled reports, adapting well into the context of mobile network signal fluctuations. the application utilized only the basic java functionalities, such that it could be installed on inexpensive low-end phones. all district officers in the 34 districts were provided with the mobile tool, so that they could submit data for facilities in their areas. data transmission was via sms. in addition to the 34 districts, 13 facilities were chosen as pilot sites. the participants were trained on the mobile application and the hmis forms, and the system was piloted the following month (october 2009). as forms were now budgeted for, advocacy to the state to print forms yielded results. the ngo also printed forms for distribution. with the transition in focus from sentinel to the routine hmis, the national hmis forms were introduced for the first time in katsina. this meant that the mobile training also included training on the content of the form. the contents included all major aspects of phc, as the state wanted to include items on antenatal care, pregnancy outcome, mortality, family planning, immunization, child nutrition, community outreach in total, 48 data elements. http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 health workers / district information officers submit health facility report using mobile phone text message (sms) over mobile network sms server with dhis software at hmis office figure 2 shows the architecture of the mobile data collection system. on evaluation, positive results were found showing that the implemented mobile solution had a good technical adaptation to the data collection challenges. a simple questionnaire administered to evaluate participant acceptance of the mobile application showed that the application was perceived as useful and appropriate. the district officers and the heads of the pilot facilities were enthusiastic and excited about the system. for the first time, data report submissions were timely. in addition, it obviated the need for long travels (sometimes up to six hours) just to submit a paper form. overall, a major finding was that the application was well received. as one of the state resource persons said, “let us not call this a pilot because it is bringing very useful results and is now part of the system.” one-hundred percent of the district workers and pilot facilities reported that they preferred this way of reporting as it saved time -they did not need to travel to report and it was more efficient. institutional logics at play ownership and sustainability with this acceptance and adoption by participating facilities, districts and the state levels there was increased interest in the sustainability and ownership of the system. of concern was that in these first months the mobile (sms) server for receiving data was still hosted with hisp outside the state. considering the logic of local ownership and the rationality of strengthening the state's routine hmis infrastructure, it was considered appropriate to move the sms server to the state hmis office so that all data were locally hosted. as one of the state hmis officers said, “we need to be able to run this system here and without your assistance, so that when the project ends we can continue to run it.” it is important to understand the context; this was a region involved in a disagreement few years before over the safety of polio vaccines, pitting community and islamic leaders against the who, un, unicef, and federal authorities over fears that polio vaccines were a weapon by western and international powers (52). the dominant logic from the state was that the system needed to be owned and controlled by the state, and that ownership would lead to sustainability (and perhaps more trust in the system). ownership here was defined as physically hosting the server (at the moh). however, the logic from the side of the implementers was on concerns with server stability, maintenance and uptime in the ministry. yet, the ngo and state agreed on the need for the state to host the sms server locally. the next phase involved navigating the http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 institutional context of the hmis and exploring how the system could be institutionalized at the state hmis. phase 3: attempting ownership and institutionalization mobile server moved to the moh the next line of action was to move the server to the state hmis office as part of an institutionalization effort. the sms server was transferred to the state hmis office and the state hmis officer trained on its use and management. the move of the server to the state moh was fraught with a number of issues, including: poor and erratic power supply for the server, short storage of sms by the mobile network companies, work culture issues, poor server care/maintenance and unauthorized tampering/fiddling. these are explained further. power supply is a perennial problem in katsina state. for the project and the ministry hosting the server, this meant running power generators. however, this introduction brought with it a significant amount of new work for the ministry. they had never been expected to have electricity daily. the ministry was unable to maintain power supply for the sms server as fuel for the generators was unavailable most of the time because of budgeting/funding and logistical issues. to compound the problem, as was discovered subsequently, sms’s were not stored by the mobile network for longer than 24-72 hours (depending on the network used). because of this, not putting the server on for at least 72 hours meant a loss of data. in addition, there were work culture issues; it appeared that coming into the office daily for the state hmis officer was a challenge as the work culture was lax. the challenges with a poor work culture in the nigerian civil service are well documented elsewhere in literature (53; 54). it also was observed that the server was poorly maintained. the server was tampered with; for example, the sms modem driver was uninstalled occasionally and improperly reinstalled, or reconfigured for internet use. because of these issues, reporting rates fell by 75%. by the seventh month, data reports received were only 25% of the total, of which half were lost because of inadvertent deletions/disruptions by the state hmis staff. the next plan of action was to increase server stability and security by dedicating the server for use for only the mobile project; restricting use by setting and frequently changing passwords. this prevented unauthorized use by outsiders. improving the power solution was attempted by advocating for increased funding for fuel (for the hmis office generator), but this did not yield much because budgeting was done in another division (outside the hmis) and it was difficult to adapt to exigencies. at that time, the budget for the year had not been signed into law. the lack of a budget by the state hmis precluded other options such as procuring inverters and/or rechargeable dry cell batteries, using solar panel systems or even acquiring another server to run as a backup, a parallel server or a middle tier gateway. in summary, many of the technical options were not possible and the bureaucratic nature of work did not permit much flexibility around the issues. as the process of salvaging the situation continued, the period of funding for the mobile project by the ngo elapsed, but the team continued with the training of hmis staff on data analysis, presentation and use. however, on evaluation, despite issues with the server at the ministry, the project was considered by many users in the facilities, districts and the state as a huge success, because it successfully demonstrated that even in the remotest of places, timely and good quality http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 data collection was possible. consequently, demands increased from other health organizations in the state for the system to be replicated in their organizations. in addition, the districts requested to host their own servers. conflicting logics conflicting logics can be observed here. on one hand was the logic for physical “ownership” and physical control of the server as a requirement for sustainability in the long-term. on the other was the implementation team’s technical rationality for objectively improving the data collection system, irrespective of server location or approach. the choice appeared to be between having an externally hosted system (either within the country or outside the country) that performed well, or having an internally hosted (and moh owned) system that was dysfunctional or, at best, difficult to maintain. during the evaluation, a pertinent issue arose. it was considered inappropriate (by the districts) for the pilot facilities to submit data directly to the since the dominant decentralization logic for primary health care held that districts needed to manage and utilize data collected within their communities. furthermore, it was considered important to strengthen the districts, rather than bypass them when health facilities send data. this came up in discussions with the various districts. as one of the district managers said, “the system is not well structured. no district will accept a system where the health facilities report directly to the, bypassing them”. according to one of the state managers, "it is important for districts to receive and analyze all the data sent from facilities in their geopolitical areas, districts should have their own modem and run the server. they should have more capacity than simply sending data from the mobile." the districts wanted their own servers so that they could also receive data from the health facilities, after which they would submit summaries to the state. however, with the ministry unable to handle the server, the districts were considered even less capable. it was argued that the districts were unable to maintain their desktop systems let alone handle sms servers. this can be seen as a conflict between the logic of bureaucratic hierarchy with the need to maintain the existing power and control structure by the districts, and the efficient but networkcentric logic of new networked technologies such as mobile technology. having a central server where all data were submitted from health facilities leading to bypassing the district level appeared to be in tension with the hierarchical structure of the hmis institution. this networkcentric logic versus hierarchical organization logic is an important one. additionally, related to this was a conflict between the decentralization and centralization logic. this is because a key rationality in public health is decentralization (68-77), such that modes of work that introduce centralized data collection are likely to encounter some tensions. this was exemplified by the districts asking to host or at least control aspects of the system. these related conflicts set the stage for the next phase, involving centralizing the server but decentralizing control and access. phase 4: resolving conflicts – decentralizing access using mobile modems and web-based dhis2 based on a review of the aforementioned conflicts, the challenge was framed as questions of balance. for example, how could the system be locally owned yet performing efficiently and http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 reliably? how could the centralized collection system permit decentralized control by the districts? how could the network-centric technology support the strongly hierarchical nature of work within the state hmis? the solution proposed was to embed control into the information system such that the server was remotely hosted yet controlled by the states and districts through administrative access, restricted based on their roles and area of coverage. fortunately, there was a new dhis system (dhis version 2), which was web-based and permitted the districts to access and assess activities from the health facilities that fell under their hierarchy. in addition, at this time the telecommunication companies in nigeria were in a price war over mobile internet market dominance. this led to a price crash on mobile internet modems, making them cheap enough for the ngo and the ministry to consider. the thinking was that being able to situate the server on the internet (for centralized management) would be desirable to the districts, which all wanted distributed access and control of their hierarchy (for decentralized control). in line with this, a dhis2 server was configured and installed for katsina and mobile internet modems as well as notebook computers were provided for all of the district health offices. data forms also were migrated from the dhis 1.4 (ms access-based system) to the new web-based system. the migration to the web-based dhis hosted in the cloud retained the advantages of the mobile system. for example, the issues with the difficulty in transportation that the mobile phones were intended to solve were similarly solved, as data transfers could be possible from the districts through the mobile internet modems. the new dhis 2 also came with a built-in mobile web interface as well as a gprs-based client. this meant that issues with the sms short storage could be averted. in addition, it helped to spread the coverage of the hmis to all the districts (as the mobile did) but also relieved the state hmis of server maintenance work. in sum, this approached allowed the resolution of a couple of conflicting logics. the centralization-decentralization tension was resolved by allowing decentralized control within a centrally managed information system. however, the network-centric versus hierarchical organization tension has continued to be a challenge. nevertheless, a step towards resolving or ameliorating it has been to emphasize the supervision of local health facilities by their districts, using the hosted dhis. for example, web reports can be reviewed and approved through data review meetings, and reports printed from the dhis2 application are forming the foundation for continuing the tradition of the hmis’ hierarchical workflow. in general, the state and the districts were satisfied with being able to have distributed access to data as well as monitoring and supervising health facilities. this was due to the centralized web-based management and the ease of keeping up with modifications and submission from the different districts and facilities. however, with the dependence on the internet by the new web-based hmis and the unequal distribution of quality mobile internet access across the districts, issues of universal coverage and equity were again becoming a concern, especially from the districts with poorer access. universal coverage and equity are foundational logics in public health, and a key aspect of what defines success within the hmis context. these emerging conflicts will (again) need to be navigated as the project continues to evolve. universal and equitable coverage of the state was a http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 motivation for the transition from the sentinel system to the hmis in earlier phases of the project. therefore, it appears that it has manifested in another form in this later stage. analytical summary of findings the preceding description with analytical insights has followed the evolution of an hmis implementation within a challenging context, highlighting the interplay between the technological solution, the institutional arrangements and stakeholder perspectives. it has exposed multiple institutional logics including where they conflicted, how they shaped the implementation process, and how they were resolved were possible. figure 3 below is a case dynamics matrix (41) showing the challenges and the actions planned and taken in each phase. included are highlights of the dominant institutional logics and conflicts in each phase of the project. · no existing hmis data collection · sentinel system limited to few sites and small scope · need to ensure ownership and sustainablity · districts bypassed · need for decentralization, ownership and control · unreliability ministry server · prioritization of few sentinel facilities and implementation of data system · transition sentinel system support into state-wide hmis · reliable external server · centralized server management, decentralized access and control · permitting hierarchy · locally and physical host system at state ministry · project-centric logic by ngo · long term state hmis infrastructure building · universal coverage & equity · ownership for long term sustainability · decentralization · bureaucratic hierarchical control · it centralization and network-centric paradigm phase 4phase 3phase 2phase 1 challenge action planned and taken dominant logic(s) project centric vs infrastructure building · short term vs long term · small scale vs universal (statewide) coverage · vertical (1 program) vs horizontal (multiprogram) conflicting institutional logics · ownership vs performance · decentralization-centralization tensions · hierarchical vs network-centric analysis case dynamics figure 3. case dynamics matrix showing the dynamics of the case discussion this case has three major analytical findings: firstly, it revealed the institutional logics that played a role in the hmis implementation process; secondly, it highlighted how the evolution of the implementation through the action research is fueled by conflicting logics and the drive to resolve them; lastly, it highlighted the approaches for resolving these conflicts. i discuss these three aspects briefly. institutional logics in the hmis http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 this study has highlighted institutional logics that are some of the most fundamental logics in hmis implementation. this claim is substantiated by the fact that previous researchers have highlighted the role and importance of these logics in shaping other cases of hmis implementation, especially in the context of developing countries. for example, the short term project-centric quick win logic in ngo internationally funded projects has been exemplified and discussed by many researchers (21; 55; 56). avgerou, for instance, highlights this project-based rationality when she discusses a similar case of international donor-led hmis implementation in the jordanian public health sector (21). other researchers have followed this through from the perspective of sustainability and describe how hmis projects are driven by the fundamental logic of sustainability (57–62). a dominant logic linked to this is that sustainability can be achieved through local ownership of implemented hmis systems (63–66). along the same lines, hmis implementers and researchers have emphasized the logic of local control extensively discussed as decentralization (56; 67–77). furthermore, the institutional logic of universal coverage, which we saw as translating to the need for state-wide coverage of the hmis, is one that has been described as “the single most powerful concept that public health has to offer” (78) and one that requires hmis to scale (79). additionally, other hmis cases have described the institutional logic of rigid bureaucracy and maintaining hierarchical power structures (19). less reported though has been the logic of network-centric organization (80–83) where network technologies such as web and mobile-based hmis disrupt existing power structures because they allow more communication. overall, using the case, the author exemplifies and identifies what can be considered as foundational logics of which hmis implementation planners and designers need to be concerned and aware. conflicting logics perhaps, even more important to recognize is that fundamentally, these logics sometimes conflict, thereby increasing the risk of implementation failure (2). however, conflicting logics can be a major enabler and driver for organizational change processes (6). the case described here suggests that the challenges and reactions in each phase were motivated by the need to resolve conflicting logics that emerged from the previous phase. in this way, conflicting logics can be seen as enablers and drivers for an implementation’s change process. identifying the conflicts that arose within the implementation process was a key problem-solving step, as well as an important change management process for the implementation. thus, i agree with researchers (17; 19; 84) who contend that identifying these logics is a major step towards improving implementations. this case provides examples of conflicting logics that implementers can watch for in hmis implementation (see figure 3 above and table 2 below). these conflicting logics appear consistent with prior research. for example, some researchers (85, 86) have similarly emphasized conflicting institutional logics arising between a project’s short term quick win focus and a long-term infrastructure-building perspective, similar to the issue here between the ngo’s initial quick win focus and the state’s long range focus. the conflict between focusing small scale and going large scale is crystallized in the struggle between running pilot projects that then struggle to scale in line with the goals for universal coverage (3; 87). this was also the situation in the jordanian hmis case (21). the third conflict — between focusing on small/vertical scope (one disease) or focusing broadly on the larger primary health system (broad horizontal scope) is also supported in hmis literature (88–90). the conflict between ownership and performance is one that has been a bane of many hmis projects in developing countries, such that the goal of http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 local ownership often ruins the objective of performance where the capacity for maintaining the performance is unavailable (91; 92). the other issues from this case on balancing decentralization-centralization (68; 70; 77; 93–96) and the conflict between hierarchical vs. network-centric logics (81; 82; 97) are also important reported hmis concerns. table 2 below is a succinct summary of these conflicting logics and how they were resolved. the resolution strategies are discussed below. table 2. conflicting institutional logics, examples from the case and resolution strategy. conflicting institutional logics example from case resolution strategy type of resolution short term vs. long term quick win sentinel system vs. long term hmis building transitioned from short term thinking to long term transitional resolution small scale vs. universal scale sentinel system in only 14 facilities vs. statewide hmis in all districts transitioned from sentinel system to statewide hmis transitional resolution vertical (program) vs. horizontal (whole systems) immunization-focused sentinel system vs. hmis focused on broad scope of primary health care transitioned from vertical immunization system to broad hmis transitional resolution ownership vs. performance locally 'owned' and (state) hosted server performed poorly, while externally hosted server performed well found a balance: a locally controlled internet server satisfied both local ownership and gave good performance dialectical resolution hierarchical vs. network-centric submissions from facilities over the mobile network bypassed the districts disrupting existing hierarchical structure found a balance: districts have access to data from facilities and have sufficient power to assess/approve data for facilities under them dialectical resolution decentralizationcentralization centralized state server vs. districts wanting to host their own server and achieve control of health facilities a balance: centralized internetbased server management actually helped decentralize access and control dialectical resolution http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 resolving conflicting logics the third set of analytical findings relate to how conflicting logics were resolved. understanding the resolution of these conflicts has been a major concern for practitioners and researchers (5; 6; 19; 98; 99) largely, the existing literature points to deinstitutionalization. however, this paper suggests that there are alternative strategies. deinstitutionalization refers to the process by which institutional logics erode and disappear (25). particularly, sahay et al (19) have used this to explain how the logic of paper use in the tajikistan hmis was deinstitutionalized (or removed) through the introduction of computer-based hmis. in our case, the changeover from the shortterm sentinel focus to a broad, ‘universal coverage’-seeking approach involved changing from one dominant logic to another. rather than describe this as the total removal (deinstitutionalization) of an institutional logic, it appears more appropriate to see it simply as conflict resolution through switching the project from one logic to the other without necessarily destroying the other logic, which, perhaps, continues to exist elsewhere. i have termed this approach to resolving conflicting logics as changeover-resolution. it can be understood better when seen in contrast to another approach i have termed dialectical resolution, which often occurs when resolution occurs not through changeover but by balancing two competing interests. for example, in this case, by centralizing the mobile server management on the internet (phase 4), the implementation decentralized access to the server, balancing and creatively combining both centralization and decentralization. the conflict was not resolved by changing from one logic to another, but rather by synthesizing both sides and finding a combinational balance. thus, dialectical resolution involves mindfully balancing between competing logics rather than taking them as if in a black-white dichotomy. a more extensive discussion of dialectics can be gleaned from the literature (28; 30; 33; 100). an inference for practice is that implementers need to look out for conflicts and build bridges between them. these bridges could take different forms: either a bridge that permits a changeover-resolution, or a bridge that permits the marriage between two sides (dialectical resolution). the third strategy deinstitutionalization appears to be an approach to building a bridge to allow crossover, and then burning one side of the bridge in attempt to exterminate one logic. in sum, this paper proposes an understanding of the resolution of conflicting institutional logics as consisting of three possible strategies deinstitutionalization, changeover resolution (logical switching/transition) and dialectical resolution (see summary in table 3 below). table 3. three strategies for handling conflicting institutional logics deinstitutionalization (as in sahay et al. (19) ) changeover resolution (as proposed in this paper) dialectical resolution (as described in this paper) when one logic needs to be obliterated, to give way to a new paradigm when actors need to focus on one logic, and compromise the other (not necessarily obliterate it) the conflicting logics need to be both adopted and combined within the implementation http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 further implications in this section, i summarize the implications of the analysis for hmis practice and research and outline the contributions made to institutional logics research. looking back at the approach and setting of this study, like other context-sensitive studies, generalization from this single case has the risk of being weak or limited. however, as i have shown in the preceding sections with numerous references to previous research, this case is typical of hmis implementation in developing countries. the findings and the interpretations i have made from this case clearly sit well within the framework of previous studies and the body of literature, and there is substantial support for taking these broadly, in a way reusable and applicable to similar situations. building on this, the analysis has implications not just for hmis implementation in nigeria but also similar contexts where conflicting institutional logics play a role. many hmis implementations, by virtue of involving multiple actors and interests, will involve an interaction of many institutional logics with some conflicting in the implementation process. from a practical perspective, implementers need to take these logics more seriously. the analysis of this case sensitizes us to the role that conflicting institutional logics play by increasing the risk of project failure, when not resolved. this is particularly important as success or failure often is constructed by the actors and through the logic with which they perceive the implementation process. therefore, it appears that for implementations to be successful, implementers may need to negotiate and navigate the different logics of the participating actors. in cases where these negotiations were not possible e.g. in the cases described by silva and hirschheim (101), avgerou (21) and kaduruwane (92), the implementation was unsuccessful. in developing countries, especially with limited resources, if we can improve in the negotiation of the conflicting logics that arise in implementations, we may significantly improve the implementation of hmis, and improve health systems and foster socioeconomic development down the line. in relation to the foregoing, another implication is that hmis, and by extension, information system design and development within the context of conflicting logics, may need to be done in ways that support the transition between logics or in ways that support the coexistence of multiple logics. in the case discussed here, the ability to navigate the different logics was partly because of the flexibility of the information system and how it could differentially support multiple, and even conflicting, logics. in conclusion, through the application of the notion of institutional logics and its related concepts, this paper has extended our understanding of hmis implementation. the paper has typified the institutional logics involved in hmis implementation, including where they may conflict. in addition, it has expanded our understanding of mechanisms for resolving implementation conflicts – by proposing the concepts of changeover resolution and dialectical resolution, as an addition to the existing concept of deinstitutionalization. i reckon that these are pertinent contributions, and it may be beneficial for researchers to extend these concepts further and broaden our understanding in other contexts. http://ojphi.org/ understanding hmis implementation in a developing country ministry of health context an institutional logics perspective online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 while hmis implementation generally is seen as a difficult and complex process, i believe that understanding the dominant and conflicting logics at play can provide implementers with deep insight into aspects to examine when performing needs and situational analyses, as well as when developing implementation and change management strategies. this paper has already highlighted some of the logics that should be considered – e.g. the need for decentralization, hierarchy and centralization, long-term sustainability, ownership, universal coverage (and scale up) and short-term project focus and the conflicts that may arise between them. preempting and planning for these may enhance the prospects of hmis implementation success, especially in 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[101] silva l, hirschheim r. fighting against windmills: strategic information systems and organizational deep structures. mis quarterly 2007;31(2):327–354. http://ojphi.org/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts tracking dynamic water-borne outbreaks with temporal consistency constraints skyler speakman*, yating zhang and daniel b. neill carnegie mellon university, pittsburgh, pa, usa objective we describe a novel graph-based event detection approach which can accurately identify and track dynamic outbreaks (where the affected region changes over time). our approach enforces soft constraints on temporal consistency, allowing detected regions to grow, shrink, or move while penalizing implausible region dynamics. using simulated contaminant plumes diffusing through a water distribution system, we demonstrate that our method improves both detection time and spatial-temporal accuracy when tracking dynamic waterborne outbreaks. introduction space-time scan statistics are often used to identify emerging spatial clusters of disease cases [1,2]. they operate by maximizing a score function (likelihood ratio statistic) over multiple spatio-temporal regions. the temporal component is typically incorporated by aggregating counts across a given time window, thus assuming that the affected region does not change over time. to relax this hard constraint on spatial-temporal “shape” and increase detection power and accuracy when tracking spreading outbreaks, we implement a new graph-based event detection approach which enables identification of dynamic clusters while enforcing temporal consistency constraints between temporally-adjacent spatial regions. methods in the subset scanning framework, temporal consistency constraints may be interpreted as influencing the prior probability pi t of location i being included in the optimal spatial subset at time t. we model this prior probability for each location as log(pi t/(1-pi t)) = b0 + b1xi t-1 where xi t-1 is 1 if location i was included in the previous time step and 0 otherwise, and maximize the penalized log-likelihood ratio over dynamic spatio-temporal regions. our efficient algorithm incorporates these constraints into the graphscan method [3] by iteratively optimizing the spatial subset for each time slice conditioned on the previous and next slices. each individual optimization step is made possible by expressing the score function as an additive function (conditioned on the relative risk), which enables the priors to be included while maintaining computational efficiency. results outbreak plumes were simulated in a water distribution system for 12 one-hour periods. we assumed noisy binary sensors (with 10% false positive and 90% true positive rates) observed hourly at each pipe junction. our method (“dynamic”) was compared to the “static” method, which aggregates counts across time for each spatial region and is therefore constrained to only return temporal cylinders, and the “independent” method, which separately optimizes the spatial subset for each time slice without taking temporal consistency into account. the methods were evaluated on spatial-temporal overlap (figure 1), defined as the number of sensors contained in both the detected and affected space-time regions divided by the number of sensors in either the detected or affected space-time regions. a measure of 1 is a perfect match of spatial subsets across each time window and 0 would reflect disjoint space-time regions. additionally, average time to detect an outbreak (at a fixed false positive rate of 1/month) was 4.24, 4.56, and 6.65 hours for the dynamic, static, and independent methods respectively. conclusions relaxing constraints on spatial-temporal region shape must be done carefully. allowing independent selection of spatial regions loses important temporal information while hard constraints on the spatial-temporal region will fail to capture the dynamics of the outbreak. our approach for detecting dynamic space-time clusters, while incorporating temporal consistency constraints, addresses these issues and results in higher spatial-temporal accuracy and detection power. spatial-temporal overlap for three competing detection methods. keywords outbreak detection; space-time scan statistics; dynamic event tracking; penalized likelihood ratio acknowledgments this work was partially supported by nsf grants iis-0916345, iis0911032, and iis-0953330. references [1] kulldorff m. a spatial scan statistic. communications in statistics: theory and methods, 1997, 26(6): 1481-1496. [2] neill db. fast subset scan for spatial pattern detection. journal of the royal statistical society (series b), 2012, 74(2):337–360. [3] speakman s and neill db. fast graph scan for scalable detection of arbitrary connected clusters. advances in disease surveillance, 2010. *skyler speakman e-mail: skylerspeakman@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e15, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the natural reservoirs of salmonella enteritidis in populations of wild birds olga obukhovska* iecvm, kharkiv, ukraine objective the aim of our study was to identify possible natural reservoirs of salmonella enteritidis among wild birds. introduction salmonella enteritidis is dangerous for human due the reason of toxicoinfaction. these pathogen demonstrate high virulence for small children and people with chronic pathologies and can causes people die. the main source of infection to humans is birds (poultry and wild). wild birds represent the natural reservoir of same bacterial pathogens. it is known that salmonella can occupy an intestinal tract of birds. this colonization in general is constant, sometimes proceeds with an alternating fever, and usually, without clinical signs. infected birds can transmit pathogens to other isolates in close contact. this usually occurs on the nesting during seasonal migrations. in the southern region of ukraine are several points of intersection of migration routes of wild birds on the way from europe to africa and asia (national park “askania nova”and others). methods the study was conducted in populations of wild birds in national park “askania nova” and peninsula “arabat arrow” (the azov sea coast). from bird selected samples of blood serum and egg yolks for research in serum plate agglutination test (spa) and litter samples for bacteriological research. results the serological monitoring in populations of wild waterfowl in national park “askania nova” (ichthyaetus relictus, sterna nilotica, sterna herundo, casarca ferruginea) has shown the presence of seropozitive individuals in adult birds (average 18%) and egg yolks (avarrage 12%). the bacteriological investigations confirmed circulation of salmonella in this group of birds. 32.3% of all bacterial pathogens was salmonella and more then half of them was the reprezentatives of serovar salmonella enteritidis. similar studies were conducted on territory of peninsula “arabat arrow” (the azov sea coast). the serological monitoring among of wild waterfowl (ardea cinerea, sterna caspia, phalacrocorax carbo, podiceps cristatus, anas platyrhynchos, cygnus olar) revealed the presence of antibodies in blood serum (avarrage 17%) and egg yolks (avarrage 10%). from litter samples was isolated a great deal of enterobacteria (escherichia coli. salmonella, citrobacter, enterobacter), havever 34.8% of them were salmonella and near half of salmonella (53.2%) was reprezentatives of serovar salmonella enteritidis. conclusions it is proved that m. gallisepticum can persist among decorative waterfowl for her welfare with galliformes, while waterfowl is a reservoir of the pathogen. also natural reservoirs of mycoplasma can be wild waterfowl (casarca ferruginea). such groups (populations) of birds may serve as a source of infection for commercial herds. this shows that wild waterfowl are the natural reservoir for these dangerous pathogens like salmonella enteritidis. the carriers may account for 17-18% of all individuals in the population. in nesting different species of wild birds may be infected by salmonella enteritidis. in the process of migrating wild birds can carry salmonella enteritidis over long distances and is a threat to commercial poultry flocks and humans. keywords salmonella enteritidis; natural reservoir; wild birds acknowledgments b. stegniy, d. muzika, k. glebova; dept. of bacterial infection, national science center “institute of experimental and clinical veterinary medicine”, kharkiv, ukraine references 1. suzuki s. pathogenicity of salmonella enteritidis in poultry int j foot microbiol 1994.-21(1-2);89-105. 2. boonmar s, bangtrakulnonth a, pornrunangwong s, terajima j, watanabe h, kaneko k, ogawa m. epidemiological analysis of salmonella enteritidis isolates from humans and broiler chickens in thailand by phage typing and pulsed-field gel electrophoresis. j clin microbiol.-1998 36(4):971-4. 3. henzler dj, ebel e, sanders j, kradel d, mason j.salmonella enteritidis in eggs from commercial chicken layer flocks implicated in human outbreaks. avian dis 1994.-38(1):37-43 4. characterization of salmonella enteritidis isolated from human samples de oliveira f.a., pasqualotto a.p., da silva w.p., tondo e.c. food research international vol45, iss2 2012 p1000-1003 *olga obukhovska e-mail: olgaobuhovskaya@rambler.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e171, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts u.s. dept. veterans affairs (va) smec-bio reporting for leadership decision support shantini d. gamage*1, 2, loretta a. simbartl1, stephen m. kralovic1, 2, 3, katherine s. wallace4 and gary a. roselle1, 2, 3 1national infectious diseases service, veterans health administration (vha), washington, dc, usa; 2university of cincinnati college of medicine, cincinnati, oh, usa; 3cincinnati va medical center, cincinnati, oh, usa; 4office of operations, security and preparedness (osp), va central office, washington, dc, usa objective to assess reports sent from the united states va subject matter expertise center for biological events (smec-bio) – a proof-ofconcept decision support initiative – to the va integrated operations center (va ioc). introduction va is the u.s. federal agency responsible for providing services to america’s veterans. within va, vha is the organization responsible for administration of health care services. vha, with 152 medical centers and over 900 outpatient clinics located throughout the u.s. and territories, provided care to over 5 million patients in 2011. after the 2009 h1n1 influenza pandemic, osp, which oversees va senior level briefing of preparedness issues, conceptualized and initiated smec-bio as a protocol-based mechanism to incorporate timely vha subject matter expertise into leadership decision making via the va ioc. previous work has examined collection and integration of data from va and interagency sources for trend and predictive analyses (1). this current work is an initial assessment of smec-bio reporting, which has been in development for the past year and functions on an ad hoc basis for decision support; needs and gaps can be assessed toward a formalized communication plan with the va ioc. methods in may, 2011, smec-bio designed a report template. all smecbio reports submitted to the va ioc using the template were assessed based on reason for the report, timing, data sources used, and outcome. a gap analysis was conducted to identify areas for further improvement. results eight smec-bio reports were produced since the template was initiated in may, 2011. the reasons for reporting fell into the following categories: 1) briefings of interagency protocol activations [e.g. national biosurveillance integration system (nbis) protocol]; 2) requests for information (rfis) from the va ioc regarding specific biological events (e.g. 2012 h3n2v influenza associated with swine at fairs); 3) rfis from the va ioc on general infectious diseases issues (e.g. 2011 dust storm in arizona); and 4) smec-bio-initiated reports to provide situational awareness to the va ioc on a biological event (e.g. the measles outbreak at the time of the 2012 super bowl in indiana). reports in response to rfis were all submitted within the day, often within hours including those that required data collection and interpretation, indicating that smec-bio can be a viable source for timely decision support to senior leadership. some reports, such as the one on possible infectious diseases issues after hurricane irene in august, 2011, were subsequently shared by va ioc with vha operations and with field facilities, thus highlighting the potential for facilitating provision of timely subject matter expertise for local response. the primary information source for reports was the centers for disease control and prevention website, press releases, and interagency briefings. data sources used were the vha centrally-administered electronic health records system and syndromic surveillance via va-adapted essence. gap analysis results included common themes for biopreparedness: uncertainty in data quality and interpretation, communication of results and confidence levels to leadership, and coordination among stakeholders. furthermore, the development of a decision tool to guide selection of events for reporting will be a critical initial requirement of a formal communications plan. conclusions as smec-bio progresses from proof-of-concept phase to development status, knowledge gained from ad hoc reporting, as described in this work, will be critical for developing a routine and effective communications plan. other ongoing work that will support communications include staffing assessments, development of analysis tools, and incorporating automated report capabilities. keywords reporting; decision support; communications acknowledgments the authors thank jc cantrell, va ioc director, and va ioc staff for helpful interactions regarding reporting. references 1. wallace, ks, et al. u.s. department of veterans affairs integrated operations center (va ioc): collaborations for surveillance, analysis, and prediction for infectious disease threat preparedness — pilot review of dengue occurrence. emerg. health threats j. 4:s155, 2011 *shantini d. gamage e-mail: shantini.gamage@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e185, 2013 a brief review of vaccination coverage in immunization registries 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 a brief review of vaccination coverage in immunization registries neal d goldstein 1 , brett a maiese 1 1 drexel university school of public health abstract immunization registries are effective electronic tools for assessing vaccination coverage, but are only as good as the information reported to them. this review summarizes studies through august 2010 on vaccination coverage in registries and identifies key characteristics of successful registries. based on the current state of registries, paper-based charts combined with electronic registry reporting provide the most cohesive picture of coverage. to ultimately supplant paper charts, registries must exhibit increased coverage and participation. mesh keywords: immunization/statistics & numerical data; registries; information systems; patient compliance; immunization programs/utilization introduction it is well known that vaccination is one of the most successful public health initiatives to date, and having a successful immunization program is paramount to preventing vaccine-preventable diseases (1-3). however, today’s vaccination delivery system is insufficient to keep up with the demands of an ever-changing landscape (3,4). to address this need and ensure adherence to recommended vaccination schedules, immunization registries are increasingly being utilized. this brief defines immunization registries (more recently known as immunization information systems) as population based electronic information systems that minimally capture and report vaccination events. studies have already demonstrated the use of regional registries increases vaccination coverage and documentation (5,6). additionally, decision support capabilities, such as patient and provider reminders, provided through electronic information systems can improve up-to-date (utd) rates (7). participation in registries, or the number of children with vaccination records, has been steadily increasing (8). despite this, the question remains among children recorded in the registry, are they utd on their vaccinations? and, if a higher percentage of utd vaccines indicate a more robust registry, what are the contributing factors to this increased coverage? this brief considers these two questions. a brief review of vaccination coverage in immunization registries 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 methods relevant articles were identified via a pubmed search using the following keywords: (immunization registries or immunization information systems) and (utd or up-to-date). the keywords “accuracy,” “completeness,” and “coverage” were used in lieu of “utd or upto-date” for subsequent searches and generated additional articles. literature searches were limited to studies in humans and published in english. as no date limits were applied, studies available through august 2010 were eligible. figure 1 is a graphical representation of the methodology. utd = up-to-date, nutd = not up-to-date figure 1: pubmed search methodology to identify literature on vaccination coverage in immunization registries a brief review of vaccination coverage in immunization registries 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 three inclusion criteria were applied. first, the study setting was based in united states. second, the study investigators included a vaccination up-to-date percentage, or the raw numbers from which the percentage was calculated, and third, the immunization registry was population based (that is, a hospital’s electronic medical record was excluded). these inclusion criteria yielded seven articles which we believe represent the body of published work on utd coverage in an immunization registry at the time of writing (9-15). results the seven articles were all unique studies covering various geographic regions, and consequently various regional and statewide immunization registries. therefore these results are experiences with not a single registry, but myriad registries each with its own population of patients and providers. table 1 summarizes the seven articles by study type, population, and setting; outcome measure; and utd results. table 1: summary of articles for vaccination coverage in immunization registries review author (year) study type, population & setting outcome measure reviewed results kolasa et al. (2006) cross-sectional survey in philadelphia, pa of private practices where children were at risk for underimmunization. children age 19 to 35 months. utd for 4:3:1:3 series in registry versus provider charts. 62% utd coverage in registry; 80% utd coverage in chart. boyd et al. (2002) cross-sectional survey in bexar county, tx of clinics participating in the vaccine for children program. children aged 12 to 35 months utd for 4:3:1 series in registry versus clinic provider charts. 64.1% coverage in registry; 39.8% coverage in clinic charts. stille et al. (2000) cohort study in hartford, ct of infants younger than 1 month tracked through 7 months. source population was three primary care facilities serving >80% medicaid population. utd at seven months defined as 3 dtap, 2 polio, 3 hib, 2 hep b in provider charts versus charts plus registry. 53% utd at chart review for cohort; 58% utd after chart plus registry review for cohort. davidson et al. (2003) cohort study in denver, co at denver health medical center. two birth cohorts from 1993 and 1998. utd for 3:2:3 series in registry assessed at 12 months. 83.1% utd for 1993 cohort; 78.9% utd for 1998 cohort. khare et al. (2006) cross-sectional survey of four mature registries located in the us. children aged 19 to 35 months. utd for 4:3:1:3 series in registry versus provider charts. 31.7%, 65.4%, 71.9%, and 61.8% coverage at each site based on registry; 65.6%, 78.8%, 81.6%, and 77.0% utd coverage at each site based on charts, respectively. callahan et al. (2004) cohort study in syracuse, new york at university hospital. patients < 11 years presenting in the emergency department. utd per advisory committee on immunization practices, depending on age. 61% utd in cohort. a brief review of vaccination coverage in immunization registries 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 author (year) study type, population & setting outcome measure reviewed results kolasa et al. (2009) cross-sectional survey in philadelphia, pa of all children born between november 1, 2003 and october 1, 2004, and living in areas served by two community-based outreach organizations. study population was nutd according to the registry at 10 months of age. utd at 10 months defined as 3 dtap, 2 polio, 2 hib, 2 hep b (+1 at birth not recorded) in registry. 64% utd post-outreach, despite being marked as nutd in registry. utd = up-to-date, nutd = not up-to-date; dtap = diptheria, tetanus, acellular pertusis, hib = haemophilus influenzae type b, hep b = hepatitis b when determining immunization status, the study investigators have used a subset of the u.s. department of health and human services recommended vaccinations (16) depending on various factors, such as subject age, ability to track antigens in the registry, antigen availability, and local preferences. while the studies reviewed mainly used different vaccination series dependent mostly on age, a direct comparison is still valid as we are tracking immunization completeness in the registry, and are not interested in comparing antigens tracked per registry. supplementing u.s. vaccination policy, the world health organization (who) has issued the global immunization vision and strategy aiming to achieve a 90% national coverage and 80% local coverage (17). although desirable to evaluate the results in table 1 against who criteria, a direct comparison may be misleading as children in registries represent a specific subset of the total population. the two most common series based on the study subjects age included the 4:3:1(:3) series and the 3:2:2 series. the 4:3:1(:3) includes 4 diptheria, tetanus, acellular pertusis (dtap); 3 polio (oral or inactivated); 1 measles, mumps, rubella (mmr); and 3 haemophilus influenzae type b (hib). the 3:2:2 series includes 3 dtap, 2 polio, and 2 hib. the 3:2:2 series may also be recorded as 3:2:3 indicating a third hib vaccination, depending on the age and eligibility of the child. additionally, for both series, the hepatitis b vaccination may or may not be considered in the study. discussion the current state of registry coverage was ascertained from the available literature. registry completeness ranged from 31.7% to 83.1%, whereas paper charts ranged from 39.8% to 81.6%. in studies where registry data were compared to provider charts, the charts were more inclusive of vaccination events. in all cases, when provider charts were supplemented with registry data, the utd percent increased. this is an important finding; registries can augment the patient chart to provide a more inclusive look into vaccination utd rates, particularly for children that have switched providers who may have incomplete histories. a brief review of vaccination coverage in immunization registries 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 many factors can affect vaccination coverage both in paper charts and electronically, and it is currently not clear which have the greatest effect. one study that attempted to determine which factors were associated with low coverage found that the level of coverage was not well predicted by number of providers per capita, a common assumption (18). specific reasons were attributed to registry incompleteness. kolasa observed lack of electronic data submission resulted in a disparity of completeness between the registry and charts (9). studies have shown electronic submission results in greater accuracy (8,9,19). additionally, kolasa found that hospital-based practices, which typically have a more robust infrastructure, have a higher utd percentage versus smaller practices. davidson posited the infancy of a registry explains why historical immunization events captured in provider charts are not electronically accessible (12). it stands to reason over time that electronic immunization data will increase. legislation may also affect differences in registry coverage. a survey of state-level immunization information system legislation found wide variability in whether or not states had laws authorizing an immunization information system, mandating reporting to that system, addressing sharing of immunization information (and healthcare information in general) and the type of consent required to share information (20).in cases where switching providers was common, or registry use was mandated, utd completeness of the registry eclipsed paper charts (10,12). there are several limitations to these studies. first, by consulting a registry, the practitioner assumes accuracy of the reported data. callahan notes “[t]his has been shown to be a problem with registries in their current state of development” (14,p300). indeed, kolasa found among children listed in the philadelphia registry as being not utd on their vaccinations, 64% were found to be utd from charts (15). stille had similar qualms with the connecticut state registry; specifically vaccinations received by un-identified providers cannot be tracked and children who have relocated outside the reaches of the registry may be erroneously reported as not utd (11). second, study populations were frequently drawn from underimmunized, “at risk” populations who visit public providers, potentially affecting the external validity of the results. however, since the underlying technology of the registry is the same regardless of the provider, it is reasonable to expect the findings to generalize. an opportunity exists for additional studies to assess particular “at risk” populations, such as low birth weight and immunocompromised children, and use of electronic registries to decrease mortality. last, utd percent may not be an accurate metric for studying immunization coverage. in a study by robison et al., the utd measure served as a general guide, but does not provide a reason for the low coverage (21). there are limitations to the methods used to conduct this literature review. the registries considered for inclusion had to be population based. by disqualifying a hospital’s electronic medical record, we feel the population is more inclusive of the total vaccinated population, not just children that have presented to a hospital likely for other indications. next, our search strategy may have excluded relevant articles, although we feel the searches incorporated the totality of keywords used to index these articles. finally, only studies that were u.s.-based were eligible. given the immunization policy and tracking differences observed between countries, this allowed us to draw conclusions specific to the u.s. a brief review of vaccination coverage in immunization registries 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 percentages of utd children in immunization registries are lagging compared to provider charts. to accurately assess a child’s true vaccination status, a combination of the registry and providers charts provides the best picture. registries that offered decision support, broad participation, and efficient electronic reporting of vaccination events tended to have a higher proportion of utd children. conflicts of interest the authors have no conflicts of interest to disclose. correspondence neal d goldstein, drexel university school of public health email: ng338@drexel.edu references 1. centers for disease control and prevention (cdc). impact of vaccines universally recommended for children--united states, 1990-1998. mmwr morb mortal wkly rep. 1999 apr 2;48(12):243-8. 2. stern am, markel h. the history of vaccines and immunization: familiar patterns, new challenges. health aff (millwood). 2005 may-jun;24(3):611-21. 3. orenstein wa, douglas rg, rodewald le, hinman ar. immunizations in the united states: success, structure, and stress. health aff (millwood). 2005 may-jun;24(3):599-610. 4. hammer ld, curry es, harlor ad, et al. increasing immunization coverage. pediatrics. 2010 jun;125(6):1295-304. 5. wilcox sa, koepke cp, levenson r, thalheimer jc. registry-driven, community-based immunization outreach: a randomized controlled trial. am j public health. 2001 sep;91(9):1507-11. 6. kempe a, steiner jf, renfrew bl, lowery e, haas k, berman s. how much does a regional immunization registry increase documented immunization rates at primary care sites in rural colorado? ambul pediatr. 2001 jul-aug;1(4):213-6. 7. jacobson vj, szilagyi p. patient reminder and patient recall systems to improve immunization rates. cochrane database syst rev. 2005 jul 20;(3):cd003941. 8. centers for disease control and prevention (cdc). progress in immunization information systems united states, 2008. mmwr morb mortal wkly rep. 2010 feb 12;59(5):133-5. 9. kolasa ms, chilkatowsky ap, clarke kr, lutz jp. how complete are immunization registries? the philadelphia story. ambul pediatr. 2006 jan-feb;6(1):21-4. 10. boyd td, linkins rw, mason k, bulim i, lemke b. assessing immunization registry data completeness in bexar county, texas. am j prev med. 2002 apr;22(3):184-7. 11. stille cj, christison-lagay j. determining immunization rates for inner-city infants: statewide registry data vs medical record review. am j public health. 2000 oct;90(10):16135. 12. davidson aj, melinkovich p, beaty bl, et al. immunization registry accuracy: improvement with progressive clinical application. am j prev med. 2003 apr;24(3):276-80. a brief review of vaccination coverage in immunization registries 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 13. khare m, piccinino l, barker le, linkins rw. assessment of immunization registry databases as supplemental sources of data to improve ascertainment of vaccination coverage estimates in the national immunization survey. arch pediatr adolesc med. 2006 aug;160(8):838-42. 14. callahan jm, reed d, meguid v, wojcik s, reed k. utility of an immunization registry in a pediatric emergency department. pediatr emerg care. 2004 may;20(5):297-301. 15. kolasa ms, lutz jp, cofsky a, jones t. provider chart audits and outreach to parents: impact in improving childhood immunization coverage and immunization information system completeness. j public health manag pract. 2009 nov-dec;15(6):459-63. 16. centers for disease control and prevention. recommended immunization schedules for persons aged 0 through 18 years---united states, 2010. mmwr morb mortal wkly rep. 2010 jan 8; 58(51&52);1-4. 17. world health organization. immunization, vaccines and biologicals > global immunization vision and strategy. available at: http://www.who.int/immunization/givs/goals/en/index.html. accessed may 19, 2011. 18. rosenthal j, rodewald l, mccauley m, et al. immunization coverage levels among 19to 35-month-old children in 4 diverse, medically underserved areas of the united states. pediatrics. 2004 apr;113(4):e296-302. 19. kolasa ms, cherry je, chilkatowsky ap, reyes dp, lutz jp. practice-based electronic billing systems and their impact on immunization registries. j public health manag pract. 2005 nov-dec;11(6):493-9. 20. centers for disease control and prevention, national center of immunization and respiratory diseases. survey of state immunization information system legislation. available at: http://www.cdc.gov/vaccines/programs/iis/privacy/legsurv.htm. accessed may 19, 2011. 21. robison sg, kurosky sk, young cm, gallia ca, arbor sa. immunization milestones: a more comprehensive picture of age-appropriate vaccination. j biomed biotechnol. 2010;2010:916525. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluating utility of cold-injury syndromic surveillance data in new york city kathryn lane*1, ramona lall2, katherine wheeler1, kazuhiko ito1 and thomas matte1 1bureau of environmental surveillance & policy, nyc department of health & mental hygiene, new york, ny, usa; 2nyc department of health & mental hygiene bureau of communicable diseases, new york, ny, usa objective 1) develop cold exposure-related injury syndromic case definitions 2) use historical data to compare trends among cases identified in syndromic surveillance and cases identified in ny statewide planning and research cooperative system (sparcs) hospital discharge data to evaluate representativeness and 3) develop regression models to examine relationships with cold weather conditions, and compare relationships across case definitions and data sources. introduction cold weather exposure-related injuries range from hypothermia to less severe conditions such as frost bite, trench foot, and chilblains, which are all preventable causes of mortality and morbidity. in recent years, nyc has successfully used syndromic surveillance of heat-related ed visits to inform emergency response during heat waves. similar timely surveillance of cold-exposure related injuries could also inform public health protection measures during severe winter weather or cold season power outages. we conducted a retrospective analysis to compare hypothermia and cold-injury patient case characteristics, as well as temporal and meteorological correlates, between syndromic surveillance data and hospital discharge data. methods using chief complaint key words, we developed syndromic case definitions for 1) hypothermia only and 2) all injury caused by environmental cold exposure. case definitions were applied to an archive of 2008-2010 cold season (october to april) syndromic surveillance data reported from a subset of nyc emergency departments (ed ss), representing 95% of all ed visits in nyc. relevant icd-9 codes (991, e901.0, e901.8, e901.9, e988.3) were applied to ed discharge data (ed dx) to detect hypothermia and cold injury cases. age, gender, and alcohol involvement were compared using tests of proportion to determine whether characteristics of cases identified through ed ss were representative of cases identified in ed dx data. poisson regression models were fit to estimate the relation of daily ed ss and dx counts with daily temperature, snow depth, and other weather conditions. models were adjusted for month, holiday, day of week, and year to account for potential temporal confounding. results fewer hypothermia and cold injury cases were detected with ed ss than with ed dx but the two populations did not differ significantly with respect to age and sex. from 2008-2010, there were 292 hypothermia cases with an average annual rate of 1.2 per 100,000 people, and 445 cold injury cases (1.8 per 100,000) identified in ed ss data. over the same time period, there were 566 hypothermia cases (2.3 per 100,000) and 933 cold injury cases (3.7 per 100,000) identified in ed dx data. daily counts of hypothermia and cold injury were correlated across data sources. in preliminary analyses using both case definitions, minimum daily temperature was associated with increases in daily ed ss and ed dx counts. mean daily snow depth was associated with ed ss and ed dx cold injury cases, although not with hypothermia counts. risk increased at lower temperatures for both case definitions. conclusions there were no meaningful differences between ed ss and ed dx weather models. minimum temperature is associated with both case definitions. snow depth is associated with cold injury. daily minimum temperature and mean snow depth are potentially useful in determining timing of surveillance. syndromic surveillance data are a timely means for monitoring hypothermia and other cold-related injuries, and could provide health departments with useful information during severe winter weather to guide prevention. keywords syndromic; cold-injury; environmental surveillance acknowledgments data analysis and syndromic surveillance unit, bureau of communicable diseases, nyc dohmh *kathryn lane e-mail: klane1@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e122, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using cloud technology to support monitoring during high profile events megan patel*1, enyinnaya adighibe1, joseph lombardo2, wayne loschen2, miles stewart2 and michael o. vernon1 1cook county department of public health, oak forest, il, usa; 2johns hopkins university applied physics laboratory, laurel, md, usa objective in may 2012, thousands of protesters, descended on chicago during the nato summit to voice their concern about social and economic inequality. given the increased numbers of international and domestic visitors to the windy city and the tension surrounding protesting during the summit, increased monitoring for health events within the city and chicago metropolitan region was advised. this project represents the first use of cloud technology to support monitoring for a high profile event. introduction hospital emergency departments in cook and surrounding counties currently send data to the cook county department of public health (ccdph) instance of essence on ccdph servers. the cloud instance of essence has been enhanced to receive and export all meaningful use data elements in the meaningful use format. the nato summit provided the opportunity for a demonstration project to assess the ability of an amazon govcloud instance of essence to ingest and process meaningful use data, and to export meaningful use surveillance data to the cook county locker in biosense 2.0. methods in the three weeks leading up to the nato summit, hl7 data extracts were sent to biosense 2.0 and a data feed was established to the amazon govcloud instance of essence. queries specific to anticipated health events associated with the summit such as injuries, tear gas exposure, and general exposure, were developed. several features of the cloud instance of essence enhanced the ability of ccdph staff epidemiologists to conduct analyses, including the sharing capabilities of queries and the myessence dashboard feature. the sharing capabilities within the cloud instance of essence allowed queries to be easily shared with multiple staff epidemiologists and across health jurisdictions. the myessence dashboard feature was used to create dashboards of surveillance results, including time series graphs, maps, and records of interest for relevant queries, that were shared with public health staff monitoring population health during the summit. this information was used to provide situational awareness on a daily basis in the chicago metropolitan region. results data feeds to biosense 2.0 and the amazon govcloud instance of essence were successful. the nato summit did not produce any remarkable public health concerns in suburban cook county. the use of the cloud instance of essence enhanced the timeliness of generating situational awareness reports for distribution to public health partners in the chicago metropolitan region. conclusions while further evaluation of cloud resources to conduct syndromic surveillance is warranted, use of the cloud instance of essence during the nato summit demonstrated the ability of the cloud to support surveillance for both routine and high profile events. keywords cloud; meaningful use; surveillance; public health acknowledgments this project was supported by the wide area recovery and resiliency program of the department of homeland security under contract n00024-03-6606. cloud services were provided by the cdc’s biosense 2.0 program under contract 200-2011-40934. *megan patel e-mail: mctoth@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e73, 2013 5055-37977-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 58 (page number not for citation purposes) isds 2013 conference abstracts challenges of elr implementation: moving toward semantic understanding through vocabulary validation daniel b. golson*, natalie raketich and erin holt communicable and environmental diseases and emergency preparedness, tennessee department of health, nashville, tn, usa � �� �� �� � � �� �� �� � objective 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� �� �7������������9� ������ ����� ����� ���������� �������� ������������ � ��� ��� ���� � ������ ������������� �" ����� � �� ����������� �� ������� ���������� �������� ������������������ ���������� ��������������������� �<���� �� ��� ���� ������ � � ��� � �� ����������� ������ ���� ���������� �� ������ ������������������������ ��������� ������������������ ��� ���� � �� ���� ��� ������#� ���� � � ���� ������ �� ��� �� ��� �������� ����� ����� ����� � � �� ������� �������� ����������������� ��� ��� � ,�� ������ ��� � ���� ����� ������ ��� �� ����� ���� ������� � ��� ���� ��� �������� ���� � �����#����� ��� �� ����������������� ���� ��� ������� ����� ��������������������� �� �� ����������� ���� keywords ����������� � ��� ����� �� ������=� /� �� �� =� (������ *� ���� /������� ���=�8�� ��� ��=�) � �>� ���� *daniel b. golson e-mail: daniel.golson@tn.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e23, 2014 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts use of rapid online data collection during a large community enteric outbreak in toronto, canada anne arthur, effie gournis and kaitlin bradley* communicable disease control, toronto public health, toronto, on, canada objective to describe the use of an online survey tool to rapidly collect data from a large community outbreak of enteric illness in toronto, canada. introduction in the early morning of friday january 20, 2017, toronto public health (tph) was notified of several reports of acute vomiting, diarrhea, and stomach pain/cramps among students living in residence at a post-secondary institution in toronto, canada. a public health investigation was initiated and it was quickly determined that a large number of students and visitors to the campus were affected. following considerable media coverage, tph began receiving an overwhelmingly high volume of reports from ill individuals who lived, visited, or worked at the college campus and had experienced gastrointestinal illness. methods gastrobusters – an established online foodborne illness reporting tool was quickly adapted to support the outbreak investigation. gastrobusters was rapidly updated to include a screening question allowing ill individuals connected with the outbreak location to self-identify and report their symptoms, onset dates and times, and food histories to tph securely online. the necessary updates were developed, tested, and implemented in less than one hour. ill individuals were directed to the gastrobusters website – tph. to/gastrobusters by college administrators and through media messaging. those who were ill and reported to tph through other methods (e.g., by phone) were interviewed by tph investigators to collect comparable data, which were entered by staff into an online survey that mirrored the structure of the gastrobusters questions. these two data sets were merged and descriptive analyses were conducted using ms excel and sas v9.2. results in total, 354 reports associated with the outbreak were received by tph 232 who self-reported through gastrobusters, and 122 reported through other methods who were interviewed by tph. use of gastrobusters allowed ill individuals to report at a time convenient to them 204 (88%) reports were submitted outside of tph’s business hours. as well, by providing ill individuals a method to self-report, tph was able to rapidly collect, analyze and interpret data over the weekend while minimizing use of tph staff resources. a summary report was available on monday january 23, 2017 by 9:00 am, describing 236 confirmed and probable cases whose data were collected via both online surveys (gastrobusters and tph data collection tool), between friday and sunday evenings. these data supported the hypothesis that the source of illness for the outbreak was likely norovirus; this was later confirmed through laboratory results. conclusions this investigation provides a successful example of how an existing online reporting system for foodborne illness can be used for rapid data collection during a large-scale community enteric outbreak, where the exposed population could not be easily defined and the source of illness was unknown. advantages of using this approach included: 1) rapid and robust data collection resulting in prompt analysis, and 2) efficient use of public health resources given the volume of reports otherwise processed by a public health investigator. moreover, the investigation coincided with a weekend when there are fewer staff available and large amounts of overtime costs would have been accrued. tph is currently developing standards for the use of similar tools in the future. keywords foodborne illnesses; surveillance; epidemiology; infectious disease reporting acknowledgments we would like to acknowledge the leadership and significant contribution of several tph outbreak leads including: dr. michael finkelstein and debra hayden. additional gratitude is extended to tph communicable disease control and health environments staff, and to our external partners at the college for their cooperation in the investigation of this outbreak. we would also like to acknowledge sylvia ota, for providing expertise in the use of gastrobusters during the outbreak. references 1. toronto [internet]. toronto: city of toronto; c1998-2017. gastrobusters; [cited october 2, 2017]. available from: tph.to/ gastrobusters *kaitlin bradley e-mail: kaitlin.bradley@toronto.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e200, 2018 title : 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 electronic data collection and management system for global adult tobacco survey sameer j pujari 1 , krishna m palipudi 2 , jeremy morton 2 , jay levinsohn 3 , steve litavecz 3 , michael green 4 on behalf of the gats collaborative group 1 tobacco free initiative, world health organization, geneva switzerland 2 global tobacco control branch, centers for disease control and prevention, atlanta usa 3 rti international, usa 4 cdc foundation, atlanta usa abstract introduction: portable handheld computers and electronic data management systems have been used for national surveys in many high-income countries, however their use in developing countries has been challenging due to varying geographical, economic, climatic, political and cultural environments. in order to monitor and measure global adult tobacco use, the world health organization and the us centers for disease control and prevention initiated the global adult tobacco survey, a nationally representative household survey of adults, 15 years of age or older, using a standard core questionnaire, sample design, and data collection and management procedures. the survey has been conducted in 14 lowand middle-income countries, using an electronic data collection and management system. this paper describes implementation of the electronic data collection system and associated findings. methods: the survey was based on a comprehensive data management protocol, to enable standardized, globally comparable high quality data collection and management. it included adaptation to specific country needs, selection of appropriate handheld hardware devices, use of open source software, and building country capacity and provide technical support. results: in its first phase, the global adult tobacco survey was successfully conducted between 2008 and 2010, using an electronic data collection and management system for interviews in 302,800 households in 14 countries. more than 2,644 handheld computers were fielded and over 2,634 fieldworkers, supervisors and monitors were trained to use them. questionnaires were developed and programmed in 38 languages and scripts. the global hardware failure rate was < 1% and data loss was almost 0%. conclusion: electronic data collection and management systems can be used effectively for conducting nationally representative surveys, particularly in lowand middle-income countries, irrespective of geographical, climatic, political and cultural environments, and capacity-building at the country level is an important vehicle for health system strengthening. http://ojphi.org/ 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 key words: electronic data collection and management, tobacco control surveillance, low and middle income countries, handheld, general survey system, global adult tobacco survey. 1. introduction portable handheld computers and electronic data management systems have been used for surveys in many high-income countries [1-3]. however, information on the use of mobile handheld computers for national surveys in developing countries is limited [4-7] and implementation can be a challenge both administratively and technically because of the varying geographical, economic, climatic, political and cultural environments [1]. most surveys in developing countries use paper questionnaires with manual input into a computer database for collation and statistical analysis [figure 1.1]. this method can be timeconsuming, error-prone and expensive, and a barrier to increasing the volume of data and speed of collection and analysis. figure 1.1: paper questionnaire data management model as part of a plan to develop methods to monitor and measure global adult tobacco use, the world health organization (who) and the us centers for disease control and prevention (cdc) initiated planning and implementation of the global adult tobacco survey (gats) [8], as a component of the global tobacco surveillance system (gtss). gats is a nationally representative household survey of adults, 15 years of age or older, using a standard core questionnaire, sample design, and data collection and management procedures that have been reviewed and approved by a committee of international experts. http://ojphi.org/ 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 tobacco use is a major preventable cause of premature death and disease worldwide. it kills nearly six million people each year and causes hundreds of billions of dollars of economic damage worldwide. should current trends continue, this figure is expected to increase to more than eight million a year by 2030 [9]. if efforts to mitigate this epidemic are to succeed, there must be an efficient and systematic surveillance mechanism to monitor and manage the epidemic [10]. gats was designed to produce high quality, globally comparable national and sub-national estimates on tobacco use, exposure to secondhand smoke, and quit attempts among adults in countries surveyed and to enhance the capacity of these countries to design, implement, and evaluate tobacco control and prevention programs [8]. gats has been designed to produce national and regional estimates on tobacco use and tobacco control measures among adults. gats is a face-to-face interview survey of civilian, non-institutionalized men and women aged 15 years or older who consider the surveying country to be their primary place of residence. gats uses a stratified multistage cluster sampling approach in which probability-proportionalto-size random selection is used to successively select the sample of households in one or more stages to ensure adequate coverage of the entire target population while simultaneously minimizing the costs of data collection. after each sample household is selected, one eligible resident from each selected household is chosen electronically for the full survey interview. the selection is made by having the handheld computer generate a random number that corresponds to one member of the household [8]. during 2008-2010, gats was conducted in 14 lowand middle-income countries [11-24] (bangladesh, brazil, china, egypt, india, mexico, philippines, poland, russian federation, thailand, turkey, ukraine, uruguay and viet nam) representing about 3.6 billion people [25]. portable handheld computers were employed for data collection and the general survey system (gss) was the electronic data management system used [figure 1.2] . this paper describes the implementation of electronic data collection during gats and discusses the challenges and future directions for the use of handheld computers for data collection and electronic data management. figure 1.2: electronic data management model used in gats http://ojphi.org/ 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 2. methods gats data collection was conducted using portable handheld computers. to create and adapt this data collection and management system for gats [figure 2.1], a comprehensive data management protocol [8, 26-29] was developed, which included the following components:  developing and adapting the data management implementation plan to specific country needs  selection of appropriate portable handheld computers  software customization, using the case management system (cms) and the general survey system (gss), for the portable handheld computers  training, capacity building, and technical support. questionnaire programming using gss ide case file creation using gss ide setup handhelds quality control conduct training, load case file and distribute handhelds collect and transmit data to regional / national data center supervisor monitors interview process create master national dataset & reports using gss ide conduct field work provide feedback review data and provide feedback send final master aggregated file and master sdf file to dcc end of data collection start post data collection process translate finalized questionnaire develop, review and finalize questionnaire process diagram : gats data collection activities quality control figure 2.1: process diagram: gats data collection activities the process of programming the questionnaire, planning and testing the data collection started after the questionnaire instrument and the sample had been adapted and approved to suit countryspecific needs. http://ojphi.org/ 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 2.1 developing the data management implementation plan: the following three data transmission models were developed for countries to adapt to their specific needs: model a web-based data transmission: this model (figure 2.2) was designed for countries with experience in electronic data collection systems and a wide reach of access to wireless internet, which was considered a relatively high level of infrastructure and technical capacity. the sample was loaded onto the handheld computers from the national center to the field via wireless internet using a web interface and the data from the field were transmitted back to the national data center using the handheld devices and wireless internet connections. of the 14 countries, only one (poland) implemented gats using the web-based data transmission model [table 1]. figure 2.2: model a – web-based transmission model b combination of web and card based: this model (figure 2.3) was designed for countries that did not fit the model a requirements but had field internet capabilities similar to most countries, which was considered an intermediate level of infrastructure and technical capacity. the data were transmitted using secure digital (sd) cards for loading the sample from the national level to the field level and over the internet from the field to the national level, either via email or secure file transfer protocol (ftp) sites. all countries other than poland used model b, either fully or partially, for data management of gats [table 1]. figure 2.3: model b – card –based with field internet capabilities http://ojphi.org/ 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 model c card-based data transmission: this model (figure 2.4) was designed for countries with no internet access, considered the weakest level of infrastructure and technical capacity. the data were transmitted manually in both directions using sd cards, from the national center to the field interviewer’s portable handheld computers and back, or in some cases via ftp file transfers. none of the 14 countries used this model exclusively; china and egypt used a combination of models b and c for some areas with very poor infrastructure and no internet access [table 1]. figure 2.4: model c: card-based data transmission 2.2 selection of appropriate portable handheld computers for the first phase, two main models of hp ipaq® portable handheld computers were used for data collection in most countries. the hp ipaq hx2490c was used initially and then after it went out of production, the hp ipaq 210/211 series was used. figure 2.5: handheld computers used for gats the hardware was procured centrally to avoid country-specific procurement delays. each portable handheld computer was accompanied with two sd cards and one extra battery as backup. brazil used already existing handhelds with the windows 5 operating system, which had been used for previous surveys [12]. a 10% hardware contingency was provided. after the completion of data collection, the handheld computers and other equipment were donated and remained in each country, so that both hardware and software could be reused to conduct other research or surveys as needed. http://ojphi.org/ 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 2.3 software customization using general survey system (gss) for the handheld computers programming and data management [26] the general survey system (gss) used in gats is open-source software, which meant it could be used for data collection in any national or sub-national surveys. the database was stored on the handheld computers in an encrypted structured query language (sql ) server, compact edition database file (sdf) format. gss had two main components, as described below: 2.3.1 gss integrated development environment (ide) suite (desktop computer interface) the desktop application had a variety of tools to adapt the handheld computer questionnaire to country-specific settings, as well to aggregate, monitor, and report data during and after field data collection. some of the key tools were:  the questionnaire designer: this user-friendly tool enabled users to develop questionnaires in multiple languages and build the files needed for loading the handheld computers. it also had tools for version control.  sample management (case file tools): in gats the household sample was preloaded on to the handheld computers with all household identification details. case file tools in gss program are then used for creating and managing the case files.  data aggregation tool: data collected from field were aggregated for analysis and fieldwork monitoring using this tool.  data viewing tool: every input was tracked by the software to provide the highest data quality standards. the data viewer allowed the central data hub to view data entry values without aggregating or converting the database to another format.  reports tool: daily progress and status reports along with basic analysis could be generated by just reading the database and selecting the method of analysis. fast and easy reporting of progress and/or issues enabled smoother and more efficient data collection, while any potential issues throughout the fieldwork could be traced and addressed immediately instead of having to wait for completion of data collection and entry, as is the case in most paperbased surveys. figure 2.6: general survey system software http://ojphi.org/ 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 this comprehensive software component evolved over the two years of gats implementation and proved to be a very effective tool for capacity building and helping countries use the technology for other survey/research needs. 2.3.2 gss engine and a case management system (cms) (handheld computers component) the gss handheld computers component had various tools, described below, the main one being :  the gss engine: this engine included a folder with system files that enabled a questionnaire designed using the gss desktop application to be read and displayed on the handheld computer. the gss engine was also responsible for data security, encryption and backup tasks on the handheld computers. figure 2.7: general survey system software  the data were stored in real time in the handheld computer’s memory. in addition, a copy of the dataset was backed up onto the sd card in the handheld computer at the end of each data collection session, thus minimizing data loss. http://ojphi.org/ 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 2.4 training, capacity building, and technical support a standardized training mechanism was used for training in-country it/fieldwork personnel. the data management team was extensively trained on each component of the survey system so they could train fieldwork personnel and maintain gats specific data collection and quality control procedures. this also insured that countries would be able to conduct future surveys/research using the software and hardware. the training package consisted of three main components: 1) training for pre-test and full survey implementation: each country conducted a pretest prior to the actual fieldwork to test the data management model, software and hardware system, and questionnaire used in gats, in order to extract lessons for effectively and systematically implementing the full survey. the training, ranging from three to five days, was provided to the country implementation team, it and data management teams, and fieldwork personnel, using standardized training procedures with a specific focus on data collection and management with handheld devices [8]. in some countries (e.g., bangladesh), refresher training was conducted in addition to the full survey training to meet country-specific needs and address changes in the data collection implementation mechanism. other countries (e.g., russia) were able to conduct the full survey with remote support after the in-country pre-test training. 2) manuals and guides: specific manuals for data management were developed and adapted for each country as needed. these followed the standard protocol and were an important instrument for maintaining systematic and standard procedures, as well as sustaining capacity. they included:  programmer’s guide to the general survey system [26]  core questionnaire programming specifications [27]  data management implementation plan [28] 3) technical support: an ongoing support mechanism via an international team of technical experts was in place to manage any challenges in the large-scale field implementation and provide remote and in-country technical knowledge for all countries. 3. findings gats was successfully conducted in all 14 phase i countries, representing over 3.6 billion people or over half of the world’s population, using handheld computers for data collection, between 2008 and 2010. data were collected on approximately 303,000 households, representing more than 600 million smokers. in total, approximately 4300 field interviewers, supervisors and monitors were trained to use portable handheld computers, out of which, excluding brazil, almost 2700 field interviewers alone trained in using these devices for data collection. approximately 3000 portable handheld computers used in the survey were: • programmed to work with nearly 40 languages and dialects including english. • programmed to work in various scripts, both western and non-western characters, including arabic, hindi, latin, mandarin, and cyrillic. • implemented in extreme environmental conditions, including • high altitude areas in china http://ojphi.org/ 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 • freezing winter in ukraine and poland • hot and dry summer in egypt • high humidity in coastal india • monsoon season in bangladesh. data quality, data availability, data security, data usability, cost-effectiveness and sustainability were observed to be the major advantages of using the electronic data collection and management system, as explained in detail below. 3.1 data quality paper-based systems have been used for a long time in developing countries, but with many quality issues, such as transcription, data entry, and editing errors, skip logic errors, out-of-range values, and recording of ambiguous values. this has been especially true where the interviewer had to refer to answers to previous questions (sometimes in previous sections) in order to determine what question to ask next, based on the logical flow of the questionnaire. these errors were usually found during data entry from the paper forms and edited out with a good data entry and cleaning program, however it was difficult to rectify them at the entry level. the use of portable handheld computers allowed quality control at the time of data capture while the respondent was still present. the use of skip programming, range checks, validation and other data checks for valid data entry on portable handheld computers has proven to be highly instrumental in enabling high data quality and standardized entry at the collection stage. in-built skip and logic checks increased efficiency and reduced interview time. in gats, the data were stored digitally on the handheld computer and backed up on an external memory card, thus no data entry was required at the end of the survey. the data were transmitted during fieldwork, using the web or emails on a daily or weekly basis, allowing early monitoring and quality control of the fieldwork at the regional and/or national level, which was not easily possible in paper-based surveys. overall the electronic data collection mechanism enabled better data quality control, as the data were verified at the entry level from the original data sources. in addition, rapid availability of the electronic files made early monitoring and review possible. 3.2 data availability use of portable handheld computers in gats proved to be both timeand cost-effective. it also reduced error during risk-prone processes, such as administration of a huge number of paper questionnaires, shipping the equipment, and data entry and data cleaning. based on feedback from the countries, the fieldwork was usually slower than average in the first two weeks, as fieldworkers learned to use the technology. however over time the pace picked up and overall timing actually improved by the end of the survey. most countries completed their fieldwork on or before the scheduled end date. http://ojphi.org/ 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 the process was reported to be more efficient and timely overall as compared to paper and pencil methods. one reason was the much reduced manual work required during interviews when using fully programmed electronic questionnaires. the automated data checks also saved interviewing time and costs. decreasing the time between data collection and data reporting had many advantages. it reduced costs, allowed better tracking of survey completions, helped estimate time to project completion, promoted early analysis, and allowed issues with questionnaires to be reported and corrected more swiftly. electronic data capture reduced the time between data collection and reporting by putting the data in a clean, electronic format as soon as they were collected. gats showed that electronic data collection could be optimally efficient for large-scale activities, given the rapid availability of data for reporting, analysis and prompt action. 3.3 usability the portable handheld computers were easy to use and carry. the electronic data collection system using portable handheld computers proved to be very manageable administratively over the entire span of the data collection, especially for large-scale surveys. a 10% hardware contingency was planned, however, the actual hardware failure rate gats was less than 1%. battery life, which was an early concern, proved not to be a problem in the field as the instructions to charge the equipment every night were followed and monitored, and there was proper advance planning, especially for fieldwork in remote and rural areas. 3.4 data security handheld computers provided a more secure environment for data because they were validated and stored electronically at the entry stage, with backup on the device as well on an sd card in a secure encrypted format. this ensured safety, security and anonymity of the data. the electronic data collection system used in gats resulted in 0% data loss globally. it provided an accurate and stringent audit trail of response recording, which facilitated early monitoring of data integrity issues at the field level, highlighting any unconventional changes in data values. 3.5 cost effectiveness the initial cost to purchase equipment was a prominent budget item. it seemed likely that the use of handheld computers would be cost effective only if they were used for multiple surveys or other data collection initiatives. although the initial cost of equipment was higher, there were other major areas of cost savings, including paper, printing, and dispatch and handling of paper questionnaires, in addition to savings associated with data entry software purchases, data entry forms programming and development, and labor costs for data entry and editing. the entry-level checks with enhanced data quality may have provided greater precision with smaller sample sizes, compared to paperbased systems where the data cleaning removes invalid data thus requiring additional data to achieve similar precision. overall this methodology allowed for faster data availability, enhanced http://ojphi.org/ 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 data quality, and reduced administrative and logistical efforts and costs. the higher up-front costs of the electronic systems were recovered or mitigated by reducing back-end costs (e.g., keying and editing) and speeding up access to the data for analysis. in addition, it improved data quality. it was expected that a significant issue in using handheld computers for gats would be the initial training and technical support. this was addressed by adding additional one or two days of structured training for interviewers in the use of handheld computers for data collection. although this was an extra initial cost, one day of extra training time was gained because less training was needed on questionnaire skips and quality checks, as these were built into the system. any extra time and cost was easily compensated by the faster fieldwork using electronic data collection. 3.6 sustainability the gats partners facilitated significant technical capacity building at the country level in the implementation of nationally representative surveys. sustainable technical skills and the electronic data collection and management system together have proven to simplify and shorten the data collection process, enhance data quality and facilitate health system strengthening at the country level. this has been demonstrated by gats countries having subsequently successfully conducted other national or health surveys using the new system (e.g., egypt used the system for an economic survey, bangladesh used the portable handheld computers for a noncommunicable disease risk factor survey, china used a similar system for its behavioral risk factor survey, and india has conducted a national feasibility review for implementing this system across all health surveys in that country). 4. conclusions gats was one of the largest global public health surveys. the use of an electronic data collection and management system provided data at a fast pace to meet programmatic needs with the highest quality of data. successful use of this technology in gats has proven that the use of handheld computers for national surveys in developing countries can significantly enhance the quality of data collection and management processes. this system is could be used in most nationally representative surveys. it has been shown to expand the technical capacity of the host country and strengthen the overall health systems by providing effective tools for in-country staff, both in tobacco control and in other areas of survey research, thus acting as a strong vehicle for health system strengthening. http://ojphi.org/ 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 table 1: summary of participating gats countries countries year data management model implemented number of handhelds number of country specific languages number of field interviewers trained number of households contacted bangladesh 2009 b 87 1 72 11,200 brazil* 2008 b n/a 1 n/a 51,011 china 2010 b+c 245 1 245 15,000 egypt 2009 b+c 160 1 100 23,760 india 200910 b 500 19 500 79,690 mexico 2009 b 175 1 177 18,540 philippines 2009 b 205 6 189 12,086 poland 2009 a 140 1 187 14,000 russian federation 2009 b 260 1 447 12,000 thailand 2009 b 147 1 109 22,780 turkey 2008 b 275 1 275 11,200 ukraine 2010 b 130 2 94 13,833 uruguay 2009 b 135 1 135 6,558 viet nam 2009 b 185 1 104 11,142 total 2,644 † 38 2,634 † 302,800 * in brazil gats was conducted on a sub sample as a part of national household sample survey (pnad) n/a – not available † excluding brazil http://ojphi.org/ 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 acknowledgements the authors would like to thank dr. samira asma from the us centers for disease control and prevention and dr. edouard tursan d’espaignet from the tobacco free initiative, who geneva, for their scientific review of the manuscript. funding for the global adult tobacco survey (gats) was provided by the bloomberg initiative to reduce tobacco use, a program of bloomberg philanthropies. the government of india contributed to gats implementation in india and the bill and melinda gates foundation provided additional funding for gats implementation in china. finally, we thank the thousands of fieldworkers for their contributions and the several hundred thousand respondents for their cooperation, without whom this work could not have been realized. disclaimer ®hp ipaq is either a registered trademark of hewlett packard us and/or other countries. the views expressed in this article are solely those of the authors and not necessarily those of the gats partner organizations. conflict of interest all authors declare no conflict of interest. corresponding authors: name: sameer j pujari title: technical officer, tobacco free initiative, world health organisation, geneva switzerland email address: pujaris@who.int mailing address: avenue appia 20, 1211 geneva 27 switzerland. telephone: +41 22 791 3314 name: krishna mohan palipudi title: senior survey statistician, global tobacco control branch, centers for disease control and prevention, atlanta usa. email address: kpalipudi@cdc.gov mailing address: 4770 buford hwy ne, ms k-39, atlanta, ga 30345, usa telephone: 770-488-5648 http://ojphi.org/ mailto:pujaris@who.int mailto:kpalipudi@cdc.gov 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 summary table what was already known on the topic: what this study added to our knowledge:  use of electronic data collection and management is effective and provides better data quality and faster data availability for policy making and action.  paper based surveys have been used for long time to collect data for nationally representative health surveys in middle and low income countries.  portable handheld computers and electronic data management systems have been used for national surveys in many high-income countries.  the challenge, in the implementation of a standardized global survey across multiple middle and low income countries is the heterogeneity of geographical, economic, climatic, political and cultural environment of countries.  the electronic data collection and management system used for gats illustrates that a standardized electronic data collection and management systems can be used effectively in a number and variety of middle and low income countries for conducting nationally representative health surveys.  for an electronic data management system to be efficient in multiple countries, a standard comprehensive protocol is a very critical instrument.  the capacity built in-country staff for electronic data collection and management system acts as a strong vehicle for health system strengthening both in tobacco control and in other areas of survey research references 1. world health organization. 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http://ojphi.org/ http://www.cdc.gov/tobacco/global/gats/countries/amr/fact_sheets/uruguay/2009/pdfs/uruguay_2009.pdf http://www.cdc.gov/tobacco/global/gats/countries/amr/fact_sheets/uruguay/2009/pdfs/uruguay_2009.pdf http://www.who.int/tobacco/surveillance/en_tfi_gats_vietnam_report.pdf http://esa.un.org/unpd/wpp/index.htm http://www.who.int/tobacco/ 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 gats collaborating group national agencies and collaborators country name agencies collaborators bangladesh ministry of health and family welfare (mohfw), national institute of preventive & social medicine (nipsom), bangladesh bureau of statistics (bbs) national institute of population, research & training (niport) md. amirul hasan (nipsom) brazil ministry of health-secretariat of health surveillance (svs), brazilian institute of geography & statistics (ibge), national cancer institute (inca), the national health surveillance agency (anvisa) deborah carvalho malta (moh-svs), eduardo pereira nunes, marcia quintslr, cimar azeredo (ibge), liz maria de almeida (inca), humberto martins (anvisa) china ministry of health (moh), chinese centers for disease control (china cdc) yang gonghuan, yang yan, xiao lin, li qiang (china cdc) egypt ministry of health (moh) central agency for public mobilization & statistics (capmas) sahar latif labib (moh), awatef hussein (capmas) india ministry of health & family welfare (mohfw) ― government of india, international institute for population sciences (iips) anuradha vemuri, jagdish kaur (mohfw), f. ram, sulabha parasuraman (iips) mexico ministry of health (moh) national institute of public health (insp) mauricio hernandez avila (moh), luz miriam reynalesshigematsu (insp) philippines department of health (doh), national statistics office (nso) agnes segarra (doh), glenn barcenas, benedicta yabut (nso) poland ministry of health (moh), maria skłodowska-curie cancer center institute of oncology, medical university of warsaw, pentor research international tadeusz parchimowicz (moh), witold zatonski, krzysztof przewozniak (cci), filip raciborski (wmu), krzysztof siekierski (pentor) russian federation ministry of health & social development (mohsd), federal state statistics of russia (rosstat), pulmonary research institute (pri) maria shevireva, natalya kostenko, (mohsd), vadim nesterov, tamara chernisheva, tatiana konik (rosstat), galina sakharova (pri) thailand ministry of public health (moph), national statistical office (nso), tobacco control research & knowledge management center (trc) at mahidol university sarunya benjakul (moph), lakkhana termsirikulchai, mondha kengganpanich (trc), areerat lohtongmongkol, hataichanok puckcharern, chitrlada touchchai (nso) turkey ministry of health (moh), turkish statistical institute (turkstat), hacetteppe university (hu) hüseyin i̇lter (moh), enver tasti, ramazan celikkaya, guzin erdogan (turkstat), nazmi bilir, hilal özcebe (hu) ukraine ministry of health (moh), kiev international institute of sociology (kiis), school of public health, national university of kyiv-mohyla academy (sph) alla grygorenko, konstantin krasovsky (moh), natalia kharchenko, volodymyr paniotto (kiis), tatiana andreeva (sph) uruguay ministry of health (moh), national statistics institute (ine) winston abascal, ana lorenzo (moh), franco gonzález mora (ine) viet nam ministry of health (moh), viet nam standing office on smoking and health (vinacosh), general statistics office (gso), hanoi medical university (hmu) phan thi hai (moh), nguyen the quan (gso), hoang van minh (hmu), kim bao giang (hmu) http://ojphi.org/ 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 international agencies and collaborators world health organization (who) ─ tobacco free initiative headquarters: douglas bettcher, lubna bhatti, edouard tursan d’espaignet, sameer pujari, ayda yurekli afro: a.e. ogwell ouma, nivo ramanandraibe amro-paho: adriana blanco, roberta de betania caixeta country offices: brazil: enrique gil; mexico: carlos gamez; uruguay: julio vignolo emro: fatimah el awa, heba fouad country office egypt: randa abou el naga euro: kristina mauer-stender, rula khoury country offices: poland: anna koziel; russian federation: luigi migliorini, oleg storozhenko; turkey: toker ergüder; ukraine: nataliya korol searo: dhirendra n. sinha country offices: bangladesh: sohel choudhury, m. mostafa zaman; india: vineet munish gill; thailand: chai kritiyapichatkul wpro: susan mercado, james rarick country offices: china: sarah england; philippines: marina miguel-baquilod; viet nam: pham thi quynh nga, pham huyen khanh u.s. centers for disease control and prevention (cdc) global tobacco control branch, office on smoking and health (osh) linda andes, samira asma (branch chief), glenda blutcher-nelson, felicita david, peter edwards, thomas r. frieden (cdc director), jason hsia, deliana kostova, ronney lindsey, charity “nikki” mayes, timothy mcafee (osh director), sara mirza, jeremy morton, krishna mohan palipudi, terry pechacek, edward rainey, dana shelton, yang “sophia” song, raydel valdés salgado, brian taitt, luhua zhao cdc foundation william parra, brandon talley, connie granoff, michael green johns hopkins bloomberg school of public health joanna cohen, rajeev cherukupalli rti international steve litavecz experts & scientific advisors questionnaire review committee benjamin apelberg, jeremy morton, marina miguel-baquilod, ron borland, gary giovino, prakash c. gupta, daniel ferrante, ahmed mandil, mostafa mohammed sample review committee michael bowling, william kalsbeek, krishna mohan palipudi, t. k. roy http://ojphi.org/ 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 scientific advisors sonia angell , neeraj bhalla, frank chaloupka, prabhat jha, judith mackay, sir richard peto, jonathan samet, gajalakshmi vendhan, witold zatonski bloomberg philanthropies – bloomberg initiative to reduce tobacco use kelly henning, jennifer ellis http://ojphi.org/ layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts establishment of a one health surveillance initiative in the ca/baja ca border region sarah c. marikos*1, karen l. ferran1, esmeralda iniguez-stevens1 and francisco javier monge navarro2 1early warning infectious disease surveillance (ewids), california department of public health, san diego, ca, usa; 2universidad autónoma de baja california, veterinary school, mexicali, mexico objective to showcase one border one health, a binational, multidisciplinary initiative in the california/baja california (ca/bc) border region whose aim is to reconfigure traditional species-specific approaches to surveillance for emerging and re-emerging pathogens. introduction the ca/bc border region encompasses a wide range of ecosystems, topography, dense urban areas, and agricultural developments that coexist in a limited geographic area and create numerous humananimal-environmental interfaces. the region is recognized for its high biodiversity, the presence of over 85 endangered plant and animal species, its importance on the pacific migratory pathway, high levels of population mobility, and hosts the busiest international border in the world. these interfaces pose a significant risk to animal, human, and environmental health, as evidenced by frequent wildlife die offs, antibiotic resistant bacteria in streams, beach closures due to fecal contamination, pesticide toxicities, zoonotic infectious disease outbreaks, and vector borne diseases. in the marked absence of any organization comprehensively addressing the health risks posed by these complex interfaces and recognizing that these issues necessitate a binational, cross-sectoral one health approach, the early warning infectious disease surveillance program (ewids) founded one border one health (oboh) in 2011. oboh recognizes that early warning systems should systematically monitor animal, human, and environmental health and that early detection is key to control. hence oboh’s primary aim is to create and integrate early warning surveillance systems that gather data from disparate sources in order to protect and improve animal, human, and environmental health. this information can be used to inform decision makers about important public health events in the ca/bc border region. methods oboh is a unique multi-disciplinary initiative comprised of over 30 institutions from mexico and 60 institutions from the united states, with representation from government, academia, non-profit, private and military sectors. professionals with expertise in public health, veterinary medicine, ecology, biology, urban planning, epidemiology, wildlife health, and environmental health are working in concert rather than in the traditionally isolated human, environmental health, domestic animal and wildlife sectors. oboh is actively seeking to translate one health theory into practice through its diverse, binational network. this demonstration presents oboh’s surveillance, informatics, and education activities, focusing on its strengths, challenges, and future directions. conclusions to the authors’ knowledge this is the first trans-border regional network established to enhance cross border epidemiologic information exchange and surveillance using one health concepts in north america. despite the large disparities between health systems, cultures, languages, socioeconomics, politics, animal management strategies, industries and ecosystems in the ca/bc border region, professionals from diverse disciplines are dedicated to oboh and to the creation of a sustainable integrated surveillance system. oboh is building the infrastructure for an early warning system in the border region, while improving regional infectious disease surveillance capacity and educating a new cadre of students and professionals about the importance of a one health approach. challenges include identifying cross-sectoral/multi-disciplinary funding opportunities to support activities, systematically operationalizing one health without such funding, identifying and involving partners from different sectors, promoting data exchange, and maintaining an equal understanding of one health surveillance within the initiative as membership increases. this demonstration provides recommendations on how to initiate and sustain cross-border, multidisciplinary, cross-sectoral surveillance engagements in resource-constrained environments. keywords cross-border surveillance; emerging and re-emerging pathogens; one health; collaboratives; early warning systems acknowledgments the authors acknowledge dr. helen engelke (western university of health sciences) and dr. nikos gurfield (san diego county, department of environmental health) for their rich contributions to one border one health. the authors are also extremely grateful for all the contributions by one border one health partners to this initiative. *sarah c. marikos e-mail: smarikos@cdph.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e205, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using gi syndrome data as an early warning tool for norovirus outbreak activity erin e. austin*, jun yang and tim powell division of surveillance and investigation, virginia department of health, richmond, va, usa objective to assess the relationship between emergency department (ed) and urgent care center (ucc) chief complaint data for gastrointestinal (gi) illness and reported norovirus (nv) outbreaks to develop an early warning tool for nv outbreak activity. the tool will provide an indicator of increasing nv outbreak activity in the community allowing for earlier public health action to mitigate nv outbreaks. introduction norovirus infection results in considerable morbidity in the united states where an estimated 21 million illnesses, 70,000 hospitalizations, and 800 deaths are caused by nv annually (1). additionally, nv is responsible for approximately 50% of foodborne outbreaks (1). between january 2008 and june 2012, 875 nv outbreaks were reported to the virginia department of health (vdh). to assist in detecting possible disease outbreaks such as nv, vdh utilizes the web-based electronic surveillance system for early notification of community-based epidemics (essence) to monitor and detect public health events across virginia. essence performs automated parsing of chief complaint text into 10 syndrome categories, including a non-specific gi syndrome that serves as a proxy for gi illnesses like nv. methods ed and ucc chief complaints parsed into the essence gi syndrome category were compared to confirmed and suspected nv outbreaks across four years. in this study, the analysis periods were defined as week 21 through week 20 of the subsequent year. gi syndrome visits as a proportion of all ed and ucc visits and nv outbreak counts were aggregated by week. time-series, correlation, and logistic regression analyses were performed. low nv outbreak activity weeks were defined as those with 4 or fewer outbreaks, and high nv outbreak activity weeks were those with 5 or more outbreaks. based on low nv outbreak activity weeks, baseline and threshold values for the weekly percent of gi syndrome visits were calculated for each analysis period. baseline calculation was the average weekly percent of gi syndrome visits from week 21 to week 31 and threshold value was baseline plus two standard deviations. weekly percent of gi syndrome visits was compared to the threshold value to serve as an indicator of increasing nv outbreak activity. results the study period was from may 18, 2008 to may 19, 2012 (fig 1). a total of 1,425,728 gi syndrome visits and 804 confirmed and suspected nv outbreaks were analyzed. weekly visits to ed and ucc facilities with gi syndrome were highly correlated with outbreaks of nv in the community (r =0.809, p <.0001). median and mean number of nv outbreaks per week were 2 and 4, respectively (range 023). nv outbreaks were more prominent during the winter months with peaks occurring between weeks 3-9. median and mean percent of gi syndrome visits per week were 10.2% and 10.5%, respectively (range 8.9%-12.8%). weeks with high nv outbreak activity were more likely to occur when the weekly percent of gi syndrome visits had surpassed the threshold value (or =110.7, 95% ci: 31.9-384.8). on average, weekly percent of gi syndrome visits surpassed the threshold value 1.25 weeks prior to the start of high nv outbreak activity weeks (range 0-3). conclusions these results support the use of syndromic surveillance gi illness data as an early warning indicator of increasing nv outbreak activity in virginia. this study identified that gi syndrome visits crossed a calculated threshold value on average 1.25 weeks before the initiation of high nv outbreak activity. although nv outbreaks occur year round, this study attempted to identify an indicator to trigger meaningful risk communication to the community immediately prior to high nv outbreak activity with the goal of reducing the magnitude of nv outbreaks. this early warning tool for nv outbreak activity will be implemented in the following year to validate its effectiveness and timeliness in mitigating nv outbreaks in virginia. percent of emergency department and urgent care center visits with gi syndrome and reported norovirus outbreaks, virginia, may 2008-may 2012. keywords syndromic surveillance; essence; norovirus; gi illness references centers for disease control and prevention (2012). burden of norovirus illness and outbreaks. retrieved september 5, 2012, from http://www.cdc.gov/norovirus/php/illness-outbreaks.html. *erin e. austin e-mail: erin.austin@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e69, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts multiagent simulation of the hepatitis b epidemic process tetyana chumachenko*1, dmytro chumachenko2 and olexandr sokolov2 1epidemiology department, kharkiv national medical university, kharkiv, ukraine; 2national aerospace university “kharkiv aviation institute”, kharkiv, ukraine objective to develop multiagent model of hepatitis b (hbv) infection spreading. introduction the standard approaches to simulation include solving of differential equation systems. such approach is good for obtaining general picture of epidemics (1, 2). when the detailed analysis of epidemics reasons is needed such model becomes insufficient. to overcome the limitations of standard approaches a new one has been offered. the multiagent approach has been offered to be used for representation of the society. methods of event-driven programming give essential benefits of the processing time of the events (3). methods for model development c# computing language has been used. we have used demographical data, the incidence rate of hbv infection of all population and different population groups (age, professional and other groups), coverage of hepatitis b vaccination, the proportion of hbv carriers in population, the prevalence rate of chronic hbv infection, percent of dominated transmission routes and factors and other rates in kharkiv region. all parameters, expressed in the model were estimated using sero-surveys data and data of epidemiological surveillance of kharkiv region sanitary-epidemiological station. also the theoretical knowledge about hbv infection has been used. 26 conditions have been derived from the problem domain. the transition from one condition to another depends on stochastic value and time of the event change. all events are organized in priority queue which results in high rate of computation performance. the dependence on time and random value determines automata theory conceptions. results the prototype of software system, which includes a subsystem of the multiagent simulation and specialized statistical and mathematical sub-system which can process the simulation results and perform a conditional optimization of the selected objective functions (morbidity, the effectiveness of specific preventive and control activities and their price, measure of reducing the socio-economic impact of hbv infection, etc.) have been developed. screen form is presented in figure 1. conclusions the multiagent simulation model of the hbv infection epidemic situation development, based on data obtained in kharkiv (ukraine) has been created. the simulation results allow us to: 1. predict the dynamics of the epidemic process in time in a particular area, taking into account specific epidemic situation; 2. test the effectiveness of various preventive measures (sterilization of instruments, coverage of hepatitis b vaccination of certain groups of people, etc.). using the present model in public health system suggests improvment of the epidemiological diagnostics of hbv infection and of the quality of management decisions about epidemiological surveillance. the evolution of multiagent simulation in epidemiology will broaden the possibilities of epidemiological surveillance and control. fig. 1. the main panel of simulation management and graphic visualization. keywords multiagent modeling; event-driven programming; predictive disease modeling; epidemiological surveillance references 1. shoujun zhao, zhiyi xu, ying lu.: mathematical model of hepatitis b virus transmission and its application for vaccination strategy in china. international journal of epidemiology. 2000;29:744-52. 2. o’leary, c., hong, z., zhang, f., dawood, m., smart, g., kaita, k., wu, j.: a mathematical model to study the effect of hepatitis b virus vaccine and antivirus treatment among the canadian inuit population.eur j clin microbiol infect dis. 2010 jan;29(1):63-72. 3. hethcote, h.w.: the mathematics of infectious diseases. siam rev. 2000; 42(4): 599–653. *tetyana chumachenko e-mail: tatalchum@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e178, 2013 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 130 (page number not for citation purposes) isds 2015 conference abstracts a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 1epidemiology, nys department of health, albany, ny, usa; 2ntt data, albany, ny, usa objective improved methods for user analysis of communicable disease surveillance data in new york state (nys), excluding new york city (nyc). introduction nys (excluding nyc) has a very robust communicable disease electronic surveillance system (cdess). this system provides disease specific modules, as well as a tracking system for contacts, and a perinatal infant tracking system. this system provides an easy way for users to quickly download a file with all of their data. nys (excluding nyc) tracks, on average, 300 infants of hepatitis b surface antigen (hbsag) positive mothers annually. cdess provides an infant tracking module for local health departments (lhds) to enter and monitor vaccine information, add multiple infants per mother, and track patient movement and loss to follow-up. the tool allows lhds to analyze infants’ data by birth year cohort, with all of their current vaccination and serology information available in one record. in 2013 and 2014, more than 13,000 cases of gonorrhea were reported to cdess in nys (excluding nyc). from november 2013 through may 2014, only 61% of cases were adequately treated with a regimen recommended by the centers for disease control and prevention (cdc) std treatment guidelines for gonorrhea1, and 29% were missing treatment information. the cdess system allows the lhds to track patients who have inadequate and/or missing treatment information. methods the infant’s birth information, mother’s information, and the provider information in cdess are prepopulated from newborn screening for each infant born to an hbsag positive mother. lhds enter the infant’s vaccination record and the infant’s serology record. to access the infant’s data on cdess, the lhd needs to know the mother’s information: either the unique identifier or complete name and date of birth. gonorrhea investigations are automatically created by cdess by data from the electronic clinical laboratory reporting system (eclrs). demographic, provider, and laboratory information is prepopulated by eclrs. lhds create the case upon review and perform follow-up and update the case supplemental form with information regarding risk, treatment, and additional provider data. the cdess data resides in oracle tables. using sas v9.3, the complex relational data is collated into one record per infant for the perinatal data, and one record per patient for the gonorrhea data. this data is then converted to one comma separated values (csv) file for each county and birth year cohort for perinatal, and county and case year for gonorrhea. these csv files are pushed to the oracle database by using the oracle sql*loader utility. this loads the csv files as a character large object (clob) into an oracle table in the cdess database. sas runs automatically every night and sends the updated csv files to cdess. the updated csv files are immediately available for download by the lhds. using these files county users can filter their data by provider, treatment, vaccine dosage, and age to be more proactive in protecting the health of new yorkers. results the simplicity of accessing one file removes multiple steps in finding the perinatal infants’ vaccination and serology information. using these csv files, we can easily see that there are 259 babies currently being followed in the 2014 birth cohort. twenty babies of the original 285 birth cohort moved out of jurisdiction, while six cannot be located. of the 259, five did not receive hbig at birth and four did not receive their first dose at birth. by eight months of age, 219 (85%) had received three hepatitis b vaccinations. thirty-eight percent have already had their pvst. using the gonorrhea files, the lhds can assess who is in need of treatment. from november 2014 through may 2015, 92% of cases received adequate treatment, with only 2.6% missing data. twenty percent fewer patients were considered to be inadequately treated because they received only one treatment in 2014-2015 than in 2013-2014. conclusions removal of multiple steps in data retrieval saves time for the lhds. the lhds now have an easy way to analyze their own data and be more proactive on their follow-up with physicians and families. all infants and patients within a jurisdiction can be monitored simultaneously. user-friendly systems and simple data analysis processes improve the overall quality of data collection. keywords surveillance; communicable disease; gonorrhea treatment; perinatal hepatitis tracking references 1 centers for disease control and prevention. update to cdc’s sexually transmitted diseases treatment guidelines 2010: oral cephalosporins no longer a recommended treatment for gonococcal infections. mmwr 2012;61(31): 590-94. *candace m. noonan-toly e-mail: candace.noonan-toly@health.ny.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e71, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and 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related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak 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tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic 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supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts towards interoperability for public health surveillance: experiences from two states brian e. dixon*1, 2, 3, jason a. siegel5, tanya v. oemig5 and shaun j. grannis4, 2 1indiana university, indianapolis, in, usa; 2regenstrief institute, indianapolis, in, usa; 3center of excellence on implementing evidence-based practice, department of veterans affairs, veterans health administration, health services research and development service, indianapolis, in, usa; 4indiana university school of medicine, indianapolis, in, usa; 5atlas development corporation, calabasas, ca, usa objective to characterize the use of standardized vocabularies in real-world electronic laboratory reporting (elr) messages sent to public health agencies for surveillance. introduction the use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. for health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary. semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. there are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through elr (2). logical observation identifiers names and codes (loinc) identify the specific lab test performed. systematized nomenclature of medicine-clinical terms (snomed ct) identify the diseases and organisms tested for in a lab test. many commercial laboratory and hospital information systems claim to support loinc and snomed ct on their company websites and in marketing materials, and systems certified for meaningful use are required to support loinc and snomed ct. there is little empirical evidence on the use of semantic interoperability standards in practice. methods to characterize the use of standardized vocabularies in electronic laboratory reporting (elr) messages sent to public health agencies for notifiable disease surveillance, we analyzed elr messages from two states: indiana and wisconsin. we examined the data in the elr messages where tests and results are reported (3). for each field, the proportion of field values that used either loinc or snomed ct codes were calculated by dividing the number of fields with coded values by the total number of non-null values in fields. results results are summarized in table-1. in indiana, less than 17% of incoming elr messages contained a standardized code for identifying the test performed by the laboratory, and none of the test result fields contained a standardized vocabulary concept. for wisconsin, none of the incoming elr messages contained a standardized code for identifying the test performed, and less than 13% of the test result fields contained a snomed ct concept. conclusions although wisconsin and indiana both have high adoption of advanced health information systems with many hospitals and laboratories using commercial systems which claim to support interoperability, very few elr messages emanate from real-world systems with interoperable codes to identify tests and clinical results. to effectively use the arriving elr messages, indiana and wisconsin health departments employ software and people workarounds to translate the incoming data into standardized concepts that can be utilized by the states’ surveillance systems. these workarounds present challenges for budget constrained public health departments seeking to leverage meaningful use certified technologies to improve notifiable disease surveillance. table 1 – proportion of “raw” elr data samples with loinc or snomed ct concepts keywords standards; interoperability; electronic laboratory reporting; public health surveillance; computerized medical records systems acknowledgments the authors further thank shahid khokhar of the regenstrief institute and keith michaelson of atlas public health for their help with the extraction of the elr message data. this work was supported, in part, by the indiana center of excellence in public health informatics through a grant award (501hk000077) from the u.s. centers for disease control and prevention. references 1. dolin rh, alschuler l. approaching semantic interoperability in health level seven. j am med inform assoc. 2011;18(1):99-103. 2. wurtz r, cameron bj. electronic laboratory reporting for the infectious diseases physician and clinical microbiologist. clin infect dis. 2005;40(11):1638-43. 3. dixon be, mcgowan jj, grannis sj. electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. amia annu symp proc. 2011;2011:32230. *brian e. dixon e-mail: bdixon@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e51, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts does antimicrobial prescription data improve influenza surveillance in va? patricia schirmer*1, carla winston1, russell ryono1, cynthia lucero-obusan1, gina oda1 and mark holodniy1, 2 1department of veterans affairs, office of public health surveillance and research, palo alto, ca, usa; 2stanford university, division of infectious diseases and geographic medicine, palo alto, ca, usa introduction antimicrobial prescriptions are a new data source available to the veterans health administration (vha) biosurveillance program. little is known about whether antiviral or antibacterial prescription data correlates with influenza icd-9-cm coded encounters. we therefore evaluated the utility and timeliness of antiviral and antibacterial utilization for influenza surveillance. methods antiviral (oseltamivir, zanamivir) and antibacterial (azithromycin) outpatient (op) prescriptions and op essence coded respiratory syndrome, influenza-like-illness (ili) or influenza-specific icd-9cm coded visits were analyzed covering the 2010-2011 and 20112012 influenza seasons (july 1, 2010-july 31, 2012) for 152 va medical centers and 971 outpatient clinics using va corporate data warehouse and essence biosurveillance tool. correlation analysis and peak comparisons were performed. results for this time period, there were 2,880,415 respiratory, 1,578,421 ili, and 5,158 influenza-specific coded visits. for both influenza seasons, respiratory and ili visits peaked at weeks 1-2 whereas influenza-specific visits had two peaks between weeks 37-40 and weeks 6-11 (see figure 1 and 2). the total number of prescriptions was 631,272 azithromycin; 8,362 oseltamivir; and 88 zanamivir (see figure 2). spearman rank correlation coefficients for daily antiviral prescriptions and influenza-coded visits were (0.70); ili visits (0.64), and respiratory illness visits (0.62), respectively; and for azithromycin prescriptions 0.77, 0.98, and 0.97 respectively. oseltamivir and zanamivir prescriptions only increased in 2010-2011 starting with week 51 and peaking week 6 and in 2011-2012 starting with week 8 and peaking week 14. however, azithromycin prescriptions tracked better across the entire influenza season (peaking at weeks 1-2 for both influenza seasons). conclusions va outpatient prescription data indicated that significantly more ili and respiratory syndrome visits occurred compared to antiviral prescriptions dispensed with marginal temporal correlation between visits and antiviral prescriptions. reasons for this finding require further investigation. although we did not chart review the visit code and antimicrobial prescription in individual records, possible factors may be related to later presentation of cases, perceived lack of efficacy of antivirals, or insufficient coding of influenza. thus, antiviral prescription data provided minimal additional information for influenza trend monitoring in va although may still be useful a marker of more severe illness. interestingly, azithromycin use tracked better with the onset and peaks of the influenza season. further investigation is also needed to determine whether patients with influenza-specific coded encounters were also prescribed azithromycin and why relatively few encounters were coded with an influenza-specific code. figure 1: daily respiratory (resp) [green], influenza-like illness (ili) [purple], and influenza-specific [yellow] encounters compared to azithromycin (azithro) [blue] and oseltamivir and zanamivir [red] orders in va facilities nationally from july 1, 2010-july 31, 2012. figure 2: detailed view of daily influenza-specific coded encounters [yellow] and azithromycin (azithro) [blue], oseltamivir and zanamivir [red] orders in va facilities nationally from july 1, 2010-july 31, 2012. keywords essence; surveillance; influenza; veterans; antimicrobials *patricia schirmer e-mail: patricia.schirmer@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e35, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts flu near you: an online self-reported influenza surveillance system in the usa rumi chunara*1, 2, susan aman2, mark smolinski3 and john s. brownstein1, 2 1harvard medical school, boston, ma, usa; 2boston children’s hospital, boston, ma, usa; 3skoll global threats fund, san francisco, ca, usa objective to develop a participatory system for monitoring the activity of influenza-like-illness among the united states general population. introduction the emergence of new influenza strains including h1n1, h5n1, h3n2v as well as other respiratory pathogens such as sars, along with generally weak information about household and community transmission of influenza, enforce the need for augmented influenza surveillance. at the same time, internet penetration and access has grown, with 82% of american adults using the internet [1], enabling transfer and communication of information that can be collected and aggregated in near real-time. surveillance targeted towards influenza in other countries, and towards malaria in india, has previously been executed with good user engagement [2,3]. in this study, we created an online participatory influenza surveillance tool in the united states, called flu near you. methods volunteer users were primarily solicited via collaboration with the american public health association and their members’ networks starting oct. 24, 2011. upon registration, each user is sent a weekly email, taking them to the flu near you website. on the website they fill in a short survey asking if they had any of 10 symptoms: fever, cough, sore throat, shortness of breath, chills/night sweats, fatigue, nausea or vomiting, diarrhea, body aches and headache, in the last week. users can also enroll their household members and enter information in for them weekly. a map of influenza activity is made available to users, and anyone accessing the website [figure 1]. on the map, the number of individuals reporting with no symptoms, some symptoms, or influenza-like illness are visualized, aggregated to the zip code level. users can also compare the contributed data with other surveillance systems: the centers for disease control and prevention, and google flu trends for the same time period [figure 1]. we also obtained user feedback through a survey in early july 2012. results as of august 21, 2012, there are over 9300 flu near you users, from all 50 states. 94.0% of users are between 20 and 70 years, although 37.2% of household members are < 20 years old. overall 62.0% of members (users and household) were female. we found that 46.4% of users answered 3 or more surveys. qualitatively from survey responses, we learned that simple feedback and an emphasis on public health education are important in this type of system. conclusions we found that it is possible to engage users in a symptom self-reporting system and augment information about influenza for the nation. increased uptake would increase the value of the system for the public and public health professionals. flu near you is expanding in its second season, working to increase user participation. other connected projects are also examining the expansion into other diseases with high prevalence and in need of augmented surveillance. with a larger user base and through a longer period of execution, systems like flu near you will help to improve our understanding of influenza epidemiology as well as guide implementation of relevant and timely public health interventions, for example in estimating vaccination rates and efficacy among different demographic groups. information reported by individuals can augment traditional public health surveillance methods for more timely detection of disease outbreaks, monitoring disease activity and increasing the public’s engagement in their own and population’s health. keywords influenza; surveillance; crowdsourcing acknowledgments all of the flu near you users who have contributed information. references [1] pew internet & aerican life project. available at: pewinternet.org. accessed aug. 22, 2012. [2] marquet rl, et al. internet-based monitoring of influenza-like illness (ili) in the general population of the netherlands during the 20032004 influenza season. bmc public health. 2006; 6: 242. [3] chunara r, et al. (2012) online reporting for malaria surveillance using micro-monetary incentives, in urban india 2010-2011. malaria j 11: 43. *rumi chunara e-mail: rumi@alum.mit.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e133, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using cultural modeling to inform a nedss-compatible system functionality evaluation olympia anderson and miguel torres-urquidy* centers for disease control and prevention, atlanta, ga, usa objective the culture by which public health professionals work defines their organizational objectives, expectations, policies, and values. these aspects of culture are often intangible and difficult to qualify. the introduction of an information system could further complicate the culture of a jurisdiction if the intangibles of a culture are not clearly understood. this report describes how cultural modeling can be used to capture intangible elements or factors that may affect nedsscompatible (nc) system functionalities within the culture of public health jurisdictions. introduction the national notifiable disease surveillance system (nndss) comprises many activities including collaborations, processes, standards, and systems which support gathering data from us states and territories. as part of nndss, the national electronic disease surveillance system (nedss) provides the standards, tools, and resources to support reporting public health jurisdictions (jurisdictions). the nedss base system (nbs) is a cdc-developed, software application available to jurisdictions to collect, manage, analyze and report national notifiable disease (nnd) data. an evaluation of nedss with the objective of identifying the functionalities of nc systems and the impact of these features on the user’s culture is underway. methods we used cultural models to capture additional nc system functionality gaps within the culture of the user. cultural modeling is a process of graphically depicting people and organizations referred to as influencers and the intangible factors that affect the user’s operations or work as influences. influencers are denoted as bubbles while influences are depicted as arrows penetrating the bubbles. in the cultural model, influence can be seen by the size and proximity (or lack of) in the model. we restricted the models to secondary data sources and interviews of cdc programs (data users) and public health jurisdictions (data reporters). results three cultural models were developed from the secondary information sources; these models include the nbs vendor, public health jurisdiction (jurisdiction) activities, and nedss technical consultants. the vendor cultural model identified channels of communication about functionalities flowing from the vendor and the nbs users with cdc as the approval mechanism. the jurisdiction activities model highlighted perceived issues external to the organization that had some impact in their organization. key disconnecting issues in the jurisdiction model included situational awareness, data competency, and bureaucracy. this model also identified poor coordination as a major influencer of the jurisdiction’s activities. the nedss technical model identified major issues and disconnects among data access, capture and reporting, processing, and elr functionalities (figure 1). the data processing functionality resulted in the largest negative influencer with issues that included: loss of data specificity, lengthy submission strategies, and risk of data use. collectively, the models depict issues with the system functionality but mostly identify other factors that may influence how jurisdictions use the system, moreover determining the functionalities to be included. conclusions by using the cultural model as a guide, we are able to clarify complex relationships using multiple data sources and improve our understanding of the impacts of the nc system functionalities on user’s operations. modeling the recipients of the data (e.g. cdc programs) will provide insight on additional factors that may inform the nedss evaluation. figure 1. cultural model from a nedss technical consultation meeting. keywords evaluation; cultural modeling; functionality; nedss acknowledgments we acknowledge the leadership and staff of cdc’s stakeholders and the division of notifiable disease and healthcare information. references beyer h, holtzblatt k. contextual design: defining customer-centered systems. san diego (ca): academic press; 1998. *miguel torres-urquidy e-mail: jvu5@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e84, 2013 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts biosurveillance ecosystem (bsve) workflow analysis timothy dasey*1, hayley davison reynolds1, nancy nurthen2, christopher kiley2 and john silva3 1chemical and biological defense systems, mit lincoln laboratory, lexington, ma, usa; 2dtra chemical & biological technologies directorate, ft. belvoir, va, usa; 3silva consulting services, llc, sykesville, md, usa introduction the defense threat reduction agency chemical and biological technologies directorate (dtra cb) has initiated the biosurveillance ecosystem (bsve) research and development program. operational biosurveillance capability gaps were analyzed and the required characteristics of new technology were outlined, the results of which will be described in this contribution. methods work process flow diagrams, with associated explanations and historical examples, were developed based on in-person, structured interviews with public health and preventative medicine analysts from a variety of department of defense (dod) organizations, and with one organization in the department of health and human services (dhhs) and with a major u.s. city health department. the particular nuanced job characteristics of each organization were documented and subsequently validated with the individual analysts. additionally, the commonalities across different organizations were described in meta-workflow diagrams and descriptions. results two meta-workflows were evident from the analysis. in the first type, epidemiologists identify and characterize health-impacting events, determine their cause, and determine community-level responses to the event. analysts of this type monitor information (primarily statistical case information) from syndromic or disease reporting system or other sources to determine whether there are any unusual diseases or clusters of disease outbreaks in the jurisdiction. this workflow involved three consecutive processes: triage, analysis and reporting. investigation and response processes to disease outbreaks are both parallel and overlapping in many circumstances. in the second meta-workflow type, analysts monitor for a potential health event through text-based sources and data reports within their particular area of responsibility. this surveillance activity is often interspersed with other activities required of their job. they may generate a daily/weekly/monthly report or only report when an event is detected that requires notification/response. there are similar triage, analysis and reporting workflow stages to the first meta-workflow type, but in contrast these analysts are focused on informing leadership and response in the form of policy modification. they are also subject to answering leadership-driven biosurveillance queries. conclusions in these interviews, analysts described the shortcomings of various technologies that they use, or technology features that they wish were available. these can be grouped into the following feature categories: data: analysts want rapid access to all relevant data sources, advisories for data that may be relevant to their interests, and availability of information at the appropriate level for their analysis (e.g., output of interpretations from expert analysts instead of raw data). enhanced search: analysts would like customization of information based on relevance, selective filtering of sources, prioritization of search topics, and the ability to view other analysts searches. verification: analysts want indications of information that has been verified or discarded by other analysts, a trail of information history and uses, and automatic verification (e.g., data quality editing) if possible. analytics: analysts want access to forecasting models, services to suggest analysis methods, pointers to other analysts’ expertise, methods, and reports, and tools for “big data” exploitation. collaboration and communication: analysts want assistance identifying people who may have needed information, real-time chat, the ability to compare analyses with colleagues, and the ability to shield data, results, or collaborations from selected others. archival: analysts want automation to provide lessons learned, methods and outcomes for related events, the ability to automatically improve baselines with analyzed data, and assistance with reporting on interim analytic decisions. the current understanding of the biosurveillance analyst’s functions and processes, based on the results of these interviews, will continue to evolve as further dialog with analysts are combined with results of evaluations during subsequent phases of the new bsve program. keywords biosurveillance; workflow; collaboration; operations acknowledgments this work is sponsored by the defense threat reduction agency under air force contract #fa8721-05-c-0002. opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the united states government. *timothy dasey e-mail: timd@ll.mit.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e86, 2013 determining community health status priorities in an online analytic processing (olap) environment 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi determining community health status priorities in an online analytic processing (olap) environment james studnicki 1 , john w. fisher 1 1 university of north carolina, charlotte, college of health and human services, department of public health sciences abstract introduction: the determination of priorities is an essential component of community health status assessment. yet, there is an acknowledged need for a systematic method which will utilize data in standardized comparisons to yield priorities based on objective analyses. method: we have deployed a web-based system with: a flexible online analytic processing (olap) interface; multiple sources of event-level data conformed to common definitions in a data warehouse structure; and, centralized technical infrastructure with distributed analytical capabilities. the prioritization tool integrated into the system takes full advantage of the granularity of multidimensional sources of data to: apply a series of defined objective criteria; vary the weight of those criteria and detect the reordering of the rankings in real-time; and, apply the prioritization algorithm to different categories of health status outcomes. results: in our example, mortality outcomes for miami-dade county, florida, were considered with three different weighting combinations of the four primary ranking criteria. the resultant analyses return markedly different mortality priority rankings based upon the selection and weighting of the criteria. conclusion: rankings of community health outcomes based on a static set of criteria with fixed weighting factors may not provide sufficient information necessary for priority setting and may, in fact, be misleading. mesh keywords: public health information, public health practice, community health status assessment, community health priorities. correspondence: jstudnic@uncc.edu copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:jstudnic@uncc.edu determining community health status priorities in an online analytic processing (olap) environment 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi introduction community health status assessment is now widely practiced by not-for-profit hospitals seeking to justify their tax exempt status, by community applicants for many federal programs, and by a majority of the nations 2,500 local health departments. 1 an important component of these assessments is some determination of the subject communities’ health status priorities. priority setting should enable the allocation of resources among competing programs, groups or individuals. 2 however, there is very little empirical evidence on priority setting in public health, knowledge of how priority-setting decisions are made is still rudimentary, and many health care and public health resources are expended without a clear understanding of priorities. 3,4,5 unfortunately, there is also evidence that local health officials are more likely to use subjective criteria rather than evidence based objective criteria when deciding on the most important health issues in their communities. 6 thus, there is an acknowledged need for a pragmatic and systematic method which will utilize objective data in standardized comparisons allowing decision makers to rely more on the use of hard evidence. 7,8,9 evidence-based approaches should lead to more consistent and efficient decision making and ultimately result in more strategic allocation of public health resources. 10 in separate deployments in the states of north carolina (catch) and, more recently, florida (healthtrac) investigators have established a web-based analytical environment with: a flexible and powerful online analytic processing (olap) interface; multiple sources of multidimensional, event-level data fully conformed to common definitions in a data warehouse structure; enabled utilization of publicly available decision support software tools; and distributed analytic capabilities with centralized technical infrastructure. 11 utilizing the analytical capabilities of the olap analytical environment, the challenge was to create a community health status prioritizing system which would meet these functional requirements: 1) it must systematically apply a series of defined objective criteria; 2) it must be able to rank different types of health status outcomes (e.g., mortality rates, hospitalization rates); and 3) it must provide flexibility in the weighting of the evaluation criteria to enable iterative, “what-if” types of analyses. methods the healthtrac/ catch data warehouses provide a unique opportunity to simultaneously parse community health outcomes across the full set of demographic dimensions at analyst-controlled levels of granularity, and then to rank-order them according to analyst-defined criteria. these capabilities are possible because of the following functional characteristics of the system:  event-level health outcomes are available in five data sets – births, mortality, hospitalizations, emergency department visits, and the cancer registry.  using icd-10 codes, mortality indicators may be aggregated by icd chapter and subchapter, the clinical classification system (ccs), or the nchs 113 common causes of death. rankings may be based on crude rates, age-adjusted rates, or years of potential life lost.  similarly, hospitalizations may be aggregated according to primary diagnosis icd-9-cm codes using the ccs disease definitions. rankings may be based on crude rates, age-adjusted rates, length of stay total days, or total charges, depending on the analyst’s definition of impact. determining community health status priorities in an online analytic processing (olap) environment 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi  cancer registry data affords access to cancer incidence data aggregated based on site (e.g. breast, lung) definitions, with rankings based on crude rates, age-adjusted rates, and stage-ofdiagnosis measures.  potential issues with small cell sizes can be addressed by aggregating across time, diagnosis, and/or selected demographic dimensions. we assume three nearly orthogonal dimensions compose the concept of “priority”: impact; departure from norms; and time. these dimensions are defined and operationalized in the following manner:  the impact dimension measures the effect of the event on the population. mortality rates (number of deaths per unit of population) and ypll, lost years of potential life, are measures of the impact of premature mortality. for hospitalizations, impact measures may include admission rates, total length of stay days, or total charges.  the second dimension, departure from norms, measures how much the measure differs from some benchmark. typical standards include state or peer averages, or standard deviations from the mean of all measures in a selected geography (e.g., county or hospital service area).  a third dimension temporal rate of change simply indicates whether an indicator is measurably improving, deteriorating, or not reflecting any significant change trend. the relative importance ascribed to each of these dimensions can have a major effect on the rankings of the indicators, and this is a major limitation of static conventional ranking methodologies that use county-level comparative rankings as the sole measure. a common problem with many county-level ranking systems is their attempt to aggregate mixed-outcome types into a single “health” index or ranking. combining rankings representing mortality, hospitalizations, health resource availability, disease morbidity and other determinants of health requires at least an implicit relative weighting of the various outcome types. assigning an explicit comparative factor to each outcome type is a highly subjective exercise; absent such an assignment, each of these indicators is implicitly assumed to have equal effect on the health of the community. the healthtrac/catch prioritization tool avoids this conundrum by first dividing the indicators into five separate categories representing the disparate outcome types:  mortality – computed from vital statistics death records using primary cause of death;  morbidity – selected from county-level reports of infectious and sexually transmitted diseases;  hospitalizations – inpatient (at least overnight admissions) hospitalization rates using the primary diagnosis icd-9-cm code aggregated using the clinical classification system hierarchy;  behavioral – county-level rates of conditions/behaviors selected from the annual behavioral risk factor surveillance system (brfss) survey;  correlates of health (violent crime, high school drop-out rates, uninsured rate, etc) – amalgamation of county-level indicators from a wide variety of public data sets. the tool then employs a conventional top-down approach using county-level aggregates and four measures representing the three priority dimensions of impact, departure from norms, and time: determining community health status priorities in an online analytic processing (olap) environment 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi  impact is measured by counting the number of individuals affected. additional measures warranting investigation include length of stay and total charges for hospitalizations, and years of potential life lost (75) for mortality.  departure from norms is calculated as rate differences between the subject county and the rates for both the state and the county’s peers. additional benchmarks available include the national average and any published goals, such as healthy people 2020.  the temporal dimension is captured by measuring any significant trend over a selected period such as the most recent 3 years or 5 years. trend lines are computed using a linear regression curve and an adjustable minimum r 2 value for significance. the challenge of normalizing the dimensional variances to allow their aggregation by simple addition after being weighted is addressed by assuming a normal distribution and converting the values to standard deviations (z-values). for instance, for the mortality category, the number of deaths due to each of the top common causes of death vary widely within a given county. by computing the distance in standard deviations from the median number of deaths for all causes of death, the very wide differences are reduced to a much more manageable range and one that is common to the other criteria as well. results once the prioritization tool is activated, the user will be prompted to select the category of outcomes to be rank ordered. in our example, the user selects the mortality outcomes (figure1). on the left hand side of the screen, four screening criteria previously defined are listed: number affected; trend; magnitude of difference (peer); and magnitude of difference (state). a fifth criterion, community support, may also be activated. this enables any special influence which may be specific to the community environment to be considered in the prioritization process. to the right of the criteria are slide bars which serve to determine the weight given to each of the criterion. the analyst locates the slide bar at the exact point to represent the relative contribution of each criterion to the ranking algorithm. the total weight of all criteria combined is exactly 1. determining community health status priorities in an online analytic processing (olap) environment 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure1. health outcome category selection in our first example (figure2), we have weighted the number of persons affected and the direction of the trend in the outcome as about equally important in the ranking. the difference between the values in the subject community (miami-dade) versus the values in a sociodemographically equivalent peer group of counties, and the florida state average values are not considered in this ranking. this weighting combination produces a ranking with chronic and acute lower respiratory disease death rates as the highest mortality priorities. determining community health status priorities in an online analytic processing (olap) environment 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 2. criteria weighting combination no.1 in our next iteration (figure 3), we eliminate the influence of temporal trend and weight the algorithm to produce a ranking of causes of death solely on the number of persons affected. the ranking of mortality outcomes, now strikingly different, places the two measures of premature death (years of potential life lost 65 and 75) at the top of the rankings because they aggregate deaths from all causes. note also, that the familiar major causes of death (heart disease, cancer, stroke, cerebrovascular disease) emerge as high priority mortalities in this ranking. lower respiratory diseases remain in the listing but are no longer top ranked. determining community health status priorities in an online analytic processing (olap) environment 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3. criteria weighting combination no.2 finally, we allow the rankings to be solely determined by miami-dade’s mortality profile compared to their peer counties and the state average (figure4). this time the number affected and temporal trend are not considered. incidentally, this is the way most of the static community report cards primarily derive their rankings. again, the rankings are markedly changed with hiv/aids, homicide and diabetes emerging at the very top of the rankings. deaths from lower respiratory diseases have disappeared from the top ten. determining community health status priorities in an online analytic processing (olap) environment 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 4. criteria weighting combination no.3 discussion clearly, variation in the explicit weighting of objective criteria can dramatically change the rankings of health outcomes which are used to derive community health status priorities. priorities based upon a static ranking of a number of fixed indicators are unable to reflect the unique and changing circumstances and characteristics of local populations. the prioritization tool herein described utilizes county level aggregates to identify the high priority issues, and then assumes that the olap drill down capabilities will be used to investigate the distribution and patient characteristics of high-priority indicators. in the case of premature death as indicated by years of potential life lost (ypll), for example, the olap tool is available to identify the contribution made by various causes of death (e.g., infant mortality, heart disease, homicides), in specific age groups, by race, in various geographic locations within the county. thus, the analyst can produce actionable information about the problem. in the absence of an olap enabled warehouse of event-level data sources, this macro-micro sequential analysis is not possible. in the future, olap capabilities will allow for a “bottom-up” rather than the current “top-down” approach. the proposed approach starts at the lowest grain of geography using a default weighting scheme to allow the analyst to roll up dimensions (e.g., collapse the race and/or gender determining community health status priorities in an online analytic processing (olap) environment 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi dimensions) to measure statistical power. this approach will produce high priority populations rather than causes of death or reasons for hospitalization. the populations will be defined by the outcome, but also age, race, location, and other characteristics. why is this capability necessary in understanding community health status priorities? effective health outcome interventions are local; that is, they address specific health issues affecting identifiable sub-groups of the population defined by geography, race, age, and gender. prioritizing population health outcome issues for interventions requires identifying the outlying health outcomes for these sub populations. as we have seen, this has been accomplished by comparing county-level values for a limited number of generic indicators against other counties, and then “drilling down” to identify variations within the county to identify the potential outlying sub populations. this approach works best for small-to-mid-size, fairly homogenous counties. for large, heterogeneous counties, the extreme outliers will tend to “wash out” each other, and the county level average value may not in any way reflect the true depth of the problem for the most vulnerable segments of the population. consider that for florida, there are some 1,000 zip codes. each zip code population can be subdivided by race (3 values black, white, and other), 2 genders (male, female), and perhaps 5 age bands. this yields 30,000 sub population groupings. using the nchs 113 common causes of death, the mortality possibilities alone total over 3.3 million possible " sub-population "problem outcomes (113 x 30,000). searching this space manually is simply infeasible. yet, the event-level data sources and information technology is available to deliver this type of analytical capability to every us community. acknowledgment we acknowledge the technical assistance of kathy belk, director of health analytics, medassets, inc. corresponding author jams studnicki, sc.d. irwine belk endowed chair and professor 1 jstudnic@uncc.edu phone: 704-687-8981 fax: 704-687-6122 references [1] national association of county and city health officials. 2005 national profile of local health departments. washington, dc, 2006. [2] mckneally m, dickens b, meslin e, et.al. bioethics for clinicians: resource allocation. canadian medical association journal. 1997;157(2):163-167. [3] martin d, singer p. a strategy to improve priority setting in health care institutions. health care analysis. 2003;11(1):59-68. [4] martin d, abelson j, singer p. participation in health care priority-setting through the eyes of the participants. journal of health services research and policy. 2002;7(4):222-229. mailto:jstudnic@uncc.edu determining community health status priorities in an online analytic processing (olap) environment 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [5] woolf s, stange k. a sense of priorities for the healthcare commons. american journal of preventive medicine. 2006;31(10):99-102. [6] platonova e, studnicki j, fisher j. local health department priority setting: an exploratory study. journal of public health management and practice. 2010;16(2):140-147. [7] mitton c, donaldson c. tools of the trade: a comparative analysis of approaches to priority setting in healthcare. health services management research. 2003;16(2):96-105. [8] mitton c, patten s, waldner h, et.al. priority setting in health authorities: a novel approach to a historical activity. social science and medicine. 2003;57:1653-1663. [9] vilnius d, dandoy s. a priority rating system for public health programs. public health reports. 1990:105(5):463-470. [10] sibbald s, singer p, upshur r, et.al. priority setting: what constitutes success? a conceptual framework for successful priority setting. bmc health services research. 2009;4:43.doi:10.1186/1472-6963-9-43. [11] studnicki j, fisher j, eichelberger c, et.al. nc catch: advancing public health analytics. online journal of public health informatics. 2010; 2(3). ojphi-06-e116.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 53 (page number not for citation purposes) isds 2013 conference abstracts a data mining approach to identify climatic determinants of dengue fever patterns in french guiana claude flamand*1, mickael fabregue2, sandra bringay3, vanessa ardillon4, philippe quenel5, jean-claude desenclos6 and maguelonne teisseire7 1institut pasteur de la guyane, cayenne, french guiana; 2lirmm, cnrs, umr 5506, montpellier, france; 3miap department, university paul-valery, montpellier, france; 4regional epidemiology unit of institut de veille sanitaire, cayenne, french guiana; 5institut pasteur de la guyane, cayenne, french guiana; 6institut de veille sanitaire, paris, france; 7laboratory department of information system, irstea-tetis, montpellier, france � �� �� �� � � �� �� �� � objective ��� ����� �� �� �� ���� � � ���� ������ ��� ������ ���� �� ����� ��� ��� ���� ������ ���������� � ������ ������������������� ������������� �� ��������������������� introduction ����� ������� ���������� ������������������������� ������� ����� ������� �������� � ���� ���������������� �� ������������������������ ��� �� ���������� � ������ ����!"#��$���������� �� �������� � ����� ������ ������������������������ ���� �������� ����������� ���� ������ ���� ����������� ������� �� � ������ ���� ����������������� �� ��� ��������� ������������ ������� ������ ���� ���� �!%#� ���������� �� ��� ����� ���� � ������ ����� �� ��� � �� ���� ���� �� �� ���� �������� ��� ��� ����������� ����&�� methods ����� ���� ����� �������� ���������������� ���������� ���������� ��� �%''(� ��%'""��)�� � ������������������� ��� ��� ������������ � ��� � � ���� ��� � � � ���� ���� � ���� ��&�� ����� �� � ����� � ��� � � ��� � ����� ��� � ����� ������ ��� ������ ����������� �������������� ��������� � � ������ ������ � ��������*+ +���������� �������������� �� ���������� ������ ������ ��� ���� ����������!,#� ������� ���� ��������� � ������ ������� �� ������ ���� ��� �� ��� � ������� ���� � ������ ����������� �������� ������������� � � ��� ������� ���������� ����� ��� ����� ��������� �������� ���� ������������������ ������ �������������������� $��� � ������ ����������������,�� ���-� �� . ���"-�$������ �� � ������������ �������� �������� ���� � �������� �� ��������������/ �� . ���%-�$������������������������ � ������ ������� ��� ��������� ���� ������������������� �/ �� . ���,-�$��������� ������ ������� ��� ���� �������� ��� ��� ������� ������������������ �������������� �� � results ��� � ����� ����� ����%''(� ��0�����%'"" �,1 234���������������� ���� "" ",,� ����� ������� ������ ��� ������ ����� ��������� ��� ������� ����������������� ��� ��������������� ��� �����������������,� �5��� �� ����&������� � ���� ������������$������� ������� ���������� ���� ���������� �,3� ��6"����&�� $��� �� ����� � ��� ��� �� ����&� ���� ������� ��� ������� ��� �� �� �� 6����&��� ������ ������� ������������ ��� ������������� ��� ��� � � ���� ���� �� ��� � � �� ��������� ��� ��� ��� � � � ���� ���� 7�� ��� �� ���&����������"����%� �� ���������� ���� �� ���� �������� ��8 ����� �� ���� ��� � ����� �� �������������� ��� ��������������$�������� ���� ��������������� ��������� ����������� �������� ����� �� �������� ��� ���&� �������������� ���������� ����� ��� ����������������������� conclusions ����� ��� �� ��� ������������� �� ���������� ������ ����� ���� � ��������� ���� ����� �� ���� ����� ������ �� � �������������������� ���������������������������)� �������� �� ���� ������ ����� ������ ��� ��������� ���� �������� ������� � ���� � �� �� � ������ � ���� �������� � ������ �� ������� ���� ��� ���� ��� � � ���� � � ��� ��� �� ��������� �� ���� �� ����� ��� �� ����� �� ��������� ����������� �� �� �� ������ �� ������ ����������� �� ���� ������ � �������� �������&����������� � �� ����� �� ���������������������������� ����� ���������� ��������������� ��� � �� ������� ������ ����� keywords 9�� ��� �����/� 9� �� *���� /� :���� ����� ��������/� ������� ������/� *� ������ �������� ��� acknowledgments ��� ���� �� ����� �� ���� ��� ��� ��������� ���� ��������� ��� ��� ������������� ��� � � ��� �������� ���;� ����������� ���� ��<�� ���������� �� ���&� *� �������������� ������������ �� ��� � ������ ������� �������� ���� references !"#�;����=�> �;� ����; �$�� �. �?�����.������������������ ��������� �����-������������������ ���� ����� ���������������������� ���� � ��� @a�.�b� ��$����9���%'"%/(728-�"(63� !%#� 0 ����� � ;� � .��&�� � ;�� *���� � ������ ���� �� ������ :�-� c� � @�.� � )��� �0�.�@��7����8������� ��:� ���� ������)�������������9� ���� �� ������ ��:����)� �� ���.���� ��@���� �"112� !,#�;��� �� �d �e��� �� �. �@������ �@��)�� �� ����.����� ����@� ����*��� �� ��:�-�:�����%'"'�:����:� ���� ������)�������������9� ��*���� � ���&�������%'"'-13"f133� *claude flamand e-mail: cflamand@pasteur-cayenne.fr� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e116, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts sequential bayesian inference for detection and response to seasonal epidemics michael ludkovski* and junjing lin statistics and applied probability, uc santa barbara, santa barbara, ca, usa objective development of a sequential bayesian methodology for inference and detection of seasonal infectious disease epidemics. introduction detection and response to seasonal outbreaks of endemic diseases provides an excellent testbed for quantitative bio-surveillance. as a case study we focus on annual influenza outbreaks. to incorporate observed year-over-year variation in flu incidence cases and timing of outbreaks, we analyze a stochastic compartmental sis model that includes seasonal forcing by a latent markovian factor. epidemic detection then consists in identifying the presence of the environmental factor (“high” flu season), as well as estimation of the epidemic parameters, such as contact and recovery rates. methods anticipating policy-making applications, we consider sequential bayesian inference. to focus on intrinsic model uncertainty, we assume full observation of all individual status changes, but unobserved seasonal factor m underscore “t” and unknown reaction rates. using theory of nonlinear filtering of point processes, we derive analytic expressions for conditional likelihoods of latent factor trajectories. we then utilize a sequential monte carlo approach based on particle learning (pl) [1] to infer the epidemic parameters in conjunction with online filtering of m underscore “t.” these tools extend the pl method to continuous-time jump-markov models and are widely applicable in generic stochastic chemical kinetic models. using the developed inference methods, we then investigate costefficient sequential policy making. we analyze and compare several heuristic counter-measure strategies that work by modifying the duration/frequency of the high epidemic season. results the proposed algorithm was implemented in r and extensively tested on simulated data [2]. we find that the pl method is able to efficiently carry out joint inference. we also find that counter-measures incorporating sequential learning are generally more efficient that other inference-free policies. conclusions we developed a new bayesian approach to joint inference of parameters and latent factors in continuous-time stochastic compartmental models. there is ongoing work [3] to adjust our methods for more realistic observation schemes. fig: response strategy based on the bayesian posterior probability $\pi^2_t$ (use math notation) of high flu season $\{ m_t = 1\}$. countermeasures begin when prob > 95% and end when prob <5%. keywords bayesian inference; stochastic compartmental models; seasonal epidemics; hidden markov models acknowledgments we thank jarad niemi for useful discussions. references [1] carvalho, c. m.; johannes, m.; lopes, h. f. and polson, n. particle learning for sequential bayesian computation. bayesian statistics, 2011, 9, 317-360. [2] lin j, and ludkovski m., sequential bayesian inference in hidden markov stochastic kinetic models with application to detection and response to seasonal epidemics, submitted, 2012. [3] ludkovski m, niemi j. optimal disease outbreak decisions using stochastic simulation. in: proceedings of the 2011 winter simulation conference. jain s, et al eds. *michael ludkovski e-mail: ludkovski@pstat.ucsb.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e7, 2013 ojphi-06-e90.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 108 (page number not for citation purposes) isds 2013 conference abstracts arizona review of reported binational cases with mexico orion mccotter*1, andrea mannell1, catherine golenko1 and nubia hernandez2 1office of border health, arizona department of health services, tucson, az, usa; 2secretaria de salud de sonora, 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�������������� � � ����#� ��� ���������� ��� ��� ��� ������������������������������� �������� ���������������� ���� � �������������������������'�(����������� � ��#��� ���� ������� � � �������� �� ����� ���������� � ������������������������������ ������� ���� ��0��� �� ����� ������� ������� �������� ������ ������� ��� ���� ��� ������� �� ���� ������� ��������������� ������������� ��������� ���� ����������� � � keywords /���������+��� ���������+�/� � +�� �����+����� � acknowledgments ����/".����������� ���0���������0����������������� � ������������� ����� ����� �������������� ���� � � � �������#��� �����'-.�"��������� �������� �����������������������������0������� �������� *orion mccotter e-mail: orion.mccotter@azdhs.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e90, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using change point detection for monitoring the quality of aggregate data ian painter*, julie eaton and bill lober university of washington, seattle, wa, usa introduction data consisting of counts or indicators aggregated from multiple sources pose particular problems for data quality monitoring when the users of the aggregate data are blind to the individual sources. this arises when agencies wish to share data but for privacy or contractual reasons are only able to share data at an aggregate level. if the aggregators of the data are unable to guarantee the quality of either the sources of the data or the aggregation process then the quality of the aggregate data may be compromised. this situation arose in the distribute surveillance system (1). distribute was a national emergency department syndromic surveillance project developed by the international society for disease surveillance for influenza-like-illness (ili) that integrated data from existing state and local public health department surveillance systems, and operated from 2006 until mid 2012. distribute was designed to work solely with aggregated data, with sites providing data aggregated from sources within their jurisdiction, and for which detailed information on the un-aggregated ‘raw’ data was unavailable. previous work (2) on distribute data quality identified several issues caused in part by the nature of the system: transient problems due to inconsistent uploads, problems associated with transient or long-term changes in the source make up of the reporting sites and lack of data timeliness due to individual site data accruing over time rather than in batch. data timeliness was addressed using prediction intervals to assess the reliability of the partially accrued data (3). the types of data quality issues present in the distribute data are likely to appear to some extent in any aggregate data surveillance system where direct control over the quality of the source data is not possible. in this work we present methods for detecting both transient and long-term changes in the source data makeup. methods we examined methods to detect transient changes in data sources, which manifest as classical outliers. we found that traditional statistical process control methods did not work well for detecting transient issues due to the presence of discontinuities cause by long term changes in the source makeup. as both transient and long-term changes in source makeup manifest as step changes, we examined the performance of change point detection methods for monitoring this data. these methods have been previously used for detecting changes in disease trends in data aggregated from distribute (4). following kass-hout (4), we used the bayesian change point estimation procedure of barry (5) as implemented in the r package bcp (6). we examined both offline and online detection using time series held at a constant lag. results we found that transient problems could be detected offline as neighboring change points with high posterior probability. when multiple outliers exist close together, detection can be improved by iteratively removing flagged data points and re-running the change point detection on the reduced data. following the removal of outliers, remaining change points indicated long-term changes. to enable realtime monitoring for data quality problems we modified this offline detection process to in addition flag individual change points (rather than pairs of change points) detected in the most recent 5 days. keywords data quality; surveillance; changepoint methods; distribute acknowledgments we would like to thank the markle foundation and the centers for disease control for providing funding for the distribute project. references 1. olson dr, et al. applying a new model for sharing population health data to national syndromic influenza surveillance: distribute project proof of concept, 2006 to 2009. plos currents influenza. 2011 sep 12. 2. painter i, et al. how good is your data? 2011 isds conference abstract. emerging health threats journal 2011, 4 3. painter i, et al. generation of prediction intervals to assess data quality in the distribute system using quantile regression. jsm proceedings, section on statistics in defense and national security. 2011 dec. 4. kass-hout ta, et al. application of change point analysis to daily influenza-like illness emergency department visits. jamia. 2012 jul 3. 5. barry d, hartigan ja. a bayesian analysis for change point problems. j am stat assoc 1993;35:309–19. 6. erdman c, et al. bcp: an r package for performing a bayesian analysis of change point problems. journal of statistical software 23(3). 2007. *ian painter e-mail: ipainter@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e186, 2013 wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 1 ojphi wa notify: the planning and implementation of a bluetooth exposure notification tool for covid-19 pandemic response in washington state tiffany chen1, janet baseman1*, william b. lober2, debra revere3, rebecca hills1, nola klemfuss4, bryant t. karras5 1department of epidemiology, school of public health, university of washington, seattle, wa 2biobehavioral nursing & health informatics, school of nursing, university of washington, seattle, wa 3department of health services, school of public health, university of washington, seattle, wa 4brotman baty institute for precision medicine, school of medicine, university of washington, seattle, wa 5office of science, health and informatics, washington state department of health, olympia, wa abstract bluetooth exposure notification tools for mobile phones have emerged as one way to support public health contact tracing and mitigate the spread of covid-19. many states have launched their own versions of these tools. washington state's exposure notification tool, wa notify, became available on november 30, 2020, following a one-month seattle campus pilot at the university of washington. by the end of april 2021, 25% of the state's population had activated wa notify, one of the highest adoption rates in the country. washington state's formation of an exposure notification advisory committee, early pilot testing, and use of the en express system framework were all important factors in its adoption. continuous monitoring and willingness to make early adjustments such as switching to automated texting of verification codes have also been important for improving the tool’s value. evaluation work is ongoing to determine and quantify wa notify’s effectiveness, timeliness, and accessibility. keywords: covid-19, smartphone, mobile applications, contact tracing, communicable disease control abbreviations: association of public health laboratories (aphl), bluetooth low energy (ble), case risk exposure and risk surveillance tool (crest), department of health (doh), electronic lab reportable (elr), environmental health and safety (eh&s), exposure notification (en), en express (enx), google|apple exposure notification (gaen), local health jurisdiction (lhj), university of washington (uw), washington state (wa state) doi: 10.5210/ojphi.v13i1.11694 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 2 ojphi introduction contact tracing and case investigation have been some of the most essential tools to control the spread of infectious diseases, beginning as early as the late 19th century [1]. during a case interview, patients with confirmed or suspected cases of an illness are given guidance and support on what to do next and interviewed about who they may have been in contact with. these contacts are then "traced" and informed of their exposure for them to take protective measures such as quarantine [2]. in modern times, contact tracing is commonly used for diseases like tuberculosis, measles, and sexually transmitted diseases such as hiv. contact tracing has been important for controlling outbreaks of diseases like ebola and smallpox, and novel viruses such as h1n1 in the past [3-5]. rapid contact tracing is one of public health’s most effective tools in limiting transmission of sars-cov-2, however, contact tracing personnel require specialized training. existing contact tracing workforce and public health infrastructure have struggled to support the large numbers of cases, leading many states and countries to hire and train thousands more. however, with new advancements in technology and the abundance of smartphones, digital solutions have presented new opportunities to contribute to public health epidemic response. digital contact tracing first emerged as a way to speed up the traditional contact tracing process and supplement public health’s efforts by using smartphone technology for tasks like case management, symptom monitoring, and proximity tracking [6]. exposure notification (en) is a subset of digital contact tracing and is not meant to replace the traditional contact tracing infrastructure and process. en refers to part of the contact tracing process in which close contacts of a confirmed case are informed that they have been exposed to a case. a variety of technologies and approaches have been used in different countries and states, each with different approaches to privacy protection and system capabilities. en uses the bluetooth low energy (ble) technology built into smartphones to detect mutual proximity, without recording location information [7]. this method assumes proximity, measured by signal strength, as a proxy for distance (reflecting the cdc’s distance per time quantification of meaningful exposure to define a close contact) while still preserving user privacy. on april 7, 2020, university of washington (uw) researchers and other collaborators released privacy sensitive protocols and mechanisms for mobile contact tracing (pact), proposed protocols for how to best utilize mobile phones to supplement contact tracing while preserving privacy and minimizing risks [8]. this guidance had some influence on google and apple’s exposure notification (gaen) protocol, released may 20, 2020 [9]. gaen generates random identifiers every 10-20 minutes (figure 1). strength of the ble attenuation is used to approximate a 6-foot distance for 15 minutes. this allows a device to determine whether another it has been “exposed” to another device and exchange identifiers with another phone. understanding of appropriate configuration settings is still evolving. the recorded identifiers do not contain any information about the user or the location and are stored on the device for 14 days. both the time and distance for determining an exposure can be changed. when a user tests positive for covid19, local public health authorities share a verification code that, when voluntarily entered into that user’s smartphone, causes the device to upload to a central server the coded identifiers it has broadcast over the past 14 days. these anonymized identifiers are then downloaded periodically wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 3 ojphi by other devices, who compare them to their own record of which device codes they have been “exposed to,” based on a combination of signal strength and time. a match generates a notification of possible exposure on the receiving device. this leads to rapid contact notification, notification of contacts who could be unknown to the case and would not be reported during a case interview and helps combat recall bias. early modeling suggested that with only a 15% adoption rate, infections could be reduced by 8% and deaths by 15% [10]. figure 1. wa notify uses google apple exposure notification technology to inform users of potential exposures [23]. currently, over 20 states and u.s. territories have adopted gaen technology to help slow the spread of covid-19 [11]. however, washington state (wa state) is an important case study for several reasons. wa state was the first u.s. state to report a covid-19 infection on january 20, 2020 and one of the earliest states to be hit hard by the growing epidemic [12]. wa state was one of the earliest states to consider adopting the smartphone-based en technology to support covid19 responses, and sequentially developed and evaluated a locally developed app based on the pact protocol, the use of an open-source gaen app, and the implementation of wa notify as a configuration of the google/apple exposure notification express (enx) embedded capabilities in ios and android phones [13]. considerations included strong, thoughtful collaboration and planning between the wa state doh, the uw and microsoft research, robust data collection, and pilot testing. these aspects of planning and rollout may have contributed to the success and rapid adoption observed in wa state compared with other states. this report will describe the implementation of bluetooth enx technology in wa state, from the planning and decisionmaking to the pilot testing, adoption, and early adjustments made (figure 2). these lessons may be useful in evaluating the success and value of this technology, for other states still considering adoption of en technology and in considering how to best implement similar solutions in the future. wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 4 ojphi figure 2. wa notify planning, piloting, and implementation timeline and major milestones. pre-implementation planning early design and decision making after the release of gaen at the end of may 2020, and the anticipation of the second wave of covid-19 cases after the initial spring peak, governor jay inslee and wa state doh leadership convened the covid-19 exposure notification advisory committee to provide guidance, oversight, and recommendations regarding the implementation of this technology. at the time, no other state had formed a committee to oversee their bt en work and make recommendations. the committee, formed in june 2020, consisted of experts in key areas including mobile technology, public health, outbreak investigation, ethics, equity, and security. the committee also included representatives from community stakeholders. the committee met biweekly until midoctober and discussed various concerns and considerations about the use of this technology. recommendations from the committee included making accessibility and equity a priority, with many concerned that technology-based solutions may exacerbate existing disparities. these recommendations played a large role in informing ongoing evaluation work. the committee was also a space for representatives from important communities in the state, such as farm workers, people with disabilities, and tribes, to contribute their voices and ask questions. ultimately, governor inslee and wa state decided to move forward with the use of bt technology for a statewide en implementation for a smartphone. wa notify pilot testing near the end of summer 2020, the uw decided to move many classes for the autumn quarter online, but still allow for limited in-person classes. several thousand students returned to campus to live in residence halls or greek residences. the start of the autumn quarter created a perfect opportunity to pilot test the existing bt implementation among students, staff, and faculty. the wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 5 ojphi wa notify team saw this as an opportunity to collect information about potential technical issues, concerns and questions users would have, and to help inform integration of the tool into public health workflow. in early september 2020, apple and google announced en express (enx), an option that allowed state public health authorities to use gaen technology without having to dedicate time and resources to building out their own tools. each state can tune the parameters of the tool to meet statewide needs. wa state chose to adopt this method and was the second state to launch an enx tool, shortly after colorado. the "days since exposure threshold" was set at 14 days. parameters were also set to approximate 6-feet proximity based on bluetooth attenuation strength for 15 minutes, consistent with doh’s definition of a close contact as per cdc protocols. since the start of the pandemic, the uw environmental health and safety (eh&s) department has acted as the public health agency for the university population in collaboration with the state and local public health authorities. for covid-19 cases among students or staff on campus, eh&s would conduct surveillance, case investigation, contact tracing, and outbreak investigations. the eh&s contact tracing team was trained on how to register with the association of public health laboratories (aphl), generate verification codes, and provide the codes to pilot participants who tested positive during the case investigation interview. the verification code, once entered by the user into the tool loaded on a smartphone, would alert other pilot users who had been in close contact with the confirmed case within the past 14 days. the pilot version of the en implementation was launched for the uw student population on november 2, 2020, and for employees on november 19, 2020. the uw marketing and communications department sent out invitation emails to eligible students and employees with information about the pilot and instructions on how to participate. both apple and android users had a more complex installation process for the pilot than latter users had in the statewide deployment. apple users were required to download a profile before downloading the tool, and android users were given a link to the hidden tool on the google play store. supporting documentation including background information about the tool and an faq page were made available. during the initial recruitment period, 23,324 students were invited to participate via email. this invitation was limited to students living in campus housing, greek housing, or attending classes in person. of those invited, 4,140 (18%) clicked the link and were directed to the pilot website, and 2,238 (54%) of those started to download the tool. the majority of download attempts used the apple profile (1,771, 79%) compared to the google play store (467, 21%). among students who started a download, only 1,490 (65%) completed the installation of the tool, resulting in an overall campus adoption rate of 6%. adoption patterns were similar in the employee group. on november 19th, 37,128 employees were invited to participate, with 3,522 (9%) people visiting the website. out of these, 2,087 (80%) initiated the tool download and 1,480 (71%) of them successfully downloaded the tool, resulting in an employee adoption rate of 4%. the adoption patterns indicate that each additional step of download decreased the level of user engagement. providing users with direct access to tool download would likely increase adoption. engagement and activations were highest directly after wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 6 ojphi the initial invitation on november 2nd, with another increase after a reminder email to students on the 10th (figure 3). cumulative installations also increased directly after staff and faculty were invited to participate on the 17th. figure 3. cumulative wa notify activations during university of washington campus pilot over time, beginning november 2, 2020. feedback and user requests were tracked and analyzed by the uw pilot team. questions and comments from pilot participants focused primarily on download and activation issues as well as questions about device and version compatibility. these comments and troubleshooting details helped shape the faqs and other educational materials for the statewide launch. wa notify integration with public health wa notify relies partly on the existing public health and case investigation infrastructure to issue codes to positive cases. thus, it was important to have buy-in and appropriate training and integration of this process before statewide implementation in a way that would not be overly burdensome. in wa state, local health jurisdictions (lhjs) manage contact tracing and case investigation for residents of their jurisdictions, with support from doh provided as needed. some state lhjs use the state’s case risk exposure and risk surveillance tool (crest), a tool developed in the summer of 2020 to facilitate case interviews and data management, and other lhjs use a platform of their choosing. lhjs were provided with training and scripts on how to ask if a case was a wa notify user. interviewers generated the 8-digit verification code and walked the user through the process of entering it into the tool. these codes expire after 15 minutes. training materials differed between crest and non-crest lhjs, as code generation was integrated into the crest wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 7 ojphi platform. case investigation interviews included additional questions regarding whether cases had an en tool on their smartphone and if receipt of an en was a reason for getting tested. statewide implementation wa notify became available across the state on november 30, 2020. the media and public relations plan for the launch of wa notify consisted of four stages: initial launch, milestone announcements at certain adoption increments, new year’s reminders, and testimonials from users and endorsements from notable public personalities. the initial launch included a press conference by the governor, radio spots, social media posts, educational videos, and fact sheets. these media assets were made available in both english and spanish. the initial launch also included push notifications sent to ios devices on november 30, alerting apple users to the tool’s availability. several more notifications were pushed to ios and android devices throughout early december. to increase its reach, wa notify was made available in 29 different languages. initially, the tool was only available to those with ios version 13.5 or later, and android version 6 or above. ios compatibility was later expanded to version 12.5 in december to include older models of apple devices. activations were high immediately following the launch, with over one million total installations of wa notify within the first week (figure 4). by the beginning of april, there were 1.9 million activations in the state, 86% of which came from apple devices. the large majority of activations occurred shortly after the initial launch, with a much slower increase over time. given a population size of 7.6 million people, this resulted in an overall adoption rate of 25% of the total population in wa state [14]. however, smartphone ownership by state is difficult to ascertain. assuming smartphone ownership in the state is equal to the overall american average of 85%, the approximate adoption rate in smartphone owners in wa state is 29% [15]. the original adoption target was at least 15%, based on the university of oxford’s modeling estimates of adoption effectiveness [10]. by the end of february, wa state successfully surpassed the original goal for adoption. wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 8 ojphi figure 4. wa notify total activations by week, starting the week of november 29, 2020, until the week of march 14, 2021. data on installations by ios vs. android, notifications, counts of verification codes, and other summary statistics were collected and tracked in an internal google sheets-based data repository and dashboard. this repository is updated daily with data from several sources. the uw wa notify team continued to monitor and provide support to end users and answer their inquiries during the transition from the pilot to the statewide rollout, eventually transferred this responsibility to doh at the end of december. user inquiries, requests and responses were documented and cataloged. early adjustments: introduction of rapid automated texting after monitoring the initial adoption throughout the month of december, as well as covid incidence overall, it became clear that high caseloads would put a strain on the contact tracing infrastructure and sheer numbers may cause delays in the process. after approval from leadership, planning began in december to develop a system for sending activation verification code messages to cases based on hl7 electronic lab reportable (elr) messages rather than relying solely on case investigators interviews. on january 11, 2021, wa notify began using automated texting to issue verification codes (figure 5). this method generates text messages in the aphl server that are sent to the phone number on file for all covid-positive lab results each day. each text includes an individualized code and information on what to do next. the codes expire after 24 hours, however, codes can still be issued during the case investigation if necessary. wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 9 ojphi figure 5. wa notify verification code generation and notification process [24]. red arrows indicate changes in the process when automated texting is used. red dashed lines indicate that public health does not need to manually request and send the user the verification code. instead, the server generates codes that are automatically sent to users' phones via text message. getting codes to users who test positive as quickly as possible ensures that possible close contacts receive a notification sooner and can take appropriate actions. automated texting also minimizes the involvement of public health personnel, which is especially critical when resources are limited. it results in less need for codes to be issued person-to-person during a case interview, and fewer calls to public health to ask about codes, as seen in colorado. this method still allows for codes to be issued in crest if necessary. risks of the transition to automated texting were weighed heavily against its benefits. positive cases may receive the text before even being notified of their test result. receiving the text may lead to confusion, especially by those who are not wa notify users. additionally, it is not known for sure that phone numbers provided can receive texts or that it belongs to the person who tested positive. these risks were carefully weighed, and mitigation strategies were prepared. the transition to automated texting resulted in a large spike in the number of codes being issued (figure 6). before the switch, less than 1,000 codes were being issued per week. in the initial week of automated texting, over 32,000 codes were issued and 6.1% of these codes were claimed. in the following weeks, the number of codes issued decreased slightly and eventually leveled off to around 4,000-5,000 codes per week. the percent of codes claimed over the following weeks ranged from 6-10%. wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 10 ojphi figure 6. number of wa notify verification codes issued and claimed by users by week, from november 30, 2020, to the week of march 7, 2021. automated texting of verification codes was enabled on january 11, 2021. discussion after careful consideration and testing, wa state launched its bluetooth enx implementation, wa notify, in november of 2020 to help mitigate the spread of covid-19. as of april 2021, over 1.9 million wa state residents or 25% of the population and almost 30% of smartphones have activated the tool on their smartphones, making it one of the most wide-reaching en tools in the country. several reasons may underlie the success of its implementation: use of the enx framework, the uw pilot study, and the formation of the exposure notification advisory committee. despite wa state being one of the earliest to consider the use of this technology, it was not one of the earliest states or countries to implement bluetooth en using the gaen protocol. taking the time to develop community input and advice took time but was important. the choice to use enx, a free system embedded in the phone’s operating system, provided substantial benefits, despite the delay in implementation. these included a considerable decrease in the need for upfront development of both app and infrastructure and decreased investment by public health. also, the enx system simplified both the user experience, once we were beyond the pilot phase, and allowed for easier push notifications to be sent directly to the device. several states launched their own en tools prior to the availability of enx and before wa state: six over the summer and four more by october. states who implemented early, like alabama, pennsylvania, and north carolina, had adoption rates of less than 10% of the adult population in one month [16]. wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 11 ojphi other states using enx also reported much higher adoption. connecticut reported that their tool, covid alert ct, had been activated in about one-third of the state’s population in january 2021 [17]. washington d.c. reported that 53% of residents had downloaded their dc can [18]. after adopting the enx framework in february, virginia doubled its number of activations in just a few weeks, bringing total adoption to 22% of the population [19]. however, it is difficult to compare adoption rates across different states due to different denominators (total population, adult population, smartphone owners) and lack of publicly available, up-to-date data. development of a bt enx implementation for a smartphone such as wa notify can be extremely expensive. uk nhs officials noted their tool costs total over £35 million ($48m usd), and new york’s cost around $700,000 [20,21]. by using the free enx platform, rather than investing in duplicative app development and customization, wa state was able to put $2 million alone into a marketing and communications campaign [22]. furthermore, the uw pilot enrolled almost 3,000 early adopters who formed an initial set of users when the system was expanded to statewide implementation. the pilot also allowed for user troubleshooting, allowed the team to understand what to expect from the launch, and increased public awareness of the tool. the creation of the exposure notification advisory committee allowed for careful consideration of the risks and potential benefits of the use of the technology. while some states tried to launch their en tools as quickly as possible without fully considering the implications of the decision, wa state’s foresight paid off in the long run. advisory committee discussions, concerns, and recommendations helped form the foundation of the ongoing evaluation work. limitations wa notify and other gaen-based solutions prioritize user privacy and do not collect information on location or identity. this may allow for higher acceptability and adoption, however, this also presents challenges. the impact of privacy on user adoption and the tradeoff enx users prefer between privacy and functionality, has not been studied. evaluation of wa notify, its effectiveness, and user experiences are difficult because the identity of those receiving notifications is completely unknown. since activating and using wa notify is completely voluntary and anonymous, demographic information of the users is also not directly available. this makes it difficult to examine equity and adoption across groups, especially those disproportionately affected by covid-19. access to smartphone technology, as well as cell service, is unevenly distributed across the state, so wa notify is not equally accessible to every single person in the state. further work is needed to understand the impact of this inequity and how to mitigate it. additionally, due to the novel nature of the technology, there is no previously existing framework on which to base the implementation and evaluation processes. next steps wa notify and similar tools promise to supplement the traditional contact tracing process and use existing technology to help mitigate the spread of covid-19. however, whether bluetooth exposure notification has been successful and delivered on these claims is still yet to be fully investigated. the privacy-preserving nature of the tool makes it even more difficult to answer this question. a research group at the uw has been planning and developing the evaluation of wa wa notify: the planning and implementation of a bluetooth exposure notification tool for covid19 pandemic response in washington state online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e8, 2021 12 ojphi notify since the summer of 2020. this robust evaluation addresses many of the limitations the tool brings by integrating a variety of data sources, including contact tracing data, population-based surveys, and interviews. the evaluation aims to assess wa notify’s public health value, unintended consequences, accessibility and equity, impact on contact tracing workflows, and more. the project will also investigate potential further uses of the technology. as we move forward, there is potential for public health agencies to adapt en technologies to be used for diseases other than covid-19. conclusion the covid-19 pandemic has posed unprecedented challenges to public health infrastructure and systems. due to a lack of funding and resources, public health agencies are often slow to adapt technologically. this crisis, however, has led to public-private partnership and engagement that has never before occurred on such a large scale. bluetooth en is a truly novel approach to controlling the age-old threat of pandemics. partnership with multistate shared efforts and integrated enx solution led to high adoption rates and cost savings that could be applied to outreach and evaluation. the lessons learned from the process of launching wa notify and similar population-based interventions may be useful for future tools and technology. digital tools and technology have the potential to play a further role in supporting public health, whether it comes to communication, immunization, or public outreach. acknowledgments we would like to thank investigators from the university of washington paul g. allen school of computer science and engineering and microsoft research, whose work established the feasibility of bt en and gaen within wa state and enabled the subsequent state-wide adoption of wa notify. we also thank university of washington leadership for their support of the wa notify pilot work on the university campus and the campus pilot technical team, notably daniel lorigan and jenney lee. we acknowledge the contributions of team members in scoping and providing feedback on this manuscript, notably courtney segal, iris jia and tyler bonnell. this project was supported through an inter-agency agreement between washington department of health and the university of washington. financial disclosure the authors do not have any financial disclosures to report. references 1. mooney g. 2020. “a menace to the public health” — contact tracing and the limits of persuasion. n engl j med. 383, 1806-08. pubmed https://doi.org/10.1056/nejmp2021887 2. cdc. case investigation and contact tracing : part of a multipronged approach to fight the covid-19 pandemic [internet]. centers for disease control and prevention. 2020 [cited https://pubmed.ncbi.nlm.nih.gov/32877577 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[cited 2021 mar 30]. available from: https://developer.apple.com/documentation/exposurenotification/supporting_exposure_notifi cations_express. layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts operational experience: integration of aspr data into essence-fl during the rnc carrie eggers*1, dina passman2, aaron chern1, dara spector2, aaron kite-powell1, tim davis2, wayne loschen3, joe lombardo3, douglas char2 and janet hamilton1 1florida department of health, bureau of epidemiology, tallahassee, fl, usa; 2health and human services, office of the assistant secretary for preparedness and response, washington, d.c., dc, usa; 3johns hopkins university applied physics laboratory, laurel, md, usa objective the florida department of health (fdoh), bureau of epidemiology, partnered with the u.s. department of health and human services (hhs) office of the assistant secretary for preparedness and response (aspr) to improve surveillance methods in post disaster or response events. a new process was implemented for conducting surveillance to monitor injury and illness for those presenting for care to aspr assets such as disaster medical assistance team (dmat) sites when they are operational in the state. the purpose of the current work was to field test and document the operational experience of the newly implemented aspr data module in essencefl (syndromic surveillance system) to receive near real-time automated data feeds when aspr federal assets were deployed in florida during the 2012 republican national convention (rnc). introduction florida has implemented various surveillance methods to augment existing sources of surveillance data and enhance decision making with timely evidence based assessments to guide response efforts post-hurricanes. historically, data collected from deployed federal assets have been an integral part of this effort. however, a number of factors have made this type of surveillance challenging: logistical issues of field work in a post-disaster environment, the resource intensive manual data collection process from dmat sites, and delayed analysis and interpretation of these data to inform decision makers. the essence-fl system is an automated and secure web-based application accessed by fdoh epidemiologists and staff at participating hospitals. methods essence-fl was configured by the johns hopkins university applied physics laboratory (jhu/apl) to receive aspr electronic medical record (emr) data. a scheduled program to generate data files for fdoh was created using sas enterprise business intelligence (ebi) software and a script was set up on the aspr server to send an updated file via secure file transfer protocol (sftp) every 15 minutes. a case definition was created by aspr field teams to identify which encounter visits would be entered into the electronic medical record (emr) and received in essence-fl. to assess completeness of data elements and total patient encounters received in essence-fl, dmat field teams maintained excel line lists of patient encounters and emailed them to fdoh three times daily during the rnc. aspr data were reviewed and analyzed by fdoh staff multiple times a day in near real time utilizing the existing essence-fl robust analysis tools. results three separate aspr missions were deployed to florida to support the rnc. aspr emr data files were received at 15-minute intervals by essence-fl from the aspr central server during each day of the 2012 rnc (august 26-31). reduced patient counts within essence-fl as compared with dmat-maintained excel line lists indicated an incomplete input, upload, or transfer of patient data from one of two aspr sites to the central aspr servers. although only 11 of 34 total patient encounters were received by essence-fl during the event, the system design enabled users to run specific queries and display the results of their queries in time series graphs, pie and bar charts, gis maps, dashboards, and statistical tables. conclusions there is a great need to have timely access to data sources to enhance disease surveillance efforts and help guide decision makers’ situational awareness and disease control efforts during a response. the fdoh, bureau of epidemiology’s collaboration with jhu/apl and aspr takes advantage of aspr’s emr-s to make data sharing and analysis efficient as evidenced during the rnc. automated data feeds to essence-fl removed resource intensive manual data collection by public health, improved standardization of syndrome and demographic categorizations, increased access to these data by local, state, and federal epidemiologists in a timely manner, and expedited analysis and interpretation for situational awareness. future recommendations include pre-event testing of the entire data flow process, establishing an on-site specialist to immediately assist with any issues, greater understanding of the field team use of the emr-s, and ensuring field staff is aware of data quality needs for effective public health surveillance. keywords surveillance; response; disaster *carrie eggers e-mail: carrie_eggers@doh.state.fl.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e71, 2013 application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources application of geographic information systems and asset mapping to facilitate identification of colorectal cancer screening resources clement kudzai gwede1, beverly g ward2, john s luque3, susan t vadaparampil1, desiree rivers4, dinorah martinez-tyson4, shalewa noel-thomas1, cathy d meade1 1 moffitt cancer center, department of health outcomes and behavior, division of population sciences, tampa, fl 2 bgw associates, llc, tampa, florida 3 georgia southern university, jiann-ping hsu college of public health, georgia 4 university of south florida, tampa, fl abstract objective: we sought to identify and map the geographic distribution of available colorectal cancer screening resources; following identification of this priority within a needs assessment of a local community-academic collaborative to reduce cancer health disparities in medically underserved communities. methods: we used geographic information systems (gis) and asset mapping tools to visually depict resources in the context of geography and a population of interest. we illustrate two examples, offer step-by-step directions for mapping, and discuss the challenges, lessons learned, and future directions for research and practice. results: our positive asset driven, community-based approach illustrated the distribution of existing colonoscopy screening facilities and locations of populations and organizations who might use these resources. a need for additional affordable and accessible colonoscopy resources was identified. conclusion: these transdisciplinary community mapping efforts highlight the benefit of innovative community-academic partnerships for addressing cancer health disparities by bolstering infrastructure and community capacity-building for increased access to colonoscopies. 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources introduction colorectal cancer is the third most common cancer and the third leading cause of cancer deaths in american men and women [1] with an estimated 146,970 new cases and 49,920 deaths expected in 2009. the mortality burden is greatest in african americans/blacks, the uninsured and other socio-economically disadvantaged groups. with regards to reducing disease burden, colorectal cancer screening is recommended for men and women over the age of 50 years in the u.s. survival after a colorectal cancer diagnosis depends on a multitude of factors including the stage of disease at diagnosis. while five-year survival rates exceed 90% if disease is detected and treated in the early stages, only about 39% of colorectal cancers are diagnosed in the early stages. high among the barriers to early detection is lack of access to affordable and acceptable screening resources for the general population, and especially for the uninsured and medically underserved communities [1]. the institute of medicine (iom) and the department of health and human services (dhhs) have identified the need for relevant strategies to improve access to healthcare services and to support the elimination of health disparities [2-6]. cancer health disparities result from the complex interplay of economic, social, behavioral and environmental circumstances that impact differential access. as freeman relates, “disease often occurs within the context of human circumstances, including social position, economic status, culture, and environmental context” [7]. hence there is a growing literature documenting cancer related health disparities from a geographic perspective [3, 8, 9]. however, with few exceptions [10-13], not much geographic analysis has been done for colorectal cancer, a detectable, preventable and treatable malignancy characterized by marked racial-ethnic disparities. in this paper, we share our experiences applying geographic information systems (gis) and asset mapping to explore availability and accessibility of culturally responsive colorectal cancer screening services in the tampa bay community cancer network (tbccn) collaborative. we describe two examples of gis and asset mapping among tbccn partner organizations and service region, describe the methods of data capture and preparation for mapping, offer step-by-step directions for gis and asset mapping, and present preliminary findings that have informed research and practice decisions related to accessing colorectal cancer screening and related services. the challenges, lessons learned and implications for other local or national cancer control programs seeking to map similar indicators for community engagement are also discussed. contributions of gis and asset mapping: integrating new technology and traditional data tools an emerging technology in cancer health disparities research [3], gis can be combined with another instrument, asset mapping to understand and visualize specific social and health conditions and resources that impact health disparities in the context of geography. gis technology provides researchers, practitioners, policy makers and other decision makers the ability to integrate health data with mapping functions and allow for in-depth investigation, exploration, visualization, and modeling of health outcomes patterns [3, 14, 15]. evidenced by its 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources broad application in many sub-disciplines of public health, including understanding patterns of and planning hiv interventions, mapping injuries and environmental injustice, and mapping distribution of practitioners [8, 9, 16-26], gis has benefited communities and moved the science of public health forward. in addition, there is a growing gis literature mapping cancer epidemiology data by race, ethnicity, gender, disease and healthcare access [3, 14, 15, 27-29]. the utility of gis mapping technology is its versatility in the use of traditional health data (e.g., incidence, mortality, resources) allowing researchers to present them in more dynamic ways to varied audiences such as community research partners, decision makers, or other stakeholders or other interested general public constituencies. however, the application of gis to understand local cancer screening resources is less common. thus, it is important to provide examples of how gis and asset mapping are employed as integral tools in the context a local community-academic collaborative seeking to increase access to cancer screening resources for medically underserved populations. gis uses data generated at many levels of society and geography and one method of gathering and organizing these data is the asset mapping method. asset mapping (community resource mapping, environmental scanning, social mapping, youth mapping) is the process of cataloging and highlighting the positive resources (rather deficits) in a community [30-34] to create awareness of community resources, identify connections among community providers, and meet community needs. thus, in this context, the use of gis is intended to directly address the needs of tbccn partners and to benefit the community. our application of gis and asset mapping in a local community-academic partnership network, represents a unique and rich opportunity for analysis of salient data to inform decisions. the overall goal of the tbccn is to create a solid, robust and sustainable core organizational network infrastructure through collaborations, partnerships and community-capacity building activities. funded by nci’s center to reduce cancer health disparities, tbccn aims to address critical access, prevention and control issues that impact medically underserved, lowliteracy and low-income populations in selected areas of an ethnically diverse tri-county area which includes a growing number of foreign-born residents. meeting this goal involves cancer awareness and education activities, the use of cbpr methods, and the creation of sustainable collaborations and partnerships that improve access and utilization of cancer screenings. specifically, a formal assessment of the tbccn partners [35] identified an unmet need to increase access and availability of colorectal cancer screening resources. access and affordability to these beneficial screening resources was prioritized by the tbccn membership, therefore setting the stage for the gis and asset mapping efforts reported here. in a related partnership, the tampa-based patient navigator research program (pnrp) (u01 ca117281) also strives to address access and utilization of cancer screening services for the medically underserved. this study seeks to mitigate barriers and facilitate timely access to diagnosis and appropriate care using existing community resources. as such, both cancer health disparities collaborative programs benefit from the use of gis and asset mapping to improve identification, accessibility and use of affordable colorectal cancer screening resources. 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources methods several factors informed our decision to consider the use of gis and asset mapping to the investigation of colorectal cancer resources. first, colorectal cancer is among the top cancers diagnosed in both men and women in the tampa bay area [36]. second, the american cancer society identified colorectal cancer as a major priority and published a document to motivate the public and medical communities to eliminate the unnecessary suffering caused by colorectal cancer [37]. third, our community partners recognized and related a significant gap in the availability of colorectal screening and diagnostic services in the area according to a partner needs assessment [35]. finally, the need for locating and accessing colorectal cancer screening and related services was further reiterated by feedback from navigators and investigators from the pnrp study who documented longer waiting times and limited sources for receipt of colonoscopies as compared to mammographies for similar patient populations. unlike for breast and cervical cancer, there is no state funded program for colorectal cancer screening and early detection for medically underserved in florida. thus, tbccn investigators sought to map specific geographic areas with population characteristics, resource users with resources or access points, and spatial locations of different types of resources e.g., hospitals providing colonoscopies. by integrating and analyzing information in this way, users can assess local socio-demographic characteristics, assess the supply of resources, generate ideas for coordination of resources, assess relationships between resources and resource users (e.g., high risk groups and medical service locations), and generate maps that illustrate these relationships in a visually compelling way. in order to accomplish this, we were guided by strategies for engaging community members in processes that were consonant with a cbpr theoretical perspective and participatory action research [38, 39] to effect sustainable, positive health change. community member input was integral throughout our efforts as the problem was first identified, as the processes became defined, and as the results were found. institutional review board (irb) approval and consent of participants were obtained before initiating data collection. step-by-step: how to “map” community assets asset mapping begins with defining the community, including the geographic boundaries and target population (e.g., inner-city, racial/ethnic, elderly, persons with disabilities, medically underserved, or by specific cancer diagnosis). the next step should include discussions on how the information collected will be used. if, for example, the data will be used to create a community resource guide, there may be a need to discuss the distribution scope and plan resources for printing and updating the materials. the third step is to decide which assets will be identified. this may include building on existing data sources and identifying and modifying inventory tools for data collection. the process of asset mapping is made with and from individual and community data. two research strategies may reduce health disparities: community participation and the use of geographic information systems [13]. when combined with community participation, geographic information systems approaches, such as the creation 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources of resource or population maps that connect disease focused resources/assets and with community context, can catalyze action to reduce health disparities. resources of interest to tbccn include providers, cancer screening facilities/services (e.g., colonoscopies, mammograms), cancer treatment programs (e.g., hospitals, cancer centers, other outpatient treatment facilities), and other cancer-related support services (navigators, support groups, hospice, transportation, lodging). all these can be identified and assessed for their feasibility and utility (e.g., fee structure, payment plans, accessibility, efficiency) before they are included on a list or database to be mapped or distributed. with an understanding of the types of assets in communities, a plan is developed to collect the information. some considerations include target dates for data collection, method of data collection, types of resources needed to collect the data, e.g., people, copying, database creation and entry, and facilitators. much of the data collection can begin with existing information resources that build on previous surveys or asset map-type activities. lacking these resources, one can begin by using existing macro-level gray data, e.g., censuses, directories, etc. this helps to provide a rapid assessment of available resources. reliable sources of gray data include • city, county, and agency websites; • phonebooks and city directories; • the internet (google map is a great resource); • local newspapers; • other inventories or databases including local, state and national (e.g. the state cancer registries, state vital statistics, u.s. census, national cancer institute and centers for disease control and prevention databases). • local libraries; • community partners’ databases. finally, the process and results should be evaluated. major aspects of evaluation include identifying the most and least effective techniques and processes, the tasks that were completed, and the percent of the target population reached. the final step of evaluation includes assessing outcomes with respect to products created, community benefit and extent that individuals/agencies in the community were included in the process of mapping, additional financial resources acquired based on mapping process, and improvements in access to care or reduction in disparities. for long-term sustainability, one central question is: how will changes be captured, inventoried, and the information disseminated? a well delineated, iterative process should be developed to facilitate adding new partners and assets/resources, removing non-functional resources from the databases, and production of updated products such as maps or resource guides for distribution to stakeholders. 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources results example #1: mapping colonoscopy resources in tbccn we began with a baseline inventory of the black population in hillsborough, pasco, and pinellas counties by census tract from the 2000 decennial census [40] as logical population of reference for this illustration. these data were put into a gis program and projected to show the black population as a percentage of the total population across the three counties. as shown in figure 1, there is considerable variation among and between the counties in the distribution of the black population. hillsborough county had the largest black population, nearly 15% of county’s total; pasco county had the smallest, slightly more than two percent. within the counties, the black population was concentrated among relatively few census tracts, with some tracts in hillsborough and pinellas counties, ranging from 60 to 99% black. we then took the dataset of tbccn partners, matched or geocoded the partners’ addresses, and projected the dataset in a layer over the already mapped black population. geocoding is the process by which non-spatial data (address files) are linked to geographic coordinates and converted to map data (points on a map showing address locations). most commercial gis software packages have the ability to geocode and numerous geocoding services are also available on the internet. in this case, geocoding of partners’ addresses provided an initial assessment of the target population’s access to referral and other supportive services provided by tbccn partners. finally, we added a layer of the hospitals/facilities within the counties and a 25-mile radius. this dataset was created by the american hospital association and was included in the gis software. it served as a potentially available pool of providers for colorectal cancer screening resources for the target population. building on the information from the rapid assessment showing all hospitals and facilities (figure 1), the research team identified a need for colorectal cancer screening resources or referral guides for uninsured and underinsured residents in hillsborough county as a starting point. 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources figure 1. black population, tbccn community partners, and hospitals/facilities in three florida counties (hillsborough, pinellas and pasco) in addition to the potential hospitals, a google™ search using the term “colorectal cancer screening” was conducted to identify other potential screening assets. however, colonoscopies are the most difficult to receive due to cost and facility requirements for this procedure. so we also searched google ™ using the term “colonoscopy”. the results of the searches were downloaded into a table (excel), imported into a gis database, and geocoded. these assets were then projected over a map of the black population of hillsborough county. (see figure 2.) 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources figure 2. colonoscopy hospital/facilities googletm assets, hillsborough county example #2, pilot research project: “colorectal cancer in ethnic subgroups of u.s. blacks: exploring disparities, u01 ca114627 s1.” as part of the cnp competitive pilot grants mechanism, the “colorectal cancer in ethnic subgroups of u.s. blacks: exploring disparities, u01 ca114627 s1” study was funded to explore perceptions of colorectal cancer in three subgroups of blacks residing in medically underserved areas. race-ethnicity was self-declared by participants. the long-term goal was to generate pilot data and set the stage for a future larger scale comparative or intervention study to increase colorectal cancer screening. in this application, we geocoded the addresses of participants in the study and projected this layer over the map of the black population and colonoscopy resources in hillsborough county. (see figure 3.) this application facilitated assessment of the geographic locations (neighborhoods) from which the sample was obtained relative to location of colonoscopy resources. this mapping also shows that the distribution of the sample doesn’t differ by ethnic subgroup and geography. 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources figure 3. enrollment to a tbccn pilot study of three ethnic subgroups of blacks, hillsborough county we also used data from the 2000 census to identify by census tract concentrations of the black population by ethnic subgroups, including sub-saharan africans, caribbean blacks, and blacks 35 years of age and older. (see figure 4.) 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources figure 4. black population by ethnic subgroups and age 35 years and older, hillsborough county the implications of these asset maps are significant. first, mapping of population and recruitment data informs our recruitment strategies for the future larger scale study. in addition, tbccn researchers and community partners serving residents of historically underserved black populations to gain a better appreciation of the diversity and location of these population subgroups. discussion and conclusions challenges and lessons learned our experience with gis and asset mapping has facilitated several outcomes related to understanding and addressing health inequalities. specifically, we identified resources available for colorectal cancer screening (assets) and identified a clear need to compile and present these assets in several ways to provide accessibility to community providers and members. second, gis also facilitated our understanding of the distribution of the black population and pinpointed 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources accrual to our pilot study on colorectal cancer. this provides the tbccn with examples of how to combine multiple levels of data (population, cancer screening resources, and geographic distribution of study participants) using new and established tools, informed by a transdisciplinary team of researchers and community members. ultimately, to be useful to the stakeholders, gis and asset mapping should inform, improve understanding, and facilitate interventions that benefit the community directly in terms of service delivery and other ways of reducing inequalities in access to cancer care [13, 41]. the use of gis and asset mapping initiatives provided us with notable challenges and lessons learned which may be helpful in transferring these methodologies to other settings. three key challenges and lessons are discussed here: (1) finding accurate data sources to identify community assets (resources) for colorectal cancer screening; (2) developing the skills and capability to implement gis in a meaningful way; and (3) involving community stakeholders in building, disseminating, and maintaining a useful database of the resources. lesson #1: identifying community assets and accurate sources of data. colorectal cancer screening resources were scarce particularly with regards to screening and diagnostic colonoscopies that are accessible and affordable for uninsured, or other medically underserved persons. as mentioned earlier our community assessment [35] identified many priority areas to address but it was important to narrow our scope and focus on one problem, one county at a time. consequently, we focused on hillsborough county, the largest of the three counties in population and area. we generated lists of colorectal cancer screening from many sources including resources available in the phone book, in google™ search, and from our community partners based on prior relationships with these colonoscopy providers. a key challenge was that a large number of these assets needed to be verified to determine accuracy and accessibility in terms of fees and terms of payment, hours of service, and distance, for example. the academic partner assigned two staff members to call each identified colonoscopy provider, before retaining them on the list. nevertheless, the cnp partners are committed to updating the list as information changes in the future. lesson #2: developing skills to implement gis in a meaningful way. for this part we developed a mutual collaboration with a gis expert available at the university (bw). this individual had a strong interest in cancer control and a strong desire to reduce cancer health inequities having worked in related areas of health disparities and geography. this individual provided both technical and conceptual contributions. however, if one is not able to readily identify gis expertise, there are many trainings available (search the internet) that staff and investigators can complete. two of the tbccn co-investigators participated in a basic gis workshop. lesson #3: involving the community stakeholders in building, disseminating, and maintaining a useful database. building and maintaining the resource databases and ensuring community benefit from the gis mapping and related endeavors is a daunting task. it requires expressed ownership and committed participation by both the academic partners who provide the gis/mapping expertise, and the community partners who use and have knowledge and 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources experience with the availability, affordability, accessibility and functionality of the resources. based on these lived experiences, the community partners can advise the academic partners on what changes to make to the resource directory and related gis maps to maintain the accuracy and usefulness of this resource. like in many other academic-community endeavors, time, flexibility and shared purpose ensure success in these collaborative often unfunded endeavors. the tbccn collaborative is currently poised to participate in a number of policy initiatives to address statewide and national funding for colonoscopy screenings, diagnostic, and treatment resources for the uninsured. in partnership with a health policy advocate involved in the pnrp study, tbccn is poised to share its mapping findings with state and federal legislators to bring awareness about the lack of these resources. additionally, tbccn is positioned to provide this information to state health agencies to support funding applications for service initiatives for colorectal cancer screenings similar to the florida breast and cervical cancer early detection program for the medically underserved. in the meantime, our efforts are focused solely on completing mapping and evaluating its benefits. our experience will certainly inform similar efforts of the challenges, how to best avoid pitfalls, and what benefits to expect. mapping future directions our application of gis and asset mapping yielded asset maps that help users and practitioners to recognize and value the colorectal cancer screening resources within affected communities [42]. the pictorial representations of available resources (and inherent gaps) have facilitated discussions with resource managers and policy makers or redistribution of services that improve the health of medically underserved. elimination of cancer health disparities is a national imperative [2-6, 13, 41] that requires integration of new and emerging technologies with other innovative perspectives not commonly employed in cancer control. given, the availability of proven and beneficial early detection and treatment modalities for colorectal cancer and the heightened focus on attenuating cancer health disparities, the use of gis and asset mapping in colorectal cancer screening represents a timely and innovative strategy in meeting the laudable goal of eliminating health inequalities. moreover, it is important to recognize that communities have many assets--individual, associational, institutional and economic. with that said, more resources and funding for colorectal screening and other cancer–related services are needed in our collaborative and other communities nationwide. challenges to reducing inequalities and barriers to access are abundant. however, this process has provided baseline information to identify existing resources and to develop a resource guide for users and our partners. we have identified pertinent examples on using resources and gis technology to improve our understanding of the inequitable access and derive data driven and informed solutions for reducing pervasive barriers to colorectal cancer screening. similar methodologies may be employed to address barriers for other cancers and other health conditions locally, regionally or nationwide [2-5, 13, 41]. future directions include more research and evaluation to better pinpoint community benefit, and identify the impact of gis/resource mapping on access to screening and diagnostic colonoscopies to reduce related inequalities. 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information 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[2] institute of medicine, unequal treatment: confronting racial and ethnic disparities in health care ed. b.d. smedley and a.r. nelson. 2002, washington, dc: national academies press. [3] dhhs, geographic information systems (gis) and cancer research. 2008, department of health and human services, national institutes of health, national cancer institute: bethesda, md. [4] artinian, n.t., et al., advancing the science of health disparities research. ethn dis, 2007. 17(3): 427-33. [5] tarlov, e., et al., characteristics of mammography facility locations and stage of breast cancer at diagnosis in chicago. j urban health, 2009. 86(2): 196-213. [6] warnecke, r.b., et al., approaching health disparities from a population perspective: the national institutes of health centers for population health and health disparities. am j public health, 2008. 98(9): 1608-15. 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[13] beyer, k.m. and g. rushton, mapping cancer for community engagement. preventing chronic disease, 2009. 6(1): p. a03. [14] graves, b.a., integrative literature review: a review of literature related to geographical information systems, healthcare access, and health outcomes. perspect health inf manag., 2008. 5: p. 11. [15] brewer, c.a., basic mapping principles for visualizing cancer data using geographic information systems (gis). american journal of preventive medicine, 2006. 30(2, supplement 1): p. s25. [16] krieger, n., et al., painting a truer picture of us socioeconomic and racial/ethnic health inequalities: the public health disparities geocoding project. am j public health, 2005. 95(2): p. 312-23. [17] maclachlan, j.c., et al., mapping health on the internet: a new tool for environmental justice and public health research. health place, 2007. 13(1): p. 72-86. [18] olivet, m., et al., [health services provision and geographic accessibility]. med clin (barc), 2008. 131 suppl 4: p. 16-22. [19] basara, h.g. and m. yuan, community health assessment using self-organizing maps and geographic information systems. int j health geogr, 2008. 7: p. 67. [20] duncan, m.j., h.m. badland, and w.k. mummery, applying gps to enhance understanding of transport-related physical activity. j sci med sport, 2009. [21] endacott, r., et al., geographic information systems for healthcare organizations: a primer for nursing professions. comput inform nurs, 2009. 27(1): p. 50-6. 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources [22] grinzi, p., a. bazemore, and r.l. phillips, jr., navigating general practice. the use of geographic information systems. aust fam physician, 2008. 37(10): p. 855-8. [23] kazda, m.j., et al., methodological complexities and the use of gis in conducting a community needs assessment of a large u.s. municipality. j community health, 2009. 34(3): p. 210-5. [24] nelson, m.j., et al., a geospatial analysis of persons opting out of an exception from informed consent out-of-hospital clinical trial. resuscitation, 2009. 80(1): p. 89-95. [25] vanmeulebrouk, b., et al., open source gis for hiv/aids management. int j health geogr, 2008. 7: p. 53. [26] francois, f., et al., colon cancer knowledge and attitudes in an immigrant haitian community. j immigr minor health, 2009. 11(4): p. 319-25. [27] krieger, n., et al., race/ethnicity and changing us socioeconomic gradients in breast cancer incidence: california and massachusetts, 1978-2002 (united states). cancer causes control, 2006. 17(2): p. 217-26. [28] xiao, h., et al., analysis of prostate cancer incidence using geographic information system and multilevel modeling. j natl med assoc, 2007. 99(3): p. 218-25. [29] hopfer, s., et al., assessment of training needs and preferences for geographic information systems (gis) mapping in state comprehensive cancer-control programs. health promot pract, 2008. 1: p. 1. [30] goldman, k.d. and k.j. schmalz, "accentuate the positive!": using an asset-mapping tool as part of a community-health needs assessment. health promot pract, 2005. 6(2): p. 125-8. [31] goldman, k.d. and k.j. schmalz, "as you likert it": conducting gap-based needs assessments. health promot pract., 2007. 8(3): p. 225-8. [32] goldman, k.d. and k.j. schmalz, being well-connected: starting and maintaining successful partnerships. health promot pract, 2008. 9(1): p. 5-8. [33] kretzmann, j. and j. mcknight, building communities from the inside out : a path toward finding and mobilizing a community's assets. , ed. n.i.n. center for urban affairs and policy research, northwestern university. 1993, evanston, ill; chicago, il: acta publications [34] u.s. epa, o.o.w., community culture and the environment: a guide to understanding a sense of place. 2002, u.s. evironmental protection agency. [35] author, et al., strategies to assess community challenges and strengths for cancer disparities participatory research and outreach. health promotion practice, online frist. , 2009. [36] fcds. florida cancer data system. 2009 [cited 2009 03/31]; available from: http://fcds.med.miami.edu/inc/statistics.shtml. [37] acs, colorectal cancer facts & figures 2008-2010. . 2008, american cancer society: atlanta: ga. [38] greenwood, d.j., w.f. whyte, and i. harkavy, participatory action research as a process and a goal. human relations, 1993. 46(2): p. 175-192. [39] israel, b.a., et al., review of community-based research: assessing partnership approaches to improve public health. . annu rev public health 1998. 19: p. 173-202. [40] united states bureau of census. census 2000 summary file 1 (sf1). 2007 [cited 2009; available from: http://factfinder.census.gov/servlet/dtgeosearchbylistservlet?ds_name=dec_2000_sf1_u&_lang =en&_ts=1972084495092007. [41] graves, a., a model for assessment of potential geographical accessibility: a csae for gis. online journal of rural nursing and health care, 2009. 9(1): p. 46-54. [42] queralt, m. and a.d. witte. a map for you? geographic information systems in the social services. social work., 1998. 43: p. 455-14. 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ http://fcds.med.miami.edu/inc/statistics.shtml http://factfinder.census.gov/servlet/dtgeosearchbylistservlet?ds_name=dec_2000_sf1_u&_lang=en&_ts=1972084495092007 http://factfinder.census.gov/servlet/dtgeosearchbylistservlet?ds_name=dec_2000_sf1_u&_lang=en&_ts=1972084495092007 application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources doi: 10.5210/ojphi.v2i1.2893 cite this item as: gwede, c., ward, b., luque, j., vadaparampil, s., rivers, d., martineztyson, d., noel-thomas, s., & meade, c. 2010 apr 9. application of geographic information systems (gis) and asset mapping to facilitate identification of colorectal cancer screening resources. online journal of public health informatics [online] 2(1):e6. 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 2(1):e6, 2010 http://ojphi.org/ application of geographic information systems and asset mapping to facilitate identification of colorectal cancer screening resources clement kudzai gwede1, beverly g ward2, john s luque3, susan t vadaparampil1, desiree rivers4, dinorah martinez-tyson4, shalewa noel-thomas1, cathy d meade1 abstract conclusion: these transdisciplinary community mapping efforts highlight the benefit of innovative community-academic partnerships for addressing cancer health disparities by bolstering infrastructure and community capacity-building for increased acce... introduction step-by-step: how to “map” community assets results our experience with gis and asset mapping has facilitated several outcomes related to understanding and addressing health inequalities. specifically, we identified resources available for colorectal cancer screening (assets) and identified a clear ne... mapping future directions references enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi enhancing rural population health care access and outcomes through the telehealth ecosystem™ model brenda a. leath1*, lucenia w. dunn2, antwon alsobrook3, madeline l. darden4 1. westat, rockville, md 2. tuskegee macon county community foundation, inc., tuskegee, al 3. a2d, inc., atlanta, ga 4. health marketing concepts international, alexandria, va abstract the article highlights the telehealth ecosystem™ model, a holistic cross-sector approach for socioeconomic revitalization, connectivity, interoperability and technology infrastructure development to address health equity for rural underserved communities. two guiding frameworks, community & economic development (ced) and collective impact, provided the foundation for the telehealth ecosystem™ model. public and private organizational capacities are addressed by comprehensive healthcare and social service delivery through stakeholder engagement and collaborative decision-making processes. a focus is maintained on economic recovery and policy reforms that enhance population health outcomes for individuals and families who have economic challenges. the telehealth ecosystem™ utilizes an intranet mechanism that enables a range of technologies and electronic devices for health informatics and telemedicine initiatives. the relevance of the intranet to the advancement of health informatics is highlighted. best practices in digital connectivity, hipaa requirements, electronic health records (ehrs), and ehealth applications, such as patient portals and mobile devices, are emphasized. collateral considerations include technology applications that expand public health services. the ongoing collaboration between a social science research corporation, a regional community foundation and an open access telecommunications carrier is a pivotal element in the sequential development and implementation of the telehealth ecosystem™ model in the rural southeastern region community. keywords: community and economic development; collective impact; telehealth; care coordination; research; intranet abbreviations: hipaa (health insurance portability and accountability act), ehrs (electronic health records), tmccf (tuskegee macon county community foundation), ced (community & economic development) *correspondence: brenda leath, email: leathbrenda2@gmail.com doi: 10.5210/ojphi.v10i2.9311 mailto:madelineldarden@yahoo.com enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi introduction health equity is an essential element in the global quest for social justice, human rights and environmental sustainability [1]. unfortunately, rural communities in the united states are often disproportionately impacted by socioeconomic conditions and health disparities. approximately one-fifth of the u.s. population resides in rural america, and health care providers encounter a patient base that is generally older, sicker, and less affluent than their urban counterparts [2-4]. according to a recent rural healthy people 2020 report, “rural health challenges are complex, reflecting both significant disparities across rural populations residing in the united states and unique regional, political and social differences.” [5] the former director of the u.s. office of rural health policy asserts that the country “lacks strong infrastructure in rural population health” and that “rural demography [can] seem as complex and locale-specific as fingerprints.” [5] community mobilization geared toward local and region-centric innovations and solutions is an optimal approach to addressing the variability in rural health care issues. “understanding these differences is critical to taking steps to improve health and well-being in rural areas and to reduce health disparities among rural populations.” [3] macon county, alabama, is one of several counties in alabama’s black belt region [6] that are rich in historical events and legacies—particularly regarding african american communities— along with the topography and natural resources. located in a southern rural region, the city of tuskegee and macon county, alabama (al) continue to endure intractable socioeconomic hardships despite a rich and storied history of academic, entrepreneurial, cultural and scientific achievements. economic distress is prevalent in multiple arenas including housing, commerce, unemployment, telecommunications, transportation infrastructure and health care access [7,8]. wide-scale disparities prevail as a result of a decades-long population exodus; diminishing financial and environmental infrastructure; and limited resources to address community needs. the “black belt” designation originally referenced the rich soil and verdant landscape that drew whites as landowners to the area and who built large plantations with an emphasis on growing cotton and other agricultural products. the regional descriptor evolved to later encompass the large concentration of african american residents who were slaves on those plantations and, upon emancipation, were left with an impoverished status that continues to this day.9,10,11 this phenomenon is encapsulated in a journal of southern space article that describes the “historicalgeographical black belt, beginning as a rich, dark-soil, cotton-growing region of alabama occupied by slaveholders in the 1820s and 30s, and becoming, over time, a more generalized designation for a region or place with a majority black population. by the late twentieth century, the alabama black belt, as a region of insurgent african american aspirations, made a strong claim to take over the meaning of the term from its older and other senses.” [9] copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi catchment area demographics macon county, alabama, is situated in the east-central portion of the state and bordered by elmore, tallapoosa, lee, russell, bullock, and montgomery counties. macon’s county seat is located in the city of tuskegee, al. geographically, macon county consists of 611 square miles comprising 1.2% of the total land area in the state. according to the u.s. census bureau, total population estimates for macon county are said to be approximately 18,963 [10]. demographic indices reflect the population composition as follows: 16.4% whites, 81.4% black/african americans, 0.3% american indian, 0.5% asian, 1.5% person with two or more races, 1.5% hispanic or latino, and 15.6% white alone, not hispanic. historical perspective conditions in the historic black belt remain some of the worst in the united states [11]. current indices clearly highlight significant cumulative demographic and economic changes that are indicative of disparities and marked distress factors. the robert wood johnson foundation’s (rwjf’s) [12] recent jurisdictional comparative study ranked macon county 62nd of 67 alabama counties in terms of population health outcome factors, predicated on weighted scores for health behaviors, clinical care, social, and economic data. macon county was also ranked 54th out of 67 counties comparatively in the category of overall health factors in this rwjf report. the region also suffered historical declines in national and local agriculture/agri-business—including traditional and non-conventional farming, forestry, livestock commerce, and the export trade markets. market deficits related to the agricultural industry in macon county have impacted the african american and population in particular [13]. rural inequities such as these are an excellent example of the intersectionality of the factors that contribute to disparities [3]. the use of history and culture can be tremendous assets for rural communities and can be leveraged to improve health outcomes [3]. as noted earlier, macon county enjoys a storied history of cultural, intellectual and enterprise achievement juxtaposed against the cumulative effects of decades-long socioeconomic decline. yet, multiple systemic factors and related disparities prevail in the region. the city of tuskegee, in particular, boasts a number of historically black institutions, cultural icons and intellectual achievements. highly recognized icons include the world-renowned tuskegee institute (now tuskegee university) and the famed tuskegee airmen, who served the country during periods of war and peace. on a more poignant note, the rich legacy of achievements during the early years of the tuskegee veterans administration hospital are too often overshadowed by the infamous, and highly publicized, syphilis research studies [14]. while taking into consideration the disparity factors highlighted above, a foremost concern for the region involves insufficient access to health care—particularly emergency and specialty services. tuskegee has not had a full-service hospital for the public since the closing of john a. andrew hospital, located on the campus of tuskegee university [15]. in fact, at one point tuskegee/macon county had three hospitals: one for the county, one for the public, and one for veterans. each, over time, either closed or downsized significantly. health care was a main economic driver for the community. as is so often the case in communities impacted by the loss of a rural hospital [16], enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi the facility’s closing precipitated a slow deterioration of the community due to a loss of jobs, population and no viable economic replacement. the tuskegee veterans administration (va) hospital was a major, and historical, source of employment and served as a cornerstone of economic, intellectual, and public health innovations and stability. the hospital provided an aggregate of more than 2,000 employees and 23 medical services with training programs for nurses, medical doctors, psychiatrists, rehabilitation specialists, and other medical personnel. over time, the institution expanded in scope to include medical, psychiatric, and rehabilitation services for african american soldiers who served in world war i as well as world war ii, korean and vietnam war veterans. following a directive from president dwight d. eisenhower in the early 1950s, the hospital integrated and services were available to both black and white former soldiers [17]. under a direct order from president clinton to cut costs, a 1996 merger between the tuskegee va and the montgomery va resulted in unfavorable economic impacts—affecting not only the tuskegee va hospital proper but surrounding cities and counties as well. as a consequence, major job losses occurred in a community previously dependent upon the va hospital as a source of employment for almost 100 years. renamed the east campus of the central alabama veterans health care system, the tuskegee facility complex joined the national register of historic places in 2012 [18]. a rural underserved population: response to prevailing issues preliminary assessments, augmented by the literature and empirical observation, suggest that broad-based, systemic, and sustainable efforts are needed in macon county and surrounding black belt counties to bring about socioeconomic parity and enhance population health outcomes. the “enhancing rural population health care access and outcomes through the telehealth ecosystem” model is an initiative emanating from the visionary platform of the tuskegee macon county community foundation, incorporated (tmccf), a regional nonprofit in partnership with westat (a social science research corporation) and a2d, inc. (a telecommunications/engineering/open access carrier). tmccf and westat formed a partnership in early 2014 to collaborate on a shared vision for active pursuit of rural health and community and economic development (ced) initiatives in macon county, alabama, and this partnership subsequently evolved to include a2d, inc., in late 2014. two guiding theoretical frameworks—community & economic development (ced) and collective impact—and the introduction of intranet and telemedicine technology helped shape the conceptualization and implementation planning for the telehealth ecosystem™ model. as a catalyst for reform, the telehealth ecosystem™ model (see figure 1 below) offers a holistic crosssector mechanism for health and social services delivery, digital connectivity, interoperability and technology infrastructure enhancement. the schematic highlights the underlying impacts of social determinants in population health outcomes and aligns with elements of the rwjf county health rankings & roadmaps (chr&r) model [19]. knowledge of the region’s historical context offers a deeper understanding of tuskegee macon county’s protracted period of socioeconomic decline. accordingly, the collaborative team focused on the importance of understanding health indices and health care within a local context and explored avenues to address the underlying causes of disparities within and across geographical enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi areas. predicated upon best practices in health care, evaluation research, health technology and collaborative engagement, the telehealth ecosystem™ model is designed for implementation in iterative phases. the focus of the effort is to strengthen and expand existing health systems; improve technological infrastructure; and foster sustainable, community networks that drive change. figure 1: telehealth ecosystemtm modell – social determinants of health factors in macon county, citizens who are relatively poor and suffer from chronic diseases are more likely to not visit their doctor regularly and wait until they have an emergency to get proper medical attention. without a hospital, such residents rely primarily on local clinics to provide primary health care services. most of macon county’s citizens have to commute 25-40 miles to the nearest hospital in neighboring lee or montgomery counties to get suitable specialty acute, emergency and follow-up care. the average fixed/low-income patient simply cannot justify spending the time/money/resources to commute long distances to the nearest hospital and wait hours solely for enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi a 30-minute medical check-up [20]. because there is no hospital available within a reasonable proximity to this rural economically disadvantaged county, a viable alternative is to deploy technology solutions. applications such as telemedicine [16], tele-dentistry [5], and emobile health can facilitate access to medical care and help lower the overall cost of health care while improving the quality of service delivery and access to appropriate specialists. just as important, there is a growing awareness among medical providers of the viability of electronic data platforms such as ehrs and health exchanges, health informatics applications and technology-related mechanisms for improving population health outcomes [5]. to address this problem, tmccf and its core partners embrace technology-enabled care such as telehealth and mobile health applications which aligns with best practices and emerging solutions in the relevant literature. a recent school, health & libraries broadband coalition (shlb) website article highlights the viability of local and regionally based telecommunications access initiatives. the article recommends “community owned internet infrastructure through municipalities, public utilities, co-ops, or public private partnerships. …community ownership can be easier and more profitable when telehealth and telemedicine is a primary goal of the network.” 24telemedicine can deliver care to patients regardless of their physical location. it is cost effective not only to families, but hospitals and clinics as well. research has shown that patients who receive such care are more likely to have better health outcomes and are less likely to be admitted or readmitted to the hospital, thereby curtailing unnecessary expenditures of funds and personnel through the misuse of emergency hospital services. the telehealth ecosystem™ model’s sequential development process consists of three phases. phase i entails incremental tailored development and expansion of a health and human service provider network to support the telehealth ecosystem™ model initiative. initial phase i efforts, to date, are yielding noticeable gains in cross-sectorial engagement including awareness building and outreach and partnership development, along with a vertical network of organizational participants. slated for implementation in phase ii is an intranet mechanism that utilizes multiple technologies and is a critical second stage component of the broad-based telehealth ecosystem™ model. this will incorporate intranet technologies to facilitate equalitarian access to information [21]and services in underserved communities. expanded iterations of the telehealth ecosystem™ model are slated for phase ii of the developmental process. with an ever-evolving industry and regulatory environment to consider, phase iii will focus on replication and sustainability with an aim at harnessing the technical capacity to communicate, analyze, and disseminate user-centered multi-sector models for service provision as well as costeffective access and services. by virtue of its proposed configuration, upon full implementation, the telehealth ecosystem™ model will provide a platform to design, test, and evaluate the risks and rewards of locally conceived and/or replication of evidence-based approaches to rural health systems. phase i remains in the developmental stages and is the focus of this article. the telehealth ecosystem™ model will evolve along a continuum, and a series of initiatives are also projected in future phases to support job creation in the health care, education, and housing sectors, along with interoperability connectivity for law enforcement and emergency first responders, in communities throughout macon county. it will bring also media to the people and help transcend enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi transportation, digital connectivity and social barriers that might hamper some recipients. the telehealth ecosystem™ model will serve as a virtual community technology network that can be deployed to schools, community/youth centers, neighborhood associations, and other centrally located sites throughout the city of tuskegee and the macon county region. satellite media centers can be equipped with a live television broadcast line and expansion capabilities with the addition of supplemental equipment and e-learning applications. the core partners the tuskegee macon county community foundation (tmccf) was founded in october 2002 and reorganized in june 2012 with the new mission of enhancing the quality of life in disadvantaged communities in rural and urban areas. currently, a particular focus is on a rural area region that includes the city of tuskegee and other communities in macon county. it sees as its major goals developing appropriate partnerships that address critical issues and challenges in disadvantage communities, and building and sustaining philanthropic capital. tmccf sees its fundamental responsibility as facilitating research and documentation as a means of improving lives by investigating cause and effect so that, where appropriate, models can be replicated in other economically stressed communities. consistent with its mission, tmccf plays an integral role in marshalling resources, both people and financial, to address a community’s needs such as the reduction of health disparities. community foundations have been recognized as vital champions in revitalization efforts in resource-poor areas, especially in southern communities [22-24]. as a community foundation, tmccf leverages its expertise and relationships with local, state, and national stakeholders to convene and engage key decision makers in dialogue and action-oriented planning focused on capacity-building, impacts, and outcomes. tmccf also engages in outreach to diverse stakeholders to collaborate on the development of a strategic agenda for community and economic development, particularly in rural and urban poor communities. to meet the goal of cost-effective health and human service delivery in macon county, tmccf engaged westat and a2d as critical partners in meeting the health care challenges of the tuskegee macon county region; this collaboration evolved into the telehealth ecosystem™ model. established in 1963, westat is a nationally acclaimed social science research corporation with a rich history of conducting multisector evidence-based research and evaluation projects of national and global significance. the company’s portfolio is varied, spanning a range of health, human service, and socioeconomic domains, including those within the health disparities and health equity arenas. many of these efforts include studies that have contributed to the advancement of health equity and population health outcomes. within westat, the center on health disparities & health equity research (hereafter, “the center”) engages in transdisciplinary research and works with the center’s resource network of diverse partners. the center provides technical assistance and project development consultation with a focus on planning, stakeholder engagement, and information dissemination/project replication capacities. in doing so, the center focuses on evidence-based practices and innovations that have proven helpful in reducing disparities and promoting health equity. the center helps to foster communication and stakeholder engagement; share and exchange best practices and experiences to strengthen community-based programming; and advance the development of appropriate community-based tools and resources. the center’s guidance on performance measure development helps shape a “real world” analytics platform so that the telehealth ecosystem™ model can be meaningfully assessed by policy, funding and enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi regulatory decision makers. westat has been charged with developing an evaluation/assessment methodology that will provide clear insight into the telehealth ecosystem™ model in preparation for replication in both rural and urban underserved communities with low socioeconomic indices. a2d, inc., a competitive local exchange carrier (clec), is on the forefront of bridging the digital divide in rural and low-income urban areas. a2d is uniquely able to connect citizens who can’t afford internet access directly to community-based content, services and resources, without the internet. by removing the burden of affordability, community-based resources can now focus on providing services (in person and remotely) to all at-risk citizens. as a result, the fundamental goal of the collaborative team effort is developing replicable strategies to help multi-sector entities deliver social services across a no-cost digital medium (intranet) to help mitigate socioeconomic disparities. central to this thrust is enabling macon county’s health care provider resources to identify and secure public-private funding resources to: a) modify existing programs; b) establish centralized data sharing and analytics depositories; and c) help analyze impact data to help refine how health resources can be delivered effectively to all citizens to enhance population health outcomes. a2d’s network model has endured intensive vetting by a multitude of federal, state and municipal agencies. as such, a2d has maintained a focus on making connectivity available to all citizens regardless of their economic status as well as working with social service providers to help them leverage the network to deliver enhanced services. through the introduction of technology as an equalizer, the telehealth ecosystem, powered by a2d’s network, represents a realistic paradigm for digital connectivity in health care and human services delivery. additional early key stakeholders and organizations included the macon county health care authority, the tuskegee macon county community development corporation (cdc), southern christian leadership foundation (sclf), tuskegee university college of engineering, the tuskegee university graduate program for public health, the national association of nurses, tuskegee chapter, the retired nurses association, the community hospital of tallassee, the telehealth work group of alabama, tuskegee university bioethics and medical research center, and the department of psychology and sociology, tuskegee university. all were among key collaborators in strategic planning and project development. methods and approach the theoretical frameworks and methodological approach were based on best practices from the literature, empirical observations and real-world experiences deemed successful in enhancing rural population health outcomes. tmccf, westat’s center on health disparities & health equity research, and a2d embraced a community and economic development (ced) framework with an unwavering fidelity to addressing social determinants of health. augmentative theories such as organizational development, dissemination and adoption, and systems thinking align with the core partnership’s objectives. a combined ced framework and collective action framework overlay was subsequently adopted. the overlay of an integrated ced and collective impact framework also helps to acknowledge the relationships among economic distress, social determinants of health, the geo-political landscape, and the needs assessments/asset mapping required to move from concept to action and sustainability. this composite approach currently drives the telehealth ecosystem™ model’s rural initiative. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi in rural america, the correlation between local health care systems and the vitality of the community is strong, and high-quality health care indices can serve to leverage economic and community development initiatives [25]. in this context, population health status is viewed as an economic engine [26]—and can be a reciprocal driver for helping to bring about health equity. health care industry efforts can also serve as a bridge between diverse sectors in ced initiatives. community development has been operationally defined as a “set of processes or efforts to create community change at the local level . . . increasing awareness of issues … and enhancing community member participation in addressing these issues.” [27] a traditional ced approach offers strategic investments on a competitive merit basis to support economic development, foster job creation, and attract private investment in economically distressed areas. correlate concepts often associated with both ced and collective impact frameworks include culture and community engagement, community organization, community participation, capacity building, constituent engagement, community empowerment and coalition building [28]. initially introduced in the united states in 2011 by the foundation consulting firm fsg, the collective impact concept “provides a useful framework for community change and is situated within the broad frame of collaborative efforts focused on systems and policy change.” [29] the collective impact framework is increasingly being applied to address socioeconomic disparities, and the concept received wider dissemination in the literature in a stanford social innovation review winter 2011 article [30]. the collective impact approach is described as “the commitment of a group of important actors from different sectors to a common agenda for solving a specific social problem.” in differentiating collective impact from other forms of collaboration, the stanford review authors contend that even though the “social sector is filled with examples of partnerships, networks, and other types of joint efforts,” this approach is both distinct and flexible in format. the core partnership team embraced the collective impact framework as an overlay to the foundational ced framework, as this augmentation underscores tmccf’s efforts as the regional anchor organization. supporting backbone infrastructure is essential to ensuring the collective impact effort maintains momentum and facilitates impact [31]. a defining feature of the collective impact framework is the utilization of a backbone, or anchor, organization—a separate entity dedicated to coordinating the various dimensions and collaborators involved in initiatives. moreover, fundamental principles of the collective impact framework highlight the relevance of a common agenda, performance measurement development, sustainable activities, ongoing dialogue, and having a core anchor, or nexus, to catalyze the efforts [32]. case studies of select collective impact framework project applications underscore the vital role played by anchor institutions. the literature emphasizes the importance of “nonprofit management organizations that have the skills and resources to assemble and coordinate the specific elements necessary for collective action to succeed . . . and that power of collective action comes not from the sheer number of participants or the uniformity of their efforts, but from the coordination of their differentiated activities through a mutually reinforcing plan of action.” [30] two leading canadian-based organizational proponents of the collective impact framework and adaptive leadership concepts, the tamarack institute and collaboration for impact, contend that adaptive leadership principles are compatible with complex systemic issues: “adaptive problems . . . are complex . . . reaching an effective solution requires learning by the stakeholders involved in the problem, who must then change their own behavior in order to create a solution.” adaptive enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi leadership proponents contend that “the role of leading and coordinating collaborations is vastly different . . . one requires us to respond, the other requires us to change.” [29] augmentative theories and principles in addition to socioeconomic indices and geopolitical factors, human poverty is also contextual. poverty and inequities cannot be measured only by statistical or quantitative methodology. rather, they must be evaluated in concert with those conditions that drastically impact the very quality of one’s existence—and which are not solely dependent on socioeconomic variables. determinants of health reach beyond the boundaries of traditional health care. as such, life expectancy, health status, lack of addictions, personal security, emotional well-being, access to knowledge, environment, personal security, and political empowerment are equally crucial [21,33]. social determinants of health refer to access to power, money, and resources and the conditions of daily life that affect health and well-being for groups of people [27]. social determinants of health also correlate with the set of factors that contribute to the social patterning of health, disease, and illness the interrelationships among these factors determine individual and population health status [34,35]. systemic and structural inequities [36] such as economic disenfranchisement, racial disparities, sub-standard environmental conditions, illness and workforce-related stressors can undermine one’s overall health and well-being. moreover, “dealing with these conditions from a position of limited control can result in chronic stress on individuals, which is an underlying cause of many health conditions.” [30] education, housing, transportation, agriculture, and environmental sectors can be important allies in improving population health. accordingly, interventions that target multiple determinants of health are most likely to be effective. implementation planning and assessment processes, such as asset mapping, focus groups and participatory forums, align with emerging and best practices for engaging community leaders in participatory dialogues that are designed to enhance the information landscape [37,38]. social constructs such as lewin’s model of change [39] also informed the conceptualization of the telehealth™ ecosystem model—particularly regarding community engagement and health equity promotion aspects. lewin’s model offers avenues to combat barriers to intersectoral collaboration via a “process known as unfreezing . . . by understanding the “professional logic” of potential partners . . . pointing out the benefits of partnership to them.” this methodology, in essence, helps in “overcoming inertia and dismantling the existing mindset.” [40] perspectives on digital connectivity in reducing health and socioeconomic disparities • the telehealth ecosystem™ model focuses on strengthening community capacities as a critical variable for disparities reduction and promotes discerning media access and consumption. the model also embodies select recommendations from a knight foundation commission report [38] on media communications, information dissemination and civic engagement, which called for: a) development of systematic quality measures of community information ecologies; b) assessment of impacts on social outcomes; c) support for information providers in reaching local audiences; d) dissemination of quality content through diverse media (e.g., enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi mobile phones, radio, public access cable, new platforms); e) provision of continuous affordable high-speed internet service; and f) engagement of citizens in acquisition and knowledge sharing within and across social networks. • a stanford social innovation review article titled tech and innovation to reengage civic life provided the following caveats regarding the relevancy of information access to socioeconomic parity and community well-being: a) “sometimes even the best-intentioned policymakers overlook the power of people. and even the best-intentioned discussions on social impact and leveraging big data for the social sector can obscure the power of everyday people in their communities,” and b) “well-structured civic engagement creates the space and provides the tools for people to exert agency over policies. when citizens have concrete objectives, access to necessary technology (whether it’s postcards, trucks, or open data portals), and an eye toward outcomes, social change happens.” [41] • a federal website blog article titled advancing health equity in the digital age [42] highlighted the potential for health information technology (health it) to improve chronic disease management and care coordination efforts as well as mitigate against health issues that disproportionately impact communities of color. citing “. . . lack of access to quality, preventive health care, cultural and linguistic barriers, and limited patient-provider communication as factors that aggravate health disparities,” the author asserts that “ . . . limited financial capital and lack of systems that can communicate effectively with each other widen the digital divide between providers and other clinicians who provide health services to a significant number of minority communities.” • former federal communications commission (fcc) commissioner mignon l. clyburn spearheaded the establishment of the connect2health fcc task force, which is “a dedicated, interdisciplinary team, focused on the intersection of broadband, advanced technology, and health. clyburn’s remarks at a recent nhithimss leadership conference highlight the digital divide and resulting impacts on population health. clyburn asserts that “. . . broadband deployment . . . provides the necessary foundation for creating the gateway to new and sustainable models for meeting longstanding health goals. . . . these efforts can further spur the u.s. economy and help to close the digital divide, while at the same time be an oasis in a health care and wellness desert. …the data clearly shows that the picture of health is vastly different in connected communities versus those in digitally-isolated areas. this holds true when it comes to access to care, quality of care, and health outcomes . . . almost half of u.s. counties are ‘double burden’ counties, where there are elevated levels of chronic disease and lower levels of broadband connectivity. perhaps it’s time for us to evolve our thinking, grow our vision, and create a ‘broadband health safety net,’ for underserved groups in america.” [43] results formalization of the telehealth ecosystem™. the ecological network concept emerged as the macon county telehealth ecosystem™ in 2014. development of a charter served to codify and define the ecosystem’s mission, membership, and operating guidelines. with its work undergirded enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi by the integrated frameworks of ced and collective impact, the developing telehealth ecosystem™ model embraced collaborative processes that reflect a common vision, commitments to share information and resources, and active participation in contributing to ongoing developmental and capacity building efforts. overall impacts for phase i, to date, include noticeable gains in cross-sectoral engagement, including awareness building, outreach, and partnership development. several processes represented milestone information and dissemination opportunities and a pattern of meaningful results-oriented impacts in the evolution of the telehealth ecosystem—health equity roundtable forum and tmccf asset mapping activities. these initiatives yielded a variety of meaningful results-oriented impacts that reflect opportunities for facilitating and disseminating vital information across disciplines, venues, and audiences in the health and human service sectors— with an emphasis on what might be done jointly through expanded collaborative efforts. telehealth™ ecosystem model derivatives in expanded phases are projected to include evaluation studies, development of regional ced performance measures, publications, conferences and dissemination of tools that inform policy and decision-making. phases i through iii expansion plans include: • programs such as telehealth, distance learning, social services and virtual workforce training will be highlighted; • the intranet expansion will be designed to incorporate multiple stakeholders and organizations operating in tandem within an umbrella intranet and telehealth ecosystem to increase efficiency, effectiveness, and scale; and • participating entities will be connected to each other, to regional/state/national resources, and to the internet at minimum costs. the development process will address not only the installation of digital connectivity and intra/internet equipment for telehealth, but will address other issues that are important to developing a wholesome environment to live, work and play. the core partners used a tiered and phased approach to guide the developmental work of the telehealth ecosystem and operationalize processes in support of its mission. methods used in phase i of the telehealth ecosystem™ include the activities detailed in the following sections. federal, state, local government, and corporate entity consultation on telehealth initiatives understanding the telehealth landscape at the federal, state, and local levels was fundamental to codifying strategic plans and spurring the evolution of the telehealth ecosystem’s approach to telehealth services in macon county. selected outcomes from these efforts included: • receipt of technical assistance from various health resources and services administration’s (hrsa’s) telehealth program offices; • receipt of technical assistance support from southeastern telehealth resource center (setrc); enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi • setrc intervention led to an invitation for tmccf to join the alabama partnership for telehealth (apt) state workgroup; and • confirmation of financing and reimbursement guidelines and requirements from the alabama office of state medicaid and blue cross blue shield corporate offices. among other consultations in the private sector included exploration of various care coordination service models, such as the pathways community hub model. other organizations played a key role as informants about the targeted population and the realties facing them. this included but is not limited to the retired nurses association, macon county nurses organization, macon county health care authority, tuskegee university department of sociology and psychology, tuskegee housing authority, macon county ministers association, tuskegee medical and surgical center, and a2d. all of the named organizations had a major role as part of the strategy to improve connections of at-risk populations to health and human services needed to optimize their access to care and subsequent health outcomes. public and private sector partnerships/engagement of diverse stakeholders as the champion of this effort, tmccf recruited and engaged multi-sector provider and stakeholder organizations as members of a vertical network. as the membership of this network expanded, its operations increasingly aligned with those of a coalition. specifically, operations focused on embracing a common vision and using collaborative decision-making processes to inform our strategic approach. other information dissemination activities were conducted using various forums and media (i.e., radio talk shows, printed flyers, public tv) to heighten awareness of the problems that providers and researchers identified as needing to be addressed. information dissemination and community outreach efforts were conducted to provide the community with an opportunity to have their voices heard in decision-making. such conversations helped align the preliminary assessments with the community’s priorities. at the same time, the community would have an opportunity to gain insight into how the telehealth initiative could result in a range of benefits that were not limited only to transforming the service delivery system, but also to improving the quality of patient-centered care and reinvesting in the local economy. formal workgroup development cross-sector discussions were achieved through participation in local organizational meetings, a national roundtable series, community conversations, and inviting unique perspectives drawn from the diverse disciplines that they represented. engaging ecosystem members in an asset-based resource mapping activity to identify available resources and service gaps was an important step in model development. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi health equity roundtable event iv dissemination of information and outreach to the community. engaging the community was pivotal to this effort in light of the localized nature of the telehealth initiative. the community’s participation in a televised roundtable event on april 26, 2017, co-sponsored by westat’s center on health disparities & health equity research and the rockville institute, featured the tuskegee macon county community foundation (tmccf) and the national center for bioethics in research and health care at tuskegee university as collaborators. this event, titled “innovations in health policy & practice: a conversation in reinvesting in rural health,” explored processes by which ideas and methods transform into policy and practice within ced. the collective impact roundtable iv culminated westat’s national roundtable series. this approach was perhaps the most useful among the previous events because it directly engaged health care providers, citizens, government officials, university students and faculty as well as ordinary citizens in a rich dialogue for the first time in generations. the forum provided a unique and compelling “point of entry” for audience engagement and dialogue. it brought together a diverse group of 70 or more local leaders, stakeholders and laypersons — men and women of all ages from the public, private and nonprofit sectors, including representatives from local and state government, media, businesses, academia, nonprofits, universities, health providers and schools. discussions highlighted current and emerging programs for addressing health disparities and current and emerging programs for addressing health disparities and health equity. the roundtable events served as a motivation for presenters, facilitators and attendees (virtual and on-site) to explore other major programs and sectors as potential partners and to examine models, strategies, and tools for use in existing cross-sector programs. community processes are most successful when they encourage the richest possible dialogue among participants [37]. an interactive, multi-sector, community-based conversational forum was deployed to spawn momentum in anticipation of action-oriented responses. the insights gleaned from this this event provided a great platform for civic engagement and action. it explored processes by which ideas and methods transform into policy and practice within ced and collective impact frameworks. the robust dialogue that ensued from session iv served as a catalyst for future brainstorming, strategic planning and focused reflection. capturing new ideas in real time is one way to sustain the momentum of the changes that people want in their communities. multiple accounts to date continue to inform us that roundtable session iv participants left the convening feeling energized, and hopeful, about the opportunities ahead even in the face of formidable challenges. as noted earlier, while a rich and storied history provided a historical context for this event, macon county continues to face significant socioeconomic and population health challenges with insufficient resources to address these deficits. yet, the community abounds with creativity and commitment to transform the current state of affairs. this roundtable represented an opportunity for a rural community to articulate health challenges as well as make a cursory assessment of its resources, which would inform action steps. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi with tmccf’s leadership, local stakeholder engagement for event 4 began with recruitment of innovators to participate as panelists and enlisting local media personalities to help educate the community about the event and planned agenda topics. radio and community television promotional events were held leading up to event. outreach efforts were augmented by tmccf through various academic departments at tuskegee university as well as tmccf/westat inperson meetings with local tuskegee leaders and with the director of the tuskegee university’s archives. collectively, these efforts provided additional historical context for roundtable preparatory activities and information for ongoing strategic planning efforts. these preliminary conversations also helped to heighten awareness and encourage community participation in the event. westat and tmccf met with a2d to gain insight and understanding about the role of technology and how it can be deployed to reach the goals of connectivity to provide a comprehensive healthcare service for a rural community. several meetings were held in atlanta, ga, in the office of a2d to gain hands-on exposure to the process by which underserved communities can receive direct benefits through broadband connection. these sessions brought into sharp focus how the telehealth ecosystem can be a viable and practical model for health service delivery. health asset mapping group a major outcome of the roundtable event was the development of a health asset mapping group. the asset mapping group consisted of an informal gathering of health care providers who engaged in participatory dialogue to share their programs, projects, activities and concerns. what started out as a one-time meeting to develop a formal description of health care resources became a regular monthly meeting. participants included retirees from the health care profession. everyone felt that it was important to play a part in the revitalization of the city of tuskegee and macon county at large and acknowledged that having reliable and competent health care services was critical in making this rural community a better place to live. the group begin meeting monthly in june 2017 via rotational tours of their provider facilities, sharing programs, services and resources. one of the critical outcomes of the mapping group is learning about each individual, thereby engendering trust. gaining trust fostered participatory engagement and collaboration—a signature requirement in developing the telehealth ecosystem. the mapping group has become the foundation for initiating the vertical network and utilizing the collective impact theory. technical assistance: needs assessment, identification of effective practices, performance measurement the center provided guidance on performance measure development and helped shape a real world analytics platform for meaningful assessment of the telehealth ecosystem™ model and dissemination of outcomes for review by policy, funding and regulatory decision makers. the importance of community context in health and social science research and evaluation studies is highlighted in the summary proceedings from a workshop titled “applying a health lens to decision making in non-health sectors.” the sparseness of the population in rural communities can make it more difficult than in urban areas to gather data and expand programs that address enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi health disparities [3]. this is particularly so in scenarios involving randomized trials and control studies. our needs assessment approach is flexible and predicated upon community-based participatory research methods undergirded by digital connectivity and telehealth applications. a shared measurement system is a fundamental element of the collective impact framework: “agreement on a common agenda is illusory without agreement on the ways success will be measured and reported.” [30] a publication from the institute of medicine emphasizes the “complex interactions between these factors”; the relevance of adaptive management “taking actions and defining metrics, and then making adjustments to those actions based on feedback over time”; and noting “impact assessment is not about making definite predictions.” [44] examples of westat-led technical assistance activities conducted in phase i and slated for phases ii through iii include: phase i • an environmental literature scan • analysis of secondary demographic and health data to document need • website analytics: website performance metrics (including data on web traffic and/or click-throughs) • social media analysis: assessment of reach, engagement and/or sentiment expressed on social media platforms (e.g., facebook, twitter, flickr and youtube) • online polls: questions delivered to readers or users to gather data on knowledge, attitudes, or behaviors. • identifying and creating structures for communication and collaboration among stakeholders phases ii-iii • process and outcome evaluation • intervention research • service delivery research discussion the telehealth ecosystem™ model concept emanated from tmccf’s fundamental vision for restoration and socioeconomic renewal in macon county and was facilitated by partnership engagement. the model is a dynamic, evolutionary approach to embracing health informatics and related technologies as vital elements in wide-scale systems change. envisioned as a vehicle to address health disparities, the telehealth ecosystem™ model advances multi-sector engagement, implementation planning and novel intranet technologies for communication and interoperability between public and private health and social service organizations. informed by best practices in health care and information technology, business, health care and human service sectors derive mutual benefits from the alignment of resources for digital connectivity and access [45]. the telehealth ecosystem™ model’s phase ii intranet mechanisms will enable provider networks to: enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi • explore collaboration as content experts; • engage in mutually sponsored health equity promotional efforts; • develop health equity products for dissemination within the digital community; • explore project-based partnerships; and • explore participation in expanded internal and external organizational networks. promoting individual and organizational engagement entails generating opportunities and motivation for involvement. fulfillment of basic information needs is paramount, including information about jobs, housing, taxes, safety, education, transportation, recreation, entertainment, food, shopping, utilities, child care, health care, religious resources, and local news. seeking to contribute to overall information ecology, our telehealth ecosystem model embraces health and media literacy best practices [46] and aligns with systems theory and information dissemination constructs. a definitive report from the oecd directorate for employment, labour and social affairs recommends that legislative strategies aimed at socioeconomic disparities reduction should be grounded in policies that foster wide and equal access to education, information and highquality public services [47]. current implementation planning strategies for the telehealth ecosystem™ model are derivatives of a shared vision for restoration of a once-vibrant tuskegee macon county community. the team’s approach aligns with promising and emerging best practices in stakeholder engagement, care coordination, systems thinking, dissemination and adoption of theories and health equity promotion: “when a wider vision incorporating concepts of well-being and taking a more societal view is deployed, the patterns of relationships with other sectors change, reflecting the need for the work of the sector(s) to be ordered by the needs of people and not by sectoral objectives.” [48] the integrated ced framework/collective impact framework overlay enables sustainable change in the tuskegee macon county health care system by bringing together stakeholders from multiple sectors who may appear to have conflicting approaches but agree to set aside their personal or organizational agenda for the purpose of accomplishing an agreed-upon goal [49,50]. the advocacy is a holistic approach to problem solving, and it will take the minds and concerns of everyone working together diligently and consistently to solve the myriad issues and problems of health care in a rural community. this focus is particularly timely. in a university of georgia publication titled “an economic analysis of georgia’s black belt counties,” authors brigid doherty and john mckissick highlight the demographics, health and socioeconomic disparities [11] indices that plague the black belt region. the academic literature reflects the black belt as an area of keen focus for public health, economic development and educational researchers, legislators and policy analysts.55 “the promotion of good health is necessary across the lifespan and cannot be achieved without joint efforts and partnerships with stakeholders.” [1] collaboration can provide opportunities for determining the catchment areas and groups for resources by: collecting data about specific deficits in specific services; creating differentiated strategies to address the identified catchment areas; developing resources for evaluating the success of strategies; spreading the word about those strategies that are shown to work; and sustaining the efforts of the collaborative. “stakeholder engagement is very important in addressing the social determinants of health and health inequities . . . tackling health inequalities and ensuring equity from the start is an ambitious and enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi complex task that requires coordinated action from a wide range of stakeholders . . . it requires shared responsibilities across sectors.” [1] importantly, a collaborative can connect providers, share techniques for retaining workers, and identify promising practices in a range of areas. the westat center on health disparities & health equity research is closely aligned with its core partners, tmccf and a2d, inc., regarding future research efforts to assess the impact and effectiveness of the telehealth ecosystem model and its associated frameworks. the team recognizes that while there have been major milestones accomplished to date, there is tremendous potential to help advance the field of telehealth and public health informatics. rigorous study of our current efforts is necessary so that lessons learned may emerge and be used to inform the replication of innovative practices in other economically distressed communities. from all indications to date, this initiative portends development of a robust telecommunications infrastructure, quantitative and qualitative performance measures, enhanced population indices, and health informatics and health it platforms to address the social determinants of health. these indicators are reflective of the partners’ collective vision for enhanced population health outcomes. to this end, we plan to use future research to address the following limitations of our current efforts: • the telehealth ecosystem is in the developmental stages in tuskegee, alabama. while the ecosystem model incorporates several evidence-based practices, formal research involving assessing its effectiveness and impact in this specific community has not yet been conducted. • the current efforts have not yet used dissemination and implementation science research to validate the telehealth ecosystem approach to systematically address health equity. • scientific investigations are needed to provide a foundation for assessing the internal and external validity of the innovation for widespread adoption in other communities that have a focus on addressing health equity. conclusion over a period of years along a continuum, stakeholder engagement, implementation planning, technical assistance, and organizational collaboration laid the groundwork for a virtual telehealth provider network infrastructure (telehealth ecosystem) to facilitate future digital connectivity and health services delivery. we have determined that an intranet-based solution is the most cost-effective and efficient solution for this rural community. this preliminary work will inform future expansion of the telehealth ecosystem’s virtual intranet mechanism through its multi-dimensional operational features and functions with particular relevance to health informatics applications. examples of its anticipated uses include enhanced medical care coordination by clinicians, quality of care assessments for quality improvement initiatives by clinical provider organizations, disease surveillance by public health agencies, hipaa compliant data management and access for clinical research and evaluation by the academic research community, and community engagement by communityacademic partnerships. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi the collective impact of these foundational action steps has helped to advance understanding of the needs and capacity of the rural underserved community population and the health centers that serve them, improve health outcomes, reduce health disparities, and increase access to health care for underserved populations. these formative steps will be pivotal to the telehealth ecosystem model’s next phase of work. key action steps during phase ii will involve leveraging the telehealth ecosystem’s capacity using the intranet as a means to create a responsive system of health care for macon county residents. operational definitions black belt counties. the black belt is a region in the southern united states. the literature notes that the term originally described the prairies and dark soil of central alabama and northeast mississippi and now references a broad agricultural region in the american south characterized by historical agriculture in the 19th century and a high percentage of african american residents. community (locality) development. community development is a set of processes or efforts to create community change at the local level. it involves strengthening social ties, increasing awareness of issues affecting the community, and enhancing community member participation in addressing these issues [27]. health disparities refer to differences in the existence and frequency of health conditions and health status among groups. health disparities are referred to as health inequities when they are the result of the systematic and unjust distribution of these critical conditions. health inequities are “avoidable inequalities in health between groups of people within countries and between countries (world health organization, 2010).” health equity, then, is when everyone has the opportunity to “attain their full health potential” and no one is “disadvantaged from achieving this potential because of their social position or other socially determined circumstance.” [27] media advocacy. media advocacy refers to the strategic use of print, broadcast, and social media to encourage social, economic, or environmental change. social determinants of health refer to access to power, money, and resources and the conditions of daily life that affect health and well-being for groups of people (solar, irwin, who 2010) [51]. telehealth is generally defined as: “the practice of healthcare delivery using telecommunications technology including but not limited to diagnosis, consultation, treatment, transfer of medical data, education, dissemination of public health alerts and/or emergency updates.” the term telemedicine, on the other hand, usually references a more specific “use of telecommunications technology to deliver clinical diagnosis, services and patient consultation.” financial disclosure the authors have no financial or other interests to disclose. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi competing interests the authors have declared that no competing interests exist related to this publication. disclaimer the findings and conclusions in this manuscript are those of the authors and do not represent the official positions of westat, the tuskegee macon county community foundation, a2d, inc., or health marketing concepts international. references 1. first eu-wide stakeholder debate of the equity action. together for health equity from the start. may 2012. budapest, hungary. available from: http://www.health-inequalities.eu/wpcontent/uploads/2016/05/1_1_e_documentation_1st_eu_debate.pdf 2. commins j. population health poses unique challenges in rural areas. health leaders. october 28, 2015. available from: https://www.healthleadersmedia.com/strategy/population-healthposes-unique-challenges-rural-areas 3. olson s, anderson k. achieving rural health equity and well-being: proceedings of a workshop. national academies of sciences, engineering, and medicine. 2018. washington, dc: the national academies press. available from: http://nap.edu/24967 4. sumners c. why living in a rural area could be considered a health risk. vital record. texas a&m health science center school of public health. june 19, 2017. available from: https://vitalrecord.tamhsc.edu/why-living-in-a-rural-area-could-be-considered-a-health-risk/ 5. bolin jn, bellamy g, ferdinand ao, kash ba, helduser jw, eds. (2015). rural healthy people 2020. vol. 1. college station (tx): texas a&m health science center school of public health, southwest rural health research center. available from: https://srhrc.tamhsc.edu/docs/rhp2020-volume-1.pdf 6. alabama black belt counties map. auburn university scholarship/resources. available from: http://auburn.edu/scholarship/resources/alabamablackbeltcounties.pdf 7. the robert wood johnson foundation (rwjf)/university of wisconsin population health institute (uwphi). 2018 county health rankings report alabama. available from: http://www.countyhealthrankings.org/app/alabama/2018/rankings/macon/county/outcomes/o verall/snapshot 8. united states census bureau. income to poverty ratio. macon county, al. available from: https://data.census.gov/cedsci/results/all?q=macon%20county,%20alabama%20income%2 0and%20poverty&t=income%20and%20poverty&g=0500000us01087&ps=search*suggest ions@false enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi 9. tullos a. the black belt. southern spaces journal. emory university. april 19th 2004. available from: http://southernspaces.org/2004/black-belt. 10. united states census bureau. quick facts, macon county, alabama, available from: https://www.census.gov/quickfacts/fact/table/maconcountyalabama/pst045216 11. doherty b, mckissick k. an economic analysis of georgia’s black belt counties. the university of georgia, center for agribusiness and economic development and college of agricultural and environmental sciences april 11, 2006. available from: http://athenaeum.libs.uga.edu/xmlui/bitstream/handle/10724/18790/cr-0206.pdf?sequence=1 12. robert wood johnson foundation (rwjf), university of wisconsin population health institute. 2018 county health rankings for the 67 ranked counties in alabama. available from: http://www.countyhealthrankings.org/sites/default/files/state/downloads/chr2018_al.pdf ~ http://www.countyhealthrankings.org/app/alabama/2018/overview 13. federation of southern cooperatives/land assistance fund. available from: www.federation.coop 14. reverby sm. 2010. examining tuskegee: the infamous syphilis study and its legacy. chapel hill, nc. university of north carolina press. soc hist med. 23(2), 432-33. https://doi.org/10.1093/shm/hkq026 15. rice mf, jones w. the decline of black hospital and contemporary public policy in public policy and the black hospital: from slavery to segregation to integration. chapter 5: greenwood press. westport, connecticut. 1994 (p 101). available from: https://books.google.com/books?id=puc6k2kgfbwc&pg=pa101&lpg=pa101&dq=john+ a+andrews+hospital+in+tuskegee,+alabama&source=bl&ots=wa2rjspfg&sig=kndcg cngdrrglibh5_aofq3hqss&hl=en&sa=x&ved=0ahukewj_pux0k8jbahvdxvkkhevhd _o4chdoaqg9mau#v=onepage&q=john%20a%20andrews%20hospital%20in%20tusk egee%2c%20alabama&f=false 16. texas a&m university rural & community health institute (rchi). (2017). what’s next? practical suggestions for rural communities facing a hospital closure. available from: https://www.rchitexas.org/ 17. rhodes g, ed. director spells out history of tuskegee va during eggs and issues. the tuskegee news. december 16, 2010. available from: http://www.thetuskegeenews.com/news/director-spells-out-history-of-the-tuskegee-vaduring-eggs/article_a1d5ffe1-8fa0-5526-86bf-e89b8856a9fd.html 18. tuskegee va hospital listed on national historic register. the tuskegee news. tuskegee, al. apr 12, 2012. available from: http://www.thetuskegeenews.com/articles/2012/04/12/news/doc4f85a190bbc64595347664.t xt https://doi.org/10.1093/shm/hkq026 enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi 19. the robert wood johnson foundation (rwjf), university of wisconsin population health institute (uwphi). county health rankings 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partnerships between regional development organizations and community foundations. https://www.nado.org/wp-content/uploads/2012/07/collaborative-leadership.pdf 24. schlegel r, peng s. national committee for responsive philanthropy and grantmakers for southern progress (gsp). as the south grows, so grows the nation: executive summary. june 12, 2018. available from: https://www.ncrp.org/wp-content/uploads/2018/06/as-the-southgrows-so-grows-the-nation-executive-summary.pdf 25. rural information hub. community vitality and rural healthcare. available from: https://www.ruralhealthinfo.org/topics/community-vitality-and-rural-healthcare 26. weinstein j, geller a, negussie y, baciu a, eds. national academies of sciences, engineering, and medicine. 2017. communities in action: pathways to health equity (chap 7: partners in promoting health equity in communities). washington, dc: the national academies available from: http://nap.edu/24624 27. community toolbox. chapter 17. addressing social determinants of health in your community. center for community health and development at the university of kansas. available from: https://ctb.ku.edu/en/table-of-contents/overview/models-for-communityhealth-and-development/social-determinants-of-health/main 28. mccloskey dj, mcdonald m, cook j. heurtin-roberts, updegrove s, sampson d, gutter s, eder m. principles of community engagement (chapter 1). community engagement: definitions and organizing concepts from the literature. centers for disease control agency for toxic substances and disease registry available from: https://www.atsdr.cdc.gov/communityengagement/pdf/pce_report_chapter_1_shef.pdf enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi 29. tamarack institute for community engagement. collective impact. 2018. available from: https://www.tamarackcommunity.ca/collectiveimpact 30. kania j, kramer m. collective impact. stanford social innovation review. winter 2011. stanford center on philanthropy and civil society. available from: https://ssir.org/articles/entry/collective_impact 31. collaboration for impact. the backbone organization. available from: http://www.collaborationforimpact.com/collective-impact/the-backbone-organisation/ 32. centers for disease control. principles of community engagement: definitions and organizing concepts from the literature. 2nd ed. nih publication no. 11-7782 printed june 2011, available from: https://www.atsdr.cdc.gov/communityengagement/pdf/pce_report_508_final.pdf 33. organization for economic co-operation and development. tackling high inequities: creating opportunities for all. june 2014. available from: https://www.oecd.org/unitedstates/tackling-high-inequalities.pdf 34. centers for disease control and prevention. social determinants of health: know what affects health. january 2018. available from: https://www.cdc.gov/socialdeterminants/ 35. brennan ramirez lk, baker ea, metzler m. promoting health equity a resource to help communities address social determinants of health. atlanta: u.s. department of health and human services. centers for disease control and prevention. 2008. available from: https://www.cdc.gov/nccdphp/dch/programs/healthycommunitiesprogram/tools/pdf/sdohworkbook.pdf 36. powell j, reece j, rogers c, gambhir s. communities of opportunity: a framework for a more equitable & sustainable future for all. the kirwan institute for the study of race and ethnicity. ohio state university. 2007. available from: http://www.racialequitytools.org/resourcefiles/powell1.pdf 37. james l. knight foundation. building stronger communities through information exchange: planning for action toolkit. available from: https://www.knightfoundation.org/media/uploads/publication_pdfs/2011_kf_community_i nformation_toolkit.pdf 38. harwood r. assessing community information m\needs: a practical guide. washington, dc: the aspen institute. october 2011. available from: https://www.knightfoundation.org/media/uploads/publication_pdfs/assessing_community_i nformation_needs_10.6.11.pdf 39. mind tools. lewin’s change management model: understanding the three stages of change. available from: https://www.mindtools.com/pages/article/newppm_94.htm enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi 40. second eu-wide stakeholder debate of the equity action. together for health equity from the start. november 2012. berlin, germany. available from: http://www.healthinequalities.eu/wp-content/uploads/2016/05/documentation_second_stakeholder_debate.pdf 41. gilman h. 2015. tech and innovation to re-engage civic life. stanf soc innov rev. 24(nov), •••. https://ssir.org/data_for_community_driven_solutions/entry/tech_and_innovation_to_re_eng age_civic_life#. 42. desalvo k, garcia n. advancing health equity in the digital age. health it buzz. office of the national coordinator (onc) for health information technology. available from: https://www.healthit.gov/buzz-blog/from-the-onc-desk/advancing-health-equity-digital-age/ 43. clyburn m. leveraging health it to address health disparities: remarks of commissioner mignon l. clyburn nhit-himss leadership conference: las vegas, nv. march 7, 2018. available from: https://www.fcc.gov/document/commissioner-clyburn-remarks-himss-2018conference 44. wizemann t. (rapporteur). institute of medicine. 2014. applying a health lens to decision making in non-health sectors: workshop summary. washington, dc: the national academies press. available from: http://nap.edu/18659 45. the aspen institute. informing communities: sustaining democracy in the digital age. knight commission report. (a project of the aspen institute communications and society program and the john s. and james l. knight foundation.) 2009. washington, dc: the aspen institute. available from: https://assets.aspeninstitute.org/content/uploads/files/content/docs/pubs/informing_commun ities_sustaining_democracy_in_the_digital_age.pdf 46. hobbs r. digital and media literacy: a plan of action. knight commission report on the information needs of communities in a democracy. washington, dc: the aspen institute. available from: https://files.eric.ed.gov/fulltext/ed523244.pdf 47. organization for economic co-operation and development. tackling high inequities: creating opportunities for all. social policy division of the oecd directorate for employment, labour and social affairs. june 2014. available from: https://www.oecd.org/unitedstates/tackling-high-inequalities.pdf 48. health inequalities unit – department of health uk. equity action: tools – to improve the health equity focus in cross-government policy making. available from: http://www.healthinequalities.eu/projects/past-projects/equity-action/ 49. splansky-juster j. new research study: when collective impact has an impact. march 1, 2018. collective impact forum. available from: http://collectiveimpactforum.org/blogs/700/newresearch-study-when-collective-impact-has-impact enhancing rural population health care access and outcomes through the telehealth ecosystem™ model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e218, 2018 ojphi 50. collective impact forum. what is collective impact? available from: http://collectiveimpactforum.org/what-collective-impact 51. solar o, irwin a. a conceptual framework for action on the social determinants of health. social determinants of health discussion. paper 2 (policy and practice). 2010. http://www.who.int/sdhconference/resources/conceptualframeworkforactiononsdh_eng.pd f 52. about the alabama black belt heritage area. the official website of the alabama black belt heritage area. available from: http://alblackbeltheritage.org/about-alabama-black-beltheritage-area 53. the black belt community foundation vision. a transformed black belt. http://blackbeltfound.org/about-us/our-vision/ 54. united states census bureau. official poverty measure. macon county, al. available from: https://data.census.gov/cedsci/results/all?q=official%20poverty%20measure&t=official%2 0poverty%20measure&g=0100000us&ps=search*suggestions@false 55. settles c. how to create a tele-healthcare hub, school, health & libraries broadband coalition (shlb): promoting broadband for anchor institutions and their communities. posted january 10, 2018. available from: https://shlb.siteym.com/blogpost/1628552/292608/how-to-create-a-tele-healthcare-hub 56. the university of alabama institute for communication and information research and the black belt community foundation. (various studies.) available from: https://cchs.ua.edu/college-works-with-rural-communities-to-reduce-obesity/. https://cchs.ua.edu/research-roundup-an-intervention-program-for-childhood-obesity/. https://cchs.ua.edu/researchers-receive-800000-grant-for-community-based-participatoryresearch/. https://cchs.ua.edu/people/cynthia-moore/. https://cchs.ua.edu/pickens-countyhealth-scholars-welcomed/. enhancing rural population health care access and outcomes through the telehealth ecosystem™ model abstract introduction catchment area demographics historical perspective a rural underserved population: response to prevailing issues the core partners methods and approach augmentative theories and principles perspectives on digital connectivity in reducing health and socioeconomic disparities results federal, state, local government, and corporate entity consultation on telehealth initiatives public and private sector partnerships/engagement of diverse stakeholders formal workgroup development health equity roundtable event iv health asset mapping group technical assistance: needs assessment, identification of effective practices, performance measurement phase i phases ii-iii discussion conclusion operational definitions financial disclosure competing interests disclaimer references evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management catherine j staes, bsn, mph, phd 1 , rita altamore, md 2 , eun gyoung han, ms 1 , susan mottice, phd 3 , deepthi rajeev, ms 1 , richard bradshaw, ms 1 1 department of biomedical informatics, university of utah, salt lake city, utah 2 washington state department of health, olympia, washington 3 utah department of health, salt lake city utah. abstract to control disease, laboratories and providers are required to report conditions to public health authorities. reporting logic is defined in a variety of resources, but there is no single resource available for reporters to access the list of reportable events and computable reporting logic for any jurisdiction. in order to develop evidence-based requirements for authoring such knowledge, we evaluated reporting logic in the council of state and territorial epidemiologist (cste) position statements to assess its readiness for automated systems and identify features that should be considered when designing an authoring interface; we evaluated codes in the reportable condition mapping tables (rcmt) relative to the nationally-defined reporting logic, and described the high level business processes and knowledge required to support laboratory-based public health reporting. we focused on logic for viral hepatitis. we found that cste tabular logic was unnecessarily complex (sufficient conditions superseded necessary and optional conditions) and was sometimes true for more than one reportable event: we uncovered major overlap in the logic between acute and chronic hepatitis b (52%), acute and past and present hepatitis c (90%). we found that the rcmt includes codes for all hepatitis criteria, but includes addition codes for tests not included in the criteria. the proportion of hepatitis variant-related codes included in rcmt that correspond to a criterion in the hepatitis-related position statements varied between hepatitis a (36%), acute hepatitis b (16%), chronic hepatitis b (64%), acute hepatitis c (96%), and past and present hepatitis c (96%). public health epidemiologists have the need to communicate parameters other than just the name of a disease or organism that should be reported, such as the status and specimen sources. existing knowledge resources should be integrated, harmonized and made computable. our findings identified functionality that should be provided by future knowledge management systems to support epidemiologists as they communicate reporting rules for their jurisdiction. mesh key words: disease notification; knowledge bases; decision support systems, clinical; public health practice http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 introduction timely and complete disease reporting is critical for detecting and controlling emerging health threats, particularly infectious diseases. in each us state, clinicians and hospitals, laboratories, veterinarians, daycare providers and others are required by law to report to public health authorities when they identify a reportable condition, such as anthrax, hepatitis a or lead poisoning. (1-4) depending on the condition, reporting may lead to public health investigation, immunization, and prophylaxis of susceptible contacts, treatment of infected contacts, implementation of control measures to prevent further spread, and identification of trends and outbreaks. thus, public health reporting is a key step in the chain of events to initiate control efforts and prevent new instances of disease. figure 1: use case for public health case reporting illustrating the actors and actions involved defining and publishing reporting specifications is the very first step in the public health reporting process (figure 1). to implement public health reporting, clinicians, hospital, laboratories and others need information about what, when, how, and where to report. for reporters to ascertain this knowledge, public health authorities must specify reporting requirements and communicate those requirements to the target audience in a usable manner. there are several problems with the current processes. first, the unique reporting requirements for jurisdictions (such as cities, counties, states, and territories) are published in paper-based documents that are mailed/emailed and posted on clinic walls, and the requirements are listed on health department websites. (1, 2, 5-7) the reporting requirements may not be readily accessible or may become out of date, and the specific criteria used to identify reportable events is defined http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 by the reporter after interpreting the requirements. public health reporting criteria are not provided in computable formats that allow implementation in automated systems. the reporter may or may not interpret the requirements as intended by public health authorities. second, while websites typically list the name of reportable events in an effort to specify ‘what’ to report, the lists do not include the clinical and/or laboratory criteria that public health authorities want reporters to use to identify reportable events (1, 2, 5-7) and there is variation in the naming of events and the level of explicitness with which events are specified.(4) finally, lack of knowledge about reporting requirements and inefficiencies associated with manual processes may contribute to the well-documented problems with delayed and incomplete reporting. (8-12) recently, there have been major advances in the implementation of standards and clinical information systems, and changes in policies that increase opportunities to automate all or part of the public health reporting process. hl7 standards for vocabulary, messaging, decision support, and knowledge management are actively being developed and implemented in clinical and public health environments.(13) the inclusion of electronic laboratory reporting from healthcare settings to public health as a financial incentive for healthcare organizations to meet their ‘meaningful use’ requirements further advances the opportunities to automate public health reporting.(14) similarly, there have been improvements in the standardization of knowledge about public health reporting requirements, including knowledge represented in council of state and territorial epidemiologists (cste) policy documents (e.g., position statements) (15), the cdc/cste state reportable condition assessment (srca)(1), and the recently published reportable condition mapping tables (rcmt) developed by the standards workgroup of the cdc/cste joint electronic laboratory reporting (elr) task force.(16) the cste position statements were enhanced in response to efforts in 2008 by the federal american health information community to support real time nationwide public health event monitoring and rapid response management. (17) in order to improve the specification of reporting criteria and leverage advances in technology, the position statements were redesigned to include clinical and laboratory criteria and case report content for public health reporting of conditions that should be reported locally and included in national surveillance.(15) in 2009 and 2010, 68 new position statements concerning public health case reporting of infectious and non-infectious conditions were balloted and approved. (15) the position statements define national policy, but they do not address all the criteria required to implement reporting to local and state health departments, do not address emerging health threats, and are not accessible to automated systems. the srca provides knowledge on a website that summarizes the list of reportable conditions by state and territory, with links to relevant information on other websites. the information is updated annually and is especially useful and designed for defining the populations under surveillance (i.e., for establishing denominators) to calculate incidence rates of disease. the scra does not include specific reporting criteria nor is the knowledge currently in a computable format. the reportable condition mapping tables (rcmts) were published in 2011 and represent knowledge about laboratory reporting criteria. (16) the rcmts contain lists of standard codes for conditions reported nationally, and represents a major effort to harness domain knowledge from experts in standards, laboratory science, and the reportable diseases. the rcmts provide computable knowledge about laboratory criteria but may or may not represent the criteria for reporting laboratory results in a specific jurisdiction and do not address clinical criteria or the other knowledge required about when, how, and where to report. each knowledge resource is valuable http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 and meets a specific need. however, they do not currently meet the need of a laboratory, clinician, or other type of reporter needing a single resource for information about ‘what’s reportable where? and ‘how, when, and what should i report?’. the problem is compounded for laboratories and healthcare delivery systems that must report to more than one jurisdiction. to benefit from improved specification of reporting requirements and leverage automated decision support systems, knowledge such as that found in the position statements needs to evolve through three interacting life cycles (page 19)(18): 1) knowledge generation and validation, 2) knowledge management and dissemination, and 3) clinical decision support implementation and evaluation. the collaborative effort among epidemiologists and the improved representation of reporting specifications in the cste position statements reflect this first life cycle. as described by greenes, the first life cycle concerns knowledge that is often “initially unstructured and unassembled, or even only implicit, and must be extracted (from experts, from databases, or from the literature), organized and synthesized, analyzed for consistency and accuracy, and represented in an unambiguous form that can be computerinterpretable and acted upon (page 19).” (18) the knowledge generated from this effort should be evaluated to ensure its readiness for the knowledge management and dissemination life cycle. during the knowledge management and dissemination life cycle, greenes describes continuous cycles of: a) curation and content management, b) collaborative authoring and editing, c) versioning and tracking of changes, d) standards-based dissemination, and e) localization and updates.(18) financial, technical and governance resources are required to support these processes, particularly when the knowledge must be authored by distributed domain experts, undergo formal review and approval, be accessible to multiple and heterogeneous information systems, and may need to be modified (localized) to represent the needs of a subset of users. for example, in the public health reporting use case, there is the need to localize nationally-curated logic to represent unique reporting criteria relevant for a jurisdiction, such as a city, country, state, or territory. all of these life cycle processes and issues are relevant for managing the knowledge associated with public health reporting specifications concerning ‘what’s reportable where’. for example, the cste position statements define national policy and the rcmt contains candidate codes for logic, but it is necessary to allow the reporting logic to be ‘localized’ to address local needs and emerging health threats. in addition, knowledge from various sources needs to be harmonized. researchers with the rocky mountain center of excellence in public health informatics have been designing and developing a prototype system to author and manage knowledge about ‘what’s reportable where’ for human review and automated systems. our goal is to demonstrate a knowledge management tool that allows domain experts (e.g., public health epidemiologists) to author, curate and communicate their specifications in a manner that is unambiguous and computer-interpretable, while acknowledging the business processes associated with public health reporting and the heterogeneous knowledge needs. therefore, the objectives of the research reported in this paper were to reveal evidence-based requirements for authoring knowledge by: 1. evaluating the tabular reporting logic in the cste position statements to assess its readiness for automated systems and identify features that should be considered when designing an authoring interface, http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 2. evaluating the codes in the rcmt relative to the nationally-defined reporting logic to identify authoring requirements, and 3. describing the high level business processes and knowledge required to support laboratorybased public health reporting. we focused on the hepatitis-related position statements because they were undergoing revision during the spring of 2011 and allowed us the opportunity to provide and obtain feedback from epidemiologists and cste staff. in addition, hepatitis a, b, and c represent conditions that range from immediately reportable with a single laboratory result (i.e., hepatitis a) to those that may require multiple laboratory results performed months apart and at different laboratories (i.e., chronic hepatitis b or c). methods systematic evaluation of reporting logic in position statements during the spring of 2011, we evaluated the knowledge that describes public health reporting logic in the cste position statements balloted in 2010 for ‘hepatitis a’, ‘acute hepatitis b’, ‘chronic hepatitis b’, ‘acute hepatitis c’, and ‘past and present hepatitis c’.(15) specifically, we evaluated the tabular logic in section vi-b concerning reporting from healthcare and laboratory settings. the tabular logic is organized such that each row in the table represents a unique criterion and each column represents a disjunctive (or) set of criteria. the status of each criterion is defined in the position statements by the following guiding principles:  s: this criterion alone is ‘sufficient’ to report a case  n: this necessary criterion in conjunction with all other ‘necessary’ and any ‘optional’ criteria in the same column is required to report a case.  o: at least one of the ‘optional’ criteria in each category in the same column (e.g., clinical, laboratory or epidemiologic findings), in conjunction with all other ‘necessary’ criteria in the same column, is required to report a case.  a: this criterion must be ‘absent’ we evaluated the harmonization of criteria across position statements for similar concepts, and evaluated the criteria for ambiguity.(19) we developed an automated logic verifier tool using an excel spreadsheet as the user interface for rule definition. the format of the spreadsheet used to input logic mimics the format of the tabular logic found in the position statements (figure 2). for example, each column is a disjunctive (or) set of criteria and each row is a unique criterion. we implemented the logic described in the position statements by combining the terms in the following manner: ((sufficient or (necessary and optional)) and not present) or in pseudo-code: (s ||(n && o)) && !a). http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 2: user interface displaying tabular logic for hepatitis-related 2010 cste position statements. a java program was used to interface the excel file with the logic, process the logic rules, and run simulated test cases through the logic rules. the java program automatically converted the tabular logic into tables that internally represent boolean logic using bit-vectors. the resulting binary boolean logic allowed us to analyze simulated case-patient data to determine which test cases would trigger a report. the optimized internal format allowed for efficient comparisons of numerous simulated test cases. we simulated test cases for every conjunctive combination of criteria that would trigger a report according to the tabular logic. this case generation step was repeated for every disease, resulting in a set of report-generating test cases belonging to each disease. a simulated patient who triggered multiple reports did so because of ambiguous reporting logic. the tool ran exhaustive comparisons between the test cases to detect overlapping reports. first, we looked for overlap that would result in the generation of reports for more than http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 one reportable events. we assumed that each test case was applicable for only one reportable event. for example, in our sample, we assumed that a simulated case-patient had no more than one variant of hepatitis (a, acute b, chronic b, acute c, or chronic c) and should only meet the criteria for one reportable event. to locate duplicate reports among different reportable events (i.e., ‘false positive’ reports), we used a pairwise disjointness detection algorithm. we tested for individual pairwise overlap for every pair of reportable events. we estimated the induced false positive rate of one event’s reporting criteria on another by calculating the fraction of test cases for the first reportable event that generated ‘false positive’ reports for the second reportable event. second, we calculated a total overlap ratio using the following formula: total verlap atio ( ) ∑ ∑ : overlap ratio between disease i and j (i th row and j th column in the table). : 5 in this case because we were assessing five reportable events. during two conference calls with the epidemiologists that authored the logic in april and may of 2011, we reported our findings, recommended improvements to draft updated position statements (not published), and obtained general feedback about the spreadsheet interface for testing and viewing the logic. the spreadsheet input format was flexible, a feature that allows the user to experiment with combinations of logic. evaluation of codes in the rcmt relative to hepatitis reporting logic we accessed the reportable condition mapping tables (rcmt) available on the phin vads website on december 12, 2011(20), and downloaded the associated laboratory tests for ‘viral hepatitis, type a’, ‘type b viral hepatitis’ , and ‘viral hepatitis c’. the tests are logical observation identifiers names and codes (loinc®) (21). we grouped the loinc® codes by the laboratory criteria included in the five hepatitis-related position statements balloted in 2011 and describe the proportion of loinc® codes relevant for each criterion, and those not related to the criteria. in 2011, the laboratory criterion in the hepatitis-related position statements were the same as those in 2010, with the exception of an additional criterion for any ‘antibodies to hepatitis c virus (anti-hcv) screening-test-positive’ regardless of whether they meet the cdcdefined cutoff. this addition does not change the test codes that should be used to identify reportable events. we did not review the loinc database for additional relevant codes. high-level business process analysis to improve our understanding of the knowledge required to support laboratory-based public health reporting, we evaluated business processes associated with reporting knowledge in both the laboratory and public health setting. to understand reporting processes at a laboratory, we directly observed the work processes of the compliance officers tasked with public health reporting at a major reference laboratory. we diagramed the information gathered, and validated and improved the generalizability of the documented processes by conducting structured interviews with compliance officers at the reference laboratory and the central laboratory for a multi-hospital healthcare network. to understand the business process of reporting from the perspective of a public health agency, we conducted interviews using storyboards with http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 epidemiologists from three public health programs in utah. the objective of the interviews was to understand the processes related to handling incoming reports. in addition, to understand the parameters for criterion that may need to be authored by epidemiologists, we identified situations when reporting logic may need to change and reviewed more detailed reporting criteria being developed by the epidemiology program. results systematic evaluation of reporting logic in position statements using the automated tool to systematically evaluate the tabular reporting logic in the five hepatitis-related position statements (figure 2), we identified problems with overlapping and overly-complicated logic. first, tabular logic was true for more than one reportable event. for example, half (52%) of the test cases simulated to meet the criteria for chronic hepatitis b also generated a report for acute hepatitis b, and vice versa. most (90%) of the test cases simulated to meet the criteria for acute hepatitis c generated a report for ‘past and present hepatitis c’, and vice versa. there was no overlap between viral hepatitis type a, b, and c. second, the automated tool uncovered tabular logic that was unnecessarily complex. sufficient conditions often superseded necessary and optional conditions. for example, the logic for hepatitis a defined in columns 2 and 3 in figure 2 are not necessary given the criteria in column 1. a positive test for igm antibodies to hepatitis a was sufficient to report a case to public health therefore the more complicated logic was irrelevant. similarly, for acute hepatitis b, columns 3 through 6 in the tabular logic were subsumed by the ‘sufficient’ hepatitis b laboratory criterion in columns 1 and 2. when a criterion is ‘sufficient’ for reporting, it is not necessary to include other sets of criteria that contain the already-‘sufficient’ criterion. when we removed the superseded conditions from the logic for the hepatitis variants, and reanalyzed the pairwise overlap in the logic, the number of cases detected and the total overlap ratio did not change, indicating that the superseded conditions did not contribute to case finding. manual review of the tabular logic (figure 2) revealed additional problems: a) tabular logic occasionally violated the guiding principles (e.g., optional criteria for acute hepatitis b did not always occur with a necessary criterion); b) some criterion were ambiguous and included more than one concept (e.g., hepatitis a logic included the criterion: ‘elevated alt >200 or bili’); c) apparently similar criterion varied across different position statements; and, d) laboratory criteria needed more definition to be actionable (e.g., temporal parameters are required to make the hepatitis c criterion of ‘elevated alt >400, acute onset’ actionable). when we reviewed the logic and our findings with the position statement authors, we identified features of the analyzer tool that should be considered when developing tools to author and manage reporting logic in the future. the position statement authors indicated that the spreadsheet input for the logic of multiple reportable events provided new insights into problems with the logic and lack of harmonization of criteria. they mentioned that they had never before seen all of the rules on one page and they found it useful. we were able to provide the epidemiologists with a spreadsheet to formulate and modify logic by inserting n, o, s, and a for applicable cells, and then retest the impact on overlapping logic within and between reportable diseases. http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 evaluation of associated laboratory test codes in the rcmt the proportion of hepatitis variant-related codes included in rcmt that correspond to a criterion in the hepatitis-related position statements varied between hepatitis a (36%), acute hepatitis b (16%), chronic hepatitis b (64%), acute hepatitis c (96%), and past and present hepatitis c (96%). among the 22 codes for hepatitis a virus (hav), only 8 (36%) were for igm antibodies, the reporting criterion in the position statement; the remaining codes were for hav igg antibodies (n=5), hav antibodies not further specified (ab) (n=7), hav rna (n=1), and hepatitis panels used to order tests (n=2). among the 129 codes for hepatitis b virus (hbv), 89 (69%) were for criteria found in one or both of the hbv-related position statements. the remaining codes were either not related to any reporting criterion (n= 37 igg or antibody tests comprising 29% of the total), potentially useful tests that may not yet have been considered by the position statement authors (n= 2 codes for rrna or core ag), and a hepatitis panel used to order tests (n=1). among the 90 codes for hepatitis c virus (hcv), 86 (96%) were for criteria found in both of the hcv-related position statements. the two hepatitis c related position statements use the same set of tests to trigger a report, but the acceptable values differ. specifically, test result values for antibodies to hcv (anti-hcv) screening tests must exceed the cut-off ratios defined by cdc to meet the criteria for acute hcv results, whereas the result values are not required to meet these criteria for establishing ‘past or present hepatitis c’. the remaining codes were potentially useful tests that may not yet have been considered by the position statement authors (n= 3 codes for rrna or ag) and a hepatitis panel used to order tests (n=1). there were no laboratory criteria in the five position statements without corresponding loinc codes. business process analysis relevant to knowledge authoring needs consistent with figure 1, public health reporting starts with the process of a public health agency communicating reporting requirements to a laboratory and other reporting entities. the utah department of health and the county health departments publish reporting requirements on their websites, including pdf files that may be downloaded, and call laboratories by phone when necessary. while reporting requirements have been fairly stable over time, we identified a variety of situations when reporting requirements did or could change thus requiring the ability to add new, remove, or update reporting requirements. for example, new reportable events were added when: a. a new condition emerged (e.g., west nile virus h1n1 influenza,). thus authoring tools would require the ability to add a new condition and any associated lab tests. note that the condition may not yet exist in snomed-ct and laboratory tests may not yet be in loinc. there is often an urgency associated with publicizing this new requirement. b. a problem with an existing reportable condition emerges, requiring an expansion to the organisms under surveillance and a new event with its own reporting specifications. for example, the h1n1 influenza strain was reported separately from the “seasonal” strain routinely reported among hospitalized persons. http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 c. new control measures were available so case finding was important and prevention strategies need to be evaluated (e.g., varicella zoster). reportable events need to be able to be updated when the health department: a. seeks a denominator for situational awareness requesting that all tests performed, not only ‘positive results’, be reported (e.g., during a large cryptosporidium outbreak) ; b. relaxes constraints associated with current reportable events to improve case finding and for situational awareness (e.g., requesting all persons diagnosed with influenza be reported, rather than only those that are hospitalized). c. wants more timely reports for an emerging problem associated with an existing reportable condition, thus requesting preliminary as well as final results. d. increases specificity for a reportable event by specifying that only organisms resistant to antibiotics be reported. e. needs to add synonyms to the name of a clinical condition, or make other changes based on improved understanding about associated organisms. reportable events were removed when priorities changed and receiving reports was no longer indicated (e.g., mrsa, kawasaki, rheumatic fever, cmv). finally, the epidemiologists indicated other needs, such as the ability to change the reporting time frame, where reports may be sent, and the methods available for receiving reports, but these are beyond the focus of this paper. figure 3: example business processes and logic required to communicate reporting requirements and implement public health reporting within a laboratory http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 to manage public health reporting in the laboratories, we identified three distinct processes that each use a different set of knowledge (figure 3). first, the laboratories identified the test results associated with reportable events (i.e., using ‘evidence detection logic’). this knowledge was implemented using the laboratories ‘local’ codes that had been mapped to reportable events. potentially, this could be handled by the code sets provided by rcmt, but not all laboratories have mapped their local codes to loinc ® codes and new laboratory tests may not yet have an assigned loinc ® code. second, the laboratories applied constraints, such as age, pregnancy status, test results, or hospitalization status, to identify reportable events relevant for a given jurisdiction (i.e., using ‘reporting specification logic’). for example, the age and blood lead level criteria for reporting ‘childhood lead poisoning’ varies by state. finally, after establishing that an event is reportable in a given jurisdiction, the laboratories created a report and transmitted the information using knowledge concerning how, when, and what to include in a report relevant for a given jurisdiction. finally, we identified two sets of knowledge to complete the reporting process at the public health agency. first, the health department requires ‘evidence response logic’ to triage incoming reports, handle duplicate reports from multiple sources (e.g., reports from laboratory and healthcare sources for the same person), and prioritize those reports that require investigation. the response to an incoming report may be ‘reject the evidence’, ‘hold for more evidence’ (e.g., repeated hepatitis b surface antigen results required to establish chronicity), ‘respond to evidence in an automated manner’, or ‘respond to evidence with action by public health personnel’. finally, health departments require logic to classify events for surveillance, which is similar but different than the reporting logic communicated to reporting entities. figure 4: example business processes and logic required to manage incoming reports at the health department and apply logic for surveillance receive public health 'evidence report' (elr message, cda, other format) proposed business process for public health to manage incoming reports of public health reportable events inputs action apply evidence response logic apply classification logic hold for more evidence (repeated hbsag result, vaccine hx, etc) respond to evidence in an automated manner respond to evidence with action by public health personnel reject evidence from page 1 perform surveillance public health initiate investigation http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 during the course of this research, epidemiologists with the utah department of health defined laboratory findings they wished to receive by defining criteria using ‘lab findings by method’ (e.g. hav igm) as shown in the position statements, and including the specimen source, whether preliminary or final results are expected, and the results to be reported. discussion our evaluation revealed strengths and limitations of two important sources of knowledge for public health reporting, and identified features that should be considered when designing and implementing a knowledge management system to support public health reporting. each resource, and the information provided on health department websites, contains critical information that needs to be integrated and shared in both a human-readable and computerinterpretable format. some of the knowledge may be curated on a national level, but epidemiologist need to be able to modify requirements based on the needs of their jurisdiction. systematic evaluation of the tabular reporting logic in the hepatitis-related cste position statements revealed problems but also specific opportunities for improvement. the cste should be applauded for their efforts to structure reporting logic and use a collaborative process to gather input from domain experts distributed throughout the us and from federal, state and local public health agencies. to date, this knowledge engineering task to create reporting logic has been performed using text-based position statement documents that summarize one reportable event at a time. the knowledge is then verified by experts who review and vote on the text-based documents. this task would likely benefit from a tool that allows authors and reviewers to: a) create and test various combinations of logic using standard concepts, and b) view the logic for a variety of diseases in one view to improve logic harmonization. better tools are needed to support the early steps in the lifecycle of ‘knowledge generation and validation’(18) and eliminate wasted effort creating and negotiating overly complicated and redundant logic. better yet, the tools should go on to support the knowledge management and dissemination phase. while the reporting logic in the position statements was structured, we identified several problems that limited the readiness of the logic to be adopted for automated systems. the tabular logic were sometimes overly complicated given the presence of simpler, sufficient logic and would come true for more than one reportable event. the overlapping logic appears to be unintended for acute and chronic hepatitis b, but unavoidable for acute and ‘past and present’ hepatitis c. this situation will require that reporting systems and public health receiving systems have the ability to reconcile reports for the two reportable events associated with the same person. to be fully ready for automated systems, the logic needs to be unambiguous and computerinterpretable. while narrative logic may be easier to create because domain experts can use their own words and purposefully express ambiguity, the tabular logic was one step closer to the computationally useful form of logic needed for automated systems. additionally, in comparison to the narrative logic, the tabular logic was more structured, unambiguous, and explicit, and was easier to a) compare tabular logic across a set of reportable conditions, and b) expose problems with narrative ‘and/ ’ logic statements and misplaced modifiers. finally, it is possible to automatically generate narrative text from the tabular logic to allow domain experts to verify the tabular logic in a narrative form. http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 use of the prototype tool with the epidemiologists revealed features that may be useful for future systems. the tool presented the user with a user-friendly interface that mimics the original source of the knowledge (the position statement). the interface was a spreadsheet that could be manipulated in any spreadsheet application, such as ms excel or openoffice. this provided freedom and flexibility to efficiently compute new rules on the fly and test different logical propositions. additionally, the tool allowed the user to verify overlapping reporting rates and interference among other diseases’ reporting logic, and may be used to test logic within any of the other 68 disease-specific cste position statements. we hypothesize that the views and output generated by the tool could save time during the logic development process if it were available to the authors. unfortunately, the tool requires manual entry of the logic. it would be best to implement this functionality within a knowledge management system that supports rule authoring, thus allowing authors to define, test, and share the logic in one application. the rcmt represents a major effort by experts in standards, public health, and laboratory science to identify and standardize the code sets that could be used for detecting reportable events. we identified variation in the specificity of codes for the criteria included in the five hepatitis-related position statements. we suspect that similar issues would be found for other conditions. it is unreasonable to expect that one set of codes would satisfy all uses. therefore, we recommend that loinc codes be classified to the level commonly used for reporting criteria (e.g., igm, igg, total antibody, etc). this would allow epidemiologists to select criterion and automate the process of identifying the relevant loinc codes. automatically classifying the loinc codes has been addressed in the past(21), but is not yet solved. limitations our investigation has limitations. first, our study is limited to the ‘what’ in ‘what’s reportable where’, and does not address the full spectrum of reporting specifications concerning ‘when’, ‘how’ and ‘where’ to report. lack of standardization in these criteria have been identified by others (5) and our team (unpublished data), but this is not the focus of this paper. the specifications for ‘when’ and ‘how’ are not composed at the national level and specified in position statements. that knowledge must be generated in the context of a given state or other jurisdiction. second, we limited our investigation to the hepatitis-related position statements. while it is possible that the problems we identified are unique to hepatitis, we found similar problems with other position statements when graduate informatics students evaluated 26 other position statements as part of class assignments during 2009 and 2010. despite the limited focus on hepatitis, we believe that the problems and requirements we identified are generalizable to other reportable events and position statements. use case development based on our findings, we developed a high-level use case for authoring and accessing public health reporting specifications (figure 5). the use case includes key actors involved in the process of authoring and accessing reporting specifications, and the major system functionalities that would be required to meet the system goals. we included major existing knowledge resources (including the cste position statements and the public health information network vocabulary access distribution service (phin vads)) that may provide sharable content for public health reporting specifications. the actors and required functionality were identified iteratively as we gathered information during the business process analysis and evaluation of http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 current resources described above. the goal is to allow public health authorities to develop and access sharable knowledge (typically curated at a national level), while allowing public health authorities in state or other jurisdictions to use the sharable knowledge and author reporting specifications that meet their needs (i.e., ‘localizing content’). as shown in figure 5, public health authorities need to author as well as access specifications, but their role becomes specialized at the jurisdiction level (e.g. state level) as the specifications must meet the jurisdiction-specific legal reporting rules. because reporting specifications vary by jurisdiction, any efforts to standardize the detection logic at the national level must support the capability for localization at the jurisdictional level (e.g., city/county, state, territory). the custodian of the reporting specifications (e.g., a state epidemiologist or state health officer) could designate a domain expert to perform two functions: 1) author the jurisdiction-specific reporting actions concerning when, where, how and why to report, and 2) link this information to reporting criteria. the reporting criteria may be adopted from the sharable source, or may need to be localized to, for example, modify age, hospitalization, or laboratory criteria to meet local needs. the public health information network vocabulary access distribution service (phin vads)) may be used to provide sharable content for public health reporting specifications. figure 5: high level use case for authoring and accessing reporting specifications using national resources for sharable content while incorporating jurisdiction-specific content required by reporting systems. http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 recommendations we identified numerous features that should be requirements for systems that manage the knowledge associated with ‘what’s reportable where’. the requirements we identified compliment those identified using user-centered design and other methods reported elsewhere. in particular, knowledge authoring and management tools should allow epidemiologists to:  systematically assess reporting logic to identify logic that is overcomplicated, or overlapping with other logic for the same or a different reportable event.  specify laboratory reporting criteria at the level of granularity described in the position statements (typically, a combination of laboratory finding and method), allowing for linkage to relevant knowledge from the rcmt (i.e., the corresponding combinations of relevant loinc® and snomed codes).  identify problematic logic and experiment with improved logic before balloting and publishing reporting requirements  track the provenance of knowledge and show the history of decisions that were made as the knowledge was developed over time. for example, a feature should be included that allows authors and reviewers to comment on the knowledge and the system should track the users and dates associated with the comments  collaboratively author reporting criteria that meet national specifications (e.g., as reflected in the position statements), but allow the logic to be localized to meet state or other jurisdictional needs. in addition, when researchers validate and publish reporting logic, as for example, klompas et al have done for acute hepatitis b (22), then this further validated logic should be incorporated into the knowledge management system for review and adoption by others. conclusion the logic in the hepatitis-related position statements was insufficient to discriminate reportable events and may be overly complicated given the presence of simpler logic to trigger reports. however, the tabular logic can be assessed and improved using automated tools, is one step closer to the computable form, and represents a major effort within the ‘knowledge generation and validation’ phase. we recommend classifying the loinc codes in the rcmt to the criteria included in the position statements to allow epidemiologists to select the codes they prefer to use for reporting in their jurisdiction. we suspect variation will decrease over time as users understand the variation they are imposing on reporting facilties. in addition, we recommend using knowledge management tools to author, verify, improve, and authenticate logic, and continually incorporate improved logic that has been validated in clinical systems. acknowledgements this research was supported by cdc grants coe1: 5p01hk000030 and 5p01hk000069. p.i. matthew samore, md, university of utah. in addition, coauthors (egh) were partially funded through support from the national library of medicine (nlm training grant no.t15lm007124). we acknowledge lisa ferland and monica huang at cste, the authors of http://ojphi.org evaluation of knowledge resources for public health reporting logic: implications for knowledge authoring and management 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 the position statements, and the cste/cdc case report standardization workgroup for their support with this investigation. corresponding author catherine staes, bsn, mph, phd dept of biomedical informatics, university of utah catherine.staes@hsc.utah.edu conflict of interest there are no conflicts of interest. references 1. state reportable conditions website [database on the internet]. council of state and territorial epidemiologist. 2011 [cited december 4, 2011]. available from: http://www.cste.org/ dnn/programsandactivities/publichealthinformatics/statereportableconditionsqueryresults/ tabid/261/default.aspx. 2. utah administrative code. rule r386-702. communicable disease rule. effective february 1, 2008. 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available from: http://www.hl7.org/index.cfm. 14. cms. ehr incentive program. baltimore: centers for medicare and medicaid services; 2011 [cited 2011 december 10]; available from: https://http://www.cms.gov/ ehrincentiveprograms/. 15. cste. 2010 position statements. council of state and territorial epidemiologists; 2010 [cited 2011 september 16]; available from: http://www.cste.org/dnn/annualconference/ positionstatements/2010positionstatements/tabid/422/default.aspx. 16. cdc. reportable condition mapping tables (rcmts) another step toward standardizing electronic laboratory reporting (elr). 2011 [updated july 22, 2011; cited 2011 september 30]; available from: http://www.cdc.gov/ehrmeaningfuluse/rcmt.html. 17. cste. cste official list of nationally notifiable conditions. atlanta: council of state and territorial epidemiologists; 2007 [updated 2007; cited 2011 october 5]; available from: http://www.cste.org/ps/2007ps/2007psfinal/ec/07-ec-02.pdf. 18. greenes ra, ed. clinical decision support: the road ahead. boston: elsevier, inc; 2007. 19. cimino j. 1998. desiderata for controlled medical vocabularies in the twenty-first century. methods inf med. 37(4-5), 394-403. 20. cdc. phin vocabulary access and distribution system. atlanta: cdc; 2011 [cited 2011 december 11]; available from: https://phinvads.cdc.gov/vads/searchvocab.action. 21. steindel s, loonsk jw. 2002. introduction of a hierarchy to loinc to facilitate public health reporting. amia annu symp proc. •••, 737-41. 22. klompas m, haney g, church d, lazarus r, hou x, et al. 2008. automated identification of acute hepatitis b using electronic medical record data to facilitate public health surveillance. plos one. 3(7), e2626. http://www.ncbi.nlm.nih.gov/pmc/articles/ pmc2440348/?tool=pubmed. http://dx.doi.org/10.1371/journal.pone.0002626 http://www.hl7.org/index.cfm http://www.cms.gov/ehrincentiveprograms/ http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx http://www.cdc.gov/ehrmeaningfuluse/rcmt.html http://www.cste.org/ps/2007ps/2007psfinal/ec/07-ec-02.pdf http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2440348/?tool=pubmed) http://ojphi.org http://www.hl7.org/index.cfm.14 http://www.hl7.org/index.cfm.14 https:// http://www.cms.gov/ehrincentiveprograms/.15 http://www.cms.gov/ehrincentiveprograms/.15 http://www.cms.gov/ehrincentiveprograms/.15 http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx.16 http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx.16 http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx.16 http://www.cdc.gov/ehrmeaningfuluse/rcmt.html.17 http://www.cdc.gov/ehrmeaningfuluse/rcmt.html.17 http://www.cste.org/ps/2007ps/2007psfinal/ec/07-ec-02.pdf.18 http://www.cste.org/ps/2007ps/2007psfinal/ec/07-ec-02.pdf.18 https://phinvads.cdc.gov/vads/searchvocab.action.21 https://phinvads.cdc.gov/vads/searchvocab.action.21 http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2440348/?tool%ed%af%80%ed%b0%a0pubmed http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2440348/?tool%ed%af%80%ed%b0%a0pubmed http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2440348/?tool%ed%af%80%ed%b0%a0pubmed layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts evaluation of heat-related illness surveillance based on chief complaint data from new jersey hospital emergency rooms michael berry1, jerald fagliano1, stella tsai*1, katharine mcgreevy1, andrew walsh2 and teresa hamby1 1njdoh, trenton, nj, usa; 2health monitoring systems, inc., pittsburgh, pa, usa objective the purpose of this evaluation is to characterize the relationship between a patient’s initial hospital emergency room chief complaint potentially related to a heat-related illness (hri) with final primary and secondary icd-9 diagnoses. introduction the nj syndromic surveillance system, epicenter, developed an algorithm to quantify hri visits using chief complaint data. while heat advisories are released by the national weather service, an effective hri algorithm could provide real-time health impact information that could be used to provide supplemental warnings to the public during a prolonged heat wave. methods data on nj hospital emergency room visits were evaluated using two data sources: 1) the epicenter syndromic surveillance system of emergency room visits; and 2) the uniform bill-patient summaries (ub) system containing diagnosis data on all hospital visits. three years of data (2009-2011) were selected, for the time window of may 1 to september 30. the ub data used for matching with the epicenter data were limited to facilities participating in epicenter during the evaluation period. (epicenter facilities captured about 1/3 of all heat-related diagnoses in 2009, increasing to about 2/3 in 2011.) the icd-9 codes of interest included 992.0-992.9 and external cause of injury codes e900.0 and e900.9. we evaluated the sensitivity and positive predictive value (ppv) of the epicenter algorithm in relation to the patients’ eventual diagnoses coded in the ub data. results during the 15 months of data examined, there were a total of 871 people identified with hri visits based on the epicenter algorithm. over the same time period in the same emergency room facilities, there were a total of 2,146 people with a primary or secondary hri diagnosis in ub. the algorithm for the epicenter’s hri definition had a sensitivity of 16% (348/2,146) when any primary or secondary icd or e-code matched; the ppv was 40% (348/871). when data during a major heat event (july 21-23, 2011) was examined separately, both sensitivity (23%) and ppv (59%) improved. graph 1 presents the 2011 daily number of hri visits from epicenter data and the subset of ub data from facilities also reporting to epicenter. the pattern in the epicenter data tracked with the ub data for hri visits and correctly identified several major episodes in 2011. the major heat-related illness episode of july 2011 was selected to evaluate the non-matched epicenter and ub data. a total of 210 (95%) of the non-matched ub cases were able to be matched to epicenter chief complaint data. the epicenter information displayed a diverse range of general complaints, including syncope, dizziness, weakness, and headache. similarly, non-matched epicenter data were compared to ub data to examine diagnoses, and 22 (48%) of the epicenter hri cases were matched to ub diagnostic data. diagnosis codes for these cases were for a variety of conditions classified under “general symptoms”; fluid balance disorders; asthma; diabetes; and unspecified hypertension. conclusions the evaluation found that using chief complaint data to monitor hri was relatively insensitive in comparison to the ub diagnosis codes, with a sensitivity of just over 16% for any ub hri diagnosis. sensitivity and ppv improved during a peak heat event. the evaluation of the non-matched data (both epicenter and ub) provided little guidance for modifying the algorithm. while expanding the algorithm to include complaints such as syncope, dizziness, or weakness may capture a few more hri cases, it would also likely result in a greater number of false positive cases (i.e., higher background noise). though not especially sensitive, epicenter data did identify all major episodes of hri in 2011. the degree of correspondence indicates that the epicenter hri algorithm provides a useful real-time gauge of the daily hri trends. graph 1. 2011 hri visits identified by epicenter data and ub data subset for the same epicenter reporting facilities. keywords chief complaint; heat related illness; ub data acknowledgments this evaluation was supported in part by the environmental public health tracking program, centers for disease control and prevention, cooperative agreement #5u38eh000196. *stella tsai e-mail: stella.tsai@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e125, 2013 d2805 infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak wayne loschen 1 , richard seagraves 1 , rekha holtry 1 , lang hung 1 , joesph lombardo 1 , sheri lewis 1 1 johns hopkins university, applied physics laboratory abstract the 2009 presidential inauguration and h1n1 outbreak called for real-time electronic information-sharing and surveillance across multiple jurisdictions to better understand the health of migrating populations. the infoshare web application proved to be an efficient tool for users to share disease surveillance information. during both high profile events, public health users shared information within a secure access-controlled website across regions in the u.s. and among agencies. due to its flexible design, infoshare was quickly modified from its 2009 inauguration interface to an interface that supports h1n1 surveillance. through discussions and post-use surveys, a majority of infoshare users revealed that the tool had provided a valuable and needed function. infoshare allowed individual jurisdictions to receive timely and useful information, which, when merged with neighboring jurisdictions, significantly enhanced situational awareness for better decision-making and improved public health outcomes. background public health has conducted enhanced disease surveillance activities during high profile events for many years. the utilization of emergency operations centers, aid stations, and increased staffing for political events or large gatherings permits public health officials to keep an up-todate picture of the community’s health during these high profile events; this enhanced level of knowledge is also known as situational awareness. a real-time electronic disease surveillance system may provide utility in monitoring the health of a population during mass gatherings or special events. in special cases, the event will affect multiple jurisdictions [1]. information or data sharing among jurisdictions provides an understanding of the health of the migrating populations. large-scale public events with attendees from multiple regions, such as the super bowl and the olympics, are examples of events can that can benefit from cross-jurisdictional electronic surveillance to assess the health risks for the gathered and returning populations [2]. this article describes two recent multi-regional events that used information-sharing technology to enhance the disease surveillance capability: the 2009 presidential inauguration and the 2009 h1n1 influenza pandemic. infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 the 2009 u.s. presidential inauguration attracted an estimated 1.8 million people to the national capital region (ncr) of maryland, virginia, and the district of columbia [3]. this mass gathering event called for ongoing public health awareness across the regions where inaugural activities occurred. similarly, the h1n1 outbreak in spring 2009 resulted in increased influenzalike infection activity within the u.s. and throughout the world [4], leading the world health organization (who) to declare a pandemic of widespread human infection [5]. as a result, public health departments across the u.s. continually monitored the health of their communities. these two events brought to light the need for real-time electronic information-sharing and surveillance across multiple jurisdictions. methods sharing information v. sharing data based on the types of public health disease information that is used for community disease surveillance, patient health-indicator data can be categorized in two fundamental ways: data and information. raw line-listings, such as hospital emergency department chief complaint records, are considered ―data‖ because they are original facts. data in original form, after removal of duplicate records or erroneous entries, or when aggregated by particular attributes can be associated with individual patients with minimal or no effort. on the other hand, derived outputs such as univariate and multivariate statistical analysis outputs from aberration-detection systems and epidemiological interpretations are considered ―information.‖ here, the output is not listed at an individual level and the information is no longer traceable to patient records. because ―data‖ are gathered at the patient-level, depending on the amount of information required on each patient, they can be potentially difficult to attain and share due to regional/institutional policies and laws that protect individual privacy [6]. however, ―information‖ refers to summaries, opinions, or conclusions about data, and therefore may be less challenging to attain and more suited for sharing across jurisdictions and agencies to enhance situational awareness. sharing information instead of data provides several advantages. first, sharing information – essentially sharing statistical outputs or opinions about situations – is less restrictive and requires simpler agreements. second, when considering disease surveillance that spans across jurisdictions, interpretations of public health data anomalies by local users are more likely to provide better-informed explanation of findings because of detailed knowledge of the data and baseline disease patterns in the community they serve. if local users provide short interpretations of disease surveillance findings, users in other jurisdictions can quickly review events and focus on relevant material without having to duplicate efforts. moreover, sharing information can provide an increased sense of collaboration and trust among communities. as informationsharing among users across jurisdictions becomes commonplace, beneficial partnerships can be formed that promote exchange of new ideas, successful protocols and practices, diseasemonitoring techniques, and tools related to disease surveillance. on the other hand, there are some disadvantages to sharing information. sharing information instead of data results in loss of specificity. specificity of the data inputted into anomalydetection algorithms determines their validity. because of the non-specific and processed nature of ―information,‖ statistical algorithms have reduced capability for anomaly detection. it would be possible, however, to use shared information to fine-tune alerting algorithms. for example, if information is shared about a possible respiratory disease outbreak in a particular jurisdiction, the infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 adjacent jurisdictions may be able to increase sensitivity in local respiratory disease detectors to capture similar cases. the heightened awareness would be useful in prompting public health officials in neighboring or epidemiologically-linked regions to investigate a local event more closely; however, they will be unable to distinguish records that led to the alert beyond the summaries or interpretations provided in the shared information. infoshare infoshare is a tool developed by the johns hopkins university applied physics laboratory (jhu/apl) that allows public health officials to share concerns and preliminary investigation results among multiple jurisdictions and agencies. the tool is designed such that messages/information being shared adequately relay the situation without requiring aggregated or identifiable data to be exposed, yet allowing the user to enter more detailed information if needed to further describe the event. the model promotes the entry of information into structured fields to enable computer readability. this allows the information to be both viewed visually and used systematically by algorithms, decision support, and messaging tools. moreover, the computer readable fields can be automatically populated by other systems to help users generate messages easily and maintain control on the level of information shared. infoshare architecture figure 1. the infoshare architecture consists of two main components: the database and web application. the infoshare database holds the meta-information table, which defines fields infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 included in the current web application. the inaugural instance included fields of message type, age group, sex, size of event, excess cases, latitude, etc. the infoshare web application enables posting of structured fields by allowing users to select the appropriate single or multiple values from drop-down or list boxes. unstructured fields also enable users to enter free text to fully describe the event and to facilitate discussion on the events. users can also attach files (e.g., images, documents, etc.) to the posted event. all information on this web application is communicated securely over secure socket layer (ssl). infoshare web interface create event form figure 2. the infoshare web interface allows end users to enter data related to the event using structured and unstructured fields. it is important to note that the infoshare web interface allows end users to rank the concern level of the event. there are currently five ranks for each event (figure 3). infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 responding – responding to high magnitude/severity outbreak/disease. cluster has been identified or is highly likely investigating – investigating potential outbreak/new disease-clusters monitoring – closely monitoring some unusual patterns noted in data not concerned – no unusual patterns noted in data; no action suggested info – for information purposes only figure 3. the infoshare web interface allows users to rank the level of concern for the event. there are five rankings, ranging from high magnitude outbreaks to informational purposes only. linkage within essence in recent years, it has become increasingly common for public health entities to use electronic disease surveillance systems that employ health-indicator data for timely recognition of events of public health significance and for tracking disease trends across time. the civilian electronic surveillance system for the early notification of community-based epidemics (essence) is one such system used by some u.s. public health departments [7]. to assist with the disease surveillance and outbreak investigations, these systems contain multiple statistical alerting and data mining capabilities. in addition to automated alerting, essence users in particular have the ability to write custom queries of varying complexity into databases for dynamic processing. if a statistical alert is noted, essence users have the ability to view data at a detailed level to characterize the alert more thoroughly and to interpret findings in its proper context. the end product of conducting electronic disease surveillance is a wealth of timely public health information. because community disease patterns and outbreaks are not confined by geographic boundaries, this derived information can be useful to jurisdictions with primary responsibility, and sometimes to adjacent regions and larger jurisdictions as well. with this mindset, infoshare was tested among essence users in spring of 2009 to facilitate simplified information-sharing across public health agencies. to accomplish this, the infoshare website was made accessible via two methods. within essence, ―share‖ buttons were added on specific alerts that enabled users to easily share information about potential health risks (see figure 4). this allowed users to create infoshare messages directly from essence with most of the messages pre-filled with information. alternatively, authorized users also had the option of accessing the infoshare website directly to post and read information. in addition to the website modifications, users in the ncr developed a set of specific queries that they agreed to monitor during the heightened surveillance period. these shared queries utilized the ―advanced query tool‖ feature of essence and utilized free-text chief complaint searches to look for specific diseases such as meningitis, hypothermia, dehydration, and others. infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 figure 4. infoshare was linked to essence with the addition of a new ―share‖ feature. infoshare use during the 2009 inauguration ten days before and after the 2009 presidential inauguration, public health officials in maryland, virginia, the district of columbia, and the centers for disease control and prevention (cdc) participated in information-sharing using infoshare. ncr users monitored their respective populations using emergency room chief complaints, over-the-counter drug sales, aid station reports, poison control reports, and school absenteeism in essence [8]. cdc users monitored the ncr by reviewing military office visits, veteran’s affairs office visits, emergency room chief complaints, and laboratory data via the national biosense surveillance system. cdc users had the ability to securely log into the information sharing website to post messages regarding potential health risks they discovered. users from both systems were able to view and share additional findings using this model for information sharing in a prototype message exchange system (see figure 3) [9]. the web interface supported secure communications over ssl and provided users with the ability to: 1) create events and comments using structured and unstructured fields; and 2) attach additional files (e.g., images and documents). the style of the interface mimicked microsoft outlook and allowed users to view existing events in the upper-right corner, filter those events using the left-side, and view the contents of the selected event in the bottom-right corner (see figure 5). users had the ability to provide comments to listed events and include their own concern level to an event. users could filter the events by any parameter and view a description of the filtered parameters. these quick filters were included on the left-side bar by syndrome categories: botulism-like, fever, gastrointestinal (gi), hemorrhagic-illness, localized lesions, lymphadenitis, neurological, other, rash, respiratory, and sudden illness/death. the infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 aforementioned syndromes were predetermined by expert consensus and may be influenced by several factors such as data source characteristics, surveillance focus area, public health practitioner/agency priorities, etc [10]. inaugural web interface figure 5. the infoshare inaugural web interface allows users to view events posted by other public health officials. infoshare for h1n1 surveillance when the h1n1 outbreaks occurred in spring of 2009, the infoshare system used for the presidential inauguration was easily transitioned for use in h1n1 surveillance. due to the flexible design of infoshare, in less than an hour the application was adjusted to support h1n1 surveillance. along with other changes in the original data fields, the web interface was modified to provide quick filters by organizations instead of diseases. in addition, a user registration capability was enabled to help handle the influx of new users (see figure 6). infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 infoshare for h1n1 web interface figure 6. the web interface continued to support the previous information exchange functions with the quick-filter categories being changed from diseases to organizations, modification of some collected data fields, and the addition of the user registration capability. an email was drafted and sent to many essence installations, including: local health departments, state health departments, department of defense (dod), department of veterans affairs (va), and to the cdc. the email described previous infoshare accomplishments during the presidential inauguration, along with the tool’s capabilities and protocols for use of the site. accounts were created for ten states (wisconsin, washington, california, illinois, indiana, missouri, maryland, virginia, florida, and texas), district of columbia, dod, va, and cdc users. infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 infoshare participants during the 2009 inauguration figure 7. this figure depicts participants nationwide who used infoshare during the 2009 h1n1 surveillance event. during both events in which infoshare was used, a set of collectively accepted protocols were observed by participants:  accessing the infoshare site requires a login and password.  information posted to the infoshare site was only viewable by collaborating partners.  the information contained in a posting was confidential, to be used only by collaborating partners to perform authorized public health functions.  prior to sharing any information with a public health or other entity outside the collaborative group, the posting was first to be verified or clarified by contacting the author and authorized by the public health agency.  information posted on the website was provided for informational purposes only; users were required to recognize that professional interpretations could vary. results with use of infoshare during the 2009 presidential inauguration period, 33 events and 12 additional comments were created. of these events, 30 had a concern level of ―not concerned‖ or ―information,‖ the remaining 3 events were ―monitoring.‖ during this 21 day period, the local essence system was monitoring the same 10 geographic regions, 10 syndromes, and 7 age groups of interest, amounting to 14,700 possible statistical alerts. during this time, infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 essence generated 886 statistical alerts (6% alert rate); 314 were red alerts (representing the highest concern level of an automated detector) and the remaining 572 were yellow warnings (representing a lower concern level). this means that comments were generated on infoshare for 0.3% of the alerts generated by essence. completion of a user’s survey determined that about 63% (10/16) of users utilized the shared queries at least once a day. about 94% (15/16) of infoshare users felt the system had value. approximately 70% (7/10) felt a need for formal inter-agency policies and 75% (12/16) were able to access useful information. the h1n1 infoshare site had 14 events created between 4/30/2009 to 6/25/2009. ten of these events were considered ―information,‖ two were ―monitoring,‖ and two were ―investigating.‖ in addition to the events, there were five comments associated with those events. no formal survey was conducted during or after the h1n1 event. discussion there were many lessons learned from using the infoshare system during the inauguration and h1n1 emergence of 2009. first, it became evident that posting events must be an easy process for public health practitioners. it was important for one to be able to disperse information easily in a time of urgent need. many users were interested in the speedy posting process of the ―share‖ feature because information was pre-populated automatically. second, a protocol document – serving as a reference for users describing the methods for reviewing, posting, and sharing information on the site – was critical. this became the most important factor when the infoshare site was moved from the initial trial during the inauguration to the secondary use of the h1n1 outbreak. having a set of formal rules of use gave many users a higher comfort level with posting initial impressions to users outside their jurisdictions. third, sharing query definitions were found to be valuable during events. in addition, many users found the added communication with regard to outbreaks, whether it be current or potential, to be extremely valuable. this enabled infoshare users to work efficiently, without having to duplicate efforts. another finding was that the infoshare system needed to accommodate non-specific and specific information fields. this capability was required within the ―size of event‖ and ―excess cases‖ fields, which allowed the user to input the magnitude of the event in question. some users wanted to report specific numbers in these fields, while others preferred non-specific reporting. to accommodate both preferences, the user interface provided pull down choices such as 1’s, 10’s, 100’s, 1,000’s, 10,000+, and also allowed the user to type in their own value. during the h1n1 activity period, many users of the h1n1 infoshare application were also involved with the distribute project from the international society of disease surveillance [11]. the distribute project used an email-based listserv for communication. during this period of time, distribute listserv usage increased and a large amount of information was shared between everyone on the listserv. when the issue of using non-secure email to share public health data was raised, some users acknowledged this limitation and admitted that a tool such as infoshare would have been a safer choice. regardless, the popularity gained by distribute during h1n1 and the extent to which it was used highlights the following: ease-ofuse of the information-sharing tool – coupled with its incorporation into day-to-day work practices – is paramount to successful implementation. when information sharing across infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 jurisdictions is purely voluntary, it is difficult for a system such as infoshare to compete with the simplicity of email-based systems, despite known dangers from information loss and theft inherent to such communications. conclusion mass gathering and large event health surveillance are multi-jurisdictional challenges. individual jurisdictions are receiving timely and valuable information using real-time electronic disease surveillance systems, that when merged with neighboring jurisdictions, can significantly enhance situational awareness. this heightened awareness can lead to better decision-making and improved public health outcomes. the infoshare web application was developed by jhu/apl and proved to be an efficient tool for users to share disease surveillance information. public health users shared information within a secure access-controlled website across regions in the u.s. and among agencies. several features were in place that simplified the creation of structured and unstructured entities. by grouping and displaying postings, thereby making them simple to access and easy to process, the user interfaces were particularly user-friendly. the frequency of infoshare use during the events discussed was fair. in addition, post-use surveys revealed that the tool provided a valuable and needed function for a majority of the users. however, reviewing the use of a separate email-based data-sharing tool deployed during the h1n1 outbreaks revealed that infoshare cannot compete with standard email for familiaritybased ease-of-use and simplicity. it was evident also that because of its public health eventfocused design to collect, organize, and display postings, as well as its capability for capturing information in structured and unstructured form, fitting metrics must be used to gauge infoshare’s value for disease surveillance. more importantly, the infoshare application provided a safe forum for sharing disease surveillance information, which if stolen or misused from an insecure environment could have serious consequences. finally, use of infoshare during these public health events brought to light the need for statistical alerting capability of shared postings. hence, future enhancements should include automated analysis of the information posted from standardized and structured fields. acknowledgements this work was made possible through grant number 1u38hk000062-01 from the us department of health and human services (hhs) centers for disease control (cdc). its contents are solely the responsibility of the authors and do not necessarily represent the official views of the cdc. the authors would like to acknowledge the exceptional cooperation of the maryland department of health and mental hygiene (dhmh), the virginia department of health, division of surveillance and investigation (vdh/dsi), district of columbia department of health, bureau of epidemiology and health risk assessment (dcdoh), the eight local health departments represented in the ncr enhanced surveillance operating group (esog), and the biointelligence center (cdc). infoshare – an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 references [1] cifor. council to improve foodborne outbreak response. special considerations for multijurisdictional outbreaks. www.cste.org/dnn/portals/0/cifor%20guidelines%20chapter%207.pdf (last visited may 17, 2010). [2] lombardo js, sniegoski ca, loschen wa, westercamp m, wade m, dearth s, zhang, g. public health surveillance for mass gatherings. johns hopkins apl technical digest 2008; 27(4): 347-355. [3] published by the washington post and available at http://www.washingtonpost.com/wpdyn/content/article/2009/01/21/ar2009012103884.html. (last visited mar. 10, 2010). [4] centers for disease control and prevention (cdc). update: influenza activity—united states, august 30, 2009-january 9, 2010. mmwr. 59(02):38-43. http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5902a3.htm. (last visited mar. 19, 2010). [5] world health organization (who). world now at the start of 2009 influenza pandemic. http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_phase6_20090611/en/in dex.html. (last visited mar. 19, 2010). [6] the health insurance portability and accountability act of 1996 (hipaa). http://www.hipaa.org. (last visited june 25, 2010). [7] lombardo js, buckeridge dl, editors. disease surveillance: a public health informatics approach. hoboken (nj): john wiley & sons, inc.; 2007. [8] lombardo js. the essence ii disease surveillance test bed for the national capital area. johns hopkins apl tech dig. 2003;24(4):327-334. [9] loschen wa. methods for information sharing to support health monitoring. johns hopkins apl technical digest 2008; 27(4): 340-346. [10] lombardo j, burkom h, elbert e, magruder s, lewis sh, loschen w, et al. a systems overview of the electronic surveillance system for the early notification of community-based epidemics (essence ii). j urban health. 2003;80(2):i32-i42. [11] distribute project – influenza surveillance system. http://isdsdistribute.org/ (last visited june 16, 2010). correspondence: wayne, loschen, ms wayne.loschen@jhuapl.edu http://www.cste.org/dnn/portals/0/cifor%20guidelines%20chapter%207.pdf http://www.washingtonpost.com/wp-dyn/content/article/2009/01/21/ar2009012103884.html http://www.washingtonpost.com/wp-dyn/content/article/2009/01/21/ar2009012103884.html http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5902a3.htm http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_phase6_20090611/en/index.html http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_phase6_20090611/en/index.html http://www.hipaa.org/ http://isdsdistribute.org/ appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care rosann o’dell 1 1 a. t. still university, arizona, usa abstract health care consumers increasingly obtain health information from the internet to inform their health care; the health care consumer, who also has the role of patient, maintains the right to access information from sources of their choosing for this purpose. however, noteworthy considerations exist including information appraisal skills, health literacy and the patient-provider relationship. awareness and education are warranted to assist the health care consumer in achieving proficiency as they turn to the internet for health information. keywords: consumer health information, patients, internet, usability of health information introduction the current internet landscape offers health care consumers copious websites devoted to providing health information. a potential benefit resulting from consumer access to health information on the internet is the possibility that patients become more informed about their health and associated care decisions. in addition, it would seem that a patient armed with health information could ideally become a more active participant in their health care. noteworthy considerations exist, however, including information appraisal skills, health literacy and the patient-provider relationship. the health care consumer and landscape of internet-based health information consumers may feel empowered when they turn to the internet for health information. despite potential empowerment related to this type of information seeking, a culture of omission on the part of health care consumers exists in which they neglect to inform their physician about health information they have reviewed. the former is problematic because health care consumers obtaining health information from the internet too often rely on this information to formulate a first opinion about their health; when they present themselves as patients to their physicians, they are ultimately considering the professional medical advice they receive as second opinions (1). appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 this article defines health care consumer as individuals seeking and obtaining health care in regions of the world where freely exercised consumerism exists. in addition, this definition assumes that the internet is available to these consumers and sources of information on the internet are not restricted. health care consumers have become active participants in their health care, resulting in a patient population seeking more information about diagnoses and treatment options. it is suggested that in fact, health care consumers are largely encouraged and often expected to be well informed and responsible (2). current literature demonstrates that consumers are obtaining health information from online sources (3 – 8). with online health information readily available and a consumer population turning to these sources, it seems relevant to examine noteworthy considerations that result from this phenomenon. while health care consumers deserve respect for their right to obtain information to inform their care, challenges exist within this context. health information defined, for the purpose of this article, is information about diseases and medical conditions, as well as their diagnostic, prognostic and treatment considerations available on websites hosted by for-profit and non-profit organizations, as well as government agencies. the internet has evolved into a commonly used tool for those seeking all types of information. therefore, it should be no surprise that health information is widely available to health care consumers through this medium. in recent years, there has been an increase in health-related websites (2). through abundant sources offering health information via the internet, such information is readily available to health care consumers. the quality of online health information the quantity of health information available online is increasing. whether consumers seek information related to medications, treatments and/or diagnoses, it is worthy to question if the information sought to inform their health care is valid. it is noteworthy that online health information as described in this article is generally non-refereed. health care consumers too often lack the ability to accurately assess the quality of online health information, yet explore this type of information in isolation as a means render a diagnosis (1). reliable health information may enhance the ability of health care consumers to be knowledgeable participants in their own care; however, the quality of online health information, as argued by some scholars, is questionable (2, 3, 9). websites offering health information to consumers may also display a variety of information. for example, some of the information may be useful and valid, yet there are often links for advertisers and sponsors; meanwhile, the website may be constructed in a fashion that makes it difficult for the lay consumer to discern where reliable health information ended and an advertisement for a product began (1). the accuracy and appropriateness of online health information is worthy of consideration. certainly, some sources of health information on the internet offer more credibility; academic institutions and governmental websites come to mind as sources that are more authoritative. despite the existence of trustworthy sources offering health information to consumers, many individuals lack the ability to discern credibility among sources of online health information. appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 indeed, health care consumers exist within the celebrated principle of autonomy. as capable patients, they can make health care decisions based on whatever information they choose. however, this fact does not eradicate concern from the equation. considering varying quality among health information on the internet, the challenge to health care consumers becomes their ability to fine-tune their information appraisal skills. appraising internet-based health information compounding the issue of quality (or lack thereof) of online health information, the information appraisal skills of health care consumers is lacking. one study (4) examined the influence of inaccurate online health information. in their study, 59 percent of participants believed they could locate websites with accurate health information when in fact, the websites contained inaccurate information. while the population examined in this study was of secondary school age, their experience is still noteworthy because they are of ideal age to utilize technology. other researchers (10) examined consumer experiences with seeking online health information. their study concluded that lay individuals do indeed experience difficulty conducting online health information searches. the authors point out that access to health information does not necessarily empower health care consumers. in terms of obtaining and evaluating online health information, difficulties exist for lay individuals. another research study (8) notes that the appraisal skills of health care consumers are important because it reflects their ability to select and assess online health information pertaining to medications. the researchers noted that opinions on credible sources of information varied among participants. many respondents reported limited awareness on how they found and evaluated online health information. the researchers found that online health information not subject to quality control further challenged appraisal skills of respondents. adding to the complexities arising from non-referred internet sources providing health information and consumers challenged with comprehending such information, certain demographic characteristics may place some health care consumers at unique risk. another published study (6) found that socioeconomic status influenced the ability for patients to determine credible sources of online health information. those of higher socioeconomic status demonstrated greater ability than those of lower socioeconomic status to evaluate sources of online health information for trustworthiness. if certain health care consumers are already challenged in their pursuit of health care services, but also cannot adequately inform themselves, they may experience disenfranchisement in their role of health care consumer. with the literature suggesting that health care consumers generally lack appropriate information appraisal skills, health care consumers may find themselves at risk. there are potential consequences when inaccurate health information informs crucial health care decisions; for example, compromised safety and long-term health of a patient is possible. in addition, patients experience insecurity related to discussing medical information with skilled health care providers and fear that they will appear as a “cyberchondriac” in the eyes of their physician (1). a continued environment in which patient’s desire empowered consumerism, yet hesitate to engage appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 in information sharing with their health care provider, is one in which unknown and misunderstood aspects of the patient-provider relationship exists. views from physicians limited research has examined views of health care providers in regards to patient access to internet-based health information. while provider views on the issue of patients presenting for health care services with internet-based health information is not implicit, some research exists. available research suggests that physicians recognize the reality of health care consumers obtaining health information from the internet, for example. however, physicians report concerns about this phenomenon (2, 7). views by physicians on this topic are relevant because internet health information has the ability to influence patient safety, health care quality and health outcomes, as well as the patient-provider relationship. a group of researchers (11) sought to understand the influence of online health information on routine medical consultations from a physician perspective. their study found that physicians may perceive online health information as problematic. specifically, the participants felt that online health information contributed to misinformed patients, confusion, distress or patients being inclined to self-diagnose and self-treat. aside from the risks to the safety and well-being of patients as expressed in the previously mentioned study, the potential challenge to patient-provider relationships is a worthy consideration. another group of researchers (7) studied the influence that online health information has on physician-patient relationships. the researchers reiterated that the quality of online health information is paramount. while they acknowledged that accurate and relevant information is beneficial, that which is inaccurate is harmful. their findings concluded that physicians acquiesce to requests by patients because of the patient obtaining online health information supporting their requests; participants suggested that they were even likely to acquiesce in instances where there was potential harm to the patient. physicians did this, in part, due to the fear of damaging their relationship with their patient. in their medical education and training, physicians are not necessarily prepared to educate and mentor patients into the role of informed health care consumer. because many patients neglect to inform their physician about health information they view prior to office visits, the views of physicians and their understanding of the uses of online consumer health information are increasingly relevant. health literacy health care professionals and advocates should have an interest that health care consumers can obtain quality health information, appraise it and use it as a tool to enhance their role as a member of the medical team who is equipped for constructive participation in conversations about their health care. appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 lacking health literacy among adults receives attention in current literature. most patients lack knowledge, experience, and subject matter objectivity related to health information; the former attributes are associated with lacking health literacy skills (1). succinctly defined, health literacy is the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions (12). low health literacy is problematic and presents challenges to health care quality and outcomes (13, 7). the literature has reported on health literacy, as well as multidimensional factors resulting in health literacy challenges (15, 16, 17). regardless which factors we consider, inadequate health literacy compounds the issue of lacking information appraisal skills among health care consumers. health literacy also provides another consideration in the context of the changing patientprovider relationship resulting from health care consumers increasingly active in their own health care. the need to enhance the role of patients as members of the health care team warrants awareness and education to ensure optimal information appraisal skills and improved health literacy for health care consumers. awareness, education and furthering the conversation in an environment where health care consumers turn to various sources on the internet to inform their health care, information appraisal skills and health literacy concerns exist. in addition, the engaged role of patients continues to modify the patient-provider relationship. increasing the understanding of these issues warrants education for health care consumers. health care professionals may be familiar with the previously mentioned issues, yet health care consumers may not. while government agencies often play a role in informing the public about relevant health care topics, the influential nature of these issues deserves due diligence from health care organizations and private practitioners to inform health care consumers on ways to be successful self-advocates and informed consumers. . at all levels of health care delivery, solutions advancing awareness and education for the health care consumer may include (but are not limited to) promotional/informational print pieces intended for the health care consumer, online tutorials/courses, classroom style presentations and focused conversations with patients. physicians and other primary health care providers can also provide education to their patients during health care encounters. these professionals can offer clarification on the differences between symptoms and diagnoses, as well as suggest credible sources of online health information (1). in addition to introductory work on the awareness and education front, conversations and research in academia are necessary. in the long-term, the ability to assess awareness promotion and education efforts for their relevance and success is important. these efforts will indicate necessary areas for improvement and proven best practices to ensure the health care consumer population better understands seeking and obtaining credible health information on the internet, as well as considerations in terms of using this type of information when making health care decisions and interacting with physicians and other health care professionals. appraisal skills, health literacy and the patient-provider relationship: considerations as the health care consumer turns to the internet to inform their care 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 conclusion patients are autonomous agents. the vast reach of the internet provides health care consumers with easy access to health information. health information hosted on the internet comes from a variety of sources with varying intents. the ability for health care consumers to embrace the role of informed patient is important; in many regards, this is already occurring and could very well be the norm in the future. given this trend, it is important to consider existing issues within the context of health care consumers turning to the internet for health information to inform their health care. currently, information appraisal skills, health literacy and the evolving patient-provider relationship bring about reasons to consider how we can help health care consumers achieve competence in locating, assessing and using internet-based health information. at a minimum, awareness and education geared toward the health care consumer may help individuals achieve proficiency in terms of obtaining health information, assessing it for credibility and using it appropriately to inform their care; ultimately, improving these skills has the ability provide results that are more favorable for the consumer in their patient care experiences. furthermore, ongoing discourse and research in academia will help ensure that these experiences can only improve for the health care consumer. corresponding author rosann o’dell student, doctor of health sciences program a. t. still university, arizona, usa email: rodell@atsu.edu references 1. gualtieri, l. (2009). doctors as the second opinion and the internet as the first. chi april 4 – 9, 2009. boston, ma: usa. 2. alpay, l., verhoef, j., xie, b., te’eni, d. and zwetsloot-schonk, j. (2009). current challenge in consumer health informatics: bridging the gap between access to information and information understanding. biomedical informatics insights, 2(1): 1-10. 3. laurent, m. and vickers, t. (2009). seeking health information: does wikipedia matter? journal of the american medical informatics association, 16(4): 471-479. 4. kortum, p., edwards, c. and richards-kortum, r. 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(2010). the literacy divide: health literacy and the use of an internet-based patient portal in an integrated health system – results from the diabetes study of northern california (distance). journal of health communication, 15(supplement 2). doi: 10.1080/10810730.2010.499988 ojphi-06-e34.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 45 (page number not for citation purposes) isds 2013 conference abstracts development of an r package for syndromic surveillance: translating research into tools for the veterinary epidemiologist fernanda c. dórea*1, stefan widgren1, ann lindberg1 and crawford w. revie2 1swedish zoonoses centre, national veterinary institute, uppsala, sweden; 2atlantic 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$ 3� ����&')'-�1",#d�(1,,� &��$���� �.*��� �@*�. +�e*�: �� � � �;*�$ �� �� �a*�f g����>*�! � �� ����:��b�� �����!� ����b%����/������ �� ���������� �3��%���� ���� c����� �a ��������� ������ ���� ���/�� ������� ��/�������?� ���%���� � ���������� �� ���?:.�:������ ���/�����: ��&'))-�))d16� *cynthia a. lucero-obusan e-mail: cynthia.lucero@va.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e151, 2014 assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi assessing the ehealth literacy skills of family caregivers of medically ill elderly ali soleimaninejad1, saeideh valizadeh-haghi2*, shahabedin rahmatizadeh3 1 student research committee, faculty of public health and safety, shahid beheshti university of medical sciences, tehran, iran. 2 department of medical library and information sciences, school of allied medical sciences, shahid beheshti university of medical sciences, tehran, iran. 3 department of health information technology and management, school of allied medical sciences, shahid beheshti university of medical sciences, tehran, iran. abstract objectives: the purpose of current research is to assess the ehealth literacy level in the family caregivers of the elderly with hypertension and type-ii diabetes. methods: a total of 160 caregivers completed the eheals questionnaire. the effect of participants' gender, education, and age on ehealth literacy was evaluated. for evaluation of the correlation between the accession of health information importance and the internet usefulness for decision-making, spearman’s correlation coefficient was applied. results: the participants ehealth literacy mean score was 26.163(sd=8.83). the age of participants had a meaningful impact on the level of ehealth literacy (t=6.074; p<0.001). furthermore, among variant education levels in terms of ehealth literacy score significant differences existed (f=5.222; p=0.001). discussion: the family caregivers have a poor level of ehealth literacy. ehealth information is more important for family caregivers with a higher ehealth literacy, which may be due to their higher skills in obtaining health and medical information from the internet. caregivers' age should be considered once recommending them for the internet using to obtain health information, as the age was an affecting factor. conclusion: health centers and authorities in charge of the elderly health are recommended to train caregivers with proper skills to use online health information, such that the elderly enjoy the benefits, including improved care conditions and savings in terms of treatment costs and time. keywords: ehealth literacy; family caregivers; online health information; consumer health information; patient education, ehealth, health information, elderly, aged abbreviations: electronic health (ehealth) *correspondence: saeideh valizadeh-haghi, email: saeideh.valizadeh@gmail.com doi: 10.5210/ojphi.v11i2.10149 mailto:saeideh.valizadeh@gmail.com assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi introduction the dramatic increase in the world's elderly population and occurrence of chronic debilitating diseases such as hypertension [1] and diabetes [2] among others have forced the elderly to use nursing care and become homebound [3,4] and have made the care requirements of this population group inevitable. diabetes and hypertension are two major risk factors for cardiovascular diseases in the elderly and adult population [5,6]. due to their chronic and debilitating nature and since they require lifelong care, these diseases badly affect the patients' quality of life [7]. moreover, the family assumes an important part of the responsibility of elderly care, including the continued intake of medications, the interpretation of medical information, following up on future visits, visiting specialists regarding their health issues and making decisions about using health services [8,9]. in fact, families assume more than 80% of the responsibility for any care the elderly need [10]. the main reasons for the emergence of the phenomenon of family care include the increase in the elderly population and their affliction with chronic diseases, changes in the health system, especially regarding the health of family caregivers, and the rising costs of hospitalization. nevertheless, families are faced with challenges in providing services and taking care of the elderly at home. researches conducted on the family caregivers of the elderly mostly confirm that promoting caregivers' knowledge and information and increasing their understanding of old-agerelated problems and the optimal ways of dealing with them reduce caregivers' occupational stress and increase their motivation to provide the elderly with quality services [11]. one of the ways for training caregivers is through electronic technologies and the internet. the internet is one of the information sources used by caregivers to obtain health information. the health information available on the internet can have a major role in caregivers' decision-making about the provision of better care services to the elderly. moreover, using the health information obtained from this medium improves caregivers' mental health [12] and can be effective in the process of elderly's treatment and help family caregivers better manage their provision of health and medical services to the elderly. caregivers' internet access and use seem to be affected by demographic and socioeconomic factors. in particular, the likelihood of using health information available on the internet is increased in young and educated caregivers with a higher income who spend less time on caregiving [13]. an important point about obtaining information through the internet is that inadequate skills in searching for and understanding health information impede optimal access to online/electronic information or lead to their inappropriate use [14]. the proper use of the internet requires the ability to read, use the computer, search for information and understand and utilize the health information gathered [15]. what matters in the use of electronic health information sources is electronic health literacy (ehealth literacy), which has been characterized as "the individual's ability to search, find, understand and evaluate health information from electronic sources and use this knowledge to solve health problems" [14,16]and includes computer literacy, information literacy, media literacy, health literacy, traditional literacy and scientific literacy [17]. electronic health literacy can help establish better communication between service providers and copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi patients and facilitates the access to health and medical care [18]and ultimately improves individuals’ health [19,20]. according to various studies, people with inadequate skills to search for ehealth information will have difficulty in evaluating the quality of such information [21,22]. the health information provided by health websites are of different reliability and quality [23-25]and people have different abilities in understanding the reliability of web-based health information and properly using such information [26,27]. therefore, attention to people's ehealth literacy is highly important, because the health information available on the internet can affect people's self-care and health-related decisions and lead to doctor visits or the seeking of consultation about health matters in 12%-14% of the cases [28,29]. as a result, proper understanding of people's capability to identify and correctly usage of credible online information is mandatory by heath specialists. to do so, prior to advising their patients to obtain health information on the internet, the ehealth literacy of the patients should be evaluated. in asian countries, including iran, family caregivers have a major share of the care provided to the elderly. in line with other countries, iran is also experiencing a growing phenomenon of aging [30]. according to iranian beliefs, caring for the elderly is a sacred endeavor [31]. an estimated 80% of the patients with chronic diseases are cared for at home only by a family member, friend or relative [32]. diabetes and hypertension are among the most common diseases of old age. cardiovascular complications are the most common causes of death in the elderly, and this rate increases as blood pressure rises [33,34]. given caregivers’ need for information about elderly’s diseases and the use of the internet as a source of information [35], it is important to understand the level of family caregivers' ehealth literacy and its contributing factors. nevertheless, in this age of modern technologies, there seems to be a research gap between caregivers’ ehealth literacy and their skills. thus, the current research was performed to (a) determine the level of ehealth literacy in the family caregivers of the elderly with hypertension and type-ii diabetes, and (b) assess the importance and usefulness of access to ehealth information in view of family caregivers and determine the factors affecting the level of ehealth literacy in this population group. methods in the present cross-sectional study, the family caregivers of the elderly covered by the health base of varamin were selected from april to june 2018 through purposive sampling. the city of varamin is the administrative center of varamin township in tehran province located in the southeast of tehran city and has a population of about 225000. varamin health center is responsible for providing the residents of this city with all types of primary health services. to conduct this research, first, all the elderly with hypertension and type-ii diabetes visiting this center for receiving health and medical services were assessed in terms of having a family caregiver. then, all their family caregivers were assessed in terms of meeting the standards of: living with the elderly, their familiarity with the internet, willingness to take part and absence of severe physical deficiencies (blindness, deafness and other physical or mental disabilities. a total of 160 caregivers (aged 18 to 60 years) were eligible and selected as the study subjects in the study. many researchers divide adults into three age ranges, including young adults (under 40), middle adults (over 40) and older adults (over 65) [36]. thus, regarding the participants` age range in this assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi study (aged 18 to 60 years), they were divided by age into two groups including middle adults (over 40 years old) and young adults (below 40 years old). data collection tools data were collected using the ehealth literacy scale (eheals), which measures the individual's skills and confidence in the internet using to gather health-related information [15]. the eheals comprises 8 items scored according to the 5-point likert scale (from ‘completely disagree’ =1 to ‘completely agree’ =5), with the range of total score from eight to 40 and greater scores representing a ehealth literacy higher level [15]. the “importance of access to the health information available on the internet” and “the usefulness of the internet in health decisionmaking” are two supplementary items of eheals scale which are designed to assess the participants’ general interest in using ehealth information. these items are scored based on a 5point likert scale (from ‘totally useless’ =1 to ‘totally useful’ =5), with the total score ranging from 2 to 10 [15]. the eheals internal consistency was evaluated and approved with 0.82 cronbach's alpha, that agrees with other studies were reported [15,37,38]. the researchers distributed the eheals questionnaire in printed format among the research population. to observe the ethical principles, all the questionnaires were anonymous, and informed consent was acquired from all the participants, who were also ensured of the confidentiality of the data and the researchers’ compliance with ethical principles. current research has been approved by ethics committee of sbmu (ethics code: ir.sbmu.retech.rec.1397.389) data analysis the data analysis was performed using spss-18. the effect of participants' gender, education, and age on ehealth literacy was evaluated. by absolute and relative frequencies, the qualitative variables were defined and by the standard deviation and mean the quantitative variables were described. the ehealth literacy mean scores were compared with gender and age by the independent t-test and with education by the anova. the association of "usefulness of the internet for decision-making", "the importance of access to ehealth information", "ability to differentiate between quality and reliable online health information resources and poor-quality and unreliable ones", and "adequate self-confidence in using online information for medical and health decision-making" with gender and age was measured by mann-whitney’s test and with education was measured by the kruskal-wallis test. the spearman correlation coefficient was applied for evaluation of the correlation between importance of access to health information and the usefulness of the internet for decision-making. in all tests, a significant level of 0.05 was considered. data were described using mean, standard deviation and frequency indices. results: the majority (61.9%) of the participants were female, and only 22.5% had a different level of education at the university. most of these family caregivers (66.3%) were younger than 40 years (table 1). assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi table 1: demographic characteristics of study participants (n=160) demographics n (%) gender female male 99 (61.9) 61 (38.1) education high school dropout high school graduate associates degree bachelor’s degree master’s degree 71 (44.4) 53 (33.1) 18 (11.3) 16 (10.0) 2 (1.2) age group 18-40 >40 106 (66.3) 54 (33.8) participants' mean score of ehealth literacy was 26.163 (sd=8.83). given that the maximum attainable score in this questionnaire is 40, this finding shows that the family caregivers had a poor level of ehealth literacy. in the present study, the question "i am able to differentiate quality and reliable health information sources from poor-quality and unreliable internet sources” obtained the lowest score (3.138±1.130), which shows that participants' biggest problem was differentiating quality sources from poor-quality ones (table 2). the ehealth literacy level was also measured by gender. the findings showed that although the female caregivers had higher ehealth literacy scores (26.81±8.96) compared to the male caregivers (25.09±8.56), this difference was not statistically significant according to the independent t-test (t=1.198; p=0.233). table 2: the mean and standard deviation of the score of each item of eheals ehealth literacy scale mean sd median i know what health resources are available on the internet 3.394 1.122 4 i know where to find helpful health resources on the internet 3.231 1.011 3 i know how to find helpful health resources on the internet 3.181 1.069 3 i know how to use the internet to answer my questions about health 3.250 1.076 4 i know how to use the health information i find on the internet to help me 3.188 1.065 3 i have the skills i need to evaluate the health resources i find on the internet 3.194 1.067 3 i can tell high quality health resources from low quality health resources on the internet 3.138 1.130 3 i feel confident in using information from the internet to make health decisions 3.150 1.030 3 differences in the internet using for decision-making, the capability to discriminate between reliable and unreliable resources of online health information and the self-confidence to use the online information to make health decisions were assessed between the two genders using mannwhitney’s test. the results demonstrated that though men obtained lower mean scores than women in the noted items, the difference among them was not statistically meaningful (table 3). assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi the difference in mean score of the importance of access to ehealth information between the two genders was also assessed by mann-whitney test and the results showed that the average score of men (1.204 2.87) was lower than women (1.091 ± 3.25) and this result was statistically significant (z = -1.98, p-value = 0.048). table 3: the importance of access to ehealth information and the use of the internet for decision-making by gender p-value mean score ± std. median variable male female 0.048 2.87±1.204 3 3.25±1.091 3 the importance of access to ehealth information 0.498 3.30±1.418 4 3.52±1.190 4 the usefulness of the internet in decision-making 0.912 3.13±1.231 3 3.14±1.069 3 the ability to differentiate between reliable and unreliable online health information resources 0.247 3.02±1.118 3 3.23±0.967 3 self-confidence in using information from the internet for medical and health decision-making the results of the one-way anova showed a significant difference between the different education groups by ehealth literacy level (f=5.224; p=0.001). the scheffe post-hoc test was used to follow-up on the differences between the education groups. its results demonstrated that the mean ehealth literacy score was significantly lower in the high school dropout group (23.04±7.69) compared to the high school graduate group (28.01±10.62; p=0.03) and the group with a bachelor's degree (30.93±5.60; p=0.02). furthermore, the kruskal-wallis test results revealed the significant differences between the different education groups in terms of the importance of access to ehealth information, the ability to differentiate reliable and unreliable online health information resources and the self-confidence in using the information available on the internet to make health decisions (table 4). assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi table 4: the ehealth literacy level, importance of internet access, ability to differentiate and self-confidence to use the information available on the internet based on participants' education level pvalue mean score ± std. median variable master’s degree bachelor’s degree associates degree high school graduate high school dropout 0.001 36.00±5.657 36 30.94±5.603 32 27.67±4.899 28 28.02±10.623 28 23.04±7.696 22 ehealth literacy level 0.034 4.50±0.707 4 3.69±1.138 4 3.17±0.985 3 3.19±1.093 3 3.28±3.765 3 the importance of access to ehealth information 0.144 5.00±0.00 5 3.63±0.885 4 3.50±1.383 4 3.55±1.280 4 3.24±1.325 3 the usefulness of the internet in decisionmaking 0.016 4.50±0.707 4 3.75±0.856 4 3.28±0.958 3 3.21±1.044 3 2.87±1.218 3 the ability to differentiate between reliable and unreliable online health information resources <0.001 4.50±0.707 4 4.06±0.860 4 3.11±0.900 3 3.13±1.057 3 2.93±0.990 3 self-confidence in using information from the internet for medical and health decision-making the independent t-test was used to calculate the mean ehealth literacy score in both the age group above 40 and the group aged 40 and less. the results showed that the mean ehealth literacy level in the group aged 40 and less was significantly higher (8.336±28.896) compared to the group above 40 age (7.212±20.796); (t=6.074; p<0.001). discussion the ehealth literacy impact on the health outcomes have been confirmed in several researches. knowledgeable people make better use of the medical facilities available to them and less frequently request medical tests [39]. a good knowledge about health and make better efforts to identify and perform screening tests, is probably associated with high level of ehealth literacy [40]. given that family caregivers have a major role in ensuring the provision of health services to the elderly, the current research was conducted to evaluate ehealth literacy in the family caregivers of the elderly with hypertension and type-ii diabetes. assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi the current results demonstrated that family caregivers have a poor level of ehealth literacy (table 4). a poor level of ehealth literacy was also reported in similar studies conducted on different groups, including the students [22] and patients referring to a military hospital [41]. nonetheless, a study conducted on dental patients showed a good level of ehealth literacy among this group [26]. further studies on different groups of the population are thus necessary. the present findings also showed that the caregivers of the elderly have deficiencies in differentiating quality health information from poor-quality and their use to make decisions, which is the main factor involved in the poor ehealth literacy of the study population (table 2). this result concurs with the results obtained in studies by park [21], dashti [22] and tubaishat [42]as well as many other studies [21,43]. since the family caregivers of the elderly use the internet to obtain health information, it is essential for this group to acquire the necessary knowledge about searching and assessing online health information, so that the risks of using incorrect medical information can be eliminated, because using incorrect information may increase the economic burden on the patients and health systems. it is also necessary for health professionals to raise elderly caregivers' knowledge and help them in identifying reliable information sources. experts should also introduce websites containing quality health-related information to caregivers, so that they can use the information provided through reliable and quality websites to provide the elderly with care services. the present study also showed that access to ehealth information is more important for family caregivers with a higher ehealth literacy, which may be due to their higher skills in obtaining health and medical information from the internet. such an approach, and people's increasing dayto-day need to use the internet, especially in the health sector, necessitate experts’ adoption of strategies to improve the access to the internet. the relationship between the study variables and ehealth literacy in the family caregivers of the elderly: this part of the study presents the factors related to ehealth literacy in the family caregivers of the elderly with hypertension and type-ii diabetes. variables including age, gender and education were assessed in this study. the present research revealed a significant relationship between age and the ehealth literacy level, such that the group above 40 age had lower ehealth literacy compared to the group aged 40 and less. this finding may be since compared to middle adults, access to digital media is easier for young adults and they are more experienced in surfing the internet (7). this finding are in line with a study results on ehealth literacy level of greek citizens as well as the results of a study on ehealth literacy of dental patients [26,44]; however, some other studies did not find this relationship to be significant [21,42]. according to the present findings, further studies are required to ascertain the relationship between age and ehealth literacy. hence, while advising the caregivers to use the internet, their age (especially in the case of the age group above 40 age) should be considered and encourage them to consult the elderly physicians before using e-health information to make decision on elderlies’ health. it does not emphasize that direct education for the elderly should be ignored; instead, elderly people should be encouraged to speak with their caregivers about electronic health information and use this information. assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi as for participants' gender, the present study showed that although female caregivers had higher ehealth literacy scores than male caregivers, the difference among them was not statistically significant, which agrees with the results obtained by park et al. [21]. the present study also revealed that using the internet to make health-related decisions was more important to women than men, but it was not statistically significant (table 3). furthermore, difference among the gender groups in terms of “the importance of access to ehealth information” was statistically significant (table 3). it demonstrates that access to internet is more important for women rather than men. it should be noted that more studies are needed to ascertain the relationship between gender and their tendency to apprise health information in decision making. given this importance and regarding that women have a greater role in searching for health-related information [21], the female caregivers of the elderly ought to receive more training on how to search for information and its proper use. moreover, since women tend to prioritize the needs of other family members over their own and have a greater tendency to give care and assume responsibility [45], women's role should be particularly emphasized in planning elderly care. the current research showed a significant relationship among the different education groups by the ehealth literacy level (table 4). the higher ehealth literacy level was associated with higher education level. this finding is in line with the finding of a study on baby boomers and older adults [46]. as expected, in this study, people with higher education levels were better able to differentiate quality from poor-quality health information sources (table 4), which concurs with the results of other studies [42,43,47]. in a study conducted by hanik et al. on master's degree students, the senior students had a higher ehealth literacy compared to the first-year students, which confirms the positive effect of education on ehealth literacy [48]. electronic health literacy level therefore seems to be associated with the level of education. caregivers' education level should therefore be considered when advising them to use online health information and when giving instructions on the proper use of this information. it should be noted that the result of this study is based on selfreport abilities and further research is needed to understand the actual ehealth literacy skills of caregivers and its correlation with education level. the relevant health and medical organizations should take actions such as providing infographics on searching for health information and introducing books on how to search for information on the internet as well as introducing reliable websites to caregivers so that they can provide the elderly with health services and thus improve their quality of care. conclusions the present study showed that the family caregivers of the elderly had a poor level of ehealth literacy. since, in the modern times, health information technology is expanding, ehealth literacy is regarded as a very important part of health literacy. identifying and evaluating the level of ehealth literacy in the family caregivers of the elderly may be an effective measure in advancing the strategies for improving the quality of life of the elderly. moreover, ehealth literacy can increase family caregivers' ability to access information about common old-age diseases such as hypertension and type-ii diabetes and enable them to manage the challenges of daily living assessing the ehealth literacy skills of family caregivers of medically ill elderly online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e12, 2019 ojphi experienced by the elderly. experts also believe that one of the practical strategies that family caregivers, especially children, can employ to help the elderly with chronic diseases is to provide them with information, which can enhance the independence and quality of life of the elderly [49]. the authorities in charge of elderly health are therefore recommended to familiarize caregivers with proper ways of using online health information. so that both the elderly and their families may reap some benefits, including improved care conditions and savings in terms of treatment costs and time. limitations the present study had a number of limitations. first, it was conducted in one of the health centers in the province of tehran; conducting the same study in other centers is likely to present different results. in addition, the present study used eheals, which is a questionnaire that determines participants' ehealth literacy through self-reporting, and the respondents’ actual performance may have differed from their self-reported skills. finally, conducting the same type of study in other cities with larger sample sizes can complement the present findings. conflicts of interests no conflicts of interest. acknowledgements this study is related to the project no. 1397/44121 from student research committee, shahid beheshti university of medical sciences, tehran, iran. we also appreciate the “student research committee” and “research & 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reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts tracnet: a national phone-based and web-based tool for the timely integrated disease surveillance and response in rwanda kayumba kizito*1, kabeja adeline2, koama jean baptiste3, asiimwe anita2, binagwaho agnes4, pamela johnson5 and nyatanyi thierry2 1voxiva sarl, kigali, rwanda; 2rwanda biomedical center, kigali, rwanda; 3cdc rwanda, kigali, rwanda; 4ministry of health, kigali, rwanda; 5voxiva inc, washington, wa, usa objective (1) to describe the implementation of the electronic system for integrated disease surveillance in rwanda. (2) to present the sensitivity and specificity of the electronic reporting system to detect potential outbreaks introduction in rwanda, communicable diseases are the mostly predominant representing 90% of all reported medical consultations in health centers. the country has often faced epidemics including emerging and re-emerging infectious diseases. to enhance its preparedness to identify and respond to outbreaks and prevent epidemics, the government of rwanda has developed and deployed an electronic integrated disease surveillance and response (eidsr) working with voxiva with funding from the u.s. centers for disease control and prevention(cdc). methods the eidsr is built on rwanda’s existing national phone and webbased hiv-reporting system, “tracnet” that has been operating nationwide since 2004. data is collected for 23 communicable diseases under surveillance in rwanda categorized into immediately and weekly reportable. if a lab test is required, the sample is taken and sent to laboratory for testing. immediate, weekly, lab request and lab results forms are completed before submitting data in the system. data is entered using phone or web based application and is stored in the central database. results the design of eidsr module was completed in november 2011. as of september 2012, 252 out of 457 health facilities in rwanda have been trained and are using the electronic system (over 50% of coverage); the national roll out is still going on with complete coverage planned for december 2012. the system sends sms reminders for due and overdue reports. the timeliness and completeness of reporting are 98% and 100% respectively. notifications are sent to the concerned personnel when the threshold for outbreak detection is reached. when lab results are available and entered in the system, the results are automatically communicated to the health centers originating samples. data is automatically summarized in predefined tables, graphs, dashboards and maps. as of september 3rd, 2012, a total of 5813 reports including 1325 immediate reports and 4488 weekly reports were submitted electronically. out of 1325 immediate reports submitted, 406 potential outbreaks were detected and immediately notified and 7 of them were confirmed for cholera, rubella, influenza-like illness (h1n1), measles and food poisoning. from these data, the eidsr system shows a sensitivity of 100% and a specificity of 70% for outbreak detection. the early notification of probable outbreaks stimulated the early investigations and the quick response to outbreaks within the country and across the borders. conclusions the electronic disease surveillance system has improved timeliness and completeness of reporting and extremely supports early detection and notification of outbreaks for timely response. this system should be a model for the east african region as it has demonstrated advantages in the cross-border disease surveillance. keywords disease surveillance; informatics; m-health acknowledgments the government of rwanda for having adopted ict as pivotal for development. moh/ rwanda biomedical center for promoting innovations in health sector including m health technology. the us centers for disease control and prevention (cdc) for funding tracnet under the cooperative agreement. voxiva &other stakeholders who worked to implement tracnet: nrl, hiv division, eid division, sph, icap, kist, mtn. all partners in ict: rapid sms, jembi, pih. references a. kabeja: eidsr in rwanda. east africa public health laboratory network project workshop, kigali, rwanda (april 2012). box, g. e. p.; jenkins, g. m.; and reinsel, g. c. (1994). time series analysis, forecasting and control. prentice-hall, englewood cliffs, nj. harris, t.j. and ross, w.h.: statistical process control procedures for correlated observations. the canadian journal of chemical engineering, 69, 48-57. (1991) *kayumba kizito e-mail: kkayumba@voxiva.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e202, 2013 ojphi-06-e55.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 54 (page number not for citation purposes) isds 2013 conference abstracts newborns with delayed hearing screening prior to hospital discharge: high risk populations of hearing loss mahmoud a. gaddoury*1, tri tran2, 3, mary jo smith2, jeanette webb2, terri mohren2 and melinda peat2 1tulane univrsity, new orleans, la, usa; 2louisiana dhh oph cshs early hearing detection and intervention program, new orleans, la, usa; 3lsuhsc school of medicine, department of pediatrics, new orleans, 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(http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 189 (page number not for citation purposes) isds 2013 conference abstracts ��������� ��� ���,������) ��� �������������� �7���������������� ����������,������7���������������� ���!��������7"���#����$������!� !��������������%&&'�)� ��(����#��)���*����� �����������"�����(��7� +���%&�, �-./0��;'-',� %��7���������8����!!������������!�5�7�� ���������������������������"���#�� !����������������1�������������������%&&'�)� �� ����#������������ � �����������"�����(��7�+���%&�% �=.-0��/&'-/� *ying zhang e-mail: yz62@georgetown.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e44, 2014 ojphi-06-e124.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 109 (page number not for citation purposes) isds 2013 conference abstracts comparing findings from syndromic surveillance systems at a european level sylvia medina1, alexandra ziemann2, céline caserio-schonemann1, céline dupuy3, anette hulth4, helena medeiros1, kare molback5, thomas krafft2 and anne fouillet*1 1french institute for public health surveillance (invs), saint maurice, france; 2dept. of international health, school of public health and primary care (caphri), faculty of health, medicine and life sciences, maastricht university, maastricht, netherlands; 3french agency for food, environmental and occupational health safety, lyon, france; 4smittskyddsinstitutet, stockholm, sweden; 5statens serum institut, copenhagen, denmark � �� �� �� � � �� �� �� � objective �������� ������������ ��������� � � ���� 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���"�>� �� �7"�?����%��� �����8 �� ����� ��� ���������������� ��� ��� ����� ������������ ������&'(&��� �699�� ��������������� �����9 ������9� �����98 �� ���9�����������@,@ �������@� �� ���@����� ��� *)+�:������"�0�� ��� "�a� �� ��"�a���3�������#��3��b"�?��� ��7"� 5������ � "�� �����8 �� ����� ��� ��� ������ ���������������� ���� � ��� ������ ���������������������#�� �6������ ��� �� �� �� ��������� ����� <����c� �7����&'()�����(/(((�)�,�6&&'�= *anne fouillet e-mail: a.fouillet@invs.sante.fr� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e124, 2014 the title of the article real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 real time alert system: a disease management system leveraging health information exchange vibha anand 1, 2 , meena e. sheley 1 , shawn xu 1 ,stephen m. downs 1, 2 1 children’s health services research, indiana university school of medicine, in 2 regenstrief institute for health care, indianapolis, in abstract background: rates of preventive and disease management services can be improved by providing automated alerts and reminders to primary care providers (pcps) using of health information technology (hit) tools. methods: using adaptive turnaround documents (atad), an existing health information exchange (hie) infrastructure and office fax machines, we developed a real time alert (rta) system. rta is a computerized decision support system (cdss) that is able to deliver alerts to pcps statewide for recommended services around the time of the patient visit. rta is also able to capture structured clinical data from providers using existing fax technology. in this study, we evaluate rta’s performance for alerting pcps when their patients with asthma have an emergency room visit anywhere in the state. results: our results show that rta was successfully able to deliver “just in time” patientrelevant alerts to pcps across the state. furthermore, of those atads faxed back and automatically interpreted by the rta system, 35% reported finding the provided information helpful. the pcps who reported finding information helpful also reported making a phone call, sending a letter or seeing the patient for follow up care. conclusions: we have successfully demonstrated the feasibility of electronically exchanging important patient related information with the pcps statewide. this is despite a lack of a link with their electronic health records. we have shown that using our atad technology, a pcp can be notified quickly of an important event such as a patient’s asthma related emergency room admission so further follow up can happen in near real time. introduction with the passing of the patient protection and affordable care act of 2010, there is a greater thrust towards prevention and management of chronic conditions in primary care settings. however, the rates of delivery of preventive care and disease management services are suboptimal and there is much room for improvement in primary care settings. [1-3] one possible improvement is implementation of evidence based care guidelines in routine practice. [4] however, assessment and implementation of evidence based care guidelines in primary care http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 settings requires a significant level of knowledge and expertise not only among clinicians but also the clinical staff. additionally, there are many formidable clinical workflow challenges to overcome for successful implementation. for example, significant amounts of time are needed to assess which guidelines and services may be applicable for a given patient at each visit. [5] in the past, health information technology (hit) tools such as computerized clinical decision support systems (cdss) coupled with electronic health records (ehr) have been effective for care guidelines implementation. the cdss work by applying electronically coded care guideline recommendations, such as checking for drug dosing or drug-drug interaction, against patients’ electronic health records (ehr) to alert or remind the physician. however, most past successes with cdss are attributed to direct interfaces such as those enabled through computerized physician order entry (cpoe) or note writing and mostly in inpatient settings. [6] a reminder to the provider at the time of note writing or order entry is often too late in primary care settings as events frequently take place after the primary care provider (pcp) has completed the visit. therefore, what is needed are hit tools which monitor real or near real time events and are able to couple with cdss to deliver “just-in-time” (jit) information, at or around the time of the patient’s visit. however, such tools are feasible when an advanced infrastructure such as that for electronic delivery of information across a network of practices and providers exist, i.e. a health information exchange (hie) that is both functional and robust. our state has a robust and functionally advanced hie, the indiana network for patient care (inpc) to provide the needed functionality. the inpc is a 16-year-old health information exchange operated by the indiana health information exchange (ihie). using standards, interoperability, and the interchange of clinical data for clinical, public health, and research purposes, investigators at the regenstrief institute created the inpc in 1995. the inpc includes clinical data from 45 hospitals, as well as from public health departments, laboratories, and imaging centers, and a few large-group practices closely tied to hospital systems. the inpc is continuing to expand. the hie data repository carries over 4 billion pieces of clinical data, including over 79 million text reports. in addition to data from clinical institutions, the inpc also receives data from healthcare payers. the system is used for patient care, public health, and research. in the inpc, events such as patient registration or discharge generate standard health level 7 (hl7) messages. the hl7 messages are delivered from participating practices and hospitals from across the state to the inpc. [7] the existing near real time delivery of standard hl7 messages to the inpc on events such as patient registration or discharge creates unique opportunity for improving care processes [8], for example, the registration messages from inpc may be used to trigger alerts and reminders to pcps in order to provide evidence based preventive and disease management services that are due at a patient’s visit. additionally when coupled with a cdss, the infrastructure can connect pcps and practices from across the state for patients’ follow up care [9, 10] perhaps using a jit information approach, for example, the reminders and alerts to pcps can be customized based on patient’s previous labs and test results from their ehr. however, even with an advanced infrastructure such as the inpc, there are practical limitations in implementing automated event monitoring and alerting systems in practice. one http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 such crucial limitation is the delivery of information to the last mile i.e., to a practice in the field and back from it to the hie. much like the rest of the country [11], a majority of practices that participate in data sharing into inpc do not employ ehr in routine practice and therefore are not amenable for electronic delivery of information. additionally, their current participation in the hie consists of either receiving results through office fax machines or by logging on to hie portal to download this information. thus, despite our advanced infrastructure, a lack of a functional bi-directional interface makes it unfeasible to implement any automated event monitoring or alerting solution. unless the last mile limitation is addressed in some way, our current infrastructure may be under-utilized to improve delivery rates of preventive care and disease management services across the state. therefore in the past two years, our research group has developed a mechanism for implementing a bi-directional interface between the hie and practices in the field using existing fax technology. we chose to use fax as a medium for receiving data from practices in the field for two reasons. first and foremost fax machines are ubiquitous in physician offices and are already being used in patient care, making them easily accessible to securely exchange protected health information (phi). [12] secondly any alternative electronic media use such as an email would not pass hipaa legislation’s [13] phi security criteria, unless a institutional email system capable of exchanging secure messages electronically was established across all practices. [14] as most practices in our healthcare system reside outside our institutional email system, an email would not have been a secure way for receiving phi and was not considered hipaa safe for this study. moreover, our fax interface which is described in detail elsewhere [15] has been previously used for linking clinicians in our pediatric practices to the state department of public health for test results of newborn screens. in this paper, we describe our use of this interface to develop and evaluate an event monitoring cdss, the real time alert (rta) system to coordinate follow up care of an asthma related emergency room (er) admission across practices throughout indiana. methods the rta system the rta system is a cdss that intercepts incoming patient discharge events from inpc participating practices to generate alerts and reminders based on patient’s record in the ehr. rta draws on the rich legacy of data already contained within inpc. [16] using these data, rta is able to generate reminder alerts that apply to a particular visit for a given patient and deliver those results to pcps and practices across the state in real time or near real time. rta is able to deliver these reminder alerts to the pcps using inpc’s existing document delivery service, docs4docs® or d4d. [17] d4d is a messaging service that securely and electronically delivers documents, such as laboratory results, in pdf format to providers across the state using their national provider index (npi) number. although in a few cases d4d delivers messages directly into an ehr, in most cases, the documents are sent to a d4d secure email box or fax machine. the asthma module of the rta system is designed to alert pcps if their patients’ have any asthma related er admission. international classification of diseases-ninth revision (icd9) is used for the purposes of detecting patient’s er visit as follows. the asthma module http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 monitors hl7 messages from ers into the inpc that include a discharge diagnosis of 493.* (asthma). when such a message is detected, rta alerts the patient’s pcp so that he or she can arrange a return visit, for example, to step up the asthma therapy in accordance with published asthma care guidelines. [18, 19] in addition, to reduce the burden of care coordination, the pcp alert is also accompanied by a pre-printed letter that the practice can send to the patient. finally, rta also provides a form on which pcps and practices across the state can indicate receipt and use of the alert and update the inpc by checking boxes and faxing the form. the forms are then read by rta, using optical character recognition and optical mark recognition (ocr/omr) software. the rta system was initiated on january 9 th 2012 and has been operational for asthma care follow up since then. the data we evaluate for this study are from june 2012 to november 2012. below, we describe rta’s workflow, but first we briefly describe the adaptive turnaround documents (atad) interface used for this study. adaptive turnaround documents (atad) adaptive turnaround documents (atad) are printable forms that are dynamically generated by the rta system. [20] (figure 1) the atad in figure 1 is for notifying the pcp of their patient’s previous er admission. when the rta system detects an hl7 message from an er with an asthma discharge diagnosis, rules trigger the generation of the physician alert. the rules in the rta system are encoded as arden syntax medical logic modules (mlm) [21-23] and the alerts and reminders are generated as atad. atads contain patient specific information for the pcp. the atads are sent to d4d queue and delivered to pcps either using faxes or electronically. depending on practice preference, the personnel in pcp practices are able to view and print the atads or are able to view them by logging on to the secure hie portal. specifically, the two atads that get generated by the rta system for asthma follow up care are – 1) er admission notification atad and 2) a preprinted letter atad that is addressed to the patient as a convenience for the practice. they are described below.  er admission notification atad this atad contains information about patient’s er admission – date, time and location. (figure 1) it also contains contact information about the patient and the provider (pcp). this atad notifies the pcp of the patient’s er visit so that the pcp may decide how best to respond to the information. the system does not generate specific recommendations but only alerts the pcp to follow up. in response to this atad, the physician can respond to the rta system by checking a box indicating any or all of the following: “this is not my patient”, “we have sent this patient a letter”, “we have seen this patient since the er admission”, or “we have called this patient”. for example in figure 1, the pcp responded that they have sent the letter and also called the patient regarding their er admission. http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 to receive this and similar atads securely, the rta system is setup with a fax line. pcps are requested to return the er admission notification atad to the rta system by fax. (figure 1 shows a received atad by fax) the returned atads are optically read and interpreted by a third party ocr/omr software (www.verity.com) suite. structured data captured from the check boxes on the returned forms is stored electronically by the rta system.  letter for the patient atad included with the er admission notification atad is an addressed letter that the physician may send to the patient, inviting him or her to come to the clinic for a follow-up visit. (figure 2) this letter is provided only as a convenience to the practice so that it can be sent to the patient’s home address with the pcp’s approval. it includes both the pcp’s and patient’s name as well as an invitation to the patient to schedule a clinic visit. http://ojphi.org/ http://www.verity.com/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 1: er admission notification atad http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 2: letter for the patient atad http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the rta workflow the rta workflow is described in figure 3. when an hl7 message, containing a diagnosis of asthma (icd-9 code 493.*) in any valid segment of the hl7, for example dg1, ft1, or the obx segment, arrives into the inpc from any er, then our infrastructure is programmed to query an existing pcp attribution service to identify the pcp, using data within inpc. in brief, the pcp attribution service uses statistical methods to infer who the pcp is for a given patient and a description of those details are outside the scope of the current study. if a pcp attribution is successful, an inpc process looks up the provider’s npi in a table that locates the pcp for a d4d delivery. the inpc process then constructs an outbound hl7 adt message (step 1 of figure 3) to the rta system to trigger the processes in the rta system. the trigger message contains both the provider and the patient information the npi of pcp in the pv1 segment (as required by the d4d service for message delivery) and the patient’s information in the patient identifier (pid) segment. on receiving the trigger message, rta generates the two previously described atads (figures 1 and 2). the atads are generated as pdf documents and prepared for delivery to the inbound queue of the d4d service as follows. an outbound hl7 obr message is constructed with the provider and the patient information transferred from the trigger message; the two atads are base-64 encoded (as required by d4d) and attached to the message (in steps 4 and 5 of figure 3) and finally an exporter task in the rta system sends the message to ihie’s d4d inbound queue. the d4d service periodically polls its inbound queue and delivers the queued messages to the participating providers and practices across the state (as listed in the queued messages). the messages are delivered to the pcp’s d4d inbox by default, though a pcp may also elect to receive the d4d messages by fax. thus the message is delivered to the pcp’s (that was indicated in the hl7 trigger message) d4d inbox informing them of their patient’s recent er admission for asthma care. all hl7 messages that are exchanged from rta with inpc and d4d are protected behind institutional firewalls. to indicate their follow up, pcps are encouraged to complete and fax the er admission notification form back to the fax line in the rta system. the faxes are electronically read and data from them are extracted using ocr/omr software. [20, 24] the structured data captured using omr is then processed and deposited back to the inpc repository. these steps are detailed in steps 6 -10 of figure 3. http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 3: rta workflow results during the five month study period (june 2012 to november 2012), 2120 trigger messages pertaining to an asthma related er admission were received by the rta system from inpc. of these trigger messages, 1958 (92%) messages were processed. one hundred and sixty two (7%) trigger messages were not processed because they were either duplicates or were received in an invalid format. of those processed, 676 (35%) were missing a npi needed to identify the pcp and, therefore, were not acted upon by the rta system, and 7 (< 1%) were undeliverable. therefore, of the processed trigger messages, 1275 (65%) resulted in an er admission notification to 538 unique pcps across the state. (figure 4) of those pcps who received an er admission notification atad (figure 1), 219 (41%) received more than one, and 42 (8%) received more than five notifications during the study. these notifications were either for the same or different patients suggesting that these practices see a high number of asthma patients who had asthma exacerbations. http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 collectively pcps faxed 126 (10%) er admission notifications back to the rta system. of those, 31 (25%) atads were unreadable by the computer using automated omr methods due to misaligned or poor quality faxes. of those pcps who returned an er admission notification, 79 (15%) indicated they had taken action in response to the alerts. furthermore, the forms returned by the pcps were analyzed by extracting data from rta’s database. the pcps responses on these forms are not considered mutually exclusive for our analyses and the details of this analysis are in figure 4. there were 95 (75%) forms that were automatically interpretable using omr methods by the computer. of those 33 (35%) indicated that the pcp found the provided information helpful and 19 (20%) indicated that the pcp did not find the information helpful. however, 23 (24%) returned forms indicated that the pcp made a phone call to follow up with their patient and 4 (4%) forms also indicated that the pcp sent the attached letter to the patient in response to this notification. ten (11%) returned forms indicated that the pcp had seen the patient post their er admission and 32 (34%) forms indicated that the patient they were contacted for was not their patient. the average time between when the rta received a trigger message and when the pcp was notified with a message was 2.6 hrs. the average time from when the er admission notifications were sent to the pcp to when they were returned to rta was 4.6 days in this study. figure 4: rta evaluation results http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 discussion in this study, we have demonstrated the feasibility of electronically exchanging important patient related information with primary care providers despite the lack of a link with their ehr. we have shown that using our atad technology, a pcp can be notified quickly of an important event such as a patient’s asthma related er admission so further action can be taken in near real time. we have also shown that, using an existing health information exchange and everyday office equipment like fax machines, alerts can be delivered to any pcp across the state with simple technology. our ability to interpret the value of these notifications to physicians is limited by both the poor response rate from the clinics we asked to fax us the forms and by limitations of our pcp attribution service. experience in our hie has shown that pcp attribution requires ongoing manual correction, usually by physicians indicating that a patient is not theirs. for example, our rates of “this is not my patient” response from pcps dropped considerably (63% to 34%) when information was corrected. on visually examining the provider returned forms , we found that many indicated that they were the patient’s specialist, for example an eye doctor or their gynecologist but not their pcp. nonetheless, based on the forms we did receive, we believe that when notified, pcps find such information helpful more than a third of the time. furthermore, the majority of pcps receiving the information and responding actually reached out to their patients by making a phone call, emphasizing the nature of the alert and immediacy of follow up. as with all such research, our methods have both advantages and limitations. the advantage of our approach is that primary care practices with only a fax machine are able to share data with the hie. however, this approach does push the burden and complexities of sharing information to entities running hie and systems like rta. for example, though most faxes are computer interpretable without human intervention, still personnel are needed to verify and review faxes manually if a greater accuracy is desired and if all data that is exchanged needs to be captured. conclusion despite its drawbacks, we believe that the rta bi-directional information exchange method using existing fax technology is both feasible and innovative for follow up care of chronic illness. it can also be used for management of many other chronic diseases such as diabetes or hypertension. we also believe it can bridge the last mile barrier for adoption and tight integration of ehrs across pcps and practices. [15] furthermore, our method provides a pragmatic mechanism for exchange of information until universal identifiers for linking patients in different ehr systems can be determined. [15, 25] http://ojphi.org/ real time alert system: a disease management system leveraging health information exchange online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 corresponding author vibha anand, phd indiana university email: vanand@iupui.edu references 1. fani marvasti f, stafford rs. 2012. from sick care to health care reengineering prevention into the u.s. system. n engl j med. 367(10), 889-91. http://dx.doi.org/10.1056/nejmp1206230 2. provost s, et al. 2010. does receiving clinical preventive services vary across different types of primary healthcare organizations? evidence from a population-based survey. health policy. 6(2), 67-84. 3. lalonde l, et al. 2012. priorities for action to improve cardiovascular preventive care of patients with multimorbid conditions in primary care--a participatory action research project. fam pract. http://dx.doi.org/10.1093/fampra/cms021 4. elson rb, connelly dp. 1995. computerized patient 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obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels helena korjonen 1 1 national heart forum, london, uk abstract objectives: develop a website, the olc, which supports those people who work on promoting a healthy weight and tackling obesity. research shows that original networks where sharing of information and peer interaction take place create solutions to current public health challenges. methods: considerations that are relevant when building a new information service as well as the technical set up and information needs of users were taken into account prior to building the olc and during continuous development and maintenance. results: the olc provides global news, resources and tools and link out to other networks, websites and organisations providing similar useful information. the olc also uses social networking tools to highlight new and important information. discussion: networks contribute to a stronger community that can respond to emerging challenges in public health. the olc improves connections of people and services from different backgrounds and organisations. some challenges exist in the technical set up and also because of other aspects, e.g. public health information and differing information needs. conclusion: public health work programmes should include networking opportunities where public policy can be disseminated. the provision of necessary tools and resources can lead to better decision-making, save time and money and lead to improved public health outcomes. key words: public health, obesity, information services, website introduction in the last 25 years, overweight or obesity in england has tripled with a forecast 24% of boys being classed as obese in 2025 (1). obesity increases the risk of chronic life limiting conditions including diabetes, cardiovascular disease and cancer. obesity is not caused by one contributing factor but is a very complex area to understand and address. we live in an obesogenic environment where we are surrounded by factors that affect our health and http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 weight. obesity has been described as a ‘health time bomb’(1) costing the healthcare services in england millions of pounds and other indirect costs are thought to be around £3.6 billion (1). no single solution exists to solve the obesity problem but there is a need to build up evidence of what interventions work and involve multiple disciplines to address obesity in local areas. research shows that networking activity, such as exchanging ideas, practices and experiences with peers improve practice and quality of working life (2) and that locally conducted and published initiatives play an important role in changing the professional practice of health care providers (3). sharing information can create a sense of collaboration and trust amongst individuals and beneficial partnerships can form that promote the exchange of ideas, practices and tools (4). research shows that investment in networks may provide a cost-effective solution to disseminate information, policies and programmes (3). by sharing local initiatives and information it will reduce the reinvention of initiatives and therefore reduce cost and effort at the point of set-up. public health programmes should include a plan for creating opportunities for network interaction in order to improve adoption and diffusion of public policy and to avoid delays and diminished public health investment (3). one successful model where new ideas and practices rapidly spread through interpersonal communication was the implementation of the framework convention for tobacco control (fctc tobacco) through the globalink 1 network, which first began as an email discussion list and then became a website. many websites have since been built based on this proven network model and they effectively bridge the gap between research, policy and practice, they are able to translate research into action. obesity learning centre the national heart forum (nhf) 2 , a charitable alliance committed towards co-ordinating public health policy development towards reducing the risk of coronary heart disease and related conditions, received funding from the department of health and department for education in england to develop a website that supports those people who work on promoting a healthy weight and tackling obesity, the obesity delivery chain. this website became the obesity learning centre (olc) 3 . we aimed to build on the success of original networks such as globalink and findings of research where sharing information and peer interaction are known to create solutions to current public health challenges. the olc website is also a key component of the development of chronic disease prevention information services at the nhf. the olc sets out to strengthen and support the obesity delivery chain, e.g. local capacity and capabilities, to prevent and treat overweight in children and adults. the website is aimed at practitioners from a variety of backgrounds, those working in schools and nurseries, academics and researchers, health care professionals, local government employees who may 1 www.globalink.org/ (accessed 21 october 2010) 2 www.heartforum.org.uk (accessed 27 january 2011) 3 www.obesitylearningcentre-nhf.org.uk (accessed 27 january 2011) http://www.globalink.org/ http://www.heartforum.org.uk/ http://www.obesitylearningcentre-nhf.org.uk/ http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 work on a wide range of activities such as planning green space and urban environments, developing opportunities for physical activity, planning school menus or are conducting research in obesity. initial discussions with relevant stakeholders (table 1) took place starting in the autumn of 2008 and throughout 2008 until the site went live. the discussions consisted of presentations and open questions and answers sessions as well as telephone interviews with specific individuals. the discussions revealed that these individuals want to share local and national initiatives with one another and have the opportunity to discuss their work with peers in an online environment. it was also clear that the website should be independent from any influences and have no commercial links. although the site has a key role in supporting professionals in england in line with a national obesity strategy, it is also aimed at an international audience hoping that it may lead to collaboration and learning between the different users. table 1: stakeholders that were consulted during project set up health professionals (e.g. doctors and nurses) local government/authorities relevant committees/groups (e.g. healthy towns) primary care trusts (pcts), public health observatories government and government agencies voluntary and community sector (charities, ngos, etc.) dietitians/nutritionists policy civil servants professional bodies for health (e.g. royal colleges, ukpha etc.) schools physical activity and sports professionals academia international, intergovernmental organisations and ngos information providers/libraries (e.g. public health and nhs libraries) methods considerations on the role of the olc there were a number of considerations that needed to be kept in mind during the set up of the olc. we have already mentioned the need for the site to remain free from commercial and other influence (e.g. government, political, economic). the reason behind this is that individuals expressed a concern for them being influenced by advertising seen on the site, or the potential influence on content or on their opinions expressed in the discussion forum. it was also important that the olc did not aim to replace any existing information service or website. other services exist that provide content that is relevant, e.g. obesity data, local or regional information aimed at the public or educational websites. it was clear that the olc was a website aimed at guiding professionals to the right information as well as highlighting http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 new information but then directing users to the right location for further information. the olc works particularly closely with the obesity regions around england, some of who have their own websites, the national obesity observatory (noo) who analyse obesity data and intelligence and change4life, a national social marketing campaign aimed to improve the eating and physical activity habits of the public. it was also clear that the olc needed to build on existing information services provided by the nhf and compliment our work. the olc concentrates on providing content on obesity, but the other information services are extended to other avoidable chronic disease topics including obesity. we recognise the success of the likes of globalink and wanted to make improvements to the model, move it in line with new developments in web 2.0 and 3.0 making the site more interactive and user driven. for us, the success of the website is when the users drive the development of the content and functionality and the site almost becomes a user managed entity with vibrant content. of course we need to provide some information, e.g. news and the latest resources we harvest, but we want the site to be interactive and for the users to contribute to the content. the olc must be able to bring networks and groups together, allowing them to share discussion and material within a closed network. many expert groups exist that physically meet on a regular basis, but information and knowledge is not gathered, classified and stored together in an accessible manner. closed network pages on the olc allow them to manage their own content with editorial rights to do so. examples of closed networks are the regional obesity leads and healthy towns network members across england. a key requirement is that the olc must enable the users by providing information and resources relevant to obesity. it needs to allow the user to search and find that information. it must also allow the user to select how s/he accesses the information, by browsing the website, by signing up for newsletters or rss feeds. a topic which was discussed at length during the set up of the website was the need for users to register and login to access content. the original plan was to ensure that the user was protected through a login mechanism and that a stringent registration process was in place which included checking the validity of information provided by a new user through a referee process. a heavy administration burden as well as being tedious for users registering and waiting to be registered, it was fortunate that a change in government requirements for websites of this nature came about which allowed us to relax the registration process. technical set up and selection of software at the beginning of the project, presentations were given to groups of people followed by informal discussions and interviews with a selection of stakeholders to get their impressions of what functionality the website might have and what it might look like. this formed the initial build specification used to source appropriate software to build the website. the initial technical team consisted of two it consultants who sourced and managed the website contractual phase of the project set up. it was agreed that a content management system (cms) was necessary to build the site to allow for non-technical staff to edit the site in the future. the website was built during august and september 2009 and the site went live on 2 http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 november 2009 as agreed with the funders. very early on in the project it was realised that the cms system selected for the project was not as flexible with its functionality as was anticipated. it was clear that the software required significant technical development and updating the site proved complex. this caused additional stress on the project as a new cms had to be sourced as well as the continuation to build with the existing cms in order to go live at the agreed date. in early 2010, we sourced a suitable cms that we were confident could grow with the project and the nhf as an organisation. the selected cms easysite by eibs is an off the shelf cms with modules that can be selected and bolted on when required in the project. the olc was rebuilt and content migrated during march and april 2010 going live in may 2010. the selection of the cms for technical platform was an important decision. it was anticipated that we needed a cms that would also be used for our other information services, including the nhf website. the cms needed to be compatible with our existing systems and new systems to come during the development of our information services. information needs and benchmarking– evaluation methods it is important to conduct regular information needs analyses for all information services to ensure we are addressing information needs of those working in public health. research has shown the information needs of public health practitioners are not met at all (5) and there needs to be a strategy in place to ensure needs are met. assessing information needs of public health practitioners is difficult as they often do not know what they are missing (6). research shows that as information providers we need to raise awareness, increase access and decrease barriers of use, improve information seeking behaviour (6;7). revere et al. (8) identified a number of improvements that can be made to meet the information needs of public health practitioners in order to design an interactive knowledge management system and those findings are a useful basis on which to build an evaluation strategy. we drafted an evaluation policy of how we meet information needs and the methods used are a combination of:  analysing statistics of what users do on the site  run user needs surveys using online questionnaires twice a year  use a built in poll mechanism to ask specific questions of users  ask users to rate on and comment on pages  regular contact with our users through email, telephone or face-to-face meetings. we also use various benchmarks which are appropriate for information projects (this list is not exhaustive):  comparisons to be made with competitors, collaborators and the information community  compliance with copyright act and the data protection act.  best practice in dissemination of information  monitor information standards  conduct research into information provision and new technology to continuously develop the technological capabilities http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011  using google analytics and reporting tools for traffic, referrals, access, users and content data  quality testing standard developed in-house  accessibility – benchmark against available standards on accessibility  cost-effectiveness we also meet regularly with our funders to discuss information needs, benchmarking and development requirements. regular reports are submitted and published on the website for transparency. establishing information needs, whether the olc meets those needs, and benchmarking all contribute to our evaluation plan of the project and ultimately if it can be considered a ‘success’. results: the olc (figure 1) went live first on 2 november 2009 and with a new cms in may 2010. the site has had around 1,100 visitors each month with visitors from around 40 countries. on average the site sees around 700 new visitors 4 (approximately 60% of all users) each month. figure 1: obesity learning centre – homepage 4 unique ip addresses http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 the most popular pages on the olc are the resources directory, elearning, case studies, obesity general news, network (open and closed) pages and events calendar. it is impossible to within this paper describe all pages and content available on the olc but we will highlight certain aspects of the site that we think will be of particular interest. in order to make it easier for the user to find content, we categorise all the content according to job role of those who may be interested in that particular item, according to resource type (report, guideline, media etc.) and by topic (commissioning, planning, education, environment etc.). this (a) allows us to use internal feeds and directories that pull the information from the categories onto pages (b) allows the users to search for information (c) allows the user to browse for information according to their job role and (d) allows the user to sign up for relevant automated feeds that arrive in their inbox, on their mobile or their rss reader. figure 2 shows our resources directory which demonstrates these features as well as provide free text search fields. figure 2: category driven content: according to role, resource type and topic the case studies (figure 3) are local, regional and national initiatives of what has been tried and tested in regions. case studies can be submitted to us by uploading them to the directory where we categorise them. like with all other content, case studies can be found by selecting a topic, searching using free text search fields or by browsing all case studies. http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 figure 3: case studies on the olc the olc provides current global news in obesity and all news items are archived in a news directory (figure 4). the news is displayed on the homepage (figure 1) and users can sign up for rss feeds to receive news as they are added. we also use twitter to highlight new and important news items and resources that have gone up on the olc. when selecting news items for the news directory on the website we follow an internal appraisal and selection policy. we do not report on news that is promotional in nature (e.g. for commercial gain) or is judged to have a potential commercial bias. we also do not report opinion pieces. the olc blog (9) is used for discussing issues related to obesity and opinions of obesity experts that contribute to the blog, again where they are free of commercial ties or promotional intention. http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 figure 4: news directory discussion many websites provide information around obesity, but it is difficult for the user to find that information with the limited time that they have in keeping up-to-date. improving access to useful information is much more challenging in publish health than in biomedicine as public health professionals need to cover a broad range of subjects and sources (7) and do not always know what they are missing (6;7). it has also been stated that the public health workforce do not just need data or citations, they need filtered and synthesised information with training and support on how to use it in the form of information literacy training (6). the olc is a customised toolkit which sources and publishes documents, news, policy updates and provides information in various push and pull formats by allowing the user to search for and download information, sign up for feeds or email alerts and providing other customised ways of finding information by their job role or by topic category. the olc also harvests and publishes case studies, tools and local, national and international initiatives of work that have been tried and evaluated. by improving connections of people and services through networks; innovation and local, regional and national initiatives can be shared with ease and therefore improve quality of services. sharing existing practice enables health initiatives to progress more quickly with lower set up costs. networks can also support professional development via peer support contributing towards a stronger community that can respond to emerging challenges. the olc supports a http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 discussion forum and closed network pages where discussion about obesity issues is encouraged, although it has to be said that the discussion forum is proving to be a challenging development and few discussions are taking place. there were challenges during the build of the olc, some which cannot be anticipated at the start of a project. during the build of the olc the branding and designing visuals of the site was a lengthier process than anticipated. in general involving one or several partners mean that more people need involvement in all aspects of the set up and approval processes of the website. it was also important to build relationships with existing information providers in obesity and address any concerns that arise as early as possible. initial difficulties can be around potential overlaps of websites or existing services with that of a new service and even a resistance to the set up of such a service if it is seen as competitive or having an impact on another organisation’s objectives. we had several meetings with organisations to reassure that we were not replacing any service or intending to occupy their space and regular communication between organisations continues. another challenge is that the olc aims to reach different kinds of users all with differing levels of information literacy skills but also a workforce with differences in responsibilities and information needs. the majority users of the site are from the health sector, academia and government agencies but we also have users from the local government, schools, nongovernment organisation and community organisations. the mixture of users will require different types of information from a variety of sources. we also know that around 80% of our users are from the uk which means that 20% are international users with different needs. it is difficult to ensure that all users feel that the olc is providing what they need and we continuously review our development plan and run user needs surveys to ensure that we meet the needs of the community. in our last survey, 97% of our users told us that they find exactly what they need or at least related information to what they need on the site (10). however, a question is, do we reach everyone in the audience that ought to access the olc? it is still uncertain how many professionals interested in public health issues are unable to access the internet, or do not have time to access it, and therefore miss important content to their work. much public health information is grey literature disseminated electronically and very little such information is likely to be using the traditional print methods, due to the time it takes to send it out and also cost implications. also, the amount of grey literature public health information that is generated by various public and independent bodies today is so large that it would not be logical to disseminate this information in print. it would be useful future research to examine the quantity and format of public health information generated and also examine the information needs of public health professionals, including access issues. government election time is a challenge when funding is provided from government sources. during spring of 2010 the election announcement was made. the pre-election period in the united kingdom (uk) is described as purdah, the time between an announced election and the final election result, during which time government parties discuss policy development with civil servants and certain aspects of work, such as consultations, are put on hold. any work that is funded by existing government departments need to be put on hold as any decisions with regards to policy are postponed until after the election period. the olc was not updated during the purdah period and afterwards subject to review with the existing obesity strategy by the new government. http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 the new forming government may take some time in reviewing existing policies and the purdah period is a challenge when managing an information service. it may also be that there are changes in strategy and restructuring of workforce, which changes our audience or the objectives of the olc to align with new policy. however, as we have an international audience, we need to ensure that the olc is not alienating those users. therefore the development plan of the olc is constantly reviewed and critiqued to ensure that it meets the needs of our users. our recent survey (10) shows that users want to see more obesity news, peer-reviewed research, tools and case studies with respondents saying that the site provides relevant and up-to-date information and covers the topics they find useful. the survey also showed that we need to do some work in communicating with our users and promoting the site. conclusion the olc was developed by the nhf and has proved to be an efficient resource for users to get obesity information but also to share information with other like-minded colleagues or across affiliations. obesity professionals can access and share local, regional, national and international information and find or receive information in a variety of ways and sort the information according to type, topic or their job role. this makes information more userfriendly. our information needs surveys reveal that the users consider the website a valuable and needed function for their work in tackling obesity. future development work is dictated by where the needs are for the obesity workforce. these needs vary as and when strategy and policy are reviewed and when new evidence comes to light. this means that development plans need to be revisited on a regular basis and we need to frequently run user needs surveys to ensure we meet the current information needs. we believe that any public health programme should include networking opportunities for the workforce where public policy can be disseminated with the goal that uptake of evidencebased policy is improved. informal networks and communities can provide the necessary tools, partnerships and resources that could lead to improved public health outcomes. glossary case studies = a study that attempts to determine what factors led to success or failure, e.g. obesity initiatives implemented and evaluated in local or regional areas. content management system (cms) = designed to simplify the publication of web content to web sites and mobile devices—in particular, allowing content creators to submit content without requiring technical knowledge of html. grey literature = information produced in electronic and print formats not controlled by commercial publishing, e.g. reports, presentations, white papers etc. green space = land covered with vegetation, e.g. parks, fields and other open land within built-up area. information literacy = the ability to locate, understand, evaluate, utilize, and convey information http://ojphi.org the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 obesogenic environment= set of circumstances that encourages people to eat and drink more calories than they expend and to become obese. purdah = in the uk, the time between an announced election and the final election result, during which time government departments develop guidance and policy. rss feeds = is a family of web feed formats used to publish frequently updated works, e.g. blog entries, news headlines, audio, and video, in a standardized format. urban environment = cities, towns with higher population density compared to areas surrounding it. tools = encompass entities that facilitate effective action, e.g. programmes, resources or applications that can be used when implementing initiatives. conflicts of interest: no conflict of interest has been declared. correspondence helena korjonen, associate director information services, national heart forum, victoria house, 7th floor, southampton row, london,wc1b 4ad, england. email: helena.korjonen@heartforum.org.uk references [1] uk government foresight programme. tackling obesities: future choices (the foresight report). 2007 oct 17. [2] guindon ge, lavis jn, becerra-posada f, malek-afzali h, shi g, et al. 2010. bridging the gaps between research, policy and practice in lowand middle-income countries: a survey of health care providers. cmaj. 182(9), e362-72. http://dx.doi.org/10.1503/cmaj.081165 [3] wipfli hl, fujimoto k, valente tw. 2010. global tobacco control diffusion: the case of the framework convention on tobacco control. am j public health. 100(7), 1260. http:// dx.doi.org/10.2105/ajph.2009.167833 [4] loschen w, seagraves r, holtry r, hung l, lombardo j, et al. infoshare an information sharing tool for public health during the 2009 presidential inauguration and h1n1 outbreak 2010 [cited 2011 jan 6]; 2 available from http://firstmonday.org/htbin/cgiwrap/bin/ ojs/index.php/ojphi/article/viewfile/3031/2751 [5] lapelle nr, luckmann r, hatheway simpson e, martin e. 2006. identifying strategies to improve access to credible and relevant information for public health professionals: a qualitative study. bmc public health. 6(89). [6] rambo n. 2001. public health outreach forum: lessons learned. bull med libr assoc. 89(4), 403-06. [7] lasker rd. 1998. challenges to accessing useful information in health policy and public health: and introduction to a national forum held at the new york academy of medicine. j urban health. 75(4), 779-84. http://dx.doi.org/10.1007/bf02344507 mailto:helena.korjonen@heartforum.org.uk http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/ojphi/article/viewfile/3031/2751 http://ojphi.org http://dx.doi.org/10.1503/cmaj.081165 http://dx.doi.org/10.2105/ajph.2009.167833 http://dx.doi.org/10.2105/ajph.2009.167833 http://firstmonday.org/htbin/cgiwrap/bin/ http://dx.doi.org/10.1007/bf02344507 the obesity learning centre (olc) – a website supporting those working towards a healthy weight and reducing obesity levels 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 [8] revere d, turner am, madhavan a, rambo n, bugni pf, et al. 2007. understanding the information needs of public health practitioners: a literature review to inform design of an interactive digital knowledge management system. j biomed inform. 40, 410-21. http:// dx.doi.org/10.1016/j.jbi.2006.12.008 [9] obesity learning centre. olc blog 2011 jan 21 available from http:// obesitylearningcentre.blogspot.com/ [10] obesity learning centre. obesity learning centre user needs study. national heart forum; 2011 jan 20. http://obesitylearningcentre.blogspot.com/ http://ojphi.org http://dx.doi.org/10.1016/j.jbi.2006.12.008 http://dx.doi.org/10.1016/j.jbi.2006.12.008 http://obesitylearningcentre.blogspot.com/ http://obesitylearningcentre.blogspot.com/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts digital disease detection dashboard: rapid detection & outbreak management tool karina n. alvarez*, catherine ordun, jane blake, kirsten a. simmons, keith hansen, dan baker, lynda rowe, yusra ahmad, donald m. eby, dimitrios koutsonanos, steve escaravage and kc decker booz allen hamilaton, atlanta, ga, usa objective to develop a web-enabled digital disease detection dashboard (d4) that allows users to statistically model and forecast multiple data streams for public health biosurveillance. d4 is a user-friendly, cloudenabled, and r shiny-powered application that provides intuitive visualization enabling immediate situational awareness through interactive data displays and multi-factor analysis of traditional and non-traditional data feeds. the objective of d4 is to support public health decision making with high confidence across all four aspects of the biosurveillance continuum—detection, investigation, response, and prevention. introduction booz allen hamilton is developing a novel bio-surveillance prototype tool, the digital disease detection dashboard (d4) to address the questions fundamental to daily biosurveillance analysis and decision making: is something unusual happening (e.g., is an outbreak or novel disease emerging)?, what is the probability that what i’m seeing is by chance?, how confident am i that this data is really detecting a signal?, why is this happening and can i explain it?; and how many cases should i expect? (e.g., magnitude of event over time). these questions focus on detection, confidence, variance, and forecasting and d4 integrates a number of diverse analytical tools and methods that are crucial to a complete biosurveillance program. description d4 utilizes a big data cloud-based reference architecture and is comprised of widgets each built in r shiny studio using r code. d4 includes a user interactive map with “hot spots” of potential adverse events, customized databases with rapid query data visualizations, searchable social media (twitter) using regular stacked expressions, and forecast models. central to d4 is the disease correlation model (dcm), which uses linear regression to test the statistical significance of the relationship between the outcome of interest and explanatory factors (e.g., city, climate, and social media data). included is an adjusted r2 widget and p-value output to quickly quantify a dcm output goodness-of-fit. in addition, a forecasted widget calculates the number of estimated cases per day, over the number of days in the future by using estimates from a linear regression model that only includes time as the explanatory factor. for disease detection data generated from high-throughput laboratory analyses (e.g., real time pcr detection assays), a laboratory quality status widget evaluates all associated data generated though quality management systems and outputs a boolean (true/false) test result to provide the operator with a rapid quality assurance validation to laboratory data. d4 integrates multiple data streams into an r coding environment that enables open-source and plug-play statistical and widgetdevelopment capabilities. d4 backend resides in an amazon cloud instance allowing for scalability, speed, and controlled distributed access. as a prototype, d4 currently injects examples of the multiple data streams that may be of interest such as social media (twitter), climate data (precipitation, temperature), 911 call data, and two explanatory data sets that are epidemiological data (foodborne illness cases, flu patient counts) and other laboratory diagnosis data. d4 is currently modeling three us cities and its architecture enables access and filtering for hundreds of historic and real-time data feeds via representational state transfer (rest) application programming interface (api) calls, enabling incorporation of standardized, reliable, and timely information. audience engagement d4 is bringing new innovations and analytical tools in the area of bio-surveillance, disease detection, and disease reporting. depending on the source and quality of data, it can provide forecasting models with high accuracy accounting for different parameters and characteristics different types of disease demonstrate. besides the advanced analytical tools d4 offers, what makes this platform distinct and innovative is the ability to accept and handle data from multiple sources and formats and allows for increased sensitivity and specificity of the system. keywords biosurveillance; disease detection; open-source analytics; visualizations; r acknowledgments jr charlton and kris ford *karina n. alvarez e-mail: alvarez_karina@bah.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e2, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 120 (page number not for citation purposes) isds 2013 conference abstracts sero-prevalence of brucellosis in humans and their animals: a linked cross-sectional study in two selected counties in kenya eric ogola*1, samuel thumbi2, eric osoro3, peninah munyua4, sylvia omulo1, peter mbatha1, linus ochieng1, doris marwanga1, ian njeru3, 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��� ����� ������ �7� � ��9��� ��b�� ��� �� *sara a. taylor e-mail: sara@ehlert.org� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e115, 2014 ojphi-06-e112.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 134 (page number not for citation purposes) isds 2013 conference abstracts using social media for biosurveillance: gap between research and action tera reynolds*1, mark cameron2, mike conway3, amy ising4, eric h.y. lau5, jennifer olsen6, julie pavlin7, bill storm8, katie suda9 and courtney corley10 1international society for disease surveillance, boston, ma, usa; 2commonwealth scientific and industrial research organisation, acton, act, 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0��-."$&/��� �)gg������������ � ���� ����g� *tera reynolds e-mail: treynolds@syndromic.org� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e112, 2014 ojphi-06-e168.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 63 (page number not for citation purposes) isds 2013 conference abstracts the use of open source software to enhance public health initiatives erin hahn*1, sheri lewis1 and david blazes2 1national security analysis department, johns hopkins univ applied physics lab, laurel, md, usa; 2navy medicine professional development center, bethesda, md, usa � �� �� �� � � �� �� �� � objective ������ � ���� ��� ��� ���������� ������� ��� �� � 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&�����! ������� ��' ������&!���$�� �� ���� ��! ����( ��� ������ �$!(�����)**+�� ,��� ��� �� ��������� ��������� � ���� �� ������ � �������� ����� � ���� �����-�� �)*.)��#� � ����������� ���� ����� � ����� ���� ���� ��� ����������� ��� � ��� ����� �$!(����� � �� � ������ ����� � �� ����� ���������� ��������� ��� ������ � � ���� � �� � ��������� �� � � ������ � �� ��� �� � ��������������� ��� � �� � ����� � ���� �� ���� ,��� � ���� methods /�� �� ��� ������������ ��������� �� ��� ��������� �������� � ������ �� �� ��������� ����� ������ �� 0��� �����/�� �� ��� � ����� �� � ������ �� ���� ����� ������� ��������� ������ � ���� ��� ���� �� �� �� �� ����� �� ����� ���� � �������� ���� �� ��#����� �� ��� �� �� �� ��� �� �� �� �����' ��� � ����� ������������/�������� ��� ����� ��� ���� � �� ������� ����� ��� �$!(�� ����� �� 1 ��� ���� � � ���� �� � ���� ��� ��� � ����� ���� � ,� � � �� ��� �� ������ ��� ����� ��� ���� ��� ����������� ������� � �������� � ���� ���������������� ���� � �� ��� 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�23����� ����� � �� ��� � �� �� ���� �� ������� ��� �� keywords �� ������� ��� �� � 4� $�� �� ���� ��! ����( ��� �����4�3��� �� ! ����� ������ acknowledgments #�������1������ �� ������ � ����� ��� �������������� �� ���� ���5 ����� �!������� �� � � ���-�����!��1����6����/���� ����������5 �4� ��� 7���7 ����8� ' ��� ���� ���#����� �� ����� ��2 ��� ����� ���� �� ���� ��7 � ���� ���9 �� �� references 5 �����5��% ���� ��8!��5���� ��&/��&�:��1�(/���1�� �-%�� �� ����)*..�� �/3;�<�/����� �� �%� ���/� �� �� ��� �� � �#����� ���;� �������� 7�� � ����� ��� �� ����( ����� �5���� ��� ���������5����2;�=�+�<� .>?+*�� �� � (���/�3��� �� ��� ��( ���������� ��9��� ��� ��� <�% ������$�� �� ���������� �� ���$��� � �� ������2 � � �/�� �� �� ��( :����� 7 ���9���������8��1 � ������� ���8���������8� ��2��7� �� ����� &�����! ������� ��' ������$�� �� ���� ��� ����( ��� �������)**+�� ( ���������&!/+@�a�( �������� ��� �$�� �� ���� ��! ����( ��� ����� �)**+�� ���( ���������&!/=.�)�$��� � �� ������ ��� �$�� �� ���� �� ! ����( ��� �������)**+����)**@��� �$!(�2 ����b� �� ����8��� ���<�$!(����� � �� ��������������� �<�;"� � ��������� ���� �� �������� ��� ����� ��&!���������������������� ��� � � ����&�����! ������� ��' ������)@�� �� �� ��)*.)� *erin hahn e-mail: erin.hahn@jhuapl.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e168, 2014 ojphi-06-e122.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 163 (page number not for citation purposes) isds 2013 conference abstracts assessing best practices for grouping and analyzing urgent care center (ucc) and emergency department (ed) data sources within syndromic surveillance systems melinda c. thomas*, david atrubin and janet hamilton bureau of epidemiology, florida department of health, tallahassee, fl, usa � �� �� �� � � �� �� �� � objective ������� � ���� ����� ��� � ��������������������������� ��� ����� ����� �������� ������� ��������� ������������ � ��� introduction ����������������� ��� �� ����� ������������� ����������������� � �� ������� ��������� ����� ���� �������� �� ������������������ ! ��� ��"����� �� �������� ������� ����� �� ����#$�� �� �%&����� �� � �������� �"��� ������'������������'� ��� ��� ���"�����(� � ��� ���� � � ������ �!�� ���"������� ��"''"( "!�)���"���� ���� ��� �������� �� ������ ������� ��������"''"( "�'���������� ������ � �� ��������� ����� ����� ��������� ��� �������� ��� ������� ���� **������ ��������� �� ����� ������ �� �����������+����� ���"���� �� ����� ����,%,��� �� �� ����� ������� � ��� � �������� �������+� ��� �� �� ���� �� �� -�� ����� ��������������������-��� ��� �������� �� �+����� ������� ��� ������ ���������������� ���� ��������� ��� � 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����-� ��� ��� ����������� �� �� ���� ������"���� ��� ?� ��� �-� �������� ��������� �� � ������ �����4������� ���4������� ������������� ����� �5)5� ���������� � ���� ���� � ������� ���"��� �������� ��������� � ��������� �� ���5)5� ���������� ������-���� �������4���� ����������� ���-� �� ���� � ���� � ����� � ���"���� � �� �������� ���� ���� ���������������� ����� ����� ��������� �������� �� �� ���� ������ ���������� ���� �� ������� �� �� +�-����� ����� �� ����� ������� ���������������� ��� ���� �������� � ������ � +� ����� ������������ �� ��� ������ � �� ������� ���� �� ����� ������ ���/���� � ��� ������������������ �������� ���� ��� ���������-���������! �� ����� ��� ����/���� ��� �� keywords '��������� �����������@� ?������ ���� �� �� ���� �� @� ����� � ����� ��� ��@�"�������������� ��� acknowledgments ��� ����.�� �-� � ����� ��������� ������������� ��� � �� ���8�������?��! �������� ��5� ���� �� ����������?������������� �������� '�"����� ��������� ��� �� �� � ����� �� �� �� ��������?����� ����� � �� ���! ��� ����8������� �$�$>�a777#,#!7#6,� *melinda c. thomas e-mail: melinda.thomas2@flhealth.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e122, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts a framework for detecting and classifying outbreaks of gastrointestinal disease kathryn morrison*, katia charland, anya okhmatovskaia and david buckeridge epidemiology & biostatistics, mcgill university, montreal, qc, canada objective to develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of montreal, with the goal of improving early outbreak detection using simulated surveillance data. introduction outbreaks of waterborne gastrointestinal disease occur routinely in north america, resulting in considerable morbidity, mortality, and cost (hrudey, payment et al. 2003). outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. we seek to develop a framework for both detection and classification of outbreaks using information in both space and time. outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection. methods to generate outbreak data, we used a previously validated microsimulation model depicting waterborne outbreaks of gastrointestinal disease (okhmatovskaia et al. 2010). the model is parameterized based on outbreak characteristics such as concentration and duration of contamination, and calibrated to produce realistic outbreak data (e.g., emergency department visits from gi-illness, laboratory reporting to public health) in space and time. we are interested in identifying unique space-time signatures in the data that would allow not only detection, but also classification based on outbreak type. for example, to be able to detect and classify an outbreak as due to a water plant failure versus an food-borne illness based on unique space-time patterns, even though symptoms and temporal outbreak patterns may be similar. for the detection step, we use a hidden markov model (hmm) that accounts for spatial information through a spatially correlated random effect with an exponential decay. hmms have been used previously in disease mapping (green 2002) but not widely in space-time disease outbreak detection. for the classification step, we use a supervised clustering algorithm to classify the outbreak by source (e.g., water plant location) and type (e.g., disease). results preliminary results for the detection step show that the hmm can distinguish accurately between regions in an outbreak state versus those in a normal state at each time period. ongoing work for the detection step includes further evaluation of the hmm accuracy as a function of outbreak characteristics. for the classification step, we are evaluating the suitability of different supervised clustering algorithms for identifying the type of outbreak from the hmm results. conclusions if outbreaks are detected rapidly, interventions, such as boil-water advisories, are available to quickly and effectively limit the human and economic impacts. traditional public health surveillance systems, however, frequently fail to detect waterborne disease outbreaks. every disease outbreak has unique characteristics; simulation is the best method to estimate the capacity of syndromic surveillance to more efficiently detect different types of enteric disease outbreaks based on a variety of parameters. outbreak detection can be improved with advances in data availability, such as syndromic surveillance data that will increase timeliness of detection, and space-time information to allow for simultaneous detection and classification of outbreaks by important characteristics (type of outbreak, source of outbreak). keywords syndromic surveillance; disease outbreak detection; waterborne disease acknowledgments this research is supported by the canadian institutes of health research (cihr). references green pj. (2002). “hidden markov models and disease mapping.” journal of the american statistical association 97(460): 1055-1070. hrudey s., p. payment, et al. (2003). “a fatal waterborne disease epidemic in walkerton, ontario: comparison with other waterborne outbreaks in the developed world.” water science & technology 47(3): 7-14. okhmatovskaia a, verma ad, barbeau b, carriere a, pasquet r, buckeridge dl. (2010). a simulation model of waterborne gastro-intestinal disease outbreaks: description and initial evaluation. amia annual symposium. *kathryn morrison e-mail: kt.morrison@mail.mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e115, 2013 health information technology during covid-19 epidemic: a review via text mining ojphi health information technology during the covid-19 epidemic: a review via text mining meisam dastani1, alireza atarodi2* 1 social determinants of health research center, gonabad university of medical sciences, gonabad, iran. 2 department of knowledge and information science, paramedical college and social development & health promotion research center, gonabad university of medical sciences, gonabad, iran. abstract background: due to the prevalence of the covid-19 epidemic in all countries of the world, the need to apply health information technology is of great importance. hence, the study has identified the role of health information technology during the period of covid-19 epidemic. methods: the present research is a review study by employing text-mining techniques. therefore, 941 published documents related to health information technology's role during covid-19 epidemic were extracted by keyword searching in the web of science database. in order to analyze the data and implement the text mining and topic modeling algorithms, python programming language was applied. results: the results indicated that the highest number of publications related to the role of health information technology in the period of covid-19 epidemic was respectively on the following topics: “models and smart systems,” “telemedicine,” “health care,” “health information technology,” “evidence-based medicine,” “big data and statistic analysis.” conclusion: health information technology has been extensively used during covid-19 epidemic. therefore, different communities could apply these technologies, considering the conditions and facilities to manage covid-19 epidemic better. keywords: health information technology, covid-19, hit, text mining, topic modeling. doi: 10.5210/ojphi.v14i1.11090 *correspondence: alireza atarodi copyright ©2022 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction coronaviruses are large groups of viruses causing mild to severe diseases from the common cold to severe acute respiratory syndrome (sars) and middle east respiratory syndrome (mers). in december 2019, an emerging infectious outbreak was found in wuhan, hubei province, china, which was caused by the novel coronavirus (2019-ncov) [1]. the pandemic was spreading across the whole world [2]. there were human-to-human and healthcare worker transmissions; however, the source of the coronavirus disease [covid-19] has not been found, health information technology during covid-19 epidemic: a review via text mining ojphi and the route of pandemic transmission has not been fully understood. it is also probable that this virus to continue its mutations. the 2019-ncov has a long incubation period and strong infectivity; therefore, the prevention and control of covid-19 pandemic have faced great challenges. compared with the severe acute respiratory syndrome (sars) and middle east respiratory syndrome (mers), covid-19 has some new and different features; it has spread more rapidly due to increased globalization, a longer incubation period, and hidden symptoms [3]. due to the present circumstances, the use of technologies can help resolve the current crisis and make it simpler to manage [4]. as a center for disease control and prevention, the cdc also considers the use of information technology to manage covid-19 to be necessary [5]. health information technology is one of the new and interdisciplinary scientific areas in medical sciences that has attracted researchers' attention from various fields. health information technology includes different types of information and communication technologies to collect, transmit, display, and store patient data [6], that includes an extensive range of products, technologies, and services such as telehealth technology, cloud-based services, medical devices, remote monitoring devices, and sensors [7,8]. the use of information technology in health is called electronic health and has been extensively applied in the health care system for many years [9, 10]. the governments and health organizations can use smart approaches to overcome this epidemic due to the development and advancement of existing technology infrastructures; hence, the use of technologies to fight against this epidemic has increased during the covid-19 crisis [11]. at present, in line with the challenging and global spread of covid-19 (new coronavirus 2019), medical researchers are conducting many studies on the prevention and treatment of this disease, and the results of their studies are presented at conferences and are published in credible scientific journals [12]. moreover, much research has been carried out in health information technology and covid-19 [13, 14]. therefore, the rapid growth and variety of topics discussed in health information technology on one hand and the participation of experts of other fields in the studies related to health information technology, on the other hand, the analysis of published topics related to the application of information technology and covid19 is of particular importance for medical professionals, researchers, and policymakers. due to the increasing number of scientific papers and the considerable volume of published articles, evaluating and reviewing the articles one by one and manually extracting information and knowledge from this huge volume of texts is cumbersome or impossible. however, identifying patterns and extracting potential knowledge in large volumes of textual data is an important issue in various scientific fields [15]. the way to review these scientific texts quickly is through topic modeling and keyword analysis of the articles using automated text mining. topic modeling and text mining are statistical techniques that assess the publications and documents to identify their topic [16, 17]. topic modeling is a type of statistical modeling that explores latent patterns in texts and discovers the connections in a set of textual documents using machine learning [18, 19]. this type of modeling also provides a method and framework for smart review and exploratory analysis to researchers. scientists can apply this approach to review publications and documents to make a transparent and highly reliable analysis of a large volume of publications and documents in the shortest possible time [20]. health information technology during covid-19 epidemic: a review via text mining ojphi thus, the present study has evaluated the publication texts related to the use of information technology during the covid-19 epidemic by applying text mining techniques and identifying the published topics. methods this section consists of three steps: data collection, topic modeling, and topic analysis. -data collection the statistical population is all publications related to health information technology and covid-19, which has been indexed in the citation database of woscc (web of science core collection). the woscc advanced search was then applied to retrieve related publications. since the web of science is the most authoritative, extensively used, and the oldest citation database in the world [21], the validity and reliability of the retrieved data are valid. in the next step, the designed search strategy was searched in the advanced search of woscc on july 31, 2020. the search strategy applied in the present research was as follows: ts=((telemedicine) or (tele-medicine) or (telehealth) or (tele-health) or (mobile applications) or (mobile apps) or (m-health) or (mhealth) or (mobile health) or (ehealth) or (geographic information systems) or (geographic information system) or (gis) or (global positioning systems) or (global positioning system) or (gps) or (registries) or (registry) or (machine learning) or (deep learning) or (artificial intelligence) or (medical order entry systems) or (cpoe) or (computerized provider order entry) or (computerized physician order entry) or (medication alert systems) or (decision support systems, clinical) or (clinical decision support systems) or (cdss) or (clinical decision support) or (cds) or (information technology)) and ts= (covid-19 or covid19 or (coronavirus disease 2019) or (coronavirus disease-19) or (2019 novel coronavirus infection) or (2019-ncov disease) or (2019 novel coronavirus disease) or (2019-ncov infection)) the result of the advanced search was the retrieval of 941 articles; then, the title, abstract, and keywords of these articles were extracted for text mining analysis. topic modeling the extracted documents were then investigated by the text mining method and topic-modeling algorithm. as one of the most popular text mining methods, topic modeling is an efficient approach to analyzing many documents [22]; topic modeling is also applied in some review studies [20,23]. topic modeling is a machine learning approach to discover patterns or topics within a set of documents. latent dirichlet analysis (lda) is one of the implementation methods in topic modeling [24, 25]. lda is one of the best and extensively used algorithms and highly effective in identifying related semantic issues in scientific texts [26], and outperforms many other algorithms [27]. in contrast to its advantages, the lda algorithm has a limitation in predicting the number of topics. in this study, the number of predicted topics and the lda limit were health information technology during covid-19 epidemic: a review via text mining ojphi eliminated using the logarithmic (log) criterion (umass coherence) [28]. moreover, in the present research, six topics were selected for articles related to health information technology and covid-19 using umass criteria. it is noteworthy that selecting an excessive number of topics will lead to a large number of small and considerably similar topics [29, 30]. a higher number of topics also leads to the unavailability of no additional topic data. furthermore, due to the dispersion of keywords between topics, the interpretation of topics becomes more difficult [31] and the lower number of topics facilitates the interpretation of results [32]. the python programming language and gensim library have been applied to implement the topicmodeling algorithm [33]. the gensim library is an open-source topic-modeling tool that is compact and versatile, possesses a simple syntax, is easy to develop, and provides various libraries for working with texts [33]. numerous studies have also applied gensim to implement lda [34-36]. topic analysis the lda algorithm determines the optimal number of topics, the frequency distribution of each document in the selected topics, and the list of keywords related to each topic. however, it is not capable of automatic labeling; hence, topic labels are defined and specified manually [25, 37]. accordingly, the topics resulting from the implementation of the lda algorithm were labeled and interpreted using the most important words and articles of each topic and consulting health information technology professionals. results the results of implementing the lda topic modeling algorithm are presented in table 1, in which the six obtained topics are shown along with the most important words and related articles of each topic. table 1. topics of articles published in health information technology and covid-19. topic label top keywords top relevant articles topic 0 health information technology and management health, information, response, practice, visit, outbreak, experience, child, technology, lock-down a comprehensive review of the covid-19 pandemic and the role of iot, drones, ai, blockchain, and 5g in managing its impact[38] cyber security responsibilization: an evaluation of the intervention approaches adopted by the five eyes countries and china [39] effects of the covid-19 crisis on survey fieldwork: experience and lessons from two major supplements to the us panel study of income dynamics[40] topic 1 models and smart systems model, base, disease, system, health, case, a modeling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the covid-19 pandemic[41] health information technology during covid-19 epidemic: a review via text mining ojphi diagnosis, test, medical, spread tracking the covid zones through geo-fencing technique [42] multi-tiered screening and diagnosis strategy for covid-19: a model for sustainable testing capacity in response to pandemic [43] topic 2 big data and statistic analysis case, analysis, factor, infection, cluster, identify, risk, mortality, predict, death interdependence assessing for networked readiness index economic and social informative factors [44] main factors influencing recovery in mers co-v patients using machine learning [45] spatiotemporal clustering of middle east respiratory syndrome coronavirus [mers-cov] incidence in saudi arabia, 2012-2019 [46] topic 3 health care care, health, mental, risk, service, disease, disorder, infection, face, current ensuring mental health care during the sarscov-2 epidemic in france: a narrative review [47] the silver lining to covid-19: avoiding diabetic ketoacidosis admissions with telehealth [48] cardiac patients and covid-19: what the general practitioner should know [49] topic 4 telemedicine care, health, telemedicine, new, surgery, clinical, change, practice, challenge, technology navigating telemedicine for facial trauma during the covid-19 pandemic[50] telemedicine in the time of coronavirus [51] telehealth transformation: covid-19 and the rise of virtual care [52] topic 5 evidencebased medicine trial, clinical, report, participant, treatment, risk, evidence, drug, therapy, infection convalescent plasma or hyperimmune immunoglobulin for people with covid-19: a rapid review [53] effect of hydroxychloroquine on prevention of covid-19 virus infection among healthcare professionals: a structured summary of a study protocol for a randomized controlled trial [54] impact of pantoprazole on absorption and disposition of hydroxychloroquine, a drug used in corona virus disease-19 (covid-19): a structured summary of a study protocol for a randomized controlled trial [55] health information technology during covid-19 epidemic: a review via text mining ojphi fig.1. word cloud of topics of articles published in health information technology and covid-19. figure 1 also illustrates the ten most important words of each topic in the form of word clouds. in a word cloud, the words with larger fonts are more important and useful in the related topic. word clouds provide a unique way to summarize the content of text documents. the word's size in a word cloud is proportional to its importance and application in the whole text collection [56]. health information technology during covid-19 epidemic: a review via text mining ojphi fig.2. contribution of published articles in each topic. figure 2 indicates the rate of publications on each topic and shows that the topics of "smart systems models" and "telemedicine" had the highest number of publications, and the topic of "big data and statistic analysis" had the lowest number of publications. discussion the topic modeling acts as a text mining tool for processing, organizing, managing, and extracting knowledge, and is commonly applied to identify basic “topics” in texts [55] and provides a practical and useful representation of a very large collection of documents, publications, and the relationships between them [18]. the results obtained from topic modeling have identified six main topics for articles in health information technology. the titles of each topic in order of most publications of each topic include “models and smart systems,” “telemedicine,” “health care,” “health information technology and management,” “evidence-based medicine,” “big data and statistic analysis.” the topics obtained from the present investigation show the role and application of information technology during the covid-19 epidemic. the topic of “models and smart systems” had the highest number of published documents, which is about the application of models, algorithms, and neural networks in patients with covid-19. in this topic, medical data and images are categorized in more detail to provide a more accurate diagnosis of the disease. researchers also use machine learning models and algorithms, image recognition, semantic analysis, and other technologies and methods to conduct in-depth research on information systems, decision-making support, medical imaging, biomedicine, etc. [58-61]. apart from these cases, the optimization of algorithm performance is used increasingly in medicine due to the increasing volume of health-related information [62-64]. feng also stated that these topics were among the primary, interdisciplinary, and innovative topics in medical informatics [65]. kim and delen also considered the use of algorithms, neural networks, and computational technology for categorization/classification of diseases and symptoms as the most important issues identified in the topic of models, so that the possibility health information technology during covid-19 epidemic: a review via text mining ojphi of detecting anomalies by evaluating patterns has been shown [66]. moreover, amiri indicated that researchers were interested in carrying out studies in the field of smart systems and their awareness of the application and power of these related algorithms [13]. due to the impact of employing artificial intelligence models in detecting many diseases [6769], this issue's importance becomes more apparent and is an important reason for researchers to be interested in this topic. “telemedicine” is another topic with a considerably high number of publications. feng et al. also showed “telemedicine” as one of the innovative topics in medical informatics that has attracted the researchers' interests [65]. other studies have also reported the growth of the number of publications on this topic [70-72]. given that the purpose of telemedicine is to provide equal access to medical care regardless of geographical location [73], it is of interest to health organizations around the world. the continued advancement of internet-based audio and video communication technologies along with patients' desire for easier and more efficient ways to receive health care has led to the significant expansion of telemedicine functions over the past two decades, including teleconsultation, intensive care services, mental health monitoring as well as chronic disease management, as a supplement or an alternative to visiting doctor's office [73]. telemedicine was also a successful technology in epidemic diseases [74]. the present study also demonstrated the importance of telemedicine in covid-19 disease. “health care” is another topic that has been identified in the present study. since no specific treatment has been identified for covid-19, the need for self-care and self-control to prevent the spread of the disease is of great importance. people and individuals in the affected communities must learn to protect themselves from the potential dangers and harms of the outbreak of this mysterious and unknown new virus. therefore, the topics related to health information technology and health care are also important about covid-19 disease. health care is also an important issue in health information technology, and special attention has been paid to which in health information technology in different countries (75). “health information technology and management” is another topic that has been identified in the present study and refers to the application of health information technology in covid-19. health information technology provides the facilities to the medical personnel for managing numerous activities such as prescriptions for the patients, creation of electronic health records, testing and analysis data, etc. (76). the use of health information technology is also useful in the control and management of covid-19 (77). sedoghi et al. have also indicated that health information technology and health information systems were among the topics discussed in the articles in the field of information management and health informatics; the most central topic in this issue is dedicated to electronic health records [78]. kim and delen have also identified "adoption of hit" as one of the main clusters of medical informatics research, which has included topics such as electronic systems for recording patients' information, electronic prescriptions, data sharing, and electronic reminder system for health services [66]. health information technology during covid-19 epidemic: a review via text mining ojphi “evidence-based medicine” was the next topic identified in the present study; this concept refers to the presentation of experimental reports and evidence-based studies for better treatment and health care. evidence-based medicine is extensively promoted as a tool to enhance clinical outcomes, which refers to medical operations based on the best scientific evidence. the scientific literature is the main source of evidence for evidence-based medicine, although evidence-based literature should be completed by local evidence, the practice-based operation for individual and site-specific clinical decision-making [79]. for medical informatics research, kim and delen also discussed a topic named knowledge presentation which included a classification of medical texts in reporting vaccination side effects, a semantic classification of diseases, the use of medical notes provided in the ehr system for evaluating the symptoms of heart disease, better understanding of the methods of early diagnosis of diseases, management of clinical records, classification of medical report texts and analysis of texts, the discovery of knowledge and its reuse [66]. feng has identified the topic of “evidencebased medicine” in medical informatics studies and has stated that it is less important than other topics on medical informatics [65]. “big data and statistic analysis” was the topic with the least number of publications in the present study suggesting that researchers apply existing data and its statistical analysis to diagnose, control, and make related predictions. undoubtedly, after the global outbreak of covid-19, a great deal of information about this virus, such as the number of people infected and or killed by covid-19, epidemiological reports, the structure and activity of the coronavirus, the way of transmission of the virus, mostat-risk population, infection clusters, and symptoms of infected people have been available on official websites, scientific databases, hospitals, and other resources. researchers can analyze these data to extract different models of the virus for identifying unknown aspects of the virus [14]. conclusion the covid-19 epidemic is currently showing a global pandemic trend, and our understanding of the new coronavirus is deepening. the practitioners of global health information technology should be proactive and use their professional skills to respond to the covid-19 epidemic. the results indicate that health information technology has been extensively used during the period of the covid-19 epidemic. therefore, different communities can use these technologies to manage the covid-19 epidemic better, considering the circumstances and facilities. the present investigation also showed that using models and smart systems and telemedicine in covid-19 disease were the most important topics published in articles related to health information technology. limitations the present study has categorized scientific publications related to health information technology and covid-19 using text mining and topic modeling techniques. the data were obtained by searching for keywords related to covid-19 from the web of science database. for this purpose, the keywords related to health information technology have been selected based on the most important and popular applications of health information technology; however, some new or less widely used technologies in these keywords might not be searched. health information technology during covid-19 epidemic: a review via text mining ojphi since this study has identified six main topics and applications of health information technology in covid-19 automatically and by applying text mining techniques and according to published scientific texts; therefore, each topic may have several sub-topics that have not been mentioned in this study. therefore, it is suggested that each of the main issues be specifically evaluated and the sub-topics related to them be identified and analyzed in future studies. given that the data of this study is related to the first eight months of the covid-19 epidemic, there may be other applications of information technology associated with this disease at other times, and researchers need to do similar studies for these situations. due to the fact that the data of this study was extracted only from the web of science database, some scientific publications that were published in other databases may not have been included. the data of this study have been reported without separation of countries and geographical regions; here, there it is needed to conduct some studies on the 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of i-kelahiran – sabah’s experience 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi the birth of i-kelahiran – sabah’s experience b.r. dhesi1*, w.l. cheah1 1. department of community medicine and public health, faculty of medicine & health sciences, universiti malaysia sarawak abstract though over the years, sabah has recorded an increase of childbirth with better healthcare indicators, improving maternal and childcare has always been a major challenge. therefore, with the aim of addressing the current issues of birthing discrepancy, delayed reporting of high risk pregnancy and maximum immunization coverage within the state of sabah, “i-kelahiran”: inovasi kelahiran: was developed in june 2012. this computerised birthing system acts not only as an online storehouse of information, it also traces data and generates reports to reduce enormous duplication, save cost and time, as well as eliminating delays and confusion on management of health information. the system also helps to overcome the issue of collecting data from rural health personnel, particularly with the extreme geographical terrain in sabah. this paper discusses how i-kelahiran, a health information system was developed under the sabah health department and shares its experiences in implementation. the experience and feedback from this system will help to build a full-fledged system capable of handling childbirth data at the higher level in borneo. keywords: healthcare, maternal and childcare, health information system, borneo abbreviations: inovasi kelahiran (i-kelahiran) correspondence: dr.dhesi@gmail.com* copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction malaysia is a southeast asian country with thirteen states, consisting of two different geographical regions that are divided by the south china sea. the rapidly growing economy has made a major impact, resulting in some commentators referring to it as the "asian dragon" with a total population of 28,401,00 and a gross national income per capita of 16,530 intl. us$ (world bank, 2013). healthcare in malaysia has undergone some radical transformations. the earliest pre-colonial medical cases were confined mostly to those traditional remedies that are evident today in chinese, malay, indian and other ethnic groups. however, with the birth of colonialism, more modern and westernized medical practices were slowly introduced to the country. the total expenditure on health per capita (intl $, 2011): 616, and total expenditure on health as % of gdp (2011) is about 3.8% (world health organization, 2012a). at present, malaysia's healthcare system is divided into two sectors—the public sector and the private sector whereby the government places importance on the expansion and development of health care, putting 5% of the government social sector development budget into public http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi healthcare — an increase of more than 47% over the previous figure. this has meant an overall increase of more than rm 2 billion to improve in many areas including the refurbishment of existing hospitals, expansion of the number of polyclinics, and improvements in training and application of information and communication technology (ict) tools in the healthcare network (world health organization, 2013a). sabah, is the second largest state in malaysia, is situated at the northern part of the island of borneo, the third largest island in the world. it covers an area of 72,500 sq. kilometers with a coastline of 1,440 kilometers long washed by the south china sea in the west, the sulu sea in the northeast and the celebes sea in the east (see figure 1). figure 1: sabah, malaysia (source: http://www.malaysia-maps.com/sabah.htm) known to the world as "the land below the wind". sabah is rich not only in natural beauty and resources, but also in the cultural heritage of its people. sabah is also endowed with a heterogeneous population. the indigenous populations are made up of some 30 groups using more than 50 indigenous languages and not less than 80 dialects. the main ethnic groups are: the dusun/kadazan the largest group who make up nearly one third of the population, the murut, the paitan and the bajau (refer figure 2). other indigenous groups include the bonggi, the iranun, the ida'an and the brunei. in addition, the chinese make up the main non-indigenous group. healthcare in sabah in relation to healthcare indicators, the mortality rate for children under five years of age in sabah was 9.8 per 1,000 live births, or 525 deaths on 2010. this figure is still higher than the national average of 8.0 per 1,000 live births in 2008. on the other hand, sabah recorded infant mortality rate of 8.8 per 1,000 live births on for 2010, equivalent to 471 infant deaths. this is, however, still higher than the national average of 6.2 per cent per 1,000 live births in 2008. in terms of maternal mortality rate, sabah recorded 56 cases, or 104.4 per 100,000 live births for 2010. according to the national data, the reduction in maternal mortality rate has been progressing well from 44 maternal deaths per 100,000 live births in 1991 to 28.1 deaths in 2000 (jkn sabah, 2010). http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi figure 2: population & ethnicity in sabah (source: department of statistic malaysia, 2010-2012) the millennium development goals (mdgs) are eight international development goals that all 189 united nations member states and at least 23 international organizations have agreed to achieve by the year 2015. mdg 4 & 5 respectively focuses to reduce child mortality by twothirds, the under-five mortality rate between 1990 and 2015 and to improve maternal health by reducing three quarters, the maternal mortality ratio between 1990 and 2015. improving maternal health care and to achieve 100% coverage of immunization, has always been a major challenge in view of achieving the millennium development goal 4 & 5 (world health organization, 2013b). according to the national health morbidity survery 2 (nhms ii, 2006), 60% of sabah’s vast majority people are living in rural areas. the distance between health facilities is 7.2 km (peninsula malaysia, 5km) and distance between homes and health facilities is about 5.1 km (peninsula malaysia 2.5km) (nhms ii, 2006). extreme geographical terrain has always been a challenge for community health nurses to collect data and reports from hospitals and district health facilities. the non-citizens in sabah accounts to 25% of the general population in sabah. the high rate of transmigration of illegal immigrant has also been another challenge to the sabah health department (refer figure 2). national birthing national birthing report and post natal tracing are done by the public health division. standard operating procedure (sops) of the malaysia ministry of health requires that all pregnancies or deliveries have to be notified and reported to the public health division in respective states. the reports are handed over manually throughout malaysia leaving the public health division to take http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi full responsibility on data collection from all the hospitals and private birthing centres. a designated nurse from each community health clinic is required to travel to government hospitals and private birthing centre to collect birthing data and also to trace high risk pregnancies, post natal home visits and immunization data. the information is then compiled at the end of the month to be submitted to the district health office and later to the state health department. this has always been a long tedious process for all nurses, matrons and district health officers, to validate all reports at the end of the month. reports produced by the manual way seem to be impractical due to time and cost involved. since this process itself takes half of the nurses’ time to travel, collect and compile data, this has somehow reduces their time in the clinical work. the details on the methods and issues are presented in figure 3 & 4. figure 3: conventional method of reporting and tracing of birth & immunization data http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi figure 4: factors contributing to service issues in sabah state health department furthermore, the existing state national birthing statistic is compiled by various sources: 1) health management integrated system (hmis), which is based on place or origin of a mother. for example, if a mother is from kota kinabalu and decides to deliver in sandakan, the delivery information captured should be returned to kota kinabalu as place of origin. however, in most cases, the data was found to be in both places; 2) tbis (tuberculosis information system) birthing data which is captured from bcg immunization that are given to children. these data are extracted based on place of delivery (occurance), irrespective from where the mother lives; 3) hospital delivery. all inward deliveries from the hospital services. these resources give three different figures which has always been a “haunting problem” in every state department of health in terms of gathering information on birth and vaccination coverage (refer to table 1). table 1: birthing data as reported by different sources 2011 (bpkk, jkn sabah, 2011) no resources birth 1. tbis 63,228 2. hmis * e-reporting 2011 60,515 3. still birth 59,949 4. registered birth (department of registration) 60,515 tbis=tuberculosis information system, hmis= health management integrated system i-kelahiran information and communications technology (ict) has been referred to as a ‘key instrument’ in healthcare delivery and public health internationally (drury, 2005). when designed and implemented effectively, ict can improve access for geographically isolated communities; provide support for healthcare workers; aid in data sharing; provide visual tools linking population and environmental information with disease outbreaks; and is an effective electronic means for data capture, storage, interpretation and management. in this context, ict for health refers to any tool that facilitates the communication, processing or transmission of information by electronic means for the purpose of improving human health (bukachi & pakenham-walsh, 2007). it has also been shown to be increasingly important in the education and professional practice of health care workers. the world health organization (who) discusses the benefits of using ict in the primary healthcare setting in terms of better access to information, improved communication between colleagues, facilitating continuing professional development and providing learning tools for healthcare professionals, patients and the community as a whole. (rowe, 2008) it is widely recognized that the role of ict in the near future of healthcare will be significant and that healthcare professionals will need to be computer literate in order to function effectively in an increasingly digital environment (national health service, 1998). one estimate is that by 2010, 30% of a medical practitioners time will be spent using ict (skinner, biscope, poland, 2003), since it is thought to have the potential to transform healthcare delivery and improve the quality of care. today, hundreds of healthcare information systems are used in hospitals and community clinics to serve numerous groups of healthcare professionals in their daily work with patients. in hospitals, healthcare information technology has already been shown to improve http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi quality by increasing adherence to guidelines, enhancing disease surveillance, and decreasing medication errors (chaudhry, 2006). during the past few years, the greatest achievement in health informatics under the health care system of malaysia is the ability to monitor and control the emergence of communicable diseases, to respond in a more timely and effective manner (e-notifikasi, e-notis, mytb) and also to enable remote areas in the country to gain clinical consultations via tele-medicine. this two great achievement has brought malaysia to a different level of paradigm shift in health care services. in june 2012, the sabah state health department developed and subsequently implemented a new health informatics system called i-kelahiran: inovasi kelahiran. this system was created to intervene the current issues of birthing discrepancy, delayed reporting of high risk pregnancy as well as the immunization coverage within the state. i-kelahiran is a computerised birthing system which creates an online storehouse of information for tracing and reporting. it also reduces enormous duplication, saves cost and time, as well as eliminates delays and confusion associated with the collection or utilisation of health information that is scattered in health institutions, clinics and hospitals around sabah. it is the hope of sabah state health department that by introducing i-kelahiran as a new health informatics system, it will evoke positive changes and produce effective solution in managing the current health care issues. what is remarkable is that there are no special extra inputs and financial implications to track every mother, child and delivery service since it is a web based programme with open source by providing a suitable online bridge. the signal can be sent to any remote place in a typical sabah extreme geographical terrain with the help of broadband/wan. the characteristic of the i-kelahiran system are: 1. storehouse of information on deliveries, high risk pregnancies and immunization 2. information stored by a specific health centre/hospital can be automatically managed by the system to deliver the reports to another specific health centre or hospital 3. special pop up alerts to respective community clinics or district health office on high risk pregnancies, delayed post natal visit or due vaccination of a client 4. captures, and detects client who transfers from one health clinic to another or to a different district in a state to deliver and inform the local health clinic of that particular district that a client not within their operational range is currently under their care (post-natal, vaccination) 5. auto generated immunization online calculator of real time which estimates a districts vaccination achievement. this report can be auto-generated and exported to microsoft excel software for reporting purposes (tbis 103) 6. auto-generated birthing report (kib 103) 7. daily, weekly, monthly and selective reports can be printed as required and accordingly 8. each step taken by individual staff involved in the processing of the data entry will be recorded in the verification information system for security purpose http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi 9. enhance the accuracy of data as data is input at a single point of entry at responsible center to avoid duplication and overriding of information. 10. the use of personal id and password for each health personnel in the system tightens the control of the confidentiality of the data. 11. electronic delivery documents for non-malaysian can be created and printed under the syste, can be legally used to replace the manual sheet of documents. 12. easy to search menu: birth records of clients or baby in any facility in the state to prevent in the delay of post-natal visit and immunization follow up. technical info on i-kelahiran figure 5: technical architecture of the i-kelahiran system the software design, application and implementation (open source) was done by the author and the ict unit of hospital likas and sabah state health department. existing server host was used in order for it be accessed by users from all over sabah. web hosting by (php,mysql, and javascript) which can be accessed at: http://ikelahiran.jknsabah.gov.my. the system was built; • php 5, mysql • css javascript • apache & aws webserver free + php integrated • heidi 6 mysql administrator & browser • phpmyadmin end users will be able to access with web browser ; ie7/ie8/firefox/chrome browser which permits javascript and jquery, css minimum 1.0 and xml. network access used: http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi mohnet, 1govnet, streamyx, broadband by various operators and even android and apple mobile devices. the author and ict team has design in such a way that end users can have various methods to access the system due to poor internet connection by local service providers in certain part of malaysia. limitations in order to make sure this system is successful, few important issues need to be addressed and overcome. these include the integration and sharing of data between hospitals and primary healthcare providers, and establishment of a proper work flow in disseminating institutional based data. sharing data across multiple platforms within the health clinics and hospitals setting is a way to add value to the system since these data can be used as part of the information for better decision-making. however, data collected from different entities within the ministry is not routinely shared. furthermore, the data gathered did not include those births from the private clinics and hospitals. thus, the data might not be representative of the entire nation. in addition to that, issue of privacy and confidentiality is often being highlighted as this concern the sharing of individual health records and personal information. therefore, it is important that such system should be handled with strong technological infrastructure that would protect the right of each patient. conclusion i-kelahiran was introduced with the hope to evoke more positive changes and instill more effective approach in improving current birth data management. with this new technology, it is hope that all enormous duplication and errors of manual reporting can be reduced and it further improves the productivity and job effectiveness of the nurses in tracing high risk pregnancies and immunization coverage in line with the millennium development goals. there is still a lot of work to be done, nevertheless the spirit of the implementation team is high and optimistic. currently, the plan to create and offline data base which automatically sends the required information to respective hospitals and health clinics are in the process of planning. acknowledgements we would like to express our deepest appreciation to sabah state health director, sabah deputy state health director, all staff of sabah state health department for making this project successful. financial disclosure no financial disclosure. competing interests no competing interests. http://ojphi.org/ the birth of i-kelahiran – sabah’s experience 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e195, 2014 ojphi references bukachi f, pakenham-walsh n. 2007. information technology for health in developing countries. chest. 132(5), 1624-30. pubmed http://dx.doi.org/10.1378/chest.07-1760 chaudhry b. 2006. impact of health information technology on quality, efficiency, and costs of medical care. ann intern med. 144(10), 742-52. pubmed http://dx.doi.org/10.7326/00034819-144-10-200605160-00125 drury p. 2005. the e-health agenda for developing countries. world hosp health serv. 41(4), 38-40. pubmed department of statistic malaysia. 2011. population distribution and basic demographic characteristics. available at: http://www.statistics.gov.my/portal/download_population/ files/census2010/taburan_penduduk_dan_ciri-ciri_asas_demografi.pdf rowe m. 2008. information & communication technology in health. journal of community and health sciences. 3(1), 68-77. sabah state health department immunization and birthing report. 2010-2012. laporan tbis. bahagian pembangunan kesihatan keluarga. kesihatann awam, jkn sabah. 103, 20102012. sabah state technical meeting. 2012. laporan bulanan perjawatan (kesihatan awam) bahagian pembangunan keluarga keluarga, jkn sabah. edisi jun-oct 2012. jabatan kesihatan negeri sabah. skinner h, biscope s, poland b. 2003. quality of internet access. barrier behind internet use statistics. social science & medicine. 57(5), 875-80. pubmed http://dx.doi.org/10.1016/s0277-9536(02)00455-0 world bank. 2013. world development indicators database. available at http://data.worldbank.org/data-catalog/world-development-indicators. world health organization. 2012a. malaysia. available at: http://www.who.int/countries/mys/en/. world health organization. 2013a. malaysia health system review. health systems in transition. 3(1), 1-103. world health organization. 2013b. millennium development goals (mdgs). available at: http://www.who.int/topics/millennium_development_goals/about/en/ http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17998362&dopt=abstract http://dx.doi.org/10.1378/chest.07-1760 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16702590&dopt=abstract http://dx.doi.org/10.7326/0003-4819-144-10-200605160-00125 http://dx.doi.org/10.7326/0003-4819-144-10-200605160-00125 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16512063&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12850112&dopt=abstract http://dx.doi.org/10.1016/s0277-9536(02)00455-0 the state of information and communication technology and health informatics in ghana 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 the state of information and communication technology and health informatics in ghana emmanuel kusi achampong 1 1 university of cape coast, ghana abstract information and communication technology (ict) has become a major tool in delivery of health services and has had an innovative impact on quality of life. ict is affecting the way healthcare is delivered to clients. in this paper, we discuss the state of ict and health informatics in ghana. we also discuss the state of various relevant infrastructures for the successful implementation of ehealth projects. we analyse the past and present state of health informatics in ghana, in comparison to other african countries. we also review the challenges facing successful implementation of health informatics projects in ghana and suggest possible solutions. keywords: information and communication technology, health informatics. introduction ict presents many opportunities for improving the performance of health systems in developing countries. this has been demonstrated in several pilot projects across the developing world 1 . the availability of affordable equipment has also contributed to the several initiatives aimed at improving the effectiveness of health care providers, the efficiency of health care managers and new opportunities for health care clients. ict has assisted in driving down healthcare costs 2 ; and improved the delivery and effectiveness of healthcare services through help in disease management, improved patient safety and decision support for practitioners 3 . various systems have been developed to aid health care delivery such as local area network based patient information systems 4 , and online health information for patients and medical personnel. state of ict in ghana we live in a world in which all aspects of life are influenced by ict. in this paper, we discuss ict as the driving force of health informatics. ict is a tool that can aid dissemination of information through electronic media. we define ict as a tool or technology for gathering, storing, retrieving, processing, analysing and disseminating information electronically. ict is a fusion of telecommunication and computing with the aim of processing and disseminating information. there are many factors that determine the implementation and use of ict such as ict expert knowledge, user’s attitudes, etc. http://ojphi.org/ the state of information and communication technology and health informatics in ghana 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 since the first internet connection was set up in 1989, ghana has been a regional leader in ict 1 . over the past two decades the government and the private sector have worked together to maintain this lead. the state’s on-going ict for accelerated development (ict4ad) programme has resulted in numerous improvements in the sector and the economy as a whole. the plan which is now being redesigned to meet the current needs of the rapidly changing ict sector is expected to play a major role in the coming years. the private ict sector is highly competitive. ghana is home to about 180 internet service providers (isps), though business is concentrated among a handful of large firms, and only around 30 companies are currently active. internet penetration estimates range from 4% to 6%, but most major local players and multinational ict monitoring organisations concur that the nation has one of the highest ict penetration rates in sub-saharan africa. most people access the internet at the country’s numerous internet cafes, though thanks to rising incomes throughout the country a steady increasing number of ghanaians are able to afford an internet connection at home 1 . the nation serves as a landing point for a number of huge submarine communications cable projects such as glo1, sat3, and wasc. these will boost overall connectivity and network speeds and push down prices. additionally, both the government and private sector players are working to increase internet awareness among the wider population. with these efforts in mind, internet penetration is expected to increase dramatically over the next decade 1 . government influence in ict a number of state organisations are involved in ict in ghana. the ministry of communications (moc) oversees general policy and state-sponsored initiatives. the national information technology agency (nita), established in 2008, is the moc’s ict development and implementation arm. as of early 2011 nita was working to revitalise ict4ad to better reflect the country’s current needs 1 . the ict sector is regulated by the national communications authority (nca), which was set up in 1997 as the result of new telecommunications legislation passed the previous year. ghana’s reputation as one of the most liberal ict markets in africa is largely the result of pro-competition policies put in place by the nca. in march 2011, the nca introduced a bill that would give the authority new powers over ict and telecoms companies, including the right to approve acquisitions and other large share purchases. the bill is currently under discussion in parliament. in addition, nca is also working to advance the spread of highspeed internet services throughout ghana. ownership of computers personal computers (pc) and laptops are largely the main equipment used to exploit the potential of the internet and networked services. therefore, it is important to determine the level of ownership of computers. from a survey, only 5.0% of the sample from ghana had a pc/laptop at home 5 and this formed one-third of the situation in south africa. though there has been slight improvement in the ownership of pc from an earlier survey which showed that only 4% of the sample had computer/laptops at home 6 , the fact still remains that ownership of the technology is very poor in the country. in spite of the global fall in prices and a seeming growing trade in used computers in the country, these have not significantly http://ojphi.org/ the state of information and communication technology and health informatics in ghana 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 improved ownership of computers in the country. becoming computer literate is one of the basic prerequisites of participating in the digital revolution as well as achieving the objective of the national ict4ad policy. regretfully, most household computers are not connected to the internet. a survey showed that only 0.3% of the households in ghana had internet connection at home 5 . this is far below countries such as south africa (4.8%), namibia (3.3%), kenya (2.2%) and cameroon (1.18%). this has serious implication in widespread use of the service at the household level. internet at home will help students, as well as parents to utilise the vast potential of the service and its lack at residential level is a major limiting factor to exploiting networked services 5 . notwithstanding, computers have been available at various workplaces in hospitals and health centres. the challenge has been with the skill to use these computers for its intended purpose. secondly, connectivity at the various health centres has been a major challenge. ict infrastructure computing equipment (desktops, laptops, servers), networking devices (switches, routers, wireless access points, firewalls, local area networks (lans) and wide area networks (wans)), multimedia systems (television sets, vcd and dvd players, camera’s and camcorders), mobile telephony and communication (personal digital assistants (pdas), cell phone, landlines, fax machines etc.), imaging (desktop and network printers) and internet systems (gprs, adsl, vsat and modems) form the basis for ict infrastructure 1 . mostly every health facility has computing equipment, multimedia device, imaging and printing system, communication and internet system. the existing ict infrastructure have not been fully integrated and networked in a manner to support the healthcare delivery system 1 . apart from few hospitals with a fully functional local area network (lan), most of the healthcare providers have restricted their lans to the front office, records and pharmacy departments/units of their facilities. the lans are mainly used to support the automation of pharmacy department/units and front office operations like patient registration and records keeping 1 . multimedia systems are employed for playing back medical and non-medical documentaries and movies at the front desks. routine planned preventative maintenance is not carried out because of budgetary constraints. service level agreement is not used to define the nature and quality of ict services outsourced. ict policies in ghana there are a number of ict policies that have been put in place to facilitate the quick implementation of ict projects in the country. there is the information and communication technology for accelerated development (ict4ad) policy which is geared towards improving the socio-economic status of the http://ojphi.org/ the state of information and communication technology and health informatics in ghana 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 citizens including healthcare. the ict4ad policy represents the vision for ghana in the information age. there is the health sector ict policy and ehealth policy for the country. the goal of the ehealth strategy is to harness the potential of ict to improve the health status of people living in ghana. telephony system in ghana the telecommunication sector of ghana is one of the most liberalised markets in africa. the market has two national fixed-network operators and six operating mobile telephone companies. the fixed-line telephone segment is almost a monopolistic market since vodafone ghana controls almost 98% of the market, while airtel, the second network provider, has only 2% market share. unlike the fixed-line telephone market, there is rigorous competition in the mobile telephone market and this has contributed to an improved penetration rate in the country. in 2008, the telephone penetration stood at 52.4%, of which mobile telephones contributed 99%. the analysis showed that the deployment of fixed-line telephones has been on the decline and between 2003 and 2008; it experienced a negative compound annual growth rate (cagr) of 13.1, while mobile telephones had a positive cagr of 70.8% 7 . internet in ghana in august 1995, ghana became the second country in sub-saharan africa to have full internet connectivity. the country is directly connected to the world’s first submarine fibre-optic cable system, sat-3/wasc/safe, which links africa to europe and asia 12 . network computer systems ltd (ncs) established the first connection. ncs was given its own vsat (very small aperture terminal) gateway as a result of the constraints experienced by the then ghana telecom (gt). by 2002, the national communication authority (nca) had licensed 52 internet service providers (isps), although only about ten were operational at that time 8 . there have been several e-mail systems in ghana over the years, which have been based primarily on fidonet and unix to unix copy (uucp). uucp refers to a suite of computer programs and protocols allowing remote execution of commands and the transfer of data files and e-mail between computers. ghana gained access to fidonet, an early international bulletin board system, in 1989, under the aegis of a programme sponsored by the un’s economic commission for africa and the canada-based international development research centre. in the early 1990’s the system was upgraded, and access was expanded to include 23 scientific, governmental and educational entities. by 1995 the “.gh” domain name had been registered with the internet assigned numbers authority (iana), the international organisation that coordinates top-level internet protocol resources. much of this development was undertaken by the then ghana telecom (gt), which was launched by the government in1974 and served as the national telecoms operator until 2008, when 70% of the firm was sold to vodafone 1 . as of mid-2009 there were around 47,000 internet subscribers in ghana, which makes for a penetration rate of just 0.2%, according to internet research, a local ict research firm. most internet subscriptions are held by businesses or internet cafes, and so are used by multiple individuals. consequently, the total number of internet users in ghana is actually much http://ojphi.org/ the state of information and communication technology and health informatics in ghana 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 higher, though estimates vary widely. according to the international telecommunication union (itu), the un’s ict development agency, as of june 2010 ghana had 1.3m internet users, which translates as a penetration rate of 5.3%, up from 997,000 (4.2%) in 2009, 880,000 (3.8%) in 2008, 401,300 (1.8%) in 2006 and just 30,000 (0,2%) in 2000. the 2010 penetration rate is among the highest in the sub-saharan region, but lower than the african average of 10.9%, though this is skewed by the north african countries, the majority of which have internet penetration of 20-30% 1 . in 2009, 58% 27,399 subscribers – of all internet subscriptions were for high speed broadband packages, an overall penetration rate of around 0.1%, according to internet research, a local ict firm. by 2015, national broadband strategy, which falls under the aegis of ict4ad, seeks to increase national broadband penetration to 50%, reduce the cost of broadband by 80% and lower the cost of broadband-related hardware, including pcs, by 90%. in addition to improving high-speed internet access and the programme is also expected to boost gdp growth 1 . outside ghana’s urban centres, internet penetration falls off dramatically. most of ghana’s rural inhabitants access the web through their mobile phones or not at all. estimates for internet penetration in rural ghana hover around 1% 1 . the internet has become a very useful infrastructure in utilising the opportunities of the digital revolution. technological convergence and the development of multi-media services and increasing business applications of the internet have made access and usage a fundamental issue in the participation in the information society. ghana health informatics: past and present health informatics in ghana is at the infant stages. a lot of informatics projects are disjointed and there is no coordination between these projects. there are whole lots of pilot projects that are running in the country. pilot projects are being implemented in some hospitals with respect to health management information system (hmis). most hospitals in the country are partially electronic. efforts to establish an adequate management information system throughout the health sector have been on-going for some time now. examining the current state within the health sector, it can be concluded that the essential building blocks for establishing a proper hmis are in place:  hmis strategic plan  policy and legal framework for health data reporting  medical records policy  framework for a central data repository  computerised district health management information system  the establishment of a centre for health information at central level health management information systems the use of health management information system is not widespread. there are currently two main applications used for information management. one version is used for the management of clinical business process whilst the other supports the collection and aggregation of data, http://ojphi.org/ the state of information and communication technology and health informatics in ghana 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 and is mainly used for reporting. pdas are sometimes used for collecting data at the district and community levels. the district health management information system (dhims) software application was primarily used for data capture, aggregation and generation of management reports. it has been replaced with the district health information system (dhis2) which is being deployed for data capture, aggregation and generation of management report. different software is being used by various hospitals. these software applications are modular in nature and do not support the full processes in the hospitals. the extent to which these processes are supported by information systems varies from hospital to hospital and from activity to activity. the characteristics of the hospital processes are as follows:  documentation on admission, capturing of treatment regimen and discharge summaries are all done manually and kept in folders  key elements of the patient record in hospitals remain exclusively paper-based.  imaging and diagnostic results are not available electronically and are also not accessible from remote locations.  electronic versions of prescription history is not available  there is no electronic logistics and supply chain management system in place for medicines and non-consumables. most activities are carried out manually.  there are no systems for the generation of electronic medical records. this impact significantly on the arrangements for referrals.  long waiting time for patients  inadequate staffing levels clinical and non-clinical  challenges with financial management reporting national health insurance scheme (nhis) the government’s health insurance scheme plays a vital role in the financing arrangements for the health care providers. the national health insurance authority (nhia) has deployed an ict infrastructure for the automation of health insurance services. all accredited healthcare providers operate a common ict platform with common protocols for patient’s authentication and claims management. there are also plans to deploy an online claims management system. the ict platform for the national health insurance does not currently support any shared services. it meets only the business processes and needs of the nhis. human resource system the availability of staff with clinical and non-clinical background and a desirable level of it literacy are crucial in any health informatics project. the ministry of health (moh) does not have adequate staffs with skill sets in health informatics systems. the few staff who have undergone professional it training are not involved in mainstream ict related activities because the existing human resource establishment post does not have a structure for ict professionals. http://ojphi.org/ the state of information and communication technology and health informatics in ghana 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 there is no routine or formal training for professionals with clinical and non-clinical background in ict related courses like networking, systems administration, database administration, security, fundamental of computing and web-based systems, etc. diagnostic information systems several health facilities have diagnostics and imaging equipment. this equipment is used for the conventional laboratory examinations and x-rays. the use of paper-based documentation for the diagnostics and films for x-rays are the standard practice. technicians usually carry out tests whilst physicians or other specialist handle the interpretations of results. even though some diagnostic and imaging equipment support automation and have the capacity for digital imaging they are hardly used for that purpose. telemedicine and elearning telemedicine is the use of information and communications technology to deliver healthcare services, promote healthy lifestyles and education, where the patients or clients are geographically separated. telemedicine facilitates clinical consultation including patient assessment, diagnosis and treatment, continuing professional education, health promotion, and healthcare management. there is currently a national coverage for telecommunication via landlines and cell phone. internet connectivity is accessible nationally. it is available via adsl, gprs and vsat. all the isps have internet enable features on their network. the ministry of communication is rolling out a programme for optical fibre connectivity for all regional hospital and selected district hospitals. a national wide area network (wan) is also being deployed by the ministry of communication for use by the municipal and district assemblies (mdas). there are however challenges with bandwidth and cost. there is clear commitment from the top management to establish a robust and dependable telecommunication infrastructure throughout the country. in spite of the on-going activities, there is still a large “digital divide” delineated by geography, income, education level, literacy, etc. elearning is practiced on a very small scaled. even though elearning is recognised by the moh as one of the means of carrying out cost effective, efficient and convenient medical education, the medical schools and training centres have been slow in changing their teaching methodologies. no training institution has an elearning-enabled training curricular with interactive features and online tutor support functionalities. the only elearning project is the pan african enetwork project at the kwame nkrumah university of science and technology (knust). the university as an academic institution and the university’s hospital are being used as the project site. participation and connectivity to the elearning project is restricted to a few selected sites in africa and india. other local hospitals and universities are not allowed to connect to the network. security, data protection and confidentiality http://ojphi.org/ the state of information and communication technology and health informatics in ghana 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 there is currently no policy guideline with respect to electronic data interchanges and patient identifiable information in the health sector. ownership of electronic data and information is not defined. mobile technology for community health (motech) motech explores the potential for health service benefits of information technology. the goals of the project include:  reviewing existing health information systems and assessing community health service information needs  developing a community service delivery system for testing mobile phone-based information systems for improving maternal, new-born, and early childhood care;  developing the district health management system software that is needed to accommodate mobile phone-transmitted service information;  evaluating the impact of mobile phone technology introduction on health service volume and delivery; motech utilises a versatile platform that is not specific to maternal or child health care needs. motech uses openmrs, which is an open source medical record system as the basis of the medical records management. it has added “rules engine” and other components to process both inbound and outbound text and voice messages 9 . the motech program provides expecting and recent mothers with valuable resources including:  the mobile midwife initiative: mobile midwife is an innovative program which provides expecting or new mothers accurate health education and reminders of upcoming clinic check-ups for themselves or their babies,  the option to use either sms or voice facility  several language choices including english, kassim, nankam (local languages at the pilot project’s locations)  alerts or notifications about upcoming appointments at the clinic. this in turn reduces the number of defaulters (patients overdue for check-ups)  motech enables nurses to directly enter patient encounter information into their mobile phones. this information is then used in the generation of the nurses’ monthly reports. this automatic generation of reports saves the nurses’ valuable time and improves report accuracy.  the motech system’s search component further allows nurses to search for patients’ records through their mobile phone. on an individual level, a nurse can look for specific information on a patient, such as their due date or contact information. the system also allows for nurses to search information about groups of patients in their catchment area. an example of this would be a search for all defaulters (patients overdue for check-ups) or a listing of women with upcoming due dates.  another valuable tool that motech provides nurses with is the alert or automated sms functions. on a weekly basis nurses are notified via text messages on various patient updates such as new defaulters and both upcoming and recent deliveries. this can allow for nurses to work together more efficiently in planning ahead for upcoming deliveries of care 9 . http://ojphi.org/ the state of information and communication technology and health informatics in ghana 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 sene pocket digital assistant (pda) projectan e-health initiative in ghana the use of mobile devices in the health sector in ghana has been increasing over the past five years. the sene pda project is one of the pioneer mobile health projects in ghana and indeed the very first mobile project within the ghana health service. the sene district pda project involves the use of pocket digital assistants (pdas) to collect public health service data at the lowest level of service delivery in ghana – community-based health planning and services (chps) zones. it is in the form of medical records to aid in following up clients to ensure continuity of care. the project also aims to produce accurate service data and reduce the time spent by service providers to compile inaccurate monthly data. the project has reached an advance stage in collecting data and use of data collected to ensure that every registered child completes his/her immunisation. the safe motherhood aspect of data collection i.e. antenatal care supervised delivery and postnatal care has been started but it is still in the rudimentary phase 9 . data is collected by the community health officers at the chps zones by registering each child who receives immunisation service. the demographic details of the children are taken using the pda. the children are given unique identification numbers generated by the community health nurse. address of the child is captured so that the child can be traced for home visits. every month this register is used to follow up children who are due for immunisation in the communities. for each community visit that the community health officer makes for an outreach clinic, she queries the database and has the names of the children who are due to be vaccinated and the type of vaccines they will be taking. this helps in the preparation in getting adequate vaccines for an outreach and also ensuring that she identifies and vaccinate every child in the community who is due to be vaccinated. this same data is synchronised with a computer at the district health directorate and an interface is used to generate the monthly immunisation facility report as required by the program. the generation of this electronic monthly immunisation reports has been estimated to save the community health nurse about five working days every month of report preparation, which can be used for service delivery. district health management teams also gets immunisation reports promptly in order to make decisions on which community health nurses need extra support to reach their target populations 9 . health informatics: comparing ghana and nigerian experiences in the context of developing nations, the use of ict can potentially improve delivery of health care, patient care and reduce cost of running hospital 10 . ghana is still behind because of infrastructural issues that confronts the nation in respect to telecommunications. other challenges include unstable power supply, inadequate telecommunication facilities, unskilled staff and many others. in ghana, all regional hospitals have some form of computer networking within their facility with the intent of creating 100% connectivity. http://ojphi.org/ the state of information and communication technology and health informatics in ghana 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 most major hospitals in ghana have either computerised their front desks, records or pharmacy departments. there are many partially computerised systems in the country where one of many departments is computerised to make the department’s work easy. although various hospitals have computerised record sections, paper folders are still used by patients. very few hospitals in the country have fully functional electronic health records system. the national health insurance scheme in ghana which was introduced in 2006 has introduced computer processing of claims into their processes. computers have been secured for this online claim processing at the various major hospitals. in 2005, the nigerian government also embarked on a similar system which is called the national health insurance scheme (nhis). arguably, establishment of the scheme ought to have begun with the introduction of ict in hospitals because ict will reduce the cost of running the hospital and improve the delivery of healthcare system if fully implemented 3 . in nigeria, most hospitals have no electronic patient records; files are kept at the medical records section of the hospital. sometimes, some of the files go missing within the hospital, making it difficult for physicians to trace the medical history of patients. although few researchers 11 have developed systems to solve referral system in the hospitals but the system were yet to be implemented because the nigerian government has yet to show interest in any ict investment in any hospital. in nigeria, the physicians in the same hospital cannot share a patient’s file within the hospital and an electronic booking service is not available in any hospital in nigeria. sharing of patient’s file is very difficult in most ghanaian hospitals which still use paper records. there are no medical libraries to refer to by the physician online since most of our hospitals are not connected to the internet. this type of facility is not available in nigeria. evidence-based medicine which is being championed in the west has still not being implemented since health care facilities are not ready for such purpose. there are no computer-based appointments systems in both ghana and nigeria; patients are always expected to visit the hospital to see the doctor without any prior communication with the doctor. the availability of internet has made it easy and fast to access health information, health advice and care through online information systems aimed at the public. ghana has over 95% penetration rate of mobile telephony and this is being exploited to provide quality health care to patients. all the teaching hospitals in ghana have some computer network facility that is being used for internal communication. nowadays, hospitals could potentially use mobile phone technology to offer services to patients, but maintenance of the service requires funding. challenges to health informatics in ghana in ghana, there are four main challenges to the use of ict and the successful implementation of health informatics projects. these challenges stem from three factors, namely people, government and ict infrastructure. these challenges are discussed as follows. i. electric power supply ghana has a moderately stable power supply as compared with other neighbouring countries. any country that finds it difficult to provide uninterrupted power supply (ups) to its citizens will definitely have problems with deployment of good ict services. in ghana, one cannot fully rely on the electric power being supplied by the electricity company of ghana since at any time without notice power can go off. ict equipment was made to function with http://ojphi.org/ the state of information and communication technology and health informatics in ghana 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 other infrastructure such as electricity under “controlled conditions” that is when electricity supply is stable and constant. ii. internet connectivity the internet help in controlling cost and more importantly it transform the flow of information in health sector. healthcare organisations use internet for business processes due to cost reduction which was estimated at 10:1 to 100:1 in routine transactions 3 . there is a national fibre optic project that is being implemented by the government which will connect all regional and district hospitals. this is being done as part of the e-governance project which is being implemented by the government of ghana. iii. resistance to new technology the introduction of new technology is related to the user of the technology which may be positive and negative. many ghanaians, like any other citizens of the world, will resist new technological developments which may threaten their job. the ideal is for new technology to be associated with new knowledge and skills, through training on how to use the technology. workers expect training on how to use new technology and a corresponding increase in their income, while the organisation introducing new technology expectation may be to reduce staff strength and cost of operation. often in ghana, downsizing is the issue to be raised before the introduction of new technology and this always leads to resistance by the workers, because of the fear of losing their job. in order to use ict in ghanaian hospitals, government should train hospital staff on how to use it and not lay them off and employ those with ict skills. iv. lack of maintenance culture lack of maintenance culture is another problem; even government agencies find it difficult to maintain ict equipment in ghana. both preventive and corrective maintenance is very important for any ict equipment. the financial plans for purchases of any equipment should encompass the maintenance of such equipment, and allowances for depreciation in value which is not the case in many organisations in ghana. this could be enforced by an ict policy banning any organisation from importing, supplying and installing any ict equipment without maintenance agreement. benefit of ict in health sector ict has benefited the health sector both in developed nations and developing nations, the benefits affect the hospital’s stakeholders: management of the hospital, health personnel and patients. the use of ict in health sector reduces the cost of running hospitals 2 . ict introduces the potential sharing of patients’ files easily without any threat to patient privacy. it is used for hospital management such as admission and appointment management. ict can also improve the efficiency of medical personnel by reducing waiting times and minimising paperwork. ict makes information available for the use of hospital personnel in an easily readable form. the result of patients’ test can be added to the patients’ case file as soon as they are ready. to the patients, ict gives 24-hour access to health information and through encryption and password protection can help to keep patients’ data confidential. http://ojphi.org/ the state of information and communication technology and health informatics in ghana 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 conclusion the state of ict in ghana is comparable with other developing nations. out of all the ict indicators, mobile phone has the highest number of users in ghana. in ghana, mobile phone has the highest number of users 7 followed by the computer 5 . in this paper, we have discussed the challenges facing the use and implementation of ict in ghana which also have direct effect on health informatics in ghana. since “health is wealth”, there is a need for government of ghana to make use of ict to improve the delivery of healthcare so as to reduce poverty. nita should be tasked to oversee the implementation of all ict projects in the various regional and district hospitals in the country. corresponding author emmanuel kusi achampong university of cape coast, ghana email: eachampong@ucc.edu.gh references 1. ghana ehealth policy. ministry of health, ghana. (2010). 2. remlex d. information and communication technology in chronic disease care. medical care research and review. 64(2). (2007). p.123-147 3. o’carroll, p. w.; yasnoff, w. a., ward m. e.; ripp, l. h.; martin, e. l.(eds.). public health informatics and information systems, springer. (2003). 4. modai i., ritsner m., silver h., & kurs r. a computerized patient information system, psychiatric service, american psychiatric association, volume 53, (2000). p. 476-478 5. frempong. g. developing information society in ghana. how far? electronic journal of information systems in developing countries. (2011). 6. frempong, g.k., esselaar, s., stork, c. & anyimadu, a. household and individual ict access and usage in ghana, in gillwald, a. (ed.), african e-index, axius publisher, south africa. (2005). 7. itu. measuring the information society: the ict development index. (2009a). http://www.itu.int/itu-d/ict/publications/idi/2009/index.htm. 8. lundkvist. p., habchi, d., soderberg, b., jensen, m., akrof, e.o. & spintrack, c.d.e.. fostering and facilitating access on the sat-3/wasc/safe fibreoptic cable in west africa: improving west african internet connectivity using fibre optic cable. stockholm: spintrack it advice. [online]. (2004). available: www.spintrack.com/itadvice. 9. ghana health service publications http://www.ghanahealthservice.org/includes/upload/publications/mobile%20techno logy%20for%20community%20health.pdf 10. mbananga, n., madale, r. & becker p. evaluation of hospital information system in the northern province in south africa using outcome measure, report prepared for the health systems trust, the medical research council of journal of health informatics in developing countries www.jhidc.org vol.2 (2002). no.1 • jan 08 page 23 south africa, pretoria. http://ojphi.org/ http://www.ghanahealthservice.org/includes/upload/publications/mobile%20technology%20for%20community%20health.pdf http://www.ghanahealthservice.org/includes/upload/publications/mobile%20technology%20for%20community%20health.pdf the state of information and communication technology and health informatics in ghana 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 11. idowu, p.a., adagunodo, e.r. aderounmu g. a. and ogunbodede e. o. electronic referral system for state hospitals in nigeria. ife journal of science, volume 6 (2), (2004). p.161-166. 12. research-africa.net (n.d.) ghana science and technology system: a brief profile. [online]. available: www.research-africa.net/media/pdf/ghana-st.pdf 13. frempong, g.k. & braimah, i. assessing universal access to icts in ghana, the encyclopedia of developing regional communities with ict, idea group inc., pennsylvania. (2005). http://ojphi.org/ http://www.research-africa.net/media/pdf/ghana-st.pdf the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi the population health outcomes and information exchange (phoenix) program a transformative approach to reduce the burden of chronic disease steven j. korzeniewski1*, carla bezold2, jason t. carbone1, shooshan danagoulian1, bethany foster1, dawn misra1, maher m. el-masri1, dongxiao zhu1, robert welch1, lauren meloche1, alex b. hill1, phillip levy1 1wayne state university, 2detroit health department abstract this concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. known as phoenix (the population health outcomes and information exchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. after reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead. keywords: health information exchange; data integration; epidemiology; electronic health record; translational science; social determinants of health. correspondence: *skorzeni@med.wayne.edu doi: 10.5210/ojphi.v12i1.10456 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. 1. introduction despite tremendous investment in research [1] and having the greatest annual healthcare expenditure per capita [2], population-level health indices are poorer in the us compared to peer high-income countries [3,4]. life expectancy declined in the us for the first time in nearly a quarter century in 2015 [5] and then again in 2016 [6]. cardiovascular disease (cvd) is the leading cause of death in the us [7] and hypertension accounts for the largest fraction of attributable risk [8]; however, most of the variation in mortality can be traced to social, behavioral and metabolic factors [8]. the most vulnerable among us suffer the greatest burden of chronic diseases with increasing socioeconomic inequalities widening health disparities [9,10]. consequently, there is great interest the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi in the ability to integrate data on social determinants of health with clinical information available from electronic health records [11-13]. multiple funding agencies are also focusing on data integration beyond these sources, highlighting key information compiled by public health and social service organizations as well as community stakeholders [14]. despite such interest, few programs have demonstrated the capacity to build the necessary networks or resolve technical barriers that limit health information exchange (hie) impact. while emerging approaches prompt excitement [15,16], additional research is necessary [17-20]. included among the approaches garnering enthusiasm is the concept of research universities working with health systems, governmental entities, and non-governmental organizations in collaboration to address the challenges of massive data amalgamation [21]. by serving as impartial stewards of health information, conducting research to address knowledge gaps and disseminating information about best practices, research universities can be pivotal institutions in this effort. research universities are ideally positioned to help bring about a new golden era of chronic disease prevention that will use multidimensional and multimodal geospatial data to inform the work of public health and social service agencies. indeed, we agree that academia can play a key role specifically in improving public health practice capacity via systemwide cross-sector collaboration (akin to existing collaborations between academia and industry, healthcare and national health research agencies) [22]. in what follows, we review historical context and introduce a transformative vision to reduce the population burden of chronic disease by focusing on analytics, implementation and community engagement. known as phoenix (the population health outcomes and information exchange), this approach leverages a state level hie and multiple other resources to facilitate the integration of clinical and social determinants of health information with a goal of achieving true population health management. 2. historical context 2.1 the golden era of chronic disease epidemiology sanitation during the first half of the 19th century and infection-control efforts through the first half of the 20th century achieved unprecedented improvements in population health [23]. as infectious disease epidemics subsided in much of the developed world, a focus on noncommunicable diseases emerged [24]. on the heels of world war ii, major investments in science, medical and public health research ushered in a “golden age” of chronic disease epidemiology [25]. the recognition of tobacco as a carcinogen prompted new public health policies and cultural norms. effective treatments were developed for leading causes of mortality. in turn, progress in prevention of chronic diseases contributed to a dramatic increase in u.s. life expectancy (see [26] for a review). the chronic disease era of epidemiology was inspired by the sanitarian notion that ‘mass disease’ occurs when ‘society is out of joint’ [26]. that is, when social disruptions result in crowded living situations, exposure to harmful substances, inequitable distribution of resources, and other harmful conditions. classic studies during the 1940s-50s categorized cvd as a ‘mass disease’ based on tenfold international variation of prevalence. subsequent investigations linked the global variation to differences in saturated fat intake, serum cholesterol and hypertension; smoking was also recognized as a key contributor. these findings inspired public health programming during the 1960s-70s directed at prevention of cvd. better therapies for hypertension were introduced in the the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi 1980s and statins followed in the 1990s to address high cholesterol. by year 2000, cvd incidence had declined by approximately 70% compared to 1968 [27]. however, the trends appear to be stagnating as age-adjusted mortality rates increased in the u.s. from 2000-to-2017 for both males and females ages 35-74 years [28-30]. 2.2 rising health inequalities while u.s. premature mortality rates declined from 1966 to 1980 in all socioeconomic strata defined by county income quintile and race/ethnicity, relative health inequities between whites and people of color widened over the next two decades [31]. county-level differences in life expectancy also increased from 1980to2014, and hypertension among residents age ≥ 30 years accounted for 62% of the variation [9]. these changes were primarily mediated by correlates of social disadvantage, particularly behavioral and metabolic risk factors [8,9]. in short, ‘place matters’ [32], especially for hypertensive heart disease as seen in the fourfold difference between u.s. counties with top and bottom decile mortality rates [33]. hypertension remains a ‘mass disease’ in the modern era and vulnerable groups are severely impacted. health disparities are primarily driven by differences in social determinants (e.g., poverty, inadequate access to healthcare and nutritious foods, chronic stress, etc.). however, much remains unknown about how pathophysiology is affected by social factors and their correlates (e.g., lifestyle changes, urbanization, migration and acculturation), especially for hypertension. consequently, modern frameworks call for multilevel systems approaches that pay attention to both societaland molecular-level contributors to health equity (i.e., “the absence of unfair and avoidable or remediable differences in health among social groups” [34]) [35,36]. 2.3 the precision medicine paradox history shows that health agendas tend to sway back and forth between medical care and primary prevention approaches [37]. during the waning years of the chronic disease era of epidemiology (1950-2000), the focus shifted towards technology and clinical care. this shift appears to have been driven by a revived form of genetic determinism (see [38] for a review). despite upwards of ten billion dollars of public funding in the u.s. alone, genomic research has not yet translated into substantial gains in population-level health outcomes [39-41]. nor has genomics made a substantial contribution to our understanding of population-level racial health disparities (e.g., in cvd [42]). less than 1% of published genomics studies have progressed beyond basic science or preclinical research [43]. in short, there is an ‘evidence dilemma’ supporting the role of genomics in clinical practice and public health [39,44]. this translation gap has been described as a ‘valley of death’ [45] that underlies the modern ‘paradox of precision medicine’ [46,47]; i.e., increasing technological capacity followed by decreasing application towards public health improvement. 2.4 pivot towards public health & epidemiology efforts to fill the translation gap described above have intensified in the u.s. with the launch of the precision medicine initiative and the “all of us” research program [48]. the precision medicine concept has responded to criticisms by adopting principles of public health [49] and recognizing epidemiology as “a fundamental building block of the translational research enterprise” [50]. this shift prompted the health department of western australia to coin the term the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi “precision public health” in 2013 [51]. analogous to the aims of precision medicine, precision public health seeks to provide the right interventions at the right place and time to the right population [52]. a key pivot is the increasing focus on implementation science and descriptive epidemiology [53], as precision public health “… requires robust primary surveillance data, rapid application of sophisticated analytics to track the geographical distribution of disease, and the capacity to act on such information” [54]. so-called “big data” integration is another key element [49, 55-57], though much of the discussion is theoretical. the relevance of surveillance [58] and the importance of program evaluation [59] are not new to public health. what is new is the modern landscape of health information. challenges presented by the unprecedented amount and complexity of data include: i) technical difficulties accessing, integrating and sharing health information across multiple domains [60,61] and modalities [62]; ii) ethical [63] and epistemological concerns [64-66]; iii) implications of data inaccuracies and selective measurement in routinely collected data [67,68]; and iv) a growing potential for spurious correlations in large datasets [55,65,69]. in turn, a strong foundation in surveillance and descriptive epidemiology is needed to establish an iterative process for interpreting what we know and what we don’t know from within and across scientific disciplines (and public health sectors) [55]. there is increasing focus on small-area analytics to assess the local burden of disease (e.g., www.healthdata.org/lbd) and promote effective targeting and uptake of evidence-based interventions [67]. that is, identifying county-level [70] or city-level [71] health disparities that may be masked in national metrics can inspire strategic public health action. for example, a recent study provided evidence of strong geographic differences in cardiovascular health across the u.s. by using linked ‘micromap’ plots and multilevel logistic regression models to analyze crosssectional vital statistics and administrative data [72]. importantly, status as a racial minority or low socioeconomic status explained 51% of the variation, while state-level factors accounted for an additional 28% (e.g., soda drink taxes, farmers markets, convenience stores). due to the spatiotemporal properties of social determinants of health, longitudinal studies are needed to better understand how the dynamics between societal factors, built and natural environments and individual-level characteristics contribute to health equity. equally important are finer-scale geospatial analyses to inform local public health programming and connections with clinical care [73]; e.g., by developing ‘community vital signs’ [74] and linking social determinants and service information to electronic health records [11]. fortunately, the recent technical advances have generated a massive amount of spatiotemporal data across multiple modalities at a much finer scale and resolution [75,76]. the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi 3. where do we go next? an introduction to phoenix the population health outcomes and information exchange (fig. 1) figure 1. the population health outcomes and information exchange conceptual model 3.1 overview we do not suffer from a lack of information. health care systems, researchers, government agencies, and not-for-profits collect and warehouse masses of data. however, these databases do not interact smoothly; it is labor intensive and often cost prohibitive to blend the information together. privacy laws also add a layer of complexity. therefore, existing data are not routinely shared among institutions, community members and researchers. while some repositories hold invaluable information, no single source integrates data to convey a timely snapshot of local factors that are related to health equity and outcomes. as a result, data for public health action are fragmented, incomplete, and typically lag by months or even years. consequently, programs and interventions are often guided by intuition rather than evidence. to address this, wayne state university (wsu) is developing phoenix the first shared data repository of its kind in michigan. 3.2 public health data commons supported by a grant from the michigan health endowment fund (mhef), the phoenix program design is informed by the nhlbi framework for community-engaged implementation research and action towards health equity [14,77]. beginning with a focus on southeast michigan and the metro detroit region, we are addressing critical challenges identified by nhlbi by creating a public health data commons and providing a mechanism for coordinated access to multidimensional health information (table 1). we are working with internal partners at wsu to the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi catalog existing datasets, codebooks and instructions on pursuing collaboration with investigators who maintain these resources. in parallel, we are collating publicly available administrative datasets and codebooks, either directly (e.g., using google map and places application programming interfaces (apis)) or by working with independent contractors (e.g., loveland technologies). the backbone of our shared data infrastructure is our relationship with two large hies that will provide targeted information from electronic health records (ehrs) statewide: the michigan health information network (mihin) and great lakes health connect (glhc). these two entities, which recently merged, provide complementary access to information from every health system and a large number of ambulatory care clinics in the state of michigan, with data linkage at the individual patient level across healthcare settings. table 1. example data targeted by phoenix social environment & clinical information electronic health records shared by michigan health information exchanges state of michigan vital statistics us census/ american communities survey uniform crime reports wsu investigator databases national center for education statistics patient/resident generated health data built environment (e.g., points of interest) neighborhood characteristics healthcare resources grocery stores, restaurants, liquor licenses schools and daycare facilities places of worship parks, sidewalks, alleys tax status, foreclosures, blight violations, occupancy transportation and mobility natural environment environmental protection agency data (e.g., air & water pollution—particulate matter) climate (satellite images) policy environment blight removal program rental registration compliance the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi as a first step, mihin has agreed to secure modified business associate agreements (baas) to enable data sharing with phoenix. once done, mihin will then create a data lake using information (e.g., vital signs, laboratory test results, body mass index, medications, social history, diagnosis codes) contained within admission discharge and transfer (adt) alerts and continuity of care documents (ccd) for phoenix to access. these data will be de-identified but linked at the patient level by a common key identification number. further, home addresses will be converted to geographic indicators enabling data aggregation at the zip code, census tract, and census block level (as admissible based on prevalence under local, state and national public health policies). while details of the merger with mihin are being worked out, glhc has maintained a patient-level longitudinal cohort since 2009 that is continuously updated with adt and ccd information for all new healthcare encounters. by leveraging this resource phoenix will be able to access historical data and establish robust time-trends. key to the phoenix program applications is that mihin/glhc have already standardized/integrated data elements received from different health systems, created an anonymized unique patient identifier across systems and established portals to push/pull information to an array of different end-users across the state. 3.3 applications phoenix will model the type of hipaa-compliant, cloud-based and integrated data infrastructure needed to leverage state-wide hies towards public health surveillance, community risk stratification, community engagement and program evaluation – an overarching purpose far more evolved than optimization of individual patient care data as in typical hies [78]. mihin and glhc provide invaluable services by receiving and coordinating ehrs from providers statewide. the phoenix program will complement these efforts by integrating social determinants information from multiple administrative datasets to create and share community as well as patient risk stratification scores. this will also foster more direct collaboration between health care providers, social services and public health organizations, and other community stakeholders. 3.3.1 mapping disease burden ‘hot spots’ as we have shown with a beta version of phoenix [79], one of the most accessible utilities of our shared data infrastructure will be to determine and monitor the incidence of targeted conditions that are deemed important by community stakeholders. during a recent community-engaged health assessment the detroit health department identified the need for timely, locally relevant data regarding chronic conditions such as asthma as a surveillance priority for community members and public health partners. current estimates of asthma burden in detroit and surrounding communities typically rely on information from the michigan behavioral risk factor surveillance system (mibrfss). however, the mibrfss was not designed to be representative of the detroit area. because michigan residents are mostly white and detroit residents are mostly black, and because the latter disproportionately suffer asthma, the disease burden in detroit is underestimated by mibrfss. the mibrfss is also not designed to capture neighborhood level variation in asthma, and there is a lag of more than a year in availability of data. phoenix will provide timely, relevant information about emergency department visits and hospital admissions for asthma at the zip code, census tract or census block level. because mihin and glhc accrue data on a daily basis, this approach will enable rapid ‘hot-spot’ identification and facile evaluation of potential intervention outcome benefits. by also overlaying social determinants information, we can conduct geospatial analyses to better understand interrelationships and design interventions that are specific the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi to community needs. we will similarly target additional health concerns that account for major fractions of morbidity and mortality and are deemed important by community members. to that end, we have particular interest in understanding how blood pressure impacts cvd risk, and how social determinants affect related health outcomes. 3.3.2 patient-level & community-level risk classifications recently, a machine learning algorithm using key indicators of social disadvantage, behavior and environmental conditions obtained from publicly available data sets explained about 70% of u.s. county-level variation in premature mortality [80]. machine learning modeling of data in ehrs can also predict intermediate outcomes with a fair degree of accuracy (e.g., emergency admission within 24 months [81], one-year incident hypertension [82], and left ventricular hypertrophy [83]). use of longitudinal information enhances prediction performance compared to cross-sectional models [84-86]. similar results have been produced using large clinical research datasets [87,88], with advanced deep learning techniques [89], and with classical regression techniques [90-92]. these types of information can be shared by hies. for example, a recent study provided evidence that risk scores developed using a combination of ehr and community data can identify patients in need of wraparound services for social determinants and sharing this information with providers appears to have increased referrals and uptake [16,93]. the phoenix program will develop patient-level and community-level risk stratification models for identifying susceptibility to target conditions and service needs. these models will integrate information about social, built, natural and policy environments using hipaa-compliant information shared by mihin and glhc through the data lake created for phoenix. we will develop novel solutions for specific risk classification problems by using hierarchical models, multiple machine learning algorithms (e.g., random tree forest, neural networks) and classical techniques based on evidence from previous studies including approaches employed global burden of disease study group, as appropriate. we will also adapt novel methods that involve community members in quantitatively weighting the importance of specific health topics [15]. community risk scores will be shared via the phoenix website; future efforts will emphasize incorporation of such risk scores into provider workflows either directly through their ehr or indirectly via secure web-based applications. 3.3.3. public health program targeting & evaluation the phoenix program will host a custom-made interactive website to share health information with a broad array of community stakeholders. users will be able to, i) map the incidence of targeted conditions and upstream risk factors, ii) overlay social determinant information about natural, built, social and policy environments, iii) make basic comparisons between communities, and iv) crudely estimate health equity impact and economic benefits of programs and policies that lessen the burden of disease. providing this information to community stakeholders will enhance their capacity to seek support and target interventions towards areas in greatest need. phoenix will provide community stakeholders with a unique resource to evaluate program and policy benefits guided by the cdc framework for program evaluation [59]. we will also leverage shared data to investigate changes in health equity indicators as a function of community revitalization activities (e.g., blight removal, expansion of public-private partnerships for violence prevention, the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi etc.). over time, phoenix will iteratively enable better evaluations and stronger partnerships that focus on data-driven goals. 3.3.4 benefit-cost and economic analyses phoenix will also include information about medical expenditures and indirect but associated societal costs to motivate investments that improve health equity. we can estimate potential costsavings if community exposures are removed or protective factors are enhanced. phoenix will do just that while also providing a mechanism to share information with policymakers and other stakeholders in order to motivate community investments that improve health equity. 3.3.5 expanding reach through education academic health centers are poised to lead the way in public health sciences, pioneering evaluation and implementation strategies [21]. specifically, academic health centers can leverage university resources and influence as neutral brokers of knowledge about the value, benefits, risks and costs of precision public health to cultivate community-wide understanding of ethical, legal and social implications [21]. engaging university expertise in medicine, epidemiology, health services research, environmental science, sociology, economics, health marketing, urban design, political science and other disciplines can address complex barriers to population health improvement. the phoenix program will specifically address another critical challenge identified by nhlbi: education and training of the broader local biomedical community. we propose to develop educational materials, establish curricula and develop tools to enhance training. cultivating an appreciation of social determinants of health is particularly important -a stance supported by the american medical association’s accelerating change in medical education consortium. the phoenix team will do this in part by enlisting medical students and fellows to collate information about social services that are available in local communities (as described in [94]), cataloging social determinants screening tools, and conducting prospective research and secondary data analyses. equally imperative are steps to increase health and statistical literacy among clinicians and patients alike as we move into the so-called “big data” era [95-97]. the public health data commons managed by phoenix and community outreach initiatives will also provide ample opportunities for students to conduct secondary data analyses, develop health messaging materials, evaluate programs and estimate cost benefits. phoenix will also be connected with a biobank and clinical data registry under development at wsu. 3.3.6 community engagement a strength of the phoenix project is its capacity to build on existing wsu-community collaborations. in 2015, wsu established the office for community engaged research (ocenr) under the office for the vice president of research. for the phoenix project, ocenr will engage the community through open dialogue to determine needs and employ community members to help implement targeted initiatives. ocenr is focused on sustaining health and wellbeing of community members through citizen engagement, collaboration and partnerships. as a university and community-wide resource, ocenr offers a variety of services and trainings to promote community engagement and recruitment in research. ocenr has extensive experience in the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi creating community advisory boards and conducting focus groups for variety conditions/topics with local and industry partners. ocenr works closely with the school of social work which has over 660 active community agency and organization partners through which undergraduate and graduate social work students are placed for internships. this diverse group of organizations includes, but is not limited to, community-based governmental entities, community-based not-for-profit groups, health care and mental health service providers, schools, and service providers for the elderly. ocenr also works with the school of medicine which partners with local, county and state public health departments. capitalizing on these existing relationships will enable the phoenix project to more meaningfully, quickly and effectively engage with community members. 4. controversy & challenges 4.1 promises & pitfalls of precision public health not everyone is keen on the idea of merging public health with concepts borrowed from precision medicine, especially when genomics is involved [63,98-100]. the utility of precision public health has been questioned because its goals and strategies are not fundamentally different from those of traditional public health [101]. moreover, there is concern “…that an unstinting focus on precision medicine by trusted spokespeople for health is a mistake — and a distraction from the goal of producing a healthier population” [101]. on the other hand, there is room for optimism if the recent pivot towards epidemiology and focus on social determinants [102] can produce demonstrable results that reinvigorate public health [99]. phoenix will increase the availability and accessibility of high-quality information on the distribution and determinants of health – a core public health function. phoenix seeks to better understand the social determinants that underlie the geographic distribution of population-level health equity indicators. the focus on individual healthcare is also grounded in a population perspective; for instance, efforts to develop patient-level risk stratification tools will prioritize prevalent and costly health problems that are amendable to intervention (e.g., hypertension). phoenix is not predicated on genomics per se, although we seek to develop a system that can be used to evaluate potential health benefits of interventions and programs that may include genomic information. at the outset of our program, we expect that the integration of routinely collected data will be most informative about upstream [103] socialstructural factors that might modify public health programming and clinical care outcomes [11,55]. 4.2 data quality & coverage quality of information is critical in users' assessments and perceptions of hie programs (i.e., healthcare professionals including administrators, clinicians, program supervisors and medical directors at organizations with care managers, social workers, patient navigators and health coaches who receive event notifications) [104]. clinical information we will receive is primarily going to be shared from ehrs and thus could be subject to bias from fragmented data and selective ascertainment [17,105-107]. furthermore, ehr data are limited compared to what is typically collected in prospective research studies [108]. therefore, we will perform community screening events and supplement the shared data with program participant generated information; e.g., by the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi incorporating new technologies (e.g., via smart phone applications) and capturing exposures that occur away from home [109]. we may additionally seek to develop services targeted towards lowincome residents aiming to strengthen the social safety-net (e.g., by working with local barber shops and salons [110]) [111]. indeed, we are keen to avoid steps that may widen social disparities and/or potentially increase overdiagnosis [18,68,112]. 4.3 engagement & agency we do not yet know the best way to get social determinants information into the right hands to improve population-level health outcomes. clinicians, government and elected officials, community organizations, and citizens all have a role to play and we are deliberately engaging each of these groups. many physicians report that they do not have time or resources to consider social determinants [94]. therefore, we are working with our health system and hie partners to identify knowledge gaps and determine optimal strategies to incorporate such information into clinical practice (e.g., by developing strategies that incorporate nurses/pharmacists and streamline dissemination). to that end, as part of the cdc 1817 grant program and in collaboration with the michigan department of health and human services, we are testing a pilot version of an application programming interface (api) that will make phoenix social determinant data readily available to clinicians within systemwide ehrs. our dissemination strategy also relies heavily on community stakeholders with considerable agency; i.e., persons with motivation and ability to pursue, interpret and apply health information [113,114]. on the other hand, by establishing a surveillance system we also gain the capacity to evaluate the impact of low agency interventions (e.g., environmental policies, impact of community programs). collaborations with state and local health departments will help ensure that information from phoenix informs public health programming and response. to this end, we are working with local and state public health partners and other stakeholders to establish standard operating procedures including reporting protocols that are designed to optimize the utility of the information we generate. together, our team will identify priority conditions, establish minimum reporting requirements, and develop templates for figures, tables and text that are in-line with local, state and national norms and policies. the initial demonstration of the phoenix program in southeastern michigan will focus on priority conditions among those recently identified by the detroit health department’s community-engaged health assessment that have major public health implications across the lifespan (table 2). the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi table 2. baseline priority conditions cardiovascular disease hypertension (high blood pressure) heart failure coronary heart disease (heart attack) cerebrovascular disease (stroke) mental illness and substance use disorders* diagnosable mental, behavioral or emotional disorder that causes serious functional impairment that substantially limits one or more major life activities (or child’s functioning in family, school or community activities) [e.g., depression, anxiety, post-traumatic stress and borderline personality disorders; schizophrenia]. recurrent use of alcohol and/or drugs that causes clinically significant impairment, including health problems, disability, and failure to meet major responsibilities at work, school or home. asthma pediatric and adult maternal morbidity and mortality hypertensive pregnancy disorders (e.g., preeclampsia) preterm delivery *adapted from the substance abuse and mental health service administration 4.4 privacy privacy is a critical concern. we will report information and share aggregated community-level information with appropriate protections as defined under baa that ensure hipaa compliance. we will share community-level health information with residents, public health and social service agencies as described above. likewise, we will develop technical solutions to upload patient-level health information obtained with consent in the community to hies that can inform providers and payers to enable care coordination. we also envision working within defined baa to access deidentified, limited use datasets to facilitate core public health functions, quality improvement initiatives, comparative effectiveness research and economic impact assessments as allowable under the hipaa privacy rule (see [115] for explanation of public health and research accommodations). 4.5 sustainability perhaps the greatest challenge is sustainability. public health is markedly underfunded. fewer than three percent of the u.s. healthcare budget is currently allocated towards public health [116]. the population health outcomes and information exchange (phoenix) program – a transformative approach to reduce the burden of chronic disease 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e3, 2020 ojphi compared to peak spending in the wake of 9/11 in 2002, public health’s share of total u.s. health expenditures decreased by 17% as of 2014 and a decline of 25% is projected by 2023. the decline in public health expenditures from 2008 to 2014 amounted to a $40 billion loss in just six years. consequently, many public health agencies are understaffed and ill-equipped to develop robust surveillance systems [117]. phoenix is one model of supporting local public health by partnering with university services supported by internal/extramural funds. our goal is to be able to provide evidence of potential shortand long-term financial benefits of health programs. we also hope that disseminating the information will influence policy and programmatic decisions about support for public health and social service agencies in our community [117]. with the above understanding, development of financing mechanisms beyond reliance on university resources or extramural grant support is critical. a potential strategy may be for the phoenix program to collect fees from health systems, payers, investigators and other stakeholders for services involving advanced data integration and analytics. users who pay a subscription fee would obtain restricted cloud access to de-identified and simulated datasets that are developed for specific use-case scenarios. all data would be manipulated in the cloud and no data would be exported to end users. under this model, health systems, payers and investigators would pay in order to access population-level information and avoid costs related to data management and analytics. for stakeholders without such expertise, the phoenix team would conduct analyses on a fee for service basis. early demonstrations would focus on ‘hot spotting’ [118], ‘cold spotting’ [119] and reducing waste in the healthcare system [120]. by focusing on analytics to enable population health management, we could expand the utility and impact of our hie partners [121,122]. moreover, if successful, this financing mechanism will sustain our ability to provide actional information to community members and public health/social service agencies at no cost. we hope that the phoenix program framework will be scalable to other states across the nation that choose to adapt the model to their specific needs. in-turn, this would create a synchronous network that focuses on leveraging hie to facilitate core public health activities in concert with a diverse array of national stakeholders. 5. conclusion despite significant advances in disease specific therapies and on-going, substantial expenditures on healthcare in this country, u.s. life expectancy is declining. the plateau in mortality from conditions such as cvd has prompted calls for transformative research that emphasizes data integration, use of new technologies, implementation science and community participatory research [123]. the phoenix program was developed in direct response to these calls. 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original work is properly cited. isds 2012 conference abstracts raccoons in san diego county as sentinels for west nile virus surveillance sarah c. marikos*1, karen l. ferran1, esmeralda iniguez-stevens1 and nikos gurfield2 1early warning infectious disease surveillance program (ewids), california department of public health, san diego, ca, usa; 2county of san diego, department of environmental health, san diego, ca, usa objective to investigate the potential of utilizing raccoons as sentinels for west nile virus (wnv) in an effort to guide public health surveillance, prevention, and control efforts. introduction since its detection in 1999 in new york, wnv spread westward across the continent, and was first detected in california in 2003 in imperial county (1). in california and in many states, birds, especially corvids, are used as sentinel animals to detect wnv activity. recent seroprevalence studies have shown wnv activity in different wild mammalian species (1-3); in the united states, wnv seroprevalence in some studies in raccoons has ranged from 34-46% (3,4). in addition, it has been shown that after experimental infection, raccoons can attain high viral titers and shed wnv in their saliva and feces (5). given their peridomestic nature, we investigated the feasibility of their use as sentinels for early warning of wnv and as indicators of wnv activity as a strategy to better localize wnv transmission foci in guiding vector control efforts. methods sick, injured or orphaned raccoons undergoing rehabilitation at project wildlife, one of the largest, non-profit wildlife rehabilitation organizations in the united states, located in san diego county, were tested for wnv shedding. project wildlife team members who regularly care for sick, injured, or orphaned raccoons were trained to collect oral and fecal samples for viral testing during 2011 and 2012 upon raccoons’ arrival to project wildlife. oral and fecal samples were tested using real-time pcr for the envelope gene of wnv. results to date 71 raccoons have been tested for wnv and all pcr test results have been negative. of the 71 raccoons tested from may 2011 to october 2011 and june 2012 to september 2012, 85.9% (n=61) had age classification data. the majority of these raccoons were young; 52.5% (n=32) were days or weeks old and 39.3% (n=24) were classified as juveniles. all raccoons were found primarily in urban settings at least 20 miles from the northern edge of the county. conclusions while none of the raccoon samples tested in this study were found to be wnv positive, surveillance data from san diego county suggests that wnv activity during this time period was extremely low. from january-october 2011, san diego county vector control reported all negative results for wnv in dead birds, sentinel chickens, horses, and humans for wnv; only 1 mosquito pool from the northern border region of the county tested positive for wnv (6). thus, despite wnv activity throughout the state of california, the virus did not appear to be circulating widely in san diego county in 2011 (7). to date during the 2012 season, san diego county reported all negatives for wnv in dead birds, sentinel chickens, mosquito pools, and horses; only one human case of wnv was identified in an asymptomatic male during a routine blood donation (6). further evaluation is needed to determine if raccoons are useful sentinel species for wnv surveillance. testing should continue to evaluate if raccoons may serve as a more effective early warning sentinel for wnv than birds which can travel long distances from the exposure site, and to determine if raccoons may allow better localization of wnv activity. keywords west nile virus; early warning surveillance; raccoons; sentinels acknowledgments the authors acknowledge project wildlife staff and volunteers who generously donated their time to this study, and who provide the utmost quality care for sick, injured, or orphaned raccoons. references 1 reisen, w, lothrop h, chiles r, madon m, cossen c, woods, l, et al. west nile in california. emerg infect dis. 2004;10:1369. 2 docherty, de, samuel, md, nolden, ca, egstad, kf,griffin, km. west nile virus antibody prevalence in wild mammals, southern wisconsin. emerg infect dis. 2006;12:1982. 3 bentler, kt, hall, js, root, jj, klenk, k, schmit, b, blackwell, bf. serologic evidence of west nile virus exposure in north american mesopredators. 2007;76:173. 4 blitvich, bj, juarez, li, tucker, bj, rowley, wa, platt, kb. antibodies to west nile virus in raccoons and other wild peridomestic mammals in iowa. 2009;45:1163. 5 root, jj, bentler, kt, nemeth, nm, gidlewski, t, spraker, tr, franklin, ab. experimental infection of raccoons (procyon lotor) with west nile virus. am j trop med hyg. 2010;83:803. 6 county of san diego, department of environmental health, “west nile cases in san diego county”, accessed september 5, 2012. 7 california department of public health, “west nile page”, accessed september 5, 2012. *sarah c. marikos e-mail: smarikos@cdph.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e187, 2013 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 139 (page number not for citation purposes) isds 2015 conference abstracts use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 1infectious disease clinical research program and henry m jackson foundation, uniformed services university of the health sciences, rockville, md, usa; 2navy and marine corps public health center, norfolk, va, usa; 3cnts support to armed forces health surveillance center, silver spring, md, usa; 4walter reed national military medical center, bethesda, md, usa objective to present methods of screening chief complaints and laboratory orders to find patients who presented for ebola virus disease (evd) screening, in order to determine the impact ebola concern had on the military health system (mhs). introduction since the largest epidemic of zaire ebolavirus (ebov) in recorded history began in guinea in december 2013, the epidemic has spread to neighboring countries of liberia and sierra leone resulting in an estimation of over 27,000 total cases and over 11,000 deaths to date (1). in response to the widespread social disruption caused by this epidemic in west africa, president obama committed approximately 2,000 us service members to deploy to the region and provide humanitarian aid. us military physicians were called upon to evaluate service members returning from west africa (wa) to rule out evd. the us military also has a considerable number of beneficiaries who travel to wa to visit friends and relatives placing them at risk for exposure to ebov and the development of illness upon returning to the us. we are conducting an expanded surveillance program that employs a standard questionnaire that all providers can use when evaluating a patient at-risk for evd that will also capture information from historical encounters. the data collected from the questionnaire will be used to assess the frequency with which clinicians are called to evaluate patients for evd and the resources required. however, we realize that many encounters may not be captured with this method, especially those that are not high enough risk to require consultation with infectious disease (id) specialists, and are developing ways to screen the electronic health record (ehr) to find additional patients. methods the department of defense’s (dod) electronic surveillance system for the early notification of community-based epidemics (essence) has the capability to screen the “reason for visit” field in the ehr for any combination of words. previous to this study, we determined text strings that best captured potential evd patients. screening for these words, we can scan the entire ehr in essence from january 2014 until the termination of the outbreak to determine the number of times patients presented for evd screening, basic demographic information on their age, gender, location, military status (active duty, retiree, family member), if they presented to the emergency department or outpatient clinic for care, any laboratory tests that were performed and the results and the icd-9 diagnosis given. we will also query the ehr for evidence of ebov laboratory testing to identify all patients who had ebov-specific tests administered. currently, only pcr is used by the mhs to detect ebov. comprehensive sql and sas algorithms for the electronic hl7 data were developed to identify patients with ebov laboratory tests performed. algorithms were validated against clinical care records. for these patients, we will determine the laboratory results, whether they were hospitalized, associated diagnoses, and basic demographic information. results early in the dod response to the evd epidemic, we found 76 patients who presented at 27 military treatment facilities for rule-out evd from 1 aug 14 through 17 oct 14. the majority (58%) were post-deployment evd screening, but many were single concerned individuals. for laboratory testing, from june 2014 through july 2015, 13 tests were documented on 10 patients at 4 different locations. upon review of the ehr, 2 “patients” appeared to be testing the lab system with 8 others having suspicion of exposure. no evd cases have been detected in the mhs to date. the final results of what we are able to obtain from the ehr will be presented. conclusions we have determined that various elements of the ehr, either through an existing syndromic surveillance system or through a specifically-derived method, can provide useful information on the impact of outbreaks on a health system. for evd, we found patients that did not come to the attention of the id specialists due to low risk, but still utilized mhs resources. use of these methods to find patients of concern could be expanded to other outbreaks. keywords ebola; electronic health record; screening; laboratory tests; chief complaints references 1. world health organization. ebola situation report, 12 august 2015. available at http://apps.who.int/ebola/current-situation/ebolasituation-report-12-august-2015 *julie a. pavlin e-mail: jpavlin@idcrp.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e72, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the 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delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health 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robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of 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christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a mobile medical and health apps: state of the art, concerns, regulatory control and certification 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi mobile medical and health apps: state of the art, concerns, regulatory control and certification maged n. kamel boulos 1 , ann c. brewer 2 , chante karimkhani 3 , david b. buller 4 , robert p. dellavalle 5 1 faculty of health & human sciences, university of plymouth, drake circus, plymouth, devon pl4 8aa, uk 2 dermatology residency program, mayo clinic in arizona, scottsdale, az 85259, usa 3 columbia university college of physicians and surgeons, new york, ny 10032, usa 4 klein buendel, inc, golden, co 80401, usa 5 dermatology service, denver va medical center, denver, co 80220, usa abstract this paper examines the state of the art in mobile clinical and health-related apps. a 2012 estimate puts the number of health-related apps at no fewer than 40,000, as healthcare professionals and consumers continue to express concerns about the quality of many apps, calling for some form of app regulatory control or certification to be put in place. we describe the range of apps on offer as of 2013, and then present a brief survey of evaluation studies of medical and health-related apps that have been conducted to date, covering a range of clinical disciplines and topics. our survey includes studies that highlighted risks, negative issues and worrying deficiencies in existing apps. we discuss the concept of ‘apps as a medical device’ and the relevant regulatory controls that apply in usa and europe, offering examples of apps that have been formally approved using these mechanisms. we describe the online health apps library run by the national health service in england and the calls for a vetted medical and health app store. we discuss the ingredients for successful apps beyond the rather narrow definition of ‘apps as a medical device’. these ingredients cover app content quality, usability, the need to match apps to consumers’ general and health literacy levels, device connectivity standards (for apps that connect to glucometers, blood pressure monitors, etc.), as well as app security and user privacy. ‘happtique health app certification program’ (hacp), a voluntary app certification scheme, successfully captures most of these desiderata, but is solely focused on apps targeting the us market. hacp, while very welcome, is in ways reminiscent of the early days of the web, when many “similar” quality benchmarking tools and codes of conduct for information publishers were proposed to appraise and rate online medical and health information. it is probably impossible to rate and police every app on offer today, much like in those early days of the web, when people quickly realised the same regarding informational web pages. the best first line of defence was, is, and will always be to educate consumers regarding the potentially harmful content of (some) apps. keywords: mobile apps, text messaging, smartphones, mobile tablet computers, mobile health (mhealth), telemedicine, healthcare, evaluation, regulation and certification, quality correspondence: mnkamelboulos@plymouth.ac.uk doi: 10.5210/ojphi.v5i3.4814 copyright ©2014 the author(s) http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi background smartphones, the most common “personal computer” today, have revolutionised the communication landscape. almost ‘always on’ and highly portable (carried by their users everywhere they go), smartphones provide real-time, on-demand communication, while their rich multimedia touch-displays operate with increasing speeds, delivering data services and computing power to document and improve the networked lives of their owners [1,2]. communication via smartphones is personalised: smartphones store and exchange large amounts of personal information and users are able to customise their phones to suit their personal preferences and needs. a smartphone can record a large number of details about its user’s current status and whereabouts. it can relay appropriate social support and enable realtime and asynchronous exchanges with other users via social networks and other forms of mobile communications. the latter include text messaging (short message service—sms), photography (still and video), location and other sensors (global positioning system [gps], accelerometers, ambient light sensor, etc.), built-in applications or apps (e-mail, contacts, calendar, document readers and video players, etc.) and wireless data service [2]. (‘app’, short for ‘application (program)’, refers to a self-contained piece of software coded for a specific purpose and usually optimised to run on a mobile device.) smartphones out-shipped feature phones worldwide for the first time in q1 2013 [3,4]. one of the main differences between smartphones and feature phones is that the latter, besides being less expensive than the former, offer very limited or no support for third-party, full-fledged apps. according to the ‘mobile health 2012’ report published by pew research centre’s internet & american life project, 85% of us adults own a cell phone; of them, 53% own smartphones. half of smartphone owners use their devices to get health information. onefifth of smartphone owners have health apps on their devices [5]. the mobile revolution is offering an unprecedented opportunity to provide medical support when and where people need it. large numbers and varieties of medical and health-related apps exist on the market today. a 2012 estimate puts the number of health-related apps at no fewer than 40,000 [6]. from basic apps composed of text message reminders to apply sunscreen, to sophisticated apps that coordinate the management of diabetes, apps play a multitude of functions in health and healthcare. mobile technology has several potential advantages for providing actionable medical advice, but also has its own limitations and potential problems associated with it. these aspects of mobile technology will be the focus of the rest of this paper. range of mobile applications apps for medical providers many apps are developed for a target audience of healthcare workers, including physicians, nurses and assistants. these apps are generally more sophisticated, with medical terminology and functions, and not easily navigable by non-health professionals. in a study published in this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi 2012, a group of surveyed healthcare workers indicated the most popular categories of mobile applications functions include drug-referencing tools, clinical decision-support tools, communication, electronic health-record system access and medical education materials [7]. the top apps were drug-reference guides such as ‘epocrates’ (epocrates, inc.) [8] and ‘lexicomp’ (wolters kluwer) [9], as well as clinical decision-support reference tools such as ‘uptodate’ (wolters kluwer) [10] and ‘medscape’ (webmd llc) [11]. specialty or disease-specific apps though some apps cover a broad spectrum of general medical knowledge, others may be tailored to specific specialties such as colorectal disease-themed apps in gastroenterology or goniometer apps in orthopaedics [12,13]. the ‘eye handbook’ (developed at the university of missouri kansas city, usa) is a free ophthalmology-themed app offering mobile diagnostic visual tests, a user directory of eye-care professionals, ophthalmology specific calculations, icd-9 codes and an atlas of common ophthalmic conditions [14,15]. the field of infectious disease may particularly benefit from the rapid updates of apps which provide news and updates in real time. ‘outbreaks near me’ (developed at boston children’s hospital, usa) utilises news media reports, medical e-mail list services and alerts from official national and international organisations to monitor global infectious diseases via the ‘healthmap’ database [16,17]. medical education and teaching the younger generation of technologically capable medical professionals in training, such as students and residents, harness the power of innovative apps to improve learning. the mobility of a smartphone or tablet allows students to carry a plethora of clinical resources in a convenient and searchable package. the flexibility of the mobile platform provides interactivity in teaching and a more personalised education. given the high rate of mobile technology adoption among young clinicians, mobile applications display great potential to augment traditional training. a survey published in 2012 of acgme (us accreditation council for graduate medical education) programmes demonstrated over 85% of respondents used a smartphone and over half of respondents used mobile applications on a daily basis, most commonly drug guides, medical calculators, coding/billing apps and pregnancy wheels [18]. ‘bump’ (bump technologies, inc.), a mobile app that transfers information between two mobile devices when put in very close proximity together, demonstrated enhancement of pharmacy student learning and communication in patient simulation scenarios [19]. for new trainee doctors, a mobile app, which functioned as a portable electronic library, provided a wealth of information when senior or attending physicians were not available and thus enhanced patient care [20]. german physicians adapted the german college of general practitioners and family physicians practice guidelines into an easily accessible app for physicians and medical students [21]. flashcard and subject review applications, such as ‘microbiology and immunology wiz’ (current clinical strategies publishing) [22] or ‘lange microbiology & infectious diseases flash cards’ (modality inc.) [23], allow for portable and customisable education for medical students [17]. when provided with access to smartphones, resident physicians in botswana effectively utilised several point-of-care mobile applications in delivering healthcare in a resource-limited setting [24]. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi apps for patients and the general public (including health and fitness apps) on the other end of the spectrum, there are patient-centred apps capable of performing an equally wide array of functions. current apps aid patients in managing chronic disease, lifestyle management, smoking cessation and even self-diagnosis. the diabetes mellitus epidemic is reflected in the number of apps geared towards diabetic patients. on the android platform alone, over 80 diabetes apps offer a variety of functions, including self-monitoring blood glucose recording, medication or insulin logs, and prandial insulin dose calculators [25]. another diabetes intervention app integrated communication between patients and a healthcare provider. the patient would log fasting blood sugars, daily eating behaviours, medication compliance, physical activity and emotions into a mobile online diary. a remote therapist with access to these diaries would then formulate personalised feedback to the patient [26]. the most number of apps belong to the exercise and weight loss category. the built-in camera, standard in smartphones today, allows users to record a photo diary of daily food and drink. these photos may be transferred to a server, which identifies and quantifies the food portion [27]. a weight loss trial utilised a mobile app to monitor dietary intake, body weight and objectively-measured physical activity (obtained from a bluetooth-enabled accelerometer) of its participants [28]. kamel boulos and yang surveyed dozens of mobile, location-based (outdoor) exergaming apps that harness the power of sharing through online social networks and gamification principles on gps-enabled smartphones. in these apps, the real world becomes the ‘game map’ or playground, and players can even discover and learn about new places and their geographies while burning calories and keeping fit [29]. apps are also developed for smoking cessation and alcohol addiction. at least 47 iphone apps for smoking cessation are available [30]. ‘a-chess’ (alcohol comprehensive health enhancement support system), a smartphone-based intervention for preventing relapse in alcoholic dependency harnesses mobile technology to improve treatment and motivation [31]. in patients with chronic disease characterised by life-threatening flares, apps may allow them to track and even report symptoms. the app ‘m.carat’ was developed at faculdade de medicina da universidade do porto, portugal, for asthmatic and allergic rhinitis patients to record their exacerbations, triggers, symptoms, medications, lung function tests and visits to the doctor or the hospital [32]. users can also receive disease education, medication information, task notifications, and synchronise records with an online database to better control their symptoms [33]. psychiatric patients benefit from ambulatory monitoring through an app that randomly prompts the patient to self-report psychotic symptoms multiple times throughout the day [34]. another app for sickle cell disease patients allows them to access an online diary to record pain and other symptoms [35]. monitoring symptoms in patients with copd (chronic obstructive pulmonary disease) through a mobile app alerts patients and providers to suspected disease exacerbations, thereby facilitating prompt intervention [36]. an app developed for patients with dementia, ‘iwander’, assists patients with daily living by providing audible prompts offering to direct the patient home, sending notifications and gps coordinates to caretakers, or by calling local 911 (us emergency) services [37]. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi patients may even use apps to attempt self-diagnosis without a medical visit. patients with a camera-enabled smartphone can use apps to take photographs of skin lesions and send these to a remote server for computer analysis and/or review by a board certified dermatologist [38,39]. such apps are not without their pitfalls and this will be discussed in detail later in this paper. apps may empower non-medical professionals to provide basic triage at the scene of trauma such as on the sidelines of a sporting event. an app development team with a neurosurgeon among its members created ‘concussion test’, which follows the standardised and validated sport concussion assessment tool 2 (scat2). five similar concussion apps exist for purchase as well [40]. mobile app technology has far-reaching potential in the public health domain as well. apps may be used to contribute to the care and prevention of sexually transmitted disease (std). a study of available apps demonstrated 55 unique mobile apps for hiv (human immunodeficiency virus) and std education, prevention, testing and resources [41]. text messaging text messaging, or short message service (sms), dates back to the early days of mobile phones, and most applications worked equally well on the simplest mobile phones, the more advanced feature phones, as well as full-fledged smartphones. text messaging reminder applications are easy to implement with tools such as instedd’s ‘remindem’ (free and open source) [42]. due to the pervasive nature of the mobile phone, text-messaging applications have the unique opportunity to alert patients directly regardless of location (or availability to take a voice call). evaluation of text messaging shows promising results for assisting with clinical monitoring and counselling, keeping medical appointments, smoking cessation, weight loss, chronic disease management (e.g., diabetes care monitoring), or reminding people to use sun protection to prevent sunburns in the near term and skin cancer in the long term [43-46]. adherence to therapy and disease control may be improved in psoriasis patients who receive daily educational and motivational text messages [47]. text message reminders to patients may also improve antibiotic compliance [48] or even prevent recurrent cardiovascular events [49]. on the one hand, text messaging provides nearly universal user access, particularly in lowresource settings, with decreasing costs, simple interventions to develop and use, customisable content and schedule, and a push-mode delivery that prompts users to read and possibly respond. on the other hand, text messaging offers limited interaction, with often only passive engagement, and does not leverage the latest smartphone computing power. electronic health records mobile apps may also provide access to electronic health records and patient information. kharrazi et al. mention 19 apps that allow patients to store personal health records on their mobile devices [50]. ‘healthvault’ (microsoft) acts as a platform for personal health data, including data from personal health monitoring and fitness devices, putting consumers in control of their health information, with the ability to securely share it with clinicians, caregivers, family members, or others, as needed [51]. ‘docbookmd’, an app meeting hipaa (us health insurance portability and accountability act) encryption and security http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi requirements [52], allows physicians to easily transmit text messages and images to one another [53]. telemedicine and telehealthcare the implementation of telemedicine and telehealthcare (including clinical telemonitoring of patients) through the application of mobile devices is clearly a practical and potentially lowcost choice in the delivery of healthcare, as seen, for example, in the mobile component of the caalyx/ecaalyx (complete ambient assisted living experiment/enhanced complete ambient assisted living experiment) prototype systems [2,54]. several applications have been described that harness mobile devices and apps to increase efficiency and access to care, particularly in emergency situations [55]. when time is of the essence, apps can increase speed and accessibility to critical specialist care in real time, e.g., in stroke or acute trauma. acute stroke care is made portable and accessible to non-urban centres via real-time video on smartphones [56]. the ‘i-stroke’ system was developed to transfer clinical data, computed tomography (ct), magnetic resonance imaging (mri), angiographic and intraoperative images, as well as expert opinion, all in real time [57]. ‘resolutionmd’ (calgary scientific) [58] is an fda (us food and drug administration)-cleared teleradiology app incorporated into a telestroke network, which provides remote vascular neurologists with radiographic images [59,60]. acute trauma patients also benefit from timely and efficient management. an iphone-based teleradiology program was used for the diagnosis of acute cervical trauma, examining ct scans to evaluate for the presence of fractures or displacements [61]. resource-limited settings and remote locations (e.g., distant rural areas and desert settlements) may benefit from access to specialist care and teleconsultations through mobile technology, particularly in disciplines with no locally-residing specialists, such as ophthalmology or dermatology. in one study, the iphone was used to send fundoscopic images to board certified ophthalmologists for review to detect diabetic retinopathy [62]. mobile phone multimedia messaging allowed general practitioners to send teledermatology referrals in the form of photos and relevant clinical information to specialist dermatologists for consultation [63]. in some instances, mobile apps may allow telemedicine to replace time-consuming office visits altogether. this modality may benefit specialties that require frequent follow-up care or monitoring, such as rehabilitation or post-operative care of patients. a physical therapy app provided virtual-reality-based balance exercises through a mobile device. remote physiotherapists with access to the results could adjust the level of exercises accordingly [64]. surgeons utilised remote real-time monitoring of free flaps via smartphone photography to replace in-person examination [65]. smartphone attachments several applications work in conjunction with some specialised phone attachment piece or wireless sensors in order to perform specialised activities that are not part of a phone’s standard functions, e.g., the caalyx/ecaalyx system described in [2,54]. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi for example, an adaptor with electrocardiogram (ecg) electrodes may transmit electrical data to detect abnormal heart rhythms in a non-hospital setting [66]. patients with diabetes may synchronize a glucometer attachment to their mobile device to track blood glucose and share the data through an internet connection [67,68]. the next generation of smartphone app technology may even enable users to perform routine blood testing [69]. using a windows phone with a micro lens mounted over the camera, the ‘lifelens’ app captures high resolution images of cells in a drop of blood and then analyses them to detect to existence of malaria [70]. evaluation studies of mobile medical and health-related apps ever since the first cellular phone call was placed on 3 april 1973, the mobile age has grown and continues to do so at an exponential rate, particularly during the 21st century [71]. the vast array of smartphones, mobile tablets and mobile medical and health-related apps on offer today (see the app examples presented earlier in this article) provides consumers with an unprecedented opportunity to achieve their health and healthcare goals, and overcome many obstacles along the way. however, with such a booming industry also come concerns, risk and potential dangers. it is perhaps surprising that relatively very little research has been undertaken so far (as of 2013) to investigate the validity and efficacy of these devices and apps in the contexts of health and healthcare. here, we will briefly present several evaluation and validation studies exploring the diversity of health and healthcare-related apps. the majority of these studies have been conducted on small patient populations to compare the efficacy of a smartphone/tablet app versus conventional resources. diabetes management one of the most well studied areas is that of diabetes management. a review conducted by demidowich et al. [25] investigated 42 android apps for diabetes self-management. the mean composite usability score which evaluated six standard features per app was 11.3 out of a possible 30, and no apps offered direct data input from glucometers. the study concluded that surprisingly few apps provided a comprehensive method of diabetes self-management, but did mention ‘glucool diabetes’ (3qubits) [72], ‘ontrack diabetes’ (gexperts inc.) [73], ‘dbees’ (freshware.pl) [74] and ‘track3 diabetes planner’ (coheso, inc.) [75] as recommended apps [25]. medical imaging another fascinating area of investigation is that of tablet (ipad [apple, inc.]) clinical imaging apps used to view medical and diagnostic images. such mobile apps pose particular advantages in emergency settings. several studies have been conducted to compare the efficacy of the ipad to diagnose pulmonary embolism and intracranial haemorrhage versus conventional picture archiving and communications system (pacs) or liquid-crystal display (lcd) monitor systems. the studies have found the ipad to be equivalent to conventional methods, but express the need for conducting further research to examine minor discrepancies [76-78]. in addition, the ipad is being explored as an aid in laparoscopic training for residents and for percutaneous kidney access [79,80]. much of this exploration is described as ‘case report’ experimental studies rather than full evaluation reports. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi global health and infectious diseases the need for affordable, reliable and prompt diagnostic and therapeutic measures is especially evident in the global health infectious disease arena. the ipad has been shown to be comparable to conventional pacs lcd viewing in the diagnosis of tuberculosis [81]. a study performed in rural bangladesh demonstrated that basic mobile phone technology is both efficient and effective in improving case detection and management of malaria [82]. mobile phone messaging has been proposed and investigated as a method to improve medication adherence and communication in hiv management, but a systematic review published in 2013 by van veltoven et al. examined 21 studies and determined that there is limited evidence that mobile phones are efficacious in hiv care [83]. further studies are needed to adequately assess this topic. pain management using smartphone-based diaries an innovative and novel use of smartphone technology is in the realm of pain management. as quoted in a 2011 new york times interview, dr sean mackey, chief of pain management at stanford school of medicine, usa, explains that “before we did not have good data on what is the burden of pain in our society…the number of people is more than diabetes, heart disease, and cancer combined” [84]. pain is an incredibly diverse and prevalent state that it is often hard for patients to describe, which makes it even harder for caregivers to diagnose and treat. smartphone technology has the potential to revolutionise real-time pain reporting. in a usability testing study published in 2012, a smartphone-based e-diary was successfully used by children and adolescents with sickle cell disease to report pain symptoms [34]. a randomised clinical trial has shown women with chronic widespread pain experience fewer catastrophising events (rumination, expecting the worst, and feeling helpless) when using smartphone-based diaries with immediate therapist feedback [85]. similar studies have shown successful usability of smartphone pain assessment in wheelchair users and adolescents with cancer [86,87]. dermatology the field of dermatology is also taking advantage of the technological smartphone revolution. one randomised community trial provided text message reminders to use sunscreen daily as the intervention (for sun protection, to prevent sunburns in the near term and skin cancer in the long term), and monitored dispensed sunscreen. text messages increased sunscreen use, with greater daily adherence [46]. text messaging has also been investigated as a tool for improving motivation and treatment adherence in patients with psoriasis. following 12 weeks of daily text message reminders/educational tools, patients demonstrated significantly better improvement of disease severity and quality of life, with superior adherence to therapy and optimised patientphysician communication compared to a control group with no text message intervention [47]. multimedia (text and photos) messaging (multimedia messaging service[mms]) has proven to be a promising tool in teledermatology, where it has been used to send digital photographs of skin conditions to specialist dermatologists for diagnosis. one study compared mms photographs sent to dermatologists at a university hospital versus separate face-to-face visits http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi in 40 patients. the diagnosis based on multimedia message referral was correct for 78% and provided management recommendations for 98% of patients. this study employed two dermatologists for the multimedia message diagnosis and two separate dermatologists for the face-to-face visits. interestingly, there was a lower concordance (68%) between the two dermatologists on the mms arm of the study compared to 88% between the face-to-face dermatologists [63]. another potential use of smartphones in dermatology involves apps targeted for patients. in particular, apps have been designed to aid patients with suspicious skin lesions to determine if their lesion is benign or malignant. a study published in 2013 in jama dermatology analysed four of these apps and discovered that the ability of apps to assess melanoma risk is highly variable. in fact, three of the four studied apps incorrectly classified 30% or more of melanomas as ‘unconcerning’. a major conclusion of this study was that extreme caution should be exercised when consumers use apps to assess their medical risks, since many apps are subjected to very little or absolutely no regulatory oversight [39]. such apps might still be useful, as long as there is an appropriate in-app disclaimer warning users in a clear and simple language about the app’s diagnostic limitations, the possibility and implications of ‘false negatives’, and that the app should not be taken as a substitute for proper professional clinician’s evaluation and advice. regulatory control and certification of medical and health apps studies expressing concerns about existing apps for colorectal diseases, microbiology, dermatology, asthma, diabetes and opioid converters a number of recent articles and studies have investigated the potential dangers and safety of some clinical and health apps aimed at healthcare professionals (but available to all) or aimed at the general public, and whether (and most importantly how) they should be assessed and controlled by the us food and drug administration (fda) and/or other relevant and corresponding entities in other countries such as the medicines and healthcare products regulatory agency (mhra) in england [88-91]. for example, o'neill and brady [12] recently questioned the reliability of unregulated medical apps specifically applied towards colorectal diseases. of a total of 68 individual colorectal themed apps they surveyed in their study (amongst which five were duplicates), only 29% had had user satisfaction ratings and 32% had named medical professional involvement in their development or content. with such a little medical professional involvement in the design of the majority of these apps, increased regulation of some kind is definitely required if we were to improve accountability for app content [12]. similarly, in another study by visvanathan et al. [92], the accuracy and reliability of the content of apps used in diagnosis and patient management were called into question. of 94 microbiology-themed apps they surveyed, only 34% had stated medical professional involvement. the lack of medical professional involvement in the design of the majority of these apps again undermines users' ability to be informed regarding app content quality. visvanathan et al. conclude by proposing that increased regulatory measures be introduced to safeguard patient welfare [92]. ferrero and colleagues [38] investigated the potential danger of clinical dermatology apps targeting patients, calling for fda regulation of such apps. they tested ‘skin scan’, an app created to help with the identification and management of skin cancer, against 93 clinical http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi images from the national cancer institute and fitzpatrick’s dermatology in general medicine, and found only 10.8% (10/93) were rated as high risk melanomas. ‘skin scan’ has since then been renamed to ‘skinvision’ (skinvision bv, netherlands) [93] by its developers, who have also announced they are planning a clinical trial in europe to compare the effectiveness of their app against traditional diagnostic tools [94]. likewise, robson et al. [95] and wolf et al. [39] urged caution when using melanoma risk analysis and detection apps due to their diagnostic inaccuracy. huckvale et al. [96] and mckinstry [97] expressed their concerns about worrying deficiencies in existing asthma selfmanagement apps, concluding that none combine reliable information and appropriate supportive tools, and that some are even unsafe. demidowich et al. [25] described deficiencies and other issues hindering usability in their reviewed sample of diabetes selfmanagement apps for android smartphones. o'neill and brady [98] pointed to apps where inaccuracy or inconsistency could potentially cost lives, citing their experience with opioid conversion calculators. they examined 23 different opioid conversion medical apps and found alarming inconsistencies in their outputs. apps as a ‘medical device’: regulation in usa and europe through guidance first released in draft form in july 2011, the us fda defined “a small subset of mobile medical apps that may impact on the performance or functionality of currently regulated medical devices and as such, will require fda oversight” [99,100]. such medical apps could present a real risk to patients if the apps do not work as intended. the fda has already cleared a handful of mobile medical apps [101] that are either used as an accessory to an fda-regulated medical device or transform a mobile platform into a regulated medical device (e.g., an app that turns a smartphone into an ecg machine). fda-approved apps include ‘mobile mim’ for iphone and ipad, a diagnostic radiology app by cleveland-based mim software inc. that enables a healthcare professional to view medical images on an ipad and make a diagnosis [102]. more recently, intuitive medical technologies in shreveport, louisiana, received 510(k) fda clearance for their ‘iexaminer’ adapter and companion app for the iphone. the ‘iexaminer’ attachment connects ‘welch allyn’s panoptic ophthalmoscope’ to an iphone, aligning the eye piece of the ophthalmoscope to the phone’s camera. the combined system and app allows clinicians to image the eye (fundus exams) and save the images for later review or sharing with colleagues [103]. in a must-read ieee spectrum article published in september 2012, strickland presented a list of top-five requests to the fda that came out of a survey of key industry figures on what they would like to see in fda rules for medical apps [104]. the requests deal essentially with the need to crisply define the boundaries between apps that have to be regulated by the fda and apps that do not have to go through the certification process. the surveyed industry experts called for clarifying the difference between a medical app and a wellness app, as well as the difference between diagnosing and monitoring; establishing the risk-level threshold for fda enforcement; defining the limits of the fda’s rule on apps that serve as device accessories; and making a plan for how to handle “modular” apps, i.e., multiple apps designed to work together (if one of those apps is regulated by the fda, must all the others be as well?) [104]. in europe, ‘oncoassist’ [105], an irish app for the iphone and ipad that contains prognostic tools and useful calculators for oncologists at the point-of-care (and as such falls within the http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi definition of a medical device), has received ce certification or conformité européenne, a key mark of a product’s compliance with relevant eu [european union] legislation [106] in 2013, and displays the ‘ce mark’ on its welcome screen [107]. indeed, the european medical device directive mdd 93/42/eec explicitly mentions ‘software’ in its definition of ‘medical device’ [108]. but many apps with dosage calculator functions currently marketed in the uk still do not carry the ce mark to show that they have been registered as class i medical devices with the mhra in england or one of the corresponding regulatory bodies in other eu countries [109]. the likelihood of an app being treated as a medical device by the mhra depends on what the app does and the corresponding level of patient risk associated with it. high risk apps, e.g., those performing complex calculations using patient data to aid diagnosis or treatment decisions, can be safely classified as ‘medical devices’. according to mhra, if an app is purely a record archiving and retrieval system (electronic health records), it is unlikely to be considered a medical device; however, if it includes a module that interprets data or performs some calculation, then it is likely that this particular component may be considered a medical device. decision-support apps are also generally not considered a medical device by mhra if they just provide existing (i.e., reference/evidencebased) information to enable a healthcare professional to make a clinical decision. however, if the apps perform a calculation or interpret or interpolate data and the clinician does not review the raw data, then such apps may be considered medical devices for mhra purposes. nevertheless, an app performing simple and straightforward calculations such as bmi (body mass index) should not be treated as a medical device, but a dosage calculator that recommends a dose based on individual patient details should be [109,110]. developers and publishers wanting to have the ‘ce mark’ on their apps need to notify the mhra in the uk (or a corresponding agency in other european countries), producing a ‘declaration of conformity’ that includes detailed technical documentation of how their app design conforms to the medical device directive mdd 93/42/eec. as part of the technical documentation, app developers will also need to have undertaken a controlled test and risk assessment to demonstrate that their app supports and improves upon any existing process used to present the same information or function. once all the registration documentation is ready, developers and/or publishers should submit it to the mhra with the appropriate registration fee (£70.00 gbp at time of writing) [109]. nhs (england) online health apps library: a full-fledged ‘vetted app store’ is still a far distance away the national health service (nhs) in england runs an online health apps library [111], where it lists and recommends some carefully selected apps such as ‘ibreastcheck’ (breakthrough breast cancer) [112] and the nhs’ own ‘health choices’ app [113] (not all of the listed apps can be considered as medical devices). the nhs health apps library only provides links to third-party stores hosting the actual apps. developers can submit their apps for review and possible listing in the library. visitors can also review and rate apps in the nhs health apps library. many other app directory sites (and the app stores run by amazon, apple, google, microsoft and research in motion [rim/blackberry]) allow users, including healthcare professionals, to review and rate apps, e.g., the ‘medical app journal’ directory [22]. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi it has been proposed that the nhs should provide its own full-fledged ‘vetted app store’ [114,115]. a proper app store (cf. apple itunes store or google play) would accept app submissions from developers and also deliver those apps (if approved) to users’ devices, while handling any payments that might be involved (in the case of paid apps). for the nhs, there would be the additional task of assessing and ensuring the quality of these apps from a medical/health point of view, e.g., ‘is the medical content or advice offered by an app sound, safe and up-to-date?’ this is in addition to the technical assessment of submitted apps, to determine if they behave as intended, without crashing the devices running them, and where applicable, if they are secure and protect user’s privacy. these are not trivial tasks, and could prove very demanding and well beyond the remits of the nhs, when one considers the thousands of medical and health apps that a specialised health apps store would have to deal with. beyond ‘apps as a medical device’: desiderata for successful clinical and health-related apps going outside the rather rigid and narrow limits of the definitions by the fda and mhra of ‘software (apps) as a medical device’ and the associated risks to patient safety, there are other aspects of clinical and health-related apps that deserve much attention. these aspects or factors are also equally important in the case of health-related and medical apps that are excluded from conventional (software as a) medical device regulations. these factors affect an app’s overall fitness for purpose, effectiveness and value-for-money (for paid apps), and can thus be seen as ‘desiderata or ingredients for app success’. medical and health app reviewers, such as the curators of the above mentioned nhs health apps library, should cover (and perhaps rate in some way) these aspects in any app review or assessment they undertake (where applicable), to allow end users to make informed comparisons and decisions about which apps to download and use. furthermore, we recommend that these factors should be routinely considered by app developers and publishers, perhaps in the form of a checklist to be added to their existing quality assurance (qa) procedures (see also our discussion of happtique health app certification standards below), as a kind of industry self-regulation and/or voluntary certification. these app aspects cover content quality (for health education and reference apps), usability issues (including ‘cognitive accessibility’ and associated consumers’ general and health literacy issues), device connectivity standards, as well as a number of other issues that are fully covered in happtique health app certification standards [116,117], such as app security and user privacy. regarding content quality, apps need to: (1) provide authorship information, including detailed information about authors’ affiliations and credentials and about any medical professional involvement in content preparation; (2) list all references or sources of content (attribution); (3) fully disclose any app sponsorship or other commercial funding arrangements, and any potential conflicts of interest; and (4) ensure a balanced, non-biased coverage of facts and information currency (up-to-datedness). these are the same essential criteria governing the quality benchmarking of online medical/health-related information resources and web sites in general (prior to the apps era) [118,119]. for apps serving medical images, evaluators additionally need to establish that all the relevant ethical issues, such as obtaining informed consent from patients to publish their images in a smartphone app, were duly considered in the design of these apps [120]. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi but high-quality, evidence-based content alone is of limited value, if presented in a way that does not adequately match and address the usability, accessibility, readability (reading with understanding) and health literacy needs of target audiences. an app that is perfectly usable by a younger person might be very difficult to manipulate by an older or disabled person with different and unique usability needs related to ageing and/or physical and cognitive impairment [2,121-126]. app designers and content developers tend to focus on the “more obvious” types of usability and accessibility (i.e., visual, auditory and motor), and often overlook or give little attention to the cognitive accessibility aspects of their content and user interfaces, which have to do with users’ average reading age [127] and general, digital and health literacy levels [128]. almost half of all europeans show limited health literacy, according to the european health literacy survey results published in 2011 [129], and this should be taken into consideration when designing and evaluating apps intended for consumption by the general public. presenting correct, unbiased information but in a way that is hard to understand by the intended audience not only renders this information useless, but also makes misunderstanding a likely possibility, which can have serious negative health consequences [128]. for apps that connect to devices, such as glucometers, heart rate and blood pressure monitors, integration with continua health alliance’s [130] home and mobile telehealthcare ecosystem of devices from different manufacturers and suppliers would be highly desirable and futureproof. frohner et al. [131] describe one such an app, and microsoft ‘healthvault’ apps for windows phone and windows 8/rt [132] already support continua certified devices [133]. biometric fingerprint identification is now available on some smartphones and can help verifying and ensuring the identity of the person or patient using the device or a particular app running on it. some apps allow patients to manually log and edit their health and lifestyle data before submitting these details electronically to their treating clinicians. problems may arise in relation to selective and subjective reporting of data by patients and/or patient compliance in maintaining such manual logs, resulting in incomplete data being submitted to clinicians. sensors can automate the logging of some clinical/health data and partially solve issues related to patient compliance and reporting, but patients may still forget or choose not to wear the corresponding sensor devices, particularly when perceived as intrusive, cumbersome and/or threatening their individual privacy. ‘patient-centred design’ and appropriate involvement of end users in app and device design, which are now mandatory requirements explicitly stated in fda regulation 21 cfr 820.30, can help mitigate these latter issues. happtique health app certification happtique [134] is a us mobile health (mhealth) solutions company aimed at integrating mhealth into patient care and daily life. the company released their ‘happtique health app certification program’ (hacp) in an attempt to address many of the above discussed app evaluation criteria, as well as some additional relevant and equally important aspects, beginning from where fda (and mhra) usually leave off. in other words, happtique’s scope includes, but also goes beyond, the ‘software as a medical device’ definition used by fda and mhra/‘ce mark’. hacp is meant to assist healthcare providers and consumers in identifying medical, health and fitness apps that “deliver credible content, contain safeguards for user data, and function as described”. the final hacp certification standards and associated performance http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi requirements were released on 27 february 2013. they assess operability, privacy, security (collectively referred to as the ‘technical standards’) and content (‘content standards’), and are available at [116,117,135]. app operability (op) standards (op1 to op9) cover issues such as ensuring the app installs, launches, and runs consistently on target device(s) and operating system(s). hacp has some very comprehensive privacy requisites, app privacy (p) standards (p1 to p6), requiring, for example, the app to disclose to its users the type(s) and full details of all data it (or any in-app advertiser) collects or accesses on user devices, either pertaining to the usage of the app and/or to the specific user, including user-generated data and data that are collected automatically about the user, and how and by whom all that data are used [135]. app security (s) standards (s1 to s7) deal with issues such as verifying that the app, including without limitation, any advertisement displayed or supported through it, is free of malware. app content (c) standards (c1 to c11) require (among other things) that content of apps “be written and presented in a manner that is appropriate for the intended audience”. content standards also cover in-app advertisements, demanding that “an app that contains advertisements clearly identifies the advertising and complies with any and all applicable regulatory requirements, particularly advertisements that involve or relate to products or services that are clinical or related to health” [135]. happtique has partnered with third party organisations to serve as hacp partners for the evaluation of apps against the certification standards, based upon each organisation’s area of expertise. the testing of the technical standards was assigned to intertek, a multinational inspection, product testing and certification company headquartered in london, united kingdom [136]. apps that meet all of the technical standards are then evaluated for the content standards by the association of american medical colleges (aamc) [137] and cgfns (commission on graduates of foreign nursing schools) international [138], with the help of clinical specialists selected based on the app’s specific subject matter [117]. hacp remains a voluntary programme. in order to qualify for submission, an app’s content must be written in english, run natively on ios (apple), android, blackberry or windows devices, be intended for sale or use within the us (happtique is currently solely focused on the us market), and pertain to at least one of the following areas: provision of healthcare, health monitoring and management, and/or advancement of healthcare or medical knowledge. apps requiring any public or private certification, registration, clearance or similar approval (e.g., by fda) must obtain those approvals prior to submitting an application for happtique certification [117]. apps successfully meeting all of hacp’s standards and associated performance requirements are granted the happtique certification and seal, valid for a two-year period and specifically associated with the app version that was submitted for evaluation. the app developer may then use the seal for promotional, advertising or marketing purposes, as long as the certification is valid and such use is in compliance with happtique’s guidelines. all active seals contain a link back to the ‘happtique certified app directory’ that includes relevant information about the app and its certification history [117] (cf. health on the net foundation’s honcode certificate and seal [139]. http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi conclusions apps may have deficiencies and limits. app development, support, maintenance and regular updating may entail significant costs. interactions may require substantial effort. advice may not align with users’ expectations or life activities. not all content may benefit all users, and getting users to download and engage with mobile apps is an art [1]. health disparities and low health literacy and numeracy may negatively affect use [128]. furthermore, many smartphone apps are not based on behavioural change theories or guidelines (where these could have made a significant, positive difference, if implemented) [30,44]. relatively few studies exist on the effectiveness (clinically and cost-wise) of mobile smartphone apps and more research is needed to properly address this issue. assessment of some aspects of specific apps or types of apps may require a full blown clinical trial or evaluation study and the necessary resources to conduct it, which is well beyond what can be evaluated by a single person or a few people using a checklist of criteria to look for. software applications (apps) and ‘software as a medical device’ are not new concepts. but the mobile social web is now enabling millions of people to more easily share, rate, recommend, and find software applications about almost any topic under the sun (‘there is an app for that’). before the advent of smartphones, small-form-factor tablets and the latest generations of mobile operating systems and web browsers that support the concept of apps and associated ‘app stores’, downloading and installing software (including shareware [cf. trial versions of paid apps today] and freeware [cf. free apps]) was always possible, but not as easy or as popular (among average internet users) as it is today. however, this ubiquity (ease-of-installation and popularity) of today’s apps is also bringing in additional risks to less experienced users who might find themselves tricked to download apps that contain malware, or violate their online privacy, or offer them dubious medical information and advice, hence the need to educate users, particularly the general public, and raise their awareness about the potential negative aspects of mobile apps and how to appraise the quality of an app before installing it and granting it permissions on their devices. hogan and kerin [140] consider it crucial to educate patients regarding the “unregulated and potentially harmful content of (some) apps”. voluntary app certification schemes such as hacp, while very welcome (as standard setters in the domain), are reminiscent of the early days of the web, when many “similar” quality benchmarking tools and codes of conduct for information publishers were proposed and developed to appraise and rate online medical and health information [141]. indeed, many apps on offer today are nothing more than custom informational web sites displayed in their own dedicated app screens (custom web browser screens). it is probably impossible to rate and police every app on offer today, much like in those early days of the web, when people quickly reached the same conclusion regarding web pages offering medical and health information. the best first line of defence was, is, and will always be to educate consumers. empowered patient groups and consumer leaders can play an important role in this respect, as seen, for example, in the recent release of the ‘european directory of health apps 20122013’ [142]. postscript a new class of wearable smartphone-connected devices is emerging, namely smart watches (worn like a standard watch around the wrist) and optical head-mounted displays such as http://ojphi.org/ mobile medical and health apps: state of the art, concerns, regulatory control and certification 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e229, 2014 ojphi google glass (worn like eyeglasses). these devices offer new, unique form factors and affordances, requiring dedicated apps to properly address the associated usability opportunities and limitations [143,144]. smart watches are being used as physical activity trackers [143], while google glass has been experimentally deployed as ‘surgical assistant’ and for medical education purposes [144,145]. as these wearable devices and their corresponding apps continue to develop and mature, dedicated research and expert reviews will soon become necessary to investigate and document their various potential and current clinical and health applications. authors' contributions mnkb conceived and planned the study, conducted the initial pubmed literature survey, and drafted the manuscript with contributions (mainly to the section entitled ‘range of mobile applications’) from acb, ck, dbb and rpd. all authors have read and approved the final manuscript. competing interests the authors declare that they have no competing interests. any reference herein to any specific commercial apps, devices, other products, or services by trade name, trademark, manufacturer, provider or otherwise does not necessarily constitute or imply its endorsement, recommendation or favouring by the authors. acknowledgements commercial products, services and company/brand names mentioned in this paper are trademarks and/or registered trademarks of their respective owners. references 1. heron ke, smyth jm. 2010. ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. br j health psychol. 15(1), 1-39. doi:http://dx.doi.org/10.1348/135910709x466063. pubmed 2. boulos mn, wheeler s, tavares c, jones r. 2011. how smartphones are changing the face of mobile and participatory healthcare: an overview, with example from ecaalyx. biomed eng online. 10, 24. 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decision-making courtney d. corley*1, onicio leal-neto2, craig taylor3, césar escobar-viera4 and victor del rio vilas5 1pacific northwest national laboratory, richland, wa, usa; 2epitrack, recife, brazil; 3cojengo, glasgow, united kingdom; 4university of florida, gainesville, fl, usa; 5pan american health organization (paho), rio de janeiro, brazil objective to review current trends and issues in the development and use of mobile apps for public health surveillance and decision making in settings with different resource availability and technological development. the panel discussion will address cross-cutting issues of general interest, including timeliness, recruitment, validation, and engagement by presenting innovative examples of apps conceived for various uses in human and animal surveillance. introduction an increasing number of mobile applications available for download provide biosurveillance capabilities using new and traditional data streams. biosurveillance apps span a wide range of settings, uses, technologies, and resource capacities that provide health analysts rapid and efficient means of data collection, visualization, and analyses. however, this technological “heaven” is not free from the challenges of traditional biosurveillance applications, namely validation to inform specificity and recruitment and engagement to ensure representativeness. this panel will provide guidance for future development and utility of mobile apps by illustrating how these matters are addressed in field tested mobile applications. description the panel comprises epidemiologists, data scientists, and technology partners who will address the full application lifecycle of several biosurveillance apps, from the assessment of the specific demand, resource, and technology specifications, to implementation and evaluation. the panel also describes how the apps highlighted navigated their specific setting constraints and the lessons learned in addressing standard biosurveillance issues, namely timeliness, validation, engagement and recruitment. court corley’s “data if we can get it, should we use it?” presentation covers the types of data useful for “mobile health”, if they differ from traditional public health data sources, and the issues around using novel open web data sources due to legal and ethical concerns. onicio leal-neto’s “saúde na copa app engaging users with participatory surveillance” presentation discusses methods for actively and passively engaging with mobile users to promote health surveillance and decision apps during mass gatherings. “mozziemap, a tool to enhance the decision-making process for vector abatement programs” by cesar escobar-viera describes mobile applications to improve tracking and prediction of changes in mosquito population for vector-borne diseases. finally, craig taylor describes “vetafrica: a mobile application developed by cojengo supported by the microsoft 4afrika initiative” to address the lack of access to experienced veterinary assistance on the ground, accurate real time disease surveillance data, and misdiagnosis of livestock diseases in kenya. the subsequent discussion will evaluate how the new systems improved biosurveillance and their impact on the health of the targeted populations, as well as their contribution to the development of the “mobile health” field. the panel provides the basis for an evaluation framework and constitutes a repository of practical advice for the development and implementation of biosurveillance apps. throughout the panel, the moderator will question the audience via a newly developed web app about their collective experience with biosurveillance apps. audience engagement the panel will be presented with the novel online software tool prezi so that the audience can engage with the content during the panel. for question asking and prioritization we will use multiple methods. first, we will allow the auidence to sms the moderator their questions, secondly, we will employ a webapp to be developed collaboratively by the panelists to directly engage the audience in gathering and sharing real-time feedback. in order to be dynamic to the audience needs, we provide the audience a hashtag to use when chatting about the panel in social media and the panelists and moderator will monitor the back channel conversations and adjust the discussion appropriately. keywords mobile applications; biosurveillance; engagement; timeliness acknowledgments the mozziemap app was supported by a contract from the u.s. defense threat reduction agency (dtra), joint science and technology office for chemical and biological defense under project numbers cb4045 to the pacific northwest national laboratory (pnnl). pnnl is operated by battelle for the u.s. department of energy under contract deaco576rlo 1830. the saúde na copa app was funded by the skoll global threats fund. *courtney d. corley e-mail: court@pnnl.gov online journal of public health informatics * issn 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public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e98, 2014 isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 (page number not for citation purposes) isds 2013 conference abstracts keywords �������� � ���� �� ������8� ��� � �� �� ��������#� acknowledgments ����� ��� ���<������� �� ������������� ��� �� �� references �� ����������������� ��� �� � ������ � �� ����� ������� ���������������� ��� � �� <� � ������������ �� #����� ���� ��!�=�� �������"����� #������� ��0 ��� ��� �0�� � ��� ���� ��� ����# ������$*1'2%��&������ � �� ��#����� ����� ������'�� �� ��2*�$(%���'(>�*.�� 0 ��� ������<� ��)���0 �������� �� ���#�������� �� ������ ���� ������� � ��� �� �=� ���� ������� ��*������������<� ������� ��#������ ���#��� ��� � ��� �+�� �� ������� ������� ��� ����*1''��0����'1��'.�� ���� �������� ��?������,��� �������-������������@���� ����� �� ����� � ����� ����� �� ����� �� ���� �������� � ������ ����'�� �� ���� � �� ����� � ����$*1'*%��'*,�*,/� *murtaza t. ali e-mail: murtaza.ali@aku.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e98, 2014 ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 1 an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies gianpaolo franzoso 1 1. ospedale civile s. maria regina degli angeli. adria (ro) italy abstract introduction: the purpose of the article is to share a modus operandi and a tool that allows the recruitment and management of thousands of patients and their treatment by using a simple software created by the author and made freely available to all colleague-pharmacists. the author, a pharmacist, created this database because there were no tools on the market with all the features needed to manage the treatment of patients and the orders of drugs to ensure continuity of care without waste of public money. methods: the data collection is facilitated by the software and allows the monitoring of treatment of the patients and their re-evaluation. this tool can create a table containing all the information needed to predict the demand for drugs, the timing of therapies and of the treatment plans. it is an effective instrument to calculate the optimal purchase of drugs and the delivery of therapies to patients. conclusions: a simple tool that allows the management of many patients, reduces research time and facilitates the control of therapies. it allows us to optimize inventory and minimize the stock of drugs. it allows the pharmacist to focus attention on the clinical management of the patient by helping him to follow therapy and respond to his needs. keywords: database, drug distribution, drugs delivery, patients management abbreviations: therapeutic plan (tp); italian drug agency (aifa); national health service (nhs) correspondence: gpaolofranzoso@gmail.com doi: 10.5210/ojphi.v6i2.5315 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction in italy one of the strategies which allows to save a lot of money in the management of therapies is the distribution of drugs through the hospital channel. the definition of the system of reimbursement, distribution, as well as the fixing of the price of the medicine are determined through complex procedures. in the italian context the italian drug agency (aifa) is in charge of this decision path. in italy, drugs prescribed by the national pharmaceutical formulary of medicines reimbursed by the national health service (nhs) are classified in group a (which becomes group h when medicines are issued by nhs through the hospital channel and dispensed in hospitals). alternatively, the medicines can be classified in group c. the medicines reimbursed by the nhs include essential medicines for the treatment of chronic diseases, reimbursed for each therapeutic indication authorized. in some cases there is an aifa note, that limits the reimbursement to only some of those indications. consequently, the class a drugs whose therapeutic indications are not included ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 2 in these notes are charged to the patient. given the national legislation regulating the reimbursement of medicines and their delivery system, is possible to identify different modes of delivery of medicines in the nhs to patients. drugs are prescribed in two ways: either by prescriptions by general practitioners or by treatment plans given by medical specialists working in public hospitals. while the first case involves the dispensing of drugs to the patient through contracted pharmacies, in the second case the dispensing of medicine is undertaken directly by health facilities and hospitals through direct distribution. alternatively, agreements are reached with contracted pharmacies on the distribution account. a recent law has introduced direct distribution as an alternative system for the distribution of medicines, as opposed to the conventional one. the hospital pharmaceutical management deals with the administration of medicines in hospital pharmacies in the national health service. however, for the purposes of monitoring of hospital pharmaceutical expenditure, the consumption of medicines in hospitals also includes prescribed medicines in class h and class c. in the case of h and a class medicines purchased by public hospital pharmacies, the cost incurred by the nhs coincides with the price resulting from a tender for the purchase or defined as a result of direct negotiation between the hospital pharmacy and the pharmaceutical company. in the case of medicinal products in class c, the price is defined by the pharmaceutical company and not published but is simply communicated to aifa. the governing of national pharmaceutical expenditure essentially makes use of four instruments which are: 1. fixed ceilings of pharmaceutical and hospital expenditure 2. monitoring of pharmaceutical expenditure 3. allocation of a budget to each pharmaceutical company 4. shelf procedures of the pay-back for the benefit of the regions one of the tasks undertaken solely by hospital pharmacies is the revision/update of the regional therapeutic handbook and the development of guidelines to address the prescriptive activity [1]. higher discounts are available to hospitals when ordering drugs from pharmaceutical companies. distribution directly through the hospital is, therefore, a method of economizing for the national health service. the advantages of centralized distribution are also passed on to the patient who is monitored by only one pharmacist throughout the period of therapy. this allows us to establish a relationship of mutual interaction that ensures proper monitoring of therapy. the relationship with the hospital pharmacist allows the patient to have a reference person who can answer questions about treatment. hospital pharmacist can interface with the doctor who is treating him, resolving any problems that may arise during therapy. the hospital pharmacist is a professional who can interact with doctors gaining valuable information regarding the various therapies and can get in touch with doctors picking up any suggestions that may emerge from the point of view of the prescriber. in the case of innovative therapies the hospital pharmacist can share opinions with the doctor and represent a common link between medical specialist innovative therapy the patient. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 3 methods in the service of the hospital drug distribution in adria (rovigo, italy) medication is managed in the categories previously mentioned. 1. drugs that must be distributed by hospitals (group h). 2. medications distributed from both hospitals and pharmacies in the territory on restricted medical prescription (group a that are of recent marketing). for new drugs the italian drug agency establishes special modules to monitor the prescribing indications for use and the duration of therapy called the therapeutic plan (tp). it contains patient information, diagnosis, medication, dosage and duration of treatment. the italian drug agency has also created the aifa registers: a second instrument used to monitor the appropriateness of prescribing. to categorize the concept we can say that therapeutic plans are regulatory instruments with the objective of ensuring the appropriateness of use of medications, in some cases orientating treatment decisions in favour of the most effective and proven molecules and to precisely define the clinical conditions in which the drugs are paid for by the national health service. therapeutic plans are required for the delivery of drugs: • for severe specific diseases often with pronounced economic impact • of recent marketing • for which it is necessary to carefully monitor the risk/benefit profile. therapeutic plans have a dual purpose: to address and authorize the general practitioner to prescribe and as an instrument of drug control by the hospitals. for this reason, the treatment plan must be completed correctly in all its sections (including patient demographics, the stamp and signature of the specialist who must be clearly identified). it will include the instructions of the aifa notes, the information recorded for each medication and treatment protocols identified by the regions. the aifa registers, on the other hand, represent an advanced tool developed from government defined appropriateness for the prescribing of medication. generally the products are entered in the registers immediately after their marketing authorization, or after approval of an extension of its indications [2]. the therapeutic plan can be filled out only by hospital specialists, and there are restrictions as to those who can prescribe and which centres and doctors may prescribe. there is a list of drugs that are marketed with restricted access programmes, and for different drugs, different ways of distribution and monitoring of the therapy can be specified. restrictions that require the patient to undergo re-evaluation before the continuation of therapy are often added. at the time of the clinical re-evaluation the specialist will assess the effectiveness of treatment, and establish a new therapeutic plan a copy of which is then sent to the hospital pharmacy for reference. for therapies that have no particular restrictions on dispensing, current legislation provides for a maximum therapy delivery of sixty days. in this scenario, finding the data to be sent to the ministry of health becomes challenging. the foregoing makes it clear that the direction taken is the one that leads to the identification of the exact number of packs delivered to the individual patient in a given period of time. the test of appropriateness is the second prescriptive requirement and the need to be able to identify the prescriber centre, the doctor who prescribed the medication, the dosage, the adjustments necessary, therapy etc presented itself. the problem over the years was as follows: to ensure the continuity of patient's therapy by ensuring the patient always has access to treatment. the hospital had to ensure this without buying large quantities of the ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 4 drug and without creating large inventories, which constitute a value, and may be a waste of money if the drug isn't used before the expiry date. it might seem logical to order the drug only when the patient has a doctor's prescription. in reality, what might apparently seem simple is not the case. this is due to the fact that often, patients are elderly people who do not go to the doctor for repeat prescriptions because they are convinced that the therapy does not require re-evaluation, or people so busy with their work that they do not realize that they have finished the medication until they are down to the last tablet or vial and discontinue therapy as they would have to visit the doctor to request the renewal of the prescription. this therefore, would be a tool which would allow the collection of all the patients’ data, facilitate quick access to information about their treatment as soon as the patients come into the pharmacy or need to call the pharmacy. the tool allows the management of these data to resolve the problems of the patient and of the hospital. it was necessary to make sure that the patient was reminded of the monitoring visit before the end of therapy he was undergoing. it was necessary to ensure the availability of the drug and ensure delivery within 24 hours. the author created a database named gpgest after the author’s initials. in this database, patients are entered by recording all data, telephone contact and the data of the therapy at the first visit. the database has a list of medications that the pharmacist himself can update or change at will. the software enables the rapid filling of various fields using drop-down menus. these menus can be implemented to maintain the database. this type of software was not available on the database market. it was necessary to have a tool that could calculate with certainty how long the patient would be covered with the packs or units of medication which were being prescribed, so a formula that allows (on the basis of dosing) for the insertion of the unit of drug delivered was included in the software in order to calculate the date on which the patient will exhaust the supply of medication provided. this is shown in figure 1. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 5 figure 1: screenshot of database in this way, when the patient receives the last disbursement, the database will show that for the next request the therapeutic plan will expire and advises the pharmacist of the need for re-evaluation. the patient is entered as follows. he is registered in the database. personal details of the patient are taken. the tp is checked. at this point checks are made to ensure that the drugs have been prescribed by an authorized practitioner and through an authorized centre. this data is then stored in the database. checks are made to ensure that the drug is being used for the correct application and any patient requests are noted, for example, as to the best time to be contacted by phone. the amount of drug to be delivered is calculated on the basis of the dosage indicated on the therapeutic plan. it will suffice to enter in the “supply date” box the date of dispensing of the drug or the date on which the patient finishes any left-over drug which they might have. subsequently, the unit of the drug to be dispensed should be inserted. finally, the frequency with which the patient should take the prescribed dose should be inserted. in figure 1, the patient takes one pill per day. for example if there were 2 pills per day, a 2 is inserted in the box “units to administer” and in the box “every x days administration” 1 if there were 1 phial per week, a 1 would be inserted in the box “units to administer” and a 7 in the box “every x days administration” if there were 1 pill every second day, a 1 woud be inserted in the box “units to administer and a 2 in the box “ every x days administration. finally the unit of the drug dispensed is inserted. if a pack contains 60 pills and 2 packs are dispensed, suffice to enter 120 in the “quantity” box and the database will enter the date on which the patient will exhaust the drug “end therapy date”. when the sheet is printed, the system prints a sheet which is sent to the patient. this sheet reminds him when he will run out of the drug that he is receiving and he is informed of the day on which to withdraw the next lot of medication. figure 2. the patient must hand in this sheet the next time they collect the drug form the pharmacy. when the patient returns or requires the sending of the medication to his area of residence, it will be necessary simply to update the disbursement date and amount of the drug sent. the second time the patient collects the prescription, they will be asked how the therapy is progressing or if they have anything to report. the patient will be asked if they have seen the doctor again and if the dosage has remained the same. if the situation remains unchanged, the stock date and the quantity of the drug dispensed are updated. on this sheet it is also possible to enter special advice regarding the storage instructions for the drug such as the temperature at which it should be kept. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 6 figure 2: sheet for patient ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 7 figure 3: insert advice for the patient the database has designated spaces for the recording of the dates on which the packs of the drug should be prepared and when the patient will collect them. all this is recorded in a database table that will become the record of disbursements made to each individual patient and become a tool for the pharmacist to manage patient therapy and order him his drug. the pharmacist will be able to control at fixed time intervals which and how many patients will have exhausted their supply in a given period. dates on which the prescription was dispensed can be saved. this allows us to have a clear picture of the patient and makes it possible to understand if he is following the treatment and how he is following it. the therapeutic plan is scanned and stored in a scanned copy with name and date of completion. for example: john doe 27.05.2014. in this way, with a simple search command, all documents relating to a particular patient can be retrieved. this is shown in figure 4 figure 4: individual patient document at the end of the working day, all of the prescriptions made on that day are scanned and the bundle of the disbursements saved and renamed in a pre-established manner “dd day-monthyear” to enable to locate the disbursements made on whatever day through a simple search command. example: all the disbursements made on 11/01/2013 are scanned and saved under “dd 11012013”; all the disbursements made on 15/01/2013 are scanned and saved under “dd 15012013” and so on. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 8 in this way and in a single file it will be possible even months later, to identify how much was disbursed on that particular day. this is shown in figure 5. figure 5: record of disbursements the database also has a “diary” function which allows the pharmacist to save requests from patients which have to be prepared subsequently. when a patient calls, the pharmacist only needs to look for the name with the designated command and insert the identification number of the pharmacist who takes the call in the field “call”. later on, the pharmacist will prepare this request. with the key “outstandings” the outstanding calls for the pharmacist can be highlighted. this option could be useful for hospitals which send drugs to patients’ houses, as happens in the case of this article. the database is designed to manage all users in the reference area of the hospital in adria (ro). this territory includes three satellite centres called social health centres. patients can call the pharmacy and ask for the delivery of the drug to one of these centres which could be closer to their home. another need therefore, was that for storage and management of all the calls received during the day and to manage all these requests for drugs in the shortest possible time. the database stores all incoming calls during the day and once again at the touch of a button shows which users are in need of medication and had called for medicines (outstanding). a single table incorporates all the data for all of the patients. as patients are entered into the database a table is created that contains the basic information for each patient, such as medication, the dosage, the centre prescriber, the date on which stocks of medication will run out and the date of expiry of the tp. with the filters in the programme these tables can be analyzed. by filtering a given drug in a given date range, the drug can be provided at the right time, anticipating the needs of the patient. this table can be used to show how many patients use a drug and predict how many of them will run out of the drug in a given period. in this way, checking in advance, the drug for the individual patient can be ordered. this table can also be exported and, subsequently analyzed with any statistical software to obtain the desired report. with the database, simple tables can be created which contain all the data for each user. here is how the problem of stock and thus lowered stock value. in three steps data can be extrapolated from the main table which is fed by adding patients into the database. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 9 first the table can be sorted into columns of drugs. filters are used to select the patients being treated with the drug which are then arranged by date of exhaustion of stock. thus as shown in figure 6, it can be estimated how many patients will run out of the drug within a certain time span. as illustrated in figure 6 the database shows that there are three patients in the pharmacy with abilfy 28 cpr 15 mg. isolating these three patients, it can be noted that of the three, one will finish the drug which he has on the 05/03/2014 but his prescriptions needs renewing. it could be useful to contact him to see if he has seen his doctor to renew the therapeutic plan. for the other two patients however, a prescription covering at least a month of therapy for both must be foreseen. in this way it is not necessary to buy a random number of packs of the drug, but only the exact quantity necessary in order for the two patients to complete their therapy. the data was obtained relatively quickly and this allows to order the quantity of medication necessary to cover the two patients highlighted. figure 6: predict the requirements ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 10 results the database allows • the extraction of a table containing all the information needed to predict how many and which drugs will be needed in a given period of time. • the programmation of the orders of medications in the appropriate quantity limiting inventory costs and solving the problem of expiry of the drugs. • the rapid extraction of tables of all users treated with a certain drug or all users who are monitored by a specific prescriber centre. the table thus becomes the repository of all relevant information for studies of drug epidemiology or any other statistical study. • the creation of a filter table which groups the drugs to be ordered. • the management of the available budget for the purchase of drugs that is limited and is allocated annually. it was necessary keep within budget and make sure that at the end of the year the stocks were zero. the stocks, in fact, represent a value and the hospital management required that this value be as close to zero as possible. • not to have large stocks of drugs and not simply to wait for the arrival of the patient but to foresee his needs. • to predict how much and what medication it will be necessary to buy with targeted orders and without exceeding budget. • the allocation of the right funds to the right patient in the right moment and in the right amount by ensuring him the therapy without waste. • the calculation in advance for a given patient of how much medicine he will need without having to buy drugs in excess. • the avoidance of the purchase of more medicine than is necessary for patients. • the avoidance of the expiration of the drugs not used. in this way, in the first three-year period of activity, the ulss 19 was the only one in the region which achieved an optimization of the costs of direct distribution of drugs, in contrast to a trend on the increase in other institutions as emerges from an official report from the veneto region. in figure 7 a short extracted is cited by way of an example. in the period studied, all health and hospital facilities (with the exception of ulss 19 in adria which shows a decrease of 7.3% over three years) show an increase in direct distribution expenditure [3]. obviously these results are the product of team work and guidance on the part of all of the pharmacy staff. the calibration of the quantities of drugs to be ordered has in any event contributed to decreasing costs sustained by direct distribution, and remain within budget without prejudicing the level of patient care. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 11 figure 7: expenditure for direct distribution (veneto) conclusions the advantages of this tool for the hospital pharmacy and the hospital have proved to be numerous and can be summarized as follows: • the hospital can buy only the amount of medication strictly necessary for the management of their patients. this allows a scrupulous and careful use of public funds. • stocks of medicines are unnecessary, so the value of stock will be reduced to a minimum, allowing the hospital to move funds from pharmaceutical spending to other areas which may be lacking in resources. • vastly reduces the risk of waste of public money by eliminating the possibility that the drugs purchased in excess then expire unused. at the same time, however, therapies will be guaranteed for all patients. • improve the management of the patient. the pharmacist can monitor how the patient takes medication and if it is being taken in the right dose. the pharmacist may refer the patient for a check-up with the specialist for adjustment or confirmation of therapy. all this is possible with great immediacy, for example when the patient presents to collect the prescription the pharmacist can immediately check if it is the correct time for withdrawal or if there was some problem with the therapy. • anticipating the end of the therapy the pharmacist can also manage access to the pharmacy. this allows a few pharmacists to manage many patients. 2,000 patients were able to be managed by a single operator. an appointment system was created to allow patients to find the medication ready for collection without long waits. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 12 as illustrated in figures 8 & 9 the patient is shown what he has to do for the next collection of the drug. if a patient is following a treatment which has just begun or has a long term therapeutic plan it will be indicated on the sheet which is given to him to continue with the treatment as illustrated in figure 8 figure 8: patient following treatment otherwise, as illustrated in figure 9 if with the drug prescribed, the patient arrives at the expiry date of the therapeutic plan, he is advised at that point that for the next prescription he should contact his family practitioner for a check-up. availing the patient of this information makes him take responsibility and conscious that the treatment is being monitored in accordance with the family practitioner’s instructions. the patient who presents to collect his prescription must hand in these forms which were given to him on the preceding occasion. this allows the pharmacist to see if the prescription is being collected at the right time or if it is late rather than early. in tables where it is possible to export from the database all the dates assigned to the patient for successive collections are registered. ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 13 figure 9: patient requiring medical re-evaluation. analysing the tables it is possible to highlight all of the patients who have not presented to collect their prescriptions, to contact them to enquire as to what difficulties they have encountered in following the treatment and the reason for which they have not presented in the pharmacy. • often, the ministry of health requires the submission by each hospital of the number of patients taking a particular drug, the physician who prescribes them and the centre from which he operates. switching from paper to computerized management allows the hospital to send data faster if requested. if this were more widely implemented, it could facilitate the monitoring work of the ministry of health. • to date, the ministry of health has not focused on the importance of computerized management therapies. for this reason, no software has ever been developed by computer companies. software products for hospital facilities are orientated towards the collection of data which are then used for economic and reimbursement purposes between various hospitals countrywide. no software presently monitors the treatment of the patient. the submission of data on the drug consumption of the various patients is limited to a small amount of data that can be sent by each hospital sometimes months in retrospect. often the member of staff responsible for sending this data is not part of the clinical team but of the administrative one. this prevents the use of that data to check therapies for the benefit of the patient and the preservation of public pharmaceutical expenditure. that is the reason why our corporate ojphi an effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e183, 2014 14 database and all those currently on the market are not able to predict when a patient will run out of the drug that we are delivering. knowing in advance for how many patients and for which drugs to use the money available has been the only way that has permitted us to remain within the budget allocated to our hospital by the ministry of health. it was necessary to manage already existing patients, recruit new clients, provide therapy to all patients, lower inventories and communicate patient data quickly subsequent to any inquiry or information requested by the ministry of health. this had to be managed by one operator. unfortunately in italy the recruitment of staff has been reduced dramatically and the situation as in many italian hospitals, is very common. with this method, many problems were satisfactorily resolved. the purpose of the article is to share the author’s modus operandi in the management of patients in a hospital of small size. over the past five years more than 1700 patients have been recruited starting from a few hundred existing paperbased medical records. every year objectives achieved and procedures of computerization have allowed a complete electronic archive of all disbursements to be created that can be called up with a search on the hospital server. a common language and shared resources is an important and necessary step for the development of public health. shared ideas and speaking a common language can solve many problems which presently mean that public health is singled out as a slow, imprecise, wasteful, bureaucratic slave. many of these problems are due to the slow introduction of computerization in the public health sector, which coincides with the decline in human resources in hospitals. this example aims to show how, at times, with existing resources many problems can be solved. the database will be made available free of charge to anyone who wants to use it in their service. it comes with a users’ manual in two versions. is possible to download the english version and english tutorial and also the italian version and italian tutorial on: https://www.dropbox.com/sh/2r8svetn4srikw1/aabnm4jynbqwtifwqrka2lt5a?dl=0 or request it by mail from gpaolofranzoso@gmail.com. competing interests, disclosure of potential conflict of interest please note that this paper has never been submitted elsewhere in similar form. the sole author is the above-mentioned. i am aware of submission and i agree with it. there are no potential conflicts of interest. references 1. national observatory on the use of medicines. the use of drugs in italy report osmed 2012. report for the italian drug agency (aifa). rome. report n. september 2013. 2. national observatory on the use of medicines. rome. report n. september 2013. 3. italian republic. the court of auditors. regional section control for the veneto region. deliberazione 247/2012/gest http://www.corteconti.it/export/sites/portalecdc/_documenti/controllo/veneto/2012/delib era_247_2012_gest_e_relazione.pdf (2014 accessed 29 may 2014) https://www.dropbox.com/sh/2r8svetn4srikw1/aabnm4jynbqwtifwqrka2lt5a?dl=0 isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 87 (page number not for citation purposes) isds 2013 conference abstracts using syndromic surveillance to investigate tattoorelated skin infections in nyc mollie kotzen*1, 2, robert mathes1, lillian lee1 and don weiss1 1bureau of communicable disease, new york city department of health and mental hygiene, long island city, ny, usa; 2new york university, new york city, ny, usa objective to investigate tattoo-associated skin infections due to mycobacterium chelonae using emergency department (ed) syndromic surveillance. introduction in 2012, an outbreak of mycobacterium chelonae infections in tattoo recipients in rochester, ny was found to be associated with premixed tattoo ink contaminated before distribution.1 in may 2012, a case of m. chelonae was reported in a new york city (nyc) resident who received a tattoo with ink alleged to have been diluted with tap water. when a second case of m. chelonae in a tattoo recipient was reported in march 2013, an investigation was initiated. m. chelonae is not reportable in nyc other than in clusters reported by providers or laboratories. to determine if there were additional tattoo-associated m. chelonae infections, we searched for cases using nyc ed syndromic surveillance. methods ed syndromic data is de-identified and received daily from 49 of the 52 acute care hospitals in nyc.2 patient chief complaints are routinely scanned for key words and coded into syndromes (e.g. influenza-like illness, asthma). chief complaint data containing the key word “tattoo” for the period january 1, 2008 – december 31, 2012 were selected for analysis. the data were analyzed to describe trends and identify ed visits suspicious for skin infection (chief complaints containing the words “cellulitis,” “infected,” “redness,” etc.). ed visits that met our criteria in the five months preceding the recent m. chelonae report were selected for interview (november 2012-march 2013). names and contact information associated with the chief complaint data were obtained from hospital staff. a questionnaire including symptoms and duration, location of tattoo, name of tattoo parlor, and artist information was administered to evaluate possible m. chelonae cases requiring referral to a dermatologist for diagnosis. nyc laboratories were contacted to inquire about skin or soft tissue cultures from 2012 or 2013 in which m. chelonae was isolated. results a total of 577 tattoo-related ed visits (tredv) representing 43 (88%) hospitals were identified during 2008-2012. a 26% increase in the proportion of tredv per total ed visits among ages 18-64 was noted from 2008-2012. chi square for trend was not significant (p=0.11). three-hundred eighty (66%) of these visits were identified with additional chief complaint wording suggestive of infection. thirty-one tredv were identified in the five-month period preceding the second reported case of m. chelonae. ed visits were distributed among 19 nyc hospitals (range 1-4 visits/hospital). the median age of patients was 24 (range 16-48) and 65% (20) were women. for 18 (58%) patients, the chief complaint was coded as infection, 10% (3) for rash, 10% (3) for swelling, 7% (2) for pain and 16% (5) for other (allergic reaction, redness, warmth, not specified). interviews were conducted for 14 (45%) of the tredv. reasons for unsuccessful interviews included no reply to three phone call attempts and wrong or disconnected phone numbers. ed patients interviewed did not differ significantly from those who could not be reached on age, sex, or borough of residence. thirteen (93%) interviewed patients had resolution of symptoms or a noninfectious diagnosis. one patient who had persistent symptoms was referred for additional medical care and was diagnosed with staphylococcus aureus. from january 2012 through march 2013, nyc laboratories reported 13 isolates of m. chelonae from skin or soft tissue specimens, none of which were from patients with recent tattoos. conclusions using ed syndromic surveillance data, we determined that one case of tattoo-related m. chelonae was not part of an unrecognized outbreak or cluster of cases. in response to this concern about m. chelonae infections in tattoos, the health department sent a letter to all licensed tattoo artists in new york city advising them not to dilute tattoo inks with tap water. syndromic surveillance is an option for finding cases when the event under surveillance is described by a unique and specific word or phrase, such as tattoo. this method can be similarly used for situations where diseases are either not reportable or when cases may be otherwise difficult to capture. keywords tattoo; mycobacterium chelonae; syndromic surveillance references 1. giulieri s, cavassini m, jaton k. mycobacterium chelonae illnesses associated with tattoo ink. n engl j med. 2012; 367(24): 2357-8. 2. heffernan r, mostashari f, das d, karpati a, kulldorff m, weiss d. syndromic surveillance in public health practice, new york city. emerg infect dis. 2004; 10(5):858-864. *mollie kotzen e-mail: mkotzen@health.nyc.gov scholcommuser stamp scholcommuser rectangle scholcommuser rectangle scholcommuser text box online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e172, 2014 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 67 (page number not for citation purposes) isds 2013 conference abstracts informing u.s. federal public health preparation for emerging virus pandemic threats at ports of entry andrew hickey*, diana wong, janet hendricks, michael stephens, erik pedersen, deborah carr, kandis brown, christopher grant, jamie hobson, jessica ruble, william albrecht, tajah blackburn, afif marouf, todd bodenhamer, julie waters and mark freese national biosurveillance integration center (nbic), department of homeland security (dhs), washington, dc, usa � �� �� �� � � �� �� �� � objective ������� ��� ��������� �� ��� �� ����� ����� ��� �������� ���� �� ������������ �������� ������ � ��� � �������� �� �� �� � ��� � � ���������� ��������� ����� ��������������� � ��� ���� � �������� !� "#$� %&�����!'(�)&������ �*��+, 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�������� �� ��� ������� �� �, keywords ���� ��;�'(�);��� ���� ������ ;�/ ����� ��� ��' � �����# ������� !/'#&;�� "#$� % *andrew hickey e-mail: andrew.hickey@hq.dhs.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e152, 2014 ojphi-06-e27.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 191 (page number not for citation purposes) isds 2013 conference abstracts comparison of aberration detection algorithms for biosurveillance systems hong zhou*1, howard burkom2, carla winston3 and umed ajani1 1cdc, atlanta, ga, usa; 2john hopkins applied physics laboratory, washington dc, md, usa; 3veterans health administration, palo alto, ca, usa � �� �� �� � � �� �� �� � objective ������� ����� � �� �� ��������������� ��� ������������ ����� ������� ������ � ��� � ���� ��� �� �������� ����� � ������� ��������� ���������������������������������� � ����������� �������������� ����� ���� ������������ ������� ���� ������ ����������� ���� ��� ������� ������ ���� �!� ������ �"��" ��!��������� introduction #������ ������ ���� � ���� ����� ������ ������ �� �� ������������� ���������� ���� � ���������� ��������� ���� ������������������� ��� �� � �� ��� ������ �� �����������$ ������ �����%��� �����&������ �'�( � ��� ������ ����� �)���������� �� ���� ������������������� ���� �������������������� � � ���������������� ��������� ����������� ����� ���� � ��������� � ��� � ����*������� ����� ��� ������ ����+ �" �� ���������� ��������� � ���������� �������� �������������� �� �"��" ��!��������� ���������� ��� ���� ���������� ������� ������������������ �� �������� �� ����� ������ ��� �������� ��������� �� ��� ��������� �� ��������������,� �������������� � ��� 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� ������������ � ��!� ��� ��� ��!������� � ����� �� ��� ������ ��� ����� ��������������� ����� conclusions *����� � ����������� ������� ������ � �������� ����� �� ������" ������ ���� ��� ������� ������ ��� ��!� �7 ��!�������� � ��������" �� ���� ��������������������������������"� ������ � !�������������� �#&#.� ��� ���������� �� ���� ����� ������������'���������� =� ��� � � ����������������"�� ����� ��� ������� ������ ����� �� ������� * ���8�&������ ����(4)� ��������������(� �)�������������������"� ������ � ?������ ������� �� ��� ����� keywords ���&����5���� ��������������5����� ����� � references ���= ��!� �%�,��,�@���� ��a,���������������� �������� ������� ���" � ���������������������8�$�%&b� � ���� ��#&#."� ������������" �����&� ��.���'��65�'c8;3�c"'d� '��*�! ��-2,��� !���@,�e����-,���� ���$�� ����������"�� ���������" ����� ��� ������ �� � ���� ��������� ���� �����$�� ����� 2������� '��d5�<8<;;"d� *hong zhou e-mail: fwd6@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e27, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts multiple source spatial cluster detection through multicriteria analysis luiz h. duczmal*1, alexandre c. l. almeida2, fabio r. da silva1 and martin kulldorff3 1universidade federal de minas gerais, belo horizonte, brazil; 2universidade federal de são joão del-rei, ouro branco, brazil; 3harvard medical school, boston, ma, usa objective to incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection using statistical tools from multi-criteria analysis. introduction multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods [1,2]. spatial scan statistics have been adapted to analyze multivariate data sources [1]. in this context, only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and computing its significance. a multi-objective scan was proposed to detect clusters for a single data source [3]. methods for simplicity, consider only two data streams. the j-th objective function evaluates the strength of candidate clusters using only information from the j-th data stream. the best cluster solutions are found by maximizing two objective functions simultaneously, based on the concept of dominance: a point is called dominated if it is worse than another point in at least one objective, while not being better than that point in any other objective [4]. the nondominated set consists of all solutions which are not dominated by any other solution. to evaluate the statistical significance of solutions, a statistical approach based on the concept of attainment function is used [4]. results the two datasets are standardized brain cancer mortality rates for male and female adults for each of the 3111 counties in the 48 contiguous states of the us, from 1986 to 1995 [5]. we run the circular scan and plot the (m(zi),w(zi)) points in the cartesian plane, where m(zi) and w(zi) are the llr for the zone zi in the men’s and women’s brain cancer map, respectively, and i, i=1,...,n(r) is the set of all circular zones up to a radius r>0. the nondominated set is inspected to observe possible correlations between the two maps regarding brain cancer clustering (figure 1); e.g., the upper inset map has high llr value on women’s map, but not on men’s; the inverse happens to the lower inset map. other nondominated clusters in the middle have lower llr values on both datasets. the first two examples have comparatively lower p-value (they belong to the two “knees” in the nondominated set), as computed using the attainment surfaces (not shown in the figure). conclusions the multi-criteria multivariate approach has several advantages: (i) the representation of the evaluation function for each datastream is very clear, and does not suffer from an artificial, and possibly confusing mixture with the other datastream evaluations; (jj) it is possible to attribute, in a rigorous way, the statistical significance of each candidate cluster; (iii) it is possible to analyze and pick-up the best cluster solutions, as given naturally by the non-dominated set. part of the solution set in the llr(male) x llr(female) space of the male/female brain cancer datasets for the us counties map. clusters are indicated by blue points, with the non-dominated solutions represented by small red circles. the inset maps depict the geographic location of the clusters found in the us counties map (yellow circles) for two sample non-dominated solutions. keywords spatial scan statistic; multi-criteria; attainment surface; multiple data stream acknowledgments the authors acknowledge the grants from cnpq and capes. references [1] kulldorff m, mostashari f, duczmal l, yih k, kleinman k, platt r.(2007) multivariate scan statistics for disease surveillance. stat med,26,1824-1833. [2] jonsson et al. analysis of simultaneous space-time clusters of campylobacter spp. in humans and in broiler flocks using a multiple dataset approach (2010). ij health geogr,9:48 [3] duczmal l, cançado alf, takahashi rhc (2008) geographic delineation of disease clusters through multi-objective optimization. j comp graph stat,17:243-262. [4] cançado alf, duarte ar, duczmal l, ferreira sj, fonseca cm, gontijo ecdm (2010). penalized likelihood and multiobjective spatial scans for the detection and inference of irregular clusters. ij health geogr,9:55. [5] fang z, kulldorff m, gregorio di (2004). brain cancer mortality in the united states, 1986 to 1995: a geographic analysis. neuro-oncology 03-045, may 6. *luiz h. duczmal e-mail: duczmal@ufmg.br online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e11, 2013 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 34 (page number not for citation purposes) isds 2013 conference abstracts non-parametric scan statistics for disease outbreak detection on twitter feng chen* and daniel b. neill event and pattern detection laboratory, carnegie mellon university, pittsburgh, pa, usa � �� �� �� � � �� �� �� � objective ��� ������ � � ���� �� ��� ���� ���� ��� �� 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��� ������ #����$�� ������ ��� ��� ���������� �� ���&�� ���"�"��� �#'��� �� ������� ���� ������ ������� ��������� ���� � �������� ���"�8������,�� ���� � � � �� ��� �#'�#���� #������� �&��� �����1"14�8�>� '�������� ���� ��� ����# �� ���������#'�*"<�� '���� �� ������ ��� �� �� '��� #�-� ��?� � '��# �� ���������� � �� ��� #����#��� � # ����&��## ����� " conclusions ��������� ��������� � �� ����� �� �� #� ���� � ����� � ������ ���������� #����� �� �� ��"�$ ��� ���� ��'����$�� ��������� � ��� ��������������# ��������$�� ���� " 8������*)�8�9�&�"�$�9���� �� ���� �������� � ��� 8������,)�8�9�&�"�@� ��$���� ���@ ��$��� keywords ����� � �� ������ ��� �� ���+����� #����� +����� ����� ��� � acknowledgments $ ��������� ��� � � ##'������� ����'� ��� ��� #���������8���� ����� �� � ��aa��1b*;-?<��aa��1b**1-,� ���aa��1b<---1" references *"� �)>>���"���� #"�#>&���# ��� ���������#���� >�������� ��������� ���� � �������#�� ��� >&���# ��� � � &����> *feng chen e-mail: fchen1@cmu.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e155, 2014 {{}} the electronic medical record and patient-centered care 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 the electronic medical record and patient-centered care neil j nusbaum 1 1 dept of veteran affairs, va central western massachusetts health care system abstract background: one goal in emr development should be to facilitate a patient-centered clinical encounter. much prior emr development has focused on capturing objective data, such as laboratory values and medication lists. less attention has been devoted to the more complex task of capturing and analyzing data that incorporates the patient’s concerns and preferences. methods: a literature search supplemented the author’s own various experiences with one emr (that used nationally by the department of veterans affairs) from his various perspectives of a physician, an educator, and a chief of staff. this data was used to identify both opportunities and obstacles to promoting patient-centered care in an integrated care setting that relies heavily on an emr. qualitative analysis and suggestions are offered for how the emr can individualize patient care, in support of a patient-centered approach. result: three promising target areas in efforts to develop a patient-centered emr are: elicitation of the chief complaint, conduct of health screening activities, and evaluation of health literacy. a range of strategies were identified, some of which may require information technology development, such as to facilitate patient direct entry of data into their own emr. conclusion: emr design can facilitate a more patient-centered clinical encounter. beyond the benefits to the individual patient, patient-centric modifications to the emr architecture may also facilitate quality improvement and research activities on patient centered care. in light of the widespread current discussions of a movement toward accountable care organizations that use emr, it will be especially important to ensure that the resulting care systems maintain a focus on the patient and not just on the system of care. key words: usability of health information, health promotion / disease prevention, information technology, organization and delivery of care, quality of care the electronic medical record and patient-centered care 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 introduction there has been considerable interest in making the clinical encounter more patient centered.[1] at the same time, in recent years there has been widespread exploration of electronic medical record (emr) use in both inpatient and outpatient settings.[2] clinicians wish to implement the emr in such a way that it does not retard the emotional depth of the interaction between the physician and patient.[3] accordingly, it is fruitful to consider efficient and harmonious ways in which the implementation of a patient-centered approach and introduction of emr will proceed in concert.[4 5 6 7] the challenges in implementation of a patient-centered emr differ somewhat according to the component of the patient record. the most rigidly structured components are the easiest to organize or capture into an electronic record, but the very rigidity of the structure may easily shift the center of focus to the electronic instrument and its computer interface.[8] the range of challenges and opportunities in creating a patient-centered emr can be illustrated by considering three disparate components of the health care encounter: elicitation of the chief complaint, health screening activities, and evaluation of health literacy. the chief complaint numerous observers have bemoaned the tendency of clinicians to quickly interrupt the patient’s own description of their chief complaint at the initiation of a clinical encounter. the chief complaint is intended by its nature to represent a report by the patient of their subjective concern, and therefore, it does not need to correspond to any standard pathophysiological criteria of plausibility. the chief complaint classically represents a part of the history that is driven by the patient and not the physician. among other purposes, a careful recording of the chief complaint can serve as the anchor for the rest of the patient encounter to help ensure that the patient’s concerns are addressed. fidelity of recording is particularly important where the history is gathered by one person such as a clinic nurse before the patient sees another individual such as the clinic doctor. in order to make full use of the chief complaint it is desirable to build on emerging technology to allow the patient themselves to record their chief complaint in their own words rather than relying on the nurse or physician to serve as a scribe. in order to accomplish such direct patient entry, of course, the computer architecture must be structured to provide the capability for a patient to enter data into the emr, whether from a computer keyboard, a tablet, or perhaps even by means of voice dictation software (such as could be used even by the visually impaired or those including children who lack literacy skills) some other device.[9] one then could allow the patient to electronically enter their chief complaint in response to the typical open-ended question of the main reason for their visit today. their response could be imported verbatim into the patient record as the stated chief complaint. in some cases, the clinician will find that the patient's chief complaint actually encapsulates well what the clinician perceives during the encounter to be the most the electronic medical record and patient-centered care 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 important medical issue. in other cases, the chief complaint may represent a subjective concern of the patient alone, but one which the clinician regards as not a serious threat to health. in still other instances, the chief complaint may evidence a significant misunderstanding by the patient of the pathophysiology of their disease process, a topic on which the patient might benefit from focused patient education. a shared attentiveness to the patient’s chief complaint needs to be maintained by the entire caregiving team throughout the course of the patient encounter. in particular, it is important for the ongoing physician-patient relationship that the chief complaint has been appropriately addressed by the end of the encounter. a clear and legible verbatim transcript of the patient's chief complaint, such as one recorded digitally by the patient themselves before the encounter began, could well be integrated into the process of closing the patient encounter. this might be as straightforward as the clinician, toward the close of the encounter, paraphrasing the patient's chief complaint and then asking the patient themselves whether the patient felt that the chief complaint had been addressed to the patient's satisfaction. if the patient's answer is in the negative, the clinician (depending upon clinical needs and time constraints) could further address the chief complaint in closing the current visit and/or offer the patient a follow-up visit to address the chief complaint. in general, having a legible verbatim version of the patient's chief complaint will facilitate its use not just for the current patient encounter but also as a reference point for future patient encounters. although clinicians commonly elicit a chief complaint at the current visit, it is certainly less common for clinicians to search the record of chief complaints offered by the patient at prior visits. a history of repeated similar chief complaints on multiple prior visits may be a valuable relatively objective indicator of the chronicity of a medical problem, and so complement the patient's own present recollection of when the complaint first arose. a broad array of complaints over prior visits can similarly serve as a complement to the current elicitation of the review of systems, and may offer helpful clues to the presence of chronic multisystem disease. use of the electronic medical record to target health screening a concern that regularly arises when the electronic medical record is introduced into a clinical setting is that the introduction of the electronic interface will, at least in the nearterm, reduce the efficiency of the interaction between physician and patient and thereby reduce the number of patients that can be seen during the workday. a poorly designed emr can force the physician to spend time on computercentered tasks such as screen navigation and data entry, at the expense of time for patient-centered tasks. one way to alleviate this potential time barrier to introduction of the electronic medical record accordingly is to create early opportunities for the electronic medical record to increase the efficiency of the clinical interaction. even a fairly rudimentary emr can assist in creating efficiency in mechanical tasks, such as entering the date and time into progress notes. a bigger challenge is to structure the emr so that it can support rather than impede the humanistic aspects of the physician-patient interaction. the electronic medical record and patient-centered care 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 a concern of primary care physicians is that the time constraints of the clinical encounter leave relatively little time for patient counseling about health maintenance and health screening issues. further exacerbating this constraint, bodies such as third-party payers may insist upon repeated counseling efforts upon a wide variety of topics. scattered attention at a single visit to multiple counseling topics, if it creates the impression for the patient that all the counseling topics are of equal importance, also has the potential to dilute the impact of the interaction with regard to those topics of most significant concern to the individual patient's health. an emr menu could readily maintain a menu of pending health maintenance topics, offer both the physician and the patient an opportunity to prioritize which on the list should be addressed at the current visit, and to come to an agreement on steps (such as planning for an early follow-up visit) to address those items not resolved on the current visit. a lengthy and complex list of deferred reminders could prompt the emr itself to suggest that the follow-up appointment be planned to allow sufficient length to accommodate the planned discussion agenda. the list of deferred items could be appended to the next clinic appointment as a popup screen in the emr itself. the list might also be appended to any clinic reminders that are sent to the patient before the follow-up appointment. one use of the medical record, which has already been translated into practice in some environments, is to target clinical interventions demographically. a simple example is to target clinician reminders for screening tests such as a mammogram [10] only for those patients for whom the respective test is appropriate according to their gender, age, and past medical history. likewise clinical reminders in some emr environments are automatically suppressed if they are not currently relevant to the specific patient, such as a patient who has already received their influenza immunization for that flu season. flu vaccine reminders in the emr could be translated on the patient side into automated phone calls or mail or email to eligible patients, with the mode of communication employed chosen for the given patient in patient-centric fashion, in accordance with their personal preference as it has previously been recorded in their emr. the emr similarly can structure the encounter on the clinician side, so that an offer of flu vaccination can be made accordingly to clinician preference by the intake nurse, during the physician encounter, and/or at checkout after the physician encounter. screening interventions could further be targeted by use of patient electronic responses to questions regarding risk factors. patients could respond to even very personal (or sensitive) questions using an electronic tool such as a pda to record their answers[11], and indeed some patients may feel more comfortable responding to such questions on an electronic form[12] rather than verbally face-to-face with a clinician questioner. answers to questions regarding issues such as recreational drug use or risky sexual behavior, for example, might influence whether a patient should be offered not only initial testing for hiv but also subsequent retesting if the initial test were negative. the emr may be a particularly useful device as well to share and simultaneously track the electronic medical record and patient-centered care 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 share of patient care information across sites of care for the patient, such as across the entire structure of health care networks, while simultaneously tracking that sharing for hipaa and similar regulatory purposes. sharing of information in the emr certainly should be an important component of the electronic integration of future accountable care organizations, if they are indeed to function as integrated entities. efforts to develop the use of the emr as a tool for patient centered care should embrace not just physicians and nurses, but also other clinical providers such as pharmacists. [13] ideally, the emr also should include and integrate not merely the data from face to face visits, but also patient concerns as expressed in electronic messages that may have been sent between visits. [14] it should readily be possible to further increase the targeting of clinical reminders in the electronic medical record in a patient-specific fashion. consider for example a patient with diabetes with comorbidities that is routinely seen in the office at three month intervals. if the patient has need for say four different topics to be raised (e.g. smoking cessation, depression screening, advice regarding exercise, advice regarding daily visual foot inspection) one could use the electronic medical record to sequence each of the topics to be raised one at a time over the course of a year at each quarterly visit rather than having all four topics scheduled to come up simultaneously at multiple times during the year. the sequential approach would allow each of the topics in turn to receive adequate attention. if desired, one could also include a clinical reminder to the clinician of the topic that had been raised in counseling at the last prior visit so that it could be reinforced at the current visit. if the preceding visit had included a discussion of foot care, the clinician might well wish to briefly reinforce this topic at the current visit, and pay particular attention to the feet during a physical examination. a still more sophisticated algorithm might include a prioritization of topics according to previous responses from the patient. if the patient is a smoker who has repeatedly rejected any consideration of smoking cessation then it might still be appropriate to raise the topic at least briefly at least once per year. on the other hand, if the patient had been in contemplation of smoking cessation on a recent visit, then the topic of smoking might appropriately be flagged for more detailed discussion at each of the succeeding four visits. patient health literacy a collateral benefit of the approach described above is that it would enter in machinereadable format a sample of free text provided by the patient. the patient's text entry could readily be analyzed with tools such as those already bundled in a word processing program to analyze the level of complexity of the written passage. [15] the reading level of the patient text, or indeed even the patient's failure to provide any textual material at all in response to the request for it, might offer a ready clue to the clinician that the patient's literacy skills are limited. a patient’s lack of health literacy can be hidden from the clinician if the patient is purely the electronic medical record and patient-centered care 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 a passive participant in their own health care. encouraging the patient to ask questions has been embraced as a strategy to make the patient more active in their own care. an example is the “ask me three” questions approach: “providers should always encourage their patients to understand the answers to: 1.what is my main problem? 2. what do i need to do? 3. why is it important for me to do this? ) [16] the answer to the first of the three questions, “what is my main problem?” is the main problem as identified by the clinician by the close of the encounter. that answer by the clinician may or may not resemble the chief complaint, the main problem as seen by the patient at the start of the encounter. one strategy to help improve patient health literacy, therefore, may be to focus attention on how and why the clinician statement of the main problem differs from the patient statement of the chief complaint. the emr could also be readily structured so that one task for the physician or checkout nurse is to review with the patient whether the patient has had those three “ask me three “ questions each answered to the patient’s satisfaction. one of the challenges for a clinician as educator is to provide patient education whose form, content, and level of complexity are appropriate to the patient. even for a given patient with a chronic condition, the education goals may evolve as the patient becomes more knowledgeable about that condition. [17] it might well be of interest for quality improvement or research purposes to track how the educational needs of the patient, as reflected in their chief complaint and/or in their “ask me three” questions, evolve over serial visits. it could similarly be instructive to observe from textual analysis of the chart whether the chart entries by the provider evolve in a corresponding fashion. finally, as it may become more common for patients to read their own charts, it would be of interest to learn from patients if they felt the chart reflected their own understanding of what happened at the visit, if they found the chart itself difficult to read e.g. because of jargon, and indeed if such use of jargon at the actual visit had interfered with their understanding at the visit. conclusions the emr offers a powerful tool for implementing standardized approaches to care that may be very helpful in implementing clinical protocols and other interventions to decrease clinical omissions or other errors in a highly reproducible fashion. an emr environment is particularly useful for addressing mechanical tasks, such as retrieving whether a patient has already had a flu shot this year. at the same time, however, the emr has rich potential to support appropriate personalization of care. a sophisticated emr can best meet the needs of an individual patient if it simultaneously supports care provision that is evidence based as well as care that is sensitive to the individual patient’s emotional, educational and cultural context.[18] if a personal health record [19] is to be truly personal, it should incorporate concerns relevant to the particular patient. it is essential to develop the full potential of emr implementation if the emr is to actively support the paradigm of patient centered care. note: personal opinions are those of the author, not official. the electronic medical record and patient-centered care 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 correspondence neil jay nusbaum dept. of veteran affairs email: nusbn@aol.com references [1] brennan pf, downs s, casper g. project healthdesign: rethinking the power and potential of personal health records. j biomed inform. 2010;43(5, supplement 1):s3-s5. http://www.ncbi.nlm.nih.gov/pubmed/20937482 [2] ludwick d, doucette j. adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries. int j med inform. jan 2009;78(1):22-31. http://www.mece.ualberta.ca/~doucette/publications/ludwick-doucette-ijmi-2009emr.pdf [3] chan w, stevenson m, mcglade k. do general practitioners change how they use the computer during consultations with a significant psychological component? int j med inform. aug 2008;77(8):534-538. http://www.sciencedirect.com/science/article/pii/s138650560700175x [4] ventres w, kooienga s, marlin r. ehrs in the exam room: tips on patient-centered care. fam pract manag. mar 2006;13(3):45-47. http://www.aafp.org/fpm/2006/0300/p45.html [5] reid r, wagner e. strengthening primary care with better transfer of information. cmaj. nov 2008;179(10):987-988. http://www.cmaj.ca/content/179/10/987.full [6] detmer d, bloomrosen m, raymond , tang p. integrated personal health records: transformative tools for consumer-centric care. bmc medical informatics and decision making 2008, 8:45. http://www.biomedcentral.com/1472-6947/8/45 [7] murphy j. patient as center of the health care universe: a closer look at patientcentered care. nursing economics 2011; 29(1):35-37. [8] rhodes p, langdon m, rowley e, wright j, small n. what does the use of a computerized checklist mean for patient-centered care? the example of a routine diabetes review. qual health res. mar 2006;16(3):353-376. http://www.ncbi.nlm.nih.gov/pubmed/16449686 [9] moore bj, gaehde s, curtis c. architectural choices and challenges of integrating electronic patient questionnaires into the electronic medical record to support patientcentered care. amia annu symp proc 2008; 2008:490-494. pmcid: pmc2655980 [10] baron rj. quality improvement with an electronic health record: achievable, but not automatic. ann int med 2007;147:549-552. http://www.annals.org/content/147/8/549.abstract [11] bernabe-ortiz a, curioso wh, gonzalez ma, evangelista w, castagnetto jm, carcamo cp, et al. handheld computers for self-administered sensitive data collection: a comparative study in peru bmc medical informatics and decision making 2008, 8:11. http://www.biomedcentral.com/1472-6947/8/11 http://www.ncbi.nlm.nih.gov/pubmed/20937482 http://www.mece.ualberta.ca/~doucette/publications/ludwick-doucette-ijmi-2009-emr.pdf http://www.mece.ualberta.ca/~doucette/publications/ludwick-doucette-ijmi-2009-emr.pdf http://www.sciencedirect.com/science/article/pii/s138650560700175x http://www.aafp.org/fpm/2006/0300/p45.html http://www.cmaj.ca/content/179/10/987.full http://www.biomedcentral.com/1472-6947/8/45 http://www.ncbi.nlm.nih.gov/pubmed/16449686 http://www.annals.org/content/147/8/549.abstract http://www.biomedcentral.com/1472-6947/8/11 the electronic medical record and patient-centered care 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 [12] ruland c, starren j, vatne t. participatory design with children in the development of a support system for patient-centered care in pediatric oncology. j biomed inform. aug 2008;41(4):624-635. http://www.sciencedirect.com/science/article/pii/s1532046407001116 [13] frenzel je. using electronic medical records to teach patient-centered care. american j of pharmaceutical education 2010; 74(4) article 71. [14] stiles ra, deppen sa, figaro mk, et al. behind-the-scenes of patient-centered care: content analysis of electronic messaging among primary care clinic providers and staff. med care. 2007;45(12):1205-1209. doi: 10.1097/mlr.0b013e318148490c [15] http://office.microsoft.com/en-us/word/hp051896011033.aspx . website accessed august 7, 2011. [16] http://www.npsf.org/askme3/ . website accessed august 7, 2011. [17] craft ce, fowles jb, kind ac, kind ea, richter sa. no change in physician dictation patterns when visit notes are made available online for patients. mayo clinic proceedings 2011; 86(5):397 http://dx.doi.org/10.4065/mcp.2010.0785 [18] snyder cf, wu aw, miller rs, jensen re, bantug et, wolff ac. the role of informatics in promoting patient-centered care. cancer j. 2011; 17(4):211-218. doi: 10.1097/ppo.0b013e318225ff89 [19] smith sp, barefield ac. patients meet technology: the newest in patient-centered care initiatives. health care manag (frederick). 2007;26(4):354-362. pmid:17992110 http://www.sciencedirect.com/science/article/pii/s1532046407001116 http://office.microsoft.com/en-us/word/hp051896011033.aspx http://www.npsf.org/askme3/ ojphi-06-e60.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 80 (page number not for citation purposes) isds 2013 conference abstracts priority communicable disease surveillance (pcds) in bangladesh mohammed a. kalam* research, siam health care, dhaka, bangladesh � �� �� �� � � �� �� �� � objective ������ ���� �� ��� ����� � ������ � �� ���� ���� �� ����� ����� � ������ ���� ����� ��� � ��� �������������� ������ ������ �� ������ ��� �� ������ �� ������� �������� �� ������� �� ��������� ���� ���� � ���� ������ ������� ���� � ���� �������������!��"��# �� !��$�� % & �!��'��(������ ���!��)��*��� ��!��+��,����� ����!��-��.� /� ������� ���� ����� ����� ���� ����� introduction ���� ��������� ������� ��������� ��� ��� ������������������� ���� �� ������ ����������� ������� ������ ���� � ����� ������� ���� ������ �0�(���� ���� �� ��1�� �����2 ������ � ������ ��������� �����12�������� ���������� �������� ���������&������� ���� �������� ����� 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�������������� ��.� &� � ���������������� / ������� �0�� � ������������ ����������� ��������� / ������� ������ ���� �0��( � ��"� conclusions <���1�� �����2 ������ � ������ ��������� ������������� � ��� � ���� ����� ��� ��� ��������� ���� ��� � � � ������ ��������� � ��� ��9� ������������� �� � � ���� ���� ��� �� ����������� ��� ���� ������ ������ ��������� ���� ���������� �� �������� �����0�(��� ��� �� �� ��� ��� ����� ��������� �� ����������� ������ ������� ������� ���� ����� � ������������ �����0 ( � �����8��� ��� ��12�� ( � ���"�������� �� ����� � �����1 ������2 ���� ��� �� ������3��0�56�� ����� "5��� b ����8 ������������ ��� ����� ���� ��� ��8 a�� ��������� ���� ��"5�� keywords ������ �!����� ��!��� ���� references ?,�280�3������;� ���*����� ��1�� ���9� ����� �� �����"55+ "5�� *mohammed a. kalam e-mail: med_sociology_iedcr@yahoo.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e60, 2014 dial: a platform for real-time laboratory surveillance 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 dial: a platform for real-time laboratory surveillance shamir n mukhi, phd, peng 1,2 , jennifer may-hadford, mph 2,3 , sabrina plitt, phd 2,3 , jutta preiksaitis, md 4 , bonita lee, md, msc 3 1 canadian network for public health intelligence, 2 public health agency of canada, 3 provincial laboratories for public health alberta, 4 university of alberta abstract laboratory information systems fulfill many of the requirements for individual result management within a public health laboratory. however, access to the systems by data users, timely data extraction, integration, and data analysis are difficult tasks. these difficulties are further complicated by often having multiple laboratory results for specific analytes or related analytes per specimen tested as part of complex laboratory algorithms requiring specialized expertise for result interpretation. we describe dial, (data integration for alberta laboratories), a platform allowing laboratory data to be extracted, interpreted, collated and analyzed in near real-time using secure web based technology, which is adapted from cnphi`s canadian early warning system (cews) technology. the development of dial represents a major technical advancement in the public health information management domain, building capacity for laboratory based surveillance. keywords: laboratory, surveillance, informatics, analysis, epidemiology, pandemic introduction public health laboratories are the front-line of public health. the ability of the public health system to respond to emerging public health challenges is partly dependent on how effectively public health laboratories, create, integrate and communicate testing results [1]. the recent h1n1 pandemic has confirmed the important need for timely access to comprehensive laboratory testing data in order to monitor trends and rapidly make informed public health policy decisions. historically, it has been difficult to access laboratory data for surveillance purposes as laboratory information systems (lis) focus mainly on data handling for laboratory testing including the pre-analytical, analytical and post-analytical phases relating to clinical case management [2]. moreover, the ongoing identification of new pathogens associated with clinical syndromes, technological advancements in diagnostic assays and the use of complex test algorithms to improve diagnostic sensitivity and specificity have made it difficult to interpret and present laboratory data in a format that is useful for surveillance and public health purposes. in 2007, the provincial laboratory for public health (provlab) in alberta and the canadian network for public health intelligence (cnphi) [3] partnered to create dial, data integration dial: a platform for real-time laboratory surveillance 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 for alberta laboratories, a platform allowing laboratory data to be extracted, interpreted, collated and analyzed in near real-time using secure web based technology, which is adapted from cnphi`s canadian early warning system (cews) technology [4]. alberta is a province in canada with a population of about 3.7 million. alberta’s geography is diverse, ranging from rural farming communities to areas of natural resource extraction to highly urbanized centers. all health services are publicly provided and are managed and directed under a single health authority since april 2009. provlab provides a wide array of diagnostic tests for communicable diseases, in concert with regional laboratories, as well as specialized testing, reference services and laboratory support for provincial programs such as prenatal screening and outbreak investigations. provlab also provide diagnostics services for the northern territories including the northwest territories, nunavut and parts of the yukon. the diagnostic manual at provlab includes testing and characterization of viral, bacterial, fungal and parasitic pathogens from various sample types using traditional methods, advanced molecular techniques, and serological tests.[5] historically in provlab, laboratory data was accessed through rigorous extraction routines from lis to a data management tool that enabled a simpler interaction with the data. however, customized queries and specialized tables were accomplished only by programmers and thus data was not easily accessible to medical, technical and administrative laboratory staff or public health practitioners outside the laboratory. moreover, the raw laboratory data still required interpretation and transformation by laboratory experts to become clinically meaningful data for public health stakeholders and researchers. a solution that allowed front line users to access, present and analyze collated, integrated and interpreted laboratory information in real-time was needed. we describe the solution to this need, the dial platform, focusing in depth on its various key components as displayed in figure 1. the platform has been developed to work with any disease target tested within the laboratory, however, given the complexity of laboratory testing procedures for various targets, the platform needs to be adapted as needed. at its present state, the dial platform has been configured to analyze testing data for the detection of respiratory viruses, typing of methicillin resistant staphylococcus aureus (mrsa), syphilis (serological assays and direct detection), and environmental water testing. dial: a platform for real-time laboratory surveillance 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 ade aie parser jdbc (hl7) user interface charts data table bookmarks analysis maps linelists classifications aerts filtersdemographics dial lis data centre target/pathogen based processes bookmark sharing <<ssl>> 1 2 3 web data customized secure online databases case management web service patient based analysis cnphi figure 1: dial platform components dial platform components technical configuration dial platform runs using jboss server and oracle database on a dell r900 server using vmware virtualization technology. the dial platform requires three virtual servers including: 1. database: oracle database 2. data centre: data processing facility 3. application: user interface dial has proven to be a very stable system since its launch in 2009. dial’s speed is not expected to decrease as the number of users are increased, nor is the stability expected to decline as more data or disease targets are added. data acquisition dial data is virtually real-time as laboratory information is automatically extracted using a proprietary extraction engine connected directly to the lis twice daily. lis data is organized by specimen and contains demographic and geographic information on patients and health providers as provided on requisitions accompanying the submitted specimens. the latter data is routinely entered manually as part of the pre-analytical testing process. test requests for specific analytes dial: a platform for real-time laboratory surveillance 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 are entered into lis using specific test codes and results of requested tests and supplementary or confirmatory tests, as defined by testing algorithms, are entered into the lis electronically for tests performed on interfaced instruments or manually by technologists. the extraction and provision of lis data to dial does not require any effort from laboratory personnel. the specimen data from the lis are automatically extracted into the data management tool running on a sql server database allowing seamless connection over jdbc (java database connectivity). the data is extracted using proprietary automated data extraction engine (ade) which is a data extraction approach using one line per specimen from the lis. the data is extracted using standard connection to the relational database which can be adapted to various lis platforms. all the relevant specimens data contained in the lis are extracted into the dial database. currently, the ade runs twice daily, however, it can be scheduled to execute more often if required. once extracted, the laboratory test results for the extracted specimens go through an interpretation engine and are stored in an oracle based database. dial is developed as a record based system where a record can be a specimen or a patient depending on the source data. given the complexities of compiling specimen-based data into patient-based information, especially for infectious diseases with the possibility of re-infection events occurring at variable intervals, dial currently only supports specimen level reporting and analysis. dial does not allow modification of extracted data, thus, complying with the requirement that the lis is the “owner” of the data and changes must be enacted in the lis. automatic electronic updates directly from a lis will outperform, both in time and completeness, other laboratory surveillance systems that rely on manual data entry, human interpretation and communication [6,7]. security dial is accessed via a secure, password protected web based platform using ssl (secure socket layer) technology hosted at the provlab facilities. dial’s provision over the web allows remote access to the application. there is no need for hardware or software enhancements by users. individuals applying for system access are evaluated by a provlab team to assess their needs. data access for external users is provided in accordance with health information access policies defined by health partners and stakeholders. if dial access is granted, a user agreement is signed outlining the ownership and usage of the data. current users include medical officers of health, communicable disease nurses, provincial and local epidemiologists, laboratory staff, and provlab laboratory personnel. dial supports tiered access control using proprietary registration system. access to data can be restricted by geography (e.g.: province, region), targets/pathogens (e.g.: respiratory, mrsa) and function (e.g.: trending, algorithms). dial: a platform for real-time laboratory surveillance 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 interpretation engine the detection of pathogens is often achieved by a variety of test methods which generate multiple test results for a sample. these test methods change overtime with technological advancement. for example, depending on the type of sample and the year of submission, various respiratory viruses could be tested using one or more of these methods: direct florescent antibody tests, traditional virus culture, rapid shell vial culture, in-house developed or commercial-based molecular diagnostic tests. these multiple test results generated for a sample need to be interpreted by laboratory experts to provide clinically meaningful data. similarly, serological tests frequently include complicated algorithms with sensitive screening assays to be followed by confirmatory tests. the data generated by these multi-test algorithms is complex, requiring an enormous amount of time and efforts for data cleaning and interpretation, which presents a major challenge for the laboratory experts. to address this challenge, an automated interpretation engine (aie) was developed with the content experts at provlab. the aie comprises of target specific interpretation script that analyze raw laboratory results and classify each specimen based on set rules to provide interpreted laboratory data. depending on the type of sample and tests performed, there may be multiple classifications per specimen, e.g., multiple respiratory viruses that are detected by various methods in a sample. with updates and changes in test algorithms over time, multiple interpretation algorithms may result in the same classification, e.g., influenza a can be detected by one or more methods reported using different test codes and result formats in lis. depending on the data needs, aie can also be constructed to provide detailed laboratory technical information so that the testing data can be easily accessed. this could be used to evaluate test algorithms and assay performance characteristics to support laboratory quality initiatives. the aie and its’ associated classifications translate results from complex laboratory algorithms into clinically meaningful data forming the basis for data analysis in dial. aie, which is adapted from the smart engine technology [4], uses the following functions to process the data: data receiver: allows importing of multi-format data from flat files and/or databases. in its’ current state, data is presented to the aie in a tab delimited format. data validation: applies predefined logical rules to incoming datasets to filter any records that are missing key data elements. for example, a record must have a patient identifier. data coder: converts data values into standard analyzable codes. this is where the classifications are generated. a classification can be lowest level interpretation (code representing specific set of rules based on various test results) or a group of interpretations, or other classifications, thus providing a mechanism for a hierarchical structure. data export: facilitates exporting of data in various formats to facilitate parsing the data into appropriate database table(s). dial: a platform for real-time laboratory surveillance 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 interrogation, interactivity, analysis, reporting dial data are accessible by users via an interactive web based platform. the user interface is intuitive, based on a simple point and click approach. the user interface built in java technology (jsf java server faces) is based on the following main components:  classifications: the fundamental unit of analysis in dial is the classification (or a set of classifications) assigned by the aie to each specimen. single or multiple classifications must be selected to create a “data series” for display. the portion of the interface that allows one to specify a classification within a target (e.g. influenza b positive amongst all respiratory samples in a defined time period) is organized in an expanding tree fashion with check boxes listing all possible classifications related to a given target. this enables users to seamlessly navigate through the tree and make appropriate selections.  demographics: the interface enables users to select specific settings for age, gender, geography (including provincial, regional, city and postal code) and other parameters, e.g., outbreak versus sporadic community-based specimens, enabling customized demographic, geographic and specimen type ranges for data analysis.  chart configuration: dial interface enables plotting of various types of charts including line, bar, stack and pie charts. special features such as a cumulative curve on line graphs can be added. individual charts can be constructed based on each selected classification or in a combined format with a single chart displaying all classifications simultaneously. the interface provides a mechanism to customize the colors, axis, legends, series order, titles and markers.  advanced filters: in addition to classification selection and user-defined time period, demographic and geographic settings, users are able to further filter records using predefined set of fields which could be built as simple pick lists or complex wildcard searches. the ability to filter records aids in the ability to stratify and select data, especially in data fields that allow a wide variety of content, for meaningful analysis and reporting. this reporting flexibility adds to the usability and acceptability of the system.  rates: visualizing rates is an important aspect of any surveillance system. specifically, the rate of the number of specimens within one classification groups in relation to the others through an interactive interface is of value for analyzing laboratory data. dial interface enables users to select up to two rate computations which are displayed as overlays on the main chart.  bookmarks: chart specification and customization can take significant effort, thus, repeating the process on a routine basis can be time consuming and frustrating. to alleviate this, dial allows users to bookmark charts including all the settings, customizations and filters. these bookmarks can be shared and retrieved on a routine basis when generation of similar charts is desired. dial: a platform for real-time laboratory surveillance 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010  data table: the interface includes the ability to convert a chart into a summary data table that can be exported to a comma delimited format (easily viewed using microsoft excel). further, each cell can be viewed as a detailed line list (that is, all fields of a given record). these line lists of records may be exported as a comma delimited file.  gis maps: during the parsing stage (after extraction and interpretation), all specimens are assigned a geolocator using a specific algorithm that takes into account various variables including postal code, city and health region, as available. this geolocator is stored with the specimens in the dial database. these geolocators can be plotted on a gis map (via google maps) as points or summarized at a regional level. these maps can be exported for inclusion in reports. the ability of dial to represent data on maps is valuable to communicate information and emphasize geographic relationships.  analysis: matlab analytical package has been integrated into dial to enable advanced analysis of data series. due to the potential complexity of using advanced analytical packages, the dial interface includes two options: a basic mode which includes predefined functions; and an advanced mode which enables custom scripting of functions. examples of dial applications dial has become the core infrastructure supporting laboratory based surveillance and reporting for provlab. below are some examples demonstrating dial’s utility. 1. reporting during pandemic h1n1: dial was used to provide daily surveillance reports including rates and linelist data for circulating respiratory viruses, including influenza a by h-typing results, to the province of alberta, the territories, nunavut and northwest territories. summary data was provided for national surveillance [8]. 2. test optimization: data provided by dial was used to assess the sensitivity and specificity of various test methods, including newly implemented molecular diagnostic tests specific for pandemic influenza h1n1 2009 during the first wave of the pandemic. without dial this would have been unachievable due to intense direct hands-on time required for specimen processing and result generation by laboratory staff during the pandemic. by combining real-time data on circulating viruses from dial with knowledge of test characteristics, a model was able to be established which performs cost/quality analysis of different testing algorithms thus supporting optimized and costeffective decision making with respect to the sequence of tests performed within the laboratory during the pandemic [9]. 3. mrsa analysis: provlab has characterized all first case mrsa isolates, (defined as first infection from patient within previous 12 months), forwarded by diagnostic laboratories in alberta as part of an enhanced surveillance program since june 2005. mrsa strains are reported as one of the canadian epidemic strain types (cmrsa1-10), based on pfge and spa typing. we used dial to analyze the typing data for all mrsa isolates received by provlab between june 2005 and december 2008 [10]. dial can be used by external stakeholders to access and analyze mrsa typing data. dial: a platform for real-time laboratory surveillance 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 limitations and future directions  only provlab data is currently being utilized in the dial system. for many pathogens, provlab performs all testing within the province providing population-based data. for some markers, however, other laboratory partners within alberta perform variable portions of the testing and negotiations and discussions are underway with respect to including their data on dial which will result in more comprehensive and integrated laboratory data within the province.  the development of a method to convert specimen based data to patient based data is planned. this will allow a better determination of the incidence of infection as multiple samples from one individual will be rationalized by the system. the goal is to create a patient record within dial containing all the laboratory samples for every patient every alberta resident has a unique public health number for provincial health insurance claims; this will be useful in the creation of a patient record.  an aberration detection component of the system is planned. this functionality will be developed based on confidence based aberration interpretation framework [11] ¸using matlab and java for implementation of the algorithms. a system for automated and configurable alerts will be setup so that notifications can be generated when predetermined thresholds in both time and space are exceeded enabling the early identification of outbreaks or clusters of infections.  the aie for different communicable disease targets, e.g., enteric pathogens, blood-borne pathogens, will be developed in the dial platform in the future. conclusion based on our literature survey and personal communications, other international jurisdictions [12-15] have also realized the importance of reporting summarized laboratory data for public health intelligence. it does not appear that any of the systems are identical, all having evolved to satisfy the needs of their local communities of practice. the processes used to import laboratory data are different, ranging from manual input to automatically downloaded laboratory files over the internet. the ability to perform custom queries of data appears to be similar in the systems reviewed; however the use of a global laboratory interpretation engine, a proprietary feature of the dial platform, appears to be unique. we believe that a complete and thorough systematic review of currently functional laboratory surveillance systems is needed in order to highlight best practices that could guide further development. dial has been a valuable addition to communicable disease surveillance in alberta. dial’s ability to report real-time, interpreted laboratory data proved invaluable during the recent pandemic influenza outbreak when timely and accurate data was needed for reporting and decision making. the abilities of dial platform to extract data, clean the data, transform it using automated interpretation engines, integrated the data, and to display data using graphics, tables dial: a platform for real-time laboratory surveillance 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 and maps through interactive user interface further enhances the ability to interpret and communicate information to laboratory staff, laboratory stakeholders and policy makers. acknowledgements the authors would like to acknowledge contributions of the cnphi and provlab teams. specifically, dr marie louie, the acting medical director of the provlab, for her ongoing support, and lin yan, david sanders and tony huynh for their assistance with the development of the platform. correspondence: shamir n mukhi shamir.nizar.mukhi@phac-aspc.gc.ca references [1] canadian public health laboratory network. core functions of canadian public health laboratories. accessible at: http://www.cphln.ca/pdf/2004-0914_cphln_core_functions_eng.pdf [2] reference chapter vi – laboratory information systems in “laboratory medicine: a national status report”. prepared by the lewin group for division of laboratory systems, national center for preparedness, detection, and control of infectious diseases, centers for disease control and prevention. 2008. [3] mukhi, s. n., aramini j. and kabani a. contributing to communicable disease intelligence management in canada, can j infect dis med microbiol. 2007 november; 18(6): 353–356. [4] aramini j. and mukhi s. n. canadian application of modern surveillance informatics. in: lombardo j.s. and d. l. buckridge disease surveillance: a public health informatics approach, pp. 315-328. john wiley & sons inc. publication. [5] guide-to-service version 4.3 – 2010 december 08. provincial laboratory for public health (microbiology) and medical microbiology laboratory, university of alberta hospital, edmonton , alberta. available at: http://www.provlab.ab.ca/guide-to-services.pdf [6] ward m, brandsemap, van straten e, bosman a. electronic reporting improves timeliness and completeness of infectious disease notification, the netherlands, 2003. euro surveill. 2005;10(1):pii=513 available at: http://www.eurosurveillance.org/viewarticle.aspx?articleid=513 [7] nguyen t., thorpe l. makki h., nostashair f., benefits and barriers to electronic laboratory results reporting for notifiable diseases: the new york city department of health and mental hygiene experience. am j public health v97(supplement_1): s142-s145 [8] mukhi, s.n., b. e. lee, j. k. preiksaitis, 2009: dial: an interactive web based platform for real-time interpretation and analysis of laboratory test results to support analysis and reporting during the h1n1 swine origin influenza virus (s-oiv) outbreak, ammi-cacmid, 2009. [9] lee be, louie m, chamberlin b, fenton j, flegel m, fonseca k, may-hadford j, plitt s, tellier r, mukhi s, drews s. optimization of respiratory virus test algorithms by cost and http://www.cphln.ca/pdf/2004-09-14_cphln_core_functions_eng.pdf http://www.cphln.ca/pdf/2004-09-14_cphln_core_functions_eng.pdf http://www.provlab.ab.ca/guide-to-services.pdf http://www.eurosurveillance.org/viewarticle.aspx?articleid=513 dial: a platform for real-time laboratory surveillance 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 quality during the influenza (h1n1) 2009 pandemic using a secure web-based platform. 2010 ammi canada cacmiid annual conference, edmonton, canada, may 6-8, 2010. [10] ferrato c, chui l, mukhi s, drews s, lovgren m, yan l, checkley s, lee be, louie m. a web based platform for mrsa typing surveillance in alberta. 2010 ammi canada cacmiid annual conference, edmonton, canada, may 6-8, 201 [11] mukhi s. a confidence based aberration interpretation framework for outbreak conciliation. online journal of public health informatics, issn 1947-2579, http://ojphi.org vol.2, no. 1, 2010. [12] rolfhamre p, janson a. arnebornm, ekdahl k. siminet-2: description of an internet-based surveillance system for communicable diseases in sweeden. euro surveill. 20006;11(5):pii=626 available at: http://www.eurosurveillance.org/viewarticle.aspx?articleid=626 [13] faensen d., claus h., benzler j., ammon a., pfoch t., breuer t., krause g., survnet@rkia multistate electronic reporting system for communicable diseases; eurosurveillance vol.11 issue 4-6 pp100-103. [14] domeika m., kligys g., ivanauskiene, o., mereckiene j., bakasenas v., morkunas b., berescianskis d., wahl t., stenqvist k., inplementation of a national electronic reporting system in lithuania; eurosurveillance vol.13 issue 13 pp 1-6. [15] cidr -computerized infectious disease reporting, e-health 2005 presentation accessed on april 27 at http://www.hpsc.ie/hpsc/cidr/presentations/file,1113,en.pdf. http://www.eurosurveillance.org/viewarticle.aspx?articleid=626 http://www.hpsc.ie/hpsc/cidr/presentations/file,1113,en.pdf crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 178 (page number not for citation purposes) isds 2013 conference abstracts establishing a pilot surveillance system for venous thromboembolism aaron m. wendelboe*1, janis campbell1, dale bratzler1, michele beckman2, nimia reyes2 and gary raskob1 1biostatistics and epidemiology, university of oklahoma health sciences center, oklahoma city, ok, usa; 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article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 91 (page number not for citation purposes) isds 2013 conference abstracts simulation-based testbed for bio-surveillance systems gerald larocque, taylor locke, micah lee and aaron kite-powell* mit lincoln laboratory, lexington, ma, usa � �� �� �� � � �� �� �� � objective ��������� � ���� � ��� ����� � �������� �� ���������� �� ������ ������ ����������������� ������� ������������ ������� ������������ �� ����� ������ �������� ������� �� �� ��� ����� � �� ��� �� ��� ���� � �� ��� �������������� ������������ introduction ��������� ����������� �!���� ����"������# �!"$������������ ��� � ��������� ��� ������ ������� ��� ���� ����� � ������ ���� � �� ������������� ���� � ��� �������� �����%����������� ����&����� ��� 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����������������� ������ ���� ���:�������� � *aaron kite-powell e-mail: aaron.kite-powell@ll.mit.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e107, 2014 the growth and variation of symptoms of influenza-like illness isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 152 (page number not for citation purposes) isds 2013 conference abstracts the growth and variation of symptoms of influenzalike illness: an application of the linear growth curve model in syndromic surveillance in rural china xiaoxiao song*1, tao tao1, qi zhao1, lars palm3, shaofa nie4, hui yuan2, vinod k. diwan5 and biao xu1 1dept. of epidemiology, school of public health, fudan university, shanghai, china; 2jiangxi provincial center for disease prevention and control, nanchang, china; 3future position x, gavle, sweden; 4school of public health, tongji medical college of hazhong university of science and technology, hubei, china; 5division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden � �� �� �� � � �� �� �� � objective �������� ��� � ���������������������������� ���������� ������������ ���������� ���� �� introduction ����������������� � ����� ����� ������� �� � �� ���� ��������!�� � ��������� ��������� ���� ��� ���������������� ������ ����� ����� ������"���������" �# � methods �� � ������ ���� � ��� �� ������� ��� ������� � � � ���� �� �� ����� #$%� � ������� ��������������������� �� ��������&'(&#()&#)����&*(+&()&#+� ���������"��������� ��� ���������� ���� ����,�) ���������� �� ����� ����� ���������. ��������������� ��%��������� �� � ���� ���� � ������� ����������� ����! ��� ���� ��������� 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h���0��/�9� ����/�i��j/�1�� �d�./�?�����/�1�����k��c���!������ � ��� !�!�� ����� ��� ������ ������ ����� ������ ������ � ������������ � ���������� �� � ��� �������������"����a����� ��� 5� ��� ������������ j-"����������1 ����-��)&#)�)&#)(&)(&+e#)�# a#�%� )�� ,����� ���d��-����� � ����������������� ��a�0�� �e�)&##� isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 153 (page number not for citation purposes) isds 2013 conference abstracts ��� ����� �� � � � ����������� ��� ��� ������� ��� ����������� ����� ���� � ���� �� �� ����� ����� �� ��� ���� �� �� �� ������ �� ������ !� ������� �� "�� �������� ��� ������ #�� $ ��!%&�%%%%'(�%)*+�,-./�0&&,�&&.-+�12�� 0&&,3%+0"�#!*.,�-+� )�� 4������5 �6� ������6�������7��!�������� �������������847�9�8��� ��������8� ����������7 ��������7�������� � ������� ������� ������ �� ����7 ��0&%�3.0!%:)&� *xiaoxiao song e-mail: chinasxx@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e73, 2014 process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi process mining of incoming patients with sepsis renee m. hendricks1* 1binghamton university, phd candidate, industrial and systems engineering, ny abstract data mining is a technique for analyzing large amounts of data, in various formats, often called big data, in order to gain knowledge about it. the healthcare industry is the next big data area of interest as its large variability in patients, their health status and their records which can include image scans, graphical test results, and hand-written physician notes, has been untapped for analysis. in addition to data mining, there is a newer analysis method called process mining. process mining is similar to data mining in that large data files are reviewed and analyzed, but in this case, event logs specific to a particular process or series of processes, are analyzed. process mining allows one to understand the initial baseline, determine any bottlenecks or resource constraints, and evaluate a recently implemented change. process mining was conducted on a hospital event log of patients entering the emergency room with sepsis, to better understand this newer analysis method, to highlight the information discovered, and to determine its role with data mining. not only did the analysis of the event logs provide process mapping and process analysis, but it also highlighted areas in the clinical operations in need of further investigation, including a possible relationship with patient re-admission and their release method. in addition, the data mining method of creating a histogram, of the process data, was applied, allowing data mining and process mining to be utilized complimentary. keywords: data mining, process mining, sepsis *correspondence: rhendri2@binghamton.edu doi: 10.5210/ojphi.v11i2.10151 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction in the healthcare industry, as in other industries, there are vast amounts of data collected and stored, but left unanalyzed. this is ill-timed as the data may provide historical information or trends that could assist with developing current and future methods, strategies and prediction models. this is where data mining assists as trends and patterns can be understood from these large amounts of data, as well as providing ways to classify or identify similar groups or entities. in healthcare, patients can be identified or classified based on a series of health measurements to determine who is at risk for developing a particular disease, or at what disease stage they are approaching. data mining is effective but it’s limitation may be in mining process or time-stamped datasets. mailto:rhendri2@binghamton.edu* process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi when analyzing event logs or other time-stamped data, the entities or steps are dependent and build upon other information in the same file, so this requires the event logs, such as for one patient or one medical staff, to be analyzed as a whole unit and not separated out as typically conducted with patient files for analyzing trends or prediction in disease. granted, in data mining, there can be dependent and independent variables, but the information in event logs are not well defined nor bounded variables, but a series of steps that could be labeled as variables but separating them from their preand poststeps provide no meaning and therefore requires analysis as a whole. a newer method of data analysis, called process mining, has come of interest for analyzing event log, data formats. process mining is conducted to gain knowledge about a particular process or series of processes being executed. having an accurate model of the process behavior improves the implementation and evaluation of the process as well as configuring any additional requirements not included in the system [1]. based on a dutch hospital dataset available online, this project applied process mining methods to this event log, to first understand this method and outcomes, but to also determine its relationship with data mining. is a separate mining method required for process data or can data mining methods also be utilized? are process mining and data mining complimentary? the process data for this study was a record of the patients’ steps when entering the emergency room (er) with sepsis cases, as well as their release and possible re-admission steps. process mining is a new, data method area in need of applied examples in order to understand how to use it, the expected outcomes, how to improve it, and its role with data mining. process mining when searching the topic of data mining in healthcare, there were a series of articles that referenced process mining. many of the articles were conference proceedings, which may indicate the newness of this method. process mining is defined as a method for exacting data from information systems, such as hospital information systems, to gain understanding about the processes and further refine them. healthcare processes are complex and involve steps executed by personnel from various disciplines and offices. this complexity makes it interesting yet difficult to analyze and understand. process mining is defined as extracting knowledge from data generated and stored in information systems in order to analyze them [1]. typically, event logs are utilized for analysis, as they provide timestamps of steps as well as the personnel or system users who completed the steps, along with any step completion information, such as a mouse click, data entered, or an item selected. because of the use of recorded event logs, process mining is considered an a-posterior analysis or analysis after the event(s) [2]. the authors in [1] found a plethora of information on process mining. both types of processes in healthcare can be analyzed with this method: medical (clinical) processes and organizational (administration) processes. the software tools typically utilized in process mining are prom and disco, both which can be downloaded for free. in addition, three common algorithms that are utilized in process mining are fuzzy minor, heuristics minor and trace clustering. heuristics and fuzzy minor are process discovery methods whereas trace clustering in similar to clustering in data mining, in that events or steps are clustered. process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi the authors in [1] also list many benefits of conducting process mining, especially in healthcare. process mining can assist with understanding and even predicting both the staff and patient behaviors in certain situations as well as assist in redesigning and improving the process. process mining provides information on what is causing the bottlenecks and allows one to analyze process performance and reduce times, such as patient wait times and procedure times. most importantly, process mining can determine the gap of what is supposed to happen in a process(es) versus what is actually happening in the process(es), so the process(es) can be better understood and improved. process mining case studies one of the case studies found regarding process mining determined the diagnostic, therapeutic and clinical processes from hospital admission to discharge of 368 patients who were diagnosed with a first-ever stroke at four hospitals in italy [2]. the event logs from the hospital chart system were utilized and the heuristics minor algorithm was chosen to map the process steps taken by the patients. the timeframe of this study was not provided, so the length, in weeks or months was unknown, but the study still provided valuable information as the process maps for each of the 4 hospitals were determined. this provides not only the process steps, for each hospital, but the entire, average process time from admission to discharge for the patients, along with the standard deviations among patients. this study also allowed the hospitals’ processes to be compared to understand the different steps taken by each, something that is new in the healthcare industry. further analysis of these steps may provide additional information into the clinical practices and how they can be refined. typically, this information is reviewed and utilized from a clinical operations perspective, to make sure correct protocols are followed, but this same information is not typically utilized for process understanding and improvement, making process mining a new research area and possibly an effective new method [2]. a second case study of process mining determined the common paths taken by patients arriving for an outpatient procedure. the researchers’ intent was to first determine the most common process and steps and then compare to an expert created process. the event logs were taken over a month time-period from the hospital information system. the researchers were able to extract 699,136 event logs and 123,299 patient cases [3]. in this case, both the heuristics and fuzzy minor algorithms were utilized as the researchers wanted a more refined model of the process from the use of 2 techniques, along with the use of both software programs, prom and disco. the researchers discovered in this case that the most common path taken by outpatients was: consultation registration, consultation, consultation scheduling, payment, and then outside hospital prescription printing. the researchers also found that the process developed with the algorithms matched the expert created model by 89%, a significant result [3]. the intent of process mining falls in line with process improvement and lean thinking in that the process needs to be understood, controlled and evaluated for continuous improvement. process mapping and lean thinking has been utilized for decades to sketch the process or processes under study, in order to determine the process steps and functions over time to analyze a situation, but very few individuals have reviewed event logs to see if more information can be gathered about the process. attempting to sketch a process map from a large event log, even for a week of events, would be difficult and time consuming for an individual or team, but may be efficiently completed with process mining software tools. as in lean thinking, if the process is already understood and process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi documented, then process mining may allow others to improve the process or understand the effects of a change made prior to implementation. process mining and process mapping are not seen as replacements for each other, but as compliments as both techniques provide information that can highlight and improve the process. in addition, event logs may depict a different picture that cannot be gathered from observations or interviews with participants, providing a reliable, constant source of information. methods the purpose of this study was to analyze a hospital’s event log of patients entering the emergency room with sepsis, using a process mining software tool and algorithms, to understand how process mining is conducted and to interpret the results. in addition, the researcher wanted to determine if data mining methods can assist with analysis of event logs. data description and format the data utilized for this study was from a dutch hospital event log downloaded from the 4tu.center for research data website [4]. this information is from the hospital’s enterprise resource planning (erp) system and includes 15,214 events, for 1,050 patient sepsis cases, from november 7, 2013 to june 5, 2015 [5]. in addition, the events are divided into 16 hospital activities, or classes, with one case representing one patient’s pathway through the hospital [5]. the downloaded file was compressed and of the .xes extension, which is an event log file format. because existing zip applications do not open this file, the z7-zip application was downloaded in order to extract the event log file (.xes) from the compressed file. when searching for studies that utilized this same dataset, it was discovered a few studies utilized this same data, but for only determining and mapping the process flows. data mining methods were not utilized in the previous studies but are utilized in this study. tool, initial data review, and algorithms prom tool the source tool utilized for this study was prom 6.7, revision 35885. prom is the abbreviation for process mining framework [6] and was downloaded from the process mining website [6]. prom was selected for this study as it was noted in [1], that prom is the most commonly utilized tool in healthcare. there are two versions available, a lite version, prom lite 1.1 and the prom 6.6 and 6.7. most times, a lite software version has limited usage and tools, so the 6.7 version was selected. in addition, the 6.6 and 6.7 versions are targeted for researcher use. prom also has a package manager tool, where many of the algorithm packages are selected and downloaded, so they can be utilized. this is similar in programming, when listing the libraries in a command line, so the functions and algorithms can be executed in the program. in addition, the prom tool can accept various event log file formats, including the .xes format, and also allowed the .xes file to be saved as a .csv file, which can be viewed and analyzed using tools. matlab is a current data mining and analysis tool that was also utilized in this study for a quick overview of the data in the event log. process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi initial observations of the event log data first, the sepsis event log file was imported into prom 6.7. before any analysis, the file was also exported as a .csv, for later use. prom provides a series of functions based on the data imported. available algorithms are highlighted green in a function listing. functions can also be found through a search field at the top. to understand the overall data, the view source option was selected and a dashboard of the data was provided, as seen in figure 1 below. one process is listed, with 1,050 (patient) cases, and 15,214 events, along with 16 classes (of events). this confirms that the data is as expected and matches the data description in [5]. in addition, the data time frame from november 7, 2013, to june 5, 2015 is also listed correctly. this dashboard also shows that the events per overall cases range from a minimum of 3 events (or steps), a mean of 14 events and a maximum of 185 events. the large number of events per case may be associated with the patients who were re-admitted as a multitude of events would have occurred. in addition, the events are divided into 16 event classes or activities, with a min of 3 event classes per patient case, a mean of 9 event classes or steps per patient case and a max of 12 steps (of the total 16) per patient case. this makes sense as not every event class (step) will be followed by any one patient. figure 1: sepsis event log dashboard in prom process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi after confirming the data characteristics, the summary option was selected and this provided a listing of the 16 activities or steps for the patients in this dataset. as can be seen in figure 2 below, the 16 activities consist of some of the following: er registration, ivs or tests administered, being admitted to the triage, and the series of releases a-e, and if the patient returned to the er. also, the 16 steps are listed by their occurrences from high to low, with leucocytes and crp having the highest percentages of occurrences at 22.236% (3,383 occurrences) and 22.441% (3,262 occurrences) accordingly, whereas many of the er release steps occurred in lower frequencies. leucocytes, lactic acid and crp are all measurements provided by blood tests. leucocytes are white blood cells and a high white blood count can signal an infection the body may be fighting [7]. crp, or c-reactive protein can signal inflammation in the liver if the test result is high [8]. if these patients had sepsis or another infection, blood tests are typically conducted to see if there is a sign of an infection and it determines the path forward for treatment. in addition, there are two types of admission steps a patient can take depending on their conditions. this is because the patient can be admitted to 2 units, intensive care (ic) unit, or not as intensive (nc) unit. there are also five release types, a-e, for the discharge path. figure 2: sepsis event log summary of activities in prom to test the initial analysis of the event log in matlab, the .csv format of the sepsis event log file was imported into matlab and the histogram function was selected. the output can be seen in figure 3, on the next page, and it matches the activities summary in prom, with the leucocytes and crp steps having the highest occurrences and the patient discharges having the lowest frequencies. so, it appears matlab may assist with process or event log data, at least for initial process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi analysis or frequency counts. this also confirmed that the original data file, which was converted to the .csv format, in prom, did not lose any data. figure 3: sepsis event log histogram of activities in matlab prom algorithms using the prom software tool, a series of algorithms, or functions were applied to the sepsis event log and included first determining the process map or model, along with discovering the casual matrix. to create the process map or model, the alpha minor function was selected. this feature is a discovery approach for mapping the process of the event logs. this is the first time this researcher utilized these event logs, so it is necessary to discover the path first. if past or known results were gathered, then the discovery phase could be skipped. the event logs are the steps the patient encountered while being admitted to the er and being tested for sepsis, and then possibly released or admitted to a hospital unit. the results of this function are explored in the next results section. this feature was utilized to visualize the process map of this event log before conducting any further analysis. to determine any causal relationships of the activities, the discover matrix function was selected. this calculates a value from 1 to -1 for each of the 16 activities. a 1 (ideal) value means that there is a casual relationship between the row activity and the column activity. a -1 value means there is no causal relationship between the row and column activities. process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi results the process model or sometimes called, petrinet, for the sepsis event log was created using a discovery algorithm. the results of this mapping can be seen in figure 4 below. the process starts at 1, which can lead to a series of steps, whether admission ic, crp test, as well as the erp triages, er registration, and iv liquid, as each patient follows a different route, based on their condition and encounter. the arrows represent the transition or next activity in the process. also, it appears in some cases, that after the blood test results of crp and leucocytes, patients were then released. as expected, there are patient cases where different admissions (ic and nc) were utilized, as well as different discharge types (a-e) and some cases where patients were re-admitted. the circle at the bottom of the figure is the end point of the patient process. figure 4: sepsis event log process model/petrinet in prom process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi in addition, the discover matrix function was utilized to see if there are any causal relationships between the 16 activities. a 1 value means there is a causal relationship between the row activity and the column activity. the results of this test can be seen in table 1 below. the heuristics minor, a discovery method, was utilized. the values higher than 0 are highlighted blue, as there may be a causal relationship. looking at the rows, the step, leucocytes has a series of values above 0.9, which means that this step has a causal relationship with a series of columns, which includes all the release steps and the return to er steps. this makes sense as many of the patients require the blood test to discover if there are any underling infections the body may be fighting, before discharged. many cases where there is a 0.0 value (in white), this means the step cannot be compared to itself, similar to a correlation matrix where the values are left empty. it is also not surprising that the crp test also has high causal relationship with the discharge steps as this tests for any possible infections, prior to patient release. it also appears admission nc, er registration, iv anti-biotics and iv liquids have a causal relationship with admission ic, with a value of 0.64, 0.5, 0.98, and 0.75 accordingly. it also appears that some of the releases (a, c, d, e) have a causal relationship with re-admittance into the er, which may require further analysis. it is important to understand not only the steps but the profiles of the patients that were re-admitted to the er, so as to reduce re-admission. reducing the re-admission levels reduces the work load on the medical staff, reduce the patient recovery times, along with reducing the costs incurred by all. table 1: sepsis event log casual relationship matrix in prom conclusion in conclusion, process mining methods and algorithms appear effective in analyzing the available hospital event log. not only does analysis of the event logs provide process mapping and process analysis, but also highlights areas in the clinical operations that may require further investigation. modeling the patient path helps one to understand the steps the person followed, and the steps the medical staff followed in their roles, to both learn the process or processes under investigation as well as to refine the process with the intention to reduce patient waiting times and re-admissions as well as streamline the clinical processes for the medical staff. in addition, it also appears that some data mining methods, such as creating a histogram, is also of use with process data. so, data process mining of incoming patients with sepsis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e14, 2019 ojphi mining methods and process mining may be utilized complimentary in future event log analysis. but process mining may be a division in its own right, as the event log data requires different analysis than independent variables, such as patients results, require in data mining. so, it appears, both data mining and process mining will be here to stay, for now. the limitation of this study is that one dataset, which is one hospital event log, was utilized. also, two software programs, prom and matlab, were utilized for this study. it is recommended for future research that multiple events logs are analyzed, using both process mining and data mining methods, whether for the same process, so comparisons are made for a particular process, or across processes, units or hospitals, to compare the software results, to better understand how the algorithms work with these data formats, as well as to gain insights to these processes under investigation, in order to baseline and improve them. with limited software options available when analyzing event logs, it is difficult to accurately confirm the results, repeatedly. acknowledgements i would like to thank my course professor dr. won, and my department head, dr. khasawneh because their insights provided me an interest in data and process mining and machine learning. references 1. rojas e, munoz-gama j, sepúlveda m, capurro d. 2016. process mining in healthcare: a literature review. j biomed inform. 61, 224-36. pubmed https://doi.org/10.1016/j.jbi.2016.04.007 2. mans r, et al. 2008. process mining techniques: an application to stroke care. stud health technol inform. 136, 573-578. pubmed 3. kim e, kim s, song m, kim s, yoo d, hwang h, yoo s. 2013. discovery of outpatient care process of a tertiary university hospital using process mining. healthc inform res. 19(1), 4249. pubmed https://doi.org/10.4258/hir.2013.19.1.42 4. 4tu.centre for research data. 2017. http://data.4tu.nl/repository/. accessed 30 november 2017. 5. mannhardt f. (2016) sepsis cases event log. eindhoven university of technology. dataset. https://data.4tu.nl/repository/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460 6. eindhoven university of technology. 2017. http://www.processmining.org/start accessed 15 november 2017 7. mdhealth.com. 2017. http://md-health.com/leukocytes.html accessed 10 december 2017. 8. web md. 2017. https://www.webmd.com/a-to-z-guides/c-reactive-protein-test#1 accessed 10 december 2017 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27109932&dopt=abstract https://doi.org/10.1016/j.jbi.2016.04.007 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18487792&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23626917&dopt=abstract https://doi.org/10.4258/hir.2013.19.1.42 use of a web forum and an online questionnaire in the detection and investigation of an outbreak 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 use of a web forum and an online questionnaire in the detection and investigation of an outbreak tammy l. stuart chester 1 , marsha taylor 2 , jat sandhu 3,4 , sara forsting 3 , andrea ellis 5 rob stirling 1 , and eleni galanis 2,4 1 office of public health practice, public health agency of canada, ottawa, ontario, canada; 2 british columbia centre for disease control, vancouver, british columbia, canada; 3 vancouver coastal health authority, vancouver; 4 school of population and public health, university of british columbia, vancouver; 5 foodborne, waterborne and zoonotic infections division, public health agency of canada, guelph, ontario, canada running title: web-based technologies in outbreak investigations abstract a campylobacteriosis outbreak investigation provides relevant examples of how two web-based technologies were used in an outbreak setting and potential reasons for their usefulness. a web forum aided in outbreak detection and provided contextual insights for hypothesis generation and questionnaire development. an online questionnaire achieved a high response rate and enabled rapid preliminary data analysis that allowed for a targeted environmental investigation. the usefulness of these tools may in part be attributed to the existence of an internet savvy, close-knit community. given the right population, public health officials should consider web-based technologies, including web fora and online questionnaires as valuable tools in public health investigations. mesh key words: outbreaks, internet, communications media, epidemiologic methods, public health introduction web-based technologies are increasingly being used in a wide range of public health applications such as disease surveillance, outbreak detection, research and communication of public health messaging [1-4]. still, much research remains to be done to determine when and how web-based tools can be most effectively utilized. here we outline the role that two web-based technologies played in the detection and investigation of an outbreak and potential reasons for their usefulness. http://ojphi.org use of a web forum and an online questionnaire in the detection and investigation of an outbreak 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 the study a large campylobacteriosis outbreak affecting 225 people was associated with a june 16, 2007 mountain bike race in british columbia (bc), canada. the investigation identified ingestion of contaminated mud as the likely source of illness [5]. two web-based resources were used in the outbreak investigation: an existing mountain biking web forum, and an online questionnaire. the web forum there was a pre-existing web forum for mountain biking in bc with a discussion thread specifically related to the bike race of interest. the first postings regarding ill racers appeared on june 18, two days after the race [figure 1]. further postings about ill racers prompted the race organizer to contact the local health unit on june 20th. the first reports of laboratory confirmed campylobacter jejuni among racers were received by the local health unit on june 25, at which point an investigation was initiated. between june 18 th and july 6 th 2007, there were 100 race related posts. of these, 58 entries posted by 34 individuals specifically discussed the issue of ill racers. prior to the design of the online questionnaire, the web forum was reviewed to identify potential exposures of interest. racers discussed inadvertent mud consumption, foods consumed, and the use of a common cloth to wipe riders’ faces. additional useful information posted to the forum included the fact that some racers did not complete the race but became ill, some used fenders and others did not, and that there were different types of water delivery systems used, including water bottles and personal hydration packs. additional postings and photos also informed investigators of the conditions of the race course and the extreme mud coverage of racers’ hands and faces. a qualitative analysis of the web forum discussion thread was conducted to explore possible reasons why it was a useful source of information in this investigation. some individuals who participated in the race-specific discussion joined the forum as early as 2002, revealing that they had been part of a virtual community for some time. the analysis also revealed that the thread was not moderated and that participants were willing to share personal information such as their symptoms, photos and knowledge of illness in fellow racers. for example, one rider said “did anyone else who raced get sick yesterday/today? … symptoms are (and they are ugly) non stop washroom visits that are far from normal, extreme fatigue…unable to eat, drink and sleep”. the online questionnaire an online questionnaire was designed as part of a cohort study to investigate the outbreak. the questionnaire took less than 48 hours to design, set-up on the web using ultimate survey enterprise.net (v3.0.7) software and pilot. as racers required an email address for race registration, a web link to the online survey was sent by email to the 787 race participants. race participants were predominantly male (84%) and 78% were less than 45 years of age. most participants were canadian (94%), many of whom were from the community in which the race was held. a total of 549 participants (70%) completed the questionnaire before the site closed, of which 537 were included in the cohort study and 225 (attack rate: 42%) met the ill case http://ojphi.org use of a web forum and an online questionnaire in the detection and investigation of an outbreak 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 definition [5]. persons completing the questionnaire were not significantly different when compared to all race participants with respect to age, sex and residence. responses were given a time stamp: within two hours, 69 responses (13%) were received and in 48 hours more than half of the total responses (n=293) (53%) were submitted. an increase in responses was seen on july 5 th following an email reminder sent to race participants [see figure 1]. data were quickly extracted and preliminary analysis conducted to direct environmental sampling. figure 1: dates of occurrence of significant events (outbreak detection, web forum postings and online questionnaire responses) in the investigation of an outbreak. black bars indicate onset of symptoms in cases (clinical and lab confirmed) (n=225); grey bars indicate web forum postings by racers or organizers specifically regarding illness in racers (n=58); white bars indicate number of responses to the questionnaire for which time stamps were available (n=506). http://ojphi.org use of a web forum and an online questionnaire in the detection and investigation of an outbreak 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 discussion in the present investigation, the feeling of comfort and community in the well-established, nonmoderated web forum potentially contributed to the sharing of personal and detailed information, which made the forum useful to investigators. the impact of an outside moderator in virtual communities is unclear [6,7]. moderation could possibly impact participation and the type and amount of information shared. the nature of the web forum may have led individuals who would not have sought medical attention and laboratory testing for their illness, to still discuss their symptoms in the forum. the discussion of illness in race participants in web forum postings brought the outbreak to the attention of the local health unit five days prior to notification of any c. jejuni positive laboratory results. although in this instance the investigation was not initiated earlier, it demonstrates the capacity of web fora to aid in early outbreak detection. in this outbreak, web forum postings also aided in hypothesis generation by suggesting possible routes of c. jejuni exposure and providing useful information regarding the race and mountain biking that investigators may not have originally considered. to the knowledge of the authors a web forum providing this type of contextual insight into informing an investigation has not previously been reported. online questionnaires have been used previously for outbreak investigations [8-13]. they can be efficient tools to rapidly reach a large proportion of a population. a study comparing the response rates of online questionnaires to those administered by telephone revealed no significant differences in median response rates. however, telephone administered questionnaires took significantly more time to complete [14]. here, the use of an online questionnaire eliminated the time and public health resources required to conduct phone interviews and enter data. the resulting rapid preliminary analysis enabled a targeted environmental investigation. several factors may lead to a high response rate to online questionnaires. internet access and comfort with online tools are prerequisites [14]. the target population in our study had features suggesting a familiarity with internet tools. all race participants had active email addresses as evidenced by the requirement for email registration for the race. the existence of and participation in a race-specific discussion thread on a web forum also suggests that many racers were internet savvy. the ease of completing the online questionnaire may also have contributed to the high response rate. a strong sense of community combined with high attack rates amongst members of that community may increase interest and participation in an outbreak investigation. a high response rate (96%) to an online questionnaire was seen in a norovirus outbreak linked to a dinner reunion where the attack rate was 73% and attendees likely also had a strong sense of community [12]. in the present investigation, the majority of racers were from bc, with many living in the race community, and many racers were part of a virtual community. the relatively high attack rate (42%) in this outbreak also meant that many individuals were either personally affected or knew someone who was. in contrast, in an investigation of a salmonellosis outbreak following a http://ojphi.org use of a web forum and an online questionnaire in the detection and investigation of an outbreak 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 sporting event involving athletes and spectators from numerous states and therefore likely less sense of community, coupled with a low attack rate (22%), the response rate to the online questionnaire was only 34% [10]. limitations the usefulness of web fora and other social media sites in outbreak investigations is clearly dependent upon the ability of public health professionals to identify and utilize these resources in a timely way. although it was found to be very useful, one limitation of the web forum in this investigation was that it was only one source of information from a small number of participants. our assessment of potential reasons for the success of the online questionnaire was limited by the fact that we did not ask respondents what motivated them to complete the questionnaire. additional limitations of our online questionnaire, compared to phone based interviews, include the fact that respondents could not ask for or be prompted for clarification. in order to help ensure completion we also limited the length of the questionnaire compared to what may have been created for a phone based interview. analysis of the online questionnaire response times was limited due to technical difficulties. we were only able to capture time stamp information for 506 of the 549 completed questionnaires. conclusions internet technologies can play a role in the identification and investigation of outbreaks. public health officials should consider web fora potential sources of not only early signals of outbreaks but also resources that can provide contextual insights to inform investigations. with the proliferation of social media in most facets of daily life, public health professionals need to be cognizant of their application in investigating and responding to adverse community health events such as outbreaks. the highly accessible and scalable nature, as well as immediate reach provides an essential platform for public health communication in this era. in this situation the web forum was not developed or owned by public health and public health specialists chose not to directly intervene. in certain situations identifying or developing public health informatics applications to alert a community at risk using web forums and other forms of social media could be useful. it is also important that public health professionals have the tools available to rapidly create and utilize online questionnaires when needed. online questionnaires can successfully expedite data gathering, especially among an internet savvy cohort where there is a sense of community and a high attack rate, thus making them a valuable tool for public health investigations. acknowledgements we would like to acknowledge jayne corder for her assistance with the development of the online questionnaire as well as paul martiquet, the mho with jurisdictional responsibilities for the outbreak. thank you also to cliff miller, the race organizer, for bringing the web forum to the attention of the local health authority. http://ojphi.org use of a web forum and an online questionnaire in the detection and investigation of an outbreak 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 correspondence tammy l. stuart chester, phd public health agency of canada (phac) tammy.stuart@phac-aspc.gc.ca conflicts of interest none declared references [1] brownstein js, freifeld cc, madoff lc. 2009. digital disease detection--harnessing the web for public health surveillance. n engl j med. 360(21), 2153-55, 2157. http:// www.nejm.org/doi/pdf/10.1056/nejmp0900702. http://dx.doi.org/10.1056/nejmp0900702 [2] wilson k, brownstein js. 2009. early detection of disease outbreaks using the internet. cmaj. 180(8), 829-31. http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2665960/ pdf/1800829.pdf. http://dx.doi.org/10.1503/cmaj.1090215 [3] chew c, eysenbach g. 2010. pandemics in the age of twitter: content analysis of tweets during the 2009 h1n1 outbreak. plos one. 5(11), e14118. http://www.plosone.org/article/info %3adoi%2f10.1371%2fjournal.pone.0014118. http://dx.doi.org/10.1371/journal.pone.0014118 [4] chou wy, hunt ym, beckjord eb, moser rp, hesse bw. 2009. social media use in the united states: implications for health communication. j med internet res. 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[13] kuusi m, nuorti jp, maunula l, miettinen i, pesonen h, et al. 2004. internet use and epidemiologic investigation of gastroenteritis outbreak. emerg infect dis. 10(3), 447-50. http:// www.cdc.gov/ncidod/eid/vol10no3/pdfs/02-0607.pdf. http://dx.doi.org/10.3201/ eid1003.020607 [14] ghosh ts, patnaik jl, alden nb, vogt rl. 2008. internetversus telephone-based local outbreak investigations. emerg infect dis. 14(6), 975-77. http://www.cdc.gov/eid/content/14/6/ pdfs/975.pdf. http://dx.doi.org/10.3201/eid1406.061513 http://www.eurosurveillance.org/images/dynamic/ee/v14n41/art19355.pdf http://www.cdc.gov/ncidod/eid/vol10no3/pdfs/02-0607.pdf http://www.cdc.gov/eid/content/14/6/pdfs/975.pdf http://ojphi.org http://cid.oxfordjournals.org/content/49/12/1811.full.pdf+html http://cid.oxfordjournals.org/content/49/12/1811.full.pdf+html http://cid.oxfordjournals.org/content/49/12/1811.full.pdf+html http://www.cdc.gov/ncidod/eid/vol10no3/pdfs/02-0607.pdf http://www.cdc.gov/ncidod/eid/vol10no3/pdfs/02-0607.pdf http://dx.doi.org/10.3201/eid1003.020607 http://dx.doi.org/10.3201/eid1003.020607 http://www.cdc.gov/eid/content/14/6/pdfs/975.pdf http://www.cdc.gov/eid/content/14/6/pdfs/975.pdf http://www.cdc.gov/eid/content/14/6/pdfs/975.pdf 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts tractable use cases for collaboration in public health surveillance stacey hoferka1, caleb wiedeman2, ramona lall3, michael coletta4 and howard burkom*5 1illinois department of public health, chicago, il, usa; 2tennessee department of health, nashville, tn, usa; 3new york city department of health and mental hygiene, new york, ny, usa; 4center for surveillance, epidemiology, and laboratory services, centers for disease control, atlanta, ga, usa; 5johns hopkins applied physics laboratory, laurel, md, usa objective the main objective is to broaden the collection of use cases developed by the isds technical conventions committee (tcc) to enhance effective collaboration between public health practice and analyst researchers in various disciplines and institutions. panellists will present and motivate use case concepts including requirements for practical solution methods. component objectives are to refine the presented use cases and to stimulate formation of new ones at local, state, and national levels. introduction the mission of the isds tcc is to bridge the gap between the analytic needs of public health practitioners and the expertise of researchers from other fields for the enhancement of disease surveillance, including situational awareness of chronic as well as infectious threats and follow-up activities such as case linkage and contact tracing. committee activities to achieve this mission are identifying practical use cases, refining technical specifications in open forums, obtaining benchmark datasets for controlled dissemination, validating candidate methods, and sharing method documentation. in its first 2 years, the tcc has worked on three use cases and assisted with development of data use agreements to permit posting of benchmark datasets, http://www.syndromic.org/ communities/technical-conventions. recent polling of the biosense user group indicated widespread interest in developing additional use cases. the proposed panel is intended to focus on practical applications of common interest, refine the use case development and dissemination process, and foster global interest in this process. description after a review of the tcc mission and process, panellists will present use cases of current and ongoing concern. use cases to be presented are: 1) monitoring the public health burden of extremes of heat and cold, defined by excess, all-cause morbidity and risk ratios for specific conditions in at-risk populations. 2) enhancement of state-level and county-level surveillance for arboviral infections, 3) using free-text chief complaints to categorize all emergency department visits for surveillance and situational awareness in a large metropolitan setting, 4) rapid coordination of state and local surveillance findings to inform a coherent, timely regional and national health picture, from case definition to coordination/communication tools panellists will address the following items in the use case context: a. explain the use case and its public health importance. b. define component analytic sub-problems that could be addressed. c. specify the requirements of a solution useful to their institution/ agency in terms of applicable features such as needed outputs products, software environment(s) and runtime, and necessary visualization. d. describe the envisioned application of requested methods and the benefits for workflow, reporting, or communications with other agencies. e. list information that would be needed to address the component problems, and discuss how data might be modified, truncated, simulated, or otherwise made available to provide this information for development, with or without data use agreements. audience engagement the audience will be prompted with questions on the relevance, aspects of common utility, technical feasibility, and data availability issues related to each use case. subsequent tcc development of each one may be launched according to attendee participation and interest. new and related use case ideas will also be solicited. keywords weather-related; arbovirus; chief complaint; case definition; technical conventions *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e4, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 75 (page number not for citation purposes) isds 2013 conference abstracts a health and demographic surveillance system in a low socioeconomic setting in karachi, pakistan muhammad ilyas*, muhammad i. nisar, abdul m. kazi, murtaza t. ali and anita zaidi paediatric and child health, the aga khan university, karachi, pakistan � �� �� �� � � �� �� �� � objective ���� ���� �� �� � � ���� �� ���� ��� ��� �� ����� �������� ���� �� ������������ �� ������ ���� �������� ����� �� �� �� ���� ���� ��� ��� �� ���� � �� ��� �� ���� ������������� ��� �� �� ��� �� �� � �� � ���� � �� � ����� ������� �� ���� ��� ������� ���� ������� �� �� �� �� � � �� ��� � 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non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using mobile technology to help eliminate malaria in zanzibar gordon m. cressman*1, michael v. mckay1, abdul-wahid al-mafazy2, mahdi m. ramsan1, abdullah s. ali2, issa a. garimo1, humphrey mkali2 and jeremiah j. ngondi1 1rti international, research triangle park, nc, usa; 2zanzibar malaria elimination programme, zanzibar town, united republic of tanzania objective this presentation aims to share the results of a six-year effort to use mobile health (mhealth) technology to help eliminate malaria from a well-defined geographic area. this presentation will review the history, technology, results, lessons-learned, and applicability to other contexts. introduction zanzibar is comprised primarily of two large islands with a population of 1.3 million. indoor residual spraying (irs) campaigns, distribution of long-lasting insecticide treated bed nets (llins), ensuring treatment medication is available, and use of rapid diagnostic tests (rdts) have reduced malaria prevalence from 39% in 2005[1] to less than 1% in 2011-2012. this is the third time zanzibar has been close to eliminating malaria, but there are serious challenges. these include vector resistance to pyrethroids, the shortlived efficacy of llins, and resistance to behavior change. constant traffic with mainland tanzania and foreign countries also poses the risk of outbreaks. an effective and sustained surveillance and rapid response system is essential to control outbreaks and optimize interventions. methods in 2008 the zanzibar malaria elimination program (zamep), in collaboration with the u.s. president’s malaria initiative (pmi), the u.s. centers for disease control and prevention (cdc), and rti international (rti), developed and implemented the malaria epidemic early detection system (meeds). meeds enabled 159 government primary health care units to use simple forms displayed on feature phone handsets to submit weekly aggregate case data via unstructured supplementary service data (ussd). this enabled zamep to make data-driven decisions regarding active case detection and coverage of malaria interventions. in 2012 meeds was modified to support individual case reporting. each district malaria surveillance officer (dmso) was equipped with a tablet computer running coconut surveillance, a mobile application developed by rti in collaboration with zamep. coconut surveillance receives case alerts from meeds automatically. once a dmso is alerted of a new case, he or she is guided through an active case response protocol by coconut surveillance. additional case data are entered into the tablet at the facility and the household. coconut surveillance uses the geographical positioning system (gps) capability of the tablet to record the location of the household. each household member is tested, and new cases are treated immediately. the data are synchronized at least daily with a shared cloud database. a web-based dashboard enables near real-time monitoring of active follow-up. an automated notification system helps to alert officials to system errors and problems with case follow-up. officials use near real-time maps and reports to quickly identify hot-spots and transmission patterns. results since january 2013 this integrated system has been used to respond to more than 4,000 case reports, rapid diagnostic tests (rdts) have been administered to and data collected from more than 14,400 household members, and 914 new cases have been diagnosed and treated. this provides zanzibar with near real-time case identification, geo-located data, and management information about field staff progress. continuous enhancement has added intelligence and automation to improve data quality, detect outbreaks, and alert decision makers. conclusions the integrated meeds and coconut surveillance system in zanzibar is recognized as one of the most advanced in the world, but it faces several challenges. for example, services for the meeds facility-based case reporting system are costly and, at times, unreliable. the system is not linked to the health management information system (hmis). finally, cases from the extensive private sector, migrants and the military are not captured, and the system does not capture information on intervention quality. keywords malaria; surveillance system; surveillance-response systems; mobile technology; tanzania references 1. jaenisch t, sullivan dj, dutta a, deb s, ramsan m, othman mk, gaczkowski r, tielsch j, sazawal s. malaria incidence and prevalence on pemba island before the onset of the successful control intervention on the zanzibar archipelago. malar j. 2010;7:32. *gordon m. cressman e-mail: gmc@rti.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e15, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 41 (page number not for citation purposes) isds 2013 conference abstracts assessing the impact of climate change and land use variation on microbial transport using watershed scale-modeling rory coffey*1, brian benham2, leigh anne krometis2, mary leigh wolfe2 and enda cummins1 1university college dublin, dublin, ireland; 2virginia tech, blacksburg, va, usa � �� �� �� � � �� �� �� � objective �������� ����� ��� ������ ����� �� � ������ � ������� �������� �� �� ���� � �� � �� � ����� ����� ��������������� ��� ����� �� �� introduction ��������� ������������ �� ���� �� � ����� ������ ���� ��������� ����� ���� � ��� �������������������� ����� ��������� ������������� �� ���������� ���� ��� � ����� ���� �������� ������ ���� ����������� ���� 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�������<������! ��� �� �������� "�a������������� ���� � �7��������� "�%)�<& "� ; ����� ��"�7� =��e���)"�f��� ��f"�f� �� ��5"�g���������� � �h ����d��; ����� �! � ��� ���� ������� ��� ������ � ��� ��57:� ��������� �� ����� �����! ����������� ������ ����1228"�1=�1�+$8$b$8i *rory coffey e-mail: rorypc4@vt.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e15, 2014 commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? denise harrison1*, saumya ramarao2, dinesh vijeyakumar3, james mckinnon4, kristina brown5, stanley mierzwa6 1united states agency for international development (usaid), washington, dc, usa 2population council, new york city, new york, usa 3ibm, global health supply chain procurement and supply management (ghsc-psm) project 4independent consultant, usa 5inova, washington, dc, usa. 6kean university, center for cybersecurity, union, new jersey, usa abstract stakeholders are coming together to develop a vision for increasing access to family planning (fp) by 2030. of the 923 million women in the developing world who wish to avoid a pregnancy, 218 million women are not using a modern contraceptive (guttmacher institute, 2020). in 2016, over 3.4 billion people were using the internet (https://ourworldindata.org/internet 2016). moreover, internet users in the developing world use social media more frequently than internet users in the u.s. and europe. of the many proposed actions to accelerate progress in family planning, the use of twitter should be a key component. in this commentary, we describe the use of twitter in a select group of low-and-middle-income countries that have made commitments to the family planning 2020 initiative (fp2020 countries and have the potential to leverage twitter with current and potential family planning users. we examine twitter feeds in eight key fp2020 countries, and we look at the content of tweets issued by the ministries of health in most of these same countries. our view is that it is feasible and easy to access twitter feeds in low-and -middle income countries. we base our view on the types of reproductive health and family planning terms discussed in a public forum such as twitter by current and potential users and their partners and ministries of health. we highlight two broad considerations that merit discussion among interested stakeholders, including policy makers, program designers, and health advocates. the first relates to the use of twitter within family planning programs, and the second relates to themes that require more significant research. data coupled with analytical capacity will help policy makers and program designers to effectively leverage twitter for expanding the reach of family planning services and influencing social media policy. our aim is to not only to contribute to the body of knowledge but also to spur greater engagement by program personnel, researchers, health advocates and contraceptive users. keywords: twitter; public health; family planning; global health; public health research; *correspondence: deharrison@usaid.gov doi: 10.5210/ojphi.v13i2.11094 commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi introduction sustainable development goal (sdg) 3 includes a target “universal access to sexual and reproductive health services including for family planning” https://sdgs.un.org/goals/goal3. voluntary family planning is a known strategy to reduce maternal and infant mortality and improve the health of women and their children and is considered a “best buy” [1]. today, stakeholders are coming together to develop a vision for increasing access to family planning by 2030 by influencing social systems and supporting data-informed decisions to accelerate progress. social media, including twitter, could be a key component for social influence and data generation and analysis. twitter is one of the most popular forms of microblogging. unlike other social media platforms such as facebook, instagram, or whatsapp, twitter posts are generally public and hence can be collected and analyzed. we feel there is a growing sense in the field of international family planning that social media and digital forms of communication will be able to reach health consumers more efficiently and help family planning programmers and advocates respond to their needs. family planning (fp) programs should comprehensively incorporate twitter to communicate to potential and current fp users. in this commentary, we describe the use of twitter in eight (bangladesh, india, indonesia, ghana, liberia, kenya, nigeria, and south africa low-and-middle-income countries (lmics)) and discuss the potential to leverage twitter for more engagement with current and potential users of family planning. what are people talking about? we examine twitter feeds in two different ways at various points in times in eight key fp2020 countries. first, we look at tweets found in country twitter feeds for a one-week period in 2016 (jan 1st to 8th) and in 2020 (nov 1st to 10th). copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi figure 1: tweet summary by keywords for the period november 1-10, 2020 (tweets below 100 count, not visible in the chart) figure 2: tweet summary by key words for the period november 110, 2020 (tweets below 100 count, not visible in the chart) in all countries, there were public tweets on the issue of family planning. countries such as south africa, india, and nigeria had a vibrant and active twitter conversation while others such as liberia, kenya and ghana, indonesia, and bangladesh had tweets but were not substantial. commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi we believe that many discussions on social media were occurring in languages other than english. we noted that some words such as condom and family planning were tweeted frequently while the intrauterine device (iud) was rarely used. the mention of specific contraceptives is in line with our expectation that only those that are widely used or are new to the market will likely be discussed on social media. in terms of actual methods discussed, iud use is one of the least discussed methods among the methods discussed; interestingly, we also find that “iud” is more commonly used than “intrauterine device” indicating fluency in the general population about this term. while there might be fluency about the term, it is difficult to ascertain the depth of knowledge about iuds. we found that condoms were discussed more than any other method. we speculate that this discussion could have been spurred due to greater concern about protection and prevention of hiv and stis since the tweets themselves do not make clear as to the underlying motivation, but we see a high amount of hiv/condom discussion in south africa, which has a high hiv prevalence rate. relatedly, myths around condoms and their ability to protect against hiv were found in a group of tweets on condoms in nigeria and were retweeted many times in 2016. during the second analysis in 2020, there were a lot more tweets related to iuds from non-governmental organizations (ngos), civil society organizations (csos), advocates and well as manufacturers. one tweet, “wahala for women wey get iud o” from @debo_bohor based in nigeria, complaining about the difficulty women have obtaining iuds, was retweeted over 50 times in nigeria and neighboring ghana. third, we found myths and misconceptions and an effort to dispel them as well. myths around condoms and hiv were also detected in the script, such as the belief that withdrawal prevents hiv transmission. tweets around this myth were repeated and pervasive during the first round of analyses from 2016. however, in 2020, many ngo, civil society organizations (cso), and government workers sent tweets countering myths and explaining how different methods work, especially condoms, iuds, and emergency contraception (ec). in nigeria, @chief_ozomma, based in abuja, nigeria, responsible for logistics, retweeted: @onelifeng: “withdrawal method is one of the least effective methods of all the methods of contraception. it is a recommendation to make use of condoms during sexual intercourse not only to prevent pregnancy but to also prevent stis.” onelife initiative for human development is an ibadan-based civil society organization focused on youth and sexual and reproductive health. we used the global guidance on family planning provided by the who to verify the validity of the tweets. fourth, we noted that although there appears to be general discussion about contraception and family planning, it tends to be less about specific brands. however, there was more product and service promotion by manufacturers and social marketing organizations during november 2020 than in 2016, indicating that market players could be using twitter to build demand for their products. also, hashtags were not found as often as search terms on their own, indicating there are not a lot of campaigns around contraception on twitter in these countries. when specific contraceptives are discussed, not all methods are discussed equally. finally, we learned that the media (newspaper/television) assist in family planning messaging on twitter. for example, in the 2020 feed, an analysis of popular users (tweets with most likes) found commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi that the tweets originated from the media. this suggests that twitter, combined with conventional media, can amplify family planning messaging and discussion. what do ministries of health tweet about? on reviewing tweets issued by national ministries of health during may 25 – june 24, 2019 as well as between may 14 – august 17, 2020, the majority were on ceremonial events, recounting the news of government officials and some policy announcements. some ministries, notably the ministry of health and family welfare (mohfw) in india sent primarily educational tweets. the moh of kenya had many tweets about preventing ebola and surveillance updates last year. none of the ministries of health followed many people or organizations. however, the number of followers grew exponentially from 2019 to 2020, indicating that people are following the moh more on twitter. only the ministries of health of kenya and india had tweets on family planning during the 30-day period of may 25 – june 24, 2019. @moh_kenya also tweeted advice against pre-marital sex, especially for teens. @mohfw_india issued nearly 20 tweets on menstrual hygiene management in recognition of the celebration of world menstrual hygiene day. what does this mean for family planning? based upon our exploration of the potential of harnessing twitter for fp programming, we identified two broad considerations that merit discussion among interested stakeholders including policy makers, program designers and health advocates. the first relates to the use of twitter within family planning programs for program enhancement and the second relates to themes that require greater research and analysis. by program enhancement, we mean improved reach of family planning programs to educate and orient users and potential users resulting in a more informed clientele. program enhancement: we foresee the potential for including analysis of tweets for message development and program planning and say this for two reasons. first, we anticipate that there will continue to be growth in the use of social media for discussion on family planning and related health issues. second, as adeptness in social media use increases and new forms of community conversation become commonplace, we can anticipate that consumers of family planning and health care will be engaging with the health system in different ways. ministries of health, ngos, and csos need to be ready to respond to user concerns and community chatter to design effective programs. they also need to understand twitter users’ and their networks to positively influence the conversation around family planning access and use. this implies that program designers need to plan for modern communication strategies that do not rely exclusively on traditional models of information education communication (iec). it implies that family planning programs need to include tech and media-savvy communication teams on their roster to be able to respond in realtime to user needs and priorities for engagement with the family planning program. we believe that there is potential for program managers and clinic personnel to learn about the “buzz outside the clinic,” especially for youth [2] who are using twitter more frequently, so that they can appropriately design and tailor their services and products to serve the needs of their populations. we acknowledge that program personnel may not get a comprehensive understanding commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi of the issues or underlying and less explicit concerns that consumers might have; nevertheless, they will be able to get a better sense of the range of consumer concerns and fears than they might learn in a formal provider-consumer interaction during service provision. we provide four examples of the ways in which family planning programs can employ twitter to engage with consumers. the first example is in dispelling myths. one predominant area is in timely myth-busting especially given the speed at which rumors and false information can spread. we anticipate that there is potential for twitter to dispel myths around family planning, as it has done for ebola, and is doing for covid-19 [3]. for example, the anti-vaccine campaign in the us worked through social media platforms, including twitter during the measles outbreak in 2015 [4] and more recently in 2019. although there were responses from official health agencies such as the centers for disease control (cdc) as well as mainstream media outlets, the narrative was led by grassroots campaigners. the lessons learnt from episodes such as this and the handling of the ebola epidemic provide direction on how official health agencies need to coordinate with media to counter myths and provide accurate, evidence-based information. a second way that programs can use twitter is for improving existing messaging about contraceptive supply chains and services and likely direct users to points of care, whether in the public or private sectors. the kenyan moh used twitter to announce that there were sufficient supplies of antibiotics during the studied 30-day period and could use twitter to announce the availability of fp supplies, as could other ministries of health. many contraceptives are being introduced or being offered through multiple service outlets—these include contraceptive vaginal rings, the sayana® press injectable available through many types of providers, including for selfadministration, and various hormonal intra-uterine systems. it will be necessary to support the rollout of these contraceptives with messaging aimed at consumers to make them aware of the product, the different types of outlets to seek information and services, as well as provide support in the use of the chosen contraceptive. finally, as we have seen during the covid-19 pandemic, some family planning programs have offered consultations through telemedicine; twitter can be used to inform health consumers of such service options if they are pertinent to that program setting. the third area for program enhancement is around social and behavior change communication. twitter might serve as a modern tool for promoting healthy behaviors by addressing stereotypes, increasing awareness of family planning options and creating a community of users; for example, linking people to websites and organizations that have accurate information on family planning and promoting a community of family planning users. similarly, having influencers tweet about their healthy habits might serve as an endorsement for their followers to adopt similar habits. a fourth area is around accountability for supplies. while using closed facebook pages to report fp stock-outs at the facility level occurs in some countries such as the philippines (see https://www.facebook.com/groups/commoditymonitoring/). twitter could be used as an open and transparent mechanism for accountability. women could let neighbors and government officials know when there are stock-outs at their local service delivery point. this is similar to airline passengers directly tweeting to the airline company the problems or grievances they have in their travel. relatedly, government officials and neighbors could let women know when new stocks or new methods are available at local service delivery points. twitter could also be used among health commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi workers to transmit more detailed types of data such as pictures of monthly stock reports and orders. research: in the domain of research, there is much to be learned. there is a body of research largely led by computational scientists on scanning and analyzing tweets to measure the saliency of the topic being discussed and the fervor behind the topic—in other words, the sentiment and buzz. it is intuitive and to be expected that interest in a particular topic swells in response to an event, milestone or ongoing crisis. for example, 10.5 million tweets containing the word “ebola” were sent from 170 countries between september 16 and october 6, 2014 during the time the epidemic was occurring [4]. we believe that there is an audience interested in family planning and sentiment analysis [5] could be conducted. with the increasing saturation of internet access, we anticipate that family planning will be discussed more. since twitter is a low cost and viable tool, it will be possible to use it for monitoring and assessing family planning attitudes and interests. concomitantly, it will be necessary to train family planning program managers in sentiment analysis and buzz so that they can shape interventions and develop messaging in response to twitter feed. a second area of research is that not much is known about the coverage of twitter, how and how well messages through twitter work to bring about social and behavior change, especially in matters related to family planning and reproductive health. we know from other health areas such as cancer prevention have used twitter for health messaging that some changes in attitude and knowledge are possible but no evidence of behavior change [6]. the extent to which the potential of twitter analysis can be harnessed for program design will depend on the ease with which the analysis can be done by the staff on the roster with social media abilities. possible ways to facilitate the process exist. for example, the open-source r package shiny allows users to easily build interactive web apps that can be standalone on a webpage or embedded, could be developed for governments, especially local government, civil society, ngos, and donors to facilitate following twitter feeds. a shiny app used weekly by programmers could inform fp programs and policies on a real-time, low-cost basis. shiny is a good option for fast prototyping and requires less programming capacity than other frameworks that could accomplish the same goal. for example, java is not as user-friendly for non-programmers, and development of a simple gui app can take time. call to action our view is that it is feasible to use twitter in lmic countries because there is interest in public discussion about family planning. it is no longer a topic meant solely for discussion privately within families or between consumers and health care providers. moreover, two-thirds of the world uses the internet, and new internet and social media users are growing at a faster pace in many fp2020 than in the u.s. and europe. many people access the internet through smartphones instead of computers. in 2015, smartphone ownership in the developing world grew 37% annually [7]. we believe that there is potential for the use of twitter as a communications tool that family planning programs in lmics can use. we also caution that before the wide-spread use of twitter, health programmers conduct cybersecurity assessments to identify any potential weakness and commentary: does twitter have a role in improving family planning messages and services in low-and-middle-income countries (lmics)? 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e11, 2021 ojphi develop mitigation plans to counter the threat should they occur [8]. finally, we also call for research from many program settings and geographical regions to assess practical and realistic applications. as greater fluency in the use of twitter among program planners and health consumers increases, we anticipate different media strategies will be required for each country. by doing so, family planning programs will be better prepared to serve a new generation of users who are socialized to communicate in new mediums than their predecessors. progress towards sdg 3 will thus be made easier by harnessing the potential of twitter to serve larger segments of the population. acknowledgments this research study and report was supported by the packard foundation under a grant to the population council. references 1. starbird e, norton m, marcus r. 2016. investing in family planning: key to achieving the sustainable development goals. glob health sci pract. 4(2), 191-210. pubmed https://doi.org/10.9745/ghsp-d-15-00374 2. pew research center, june, 2018, “social media use continues to rise in developing countries, but plateaus across developed ones” 3. limaye rj, sauer m, ali j, bernstein j, wahl b, et al. 2020. building trust while influencing online covid-19 content in the social media world. lancet digit health. 2(6), e277-78. doi:https://doi.org/10.1016/s2589-7500(20)30084-4. pubmed 4. luckerson v. 2014. watch how word of ebola exploded in america. https://time.com/3478452/ebola-twitter/. 5. rosenthal s, farra n, nakov p. 2017. sentiment analysis in twitter. proceedings of the 11th international workshop on semantic evaluations (semeval-2017), pages 502–518, vancouver, canada, august 3 4, 2017. association for computational linguistics. 6. gough a, hunter rf, ajao o, jurek a, mckeown g, et al. 2017. tweet for behavior change: using social media for the dissemination of public health messages. jmir public health surveill. 3(1), e14. pubmed https://doi.org/10.2196/publichealth.6313 7. pew research center. “smartphone ownership and internet usage continues to climb in emerging economies.” february 2016 http://www.pewglobal.org/2016/02/22/smartphoneownership-and-internet-usage-continues-to-climb-in-emerging-economies/ 8. mierzwa, s. ramarao, s, yun, ja. and jeong, bg. 2020. proposal for the development and addition of a cybersecurity assessment section into technology involving global public health. international journal of cybersecurity intelligence & cybercrime. 3(2), 48-61. https://doi.org/10.52306/03020420babw2272 https://pubmed.ncbi.nlm.nih.gov/27353614 https://doi.org/10.9745/ghsp-d-15-00374 https://doi.org/10.1016/s2589-7500(20)30084-4 https://pubmed.ncbi.nlm.nih.gov/32322814 https://pubmed.ncbi.nlm.nih.gov/28336503 https://doi.org/10.2196/publichealth.6313 https://doi.org/10.52306/03020420babw2272 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts applying zero-inflated mixed model to school absenteeism surveillance in rural china xiaoxiao song1, tao tao1, qi zhao1, fuqiang yang2, palm lars3, diwan vinod4, hui yuan2 and biao* xu1 1school of public health, fudan university, shanghai, china; 2jiangxi provincial center for disease prevention and control, nanchang, china; 3future position x, gavle, sweden; 4ichar, karolinska instituet, stockholm, sweden objective to describe and explore the spatial and temporal variability via zimm for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural china. introduction absenteeism has great advantages in promoting the early detection of epidemics1. since august 2011, an integrated syndromic surveillance project (issc) has been implemented in china2. distribution of the absenteeism generally are asymmetry, zero inflation, truncation and non-independence3. for handling these encumbrances, we should apply the zero-inflated mixed model (zimm). methods data for this study was obtained from the web-based data of issc in 62 primary schools in two counties of jiangxi province, china from april 1th, 2012 to june 30st, 2012. the zimm was used to explore: 1)the temporal and spatial variability regarding occurrence and intensity of absenteeism simultaneously, and 2) the heterogeneity among the reporting primary schools by introducing random effects into the intercepts. the analyse was processed in the sas procedure nlmixed4. results the total 4914 absenteeism events were reported in the 62 primary schools in the study period. the rate of zero report was 49.88% (fig.1). according to zimm, there are fixed and random effect parameters in this model (table 1). firstly, for the fixed parameters, the spatial variable (county) was not significantly different both the occurrence and intensity model, while for the temporal variable (month), the probability of absenteeism occurrence was significantly different over three months (!=-0.165, p =0.026), suggesting a decreasing of school absenteeism from april to june. meanwhile, a statistical significant difference in the intensity of absenteeism was also found over the three months (!=0.073, p=0.007). secondly, the random effect of intensity model was statistically significance (p=0.008), which strongly indicated a heterogeneity in intensity of absenteeism among the surveillance schools. whereas the random effect of occurrence model by logistic regression showed a non-statistical difference (p=0.774) among the schools suggesting the homogeneity in the occurrence of absenteeism. conclusions school absenteeism data has greater uncertain than many other sources and easier fluctuate by some factors such as holiday, season, family status and geographic distribution. thus, the spatial and temporal dynamics should be taken into account in controlling fluctuate of absenteeism. moreover, school absenteeism data are correlated within each school due to repeated measures. applying the zimm, the occurrences and intensity of absenteeism could be evaluated to reduce the bias and improve the prediction precision. the zimm is an appropriate tool for health authorities in decision making for public health events. table 1 fixed parameters and variance components estimates for the absenteeism using zimm fig. 1 absenteeism from apr. 1st to jun. 30th 2012 keywords surveillance; absenteeism; zero-inflated mixed model; occurrence; intensity references 1. lenert l, kirsh d, johnson j, aryel rm. absenteeism. in: wagner mm, moore aw, aryel rm, editors. handbook of biosurveillance burlington: academic press, 2006:361-68. 2. yan w-r, nie s-f, xu b, dong h-j, palm l, diwan vk. establishing a web-based integrated surveillance system for early detection of infectious disease epidemic in rural china: a field experimental study. bmc medical informatics and decision making 2012;12. 3. calama r, mutke s, tome j, gordo j, montero g, tome m. modelling spatial and temporal variability in a zero-inflated variable: the case of stone pine (pinus pinea l.) cone production. ecological modelling 2011;222(3):606-18. 4. tooze ja, grunwald gk, jones rh. analysis of repeated measures data with clumping at zero. statistical methods in medical research 2002;11(4):341-55. *biao xu e-mail: bxu@shmu.edu.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e92, 2013 electronic health records access during a disaster 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e232, 2014 ojphi electronic health records access during a disaster kevin horahan1, herman morchel2, murad raheem3, lee stevens4 1. u.s. department of health and human services, office of the assistant secretary for preparedness and response, office of policy and planning, division of health system policy 2. emergency trauma department, hackensack university medical center, steven j. pawlak msm, micp, nj ems task force 3. u.s. department of health and human services, office of the assistant secretary for preparedness and response, office of preparedness and emergency operations, regional emergency coordinator program 4. u.s. department of health and human services, office of the national coordinator for health information technology abstract as has been demonstrated previously, medical care providers that employ an electronic health records (ehr) system provide more appropriate, cost effective care. those providers are also better positioned than those who rely on paper records to recover if their facility is damaged as a result of severe storms, fires, or other events. the events surrounding superstorm sandy in 2012 made it apparent that, with relatively little additional effort and investment, health care providers with ehr systems may be able to use those systems for patient care purposes even during disasters that result in damage to buildings and facilities, widespread power outages, or both. correspondence: kevin.horahan@hhs.gov doi: 10.5210/ojphi.v5i3.4826 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction there are numerous practical reasons for implementing an electronic health records (ehr) system and a growing body of evidence in the literature suggesting that there are clinical and financial benefits of using ehrs in the ordinary course of providing care.1 it stands to reason that clinicians and their patients might enjoy similar benefits in extraordinary situations such as during a declared disaster or other public health emergency.2 this article describes the situation at the long beach medical center (new york) in the immediate aftermath of hurricane sandy in 2012 and how its ehr system was made available despite the facility itself having been rendered inoperable for patient care. http://ojphi.org/ electronic health records access during a disaster 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e232, 2014 ojphi background the national planning frameworks (the frameworks) address the five mission areas of preparedness – prevention, protection, mitigation, response, and recovery. 2 the frameworks focus on a “whole community” concept, a shared responsibility model where people, businesses, other groups, and government (at all levels) work together to achieve the national preparedness goal. 2 one of the basic tenets of preparedness is, to the greatest extent possible, to incorporate into everyday operations those systems, processes, equipment, and strategies that might be employed during a disaster.3 familiarity with those elements that may be employed during a large scale event eliminates one potential stumbling point. the daily use of an ehr system is an excellent example. the development and “meaningful use” of accessible, secure, and interoperable ehr systems and, ultimately, the creation of nationwide interoperable health information exchange capacity is part of the federal health information technology strategic plan.4 likewise, strategic objective number four of the national health security strategy identifies “the use of portable, standardsbased, interoperable ehrs” as an essential element of a “prepared and responsive health system”.5 the everyday use of health information technology (hit) can speed up the diagnosis of problems, help coordinate care, reduce duplicative treatments, limit the risk of medication interactions, and ultimately lower health care costs.1 accordingly, maintaining the hit infrastructure during a disaster or other large scale event may be one way to reduce stress on the health care system which could, ultimately, make it more resilient. in the fall of 2012, nearly eight years after hurricane katrina, the hit infrastructure was tested when a hurricane with even larger breadth than katrina roared up the eastern seaboard, pummeled the new jersey coastline, and sent a wall of water toward the most densely populated city in the united states.6 hurricane sandy caused 147 deaths and nearly $50 billion in damage.6 recovering from sandy’s devastation is ongoing and will continue for years to come. with more than a decade of planning since the attacks of september 11, 2001, new york state has invested deeply in the development of health information exchange. with additional support from the health information technology for economic and clinical health (hitech) act,7 the state has moved closer to the realization of the state health information network – new york (shinny).8 while water was impossible to hold back, the availability of health information before, during, and after the storm remained remarkably stable. among the users of ehrs in the greater new york city area there was only one report of records being lost, in a small clinic that was actually in the process of converting their paper records into an ehr system.9 however, there were widespread reports of paper records being lost.9 in new jersey, with fewer hospitals in the direct impact zone, the state regional extension center program planned in advance by contacting providers prior to the storm’s landfall with instructions on how to back up data stored in the their ehrs. this planning assured that patient information would be safe and accessible during and after the storm. http://ojphi.org/ electronic health records access during a disaster 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e232, 2014 ojphi discussion hurricane sandy’s impact on long beach medical center long beach medical center (lbmc) is located east of new york city on long beach barrier island, immediately adjacent to reynolds channel. the facility has a 162 bed acute care hospital as well as a 200 bed skilled nursing facility specializing in rehabilitation.10 lmbc handles over 13,000 emergency department visits in a typical year and the facility has had an emergency department ehr system in place since 2009 (c.c., written communication, march 2013).[removed hyperlink field] during hurricane sandy, lbmc was taken completely offline by the storm surge. its basement, which houses the heating, electrical, fire alarm, and communications systems as well as the food and laundry services, was flooded with more than ten feet of water.11 in an effort to provide the residents of long beach with nearby access to emergency care, the state of new york requested assistance through the emergency management assistance compact (emac).12 shortly after sandy’s landfall, the u.s. department of health and human services’ office of the assistant secretary for preparedness and response (aspr) activated the national disaster medical system13 at the request of the new york state department of health (nysdoh) and nassau county department of health. working with their state and local partners, aspr personnel established a disaster medical assistance team (dmat) base of operations at a soccer field a short distance from lbmc. this dmat allowed the long beach community to receive emergency care without having to leave the immediate area. this also reduced the number of patients seeking treatment at already overburdened facilities in the surrounding communities. to begin the process of recovery as soon as practical, aspr staff again worked with the state and local representatives to transition the dmat operations back to lbmc. reestablishing operations at the lbmc site, in whatever capacity, would allow their staff many of whom were furloughed after sandy to return to work and bring the community one step closer to full recovery. however, because the lbmc facility itself was inoperable, another option was needed to begin providing medical care. mobile satellite emergency department in 2006, the northern new jersey urban areas security initiative (uasi) began to develop a resource to help deal with a surge of patients. the intended purpose of the resource was to supplement the assets of the region’s emergency care providers – both facility and field based. at about the same time, hackensack university medical center (humc) was seeking funds from the department of defense with the intention of creating a deployable hospital that would include an emergency room, operating room, and accompanying support facilities. when the two parties learned of each other’s projects, they decided to collaborate to create one overall comprehensive program called the msed or mobile satellite emergency department.14 shortly after acquiring the msed, the humc staff identified the potential need for a high speed, secure data link to the hospital. providing services remotely – whether during a disaster or for purposes such as screening or prevention often has significant benefits. however, having remote http://ojphi.org/ electronic health records access during a disaster 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e232, 2014 ojphi access to an existing ehr system would allow the msed staff to view and update existing patient records. because of this, they researched the cost and feasibility of several data transfer technologies. testing was conducted on an unlicensed, point to point, microwave ethernet bridge system with capabilities that included data speeds of up to 300 mbps over a distance exceeding 100 miles and data security provided by aes 256 encryption. as part of the test, one unit was mounted on the rooftop of humc in hackensack, nj and the other on a van which was driven to various locations in and around hackensack. the unit operates most efficiently when in direct line of site; however non-line of site operation is possible albeit with reduced performance. the testing was highly successful and secure data links were established from multiple locations. the msed has become the centerpiece of a comprehensive surge capacity project that serves the uasi region and state. this is accomplished through an extensive collaboration process with the nj department of health (njdoh), the new jersey ems task force (njemstf), the medical reserve corps (mrc), and various hospital and health care entities. humc hosts the msed assets and provides medical staff to operate within the msed while the njemstf provides overall command, planning, and logistical support during deployments. generally, staffing is provided for prolonged operations or deployments through collaborative agreements with the mrc and other hospital entities. when new york initiated its request for emac assistance during hurricane sandy, the msed was among the assets requested. the discussion between representatives of the nysdoh and the njdoh that led to the request took place in the joint field office and was facilitated by aspr regional emergency coordinators. through a federal emergency management agency (fema) mission assignment, this operation was supported by several aspr assets. use of innovative technology health care providers are encouraged to incorporate preparedness into their everyday operations and using an ehr system is a great example. in setting up the msed at the long beach location, discussions with the lbmc providers who would be working in the msed took place regarding the use of the lbmc ehr and radiology image transfer programs. the advantages, as discussed previously, included staff familiarity, access to existing medical records for the population, archiving of medical information for new encounters in the temporary facility, and transfer of digital radiology images for interpretation by offsite radiologists. because the lbmc’s data center is located approximately one mile away from the main facility on the upper floor of a multi-story commercial office building, the ehr system was safe from the flood waters. normally, fiber optic data lines are used to link the data center with the hospital building. however, due to physical damage to the hospital infrastructure by flooding and loss of integrity of the fiber optic lines due to downed trees and other debris, the existing connections between the data center and hospital were unusable. the humc msed team proposed using the microwave link system that they had tested previously to link the lbmc data center with the temporary emergency department being set up in the medical center’s parking lot. because of the experience gained and success of the prior testing the group decided to deploy the system at lbmc; the manufacturer’s local engineering representative came to the site and provided technical support. one unit was mounted on the roof of the data center and the other on the roof of the hospital. the units use power over ethernet (poe) so a single data line http://ojphi.org/ electronic health records access during a disaster 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e232, 2014 ojphi is all that was required from the rooftop unit to the msed and tent structures. once the cable was brought inside the patient care areas, a router was used to establish a local network and the hospital’s computers, displays, and printers were installed. connection of the msed digital x-ray system to the network allowed transfer of images to offsite radiologist physicians for interpretation as needed. in short, despite the austere situation, the care provided could still approximate that provided on a ‘normal’ day. conclusion the impact of hurricane sandy put many aspects of the frameworks particularly the mitigation, response, and recovery components to the test. it also helped to identify where strategies such as employing an ehr system can cross the frameworks from mitigation to response, and into recovery. while some of the events at lbmc were serendipitous, there were a number of areas where preparedness efforts paid off. the implementation and regular use of ehr systems is clearly on that list as is the partnership developed between federal, state, local, and private entities involved in disaster planning. we have seen before, and it was true during sandy, that the use of an ehr system can facilitate record recovery if a facility is damaged or destroyed. what this experience also shows is that, with only a small amount of additional planning, it is possible to maintain access to an ehr system during a significant event even if a facility has been damaged or destroyed. disclaimer: the contents of the article represent the personal views of the individual authors and do not necessarily express the opinion or policy of the us department of health and human services (hhs), hackensack university medical center (humc), or the new jersey ems task force (njemstf). no statement in the articles should be construed as an official position of hhs, humc, or njemstf. acknowledgments: cheryl chapman, peter genova, and sharon player of the long beach medical center provided information and comments to the authors; and alicia livinski from the national institutes of health library assisted with editing and manuscript preparation. references 1. menachemi n, collum t. 2011. benefits and drawbacks of electronic health record systems. risk manag healthc policy. 4, 47-55. pubmed http://dx.doi.org/10.2147/rmhp.s12985 2. u.s. department of homeland security. national planning frameworks. washington, dc: u.s. department of homeland security; may 2013: http://www.fema.gov/national-planningframeworks. accessed november 22, 2013. 3. abir m, mostashari f, atwal p, et al. 2012. electronic health records critical in the aftermath of disasters. prehosp disaster med. 27, 620-22. pubmed http://dx.doi.org/10.1017/s1049023x12001409 http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22312227&dopt=abstract http://dx.doi.org/10.2147/rmhp.s12985 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23009722&dopt=abstract http://dx.doi.org/10.1017/s1049023x12001409 electronic health records access during a disaster 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e232, 2014 ojphi 4. office of the national coordinator for health information technology. federal health information technology strategic plan 2011 – 2015. washington, dc: office of the national coordinator for health information technology: http://www.healthit.gov/sites/default/files/utility/final-federal-health-it-strategic-plan0911.pdf. accessed august 9, 2013. 5. u.s. department of health and human services. national health security strategy of the united states of america. washington, dc: u.s. department of health and human services; december 2009: http://www.phe.gov/preparedness/planning/authority/nhss/strategy/documents/nhssfinal.pdf. accessed august 9, 2013. 6. blake es, kimberlain tb, berg rj, et al. tropical cyclone report hurricane sandy (al182012) 22 – 29 october 2012. miami, fl: national hurricane center, national weather service; 12 february 2013: http://www.nhc.noaa.gov/data/tcr/al182012_sandy.pdf. accessed august 9, 2013. 7. public law 111-5: title xiii of division a of the american recovery and reinvestment act of 2009. (123 stat. 226; date: 2/17/09). 8. new york ehealth collaborative. statewide health information network for new york. 2012; http://nyehealth.org/what-we-do/statewide-network/. accessed august 9, 2013. 9. coughlin b. in the wake of hurricane sandy: health it 1, paper records 0. november 21, 2012; http://www.healthit.gov/buzz-blog/ehr-case-studies/hurricane-sandy-healthinformation-technology/. accessed august 9, 2013, 2013. 10. long beach medical center. who we are. 2012; http://longbeachmedicalcenter.org/whoweare.aspx. accessed august 9, 2013. 11. ny town eyes hospital reopening months after sandy. march 23, 2012. http://bigstory.ap.org/article/ny-town-eyes-hospital-reopening-months-after-sandy. accessed august 9, 2013. 12. national emergency management association. emergency management assistance compact (emac). 2013; http://www.emacweb.org/. accessed august 9, 2013. 13. office of the assistant secretary for preparedness and response. national disaster medical system. http://www.phe.gov/preparedness/responders/ndms/pages/default.aspx. accessed august 9, 2013. 14. new jersey mobile satellite emergency department (nj-msed). 2012; http://www.njmsed.com/. accessed august 9, 2013. http://ojphi.org/ crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 32 (page number not for citation purposes) isds 2013 conference abstracts early detection of possible outbreaks from electronic laboratory reports hwa-gan chang*, candace noonan-toly, jian-hua chen and bryon backenson new york state department of health, albany, ny, usa � �� �� �� � � �� �� �� � introduction ������� � ����� ����������������������������������������������� ����������� ���������� ������ ����� �� ��� � �� � ������������� ���� ����������������������������������� ������������������������!�" �����# ���������� $������� ����������� ��%��������� ������ ��$� �� ���� ��������&'�������(���� �$�������������($����������� ������� ���# ������������������������%�����������!������������%���)*+�� ���������� ����������������������������%������� !�,�����%�������������������������� ������-������������ �����%���*.+�� ���������������������������� ��� ����� ����� �� ����������������� ����������!������������������ ��������� ������� ��������������� �������������������������� ������������%��# �������������������������!�" �������#�������������������������������� �������-��������������������/��%�����������������������������/�� �� � �������������� �������� ��������������������������������������������� ������� ����������� ������������#����������������������������! methods �����������������%������������������������%������� � ����01012.00� � ���� �'13012.03� �����%������������������������4��������������/� �����������/�5������/�����������/�,��������/� ���������/����� ������� ����� ��������� ���� ��#����������� ��� �������� ����� ����/� ����� ����/� ������ ����� �����������������������������!�" �������#����������# ���������������������������������� ���� ���������� �&1012.03�� ���� � '13012.03��������������6��7����������������� �����%����������� � ����������������������������� ���%������������ ��������������� ���� 01012.00����813.12.03!�" ����9���������������7������������������# ������� ��������:;�2.� ���������� ���� ��������������������20�����!� <������������������������������� ��������������������������������� ��� ����#%�����:�.!.&!��($������� �������� ��������� �������� ���� �$� �������������� ��������� ��������� ��������������������$� � � ���� ������������������ �����������������!�" �������� �������������� ������ ����� ������������������ ���������������������!�" ������������� � ������������������������������������ ����������������������������� ����������������� ����������������������������� �� ����������� ���� �����������������������������$� ���%����������������������! results " ���������2/.&.������������� �������%������������ ���'�������������# ���������������%������� ��������� ��������������!�"�����0����������� � ���3)��������������������� ���������������#���������������������� ���������/�80+�� �� ����������� �������������������� ������������� %����������������!�" ������������������ ����������������������������� ����������������� ���������������� ����2����08���������������%��������� �������! conclusions " �������#����������������������������������������������������� ��� ���������� ��������� ��������� ���� ��� ����������� ����������� ���������� ������!�" ����������������� ������ ���� ������ �����������������# ���������/������������/�,��������/� �������������� ������������������� ��������� ������������������ �������� �������������������������������# ������� ��������� ����������������������!�" �������#����������������� ������������������������%�������� ����� ����������������������! "�����0!����������$�������� ������������������������������������ �����/�&10#� '13012.03/��� keywords ���=�>������ =� �������������� *hwa-gan chang e-mail: hgc04@health.ny.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e131, 2014 strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah wu xu, phd 1,2 , warren pettey, mph, cph 2,3 , yarden livnat, phd 2 , per gesteland, md 2,4 , deepthi rajeev, ms, msc 2 , jonathan reid, mba 1 , matthew samore, md 2,3 , r. scott evans, ms, phd 2,4 , robert t. rolfs, mph, md 1,2 , catherine staes, bsn, mph, phd 2 1 utah department of health, 2 university of utah, 3 va salt lake city health care system, 4 intermountain healthcare abstract collaborate, translate, and impact are key concepts describing the roles and purposes of the research centers of excellence (coe) in public health informatics (phi). rocky mountain coe integrated these concepts into a framework of phi innovation space and stage to guide their collaboration between the university of utah, intermountain healthcare, and utah department of health. seven research projects are introduced that illustrate the framework and demonstrate how to effectively manage multiple innovations among multiple organizations over a five-year period. a coe is more than an aggregation of distinct research projects over a short time period. the people, partnership, shared vision, and mutual understanding and appreciation developed over a long period of time form the core and foundation for ongoing collaborative innovations and its successes. key words: public health partnership, innovation stage, space, and management introduction public health informatics (phi) is an action-oriented science and innovation-driven practice. partnership between academic informatics researchers and public health practitioners is crucial for successful translations of informatics research findings into practice. building sustainable partnerships over various innovation journeys and efficiently translating a product from a research laboratory into public health operations are challenges for the academic centers of excellence (coe) in public health informatics. in this manuscript, we describe the rocky mountain center of excellence in public health informatics (rmc) and put it forward as an illustrative example of a framework for successful innovation partnership between public health and academia in utah. we built the rmc upon ongoing collaborations from three related domains: informatics research, epidemiology and population sciences research, and public health practice (see figure strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 1). the research institutions include the university of utah departments of medicine, pediatrics, and biomedical informatics, and intermountain healthcare. the practice organizations include utah department of health, salt lake valley health department, davis county health department, denver health, and intermountain healthcare. regardless of a member’s background, all the collaborators had a shared vision of the rmc as a “center of excellence to rapidly translate public health informatics research results into practice, to broaden collaboration between innovative researchers and practitioners, and significantly impact and improve epidemiology and surveillance.” the rmc’s innovation priorities include informatics tools to support disease surveillance and investigation (especially food safety, pandemic influenza), public health case reporting, and secured communication. shared vision does not naturally lead to smooth partnership among different disciplinary professionals. thomas j. allen pointed out that to treat both professions (engineers and scientists) as one and then to search for consistencies in behavior and outlook is almost certain to produce error and confusion of results (1). an epidemiologist’s priority, in general, is to conduct disease surveillance, investigate outbreaks, and operate a public health system under the public health legal authority. a researcher’s priority is to conduct studies and disseminate novel discoveries directed by funding sources with time limits. aside from the disciplinary factors, collaborative informatics research among academic and public health parties often involves members of one party performing in the workspace of the other. workspace crossover may lead to tension among partners. furthermore, content of partnerships and interactions across workspace at different innovation stages are interdependent but not consequential as well (1). framework for public health informatics innovations’ spaces and stages (phi-iss) in order to translate our innovation experiences into a framework to better coordinate the research projects across research and practice spaces and project lifecycles, we used inductive reasoning methods and theorized our own experiences during the past five years. specifically, we first collected information from one project such as project deliverables, tasks, timelines, responsible parties and locations. we analyzed the information within the context of our own recalls and reflections on the partnership situations for each of project deliverables and tasks. for example, the concept of the “innovation space” was first used by a public health investigator during a heated discussion on how to balance resource commitments between research project timeline and public health practice priorities. when we summarized our research activities and epidemiology /population research informatics research public health practice figure 1: interactive domains of public health informatics strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 evidences of partnership development into a structured framework, the concepts of innovation “space” and “stage” emerged. public health informatics is a relative new sub-field of the health informatics domain, which also includes clinical informatics and has recently seen an explosion of interest in funding and conducting translational research. the paradigm of clinical translational research has a fourphase conceptual model describing clinical research progressing “from bench to bedside.” we adapted and applied this phase concept to public health informatics research arguing that the corollary is to transition public health informatics research and innovation “from campus to field.” clinical and translational research has four distinct but interdependent phases (t1-t4):  t1: research seeks to move a basic discovery into a candidate health application  t2: research assesses the value of t1 application for health practice leading to the development of evidence-based guidelines  t3: research attempts to move evidence-based guidelines into health practice, through delivery, dissemination, and diffusion research.  t4: research seeks to evaluate the "real world" health outcomes of a t1 application in practice (2). out public health informatics innovation stage and space (phi-iss) also include four stages. we also add a new dimension of “space” in each stage as follows:  iss1 research initiates to move new discoveries into candidate public health applications with practitioners’ input (space=university mainly. finished during the grant writing period.)  iss2 research and practice collaborate to assess the value of iss1 discovery for public health practice leading to evidence-guided innovation (space=university and public health)  iss3 – research translates and practice implements fully-tested iss2 innovation into public health practice through researchers’ delivery, dissemination, and diffusion efforts (space=public health and university)  iss4 research evaluates the "real world" impact of public health outcomes of iss3 implementation in practice (space=university) (3). figure 2 outlines the four innovation stages where research space is highlighted in light color and public health space in dark color. the direction and color of an arrow indicate an action initiator, role impact and location of workspace. for example, at the initiate stage i, public health is needed to identify what the problem, issue or desired outcome is. informatics researchers determines the “how.” researchers mainly work on campus and make impact on public health’s participation in the collaborate stage ii. public health makes active input on research designs in stage ii where workspace crossover begins. at the translate stage iii, researchers deliver prototypes, pilot products, and modify public health informatics infrastructure. when a research product is implemented in a public health practice, research impact will go beyond the original practicing collaborators being diffused widely. over the four stages, research activities strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 highlighted in light color gradually penatrate into public health practice indicated by the incressed dark-color areas. figure 2. innovation stage and space for public health informatics during the past five years, the rmc has performed research and development resulting in projects that, as of 2011, are at various points along the spectrum of public health informatics innovation stage and space. we now provide several examples below to illustrate the phi-iss framework. epinome: an example of transition through the continuum of innovation stage and space we describe the innovation stages and space framework using our work with a new software innovation called epinome (4, 5). epinome is a user centric visual analytics system that empowers users to visualize, explore and analyze public health data. epinome features a dynamic environment that seamlessly evolves and adapts to user tasks and focus change. translation of epinome into a public heath setting can significantly enhance the analytical capacity and usability of the reportable disease data collected using utah’s surveillance computer system (ut-nedss implemented with trisano software). = public health space= research space ii iii ivi impact evaluation outreach collaborate public health evidencebased design and development translate operationalize research product transform practice initiate idea prototype with limited public health input strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 3. epinome interface iss1 – initiate to move new discoveries into candidate public health applications (space=university): 2007-2009 started in 2007, the university of utah’s researchers conducted interviews with state and local epidemiologists on the topic of pertussis outbreak investigation, and collected input from experts in cognitive psychology, infectious disease epidemiology, and mathematical modeling of infectious disease and visual analytics. based on the identified public health needs, coe investigators developed a software prototype of the epinome novel technology. the researchers then demonstrated the prototype of epinome to public health practitioners using large scale highfidelity simulations of pertussis outbreaks. in 2009, the researchers and public health personnel jointly wrote the rmc grant application for further development of epinome system and seek to generalize and adapt the pertussis investigation model to food-borne illness and influenza. during this initial stage, activities mainly occurred on campus; public health had minimal participation and less commitment to the research project. the minimal interactions between the teams did not lead to a noticeable tension within the partnership. we applied the phi-iss framework to analyze collaborative activities and identified 15 deliverables from the epinome research team as candidates for public health adaptation and implementation. of the 15 deliverables of epinome components, nine were in the iss2 stage, four in the iss3 stage, and two in the iss4. iss2 – collaborate to assess the value leading to evidence-guided innovation (space=university and public health):2009 – 2011 at the iss2 stage, researchers and practitioners jointly assessed the practical and potential added value of epinome. we conducted contextual inquiries and observations of public health practice, strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 processes, procedures, and policy to elucidate design objectives, restrictions and requirements that may be imposed at a public health practical setting. public health was actively involved in evidence collection in this stage. the researchers conducted multiple on-site demonstrations/explorations with different groups of potential public health users of epinome to assure that epinome would meet practitioners’ needs. this effort has led to the reengineering of epinome’s functionality and it infrastructure. researchers worked with two groups of practitioners, namely epidemiologists and information technologists, on the nine deliverables for this phase of the project: 1. state epidemiologists, informaticists, and campus researchers conducted contextual inquiries on (a) food-borne disease investigation workflow at state and local health departments and (b) unmet needs from the users of the indicator-based information system for public health (ibis-ph) 2. conducted an “affinity walk” to organize and categorize similar user needs collected through contextual inquiries (an affinity diagram) 3. developed use cases based on affinity diagrams 4. develop epinome’s ontology for processing the ut-nedss data 5. integrate epinome with the state of utah’s gis service 6. develop web services for epinome 7. ensure interoperability of epinome with the udoh it environment (authentication, application/database servers) 8. mirror the udoh implementation environment (ut-nedss functionality, export schema, apache/spring server respond to user’s requests) at the university of utah’s it laboratory 9. create visualizations of diagnostic pulsed field gel electrophoresis (pfge) patterns (an alphanumeric result) that assist epidemiologists in understanding both the pathogens circulating in populations (surveillance) and for investigating the similarities and differences between the pfge patterns of cases and candidate exposures (outbreak investigation) the iss2 deliverables were workspace crossovers without significant tensions among partners as the efforts focused on information collection and knowledge exchange and were less invasive in other party’s business processes. this stage requires increased resource commitment by the public health side (e.g., face to face meeting time) to provide additional information, artifacts and consultation to researchers. at time, the research agenda and time commitments had to compete with public health operational priorities. iss3 – translate innovation into practice (space=public health and university): 2010-2011 researchers worked with utah department of health personnel on the following iss3 deliverables: 1. exported ut-nedss data according to the epinome ontology schema 2. established access to the udoh’s laboratory information management system 3. develop ut-nedss mirror functioning at udoh 4. establish apache/spring server responds to calls the iss3 is a successor fail-stage for translating epinome from campus to the public health field. to prepare for a smooth implementation in the iss3, researchers spent considerable time and resources to replicate the ut-nedss it environment and data structure in the university strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 research environment. with the institutional review board’s permission, the research team obtained access to de-identified data from the ut-nedss system, and then, to sustain a realistic dataset, matched the remaining identifiers to an in silico population to provide fictional details where identifiable details had been scrubbed. utah department of health successfully exported the ut-nedss data according to the epinome’s ontology schema. the researchers and practitioners began to integrate epinome into the state it system establishing operational communication between epinome and laboratory information management system (lims) and ut-nedss. success in the iss3 stage requires special attention to be paid to maintaining an effective and active partnership. this stage involves translation of research innovation from university workspace into public health workspace: this workspace crossover is a potential tension point. to address this concern we developed a protocol and shared vocabulary to facilitate communication, provide contextual information, and assure correct and efficient utilization of all resources. shared identification and articulation of the iss3 deliverables helped to maintain a mutual understanding assuring successful collaborative innovations across workspaces. the epinome partners jointly developed a plan for agile delivery and training, usability testing, and deployment strategies. the researchers also planned to transfer knowledge necessary (e.g. software documents, etc.) to maintain epinome in practice. public health was in process of developing a sustainability plan. at this stage, public health began to realize that the epinome research product possibly would become an operational resource for their practice and gradually developed a stewardship attitude towards the epinome. the iss3 had disproportionate effects on public health practice due to required changes in public health’s workflow, data access, and adjustment to unfamiliar visual analytical methods and data displays. the tasks in the iss3 are more interdependent across workspaces than those in other stages. investigators at the university depended upon collaborators at practice to accept, understand, and complete certain tasks before moving forward with next research tasks. however, for public health collaborator, these research tasks were low priorities for their practice and competed with their day-to-day job demands. key personnel functioned as liaisons were indispensable in overcoming these challenges. the liaisons who understand or have previous experiences in both research and practice, served as information pipelines between the campus and the field. we have avoided pitfalls and tension points due to the liaisons’ constant coordination of competing demands on personnel, communication channels and appropriate assignments for public health personnel. iss4 evaluate the impact in the “real world” (space=university and public health): 2011 there were four iss4 deliverables which comprised the following: 1. collect and analyze epinome server records (click stream analysis) to assess and describe patterns of usage 2. created and evaluated mockups for epinome special application for foodborne disease outbreak investigation the iss4 is an impact stage. the udoh institutional review board approved “opening the public health space” for researchers to evaluate the impact of adopting epinome. the partners strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 planned to jointly develop an evaluation plan and metrics by analyzing web log files to identify patterns of use and problems experienced while using the software. additionally, the team can create plans to disseminate the novel discovery, physical product, and its evaluation to research and practice communities. in summary, the phi-iss framework provides a) a priority tool for planning and administrating various tasks and deliverables, b) a new perspective to consider how to allocate resources across organizations at appropriate time, c) a communication facilitator to identify and mitigate unnecessary tension or conflict among partners, and d) a road map to measure the status of a public health informatics research project. status of other projects in 2011 relative to innovation stage and space figure 4 presents the status of other six research projects conducted by the rmc investigators or informatics graduate students mentored by the rmc faculty from 2007 to 2011. as of the end of 2011, these projects are at various points along the spectrum of public health informatics innovation stage and space. projects that were the focus of research during the first three years of funding are further along in the spectrum. the two projects currently in innovation stage iv were developed and deployed in a single environment allowing other users to access the system using the web: germwatch, a pathogen-specific surveillance system, was developed and deployed in the intermountain healthcare environment while phaccess, a secure communication and project management network, was developed and deployed at the utah department of health. in contrast, the two projects currently in innovation stage iii have required more than one environment to successfully develop and deploy the research output. epinome has been developed in the research environment but requires integration with data and systems at the utah department of health before it can become operational and impact public health. similarly, rtcend, a project transmitting case reports electronically from healthcare to public health, requires implementation in both the clinical and public health environment to realize the exchange of information between the two settings. to complicate the situation further, the public health disease surveillance system (utah-nedss) to which these applications must be linked was developed and deployed during the past few years. the knowledge management project in innovation stage ii was initiated more recently in 2010 and is currently focused on collaboration to design and develop systems that meet evidence-based public health needs. finally, while innovation stage i for the funded projects occurred during the grant writing period, the ongoing collaborations between public health practitioners and researchers and students in the academic setting have spawned new ideas and prototypes that have the potential to further advance the science and practice of public health informatics. these additional projects are described below. strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 4. status in 2011 of rmc-related projects along the innovation stage and space spectrum germwatch in the operation/impact stage germwatch (http://www.germwatch.org) is a pathogen-specific surveillance system that was initiated in 2001 by researchers in the department of pediatrics at the university of utah and supported by a utah department of health’s grant, then further developed with partial support for junior investigators with the rmc. germwatch is an information resource that provides timely (~24 hour delay) surveillance information about regional microbiological activity including viral respiratory surveillance, viral and bacterial gastrointestinal infections and antimicrobial resistance (see figure 5). the system is based on microbiological testing performed in intermountain healthcare’s large integrated healthcare delivery network that provides a majority of the healthcare for the state of utah, which affords the potential to approximate population-based rates. intermountain healthcare operates the germwatch system in partnership with the university of utah departments of pediatrics and biomedical informatics on behalf of utah clinicians and state and local health departments. the system is currently being modified to provide information more suitable for consumption by the public. germwatch greatly enhances the breadth utah’s surveillance system by adding pathogenspecific data based on microbiologic testing in utah’s largest integrated healthcare delivery system to syndromes based on emergency department chief complaints and notifiable diseases reported under state law (6). the system is also novel in the sense that the information provided is specifically geared towards meeting the information needs of healthcare providers and healthcare system administrators, as well as providing information to public health about common outbreaks that are not part of the reportable disease profile (e.g., rsv, adenovirus, enterovirus, human metapneumovirus) (7). germwatch data has been used by coe researchers to conduct cutting edge infectious disease modeling research including forecasting rsv epidemics using meteorological variables (8) and modeling seasonal variation of rsv (9). the data on antibiotic resistance is available for the entire system and can be partitioned by impact evaluation outreach collaborate public health evidencebased design and development translate operationalize research product transform practice = public health space= research space initiate idea prototype with limited public health input logic analyzer* knowledge management epinome phaccess germwatch rtcend ii iii ivi mortality surveillance* *student projects spawned by rmc collaborations strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 conditions and various populations (e.g., ambulatory pediatric urinary tract infections). ongoing research is addressing how to optimally present and provide access to this data to front line clinicians. surveys and anecdotal reports have documented substantial and sustained interest on the part of clinicians in having access to this information because they believe the data to be useful in clinical practice for improving diagnostic accuracy, improving clinical decision making (e.g., antibiotics prescribing, testing for viruses), improving communication with patients/parents about pathogens circulating in their regions(6-8, 10). administrators charged with managing healthcare resources and systems find the data valuable for making decisions about human resources (e.g., knowing when they will need to call in extra staff), implementing visitor restrictions to reduce nosocomial infections and planning and implementing rsv immunoprohylaxis for high-risk patients [personal communication phg]. public health officials report that the system is a valuable addition to their surveillance arsenal because it provides ready access to information about conditions they are not resourced to track [personal communication phg]. figure 5. germwatch interface providing a portal to pathogen-specific surveillance and brief messages to guide clinical and public health decision making phaccess in the operation/impact stage during the first three years of coe funding, the research and collaboration efforts due to the “interact” project resulted in a shared and improved understanding of the problems in communication between clinical and public health settings. for example, we documented problems among urgent care providers with understanding public health reporting requirements and their role in population health (10).urgent care providers are highly likely to be first line strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 responder during an outbreak. we also identified communication barriers between public health settings. between the various clinical and public health settings, secure transmission of information was limited to phone calls and faxed paper reports. figure 6: screenshot of the phaccess interface allowing secure access to multiple applications in 2008, the development of phaccess was initiated by a rmc research informaticist who was involved in informatics research and training but stationed in the public health environment. initially, the purpose of the application was to share information about public health issues with all 12 local health departments in utah. phaccess expanded through user input about their needs and has value for public health practitioners and agencies. it allows secure communication about current issues/outbreaks between state and local public health departments and clinical partners. there is an easy single sign-on to access secure applications, such as epi-issue tracker, ili surveillance reporting, ut-nedss and secure messaging. phaccess has a simple framework that allows for the ability to create and publish new applications with little development effort. phaccess allows secure communication between researchers and public health practitioners. finally, phaccess has the ability to easily bring on new users. the value of phaccess can be measured by usage. as of august 2011, there were a total of 898 registered users, including public health practitioners affiliated with the utah department of health (n=470), local and county health departments (n=114), as well as physicians and infection preventionists from hospitals in utah (n= 138), and other users from state, national or commercial entities (n= 175). as many as 339 epidemiologic issues have been collaboratively addressed with local health departments. finally, 57,174 secure emails have been sent using phaccess. phaccess rapidly transitioned through the stages of the iss framework and is currently available for adoption by other public health entities. strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 rtcend in the translating stage starting in 2007, rmc investigators from informatics, clinical, and public health settings collaborated to design a system to implement real-time communication of electronic notifiable (aka reportable) diseases (rtcend). the team was not addressing detection of reportable events, but rather addressing the content and structure of electronic messages for a clinical setting to report diseases such as hepatitis a or salmonella to public health. investigators collaborated (in stage 2) to understand the public health workflow and information needs, and evaluated existing hl7 and cdc messaging standards (11, 12). based on evidence derived from the investigations and the existing messaging infrastructures, a proposed health level 7 (hl7) version 2.5.1 message format was defined that was extendable to accommodate any reportable condition and allowed for the inclusion of both laboratory and clinical information in a message (see figure 7). in addition to local collaboration efforts, rmc investigators collaborated with the cste/cdc case report standardization workgroup (crswg) to provide input and be informed by the cste policy concerning the recommended core content of a case report. figure 7. rtcend reporting process currently, rtcend is in innovation stage 3 and becoming operational by intermountain healthcare and the utah department of health. prior to implementing the system, researchers evaluated the quality of electronic reporting using rtcend compared with traditional manual reporting methods, and focused on the timeliness, completeness of information content in the initial report, and completeness of transmitting case reports for recognized reportable events.(13)the prospective evaluation (performed in july 2010) and the retrospective evaluation (performed on messages sent from october 2010 to february 2011) found that electronic messages were more timely than paper reports sent from other healthcare facilities (p<0.0001) (13). the hl7 messages also included more complete information when compared to the content of paper reports from other facilities, particularly concerning hospitalization status, and the reporting contact’s name and phone number (13). strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 rtcend illustrates the challenges of implementing new informatics innovations in the operational public health environment. during the time of this project, several important changes occurred in the public health environment that required modifications to the implementation plans. to complete the research and evaluate impact on public health practice, the utah department of health must be able to receive and integrate messages into their surveillance system. during the course of this research, udoh has developed and implemented a new utah-nedss system with an outside contractor (trisano) and the reporting standards have changed. the rtcend message format is currently being modified to meet the intent of rtcend to send clinical information while fulfilling the recent meaningful use requirements defined by the hitech act to send electronic laboratory messages (14). electronic messages are currently being sent from 22 intermountain healthcare facilities to a test environment at udoh. the messages will need to be integrated into the utah-nedss before their impact on public health work flow and disease control can be assessed. once integration has been completed, intermountain healthcare will explore enhancing the messages to include other clinical data requested by public health. knowledge management in the collaborative stage in the fall of 2009, research was initiated to address the management of public health knowledge required to improve communication between public health agencies and their clinical and laboratory partners. the research is focused on the following three use cases:  public health reporting from laboratory and clinical settings,  public health notifications about alerts and “what’s going around”, and  request and response for information to support a public health investigation, particularly using structured reports (e.g. based on the hl7 clinical document architecture). the goal is to demonstrate a new model for managing public health knowledge using serviceoriented architecture (soa) and standard terminologies that a) allow public health authorities to author, store, and ‘publish’ computer-interpretable knowledge, and b) allow users to access the knowledge using context-aware information retrieval strategies, view human-readable content, download structured content using web services for execution within their own systems, and subscribe or query for updates. this project is a reference implementation to assess the feasibility and value of the proposed system. currently, we are focused on the public health reporting use case in innovation stage ii. researchers and practitioners are jointly assessing the problems and defining requirements in the public health practice space to ensure that design and development efforts underway in the research space are guided by evidence. to determine content and functional requirements, researchers reviewed existing knowledge resources and analyzed business process (15). we surveyed hospital and commercial laboratories to describe current processes, estimate their burden to comply with public health reporting, and to evaluate the business need and readiness for a service using soa to deliver standardized reporting specifications. we are using ethnographic methods to get feedback from public health, clinical, and laboratory users on design and workflow issues. strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 to develop the system, we are using the infrastructure developed for the university of utah’s federated utah research and translational health electronic repository (further). the system uses web services developed in java for intersystem communication and requires no proprietary tools by laboratory or hospital personnel/systems. the knowledge is stored in xml files located in a metadata repository. the terminology services are handled by apelon’s distributed terminology system (dts) (http://apelon-dts.sourceforge.net/) which supports national data standards. we are using altova’s xml spy and stylevision to create data models and data entry forms, but these tools are not needed by system users. the knowledge is accessed through a query interface that allows a user to specify a condition, jurisdiction and role (e.g., laboratory or clinician). the query results are returned by a service and displayed. to develop the system, we are using the infrastructure developed for the university of utah’s federated utah research and translational health electronic repository (further). the system is web-based, using web services and developed in java, and requires no proprietary tools by laboratory or hospital personnel/systems. the knowledge is stored in xml files located in a metadata repository. the terminology services are handled by apelon’s distributed terminology system (dts) (http://apelon-dts.sourceforge.net/) which supports national data standards. we are using altova’s xml spy and stylevision to create data models and data entry forms, but these tools are not needed by system users. the knowledge is accessed through a query interface that allows a user to specify a condition, jurisdiction and role (e.g., laboratory or clinician). the query results are returned by a service and displayed. we will test system usability and evaluate functionality during innovation stage 2 by exporting the reporting specifications for utah and using the knowledge to inform public health reporting from the university healthcare enterprise data warehouse. further development is limited by reduced support for the rmc. pilot projects in the initial stage a center of excellence in public health informatics requires the ongoing infusion of new ideas and new investigators to address emerging and future public health informatics needs. collaborations forged by the rmc environment have spawned a variety of new research pilots conducted by students and other investigators interested in public health informatics related problems. two illustrative examples currently underway are shown in figure 4. as public health practitioners and researchers engage in discussions about problems they face and research in progress, students and investigators identify a) new and interesting research questions, and b) opportunities to apply informatics methods used in other domains to the public health practice space. for example, in the spring of 2010, a graduate informatics student with a background in engineering identified a solution for automating the analysis of reporting logic defined in cste position statements (16). the student developed a java-based tool with a user-friendly excel file for input to test reporting logic for hepatitis (17). to lay down a solid base for this research initiative, the rmc research faculty guided the student to solicit input from three public health epidemiologists that authored the hepatitis-related position statements. the epidemiologists provided feedback about the tool and received follow-up reports about the logic being revised for re-balloting in 2011. however, more input from public health and research resources would be required to move it from the innovation stage i to innovation stage ii. strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 similarly, another graduate informatics student is developing a grid-based tool to improve the processing of cause of death narratives to enable real-time monitoring of deaths due to pneumonia and influenza and other events with public health implications (18). while this research is not funded by the rmc, its innovation journey and impact are highly relevant to the rmc’s innovation research and integral to the mission of developing sustainable partnerships and researchers that understand public health practice. other impacts of partnerships fostered by public health informatics innovations the sustained collaboration between partners resulted in other benefits that may not be obvious to outside observers. the collaborations resulted in “floating everyone’s boat”, being a force magnifier for training, engaging partners in the national standards development efforts, and improving the analysis of problems in the public health and clinical environment. for example, first, the collaboration between public health practice, research and academics resulted in a)knowledge transfer in both directions, b) integration of practitioners into the national efforts (elr), c) creating a ‘safe environment’ for public health practitioners to get up to speed in a faster way; and d) developing a model for informatics students to participate in “infoaids” during public health crisis situations. second, the coe was a force magnifier for training the public health workforce, informatics graduate students, and junior investigators. for example, public health collaborators had access to continuing education and in turn contributed as faculty for the amia 10x10 course. graduate informatics students became engaged in research leading to careers in public health informatics and had the opportunity to interact closely with public health practitioners, evaluate surveillance systems and cste position statements, and make significant contributions to the cdc/cste case report standardization workgroup (19). five junior faculty investigators in university of utah jump-started careers built on the grants opportunities and collaborations fostered through the rmc. third, improved problem analysis was demonstrated during theh1n1 outbreak in 2009. prior to establishing the rmc, many of the key players who would need to respond to this type of public health events did not know one another. as the h1n1 outbreak evolved, researchers from the center were able to rapidly and systematically evaluate real communication in the field and identify problems with duplication of effort and communication overload (20). after the first wave of the outbreak, a new communication strategy was developed in large part due to the ongoing partnership among the major stakeholders, many of whom were represented in the rmc partnership (figure 7). the revised organizational communication strategy included a taskforce to coordinate messaging and deliver a unified public health messages through chief medical officers with health care entities, and requirements were identified for future message delivery to improve response to public health threats. strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 7. timeline of response to the h1n1 outbreak illustrating integration of research and impact on practice finally, there are other tangible and intangible impacts resulting from the collaborations fostered by the rmc. practitioners and researchers from the rmc jointly participated in national standards development efforts to increase their understanding of standards and speed up the innovation process at the home organziation.sharing of ideas were also fostered through the rmc weekly virtual meetings to discuss new research, issues, and outside innovations. there was a total of 164 papers, posters, abstracts, presentations, and white papers published during the past 5 years. the collaborations reached outside of our own rmc to establish the community of innovators in epidemiology and public health informatics (coi-ephi). conclusion and discussion the rocky mountain center of excellence (coe) in public health informatics’ collaborative trajectory provides live examples of our innovation processes within the past five years. respecting each other’s working space is crucial for successful collaboration between researchers and practitioners. understanding the innovation stage advances the innovation management across the spaces. acting indifferently to needs and expectations across workspaces may hamper or even dissolve the collaboration. sometimes, workspace crossover may cause miscommunication and friction among collaborators. applying the public health informtics innovation stage and space framework to collaborative activities can help reveal potential challenges early. with mutual understanding of a common framework, we developed strategies to help project managers to anticipate potential points of difficulty and proactively reduce and mitigate potential risks for partnerships. understanding the boundary and process of practitionerparticipated research significantly improved efficiency of public health informatics innovations in utah. strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 the seven informatics researches described above also demonstrate that coe is not just a one-time collaboration of distinct research projects within one grant. the people, partnership, shared vision, and mutual understanding and appreciation developed over a long period of time are the core and base for ongoing effective innovations and its successes. acknowledgment research supported by cdc grants coe1: 5p01hk000030 and 5p01hk000069. p.i. matthew samore, md, university of utah. corresponding author wu xu, phd deputy director, center for health data director, office of public health informatics utah department of health salt lake city, utah 84114-1019 wxu@utah.gov 801-538-7072 (o) references 1. katz r, ed. "distinguishing science from technology" the human side of managing technological innovation. new york: oxford university press; 2003. 2. michigan institute for clinical & health research. clinical & translational research. 2011 [updated 2011; cited 2011]; available from: http://www.michr.umich.edu/about/ clinicaltranslationalresearch. 3. xu w, livnat y, pettey w, reid j, staes c, et al. innovation space and stage for public health informatics research and practice: a state experience. 2011 cste annual conference; 2011 june 12-16; pittsburgh, pennsylvania. 2011. 4. livnat y, gesteland ph, benuzillo j, pettey w, bolton d, et al., eds. epinome a novel workbench for epidemic investigation and analysis of search strategies in public health practice. amia 2010 annual symposium; 2010 november 13; washington, d. c.: proceedings of the annual american medical informatics association symposium; 2010. 5. livnat y, rhyne t, samore m. epinome: a visual analytics workbench for epidemiology data. computer graphics & application. 2012. 6. gesteland ph, samore mh, pavia at, srivastava r, korgenski k, et al. 2007. informing the front line about common respiratory viral epidemics. amia annu symp proc. •••, 274-78. 7. gesteland ph, allison ma, staes cj, samore mh, rubin ma, et al. 2008. clinician use and acceptance of population-based data about respiratory pathogens: implications for enhancing population-based clinical practice. amia annu symp proc. •••, 232-36. 8. walton n, poynton mr, gesteland ph, maloney c, staes c, et al. 2010. predicting the start week of respiratory syncytial virus outbreaks using real time weather variables. bmc med inform decis mak. 10(68). mailto:wxu@utah.gov http://www.michr.umich.edu/about/clinicaltranslationalresearch strengthening partnerships along the informatics innovation stages and spaces: research and practice collaboration in utah 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 9. leecaster m, gesteland ph, greene t, walton n, gundlapalli a, et al. 2011. modeling the variations in pediatric respiratory syncytial virus seasonal epidemics. bmc infect dis. 11(1), 105. http://dx.doi.org/10.1186/1471-2334-11-105 10. staes cj, gesteland p, allison m, mottice s, rubin m, et al. 2009. urgent care physician’s knowledge and attitude about public health reporting and pertussis control measures: implications for informatics. j public health manag pract. 15(6), 1-8. http:// dx.doi.org/10.1097/phh.0b013e3181af0aab 11. rajeev d, staes cj, evans rs, mottice s, rolfs rt, et al. 2010. development of an electronic public health case report using hl7 v2.5 to meet public health needs. j am med inform assoc. 17(1), 34-41. http://dx.doi.org/10.1197/jamia.m3299 12. rajeev d, zeller r, price a, reid j, staes cj, et al. evaluating the impact of electronic disease surveillance systems on local health department work processes. public health information network (phin); 2009; atlanta, ga 2009. 13. rajeev d, staes cj, evans rs, price a, hill m, et al. 2011. evaluation of hl7 v2.5.1 electronic case reports transmitted from a healthcare enterprise to public health. proc amia annu fall symp. 14. cms. ehr incentive program. baltimore: centers for medicare and medicaid services; 2011 [updated 2011; cited 2011 december 10]; available from: https://www.cms.gov/ ehrincentiveprograms/. 15. rajeev d, staes cj, young j, staggers n. a pilot usability study of public health websites in determining what conditions are reportable where. proceedings of the annual meeting of the american medical informatics association (amia) 2010. 16. cste. 2010 position statements. council of state and territorial epidemiologists; 2010 [updated 2010; cited 2011 september 16]; available from: http://www.cste.org/dnn/ annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx. 17. han e, duncan j, staes c. improving the logic for hepatitis case reporting found in the 2010 cste position statements. poster session presented at 2011 council of state and territorial epidemiologists (cste) annual conference; pittsburgh. 18. davis k, staes cj, price r, duncan j, igo s, et al., eds. improved automated encoding of deaths certificates to identify pneumonia and influenza death. amia 2011 annual symposium; 2011; washington, dc. 19. jacobs j, ganesan s, altamore r, abellera j, staes c, eds. a framework for modeling data elements used for public health case reporting (best poster awarded for the surveillance and informatics track). poster session presented at 2011 council of state and territorial epidemiologists (cste) annual conference; 2011; pittsburgh. 20. staes cj, wuthrich a, gesteland p, leecaster m, allison m, et al. 2010. public health communication with frontline clinicians during the first wave of the 2009 pandemic influenza outbreak. j public health manag pract. (accepted). http://www.cms.gov/ehrincentiveprograms/ http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx http://www.cste.org/dnn/annualconference/positionstatements/2010positionstatements/tabid/422/default.aspx social and institutional issues in the adoption of school-based technology-aided sexual health education program 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi social and institutional issues in the adoption of school-based technology-aided sexual health education program angella musiimenta 1 1 mbarara university of science and technology, and bishop stuart university kakoba abstract objective: school-based sexual health education interventions can reach young people of diverse backgrounds and equip them with knowledge and skills for protecting themselves against hiv/aids, unwanted pregnancies, and live healthy and responsible lives. however, given that school-based sexual health education intervention are health projects implemented in educational settings, variety of social and institutional issues can present challenges. this study aimed to obtain rich insights into the facilitating or inhibiting mediators for the implementation of a schoolbased sexual health education intervention in uganda. method: this study conducted 16 qualitative interviews to investigate the mediators for the implementation of the school-based sexual health education intervention based on experiences of two ugandan schools: the school which successfully completed the implementation of the intervention, and the school which abandoned the intervention half-way the implementation. results: rather than the technological aspects, results indicate that the implementation was strongly influenced by interplay of social and institutional mediators, which were more favourable in the “successful” school than in the “failure school”. these mediators were: perceived students’ vulnerability to hiv and unwanted pregnancies; teachers’ skills and willingness to deliver the intervention, management support; match with routine workflow, social-cultural and religious compatibility, and stakeholder involvement. conclusion: rather than focusing exclusively on technological aspects, experiences from this evaluation suggest the urgent need to also create social, institutional, and religious climate which are supportive of school-based computer-assisted sexual health education. evidence-based recommendations are provided, which can guide potential replications, improvements, and policy formulation in subsequent school-based sexual health education interventions. key words: sexual health education; school health education; adolescents’ sexual health; teacherstudent sex education; informatics. correspondence: angellamusiimenta@yahoo.com copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. social and institutional issues in the adoption of school-based technology-aided sexual health education program 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi introduction technology-assisted healthcare interventions have a long history of failed implementation [1] . it is generally agreed that the major attributing mediators for this repeated failure are social and organisational in nature rather than technical [2] . variety of social and organisational factors contributes to low adoption of computer-assisted health interventions. these include organizational incompatibility, and integration with routine workflows [2]. yet, numerous studies (e.g. [3],[4],[5] ) contend that social and organizational compatibility issues have been insufficiently investigated in the development and implementation of computer-assisted healthcare interventions. more attention is normally put on the technological aspects at the expense of social and organizational issues. failed sexual health and hiv/aids education interventions due to social and organisational issues are not uncommon even in the developed world [6] . the economic costs of failure of such interventions can create huge strains on the already overburdened economies of developing countries. in response to the high prevalence of hiv/aids in uganda since the early 1990’s, uganda adopted a comprehensive hiv/aids prevention strategy that acknowledged hiv as a threatening problem and involved the government and civil society in advocating abstinence, be faithful, condom use (abc). the result was a recorded success in hiv prevention. however, since 2005, hiv infections among ugandans ages 15-49 have risen from 6.4% to 7.3% in men and to 8.3% in women, and are continuing to rise [7] . reasons the increase in hiv infections particularly among young people include lack of hiv/aids knowledge and self-efficacy skills for hiv/aids prevention [8] . schools accommodate many young people of diverse backgrounds, thus, they can be used as intervention sites to equip them with sexual health and hiv prevention knowledge and skills [9],[10],[11] . as a resource-limited country that has been severely affected by hiv/aids, uganda stands to benefit greatly from the integration of sexual health education interventions in schools. however, successful integration of sexual health education interventions in schools cannot be assumed especially given that they are health projects implemented in educational settings. overall, mediators for successful implementation of school-based sexual health education programs remain under researched especially in developing countries [12],[13],[14],[15],[16] . evaluating such interventions helps in identifying the needs and expectations of the target populations, which are crucial for obtaining community support and development of culturally accepted interventions. the only government-initiated school-based sexual health education intervention that was implemented in uganda yielded no health benefits and suffered low prioritisation from schools [17] . the rest of such interventions implemented in uganda are mainly donor-funded pilot projects, which, for political reasons rarely publish results from failed interventions, yet such unsuccessful stories would provide good lessons for future interventions. this study conducted a qualitative cross-case analysis aimed to obtain rich insights into the facilitating or inhibiting mediators for the implementation of a school-based sexual health education intervention in uganda. this was achieved by investigating why the intervention implementation was completed in one school but abandoned in another. social and institutional issues in the adoption of school-based technology-aided sexual health education program 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi methods the world starts with me (wswm) intervention the intervention being evaluated by this study is known as the world starts with me (wswm). funded by the world population foundation, the wswm was developed by butterfly works, schoolnet uganda and local experts in uganda. it is a-14 lesson sexual health education intervention which is delivered to students by oriented intervention teachers using the intervention website (http://www.theworldstarts.org), printed handouts, the program cd rom, and also it has an online support centre (http://schoolnetuganda.sc.ug/wswmonlinesupport/) for answering students’ sexual health questions. first implemented in schools in uganda in 2003, the wswm has also been adapted to the local context and implemented in kenya, india, thailand, indonesia, and vietnam. in uganda, the implementation normally starts in february to target new school entrants and implementation cycle ranges from 7-12 months. topics covered include: 1) protect yourself: stis and hiv/aids; 2) hiv/aids: u have a role 2 play 2; 3) sexuality and love, and 4) pregnancy: 4 girls and 4 boys! this study set out to investigate why some schools implement the wswm intervention successful while others abandon the implementation of the intervention. to answer this question, two schools are involved in this investigation: 1) the school that successfully implemented the wswm intervention; known as the completed implementation school (cis) for the purpose of this study; and 2) the school that abandoned the implementation; known as the abandoned implementation school (ais). selection of the study schools-cis and ais study schools were selected based on the research question [18] . the first school to be selected was the cis. the cis is a government owned-army founded school based in the semi-urban western part of uganda. although there were many schools that had completed implementation of the intervention, the cis was of particular interest for three major reasons: one, i had made initial contacts with the intervention teachers of the cis during the wswm workshop. these teachers expressed much interest in the research and promised to help me carry out this investigation in any way possible. two, being a school in a military barracks with many warorphaned students and children from soldiers’ separated families, these students were particularly vulnerable to hiv/aids. three, this school was within my proximity and was therefore economically viable. by the time (sept.2009) this research started, the cis had just completed the first cycle of the wswm intervention that was implemented from feb.09-sept.09. the intervention was delivered by teachers and student peer educators using the computer website, cds and computer print-outs. next was the selection of a school suitable for taking part in a cross-case analysis. the three options provided a basis for selecting the next case [19] . these options are: 1) select a study to identify more themes for the extension of the emerging theory, and/or; 2) select a study to replicate the already selected cases in order to confirm the themes identified in the previous cases; or 3) select an extreme study that has opposing characteristics to the previous cases for the extension of the theory that is emerging. the third option was adopted in this study, and a second case study the abandoned implementation school (ais) was a polar opposite school that was selected mainly to see how http://www.theworldstarts.org/ http://schoolnetuganda.sc.ug/wswmonlinesupport/ social and institutional issues in the adoption of school-based technology-aided sexual health education program 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the themes the emerged from the cis manifest themselves in ais. particularly, after formulating an initial framework of influences for the successful implementation of the intervention from findings of the cis, i was keen to investigate how the identified influences manifested themselves in an opposite case where the implementation of the intervention was abandoned. generally, given that the intervention in the ais was abandoned half-way through its implementation, exploring this case created an opportunity to compare its abandoned story with the successful story in the cis. the ais is a protestant church-founded and a girls-only school situated in rural western uganda. this school had around 1000 students, all of whom were boarders—staying at school during school days. in 2009, the intervention was implemented up to the 7 th lesson and abandoned before completing all 14 lessons. the implementation of the intervention begun in feb.09, suffered many irregular attendances from both teachers and students, and was terminated in oct.09. by the time this research started (dec 2009), around two months had passed since its abandonment. before its abandonment, the intervention was delivered using a combination of intervention handouts, cds and the internet. there was more than one school where the implementation was abandoned, but the ais was selected on the basis of characteristics that sharply differed from that of the cis. for instance, unlike the cis which was urban-based and military-founded, the ais was a rural-based and a church-founded school. thus the school was chosen in order to explore the distinction between rural and urban respondents and between government/military-founded and church-founded, due to anticipated differences in social, institutional, cultures and beliefs, which may be central to implementation decisions. selection of participants and data collection conducted in december’09, this investigation involved 16 semi-structured interviews with teachers and heads of schools of the cis and ais. although a relatively long period of time has elapsed since the data was collected, the findings are still relevant since nothing has changed in the implementation process of the intervention. the study aimed to qualitatively investigate why the implementation of the intervention was completed in the cis and abandoned in the ais. to achieve this aim, it was necessary to select participants on the basis of their relationships with the intervention. such purposive sampling is the commonly used sampling technique in qualitative research 20 . the selected participants were: a) 4 school heads/deputy heads of the cis and the ais aged between 38 and 50, of which 3 were males, and 1 was a female; b) 4 intervention teachers who delivered the intervention in the cis and ais, of which 2 were females and 2 were males; c) i also interviewed 8 teachers from the cis and the ais in order to get objective perspectives from teachers that were not directly involved in the intervention. the average age for participants was 34. this study is part of the phd research pursued in manchester business school, the university of manchester-united kingdom. ethical approval to conduct this study was obtained from the manchester business school research ethics committee. for confidentiality purposes, schools requested not to include their real names in the findings of this study. the formulation of interview questions was guided by the prevailing related literature [11],[21],[22],[23] . questions focused on facilitators and inhibitors to the implementation of the intervention. social and institutional issues in the adoption of school-based technology-aided sexual health education program 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi interview appointments were made, after which participants were interviewed in their offices at their respective schools during normal school hours. with consent from participants, all interviews were digitally recorded. each interview lasted from 30-60 minutes. data analysis i transcribed the interviews, which enabled me to become familiar with emerging themes from different interviewees before the actual data analysis began [24] . thematic data analysis was conducted to generate the results below, the summary of which is indicated in table 1. to ensure that all the data effectively fitted within the identified themes, verbatim sections of all the interview transcripts were extracted on separate documents and arranged according to the themes. results table 1: summary cross-case comparison of responses from the cis and the ais completed implementation school (cis) abandoned implementation school (ais) themes properties perceived students vulnerability -high student vulnerability due to un-protective environments at school, at home, and at school. included hiv/aids positive students and previous sexual offenders in the school -relatively low student vulnerability due to protective environment at school, and at home teachers confidence -some challenges in tackling very sensitive issues such as condom use demonstration -feelings of embarrassment in discussion sexual issues and reservations about condom demonstrations management support and priorities -supportive school administration -the new school administration that took office after intervention launching was unsupportive match with routine workflows -although the intervention was not school’s main timetable, no extraacademic lessons collided with the intervention time -intervention not school’s main timetable, yet, the new head of school fixed academic lessons and exams after-classes during the intervention time. institutional climate -entire school environment owned and supported the intervention, e.g. other teachers voluntary involvement in the intervention activities -unsupportive school environment, e.g. other teachers claimed that the intervention teaches prostitution and that intervention teachers sexually harass students. social and institutional issues in the adoption of school-based technology-aided sexual health education program 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi teaching motivation -teachers were committed to teach without financial incentives but stressed need for some form of incentives. -teachers refused to deliver the intervention because they were not paid and stressed they could not resume teaching without being paid technology issues lacked enough computers and internet connections: had 3 computers (with internet connection), and one television set for delivering the intervention to 150 students lacked enough computers and internet connections; 30 computers of which only 5 had internet, compared to 200 students enrolled for the intervention. cultural/ religious compatibility -incompatibility issues (such as age appropriateness, homosexuality and condom advocacy) but teachers believed that breaking the taboos was more worthwhile than risking lives with hiv/aids -incompatibility issues and teachers felt intimidated by bad social/religious attitudes towards the intervention, and were strongly against condom advocacy and homosexuality. community involvement involved communities and parents community was not involved perceived students’ vulnerability: the head teachers and intervention teachers in the cis perceived the intervention to be of greater benefit due to high perceptions of students’ vulnerability to hiv/aids and teenage pregnancies. this was due to the un-protective environments that students were exposed to both at school and at home. at home, many of the students were orphans whose parents had lost their lives in wars or from aids, while others were separated from their parents due to military operations: the majority of our students lost their parents in wars, others lost their parents due to aids. and those whose parents are alive, their parents went for wars; they are in north, iraq, somalia, all over the world. and being in the barracks, they are very exposed, so, we needed the wswm to ensure our students do not fall victims of aids and pregnancies [head teacher, cis]. being a mixed gender school located in the military barrack, there were possibilities of students’ sexual harassment from barracks soldiers, city suburb dwellers as well sexual misconduct between students themselves. some students were already living with hiv/aids while others were previous sexual offenders: you see young people have a lot of challenges in this era of aids, with these ones here we used to have a lot of sexual misconducts and child pregnancies between students themselves, and sometimes between students and soldiers and even neighboring trading centre. even some of our students have aids, others were once in prisons for rape, defilement e.t.c. so we needed to counsel them, and the wswm provides a good avenue for that [intervention teacher, cis]. social and institutional issues in the adoption of school-based technology-aided sexual health education program 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the head teachers and intervention teachers of the ais considered the intervention to be beneficial to students in equipping them with reliable information for pregnancy and hiv/aids pregnancy. however, compared to students’ vulnerability in the cis, the perception of students’ vulnerability in ais was relatively low due to students’ protective environments both at school, since this was a rural-based girls-only school, and at home since students came from stable middle-income families: generally, the wswm is helpful in equipping these young girls with information to prevent themselves from pregnancies and aids. we also have very strict roles on them here at school…. most of their parents are educated middle income earners, so they should also be safe at home really [intervention teacher, ais]. teachers’ confidence: the cis teachers faced challenges of feelings of embarrassment in dealing with issues that appeared too sensitive in class. despite these challenges, teachers were able to deal with such issues during out-of-class private consultations: i am confident because as a biology teacher, i have always taught related issues in. reproductive biology. but sometimes it becomes hard to discuss very private things before students because they are like my children. yes, i am confident if students privately consult me after class [intervention teacher, cis]. however, after-class private consultations were hindered by the prevailing sexual mistrust between teachers and students. it was urged that students should consult teachers of the same sex during after-class consultations: …erm, some girls take it very far by simply coming to tempt male teachers into sex while pretending that say they have been dropped by their boyfriends and need some counselling. sometimes i wish i had only boys. i think for very sensitive issues, students should privately consult teachers of the same sex after class [intervention teacher, cis]. noteworthy however is that despite the reported confidence, intervention teachers only informed students about condoms. no demonstrations of condom use were carried out due to doubts of age appropriateness: we generally tell them about condoms but we encourage them to abstain. yea, i could demonstrate condom use but i think it is better we just let them know about condoms for now. we give them information about the role of condoms, where to buy them, and how they should first read instructions before using them [intervention teacher, cis]. teachers in the ais lacked self-efficacy and felt embarrassed and uncomfortable in both the classroom delivery of the intervention and in responding to some of the students’ sexual health queries during out-of class private consultations: social and institutional issues in the adoption of school-based technology-aided sexual health education program 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi we give students some information but sometimes it is hard to discuss information that looks too private in class. to some extent yes i can answer those queries after class, but to some extent no because still some of them are either still too sensitive or i simply don't know the answers myself [intervention teacher, ais]. the aic teachers confessed that if the implementation of the intervention had continued, they would have skipped the topic on condom use due to both lack of skills and doubtfulness of the age appropriateness of the topic: we could not have taught students about condoms… condoms should be removed from the wswm program and left for experts like sexual health experts because for them they have the ability to discuss and demonstrate condom use confidently, and at right age. [intervention teacher, ais]. management support and priorities: in the cis, the school administration was supportive of the intervention activities including attending head teachers’ orientation, financially supporting teacher training workshops, and actively participating in intervention activities such launching and exhibitions: …our head teacher is very cooperative in this program, for example he himself attended the head teachers’ oriented workshop, and he contributes to the cost of our training workshops, and he was very helpful in mobilizing the entire school during the launching of this program. and i think you yourself saw his active participation at the exhibition [intervention teacher, ais]. in contrast, the ais experienced a change of administration a few months after the intervention was launched. having never attended the intervention’s head teachers’ orientation workshop, the new head of school was administratively unsupportive of the intervention; including failure to contribute money for teachers’ training workshops and lack of active involvement in the activities of the intervention: the new head of school that joined us after the program was implemented didn't attend the head teachers’ orientation workshop. although i introduced the program to her, she kind of not prioritises it; our teachers are not facilitated to attend training workshops... [deputy head teacher]. match with routine workflow: in both the cis and ais, there was no time allocated for the intervention on the schools’ main timetable, as the intervention could not be matched with the schools’ routine workflows: …it would be good if the wswm was timetabled like any other subject really. we teach the program in the evening as an extra-curricular activity. for dayscholars, we sacrifice and teach them on saturday [intervention teacher, cis]. social and institutional issues in the adoption of school-based technology-aided sexual health education program 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi failure to put the intervention on the school’s main timetable consequently resulted in students’ inconsistent attendance due to clashes with other extra-curricular activities and housework for day-scholars: students keep coming in and out, because in the evenings when the program is run, they are already tired from the days work and end up not coming, and they also have to prepare for their night preps, and sometimes there are other things like football going on [intervention teacher. sometimes the day-scholars don’t turn up on saturdays coz they have to some work at home cis]. noteworthy, however, is that although both schools never integrated the intervention into the main timetable, workflow mismatch was more seriously felt in the ais, since the new head of school instead fixed academic lessons and exams after-classes during the same time the intervention was conducted. the adverse effect of this clash was intervention termination: we stopped at lesson 7 coz when the new head teacher came, she put academic lessons and exams in the evenings at the same time we were having our wswm lessons… so we kind of give up on the wswm because of this collision [intervention teacher, ais]. institutional climate: the overall school environment in the cis was generally supportive of the intervention. other than the intervention teachers, other teachers actively participated in the intervention activities, including voluntary engagement in intervention classroom sessions: …yea, they [other teachers] are very much involved. in fact some even come into our classes and contribute to our discussions. and this helps students to learn from real life experiences of different people that have passed through the same challenges [intervention teacher, cis]. in contrast, lack of intervention ownership from other teachers created unsupportive institutional environments for intervention implementation in the ais. rather than collaboratively embracing the intervention, other teachers instead claimed that the intervention teaches prostitution and that intervention teachers sexually harass students: …other teachers are completely not bothered about the wswm program. in fact they say we are teaching these students prostitution, and when they see students come to us for sexual guidance, they think we are instead corning them for sex [intervention teacher, ais]. teaching motivation: intervention teachers in both the cis and the ais were not paid any financial incentive for delivering the intervention. yet, in addition to delivering the intervention lessons they had to teach their routine academic load. nevertheless, teachers in the cis were still committed to delivering the intervention in order to help students with hiv and teenage prevention information. they however stressed the need for either a teaching allowance or a reduction in academic teaching workload: social and institutional issues in the adoption of school-based technology-aided sexual health education program 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi it is a sacrifice really because we want to help our students so that they don’t fall victims of aids and early pregnancies; and we have to teach our academic lessons too…we need to be given some token for teaching the program. program donors need to do something, otherwise, at some point, we will have to give up, or they can reduce for us the academic lessons we are teaching …[ intervention teacher, cis]. in contrast, in the ais, the intervention teachers’ involvement was mainly driven by financial motives. lack of financial motivation was one of the major driving mediators for intervention abandonment: …when i had about the wswm, i knew i was going to get some money for teaching it, so that's why i got involved knowing that i will not be committing my time for free. but because of no payments, we kind of pulled out. if you see project donors or leaders, tell them that they have to pay us if they ever want us to resume teaching the wswm lessons [intervention teacher, ais]. technological issues although the intervention is supposed to be web-based, there was a lack of sufficient computers and reliable internet connections in both the cis and ais. in the cis, the school relied on only one connected computer for the delivery of the intervention to 150 students: we don't have enough computers and internet. you have seen it yourself even. we need more connected computers, not just the three computers we have out of 150 students that need them…[intervention teacher, cis]. likewise, the ais had computers and internet, the number of computers outnumbered the number of students; 30 computers, of which only 5 computers were connected to the internet, compared to 200 students that had enrolled for the intervention: …there is also a problem of too many students in relation to the 30 computers we have here; and there are only 5 computers with internet [intervention teacher, ais]. lack of computers and internet connections not only de-motivated students, but also limited intervention accessibility, and made it impossible for students to fully utilise the potentials of the intervention including online discussion forums and interactive hiv/aids prevention games: …much us we try to use the few computers available, students need to be able access the program on their own and practice the things like the interactive games, and you know being able to get involved in that online discussion. such hands on experience would really be motivating to students [intervention teacher, cis]. social and institutional issues in the adoption of school-based technology-aided sexual health education program 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi lack of/unreliable internet connections interfered with email communications between the intervention leaders and teachers: …which means when our internet is off, we have to travel five miles away from here and spend our own money in internet cafes in order to read emails from project leaders [intervention teacher, ais]. cultural compatibility with sex education: despite the taboo-relatedness surrounding the subject of sexuality, intervention teachers in the cis believed that breaking the taboos was less blameworthy than risking youthful lives with hiv/aids and pregnancy: i see, but though some people are against it [sex education], the fact remains the fact; we have to give them the right information, we have to tell them to use condoms if they can’t abstain, otherwise, we can’t remain quite when aids is finishing our youth, when our girls are getting pregnancies and dropping out of schools [intervention teacher, cis]. in contrast, intervention teachers in ais felt significantly intimidated by bad societal attitudes that regarded the intervention as a sexually misleading intervention. they stressed the need for intervention tailoring to suit implementers’ differing cultures and students’ different needs and ages: it can be disturbing to teach things that other people regard as immoral. and much as i think sex education is ok, we need to be very careful what information is culturally allowed, what information we should reveal to young people of different ages and origins [ teacher, ais]. teachers in both cis and ais suggested that lesson 7 be removed or revised due to cultural incompatibility. lesson seven is about homosexuality and sexuality including different ways of playing sex and their precautions. they argue that the lesson gives students inappropriate details in addition to encouraging homosexuality, which is culturally unacceptable: …also lesson seven about sexuality should be removed or should be adjusted, it goes unnecessarily beyond what students should know, it encourages homosexuality too, which is against our culture [head teacher, ais]. religious compatibility with condom advocacy: given the fact that aids has killed many young people, who are at the same time the most economically productive age group, intervention teachers in the cis stressed the need to inform students about the alternative of using condoms despite the prevailing religious values against condom advocacy: although we are religious, we cant remain rigid about condom use when aids has killed many of our youth population, yet the youth are the future of this nation in terms of being economic productive. we need to tell them condom use as an option for those who fail to abstain [teacher, cis]. social and institutional issues in the adoption of school-based technology-aided sexual health education program 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi in contrast, for religious and moral reasons, teachers and heads of school in the ais were strongly against the intervention topic on condom use. advocating sex abstinence was the only strategy allowed in this church-founded school, since it was argued that condoms may hasten students’ sexual activity: … this being a church-founded school; teachers cannot be immoral by starting teaching young people about condoms instead of abstinence. personally, though i am in support of this program, i don’t hide the fact that i am strongly against the lesson on condom use. condoms can lead children to want to practice what they are taught [deputy head teacher, ais]. public sensitisation and involvement: the relationships existing between school and local communities impacted the adoption of the intervention. the cis created awareness in the local communities by inviting some local leaders during the launching of the intervention, introducing the intervention during parents’ meeting, as well inviting the public during end of intervention’s students’ exhibitions to witness students’ personal testimonies, poems and drama: in order to inform the surrounding communities and parents about the benefits of this program, we invited some influential members of the community when we were launching the wswm, we also briefly tell parents about the wswm when we call them for meetings. we also call them for the program exhibitions and they witness students’ educative poems, drama and personal testimonies [head teacher, cis]. consequently, sensitising and involving parents and neighboring communities was instrumental in buying intervention support from people who would otherwise negatively perceive the intervention to be teaching prostitution. some parents were taking an active role by voluntarily getting involved in intervention sessions: …i think making parents aware of the program, has kind you know informed them that we are not teaching prostitution as some thought at first, but that instead we are teaching students how to avoid aid, unwanted pregnancies, and live responsibly. i see some parents even come and attend our program sessions and join in our discussions [intervention teacher, cis]. in contrast, in the ais, there was a lack of public’s/parents’ sensitisation about the intervention. intervention teachers lacked the confidence to sensitise parents about the intervention during parents’ meetings due to perceptions of resistance and agenda-mismatch: the parents and the general public needed to be informed of existence and benefits of the wsw, but, the strategy of introducing the program during parents’ meetings could not work; you cant just introduce such stuff on people’s agenda, unless it is done by the heads of school or project leaders because us we received some objections here and there [intervention teacher, ais]. social and institutional issues in the adoption of school-based technology-aided sexual health education program 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi discussion this study intended to find out why was the implementation of the sexual health education intervention was completed in one school but abandoned in another. as discussed below, the success of the implementation of the intervention depended on the extent to which students were perceived to be vulnerable, teachers’ level of confidence in delivering the intervention, teaching motivation, management support, match with routine workflows, institutional climate, and socialcultural and religious compatibility of the intervention. perceived students’ vulnerability: perceptions of the important role of the intervention in helping vulnerable students prevent themselves from contracting hiv and suffering unintended pregnancies motivated cis to adopt the intervention. low perceptions of students’ vulnerability to hiv constrained the adoption of the intervention in the ais. this difference in perceptions of vulnerability was could have been because the cis was an army-founded mixed gender school located in the military barracks with many orphans and students from soldiers’ separated families. there were possibilities of students’ sexual harassment from barracks soldiers, and city suburb dwellers, as well as sexual misconduct between students themselves. some of the students in the cis were already living with hiv/aids while others were previous sexual offenders. in contrast, in the ais, perception of students’ vulnerability was generally low due to perceived protective environments both at school and at home. this was because, the ais was a church-funded girls-only school with students from middle income families, many of whom had both of their parents alive. generally, compared to the ais, the cis considered the implementation of the intervention a priority rather than an option. the demonstrated positive relationship between teachers’ and head teachers’ perceived benefits of adopting the intervention and the actual adoption of the intervention is consistent with contentions of behavioral scientists that stress a relationship between anticipated adoption benefits and adoption decisions [25],[26] . noteworthy however is that although the anticipated benefits implicated by the mentioned behavioural scientists focus on target audiences whose behaviours need to change, this study demonstrates that the influence of the perceived benefits extends from the individual target audience to influential stakeholders. this indicates the need to involve students, teachers and heads of schools in planning, developing and implementing school-based sexual health interventions. teachers’ confidence: teachers’ low levels of self-efficacy greatly hindered the fidelity of the intervention implementation. teachers in both the cis and the ais felt uncomfortable discussing sensitive sexual issues. however, unlike in the ais, teachers in the cis, notwithstanding some sexual mistrust between teachers and students, were confident to deal with some sensitive issues during out-of-class private consultations, and to inform students about condoms during classroom delivery. feelings of embarrassment and discomfort, and lack of skills in discussing sensitive sexual issues greatly constrained the would-be skills-based and interactive nature of the intervention and constrained role plays and condom use demonstrations. this consequently resulted in delivery of mainly factual and superficial information. although the teachers attended a 5-day intervention orientation workshop, they expressed a need for more training, as it was argued that limited training contributed to their low levels of self-efficacy in delivering the intervention. related studies report teachers’ feelings of embarrassments, lack of skills and social and institutional issues in the adoption of school-based technology-aided sexual health education program 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi confidence, mainly due to limited training, as major barriers to effective implementation of sexual health education in schools [11],[16],[21],[27] . possibilities of sexual mistrust between students and teachers are not uncommon [22] . separating classes into same-sex groups may minimise lack of confidence in classroom discussions [29] . allocating same-sex teachers to groups may minimise sexual mistrust between student and teachers especially during out-of-class consultations. noteworthy however, separating classes into same-sex groups for the entire curriculum may constrain the depth of discussions about topics that require views of mixed sexes [23] . these topics include discussions about gender norms. this approach may also constrain confidence in sex communications with the opposite sex. management support and priorities: the school administration in the cis was supportive of the intervention activities, including attending head teachers’ orientation workshop, financially supporting teacher training workshops, and was actively involved in intervention launching and exhibition. lack of support from the school management was a major cause of the abandonment of the implementation of the intervention in the ais. there was a change of school administration after the intervention was launched, and the new head teacher did not support and prioritise the intervention. she did not financially support intervention teacher training workshops, which led to teachers’ failure to attend training workshops. she also introduced institutional changes that adversely affected the intervention; including fixing evening academic lessons and exams that clashed with the intervention delivery time. although there was inconsistency in students’ intervention attendance, due to timetabling problems in both the cis and the ais, this problem was more significant in the ais than in the cis. students’ inconsistent attendance in the cis was mainly attributed to clashes of the intervention with other extracurricular activities and home works, while that of the ais was largely attributable to clashes with evening academic lessons and exams fixed by the new head of school. administrative support is a significant factor for the success of school-based sexual health education interventions [22] , and the effective implementation of technology-assisted health interventions [5] . match with routine workflows: in both cis and ais, the absence of a timetabling policy left teachers in helpless and they struggled to allocate some time for the intervention. the intervention could not be allocated time on the schools’ core timetables. delivering the intervention as an extra-curricular activity in the evenings and weekends resulted into students’ inconsistent attendance and largely contributed to intervention abandonment in the ais’s worst case scenario. inability to complete school-based sexual health education curricula in uganda due to time constraints is not uncommon [30] . worldwide, poor timetabling policy is an impediment to successful implementation of school-based sexual health education interventions [16] . even in healthcare settings, workflow mismatch between the institutional routine workflow and the technology-assisted innovation being introduced are not uncommon [4] . teaching motivation: intervention teachers complained of teaching overload, as the intervention was an extra teaching load, moreover without any financial incentives for its delivery. there seemed to be disagreements about who should motivate and facilitate intervention teachers; intervention donors expected schools to motivate their teachers while schools also expected the donors to motivate the teachers. ais teachers stressed that they were not willing to resume the intervention without financial motivation, while cis teachers cautioned that they will stop delivering the intervention if they are not getting some form of incentives e.g. social and institutional issues in the adoption of school-based technology-aided sexual health education program 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi reduction in academic load or financial motivation. in contrast, although teachers in the cis were still committed to delivering the intervention due to high perceptions of students’ vulnerability to hiv and unintended pregnancies, they also stressed the need for teaching allowance or reduction in academic teaching workload. related to this, some behavioural researchers [31],[32],[33] , suggest that incentives can motivate and encourage the adoption of behavioural change interventions. technological aspects: there was a lack of computers and reliable internet connections in both the cis and ais. in particular, the cis had only three computers with internet connection and one television set to deliver the intervention to 150 young people, while the ais had limited computers and internet connections; compared to 200 young people in the intervention, the school had 30 computers, of which only five were connected to the internet. lack of computers and internet limited accessibility to the intervention, led to partial or no coverage of discussion forums and interactive hiv/aids prevention games, and created communication barriers between the teachers and intervention leaders. yet, the technology-based would have been beneficial in many ways including providing unlimited accessibility of the intervention, offering an opportunity to tailor the intervention to students’ varying sexual health information needs, and ensuring confidentiality of the otherwise sensitive sexual health information [10] . noteworthy however is that despite the potential advantages, results indicate that compared to the technological aspects, the social and orgnisational issues played a major role in the implementation and adoption of the intervention. related literature [3],[5] acknowledge that compared to technical aspects, social and organisational issues are major determinants of effective implementation of technology-assisted healthcare interventions. social-cultural and religious compatibility with sex education and condom advocacy: teachers and heads of schools in both the cis and the ais expressed concern over the inappropriate sexual details of lesson 7 (sexuality and love) and claimed that the lesson encourages homosexuality, and gives students unnecessary details on sexuality. they argued that the contents are culturally unacceptable, and that the topic on homosexuality should be removed. despite the taboos surrounding the subject of sexual health, teachers in the cis embraced the intervention as they believed that breaking the taboos was justified compared to risking youthful lives with hiv/aids and pregnancy. they were committed to informing students about the alternative of using condoms despite the prevailing religious values against condom advocacy. in contrast, teachers in ais reported feeling significantly intimidated by the social-cultural and religious incompatibilities that associated some of the interventions topics with students’ prostitution, sexual harassment, sex experimentation and increased sex activity. they were strongly against the intervention topic on condom use and associated condom advocacy with promoting sexual immorality by encouraging sexual activity. being a church-founded school, it believed in abstinence-only interventions preferably delivered by christian role models, as they claimed possibilities of teacher-student sexual harassment in teacher-delivered interventions. although there is currently no evidence to support such claims [13] , concerns about young people’s sex experimentation as a result of sexual health education (in particular condom education) have long been reported to be the major concern of parents and teachers [22],[34] . such presumptions assume students’ ignorance of sexuality as well as inactivity in sexual issues, yet there is evidence, students as young as 11 years are sexually active [9] . apparently, with or without sexual health education, it would be hard social and institutional issues in the adoption of school-based technology-aided sexual health education program 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi to conclude other than that young people engage in sexual activity. participants stressed the need to tailor the intervention to suit implementers’ differing cultures and young people’s different needs and ages. age-appropriateness has long been an adults’ biggest concern about schoolbased sexual health interventions [35] . it cannot be assumed that young people are all at the same level of sexual activeness and neither can it be assumed that they all have the same sexual health education needs. it is generally agreed that successful implementation of school-based sexual health education programs depends on their compatibility with social-cultural and religious values prevailing among adopting communities [11],[21] . related literature [1],[2] about the adoption of technology-assisted health innovations attribute low adoption rates of such interventions to incompatibility with organisational valves and beliefs. stakeholder sensitisation and involvement: findings demonstrate that stakeholder involvement is crucial for the successful implementation of school-based sexual health education interventions. unlike the ais, the cis created awareness of the benefits of the intervention in the local communities, and involved local leaders, parents and other teachers in intervention activities such as launching, exhibition, classroom discussions. awareness about the intervention was also continuously raised during parents’ meetings. peer educators and intervention alumni were utilised as role frameworks for the advocacy and promotion of the intervention. in addition, unlike the ais, the cis environment was supportive of the intervention; for instance other teachers voluntarily actively participated in the intervention activities, including voluntary engagements in intervention classroom sessions, and encouraging young people to adopt the intervention. sexual reproductive experts from the neighbouring hospitals and health organisations were often invited to talk to students as well as to help teachers with some emerging challenging issues regarding their sexual and reproductive health. the cis approach of stakeholder involvement consequently created intervention support from the public that would otherwise have negatively perceived the intervention. its school-community links were sources of expertise advice. the cis suggested involving parents through student-parent intervention assignments i.e. giving students holiday or take home assignments that require to be discussed with parents/guardians. interactive child-parent assignments is a potential strategy for encouraging parent-child sex communication [11],[27] . overall, community involvement in the development and implementation of sexual health education programs is vital in obtaining community support and development of culturally accepted interventions. implications and conclusion school-based sexual health education interventions can reach young people of diverse backgrounds and equip them with knowledge and skills for preventing themselves against hiv/aids, unwanted pregnancies, and live responsibly. however, being a health project implemented in educational settings, many social and institutional issues can present challenges. this study has explored the facilitators and inhibitors of the implementation of the school-based sexual health education intervention (wswm) based on experiences of two schools that implemented the intervention in uganda. these schools were the cis, which completed the implementation of the intervention, and the ais, which abandoned the intervention half-way the implementation. experiences from these schools suggest that the “successful” implementation was strongly influenced by interplay of many mediators, which were more favourable in the “successful” school than in the “failure” school. these mediators were: perceived students’ social and institutional issues in the adoption of school-based technology-aided sexual health education program 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi vulnerability to hiv and unwanted pregnancies; teachers willingness to deliver the interventions in addition to their routine academic workloads, school management support; match with schools’ routine workflow and formal curricula, social-cultural and religious compatibility with sex education and condom advocacy, the level of stakeholder involvement in the intervention planning and implementation; and the extent to which the pre-existing academic teachers are professionally equipped to deliver sexual health education interventions. the intervention orientation given to teachers was considered inadequate. even with adequate training, given its sensitive nature, teaching sexual health education in school environments is a challenge that is globally experienced by teachers in both developing and developed countries [11],[16] . the biggest challenge appeared to be that of social-cultural and religious incompatibility. there were high levels of resistance from stakeholders in the ais who claimed that such interventions encourage promiscuity among young people. they believed that young people are more likely to ‘experiment out’ what they learn from such interventions e.g. condom use experimentation. based on the findings, this study recommends the following: emphasising policy-level advocacy and putting in place school level and national level policies for prioritising sexual health education, including: (1) ensuring direct implementation of the intervention by the ministry of education to improve intervention prioritisation. implementing the intervention by a donor-funded ngo was rather generally seen, by some stakeholders, to be politically motivated by their own ambitions; (2) committing schools to ensure fidelity of implementation of the intervention, otherwise, even a well-designed and comprehensive intervention may not yield positive behavioural impacts if some sessions are skipped or partially covered, as was the case of condom use education in this study. another way of improving prioritisation of sexual health education is to cover the subject under a broader compulsory health promotion subject and to ensure proper time tabling. for instance, in the united kingdom, sexual health education has recently been made part of the new personal, social and health education (pshe), and in 2011, it became a statutory part of the national curriculum [36] . supporting schools and teachers including: (1) putting strategies for ensuring availability of computers and intervention consumables; (2) setting standards for teacher-student relationships; (3) setting strategies for teacher training in skills-based and interactive sexual health education e.g. through pre-service training at teacher training institutions and in-service training offered by experienced sexual health educators; (4) implementing related but age-appropriate interventions in primary schools; (5) selecting intervention teachers on grounds of personal interest and enthusiasm in young people’s sexual health; (6) ensuring good schools’ administrative support; (7) provision of incentives to intervention teachers e.g. by giving teaching allowances and/or reducing the academic teaching load; in cases where the intervention is a major part of the school curriculum, teachers can be completely relieved from teaching other subjects; (8) teachers should have on-going access to specialists to enable continuous competency development and to help them in dealing with challenging issues that might emerge in the course of delivering the intervention. this may include linking intervention teachers to sexual health specialists from local health centres. employing a variety of intervention supplementary and activities and stakeholder involvement, such as: (1) social mobilisation through community sensitisation and involvement; (2) social and institutional issues in the adoption of school-based technology-aided sexual health education program 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi implementing parallel interventions at community level to supplement school-based interventions and cater for vulnerable young people who are not in schools; (3) sensitising and clearing away misconceptions associated with sex education; (4) involving all relevant stakeholders including young people, parents, teachers, educationists, policy makers, practitioners and religious leaders in major phases of the intervention, including training needs analysis, planning, design and implementation phases; (5) identifying network opinion leaders who can champion the intervention implementation; a multi-sector collaboration approach that includes all relevant sectors and stakeholders is a sine qua non for intervention acceptability, sustainability, and the subsequent adoption of advocated behaviours. thus, there is a need for sufficient solidarity between different sectors, policy makers, leaders of the intervention, educationalists, schools, and local communities in the planning, design and implementation of the intervention. concerns of age-appropriateness can be minimised by tailoring the intervention to better meet the differing needs of young people of different age groups [10] . rather than the generic ‘one-size fits all’ interventions which assume that young people have similar needs, interventions need to target specific preventive methods to specific individuals. in intervention tailoring: (1) messages of abstinence and delay of sex onset can be targeted at young people who are not yet sexually active; (2) messages of condom use and secondary abstinence can be targeted at those who are already sexually active. another worthwhile alternative approach can be to give young people all the correct information they need in order to protect themselves against hiv/aids and pregnancy irrespective of their needs, while at the same time warning them of the dangers of early and increased sex activity. to sum it all, this research makes important contributions on an area that has been inadequately researched. lack of rigorous evaluation of school-based sexual health education interventions in africa has been of greatest concern [13] . uganda has had only one government-initiated schoolbased sexual education program, which suffered low prioritization from schools and yielded no health benefit [17] . the wswm—the donor/ngo-initiated sexual health education intervention evaluated in this study improved students’ sexual health knowledge and attitudes but had no significant impacts on sexual behaviours [9] . yet, although knowledge and attitudes are important health attributes, the most important one is health behavioural change, which leads to health outcomes. many of the donor-initiated school-based sexual health education interventions implemented in uganda are really evaluated. those that carry out evaluations do not publish the results of failed interventions. yet, such unsuccessful stories would provide good lessons that can be used to improve the implementation of sexual health education interventions. many of the implementation mediators identified in this study have been reported in literature although not necessarily in the contexts of uganda. although often given less attention, compared to technical/technological aspects, social and institutional issues were major determinants of effective implementation and adoption of intervention. this study adds to the existing literature by employing an innovative approach that explored the implementation of the intervention by practically comparing the cis’ success story with the ais’ failure story. the evidence-based recommendations outlined above can guide potential social and institutional issues in the adoption of school-based technology-aided sexual health education program 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi replications, improvements, and policy formulation in subsequent school-based sexual health education interventions. acknowledgement this work is part of a phd research that was financially supported by the commonwealth scholarship commission in the united kingdom, and supervised by dr. donal flynn at manchester business school, the university of manchester-united kingdom. corresponding author angella musiimenta mbarara university of science and technology, and bishop stuart university kakoba email: angellamusiimenta@yahoo.com references [1] heeks, r., mundy, d. and salazar, a. (1999). why health care information systems succeed or fail. manchester school of environments and development, working paper 9, manchester, uk . available at: www.sed.manchester.ac.uk/idpm/publications/wp/igov/igov_wp09.htm. accessed september 12 2012]. [2] hare, k., whitworth, b. and deek, p. (2006). identifying barriers to it adoption in clinical health care: a pilot study. healthcare and informatics review. available at: http://hcro.enigma.co.nz/website/index.cfm?fuseaction=articledisplay&featureid=070906. accessed november 16 2012]. [3] munir, s.k. and kay, s. (2003). organizational culture matters for system integration in health care. amia annual symposium proceedings 484–488. available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1480353. accessed october 16 2012. [4] toussaint, p. and berg, m. (2000). electronic patient record as an organizational artefact. hershey, pa, usa: ideal group publishing. available at: http://portal.acm.org/citation.cfm?id=373168.373207&coll=guide&dl=guide&cfid=15 151515&cftoken=6184618. accessed november 21 2011]. [5] day, k. and norris, t. (2006). the leadership in times of crisis during change due to health it projects: health care and informatics review. available at: http://hcro.enigma.co.nz/website/index.cfm?fuseaction=articledisplay&featureid=050906. accessed august 01 2012]. [6] o’grady, l. (2006). consumer his education in hiv/aids: a pilot study of a web-based video workshop. bmc medical informatics and decision making, doi:10.1186/1472-69476-10. [7] uganda aids commission national:hiv prevention strategy 2011-2015.2011. available at: http://www.aidsuganda.org/documents/nps.pdf. accessed may 14, 2013. [8] hulton la, cullen r, k. w. perceptions of risks of sexual activity and their consequences among ugandan adolescents studies in family planning. 2000;31(1):35-46. http://www.sed.manchester.ac.uk/idpm/publications/wp/igov/igov_wp09.htm http://hcro.enigma.co.nz/website/index.cfm?fuseaction=articledisplay&featureid=070906 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1480353 http://portal.acm.org/citation.cfm?id=373168.373207&coll=guide&dl=guide&cfid=15151515&cftoken=6184618 http://portal.acm.org/citation.cfm?id=373168.373207&coll=guide&dl=guide&cfid=15151515&cftoken=6184618 http://hcro.enigma.co.nz/website/index.cfm?fuseaction=articledisplay&featureid=050906 http://www.aidsuganda.org/documents/nps.pdf.%20accessed%20may%2014 social and institutional issues in the adoption of school-based technology-aided sexual health education program 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [9] musiimenta a. a controlled before-after evaluation a computer-based hiv/aids education on students’ sexual behaviours, knowledge and attitudes. online journal of public health informatics. 2012a;4(1). 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[13] unesco. international technical guidance on sexuality education: an evidence informed approach for schools, teachers and health educators. 2009. available at: http://unesdoc.unesco.org/images/0018/001832/183281e.pdf . accessed november 2, 2012 [14] paul-ebhohimhen va, poobalan a, er. vt. (a systematic review of school-based sexual health interventions to prevent hiv/aids in sub-saharan africa. bmc public health. 2008. [15] magnussen l, ehiri je, ejeri h, pe. j. interventions to prevent hiv/aids among adolescents in developing countries: are they effective? international journal of adolescent medical health. 2004;16:303-23. [16] buston k, wight d, hart g, s. s. implementation of a teacher-delivered sex education programme: obstacles and facilitating conditions. health education research, theory and practice 2002;17(1):59-72. [17] hyde k, ekatan a, kiage p, c. b. hiv/aids and education in uganda: window of opportunity? . washington, dc: world bank.2002. [18] eisenhardt km. building theories from case study research. the academy of management review. 1989;14(4):532-50. [19] yin r. case study research. beverly hills: sage; 1984. [20] bryman a. social research methods, oxford: oxford university press; 2001 [21] smith g, kippax s, p a, p. t. hiv/aids school-based education in selected asia-pacific countries. sex education. 2003;3(1):3-21 [22] power r, langhaug l.f, nyamurera t, wilson d, bassett mt, fm. c. developing complex interventions for rigorous evaluations – a case study from rural zimbabwe. health education research, theory and practice. 2004;19:570-5. [23] wight d, c. a. from psycho-social theory to sustainable classroom practice: developing a research-based teacher-delivered sex education programme. health education research. 2000;15:25–38. [24] bryman a. social research methods, 3 rd ed. oxford: oxford university press;2008. [25] rosenstock i.l, strecher vj, mh. b. social learning theory and the health belief model. health education quarterly. 1988;15(2):175-83. [26] ajzen i. attitudes, personality and behaviour. milton keynes: open university press; 1988. [27] kirby d. d. k. recommendations for effective sexuality education programs. unpublished review prepared for unesco. 2009. http://www.hta.ac.uk/ http://unesdoc.unesco.org/images/0018/001832/183281e.pdf%20.%20accessed%20november%202 social and institutional issues in the adoption of school-based technology-aided sexual health education program 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [28] wight d, raab gm, henderson m, abraham c, buston k, g h, et al. limits of teacher delivered sex education: interim behavioural outcomes from randomised trial. british medical journal. 2002;324(1430). [29] kirby d, korpi m, rp b, hh. c. the impacts of the postponing sex involvement curriculum among youth in california. family planning perspectives. 1997;29(3):100-8. [30] kinsman j, harrison s, kengeya-kanyondo j, kanyesigye e, musoke s, whitworth j. implementation of a comprehensive aids education programme for schools in masaka district, uganda. aids care, psychological and social-medical aspects of hiv/aids. 1999;11(5):591-601. [31] kotler p, roberto e, n. l. social marketing: improving the quality of life. thousand oaks, ca: sage; 2002. [32] hill r. the marketing concept and health promotion: a survey and an analysis of "recent health promotion literature”. social marketing quarterly. social marketing quarterly. 2001;2:29-53. [33] dolan p, hallsworth m, halpern d, king d, i. v. mindspace: influencing behaviour through public policy. institute for government. 2010. available at: http://www.instituteforgovernment.org.uk/content/133/mindspace-influencing-behaviourthrough-public-policy. accessed june 20, 2012. [34] mitchell k, nakamanya s, kamali a, g. w. community-based hiv/aids education in rural uganda: which channel is most effective? bmc medical informatics and decision making. 2001;16:411-23. [35] flicker s, goldberg e, read s, veinot t, mcclelland a, saulnier p, et al. hiv positive youth’s perspectives on the internet and ehealth. journal of medical internet research. 2004;6(e2). [36] teachernet. personal, social, health and economic (pshe) education. 2009 available from: http://www.teachernet.gov.uk/teachingandlearning/subjects/pshe/. accessed december 18, 2011. http://www.instituteforgovernment.org.uk/content/133/mindspace-influencing-behaviour-through-public-policy http://www.instituteforgovernment.org.uk/content/133/mindspace-influencing-behaviour-through-public-policy http://www.teachernet.gov.uk/teachingandlearning/subjects/pshe/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 e-prescribing: history, issues, and potentials j. warren salmon 1 , ruixuan jiang 2 1 university of illinois at chicago, school of public health 2 university of illinois at chicago, college of pharmacy abstract electronic-prescribing, computerized prescribing, or e-rx has increased dramatically of late in the american health care system, a long overdue alternative to the written form for the almost five billion drug treatments annually. this paper examines the history and selected issues in the rise of e-rx by a review of salient literature, interviews, and field observations in pharmacy. pharmacies were early adopters of computerization for a variety of factors. the profession in its new corporate forms of chain drug stores and pharmacy benefits firms has sought efficiencies, profit enhancements, and clinical improvements through managed care strategies that rely upon data automation. e-rx seems to be a leading factor in overall physician acceptance of electronic medical records (emrs), although the centers for medicare and medicaid (cms) incentives seem to be the propelling force in acceptance. we conclude that greater research should be conducted by public health professionals to focus on resolutions to pharmaceutical use, safety, and cost escalation, which persist and remain dire following health reform. keywords: emr, electronic medical records, physicians, pharmacists, pharmacy, implementation, medicare incentivizing, pharmacy benefits managers, pbms, retail chain drugstores, electronic prescribing, cpoe, clinical decision support, obamacare, eprescribing, e-rx, adverse drug reactions. introduction when thinking about innovation in the healthcare system, it is almost always medical technology (pharmaceuticals, medical devices, diagnostic and surgical inventions, etc.) that comes to mind. but other aspects of technology support needs to move forward as well to provide the most effective processes for both patients and providers. a key aspect of medical technology support lies in health information technology (hit) systems, which may enable greater cost savings, efficiency, and eventually improved patient outcomes. in general, healthcare, unlike other industries, has lagged in embracing innovations in information technology; though it is apparent that this sector is now well on its way. a few federal agencies are actively promoting hit; hundreds of vendor firms are emerging; degree programs are mushrooming; and professional organizations are hopping on the bandwagon. http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 back in the 1960s pharmacies were actually in the forefront of automation in health care. as soon as computer hardware firms rolled out early computers and software--with later transmission across telephone lines--pharmacies found practical business cost savings for accounting and inventory control (over ledger books and hand-counting tens of thousands of pills). dispensing of prescriptions was matched to on-line purchasing from drug wholesale suppliers, which enabled quick shipments, cost analyses, and later patient billing through third party administrators (tpas). recently, e-prescribing technology has taken hold as an advance over hand-written drug treatments orders; e-rx, being one of the earliest adoptions in computerized physician order entry (cpoe), gained acceptance by certain physicians. prescribing errors are the largest source of preventable errors in hospitals. e-prescribing, according to the institute of medicine, can reduce the number of errors and indeed impact health outcomes. 2 this paper presents issues surrounding electronic prescribing and developments in pharmacy related to the trend of electronic medical records and examine the need for health services research for promoting better outcomes from the use of pharmaceutical use. pharmacies in the forefront today, most pharmacies use online, real time transactions for verifying the patient's coverage eligibility; the process beyond receiving the prescription to transmitting claims to payers and obtaining adjudication so that copayments and deductibles can be calculated and collected. additionally, the pharmacist can perform prospective drug utilization reviews using information received from the insurance payer to counsel patients to prevent possible downstream hazards, such as inappropriate prescribing, drug-drug interactions, first time use, compliance, mandatory counseling for medicaid patients, and more. over 3.5 million harmful errors can be potentially prevented to save payers substantially in medical costs if pharmacists in a clinical role help patients receive safer and more affordable choices. 3 tpas arose in the 1980s as government and corporate employers added drug coverage to their insurance benefit packages. by the early 1990s tpas had begun to morph into nascent pharmacy benefit managers (pbms). today, pbms number less than 50; with 5 to 8 national and regional pbms controlling over 80 percent of the national dispensing market, for nearly 5 billion prescriptions written annually overall. pbms pioneered mail order deliveries, and they politically secured medicare part d drug administration during the bush era. 4 this segment of the health industry has witnessed significant consolidation and reorganization, which is ongoing as smaller firms get quickly gobbled up. 5 put in place by the corporate giants are new pharmaceutical management strategies and clinical interventions to contain rising drug costs and utilization, while attempting to monitor and improve quality outcomes. 6 it should be noted that pbms--a few larger in revenue now than most multinational pharmaceutical firms--pose a formidable challenge to the hegemony of big phrma and drug pricing. 7 while in the late 1960s with the arrival of medicare and medicaid, hospitals began automating their insurance billings and accounting; since then, physicians remained reluctant to adopt http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 computerized protocols and clinical information systems into their practices. numerous attempts at electronic medical records (emrs) in hospitals faced rough terrain in their implementation, which inevitably soured and slowed early adopters across the nation. 8,9,10 such difficulties are to be expected as new ideas and complex systems rarely are implemented smoothly. emrs are complex systems necessitating significant input from all health care providers. physicians observed these problems and developed various resistances. 11 advocated early on by managed care pharmacists, e-prescribing became an easier positive step to help physicians identify benefits over paper prescribing pads. 12,13 prescription mistakes are rampant and under-reported. 14 according to the iom, handwritten notes, manual order entry, non-standard abbreviations, poor legibility, all lead to substandard care, including errors and injuries. 2 computerized prescribing in a way has led the medical profession into the fuller electronic medical record compliance. several e-rx implementations were pioneered by large chain drugstores principally for steering patients from doctors' offices to their individual stores for business. one chain claims significant pharmacist efficiencies and overall paper reductions, thus profit improvement. 15 managed care organizations (hmos and ppos) also initiated computerized prescribing through distribution of personal digital assistants (pdas) with their formulary (a list of preferred covered drugs) added as a built-in feature for doctors directed prescribing of chosen entities. 16 with this new tech, extra communication between pharmacists and doctors could be streamlined, thus pharmacies could have prescriptions ready for patient pickup. additionally, pharmacies can automatically conduct online communication to and from either the managed care organization or pbm to ensure formulary compliance, approval of the claim, and billing details. some managed care organizations even can direct online clinical interventions to be conducted by the retail pharmacist. overall, computerized prescribing and other pharmacy management systems technology have indeed improved efficiency in pharmacy operations. such a move forward is necessary given the huge number of added drug entities onto the u.s. pharmacopeia by drug firms, and the increased number of prescriptions written per capita today for an aging population. the scripts written for seniors and the coming cohorts of baby boomers (7.9 to 8.2 average) encompass multiple chronic degenerative conditions, with most prescribed for a lifetime use so require continuous renewals. proponents of e-prescribing technology herald such achievement as a vast improvement to the u.s. healthcare system, which is seemingly accepted as an axiom by numerous parties. yet, there may be more to the situation: while computerized prescribing has become more widespread, the pockets of medical profession reluctance to emrs in general may be due to required costs for equipment and software, and special training for their clinical staff. internet access, as well as a comfort level with changing behavior; plus working with advanced technologies and protocols are key also. since most practices must have automation today for insurance billing, this is remains a compatible software and comfort issue; yet, some physicians remain suspicious of greater encroachment upon their professional autonomy by administrators, private payers, and government. 17,18 privacy of patient information is a top concern of the medical profession as reports of hacked systems or stolen computers frequently pop up in the news. http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 purported benefits to electronic prescribing policies to incentivize e-prescribing and the purported benefits may be facilitating a positive attitudinal change toward electronic medical records (emr) adoption. of course, health information technology (hit) has become more successful with user-friendly software; successful vendors have innovated to entice greater professional acceptance, convincing them by heralding computerized prescribing benefits, along with cms meaningful use adoption. some of the so-called "magical thinking" ascribed to emrs by commentators appears to have carried over to e-prescribing. most professionals of late seem to not view hit as a panacea for quality and cost improvements, but realize a broader need for structural reforms. purported benefits of electronic prescribing have included: e-rx enhances pharmacy efficiency. for sure, electronic delivery of the prescription eliminates the tried and true problems of doctors scribbling and enables the pharmacist to prepare the prescription to ease patient pickup. e-rx promotes formulary adherence. managed care organizations find that physicians choose the drugs for which they have contracted for cheaper purchase, thus it enhances their profits and perhaps promotes some quality where their pharmacy and therapeutic committee decision-making in all intents and purposes well assesses efficacy and costeffectiveness of the various entities on the formulary. e-rx enhances prescribing errors by physicians being caught. pharmacy software can check for the proper drug being prescribed at the right dosage in many cases so medication errors may be minimized. e-rx reduces adverse drug reactions (adrs) by electronic entry into the pharmacy’s computer allowing patient allergies, past bad experiences with certain drugs, and drugdrug interactions to potentially be identified, also pending pharmacist intervention. 19, 20 e-rx may catch dosage errors, particularly in light of the differences between pediatric formulations and adult dosage levels. this can also be part of the assessment done electronically before the pharmacist prepares the prescription. e-rx decreases drug-drug interactions. much existing pharmacy software already checks the patient’s profile (assuming that patients use just a single pharmacy) to raise flags to the pharmacist before dispensing about any potential of multiple drugs interacting. e-rx helps prevent injuries and reduce health costs. alerts given to physicians reduce the likelihood and severity of adrs, according to one study in the archives of internal medicine . 21 e-rx improves quality of care and reduces malpractice claims. again, it is asserted to yield a reduction in medication misadventuring, reducing both physicians’ and http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 pharmacists’ making mistakes. most of these depend upon the pharmacist's vigilance in interacting with a well-designed clinical software system with a caring professional role. emrs in one study in the archives of internal medicine saw an association with "a significant reduction in malpractice claims against physicians." 22 e-rx increases patient pickup from the pharmacy and patient compliance. this benefit is assumed by a few reports that patients arrive at the pharmacy to receive their drugs more so when delivered electronically, rather than when they carry a piece of paper. patients with electronic prescribing allegedly pick up their drugs and take them more assiduously than those with paper prescriptions. add-on programmed dispensing devices for patients have been found to work best to alert providers of non-compliance. 23 in the list above, such “findings” may be real for some settings, but regrettably cannot be generalized across different organizations. it must be emphasized that electronic prescribing is a tool that must be launched under clinical decision support systems (mentioned above), which are necessary to provide any benefits to patient outcomes and providers alike. such support systems are not just “plug and play.” systems must be customized for each institution based on formulary needs and local prescribing practices; institutions need to constantly test, refine, update, and customize systems to keep up with changes in prescribing needs, trends, and practices. overtime, adapting vendor packages has been hard for several provider groups, but now standardized protocols seem to be coming into place. nevertheless, little health services research to demonstrate major clinical and cost value has yet to be there. “studies” are generally reports from single, unique organizational settings and often short-term snapshots. one must still be cautious of claims by vendors; observations by provider groups (group practices, hospitals, hmos, pbms) may capture some of the contributions from computerized prescribing; notwithstanding, assessments in the scientific literature are scant compared to numerous reports in the it and medical "news" coverage. of note, computerized prescribing is not just a message system, but is placed within advanced pharmacy software systems for patient care management in the new clinical direction seen in ambulatory and hospital pharmacies. e-rx is also embedded in the many medical groups' fuller emr systems. patient profiles in pharmacies have long been initiated and advanced to assess many of the above-described issues in trying to improve medication management and to promote better drug outcomes. these systems in hospital and ambulatory settings may reduce prolonged hospitalizations, and prevent morbidity and mortality, as well as minimize added utilization costs by catching medication mistakes proactively. 24 it should be pointed out that, particularly in hospital settings and some pbms, computerized prescribing is essential to hook into newer automatic dispensing robotics that greatly reduce professional pharmacist time devoted to dispensing. as the profession has sought a greater clinical role for the pharm.d. graduate, these robots in hospital basements have freed up clinical pharmacists to interact with patients as the drug expert on the medical care team. 25 where physicians were able to witness favorable computerized prescribing, doctor consideration to engage in broader electronic medical record activities was aided. 26 elsewhere physicians had http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 little choice in adoption and use when pressured by medical groups and hospital systems. while the medical profession has shown overall resistance to emr adoption, pharmacy professionals have seemed historically to be more open to health information technology. this may be due to their employee status under management dictates in chain drug stores and pharmacy benefit management firms (as might be compared to physicians in group practices or hospital-salaried). the major motivation for provider organizations to switch over to electronic medical records seems to be practice cost savings on many levels when they can be obtained. insurers have begun to provide their preferred technology to medical groups for billing purposes, which has raised questions among consumer advocates as to how the information may be used. 27 intuitively one might think that having a wealth of information at the practitioners’ fingertips would eliminate redundancies in treatment or tests and lead to speedier diagnoses. so far in the implementation of emrs, this has not been well proven. earlier studies of a few models have actually shown that emrs may not be any more cost-effective than traditional filing systems. doctors may actually order more tests when the emr is available rather than less. as to reasons behind this surge in utilization, perhaps gaps in the patients’ medical care are being pinpointed as the overabundance of information from a centralized database is at the physician's finger tips. u.s. health & human services is investigating cases of "upcoding" to inflate medicare billings. 28 because emr systems vary significantly and are still in early stages everywhere, if adverse drug reactions and avoidable hospitalizations can be minimized, then cost effectiveness in the longer run will be demonstrated for many cases. using hit with patient-centered principles "improves care, according to the bulk of evidence published during the last 14 years." 29 ongoing cms incentivizing to speed the adoption of electronic medical records, the centers for medicare and medicaid (cms) was given authority to incentivize physicians for electronic prescribing technology. medicare part d revisions passed in 2007 increase medicare reimbursements to physicians who adopt e-prescribing. bush administration officials had sought to mandate e-prescribing for all medicare scripts under the drug program, but standards were undeveloped and numerous part d providers had yet to develop capacity to follow them. 30 details for the equipment, software, and standards for operations were discussed in commissions and at conferences. provider organizations were divided on steps to take and how fast to proceed; they all chimed in with their varying perspectives. thus, the proposing of incentives formulated for medicare reimbursements for 2011 and 2012 began to attract emr compliance. physicians who adopted e-prescribing would receive a 2% bonus in their medicare reimbursements for 2009-2010. procrastinating would reduce the bonus percentage and then reductions of 1% to 2% would occur through 2014 for those who did not use computerized prescribing. e-rx was sold for reducing prescribing errors caused by illegible writing, but it was imbedded in the push for emrs to establish the physician quality reporting system (pqrs). by 2008, $92 million was paid to 85,000 physicians (up two-thirds from 2007) for reporting quality-related data. federal funds were also released for state medicaid programs to similarly have data collected under the pqrs. 31 http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 again, computerized prescribing was attractive to physicians and began to lead them to fuller emr consideration for the bonus payments. by 2010 the cms criterion for providers was to write 40% of prescriptions electronically to qualify for funding, instead of the original 75% goal. funds were available to providers to subsidize them for the cost of emr setups; cms and professional organizations undertook many efforts to support providers as congress again increased the incentives. by 2010, 269,000 eligible professionals participated in the pqrs. as far as e-rx went, cardiologists, family practitioners, and internists were chief among those relying on this e-technology. $391.6 million in incentives were given out. as implied, providers vary as to who more embraces electronic prescribing, but 131,000 were using it, up from 89,209. physicians, pharmacists, nurses, nurse practitioners, and physician assistants found e-prescribing acceptable and useful. by march of 2012, $4.5 billion in incentives were awarded by cms. 32 discussion as readers of this ojphi journal have followed development of emrs and meaningful use, there are many nuances in this rollout for the new medicare reimbursement, which go beyond the intent of this paper. public health people realize that, since the u.s. remains the only major industrialized nation not regulating prices set by drug companies, prescription drugs generally cost 25% to 40% more than other nations. adding to the cost and expenditure problematic, significant drug misadventuring 33 and the lack of understanding how pharmaceuticals and their use truly affect public health outcomes is plenty of reason to spur forward the construction of researchable databases from electronic prescribing. 34 prescription drug expenses are the single largest out-of-pocket spend. drugs cost as much as expenses for physician care, vision care services, and medical supplies combined. the misleading percentage on the annual federal pie chart grossly underestimates all pharmaceutical spending, since it is only ambulatory drugs counted, not including administrations in hospitals, nursing homes and doctor offices, which due to their exorbitant pricing and frequency far outpace the 9% ambulatory percentage. for 75% of our seniors, prescription drugs are the single biggest outlay. older americans with five or more chronic conditions incurred on average $5,300 in prescription drug costs in 2008, while medicare part d coverage was being secured. 35,36 clearly, there is a health system imperative to economize in the delivery of pharmaceuticals, and, more importantly to arrange for workable databases in order to investigate inappropriate and over-prescribing; monitor harmful adverse drug effects; prevent drug-drug, drug-herbal, and drug-otc interactions that cause mishaps. 37 the use and misuse of pharmaceuticals has untold endpoints in ers, hospital beds, and morgues. addressing this somewhat in certain managed care firms are new pharmaceutical management strategies, along with clinical interventions to contain rising costs and curb utilization, while attempting to improve quality outcomes. some analysts feel this has been only a start, however. not all of these "innovations" have warmed the hearts of physicians to computerization and its data accumulation to scrutinize their behavior. 38 http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 cms' stage 2 of meaningful use sought to focus on advancing interoperability and establish data exchange infrastructures. it has been recognized that the incompatibility of varying vendor systems have presented certain problems. the widely quoted 2005 rand study 39 suggesting $81 billion in savings from emrs have many today believing it an overblown promise due to the paucity of health services research, the hopes of it proponents, and vendor vested interests. 40 emr benefits as a fiscal brake will likely come only when they facilitate careful monitoring of clinical care with effective steps to improve quality amidst payment reforms, the strategic details of each being politically and technically quite underdeveloped as yet. nevertheless, in our profitbased american health care system, larger providers seem better positioned to reap emr economic returns on investments. 41 data mining on the use of pharmaceuticals has yet to be perfected much. using electronic data more effectively will turn disparate information into intelligence with analytics that can lead to better clinical and operational decisions. "indeed, a recent report from the institute of medicine points out that better use of data could lead to the "system-wide transformation” so sorely needed in the industry." 42 evidence-based alerts hold the most promise to aid practitioner prescribing. yet, the long trek into what former nejm editor arnold relman called the "era of assessment and accountability" will reveal that the overall stakes are high for the near future. 43 issues remaining are the electronic exchange of health data (patient summary to doctors) where physicians generally favor this, believing that it will increase their practice efficiency, and also feeling that the electronic data sharing will reduce overall costs in the health system. yet, it is obvious that there's a greater need for a national strategy with negotiated technical standards, clearer cms guidance, and support for best practices for both medicare and medicaid patients. difficulties in emrs persist: moving toward "value-based care" includes the record certifications, secure messaging, updating technical requirements, harmonizing incentive programs, penalty assessments, appeals processes, privacy of the data, downtime issues, among others to arise. moreover, the "technologicalization of medical practice" faces what all small business in america is attempting to deal with, including the impact of cloud computing, social media use, tablets, video conferencing, new apps, and the continued onslaught of innovations. 44 stage 2 criteria had among its core set of meaningful use criteria to increase cpoe medication to 60% of orders. by october 2012 over $8 billion was paid out to 165, 800 mds and hospitals; 131,000 participated in the electronic prescribing incentive program in 2010. this number was increasing by the month. 45 as mentioned, early-on emr users hit road bumps in implementation, which were perhaps due to provider-it staff mis-communications and cultural clashes. providers attempting ehrs into operation--whether successful or not--have not published much findings; so few real world lessons can be taken away with insufficient research in the literature. nevertheless, hit advances have recently reached a critical mass, with 69% of primary care physicians using ehrs. 46 in brief, does such adoption exemplify that key trends in the american health care system virtually go forward while insufficiently unexplored by health services researchers? http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 meanwhile, professional roles and relationships get reshaped by technological and organizational forces. 41 published reviews of canadian electronic medical records 47 show that in the majority of cases, emrs are considered positive for medical practices, but degrees vary with the aspect examined (e.g. cpoe, medication errors, prescriber productivity, etc.). this study quantifies “…findings suggest there is a 51% chance that an emr can improve office practice, while in 30% of the time there may not be any effect, but only 19% may lead to negative consequences." 48 the main complaint provider users seem to feel is that systems are not “intuitive”, therefore difficult to use if not a heavy regular user. 49 when e-rx and emrs fit neatly into the workflow of the practice, saving capabilities for the practice can be found. but physicians need to have realistic expectations for the emrs. 46 lessons should, of course, be read with caution because each individual system should be fully integrated and up and running for an extended period of time--and then evaluated in research by someone other than the vendor and internal biased staff. as the global market for pharmacy automation expands to a projected $3.9 billion by 2017 ($2.6 b this past year) opportunities for evaluation of newer innovations will be likely. 50 early studies of a few models suggest that ehrs may actually not improve cost effectiveness than the traditional paper filing system surprisingly due to doctors actually ordering more tests when the ehr is available to them rather than less as per expectations. 49 additionally, ehrs may generate more insurance billing (thus increasing provider revenues) for previously undocumented services to ease use for supporting hardware and software upfront costs. but the frequently quoted rand analysis 39 has been questioned for validity citing that the data extrapolation may not be scientifically merited and the comparisons drawn to the banking industry may not necessarily be valid. 40 also "personal financial ties " linking some researchers and emr producing companies suggest that available information seems thin at best; especially from a scientific point of view for more objective studies; providers may be skeptical to take on a new system. 40 patients will experience benefits from e-rx and will likely support their use. firstly, patients will see the improved service from the pharmacy and/or pbm in wait times reduced, safety more assured, and with efficiencies in the pharmacy, more professional face time for complicated regimens. patient access to their medication list has yet to become widespread so they will want to utilize the internet for fuller drug information. when properly operating, mistakes can be reduced through clinical management systems. in particular, prior authorizations (which are quite commonplace with expensive and specialty drugs) can be sped up for approvals with electronic communication of clinical data back and forth between the physician and pharmacy. in the u.k., pharmacists will soon be given access to patients full emrs, 51 a practice found only selectively in certain managed care organizations here. the british national health service thus has such an advantage for patients over the u.s. private system fragmented into competing corporate entities of hmos, pbms, and chain drug stores. the latter also poses barriers for the portability of information for the patient's benefit. http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 conclusion because ehrs are still in the early stages everywhere and quite heterogeneous if the patient can avoid hospitalization or other adverse effects (drug allergies etc.) through the medical team’s use of ehrs, then system cost effectiveness in the longer term can be realized. as one member of a family practice notes, primary care practices are key in patient monitoring for the much more expensive hospitalization stays and emergency room visits, and this point may possibly aid economizing expenditures for the nation as asserted by president obama. 40 at this junction, the quality of patient treatment may be upgraded with adherence to evidence based medicine while patients can be better monitored. but of course, health services research will need to accurately assess the overall situation with appropriate analytics in development. 52, 53,54 this then raises the issue of long-term safety in the use of ehrs. 55 given the widespread differences in emr systems and their various implementations, amidst the issue of harnessing appropriate drug use, can patient safety in drug utilization achieve the most cost effective patient-oriented care. health hazards linked to hit are beginning to be noted. 56 congressional attention may be leading hhs observation to minimize patient risks. 57 here the larger structure of health reform under the aca provokes the question of : whither go the accountable care organizations (acos) and health insurance exchanges (hies)? aco outreach to the most vulnerable populations will be key here if public health outcomes are to see remedy. 58,59 so will e-rx and ehrs really only be useful for provider cost savings and convenience, or will they promote patient-centered, patient focused care systems that will alter health outcomes in the whole population? the complications over the next few years are vast to forestall predictions. notwithstanding, public health researchers must engineer vital roles in illuminating the micro organizational issues and on the larger level, what works best in hit for specific populations. researchers can instill a population-based perspective and build toward public health outcomes measured for aging cohorts with multiple chronic conditions under poly-pharmacy regimens. if the entire healthcare system is to see serious structural reform (acos and hies) and evidence a more patient oriented focus across populations, current clinical practice requires alterations that electronic prescribing and emrs can greatly assist. smoothing the transition between clinical decision making and completion of documentation requires changed roles and new jobs to emerge. at one particular location which implemented ehrs, a representative said, “...burdens have shifted and job descriptions changed. if anything, we have increased the number of clinical staff…” 40 because the use of e-rx and ehrs is just beginning to become more commonplace, studies to examine the long-term effectiveness (both cost and safety) of these systems deserves attention. this is surely a frontier for how these technologies for cost effective and safety improvements can be properly fostered by public health professionals. the implementation of acos and hies under the accountable care act provides a complex context yet determined to the further evolution of e-rx and emrs. http://ojphi.org/ e-prescribing: history, issues, and potentials online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 conflicts of interest neither author has any conflicts to disclose. this commentary's research was unfunded. corresponding author j. warren salmon, ph.d. professor of health policy and administration school of public health university of illinois at chicago jwsalmon@uic.edu references 1. numerous newsletters have arisen to herald hit, along with a few journals (including this online journal of health informatics) including health data management, healthcare it news, technology news and analysis, mobile health news, ehr intelligence, health care informatics , journal of biomedical informatics, health it strategist, health it. gov, applied clinical informatics, and more. medscape, american medical news, and medical and pharmacy journals, along with the business press, follow hit developments regularly too. 2. institute of medicine (2001) crossing the chasm: a new health system for the 21st century, national academy press, june 29. 3. institute of medicine (2006) preventing medication errors, national academy press, july 21. 4. navarro, r., (ed.), managed care pharmacy, frederick, md, aspen publishers, 1999. 5. cnbc (2012). pharmacy benefits managers: too powerful? available from: 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(2012). changing market for pharmacy benefits. managed care. january, pp. 34-43. 8. ammenwerth, e., talmon, j, ah, j.s., bates, d.w.beuscart-zephir, m.c., duhamel, a., elkin, p.l. gradner, r.m., % geissbuhler, a. (2006). impact of cope on mortality rates-contradictory findings, important messages. methods in medicine 45(6):586-593. 9. dolan, p.l. (2012) doctors' love-hate relationship with ehrs. amednews.com, may 7. 10. stuckey, m. (2012). ehrs: despite incentives, some physicians hesitant to transition to systems. american health line, november 8. 11. wigger, u. (1992). "use of q-technique in pharmacy student selection." thesis presented to the faculty at the uic college of pharmacy. 12. spiro, r. (2012). the impact of electronic health records on pharmacy practice. drug topics april, pp. 46-54. 13. gebhart, f. 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(2012) the value of analytics in health care. ibm institute for business value ibm, global business service. 55. goedert, j. (2012) safety advocates seek national safety goals. health data management. november 5. available from: http://www. healthdatemanagment.com/news/ehr-elctronic-healthrecords-national-safety-goals-45224-1.html?zkprintable=true 56. goedert, j. (2012) report: many of top tech health hazards linked to it. health data management. november 8. available from: http://www. healthdatemanagment.com/news/health-information-technology-patient-safety-hazrds-ecri45210-1.html?zkprintable=true 57. conn, j. (2012). lawmaker seeks answers in health it safety. modern healthcare november 14. 58. markle foundation (2009). health information technology and health care reform must be well-aligned to improve health and lower costs. medical news today available from: http://www. medicalnewstoday.com/releases/157684.php. 59. obaid, h. & salmon, j.w. (2012). community-based accountable care organizations to improve health outcomes for the illinois medicaid program, poster at american public health association annual meeting, san francisco, california, october 28. http://ojphi.org/ ojphi-06-e113.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 48 (page number not for citation purposes) isds 2013 conference abstracts utility of a syndromic surveillance system to identify disease outbreaks with reportable disease data carrie eggers*, janet hamilton and richard hopkins florida department of health, tallahassee, fl, usa � �� �� �� � � �� �� �� � introduction ������� � ��� � � �� ��������� ���� �� � � �� ����� � ������ �� �� �� ����� � ��� � 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acknowledgments 3���� ��*������� �� ����� ��� ����� �� '�����*� ������� �� � ����� $�$��$"/!��3"/��������*���?�3�� ��b� %* ' �� references ������"�� ������ �$�� �� ���� � ���� ��" � � �5 � � ��� ���� �� ����� �� ������,) )!)��� ������ ��� � � � ��� �� ���!�� �)�678;) *carrie eggers e-mail: ceggers@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e113, 2014 from twitter to megaphones: seven lessons learned about public health crisis communication 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 from twitter to megaphones: seven lessons learned about public health crisis communication lisa gualtieri, phd 1 1 tufts university school of medicine in boston we took the availability and quality of our tap water for granted until may 1, 2010, when a major water pipe break interrupted water service to two million greater boston residents. information spread quickly to citizens about the problem and what to do, all the more notable because the water main break occurred on a saturday. in this age of consumer paranoia about withheld information, the massachusetts water resources authority (mwra) was in front of cameras and online, communicating what they knew and what they were doing. tufts university and the boston public health commission used communication channels ranging from twitter to megaphones to get the word out. their behind-the-scenes emergency planning processes, their response to this incident, and seven lessons learned from this short-lived crisis are applicable to many other crises. the evolution of the tufts emergency alert system: because i learned about the broken water main in a text message from tufts university, where i teach, i spoke to geoff bartlett, technical services manager in the department of public and environmental safety (dpes) at tufts about the process they used to communicate about the broken water main. first he told me how tufts emergency alert system started and evolved. following the virginia tech massacre in 2007, dpes, university relations, and university information technology invested in emergency notification system technology and developed policies for when and how it would be used. the tufts emergency alert system was initially intended for life threatening emergencies after the events on the virginia tech campus showed the need for rapid and reliable campus-wide communication. in requesting student and employee contact information, tufts made this clear since they thought people would be reluctant to participate if they anticipated inconsequential messages. tufts first used the emergency alert system to inform the campus of the status of a power outage in october 2008 because the email communication plan in place for this type of tier 2 emergency wouldn’t work because of the lack of electricity. this initial use led to the revised policy that the emergency alert system should be used aggressively for dire emergencies but less aggressively when there is no threat to health, safety, or life. almost exactly one year later, there was another power outage in october 2009, and short text messages were sent. while there was planning for h1n1, the emergency alert system was never used because there was no urgency to push messages. the third use was for the water main break. http://www.bphc.org/newsroom/pages/topstoriesview.aspx?id=155 http://www.boston.com/news/local/massachusetts/articles/2010/05/04/with_repair_mwra_crisis_nears_an_end/ http://www.mwra.com/ http://publicsafety.tufts.edu/ http://publicsafety.tufts.edu/ from twitter to megaphones: seven lessons learned about public health crisis communication 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 how tufts creates messages: while tufts considered preparing messages in advance, it didn’t seem possible to anticipate every situation. instead they created “strunk and white” guidelines for crisis communication. their three guiding principles for creating initial messages are: 1. what is happening 2. what you need to do now 3. where to go for more information. messages must be succinct because of cell phone screen size and to increase the likelihood people read them, avoid jargon and abbreviations, and be composed for easy conversion into speech. while the tufts community is tech-savvy, they are aware that not everyone is connected all the time therefore some messaging includes spreading the word. for many emergencies, especially life-threatening ones like violent criminal incident or tornado warning, content is prescripted by tufts using sources such as the massachusetts department of public health. in the case of the water main break, massachusetts emergency management agency sent out the initial message. when geoff received the message on saturday, may 1, he was in a command post on campus with police, fire, and ems personnel where they were managing the public safety aspects of the spring fling concert. because there was no reported danger or health threat, email was used initially. later in the day, after massachusetts governor deval patrick declared a state of emergency, dpes fully activated the emergency alert system. in addition, email, word of mouth, twitter, and the web were used to spread information. i asked geoff if there was concern about any health issues arising from students who drank tap water. he said that there was an faq that included the consequences of ingesting water. however the information they were receiving from the state agencies, and therefore their focus, was on the status of the water main break and what to do, such as the boil water order. student feedback after the crisis ended was largely positive but included that there were terms, like boil water order, that they didn’t understand. the boston public health commission emergency preparedness process: to see how a public health organization responded, i looked at the boston public health commission (bphc) website and spoke to susan harrington. she had guest-lectured in my online consumer health course about their use of the web and social media and i wanted to see how they deployed them in an emergency like the water main break. bphc and its partners participate in emergency preparedness exercises to refine their coordination and response. in 2007, bphc worked with the postal office on a large-scale exercise and last year they responded to the real-life h1n1 epidemic. just last month, bphc invited businesses, health care settings, and other partner organization to a flu review, where they discussed how bphc responded, including what they did well, what didn’t work, and made recommendations as they prepare for the next flu season this fall. http://emergency.tufts.edu/water http://www.mass.gov/dep/water/drinking/boilfaq.htm http://www.mwra.state.ma.us/01news/2010/boilwaterorder1.htm http://www.bphc.org/pages/home.aspx http://lisagualtieri.com/2010/01/14/bostonpublichealth/ http://onlineconsumerhealth.com/ http://www.boston.com/news/local/articles/2007/09/07/bioterror_drill_to_test_distribution_of_drugs/ http://www.boston.com/news/local/articles/2007/09/07/bioterror_drill_to_test_distribution_of_drugs/ http://www.bphc.org/flu from twitter to megaphones: seven lessons learned about public health crisis communication 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 how the boston public health commission alerted residents: susan was in a city not affected by the burst water main on the saturday the news was announced and received a call from work alerting her to the situation. working in concert with federal, state, and city agencies, the mayor’s office and bphc relayed important information and coordinated response efforts. the immediate issue was reaching people, which the bphc first did through twitter, facebook, and their website. the mayor’s office posted information on its own sites and used its reverse 911 phone system to alert residents. boston police officers drove up and down streets using megaphones and loudspeakers. bphc set up conference calls with area hospitals and staffers were sent out to food-service establishments who needed to quickly adapt their procedures for the boil water order. throughout the weekend, the mayor’s 24-hour hotline added staffers to help answer any questions residents had. the mayor’s office and bphc also called upon their partners, which included faith-based organizations, schools, and businesses, to spread the message through their own channels, and asked residents to inform the elderly who may not have access to the web and social media. the challenge was responding quickly and reaching as many people, wired or not, as possible. these techniques had been used to spread the word about h1n1 vaccine availability. twitter proved very effective at relaying up-to-the-minute news. while twitter is global, people use the #boston hashtag and other filters to get local information including traffic updates, event listings, and even local celebrity sightings. not long after boil water order was issued, the twitterverse was abuzz with the news – even dubbing a new hashtag for the emergency: #aquapocolypse. the most influential – and most followed – twitter profiles were not only pushing out timely information, but passing on questions to bphc, allowing them to respond and dispel any myths. creating fact sheets: no matter what the crisis, some people worry and they are the ones who especially need facts. one of the main bphc priorities was posting information and fact sheets to the bphc website. as a homeowner susan knew what questions she had, but she had to consider the broad demographics of boston in terms of where people live, the languages they speak, and their access to water. bphc worked with the massachusetts department of public health to create easy-to-read and culturally appropriate guidelines for the boil water order for bostonians, including translating the fact sheets into multiple languages using a professional translation company with proofing by commission staffers. these materials were later updated to reflect the lifting of the boil water order and subsequent flushing out instructions. i asked susan about the extent to which they date materials. in a crisis, knowing that an update is available and when it was issued is crucial. throughout the flu response and boil water order, they posted dates on their websites, but in a non-emergency she said it is a challenge to keep an entire website updated. fact sheets often are dated but other online materials may not be. from twitter to megaphones: seven lessons learned about public health crisis communication 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 health issues and disease tracking: i taught a course in shanghai once and remembered the advice i was given about the level of bacteria being higher in the tap water than americans are used to. i slipped back into shanghaimode and remembered to rinse my toothbrush with bottled water and the myriad of other pointers i had been given. i was curious if boston residents who drank tap water during the emergency expressed health concerns. susan said that if pathogens were in the water, people may have experienced minor gastrointestinal illness after consuming that water. a greater concern would be for residents who are immunocompromised. the bphc uses a sophisticated surveillance system to track diseases in boston. (in fact, boston has been nationally recognized for its disease tracking system.) health care settings report diseases to bphc, which in turn, conducts a follow-up investigation and identifies the source of the illness, such as food contamination. these disease patterns are tracked over time. in the case of the boil water order, there was no spike in gastrointestinal illnesses. google has a less formal process of tracking disease patterns, collecting search phrases to find trends including the spread of illness. google’s h1n1 flu trend matched up fairly well to massachusetts’ trend lines. lessons learned: susan harrington and geoff bartlett both thought the mwra did a great job of letting people know what they knew, what they didn’t know, and what they were doing to find answers and repair the pipe. this was essential not just to inform people but to allay paranoia and fears given well-publicized situations like toyota and vioxx where information was not publicly disclosed in a timely fashion. some lessons learned about rapid health communication from the water main break are: 1. develop a rubric to assess the type of crisis as it impacts your institution. when the crisis is over, review, solicit feedback, and refine using what the military call an after action review. 2. identify and coordinate with partners in advance. in the case of the water main break, an impressive number of groups coordinated efforts seemingly seamlessly and, in many cases, behind the scenes. 3. prepare a communication plan for each type of crisis. while newspapers write obituaries for famous people in advance, you can’t anticipate all eventualities. however, you can prepare guidelines and immediately use them. flexibility needs to be built in to communication plans, even to the definition of a life threatening emergency and when to select modalities that “wake you up” or more passive ones like email. 4. carefully construct messages to convey needed information succinctly. high-quality materials take time to produce because it’s important to first gather facts and then create and review accurate, appropriate, and easy-to-understand information, be they short like text and twitter messages, or less constrained by length. dating material is especially important in a crisis. 5. create messages that inform and allay unnecessary fears. think like – or talk to your target audience. be careful about jargon, although everyone in greater boston quickly became conversant quickly with “mwra” and “boil water order”, which are not http://www.boston.com/news/health/articles/2010/05/03/chance_of_getting_ill_may_be_minuscule/ from twitter to megaphones: seven lessons learned about public health crisis communication 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 3, 2010 in the common vernacular. terminology was also an issue with h1n1: swine flu was the term adopted by the press initially, but it was distracting because of the association with pigs. 6. use social media, which can be both fast and local. use emerging informal partners, who malcolm gladwell calls mavens, to facilitate the spread of messages in twitter. but even when people are wired, they aren’t always online. the low tech megaphone and word of mouth works best for some. 7. use crises to educate people. while the water main break left many people with a heightened appreciation for their tap water, it was short-lived. however there may be a missed opportunity here to educate people about water sources, safety, and conservation as well as about emergency response. correspondence: lisa gualtieri, phd tufts university school of medicine 136 harrison avenue boston, ma 02111 l.gualtieri@tufts.edu http://en.wikipedia.org/wiki/the_tipping_point http://www.boston.com/bostonglobe/editorial_opinion/oped/articles/2010/05/08/7_things_the_water_crisis_taught_me/ 5058-38705-1-ce.pdf isds annual conference proceedings 2013. this is an open access article 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���������/ ���� ���������� �� ���=�� ��������=��� ���������� ����� ������������ �����3���� ����=�� ��������12�������� ����� ����=�� ���.���������/ �� ��+.����� ����*�� ������� ��'���3�*��.�3�����3�������=��������� � �� �� ���� ����� ���� ������ ����'�2 � ���� � �+������� ������ � � ����� ���� ��������������' keywords ���� ���� � �5������ �������� 5��*�� ��� ���� ���5��� ��� ��� ���� � �� ��� � ���� references )'��������� � ��%�� ��& ���'��� ��� ��� ���� � ��$�� � �����!""#.!��� ��'�@���* 4�0���%�� ��& ���5�!""7'� �4883��������'3��'�� 8� ���/ � ����8!""786976!;)#7";)"a���'���' !'�b����b�.�(� � ��$(.�%-�������$.�2��� ���&�c'�(�������� ����� ����� �� � ���*�� �� ��� ����� ��� ��� ���� � ����� � ����.���� ���� ��.� !""9d!"))'� ������ ����� � <��� !")!� c �'� � �488�='���'���8)"':!")8 ���)7"9')!"!:)' *katrin s. kohl e-mail: kkohl@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e104, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts demographic health analysis by incorporation of census data with patient records christopher r. cuellar* and wayne loschen johns hopkins university applied physics laboratory, columbia, md, usa objective the objective of this project is to enable a deeper analysis of patient health by correlating patient health records with the census demographic data. based upon these correlations, the essence system will be enhanced with new query filtering capabilities. introduction electronic disease surveillance canonically represents analysis performed on health records with respect to their syndromes, complaints, lab data, etc. this data can tell the story of a patient’s current status but does not provide a holistic look at the where the patient is from. by incorporating census data, a deeper examination of the patient’s area can be performed which may result in discovery of risk factors associated with race, economic status, and culture. methods data was gathered from census surveys conducted by the us government in a comma delimited file [1]. datasets used included household income, poverty status in the past 10 years, and race. all data within each file was associated with a census zcta block. zcta’s, while similar to zip codes, do not share a one-to-one correlation with zip codes, and subsequently, need to be mapped appropriately to use them with zip code-based health records. the most common approach to this mapping is to calculate the centroid of the zcta region and project that onto the zip code region. we used a dataset that performed such a mapping for our zip code to zcta correlation [2]. finally, through collaboration with the florida department of health, we developed several groupings for the data. partitions of health records were then built based upon factors such as percentage of race or ethnicity in a specific zcta, median household income, and predominant race for the resident zcta block. these groupings were then incorporated into the query portal feature of the system. results users can now filter for all health data by a demographic factor, such as finding all patients from a predominantly hispanic zip code, or all fever cases from zip codes with a median income level of $50,000 or more. primary complications were due to the usage of zcta for census data and the usage of zip code for patient records. since there is no exact one-to-one mapping, it is impossible to assure that patients reside within a specific zcta without a more granular location being specified. zcta data is also subject to the number of people responding to surveys within a given area and can be unreliable for areas of low participation. the binning for census data is variable depending on the location which it is being applied, there is no perfect separation that can be applied especially when considering median household income. conclusions application of census data can be burdensome, especially as zip codes have a tendency to change in shape; however, the usefulness of census data in determining societal risk factors can be immeasurable. future work includes developing the capability to automatically determine changes in zcta and zip code representations. development of appropriate binning for census data is a problem of localization as features such as median household income can be overall higher or lower within a state compared to other states. finally, inclusion of a metric comparing cost of living should be added to get a better idea of the worth of specific median household incomes. this talk will detail the technical approaches, including complications, along with the types of data incorporated and the resulting forms of analysis possible. the datasets themselves will be discussed in terms of their granularity and what information they can provide. keywords census data; system development; query improvement references 1) u.s. census bureau; using american factfinder; http://factfinder2. census.gov;(may 2014) 2) uds mapper; http://www.udsmapper.org *christopher r. cuellar e-mail: christopher.cuellar@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e16, 201 ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 cost comparison model: blended elearning versus traditional training of community health workers mysha sissine 1* , robert segan 1 , mathew taylor 2 , bobby jefferson 1 , alice borrelli 2 , mohandas koehler 2 , meena chelvayohan 1 1. futures group international, washington dc 2. intel corporation, washington dc abstract objectives: another one million community healthcare workers are needed to address the growing global population and increasing demand of health care services. this paper describes a cost comparison between two training approaches to better understand costs implications of training community health workers (chws) in sub-saharan africa. methods: our team created a prospective model to forecast and compare the costs of two training methods as described in the dalburge report (1) a traditional didactic training approach (“baseline”) and (2) a blended elearning training approach (“blended”). after running the model for training 100,000 chws, we compared the results and scaled up those results to one million chws. results: a substantial difference exists in total costs between the baseline and blended training programs. results indicate that using a blended elearning approach for training community health care workers could provide a total cost savings of 42%. scaling the model to one million chws, the blended elearning training approach reduces total costs by 25%. discussion: the blended elearning savings are a result of decreased classroom time, thereby reducing the costs associated with travel, trainers and classroom costs; and using a tablet with wifi plus a feature phone rather than a smartphone with data plan. conclusion: the results of this cost analysis indicate significant savings through using a blended elearning approach in comparison to a traditional didactic method for chw training by as much as 67%. these results correspond to the dalberg publication which indicates that using a blended elearning approach is an opportunity for closing the gap in training community health care workers. keywords: cost of mhealth, technology costs, community healthcare worker training, mhealth, subsaharan africa, human resources for health, blended elearning correspondence: email: msissine@futuresgroup.com* doi: 10.5210/ojphi.v6i3.5533 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mailto:msissine@futuresgroup.com* ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 introduction worldwide projections indicate that to meet the current global health care demand we need to train another 4.3 million health care workers – doctors, nurses, midwives and other health care professionals [1]. the health care worker shortage is disproportionately affecting africa where 25% of the global burden of the disease resides with only 3% of the global health workforce to confront it [1,2]. the shortage for community health care workers (chw) in sub-saharan africa alone is approximately one million [3]. chws provide vital life-saving services to communities that do not have regular access to health services. as a result, human resources for health is one of the most pressing global health challenges for the development community today [1]. response from donors and government agencies has been to increase programs, advocacy, and funding for training of health professionals including chws. there is a demand for low-cost, effective training mechanisms to increase the number of chws and improve the efficiency of existing health care workers. concurrent with the growing need for health care workers there has been an increase in mobile technology, user uptake, and supporting infrastructure. in sub-saharan africa the annual growth rate for mobile technology is 19% where networks coverage and user subscriptions are increasing [4]. to benefit from the growing uptake and infrastructure in mobile technology, development agencies, national ministries, private sector and ngos are using mobile health (mhealth) tools for successful and cost effective support of health data collection, surveillance, counseling, decision support, and supply chain management [5]. the surge in mobile technology uptake and use offers many opportunities including improved training of community health workers. in an effort to explore the benefits of integrating mhealth technologies to help train the chws needed in sub-sahara africa, the dalberg global development advisors published preparing the next generation of community health workers: the power of technology for training in may 2012 [4]. the paper commissioned by the iheed institute, barr foundation, mhealth alliance, and mdg alliance, gathered input from a wide assortment of notable ngos (e.g. worldvision, unicef, save the children, partners in health, amref, jhpiego, intrahealth), technology companies (intel, hp, vodafone, dimagi, grameen, millennium villages, brac), academia (johns hopkins, open university), the ministries of health for nigeria and kenya, and the world health organization [4]. the dalberg report specifically set out to determine if technology can be “harnessed in transformative ways to address critical gaps in community health worker training in sub-saharan africa”. [4] the report explored the concept using a blended elearning approach for training health care workers, which in addition to classroom time, includes learning from content on mobile applications. the blended elearning approach mixes live training with multimedia applications as an effective pedagogical way to foster interaction, repetitive learning, supervision and monitoring. the current model for training health care workers is a didactic classroom setting for training alone [4]. when compared to the current chw training model, the dalberg report showed that the blended elearning strategy is a promising, innovative and efficient approach to training chws. in addition to reducing costs for training, the blended elearning approach could improve standardization of training materials and increase retention to course materials because of onhttp://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 demand access to revisit course materials. a blended elearning approach also includes multimedia materials, visuals and audio important for individuals with different learning styles or to assist learning for students with limited literacy and education background. further, one study [6] indicates that rich multimedia content contributes to faster and better training but it is only being used in about 10% of training environments. ninety percent of chw trainings are using paper based content like flipcharts, handouts and textbooks. the dahlberg report concluded that a blended approach to learning was a valuable tool for costeffective and sustainable training. up to eighty percent of the training content could be standardized and shared with the blended approach, and digital content is easier to transfer and localize. this is particularly relevant for the developing community where a blended elearning approach can be used to scale up much needed training initiatives to meet health care demands and fill the community health worker gap. budgets for development programs are limited and cost is a critical consideration for implementation and ongoing use of a capability solution. sustainability is determined by availability of skills to manage and support a solution and by the flexibility of a solution to adapt to evolving requirements. using the results of the dahlberg report, our team set out to explore the question: what is the cost for a blended elearning approach as suggested by the dahlberg report and how does this differ from traditional didactic training costs? to address this question our team created a costing model to forecast and compare the costs of two training methods (1) traditional didactic training and (2) blended elearning approach. we will also explore how well these solutions scale to large populations, while being flexible enough to support differing requirements. literature review in order to gather information to support the analysis and research, we began with a literature review on pubmed in april 2014. selected publications focused on research regarding cost of blended elearning for community health care workers. keywords used in the search included: model, forecasting, costs, comparative cost, mhealth, training, health care worker(s), global health, developing countries, and elearning, technology. initial search results returned over 200 articles, however most were excluded because not all studies were conducted in a global setting and were therefore not relevant to a low-income setting. themes that emerged from the literature search are: 1) a new focus and growing interest in using ehealth and mhealth to strengthen learning for medical professionals both in domestic and international setting [7-10]; 2) lack of formal outcome evaluations of these technologies in developing countries and conclusive evidence evaluating programs [6-9,11]; and 3) lack of evidence regarding the cost of these ehealth and chw training programs [9,12,13]. http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 methods costing model using the results of the dahlberg report, our team set out to explore the question: what is the cost for a blended elearning approach as suggested by the dahlberg report and how does this differ from traditional didactic training costs? to address this question our team created a costing model to forecast and compare the costs of two training methods (1) traditional didactic training and (2) training with a blended elearning approach. the cost model created is a prospective model, based on expected future in-country costs. it is not a model of current existing training programs in-country, however the inputs used to populate the model are based on real cost data from the literature [12], cost data from intel corporation and expert opinion from technical staff working with futures group in nigeria. the model was built in microsoft excel 2010. all costs are listed in us dollars. the team used input data gathered from nigeria to investigate the cost for the training of community health workers. nigeria was selected because of the population size, importance within the region, and because futures group has a local presence which allowed for better access to reliable cost data during our data collection period in february 2014. our costing model compares two scenarios. the first is the baseline training which includes the input costs required to conduct a traditional didactic community health care worker training (baseline training). the second scenario includes the input costs of a blended elearning training consisting of a reduced in-class training component, supplemented with out of the classroom elearning activities (blended elearning training). in addition to comparing elearning training costs, we include and compare costs for technology and connectivity to support the ongoing data collection needs of the chw. after running the model for training 100,000 chws across five years in each scenario we compared the results. model inputs the inputs applied to the model came from the literature [4,12], local futures group technical staff and intel corporation. the baseline training consisted of in-classroom training for 12 weeks in year one [4,12]. the blended elearning training consisted of reduced in-classroom training to 6 weeks combined with external elearning on a mobile device with interactive multimedia such as video, audio and visuals [4]. cost associated the facility, classroom supplies, instructor travel, instructor per diem, instructor lodging, chw per diem incentive, and chw salaries were based on by expert opinion by futures group technical team. we estimated chw annual salaries to be $960 per year. futures group technical staff also provided local nigeria cost data for smart phones (table 1), voice/ data connectivity and solar charging packs (table 2). costs included in the model for year 1 includes device, connectivity and solar charger costs for each chw. years 2-5 includes inflated voice and data connectivity costs. we found that the average smart phone cost in nigeria is $150 and the average data cost is $40 per month. http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 table 1: comparison of smartphone costs across providers in nigeria service provider smartphone model cost (usd) airtel nokia asha 303 127 airtel nokia lumia 510 174 airtel samsung galaxy young 125 mtn infinix race 126 glo nokia lumia 520 174 glo blackberry 9320 177 table 2: comparison of data costs across providers in nigeria service provider data allowance cost (usd) airtel 4gb 25 mtn 4gb 49 glo 4gb 37 a variety of devices and data connectivity options were reviewed to determine the best balance of technology costs and functionality to meet the needs of chws. in addition to overall cost of the training programs, consideration was given to device, connectivity, device charging requirements and device functionality to meet the ongoing data collection needs of chws. finally, we considered the use of a combination of a feature phone and a tablet computer rather than a smart phone for the blended elearning training and ongoing data collection needs of chws. based on the opinion of in-country staff, our model included the assumption that chw would already own a feature phone therefore feature phone costs were not calculated in the model. intel corporation provided cost data for tablet devices. tablets would have occasional connectivity, which offers chws participating in the blended elearning training the ability to download and upload training materials and content from a “hot spot” or wi-fi enabled area and store them for offline use. other inputs into the model included inflation rates and attrition rates. inflation rates incorporated into the model are 10.5% based on average escalation in nigeria from january 2011march 2014 [14]. attrition rates of 5% were also included in the model and based on published literature [12]. model assumptions it is important to note the following assumptions that were made in the construction of the model. • there would be one classroom for every 50 chws • there would be one instructor for every 50 chws • all instructors would need to travel to the training location and would require a per diem rate • each chw would receive a per diem incentive of $103 per month during inclassroom training http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 • each chw would own a feature phone, therefore cost for purchasing a feature phone were not included in the model • each chw would participate in a 20 hour refresher course which would take place in a classroom setting results based on our model, a substantial difference exists in total costs between the baseline and blended elearning training programs. results indicate that using a blended elearning approach for training community health care workers in nigeria will provide cost savings of 19.6 million usd or a reduction of costs by 42% in classroom costs alone. baseline training classroom costs table 3 provides details on the cost for the baseline training or traditional didactic training approach which would requires 12 weeks (3 months) of in classroom training. total classroom cost is $47,094,000 across five years for training a total of 100,000 chw. note that units are 120,000 because of the 5% attrition rate that is applied to the model. table 3: classroom cost results baseline training item units number of units unit cost (usd) total cost (usd) classroom supplies /chw 120,000 36 4,320,000 classroom facility costs /classroom 2,400 900 2,160,000 chw per diem incentive /chw 120,000 309 37,080,000 lodging for trainers /trainer 2,400 950 2,280,000 travel/transportation for trainers /trainer 2,400 285 684,000 annual refresher course (y2-y5) /training 380,000 1.5 570,000 total baseline training costs 47,094,000 blended elearning training classroom costs table 4 provides details on the blended elearning training, which supplements classroom time with out of the classroom mobile training applications that are rich with multimedia content. the in-classroom work is reduced to 6 weeks (1.5 months). the total classroom cost for this program is $27,540,000 across five years for training a total of 100,000 chw. an attrition rate of 5% is included. http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 table 4: classroom cost results blended elearning training item units units unit cost (usd) total cost (usd) classroom supplies /chw 120,000 18 2,160,000 classroom facility costs /classroom 2,400 450 1,080,000 chw per diem incentive /chw 120,000 185 22,248,000 lodging for trainers /trainer 2,400 475 1,140,000 travel/transportation for trainers /trainer 2,400 143 342,000 annual refresher course (y2-y5) /training 380,000 1.50 570,000 total blended training costs 27,540,000 classroom cost comparison one cost driver in this model are the costs associated with in-classroom training. comparing the two scenarios for training community health workers, the baseline training method is more costly than the blended elearning approach. switching over to a blended elearning training program would reduce the costs for training 100,000 chw in nigeria by 42%. these cost savings are a result of decreased classroom time, thereby reducing the costs associated with travel, trainers and supplies. device cost comparison a variety of devices and data connectivity options were reviewed to determine the best value and balance of technology costs and functionality to meet the needs of the programs. costs of device, connectivity, device charging requirements and device functionality were considered. devices and connectivity mechanisms generally fell into one of two groups (1) costs associated with using smart phones and (2) costs associated with using a combination of feature phone and tablet with wifi. table 5 provides a comparison of these two groups. with cost the only consideration, it is clear that selecting a tablet with wifi and feature phone would be the least expensive option. by selecting to use either a tablet and feature phone in a mhealth intervention, the program would eliminate the need for a monthly data package associated with a smartphone therefore reducing technology costs. recognition is made that cost savings is based on occasional free wi-fi accessibility which may not be available in all locations. table 6 depicts the cost savings once the technology is applied at scale to 100,000 chw across a five year training program. costs included in the model for year 1 include technology purchase, data/voice connectivity, and purchase of a solar charger. years 2-5 includes inflated data/voice connectivity costs. http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 table 5: technology cost comparison for the training programs cost smartphone costs (usd) tablet & feature phone cost (usd) device 150 160 connectivity (annual)* 660 180 data (monthly) 40 0 voice (monthly) 15 15 solar charger 40 40 total (device, connectivity, charger) 850 380 *annual connectivity is based on data monthly + voice monthly the feature phone provides instant communications though sms and voice without the additional costs associated with a monthly data plan as would be required by the smartphone. in addition, the tablet offers additional functionalities not found in the smartphone like access to a usb drive for medical devices, file sharing, an office suite, multimedia training, medical journals, decision making support, lab and pharmacy information, full electronic medical record system (emr) among others. the limitation of the tablet is that the chw would need to have regular access to wifi for communication of training assessments, patient status and population health data. refresher training can be accessed through flash drives and direct downloads. table 6: technology and connectivity cost comparison for trainings for one hundred thousand chws year smartphone with data (in millions usd) tablet & feature phone with voice and wi-fi (in millions usd) tablet & feature phone net savings (in millions usd) year 1 85 38 47 year 2-5 341 93 248 total 426 131 295 training costs for one hundred thousand chw by running the model including the classroom training costs with the feature phone and tablet we see that, a savings of 67% ($314.5 million) can be achieved in comparison to the baseline training program (table 7). table 7: training and supplies cost comparison for one hundred thousand chws baseline (in millions usd) blended (in millions usd) net savings (in millions usd) classroom training 47.1 27.5 19.6 smartphone 426.0 0.0 295.0 feature phone/tablet 0.0 131.0 total cost 473.1 158.5 314.5 http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 scaling up training costs to one million community health workers when our model is scaled up to training one million community health workers and includes additional costs associated with chw salaries, management and overhead expenses, the blended elearning training approach that uses wi-fi tablets is still cost saving by 25% or $3.62 billion usd (table 8). table 8: savings from a blended elearning training program for one million chws input baseline (in billions usd) blended elearning (in billions usd) savings (%) device 4.27 1.32 69 training 0.47 0.28 42 overhead 1.89 1.72 25 salaries 5.92 5.92 0 management 1.97 1.97 0 total 14.53 10.91 25 discussion for an elearning solution to be sustainable and scalable it must be low cost and flexible. it must be able to function with minimal or occasional connectivity. it must also enable measurements of its effectiveness to facilitate evidence for program evaluation and to further inform best practices. the program must also meet the requirements of local supportability and have the ability to adapt general content into local languages. as recommended in the dalberg report, mhealth content developers and elearning content should disaggregate content from underlying technologies to improve the spread of content easily. in addition, the global health community should support investments in platforms that facilitate sharing content [4]. ideally a technology solution would provide both training and job capabilities. examples of this are data collection and reporting, communications, medical diagnostics decision support, medical record keeping. limitations there are many differences between low resources countries and regions where infrastructure, culture, costs, literacy, and security of health data can vary. due to this variation, there are several limitations and the result of this study may not universally apply to other regions or programs. • first, there are infrastructure differences between countries adding complexity to generalize mhealth and elearning approaches. some countries do not have broadband infrastructure or internet access necessary to support the blended elearning approach. the dalberg report pointed out that only 9.6% of africans use the internet and 40% of the sub-saharan african population is still not covered by cellular networks [4]. • costs inputs will also vary depending upon the setting. cost data used in this analysis was from nigeria and results will likely differ in a different setting. http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 because of this, the savings as reported in this analysis may vary when applied to another country. • the assumption of occasional free wifi connectivity may not be appropriate in all settings. while the blended elearning approach does not assume constant or even regular access to wifi; even occasional access to wifi may not be realistic in certain settings. • cultural differences should be considered. in some countries gender differences may play an important role in access to technology. in some regions women do not have access to mobile devices limiting the success of a blended elearning approach to training female chws. literacy levels may also vary from country to country and can challenge some aspects of an elearning program. in addition, user experience level may make a blended elearning approach less effective for training chws. • standardization of digital learning modules can reduce costs and provide a baseline for quality assurance. the need for efficient training to meet the goals of training one million chw’s will require a coordinated approach that reduces the need for duplicate content development. conclusion the results of this cost analysis indicate significant savings through using a blended elearning approach in comparison to a traditional didactic training for the additional one million health care workers needed in sub-saharan africa. results indicate that training cost can be reduced by 42% when compared to a baseline traditional didactic approach. the cost savings are due to reductions in in-class room training time, cost associated with instructor travel, and device selection. these results correspond to the dalberg publication which indicates that using a blended elearning approach is an opportunity for closing the gap in training community health care workers. further analysis indicates that additional cost savings can be met by using a tablet with wi-fi rather than a smartphone with a data plan. when the tablet and feature phone combination was used a savings of 67% was achieved compared to the baseline approach using a smartphone with monthly data plan. using nigeria as an example, this paper reports on a cost comparison between the traditional didactic approach (baseline) and the blended elearning approach. there are however several limitations that arise when pinpointing and assigning cost data and country context. these include differences between countries with infrastructure, culture, costs, and literacy. therefore, there are many factors to consider when determining the best method for training a health care workforce in a low resource country. in the right setting, the impact of a blended elearning approach for community health care workers is substantial. in addition to the potential for cost savings, there is promise for greater impact and retention because elearning trainings can be shared and reviewed on-demand as refresher material. course work in an elearning format can also increase flexibility with scheduling and coordination [9]. the blended elearning approach also increases standardization http://ojphi.org/ ojphi cost comparison model: blended elearning versus traditional training of community health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e196, 2014 of training and translates best practices to wide audiences quickly. finally, working with educational materials in an 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unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 9 (page number not for citation purposes) isds 2013 conference abstracts public health implications of cryptococcal infection among hiv patients on antiretroviral therapy in hospital in shika, nigeria aisha o. abubakar4, beatty v. maikai*1, bolanle o. musa2 and adebola t. olayinka3 1department of veterinary public health and preventive medicine, ahmadu bello university, zaria, nigeria; 2immunology unit, department of medicine, ahmadu bello university teaching hospital, shika, nigeria; 3department of medical microbiology, ahmadu bello university teaching hospital, shika, nigeria; 4ahmadu bello university teaching hospital/aids preventive initiative nigeria, shika, nigeria � �� �� �� � � �� �� �� � objective �������� � ���� �� � ��� � ��� �������� ��� �� ���� ��� � ������ ���� ����������� 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��������*�%==)� )111��&��������#���)11+5+:��:�-7=�-=6 *beatty v. maikai e-mail: beatt18@yahoo.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e61, 2014 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 95 (page number not for citation purposes) isds 2013 conference abstracts estimation of basic reproduction number of enterovirus 71, coxsackievirus a6 and a16 in hand, foot, and mouth disease outbreaks in singapore cindy lim*1, lili jiang1, li wei ang1, stefan ma1, lyn james1 and jeffery cutter2 1epidemiology & disease control division, ministry of health, singapore; 2communicable diseases division, ministry of health, singapore � �� 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media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 1epidemiology, mcgill university, montreal, qc, canada; 2epidemico, boston, ma, usa; 3university of massachusetts medical school, worchester, ma, usa; 4computational epidemiology group, children’s hospital informatics program, devision of emergency medicine, boston children’s hospital, boston, ma, usa objective the current analysis describes the scope and trends in united states content from the vaccine sentimeter’s results, while seeking to examine any possible links between media content, vaccine coverage, and reported vaccine adverse events in the country. introduction the success of public health campaigns in decreasing or eliminating the burden of vaccine-preventable diseases can be undermined by media content influencing vaccine hesitancy in the population. a tool for tracking and describing the ever-growing platforms for such media content can help decide how and where to invest in campaigns to increase public confidence in vaccines. the vaccine sentimeter, developed from the healthmap project, aims to assist public health practitioners in maintaining or improving vaccine coverage through a real-time, online visualization tool of global media content on vaccines. methods the vaccine sentimeter collected over 9,552 online articles covering us events from 2012 to 2014, from sources such as news aggregators, government reports, and blogs. the sources were searched every hour using key terms related to vaccines, such as brand names, adverse events, or vernaculars (e.g. “shots”). through both manual and automated curation, articles were classified by date, location, vaccine, category (topic such as “outbreaks”,“research”, “costs”, etc.), and overall article sentiment (positive, neutral/unclear, or negative). data on vaccine coverage rates were obtained online from the results of the center for disease control’s national immunization survey (nis), the behavioral risk factor surveillance system (brfss), and the national health interview survey (nhis). reports of adverse events were collected from online data of the vaccine adverse event reporting system (vaers). national coverage for 12 vaccines for which sufficient reporting was found in the vaccine sentimeter articles were examined for possible links to the sentiment of media content on each vaccine. finally, the categories of media content were compared to that of the vaers to examine whether the media content about adverse events and other topics followed similar patterns as those reported in the public. results the three most frequently reported states in the media were california, new york, and texas with, respectively, 760, 517, and 393 classified reports. most frequently reported vaccines included influenza/h1n1 vaccines (n=2750), human papillomavirus vaccines (hpv) (n=1678), vaccines of the mumps, measles, and/or rubella family (mmr) (n=1363), vaccines of the diphtheria, pertussis, and/or tetanus (dpat) family (n=847), and polio-related vaccines (n=450). across the us, 10.2% of vaccine media content was classified as being of an overall negative sentiment, while 86.4% were positive and 3.4% neutral/unclear. most frequent classified categories included policy recommendations (n=1365), delivery strategies (n=907), outbreaks (n=756), and vaccine effectiveness (n=617). national coverage for vaccines of interest ranged from 57% (hpv) to 92.9% (polio vaccines). the percentage of negative sentiment for these vaccines ranged from 1.6% (meningococcal vaccines) to 21.5% (hepatitis b vaccines). national vaccine coverage was negatively but not significantly correlated (r = -0.47, p = .122) with percentage of negative media sentiment for the 12 vaccines of interest. conclusions these and future findings emphasize the importance of the vaccine sentimeter as a valid tool for public health agencies. this tool may allow them to track any loss of confidence in a variety of vaccines, at either the national or state level, at an early enough stage to allow effective policy implementation. funding for development of the vaccine sentimeter was provided by sanofi-pasteur vaccination coverage for 12 vaccines in the us plotted over the percentage of negative sentiment in media content keywords media content; vaccine; healthmap; vaccine coverage; vaccine sentimeter *guido a. powell e-mail: gdpwll@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e73, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a 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imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts effective collaboration models for statisticians and public health departments steven e. rigdon1, elena naumova2, ian painter3, yulia gel4 and howard burkom*5 1biostatistics, saint louis university, st. louis, mo, usa; 2department of civil and environmental engineering, tufts university, medford, ma, usa; 3university of washington, seattle, wa, usa; 4university of texas, dallas, tx, usa; 5johns hopkins applied physics laboratory, laurel, md, usa objective the session will explore past collaborations between the scientist panelists and public health departments to highlight approaches that have and have not been effective and to recommend effective, sustainable relationship strategies for the mutual advancement of practical disease surveillance and relevant academic research. introduction public health departments need enhanced surveillance tools for population monitoring, and external researchers have expertise and methods to provide these tools. however, collaboration with potential solution developers and students in academia, industry, and government has not been sufficiently close or well informed for rapid progress. many peer-reviewed papers on biosurveillance methods have been published by researchers, but few methods have been adopted in systems used by health departments. in a 2013 biosense user group survey with responses from users in more than 40 u.s. states, access to improved analytic methods was a top priority [1]. among the tools most desired by respondents were the essence biosurveillance system with multiple analytic tools and statistical software packages such as sas. multiple obstacles have slowed the progress of practitioners and developers who seek the development and implementation of useful analytic tools. first, the epidemiological challenges and associated operational constraints are not sufficiently understood among academic developers. many health departments do not have the resources to hire such developers beyond maintenance of information technology, and the health monitors are typically too busy to publish in peer-reviewed journals. second, data cannot be shared because of privacy and proprietary limitations with varying local rules. data-sharing has posed difficult administrative problems, both within and external to health departments, in the course of isds technical conventions committee efforts to promote interactions through use case problems [2]. third, aspects of situational awareness vary widely among health monitors at different jurisdictional levels, so analytical challenges and constraints vary widely among potential users. practitioners have pointed out that “surveillance is local”, but local operational and data environments vary widely. a fourth main issue is cross-cultural: understaffed health departments must respond to successive crises and often lack the time for requirements analysis and technical publication. such client work situations complicate interaction with academic environments of semester schedules and limited grants and transient student support. this panel brings together academic statisticians who have had successful direct relationships with public health departments to discuss how they have dealt with these challenges. description panelists will describe their experiences working on projects for health departments, including actual applications as examples. discussions will cover system studies as well as the creation and implementation of operational tools. issues discussed will include: • analysis of technical requirements and scoping technical problems for health department utility and tractability of analytic approach. • adaptation of traditional statistical approaches, such as relaxation of data distribution assumptions and management of changing data environments • communication of solution methods to non-statisticians • data-sharing barriers, their effects on progress achieved, and means for overcoming or working around them • obtaining and sustaining needed funding for collaborations panelists will derive advice for public health practitioners seeking analytical help in the areas of forming relationships, framing problems, communicating results, and seeking funding. audience engagement audience reactions and experiences will be steered to formation of models for collaboration regarding issues of analytic requirements, communication, data-sharing, and funding. keywords statistical collaboration; analytic methods; technical conventions references [1] gibson j., karras b.t., gordon g.s., biosense 2.0 governance: surveying users and stakeholders for continued development, online journal of public health informatics (2014) vol. 6, no. 1, http://ojphi.org/ojs/index.php/ojphi/article/view/5051 [2] baer a., rennick m., kite-powell a., atrubin d., guidelines for navigating human subjects review and preparing data sets for sharing with the isds technical conventions committee, online journal of public health informatics, vol 6, no 1 (2014), http://ojphi. org/ojs/index.php/ojphi/article/view/5109 *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e5, 201 crappdf.pdf 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attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts enhancing surveillance for arboviral infections in the arizona border region orion mccotter*1, frank vanskike1, 2, kacey ernst2, ken komatsu3, harold margolis4, stephen waterman5, laura tippit1, kay tomashek4, anne wertheimer2, sonia montiel5, catherine golenko1 and elizabeth hunsperger4 1arizona department of health services, office of border health, tucson, az, usa; 2university of arizona, mel and enid zuckerman college of public health, tucson, az, usa; 3arizona department of health services, phoenix, az, usa; 4centers for disease control and prevention, division of vector-borne diseases, dengue branch, san juan, puerto rico; 5centers for disease control and prevention, division of global migration and quarantine, u.s. mexico unit, san diego, ca, usa objective to enhance arboviral surveillance and laboratory capacity to establish a surveillance baseline for the emerging threat of dengue fever in the arizona-mexico border region. introduction west nile virus (wnv) and dengue virus (denv) are both arboviruses which are transmitted to humans by an infected mosquito bite during blood-meal feeding. the clinical presentations of nonneuroinvasive wnv and dengue fever are similar, and symptoms may include acute onset of high fever, headache, myalgia, arthralgia, nausea, vomiting, and often a maculopapular rash. more serious manifestations of these viruses include fatal encephalitis and meningitis in wnv patients and fatal hemorrhagic disease in dengue patients. over the last decade, wnv has spread rapidly across north america, reaching arizona in 2004, and has become a significant cause of human illness since that time. even though dengue has been described as primarily a disease of the tropics and sub-tropical areas, there is a small but significant risk for dengue outbreaks in the continental united states as evidenced by surveillance efforts in texas that identified local dengue transmission in 2005. in recent years, outbreaks of dengue have occurred in mexico border states, most notably sonora in 2010. that same year, arizona had the highest incidence of wnv cases in the u.s. including number of neuroinvasive disease cases, total cases, and number of deaths per state. the emergence of denv and wnv as important public health problems maybe have been due to non-effective mosquito control, global demographic changes (urbanization and population growth), increased air travel, and inadequate surveillance. methods vector mapping: mapping techniques will be utilized to visually depict aedes aegypti populations captured from previous seasonal public health environmental vector trapping programs. laboratory capacity: multi-state laboratory training by cdc dengue branch was held in october 2012. surveillance: the wnv cases that present to medical services for wnv testing and reported to public health officials are the most severe nueroinvasive cases. much less is understood about the non-neuroinvasive cases with often present with non-descript symptoms. results vector mapping: comparative densities of ae. aegypti with academic partners of the entomology and public health conducting a study capturing ae. aegypti may help to enhance environmental programs. laboratory capacity: the laboratory training will cover conventional serological methods as well as recently fda cleared molecular rt-pcr. participants will include public health laboratory personnel working in molecular and serology diagnostics and other binational partners. surveillance: a convenient seroprevalence study at sentinel-hospital site of symptomatic patients presenting in arizona border hospital sites will be performed to better understand circulating levels of arboviral infections. conclusions appropriate and timely response to surveillance data is the key to identification human and animal disease associated with wnv, denv, and other arboviruses. the mosquito vector ae. aegypti is well established widespread and thriving in arizona yet there is no autochthonous transmission of denv identified to date. the results from this study will identify gaps and potential prevention and control measures for emerging infectious diseases including wnv and denv in arizona. keywords dengue; surveillance; emerging infections; dengue fever; arboviral acknowledgments us-mexico border states, local health departments, sonora secretariat de salud, arizona state public health laboratory. references hayden, m.h., et al., microclimate and human factors in the divergent ecology of aedes aegypti along the arizona, u.s./sonora, mx border. ecohealth, 2010. 7(1): p. 64-77. walker, k.r., et al., human and environmental factors affecting aedes aegypti distribution in an arid urban environment. j am mosq control assoc, 2011. 27(2): p. 135-41. hoeck, p.a., et al., population and parity levels of aedes aegypti collected in tucson. j vector ecol, 2003. 28(1): p. 65-73. botz, j.t., survey of aedes aegypti eggs in and around homes in tucson, arizona. j am mosq control assoc, 2002. 18(1): p. 63-4. fink, t.m., et al., aedes aegypti in tucson, arizona. emerg infect dis, 1998. 4(4): p. 703-4. engelthaler, d.m., et al., the reemergence of aedes aegypti in arizona. emerg infect dis, 1997. 3(2): p. 241-2. *orion mccotter e-mail: orion.mccotter@azdhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e4, 2013 ojphi-06-e118.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 162 (page number not for citation purposes) isds 2013 conference abstracts a national electronic system for disease surveillance in rwanda (eidsr): lessons learned from a successful implementation nyatanyi thierry1, kabeja adeline1, asiimwe anita3, binagwaho agnes3, koama jean baptiste4, johnson pamela5 and kayumba kizito*2 1rwanda biomedical center, kigali, rwanda; 2ministry of health, kigali, rwanda; 3voxiva rwanda sarl, kigali, rwanda; 4cdc, kigali, rwanda; 5voxiva inc, washington, dc, usa � �� �� �� � � �� �� �� � conclusions ��������� � ��� �� ��������������������������� ������������� ������������� ��� ��� ������������������� ������������ � ��������� �������� ���������� ���� � ���������������������������������� � ��� �������������������� ������������������������������������������� ���� ����� ���������!����������� � �����"�!���������������������� ��!������������������������������������ ����� ������������#������� $ �!������ ����������������������%��#$�&� �������������� ��������� 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michelle lally, md3; gregory zimet, ph.d4; kristin meyer, ph.d2; samir souidi1;adolescent trials network for hiv/aids interventions 1. information technology, population council, new york, ny 2. department of epidemiology, university of california, los angeles (ucla), ca 3. alpert medical school of brown university and lifespan hospital system, providence, ri 4. indiana university, indianapolis, ia abstract we describe building an avatar-based self-report data collection tool to be used for a specific hiv prevention research project that is evaluating the feasibility and acceptability of this novel approach to collect self-reported data among youth. we discuss the gathering of requirements, the process of building a prototype of the envisioned system, and the lessons learned during the development of the solution. specific knowledge is shared regarding technical experience with software development technologies and possible avenues for changes that could be considered if such a self-report survey system is used again. examples of other gaming and avatar technology systems are included to provide further background. keywords: self-report survey data collection; acasi; capi; casi; avatar; interactive questionnaire; php; html5; hiv prevention correspondence: smierzwa@popcouncil.org doi: 10.5210/ojphi.v6i2.5347 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction researchers who are involved in self-report data collection continue to look for ways to collect better survey result data with electronic data capture systems. survey tool strategies such as acasi (audio computer-assisted self-interviewing), casi (computer-assisted self-interviewing), capi (computer-assisted personal-interviewing), and ivrs (interactive voice response system), to name a few, do currently exist, but finding other novel methods continues to be something considered by social science and epidemiology researchers. the use of avatars and gaming systems is popular with adolescent youth. these electronic technology entertainment systems tend to keep adolescents engaged. this article describes the situational need for a customized avatar-based research data collection system and takes the reader through the expected and unexpected challenges experienced. http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi background several promising hiv preventative medications are under study and offer great potential to alter the course of hiv incidence trends. however, the collection of accurate adherence records by participants has been a persistent challenge across a diversity of studies and populations [1,2]. frequently participants over-report the consistency with which they’ve adhered to a medication regimen making it difficult for researchers to assess the acceptability of promising preventive methods in real-time. methods of survey administration such as acasi (audio computer-assisted self-interviewing) data collection have resulted in minimal improvements in the accuracy of participant adherence reports in clinical trials, and they have greater effects when other sensitive behaviors are probed such as sexual practices [3]. new methods of electronic survey administration are critically needed, particularly approaches that are appealing to young men who have sex with men, as the incidence of hiv has remained highest among this group in the u.s [4]. to address this challenge, our research and technology team developed an innovative web-based data collection system that uses a participant-designed avatar to deliver the survey. research indicates that gaming systems and avatar-based programs may be especially attractive to youth. programs that allow participants to customize their own avatar may prompt greater engagement [5-7]. self-report surveys can be lengthy, and may include detailed calendar recall type questions. through the use of a self-created avatar, participants may remain engaged and complete longer, more detailed on-line surveys. through our study, we aimed to test and assess if our avatarcentered survey method might improve the accuracy of self-reported adherence to biomedical hiv prevention methods. the system needed to be accessible from multiple computer, tablet, and smartphone platforms as well as through a variety of browsers. the customized avatars would appear on each question screen, but would move to different locations on the screen and present the questions within a text bubble. the web-based self-report questionnaire system with avatars that we developed provides another potential survey tool for health researchers to utilize, particularly where there is a concern about the honesty or validity of responses. although the system was created for a specific hiv prevention project, it could also have applications in other research efforts including electronic data collection with a younger population. for example, an additional application might include health behavior research studies with efforts to solicit accurate information about cigarette smoking, illegal drug use and alcohol consumption, and perhaps even dental hygiene. in this article we will share with you our process for the design and building of such a data collection system. we will then share issues we had foreseen as well as those we did not, and finally discuss potential future uses of avatars in electronic data collection systems. methods in approaching the software development of the data collection tool, the more traditional software development life cycle (sdlc) methodology was utilized. this includes the phases of requirements specifications, software design, implementation and integration, testing, deployment and maintenance. the overarching research project included the use of a protocol document for hiv prevention. this planning document was instrumental in outlining the detailed requirements that help in the very important first step of sdlc which emphasizes planning. during the design phase, in the spirit of scrum framework, several iterations of the user interface pages were created http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi and shared with the investigative and protocol team leadership. utilizing this additional complement to the overall sdlc approach proved to be extremely important since expectations of the design were adjusted frequently before fully entering the programming phase of the effort. discussion how the requirements were established the research investigators were interested in an engaging electronic data collection system that could be used by participants located in the united states in a self-administered manner. the system needed to be available both onsite at clinics and more often, remotely from outside the clinic with connectivity to the internet via smartphones, tablets, laptops or desktop computers. participants needed to be able to create their own avatars by selecting from a variety of personal qualities and attributes. when resuming a questionnaire via subsequent login, the self-created avatars would re-appear, connected to the user’s unique profile/login. in addition, the project included a follow-up questionnaire and upon login to the second questionnaire at a different time, the self-created avatar would again be presented. site administrator staff needed to be able to technically enable surveys for participants; monitors survey completion status, and provide automated emails when surveys were completed. the ability to resume an already-started survey was necessary, but within a certain a time limit. when resuming an alreadystarted survey, the customized avatars would be available and present to the participant. security was needed for assigned user accounts, the internet or browser communication traffic and finally, storage of the resulting data. prototypes were built and demonstrated to both the research investigator teams as well as the clinic staff responsible for enabling the surveys. comprehensive staff training and ongoing technical support were critical to the successful launch and implementation of the system. the majority of the previous data collection projects our technology team has been involved in required us to provide a system that could be used in less-developed areas of the world where interrupted internet and power were frequently experienced. in this case the customized data collection system with integrated avatars would be used domestically in the united states, and these challenges were not anticipated. previous experience in building self-report data collection systems that provided foundation, and experience the population council’s information technology group has assisted with the creation of customized acasi and capi/casi (computer assisted personal/self interviewing) solutions for over ten years in a variety of research projects. these solutions involved the development of a full customized acasi module, in which respondents listen to prerecorded audio questions through headphones connected to a handheld computer and record their responses using a touchscreen [8]. our development experience includes research efforts designing data collection interfaces for semi-literate populations and implementing them in resource-poor areas. at the time of this publication, our team has overseen the technical implementation of approximately 19 distinct research projects in 10 countries and in 21 different languages. this depth of experience was one of the reasons our team was called on to assist with the creation of a web-based self-report questionnaire system that integrated participant generated customized avatars. when embarking on software development of any system, our development team http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi generally tries to first determine if any products are already available for purchase, rather than building a new system from scratch. after collecting the requirements and doing online research for existing solutions, we determined that a custom built solution was required, especially to integrate the more complex tracking and monitoring portal. building the prototype lots of trial and error and brainstorming took place during the building of the initial prototype of the avatar web-based self-report data collection system. at first the design included building separate applications that could run on traditional computers, tablets and smartphones with the variety of operating systems, such as ios, android, windows, linux and blackberry. our experience at the time indicated that users had the best user flow experience when specific programs were written for these different platforms. however, this was quickly dismissed since maintaining and building these individual programs would be time and cost prohibitive as well as creating the need to manage change and version control for each platform. ultimately we decided to create an html5 compliant website utilizing responsive web design (rwd) principles that could run on the required variety of user interface devices. the use of cascading style sheets in combination with javascript allowed for employing a rwd. the idea behind rwd is that the website would adjust automatically to the dimensions and form factor of whatever screen was being used. the site was also only reachable via ssl encryption. it should be noted that while the avatars employed were customized, they were not in the format of eca (embodied conversational agent), where the avatar can see and hear the respondent through a webcam and react to body posture and spoken language [9]. the core of the application was built using the programming language php, with jquery version 9, and the bootstrap framework. the back end database is microsoft sql server 2008, which makes exporting of the resulting data possible into a variety of formats, including excel, and the website site is running on iis 7.5. it was felt that these were the most proven technologies and had the best chance of performing as expected on the broadest range of devices. the data flow design including database updates are demonstrated in figure 1. we considered using html5 local storage, but that meant that there was a possibility that sensitive data would be stored on participants’ devices, such as their smartphones. challenges that were expected building websites that will be thoroughly supported on all web browsers is not something to be taken lightly, especially given the gamut of devices that can access the web. smartphone browsers change over time, as do most other browsers, and this can create problems. in our case the surveys were being completed on a variety of smartphones and we did see several instances where we had to make small adjustments because of an upgraded browser. when creating sites that provide data from a back-end database it is important to make sure the screen and interface response are almost instantaneous, otherwise there is a risk of delays when a user attempts to move from one screen to the next, as is the case in responding to questionnaires. we anticipated that there may be cases where a particular browser on a particular machine may not work, so we were prepared to suggest that participants try another device if they were having a problem with the site functionality. http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi figure 1: data flow diagram additionally, virtual worlds that include 3d avatars such as second life are very sophisticated in the level of imagery they use. we realized this at the onset of building the website questionnaire system, but resource and time restrictions hindered our efforts. as technology advances and programming costs shift, we would aim to make improvements to the visual appearance of future avatars and their environments to heighten their life-like qualities and capabilities. although we do not envision a system similar to second life in use, we would consider some of the future visual qualities could include the ability for greater movement and flexibility of the avatars. challenges that were unexpected and what we would do differently when you think of avatars you may imagine second life – a very rich and high-end multi-user 3d-like virtual web site that provides a surreal lifelike feel. while we knew that we would not be able to build on the same scale or on par with the graphical complexity of second life, we did not expect it would be as difficult as it was to create even the simple screens that permitted the user to create their own custom avatar. for this we may consider using a design service specialist or firm in the future if we are to create another custom avatar function. because of time constraints and development complexity, we did not incorporate some higher-end more personal and/or specific qualities such as tattoos, hair type, and ear/nose rings, although we did receive requests for users to be able to add more of those types of details. a subset and sample avatar building screen is http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi demonstrated in figure 2. audio sound was played during the customizing section and the users had the ability to turn it off. figure 2: avatar builder screen we originally envisioned that our avatars would be able to move their arms and legs and walk around the screen. initial attempts to enable the avatars to move were successful. the movement included jumping, spinning and even walking. however, when these movements were tested on a mobile device browser in 3g the performance or speed and fluidity of the avatar movement was inconsistent and displayed intermittent movement or jerkiness. in addition, the javascript libraries that made use of html5’s canvas element were not consistent across browsers during development, meaning much code duplication. because of this it was decided to adjust the avatars for less movement, in order to ensure that the core focus of the questionnaire, which is to allow self-reporting, would be done. the site used for the web-based questionnaire system was built top-down, meaning first for a general computer web-browser and then retrofitted to work on mobile or tablet computers. after having gone through that approach, we think there are advantages to building bottom-up, meaning starting from the mobile device version and scaling up to the computer web-browser. the bootstrap framework software utilized certainly has its advantages, especially since it is probably the most known and tested responsive framework. before the beginning of february of 2014 we would probably have said that we would use a framework other than bootstrap, since the version of bootstrap we used had high overhead and was not really designed mobile first. a quick google search turns up many responsive frameworks to choose from. bootstrap 3, however, is http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi getting very good press, and seems to have major advantages to bootstrap 2. of course we would still look around for the toolkit that was right for us. one of the biggest issues is that this survey was designed to take advantage of the new technologies of html5. we thought that most participants would be using their smartphones, or perhaps their tablets or apple laptops. so far the reality has proven to be that more users than anticipated chose to take surveys at the clinic – which usually meant windows desktops which do not have full html5 functionality. while the current version of internet explorer (ie) is great, html5 compatibility is only guaranteed for ie10 and above. ie9 has some partial support. many html5 elements, such as the canvas element do not degrade gracefully in older versions of internet explorer. even though we promised a working survey for html5 compatible browsers only, it is still necessary, even for an audience of young “hip” persons, to have a fallback. unfortunately, in some cases, especially for presentation and validation, that would mean writing and maintaining two versions of the survey. admittedly we didn’t expect so many users using internet explorer on windows desktops. we also found out that some types of questions simply don’t lend themselves to handheld devices, no matter what framework or design is used. for example, long grids of yes/no survey data and calendars often didn’t display correctly on some handhelds. we do believe that html5 local storage has a bright future. it’s easy to work with and provides the ability to store data offline and then send to the main server when on-line. given the nature of this survey, it was not a fit, but perhaps in future surveys we will use it and thus reduce round trips to the server. of course we must keep in mind that html5 local storage are like web site cookies, very insecure, and client side encryption (typically using javascript) will deter few people from exploiting this security weakness to obtain this potentially sensitive information. vision for future use will the use of avatars continue to expand within the area of electronic data collection? the authors consider that this may occur and there appears to be movement in some areas surrounding ehealth where this is being demonstrated. in one case, the eu-funded myhealthavatar project is trying out a digital representation of a user’s health status, designed as a lifetime companion (avatar). your health avatar will facilitate the collection of, and access to, long-term health-status information. this would empower patients, would be valuable for healthcare decision and promising for health research [10]. electronic gaming systems such as the wii, xbox and others involve the creation of avatars that are presented to you as you start playing a digital game. these digital representations are popular and fun to create, and also add a level of customization created by the end-user to the canned games. the use of exergames is being studied for aiding in rehabilitation. these are electronic games such as the wii fit that allow for in some cases home rehabilitation [11]. second life continues to be popular, although at not the same level of social networking sites such as twitter and facebook. the authors wonder what it will take to make more people aware of second life. some initial research has indicated that esmart-mh (electronic self-management resource training for mental health) has shown promise with avatar-based depression self-management http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi among young adults (18-25 years of age). participants who received esmart-mh had a significant reduction in depressive symptoms over 3 months, while individuals in the attentioncontrol group condition had no change in symptoms [12]. the uses of games are further being assessed and evaluated by researchers to see if there are ways to help people in a variety of health-related areas. a randomized trial involving adolescents receiving cancer treatments showed that participation in a video-game intervention, increased adherence to select treatments for the intervention group [13]. those participating in the videogame intervention group played re-mission at least 1 hour per week for a 3-month period. the game was designed to highlight the importance of continuing treatment during remission despite uncomfortable side-effects. the research team also measured a statistically significant increase in cancer-related knowledge by the intervention group and an increase in self-efficacy that they could successfully complete treatment [13]. in a separate study, a team at iowa state tested how memory and brain enhancement could be facilitated through video games. they found surgeons who played video games were 27 percent faster and made 37 percent few mistakes during laparoscopic surgeries than did non-gamers [14]. one of the authors has adolescent children and has been guilty of telling them to stop playing electronic video games that include customized avatars. however, after building this novel data collection system and learning more about the potential gaming and avatar-centered technologies hold for promoting healthy behaviors, maybe it is time to say “go ahead and use these consoles!” limitations although the effort to design, develop and support the web-based self-report data collection system with avatars did further expand our experience and knowledge in this area, there are some limitations that could be addressed in further research and system development efforts. future development of the system would include a more comprehensive administrative portal for allowing researchers to construct their own surveys without the need of an information technology programmer. including pre-tests with survey respondents would allow for further feedback which may result in system design or user-interface changes. in addition, a greater variety of innovative question types could be created to provide alternatives for researchers and in turn the participants taking the web-based surveys. when considering electronic self-report data collection in research projects our information technology group has often been asked about the price feasibility for performing them. this paper did not address the cost savings or efficiency that can be obtained when automating the survey process. future efforts or research to address the costs associated when comparing manual (paperbased) versus web-based avatar questionnaires could help to address this scrutiny. conclusion researchers and information technologists continue to explore and look for innovative ways to collect self-report data via electronic surveys. introducing customized avatars and gaming techniques is one new approach that has been discussed in this paper and has demonstrated it is possible to create such a web-based solution. we envision that a future generation of the tool built and discussed above would include much more life-like graphics with the introduction of more http://ojphi.org/ web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi movement; this was not attempted in this version as it created technical hurdles that would have made the system unreliable and unusable in the context of the participant survey flow. acknowledgements we thank sarah thornton and the team at westat for their collaboration in setting up the operational data collection process with the many sites involved in this research study. we are grateful for the graphic design work provided by mike vosika and the expert article editing by irene friedland both at the population council and to marco adrian sanchez and mhealthcoach for their input and guidance on several programming aspects of the created solution. this work was supported by the adolescent medicine trials network for hiv/aids interventions (atn) grant nichd 5 u01 hd 40533 and 5 u01 hd 40474, which are funded by the national institute of child health and human development, and with co-funding from the national institute on drug abuse (nida) and national institute of mental health (nimh). references 1. grant rm, lama jr, anderson pl, et al. 2010. preexposure chemoprophylaxis for hiv prevention in men who have sex with men. n engl j med. 363(27), 2587-99. pubmed http://dx.doi.org/10.1056/nejmoa1011205 2. van damme l, corneli a, ahmed k, et al. 2012. preexposure prophylaxis for hiv infection among african women. n engl j med. 367(5), 411-22. pubmed http://dx.doi.org/10.1056/nejmoa1202614 3. gorbach pm, mensch bs, husnik m, et al. 2013. effect of computer-assisted interviewing on self-reported sexual behavior data in a microbicide clinical trial. aids behav. 17(2), 790-800. pubmed http://dx.doi.org/10.1007/s10461-012-0302-2 4. ackers m-l, greenberg ae, lin cy, et al. 2012. high and persistent hiv seroincidence in men who have sex with men across 47 u.s. cities. plos one. 7(4), e34972. pubmed http://dx.doi.org/10.1371/journal.pone.0034972 5. yee n, bailenson j. 2007. the proteus effect: the effect of transformed self-representation on behavior. hum commun res. 33(3), 271-90. http://dx.doi.org/10.1111/j.14682958.2007.00299.x 6. lim s, reeves b. (2006). being in the game: effects of avatar choice and point of view on arousal responses during play. paper presented at the international communication association, dresden, germany 7. noar, seth, m., grant harrington, nancy (2012). ehealth applications promising strategies for behavior change 8. mierzwa s, souidi s, friedland i, et al. (2013). approaches that will yield greater success when implementing self-administered electronic data capture ict systems in the developing world with an illiterate or semi-literate population, population council 9. malakhoff la, jans m. (2011). towards usage of avatar interviewers in web surveys, survey practice, vol 4, no 3 http://surveypractice.org/index.php/surveypractice/article/view/104/html http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21091279&dopt=abstract http://dx.doi.org/10.1056/nejmoa1011205 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22784040&dopt=abstract http://dx.doi.org/10.1056/nejmoa1202614 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23054034&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23054034&dopt=abstract http://dx.doi.org/10.1007/s10461-012-0302-2 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22529964&dopt=abstract http://dx.doi.org/10.1371/journal.pone.0034972 http://dx.doi.org/10.1111/j.1468-2958.2007.00299.x http://dx.doi.org/10.1111/j.1468-2958.2007.00299.x web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e191, 2014 ojphi 10. http://ec.europa.eu/information_society/newsroom/cf/dae/itemdetail.cfm?item_id=13590 11. ben schouten; stephen fedtke; tilde bekker; marlies schijven; alex gekker (2013). games for health, proceedings of the 3rd european conference on gaming and playful interaction in health care 12. pinto, m. d., hickman, r. l., clochesy, john, buchner, marc. 2013. avatar-based depression self-management technology: promising approach to improve depressive symptoms among young adults. appl nurs res. 26(1), 45-48. pubmed http://dx.doi.org/10.1016/j.apnr.2012.08.003 13. kato pm, cole sw, bradlyn as, et al. 2008. a video game improves behavioral outcomes in adolescents and young adults with cancer: a randomized trial. pediatrics. 122(2), e30517. pubmed http://dx.doi.org/10.1542/peds.2007-3134 14. bilton n. (february 16, 2014). disruptions: using addictive games to build better brains, new york times 15. hewett, paul c.; mensch, barbara; erulkar, annabel s. (2004). consistency in the report of sexual behavior among adolescent girls in kenya: a comparison of interviewing methods, sexually transmitted infections 80(suppl2) http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23265918&dopt=abstract http://dx.doi.org/10.1016/j.apnr.2012.08.003 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18676516&dopt=abstract http://dx.doi.org/10.1542/peds.2007-3134 why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research brian e. dixon, mpa, phd 1,2 ; shaun j. grannis, md, ms 2,3 1 indiana university, school of informatics, indianapolis, in; 2 regenstrief institute, indianapolis, in; 3 indiana university, school of medicine, indianapolis, in abstract despite the likelihood of poor quality data flowing from clinical information systems to public health information systems, current policies and practices are pushing for the adoption and use of even greater numbers of electronic data feeds. however, using poor data can lead to poor decision-making outcomes in public health. therefore public health informatics professionals need to assess, and periodically re-evaluate, the quality of electronic data and their sources. unfortunately there is currently a paucity of tools and strategies in use across public health agencies. our center of excellence in public health informatics is working to develop and disseminate tools and strategies for supporting on-going assessment of data quality and solutions for overcoming data quality challenges. in this article, we outline the need for better data quality assessment and our approach to the development of new tools and strategies. in other words, public health informatics professionals need to ask questions about the electronic data received by public health agencies, and we hope to create tools and strategies to help informaticians ask questions that will lead to improved population health outcomes. key words: public health informatics, systems analysis, information management introduction data are the lifeblood of the knowledge economy and health care. google, microsoft, and other modern companies compete with innovative solutions that support the generation, analysis, management, and exchange of data. health care and public health similarly focus efforts on generating, collecting, analyzing, and sharing data about individual patients and populations, respectively. data are vital to joint activities including surveillance of chronic and communicable disease, population health assessments, and health care policy. effective practice requires access to representative, complete, and timely data from multiple sources (1, 2). http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 unfortunately the quality of the data stored in information systems across industries and organizations is often poor (3). typical data quality issues encountered include: inaccurate data, inconsistencies across data sources, and incomplete (or unavailable) data necessary for operations or decisions (4). for example, a large bank found that data in its credit-risk management database were only 60 percent complete, which necessitated additional scrutiny by anyone using its data (5). in health care, the completeness of data in electronic health record (ehr) systems has been found to vary from 30.7 to 100 percent (6). evidence from the information management literature on the impacts of these issues is sparse, but estimates of impacts include: increased costs ranging from 8-12 percent of organizational revenue, and up to 40-60 percent of a service organization’s expenses consumed due to poor data; poorer decisions that take longer to make; lower data consumer satisfaction with information systems; and increased difficulty in reengineering work and information flows to improve service delivery (4). impacts on health care include ill-informed decisions when humans or machines use poor quality data inputs from ehr systems (7, 8). the public health literature suggests additional impacts of poor data quality issues for public health agencies and processes. for example, spontaneous reporting rates for infectious diseases range from 9 to 99 percent and have remained relatively unchanged from 1970 – 2000 (9). while some conditions, such as sexually transmitted infections, are reported approximately 80 percent of the time, many conditions (e.g. pertussis, shigellosis) are reported less than half of the time. timeliness, another attribute of data quality (3), has also been found to be a challenge in public health reporting (10). delays in the receipt of notifiable disease data (timeliness) and the lack of a complete set of reports (completeness) impact public health agency surveillance processes, including but not limited to the ability of agencies to respond to emerging disease threats. although the available evidence suggests that data quality issues are real and can have significant impact on operational processes, personnel, and budgets, too often public health practitioners assume that the increasing flow of electronic data from various information systems used in modern practice are of equal quality. this can lead to suboptimal outcomes beyond those described above, including failed implementations of public health information systems and inefficiencies in data collection and analyses. to ensure the information and knowledge generated from electronic observational clinical data can support effective public health practice, public health agencies must develop effective strategies to accurately characterize electronic clinical data sources both during the initial deployment phase and routinely reassess data characteristics of the operational system on a regular basis. in this paper, we outline the practical needs regarding careful analysis and continuous re-examination of data and their sources. we then describe how our u.s. centers for disease control and prevention (cdc) center of excellence in public health informatics is working with public health informatics stakeholders to develop a framework that will support the operationalization of analyzing data quality continuously within an organization. we conclude with thoughts on the implications for such a framework and our next steps in the development and validation of the framework. the importance of understanding data needs in public health developing methods and operational practices for assessing data quality requires an understanding of the various public health business processes, as well as the context in which those processes occur. business processes are sometimes referred to as “use-cases.” to http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 understand a particular business process or use-case, one must ask questions about what, where, when, why, and how data are collected, stored, shared, and used to support the activities performed by public health and health care professionals involved in the business process. an example of a public health business process is syndromic surveillance. syndromic surveillance detects initial manifestations of disease before diagnoses (clinical or laboratory) are established (11-13). data and information in syndromic surveillance systems come from a variety of sources, including hospital emergency department visits, ambulatory clinic visits, school absenteeism, poison control centers, and over-the-counter medication sales (2). in syndromic surveillance, agencies have an inherent need to understand the quality of the data from emergency departments, schools, and a variety of other sources to assess whether the data are able to accurately describe emerging public health threats. poor quality data will lead epidemiologists down dead end pathways including false-alerts or missed alerts, which will waste scarce public health resources. initially syndromic surveillance focused solely on patients’ chief complaints, the primary reason for their visit to a health care provider, as they presented in the emergency department (ed). chief complaints were found to be generally representative, complete, sensitive, specific, and reliable enough to detect emerging outbreaks including the start of the influenza season and bioterrorism events (2). subsequent research demonstrated that grouping chief complaints into syndromes and adding discharge diagnosis information into syndromic surveillance analysis improves validity and reliability (14-16). the current effort to improve the meaningful use (mu) of ehr systems incentivizes ed and ambulatory providers to submit chief complaint data to public health (17, 18). before leveraging chief complaint data from all ehr source systems, the quality of new sources must be assessed for completeness, validity, and reliability. for example, chief complaints reported by specialty providers, like thoracic surgeons, may not be as useful for detecting emergent threats as those from the ed or full-spectrum primary care clinics. data from specialty providers likely are predominantly grouped into a single syndromic category and therefore likely to lack specificity and sensitivity, an issue under investigation now by the indiana state department of health and other health departments across the u.s. in addition to syndromic surveillance, mu is driving health care providers to increasingly submit electronic laboratory reporting (elr) and immunization data to public health agencies. this will dramatically increase the number of data sources providing electronic data to public health agencies over the next 5 to 10 years. for example, in indiana where we have steadily increased the number of providers utilizing elr to report communicable disease cases to the state health agency over the last 10 years, we now have over 200 discrete data feeds from laboratory information systems, and expect that number to increase substantially. continuously analyzing data quality across those feeds is a necessary but challenging task. elr monitoring is especially challenging given the frequency with which laboratories update and append new tests to their catalogues (19, 20). beyond meaningful use, public health agencies must collect, manage, and utilize data and information across a number of program areas. each program area may impose unique requirements on data captured through a general or shared collection method. recently the state health agency in indiana sent a letter to several hospital laboratories highlighting the lack of guarantor information (e.g., name and contact information for the person financially responsible for health care expenses) when submitting elr information to the state’s communicable disease http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 program. the letter originated not from the communicable disease program director but a different program within the agency responsible for following up on case reports for minors. some public health officials perceived the traditional patient contact information in the elr messages as insufficient and decided to instead use guarantor information which typically contains parental or legal guardian’s contact information. however, these decisions were not shared with the data providers nor laboratory directors until after the state had performed an analysis to determine which hospitals were sending the guarantor information and which ones were not. a lack of communication about data needs and uses across program areas within the agency resulted in confused laboratory directors contacting vendors and data partners about the letters from the state. furthermore, it’s important to note that the laboratory information systems (lis), which are commonly used to process lab results, do not readily capture patient guarantor information. instead, this is often managed by enterprise billing software. consequently, supplying guarantor information likely requires non-traditional integration of two information systems, which may require new processes outside of the typical elr information flow. furthermore many public health professionals, like data consumers in other health care segments and industries, believe that data is easily and uniformly captured and stored across the spectrum of health care services. however, data are captured for a specific purpose, and the collection of additional data elements is costly. additional data elements require staff to ask for and then record the information, which translates into additional time and labor. software systems often must be modified to accommodate the new data fields and new workflow, also a costly process. therefore data consumers must understand the impact of the cost of data collection on the characteristics of data captured in various environments, like their completeness, when making decisions about secondary use. public health officials, for example, might benefit from understanding that elements like the provider’s phone number and address have little clinical relevance to the physician receiving the results of a lab test. these fields are poorly populated by laboratory information systems. although these fields can be required according to state and federal regulations, it does not guarantee that they will be complete and available for public health surveillance processes. for example, provider addresses and phone numbers are missing in more than 95% of the electronic laboratory messages sent directly from lab systems to the indiana network for patient care (inpc), a health information exchange repository that receives lab data from over 70 hospitals and their send out facilities (21). thus policies to require additional data elements are unlikely to impact data collection processes unless laboratories and hospitals are incentivized to capture the additional data elements needed for public health surveillance processes. the above set of issues illustrates a clear need for public health agencies to assess and document their data needs and sources. without clear expectations regarding public health’s desired needs and uses of data, information systems will likely receive, analyze, manage, and generate poor quality data, information, and knowledge about population health. one pressing role for public health informatics professionals is clear: they must support agencies’ assessment of data needs and sources. this means asking questions about the what, how, and why of existing business processes as well as the data, and their sources, involved in those processes. informatics professionals should further capture and share information with public health stakeholders regarding what can and cannot be feasibly collected from clinical information systems given existing information and work flows. however, there is a paucity of practical tools and resources for informatics professionals to utilize. in the rest of this article, we describe our approach to http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 developing a framework to support better assessment of needs and sources to support more efficient use of information systems and resources. what our cdc center of excellence is doing to better understand data needs the indiana center of excellence in public health informatics (icephi) resolved to develop methods and operational practices for assessing data quality from a myriad of clinical data sources. our initial attempt involved scheduling meetings with a broad range of stakeholders within the indiana public health community. we invited public health researchers, state health officials, local health officials, and informatics professionals in both health care and public health settings. while the meetings confirmed the real-world challenges previously described, they were suboptimal with respect to outlining the necessary methods and practices for performing data quality assessment. given the demands of public health personnel, not surprisingly meetings often only contained a subset of the full stakeholders involved in icephi, so it was not possible to have every stakeholder in attendance at any given meeting. furthermore, participating stakeholders often failed to provide full details about their information needs. this was due to a variety of reasons, including individuals lacking full details for a particular workflow, individuals who are uncomfortable speaking at meetings, and individuals who primarily work with reports but do not fully understand the provenance of the data that comprise those reports. we transitioned from the meeting strategy when it became clear that this approach would not capture data quality perceptions and needs in a systematic fashion that would meaningfully inform standard methods and practices. to systematically explore data quality perceptions and needs from a diverse group of stakeholders in public health and informatics, we selected a modified delphi technique (22). the delphi technique was chosen because this method can 1) structure group communication and decision making processes and 2) foster consensus from very diverse groups of individuals (23). delphi studies have proven useful in a range of industries and settings, including medicine, nursing, and informatics (23-25). the delphi study is being conducted as the third of three phases in our systematic approach to examining data quality perceptions and needs. in phase one, we convened a diverse set of purposefully selected stakeholders from across academia, state health agencies, and local health departments to define and prioritize a limited set of public health work processes that involve data collection from health care providers and information systems. the majority of our stakeholders were selected from indiana. however, we further invited select individuals from other states who had previously collaborated with our center of excellence. in phase two, informatics researchers within icephi compiled a list of data elements required under indiana state law to be reported to the health department for the various work processes under discussion. the stakeholders were invited to review this list and propose additional data elements they felt would be useful to have in those work processes. the additional data elements were appended to the respective work process lists. these final lists represent an initial, stakeholder-driven view of the data necessary to optimally perform common work processes in public health practice. these processes included: aggregate surveillance for influenza across counties and the state; individual case reporting for meningococcal disease; and ongoing monitoring of diabetes levels at the county and state level. http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 the third and final phase of our design involves administering the modified delphi study. we used the work processes, including their full descriptions and list of data elements, from the prior phases to create an online survey instrument to record the opinions and perceptions of a much wider group of public health stakeholders. our goal is to recruit 20-30 stakeholders with expertise in various aspects of public health practice, those which align with our work processes, and achieve consensus on the need for and use of the data elements specified in phase two. individuals in the larger stakeholder group will be able to suggest additional data elements each round, and participants will be able to amend their responses based on a review of the groups collective thinking to date (this is a major component of delphi studies). in addition to helping icephi more robustly specify the work processes and necessary data elements, stakeholders will also be asked to indicate their perceptions of the quality of data they currently receive from existing sources. we will, for example, ask about the timeliness and completeness of patient contact information provided in existing case reports. here, too, we hope to achieve consensus on the perceived need for specific data elements to be timely or complete since various data sources will possess different timeliness and completeness characteristics. to date we have completed the first two phases of our project as well as the survey instrument we will use to conduct the third phase. in the coming months, icephi will recruit a sizable group of public health stakeholders and conduct the delphi study with this group. future directions and work the primary goal of our work is to utilize the product of the study to develop and evaluate systems that can improve public health practice in indiana. we aim to take the results of the delphi study and begin applying it to our work within the inpc. we will focus on the prioritized work processes and data elements to ensure the inpc is addressing these adequately for the local and state agencies engaged with data exchange activities. where deficiencies are found, we will work with icephi stakeholders to improve data capture, analysis, and reporting. we will further examine and document the challenges with meeting the consensus-based priorities of public health stakeholders given the state of clinical information systems, mu, and the capacity of the inpc. our findings and lessons will be shared with the public health informatics community to inform future public health and informatics research, practice and policy in indiana and beyond. in addition to local applications, we intend to develop a workbook or other product to provide guidance on data quality issues to other public health entities. the workbook would consist of both a general framework for addressing data quality issues in a setting where electronic data is being received from numerous information systems and templates that agency personnel could complete to identify and prioritize agency business processes and needs. the framework will be largely informed by our delphi study methods. however, we recognize that agencies will likely lack the ability to replicate a full delphi study. recognizing that simple stakeholder meetings are insufficient, the framework will identify strategies for bringing stakeholders together meaningfully to discuss and prioritize items developed initially by agency staff. the templates will guide staff members through the complex issues surrounding data quality and help them generate documents that stakeholders can review and use to establish priorities, policies, and directives. the goal for the workbook is to be practical, and its design and usage will be modeled after other practical informatics tools such as the u.s. agency for healthcare research and quality (ahrq) “health information technology evaluation toolkit” (26) and the public http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 health informatics institute (phii) “collaborative requirements development methodology” (27). finally, our aims include a comparison of measured data quality perceptions and needs with measured real-world data quality from clinical information systems. our current project will collect perceived quality of existing elr, syndromic surveillance, and other public health information system data as well as the perceived needs of those in public health working with elr, syndromic, and other information system data. icephi also includes an operational health information exchange (the inpc) with hundreds of interfaces to real-world clinical information systems. we aim to measure the quality of the data (e.g., completeness, timeliness) flowing into the inpc. we can then compare the real-world quality of the data with the perceived quality and needs of public health stakeholders. such evidence will provide insight on where we are in developing public health informatics systems in relation to where public health practitioners need them to be. conclusion the quality of data from information systems varies over time and across systems, and it is often poor. using poor data from clinical information systems can lead to poor decisions regarding patient care and public health policy. despite an abundance of poor data flowing through electronic systems, current policies and practices, knowingly or unintended, assume equal quality across all sources therefore pushing for the adoption and use of more electronic data feeds to public health. public health officials, epidemiologists, informaticians, and other professionals who recognize the unequal quality of data sources lack practical tools and resources to adequately assess quality to improve the utilization of electronic data sources that feed public health information systems. our cdc funded center of excellence in public health informatics will continue to examine the quality of data and their sources. we aim to develop novel, practical approaches for continuously assessing data quality to improve the decisions and work processes involved in modern public health practice. our findings and lessons learned should contribute to future research and policies that will improve data quality, utilization of available data sources, and the effectiveness of public health information systems. in other words, icephi will ask questions about data and information sources that will lead to improved population health processes and outcomes. this is an important role for all public health informatics centers and professionals to perform within their agencies and communities. “the art and science of asking questions is the source of all knowledge.” thomas berger acknowledgements the work of the indiana center of excellence in public health informatics reported here is supported by a grant (1p01hk000077-01) from the u.s. centers for disease control and prevention. the contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention. http://ojphi.org why “what data are necessary for this project?” and other basic questions are important to address in public health informatics practice and research 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 conflict of interest the authors declare that they have no real or apparent conflicts of interest. corresponding author brian e. dixon, mpa, phd assistant professor of health informatics, indiana university school of informatics research scientist, regenstrief institute 410 w. 10 th st., suite 2000 indianapolis, in, 46202 email: bedixon@iupui.edu references 1. buehler jw, hopkins rs, overhage jm, sosin dm, tong v. 2004. framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the cdc working group. mmwr recomm rep. 53(rr-5), 1-11. epub 05 2004. 2. lombardo js, buckeridge dl, eds. disease surveillance: a public health informatics approach. hoboken: john wiley & sons; 2007. 3. wang ry, strong dm. 1996. beyond accuracy: what data quality means to data consumers. j manage inf syst. 12(4), 5-34. 4. redman tc. 1998. the impact of poor data quality on the typical enterprise. commun acm. 41(2), 79-82. http://dx.doi.org/10.1145/269012.269025 5. bailey je, pearson sw. 1983. development of a tool for measuring and analyzing computer user satisfaction. manage sci. 29(5), 530-45. 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2007 [june 10, 2009]; available from: http:// healthit.ahrq.gov/portal/server.pt/gateway/ptargs_0_1248_807442_0_0_18/ ahrq_evaluation%20toolkit.pdf. 27. collaborative requirements development methodology (crdm) walkthrough. public health informatics institute; 2011 [cited 2011 september 8]; available from: http:// www.phiicrdm.org/methodology. http://ojphi.org http://dx.doi.org/10.1186/1471-2458-1-914 http://dx.doi.org/10.1186/1471-2458-1-914 http://dx.doi.org/10.1186/1471-2458-1-914 http://dx.doi.org/10.1016/j.annemergmed.2005.04.01215 http://dx.doi.org/10.1016/j.annemergmed.2005.04.01215 http://dx.doi.org/10.1016/j.annemergmed.2004.03.03017 http://dx.doi.org/10.1016/j.annemergmed.2004.03.03017 http://dx.doi.org/10.1016/j.annemergmed.2004.03.03017 http://www.himss.org/content/files/himss%ed%af%80%ed%b1%82summaryofarra.pdf.19 http://www.himss.org/content/files/himss%ed%af%80%ed%b1%82summaryofarra.pdf.19 http://www.millennium-project.org/frmv3%ed%af%80%ed%b1%820/04-delphi.pdf.23 http://www.millennium-project.org/frmv3%ed%af%80%ed%b1%820/04-delphi.pdf.23 http://dx.doi.org/10.1016/s0020-7489 http://dx.doi.org/10.1016/s0020-7489 http://dx.doi.org/10.1186/1472-6947-6-126 http://dx.doi.org/10.1186/1472-6947-6-126 http://healthit.ahrq.gov/portal/server.pt/gateway/ptargs%ed%af%80%ed%b1%820%ed%af%80%ed%b1%821248%ed%af%80%ed%b1%82807442%ed%af%80%ed%b1%820%ed%af%80%ed%b1%820%ed%af%80%ed%b1%8218/ahrq%ed%af%80%ed%b1%82evaluation%ed%af%80%ed%b0%8820toolkit.pdf.27 http://healthit.ahrq.gov/portal/server.pt/gateway/ptargs%ed%af%80%ed%b1%820%ed%af%80%ed%b1%821248%ed%af%80%ed%b1%82807442%ed%af%80%ed%b1%820%ed%af%80%ed%b1%820%ed%af%80%ed%b1%8218/ahrq%ed%af%80%ed%b1%82evaluation%ed%af%80%ed%b0%8820toolkit.pdf.27 http://healthit.ahrq.gov/portal/server.pt/gateway/ptargs%ed%af%80%ed%b1%820%ed%af%80%ed%b1%821248%ed%af%80%ed%b1%82807442%ed%af%80%ed%b1%820%ed%af%80%ed%b1%820%ed%af%80%ed%b1%8218/ahrq%ed%af%80%ed%b1%82evaluation%ed%af%80%ed%b0%8820toolkit.pdf.27 http://healthit.ahrq.gov/portal/server.pt/gateway/ptargs%ed%af%80%ed%b1%820%ed%af%80%ed%b1%821248%ed%af%80%ed%b1%82807442%ed%af%80%ed%b1%820%ed%af%80%ed%b1%820%ed%af%80%ed%b1%8218/ahrq%ed%af%80%ed%b1%82evaluation%ed%af%80%ed%b0%8820toolkit.pdf.27 http://healthit.ahrq.gov/portal/server.pt/gateway/ptargs%ed%af%80%ed%b1%820%ed%af%80%ed%b1%821248%ed%af%80%ed%b1%82807442%ed%af%80%ed%b1%820%ed%af%80%ed%b1%820%ed%af%80%ed%b1%8218/ahrq%ed%af%80%ed%b1%82evaluation%ed%af%80%ed%b0%8820toolkit.pdf.27 http://healthit.ahrq.gov/portal/server.pt/gateway/ptargs%ed%af%80%ed%b1%820%ed%af%80%ed%b1%821248%ed%af%80%ed%b1%82807442%ed%af%80%ed%b1%820%ed%af%80%ed%b1%820%ed%af%80%ed%b1%8218/ahrq%ed%af%80%ed%b1%82evaluation%ed%af%80%ed%b0%8820toolkit.pdf.27 ojphi-06-e35.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 139 (page number not for citation purposes) isds 2013 conference abstracts effect of 4-day school district closure on influenza-like illness rates among students and household members — kentucky, 2013 elizabeth s. russell*1, 2, yenlik zheteyeva2, hongjiang gao2, jeanette rainey2, jianrong shi2, karen wong2, douglas thoroughman1, 2 and amra uzicanin2 1division of epidemiology and health planning, kentucky department for public health, frankfort, ky, usa; 2center for disease control and prevention, atlanta, ga, usa � �� �� �� � � �� �� �� � introduction ������� ������ ����� ����� �������� ������� ������ ������ ��� � ���������� ��� ������ ���� ������� �� �������� � � ����� �� ���� ���������� ��������� ������� � �� ���� ��� ����� ������ ����� �������� ��������������� ������������������ ��� ��� ��!�"���� ��� �������� �� � �#�� �� �" ����$�� � �%�� �� �������������& �� �%�'()*���� 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�������������� ��3 ������������ *elizabeth s. russell e-mail: wjv4@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e35, 2014 quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 quality and integration of public health information systems: a systematic review focused on immunization and vital records systems joshua r vest 1 , hilary m kirk 2 , l michele issel 2 1 weill cornell medical college, new york, ny 2 university of illinois at chicago, school of public health, chicago, il abstract objectives: public health professionals rely on quantitative data for the daily practice of public health as well as organizational decision making and planning. however, several factors work against effective data sharing among public health agencies in the us. this review characterizes the reported barriers and enablers of effective use of public health is from an informatics perspective. methods: a systematic review of the english language literature for 2005 to 2011 followed the preferred reporting items for systematic reviews and meta-analyses (prisma) format. the review focused on immunization information systems (iis) and vital records information systems (vris). systems were described according to the structural aspects of is integration and data quality. results: articles describing iis documented issues pertaining to the distribution of the system, the autonomy of the data providers, the heterogeneous nature of information sharing as well as the quality of the data. articles describing vris were focused much more heavily on data quality, particularly whether or not the data were free from errors. conclusions: for state and local practitioners to effectively utilize data, public health is will have to overcome the challenges posed by a large number of autonomous data providers utilizing a variety of technologies. keywords: public health informatics; registries; birth records; information systems; vaccination; organization and administration introduction both local health departments (lhd) and state health agencies need access to quantitative information for organizational decision making, strategic and community planning, as well as for the day-to-day practice of public health. 1 for example, data exchanged by public health http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 practitioners on childhood immunizations are not only used to determine if specific individuals are up-to-date on vaccinations, but as a core public health indicator, are essential for population health surveillance and for conducting quality improvement processes. integration of their information systems (is) is a prerequisite to having access to and sharing of real-time data across organizations with different data management systems and different data elements. unfortunately, major forces have conspired to prevent easy and effective data sharing in public health from being the norm. first, public health is very much a reflection of the public health system in the us: a federation of independent states with differing relationships with their respective lhds, tasked with various aspects of data collection, storage, and dissemination. 2,3 second, the utilized information technologies (it) and is in local health departments may not stem from public health needs and requirements, but instead from a local government’s broader needs, resources and existing it/is decisions. 4 furthermore, last decade’s relatively high level of preparedness funds awarded to public health, while providing a much needed upgrade to the it/is capacities of state and local agencies, has not been sustained and pre-dated the more recent advances and focus on interoperability seen in the broader health it arena. lastly, public health informatics is a relatively new specialty within public health and still has uneven uptake across public health agencies. 5 all in all, public health organizations historically have not implemented is or adopted it that support efforts at efficient and effective storage and sharing of data. 4,6,7 the objective of this literature review is to characterize the barriers and enablers of effective use of public health is that support two key public health activities: immunizations and vital statistics. we purposefully selected these immunization information systems (iis) and vital records information systems (vris) because they reflect longstanding key public health activities, and both have undergone recent, widespread moves toward complete electronic record keeping. 8,9 furthermore, these systems reflect different types of data, data sources, organizational involvement, and uses within the public health system. with a particular focus on supporting individual vaccinations, a comprehensive iis includes information from public health organizations, primary care providers and even schools. in contrast, vris requires cooperation predominantly from local hospitals, funeral homes, and midwives, to support legal documents for individuals. the results of the literature review provide insights into the means to improve the fragmented state of public health data in the us. framework this review examines the factors related to successful or unsuccessful information sharing from the dual perspectives of is content and overall structure. content is critical as that is the aspect of the system that is most visible practitioners relying on the is for their daily public health work, whereas the structure of the is in terms of data sources and technology affects the availability and quality of data in a less obvious manner for end users. we used the aimq’s dimensions of data quality identify and categorize attributes of the data contained within iis and vris. 10,11 the fifteen data quality dimensions addressed such issues as completeness, timeliness, relevancy and understandability. the structural aspect of is intergration is aptly captured at the system-level by hasselbring’s dimensions of is integration: heterogeneity, distribution, and autonomy. 12 the myriad of technological factors that can plague large information systems that have diverse sources of data and multiple types of organizational users fall under the heterogeneity dimension. http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 problematically, barriers to interoperability originating from a lack of data and exchange standards, difficulties in networking legacy systems, and different technology platforms are commonplace in public health. for example, lhds differ substantially in their methods of record keeping, 13 and for some public health activities no national data standards exist. the distribution dimension captures the scope of the is in terms of the number of data sources, such as local and state government agencies and private healthcare providers. the autonomy dimension includes characteristics of the organizations exchanging information as those characteristics relate to interorganizational relationships, self-determination, and governance issues. as autonomous entities, organizations and individuals may exhibit idiosyncratic behaviors around actual system usage, willingness to engage in interorganizational data sharing, or even approaches to providing public health activities. methods we undertook a systematic review to identify the factors associated with successful and unsuccessful iis and vris following the suggested form of the preferred reporting items for systematic reviews and meta-analyses (prisma). 14 information sources & searching we conducted a review of the english language public health and medical research literature for research, evaluation, or case studies describing experiences with the implementation, evaluation, success, usage, or failure of immunization information systems and vital records systems in the us. searches were limited to articles published between 2005 and 2011. both immunization information and vital record systems were searched as keyword terms in pubmed, isi web of science, and the center for public health systems & services research library. in addition, we reviewed the table of contents for the same time period in relevant journals, organization’s websites and conference abstracts (a sample query and all sources are provided in the appendix). initial search results yielded 756 unduplicated records. study selection based on abstract information, we excluded from the initial search set all non-us based studies, reviews, editorials, commentaries, and those articles that did not describe an immunization information or vital statistics system or efforts to create such, or instances where no indication existed the study was about an is. two members of the research team independently reviewed each record and then arrived at the excluded set through consensus. the same team members independently read the full text of each article and determined its inclusion status. differences were resolved by consensus after a joint reading session. articles were retained for inclusion in the review if it described the barriers to, or factors supporting, the design, implementation, or effective use of an iis or vris used by state, local or government public health agencies. these criteria allowed for the inclusion of studies addressing technical structure, data quality, or evaluations of the is as used in practice. this excluded special purpose surveys, is maintained solely by private providers, systems that provided only http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 aggregated information, or articles that described the source data as part of a large research endeavor (i.e. using mortality data to supplement a hospital research study). data collection from each included article, we abstracted the type of is, study design, goals and objectives, study setting or participants, and factors identified as contributing or hindering success of the effort. we then coded each of the identified factors according to the dimensions of is integration or data quality. results study selection & characteristics we screened 756 unduplicated records and of those 210 warranted full text review (figure 1). the most common reason for excluding records after full text review was that the article did not identify any barriers to, or factors supporting, either any aspect of the is or data quality. our review process resulted in 23 total studies of which 11 reported on immunization information system (iis), 1,15-24 11 on vital record systems, 25-35 and 1 described a public health information system that integrated vital records with other information systems. 36 sixteen articles described state-level systems, but reports also included data and systems specific to atlanta, 25 boston, 20 new york city, 15,21 and philadelphia. 18,19 one study interviewed immunization program managers both in urban areas and at the state level. 1 in addition, the majority of included studies (17 out of 23) were cross-sectional analysis of the information system records, often compared to data generated in clinical settings or in other information systems. 16-21,23-28,30-35 the remaining articles were case studies, 15,22,36 a review of documents, 29 and qualitative.?? 1 http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 records identified through database searching (pubmed n = 518) (web of knowledge n = 133) (phssr library n = 7) s cr e e n in g in cl u d e d e li g ib il it y id e n ti fi ca ti o n additional records identified through other sources (table of contents review n = 97) (key website review n = 49) (apha conference abstracts n = 10) records after duplicates removed (n = 756) records screened (n = 756) records excluded (n = 546) full-text articles assessed for eligibility (n = 210) full-text articles excluded (n = 187) not us, vital stats, immunizations, or research (n = 65) not a public health system (n = 23) system providers aggregated data only (n = 3) no barriers or supporting factors (n = 96) studies included in qualitative synthesis (n = 23) immunization information systems (n = 11) vital records information systems (n = 11) multiple information systems (n = 1) figure 1. study identification and selection process. dimensions of is integration the difficulties and challenges arising from the integration of multiple technologies into a single is were noted by six studies (table 1); five of which were examinations of iis. heterogeneity was manifest as different methods of data collection, 24 differences in the structure and storage of data between data sources, 36 differences in the amount and quality of information collected from other systems based on technology, 18-20 and differences in the iis’ ability to both send and http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 receive information. 15 while electronic data repositories in the form of electronic health records or billing systems tended to make data more accessible to the iis and of higher quality, 19,20 simply having an electronic data source was not a panacea. for example, kolasa and colleagues 18 reported that just because an electronic billing system was in place did not mean the iis received all the data. furthermore, each unique system from which an iis obtains data requires its own interface, which requires time and money. 15 conversely, schauer and colleagues 22 noted the ability of each lhds to exchange information in wisconsin was a strength of the iis. table 1. characteristics and findings of research studies describing public health information system factors associated with quality and system integration, 2005-2011. citation system investigated study design & subjects system integration issues data quality issues immunization information systems kolasa, cherry, et al. (2005) 18 philadelphia kids immunizations database/tracking system cross-sectional, review of iis records & provider records heterogeneity  use of an electronic billing systems did not mean all data were transferred to iis autonomy  reporting rates varied by provider organization type & size completeness  24% of provider recorded immunizations not reported in the iis kolasa, chilkatowsky, et al. (2006) 19 philadelphia kids immunizations database/tracking system cross-sectional, review of provider records, & iis records heterogeneity  differences in submission rates based on how records submitted: electronic medical records highest & electronic billing higher than paper records autonomy  reporting rates varied by provider organization type: hospitals had highest rates completeness  provider charts more complete than iis on up to date status but varied by data entry type: o iis & direct entry: κ = 1.0 o iis & electronic medical record: κ = 0.72 o iis & electronic billing: κ = 0.66 o iis & manual billing: κ = 0.65 o iis & manual logs: κ = 0.42  provider charts more complete than iis on up to date status but varied by provider type: o iis & hospitalbased: κ = 0.81 o iis & pediatric practice: κ = 0.37 o iis & family practice: κ = 0.63 o overall : κ = 0.58 http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 citation system investigated study design & subjects system integration issues data quality issues dombkowski, leung, et al. (2007) 16 michigan care improvement registry cross-sectional, survey of physicians completeness  majority of providers found iis information complete ease of use  few practices had difficulty in accessing iis free-of-error  majority of providers found iis information accurate value-added  concerns about patients limited to medicaid only stecher, adelman, et al. (2008) 23 arizona state immunization information system cross-sectional, review of primary care records, parental recall, & iis records autonomy  mandatory reporting to iis for providers resulted in high percent of children included  providers can choose to electronically report or mail reports completeness  iis specificity for up to date: 74%  iis sensitivity for up to date: 47%  ii positive predictive value for up to date: 72%  91% of children included in the iis timeliness  mailed reports may take up to 2 week to entered & electronic reports reviewed for 1-2 days before entered mahon, shea, et al. (2008) 20 boston immunization information system cross-sectional, review of pediatric clinic records heterogeneity  fewer discrepancies in data from practices with electronic medical records autonomy  even with an electronic medical record, providers may not create electronic records for existing patients believability  records of individuals older than the iis may not be accurate completeness  manufactures, lot numbers, doses, & dates missing in iis free-of-error  manufacture & lot numbers in provider records did not match iis white, anderson, et al. (2009) 24 minnesota immunization information connection cross-sectional, review of hospital records, & interviews with hospital staff heterogeneity  iis data derived from birth certificates, direct data entry, & historical records autonomy  hospitals were not providing vaccination data beyond what was required on birth certificates completeness  iis missing birth doses administered in hospitals http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 citation system investigated study design & subjects system integration issues data quality issues schauer, maerz, et al. (2009) 22 wisconsin immunization registry case study heterogeneity  all lhds could exchange data with the iis distribution  difficult to identify provider participation given complexity of provider networks and affiliations completeness  data missing on children vaccinated by non-iis users free-of-error  records of children who have moved out of state not de-activated groom, kennedy, et al. (2010) 1 various interviews 7 state & urban area immunization program managers distribution  care obtained from multiple providers ease of use  providers have quick access to iis information dombkowski, reeves, et al. (2011) 17 michigan care improvement registry cross-sectional, analysis of reminder / recall notifications sent by lhds timeliness  outdated parental contact information papadouka, metroka, et al. (2011) 21 new york city immunization information system cross-sectional, review of patient records by lhd staff completeness  only 37% of doses recorded in iis contained lot numbers arzt, forney, et al. (2011) 15 new york city citywide immunization registry case study heterogeneity  requires multiple interfaces, “each vendor requires a separate development effort and a significant investment of time and resources”  primarily unidirectional data flow & few vendors prepared to implement bi-directional data flow vital records information systems smith, veazie, et al. (2005) 35 maryland vital records cross-sectional, comparison of death certificates & multiple occupational injury fatality systems free-of-error  death certificates had a higher sensitivity than the other sources (89%), but still did not include all cases lydonrochelle, holt, et al. (2005) 32 washington vital records cross-sectional, comparison of birth certificates, hospital discharge data, & medical records free-of-error  birth certificate maternal labor & birth events: o true positive fraction ranged from 34.4% to 81.2% o false positive http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 citation system investigated study design & subjects system integration issues data quality issues fraction ranged from 0.1% to 7.4% o positive predictive values ranged from 60.3% to 93.0% o negative predictive values ranged from 81.2% to 99.1% mann, knight, et al. (2005) 33 utah vital records cross-sectional, comparison of death certificates, hospital records, & emergency medical services records free-of-error  death certificates do not reflect all injury mortalities rodriguez, mallonee, et al. (2006) 34 oklahoma vital records cross-sectional, comparison of death certificates & injury surveillance system data on traumatic brain injury mortality free-of-error  sensitivity of death certificates = 78%  positive predictive value of death certificates = 98%  higher odds of missed cases for females & older individuals  higher odds of missed cases by cause of death coding horon (2005) 30 maryland vital records cross-sectional, comparison of death certificates, fetal death records, & medical examiner records free-of-error  38% of maternal deaths were unreported on death certificates lydonrochelle, cárdenas, et al. (2005) 31 washington vital records cross-sectional, comparison of fetal death records with medical records free-of-error  fetal death certificate maternal & perinatal conditions: o true positive rate ranged from 0.0% to 100.0% o false positive rate ranged from 0.0% to 10.5% o positive predictive values ranged from 0.0% to 100.0% o negative predictive values ranged from 33.3% to 100.0% http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 citation system investigated study design & subjects system integration issues data quality issues caveney, smith, et al. (2006) 27 texas vital records cross-sectional, comparison of death certificates, medical records, & individual interviews free-of-error  death certificate race/ethnicity reporting consistent with self-reports o percent agreement: 97.1% o sensitivity: 95.4% o specificity: 99.0% fiscella & meldrum (2008) 28 california vital records cross-sectional, comparison of death certificates & hospital discharge records free-of-error  agreement between hospital discharge records & death certificates variable by race: o white: κ = 0.76 o black: κ = 0.92 o asian: κ = 0.88 o native american: κ = 0.27 o other: κ < 0.01 brender, suarez, et al. (2008) 26 texas vital records cross-sectional, comparison of birth certificates & individual interviews free-of-error  birth certificates have high specificity with parental self-reported occupation  sensitivity of birth certificates for maternal & parental varies by occupational classification group fitzgerald, wartenberg, et al. (2009) 29 50 states’ birth & fetal death records document review of forms relevancy  most states did not collect information that would be useful for environmental exposure investigations: o duration of residence for mothers o paternal residence o parental occupation boulet, shin, et al. (2011) 25 atlanta vital records cross-sectional, comparison of birth certificates & metropolitan atlanta congenital defect program records free-of-error  low sensitivity of birth certificates for birth defects multiple content systems chapman, ford, et al. (2011) 36 virginia vital events and screening tracking case study heterogeneity  differences in data field types between systems http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 citation system investigated study design & subjects system integration issues data quality issues system autonomy  organizations create different identifiers for the same individual regardless of what technology is used to share data with public health agencies, data sharing remained subject to variation introduced by individual users and organizations. organizational and individual autonomy are illustrated at a very basic level by differences in the rate of data sharing by organizational type and size. 18,19 the challenges posed by differences in organizational practice were illustrated in three studies. specifically, organizations create different identifiers for the exact same individual, 36 individuals within an organization may choose not to use an information system, 20 or organizations may not collect all the data desired by public health organizations. 24 as a potential solution to the problems posed by organizational and individual autonomy in data sharing, the arizona state immunization information system reported a higher percentage of children were included in the iis due to mandatory reporting for providers. 23 only two studies described how the number of data sources, or the problem of distribution, affected public health data sharing. a case study of the wisconsin immunization registry noted the basic activity of identifying provider participation in the iis was complicated by the number of networks and affiliations contributing data to the system. 22 also specific to data on immunizations, groom and colleagues 1 noted how the reality of patients seeking care from multiple providers can prevent public health from having complete information. table 2. summary of distribution of issues by assessment framework (n=23 studies). dimensions of is integration immunization vital records multiple content heterogeneity 6 1 autonomy 5 1 distribution 2 data quality characteristics immunization vital records completeness 8 error free 3 10 ease of use 2 timeliness 2 believability 1 value added 1 relevancy 1 data quality issues the studies of vital record information systems (vris) were exclusively concerned with data quality and more specifically if the data were free-from-errors. under the data quality assessment framework, free-from-error is roughly analogous to misclassification bias. so most studies were http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 not concerned whether or not a vital event had occurred, but whether the data describing that event was error free. in terms of the cause of death, vris underreported injury mortality 33-35 and vris also did not accurately reflect pregnancy status 30 on death certificates. natality data in the vris were also the subject of reviewed studies, which concluded substantial variation on the recording of labor & delivery events, 32 birth defect data were not free-from-errors, 25 and parental occupations were not categorized correctly. 26 the reviewed studies were not conclusive about the extent race/ethnicity data in vris are free-from-error. for example, in a comparison of california hospital discharge records and death certificates, fiscella and meldrum 28 noted wide variation in the data agreement by race/ethnicity, but a comparison of death certificates with selfreported race/ethnicity found high agreement in texas. 27 lastly, fitzgerald and colleagues’ 29 warrants specific attention because of all the vris studies, it was the only one concerned with the relevancy of the data. instead of assessing whether data were free-from-error or complete, the authors sought what types of additional data would be useful. the reviewed articles examining iis tended to be more diverse in their treatment of data quality issues than those focused on vris. however, if the data were complete and free-from-error was still a major focus. studies reported their respective iis were missing vaccination doses, 18-20,23,24 missing manufactures & lot numbers, 20,21 or even whole individuals. 22 even when present, manufacture and lot numbers may be wrong 20 or individuals may have moved out of the state. 22 other dimensions identified in the reviewed articles included: the believability of older records in iis, 20 the timeliness of the available contact information, 17 and the ease of system use. 1 a single article requires additional comment. dombkowski and colleagues 16 presented a more comprehensive evaluation of an iis than the other studies by examining end user perceptions. while these were the perceptions of physicians users, the system was nonetheless a public health iis. they reported positive perceptions of data completeness, that it was free-from-error, the system was easy to use and that it was beneficial to use in their work. discussion the review of evidence on is in public health, as revealed through immunization and vital records, confirms the general perception that significant challenges and barriers prevent public health from leveraging is to its fullest extent. 6 in general, this review revealed public health is are struggling with issues of integrating multiple technologies, data sources and independent organizations and often not providing information of sufficient quality to public health practitioners. the is qualities reported had bearing on public health practice, policy making and research. however, beyond simply documenting challenges, this review provides insights into the interrelated nature of system features and data quality issues and highlights the need to incorporate more end user perspectives. while each dimension of system integration and data quality were assessed independently, the articles included in our review on iis illustrate how one dimension can affect another. for example, autonomy may be highly correlated with system heterogeneity as was the case in arzt and colleagues’ 15 report where independent organizations were making their own, and very different, technology vendor choices. likewise, several studies noting examples where organizations limited data sharing, choose inefficient methods of exchange or did not participate http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 fully in the system also reported issues around completeness. 18,23,24 these instances of the importance organizational and individual decisions on an integrated is, or at least a hoped for integrated is, indicate both a priority and a role for public health. although data quality is paramount for quality decision making, the priority in public health is should be from the system perspective. because public health deals with populations, if differing technologies are not rectified, if individual differences in organizational practices are not mitigated and if all components of the public health system not included, it will not be possible to compile data that is accurate, timely, complete and error-free. without a systems focus, the dimensions of is integration (heterogeneity, distribution and autonomy) are ignored and thus likely to present as longer term, but avoidable, problems. to facilitate this approach, public health agencies need to take a strong coordinating role. we intentionally use the word coordination, because no public health entity will be able to eliminate organizational autonomy. even in states with highly centralized governance structures, public health will not be able to dictate specific vendor choices or business processes. current federal policy around electronic health records demonstrates that reality. what public health can do is coordinate around specific capabilities like exchange standards and required data elements, which is consistent with broader health it policy, provide guidance to locally governed lhds, or deploy uniform solutions within centrally governed states. previous research agendas in the area of public health is noted the importance of attending to user needs. 37,38 with a few exceptions (for example 1,16 ) specific public health user perceptions or needs were not at the forefront of the reviewed literature. however, the examined literature revealed that public health data are valued as much for research purposes as for practice meaning the authors were primarily focused on researchers as their users of interest (for example 20,27,28,30 ). a dichotomization between the practice and research value of public health information does not bode well for generating evidence relevant to understanding the real-time, daily needs of lhd staff, for participatory research with practitioners, nor for generating knowledge from practice. researchers are more likely to be concerned with quality of data than on integration, compared to the concerns of practitioners. both groups ought to be equally concerns about both aspects of is. this review documents the state of two public health is examples over the past six years. however, public health is will undoubtedly undergo a transformation over the course of this decade for two very different reasons. the first is essentially self-imposed: the launch of the public health accreditation process. accreditation standards focus on data driven decision making and include requirements of data sharing with other public health organizations, 39 which should foster interests and investment in public health is. with the majority of state public health agencies intending to seek accreditation 40 and not an inconsequential level of interest among lhds, 5 the potential for increased attentiveness to is issues around the nation could increase substantially. the other development, which will have a multi-faceted impact, is the national effort to increase adoption of electronic health records under the meaningful use program. 41 for lhds providing primary care services, this program will provide unparalleled financial incentives to adopt a key, interoperable clinical information system thereby increasing the technological capacity for numerous public health organizations. for all of public health, the meaningful use criteria specific to public health reporting will dramatically increase existing http://ojphi.org/ quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 distribution and heterogeneity challenges. effectively all healthcare providers and organizations will soon be able, and required, to contribute data to public health. public health will have unprecedented access to clinical data on essentially everyone who accesses the healthcare system, but it will come at the cost of a dramatic increase in number of organizations contributing to public health as well as the data originating from numerous technologies. conclusions for state and local practitioners to effectively utilize data, public health is will have to overcome the challenges posed by a large number of autonomous data providers utilizing a variety of technologies. efforts to ensure quality information must remain attentive to the role of overarching system factors. acknowledgements this work was supported by the robert wood johnson’s dissertation and junior faculty awards in phssr in conjunction with the public health services and systems research coordinating center. corresponding author joshua r vest, phd, mph assistant professor weill cornell medical college email: jov2025@med.cornell.edu appendix search terms immunization information systems immunization registries vital records birth certificates death certificates birth records death records example search strategy for pubmed 1. all field search of “immunization information systems” 2. all field search of “immunization information system” 3. all field search of “vital record system” 4. all field search of “vital records system” 5. all field search of “vital records systems” 6. all field search of “vital record systems” 7. #1 or #2 or #3 or #4 or #5 or #6 http://ojphi.org/ mailto:jov2025@med.cornell.edu quality and integration of public health information systems: a systematic review focused on immunization and vital records systems 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 8. mesh term birth certificates 9. mesh term birth records 10. mesh term death certificates 11. mesh term death records 12. mesh term immunization 13. #8 or #9 or #10 or #11 or #12 14. mesh term information systems or mesh term computer systems or mesh term public health informatics 15. #13 and #14 16. #15 or #7 17. limits: english. year 2005/1/1 2011/12/31. abstract number retrieved = 518 relevant journals, organization’s websites and conference abstracts american journal of public health bmc public health journal of public health management & practice online journal of public health informatics public health reports centers for disease control & prevention national association of county & city health officials association of state & territorial health officials national organization of urban maternal & child health leaders (citymatch) association of maternal and child health programs 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arlington, va: 2011. 41. department of health & human services. 42 cfr parts 412, 413, 422 et al. medicare and medicaid programs; electronic health record incentive program; final rule. federal register. 2010;75(144):44314-588. http://ojphi.org/ http://www.phaboard.org/wp-content/uploads/phab-standards-and-measures-version-1.0.pdf http://www.phaboard.org/wp-content/uploads/phab-standards-and-measures-version-1.0.pdf 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts analysis of healthcare seeking behavior chrissy dangel*1, steven allgeier1, adam haas2 and amanda johnson2 1us epa, cincinnati, oh, usa; 2csc science & engineering, cincinnati, oh, usa objective this paper describes analyses of health seeking behaviors from two surveillance datastreams: poison control center (pcc) calls and emergency department (ed) visit records. these analyses were conducted in order to quantify behaviors following the development of symptoms after water contamination exposure and to understand the motivation, decision-making and timing behind healthcare seeking behaviors. introduction healthcare seeking behavior is important to understand when interpreting public health surveillance data, planning for healthcare utilization, or attempting to estimate or model consequences of an adverse event, such as widespread water contamination. although there is evidence that factors such as perceived susceptibility and benefits affect healthcare seeking behavior, it is difficult to develop accurate assumptions due to a lack of published research on this topic. current conceptual behavior models, such as the health belief model, are not easily translated into quantifiable terms. methods for the pcc analysis, data was included from 480 calls from two pccs from september 2012 to april 2013. shortly following the calls, pcc callers were surveyed and results were merged with call data. a subset of callers surveyed was preferentially selected based on the reason for call or substance described in an attempt to gather more information on situations similar to a possible water contamination event. the primary outcome of interest was the method of obtaining the pcc phone number. methods of obtaining the pcc phone number were categorized as research, professional referral, pcc sticker/ magnet, friend/family and other/non-specified. the ed visit records analysis comprised over 71,000 visit records from five eds from january 2001 to december 2011 in cincinnati, oh. in this analysis, the primary outcome of interest was means of arrival to the ed. ed arrival was dichotomized as emergency medical services (ems) utilization (yes/no). healthcare seeking behaviors were categorized by characteristics such as age, disease category, insurance type, disposition as a severity indicator and time period. for both data sets, exploratory data analysis was performed using contingency tables. differences in the proportions of methods to obtain the pcc phone number and to means of ed arrival were compared across demographic variables and visit characteristics using chi-square tests of significance. results the main finding from the pcc analysis was that insurance type was significantly associated with obtaining the pcc number (p =0.002). survey respondents with private or employer based insurance were 1.9 times as likely to obtain the number from a pcc sticker or magnet while those with government based insurance were 1.7 times as likely to obtain the number after a referral compared to other methods. the ed study found that arrival to the ed by ems was uncommon (<30%) but varied by ed (p <0.001). ems utilization was more common in the autumn season and peaked on fridays and saturdays. age, disease category, insurance type and disposition were significantly associated with ems utilization (p’s <0.001). adults at least 64 years of age were 1.9 times as likely to utilize ems compared to younger adults. children ages 15-19 were 2.8 times as likely to arrive by ems compared to younger children. patients arriving to the ed due to poisoning of drugs or toxic effects of nonmedical substances had the greatest ems utilization. the effect of insurance type on ems utilization varied by patient age. adults with government based insurance and children with commercial based insurance were the most likely to utilize ems. disposition exhibited the strongest effect on ems utilization. children who were admitted were 7.7 times as likely to arrive by ems. adult patients were 5.7 times as likely to arrive by ems if they were admitted. in a sensitivity analysis adjusting for disposition, the association between insurance type and ems utilization was no longer significant for admitted patients (p =0.105). conclusions this study better defines the health seeking behaviors of patients experiencing symptoms analogous to those following water contamination and highlights the importance of using diverse outreach methods to promote pcc and ems utilization. keywords healthcare; behavior; poison control center; emergency department; analysis references 1. sheeren, p. & abraham, c the health belief model, in predicting health behaviour (conner, m. & norman, p. eds). buckingham: open university press. 1995 *chrissy dangel e-mail: dangel.chrissy@epa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e17, 2015 mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study angella musiimenta1*, wilson tumuhimbise1, godfrey mugyenyi1, jane katusiime1, esther c atukunda1, niels pinkwart2 1mbarara university of science and technology, mbarara, uganda, 2angels compassion research and development centre (acord), mbarara, uganda,3humboldt-universität zu berlin abstract background: reducing maternal and infant mortality rates remains challenging. illiteracy, lack of reliable information, long distances to health centers continue to limit access to quality maternal healthcare in uganda. mobile health technologies could be promising affordable strategies for enhancing access to maternal health services. however, there is lack of studies assessing the experiences of illiterate rural pregnant women regarding these technologies. objective: to explore how illiterate pregnant women perceive a maternal health mobile application composed of tailored video and audio messages, appointment reminders and calling function. methods: we purposively sampled illiterate pregnant women initiating antenatal care at mbarara regional referral hospital. we carried out three focus group discussions with 14 women to elicit information on perceptions of the proposed mobile phone based multimedia application. we used stata 13 to describe study participants and their preferences. results: pregnant women anticipated that intervention would enhance maternal health by reminding them to attend antenatal appointments, enabling transport cost and time saving, providing tailored information that is easy to understand, and recall. however, financial constraints and phone sharing would limit the functionality. conclusion: mhealth application may provide acceptable and affordable alternative approaches to providing maternal health services, especially in settings where face-to-face approaches are challenging. keywords: mobile health technologies, maternal health, illiterate women, videos/audios, multimedia, appointment reminders. correspondence: * amusiimenta@must.ac.ug doi: 10.5210/ojphi.v12i1.10557 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi introduction maternal health reducing maternal and infant mortality rates remains a global priority. in many low resource countries (lrcs), maternal and child mortality rates remain unacceptably high. in 2013, 289,000 deaths occurred globally, of which 286,000 occurred in developing countries alone [1]. the maternal mortality rate in uganda is estimated at 400 maternal deaths per 100,000 live births [2]. this rate is considered to be among the highest in the world, with maternal mortality being more likely for women living in rural areas and among poorer communities. statistics about maternal health in uganda should be interpreted with caution since 62% of women deliver outside the health facility without skilled care and thus their data is not captured. for every maternal death in uganda, at least six women survive with chronic and debilitating ill health [3]. although uganda had previously registered a decline in infant and under-five mortality, recent statistics show that currently, 17 women die every day while 106 new-born babies die every day [4]. most causes of these deaths are preventable maternal-related complications such as severe bleeding, infections, unsafe abortion, pregnancy-induced hypertension, obstructed labor, and lack of access to life saving hiv treatment [5]. the report further indicated that illiteracy, poverty, distance to health care centers, and cultural practices continue to limit access to quality health care in uganda. being pregnant in uganda is nowadays believed to be ‘putting one leg in the grave’ as the chances of being alive after delivery are much lower than in other regions of the world. only about 33% of pregnant mothers receive the recommended four antenatal visits per partum [2]. many women especially those from rural areas continue to lack reliable maternal health information—they rely on information from their peers which is mixed with cultural taboos, and is not accurate since pregnancy treats each woman differently. access to essential health services and information, particularly for the vulnerable rural pregnant women becomes even more complicated given the current covid-19 pandemic due to the overwhelmed healthcare systems. potentials of mobile health technologies traditional approaches to accessing health information from the health facilities may be limited by geographical boundaries, are often expensive due to the time and transport costs involved, and may be constrained by poor communication and records management, and chronic shortages of health professionals particularly in lrcs [6,7]. the use of information communication technology (ict)-based approaches to disseminating information such as multimedia mobile applications, websites, and social media can minimize the challenges associated with face-to-face approaches [8]. rapidly expanding cellular networks across sub-saharan africa have greatly increased the capacity of cellular technology to serve as a novel solution to challenges and barriers to maternal health. according to global system for mobile communications (gsma) real-time intelligence data, there are currently over 5.13 billion people with mobile devices, and 8.97 billion mobile connections which exceeds the current world population of 7.71 billion. moreover, in uganda specifically, mobile phone reception is available across the vast majority of the country, including many rural areas where 80% of population lives of which majority are economically disadvantaged [9]. given the widespread telephone ownership and mobile network coverage in sub-saharan africa— (76% ownership in 2015 [10], mobile health-based interventions can potentially provide affordable means of providing reliable information to expectant mothers. mobile phone infrastructure is being utilized widely in multiple sectors, such as micro-banking via mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi services like mobile money and m-pesa, which has resulted in inclusive development in lrcs by unlocking opportunities to reach the otherwise underserved populations [11]. such applications can potentially provide accessible and affordable means of providing reliable maternal health information to the otherwise hard-to-reach rural women. mobile health technologies for maternal health from our prior work, we have demonstrated that using icts such as networked and mobile telephones is acceptable and feasible, and useful in bringing services to patients regardless of geographical barriers and social denials, and can cater for privacy and confidentiality aspects [1214]. although the application of mobile technologies in supporting maternal and child health is still rare especially in developing countries, they are promising tools for innovatively disseminating pregnant-related information to women [15]. while there are few attempts to improve maternal health in developing countries by use of mobile technology through innovations such as momconnect and mobile moms [16], the need for more research on the use of mobile phones in maternal health is well documented in a systematic review [17]. mobile health technologies for illiterate women there are generally limited studies that have utilized mobile health intervention to support maternal health especially in lrcs. the modest existing studies report positive benefits; such as increased antenatal attendance as a result of maternal health-related sms (short messaging service) texts sent to pregnant women [16,18-20], and strengthened women’s relationship with healthcare providers [21]. however, these studies have mainly utilized sms texts, benefit women who can read and write while leaving out illiterate women who are, in most cases, more prone to maternal health problems. the use of biweekly voice messages in local language (with maternal and child health contents) which were sent to the mobile phone of pregnant women’s spouses motivated mothers to visit antenatal postnatal clinics in india [22]. in uganda, 33.7% of women of 25 years and above have never been to school, while 41.2% only completed some primary level education [23]. rural illiterate and semi-illiterate expectant women in uganda continue to lack access to friendly, confidential, reliable, and interactive maternal health information—about what to expect, and what to do at different stages of pregnancy, good feeding habits, as well as information about the benefits of delivering from the hospital, which results in many delivering from their homes without a qualified birth attendant. against this background, this study explored how pregnant women perceive a maternal health mobile application composed of tailored video and audio messages, appointment reminders and calling function. methods study design and setting the study combines focus group discussions and surveys in a parallel mixed methods study design. participants (pregnant mothers) were recruited from mbarara regional referral hospital (mrrh), which is the largest hospital in rural southwestern uganda. the mrrh employs 11 obstetricians and 22 midwives and performs over 10,000 deliveries annually with a maternal mortality rate of 270/100,000 live birth, caesarean section rate of 30% and a perinatal mortality rate of 56/1000 [24]. sociodemographic and basic health data are captured from pregnant women during their first mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi visit, and stored in paper-based antenatal registers. each woman is given an antenatal card that contains her biodata as well as the date of the next appointment. women are expected to attend at least four antenatal appointments, and they are supposed to bring their antenatal cards on every visit. the clinic verbally provides group-based health talks to women. the talks are scheduled according the trimesters—first trimester talks are offered on tuesdays, second trimester talks are offered on wednesdays, while third trimester health talks are offered on thursdays. topics covered in these talks include nutrition and birth preparedness. there are currently no follow-up mechanism for pregnant women who miss attending their antenatal appointments. selection of study participants between october 2019 and february 2020, we purposively sampled pregnant women receiving antenatal care from the department of obstetrics and gynaecology, mrrh. inclusion criteria were as follows: a) initiating antenatal care at mrrh at the earliest presentation in the first and second trimester, b) being illiterate (not having studied beyond primary seven or elementary education), c) 18 years and above, d) residents of mbarara (within 20km of mrrh) e) willing and able to give informed consent. the mobile phone-based multimedia application for maternal health we developed the multimedia mobile health application using java programming language, while the database that hosts multimedia messages was developed using sqlite. it is an offline (standalone) application which was developed following user-centered design approaches that involved incorporating input from pregnant women as prospective users. the application was installed on affordable smart phones provided by the study. pregnant women were provided with solar chargers to supplement electricity charging. the application has three major functionalities; 1. the video/audio function; which provides locally customized videos and audios that provides personalized maternal health information to pregnant women based on their pregnancy stages. contents of the videos/audio messages include; nutrition, breast feeding, hiv testing, spouse involvement, family planning, danger signs, preparing for child birth, care during pregnancy, care during delivery and postnatal care. 2. the appointment reminder function allows the users to set the dates and reminders for their next antenatal care appointment. 3. the calling function through which the pregnant women can communicate with health workers and ask pregnancy-related queries or application related issues. it also has login module which uses pictorial password to allow access to the application. study procedures before the focus group discussion, we first oriented each participant about the potential mobile application to support maternal health. first, we explained and demonstrated how the application works including how to login to the application, view the multimedia videos, audios, set antenatal appointment reminders, and call the specialist in case of an inquiry. participants were then given mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi the application and asked to explain what it does and to practically demonstrate how it works to the researchers. data collection from a private space at a research office near the mrrh, am, wt, and gm carried out three focus group discussions with illiterate pregnant mothers (totaling to 14). each focus group discussion lasted between 40 and 60 minutes. all questions in the focus group guide were translated into the local language (runyankole) and back-translated to english by a different translator, after which the two versions were compared for accuracy. the focus group discussion were carried out in runyankole (local language). the discussions were digitally recorded, transcribed, and translated to english. we elicited participants’ opinions about the usage of a mobile phone-application to support maternal health, including anticipated benefits and challenges of using the intervention. following each interview, np and jk reviewed transcripts for quality, clarity, and detail. tw and am also administered surveys to pregnant women to collect information on socio-demographics, socio economic status, food security, hiv status, and cellular phone ownership and utilization. analysis we used inductive content analysis [25] to derive categories describing and summarizing how participants perceived the intervention. initially, am and wt reviewed and discussed 20% of transcripts for content relevant to participants’ perceptions about the intervention, anticipated benefits, and challenges. am and wt then assembled a codebook from the identified concepts, using an iterative process, which included developing codes to represent content, writing operational definitions, and selecting illustrative quotes. np, jk, and gm also reviewed and discussed the codebook. following completion of the codebook, am and wt applied codes using nvivo 11. differences in coding were harmonized through discussion. am and tw used stata 13 to describe study participants’ characteristic, their socio economic status, food security, intervention preference, pregnant-related information, and technology readiness. human subjects and ethics approval all participants provided signed informed consent before study participation. the institutional review committee of mbarara university of science and technology, the uganda national council for science and technology, approved this study. results survey results participant characteristics mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi table 1: socio-demographic and basic health status characteristics of study participants pregnant women (n= 14 (%)) median age (years) include iqr 26.00 (iqr= 11) marital status married 11(78.6) highest level of education p1p7 11(78.6) no education 3(21.4) able to read english 4(28.6) able to read runyankole 11(78.6) living with hiv 4(28.6) mean (sd) months of pregnancy 3.93 (1.730) sd=standard deviation of the 23 screened pregnant women, 9 (39.13%) were excluded. pregnant women were excluded for (individuals could have >1 criterion): unwillingness to give consent (n=3; 33.33%), lived beyond 20km from mrrh (n= 4, 44.44%), below 18 years (n=1, 11.11%) and studied beyond primary seven (n=1, 11.12%). a total of 14 pregnant women, of whom 4(28.6%) were persons living with hiv/aids, enrolled in the study between october 2018 and february 2019 (table 1). the majority of participants had not gone beyond primary seven (n=11; 78.6%) and were able to read runyankole (local language) (n=11; 78.6%). table 2: current pregnancy-related information pregnant women (n=14) current source of information about pregnant* friends and relatives 6 (42.9) health care provider 11 (78.6) internet 2 (14.3) mass media e.g. radio/television 2 (14.3) frequency of getting information about pregnant* per month 11 (78.6) more than a month 3 (21.4) form of information about pregnant received* mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi word of mouth 13 (92.9) written text 4 (28.6) video/audio 5 (35.7) pregnancy information often got* nutrition 11 (78.6) antenatal check-up reminders 10 (71.4) hiv testing 9 (64.3) birth preparedness 11 (78.6) danger signs 11 (78.6) exercises 9 (64.3) using a mosquito net 1 (12.5) as indicated in table 2 above, the majority of pregnant women reported getting pregnancy-related information from healthcare providers (n=11, 78.6%), on a monthly basis (n=11, 78.6%), and by word of mouth (n=13, 92.9%). pregnant women often get information about nutrition (n=11, 78.6%), birth preparedness (n=11, 78.6%), and danger signs (n=11, 78.6%). table 3: technology readiness pregnant women (n=14) personal phone ownership yes 12 (85.7) no 2 (14.3) type of phone feature phone 12 (85.7) smart phone 2 (14.3) phone sharing yes 5 (35.7) no 9 (64.3) ever received pregnancy information on phone yes 2 (14.3) no 12 (85.7) preferred frequency of receiving information about pregnancy on phone mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi daily 6 (42.9) weekly 7 (50.0) monthly 1 (7.1) preferred content of information to be sent on phone antenatal check-up reminders 12 (85.7) nutrition 10 (71.4) birth preparedness 12 (85.7) danger signs 9 (64.3) breast feeding 8 (57.1) immunization 7 (50.0) preferred form of receiving information to be received on phone video 11 (78.6) audio 10 (71.4) images/still photos 5 (35.7) text 4 (28.6) phone calls 1 (7.1) preferred length of the message 2 to 4 minutes 1 (7.1) 5 minutes 5 (35.7) above 5 minutes 8 (57.1) as indicated in table 3 above, the majority of pregnant women owned mobile phones (n=12, 85.7%), all of which were feature phones. only 5(35%) reported sharing their mobile phones. the majority (n=12, 85.7%) had never used their phones to receive pregnant-related information. all of participants preferred receiving tailored maternal health-related information on mobile phones in local language (runyankole). the most preferred form of messages was video (n=11, 78.6%), followed by audio (n=10, 71.4%). weekly messages (n=7, 50%) were preferred to daily (n=6, 42.9%), while monthly messages were the least preferred (n=1, 7.1%). most participants preferred receiving messages in form of antenatal appointment reminders (n=12, 85.7%), and birth preparedness (n=12, 85.7%). interview results as explained below, participants (pregnant women) perceived that the intervention would improve maternal health through: 1) providing appointment reminders, 2) enabling transport cost and time saving, 3) source of friendly, tailored, information that is easy to understand, and recall, 4) enhancing male support and involvement, and 5) enhancing feelings of being cared for, and mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi prompting self-care. all participants preferred receiving tailored maternal health-related information on mobile phones in local language (runyankole). this includes information about antenatal checkup appointments reminders nutrition, birth preparedness, danger signs, breastfeeding, and immunization. participants felt the functionality of the mobile application intervention may be limited by lack of phone ownership, sharing of phones, and financial constraints. providing appointment reminders pregnant women reported that using mobile phone-based application could remind them to attend their antenatal appointments by minimizing the possibilities of forgetting. receiving the information on a phone is better because it reminds you in case you have forgotten the next visit”. it is very easy to forget your next appointment, but when the message comes, it reminds you. enabling transport cost and time saving pregnant mothers felt that receiving maternal health-related information on their mobile phones could save them transport costs that would otherwise be incurred in travelling to the hospital to access such information from healthcare providers. this is important since some of them reported walking long distances to the clinic for antenatal checkups, which sometimes causes them to miss some appointments especially in times when they are feel too weak to walk. since i can receive the information from my phone, it saves me money that i should have spent in form of transport to the clinic. it is not easy to get transport since i am not working, and sometimes i have to walk a very long distance to come here. sometimes i fail to come when i have no transport, and i have no energy to walk. it also saves me time too, since i don’t have to prepare and travel to the seminar room to get that information. source of friendly, tailored, information that is easy to understand, and recall. pregnant women narrated how the videos are being played by happy people compared to the unfriendly and tired faces that they often encounter at the clinic. they also reported that the videos and audios contained information they could easily understand since they are visual messages played in their local language, and can be replayed and listened to, as many times as wanted. this approach was preferred compared to the current approach used by the clinic that involves providing verbal general health talks which are not necessarily tailored to their specific needs. sometimes you show up at the clinic, and the doctors and nurses welcome you with gloomy tired faces and speak rudely to you, and they make you wait for a long time before they can attend to you, which even makes you fear to return. but i like these videos because the people in them are happy and speak kindly. also, one can easily mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi forget the verbal information from the clinic, but with these videos, i can see them now and again until i have mastered them, and can also refer to them in future, and even share them with my colleagues who are pregnant. enhancing spouse support and involvement pregnant women reported that receiving maternal-health related information on a mobile application could provide opportunities for them to share the information with their spouses. they narrated that this sharing would demonstrate to their spouses the importance of the contents of the videos/audios such as nutrition, and attending antenatal appointments. the women reported that this would motivate spouses to provide the required nutritional/transport support, which they would other be reluctant to provide. when you receive information about nutrition on phone and you show that message to your husband that will encourage him to buy you the necessary food without bias. it will be easier for me to get permission from my husband to come for checkup when i show him the reminder on phone. he will know that the hospital seriously wants me to attend, so he will give me transport and allow me to come, otherwise sometimes he tells me that i should not come especially when he has no money. enhancing feelings of being cared for, and prompting self-care pregnant women associated receiving maternal health information on phone with feelings of being cared for. being seen using the application could be interpreted as having special people who care about their pregnancy. this perceived care could motivate other women to access maternal healthcare from hospitals. i would be glad to receive the information since it can make me feel cared for. even when other people see me using the application, they know that i have special people who are caring about my pregnancy so that when they become pregnant they feel convinced to go and get the same services from the hospital. women reported that receiving mobile phone-based information about their pregnancy would enhance self-pregnancy care including taking the right foods, and nutritional supplements such as iron and folic tablets. they narrated how they would take an active role in their health by complying with the information contained in the application. it will enable us to know the right foods to eat during pregnancy, as well the medicines we have to take. for example, i have just heard about the need to take folic acid in the video; this is something i had never heard about before, so i am going to see how i can start taking it. mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi participants felt the functionality of the intervention may be limited by financial constraints and sharing of phones. financial constraints may limit the effectiveness of the intervention pregnant women anticipated instances when they could receive the information but fail to respond to it due to lack of money. one woman cited a possibility of receiving nutritional information when she has no money to buy the recommended food. the problem comes in when you receive the information about nutrition yet you don’t have money to buy the recommended food. lack of personal phone ownership may constrain receipt of information pregnant women who share their phones or have no phones perceived the possibility of not receiving the information sent via the intervention. sometimes my husband uses my phone, so when you send me a message indicating the foods i need to eat, he will deny having seen the message when he has no money to buy the food, because he knows that when i see the message, i will ask him to buy those foods. discussion in this study, pregnant women reported that the multimedia mobile application for maternal health could support maternal health by reminding patients attend their antenatal appointments, which addresses forgetfulness that sometimes hinders adherence to antenatal visits. receiving videos and audios with maternal health-related information, and the opportunity to call in when need be could relieve pregnant women of the transport burdens and time that they spend travelling or sometimes walking long distances to the clinics for the same services. the application was perceived as a source of friendly, and tailored maternal health information that, given its video/audio nature is easy to understand and recall compared to verbal health talks that women often get from the clinic. accessing the videos/audio could provide an opportunity to share the information with spouses as a way of soliciting the support needed to buy the necessary foods and obtain transport to the clinic. pregnant women perceived receiving videos and audios as being cared for by people who mind about their health. the knowledge obtained from videos and audios, and from calling specialists could be used by pregnant women to take an active role in improving their maternal health including taking the right foods, birth preparedness, identifying danger signs, as well as adopting breastfeeding and immunization. however, pregnant women felt the functionality of the mobile application intervention may be limited by financial constraints, and sharing of phones. reminding pregnant women to attend antennal visits through automatic appointment reminders could potentially reduce missed appointments as a result of forgetting the appointment date. attending antenatal care from the clinic provides an opportunity to educate women about danger signs of pregnancy complication, empowers women to develop a birth plan, enables monitoring of the pregnancy, and reduces morbidity and mortality risks for the mother and the child [26]. according the uganda demographic and health survey 2016, only 58% of rural women attend mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi the recommended four antenatal visits compared to 65% of women from urban areas. this report continues to demonstrate that only 53% of women with no education attended the recommended for antenatal visits compared to 72% of women with more than a secondary education. though not meant for illiterates, a recent systematic review demonstrates some evidence that mobile phone-based sms text reminders can improve adherence to attending antenatal visits [27] in a low resource setting. being reminded by mobile phone-sms based reminders could potentially result into forming a habit of adopting a health behavior [28]. when pregnant women turn up on their scheduled appointments, they are likely to maximize their contact and communication with health care professionals. forgetting to attend an antenatal appointment requires rescheduling a missed appointment, which is challenging given the prevailing heavy workloads of health care professionals, as well as the already overburdened healthcare systems in uganda. due to fear of being blamed by health care professionals for having missed antenatal appointments, some pregnant women consequently resort to not returning to the clinic and end up being lost followups, or resort to traditional birth attendants, or only turn up again at their delivery time, which can potentially put the health of a mother and her baby at risk. the present study indicates that providing maternal health-related information through mobile phones could relieve the burden of transport costs, time commitments, as well as the hassle of having to walk for long distances to access this information from the clinics. evidence demonstrates that lack of transport to antenatal facilities, and walking long distances constrain access and utilization of antenatal care especially among the poor and remote communities [29]. related studies indicate that using mobile phone-based consultations by village health teams to support maternal health saved mothers unnecessary transportation costs and improved their attitudes towards adapting recommended maternal and child care practices [30]. compared to the verbal health talks that pregnant women often get from the clinic, the mobile phone-based application was perceived to be a source of friendly and tailored maternal health information that can easily be understood and recalled. weekly videos with a length of at least five minutes were preferred to monthly videos. watching friendly faces in the videos was preferred to the often unfriendly physical faces that pregnant women encounter from the overworked health care providers. lack of friendly maternal health services have previously been cited as a major barrier to adoption of maternal healthcare seeking behavior in low resource settings [31]. providing tailored information could potentially increase women’s knowledge, understanding, and engagement while at the same time creating a sense of empowerment to cope with pregnancy, which can prevent adverse maternal and child health outcomes [32]. since they can be watched, paused, and repeated anytime, the videos and audios provide an opportunity for self-paced learning which facilitates learning and recall, that may otherwise not be possible with face-to-face approaches. the mobile health application provides opportunities for pregnant women to share the videos, audios, and appointment reminders with their spouses which could motivate their spouses to support them and be involved in their maternal health. this support could be in form of transport/permission to the clinic for antenatal services and delivery, assistance with house chores, and nutritional and feeding support. spouse support has been previously associated with utilization skilled birth services in the same setting [33]. using mobile phone-based sms texts though not necessarily with illiterate women has previously been associated with improved spouse support which consequently has positive impacts on utilization of maternal health services [34]. mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi pregnant women associated receiving videos and audios with feelings of being cared for by people who mind about their health. this could create a sense of connectedness to health care providers which could motivate women to comply with the demands of the application including attending clinic appointments in order not to disappoint the health care providers. this emotional support could provide women with reassurance, acceptance and encouragement to utilize maternal health services, while at the same helping them overcome pregnancy-related stress emanating from long waiting queues at the clinic, that are often exhausting, frustrating, and time-consuming [35]. the maternal health knowledge gained through watching/listening to the videos/audios could enhance self-care including taking the right foods, preparing for birth, identifying danger signs, as well as adopting best practices such as breastfeeding and immunization. enhancing self-care through mobile phone-based technologies is useful in improving maternal health for example; it reduces still birth [36], facilitates healthy gestation weight gain [37], and encourages smoking caseation among pregnant women [38]. some pregnant women reported that functionality of the mobile application may be limited by financial constraints particularly in an instance when the video about nutrition highlights foods that they cannot afford to purchase. another constraint was that of sharing of mobile phones which may constrain the receipt of some information. the problem of phone sharing may not be significant since according the survey results, only a few participants shared their phones. however, given the multimedia nature of the application, it can only run on smart phones which were only owned by a couple of women. the main strength of this study is that it used a mixed-methods approach to collect both closed and open-ended data. in addition, because the study was conducted in a rural sub-saharan african area and population, it has implications for similar settings. this study also identifies important insights from pregnant women that can inform the development of mobile phone-based application for improving maternal health. this study is limited by the fact that we asked participants about perceptions before they could use the intervention in real life. although we practically demonstrated the use of the intervention to all participants, participants were not able to describe actual experiences using the application (videos, audios, appointment reminders, and calling function) as part of their daily routine. lastly, findings could be vulnerable to social desirability bias because participants self-reported their preferences and anticipated uses of the application; moreover, before the researchers and application developers. in sum, mobile health application could potentially provide useful, accessible, friendly and tailored approaches to improving maternal health. they can provide a promising alternative or complementary approach to providing maternal health information especially among the rural illiterate women who might not easily access the information from health facilities. as mobile health technologies proliferate, understanding how they are perceived by prospective users is critical to developing interventions that are acceptable, feasible and effective in improving maternal health. findings from this formative study informed the development of a mobile health intervention that is being implemented in a randomized controlled trial (nct04089800) whose primary outcome measure is adherence to antenatal visits. secondary outcome measure include adherence to postnatal visits, number of women delivering from hospital, number of maternal complications, missed abortions, premature, number of maternal and newborn deaths ad maternal mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e8, 2020 ojphi health-related knowledge, attitudes. further research is needed to understand how participants in diverse settings perceive this intervention. acknowledgment the study was funded by a grant from the german ministry of education and research, under the 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of mobile technology. subst abuse rehabil. 7, 15. pubmed https://doi.org/10.2147/sar.s84239 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29748159&dopt=abstract https://doi.org/10.2196/mhealth.9565 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26931205&dopt=abstract https://doi.org/10.1186/s12884-016-0829-8 https://doi.org/10.1016/j.wombi.2018.08.040 https://doi.org/10.1007/s41347-017-0030-6 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27110146&dopt=abstract https://doi.org/10.2147/sar.s84239 innovative technology for web-based data management during an outbreak innovative technology for web-based data management during an outbreak 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 innovative technology for web-based data management during an outbreak shamir n mukhi 1,2 , tammy l stuart chester 2 , justine da klaver-kibria 3 , deborah l nowicki 4,5 , mandy l whitlock 4 , salah m mahmud 4,5 , marie louie 6 , bonita e lee 7 1 canadian network for public health intelligence, 2 national microbiology laboratory – public health agency of canada, 3 jkk environmental-health consulting, 4 winnipeg regional health authority, 5 university of manitoba 6 provincial laboratory for public health of alberta, 7 university of alberta abstract lack of automated and integrated data collection and management, and poor linkage of clinical, epidemiological and laboratory data during an outbreak can inhibit effective and timely outbreak investigation and response. this paper describes an innovative web-based technology, referred to as web data, developed for the rapid set-up and provision of interactive and adaptive data management during outbreak situations. we also describe the benefits and limitations of the web data technology identified through a questionnaire that was developed to evaluate the use of web data implementation and application during the 2009 h1n1 pandemic by winnipeg regional health authority and provincial laboratory for public health of alberta. some of the main benefits include: improved and secure data access, increased efficiency and reduced error, enhanced electronic collection and transfer of data, rapid creation and modification of the database, conversion of specimen-level to case-level data, and user-defined data extraction and query capabilities. areas requiring improvement include: better understanding of privacy policies, increased capability for data sharing and linkages between jurisdictions to alleviate data entry duplication. keywords: outbreak, laboratory, surveillance, epidemiology, integration, data, informatics introduction infectious disease surveillance at all levels (local, provincial, federal, and international) is essential for timely identification of possible outbreaks. effective outbreak investigations and measures to reduce the impact and disease transmission are critical and require ongoing collection, management, and analysis of data, in addition to timely aggregation of data across time and jurisdictions (municipal, provincial and territorial). seamless data management during an outbreak has been problematic in the past [1]. there are a multitude of challenges related to data management including lack of data sharing agreements, limited resources and technology resulting in manually intensive processes, and lack of shared http://ojphi.org innovative technology for web-based data management during an outbreak 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 platforms leading to inability for multiple stakeholders to access real time data. as identified in the naylor report on the 2003 sars outbreak in canada[2], one of the key steps in achieving seamless outbreak management is “…uniform adoption of highly flexible and interoperable data platforms, that allow sharing of public health information, capture of clinical information from hospitals, and integration into an outbreak management database platform”. in the spring of 2003, the public health agency of canada (then, health canada) partnered with several provincial/territorial and regional public health stakeholders to improve pan-canadian public health surveillance, communications, and response through the application of new technologies. this resulted in the creation of the canadian network for public health intelligence (cnphi), a comprehensive framework of applications and resources designed to fill critical gaps in canada’s national public health info-structure [3]. in an attempt to alleviate some of the challenges of data management during outbreaks, a new technology within the cnphi platform, called web data, was developed in 2008. it provides a mechanism for non-technical users to rapidly deploy a secure and adaptable webbased system for managing data. it also has an integrated search and interrogation capabilities and can produce instant data reports. in the spring of 2009, web data technology was put to test to assist with data management during the 2009-2010 h1n1 influenza pandemic. the winnipeg regional health authority (wrha), winnipeg, manitoba and the provincial laboratory for public health (provlab) in alberta both employed web data technology. the experiences of these organizations represent the epidemiological and laboratory perspectives, respectively. this manuscript describes the design, development, application and evaluation of the web data technology that was employed by these organizations during the h1n1 pandemic. design and development of web data the web data technology provides an easy-to-use web-based interface to create customized web-based data collection tools. it allows for user-defined databases and surveys that can be used to collect data in the form of open– and closed-ended questions, both quantitative and qualitative in nature. surveys can be used to collect a one-time response from users, whereas databases can be used to collect any number of records. the technology consists of the following main components: 1. form builder: also known as the designer, allows users to interactively develop forms using a drag and drop approach providing flexibility to non-technical users for rapidly developing secure databases. the form is based on section(s), which can include many fields placed on a grid of rows and columns defined by the user. the designer includes a pre-defined set of field types categorized as text inputs, selection inputs or display fields. examples of fields include checkboxes, radio buttons, dropdowns, and a table input. the user may also specify mandatory fields as well as additional descriptive help text for each of the fields as required. each section can be controlled for read or write access or completely hidden from specific users. http://ojphi.org innovative technology for web-based data management during an outbreak 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 2. queries/reports: the tool has a simple interface and allows users to develop queries to interrogate their database. data field(s) can be interactively specified and data exported for further analysis. customized reports may also be designed if required. 3. data uploader: in order to support batch data entry, the tool includes an uploader feature that allows batch uploading of comma delimited data based on a user defined uploader configuration. 4. advanced analysis: data trending and data analysis can be enabled through seamless integration with the canadian early warning system (cews) technology [4] which provides user driven interactive trending, analysis, gis mapping, reporting and extraction. 5. record transfer: to foster collaboration and seamless data sharing between multiple implementations of a form used by various stakeholders, the web data tool allows sharing of records based on a user defined field sharing configuration. that is, a set of specific fields can be copied or transferred from one record in a specific form into a different form without re-entry of the data. 6. cluster analyzer: this is a specialized module of the web data tool which enables identification of records that meet user defined criteria based on a set of fields. that is, users may define a specific set of fields and their matching preference order to identify a group of records (i.e., clusters). the cluster is then supported by a data table and a chart to visualize the findings. the technology allows seamless access by multiple users via a secure web-based model. it provides multiple levels of access control including role (reader, writer, administrator), jurisdictional and field level controls. deployment of web data during the h1n1 pandemic the web data technology was deployed in two different ways. the wrha team used the national instance of the web data technology. since the instance was not in manitoba, the wrha team opted to not include nominal information within the database. the provlab team used a local instance of web data technology which enabled them to include nominal information, i.e. the physical hardware resided within provlab infrastructure. both instances were managed by the cnphi team. wrha users had to log into the national instance of cnphi and accessed their form on the same system, while provlab users transitioned to local instance of cnphi and used the technology on it. this provided seamless user management and access to the resources. http://ojphi.org innovative technology for web-based data management during an outbreak 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 evaluation of the application of web data during the h1n1 pandemic a questionnaire was designed to explore the strengths and limitations of the web data technology during its construction and use during the 2009 h1n1 pandemic, as well as to compare and contrast prior data management approaches with web data management. the questionnaire included open and closed ended questions regarding attributes deemed important for data management during an outbreak, the initial data management, the transition to the web data technology, and the sequence of events used when employing the new technology for data management. questions were also asked regarding improvements that were seen as well as successes and limitations of the new web data technology. the questionnaire was sent via email to representatives in provlab and wrha. after receipt of the answers to the questionnaire, follow-up telephone interviews were conducted. for alberta and wrha, the representatives who supported and coordinated the implementation of the wed data technology provided feedbacks to the questionnaire. using the information collected from the questionnaire, a flow chart detailing the steps associated with data-gathering, information sharing, data management, and data technology prior to and following the use of the web data technology was constructed. this was done for both the provlab and wrha. the strengths and limitations of the web data technology used by wrha and alberta provlab were identified and compared. results the experience of the winnipeg regional health authority (wrha): the wrha pandemic response plan was coordinated by the regional health authority. figure 1 illustrates the flow of information between wrha and its partners and the changes that occurred (green) following the introduction of web data technology. influenza a, under the public health act of manitoba, is included on the notifiable diseases list and is reportable by all laboratories in the province. cadham provincial laboratory, the public health lab for manitoba, notified manitoba health of the first positive pandemic h1n1 2009 laboratory results in early 2009. these positive cases were manually entered into a manitoba health h1n1 access database. manitoba health subsequently sorted results and faxed the positive results of winnipeg residents or non-winnipeg residents in winnipeg hospitals to the wrha communicable disease unit. demographic and laboratory information was entered into the regional integrated public health information system (iphis). a referral was sent via iphis to a community public health nurse, who then completed an h1n1 case investigation form for all community and hospitalized cases in pandemic wave 1 and hospitalized cases only in pandemic wave 2. completed investigation forms were then faxed back to the communicable disease unit and filed in a paper chart. prior to the introduction of the web data technology, data from investigation forms for hospitalized patients were captured in an excel spreadsheet and maintained by the wrha population health surveillance unit, as variables required to support http://ojphi.org innovative technology for web-based data management during an outbreak 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 surveillance could not be easily or quickly added to iphis. all completed forms and updates to forms were faxed to manitoba health who then updated their h1n1 access database. initially, the wrha critical care program was maintaining an excel spreadsheet of all hospitalized icu cases which was updated every morning by overwriting the clinical data from the day before, making new changes difficult to track. this file was emailed to the wrha population health surveillance unit every morning, and was managed and stored on one computer. an access database was later created by the population health surveillance unit and shared with the wrha audits and quality analysis team in order to track clinical updates for icu patients. these processes were manual and there were multiple layers of duplication of data transfer and entry. during the initial stages of the h1n1 pandemic the wrha identified a need for a webbased database that was flexible enough to accommodate all fields on the investigation form and allowed for real-time access by multiple users. web data technology was used to rapidly develop a secure web-based database for epidemiological, clinical and laboratory information. initial consultation to setup the system was on june 10 th , 2009 with a functional database deployed on june 12 th ,2009. data analysts and epidemiologists at the wrha manually entered non-nominal data from the h1n1 investigation forms for all cases of h1n1 in winnipeg residents and non-winnipeg residents in winnipeg hospitals. the database continues to evolve as needed and allows for interactive data querying. data were exported for further analysis using stata (version 11.0) to generate daily and weekly reports. the web data database could not contain personal identifying information due to privacy regulations (see discussion). a unique identifier for each case generated by iphis was used to link cases in iphis to the web-based database when the two datasets were merged in stata (version 11.0). due to privacy and data sharing regulations manitoba health and the wrha continued to maintain their own databases. near the end of the second wave, the wrha was able to export data and send electronic updates to manitoba health instead of faxing updated investigation forms. additionally, first nations inuit health (fnih) in manitoba was given read only access to fnih cases hospitalized in winnipeg in the web data database allowing real time access to information. http://ojphi.org innovative technology for web-based data management during an outbreak 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 population health surveillance unit manitoba health -maintains access database public health nurse -investigates community, hospital and icu cases cadham laboratory checked for missing information positive cases faxed daily positive cases faxed daily forms faxed, any updates faxed again (eventually linelists directly from web data) iphis generates referral investigation forms faxed critical care program -maintained excel spreadsheet updated every morning iphis lab/demographic data only wrha audits and quality analysis (access) -for icu data web data all variables (non-nominal) linked by client and case id first nations and inuit health (fnih) -view fnih cases only stata v11.0 linkage and analysis read-only realtime access figure 1: flow of information before and after (green) the implementation of web data technology in the wrha. information in green indicates the changes that were made. based on the survey conducted, the limitations and benefits found by employing web data technology for h1n1 case management in the wrha are as follows: benefits data access: at the beginning of the first wave, data from the investigations forms for hospitalized cases were limited to the population health surveillance unit. with the web data database, multiple users could access data in real time. the web data database provides secure access that may be audited. in addition remote access to data was easily possible with the internet-based setup. both features allowed the wrha to give specific read only access to fnih. data collection/extraction: the web data database was easily modeled after the provincial surveillance form. information could be easily extracted for analysis and then transferred to manitoba health electronically via a secure file transfer website. data storage/management: at the beginning of the pandemic, laboratory and demographic information were being entered into iphis; data from the investigations forms for hospitalized cases were being entered into an excel file and another excel file was used to track clinical updates of patients in the icu. there was no well-designed system in place to manage pandemic http://ojphi.org innovative technology for web-based data management during an outbreak 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 h1n1 data. with the web data database, it was possible to rapidly create and modify the working database, as well as adapt to revisions to the case investigation form. duplicate manual data entry of mandatory data fields was eliminated. data queries/reports: there was the ability to create and save queries and to easily update case information. basic reports could also be quickly generated. limitations data sharing/linkage: linking epidemiological, clinical and laboratory data was hindered by the lack of pre-defined processes to deal with privacy policies. working with, and understanding of, current personal health information privacy policies will enhance data sharing between regional and provincial jurisdictions, and decrease duplication of data entry by having a common shared data management system for multiple stakeholders. the experience of alberta provincial laboratory (provlab): in the province of alberta, provlab provided centralized testing for respiratory viruses and was the front-line support in confirming h1n1 cases which was critical in the coordination of pandemic response plans. provlab employed a web-based platform called dial (data integration for alberta laboratories) [5], a technology also part of the cnphi framework, specifically designed to enhance the benefits of laboratory data surveillance, and to support easy access to near real-time laboratory data. since multiple specimens may be submitted to the provlab for a single patient, it was very difficult to have an automated algorithm to translate specimen-based data into patient-based data. thus, an entirely manual process was initially adopted at provlab where a single user generated a patient-based linelist in excel for public health stakeholders (figure 2). it is important to note that figure 2 depicts the flow of information from the laboratory perspective of the provlab, i.e., not from a provincial perspective as was illustrated for the wrha experience, and therefore provincial departments have largely been omitted from the figures. as illustrated in figure 2, patient demographics, and pertinent health information, along with specimenswere submitted to the provlab for influenza/respiratory virus testing. when a specimen tested positive for h1n1, a single user would manually enter the patient demographic information of the positive specimen into an excel spreadsheet. each day the user had to check for duplicate specimens from a single patient at least once to maintain a patient-based dataset. on a daily basis, this excel spreadsheet was sent to alberta health and wellness by the single user as a province-wide patient-based linelist. the linelist was also employed to generate daily reports for public health professionals. at the same time, appropriate personnel from alberta health and wellness and other regional public health stakeholders, also maintained their own access database using information from notifiable laboratory reports for h1n1 cases that were reported by provlab. http://ojphi.org innovative technology for web-based data management during an outbreak 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 provlab -single user for data entry -manual entry of specimen-level data into excel -manual removal of duplicate specimens from same patient -single user responsible for distributing reports to province clinical/epidemiological information information provided on requisitions submitted with samples are entered into provlab laboratory information system (lis) laboratory reports with test results are sent to the submitter(s) and public health partners according to the notifiable disease reporting requirements by mail/fax/electronic generation of specimen-based, province-wide h1n1 reports and linelists alberta health and wellness and other regional stakeholders -maintains mainly an access database figure 2: provlab h1n1 case management using the previous system. the web data technology was rapidly deployed within provlab during the pandemic due to the immediate need for managing case data in a structured manner allowing for web-based secure sharing and collation (see figure 3). the first h1n1 case was reported on april 28, 2009 in alberta. the first meeting to discuss possible implementation of the web data technology as part of the pandemic response from the laboratory perspective was held in the afternoon of may 4, 2009. the database was designed with stakeholders’ input and set up quickly. data was entered and the web data technology was deployed and became functional with the first casebased line list extract distributed to public health partners on may 11, 2009. the application continues to be used for influenza case-based data in the province by both laboratory and public health partners. initially, specimen-based data was manually entered into web data to provide patient-based data. however, as the number of positive h1n1 specimens increased, modified specimen-based linelists generated using dial were batch uploaded (i.e., multiple specimens at one time) into the web data technology removing the need for manual data entry. a review process was developed to compare h1n1 positive specimen test results to patient-based information in web data. this was needed to identify duplicate positive specimens submitted from a single patient. this allowed for the maintenance of a web enabled patient-based database of all influenza a positive specimens in alberta from the start of the pandemic. the queries function of the web data technology allowed easy access and extraction of patient-based linelist for public health stakeholders. http://ojphi.org innovative technology for web-based data management during an outbreak 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 data connectivity was also established between the web data and dial so all the analytical functionalities of the dial platform could be used to analyze patient-based h1n1 data. data analysis went from non-existent to excellent for patient-based data, so that laboratory quality issues and public health questions could be answered quickly. secure and audited access to dial and web data based case management databases were available remotely through the internet. separate web databases were set up for the province of alberta, the northwest territories and nunavut to suit specific user needs in compliance with the health information act. provlab laboratory information system (lis) clinical/epidemiological information information provided on requisitions submitted with samples are entered into provlab laboratory information system (lis) automated data extraction and upload dial -extract near realtime specimen-based linelists -specimen-based data uploaded into web data -remote access -storage capabilities province wide h1n1 reports web data patient-based data -data queries -graphing and geographic analysis alberta health and wellness & other regional stakeholders -maintain database mostly using access specimen-based notifiable laboratory reports sent by mail/fax/ electronic delivery system interpreted specimenbased data figure 3. provlab perspective of h1n1 case management using web data supported by dial. based on the survey conducted, the limitations and benefits of using web data technology for h1n1 case management in the provlab are as follows: benefits: data access: data could be accessed remotely by authorized users via the internet. data collection/extraction: previous manual entry of data was practically eliminated except for a manual check of the transformed patient-based data generated from specimen-based linelists before batch upload into the web data technology. validated lab data were updated onto dial automatically twice daily. batch data support allowed for uploading of a large amount of specimen data at one time. with web data manual entry errors were largely reduced. http://ojphi.org innovative technology for web-based data management during an outbreak 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 data storage/management: data automatically transformed from specimen to case-based data. the database could accommodate an evolving h1n1 case definition and provided a relational database for patients with multiple specimens. data queries/reports: data storage and management, as well as patient-level data allowed for excellent capabilities for generating queries and reports. data sharing: all laboratory results are stored in the provlab laboratory information system with restricted user access. however, the web data technology and dial can be accessed remotely and securely. these applications allow authorized regional jurisdictions easy access to relevant data. improvements required data linkage: the case management database was used exclusively in the laboratory setting for managing cases with multiple specimens and it was not linked to clinical and epidemiological information. as a result clinical and epidemiological data could not be used to define specimentesting priorities at the height of the pandemic. discussion both the winnipeg regional health authority and alberta provlab had positive experiences with implementation of the web data technology. the main benefits included: improved and secure data access. web data allowed for multiple users to access the database in real time, such as laboratory personnel, epidemiologists, and public health officials. the database was secure and was accessible via the internet with user audit functions. improved efficiency and error reduction with batch data uploader the single user manual input of data was inefficient and errors were more easily made. automated batch uploading allowed for very efficient data uploading, removed duplication of efforts, and reduced data entry errors. there was also less strain on human resources during pandemic. electronic collection and transfer of data the electronic collection and transfer of data was efficient and reduced errors associated with manual data entry. updates could be automatically sent to the appropriate public health officials in a timely manner. rapid creation and modification of the database over the course of the pandemic, the epidemiology of h1n1 changed, requiring the modification of the h1n1 case definition on investigation forms. web data technology provided flexibility and allowed for rapid creation, or adjustment of existing variables as needed. it could http://ojphi.org innovative technology for web-based data management during an outbreak 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 accommodate clinical, epidemiological and laboratory data input needs in a timely fashion and could respond to changes in processes and algorithms. conversion of specimento case-level data case based data were needed to understand the epidemiology of the pandemic, for public health decision making and setting laboratory-testing priorities at the provlab. data extraction and queries the ability to create, save and sort within queries was possible. although the wrha and the provlab had different approaches to, and applications of, the pandemic case management tool, the limitations to web data and the areas requiring improvement were similar: compliance with privacy policies/laws protection of the privacy of personal information, which encompasses one’s health information, is paramount in both alberta and manitoba [6, 7]. typically, personal information (e.g., personal health number, name, date of birth) cannot be stored on physical hardware outside the jurisdiction where it was collected. it is important therefore, that government policies and technological advances be established to maintain privacy such that data sharing and jurisdictional collaboration occurs as efficiently as possible during pandemic response. one possible solution to this problem is to deploy a local instance within a jurisdiction. data sharing and linkage between jurisdictions it was difficult to share information across various stakeholders that employed unlinked data systems. it was clear that each group collected only a part of the data required to paint a complete picture of the outbreak and epidemiology of the pandemic required for a collaborative response. having a single database accessible to each authorized professional jurisdiction to input their data would allow for the best use of resources. having access to other geographical jurisdictions would allow for best practices and epidemiological insight to become more apparent. duplication of data entry duplication of data entry also stems from privacy policy. each jurisdiction (e.g., wrha or critical care unit) maintains its own database in order to protect the personal information of individuals. in wrha, an additional layer of coding using duplicated data entry (i.e., the linkage of iphis to web data database via stata) was applied with the usage of web data to protect privacy. comparison to other web data like tools internet has become an integral part of health care with 77% of health department staff reporting use of the internet to search for health information at least once or twice a day [8]. in 2004, while 84% of 56 health departments reported the use of web-based reportable disease lists in the united states, only 9% had secure web-based capability for case reporting [9]. a more recent http://ojphi.org innovative technology for web-based data management during an outbreak 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 survey still revealed substantial variations in the adaption of electronic system in united states with some web-based manual electronic laboratory reporting system [10]. the concept of building rapid databases is also available though publicly available epidata software, which is an open-source software that allows users to build custom data collection tool. when compared to web data technology, however, epidata is limited by single user data entry, desktop focus and is not ideal for large datasets [11]. the description of other similar electronic web-based system is limited and there is little data on the deployment and user feedback to allow direct comparison with the web data technology [1214]. conclusion the web data technology provides an innovative platform to rapidly implement, with minimal technical expertise, a secure web-based database during an outbreak. it provides an easy-to-use interface for data entry and mechanisms to seamlessly link data, query and export data, and to adapt to change. in order to maximize its effectiveness and use, there needs to be simultaneous work on resolving data sharing and privacy issues before an outbreak. acknowledgements the authors would like to acknowledge contributions of dimitri tishchenko and brad micholson of the cnphi group, wrha and provlab teams, including support from users within alberta health and wellness and alberta health services. correspondence dr shamir n mukhi, 204.771.4698, shamir.nizar.mukhi@phac-aspc.gc.ca references [1] martin sm, bean nh. 1995. data management issues for emerging diseases and new tools for managing surveillance and laboratory data. emerg infect dis. 1(4), •••. http:// dx.doi.org/10.3201/eid0104.950403 [2] learning from sars: renewal of public health in canada. a report of the national advisory committee on sars and public health. october 2003. [3] mukhi sn, aramini j, kabani a. 2007. contributing to communicable disease intelligence management in canada. can j infect dis med microbiol. 18(6), 353-56. [4] aramini j, mukhi sn. canadian application of modern surveillance informatics. in: lombardo j.s. and d. l. buckridge disease surveillance: a public health informatics approach, pp. 315-328. john wiley & sons inc. publication. mailto:shamir.nizar.mukhi@phac-aspc.gc.ca http://ojphi.org http://dx.doi.org/10.3201/eid0104.950403 http://dx.doi.org/10.3201/eid0104.950403 http://dx.doi.org/10.3201/eid0104.950403 innovative technology for web-based data management during an outbreak 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 [5] mukhi sn, may-hadford j, plitt s, preiksaitis j, lee b. dial: a platform for real-time laboratory surveillance, online journal of public health informatics, issn 1947-2579, http:// ojphi.org vol.2, no. 3, 2010. [6] government of manitoba. 2007. freedom of information and protection of privacy (foipp) act. available at http://web2.gov.mb.ca/laws/statutes/ccsm/f175e.php accessed july 2010. [7] government of alberta. 2003. freedom of information and protection of privacy (foipp) act. available at: http://www.oipc.ab.ca/pages/pipa/readact.aspx accessed july 2010. [8] turner am, petrochilos d, nelson de, allen e, liddy ed. 2009. access and use of the internet for health information seeking: a survey of local public health professionals in the northwest. j public health manag pract. 15(1), 67-69. http:// dx.doi.org/10.1097/01.phh.0000342946.33456.d9 [9] m'ikanatha nm, welliver dp, rohn dd, julian kg, lautenbach e. 2004. use of the web by state and territorial health departments to promote reporting of infectious disease. jama. 291, 1069-70. http://dx.doi.org/10.1001/jama.291.9.1069 [10] cdc. 2009. status of state electronic disease surveillance systems --united states, 2007. mmwr. 58, 804-07. [11] achonu c. epidata: a potential tool for pandemics and large scale outbreaks, http:// www.apheo.ca/resources/projects/epidata/cdsn%20epidata%20presentation%2028oct10.pdf, accessed on may 12th, 2011. [12] snacken r, manuguerra jc, taylor p. 1998. european influenza surveillance scheme on the internet. methods inf med. 37, 266-70. [13] kant l, krishnan sk. 2010. information and communication technology in disease surveillance, india: a case study. bmc public health. 10, s11. http:// dx.doi.org/10.1186/1471-2458-10-s1-s11 [14] mao y, wu z, poundstone k, wang c, qin q, et al. 2010. development of a unified webbased national hiv/aids information system in china. int j epidemiol. 39, s79-89. http:// dx.doi.org/10.1093/ije/dyq213 http://web2.gov.mb.ca/laws/statutes/ccsm/f175e.php http://www.oipc.ab.ca/pages/pipa/readact.aspx%20accessed%20july%202010 http://ojphi.org http://o%ed%af%80%ed%b1%8dphi.org http://o%ed%af%80%ed%b1%8dphi.org http://dx.doi.org/10.1097/01.phh.0000342946.33456.d9 http://dx.doi.org/10.1097/01.phh.0000342946.33456.d9 http://dx.doi.org/10.1097/01.phh.0000342946.33456.d9 http://dx.doi.org/10.1001/%ed%af%80%ed%b1%8dama.291.9.1069 http://dx.doi.org/10.1001/%ed%af%80%ed%b1%8dama.291.9.1069 http://www.apheo.ca/resources/pro%ed%af%80%ed%b1%8dects/epidata/cdsn%ed%af%80%ed%b0%8820epidata%ed%af%80%ed%b0%8820presentation%ed%af%80%ed%b0%882028oct10.pdf http://www.apheo.ca/resources/pro%ed%af%80%ed%b1%8dects/epidata/cdsn%ed%af%80%ed%b0%8820epidata%ed%af%80%ed%b0%8820presentation%ed%af%80%ed%b0%882028oct10.pdf http://dx.doi.org/10.1186/1471-2458-10-s1-s11 http://dx.doi.org/10.1186/1471-2458-10-s1-s11 http://dx.doi.org/10.1186/1471-2458-10-s1-s11 monitoring older adult blood pressure trends at home as a proxy for brain health 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi monitoring older adult blood pressure trends at home as a proxy for brain health nicole cassarino, mph*, blake bergstrom, christine johannes, md, lisa gualtieri, scm, phd tufts university school of medicine abstract even when older adults monitor hypertension at home, it is difficult to understand trends and share them with their providers. myhealthnetwork is a dashboard designed for patients and providers to monitor blood pressure readings to detect hypertension and ultimately warning signs of changes in brain health. a multidisciplinary group in a digital health course at tufts university school of medicine used design thinking to formulate a digital solution to promote brain health among older adults in the united states (us). older adults (aged 65 and over) are a growing population in the us, with many having one or more chronic health conditions including hypertension. nearly half of all american adults ages 50-64 worry about memory loss as they age and almost all (90%) wish to maintain independence and age in their homes. given the well-studied association between hypertension and dementia, we designed a solution that would ultimately promote brain health among older adults by allowing them to measure and record their blood pressure readings at home on a regular basis. going through each step in the design thinking process, we devised myhealthnetwork, an application which connects to a smart blood pressure cuff and stores users’ blood pressure readings in a digital dashboard which will alert users if readings are outside of the normal range. the dashboard also has a physician view where users’ data can be reviewed by the physician and allow for shared treatment decisions. the authors developed a novel algorithm to visually display the blood pressure categories in the dashboard in a way straightforward enough that users with low health literacy could track and understand their blood pressure over time. additional features of the dashboard include educational content about brain health and hypertension, a digital navigator to support users with application use and technical questions. phase 1 in the development of our application includes a pilot study involving recruitment of primary care providers with patients who are at risk of dementia to collect and monitor bp data with our prototype. subsequent phases of development involve partnerships to provide primary users with a rewards program to promote continued use, additional connections to secondary users such as family members and expansion to capture other health metrics. keywords: brain health, older adults, aging in place, blood pressure, digital health, patient-generated health data *corresponding author: nicole.cassarino@tufts.edu doi: 10.5210/ojphi.v13i3.11842 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:nicole.cassarino@tufts.edu monitoring older adult blood pressure trends at home as a proxy for brain health 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi introduction in the us, there is growing concern around the health of older adults, defined as those aged 65 years old and over [1]. older adults are an expanding population, living longer than ever before. as of 2018, 52 million americans were over age 65 and it is estimated that this population will nearly double to 95 million by 2060 [2]. eighty-five percent of older adults live with one or more chronic conditions, including hypertension, commonly known as high blood pressure, which affects 63.6% percent of adults over age 65 [3,4]. nearly 90% of older adults aspire to stay in their current home with some level of independence for as long as possible, a concept termed “aging in place” [5,6]. to promote healthy aging in place, public health initiatives aimed towards increasing the safety and well-being of older adults are needed. declines in brain health pose one of the biggest threats to the safety and well-being of older adults aging in place. the us centers for disease control and prevention define brain health as “an ability to perform all the mental processes of cognition, including the ability to learn and judge, use language, and remember” [7]. one of the strongest threats to brain health among older adults includes dementia, defined as “the loss of cognitive functions including thinking, reasoning and remembering, as well as behavioral abilities to a degree at which the affected individual’s daily life and activities are compromised” [8]. the most common form of dementia includes alzheimer’s disease (ad), a progressive neurological disorder characterized by a progressive decline in thinking and social and behavioral skills, ultimately inhibiting the affected individual’s ability to function independently [9].dementia begins to develop before the disease is formally diagnosed [10]. results from the national poll on healthy aging found that nearly half (44%) of americans ages 50-64 worried about developing dementia as they age [11]. it is imperative that novel public health initiatives, such as the one reported on here, are developed to detect and prevent brain health disorders which meet older adults where they are truly at: in their homes. studies have indicated that a number of behavioral factors, such as smoking, physical activity and diet, as well as physiological factors, such as blood pressure, cholesterol and fasting blood glucose, have been associated with the onset and progression of dementia [12]. many epidemiological studies have revealed a link between dementia and lifestyle-related diseases, such as hypertension [13,14]. importantly, cohort studies have revealed that hypertension in midlife (around 45-64 years old) is associated with poorer cognitive function or dementia twenty years later, during older adulthood [15]. as blood pressure is a physiological metric which can be measured and recorded at home quickly, non-invasively and relatively easily at a low cost, compared to other metrics, measuring and recording one’s blood pressure routinely in the home may serve as one means by which older adults can slow or prevent the acquisition of diseases which threaten brain health, such as alzheimer’s disease and other dementias. problem statement and process among older adults, conditions of poor brain health can lead to cognitive difficulties beyond those associated with normal aging, including dementia.as the number of american older adults living with dementia continues to grow, we recognize the utility of a digital health solution which can allow older adults to prevent brain health decline as they age in place. monitoring older adult blood pressure trends at home as a proxy for brain health 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi within the context of a graduate-level course on digital health, the authors were tasked by aarp to design a digital health solution aimed towards promoting brain health for older adults who wish to successfully age in place. after speaking with content experts from aarp as well as potential end users, we recognized the need to create a solution which would be available to users at a lowcost. we also believed that users would benefit from ease-of-use in the home and wanted to design a solution that can record and store data related to a health-focused behavior which older adults already do regularly in their homes (i.e. weighing themselves or recording periods of sleep). finally, we aimed to create a solution that would record these measurements in a way that would provide the user with insight regarding trends or patterns within their health metrics which could be shared with their healthcare providers to be used in a predictive manner. we incorporated a design thinking methodology to guide the steps taken to develop a solution to the problem. design thinking is a structured process used to achieve a deep understanding of a target audience and their needs. this iterative process typically includes the following steps taken to create a solution to the identified problem: (1) inspiration, which involves understanding the initial problem and opportunities for change, (2) definition, which involves identifying the needs, constraints, and receptivity of the defined target population, (3) ideation, during which brainstorming is done to create novel solutions for the problem at hand, (4) experimentation, at which point a potential solution is designed and flaws are identified and rectified and finally (5) evolution, the stage in which the prototype is shared with potential end users and feedback is gathered and used to modify shortcomings of the solution [16]. the authors completed these five steps in order to design and validate a digital health solution aimed towards addressing the pervasive and complex problem of helping older adults maintain brain health in response to the challenge posited to us by aarp. given the well-studied association between hypertension and poor brain health, and the relative ease with which older adults can measure and record their own blood pressure at home, we proposed a solution which empowers older adults to measure, record and monitor their blood pressure at home to detect and prevent declines in brain health. by regularly monitoring blood pressure and maintaining it within a healthy range, the blood vessels which supply nutrients to the brain would be less likely to become diseased, in turn preventing physiological damage to the brain which can lead to cognitive impairment among older adults. additionally, sharing blood pressure measurements with healthcare practitioners can give older adults and their providers information on primary users’ vascular health, which, in turn, may give insight into the status of users’ brain health and allow detection of brain health problems before they begin or progress [17]. monitoring their own blood pressure gives older adult users the additional opportunity to engage with their own health maintenance; research indicates that engaged and informed patients take more responsibility for their health, which may slow or prevent potential degradation associated with aging [18,19]. to this end, the office of the national coordinator for health information, a federal organization tasked with coordinating efforts “to implement and use the most advanced health information technology and the electronic exchange of health information” publicly announced a goal for “pghd to seamlessly and securely flow from patient to clinicians and research as part of routine care and research” by 2024 [20,21]. therefore, we recognized that preventative care for brain health can include consistent recording and understanding of patientgenerated health data (pghd) by patients and discussion of this data with their healthcare providers. monitoring older adult blood pressure trends at home as a proxy for brain health 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi target audience interest in digital health solutions the original problem focused on the needs of older adults, but a digital solution can only be successful if the target users are receptive to digital health. studies have shown that a majority of older adults and their caretakers are interested in using digital health solutions to monitor their health conditions in their homes [22,23]. one study of in-home sensor technology to detect cognitive changes and other health issues among older adults found that over 90% of participants accepted the in-home monitoring as long as the data collected would be useful to their doctors [24]. based on their high level of interest, and their role as the demographic most directly impacted by a digital health solution aimed at promoting brain health, the authors believed a digital health solution would be feasible for older adults as the primary target user population during the design thinking process. importantly, we aim to include all adults over age 45, capturing those who have not yet reached older adulthood by definition but are in the critical period during which hypertension can lead to problems with brain health. in addition to defining age, the primary user population we chose to focus on would be living in their homes,, have at least one chronic health condition as four out of five american adults over age 50 do [25], and would have internet access, basic digital literacy skills and access to a device which can access the internet, such as a smartphone or personal computer. after considering the main sources of support on whom these primary users rely for their wellbeing, we identified healthcare providers as well as caretakers as the secondary users. identifying the network of individuals who would be involved in this application of digital health led us to consider additional stakeholders in this solution. finally, we identified another potential partner to be aarp, who provided the original problem and with whom we consulted during the design thinking process for the digital health solution. from these consultations we learned that aarp would be open to working with designers of the solution to recruit potential users, as well as smart blood pressure cuff manufacturers, such as withings, from whom we could order the smart device in bulk to ultimately offer lower prices to primary users. target audience assessment following the design thinking process, to better understand our target population, we conducted a four-step process to identify the needs of older adults who wish to maintain their brain health. our first step involved developing a set of interview questions to get valuable feedback around the topics of brain health, health/technology literacy, and more from potential end users. secondly, we used feedback from these interviews to develop personas that would represent different types of potential end users who may potentially use a digital solution for brain health. we then developed a fictitious scenario for each persona which might prompt them to seek a digital solution for their brain health. finally, we ran multiple q&a sessions with content experts working at aarp in the fields of healthy aging in place and brain health to both learn what solutions to which they already have access and what gaps they have identified that we can address through our product ideation. interviews we planned and conducted four key informant interviews with potential primary users. to learn as much as possible from a small convenience sampling, we developed our interview questions around seven major categories each relevant to maintaining brain health in older adults. monitoring older adult blood pressure trends at home as a proxy for brain health 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi category 1 investigates the interviewee’s baseline healthcare practices and resources. category 2 investigates to what extent memory issues currently impact the interviewee’s life. category 3 investigates whether the interviewee has had any concerns about memory or independence issues to date. category 4 investigates the interviewee’s motivation(s) to explore a potential intervention related to brain health. categories 5 investigates the interviewee’s health literacy. category 6 investigates the interviewee’s digital literacy. category 7 investigates the interviewee’s openness about home-based health monitoring and data-sharing. to recruit interview subjects, we used a convenience sample; each interview was conducted by a member of our team with an acquaintance of choice aged 50 or older. four interviews with potential primary end users were completed via phone calls and answers were electronically transcribed. interview results by creating the interview guide and using it with potential primary end users, our team was able to further consider all stakeholder populations involved in the need for a digital health solution to preserve older adults’ brain health at home. (the complete interview guide instrument can be found in the appendix a). responses obtained from these interviews provided greater insight into the baseline healthcare practices and resources of our interviewees. from these questions, we were able to ascertain the extent to which older adults are presently involved in their own health care. all (n=3) had some degree of contact with their pcp but it varied from in-person communication (50%) to the telephone (25%) and using their patient portals (50%). all (n=3) had baseline knowledge of their own health conditions and most (75%) were motivated to be involved in their health care. those with chronic health issues self-reported to be more motivated and likely to be interested in using digital health technology to assist in managing their health. interviewees were also asked to explain any memory problems they currently experience. all older adult interviewees (n=3) expressed that they had experienced mild memory impairments recently. none (n=0) had been diagnosed with memory disorders, such as alzheimer’s disease, and none (n=0) reported not having memory issues. when asked about any concerns they had about their memory, all interviewees (n=3) expressed interest in strengthening their memories as they aged to avoid any further loss. all interviewees (n=3) expressed concerns with maintaining memory or independence issues to date. monitoring older adult blood pressure trends at home as a proxy for brain health 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi when asked about their motivation to use technology for brain health, interest varied among interviewees. from one of our interviewees, we heard that “most technology is not relevant to them, inefficient and difficult to use.” this comment underscores the importance of building digital literacy among older adults so that they can easily adopt and use digital technologies routinely to manage and maintain their health. fifty percent (n=2) of the interviewees were aware that blood pressure affects many aspects of health including memory, underscoring the need to promote health literacy among older adults. the results from our four interviews revealed varying degrees of comfort in using technology among older adults. the range was from very comfortable for half (50%) of the interviewees (i.e. commonly uses multiple mobile and web-based applications for health purposes and their patient portal) to no desire or lack of knowledge (50%) in accessing their own health information or any health technology. three out of the four (75%) interviewees had specific concerns related to privacy and security of personal health information were brought up in conversations of routine use of a potential digital solution for brain health. in addition, while each individual uses technology daily, interviewees’ experiences with digital literacy were mixed. for 25% of the interviewees, some barriers experienced in using any health technology included a lack of interest and/or motivation to monitor, record and share their own health data. despite the limitation of a small number of interviewees, the responses were helpful in defining next steps. based on the analysis, important and necessary considerations to include in our product design are: 1) creating a product that will be easy and convenient to use at home, 2) promoting patient engagement in adopting and operating the technology daily without the direct assistance of a healthcare provider, 3) providing assistance when using the technology via a digital navigator, 4) providing educational materials specific to relevant health issues, such as elevated blood pressure and, 5) maintaining the primary users’ privacy and ensuring that the data is transmitted securely between the primary user, their physician and their health insurance company. personas and scenarios after conducting interviews, we used the feedback to develop five unique personas and scenarios to represent each use case we identified that would benefit from digital solutions for brain health. these fictitious personas are distilled from the interviews and reflect the research conducted in the project’s initial stage. the first three are primary personas, which we define as user populations that are directly impacted by brain health issues and would be the main users of our digital health solution. the last two are secondary personas, whom we define as populations who are impacted by the primary users themselves; an example would be a family member who provides most of the caregiving or a doctor of a primary user. our first primary persona is victor, a 65 year old retiree living in upstate new york. he lives at home with his wife and lives a generally healthy life which includes a healthy diet and exercise habits (by traditional american standards). he has been diagnosed with hypertension but has had monitoring older adult blood pressure trends at home as a proxy for brain health 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi it managed for years by taking a daily statin. however, for the third time in a row, he has forgotten to take his wallet with him to his regular pcp appointment. he starts to worry whether this is a fluke memory issue or something more significant. internet searches make him even more worried. he plans to bring this up with his pcp during his next appointment. our second primary persona is amy, a 55 year old clinical assistant living in hanover, massachusetts. she is divorced and lives with her elderly parents, acting as a caregiver. her mother has hypertension, with which amy is very involved in managing. after a long night shift, amy comes home and realizes she forgot whether her mother took her blood pressure medication that day. she questions her mother and a verbal argument ensues between the two, with both accusing the other of being forgetful. amy starts to worry whether her ability to take care of her parents might be affected by the long hours she spends at work, or if something more serious might be affecting her ability to care for them. she starts researching online and finds that tracking data with her smartphone could help her keep an eye on both her and her mother’s patterns of memory loss. our third primary persona is allen, a 60 year old consultant who recently moved to his vacation home in the countryside of rural maine to become a full time teleworker. he lives 45 minutes away from the closest hospital. he has type 2 diabetes and hypertension, for which he takes a daily statin. he recently downloaded a medication tracking app on his phone, as he noticed he hasn’t been as consistent about taking his statin everyday as he used to. ever since the move away from the city, he also realized he hasn’t been as active since there’s no gym located nearby. in order to stay independent, he knows that he needs to keep himself as healthy as possible, physically and mentally. he decides to set up a telehealth visit to ask his doctor about remote healthcare and athome monitoring. our first secondary persona is stephen, a 35 year old software engineer that lives across the country from his parents, whom he regularly video calls. his father has always been generally healthy, but his grandmother on his father’s side died of dementia when stephen was in college. during their most recent call, as stephen’s father was about to say goodbye, he accidentally called him the wrong name. stephen didn’t think much of it but started to worry as the day went on. was his father distracted? was he messaging somebody else and accidentally said their name? or is his grandmother’s dementia genetic and has possibly been passed down to her son? stephen started to worry about being so far away from his father to help address these potential issues. our second secondary persona is dr. sevilla, a 45 year old physician in los angeles, ca and stephen’s father’s pcp. ever since epic healthcare software was installed at her clinic in 2015, she started using telehealth capabilities with her patients. during the pandemic, dr. sevilla notices that while most patients transitioned smoothly to telehealth, stephen’s father had not. she worries about him as she has heard from his son stephen about memory-related concerns and knows that stephen doesn’t have much support in monitoring it with his son across the country. she starts looking into remote monitoring technologies that could be integrated into epic. discussion with aarp subject matter experts before beginning our ideation phase, we set up three meetings with different aarp leaders in the fields of digital technology and brain health. our first meeting was with nataki edwards, senior monitoring older adult blood pressure trends at home as a proxy for brain health 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi vice president of digital marketing at aarp. below were our three prepared talking points with her: 1. what is aarp’s experience with members using their existing digital tools? what feedback (positive and negative) has aarp received about them? what are the barriers to using these tools have members expressed? 2. does aarp have a digital navigator to assist members in using their digital tools as needed? 3. does aarp have partnerships with any health monitoring device companies, such as manufacturers of smart blood pressure monitors, to offer discounts to members? our second meeting was with rachael lazarus, ph.d, a neuroscientist working for the staying sharp initiative within aarp, and kathy washa, the director of the staying sharp program at aarp. this meeting focused on getting more information about aarp’s staying sharp platform and how it was developed and implemented to support the brain health of aarp members. during this meeting, we learned how the platform encourages older adults to maintain their brain health through the following six pillars. later, during our ideation process, we took these into consideration and contemplated designing educational content integrated into our digital solution centered on how each pillar could be used to prevent hypertension and, in turn, promote brain health (figure 1). figure 1. the six pillars of brain health emphasized in aarp’s ‘staying sharp’ program. source: aarp, n.d. our third meeting was with alison bryant, phd, senior vice president of aarp research and digital equity lead. our goal for this final meeting was to understand how aarp could potentially accommodate storage of patient-generated health data (pghd) and whether the digital health solution that we have designed would be something that they could embrace and encourage for their members. ideation we began our ideation process by reviewing pre-existing studies designed to understand brain health among older adults as they age in place. our focus was on the ones we identified a number of studies centered on the use of digital solutions to promote mental health among older adults (including solutions aimed towards depression and social anxiety), but few focused on loss of monitoring older adult blood pressure trends at home as a proxy for brain health 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi cognition and/or memory, one of the most prevalent conditions among older adults [26,27]. while digital solutions have been created to use biometric data from primary users’ electronic health records to predict outcomes related to brain health, including hypertension, none have been designed to be used on a daily basis by the primary user in his or her home [28]. as such, we recognized the need to create a digital health solution which could promote brain health maintenance for older adults in their homes and take into consideration barriers such as health literacy. to our knowledge, our proposed digital solution is the first to be directly used by the primary end user, the patient, in the home to detect and alert secondary users, the primary user’s support team of caretakers and healthcare providers, to changes in blood pressure which might predict a primary user’s risk for dementia. from our interviews with potential primary target users, we identified some triggers that might prompt an older adult to use our solution. these included fears surrounding memory decline and resulting inability to stay independent as well as the inability to continue to age in place in the home. this understanding is essential for a solution to be adopted and used by the target user population. through a review of the existing literature, as well as conversations with potential primary users as well as content experts at aarp, we identified four barriers to adoption (1-3) and use (4) that their members, who are 50 years of age or older, including older adults, face in managing their health care and their ability to use technology to record and view their health data. these barriers include: 1) inability to completely understand personal health data among some primary users, due to low health literacy 2) lack of familiarity with internet-enabled devices and web-based applications due to low digital literacy 3) poor access to the internet services and/or lack of technology used to access the internet (i.e. personal computer, smartphone) can affect a primary user’s ability to record their own health data, limiting their ability to share this data with physicians. 4) concerns about privacy and security of data. all of these triggers and barriers were taken into consideration during the ideation phase of the design thinking process to promote uptake, utility and ease of use among primary and secondary end users. one of the main challenges in the development of our product design included narrowing our focus on the broad topic of “brain health” to achieve the goal of collecting and tracking data to improve brain health outcomes for older adults. we aimed to target improvement in the areas of overall health and wellness, health knowledge and digital literacy, while specifically designing an intervention centered on brain health. initially we formulated the idea of creating a platform with aarp that would collect the primary user’s blood pressure each day and provide alerts when elevated blood pressure was detected. the solution would also offer links to educational content related to high blood pressure and brain health to help users with varying levels of health literacy put their measurements into context. monitoring older adult blood pressure trends at home as a proxy for brain health 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi in our discussion with dr. alison bryant we learned that aarp would be interested in providing incentives through the aarp rewards program to encourage primary users to collect and measure their blood pressure. however, aarp declined the role of storing primary users’ data or providing medical advice to prevent liabilities on their end. from this point, we pivoted in our design process and developed our application, a service that would be instrumental in collecting health metrics, starting with blood pressure in our first iteration, to better assist primary users in monitoring their own health trends and share them with their care team. the design thinking process for our solution benefitted from the diversity of professional backgrounds of the authors. our team consisted of two clinicians: one pediatrician currently practicing at a major boston-based hospital who is currently earning a master of health informatics and analytics degree and a dentist with a master of public health degree. additionally, our team included one current master of health information and analytics degree candidate with a background in software engineering (and current employee at mitre). our team also consisted of a master of public health and current first-year medical student with a background in epidemiology research. our work was ultimately supported by our course instructor, dr. lisa gualtieri, an expert in the field of digital health and creator of recyclehealth, a non-profit organization which collects and distributes wearable activity trackers to underserved patient populations. along with content experts from aarp, we leveraged the strengths and insights of all team members to develop a comprehensive, practical and novel digital solution to promote brain health among older adults wishing to age in place. introducing: myhealthnetwork overview myhealthnetwork is the working title for the prototype of a digital health application which can store blood pressure readings as inputted by the primary user and share these data with additional stakeholders, including the primary user’s primary care physician (pcp), to detect changes that might predict the user’s risk for dementia (figure 2). the application as designed will connect to a digital blood pressure cuff via bluetooth, and users can manually input their data if they own a conventional blood pressure monitor. from the moment the primary user begins monitoring their blood pressure through the application, they will have access to a digital navigator, a chatbot, which can connect to a live person available for assistance to help answer non-urgent questions related to health and assist in using and connecting the app and blood pressure monitor. monitoring older adult blood pressure trends at home as a proxy for brain health 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi figure 2. flow chart describing the transmission of patient-generated health data from primary user into myhealthnetwork to additional stakeholders. after using myhealthnetwork for at least thirty days, the primary user is alerted if their blood pressure readings diverge from normal values and will be encouraged to continue to monitor daily or seek medical care as needed. there will be an option to download the raw data onto their personal mobile device so that primary users can show this information to their pcp during their next appointment. during the later stages of piloting, myhealthnetwork will be integrated into electronic health records so that the patient-generated health data will become part of the patient’s existing medical record. during times where blood pressure readings are consistently higher or lower than expected (but within non-life threatening levels), primary users will be prompted to contact their pcp to review the data. in cases where blood pressure readings reach dangerously high or low levels, primary users will be alerted to seek medical attention immediately. myhealthnetwork offers a feature through which the primary user can choose to share their blood pressure readings with family members and friends. the application was designed with this feature in mind based on a large body of research which indicates that older adults who have stronger social support tend to have healthier blood pressure levels [29,30]. through conversations with aarp representatives, we learned about the aarp loyalty rewards program which currently provides rewards for aarp members who provide data from their fitness trackers. as the aarp loyalty rewards program is well-established, it would be a useful tool which could be integrated into our solution to encourage older adults to monitor their blood pressure and provide incentives to continue monitoring. our app sends metadata (no usergenerated raw data) to the aarp rewards program on how often they are checking their blood pressure weekly. the primary user receives an alert from the app on the total number of points they have accumulated and could redeem a reward at the end of each month of monitoring. monitoring older adult blood pressure trends at home as a proxy for brain health 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi in addition to aarp, the back-end of the application links to the primary user’s health insurance provider. initially, we will partner with united healthcare, as we learned from our conversations with aarp content experts that aarp already has an existing partnership with united healthcare and trusts the health insurance company to securely store raw health-related data. ideally, additional health insurance companies would consider collecting, storing and monitoring data output from myhealthnetwork and potentially decrease annual premiums for primary users who demonstrate intentionality in maintaining their own health through consistent use of the product. dashboard prototype the authors developed a prototype to display the look and functionality of the wellness dashboard. the dashboard is a simple web application with two tabs, one depicting a view that the patient could access (see figure 3) as well as a view for their physician (see figure 4). both views share a similar layout with a temporal plot of blood pressure data in the top half (with adjustable date ranges) and then three different summaries of data/information on the bottom half. the goal for both views is to allow for quick and easy interpretation of data relevant to each party. the patient view tab was designed with health literacy in mind, as interpreting blood pressure trends can be a complicated subject with multiple values to monitor over time. the bottom three summaries have large and easy to read text, covering the following three areas from left to right: (1) “alerts”: displays either automated warnings if certain negative trends can be seen or messages directly from the doctor, (2) “your weekly summary” shows the percent change in blood pressure in the past week as well as the average value, and (3) “have questions?” provides links to multiple resources, including technical support and educational modules centered on brain health and blood pressure. through an aarp partnership, the staying sharp initiative, which is designed to offer curated educational content on brain health to older adults, would be linked here as well as an additional resource. for the application to be successful, one of the most important features is the patient view of blood pressure data. to be effective with users of varying degrees of health literacy, we knew a graphical approach was needed. further, we recognized the necessity of having a display of long term trends in blood pressure since the underlying point is to see changes and trends in data. after examining many mechanisms to help patients understand blood pressure readings, we believed that the color-coded blood pressure categories provided from the american heart association were the easiest to interpret, as they include a simple green, yellow, orange, red, and dark red scale for the following categories: normal blood pressure, elevated blood pressure, hypertension stage 1, hypertension stage 2, and hypertensive crisis. each category is based on the value of both the systolic blood pressure (sbp) and diastolic blood pressure (dbp), with specific conditions that can be found at the american heart association’s website [31]. however, as authors we were challenged by the fact that there was no literature found on how to display these categories in a temporal view. the two main challenges included: (1) the need to include two inputs as opposed to one (sbp and dbp), meaning a single line for blood pressure on a plot over time could not be used, and (2) the american heart association blood categories do not have a numerical output that can be plotted on a y-axis over time. therefore, we developed an algorithm to display the trends of a patient’s blood pressure readings in a single line over time. monitoring older adult blood pressure trends at home as a proxy for brain health 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi the algorithm is based on taking in both blood pressure inputs (sbp and dbp) and calculating what is being defined as an alpha value, which is a number between 0 and 5 and will act as the yaxis value on a plot to describe what blood pressure category the two given inputs indicate. the final plot will have an x-axis for dates and a y-axis for blood pressure categories on a numerical scale from 0 to 5, with each category taking up a span of 1 unit. this results in the green normal category spanning from 0 to 1, the yellow elevated category spanning from 1 to 2, the orange hypertension stage 1 category spanning from 2 to 3, the red hypertension stage 2 category spanning from 3 to 4, and the hypertensive crisis category spanning from 4 to 5. a line reflecting the seven day moving average of each value will be fitted to account for outliers. finding the alpha value is dependent on another value, delta, which takes into account where exactly the current blood pressure inputs fall within their category range (for example, is the reading in the middle of the category’s range [i.e. sbp=125 in a scale of 120-129] or is it closer to the border of this category [i.e. sbp=128 along the same scale]). therefore, we created the delta value (𝛿), a numerical value to represent a given blood pressure input’s position in its category’s range. there will be a delta value for both sbp and dbp, with the general equation for equating the delta value being the following: 𝛿 = 𝐵𝑃 − 𝑙𝑜𝑤𝑒𝑟 𝑙𝑖𝑚𝑖𝑡 𝑢𝑝𝑝𝑒𝑟 𝑙𝑖𝑚𝑖𝑡 − 𝑙𝑜𝑤𝑒𝑟 𝑙𝑖𝑚𝑖𝑡 in the delta equation, the “lower” and “upper” limits reflect the lowest and highest blood pressure readings in a given blood pressure range, respectively” for a given set of inputs, there will be a delta for both the sbp (δs) and for the dbp (δd). table 1 summarizes how each delta value would be calculated for both sbp and dpb, showing the specific upper and lower limit values denoted by the american heart association (aha). one issue that we encountered, however, was that the highest category (hypertensive crisis) defined by the american heart association did not have upper limits, which are required for this algorithm to work. therefore, upper limits were chosen based on the ranges of the previous category (hypertension stage 2). the total range for sbp in hypertension stage 2 was 40 mmhg, therefore that same range was used for δs, hypertensive crisis. the total range for dbp in hypertension stage 2 was 30 mmhg, therefore the same range was used for δd, hypertensive crisis. monitoring older adult blood pressure trends at home as a proxy for brain health 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi blood pressure category δs δd normal 𝑆𝐵𝑃 − 0 𝑚𝑚𝐻𝑔 119 𝑚𝑚𝐻𝑔 − 0 𝑚𝑚𝐻𝑔 𝐷𝐵𝑃 − 0 𝑚𝑚𝐻𝑔 79𝑚𝑚𝐻𝑔 − 0 𝑚𝑚𝐻𝑔 elevated 𝑆𝐵𝑃 − 120 𝑚𝑚𝐻𝑔 129 𝑚𝑚𝐻𝑔 − 120 𝑚𝑚𝐻𝑔 𝐷𝐵𝑃 − 0 𝑚𝑚𝐻𝑔 79 𝑚𝑚𝐻𝑔 − 0 𝑚𝑚𝐻𝑔 hypertension stage 1 𝑆𝐵𝑃 − 130 𝑚𝑚𝐻𝑔 139 𝑚𝑚𝐻𝑔 − 130 𝑚𝑚𝐻𝑔 𝐷𝐵𝑃 − 80 𝑚𝑚𝐻𝑔 89 𝑚𝑚𝐻𝑔 − 80 𝑚𝑚𝐻𝑔 hypertension stage 2 𝑆𝐵𝑃 − 140 𝑚𝑚𝐻𝑔 179 𝑚𝑚𝐻𝑔 − 140 𝑚𝑚𝐻𝑔 𝐷𝐵𝑃 − 90 𝑚𝑚𝐻𝑔 119 𝑚𝑚𝐻𝑔 − 90 𝑚𝑚𝐻𝑔 hypertensive crisis 𝑆𝐵𝑃 − 180 𝑚𝑚𝐻𝑔 219 𝑚𝑚𝐻𝑔 − 180 𝑚𝑚𝐻𝑔 𝐷𝐵𝑃 − 120 𝑚𝑚𝐻𝑔 149 𝑚𝑚𝐻𝑔 − 120 𝑚𝑚𝐻𝑔 table 1. delta values are defined as a numerical representation of where a given blood pressure value falls within its blood pressure category’s range on a scale of 0 to 1. this table represents the expressions used to calculate the delta value for both sbp (δs) and dbp (δd), which are based off of the american heart association’s blood pressure category chart. because there are two delta values and only one can be used, we decided to choose the larger of the two values for the final delta value of a given category. this was decided on the basis that the goal of the visual display is to highlight potentially increasing trends in blood pressure readings and hoping to catch the user’s attention whenever possible. with a delta value found, the final alpha value will be that delta value added to a constant related to its given blood pressure category, since the final scale is from 0-5. below, the algorithm will be displayed three different times, with each successive time providing more granularity than the former. the following version is the highest level view, explaining the process of how an alpha value is calculated for a given input. it essentially includes five different “if” statements, with each one representing a different blood pressure category. as seen below, the delta value chosen is added to 0 for normal blood pressure category, 1 for elevated blood pressure category, and so on. for example, if the delta value of a given set of blood pressure inputs was found to be 0.6 and fell within the hypertension stage 1 category, its final alpha value would be 2 + 0.6, which would result in a y-value of 2.6 on the final graph. 𝑖𝑓 (𝑁𝑜𝑟𝑚𝑎𝑙 𝐵𝑙𝑜𝑜𝑑 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦) 𝛼 = 0 + 𝑒𝑖𝑡ℎ𝑒𝑟 𝛿𝑆,𝑁𝑜𝑟𝑚𝑎𝑙 𝑜𝑟 𝛿𝐷,𝑁𝑜𝑟𝑚𝑎𝑙 , 𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟 𝑖𝑠 ℎ𝑖𝑔ℎ𝑒𝑟 𝑖𝑓 (𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 𝐵𝑙𝑜𝑜𝑑 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦) 𝛼 = 1 + 𝑒𝑖𝑡ℎ𝑒𝑟 𝛿𝑆,𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 𝑜𝑟 𝛿𝐷,𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 , 𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟 𝑖𝑠 ℎ𝑖𝑔ℎ𝑒𝑟 𝑖𝑓 (𝐻𝑦𝑝𝑒𝑟𝑡𝑒𝑛𝑠𝑖𝑜𝑛 𝑆𝑡𝑎𝑔𝑒 1 𝐵𝑙𝑜𝑜𝑑 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦) monitoring older adult blood pressure trends at home as a proxy for brain health 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi 𝛼 = 2 + 𝑒𝑖𝑡ℎ𝑒𝑟 𝛿𝑆,𝑆𝑡𝑎𝑔𝑒 1 𝑜𝑟 𝛿𝐷,𝑆𝑡𝑎𝑔𝑒 1, 𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟 𝑖𝑠 ℎ𝑖𝑔ℎ𝑒𝑟 𝑖𝑓 (𝐻𝑦𝑝𝑒𝑟𝑡𝑒𝑛𝑠𝑖𝑜𝑛 𝑆𝑡𝑎𝑔𝑒 2 𝐵𝑙𝑜𝑜𝑑 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦) 𝛼 = 3 + 𝑒𝑖𝑡ℎ𝑒𝑟 𝛿𝑆,𝑆𝑡𝑎𝑔𝑒 2 𝑜𝑟 𝛿𝐷,𝑆𝑡𝑎𝑔𝑒 2, 𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟 𝑖𝑠 ℎ𝑖𝑔ℎ𝑒𝑟 𝑖𝑓 (𝐻𝑦𝑝𝑒𝑟𝑡𝑒𝑛𝑠𝑖𝑣𝑒 𝐶𝑟𝑖𝑠𝑖𝑠 𝐵𝑙𝑜𝑜𝑑 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦) 𝛼 = 4 + 𝑒𝑖𝑡ℎ𝑒𝑟 𝛿𝑆,𝐶𝑟𝑖𝑠𝑖𝑠 𝑜𝑟 𝛿𝐷,𝐶𝑟𝑖𝑠𝑖𝑠, 𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟 𝑖𝑠 ℎ𝑖𝑔ℎ𝑒𝑟 the next version of the algorithm includes two more levels of granularity. the first inclusion is the specifics of what each blood pressure category is defined as based on sbp and dbp inputs. the second shows the math behind choosing the higher value between δs and δd, which is based off of the general equation below: 𝑀𝑎𝑥(𝑎, 𝑏) = 1 2 (𝑎 + 𝑏 + |𝑎 − 𝑏|) this equation is utilized below: 𝑖𝑓 (0 ≤ 𝑆𝐵𝑃 < 119 & 𝐷𝐵𝑃 < 80) 𝛼 = 0 + 1 2 (𝛿𝑆,𝑁𝑜𝑟𝑚𝑎𝑙 + 𝛿𝐷,𝑁𝑜𝑟𝑚𝑎𝑙 + |𝛿𝑆,𝑁𝑜𝑟𝑚𝑎𝑙 − 𝛿𝐷,𝑁𝑜𝑟𝑚𝑎𝑙 |) 𝑖𝑓 (120 ≤ 𝑆𝐵𝑃 ≤ 129 & 𝐷𝐵𝑃 < 80) 𝛼 = 1 + 1 2 (𝛿𝑆,𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 + 𝛿𝐷,𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 + |𝛿𝑆,𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 − 𝛿𝐷,𝐸𝑙𝑒𝑣𝑎𝑡𝑒𝑑 |) 𝑖𝑓 (130 ≤ 𝑆𝐵𝑃 ≤ 139 𝑂𝑅 𝐷𝐵𝑃 < 80) 𝛼 = 2 + 1 2 (𝛿𝑆,𝑆𝑡𝑎𝑔𝑒 1 + 𝛿𝐷,𝑆𝑡𝑎𝑔𝑒 1 + |𝛿𝑆,𝑆𝑡𝑎𝑔𝑒 1 − 𝛿𝐷,𝑆𝑡𝑎𝑔𝑒 1|) 𝑖𝑓 (140 ≤ 𝑆𝐵𝑃 ≤ 179 𝑂𝑅 80 ≤ 𝐷𝐵𝑃 ≤ 89) 𝛼 = 3 + 1 2 (𝛿𝑆,𝑆𝑡𝑎𝑔𝑒 2 + 𝛿𝐷,𝑆𝑡𝑎𝑔𝑒 2 + |𝛿𝑆,𝑆𝑡𝑎𝑔𝑒 2 − 𝛿𝐷,𝑆𝑡𝑎𝑔𝑒 2|) 𝑖𝑓 (𝑆𝐵𝑃 > 180 𝑂𝑅 𝐷𝐵𝑃 > 120) 𝛼 = 4 + 1 2 (𝛿𝑆,𝐶𝑟𝑖𝑠𝑖𝑠 + 𝛿𝐷,𝐶𝑟𝑖𝑠𝑖𝑠 + |𝛿𝑆,𝐶𝑟𝑖𝑠𝑖𝑠 − 𝛿𝐷,𝐶𝑟𝑖𝑠𝑖𝑠|) and finally, the equation below provides the highest level of granularity, where each delta value is replaced with the simplified expressions from table 1. this is the final version that was used in the app development, with the only variables being the two inputs of sbp and dbp. monitoring older adult blood pressure trends at home as a proxy for brain health 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi 𝑖𝑓 (0 ≤ 𝑆𝐵𝑃 < 119 & 𝐷𝐵𝑃 < 80) 𝛼 = 0 + 1 2 ( 𝑆𝐵𝑃 119 + 𝐷𝐵𝑃 79 + | 𝑆𝐵𝑃 119 − 𝐷𝐵𝑃 79 |) 𝑖𝑓 (120 ≤ 𝑆𝐵𝑃 ≤ 129 & 𝐷𝐵𝑃 < 80) 𝛼 = 1 + 1 2 ( 𝑆𝐵𝑃 − 120 9 + 𝐷𝐵𝑃 79 + | 𝑆𝐵𝑃 − 120 9 − 𝐷𝐵𝑃 79 |) 𝑖𝑓 (130 ≤ 𝑆𝐵𝑃 ≤ 139 𝑂𝑅 𝐷𝐵𝑃 < 80) 𝛼 = 2 + 1 2 ( 𝑆𝐵𝑃 − 130 9 + 𝐷𝐵𝑃 − 80 9 + | 𝑆𝐵𝑃 − 130 9 − 𝐷𝐵𝑃 − 80 9 |) 𝑖𝑓 (140 ≤ 𝑆𝐵𝑃 ≤ 179 𝑂𝑅 80 ≤ 𝐷𝐵𝑃 ≤ 89) 𝛼 = 3 + 1 2 ( 𝑆𝐵𝑃 − 140 39 + 𝐷𝐵𝑃 − 90 29 + | 𝑆𝐵𝑃 − 140 39 − 𝐷𝐵𝑃 − 90 29 |) 𝑖𝑓 (𝑆𝐵𝑃 > 180 𝑂𝑅 𝐷𝐵𝑃 > 120) 𝛼 = 4 + 1 2 ( 𝑆𝐵𝑃 − 180 39 + 𝐷𝐵𝑃 − 120 29 + | 𝑆𝐵𝑃 − 180 39 − 𝐷𝐵𝑃 − 120 29 |) the physician view was designed to integrate seamlessly and efficiently into clinical workflows with the ultimate goal of allowing a physician to get actionable insights on a patient’s blood pressure trends in under 30 seconds. as can be seen in figure 4, the top graphical section shows the actual values for sbp (black) and dbp (blue) over time. the dots show the raw data inputs and the lines reflect the seven-day moving averages, allowing for outliers to be incorporated into the long term trends. the dotted black and blue dotted lines correspond to respective patientcentered sbp and dbp goals that can be set by the physician. the bottom summaries provide information that was deemed most important from conversations with physician colleagues on this team. the bottom left shows the patient’s average blood pressure as well as percent change for the chosen date range. the middle summary shows how many days and what percentage of days a patient has been in each blood pressure category in the given timeline. this summary also updates whenever a date range is chosen. and finally, the bottom right summary lists all medications the patient is currently taking. monitoring older adult blood pressure trends at home as a proxy for brain health 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi figure 3. patient view tab of the wellness dashboard prototype. figure 4. physician view tab of the wellness dashboard prototype. development process to ensure the utility and technical fidelity of our digital health solution, we created a four-stage development process to introduce, test, refine and expand our product over a period of three years (figure 5). phase one will include a pilot program during which we will recruit the following monitoring older adult blood pressure trends at home as a proxy for brain health 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi groups to test the solution: primary end users (older adults aged 50 and over at risk for dementia) and primary care physicians interested in a digital solution for predicting adverse brain health conditions for their older adults. in phase one, primary users will collect and store their blood pressure data using the beta version of our application. feedback will be collected from both primary users and their healthcare providers in the form of a survey during this time so that the application, still in development at this point, can be refined in response. importantly, surveys dispersed to physicians will inquire as to how the data collected by the digital solution can best confer value to the physicians and their patients and minimize the burden of incorporating patientgenerated data into their clinical workflow. during phase two of development, educational content will be created and integrated into the front end of the application for primary users and their caretakers to consume for the purpose of learning more about brain health and blood pressure. educational content will be rooted in the six pillars of aarp’s staying sharp platform. (together, phases one and two will last for about one year after the beta version of the application has been developed). during phase two, we will work with aarp to integrate incentives to use myhealthnetwork into the preexisting aarp rewards program. potential incentives might include reduced annual aarp membership fees, the ability to earn points, redeemable for prizes or discounts at major retailers, and more. finally, phase two will also include the development of advertisements targeted towards potential primary users and the secondary users who support them (including clinicians and the caretakers). by the end of phases one and two, we plan to have built our primary user base. phase three will begin during the second year of development. phase three will focus mostly on engaging secondary users. during this phase, we will test sharing the data input by primary users with main secondary users: primary users’ healthcare providers and their caregivers at home. additionally, this phase will be used to develop partnerships with physicians (mostly primary care providers for older adults), electronic health records vendors and health insurance companies, with all of whom we aim to share and store primary users’ recorded data. during this phase, we would survey healthcare practitioners once more to gather their impressions around the ease of use and utility of the patient-generated health data captured by myhealthnetwork in their clinical workflow. in phase four, we would begin the fourth and final phase of development: data expansion. based on feedback from the previous three phases, we would design and implement a second piloting process to test collection of additional health data metrics to predict brain health outcomes. these metrics could include weight, activity levels, heart rate, adherence to prescribed medication and more. by expanding the myhealthnetwork application to store additional health metrics related to brain health, we aim to eventually create a holistic and comprehensive digital health solution which can mitigate the public health problem of poor brain health among older adults in the us. addressing barriers to adoption and/or use collectively, the four phases of development of our digital health solution address the four aforementioned barriers that the authors identified through literature review as well as interviews and conversations with potential primary users and aarp content experts, respectively. the first barrier we identified included the inability to understand health data among primary users with low health literacy. the inclusion of educational media in the application will enable primary users monitoring older adult blood pressure trends at home as a proxy for brain health 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi to find answers to their health-related questions quickly and conveniently without the need for consulting sources outside of the application. secondly, we expect that using myhealthnetwork consistently will be challenging at first for primary users with low digital literacy, defined as the ability to use information technologies for learning, work, and recreation [32]. for this reason, we have included a feature which guarantees primary users around-the-clock access to a digital navigator, an on-demand chatbot with whom users can ask technological questions around the proper use of the myhealthnetwork application. users will have the option to speak to a human digital navigator if they prefer it to the chatbot. additionally, we expect that potential primary end users with lack of access to devices able to access the internet for the purposes of health data input may be unable to fully utilize myhealthnetwork. to counter this, we plan to initially provide smart blood pressure cuffs to the users in the pilot study at a reduced cost to primary users with financial need by pairing with smart blood pressure cuff companies, such as withings, to purchase the devices in bulk at below market price. finally, we learned that primary users and other stakeholders might have concerns about the security and primary of users’ personal and health data. to address this, we plan to partner with health insurance companies, beginning with united healthcare with whom aarp has a preexisting relationship and trusts to store sensitive user data. to address similar concerns among clinicians who will integrate myhealthnetwork into their workflow, we plan to partner with credible ehr vendors whose operational practices meet our high standards for security and privacy of patient data. figure 5. phases of development for myhealthnetwork (mhn) limitations a number of limitations to our design thinking process should be noted, particularly within the context of this exercise being completed in a graduate course on digital health. our most significant limitation includes our inability to launch and test the prototype of myhealthnetwork with both primary end users and their healthcare providers in their homes and clinical monitoring older adult blood pressure trends at home as a proxy for brain health 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi environments, respectively. we were limited by the short time frame (one semester) during which the entire design thinking process took place. to address this limitation, we capitalized upon opportunities to gather feedback similar to that which we may have received through a true implementation of our solution, including interviews with potential primary users, conversations with aarp content experts and creation of a rudimentary prototype of myhealthnetwork’s primary user-facing dashboard. additional limitations of our study include the use of a convenience sample for interviews of a small number of potential primary end users (n=4). however, we did make it a priority to represent a variety of races, sexes, ages, locations and literacy levels in our small sample of interviewees. reaching new customers might present another challenge as the user base might reflect mostly individuals who are health-literate, digitally-literate and have a membership to the aarp; therefore, reaching potential primary users without a membership or with low health and/or digital literacy might be challenging. while we recognize that this might limit our ability to reach a broader group of users, we only limit our recruitment to members of the aarp during the pilot testing phase of our design process, after which we would work with clinicians involved in the pilot to attract harder-to-reach older adult populations. additionally, we plan to have interactive educational video modules integrated into the myhealthnetwork platform which will explain the medical and technical aspects of our solution to older adult primary users and their caretakers, facilitating ease of use for users with varying levels of health literacy. another limitation includes the acknowledgement that this final algorithm is not a perfect solution. it is a first attempt at temporally plotting something that has yet to be done so in mainstream medicine. it is not meant to give precise data points, but to show patients with potentially low health literacy a general idea of how healthy they are and where their health may be heading. this algorithm has the potential to continuously be improved upon to find the best approximation of blood pressure category over time. one area of improvement could be with the fact that we normalized each category into a span of 1 unit, even though the blood pressure ranges for each category vary. another issue is the lack of an upper bound for the hypertensive crisis category, with other solutions potentially being a better fit. furthermore, we are presently limited by our lack of access to data that would predict how much a potential user would be willing to pay for the myhealthnetwork. however, to address this limitation, we held discussions with aarp representatives during the design thinking process to consider how we could drive down costs and ensure that access to myhealthnetwork is as inclusive as possible. some ideas included partnering with a smart medical device manufacturer, buying hardware in bulk, offering discounts to aarp members and working with health insurance companies to create discounts to users in exchange for access to their private health data. finally, the digital health solution would only have utility among users who have an internet connection and a device with connection to the internet to allow access to the platform. to address this limitation, we considered trying to partner with internet service providers who might be able to offer discounted prices to potential users in areas with low internet connectivity. future directions as previously mentioned, once a prototype of our solution is created, centered on the collection of blood pressure data, future phases of development will include expansion of the platform to capture monitoring older adult blood pressure trends at home as a proxy for brain health 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi additional metrics related to brain health including blood glucose levels, weight, physical activity, resting heart rate, sleep pattern and more [33-37]. these expanded features can capture richer insights about primary users’ brain health. we also intend to add a feature to future versions in which the user can input to the plot when each of their medications was newly prescribed, allowing for correlations to be made with different medical cocktails. conversely, myhealthnetwork can be refined over time so that users’ blood pressure readings can be used to predict additional outcomes beyond brain health alone, including heart attack, stroke and vision loss and more [38]. additionally, opportunities for primary users to strengthen and maintain brain health will be provided as they collect and record more data, as studies show that using certain digital technologies can improve memory, fluid intelligence and other cognitive abilities [39]. a second future direction will include potentially partnering with technological companies which already store users’ health data with their permission. one such potential stakeholder could include apple, who recently included the ability to securely share health and wellness data with select individuals, including loved ones and physicians, in their newest iphone software update, ios 15. through a partnership such as this, we aim to eventually integrate our user-generated health database and wellness dashboard into ios to promote ease of use for primary users who have access to an iphone. finally, we plan to expand our digital health solution to accommodate use by individuals outside of the united states. broadening our user base on a global scale may allow us to better mitigate the public health issue of poor brain health among older adults at a global level. solutions to detect and prevent dementia are heavily needed outside of the us, particularly in asian nations; for example, south korean residents are currently expected to experience the longest lifespans of any nation, with a projected life expectancy of 90 years by 2030. concurrently, cases of dementia are expected to triple on a global scale, from 47.5 million individuals affected in 2015 compared to 135.5 million in 2050 [40]. as older adult populations of this and other nations continue to grow, there will be a need for digital health solutions which can help empower older adults to maintain brain health in their homes. conclusion in using the design thinking process to address the challenge of older adults who are concerned about brain health, we chose hypertension as a proxy and developed a prototype solution to target the needs of older adults. given the well-studied association between healthy blood pressure and brain health [12], the authors hypothesized that if users actively monitor their blood pressure and review temporal trends along with their primary care physician, this could promote a healthier lifestyle and preserve their brain health. our aim was to design a user-friendly digital solution that would store and analyze the patientgenerated health data. our prototype ”myhealthnetwork” is a digital solution which allows users to: 1) input and store daily blood pressure values, 2) over time visually monitor and review their blood pressure trends and 3) share the data with their primary care provider. the dashboard view was designed using our novel algorithm developed by the authors to display the blood pressure categories in a visually straightforward representation devoid of medical jargon. the algorithm provides a 7-day moving average that most users with limited health literacy would be able to understand and follow their blood pressure trends over time. our application encourages shared monitoring older adult blood pressure trends at home as a proxy for brain health 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi decision-making between the user and physician with the goal of supporting users in their mission to maintain their brain health, age in place and ultimately lead healthier lives. one challenge that we faced included developing an uncomplicated solution that would encourage patient engagement in the process of monitoring blood pressure and yield the secondary benefits of improving users’ health and digital literacy. the design thinking process guided us through each step from initial problem to ideation and building our solution. our background research, defining user characteristics and stakeholders, real-life interviews with various points of view and discussions with aarp subject matter experts assisted us in identifying barriers for our primary end users and limitations of current technology. in addition, our group members had a variety of backgrounds in healthcare, engineering, public health research and technology, which proved advantageous in assembling our solution. future directions for our digital health solution include expansion of the platform to capture other health metrics, refinement of the algorithm to predict other health outcomes such as stroke, an increase in partnerships with technology companies for broader utilization and growth of our user base to include end users outside of the united states. our project highlights the value of involving end users and primary care physicians in the development process with the goal of continued participation in the long term. as the number of digital devices used to monitor health increases, the need to ensure that users have the health knowledge and technological skills to navigate is critical; for this reason, we incorporated a digital navigator in our application. furthermore, our digital solution illustrates the need to advance integration of digital health technology into mainstream health care. incorporating “patientgenerated health data” effortlessly into health care systems could have the positive effects of healthier individuals and potentially shift health management and treatment from reactionary to prevention medicine. acknowledgments the authors would like to thank alison bryant, phd, senior vice president, aarp research & enterprise lead, tech & digital equity, for her contribution to this study by proposing the original challenge which inspired this work. we appreciate her guidance and feedback throughout the design thinking process. references 1. office of disease prevention and health promotion. 2020. older adults. available at: https://www.healthypeople.gov/2020/topics-objectives/topic/older-adults. accessed may 22, 2021. 2. population reference bureau. 2019. fact sheet: aging in the united states. 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appendix a interview questions establishing baseline health care practices and resources 1. what do you do when you have a health question or concern? (see a doctor, go to the er, urgent care clinic, ask a friend, search the internet) 2. where do you go for health care? do you have a doctor or health care provider that you see when you are ill? do you have regular check-ups (annually) or only seek care when you don’t feel well? 3. is there anything that holds you back in having regular checkups with a doctor? (transportation, mobility, time, insurance coverage, cost) establishing awareness/impact of patient’s memory issues 4. in the last month, how often did forgetting something interfere with your daily activities? (everyday, most days, somedays, rarely, or never) 5. can you describe a specific instance of being “forgetful”? or have you been worried about someone else’s memory? (family member, friend? 6. how do you feel when you have these lapses in memory? why? (worries, anxious, frustrate) establishing interviewee’s concern about any memory/independence 7. how often are you worried about your forgetful moments? have they ever made you worry about your ability to be independent? a. if often: have you ever done anything about it? have you mentioned it to your doctor? establishing interviewee’s motivation to explore an intervention 8. how interested would you be in learning about a tool that can help your mind stay sharper? very interested, somewhat interested, or not at all? establishing health literacy 9. did you know that blood pressure has been linked with many aspects of health, including memory loss? 10. do you know how to take your blood pressure? if so, do you know how to interpret what your blood pressure means? establishing digital literacy 11. what technology do you currently use? (smartphone, wearable) a. if none: why not? (internet connection) 12. do you use any digital health application currently? (patient portal, activity tracker) a. if none: why not? establishing openness to home-based monitoring and data sharing 13. if you had a device that could monitor your blood pressure daily easily, would you be interested in doing that? a. if yes: would you want to look at the data you collected? would you be interested in sharing this information with your pcp? with you family members? monitoring older adult blood pressure trends at home as a proxy for brain health 28 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e16, 2021 ojphi 14. what other aspects of your health and daily life would you be comfortable recording and/or sharing? (weight, food, activity, mood, sleep) 15. what would you like to know before you try such a device at home? would it help to have some simple instructions on how to use it? would you prefer to have someone to talk with to go over setting up/using the device? 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts putting data linkage into first gear: lessons from firefighter injury research jennifer a. taylor*1, michael t. le vasseur1, shannon a. widman1, priya sankar2 and henry j. costo3 1department of environmental & occupational health, drexel university schoof of public health, philadelphia, pa, usa; 2office of the general counsel, drexel university, philadelphia, pa, usa; 3fire department, city of philadelphia, philadelphia, pa, usa objective the purpose of this panel is to describe the process of using data to develop firefighter nonfatal injury surveillance systems in the city of philadelphia and the state of florida through the linkage of data from workers’ compensation, inpatient and emergency department hospitalizations, human resources, and continuing education/training registries. introduction this work builds on a successful demonstration project and expands its data linkage capacity to new community partners. presently, a national non-fatal injury reporting system does not exist for the fire service. in order to tell the story of all injuries within a fire department, state, or on a national scale, we must utilize data that are available from multiple sources that do not naturally talk to each other. in this panel, we will describe the purpose of the project, its goals, and the success of its model to public health surveillance. description we will engage the audience through a panel presentation comprised of members of our team who were critical to each step in the process, from stakeholder engagement to data acquisition and linkage. we will describe, in a stepwise process, how we effectuated data agreements, educated and heard from stakeholders, and ultimately analyzed the resulting data to uncover conclusions regarding injuries in the fire service. panelists include a legal expert, a statistician, an industry partner, and project staff. the panel will begin with the discussion of the various stakeholders necessary (including leadership from national organizations) to garner agreements on the importance and benefits of data linkage. we will discuss the legal process and implications of establishing data sharing agreements and contractual relationships, especially around the access to personally identifiable health information (phi). we will explore issues such as access to personal identifiers, and how working with stakeholders to examine policy facilitators and barriers to accessing data, in order to perform data linkage between data sets that do not regularly talk to each other, is critical for driving the process forward. we will also discuss the utility of the resultant master database to the community partners, and how we elicit “givebacks” to the data stewards so that they understand the utility and relevance of the databases created. we will then move on to show how the phi data linkage was conducted using direct and probabilistic data linkage methods. we will describe our computing environment, especially as it pertains to data security and integrity. we will conclude with a description of the analyses we were able to undertake because we successfully linked data sets that had not previously talked to one another. we will also integrate one of our community stakeholders to share his perspective on the utility of the resultant data for his prevention priorities for the road ahead. audience engagement methods for cultivating relationships with target communities and stakeholders will be discussed by project staff members (jennifer taylor/shannon widman). the legal process, hurdles, and implications, regarding effectuating data sharing agreements with target sites, will be presented by a legal expert (priya sankar). data linkage, requirements and methods, will be explored by an experienced statistician (michael levasseur). the utility and usefulness of the final product resulting from the data linkage will be presented by one of our project’s stakeholders, a fire service community member (henry costo). keywords data linkage; surveillance; fire service *jennifer a. taylor e-mail: jat65@drexel.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e6, 201 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts using search volume for surveillance of medication prescribing jacob e. simmering*, linnea a. polgreen and philip m. polgreen university of iowa, iowa city, ia, usa objective to validate search volume estimation for outpatient medication prescribing. introduction investigators have used the volume of internet search queries to model disease incidence, especially influenza and general consumer behavior [1]. our group has used search volume to model interest in fda safety alerts and adverse drug event incidence. we found evidence of changes in search behavior following warnings and the expected relationship between search volume and adverse drug event incidence. thus, search volume may help provide near real time surveillance of drug use patterns to help monitor and mitigate risk to the population from adverse drug events. however, the use of search query volume as a proxy for drug use has yet to be validated. we attempt to validate search volume estimation of drug utilization in three ways: 1) explore seasonal variations in search volume and outpatient utilization, 2) monitor change between substitute drugs following patent expirations and 3) use search volume estimation methods to estimate tb incidence. methods google insights normalized search share was used to characterize interest in a drug. the estimates of drug utilization were derived from the medical expenditure panel survey (meps), a nationally representative sample of the us population. substitute drugs and notable patent expirations between 2004 and 2011 were obtained via pharmacist review. tb incidence was derived from the mmwr yearly summary of notifiable diseases. to validate the assumption that search volume relates to drug utilization, we estimated weekly utilization for 9 drugs (amoxicillin, azelastine, azithromycin, benzomatate, cefdinir, ciprofloxacin, levofloxacin, moxifloxacin and olopatadine) using meps for 20042009. the weekly utilization volume was cross-correlated with the google insights series with lags ranging from -6 to +6 months. to compare the rate of substitution between name brand and generic drugs following the expiration of a patent, we treated the generic drug search volume as the independent variable and the name brand as the dependent variable. using ols, we calculated the marginal rate of substitution between the name brand and generic search queries. preliminary work has focused on substitution of generic simvastatin for branded zocor. as tb treatment regimens usually include a fixed set of medications (isoniazid, rifampin, pyrazinamide, ethambutol), the utilization of these drugs should correspond with tb incidence. we modeled national tb incidence using ols with search volume and an indicator for the month of december. the number of reported cases in december is inconsistent with the seasonality of tb in the us and is a significant departure from the expected value given the rest of the series. we suspect this is due to a reporting artifact and include the indicator variable in our model to mitigate the effects of this inconsistency. results the seasonality of drug use is reflected in search volume. only 3 of the 9 drugs (33%, amoxicillin, azithromycin and cefdinir) had enough volume in the meps to create a reasonable time series. all 3 drugs had statistically significant positive correlations at lags near 0 and significant negative correlations at lags of +/6 months. amoxicillin, for example, had a significant correlation at lags around 0 of 0.55-0.60 and correlations at a lag of -5 or +5 months of -0.4. the magnitude of this correlation coefficient would suggest that the two series track closely. patent expirations (and the resulting emergence of generic medications with new names) are apparent in search volume as well. we find a strong negative relationship between search volume for simvastatin and zocor. specifically, a one unit increase in search volume for ‘simvastatin’ is associated with a 0.96 (p < 0.0001) unit decrease in the search volume for ‘zocor.’ the simple model for tb incidence demonstrates the utility of using drugs as queries for disease. search volume was a significant (p = 0.006) and positive predictor of tb incidence controlling for the december aberrations. conclusions the google insights search volume for a set of highly seasonal drugs is highly correlated with community utilization as measured by seasonal variance in utilization, change in search and prescribing patterns and expected prescribing following tb. the ability to estimate use of drugs from search volume presents a new method for keyword selection in search based incidence models and a method to monitor changes in the pharmaceutical market. keywords pharmacovigilance; web search; prescribing behavior references 1. goel, s. et al. (2010) predicting consumer behavior with web search. pnas 104:17486-17490 *jacob e. simmering e-mail: jacob-simmering@uiowa.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e119, 2013 ojphi-06-e92.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 166 (page number not for citation purposes) isds 2013 conference abstracts college health surveillance network: use of health services by usa college students james c. turner1, adrienne keller*1, jennifer c. bauerle1 and craig roberts2 1university of virginia, charlottesville, va, usa; 2university of wisconsin-madison, madison, wi, usa � �� �� �� � � �� �� �� � objective ���������� � � �� � ���������������� 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�������� �� ��� � ����� ����� ���� ��� ����� ������ ��� keywords ����� ��>�#��#$ #>�� ���� *rhonda a. lizewski e-mail: rhonda.a.lizewski.mil@mail.mil� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e114, 2014 can internet access growth help reduce the global burden of noncommunicable diseases? 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi can internet access growth help reduce the global burden of noncommunicable diseases? stefan kohler 1 1 institute for social medicine, epidemiology and health economics, charité university medical center, berlin, germany introduction the three leading causes of death in 1900 were pneumonia, tuberculosis, and diarrhea and enteritis. together with diphtheria, these diseases caused one third of all deaths. heart disease, already at that time the most frequent noncommunicable disease (ncd) death, was estimated as the fourth most frequent cause of death (1). nowadays, ncds constitute the leading causes of death in all regions, except africa (2). nearly 80% of ncd deaths (29 million) in 2008 occurred in lowand middle-income countries (3). cardiovascular disease, such as heart attacks and strokes, accounted for most ncd deaths (17 million) in that year, followed by cancer (7.6 million), respiratory diseases such as chronic obstructed pulmonary disease and asthma (4.2 million), and diabetes (1.3 million) (2). taken together, these four groups of ncds account for approximately 80% of all ncd deaths (30.1 million). the largest increases in ncd deaths by 2020 are projected for africa, the eastern mediterranean and south-east asia. in these regions, ncds may increase by over 20% (2). by 2030, the total annual number of deaths from ncds may increase to 55 million, i.e., exceed the 2008 death toll by 50% (3). the four most common ncds share lifestyle-related and preventable abstract noncommunicable diseases, such as cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes, are currently the leading causes of death in several regions of the world. the continuing fast increase in the global burden of noncommunicable diseases is accompanied by a speedy worldwide internet access growth. the worldwide number of internet users has doubled over the past five years. as the internet can make the access to information on a healthy lifestyle and disease prevention activities easier, internet access growth may help to promote good health. against this background, i discuss the roles the internet and access to information can play in health promotion. i also present an open access web portal on local prevention and health promotion activities. it was initiated by two german states to link health information from disparate sources and to organize this information in a user-friendly way. the web portal focuses on reducing preventable lifestyle-related risk factors associated with noncommunicable diseases, including physical inactivity, unhealthy diet, tobacco use, and the harmful use of alcohol. this local initiative has the potential for scaling up and can serve as a blueprint for other areas that have or will acquire internet access. keywords: internet, health promotion, open access, prevention, scale up, web portal correspondence: stefan.kohler@charite.de copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. can internet access growth help reduce the global burden of noncommunicable diseases? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi risk factors; notably, physical inactivity, unhealthy diet, tobacco use, and harmful use of alcohol (2,4). according to the world health organization (who), 9% of global ncds deaths can be attributed to tobacco use, 6% to physical inactivity and 5% to overweight and obesity (4). besides the advance of ncds and their influence on the causes of death, another rapidly evolving trend is shaping our lives. the number of internet users worldwide has doubled over the past five years. by the end of 2010, around 30% of the world population was online (5). the proportion of households with internet access at home and of individuals using the internet were lowest in low-income countries (1.6% and 5.7%, respectively), moderately low in middle-income countries (17.8% and 23.8%, respectively), and highest in high-income countries (74.0% and 73.6%, respectively). these disparities have decreased since 2005 because the growth rates in the proportion of households with internet access at home and of individuals using the internet from 2005 to 2010 were highest in low-income countries (300% and 418%, respectively), moderately high in middle-income countries (147% and 201%, respectively), and lowest in high-income countries (37% and 24 %, respectively) (table 1). table 1. households with internet access at home and individuals using the internet population (mil.) access (%) usage (%) 2005 2010 ∆ 2005 2010 ∆ 2005 2010 ∆ germany 82 82 0 61.6 82.5 34 68.7 82.0 19 world 6,504 6,895 6 18.8 30.3 61 15.8 30.2 91 east asia & pacific 1,893 1,962 4 9.0 19.8 120 8.3 29.8 259 europe & central asia 399 405 2 6.7 34.3 412 12.9 39.3 205 latin america & caribbean 550 583 6 9.7 20.7 113 16.5 34.0 106 middle east & north africa 304 331 9 9.1 22.7 149 8.3 21.0 153 south asia 1,517 1,633 8 1.4 4.3 207 2.5 8.1 224 sub-saharan africa 755 853 13 1.1 3.6 227 2.3 11.2 387 low income 718 796 11 0.4 1.6 300 1.1 5.7 418 middle income 4,699 4,971 6 7.2 17.8 147 7.9 23.8 201 high income 1,087 1,127 4 54.0 74.0 37 59.3 73.6 24 note: ∆ growth from 2005 to 2010 in percent data: world bank & international telecommunication union (5) observing that internet access and usage is growing in all regions of the world at a time when the global prevalence of ncds is increasing, poses the question whether internet access growth can help reduce the global burden of ncds; and if so, how? as the internet offers ways for the user to access, and for health professionals to present customized health information, its growth may help to strengthen prevention and health promotion worldwide; particularly in areas of the world that have or will obtain internet access and face an increasing burden of ncds. in this article, i discuss the roles the internet and access to information can play in health promotion. i also present an open access web portal on local health promotion and prevention activities, and its web statistics. the web portal presented was initiated by the two german states berlin and brandenburg in order to make existing information on regional disease prevention and health promotion activities more accessible and visible. its ultimate goal is to thereby motivate more people in the region to pursue a healthier lifestyle. despite the fact that the web portal was developed in a local initiative, the underlying idea and concept to inform about local health activities and a healthy lifestyle under one virtual roof have the can internet access growth help reduce the global burden of noncommunicable diseases? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi potential for scaling up and can serve as a blueprint for other areas that have or will acquire internet access. the internet, access to information and health promotion the internet facilitates access to information from various sources. it may also create access to information for the first time. the internet allows information providers to distribute information rapidly and widely. it allows fast updates of information and the possibility of organizing information in appealing ways. users are confronted with good and bad information, which they need to evaluate, for instance, by assessing the trustworthiness of the information provider or by comparing information from different, ideally independent, sources. if the challenge of assessing the quality of the information is successfully undertaken, then the information accessed through the internet enables people to make informed decisions about their life and health. the relationship between access to information, which increases with internet growth, and health was acknowledged, for example, at the first international conference on health promotion organized by the who and held in ottawa in 1986. the resulting ottawa charter for health promotion characterized health promotion as the process of enabling people to increase control over and to improve their health. it recognizes that access to information, together with a secure foundation in a supportive environment enables people because: “people cannot achieve their fullest health potential unless they are able to take control of those things which determine their health,” e.g., “a secure foundation in a supportive environment, access to information, life skills, and opportunities for making healthy choices” (6). the possible role of information and new information technology has been discussed and assessed, for example, by the who regional office for europe: “information and education […] are necessary and core components of health promotion, which aims at increasing knowledge and disseminating information related to health. […] the mass media and new information technologies are particularly important” (7,8). subsequently, the broader term of health literacy, described by the who “as the cognitive and social skills which determine the motivation and ability of individuals to gain access to, understand and use information in ways which promote and maintain good health,” has been brought into the discussion as a prerequisite of healthy behavior. “by improving people’s access to health information and their capacity to use it effectively, health literacy is critical to empowerment” (9,10). this need to not only be able to access, but also to understand and use information becomes apparent when looking at the amount of health information that can be accessed through the internet. a google search of the web for the terms “prevention” or “health promotion” currently yields about 251 million results, and about 11 million results for the corresponding german terms “prävention” and “gesundheitsförderung.” an web portal for prevention and health promotion activities as started in the german region berlinbrandenburg is an example of how information on local prevention and health promotion activities and projects can become organized in order to be more visible and accessible. the web portal for prevention and health in berlin and brandenburg the internet is widespread and popular in germany. a percentage of 82.5 of households can access the internet and 82% of households use the internet for an average time of 140 minutes per day (11,12). thus, a web portal for prevention and health promotion in this setting can potentially activate many people. amongst other things, this may have encouraged the launch of a web portal dedicated to prevention and health promotion in berlin and brandenburg (http://praeventionsatlas.de/). it was initiated and is maintained by a common health economy http://praeventionsatlas.de/ can internet access growth help reduce the global burden of noncommunicable diseases? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi network of the local governments of these two states (13). the goal of this web portal is to present information on a healthy lifestyle together with information on local health activities in an easy, accessible and visible way. the perceived need for presenting information on a healthy lifestyle and health activities in a new way, which is addressed by the web portal, is consistent with the following observation: statutory health insurance, that serves over 80% the german population, subsidizes several health prevention and health promotion activities. the social insurance code (§20 sozialgesetzbuch v) sets a target value for primary prevention expenditure, which statutory health insurance follows. in 2011, the target value of €2.86 per insurant was exceeded by 35%, resulting in a spending of €270 million on primary prevention courses and projects by statutory health insurance (14). despite low costs to all those insured, the participation rate in these subsidized prevention courses is consistently low (2.4% in 2011 and 3.0% in 2009) (15,16). this suggests that, inter alia, there might be a need to improve the access to and impact of existing information on a healthy lifestyle and health activities, in addition to offering low-cost health activities. all of the main topics under which german statutory health insurance actively sponsors lifestyle-related health courses and projects are covered by the web portal: e.g., physical activities, healthy diet, cessation of substance abuse, and stress management. brief health tips for each topic are combined with information on corresponding and locally available health courses, as well as contact people (17–19). stress is not one of the ncd risk factors enumerated by the who, but stress may aggravate unhealthy behavior, such as overeating or eating unhealthy foods, smoking or drinking, or directly trigger cardiovascular diseases (20– 22). in contrary, stress may not increase blood pressure (22) and, thus, not increase the prevalence of raised blood pressure, which the who describes as the leading ncd risk factor globally in terms of attributable deaths (4) it is expected that an increased visibility of the information on local health activities derived from the web portal will lead to an increased participation rate in those activities as well as their proliferation. if these goals are achieved through the web portal, it can help reduce common ncd risk factors and, in consequence, the prevalence of ncds. active content management on the web portal facilitates the bridging of gaps between scientific evidence on preventive interventions and prevention practiced (23). therefore, the web portal can support early communication of effective and up-to-date health advice. regular web statistics implemented within the web portal allow the monitoring and evaluation of intermediate goals, such as the number of site visits and usage of the information on the web portal. web statistics web statistics enable an ongoing evaluation of the user access of and interests in the health information provided. the informational content can be revised promptly to counter a decline in user interest, as measured by the total number of website visits, or to guide users to specific topics in prevention and health promotion. from its launch date, august 30, 2011, until july, 31, 2013, the web portal was visited by approximately one out of every 750 residents in the berlin-brandenburg region, accounting for a total of about 10,000 visits with more than three page views during an average site visit. almost 5,000 further visits originated in other regions than berlin or brandenburg. countries in which german is an official language accounted for 98% all visits. within the berlinbrandenburg region, 82% of all visits of the web portal are new visits compared to 84% worldwide. interest in the topics substance abuse (12.53%) and stress management (21.86%), was lower than interest in topics healthy diet (24.03%) and physical activity (41.58%), on average over the past 23 months. over time, interest in physical activity (+0.60%, p < 0.01) can internet access growth help reduce the global burden of noncommunicable diseases? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi and healthy diet (-0.56%, p < 0.01), as measured by monthly visits to the topic in the web portal, have changed significantly. this change may reflect that the behavior of the visitors to the website is still reaching its steady state. interest in stress management (+0.03%, p > 0.75) and substance abuse (-0.07%, p > 0.58) have remained constant (figure 1). figure 1. interests of users and trends on web portal for prevention and health promotion in berlin and brandenburg (http://praeventionsatlas.de/) data: google analytics discussion a web portal dedicated to disease prevention and health promotion activities that are available locally increases their visibility and may, thus, increase participation in those activities. in the case of germany, several promotional subsidized health activities are available. in countries with different conditions, the development of an adapted web portal to strengthen disease prevention and health promotion could accompany a government’s or organization’s attempt to introduce interventions to improve health. informing about health activities through the internet may also help to attract some social groups who are underrepresented, such as young adults and males, to these activities: in 2011, 2.4% of all those publicly insured in germany participated in subsidized prevention programs offered by their health plan. the participation rate was below average for people younger than 30 years of age, who may feel less need to prevent disease than the elderly or consider the contents of the subsidized health activities unattractive. of all participants, 21% were male and 79% were female (14,24). by contrast, the internet usage shows a different trend. in 2012, according to an annual survey of 30,195 german citizens over 14 years of age, 81% of men and 70.5% of women were internet users; internet use decreased steadily 0% 10% 20% 30% 40% 50% 60% aug 11 sep 11 oct 11 nov 11 dec 11 jan 12 feb 12 mar 12 apr 12 may 12 jun 12 jul 12 aug 12 sep 12 oct 12 nov 12 dec 12 jan 13 feb 13 mar 13 apr 13 may 13 jun 13 jul 13 physical activity healthy diet stress managment substance abuse http://praeventionsatlas.de/ can internet access growth help reduce the global burden of noncommunicable diseases? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi with age from 98.7% (14-19 years) to 60.4% (60-69 years) and dropped sharply among the 70+ age group (28.2%) (11). since internet usage by gender and age is inversely associated with participation in prevention courses by gender and age, informing about health activities through the internet may help to attract young adults and males, who are the more frequent internet users but have less interest in health promotion activities. conclusion taking into consideration the wide usage of the internet among germans, the web portal for prevention and health promotion that exists in the states of berlin and brandenburg can increase participation in local prevention and health promotion activities in this region and among specific target groups. due to the worldwide increase in internet access, the concept of an open access web portal for prevention and health promotion activities has the potential to be transferred to other regions including developing countries, which are confronted with the fastest internet proliferation and an increasing burden of lifestyle-related ncds. the number of people with diabetes, for example, is projected to more than double between 2000 and 2030 in all regions of the world, except established market economies and former socialist economies, in which the projected increase is 54% and 20%, respectively (25). this projection is consistent with a recent large epidemiological survey of 153,996 adults in three high-income countries, seven upper-middle-income countries, three lower-middle-income countries, and four low-income countries. it found that the prevalence of healthy lifestyle behavior was low among a sample of patients with a coronary heart disease or stroke event, with even lower levels in poorer countries (26). in summary, the idea and concept behind the web portal for prevention and health promotion in berlin and brandenburg have the potential for scaling up. as more and more areas of the world have or will acquire internet access, organized information on healthy behavior and available options for personal action, made accessible through the internet, are likely to help reduce the burden of ncds globally. acknowledgments no sources of funding were used to prepare this article. the author was part of a research team that developed the content of the 2011 edition of the web portal and a book on prevention and health promotion in berlin and brandenburg that were funded by the senate for economics, technology and women of the city of berlin. corresponding author: stefan kohler, phd institute for social medicine, epidemiology and health economics charitè university medical center luisenstraße 57 10098 berlin germany email: stefan.kohler@charite.de references 1. centers for disease control and prevention. achievements in public health, 19001999: control of infectious diseases. morbidity and mortality weekly report. mailto:stefan.kohler@charite.de can internet access growth help reduce the global burden of noncommunicable diseases? 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gesundheitsstandort berlin-brandenburg. berlin: kulturbuch-verlag; 2012. 51 p. 24. gesundheitsberichterstattung des bundes. mitglieder und mitversicherte familienangehörige der gesetzlichen krankenversicherung am 1.7. eines jahres [members and insured family members of the statutory health insurance on 1.7. of a year] [internet]. 2013 [cited 2013 jul 31]. available from: http://www.gbe-bund.de/ 25. wild s, roglic g, green a, sicree r, king h. global prevalence of diabetes: estimates for the year 2000 and projections for 2030. diabetes care. 2004;27(5):1047– 53. 26. teo k, lear s, islam s, mony p, dehghan m, li w, et al. prevalence of a healthy lifestyle among individuals with cardiovascular disease in high-, middleand lowincome countries: the prospective urban rural epidemiology (pure) study. journal of the american medical association. 2013;309(15):1613–21. 5169-38201-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the 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������� ��%� ����&��� ��":����+,#���� �� ����� ������� � �� ����� ���������� �.�,� ������������ ��� ������� ������� conclusions !��������� ���������������� �� ��� ������� �� ����� ��������������� ��������� �� ��%����������� �� �� �� ��������� ����� ��� �������� ��� �� �� � ��) ����� ���� ��� �������� � ����� ������ �������������������� �������� �� ����� ������������ ������� ;�!$���� ��%�8$���� ��%����� 8�� ���� ������� ������ ���� � ��� �%� ������ ��� ��� �� ��� � ���������� ���� ����� � ������ � � ������� ��� �� ������!�������� ���� � �� �� � ���� ���2��� ������������ ��� ����� ������� ������ �� ������� ������� � ����!�������)*)� �������������� �� keywords ���� ��� ��� �<�!���������!�����������<�)���������*��������� <� =����� ���2���� � acknowledgments � � � ����� �� � � �� ��� ��� �� ��� ��� �� �� ��� � ��� ���� � ���� ������!������ �>���������$ ����������� ���� ������� ������ ����� �� ��� �������������!����� ��?�����2����!����� ��)�� �� ���� references +����������!� ������/���������� �",..3#�/���������� ��� ��� �� �=���� ������/��&�� ��2���������� � ;������ � ����������� �������� ��� ��&�� � ,��@�� �������� %�a�����"2 ���%�,.+,#��=������������������� ����)���� ������������ � ����� �/���� ��*�����? ����,.+.���� � �����,..b� ,.+.�2�!��'� �������(� �����-+%�,.+,%������ ;������� �����;� ;������� ��������� �� �2����� ��/���� ���� � ,.+.�����-� �� *biru yang e-mail: biru.yang@houstontx.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e17, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts metagenomic profiling and identification of antimicrobial resistance genes from airborne microbial communities tamar dickerson*1, jonathan l. jacobs1, nicole waybright1, danielle swales2, peggy lowary1, jeanette coffin1 and joseph bogan1 1biosurveillance, mriglobal, rockville, md, usa; 2mriglobal, charlottesville, va, usa objective to assess the temporal dynamics of airborne bacterial communities in four locations around the national capital region and the dispersion of antimicrobial resistant (amr) genes present within them. introduction since the adoption of antibiotics in the early 20th century, a plethora of clinical pathogens have acquired resistance to one or more modern-day antibiotics. this has resulted in antimicrobial resistance (amr) being recognized as a severe threat to human and animal health worldwide. recent work has demonstrated that amr bacteria are widely prevalent in the environment, perhaps exacerbated by the widespread use of antibiotics for clinical or agricultural purposes. methods dry air filter units were used to collect air samples daily for a period of three months at four locations inside and outside a transit center in the national capital region. samples were concentrated with a vivaspin 100,000 dalton ultrafiltration concentrator for dna extraction, then pooled by month and pcr amplified for 16s rrna community profiling. amplicon sequencing was carried out on life technologies (thermo fisher scientific) ion torrent personal genome machine (pgm). additionally, pooled samples were subjected to shotgun metagenomic sequencing on the ion proton platform. lastly, a functional metagenomics screen is being carried out for antibiotic resistance genes against seven antibiotics: gentamycin, chloramphenicol, ciprofloxin, trimethoprim, colistin, tetracyline and penicillin. following confirmation of amr, the functional gene from each positive clone will be sequenced and compared to the existing shotgun metagenomics results. this will enable the prevalence of amr genes in each of the four airborne microbial communities to be assessed. results initial data from each of the four sites for both 16s rrna community profiling and shotgun metagenomic sequencing have shown a diverse population of environmental, human, animal and plant bacteria, with the genus pseudomonas representing the largest proportion of sequences in both data sets. furthermore, analysis of the shotgun metagenomics data suggests the presence of antibiotic resistance genes, whose biological functionality remains to be confirmed by further laboratory testing. conclusions preliminary results suggest that airborne microbial communities are complex and serve as a dynamic reservoir for the dispersion of antimicrobial resistance factors in the environment, potentially complicating the existing world-wide public health crisis to combat amr pathogens. keywords biosurveillance; airborne; bacteria acknowledgments this work is supported by a mriglobal internal research & development award. *tamar dickerson e-mail: tdickerson@mriglobal.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e18, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 42 (page number not for citation purposes) isds 2013 conference abstracts tools and apps to enhance situational awareness for global disease surveillance alina deshpande*, kristen margevicius, eric generous, kirsten taylor-mccabe, lauren castro, joseph longo and reid priedhorsky defense systems and analysis division, bioscience division, los alamos national laboratory, los alamos, nm, usa � �� �� �� � � �� �� �� � objective ��������� � ��� �������������� �� ��� ���� �� �� ���� ����� � ��� ��� ����������������� 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���� �� � �� ������ ������������ ���� ����� ��� ������ ����� ��������������� ��� �� �������� �� ������� ��� ����!���������������� ��� ����� � �����������123��������������� ����� ����� �� �������� �� ������� ������ ���4���� ��� ����� � �� �� ����������� ���� ���� ��� ��� ���� ������� �� ��� �������� ��� �������� ��� � ��� �� ��� ��� �����#�������� ������ ������ ����� �� ������� ������� ������������ ���� �������������� ����� ����� �� ��� ����� �� ���������� ����� ������������( )�� ����*#+� ����������� �� ������������������������� ����� ��� ��� ���� �������������� � � ���� ����������� ��� �� �������� �� ���������� ������������������� ����� #������� � ���������� � ���� / � ����� �������� ������������� ��� ��� ������ ��� ���� � ������������� � ��� ���5 ������� ���6� ��� � ������������ ���������� � � ������������� ���� �� �������� �*#+� ��� ���������� ���� � ���������� ���� � ��� �� ���� �� � ����� ��� ���%����������� &� ���������������� conclusions ������ �������������� ��������� ��� ��"#$"����������������� � ��� �� ���� ��� ��� ��� �� �� ���� ����� � ����� ��� � � � ����� �� � �� �� � ������� �� ����� ����� �� �������� �� ���������������� �������� � � ��������� �� � �� ���� � ��� ���� ���������������������� ����� ��� ����������� keywords ����� ����� �� ��'���� � ����� ���'������ �� ��� acknowledgments *��������� ������ ��������������������������� ��)����� ���#������ %��)#&� �7� ����� ����� ��������������8�� ���%7��8&������ ����� ��� �� �� ��9���� *alina deshpande e-mail: deshpande_a@lanl.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e111, 2014 design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi design choices for automated disease surveillance in the social web mark abraham magumba1*, peter nabende1 and ernest mwebaze2 1. department of information systems, makerere university uganda, college of computing and information sciences 2. department of computer science, makerere university uganda, college of computing and information sciences abstract: the social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale. the utility of these developments to public health use cases like disease surveillance, information dissemination, outbreak prediction and so forth has been widely investigated and variously demonstrated in work spanning several published experimental studies and deployed systems. in this paper we provide an overview of automated disease surveillance efforts based on the social web characterized by their different high level design choices regarding functional aspects like user participation and language parsing approaches. we briefly discuss the technical rationale and practical implications of these different choices in addition to the key limitations associated with these systems within the context of operable disease surveillance. we hope this can offer some technical guidance to multi-disciplinary teams on how best to implement, interpret and evaluate disease surveillance programs based on the social web. keywords: crowd sourced disease surveillance, data mining, knowledge engineering, participatory epidemiology, the social web *correspondence: magumbamark@hotil.com doi: 10.5210/ojphi.v10i2.9312 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction current global trends like increasing population densities and higher mobility of persons and goods mean epidemics have the potential to spread extremely rapidly [1,2] thereby creating a need for equally fast reporting for early detection and investigation of outbreaks. whereas traditional approaches like sentinel surveillance are still relevant, they are incapable of adequately addressing the needs of early detection due to time lags introduced by formal processes and their limited geographical reach. webbased systems have emerged as an addition to these efforts. web-based approaches offer almost instant reporting which in the case of the social web is made possible by billions of users spread out across the globe and generating a continuous deluge of information in what is probably the largest data gathering operation in the world. most of this data is in the form of natural language text and is devoid of structure mailto:magumbamark@hotil.com design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi necessitating information extraction routines before it can be converted to actionable epidemiological intelligence. it is also generated by non experts thereby raising legitimate concerns about its accuracy, reliability and verifiability. however, even long established approaches like ehr (electronic health reporting) suffer from similar problems in addition to the fact that even in highly developed countries like the united states there are many areas where they are not well deployed [3-5]. disease surveillance attempts to answer five questions about disease events of interest often abbreviated as the 5ws. these are what (the event diagnosis),where (the event location), when (the time of occurrence), who (the victim/patient) and why/how (the causal agents involved). disease surveillance requires a continuous and rigorous data acquisition operation which can be both complex and logistically demanding. regarding the amount of directed effort expended in the process of obtaining information, surveillance can be characterized as either active or passive. with active surveillance those in charge of the process actively seek information as opposed to passive surveillance where medical workers wait for cases to essentially report themselves in the normal process of patients seeking medical care or some alternative scenario. both approaches are feasible on the social web. disease surveillance can also be classified as indicator based or event based. indicator based surveillance encompasses traditional formal systems with regular, predetermined reporting times whereas event based surveillance is real-time and ad hoc and incorporates both formal and informal sources and usually entails loose case definitions. the focus of web-based surveillance has nearly exclusively been on event based surveillance and all the systems covered in this paper are event based systems. another term that has become nearly synonymous with this web-based disease surveillance is syndromic surveillance which is defined as an investigational approach where health department staff, assisted by automated data acquisition and generation of statistical alerts, monitor disease indicators in real-time or near real-time to detect outbreaks of disease earlier than would otherwise be possible with traditional public health methods [1]. the goal of syndromic surveillance is early detection and it generally employs pre-diagnostic data or more relaxed definitions of incidence referred to as syndromes. in both cases disease events are considered unconfirmed and warrant further investigation. this is the stance adopted by most implementations based on the social web. the social web has been formally defined as a set of social relations that link people through the world wide web [6]. in functional terms it encompasses several services such as microblogs (twitter, tumblr),social networking (facebook, google+, linkedin, whatsapp), video sharing (youtube, vimeo), image sharing (instagram, flickr) and blogging to mention but a few. however, the boundaries of the social web have been extended by the rise of social application programming interfaces (apis) which are programming libraries that allow third party applications to make function calls to web services, and it now potentially includes the entire world wide web. through social apis services that aren’t already social are easily “socializable” by incorporating social features like content sharing and user comments hosted on third party platforms. an example is here is google which has incorporated social services like google hot trends, google social search and google+ to mention but a few. similarly chinese search giant baidu implemented baidu space which allows registered users to create personalized homepages in a query-based searchable community and baidu bookmarks a social bookmarking service supported by baidu.com. the vast reach of the social web whose users currently number in billions [7] and the rise of social platforms means social data is probably the most complete record of current human affairs. it does not just offer the possibility of capturing users views on a multitude of subjects but their physical and mental design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi situation, geo-location, movements, connections and much more in a variety of data formats ranging from text to streaming video. this information is mostly unsolicited and is generated at an explosive pace with unprecedented variety. for instance facebook.com, a single social networking service had 2.07 billion monthly active users of which 1.74 billion were daily active users generating information at a rate of 293,000 status updates, 510,000 comments and 136,000 photo updates a minute as of november 2017 [8]. this makes the social web a uniquely well suited architecture for the implementation of web-based, syndromic surveillance systems. the key theme is user generated content and user interaction and by this criterion we shall exclude systems like medisys [9] and goarn (global outbreak and alert response network) [10] in which there is a strong distinction between the system’s primary data entrants and the general public and the direction of information flow is strictly from the system to the general public with the general public being purely a consumer rather than a source of information. design choices for automated disease surveillance systems and studies on the social web in this section we discuss some of the key high level design choices for disease surveillance systems and experimental studies. figure 1 below depicts the typical analytical pipeline for web-based disease surveillance. dashed lines imply optional steps. based on these steps we identify the following high level decisions that have to be made: the user participation model, language parsing methodology, multiplicity of data sources (whether single or multiple data sources are employed), number of diseases to be investigated, the ultimate objective (predictive modeling, real-time disease monitoring or explanatory modeling) and the choice of deployment platform. the simplest pipeline involves retrieving messages from a single source and aggregating and reporting this data usually as a map visualization. this applies to the case of explicit user participation where structured data is obtained from willing volunteers and disease reports are assumed to be true meaning there is no need for mathematical modeling or data preprocessing steps like translation, filtering and language parsing. figure 1: analytical pipeline for disease surveillance applications on the social web design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi user participation model although the social web is participatory by nature, the extent to which users may be cognizant of the purposes for which their data is used differs. in most cases users are not explicitly recruited to volunteer information for the sole purpose of public health monitoring. for instance google flu trends and google dengue trends google [11] which relied on user searches for flu and dengue fever related terms did not provide explicit notification to users that their searches were being monitored for this purpose. in this case it is assumed users have provided implicit permission for use of their data for additional purposes by agreeing to terms and conditions of social web services usually by the very act of using them. this approach raises some privacy concerns and some services like twitter have attempted to offset them by allowing users to label their messages as private to make them unavailable for these additional purposes however the default visibility is set to public meaning user messages are available to third party access unless explicitly protected by the authors. this category also entails health news aggregators like gphin (global public health network) [12] and healthmap.org which compile health related reports from a multitude of web based sources not necessarily dedicated to health reporting. another instance of implicit participation involves the use of automatically obtained data from embedded sensors like propeller health’s sensor for asthma and copd (chronic obstructive pulmonary disease). propeller health has used data from its network of asthma and copd patients to produce socalled asthma forecasts as well as track local asthma and copd conditions in real-time for american cities. the service known as “propeller air” has been packaged into an api allowing it to be integrated into other websites and web services [13-15]. it has even been integrated into alexa, gogle’s ai assistant allowing users to obtain local asthma data via a natural language interface by simply saying, “get asthma conditions from propeller”. implicit user permission appears to be the de facto stance of most systems and studies based on social media [16-22]. in this case users inadvertently report ongoing cases of interest in the course of using the system for some other purpose. where unstructured textual data is involved this is technically challenging as it requires advanced text pre-processing and language parsing to extract useful information as messages are not necessarily generated for the purpose of communicating disease occurrence. alternatively, information may be explicitly requested for from the public. this is the approach adopted by implementations like flunearyou.org [23] for the united states of america which boasts over 60,000 users and influenzanet.eu [24] which currently covers eleven european countries. these systems rely on volunteers who knowingly and voluntarily report their flu symptoms. influenzanet.eu started off as a dutch service called degrotegriepmeting.nl which was able to recruit 20,000 volunteers in the netherlands and belgium in its first season and 30,000 volunteers in 2003 its first year of operation through a vigorous marketing campaign [25] and the so called great dutch influenza survey (“de grote griepmeting” in dutch) has been carried out since then [26], the second service was gripenet.pt a portuguese service that was directly inspired by the success of the dutch system and attracted 5000 volunteers between 2005 and 2007 [27], a third service influweb.it was launched in italy in 2008 [28] and finally a fourth system grippenet.fr was launched in france in 2012 and currently has 11811 active accounts [29]. these four teams decided to collaborate on the development of a unified european system directly leading to the birth of influenzanet.eu. in 2009, the northern hemisphere h1n1pandemic accelerated plans to export the platform to a new country namely the united kingdom in the form of a fifth system flusurvey.org.uk [30,31]. the influenzanet.eu project has been extended as influensakoll.se design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi in sweden [32], flusurvey.ie in ireland [33], influmeter.dk in denmark [34] and gripenet.es in spain [35]. the appeal of explicit user involvement is that it requires less advanced language processing and is likely to generate better quality data than implicit user for instance promed-mail [36] employs volunteer networks of professionals that report high quality disease incidence data for early detection of disease outbreaks with high accuracy. that said there is currently no conclusive research that consciously reported data from volunteers is better at predicting or monitoring diseases than automatically mined data inadvertently supplied by unwitting users in their public social posts. on the basis of the literature we have come across, implementations and experiments based on an implicit user participation model such as those conducted on google flu trends and twitter data [16,17,19,37] seem to slightly outperform those based explicit volunteer reporting [26,38,39] in terms of how well they correlate with official data. this is probably as a consequence of the fact that systems based on widely deployed social platforms like twitter make up for their poor data quality by accessing a far larger number of potential “volunteers” comprising every one of the billions of social web users. furthermore, even with selfreported data problems of verifiability persist. for instance it is not possible to remotely diagnose an infectious disease and services like influenzanet.eu have to rely on so-called “syndrome definitions” which are basically groups of symptoms that if they co-occur have a high probability of being indicative of a given illness. however, even when individuals faithfully report these, there is a significant overlap between different diseases in terms of symptoms and the same symptoms could be caused by potentially unrelated conditions. furthermore, systems based on self-reported information are useless when users do not provide this information for any reason and therefore require elaborate marketing campaigns and reminder mechanisms to ensure a continuous stream of data. volunteer driven systems generally suffer from user attrition [40] and as a consequence require a concerted effort to maintain user participation and engagement in addition to continuous user recruitment. however, there is always a chance that these same users are inadvertently revealing this same information in the process of communicating with their social connections hence the utility of systems that are always listening in on user conversations for any relevant mentions of disease related words. finally, from an operational overhead point of view implicit user participation is far cheaper than explicit participation as social apis eliminate the need to explicitly enroll volunteers allowing operational details like marketing and maintaining user engagement to remain external to the surveillance process. in addition by comparison to explicit participation it has very low infrastructure requirements as there is no technical need to store disaggregated user data. there are also implementations which employ a hybrid user participation for certain aspects of the process such as crowdbreaks.org [21]which employs volunteers to annotate messages for its classification a mechanism they refer to as "user-driven data refinement". language parsing methodology where users are not explicitly aware that their conversations are being monitored for diseases surveillance messages will be highly unstructured and contain additional artifacts like emoticons, urls (universal resource locators), slang and multimedia in addition to errors like misspellings. hence some language parsing is required and from this perspective web based disease surveillance systems can be broadly categorized as rule-based systems or machine learning systems. rule-based approaches extract design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi useful data by using manually generated rules. for instance doan et al [41] use rules like the presence of references to negation and humour in conjunction with shallow parsing with the rasp grammatical parser [42] to filter twitter messages about the flu. the simplest rule based systems employ keyword matching approaches which assume messages to be relevant if they contain certain keywords [11,19,43,44]. in projects like biocaster [45], bio storm (biological spatio-temporal outbreak reasoning module) [46], medisys [47], gphin and healthmap.org [48] these keywords are have further been hierarchically structured into ontologies or taxonomies. ontologies allow for more structured definitions that may simplify tasks like dictionary maintenance and support for multiple languages by modularizing concept definitions. there are two approaches for implementing multi-lingual systems the first involves translating messages to a common language such as english and then applying some parsing rules that are usually based on an ontology, in this case you have a single ontology definition. this is the approach taken by the healthmap project which relies on the google translate service for translation. the second involves creating different versions of the ontology for different languages which was the approach that was taken by the biocaster project. the advantage of the former approach is that it is likely to support more languages as freely accessible public apis are well maintained and constantly adding new languages for instance google translate currently supports 103 languages [49] whereas biocaster supported 8 languages [50].the advantage of the latter approach is that it may provide better performance than machine translation which is still relatively inaccurate particularly for noisy data sources like social media and may not always produce reliable results. however, it is difficult to create and maintain exhaustive domain definitions using static rules. simple rules like text matching are susceptible to false positives as in many cases keywords may be mentioned in irrelevant contexts on the other hand longer more elaborate rules may be susceptible to false negatives as a result of the fact that it is impossible to write rules that account for the full variety of expressions people may use to communicate morbidity. this may result in inaccurate models as generally some positive correlative relationship is sought between the volume of keywords and disease activity on the ground. for instance equation 1 below depicts the formula employed to relate keyword volume with official ili data by fitting the log-odds of an ili physician visit with an ili related search query for the google flu trends project by ginsberg et al [37]. p represents the percentage of ili physician visits, β0 is the intercept. 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙(𝑃𝑃) = 𝛽𝛽0 + 𝛽𝛽1 × 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙(𝑄𝑄) + 𝜀𝜀 (1) given such a formulation, imprecise semantic filtering will likely negatively impact results. we are not aware of any studies that have attempted deep semantic filtering on search engine data but for microblog data such as twitter data it has been shown to be the case that more robust semantic filtering improves results. this is achieved with a second approach known as machine learning. with a machine learning approach explicit rules do not need to be written as these are automatically learned from data by some machine learning algorithm. with this approach first a keyword-based approach is applied and then messages are further filtered using machine learning algorithms. machine learning will generally impose higher infrastructure requirements due to the increased computational overheads of running machine learning algorithms. machine learning also imposes higher operational overheads as a result of the additional cycle of activities related to training and keeping language models updated. figure 2 depicts a generic natural design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi language processing (nlp) cycle for unstructured textual data typical of the social web. the nlp cycle starts with corpus generation which entails obtaining messages from which to create language models from social platforms. the key concern at this phase is ensuring that the messages obtained are actually representative of the data. additionally obtaining a sufficiently dense sample may not be straightforward. whereas there is an unprecedented amount of information on the social web, its distribution is tilted towards certain popular topics. therefore for some topics it may not be possible to obtain sufficient information to create reasonable data driven language models. however, many social platforms that have implemented apis simplify this step. for instance the twitter api allows one to obtain randomly sampled messages for a given keyword eliminating the need to create web scrapping tools to obtain messages. figure 2: activity cycle for textual analytical pipeline for disease surveillance applications on the social web the data also requires pre-processing which may involve several steps such as translation for multilingual pipelines. other typical steps include duplicate removal, data cleansing steps like removal of punctuation and other social media related artifacts like hashtags, urls and emoticons, sentence segmentation, tokenization, normalization, part of speech tagging, noun phrase chunking and finally annotation. the next step is feature engineering. in many situations the words themselves are taken as design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi the features and simply vectorized. more advanced techniques are possible like distributional word embeddings such as neural word embeddings like word2vec [51] which represent words as low dimensional vectors and are able to conserve non trivial semantic relationships for instance where v(king) is the vector for “king” it has been found that v(king) – v(man) + v(woman) returns roughly the same value as v(queen) . in addition distributional representations have been found to improve the results of nlp tasks such as classification and achieve state of the art results for other tasks like named entity recognition with methods such as recurrent neural networks. however, their application in this domain is limited to a few examples like the work by magumba et al [52] who employed word2vec embeddings learned from conceptual representations of tweets to create message classification models for general purpose disease incidence detection. the next step is training a model using these features followed by validation of the model. the entire sequence of activities required to encode a message as a vector of features has got to be repeated on the fly for every message for live systems. this includes all pre-processing and feature extraction steps in addition to decoding the message into some result using the learned model. intermediate steps like part of speech tagging and noun phrase chunking have their own pipelines and require similar language resources like annotated training data for model training and validation. given the high throughput of these systems the computational requirements of this pipeline are a real technical concern that must be addressed at design time. for text processing algorithms computational complexity is typically expressed in terms of processing time (time complexity) and memory requirement (space complexity) as a function of some property of the data. typical properties employed for complexity evaluations include the average length of a message and the number of messages. usual workarounds to computational complexity typically rely on parellization strategies in which the computing load is distributed across multiple elements like processors which undoubtedly introduces additional costs. in addition algorithmic compromises are usually made for complex algorithms like crfs and log linear models such as limiting the number of context words at a small cost to performance. furthermore approximate inference approaches like stochastic gradient descent (sgd) and coordinate descent can be employed to dramatically reduce the training time complexity of several methods such as logistic regression and svms (support vector machines). typical applications of machine learning in this domain include classification and named entity recognition. classification entails distinguishing between relevant and irrelevant messages with classification algorithms like logistic regression classifiers [16], support vector machines (svms) with polynomial kernels [18], log linear models [53], and naive bayes classification [54]. named entity recognition (ner) which entails automatic detection of entities like persons, disease events and locations with methods such as svms [54] and linear chain conditional random fields (crfs) [55]. unsupervised approaches on the other hand do not require any annotated data and instead rely on some objective function like the euclidean distance between vectorial representations of messages. these techniques are generally clustering algorithms that partition a corpus of messages and other data into clusters of closely related signals. the clusters may be used as target categories to train classification models for classifying new messages or may be employed as syndroms for generalized syndromic surveillance as with ailment topic aspect model (atam) [56] employed in healthtweets.org [20]. other techniques include latent dirichlet allocation (lda) [57], expectation maximization (em) clustering [58] and bisecting k-means algorithm [59] to mention but a few. design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi these techniques will also require some feature engineering to overcome noise like irrelevant words and achieve some level of generalization. purely data driven approaches commonly employ a vector of the most frequently occurring words as the vocabulary and mask over rare words. the size of the vocabulary is usually set by trial and error. a thresholding approach where tokens whose frequencies are below a certain value are eliminated may also be used as an alternative to setting some arbitrary vocabulary length. depending on the algorithm the resulting representation may be the same length as the original message as with distributional clustering approaches like lda or a fixed length vector with the same length as the vocabulary as with centroid based clustering techniques like k-means. in the latter case a one hot encoding is typically employed which is a binary representation indicating whether or not each vocabulary item occurs in a given message. these datasets are usually characterized by large vocabularies and short messages therefore this is usually a very sparse vector with most items set to zero. this can then be subjected to additional transformations like principal component analysis for dimensionality reduction [58]. the key difficulty with unsupervised methods is that they require one to set hyper parameters and choosing optimal values for hyper parameters could be challenging and requires one to make some potentially unjustified a priori assumptions about the data like the number of topics in addition to alpha and beta parameters that describe the distribution of words over topics and topics over documents respectively for lda. considering that word and topic distributions are not static on the social web this raises practical concerns on how to keep models up to date as good guesses of these hyper parameters have to be continuously supplied as the underlying word and topic distributions change. furthermore, sometimes there are issues of interpretability of results for instance lda will not always return topics that make sense and therefore require some human mediation. finally there are implementations that employ a hybrid human mediated automatic language parsing procedure such as gphin which automatically calculates so-called “relevancy scores” for each article. articles with a high score are automatically published but those with low scores go through a second manual check by a human analyst [60]. such a setup minimizes the possibility of false negatives. multiplicity of data sources it is a requirement for these approaches to analyze as much signal data as possible in order for them to be as representative as possible. the natural approach to achieving this has been to incorporate as many data sources as possible. this is an the approach that has been employed by systems like healthmap.org and the “outbreaks near me” mobile application which incorporate data from googlenews, promedmail, world health organization reports, geo sentinel, oie (the world organization for animal health), eurosurvillence, baidu news, soso info and the wildlife data integration network. many of these sources are themselves aggregators of multiple sources for instance google news aggregates more than 50,000 news websites [61], promed-mail itself relies on media reports, official reports, online summaries, local observers, and others. whereas there is some experimental evidence that suggests that employing multiple data sources may improve accuracy and robustness of predictive algorithms, maintaining multiple apis and data extraction pipelines imposes a significant amount of operational overhead on the process. there also additional issues that will have to be resolved such as dealing with different data representations and formats and varying spatial and temporal granularity across different data sources. design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi however, the rise of social media and social apis has made single source surveillance feasible. social networking websites like twitter.com and facebook.com are in fact aggregators in away as they allow multiple types of users to sign on. there are three key types of users. the first are individuals who number in billions. the second are organizations like news services and government agencies communicating vital information like updates on developing situations in real time. the third category comprises automated agents known as bots. for this reason social media offers a simplified route to achieving representative data source as it bypasses the complex logistics of maintaining multiple apis and data sources. single source systems include examples like biocaster which employed promed-mail alerts, flu-prediction.com, crowd-breaks.org and healthtweets.org which are based on twitter data in addition to most studies based on twitter [16,17,19,55,62-64], polgreen’s work on yahoo searches [39] and the now retired google flu and google dengue trends services [8] that employed server search logs. number of diseases monitored regarding the epidemiological focus of these works in terms of diseases of interest one can formulate two broad categories that is work that has concentrated on a singular illness such as flu-prediction.com, flusurvey.co.uk, google flu and google dengue trends that concentrated on the flu and dengue fever respectively in addition to most work on twitter which has concentrated on the flu [16-19,23]. the second category involves systems that are able to handle multiple diseases. depending on the underlying method this second category can be further subdivided into systems that handle a determinate set of diseases and generalized approaches that can potentially identify an unbounded number of ailments. the former include systems such as the ontology and taxonomy based systems which as already discussed ultimately rely on text matching to extract predefined disease entities. a second more complex instance of this category involves topic modeling based approaches such as atam based on lda [57]. as already noted these require some hyper parameters like alpha and beta parameters in this case which themselves require some prior assumptions of topic distributions the topics in this case being individual diseases. as already mentioned, further human mediation is required to determine if the resulting topics make sense. in this case the number of possible ailments is bounded by the number of topics or clusters set at training time whereupon new messages are classified using some distance measure like euclidean distance. furthermore, these approaches do not directly return ailments but rather clusters of messages with a high likelihood containing the same topics. the key limitation of employing such fixed definitions is that such systems are incapable of accommodating new health concerns such as emerging diseases without significant re-work. for a more direct and truly generalized automated approach the most promising systems are those that apply machine learning based named entity recognition algorithms such as crfs (55]. these rely on features like the order of words and other features like word prefixes and suffixes to label words as entities like persons, drugs and diseases. this approach is not limited to a reference dictionary and can potentially detect previously unseen ailments. a key drawback of this approach is that machine learning driven named entity recognition so far performs quite poorly on social data for instance the experiments by jimeno-yepes et al [55] reported a precision of 0.755 and a recall 0.62 on a data set of 11,647 tweets. this means that 25% of tokens identified as mentions diseases by the model were not and nearly 40% of tokens that referred to diseases were labeled as mentions of other entities. we would only expect performance to further deteriorate in a live implementation. however, we are not currently aware of any design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi automated disease surveillance systems that have incorporated machine learning driven named entity recognition in their analytical pipeline. as far as we know general threat monitoring has generally been deployed by human moderated systems like promed-mail and gphin. where the goal is predictive modeling, separate analytical pipelines are required for each new ailment as with google flu trends for flu and google dengue trends for dengue fever as generally predictive epidemic models are not transferable between different diseases and from this perspective a generalized data extraction interface offers little gains however where the goal is to monitor some unspecified threat such as bioterrorism in real-time it would be extremely beneficial. predictive modeling, real-time monitoring and explanatory modeling regarding the ultimate objective there are several ways to characterize the body of work, there are predictive systems that generally attempt to provide early warning for prospective disease outbreaks before they are reported by official systems, then there are systems intended primarily for monitoring the progress of outbreaks in real-time and finally there is work of an exploratory nature aimed at finding patterns and explaining disease related phenomena like causality, spatio-temporal clustering and disease dispersal networks that we refer to as explanatory modeling. assuming a population of one individual figure 3 below depicts the usual progression of disease and the applicable system objectives at each phase in terms of prediction or monitoring from the point of view of that individual. figure 3: disease forecasting, nowcasting and real-time monitoring vs. disease progression timeline however, populations comprise several individuals and in general disease will progress differently for each individual with each phase commencing and concluding at different times. as a consequence there is significant overlap between prediction and monitoring. where there is an unusually high number of individuals falling ill at roughly the same time for a given disease and population it is referred to as an outbreak. design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi in general we refer to implementations that employ pre-diagnostic data such as searches for certain keywords as predictive modeling and based on the prediction time horizon these can further be categorized into nowcasting and forecasting. nowcasting refers to tracking outbreaks as they occur but due to process lags in formal surveillance systems like ilinet (influenza-like illness surveillance network) now-casting systems have been known to “predict” official statistics up to two weeks in advance [37]. forecasting involves longer time horizons or at least is intended to for instance there are works that have employed the term forecasting for time horizons that are shorter or comparable to nowcasting systems for instance dugas et al [65] use statistical methods to make predictions that are one week in advance of official statistics. for a clearer technical distinction we can employ the shape of the model input data. assuming a simple system with a single predictor variable x and a single target y and x and y are vectors of values such that 𝑋𝑋 = (𝑥𝑥1, 𝑥𝑥2, 𝑥𝑥3 … … . 𝑥𝑥𝑛𝑛 ) and 𝑌𝑌 = (𝑦𝑦1, 𝑦𝑦2, 𝑦𝑦3 … … . 𝑦𝑦𝑛𝑛 ). for nowcasting approaches such as the google flu trends method the model input takes the form {(𝑥𝑥1, 𝑦𝑦1), (𝑥𝑥2, 𝑦𝑦), (𝑥𝑥3, 𝑦𝑦3) … … … . (𝑥𝑥𝑛𝑛, 𝑦𝑦𝑛𝑛) } . here the disease activity y is assumed to be only dependent on the value of x at that given time and the goal is to predict y given some x. for forecasting the model input is a time series corresponding to a chronological history of observed disease activity bounded by number of lag observations. for instance for a lag of two for data that is t observations long given yt denotes the value of y at time t the model input would take the form {[(𝑦𝑦𝑡𝑡−𝑛𝑛−2), (𝑦𝑦𝑡𝑡−𝑛𝑛−1), (𝑦𝑦𝑡𝑡−𝑛𝑛)] … … … … [(𝑦𝑦𝑡𝑡−4), (𝑦𝑦𝑡𝑡−3), (𝑦𝑦𝑡𝑡−2)], [(𝑦𝑦𝑡𝑡−3), (𝑦𝑦𝑡𝑡−2), (𝑦𝑦𝑡𝑡−1)]} and the objective would be to predict y at time t +1 given values of y at time t and t 1. in this case y is dependent on the sequence of previous values of y. for the purpose of forecasting, a variety of supervised machine learning approaches have been applied and these include multivariate regression models [66], ensemble prediction approaches [67], boxjenkins generalized linear models (glm) and generalized linear auto regressive moving average (garma) [65]. similar methods have been applied for nowcasting particularly regression techniques [64] in addition to aberrancy detection techniques [46]. for forecasting longest time horizons are around 2 months [68,69, 70]. however, the vast majority of work in this domain is on nowcasting and it entails systems like google flu and google dengue trends, flunearyou.org and the majority of studies reviewed here [16-19]. real-time disease monitoring is post-diagnostic meaning the condition has already been identified and it involves keeping track of suspected or confirmed outbreaks and epidemiological incidents. in these implementations there is no need to build predictive models to relate signal data to official data as these systems report cases with a high level of confidence and generally assume their sources to be true but not necessarily representative. these kinds of implementations will rely on high quality data sources like networks of public health professionals and include systems like healthmap.org, promed-mail, biocaster, gphin and the “outbreaks near me” mobile application. for implementations like the influenzanet.eu systems and flunearyou.org which rely on information provided directly by the population there is less certainty about the accuracy of diagnosis and therefore a weaker claim is made by employing more fluid syndromic incidence definitions rather than reporting cases of specific conditions. the general approach for such systems is to have users indicate their symptoms in an online form and if users exceed some number of symptoms the system takes this as a positive diagnosis for some syndrome as opposed to detecting a specific disease. for the systems mentioned above the syndrome is “flu-like illnesses”. design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi explanatory modeling is geared towards understanding the mechanics of disease emergence and spread and thereby identifying at risk individuals or populations and modeling effective interventions. with predictive modeling the mechanics are more or less irrelevant as all that is required is a positive correlative relationship between a signal variable that is usually the volume of messages about a given topic and disease activity on the ground. explanatory modeling includes work on geospatial cluster analysis [47,68,70] and social networking analysis [71]. geo-spatial cluster analysis involves determining clusters of locations with elevated level of cases during an outbreak. this is important because disease cases will not be uniformly geographically distributed and targeted interventions require more fine-tuned location data. social networking analysis investigates interconnections between individuals and may be employed to determine at risk individuals or populations. system deployment options there are two main deployment options namely web and mobile. most large scale deployments are in the form of web applications that run in a browser. the main advantage of this approach is that the service is made instantly available to most internet capable devices which normally ship with a default browser program. however, web implementations are not guaranteed to render uniformly as different vendors do not uniformly implement web standards like html (hypertext markup language). in addition, the user experience for desktop sites when accessed from smaller devices is usually inferior to that on desktop due to the smaller size of the display. a common compromise is to create a minimal version of the web site referred to a mobile site. web servers can then detect the client’s browser via information such as the “user agent” http (hypertext transfer protocol) request header and load the appropriate website version. however, as depicted in figure 4 the number of users accessing mobile internet on smaller devices like smartphones and tablets has grown quickly since their introduction eventually overtaking that of users accessing internet via desktop on first november 2016. as a consequence of this there has been a rapid upsurge in mobile applications. figure 4: proportion of worldwide internet users by access mode. source: gs.statcounter.com [102] design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi mobile applications have several advantages of web applications in terms of how they interact with the user especially where an explicit user participation model is employed. first of all most mobile applications do not explicit logon, in addition it is possible to program reminders and integrate them with the phone’s native functions like phone vibration which could be more effective tools for maintaining user engagement than mass marketing campaigns such as email broadcasts that are typically employed by web applications. in addition, a more intimate connection is expected between participants and their phones which they keep on their person for most of the day than desktops or laptops which they interact with intermittently providing more opportunities for users to engage with the application for instance research shows that young adults use their smartphones an average of 5 hours a day which is 30% of their total waking time [72]. interestingly, none of the large scale explicit user participation systems discussed so far offers a mobile website or mobile application for that matter. however, mobile website versions and applications are offered by healthmap.org whose mobile platform “outbreaks near me” is deployed on android platform and propeller health that has both android and ios implementations. both services employ an implicit user participation model. another important factor particularly for the developing world is price. low end android phones can go for as low as $25 usd [73] and can be used in areas with intermittent power or no power at all as they can be reliably recharged by off-grid options like solar chargers. they have also been applied extensively in monitoring diseases in plants and livestock where disease monitoring greatly benefits from mobile data collection units as it is impractical to transport plants and livestock to central facilities for diagnosis and treatment necessitating data collectors to actively seek information in the field. examples include implementations like “be seen be safe” for real-time monitoring and modeling of poultry outbreaks in canada which is available on both android playstore and apple’s app store and the mcrops project by makerere university in uganda that employs image processing techniques to identify cassava diseases with smartphones and relay results in real-time to data analysts [74,75]. the mcrops project has rolled out three apps on android namely whitefly detection, necrosis detect and adsurv. they are also particularly useful in fast changing environments such as the american navy’s passion project (precise at-sea ship system for indoor-outdoor navigation system) which was intended to track on board outbreaks for the american naval fleet [76], the android based system was an extension of previous work in zambia and colombia by the onr (office of naval research) and is applicable to dynamic situations like disaster relief in addition to the. an important decision for mobile application deployments is the choice of platform as unlike web deployments different versions of the application are required for every target mobile platform. developing for multiple platforms certainly imposes higher expenses and has to be clearly justified. figure 5 depicts the popularity of different mobile platforms by market share from 2009 to 2017. as shown in the figure, the vast majority of devices are running android operating system making it the default platform choice for most scenarios. in some countries like the united states the split between the two leading platforms namely android and ios is more even at 55.4% and 43% respectively [77] necessitating mobile implementations to roll out at least two versions of their applications to cater for the two options. for most large services the minimum deployment comprises a web application and at least one mobile implementation. the fact that several projects covered in this discussion have only a web deployment is indicative of the infancy of this domain. ios is generally very poorly represented in developing countries due to the fact that ios products are typically more expensive. however, even the relatively low cost of android devices may be prohibitive for many individuals in the poorest countries. in addition these devices require an internet subscription design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi and internet coverage both of which cannot be taken for granted in many parts of the world. this has led to massive interest in sms (short message service) technology in the developing world. sms based apps eliminate most of the financial hurdles associated with other technologies, in addition every single cell phone is sms capable. the downside is that feature-wise sms based interfaces are limited as they are text based. however, we are currently unaware of any efforts that have been implemented as truly social undertakings. the standard model seems to be to rely on a group of trained data entrants with a view of enhancing reporting within formal structures as opposed to sourcing health information directly from the general public [78-80]. figure 5: mobile os market share for different platforms. source: statista.com [103] summary whereas the preceding discussion has mainly focused on a textual analytical pipeline, we would like to point out that this is only because the majority of the work by far has employed textual signal data but it is not necessarily the limit of what is possible for instance garimella et al [81] used automated image processing techniques like machine annotation to track lifestyle diseases such as obesity and excessive drinking using geo-tagged instagram posts. we expect that as social media systems continue to grow in terms of users and machine learning and other analytical tools become more established more advanced forms of processing and data collection will become feasible. one such emerging field is the use of data from smart wearables and embedded sensors like the propeller health device referred to above that is attached to a user’s inhaler or bluetooth spirometer. in addition widely available devices like smart watches come packed with an array of sensors that given the right software can detect physiological signals like the wearer’s heartbeat, muscle activity and stress levels. embedded sensors make it possible to have high precision individual level surveillance of vital health signs like cardiac rhythms in a noninvasive way with very little conscious effort by the wearer. these technologies typically employ some design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi intermediate computing device like a smart phone as a gateway node for local storage and data uplink to remote servers. however, most vendors, with a few exceptions like propeller health, do not yet have dense enough networks of users to reliably execute public level health monitoring but certain product categories like smart watches are already widely deployed meaning we are quickly approaching a time when this will be feasible. figure 6 shows the number of smart wearables sold worldwide by category from 2014 to 2017 depicting steady growth in the sector. it also reveals that majority of wearables are in the form of health and fitness trackers followed by smart watches. there is currently a lot of promising research in embedded sensor technologies capable of monitoring practically every physiological signal such as smart textiles that can measure stress levels and neural muscular abnormalities through embedded miniaturised ecgs (electrocardiograms) and emgs (electromyograms) [82-84]. as the field advances these can only become cheaper, less invasive and more widespread. figure 6: yearly global sales of smart wearables from 2014 to 2017 by category from 2014 to2017. source: statista.com [104]. finally we would like to point out that some systems mentioned in this discussion that are currently not live. these include google flu trends and google dengue trends that were retired in 2014, biocaster which was retired in 2012 and healthtweets.org and crowdbreaks.org that are still in development. the google flu/dengue trends and biocaster systems have been included because they were among the first large scale projects to be deployed for web based disease surveillance and their data and methods have widely been employed as benchmarks. healthtweets.org and crowdbreaks.org have been included because they are pioneering applications of large scale machine learning for textual processing in this domain. table 1 below provides a summary of the preceding discussion. design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi table i. a comparison of the merits and demerits of different design choices for automated disease surveillance on the social web design element design option merits demerits examples in literature (live systems in bold, underlined, systems in development and prototypes in bold, retired systems in bold italics and studies in regular text) user participati on model explicit user participation -higher quality data -no text processing required limits potential pool of participants flunearyou.org [23], promed-mail, influenzanet.eu [24] (flusurvey.org.uk, grippenet.fr, gripenet.pt, influenzasakkol.se, flusurvey.ie,influweb.it) implicit user participation all social media users are a potential source of data -requires advanced text processing -potential for breaches of privacy google flu trends and google dengue trends [11], healthtweets.org [20], crowdbreaks.org [21], gphin [12], flu-prediction.com,healthmap.org [16], “outbreaks near me”, several studies [16-19,43,44,81] language parsing methodolo gy rule based parsing computationally inexpensive commonest approach of text matching is susceptible to false positives google flu trends and google dengue trends [11], biocaster [45], biostorm [46], healthmap.org [16], “outbreaks near me”, several studies [11,19,41,43,44] machine learning approaches better accuracy -computationally intensive -model development is technically complex -require labor intensive continuous generation and preparation of training data crowdbreaks.org [21], healthtweets.org [20], several studies [53-59] multiplicit y of data sources single data source low operational overhead in terms of data acquisition resulting models may be less robust than models built from multiple data sources google flu trends and google dengue trends [11], healthtweets.org [20], crowdbreaks.org [21],biocaster [45], flu-prediction.com, several studies [16-19,23,44,62-64] multiple data sources -models are likely to be more robust than those from a single data source high operational overhead regarding data acquisition for instance maintaining multiple apis healthmap.org [16], “outbreaks near me”, gphin [12], pro-med mail, several studies [66,67] system deployme web deployment -single deployment may cater for multiple devices -application may not perform optimally across different devices google flu trends and google dengue trends [11], healthtweets.org [20], crowdbreaks.org [21], biocaster design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi nt options since the application is accessible to any device with a browser and display and usability are poor on devices with smaller device -shallower platform integration limiting access device hardware features and hence limiting user interaction [45], flu-prediction.com, pro-med mail, healthmap.org [16] mobile deployment -enjoy deeper platform integration on mobile devices allowing for a more intuitive user interface for instance on android developers can take advantage of phone features like the speaker, accelerometer, vibration and gps location -unlike web deployments each platform requires its own version of the application and therefore there can be considerable development effort if it is intended to serve multiple platforms propeller health(android and ios),outbreaks near me(android only) limitations of automated disease surveillance systems and studies on the social web in this section we discuss some key caveats pertaining to the operationalization of disease surveillance based on social data. these are meant to inform implementers on the limits of these methods and guide deployment decisions and the interpretation of results. these include issues like the irregular distribution of signal and target data leading to geo-spatial gaps in data, inconsistencies in the location resolution of data, inherent data biases and the availability of data and language resources particularly for supervised machine learning method. geo-spatial gaps in data and inconsistent location resolution of data points services like google and twitter are capable of obtaining a user’s location with a resolution in excess of a millionth of latitude and longitude, an area equivalent to a thirty square feet wide circle on the ground, however most data is not geo-tagged and the distribution is such that most social web users are clustered into a few places like urban centres. furthermore, although it is technically trivial to obtain high level location data like the country of origin of a social post with the leading social apis, more fine grained geo-location usually requires users to explicitly disclose their location and most users do not. as a consequence most studies are based on data that is aggregated nationwide [11,16,17,19,44,66,67]. the problem with this is that it implicitly assumes that an entire nation or even a city is a single homogenous region. such models are limiting as these predictions are ostensibly created to allow health officials to plan in advance but in this regard they fall short as they give no information on the geographical distribution of cases. several types of heterogeneity do exist meaning such that it may be design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi more useful to view the population as a metapopulation1. that is local conditions such as the immunity of different subpopulations and the efficacy of medical interventions may so significantly alter the parameters of a geographically distributed epidemic or outbreak that it may be more accurate to model it as a collection of distinct sub epidemics that may even be out of sync with each other. as an example figure 7 below shows district level epidemic trajectories for the 2014-2015 ebola outbreak in sierra leone. it is quite clear that there were significant differences in epidemic curves for different districts in particular the curves for kenema and kailahun in the middle plot were significantly out of phase with other districts. furthermore, even though the first cases were observed in kenema and bo the number of cases shot up rapidly in kenema and rose at a much slower rate in bo peaking as the incidence had already began to decline in kenema. such effects would be concealed by a country level model. unfortunately for most locations it is currently not possible to reliably obtain sufficiently dense geotagged social data at this level of detail. however, where possible we recommend smaller models, such as district level models in this case, from which global models can be obtained by aggregation as demonstrated by dugas et al [65] who used google flu trends data to create predictions for individual medical centers. in most cases the distribution and availability of both signal and target data prohibits such an approach. design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi figure 7: district level epidemic trajectories for the 2014-2015 ebola outbreak in sierra leone. weekly incidence records for each district are shown as colored ‘x’, solid line in the corresponding color is the approximate average incidence. dates shown on the x-axis (dd/mm/yy) are endings of epidemic weeks. source: yang et al [105] for the case of unavailable target data, work by generous et al [68] has shown that in some situations models may be transferable between locations for particular diseases. they obtained positive results for influenza between japan and thailand and thailand and the united states. this means that at least in the context of availability of target data it may be possible to employ models from resource-rich locations and contexts to at least make default, fallback predictions for resource-poor locations and contexts given demonstrably similar causal factors. however, in situations where it is not possible to obtain sufficiently dense social data for a given target location these methods are completely unusable. data biases the effect of some inherent biases has not been rigorously examined. olteanu et al [85] highlight four possible biases namely population biases, behavioral biases, temporal variations and data redundancy. population biases have to do with the distribution of different demographics, this is of importance here as demographics play some role in the spatio-temporal dynamics of disease spread [86]. this compounded with the fact that social data is often heavily tilted in favor of certain demographic categories has raised some queries about its representativeness. in some studies attempts have been made to correct these distortions for instance tilston et al [39] split their data according to age category then re-weight it such that incidence frequencies for categories that were underrepresented in the data versus the population were upwardly adjusted whereas those for categories that were overrepresented in the data versus the population were downwardly adjusted. the weight of an individual wigiven 𝑃𝑃𝑖𝑖 𝑈𝑈𝑈𝑈 is the actual proportion of the individual’s population category in the population and 𝑃𝑃𝑖𝑖 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 is the proportion of the individual’s population in the survey data was then computed as follows: 𝑊𝑊𝑖𝑖 = 𝑃𝑃𝑖𝑖 𝑈𝑈𝑈𝑈 /𝑃𝑃𝑖𝑖 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 (2) however, systematic investigations into the role of these demographic distortions on the performance of these methods are generally lacking. for instance in the work by tilston et al no empirical justification is made for weighting data such as comparing the performance of the weighted approach against that of an un-weighted approach. behavioral biases refer to how users behave in different contexts for instance teevan et al [87] found that twitter queries are more related to transient, temporary relevant situations whereas web queries by users about a topic evolved as users knowledge about topics increased. in other words, users had specialized purposes for different social services. behavioral biases also include content production biases which encompass several observations such as the fact that there are geo-spatial linguistic variations within countries [88]. temporal variations refer to time dependent differences in populations and behaviors. for instance if you carried out the same exact experiment at different times it is possible to sample entirely different users [89,90]. as a consequence of all this models eventually age and become inconsistent with the reality on the ground requiring new models to be created over time. training new models over time will impose design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi additional overheads which must be anticipated at design time. it is also important that experiments employ the correct temporal granularity or risk missing instances of relevant phenomena that is for short-lived phenomena a fine grained temporal granularity is required and conversely a coarse grained temporal granularity is required for long term phenomena. for instance you would have to be mindful of disease specific temporal characteristics like communicable periods and incubation periods. as an example the incubation period (the time between infection and first onset of symptoms) of salmonella food poisoning is 6-48 hours therefore data for predictive modeling or monitoring of salmonella outbreaks must originate from a data source that is updated at least hourly otherwise predictions and estimates may lag or become out of sync with the reality events on the ground. finally, redundancies may bias the data. it is not uncommon to have the same message posted several times or have several messages which are fundamentally equivalent. for instance twitter has a “retweet” function which essentially allows users to propagate a message of interest throughout their network. care has to be taken to deal with such redundancy or it may bias the quantitative inferences made from the data. retweets are easy to deal with but where several messages refer to the same event but are lexically divergent and posted by different users advanced cross document co-reference resolution is required to determine entity mentions that are equivalent. we are presently unaware of any work on cross document co-reference resolution of disease event mentions on the social web. matters become even more complicated where multiple data sources are employed as key events like disease outbreaks are likely to be communicated via several outlets. redundancy does not only exist for major events but even at an individual user level as many users have multiple social accounts. this raises important questions about how best to architect multi-data source solutions for instance is it better to merge all the data sources into a single data stream or to create an ensemble of predictive models? such questions are currently unanswered. accessibility of data and availability of language resources where supervised machine learning techniques are applied for language parsing there is need for annotated in-domain data. given the sheer volume of messages on social media and the speed with which the lexicon evolves [11] creating representative datasets is very challenging. generally speaking work on language processing disease surveillance in the social web has dealt with comparatively small datasets for instance conway et al [91] use n-grams and semantic features to classify documents in the biocaster corpus which comprises 1000 documents, paul and dredze [18] have to rely on amazon’s mechanical turk (mturk) tool [92] to generate a corpus of 5,128 tweets labeled as related or unrelated. these are relatively small compared to standard corpora like the imdb corpus [93] which contains 50,000 annotated reviews. small datasets increase the likelihood of over fitted models which are characterized by extremely high performance on training data but unreliable performance on production data. also there is a general lack of standard datasets and in most cases studies have had to resort to creating their own data. other systems like crowdbreaks.org have attempted to circumvent this problem by relying on crowd sourced message annotation by having site visitors label messages as either relevant or irrelevant. however, this comes at the price of having inconsistent definitions of message relevancy. given the dynamic nature of social web data and users continuous and semi-automated means of corpora generation and maintenance are necessary. how to exactly arrive at a logistically optimal method for this however requires some dedicated research. in addition there is need for some dedicated effort towards creating standardized language resources which at the moment do not really exist making design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi it difficult to compare the performance of different approaches to similar tasks. for text data the usual data source is server search logs [1,44,69], for social media data the majority of the work has employed twitter’s streaming api which allows free access to 1% of the twitter feed. another open option is wikipedia article access logs [68]. however, in general terms free public access to data is uncommon and most systems require high level organizational approval for any form of meaningful access. overfitting where machine learning and statistical modelling are employed the practise is to sample as much data as possible and then split this data into training and testing datasets. the instinct is then to maximise the performance of whatever learned models on the training data however this makes two unjustifiable assumptions. the first is that the data is representative. this is problematic because of the huge amount of data involved for instance 400 million tweets only represents 1% the tweets in a year [94] and this percentage is ever getting smaller. however, as noted in the previous section, particularly with supervised approaches for language parsing the annotated training data is seldom more than a few thousand messages. it is therefore quite difficult to determine how much confidence may be assigned to such models. a second even more troublesome assumption is that the problem space is well defined. there are indeed unknown unknowns coupled with a dynamic problem space further complicating the modeling problem. furthermore, researchers are generally more eager to report results on high performance models but in many instances these models essentially just overfit the training data. this means that models are tuned to maximize performance on the training sample rather than to generalize. a well known example of a model that performed well in development but poorly on live data is that of that of google flu trends which was reported to have obtained a 0.97 mean correlation with actual data for the 2008-2009 flu american flu season in the initial paper [37] before grossly over-estimating the 2011-2012 and the 2012-2013 seasons [95,96]. conclusion whereas the preceding section highlights several limitations, it is important to note that the caveats highlighted are not unique to the social web and are more or less general issues related to electronic disease surveillance but the sheer volume, variety and velocity of social data and the unprecedented reach of the social web amplify these problems to new proportions. however, those very characteristics of social data present previously unavailable opportunities for disease surveillance. for instance, for most social platforms such as social networking sites and microblogs this data is unsolicited essentially making the social web a self-updating database. this effectively eliminates the need for elaborate data collection infrastructure as data gathering is simply a matter of implementing the relevant social api and is essentially external to the surveillance process. social apis further simplify additional tasks like geolocation and information filtering which would otherwise extremely complex to operationalize with traditional approaches. furthermore many of these pitfalls like geo-spatial gaps in data and unrepresented demographics are effectively solving themselves as a consequence of contemporary technology and social trends for instance more and more people are accessing mobile computing devices and connecting to the internet [97,98]. provided data exists in the form of signal data like search engine queries and social media messages and target data like ilinet statistics, web based techniques have been shown to have a wide range of applicability. promising results have been obtained for most disease categories by mode of transmission. these include vector borne diseases [11], airborne diseases [16-19], food borne diseases design choices for automated disease surveillance in the social web online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e214, 2018 ojphi [63,64,99], contagious diseases [69], water borne diseases [62], sexually transmitted diseases [71], non communicable diseases [100] and even psychiatric conditions like suicide risk [101]. however, data remains a huge challenge. whereas the social web alleviates many of the infrastructural burdens related with traditional formal systems it still requires target data for model development and validation. unfortunately, in many contexts electronic health reporting is poorly developed making it impossible to build predictive models or validate the efficacy of social data for monitoring purposes. in addition, as noted earlier access to data like server search logs is usually heavily restricted therefore limiting the pool of researchers and systems that can employ it to those with high level organizational clearance. regardless, major strides have been made with the little open data available and based on the enormous research interest and the evidence from the few large scale deployments we anticipate that many more stakeholders 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from:http://doi.acm.org/10.1145/2527031.2527049. 107. tseng kc, lin bs, liao ld, wang yt, wang yl. 2014. development of a wearable mobile electrocardiogram monitoring system by using novel dry foam electrodes. ieee syst j. 8(3), 900-06. https://doi.org/10.1109/jsyst.2013.2260620 1a population of populations, it comprises members of the same species and a degree of separation is assumed between the different sub populations. some level of interaction occurs as a result of some individuals moving between different subpopulations. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26559683&dopt=abstract https://doi.org/10.1098/rsif.2015.0536 https://doi.org/10.1109/jsyst.2013.2260620 design choices for automated disease surveillance in the social web abstract: introduction design choices for automated disease surveillance systems and studies on the social web user participation model language parsing methodology multiplicity of data sources number of diseases monitored predictive modeling, real-time monitoring and explanatory modeling system deployment options summary limitations of automated disease surveillance systems and studies on the social web geo-spatial gaps in data and inconsistent location resolution of data points data biases accessibility of data and availability of language resources overfitting conclusion references ojphi-06-e119.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 (page number not for citation purposes) isds 2013 conference abstracts a probabilistic case-finding algorithm for chronic disease surveillance stephanie brien*1, luke mondor1, nancy mayo2 and david buckeridge1 1mcgill university, montreal, qc, canada; 2mcgill university health center, montreal, qc, canada � �� �� �� � � �� �� �� � objective ��������� � ���� ��� �� ���� �� �� ���� ��� ����� ��� ����� ������� �� �� ����� ��������� �� �������� �������������������� �� �������� �� ����� � introduction ��������� ���� ������������� �������������� ������������������� ���� �������� �������� � �� � � ���� ��� ������������������� ����������� � ����� �� ������ ������ ������ � ����� ������ �� �������� ����� ���� ���� �������� ���������� ������� ���� ��� ��� ������ �� ��� ������� ��������� ������ ����� �� ���� ����� ��������� ������������� �� ����� ��������� ���� �������!���� ��� �� �����������"#���$%��#$����&��� �' � ���� ��������������� ����� ������������������ ������(������ � ��� � ��� ���������� � ��) ������� *�� ������ ������ ��*� ���� *�� �� ���*� �� ��� ���� ����������������+����� ���� �� ��� ����������� � ����� �� ���� ����� �������� � ��� ���* ����� �� ������ ������&��� �' � ������������ � ����� ��������� ��������������� *��� �� ����� 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attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 82 (page number not for citation purposes) isds 2013 conference abstracts effect of electronic health record systems access on communicable disease report completeness uzay kirbiyik*1, 2, brian e. dixon1, 3 and shaun j. grannis1, 4 1regenstrief institute, indianapolis, in, usa; 2richard m. fairbanks school of public health at iupui, indianapolis, in, usa; 3indiana university, school of informatics and computing, indianapolis, in, usa; 4indiana university, school of medicine, indianapolis, in, usa � �� �� �� � � �� �� �� � objective ����������� ��� ����� ������ ����� ���� � ��� ������������������ ������������ ������������������������������������� introduction ���������� ����������������� ��������� ���� ������ ���������������� � ��������!��������������� ��������������� 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���������������� ������ keywords %�������������������� ��� ��3�����������3������������������ acknowledgments ,��� ��$�� ����� ����-%���� � �� �� � ����#�� �� ��� ���� � ��� ���� �!� ����#�������� ������� ��������� ��5(�.5)5;5;� ���� ��4 ����� � � ����� �� ������� � �����b��������� ��������������������� �� �������������� � �� ����� � ���������������������� ���� �� ������� ��� ���������#��� � 4��b� references (�� ��+���7�"�-�c�#���66"�c ������.6������� ��������� ��� �������&���� ��������� ��������� ��� ���� ��� � ��������+� �� ���������� ��������� ���� � ��� ��� �� ���������4-d4�4����.����� ����)5((3)5((0:))� :5� online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e132, 2014 isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 83 (page number not for citation purposes) isds 2013 conference abstracts )�� ������� � ���� ���"� �������"��������"�������������"���������� ���������� ����������������������������������� ���������������� � ����)5((3(<0):�;� :�� ������� ��"�!�"����#�"� ����$��"���"���������%���� ���� #��%����� ��������������&��� #'��������%�������������������������������������� ����� ���(���������������&��� #���������)5(:3)�(�0��*�#��%)�(�/))< *uzay kirbiyik e-mail: ukirbiyi@iupui.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e132, 2014 tracking covid-19 burden in india: a review using smaart rapid tracker 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi tracking covid-19 burden in india: a review using smaart rapid tracker ashish joshi1*, harpreet kaur2, l. nandini krishna2, shruti sharma2, gautam sharda3, garima lohra2, ashruti bhatt2, ashoo grover4 1cuny graduate school of public health and health policy, new york, usa 2foundation of healthcare technologies society, new delhi, india 3iowa city west high school, iowa, usa 4indian council of medical research, new delhi, india abstract objective: india has seen a rapid rise in covid-19 cases. examine spatiotemporal variation of covid-19 burden tracker across indian states and union territories using smaart rapid tracker. method: we used smaart rapid tracker to visually display covid-19 spread in space and time across various states and uts of india. data gathered from publicly available government information sources. data analysis on covid-19 conducted from march 1 2020 to october 1 2020. variables recorded include covid-19 cases and fatality, 7-day average change, recovery rate, labs and tests. spatial and temporal trends of covid-19 spread across indian states and uts is presented. result: the total number of covid-19 cases were 63, 12,584 and total fatality was 86,821 (october 1 2020). more than 85,000 new cases of covid-19 were reported. there were 1,867 total covid19 labs throughout india. more than half of them were government labs. the total number of covid-19 tests was 76,717,728 and total recovered covid-19 cases was 5,273,201. results show an overall decline in the 7-day average change of new covid-19 cases and new covid-19 fatality. states such as maharashtra, chandigarh, puducherry, goa, karnataka and andhra pradesh continue to have high covid-19 infectivity rate. discussion: findings highlight need for both national guidelines combined with state specific recommendations to help manage the spread of covd-19. conclusion: the heterogeneity represented in india in terms of its geography and various population groups highlight the need of state specific approach to monitor and combat the ongoing pandemic. this would further facilitate the tailored approach for each state to mitigate and contain the spread of the disease. keywords: covid-19, india, spatiotemporal, infectivity rate, rate of change abbreviations: union territories (ut) correspondence: * ashish.joshi@sph.cuny.edu doi: 10.5210/ojphi.v13i1.11456 copyright ©2021 the author(s) mailto:ashish.joshi@sph.cuny.edu tracking covid-19 burden in india: a review using smaart rapid tracker 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi introduction the recently discovered sars-cov-2 originated in the month of december 2019 in wuhan, china, and has since caused catastrophic damages globally after being declared as a pandemic. it has spread rapidly and globally since its advent via direct or indirect contact with infected surfaces or people [1]. the infection can be asymptomatic or can primarily cause fever, dry cough and tiredness along with difficulty in breathing, body aches, diarrhoea and loss of taste or smell [2]. as of 1st october 2020, the worldwide total number of covid-19 cases stand at a staggering 33,842, 281 with usa (total cases 3741406, fatality 208433), india (total cases 6685082, fatality 103569) and brazil (total cases 4915289, fatality 146352) among the top three countries with the covid-19 infection [3]. since no vaccine or pharmaceutical drugs are available to combat covid-19, therefore, to control infections, prevent spread of the virus and reduce mortality, the governments across the globe issued various advisories, and policies and various non-pharmaceutical interventions. these interventions included home isolation, voluntary quarantine, and closure of educational institutions, lockdowns, and guidance on hygiene [4]. however, the cases continued to surge, especially in lowand middle-income countries (lmics), where the population is already at risk due to inequitable access to the health services in resource-poor settings [5]. india is the second most populated country with a population of around 1.38 billion and more than half of its population residing in rural areas [6]. the country is categorised into six zones namely – north, north-east, central, east, west and south. there are total 28 states and 8 union territories in india (figure 1) [7]. all these states and union territories have official abbreviations consisting of two letters and these abbreviations have been used in this study to present the data in a simpler form [8]. india reported its first case on 30 january 2020, the same day when who declared covid-19 as public health emergency of international concern. the case was linked, to an indian medical student who returned to kerala from wuhan. the state reported 2 more cases within a few days and the first death due to coronavirus in india was reported on 12 march 2020 in karnataka. by the time the situation was declared a pandemic by who on 11 march 2020, around 11 states and union territories of india had been affected by the novel coronavirus. although the government took swift actions to curb and contain the spread of the virus, but, despite of the early precautionary steps by the government, india had a growing outbreak with more than 6.3 million cases (as of 1st october, 2020) across its states and union territories [9]. as of 1st october 2020, the total reported deaths due to covid-19 in india were 102,685 [9,10]. with the alarming rise of covid-19 cases, the government of india imposed a nation-wide lockdown under the disaster management act 2005, on 24 march 2020 for 21 days, and subsequently implemented 3 more lockdowns till the end of may with increasing relaxations in each. india’s immediate response to the surge of cases has been one of the strictest measures as compared to other countries [11] and was also lauded by who. the non-pharmaceutical interventions (npis) in these lockdowns included travel ban for both inter-district and inter-state movements, functioning of only essential services, closing of all educational institutes, ban on public gatherings, guidance and advisories on voluntary quarantine, and home isolation. however, these npi’s also posed serious repercussions, not this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. tracking covid-19 burden in india: a review using smaart rapid tracker 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi only on the economy but, also social well-being and health of the people. the downwardly spiralling economy and unemployment has further triggered the pattern of the biggest reverse migration in the indian history. in a country like india, where the wage labourers were dependent on daily incomes, they suddenly found themselves with no job prospects which led to this migration stream. whether the lockdown was successful is a debatable issue but, it has certainly helped in slowing down the spread of infection [12]. currently where there are no recommended drugs and vaccines available to control the transmission of covid-19, mathematical estimation can help in predicting its control [13]. a study reported that the aggressive containment measures to control transmission, might have helped in reducing the ro (reproduction number), but a proper and rapid surveillance system is necessary, along with increased testing and devising strategies to control the asymptomatic cases [14]. results of one of the previous studies showed that if the lockdown was implemented seamlessly, then it may possibly have helped the containment of the spread of cases and would have been the perfect time for the central and state governments to strengthen their pandemic responsiveness and disease surveillance system [15]. the rise in the number of reported cases in india can also be attributed to increased covid-19 testing by the government. initially, there was only 1 lab in pune which has now increased to a total of 1869 labs, with over 58% (1101) owned by the government (as of 1 october, 2020) [16]. the variety of diagnostic tests have also been made available with rt-pcr, true nat, cbnaat, rapid antigen testing and rapid antibody testing [17]. the objective of this study was to track spatiotemporal variations in the spread of covd-19 across different regions of india using an internet enabled, interactive population health informatics platform smaart rapid tracker. the tracker generates covid-19 trends and insights to inform stakeholders about the spread of covid-19 virus across space and time. tracking covid-19 burden in india: a review using smaart rapid tracker 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi smaart rapid tracker visualization empowers laypeople and professionals to better track global pandemic. comparing disease’s spatiotemporal variation across different regions of india is of great interest as different states have responded differently to the covid-19 pandemic. smaart rapidtracker, a research enabled action oriented policy interventions driven by data, is an innovative policy informatics tool, aimed to track geospatial spread of covid-19 outbreak and policy actions globally. the dashboard aggregates publicly available but verified information on the burden of covid-19. there are four key modules of smaart rapid tracker including data module, policy module, digital resource and insights module. data module aims to aggregate covid-19 data and provides users an opportunity to explore, compare, sort, and rank covid-19 related data across different geographic settings (figure2). variables recorded in smaart rapid tracker include covid-19 total and new cases, total and new fatality, recovered cases, availability of lab centres and number of covid-19 tests data. information on recovery cases of covid-19 is also gathered. map source: https://mea.gov.in/india-at-glance.htm [18] (accessed on february 17, 2021) zonal classification source: http://interstatecouncil.nic.in/composition-2/ [19] (accessed on february 17, 2021) https://mea.gov.in/india-at-glance.htm http://interstatecouncil.nic.in/composition-2/ tracking covid-19 burden in india: a review using smaart rapid tracker 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure2. smaart rapid tracker modules data sources module indicates the sources of data from where information if gathered. digital resource module is a repository of evidence based digital covid-19 resources such as digital apps aimed to address the burden of covid-19 and insights module generates meaningful trends and present findings in an interactive format using maps, chart and graphs. smaart rapid tracker is designed and developed using smaart informatics framework that gathers multi-faceted, multi-level, and multi-dimensional data and analyses that data into meaningful information that is contextually relevant. new knowledge generated through use of interactive visualizations in the form of maps, charts and graphs can be leveraged to guide data driven, evidence-based decision making. methodology the smaart rapid tracker dashboard aggregates globally publicly available but verified information on the burden of covid-19. in this study, we analyse covid-19 data collected for all the indian states and union territories. this includes the total and new cases of covid19, and total and new cases of covid-19 per million, total and new fatality, and total and new fatality cases of covid-19 per million, daily and 7 day change of covid-19. further data was recorded on the covid-19 lab facilities and its testing data across each state and ut of india. the total lab facilities specified as covid-19 testing facility centres, were first categorized according to the geographical location in the state/union territory and then further categorized as per the district’s distribution within those states/ union territories. the proportion of lab facilities conducting covid-19 detection in each location, helps us realize the testing capacity in each area of the country. information was also recorded on when the first covid-19 case and fatality was reported. we also assessed infectivity rate by calculating: infectivity rate = total covid-19 cases/total reported covid-19 tests*100 [20]. each region’s infectivity rate was calculated and compared across different regions of india. we calculated time lapse between the first case and first death due to covid-19. it is the time period or the duration between the first reported covid-19 case and the first death due to covid-19. for each state and union territory, this duration was calculated and expressed in the form of total number of days. we assess the date of the index case and then count all the days till the date of the first covid-19 specific death. tracking covid-19 burden in india: a review using smaart rapid tracker 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi time lapse (number of days) = first reported covid-19 casefirst reported covid-19 fatality time lapse would help inform the distribution pattern of the covid-19 across different regions of india. to understand the transmission dynamics of covid-19, the total confirmed cases and deaths due to covid-19 were collected for each month, starting from march 2020 to september 2020 for all the states and union territories. all the data was obtained from publicly available domains of the ministry of health and family welfare, indian council of medical research, state and union territory’s health department, and the smaart rapid tracker website mainly (smaartrapidtracker.org). all data has been analysed till october1 2020. statistical analysis: we used smaart rapid tracker (smaartrapidtracker.org) to visually display the descriptive analysis of total number of cases, and fatality, cases and fatality per million, number of covid19 tests conducted, and the number of labs available in each states. descriptive analysis was also performed to report infectivity rate across different regions of india. states and uts were stratified based on infectivity rate of covid-19. smaart rapid tracker was used to display 7-day average change of new covid-19 cases across different states and uts of india. results india, 7th largest country in the world consists of 28 states and 8 union territories. according to the provisional results of the 2011 census, the literacy rate in the country stands at 74%. as per census 2011, while 28.5% population of india lies between 0-14 age group, only 8.3% are above the age of 60 years in the country. as of oct1 2020, the total number of covid-19 cases reported were 63, 12,584, and 5,216 cases per million (figure3). more than 85,000 new cases of covid-19 were reported and 72 new cases per million as of oct1 2020. as of oct1 2020, the total fatality reported in india was 86, 821. there were 1,867 total covid-19 labs throughout india. more than half of them were government labs (59%; n=1101). the total number of covid-19 tests reported as of oct 1 2020 was 76,717,728 and total recovered covid-19 cases was 5,273,201 (figure3). tracking covid-19 burden in india: a review using smaart rapid tracker 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure3. status of covid-19 in india (as of oct 1 2020). source: smaartrapidtracker.org (last accessed oct 22 2020) results show an overall decline in the 7-day average change of new covid-19 cases and new covid-19 fatality in india as of october 1 2020. (figure4a and 4b). as of october 1 2020, 7day average change of new covid-19 cases was 0.89% as compared to 4.13% on sep1 2020 (figure4a). similarly, the 7-day average change of new covid-19 fatality was 3.51% on oct1 2020 as compared to -22.66% on sep1 2020 (figure4b). number of covid-19 recovered cases per million has increased more than 50% as of oct 1 2020 (n=4357/million) compared to sep1 2020 (n=2347 per million). figure4a. 7-day average change of new covid-19 cases (april1 2020-oct1 2020) source: smaartrapidtracker.org (last accessed oct 22 2020) tracking covid-19 burden in india: a review using smaart rapid tracker 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure4b. 7-day average change of new covid-19 fatality (sep1 2020-oct1 2020) source: smaartrapidtracker.org (last accessed oct 22 2020) in india, kerala was the first state to report covid-19 case, whereas karnataka was the first state to report covid-19 related fatality. mizoram and lakshadweep islands have not reported any covid-19 fatality. however, states and uts that have reported covid-19 fatality within days of a reported covid-19 case included north eastern state of meghalaya (n=2days), southern state of karnataka (n=4 days), and an eastern state bihar (n=7 days). similarly, the southern ut andaman & nicobar islands (n=123 days) and north eastern state manipur (n=127 days) reported their first fatality after 3 months of their first reported case of covid19. results showed that by the end of the march 2020, nearly all of india (28 states and 8 union territories) had been affected by covid-19 cases (figure5). tracking covid-19 burden in india: a review using smaart rapid tracker 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure5. number of day’s difference between first reported covid-19 fatality and first case. • overall covid-19 case and fatality across india total covid-19 cases in south india were highest (n=2,281, 754) as of october 1 2020 (table2). this attributed nearly 37% of the total number of covid-19 cases in india, followed by west zone (27%) and north zone (17%) however, the total number of cases per million was highest in north india (n=82,965 per million) followed by south (n=63,438 per million) and west (44,008 per million) (table1). central zone showed the lowest number of total covid-19 cases per million (n=6069 per million). highest total covid-19 fatality was seen in west zone (n=41512) while northeast zone (n=1137 showed the lowest covid-19 fatality (table1). highest fatality per million was seen in north india (n=1282 per million) compared to central india (n=67 per million). (table1). tracking covid-19 burden in india: a review using smaart rapid tracker 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi table1. total covid-19 cases, cases per million, fatality and fatality per million comparison across different regions of india. (october 1 2020) indicators regions of india north north-east central east west south total cases 1063427 239680 236698 733269 1670899 2281754 total fatality 17904 1137 3197 7321 41512 26426 total covid-19 tests 20015123 4813757 3185406 16244588 14816225 24606962 total covid-19 labs 466 107 137 249 297 611 infectivity rate 5.31 4.98 7.43 4.51 11.28 9.27 total cases per million 82965 35099 6069 12170 44008 63438 total fatality per million 1282 216 67 104 690 919 source: smaart rapid tracker (www.smaaartrapidtracker.org) • covid-19 labs and testing data highest number of covid-19 labs were reported from south india, followed by north and west. number of covid-19 government labs were higher across all regions of india except south where private labs were greater in number compared to government labs. (figure6). figure6. number of covid-19 labs across different zones of the total cvoid-19 tests conducted in india (n=83682061), south (29%; n=24606962) and north (24%; n=20015123) regions of india attributed to the highest number of covid-19 tests. the central region of india had the lowest covid-test (3.81%; n=3185406), followed by north-east (5.75%; n=4813757) and east zone (19.41%; n=16244588). as of october 1 2020, in india infectivity rate was about 7.4% with nearly 6,22,5727 cases of covid-19 of the 83682061 reported covid-19 tests. covid-19 analysis by zone: the total covid-19 cases and fatality varied across different states within each zone. similar geographic variations were seen for covid-19 labs, tests and infectivity rate due to covid-19. a. covid-19 burden north zone tracking covid-19 burden in india: a review using smaart rapid tracker 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi • total covid-19 cases and fatality burden: total covid-19 cases were highest in the state of uttar pradesh (n=394,856). similarly, uttar pradesh also had the highest cvid-19 fatality (n=5,715) followed by delhi (n=5320). lowest total covid-19 fatality was seen in leh-ladakh (n=58), and himachal pradesh (n=183). majority of the states and ut have in the north india have shown decline in 7-day average change of new cases of covid-19 except leh-ladakh and uttarakhand (figure7b). results show that uttar pradesh surpassed delhi in total covid-19 cases around august 17 2020 (figure7a) and around sep 20 2020 for total covid-19 fatality (figure 7c). punjab is another state showing increase in total covid-19 fatality. • covid-19 labs and testing data: the state of uttar pradesh had highest number of covid-19 labs in the north zone (n=197). nearly 41% (n=122) of them were government labs. highest covid-19 tests were reported from the state of uttar pradesh (n=10,263, 709) attributing more than half of the total tests (51%) conducted in the entire north zone. • covid-19 infectivity rate: overall covid-19 infectivity rate (number of positive cases of covid-19/number of total tests reported) was 5.31% (as of oct 1 2020). chandigarh seemed to have highest covid-19 infectivity rate (15%), followed by delhi (8.81%), leh-ladakh (7.8%), uttarakhand (6.66%), haryana (6.5%) and punjab (n=6%). uttar pradesh had the lowest infectivity rate of 3.85% among the states in the north india. figure7a. covid-19 total number of cases north india source: smaartrapidtracker.org (october 1 2020). tracking covid-19 burden in india: a review using smaart rapid tracker 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure7b. 7day average change of new covid-19 cases north india source: smaartrapidtracker.org (october 1 2020). figure7c. covid-19 total number of fatality north india source: smaartrapidtracker.org (october 1 2020) b. covid-19 burden south zone • total covid-19 cases and fatality: results showed the highest number of total covid-19 cases in the state of andhra pradesh (n=687351), karnataka (n=592911) and tamil nadu (n=591943) (figue8a). more than half of the total cases of covid-19 in the south zone were attributed to the cases in the states of andhra pradesh (30%) and karnataka (26%). andaman and nicobar islands (n=3821) reported the lowest total covid-19 cases while lakshadweep islands reported no covid-19 case. results showed decline in 7 day average change of new covid-19 cases across majority of the states in south india except kerala (39.63%) and karnataka (14.86%) that showed an increase (figure8b). however, highest total covid-19 fatality was seen in tamil nadu (n=9,453) and karnataka (n=8,777). both of these states attributed more than 69% of the total covid-9 deaths in the south zone (figure8c). • covid-19 tests and lab centres: the number of covid-19 labs was highest in tamil nadu (n=188) followed by karnataka (n=144). andhra tracking covid-19 burden in india: a review using smaart rapid tracker 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi pradesh (n=96) had the highest covid-19 government labs (n=77) as compared to the states of karnataka (n= 53) and tamil nadu (n=66). the number of covid19 tests was highest in the state of tamil nadu (n=744, 1697). • covid-19 infectivity rate: states such as puducherry (13.9%), karnataka (11.86%) and andhra pradesh (11.69%) were the 3 southern states reporting highest infectivity rate. states such as kerala (6.27%) and telangana (6.27%) reported the lowest infectivity rate among the southern states of india. figure8a. covid-19 total number of cases south india source: smaartrapidtracker.org (october 1 2020). figure8b. 7day average change of new covid-19 cases south india source: smaartrapidtracker.org (october 1 2020). tracking covid-19 burden in india: a review using smaart rapid tracker 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure8c. covid-19 total number of fatality south india source: smaartrapidtracker.org (october 1 2020) covid-19 burden east zone • total covid-19 case and fatality: results showed highest number of total covid-19 cases in the state of west bengal (n=253, 768), followed by odisha (n=215676) and bihar (n=181285). jharkhand (n=82540) had reported the lowest total covid-19 cases (figure9a). an increase in the 7-day average change of new covid-19 cases was seen in bihar (18.57%) and west bengal (2.66%) (figure9b). more than half of the fatality in the east zone was attributed to west bengal (67%; n=4899). lowest fatality was reported in the state of jharkhand (9.6%) (figure9c). • covid-19 tests and lab centres: though the highest covid-19 cases and fatality was in west bengal and odisha, covid-19 testing was highest in the state of bihar (n=7,386, 521) followed by odisha (n=3, 300, 644) and west bengal (n=3271316). of the total covid-19 labs in the east zone, 41% (n=101) of them were in the state of west bengal of which majority of them were the government labs. • covid-19 infectivity rate: infectivity was lowest in bihar (2.45%) while west bengal had highest infectivity (7.76%), followed by odisha (6.5%) and jharkhand (3.61%). figure9a. covid-19 total number of covid-19 cases east india source: smaartrapidtracker.org (october 1 2020) tracking covid-19 burden in india: a review using smaart rapid tracker 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure9b. 7 day average change of new covid-19 cases south india source: smaartrapidtracker.org (october 1 2020) figure9c. covid-19 total number of fatality east india source: smaartrapidtracker.org (october 1 2020) covid-19 burden central zone • total covid-19 case and fatality: total covid-19 cases in the state of madhya pradesh (n=126043) were higher compared to the state of chhattisgarh (n=110,655) (figure10a). however, as of oct 1 2020, there was a 30% increase in the 7-day average change of new covid-19 cases in the state of chhattisgarh as compared to madhya pradesh that showed a decline of 13% (figure10b). similarly, covid-19 fatality was higher in the state of madhya pradesh (n=2281) compared to chhattisgarh (n=916). madhya pradesh attributed nearly 71% of the covid-19 fatality in the central zone. (figure10c). • covid-19 tests and lab centres: covid-19 testing was higher in the state of madhya pradesh (n=2063765) compared to chhattisgarh (n=1,121, 641). the total number of covid-19 labs was higher in the state of madhya pradesh tracking covid-19 burden in india: a review using smaart rapid tracker 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi (n=97) compared to chhattisgarh (n=40) and majority of these states had government covid-19 labs. • covid-19 infectivity rate: chhattisgarh had a greater infectivity rate (9.87%) compared to madhya pradesh (6.11%). figure10a. covid-19 total number of covid-19 cases central india source: smaartrapidtracker.org (october 1 2020) figure10b. 7-day average change of new covid-19 cases central india source: smaartrapidtracker.org (october 1 2020) tracking covid-19 burden in india: a review using smaart rapid tracker 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure10c. covid-19 total number of fatality central india source: smaartrapidtracker.org (october 1 2020) covid-19 burden west zone • total covid-19 case and fatality: total covid-19 cases in the state of maharashtra (n=1366129) were highest compared to the total number of covid-19 cases in the state of gujarat (n=135842) and rajasthan (n=133119). lowest cases were seen in goa and dadra and nagar haveli and daman and diu (figure11a). the 7-day average change of new covid-19 cases showed an increase in covid-19 cases only in rajasthan while all other states showed decline (figure11b). covid-19 fatality was reported highest in the state of maharashtra (n=36181) contributing 87% to the total fatality occurring in the west zone of india. goa (1%) and rajasthan (4%) had the lowest covid-19 fatality (figure11c). • covid-19 tests and lab centres: covid-19 testing was highest in the state of maharashtra (n=6, 87, 5451) followed by gujarat (n=4, 47, 4766) and rajasthan (n=3, 14, 3572). similarly, the number of total covid-19 labs was also highest in the state of maharashtra (n=172) followed by gujarat (n=72) and rajasthan (n=45). fifty percent of the labs in maharashtra were government labs. • covid-19 infectivity rate: goa with lowest number of total cases and fatality showed higher covid-19 infectivity (12.76%) compared to gujarat (3.04%) and rajasthan (4.23%) as of october 1 2020. however, maharashtra had the highest infectivity rate (20%) compared to all other states in india. figure11a. covid-19 total number of cases west india source: smaartrapidtracker.org (october 1 2020) tracking covid-19 burden in india: a review using smaart rapid tracker 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure11b. 7-day average change of new covid-19 cases west india source: smaartrapidtracker.org (october 1 2020) figure11c. covid-19 total number of fatality west india source: smaartrapidtracker.org (october 1 2020) covid-19 burden north east zone • total covid-19 case and fatality: results show highest covid-19 cases in the state of assam (n=177221) compared to other north eastern states (figure12a). assam (73.94%) and tripura (10.74%) contributed towards greater burden of covid-19 cases in north east india. (figure12a) 7-day average change of new cases of covid-19 showed decline in these cases only in 3 of the north-eastern states including tripura, arunachal and mizoram (figure12b). nagaland, assam and meghalaya showed the highest increase in the 7-day average change of new covi-9 cases. more than half of the covid-19 fatality was in assam (59%; n=680) followed by tripura (24%; n=277), manipur (5.7%; n=65) and tracking covid-19 burden in india: a review using smaart rapid tracker 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi meghalaya (4.13%; n=47). no covid-19 fatality has been reported in mizoram as of october 1 2020 (figure12c). • covid-19 tests and lab centres: covid-19 testing was highest in the state of assam (n=3563210) compared to tripura (n=391684), manipur (n=243870) and meghalaya (n=153177). number of total covid-19 labs was highest in the state of assam (n=35) with more than half of them being the private labs (57%; n=20). • covid-19 infectivity: nagaland (7.51%), tripura (6.57%) and sikkim (5.8%) are some of the common north-eastern states with high covid-19 infectivity rate as of oct1 2020. mizoram (2.49%), meghalaya (3.57%) and arunachal pradesh (3.81%) reported lowest covid-19 infectivity rate. though assam had the highest number of covid-19 cases and fatality, the infectivity rate was 4.9%. figure12a. covid-19 total number of cases north east india source: smaartrapidtracker.org (october 1 2020) figure12b. 7 days average change of new covid-19 cases north eastern india source: smaartrapidtracker.org (october 1 2020) tracking covid-19 burden in india: a review using smaart rapid tracker 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi figure12c. covid-19 total number of fatality north eastern india source: smaartrapidtracker.org (october 1 2020) sub-analysis we stratified the states/uts by covid-19 infectivity rate into three categories including: group1 having an infectivity rate <=5.99%; group2 having an infectivity rate between 6.00% to ≤10.00% and group 3 having an infectivity rate greater than 10% (table2). forty percent of the states/uts were in group1with an infectivity rate <=5.99%. more than half of the states (64%; n=9) were from north-east and west parts of india followed by 21% (n=3) from north india and 14% (n=2) from east india. none of the states /uts were from either south or central parts of india. highest infectivity rate was seen in west india including maharashtra (ir=19.87%), followed by north indian state of chandigarh (15.07%). lakshadweep island was one of the only ut in south india that had zero infectivity rate due to covid-19. forty three percent (n=15) of the indian states and uts were in group 2 with an infectivity rate of 6.00% to ≤10.00%. more than half of the states/uts in group2 were from north (33%) and south (26%), while 13% of them were states/uts from east, central and north-east parts of india. none of the states from west india were in group2. seventeen percent of the states and uts were in group3 with highest infectivity rate of greater than 10%. fifty percent of these states were from south india including andhra pradesh (11.69%), karnataka (11.86%) and puducherry (12.76%). tracking covid-19 burden in india: a review using smaart rapid tracker 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi table2. states/uts stratified by infectivity rates into 3 groups (as of october 1 2020) states/uts of india (n=36) group1 infectivity rate <=5.99%. group2 infectivity rate between 6.00% ≤10.00% group3 infectivity rate > 10% bihar (east) punjab (north) andhra pradesh (south) mizoram (north eastern) madhya pradesh (central) karnataka (south) gujarat (west) kerala (south) goa (west) meghalaya (north eastern) telangana (south) puducherry (south) jharkhand (east) andaman and nicobar islands (south) chandigarh (north) arunachal pradesh (north eastern) haryana (north) maharashtra (west) uttar pradesh (north) odisha (east) rajasthan (west) tripura (north eastern) manipur (north-eastern) uttarakhand (north) jammu and kashmir (north) nagaland (north eastern) dadra and nagar haveli and daman and diu (west) west bengal (east) himachal pradesh (north) lehladakh (north) results of the study show spatiotemporal covid-19 trends across different parts of india that help us inform data driven, evidence-based policy making. the geographic variation in the covid-19 cases and fatality will help us explore factors that are influencing the spread of the virus and help respond accordingly. high infective rates across different southern states of india have been observed. monitoring spread of the covid-19 virus will help policy makers take comprehensive and necessary measures sufficient to take control of the disease. discussion india has the second highest population after china which makes it more vulnerable to covid19. the first case of covid 19 was reported in india on january 29 when a kerala student returned from wuhan, china. the first 100 covid-19 cases were reached by march15 2020 and the first 1000 covid-19 cases by march 31 2020. as of october 1 2020, india had a total of 6225763 covid-19 cases and fatality of 98,678. our study outlines spatiotemporal covid-19 trends to examine the extent of states affected due to covid-19. as of october 1 2020, the states/union territory with the highest number of total covid-19 cases was maharashtra (n=1366129) and with a total of 36181 covid-19 specific deaths. the state had recorded the highest fatality in india. results show an overall decline in the 7-day average change of new covid-19 cases and new covid-19 fatality in india as of october 1 2020. tracking covid-19 burden in india: a review using smaart rapid tracker 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi results show greater total covid-19 cases in south india, but total fatality was higher in west india attributed primarily to the state of maharashtra. higher infectivity rate across several states such as maharashtra (19.87%), chandigarh (15.07%), puducherry (13.7%), and goa (12.76%) highlight need of measures such as social distancing, wearing masks, and hand washing. further it also highlights the importance to prepare healthcare facilities with essential medical and other equipment supplies. the circumstances in covid19 is highly uncertain, and hence it becomes challenging to envisage the course of the novel coronavirus. india is a vast country, and most of the indian states are quite large in the geographic area and population. analysing coronavirus infection data, considering entire india to be on the same page, may not provide us the right picture. this is so because the total cases, new infectionrate, progression over time, and preventive measures taken by state governments and the common public for each state are different. each state needs to be tackled differently and will enable the government to utilize the limited available resources in an effective manner. smaart rapid tracker displays visual trends in infection rates and can provide us whether the covid-19 is under control or not in a specific state. there are many states like maharashtra, chandigarh, puducherry, goa, karnataka, and andhra pradesh with very high infectivity rates are already at high risk. similarly, other states such as chhattisgarh, delhi, tamil nadu and west bengal are the states that may see a huge jump in confirmed covid-19 cases if preventive measures are not implemented properly. we examine covid-19 infection in each state and union territory as of october 1 2020. total cases and fatality per million are also represented for better comparison purposes. results visualize the snapshot of the pandemic on 1st october throughout the country and also presents the total number of covid-19 tests conducted for each state and the total lab facilities as on 1st october 2020. we also examined the onset of first covid-19 fatality after first reported case of covid-19. results showed variation in the number of days when first fatality due to covid-19 was reported after first reported case of covid-19. states and uts such as meghalaya (n=2days), karnataka (n=4 days), and bihar (n=7 days) showed earlier onset of covid-19 fatality. however, other states and uts such as andaman & nicobar islands (n=123 days) and manipur (n=127 days) reported their first fatality nearly after 3 months of their first reported case of covid-19. the approaches to addressing the states must be different due to limited resources. policy makers need to identify early at risk infected-clusters as quickly as possible. india due to its rich diversity and vast population, would need to look at each of the states individually to decide further actions to contain the spread of the disease, which can be crucial for the specific states only. almost all the nations including india have been greatly impacted and attempts to control and expend efforts on containment of covid-19 are ongoing. future work should incorporate additional data elements such as clinical data, covid-19 hospitalization and treatment data combined with social determinants of health data such as education, income, healthcare access to design and develop a more robust and highly responsive surveillance systems to have better ability to track the spread of covid-19 and its impact in years to come. limitations although the present study focuses on examining the spatiotemporal variation of covid-19 burden across indian states and union territories using smaart rapid tracker, but the data tracking covid-19 burden in india: a review using smaart rapid tracker 23 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e4, 2021 ojphi from the smaart rapid tracker may be used for a comprehensive inter 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[cited 2021feb17]. available from: http://interstatecouncil.nic.in/composition-2/ 20. barreto ml, teixeira mg, carmo eh. 2006. infectious diseases epidemiology. j epidemiol community health. 60(3), 192-95. doi:https://doi.org/10.1136/jech.2003.011593. pubmed https://doi.org/10.1016/j.chaos.2020.110173 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32834653&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32607504&dopt=abstract https://doi.org/10.1136/jech.2003.011593 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16476746&dopt=abstract layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts multi-dimensional problems in health settings: a review of approaches to decision making victor j. del rio vilas*1, gilberto montibeller2, l. alberto franco3 and willy aspinall4 1paho, rio de janeiro, brazil; 2london school of economics, london, united kingdom; 3university of hull, hull, united kingdom; 4university of bristol, bristol, united kingdom introduction there appears to be a growing number of prioritization exercises, for example of diseases, in health related settings (1). the decision process around these exercises involves comparing competing alternatives, i.e. diseases, and irreducible objectives. in addition to the multi-dimensional nature of the problem, the lack of reliable data, group dynamics associated to the involvement of experts, and the multiplicity of stakeholders, among other contextual factors, add complexity to the decision process. here we review trends in such prioritization exercises and applications in different settings and for different events of interest, for example the management of emerging risks. based on our findings, we discuss a conceptual framework based on multi-attribute utility theory presented to the world organization for animal health (oie) for the modification of its qualitative assessment of veterinary services performance into a quantifiable decision support system. methods we searched pubmed for articles containing the key words ‘multicriteria’, ‘multi-attribute’, ‘multi-objective’, ‘prioritization’, ‘decision making’ and their variations (e.g. without hyphenation) for the period 1990 to 2011 for human and veterinary medicine. we focused on prioritization methodologies and their sound application. results a large number of prioritization efforts in health settings aim to produce a rank order of diseases to help allocation of scarce surveillance and disease control budgets. a number of applications target the prioritization of competing health interventions against specific diseases. fewer target different events, for example emerging threats. common mistakes found in multi-attribute prioritization approaches reported in the social sciences (2) appear also in public and animal health settings. in particular, the application of linear additive models to non-preferentially independent evaluation criteria, the poor design of attributes to assess the decision alternatives, the failure to define suitable criteria scales, and mistakes in defining trade-off weights were prevalent. in addition, most decision support tools tend to be overly complex. this not only compromises their acceptability and long-term sustainability but also increases the likelihood of methodological mistakes in their design and regular application. for example, the failure to properly identify and separate ‘ends’ objectives, such as the improvement of a country’s health, from ‘means’ objectives, i.e. required resources, in the definition of the fundamental drivers in any decision process. conclusions our findings, and experience in the practical application of formal prioritization methodologies (3), informed our advice to the oie for the quantification of its tools for the assessment of veterinary services performance. the current framework used by the oie produces a purely qualitative output with ordinal scales. the suggested quantitative extension allows additional outputs not available in their current form, for example, the aggregation of assessment scores at any level within the framework to produce a country’s overall score. it also permits the assessment of marginal performance improvements for every criterion and the consideration of trade-offs among the different criteria. the final output of our extension is the identification of the best portfolio of actions that will maximize the overall capability of national veterinary services given available resources. quantification of the existing tool will deliver obvious benefits such as enhanced accountability and transparency in the decision making process, and will allow the historical analysis of a country’s veterinary services performance. the approach suggested to the oie is adaptable to similar decision problems, such as monitoring the implementation of the international health regulations in a given country. keywords prioritisation; multi-attribute utility theory; decision support references 1. krause g., 2008. prioritisation of infectious diseases in public health – call for comments. euro surveillance 13, 40. 2. keeney r.l. (2002). common mistakes in making value trade-offs. operations research, 50, 6, 935–945. 3. del rio vilas v.j., voller f., montibeller g., franco a., sribhashyam s., watson e., hartley m., gibbens j. an integrated process and management tools for ranking multiple emerging threats to animal health. preventive veterinary medicine (in press). *victor j. del rio vilas e-mail: vdelriovilas@yahoo.co.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e117, 2013 northwest public health information exchange’s accomplishments in connecting a health information exchange with public health the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 1 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 1 dobbs d, 1 trebatoski m, 2 revere d 1 science applications international corporation, mclean, va 2 center for public health informatics, university of washington, seattle, wa abstract: in 2007 the centers for disease control and prevention (cdc) issued a request for proposal for the “situational awareness through health information exchange” project. the situational awareness project’s goals are to connect public health with health information exchanges (hies) to improve public health’s real-time understanding of communities’ population health and healthcare facility status. during this same time period the health and human services’ office of the national coordinator for health information technology released several reports identifying the growing number of communities involved in health information exchange and outlining the requirements for a nationwide health information network (nhin). cdc saw the possibilities of using hies and the nhin to accelerate the real-time sharing of clinical and facility-based resource utilization information to enhance local, state, regional, and federal public health in responding to and managing potentially catastrophic infectious disease outbreaks and other public health emergencies. hies would provide a unified view of a patient across health care providers and would serve as data collection points for clinical and resource utilization data while nhin services and standards would be used to capture hie data of importance and send those data to public health. this article discusses how automated syndromic surveillance data feeds have proven more stable and representative than existing surveillance data feeds and summarizes other accomplishments of the northwest public health information exchange in its contribution to the advancement of the national agenda for sharing interoperable health information with public health. keywords: data collection; electronic health records; health information exchange; information management; medical record linkage; public health the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 2 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 introduction the situational awareness project’s goals are to connect public health with health information exchanges (hies) to improve public health’s real-time understanding of communities’ population health and healthcare facility status. during this time period (2007) health and human services (hhs) office of the national coordinator for health information technology (onc) released several reports that identified the growing number of communities involved in health information exchange, the requirements for a nationwide health information network (nhin) [1] and a summarization of nhin prototype architectures [2] that included recommendations for a set of nhin services and capabilities. cdc saw the possibilities of using hies and the nhin to accelerate the real-time sharing of clinical and facility-based resource utilization information to enhance local, state, regional, and federal public health in responding to and managing potentially catastrophic infectious disease outbreaks and other public health emergencies. hies would provide a unified view of a patient across health care providers and would serve as data collection points for clinical and resource utilization data. nhin services and standards would be used to capture hie data of importance and send those data to public health. science applications international corporation (saic) conducted a thorough search process to find suitable hies and project stakeholders to participate in the cdc’s situational awareness project. to get a bearing on the state of the hie market saic reviewed a sample of regional health information organizations (rhios) in california conducted by calrhio in 2006, which showed that only 13% of the rhios were operational and actively sharing data. [3] many rhios/hies that saic investigated were simply marketing and training organizations that were early in the process of evangelizing health data exchange concepts: some of the hies that surveys and articles labeled as “operational” had only rudimentary clinical messaging systems, and weren’t capable of providing information required as part of the minimum data set; others were pilot infrastructures that had minimal hospital, physician, and patient enrollment populations; many relied heavily on grants rather than user fees, and saic determined they may not be self-sustaining over the life of the cdc contract. based on these findings saic created criteria for selecting an hie team. these criteria included team members that would: be viable for the duration of the contract; have significant existing health care data exchange; include strong representation of state and local public health; have established external data sharing precedents to enable rapid implementation of the minimum biosurveillance data set (mbds); and have experience in developing local public health tools. table 1 provides a high-level description of the main roles and responsibilities of each nw-phie member. the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 3 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 table 1. nw-phie roles and responsibilities organization role/responsibility idaho department of health & welfare (id dohw) provide idaho requirements for biosurveillance, case reporting and public health alerting. assist in getting biosurveillance and case reporting information to id local public health. inland northwest health services (inhs) serve as the main point of interface into clinical care through its 34 hospitals, 3,500 beds, numerous laboratories, two regional imaging centers, 400 physicians’ offices, and 2.6 million longitudinal electronic health records. science applications international corporation (saic) serve as prime contractor for the nw-phie team on the cdc situational awareness contract. saic has leveraged its extensive experience in connecting clinical care to public health and developing disease surveillance systems. spokane regional health district (srhd) serve as a focal point for connecting public health with spokane practitioners. participate in understanding how the minimum biosurveillance data set (mbds) can assist local health departments and develop bidirectional communications between public health and clinical care practitioners. uw center for public health informatics (uw cphi) assist state and local public health departments in using the clinical data streams created by this contract to improve public health practices and outcomes. washington state department of health (wa doh) provide state and local public health requirements to the nw-phie team and integrate the clinical data streams created under this contract into existing doh surveillance systems. inland northwest health services (inhs) is the hie platform for nw-phie. inhs is a non-profit regional collaboration to improve patient care through innovative technology solutions and shared services. inhs has electronically linked 34 hospitals, with more than 3,500 beds, across washington and idaho. these facilities share a common information system featuring a unified master patient index (mpi). clinics outside the network receive data via standard electronic messages, and physicians can view the data via the internet or wirelessly in the hospitals. laboratories and imaging centers contribute data to the integrated system. the same shared services approach has been used to develop a centralized electronic medical record service for physician offices, currently serving more than 700 physicians across the region. inhs also operates an extensive telehealth network, linking 68 hospitals, clinics and public health agencies around the region. this system is used to deliver health care and continuing education to rural patients and practitioners. the regional integrated information system and telehealth has allowed the implementation of a wide variety of technical innovations, including computerized physician order entry, nursing documentation, bar-coded medication the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 4 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 verification systems, and tele-er. inhs also provides other shared services, including a regional air ambulance service, community health education, and physical rehabilitation programs. review of accomplishments and current activities project outreach to stakeholders: an important initial step was to educate the clinical community about the goals of the situational awareness project and gain their commitment to participate. inhs assembled a kickoff meeting for hospital administrators and health information managers from many of their 34 member hospitals with representatives of cdc, wa doh and srhd. this meeting allowed the hospital representatives to better understand project goals and benefits, as well as provided a forum for answering the hospital representative’s questions. inhs staff also met multiple times with a regional coalition of health information managers to review the project and answer questions. while hospitals in the region were accustomed to reporting notifiable conditions to public health agencies and have a long history of working cooperatively with public health, they were not familiar with the concept of syndromic surveillance and were hesitant about a project that required routine reporting of so much data on almost all of their patients. they were particularly uncomfortable with the requirement for pseudo-anonymization of patients and questioned whether or not their patients’ privacy would truly be protected. inhs worked with srhd to help the hospitals understand how syndromic surveillance fits within the framework of the existing washington state notifiable condition regulations. srhd was also very effective at leveraging its relationship with hospital infection control staff to decrease concerns about the new project and to establish a patient re-identification process that would fit within the hospital’s current processes and reassure the hospitals about their own ability to protect their patients’ privacy. based on this background work, data sharing agreements were signed with sacred heart medical center (shmc), the largest hospital in the region. once shmc agreed to participate, other hospitals quickly agreed to sign data sharing agreements. collaboration with other hies and public health agencies: an important aspect of our project is to promote the benefits to public health of collecting health information from health information exchanges. to support this we presented our work efforts at numerous healthcare, health informatics and public health conferences (see table 2). these presentations helped us disseminate information on the methods and practices we employed in connecting hies to public health. the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 5 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 table 2. presentations on the benefits of collecting syndromic surveillance data from hies meeting date objective cdc hie community of practice feb 2009 presentation of nw-phie project to the cdc’s hie community of practice. himss 2009 hie presentation april 2009 participation in joint cdc sa awardees’ presentation on our hie project. american medical informatics association (amia) 2009 spring congress may 2009 participation in a panel discussion on hies and public health.[4] sharing summarized syndromic surveillance data meeting in nyc june 2009 meeting with cdc, ny, nyc, in and wa doh at the nyc department of health and mental hygiene (dhmh) to discuss summarized syndromic surveillance data. this meeting resulted in the development of the geocoded interoperable population summary exchange (gipse) specification. council of state and territorial epidemiologist (cste) 2009 conference june 2009 participation in a presentation and question and answer session on hies and public health.[5] phin 2009 conference hie presentation sept 2009 participation in a joint cdc situational awareness (sa) awardees’ presentation on our hie project entitled “public health and hies: developing a common roadmap to success”.[6] american medical informatics association (amia) 2009 annual symposium nov 2009 participation in a joint cdc sa awardees’ presentation on hies and public health. international society of disease surveillance (isds) 2009 annual conference dec 2009 poster presentation comparing nw-phie’s influenza surveillance data to data provided through wa doh’s influenza-like-illness network (ili net) providers. [7] cste 2010 conference june 2010 participation in a presentation and question and answer session on hies and public health.[8] national association of city and county health officials (naccho) july 2010 demonstration of how our project helped state and local public health organizations. in addition to presenting at conferences we also demonstrated current and leading edge technologies that we are using (or hope to use) as part of our technical infrastructure for connecting hies to public health. the goal is to reinforce with the healthcare and public health communities that the technologies for connecting hies to public health already the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 6 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 exist and to demonstrate how those technologies work. table 3 lists the technology demonstrations in which we have participated. table 3. nw-phie’s technology demonstrations meeting date objective nhin gateway demonstration in washington, d.c. dec 2008 demonstrate how nhin gateway can support the sharing of information between hies and public health. himss 2009 connectathon testing feb 2009 test interoperability capabilities with ehr vendors in preparation for the 2009 himss interoperability showcase. himss 2009 conference interoperability showcase april 2009 demonstrate how the nhin gateway can be used to send syndromic surveillance data encoded using the hitsp biosurveillance format to the cdc. also demonstrated ehr public health alerting using integrating the healthcare (ihe) and health information technology standards panel(hitsp) profiles. phin 2009 conference interoperability showcase sept 2009 two phin interoperability showcase scenarios: (1) emr public health alerting and (2) influenza surveillance using summarized counts (gipse) and the nhin gateway. capital hill health information technology week sept 2009 demonstrate a biosurveillance scenario using gipse and the nhin gateway to congressional members and staff. ihe connectathon testing jan 2010 test interoperability capabilities with ehr vendors in preparation for the 2010 himss interoperability showcase. himss interoperability showcase feb 2010 participate in demonstration scenarios that showed how hies and public health can communicate to identify and manage disease outbreaks using decision support tools and web-based public health alerting systems. capturing relevant clinical data: saic took the lead in developing the technical solution for collecting biosurveillance data and sending it to public health. saic’s solution, called the biosurveillance integrator, works by filtering clinical information to find relevant biosurveillance data, removes personally identifiable information, creates standardized biosurveillance messages and sends them to the wa doh, srhd and eventually on to the cdc. the biosurveillance integrator takes as input clinical information (hl7 messages, ehr export files, etc.) and filters those data based on the rules specified in the american health information community (ahic) biosurveillance use case. the biosurveillance messages that are created conform to the hitsp biosurveillance message standards. inhs configured their cloverleaf integration engine to send existing hl7 messages to saic’s biosurveillance integrator and also created the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 7 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 custom file extracts from their resource utilization system and emergency department system. the team installed, configured and tested the public health information network messaging system (phinms) to securely send the biosurveillance files from inhs to wa doh. phinms compresses, encrypts and digitally signs files before transporting them over the internet to wa doh. within six months from project inception the project team was testing the biosurveillance integrator. in december 2008, nine months after project inception, nw-phie went live with the shmc, the 2 nd largest hospital in wa state, sending biosurveillance messages to public health based on admission/discharge/transfer (adt) messages. in january 2009, the transmission of data for shmc was expanded to cover laboratory orders and results. approximately nine thousand adt-based and seven hundred fifty order and result biosurveillance messages were being sent to public health daily based on the shmc implementation. in the next year of the project nw-phie implemented real-time syndromic surveillance data feeds to public health for thirteen additional hospitals. these hospitals are sending a detailed set of biosurveillance information including:  facility-level census and resource utilization information  patient-level o demographic information o emergency department chief complaint, working and final diagnosis o inpatient admission reason and final diagnosis o outpatient reason for visit and final diagnosis most of the nw-phie’s efforts have been focused on analyzing the patient-level data. inhs hospitals had text-based laboratory results that are hard to programmatically analyze. in order to better analyze the laboratory data the nw-phie implemented eleven discrete hospital laboratories interfaces. these discrete laboratory interfaces not only provide better syndromic surveillance data, they also provide a foundation for performing reporting on laboratory results for notifiable disease conditions. to further improve the ability to analyze laboratory test orders and results we mapped local laboratory test code to logical observation identifiers names and codes (loinc®) laboratory test codes and local test results (e.g., organisms, result codes) to codes from the systemized nomenclature of medicine-clinical terms (snomed ct®). nw-phie’s population coverage: the map in figure 1 shows nw-phie’s patient catchment area in washington, oregon, idaho and montana. based on the patient’s zip code, the map depicts the number of patient encounters per capita nw-phie filtered from november 6, 2009 through february 5, 2010 (3 months). darker colors indicate that a higher per capita number of patient encounters were filtered from that zip code. prior to the initiation of our project there was no automated filtering of patient encounters for public health situational awareness in these catchment areas. the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 8 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 wa or id mt figure 1. nw-phie’s per capita patient encounters for the 4th quarter of 2009 (based on the patient’s zip code) throughout the life of the project we have steadily increased the number of patient encounters filtered as can be seen in figure 2. 0 50,000 100,000 150,000 200,000 250,000 # o f e n c o u n te rs year 1 (12/18/08 2/5/09) year 2 -q1 (2/6/09 5/5/09) year 2 q2 (5/6/09 8/5/09) year 2 q3 (8/6/09 11/5/09) year 2 q4 (11/6/09 2/5/10) year 3 q1 (2/6/10 5/5/10) patient encounters filtered for public health use outpatient ed inpatient figure 2. number of patient encounters filtered by project quarter analyzing biosurveillance data for public health purposes: the biosurveillance information being sent to public health is patient-level, transactional data. uw developed a process for taking the transactional data and creating a patient encounter record which then gets analyzed to identify incidents of importance to public health. the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 9 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 in the spring of 2009 the h1n1 pandemic occurred. this was a galvanizing event for public health resulting in a full-court press on gathering information to characterize h1n1 outbreak and help manage the response. nw-phie, in concert with epidemiologist and informaticists from cdc, ny and in, quickly developed algorithm specifications for filtering clinical encounter information to identify cases of influenza, influenza-likeillness, and pneumonia as well as a file format to convey the indicator counts. these filter algorithms are called indicators and can be based on:  words or phrases in chief complaints, nurse triage notes or admission reasons  icd-9 diagnosis codes  test/procedure codes  test order or result codes in order to compare rates we developed both numerator and denominator indicators. numerator indicators identify the number of patients with a given condition and denominator indicators provide a total count of the population of patients surveyed. because of the variability of patient-level data captured by different organizations it was important to define numerous indicators that cover the breath of possible data captured. over forty different numerator indicator specifications for influenza, ili, and pneumonia were developed and four different denominator definitions. the file format for conveying the indicator counts is called the geocoded interoperable population summary exchangefile and is designed to allow the electronic exchange of health indicator counts that can be stratified by a number of variables, including age group, gender, patient class, discharge disposition and geography (zip) [9]. on september 1, 2010 nw-phie began sending a gipse file of 10 different influenza indicators to the wa doh, srhd and cdc. this file has a rolling 30 days of gipse indicators stratified by three digit zip, gender, eight age ranges, patient class and a ub40based discharge disposition. these data can easily be imported to create epidemiology curves such as the one depicted in figure 3. figure 3. rate of influenza-like-illness chief complaints for ed patients by age group based on nw-phie gipse file from october 2009 the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 10 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 while these data were useful to epidemiologists in understanding the size of the influenza outbreak they did not answer questions about possible changes in the severity of illness over time and the rate of influenza vaccinations and the vaccine’s effectiveness. to help answer these questions nw-phie met with emergency department personnel and developed a means to capture a patient’s temperature and blood oxygen saturation to help characterize severity of illness, and a patient’s self reported seasonal and h1n1 vaccination status to help characterize vaccination rates and effectiveness. to cover these additional data elements nw-phie began sending four additional indicators:  hypoxemia initial pulse oximetry reading saturation < 95%  fever measured temperature ≥ 100  self-reported seasonal influenza vaccination  self-reported influenza a/h1n1 vaccination we also combined these four new indicators with several of our existing influenza indicators to create twelve new composite indicators, as seen in table 4. table 4. composite indicators for severity and vaccination status influenza indicator hypoxemia fever self reported vaccination seasonal influenza h1n1 influenza-like-illness chief complaint     influenza-like-illness diagnosis     influenza diagnosis     these additional indicators have proven extremely helpful in more fully characterizing the h1n1 outbreak. comparing gipse influenza indicators to ilinet counts: wa doh undertook a study to compare the utility of nw-phie’s influenza surveillance indicators to those of the existing influenza-like-illness network (ilinet) data from wa sentinel providers. the comparison metrics include representativeness, stability and timeliness of the data. figure 4 plots the percent of patient encounters indicating ili (rate of ili) from both the ilinet sentinel providers and from nw-phie emergency department and outpatient encounters. these rates are overlaid on a plot of the number of laboratory confirmed cases reported to wa doh during the same week. the chart shows that nw-phie’s ili rates are more stable over time and align more closely with the trend of laboratory confirmed cases than ilinet’s. the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 11 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 figure 4. ili rate from nw-phie and ilinet overlaid with laboratory confirmed influenza cases in analyzing the stability of the ili data feeds wa doh found that nw-phie data are resistant to the seasonal fluctuations seen in reporting among ilinet reporters and, because of the large number of visits monitored by nw-phie, the data are not significantly affected by events at a single site. nw-phie ili data were more timely with a consistent one day delay in reporting to wa doh (i.e., the prior day’s results are reported the following morning to wa doh) versus a median of 11.8 days delay for ilinet reporting to the wa doh. nw-phie’s ten day improvement in reporting timeliness can be essential in helping public health respond to an outbreak. current activities: the final major effort of nw-phie is to implement automated reporting to public health of laboratory notifiable findings from inhs hospital laboratories. in public health’s parlance this capability is referred to as electronic laboratory reporting (elr). elr is a complex undertaking. wa state law identifies seventy-seven disease conditions that are notifiable to public health and each county may have additional disease conditions that require notification to public health. saic is developing and implementing an elr solution that builds on the standardization of the laboratory results messages that occurred as part of the biosurveillance data feeds to public health. additional efforts required to implement a full the elr solution include:  expanding the laboratory result messages analyzed past the microbiology-based results that are analyzed for biosurveillance. these lab results need to have their test and results codes mapped to loinc and snomed codes, respectively.  developing a knowledgebase that stores the rules for filtering laboratory results to identify notifiable disease conditions. the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 12 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010  creating and maintaining an encounter summary database that contains patient demographic, visit and physician information required on an elr report but not available in a laboratory result messages.  creating standardized elr messages to public health that are encoded using standardized vocabulary. of particular importance is the development of a knowledgebase that stores the rules for filtering public health results. the knowledgebase saic has developed stores the relationship of notifiable conditions to loinc laboratory test codes. for each laboratory test that can be used to identify a notifiable disease condition the knowledgebase stores the possible result types (text, coded entry, numeric, structured numeric) and result values that indicate the presence of the notifiable disease condition. in developing this knowledgebase saic made use of the council of state and territorial epidemiologists’ (cste) technical implementation guides (tig) for notifiable disease conditions, the notifiable disease condition mapping tables (ncmt) published by cdc and the regenstrief loinc mapping assistance (relma). the saic developed elr filtering rules knowledgebase has several thousand entries. saic and inhs plans call for implementing elr for an initial hospital in the fall of 2010 and with an additional implementation by the end of 2010. conclusion nw-phie has greatly improved public health’s ability to monitor and respond to health emergencies in central and eastern wa. prior to the initiation of nw-phie’s situational awareness project no syndromic surveillance data feeds were being sent to public health from hospitals in central and eastern wa. within the first two years of the project nwphie has implemented fourteen hospitals, representing close to a million patient encounters a year, that are sending a full set of syndromic surveillance data to public health. two additional hospitals will begin sending syndromic surveillance data feeds to pubic health in the fall of 2010. these syndromic surveillance data feeds have proven vitally important in helping manage the recent h1n1 outbreak and are available to help manage future outbreaks whether from ili or other types of disease. saic has designed and developed an automated process for performing elr for wa state’s seventy-seven notifiable disease conditions. to support the elr process nwphie has implemented eleven discrete hospital laboratory feeds. saic’s elr solution is being implemented for an initial hospital laboratory in the fall of 2010 with another hospital laboratory implementation planned by year end 2010. in addition to making a difference in improving the sharing of clinical information among clinical care and public health nw-phie’s situational awareness project has advanced the national agenda for sharing interoperable health information with public health. nwphie has implemented hitsp biosurveillance standards, developed a specification for the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 13 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 sharing summary syndromic surveillance data and demonstrated how the nhin gateway and services can be used to support public health surveillance. our project, along with those from in and ny, has provided a roadmap for how public health can participate and derive value from hhs funded state hies. our efforts should also help clinical care understand ways in which they can achieve the public health reporting requirements of ehr meaningful use. references [1] cohn sp and national committee on vital and health statistics. report to the secretary of the u.s. department of health and human services on functional requirements needed for the initial definition of a nationwide health information network (nhin). 10/31/2006. http://www.ncvhs.hhs.gov/061030lt.pdf [2] rishel w, riehl v, blanton c. summary of the nhin prototype architecture contracts. a report for the office of the national coordinator for health it. 05/31/2007. http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_10731_848093_0_0_18/sum mary_report_on_nhin_prototype_architectures.pdf [3] sutherland j. regional health information organization (rhio): opportunities and risks. v 2.1. 11/2005. http://www.himss.org/content/files/sutherland_rhio_whitepaper.pdf [4] safran c, garrett n, dente m, le l, grannis s, karras b. panel: translating public health information into clinical action: a national demonstration project / connecting public health to clinical care through health information exchange implementation of ahic biosurveillance. amia spring congress, may 2009. available at http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_onsite_program.pdf [5] magruder c, johnson g, grannis s, karras b. enhancing public health practice through health information exchange – new york, indiana, and washington/idaho perspectives. 2009 cste annual conference, june 2009. available at http://www.cste.org/dnn/annualconference/2009annualconferencearchive/tabid/321/d efault.aspx [6] magruder c, dobbs d, karras b, blake pa, grannis s, johnson gs. public health and health information exchanges: developing a common roadmap to future success. 2009 phin annual conference, 2009. available at http://cdc.confex.com/cdc/phin2009/webprogram/paper21174.html [7] close n, lofy k, sandifer t, lowe p, karras bt. utilization of washington state’s public health emerging event detection system (pheeds) for 2009 h1n1 surveillance. isds meeting, 2009. [8] karras b, gibson j, johnson g. public health and health information exchange: lessons learned a centers for disease control and prevention sponsored initiative in new york, indiana and washington/idaho. 2010 cste annual conference, june 2010. available at http://cdc.confex.com/cdc/phin2009/webprogram/paper21174.html [9] hiem/profile development work group. geocoded interoperable population summary exchange (gipse). profile definitio. v 1.0. 09/03/2009. available at http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_10741_909195_0_0_18/gip seprofilespecification.pdf http://www.ncvhs.hhs.gov/061030lt.pdf http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_10731_848093_0_0_18/summary_report_on_nhin_prototype_architectures.pdf http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_10731_848093_0_0_18/summary_report_on_nhin_prototype_architectures.pdf http://www.himss.org/content/files/sutherland_rhio_whitepaper.pdf http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_on-site_program.pdf http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_on-site_program.pdf http://www.cste.org/dnn/annualconference/2009annualconferencearchive/tabid/321/default.aspx http://www.cste.org/dnn/annualconference/2009annualconferencearchive/tabid/321/default.aspx http://cdc.confex.com/cdc/phin2009/webprogram/paper21174.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21174.html http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_10741_909195_0_0_18/gipseprofilespecification.pdf http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_10741_909195_0_0_18/gipseprofilespecification.pdf the northwest public health information exchange’s accomplishments in connecting a health information exchange with public health 14 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 acknowledgments this work was funded by the centers for disease control and prevention through the “accelerating public health situational awareness through health information exchanges” contract #200-2008-24369 and was made possible by the efforts of the entire nw-phie collaborative. correspondence david dobbs, science applications international corporation, mclean, va. dobbsd@saic.com conflicts of interest the authors do not report any conflicts of interest. mailto:dobbsd@saic.com perceived value of applying information communication technology to implement guidelines in dveloping countries; an online questionnaire study among public health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e180, 2014 ojphi perceived value of applying information communication technology to implement guidelines in developing countries; an online questionnaire study among public health workers pasipanodya ian machingura1, olawumi adekola2, eunice mueni3, omo oaiya4, lars l gustafsson5, richard f heller6 1. college of health sciences, university of zimbabwe, zimbabwe 2. international health management services, nigeria 3. african institute for development policy, kenya 4. west and central african research and education network, nigeria 5. division of clinical pharmacology, department of laboratory medicine, karolinska institute, sweden 6. people's open access education initiative (peoples-uni), uk abstract introduction: practice guidelines can be used to support healthcare decision making. we sought to identify the use, and barriers to the implementation, of electronic based guidelines to support decisionmaking in maternal and child healthcare (mch) and the rational use of medicines, in developing countries. methods: graduates who had gained the master of public health degree through the peoples-uni (postgraduate public health education in developing countries) were sent an online survey questionnaire which had been piloted. two reminders were sent to non-respondents at intervals of 10 days. results were explored using descriptive analyses. results: 44 of the potential 48 graduates from 16 countries responded – most were from africa. 82% and 89% of respondents were aware of guidelines on mch and the rational use of medicines respectively. electronic guidelines were more available in university hospitals than in provincial hospitals or rural care. all respondents thought that guidelines could improve the delivery of quality care, and 42 (95%) and 41 (93%) respectively thought that computers and mobile or smartphones could increase the use of guidelines in service delivery. lack of access to computers, need to buy phone credit, need for training in the use of either computerized or phone based guidelines and fear of increased workload were potential barriers to use. conclusion: there is support for the use of electronic guidelines despite limited availability and barriers to use in developing countries. these findings, and other literature, provide a guide as to how the further development of ict based guidelines may be implemented to improve health care decision making. keywords: clinical practice guidelines, information communication technology (ict),maternal and child health (mch), rational use of medicines. . abbreviations: maternal and child health (mch), information communication technology (ict) correspondence: rfheller@peoples-uni.org doi: 10.5210/ojphi.v6i2.5368 copyright ©2014 the author(s) perceived value of applying information communication technology to implement guidelines in dveloping countries; an online questionnaire study among public health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e180, 2014 ojphi introduction clinical practice guidelines have the potential to enhance the quality of healthcare by promoting consistent clinical decision making based on best evidence. guidelines can be made available either electronically or in print. guidelines in a printed form may not be updated promptly enough to keep pace with rapidly emerging evidence. in addition, the publication, dissemination and receipt of feedback from stakeholders create other physical limitations to the value of printed guidelines [1] [2]. an important requirement for the development of both electronic and printed guidelines is that they should involve the key stakeholder groups that are going to implement the guidelines [3] [4] [5]. well developed and interactive guidelines using information and communication technology (ict) may be able to at least partly meet these challenges [6] [7]. the world has had a proliferation of ict systems revolutionising how human knowledge is recorded, stored and retrieved. pre-ict era information retrieval would entail significant time in a library and physical access to hard copy material. however, use of ict currently enables an individual to carry out a search and obtain the information on a computer or mobile device. healthcare professionals in developed countries have benefited immensely from the informatics revolution. on the other hand, low and middle income countries are the least advanced in ict infrastructure and service provision, whilst they have the burden of the majority of world’s health issues as well as a shortage of healthcare workers [6] [8]. however, the potential of mobile phones and the internet revolution are apparent across all continents including rural parts of africa and asia. translation of clinical practice guidelines into an electronic form (whether on computers or smartphones) may utilise this revolution and enhance their accessibility [9] [10]. it was observed in botswana that hard copy versions of guidelines were kept locked in rooms in healthcare facilities and rarely referenced by clinicians, while smartphones loaded with medical applications and resources were a useful information retrieval tool for clinicians working in remote areas [6]. in a study among medical professionals in a national hospital in kenya, some of the participants indicated awareness of only a few specific guidelines from the ministry of health [11]. similar poor access to printed guidelines was recently documented in three african countries (burkina faso, ghana and tanzania) [2]. this study sought to find out, amongst a group of developing country healthcare professionals mainly from africa, if guidelines for maternal and child healthcare (mch) as well as guidelines for the rational use of medicines were available either in print or electronically. the study also sought to discover whether the respondents were aware of the availability of guidelines, whether they used the guidelines in practice and what they considered to be the potential future value and limitations to the use of computers and smartphones to access guidelines to support decision making. methods the people's open access education initiative (peoples-uni http://peoples-uni.org) [12] provides online education in public health to health professionals in developing countries. the first set of alumni who had graduated with the master of public health degree, awarded by a partner this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. perceived value of applying information communication technology to implement guidelines in dveloping countries; an online questionnaire study among public health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e180, 2014 ojphi manchester metropolitan university in 2012 and 2013 provided the sampling frame for this survey. approval for the study was granted by the peoples-uni leadership group. a questionnaire, based on an initial literature review [2] [4] [13] [14], was developed and piloted informally among the author members of the alumni group. the questions included demographic information about the respondents, as well as information about considered value of ict based guidelines in general as well as in relation to mch and rational use of medicines. an online survey form was prepared, using eko-konnect (a cluster of the nigerian research and education network, (ngren) http://eko-konnect.org.ng/). all potential respondents were sent a message via the discussion forum to which they are subscribed as alumni or students, and two reminders sent to non-responders at intervals of 10 days. data collection was completed within one month in the second half of 2013. results were explored using descriptive analyses. results responses were obtained from 44 of the 48 potential respondents, working in 16 countries. most of the respondents came from africa, see table 1. table 1. region in which the respondents worked (n=44) region of respondent number west africa 21 east africa 11 southern africa 7 asia 5 total 44 19 of the respondents were physicians, 3 were nurses, and 11 were non-clinical public health personnel. the other 11 respondents came from various health related areas including pharmacy, laboratory science or worked as clinical community or public health personnel. 13 worked directly in maternal health, 25 in child health, and the remainder in a number of clinical or nonclinical areas, although there was overlap between the groups with some respondents reporting more than one area of work. the respondents reported multiple roles which included 20 as managers, 16 clinicians, 11 teachers and 24 researchers. the majority completed the survey themselves, although 3 asked others to assist. guidelines were given as the most frequent sources of information to guide mch, with each of the other options given (continuing professional education, reliance on training and asking colleagues) also being quoted in that order. 36 (82%) of the respondents said they were aware of guidelines on mch service delivery, and 39 (89%) were aware of guidelines for the rational use of medicines or the essential drugs list. table 2 shows that guidelines were available in paper or electronic form, with fewer available in electronic form for rational use of medicines than for mch, and fewer in rural than provincial or university hospital care. perceived value of applying information communication technology to implement guidelines in dveloping countries; an online questionnaire study among public health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e180, 2014 ojphi table 2. format in which guidelines were reported to be available. percentage calculations based on 44 total respondents) type of care facility maternal and child healthcare rational use of medicines paper electronic both any electronic form don't know paper electronic both any electronic don't know university hospital care (private or public) 8 5 21 26 (59%) 2 16 3 17 20 (45%) 3 provincial hospital care 17 2 13 15 (34%) 4 19 3 9 12 (27%) 8 rural care 18 3 10 13 (30%) 6 21 1 5 6 (14%) 12 note: numbers add to more than 44 in rows for both mch and rational use of medicines, where guidelines were available in more than one format. 34 (77%) and 35 (80%) of the respondents had access to the guidelines for mch and rational use of medicines, respectively. all of the respondents thought that guidelines could improve the delivery of quality care, and 42 (95%) and 41 (93%) respectively thought that computers and mobile or smartphones could increase the use of guidelines in service delivery. hard (printed) copies of guidelines were thought likely to be the most effective guideline format by 11 (25%) of the respondents, while 25 (57%) thought that electronic or computerised formats, and 10 (23%) thought that mobile or smartphones were the preferred option. a number of respondents mentioned that this would depend on local circumstances. twenty six (59%) of the respondents were aware of the guidelines for the integrated management of childhood illness. others mentioned that they were aware of guidelines relating to malaria, hiv/aids, neonatal resuscitation, health management information (weekly surveillance) and who and unaids guidelines. table 3 shows the challenges to the implementation of guidelines lack of access to computers would be a major limitation to the implementation of a guidelines system, and training would be required. lack of funds to purchase phone credit could be a limitation to the use of phone based guidelines. fear of increased workload was thought to be a limitation by only around one third of respondents. other reasons given included lack of access to maintenance, connectivity, electricity or phone networks. perceived value of applying information communication technology to implement guidelines in dveloping countries; an online questionnaire study among public health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e180, 2014 ojphi table 3. challenges forseen in the implementation of guidelines using computers and mobile or smartphones. numbers (percentage of total 44 respondents). potential challenges computer mobile/smart phone lack of access 33 (75%) 16 (36%) lack of ability to use 21 (48%) n/a lack of funds to purchase credit on the phone n/a 28 (64%) requires training of health care providers 33 (75%) 25 (57%) fear of increased workload 15 (34%) 12 (27%) n/a – not asked discussion there was good response rate among the alumni of the online mph programme, who were working in a number of countries – mostly in africa – and in fields relevant to the scope of the survey. respondents were aware of the existence of guidelines for both mch and rational use of medicines. availability seemed to vary according to the site of the care facility, with rural care being more likely to have paper based guidelines and less likely to have electronic guidelines than urban university hospitals. all respondents thought that guidelines could improve the quality of care and most that this applied to those available through either computers or mobile phones. the most effective mode of delivery was thought to be the computer, with fewer picking hard copies or phone, although this could vary according to local circumstances. lack of access to computers, however, would be a limitation, as would the need to buy phone credit. practitioners would require training in the use of either computerised or phone based guidelines, but only one third and one quarter of the respondents respectively thought that a fear of increased workload would be a barrier to their use. these results are consistent with a study among healthcare workers in burkinao faso which found that there was a willingness to use modern technology and guidelines in the workplace, but a concern about the working time required and the training necessary [9]. the need for training is a consistent finding, and any attempt to increase the use of ict based guidelines should be accompanied by appropriate training. the costs of implementing such a system should therefore include the costs of training. in addition, consideration of how to increase access to ict based guidelines to improve decision support in healthcare should include the ease of use of the system and its perceived usefulness by the healthcare workers [4] [5] [6] [15]. there have been calls over the years for better access to information using the internet [15] and specifically how guideline use might enter the digital age [1]. from the developing world, a kenyan study found an expressed desire for the increased availability of ict to help with access to health information [11], although a study among rural nigerian mch workers found that there perceived value of applying information communication technology to implement guidelines in dveloping countries; an online questionnaire study among public health workers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e180, 2014 ojphi were a number of endemic barriers and end-user preferences that would limit the adoption of ict in health care [16]. there is potential for ict to make a contribution to health care, and hence the health of the population, if it is made more readily and openly available. involvement in the planning and implementation [4] [5] and belief in the benefits [5] have previously been shown to improve intentions to use ict based decision support systems. it is difficult to demonstrate the potential benefit of guideline implementation to improve health outcomes [13]. mobile phone reminders have, however, been shown to improve antenatal care attendance [17]. education, audit and outreach have been shown to improve mch outcomes substantially [18] [19], but these were in the context of a package of interventions. ict based guidelines, if they are to be beneficial, are likely to need to be part of a broad package of interventions aimed at improving care and the use of evidence based interventions. study limitations include the relatively small sample size and the lack of validation of the answers to the survey questions. hard copies of the survey form were not used in this fully online survey. the use as the sampling frame of the graduates of a high quality online mph programme, working themselves in various parts of the health care system in developing countries, has a number of advantages. they have expertise in the critical evaluation of information and the implications of ict for education, are used to collaborating and sharing information online, and understand the realities of healthcare in developing countries. our study also fills the lack of knowledge on the extent to which various guidelines are available in resource strained countries and the attitudes to make them available in digital format. conclusion our study adds to the literature in that it comes from a wide spectrum of developing world settings amongst a group of health professionals well trained through an mph programme. we report strong support for the use of guidelines, as well as for the use of ict delivery to help their implementation. currently electronic guidelines have limited availability other than in university hospitals, and the barriers to use identified offer a template for action. these findings, together with other literature and various initiatives for action, provide a guide as to how the further development of ict based guidelines may be implemented to improve health care decision making. financial disclosure no funding was sought or obtained for this study, other than support to llg as acknowledged below. competing interests no competing interests perceived value of applying information communication technology to implement guidelines in dveloping countries; 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isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 101 (page number not for citation purposes) isds 2013 conference abstracts utility of potential misdiagnoses in predicting foodborne outbreaks lucia lucia*1, artur dubrawski2 and lujie chen2 1singapore management university, singapore, singapore; 2carnegie mellon university, pittsburgh, pa, usa objective to investigate utility of using inpatient and emergency room diagnoses to detect outbreaks of salmonellosis in humans. to quantify the impact of including in the analysis cases diagnosed with conditions that may have physiological appearance similar to salmonellosis. introduction reliable detection and accurate scoping of outbreaks of foodborne illness are the keys to effective mitigation of their impacts. however, relatively small number of persons affected and underreporting, challenge the reliability of surveillance models. in this work, we correlate a record of identified outbreaks and sporadic cases of salmonellosis in humans retained in pulsenet1, and diagnosis codes in hospital claims collected in california from 2006 to 2010. we hypothesize that the data support and reliability of detection could be improved by including cases in which salmonella infection may be confused2. methods we join the data in a table indexed with dates and locations, containing counts of inpatient and ed patients diagnosed with salmonellosis and related diseases, also counts of cases involved in outbreaks, aggregated by day (the admission date or the isolation date) and location (the county of hospital locations or the county where the outbreaks occurred). 9.5% of the 66,845 rows in the table involve sporadic cases and identified clusters. to quantify predictive utility of potential misdiagnoses, zero-inflated poisson regression (zip) model3 is trained to predict the number of cases in epidemiological data. among salmonellosis (counts in inpatient and ed) and 12 potential misdiagnoses, the best combination of input features is found by exhaustive search to minimize 10 fold cross validation zip prediction error. the chosen model is then trained using thusly selected features using all data. similarly, we train a random forest (rf) binary classifier4 that also includes spatio-temporal predictors (county and month) to discount seasonality and spatial propensity of outbreaks. results we found that 8 diagnoses related to salmonellosis have non-trivial impact on outbreak predictability (only celiac is insignificant with p-value>0.05). their contributory effect is indicated by positive coefficients of zip count model and negative coefficients of zip zero model, as shown in the table. including counts of these diagnoses improves predictability of the occurrence of outbreaks vs. using salmonellosis diagnoses only. the auc score of the rf model increases from 57% to 87%. adding spatio-temporal factors improves the predictability to 91% auc. the model discovers 71% of actual outbreak cases at 7% false positive rate (fpr) and correctly recalls 4.5 as many outbreak cases at 1% fpr as when using salmonellosis diagnoses only. we found 37% of the predictions can be made 1 to 7 days earlier than the recorded isolation date, increasing precision to 89%. this suggests a potential early warning utility. it is also possible to spot outbreaks not revealed in pulsenet. for instance, 22 out of 35 outbreak predictions in yolo county are not in pulsenet; 60% of these 22 have at least 40% of nearby counties showing positive predictions or actual cases in pulsenet in the same periods of time. conclusions empirically found informative correlation between the counts of hospital patients diagnosed with diseases that may have physiological appearance similar to salmonellosis, and epidemiologically recorded cases of salmonellosis. this suggests that tracking these diseases could support accuracy of foodborne illness surveillance. further study is yet required to verify the actual extent of clinical misdiagnosing, and if there are other factors explaining the apparent correlation. keywords foodborne outbreaks; misdiagnosis; predictive analytics acknowledgments this work is supported by the national science foundation (awards 0911032, 1320347), and the singapore national research foundation under its international research centre @singapore funding initiative and administered by the idm programme office, media development authority. references 1. pulsenet. http://www.cdc.gov/pulsenet/about/index.html 2. rightdiagnosis. http://www.rightdiagnosis.com 3. lambert d. zero-inflated poisson regression, with an application to defects in manufacturing. technometrics. 1992.34(1): 1-14. 4. breiman l. random forests, machine learning. 2001.45(1). *lucia lucia e-mail: lucia.2009@phdis.smu.edu.sg scholcommuser stamp scholcommuser rectangle scholcommuser rectangle scholcommuser text box online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e173, 2014 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 114 (page number not for citation purposes) isds 2013 conference abstracts leveraging public health emergency informatics during the fungal infections outbreak, tennessee—2012 rendi murphree*1, 2, paul petersen1, laina stanford1, jeff c. sexton1, tonya mckennley1, james milliken1, tristan victoroff1, 2, robert newsad1, 2 and joseph roth1, 2 1tennessee department of 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developing the foundation for syndromic surveillance and health information exchange for yolo county, california 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 developing the foundation for syndromic surveillance and health information exchange for yolo county, california osama chaudhary, mph 1,2,3 1 university of illinois at chicago, school of public health 2 yolo county health department 3 sacramento county department of health and human services abstract this report delineates yolo county health department’s process to ascertain its optimal methods of participation in syndromic surveillance and health information exchange. as a health department serving a county of just 200,000 residents, yolo county health department needed to operate within strict financial constraints. meaningful use legislation enabled it to pursue both syndromic surveillance and health information exchange participation whilst complying with its budgetary restrictions. the health information technology for economic and clinical health (hitech), a segment of the american recovery and reinvestment act of 2009, has incentivized the ‘meaningful use’ of electronic health records (ehrs) by providing incentive reimbursements and non-compliance penalties. the meaningful use of ehrs is to take place over 3 stages: stage 1 has begun, stage 2 is imminent, and stage 3 is currently being discussed. having been solicited by both health information exchange and syndromic surveillance options which were cost-prohibitive, yolo county health department focused attention on biosense 2.0, a meaningul use-ready and virtually free syndromic surveillance program developed by the federal centers for disease control and prevention. in collaboration with sacramento county department of health and human services, and with support from several other area counties, yolo county health department submitted a funding opportunity application for biosense 2.0 regional implementation. through this collaboration, yolo county health department has begun participating in the formative stages of the sacramento area center for advanced biosurveillance (sac-b). via sac-b, yolo county health department will be able to participate in syndromic surveillance in the biosense 2.0 program, and simultaneously expand its electronic health data sharing towards a more comprehensive health information exchange. lastly, over the course of these projects, three other methods of participating in electronic health data sharing became available to yolo county health department: all three methods were the direct result of meaningful use legislation. key words: county health department; health information exchange; meaningful use, syndromic surveillance http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 introduction yolo county sits upon 1,021 square miles of agricultural terrain directly to the west of sacramento, the state capital of california. most of yolo county’s 200,000 residents reside in one of four incorporated cities: davis, woodland, west sacramento, and winters. some of the recognizable institutions in the county include the university of california, davis, ranked in us news and world report’s top 40 universities, the port of sacramento, and the triple-a baseball affiliate of the oakland athletics, the sacramento rivercats [1]. yolo county is the ranked 7 th out of 58 california counties for overall health, boasting a 76% high school graduation rate, a 93% rate on residents’ access to healthy foods, a 16% rate of residents without health insurance, and an equally low 16% rate of physical inactivity in residents [2]. figure 1: california and counties of the greater sacramento area yolo county health department, located in woodland, is composed of 9 divisions: communicable disease, environmental health, indigent health medical services, public health education, public health emergency preparedness, public health nursing, public health laboratory, vital records, and wic: women, infants, & children. in may of 2012, the two projects detailed in this report were initiated under the supervision of both the division of public health emergency preparedness supervisor and the yolo county health department epidemiologist. purpose and objectives the first project was to ascertain the optimal methods for yolo county health department to participate in health information exchange; the second was to ascertain the optimal methods for yolo county health department to engage in syndromic surveillance. health information exchange (hie) is the process of reliable and interoperable electronic health record (ehr) sharing, conducted in a manner that protects the confidentiality, privacy, and security of the information. the american recovery and reinvestment act 0f 2009 (arra), commonly referred to as the stimulus or the recovery act, allotted over $155 billion for the purpose of enhancing healthcare delivery, with over $33.5 billion provided for the pupose of optimally utilizing information technology in the healthcare delivery process. the crux of http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 information technology’s role in healthcare is the use of ehrs. an ehr is a “longitudinal electronic record of patient health information generated by one or more encounters in any care delivery setting. included information are patient demographics, progress notes, problems, medications, vital signs, medical history, immunizations, laboratory data and radiology reports” [3]. arra has incentivized utilization of ehr transmission through the use of payment distribution and non-compliance penalties associated with deadlines for which “meaningful use” of ehrs must be fulfilled in healthcare organizations. this is thus an extremely exciting, yet also possibly distressing, time for healthcare organizations. concerns about meeting meaningful use requirements and deadlines may initially overshadow the vast benefits that meaningful use adoption of ehrs will provide for both providers and patients. an ehr’s use can actually be as simple as a laboratory electronically transmitting test results to a clinic. as defined by the federal centers for disease control and prevention (cdc), syndromic surveillance is the use of “individual and population health indicators that are available before confirmed diagnoses or laboratory confirmation to identify outbreaks or health events and monitor the health status of a community” [4]. syndromic surveillance conductors can now also leverage meaningful use regulations in seeking to obtain data from healthcare providers. literature review yolo county health department required the planning of its potential hie and syndromic surveillance participation to take into account it’s extremely limited, and virtually absent, financial resources. the resultant quest for seeking potential sources of cost-free resources began with perusing such options as provided for through federal legislation. arra’s health information technology for economic and clinical health (hitech) act created incentive payments, to be distributed in the form of medicaid and medicare reimbursements, to eligible providers (eps), eligible hospitals (ehs), and critical access hospitals (cahs), pending their demonstration of meaningful use of ehrs. meaningful use was designed to be demonstrated in 3 stages. stage 1 reporting for meaningful use of ehrs began in 2011. stage 2 reporting will begin in fiscal year 2014 for ehs and cahs, and calendar year 2014 for eps. stage 1’s most direct impact with regards to public health was its requirement that one of the following three capabilities was addressed by eps, ehs, and cahs: syndromic surveillance, immunization registries, or electronic laboratory reporting [5]. stage 2’s most direct impact with regard to public health is that all three of the previous public health components have been proposed to be made mandatory. in addition, stage 2 has been proposed to include reporting to a cancer registry as a mandatory fourth public health component. finally, stage 2 has been proposed to delineate mandatory technological requirements with which these four public health components should be fulfilled [5]. the meaningful use objective for syndromic surveillance is the capability to submit electronic syndromic surveillance data to public health agencies except where prohibited, and in http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 accordance with applicable law and practice. the statutory authority is found in section 170.314(f)(3), with regards to public health surveillance, and in section 170.314(f)(4), with regards to electronic data transmission to public health agencies [5]. stage 2’s proposed selection of the technological requirement for electronic data exchange is health level 7 version 2.5.1 [6]. stage 3 requirements for meaningful use will likely include extensive utilization of clinical decision support systems, recording advanced directives for elderly patients, expansion of demographic data, increased delineation of drug interaction effects, and further emphasis on sharing care summaries [7, 8]. the preview of stage 2 requirements was welcome news to yolo county health department, providing a viable framework upon which to proceed on both the hie and syndromic surveillance projects. yolo county health department would be able to leverage the meaningful use requirements in order to facilitate the data exchange necessary for hie and syndromic surveillance, whilst simultaneously operating within their budgetary constrictions. methods and analysis at the time of the commencement of the projects, yolo county health department had been asked to join the formative stages of what was tentatively being termed the north east bay health information exchange services. other areas being courted for involvement included napa county, solano county, and sonoma county: all lay eastward from yolo county. although the invitation for the north east bay hie services group was yolo county health department’s first formal brush with potential involvement in hie, and thus may have appeared enticing, two aspects of the invitation prompted a cautious approach. the first was that yolo county health department was being asked to contribute $3,000 in initial participation fees, with likelihood of recurring annual fees. the second factor was that most of yolo county’s residents and healthcare organizations were either based in, or operated towards, sacramento, which is due west. after consultation with the yolo county health department director, the yolo county health department fiscal advisor, and the coordinator for the north east bay hie services, it was decided that no imminent action would be taken, due both to the requested fiscal obligations and the lack of assurance that participation would benefit the residents of yolo county. assessment was simultaneously being undertaken of syndromic surveillance possibilities. an electronic data vendor, health monitoring systems, had been soliciting yolo county health department for its business for several months. fiscal constraints, however, once again proved prohibitive, and curtailed serious consideration regarding purchase of the syndromic surveillance programs bring proffered. fortuitously, however, biosense 2.0, a cdc-led syndromic surveillance program, began to emerge as not only an extremely viable, but potentially extremely beneficent, option for yolo county health department’s future syndromic surveillance undertaking. http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 biosense 2.0 is a “[national public health surveillance system for the early detection and rapid assessment of potential public health threats. by using statistical aberration detection methods on health data supplied to health departments from a variety of sources, public health officials are able to identify and investigate anomalies both temporal and spatial, providing insight on the health and potential threats to the health of communities and country]” [9]. cdc oversees the biosense 2.0 program in cooperation with the association of state and territorial health officials, the national association of county and city health officials, and the council of state and territorial epidemiologists. biosense 2.0 offers meaningful use-ready, and hipaa-compliant, syndromic surveillance capabilities. its monumental benefits include both the fact that it has the support of, and contributions from, the aforementioned national organizations, and that it is virtually cost-free. as biosense 2.0 utilizes the cloud computing structure, there are no hardware or software costs or requirements for participants. all data is stored in what is referred to as the cloud environment. this cloud environment is the result of remote servers, owned by amazon web servers, hosting the information, whilst users can effectively query, manipulate, and analyze this information via internet connected computers. as it became evident that biosense 2.0 could be utilized for syndromic surveillance by yolo county health department, the next ascertainment to be made was to determine both how beneficial it could be, and in which manner it might be most advantageously utilized. just as preparation to assess these possibilities began, cdc announced a biosense 2.0 funding opportunity. funding opportunity number cdc-rfa-oe12-1202, entitled “biosense 2.0: building state, local, tribal, and territorial surveillance capacity to enhance regional and national allhazards public health situation awareness” was announced at the end of may. the application deadline for the $900,000/3-year grant, however, was june 26, thus leaving essentially a three week turnaround time for funding opportunity investigation and application planning, design, creation, revision, and submission (with the additional bureaucratic requirements such as obtaining the associated departmental approvals). as yolo county is home to only 200,000 residents, it seemed unlikely that the biosense 2.0 funding opportunity would be awarded solely to yolo county health department for countylevel use. as part of the sacramento metropolitan statistical area, however, yolo county is part of a 5,000 square mile area which is home to over 2.1 million residents. the decision was made to solicit collaboration from the surrounding areas, beginning with the most prominent in terms of population and public health significance, sacramento county. upon consulting with representatives from sacramento county department of health and human services, el dorado county health department, nevada county health department, placer county health department, sutter county health department, and yuba county health department, it was agreed that a greater sacramento area collaborative effort at both participating in biosense 2.0 and applying for the biosense 2.0 funding opportunity would be preferable to attempting these efforts at individual county levels. http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 epidemiologists from these and adjacent counties, representing over 2.5 million residents across 10 counties, had been meeting regularly in a regional, interactive, and supportive process; the foundation for the collaboration was thus already laid. the regional approach to syndromic surveillance, however, still required a vision for the future. as syndromic surveillance is but one aspect of biosurveillance, and the county health representatives did not wish to be limited in subsequent endeavors, it was decided that all facets of biosurveillance should be sought to be supported. a new organization would be created: the sacramento area center for advanced biosurveillance (sac-b). sacramento county department of health and human services (sac county dhhs), being the most prominent organization amongst those participating, would be the lead agency in both forming sac-b and in applying for the biosense 2.0 funding opportunity. a grant-writing team commenced intense communication with the biosense 2.0 redesign support team, seeking guidance for planning and writing the funding opportunity application. after soliciting letters of support from health officers of the pertinent counties (some were unable to submit the letters in time for the submission), the funding opportunity application was submitted on june 22. results and discussion the two projects, the hie and syndromic surveillance assessments, thus partially merged into one project. as syndromic surveillance is a foundation of biosurveillance, and as biosurveillance is a significant segment of hie, this was a favorable, and possibly to-be-expected, outcome. in order to assess yolo county health department’s future in hie, an hie survey was created and sent to over 300 of yolo county health department’s partner organizations in healthcare and/or community service. responses to this survey were still being received and collated as of the end of july, 2012. within two and a half months, however, yolo county health department has progressed from regarding syndromic surveillance as simply a goal for which research should be undertaken, to membership in a regional collaboration for biosurveillance. as of the end of july, 2012, the funding opportunity application had still not been responded to by the cdc. members of both yolo county health department and sac county dhhs were optimistic, however. despite the absence of notification regarding the funding opportunity application’s status, sacb began coalescing. health officers and epidemiologists from the participating counties had been joined by representatives from butte, shasta, and san joaquin counties in discussing how the regional approach to biosense 2.0 might be undertaken. to this end, relevant guidelines were collected from tarrant county, texas, a national leader in the regional approach to biosurveillance. sample data use agreements, material regarding hipaa concerns from potential data suppliers, and a delineation of the various options for monitoring biosense 2.0 for anomalies, were distributed to the various counties. http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 sac-b will leverage existing inter-stakeholder goodwill, meaningful use requirements, fiscal and quality-of-care improvements, standards and interoperability, data aggregation services, and member expertise to recruit and guide local electronic data providers for biosense 2.0 participation. the objective of the sac-b is to develop into a center of excellence which implements best practices that facilitate early detection and response to novel infectious disease pathogens, emerging diseases, disease outbreaks, bioterrorism, and urgent care departments. thus sac-b will serve as the foundation for developing a thriving health information exchange for the greater sacramento area. this process is the type of regional multi-stakeholder effort that the office of the national coordinator for health information technology has envisaged in its plan to form a nationwide health information network [8]. finally, during the undertaking of these projects, the california reportable disease information exchange (calredie), the california immunization registry (cair), and the california cancer registry (ccr) all became meaningful use-ready. these organizations were thus able to provide electronic health data regarding electronic laboratory reporting, immunization registry, and a cancer registry, respectively. thus, yolo county health department now has avenues for participating in electronic health data exchange for all four of the proposed stage 2 meaningful use public health components. public health significance syndromic surveillance is gaining acceptance as a crucial, if not necessary, tool, in early outbreak detection and bioterrorism prevention. characteristics of the greater sacramento area lend support to the need for a regionalized syndromic surveillance approach. “the greater sacramento area has experienced overall population growth throughout the recent economic recession. this includes a large population of seasonal migrant farm workers who reside in migrant camps throughout the region. health inequity in access to care and health outcomes may increase incidence and propagation of novel or emerging disease transmission in such sub-populations. the area is home to the sacramento international airport, the international port of sacramento, four interstate freeways (i-5, i-505, i-80, i-50) and numerous other highways and railways. routine travel to and from nevada, oregon, the san francisco bay area, los angeles, and mexico, involving thousands of travelers, hazardous materials, and freight occurs daily. the area is also home to the university of california at davis, california state university at sacramento and a number of other colleges and universities. these institutions are at increased risk of introduction of novel or emerging disease threats through international travel to and from campuses or may themselves present as targets for bioterrorism. the swath of agricultural land surrounding the city of sacramento produces a significant volume of the food produced in the us. rice, tomatoes, fruit, and a variety of nut, vegetable, and grain crops originate in the area. as a result, large and potentially vulnerable food processing, storage, and distribution centers are located throughout the region and may present an intentional or http://ojphi.org/ developing the foundation for syndromic surveillance and health information exchange for yolo county, california 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 unintentional opportunity for large-scale food contamination and food-borne disease transmission. finally, the state legislature and 22 state governmental agencies are located in sacramento. federal agencies are also located in and around the region. the largest us postal service postal center in northern california (one of three such centers in the entire state) is also located in the sacramento area”[10]. limitations the chief limiting factor of these two projects was dearth of time for completion. health information exchange is a complex endeavor, requiring cooperation and coordination amongst a multitude of organizations on a multitude of levels, including two significant obstacles: legal and fiscal determinations. thus, yolo county health department will continue to communicate with its partner healthcare organizations in an attempt to hone a path towards increased hie participation and/or recruiting. the optimal route is through participation in sac-b and the leveraging of meaningful use legislation. sac-b had been serendipitously birthed partly out of the major time limitation of the syndromic surveillance project, as it was evident that collaboration amongst the area counties was necessary in order to increase chances for receiving the funding opportunity. sac-b will now, however, continue to proceed towards active and collaborative regional biosurveillance activities in spite of the result of the funding opportunity application, and yolo county health department thus has an avenue through which to develop syndromic surveillance capability and hie participation. acknowledgements the author would like to thank dr. cassius lockett, tim wilson, ms, and dana carey, bs, for their support and guidance in this project. dr. lockett and mr. wilson co-designed and coauthored the cdc funding opportunity referenced in this report; ms. carey supervised each phase of the project. the author would also like to thank dr. edward mensah and dr. kshitij nawal for their oversight and support during this project. conflicts of interest the author declares that he has no conflicts of interest. correspondence osama chaudhary, mph 12952 riley ct. rancho cucamonga, ca 91739 email: osamachaudhary@gmail.com http://ojphi.org/ mailto:osamachaudhary@gmail.com developing the foundation for syndromic surveillance and health information exchange for yolo county, california 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 references 1. u.s. news & world report. national university rankings. available at: http://colleges.usnews.rankingsandreviews.com/best-colleges/rankings/nationaluniversities/data/page+2. accessed june, 2012. 2. robert wood johnson foundation. county health rankings & roadmaps. available at: http://www.countyhealthrankings.org/app/california/2012/yolo/county/1/. accessed july, 2012. 3. healthcare information and management systems society. ehr: electronic health record. available at: http://www.himss.org/asp/topics_ehr.asp. accessed july, 2012. 4. centers for disease control and prevention. syndromic surveillance. available at: http://www.cdc.gov/ehrmeaningfuluse/syndromic.html. accessed july, 2012. 5. department of health and human services. centers for medicare & medicaid services. medicare and medicaid programs: electronic health record incentive program: final rule. 6. department of health and human services. centers for medicare & medicaid services. medicare and medicaid programs; electronic health record incentive program-stage 2; proposed rule. 7. mosquera, m. first glimpse at meaningful use stage 3 measures. government health it. available at: http://www.govhealthit.com/news/health-it-panel-explores-draft-mu-3measures. accessed july, 2012 8. ihealthbeat. policy committee talks about proposals for meaningful use stage 3. available at: http://www.ihealthbeat.org/articles/2012/8/3/policy-committee-talks-aboutproposals-for-meaningful-use-stage-3.aspx. accessed august, 2012. 9. kass-hout, t., and xu, z. biosense 2.0 analytics: change point analysis. june, 22, 2012. available at: https://sites.google.com/site/biosenseredesign/community-forum. accessed july, 2012. 10. chaudhary, o., lockett, c., and wilson, t. sacramento county department of health and human services, public health division, epidemiology and disease control. biosense 2.0: building state, local, tribal, and territorial surveillance capacity to enhance regional and national all-hazards public health situational awareness. funding opportunity number cdc-rfa-oe12-1202. june 26, 2012. http://ojphi.org/ http://colleges.usnews.rankingsandreviews.com/best-colleges/rankings/national-universities/data/page+2 http://colleges.usnews.rankingsandreviews.com/best-colleges/rankings/national-universities/data/page+2 http://www.countyhealthrankings.org/app/california/2012/yolo/county/1/ http://www.himss.org/asp/topics_ehr.asp http://www.cdc.gov/ehrmeaningfuluse/syndromic.html http://www.govhealthit.com/news/health-it-panel-explores-draft-mu-3-measures http://www.govhealthit.com/news/health-it-panel-explores-draft-mu-3-measures http://www.ihealthbeat.org/articles/2012/8/3/policy-committee-talks-about-proposals-for-meaningful-use-stage-3.aspx http://www.ihealthbeat.org/articles/2012/8/3/policy-committee-talks-about-proposals-for-meaningful-use-stage-3.aspx https://sites.google.com/site/biosenseredesign/community-forum 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts ratio of excess ed ili visits to seroprevalence, influenza a/h1n1 infection, fl, 2009 richard hopkins*2, 1, aaron kite-powell1, kate goodin1 and janet j. hamilton1 1epidemiology bureau, florida department of health,, tallahassee, fl, usa; 2university of florida, gainesville, fl, usa objective to estimate the number of infections due to the novel 2009 influenza a/h1n1 virus corresponding to each ed visit for ili in a four-county area of florida. knowing such ratios, one could (in future similar situations) estimate the cumulative number of infections due to a novel influenza virus in a population. introduction a seroprevalence survey carried out in four counties in the tampa bay area of florida (hillsborough, pinellas, manatee and pasco) provided an estimate of cumulative incidence of infection due to the 2009 influenza a (h1n1) as of the end of that year’s pandemic (1). during the pandemic, high-level decison-makers wanted timely, credible forecasts as to the likely near-term course of the pandemic. the cumulative percentage of people who will be infected by the end of the epidemic can be estimated from the intrinsic reproductive number of the viral strain, its r0, which can be measured early in the epidemic (2). if the current cumulative number of infections can be estimated, then one can determine what fraction of the eventual total number of infected people have already been infected. methods excess emergency department (ed) visits for influenza-like illness (ili) during the pandemic period (compared to the four nonpandemic years 2007, 2008, 2010, and 2011) were estimated using the essence-fl syndromic surveillance system for the four-county area. for each week, the percentage of visits that were for ili, by age group, was compared between 2009 and the average of the other four years. the difference, the excess in percent ili, was then applied to the observed number of ili visits for each week and age group to calculate the excess number of visits. these excess visits were summed for the epidemic period, and compared to the estimated number of infected residents of the four-county area obtained by applying the prevalence from the serosurvey to the census population of the four-county area, by age group. results there were an estimated 44 infections for every ili ed visit. agespecific ratios rose from 19.7 to 1 for children aged <5 years to 45.4:1 for those aged 5 to 17, to 47.3:1 for those aged 18 to 24, 36.8 for those aged 25 to 49, 55.4 for those aged 50 to 64, and 143.8 to 1 for persons aged >64 years. the proportion of infections by age group is similar but not identical to the proportion of excess ed visits. for example, 13.1% of excess ed visits were for children under age 5, while 9.7% of estimated infections were in that age group. persons aged 65 years and older, accounted for 2.3% of excess ed visits and 6.5% of infections. conclusions these ratios provide a way to estimate cumulative incidence of influenza in an epidemic, using excess numbers of ili visits to emergency departments. these can be used in real time for planning and forecasting, when carrying out timely seroprevalence surveys is not practical. syndromic surveillance data allow age and geographic breakdowns in estimates of ili visits and cumulative infection rates. if r0 has been estimated independently, then the seroprevalence at which transmission will slow and stop can be compared to the current prevalence estimated from ed visits using these ratios. similar ratios should be calculated elsewhere if both population-based ed visits for ili and seroprevalence data for an epidemic strain of influenza are available. influenza-like illness visits to florida emergency departments by week, 2007 to 2011. keywords influenza; prevalence; emergency department visits references (1) cox cm, goodin k, fisher e, et al. prevalence of 2009 pandemic influenza a (h1n1) virus antibodies, tampa bay florida — november–december, 2009. plos one 2012 6(12): e29301. oi:10.1371/journal.pone.0029301 (2) nokes dj, anderson rm (1988) the use of mathematical models in the epidemiological study of infectious diseases and in the design of mass immunization programmes. epidem. inf 1988; 101, 1-20 *richard hopkins e-mail: hopkinsrs@comcast.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e29, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts developing a guidance document to improve public health surveillance during disasters nicole nakata*1 and amy wolkin2 1orise research participant at cdc, atlanta, ga, usa; 2cdc, atlanta, ga, usa objective our objectives are to describe and receive feedback on a disaster surveillance guidance document that can be used by state and local health departments, to fill the gaps in public health (ph) disaster morbidity and mortality surveillance. introduction during all phases of the disaster management cycle, ph surveillance plays a valuable role. surveillance provides ph officials and stakeholders the information they need to respond to disasters and take action in an appropriate and timely manner. despite the fact that surveillance provides a valuable function in disasters, a study by the disaster epidemiology subcommittee of the council of state and territorial epidemiologists (cste) found that there are still significant differences, across states, in their use of disaster surveillance1. further, there is no standardized guidance on implementing or modifying surveillance for a domestic disaster. this document seeks to fill this gap, providing guidance on planning, initiating, conducting, and evaluating disaster ph surveillance in the u.s. methods we developed a draft disaster surveillance guidance document based on general ph surveillance best practices as well as recommendations for epidemiologic activity from disaster response handbooks, field manuals, and other government and non-governmental organization documents. we are also seeking input from stakeholders, including cste and isds. during this presentation we will present the concept of our draft disaster surveillance guidance document. we will give a brief overview on the following document sections: 1) introduction and purpose of disaster surveillance, 2) disaster surveillance systems and practices for implementing them, 3) analysis and dissemination of data, and 4) evaluating the effectiveness of disaster surveillance systems. we will also discuss useful resources such as standardized documents for data collection to include in the guidance. results we hope to gather feedback to better understand what tools and resources are needed to increase disaster surveillance capacity and action. this will enable us to gather valuable input from our stakeholders, strengthen our document, and ensure we are meeting the needs of our state and local health departments. we also hope to increase awareness of and improve surveillance tools to aid in planning for and implementing disaster surveillance. conclusions as disasters continue to occur with increasing magnitude and frequency, the use of this disaster surveillance guidance document may help state and local health departments plan for and implement ph disaster surveillance. additionally, the use of standardized data collection forms and case definitions may aid in providing data useful beyond the response phase of a disaster for comparison and further study in the future. keywords disaster surveillance; disaster management; guidance; morbidity; mortality references 1. simms e, miller k, stanbury m, heumann m, miller t. (2013) disaster surveillance capacity in the united states: results from a 2012 cste assessment, council of state and territorial epidemiologists. accessed 7 february 2014 from http://c.ymcdn.com/sites/www. cste.org/resource/resmgr/environmentalhealth/disaster_epi_ baseline731km.pdf *nicole nakata e-mail: wng9@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e43, 2015 experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi experience of using information systems in public health practice: findings from a qualitative study joshua r vest, phd, mph1, l. michele issel, phd, rn2, sean lee, mph2 1. center for healthcare informatics & policy, division of quality and medical informatics, department of public health, weill cornell medical college 2. community health sciences division, school of public health, university of illinois at chicago abstract objective: data collection and management by local health departments (lhds) is a complex endeavor, complicated by system level and organizational factors. the purpose of this study was to describe the processes and use of information systems (is) utilized for data collection, management, and sharing by lhd employees. methods: we interviewed a purposive sample of 12 staff working in the key public health practice areas of communicable disease control, immunizations, and vital records from three lhds in different states. our interview questions addressed job descriptions, daily activities, and the use and perceptions of both data and is in support of their work. a content analytic approach was used to derive themes and categories common across programmatic areas. results: local public health involves the use of mix of state-supplied and locally implemented is supported by paper records. additionally, each lhd in this study used at least one shadow system to maintain a duplicate set of information. experiences with is functionality and the extent to which it supported work varied by programmatic area, but inefficiencies, challenges in generating reports, limited data accessibility, and workarounds were commonly reported. conclusions: current approaches to data management and sharing do not always support efficient public health practice or allow data to be used for organizational and community decision making. many of the challenges to effective and efficient public health work were not solely technological. these findings suggest the need for interorganizational collaboration, increasing organizational capacity, workflow redesign, and end user training. key words: information systems, immunization, public health informatics, vital statistics, public health administration correspondence: jov2025@med.cornell.edu doi: 10.5210/ojphi.v5i3.4847 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health info rmatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi introduction public health services delivery depends on data and information for enumeration and reporting as part of disease surveillance [1], preventive and medical service delivery [2], local decision making, strategic planning, and quality improvement [3-5]. such activities make reporting and managing data substantial portions of local public health practitioners daily job activities [6]. however, local public health practitioners work within systems and organizations that can present challenges to the effective collection, management, and sharing of data [7]. data collection and management in public health practice is a complex endeavor. these efforts require exchanging data with multiple organizations due to overlapping jurisdictional boundaries, shared responsibilities, and mobile populations [7-10]. generally, increasing the number of data sources increases challenges around maintaining information quality [11]. additionally, each lhd is often home to a variety of different data management approaches [12], which can include multiple or programmatic-specific information systems (is) that are not capable of electronically sharing information in a standards-based structured fashion, or reliance on a combination of paper and is [13-17]. this too creates complications as increasing the number of is which an individual must use increases the complexity of work and negatively affects productivity [12]. also, that insufficient or non-interoperable is can have negative effects on the ability of public health organizations to effectively plan, respond in a timely manner to events, or operate efficiently [18-20]. in fact, evidence indicates the inability to electronically transmit or receive data is the norm for many public health activities [21]. the interaction of individual need for information and presence of a complex arrangement lead us to enrich our understanding of lhd data use, management, and is characteristics through insights from the lived is experiences of public health practitioners. we specifically framed our investigation in the context of data gathered and shared between lhds and state health agencies (sha) in order to focus on the use of information that is a product of the entire public health system. we selected the activities of immunization delivery, communicable disease control, and vital records as they are performed by a majority of lhds [17], require data gathering and sharing by multiple public health entities, and these program areas are subject to structural barriers to information sharing [21]. we specifically sought to characterize the perceived is needs and barriers, as well as is uses and work-around solutions to accomplish the program goals. methods the qualitative study design involved a purposive sample of lhd employees and open-ended interviews. a content analytic approach was used to derive themes and categories common across programmatic areas. sample in mid-2012, one member of the research team (a1) interviewed 12 staff working in the areas of communicable disease control (n=4), immunizations (n=4), and vital records (n=4) from three http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi lhds. the three sites were each in different states and included an urban, a rural, and a suburban lhd (table 1). the sites were selected through our existing organizational contacts with the only requirement that they conducted all three public health activities. informants had the various titles of epidemiologist, program coordinator, nurse, manager, or registrar. we purposefully interviewed those who were responsible for gathering and data sharing. data collection data were collected on-site in order to observe is and data collection forms. the semi-structured, open-ended interview guide addressed job descriptions, activities, and the use and perceptions of data and is in support of work. research into the high prevalence of data sharing gaps [21] and existing instruments measuring is quality [22,23] informed questionaire development. interviews lasted approximately 45 minutes. consent to interview personnel was first obtained from each lhd’s chief administrative officer and then from each interviewee. data analysis analysis followed a general inductive approach [24]. independently, jv and mi read the transcripts and employed open coding to identify tentative categories. both team members have worked in local public health. the independently derived codes were reduced by consolidating overlapping categories and identifying higher-level themes. discussion resolved differences and resulted in a category labels and descriptions. to validate the thematic codes, sl independently conducted closed-coding of all transcripts. a sample set of documents from each program area indicated >90% agreement between three sets of coders. lastly, we also conducted member checking; one informant reviewed their transcript and concurred with the assigned coding. results lhds used a mix of multiple is supported by traditional paper forms, telephone calls, and faxes to collect and share all the data necessary to complete their daily work. the is included both state-provided and locally implemented systems as well as business process oriented is like practice management systems (table 1). as indicated, the is in use were very specific. each programmatic area used its own is and for the communicable disease programs the is were frequently disease-specific. table 1. characteristics and information systems in use at interviewed local health departments by programmatic area. data management or information systems (is) used1 health department number of staff interviewed communicable disease immunization vital records rural 3 electronic labs practice management2 paper records iis3 practice management vris4 paper records spreadsheets http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi suburban 4 surveillance is5 std is local database spreadsheets paper records iis3 mch is6 practice management paper records vris4 paper records spreadsheets large urban 5 surveillance is5 std is5 hiv is5 spreadsheets paper records iis3 statewide client management is inventory management is vaccine for children provider is practice management vris4 paper records local database 1quality, functions, interoperability, or perceptions of is are not reported here. 2practice management includes billing & scheduling functions 3immunization information system (statewide system) 4vital records information system (statewide system) 5 statewide communicable disease (and hiv/std) surveillance systems 6maternal child health information system (statewide system) we identified 46 categories within 11 themes (see appendix). we present the four most salient to the majority of interviewees in detail with illustrative quotes in table 2. table 2. themes and corresponding categories regarding public health information systems (is) and technology. selected theme selected category specific areas & example quote factors affecting information system quality descriptions of factors, circumstances, or conditions that affect specific quality characteristics or overall quality of the data within the is information system quality reporting / output capability “…it’s not a report writing system in the sense that i would think it is where it generates aggregate output…when we run a report we’re basically creating another data file.” epidemiologist, urban lhd “we do have the opportunity to run some reports. but, to be honest with you, it's so difficult to run a report that no one does it.” – communicable disease, suburban lhd information system quality interoperability “our health department providers that use [the iis], they're like, "really? we have to input everything into [the iis] and then at the end of the month we have to do it again into [vaccine management system]?" and it would be a lot easier, yeah, if they talked to each other…” – immunization coordinator, urban lhd barriers to data the system level context, organizational level factors, or situations that affect the need or ability of staff to http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi acquisition from others get information from other organizations or sources jurisdictionally defined work “the access we have now for neighboring counties is just that we can put a name in and we can see it's in there, but we can't necessarily see the disease or see what's going on there.” – nurse, urban lhd “you just don't have the ability to see everything that's going on, because some things are blocked.” communicable disease, suburban lhd mobile populations “confidentiality. they don't wanna be known wherever they're going. so if they feel like they can't have the confidentiality there in [city in neighboring state], then they'll come here and be tested.” – public health nurse, rural lhd data ownership “we need our data back, and we need it back immediately…[the sha is] looking at it simply as data… what that means to us is much more important.” – registrar, urban lhd barriers to effective data sharing (to others) experienced and reported difficulties, challenges or factors/situations that need to be overcome/addressed in order to provide data to others reporting back “so it seems like our staff in the unit have to pull information from [is], put it on a separate piece of paper, and then send it to the state. so i’m not sure why we have to add that extra step when i feel like, in an ideal world, we would be able to use [the is] to report on the information that they need since there already is a way for us to collect it.’ – epidemiologist, urban lhd consequences of data sharing barriers and isq problems all consequences or outcomes associated with the inability to efficiently secure desired information from other sources and of having poor data quality duplication of work/re-work / inefficient work “if you got a parent that’s not a good steward of records, they could possibly have that same child immunized about 3 or 4 times by the certain age and they don’t necessarily need all those vaccines.” – immunization staff, rural lhd workarounds “we were having to write everything in the comment field for zoonosis.” – public health nurse, urban lhd shadow is “we're duplicating our reporting. we do one for in house to help us keep track, and then we use the state system.” – communicable disease, suburban lhd http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi factors affecting is quality this theme encompassed factors, circumstances, and conditions that effected specific quality characteristics or overall quality of the data within the is. also included in this theme are assessments or views on the quality of the is itself in terms of the user experience, available data, and reporting features. reporting functionality is important for public health is since being able to generate aggregated statistics or line listings from data is critical to case investigation and community assessments. interviewees, however, frequently mentioned difficulty using the reporting capabilities of their is: the system did not produce reports in a desired format; reports were too difficult to obtain; or the capability to run reports wasn’t present at all. reporting was generally absent for vital records. the registrar in the rural lhd did not run reports, but neither did the sha share reports with her. the complete absence of reporting functionality at the suburban lhd forced vital records staff to do manual counts off screen displays. the urban lhd could not run reports from their sha-supplied is either. instances of interoperable is supported work existed, with immunization programs more integrated than other programmatic activities. interoperable is use standards to ensure the meaning and usability of data are preserved when exchanged. the rural lhd used a public health-specific practice management system that could export data into the state’s immunization is (iis) eliminating the need for double data entry. however, the urban and suburban’s iis did not have true-bidirectional data sharing with other applications. interoperability was limited to data only being able to share in one direction, restrictions on having records in one system first, or only being able to share data for children and not adults. as a result, those iis were not truly comprehensive sources of information on immunizations or data had to still be actively managed and re-entered by staff. due to the high degree of centralized control over registration, the vital records is were near to being true enterprise-wide systems, e.g. a single is served the entire state. as a result there were no other “official” public health systems with which the is had to be interoperable. barriers to data acquisition & sharing more than any other theme, the categories of data in this theme focused on the role of public health system and organizational level factors in data management. issues fundamental to public health, like jurisdictionally defined work, measurement of populations, and data ownership, each affected how practitioners obtained or shared data. this theme was evident across all programmatic areas and lhds. often practitioners knew relevant data existed elsewhere, but could not access it. for one, interviewees confirmed mobile populations fragmented client data. for example, a nurse from the rural lhd explained that a sizable percentage of her clients were actually from a large city in a neighboring state. additionally, jurisdictional boundaries translated into restrictions on data access. this was true even for programs with shared is. if a nurse at the urban lhd reported a case, but investigation determined the individual lived in another county, eventually her ability to view much of the detail on the case would be limited. the suburban lhd reported the same http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi issue. for vital records, viewing and editing were also curtailed for out of jurisdiction individuals. the exception among the group was immunizations: the capability to get or view out-of-jurisdiction information was less of an issue due to shared iis. issues around which agency controls data, regardless of how collected, were most pronounced at the urban lhd. for communicable diseases and vital records, staff did not have direct control over, or even direct access to the data they collected on their community. instead they relied on extracted datasets from the sha. despite the lack of control, the lhd believed they owned the data. few practitioners reported barriers to getting data to the sha, which was not surprising since the sha supplied most of the main is or the sha was receiving paper forms. instead, practitioners mentioned idiosyncratic work processes, the complexity of dealing with multiple departments, and narrow reasons for sharing data. for example, reportable condition staff at two lhds mentioned having to run reports for the state when the information was already available in a shared is. lastly, for most locals, data sharing with the sha was part of business processes: immunization programs for monitoring inventory and compliance; vital records for registration and issuance; and communicable disease programs were “required”. while local practitioners valued data, they generally did not recognize value in sharing data with the sha. consequences of data sharing barriers and is quality problems many examples of inefficient and wasted effort could be expected from challenges with technology: double data entry, submitting extra paper copies of forms along with electronic data, multiple phone calls, and duplicated information requests. is could even complicate the relationships with local providers if they too were forced to do double data entry. likewise, workarounds existed to both inputting and retrieving data. for example, communicable disease staff used comment fields to record information in the sha provided is. this practice was disease specific as some conditions had is fields that corresponded to all the information captured on paper field records, but others did not. getting data back out of systems was often difficult, because not all fields could be queried or local staff did not have access rights. to get around this challenge, communicable disease staff would call sha employees with the sufficient access to request custom reports or for specific inquires. each lhd in this study used at least one shadow system, a parallel is that only existed to provide easy access to data already stored somewhere else [25]. these additional is ranged from spreadsheets listing cases to local relational databases designed to manage all aspects of public health reporting and analysis. the origins of the systems were primarily linked to inaccessible data, the need to retain data that could not be entered into state is, or differences between local’s and the state’s preferences for data management and recording. public health practitioners easily justified the use of shadow systems: their “home-grown” systems provided timelier, more complete, more accessible, more accurate, and more useful data than the “official” state repositories. http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi use of information across all programmatic areas, public health agencies collect data for action. interviewees recognized the value of data to both their agencies and their constituents. additionally, they identified applications for the data whether it was for managerial decision making, information sharing with the sha, or for the potential to improve public health. unfortunately, interviews revealed is quality, data sharing barriers, and organizational capabilities individually or combined made the turning of data into information difficult. some of the most striking limitations on the use of information were around management decisions and strategic planning (table 3). lack of reporting capabilities or insufficiently detailed reports limited the ability of the lhds to use that information for broader planning purpose. also the lack of integration between the systems did not allow staff to “pull that information out and utilize it effectively”. sometimes it was employee’s skillsets or competing responsibilities, but other times practitioners did not see the value in aggregated information. discussion the process of acquiring, managing, and sharing public health data at the local level is complex. numerous is of varying quality and capability both support and complicate the process. our interviews with public health practitioners revealed a need to improve data sharing efforts and activities in order to promote efficient public health practice, support decision making, and ensure confidentiality and security. lhds are obligated to share data on their communities with their sha [26] and the interviewed lhds were meeting that obligation. however, difficulties and inefficiencies permeated the entire process. these challenges are not surprising and almost natural outcomes of the complicated and multi-faceted mechanisms and processes by which lhds collect and manage data. for example, each program needed multiple is in order to provide services or public health activities. the use of multiple, different is complicates work through multiple passwords, log-ins, and switching between systems [12]. if interoperability is absent in a multi-system environment, as was often the case in our observations, then double data entry and other inefficiencies result. lastly, the continued reliance on paper as an important part of data management also contributes to inefficient work. hybrid paper-is data management approaches are slower and less productive than is alone [27,28]. table 3. quotes explaining the challenges to turning data into information for public health practice according to local health departments staff position, lhd quote communicable disease supervisor, suburban lhd “i feel like there's a lot of data that comes in, but there's not a lot of data that goes back out into the community…why are we collecting all this data if we're not informing people of what we're finding?” vital records staff, suburban lhd “the [lhd director] wants the information for statistics and sharing with city planning to see where risk areas may be…the http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi state does have canned reports that we can request, but he wants more specific and that we cannot get it from the state or we can’t generate it ourselves without individually going through each birth certificate and pulling out the information we need.” epidemiologist, urban lhd “for the most part, the state reports, they don’t always have the data that we need at the local level. and since we can’t run reports at the local level, we do have to go back to the state to request the information.” epidemiologist, urban lhd “…people at the state have found that sometimes their own data release policy is too restrictive and there’s definitely people who work there that do realize that there is some value in releasing some aggregate data so any of the stakeholders who are interested in it could learn something from it.” communicable disease supervisor, suburban lhd “i don't know how to run reports well enough for std yet to be able to feel comfortable pulling the data and using it for strategic planning. i would rely on our old manual paper because i know that is accurate.” nurse supervisor, suburban lhd “i’m so busy dealing with the day-to-day activities that it’s like, ‘okay you need this report, fine i give it to you whatever,’ but truly right now i’m just kinda like overwhelmed ...” communicable disease staff, rural lhd i've heard them talk about making reports...but i really don't have the need for it right now.” immunization program director, urban lhd “the biggest challenge we have is the overall integration of the data systems. so it's not just immunizations, it's std and communicable disease and everything that they're doing, there's health information in there, but i don't think there's anybody that really knows how to pull that information out and utilize it effectively. we have such a patchwork that there's no way, without an incredible amount labor and resource, to sort of pull those things together.” several is characteristics thwarted public health professionals efforts to turn data into information for planning and decisions making purposes. lack of interoperability and multiple is did not support obtaining a complete picture of the health of a community. also important was the absence or ineffective reporting features in several is. the ability to access information in a meaningful and easy manner is a marker of is quality [22]. more importantly, without accessible information lhds were limited in their ability to use evidence-based decisionmaking, engage in strategic planning, or undertake quality improvement efforts [3-5,29]. when lhds do not possess sufficient internal data capabilities, they rely on the sha [30]. in these instances, lhds need to work with cooperatively with their sha counterparts to identify reports that meet their local needs. while is quality complicated work, our finding suggest that attempts to improve data management in public health practice need to adopt a socio-technical perspective. sociotechnical theory emphasizes the interplay between is, individuals, and their broader contextual work environments to improve is effectiveness [31,32]. as an example of this dynamic, we http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi documented the influence of state policies on data management and usage issues. communicable disease and vital record staff routinely employed is that contained information to which they felt they needed access in order to effectively do their jobs, or it was information they themselves had created. yet, that information was unavailable solely due to policy and not due to technology. likewise, practitioners did not always possess the skills to effectively use all of the is capabilities. under these types of contextual constraints or skills, problems will not be addressed by simply upgrading or buying a new is. given financial limitations facing many agencies nationwide, upgrades or new technologies may not be feasible anyway. instead solutions to these problems will come from inter-organizational collaboration, increasing organizational capacity, workflow redesign, and end user training. the prevalence of shadow systems represents an area of concern. shadow is are a consequence of a failure, or perceived failure, of the enterprise is to meet users’ needs [25,33]. these needs can be access to information or desired analytics and reporting capabilities [34]. in this way, shadow systems are an extreme case of a workaround; practitioners want to do their job enough that they are willing to duplicate entire systems. shadow systems, even rudimentary spreadsheets, come at a cost: they must be created, they often require double data entry, and they must be supported and maintained. as another potential cost, they are a security and a privacy threat [35]. older systems do not have the same security protections as newer is and accidental disclosure of confidential public health information has happened in the past, because sensitive information was recorded on spreadsheets [36]. finally, is presence, quality, functionality, and governance differed by programmatic area. as a result, within each lhd, is capabilities and experiences varied widely and staff even functioned under different access policies for patients or cases outside their jurisdiction. the categorical and disease-specific nature of public health funding [37,38] may contribute to these different experiences. for example, immunization staff had greater capabilities and policies facilitating information access, probably due to the decade long investment and national priority around childhood immunizations and iis [39]. current trends may address these differences in is; specifically, public health accreditation places an emphasis on understanding data sources, technology; and community planning requires data [40]. a coupling of improved organizational awareness with flexibility around funding and investments would help address the wide variation in is [38]. study limitations we interviewed a diverse set of lhds and explored three major program areas, but the findings may not generalize to other departments or activities. our small sample may not be nationally representative of the experiences of practitioners, where is and data quality issues may be common [6,7,12]. we also acknowledge that or perspective on data sharing is limited; we have no data from the sha perspective. this would be an important avenue for future data collection as shas tend to have more advanced is capabilities than lhds, but have more partners with which to share data. additionally, is is a critical mechanism to link the efforts of public health and the healthcare system [41], but our interviews did not fully investigate that area. given the current national emphasis on health information exchanges and electronic medical records [42], we http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi acknowledge that the results reflect a current reality that might be rapidly changing in many lhds. conclusions local public health involves the use of multiple is supported by paper records. current approaches to data management and sharing do not always support efficient public health practice nor allow data to be used for organizational and community decision making. some of these challenges can be addressed through shas cooperatively working with lhds in the state to define standard work processes and to establish is governance that supports local practice. acknowledgements the project was approved by the institutional review boards of georgia southern university and weill cornell medical college. we thank julie beth heiniger for her assistance with data management. the contributions of l. michele issel, sean lee and julie beth heiniger were supported in part by hrsa bureau of health profession, division of nursing, grant # d11hp14605. conflict of interest the authors have no conflicts of interest to declare, financial or otherwise. references 1. lee lm, thacker sb. 2011. the cornerstone of public health practice: public health surveillance, 1961--2011. mmwr surveill summ. 60(suppl 4), 15-21. pubmed 2. schauer sl, et al. 2009. the use of an immunization information system to establish baseline childhood immunization rates and measure contract 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using information systems in public health practice: community out-of-hospital cardiac arrest patterns 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi 42. burke t. 2010. the health information technology provisions in the american recovery and reinvestment act of 2009: implications for public health policy and practice. public health rep. 125(1), 141-45. pubmed appendix. themes and codes with definitions used identified from content analysis of interviews with local public health practitioners. theme code definition barriers to data acquisition from others the system level context, organizational level factors, or situations that affect the need or ability of staff to get information from other organizations or sources jurisdictionally defined work differing roles and is responsibilities based on political areas/jurisdictions or geographic areas of program implementation/oversight, as it pertains to subsequent acquisition of data mobile populations citizens/patients receive services at various locations which fall under different jurisdictions with different is/ forms/ policies, as explanation of why data acquisition is difficult data ownership description of which organization(s) or departments control/owns which data elements or overall data that are needed, as it pertains to subsequent acquisition of data data access control nontechnical aspects and policies of departments that determine who can access /use what information in existing is; blocked access to needed data elements barriers to effective data sharing (to others) experienced and reported difficulties, challenges or factors/situations that need to be overcome/addressed in order to provide data to others multiple data partners at state having to deal with different state offices / agencies / departments for data related to a given health topic more than 1 is to do job job or single task requires the access / use of more than 1 information system task technology fit issues regarding the match or appropriateness of the design of the is to public health work, including fragmentation of the is across agencies/departments organizational capabilities skills within the organization /department (analytic, technical) to be able to use information, as antecedent to sharing data or reports reporting back lack of information flow back from other organizations and departments with whom data had been shared (ie, reports) regarding use or quality of those data; no http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20402207&dopt=abstract experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi feedback loop organizational sharing partner attribute code to clarify / describe who data sharing is occurring with. other lhd other local health departments in other jurisdictions. state health agency – general the state health agency in general – not specific to any unit or department with the agency state counterpart department the counterpart department with the state health agency (e.g. immunizations, communicable disease, vital records) providers any healthcare organization, provider or physicians other all other organizations (funeral homes, charities, social services, etc) factors affecting isq descriptions of factors, circumstances, or conditions that affect specific quality characteristics or overall quality of the data within the is sources of error explanations of how errors are introduced into the data, general comments regarding sources or extent of errors data logistics/work processes descriptions of the work process of collecting & reporting information and the ways those work processes are related to specific or overall data quality use paper for job instances where paper is required or used in parallel with is (both forms & as a paper-based record keeping) in order to have complete data (not shadow system) isq timeliness issues affecting or perceptions of the timeliness of the data in the is isq missing info the type or extent of information that is missing in the is, either specific data elements or entire records isq accuracy the type or extent of information inaccuracies, such as wrong values or unbelievable information isq accessibility technical and software factors related to the availability and retrieval of information from the is; user friendliness of the is interface security and confidentiality issues related to assuring the security and confidentiality of the data as they effect data quality (ie, ability to edit and correct data), irrespective of data ownership isq multiple data sources factors related to the quality of the information due to multiple users (ie data managers, data entry personnel, providers) or multiple sources of the information isq interoperability factors related to the ability of the is to export/import data from other information and computer systems isq reporting / output capability ability to manipulate the data or generate output/reports using the existing software isq inclusion rules what makes individuals eligible to have their data included in the is http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi consequences of data sharing barriers and isq problems all consequences or outcomes associated with the inability to efficiently secure desired information from other sources and of having poor data quality duplication of work/re-work / inefficient work repeat of work or use of inefficient work practices (ie calling to get missing data) stemming from having poor is quality or data logistic procedures workarounds additional work processes and communication efforts developed and used to overcome /get around /avoid barriers encountered in data availability or use shadow is creation and use of additional is or duplicate is, databases, or repositories, due to accessibility or functionality issues, in order to store and use information already available in other system, such that staff do not work from a single is effects decision making limits strategic planning, community planning, environmental scanning, or community assessment resulting from incomplete data effects health problems negative effects for health of individuals or populations resulting from is problems hardware considerations descriptions of concerns, issues, or experiences specifically related to computer hardware and its maintenance hardware & data backups comments regarding the characteristics of the hardware which affects its usefulness (ie interface of parts), and of the degree of capability to maintain backups system stability comments regarding the reliability of the hardware in terms of having an overall stable computer system (ie, not crash) data quality focused solutions descriptions of anticipated or actual ways identified to correct or overcome the known problems with the actual data elements identify & correct error actions to pin-point the incorrect data element or record, and the associated actions to correct that specific error in the data technical support support options available to help with issues / correct mistakes or provide analytics sharing focused solutions descriptions of anticipated, potential or actual ways identified to correct or overcome the known information sharing problems regionalization changing of jurisdictional limitations to focus on larger community areas as means to improve data sharing benefits of is descriptions of perceived or actual advantages to individuals and populations, and organizations from http://ojphi.org/ experience of using information systems in public health practice: community out-of-hospital cardiac arrest patterns 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e227, 2014 ojphi having an is customer benefits benefits seen by customers, citizens, society from having accurate, timely data use of information descriptions and explanations of how public health practitioners apply (or fail to) information useful information comments on the overall usefulness/not of the data in is management executive, administrative or managerial uses of information to help the work and operation of the lhd community partners sharing of information and reports with the community practitioners how individual practitioners apply information to public health activities other government partners sharing of information with other government partners required state reports use of information in required reports to the state required sharing instances of mandatory or obligatory reporting to other agencies is uers’ views descriptions of personal views, opinions and perspectives on the current and future of the is that individual is working with personal responses to is emotions (positive and negative) triggered by working with the is meaning of information & data distinctions made or differences mentioned between data and information full vision re-thinking about how it/is should support public health and what changes should occur to new systems http://ojphi.org/ 5060-37987-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 40 (page number not for citation purposes) isds 2013 conference abstracts combs: a biosurveillance ecosystem (bsve) prototype michael e. cleary*, erik t. antelman, natalya markuzon, sarah m. miller, timothy a. postlethwaite and zahar prasov the charles stark draper laboratory, cambridge, ma, usa � �� �� �� � � �� �� �� � objective ������� �� ���� ������������ ������������������������������ ����������� ����� ���� ���������������!���� ����� ���� ������������������� ��� ����� ������� ������������ �"�������������������� ��� ������ ����#� �$��������%�����������&� ��' �� ���������� ������������ �#%&���� ��������������� ������(��$�������� ���������������' $� �����"� �� �����)� �$��� ��� �������� ��� ������������ ��������� ������*����)� � ��� ���$ ������ ����������������� ��� ��� �� �����(���" introduction #%&������ ��� ����������� ����)��+ �)���� ��� �$�������*������ �� ���� ������������������������ ���$��'�����*�� ������ ����� ��������� ��)�����$� �������� ������� �����*��� ������ ������������������ ����� )� ����������� �������(����������������$���� ������ ���� ����������' ������������)����� ��)�� ��������������� ������������������������� �� �$������� �" methods #%&��� ����� ��� ������ +��� ������$������� �,������ -�"� ,����*� #%&���)� ����� ������(��������� ������.�$�$������� �������������� �� ��������� � ������������ ������������������� ���*��� ���� ���� � ����� ����� ������ ���*������ �(���*��� ��������������������� ��������� � ����� ���"������ *�����#%&��� ������������� �� ��� �����������)� � ���$ ����������� �(�������� ��/��������� ������������������������� ���+��� ����0�1������������������*����*���������� � ������ � ��� ������� ���*��� ����� ���������*��� �������$ ��"�,��� �*�#%&��� ����� ��������������� ����� '���������������������������������� ��� ��������� �(������� � �����������*���� ������� ������ �(��������� �*� �� ��� ����� ���������" 2���� ����������� �������+��� ��������� ����������� ���������� �����������������������������������������������)����� ��)� �������� #%&�������������������� �$ ��)������� � "�2��������������� � �� ��� � �����������������)� ����������� ��������� ��� ������������� � ����$� �������������������� ��������������� ��������)�� ��������"� #���������)� � �� ������ �����+����������� ��������"����� ������ )�� ��������������)� �$���$ ���������� �� ����)����� ��� ���� �������' ������������ ���/����"�3���������� ��� ������)� ������������������ ���������� ��+���� ������ �������������������������������� ������ � � �� ��������" results ����������������������������� ��456����� ���*�����#%&��� �������� ��� �� � � ��� �������� ���������������$� �����7������������*� ������*����� �������*�#� �$��������8�#������������*� �� �����8� ��� �*��� ����������"������ ��������������������������� ������������� ���� ���� "� ��������������� �� '$��� �#%&��� ���$��� ��,������4�����)����� ������ ����)� �������������� ����� �)��+����������������$���� ������� ����� ������ �����������������"����� ���$��� ���� � ���������(�$ �*� ���$ �������������� ������ � ��������������� �����������)� ����$ ��+��� ����$� �����" conclusions ������������������� ��� ������#%&������ ��� ��������� ���� ����' ������ ��� ������� �������������������$�� ����� ��������$������� ���7 '� � �/�$ ��� �� '$��� � ����������������� �� ��������������� �� ��������� �������������� �$�������� ������� ��������� ��������' ������� �1���������� ��� ������������������� � ��������� ��� ����� � ��)����������* '� �����'�������(�$ �*�����'��'������������ ����)��+$����* '� #� �$����������� �������������.�������� ����*��� ��������' ������ ����������� �$��� ����*��� '� �������� '����������������������)������������� �(�' ������ � �����������*���� ������� ������ �(��������� �*��� �� ���������" ,������-7�#%&���%��������� ����) ,������47�#%&���2�������������$��� �#������ keywords $�������� ����9���$ ������ ��9�� ���� ���������9���� ����)��+$���� acknowledgments �����)��+������������ �$�������� �������������� ������� ���������� �*� �����������������:;;<<-'-='#'4<45*�����$ ���� ��� �� ��������� �$������ �������� �:��� �>�� ������������#������2��� ��" #���������?�4<-=�$������#��� �������+��������@�$�������*�3��"� ����' �������� ����������� ������)��+��������� � �����������������������������*��� � � ����������� ���������������� �>��+������$��� ��� �� �$� � ���������)���� � ���� �� " *michael e. cleary e-mail: mcleary@draper.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e26, 2014 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 150 (page number not for citation purposes) isds 2015 conference abstracts syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemiology, florida department of health, tallahassee, fl, usa objective to determine if emergency department (ed) based syndromic surveillance can be utilized to characterize in near real-time influenza infection in three high-risk populations: a) adults > 65, b) pregnant women, and c) children < 5. introduction near real-time emergency department chief complaint data is accessed through florida’s syndromic surveillance system: electronic surveillance system for the early notification of communitybased epidemics-florida (essence-fl). the florida department of health relies heavily upon these data for timely surveillance of influenza and influenza-like illness (ili). hospital discharge data available from the florida agency for health care administration (ahca) captures information about influenza-associated ed visits and is considered complete. the delay in receiving the data (up to a year) hinders timely evidence-based decision making during the influenza season. previous analyses (comparing the complete ahca hospital discharge data to the essence-fl ili syndrome and influenza sub-syndrome) have shown essence-fl is a timely, effective tool to monitor influenza activity in the state and that the influenza sub-syndrome most closely approximates influenza season activity in florida. adults > 65, pregnant women and children < 5 are at increased risk for morbidity and mortality from influenza infection. this investigation aims to determine if syndromic surveillance can be used to characterize in near real-time influenza infection in adults > 65, pregnant women, and children < 5 by comparing ed visits for influenza and ili in essence-fl to historical ahca records of people who incurred ed charges at a florida hospital with diagnosed influenza. methods florida ahca data for hospital admissions and ed visits from 2008-2014 were queried for patients with diagnosed influenza (icd9-cm code: 487 or 488) in the following groups a) adults > 65, b) pregnant women and c) children < 5. in the ahca dataset, pregnant women were identified by querying for women aged 14-49 with a diagnosis code of influenza and a pregnancy diagnosis code (icd9-cm code: v22 or v23). essence-fl ed chief complaint data for the three target populations categorized into the ili syndrome or the influenza sub-syndrome (or both) for the same time period. in essence-fl, ed visits in pregnant women were identified using a free text query for “preg” in the chief complaint field among women aged 14-49. a time series by week was used to compare the essence-fl ili syndrome data and the influenza sub-syndrome data to the influenza diagnosis data from ahca for each of the three target populations. subsequently, ahca data were compared to essence-fl ili syndrome data and the influenza sub-syndrome (among each of the three target populations) by week to determine the correlation between the syndromic surveillance categories and ahca discharge diagnoses. results in the ahca data set a) 29,503 adults > 65, b) 858 pregnant women, and c) 71,807 children < 5 with influenza diagnoses incurred ed charges from florida hospitals. in the essence-fl dataset the ili syndrome identified: a) 62,015 visits from adults > 65, b) 1,841 visits from pregnant women, and c) 288,033 visits from children < 5; use of the influenza sub-syndrome identified: a) 36,345 visits from adults > 65, b) 1,217 visits from pregnant women and c) 25,858 visits from children < 5 years of age. in all three sub-populations, trend (season onset, duration, and end) and volume analyses showed that ed visits categorized into the essence-fl influenza subsyndrome best matched the acha data for approximating the impact of influenza in the target populations. correlation analyses found that the essence-fl influenza sub-syndrome had higher correlation coefficients than the ili syndrome when compared to the ahca data by week (albeit these gains were very small for the adults over 65 and pregnant women groups). none of the queries had a correlation coefficient lower than 0.76. conclusions syndromic surveillance can be used not only to monitor overall influenza trends, but is also effective for timely surveillance and estimation of influenza activity in three target populations: a) adults > 65, b) pregnant women, and c) children < 5 in florida. while rich, complete ahca data may be up to one year old, which does not allow for timely and informed decision-making and prioritization of resources during influenza season. keywords influenza; syndromic; at-risk populations *heather rubino e-mail: heather.rubino@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e74, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city 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cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana bruce macleod 1 , james phillips 2 , allison e. stone 2 , aliya walji 3 , john koku awoonor-williams 4 1 mailman school of public health, columbia university, new york, ny and computer science department at the university of southern maine, portland, maine 2 mailman school of public health, columbia university, new york, ny 3 grameen foundation, seattle wa 4 upper east regional director of health services, ghana health service abstract this paper describes the software architecture of a system designed in response to the health development potential of two concomitant trends in poor countries: i) the rapid expansion of community health worker deployment, now estimated to involve over a million workers in africa and asia, and ii) the global proliferation of mobile technology coverage and use. known as the mobile technology for community health (motech) initiative, our system adapts and integrates existing software applications for mobile data collection, electronic medical records, and interactive voice response to bridge health information gaps in rural africa. motech calculates the upcoming schedule of care for each client and, when care is due, notifies the client and community health workers responsible for that client. motech also automates the aggregation of health status and health service delivery information for routine reports. the paper concludes with a summary of lessons learned and future system development needs. key words: maternal, child health, electronic medical records, mobile phones, low resource settings introduction mounting evidence that community-based primary health care can improve the survival of mothers and children have fostered international commitment to expand community health programs in poor countries 1 . over a million paid community health workers now work in village http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 locations in asia and africa 2 . this trend coincides with an acceleration of access to mobile telephones, currently estimated to number in the range of five billion globally 3 . these simultaneous developments have generated international interest in potential synergies that could improve the quality and coverage of primary health care. in 2009, columbia university, the grameen foundation, and the ghana health service responded to this opportunity by launching a program of technology development and research designed to evaluate the potential uses of mobile technology in supporting community health operations. known as the “mobile technology for community health” (motech) initiative, the project focuses on leveraging mobile phone technologies to improve the health of the pregnant women and young children in the upper east region of ghana –an impoverished and isolated locality where childhood tropical diseases such as malaria are hyper-endemic, mortality is generally high, and risks associated with childbirth are extreme. this paper describes the context that motivated the design of motech and the software architecture of a pilot system that is now in operation in one district in northern ghana. the problem high quality and timely health information is not readily available to pregnant women and new mothers in the upper east region. internet access is virtually non-existent, literacy rates are low, and travel distances to clinics can be significant for women. the need for quality health information in the upper east region is especially important. local beliefs and superstitions oftentimes guide women where there are no other information alternatives reflecting international norms for best practices 4 . for example, in kassena-nankana west, elder female relatives encourage younger pregnant women to labor at home for as long as they can stand before going to a health facility, as this is a mark of strength and willpower 5 . for these reasons and others, ghana health service encourages women to visit local clinics at least four times before delivery. in the women’s first visit to the clinic, they are given a small paper booklet that provides basic health information for the pregnancy and includes a schedule of upcoming care. unfortunately, in many cases the woman is simply unable to access the information in this booklet because she is illiterate. information about infectious diseases is limited, even for conditions that are hyper endemic. in particular, malaria remains the major cause of death among under-5 children, despite low cost and effective means of preventing and curing illness 6, 7 . moreover, the proper use of intermittent preventative treatment during pregnancy (iptp) can reduce maternal anemia, fetal loss, and dangerous complications for the unborn child. frontline health workers in ghana use five large 11x17 inch notebooks, termed “registers” by the ghs, for recording health service delivery information. the daily register maintains basic information about the patient, date, and the nature of the visit. there are also registers for more detailed care information about antenatal care (anc) for pregnant mothers, postnatal care (pnc) for young children, and family planning. the proper use of these registers can be compromised in a number of ways. when frontline health workers are working outside the clinic, we found that many nurses use their own, smaller, notebooks to record information. this data from the community outreach sessions is then transferred to the registers on their return to the clinic and it is not surprising to expect problems in linking the individual level data from the two sources. when a register is full and cannot hold http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 additional information, the health worker is instructed to transfer information regarding currently active patients into a new register, creating the possibility of inaccurate transfers or missing information. nurses are expected to determine when clients do not meet the recommended appointments for routine antenatal and postnatal care. determining these lapses in care would require regular care schedule calculations for each client and this work is not always done due to low prioritization, inadequate training, or insufficient supervision. finally, the proper linkage of longitudinal care information for a patient can be challenging when different frontline health workers, such as nurses and midwifes, both access and update information in the registers, and patient identification is not always obvious. health administrators and planners benefit from a clear and accurate picture of health service delivery operations and the health status of the population. to obtain this information, nurses in ghana are asked to summarize anc, pnc, family planning, morbidity, and mortality counts by gender and by age group. this data reporting activity usually requires around three days of work every month. . the quality of the data reported can be easily compromised by not recording all health service delivery or not performing the correct calculations. more fundamentally, this data management task is not central to the routine operations of the nurse and it can be delayed when the nurse prioritizes patient care over the reports. all of this combines to create information needs and bottlenecks for all participants in the health system (figure 1). in particular, clients: mothers and their children community health officers appropriate care schedules managers clients due for care figure 1: health information needs and wants http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012  clients seek information to guide health behavior, but clinics can be remote and accessing them detracts from farming or domestic work responsibilities. in addition, health workers can be preoccupied with clinical duties with little time for household outreach. clients may not be conversant with english and workers may not understand local languages and this linguistic divide can impede the flow of essential health information.  frontline health workers use paper based registers to document health service delivery. ideally, nurses should follow up on patients who do not meet their appointment schedule, but workers often either 1) are not trained adequately to identify defaulters in their registers; 2) neglect this work due to lack of time or motivation; or 3) lack supervisory support and oversight in this work.  health administrators seek information on the health status of the population and the extent of front line health operations. while each nurse will aggregate service delivery and morbidity information from the paper registers every month, this involves calculations for hundreds of different data points because data needs to be reported by age group, gender and the type of service delivery or disease. the quality and timeliness of the reports can be compromised as a result. the context the motech pilot is located in one district of the upper east region, known as kassenanankana west, a locality on ghana’s border with burkina faso. in 2010, the population of the district was approximately 74,000. the ghana statistical service ranks the upper east region as the poorest of the 10 regions in the country and the most rural (84 percent). the kassenanankana west district where motech is under trial remains isolated, rural, and remote. the practice of traditional religion is widespread; rites, rituals, and beliefs tend to reinforce and sustain institutions of patriarchy, lineage, and male dominance 8, 9 . a strong cultural emphasis on the lineage, in turn, emphasizes the role of the corporate extended family in arranging marriage for the purpose of perpetuating the lineage. over one half of married women were in polygamous unions in the baseline period. geography and human ecology of the area accentuate social isolation and complicate efforts to organize health and human services in the locality. the settlement pattern is highly dispersed, with no towns, modern markets, or industries that would develop centers of trade and communication. there are three primary languages in kassena-nankana west district and two of three languages have no established written form. most of the populations are kassim or nankam speakinglanguages that provide only fragmentary communication links to ghana's southern cultures, restricting exposure of the population to outside ideas more generally. although female literacy is increasing, the rate was only 13 percent in the 1990s. health conditions are known to be improving, owing to the successful introduction of community-based primary health care in 1996 by a project of the navrongo health research http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 centre. termed the “community-based health planning and services” (chps) initiative, this program provides free basic health care for pregnant women and young children in community and doorstep services that include anc services such as tetanus injections, anti-malarial medications (ipti) and iron supplements. chps has been an important source of primary health care in the uer because the region generally is lacking medical doctors, with substantially less than the national average of 1 doctor per 8500 people. as in most of ghana, trained nurses deliver much of the routine health care. in response to the critical need to address manpower shortages and service accessibility problems, chps places nurses in village posts to provide primary care where they are known as community health officers (chos). they provide care to small catchment areas consisting of a few thousand individuals known as “chps zones.” while the navrongo project resulted in steep declines in child and maternal mortality in the 1990s 10 , evidence suggests that its mode of operation has been changing as it has been scaled up in recent years. immediately prior to the introduction of motech, baseline research found many chos were not placing a priority on community outreach, but were instead waiting for clients to come to their facilities 11 . one of motech’s premises is the hypothesis that as chps developed a focus on the construction of health posts, supervisory attention on household services waned in the decade following 2000, and eroding emphasis was placed on community outreach activities 12 . during this period, cell phone coverage for this rural, agrarian, society has been expanding rapidly. as in much of africa, the lack of investment in fixed line telecommunications has allowed the companies to quickly introduce advanced wireless communication and fill an immediate need for communication services 13, 14 . in this region, the telecommunications networks support the gprs data service in most areas which allows basic communication between cell phones and internet servers 15 . baseline research found that in kassena-nankana west, 23 percent of women of reproductive age personally own a phone; 33 percent have spouses that own a phone; 15 percent have access to a phone owned by other members of their family compound; and 12 percent said they could access a phone in their community if they need to. only 16 percent of women said they had no access to a mobile phone. frequency of cell phone use varies significantly with type of phone access. more than 95 percent of respondents with personal access used their phones at least weekly vs. 40 percent of women with spousal access to a phone, 25 percent of women with access in their compound and 12 percent of women with community access. methods bridging the health information gaps motech integrates the mobile phone into the rural health system to bridge key health information gaps. pregnant women and new mothers who own a phone or have access to a phone can get relevant, personalized health information from motech. nurses are given a low cost mobile phone (less than $50) to enter data and receive motech system generated messages about patients who are overdue for routine health care. the data entered by the nurse is also used to automate the generation of reports for district and regional health administrators and http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 planners. some of the particular usage scenarios from the perspective of the key motech target groups include:  clientele: pregnant women and caregivers of children under one year can register to receive messages from motech. once registered, they receive weekly health educational voice messages (in their local language) that correspond to the gestational age of their pregnancy or the age of their child. for example, a woman in early pregnancy might receive a message explaining that nausea and/or vomiting is expected and normal during this stage. if the client is due for scheduled care, then an early notification of the upcoming clinic visit is included with their weekly educational message. if a patient misses a phone call, or if they do not have regular access to a phone, they can “flash” the system and they will be immediately called back. then, by entering their unique motech id number, they can retrieve their personal messages.  frontline health workers: nurses send patient data to the motech system using data collection software that operates on low cost mobile phones. currently, there are sixteen different types of data entry forms pertaining to various client encounters, such as patient registration, antenatal care visits, postnatal care visits, and delivery information. the mobile phone application also includes various query forms that allow real-time retrieval of information regarding clients who are overdue for particular services in the nurses’ chps zone. nurses receive a weekly sms message from motech about clients who are overdue for routine scheduled care. this includes a list of any pregnant women that have missed antenatal care visits and children that are overdue for receiving specific immunizations. with these reminders, motech makes information about clients in need of care readily available to health workers, with the aim of increasing their outreach to provide these services. community health volunteers also have a role to play in motech: when they become aware that a home birth has occurred in the village, they can contact the motech call center to report the delivery. once motech is notified of a birth, it will send an sms message every six hours to the health worker responsible for the community where the birth occurred to ensure postnatal care is promptly provided. this alert is repeatedly sent until the nurse sends detailed information about the birth and provision of postnatal care to the system, after traveling to the village to see the new child.  health administrators and planners: the data captured by motech (via mobile phonebased entry by health workers) is aggregated to generate the monthly reports that are submitted to district health managers. these reports are normally produced by health workers scanning their paper registers, keeping a tally of health services provided throughout the month, and writing the reports by hand. in motech, if nurses accurately capture all of their health services data in the motech phone, then they can use the monthly reports generated by the motech system rather than completing these monthly reporting forms by hand. health administrators and planners now receive reports that are generated from information that is used as part of routine health service operations. in addition, the regular inflow of patient data has allowed for the development of new types of reports that give a real-time monitoring of the health service delivery activities in rural locations. http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 for motech to address all of these concerns, its design and implementation requires nothing less than a systemic change in ghana’s existing health information system process (figure 2). new ways of conveying best health practices to clients are needed, the burden of paper-based reporting should be reduced, and the information systems support for nurses needs to be strengthened. these factors are interrelated, so solving only a piece of the problem risks developing an approach that will not scale and misses key opportunities for synergy. an approach that jointly addresses the health information needs of clients, health workers, managers and policy makers is poised to make the best use of limited resources and have the greatest potential for sustainable results. as figure 2 illustrates, data capture is managed by front line workers who have limited capacity mobile phones that update a server database. the server, in turn, supports workers and mothers with alerts and reminders, and feeds back reports to workers, supervisors, managers and policy-makers, mechanizing previously cumbersome capabilities for information to support services, supervision, management, and planning. thus, the central goal of motech is to transform health management information system operations from a data extraction operation to an information support system for all key stakeholders in ghana health service operations. clientele frontline health workers policy makers and health administrators reporting of key events personalized alerts via voice/sms messages aggregated data reporting automated supervisory reports cell phone data capture automated reminders & alerts motech server community health volunteers informationbased services figure 2: motech as system for addressing information needs of workers and mothers while there are many health priorities in this region, the project made the strategic decision to focus on the needs of individuals who are most associated with the unaddressed burden of disease in this setting: i) pregnant women or young mothers, ii) women at times of delivery, a majority of whom are delivering at home, iii) neonates, particularly those requiring outreach in the first 48 hours of life, and iv)children less than one year of age who require immunization and curative health services. given the significant levels of maternal and child mortality in the upper east and also that the health care needs of these groups are frequent and time-sensitive, improved information for health workers and clients in these groups holds the most potential for increasing positive health outcomes. further, motech aims to accumulate knowledge from the http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 development and deployment of this system that can be used for other health problems and in other low income countries. the motech system architecture the motech system architecture evolved from a strategic decision to build on top-tier software applications that have been field tested elsewhere in africa. the mobile phone data collection system is based on openxdata (www.openxdata.org). this innovative, open source, software application implements a significant portion of the w3c xform standard (http://www.w3.org/markup/forms/) with a relatively small memory footprint on the mobile phone. as a result, we were able to provide the nurses with relatively low cost (<$50) phones and the software development team was able to quickly design and deploy data entry forms. motech maintains health information about women and children in the openmrs medical record software application (www.openmrs.org). the unique extensible features of openmrs allowed us to quickly develop an appropriate data model and to layer project specific software in the medical record application. finally, the task of delivering voice messages to patients was provided by the intelliivr (www.yo.co.ug) application. the ugandan based company that developed and supports this interactive-voice-response (ivr) application provided expertise in setting up and adapting to the telecommunications situation in ghana. each of these applications brings considerable value to motech and a significant amount of project risk was eliminated because these systems have undergone multiple iterations of deployment and revisions in environments similar to those in north-eastern ghana. the electronic medical record for each women and child is a central element in the motech architecture. information about the health status and service delivery of each client is maintained in an electronic health record. this includes pregnancy due date, visits to the clinic, anti-malarial drugs and childhood immunizations. with this individual health record, motech calculates the upcoming schedule of care for each client and, when care is due, it notifies the client and the health workers responsible for that client. the individual level data also addresses the problem of constructing aggregate level health statistics. the onerous, monthly task for each nurse of manually calculating aggregate health status and health service delivery by age group and gender is eliminated because the system can automate these computations. while implementing electronic health records in africa may seem far in the future, mobile phone data applications and openmrs makes it possible today and these electronic health records provide the foundation for patient messaging and reporting. motech augments the individual level health record with information necessary for client messaging. for each client, data about registration status in the messaging program, phone#s, who owns the phone, language, and time preferences for phone calls are maintained. each client also has a unique seven digit system generated id that is used by both the nurse and the client for identification purposes. nurses use it to identify who is receiving care and clients use it when they call back into the motech system to retrieve their client-specific messages. the most interesting (and challenging) software development activities were centered on two tasks: integrating the three major software applications and providing code to efficiently http://www.openxdata.org/ http://www.w3.org/markup/forms/ http://www.openmrs.org/ http://www.yo.co.ug/ http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 calculate when and where a message should be sent. while each of the three applications provides great support for tailoring the content of the application, in general they were missing the interoperable components that this project needed. therefore, a considerable portion of our time was spent developing robust, fault-tolerant, code that wired the applications together. we expect that interoperability capabilities will improve in these software applications and we see considerable value in building these features into the applications. two specifications, a recommended care schedule and a messaging schedule, are used to determine when a message should be sent to a patient or a nurse. the recommended care schedule provides the timing for recommended service delivery, for example the schedule of immunizations for a child. the messaging schedule is independent of the care schedule which allows for variations in messaging timing based on the message recipient role. from these specifications, motech builds a calendar of upcoming events for each patient. when new data comes into the medical record, the schedules are updated for that patient if the information will alter the upcoming schedule. for example, if the first dose of tetanus is given, the second tetanus shot should be given at least four weeks from the date of the first dose. a periodic task in motech evaluates when a message should be sent from the messaging time specification and the calendar of upcoming events. the recommended care schedule and messaging schedule are defined using xml syntax with the goal of making it easy to use motech in other health domains. there are a number of other, smaller, components of the motech application. figure 3 illustrates some of the key components of the architecture: http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 system administrators administrators, policy makers m-forms upload adapter inbound message processor modules web forms messaging event engine reports patients, clinics, medical staff concept dictionary web server: apache tomcat <<sms gateway>> <<java mobile phone>> <<ivr system>> voice deliverymotech mforms outbound message processor data entry query results nurse mother sms text messages voice alerts & reminders motech call openmrs figure 3: the architecture of motech the extensible features of the electronic medical records system, openmrs, provided a solid foundation to build on. the openmrs concept dictionary allowed the project to combine wellestablished health definitions (anemia in pregnant women) with project specific data fields (i.e., patient telephone number). the extensible module architecture of openmrs allowed us to layer project specific logic onto the medical record system. we used this capability to develop motech modules for web based data management, nurse and patient messaging logic, an event engine that calculates a schedule of upcoming care for each patient, and administrative reporting. we also added a small amount of logic to the core codebase of openmrs. the modification allowed for an efficient recalculation of upcoming care for a client when new service data about that patient was entered in the system. for example, when openmrs receives information about a child immunization, the timetable for upcoming immunizations for only this child is updated based on new data. in this respect, the open source nature of openmrs was critical to our project. the mforms mobile data collection application noted in figure 3 is built from openxdata. this open source software includes a mobile client that runs on low-cost phones, a form designer, and server side data upload features. openxdata implements a significant portion of the xforms http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 standard which uses xml to represent the structure of data entry forms. this gave us the ability to quickly define and revise mobile data entry forms and played a critical role in delivering new functionality in a timely manner. we also modified the openxdata application software to include the ability to report inconsistent data that could only be determined on upload to the server (for example, an invalid patient id). again, the ability to add functionality to existing application with many thousands of lines of code was critical to our success. figure 3 illustrates the data flows that motech software integrates into a cohesive system:  recording health service delivery: when a nurse (upper right in diagram) provides care to a patient; this information is recorded in the appropriate form on the mforms mobile phone application. when cell phone coverage is available, the nurse can choose to upload all stored forms to the server using the gprs telecommunications standard. the upload adaptor component parses the data, performs consistency logic, and notifies the client phone of errors associated with invalid forms. invalid form entries are kept on the cell until the problem is resolved or the data is deleted. the upload adaptor also converts the data into format appropriate to openmrs and updates the patient record. as previously mentioned, when this update is made a new schedule of upcoming care is recalculated based on the date of the new data.  messages sent to clients: when a client registers for motech messages, a calendar of expected care events are generated by the messaging module. the event engine module periodically checks this calendar to see if any clients are due for messages. this calculation is based on the due date and an evaluation of the medical record to determine if the service has been provided. using declarative specifications (with xml syntax), motech will send messages to clients just before the health service (i.e. immunization) is due and continue to send messages for a designated period of time until the medical record system is notified that the service was delivered to the client. the language used for the message and the day and time the message is sent are based on patient preferences. once the event engine determines that a message should be sent to a client, it forwards the request to the outbound message processor which holds the message until the correct time of day and then forwards the message request with the intelliivr voice application that plays the message to the client. if the call is not completed, then the message is stored to be sent again later. motech clients can also retrieve their messages at any time by contacting the system toll-free and then entering their id number. clients can listen to their messages as many times as they would like.  weekly defaulters list is sent to nurses: the event engine will also calculate, on a weekly basis, all clients who are overdue for health service in a particular nurse’s catchment area and send this list to the outbound message processor which then forwards the information to the nurse via an sms gateway. nurses will receive these defaulter messages regardless of whether the client is registered to receive motech messages.  query support for nurses: nurses can obtain information from motech via the query forms of mforms. for example, a nurse may be interested all pregnant women who are due for an antenatal care visit in a particular village. the village name and type of service is entered into an mforms form, uploaded to the mforms upload adaptor which then sends the request to the messaging module. the messaging module uses the event engine to determine all clients who have defaulted on the designated health service in that chps zone http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 and sends the results to the outbound message processor which, in turn, sends the defaulter list to the sms gateway. the nurse will receive the results via sms on their phone.  district health information system reports: data from routine service delivery by nurses, sent from mobile phone data-entry forms and inserted into the client medical records is used to generate monthly reports on service provision. i) choosing a form ii) selecting a field iii) entering data iv) figure 4: the community health officer’s motech data management sequence by the end of 2011, the motech technical team had completed multiple iterations of deployment, testing, and revision so that the system was fully functional in one pilot district. nonetheless, experience with the system has provided insights into ways that its architecture could be improved. in general, the interoperable software logic between the software applications should be less tightly coupled and the configuration of the system for new client messages, mobile data form content, and report generation require too much software developer expertise. results initial lessons learned from implementation in the upper east: preliminary reactions from clients who have received motech messages have been mostly positive. women have said that motech alerts informing them that they were approaching their expected delivery date allowed them to be more prepared when they began labor. client interviews have also determined that motech informational messages promoting facility-based http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 delivery and getting to a health facility as soon as possible after the onset of labor have influenced their delivery preferences and experiences. some women have commented that these messages have empowered them to insist on accessing health services in ways that are sometimes discouraged by traditional beliefs and elder relatives. for example, one motech client said that during a previous delivery she labored at home for hours before going to a health facility, because elder female relatives said this demonstrates a woman’s strength. but during her most recent delivery, after hearing motech messages, she insisted that she go to the nearest health facility as soon as she started feeling labor pains 16 . despite this evidence of success, motech has also encountered significant operational challenges. since electrification has yet to reach most kassena-nankana villages, there are limited locations where telephones can be charged. moreover, charging involves paying a fee to phone charging providers. for this reason, it is commonplace for users to keep their phones turned off until they need to make a phone call. because cell phone use is a valuable commodity, gender problems that constrain women’s access to familial resources apply as well to access to phones. men and senior women in extended families are reluctant to lend phones to young women who might leave their phones on and consume battery power. constrained access complicates motech initiation of the transmission of client messages. for the system to work, women must call the system to retrieve their messages. but, calling back into the system is not always easy for rural women who have little experience with navigating phone ivr routines. these challenges have been noted in other research projects and this is clearly an area where more research and investigation is needed 17 . we are also in the process of developing a better understanding the operational challenges and benefits to frontline health workers. in some cases, better training is needed. for example, up until a couple of months ago, most community health volunteers were not reporting deliveries in the community. for the community health officers (nurses), the extra burden of entering patient data needs to be weighed against the benefits of automated report generation. some chps zones have been very diligent and proactive, but not all. more research, and quite possibly software refinements, is needed here as well. evaluation plan motech is a complex system with several different processes and engagement with multiple target groups. understanding the impact of such a system requires a multi-dimensional evaluation approach utilizing quantitative and qualitative methods, examining the effects of the intervention on health workers, clients, managers, and policy makers. columbia university’s mailman school of public health, in collaboration with the navrongo health research centre located in the upper east, has designed an evaluation framework that seeks to understand the impact of the motech pilot on the health-seeking behavior by clients and the productivity, effectiveness, and the quality of data reported by health workers. research also seeks to determine any challenges or barriers in using the system and any other unpredicted effects of the system on health workers and clients. http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 discussion moving towards a more general health messaging system as motech became operational, we received requests to adapt the application to support data collection and messaging for other health problems and in other regions. many of the key extensible elements are in place. openmrs supports the definition of new data concepts, location hierarchies, and health system providers. openxdata has a form designer and data upload features that can be used without any custom programming. finally, the design of the motech messaging uses an xml specification that allows users to declaratively specify which clients are eligible for a message and what the timing of those messages should be. still, there is much work to do before we can realize the vision of a health messaging system that will require little or no programming. changes are needed in some of the components we use and in some of the software that resides at the interface of the larger components. these include:  use of an open source ivr system that uses the vxml (voice xml) standard. the current use of a proprietary ivr system would require license charges and, quite possibly, customizations for new applications. relying on the vxml standard for the motech ivr interface would allow multiple ivr solutions to be used in motech. options include deploying a project-owned ivr system (for example, the open source asterik/voice glue software, www.asterik.org and www.voiceglue.org, is vxml compliant) or using a ivr service provider like voxeo (www.voxeo.org) that supports the vxml standard.  an integrated motech content manager could help new projects to manage the message content that is sent to clients. the client content evolves through time and, currently, making changes requires software developer support. an integrated content manager would allow these changes to happen with programming.  improving the interface between cell phone data collection and openmrs. currently, software needs to be written to map fields from mobile data collection forms to the openmrs data model. ideally, this could be done without requiring software to be written. one possibility is to use a data integration tool, such as mirth (www.mirth.org).  the event engine module of motech may be better implemented with rules engine software. it is likely that new applications of motech will specify different schedules of health care and client messaging. without a rules engine, this would require software development. rule engines generally have a mechanism to specify a collection of rules using a graphical user interface and collections of facts can tested against the rules. for motech, the client care history is a collection of “facts” that can be evaluated against the recommended schedule of “rules” for sending messages to the client. the open source jboss project (www.jboss.org) includes a combined workflow and rules engine that looks particularly appropriate for specifying the messaging sequence logic in motech.  the motech district reporting system is built from a large collection of custom coded sql (structured query language) statements. evolving standards like sdmx-hd (www.sdmxhd.org) may provide a less complex specification system for data aggregation. http://www.asterik.org/ http://www.voiceglue.org/ http://www.voxeo.org/ http://www.mirth.org/ http://www.jboss.org/ http://ojphi.org http://www.sdmx-hd.org http://www.sdmx-hd.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012  analysis and a strategy for scaling up the system for a larger pool of users. our current system is used for a population of approximately 74,000. we do not know what the limitations of the current system are, so an analysis is needed to identify some of the performance bottlenecks. the analysis would be the first step in identifying a strategy for scaling up to a national level. many of the above tasks are being addressed in an on-going motech re-engineering effort (http://code.google.com/p/motech/). the focus has been on developing support for larger populations, applying the ideas to different health systems, and developing a broader range of functionality with features such as pill reminder calls. conclusions motech aims to address health information problems that are commonplace in africa, where the flow of timely and accurate health information is often compromised by limited resources, households do not have formal addresses, and there are limited options for the distribution of quality health information. the application is addressed to the information needs of pregnant women and young mothers, frontline health workers, and district health managers. pregnant women and young mothers can receive regular voice calls from motech that convey best health practices and remind of upcoming appointments. nurses will enter client service delivery information into low cost mobile phones and this information will be used to automate the generation of monthly reports. the software architecture that provides this functionality is built by integrating well-tested applications for mobile phone-based data collection software, an electronic medical record system, and an interactive voice response application. motech is the initial step an iterative process of health information systems improvement in poor, rural regions around the world. ultimately, we hope that providing timely and accessible health information to the key participants of the health care system will have a transformative impact on the effectiveness of health care delivery. while building these systems requires advanced software development skills, attention to standards based specifications and configurable design will make these systems more readily available to groups that can make use of them. acknowledgements the motech pilot was funded by the bill and melinda gates foundation. motech is a project of the ghana health service (ghs). motech would not have been possible without the leadership and contribution of drs. frank nyonator, director of policy planning monitoring and evaluation division of the ghs and the full support of the district health management team and frontline workers in kassena-nankana west district of the upper east region. the openmrs and openxdata open source software communities not only provided well-tested source code, their advice and encouragement during the year and half of motech development was invaluable. we are extremely grateful for all of the support and we hope that these communities can share in any success we may have. http://code.google.com/p/motech/ http://ojphi.org the architecture of a software system for supporting community-based primary health care with mobile technology: the mobile technology for community health (motech) initiative in ghana 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 corresponding author bruce macleod mailman school of public health and the computer science department of the university southern maine 96 falmouth street portland, maine 04104 phone : 207 780 4285 email: macleod@usm.maine.edu 1. perry h, freeman p, gupta s, rassekh bm. “how effective is community-based primary health care in improving the health of children?” report of the community-based primary health care working group, international health section, american public health association, franklin west virginia: future generations. 2009 2. earth institute of columbia university technical task force on community health workers. one million community health workers. technical report of the earth institute: new york: columbia university. 2011 3. international telecommunication union. “global mobile statistics 2011,” in the unpublished web report mobithinking. http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats. 2011 4. kwapong oatf. 2008. the health situation of women in ghana. rural remote health. 8, 963. http://www.rrh.org.au. 5. ngom p, debpuur c, akweongo p, adongo p, binka fn. 2003. gate-keeping and women's health seeking behavior in navrongo, northern ghana. afr j reprod health. 7(1), 17-26. http:// dx.doi.org/10.2307/3583341 6. snow rw, omumbo ja. “malaria.” chapter 14 in jamison dt, feachem rg, makgoba mw, et al., editors. disease and mortality in sub-saharan africa. 2nd edition. washington (dc): world bank; 2006. 7. binka, fred n. fighting malaria in africa, the lancet, 371(9622):1409. 26 (april) doi: 10.1016/s0140-6736(08)60614-5. 2008 8. adongo p, phillips jf, kajihara b, fayorsey c, debpuur c, et al. 1997. cultural factors constraining the introduction of family planning among the kassena-nankana of northern ghana. soc sci med. 45(12), 1789-804. http://dx.doi.org/10.1016/s0277-9536(97)00110-x 9. adongo pb, phillips jf, binka fn. 1998. the influence of traditional religion on fertility regulation among the kassena-nankana of northern ghana. stud fam plann. 29(1), 23-40. http:// dx.doi.org/10.2307/172179 10. binka fn, bawah aa, phillips jf, hodgson a, adjuik m, et al. 2007. rapid achievement of the child survival millennium development goal: evidence from the navrongo experiment in northern ghana. tropical disease and international health. 12(5), 578-93. http:// dx.doi.org/10.1111/j.1365-3156.2007.01826.x http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats http://www.rrh.org.au/ http://www.worldbank.org/ http://www.thelancet.com/journals/lancet/issue/vol371no9622/piis0140-6736%2808%29x6019-2 http://ojphi.org mailto:macleod@usm.maine.edu http://www.rrh.org.au.5 http://www.rrh.org.au.5 http://www.rrh.org.au.5 http://dx.doi.org/10.2307/35833416 http://dx.doi.org/10.2307/35833416 http://dx.doi.org/10.2307/35833416 http://dx.doi.org/10.1016/s0277-9536 http://dx.doi.org/10.2307/17217910 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http://authormapper.com/search.aspx?val=journal%3aaids+and+behavior&val=name%3ahaberer%2c+jessica+e.&val=name%3akiwanuka%2c+julius http://authormapper.com/search.aspx?val=journal%3aaids+and+behavior&val=name%3ahaberer%2c+jessica+e.&val=name%3anansera%2c+denis http://authormapper.com/search.aspx?val=journal%3aaids+and+behavior&val=name%3ahaberer%2c+jessica+e.&val=name%3awilson%2c+ira+b. http://authormapper.com/search.aspx?val=journal%3aaids+and+behavior&val=name%3ahaberer%2c+jessica+e.&val=name%3abangsberg%2c+david+r. http://www.springerlink.com/content/1573-3254 http://ojphi.org http://maps.mobileworldlive.com/network.php?cid=134&cname=ghana http://maps.mobileworldlive.com/network.php?cid=134&cname=ghana http://dx.doi.org/10.1007/s10461-010-9720-1 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 66 isds 2014 conference abstracts vetsyn: veterinary syndromic surveillance streamlined into one r package fernanda c. dórea*, stefan widgrén and ann lindberg department of epidemiology and disease control, national veterinary institute, uppsala, sweden objective to describe an r package that was designed to provide ready implementation of veterinary syndromic surveillance systems, from classified data to the generation of alerts and an html interface. introduction the field of veterinary syndromic surveillance (vss) is developing fast, with countries exploring a great variety of data sources [1]. after implementing two vss systems we have demonstrated that the steps from classified data to full system implementation can be streamlined, and published a guideline for implementation [2]. all the steps described have been made available in an r package (https:// github.com/nandadorea/vetsyn). we aim to demonstrate the utility and potential of this streamlined approach. methods the vetsyn package takes advantage of object-oriented programming in r. using s4 classes, we have defined a syndromic class, which stores in a single object all the data needed and produced during syndromic monitoring. the object contains the following slots: • @observed: a matrix storing the observed event counts of one syndrome per column, and one time point (day or week) per row (t x s). this is constructed with a single function starting from a dataset of health events, where the events have been classified into syndromes. • @dates: a data frame storing date related information for each time point in the dataset (rows in all other slots). • @baseline: a t x s matrix which serves as a training baseline for outbreak-signal detection algorithms. during the implementation of the system a function can be used to construct this baseline from historical data, removing outliers based on parametric (generalized linear models) or non-parametric methods (moving percentiles). after the system is implemented the baseline continues to grow by automatically removing detected outbreak-signals from observed data [3]. • @ alarms: a t x s x a array (t x s x algorithms for detection), in which a third dimension is added to accommodate for the parallel use of multiple aberration detection algorithms. users can choose to use control charts (ewma, cusum and shewhart) or the holtwinters exponential smoothing to detect outbreak-signals, and those can be coupled with parametric (generalized linear models) or nonparametric (differencing) pre-processing to remove temporal effects. the user can also set up multiple detection limits, in which case for each syndrome, time-point, and algorithm used, the stored result is a detection score, rather than a binomial indicator of whether an alarm happened. • @ucl: a t x s x a array, which stores, for each syndrome, timepoint and algorithm used, the upper confidence limit of the detection method chosen, that is, the minimum value that would have generated an alarm. one function helps the user set up email alerts to be generated in case of an alarm. another function allows the user to generate an html interface where all syndromes monitored are tabulated, an alarm score is plotted for each syndrome (figure), and time series graphs show any alarms detected. conclusions we have combined several statistical analyses methods already tested for use in animal data streams, and r functions to support data management and output visualization. a full tutorial is available, which further helps streamlining the process, facilitating the implementation of syndromic surveillance systems by veterinary epidemiologists. figure: example of the html interface generated to display the information stored in a syndromic object created with the vetsyn package. keywords animal health; syndromic surveillance; r programming references [1] dupuy c, bronner a, watson e,et al. inventory of veterinary syndromic surveillance initiatives in europe (triple-s project): current situation and perspectives. prev vet med 2013 (111) 3-4 220-9 [2] dórea fc, lindberg a, mcewen bj, et al. syndromic surveillance using veterinary laboratory requests: a practical guide informed by experience with two systems. prev vet med 2014 .doi: 10.1016/j. prevetmed.2014.04.001 [3] dórea fc, mcewen bj, mcnab bw, et al. syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation. j royal soc interface. 2013 doi:10.1098/ rsif.2013.0114 *fernanda c. dórea e-mail: fernanda.dorea@sva.se online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e19, 2015 ojphi-06-e62.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 74 (page number not for citation purposes) isds 2013 conference abstracts racial disparity in birth defects: who has higher risk? ayan ibrahim*1, tri tran2, 3, dionka pierce2, julie johnston2, nicole richmond2, 3 and susan berry2, 3 1tulane school of public health &tropical medicine, new orleans, la, usa; 2louisiana dhh oph children and youth with special health needs program, new orleans, la, usa; 3lsuhsc school of medicine, department of pediatrics, new orleans, la, usa � �� �� �� � � �� �� �� � objective �������� ���� ���� ������ �� ������������� �� ����� � ������� � �� �� � �� ������������� � �������������������� ��������������� � � �������� ������ � ���! introduction "�� ���� � ����� �������� ������������� �� ����� � ������� ��� � ��� ������� � ���!�#��� $���� %����� �� ��� ������ ����� 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������������� ��������� ������������������ !�#���� ����� ������� � ��� +������ ������ � � ������ � ������� ��������+��������������� �� � ���������� ��� � � ���������������� ������ ������� ���������� � ������� ��������! keywords (����� � ���a�2���� ����� a�)���� ��+�� references ?'@�b�������� c%�d���+�ce%�&�� �����0c%�;�� �/2%�0��� ��&!����.!�e�� � ������ ���������������������� �����������&������%�'9����������� ����!�(�����) � ����/ ��&�76>'77�'�6! ?�@� �� �5��)(%�0��� ��&%�e���+����c)!����'!�/�������� �� ������������ ������������ ��� ��� �� ����� ���� � ���!�4 ��������'�7,6->�'��! ?6@�0 �� �������)�� �� �0��������� �4� � �����!����7!�" �����������&�� ������0��� ������) � ����4�������) � ���0� � ���%�f ����������7!� / ��� � ����������>�����!� �!������� ������ � ���� ���� ���� ��� ��� ���7!� �%�"� %���'6 *ayan ibrahim e-mail: ayansaid3@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e62, 2014 ojphi-06-e145.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 110 (page number not for citation purposes) isds 2013 conference abstracts guidelines to implement or improve syndromic surveillance systems sylvia medina1, marta sala-soler1, duncan cooper2, mark kanieff3, céline caserioschonemann1, céline dupuy4, alex elliot5, gillian smith5, anette hulth6, luise muller7, alexandra ziemann8 and anne fouillet*1 1dcar french institute for public health surveillance (invs), saint maurice, france; 2national health service, leeds, united kingdom; 3istituto superiore di sanità, rome, iceland; 4french agency for food, environmental and occupational health safety, lyon, france; 5public health of england, birmingham, united kingdom; 6smittskyddsinstitutet, stockholm, sweden; 7statens serum institut, copenhagen, denmark; 8dept. of international health, school of public health and primary care (caphri), faculty of health, medicine and life sciences, maastricht university, maastricht, netherlands � �� �� �� � � �� �� �� � objective �������� � ���� ����� �������������� � ���� �������� ������ �� ������������ ����� ��� � �� ��������� ������������������ introduction ����� � ������� ��� ����� ���������� � ��� ��� ��� !�� ���� "�� �������#��� ��� ���� � ������ ��� ����� �������������$�� � ��� �������� ������� ���� ������"������� � �%������ ����� ������� ������%����� ������ �&'('�������)�������������*(+��, �� ������� &-����� ��� �� �������()��� �������������$�� .���� ���� ��������� �� � ������� ��� ����� ������� ����������� ����� ������� ���� ���� �� ��� ����� ������� ��� ��� �����!��� ���� �� �!��� ������� ������ ��� ��� ��� /������ �� �� ��������� ����� �������� ����*&0)+������ ��� � ������� ��*-+�� ���!��� ���������� 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attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 156 (page number not for citation purposes) isds 2013 conference abstracts on to meaningful use stage 2: refining public health readiness guidelines sanjeev tandon*1 and charlie ishikawa2 1centers for disease control & prevention (cdc), atlanta, ga, usa; 2international society for disease surveillance (isds), boston, ma, usa � �� �� �� � � �� �� �� � objective ��� ���� ��� �� � � �� � ��� �� ���� �� ��� �� ������� � � ���� ��������� ���� �� ��� �������� ����� � ������ ���� �� � ������ ���� �������������� �� � � ���� ��� � ������������������ ������ � ����� � ����������� ��� ��� ���� � ��������� �� ������������ ���� ���� � � ���� �� � introduction �������!�������� ������� � � ��� ������ �� �������� � ������ �� ���������� ����� �����������"#$%&����� ����'� ��� ���� �� 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journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e84, 2014 ojphi-06-e86.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 44 (page number not for citation purposes) isds 2013 conference abstracts towards one health: increasing awareness of animal health among public health stakeholders fernanda c. dórea*1, céline dupuy2 and judy e. akkina3 1swedish zoonoses centre, national veterinary institute, uppsala, sweden; 2french agency for food, environmental and occupational health and safety (anses), lyon laboratory, epidemiology unit, lyon, france; 3united states department of agriculture, animal and plant health inspection agency, veterinary services, center for epidemiology and animal health, fort collins, co, usa � �� �� �� � � �� �� �� � objective ������� ���� ��� ����������� ����������� ����������� �� ����� � �� ������������� ��� ������������ �������� �� ����������������� �������������� �� ������ ������������ introduction �������� ����� ��� ������� �������� � ��� �� ��������� ������ � � �� ������������� ��������������� ������������������ �� ����� � � ����� �����������!�� �������� ������ ������������� �������������� ��� �� ����"�� ������ ��������� ������������� ���� ��� ������������������ ������������������ �� � � ����� ����������� ���� ����� ����������� � �� �������� ���� ����������������� ���� ��������� ��������� �� ������������ ��� ������������� ���� ����� � ����� ���� ���������� �� �� ������ ���� �� ���� ������� ����� � ���� ������������ ���� �# ����� ���� �������� ������ �� ��� ������������ ������ �� ���$�������� ����� ���������������������������� ���� ��������������� ������������ ���������������� �� 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������������� �� ��� �� ��������������� ����� �������������� ���� �� ��������� ������� ����������� � ����� ���� �� �� �������� ������ ������������������ � ������ �����������*�1������������������������������������ ������ ������������� � ������ ��� �����������)�1����������� ���� ������ ������������������������������������ �����������) 2������������ �� ��� ����������������������� ��� ���������� ���� ���� ��������� ����� ���� ������� ������ ������������ ����������������� $����� � ������������� ���������������� ���� ������� �����������)� 1������ �������� ����������������)�$����� ���� ��������������� � ������ )�1������������� ��� �������� ����� �������� ������������� ������������) �������������������������� ����������������� ���� ���� ������������� ��� ������� �������� �������������������� ���� ������������ �� ���� �������� ����� ���� �� ������������� ��� ������������������������� ���������� ���������� �������� ����� ������� ������� ������������������� ������� ������������������ � ����� ���� �����&'('����� ����� �� ��� �������� �������� �������� ��������� �� keywords � �����������3� �����������3�� �������� *fernanda c. dórea e-mail: fernanda.dorea@sva.se� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e86, 2014 ojphi systems epidemiology: what’s in a name? 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 systems epidemiology: what’s in a name? o. dammann 1,2* , p. gray 1 , p. gressens 3,4 , o. wolkenhauer 5,6 , a. leviton 7 1. dept of public health and community medicine, tufts university school of medicine, boston, ma 2. perinatal epidemiology unit, dept. of gynecology and obstetrics, hannover medical school, hannover, germany 3. inserm, u676, paris, france 4. department of perinatal imaging and health, department of division of imaging sciences and biomedical engineering, king’s college london, king’s health partners, st. thomas’ hospital, london, united kingdom 5. department of systems biology and bioinformatics, university of rostock, rostock, germany 6. stellenbosch institute for advanced study (stias), stellenbosch, south africa 7. neuroepidemiology unit, children’s hospital, boston, ma abstract systems biology is an interdisciplinary effort to integrate molecular, cellular, tissue, organ, and organism levels of function into computational models that facilitate the identification of general principles. systems medicine adds a disease focus. systems epidemiology adds yet another level consisting of antecedents that might contribute to the disease process in populations. in etiologic and prevention research, systems-type thinking about multiple levels of causation will allow epidemiologists to identify contributors to disease at multiple levels as well as their interactions. in public health, systems epidemiology will contribute to the improvement of syndromic surveillance methods. we encourage the creation of computational simulation models that integrate information about disease etiology, pathogenetic data, and the expertise of investigators from different disciplines. keywords: systems theory, systems biology, computer simulation, epidemiology correspondence: olaf.dammann@tufts.edu* doi: 10.5210/ojphi.v6i3.5571 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mailto:olaf.dammann@tufts.edu ojphi systems epidemiology: what’s in a name? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 introduction risk factors have always been the main focus of epidemiologists’ search for causes of disease. nevertheless, we also have a long history of incorporating information about mechanisms of disease. attention to lipid profiles in blood and tissue dates back to the 1950s [1]. this was soon followed by efforts to understand the role of lipids in coronary artery disease [2] and a clinical trial to test if lipids really were in the causal chain [3]. some epidemiologists were quick to incorporate molecular information provided by microarrays [4,5]. what we offer in this paper is encouragement to continue in this tradition to improve what we do by incorporating improved sources of information. the integration of intra-individual processes with population-based knowledge challenges the current separation of experimental and theoretical approaches. basic laboratory research in the life sciences can be conceptualized as the search for mechanisms [6,7]. the idea is that a formal description of the mechanism underlying a biologic phenomenon will contribute to a causal explanation for its occurrence. the notion of mechanisms is particularly dominant in systems biology, where intraand intercellular processes are frequently experimentally and theoretically investigated in terms of networks and pathways. knowledge of the structure of the network/pathway and the biophysical details of the interactions allows better understanding of the cellular mechanisms underlying cell functions [8]. the notion of mechanism appears to play a less important role in epidemiology, although this is slowly beginning to change [9]. for example, epidemiologists and those who study the epigenome are working together to gather data that are likely to allow modeling of disease occurrence based on data collected over decades [10]. also gaining acceptance is the recognition that the contributions of social interactions to psychiatric disorders and behavioral dysfunctions are via epigenetic phenomena [11]. to contribute to advanced mechanistic knowledge in this area of study, epidemiologists need the assistance of those who are most knowledgeable about measuring epigenetic phenomena and mechanisms. perhaps part of their reluctance to talk about mechanisms reflects epidemiologists’ preference for the probabilistic view of causal inference [12], while causal mechanisms are often considered deterministic in biology, although computational scientists who study biological regulatory networks begin to appreciate both deterministic and stochastic approaches [13]. however, we agree with glymour and cheng who wrote in 1998 that “a disconnection between mechanisms, on the one hand, and probabilistic patterns, on the other, puts everything on a false footing”. [14] still, the predominant focus on individual component causes of disease in both pathogenesis and etiology research might miss the many potential interactions between components [15]. even gene-environment interaction studies tend not to go beyond two components. consequently, they contribute minimally to the elucidation of network dynamics of disease causation as a whole. mathematical modeling can expose erroneous assumptions, define and validate working hypotheses, and guide the design of novel experiments and studies [16]. in systems biology, the structure of interaction maps encodes feedback mechanisms that can be used to explain biological phenomena in terms of the system's robustness, sensitivity, stability. to this end, the models do not have to be based on biochemical or biophysical principles but can be http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 phenomenological [17]. the pharmacologist james black poignantly explained in his nobel lecture that “models [...] are not meant to be descriptions, pathetic descriptions, of nature; they are designed to be accurate descriptions of our pathetic thinking about nature.” however, it is also possible, that just as weather forecasting still has limitations despite the use of cutting edge technology and multivariable prediction algorithms, systems epidemiology will not achieve the promise that some hope for it. in this paper, we suggest that systems epidemiology might be a promising way to supplement systems biology, with the goal of reducing disease burden at the individual [18] and population levels [19]. in essence, we propose a systems epidemiology modeled after systems biology [2024] and conceptualized as an integration of good old-fashioned black box epidemiology [25] with mathematical modeling and computational simulation [26] (figure 1). figure 1: proposed discovery process integrating pathogenetic and etiologic aspects of illness causation. we define “systems epidemiology” as an epidemiologic approach to risk identification that includes (i) systems-level (e.g., omics) exposure measurements at (ii) multiple levels (sociodemographic, clinical, biological, etc), (iii) network analyses of inter-relationships among risk factors, and (iv) computational simulation of risk scenarios in parallel to data-driven http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 biostatistical risk modeling. as such, systems epidemiology need not be seen as a replacement for conventional epidemiologic methods. rather, systems epidemiology should be seen as a supplemental approach that has advantages that conventional epidemiologic studies lack. in essence, we view systems epidemiology rather broadly. some components of studies that are needed for a full systems epidemiology study can be viewed as getting closer to that goal. for example, epidemiologic studies that collect data that can be used by molecular biologists enable the expansion and transformation of such a study from an epidemiologic study of proteins, dna methylation, or gene activation in prospective cohorts to a systems epidemiology study that allows an evaluation of the complexity of the relationships among exposures, biomarkers, and diseases [27,28]. even limited studies can have the capacity to evaluate relatively simple models, such as multi-hit models of disease risk [29]. in the next section, we introduce the main concepts from general systems theory, systems biology, and systems medicine, as well as define some of the words used in these fields (e.g., systems, network, complexity). in the subsequent section, we review and discuss the six publications that emphasize a conceptual definition of “systems epidemiology” by using the term in the title. in the fourth section, we offer our vision of systems epidemiology as an integration of epidemiologic methods and computational simulation. systems theory, biology, and medicine systems science is rooted in systems theory, which was developed in the first half of the 20th century (see [30] for a brief historical overview and [31] for a philosophical perspective.) a system can be defined as “a complex of interacting components together with the relationships among them that permit the identification of a boundary-maintaining entity or process”. [32] in the previous sentence, “system” is defined as a complex of components. this meaning of the word “complex” goes back to ludwig von bertalanffy, who coined the term “general system theory” [33] and wrote about “complexes of ‘elements’” in his chapter on mathematical underpinnings of system concepts. he considered the analysis of biological systems one of the main goals of general system theory [33]. applied to living systems, the concepts of organization, hierarchy, and interaction of biological phenomena lead to the notions of feedback loops and mechanistic processes [30]. the method of classical science has relied on the strategy of 'reduction to components,' an approach that emphasizes the study of how one molecule, one cell, or one organ responds to a stimulus [32]. in contrast, the systems approach promotes ‘expansion to dynamics,’ which can identify how complex sets of components behave when exposed to a complex set of influences. this emphasis on relationships between system components (rather than the components themselves) gives rise to the concept of emergence, “the appearance of novel characteristics exhibited on the level of the whole ensemble, but not by the components in isolation”. [32] in essence, part of the “systems view” in science is that by studying networks of interacting phenomena, we can understand their function and their emergent properties better than by studying each component (or a few of them) in isolation. this emphasis on relationships among components is familiar to the epidemiologist who studies context and interactions [34,35], as well as to those who study non-linear relationships [36-38]. thus, epidemiology appears to have incorporated elements of a systems approach without calling attention to this transition. for example, the concept that obesity and other disorders can “spread http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 through social networks” has been well recognized by those who study [39,40] and model [41] social transmission dynamics. we see the modeling of social dynamics as contributing to a better understanding of the disease occurrence in populations. applying a systems level approach to biological research was proposed at the turn of the millennium [20-24]. hiroaki kitano wrote in 2002 that “cells, tissues, organs, organisms and ecological webs are systems of components whose specific interactions have been defined by evolution; thus a system-level understanding should be the prime goal of biology. … a combination of experimental and computational approaches is expected to resolve this problem [of the intrinsic complexity of biological systems]”. [24] the goal is to achieve a systems-level understanding of both “interrelationships (organization or structure) and interactions (dynamics or behavior) of genes, proteins, and metabolites”. [20] thus, systems biology focuses on the interaction of components of biological systems, and their components. moreover, it proposes that experimental and computational work be conducted in parallel. this approach has been implemented most impressively at the cellular level, for example, with biochemical receptor signaling pathways [42-44]. one recent example of a research program that goes beyond the single cell level is the integration of experimental and computational studies designed to better understand sprouting angiogenesis, a concept crucially important, for example, in developmental processes and cancer pathogenesis [45]. the application of systems biology principles in complex disease etiology research and diagnostics has been called “network medicine”. [46] others suggest that systems biology be brought to the bedside under the name “systems medicine”. [47-50] as advances in highthroughput technologies push the many “omics”-perspectives in biomedical research from structure to function, their utilization in systems biology and systems epidemiology might help identify organizing principles [51]. systems medicine applies the tools and concepts from systems biology and addresses complexity in two key ways. first, systems medicine uses molecular diagnostics to stratify patients and diseases to better characterize and understand disease complexity [47]. by applying a networklevel view of disease to create disease networks, systems medicine will overcome current limitations in drug discovery by identifying important functional and regulatory modules within these networks [52]. then, by analyzing and targeting hubs—the most highly interconnected nodes—within these regulatory networks, and enzymatic activity in metabolic networks, network-based approaches will be able to explore the effects of various drugs in mathematical models [53]. such studies might improve not only our understanding of drug-response phenotypes, but will hopefully also help us tailor treatments to an individual’s drug metabolism. “systems epidemiology” in the literature often described as the “basic science” science of public health, epidemiology is concerned with the distribution, determinants and deterrents of disease occurrence in human populations [54]. with the ever growing number of newly identified risk factors, the single level approach of black-box epidemiology continues to face criticism [55,56]. by focusing on individual risk factors of disease, studies are unlikely to appreciate potentially important interactions between risk factors and the resulting alterations to network dynamics [57]. feedback loops among risk factors, for example, might remain undetected. new analytical and systematic methods are needed to help those who want to uncover complex biochemical pathways [58]. we are http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 confident that a systems approach to illness causation research will improve our understanding of etiologic mechanisms, and help translate complex data into knowledge relevant for medicine and public health. while systems thinking has been embraced in biology and medicine, its adoption by public health has yet to take shape [19]. as it has surfaced in the literature, the term “systems epidemiology” has been given various meanings. before we discuss our own vision of “systems epidemiology”, we briefly review and discuss six publications whose authors emphasize a conceptual definition of “systems epidemiology” by using the term in their title [56,57,59-62]. in what follows, we offer a few brief paragraphs trying to extract the meaning of “systems epidemiology” as used by the authors of these publications. we then attempt to integrate their commonalities into a framework for etio-pathogenetic research at the systems level. in the field of cancer epidemiology, lund and dumeaux define “systems epidemiology” as “a new research discipline that seeks to integrate pathways analyses into observational study designs to improve the understanding of biological processes in the human organism”. [62] although the authors consider systems epidemiology the “observational counterpart to systems biology”, they do not go beyond the “expansion of gene-environment epidemiologic studies with analyses of the transcriptome”. they suggest that in what they call a “globolomic design”, mrna and mirna analyses will “open the black box” and provide insight into biologic pathways, and even add “understanding to the concept of causality in epidemiology.” study designs that consider the trajectory of gene expression in multistep carcinogenesis and changes in lifestyle of study participants would shed light on the interplay between risk factors and the effects that changes in risk factors have on each other. similar to lund and dumeaux in cancer research, frank hu proposes incorporating metabolomics data in epidemiological studies of diabetes [56]. he suggests that incorporating recent advances in high-throughput analytic techniques into human observational studies of novel metabolite biomarkers might allow a shift “from the traditional black-box strategy to a systems approach”. nevertheless, he closes on a cautionary note by saying that “although the systems epidemiology approach can offer deeper understanding of molecular pathways underlying epidemiologic observations, whether it can improve early disease detection, clinical diagnosis, and prognosis, and contribute to personalized prevention and treatment remains to be seen.” still, hu and cornelis suggest that nutritional systems epidemiology will lead to “improved personalized optimal nutrition for prevention and treatment of disease” (emphasis in original) [63]. in order to bridge the gaps between systems biology and traditional epidemiology, we must go beyond the data-rich environment of the human genome, and include data from socioeconomic and environmental levels of interaction. infectious disease researchers have proposed a role for systems epidemiology in developing a more comprehensive understanding of tuberculosis [60,61]. while systems biology addresses biological aspects of infectious disease, systems epidemiology incorporates epidemiology, sociology, evolutionary biology, and ecology to describe physical and social environments. it also has the potential to provide a better understanding of host and pathogen interactions at levels beyond pathogenicity. high through-put microarrays of proteins, gene expression, and epigenetic markers, as well as other biomarkers, provide the potential to extend phenotyping to include the response of a cell or tissue to stimuli. add high-resolution imaging techniques with their capability to assess function http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 as well as structure and you have the potential “to derive genome-wide molecular networks of genotype-phenotype associations” called ‘‘phenomics” [64] and “deep phenotyping” [57,65]. the latter term reflects a tendency in the high-tech community, to apply the adjective ‘deep’ to mean ‘comprehensive’ and ‘thorough’. one important benefit of ‘deep phenotyping’ would be its potential to reduce misclassification by refining risk classification [57]. finally, joffe and colleagues describe the use of diagrammatic modeling in epidemiology and propose the adoption of system-wide models to include multiple interactions in causal analyses [59]. this version of systems epidemiology addresses the philosophy of causation, a key challenge for the progress of illness causation research. three themes, plus one three themes emerge from the views of systems epidemiology provided in the previous section. perhaps a bit surprisingly, all three are not really novel to epidemiologic theory. first, multiple authors think that systems epidemiology will open the black box between exposure and outcome. while some have postulated that traditional epidemiology is incapable of doing this [66], others think it can [25,67,68]. in particular, we are in full agreement with hafeman and schwartz, who suggest that we should not discard the ‘black box’ approach altogether, but instead use existing methods to open it, e.g., mediation analysis [68]. one textbook of molecular epidemiology [69] suggests that measurement of multiple consecutive molecular markers of disease progression, together with markers of susceptibility can provide some insight into the contents of the black box [70]. apparently, what is now proposed under the heading of systems epidemiology as “globolomics” [62] or “deep phenotyping” [57] might be not much more than molecular or genetic epidemiology, albeit at a finer resolution. second, at least two groups of authors stress the point that systems epidemiology should not only be thought of as the measurement of molecular markers, but should also include the study of “behavioral, sociodemographic, and group levels that may influence health and disease” [57], as well as ecologic and evolutionary factors [61]. this integration of multiple levels, layers, or scales of phenomena to be explained is familiar to epidemiologists and statisticians [71-73], as well as to those who think about general concepts of computational disease modeling [74-76]. systems epidemiology as described in the six papers cited above is a blend of traditional genetic and molecular epidemiology that includes biomarker-environment (inter-level) interaction analyses. although these concepts are certainly encouraging, the question remains how to design and perform analyses of intra-level interactions. third, the “paradox of ever-increasing measurement capabilities followed by decreasing abilities to translate basic mechanistic knowledge into clinically effective therapeutics " [57] cannot be tackled without the development of new strategies that guide us in our interpretation of data. the need to clarify how we think about complex systems studies has encouraged some to think and write about the issue of causation and causal inference in systems epidemiology [59,77]. current work in this area [78-81] continues a longstanding tradition in epidemiologic thinking [15,82,83]. while black box elucidation, multilevel analysis, and causal inference appear to be of overall importance for systems epidemiology, one key hallmark of systems biology methods, computational modeling, is prominently absent from all six conceptions of systems http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 epidemiology. just as computational modeling is considered crucial for systems biology in order to compare results from top-down computational models with data from bottom-up experimental studies [20], we suggest that modeling and simulation are crucial for systems epidemiology (figure 1). we proceed with the expectation that computational studies will help improve our understanding of disease processes, in part, by enabling us to go back and forth between epidemiologic observation and computational model, iteratively comparing what has been simulated in silico to what we observe in vivo. the result is either a validation of the current computational model, or a modification that brings the model closer to reality. yes, it will be difficult or even impossible to simulate multilevel study designs in detail. however, we are convinced that certain modeling techniques will be helpful. for example, the recursive bayesian net formalism [84], although based on acyclic graph theory, allows for a causal relationship to cause the elements of a similar relationship at a different level, thereby enabling us to model quasi-feedback linkage points between levels. most importantly, we hold that computational studies will provide systems epidemiologists with the capacity to model the interactions between observed risk factors, thereby elucidating purportedly complex interactions among exposures and the resulting processes that lead to human disease [57]. indeed, the idea of integrating mathematical modeling/simulation with epidemiologic studies has a longstanding tradition in infectious disease research [85-87] that continues in cancer epidemiology [88], prevention research [26], environmental epidemiology [89], and chronic disease epidemiology [90]. none of these recent citations features the term “systems epidemiology”. any kind of biologic simulation needs to be validated appropriately. simple and complex ways of model verification and validation need to be considered and applied in an ongoing fashion over the model’s entire “life cycle”. [91] how can systems biology and systems epidemiology be integrated? we expect that computational models of pathogenetic mechanisms from systems biology will soon be included in computational etiology models that simulate occurrence of disease in humans. such an integration of methods will likely help usher in the next epoch of disease causation research, accommodating both theory and methods decades old and state of the art data analysis/modeling. critical to these efforts will be an ongoing interdisciplinary discourse (figure 1). only if systems biologists and epidemiologists intensify their conversation can all this be achieved. we acknowledge that a general level of understanding will be difficult to achieve due to the very different underlying assumptions, modes of thinking, and scientific languages spoken in wet and dry labs. nevertheless, improved communication between experimentalists and modelers in systems biology, between epidemiologists and modelers in systems epidemiology, and among all four groups of investigators should lead to a common vocabulary and mutually-beneficial enlightenment. many experimentalists have a hypothesis before they do their experiments. in essence, they accept the iterative approach of modifying their hypothesis based on what they most recently found. in systems biology this process is realized in iterative cycles of data-driven modeling and model-driven experimentation. in essence, hypotheses can be formulated and validated with computational modeling. however, the goal of modeling is more than prediction. instead, http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 modeling provides a way of thinking, a workflow, that amongst other things guides data collection and improves understanding [92]. the term “in silico,” coined little more than two decades ago, is applied to computer simulation [93]. two important characteristics of many of these analyses are the concepts of homeostasis and 'feedback'. homeostasis is the regulation of a system to maintain a stable, constant condition. in order to induce, prevent or recover from a change or perturbation, information of the state of the system must be 'fed back'. feedback therefore implies a 'before' and 'after', which is why we can only understand cellular functions and their underlying mechanisms by treating them as dynamic systems. homeostatic regulation allows an organism to function effectively in a broad range of environmental conditions and many diseases are considered a disturbance of homeostasis (homeostatic imbalance). in this context, allostasis is described as the process of achieving or reestablishing homeostasis through physiological change. while homeostasis emphasizes the resting condition and resistance to change, allostasis emphasizes change as a process to regain balance. in essence, allostasis is the resetting of the homeostat at a new set point. concepts like homeostasis, allostasis (through feedback control and regulation), and harmony (through coordination) are used to explain some form of functional stability in biological systems – the ability of a subsystem to retain its function when it is subjected to perturbations. the dynamics of (sub)systems any level of functional organization (reactions to tissue physiology) are integrated across levels of structural organization, from molecules and cells, to tissue and organ systems. a complex system, such as an organ is thus dynamic at different levels. a more general and thus more appropriate principle to explain functional stability in organisms is homeodynamics, understood as a dynamic version of homeostasis across multiple levels of organization (e.g. from cell to tissue and organ system). generally, a shift of focus from stasis to dynamics places the focus on adaptation, rather than stability. in epidemiology, these conceptual developments still await proper recognition [94]. limitations of this discussion the small number of publications on the specific topic of systems epidemiology might be seen as a limitation of this review. on the other hand, it is precisely because so little has been written that we felt the need to spread the word, and to provide our own perspective of what might contribute to advancing this field. perhaps the main limitation of this review is the paucity of proposed strategies that can be readily incorporated into public health research. we fully acknowledge that the proposed strategies are likely to need considerable improvement/refinement before they can be used to maximum benefit. nevertheless, the combined “systems endeavor” discussed above holds the promise of helping to integrate pathogenetic and etiologic research elucidate illness causation mechanisms. the epidemiologic literature in this field has been very rich and diverse [12,15,79,80,82,83,95,96], with a recent emphasis on probabilistic causation [9,78,97]. despite progress, no agreed-upon criteria are available to help prove causation. the ongoing phenomenal successes in biomedicine despite this impossibility of causal proof suggests that going with strong correlations may be sufficient and that causal proof is not needed. http://ojphi.org/ ojphi systems epidemiology: what’s in a name? 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *6(3):e198, 2014 causation can only be inferred, not observed [79]. consequently, we have the widespread contention that observational studies cannot prove causation, but only demonstrate statistical associations. we agree with david savitz, who suggests that ”the cliché that epidemiologic studies generate only measures of association, not causation is meaningless ... even experiments just generate measures of association as well”. [98] perhaps part of the problem is the existence of multiple kinds of causation [99], and each might deserve its own taxonomy. for the time being, we recommend acceptance of the currently rather successful counterfactual, probabilistic [12], and manipulationist (or interventionist) [100] causal model in clinical epidemiology, the randomized controlled trial (rct). such rcts, designed to mimic a laboratory experiment, can test the hypothesis that a certain intervention does better than another one, and are clinically useful. simulated interventions based on observational epidemiologic studies, together with observational data might offer a stronger rationale to conduct an rct than observational data alone. in this way, a joint research program that includes both systems biology and systems epidemiology would be a rather powerful tool for causal inference. conclusion: moving forward with “systems epidemiology” systems-type thinking about multiple levels of causation will allow us to better characterize the diverse range of factors influencing complex diseases. the application of complex systems methods in epidemiology is beginning to take shape. computational models that incorporate human genomic, transcriptomic, proteomic, and metabolomic data integrated with global measurements from observational epidemiologic studies will allow epidemiologists to identify contributors to disease at multiple levels as well as their interactions. the key to success on this route will be 'integration', the integration of data from a wide range of sources, the integration of models from different levels of structural and functional organization, and the integration of expertise from different disciplines. acknowledgements the authors acknowledge support from the national institute of neurological disorders and stroke (5u01ns040069-05 and 2r01ns040069 06a2), a grant from the national eye institute (1r01ey021820-01), and a center grant award from the national institute of child health and human development (5p30hd018655-28). references 1. steiner a, kendall fe, mathers ja. 1952. the abnormal serum lipid pattern in patients with coronary arteriosclerosis. circulation. 5(4), 605-08. pubmed http://dx.doi.org/10.1161/01.cir.5.4.605 2. scarborough wr, smith ew, baker bm. 1960. studies on subjects with and without coronary heart disease. serum lipid, lipoprotein, and protein determinations and their relation to ballistocardiographic findings (a preliminary survey). am heart j. 59, 19-35. pubmed http://dx.doi.org/10.1016/0002-8703(60)90381-1 http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14916478&dopt=abstract http://dx.doi.org/10.1161/01.cir.5.4.605 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14442227&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14442227&dopt=abstract http://dx.doi.org/10.1016/0002-8703(60)90381-1 ojphi systems epidemiology: what’s in a name? 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completeness patrick t. lai*1, kavya r. gujjula1, shaun j. grannis2, 3 and brian e. dixon1, 3 1indiana university, school of informatics and computing, indianapolis, in, usa; 2indiana university, school of medicine, indianapolis, in, usa; 3regenstrief institute, center for biomedical informatics, indianapolis, in, usa � �� �� �� � � �� �� �� � objective ������� ������ ������� ����������������� ������� �������� �� � � ������ ����������� ��� ���������� �������� ���������������������� �� ����������� ������������������ �� introduction �������� ����������� �������� ������ � � ������ � ������������ � �������������� ����� ���� �������������������� ��� ����������� �� �� ������� ����� �������� ���� �������� ������������������ ������� ���� ��������� ����������� ����� � ��� ������������� ��������������� ���� ���� ������ �� ��� �� ������������� � �!������ "�� ������ � � ��� �� � ���� ������ ��������� �� � � ������� ������������� � �� �� �� ����� ��#$%��&�� � � ������ ������������� � ������ ������� 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������� � �� ����������0������� ���������������� ������ ����������� ��� �� results ������,���� ���������������� ������� ���������������� ��������� ����� ���� ������������������� ���������������� ������������ ������ � ��������� ������� ������� ������������ ��� ��� ������� ������ �� � ������������ � ����������� � ������ � �#������ ������������%���� � ���� ������������� ��#������ ����� � ��������%��������!�� ������ ���������� ���������� � ����������� ������� ������ "������������� ���������� �������� conclusions 1�� �� ������ ����������� ���������������� ��������� ������������ � ��� ������� ������� �������� ��� ��� � �������� ���� ������ � � ��� �������������� ���� ���� ��� �� ���� ��������'����������� ��� ���� "�� � ��� � ���� ����� �� ������ � � ������������� � ������� � ������ ������ ������� ����������������� ���� ������ � ������� ��� �� ��� ����������������������� ������ ��� � ����������� ������ ������ ������� ������������� ��� ������������������� ��� � ����������������� � �� ���� ����� �� � ������ ������� ������$�(���� � �� �� ���� � � ���� ������� ����������� �� keywords 2��� �3������4���� ��� �0�( ������5�� � �� � 0�(����6��� ��0� �������� ���0������ �����( �������� ���� acknowledgments �� �����!� ���������������������� �� ������78$348,8)8)���������� &�� ������3����� ����7����� ��� ��6��� �������� � �� �� ������������� ����� � � � ������������������� ������� ��� � ����� ���������� ��������� ��� � �������&376� references $%�(�������9��:�� �� ;��:���� ������4<���������� ������� �� � ����� � �� � ����� ������������ �� �����= ����4�����>�� �� ���� ���� ��������� ��� ����&��9�?� ��� ����,88,� ���$0$**#)%>@aa�+b� ,%�1 � � ���� � �� � ���7�������������� �����( �������4�����/���� b.@,.�#7,c$$�)a%������>cc������ ����c(�� ���������d �e*8@, .%�( �� ��-?��<� ��2�4��:�� ���4��f�� �� � � �1 ������ � �5������ �� 6��� ��>�1��� �� � ������2��� �3������4���� ��� ��� ������ ��� 1 ������ ���& 1&�& ��4����2�� ��,8$.��/���� �� � *patrick t. lai e-mail: ptlai@imail.iu.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e139, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts the distribution of infectious related symptoms in an internet-based syndromic surveillance system in rural china qi zhao1, fuqiang yang2, lars palm3, hui yuan2, weirong yan4 and biao xu*1 1school of public health, fudan university, shanghai, china; 2jiangxi provincial center for disease control and prevention, nanchang, china; 3future position x, gavle, sweden; 4division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden objective to describe the distribution of the infectious related symptoms in an internet-based syndromic surveillance system reported by doctors in village health stations, township and county hospitals in rural jiangxi province, china, and to identify the major infectious diseases for syndromic surveillance in different levels of health facility. introduction syndromic surveillance system, which collects non-specific syndromes in the early stages of disease development, has great advantages in promoting early detection of epidemics and reducing the burden of disease confirmation (1). it is especially effective for surveillance in resource-poor settings, where laboratory confirmation is not possible or practical (2). integrating syndromic surveillance with traditional case report system may generate timely, effective and sensitive information for early warning and control of infectious diseases in rural china (3). a syndromic surveillance system (issc) has been implemented in rural jiangxi province of china since august 2011. methods doctors and health workers in the healthcare surveillance units of issc, including village health station, township hospital and county hospital, used an internet-based electronic system to collect information of daily outpatients, which included 10 categories of infectious disease related symptoms, i.e., cough, fever, sore throat, diarrhea, headache, rash, nausea/vomit, mucocutaneous hemorrhage, convulsion and disturbance of consciousness. the data from august 1st to december 31st 2011 were extracted from database and analyzed using spss 16.0. the combination of symptoms was also analyzed to identify patients with the syndrome of influenza-like illness (ili) and fever-gastrointestinal syndrome (fgs). ili were composed by fever (>=38 degree centigrade) plus cough or fever plus sore throat, and fgs were defined as fever plus vomit or diarrhea. results two county hospitals (ch), 4 township hospitals (th) and 50 village health stations (vhs) were selected as surveillance unites in the pilot study during 2011/8/1 to 2011/12/31. in total, 152270 outpatient visits were reported, and 35395 patients had a chief complain of at least one surveillance symptom. of these symptomatic patients, 24130 (68.2%) were from vhs, 4995 (14.1%) from th and 6810 (19.2%) from ch. the proportion of patients with targeting symptom accounted for 15.5%, 66.4% and 23.9% of total outpatients in ch, th and vhs respectively. the first 3 reported symptoms were cough (61.8%), fever (28.4%), and sore throat (23.4%), whereas mucocutaneous hemorrhage, convulsion and disturbance of consciousness were the least frequently reported symptoms in all surveillance units. overall 3582 ili and 1160 fgs cases were reported accounting for 35% and 11% of fever cases respectively. of the reported ili and fgs cases, 75% ili and 55.9% fgs cases were reported by health workers in the vhs. conclusions cough, fever and sore throat were the top surveillance symptoms, and the respiratory infectious diseases had more chance to be reported in syndromic surveillance system in rural jiangxi province. training on infectious disease diagnosis especially respiratory diseases for village health workers should be enhanced since large numbers of patients are likely to visit the village health stations. figure1 distributions of symptomatic patients in different level health facilities of rural china keywords syndromic surveillance; rural; influenza-likes illness; fever-gastrointestinal syndrome acknowledgments this study was funded by [european union’s] [european atomic energy community’s] seventh framework programme ([fp7/2007-2013] [fp7/2007-2011]) under grant agreement no. [241900]. references 1. henning kj. what is syndromic surveillance? mmwr morb mortal wkly rep 2004;53 suppl:5-11. 2. siswoyo h, permana m, larasati rp, et al. ewors: using a syndromic-based surveillance tool for disease outbreak detection in indonesia. bmc proceedings 2008, 2(suppl 3):s3 3. reingold a. if syndromic surveillance is the answer, what is the question.biosecur bioterr 2003;1:1–5. *biao xu e-mail: bxu@shmu.edu.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e77, 2013 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems patrick t.s. lai*, m.p.h.1, jeffrey wilson, ph.d2, huanmei wu1,3, ph.d, josette jones1, ph.d, brian e. dixon, ph.d., mpa4,5 1. school of informatics and computing, indiana university, indianapolis, in 2. school of liberal arts, department of geography, indiana university, indianapolis, in 3. purdue school of engineering and technology, purdue university, indianapolis, in 4. richard m. fairbanks school of public health, indiana university, indianapolis, in 5. regenstrief institute, inc., center for biomedical informatics, indianapolis, in, usa abstract background: health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases. objectives: to measure and identify inequalities among chlamydia and gonorrhea rates using gini coefficient measurements and spatial visualization mapping from geographical information systems. additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county. methods: chlamydia and gonorrhea data from january 2005 to december 2014 were collected from the indiana network for patient care, a health information exchange system that gathers patient data from electronic health records. the gini coefficient was used to calculate the magnitude of inequality in disease rates. spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. a multiple comparisons anova test was conducted to determine if gini coefficient values were statistically different between townships and time periods during the study. results: our analyses show that chlamydia and gonorrhea rates are not evenly distributed. inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. inequality in gonorrhea rates were higher than chlamydia rates. disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152). conclusion: the ability to use gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi introduction health inequality, a term that describes an adverse difference in health among advantaged and disadvantaged groups in a population, is a serious, ongoing, and persistent problem that negatively affects the health of populations in the united states [1,2]. the consequences of health inequalities are detrimental as it leads to increased mortality and sickness, increased health care costs, decreased productivity, and lower control over personal health and development [3,4]. with the presence of health inequalities, not everyone is benefiting from the advances in healthcare and medical treatment that is essential in maintaining well-being and optimal health. different subsets of the population remain highly vulnerable to disease risk and infection, and various geographical areas have extreme inequalities in disease rates that require attention and focus. the need to identify high risk areas remains paramount for clinicians to effectively locate vulnerable populations in the effective management and treatment of disease. more importantly, there is a necessity to establish accurate methods to measure health disparity and inequality for identifying and creating policy interventions, prioritizing essential health resources, and understanding how communicable diseases are transmitted [5]. because of this, analysis of health inequalities remains a critical focal point for clinical health research especially when monitoring trends in disease risk, establishing novel research methodologies, and creating interventions that improve the current health status of the population. one method to measure inequality is the gini coefficient or gini index. in 1912, an italian economist by the name of corrado gini developed the gini coefficient to measure statistical dispersion in representing the distribution of data [6]. when the gini coefficient was first established, its initial sole purpose was to measure the magnitude and degree of inequality in income distribution within a population which was represented with a numeric value between 0 and 1. values closer to 0 reflect greater equality or uniformity in income distribution while values closer to 1 reflect greater magnitudes of inequality [7]. this coefficient can also be visualized graphically on an x-y graphical plot using the lorenz curve which is a line that represents the investigating health inequalities. knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs. keywords: sexually transmitted diseases, data analysis, data visualization, health inequalities, gini coefficient abbreviations: geographical information systems (gis), sexually transmitted diseases (stds), american community survey (acs), indiana network for patient care (inpc), local indicators for spatial autocorrelation (lisa) *correspondence: patrick t.s. lai ptlai@iupui.edu doi: 10.5210/ojphi.v11i2.10155 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:ptlai@iupui.edu measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi cumulative proportion of income (y-axis) versus the cumulative proportion of the population (xaxis) as shown in figure 1 [8]. on the same plot, a hypothetical diagonal line of equality is drawn to represent perfect equality of income. the gini coefficient represents twice the area between the line of equality and the lorenz curve. higher gini coefficients representing greater inequality present lorenz curves that deviate the furthest away from the line of equality while the smallest gini coefficients present lorenz curves that are closest to the line of equality [9]. thus, the gini coefficient provides a convenient way to calculate the uniformity or concentration of values in a dataset. the value and usefulness of the gini coefficient measurement of inequality can greatly be expanded by identifying and locating where the highest inequalities persist and where they reside. spatial data visualization techniques and geographic information systems (gis) can assist in locating inequalities as they can visualize the distribution of the data and detect anomalies contained in the data of interest [10]. furthermore, using these spatial data visualization tools can also aid in the ability to examine relationships and patterns of different health outcomes and the determinants that influence these outcomes [11]. as a benefit, these tools generate new information about disease risk where data is limited or where there is a lack of disease surveillance [12]. as a result, spatial data visualizations create health information about geographical locations that is easy to analyze, interpret, and organize [13]. figure 1 graphical representation of the lorenz curve the gini coefficient itself has been widely used initially in the field of economics to measure income distribution, but it has also been applied to other applications outside of economics. in measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi geography for example, the gini coefficient was applied in the measurement of cluster size for spatial clustering and concentration [14]. for healthcare and medicine, the gini coefficient was utilized to analyze the relationship between health disparities and income inequality, measure fairness in health ethics and equity, calculate the distribution of healthcare personnel, and analyzing trends of geographical disparities in health care provision [9,15-17]. a limited number of studies have incorporated the use of the gini coefficient in the study of sexually transmitted diseases. in an article from althaus et al., authors used the gini coefficient to measure the distribution of chlamydia infections in determining sexual behavior in great britain [18]. another study from leichliter et al. also incorporated the usage of the lorenz curve and gini coefficient to measure the concentration of sexual behaviors and sexual partners by gender and race using a probability sample of the united states population [19]. one such study from kerani et al. using data from non-electronic disease reporting gave a comprehensive approach of measurement with the gini coefficient to compare concentration levels and clustering between four different sexually transmitted diseases (syphilis, chlamydia, gonorrhea, and genital herpes) for king county, washington in 2005 [20]. their study concluded that among the four diseases, genital herpes had the lowest concentration of cases followed by chlamydia, gonorrhea, and syphilis having the highest concentration. because of these studies, the gini coefficient presents many applications and has become quite versatile in analyzing and understanding the distribution of various information. when conducting health disparity research, the routine investigation of sexually transmitted diseases (stds) particularly chlamydia and gonorrhea presents an excellent opportunity to analyze and measure health inequalities using the gini coefficient. untreated std infections can lead to serious health conditions and complications such as blindness, infertility, pelvic inflammatory disease, inflammation of reproductive organs, and possible cervical cancer in women [21-25]. numerous challenges exist to effectively control and reduce the transmission of stds with the presence of health inequalities being a prime factor. in many instances, chlamydia and gonorrhea rates are unevenly distributed geographically leading to areas where disease rates occur disproportionally resulting in segments of the population to be much more susceptible to infection. this creates a challenge on where and to what degree interventions should be placed for these susceptible or high-risk groups. besides the research by kerani et al., no other studies have examined or measured the distribution and inequality of both chlamydia and gonorrhea disease rates over time using the gini coefficient at the county level [20]. additionally, there are very few studies that incorporated the ability to visualize these inequalities in accordance with such inequality measures. our study on chlamydia and gonorrhea inequality provides a novel approach to present how the combination of inequality measures and spatial data visualizations can be used to locate and quantify the degree of disease inequality from clinical data sources. in our study, we seek to accomplish three goals in understanding inequality among chlamydia and gonorrhea disease rates in an urbanized county in central indiana using longitudinal data. first, we seek to quantify the magnitude of inequality among different areas of the county using the gini coefficient. secondly, we seek to identify and locate areas across the county with the greatest inequality of disease rates using spatial data visualization techniques. finally, we aim to determine if there are differences in chlamydia and gonorrhea disease rates over a ten-year period and by geographical locations within the county. the main outcome of this paper will assist healthcare measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi professionals to identify disease patterns and understand distributions of health inequality in sexually transmitted diseases. methods setting the geographical area of interest in this study is marion county, indiana, which is the location of the major capital city of indianapolis along with several smaller incorporated cities. the county has a total area of 403.01 square miles and a total population of 919,336 as of 2014 according to the american community survey (acs) 5-year estimates [26]. the county is comprised of a total of 9 townships as depicted in figure 2 with a total of 224 separate census tracts. the townships starting from northwest to southeast with the number of census tracts are pike (n = 17), washington (n = 37), lawrence (n = 23), wayne (n = 35), central (n = 55), warren (n = 23), decatur (n = 5), perry (n = 21), and franklin (n = 8). the downtown core area of the city of indianapolis is located in central township. according to the united states census bureau, a census tract is defined as a geographical feature that is comprised of about 1,200 to 8,000 people, a characteristic roughly similar to the size of a neighborhood [27]. the boundaries of census tracts are considered relatively permanent subdivisions of geographical units for statistics and population enumeration. multiple census tracts can be grouped together to form a township which represent subdivisions of a county. marion county and the indianapolis metropolitan area had one of the highest disease burdens of chlamydia and gonorrhea infection according to the centers for disease control and prevention. the reported rate of chlamydia increased from 605.7 cases to 683.6 cases per 100,000 population while the reported rate of gonorrhea also increased from 185.1 cases to 270.9 cases per 100,000 population during the time period between 2013 to 2017 [28,29]. these rates are much higher than the average rates for all major metropolitan areas studied. as a result, this geographical region has consistently been ranked in the top six in terms of highest chlamydia and gonorrhea disease rates among all major metropolitan areas. figure 2 geographical representation of marion county, indiana with the nine townships measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi data source and computation of disease rates clinical data on chlamydia and gonorrhea cases were extracted from the indiana network for patient care (inpc), a large health information exchange system network that collects and manages electronic health record data from over 100 participating hospitals and 38 health systems within indiana [30,31]. healthcare entities, hospitals, and providers who participate in this system submit their reports and patient information to the inpc. a case of chlamydia and gonorrhea is defined as an individual with a positive laboratory test for the disease [32]. if an individual has two positive confirmed tests that are more than thirty days apart (> 30 days), these tests were counted as two separate cases [33]. as both chlamydia and gonorrhea are considered notifiable diseases, hospitals and providers are required to report these diseases to public health within seventy-two hours as mandated by indiana law [34]. a period of ten years of chlamydia and gonorrhea positive cases were retrospectively collected from january 1, 2005 to december 31, 2014 from the inpc. each positive case was linked to the census tract of the corresponding patient’s residential address to ensure patient confidentiality. positive cases that were not able to be geocoded or had an undetermined address were excluded in this study. the number of positive cases in the census tracts were aggregated together to create a total sum of cases for each of the nine townships. similarly, the number of unique individuals with a record in the inpc system was tabulated for each census tract and are grouped together for each township. this is referred as the inpc population. if a patient had multiple visits with a provider who participated in the inpc in the same year, they will still be counted as one unique record in the inpc. the total numbers of chlamydia and gonorrhea cases that were geocoded and used in this study are tabulated in a frequency table including the number of individuals with a unique inpc record for each year. to compute chlamydia and gonorrhea incidence rates, we use the number of positive cases as the numerator divided by the number of unique individuals with a record in the inpc system as the denominator for that given year. this computation of rates was applied for both census tract and township levels. for each township, the lowest and highest rates of chlamydia or gonorrhea were recorded with the descriptive statistics for chlamydia and gonorrhea rates. visualization of disease rates chlamydia and gonorrhea disease rates for each census tract were represented using a choropleth map for chlamydia and gonorrhea. darker colors represented higher rates of disease while lighter colors represented lower rates of disease. to depict the visualization of disease rate disparity across the county, individual census tracts were grouped into ten deciles in order from lowest rates to highest rates separately for each disease. the first decile group represented census tracts with the lowest 10% of disease rates (dark blue) while the tenth decile group represented census tracts with the highest 10% of disease rates (dark red). census tracts with moderate disease rates or middle decile groups are represented with lighter colors. mapping of disease inequality was conducted for all ten years of study from 2005 to 2014. all spatial data visualizations were created using arcgis desktop 10.6. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi the gini coefficient to analyze and measure inequality we apply the methodology of the lorenz curve from an income inequality perspective to the inequality of disease rates in a geographical area. in our study, the lorenz curve is the line that represents the cumulative proportion of either chlamydia or gonorrhea disease cases (y-axis) versus the cumulative proportion of the population (x-axis). a diagonal line of equality represents a complete uniform distribution of disease rates. a lorenz curve that is flatter or closer to the line of equality would result in more uniformity of disease rates in the context of disease. a lorenz curve that is furthest away from the diagonal line of equality signifies increasing inequality or disparity in disease rates over a geographical area. this could represent that the rate of disease is highly variable in the population and is highly concentrated in a specific area of a region resulting in higher inequality [35]. the gini coefficient examines the degree of departure from a uniform equal distribution of values and to determine inequality among the values. to calculate the gini coefficient for each township, census tracts were ordered from the lowest to highest rates within each township for each disease. from the ordered census tracts, the number of cases were added together to give the cumulative proportion of cases starting with the census tracts with zero cases followed by census tracts with one case, and so forth. the inpc population from these ordered census tracts were also aggregated together to determine the cumulative proportion of population. thus, the gini coefficient was calculated using the formula: 𝐺 = 1 2 ∑|𝑋𝑖 − 𝑌𝑖 | 𝑘 𝑖=1 where xi is the proportion of positive disease cases, yi is the proportion of the inpc population, k is the number of census tracts in a township, and g is the gini coefficient [15]. the gini coefficient was calculated as twice the area between the diagonal line of equality and the lorenz curve as depicted in figure 1 [36]. this approach to measure inequality of sexually transmitted disease cases has been adopted using a similar methodology from a previous study by kerani et al. [20] gini coefficients closer to 0 represent greater equality and uniformity among disease rates while gini coefficients closer to 1 represent significant inequality where only a small specific group in a population receive a very large amount of disease cases in a geographical area. mathematical calculations of all gini coefficients were performed using a programmable statistical software called rstudio version 1.1.442 using the desctools package. statistical analyses to determine if there is a difference in the inequality of disease rates by year and by township for both diseases, we administered a one-way anova analysis along with a post-hoc multiple comparisons test to determine which years and townships are statistically different from each other. in performing this analysis, we used a longitudinal collection of data spanning ten years to determine the trends of chlamydia and gonorrhea disease rates from 2005 to 2014. all p-values were adjusted using the tukey correction to limit the type i error involved in conducting multiple measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi t-tests with the alpha level starting at α = 0.05. statistical analyses for the one-way anova and multiple comparisons test were performed using sas 9.4 software. results table 1 enumerates the frequency counts of chlamydia and gonorrhea cases along with the total number of individuals who were in the indiana network for patient care (inpc) health information exchange system between 2005 to 2014 for each township in marion county. most of the cases for chlamydia and gonorrhea were found in central township over the ten-year period while decatur and franklin townships on the southernmost portion of the county had the least number of cases. there is also a slight upward trend in the number of cases recorded for chlamydia from 2005 to 2012 and gonorrhea from 2005 to 2011 followed by a downward trend for both diseases. the descriptive statistics and characteristics of chlamydia and gonorrhea rates for each township between 2005 to 2014 are presented in table 2. in general, the highest rates for both diseases were found in central township while the lowest rates of disease were observed in the southernmost townships of decatur, perry, and franklin. for the county, the disease rates showed a slightly downward trend for chlamydia from 2005 to 2010 followed by an increasing upward trend starting in 2011. for gonorrhea, there was no distinctive trend occurring as the rates were observed to fluctuate over the ten-year period. the overall gonorrhea rates, however, are indeed lower compared to chlamydia rates. spatial data visualizations of chlamydia and gonorrhea incidence rates are represented in figure 3 and figure 4, respectively for the ten-year period. furthermore, a different approach to visualization was applied to identify and examine disease rate inequality by using decile grouping of disease rates as shown in figure 5 and figure 6. from these visualizations, many of the highest disease rates were situated in the central part of the county, particularly along the border between south lawrence-north warren, south washington-north central, and southeast pike-northeast wayne townships for both diseases. these areas contained census tracts that were ranked in the highest decile groups for disease rates (dark red). conversely, the lowest disease rates were predominantly found on the outer edges of the county including southeast franklin and northeast lawrence townships. these areas were characterized as having census tracts that were ranked in the lowest decile groups for disease rates (dark blue). middle decile groups representing moderate chlamydia or gonorrhea disease rates (lighter colors) were mostly scattered throughout the county with no distinct pattern or consistency. central township had the most census tracts with higher decile groups of disease rates while decatur, perry, and franklin townships exhibited the least amount. however, a few census tracts exhibiting moderate disease decile groups were observed in the three southernmost townships for gonorrhea. some townships particularly washington and lawrence had a distinctive gradient in having census tracts with the highest decile groups in the southernmost portion of the townships and the lowest decile groups in the northernmost portion of the townships. this finding was evident for both diseases which may signify high inequality in disease rates within those townships. each of the ten decile groups from 2005 to 2014 was quantified for the percentage of population and the percentage of cases within each decile group. the results are found in table 3 for chlamydia and gonorrhea. table 3 also depicts the gini coefficient inequality of disease rates for the entire county. for most of the years, the highest decile group (highest 10%) had roughly 19.1% measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi to 23.7% of all chlamydia cases within the county while the lowest decile group (lowest 10%) contained between 1% to 1.6% of all cases. the top half which represents the 6th decile to the highest 10% decile groups had about half of the population in the county but contained roughly 73.6% to 77.8% of all chlamydia cases. the disparity between the highest decile and lowest decile group for gonorrhea was even greater where the highest decile group had roughly 23.1% to 26.6% of all cases while the lowest decile group contained 0.3% to 0.8% of all cases. furthermore, the gini coefficients over the ten-year period for gonorrhea were higher than the gini coefficients for chlamydia. the overall gini coefficient range of inequality for chlamydia was from 0.344 to 0.392 while the range of inequality for gonorrhea was from 0.417 to 0.454. for both diseases, there were no large fluctuations or variability in the gini coefficients from year to year. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi table 1 frequency tables of chlamydia and gonorrhea cases and individual indiana network for patient care (inpc) records for marion county between 2005 to 2014 number of reported chlamydia incidence cases for marion county and the nine townships from inpc year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 4,281 4,205 4,410 4,754 5,123 5,075 5,258 5,301 4,793 4,425 pike 296 326 322 340 366 411 477 465 404 366 washington 425 388 383 448 473 495 518 584 427 428 lawrence 591 592 573 651 705 649 713 673 613 559 wayne 762 729 806 862 880 901 936 987 948 867 central 1,263 1,216 1,318 1,359 1,494 1,447 1,402 1,427 1,291 1,155 warren 568 568 602 641 670 656 705 709 648 590 decatur 85 76 73 66 113 107 104 86 74 108 perry 226 228 248 286 315 323 308 277 286 274 franklin 65 82 85 101 107 86 95 93 102 78 number of reported gonorrhea incidence cases for marion county and the nine townships from inpc year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 2692 2759 3038 3044 2898 2633 2504 2445 2225 2022 pike 167 192 192 203 178 181 215 201 170 167 washington 265 242 265 294 270 257 244 241 195 184 lawrence 415 391 388 435 420 357 353 338 313 261 wayne 413 443 501 515 464 454 405 414 389 352 central 849 889 1028 946 907 763 744 753 704 597 warren 374 377 428 406 438 364 357 334 296 316 decatur 53 46 42 40 45 44 37 35 24 26 perry 121 129 143 149 126 168 116 97 113 99 franklin 35 50 51 56 50 45 33 32 21 20 population of individuals with a unique record in the inpc electronic health record system for marion county and each township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 555,034 589,968 635,742 654,213 710,340 721,201 695,418 676,883 642,296 548,170 pike 47,086 45,306 47,956 49,543 55,874 57,932 55,451 54,045 51,474 43,897 washington 70,592 68,544 73,883 76,771 85,399 89,273 86,270 85,179 78,027 63,435 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi lawrence 70,367 74,544 80,721 83,705 90,581 92,742 89,817 87,683 80,315 67,129 wayne 88,266 89,164 97,508 101,466 110,280 112,447 107,435 102,831 100,661 88,338 central 113,185 118,274 123,252 123,590 135,748 136,688 131,614 125,031 120,082 106,029 warren 70,066 74,364 79,144 81,621 87,825 88,146 84,537 82,314 77,682 67,551 decatur 17,414 20,180 22,206 23,012 24,082 24,021 23,011 23,038 22,143 19,560 perry 54,226 68,367 75,488 78,280 82,581 82,282 80,225 79,938 77,298 64,435 franklin 23,832 31,225 35,584 36,225 37,970 37,670 37,058 36,824 34,614 27,796 table 2 descriptive statistics for chlamydia and gonorrhea rates for marion county between 2005 to 2014a,b range of lowest to highest chlamydia rates for marion county and census tracts within each township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 0.0 – 24.8 0.0 – 22.3 0.0 – 24.2 0.0 – 26.4 0.0 – 25.1 0.0 – 20.5 0.0 – 23.3 0.0 – 26.9 0.0 – 26.3 0.0 – 22.7 pike 0.6 – 12.4 0.6 – 15.5 1.0 – 13.2 1.5 – 13.6 0.9 – 12.2 1.8 – 12.8 1.4 – 18.0 1.8 – 16.0 1.0 – 14.8 1.3 – 16.1 washington 0.0 – 17.5 0.0 – 18.9 0.0 – 15.9 0.0 – 18.7 0.0 – 19.3 0.0 – 18.9 0.0 – 20.7 0.0 – 25.6 0.0 – 22.1 0.0 – 22.5 lawrence 0.0 – 19.3 0.4 – 19.7 0.0 – 18.7 0.3 – 21.0 0.0 – 25.1 0.4 – 19.7 0.3 – 18.8 0.8 – 18.7 0.0 – 16.8 0.6 – 20.1 wayne 1.9 – 18.0 0.0 – 18.4 1.7 – 17.5 2.3 – 18.1 0.8 – 14.0 2.1 – 15.7 0.8 – 17.3 3.5 – 19.8 3.2 – 17.0 2.3 – 18.0 central 2.2 – 24.8 1.5 – 22.3 2.4 – 24.2 2.3 – 26.4 3.3 – 24.9 2.5 – 20.5 4.0 – 23.3 1.9 – 26.9 2.5 – 26.3 2.7 – 22.7 warren 1.0 – 19.2 0.0 – 19.2 0.0 – 18.9 0.8 – 19.4 1.6 – 23.1 2.7 – 17.1 3.1 – 17.0 3.1 – 16.9 3.4 – 16.7 2.2 – 19.0 decatur 2.6 – 7.0 2.2 – 4.7 1.3 – 6.8 0.8 – 4.2 2.5 – 6.7 2.8 – 6.6 4.5 – 4.6 0.7 – 7.0 1.3 – 6.2 4.7 – 6.7 perry 0.6 – 10.8 0.0 – 9.8 0.5 – 8.9 0.7 – 8.9 1.1 – 8.6 0.5 – 11.6 0.0 – 8.5 1.0 – 7.4 1.2 – 8.0 1.3 – 6.6 franklin 0.8 – 4.0 1.1 – 5.7 0.0 – 4.9 0.8 – 5.1 1.6 – 4.3 1.3 – 3.2 1.6 – 4.0 1.3 – 4.1 1.4 – 4.8 2.0 – 4.1 range of lowest to highest gonorrhea rates for marion county and census tracts within each township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 0.0 – 16.1 0.0 – 16.8 0.0 – 19.6 0.0 – 16.1 0.0 – 16.7 0.0 – 13.8 0.0 – 16.2 0.0 – 17.6 0.0 – 15.6 0.0 – 14.7 pike 0.0 – 8.0 0.0 – 12.2 1.0 – 8.4 1.0 – 9.0 0.0 – 5.4 0.0 – 7.1 0.0 – 8.4 0.0 – 8.2 0.5 – 6.0 0.3 – 7.4 washington 0.0 – 13.8 0.0 – 14.9 0.0 – 10.2 0.0 – 14.0 0.0 – 12.3 0.0 – 11.8 0.0 – 11.9 0.0 – 11.1 0.0 – 11.6 0.0 – 10.9 lawrence 0.0 – 15.5 0.0 – 14.7 0.0 – 13.7 0.0 – 15.8 0.0 – 15.5 0.0 – 13.8 0.0 – 10.6 0.0 – 11.0 0.0 – 8.7 0.0 – 12.5 wayne 0.0 – 14.1 1.0 – 13.9 0.8 – 13.6 0.8 – 13.0 0.4 – 9.4 0.6 – 9.2 0.0 – 9.2 0.0 – 10.8 0.0 – 8.9 0.0 – 9.5 central 1.1 – 15.6 0.3 – 16.8 2.1 – 19.6 0.9 – 16.1 1.5 – 16.7 0.9 – 12.7 1.2 – 16.2 0.0 – 17.6 1.0 – 15.6 0.0 – 14.7 warren 0.0 – 16.1 0.9 – 14.5 0.0 – 13.6 0.0 – 13.8 1.2 – 15.2 0.4 – 10.7 0.0 – 8.3 0.0 – 10.7 0.0 – 9.7 0.6 – 10.5 decatur 1.6 – 5.7 0.4 – 4.1 0.4 – 3.8 0.9 – 3.1 0.3 – 2.8 1.1 – 3.0 0.4 – 2.7 0.0 – 3.1 0.5 – 1.9 0.7 – 2.7 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi perry 0.5 – 5.6 0.0 – 4.1 0.0 – 4.1 0.2 – 5.8 0.0 – 5.1 0.0 – 6.0 0.0 – 3.6 0.0 – 3.4 0.0 – 3.6 0.0 – 3.3 franklin 0.0 – 4.0 0.8 – 3.4 0.0 – 3.2 0.4 – 3.7 0.4 – 3.0 0.5 – 1.9 0.0 – 2.2 0.0 – 2.1 0.0 – 1.2 0.3 – 1.2 chlamydia rate for marion county and the nine townships year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 7.71 7.13 6.94 7.27 7.21 7.04 7.56 7.83 7.46 8.07 pike 6.29 7.20 6.71 6.86 6.55 7.09 8.60 8.60 7.85 8.34 washington 6.02 5.66 5.18 5.84 5.54 5.54 6.00 6.86 5.47 6.75 lawrence 8.40 7.94 7.10 7.78 7.78 7.00 7.94 7.68 7.63 8.33 wayne 8.63 8.18 8.27 8.50 7.98 8.01 8.71 9.60 9.42 9.81 central 11.16 10.28 10.69 11.00 11.01 10.59 10.65 11.41 10.75 10.89 warren 8.11 7.64 7.61 7.85 7.63 7.44 8.34 8.61 8.34 8.73 decatur 4.88 3.77 3.29 2.87 4.69 4.45 4.52 3.73 3.34 5.52 perry 4.17 3.33 3.29 3.65 3.81 3.93 3.84 3.47 3.70 4.25 franklin 2.73 2.63 2.39 2.79 2.82 2.28 2.56 2.53 2.95 2.81 gonorrhea rate for marion county and the nine townships year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 4.85 4.68 4.78 4.65 4.08 3.65 3.60 3.61 3.46 3.69 pike 3.55 4.24 4.00 4.10 3.19 3.12 3.88 3.72 3.30 3.80 washington 3.75 3.53 3.59 3.83 3.16 2.88 2.83 2.83 2.50 2.90 lawrence 5.90 5.25 4.81 5.20 4.64 3.85 3.93 3.85 3.90 3.89 wayne 4.68 4.97 5.14 5.08 4.21 4.04 3.77 4.03 3.86 3.98 central 7.50 7.52 8.34 7.65 6.68 5.58 5.65 6.02 5.86 5.63 warren 5.34 5.07 5.41 4.97 4.99 4.13 4.22 4.06 3.81 4.68 decatur 3.04 2.28 1.89 1.74 1.87 1.83 1.61 1.52 1.08 1.33 perry 2.23 1.89 1.89 1.90 1.53 2.04 1.45 1.21 1.46 1.54 franklin 1.47 1.60 1.43 1.55 1.32 1.19 0.89 0.87 0.61 0.72 standard deviation of chlamydia rates for marion county and census tracts within each township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 4.9 4.9 5.0 5.3 5.2 4.7 5.0 5.2 4.9 4.9 pike 3.9 3.9 3.2 3.5 3.1 3.4 4.1 4.3 4.0 3.7 washington 4.4 4.4 3.9 5.2 4.9 4.8 5.2 5.1 4.4 5.2 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi lawrence 5.8 6.1 5.9 6.6 7.1 5.5 5.9 6.0 5.4 5.7 wayne 4.4 4.2 4.3 4.0 3.6 3.4 3.9 4.2 3.7 4.3 central 4.4 4.2 4.8 4.9 4.8 4.0 4.7 4.9 4.5 4.2 warren 5.2 4.7 4.8 5.1 5.0 4.7 4.2 4.3 4.2 4.5 decatur 1.9 1.1 2.3 1.3 1.7 1.4 0.0 2.4 1.9 0.9 perry 2.3 2.3 2.3 2.4 1.8 2.8 2.1 1.9 2.1 1.5 franklin 1.1 1.7 1.7 1.6 1.1 0.7 0.7 1.0 1.2 0.6 standard deviation of gonorrhea rates for marion county and census tracts within each township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 marion county 3.8 3.8 4.1 3.9 3.6 2.9 3.1 3.2 3.0 3.0 pike 2.3 3.1 2.0 2.3 1.7 1.9 2.2 2.5 1.7 1.9 washington 3.2 3.4 3.3 3.8 3.2 2.9 3.1 2.8 2.9 2.6 lawrence 4.8 4.5 4.3 4.5 4.5 3.4 3.5 3.5 2.8 3.3 wayne 3.4 3.1 3.2 2.8 2.3 1.7 2.1 2.0 2.2 2.5 central 3.5 3.5 4.1 3.6 3.7 2.9 3.5 3.4 3.0 3.0 warren 3.7 3.5 3.3 3.9 3.5 3.0 2.4 2.8 2.7 2.8 decatur 1.7 1.6 1.3 0.9 1.0 0.8 0.9 1.0 0.6 0.8 perry 1.4 1.3 1.3 1.6 1.1 1.4 0.9 0.9 1.2 1.0 franklin 1.5 0.9 1.0 1.0 0.8 0.5 0.7 0.7 0.4 0.4 arates are designated as positive cases per 1,000 population found in the inpc bthe denominator to calculate rates is derived from the population of individuals with a record in the inpc electronic health record system for each township. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi figure 3 spatial visualization of chlamydia rates per 1,000 people for each census tract in marion county from 2005-2014 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi figure 4 spatial visualization of gonorrhea rates per 1,000 people for each census tract in marion county from 2005-2014 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi figure 5 spatial visualization of chlamydia decile grouping for each census tract in marion county from 2005-2014 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi figure 6 spatial visualization of gonorrhea decile grouping for each census tract in marion county from 2005-2014 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi to investigate the magnitude of inequality of disease rates by individual townships, table 4 represents a colored display of the gini coefficients for chlamydia and gonorrhea. gini coefficients that are more distributed evenly with lesser inequality were shown in darker green colors while gini coefficients that have greater inequality were shown in darker red colors. the highest gini coefficients for both diseases were found consistently in washington and lawrence townships while central township had one of the lowest gini coefficient values during the ten-year period. although there were periodic spikes in inequality for decatur and franklin townships, both locations had low values in the gini coefficients for specific years such as 2011 and 2014 for chlamydia and 2010 for gonorrhea. additionally, decatur and franklin were the two townships that experienced more fluctuation in gini coefficients over time compared to other townships. this is exhibited between 2006 to 2007, 2011 to 2012, and 2013 to 2014 for chlamydia for decatur township and in the time period between 2005 to 2006 and 2010 to 2011 for gonorrhea for franklin township. overall throughout the nine townships, the individual gini coefficients for gonorrhea were mostly higher than those for chlamydia. finally, to determine if there was a difference in the average gini coefficient within each year and within each township, a one-way anova test was calculated for both diseases. for chlamydia, the average gini coefficients for each township in the ten-year period were as follows: pike = 0.312, washington = 0.471, lawrence = 0.476, wayne = 0.269, central = 0.231, warren = 0.323, decatur = 0.249, perry = 0.322, and franklin = 0.254. when the gini coefficients were compared by year, the overall results were not statistically significant (f(9, 80) = 0.92, root mean squared = 0.106, p = 0.5152). however, when the gini coefficients were compared between the nine townships or geographical locations, there was an overall statistically significant result (f(8, 81) = 21.40, root mean squared = 0.063, p < 0.0001). a post-hoc multiple comparisons test with a tukey adjustment was applied to determine which townships were different from each other in terms of the average gini coefficient as shown in table 5. the results showed that some townships especially lawrence and washington exhibited differences in average gini coefficients compared to all other townships (p < 0.0001). consequently, lawrence and washington did not have significant differences in gini coefficients when compared to each other. other statistically significant differences in gini coefficients were observed between central and warren townships (p = 0.0228) and between perry and central townships (p = 0.0255). for gonorrhea, the same multiple comparisons test was applied, and results are shown in table 5. the average gini coefficients for each township in the ten-year period were as follows: pike = 0.364, washington = 0.564, lawrence = 0.529, wayne = 0.330, central = 0.283, warren = 0.370, decatur = 0.366, perry = 0.406, and franklin = 0.395. when the gini coefficients were compared by year, there was no statistically significant result in the differences of the gini coefficients (f(9, 80) = 0.20, root mean squared = 0.103, p = 0.9940). however, when the gini coefficients were compared within the nine townships or geographical location, there was an overall statistically significant result (f(8, 81) = 30.93, root mean squared = 0.051, p < 0.0001). using the same posthoc multiple comparisons test, the township pairings that exhibited statistically significant findings for chlamydia were also statistically significant for gonorrhea. in addition, there were additional statistically significant township pairings that were not previously found in chlamydia including central-pike townships, wayne-perry townships, central-franklin townships, and centraldecatur townships. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi table 3 summary statistics of chlamydia and gonorrhea decile grouping among marion county census tracts from 2005 to 2014a,b,c,d chlamydia year 2005 2006 2007 2008 2009 decile group % pop % cases % pop % cases % pop % cases % pop % cases % pop % cases 1 st decile 8.8 1.4 8.8 1.0 9.8 1.1 9.5 1.1 9.4 1.5 2 nd decile 9.8 3.0 9.9 2.9 10.8 2.8 9.6 2.6 10.0 3.4 3 rd decile 10.6 5.1 11.5 5.2 10.1 4.5 12.4 5.6 12.0 5.6 4 th decile 9.9 6.4 11.4 7.1 11.2 7.1 11.7 7.4 9.9 6.0 5 th decile 10.0 8.2 10.1 8.2 8.4 6.7 10.2 7.8 10.6 8.1 6 th decile 10.6 10.2 10.3 10.7 10.5 10.5 8.7 8.6 9.5 9.3 7 th decile 9.9 11.8 9.0 11.0 10.0 12.2 9.8 12.3 10.7 13.0 8 th decile 10.6 15.4 8.7 12.4 10.5 15.5 8.9 13.7 10.4 15.4 9 th decile 10.1 17.3 10.1 17.8 10.4 19.0 10.1 18.5 9.1 16.4 10 th decile 9.6 21.2 10.2 23.7 8.3 20.6 9.2 22.4 8.3 21.3 top half 50.8 75.9 48.3 75.6 49.7 77.8 46.7 75.5 48.0 75.4 county gini coefficient 0.362 0.380 0.392 0.394 0.383 year 2010 2011 2012 2013 2014 decile group % pop % cases % pop % cases % pop % cases % pop % cases % pop % cases 1 st decile 10.0 1.5 8.9 1.4 9.7 1.5 9.7 1.6 8.1 1.5 2 nd decile 11.0 3.7 11.8 4.2 11.2 3.7 12.5 4.3 9.8 3.4 3 rd decile 11.1 5.4 11.2 5.6 12.2 5.8 9.6 4.8 11.2 5.7 4 th decile 10.9 6.6 11.8 7.7 11.3 7.6 10.2 6.8 11.3 7.4 5 th decile 9.6 8.1 8.3 6.8 9.0 7.8 10.2 6.8 10.0 8.1 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi 6 th decile 9.3 9.8 8.5 8.6 8.1 8.2 9.6 9.8 9.3 9.2 7 th decile 9.5 12.1 10.4 12.3 10.6 12.9 9.2 11.4 9.7 11.3 8 th decile 9.5 14.6 9.2 13.9 9.0 14.2 9.9 14.4 9.7 13.5 9 th decile 10.4 18.6 11.6 20.3 9.2 16.4 9.8 17.6 10.5 17.5 10 th decile 8.7 19.6 8.3 19.1 9.5 21.9 9.3 20.8 10.4 22.4 top half 47.4 74.7 48.0 74.2 46.4 73.6 47.8 74.0 49.6 73.9 county gini coefficient 0.365 0.361 0.362 0.356 0.344 gonorrhea year 2005 2006 2007 2008 2009 decile group % pop % cases % pop % cases % pop % cases % pop % cases % pop % cases 1 st decile 8.3 0.8 8.9 0.6 9.5 0.5 8.9 0.7 9.5 0.6 2 nd decile 8.8 2.0 10.8 2.6 10.4 2.3 10.6 2.3 9.6 2.2 3 rd decile 11.5 4.2 10.7 3.8 11 4.3 11.7 4.2 11.5 4.4 4 th decile 9.7 5.2 9.4 5.0 10.4 5.8 12.0 6.7 11.2 5.8 5 th decile 10.7 7.1 10.0 7.1 11.6 9.0 10.4 7.7 10.4 7.1 6 th decile 10.3 9.2 10.9 9.8 9.0 8.5 7.9 7.1 8.6 8.0 7 th decile 10.7 13.0 10.9 12.7 10.9 13.3 10.5 12.8 11.6 13.4 8 th decile 10.5 15.8 10.1 15.8 10.4 16.6 9.6 15.7 9.1 13.7 9 th decile 9.5 17.0 9.0 18.3 8.9 17.7 10.2 20.0 9.7 18.3 10 th decile 10.0 25.7 9.1 24.3 7.8 22.0 8.3 22.8 8.8 26.5 top half 51.0 80.7 50.0 80.9 47.0 78.1 46.5 78.4 47.8 79.9 county gini coefficient 0.431 0.440 0.442 0.441 0.454 measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi year 2010 2011 2012 2013 2014 decile group % pop % cases % pop % cases % pop % cases % pop % cases % pop % cases 1 st decile 9.5 0.6 7.9 0.4 8.9 0.4 8.7 0.3 8.7 0.3 2 nd decile 10.1 2.7 10.5 2.3 10.5 1.9 11.0 2.0 9.2 1.8 3 rd decile 13.1 6.0 13.3 5.1 12.6 4.6 10.4 3.6 10.8 3.4 4 th decile 10.1 6.2 10.4 5.9 11.7 6.7 12.4 6.5 11.6 5.7 5 th decile 9.1 7.0 10.0 7.5 8.6 6.4 9.4 7.0 9.2 6.7 6 th decile 10.3 9.8 9.8 9.3 10.4 9.7 10 9.9 10.6 10.2 7 th decile 10.1 11.7 10.2 12.1 9.3 11.2 9.8 12 10.5 13.1 8 th decile 8.1 12 9.5 14.6 9.6 14.6 10.1 16.3 9.2 13.6 9 th decile 10.9 20.6 10.3 19.6 9.3 18.7 9.9 19.4 9.9 18.4 10 th decile 8.8 23.4 8.2 23.3 9.1 25.9 8.3 23.1 10.1 26.6 top half 48.2 77.5 48.0 78.9 47.7 80.1 48.1 80.7 50.3 81.9 county gini coefficient 0.417 0.445 0.452 0.452 0.445 a1st decile grouping represents the 10% of census tracts with the lowest disease rates. b10th decile grouping represents the 10% of census tracts with the highest disease rates. c % pop is percent population which is derived from the population of individuals with a record in the inpc electronic health record system for marion county. dtop half represents the 6th decile to the highest 10% decile groups. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi table 4 gini coefficients of chlamydia and gonorrhea rate inequality among townships between 2005 to 2014a chlamydia township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 pike 0.377 0.343 0.306 0.315 0.293 0.299 0.275 0.312 0.319 0.274 washington 0.434 0.466 0.453 0.517 0.501 0.496 0.498 0.428 0.44 0.471 lawrence 0.444 0.489 0.511 0.516 0.518 0.470 0.451 0.464 0.452 0.452 wayne 0.289 0.292 0.307 0.270 0.272 0.245 0.265 0.251 0.227 0.261 central 0.218 0.221 0.246 0.244 0.239 0.211 0.240 0.240 0.228 0.216 warren 0.360 0.342 0.344 0.356 0.339 0.339 0.280 0.277 0.287 0.298 decatur 0.256 0.189 0.430 0.310 0.243 0.198 0.005 0.415 0.333 0.101 perry 0.295 0.355 0.379 0.361 0.277 0.391 0.307 0.314 0.308 0.220 franklin 0.220 0.369 0.430 0.360 0.22 0.181 0.144 0.232 0.255 0.130 gonorrhea township year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 pike 0.388 0.470 0.318 0.337 0.340 0.365 0.333 0.427 0.336 0.314 washington 0.479 0.542 0.547 0.571 0.564 0.573 0.596 0.595 0.623 0.554 lawrence 0.518 0.544 0.531 0.530 0.557 0.526 0.528 0.526 0.465 0.545 wayne 0.418 0.340 0.347 0.313 0.328 0.237 0.312 0.280 0.335 0.380 central 0.259 0.253 0.268 0.265 0.303 0.287 0.321 0.302 0.282 0.290 warren 0.376 0.373 0.331 0.426 0.360 0.391 0.326 0.360 0.385 0.359 decatur 0.326 0.490 0.454 0.325 0.394 0.265 0.381 0.360 0.323 0.341 perry 0.347 0.383 0.396 0.437 0.410 0.387 0.379 0.434 0.478 0.404 franklin 0.555 0.301 0.439 0.404 0.361 0.227 0.439 0.484 0.437 0.296 agini coefficients that are closer to equality have values closer to 0 (darker green shades) while coefficients that are closer to inequality have values closer to 1 (darker red shades). measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi table 5 multiple comparison analysis of gini differences for chlamydia and gonorrhea between the nine townships from 2005 to 2014a,b chlamydia township pike washington lawrence wayne central warren decatur perry franklin pike <.0001* <.0001* 0.7913 0.0715 1.0000 0.3129 1.0000 0.4254 washington 1.0000 <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* lawrence <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* wayne 0.8823 0.5249 0.9978 0.5504 0.9997 central 0.0228* 0.9989 0.0255* 0.9941 warren 0.1353 1.0000 0.2034 decatur 0.1473 1.0000 perry 0.2195 franklin gonorrhea township pike washington lawrence wayne central warren decatur perry franklin pike <.0001* <.0001* 0.8627 0.0194* 1.0000 1.0000 0.6657 0.9137 washington 0.8425 <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* lawrence <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* wayne 0.5193 0.7214 0.8209 0.0364* 0.1243 central 0.0087* 0.0149* <.0001* 0.0002* warren 1.0000 0.8209 0.9747 decatur 0.7214 0.9398 perry 0.9999 franklin aall p-values were tabulated using the tukey adjustment. bstatistically significant p-values are represented by an asterisk at α = 0.05. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi 5. discussion interpretation of findings this study presents a method of measuring and visualizing inequality of chlamydia and gonorrhea rates longitudinally for an urbanized county using the gini coefficient for a period of ten years. our findings show that at the county level, the distribution of disease rates for both chlamydia and gonorrhea are not distributed uniformly. the level of inequality for the county was not extreme, yet the gini coefficients show that disease rates are far from homogenous. this result is consistent with past literature that displayed heterogeneous patterns in disease rates for sexually transmitted diseases [37-39]. areas of high disease rates exist, particularly in the central part of the county, while outlying areas near adjacent bordering counties have lower disease rates. the patterns of varied and uneven disease rates in the county were evident over the course of the entire ten years of study. additionally, our results indicate that gonorrhea has higher inequality in the distribution of disease rates than chlamydia. this was reflected in the very large percentage of the gonorrhea gini coefficients. in some locations where disease rates were very high, the gonorrhea disease rates were more concentrated and clustered than those for chlamydia which is consistent with previous studies that indicate gonorrhea being less evenly distributed [20,38]. it may indicate that gonorrhea represented distributions containing many census tracts with low disease rates and a few census tracts with extremely high disease rates. this would portray high inequality where only an extremely small fraction of the population receives high levels of cases compared to the rest of the population. furthermore, our study showed that the patterns of inequality of both chlamydia and gonorrhea disease rates in the county were not drastically different from year to year. these observations were consistent as areas with concentrated high rates of disease and very low rates of disease were expected to occur in the same general location over the entire ten-year period. there was no major change in where the concentrations of high rates of disease abruptly shift or migrate towards other areas. this finding shows that high risk infection areas are endemic to specific locations within the county, and these locations that have high infection rates are ideal for localized targeted interventions. one note of interest was that central township exhibited some of the lowest gini coefficient indices despite having different magnitudes of disease rates compared to some of the southernmost townships (decatur, franklin) that also showed generally low gini coefficient values. in the visualizations, central township was characterized as having many census tracts with moderate and high disease rates while the southernmost townships predominantly had census tracts with much lower disease rates. both of these different locations did portray smaller inequality in disease rates than other areas of the county. from this result, it appears that using the gini inequality measurements may not be a good indicator to differentiate or measure the magnitude of the disease rates but is sufficient to measure the dispersion of those disease rates within the townships. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi by using decile groups, our study provides a strength in understanding the characteristics of different inequality categories within our data when chlamydia and gonorrhea are compared against each other. for all ten years of study, the percent population of the top half decile groups (6th to 10th decile group) were roughly similar representing close to half of the population. however, the gini coefficient was higher when the percentage of cases was greater within the top half of the population as exhibited for gonorrhea. coincidently, within the 1st decile group, the percent population were also roughly consistent across all ten years. yet, the gini coefficient was higher when the percentage of cases within the 1st decile group was smaller. these results may indicate that higher percentages of cases in the highest decile groups play a role in influencing and increasing the values of gini coefficients. implications for clinicians there is tremendous opportunity that the measurement and identification of health inequalities provide beneficial applications and advantages for clinicians. by using inequality methods such as the gini coefficient along with spatial data visualizations, clinicians can determine and identify disparities and gaps in screening coverage in high risk locations. if a patient is asymptomatic for a sexually transmitted disease, they are more likely to not seek treatment which would propagate the disease further to other sexual partners [40]. the increasing potential for proactive screening could detect these patients ahead of time reducing the risk of complications caused by chlamydia and gonorrhea. another important benefit for clinicians is to generate knowledge in implementing effective targeted intervention programs. providers may need to decide whether chlamydia or gonorrhea intervention programs over a large area would be effective or whether they may need to succinctly target localized small areas to maximize the benefit of their programs. when the inequalities were measured by geographical areas in our study, there were differences in the distribution of disease rates within townships. this could signify that specialized targeted intervention programs that are specific to the individual high-risk community or neighborhood may perhaps work more effectively than having the same program for the entire county. knowledge of the distribution of disease rates can guide clinicians to adjust their intervention programs, create additional healthcare services, or family planning programs according to the needs of the community or whenever their current programs are not working effectively [41]. as a result, educational activities that are included in the intervention programs can be implemented to inform patients about safe sexual health practices, understand sexually transmitted disease risks, and to seek treatment [42]. implications for public health organizations along with clinicians, public health organizations can benefit from the knowledge gained from using the measures of inequalities for sexually transmitted diseases. first, understanding inequalities can help assess where the demand for urgent health provisions and resources should be administered. segments of the population that are marginalized, limited to access of healthcare, or have a demand for health programs or services can be prioritized or targeted by government agencies to increase more health funding. this could greatly assist policy makers to approve targeted resources for those at-risk individuals and communities. in some instances, having localized interventions that target a small specific area could be better than comprehensive measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi interventions that target a massive geographical area. valuable health resources would be directed to focus on highly critical infectious communities and core groups to minimize further spread of sexually transmitted diseases [19]. using inequality measures such as the gini coefficient can determine if public health resources are properly administered. high numbers of cases that occur disproportionally in a small geographical area could suggest that existing public health services and delivery across the county are inconsistent or ineffective [43]. knowing that public health funds are often limited, it is necessary to have appropriate allocation and proper management of services. thus, public health organizations can use information of inequality methods to better evaluate and improve these services. the use of inequality measures such as the gini coefficient can assist in monitoring trends in disease patterns over time and improve longitudinal collection of data for surveillance purposes. it can also help determine where abrupt and abnormal outbreaks of infection can occur with unusual changes in disease rates over a community or geographical area. combined with spatial data visualization techniques, this can assist in identifying how affected populations are concentrated or dispersed as well as recognizing the factors that contribute to these characteristics of inequality. finally, the incorporation of methods in health inequalities can enhance and improve our knowledge of data from communicable diseases. usage of electronic health records and health information exchange systems to capture communicable disease data will continue to increase, and the ability to perform analyses on existing reported data and identifying disparities is crucial in knowing how to manage and effectively reduce the health burdens of disease. the effectiveness of using and understanding data from health information technology to develop ongoing innovative methodologies will determine how public health organizations can combat and reduce the existing gaps of health disparities that is occurring in the health of our population. limitations in our study, there were some limitations that need to be considered. our study only represented cases that were both geocoded and reported through the inpc health information exchange system. cases that did not have an address or were not able to be geocoded were not included in this study which would limit the number of actual positive cases. there is also the issue of inaccurate reporting by providers as some providers may not send or report actual positive cases to public health. some providers who report cases may not participate in the inpc which also reduces the positive cases recorded. additionally, reported rates of disease are generally influenced by diagnostic, screening, and testing behaviors. when these factors are combined with the asymptomatic nature of chlamydia and gonorrhea, the true disease rates for both chlamydia and gonorrhea could be higher than what is given in our study. however, our study provides an opportunity to estimate sexually transmitted disease rates given by the information received from health information systems to record communicable disease data. as more clinicians and healthcare providers continue to use health information systems and electronic health records to capture patient data, the ability to use accurate data from these entities to produce relevant treatment interventions is vital. measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi furthermore, consideration must be made if the results can be generalized to other geographical areas that do not have the same characteristics as our urbanized county under study. these areas may include less populated rural or suburban locations with different demographics and patterns of disease. the level of chlamydia or gonorrhea inequality may not be as pronounced or similar to the findings in this study. however, the application and the concept of analyzing and using inequality measures are still important for rural areas to measure disparities in healthcare coverage and availability of health services [44]. there are potential considerations that must be addressed when performing the gini coefficient calculations. the gini coefficient only gives a relative measure on the magnitude of inequality in a given distribution. it does not locate or designate where inequalities occur in a given geographical area. as a result, identifying inequalities require spatial data visualization techniques to accompany the degree of disparity involved in different locations which is presented as a strength to our study. secondly, the gini coefficient only differentiates the characteristics of the distribution of disease rates, but it does not differentiate the magnitude of the disease rates itself. a township that has an equal distribution of high disease rates might have a similar gini coefficient as a township that contains an equal distribution of low disease rates. in this situation, spatial data visualization again provides the benefit to visualize and compare the differences in disease rates for different geographical locations. finally, closer inspection must be made for townships or geographical entities that contain a very small number of census tracts or aggregated features. in particular, decatur and franklin townships exhibited large fluctuations in gini coefficients during the ten-year period of study which may be attributed to the instability of disease rates. additionally, these two townships also had the fewest number of census tracts and cases compared to other neighboring townships. caution must be given for small sample sizes as the gini coefficient is susceptible to variation in indices when calculated over time. future studies our work can provide opportunities for additional health disparities research. although our study only examined the inequality of disease incidence rates for the population within the health information exchange system as a whole, new insights in information can be generated when the data can be stratified by race, gender, or age group to discover if there are differences in inequality trends over time from these groups. for example, previous studies have denoted racial segregation as a major factor in influencing inequality rates in sexually transmitted diseases [45-47]. it may be interesting to determine if the inequality in disease rates would be much higher in our townships if race was a contributor to the disease disparity. additionally, further investigation can be conducted to determine which factors or social determinants of health contribute to the large disparities or inequalities in disease rates. gathering this knowledge can assist clinicians and public health organizations in understanding underlying factors such as race, economic status, unemployment, household characteristics, or the number of sexual partners that lead to increased risk of chlamydia and gonorrhea infection. more emphasis and focus can be directed on understanding how communities and neighborhood structures operate to understand disease outcomes [48]. furthermore, investigation of the social determinants of health can identify barriers to routine screening of sexually transmitted diseases whether that be the lack of knowledge of disease symptoms or inability to pay for screening from the patient measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi [49,50]. clinicians can address these social determinants of health to determine the mechanisms and causes of why large health inequalities in sexually transmitted diseases exist in the population. thirdly, although we used the most basic but effective features to visualize and map inequalities using decile groups, our study can introduce other opportunities to explore additional advanced mapping techniques or even forecast where future inequalities will occur. geovisual analytics and geostatistics, which are branches of knowledge that focuses on making inferences from visual representations and patterns from data involving geographical features, can be applied to identify interesting phenomena in the data and visualizations being presented [51]. examples of geovisual analytical techniques include spatial autocorrelation measurements, the local indicators for spatial autocorrelation (lisa) method to detect outliers and anomalies in maps, and spatial regression methods. when used effectively, geovisual analytical tools can enhance decision support for clinicians to allow exploration, identification, and analysis of relevant information that can meet problem-solving needs [52]. finally, the application of the gini coefficient can be translated to determine inequality rates in other diseases that use longitudinal data. although sufficient comprehensive data is needed especially when comparing inequality over time, the broad applicability and straightforward calculation makes the gini coefficient an opportune choice to assess disease distributions within different population groups. conclusion inequality measures such as the gini coefficient combined with spatial data visualization mapping provide a unique opportunity to visualize and measure health inequality in sexually transmitted diseases while exploring underlying patterns in longitudinal data from clinical information systems. the measurement and examination of health inequality is necessary in understanding the distributions of disease incidence to improve delivery of valuable health resources and to identify population groups who are vulnerable to increased infection. as the collection of clinical data from health information systems continue to increase, the ability to explore, investigate, and analyze trends of disease incidence will be critical in reducing health disparities, improving health quality, and creating the most effective treatment interventions to alleviate the burdens of sexually transmitted diseases. acknowledgements the authors acknowledge and thank dr. jane wang, from regenstrief institute for her support in extracting the clinical data and preparing datasets for analysis. additionally, we would also like to thank the regenstrief institute for facilitating access to and use of the data from the indiana network for patient care and for providing the capital resources to pursue this research." financial disclosure the research reported in this publication was supported by the centers for disease control and prevention (cdc), u.s. department of health and human services (hhs), under contract measuring and visualizing chlamydia and gonorrhea inequality: an informatics approach using geographical information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e8, 2019 ojphi number 200-2011-42027. the content is solely the responsibility of the authors and does not necessarily represent the official views of cdc or hhs. competing interests the authors have no competing interests to report. references 1. carter-pokras o, baquet c. 2002. what is a" health disparity"? public health rep. 117(5), 426. pubmed https://doi.org/10.1016/s0033-3549(04)50182-6 2. braveman p. 2006. health disparities and health equity: concepts and measurement. annu rev public health. 27, 167-94. pubmed https://doi.org/10.1146/annurev.publhealth.27.021405.102103 3. baquet cr, carter-pokras o, bengen-seltzer b. 2004. healthcare disparities 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david.carter@cnrc-nrc.gc.ca� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e126, 2014 isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 66 (page number not for citation purposes) isds 2013 conference abstracts utilization of emergency department data for drug overdose surveillance in north carolina katherine j. harmon*1, scott proescholdbell2, steve marshall1 and anna waller3 1injury prevention research center, chapel hill, nc, usa; 2north carolina division of public health, raleigh, nc, usa; 3carolina center for health informatics, chapel hill, nc, usa objective to describe the epidemiologic characteristics for emergency department visits due to drug overdoses in north carolina. introduction in north carolina there has been an escalation of poisoning deaths. in 2011, the number of fatal poisonings was 1,368 deaths, with 91% classified as drug overdoses with the majority of those due to opioid analgesics.[1] far greater numbers of drug overdoses result in hospitalization, emergency department (ed) or outpatient clinic visits, or resolve without the individual seeking medical attention. although public health authorities have long employed death data for drug overdose surveillance in nc, little attention has been paid to the use of ed data for this purpose. through the north carolina disease event tracking and epidemiologic collection tool (nc detect), nc collects information on 99.5% of all acute-care ed visits across the state, primarily for syndromic surveillance purposes. despite the timeliness and completeness of this data system, drug overdose surveillance is a challenge due to lack of a standardized definition for the positive identification of opioid overdoses. in this study we used nc detect ed data to describe visits due to drug, and more specifically, opioid overdoses. methods we performed a nc population-based study of all 2011 ed visits due to drug overdoses captured by the nc detect system using a surveillance definition based on the centers for disease control and prevention state injury indicators.[5] we identified ed visits as drug overdoses if the visit contained an international classification of diseases, 9th edition clinical modification (icd-9-cm) diagnosis code in the range of 960-979 (.0-.9) and/or an external cause of injury code (e-code) in the range of e850-e858 (.0-.9), e950.0-e950.5, e962.0, or e980.0-e980.5 listed in any position. an ed visit was flagged as an opioid overdose if the visit contained a diagnosis code in the range of 965.0 and/or an e-code in the range of e850.0-e850.2. we generated summary statistics for all drug and opioid overdoses and looked at rates of ed visits by sex and age. results in 2011, there were 35,193 ed visits due to poisonings; 22,992 of the ed visits were characterized as drug overdoses (56.6%). the 2011 rate of ed visits due to drug overdoses was 238.1 ed visits per 100,000 person-years. we identified 3,251 ed visits as opioid overdoses with 312 visits due to heroin, 312 visits due to methadone, and 2,501 ed visits due to other or unspecified opioids. slightly over one-half of these ed visits were among women (1,722 ed visits). the 2011 rate of ed visits due to opioid overdose was 33.7 ed visits per 100,000 person-years. among men, opioid overdose rates peaked at ages 20-24; among women, rates peaked at ages 45-54. among opioid overdoses in which the intent was reported, 58.4% were unintentional, 24.7% were due to self-harm, and 15.2% were of undetermined intent. patients presenting as an opioid overdose were likely to be admitted to the hospital, with 44.9% admitted to the hospital, 4.7% admitted to the intensive care unit (icu), and 1.8% admitted to psychiatry; only 38.1% were discharged from the ed. nearly one-half of ed visits due to opioid overdoses had an expected source of payment by the government, with 25.4% expected payment by medicare, 21.1% by medicaid, and 2.6% paid by some other governmental source. another 26.2% of ed visits had an expected source of payment of self-pay. conclusions the us is in the midst of a drug overdose epidemic, primarily due to opioid analgesics. population-based data describing opioid overdoses in the ed is useful for describing the burden of these injuries and for the development of public health prevention strategies. keywords drug overdose; public health surveillance; emergency department data acknowledgments these data were obtained from the nc dhhs/dph nc detect system under a data use agreement. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, statistical analyses, results, or conclusions presented. references 1.nc injury and violence prevention branch. prescription and drug overdoses factsheet [internet]. nc dph, nc dhhs, raleigh (nc): 2013 january [cited 2013 august 20]. available from: http://injuryfreenc. ncdhhs.gov/about/poisoningoverdosefactsheet2013.pdf. 2.thomas ke, johnson rl. state injury indicators report: instructions for preparing 2010 data [internet]. ncipc, cdc, atlanta (ga): 2012 [cited september 5]. available from http://www.cdc.gov/injury/ pdfs/2010_state_injury_indicator_instructions-a.pdf. *katherine j. harmon e-mail: kjharmon@live.unc.edu scholcommuser stamp scholcommuser rectangle scholcommuser rectangle scholcommuser text box online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e174, 2014 your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 1 ojphi your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us ana aleksandric1,2, mercy jesuloluwa obasanya mph1, sarah melcher1, shirin nilizadeh phd2, gabriela mustata wilson phd1* 1 the university of texas at arlington, multi-interprofessional center for health informatics, arlington, tx, usa 2 the university of texas at arlington, department of computer science and engineering, arlington, tx, usa abstract objective: the aims of the study were to examine the association between social media sentiments surrounding covid-19 vaccination and the effects on vaccination rates in the united states (us), as well as other contributing factors to the covid-19 vaccine hesitancy. method: the dataset used in this study consists of vaccine-related english tweets collected in real-time from january 4 may 11, 2021, posted within the us, as well as health literacy (hl), social vulnerability index (svi), and vaccination rates at the state level. results: the findings presented in this study demonstrate a significant correlation between the sentiments of the tweets and the vaccination rate in the us. the results also suggest a significant negative association between hl and svi and that the state demographics correlate with both hl and svi. discussion: social media activity provides insights into public opinion about vaccinations and helps determine the required public health interventions to increase the vaccination rate in the us. conclusion: health literacy, social vulnerability index, and monitoring of social media sentiments need to be considered in public health interventions as part of vaccination campaigns. keywords: covid–19, health literacy, covid–19 vaccine hesitancy, social vulnerability index, social media, social determinants of health abbreviations: health literacy (hl), social vulnerability index (svi), social determinants of health (sdoh), united states (us) doi: 10.5210/ojphi.v14i1.12419 *correspondence: gabriela mustata, gabriela.wilson@uta.edu copyright ©2022 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:gabriela.wilson@uta.edu your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 2 ojphi introduction vaccines remain one of the most significant advancements and achievements in public health for disease prevention and control and one of the most cost-effective and successful interventions to improve health outcomes. unfortunately, vaccine hesitancy from the public is a serious threat to maintaining herd immunity and preventing outbreaks [1]. the delay in acceptance or refusal of vaccines despite the availability of vaccine services is influenced by complacency, convenience, and confidence [2] and may be fueled by health information obtained from various sources, including social media [1]. a national survey in 2020 suggested that the public’s willingness to vaccinate against covid-19 was low and may be insufficient to provide herd immunity [3]. in the us, as of january 2021, about 20% of the population remained hesitant to get the vaccine, and 31% say they will wait to see how it is working for others before getting the covid-19 vaccine [4]. although social media is a valuable tool for disseminating and receiving relevant health information for patients, clinicians, and scientists [5], it has also negatively impacted vaccination rates and public health promotion. the substantial spread of negative posts across different social media platforms, such as twitter and facebook on the vaccine's safety, has fueled the public’s hesitation in getting vaccinated [6]. a relevant example is a study by ahmed et al. that demonstrated that using twitter and facebook as sources of information related to the influenza virus has a significant inverse association with influenza vaccine uptake [6]. the anti-vaxxer propaganda has also seen a rise in the community on social media and has potentially magnified the hesitancy [7]. since the connection between social media and vaccine hesitancy has already been established in previous studies [6], it is hypothesized that a similar pattern occurs related to covid-19 vaccine uptake. during the covid-19 pandemic and the country's lock-down, people had to rely on social media to keep social interactions and connections going as they could not do so in person. at the same time, misinformation regarding covid 19 emerged in other regions around the world [8]. unfortunately, this exposed the public to unsubstantial rumors regarding protective measures against the spread of the virus and covid-19 vaccines. in particular, studies suggest that the use of social media as a source of information about covid-19 has been correlated with stronger beliefs in conspiracy theories, adverse information, and less-protective behaviors during the pandemic [5]. some of the false information being spread is that 5g mobile networks were connected to the spread of the virus, that vaccine trial participants have died after taking a covid19 vaccine, and that the pandemic is a conspiracy or a bioweapon [9]. as a result, a decreased confidence in the vaccine's efficacy and willingness to take the vaccine once available have been observed. therefore, it is crucial to understand the impact of social media posts on vaccination rates, which can help identify intervention areas and address misinformation and disinformation. it is also essential to investigate the association between vaccination rates, health literacy, and social determinants of health, which refers to conditions in the places where people live, learn, work, and play that affect a wide range of health and quality-of life-risks and outcomes [10]. these factors could impact both health and health care disparities outcomes [11] and potentially influence vaccination rates in the united states. your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 3 ojphi while there have been studies/reports on social media and vaccine hesitancy [1,3,5,9], no study has examined the association between sentiments (positive and negative) and vaccination rates using primary data collected from a social media platform. this study addresses this gap by obtaining sentiments of vaccine-related tweets on twitter and conducting a multivariant regression analysis to examine the impact on vaccination rates. the study also examines the association between social determinants of health factors – health literacy and social vulnerability index, with vaccination rates and analyzes how social media sentiments correlate with covid-19 vaccine hesitancy and vaccination rates in the united states. it also contributes to a better understanding of the impact of social media and its potential role in healthcare communication and identifies opportunities for interventions such as addressing miscommunications and increasing health literacy to improve health. it also demonstrates the importance of incorporating social determinants of health factors (health literacy and social vulnerability index) in public health interventions in the future. methods data sources the various data sources used in this study are listed in table 1. table 1: data sources used in the analysis data data source tweets covaxxy dataset. http://arxiv.org/abs/2101.07694 health literacy the university of north carolina at chapel hill. http://healthliteracymap.unc.edu/# social vulnerability index cdc https://www.atsdr.cdc.gov/placeandhealth/svi/index.html population per census block group policy map https://www.policymap.com/newmaps#/ population per state policy map https://www.policymap.com/newmaps#/ population percentages united states census bureau. https://www.census.gov/data/tables/time-series/demo/popest/2010sstate-detail.html study design this study focuses on one of the most popular microblogging and social networking service platforms, twitter [12]. the dataset used herein comes from the covaxxy dataset [13], an extensive collection of vaccine-related tweets collected in real-time between january 4 may 11, 2021, when the covid-19 vaccine was approved and vaccination started. the primary goal of this research was to determine how tweets sentiments impacted the vaccination rates in the united your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 4 ojphi states and determine what other factors, such as health literacy (hl) and social vulnerability index (svi), might influence vaccination rates. the study was also performed to find the association between hl and svi, as well as between hl, svi, and state demographics (percentages of white, black, hispanic, and asian populations per state). as the assumption is that hl and svi represent significant variables that should be taken into consideration when investigating the association between social media sentiments and vaccination rate, it is relevant to explore the relationship between them, as well as their relationships with demographic composition. thus, examining such associations can help understand social media activity and its influence on the vaccination rate. study population the dataset was comprised of tweets from 51 individual states within the us, and from the collection of 85,100,935 vaccine-related tweets between january 4-may 11, 2021, 322,035 tweets were extracted with the specified location within the united states, belonging to 117,258 unique users. twitter provides location as a bounding box of four points, each having longitude and latitude. however, for the purpose of this analysis, it was important to identify the state where the tweet was posted from. therefore, the middle point of the bounding box (see figure 1) was selected, and the federal communication commission getarea api [14] was used to determine the state of every tweet. not all tweets include such information; therefore, the tweets that received ‘none’ as a location were discarded from the dataset. figure 1: tweet’s bounding box location visualization; computing the middle point one caveat is that bots can automatically generate tweets, which might not represent the opinion of people. therefore, to remove any bias from the dataset, botometer v4 [15] was used to remove any tweets belonging to the bot accounts. according to botometer creators, between 9 and 15 percent of twitter accounts are bot accounts, while 85 percent of the accounts are human [16]. therefore, the 85th percentile of scores was computed on all the accounts scores in the dataset, being 0.69, which is then used as a threshold. afterward, every tweet belonging to the account with a score higher than the threshold was removed from the dataset. this led to the removal of 58,263 tweets belonging to 17,597 users. secondly, since the primary purpose of this research was to establish a connection between tweet sentiments and vaccination rates, all the tweets originating from the accounts representing organizations (e.g., pharmaceutical companies) were removed your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 5 ojphi using humanizr, a tool that can distinguish between personal and organizational twitter accounts considering different account features [17]. the filtered dataset included the total number of 243,202 tweets belonging to 95,292 unique users. figure 2 highlights the process of data collection and the cleaning process. figure 2: data collection and cleaning process dependent and independent variables vaccination rate: the main dependent variable in this analysis is the vaccination rate, as the objective is to see if tweets’ sentiments and social determinants of health impact the vaccination rate in the united states. the vaccination rate shown in figure 3 is per hundred people on the last day of our data collection, may 11, 2021. the vaccination rate was extracted from covid-19 vaccine data, collected daily in real-time by indiana university’s observatory on social media (osome) in collaboration with colleagues from politecnico di milano [13]. figure 3: vaccination rate in the united states per 100 people as of may 11, 2021 (figure generated with microsoft excel) your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 6 ojphi tweets’ sentiments: the sentiment of each tweet was extracted using vader [18], a machine learning tool that allows for deriving sentiments of social media posts in multiple languages. vader showed as the most accurate tool used for sentiment analysis on twitter data [19] due to its ability to take the social media language common features into consideration. this tool accepts a tweet text as an input and returns a compound score in the range from –1 to 1, where scores closer to 1 are more positive, and scores closer to -1 are more negative. health literacy: health literacy is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others [20]. health literacy was used in the analysis to provide a better understanding of how the ability of the community to receive and understand health information is impacting the public health interventions. the assumption is that vaccine hesitancy is lower in the communities with high health literacy and higher in the communities with lower health literacy. several previous studies have been conducted to examine the connection between health literacy and vaccination hesitancy. one study in france explored the covid 19 vaccine and vaccine hesitancy in relation to fake news. this study suggests that there is an association between low health literacy levels and vaccination rates [21]. a different study conducted in the us found that individuals with low health literacy would be less willing to believe credible sources and less likely to receive a vaccine [22]. finally, a systematic review of health literacy and vaccination rates indicated that there could not be a universal standard as many different factors come into play for each study [23]. health literacy data was obtained from the university of north carolina at chapel hill at the census block level [24]. a weighted average of health literacy indexes having census group population as weights was performed to calculate health literacy at the state level. the census group population was based on the 2010 census since health literacy data corresponds to 2014, which means health literacy data is calculated based on the 2010 census group data. social vulnerability index: the social vulnerability index refers to the potential negative effects on communities caused by external stresses on human health [25]. such strains include natural or human-caused disasters or disease outbreaks. a low index means communities are especially at risk during public health emergencies because of socioeconomic status, household composition, minority status, or housing type and transportation [25]. the center for disease control and prevention (cdc) noted that areas with a high social vulnerability index had lower vaccination rates [26]. the social vulnerability index (svi) data used in this study originate from the cdc’s svi at the county level. similarly, as in the case of hl, a weighted average of social vulnerability indexes was computed, having county population as weights to obtain svi on the state level. given that population data were unavailable for the oglala lakota and kusilvak, these counties were excluded from the calculation of weighted svi per state. the latest svi dataset available is from 2018, which means it was also calculated per 2010 census block groups. as specified earlier, the 2010 population data were used to calculate the weighted average to obtain svi per state. your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 7 ojphi control variables the control variables used in the statistical analysis of this study are at the state level and include the following: population, the total number of tweets, number of unique users, and state race and ethnicity composition (%). for example, some states might have a higher population, and with that, it is very likely that more tweets will be collected in these states. these variables were included to avoid any bias that could appear due to the different numbers of tweets/sentiments in various states. state demographics include percentages of white, black, hispanic, and asian populations per state, and they were also included to test if race and ethnicity play a role in the overall vaccination rate, hl, and svi. population and population percentages were calculated based on 2010 census group data since hl and svi were also calculated per census 2010 data (2020 data was not available when hl and svi were calculated). statistical analysis various hypotheses were established to be tested by statistical analysis, such as: hypothesis 1: positive tweets are associated with a higher vaccination rate at the state level. hypothesis 2: social determinants of health play a significant role in vaccine rate. hypothesis 3: a higher social vulnerability index is associated with lower health literacy. hypothesis 4: percentages of minority populations are lower in states where health literacy is higher. hypothesis 5: percentage of minority populations is higher in states where the social vulnerability index is more elevated. the analysis consists of different statistical tests to evaluate each hypothesis. to test the first two hypotheses, a multivariant linear regression model was used to predict vaccination rate as a dependent numerical variable and tweet sentiments, health literacy, and social vulnerability index as independent variables. all the control variables mentioned were included in the models. for each user in the dataset, an average sentiment was calculated for that user per week. for that particular user the following data was available: location at the state level from which tweets were generated, hl & svi for that state, vaccination rate per state for that particular week, that state race and ethnicity composition (percentages of white, black, hispanic and asian population), population, the total number of tweets, and the number of unique users. the third hypothesis was also tested using multivariant linear regression, with svi as dependent and hl as independent variables. finally, the last two hypotheses were also tested using two separate linear regression models, one containing hl as dependent variable, and the second one containing svi as dependent variable. in both models, independent variables were state race and ethnicity composition (%). your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 8 ojphi results association between vaccination rate and tweet sentiments and impact of sdoh on vaccination rate the linear regression analysis showed a strong positive correlation between vaccination rate and tweets sentiments with the p-value < 0.05. this result supports hypothesis 1, that positive tweets are associated with a higher vaccination rate. the results in table 2 show that both health literacy (hl) and social vulnerability index (svi) play a significant role, with hl yielding a strong positive correlation while svi is yielding a negative correlation. this means that for each unit increase of health literacy, the vaccination rate increases by 0.458 (p-value < 0.001). similarly, for each svi unit increase, the vaccination rate decreases by 14.1 (p-value < 0.001). these results support hypothesis 2 that social determinants of health play a significant role in vaccination rates. note that a similar analysis was performed using average tweet sentiments per user per day, and similar results were obtained. table 2: association between vaccination rates and selected independent variables independent variables estimate std. error t-value p-value sentiment 0.2734 0.1388 1.970 p < 0.05 health literacy 0.4578 0.0586 7.812 p < 0.001 social vulnerability index -14.0801 1.2823 -10.980 p < 0.001 white population (%) -9.4716 3.6687 -2.582 p < 0.05 black population (%) 3.1385 3.4969 0.897 p > 0.05 hispanic population (%) 27.1188 1.6105 16.838 p < 0.001 asian population (%) 31.0692 6.1772 5.030 p < 0.001 population 0.0000 0.0000 4.384 p < 0.001 number of unique users -0.0042 0.0003 -16.480 p < 0.001 total number of tweets 0.0013 0.0001 20.105 p < 0.001 association between hl and svi another linear regression model was performed to test hypothesis 3, having hl as explanatory variable and svi as response variable. the results suggest that there is a significant negative correlation (p < 0.001) between hl and svi. thus, the social vulnerability index is lower in the states where health literacy is higher, suggesting that the areas with a higher ability to receive and understand health information are less vulnerable. this finding implies that public health interventions need to be performed in more vulnerable areas to educate communities and increase your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 9 ojphi the vaccination rates. the relationship between hl and svi is presented in figure 4, confirming hypothesis 3. figure 4: linear relationship between svi and hl association between hl/svi and state race and ethnicity composition final linear regression models were used to test hypothesis 4 and hypothesis 5, where dependent variables were hl in the first model, and svi in the second model. the independent variables in both cases were state race and ethnicity composition (percentages of white, black, hispanic, and asian population). the result confirms hypothesis 4 by indicating the negative significant association between hl and percentages of black and hispanic population per state. meaning, the hl is lower in the states with a higher percentage of hispanic (p < 0.001) and black (p < 0.05) populations. similarly, there is a significant negative relationship between svi and white and asian population percentage, and a significant positive correlation between svi and hispanic population percentage per state, confirming hypothesis 5. this means that states with a higher social vulnerability index have a higher percentage of the hispanic population (p < 0.001), while states with a lower svi have a higher percentage of white (p < 0.05) and asian (p < 0.05) populations. this suggests that public health organizations need to dedicate resources to improve health education and increase vaccine awareness in vulnerable areas where there is a higher percentage of minority populations. results of the models are presented in table 3. your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 10 ojphi table 3: association between hl (above) and svi (below) with state race and ethnicity composition independent variable (hl) estimate std. error t-value p-value white population (%) 13.477 9.231 1.460 p > 0.05 black population (%) -20.556 8.634 2.381 p < 0.05 hispanic population (%) 31.423 3.673 8.555 p < 0.001 asian population (%) 3.839 16.750 0.229 p > 0.05 independent variable (svi) estimate std. error t-value p-value white population (%) 0.9156 0.3830 2.390 p < 0.05 black population (%) 0.0199 0.3583 0.056 p > 0.05 hispanic population (%) 0.8704 0.1524 5.711 p < 0.001 asian population (%) 1.5058 0.6950 2.166 p < 0.05 discussion the purpose of this study was to examine the association between social media sentiments surrounding covid-19 vaccination, the association between sentiments and vaccination rates in the us, and the importance of other social determinants of health factors that contribute to the covid-19 vaccine hesitancy. the most important finding of this study from the public health perspective is the association between the state's vaccination rate and the tweets’ sentiments. this indicates that social media could provide helpful information on vaccine acceptance, informing policymakers on what type of message would be beneficial for public health interventions on social media platforms. the use of social media by public health professionals could increase vaccine awareness and provide more detailed information about vaccination, contributing to the growth of vaccination rates. in addition, the analysis of social media activity could give early warnings about disease outbreaks, which public health organizations could use to prepare and guide their region/community-specific interventions proactively. the potential future work would be identifying areas where social media sentiments are more negative than in others while analyzing the social vulnerability index, health literacy, demographics, and vaccination rates in these areas. once those areas are determined, the next step would be to identify where health information can be distributed. one helpful tool to assist in this effort would be the health intelligence atlas, a dashboard with multiple layers created by the research team that includes locations of public health agencies, libraries, places of worship, medically underserved areas and populations, etc.) [27]. using a similar approach, it would be possible to identify vulnerable areas and perform public health interventions to increase the your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us 11 ojphi vaccination rates in these areas. this would be beneficial because the data found in this study can be added to this dashboard which might be used for future public health emergencies. the study described herein offers a unique perspective using the social vulnerability index and health literacy to understand better the association between vaccination rate, social media sentiments, and these other potential factors that might impact them. the main strength of this study is that it was conducted on one of the most popular social media platforms, twitter, a platform that can represent the public’s opinions on vaccination. another strength is that both tweets and vaccination rates data were collected daily in real-time, giving insight into trends surrounding vaccination and providing value to the analysis described in the study. finally, all datasets were extracted from credible sources such as the cdc and the university of north carolina. with the significant results from the analysis, this study is not without limitations: (1) the dataset consists of english tweets only, which might not reflect the vaccine hesitancy that is being discussed by minorities speaking a different language (e.g., spanish, vietnamese, etc.); (2) even though all the posts are related to the vaccination, some tweets might not represent an actual opinion of the user about it. despite the limitations mentioned above, this study shows practical applications and results that are critical to future public health interventions. conclusion the primary purpose of this observational study was to investigate if any correlation exists between social media sentiments and the covid-19 vaccination rates in the united states and identify other factors that impact the vaccination rate in the us. the regression analysis showed that social media sentiments are significantly associated with the us covid-19 vaccination rate. in addition, health literacy and social vulnerability index play an essential role in the covid-19 vaccination rate. social media might be used as an effective tool to increase the overall acceptance of public health interventions, such as the covid-19 vaccination. acknowledging that there are limitations, the results shown are relevant for future interventions and should be considered. public health professionals should incorporate social media listening tools to analyze and address the spreading of negative posts about health interventions on social media and improve health literacy in socially vulnerable areas. as shown in this study, higher health literacy and more positive social media sentiments are significant factors in increasing the vaccination rate in the us. therefore, social media is a reliable resource for informing the population about emerging threats/events and interventions, which is crucial in reducing misinformation and disinformation and building trust in the public health system. references 1. puri n, coomes ea, haghbayan h, gunaratne k. 2020. social media and vaccine hesitancy: new updates for the era of covid-19 and globalized infectious diseases. hum vaccin immunother. 16(11), 2586-93. 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us 14 ojphi 25. centers for disease control and prevention/ agency for toxic substances and disease registry [internet]. cdc/atsdr’s social vulnerability index (svi). 2021. available at: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html 26. hughes m, wang a, grossman m, pun e, whiteman a, et al. 2021. county-level covid19 vaccination coverage and social vulnerability — united states, december 14, 2020– march 1, 2021. us department of health and human services/centers for disease control and prevention [internet]. 2021. 70(12). available at: https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7993557/pdf/mm7012e1.pdf 27. wilson gm, ball mj, szczesny p, haymann s, polyak m, et al. 2021. health intelligence atlas: a core tool for public health intelligence. appl clin inform. 12(04), 944-53. epub oct 2021. doi:https://doi.org/10.1055/s-0041-1735973. pubmed https://www.atsdr.cdc.gov/placeandhealth/svi/index.html https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7993557/pdf/mm7012e1.pdf https://doi.org/10.1055/s-0041-1735973 https://pubmed.ncbi.nlm.nih.gov/34614518 your tweets matter: how social media sentiments associate with covid-19 vaccination rates in the us abstract introduction methods data sources study design study population dependent and independent variables control variables statistical analysis results association between vaccination rate and tweet sentiments and impact of sdoh on vaccination rate association between hl and svi association between hl/svi and state race and ethnicity composition discussion conclusion references ojphi-06-e36.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 181 (page number not for citation purposes) isds 2013 conference abstracts effectiveness of using a chief complaint and discharge diagnosis query in essence-fl to identify possible tuberculosis patients and contacts in hillsborough county, florida michael wiese*1, david atrubin2, warren mcdougle1 and jylmarie lewis1 1epidemiology, fdoh-hillsborough, tampa, fl, usa; 2florida department of health, tallahassee, fl, usa � �� �� �� � � �� �� �� � objective ������� �� ������������� ���� ����������� ���� ��� ������ ��� � ������������� � �� ���������� ����������� ����� �������� ��������� � � � ��� ����������������� ��� � ��� ��� ������ ���� �� ��������� ���������� ��� ��������� ������� ������������� �� ���������� ������ �� � ���� � � �!��"�������� � ���#��������� ���� �� ��������� ���������� ��� ���������������� �� ������ �������� ���������������� � ��� ���� ������������� ������������ ������� � ������$������ �� � �%� ������ ������� ��� ��������������� �����&����������'��� � ������� ������ ����� ��������� ��� ����� ��� �� ��������������� ���� ����� ���� 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health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e36, 2014 information management in cancer registries: evaluating the needs for cancer data collection and cancer research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi information management in cancer registries: evaluating the needs for cancer data collection and cancer research iris zachary1,2,3*, suzanne a boren1,2, eduardo simoes1,2,3, jeannette jackson-thompson 1,2,3 , j wade davis1,2, lanis hicks1,2 1. department of health management and informatics, school of medicine, university of missouri, columbia, missouri 2. mu informatics institute, university of missouri, columbia, missouri 3. missouri cancer registry and research center, health management and informatics, school of medicine, university of missouri, columbia, missouri abstract cancer registry data collection involves, at a minimum, collecting data on demographics, tumor characteristics, and treatment. a common, identified, and standardized set of data elements is needed to share data quickly and efficiently with consumers of this data. this project highlights the fact that, there is a need to develop common data elements; surveys were developed for central cancer registries (ccrs) and cancer researchers (crs) at nci-designated cancer centers, in order to understand data needs. survey questions were developed based on the project focus, an evaluation of the research registries and database responses, and systematic review of the literature. questions covered the following topics: 1) research, 2) data collection, 3) database/ repository, 4) use of data, 5) additional data items, 6) data requests, 7) new data fields, and 8) cancer registry data set. a review of the surveys indicates that all cancer registries’ data are used for public health surveillance, and 96% of the registries indicate the data are also used for research. data are available online in interactive tables from over 50% of crs and 87% of ccrs. some other survey responses indicate that ccr treatment data are not complete for example treatment data, however cancer researchers are interested in treatment variables from ccrs. cancer registries have many data available for review, but need to examine what data are needed and used by different entities. cancer registries can further enhance usage through collaborations and partnerships to connect common interests in the data by making registries visible and accessible. keywords: public health; disease registries; disease reporting correspondence: zacharyi@missouri.edu doi: 10.5210/ojphi.v7i2.5664 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:zacharyi@missouri.edu information management in cancer registries: evaluating the needs for cancer data collection and cancer research 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi introduction cancer registries are information systems designed for the collection, management, storage, and analysis of data on individuals diagnosed with cancer [1]. cancer registries collect data elements that include demographics, diagnoses, tumor histology, treatment, and outcome information [2]. there are three types of cancer registries: 1) facility-based registries that collect information about patients diagnosed and/or treated at that facility, 2) specialty registries that collect information on one type of cancer (e.g., brain tumors, pediatric cancers) and 3) central cancer registries (ccrs) that collect information about cancer patients in a particular geographic area (e.g., a region, state or territory) [3-5]. cancer registries have a long history, with the first registries going back to the early 1900s when individual physicians or institutions formed case-based cancer registries [1]. the first hospital cancer registry was established at yale in new haven, connecticut in 1926. the first ccrs were established in 1935 in connecticut and 1946 in california. public law 92-218, the national cancer act of 1971, directed national cancer institute (nci) to “collect, analyze, and disseminate all data useful in the prevention, diagnosis, and treatment of cancer.” [6] this led to the establishment of the surveillance, epidemiology and end results (seer) program in 1973. the first national cancer registry in the united states, the seer program covered only a few states, representing only about 10% of the total us population. congress established the national program of cancer registries (npcr) (public law 102-515) in 1992 to be administered by the cdc [7]; the cdc’s charge was to establish a central cancer registry (ccr) in states without a registry and enhance registries in states with an existing registry, thus covering the entire nation. high-quality cancer incidence data are available for over 96% of the us population as a result of these public laws. seer and cdc-npcr, along with the commission on cancer (coc), are standard setters for cancer registries. other groups that work with and make recommendations to standard setters are the american joint committee on cancer (ajcc), the north american association of central cancer registries (naaccr), the american cancer society (acs), the national cancer registrars association (ncra) and the world health organization (who) [6]. cancer data collection and analysis is required, but only complete, accurate, and timely data can make a significant difference in the field of cancer research and further efforts in the evaluation, surveillance and prevention of cancer [8,9]. parkin (2008) emphasizes that the role of cancer registries has expanded in the last two decades to include not only collection of cancer diagnosis and treatment data, but also planning and evaluation of cancer control activities, involvement in patient care and survival data. the expansion of cancer registry data collection will become an important factor in the global fight against cancer [10-12]. furthermore, cancer registration is a continuously changing field that is heavily impacted by the rapidly advancing medical field, expansion of the medical knowledge base, and coding of disease and treatment [10,13-15]. being responsive to these changes often means that data fields are added or modified, making accurate and complete data collection even more challenging. the standardization of cancer registry data is an important part of cancer registration, and registries have expanded from approximately 25 required data elements to more than 200 required data elements within the last two decades. however, by continuously adding data elements, data collection may be getting too specific and consequently lacking in completeness. evaluation, surveillance, and prevention of cancer rely on the statistics that are obtained from cancer registry data. data in the information management in cancer registries: evaluating the needs for cancer data collection and cancer research 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi ccr are necessary to support public health efforts and to provide surveillance studies that give us information on long-term effects of cancer. this project examines the theory that, among the data elements currently collected by registries, there is a shared utility of certain elements for ccr and registry data consumers; the project further theorizes that a collection of these core, meaningful data elements could help further the mission of these ccrs and better serve consumers. the project also attempts to identify problems with data collection and aims to support the idea for development of a core dataset that can better meet public health surveillance and research requirements. the overarching question is: what data elements are needed for public health surveillance, and what data elements are needed for research will need to be addressed in detail in a future study. methods survey development and content investigators developed two surveys, one for the ccrs and one for cancer researchers, with input from experts in the field of cancer registry content, survey research, health information technology, and cancer research. based on the focus of the research, the evaluation of the research registries and databases, and systematic review of the literature, the survey was developed and pilot tested. the pilot survey was sent to five internal and ten external participants. five of the internal and six of the external participants responded and gave suggestions for clarification of some of the questions in the surveys. the surveys pose questions about the data elements essential to the cancer research community, barriers to data use, and necessity of data for the cancer registry and the cancer researcher. the survey content was reviewed internally at the missouri cancer registry and research center and approved by the health science institutional review board at the university of missouri. the survey instrument for ccrs contained 41 questions on eight topics: 1) research, 2) data collection, 3) database/ repository, 4) use of data, 5) additional data items, 6) data requests, 7) new data fields, and 8) cancer registry dataset. the survey instrument for researchers contained 32 questions on topics 1 through 6. the 50 state ccrs as well as the district of columbia comprised one core group. forty-three out of 51 registries were represented. another core group included researchers at nci-designated comprehensive cancer centers (41) and national cancer institute cancer centers (26). there was no randomization because of the small sample size. the two surveys were administered simultaneously between october 2011 and march 2012. both groups received invitation by email to participate in the web based surveys. the participants were provided with a web link to access the surveys hosted on survey monkey. non-responders received two reminders each after four weeks. in february 2012, the survey was also sent to two scientific working groups (amia cri-wg and amia phi-wg) to expose the survey instrument to a broader audience. the survey was closed in may 2012. data were collected and checked for errors, such as missing data, as well as editing and data entry errors. descriptive statistics, including the total number and the geographic areas of the respondents, describing the groups that were studied are presented in this section. the descriptive statistics give information about the specific groups that are studied but cannot be generalized to any larger group due to size. in total, 43 cancer registry personnel responded to the central cancer registry survey and 28 responded to the cancer research survey. of the responders, 35 (88%) were from npcr registries, four (10%) were from seer registries and one (2%) was a seerand npcr-funded registry. the response rates to questions from nci-designated research centers were more limited (43%). survey answers that included “not sure” or “don’t know” were included in the response count [16,17]. additionally for information management in cancer registries: evaluating the needs for cancer data collection and cancer research 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi this study, the project coordinators calculated the average based on the number of people who responded to each question. each question was not answered by each respondent. none of the questions were required to be answered, some questions allowed multiple answers. results a summary of the survey results identifies common use of data elements between cancer registries and researchers. findings from the survey identify data elements that are necessary for research, such as surveillance, and data collection, and did help to clarify some barriers to the use of cancer registry data. based on the review of the answers, it is apparent from the two surveys that a gap exists between the data elements collected and the data elements needed for surveillance and research. the data elements required for public health surveillance and research do overlap, (e.g. date of diagnosis, diagnosis, site, histology), however they are needed for different reasons. for example, the date of diagnosis is important for any longitudinal or public health-related study, while the date of diagnosis can also be needed by the researcher in order to determine if a patient could be included in studies relating to treatment, follow up or possible trial studies. central cancer registry survey the overall response for ccrs was 43 out of 51 surveys (84.3%). the respondents were evenly distributed over the united states: 11 (27%) from the northeast, 12 (29%) from the midwest, 10 (24%) from the south and eight (20%) from the west. thirty-five (88%) respondents are funded by npcr, four (10%) are funded by seer, and one registry is funded by both npcr and seer. twenty-three (66%) registries are located at a state health department and 12 (34%) are located at universities. table 1 cancer registry region and location survey item response (n=41) in which geographic region is your registry located? northeast 11 (27) midwest 12 (29) south 10 (24) west 8 (20) where is your registry located? state health department 23 (66) university 12 (34) not sure 0 *not all respondents answered all questions according to the survey results, registries have good data on demographics as well as stage, size and histology as a part of registry certification, but are lacking data on treatment variables. additionally, respondents pointed out that accuracy and completeness is heavily dependent on site and histology information management in cancer registries: evaluating the needs for cancer data collection and cancer research 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi even for certified registries. capturing all cases can be challenging for sites that are treated outside the hospital setting, such as melanoma of the skin, prostate cancer, and blood disorders related to cancer. in one question, the registries were asked about the top five statespecific items collected in addition to the naaccr/seer required fields. twelve (100%) respondents collect tobacco, tobacco history, tobacco years, last name and first name, address at diagnosis, blood quantum, and one of the 12 respondents did not collect any state-specific items. when asked how many years of data the ccrs have available, 20 (87%) respondents said that they had more than 10 years of data available, two (9%) had between six and 10 years of data available, and only one respondent's registry has zero to five years of data available. fifteen (68%) respondents reported that they receive updated information on vital status and tumor status for each case. one respondent indicated that they receive vital status only through linkage with state vital records. table 2 shows the use of cancer registry data. respondents were able to choose multiple answers in the survey process. when asked if the cancer registries could fill all data requests they receive, 15 (68%) answered that they could not fill all data requests, six (27%) answered that they could fill all data requests, and one (5%) was not sure if they could fill all data requests. table 2 cancer registry data requests survey item response (n=22) can you fill all data requests the cancer registry receives? yes 6 (27) no 15 (68) not sure 1 (5) if you cannot fill all data requests, specify the reason(s)? (check all that apply) 12* data elements are not collected 12 data elements are not available 5 data elements are not reliable 7 data elements are not complete 8 data elements have missing /unknown value 4 *not all respondents answered all questions *data elements not complete include unknown and known values that may or may have been entered but are not complete; data elements missing or unknown value is a value that is missing or unknown and cannot be entered because it is unknown survey participants who indicated they could not fill all data requests were asked to provide a reason. twelve (100%) replied one possible reason was that the data elements are not collected, five (42%) said data elements requested are not available (for any reason), seven (58%) said data elements are not reliable, eight (67%) answered that data elements requested are not complete, and four (33%) responded that the data elements have missing and unknown values. several respondents mentioned that they do not have adequate staff to fill certain data requests or the necessary approval for the data requests may be unavailable. when asked how they felt about the number of data items required to be collected, 17 (77%) indicated that too many were required, five (23%) answered information management in cancer registries: evaluating the needs for cancer data collection and cancer research 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi about right, none answered too few, and some respondents placed emphasis not on the number of data items but on type and quality of those items. when asked if they were interested in collecting additional data items, 19 (86%) responded no and three (14%) responded yes, pointing out that areas such as socio-economic factors, family history, genomic assays, tumor and bio markers are needed to keep up with developments in the field. one respondent suggested that data collection for cancer registries has shifted due to the change in healthcare from inpatient to outpatient diagnosis and treatment of patients and the expanded role and additional work that registries are covering now that include many informatics and additional data collection activities. this brings up the issue of collecting data that is needed. this project asks if we should look at our datasets and explore the utility and use or need of data collected in the registry. cancer registry respondents indicated that the data are used for public health surveillance (23; 100%), database linkages that include programs like breast and cervical cancer control programs (22; 96%), and research (21; 91%), followed by cancer inquiries (20; 87%), special projects (19; 83%), next-of-kin requests (11; 48%), and clinical trials (4; 17%). a few respondents named additional needs for information related to tissue repository of bio-specimens, fda monitoring projects, and program planning. cancer registry data are available online in interactive tables at the county level by 13 (59%) ccrs. researchers focus on treatment and need treatment variables for many studies, where the public health and surveillance focus is on the actual cancer diagnosis and follow up, as well as vital status to determine planning, survival and cancer control. the continuous addition of data elements by certifying agencies introduces the discussion of specificity versus completeness of the registry dataset. as mentioned earlier, data that are collected in registries are most beneficial for data analysis and research, public health, and surveillance when the data are accurate, timely, and complete. eighty-seven percent (87%) of cancer registry respondents have more than 10 years of population based data available for use. table 3 cancer registry data and data availability survey item response (n=22) yes no not sure do you receive updated information on vital status and tumor status for each case? 15 (68) 7 (32) 0 do you make the cancer registry data available for data requests? 22 (100) 0 n/a are registry data available online in interactive tables? 13 (59) 9 (41) n/a can you fill all data requests the cancer registry receives? 6 (27) 15 (68) 1 (5) do you consider naaccr silver certification as research quality data?** 14 (70) 6 (30) n/a information management in cancer registries: evaluating the needs for cancer data collection and cancer research 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi are you interested in additional data items or elements that are not mentioned? if yes, what additional data items are you interested in? 3 (14) 19 (86) n/a *not all respondents answered all questions **silver certification meets: case ascertainment has achieved 90% or higher completeness. a death certificate is the only source for identification of fewer than 5% of reported cancer cases. less than 0.2% duplicate case reports are in the file. all data variables used to create incidence statistics by cancer type, sex, race, age, and county are 97% error-free. less than 3% of the case reports in the file are missing meaningful information on age, sex, county. less than 5% of the cases in the file are missing meaningful information on race (us only).the file is submitted to naaccr for evaluation within 23 months of the close of the diagnosis year under review. cancer research survey we had 28 respondents for the cancer research survey of 66 nci-designated cancer centers. the respondents were fairly evenly distributed over the united states, six (21%) from the northeast, nine (32%) from the midwest, seven (25%) from the south and four (14%) from the west. eighteen (72%) of the respondents are affiliated with a university or teaching hospital, five (20%) with a hospital, two (8%) with a physician group. others included were a state health department, federal government agency, clinical research company or board of health. table 4 cancer research region and location survey item response (n=26) in which state is your facility located? northeast 9 (32) midwest 9 (32) south 7 (25) west 4 (14) are you affiliated with? hospital 5 (20) university/ teaching hospital 18(72) physician group 2 (8) not applicable 2 (8) *not all respondents answered all questions when asked how many years of data they have available in their database, the majority responded with greater than five years. the cancer researchers collect demographic data including race, ethnicity, and date of birth. table 5 cancer research availability of variables survey item (n=16) response information management in cancer registries: evaluating the needs for cancer data collection and cancer research 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi which of the following demographic data items do you collect? yes no not sure name 16 8 (50) 6 (38) 2 (12) ssn 16 6 (38) 8 (50) 2 (12) date of birth 16 12 (75) 3 (19) 1 (6) ethnicity 16 12 (75) 3 (19) 1 (6) race 16 12(75) 3 (19) 1 (6) address at diagnosis 16 8 (50) 6 (38) 2 (12) current address 16 6 (38) 8 (50) 2 (12) *not all respondents answered all questions the group of cr respondents noted less than 5% missing or unknown values in those fields, respectively; 50% (eight) for date of birth; 46.7% (seven) and 33.3% (five) for ethnicity in figure 1. figure 1: what percent of the variables have missing or unknown values? when asked what other data elements or fields are of interest to them, the cr respondents listed treatment, staging, outcomes, diagnosis information, number of hospital stays, occupation and industry, comorbidity, family history, biomarkers, diet, exercise, treatment failure, ajcc staging, diagnostic evaluations and cause of death. a few researchers mentioned a preference for staging, diagnostic evaluations, and diagnostic information cancer registries can provide. cancer research respondents were able to obtain information on tumor and treatment variables, but have missing or unknown values in both categories. the majority did not know how many data elements have missing or unknown values in these two categories. eight (62%) of the respondents have less than 5% missing or unknown values for the data element of tumor site. table 6 cancer research data survey item response (n=14) yes no not information management in cancer registries: evaluating the needs for cancer data collection and cancer research 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi sure do you collect information on vital status and tumor status? 10 (71) 1 (7) 3 (21) are your data complete for all fields in most of your studies? 7 (63) 3 (27) 1 (9) are the data standardized? 11 (92) 1 (8) 0 are the data deduplicated? 8 (67) 2 (17) 2 (17) do you know what data elements are available from the state? 8 (67) 2 (17) 2 (17) *not all respondents answered all questions discussion data collected in the field of cancer registry are most useful for research, public health surveillance, evaluation, etc., when the data are accurate, timely, and complete [7,8]. the challenges to serve public health, surveillance and research include difficulties in collecting complete, accurate and timely data for all elements. responders of the surveys pointed out some data elements that are essential for the cancer research community, as well as barriers and needs for the cancer registry and the cancer researcher. an important issue addressed was the continuous addition of new data elements to the required data set as required by national standards. for example, treatment fields are wanted, but other fields are added that may or may not provide the data elements that could support research. therefore it is clear that some data needed are already collected but some data elements that are needed are not collected. over 70 percent of ccrs agree that too many data elements are required, yet data requests often cannot be filled because researchers want data elements that are not collected or not available. more dialogue is needed with stakeholders to identify needs. additionally, ccrs collect data fields that are state-specific and dependent on need for clinical research (i.e., special studies for a specific cancer site for a preset amount of time). ccrs have valuable data that should be available and accessible, not only for public health and surveillance, but also for research. cancer registries have some of the data readily available for use that are needed and wanted by cancer researchers that include for example date of diagnosis, histology and cancer site. continuous collaborations and new partnerships can be beneficial to the cancer registries and to the cancer researchers. respondents of the crs survey specifically asked for several data elements that are currently available from the cancer registries. some of the named fields (e.g., treatment, staging, outcomes, diagnosis information, number of hospital stays, occupation and industry, comorbidity, family history, biomarkers, diet, exercise, treatment failure, ajcc staging, diagnostic evaluations and cause of death addition) are available in some hospital and cancer registries as part of special studies, others such as diagnosis information or cause of death addition are readily available. in addition do cancer registries with new developments in gene discoveries, tumor, and biomarkers have to rethink their collection and dataset? can cancer registries become new leaders by working hand in hand for example with the bioinformatics laboratories in forming partnerships that rely on data exchange for necessary research and work in the cancer field? the gap and challenge of meeting the needs of public health, surveillance, and research for the future opens up new opportunities for new collaborations and partnerships for health and bioinformatics. information management in cancer registries: evaluating the needs for cancer data collection and cancer research 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi recommendations most ccrs have complete, timely, and accurate data for all the fields that are required by standard setters, but may lack other variables important to research. a possible solution is that population based registries focus on making a much smaller minimum core data set available that is timely, accurate, and complete and fills the need for public health and surveillance and also accommodates some additional data elements useful for research. further studies are needed to determine what is needed in a minimum dataset that fulfills the mission of a registry dataset and yet allows for collection of special data elements that are specifically interesting to researchers. analysis to determine what data fields are mostly used for public health and surveillance are needed to determine a minimum core dataset. limitations the limitations of this study are the small group of participants for the pool of central cancer registries (e.g. one central registry per state). further, participants of the nci designated cancer centers were not always directly involved in research. this resulted in a much lower response rate for the participant group. because of the small population, only limited results could be included to maintain anonymity of the registries. because the surveys were planned for researchers and cancer registries, the questions had to be somewhat similar to be comparable. this resulted in less specified questions. the research was to understand and identify views of both groups on the utilization and availability of data collected on cancer. additional research is necessary to identify data needs and use for both groups. conclusions survey responses indicate that a more in-depth study is warranted to determine registry and researcher needs and use of data elements. furthermore, the role of ccrs is expanding with advancements in genomics, tumor marker information that is dependent on site, pathways and predictive markers that have prognostic significance. with those advancements ccrs will play important roles for cancer surveillance, cancer control, and research. additionally this information and change in the type of data, the amount of data, the importance of data fields for these specific purposes needs to be considered when developing a core data set for registries. the ccrs will be holding a wealth of information that pertains to cancer diagnosis, origin of cancer, and treatment. ccrs can become the information broker but also information holder for the purpose of public health, surveillance and research all at the same time. references 1. training modules seer. brief history of cancer registration. u. s. national institutes of health, national cancer institute. 10/15/2012 http://training.seer.cancer.gov/ 2. wingo pa. 2005. a national framework for cancer surveillance in the united states. cancer causes control. 16, 151-70. pubmed http://dx.doi.org/10.1007/s10552-004-3487-5 3. menck h, smart c, eds. central cancer. registries, design, management, and use. (hargood academic publishers, switzerland, 1994). http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15868456&dopt=abstract http://dx.doi.org/10.1007/s10552-004-3487-5 information management in cancer registries: evaluating the needs for cancer data collection and cancer research 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e213, 2015 ojphi 4. menck hr, gress dm, griffin a, mulvihill l, hofferkamp j, et al. cancer registry management: principles and practices for hospital and central registries. third edition. dubuque, iowa: kendall hunt; 2011. 5. austin df. cancer registries: a tool in epidemiology. rev cancer epidemiol, ed. lilienfeld am, elsevier north-holland, pub, ny, ny 2:119-140, 1983. 6. havener l, thornton m, eds. standards for cancer registries volume ii: data standards and data dictionary, thirteenth edition, version 11.3. springfield, il: north american association of central cancer registries, april 2008. 7. bray f, parkin dm. 2009. evaluation of data quality in the cancer registry: principles and methods. part i: comparability, validity and timeliness. eur j cancer. 45(5), 747-55. pubmed http://dx.doi.org/10.1016/j.ejca.2008.11.032 8. das a. 2009. cancer registry databases: an overview of techniques of statistical analysis and impact on cancer epidemiology. methods mol biol. 471, 31-49. pubmed 9. volkers n. 1993. national statistics source reaches 20th anniversary. j natl cancer inst. 85(23), •••. pubmed 10. parkin dm. 2008. the role of cancer registries in cancer control. int j clin oncol. 13(2), 10211. pubmed http://dx.doi.org/10.1007/s10147-008-0762-6 11. wagner g. history of cancer registration. in: jensen om, parkin dm, maclennan r, muir cs, skeet rg, eds. cancer registration, principles and methods. lyons: international agency for research on cancer, 1991:3-6. (iarc scientific publication no 95.) 12. heinze o, ihls a, bergh b. development of an open source provider and organization registry service for regional health networks. healthinf 2010 3rd international conference on health informatics, proceedings, p 535-537, 2010. 13. parkin dm. 2006. the evolution of the population-based cancer registry. nat rev cancer. 6(8), 603-12. pubmed http://dx.doi.org/10.1038/nrc1948 14. bray f, moller b. 2006. predicting the future burden of cancer. nat rev cancer. 6, 63-74. pubmed http://dx.doi.org/10.1038/nrc1781 15. biancioni f, brunori v, valigi p, stracci f, larosa f. cancer registry and information technology: a new management system for integrating cancer registry and oncology departments. 2010 ieee workshop on health care management, whcm 2010. 16. salant p, dillman da. (1994) how to conduct your own survey. new york: wiley. 17. sorra j, franklin m, streagle s. survey user's guide: hospital survey on patient safety culture. ahrq publication no. 08-0060, september 2008. agency for healthcare research and quality, rockville, md. http://www.ahrq.gov/qual/nhsurvey08/nhguide.htm http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19117750&dopt=abstract http://dx.doi.org/10.1016/j.ejca.2008.11.032 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19109773&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=7901423&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18463952&dopt=abstract http://dx.doi.org/10.1007/s10147-008-0762-6 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16862191&dopt=abstract http://dx.doi.org/10.1038/nrc1948 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16372017&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16372017&dopt=abstract http://dx.doi.org/10.1038/nrc1781 information management in cancer registries: evaluating the needs for cancer data collection and cancer research introduction methods survey development and content results central cancer registry survey cancer research survey discussion recommendations limitations conclusions references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts comparison of three critical syndrome classifications: louisiana vs. biosense jenna iberg johnson* infectious disease epidemiology, louisiana office of public health, new orleans, la, usa objective to compare the results of biosense and louisiana syndrome classifications for influenza-like-illness, gastrointestinal, and upper respiratory infections applied to louisiana emergency department data. introduction the louisiana office of public health (oph) infectious disease epidemiology section (idepi) conducts emergency department (ed) syndromic surveillance using the louisiana early event detection system (leeds). idepi has the capability to define and change syndrome definitions in leeds based on surveillance needs and quality assurance activities. idepi submits all of the ed data to biosense, which uses different syndrome definitions than leeds. both biosense and leeds use text and icd code searches in any available chief complaint, admit reason and diagnosis data. the results of leeds and biosense syndrome classifications for influenza-likeillness (ili), gastrointestinal (gi), and upper respiratory infections (uri) applied to louisiana’s ed data were compared to examine if the different syndrome definitions yield similar results when applied to the same data. methods daily electronic ed data is imported to both the leeds and biosense databases and processed for syndrome classification. idepi queried the leeds database and the biosense front-end application to pull weekly visits classified as influenza-like-illness, gastrointestinal, and upper respiratory infections for the period of cdc week 1327 through week 1426 (6/30/13-6/28/14). the syndrome percentage means of biosense and leeds syndrome pairs were compared with paired t-tests. the linear relationship between biosense and leeds syndrome pairs were measured with pearson correlation coefficients. the syndrome results were also split into the age groups used by the biosense front-end application and pearson correlation coefficients were calculated for each syndrome age group pair. the early aberration reporting system (ears) c2 method was applied to all syndrome results to examine if alerts were generated during corresponding weeks for each syndrome pair. weekly data were exported from leeds and biosense and analyzed in r statistics package. results the syndrome percentage means of biosense ili and leeds ili were significantly different (paired t-test, p<0.000). the correlation coefficient for biosense ili and leeds ili was 0.98 and age group correlation coefficients ranged from 0.83 to 0.99 (pearson’s correlation, p<0.000). c2 generated eleven alarms for biosense ili and twelve for leeds ili, of which nine occurred on corresponding weeks. the syndrome percentage means of biosense gi and leeds gi were significantly different (paired t-test, p<0.000). the correlation coefficient for biosense gi and leeds gi was 0.90 and age group correlation coefficients ranged from 0.69 to 0.96 (pearson’s correlation, p<0.000). c2 generated two alarms for biosense gi and one for leeds gi, of which one occurred on a corresponding week. the syndrome percentage means of biosense uri and leeds uri were significantly different (paired t-test, p<0.000). the correlation coefficient for biosense uri and leeds uri was 0.96 and age group correlation coefficients ranged from 0.81 to 0.97 (pearson’s correlation, p<0.000). c2 generated six alarms for biosense uri and seven for leeds uri, of which six occurred on corresponding weeks. conclusions the results of biosense and leeds syndrome classifications for influenza-like-illness, gastrointestinal, and upper respiratory infections applied to louisiana emergency department syndromic surveillance data were highly correlated for each syndrome however the syndrome percentage means were significantly different for each syndrome pair. therefore, while percentages of total visits attributed to a syndrome as a measurement of syndrome burden may not be comparable, trends over time are comparable. in addition, the majority of c2 alerts were generated on corresponding weeks for each syndrome pair, providing confidence in the use of c2 applied to current syndrome definition results as a means of aberration detection. as public health jurisdictions work towards developing common syndrome classifications to increase data comparability across jurisdictions, this analysis provides evidence that the current differences in syndrome definitions between jurisdictions may not hinder comparability of trends over time. keywords syndromic surveillance; syndrome classification; syndrome definition; biosense *jenna iberg johnson e-mail: jenna.ibergjohnson@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):30, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts electronic health records and environmental public health tracking gonza namulanda* centers for disease control and prevention, atlanta, ga, usa objective this presentation will discuss how the tracking network is exploring the use of ehr to meet tracking network surveillance challenges and provide other opportunities to enhance environmental public health surveillance introduction the environmental public health tracking network (tracking network) is a national surveillance system that integrates environmental hazard, exposure, and health outcome data into one system. the tracking network launched in july 2009, and has since been receiving data from 23 funded state and local health departments, and several national partners, e.g., environmental protection agency. despite this success, some challenges exist in obtaining more timely and complete data to link risk factors, and assign exposure for health outcomes with long latency periods before their detection. the health information technology for economic and clinical health (hitech) act (2009) facilitates the adoption of electronic health records (ehr) through incentivizing the meaningful use of certified ehr technology. meaningful use is a set of specific objectives and data exchange standards that eligible healthcare professionals and hospitals must achieve to qualify for the centers for medicare and medicaid services (cms) electronic health records incentive programs. public health agencies in turn need to have the capacity to accept these data in the mandated standard and determine the potential and use of this increased data. this presentation will discuss how the tracking network is exploring the use of ehr to meet tracking network surveillance challenges and provide other opportunities to enhance environmental public health surveillance. we will also present some results of these efforts. methods the tracking network initiated an exploratory project with the goals of determining: i) the data that the tracking network can use from ehr; ii) information that can be included in ehr to enhance environmental public health surveillance; and iii) potential use of tracking network data in an ehr e.g., for community health needs assessments. we interviewed eight public health informaticians, public health professionals, and disease surveillance experts. we also participated in cdc’s ehr initiatives such as cdc and office of the national coordinator for health information technology’s (onc) standards and interoperability (s&i) framework initiatives namely, structured data capture (sdc) whose aim is to define requirements and standards to facilitate the collection of supplemental ehr-derived data, and data access framework (daf) whose aim is to facilitate the query of aggregate data from ehr. results through this work, we have been able to prioritize data that the tracking network could utilize from ehr; identify a list of possible partners for a pilot project to further develop and test how these data can be reported from an ehr; identify priority supplemental data that can be added to ehr to enhance environmental public health surveillance; and develop use-cases for the sdc and daf initiatives. conclusions electronic health records have the potential to enhance public health surveillance by providing more comprehensive, accurate and timely data. however several challenges exist such as: policy and data privacy issues, and the lack of common data elements among others. keywords environmental public health tracking; environmental public health surveillance; electronic health records; meaningful use acknowledgments we would like to thank the tracking program grantees and contractors who contributed to this work, all the experts who allowed us to interview them, and participants on the s&i framework initiatives. *gonza namulanda e-mail: fos0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e44, 2015 ojphi-06-e120.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 37 (page number not for citation purposes) isds 2013 conference abstracts a systematic review of influenza forecasting studies jean-paul chretien*1, dylan george2 and ellis mckenzie3 1armed forces health surveillance center, silver spring, md, usa; 2biomedical advanced research and development authority, washington, dc, usa; 3fogarty international center, bethesda, md, usa � �� �� �� � � �� �� �� � objective ������������ �������� � �������������������������� ��������� ���� ���� �������������� introduction ������������ 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journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e120, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts real-time laboratory-based influenza surveillance with xpert flu andrea dugas*1, howard burkom2, richard rothman1 and on behalf of the national emergency department influenza consortium1 1emergency medicine, johns hopkins university, baltimore, md, usa; 2johns hopkins applied physics lab, baltimore, md, usa objective to demonstrate the feasibility and validity of a novel electronic surveillance system utilizing a cloud-based interface that consolidates laboratory test results and geographical information in real-time. introduction previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance 1,2. current laboratory surveillance has an approximate 2-week lag due to laboratory test turn–around time and data collection 3. in order to provide real-time access to aggregated test results, we utilized direct cloud connectivity with a rapid pcr-based influenza test, xpert flu, to centrally consolidate test results along with gis data. on-site, type-specific results were available to physicians and uploaded for public health awareness within 100 minutes of patient nasopharyngeal swab. methods we implemented a real-time laboratory-based influenza surveillance system at two academic emergency medical departments. from november 2013 – april 2014 we systematically enrolled and tested patients with an acute respiratory illness using cepheid xpert flu, a rapid highly sensitive pcr-based assay that provides significant improvement over traditional rapid antigen tests 4. test results were instantaneously uploaded to a cloud-based data aggregation system including the test result, time and date, and geolocation. results from november 2013 – april 2014, the cloud-based laboratory surveillance system automatically uploaded test results and associated data which were immediately available to affiliated clinicians and local, state, and national public health end users. a total of 1032 subjects were tested and 82 cases of influenza were identified as displayed in figure 1. results from these two eds differentiated in real time the regional epidemic curves and showed the type-a virus wave followed by the smaller type-b virus wave. conclusions this novel, real-time laboratory-based surveillance system automatically uploaded and aggregated flu test results and the associated times and locations. expansion of this real-time capability to sentinel facilities could improve both local and national surveillance and response, reducing the need for syndromic influenza surveillance. patient demographic detail will be added to real-time flu results in the upcoming season for enriched public health utility. figure 1: influenza test results by week throughout the 2013-2014 influenza season keywords influenza; clinical decision support; surveillance acknowledgments this work was supported by the cooperative agreement idsep13001401-00 from the assistant secretary for preparedness and response. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the assistant secretary for preparedness and response (within the u.s. department of health and human services). references 1. baumbach j, mueller m, smelser c, albanese b, sewell cm. enhancement of influenza surveillance with aggregate rapid influenza test results: new mexico, 2003-2007. am j public health. 2009;99(suppl 2):s372-377. 2. ghosh ts, vogt rl. active influenza surveillance at the local level: a model for local health agencies. am j public health. 2008;98(2):1315. 3. centers for disease control and prevention. the national respiratory and enteric virus surveillance system (nrevss). 2011; http://www. cdc.gov/surveillance/nrevss/. accessed nov 25, 2011. 4. dimaio ma, sahoo mk, waggoner j, pinsky ba. comparison of xpert flu rapid nucleic acid testing with rapid antigen testing for the diagnosis of influenza a and b. j virol methods. 2012;186(1-2):137140. *andrea dugas e-mail: adugas1@jhmi.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e20, 2015 phs 398 (rev. 06/09), continuation page probabilistic case detection for disease surveillance using data in electronic medical records 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 probabilistic case detection for disease surveillance using data in electronic medical records fuchiang tsui, phd 1 , michael wagner, md, phd 1 , gregory cooper, md 1 , phd, jialan que, ms 1 , hendrik harkema, phd 1 , john dowling, md, ms, thomsun sriburadej, ms 1 , qi li ms 1 , jeremy u. espino, md 1 , ronald voorhees, md 2 1 center for advanced study of informatics in public health, department of biomedical informatics, university of pittsburgh 2 graduate school of public health, university of pittsburgh abstract this paper describes a probabilistic case detection system (cds) that uses a bayesian network model of medical diagnosis and natural language processing to compute the posterior probability of influenza and influenza-like illness from emergency department dictated notes and laboratory results. the diagnostic accuracy of cds for these conditions, as measured by the area under the roc curve, was 0.97, and the overall accuracy for nlp employed in cds was 0.91. key words: case detection, disease surveillance, influenza, electronic medical records 1 introduction in disease surveillance, the objective of case detection is to notice the existence of a single individual with a disease. we say that this individual is a case of the disease. the importance of case detection is that detection of an outbreak typically depends on detection of individual cases. 1 in current practice, cases are detected in four ways: by clinicians, laboratories, screening programs and computers. some of these methods of case detection employ case definitions. a case definition is a written statement of findings that are both necessary and sufficient to classify an individual as having a disease or syndrome. more commonly, however, the determination of whether an individual has a disease (or syndrome) is left to the expert judgment of a clinician. clinicians detect cases as a by-product of routine medical and veterinary care. the strength of case detection by clinicians is that sick individuals seek medical care. further, clinicians are experts at diagnosing illness, which is fundamental to case detection. however not every sick individual sees a clinician. also, clinicians may not correctly diagnose every individual they see. clinicians may forget to report cases or fail to report cases in the time frame required by law. 2, 3 even when a http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 clinician reports a case, the reporting may occur relatively late in the disease process. with some exceptions (e.g., suspected meningococcal meningitis, suspected measles, suspected anthrax), clinicians report cases only after they are certain (or almost certain) about the diagnosis. a variant of clinician detection is the sentinel clinician approach. 4-9, 10 a sentinel clinician reports the number of individuals he or she sees who match a case definition, e.g., for influenza-like illness (ili). the strength of sentinel clinician case detection is its relative completeness of reporting. its limitations include that some cases may not be reported and those cases that are reported may be delayed due to its being a manual process. 11 another variant of clinician detection is drop-in surveillance. drop-in surveillance refers to the practice of asking physicians in emergency rooms to complete a form for each patient seen during the period surrounding a special event. 12-19 the clinicians record whether the patient meets the case definition for one or more syndromes of interest. the strength of drop-in surveillance (and sentinel clinician surveillance) is that it detects sick individuals on the day that they first present for medical care. a limitation is that it is labor intensive. laboratories detect cases also as a by-product of their routine operation. laboratories often become aware of cases of notifiable diseases either before or at the same time as the clinician who ordered the test. the strength of laboratories-as-case-detectors is that they are process oriented; therefore, they may report cases more reliably than busy clinicians. a weakness is that there is not a definitive diagnostic test for every disease, and there may not be a test with 100% sensitivity for a disease. additionally, a laboratory cannot detect a case unless a sick individual sees a clinician, who must suspect the disease and order a definitive test. lag times for the completion of laboratory work can be substantial. screening programs detect cases by interviewing and testing people during a known outbreak to identify additional cases (or carriers of the disease). screening is most often used for contagious diseases in which it is important to find infected individuals to prevent further infections. finally, computers detect cases by applying case definitions or other algorithmic approaches to routinely collect clinical data. the earliest use of automatic case detection was for hospital infections, 20-26 followed by notifiable conditions 27-30 and syndromes 31-43 . the case definitions used in case detection may be represented either using boolean logical statements or probabilistic statements. boolean approaches include the clinical findings that are both necessary and sufficient to classify a case, such as a case of ili. the statements include and and or operations. on the other hand, a probabilistic case definition states evidence that supports or refutes a diagnosis using conditional probabilities and provides probability thresholds above which the diagnosis is considered either confirmed, likely, or suspected, e.g., a confirmed case of a disease might be defined as p(disease | data) > 0.99. in this paper, we describe and evaluate an automated case detection system (cds) that uses bayesian network models of diagnosis to represent case definitions. it first derives the likelihood of a disease given the symptoms, signs, and findings (data) for a patient, namely, p(data | disease). it then combines such a likelihood with a prior probability distribution of disease, p(disease), to derive the posterior probability of disease given the data, p(disease | data). a bayesian network is a compact representation of a joint probability distribution among the nodes in the network. when a bayesian network is used to represent the medical diagnosis of a disease, the variables (nodes) include the diagnosis and findings that a physician would use when http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 diagnosing the disease, including significant negative findings that the physician might count against some disease being present. for example, negative lab tests that usually have high sensitivity can help physicians rule out a diagnosis. 1, 44, 45 similarly, positive results of tests with high specificity can help rule in a diagnosis. 44, 45 the relevance of bayes rules to medical diagnosis was first introduced theoretically by ledley and lusted in 1959 46 and was used early on in a diagnostic expert system by homer warner in 1961. 47 developers of diagnostic expert systems continue to use the same methods as did warner, as well as more complex bayesian methods. several theoretical advantages of bayesian case detection over boolean case detection include: (1) it can use the prior probability of a disease, (2) it can represent the sensitivity and specificity of tests and findings for a disease, (3) it can represent an expert’s knowledge of disease diagnosis in the form of conditional probabilities, (4) it parallels a physician’s diagnosis of reasoning under uncertainty by computing posterior probabilities of diseases, and (5) it assists in decision making when new information becomes available. the current state-of-the-art automated cdss are (1) electronic laboratory reporting (elr) systems that are based on laboratory reports, and (2) syndromic surveillance systems that are based on chief complaints. 43, 48 however, the two systems fall into two extremes on diagnostic accuracy and timeliness spectrums. in regards to diagnostic accuracy, electronic lab reporting is at one extreme of generally being very accurate, whereas syndromic surveillance is generally less so. regarding timeliness, syndromic surveillance can be immediately available at the time of a patient visit, whereas an elr can be delayed for days from the time a lab was drawn. 49 cds is a component in the probabilistic, decision-theoretic disease surveillance and control system described in an accompanying paper in this issue of the journal. bayesian networks have not only been used for case detection but also for outbreak detection during the past decade. as a representative example, mnatsakanyan et al. 50 developed bayesian information fusion networks that compute the posterior probability of an influenza outbreak by using multiple data sources, such as aggregate counts of emergency department (ed) chief complaints that are indicative of influenza and counts of relevant icd-9 codes from outpatient clinics. as another example, cooper et al. 51-53 developed the panda system and its extensions that derive the posterior probabilities of cdc category a diseases (including anthrax, plague, tularemia, and viral hemorrhagic fevers) using ed chief complaints and patient demographic information as evidence. in this paper, we use the diagnoses of influenza and influenza-like-illness as examples, although the approach is general and can be applied to other notifiable conditions or syndromes. 2 methods this section describes (1) the bayesian cds, and (2) an evaluation of its diagnostic accuracy for the diagnosis of influenza and ili. 2.1 bayesian cds the bayesian cds includes (1) a natural language component that process free-text clinical reports and chief complaints, (2) disease models in the form of diagnostic bayesian networks, (3) a http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 bayesian inference engine, and (4) a time-series chart reporting engine (figure 1). the software components, including the inference engine, are implemented in java. hl7 patient data feeds terminology service *: narrative reports ontology service oracle database phoenix admission physician reports* radiology reports* lab. and micro. pharmacy bayesian inference engine disease models bayesian case detector (cds) ods probabilistic outbreak detection and characterization population analysis population disease models natural language processing tools nurse investigator public health epidemiologist charting engine upmc domain differential diagnosis of a patient p(influenza|data)= 0.089 p(shigellosis|data) =0.002 ... disease likelihoods p(data|influenza)= 0.115 p(data|shigellosis) =0.007 ... figure 1: cds and its relationship to other components in a probabilistic, decision-theoretic system for disease surveillance and control. cds currently operates on clinical data from the upmc healthcare system. the blue boxes represent software components and hexagons represent models. cds sits between clinical data and ods, an outbreak detection and characterization system. a component called phoenix, described in an accompanying paper, receives data from an electronic medical record (emr) system via hl-7 messaging, converts any proprietary codes to loinc and snomed codes, stores the data, and processes requests from cds. in general, cds passes the likelihoods p(dataj | diseasei) to ods for each modeled disease i and for each patient j in the monitoring period. for example, for a given patient, cds would send the probability p(data | influenza) to ods, where data denotes the symptoms, signs, and other findings of that patient. an accompanying paper in this issue describes ods in more detail. in addition, cds can output the posterior probabilities of modeled diseases for end users, as shown in figure 1. our design criteria for cds included computational efficiency sufficient to keep up with the volume of new patient data in a large healthcare system, and portability. 54 cds uses the computationally efficient junction tree algorithm 55, 56 for bayesian inference, which is also used in popular commercial bayesian inference engines such as hugin® and netica® . we have operated cds since 2009. 57 it generates daily reports of influenza and ili and sends them to the allegheny county health department (achd) by way of email (figure 2). the daily report http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 includes a graph of the daily counts of expected influenza cases, which is derived as ∑ ( ) . it also includes in the graph a daily time-series plot of boolean-based ili cases, influenza test orders, and influenza positive cases. note the boolean ili counts in the daily chart are based on the boolean case definition (fever) and (cough or sore throat), where the symptoms or findings are extracted by nlp. public health officials in the achd have indicated that they find cds to be useful. the charts shown in figure 2 illustrate three areas of impact on practice at achd. 57 first, cds provided achd with daily updates instead of weekly reports from sentinel physicians. second, achd could provide the charts to local media on a regular basis. 58 finally, achd reduced staff time since they no longer had to manually compile ili reports from sentinel ili reports (2 days of work for each weekly report). figure 2: influenza and ili summary chart for february 15, 2010 (showing data from aug. 1, 2009 to feb. 14, 2010) in a daily email report to the allegheny county health department. it comprises daily fever counts (from nlp), accumulated influenza posterior probability counts from bayesian cds, ili counts (from nlp), and influenza (flu) test positive counts. 2.1.1 disease models one of the core components in cds is a knowledge base that contains disease models represented as bayesian diagnostic networks. a disease model can include symptoms, signs, diagnosis, radiology findings, and laboratory test results (which we refer to as all-data), or it may use selected data, such as laboratory results, in which case we refer to the network as lab-only. cds has one bayesian diagnostic network (disease model) per disease. http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 cds uses an existing bayesian network design tool named genie 59 as a front end graphical user interface for disease model editing. genie, which was developed at the university of pittsburgh, can be downloaded from the web 60 for free. genie can convert proprietary bayesian network file formats used by hugin® and netica® (two of the most popular commercial bayesian inference engines) into an xml file that can be then fed into cds, allowing cds users to import networks already developed by other groups. note that the genie tool is only needed when a user wishes to revise or create a bayesian network. figure 3 shows the genie graphical user interface that allows an physician expert in clinical infectious disease to construct the influenza diagnostic model shown in right panel. for portability, cds disease models use standard terminology. for variables in a disease model representing symptoms and signs, we use concept unique identifiers (cuis) from the umls. both nlp tools in cds-the well-known medical language extraction and encoding system (medlee) 61 , and a locally developed topaz, use cuis to represent extracted symptoms and findings; note that we used topaz results in this paper. for laboratory tests, we use logical observation identifiers names and codes (loinc). 2.1.1.1 lab-only diagnostic bayesian network figure 3 shows a lab-only diagnostic model for b. anthracis. the laboratory tests in this model come from the reportable condition mapping tables (rcmt). 62 this disease model comprises 33 nodes that represent 32 lab tests for b. anthracis. the names and results of the tests are represented using the logical observation identifiers names and codes (loinc) and systematized nomenclature of medicine (snomed) coding systems. the parent node, labeled anthrax, denotes whether the diagnosis of anthrax equals true or false. the 32 child nodes denote, for each laboratory test, whether the result was positive, negative, or unknown (because it has not been obtained). the structure of this particular model indicates that we are assuming that the tests are independent, given the diagnosis. any dependencies among tests can be modeled in hidden nodes or by the inclusion of direct arcs among the nodes that denote tests. we can apply this network to report cases in a manner similar to current electronic laboratory reporting systems. the conditional probability distributions in the network represent the sensitivity and specificity of each laboratory test for the disease anthrax. let r denote the results of a set of laboratory tests for a given individual. we can perform inference on the network to derive p(anthrax | r). if that probability is above a threshold tanthrax, then the case is reported. if the specificities of the tests are assumed to be 1, then any positive test result will lead to a probability of anthrax of 1, which will result in the reporting of the case if tanthrax < 1. more generally, however, the sensitivities and specificities of the tests will not be 1, and in turn the probability of anthrax given test results will not be 0 or 1. thus, in general, there is a need for case reporting that is based on probabilistic modeling and inference. http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 3: anthrax lab-only diagnostic network. a bayesian network model for detection of anthrax cases using only laboratory results. 2.1.1.2 all-data diagnostic bayesian network for influenza we developed an all-data influenza/ili diagnostic bayesian network that comprises flu symptoms, findings, and lab tests defined in the rcmt (figure 4). the symptom and sign nodes and their corresponding conditional probabilities were initially built by author jd, who is board-certified in infectious diseases. the network comprises a total of 368 nodes including 29 symptom nodes, 337 lab test nodes, one test-order node, and one disease node (influenza), which can take the values “true” or “false”. the 337 lab nodes are those tests defined as reporting conditions in rcmt. 63 note that an nlp algorithm extracts symptoms and signs from free-text clinical reports, and they are used to set the values of the finding nodes. http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 4: influenza all-data diagnostic network. a bayesian network model for diagnosing influenza. the network utilizes data from free text clinical reports, orders for laboratory tests and the results of laboratory tests. 2.1.2 parameter estimation each model we built has two sets of parameters: expert assigned conditional probability tables (cpts) and machine learning estimated cpts. we have access to a large corpus of emrs through the upmc health system. we implemented a variation of the well-known expectation maximization-maximum-a-posteriori (em-map) algorithm 56 for learning network parameters from data. the em-map is implemented in java. the algorithm is able to learn network parameters by combining the data with prior knowledge (e.g., from our infectious disease experts and the literature), while being tolerant of missing values in the data. 2.1.3 natural language processing we developed an nlp application called topaz that determines the presence, missing, or absence (negation) of 51 findings (e.g., signs, symptoms, and diagnoses) that are expected in influenza and shigellosis cases, or that are significant negative findings. note that cds will not assign any value for a variable in a disease model when the variable identified by topaz has a value missing. topaz comprises three modules. module 1 looks for relevant clinical conditions and annotates all instances of those conditions in the report. module 2 determines which annotations are negated, historical, hypothetical, or non-patient. module 3 integrates the information from the annotations in the first two steps to assign values of present, absent (negated), or missing to each clinical condition for each patient. 2.1.4 user interface/data viewer figure 5 is a screen capture of a data viewer, which gives a patient care-episode view of the data for internal development purposes and serves as a prototype for a health department end-user http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 interface. it displays all data associated to a patient’s visit, including extracted symptoms and signs from free text reports, lab findings and cds output (posterior probabilities). figure 5. case review web page. a web page that allows users to review disease posterior probabilities (cds output) and patient data including lab reports, free text reports. the posterior probabilities are displayed in a descending order with the highest disease probability on the top. 2.1.5 event driven process an event driven process is a software process that defines how a system reacts to an event. 64 we define an event as data that triggers the execution of cds, such as a laboratory test report or an ed report for a patient’s visit. when an event is available to cds, cds computes the posterior probabilities of the patient. since a patient’s visit may have multiple events (such as chief complaint, ed reports, laboratory test reports) that are available at different points in time, a disease’s posterior probability may change over time. for example, a lab report followed by a free text discharge report could raise the influenza posterior probability from 0.5 to very close to 1 when the lab report states an influenza test is positive. note that a free-text ed report could be available a few hours after the patient visit whereas a lab report could take days. http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 to obtain an accurate patient diagnosis, when an event becomes available, cds retrieves all patient events across different types up to the current time of the patient visit by using a data linkage key. in particular, cds uses the visit number as the data linkage key. 2.2 evaluation of bayesian cds we evaluated bayesian cds in two ways: 1) case detection performance for one illness (i.e., influenza) from processing one data type, namely ed reports, and 2) nlp (topaz) performance for extracting findings from ed reports. 2.2.1.1 diagnostic bayesian network study for the study of case detection performance, we evaluated two influenza (all-data) bayesian networks: 1) an expert influenza network constructed by a board-certified infectious disease domain expert, who assessed both the structure and parameters and the bayesian network, and 2) an em-map trained influenza network. note that both networks share the same structure but different parameters. 2.2.1.1.1 training and testing data for case detection evaluation in this study, we used ed reports from upmc heath system to measure the cds performance for influenza case detection. all the ed reports used for evaluation were de-identified by an honest broker using the de-id tool. 65 the training data comprised 182 influenza cases and 47,062 non-influenza cases. the test data consisted of 58 influenza positive cases and 522 non-influenza cases. all cases were selected randomly from emrs in the upmc hs. we considered a patient to have influenza if: 1) a polymerase chain reaction (pcr) test was positive, and 2) the linked ed reports had the keywords of flu, influenza, or h1n1 in the impression section or diagnosis section. we considered a patient to not have influenza if: 1) no flu tests were ordered, and 2) the ed visits were during july 1, 2010 through august 31, 2010 for the training data, and during july 1, 2011 through july 31, 2011 for the test data. 2.2.1.1.2 evaluation metrics the evaluation metrics used in this study include: roc curves, area under a roc curve (auroc), probability of data given each of two diagnostic bayesian networks as stated in the above paragraph, and the average speed for processing one case. 2.2.1.2 topaz (nlp) evaluation we randomly selected 201 ed reports with flu pcr tests positive. the gold standard for evaluating topaz was experts’ annotation. three board certified physicians annotated the ed reports for a set of 51signs, symptoms, and other findings that are expected in influenza and shigellosis cases. to ensure reliability, all the three annotators first went through training sessions; when the measured kappa value was above 0.8, they started annotating the 201 ed reports. the evaluation metrics used in this study include kappa values, accuracy, and recall and precision. http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 3 results this section provides the evaluation results. 3.1 diagnostic bayesian networks figure 6 shows the two roc curves for the expert model and the em-map trained model for the total 580 test cases. the expert model has auroc 0.956 (95% ci: 0.936-0.977) and the em-map model has auroc 0.973 (95% ci: 0.955-0.992). we measured the computational speed for computing the posterior probabilities and em-map training. the average run time for computing influenza posterior probability is 15 milliseconds per case. the speed performance was measured on a desktop computer with intel ® core™ 2 quad cpu q9550, 2.83ghz and 4gb ram. figure 6. roc curves for two influenza bayesian networks. the blue line (with dots) represents the influenza model with parameters assigned by a domain expert and the pink line (with dashes) represents the influenza model with parameters learned by em-map algorithm. 3.2 topaz table 1 summarizes the performance of topaz. the kappa value between the gold standard and topaz was 0.79. the overall accuracy including absent (negated), present, and missing findings was 0.91 and the accuracy for only absent and present was 0.77. http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 table 1. topaz performance recall precision absent (negated) 0.82 (1022/1249) 0.84 (1022/1220) present 0.73 (1109/1526) 0.84 (1109/1319) missing 0.96 (7205/7476) 0.93 (7205/7712) 4 discussion the results of the evaluation of the two influenza bayesian networks (expert model and em-map model) show high diagnostic accuracy. additionally, augmenting the expert’s conditional probability distributions used in the model with empirical data about the distributions improves the diagnostic accuracy for influenza case detection. the performance of the topaz natural language processing algorithm for influenza findings approaches that of medical experts, as indicated by the kappa value 0.79 and overall accuracy of 91%. a limitation of the evaluation study of bayesian diagnostic models is as follows. although we obtained non-influenza cases from patient visits that occurred in the summer and were not associated with an order for an influenza test, it is possible that there are influenza cases in the non-influenza training and testing data. however, any such contamination would be expected to bias the experiment against finding good diagnostic accuracy. we also note that our current influenza model (figure 4) should be modified to distinguish between influenza a and b, which we plan as future work. of the four types of case detection discussed in the introduction—clinician, laboratory, screening, and computerized—the principle role of bayesian cds is in computerized (automatic) case detection. cds can be used to augment laboratory, clinician, and screening case detection systems. to assist clinical diagnosis, the differential diagnoses output by cds can be fed back directly to clinicians, or to other computer systems that provide decision support to clinicians at the point of care—reminding clinicians of diagnoses, notification requirements, vaccination, and history items to obtain or laboratory tests to order. for laboratory-based case detection, the lab-only approach for bayesian case detection discussed in this paper is a superset of current elr approaches, which has the advantage of being able to represent the uncertainty associated with lower sensitivity or specificity tests. for screening, the ability of the bayesian cds to represent a probabilistic case definition could be a significant advantage for emerging diseases that have case definitions that may be evolving or are dependent on constellation of symptoms and signs. 5 conclusion we developed an automatic case detection system that uses bayesian networks as disease models and nlp to extract patient information from free-text clinical reports. the system computes http://ojphi.org probabilistic case detection for disease surveillance using data in electronic medical records 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 disease probabilities given data from electronic medical records. the system is in use for influenza monitoring in allegheny county, pa, automatically reporting daily summary charts to public health officials. 57 the bayesian cds can function as a probabilistic elr system or an all-data case-detection system. cds is capable of integrating diagnostic information about a patient with prior probabilities of diseases to compute a probabilistic differential diagnosis that can be used in clinical decision support. the case probabilities derived by cds can also be used as a key component for a system that detects and characterizes outbreak diseases in the population; a companion paper in this issue discusses a system called ods that does just that. acknowledgements this research was funded by grant p01-hk000086 from the centers for disease control and prevention in support of the university of pittsburgh center for advanced study of informatics. corresponding author fuchiang tsui, phd parkvale building suite m-183, room 140 200 meyran avenue pittsburgh, pa 15260 ph: 412-648-6755 fax: 412-802-6803 email: tsui2@pitt.edu references 1. wagner m, gresham l, dato v. chapter 3 case detection, outbreak detection, and outbreak characterization. in: wagner m, moore a, aryel r, editors. handbook of biosurveillance. new york: elsevier; 2006. 2. ewert dp, frederick pd, run gh, mascola l. 1994. the reporting efficiency of measles by hospitals in los angeles county, 1986 and 1989. am j public health. 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wa 4invesco ltd., atlanta, ga abstract objectives: the noncommunicable disease (ncd) mobile phone survey, a component of the bloomberg philanthropies data for health initiative, determines the prevalence of ncds and their associated risk factors and demonstrates the use of mobile phone administered surveys to supplement periodic national household surveys. the ncd mobile phone survey uses surveda to administer the survey; surveda is an open-source, multi-modal software specifically developed for the project. the objective of the paper is to describe surveda, review data collection methods used in participating countries and discuss how surveda and similar approaches can improve public health surveillance. methods: surveda features full-service survey design and implementation through a web application and collects data via short messaging service (sms), interactive voice response (ivr) and/or mobile web. surveda’s survey design process employs five steps: creating a project, creating questionnaires, designing and starting a survey, monitoring survey progress, and exporting survey results. results: the ncd mobile phone survey has been successfully conducted in five countries, zambia (2017), philippines (2018), morocco (2019), malawi (2019), and sri lanka (2019), with a total of 23,682 interviews completed. discussion: this approach to data collection demonstrates that mobile phone surveys can supplement face-toface data collection methods. furthermore, surveda offers major advantages including automated modeswitch, question randomization and comparison features. conclusion: accurate and timely survey data informs a country’s abilities to make targeted policy decisions while prioritizing limited resources. the high acceptance of surveda demonstrates that the use of mobile phones for surveillance can deliver accurate and timely data collection. keywords: mobile phone survey, noncommunicable diseases, lmics *corresponding author: vcl7@cdc.gov doi: 10.5210/ojphi.v12i2.10574 mailto:vcl7@cdc.gov using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi introduction reducing the burden of non-communicable diseases (ncds), including cardiovascular diseases, cancer, diabetes, and chronic lung diseases, is a major challenge in international development of the 21st century, and in 2011, the united nations (un) declared ncds as such [1-3]. furthermore, in 2012, the world health assembly endorsed the who global action plan 2013-2020, which set for member states the voluntary target of a 25% relative reduction in premature mortality from ncds by 2025 [4]. given the burden of ncds, one of the un sustainable development goals also calls for a one third reduction in premature mortality from ncds by 2030 [5]. in 2015, 70% of global deaths (39.5 million out of 56.4 million) were due to ncds. the burden of these diseases rests disproportionately with lowand middle income countries (lmics), where more than 75% of ncd deaths occur [6]. ncds are the leading causes of death in developed countries and will increasingly dominate the global pattern of death [7,8]. in addition, the estimated output loss, including loss of productivity and healthcare costs, attributed to ncds is us$47 trillion if they are not addressed [4]. despite growing evidence of the epidemiological and economic impact of ncds, the global response to the problem remains inadequate in terms of financing and attention [9]. for example, while ncds contribute to 50% of global disability adjusted years, the conditions only received 1% of total donor assistance for health in 2011 [10]. the mobile phone industry is making striking contributions to cross-sector innovations, including the health sector with ncd management and prevention, accomplished by mhealth methods such as health education, email and text reminders and data collection [11]. the existing mobile phone technology landscape can serve as a catalyst to scale up ncd data collection, dissemination and use. the systematic monitoring of risk factors is essential for a country’s ability to prioritize essential resources and make sound policy decisions to address the growing ncd burden. in many lmics, the systematic monitoring of risk factors is completed using household surveys, which means that data is collected via face-to-face interviews conducted in respondents’ homes [12,13]. this method can be labor intensive, cost-prohibitive, and infrequent. mobile phone surveys offer an opportunity to supplement traditional household health surveys given mobile phones can produce high-quality data for short and frequent surveys more cheaply [14]. according to the international telecommunications union (itu), in 2018 there were more mobile phone subscriptions than people with 108 mobile-cellular subscriptions per 100 inhabitants globally and in lmics there is one mobile-cellular subscription on average for every inhabitant [15]. as mobile phone access and ownership continue to increase globally, mobile phone surveys could produce timely, affordable and accurate data to monitor and address ncd trends. the bloomberg philanthropies data for health initiative, launched in 2015, aims to strengthen the collection and use of critical public health information through multiple components. the ncd mobile phone survey component of the initiative determines the prevalence of common ncds and their associated risk factors and demonstrates the feasibility of using mobile phone surveys as an interim data collection method to supplement periodic and often infrequent national ncd household surveys. the ncd mobile phone survey is a representative survey of adults 18 years of age and older. the survey uses copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi standardized instruments and procedures, including a core questionnaire with optional questions and survey design using random digit dialing (rdd). the surveys are implemented by participating countries and ministries of health (mohs) in collaboration with relevant ministries of information and technology, with technical assistance from centers for disease control and prevention (cdc), rti international, and innovative support to emergencies diseases and disasters (instedd). the ncd mobile phone survey data collection utilizes surveda, an open-source, multi-modal mobile phone survey tool specifically developed for the project. it is a flexible platform that can deploy mixed modal surveys (including short messaging service [sms], interactive voice response [ivr] and mobile web), perform comparisons of questionnaires and mode sequences, uphold the highest data privacy standards, simplify the user experience, enable local and cloud hosting capacities, and contribute to the open-source community. surveda is designed to be easily implemented in any data hosting environment and to quickly and efficiently deploy mobile phone surveys at scale. since 2015, surveda has been used to collect ncd data in 7 countries, and data for 5 countries (zambia [2017], the philippines [2018], morocco [2019], malawi [2019], and sri lanka [2019]) are presented here, including the number of mobile phone number dialed, the number of surveys completed, response rates, and key demographics of respondents. this paper also describes in detail the survey technology tool, surveda, reviews methods used for the ncd mobile phone survey data collection in participating countries, and discusses how surveda and similar approaches can improve public health surveillance. methods survey implementation the ncd mobile phone survey is a cross-sectional survey with the target population being adults 18 years of age or older with a mobile phone number within the project site’s mobile phone number series. the core questionnaire includes demographics, questions to assess how many mobile phones the respondent uses and whether anyone else uses the mobile phone they are using to respond, and questions on ncds and related behavioral risk factors. the questionnaires are adapted to each country’s context before implementation. during data collection, a respondent is contacted via their mobile phone through a call or sms and consent (opt in or out) is obtained before they are screened to determine eligibility. once eligibility (age 18 years and above) is determined, the respondent is asked to answer questions on ncds and associated key behavioral risk factors, including tobacco and alcohol use, diet (fruits, vegetables, salt), diabetes, and hypertension. a specific number of recontacts are set for each survey if the respondent does not answer or finish the survey on the first contact. once the recontacts are exhausted, no additional attempts were made to contact the respondent. the ncd mobile phone survey used a two-phase sample design where in phase 1, a random sample of mobile phone numbers were selected using simple random sampling, and in phase 2, from the sample of mobile phone numbers, each mobile phone number was allocated to the age and sex strata to which it is a member. once the sample size for an individual stratum was met, any respondents meeting the criteria for a filled stratum were thanked for their participation and the interview was terminated. data regarding their age and sex was retained for sampling weight adjustments. this survey design yielded nationally using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi representative prevalence estimates of ncds and key behavioral risk factors for males and females over the age of 18 years. ethical clearance for survey implementation was obtained in all five countries. mobile phone data collection tool features surveda was designed to help mohs conduct surveillance of ncds in line with the objectives of the ncd mobile phone survey but can be used for any type of survey. it features full-service survey design and implementation through a web application. currently, surveda can collect data via sms, ivr, mobile web, and mixed modes. the mode(s) of the surveys, the way or method which the surveys are sent out by, in individual countries are determined by literacy rate, smartphone penetration, and overall technology usage. in an sms survey, respondents receive and answer questions via text messaging. in an ivr survey, respondents listen to prerecorded questions and select responses by pressing numbers on the mobile phone keypad. in a mobile web survey, respondents receive a text containing a link that opens the survey within a webpage. while the former two modes can be used across all types of mobile phones, the latter, mobile web, can only be used on web browser enabled phones, such as a smartphone. the surveda tool (gnu general public license 3) is a web application that is built on top of existing open-source tools specifically instedd's verboice and nuntium, and leverages popular open-source web technologies including reactjs, phoenix web framework, and mysql databases. surveda’s unique functions and features are designed to uphold the highest standards of data quality according to best practices: • mixed mode survey deploymentsurveys can be deployed in a combination of two modes, one primary mode and one fallback mode, configured by the user. if a participant does not respond to the primary mode of contact, surveda triggers the fallback mode to deploy the survey. this functionality leverages the strengths of the modes to reach participants [16]. • comparisonsmode sequence and questionnaire can be assessed by running two surveys in parallel and evaluating data quality (e.g., response rate) of the two. • languagescurrently more than 400 languages are supported by the tool for international use. • quota targetsthe number of completed interviews for specific categories (i.e. strata) can be set in surveda, such as age, gender, etc. • schedulingthe user can determine the days of the week, time zone, hours of the day, and days to block out such as national holidays. • call-back sequencethe number of times a phone number is contacted via each mode that is configured and the time interval between each contact can be set. users can include the number of times that the primary mode is tried as well as the number of times a secondary mode will be tried (if a secondary mode is used). • question randomizationsurveda includes a function to randomize the sequence of questions each participant is sent. this feature aims to reduce question item non-response due to survey fatigue. users create sections within which a question or questions are contained and then these are randomly shuffled during survey execution. users can also specify sections that will not be randomized. figure 1 summarizes the available functions of surveda. users can design or upload questionnaires, upload sample of mobile phone numbers, configure channels and modes, set schedules, define call-back using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi protocol and timelines, and deploy surveys. surveda also employs survey management features including real-time survey monitoring and survey progress visuals as well as data downloads in several formats. respondents receive and respond to surveys using their mobile phones and their data are sent to the tool and stored securely. for the ncd mobile phone survey, the survey design inputs are defined within an implementation plan by the mohs in collaboration with all partners. figure 1. functions of surveda survey design using surveda surveda’s survey design process employs five main steps: creating a project, creating questionnaires, designing and starting a survey, monitoring survey progress, and exporting survey results. these steps are sequential, as portrayed in figure 2. figure 2. surveda survey design process the first step in this process is to create a new project. a project is the top level of organization used to store and execute surveys. in a single project, a user may have multiple surveys—for example, separate surveys on diet, physical activity, and alcohol use. for the ncd mobile phone survey, the in-country ministries of health took the lead in creating a project, which stores all the components of the survey including questionnaire, channels, and data. the next step in the process is to create a questionnaire. the questionnaire contains the questions, translations, responses, skip logic, and question randomization set up. for survey deployment, users must create questionnaires tailored to the mode selected (i.e., sms, ivr, mobile web) for the survey. using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi next, using the interactive, step-by-step tool, users can design the survey by selecting a created questionnaire, uploading the sample of mobile phone numbers, selecting the survey mode, setting days and times for sending invitations to potential respondents, setting the number of re-contacts, and time between re-contacts for both the primary and secondary modes. to use the surveda tool at scale, typically it is necessary to set up agreements with mobile network operators (mnos) or aggregators to establish channels that allow surveda to exchange messages and calls with respondents. channels established through mnos or aggregators would typically result in the ability to dial 20 or more calls concurrently. users can monitor the survey progress through surveda’s dashboard, which displays the real-time status of all calls sent through the established channels. in addition, the dashboard displays the number of completed interviews, partial interviews, and other types of non-response (e.g., call failed, refusal). this allows survey managers to gauge the overall performance of the survey, estimate how long the survey will take to achieve the desired sample size, adjust course if necessary, and report on survey progress. in addition, survey data can be exported even as the survey is still in progress. surveda can export survey data for multiple purposes. a menu of four data files can be exported including the survey results from completed surveys, the disposition history or call status for each survey respondent, phone numbers for respondents who completed the survey for incentive distribution, and the interactions records between surveda and the respondent. currently, all exports are available in a comma delimited (csv) format. data hosting for this project, there are two types of data hosting: permanent data hosting and temporary data hosting. permanent data hosting refers to the long-term storage of completed survey results, and temporary data hosting refers to storage of the data within surveda while the survey is running. the temporal data hosting is cloud based using amazon web services (aws). from a technical standpoint, the project protocol recommends that permanent data is hosted on a 3rd party cloud provider environment (e.g., aws, microsoft azure). however, if the implementing agency has an existing system for storing and managing these data and the in-country policies requires a data hosting solution other than cloud hosting, an alternative solution may be created. ultimately, the implementing agency determines where the survey data are stored permanently as they own the data. privacy under any of the hosting options is upheld to the latest security requirements and maintenance protocols. under all the options, subscriber mobile phone numbers were only visible during sample upload; all data are unlinked, and de-identified in the tool as well as in data exports. analysis data on call flow including the number of mobile phone numbers dialed to the number of surveys completed are presented in the results section below. the american association for public opinion research (aapor)-defined response rate, cooperation rate, refusal rate, and contact rate were calculated according to definitions and formulas in appendix i. the number and proportion of respondents who completed the survey in primary and secondary modes are presented in the results section. the age, sex, using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi and education of respondents who completed the survey in primary and secondary modes are also presented. in addition, we used the chi-square test to test if the aforementioned characteristics of respondents who completed the survey were associated with switching from the primary mode to the secondary mode. results country specific implementation the ncd mobile phone survey has been successfully implemented in five countries: zambia (2017), the philippines (2018), morocco (2019), malawi (2019), and sri lanka (2019). the surveys were programmed in 15 languages across the five countries. furthermore, surveys were programmed in various non-roman scripts such as arabic and hindi. surveys were deployed via sms, ivr and mobile web. to date approximately 112 ministry of health staff, across seven countries, have been trained on using surveda. on average, data collection lasted 56.4 days in each country, with 84.0 interviews completed per day. in three countries (zambia, philippines and morocco), due to unavailability of a list of active mobile phone numbers, implicit sampling frames that contained all possible mobile phone numbers were used. this type of sampling frame will contain a large proportion of non-active mobile phone numbers. explicit sampling frames were used in malawi and sri lanka with known active subscribers. an incentive of $1 usd was provided to participants who completed the survey in all countries except for sri lanka. table 1 below summarizes the call flow for each country. table 1. mobile phone numbers dialed and interviews obtained out of 2,102,060 mobile phone numbers dialed across five countries, 153,602 (7.3%) resulted in contact with potential respondents, of which 89,588 (58.3%) consented to participate. of those who consented, 58,512 (65.3%) completed the screening questions about age and sex. ultimately, out of the 26,042 respondents who were eligible to participate, 23,682 (90.9%) provided an interview. these include both fully completed and partial interviews, with partial interview defined as having completed the demographic module and one ncd question. more information on the sampling strategy for the survey can be found on www.ncdmobile.org. using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi table 2. aapor rates table 2 shows the response rates, cooperation rates, refusal rates, and contact rates in each country. response rate ranged from 0.5% in the philippines to 3.3% in malawi. cooperation rate was lowest in sri lanka at 31.8% and highest in the philippines at 84.8%. refusal rate stayed below 5% for all countries, ranging from 0.1% in the philippines to 4.9% in sri lanka. the highest contact rate was found in sri lanka at 7.2% and lowest was 0.6% in the philippines. mixed mode implementation table 3. completed interviews by mode sonly one out of the three mno’s subscribers reached during primary mode were contacted using secondary mode. note: sri lanka is not included in the table as the survey was conducted in only one mode. as shown in table 3, across all four countries, most interviews were completed in the primary mode; however, an additional 7,189 interviews were completed due to the availability of a secondary mode, with 37.7% of all interviews completed in second mode. table 4 shows completed interviews by primary and secondary mode, age, gender, and educational status. with the availability of a secondary mode, 199 to 888 additional interviews in the oldest (45+) age group were completed, which is consistently the hardest group to contact via mobile phone in lmics [17]. regarding gender, there was a range of 297 to 1,277 additional females who completed the survey due to the addition of a secondary mode. lastly, the secondary mode allowed additional completed interviews in the lowest education range, which can often be under-represented in mobile phone surveys. using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi table 4. completed interviews by mode, age, gender and educational status *the chi-square test was used. athis refers to no education across all four counties. bthis refers to primary education in zambia and morocco; elementary education in the philippines; and standard 1-8 in malawi. cthis refers to secondary and more than secondary education in zambia; secondary, post secondary and college in the philippines; high school and college in morocco; and secondary and tertiary in malawi. data hosting in the five countries that have completed the survey, various temporary data hosting solutions were employed (see table 5 below). using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi table 5. temporary data hosting solutions in the countries where the project was implemented, different data hosting solutions were used including: local hosting with mno, aws, and ona. hosting solutions were determined based on local laws and regulations with the hosting environment as well as capacities to provide best practices for data hosting. for example, in zambia, it was required that data from governmental surveillance efforts be hosted at the national data hosting center, zambia national data center. all other countries used aws, of which a subset of countries used ona as a final data destination. ona is a data hosting platform that serves as a data destination, which has been in use by some of our in-country governmental partners. discussion this approach to data collection demonstrates that mobile phone surveys can be used to supplement more traditional methods of data collection. below are lessons learned based on conducting the ncd mobile phone survey in five countries. country implementation the high contact rates in sri lanka and malawi are likely due to the usage of a list of active mobile phone numbers rather than using lists of all possible mobile phone numbers generated by known prefixes within each country. it is interesting that even though the contact rate is high in sri lanka, its cooperation rate is the lowest among all countries. this could be attributed to sri lanka being the only country that did not distribute incentives to participants who completed surveys. this finding suggests that incentive-based surveys are more successful than non-incentive based surveys, as shown by previous studies [18,19]. another finding of note is that while the philippines had the highest contact rate and lowest refusal rate among all countries; it also had the lowest response rate. one explanation is that the response rate is calculated using all the mobile phone numbers dialed as the denominator and a large percentage of the numbers that were dialed in the philippines were likely not active numbers. however, once someone is contacted at an active mobile phone number, they are likely to cooperate and complete the survey. this elucidates the importance of using an explicit sampling frame versus an implicit frame which contains vast amounts of inactive numbers, given sri lanka and malawi had the highest response and contact rates. we must dial more numbers to reach a working number when inactive numbers are not excluded from the frame. including these sampling units in the calculation that are incapable of responding lower the using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi response rate. additionally, explicit frames screened for active mobile phone numbers generally increase efficiency in survey implementation by reducing data collection time. mixed modes previous literature suggests that mixing modes can increase response rates and improve the quality of mobile phone survey data [15]. our results from the implementation of mobile phone surveys in zambia, the philippines, morocco and malawi support the use of mixed modes for survey deployment as it yielded a larger number of completed interviews and increased response rates, thus potentially reduced bias and non-representative samples [15]. using a secondary mode in addition to a primary mode also showed that the secondary mode was able to capture different populations based on age, gender and education in the majority of the countries that implemented the ncd mobile phone survey. for example, in the philippines, more women completed the survey in the second mode than men, whereas in the first mode more men completed the survey. it is also evident in morocco that the second mode was able to capture more people of lower education status, who are under-represented in the first mode. however, it should be noted that in morocco, only a fraction of respondents were recontacted using the secondary mode given agreement was only reached with one out of the three mnos available for primary mode. this could have impacted the percentage and characteristics of respondents who completed the survey using the secondary mode. each mode of survey delivery has its advantages and disadvantages that could attract different populations. for example, mobile web surveys can only be executed on web browser enabled mobile phones, which can be more expensive to own. sms surveys require the respondent to be literate, which ivr surveys do not. therefore, delivering surveys using mixed modes should be able to capture a more representative sample, which can be seen from our survey findings and previous literature [16]. based on a landscaping of the current data collection software available, surveda is the first tool to employ and automate the use of a primary and fallback mode using sms and ivr. sustainability surveda, as a data collection tool, is sustainable due to the reusability of the infrastructure, acceptance by countries, and ease of use, as assessed by in-country partners during user feedback sessions. sustainability is also one of the objectives of the project given there has been a proliferation of short-lived digital health tools in recent years [20]. based on engagement with the countries during protocol implementation, it is evident that in-country partners are interested in this innovative approach to collect site-representative ncds-related information and beyond. to that end, the mno channel infrastructure is configured for the mohs to reuse for the duration of the contracts. this includes maintenance of hardware, rack space, vpn, smpp, and technical configurations. the same strategy is utilized for data hosting so that configurations for server, surveda software, and vpn remain and future data collection efforts can be easily accomplished. three of the five countries that have completed the ncd mobile phone survey are planning to implement a second round of the survey, utilizing mno channels established during implementation. the mobile phone data collection method, facilitated by surveda, successfully simplified and performed procedures that are traditionally employed by household surveys. for example, staffing for the mobile phone survey was minimal, requiring 5-10 core staff members. given that surveys are deployed via sms and ivr, no interviewers nor field staff supervisors were hired. once the channels to deploy sms or ivr using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi are established, mohs are able to reuse the channels for repeat ncd surveillance or other public health data collection efforts. country implementing teams trained on surveda are equipped to design future surveys using the process outlined above to deploy and monitor the surveys, all in one tool. throughout the implementation of the ncd mobile phone survey, activities were undertaken with the aim of enabling moh staff’s subsequent use of the survey methods and tools. for example, step-by-step hands-on training on surveda were provided to the core implementing in-country teams to build capacity for using the tool. in-country partners understand the flexibility of the surveda tool for other use cases, such as community and facility-level surveillance. for example, zambia moh has expressed interest to use surveda for a schistosomiasis survey and the municipal corporation of greater mumbai is considering employing the tool for a livability index. ecuador and sri lanka’s mohs have recently conducted covid-19 mobile phone surveys using surveda and existing infrastructure set up for their ncd mobile phone surveys. these surveys demonstrate the speed and utility of gathering population-level data to inform effective and rapid decision making during a pandemic. it is promising that several countries’ core implementing teams have used the tools and methods of the ncd mobile phone survey for other topics of interest outside of ncds to better the health of their countries’ population. open-source there has been increasing demand to better coordinate existing digital health initiatives for potential collaborations [21]. surveda is an existing global technology platform that can strengthen the value and impact of digital health investments, improve coordination and facilitate scale. one important feature of surveda that offers potential for collaborations and enables its sustainability is that it is an open-source tool, which means that surveda is publicly accessible to individuals and organizations interested in creating their own surveys. the source code for surveda is freely available online and can be modified as needed. open-source software produces cost savings and provides researchers the opportunity to develop software for their own specific needs, then share it with others doing similar work. data hosting using cloud hosting services was proven to be the fastest method to begin deploying surveys. it enabled the technical team to standardize the system's setup procedures across different country implementations, making it easier and cheaper to maintain over the project's lifecycle. different hosting providers offered a wide variety of systems and protocols to setup and access the servers they provide, with different sets of rules and conditions that increase the complexity of software deployment and maintenance. therefore, cloud hosting reduced risk, improved the sustainability and security of implementations, and is the most cost efficient of all the hosting options used for this project. the use of cloud computing has been garnering attention for its aforementioned benefits and is increasingly being implemented [22]. mobile network connections and scalability to send out surveys on a national scale, setting up agreements and establishing connections with the mnos was a necessity. this process was often challenging and is a limitation, which contributed to prolonging the timeline of survey implementation. in the planning stages of survey implementation, several options were usually considered in each country: mnos, local aggregators, and global aggregators. during the beginning stages of implementation, an extensive landscaping process was using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi conducted to determine the available options, given the diverse technology and regulatory environments that varied from country to country. therefore, there was no one-size-fits-all standard solution available. for the ncd mobile phone survey, local aggregators were typically preferred due to their existing connections to all in-country mnos. in some cases, however, they did not always meet the project’s technical requirements, so mnos and local aggregators were both used depending on the country. working with global aggregators (e.g. twilio) was another option but they have different levels of reliability in different countries and are potentially cost-prohibitive at large scales. working with local mnos provides more reliable and cost-effective connections but requires extensive one-time efforts in setting up and testing the connectivity, while dealing with a great variety of regulations, contractual agreements, technical solutions, response times and bureaucracy. the itu has explored the importance of these partnerships in the context of information and communication technology for development, specifically for meeting the sustainable development goals and provide useful guidance on establishing these partnerships [23]. one of the lessons learned is that for ivr it is usually enough to work with a single mno in the country, meanwhile sms tends to require working with each mno (or an aggregator) to setup short codes and reverse billing, which prevents respondents from being charged for text messages while responding to the survey. mobile web could also be a simple approach, if without ip whitelisting. this means respondents will be charged for data usage. however, testing confirms that data is minimal to complete the entire survey. to a smaller-scaled data collection effort, these may not be obstacles. limitations the results of the mobile phone surveys are representative of mobile phone owners rather than the general public. however, as mentioned, mobile phone ownership is generally high in the countries where the survey was implemented. an additional limitation is that the information collected is based on self-report and may be subject to bias. given the anonymous nature of the data collection, however, the risk of social desirability bias is reduced. lastly, sending out surveys on a national scale involves setting up agreements and establishing connections with mnos, which is often challenging. conclusion the bloomberg philanthropies data for health initiative successfully completed the first large scale ncd mobile phone survey in five lmics. the innovative mobile phone data collection methodology yielded a large sample in a relatively short period without the logistical efforts a traditional door-to-door survey would require. the rapid availability of data allows for the potential for speedy dissemination and use of the results. accurate and timely data is essential for a country’s ability to make policy decisions while prioritizing limited resources. the successful use of this technology demonstrates that the use of mobile phones for surveillance can deliver accurate and timely data collection. worldwide, utilization of mobile phones for data collection and research is increasing, including for applications such as public opinion surveys, health interventions, and citizen feedback [15]. although mobile phone surveys are unlikely to fully replace door-to-door demographic and health surveys, monitoring and evaluation staff should consider the utility of surveda, and mobile phone data collection as a supplemental method to provide timely data. these data add to the limited, but growing research base using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi documenting that mobile phone survey research in lmics is feasible, fast, and potentially cost-effective for collecting data on ncds and related risk factors and other priority topics critical for population health. surveda can be especially useful during situations where face-to-face data collection is not feasible. for example, during the covid-19 pandemic, using surveda, ecuador and sri lanka governments were able to quickly set up and conduct mobile phone surveys on covid-19 topics to inform program and policy implementation. with the growth of the digital community, surveda has the potential to become an integrated component of a government-led harmonized approach to monitoring and achieving the sustainable development goals and can even one day contribute to the non-health sectors as an agnostic data collection tool. acknowledgements the authors wish to thank the governments of zambia, morocco, philippines, malawi, sri lanka, ecuador and mumbai for implementing the ncd mobile phone survey. financial disclosure funding for the data for health initiative ncd component and ncd mobile phone survey is provided by the bloomberg philanthropies and the government of australia, foreign affairs. references 1. hosseinpoor ar, bergen n, mendis s, harper s, verdes e, et al. 2012. socioeconomic inequality in the prevalence of noncommunicable diseases in lowand middle-income countries: results from the world health survey. bmc public health. 12, p. 474. 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[cited august 2019]. available from: https://ojs.ub.unikonstanz.de/srm/article/view/7375/6821. 18. gibson dg, wosu ac, pariyo gw, ahmed s, ali j, et al. 2019. effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in bangladesh and uganda. bmj glob health. 4(5), e001604. pubmed https://doi.org/10.1136/bmjgh-2019-001604 19. leo b, morello r, mellon j, peixoto t, davenport st. 2015. do mobile phone surveys work in poor countries? center for global development working paper, (398). 20. world health organization. 2019. who guideline: recommendations on digital interventions for health system strengthening. world health organization. [cited september 2020]. available from: https://www.ncbi.nlm.nih.gov/books/nbk541905/. 21. world health organization. digital health atlas. [cited september 2020]. available from: https://digitalhealthatlas.org/en/-/. 22. maaref s. (2012). cloud computing in africa situation and perspectives. telecommun. dev. sect.itu, 70. [cited september 2020]. available from: http://www.itu.int/itu-d/treg/publications/cloud_computing_afrique-e.pdf. 23. international telecommunication union. 2018. m-powering for development: turning opportunities into reality, 2018. [cited september 2020]. available from: https://www.itu.int/en/itu-d/initiatives/m-powering/documents/m powering_report_2018_210918.pdf. appendix i response rate 2 (rr2) = (i + p) / (i + p + r + nc + o + uh + uo) cooperation rate 2 (coop2) = (i + p) / (i + p + r + o) refusal rate 1 (ref1) = r / (i + p + r + nc + o + uh + uo) contact rate 1 (con1) = (i + p + r + o) / (i + p + r + o + nc + uh + uo) reference: https://www.aapor.org/aapor_main/media/mainsitefiles/standarddefinitions2015_8thed.pdf https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=31565406&dopt=abstract https://doi.org/10.1136/bmjgh-2019-001604 https://www.itu.int/en/itu-d/initiatives/m-powering/documents/m https://www.aapor.org/aapor_main/media/mainsitefiles/standard-definitions2015_8thed.pdf https://www.aapor.org/aapor_main/media/mainsitefiles/standard-definitions2015_8thed.pdf using mobile phone data collection tool, surveda, for noncommunicable disease surveillance in five lowand middle-income countries 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e13, 2020 ojphi corresponding surveda disposition state 1. interview 1 2. eligible, non-interview 2 refusal and break-off (r)* 2.1 demographic questions completed and respondent eligible breakoff or started answered age and sex questions but did not answer any ncd questions 3. unknown eligibility, non-interview 3 unknown if housing unit 3.1 this code has no applicable definition in the mps. always busy 3.12 phone busy or network busy/down telephone answering device (don’t know if housing unit) 3.14 voicemail telecommunication technological barriers, e.g., call-blocking 3.15 call blocking technical phone problems 3.16 bad audio quality (i.e., static, poor reception), unable to connect because of network issues, breakoff by respondent due to technical difficulties before demographic questions began ambiguous operator’s message 3.161 ambiguous error or isdn code 4. not eligible 4 fax/data line 4.2 dedicated fax/data line nonworking/disconnected number 4.3 nonworking number 4.31 disconnected number 4.32 temporarily out of service 4.33 pagers 4.44 nonresidence 4.5 business, government office, or other organization 4.51 institution 4.52 group quarters 4.53 person not household resident 4.54 other 4.9 phone or sim (subscriber identity module) card not used no eligible respondent 4.7 less than 18 years under age 18 quota filled 4.8 respondent is 18 years or older but the stratum sample size has been attained. these respondents are rejected. answered age and sex questions but quotas were full ineligible rejected no answer (uh) 3.13 subscriber status unknown no answer, possibly nonworking/nonactive number other (uo) 3.9 breakoff before demographic questions were complete/eligibility determined, pressed 3 to refuse the interview, unable to understand language of interview, immediate hang up, temporarily out of service, or part-time fax/data line, out of coverage area refused consent or answered some questions but stopped before age or sex unresponsive and failed refused or breakoff or started other (o) 2.3 this code has no applicable definition in the mps. not attempted or worked 3.11 call failed partial (p) 1.2 demographic questions completed plus one ncd question answered at least once ncd question but did not finish the survey non-contact (nc) 2.2 this code has no applicable definition in the mps. partial and interim partial final disposition codes for rdd telephone surveys code conversion for mobile phones surveda definition complete (i) 1.1 answered all survey questionscompleted 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts impact of demographics on healthcare utilization andrew walsh* health monitoring systems, inc, pittsburgh, pa, usa objective to determine if data collected for syndromic surveillance can inform policy questions related to emergency department utilization and inpatient readmission introduction the affordable care act (aca) was promoted with two goals: expanding health insurance coverage and reducing healthcare costs. expanded coverage is expected to partially reduce costs. emergency department (ed) visits are costlier than comparable primary care physician visits. if uninsured patients use the local ed more often than insured patients with comparable conditions, insuring them may change usage and lower costs. some reports in the literature do not fit this model of ed usage. in one study, nonurgent ed visits were mainly the result of patient uncertainty about the severity of their condition. while trained medical personnel distinguished urgent and nonurgent cases after the fact, initial presentations were similar. in oregon, an expansion of medicaid increased health insurance coverage; ed usage increased rather than decreased. thus, the motivating narrative about insurance coverage and ed usage informing the aca may not be the complete story. reduction of hospital readmissions is also expected to cut costs under the aca. hospital process improvements are expected to realize this reduction. recently it was reported that up to 60% of hospital readmissions are predicted by patient demographics, raising questions about how much control a hospital has over its readmission rate. this research will examine whether data collected via syndromic surveillance can corroborate these findings. methods us census data was obtained for new jersey (nj), ohio (oh), and pennsylvania (pa) and aggregated to zip code tabulation area (zcta) for comparison with hospital data which was only available at that granularity. ed registration counts were obtained from 312 hospitals in nj (48), oh (167), and pa (97) for 2011-2013. ed usage rates for the zctas were calculated using the maximum of total population values for that zcta and adjusted for days reporting. readmission rates were obtained from a subset of these hospitals which send inpatient data via their syndromic surveillance feed. regression analysis used r v3.1.1, with packages lme4 v1.1-7 and mass v7.3-33. results ed registrations were tabulated for 3,537 zctas in nj, oh, and pa. a total of 34,412,761 registrations were observed for a usage rate of 0.337 registrations per person per year. figure 1 shows the percentage of the zcta population with public insurance coverage as an indicator of ed usage. zctas were assigned to quartile groups based on public insurance coverage rate. the more people in a zcta with public insurance, the more ed visits. regression confirmed these differences as statistically significant. the best fitting multivariate regression model of ed visits included several insurance covariates. demographic data accounted for 59% of ed usage variation between zctas. inpatient admissions and readmissions for 9 months in 2013 and 2014 were tabulated for each zcta in nj, oh, and pa; 1,736 zctas were represented in the dataset. a total of 425,291 admissions and 60,501 readmissions were observed for a readmission rate of 0.142 readmissions per admission. regression models were also created for readmission rates. several insurance covariates were also determined to be significant for these models. demographic data accounted for 53% of the variation in readmission rates between hospitals. conclusions the consistent association between insurance coverage within a zcta and healthcare utilization of residents from that zcta warrants further investigation. increasing coverage may not have the expected effect on healthcare usage and costs. the influence of demographic factors also indicates that hospitals may have limited ability to improve the targeted outcomes. as with any study of aggregate data, there is the possibility of conflating group observations with individual effects. before implementing policy changes, it is necessary to replicate these correlations at an individual level. keywords affordable care act; census data; readmission; medicaid acknowledgments we thank the new jersey, ohio, and pennsylvania deparments of health for funding support and data for this work. *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e57, 201 ojphi-06-e100.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 119 (page number not for citation purposes) isds 2013 conference abstracts meeting mandatory infectious disease reporting requirements: the department of veterans affairs’ plan brooke o’hanley selzer*, annette lin, gina oda and mark holodniy office of public health surveillance and research, department of veterans affairs, palo alto, ca, usa � �� �� �� � � �� �� �� � objective ��������� ��� ����� � ��� �������������������������������������� ��� ���� ��������������� � �!�"������#�����$�������%��������&��� ��� �%&"� ��������# ���� '����(� � ���$�������)������� ������ ����� ���� � ����������'�� ������*� ��������������� � ����%�&�# ���� '��� �+ �",�������$����� $���$����� ���������� �������� ����� �����*� ��������� ��� ������������)����� $ .������(��� �)�������$�������������������+ ��������$ � $ .����� � �����/��0�*����������%&������, introduction 1�����������������)��������������/��$����� ������������� ������ �+ ���� ����� ������������*� ��������������� � ��,� ����� �����(� ��$����� ��� 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�����)���� ���� ���� �������������� ������� � � ����������%&, results �&����� ���� �������*����������������� ��� ����� ����� ���������� ��*� ��������������� � ��,�&���+���������� ����� ����� �����������&�� ����������'��*����*� �������������������)�*� ���������,�9������������� ������������*)��&������ ������*�������$ ����������*� ���������������+ � ��� ����������/��/�)�<�� "�����$�� ����)��������������� ��������+ � ' �)� ������*� �����/ �����*� ��������������� � ��"������"�' ��$� ����� ��-,�1������$���������'�������'��������?� �� ������+� $����� $ ������ ����������� ��������*���������������%�������2�����,�1���%&� �����+ �����)����� ����)����$ �����'� �������������������� ����)��������6��+ ����� ����# ������6�����������3��'��� ��2��@ ����������'� ������� �)���$��� ����������a�'�����$�������,�b����������� ������������)$���� �����������*� ��������������� � ��� ������ ��, conclusions 1����� � .�� �������&�������� �'�������������<�� "�����/������%&� �����$��)�/ ��� ����/��# ���� '������/�������/ ������� ����������� ��� ���� ��>���"�����������������������������������*� ��������������� � ��� ��� �'����$������$������� ��������� ����� ����� ������>���"�����/�� $�� ��� ������ ����� ����� �������/ �� ������%&��������� �������'��>� �����c"�����������%&���$��)�/ �������4��� ������8���$�������� �� ��� � ������������� ��������� ��������*������*� ����, keywords ������ ����� ������������ ��>�4��� ������8��>�#�����$�������%�������� &��� �� *annette lin e-mail: brooke.o’hanley@va.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e100, 2014 evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency sripriya rajamani1*, ann kayser2, emily emerson2, sarah solarz2 1. informatics program, school of nursing, university of minnesota, minneapolis, minnesota 2. minnesota electronic disease surveillance system (medss) operations, infectious disease epidemiology prevention and control division, minnesota department of health, st. paul, minnesota abstract background: past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. the critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (elr). many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts. objectives: the study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. this was conducted in context of minnesota electronic disease surveillance system (medss), the public health information system for supporting infectious disease surveillance in minnesota. data quality (dq) dimensions by strong et al., was chosen as the guiding framework for evaluation. methods: the process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the medss system. interviews were conducted in an open-ended format from november 2017 through february 2018. based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. this in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. this was followed by analysis of data in medss by various criteria identified by the informatics team. results: all dq metrics (intrinsic dq, contextual dq, representational dq, and accessibility dq) were impacted in the data exchange process with varying influence on dq dimensions. some errors such as improper mapping in electronic health records (ehrs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. some dq dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. the study revealed that data quality is dynamic and on-going oversight is a combined effort by medss informatics team and review by technical and public health program professionals. evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi conclusion: with increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. this study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future. keywords: public health informatics, public health surveillance, disease notification, communicable diseases, electronic laboratory reporting, electronic health records *correspondence: sripriya@umn.edu doi: 10.5210/ojphi.v10i2.9317 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction past [1] and present [2] national initiatives that promote electronic health records (ehrs), also advocate for the electronic exchange of data across various healthcare sectors using nationally recommended standards [3]. the critical role of public health, in the context of disease surveillance is recognized by these regulations, with recommendations for electronic laboratory reporting (elr). elr refers to the electronic transmission of labs related to reportable conditions to public health [4]. the emphasis on interoperability in recent legislations [5] and roadmaps [6] is facilitating the focus on electronic movement of data across healthcare settings. many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts. given this landscape, it is essential to understand the process of data exchange and its impact on quality of data being transmitted, as this is a crucial step in interoperability. in addition, this holds broad implications for future priority transactions such as electronic case reporting to public health. initial research around elr focused on comparison of paper-based reports to electronic transmissions and found predominantly positive impact of elr [7,8] on specifically two metrics of data quality: timeliness and completeness. subsequent studies have assessed the role of intermediaries such as health information exchanges (hie) [9-11] to facilitate elr and reported better completeness of data with hie support. presently, studies have begun to focus on provider reporting of notifiable diseases [12,13], as moving to electronic case notification [14-16] along with elr will be great progress to support overall public health disease surveillance. challenges in adoption and use of recommended codes [17-19] and need for an informatics savvy workforce [20] were identified as some of the issues in the move towards elr [21]. a recurring theme across these studies was assessing the quality of data, including exploring new venues to measure [22-24] and improve [25] it. timeliness and completeness were the two dimensions of data quality (dq) which were often evaluated. metrics from dq frameworks published in literature can be used as guidance in identifying additional parameters for assessment. mailto:sripriya@umn.edu evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi data quality assessment framework by kahn et al. [26], identifies three dq categories: conformance, completeness and plausibility, along with verification and validation as two dq assessment contexts. dq framework by strong et al., proposes a broad conceptualization of the quality of data from perspective of data consumers. it defines high quality data as one that is fit for use and emphasizes context around data production and usage. strong’s framework proposes four dq categories (intrinsic dq, contextual dq, representational dq, accessibility dq) comprising of fifteen dq dimensions [27,28]. these include intrinsic dq (accuracy, objectivity, believability, reputation); contextual dq (relevancy, value-added, timeliness, completeness, amount of data); representational dq (interpretability, ease of understanding, concise representation, consistent representation); accessibility dq (accessibility, access security). the strength of this framework is the breadth of dq characteristics. data quality is a multi-dimensional concept dependent on multitude of factors and adoption of data standards does facilitate dq, but does not guarantee it [29]. good quality data that meet many of dq dimensions are critical for public health surveillance purposes. with increasing electronic data exchange and emphasis on interoperability, it is essential to understand impact of various facets of data exchange on various dimensions of dq. the minnesota electronic disease surveillance system (medss) [30] is the public health information system for supporting infectious disease surveillance at a state level for minnesota and operational since 2008. it holds data on reportable conditions and receives elrs submitted to the state public health agency. medss is used for case management, contact tracing and to support outbreak investigations. its scope has expanded to include non-infectious diseases such as blood lead surveillance and birth defects. it’s a person-centric surveillance system which currently holds ~1,279,986 events across infectious diseases, lead and community and family health programs. approximately 153,880 lab tests/results were reported electronically for 2017 across six health systems and four reference labs. many healthcare systems are currently on a waiting list for either onboarding/move to electronic exchange or upgrade to better version of reporting standard. nationally recommended standards for elr [4] comprise of hl7 2.5.1 for message format and loinc [31] and snomed [32] codes for representation of lab tests and results respectively. with increasing demands for electronic data exchange for incoming data to medss from clinical sectors and for outgoing data to centers for disease control and prevention (cdc), new informatics tools to support data validation and exchange were implemented. the objective of this study was to assess the data exchange process and to understand its impact on the quality of data in medss. the overarching goal is to utilize findings for improvements in informatics tools and processes to enhance the value of medss by providing good quality data to support various public health purposes including disease surveillance. methods the process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the medss system. various subject matter experts (n=9) were identified spanning across the informatics team that supports medss operations, public health program professionals who are users of the medss system and its data, and the information technology (it) team which supports the data exchange process. the focus included both onboarding (process evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi of shifting to electronic exchange for either new reporting or migration/upgrade to different standard) and on-going submissions. elr is unique in that reporting can occur from either ehr or from lims (laboratory information management system) and can occur from healthcare delivery organization or from reference laboratories and these were taken into consideration. interviews were conducted in an open-ended/discussion format and were done over time frame of november 2017 through february 2018. based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. figure 1 displays the elr onboarding process and includes the testing and validation suite of tools offered in public domain by the national institute of standards and technology (nist) [33]. the six identified key processes that influence quality of data are numbered a through f (a mapping of tests and results to appropriate codes, b nist test bed for testing of messages, c submit test hl7 messages, d solicit hl7 messages with test cases (e.g. specific tests, seasonal diseases), e technical review, f program review). figure 2 displays the internal data exchange routing process which includes the phin messaging system (phin ms) [34], a cdc provided software that serves as a transport mechanism for effective movement of messages. this part comprises of four main components numbered g through j (g phin ms, h lab code list database validation, i – rhapsody® integration engine [35] rules, j mapping in medss). figure 1: overview of elr onboarding process the potential influence of the ten identified critical steps in the data exchange process and its impact on quality of data was identified through expert input using strong’s dq framework as a evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi guidance. this was followed by analysis of data in medss by criteria identified by the informatics team. evaluation of messages not mapped to any disease program in medss was identified as a priority. next, assessment of completeness of race and ethnicity fields before and after implementation of demographic data import feature in elr was completed. using influenza reporting as a scenario, the number of non-reportable tests that get submitted and added to data in medss was examined. finally, the number of incoming messages which get rejected due to errors was examined to quantify the need for additional technical assistance. figure 2: overview of internal data exchange routing process results the process of exchanging data electronically is iterative and is initiated with numerous rounds of message testing and varying gradation of technical assistance based on data submitter need and capabilities. each step in the process was deemed critical in its impact on the quality of data which moves across clinical sector and public health. table 1 lists the six identified key processes for elr onboarding, relevant sub-processes/notes and their influence including both dq metric and dq dimension. all dq metrics (intrinsic dq, contextual dq, representational dq, and accessibility dq) were impacted with varying influence on dq dimensions. some errors such as improper mapping on ehr end had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. some dq dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. table 1: onboarding for electronic lab reporting and data quality evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi data exchange process for elr onboarding data quality metric and data quality dimension impact a. mapping of tests and results to appropriate codes completed in the clinical healthcare space (ehr system and lims) intrinsic dq (accuracy, objectivity) b. test messages using nist test bed ability to map content to hl7 fields capability to submit data in r (required) fields contextual dq (completeness) c. submit hl7 test messages to medss capability to submit data in r (required) fields complete re (required, but may be empty) and o (optional) fields contextual dq (completeness, value-added data) d. solicit hl7 messages with specific tests, seasonal diseases checking for message formats and codes which may not be present in current hl7 test feeds contextual dq (completeness, relevancy) e. technical review hl7 format checks review of loinc codes review of snomed codes review of loinc-snomed pairs mapping of code pairs with appropriate disease contextual dq (completeness), representational dq (consistent representation, interpretability) f. program review confirm mapping of code pairs with diseases check for positive and negative test results check for odd messages intrinsic dq (objectivity), contextual dq (completeness), representational dq (interpretability) table 2 lists the six identified key processes related to on-going production submissions using the internal data exchange routing and their influence on data quality. similar to the on-boarding process, all dq metrics (intrinsic dq, contextual dq, representational dq, and accessibility dq) were impacted with varying influence on dq dimensions. the three steps labelled h. (lab code list database validation), i. (rhapsody integration engine rules) and j. (mapping in medss) were deemed critical with high level of need for on-going maintenance. laboratory tests are constantly evolving along with new lab codes (loinc) and organisms detected (snomed) and their combinations to determine disease changing, some processes (h. i. j.) require frequent review. the analysis also revealed the need for collaboration and some processes are dependent on coordination across medss informatics team, information technology (it) staff and public health program professionals. evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi table 2: data quality impact of internal data exchange routing process internal data exchange routing process influence on data quality metric and data quality dimension g. phin-ms transport secure messaging platform for transport of messages accessibility dq (access security) h. lab code list database validation check to ensure that message contains approved code pairs or rules for exemption update codes and code pairs based on new tests and results contextual dq (completeness), representational dq (consistent representation) i. rhapsody integration engine rules fixes format of incoming messages as per rules converts messages into medss accepted format representational dq (consistent representation) j. mapping in medss assignment of messages to diseases contextual dq (relevancy), representational dq (interpretability) the results from analysis of data in medss by various criteria identified by the informatics team is presented in table 3. evaluation for cases which are not mapped to any disease program and assigned to “other/unknown” category yielded 952 cases. assessment of messages for these cases noted an absence of loinc and/or snomed codes and their combination pair for disease assignment. next, the analysis focused on submission of non-reportable respiratory diseases along with reportable conditions (influenza) due to issues with special lab test panel, and this identified 366 cases. this was followed by evaluating the number of incoming messages which get rejected due to errors and there currently isn’t any process that keeps track of it. the corresponding impact on data quality metrics due to these identified issues are also presented in table 3. an enhancement was implemented in january 2018 to import demographic data (race, ethnicity) from elr feeds and this evaluation presented in table 4. of the total of 3,651 electronic lab messages received from january through february 2018, data on race was present in 2,310 messages and 1,680 messages received in that time frame had data on ethnicity. comparison of this new data with already existing race and ethnicity data in medss obtained through case reporting and follow-up investigations revealed 270 number of messages wherein race from elr feed was different than one currently recorded in medss. table 3: identified issues, data quality impact and correlations with data exchange processes evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi identified issue # of cases (time frame) data exchange process dq impact non-assignment of messages to diseases lack of loinc and/or snomed codes 952 (currently)  testing during elr onboarding  validation checks with lab code list database contextual dq (completeness, value-added data), representational dq (consistent representation) loinc – snomed pair missing / not mapped 952 (currently) submission of non-reportable diseases presence of numerous non-reportable respiratory pathogens (e.g. adeno virus, corona virus) 366 (over 1 year)  testing during elr onboarding  screening with rhapsody integration engine rules contextual dq (relevancy) missing messages due to rejections rejection of messages due to format and code issues ? approx. few/day (not tracked)  validation checks with lab code list database  screening with rhapsody integration engine rules contextual dq (value-added data) table 4: demographic data from electronic lab reports and influence on data quality data imported from elr number (jan – feb 2018) data quality enhancement race data 2,310 / 3,651 (63%) contextual dq (completeness, value-added data) ethnicity data 1,680 / 3,651 (46%) discussion federal regulations and incentives have offered the needed momentum towards electronic reporting to public health. but, there are differences in public health measure reporting [36] with elr lagging behind immunization reporting due to complexities around multitude of labs associated with reportable conditions, slow adoption of recommended codes and multiple entities/professionals involved in exchange such as clinical labs, reference labs, ordering provider, infection control practitioner and disease epidemiologists. another key factor to consider is that elr can be generated from ehrs or from laboratory information systems (lis) in reference labs evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi or in healthcare settings. this study also portrays the need for constant updates to the various validation tools to ensure errors are not being propagated across the data exchange chain. this research points to the complexity of the data exchange process by illustrating the numerous stakeholders involved and the critical role each one plays in moving towards interoperability. it also pointed to the need for all data exchange partners to be informed of evolution of standards, both message formats (e.g. hl7) and codes (e.g. loinc, snomed). some of these exchange mechanisms require technical assistance for either submitter (e.g. labs, providers) and the receiver (e.g. public health) or both of them. national projects such as digital bridge [37] and aphl informatics messaging services (aims) [38] are aimed to assist in data exchange across jurisdictional boundaries in public health. the data exchange process could be set such that messages get rejected if they fail any of the checks, but will require manual intervention by public health or the data reporters to understand quality issues around rejection and fix them. the study also presents various testing tools (nist test bed) and validation engines (rhapsody, lab code list validation database) that help to automate quality checks and monitor various dq dimensions. approaches from other public health reporting such as immunizations wherein provider quality reports [39] are generated could be tried in the context of elr. likewise open source software tools have been proposed to support data quality checks for both immunization reporting [39] and elr [23,40]. implementation and maintenance of these tools require both financial and technical resources. importantly, there needs to be overarching guidance and support from national organizations such as cdc to ensure standardization and to facilitate sharing of tools/resources across jurisdictions. the study revealed that data quality is dynamic and on-going oversight is a collaborative effort by medss informatics team, technical and public health program professionals. overall, maintenance of good data quality in context of elr needs a multipronged approach with automated tools, data exchange partners education, technical assistance, regular updates of codes/tools, organizational commitment and national guidelines along with support by informaticians/data quality analysts. this research depicts the details of processes, people and technology and the need for all the parts to align to make an electronic data exchange truly meaningful by providing good quality to data that fits the purpose (public health surveillance in this case). it highlights the benefits of standardization of data exchange processes which can be applied to other public health transactions. many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts. it underscores the value of a public health informatician to be part of electronic exchange of data across various sectors (clinical care, labs) and public health. finally, this study presents a compelling picture of the interoperability endeavor as a team effort and underscores the critical role an informatics team can play in facilitating the data exchange process. limitations the study has some limitations and focus on some dimensions of data quality by strong et al., is one of them. some dq aspects such as accessibility are not integrated with exchange process and hence were excluded. the research emphasis was determined by criteria outlined by medss evaluation of data exchange process for interoperability and impact on electronic laboratory reporting quality to a state public health agency online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e204, 2018 ojphi informatics team, and was limited based on available data during study period. some metrics were not tracked and certain tool enhancements were implemented recently by it support team and thus evaluation was limited. another limitation is that currently a large volume of elr submitters are reference labs which are not required to collect race and ethnicity data and hence completeness of those data fields through elr is limited. some dq errors are attributed to frequency of upgrade of codes/validation engine that are driven by organizational resources (finances, trained personnel) / institutional priorities and 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proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 (page number not for citation purposes) isds 2013 conference abstracts validation of analytic methods for combining evidence sources in biosurveillance howard burkom*, yevgeniy elbert, liane ramac-thomas and christopher cuellar johns hopkins applied physics laboratory, laurel, md, usa objective this presentation aims to reduce the gap between multivariate analytic surveillance tools and public health acceptance and utility. we developed procedures to verify, calibrate, and validate an evidence fusion capability based on a combination of clinical and syndromic indicators and limited knowledge of historical outbreak events. introduction recent years’ informatics advances have increased availability of various sources of health-monitoring information to agencies responsible for disease surveillance. these sources differ in clinical relevance and reliability, and range from streaming statistical indicator evidence to outbreak reports. information-gathering advances have outpaced the capability to combine the disparate evidence for routine decision support. in view of the need for analytical tools to manage an increasingly complex data environment, a fusion module based on bayesian networks (bn) was developed in 2011 for the dept. of defense (dod) electronic surveillance system for the early notification of community-based epidemics (essence). in 2012 this module was expanded with syndromic queries, data-sensitive algorithm selection, and hierarchical fusion network training [1]. subsequent efforts have produced a full fusion-enabled version of essence for beta testing, further upgrades, and a software specification for live dod integration. beta test reviewers cited the reduced alert burden and the detailed evidence underlying each alert. however, only 39 reported historical events were available for training and calibration of 3 networks designed for fusion of influenza-like-illness, gastrointestinal, and fever syndrome categories. the current presentation describes advances to formalize the network training, calibrate the component alerting algorithms and decision nodes together for each bn, and implement a validation strategy aimed at both the essence public health user and machine learning communities. methods hierarchical bn training was automated with an optimization method to seek conditional probability table (cpt) entries for each subnetwork that would produce desired results from given combinations of algorithm output states. for example, a combination of red alert for laboratory test orders, multiple recent yellow alerts for filled prescriptions, and multiple recent red alerts for icd-based visits would produce a level of concern above 90% for outbreak investigation using the network, based on results of algorithms applied to the syndromic data streams for each evidence type. the derived cpts minimize the difference between the desired and computed bn outputs for all state combinations. for validation in the absence of sufficient reported events, we searched algorithm results from all selected data streams in 3.75 years of data from 289 military facilities to derive ~100 historical events for each fusion type. we adopted a cross-validation strategy by partitioning these events into 10 sets, deriving the optimal set of bn and algorithm thresholds with each 10% removed, and applying these thresholds to the remaining 10%. each optimal threshold combination maximized the median odds ratio of the bn outbreak decision node, for alerting during outbreak vs non-outbreak days, over all training events. a single set of thresholds was then used for external validation using the 39 reported events. results within each fusion type, the cross-validation strategy produced consistent combinations of algorithm and bn decision thresholds, and the resultant overall threshold choices yielded 88% sensitivity to reported events, a 10-15% improvement over the original demonstration module. conclusions we implemented training and validation methods combining methods of the disease surveillance and machine learning communities. the automated bn methods yielded an increase in sensitivity over previous heuristic methods, and the cross-validation supported the bn strategy for prospective use in essence. increased epidemiologist input is needed for more granular, effective use of data and community acceptance. extension to other surveillance environments (e.g. civilian state or province) will require expert involvement regarding both available data and the objectives/constraints of those environments. keywords bayesian network; validation; multivariate; essence references burkom h, elbert y, ramac-thomas l, cuellar c, hung v, refinement of a population-based bayesian network for fusion of health surveillance data, (2013) online journal of public health informatics, 5(1), doi: http://dx.doi.org/10.5210%2fojphi.v5i1.4413 *howard burkom e-mail: howard.burkom@jhuapl.edu scholcommuser stamp scholcommuser rectangle scholcommuser rectangle scholcommuser text box online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e175, 2014 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 70 (page number not for citation purposes) isds 2013 conference abstracts openessence: disease surveillance through medical record system integration with openmrs charles hodanics* jhu 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�������������;�2��)��������<��� 9�� �8��)� ���������=�� �� �<�����������<��������,��6��% *charles hodanics e-mail: charles.hodanics@jhuapl.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e156, 2014 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts sursaud® software: a tool to support the data management, the analysis and the dissemination of results from the french syndromic surveillance system anne fouillet*1, nelly fournet1, nadege caillère1, arnaud musset1, lucas mercier1, cécile durand1, céline caserio-schönemann1 and loic josseran2 1french institute for public health surveillance (invs), saint maurice, france; 2université de versailles st-quentin-en-yvelines (uvsq), montigny-le-bretonneux, france objective the presentation describes the design and the main functionalities of the software developed to support the data management and data analysis of the french syndromic surveillance system. introduction the french syndromic surveillance system sursaud® was set up by the french institute for public health surveillance (invs) in 2004. the system is based on three main data sources: 1/the attendances in the emergency departments (ed), 2/the consultations to emergency general practitioners’ associations sos médecins 3/the mortality data from civil status offices and e-certificates. in 2012, 400 of the 710 ed and 59 of the 62 gp’s associations are involved in the system. 80% of the national mortality is also collected. given this large database and the need to analyze data in a short delay to reach the early warning objective of the system, a specific software has been developed. methods the application has been designed in order to support the users with automatised process of the three main following steps: 1. data integration in a database, data management and the control of data quality transmitted by the providers 2. the statistical analysis of the data, according to different demographic, geographic and syndromic criteria. individual and aggregated data are consultated using reporting tools (graphs and tables). 3. the output and reporting for decision makers and feedback for data providers through epidemiological bulletins and dashboards. each night, xml files containing data recorded during the previous day are sent from the ed and gp’s association sos médecins to invs. all files are automatically integrated in the national database. a data quality process is done to check the completeness and the validity of the transmitted data. the data are managed by aggregating by age groups, syndromes, by day and ed or association. after the data management, bulletins and dashboards are automatically generated. those word® documents contain graphs, tables or maps with the most recent data. this functionality allows producing already formatted documents, including an updated overview of the health situation of the previous days. through those documents available at 10:30 am every day, the epidemiologists can analyse, add comments of the results and publish their bulletins in a short delay. the users can also consult aggregated data through a bi tool to have a more in-depth analysis of the public health. such tool can also support the investigation of abnormal signals. results sursaud software is a login and password-protected and secured internet web site. since its deployment in 2010, about 300 users have had an access to the software and 25% are data providers. in august 2012, almost 35,000 new patients are daily caught by the system and nearly 50 millions of patients are recorded in the database since 2004. to analyse data, the diagnosis coded either in icd10 for ed visits or with specific thesaurus for gp’s calls are pre-aggregated in about 200 syndromes covering a large part of the medical diagnosis and call reasons collected in the system. 22 age groups are also available with a focus on the most frailty population, like the youngest and the elderly. finally, various geographic levels can be chosen, from the local ed or association to the national level. an average of 25 bulletins or dashboards is daily produced by the software and about 10,000 documents have been produced since the deployment. they support the analysis of general overviews of population health or are focused on the surveillance of specific health situations, such as epidemics, emergent pathologies or exceptional events (disasters, mass gatherings,…) conclusions the software is a fundamental support for the french syndromic surveillance system. a new version is being developed in order to add improvements and integrate the mortality data. the deployment of this second version is expected in 2013. keywords software; france; emergency department, general practitioners references 1. broome c, loonsk j. public health information network – improving early detection by using a standards-based approach to connecting public health and clinical medicine. mmwr (2004) vol53. supplement:199-202. 2. josseran l, nicolau j, caillère n, astagneau p, brücker g. syndromic surveillance based on emergency department activity and crude mortality: 2 examples. euro surveill 2006,11(12):225-29 *anne fouillet e-mail: a.fouillet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e118, 2013 ojphi-06-e161.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 145 (page number not for citation purposes) isds 2013 conference abstracts rapid measles exposure assessment in an urban emergency department using a syndromic surveillance system christopher sikora*1, 2, kerri fournier1, hussain usman1, angela jacobs1, bryan wicentowich1 and james talbot3, 2 1alberta health services, edmonton, ab, canada; 2university of alberta, edmonton, ab, canada; 3alberta health, edmonton, ab, canada � �� �� �� � � �� �� �� � objective �������� � ������� ��� � ����� �� ���� � ���� ���� ����� �� � �� ����� � ��� ����������� ���� ��� ���� ������������ �������� ������ ���� �� ������ ���� ���� � �������������� � �� ���������������� ����� �� � � � ������� �� �� �� ��� ������������� ��� ������ �������� �� ������ �� ���� � ��� � �!� �������� �� ���� ��������"������������ ���# ��� �� ����� ���$�� �� �% �� �� �&���������&��# ��� �� �' �� �$% &&'��� � ������� ���������������������� ����(�)������ � ��� � ����� � � 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���� ����� �� ���������� �� �� /����� �� keywords &��������� &��# ��� �� ;� <���� �� 6 � � ��;� $���� ����;� =������� �� ���� � �=������ *christopher sikora e-mail: christopher.sikora@albertahealthservices.ca� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e161, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts epidemiology of gunshot-related injuries in nyc emergency departments from 2004-2014 mansi agarwal*, nimi idaikkadar and don weiss new york city department of health and mental hygiene, long island city, ny, usa objective to describe epidemiological characteristics of gunshot-related injuries in new york city (nyc) using syndromic surveillance data introduction syndromic surveillance has demonstrated utility for situational awareness for non-infectious conditions, including tracking and monitoring gun-related violence and homicides1. while new york state reports an overall decrease in gun-related crimes2, in nyc identifying smaller scale aberrations of gunshot-related violence can prompt more efficient response by city groups. we examined the temporal and borough-level characteristics of gunshot-related emergency department (ed) visits in nyc. methods the nyc syndromic surveillance system captures ed visits from 49 hospitals on a daily basis. chief complaints for each visit from january 1, 2004 through july 31, 2014 were scanned for gunshot wounds. gunshot-related visits were identified by scanning chief complaints for the following keywords — “bullet”, “gsw”, “gun”, “shot”, “firearm”, “rifle”, and icd9 code “e922.9.” the scan algorithm was designed to include common misspellings and to exclude other types of shot-related complaints, such as vaccination and bb guns. we calculated relative risks and 95% confidence intervals for gunshot-related ed visits compared with other ed visits for temporal, spatial and weather-related characteristics. hour of day was categorized into 3 categories, night (8pm-4am), morning (4am12pm), and afternoon (12pm-8pm). a 4-level categorical variable for season and a binary variable for weekend were used to examine seasonal and day-of-week effects. borough of patient residence was examined to identify spatial variation in gunshot-related injuries. average daily temperature was categorized into a binary variable with 75°f classified as hot and the analysis was restricted to summer months of june, july, and august. results there were 16,013 gunshot-related injuries identified during our study period. men accounted for 91% of the visits but only 55% of all other ed visits. the median age of the patients was 24 years (mean 27 years; range 2 to 95 years). from 2004 to 2014, nyc eds have seen a 60% decline in the annual proportion of gunshot-related visits. visits were elevated in summer months (rr = 1.73, 95% ci = 1.66-1.81) as compared with winter months, with smaller but still significantly higher elevations in spring (rr = 1.12, 95% ci = 1.07-1.18) and fall (rr = 1.35, 95% ci = 1.28-1.41). gunshot-related visits were elevated at night as compared with afternoons (rr = 3.12, 95%ci = 3.01-3.24) and on weekends as compared with weekdays (rr = 1.86, 95% ci = 1.80-1.92). in the summer, average daily temperature was also found to be a potential risk factor, with hotter days ( 75°f) having higher rates of visits as compared with cooler days (<75°f) (rr = 1.09, 95% ci = 1.05-1.13). overall, there has been a decline in gunshot-related ed visits in all boroughs from 2004 to 2014, with brooklyn experiencing a 50% decrease in the annual proportion of gunshot-related visits. there was a significant association between borough and gunshot related visits. compared with manhattan, brooklyn had the highest rates of gunshot-related visits (rr = 2.07, 95% ci = 1.97-2.18) followed by the bronx (rr = 1.12, 95% ci = 1.06-1.18), staten island (rr = 1.12, 95% ci = 1.01-1.24), and queens (rr = 0.96, 95% ci = 0.90-1.02). conclusions syndromic surveillance identified temporal and spatial patterns in gunshot related visits that are comparable to findings from the new york police department, suggesting that ed syndromic data have potential to be an accurate near-real time tracking system of gun-related injuries. syndromic data may not capture all fatal gunrelated violence but do include patients who were dead on arrival. as gunshot-related injuries are typically emergencies, patients may be using other health care facilities for these visits. therefore, ed data can be used to evaluate and improve interventions targeting gunrelated injuries. continued monitoring of gunshot-related visits can be beneficial in identifying local hot spots in gun violence around nyc for an efficient public health response by health professionals, law enforcement, policy makers, and community-based organizations. keywords gunshot-related injuries; syndromic surveillance; nyc references 1. zeoli, am, pizarro, jm, grady, sc, melde, c (2012). homicide as infectious disease: using public health methods to investigate the diffusion of homicide. justice quarterly 31:3, 609-632. 2. division of criminal justice services. office of justice research and performance (2014). crime, arrest and firearm activity report: data reported through june 30, 2014. 4 aug. 2014. accessed 15 aug. 2014. *mansi agarwal e-mail: magarwal@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e7, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts spatial analysis when the location of infection is uncertain: an innovative approach using an animalherd-level weighted analysis céline dupuy*1, 2, claire morlot3, pierre demont4, marie-pierre callait-cardinal4, christian ducrot2, didier calavas1 and emilie gay1 1anses, lyon, france; 2inra, saint genès champanelle, france; 3direction générale de l’alimentation, paris, france; 4vetagrosup, marcy l’etoile, france objective spatial analysis of infectious diseases enables identification of areas at high risk for infection, a useful tool for implementation of risk-based surveillance. for chronic diseases, the period between infection and detection can be long and when animal movements are important, identifying the place of infection is difficult. the objective of this study is to propose an innovative approach for spatial analysis that takes into account uncertainty regarding the location where animals were infected. an animal-herd-level weighted analysis was used and applied to bovine cysticercosis in france. introduction bovine cysticercosis is a zoonotic foodborne disease caused by taenia saginata involving cattle as the intermediate host and humans as the final host. humans are infected by eating raw or undercooked meat of infected cattle. cattle are infected after grazing on pasture infected by human feces. disease detection in cattle is performed during post-mortem meat inspection at the slaughterhouse through the identification of cysts in muscle tissue. cysts develop from a viable stage to a degenerated stage in one to nine months, both stages being visible and distinguishable in cattle muscle. due to the slow development of cysts and the complexity of cattle movements (up to ten different herds from birth to slaughter in france), there is a strong bias to consider the last farm location before slaughter as the location of infection. methods a spatial analysis was performed using a spatial scan statistic [2]. the background population was defined as the number of cattle slaughtered in each commune (french administrative area). the case population was defined as the number of animal-herd weights aggregated by commune. animal-herd weight was defined as the probability that the animal was infected in a given herd. for instance, an animal that spent time in three different herds from birth to slaughter was assigned three probabilities to have been infected in one of these herds: p1, p2 and p3 (p1+p2+p3=1). based on the chronology of cyst development, rules to attribute these probabilities have been defined. the model was adjusted for age and sex, since these variables are associated with bovine cysticercosis [1]. results the data used came from a national survey conducted in france in 2010 on all the cattle slaughterhouses and from the national cattle database for cattle movements. after removal of missing data, information regarding 4,557,593 cattle slaughtered in 181 slaughterhouses was used (91.1% of cattle slaughtered in france in 2010). among these, post mortem inspection allowed the identification of 6,431 cattle harboring at least one cyst (i.e. regardless of its stage of development) and 603 harboring at least one viable cyst. three significant clusters for cattle with all types of cysts were detected (figure a). only one significant cluster was detected in eastern france when cattle with viable cysts only were taken into account (figure b). conclusions formerly, only cattle with viable cysts, i.e. acute lesions, would have been considered for spatial analysis so as to limit bias regarding the location of infection. the difference in location of the clusters detected, when considering only cattle harboring viable cysts or cattle harboring all types of cysts, proved the relevancy of this novel approach. animal-herd-level analysis could be useful for other chronic diseases, such as bovine tuberculosis, for which the period between infection and detection can be long and variable. dedicated rules to attribute probabilities taking into account the evolution of the disease would then have to be defined. apparent prevalence (%) of bovine cysticercosis (a: with viable or degenerated cysts; b: with viable cysts only) and significant clusters of cattle harboring viable or degenerated cysts (a) and viable cysts only (b). rr= relative risk. keywords animal health surveillance; public health; spatial analysis; zoonosis references 1.dupuy, c., et al. (2014). construction of standardized surveillance indicators for bovine cysticercosis. prev vet med, 115, 208-292. 2.kulldorff, m. (1997). a spatial scan statistic. theory and methods, 26(6), 1481-1496. *céline dupuy e-mail: celine.dupuy@agriculture.gouv.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e21, 2015 ojphi-06-e146.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 173 (page number not for citation purposes) isds 2013 conference abstracts identifying clusters of rare and novel words in emergency department chief complaints andrew walsh*1, teresa hamby2 and tonya lowery st. john3 1health monitoring systems, inc, pittsburgh, pa, usa; 2new jersey department of health, trenton, nj, usa; 3hawaii state department of health, honolulu, hi, usa � �� �� �� � � �� �� �� � objective �������� � ���� ����� ���������������������� �� ��� ���� ������ ������������������ ���������������� �� ������������� ��������������� �������������� � ���� introduction ���� ���������������� ����������� ����������� ������� ���������� ����� ������ �������������������� ������������� �������������� �������������� ��������� �������������� ����� ������ �������� �� ����������� ����� �������� ��� ������ ������ ���������� �������� �� �� ������������ � �� ��������������� ���� ����� � ���������� � �������� � ������� ���������� � ��������� ������������� ����������������� �� ����������� ��������� ����� ���� ���� ���������� ����� ������� �� ������������� ����� � � �������������������������� ����� � ����� ��� ��� ���!� � ���� � �������� �������� �������� ������������������������� ��� ������ ��� ������������ ��� �������� ������������ ����������������� � � ����� ����� ��� �������� � �������� � ����� �"��#�������������� ����������� ������� � ���� ������� � ��������������������� ������� 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license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 155 (page number not for citation purposes) isds 2013 conference abstracts translating heat-related illness surveillance into action through partnerships david j. swenson*, kenneth a. dufault and matthew j. cahillane dhhs, concord, nh, usa � �� �� �� � � �� �� �� � objective ������� ��� �� � � � ���� ��� ��������� ��� ������� � �� � � ���� �� ���������������� �� � ���� �������� � ��� � ������� �� � �� ����� ���� ������ �� ����� � ���� � �� � ���� � ������� � �� �� �� ���! ��� ��� � �������� ���������" ��� ����� � ��� ����� #����� � ���$ introduction %����� �������� � ��� � ��� � &� ���� � � �� ����� ����� �� �� ����� �� ������ ���� ��� ����������������� ������ ����� �'()(� ������ �� ����� � ���� �������� ��� �#�� ��� ������������ #����� ��� ��� #���������� �� �������� &� ���� � � $���� �������# ���� ��! 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��������������������������� ��8�� ������$������ ����� $���;�����-���� ���<;$7,1)�=5�-�===1��> ������������� ��� ����������� ������ ������ �������� ������ ���������������� ������ ��� ������������� � ���������������������?������&� ����@��������� references ?�&���� ���������� � ����a� � ��&���������%@ �� ����8�� ������ b����������������������� ��������#��������� � ����"����� ��������$�� ���� ������������� � ����"�� � ����#��� ������� ���$��� �� �������$��� . "#$$/�#��� ���8 ���'�1=)=��(>�����*��$� �� ���c���� cdd�������� ���d���d ��� ��d�� d ������ �����d���������������d������������ � ����d� �� ��������<�� �� ����($��������)5�$ � ���1=)2*� *aaron kite-powell e-mail: aaron.kite-powell@ll.mit.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e127, 2014 mhealth: using mobile technology to support healthcare 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi mhealth: using mobile technology to support healthcare senanu okuboyejo1, omatseyin eyesan1 1. covenant university, department of computer and information sciences, nigeria abstract adherence to long-term therapy in outpatient setting is required to reduce the prevalence of chronic diseases such as hiv/aids, diabetes, tuberculosis and malaria. this paper presents a mobile technology-based medical alert system for outpatient adherence in nigeria. the system makes use of the sms and voice features of mobile phones. the system has the potential of improving adherence to medication in outpatient setting by reminding patients of dosing schedules and attendance to scheduled appointments through sms and voice calls. it will also inform patients of benefits and risks associated with adherence. interventions aimed at improving adherence would provide significant positive return on investment through primary prevention (of risk factors) and secondary prevention of adverse health outcomes. keywords: medication adherence; chronic diseases; mobile technology; sms; voice; nigeria; health care. correspondence: sena.okuboyejo@covenantuniversity.edu.ng doi: 10.5210/ojphi.v5i3.4847 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. 1. introduction the most common contributors to the disease burden in nigeria are malaria, tuberculosis (tb) and hiv/aids [1]. these diseases are chronic, infectious/non-infectious, and highly prevalent. the current goals and objectives of the health sector include: reduction of the disease burden from hiv/aids, control and eradication of malaria and tuberculosis (which are prevalent and chronic). chronic diseases are diseases which have one or more of the following characteristics: they are permanent, leave residual disability, are caused by non-reversible pathological alteration, require special training of the patient for rehabilitation, or may be expected to require a long period of supervision, observation or care [2]. most of the cares needed for chronic conditions are based on patient self-management (usually requiring complex multi-therapies) coupled with strict adherence to medication and will require health system support. adherence to long-term therapy in outpatient setting is required to reduce prevalence of these diseases. adherence is generally described as the extent to which patients take medications as prescribed by their health care providers [3]. rates of adherence for individual patients are usually reported as the percentage of the prescribed doses of the medication actually taken by the patient over a specified period. some investigators have further refined the definition of http://ojphi.org/ mhealth: using mobile technology to support healthcare 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi adherence to include data on dose taking (taking the prescribed number of pills each day) and the timing of doses (taking pills within a prescribed period). interventions aimed at improving adherence would provide significant positive return on investment through primary prevention (of risk factors) and secondary prevention of adverse health outcomes [4]. health outcomes cannot be accurately assessed if they are measured predominantly by resource utilization and efficacy of interventions. poor adherence to long-term therapies severely compromises the effectiveness of treatment making this a critical issue in population health both from the perspective of quality of life and of health economics. 1.1 mobile technology for medication adherence health challenges have been found to be a significant barrier to development in nigeria. the vulnerability of nigeria’s rural population is summarized with the following reasons: epidemics, late diagnosis of ailment, lack of good health care infrastructure and delay in transport time to urban health care facilities, and inexperienced primary health-care providers in rural areas. ict diffusion in health care offers the potential to address these concerns and to save the patient extra costs associated with treatment, such as travel and other living expenses. the latest subscriber data released by the nigerian communications commission (ncc) has shown that nigeria's telecommunications industry’s teledensity has hit 73.12 per cent as at the end of june 2012, while the total number of mobile connected lines in the country has climbed to 136.04 million. this rise validates the report by the association of worldwide mobile phone operators that africa (of which nigeria is a major player) tops the continents using mobile phones in the world. teledensity is the number of landline telephones in use for every 100 individuals living within an area [5]. the explosive growth and deep penetration of mobile communications in these areas, provides millions of rural dwellers access to reliable technology for communication and data transfer. this growing ubiquity of mobile phones is a promise of the use of mobile technologies for providing mobile health interventions. one advantage, however, of telephones with respect to medication adherence in chronic care models is its ability to create a multi-way interaction between patient and health care provider(s) and thus facilitates the dynamic nature of this relationship [6]. the need to improve and provide efficient health services has resulted in the increased use of information and communication technology-based solutions in the healthcare sector. the use of text messaging in health has garnered increasing attention as a means to track disease outbreaks, monitor patient treatment, diagnose patients, educate patients, collect and transmit data through basic mobile phones. various studies have highlighted the potential of mobile phones to disseminate public health information and mobilize attendance to vaccination programs particularly in developing countries as well as to manage the treatment of diabetes in scotland [7]. recently, the patient care messaging service for pharmacies provided by iplato has been implemented in london pharmacies, using texts to verify patients’ smoking status and invite them to take part in smoking cessation services and follow-up treatment. other health-related sms-based systems are currently being implemented throughout low and middle income countries. in 2007, a program of text message reminders was being designed with a large teaching hospital in johannesburg in an effort to make more efficient use of overworked healthcare workers [8]. sms text messaging has also been highlighted as a preferred means of communication for mobilizing support and communicating during emergency and disaster http://ojphi.org/ mhealth: using mobile technology to support healthcare 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi situations [9]. frontline sms has developed a system using mass texting for surveys and community mobilization, which is free for non-governmental organizations (ngo) [10]. with mobile technologies, medical practitioners are able to instantly update and retrieve patients’ records from anywhere within a telephone network coverage. this ensures timely update of patients’ medical records. physicians with up to date information are likely to make better prescription decisions. the adoption of mobile technology-based application could eliminate redundant paperwork, thereby facilitating more efficient and effective delivery of patient care. the objective of this paper is to provide a framework for voice-based medication adherence system. we also provide a review of the potential of mobile technologies for supporting medication adherence especially in outpatient settings. it provides instances of interventions provided via mobile technologies and its outcome measures. 2. related works many projects have applied the short message service (sms) technology in ensuring patient adherence, such projects include: the mobile med alert, a mobile medical alert system that sends sms to patients, prompting them to take their drugs. it will design architecture for mobile health interventions and develop a prototype sms-based system to improve out-patient adherence. mobile med alert was developed using programming tools such as extensible hypertext markup language (xhtml), hypertext processor (php), mysql and the integration of ozeki sms gateway. its main features includes: it can alert patients about potential drug in-take at a scheduled time, in accordance to drug regimen; it allows for feedback mechanism whereby the patient can respond appropriately to alert messages. in both cases, the application aims at increasing patients’ compliance to treatment and in the long run, reduces the rate of noncompliance in relation to drug regimen [11]. another research work considered a mobile-phone based patient compliance system (mpcs) that can reduce the time-consuming and error-prone processes of existing self-regulation practice to facilitate self-reporting, non-compliance detection, and compliance reminder among patients in nigeria. the uniqueness of this work is to apply social behavior theories to engineer the mpcs to positively influence patients’ compliance behaviors, including mobile-delivered contextual reminders based on association theory; mobile triggered questionnaires based on selfperception theory; mobile enabled social interactions based on social construction theory, also explained how mobile phone can help patient to comply to their medication treatment; the existence of mobile phones and its uses in health sectors in nigeria [12]. also projects such as weltel have applied wireless technology in ensuring patient adherence: the weltel kenya1 was a multisite randomized clinical trial of hiv-infected adults initiating antiretroviral therapy (art) in three clinics in kenya. patients were randomized (1:1) by simple randomization with a random number generating program to a mobile phone short message service (sms) intervention or standard care. patients in the intervention group received weekly sms messages from a clinic nurse and were required to respond within 48hours. randomization, laboratory assays, and analyses were done by investigators masked to treatment allocation; however, study participants and clinic staff were not masked to treatment. primary outcomes were self-reported art adherence (>95% of prescribed doses in the past 30 days at both 6 and 12 month follow-up visits) and plasma hiv-1 viral rna load suppression (<400 copies per ml) http://ojphi.org/ mhealth: using mobile technology to support healthcare 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi at 12 months. the primary analysis was by intention to treat. patients who received sms support had significantly improved art adherence and rates of viral suppression compared with the control individuals. mobile phones might be effective tools to improve patient outcome in resource-limited settings [13]. in norway, sms messages are sent to educate parents with type 1 diabetic children. these messages included definitions, facts and tips for managing diabetes [14]. text to change (south africa) project employs an sms-based quiz to test users’ knowledge of hiv/aids and encourage testing and counseling [4]. wedjat is a mobile medication reminder and monitoring system. it is a smart phone application designed to help remind its users to take the correct medicines on time and record the in-take schedules for later review by healthcare professionals [15]. the uganda-text to change program aimed to encourage citizens to seek voluntary testing and counseling for hiv/aids. multiple choice quizzes were administered through sms to subscribers of celtel (airtel). the program offered free airtime to users to encourage participation in the program. the quiz was interactive. when participants gave a wrong answer they received an sms with the correct answer from the cell phone provider. the program motivated participants to go for voluntary testing and counseling at the local health center [4]. “south africa-aftercare” program by cell-life a non-governmental organization in cape town works with the public health system and health workers to collect patient information using mobile phones during home based care visits for hiv/aids patients receiving art [16]. the solution is effective in that it eliminates the need for paperwork and enables the logging of accurate data on a large scale with minimum cost. in [17], a voice-based mobile prescription application (vbmopa) was designed and implemented to improve health care services. the application can be accessed anyplace anytime, anywhere through a mobile phone by dialing an appropriate number, this connects users to an eprescription application that is resident on a web server. this system could lead to costs and life savings in healthcare centers across the world especially in developing countries where treatment processes are usually cumbersome and paper based. other projects include: tricks (text reminders for immunization compliance in kids) which sends out text messages of immunization reminders prior to immunization dates [18]; text messaging to motivate walking in older african-americans [19]; text4baby, the first free national health text messaging service in the united states that aims to provide timely information to pregnant women and new mothers to help them improve their health and the health of their babies [20]; a mobile phone text message program to measure oral antibiotic use and provide feedback on adherence to patients discharged from the emergency department [21] and the use of text messaging to increase the receipt of influenza vaccine among low-income urban children [22]. studies have also been carried out to measure the effectiveness of electronic reminders, sms, and phone calls in improving patient adherence [23-25]. heartsaver, a mobile medical device for real-time monitoring of a patient's electrocardiogram (ecg) and automatic detection of several cardiac pathologies was developed in [26]. another study investigated the potential of short message service (sms) reminders at reducing nonattendance in physical therapy outpatient clinics. the primary outcome was rate of nonattendance without cancellation. secondary outcomes were cancellation and attendance rates http://ojphi.org/ mhealth: using mobile technology to support healthcare 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi and exploration of other factors associated with nonattendance [27]. the cameroon mobile phone sms (camps) trial; a randomized trial, tested the efficacy of weekly reminder and motivational text messages, compared to the usual care in improving adherence to highly active antiretroviral treatment in patients attending a clinic in yaoundé, cameroon [28]. another study developed and tested memo; a mobile phone depression prevention intervention for adolescents. the study developed and tested the novel mobile phone delivery of a depression prevention intervention for adolescents [29]. 3. system design and implementation this paper introduces voice medalert, a mobile medical alert system that calls and sends sms to patients, reminding and prompting them to take their drugs. patients receive alerts on drug intake methods, description and dosages, in order to ensure adherence to drug regimen and prescriptions via voice calls and text messages. furthermore, patients can reply sms alerts indicating whether they have taken the drugs or not. an incremental approach to software development is used for the development of the system. the system consists of the voice user interface (vui) design and modeled with unified modeling language (uml). it was developed using tools such as html (hypertext markup language), hypertext preprocessor (php), mysql and the integration of the twilio rest api. its main features includes: sending sms messages to patients about potential drug in take at a scheduled time; sending sms reminders to patients about appointments some hours before the scheduled appointment, placing voice calls across to patients to also remind them of scheduled appointments and dosing times. the system administrator enters information about prescription and appointments to be taken by the patients. the cron job checks the database every minute and if the prescription or appointment time conforms to the current system time, it then calls the scheduler which initiates the twilio rest api in sending sms alert. 3.1 vui design the vui used for this system is the twilio rest web service interface. it is a voice application program interface (api) that allows you to query meta-data about your phone numbers, calls, text messages, and recordings. since the api is based on rest principles, it's very easy to write and test applications. its features include: • ability to send and receive sms messages; • conversion of text to speech; • playing of audio files; • recording and storage of calls. • it also enables you to create conference calls with up to 40 people in any one conference. • in addition, twilio rest has a speech to text engine that converts spoken words into text. http://ojphi.org/ mhealth: using mobile technology to support healthcare 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi 3.2 system architecture the system is 3-tier client and server architecture as shown in figure 1. it contains client devices, servers and a back-end running mysql database. • client devices: the client systems include vui devices such as mobile phones (personal digital assistant (pda), cell phone and smart phone and other handheld communication devices). • servers: the server contains the voice (twilio rest web service interface) and web server. the twilio rest contains the speech server (gateway) which has textto-speech (tts) and voice browser. the web/application server stores the voicebased health-care content information. it uses http to maintain internet connectivity with the voice gateway. • databases: the database used is mysql. it stores the information about appointments and prescriptions. mysql is a server application (for database) able to carry out a great number of sql commands. figure 1. a framework for voice medalert. 3.3 system implementation the prototype was developed using programming tools such as extensible hypertext markup language (xhtml), ozeki sms gateway, twilio rest for the voice user interface (vui), php for web user interface (wui), apache as middle-ware, and mysql database as backend. the choice for these tools is because of their advantage as free and open source software [30]. the user interfaces for interacting with the system are shown in figures 2 and 3. figure 4 shows a screenshot of the text message on a mobile phone. http://ojphi.org/ mhealth: using mobile technology to support healthcare 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi figure 2: screenshot of the “book appointment” module of voice medalert figure 3: screenshot of the “prescribe drugs” module of voice medalert figure 4: screenshot of the text message on a mobile phone http://ojphi.org/ mhealth: using mobile technology to support healthcare 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e233, 2014 ojphi 4. conclusion the mobile technology-based alert system discussed in this paper, allows patients receive sms and voice alerts for appointments and medications. the work is in progress. the prototype is at the deployment and evaluation stage. the ubiquity of mobile phones and its current integration in health care has made it a worthy tool to this effect. the system will reasonably reduce humanto-human (h2h) contact (such as doctor to patient) by replacing it with human-to-system (h2s) interactivity. this system will lead to costs and life savings in healthcare centers’ in developing countries where to ensure adherence to treatment, patients must walk many miles to clinics to receive and take medication and this is often not possible because of distance, lack of transportation, bad weather or a worsening condition that prevents them from leaving home. the benefits of this intervention notwithstanding, it will not be a substitute for the patienthealthcare provider relationship. the extension and integration of the intervention into hospital information management systems (hims) will increase the potentials of such interventions in the provision of efficient and effective adherence strategies. this will make healthcare accessible and available to all – a giant step in the direction of the achievement of the mdgs on healthcare for all. conflicts of interest "the authors declare no conflict of interest". references and notes 1. labiran a. mafe, m. onajole, b. and lambo, e. 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learned from the nw-phie experience methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 1 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 1 trebatoski m, 2 davies j, 3 revere d, 1 dobbs d 1 science applications international corporation, mclean, va 2 inland northwest health services, spokane, wa 3 center for public health informatics, university of washington, seattle, wa abstract: the intent of this article is to provide public health and health information exchanges (hies) insight into activities and processes for connecting public health with clinical care through hies. in 2007 the cdc issued a request for proposal (rfp) for the “situational awareness through health information exchange” project. the project’s goals are to connect public health with health information exchanges (hies) to improve public health’s real-time understanding of communities’ population health and healthcare facility status. this article describes the approach and methodology used by the northwest public health information exchange to achieve the project’s goals. the experience of the nwphie collaboration provides an organizational and operational roadmap for implementing a successful regional hie that ensures secure exchange and use of electronic health information between local and state public health and health care entities. keywords: data collection; electronic health records; health information exchange; information management; information services; medical record linkage; public health; public health informatics introduction in 2007 the cdc issued a request for proposal for the “situational awareness through health information exchange” project which aims to connect public health with health information exchanges (hies) to improve public health’s real-time understanding of communities’ population health and healthcare facility status. a team consisting of five participants was assembled by the science applications international corporation (saic) to form the northwest public health information exchange (nw-phie): inland northwest health services (inhs); washington state department of health (wa doh); university of washington center for public health informatics (uw cphi); spokane regional health district (srhd); and idaho department of health and welfare (id dohw). more background on the process conducted by saic to recruit project participants can be found in the article, “northwest public health information exchange’s accomplishments in connecting a health information exchange with public health” in this issue. methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 2 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 saic received a contract award from cdc in early 2008 and began the process of creating collaborative relationships among nw-phie participants. the goal was to make sure that all nw-phie participants understood their roles and responsibilities and that effective lines of communication were developed. methods to provide a forum for effective project communication bi-weekly meeting were established where the project leads from each of the member organizations discuss project priorities, activities, risks and issues. this group sets the vision and direction for nw-phie. implementation teams are assigned to carry out specific project activities and hold their own working meetings. a comprehensive project plan coordinates all project activities and tracks project progress. a project collaboration portal was established to share information such as work products, deliverables, project plans and status reports. collecting clinical data and making it useful to public health: to realize the potential of tapping into inhs’ rich set of clinical data to improve public health surveillance and situational awareness the nw-phie project team developed a structured methodology for defining public health’s functional and data requirements and implementing the needed technology solution. this methodology is depicted in figure 1 below. figure 1. nw-phie’s requirement’s definition and it development methodology through the use of a structure requirements definition and it implementation methodology nw-phie created a repeatable and efficient process that reduced project costs and risks. our methodology helps ensure that the proper clinical data is collected and sent to public health and that public health can extract value from the data. requirements definition for public health: we began with documenting public health’s requirements for collecting clinical data from hies. to define project requirements, epidemiologists, public health stakeholders and informaticists documented answers to a fairly straightforward set of questions, including: methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 3 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 1. what are the over-arching objectives public health is trying to achieve by collecting information from hies? 2. what questions will public health try to answer with the collected data? 3. what clinical information is needed to answer public health’s questions? 4. what are the timeliness, quality and reliability requirements of the data? 5. how does the data need to be analyzed to answer public health’s questions? nw-phie’s initial efforts were to capture syndromic surveillance data from inhs member hospitals and provide this information to public health. the starting points for nw-phie’s syndromic surveillance requirements were the american health informatics community’s (ahic) biosurveillance use case and the mbds. the biosurveillance use case provides requirements for the transmission of pseudoanonymized ambulatory care, inpatient and emergency department (ed) visit, utilization, and lab result data from health care organizations to authorized public health agencies with less than one day lag time. pseudo-anonymization removes patient identifying characteristics from the data and tags the patient level data with a system generated number (pseudo-anonymized identifier). in a public health emergency, authorized pubic health officials can request that the healthcare organization re-identify the patient using the pseudo-anonymized identifier. the biosurveillance use case identifies a minimum biosurveillance data set (mbds) that contains the clinical and resource utilization data elements that are deemed the minimum list needed to support local, state and federal public health syndromic surveillance functions. these documents provided a solid foundation of requirements and a starting clinical data set for the situational awareness project. nw-phie augmented the requirements derived from the ahic documents with additional requirements from local and state public health agencies. within washington state outbreak investigations are initiated by the local health jurisdiction (lhj). the wa doh assist lhj staff by facilitating testing done through the state public health laboratory and/or cdc as well as provide coordination and/or staffing support with outbreaks that involve multiple lhjs or other states. critical to timely management of outbreaks is early identification of the outbreak along with case identification. many outbreaks of public health interest are not notifiable based upon case-based mandatory reporting by health care providers, health care facilities or laboratories. common pathogens that are not reportable as isolated cases would include influenza, varicella, rsv, and norovirus. wa state and lhj’s were very supportive of using automated surveillance systems utilizing hie data to improve public health response time in identifying outbreaks. being able to then identify cases based upon being associated with a syndromic surveillance/notifiable condition cluster would provide lhjs and the wa doh more time to identify the source of the outbreak as well as additional time for contact investigation/management. the h1n1 epidemic provided a test case for confirming our syndromic surveillance requirements. during this epidemic public health wanted to understand not only the size and spread of the epidemic but also the severity of illness, the rate and efficacy of influenza vaccinations, and instance of influenza in sensitive groups such as pregnant women. nw-phie assembled epidemiologists, informaticists, and ed nurses to discuss methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 4 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 and design how these additional public health data requirements were collected in the clinical setting and how they could be sent to public health. during the life of the project a public health requirements and data collection document has been maintained. this document describes all clinical data collected across patient types (ed, inpatient and ambulatory care) and serves as a data dictionary for the clinical data being sent to public health. identifying data sources and developing a data collection strategy: after documenting the clinical data of importance to public health nw-phie created a strategy and process for collecting each type of data by identifying and analyzing existing information stores (i.e., hospital and laboratory information systems) and information flows within inhs’ hie. common sources of data include health level 7 (hl7) admission/discharge/transfer (adt), orders and results messages from participating data sources. in addition, hl7 messages from abstracting and financial systems provided data that is not routinely sent in with adt based messages. for a preponderance of the syndromic surveillance data the strategy was to subscribe to existing hl7 clinical data information flows. custom extracts were required for some of the data types. table 1 describes the types of syndromic surveillance data nw-phie collects and our collection method. table 1. syndromic surveillance data types and methods of collection data type method of collection base facility generally static and submitted at baseline. updated as necessary. daily facility summary (reflects the current status of the facility to help identify developing conditions and resource capacity ) creation of nightly census reports as well as custom extracts from a community-wide resource utilization system. deidentified patient demographics (for example, gender, age, zip, state) captured through hl7 admit/discharge/transfer (adt) transactions. clinical (for example, patient class, chief complaint, clinical diagnosis, billing diagnosis, temperature, pulse oximetry, discharge disposition) obtained by monitoring hl7 messages and facility identifier. use of the pseudo-anonymized linker has been associated with the clinical data element record. additional data elements have been obtained through the use of system extracts where hl7 messages are not supported by the source systems (e.g., ed clinical diagnosis). laboratory orders (for example, ordered procedure name and code) obtained by monitoring hl7 order messages. laboratory results (for example, ordered data/time, laboratory, test name, test results, result status) obtained by monitoring hl7 result messages. methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 5 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 an overview of the information exchanged between the inhs and public health is provided in figure 2. this view reflects the work which is currently underway to convert the transmission of data to cdc using the secure data transport capabilities within the nhin connect gateway. health information exchange meditech/sunquest lab data biosurveillance analytics ` gipse summarized data meditech hospital system public health repository enhanced viewing capabilities de-identified patient data state lab essence syndromic surveillance nhin connect adt/lab data adt/lab/ clinical data state lab results adt/lab/ clinical data figure 2. overview of information flow between inhs and public health creating the data format specification: an important step in capturing the requisite clinical information from inhs’ hie was developing a data format specification that describes how the clinical data will be transmitted to public health. best practices for sharing clinical data are based on sending the clinical data using accepted industry standards such as those from hl7 or clinical data interchange standards consortium (cdisc) and encoding those data using standard-based terminology. the format nw-phie used for sending syndromic surveillance data to wa doh was based on the health information technology standards panel (hitsp) interoperability specification 02 (is02) for biosurveillance which covers the data elements in the mbds described above. in particular, we decided to implement the hl7 message components for hitsp is02 as these complemented our data collection strategy of filtering hl7 messages for the mbds data elements. hitsp is02 is an overarching framework for biosurveillance that consists of a complex array of documents that reference a multitude of hitsp capabilities, service collaborations, transaction packages, transactions and components. is02 also references integrating the healthcare environment (ihe) profiles and base hl7 standards. because of the complexity of the is02 specification, nw-phie took the lead, in collaboration with the indiana and new york hie project teams, for developing a concise implementation guide that pull together all the disparate information from the is02 specification into one document, the “hitsp biosurveillance message implementation guide – hl7 version 2.5”. this document includes requirements for specific methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 6 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 administrative, demographic and clinical care information and the sharing of this data with public health organizations to support syndromic surveillance needs. analyzing sample data and mapping to the data format specification: we performed a detailed review of a sampling of production-like hl7 v2.1 and v2.3 messages for each of the data sources being considered. this allowed us to evaluate the availability of data elements and coding practices and provided insights into data quality and reliability. we noted differences in data availability and quality based on patient class (inpatient, ed, outpatient), clinical documentation processes and operating procedures. these differences varied from facility to facility and necessitated some facility-based variation in data collection practices. based on this analysis, patient filtering logic (partially derived from ahic biosurveillance use case and the hitsp biosurveillance messaging guide) was developed to screen out certain types of patients and encounters including preadmissions, recurring patients (e.g., dialysis patients), obstetric and psychiatric patient visits. this logic was based on analyzing specific fields in the hl7 messages; the patient class (hl7 field pv1-2), patient type (pv1-18) and patient location (pv1-3) fields. these filtering criteria were then reviewed and validated for each of the inhs hospitals implementation. next, we mapped the data fields in the hl7 messages to the implementation guide developed as part of the initial requirements definition process. this provided the necessary specifications to allow mapping of the clinical care information provided by the various data sources to the required message standards and terminology to meet the data output specifications. obtaining data use agreements from facilities: the facilities that participate in the inhs network do so for the purposes of delivering improved and better coordinated health care. each facility signs agreements upon entering the network that support common data use, access and security policies. those standard agreements do not address electronic submission of data to public health agencies. while inhs had delivered health care data to public health agencies in the past, it had been limited to very circumscribed situations such as birth records and disease registries or ad hoc one-time data requests related to a specific public health study. in each of those cases inhs developed a customized data use agreement with each facility wanting to submit data to public health, focused specifically on the elements of that particular data request. nw-phie presented a very different scenario. hospitals would be asked to regularly submit a large data set containing information that was not mandated by any state or county law or regulation. inhs did not have the authority to release the data without full support and signed data use agreements from each participating facility. this was the first syndromic surveillance system many of the hospitals had been asked to participate in. while they were not unwilling to support the project, they did need a clear understanding of the project’s purpose and scope and comprehensive assurances that the patient data they held in trust would not be compromised. methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 7 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 inhs worked with epidemiologists from the spokane regional health district (srhd) to develop a document that explained the project and also clarified how it was authorized (although not mandated) under existing state regulations. inhs staff then met with representatives of the health information management (him) office and infection control staff at each hospital to discuss the project and answer questions. these meetings were most effective when an epidemiologist from the srhd also participated. the srhd epidemiologists already had a strong relationship with the hospital staff from prior public health investigations and helped add credibility to the request for data. the data use agreement itself was designed with standard language authorizing inhs to deliver data to public health agencies on behalf of the participating facility for a period of time stipulated by the facility. specific data elements to be released and any pertinent methodologies were included as an appendix. this approach allowed senior management from the hospital to sign the overall data use agreement and other staff, usually the him director, to sign the appendix and any updates to the appendix over time. information technology development cycle: our requirements definition phase made sure we clearly understood public health’s needs for collecting clinical data and the strategy we would employ to collect those data. during the it development cycle we finished our technical design and developed the needed technology processes for collecting and standardizing the relevant data. our process design for collecting the clinical data is depicted in figure 3 below. cdc inhs data center inhs hospitals washington dept of health sdn (ssl) inhs cloverleaf interface engine biosurveillance integrator transport queue phinms receiver phinms sender data repository situational awareness app. phinms receiver vocabulary mapping linker database phinms sender srhd epi work queue web viewer figure 3. inhs architectural view of biosurveillance solution creating the development specifications and process: we created development specifications for each of the components listed in figure 4. this gave us a very specific understanding of how the different system components interacted with each other methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 8 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 including: input format and content; processing logic; and out format and content. this holistic design process enabled us to efficiently develop processes and ensured that once all process components were assembled they would reliably work together to collect and transmit the requisite clinical data to public health. developing the data collection processes: saic developed an integration engine tool known as the biosurveillance integrator that receives hl7 messages in real time from inhs’s cloverleaf integration engine via tcp/ip. inhs cloverleaf engine performs some filtering of messages to limit the number of data elements exchanged and to exclude patient visits which are not routinely associated with an encounter involving an infectious agent (ex. re-occurring visit for physical therapy). inhs also performs filtering of the laboratory data to provide a desired subset of the lab orders and results which are valuable in the identification of infectious diseases, where the approach is to accept all results from the hospital lab that contain these results, even though this approach may result in capturing data beyond the scope of what is desired. inhs provides the data through hl7 messages and several flat file data extracts to populate the admit/discharge/transfer (adt), observation result message (orm), observation result unsolicited (oru), daily census, facility utilization and clinical data to the biosurveillance integrator for message transformation. the biosurveillance integrator takes these input data streams and transforms them into well-formed hl7 messages that conform to the hitsp biosurveillance implementation guide. this transformation process includes removing extraneous information such as patient identifiers, personally identifiable information and unneeded clinical information. the process also standardizes the vocabulary using a set of mapping tables. currently these vocabulary mapping tables are maintained by a programmer. our plans call for the implementation of a full-fledged vocabulary server so that an end user can maintain the vocabulary mapping process. the output hl7 messages are encoded with a system generated patient identifier that allows public health department to reassemble the syndromic surveillance messages for a given individual without having to know the facility’s actual mpi and visit number. the biosurveillance integrator stores a cross-reference between the facility’s patient master patient index (mpi) and the system generated patient identifier in a database. hims have access to this database and can re-identify patients to public health officials when an authorized request is received. the output hl7 biosurveillance messages are stored into a file message queue that is picked up every 15 minutes by a public health information network messaging system (phinms) which compresses, encrypts and digitally signs them before transporting them to the wa doh over the internet. the wa doh unpacks the messages and puts them into a work queue. creating the test plans: to help ensure objective, credible, timely and high quality work, the nw-phie team utilized a combination of project management, integration development, and quality control strategies and techniques. a key project management strategy has been to develop detailed test plans based on public health requirements, the methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 9 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 hitsp biosurveillance implementation guide and our system design specifications. these have given us a proven and repeatable set of processes for system testing our data collection processes and for certifying data feeds from hospitals as they are activated. to develop our test plans we created a matrix of all types of input data by patient class (ed, inpatient and outpatient) and input format (adt, orus, flat files, etc.). we developed test cases within each of those data types based on our data mapping and development specifications and documented expected results for each test case. our testing procedures defined pre-testing requirements as well as technical processes for testing each of the test cases. having detailed test plans reduced our project risk and provided a framework for ensuring a consistent level of data quality across hospital activations. conducting system testing of the solution: we performed multiple levels of testing to ensure the quality of our end-to-end data collection processes. we began by unit testing each process component to ensure that it performed according to its design specifications. after successfully completing unit testing we strung together all of our process components (see figure 3 above) into a system test. the system test was guided by our test plans and scenarios. system testing was performed in multiple test cycles. for each test cycle information was entered into a hospital’s test meditech his that covered the test cases within that test cycle. these data were then flowed through our biosurveillance integrator to ensure that that it could properly process and transform those data into the biosurveillance messages. upon completion of the system test cycle testing results were documented in a testing report spreadsheet. this test report also contains information about the test cycle, the testing environment, the facilities being tested, and any other appropriate configuration data required during the testing process. following the successful system testing for a hospital, the data activation activities were scheduled and a hand-off document containing system support responsibilities and contact information was created. the result of these activities is data activation to make the new syndromic surveillance data feeds available to end users and to prepare the production systems support team for assuming responsibility for the newly implemented data feeds. results nw-phie used a structured activation process that ensured syndromic surveillance data activations were coordinated along the entire processing chain from hospital, to inhs hie personnel, to local, state and federal public health – resulting in standardized clinical data being made useful to public health and the needs of state doh and lhjs being met. activities related to the activation of the syndromic surveillance data feeds consist of the following three steps: methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 10 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 1. preparation for activation – including completing certification of data feed based on testing/acceptance protocols; completing user and operational support training; obtaining signoff from the facility to activate the data feed; scheduling the activation and notifying data recipients of activation schedule; distribution of activation checklist, resource assignments, and technical documentation for system support; completing preparation of the production environment to receive the data; and conducting a pre-activation meeting to verify activation task assignments/status. 2. system activation – including completion of necessary system backups/recovery plans; conducting a checkpoint meeting prior to start of deployment; executing the development plan for migrating software to production environment; performing verification of an initial batch of data related to activation; validating a data sample from existing data feeds to confirm changes have not impacted existing messages; conducting a checkpoint meeting prior to activation of data feed in production; enabling the data feed and monitoring successful transmission and receipt to data consumer systems; and documenting issues and notifying stakeholders and users of activation. 3. support/maintenance – including monitoring the data feed for initial period, based on volumes/frequency of data; performing data quality analysis on large data sampling for possible vocabulary exceptions and issues; reviewing the activation plan and documenting lessons learned for incorporation into future activations; transferring responsibilities of monitoring and support to performing organization; and providing hands-on assistance for level two support by development team. wa doh receives the biosurveillance hl7 messages in 15 minute increments. these messages are stored into a data queue that is immediately indexed into an hl7-centric database schema. information for a single patient is split into many discrete biosurveillance hl7 messages triggered by events at the facility such as registration, a lab order, a lab result, etc. in order to make clinical data useful for population health purposes several steps need to be followed. first, the individual messages need to be reassembled into a longitudinal, comprehensive view of a visit encounter. this is done by using the system generated visit identifier to link adt, lab orders, results, vital signs, etc. to reconstruct the comprehensive data for a visit. next, the encounters need to be classified according to surveillance criteria, and then counted over a fixed time interval, and paired with appropriate denominator, such as total visit volume or catchment population. each component can be seen in the entity relationship diagram in figure 4 which details how the longitudinal visit record is generated from line-level hl7 messages. methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 11 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 figure 4. entity relationship diagram using indicator definitions such as those listed in table 2, encounters are classified according to surveillance criteria. table 2. indicator classifications and definitions (partial example) indicator identifier indicator name mbds field(s) indicator definition 1001 influenza-like illness chief complaint (ili-1) chief complaint 1003 or (1004 and (1005 or 1006)) 1002 influenza-like illness chief complaint (ili-2) chief complaint 1004 and (1005 or 1006) 1003 influenza chief complaint (icc-1) chief complaint flu-like or influenza (not influenza vaccinations or intestinal flu or spinal flu or stomach flu) methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 12 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 indicator identifier indicator name mbds field(s) indicator definition 1004 fever chief complaint (fcc-1) chief complaint chills or fever or febrile or rigors or temperature 1005 cough chief complaint (ccc-1) chief complaint cough 1006 uri chief complaint (ucc-1) chief complaint achy throat or epiglottitis or head pressure or inflamed throat or nasal congestion or pharyngitis or rhinitis or runny nose or scratchy throat or sinus pain or sinusitis or sneeze or sore throat or stuffy nose or tonsillitis or upper respiratory infection or burning in throat or inflamed tonsil or throat pain or pharyngotonsillitis or strep or swollen throat or swollen tonsil or swollen uvula or throat drainage or throat dry or throat infection or throat irritation or throat itch or throat tingling or tonsil infection or tonsil pain or cold encounters are then aggregated over a fixed time interval and paired with appropriate denominator, such as total visit volume or catchment population. from these data absolute counts and rates of illness within the patient population are obtained. within washington state, lhjs initiate outbreak investigation and wa doh assists lhj staff by facilitating testing done through the state public health laboratory and cdc as well as provides coordination and staffing to support outbreaks that involve multiple lhjs. wa doh is also tasked with understanding the state-wide implications of outbreaks and providing state-wide reporting. critical to timely management of outbreaks is early identification of the outbreak along with case identification. many outbreaks of public health interest are not mandated to be reported to public health based on state notifiable disease reporting laws. common pathogens that are not reportable as isolated cases include influenza, varicella, rsv, and norovirus. nw-phie’s automated syndromic surveillance systems sends de-identified data to the wa doh and create summary statistics to identify outbreaks. the demethods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 13 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 identified syndromic surveillance data provided to the wa doh is sufficient to allow the state to perform its state-wide monitoring and reporting responsibilities. once an outbreak has been identified lhjs oftentimes need to re-identify patients to perform their outbreak investigation. this re-identification is performed by lhj personnel calling the health information manager (him) at the facility and providing the system generated biosurveillance linker id. the him logs on to the biosurveillance integrator and queries to find the medical record number and name of the patient associated with the biosurveillance linker id. this information is provided to the lhj to assist in their outbreak investigation. this rapid identification of outbreaks and identification of patients associated with outbreaks provides ljhs with a needed head start on containing the outbreak by performing case and contact investigations. discussion and conclusion the experience of the nw-phie project provides an organizational and operational roadmap for implementing a successful regional hie that ensures secure exchange and use of electronic health information between local and state public health and health care entities. given the role hies are expected to play as building blocks for a proposed national health information network as well as in the office of the national coordinator for technology (onc) plan for a national health information exchange model, it is important to capture the lessons learned from the nw-phie experience. from our experience we have extracted five significant lessons we believe need to be included to achieve hie success: lesson 1. contracts having a contract for the nw-phie work that was written through a participatory and consensus process involving all key stakeholders focused our efforts by providing clear goals and deliverables. having each stakeholder contribute to the contract enabled an investment in the nw-phie’s success. however, it is important to note that this contract also allowed room for flexibility (see “creativity” below). in addition, data use agreement contracts provided evidence of the nw-phie’s openness and transparency as well as respect for and protection of the core component of the nw-phie: data. lesson 2. collaboration a key lesson learned was the benefits of collaborating, in particular involving local and state public health as a fully participating partner at the beginning of the project. for example, after collecting the mbds data elements, state and local epidemiologists were given access so they could provide input on system requirements, participate in tool design and have their work process needs reflected by these tools. perhaps most importantly, once the epidemiologists had the data not only was it easier for them to see the project’s value but they took the lead in fleshing out requirements. this collaboration cemented a buy-in for epidemiologists that would not have occurred if we had started with greenfield requirements. an additional point on collaboration is that the nw-phie team was structured to include a blended set of technical, public health, clinical, project management, and research skills as represented by an hie, multiple health departments, a methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 14 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 systems integrator and an academic research university. the skills and assets of each group were leveraged throughout the nw-phie project. lesson 3. consensus achieving consensus on clearly defined shortand long-term goals that addressed the needs and priorities of all stakeholders was a key to ultimate success. it was also important to incorporate consensus-based data sharing policies and practices. an example of this is the mbds definition which was developed through expert opinion, informed by syndromic surveillance systems across the country and national groups sponsored by ahic, and thus provided the nw-phie team with a trusted and clear definition of the data elements to collect. we also found that it was important to determine “how” and “what” was needed out of an existing ehr, as opposed to expecting the ehr to change or create new elements as needed. achieving consensus on these requirements involved all partners. lesson 4. communication the team participated in regular bi-weekly conference calls that provided a level of governance, oversight and a forum for regular participation by all team members. these regular communications focused on the project milestones and deliverables but also allowed time for creative problem-solving. in addition, the larger hie grantees held regular conference calls that the nw-phie team was invited to participate in, which assured transparency and helped build a larger hie community. lesson 5. creativity being able to try out different approaches to exchanging and analyzing the data was critical. public health benefitted from the rapid and throw-away testing “sandbox” provided by the university in which data could be analyzed and modified until solutions were tested and developed. and although we had a contract with clear goals and deliverables, there was enough flexibility in the contract for creativity and response to crisis situations (e.g. the h1n1 outbreak) and opportunities to participate in conferences and demonstrations. it is also important to note that the h1n1 public health events which occurred during the project enabled our activities to receive a higher priority within healthcare organizations and energized the project. as of publication of this article, the flexibility that rapid role out provided – in combination with the ability to academically review data and implement new features – continues. acknowledgments this work was funded by the centers for disease control and prevention through the “accelerating public health situational awareness through health information exchanges” contract #200-2008-24369. we also wish to acknowledge helpful feedback from michael davisson and bryant karras at wa state doh; paul bugni and bill lober at uw; and mark springer at srhd; along with the work of the entire nwphie collaborative. methods for leveraging a health information exchange for public health: lessons learned from the nw-phie experience 15 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 correspondence michael trebatoski, science applications international corporation, healthcare systems, mclean, va. trebatoskim@saic.com conflicts of interest the authors do not report any conflicts of interest. mailto:trebatoskim@saic.com paper details load balancing at emergency departments using ‘crowdinforming’ 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 load balancing at emergency departments using ‘crowdinforming’ marcia r friesen 1 , trevor strome 2 , shamir mukhi 3 , robert mcloed 1 1 university of manitoba, canada 2 winnipeg regional health authority 3 canadian network for public health intelligence abstract background: emergency department (ed) overcrowding is an important healthcare issue facing increasing public and regulatory scrutiny in canada and around the world. many approaches to alleviate excessive waiting times and lengths of stay have been studied. in theory, optimal ed patient flow may be assisted via balancing patient loads between eds (in essence spreading patients more evenly throughout this system). this investigation utilizes simulation to explore “crowdinforming” as a basis for a process control strategy aimed to balance patient loads between six eds within a mid-sized canadian city. methods: anonymous patient visit data comprising 120,000 ed patient visits over six months to six ed facilities were obtained from the region’s emergency department information system (edis) to (1) determine trends in ed visits and interactions between parameters; (2) to develop a process control strategy integrating crowdinforming; and, (3) apply and evaluate the model in a simulated environment to explore the potential impact on patient self-redirection and load balancing between eds. results: as in reality, the data available and subsequent model demonstrated that there are many factors that impact ed patient flow. initial results suggest that for this particular data set used, ed arrival rates were the most useful metric for ed ‘busyness’ in a process control strategy, and that emergency department performance may benefit from load balancing efforts. conclusions: the simulation supports the use of crowdinforming as a potential tool when used in a process control strategy to balance the patient loads between eds. the work also revealed that the value of several parameters intuitively expected to be meaningful metrics of ed ‘busyness’ was not evident, highlighting the importance of finding parameters meaningful within one’s particular data set. the information provided in the crowdinforming model is already available in a local context at some ed sites. the extension to a wider dissemination of information via an internet web service accessible by smart phones is readily achievable and not a technological obstacle. similarly, the system could be extended to help direct patients by including future estimates or predictions in the crowdinformed data. the contribution of the simulation is to allow for effective policy evaluation to better inform the public of ed ‘busyness’ as part of their decision making process in attending an emergency department. in effect, this is a means of providing additional decision support insights garnered from a simulation, prior to a real world implementation. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 1. background 1.1 emergency department overcrowding emergency department (ed) overcrowding, manifest in long patient wait times and excessive lengths of stay in hospital eds, is a chronic public health concern in that it negatively impacts patient experiences and, potentially, their health outcomes, healthcare worker experiences, and system efficiencies. reducing waiting times and potentially improving efficiencies can be fostered through the use of information and communication technologies in the delivery of services and resource management, as demonstrated by the prevalence of e-health initiatives that span the off-site delivery of care (telehealth), robotic surgery, mobile health applications, electronic medical records and region-wide records integration. patient wait times, excessive lengths of stay, and ed overcrowding receive considerable attention within healthcare administration and from the general public. debates on how best to address these problems have been ongoing for several decades [1][2][3] and will likely continue into the foreseeable future. factors related to ed overcrowding and excessive wait times are known to be complex. they include internal factors such as eds that are too small, difficulties in transferring admitted patients out of the ed, staff shortages, and delays in consultative and diagnostic services; external factors include increasing patient volumes and increasing complexity and acuity of cases [4][5][6][7]. many of the approaches that have been tried to reduce ed wait-times and lengths of stay involve addressing intra-ed processes. familiar approaches include minor treatment streams (‘fast-tracks’) for low-acuity patients [8][9][10], and re-conceptualization of the treatment model [11], such as team triage [12]. it is also known that patient perceptions of wait times and service delivery are important factors to consider [13]. another approach to address these issues is controlling arrivals at eds. for example, emergency medical services (ems) dispatchers may direct crews to a particular ed based on locale, patient acuity, and facility specialization. more recently, a pilot has been initiated in the region to transport stable and non-emergent patients to an urgent care facility as opposed to an ed; it is these patients who previously waited in an ed at the non-acute end of the triage scale [14]. this may have the effect of improving patient flow and helping to alleviate ems offload delays and getting paramedics on the road faster. 1.2 measures of overcrowding for modeling and simulation modelling and simulation have been available to the study of the dynamics of emergency care for several decades, yet the resultant efforts are sparse and relate to many aspects of ed operations, including staff scheduling, patient flow, and departmental performance metrics [15][16][17]. several studies also address patient loads in particular, although the underlying modelling methodologies and approaches are dissimilar from this work [18][19][20]. in model-building, researchers agree that usable and standardized definitions of overcrowding need to be established [21][22], as well as for the concepts inherent in overcrowding, namely ‘busyness’ of an ed, and what constitutes a patient surge [22]. various researchers have put forward proposals for measures of crowding, generally inclusive of patient loads or volumes, patient acuity distributions, physician resources, and m. friesen et al. / load balancing at emergency departments using “crowdinforming” 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 bed resources [24][26][27]. in all cases, it is clear that variables associated with crowding and busyness are complex and interact with one another, and efforts are focussed on building reliable conceptual frameworks [27]. in [24][25], patient arrival rates were used in quantifying measures of crowding and busyness, which this current work also supports. this work develops a simulation model based on a process control strategy that relies on crowdinforming as an input; it is one of the first efforts at using real ed data in a feedback control manner to better understand the outcome of making real-time ed data available to the general public. in practical manifestation, such a strategy would provide patients with information to help with their decision to visit a hospital ed. in theory, actual patient loads at and between eds may be balanced (resulting in streamlined patient flow), and patients’ perceptions of their ed experience may be enhanced as a result of improved patient flow due to the load-balancing effect of crowdinforming. simulations are an appropriate means of gaining this insight because potential impacts can be determined and evaluated without impacting real eds or patients. when using actual patient data to feed a simulation, one must consider that the results may not generalize to other regions. if this investigation were to be replicated elsewhere, different and/or additional characteristics would need to be evaluated [21]. the use of simulation is justified; however, as our data appears consistent within the regional intuitions, so work to develop the model was undertaken. the balancing of ed loads is a complex multifaceted problem; redirection of patients is just one piece of the solution. the patient redirection simulated here would be typical of patients with non-emergent conditions able to consult a webor smart-phone based service that provides insight into the “busyness’ of an ed prior to deciding upon which to attend. the aspect being explored deals with using data that is becoming available, such that an informed decision can be made in the case of lower acuity patient self redirection. 1.3 crowdinforming the concept of crowdinforming refers to the delivery of relevant information to the general public, which in non-critical cases can be accessed prior to a pending ed visit. this information is then used to decide which ed to attend. crowdinforming is derived as a variant of the better-known concepts of crowdsourcing and crowdcasting, which recently have been used in biosurveillance applications [28][29]. one of the fundamental tenets of crowdsourcing is that the feedback loop needs to be closed, as information mined through crowdsourcing flows back to the crowd that generated it (crowdcasting), presumably to accrue benefit. crowdsourcing has become a standard marketing practice enabled through the internet and as such is becoming a familiar process from a user perspective. there are emerging healthcare examples of the crowdinforming concept in practice. these include pre-hospital ambulance redirection based on internet-accessible real-time ed loads in western australia [30], and a service by which cellular phone users in texas, usa can text a common number and receive a return text indicating the expected wait time at the nearest ed [31]. the crowdinforming simulation presented here would augment the types of smartphone apps that are being developed to assist people in selecting the closest ed [32]. overall, direct communication with the public is recommended as a way to enhance the responsiveness to directives regarding patient flow [33]. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 this work contributes to efforts in understanding and reducing ed overcrowding: this work illustrates one potential avenue of contributing to this goal by simulating the effect of making information available to the public, allowing them a degree of informed choice on which ed to attend. overall, finding policies that balance loads among collaborating ed facilities is part of efforts in many systems that explicitly employ algorithms for that purpose. in addition, there is little counter evidence in any realm whereby an unbalanced system would behave in an optimal manner. ed load balancing is complicated by the highly stochastic and unpredictable behavior of patients and their needs. the system we are proposing is a negative feedback control system as opposed to running open loop. with extremely high probability, these negative feedback systems are always preferable to open loop control systems. as such, if these techniques can be employed they should prefaced by insight gained by modeling and simulation as more analytical solutions are likely not to exist, certainly not with the ease of which a model and simulation can be developed. 2. methods the objective of this exploratory study was to investigate the application of existing available data and emerging data feeds towards developing an auxiliary ed process control strategy. a further objective was to integrate crowd-informed data into the strategy, this will allow for the exploration of the potential effect (via simulation) of the process control strategy on the balancing of patient loads between hospital eds. patient load balancing was considered primarily as the relative equalization of patient loads between individual facilities (according to respective capacities). patient load balancing should also consider the ‘smoothing’ of variability in patient loads at a facility over time (i.e. surge mitigation). however, this work considered primarily the inter-institutional impacts (vs. intra-institutional impacts) as a primary validation of the model. the work progressed in three stages: (1) statistical analyses of the data (descriptive statistics), to better understand the data and to test some intuitive expectations relative to variable relationships (section 2); (2) the iterative development of a preliminary process control strategy, built upon crowdinformed parameters (section 3.1); and, (3) the simulation of the process control strategy, and preliminary outcomes (section 3.2). data for this investigation were based on six months of patient visit data taken from the emergency department information system (edis) of the health authority of a mid-sized canadian city, spanning january through june 2009. the source included approximately 120,000 patient visits from six eds. data included gender and age of the patient, and timestamps of various milestones related to arrival, triage and registration, and discharge time. the data essentially comprise a trace file of events that have taken place. initially and intuitively the authors expected the most meaningful input into a strategy for self-initiated patient redirection to be patient waiting times and length of stay. data are now being collected that, in addition to the above, includes timestamps of the beginning and the duration of individual treatment events, specialty consultation, and/or diagnostic services. at the time of writing, only the more rudimentary data set was available, which nonetheless was the appropriate starting point for modeling efforts aimed at improving m. friesen et al. / load balancing at emergency departments using “crowdinforming” 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 the efficiencies between eds (inter-institutional). the forthcoming, expanded data which include specific trajectories through a particular ed will potentially facilitate intrainstitutional modeling efforts. data were fully anonymized and blinded to ensure patient anonymity, and data handling and storage security measures were taken to ensure patient confidentiality. any patient identifiers were removed prior to receipt of the data, and ed sites were masked by labels a through f. for purposes of this initial investigation, all sites were treated equal in terms of bed capacity, although it is known that the health region consists of two larger tertiary centres and four community centres. this was done for modeling and visualization purposes only with the conjecture being that if load balancing was not achievable in this simplified modeling scenario, it is unlikely to be effective once varying capacities were included. 3. results and discussion 3.1 initial data analysis descriptive statistics were applied on the edis data set, calculating measures of central tendency and variability for various parameters. this was done to gain insights into basic variable relationships and to confirm the fidelity of the data. fundamental to the concept of patient load-balancing is a concept or metric of ‘busyness’ of an ed, which is often manifest in circumstances of excessive patient wait times, facility overcrowding, and excessive lengths of stay. the preliminary analyses attempted to conceptualize ed ‘busyness’ relative to a range of variables in the available data, in order to determine the most meaningful variable or combination of variables for further use in a process control strategy for this particular data. as an initial insight into the nature of the data, a small sample (two months) of the data set is summarized in table i. table i summarizes the average patient length of stay (los) over 4hr non-overlapping windows for each ed. length of stay was calculated from the data as the total time from initial registration and triage to the time of patient disposition (i.e., admission, discharge, transfer, deceased). since limited data were available that broke down the total los into wait time and treatment time, we initially elected to use the overall los as a correlation to patient waiting time. other time intervals (such as door-to-triage, or time waited to see a physician) could have been employed, but at time of writing, such levels of granularity were not as reliable within the data as the parameters from which to calculate total los. causes of variation in the data include site-based process differences and individual user practices (for example, batch data entry by healthcare workers after a set of patients, vs. more automated data entry after each individual patient, resulting in different timestamps for equal service processes), and simple data entry and system usage errors. these factors also confound a comparison of intra-institutional impacts, and were a further motivation to maintain the current focus on the inter-institutional scale. all facilities exhibited similar trends, that being very little variation in patient los. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 1: average length of stay and average number of patients facility avg. los (hrs) st.dev. of los (hrs) avg. number patients a 3.8 1.43 19.7 b 7.9 2.35 23.4 c 9.2 4.49 12.8 d 6.9 2.68 15.0 e 9.7 4.88 13.5 f 9.0 3.01 18.1 an intuitive approach to establishing a ‘busyness’ metric would be to consider associating los with the number of patients waiting. this observation in addition to being intuitive has also been reported elsewhere [27]. the data facilitated initial investigations of a model that would fit a length of stay duration to the number of people waiting. in this manner when a redirection to another facility occurs, the original data are modified to reflect a reduced los at the facility the person would have initially chosen, as well as a slight increase in the los at the facility of chosen destination. this was the basis of the preliminary conjecture that average los, as a function of patients waiting, would be a meaningful ‘busyness’ metric and a meaningful variable in a process control strategy. figure 1: average los at facility b vs. patients waiting figure 1 shows a representative plot of this fit of average los and number of patients for facility b, based on six months of edis data. there is no directly observable positive linear relationship between number of patients waiting and the los (r 2 = 0.008). this is counterintuitive and not as reported in [27]. this may reflect that during peak ed hours (between 10:00am and 10:00pm) there are increased staffing levels in the ed and ancillary departments. also, several sites operate minor treatment areas (also known as “fast tracks”) to deal with increased volumes of less-acute patients during peak ed hours. similar results were found for each of the six hospitals within the regional health authority, in which the best fit line was relatively horizontal. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 the initial exploratory strategy of directly using average los data as a control input would result in patients regionally being redirected to facility a, with no concomitant reduction in length of stay at the remaining facilities. this is intuitively untenable, and the next analysis took time-of-day into account, to reflect time-varying healthcare worker resources (staffing levels). with this inclusion, figure 2 reflects the average los at facility f between 0:00-4:00 am and between 12:00-4:00 pm. even though the los is comparable, the throughput is considerably higher during the day, assumed to be a consequence of increased staff, increased availability of ancillary services and a greater number of non-urgent patients. figure 2: length of stay at facility f, 0:00-4:00 am and 12:00-4:00 pm again, the anticipated positive linear relationship between patient load and los was not borne out by the data. the follow-on analysis refined the los estimates by stratifying triage scores. (canadian eds typically employ the canadian triage acuity scale, ctas, which is a 5-point scale with 1 being most acute or serious and 5 being the least acute.) ctas level 3 was chosen for analysis, as it captures the single largest group of patients (34% of all ed visits within the data). ctas 3 also reflects the triage score most often assigned to patients presenting with influenza-like illness, and the issues of ed load-balancing (surge mitigation) become particularly relevant in outbreaks of ili. data were also culled for outliers, defined as data points more than three standard deviations removed from the mean. the results analysing the wait time at an ed for a specific triage score (3) continued to discount a positive linear relationship between los and patient load, where this relationship was the basis of the preliminary conjecture. this is unfortunate from a control perspective, indicating that the number of patients in the ed is not necessarily the best measure of “busyness”. it could be argued that a better measure of “busyness” may be more easily associated with a patient’s wait to be seen (wtbs). although wtbs also seems intuitively reasonable, it unfortunately suffered from similar drawbacks as the los as a measure of busyness. as a check on this conjecture, more extensive data were used from a later data set where the total los was detailed to include wtbs. for all six hospital eds, the wtbs and los are m. friesen et al. / load balancing at emergency departments using “crowdinforming” 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 moderately correlated with coefficients ranging from 0.42 to 0.62. figure 3 illustrates a running average wtbs and los at facility b with a 10-patient running average (as an average of those waiting and for display). figure 3: average waiting to be seen and length of stay (running average) over a one month period. based on these initial investigations, average los and average wtbs durations were discounted as possible control parameters. a known drawback is that neither average los nor wtbs is useful as a real-time control parameter in this data set, as these averages lag behind data that would be available at a given instant of time. nonetheless, average los and average wtbs up to a point in time (running average) were investigated, with a correlation coefficient of 0.04 for the complete data set, and a correlation coefficient of 0.45 when controlled for triage scores. in a further exploration of a meaningful metric of ‘busyness’, patient arrival rates and departure rates from an ed were considered and found to have some degree of exploitable structure. patient arrival rates’ influence on busyness was also supported by other researchers [24][25][26]. in this work, regression analyses are in progress to quantify this influence for the current data set. figure 4 illustrates the near instantaneous patient arrival and departure rates as seen by facility a for a representative 180-hour slice of data. this plot becomes slightly less erratic if the arrivals are shifted by the average los, which in the case of facility a is approximately 4 hours. as expected, the arrival rates and departure rates are correlated to one another, albeit shifted in time. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 4: arrival and departure rates at facility a. the preceding discussion outlined efforts to obtain understanding of the data. initial conjectures that average los and wtbs would be meaningful metrics of ‘busyness’ and thus meaningful variables in a process control strategy for the purpose of load balancing were not borne out by the data. several other analyses were performed but not presented here, as they likewise discounted the original conjectures. a summary of the exploratory analysis is provided in table ii. it is acknowledged that these results are counter-intuitive and at times contrary to other published findings; however, these results are reflective of the particular data set used. the approaches considered above illustrate the difficulty of developing a control strategy that relies on straight forward estimates of ‘busyness’. table ii summarizes the interim results. table 2: potential ‘busyness’ metrics and their utility in redirection ‘busyness’ metric meaningful here as a redirection parameter rationale number of patient waiting no los not a function of number waiting average los no los not a function of number waiting average wtbs no somewhat correlated to average los patient arrival rate potentially literature [21][27][35], intuition going forward, a metric of ‘busyness’ through a process of elimination is taken to be the patient arrival rates as opposed to absolute quantities such as average length of stay or total waiting hours, as this metric appears to have at a qualitative relationship to patient load. the arrival rates from various hospitals were used from the trace file (original data) to re-evaluate a patient-initiated redirection control strategy. modelling control strategies based on trace file data is common in many areas of optimization [34]. in the case of internet or web traffic for example, one often has a trace file or log to work with, using it as input to a simulation to better evaluate the performance of an algorithm one would like to investigate. a proposed m. friesen et al. / load balancing at emergency departments using “crowdinforming” 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 algorithm (control strategy) then works from the trace file to see if throughput can be improved. during the process, the trace file is modified as if requests were sent to alternative services. in effect, this can be articulated as a system for simulating dynamic load balancing on various services produced using trace files of real service loads. an additional consideration in using arrival rate as our “busyness” metric was derived from [21] where the authors indicated that “autocorrelation in the models for wait time and los is consistent with our intuition that process times are affected by prior patient arrivals” and that “future work will focus on discrete event simulation modeling”. this is similar to the case presented here; we have data that appears consistent within the regional intuitions and are using the data as input to simulation. further consideration also came from the literature [27][35], where in particular number of patients from the ed was associated with the daily mean length of stay for all ed patients [35]. 3.2 process control strategy development built upon crowdinforming based on the initial insights, a preliminary process control strategy upon which to apply a crowdinforming simulation was developed. intuitively, los, wtbs, arrival rates, origin of enquiry, and ed capacity should lend themselves to a model for crowdinforming to be built with increasing fidelity. however, it is crucial that ‘busyness’ metrics be extracted from real data, as they may be context-specific and/or counter intuitive as evidenced here. the model for patient decision making is a simple discrete event simulator, where each patient is individually governed by the stochastic decision process of figure 5. the process of figure 5 is essentially for fair weighted crowdinforming diversions. a person being modeled either proceeds to the ed they would have gone to normally or consults a service and makes a weighted fair decision as to which ed to attend. in this model they will probabilistically attend the least busy. figure 5: patient redirection decision process m. friesen et al. / load balancing at emergency departments using “crowdinforming” 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 in conceptualizing ‘busyness’ in terms of patient arrival rates, some of the anticipated effects of patient-initiated redirection were observed. the redirection policy model was based on five minute intervals of data availability and updates. the redirection policy in this simulation was straight-forward: a percentage of patients (e.g. 75%) were set to consult the posted arrival rates and then probabilistically self-redirect to the facility with the lowest present arrival rate. figure 6 illustrates the raw arrival rates at the six regional eds. the horizontal axes in figures 6 indicate serial 12-hour increments. figure 6: arrival rates at six facilities with no crowdinformed redirection. the main observation is that without any form of patient redirection modeled, there is a reasonable degree of diversity in the arrival rates at any particular facility. the spikes represent arrival surges at the individual facilities that have been conjectured to contribute to crowding and increased waiting times [22][36]. the output from the decision process (figure 5) then modifies the trace file accordingly. figure 7 is the result of running the individual stochastic decision process (figure 5) with input from the trace file to provide the weighting for the decision process. in this manner a patient probabilistically self redirects to alternative facilities weighed by the arrival rates as illustrated in table iii. without incorporating capacity the ideal output would be for all facilities to identical with little variation about the moving average. figure 7 approximates the former but illustrates that the latter is not substantively impacted by the control strategy modeled. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 7: arrival and departure rates at six facilities with weighted fair crowdinforming redirection (multiple passes). as the data are time varying, samples from weighted fair redirection, sorting redirect and the raw trace file were transformed to the frequency domain (fft) and digitally filtered, essentially removing the circadian fundamental. as the data clearly exhibited a circadian autocorrelation component, transformation to the frequency domain was the simplest means of its removal. more complicated analyses could have been undertaken but as we were only interested in the variance about the moving average, this was the most straightforward. this allows one to estimate the variance from the periodic moving average. the standard deviation arising from the inverse transform was approximately 0.41 for the raw trace file, 0.50 for the weighted fair redirection and for hospital b (selected arbitrarily). it was somewhat unexpected that the weighed fair redirection performed more poorly in terms of variance than the original trace file, even though an element of load balancing appears evident from figure 7. figure 7 re-adjusts the trace file over several passes through the trace file to more fully represent the steady state associated with this control strategy as opposed to more transient behaviour as would be the case if the crowdinforming policy were instantly switched on. in a more refined model to be explored in future work, the data would allow for an aspect of travel time to be considered, as an edis patient record can be configured to include prefixed postal code information (approximate neighbourhood of origin); for this study, the postal code was sanitized from the records. also, although not investigated here, an exponential weighted moving average (ewma) of arrival rates might improve the outcomes and be easily extracted as well. at the time of writing, only one of the hospitals included in this study has an ed wait-times display “dashboard” deployed in the ed waiting room. currently, this dashboard displays the number of patients in the ed waiting room, average waiting time of patients in the waiting room, and expected wait time to see a physician. information on this dashboard is updated in near-real-time from the edis database, and the dashboards are expected to roll-out to all eds in the region within the next several months. this dashboard has been found to be of value to both patients and staff within these eds and our basic premise is that this data may also be of use outside of the eds themselves. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 our model extends this notion of a wait-times dashboard and conjectures that in a reasonably short period of time, these data could be made available through a web service, such that an individual would be able to query the hospital “dashboards” from an “ed wait times” website over the internet with any browser, be it mobile cellular or wired. in addition, a text service could be established where a user would text a notable number (for example, 311edwait) and receive a list informing of anticipated waits or load at various facilities. in late 2009, real-time displays of the ed dashboard data were installed in the dispatch centre of the city’s fire and paramedic services, in order to facilitate pre-hospital re-direction of ambulances and emergency vehicles. however, when considering large-scale crowdinforming to an entire city’s population, modelling and simulation provide an exploratory investigation into the potential effects of a crowdinformed system, without the cost and potential disturbances to a system under endemic stress. in this simulated crowdinforming scenario, a proactive decision is made by a relatively lowacuity patient who intends to visit a hospital ed for a non-emergent medical issue. in addition to the stochastic process of deciding upon going to an emergency department, each patient is also provided with estimates of load, proxied by patient arrival rates (for simulation purposes). these data are available and easily instrumented within the controls being modelled. as an illustration, assume there are four hospitals with average arrival rates of 5, 10, 15, and 20 patients/hr. in the model a potential patient would create a biased roulette wheel to guide their probabilistic decision once they have elected to go to an emergency department. the associated normalized probabilities associated with informed self-initiated redirection are shown in table iii. the arrival rates (table iii) would be provided by the hospitals and made available to the public through a web service, smart phone app or text service as discussed above. the “arrival rates +1” models the impact of adding the decision-making patient to each facility, as if the patient were attending all eds. the table then calculates the probability of the individual patient actually going to the respective facilities. that is to say, the facility becomes a slightly less attractive destination if you choose to direct yourself there, in effect biasing that facility to be slightly busier. from a numerical perspective, it also prevents a division by zero in the event the arrival rate at a facility is zero arrivals/hour. in reality, the numbers are only for simulation purposes only. conveyance of a selected ‘busyness metric’ (whatever it is) would have to be considerably more user friendly, such as number of signal reception bars as seen on a cell phone, or a redyellow-green-light scale. table 3. informed emergency department self redirect probabilities emergency department 1 emergency department 2 emergency department 3 emergency department 4 total arrival rates (/hr) 5 10 15 20 arrival rates +1 (/hr) 6 11 16 21 1/over 1/6 1/11 1/16 1/21 0.368 probability 0.45 0.25 0.17 0.13 1.0 m. friesen et al. / load balancing at emergency departments using “crowdinforming” 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 ideally, table iii would combine estimates of travel time and patient load in an additive manner as a linear combination weighted appropriately as in equation (1). within the general population, a person presented with statistical data would likely preferentially choose the facility with the lowest arrival rate, as people are typically not probabilistic in nature. if this were the case, the situation would by default look similar to where a simple sort was used for redirection. additionally, equation (1) accounts for varying perceptions of equal durations of wait time: people may perceive 30 minutes travel time more favourably than 30 minutes of wait time, perhaps due to the psychology of the perception of control (actively driving a car vs. passively sitting in a chair). in equation 1, n represents the number of ed facilities. a weighting has been assigned to reflect a probabilistic decision that incorporates both travel time and arrival rates. here reflects a person’s decision to consider redirection. the term  weights the probabilistic component associated with patient arrival rates as indicators of “busyness”. the term  weights the probabilistic component associated with travel time associated with distance di, where di represents the distance to facility i. the parameter that would be crowdinformed from a regional hospital authority would be the term associated with ‘busyness’. in this data set, the ‘busyness’ metric is patient arrival rates denoted i, wherei represents the arrival rate at facility i. if used in a mobile or web based application, the distance and estimated travel time can be extracted from many geolocation travel time estimation services readily available. for example, if one were to discount travel time and only use the measure associated with busyness (i.e. patient arrival rates),  =0. if the data as illustrated in table iii were then used, the person would choose ed1 with a probability of 0.45. if in addition,  i.e., was a 75% chance that the person would consider self redirection) the probability of the person choosing ed1 would be 0.75*0.45 = 0.33. this would model approximately a 33% chance that the person would redirect to ed1. this also presents an opportunity to introduce a capacity parameter as in equation 2. the nice feature about this type of measure is that it is relative between facilities. the capacity parameter would be arrived at experientially for each institution, although back-casting from available data may be a starting point. as capacity would is not subject to change at an institution for prolonged periods of time, its actual value in this simulation is arbitrary. (1) using such a method, the patients make a probabilistic decision weighted by the least anticipated wait, inferred from the busyness of the eds derived from the near real-time patient arrival rates. as a consequence, the overall surge seen between hospitals is dampened by the behaviour of informed individuals. (2) a statistical measure associated with mean and variance indicates that the load balancing is statistically significant given our behavioural assumptions, where an inference of estimated m. friesen et al. / load balancing at emergency departments using “crowdinforming” 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 “busyness” probabilistically influences a person’s decision in attending a particular emergency department. the overall effect is that of a low pass filter, smoothing out the peaks and valleys in space and, to some extent, in time. filtering in time is expected to result from incorporation of an estimate of travel time as indicated in equation 1. although preliminary, this work represents one of the first modelled instances of the potential of crowdinforming in providing a policymaker with a simulation to aid decision support. the decision would be to create public access to "dashboard" type information that is currently available in emergency departments or electronic records. the publicly available “dashboard” would be accessible via the web or 3g smartphone. crowdinforming serves to filter the simulated patient surges at a given facility by balancing patient load between facilities (to each facility’s respective capacity). as the win-waiting-room “dashboards” are a relatively new concept currently under evaluation it is unlikely that exactly the same data would be presented to a patient considering self redirection at this time. 3.4 limitations the model is preliminary and currently uses one metric – patient arrival rates – as an indicator of ‘busyness’ and as the basis for crowdinforming. intuitively expected metrics such as the number of patients waiting as well as average los were not found to be effective as an input into a process control strategy for patient redirection. additional statistical analysis needs to be carried out on the trace file data, to facilitate a refinement of the model to account for the interplay of multiple variables. additionally, forthcoming edis data will provide further breakdowns of various waiting and treatment phases, which will facilitate a more precise model. it is unlikely that a simple unified model of “busyness” across all eds across varying hospital authorities would suffice, particularly in scenarios where some indication of real time “busyness” is to be fed back to the general community. a further significant limitation associated with research of this nature is that stochastic models of behaviour have to be estimated. modelling social dynamics is a difficult and messy problem; however, insights garnered from simulation and modelling are nonetheless useful and would be used in support of an otherwise best-guess decision. not all patients, in reality can easily redistribute themselves to all sites equally. for example, the most acute patients (i.e., ctas 1 or 2) need to get to the nearest or specialized ed immediately, and most likely by ambulance. additionally, these cases wait very little once they arrive at a hospital, regardless of which site. to further complicate this, certain types of conditions may get directed to a specific site. in the health authority represented by the data, trauma and stroke cases are most often directed to site b whereas cardiac cases are most likely sent to site f when practical and in the best interests of the patient. patients assessed at ctas level 3 are most likely sick enough to desire to go to the nearest ed, whereas the patients assessed at ctas level 4 and 5 (i.e., least acute or serious) are likely the most sensitive to wait times and length of stay issues and would benefit by targeting an ed with a minor treatment area (mta). in addition, several of the larger and busier sites have resources/services not available at smaller sites, so a length of stay at one site may not be same if a patient chooses a different site with a different scope of services available. future models would likely need to take into account these considerations. other types of data may also be useful to the person using a crowdinforming service could also reflect resources at the facility. for example, in the case of a major laceration whether a plastic surgeon were in site m. friesen et al. / load balancing at emergency departments using “crowdinforming” 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 or in the case of a broken arm or wrist whether a physician were on site who would be able to set the break. the present framework appropriately considers system-wide impacts with simplified assumptions on respective ed capacity, prior to intra-institutional simulation and other institutional refinements. the framework developed here allows policies to be simulated and evolved, adding a qualitative assessment to decisions that may otherwise be experiential or best intent. this work is one of the first demonstrations of the crowdinforming intervention as a variant of the broader concept of crowdsourcing, and it demonstrates the role that similar technologies which provide crowdinformed decisions will continue to play in the future. it is important to note that this work did not set out to develop a model to fit observed data, nor to develop a ‘busyness’ model of an ed. the objective was to develop a process control strategy built on crowdinforming, and in doing so, a meaningful metric of ‘busyness’ is required, which was expected to be somewhat self evident. thus, this work included an exploration into the data in order to define a meaningful metric for this data set. this exploration is a pre-requisite to the development of a meaningful process control strategy for crowdinforming. the work implied that the input parameters (in this case, a ‘busyness’ metric) may be ‘data-unique’ and must be defined and verified for each respective data set. furthermore, the validity of patient arrival rates as an input parameter has not been established definitively; however, the work did indicate that other parameters are – surprisingly – not necessarily a good metric within this particular data set within this problem domain. this study was limited to data from one regional health authority. specific results of similar analyses at other regions would likely be different. a limitation of modeling is that the assumption of ‘busyness” (wait time and los) is consistent with our intuition that process times are affected by prior patient arrivals. these limitations imply that data collected from a multiple-ed system have to be carefully considered as issues and conditions at other regions are likely to be different (as they are, often, between eds within the same region). however, as data become more widely available, there will be increased opportunity and additional tools available for its analysis and improved interpretation of causality concerning “busyness”. similar considerations also apply to the feedback control strategy one may be interested in. fortunately, there are an increasing number of modeling and simulation tools and methods readily amenable to this type of policy modeling. ideally for patient self redirection one of the means of providing a meaningful “busyness” metric would be streaming video of each facility. the technology now exists to do this but is unlikely to ever be widely implemented for a variety of reasons (primarily related to patient privacy and security). other alternatives are to crowdsource ed visits directly from patients at various eds, mobile applications and back end web services already exist for this type of collection and dissemination. limitations again would be related to the trustworthiness of the reporting (as patients may be tempted to overstate their wait in an effort to redirect others away from the facility in the attempt to reduce their wait and service time) as well as the potential skew due to specific demographics that are more apt to use mobile communications and to participate in crowdsourced efforts. m. friesen et al. / load balancing at emergency departments using “crowdinforming” 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 as previously mentioned, this problem domain is very complex with myriad variables. as data systems improve, and the ability to extract consistent and meaningful data from these systems follows suite, a more detailed insight into the complexities of ed operations will be gleaned from the data. as more data is available, investigators will be able to exploit stein’s paradox where an estimate of “busyness” is made more accurate when three or more parameters are estimated simultaneously. these parameters may be apparently unrelated, but for example could include arrival rates as discussed above augmented with the number of paramedics waiting for their patients to be seen by an ed doctor. a final limitation of the model presented here is that since the input was extracted from real arrival rates at various facilities (trace file), there is not a directly observable output such as resulting average los as a consequence of the redirection. the indirect output is a reduction in arrival spikes associated with arrivals without patient self-redirection, as compared to the arrivals associated with patient self-redirection. although the modelled process is stochastic, the underlying principle is that of negative feedback control system thereby balancing the ed loads across facilities and attenuating arrival spikes. 4. conclusions this work illustrates the role that data-driven modelling can play in developing policy or supporting public health decisions. the investigation derives a preliminary process control strategy upon which to model the effects of self-initiated patient redirection on hospital patient loads. the work illustrates how one specific intervention – that of proactive information dissemination or crowdinforming – can potentially provide a degree of process control in the form of patients’ self-initiated re-direction, resulting in load balancing at emergency departments. the work illustrates that meaningful strategies rely on appropriate dataor context-specific choices in the variable or variables chosen as metrics of ‘busyness’ and consequently to their broadcast through crowdinforming. the results of this investigation are not intended to be specific recommendations for a regional health authority, but rather illustrate a flexible framework around which applications of this type can be better evaluated and their potential outcomes assessed. the benefit of simulation is to allow a policy or decision maker to gain insight into what may occur and be better prepared than in the implementation of a crowdinforming policy without modeling or simulation. the preliminary model was derived from simple statistical analyses of the available edis data, in order to test support for preliminary conjectures. as the initial conjectures were discounted by the data, the model was refined. the application of a stochastic patient decision process representative of crowdinforming was demonstrated to contribute to load balancing at individual hospital emergency departments. the evidence for the findings was the trace file data associated with real ed visits within a regional health authority, and the balancing of arrival rates as a consequence of self redirection when a metric of ed ‘busyness’ is presented to the public. it was noted however, that although loads were being balanced across the facilities, the simulation did not indicate any alleviation in the variance associated with the arrival rates. in this regard, an increase in variability may detrimentally contribute to the patient flow within the various eds. future work will include the utilization of running averages of the wtbs time, los and arrival rates once more data become available, as well as multiple regression analysis to learn more about predictor variables and a dependent “busyness” variable. preliminary analysis indicates that several parameters and their interactions could be included in a more detailed m. friesen et al. / load balancing at emergency departments using “crowdinforming” 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 estimate of “busyness”. the stochastic decision process and control model would remain the same. competing interests the authors declare that they have no competing or conflicting interests. acknowledgement the authors would like to thank the p.t.h. thorlakson foundation for a research grant in support of models for healthcare re-engineering. the authors would also like to thank the reviewers for their thoughtful comments. correspondence robert mcloed university of manitoba email: mcloed@ee.umanitoba.ca references [1] d.a. thompson, p.r. yarnold, d.r. williams, & s.l. adams, effects of actual 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[36] j.l.wiler, et al., optimizing emergency department front end operations, an information paper, american college of emergency physicians, jan. 2008. http://www.fiercehealthcare.com/story/patients-use-smartphone-apps-find-nearest-er/2010-11-23 http://www.fiercehealthcare.com/story/patients-use-smartphone-apps-find-nearest-er/2010-11-23 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts tiny footprints embedded on grieving mother’s heart: stillbirths in the health and demographic surveillance system muhammad ilyas*, murtaza ali, muhammad imran nisar, fyezah jehan and abdul momin kazi pediatrics, the aga khan university, karachi, karachi, pakistan objective objective: to describe characteristic of stillbirth in a diverse population in karachi health and demographic surveillance system. introduction stillbirth is an unfortunate event in a woman life which remains uncounted in developing countries, thus, seldom caught attention until recently. among 3.2 million stillbirths globally, 98% occurs in lmics with majority in south asia, and 75% of those are preventable. globally, it counts as equal to neonatal deaths and is not mentioned in mgds, global charters and programs priority. besides immense information gap, it is mostly not part of vital registration system. mostly, the data for stillbirths is mostly collected in demographic surveys, clinical studies or retrospective records, underestimating the counts. besides, lack of optimal national vital registration system, pakistan has highest rate of stillbirth. hence, to collect prospective data, efforts are made by department of paediatrics of aga khan university to maintain a demographic & health surveillance system (dhss) at karachi to provide more robust data over years. methods our catchment area is 19 sq km with a population of around 274,856 with 69699 females between 15 to 49 years of age. surveillance catchment area is divided into clusters/blocks of about 200 to 250 structures, each marked with a unique number. each married woman, pregnant woman and <5 year child’s unique id is collated with gps coordinate of structures they live in. block boundary is mapped using gps track log technique. structures, streets and landmarks are digitized through gis. our community health workers (chws) visit each household quarterly in dhss area to identify new pregnancies and follow pregnant woman until pregnancy outcome. further verbal autopsies (va) are conducted for all stillbirths, <5 years children deaths and adult female deaths (13-49 years) including maternal deaths to obtain cause-specific mortality. va’s are attempted after allowing 2 weeks of grieving time to family. results during 2012 we captured 13979 pregnant women, 9136 live births and 239 still births yielding stillbirth rate of 25.5 per 1000 births. vas for 79% stillbirths was completed. in 82% of va interview respondent were mothers. majority stillbirths were males (n= 107) 58%. most number of stillbirths occurred in hospitals 47.5% and 43% occurred in multi gravida women. 33% pregnant women with sb reported high blood pressure, 2.1% had epilepsy during pregnancy and 2.7% had convulsion during last month of pregnancy. furthermore 6.6% of the women with sb had excessive vaginal bleeding during first six month of pregnancy, 9.3% during last month of pregnancy, and nearly 25% during the intrapartum period. 72% of sbs were delivered by doctors, nurses or midwives and 28% by tradition birth attendants (tbas). 88% women had normal vaginal deliveries, 12% had caesarian section; 22.4% were premature and 89% were fresh stillbirths. conclusions majority of still births occur near to birth and can be prevented by improving the quality of obstetric care especially around the time of birth. keywords stillbirths; demographic & health surveillance system; verbal autopsy; pakistan references ilyas, m., nisar, m. i., kazi, a. m., ali, m. t., & zaidi, a. (2014). a health and demographic surveillance system in a low socioeconomic setting in karachi, pakistan. online journal of public health informatics, 6(1). ali, m. t., kazi, a. m., nisar, m. i., ilyas, m., & zaidi, a. (2014). geospatial reporting of health demographic surveillance in a peri urban setting. online journal of public health informatics, 6(1). stillbirth collaborative research network writing group. causes of death among stillbirths. jama: the journal of the american medical association306.22 (2011): 2459. lawn, joy e., et al. stillbirths: where? when? why? how to make the data count? the lancet 377.9775 (2011): 1448-1463. *muhammad ilyas e-mail: ilyas.muhammad@aku.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e31, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 206 isds 2014 conference abstracts using ambulatory syndromic surveillance data for chronic disease: a bmi case study andrew walsh* health monitoring systems, inc, pittsburgh, pa, usa objective to demonstrate the utility of ambulatory syndromic surveillance data to public health domains beyond communicable diseases introduction syndromic surveillance is one of the meaningful use public health menu set objectives for eligible professionals. the value of this data for syndromic surveillance as an adjunct to the more widely adopted emergency department registrations has not been studied extensively. it may be that it would improve the sensitivity or timeliness of detecting certain communicable disease events, or it may just contain signals comparable to what is available via other syndromic surveillance data streams. the value of making the effort to collect this data is considered contingent on the answer to that question. public health is concerned with more than just communicable diseases, however. chronic diseases and their underlying causes are also a significant public health concern. obesity alone is estimated to be responsible for 2.5% of the global disease burden, and represents a higher fraction in many developed nations. since chronic diseases are not associated with singular events of brief duration, they are difficult to track with traditional surveillance methods. they are also not typically managed via emergency departments, so syndromic surveillance does not capture them well either. chronic diseases are often treated by physicians at ambulatory practices. thus data from eligible professionals may provide a means for monitoring chronic diseases, or metrics associated with chronic diseases, that would not otherwise be as feasible. as a proof of concept, this study seeks to determine if body mass index (bmi), the standard measure of obesity, can be obtained from ambulatory syndromic surveillance messages. methods syndromic surveillance messages were collected from approximately 100 eligible professionals at 21 practices in the greater philadelphia metropolitan area over a period of 1 month. these were received and processed by the epicenter syndromic surveillance system. height and weight were extracted from obx segments and used to calculate bmi. the study population was restricted to adults, as determined by visit date relative to the patient’s reported date of birth. the data were also filtered to exclude outlier height and weight values, as determined by the distribution of these values in the dataset. these extreme values appeared to represent data entry errors. the mean height and weight for each patient were then calculated from all remaining observations, and those means were used to calculate the patient bmi. results a total of 194,523 messages were received from 21 practices over the 1 month study period. these represented 24,076 unique visits from 18,858 patients, as determined by the identifiers sent by the practices. of these, 18,047 were adults (95.7%). of those, 18,022 (99.9%) had height and weight measurements determined to be within the limits determined from the distributions. bmi was calculated for those 18,022 patients. it ranged from 10.5 to 122.7. the median bmi was 27.1 and the mean was 28.1. the full distribution of bmi is shown in figure 1. 270 patients (1.5%) had an underweight bmi, 6,194 patients (34.4%) had an overweight bmi, and 5,679 (31.5%) had an obese bmi. conclusions this study successfully demonstrated that bmi can be extracted from ambulatory syndromic surveillance messages. minimal data processing was necessary to achieve high data quality. expanding to include children would make it more difficult to set lower limits for filtering errant height and weight values, but in this setting that filtering did not substantially affect the results. taking the mode of values from a given visit rather than the mean might provide an alternative method to exclude some errant values. a longer, longitudinal study would be necessary to determine whether monitoring patient bmi in this fashion could actually be used to detect meaningful trends of interest to public health, or used to improve significant public health outcomes. keywords ambulatory data; chronic disease surveillance; bmi; syndromic surveillance acknowledgments we wish to thank the pennsylvania department of health for funding support and data for this work. *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e58, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 176 (page number not for citation purposes) isds 2013 conference abstracts evaluating the ability of a syndromic surveillance system to detect heat-related illnesses in houston, tx, 2009-2012 nathan wang*, biru yang, wesley mcneely, salma khuwaja and raouf arafat houston department of health and human services, houston, tx, usa � �� �� �� � � �� �� �� � objective ������� � �� ����� � �������������� ��� ��� ���������� ��� ���� �� � ��� ����� ��� ���������� � �������� ������ ���� � ������� �� ��� ��� ������ ���� � ��� �� ���� ������� ��������� ����� �������� ���� ���� �� ��� ��������� introduction �� �������� ����� ������� ������ �� ��������������� � ��� �� �� �������� ��� �������� �� ���� ������������ ����� ������� � ������ ��� �� ��������� ���������!"��#��$ � ��� �������� ��������%��� �� ������ ��� ����������� ������ �������� ����� �� �� ���������������� � ����"&&'%�� ��( ""��� ��� ����� �� �� ���������������!)#�������� ����%������� ��������� �������� ����� ��������� �� ��� �� ������� �� ���� �� ������ �� ����� ������������ ����� � ���$���� ��� �������� �� ����� ����*��� ����!�$��*#��* 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������ ��������� �������� �� ��� ��������� �� conclusions +���� � ������������ ��� ��������� ������ ��������� �������� � � ��� ��������� �� keywords ������� ��� ��� ������>���� ����� ��� ������>��������� �������� � acknowledgments �� �� ���/� ���� � ���� ��� ��� ��� �� � ��� $���� ��� � ��� ���� �� ���� � ����*��� ����? ��� ������ ��� ������ ��� ��������� ������ ���$�� ��� ��� �������������*�� � ��@�����5����*�� � ��:� � ��� references "��-������-a%������$b%�* ����a+%��������2%�����c�����82������ �� ������� �/������� ����� ��� ������� ������ ������������������ ��� �� � � ��� ���"''<�+� �������� �� ��� ����a� ��������2�� ����*�� ����� "'''>�(�!"#6)"1<4� (��$�� �� �ad%�+���������e�%�9 ��� ���+%������ �?��@��������� ��� ��� ����� ������ ���� � ��� ���( ����� ������ ��b�� �6������� ������ ������������������ ��� ���+� ����+�����( )>�&!"#6"1(� ���a��������8%�+� ������f%�?� ��f���$%�-� ����a%�d ��� ��8%�?� �/���9%� ����5� ����� �b��*������ ��� ��� �������������� ���������� � �6� ��� �� �� ��������������������������� ��� �� ��d�������?2+�2��� :����������$�� � ���2�/ ����( '>�'6")� )��f� �����g�� ����*��� ����* �����( ""������� � ��3��5���� ����� � �� �* �������� � ��=9����� ��%��h�i� ���( ""�.� �����"<j>�5�� �� ���������6�� �6==����������������=���=k�l* ����( ""-������ *nathan wang e-mail: nathan.wang@houstontx.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e41, 2014 stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e2, 2021 ojphi stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool hannah a. burkhardt1*, pascal s. brandt1, jenney r. lee1, sierramatice w. karras, paul f. bugni, ivan cvitkovic, amy y. chen, william b. lober 1university of washington, seattle, wa abstract as the covid-19 pandemic continues to unfold and states experience the impacts of reopened economies, it is critical to efficiently manage new outbreaks through widespread testing and monitoring of both new and possible cases. existing labor-intensive public health workflows may benefit from information collection directly from individuals through patient-reported outcomes (pros) systems. our objective was to develop a reusable, mobile-friendly application for collecting pros and experiences to support covid-19 symptom self-monitoring and data sharing with appropriate public health agencies, using fast healthcare interoperability resources (fhir) for interoperability. we conducted a needs assessment and designed and developed stayhome, a mobile pro administration tool. fhir serves as the primary data model and driver of business logic. keycloak, aws, docker, and other technologies were used for deployment. several fhir modules were used to create a novel “fhir-native” application design. by leveraging fhir to shape not only the interface strategy but also the information architecture of the application, stayhome enables the consistent standards-based representation of data and reduces the barrier to integration with public health information systems. fhir supported rapid application development by providing a domainappropriate data model and tooling. fhir modules and implementation guides were referenced in design and implementation. however, there are gaps in the fhir specification which must be recognized and addressed appropriately. stayhome is live and accessible to the public at https://stayhome.app. the code and resources required to build and deploy the application are available from https://github.com/uwcirg/stayhome-project. keywords: covid-19, health information interoperability, patient-reported outcomes, mobile applications, epidemiological monitoring correspondence: haalbu@uw.edu* doi: 10.5210/ojphi.v13i1.11462 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:haalbu@uw.edu* stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e2, 2021 ojphi introduction sars-cov-2, a novel coronavirus which causes the disease covid-19, was first reported in december 2019 in china [1] and quickly spread globally, causing millions to contract the virus and fall ill. in the us, the disease was first discovered in a washington state nursing home in february 2020. as of august 2020, the virus has claimed over 170 thousand lives in the us [2]. starting in april 2020, states imposed severe limitations on businesses and individuals in an attempt to curb spread. as of august 2020, states have relaxed stay-at-home orders and are experiencing diverse outcomes from reopened economies. blueprints to minimize the increased outbreaks include public health efforts such as widespread testing, identification of cases, and intensive contact tracing [3]. states are rapidly scaling up the number of individuals conducting contact tracing investigations to meet this need, which means that the contact tracing workforce is comparatively inexperienced, yet meets with high caseloads. an additional exacerbating factor is that many sources of essential data, such as community-based testing, medical records, and public health records are managed in ways that inhibit their effective linkage. informatics solutions may ease and support this labor-intensive, manual work. we can draw on the experiences of patientcentered systems in clinical informatics, which recognize that often the patient and their reported experiences may be the best links between information managed in disparate locations and systems. in the context of covid-19, this may include events such as testing and clinical visits, as well as personal information such as contacts and travel, and clinically relevant symptom monitoring. individuals are encouraged to wash their hands, wear masks, maintain physical distance from others, and self-monitor for symptoms in order to recognize when further steps become necessary. proactively tracking symptoms may allow recognition of emerging symptomaticity, allowing earlier self-isolation and reducing transmissions, and is therefore a core need of the community. maintaining a diary of symptoms, possible exposures, virus and antibody testing, and travel history has the additional potential benefit of providing public health agencies with reliable information to conduct case investigations, should that need arise. the more people keep and are willing to share such records, the greater the benefit may be to the community. symptom trackers and exposure diaries should therefore be easy to use and accessible to everyone, including vulnerable populations. choosing a mobile-accessible design supports this goal due to the ubiquity of mobile phones and the potential to reach more individuals from vulnerable communities. in 2019, over 80% of individuals and over 50% of older adults in the united states owned a smartphone; 17% had internet access only on their phone, with higher proportions for poor populations (26%) as well as black (23%) and hispanic (25%) minority groups [4]. individuals should be able to share diaries of covid-relevant observations directly and easily with the relevant agencies if they so choose. using health data standards can lower the barrier to sharing symptom tracker and exposure diary data with third parties. the fast healthcare interoperability resources (fhir) [5] standard may facilitate data sharing, both with electronic health records (ehrs) for use in clinical care, and with public health to benefit contact tracing. stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e2, 2021 ojphi representative covid-19 apps the covid-19 pandemic has prompted the development of numerous consumer applications intended to support individuals, to complement traditional public health surveillance efforts, or both. some examples of these apps, grouped into broad categories, are shown in table 1. as a longitudinal symptom tracker, stayhome represents a less common application type, in contrast with “low-tech” symptom screeners and “high-tech” contact tracing apps. yet, there have been examples of such applications seeing high adoption and even enabling disease research: a symptom tracker launched in the uk on march 24, 2020 (“covid symptom study app”) gained 700,000 users within 24 hours and ultimately contributed to the reporting that loss of sense of taste or smell is associated with covid-19 infection [6-8]. stayhome first and foremost supports self-monitoring by individuals. supporting public health efforts is a secondary goal enabled by stayhome if a need arises. table 1. comparison of covid app modalities description example data collected longitudinal symptom checker/ screener allows the user to enter their symptoms and receive medical advice. uses the responses to recommend one of several possible next steps, e.g. continuing to practice social distancing or contacting a healthcare provider. apple’s “covid-19 screening tool” [9] cdc’s “symptom selfchecker” [10] symptoms associated with covid-19 (e.g. fever, cough), risk factors (e.g. age, comorbidities), recent travel, possible exposures. no. applications are designed for one-off use and do not collect data longitudinally. contact tracing apps keeps track of people users have been in contact with by collecting location and proximity data, and alerts users of possible exposures. may utilize a range of bluetrace (singapore) [11] corona-warnapp (germany) [12] collects records of which other devices a device was near over the past days or weeks, enabling alerting of possibly exposed individuals in yes. applications track location or proximity information over time. stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e2, 2021 ojphi technologies, e.g. bluetooth, gps, or google and apple’s joint api. the case of a positive test. longitudinal symptom tracker/contact diary helps users track symptoms, possible exposures, and other data over time, potentially enabling discovery of longitudinal trends. covid symptom study app (uk) [7] stayhome symptoms associated with covid-19 (e.g. fever, cough), risk factors (e.g. age, comorbidities), recent travel, possible exposures. data is collected repeatedly (e.g. once daily) yes. applications track symptoms and other data over time. fhir lack of standardization can both delay access and reduce the quality of the data available to public health agencies. to address broad issues of health data interoperability, health level seven international (hl7) has published the fhir standard. applicable healthcare organizations are required to implement and maintain the fhir application programming interface (api) per the interoperability and information blocking rule of the 21st century cures act (effective june 2020) [13,14]. fhir describes a standard representation for many common entities in the healthcare domain, defines relations between these entities, and describes a variety of computational methods for operating on these entities. the fhir specification describes recurrent healthcare application business requirements in dedicated modules, accessible via the documentation index on the fhir website (https://www.hl7.org/fhir/). two examples are the workflow module and the clinical reasoning module. the workflow module describes how care plans and specific care-related workflows can be characterized, scheduled, and executed. the clinical reasoning module provides a mechanism to represent and evaluate clinical knowledge in an entirely fhir-native way, that is, using fhir-compliant data structures, operations, and logical expression languages like fhirpath [15] and the clinical quality language (cql). the fhir specification is flexible in how it can model data and workflows. supplementary standard operating procedures and constraints are published in implementation guides (igs), supplying additional details and restrictions required for true interoperability, and offering guidance on how to use fhir to solve particular problems. for example, the us core ig places restrictions on entity attributes and terminologies (e.g. icd-10-cm). additionally, the structured data capture (sdc) ig guides the interoperable and fhir-compliant implementation of data entry forms. given the broad nature of the fhir specification and the existence of mature igs that stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e2, 2021 ojphi address the requirements of the stayhome application, there is an opportunity to use fhir as the underlying model for both application data and business logic. significance to address the need for community-based self-monitoring and exposure tracking, to support individuals’ decision making and contact tracing efforts in case of infection, we developed stayhome, a reusable, mobile-friendly, longitudinal symptom tracker designed and developed following user-centered design principles. stayhome allows regular logging of symptoms and other information, and review of data over time. stayhome is unique in that it is implemented not just to exchange data using fhir, but to adopt fhir resources for internal representation of data and business logic, a design approach we call “fhir-native”. traditionally, fhir and its predecessors, such as the hl7v2 messaging standard, are used first and foremost to support an interoperable interface strategy. in line with this primary goal of fhir, stayhome’s use of the standard enables interoperability with other health informatics systems, such as electronic health record (ehr) systems and public health informatics systems. however, stayhome also leverages fhir as internal information architecture, using the standard’s domain models to represent both data and business logic. this holistic use of fhir makes the application a generic pro tool independent from any specific health problem, pro use case, or host system. stayhome is open source and freely available to anyone to use, modify, and implement for clinical and consumer health informatics applications (under the bsd 3-clause license) at https://github.com/uwcirg/stayhome-client and associated repositories. with fhir adoption increasing, informaticists are exploring how best to use it to address important issues in health informatics. we share lessons learned from developing and deploying this application, which we hope will benefit others in designing and developing applications using this new standard. methods needs assessment & design in february 2020, before washington state’s stay-home-orders were put in place and before the outbreak was officially characterized as a pandemic, we recognized the potential benefits of a symptom tracker application. to inform the requirements for such an application, we solicited input from students in an undergraduate class about science, evidence, and health, conducted by one of the authors (wl), where covid-19 had become a frequent topic of discussion. this process included informal conversations with the students as well as a structured survey asking participants about their covid-19 concerns, and how a smartphone application might help them with those concerns (hb, wl, sk). project team members (sk, hb, wl) then tabulated survey results and used content analysis methodology to identify and prioritize user-centered design features. the university of washington irb determined on july 6, 2020 that ethics approval was not required for this project. stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 6 with the pandemic actively unfolding, there was an immediate need for the application. we therefore aimed to keep the user experience simple, using standard mobile ui components and interaction patterns where possible, while maintaining a user-centric approach to application design by conducting user tests and revising based on feedback in an iterative fashion. fhir in addition to using fhir resources to represent our data model, we also made use of the fhir restful api [16] to create and update both metadata (e.g. questionnaire resources) and data (e.g. patient resources) and the fhir search api for data retrieval. concepts from the workflow module (e.g. definitional resources) were used for pro workflow execution. we dynamically encoded logic via fhirpath expressions as part of questionnaire display items, per the clinical reasoning module and sdc guidance. internationalization was implemented using guidance from the implementation support module. the consent resource was employed in conjunction with guidance from the security and privacy module to record data sharing preferences. we referenced the terminology module to implement a custom code system for app-internal messages/notifications. extended operations, as described in the exchange module, were used to expand answer option value sets. development and deployment mobile applications exist within several disjoint consumer app ecosystems characterized by different hardware, software, and process constraints, which is a barrier to mobile app development and maintenance. cross-platform mobile application development frameworks are available to address this challenge, allowing the use of a single codebase for developing android, ios, and web applications. this work utilized google’s flutter [17] cross-platform mobile application development framework. figure 1 shows an overview of the overall system architecture. in addition to the client applications (community facing and administrative), the system includes keycloak [18] as an open id connect identity provider, a reverse proxy for role-based access control (“map-api”), and hapi fhir version 4.2.0 as a fhir api server and persistent data store. amazon web services (aws) cloud infrastructure provides high availability and scalability for each server component, and docker supports continuous integration (ci) and deployment. stayhome is internationalized to english, spanish, haitian creole, and german, with automated string export and import processes. gitlab and github were used for version control and to make our work publicly accessible. the full code for the stayhome app, the authorization reverse proxy server, and docker files needed for deployment are open-source and freely available. stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 7 figure 1. system architecture. in addition to the client applications (community facing and administrative), the system includes keycloak as an identity provider, a reverse proxy for role-based access control (“map-api”), and hapi fhir version 4.2.0 as a fhir api server and persistent data store. we began software development in feburary 2020 and published version 1.0 on march 27, 2020. stayhome is live and accessible to the public at https://stayhome.app. a companion dashboard application (“stayhome-dashboard”) was developed to allow administrators to view and manage data and users. this application supports public health surveillance and reporting workflows by providing search and filter functionality and allowing review of usage statistics and user level-information such as location (for users who choose to share that information). the appearance of user data from the client-app is based on “opt-in” permissions given by the user of the client app. further, the dashboard provides a venue to host future functionality, such as advanced reporting and data visualization. results needs assessment & design there were 63 undergraduate students enrolled in the class; about 40 students were present in class when we conducted the assessment in february 2020. participants indicated that they were concerned about infection and developing the disease and that they were unsure of how to protect stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 8 themselves and others. many were wondering how they would be able to tell if they had contracted the virus. some participants indicated that they measured their body temperature daily. with word of mouth being a major information source and official sources recommending caution or even deemphasizing the seriousness of the outbreak, participants expressed uncertainty that fueled a need for reliable information. based on these conversations with potential users, it became apparent that we had the opportunity to develop an application to address the need for daily self-monitoring of symptoms and clinical signs. we aimed for a simple, functional design. however, navigation design was challenging and the subject of several design iterations. our goal was to streamline the user experience so users could quickly find frequent actions, while still allowing easy access to less-frequently used functions. for example, the symptom assessment questionnaire might be completed daily. in contrast, the risk factors and exposures questionnaires would only be filled out once (or in the case of a relevant event). additionally, we wanted to avoid confusion and provide reassurance in how data would be collected and used, so we opted to display lay-person-friendly terms and conditions and detailed explanations in-line with the controls that asked for such information. figure 2 shows screenshots of the current user interface. figure 2. stayhome application ui. from left to right: login/start screen, home screen, questionnaire screen, trends review screen. fhir the use of fhir supported the rapid development of the stayhome application by providing a domain-appropriate data model and query framework. fhir specifies the general structure (i.e. applicable data elements) of resources describing various concepts in health and healthcare applications; there is, however, significant flexibility in how these resources can support individual use cases. this section gives an overview of some of the parts of the fhir specification used, and stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 9 how they support the stayhome use case. see figure 3 for an overview of how resources work together. figure 3. fhir resources used by stayhome. resources patient. the patient resource links individual users’ personal information and settings with their account login managed by the external identity provider service. stayhome uses patient resources to manage demographics, contact information, and language preference (the application does not require users to enter this information). careplan. this resource specifies a set of questionnaires. a definitional resource is defined on the application level, which is then instantiated for each new user. instantiation allows subsequent, person-specific adjustment of the set of questionnaires to be administered and provides the opportunity to personalize the frequency of individual instruments. careplan resources reference the patient they belong to via the subject element and list the questionnaires in the activity element. questionnaire. this resource defines the content of each pro instrument and is thus central to stayhome. it consists of a list of display items, which can be either an answerable question or help stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 10 text. each item includes answer options coded for interoperability (e.g. loinc codes), selection behavior (e.g. single answer vs. multiple answer), and formatting information (e.g. section headers vs. help text), as appropriate. items can also represent calculated quantities (e.g. score totals), conditionally displayed items to support branching logic, and conditional user messages (e.g. a “high risk” and a “low risk” message, presented based on the calculated score total). for an excerpt from the symptom assessment questionnaire, see figure 3. questionnaireresponse resources record responses to questionnaires. consent. stayhome enables users to indicate their data sharing preferences using consent resources. each of these resources indicates whether an individual user consented to share their data with the stated organization. organization. this resource type is used to identify entities to whom sharing consent can be given, e.g. public health agencies, researchers, and potential community partners, and is referenced by consent resources. query api stayhome utilizes fhir’s built-in search apis in several ways. first, resources are queried by id in cases where that id is known, e.g. in careplans, which specify their component questionnaires by id. second, the application retrieves relevant resources with simple search queries, e.g. to retrieve the patient resource corresponding to the logged-in user. finally, multiple combined search and filter criteria are used to retrieve bundles of resources for specific use cases; for example, to plot response history for individual questions. the administrative dashboard uses such a query to retrieve records. the “map-api” reverse proxy ensures that only resources with proper authorization access are returned, i.e. those belonging to the logged-in user, or on the dashboard, those referring to patients who consented to share data. internationalization to display questionnaires in different languages according to user choice, we utilized both the translatable and translation extensions following guidance from the implementation support module (internationalization section). the translatable extension tags strings as user-facing. we added an automatic extraction step to push untranslated english strings to the ci pipeline. after receiving translations from translators working in translation software such as transifex [19], translations were inserted back into the fhir resources using the translation extension (code snippet 1). code snippet 1. json example of a string element with two translations. the string is tagged as user-facing with the translatable extension. "text": "start date", "_text": { "extension": [ { "url": "http://hl7.org/fhir/structuredefinition/elementdefinitiontranslatable", stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 11 "valueboolean": true }, { "url": "http://hl7.org/fhir/structuredefinition/translation", "extension": [ { "url": "lang", "valuecode": "de" }, { "url": "content", "valuestring": "anfangsdatum" } ] }, { "url": "http://hl7.org/fhir/structuredefinition/translation", "extension": [ { "url": "lang", "valuecode": "es" }, { "url": "content", "valuestring": "fecha de inicio" } ] } ] }, calculated questionnaire items the fhir structured data capture (sdc) ig describes how to use expressions and extensions to support calculated display items, such as score totals. stayhome employs fhirpath as an expression language, and the ordinalvalue extension attaches numeric values to categorical response options. the freely available fhirpath.js [20] library is used to calculate the value of the expressions in real-time as the user enters questionnaire responses. the questionnaire resource includes the expression shown in code snippet 2 for this purpose. code snippet 2. fhirpath expression used by stayhome to calculate the sum of all selected answers’ ordinal values (score total). questionnaireresponse.item.answer.extension.where(url.contains('valueordinal') ).valuedecimal.aggregate($this + $total, 0) discussion in this work, we designed, developed, and deployed a mobile-friendly, fhir-native, pro tool with application to covid-19 symptom assessment and exposure diary functions. this stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 12 information system administers questionnaires and organizes information using fhir for the representation of both application content and behavior. fhir-native approach advantages using fhir in general and in a fhir-native application context has significant advantages (table 2). the fhir standard emerged from the modeling work of many experts and thus provides an excellent starting point for an application-specific data model in the health domain. while our application’s needs are comparatively simple, starting with data structures capable of accommodating advanced data and process models obviated the need to restructure the data model in the face of new business requirements. existing fhir server implementations such as hapi provide routine functionality, such as create, read, update, and delete (crud) apis, as well as advanced domain-specific tooling, such as cql support, allowing developers to focus on their application’s unique requirements. because fhir specifies the interface between clients and servers, implementations are interchangeable as long as they comply with this interface. this allows the operation of apps directly against either standalone fhir servers or other fhircompliant servers, e.g. those belonging to ehrs or public health information systems. fhir also facilitates the exchange of information between systems, known as interoperability, though carefully following implementation guides is required to achieve semantic interoperability. beyond data exchange, we leveraged fhir as the internal information architecture. this approach facilitated rapid application development by reducing the need to design and refine a data model, as well as the tooling to support it. because fhir resources (e.g. definitional resources and questionnaires) drive application content and behavior, pro functionality is reusable for any fhir-compliant questionnaire. the application requires no custom server code and operates entirely within the fhir specification, facilitating the reuse of stayhome as a generic fhirenabled questionnaire administration tool. fhir can accommodate complex business logic in an elegant, standardized way, making possible a wide range of applications that require no server code. disadvantages there are disadvantages to using fhir, which may intensify for fhir-native applications. though the ability to use existing server implementations and tools offsets the time investment required to use fhir, the complexity and size of the fhir specification and its extended documents may be a barrier to adoption. additionally, in some domains, e.g. in the consumer health application realm, fhir may not be widely adopted, reducing the immediate benefit of interoperability-readiness. further, many use cases, clinical and non-clinical, are not modeled by fhir. while the standard is flexible and extensible to new scenarios, there is a risk of tipping the balance from using it appropriately to counterproductively shoehorning application data and business logic into fhir. for example, if resources or resource elements cannot be understood outside of the stayhome context or are not compatible with recommendations laid out in commonly used igs such as us core, the use of fhir may turn out to be a burden. stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 13 finally, despite the guidance from fhir modules and igs, many implementation and representation choices remain with the developer, and these design choices may be imperfect. for example, we considered using a single, unmodifiable careplan resource to define the set of questionnaires at the application level. we instead chose to instantiate a user-level careplan resource with reference to an application-level definitional resource (template) to allow personalization of app content. this instantiation introduced additional barriers for content maintenance: adding new questionnaires to the definitional resource required updating every user’s careplan instance as well (previous versions are still accessible via the history api). this experience underscored the need to make representational choices carefully and with specific use cases in mind; to ground decisions in implementation guides, which address many common difficulties, as much as possible; and to accept the fact that updating large numbers of resources as part of application updates may ultimately be unavoidable as business requirements evolve. table 2. fhir/fhir-native advantages and disadvantages. pros cons ● represents modeling work of many experts ● specifies common requirements and corresponding tools/apis (e.g. storage functionality, queries, versioning, crud operations, data formats, localization) ● specifies advanced functionality; specific implementations (e.g. hapi) might provide further functions that become automatically available to fhir-native apps (e.g. async to export bulk data, terminology services to translate codes, advanced searches, subscriptions, cql) ● in theory, ehr workflow integration out of the box with smart [21] ● in principle, standard data format enables immediate interoperability with other systems: resources can be consumed by other apps and other apps’ resources could be consumed by stayhome ● igs help with solving common challenges and further semantic interoperability ● fhir implementations are interchangeable (e.g. hapi, azure and google cloud fhir servers, ehr with fhir apis, public health system with fhir apis) ● complex: it can be challenging to determine the intent of different pieces, and what applies to a particular use case; takes time to work through; developing compliant applications requires consideration of constraints from different places ● specific application constraints and requirements may not have been considered when developing the spec ● does not specify all functionality required for a complete app, e.g. access control, workflows for non-clinical use cases ● fhir does not model some specialized/complex clinical use cases ● risk of forcing data and business logic into fhir in a way it wasn’t intended for, compromising interoperability and application reusability in practice ● may currently be limited in some application domains, e.g. wearables and other consumer health applications stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 14 ● clients are independent of the server / can operate out of any fhir-compliant server (e.g. a standalone system or an ehr). ● pro functionality is reusable to any questionnaire that can be specified as fhir ● built-in backward compatibility (between the data model and different versions of client apps) ● standard terminologies enable translating the application to work in a new context (e.g. to use loinc instead of snomed) development and deployment for our user-facing application, we chose flutter as a solution to cross-platform mobile application development, but quickly felt the impact of using emerging technologies. for example, there was no fhir resource library for the dart programming language, requiring us to generate and test the data model and serialization code. on the other hand, using an actively evolving framework enabled us to take advantage of cutting-edge technology. for example, flutter web was first released in the beta channel in december 2019. not long before our initial release, we recognized that our group did not have the necessary resources and expertise to overcome the challenges of mobile app publishing (e.g. registering as a non-profit entity, integrating ios application builds in our ci pipeline), within an acceptable time frame. flutter web, while suffering idiosyncrasies typical of beta-stage software, was an alternative that became available just in time for us to publish stayhome as a web application instead. web applications have many benefits over native applications. they provide the same experience on different platforms, including desktop, maximizing reach while minimizing the additional development effort required to support many different platforms. deployments (such as when a new version is published) affect end-users minimally, as client browser apps will automatically retrieve the latest version of the web application when reloading, eliminating the need to update local application installs. additionally, some development teams may find deploying a web application to be straightforward compared to completing the steps required to get a native mobile application approved and published on each of the major platform app stores. while web applications may provide suboptimal ux compared to native apps, there is an opportunity to narrow that gap via progressive web application (pwa) or “installable web application” setups. other drawbacks of non-native applications include limitations for integration with local hardware (e.g. bluetooth, camera) and barriers in using notifications. authentication and authorization for fhir were challenges, as fine-grained data access control is an infrastructure requirement not adequately addressed by fhir. while solutions such as smart on fhir and interceptors for hapi fhir were a possibility for authentication and authorization, we instead implemented a lightweight reverse proxy server as a single solution for both authentication and authorization. this approach removed the need to manage and secure stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 15 credentials within the stayhome app and allowed us to keep the server implementation (e.g. hapi vs. ehr) interchangeable and independent of the client app. while comparatively simple conceptually and in practice, this “non-standard smart on fhir” setup adds a custom layer in our system architecture. we are actively investigating the use of a so-called standalone smart on fhir application setup to simplify our authorization system. future work future work includes implementing a fully smart compliant launch flow. additionally, there is an opportunity to further leverage fhir to support more advanced functionality, for example, by using extraction as described in the sdc module to create observation resources from questionnaireresponse resources. while we provided the application for use to members of our immediate community to support community members as soon as possible and to collect further feedback, accomplishing broad adoption of the app requires resources and expertise, including marketing and consumer support, that lie outside of our capabilities and resources. however, the application may be useful for specific programs, such as research projects and community health programs, with well-defined objectives and user groups. such smallto medium-scale implementations thus represent an important future direction for this work. conclusion we presented stayhome, a fhir-native pro tool applied as a covid-19 symptom tracking application and public health reporting tool. we deployed this tool in a software system that includes a community member-facing web application, authentication/authorization layer, fhir server, and administrative dashboard application. code and resources are open source under the terms of the bsd 3-clause license. as a mobile-friendly web application, stayhome maximizes reach. stayhome addresses core community needs by providing users with a tool to self-track possible symptoms and exposures and supports them in following public health recommendations for their personal health. the use of fhir enables user-controlled interoperability with, for example, public health contact tracing and case investigation systems, an area the authors are actively working on with the washington state department of health through the commoncircle initiative. we innovatively operationalized fhir beyond its use as a messaging standard, leveraging it as an internal information architecture to represent application content and behavior. thus, we created a questionnaire administration tool that is easily reusable for other use cases by simply updating fhir resources. the tool can operate out of any host system, e.g. a standalone fhir server, an ehr, or another fhir-compliant server. during this process, we found that there are advantages and disadvantages in the use of fhir and the fhir-native approach. we applied fhir in a novel way by using it as internal information architecture, but also found that the right level of fhir use can be a balancing act that depends on individual application requirements and constraints. we used emerging technologies, tried-and-true technologies, and custom system components, finding benefits and tradeoffs for each. we also recognized that pre-existing expertise is a factor in deployment considerations. we provide our code in several open-source repositories, freely available under the bsd 3-clause license at https://github.com/uwcirg/stayhome-client and in associated repositories. we hope that the stayhome application benefits the community in stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool 16 working to stay safe and flatten the curve. additionally, we hope that the code and resources found in our github repositories and the considerations shared in this manuscript benefit others in their efforts to develop and operationalize fhir applications in general and pro applications in particular. acknowledgements the authors conducted this work through the uw clinical informatics research group (cirg), and gratefully acknowledge the contributions of cirg staff, especially justin mcreynolds. funding this work was partially supported by the national library of medicine training grant (t15lm007442). references 1. timeline of who’s response to covid-19. accessed july 1, 2020. https://www.who.int/news-room/detail/29-06-2020-covidtimeline 2. dong e, du h, gardner l. 2020. an interactive web-based dashboard to track covid-19 in real time. lancet infect dis. 20(5), 533-34. doi:https://doi.org/10.1016/s14733099(20)30120-1. pubmed 3. mcclellan m, gottlieb s, mostashari f, rivers c, silvis l. a national covid-19 surveillance system: achieving containment. published online 2020. 4. andersen m. mobile 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flutter beautiful native apps in record time. accessed june 9, 2020. https://flutter.dev/ 18. keycloak. accessed august 17, 2020. https://www.keycloak.org/ 19. localization platform for translating digital content | transifex. accessed august 17, 2020. https://www.transifex.com/ 20. hl7/fhirpath.js: javascript implementation of fhirpath. accessed august 11, 2020. https://github.com/hl7/fhirpath.js/ 21. mandl kd, kohane is. 2009. no small change for the health information economy. n engl j med. 360(13), 1278-81. doi:https://doi.org/10.1056/nejmp0900411. pubmed https://doi.org/10.1056/nejmp0900411 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19321867&dopt=abstract stayhome: a fhir-native mobile covid-19 symptom tracker and public health reporting tool abstract introduction representative covid-19 apps fhir significance methods needs assessment & design fhir development and deployment results needs assessment & design fhir resources query api internationalization calculated questionnaire items discussion fhir-native approach advantages disadvantages development and deployment future work conclusion acknowledgements funding references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts identifying emerging novel outbreaks in textual emergency department data mallory nobles1, lana deyneka2, amy ising3 and daniel b. neill*1 1carnegie mellon university, event and pattern detection laboratory, pittsburgh, pa, usa; 2north carolina department of health and human services, communicable disease branch, raleigh, nc, usa; 3university of north carolina, chapel hill, nc, usa objective we apply a novel semantic scan statistic approach to solve a problem posed by the nc detect team, north carolina division of public health (nc dph) and unc department of emergency medicine carolina center for health informatics, and facilitated by the isds technical conventions committee. this use case identifies a need for methodology that detects emerging, potentially novel outbreaks in free-text emergency department (ed) chief complaint data. introduction typical approaches to monitoring ed data classify cases into pre-defined syndromes and then monitor syndrome counts for anomalies. however, syndromes cannot be created to identify every possible cluster of cases of relevance to public health. to address this limitation, nc detect’s approach clusters cases by arrival times and monitors the textual chief complaint data associated with each identified cluster for relevant similarities [1]. this approach is time consuming and limited in its ability to detect emerging outbreaks that are dispersed across time. a new method is needed to automatically identify clusters of interest that would not be detected by existing syndromes. clusters may be based on symptoms, events, place names, arrival time, or hospital location. the nc dph dataset describes 198,511 de-identified ed visits over one year at 3 north carolina hospitals. the data include chief complaint, altered date and time of arrival, hospital a/b/c, and age group. about 40 simulated outbreaks were injected into the data set by the nc detect team. for example, an inject cluster might consist of 4 patients who report getting sick after eating at a particular restaurant. methods our semantic scan approach [2] is well suited to this problem. here, we first infer a set of topics (probability distributions over words) from the free-text data using latent dirichlet allocation [3]. then, we assign each case to its most likely topic and use a variant of spatial scan [4] to identify anomalous counts of these topics. our approach learns one set of topics using past data and a second set of topics for the most recent data (3-hour moving window). the first set of topics describes commonly occurring illness types (e.g., fever or rash). the second set of topics are chosen to be maximally different from the first set, and thus can capture clusters related to one-time events (e.g., common-source exposure) or novel disease types. scans can be performed over combinations of other data attributes (e.g., age groups) to identify the outbreak type and affected subpopulations. results our methods successfully identified clusters of cases referring to specific locations, unusual sets of symptoms, or affected subpopulations. for example, we found a cluster of 10 cases that all mentioned a local middle school within a 4 hour span. other detected clusters had related chief complaints, like a chemical spill, motor vehicle accident or contagious disease such as head lice or scabies. some discovered clusters identified a location and symptoms, such as a sudden onset of rashes at the beach. other clusters found specific subpopulations, e.g., 7 young adults complaining of smoke inhalation. conclusions the topics learned by our semantic scan approach act as illness categories and eliminate the need for classifying cases into predefined syndromes. since topics dynamically adapt to current data, semantic scan can identify emerging clusters that public health officials could not have predicted in advance. finally, semantic scan is automated and sufficiently fast to identify outbreaks as they occur. we expect that the method could also be useful to other public health surveillance problems. keywords text mining; event detection; semantic scan acknowledgments this work was supported by nsf grants iis-0916345, iis-0911032, and iis-0953330. data was provided by the nc dhhs/dph nc detect system. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. references 1. li m, loschen w, deyneka l, et al. time of arrival analysis in nc detect to find clusters of interest from unclassified patient visit records. online j pub health inform 2013; 5(1): e13. 2. murray k, dyer c, liu y, neill db. a semantic scan statistic for novel outbreak detection. tech. rept., carnegie mellon university, 2014. 3. blei d, ng a, jordan m. latent dirichlet allocation. j mach learn res. 2003; 3:993-1022. 4. kulldorff m. a spatial scan statistic. commun stat theor meth. 1997; 26:1481-96. *daniel neill e-mail: neill@cs.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e45, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, 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117 (page number not for citation purposes) isds 2013 conference abstracts a tool for interactive disease outbreak visualization, detection, and forecasting jarad niemi* iowa state university, ames, ia, usa � �� �� �� � � �� �� �� � objective ������� � ��� �������������� � ��� ������� � ������ ��� ������ ����� ������ � ��� �� ���������� �� ������� � �������� introduction ���� �������������� ����� ��� �������� ��� ������� � ���� ������� ��� ������� �� ����������� ��������� �������������� ������� ������ !��� �� �"� � �#� ���$ �%�� ��� ����� �����& ������������������� � ��� ������ ������ ����������������� �������������� ����������� � ������ ������ �� � ���� � ��� ������� � ������� ���� � ��������� ���� �� ��������� ��!� ��& ������������� �������#� ���$ ������ �� ���� �� ���������� ��������������� ��������� �'� ���(���� ������������ �&� ��������������� �� �������������� ���������������� � ���� ������ �� ��� �������� � ���������� ��� �� ���������� ����������� ����������� ����� � ��� � 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*jarad niemi e-mail: niemi@iastate.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e7, 2014 will spatial algorithms fail to detect disease outbreaks during a pandemic spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increases during 2009 h1n1 pandemic jialan que 1,2,3,* , fu-chiang tsui 1,2 1 rods laboratory, department of biomedical informatics, university of pittsburgh 2 intelligent systems program, university of pittsburgh 3 division of biomedical informatics, university of california, san diego abstract background spatial outbreak detection algorithms using routinely collected healthcare data have been developed since the late 90s to identify and locate disease outbreaks. however, current wellreceived spatial algorithms assume only one outbreak cluster present at the same point of time which may not be valid during a pandemic when several clusters of geographic areas concurrently occur. based on a retrospective evaluation on time-series and spatial algorithms, this paper suggests that time series analysis in detection of pandemics is still a desirable process, which may achieve more sensitive performance with better timeliness. methods in this paper, we first prove in theory that two existing spatial models, the likelihood ratio and the bayesian spatial scan statistics, are not useful if multiple clusters occur at the same point of time in different geographic regions. then we conduct a comparison between a spatial algorithm, the bayesian spatial scan statistic (bss), and a time series algorithm, the wavelet anomaly detector (wad), on the performance of detecting the increase of the over-the-counter (otc) medicine sales during 2009 h1n1 pandemic. results the experiments demonstrated that the bayesian spatial algorithm responded to the increase of thermometer sales about 3 days later than the time series algorithm. conclusion time-series algorithms demonstrated an advantage for early outbreak detection, especially when multiple clusters occur at the same time in different geographic regions. given spatialtemporal algorithms for outbreak detection are widely used, this paper suggests that epidemiologists or public health officials would benefit by applying time series algorithms as a complement to spatial algorithms for public health surveillance. keywords: biosurveillance, disease outbreak detection, time series spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 introduction over the past decades, infectious disease outbreaks routinely devastated the world’s urban and suburban population. the release of anthrax in 2001, the severe acute respiratory syndrome (sars) outbreaks in 2002, and recent h1n1 swine flu outbreaks in 2009 are examples [1,2,3]. lessons learned from those outbreaks include development of disease specific vaccines and effective outbreak detection algorithms that can be employed in biosurveillance systems [2,4,5]. background currently, principal approaches for outbreak detection include temporal analysis, spatial analysis and tempo-spatial analysis. temporal analysis using time series algorithms is a conventional approach due to its simplicity when compared with spatial algorithms, which require additional geographical information. one of the algorithms, the wavelet anomaly detector (wad) algorithm [4] developed by the university of pittsburgh, is used in the realtime outbreak and disease surveillance (rods) system. it computes a score for each unit (e.g., zip code) area based on how many standard deviations that the number of the cases from that area in the most recent day is elevated from the expected, and then reports all the areas with higher-thanthreshold scores. some other algorithms applying temporal analysis have also been explored in [5,6,7]. in addition to time series algorithms, researchers have developed spatial and tempo-spatial algorithms to take into account geographical information in the belief that the additional spatial information may lower false alarm rates and better localize outbreaks. there are two approaches for spatial and tempo-spatial algorithms: the frequentist approach and the bayesian approach. a representative algorithm using frequentist approach is the spatial scan statistic (kss) developed by kulldorff et. al. in 1997 [8], which scans the region of interest for clusters using circular windows of various sizes. each scanning window may cover a number of zip code areas and is considered as a cluster candidate. this frequentist approach uses a likelihood-ratio test, a statistical test used to compare the fit of two models: the null hypothesis (no outbreaks in a region) model and the alternative hypothesis (an outbreak in a region) model, to find a window (cluster) with maximum likelihood ratio. the derivatives of kss include the elliptic spatial scan statistic and the flexible spatial scan statistic (fss), which are derived by relaxing the constraint of a circular cluster shape (window) [9,10]. a representative spatial algorithm using the bayesian approach is the bayesian spatial scan statistic (bss) developed by neill et. al. [11]. it computes the posterior probability of the alternative hypothesis h1, p(h1(s)|data), in a region s. it creates a conjugate gamma-poisson model to compute the posterior probability of having an outbreak in region s. bss employs a rectangular scanning window (aligning with x and y axes) to search for clusters over a grid covering the whole region of interest. each window can comprise one or more grid cells and is considered as a potential cluster. the algorithm identifies the outbreak cluster with the highest posterior probability of having an outbreak. another spatial clustering algorithm recently developed by the authors, the rank-based spatial clustering (rsc), employs a different searching scheme which has been demonstrated to improve computational complexity [12]. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 problem and objective several previous studies have demonstrated that the aforementioned spatial algorithms are able to identify and localize outbreaks and they are preferable in some ways over time series algorithms when applied to different data sources [8,10,11,13,14,15]. nonetheless, those studies, regardless of whether they examined frequentist or bayesian models, share the same assumption: only one outbreak cluster at a same point in time exists in the entire study region. the fact is that such an assumption may not hold during an outbreak or a pandemic, when disease activities can be found in multiple geographically separate places across a large region. in this paper, we studied the applicability of spatial algorithms based on the single outbreak cluster assumption for detecting h1n1 pandemics. the methods section provides 1) a theoretical derivation of the deteriorated performance of the spatial algorithms when the single cluster assumption is no longer true, and 2) an evaluation scheme using real-world otc data collected from texas state during april 2009 (the starting period of the h1n1 pandemic) to compare the performance of a spatial algorithm and a time series algorithm. the hypothesis in this paper is that current existing spatial disease detection algorithms cannot detect outbreaks earlier than the time series algorithm if their underlying statistic models have the one-cluster assumption. methods spatial algorithms for outbreak detection the common statistical models for spatial detection algorithms include the likelihood ratio (used in kss and fss) and the bayesian posterior probability (used in bss and rsc). both models presume that there is only one cluster of an outbreak at a time possible within the whole study region [8,11,12]. however, this assumption would be violated in circumstances where a disease simultaneously spread from multiple separate geographic areas. in the following, we prove that both models will be defective given the assumption of one outbreak cluster is violated. table 1 lists the main symbols used in this paper and their respective meanings. table 1: list of symbols. symbol meaning a region of interest the total observed counts in the entire study region the summed observed counts in the areas within cluster the summed observed counts in the areas outside cluster the total expected counts in the entire study region the summed expected counts in the areas within cluster the summed expected counts in the areas outside cluster the observed counts in area the expected counts in area ⁄ , infection rate within cluster ⁄ , infection rate outside cluster ⁄ , infection rate within the entire region spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 1) likelihood ratio model the frequentist approach in spatial scan statistics models uses likelihood ratio ( ) and pvalue to determine an outbreak region. equation (1) is the likelihood ratio ( ) of having an outbreak in a region against having no outbreak in the entire study region [8]. if is greater than 1 and its p-value is the most significant in the randomization test, it is likely that the cluster is the one having an outbreak. ( ) ( ) ( ) (1) under the one outbreak cluster assumption, region , with more observed counts due to the outbreak and no outbreaks outside of , is likely to have given and . however, when we consider a scenario where multiple ( ) outbreak clusters are occurring concurrently in several geographically separate regions, we define ⁄ as the infection rate for each cluster region and , and we assume for . the likelihood ratio for region becomes close to 1 as the cluster number becomes greater (e.g., ) and the outbreak regions become to cover the entire study region, ∑ , as shown in eq. (2): ( ∑ ∑ ∑ ∑ ) ( ∑ ∑ ∑ ∑ ) ( ∑ ∑ ) ( ∑ ∑ ) ( ∑ ∑ ) (2) equation (2) shows that given any cluster region with and a large , the likelihood ratio of having an outbreak approaches 1, which means there is no difference between the nonhypothesis and the alternative hypothesis even though both and are much higher than 1 (e.g., their observed values are much higher than baseline values); consequently, this leads to the incorrect result of no found outbreak instead of multiple probable outbreak clusters. 2) bayesian gamma-poisson model the bayesian approach in spatial scan statistics uses the posterior probability of a region having an outbreak to determine an outbreak region, as shown in eq. (3), which requires three variables: , and . in eq. (4) is the likelihood of the alternative hypothesis (i.e., having an outbreak in region ) based on a gamma-poisson model. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 is the prior probability of having an outbreak in any region , while represents the prior of having no outbreaks. in our study, we estimated ( ) ⁄ by assuming a uniform distribution among all possible regions having an outbreak and used to denote the prior probability of having an outbreak cluster in the entire study region. given ∑ where , the probability of data in eq. (5), , is computed as the sum of two components: one is the sum of the likelihood of all possible clusters multiplied by their priors ( ) and the other is the sum of the likelihood of the nonhypothesis multiplied by the prior . ( | ) (3) ( | ) (4) ∑ ( | ) , (5) where is a constant factor. we will focus on the computation of the likelihood since is a constant across any cluster in eq. (3). consider a specific case where there are ( ) outbreak regions and the summed time series for each region follow a poisson distribution with the same conjugate gamma priors (i.e., ) and have expected values and observed values which are close to each other (e.g., and , where ), respectively. the likelihoods of all the candidate clusters will thus result in the values, ( | ) , ( | ), , ( | ), where ( | ) ( | ( )) for which we denote ( | ) for any ( | ). the posterior probability of each one of the regions in eq. (3) can be derived in eq. (6) when becomes large, where is the summation of the likelihood of all the possible regions other than the clusters multiplied by the priors. ( | ) ( | ) (6) if is large, such as during a pandemic period, the posterior probability ⁄ for each examined region would approach zero, which indicates false negatives for outbreaks. although it is an extreme example, we are addressing the issue of lowered posterior probabilities of outbreak regions due to the violation of one-outbreak-region assumption. thus, the bayesian gammapoisson model can be challenged as well. an example: 2009 h1n1 flu pandemic beginning in the state of veracruz, mexico, the 2009 h1n1 flu outbreak spreads quickly and globally. in the u.s., within about 3 weeks, the h1n1 virus became widespread in 8 states and spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 infected about 5,000 people [16]. compared with the previous year, this outbreak provided much stronger signals captured in the cdc seasonal ili trend (fig. 1), thus it was selected as a real-life example to test our hypothesis. figure 1: percentage of visits for influenza-like illness (ili) reported by the u.s. outpatient ili surveillance network (ilinet), weekly national summary, september, 2008 – april, 2009 (http://www.cdc.gov/flu/weekly) we applied bss (a spatial algorithm) and wad (a time series algorithm) to detect the significant increase of thermometer sales which may indicate the onset of the h1n1 pandemic that occurred in the state of texas at the end of april, 2009. the reasons for choosing these two algorithms for this study are 1) bss is a spatial algorithm preferred over the frequentist algorithms and has been tested and applied in the applications of prospective disease surveillance [11,13,17]; and 2) wad is a well established time series algorithm which has been evaluated in multiple studies and found favorably compared with other time series algorithms [7,18]. the goal for these two algorithms in this study is to find emerging clusters of geographic areas having significantly increased thermometer sales which may indicate increased h1n1 infections in the early stage of the pandemic. 1) study dataset in this study, we used routinely collected over-the-counter (otc) sales data as the data source for detection of flu outbreaks. because previous studies from literature have demonstrated that otc data such as cold remedies and diarrhea remedies sales can serve as good indicators for spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 outbreak detection and are more timely than physician diagnosis, these data were used as an influenza outbreak indicator [19,20,21,22,23,24]. we obtained the dataset from the national retail data monitor (nrdm) system [25] developed by the rods laboratory, which has been collecting otc sales data from 30,000+ retail stores across the country on a daily basis since 2003. the nrdm classifies each retail product sale into one of twenty three categories, taking into account both purpose of the treatment and consumer age group, such as anti-fever adult or cold relief pediatric. we chose to study the data from texas for the following reasons: 1) texas is one of the two states (the other is california) where h1n1 pandemic was confirmed and identified in the early stages, 2) texas shares the longest border line with mexico, where h1n1 was first identified, and 3) texas is the 2 nd largest state and it also has the second largest population in the u.s.. among the 23 otc categories nrdm provides, we chose the thermometer sales category as our indicator of the flu outbreak for three reasons. first, the rods disease surveillance system signaled enormous spikes in the time series data of over-the-counter (otc) thermometer sales in texas at the end of april corresponding to the chronology of the 2009 h1n1 pandemic (fig. 2). second, we found a strong correlation (correlation coefficient is 0.91 with 95% confidence interval [0.89,0.92]) between patients with constitutional syndrome visiting emergency departments (eds) and otc thermometer sales in pennsylvania in the past flu seasons as shown in fig. 3. finally, villamarin et al. also demonstrated high correlation (0.89) between actual and predicted ed visits using thermometer sale data [26]. figure 2: total thermometer sales in texas collected from nrdm between 4/23/09 and 5/15/09. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 figure 3: time series of patient ed visits with constitutional syndrome and counts for otc thermometer sales in the state of pennsylvania in 2009. our experimental dataset covers purchases made between march 1, 2007 and may 31, 2009. the thermometer sales data included records from 1,413 pharmacy stores in 581 zip code areas. the data prior to april 1, 2009 were used to train algorithms and estimate false alarm rates while data from april 1, 2009 to may 31, 2009 was used to evaluate the algorithms (evaluation period). although we conducted a retrospective study, a prospective analysis was mimicked by incrementally adding each day’s data to the algorithms as during the evaluation period. for each day, the algorithms use previous 730 days to predict the current day’s sales and estimate the values of prior parameters (e.g., ’s and ’s in gamma distribution used in the bayesian spatial detection algorithm). in order to define the alert threshold, , used by the detection algorithms, we applied the analysis to each of the 365 days between april 1, 2008 and march 31, 2009 and recorded the highest score for each day. this allows us to calculate the false alarm rate as ⁄ if is the -th greatest in the set of 365 recorded scores. the assumption here is that during the 3-year period before april 1, 2009, there were no h1n1 pandemics. but note that the ignored other strains of flu that did occur in this period will result in an underestimation of algorithm performance. 2) date of outbreak onset figure 4 shows the accumulation of confirmed h1n1 cases in texas after april 23, 2009 (when cdc started counting cases) as posted on cdc’s official website [16]. within about 3 weeks, the h1n1 virus spread quickly and infected more than 500 people in texas. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 figure 4: cumulative number of confirmed h1n1 cases in texas reported to cdc between 4/23/09 and 5/15/09 in this study, the date of april 24, 2009 is considered to be an indicator of the onset of the h1n1 flu pandemic hitting texas and is used to evaluate the detection algorithms. on this day, the world health organization (who) issued the first disease outbreak notice on the h1n1 flu pandemic, confirming the infection of a number of people in mexico and the united states [27]. on the same day, the us centers for disease control and prevention (cdc) announced that 7 of the 14 mexican samples contained the same virus strain as that found in california and texas and suggested that the containment in the usa was “not very likely” [28]. another appropriate date can be considered as the pandemic onset is the date when the first case was confirmed in texas. since we are comparing the relative timeliness between different detection algorithms, the absolute difference between each algorithm and the real onset date will not affect our findings. 3) algorithm evaluations we evaluated the bayesian spatial scan statistic (bss) and the wavelet anomaly detection (wad) algorithms using data from the real otc thermometer sales data from the 581 zip code areas in texas where reporting stores were located. we applied wad to these 581 zip code areas and chose the threshold based on the false alarm rate computed from the training data set. bss was applied to the same data sets. we laid a 24x24 grid (576 grid cells) over a texas state map that included the 581 zip code centroids. all of the resulting rectangles, of varying sizes and covering different locations, were examined. the baseline (expected count) for each zip code was estimated using a wavelet transform from the previous two years of data (as used in wad). by assuming a uniform prior distribution, this approach computes the posterior probability of having an outbreak using the gamma-possion model we described earlier in the section. the cluster with the highest posterior probability is reported if it exceeds a threshold, which is estimated based on a preset false alarm rate described in the subsection of study dataset. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 we examined the detection timeliness of both the wad (wavelet anomaly detector) and the bss (bayesian spatial scan statistic) algorithms, respectively. we chose two false alarm rates: one false alarm per two months and one false alarm per month, allowing us to evaluate the two algorithms from a practical perspective as resources are limited to public health officials. the false alarm rate reflects how often an alarm is triggered by chance, assuming that analyses are repeated on a regular basis with a periodicity equal to the specified time interval length, e.g., on a daily basis. results the results of wad are illustrated in fig. 5 and fig. 6. fig. 5 shows the number of significantly elevated zip code areas in texas between the april 23 and may 15 in the evaluation period. in addition, fig. 6 shows the geographical distribution of the zip code areas with significantly elevated thermometers sales from april 25, 2009 to april 30, 2009, using the same color scheme to represent the two significance levels. as shown in both figures, the number of significantly elevated zip code areas stayed low (less than 20 zip codes) before april 27. from our past experience, the spikes shown in these areas probably resulted from an imperfect data collection process or from some other stochastic reasons (e.g., non-continuous data reporting from some stores) since the spikes are distributed randomly and only last for a day (see fig. 6(a), 6(b)). however, starting from april 27, 66 zip code areas simultaneously signaled alarms. furthermore, the thermometer sales stayed significantly high for several days, until around may 7, and the number of significantly elevated zip codes exceeded 100 on april 29 and april 30 (in fig. 6(cf)). more specifically, the same zip codes in the counties nueces, travis, bexar, collin, dallas and later el paso, bowie, tarrant and cameron, repeatedly reported significantly elevated thermometers sales within those days. these results suggest that wad was able to detect the significant increase on april 27, 2009, by identifying 66 zip code areas in about 20 counties showing a significantly elevated amount of thermometer purchases from those drug stores under surveillance. figure 5: the number of significantly elevated zip code areas in texas analyzed by wavelet transform between 4/23/09 and 5/15/09. the orange bars represent the numbers of elevated zip codes with scores equal to or larger than the threshold score, which allows only one false alarm spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 (fa) per two months by chance. the yellow bars represent the numbers of the zip codes having a significant increase of cluster posterior probabilities corresponding to fa between once per two months and once per month. bss, on the other hand, showed different results from wad when analyzing the same set of data. the first day bss fired an alarm was april 30, 2009 which was 3 days later than wad did. it reported a cluster including 409 out of total 581 zip codes with the posterior probabilities above the threshold corresponding to one false alarm per month (fig. 7(a)). the cluster was located in the eastern central part of texas. on the next day, may 1, 2009, bss fired the 2 nd alarm with a cluster comprising 272 zip codes with significance of one false alarm per two months (fig. 7(b)). fig. 7 shows that the cluster had become more localized and moved to the southeast. however, during the first 6 days of the real outbreak, from april 24, 2009 to april 29, 2009, bss did not identify any significant cluster. it is also worth noting that after may 1, although the counts of thermometers sales were still high, bss did not fire any alarms at all for the rest of days within the evaluation period (except for may 5, when a 3 zip code cluster was found but was believed to be a false alarm). discussion in the example described above, we found that wad was able to detect the significant increase of thermometer sales, which may indicate the onset of h1n1 pandemic, 3 days earlier than bss did. the slow timeliness of bss in this study compared with wad can be attributed to the violation of the basic assumption that only one outbreak cluster can occur at the same point in time in different geographic regions. therefore, in order to rapidly detect a pandemic, such as the 2009 h1n1 flu outbreak, which takes place in multiple distant places in a sudden and simultaneous way, our results show that the spatial models with the one-cluster assumption are not preferred. (a) april 25, 2009 (b) april 26, 2009 spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 (c) april 27, 2009 (d) april 28, 2009 (e) april 29, 2009 (f) april 30, 2009 figure 6: the counties in texas with zip codes having significant elevated counts on the sales of thermometers. orange areas represent the zip code areas with significance level of 1 false alarm per 2 months and yellow areas represent the zip code areas with significance level of 1 false alarm per 1 month. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 (a) april 30, 2009 (b) may 1, 2009 figure 7: two significant clusters detected by bss on april 30, 2009 and may 1, 2009. the zip code areas in lime are the areas covered by nrdm; the zip code areas in yellow are the ones in the cluster with significance level of 1 false alarm per month detected by bss and the areas in orange are the zip code areas in the cluster with significance level of 1 false alarm per 2 months. however, although the time series algorithm, wad, was able to respond 3 days earlier than the bayesian spatial algorithm, bss, which suggests the time series algorithms are more sensitive to pandemic detection, it also has a few drawbacks. time series algorithms are less robust on noisy data (e.g., caused by imperfect data collection process, etc.) since they signal alarms whenever the deviation of the observed counts from the expected counts in an area exceeds a threshold. thus, a secondary analysis is recommended to be performed. a secondary analysis may include 1) considering the output for the previous days as well to determine if the newly alarmed areas are correlated with the previous ones; 2) studying if there is a data quality problem that may cause an abnormal increase of sales; and 3) waiting for the outcome from the next time period to make better decision. it is also important to address how strong the otc data used in this study have the signal of real h1n1 pandemic. in the study dataset, the number of thermometer sales is much higher than the number of confirmed cases each day at the end of april. this implies that a big proportion of the elevated thermometer purchases possibly were not due to h1n1 infection but other reasons. although the literature has shown that otc data can be used to predict number of cases in emergency departments [26], research on the relationship between otc sales and medical treatment seeking is still needed especially by the people from the public health or social behavioral fields, such as the study on the relations between hospitality and behaviors during outbreaks [29,30]. spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 limitations both the experimental data and the applied algorithms limited this study. our experimental design was restricted to analyzing only one type of data (i.e., the otc thermometer sales) due to data availability. some public health organizations or biosurveillance systems, however, may have more than one possible data source (e.g., emergency department patient visits, etc.) available, which to some extent may contain better signals of h1n1 pandemic. also it would be ideal to analyze multiple states of data and to use ed data to create a better gold standard in terms of deciding outbreak onset period. we hope in the future to use isds distribute project data for a larger scale study. furthermore, the comparison in this study was only performed between one spatial algorithm and one time series algorithm. evaluation using other algorithms (e.g., moving average, the spatial scan statistic, etc.) would be helpful to support our findings. in addition, this study only used one known h1n1 pandemic to test the performance of the two algorithms; thus confidence interval for timeliness is not available. conclusion we have conducted a study on the detection of significantly increased thermometer sales which may indicate 2009 h1n1 pandemic in texas by applying a time series algorithm and a spatial algorithm. although the spatial algorithm was more robust (fewer false alarms) and informative (suggesting geographical distribution of outbreaks) than the pure time series algorithm, our results suggest that the time series analysis is still desirable in detection of pandemics as it may achieve a more sensitive performance with better timeliness. the use of time series algorithms, therefore, is still necessary for rapid outbreak detection, especially in scenarios where the singlecluster assumption does not hold. instead of replacing the time series algorithms, we suggest epidemiologists or biomedical informaticians apply time series algorithms as a complement to current spatial algorithms for public health surveillance purposes. acknowledgement the project was funded by cdc grants p01 hk000086 and 1u38 hk000063-01, and pa department of health grant sap #40000012020. corresponding author jialan que department of biomedical informatics university of pittsburgh, pa email: jiq4@pitt.edu mailto:jiq4@pitt.edu spatial and temporal algorithm evaluation for detecting over-the-counter thermometer sale increasing during 2009 h1n1 pandemic 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 references 1. amerithrax or anthraxiinvestigation., u.s. federal bureau of investigation. 2. hung ls. 2003. the sars epidemic in hong kong: what lessons have we learned? the royal society of medicine. 96(8), 374-78. http://dx.doi.org/10.1258/jrsm.96.8.374 3. who. influenza-like illness in the united states and mexico. 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[online].; 2009. available from: http://www.cdc.gov/media/transcripts/2009/t090424.htm. 30. isaacs d. 2010. lessons from the swine flu: pandemic, panic and/or pandemonium? j paediatr child health. 46(11), 623-26. http://dx.doi.org/10.1111/j.1440-1754.2010.01912.x 31. wong lp, sam ic. 2011. behavioral responses to the influenza a(h1n1) outbreak in malaysia. j behav med. 34(1), 23-31. http://dx.doi.org/10.1007/s10865-010-9283-7 http://www.cdc.gov/media/transcripts/2009/t090424.htm towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 towards an effective health interventions design: an extension of the health belief model rita orji 1 , julita vassileva 1 , regan mandryk 1 1 department of computer science, university of saskatchewan, canada abstract introduction: the recent years have witnessed a continuous increase in lifestyle related health challenges around the world. as a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. the designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. several health behavior theories have been used to inform health intervention designs, such as the theory of planned behavior, the transtheoretical model, and the health belief model (hbm). however, the health belief model (hbm), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. however, the effectiveness of this model is limited. the first limitation is the low predictive capacity (r 2 < 0.21 on average) of existing hbm’s variables coupled with the small effect size of individual variables. the second is lack of clear rules of combination and relationship between the individual variables. in this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the health belief model by introducing four new variables: self-identity, perceived importance, consideration of future consequences, and concern for appearance as possible determinants of healthy behavior. (2) we exhaustively explored the relationships/interactions between the hbm variables and their effect size. (3) we tested the validity of both our proposed extended model and the original hbm on healthy eating behavior. finally, we compared the predictive capacity of the original hbm model and our extended model. methods: to achieve the objective of this paper, we conducted a quantitative study of 576 participants’ eating behavior. data for this study were collected over a period of one year (from august 2011 to august 2012). the questionnaire consisted of validated scales assessing the hbm determinants – perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy – using 7-point likert scale. we also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. to analyses our data, we employed factor analysis and partial least square structural equation model (pls-sem) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. we tested for the validity of both our proposed extended model and the original hbm on healthy eating behavior. finally, we compared the predictive capacity of the original hbm model and our extended model and investigated possible mediating effects. results: the results show that the three newly added determinants are better predictors of healthy behavior. our extended hbm model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. this shows the suitability of our http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 extended hbm for use in predicting healthy behavior and in informing health intervention design. the results from examining possible relationships between the determinants in our model lead to an interesting discovery of some mediating relationships between the hbm’s determinants, therefore, shedding light on some possible combinations of determinants that could be employed by intervention designers to increase the effectiveness of their design. conclusion: consideration of future consequences, self-identity, concern for appearance, perceived importance, self-efficacy, perceived susceptibility are significant determinants of healthy eating behavior that can be manipulated by healthy eating intervention design. most importantly, the result from our model established the existence of some mediating relationships among the determinants. the knowledge of both the direct and indirect relationships sheds some light on the possible combination rules. keywords: health belief, models, health behavior, health interventions, theories, determinants. introduction the growing increase in lifestyle-related health problems has motivated a shift from treatment-and-prescription centric (reactive) healthcare system to a patient-centric (proactive) system that is based on prevention and promotion of healthy behavior around the world. a variety of health behavior interventions have been designed with a preventive standpoint toward diseases in mind. a typical example is rightway café, a game designed to encourage healthy eating in young adult (peng, w., 2009). similarly, lunchtime is persuasive health application designed to teach people how to make healthy eating choices when eating in the restaurants (orji et al., 2012). the designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. this is because interventions that are informed by theories and models tend to be more successful than those based on intuition (glanz et al., 1997). several health behavior theories have been used to inform health intervention designs, such as the theory of planned behavior (ajzen, i., 1991), the transtheoretical model (prochaska et al. 1992), and the health belief model (rosenstock, 1966). however, the health belief model (hbm), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories in the design and evaluation of health behavior interventions (glanz and lewis, 2002; national cancer institute, 2003). the hbm was developed to address problem behaviors that evoke health concerns. it postulates that an individual’s likelihood of engaging in a health related behavior is determined by his/her perception of the following six variables: perceived susceptibility (perceived risk for contracting the health condition of concern); perceived severity (perception of the consequence of contracting the health condition of concern); perceived benefit (perception of the good things that could happen from undertaking specific behaviors); perceived barrier (perception of the difficulties and cost of performing behaviors); cue to action (exposure to factors that prompt action); and selfefficacy (confidence in one’s ability to perform the new health behavior). these six health determinants identified by hbm together provide a useful framework for designing both long and short-term health behavior interventions (glanz, 1995). hbm focuses mainly on health determinants; therefore, it is most suitable for addressing problem behaviors that have health consequences (e.g., unhealthy eating and physical inactivity). hbm has been adapted and successfully applied in the design of health interventions (for example see peng, w., 2009; orji et al., 2012). however, despite the success of hbm in informing and predicting a range http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 of behaviors with health outcomes, previous research shows that hbm’s determinants are insufficient predictors of behavior (norman & brain, 2005). this is due to two main limitations of hbm: the low predictive capability of the determinants; their small effect size; and the lack of clear rules for combination of the variables and the relationships between them. on average, hbm’s determinants predict approximately 20% (r 2 < 0.21, see cohen, 1988, 1992) of variance in healthy behavior, leaving 80% of the variance unaccounted for. this points to the need to investigate other determinants that were not accounted for by hbm. in addition, most hbm researchers assume that the individual determinants are only directly related with healthy behavior and no indirect or mediating effects exist between the variables. in response to these two limitations, many researchers have extended the original hbm to increase its predictive capacity. for instance, self-efficacy was added as an extension to the original hbm (rosenstock et al., 1988). in recent years, there has been renewed interest in adapting and extending the hbm. for example, reece (2003), in a study of hiv-related mental health care extended the hbm to include ‘hiv-related stigma’ variable. however despite these extensions, the average predictive capacity of hbm remains considerably low, ranging from 20% to 40% (conner & sparks, 1996; conner & armitage, 1998). moreover, most of the new variable(s) added to the model are application area specific and thus only suitable for a particular health behavior domain under investigation. therefore, the extended models may not be applicable in other health domains. investigating other determinants that affect a range of health behaviors is essential for improving the effectiveness of health promotion intervention designs based on hbm. to address these limitations and also further our effort towards developing an effective persuasive technological intervention for behavior change, we extend the hbm to include four new variables as determinants of healthy behavior: self-identity, perceived importance, consideration of future consequences, and concern for appearance. to test the suitability of our model, we validated it on healthy eating domain. we conducted a quantitative study on 576 participants and employed structural equation modeling (sem) to exhaustively explore the interaction/relationships between the variables of the extended hbm. as a secondary research objective, we validated the predictive capacity of the primary hbm (with susceptibility, severity, benefit, and barrier as main determinants) on healthy eating behavior. we also examined the predictive utility of self-efficacy alongside cue to action to the hbm. finally, we compared the results from the three models (the primary hbm, the primary hbm with self-efficacy and cue to action added, and our extended hbm). the results from our models show that the four new variables we added to the hbm are in fact better predictors of healthy behavior than all the previously proposed variables (susceptibility, severity, benefit, barrier, and cue to action). self-efficacy however, remains the strongest determinant of healthy behavior in all the models, confirming its predictive utility. our extended hbm model led to approximately 78% increase (from 40 to 71%) in predictive capacity in comparison with the old models. most importantly, the result from our model established the existence of some mediating relationships among the variables. the knowledge of both the direct and indirect relationships sheds some light on the possible combination rules. these findings have both practical and theoretical implications which we discuss later. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the remainder of this paper is organized as follows: section 2 reviews the health belief model and some other determinants of healthy behavior. in section 3, we describe our research methodology: research approach and measurement instrument, research participants, and data validation. section 4 contains the result analysis: test of the old model and our proposed model. in section 5, we discuss our results and its implications. finally section 6 concludes the paper with future research directions. related work behavioral and social science theories provide a basis for understanding health behavior. the health belief model (hbm) has been widely adapted and applied in various health domains. despite its popularity, the hbm has limitations, which stems from its low predictive capability. we begin this section with an overview of the health belief model (hbm) and its limitations. we then discuss some other relevant health behavior determinants that could possibly extend the hbm and improve its predictive capability. this work is an effort towards designing an effective persuasive technological intervention for healthy behavior motivation. 1.1 health belief model the hbm is the first theory that was developed exclusively to explain health-related behaviors. as one of the oldest and the most widely used theories of health behavior (glanz and lewis, 2002; national cancer institute, 2003), it is regarded as the origin of systematic and theory-based research in health behavior (hochbaum, 1992; kharrazi, 2009). hbm was developed as a systematic method to identify, explain, and predict preventive health behavior (janz and becker, 1984; rosenstock, 1974). according to rosenstock (1966), the original goal of the developers of the hbm was to focus the effort of researchers who aim to improve public health by understanding why people do not take preventive measures to health promotion. since its development, hbm has been employed in a variety of public health settings over the years. for example, hbm has been applied to help increase voluntary screening rates for cervical cancer, e.g., undergoing pap-test (hay et al., 2003) and breast cancer, e.g., mammography;(simon and das, 1984), breast self-examination (umeh and rogan-gibson, 2001), for smoking cessation (li et al., 2003), contraceptive use (lowe and radius, 1987), osteoporosis prevention wallace (2002), dental-care (chen and land, 1986) , and healthy eating (deshpande et al., 2009). the model’s ability to explain and predict variety of health related behaviors has been validated across various domains and among wide range populations (janz & becker, 1984; carpenter, 2010). the model has also been used in designing many successful health interventions (arik and boeijen, 2009; kharrazi, 2009). the hbm postulates that an individual’s likelihood of engaging in a health-related behavior is determined by his/her perception of the four variables: perceived susceptibility; perceived severity; perceived benefit; perceived barrier. each of these variables, individually or in combination, has been used to explain health behavior. these four variables have been broadly categorized into two main aspects of individuals’ representations of health and health behaviors: perceived threat and behavioral evaluation (abraham and sheeran, 2005). we discuss each of these categories and their associated variables in detail below. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 1.1.1 the perceived threat the hbm posits that an individual is likely to perform a behavior if he/she perceives a threat from a disease or health condition. the threat perception is based on two beliefs: the perceived susceptibility of the individual to the disease and the perceived severity of the consequences of the disease for the individual. perceived susceptibility refers to the probability that an individual assigns to personal vulnerability to developing the health condition. in order words, it is the subjective belief a person has regarding the likelihood of acquiring a disease or harmful state as a result of indulging in a particular behavior. perceived susceptibility explains that people will be more motivated to behave in healthy ways if they believe they are vulnerable to a particular negative health outcome (rosenstock, 1966). the personal perception of risk or vulnerability has been found to be an important perception in promoting the adoption of healthier behaviors (abraham and sheeran, 2005). individuals vary widely in their perception of susceptibility to ill health condition or disease. often, the higher the perceived risk, the higher the likelihood of an individual engaging in behaviors that decrease the risk. for example, the likelihood that an individual will engage in precautionary behavior to prevent weight gain (e.g. exercise and low calorie diet) may depend on how much they believe that they are at risk of obesity. perceived susceptibility has been found to be predictive of a number of health-promotion behaviors including smoking cessation, breast self-examination, healthy dental behaviors, and healthy diet and exercise (abraham and sheeran, 2005). however, in general, it has been found that people often underestimate their own susceptibility to disease (redding and rossi, 2000). perceived severity refers to how serious an individual believes the consequences of developing the health condition will be. it deals with an individual’s subjective belief in the extent of harm that can be caused from acquiring the disease or unhealthy state, as a result of a particular behavior. an individual is more likely to take an action to prevent gaining weight if s/he believes that the possible negative physiological, psychological and social effects resulting from becoming obese pose serious consequences (e.g., death, physical impairment leading to other health condition, financial burden, pain and discomfort, and difficulties with family and social relationships). specifically, if the undesirable health outcome will not have a large impact on individual’s life, s/he will not be motivated to act to avoid it even when s/he is at risk. although the perception of seriousness of any health condition may be based on medical knowledge, it may also come from one’s belief about the difficulties a disease would create or the effects it would have on his or her life in general (mccormick-brown, 1999). 1.1.2 behavioral evaluation hbm also proposes that an individual is likely to perform a behavior if s/he perceives that performing the behavior will supposedly reduce the negative health outcome. the behavioral evaluation is based on two beliefs: the perceived benefit or efficacy of the target health behavior and the perceived costs or barrier to performing the target behavior. perceived benefit refers to an individual’s subjective opinion of the value or usefulness of enacting a health behavior to offset the perceived threat. under perceived benefit, motivation to take action to change a behavior requires the belief that the precautionary behavior will effectively prevent the condition. the individual must perceive that the target behavior will provide strong positive benefits. specifically, the target behavior must have the tendency of preventing the negative health outcome. for instance, individuals who are not convinced that http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 there is a relationship between eating and gaining weight are unlikely to adopt a healthier eating behavior for the mere purpose of reducing their chances of getting obese. perceived barrier refers to an individual’s subjective evaluation of the difficulties or the hindrances associated with the target behavior. with perceived barrier, an individual may not perform a behavior despite his/her belief about the effectiveness (benefit) of taking the action in reducing the threat if the barrier outweighs the benefit (rosenstock, 1966). the barrier often relates to the characteristics of the health promotion measure. it may be expensive, painful, inconvenient, and unpleasant. these characteristics may lead one away from adopting the behavior. to adopt the new healthy behavior, people have to believe that the benefits by far outweigh the consequences of continuing the old behavior (center for disease control and prevention, 2004). 1.2 extensions to the original hbm the original hbm consisting of the four primary variables (susceptibility, severity, benefit, and barrier) has been modified my researchers. in this subsection, we discuss how the original hbm has been extended over the years with new variables cue to action: in addition to the four primary variables mentioned above, rosenstock (1966) suggests that a combination of threat and behavioral evaluation variables could reach a considerable level of intensity without resulting in overt action unless an event occurs to trigger action in an individual. thus, cue to action determinant was added to the model to denote a trigger for health behavior when appropriate beliefs are held (rosenstock, 1966). in rosenstock’s original formulation, cues to action could include external cues such as a mass media campaign, social influence, or internal cues such as a negative change in bodily state or perception of symptoms. more generally, cues to action can be events, people, or things that spur people to change their behavior. although, cue to action have been identified as an important behavioral determinant, it is the most underdeveloped and rarely measured or researched variable of the model (janz & becker, 1984; rosenstock, 1974) self-efficacy was added to the hbm in 1988 by rosenstock et al. it is a term that is used to describe an individual’s belief about his/her ability to perform the behavior in question (bandura, 1977). generally, people may not want to attempt to do something new unless they think that they can do it. for instance, if someone believes that a new behavior is useful (high perceived benefit), but does not think that s/he is capable of doing it (low self-efficacy), chances are that s/he will not try the new behavior. while it seems intuitively clear that selfefficacy is a significant determinant of health-behavior following the wide adoption by health-promotion researchers, it is necessary to examine its impact in relation to other health determinants. table 1 presents a summary of the primary hbm constructs and the later extensions with possible strategy for applying them. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 table 1: health belief model variable summary and related intervention strategies variables definition possible intervention strategy perceived susceptibility an individual’s assessment of his or her chances of getting the disease use self-monitoring, simulation, and personalization/tailoring strategies to help individuals develop accurate perceptions of own risk. perceived severity an individual’s judgment as to the seriousness of the effects of contracting the health condition use systemic desensitization, vicarious reinforcement, and biofeedback technique to help individuals develop a realistic perception of the consequences of a condition and recommended action. perceived benefits an individual’s evaluation of the positive things that will happen as a result of enacting the health behavior use gain-framed appeal and positive reinforcement/reward mechanism to portray the potential benefits of adopting healthy behavior. perceived barriers an individual’s opinion regarding the difficulty or cost of adopting the new behavior teach problem solving and decision making strategies to overcome the perceived barrier of enacting healthy behavior cue to action this consist of both internal and external prompts that will trigger an individual to performing the target behavior employ reminder and suggestion strategies as an external prompt to performing the target behavior. biofeedback strategy could be used as an internal trigger. self-efficacy personal belief on one’s own ability to enact the desired behavior use role-playing, modeling, incremental goal setting strategies to build an individual’s believe about his/her ability to adopt healthy behavior. 1.3 strengths and weaknesses of hbm the original hbm has some recognized strengths and weaknesses which we discuss below. strengths the main strength of the hbm is its use of simplified health-related constructs that make it easy to implement, apply, and test (conner, 2010). the hbm has provided a useful theoretical framework for investigating the cognitive determinants of a wide range of behaviors for over three decades. again, it has focused researchers’ and health care professionals’ attention on variables that are prerequisites for health behavior. hence, it has formed a basis for many practical interventions across a range of behaviors (jones et al., 1987). however, it’s not without some limitations. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 weaknesses there are two main criticisms of hbm: first, the model did not explicitly spell out the relationships between the variables and no clear rules for combining the formulated variables (armitage and conner, 2000; sheeran and abraham, 1996). however, this weakness can also be viewed as strength, because lack of strict rules of combination offers flexibility that makes the hbm adaptable and applicable to many health behavior and population groups. the second and a major weakness of hbm is its predictive capability. the results from quantitative reviews of the hbm, suggest that the primary variables (susceptibility, severity, benefits, and barriers) were significant predictors of health-related behavior in most cases. however, their effect sizes are usually very small (harrison et al., 1992; abraham and sheeran, 2005). this suggests that there are other important variables that determine healthy behavior that have not been accounted for by hbm. thus, the model is incomplete, despite its high adoption by healthy behavior promotion researchers. in response to this limitation, researchers have identified other variables that are probably stronger determinants of health behavior than those identified by the hbm. for instance, rosenstock et al. (1966, 1988) extended the hbm with cue to action and self-efficacy, which generally improved the predictive power of the model. similarly, several other researches have adapted and extended hbm in various contexts. for example, king (1982), in a study of screening for hypertension extended the hbm to include a measure of individuals understanding of high blood pressure. similarly, reece (2003) extended the hbm to access hiv-related mental health care with the addition of ‘hiv-related stigma’ variable. according to reece (2003), the addition of ‘hiv-related stigma’ significantly increased the model’s predictive capacity from r 2 = 0.29 to r 2 = 0.63 showing again that there exist some room for improving the predictive effectiveness of hbm. although these researchers have attempted to improve the predictive capability hbm, most of the extended variable(s) are application area specific (only suitable for that particular health behavior under investigation). therefore, the extended model may not be suitable for application in a range of other health behavior. our work therefore, aims to develop an extended hbm model that can be applied across several health domains. in summary, the hbm provides a useful framework for investigating health behaviors. in general, all the model’s components are seen as independent predictors of health behavior (armitage and conner, 2000). high-perceived threat, low barriers, and high perceived benefits to action increase the likelihood of engaging in the recommended behavior (berker and maiman, 1979). however, according to bandura (1977 as cited in munro et al., 2007), perceived severity might have a weak correlation with health action and might even result in avoidance of protective action. the perceived severity therefore, may not be as important as perceived susceptibility. similarly, in a review by harrison et al (1992) susceptibility and barrier were the strongest predictors of behavior. an individual’s perception of perceived susceptibility and seriousness provide the motivation to act while benefits (minus barriers) provide the path of action. however, it may require a cue to action for the desired behavior to occur. the hbm differs from other models (e.g., the theory of planned behavior (tpb)) in that there are no strict guidelines on how the different http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 variables combine to predict behaviors. instead, the hbm proposes that the individual independent variables are likely to contribute to the prediction of health behaviors (sheeran & abraham, 1996). thus, hbm has been widely employed in predicting a range of behaviors with health implications. for example, janz and becker (1984) found that across 18 studies, the 4 primary hbm variables were nearly always significant predictors of health behavior. susceptibility, severity, benefits, and barrier significantly predicted health behavior for 82, 65, 81, and 100% of the studies respectively. this results show that barriers and susceptibility are the most reliable predictor of behavior followed by benefits, and finally severity. this finding was further supported by the review conducted by harrision et al. (1992) with more stringent inclusion criteria. harrison et al. (1992) reported that susceptibility and barriers were the strongest predictors of behavior. 1.4 other important health behavior determinants researchers have identified some other important variables that affect the tendency of performing a behavior. most of these variables have not been examined in the context of any existing theoretical framework and therefore, have not been widely employed by health behavior intervention designers. in this section, we review some of these determinants that could possibly improve the predictive capability of existing health behavior theories. consideration of future consequences one of the main difficulties one encounters when attempting to motivate people to adopt a healthy behavior is the invisible immediate and short-term benefit and consequences of many health behaviors. health-related behaviors are often characterized by immediate effort for possible future gain. a rational decision to adopt healthy behavior may require that the value attached to the future health benefits outweigh the immediate cost in terms of time, money, or pleasure foregone to achieve longer-term health benefit (adams, 2012). how one considers the future outcomes of the present behavior may also play a role in adoption of healthy behaviors. given this, strathman et al. (1994) proposed a new variable: consideration of future consequences (cfc). cfc is used to measure “the extent to which people consider the potential distant outcomes of their current behavior and the extent to which they are influenced by these potential outcomes” (strathman et al., 1994). research suggests that cfc is a reliable, stable, and a valid predictor of a range of significant behaviors. strathman et al. (1994) validated the cfc variable and reported internal reliability for the 12-item scale ranging from .80 to .86. furthermore, joireman et al. (2006) examined the relationship between cfc and some other related constructs and provided evidence for its convergent and discriminant validity. for instance, people who scored high in cfc also scored higher in delayed gratification and higher levels of conscientiousness (strathman et al., 1994). since its development, cfc has successfully predicted a range of behaviors, including health concern, environmental behavior, and cigarette or alcohol use (strathman et al., 1994). research has shown that individuals high on the cfc scale generally reported greater concern for health and lower use of alcohol and cigarettes. similarly, subsequent research has demonstrated that individuals scoring high in cfc reported that they exercised more frequently (uuellette et al., 2005), are more likely to get an hiv test, and less likely to engage in risky sexual practice (dorr et al., 1999). while the validity of cfc to predict range of behaviors has been examined, promising past studies have not tested the validity of cfc within any well-known http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 health theoretical framework. a study by orbell et al. (2004) was one of the few that studied cfc under an existing theoretical framework. they utilized the theory of planned behavior (tpb) framework to illustrate the mediating role of cfc on tpb variables. we propose to examine whether the cfc will affect the adoption of a healthy behavior and whether the interaction between cfc and healthy behavior is mediated by hbm determinants by integrating it into the hbm model as one of the health behavior determinants. self-identity self-identity is another predictor of behavioral intention and actual behavior that has been suggested by identity theorists and has been empirically tested by researchers (stryker, 1987). self-identity is a term used in describing some salient and enduring part of one’s selfperception in relation to a particular behavior (e.g., “i think of myself as a health conscious person”) (sparks, 2000). research has shown that self-identity plays a role in motivating human behavior. for instance, according to sparks and gutherie (1998), individuals who perceive themselves as health conscious tend to positively associate with health behavior. there are other evidences linking self-identity to actual behavior or behavioral intention in several domains, including exercising (theodorakis, 1994). in relation to healthy behavior, some work has identified that self-identity influences the tendency of one adopting a healthy behavior. according to szalavitz (2012), one of the best ways to change health behavior is to change a person’s self-identity. “” szalavitz (2012), when a smoker begins to view herself as a nonsmoker or a teen sees binge-drinking as something people like me don’t do, behavior change is typically more lasting than if the person’s sense of identity is not invoked. it has been argued that measures of self-identity can enhance models of the cognitive antecedents of behavior (eagley and chaiken, 1993). for example, spark and shepherd (1992) examined the role of identity in relation to the theory of planned behavior and found that individuals who see themselves as green consumers (i.e., green identity) had stronger intentions to consume organic vegetables, and their self-identities contributed significantly to the prediction of intention over and above other tpb variables. furthermore, spark et al (1995) reported that self-identity had an independent predictive effect on intentions in relation to five dietary changes associated with reducing the amount of fat in the diet. thus, self-identity has been shown in many studies to be a useful addition to tpb variables in predicting different dietary behaviors. several other research works in domains ranging from exercise, eating behavior, to sexuality, and drug use suggests that having one’s identity wrapped up in a particular behavior is a crucial motivating factor to sustaining the behavior (szalavitz, 2012). the reverse is also possible. for example, a person whose identity and selfsense are tied directly to unhealthy behavior will likely continue performing the behavior. concern for appearance several research findings have shown that people who are concerned about their health believe that they are responsible to engage in protective health behavior (orji et al., 2012). however, other processes may also be operating. people may eat properly, not smoke, exercise, watch their weight, and practice other preventive health behavior for reasons unrelated to health concern. research has shown that people are motivated by their concern for appearance, attractiveness, and popularity more than by the health consequences of their behavior (hayes and ross, 1987). the society tends to attach a lot of importance on an individual’s physical appearance. this is evident in public media and advertising sectors where several actions and products are symbolized with physically attractive models, actors http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 and actresses suggesting that they are the ideal that the public should seek to achieve (hayes and ross, 1987). according to kai-yan (2002) people believe that physical attractiveness is linked to life of happiness, success, and social acceptance while fatness is associated with laziness, stupidity, and chaos. in general, physically attractive people have more positive social contacts and more success in manipulating their social environment than unattractive people (barocas and karoly, 1972). concern with appearance has had a long-term research history. several research findings have shown that concern with appearance exerts a great influence on human behavior and decision making. for example, as early as 1960’s walster et al. (1966) found in a study of 752 students that physical attractiveness emerged as the only predictor of an individual’s liking for and desire to subsequently date a potential partner. similarly, concern with appearance has successfully predicted health behavior in many domains including dieting (hayes and ross, 1987). similarly, increased physical activity seems to be associated with concern about appearance. according to the analysis by hausenblas and fallon (2006), exercisers have a more positive body image than nonexercisers. despite the increasing evidence of the widespread impact of appearance concern, it has not been widely adopted by health behavior promotion researchers. however, concern for appearance ought to be taken more seriously because people’s feelings about their appearance can have significant effect on their self-perception, their health behavior, wellbeing, and even adherence to treatment. thus concern with appearance may be a motivating factor in preventive health behaviors. perceived importance research has shown that perceived importance could also be a significant predictor of behavior (deshpande et al., 2009). perceived importance, a term suggested and validated by robin at al. (1996), describes how much value a person attaches to the outcomes of a particular behavior. it is different from perceived benefit in that benefit is more concerned with the good things that will happen as a result of performing the behavior in question to avert the threat. perceived important on the other hand is more about the value an individual attaches to the outcomes of a particular behavior. these outcomes can be either positive (as a result of performing the desired health behavior) or negative as a result of not performing the behavior (or indulging in the unhealthy behavior). perceived importance unlike benefit is not necessary evaluated based on its ability to prevent the threat. for example, exercising 30 minutes daily may be perceived by an individual to be of benefit (because of its ability to keep one from gaining excessive weight), however, s/he may likely not exercise if s/he perceive that the various benefits accrue to exercising as unimportant to him/her. perceived importance has been shown to successfully predict ethical behavior intention (moral judgment) (robin et al., 1996), dietary behavior (deshpande et al., 2009) and honjo and siegel (2003) showed that perceived importance impact on weight concern and smoking initiation. the impact of perceived importance on health behavior is underexplored. therefore further research is needed to investigate the impact of this variable on various health behaviors both independently and in the context of other known theoretical frameworks. in summary, behavioral or social science theories provide the basis for understanding health behavior. these theories have been proposed as a framework for designing interventions, understanding how the interventions work to promote change in behavior, and for evaluating the effectiveness of interventions. however, the theories are limited by the percentage of variance in behavior they explain. therefore, there is a need for research on other variables to account for the missing variance. on the other hand, the variables discussed above http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 (consideration of future consequences, self-identity, concern for appearance, and perceived importance) have been validated as independent predictors of various behaviors. however, they have not been examined in context of any known health theories to know their relationships with other variables and their ability to increase the predictive capacity of these theories. this work therefore seeks to examine the predictive capacity of these variables in the context of hbm, one of the widely employed theories of health behavior research method the data reported in this paper is part of a project aimed at designing theory-driven technological interventions for promoting healthy behavior that was approved by university of saskatchewan ethics board. research approach and measurement instrument this study employed a quantitative method of data collection which involved the collection of primary survey data from a large number of participants. to collect data for our model, we developed an online survey version of the hbm scale, concern for appearance; consideration of future consequences; self-identity; and perceived importance scales posted announcements in high traffic websites and forums. the survey was developed after an extensive review of hbm, their application areas and their effectiveness and was pilot tested (n=10) for refinement. we chose dietary behavior as a case study to validate our research instrument and to test our model because healthy eating is a desirable behavior with wide range of both mental and physical health implications. good dietary behavior can delay or even prevent the onset of many diseases, including diabetes 2 diabetes and obesity. as a result, interventions aimed at modifying dietary behavior have been identified as the cornerstone treatment for these health conditions (lau et al., 2007). accordingly, several health promotion and diseases control programs (for example see peng, 2009; fujiki et al., 2008; orji et al., 2012) are focused on promoting healthy eating and physical activity. the survey instrument consists of questions assessing (1) participant demography; (2) perceived benefit of healthy eating; (3) perceived barrier to healthy eating; (4) perceived susceptibility; (5) perceived severity; (6) cue to action; (7) self-efficacy (8) likelihood of healthy eating behavior (9) concern for appearance; (10) consideration of future consequences (11) self-identity; (12) and perceived importance, where (9), (10), (11) and (12) are new variables, that we propose as extension to the hbm model. the questions used in measuring the hbm variables (questions (2) to (8) listed above) were derived from abraham and sheeran (2005) and most of the questions have been validated on healthy eating by deshpande (2009) and sapp and jensen (1998). all the hbm variables were measured using a 7-point likert scale ranging from “1 = strongly disagree” to “7 = strongly agree”. an example of a question in the perceived susceptibility variable category is requesting the participants to state their level of agreement with the statement “if i don’t stick to a healthy diet, i will be at high risk for some diet-related diseases”, extending hbm variable following from the discussion in section 2.4, we extended the hbm model by including consideration of future consequences, concern for appearance, self-identity, and perceived importance. consideration of future consequences (cfc) has been increasingly acknowledged as being important behavior determinant. the effect that the current health behavior and attitudes has on future health and well-being can be profound and, research has shown that consideration of future consequences impacts health behavior (uuellette et al., 2005; joireman et al., 2006). http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 cfc was measured using 12-item questions developed and validated by (strathman et al., 1994). respondents were required to indicate to what extent each item characterized them on a 5-point likert scale ranging from “1 = not at all” to “5 = extremely well”. some examples of questions are “i often consider how things might be in the future and try to influence those things with my day to day behavior,” “i only act to satisfy immediate concerns, figuring the future will take care of itself,” and “i think that sacrifice now is usually unnecessary since future outcomes can be dealt with at a later time.” concern for appearance is included based on previous research findings that people are motivated by their concern for appearance, attractiveness, and popularity more than by the health consequences of their behavior (hayes & ross, 1987). concern for appearance was measured using validated scales adapted from hayes & ross (1987). typical questions for this variable ask the participants to rate how important it is for them to: “look attractive” and “have good posture”. the questions were measured using a 5-point likert scale ranging from “1 = not at all important” to “5 = very important”. self-identity is used to describe one’s perception about him/herself. research has shown that self-identity plays a role in motivating human behavior. individuals who perceive themselves as health conscious tend to positively associate with healthy behaviors (sparks & gutherie, 1998). we measured self-identity using a validated scale adapted from sparks & gutherie (1998). an example of a question in this category is “i think of myself as someone who is concerned with healthy eating”. the participant states their level of agreement with each item using a 5-point likert scale, ranging from “1 = strongly agree” to “5 = strongly disagree”. perceived importance: research has shown that perceived importance could also be a significant predictor of behavior. perceived importance, a term suggested by robin et al. (1996), describes how much value a person attaches to the outcomes of a particular behavior. it was added following research from deshpande et al. (2009) that showed that perceived importance is a determinant of healthy eating. perceived importance was measured using a validated scale adapted from deshpande et al. (2009). a typical question is “how important is it for you to eat a diet high in nutrition?” the questions were measured using a 5-point likert scale ranging from “1 = not at all important” to “5 = very important”. 1.5 research participants the participants consisted of 576 adults recruited from the internet. there were 559 usable responses (responses from participants that are at least 18 years of age). the data were gathered over a period of eleven months in from august 2011 to august 2012. the eligibility criterion was that the participants were at least 18 years old at the time of data collection. the eligibility criterion was in compliance with the study ethics approval that ensured that the participants were of legal age to make decisions independently (including decisions on what to eat). the participants’ demographics is as summarized in table 2. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 table 2: summary of participants’ profile variable frequency (n = 559) percent (%) gender female 269 48 male 290 52 age 18-25 196 35 26-35 203 36 36-45 77 14 over 46 83 15 education less than high school high school graduate 76 14 college diploma 69 12 bachelor’s degree 189 34 master’s degree 165 30 doctorate degree 40 7 others 14 2 geographical territory africa 181 32 north america 176 32 south asia 124 22 western europe and uk 39 7 middle east 11 2 south and central america 6 1 east europe and russia 4 1 southern europe/mediterranean 8 1 australasia 1 0 others 9 2 1.6 data validation to ensure reliability and validity, we selected an analytical method that explicitly models the linear and quadratic effect (non-linear relationships) between the measured variables. we used both spss 19 and smartpls 2 (ringle et al., 2005) structural equation modeling (sem) tool to exhaustively explore the interaction between the variables and to simultaneously solve the multiple equations. instrument validation: to determine the validity of the survey instrument we conducted principal component analysis (pca) using spss 19. before conducting pca, the kaisermeyer-olkin (kmo) and bartlett sphericity tests were used to measure the sample adequacy (kaiser, 1970.). the kmos were all greater than the recommended threshold of 0.5 and the result of bartlett sphericity tests were significant at <0.001. thus, the data was suitable to conduct factor analysis (guo, 1999). the factor loadings and the corresponding factor scores (weights) for each variable were generated. the factor loading resulted in removal of some questions and the remaining questions have larger loading on their corresponding factor (≥0.7) than cross-loadings on other factors (≤0.4) (gefen et al., 2000). thus, these questions http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 could effectively reflect factors because they have good validity including convergent and discriminant validity. reliability of the variables and indicators: we examined the data for reliability using both spss and smartpls tool. to check the reliability, we used cronbach’s α, which ranges from 0 to 1. according to peter (1997), cronbach’s α should be ≥ 0.7, but for 2-3 indicator variables, a cronbach’s α ≥ 0.4 is acceptable. as shown in tables 3 (column 4), the cronbach’s α of the variables satisfies these conditions (susceptibility and severity have two indicators each, therefore, their cronbach’s α are within the acceptable range of ≥ 0.4) results analysis after the validation of the data, we developed and tested the path model presented in figures 1, 2, and 3 using sem in smartpls tool, which allows for simultaneous measurement (of indirect and direct influences of the variables) and structural models. in contrast to previous work in which sem was used to confirm or test hypotheses, our goals are: 1) to validate our extended hbm in the healthy eating behavior domain; 2) to test the predictive capability of the extended hbm model by generating a predictive model of healthy eating; 3) to exhaustively examine the interactions between the extended hbm variables and the original hbm variables; 4) to validate the old hbm and confirm its performance on healthy eating behavior, and 5) to compare the predictive capability of the original hbm variables and the extended hbm variables. to achieve these aims, we systematically examined the interactions and the impact of the 10 variables (susceptibility, severity, benefit, barrier, cue to action, self-efficacy, perceived importance, consideration of future consequences, appearance concern, and self-identity) on healthy behavior. this enabled us to exhaustively explore the importance of each variable in determining healthy eating behavior. we chose to validate our model on eating behavior because it is associated with many health implications. 1.7 test of proposed path model partial least square (pls) model analysis essentially proceeds through two stages. the first stage deals with reliability and discriminants validity analysis of the indicator items and their associated independent variables in the outer model. in the second stage, the relationships between the dependent variables in the inner model are estimated through bootstrapping procedures. our analysis rigorously followed these two stages to confirm both discriminate and convergent validity and internal consistency. the model fit indices of the structural equation model are presented in table 3. the square root of average variance extraction (ave) coefficients from the smartpls output is a key statistic at the first stage of the path analyses as it represents the variance extracted by the variable from its indicator items. as http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 shown in table 3, the ave indices for all the variables are above the ideal value of 0.5. the cronbach’s α values and the composite reliability that analyzes the strength of each indicator’s correlation with their variables are all higher than their threshold values. specifically, the high cronbach’s α values of our newly added variables of consideration for future consequence, perceived importance, self-identity, and appearance concern (0.81. 0.87, 0.88, and 0.80 respectively) shows suitability. similarly, redundancy values are greater or equal to “0”. the t-values that measure the significance of the path coefficient are all greater than the recommended threshold value of 1.96. all the interactions (path models) presented in the models are statistically significant at p≤ 0.01. overall, our proposed model’s variables predict 71% of variance in healthy eating behavior (see figure 1). this shows the high predictive relevance and the suitability of the extended hbm. to measure the shared variance between the variables and their measures, we evaluated the discriminate validity of the model. the discriminate validity further confirmed that the diagonal values were significantly higher than the off diagonal values (i.e., correlation values) as shown in table 4. all variables had diagonal elements (ave) greater than the recommended value of 0.5, and greater than the correlation values; the data demonstrates successful discriminate validation. table 3: scale reliabilities table 4: ave and latent variables correlation matrix http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 1.8 proposed model figure 1 represents the extended hbm proposed by us, figure 2 represents the primary hbm path model with added variables self-efficacy and cue to action (henceforth referred to as the “intermediate model”) and figure 3 represents the primary hbm path model (henceforth referred to as the “baseline model”). the baseline model consists only of the four primary determinants (benefit, barrier, susceptibility, and severity) of hbm. in the intermediate model the six variables: benefit, barrier, susceptibility, severity, self-efficacy and cue to action are the determinants of the healthy eating behavior. in the extended hbm models (figure 1), perceived importance, consideration of future consequences, self-identity, and appearance concern were added as an extension to the intermediate model’s variables (benefit, barrier, susceptibility, severity, self-efficacy and cue to action). the variables (benefit, barrier, susceptibility, severity, self-efficacy, cue to action, perceived importance, consideration of future consequences, self-identity, and appearance concern) serve as independent variables that influence (i.e., are the determinants of) healthy eating behavior in our extended model. 1.8.1 the influence of the models’ variables on healthy eating behavior the structural model determines the relationships between the determinants in the models. important criteria for evaluating the structural model are the coefficient of determination (r 2 ) measures the percentage of variance that is explained by the independent variable of a model, as well as the path coefficients () and their corresponding significance level (pvalue), which were derived from the t-test (hair and ringle, 2011). the structural models and their corresponding are r 2,  are as shown in figures 1, 2 and 3 and summarized in table 5. the extended hbm model, the intermediate, and the baseline model yield r 2 value of 71, 40, and 20% respectively. the p-values as shown in table 3 are all ≤ 0.01. as can be seen from the three figures, numerous interactions exist among the many variables involved in the extended model. however, in designing theory-driven interventions for health promotion and disease prevention designers often need to select from the various variables of hbm since it may not be feasible to implement all the variables in a particular intervention. the practical question, therefore, is which of the variables or which combinations of variable from the hbm will provide the most effective result? to answer this question, we explored the effect of each variable on healthy behavior by exploring the performance of our model with and without each of the ten variables from our extended hbm. this gives an insight on the proportion of the variance in the dependent variable that is predictable from each independent variable f 2 as shown in table 6. we also tested for mediating effect in pls-sem and tested for significant mediation using sobel test (sobel, 1982). the test establishes that the effect consideration for future consequences, self-efficacy, and self-identity on healthy eating behavior are partially mediated by hbm determinants. 1.8.1.1 the performance of the baseline and the intermediate hbm the old hbm model comprises of the baseline and the intermediate model. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the structural models of the intermediate and the baseline model are as shown in figures 2 and 3 respectively and summarized in table 5. in the baseline model, perceived barrier emerged as the strongest determinant of healthy behavior ( = -0.42, p<0.01). it is followed by susceptibility with only a weak effect ( = 0.11, p<0.01). the independent variables in the baseline model predicted only 20% of the variance in healthy eating behavior. in the intermediate model, with the exception of self-efficacy and perceived barrier, all the hbm variables show only weak association with healthy eating behavior (with  value ranging from 0.02 to 0.08). susceptibility ( = 0.06, p≤ 0.01), severity ( = 0.05, p≤ 0.01), benefit ( = 0.02, p≤ 0.01), and cue to action ( = 0.08, p≤ 0.01). self-efficacy emerged as the only strong positive and significant determinant of healthy behavior with  = 0.53 and p≤ 0.01. on the other hand, perceived barrier remains the only variable that influences healthy behavior negatively with  = -0.20 and p≤ 0.01. the independent variables in the intermediate model accounted for 40% of the variance in healthy eating behavior. 1.8.1.2 performance of the extended hbm the new variables (perceived importance, consideration of future consequences, self-identity, and appearance concern) added as an extension to the hbm passed both the validity and reliability test. their test scores exceeded the recommended threshold for all the measured components (see table 3 and 4). this shows that they are adequate to be used as behavior determinants with the hbm. the structural model of the new extended hbm is as shown in figure 1 and summarized in table 5. all the new added variables to the hbm are positively and significantly associated with healthy behavior with path coefficient () value ranging from 0.10 to 0.37. surprisingly, our model shows that the newly added variables are better predictors of healthy behavior than the variables from the baseline and the intermediate hbm, with the exception of self-efficacy ( = 0.39, p≤ 0.01, , f 2 = 21%) which is part of the intermediate hbm . the contributing effect of each individual variable (effect size) f 2 of the newly added variables ranges from 15% to 20% (see table 6). perceived importance ( = 0.32, p≤ 0.01, f 2 = 15%), consideration of future consequences ( = 0.20, p≤ 0.01, f 2 = 15%), self-identity ( = 0.37, p≤ 0.01, f 2 = 20%), and appearance concern ( = 0.10, p≤ 0.01, f 2 = 16%). perceived barrier emerged as the only variable that influences healthy behavior negatively. again, in addition to the direct impact of the variables on healthy behavior, our model also shows that the primary hbm variables (susceptibility, severity, benefit, and barrier) mediate the effect of consideration of future consequences and self-identity on healthy behavior. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 1. the extended health belief model predicting healthy eating behavior. the ‘ ’denotes the interactions and the associated no. represents the β values. the ‘ ’ denotes the mediating effects. figure 2. intermediate model predicting healthy eating behavior figure 3. baseline model predicting healthy eating behavior http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 discussion and implications a major limitation of hbm as identified by research and confirmed by our model (see figure 3) is the low predictive capacity of its primary variables (abraham and sheeran, 2005). the hbm variables predict approximately 20% of the variance in healthy behavior on average. this suggests that there are other important determinants of healthy behavior not yet accounted for by hbm. our work responds to these shortcomings by discovering new variables (consideration of future consequences, perceived importance, appearance concern, and self-identity that could extend the capability of hbm. again, as can be seen from figures 2 and 3, the majority of research involving hbm assumes the existence of only direct relationships between the variables. this is as a result of lack of clear rule of combination of variables and their relationships. our model established the existence of both direct and indirect (mediating) relationships among the variables in the hbm. table 5: summary of the interactions between the determinants and healthy eating behavior table 6: magnitude of variance on behavior accounted by each independent variable (effect size) http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 1.9 the baseline and intermediate hbms from the sem results shown in figures 3, among the four primary variables (susceptibility, severity, benefit, and barrier) in the baseline model, barrier emerged as the strongest predictor of behavior (with path coefficient  = -0.42 and p ≤0.01). this is followed by susceptibility (with  = 0.11 and p ≤0.01), finally followed by severity and benefit with  = 0.08 and p ≤0.01 each as shown in figure 3. this is in agreement with previous results that have identified barrier and susceptibility as the best predictor of healthy behavior (harrison et al., 1992; janz and becker, 1984). the baseline model predicted a total of 20% (r 2 = 0.20) of variance in healthy behavior. this is again comparable to earlier research focusing on a variety of health behaviors. the variance explained has ranged from 20% to 40% (conner and sparks, 1996; conner and armitage, 1998). a sizable amount of variance (approx. 80%) could not be explained by the baseline model. however, the addition of the two variables cue to action and self-efficacy in the intermediate model tremendously increased the predictive capability of the model by 100% (from 20% to 40%) as shown in figure 2. this shows that these two variables account for as much variance as the four primary hbm variables combined. interestingly, self-efficacy emerged as both the strongest and the most significant determinant of healthy behavior with  = 0.53, and p ≤0.01. it is followed by barrier with  = -0.20, and p ≤0.01. this shows that health intervention designers should pay more attention to designing interventions that increase the user’s feeling of self-efficacy. 1.10 the extended hbm to improve the predictive capability of the hbm which is the major limitation of hbm, we added the four variables consideration of future consequences; self-identity; perceived importance; and appearance. as shown in figure 1, including these four variables significantly increased the predictive capacity of the model by approximately 78 % (r 2 increased from 40% to 71%). the statistical finding shows that the four new variables added to the hbm have their place as determinants in hbm model. the model shows that selfidentity, appearance, consideration of future consequences and perceived importance (listed in decreasing order of magnitude of effect) yield substantial improvements (see table 6). thus we expect that healthy behavior intervention based on our extended hbm variables will be more effective. in addition to that, within the extended hbm, self-efficacy still emerged as the strongest and most significant determinant of healthy behavior (with  = 0.39, effect size f 2 = 21% and p ≤0.01) (see figure 1 and table 6). this again confirms the importance of designing to promote self-efficacy. however, benefit, severity, and cue to action have weak association with behavior with effect size f 2 = 1%, 1%, and 0% respectively. this shows that severity, benefit, and cue to action may not be as important as the other variables in promoting the health behavior (e.g., perceived susceptibility). this is in line with bandura (1977 as cited in munro et al., 2007) “perceived severity might have a weak correlation with health action and might even result in avoidance of protective action.” http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 1.10.1 variables interactions interaction with cue to action: cue to action was introduced by resenstock (1966) based on the assumption that certain cues would activate/stimulate an individual’s perception of threat from certain health condition by influencing the perceived severity, susceptibility, or both. with more powerful cues or accumulation of cues (especially those with more personal relevance), a person is stimulated to take action. surprisingly, from both our extended model and the intermediate model (see figures 1 and 2 respectively), it was found that cue to action has weak or no effect (with  = 0.03 and 0.08, and p≤ 0.01) on health behavior. as shown in table 6 cue to action has very weak effect (f 2 = 0%) on health behavior. this finding was unexpected. however, we observed that the addition of the four new determinants in our extended model decreased the association between cue to action and healthy behavior (i.e.,  from 0.08 to 0.03, see figures 1 and 2). one possible explanation is that the introduction of the variables self-identity, consideration of future consequences, importance, and appearance reduce the tendency of any form of cues (both external and internal) to trigger behavior performance. another possible explanation according baranowski et al. (2003) is that people may not rate the importance of cue to change accurately. little research has been focused on the impact of cue to action. however, internal cues, such as feeling better physically or mentally after adopting a healthy behavior were rated as the most likely to prompt action (baranowski, et al., 2003). interaction with perceived threat: research on hbm and its applications so far has focused mainly on manipulating an individual’s perception of threat (susceptibility and severity). this is because perceived susceptibility and severity have been considered as the primary motivation to change for most individuals. our model, however, shows that perceived severity has only a weak relationship with health behavior (with  = 0.08, f 2 = 1%, and p≤ 0.01) and susceptibility shows only a moderate association with the behavior ( = 0.17, f 2 = 5%, and p≤ 0.01) as shown in figure 1 and table 6. this ordering confirms previous findings that have identified susceptibility as a stronger predictor of healthy behavior when compared with severity (harrison et al., 1992; janz and becker, 1984) and severity as weak predictor that might even lead to avoidance of the health behavior (bandura, 1977 as cited in munro et al., 2007). this implies that perceived severity may not be as important as perceived susceptibility in motivating behavior change. this also implies that health intervention developers focusing on only these key variables (susceptibility and severity) have only a limited chance of being successful (approximately 6% effect size). it also means that perception of threat alone is not enough to motivate healthy behavior adoption. interaction with perceived benefit and barrier: the perceived benefit and barrier are among the primary variables of the hbm that have been reasonably well researched. the hbm posits that an individual is likely to perform a behavior if s/he perceives that performing the behavior will reduce the negative health outcome (perceived threat). our data shows that among the four primary variables of hbm (susceptibility, severity, barrier, and benefit), barrier is the strongest and most significant determinant of behavior (with  = -0.42 and p≤ 0.01). however, the addition of self-efficacy and cue to action in the intermediate modelreduces the inhibiting effect of barrier (from  = -0.42 to  = -0.20), as shown in figure 2. this can be explained by the fact that increasing an individual’s feeling of confidence about a particular behavior will reduce the perceived difficulty associated with the performance of that particular behavior and increase the tendency of performing the behavior. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 our model shows that self-efficacy, self-identity, and consideration for future are mediated by perceived barrier (see figure 1). therefore, health intervention designers aiming at reducing the inhibiting effect of perceived barrier associated with a particular health behavior should design an application that increases the individual’s self-identity, concern of future, and selfefficacy about his/her ability to perform a behavior. on the other hand, benefit, just like severity, has a weak relationship health behavior (with  = 0.08, f 2 = 1%, and p< 0.01). however, it mediates the relationships between self-identify and consideration for future on behavior (see figure 1). similarly, any intervention designed to increase self-identity and consideration for future will increase the perceived benefit associated with a particular health behavior. interaction with self-efficacy: self-efficacy describes an individual’s confidence in his/her ability to perform the health behavior. the hbm proposes that an individual is more likely to perform a behavior if s/he believes that s/he is able to perform it. our extended model shows that among all the ten variables that emerged as determinants of healthy behavior, selfefficacy is the strongest and the most significant determinant ( = 0.39, f 2 = 21%, and p≤ 0.01) as shown in figure 1. interestingly, self-efficacy has both a direct relationship with healthy behavior and an indirect relationship via barrier (with  = -0.45 and p≤ 0.01) as shown in figure 1. this means that self-efficacy not only increases an individual’s tendency of adopting a healthy behavior but also reduces the inhibiting effect of barrier on behavior performance. the strong and significant negative association of self-efficacy with barrier ( = -0.45 and p≤ 0.01) means that designing intervention to increase the feeling of self-efficacy might be the most effective way to reduce an individual’s perceived difficulties associated with a certain behavior. this highlights the need for both health behavior intervention designers and behavioral theorists to pay special attention on self-efficacy and to design their interventions to emphasize self-efficacy. interaction with self-identity: self-identity, which describes one’s perception about him/herself, emerged as the second strongest and significant determinant of healthy behavior (with  = 0.37, f 2 = 20%, and p≤ 0.01), following self-efficacy. among the four new variables that we added to extend the hbm self-identity is the best determinant. the strong relationship between self-identity and healthy behavior is in line with the cognitive dissonance theory (festinger, 1957), which suggests that people try to be consistent with their existing views to reduce dissonance. our results show that people value consistency. therefore, if an intervention can be designed in a way that associate individuals with certain health behavior and make them commit to a behavior, they are likely to stick to it. therefore, designers aiming at increasing health behavior can use commitment, consistency, and goal setting to make the user identify with the desired health behavior. tracking and comparing behavior with stated goals and commitments makes the deviations observable, and will cause dissonance, which could motivate the desired behavior performance. it is also worth noting that self-identity has both direct and indirect relationship with the healthy behavior. susceptibility, severity, benefit, barrier, and cue to action mediate the relationship between self-identity and healthy behavior (with  = 0.17,  = 0.16,  = 0.21,  = -0.24, and  = 0.16 respectively) as shown in figure 1. this implies that designing to increase self-efficacy will invaluably increase the perceived susceptibility, severity, benefits, and cue to action while reducing the perceived barrier interaction with perceived importance: perceived importance describes how much value a person attaches to the outcomes of a particular health behavior. perceived importance is a significant determinant of healthy behavior from our model (with  = 0.32, f 2 = 15%, and p≤ http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 0.01). this is in line with findings in previous work that stated that importance is positively associated with healthy behavior (deshpande et al., 2009). this positive association of importance with healthy behavior shows that the value an individual attached to outcomes associated with a particular behavior is a better determinant of behavior performance than the ability of the behavior to avert perceived threat (benefit). perceived importance is not necessarily associated with threat. however, since several health behaviors (e.g., healthy eating) often have multiple benefits, a prerequisite to designing to increase the perceived importance should be to identify the outcome that is of important to an individual or group of individuals. designers should, therefore, design personalized interventions that motivate individuals by linking behavior performance to outcomes that are of important to each individual. interaction with consideration of future consequences: a major challenge in motivating people to adopt healthy behavior is the invisible immediate and short-term benefit of many health behaviors. adopting and maintaining a healthy behavior is a difficult task that has almost no immediate health effect. therefore, consideration of future consequences measures the extent to which people consider the potential distant outcomes of their current behavior. our model shows that consideration of future consequence is a significant determinant of healthy behavior (with  = 0.20, f 2 = 15%, and p≤ 0.01). the positive relationship between consideration of future consequences and healthy behavior confirms previous study results that consideration for future plays a role in the adoption of healthy behavior (strathman et al., 1994). thus, to motivate an individual to adopt a healthy behavior, intervention designers should make the long-term effects of healthy behavior observable. another interesting finding from our model is that consideration of future consequences has both direct and indirect relationship with healthy eating. the relationship between consideration of future consequences and healthy behavior is mediated by susceptibility, severity, benefit, and barrier (with  = 0.08,  = 0.13,  = 0.09, and  = -0.07 respectively) as shown in figure 1. this implies that any intervention that is designed to increase consideration of future consequences will also increase the perceived susceptibility, severity, and benefits while reducing the perceived barrier. interaction with appearance: people may adopt healthy behavior for reasons that are unrelated to health. for instance, concern for appearance has been identified as one of those reasons. from our model, appearance is positively associated with healthy behavior ( = 0.10, f 2 = 16%, and p≤ 0.01). this confirms previous research that shows that people are motivated by their concern for appearance and attractiveness (hayes and ross, 1987). this is because people believe that attractiveness is linked to life of happiness. our results also confirm the previous study that shows that weight concern (which can lead to reduced attractiveness) is an important consideration in peoples’ decision to adopt a healthy behavior (orji et al., 2012). therefore, physical self-presentation is important for motivating healthy behavior change. designers could emphasize reduced attractiveness as a potential risk of unhealthy behavior. the mediating roles on consideration of future consequences, self-identity, and selfefficacy: one of the limitations of hbm is the lack of clear rules of combination (armitage and conner, 2000). the model did not explicitly spell out the relationships between the variables. a novel contribution of this work is the establishment of the mediating role played by the five hbm’s variables (susceptibility, severity, benefit, barrier, and cue to action) on consideration of future consequences, self-identity, and self-efficacy. as shown in figure 1, http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the four variables susceptibility, severity, benefit, and barrier partially mediate the relationship between consideration for future consequences and healthy behavior. introducing consideration for future consequence variable will increase an individual’s perception about susceptibility, severity, benefit and reduces the perceived barrier associated with behavior performance. for example, an intervention that increases an individual’s consideration of future is more likely to make the individual have a favorable evaluation of the perceived susceptibility, severity, benefit, and barrier associated with a particular behavior. similarly, as shown in figure 1, along with perceived susceptibility, severity, benefit, barrier, and cue to action partially mediates the relationship between self-identity and healthy behavior. this implies that any intervention that causes one to associate him/herself positively with a certain health behavior (e.g., “i am a healthy eater or health conscious person”) will be more likely to increase the likelihood of responding to various cues to action with regards to that particular behavior. for example, an individual who encounters a health message that associates him/her with certain health behavior may be more likely to respond to various cues to action with respect to that particular behavior; see more of the benefits as opposed to the barriers associated with performing the target behavior; and finally have an increased evaluation of perceived threat of the unhealthy behavior. this will lead to an increased adoption of healthy behavior. also worth noting is the fact that barrier mediates the relationship between self-efficacy and healthy behavior. as shown in figure 1, self-efficacy reduces the negative influence of barrier on healthy behavior. this implies that designing to increase the feeling of selfefficacy using various technological intervention strategies such as role-playing, incremental goal setting, and modeling will reduce the hindering effect of barrier on healthy behavior adoption. establishing the existence of mediating relationship contributes to both the theory and the practical application of the theory in intervention design. theoretically, it provides a holistic understanding of the interaction between the variables (both direct and indirect interactions). in practice, it gives intervention designers an idea of how to combine the variables of the extended hbm to amplify their effect. limitations although this study enhances our understanding of the factors determining the healthy behavior, there are some limitations that warrant further research. most of the survey participants (approximately 60%) in the present evaluation are in the age range of 18-35 years; therefore, care should be taken in generalizing our result for all age groups. a further bias might be caused by the relatively high level of education of the participants – approximately 70% of our participants are at least a bachelor’s degree holder. future work will try to expand the participants group so it is representative of all age groups. including younger participants would ensure also a wider representation of people with lower education levels (e.g. not yet completed high-school, or middle school), where healthy eating interventions would be particularly important. http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the present study tested our proposed model in the healthy eating domain only; there is still the need to validate the extended hbm model in other health behavior domains. in principle, our model could be applied to various health behavior domains (e.g. exercising, smoking cessation, dealing with various addictions). future studies can also expand this model by adding some other variables that can account for the remaining 29% variance in healthy behavior. in the future, we would like to conduct a more comprehensive evaluation of the proposed model. we will use the model to develop an application to motivate healthy eating behavior and evaluate its effectiveness. conclusion behavioral theories play an important role in the design, implementation, and evaluation of health behavior interventions. in recent years, a number of behavior theories have emerged. the hbm is of particular interest, because of its wide adoption and application in several health domains. several health interventions have been developed that are based solely on the primary variables (susceptibility, severity, benefit, and barrier) proposed by the hbm. despite the widespread adoption of hbm, it has failed to provide consistent evidence of success in health behavior promotion. therefore, it has been argued that the existing hbm’s variables are limited in guiding the design of health behavior interventions for two main reasons: (1) the predictive capacity of hbm is low. the primary variables account for less than 21% (r 2 < 0.21) of variance in healthy behavior change and the effect sizes of the individual variables are very small. (2) there no is clear rule of combination of the hbm variables. the relationships between the individual variables are not specified. these limitations are the cause (at least to some extent) of the inability of hbm to provide substantial health behavior improvements. in this paper, we took a first step towards overcoming these limitations. we improved the predictive capability of hbm by extending it to include four other healthy behavior determinants: self-identity, perceived importance, consideration of future consequences, and concern for appearance. we also explored the interactions between the variables and established some mediated relationships. in the process of ascertaining the suitability of each variable to act as a health determinant within hbm, several theoretical questions were answered and some other findings validated to show the suitability of our proposed hbm extension, we validated our model in the healthy eating domain. the statistical findings show that the four new variables (self-identity, perceived importance, consideration of future consequences, and concern for appearance) added to the hbm do in fact all have their place as determinants within the hbm by showing some significant association with healthy behavior. our newly added variables appeared to be better predictors of healthy behavior than all the previously proposed variables with exception of self-efficacy with remains the strongest determinant of healthy behavior. our extended hbm model led to approximately 78% increase (from 40 to 71%) in predictive capacity from the old model. this shows the suitability of our extended hbm to predict healthy eating behavior and to direct health intervention design. finally, to give some insight on the possible combination and relationship between the hbm variables, we systematically explored the model for possible interactions between the variables. the results show that there exist some mediating relationships between some http://ojphi.org/ towards an effective health interventions design: an extension of the health belief model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 variables of hbm. the primary hbm variables susceptibility, severity, benefit, and barrier do in fact mediate the relationship between consideration of future and self-identity alongside with cue to action. another novel finding is that perceived barrier mediates the relationship between self-efficacy and healthy behavior. this work contributes to both health behavior theory and health intervention design domains. in the theory front, we extended the hbm, validated the extended model and showed that the extended model predicted 71% variance in health behavior, in contrast to the 21% variance predicted by the original hbm. we also compared the old models (the baseline and the intermediate models) with our extended model and showed that our four proposed variables along with self-efficacy are better predictors of healthy behavior than all the original variables of the hbm. our extended hbm significantly increased the predictive capacity. we hope that this new model will spur research on investigating the influence of our proposed variables within other theoretical frameworks (e.g., the tpb). on the practical side, our discovery of mediating relationships between the extended hbm variables is intended to allow for a more straightforward and informed combination of variables in health intervention design, leading to higher effectiveness of interventions . the mediating effect shows that some variables function as an antecedent to others and, therefore, will produce a better effect when applied together either in succession or simultaneously. for instance, self-identity can be applied alongside susceptibility, severity, benefit, and cue to action to increase their effect. barrier can be significantly reduced following successful implementation of self-identity and self-efficacy. the increased predictive capacity of our extended hbm also means that interventions designed based on this model have a greater chance of success than those based on the original hbm. although most of the variables are important, some variables like self-efficacy, self-identity, and perceived importance are obviously more important determinants of healthy behavior. calculating the effect size f 2 (as shown in table 6) ensures that intervention designers can easily make an informed decision on the choice of variable or a combination of variables to implement in a design. acknowledgements the first author of this paper is being sponsored by the nserc vanier canada graduate scholarship. many thanks to dr. ebele osita, fidelia orji, and fr patrick ampani for their assistance with the data collection and to the reviewers for their insightful comments. corresponding author rita orji university of saskatchewan, canada email: rita.orji@usask.ca references [1] abraham, c., and sheeran, p., 2005. 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[69] walster, e., aronson, e., abrahams, d. and rottman, l., 1966. the importance of physical characteristics in dating behaviour. journal of personality and social psychology, 4, 508-16. http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 11. french institute for public health surveillance, saint-maurice cédex, france; 2sos médecins, paris, france objective to illustrate the complementarity and added value of the gp’s emergency network “sos médecins” through an example of an epidemic of gastroenteritis (ge). introduction in france, the surveillance of ge is performed by several complementary systems including specific and syndromic surveillance systems. the gp’s emergency associations “sos médecins” are part of the french syndromic surveillance system sursaud since 2006. sos médecins functions as a liberal medical regulation. in 9 years, the network has become almost exhaustive and contribute to the surveillance of seasonal and non-seasonal health events at different geographical scales, in the fields of infectious diseases and environmental health. ge is one of the 50 indicators daily followed by the by the french institute for public health surveillance (invs) syndromic surveillance unit. methods in july 2015, 60 of the 62 associations (about 1000 gp’s) transmit daily data to invs, representing an average daily volume of about 10,000 consultations. national coverage is nearly exhaustive and distribution is homogeneous all over territory including overseas. 80% of the population living in urban areas is estimated to potentially have access to this type of structure. at a national level, the mean proportion of coding for medical diagnoses is 84% in 2015 (only 60% in 2010). the daily national analysis is performed based on the number of acts, 7 days moving average and the proportion of the tracking indicator among coded acts. similar analysis are performed at regional level. data analysis is performed daily and weekly using dashboards and control charts that are generated by a business intelligence application. results in september 2014, an increase of the proportion of ge diagnoses in all-coded diagnosis was observed at a national level through daily dashbords. the regional analysis revealed that some regions were more impacted such as normandie and pays de la loire (both west-north regions). the early signal was confirmed after querying practionners of sos médecins associations in basse-normandie. the analysis of the specific surveillance data (gp’s in-hour nor laboratory testing) or the syndromic oscour emergency department data did not showed any increase in their ge indicators. the syndromic network of public health england (phe) was contacted and reported an increase in the number of cases of ge. the clinical presentation of ge observed in france and uk were mostly upper gastro-intestinal presentation with vomiting symptoms. this episode affected all age groups and particularly children under 15 years old and young adults conclusions sos médecins data was the only data source to identifiy an increase in ge in two french regions. the clinical presentation did not fit with the various case definitions adopted. in fact, the specific monitoring systems could not detect the increase because they use a restrictive case definition only including acute diarrhea. also, this outbreak has not been detected in the emergency because symptoms were not serious in all age groups. this episode allowed to discuss the relevance of the different case definitions used in france for ge surveillance. this data source is essential for syndromic surveillance system of invs: data quality and geographical coverage of the network allows, in addition to emergency services, to obtain accurate analysis epidemic phenomena (eg flu or heat wave). keywords acute gastro-enteritis; sos médecins; gp’s acknowledgments to sos médecins associations for providing data *marc ruello e-mail: m.ruello@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e75, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 151 (page number not for citation purposes) isds 2013 conference abstracts ehr conformance testing for enhanced syndromic surveillance system interoperability robert snelick1, charlie ishikawa2, rebecca zwickl2 and sheryl taylor*3 1national institute of standards and technology, gaithersburg, md, usa; 2international society for disease surveillance, brighton, ma, usa; 3booz allen hamilton, vienna, va, usa � �� �� �� � � �� �� �� � objective ������ �� ��� ���� ��������� � � ������ ��������������� ��������� ���������������������������������������� ����� ������� ����!!�� �� ������������� �� ��������������� ������ ��� ���������"#$���� ���!��% � ��� ������ ��������!� ���� ��� ����������������!� ��� ������� ��&�' introduction (��)�����!���*������ ��� �*������������� ������� �������� �������� � ��� ��� ������������������������������������� ���� ��!�������!� % ���� ��� ��� ��� �����+#,����� � ������������� �������������� �� ��� � ��� ������ �����'�"��� ������������� ���� �� ��������� ���� ����� ��������� ���������� �������� ��� ���� �������� ������������� ����% ����������������'�� ������*� ������*����������� ����� ������� �� ������!��)������������ � �� ��� �����!��������'���� ��&�*����% �����!�������� ����!�����!� ��� �����!�!��� ���� ��� ��� ���������� 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��������������������������'�� ��,�(�� �� ��� ����"#$�������� �����%� �'�8��� ��)�� �� � ���� ����������� ��� � �"#$� �� % �������������������� �������!�9��1�� ���� ������ ��� ��� �� ��)� ���� � ��� �!��������! �����:��!�� ���������������� ���������� �� ���� ��� �����;��/������� ��������� �� ������������ ����-����% ��� ������� �������;��7���!!����� �� ���� 5�� ��� ����4!�� ���� � � ������ ��� �� ����;������<��!���� ������ ���� ������������� �� ��!� ����%��������!����� � ����' conclusions � �����%� �*�������� �������� �� ��,�(�� �� �� �"#$� �� ���% ������������� ����� ���������������� �� ��� �����!��������� �������� �� ���� ��������������� �������������� ���������� �� ��+#,�*���� ��!���� �������� ������� ��� ������ ������������� ���!��� ��� ��������� �� !���� ���������� ���!�� ��������� ���=� ���������������% �����!� ���� ��� ��������������������� ���' keywords ��� ����� ���� �� ���;� ���� ������ ��� � �������;� ����������� ���;� ������������ �� ���;�� ������� *sheryl taylor e-mail: taylor_sheryl@bah.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e133, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts utility and acceptability of influenza surveillance amongst emergency providers andrea f. dugas*1, howard burkom2, anna l. duval1, richard rothman1 and a national emergency department influenza consortium1 1johns hopkins university, baltimore, md, usa; 2johns hopkins applied physics lab, baltimore, md, usa objective to evaluate the utility and acceptability of a real-time cloud based influenza surveillance tool amongst emergency department (ed) providers. introduction each year, influenza affects approximately 5-20% of the united states population causing over 200,000 hospitalizations and 3,000 – 49,000 deaths 1-3. as a key point of entry to the health care system, eds are responsible for the initial management and treatment of a substantial proportion of these influenza patients, thus directly impacting overall public health. as the front line of influenza diagnosis and treatment, ed providers may benefit from real-time easily shared influenza surveillance information. methods we created a real-time laboratory-based influenza surveillance system at two us academic emergency medicine departments. from november 2013 – april 2014 we systematically tested patients with acute respiratory illness at these two eds using cepheid xpert flu, a rapid highly sensitive pcr-based assay that provides significant improvement over traditional rapid antigen tests 4. test results were instantaneously uploaded to a cloud-based data aggregation system available to ed providers via a web-based interface. providers also received bimonthly email updates summating year to date results. ed providers were surveyed prior to the start, and after the conclusion of the influenza season, to assess providers views regarding acceptability and utility of the internet and email-based surveillance provided throughout the influenza season. results influenza surveillance at the 2 sites identified 82 subjects with confirmed influenza among 1032 enrolled patients. of 198 providers contacted, 151 (76%) responded to the pre-survey; and 86 (57%) of those completed the post-survey. of the included participants, 42% were female, 11% were midlevel providers, 48% were resident physicians, and 40% were attending physicians. on the pre-survey, the majority of providers indicated that they sporadically obtain influenza surveillance actively (62%) and passively (48%), and that additional information on influenza prevalence would be useful (75%). on the post survey, most providers reported that they did not go to the provided surveillance website (54%), but the surveillance emails impacted their general awareness of influenza (72%), clinical diagnosis of influenza (24%), decision-making to test for influenza (31%), and decision-making to treat influenza (24%). overall, the additional surveillance data impacted the providers’ influenza testing (66%) and treatment (51%) practices. conclusions the majority of ed providers found surveillance data useful and indicated the additional information impacted their clinical practice. providers are more receptive to obtaining surveillance information via passive means such as emails than via active means such as visiting a website. accurate and timely surveillance information, distributed in a provider-oriented format, can impact ed provider management of patients with suspected influenza. keywords influenza; surveillance system; clinical decision support acknowledgments this work was supported by the cooperative agreement idsep13001401-00 from the assistant secretary for preparedness and response. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the assistant secretary for preparedness and response (within the u.s. department of health and human services). references 1. centers for disease control and prevention. seasonal influenza. 2011; http://www.cdc.gov/flu/about/qa/disease.htm. accessed feb 8, 2011, 2011. 2. thompson ww, shay dk, weintraub e, et al. mortality associated with influenza and respiratory syncytial virus in the united states. jama. 2003;289(2):179-186. 3. thompson wws, d.k., weintraub e, brammer l, bridges cb, cox nj, fukuda k. influenza-associated hospitalizations in the united states. jama. 2004;292(11):1333-1340. 4. dimaio ma, sahoo mk, waggoner j, pinsky ba. comparison of xpert flu rapid nucleic acid testing with rapid antigen testing for the diagnosis of influenza a and b. j virol methods. 2012;186(1-2):137140. *andrea f. dugas e-mail: adugas1@jhmi.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e71, 2015 5127-38119-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 183 (page number not for citation purposes) isds 2013 conference abstracts missouri emergency department visits for carbon monoxide poisoning fei wu* and carol braun missouri department of health and senior services, jefferson city, mo, usa � �� �� �� � � �� �� �� � objective �������� �� ���� � ������ ������� � ���� ��� ���� ���������� �� � ������ �� ������ ��� � ��� � ������������������� ��������� � ��� ������������������� � ������ ���� ����� ���� ��� � ��� �� �� �� ������������ � �� ��� ����� ��� !����������� ����������� �� � ��� ���������� introduction �������� � ����� ������� ��� � ������� �� ��������� ��"������� � � ���� #�� ������ ��� ����� ��� �� ��� ��������� � ��� ��� �$ �� �� %��� ���& � �� ��� ����� ���� ����� ������ ���������� ���������� �� � ��� ���� ����� ���������� � �� methods !� ����� '��� � ���( �� ����� �� � ��������� � ��� � � %��� ��� �� �%��� �������� � �����)����� ���� ��������� ������� �� ��� � �� ��� %% )� ����� � !� �� �� ������ �������������� � ��� � %% )� � � ��� �* �'������� �����+ ���� ,����+�� ���� ,���� � ��������������%���� �� �&� ��� ��� ��� ��" �������� ����� ����� � ���-%&"���������� �'����� ����� ��� ���� ���� ��� �������� ������� �� ��������� � �� �� ���%������� ���& �������%���'�� ��%&%���� ���� � .�/��'�������� ������������ �� ���� ��� ������������ results �����������'���� ����� ����� ������ ��� � !�������������0� ����� 1#�2312���������0� �/3#�231/�� ������������������� ��� ���'��*�� � ��� �#� � �'��*�� ��� �#�� � ��� ��#�� � � ��� ��#���� #� � ���� �� �� � * �' ������������� � ����� � �' � �11.� !������������ � ���� �� �� ������������� � ��� �������������� ���������� �� ����������� !� ������������������� � ��' � ���� ����������43�56#�17�58�� �������� � ��'��� ���59�46���)����� ��� � ������ � �'������ �� ��� �' � ��� ��� ��� ��� ������ ������ � ���� ��� ������� �� �� � ������� � '� � ���� ����� ���� � ������� �'� � ���� ���#�'�� ��' � �� ��� �� � ��: ������#�� �� ��� ����%���;�������� ��#�%���;����������� ��0� *� �� ���� ����� ��� ������ ������� ����<� ���������������� ����� ��� ��� �������������� � ������ ���� ���������� �� ������&����� � !� �������� ���������������� ������� ������� ����-���� � ����������� #� �� �836�� ��� ������ ���-��� ����'���/�36� conclusions ������������ �� ����� ���� ���������� ��������� ���� !��������� ������ ���� ��������� �� ������������� � ��' � ��� ���� ��� � %� % )� ������' � � ����� ���� ��� ��� ������� ����� ���� ����� �� ���-%&"������ ������������� �� ����� ���� ���� �������� !������ ����������� �� � ���� �� �� ��� #���� ����� � ���� � ��� ������ �� �� ������������ � �� keywords ����� � �� ���� � =���� � �>� � �� �� ! ����� �� ������>� %% )� acknowledgments ��� *�������� � �!� �� #�;� �� �?���� ��#�;����-������� ��&���:������ ����� ����������! ����� ����������� ��% ����% ��� ������� �� '� �� ����������� �� *fei wu e-mail: fei.wu@health.mo.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e50, 2014 sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 1 ojphi sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 carla bezold1, erin sizemore2, heather halter1, diana bartlett2, kelly hay1, hammad ali2 1. mitre corporation, united states 2. centers for disease control and prevention, united states abstract objectives: health department personnel conduct daily active symptom monitoring for persons potentially exposed to sars-cov-2. this can be resource-intensive. automation and digital tools can improve efficiency. we describe use of a digital tool, sara alert, for automated daily symptom monitoring across multiple public health jurisdictions. methods: eleven of the 20 u.s. public health jurisdictions using sara alert provided average daily activity data during june 29 to august 30, 2021. data elements included demographics, communication preferences, timeliness of symptom monitoring initiation, responsiveness to daily messages, and reports of symptoms. results: participating jurisdictions served a u.s. population of over 22 million persons. health department personnel used this digital tool to monitor more than 12,000 persons per day on average for covid-19 symptoms. on average, monitoring began 3.9 days following last exposure and was conducted for an average of 5.7 days. monitored persons were frequently < 18 years old (45%, 5,474/12,450) and preferred communication via text message (47%). seventy-four percent of monitored persons responded to at least one daily automated symptom message. conclusions: in our geographically diverse sample, we found that use of an automated digital tool might improve public health capacity for daily symptom monitoring, allowing staff to focus their time on interventions for persons most at risk or in need of support. future work should include identifying jurisdictional successes and challenges implementing digital tools; the effectiveness of digital tools in identifying symptomatic individuals, ensuring appropriate isolation, and testing to disrupt transmission; and impact on public health staff efficiency and program costs. keywords: covid-19, digital health, contact tracing, public health practice, informatics, symptom assessment abbreviations: code of federal regulations (cfr), coronavirus disease 2019 (covid-19), standard deviations (sd) doi: 10.5210/ojphi.v14i1.12449 copyright ©2022 the author(s) sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 2 ojphi this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction contact tracing, including active monitoring of identified contacts, is a key public health strategy to contain sars-cov-2, the virus that causes coronavirus disease 2019 (covid-19) [1]. close contacts of persons with covid-19 are recommended to quarantine and monitor symptoms for 14 days following exposure; under certain conditions, options exist to end quarantine after 7–10 days [2]. daily active monitoring of persons in quarantine can help improve compliance with quarantine and promptly identify symptomatic persons who may need clinical evaluation [3]. effective monitoring must be initiated in a timely manner [4]. daily monitoring is resourceintensive for state, tribal, local, and territorial public health staff [1, 4, 5]. because of resource constraints, some health departments use technology, including digital tools, to automate aspects of daily monitoring. digital tools can expedite initiation of symptom monitoring, streamline communication with monitored persons, and integrate data about their status into other public health information systems [6]. sara alert (mitre corporation, mclean, va) is an open-source [7], web-based, automated symptom monitoring tool launched in april 2020 [8]. this digital tool can be used for daily symptom monitoring of persons with covid-19, close contacts recently exposed to persons with covid-19 [9], and other groups with potential exposure to sars-cov-2, including travelers [10], students in k-12 schools [11], and critical infrastructure employees [12]. each public health jurisdiction determines how to incorporate sara alert into their contact tracing workflow [13]. the objective of this report is to describe the use of sara alert across multiple jurisdictions for monitoring for covid-19 symptoms following exposure in terms of the persons monitored, timeliness of initiation in daily monitoring, responsiveness to daily messages, and reports of symptoms. methods automated monitoring sara alert can be used to monitor symptoms following confirmed or suspected exposure to sarscov-2 (“exposure monitoring”) or following identification of a confirmed or probable case of covid-19 (“isolation monitoring”). each jurisdiction determines the population to be monitored via sara alert, and implements procedures to initiate monitoring, including securing permissions as appropriate. monitored persons must have access to a telephone, mobile phone, or computer to interact with sara alert. monitored persons are sent daily reminders to report symptoms during their monitoring period, via their preferred method (telephone call, text message, texted weblink, or emailed weblink). monitored persons can respond directly, through a parent or other proxy, or a public health staff member can enter responses collected through follow-up contact. public health staff are alerted to persons reporting potential covid-19 symptoms or not responding to that day’s sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 3 ojphi reminder [13]. once monitoring is completed and a record is closed, potentially identifiable data are purged to maintain privacy. data source jurisdictions actively using sara alert during summer 2021 were invited to participate in this study (n=20). only those providing consent to share data are included. data on persons refusing automated monitoring (opt-out) are not consistently available through sara alert. notable data elements and collection methods for this report are: (1) monitored persons or a designated proxy (e.g., parent or guardian) self-report date of birth (required), and sex, race, and ethnicity (all optional); (2) preferred mode of communication (required); (3) preferred contact time and primary language (optional); and (4) public health staff enter last date of exposure during enrollment when known, otherwise, date of last exposure defaults to date of enrollment into sara alert. additional data elements details are available in the data dictionary [14]. data analysis we extracted aggregate data on currently monitored persons daily from june 29 to august 30, 2021. to focus on monitoring of symptoms during a quarantine period following a confirmed or possible exposure (as opposed to continuous monitoring of persons with ongoing exposure), we limited our analysis to jurisdictions where automated monitoring was conducted for a mean of ≤ 14 days over the duration of our data collection period, aligning with recommended duration of quarantine for close contacts following sars-cov-2 exposure at the time of analysis. we determined the number of monitored persons, summarized their demographic characteristics, calculated symptom monitoring timeliness initiation, examined responsiveness to daily messages, and summarized use of the digital tool to report potential covid-19 symptoms. we totaled categorical data (sex, race, ethnicity, age group, and contact preferences) across jurisdictions and calculated percentages among all persons monitored. we weighted continuous data by the proportion of persons contributed by each jurisdiction and calculated weighted means and standard deviations (sd). this non-research activity was reviewed by cdc and conducted consistent with applicable federal law (45 cfr part 46 did not apply) and cdc policy. results of the 20 jurisdictions invited to participate: five were excluded because automated monitoring was conducted for a mean >14 days; three did not respond by consent deadline; and one declined to participate. eleven jurisdictions (5 state, 1 territorial, 5 local) contributed to this analysis: arkansas; commonwealth of the northern mariana islands; weld county, colorado; berrien county, michigan; clay county, missouri; jackson county, missouri; montana; vermont; virginia; washington; and teton county, wyoming. the population within the 11 participating jurisdictions is 22,369,182 [15]. in these jurisdictions, each day on average, public health staff sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 4 ojphi initiated automated monitoring of 1,318 persons, monitored 12,450 persons automatically, and sent 70,218 automated messages. among 11,165 persons with sex reported, the majority were female (5,823, 52%). nearly half (5,474, 45%) of persons monitored were aged ≤ 18 years (table 1). race and ethnicity were not available for 64% and 30% of monitored persons, respectively. monitoring preferences were to be contacted via text message (47%), in the morning from 8:00-12:00 am (51%). among 10,616 persons with a recorded self-reported primary language, their preference was predominately english (10,350, 97%%). table 1. demographic characteristics and communication preferences among persons monitored for covid-19 symptoms using an automated symptom monitoring tool (sara alert), 11 u.s. public health jurisdictions, june 29-august 30, 2021 (n=12,450) characteristic monitored persons, no. (percent)a sex male 5,342 (43%) female 5,823 (47%) unknown 33 (<1%) not reported 1,252 (10%) race white 3,316 (27%) black or african american 917 (7%) american indian or alaska native 15 (<1%) asian 146 (1%) native hawaiian or pacific islander 10 (<1%) multiple races 111 (1%) not reported 7,935 (64%) ethnicity hispanic or latino 698 (6%) non-hispanic or [not] latino 7,879 (63%) not reported 3,735 (30%) ageb group, years sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 5 ojphi ≤ 18 5,474 (45%) 19-29 1,540 (13%) 30-39 1,604 (13%) 40-49 1,426 (12%) 50-59 1,044 (9%) 60-69 652 (5%) 70-79 299 (2%) ≥ 80 100 (1%) probable miscalculationb 312 (3%) preferred mode of communication from public health jurisdiction telephone call 833 (7%) text message 5,900 (47%) texted weblink 4,275 (34%) emailed weblink 1,442 (12%) preferred contact time morning (8:00 to 12:00) 6,317 (51%) afternoon (12:00 to 16:00) 3,226 (26%) evening (16:00 to 20:00) 1,111 (9%) not reported 1,796 (14%) primary language english 10,350 (83%) spanish 247 (2%) otherc 19 (<1%) not reported 1,832 (15%) anumbers and percentages may not sum to 100 due to rounding, since values are daily averages across the study period. bsara alert calculates age as the difference between date of birth and the current date. outlier records with calculated age > 110 years are flagged as probable miscalculations. cas of august 2021, sara alert also supports french, somali, and puerto rican spanish messaging from the health department. sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 6 ojphi public health staff-initiated symptom monitoring in sara alert a weighted mean of 3.9 days (sd = 2.8; range 0-7.8) after last exposure, and persons were monitored for a weighted mean of 5.7 days (sd = 5.8; range 1.7-8.3). within an individual’s monitoring period, 49% (6,159/12,450) responded to every automated message, and 74% (9,242/12,450) responded to at least one automated message. of those responding, 86% of responses were submitted directly via the digital tool (5,281/6,159 for every message; 7,944/9,242 for at least one message). of the 51% (6,291/12,450) who did not respond to every automated message sent during their monitoring period, a weighted mean of 3.6 (sd = 0.7; range 1.4-6.7) consecutive days passed without response. eleven percent (1,313/12,450) of monitored persons reported potential covid-19 symptoms [16] during their monitoring period. discussion public health staff in 11 jurisdictions used sara alert to automate symptom monitoring for an average of more than 12,000 persons per day from june 29 to august 30, 2021. the high number of automated messages sent, and large number of individuals engaging directly with the tool suggests the potential benefit of digital tools to increase public health staff capacity and efficiency for symptom monitoring beyond what is achievable through traditional, non-automated methods. our findings build upon previously published experience with sara alert from maine [9]. our study incorporates a broader geographic area and monitoring experiences 18 months after the world health organization’s declaration of the covid-19 pandemic. the jurisdictions included in this analysis have a combined population of more than 22 million persons and include areas of higher overall covid-19 incidence than maine. both the report from maine and our study explore use of the tool during a time when covid-19 case counts were increasing towards a single day peak, namely the july 2020 peak for maine and september 2021 peak for our study [17]. collectively, our findings suggest that use of a digital tool for symptom monitoring has utility, both for public health staff and monitored persons, throughout the duration of the covid-19 pandemic and across a broad geographic area. uniquely, our study contributes data on engagement by monitored persons with a digital tool for symptom reporting. only 49% of monitored persons responded to every automated message. we think this finding reflects the difficulty in getting complete monitoring data, despite using a digital tool. however, 74% of monitored persons responded to at least one automated message. this number is higher than what has been reported in other studies examining the use of automated monitoring tools for other infectious diseases [18, 19]. the majority persons monitored that responded to automated messages did so directly via sara alert; suggesting that persons monitored accepted use of a digital tool to report symptoms. interestingly, we found that 45% of monitored persons were 18 years of age or younger. considering trends in case counts during july and august, 2021, where the age groups with the highest incidence of cases were among children or persons of reproductive age [17], combined with lower vaccination coverage in persons <18 [17, 20], it is plausible that a large portion of close contacts requiring symptom monitoring by public health departments would be 18 years of age or sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 7 ojphi younger. additionally, k-12 schools resumed in-person classes in some jurisdictions during our study period, potentially reflecting increased exposures for children attending school [21]. limitations a key limitation of this report is the fact that we are unable to document the reason each monitored person was enrolled in sara alert, and this report likely includes persons not identified as close contacts following a sars-cov-2 exposure. the populations eligible for automated symptom monitoring via sara alert shifted over time due to jurisdictional changes in policy, community transmission levels, and resource availability [9]. based on their evolving covid-19 response needs, some jurisdictions primarily used sara alert for symptom monitoring of close contacts, while others used it to monitor travelers entering their jurisdiction or persons with ongoing exposures such as critical infrastructure employees. information to differentiate between these uses is not captured consistently across jurisdictions. however, persons were enrolled in sara alert based on health department policies and priorities, and as such our findings can provide relevant insights regarding the acceptability of automated symptom monitoring for a range of purposes. this report is subject to at least three additional limitations. first, as our analysis period represents a snapshot of the covid-19 pandemic after initiation of vaccination of persons 12 years and older and during a time of increasing but non-peak case counts, findings may not be generalizable beyond similar epidemiologic settings. second, our findings are neither representative of all jurisdictions using sara alert nor of the united states. generalizability to the u.s. population is also limited by available data on race and ethnicity of monitored persons and overrepresentation of persons aged 18 years and younger compared with the u.s. population (45% v. 22% in 2019 u.s. census). third, because deidentified aggregate data extracted from sara alert were used in this analysis, we were not able to link to other data sources (e.g., laboratory results, case surveillance) or verify values (e.g., last date of exposure, which might be biased toward zero because of system defaults). conclusion numerous digital tools emerged to support contact tracing during the covid-19 response [6], but limited data describing their use are available. this report shows that incorporating this digital tool into public health workflows might improve public health capacity for daily symptom monitoring, allowing staff to focus their time on interventions for persons most at risk or in need of support. evaluation priorities for future work include implementation lessons learned, challenges and successes; the effectiveness of digital tools compared to traditional non-automated monitoring methods in identifying symptomatic individuals, ensuring appropriate isolation, and testing to disrupt transmission chains; and how these digital tools helped health departments use human resources more efficiently, resulting in public health cost savings. correspondence: erin sizemore, esizemore@cdc.gov sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 8 ojphi acknowledgements we would like to thank all monitored persons who enrolled in sara alert; the public health staff at each jurisdictions contributing data to this report; margaret (peggy) honein, melanie taylor, nicholas deluca, jody mclean, lauren finklea and calvin hightower for their support and contributions to sara alert through cdc’s covid-19 emergency response; dawn heisey-grove for critical review of this manuscript; adam holmes, maeve kokolus, and kathy lewis for assistance acquiring data; paul jarris and suzette stoutenburg for contributions to sara alert as mitre project leadership; and association of public health laboratories, association of state and territorial health officials, cdc foundation, council of state and territorial epidemiologists, and national association of county and city health officials for their partnership in sara alert. financial disclosure the author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work, including no-cost, jurisdictional implementation of sara alerttm and technical assistance, was supported by the centers for disease control and prevention [contract 75fcmc18d0047]. the sara alert trademark is currently registered with the united states patent and trade office. this technical product was produced for the u.s. government under contract number 75fcmc18d0047, and is subject to federal acquisition regulation clause 52.227-14, rights in data-general. no other use other than that granted to the u.s. government, or to those acting on behalf of the u.s. government under that clause is authorized without the express written permission of the mitre corporation. for further information, please contact the mitre corporation, contracts management office, 7515 colshire drive, mclean, va 22102-7539, (703) 983-6000. (c) 2021 the mitre corporation all rights reserved. approved for public release 21-3316. competing interests no competing interests. references 1. spencer kd, chung cl, stargel a, shultz a, thorpe pg, et al. 2021. covid-19 case investigation and contact tracing efforts from health departments united states, june 25july 24, 2020. mmwr morb mortal wkly rep. 70(3), 83-87. pubmed https://doi.org/10.15585/mmwr.mm7003a3 https://pubmed.ncbi.nlm.nih.gov/33476317 https://doi.org/10.15585/mmwr.mm7003a3 sara alert: an automated symptom monitoring tool for covid-19 in 11 jurisdictions in the united states, june – august, 2021 9 ojphi 2. centers for disease control and prevention. when to quarantine. available at: https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine.html. 3. centers for disease control and prevention. science brief: options to reduce quarantine for contacts of persons with sars-cov-2 infection using symptom monitoring and diagnostic testing. available from: 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covid-19 cases, emergency department visits, and hospital admissions among children and adolescents aged 0-17 years — united states, august 2020-august 2021. mmwr morb mortal wkly rep. 70(36), 1249-54.pubmed. https://doi.org/10.15585/mmwr.mm7036e1 https://saraalert.org/blog/user_guide/data-dictionary/ https://data.census.gov/cedsci/ https://cdn.ymaws.com/www.cste.org/resource/resmgr/ps/positionstatement2020/interim-20-id-02_covid-19.pdf https://cdn.ymaws.com/www.cste.org/resource/resmgr/ps/positionstatement2020/interim-20-id-02_covid-19.pdf https://covid.cdc.gov/covid-data-tracker/#datatracker-home %20pubmed https://doi.org/10.2196/10842 https://pubmed.ncbi.nlm.nih.gov/27522128 https://pubmed.ncbi.nlm.nih.gov/34473680 https://doi.org/10.15585/mmwr.mm7035e1 pubmed https://doi.org/10.15585/mmwr.mm7036e1 ojphi: vol. 3 issue 3: journal information journal id (publisher-id): ojphi issn: 1947-2579 publisher: university of illinois at chicago library article information ©2011 the author(s) open-access: this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. electronic publication date: day: 22 month: 12 year: 2011 collection publication date: year: 2011 volume: 3 issue: 3 e-location id: ojphi.v3i3.3794 doi: 10.5210/ojphi.v3i3.3794 publisher id: ojphi-03-18 harnessing electronic health records for public health surveillance michael klompas, md mph1 michael murphy, ba1 julie lankiewicz, mph1 jason mcvetta2 ross lazarus, mbbch1 emma eggleston, md mph1 patricia daly, ms rn3 paul oppedisano, mph3 brianne beagan, mph3 chaim kirby, jd ma4 richard platt, md msc1 1 harvard medical school and harvard pilgrim health care institute, boston, ma 2 heliotropicinc, los angeles, ca 3 massachusetts department of public health, boston, ma 4 children’s hospital, boston, ma correspondence: corresponding author: michael klompas, department of population medicine, harvard medical school and harvard pilgrim health care institute, 133 brookline avenue, 6th floor, boston, ma 02215, phone: 617-509-9991, fax: 617-859-8112, email: mklompas@partners.org abstract electronic medical record (emr) systems are a rich potential source for detailed, timely, and efficient surveillance of large populations. we created the electronic medical record support for public health (esp) system to facilitate and demonstrate the potential advantages of harnessing emrs for public health surveillance. esp organizes and analyzes emr data for events of public health interest and transmits electronic case reports or aggregate population summaries to public health agencies as appropriate. it is designed to be compatible with any emr system and can be customized to different states’ messaging requirements. all esp code is open source and freely available. esp currently has modules for notifiable disease, influenza-like illness syndrome, and diabetes surveillance. an intelligent presentation system for esp called the riskscape is under development. the riskscape displays surveillance data in an accessible and intelligible format by automatically mapping results by zip code, stratifying outcomes by demographic and clinical parameters, and enabling users to specify custom queries and stratifications. the goal of riskscape is to provide public health practitioners with rich, up-to-date views of health measures that facilitate timely identification of health disparities and opportunities for targeted interventions. esp installations are currently operational in massachusetts and ohio, providing live, automated surveillance on over 1 million patients. additional installations are underway at two more large practices in massachusetts. introduction the harvard center of excellence in public health informatics has developed an electronic medical record (emr) based system for comprehensive public health surveillance: the electronic medical record support for public health (esp) platform (http://esphealth.org).1, 2esp organizes raw data extracted from emr systems, maps them to heuristic concepts, analyzes these data for conditions of public health interest, and electronically transmits case-level or population-level data to public health agencies. esp is designed to be compatible with any emr system. all source code is available free of charge under a library general public license. esp currently provides notifiable disease reporting for selected infectious diseases, syndromic surveillance for influenza-like illness, and chronic disease surveillance using diabetes mellitus as the demonstration example. infectious disease surveillance esp was originally designed to identify and electronically report patients with notifiable diseases such as chlamydia, gonorrhea, active tuberculosis and acute viral hepatitis. in contrast to electronic laboratory reports, esp leverages the full breadth of data present in emrs to do more than simply report positive test results. for example, esp algorithms distinguish acute versus chronic infections and active versus latent tuberculosis.3 esp algorithms also seek clinical diagnoses that may not trigger positive laboratory tests such as early lyme disease and culture-negative tuberculosis.4 once a case is identified, esp uses the wealth of data in emrs to prepare hl7 electronic case reports that include electronically determined symptoms, pregnancy status, and treatments prescribed. esp also includes a syndromic surveillance module for influenza-like illness. this module counts patients who fulfill the centers for disease control and prevention syndromic definition for influenza-like illness. results are stratified by age and sex and then sent to the massachusetts department of public health. the health department merges esp’s data into the state -wide sentinel report for integration into the centers for disease control and prevention national influenza-like illness surveillance program for hhs region 1: (http://www.cdc.gov/flu/weekly/regions2010-2011/hhssenusmap.htm). diabetes surveillance we are currently working on methods to apply the disease detection protocols we pioneered for notifiable disease detection to surveillance for diabetes prevalence, incidence, care, and complications. as with infectious diseases, integration of laboratory data with current and prior diagnoses and prescriptions facilitates more complete and more granular surveillance. for example, we found surveillance for gestational diabetes by assessing oral glucose tolerance test results alone misses a third of cases. one reason is that clinicians may make the diagnosis in unconventional but clinically reasonable ways. for example, the patient may have a history of gestational diabetes from a prior pregnancy and may spontaneously start checking her glucoses on her own and find high results. an algorithm that assesses pregnancy, diagnosis codes, and prescriptions for test strips or lancets captures these extra cases.5 similarly, our algorithm for frank diabetes looks for patients with positive hemoglobin a1cs, elevated fasting glucose and/or random glucose values, new prescriptions for insulin or oral antiglycemic agents, or recurrent diagnosis codes for diabetes. including all these criteria increases case capture by 28% compared with assessing diagnosis codes alone (the current de facto standard for electronic population level surveillance) and by 54% compared to using hemoglobin a1cs alone (an alternative method of population surveillance for diabetes currently mandated in new york city).6 we have also developed an algorithm to distinguish between type 1 and type 2 diabetes.6 this is a major advance since most current population level surveillance tools, such as administrative codes or the behavioural risk factor surveillance system (brfss) do not routinely distinguish between these two very different diseases (some states do add supplemental questions asking patients to self-report diabetes type but this is not a core component of brfss). our algorithm is nested within the population identified by our frank diabetes algorithm and incorporates current and historical icd9 codes, laboratory tests, and prescriptions. the algorithm’s sensitivity and positive predictive value for type 1 diabetes is 100% and 94% respectively. benefit to public health practice esp provides public health practitioners with more detailed and timely data to identify priority areas for intervention compared to traditional surveillance tools. public health departments currently rely primarily on voluntary, anonymous telephone surveys such as the behavioral risk factor surveillance system (brfss) to assess chronic disease and health behavior patterns.7, 8the brfss has significant limitations, including cost, reliance on self-reports, restriction to respondents with telephones, limited language coverage, and capacity for only a limited number of questions. estimates are not possible for certain populations or geographic areas because of sample size limitations. brfss also lacks clinical information such as medications, laboratory tests or vital signs. esp overcomes many of these limitations by harnessing the wealth of data routinely captured by emr systems. esp is able to provide comprehensive surveillance on very large numbers of patients with detailed data on patient demographics (age, sex, race/ethnicity, location), important clinical traits (e.g. body mass index, pregnancy status), patterns of care (e.g. medication prescriptions, use of screening tests, referral to nutrition therapy), health outcomes (hemoglobin a1c, lipid profiles, blood pressure control), and complications (e.g. hypoglycemic episodes, chronic kidney disease, retinopathy, etc.). these data can help health departments identify disparities in health status, care patterns, and outcomes and inform targeted interventions for the most vulnerable members of the population. lessons learned effective use of electronic medical record data for public health purposes requires sophisticated understanding of clinical data sources in order to faithfully capture and meaningfully map native data to universal concepts. surveillance algorithms integrating multiple components of the medical record (diagnosis codes, laboratory test results, and medication prescriptions) are often more sensitive and specific than any of these components alone. developing sensitive and specific algorithms is painstaking work requiring access to rich clinical data, sophisticated programming staff, engaged clinical staff that can validate electronic cases against manual reviews, and sufficient time and patience to iteratively repeat this process to optimize performance.9 limitations there are important limitations to surveillance using emr data. examples include discrepancies in coding practices between physicians and practices that may affect the performance of case identification algorithms; incomplete data on patients’ diagnoses, lab tests, and prescriptions when patients seek care from multiple providers, some of whom may be outside the practices covered by esp; difficulty determining accurate denominators for incidence and prevalence calculations since some individuals at risk never seek medical care and some individuals seek care from multiple practices and therefore may count in the denominators of both practices but in the numerator of only one, both or neither practice depending on how the specific array of diagnosis codes, labs, and prescriptions accrued at each practice; difficulty keeping concept mapping current in the face of rapidly changing coding nomenclatures; and limited capacity to identify important contextual data and risk factors that are poorly recorded in emr systems such as incarceration, restaurant work, sick contacts, and recent travel. translating research into practice we have developed comprehensive reports for pre-diabetes, gestational diabetes, and frank diabetes that describe demographics (age, sex, race, location), clinical parameters (hemoglobin a1c, body mass index, lipid profile, blood pressure), and indicators of care (prescriptions, nutrition referrals, follow-up testing, changes in glucose control over time). these reports were custom built in collaboration with the massachusetts department of public health to highlight the parameters of greatest interest and concern to public health practitioners. these reports can also be used by the medical practices themselves to study their patterns of care and identify targets for quality improvement initiatives. collaborations there are four esp installations at various stages of maturity. the core installation is in atrius health, a multispecialty practice with 700 physicians serving over 700,000 patients in 25 sites in massachusetts. the atrius health esp server resides in the practice’s central data processing center. it is populated nightly with text files extracted from atrius’s epic care emr. these contain clinical information on every patient encounter from the preceding 24 hours. a second mature installation is in metrohealth, an integrated ambulatory and hospital system serving over 350,000 patients in cleveland, ohio. it is also populated with text files extracted from metrohealth’s epic care emr. these two installations have together reported over 12,500 notifiable disease case reports to their respective state health departments since inception. two additional installations of esp are currently underway. one is in the northern berkshires regional health information exchange in north adams, massachusetts. this installation is populated by hl7 messages generated by eclinical works emrs. it shows esp’s compatibility with different ehr systems and the feasibility of installing esp in a health information exchange to serve an entire community. the fourth installation is in the cambridge health alliance, an integrated health care system and safety net provider affiliated with the city health department that provides care in hospitals and health centers for the city of cambridge. benefit to public health informatics all software and protocols developed by the center are freely available for use by medical practices and public health agencies across the state and nation. esp can be readily adopted by any medical practice and is fully extensible, allowing users to develop and implement new surveillance targets and reports. esp is compatible with different electronic medical record systems. it is currently populated by extract-transform-and-load from epic care, hl7 messages generated by eclinical works, and sql queries from epic care’s clarity system. the center’s work is the platform for a new competitive award from the office of the national coordinator for health information technology to build and deploy mdphnet, a distributed network to support bi-directional communication between the state health department and practices that have installed esp. mdphnet will help integrate surveillance results from distributed esp installations. it will also facilitate custom queries from health department officials to run in parallel on distributed esp systems. impact on public health practice the culmination of esp’s data extraction and analysis is intelligent presentation. we aim to make esp data as intuitive and impactful as possible for users. in collaboration with our center of excellence partners at children’s hospital, we are creating the riskscape, a web interface to graphically display population level surveillance summaries (figure 1). the riskscape is built to be generalizable to any population level surveillance target (e.g., diabetes, asthma, heart disease, influenza like illness), to allow users maximal flexibility to stratify the data in whatever way they wish, and to be as user friendly and visually appealing as possible. the intent of the riskscape is to highlight geographic regions and population groups that could benefit most from targeted public health interventions. for example, a riskscape user can specify a report of the rates of postpartum testing for frank diabetes amongst gestational diabetics stratified by zip code, race / ethnicity, and age. a report of this nature could highlight hispanic women under age 20 in the southern neighborhoods of boston as having disproportionately lower rates of postpartum testing. this in turn can inform a targeted public health campaign to increase postpartum testing for this well-defined population. [figure id: f1-ojphi-03-18] figure 1  riskscape screenshot. the figure depicts a heat map of the proportion of women with gestational diabetes who receive nutrition counseling from a registered dietician by 3-digit zip code. the figure highlights regions of the state where disproportionately fewer women are getting counseling from a dietitian. analyses of this sort can help public health departments develop targeted interventions for the population groups and regions at greatest need. conclusions esp demonstrates the vast potential of emrs to change the face of surveillance by improving the accuracy, completeness, efficiency, and granularity of surveillance with relatively little marginal cost for new infections and conditions. the esp system is customizable and extensible. new surveillance targets such as immunization registries, clinical care monitoring, and drug safety all have the potential to be integrated into esp. much unexplored territory remains. references 1.. centers for disease control and preventionautomated detection and reporting of notifiable diseases using electronic medical records versus passive surveillance--massachusetts, june 2006–july 2007mmwr morb mortal wkly rep 2008;57(14):373–376. 2.. lazarus r, klompas m, campion fx, et al. electronic support for public health: validated case finding and reporting for notifiable diseases using electronic medical dataj am med inform assoc 2009;16(1):18–24. 3.. klompas m, haney g, church d, lazarus r, hou x, platt r. automated identification of acute hepatitis b using electronic medical record data to facilitate public health surveillanceplos one 2008;3(7):e2626. 4.. calderwood ms, platt r, hou x, et al. real-time surveillance for tuberculosis using electronic health record data from an ambulatory practice in eastern massachusettspublic health rep 2010;125(6):843–850. 5.. klompas m, mcvetta j, eggleson e, et al. automated surveillance and public health reporting for gestational diabetes incidence and care using electronic health record data (abstract)emerging health threats journal 2011:4. 6.. klompas m, eggleson e, mcvetta j, et al. automated detection and classification of diabetes using electronic health recordspaper presented at: cdc diabetes translation conference2011minneapolis, mn 7.. chowdhury p, balluz l, town m, et al. surveillance of certain health behaviors and conditions among states and selected local areas behavioral risk factor surveillance system, united states, 2007mmwr surveill summ 2010;59(1):1–220. 8.. hughes e, kilmer g, li y, et al. surveillance for certain health behaviors among states and selected local areas united states, 2008mmwr surveill summ 2010;59(10):1–221. 9.. klompas m, bialek sr, kulldorff m, vilk y, harpaz r. herpes zoster and postherpetic neuralgia surveillance using structured electronic datamayo clin proc. 2011 in press. article categories: articles 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts surveillance to manage disease on canadian swine farms john berezowski*1, chris byra2, egan brockhoff3, dan hurnik4, christian klopfenstein5, harold kloeze6, luc bergeron7, george charbonneau8, francois cardinal9, iqbal jamal10 and travis herntier11 1university of bern, bern, switzerland; 2greenbelt swine veterinary services, chilliwack, bc, canada; 3prairie swine health services, red deer, ab, canada; 4atlantic veterinary college, charlottetown, pe, canada; 5le centre de développement du porc du québec, quebec, qc, canada; 6canadian food inspection agency, owen sound, on, canada; 7ministère de l’agriculture, des pêcheries et de l’alimentation du québec, québec, qc, canada; 8south western ontario veterinary services, stratford, on, canada; 9consultants avi-porc, drummondville, qc, canada; 10aql management consulting, edmonton, ab, canada; 11fd solutions, winnipeg, mb, canada objective to improve swine farmers and veterinarians ability to manage disease introduction on a day to day basis, farmers and their veterinarians deal with many diseases without the benefit of surveillance for early outbreak detection, or coordinated outbreak responses. without this support, highly contagious pathogens such as porcine epidemic diarrhea virus (pedv) can spread quickly and potentially cause significant harm. the purpose of this project was to develop a surveillance system to help canadian swine farmers and veterinarians to deal more effectively with diseases methods the project team developed some unique approaches to animal disease surveillance. they adopted a unique organizational structure in which the data and information providers (veterinarians and farmers) were also directly involved in the decision making process. they designed an infrastructure that integrated two components 1) a social network and 2) a data collection, analysis, event detection and reporting system the social network or swine veterinary network (svn) includes veterinarians and swine specialists. the svn consists of 3 regional: 1) the western provinces, 2) ontario plus the maritimes and 3) quebec; and one national network. each network meets quarterly via the internet. one week prior to the meetings each veterinarian participating in the cshin is asked to fill out a web survey where they identify important, changing or emerging disease issues seen in their practice. the data is collated and presented at the meetings. the networks discuss important health issues; develop strategies for dealing with diseases and communicate this intelligence across the country. the second component is a practice based surveillance (pbs) network. it collects, analyses and reports syndromic data from veterinary practitioners in real time via the internet for early disease detection and to provide other health related information. because swine farms are relatively large populations in themselves participating veterinarians report farm level data such as the prevalence of syndromes (coughing, diarrhea, neurological etc) within production groups (sows, boars, piglets, etc), as well as individual clinical and laboratory diagnosis both networks produce swine health intelligence that is delivered directly to swine farms by participating veterinarians and to other stakeholders through various communication networks. a governance model was developed that allows for control of data by data providers and regional and national decision making processes that include farmers, veterinarians, as well as industry and government representatives. results communication was greatly enhance by quarterly meetings and through quarterly regional and national disease updates. the cshin was the only national source for pedv intelligence during the outbreak in canada, providing daily electronic updates to industry subscribers across the country the pbs detected a sudden regional increase in swine influenza 3 weeks before it was identified by laboratory submissions. streptcoccus suis a zoonotic pathogen has been identified as the most common clinical diagnosis on pig farms, providing valuable information to advocate for resources for disease control. porcine circovirus 2 was shown to be occurring on swine farms in spite of vaccine use, with improper vaccination being identified as the cause conclusions the cshin infrastructure has been developed and the program rolled out across canada. it has demonstrated that it can provide value to individual famers and the industry as a whole. next steps are to secure long term funding keywords animal health surveillance; syndromic surveillance; social network; alternative data source acknowledgments the cshin was developed by the canadian swine health board with funding from agriculture and agri-food canada *john berezowski e-mail: john.berezowski@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e8, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts establishing a plan of action implementing integrated disease surveillance in sudan hayat khogali1, ngozi a. erondu*2, 4, betiel h. haile4 and scott j. mcnabb3, 4 1sudan federal ministry of health, khartoum, sudan; 2london school of hygiene and tropical medicine, london, united kingdom; 3emory university, rollins school of public health, atlanta, ga, usa; 4public health practice, llc, atlanta, ga, usa objective this presentation aims to discuss the need and share the results from an international, multi-disciplinary mission to assess public health surveillance (phs) in sudan and develop a plan of action (poa) to strengthen phs through integration. we will present the findings of the mission and the resulting poa developed for public health surveillance strengthening (phss) designed to rapidly detect, report and respond to infectious diseases and establish sustainable, integrated one health concepts. introduction integrated disease surveillance and response (idsr) is a strategy conceived and adopted by the world health organization regional office for africa in 1998. the goal of idsr is to support phss in africa and enhance efficiency and communication across all levels of the national public health system. idsr emphasizes the commitment of the revised international health regulations [ihr (2005)] to strengthen and maintain core capacities to detect, assess, report, and respond to public health events of international concern. in february 2014, the sudan federal ministry of health (fmoh), requested an assessment of phs with attention to phss integration opportunities and enhancing their ability to meet ihr (2005) requirements. a who regional office for the eastern mediterranean (who/emro) team of consultants performed this mission in collaboration with the fmoh. methods the mission drew observations and conclusions and framed recommendations, derived from document reviews, semi-structured interviews with key informants, discussions with heads of diseasespecific programs and through observations on how phs data were collected and reported from the peripheral levels to higher levels, as seen during the field visits. the findings of this technical mission and the recommendations were formulated into a country report and poa. results the mission found that phs in sudan consisted of multiple, siloed, vertical disease phs systems, each using separate data collection and reporting paper-based forms. this resulted in duplication of data collection and inconsistencies in reported data from various sources. it also creates a burden on staff. a poa to implement and sustain phss through integration in sudan was developed and recommended to the sudan fmoh. the poa advocated a strategic approach of phss, which incorporated ihr (2005) and one health concepts. this poa included technical, training, and management support services required to establish phss, e-surveillance, and a field epidemiology training program (fetp) in sudan. the main activities of this approach included: • facilitate coordination of national entities for one health phss with an objective of early warning and response • develop and modernize phs tools, standards, guidelines and policies, and streamline reporting channels • develop policies, and framework for national laboratory phss, including standards and guidelines • train health and phs officers on phss components • develop an fetp introduction plan conclusions empowering phss will equip a motivated health staff with a strategy to streamline phs functions; coordinate an “all-hazards” monitoring and alert approach; and provide national consistency for detection, analyses, and reporting. if implemented, the recommended poa supports a long-term goal to shift – in a phased manner – from the present fragmented, disease-specific, phs focus, to an integrated one with full implementation capacity at the community, local, state, and national levels. keywords idsr; ihr(2005); sudan; plan of action; one health acknowledgments the authors would like to greatly acknowledge the original mission members dr. hoda mansour [hm] (namru-3, cairo) and dr. margaret lamunu [ml] (who/hq) as well as the support received from the csr team of the who coordinating office and the sudan fmoh. references mcnabb, s.j.n., s. chungong, m. ryan, t. wuhib, p. nsubuga, w. alemu, v. carande-kulis, and g. rodier. conceptual framework of public health surveillance and action and its application in health sector reform. bmc public health 2002,2:2 mcnabb, s.j.n., s. chungong, m. ryan, t. wuhib, p. nsubuga, w. alemu, v. carande-kulis, and g. rodier. conceptual framework of public health surveillance and action and its application in health sector reform. bmc public health 2002,2:2 technical guidelines for integrated disease and response in the african region (2nd edition) october 2012 *ngozi a. erondu e-mail: ngozierondu@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e32, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts development of national disability surveillance system in sri lanka inoka e. weerasinghe*1, 2 and sumedha jayatilake3 1ministry of health, colombo, sri lanka; 2center for injury research and policy, johns hopkins university, baltimore, md, usa; 3faculty of dentistry, university of peradeniya, peradeniya, sri lanka objective to review the national disability statistics and to develop a methodology for the establishment of a national disability surveillance system in sri lanka. introduction a major drawback in disability prevention and rehabilitation in sri lanka is the lack of complete, accurate and timely data. national disability surveys conducted as a part of the decennial census lack complete and scientifically accurate information. further, sri lanka has no established disability surveillance system. this review was conducted to explore the national disability data and to develop a methodology for the establishment of a disability surveillance system in sri lanka. methods medline and google data bases were searched for published data from 1984 to 2014 using the key words; disability, national disability data, national surveys, national/international disability surveillance and sri lanka. further, disability statistics were reviewed using publications available in census department, central bank, postgraduate institute of medicine and other governmental and nongovernmental organizations and institutes of sri lanka. in addition, both local and international experts in the field of disability, health care surveillance and bio-informatics were interviewed. results according to the initial survey on disability in sri lanka-2001 conducted by the census and statistics department, prevalence of disability was 1.6%1. the central bank of sri lanka in 1996/97 and 2003/04 conducting consumer finance and socioeconomic surveys has reported that the prevalence of disability in sri lanka was 2.3%2. a survey by the united nations revealed that the total disability prevalence in sri lanka was 2% in 19863. the highest disability prevalence for sri lanka was reported by the world health survey, 2002-2004 which was 12.9%4. all above surveys have categorized disability basically according to gross anatomical defects or functional disability and none of them had measured disability using an internationally comparable standard. limited number of studies has been conducted on specific types of disabilities confined to local areas. however, only a few has used a standard disability measurement such as international classification of functioning, disability and health (icf)5. standard measurements in the national disability data collection helps to compare the data locally and internationally. therefore, following initial steps were identified as key points in the establishment of a national disability surveillance system in sri lanka. they are planning icf based disability surveillance system, piloting the disability surveillance system, testing the possibility of coding and feasibility at selected test sites. formulation of a commonly agreed icf based information sheet to report functioning and disability data to an identified central body, setting up of a locally based icf training for the primary health care workers and curative and rehabilitation care workers, information collection to be done by the primary health administrative areas (medical officer of health areas) which collect the community field data and from the hospital and rehabilitation centers and to send the information to the central body, method of sending information either via post or e-mail, working out an analytical methodology to utilize the information produced and later on to develop a software to report and analyze the data. conclusions sri lankan disability data differ from one data source to the other. current disability data in sri lanka are non-comparable locally and internationally because they are not based on a scientific disability classification system. this highlights the great need of a national disability surveillance system using scientific methods such as icf. keywords disability surveillance; sri lanka; public health acknowledgments authors would like to acknowledge all the experts supported. references 1. brief analysis of the characteristics of the disabled persons, sri lanka, department of census and statistics, 2009. 2. consumer finances and socioeconomic survey report 2003/04 part 1, central bank of sri lanka, colombo. central bank of sri lanka, 2005. 3. united nations disability statistics database, united nations, 2011, new york. 4. world health survey, geneva, world health organization, 2002-2004 5. international classification of functioning, disability and health, geneva, world health organization, 2001. *inoka e. weerasinghe e-mail: eranganie@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e59, 201 testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 testing an extended theoretical framework to explain variance in use of a public health information system abstract objectives: this study examined determinants of using an immunization registry, explaining the variance in use. the technology acceptance model (tam) was extended with contextual factors (contextualized tam) to test hypotheses about immunization registry usage. commitment to change, perceived usefulness, perceived ease of use, job-task changes, subjective norm, computer self-efficacy and system interface characteristics were hypothesized to affect usage. method: the quantitative study was a prospective design of immunization registry endusers in a state in the united states. questionnaires were administered 100 end-users after training and system usage. results: the results showed that perceived usefulness, perceived ease of use, subjective norm and job-tasks change influenced usage of the immunization registry directly, while computer self-efficacy and system interface characteristics influenced usage indirectly through perceived ease of use. perceived ease of use also influenced usage indirectly through perceived usefulness. the effect of commitment to change on immunization registry usage was insignificant. conclusion: understanding the variables that impact information system use in the context of public health can increase the likelihood that a system will be successfully implemented and used, consequently, positively impacting the health of the public. variables studied should be adequate to provide sufficient information about the acceptance of a specified technology by end users. keywords: public health informatics; technology acceptance model; tam, immunization registry; public health; health information technology introduction researchers, investors, managers and practitioners are just a few among many others attempting to understand why end-users do not use adopted information systems even when the systems appear to promise substantial benefit. understanding why people use or, reject computers is one of the most challenging issues in information system (is) research [1]. even with improvement in application usability, lack of use remains a challenge, and has led many organizations to fail in achieving the benefits reaped from http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 implemented systems [2]. as public health organizations continue to invest in information systems such as immunization registries, the expectation is that end-users are using the systems in order for benefits to be realized. the national vaccine advisory committee (nvac) recommended that strategies geared toward improvement of immunization coverage and reduction of vaccine-preventable diseases should include immunization registries as key strategy [3]. individual organizations were developing registries as far back as 1980s [3]. however, it was only in the early 1990s that population-based registries were promoted [3]. public health organizations are aware that immunization registries can facilitate the realization of their public health objectives, and therefore they continue to develop and to promote the adoption of the registries. however, once organizations adopt the immunization registries, there is no empirical evidence in the literature demonstrating meaningful use. immunization registries are confidential, population-based, computerized information systems that contain data about children in a geographic area [4]. the centers for disease control and prevention (cdc) defines an immunization registry as a key tool used to increase and sustain high vaccination coverage by providing complete and accurate information on which to base vaccination decisions. an immunization registry with added capabilities such as vaccine management, adverse event reporting, lifespan vaccination histories and linkage with other electronic data sources is referred to as an immunization information system (iis). though ‘immunization registry’ and ‘iis’ are used interchangeably in this article, the system studied is an iis. iis are beneficial in providing information about immunization coverage levels by child, by immunization provider, and by geographic area [5]. they are of benefit to a wide range of stakeholders some of which include: children, parents, doctors and nurses, health plans, schools, communities, and states. the iis has functionality for reminders and recalls. parents can be reminded when an immunization is due and recalled when an immunization is missed (overdue). healthcare providers are able to obtain a consolidated report on a child’s immunization history because the registry consolidates records from multiple providers. healthcare providers are also able to use the iis to obtain the most current recommendations for immunization practice and to determine what vaccine to administer. the iis can also track contraindications and adverse events that are immunization-related. managed care and other organizations can get coverage reports from the iis. immunization information systems are used for additional purposes such as clinical assessments and surveillance activities. iis reduce the time needed by school nurses and administrative staff to check immunization status by providing automatic printouts of immunization status. the iis also promotes greater accuracy of records avoiding duplication of immunizations [5]. iis can help to prevent disease outbreak and control vaccine-preventable diseases by identifying under-immunized children (children at risk for vaccine-preventable diseases). information on community coverage rates is included in iis [5]. more comprehensive data can be made available in the iis than on paper because the iis can be linked to other databases such as newborn and lead screening or other state registries [5]. http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 no study has tested a widely accepted theoretical framework on the use of immunization registries. a review provided evidence that the technology acceptance model (tam) (figure 1.) which associates system usage with acceptance was the most influential model of more than 20 computer use models [6]. this was attributed to the fact that tam is powerful in describing information system use behavior [7-14]. this research therefore has its theoretical grounding in the tam which has its roots in the theory of reasoned action (tra) [15].tra explains behavior and was formulated by ajzen and fishbein in 1967, and has its origins in social psychology. the initial tam included attitude as a construct that mediated between peruse, peou and behavior intent. attitude was not found to be significant and was removed resulting in a revised tam in which behavior intention (bi), a proxy for actual use was a function of perceived ease of use (peou) and perceived usefulness (peruse). both the determinants of bi have been found to be statistically significant in explaining use, but peruse was found to be a stronger determinant than peou [9]. tam also postulated that peruse and peou mediated external variables. figure 1. the original technology acceptance model though tam is popular and valid for explaining technology usage behavior; it is a decontextualized theory, hence failing to include important contexts such as social, organizational and work context. the need to extend tam with other contexts is reiterated throughout the literature, even by the developers of tam. for example, researchers admit to having not included the social context in tam and recommend extending tam to explore the social context [9, 10]. in this study, additional variables are therefore added to tam to contextualize it so that usage is examined as a function of elements of other contexts. in addition to examining the relationship between system usage, peou and peruse, the study also seeks to examine whether these new variables introduce moderating effects. the research model is thus further extended to test whether any of the added variables influence the strength and or direction of the relationship between the peou and peruse and system usage. intention to use perceived usefulness (peruse) perceived ease of use (peou) attitude to using actual system use http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the study research model postulated that, immunization registry usage is indirectly related to (i) system interface characteristics (sic), (ii) computer self-efficacy (cse) and directly and indirectly related to (iii) peou. peou is a function of system interface characteristics (sic) and computer self-efficacy (cse). this study examined whether sic and cse affected su indirectly through peou and through peou’s effect on peruse. the study also examined if peou is directly related to immunization registry usage. the study research model also postulated that, immunization registry usage is only directly related to (iv) perceived usefulness (peruse), (v) commitment to change (ctc) (vi) subjective norm (sn) and, (vii) job tasks change (jtc). this research model examined immunization registry behavior as a function of perceived usefulness (peruse) and perceived ease of use (peou). peruse and peou were both proposed mediators in the model and variables they mediate were included in the model. davis (1989) demonstrates that peruse mediates peou’s effect on system usage. the study also tested whether peruse mediated peou, hence attempting to validate davis’s (1989) findings. the study tested whether peou mediated system interface characteristics and computer self-efficacy (both antecedents of peou). the constructs were all conceptualized. peou was the degree to which an end-user believed using a system to be free of effort [9]. sic were features of the system interface that had an influence on mental effort. cse was found to affect peou [16, 17]. cse was the end-user’s self-assessment of his/her ability to use information and computer technologies in general [16]. peou influenced peruse and, both peou and peruse influenced system usage. perceived usefulness was the degree to which an end-user believed that using the information system would enhance his or her job performance [9]. useful was defined as capable of being used advantageously [9]. performance benefits were assessed by measuring a person’s anticipated consequences of using the system (peruse). the inclusion of peruse in this study’s research model was also supported by behavioral decision theory. commitment to change was the end-user’s psychological attachment to using the system as implementation unfolds [18]. system usage was the dependent variable in this study and actual usage behavior was examined rather than intention to use a system in order to expand the scope to accommodate situations where use of a system was mandated. subjective norm also affected system usage behavior. subjective norm was defined as “an end-user’s perceptions that people who are important to him/her think he or she should or should not use the system” [19]. using an information system is a social process; hence behavior towards the information system can be based on social influence from others [20]. job characteristics also influenced system usage. when job-tasks change, it influences system usage. job level changes were the changes that occurred due to a collection of tasks and non-task related factors. task level changes are the changes that occurred to specific (or individual) tasks. few studies have examined how changes in the job and tasks influence system usage. the impact of job-tasks change on expert system use was studied and showed a strong negative relationship between job –tasks change and use [21]. end-users will use a system more effectively if it does not impose changes to tasks they perform for their job. in this study, job-tasks changes were therefore the specific work-related tasks that changed upon implementation of a system. http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 methods the behavior of using an iis is an important variable to be studied especially because many studies have found usage to be associated with system acceptance [22, 23]. the overall purpose of this study was to test a theoretically sound research model through an empirical study that answers the following research question: what factors significantly predict the use of a web-based immunization registry/iis? this study was conducted in a us state and the unit of analysis was the individual immunization registry end-user. all the organizations that had implemented the immunization registry developed by the state health department were contacted for recruitment. when calls were made to each organization, the specific contact information for the point person/people for the immunization registry was/were requested. the individuals were contacted and through the contact person additional prospective study participants were solicited. 100 study participants were then randomly selected from the provided prospective study participants. to be considered for the study, individuals had to meet the inclusion criteria of being a recent end-user of the immunization registry who had recently (within 3-6 months of training) been trained on the use of the registry. additionally, the individual had to have worked at the agency for at least 2 years and also had to be able to participate in two phone interviews, one occurring about 3 months after the initial interview. the goal of the data collection was to gather data that was relevant and sufficient to test the study’s hypotheses and hence answer the study’s research question. data was collected by means of surveys administered through phone interviews. in this study, two surveys were administered to respondents. the first survey was administered to participants after system training, but before extensive use of the system. the second survey was administered to assess use of the system 3 months later. the constructs that were operationalized in the surveys to test the research hypotheses were: commitment to change (ctc), job-tasks change (jtc), system interface characteristics (sic), computer self-efficacy (cse), perceived usefulness (peruse), perceived ease of use (peou), subjective norm (sn) and system usage (su). the questions with the exception of demographic and context questions were drawn from the literature. though the survey was tested for content validity and the survey items were modified to be representative of the content domain. this study tested the following hypotheses: h1: sic is positively related to peou h2: cse is positively related to peou. h3: peou is positively related to peruse. h4: peruse is positively related to system usage. h5: peou is positively related to system usage. h6: commitment to change is positively related to system usage. h7: subjective norm is positively related to system usage. h8: job-tasks change is negatively associated with system usage http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 in this study, additional variables are therefore added to tam to contextualize it so that usage is examined as a function of elements from other contexts. in addition to examining the relationship between system usage, peou and peruse, the study also sought to examine whether these new variables introduce moderating effects. the research model was thus further extended to test whether any of the added variables influenced the strength and or direction of the relationship between the peou and peruse and system usage. the conceptual model extended tam with social (through subjective norm), organizational (through commitment to change), and work (through job-tasks change) contexts. the social dimension can be viewed as influence from peers whereby a user’s behavior is shaped by peers hence becoming an enabler or deterrent of technology acceptance. the peer dimension, involves the individual conforming to others’ beliefs, namely, normative beliefs. subjective norm is the peer dimension examined in this study. though examining subjective norm is necessary, in situations where the external influence of peers does not influence behavior, an individual’s own beliefs such as how committed they are to the change will prevail, and is therefore more sustainable [18]. an individual working in an organization will exhibit this organizational variable; hence commitment to change is introduced in the conceptual model, as a determinant for system usage. lastly, technology is known to change work especially if the system is not designed to support an organizations business practices and workflow. this can therefore be an implementation barrier and for this reason in examining use, it is critical to examine this potential barrier to system use. tam is therefore extended by adding the job-tasks change variable in the conceptual model. the data was analyzed in spss v. 15.0. cronbach’s alpha was used to investigate internal consistency of the items measuring each concept. measures for latent variable were created by aggregating (by averaging) the items for: perceived ease of use (peou), perceived usefulness (peruse), job tasks change (jtc), system interface characteristics (sic), commitment to change (ctc), subjective norm (sn). cronbach’s alpha and other reliability measures were computed to measure item consistency/reliability. a high cronbach’s alpha (greater than .70) demonstrated that the items were measuring the same underlying construct and had good reliability. pearson’s r correlation was computed to measure how closely related variables are by showing the degree of linear relationship between two variables. analysis involved using frequencies to display distribution of values for a variable and to check for any data entry or coding errors. frequencies generated both statistical and graphical displays. variability of responses was critical and variables that did not have enough variance in responses were not included in the analysis. descriptive statistics that included, means, standard deviations and correlations were generated. http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 regression analysis was applied to estimate the linear relationship between a dependent variable and independent variables. in cases where there was more than one independent variable predicting the dependent variable, the regression equation applied was: y = a + b1*x1 + b2*x2 + ... + bn*xn.. where there was only one variable the regression equation applied was: y=a + b * x (y = dependent variable, x = independent variable, a=constant/intercept, b=slope /regression coefficient the independent contributions of each independent variable to the prediction of the dependent variable. the analysis included the following equations: equation 1: h1, h2: peou = b0 + b11sic+ b12cse +control variables equation 2: h3: peruse = b0 + b21peou+ b11sic+ b12cse +control variables equation 3 and 4: h4, h5, h6, h7, h8: su= b0 + b31peruse+ b32ctc+ b33jtc+b34sn+ b21peou+ b11sic+ b12cse + control variables (su was measured through functions and through the frequency of use, therefore the analysis generated results for both models) this study sought to examine whether any of the variables that were added to the theoretical framework had moderating effects on the relationships between peruse and system usage, and peou and system usage. the following interaction effects were tested if the variable was found to be significant in the regression model: 1) peou * ctc 2) peou * sn 3) peou * jtc 4) peruse * ctc 5) persue * sn 6) peruse * jtc 7) each of the models included the following control variables: organization size (fulltime + part-time employees), education, job category, organization type, job tenure, number of hours work, gender, previous computer experience, number of years using a computer, previous experience collecting immunization data electronically and total number of computer applications used. control variables were used to reduce the possibility of spurious relationships. the research sought to include non-technical variables in the research model as well, realizing that though technical inadequacies can lead to ineffective use of the system, non-technical issues also have an effect. expert systems that fell into disuse, respondents were more likely to cite problems of a non-technical, non-economic nature than of a technical nature [21]. the models adjusted for clustering effects, potentially introduced because some of the study participants worked in the same organization. the study had 100 participants from 30 organizations. the analysis adjusted for the possibility of clustering effect of organizations on the data. the need to test for a possible clustering effect was based on the notion that individuals working at the same organization may have more similar responses than those in other organizations. if this effect had not been taken into account, the statistical tests would have underestimated the standard errors of parameter http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 estimates. to avoid this, the adjustment was made by applying the huber –white correction, a robust estimator of variance. the estimator relaxed the ols assumption of independence of observations within clusters and only required that observations be independent across (between) clusters. the multiple regression analysis involved selecting the dependent and independent variables to include in the regression in spss. the r-square was computed and it showed the proportion of variance in the dependent variable, which could be predicted from the independent variables. this measured the overall strength of association and did not show the extent to which a specific independent variable was associated with the dependent variable. the adjusted r-square was computed as well, indicating that as predictors are added to the model, each predictor will explain some of the variance in the dependent variable. the standard error of the estimate (root mean square error) which indicates the standard deviation of the error term was computed. the variance explained (sums of squares for the regression/model) by the independent variables and the variance not explained (sums of squares for the regression residual or error) by the independent variables was computed. the total sums of squares were also computed. the degrees of freedom (df) associated with the variances were also computed. the mean squares (sum of squares/ df) and f-value were also computed. the f-value was computed by dividing the mean square regression by the mean square residual. an associated p-value indicated whether the independent values significantly predict the dependent variables. a p-value less than 0.10 for the model indicated that together the group of independent variables showed a statistically significant relationship with the dependent variable. a p-value was also calculated for each independent variable, and indicated that the specific independent variable showed a statistically significant relationship with the dependent variable. multiple regression assumes that residuals (predicted minus observed values) are distributed normally, hence follow a normal distribution. the distribution of the variables was examined in this study by plotting normal probability plots and histograms to inspect the distribution of the residual values. collinearity was investigated to determine whether any of the independent variables were correlated. the vif, tolerance and condition index were examined in this study to assess collinearity. big values of vif greater than 10 were considered potentially a problem. however, this was confirmed by examining the proportions of variance. collinearity was also considered evident if 2 or more variables had large proportions of variance (0.50 or more) that correspond to a large condition indices (greater than 30). results the response rate for this study was 77%. the 23% did not participate for a variety of reasons, some of which included, work load, their belief that they incapable to respond to questions because they did not use the immunization registry enough, or because upon several attempts to contact the individual for the interview, the person could not be reached or did not return the call. multiple regression analysis was applied to the data and generated estimates for the following four models: http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 model 1: h1, h2: peou = b0 + b11sic+ b12cse +control variables model 2: h3: peruse = b0 + b21peou+ b11sic+ b12cse +control variables model 3: h4, h5, h6, h7, h8: su (functions) = b0 + b31peruse+ b32ctc+ b33jtc+b34sn+ b21peou+ b11sic+ b12cse + control variables model 4: h4, h5, h6, h7, h8: su (frequency) = b0 + b31peruse+ b32ctc+ b33jtc+b34sn+ b21peou+ b11sic+ b12cse + control variables the instrument was tested for both content and construct validity. inter-item correlation for items measuring the constructs was calculated for each construct and had a high cronbach value for each ranging from 0.91 to 0.97. collinearity amongst variables was also tested for. the vif values were all below 10. examining the proportions of variance validated this result. collinearity was considered evident if 2 or more variables had large proportions of variance (0.50 or more), which corresponded to large condition indices (greater than 30). the results showed that the collinearity was evident, but it was amongst the control variables and not in the theoretical constructs. since inferences were being made about theoretical constructs and not control variables this did not have negative implications on the study results and conclusions. normality was also examined by plotting regression residual histograms and plots. a generalized estimating equations model generated 2 different models, one model applied the robust estimator correction for the correlation matrix and the other did not. the former represented the correction for clustering effects and the latter did not. the goodness of fit statistics (quasi likelihood under independence model criterion – qic) indicated which model was better. the models did not differ in goodness of fit suggesting that the clustering effect was insignificant in this study. the significance of each model was determined from the p-value. a p-value for the model of <0.10 was significant and indicated that the independent variables (as a group) reliably predicted the dependent variable (also, show a significant relationship with the dependent variable). a p-value of <0.10 was also considered significant because of the small sample size used to test the hypothesis. the p-value for model 1 was <0.10, therefore, the independent variables reliably predicted perceived ease of use. the rsquare of 0.513 for this model indicated that approximately 51% of the variability of perceived ease of use was explained by the variables in the model. the adjusted r-square of 0.42 indicated that approximately 42% of the variability of perceived ease of use was accounted for by the model even after taking into account the number of predictor variables in the model. in model 1, computer-self efficacy and system interface characteristics were the only significant predictors influencing perceived ease of use positively. consequently, the hypotheses that system interface characteristics are positively related to perceived ease of use (h1), and that computer self-efficacy is positively related to perceived ease of use (h2) were supported. the p-value for model 2 was < 0.10 (p=0.69) indicating that the independent variables reliably predict perceived usefulness. the r-square of 0.255 for this model indicated that approximately 26% of the variability of peruse was explained by the variables in the http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 model. the adjusted r-square of 0.101 indicated that approximately 10% of the variability of peruse was accounted for by the model even after taking into account the number of predictor variables in the model. in model 2 only perceived ease of use was a significant predictor. this predictor’s coefficient was significantly different from 0. the result supported the hypothesis that peou is positively related to peruse (h3). the pvalue for model 3 was <0.10 indicating that the independent variables reliably predicted system usage. the r-square of 0.829 for this model indicated that approximately 83% of the variability of su was explained by the variables in the model. the adjusted r-square of 0.783 indicated that approximately 78% of the variability of su was accounted for by the model even after taking into account the number of predictor variables in the model. in model 3, the estimates showed that only perceived usefulness, perceived ease of use, subjective norm, and job-tasks change were significant predictors while the remaining were not. these predictors had coefficients that were significantly different from 0. of the theoretical constructs, perceived usefulness, perceived ease of use, subjective norm and job-tasks change were significant predictors in model 3 and commitment to change was not. the significant variables influenced system usage positively with the exception of job-tasks change, which influenced system usage negatively. therefore, the hypotheses that postulated peruse is positively related to system usage (h4), peou is positively related to system usage (h5), jtc is negatively related to system usage (h8), and sn is positively related to system usage (h7) were supported. the hypothesis that postulated ctc is positively related to system usage (h6) was not supported. the p-value for model 4 was > 0.10 indicating that the independent variables did not reliably predict system usage and the model was not significant. therefore, all of the hypotheses were not supported. frequency of use does not appear to be an adequate measure for system usage in a mandatory use context, reflected by the insignificance of the model and all of its variables. in a mandatory use context, the model that conceptualizes usage in relation to quality of use, as ‘functions for which the system is used for’ is a significant model and explains 73% of the variance in immunization registry usage. this percentage is higher than the percentage (45%-57% and lower for field studies) stated in the literature when tam is not extended [24]. system interface characteristics and computer self-efficacy does not uniquely contribute to the prediction of system use, but both variables influenced peou (a significant predictor for system use), demonstrating a mediating effect through peou. the mediation effect of peruse is also evident, peou is a significant determinant of peruse and peruse is a significant predictor of system use. therefore, peruse mediates between peou and su. moderating effects on peruse and system usage, and peou and system usage are not demonstrated. http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 0.434* 0.309* figure 2. graphical presentation of study findings model estimates and r 2 shown. parameter estimates from model 1, 2 and 3 are shown on this diagram and are preceded by * , indicating their significance level (*p<0.10 (significant). r 2 is also shown and it is the proportion of variance in the dependent variable explained by the independent variables. note: the parameter estimates showing the direct effects on system use are from model 3 (functions system used for – dependent variable). discussion understanding factors that influence the use of an implemented public health information system such as an immunization registry is of great important to those implementing the system and those interested in the positive impact of using the technology for positive public health outcomes. use was examined from two perspectives in this study, 1) through frequency of use 2) through functions for which the system was used. the study argued that attempting to conceptualize or operationalize usage by examining the more widely used approach that based the operationalization on volume of usage (frequency of system use), would not be useful in a situation where system use was mandatory. the results supported this argument, showing that the model with the more comprehensive system usage (su) r 2 =0.78* (functions) r 2 =0.07 (frequency) (frequency) perceived usefulness (peruse) *r 2 =0.10 system interface characteristics (sic) computer selfefficacy (cse) perceived ease of use (peou) r 2 =0.42* commitment to change (ctc) subjective norm (sn) 0.056 1.008* 0.825* -1.014* 0.266* 0.285* job-tasks change (jtc) http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 operationalization of usage was significant and the model that applied frequency of use as the dependent variable was not. this study demonstrated the applicability and predictive power of an extended and modified technology acceptance model (ctam) in predicting the actual use of an immunization registry. the percent of the variance explained by the extended tam exceeded the percent of variance when tam is not extended demonstrating that the independent variables were predicting a higher proportion of variance in system usage in this study than when tam was not extended. clearly, tam proved to be an appropriate initial model for this study and it is evident that extending tam increased its predictive power. extending tam to include context variables extended the theory. all the context variables with the exception of commitment to change influenced the use of the immunization registry. therefore, contextualizing tam introduced new associations, making it evident that the social context and work context contribute to explaining why an immunization registry is used or not used. this model was not only extended but it was modified to make it applicable in a mandatory use context. the high r-square of the model suggests that this research model is appropriate for mandatory use contexts. the research question asked what factors significantly predict the use of a web-based immunization registry/iis? perceived usefulness, perceived ease of use, subjective norm, job-tasks change, system interface characteristics and computer self-efficacy significantly predicted the use of this study’s immunization registry. as theorized perceived usefulness, perceived ease of use, subjective norm and job-tasks change were found to have a significant influence on the end-user’s use of the immunization registry. perceived usefulness, perceived ease of use and subjective norm were positively related to immunization registry/iis use. job-tasks change was negatively related to immunization registry/iis use. contrary to our postulation, commitment to change did not influence the use of the immunization registry. this finding can be attributed to the fact that the end-user’s cognitive dissonance did not need to be reduced because system usage was mandated. as postulated, perceived ease of use mediated the influence of system interface characteristics and computer self-efficacy on the use of the immunization registry. additionally, as postulated, perceived usefulness was found to mediate the influence of perceived ease of use on the use of the immunization registry at a significance level of 0.10. in this study, subjective norm influenced immunization registry use the most. this finding was not surprising given that study participants in clinics and the local health departments view colleagues in the state health department more important due to their governmental status. the immunization registry was implemented free of charge making the environment even more favorable for end-user’s to conform to other’s views. the study results also showed that the relationship between peruse and system usage as well as the relationship between peou and system usage were not moderated. http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the study findings therefore show that end-users of immunization registries are therefore found to use an immunization registry if they think it is useful, easy to use, and if they are committed to the implementation. however, the end-user’s use of a system is independent of the influence by others more important to them, or by whether the system implementation changes job tasks. end-users also perceive a system easy to use if its ease is evident through a well-designed system interface that minimizes the cognitive effort and disorientation as the end users interact with the immunization registry’s interface. if end users of an immunization registry have a strong sense of their ability to use the registry, hence a high computer self-efficacy, they find the immunization registry easy to use. an end-user who believes that the immunization registry is easy to use believes that using the system will enhance his or her job performance. high computer self-efficacy, well designed immunization registry interface and belief in the benefits of the immunization registry indirectly then contribute to end-users using the implemented immunization registry as intended to perform job functions. some of these variables had been studied previously and the results in this study were consistent with other findings. for instance, four different systems were studied in four organizations and two involved voluntary usage and the other two involved mandatory usage [14]. it was found that in both the voluntary and mandatory context, perceived usefulness and perceived ease of use significantly influenced system use behavior directly. studies have shown inconsistent results about how perceived ease of use influences usage behavior [9, 25]. for instance, it was found that perceived ease of use influenced system usage indirectly but not directly [26]. another study showed that perceived ease of use did not influence system usage directly or indirectly [27]. nevertheless, most studies have confirmed that perceived ease of use predicts system usage through perceived usefulness [9, 25, 28]. to increase the validity of this study’s findings, in conducting the study it was imperative that spurious effects were controlled for. the study sought to minimize any spurious effects by introducing control variables. control variables were introduced in this study to establish that the predictor (independent) variable was the sole cause of the observed effect in the dependent variable. in the significant models, it appeared that the majority of control variables did not contribute to the observed effect in the dependent variable. the study controlled for other biases to minimize the likelihood of inconsistent estimates ensuring the hypotheses tests were reliable. in this study self-selection error was controlled for. a web-based survey was not chosen as a data collection tool because the data collected on the internet could potentially suffer from self-selection. the use of the internet by individuals varies and certain individuals are more likely to be on the internet than others, and are therefore more likely to fill-in the web-survey. http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 also, this study minimized the error of recall by asking users questions about recent events. the users are surveyed immediately after training of the system (before actual use of the system) and 3 months after use of the system. using expert knowledge, empirical evidence and theory to ensure that constructs were represented by valid and reliable measures minimized bias through measurement error. the high cronbach’s alphas demonstrated that the items were measuring the same underlying construct and had good reliability. lack of exogeneity assumption of regressors is observed when key aspects in research models are omitted. this study drew from literature and theory to include exogenous variables in the research model, extending tam. experts confirmed content validity through the survey pre-test and focus groups. questionnaire items to measure perception, beliefs, attitudes, judgments, or other theoretical constructs are likely to reflect measurement error because of absence of physical measures corresponding to these variables. in this study however, using behavior measures for the variables in the regression model mitigated this problem. systematic error, which is error that makes survey results unrepresentative of the target population by distorting the survey estimates in one direction, was controlled for in this study. this error can distort the results in any direction but tend to balance out on average. though this error cannot be measured directly response rate was viewed as an appropriate the indicator in this study. in this study, the response rate of 77% was good and the sample size adequate, thereby minimizing this error. random error such as error introduced through data capture was minimized in this study. controlling for this was critical in ensuring that results are not overestimated or underestimated. data was captured on paper and transferred to spss. though it was transferred and possibly prone to data entry errors, the data was checked three times against survey data. double verification was also applied and it entailed entering the same data in spss on two separate occasions and comparing frequency data. lastly, survey testing error was controlled for by pre-testing the survey to minimize this error. this study has both theoretical and practical implications. this study conceptualized and operationalized use in a manner that was meaningful and applicable beyond the scope of volitional use, thus in a manner that signified use of the immunization registry to support job functions in a mandatory use context. the more common conceptualizations of use in the literature had been limited to a conceptualization that was specific to voluntary system use contexts, and one that did not measure use adequately in mandatory use context. researchers have empirical evidence from a study that explored use from a mandatory use context. this research has also provided other researchers with measures and data collection tools that can be used in the context of mandatory use context. most of the previous operationalizations of use lacked comprehensiveness, and few of the studies related use to job functions. to measure use as conceptualized in this study, the operationalization needed to be granular and needed to measure the functions that the http://ojphi.org/ testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 immunization registry is used for. in this study it was paramount that the conceptualization of use, represented system use of the immunization registry for specific job functions, and that the operationalization adequately measured this use. there is no agreed upon way of conceptualizing or operationalizing the use construct in the literature. by focusing on functions rather than the common simpler operationalization such as frequency of use, this operationalization is applicable in a mandatory use context. the majority of studies have operationalized system usage for volitional contexts, therefore, there is no doubt that this study presents a new operationalization that future researchers seeking to study use in a mandatory context can apply. conclusion as more public health data becomes available and accessible through information systems, it will be critical to understand what factors could potentially hamper effective and meaningful use, and to mitigate those beforehand. this study identifies variables able to influence use and serves as a useful guide for researchers and practitioners. the present national focus on meeting meaningful use requirements of electronic health records in the united states is mostly prevalent in clinical settings. however, public health agencies should adopt similar best practices and begin defining what is considered meaningful use and measuring what could potentially impact that use. this study is among few informatics studies based on a comprehensive and explicitly presented theoretical framework. the extended theoretical framework can be generalized across disciplines and should be tested in other contexts as well. conflict of interest statement the author of this article, dr. wangia declares “i have no financial and personal relationships with other people or organizations that could inappropriately influence (bias) my work.” corresponding author victoria wangia research assistant professor university of kansas medical center email: vwangia@gmail.com http://ojphi.org/ mailto:vwangia@gmail.com testing an extended theoretical framework to explain variance in use of a public health information system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 references 1. swanson eb. information system implementation: bridging the gap between design and utilization. 1988, homewood, il: irwin. 2. hasan b. 2003. the influence of specific computer experiences on computer self-efficacy beliefs. comput human behav. 19(4), 443-50. http://dx.doi.org/10.1016/ s0747-5632(02)00079-1 3. freeman va, defriese gh. 2003. the challenge and potential of childhood immunization registries. annu rev public health. 24, 227-46. http://dx.doi.org/10.1146/ annurev.publhealth.24.100901.140831 4. nvac. 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theories. inf manage. 39(4), 297-311. http://dx.doi.org/10.1016/s0378-7206(01)00098-2 http://ojphi.org/ http://dx.doi.org/10.1287/isre.2.2.143 http://dx.doi.org/10.2307/249658 http://dx.doi.org/10.1287/ http://dx.doi.org/10.2307/249291 http://dx.doi.org/10.2307/249291 http://dx.doi.org/10.2307/249577 http://dx.doi.org/10.2307/249577 http://dx.doi.org/10.1016/0167-9236 http://dx.doi.org/10.1016/0167-9236 http://dx.doi.org/10.1016/s0378-7206 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts towards a framework for data quality properties of indicators used in surveillance ian painter*1, lauren carroll1, david buckeridge2 and neil abernethy1 1university of washington, seattle, wa, usa; 2mcgill university, montreal, qc, canada introduction effective use of data for disease surveillance depends critically on the ability to trust and quantify the quality of source data. the scalable data integration for disease surveillance project is developing tools to integrate and present surveillance data from multiple sources, with an initial focus on malaria. consideration of data quality is particularly important when integrating data from diverse clinical, population-based, and other sources. several global initiatives to reduce the burden of malaria (presidents malaria initiative, roll back malaria initiative and the global fund to fight aids, tuberculosis and malaria1) have published lists of recommended indicators. values for these indicators can be obtained from different data sources, with each source having different data quality properties as a consequence of the type of data collected and the method used to collect the data. our goal is to develop a framework for organizing the data quality (dq) properties of indicators used for disease surveillance in this setting. methods we examined selected malaria indicators for the country of uganda calculated from four sources: uganda health management information system (hmis); uganda malaria surveillance project (umsp) sentinel site surveillance data, the uganda 2010 demographic health survey (dhs) and uganda indoor residual spraying (irs) program data. because different malaria indicators can be calculated from varied data sources, we organized the dq properties according to the provenance of the data sources used to calculate each indicator. using a hierarchical system, we grouped dq properties at different levels of the process used to obtain an indicator: properties common to (or inherited from) the system generating the data, those inherited from the data source, those arising from data processing steps, those inherited from data fields used to calculate an indicator, and those specific to the calculation method. results for any indicator, meta-data on the provenance of that indicator can be used to retrieve data quality properties from the appropriate level of the hierarchy. for example, the indicator “malaria test positivity rate” calculated for inpatient data from the umsp data source is calculated from the ratio of two other indicators: “any positive lab test for malaria” and “number of tested cases”. each of these indicators is in turn calculated from multiple specific data fields as an aggregate of case summaries from the 6 umsp sentinel sites. the data quality properties of the “malaria test positivity rate” then consist of dq properties specific to: 1. calculation of the indicator, 2. calculation of the two component indicators, 3. each field used in the calculations, 4. processing of the data used in the calculations, 5. sites providing the source data, and 6. the umsp data source. examples of dq properties at each level include standard errors for the ratio indicator and component indicators (1, 2), missing data rates for the each field (3), procedure for handling incomplete data forms (4), site specific sensitivity and specificity of microscopy detection of malaria, as measured at the start of the sentinel site program (5), and details on the training and quality assurance used in the program (6). conclusions our process captured meta-data elements relevant to provenance beyond those typically considered as dq properties. using a broad definition of dq (e.g. “the totality of features and characteristics of an entity that bears on its ability to satisfy stated and implied needs” iso 849201986), we consider the meta-data elements relevant to provenance to be important dq properties. for example, the dq measures of source data elements might indicate that the numerator of an indicator is overestimated, while the denominator is underestimated. by propagating this knowledge to the calculated indicator, we can determine that there is a qualitative risk of overestimation of the given indicator. decision makers utilizing surveillance data need to be able to rely on the quality of data, to inspect that quality, and when possible, to quantify the quality. by developing a reusable framework for data quality and provenance meta-data, we hope to enable diverse decision makers to consistently and confidently interpret available surveillance data, indicators, and the analyses based on them. keywords data quality; biosurveillance; global health; secondary data; malaria acknowledgments the sdids project is supported by the bill and melinda gates foundation. references reithinger, r. (2014). global malaria efforts. trans royal soc trop med & hyg,108, 247-248 *ian painter e-mail: ipainter@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e46, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts developing a national database of radon test data in collaboration with epa: a pilot project to ascertain feasibility carrie eggers* national center for environmental health, centers for disease control and prevention, atlanta, ga, usa objective test the feasibility of a publicly accessible national radon database by conducting a pilot project to standardize previously nonsystemized, uncoordinated state and local health department radon data sources into a nationally consistent radon information resource. introduction lung cancer is the leading cause of cancer death in the u.s. with radon exposure as the second leading cause of lung cancer after smoking and the number one cause of lung cancer among nonsmokers. the environmental protection agency (epa) estimates that one in fifteen homes nationwide has elevated radon levels. although public outreach efforts promote radon testing and subsequent mitigation when unsafe levels are found, data are non-standardized largely because of varying regulations among states, making targeted public health actions challenging. in accordance with the federal radon action plan to demonstrate results of radon risk reduction, epa is collaborating with cdc’s environmental public health tracking program. the tracking program has existing relationships with state and local partners to provide various environmental and health data, an established process for managing the data, and robust tools to analyze and visualize the data that are made publicly accessible via a web-based system (tracking network). methods for the pilot, a work group was formed with participants from epa, cdc’s tracking program, and environmental health and radon control programs from six state tracking programs. the work group provides guidance on developing the capability for receiving and displaying radon data on the tracking network. required and optional standardized data elements are identified for data submission. pilot participants agree to supply minimum required elements such as county or zip code, unique test identifier, test result, and whether the test occurred before or after mitigation. optional elements incorporate more detailed descriptors: building type, test location within the building, and test device type. participants will also provide metadata for its submissions, detailing key attributes to ensure understanding about the data. the data transport process uses a common schema and the tracking program’s established data submission protocol, modified slightly for the pilot. results during the data call, two states will submit radon test data for 1993-present, one state will submit data since 2005, and the remaining three states 1-5 years of data. via secure access, states will view their own record-level data along with aggregated data as elevated radon incidence by county or zip code, by year. data elements will range from the minimal required elements to records with almost all optional fields completed. lessons learned from the pilot will be documented and examined for viability of scaling up to a national level database. conclusions advantages of a national system include improving data compatibility through establishment of data standards, reducing the burden on national testing laboratories by providing a common repository for test results, and making these data available to states, particularly to those without local radon surveillance systems. future considerations include expanding the pilot to involve more states, developing data sharing relationships with radon testing labs, and addressing how to obtain and make available finer geographic resolution data for more effective public health actions. as additional data (e.g., geological) are added and more states participate, nationally consistent data and measures related to radon exposure potential, testing rates, and mitigation effectiveness may be made publicly available as part of demonstrating radon risk reduction. keywords standardize; data integration; national database *carrie eggers e-mail: ceggers@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e22, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 158 (page number not for citation purposes) isds 2013 conference abstracts real-time warning and signal verification for infectious disease outbreaks using syndromic surveillance system in rural jiangxi province, china tao tao*1, changming zhou1, xiaoxiao song1, qi zhao1, per andersson2, weirong yan3, 4 and biao xu1 1school of public health, fudan university, shanghai, china; 2invotech solutions, gävle, sweden; 3division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden; 4department of epidemiology and biostatistics, school of public health, tongji medical college, huazhong university of science and technology, wuhan, china � �� �� �� � � �� �� �� � objective �������� � ���� ����� ��� ������������ �������� ��������� � � �� ����������� ����������� �������� �� 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an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 128 (page number not for citation purposes) isds 2013 conference abstracts orbit – the oak ridge biosurveillance toolkit laura pullum* and arvind ramanathan computational data analytics, oak ridge national laboratory, oak ridge, tn, usa � �� �� �� � � �� �� �� � objective ������� �� ���������� �� ������� ����������� ��������������������� ��� �� ����� ������������� ��������������������� ����� ���� �������� �� ��� ����������������������������������������� ���� �������������� �� ��� � ������������ �������������� � introduction �!"�#�������� � �� ���������������� ��������� ���������� ���������� ������������ ���� $���������%������������$ ���� ����#���������������� �� ��������������� �������� $ 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�������� ������������� ����� �� ��� �����$� keywords % � ��� � ��������+�;�� ����� �����$��+�<������������� � acknowledgments �!23������ ��� �����=#�"��� �� ��33>�������� �= 7 �& ������;� ������� �� ����� ��&;�.>?@�??�!��a�@ �#� �= 7 �b� ��� ���*=7b��� ���������� �� �������� ������� �������� ����� � ���������� �������� $���� �� ������� �� �=7b�� ������������ � ���� �������������� � ��� ���������� ��� �� � ����������(� ����� ��� �������� ������� ������������������ ������������� ����=7b������� � references /�0� 5 �!������ '�����c �7���� � � �;��������������� ���������� ����$����� ����� �� ���������� ����$�������������������� �c�,���� ���9*��)�de9��� �??d � /�0� : �>������ �2�������������� ���� ������������ ����� �%������$� ���"����� ����� �;�� � ��,� ���??@ /60� . �!�����������. c �7� ����4 f �.������������> 7 �>� ��������� � ; ��� � � ����� ���� �������� � ��� � ��� ����� ������� ����� ���� ��������� ������ ���� ����� �� �������� �)� .����� ������ ��� �'�� � ��� ����� � $���� � 4��� ���)� 7��� ��� �� -�� ������ ���� "����������� ��� g?*���)�@6he�@@����?�� � /90� > �>� ��������������. �c ���� �;�� � ���)�%������ ����� �� ������� ������ ����� ���� ���� �,��.� �� �����2 �����,�����������4�� ������ 7��� ���*2,47������hgdehdh���??6 *laura pullum e-mail: pullumll@ornl.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e157, 2014 development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan kamran ahmed1, muhammad arish salam bukhari2, mohammad dauod altaf3, peter clement lugala4, ghulam rabani popal5, alaa abouzeid6, margaret lamunu7 1. world health organization, ex. edews project team lead for yemen, pakistan, somalia and liberia 2. world health organization, technical officer information systems, regional office for africa (afro), brazzaville 3. world health organization, team leader who health emergencies, country office, afghanistan 4. world health organization, officer in charge / acting who representative, country office, nigeria 5. world health organization, senior advisor to the regional director, regional office for the eastern mediterranean (emro), cairo 6. world health organization, emergency operation manager / team lead operational partnerships, regional office for the eastern mediterranean (emro), cairo 7. world health organization, health emergency officer, headquarter, geneva abstract objective: to share lessons learned with experience in concept development of electronic disease early warning system (edews) as a standardized informatic tool for optimal disease surveillance for early warning and response network (ewarn) during humanitarian crisis. methods: we did literature search, review and analysis to document system attributes of existing electronic tools being used for disease surveillance, early warning and health management information system (hmis). we generated baseline information and conducted multiple planning sessions with stakeholders for ewarn system requirement elicitation and validation to inform concept development of standardized electronic tool. results: we identified 98 electronic health projects, classified 22 projects under ‘disease and epidemic outbreak surveillance’ theme, whereas only four electronic tools met our selection criteria and were reported to be implemented in humanitarian settings complimentary to ewarn. baseline information was obtained to guide work on requirement gathering and analysis process, and development of concept for a standardized electronic tool for ewarn. discussion: the edews was enhanced with an objective to develop standardize electronic tools and data collection procedures to monitor diseases and health events for alert detection in global development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi introduction displacement of population due to conflict and natural disasters, inadequate shelter, insufficient and unsafe water, and inadequate sanitation poses significant risk factors associated with potentially life-threatening infectious disease outbreaks. the extensive population movements during humanitarian crisis exacerbate the circumstances with significant numbers of internally displaced populations (idps), returnees and refugees congregating in urban centres and the outskirts where basic service provision and infrastructures are unable to absorb the additional burden, and services are overwhelmed or simply not available to address mounting needs [1,2]. such poor conditions frequently result in increased morbidity and mortality during the crises, particularly in countries with poor disease surveillance mechanisms [3]. the routine public health surveillance systems of many lowor middle-income countries may be underperforming or nonexistent and may be disrupted during the humanitarian emergencies. the world health organization (who) recommends implementation of ewarn within two weeks of onset of an acute event as one of the priority interventions to mitigate the negative health consequences resulting from the acute emergency or humanitarian event. the ewarn system facilitates collection of essential, minimal data on prioritized epidemic prone or selected diseases with significant public health consequences, where rapid analysis of trends for outbreak or event humanitarian settings. the enhanced system could be harnessed as a powerful tool by outbreak response teams in getting vital epidemiological information for appropriate and timely response during emergencies. conclusion: edews experiences in yemen, somalia, liberia and pakistan offers an opportunity to learn and apply lessons to improve future health informatics initiatives or adapt edews as a feasible standardized approach to enhance ewarn implementation during humanitarian crisis, and potential integration into routine surveillance systems. keywords: electronic data collection, alert notifications, disease early warning, electronic tools, edews, humanitarian crisis. abbreviations: data summarization technique (dst), electronic disease early warning system (edews), electronic integrated disease early warning system (eidews), early warning and response network (ewarn), early warning and response system (ewars), early warning and response (ewar), global system for mobile communications (gsm), health management information system (hmis), internally displaced populations (idps), information technology (it), short messaging service (sms), world health organization (who) corresponding author: kamran ahmed drkamranrajput@gmail.com doi: 10.5210/ojphi.v11i2.10157 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:drkamranrajput@gmail.com development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi detection for prompt response and intervention is instrumental for mitigating the potential high morbidity and mortality that may be attributed to the even. the implementation of ewarn in emergencies or for disaster response is recommended for emergency phase of the response and should be discontinued in the event of implementation or activation of routine disease surveillance system as the affected populations recovers from the emergency and /or disaster effects [4,5]. ewarn has been implemented as adjunct to the routine public health surveillance systems as a part of an earlier efforts to strengthen surveillance and response during the humanitarian emergencies, but it continued to use traditional paper recording and reporting tools to manage epidemiological data in several lowand middle-income countries such as yemen, somalia, liberia, iraq and pakistan [6]. this has posed unique challenge to achieve ewarn objectives, consequently resulting in worsening of the health status of affected communities and populations at risk, when outbreaks of epidemic prone diseases remained undetected or detected too late to allow for effective public health response [4,6,7]. recent advances in public health informatics have however, led to the development and implementation of various electronic tools for different settings and context that facilitate real time reporting, transmission and processing of epidemiological data for timely detection, verification and prompt action. the electronic data capture using these tools have been aided using electronic devices such as mobile phones, tablet devices and other touch screen interfaces in certain settings utilizing existing it infrastructure and communication means [8,9]. the electronic tools developed so far for disease surveillance or for similar purposes, are either in use or at various stages of development or evolution and with varying capabilities. such disparate electronic tools with different functionalities and specificities, have been developed independently on different platforms using different architectures. consequently, there is duplication of efforts, loss of time, lack of standardization and harmonization, leading to inefficient use of limited resources, including time. also, disparate tools receive less feedback for improvement than if a single standard tool were recommended and broadly adopted [6,10-14]. furthermore, published data on such tools is very limited [6,11,13,15-17]. while multiple challenges have been reported with implementation of ewarn in humanitarian setting, the need for standardization of electronic tools for data collection, analysis and reporting for use in humanitarian settings when existing public health surveillance systems are overburdened, underperforming or non-existent at all remains a critical necessity for public health workers in such settings. equally, important is the need to address concerns over data privacy, protection, ownership and trust issues among stakeholders including governments of affected countries [4,6]. this paper presents our efforts in conceptualizing, development and use of a standardized online database system with web-based and mobile/smart phone data collection application package, called ‘electronic disease early warning system (edews), and sharing lessons learned during its implementation in various context that include during acute, protracted and post emergency phases in humanitarian settings of yemen (civil unrest), somalia (civil unrest), liberia (ebola outbreak), iraq (civil unrest) and pakistan (flood emergency) [13,15-18]. development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi methods various electronic tools with different functionalities and specificities (and using different architectures) have been developed and used for the purposes of ewarn implementation in humanitarian settings but published data on such tools is very limited [6,11,13,15-17]. to increase our understanding and knowledge on existing electronic tools, we did a preliminary work to analyze existing tools for system attributes, processes and lessons learned and later developed requirement specification document during planning sessions with stakeholders for the development of an online disease early warning database and electronic tools as a standardized data collection, analysis and reporting approach. the aim of development of standardized tool and data collection procedure was to enhance implementation of ewarn during humanitarian crisis with a potential to integrate into routine surveillance systems or fill gaps where no routine surveillance system exists. we generated baseline information on existing electronic tools implemented for ewarn or similar purposes in various locations, as part of the generation and documentation of requirements for the development of standardized electronic tools and online database system. for this purpose, we did literature search and performed review and analysis to document functionalities and specifications of different electronic tools being used for various needs in health, including for early warning, disease surveillance and health management information systems. we used snowball literature search method to search available resources on existing electronic healthcare projects in pubmed/medline, science citation index (web of science) and google scholar databases, including who library database for publicly available contents and writing to professional contacts working in humanitarian settings [19]. we used the keywords “electronic data collection tools” and (“early warning” or “alert notification” or “ehealth” or “esurveillance” or “mhealth” or “epidemics” or “emergencies” or “disease surveillance” or “outbreak response” or “humanitarian response” or “low income” or “middle income”). the results of the above search were reviewed to identify tools meeting selection criteria and removed articles with no accessible abstracts and/or full-text versions. we reviewed 100 articles and generated list of seven application areas in public health practice outlined below, where electronic tools have been developed and used to support various healthcare interventions: 1. disease and epidemic outbreak surveillance 2. health education and awareness 3. health financing 4. health management and information system 5. health human resource (hrm) mlearning 6. patient monitoring and support 7. point of care, support and diagnostics at least 23% of listed electronic tools were classified under ‘disease and epidemic outbreak surveillance’ theme. next, we filtered the list to select relevant electronic tools used in development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi humanitarian emergency context and for more focused review and analysis using the following criteria; 1) surveillance of infectious disease epidemics 2) complementing routine surveillance systems 3) outbreak detection and alerting 4) use in humanitarian response to obtain baseline information to guide efforts in gathering and analysis of requirements, process, and development of a standardized electronic tool to enhance implementation of ewarn in emergencies, we then performed an objective analysis and evaluation of specifications, functionalities, outputs, software details and positive attributes of individual tools, including gap analysis and challenges. (6,11,14,16). results we identified 98 health projects that have developed and implemented electronic tools in order to improve overall health care capacity and capabilities in various developing countries since 2000. table 1 shows distribution of identified tools classified under different application areas for electronic health projects in public health practice. table 1: classification of electronic tools being used in public health settings (n=98) application areas number of initiatives disease and epidemic outbreak surveillance 22 health education and awareness 17 health financing 3 health management and information system 15 health human resource (hrm) mlearning 3 patient monitoring and support 24 point of care, support and diagnostics 14 total articles selected 98 following is the list of selected four electronic tools chosen from ‘disease and epidemic outbreak surveillance’ theme based on selection criteria for more focused review and analysis to obtain baseline information: 1) district health information system (dhis2) 2) surveillance post extreme emergencies and disasters – philippines (speed) 3) electronic disease surveillance system for cisdcp china 4) electronic disease early warning system – pakistan development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi the specification requirement document was generated as a result of review of these four tools and used in concept development process of edews as a standardized tool to enhance ewarn implementation. the edews was then developed and implemented during humanitarian crisis in pakistan, yemen, somalia and liberia, and adapted for ewarn implementation in iraq. the edews attributes and lessons learned with experience in edews implementation in these humanitarian settings are discussed in the next sections. edews application concept, exit strategy and integration into routine surveillance system the edews is an initiative that was designed for the first time during the flood emergencies in pakistan in 2010 as a result of recommendations from external evaluation of disease early warning system [7,20]. edews was developed with an objective to collect data using electronic data collection procedure to monitor diseases and health trends and could be harnessed as a powerful tool by health emergency managers in getting vital epidemiological information for appropriate and timely response during emergencies and disasters. edews uses online database system complimented with web-based and mobile/smart phone-based user interfaces that allow communities, field epidemiologists and health workers in health facilities or selected sentinel sites to send daily or weekly disease data to the central database system through secure and timely electronic transmission for timely analysis. built-in short messaging service (sms) alert notification module in edews analyzes data automatically and generates automatic sms alert notifications to outbreak response teams for immediate verification and response. to generate reports and visualizations, health staff also validates data using advanced validation module within user interfaces before the submitted data is analyzed automatically to generate tables, graphs, epidemiological bulletins/reports and interactive data visualizations on dashboard including live maps based on time, place and person. the electronic data collection application for edews is developed considering compatibility with all operating systems include edews mobile app for android/ios (with both online and offline data collection modes), desktop app for use in windows/mac/linux operating systems, satellite app and sms or global system for mobile communications (gsm) approach for settings where gsm technology exist without mobile internet. all these apps are optional to be selected for specific context during the edews implementation and included in one package that allows higher flexibility for customization of edews and launching this application considering budget constraints. these optional features can be activated anytime based on needs and resources availability during the humanitarian crisis or while integrating edews into routine surveillance system, figure 1. development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi figure 1: technical diagram showing edews workflow and integration with lab-information systems, national surveillance system and hmis the edews has proven as an effective in its core functions of alert detection and early outbreak containment and able to transmit data from any geographical location with or without internet. satellite apps don’t require expensive satellite phones or systems but can upgrade smart phones to satellite data collection devices using cost effective boosters. data summarization tool data summarization technique (dst) is an innovative and low-cost simple analytical method that was developed to assess the disease situation for early detection of epidemics while launching implementation of edews application during humanitarian crises in resource poor countries. this approach can easily bridge the gap between onset of emergency, planning, configuration and development phase for edews implementation and final launch of edews application as a standardized data collection and analysis procedure that takes two weeks to one month on average. the dst approach is easily implementable in practice and has been developed on advanced time efficient ms-excel pivot table technique that requires basic excel skills at end user level and is customizable for better integration with other databases (example epi info & access) of vertical health programs to generate semi-automated epidemiological reports on timely and ad-hoc basis, an important public health informatics approach to perform well during emergencies. local capacity is easy to build to utilize dst tools for complete data analysis using simulation exercises where basic ms.excel knowledge was considered essential for health staff participation in trainings. dst approach also improved timeliness of sharing of information on epidemiological trends and feedback back to stake holders and reporting facilities from weeks with paper recording system to few hours with dst in humanitarian settings of yemen, somalia and pakistan [16]. development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi edews attributes developed and implemented during humanitarian crisis table 2 shows the summary of attributes developed and configured in developed edews based on requirements specifications gathered and analyzed using baseline information from objective analysis and evaluation of selected electronic tools, and later used in various scenarios considering local context, surveillance needs, availability of resources and political considerations in humanitarian settings. the edews application was fully implemented by who during the emergencies in yemen, somalia, liberia and pakistan and later adapted edews for development and configuration of similar application to compliment ewarn implementation in iraq in 2015 [15]. development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi table 2. summary of system attributes of edews implemented for ewarn enhancement in humanitarian settings of yemen, somalia, liberia, pakistan and iraq*. features and functionalities pakistan (201112) yemen (2013present) somalia (20132015) liberia (2015-2016) iraq (2015) * reporting user interfaces web, smart phone web, smart phone web, smart phone web, smart phone, sms and satellite web, smart phone mobile app modes internet, offline sync internet, offline sync internet, offline sync package (internet, offline sync, sms/gsm, satellite) internet, offline sync automated epidemiological analysis dst, web-based dst, web-based dst, web-based web-based web-based alert notification module sms notification sms notification sms notification sms/email notification sms/email notification data transmission internet, sms/gsm internet, sms/gsm internet, sms/gsm internet, sms/gsm, satellite internet, sms data integrity and protection yes yes yes yes yes database and apps used open source open source open source open source n/a form customization feature yes yes yes yes no app for communication with end users yes yes yes yes no data visualization live interactive live interactive live interactive live interactive live interactive development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi epidemiological bulletin online, downloadable in pdf online, downloadable in pdf online, downloadable in pdf online, downloadable in pdf n/a reporting frequency daily, weekly daily, weekly daily, weekly daily, weekly daily, weekly current status exit integrated into routine health system exit exit n/a data storage local physical server local physical server local physical server local physical server n/a import / export data yes (excel, csv, xml) yes (excel, csv, xml) yes (excel, csv, xml) yes (excel, csv, xml) n/a built-in data validation & verification modes yes yes yes yes yes gis mapping interactive and realtime interactive and realtime interactive and realtime interactive and realtime interactive and realtime access and permission granular permissions granular permissions granular permissions granular permissions n/a lab support integration yes yes no no no outbreak investigation module yes yes yes yes no application programming interface (api) yes no no yes n/a development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi reference guides and faqs yes yes yes yes n/a integration with routine surveillance systems yes yes no no n/a embedded technical guides yes no no yes n/a * lack of access to the adapted edews electronic tools and its documentation for ewarn in iraq precluded meaningful comparison between edews adaptation in iraq with edews implementation for ewarn in other humanitarian settings. development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi challenges in setting up of edews in humanitarian settings the key limitations and challenges we experienced during the edews development and implementation included lack of pre-disaster preparedness, funding constraints, user retention, limited or no users technical awareness, human resource, accessibility to telecom networks, issues related to infrastructure/communication and it, power availability, mobile phone subscriptions, delays in logistics, lack of motivation due to incentive based culture for added roles/responsibilities, and challenges in training delivery and follow-up at the district and health facility level. it may take from 15 to 30 days to set up edews surveillance system due to mandatory time required for environment scanning such as rapid assessment of it infrastructure/communication and accessibility, resource mapping and needs assessment, requirements analysis, and finalizing logistics including local physical server that all are crucial steps to be considered before implementing any electronic tool for ewarn. these steps ensure higher success of project compared to projects implemented without understanding health informatics environment that involve people, process and technology. moreover, such planning results in higher acceptance rate among end users, stake holders and government with sense of ownership due to local physical server since data resides within country and not shared on third country cloud server, a main concern raised by multiple government officials during edews implementations in emergencies. this also encourage governments to switch and integrate edews application as a part of who’s recommended ewarn exit strategy and integrate into their routine disease surveillance system as evident from edews integration into national disease surveillance system in yemen [18,21,22]. discussion this paper presents our efforts to conceptualize and develop standardized informatics tools for disease early warning and share experiences in the use of edews as a potential solution for standardized electronic data collection and reporting procedures during emergencies. edews has supported implementation of communicable disease surveillance and early warning systems in humanitarian settings of pakistan, yemen, somalia, liberia and adapted in iraq. the successful implementation of edews in these countries demonstrates its potential to be adapted as a standardized electronic tools and data collection procedure for easy deployment during emergencies. the edews application has been upgraded and improved based on lessons learned, best practices and experiences from its implementation in various countries since its first development and testing during 2011 flood emergency in pakistan. furthermore, recent upgrades are released considering various emergency scenarios, geographical terrains, communication and technological barriers and other technical challenges in order to save time and resources. edews has also demonstrated successfully that it was able to meet its objectives in all humanitarian settings of generating timely notifications, outbreak containment, needs for automated epidemiological analysis, reporting and visualization, complementing routine surveillance, enhancing ewarn and filling gaps of disease surveillance in settings with non-existing surveillance systems. currently, edews program is functional in yemen as integrated part of national integrated surveillance system [18,19,21,22]. development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi if edews is considered for implementation or adaptation as a standardized tool for ewarn during emergencies, the full deployment of edews takes two weeks to a month from the beginning of an emergency response. this time includes environmental scanning, assessment, resource mapping, requirements analysis, prioritization, logistics, trainings, configuration of online database and app for smart screen devices when cloud computing is not an option for government. these steps are extremely crucial to set up any electronic tool during the emergencies and provides better understanding of local context and considerations such as technical, financial, cultural and political aspects that may otherwise result in project acceptance issues, government ownership or underperforming electronic tools and system, not meeting the objectives of ewarn system during emergencies. to understand such challenges, address them properly and gather all necessary support for the edews success, initial environment scanning was a part of edews planning phase before launch, and we recommend applying similar approach in other similar interventions for successful implementation of electronic surveillance in lowand middle-income countries, especially during emergencies. we successfully addressed technical considerations in all settings such as it infrastructure issues with development of mobile applications package, capable of transmitting data electronically from any geographical location such as good coverage (both internet and gsm/sms services available), poor coverage (no internet but sms/gsm service available) and no coverage (no internet or gsm/sms service). the costs of such services were very well controlled to keep data transmission cost-efficient. to address power issues, we provided power banks that could provide continuous power supply to keep electronic devices charged for data collection purpose. the only issue we faced with this option was logistics to supply batteries but was manageable. another main issue in using electronic means was monthly subscription process of mobile phone and internet services but we managed it centrally with memorandum of understanding with cellular companies, maintaining a log trail, better monitoring and documentation for accountability in edews that helped in enforcing fair use policy. for liberia, we have noted special challenges with data transmission in situations of complete reliance on phone and electronic transmission of data for disease early warning purpose particularly in hard to reach areas, following a large-scale ebola outbreak with significant impact. for such situation, we used portable booster satellite solution that allows satellite data transmission using smart phones, much cheaper than existing standard satellite phone options for electronic data transmission, to overcome challenges with data transmission in the event when communication networks are inoperable. provision of such boosters ensured timely data transmission for an alert detection and notification purpose, a critical function of ewarn disease surveillance, aligned with who’s ‘no regrets’ policy [5]. this approach also helped in bringing better solution to replace concept of “runner” being used by various ehealth projects implementing electronic tool in humanitarian settings when all communication systems had been destroyed. in this runner concepts, staff assigned as “runner” physically visit each of the reporting centers daily, collect data in electronic devices and transmits data electronically by visiting the nearest network coverage area. our results indicate that dst is very efficient and handy analytical approach to monitor health situation without any delay during humanitarian crises where poor capacity and issues with access development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi and resources always cause delays in the implementation of electronic tools for disease early warning system. furthermore, this innovative approach can strengthen overall public health information management capacities and capabilities in resource poor settings during emergencies due to its flexibility and simplicity in order to overcome the challenges in absence of electronic automated analysis tools and with limited skilled human resource capacities. the dst was introduced to compliment edews system to fill the gap period required to set up edews from the onset of emergency and can be used in parallel to setting up of ewarn system immediately upon emergency onset, thus covering bridging this essential gap. development and setting up of edews system used emergency response funds in various humanitarian settings. the system was implemented with limited funds and an objective to be deactivated with ewarn when routine surveillance system resumes its full functionality, otherwise edews has full potential to be integrated into routine surveillance system in a manner that ensures government ownership and sustainability but requires continuous support for maintenance cost that is much lower than the first time setting up cost. hence, the deactivation of edews project was more significantly driven by the lack of funds for continuation and its need in future by government or health sector. on contrary, edews implementation in yemen has resulted in successful integration of edews system with national surveillance system, where project was initially pilot tested with limited funds in four governorates in 2013 but later secured more funding for project continuation and scaled up to cover all yemen’s governorates. this rapid expansion of the sentinel sites in all yemen’s governorates amid security challenges and limited resources has been documented as a considerable achievement and resulted in integration of edews with national surveillance system into one system, called ‘electronic integrated disease early warning system’ (eidews). this integration of edews into national surveillance system has been found very effective that ensures the speed and efficiency of data collection, analysis and public health response to outbreaks [18,21,22]. conclusion this study demonstrates the feasibility of using edews as standardized electronic tool to enhance ewarn implementation and other similar public health interventions requiring time efficient and cost-effective electronic tools for ‘data into action’ purpose in global humanitarian settings and incorporate as part of a regular emergency preparation programs. furthermore, the recent integration of edews into national routine surveillance system by yemen demonstrate the potential of edews to be integrated into national surveillance program (eidews) to ensure the speed and efficiency of data collection, analysis and public health response to outbreaks. such integration to ‘one national integrated surveillance and response system’ can only be achieved successfully through high level determination, commitment, trust building, strong collaboration and working relationships between government and health sector. limitations lack of access to the adapted edews electronic tools in iraq precluded meaningful comparison between edews adaptation in iraq with edews project implementation in other humanitarian settings. the presented data on adapted edews for ewarn in iraq is taken through email development and implementation of electronic disease early warning systems for optimal disease surveillance and response during humanitarian crisis and ebola outbreak in yemen, somalia, liberia and pakistan online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e11, 2019 ojphi communications and contacting teams supported edews efforts to enhance ewarn in iraq, thus limited in table presented in this paper. currently, two slightly different naming approaches are in use for the same concept of who’s early warning and response (ewar) during crisis in various humanitarian settings across the globe. these names are early warning and response system (ewars) and early warning and response network (ewarn). to keep consistency across paper, we have used ewarn name, but the concept is same as ewars and should not be confused. acknowledgements we are grateful to 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medium, provided the original work is properly cited. 157 (page number not for citation purposes) isds 2013 conference abstracts evaluating the performance of syndromic surveillance system using high-fidelity outbreak simulations tao tao*1, qi zhao1, shaofa nie2, lars palm3, vinod k. diwan4 and biao xu1 1school of public health, fudan university, shanghai, china; 2department of epidemiology and biostatistics, school of public health, tongji medical college, huazhong university of science and technology, wuhan, china; 3future position x (fpx), gävle, sweden; 4division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden � �� �� �� � � �� �� �� � objective �������� � �� ��������������������� ��� ������ ������� � ����� � ������������� ������� � �� ������� �� ����� �� ����� �������� ���� ��� �� �� �� �� introduction �� � � �� ����� ��� ����� �� � � ������ � ����� �� � � � �� �� � � ������ �� �� ���� � � �� ������������ ��� �� �� �� 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������� �� ���� ����������� � ��� ������ � ������������������������ ��� � ������ keywords ����� ����b�� ����� �� b���� ��� �� acknowledgments ��������� �� ������ ��� �4���� ��c����,��! � ����+� � �����*��� �� �� �%6+*>7"##>�"#$/8�6+*>7"##>�"#$$8(���� ���� ��� �� � ������� 6"0$@##8� *tao tao e-mail: ttsuper2000@hotmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e140, 2014 ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 patterns and correlates of public health informatics capacity among local health departments: an empirical typology j. mac mccullough 1,2* , kate goodin 2 1. arizona state university 2. maricopa county department of public health abstract objective: little is known about the nationwide patterns in the use of public health informatics systems by local health departments (lhds) and whether lhds tend to possess informatics capacity across a broad range of information functionalities or for a narrower range. this study examined patterns and correlates of the presence of public health informatics functionalities within lhds through the creation of a typology of lhd informatics capacities. methods: data were available for 459 lhds from the 2013 national association of county and city health officials profile survey. an empirical typology was created through cluster analysis of six public health informatics functionalities: immunization registry, electronic disease registry, electronic lab reporting, electronic health records, health information exchange, and electronic syndromic surveillance system. three-categories of usage emerged (low, mid, high). lhd financial, workforce, organization, governance, and leadership characteristics, and types of services provided were explored across categories. results: low-informatics capacity lhds had lower levels of use of each informatics functionality than high-informatics capacity lhds. mid-informatics capacity lhds had usage levels equivalent to highcapacity lhds for the three most common functionalities and equivalent to low-capacity lhds for the three least common functionalities. informatics capacity was positively associated with service provision, especially for population-focused services. conclusion: informatics capacity is clustered within lhds. increasing lhd informatics capacity may require lhds with low levels of informatics capacity to expand capacity across a range of functionalities, taking into account their narrower service portfolio. lhds with mid-level informatics capacity may need specialized support in enhancing capacity for less common technologies. keywords: public health informatics, local health department, information systems, typology. abbreviations: local health department (lhd), information systems (is), information technology (it) correspondence: mccullough@asu.edu* doi: 10.5210/ojphi.v6i3.5572 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 introduction public health informatics and information systems have long been cited as a way to strengthen the work of public health departments [1,2]. a strong body of evidence exists that shows positive benefits to public health in using a wide range of informatics-based systems [3-7]. in addition, adoption of health information technologies by hospitals and providers in the clinical sector [8,9] may present an even greater opportunity for public health departments to leverage informatics to improve population health [10]. a large proportion of the work to assure, assess, and develop policies to promote population health is undertaken by local health departments (lhds) [11]. lhds can use information technology to enhance capacity for data collection, examination, and dissemination [12]. the science of systematically applying information technology and information systems to public health practice, research, and learning is known as public health informatics [12,13]. evidence suggests that, relative to other industries, lhds may be lagging in use of informatics and adoption of information systems [6,14,15]. some of the most recent large scale evidence on overall levels of informatics capacity among lhds comes from surveys conducted in 2008 and late 2009. vest et al. determined that while overall use of information systems (is) and information technology (it) by lhds was low in 2008, there is clear evidence that lhd structure, governance, finance, and types of service provided characteristics were associated with is and it usage [14]. this provides empirical evidence supporting the conceptual linkage between lhd characteristics, public health service provision, and health is/it usage. a second survey focused on lhd informatics reiterated the link between lhd jurisdiction size and use of certain informatics functionalities [16]. more recent evidence suggests that, as in the rest of the health care sector [9,17], health is utilization by lhds, and in particular electronic health records, has changed substantially since 2010 [18]. accompanying this increased system-wide capacity for capturing, storing, and transmitting data electronically is a call for lhds to become informatics-savvy [19]. yet currently, very little is known about which informatics systems are actually being used by lhds [12]. even less is known about interrelationships between systems, whether informatics capacities are symbiotic, where use of one system can facilitate or enhance the use of other system(s), or whether informatics systems operate on a zero-sum basis and tend to crowd out one another. building informatics capacity may mean that lhds will need to coordinate informatics needs and resources across programs and services, implement new or improved is/it systems, or enhance their informatics workforce [19,20]. this means that, in addition to exploring the presence or absence of individual informatics functionalities, it may be beneficial to measure and explore broad-based measures of lhd informatics capacity. the purpose of this study was to test for patterns in the presence of public health informatics functionalities within lhd through the creation of an empirical classification of lhd informatics capacities. this empirical classification was then used to explore correlates of low-, mid-, and high-informatics capacity lhds. http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 methods conceptual model several primary factors were hypothesized to motivate lhds to establish high capacity in public health informatics—1) lhd financial and workforce characteristics [21]; 2) lhd organization, governance, and leadership [22,23]; and[removed hyperlink field] 3) the types of services provided by an lhd [24-27]. each of these three categories have been shown to correlate with lhd adoption and use of electronic health records [18], an important informatics functionality. data data from the 2013 national association of county & city health officials (naccho) profile survey were used. the profile survey methodology is described in detail elsewhere [28]. the profile survey is a census of lhds conducted approximately every three years. all lhds receive a core questionnaire and a stratified random sample of lhds are also assigned one of two modules to complete. informatics questions are included in one of the modules, so only a subsample of lhds report data on this topic. in 2013, the module included questions about informatics usage provided data for 505 lhds (79% overall response rate). in addition to informatics data, the naccho profile survey also contains information on lhd financial, workforce, organization, governance, and leadership characteristics and types of services provided. measures primary measure of interest the primary measure of interest was a lhd’s informatics capacity. this measure was operationalized through the creation of an empirically-based typology. naccho profile survey data on lhd awareness, consideration, and implementation of five informatics functionalities: electronic health records, health information exchange, immunization registry, and electronic disease registry. for each system, survey response options included: no activity, have investigated, planning to implement, have implemented. in addition, use of electronic syndromic surveillance was assessed through a dichotomous measure (yes/no). hierarchical cluster analysis methods were used to categorize lhds according to public health informatics capacity. the ward method was used to group lhds into clusters, with similarities assessed using the squared euclidean distance method [29]. a three-cluster measure was determined to provide the optimal combination of data fit and parsimony. this approach is consistent with previous typology developments within lhds [30]. to test the reliability of this novel typology of public health informatics capacity, the data were randomly partitioned into two mutually exclusive ‘training’ and ‘validation’ datasets. empirical clusters were re-calculated for each of the two datasets. the resulting samples were then compared along lhd financial, workforce, organization, leadership, and governance characteristics, and types of services provided, as shown in the appendix table a1. this method has been used in other public health informatics research as a way of testing the sensitivity of model calculations to the specific data included in the analytic sample [31]. due to changes in http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 the naccho profile survey questionnaire, it was not possible to use data from other survey years to perform reliability testing. future profile surveys containing data on these six informatics functionalities may enable these comparisons in the future. predictors of interest several variables were used within the three broad categories hypothesized to motivate lhds to establish high capacities for public health informatics. all predictors of interest were obtained from the 2013 naccho profile survey. lhd finances were assessed through per capita expenditures in five categories: total, local sources, state and federal sources, medicare/medicaid sources, and other clinical sources. all expenditure categories were divided by the lhd’s population served. lhd workforce was assessed through two measures: total full-time equivalent (fte) positions per 10,000 population, and whether the lhd employed informatics personnel (termed “information systems” personnel on the survey instrument). the organization and governance of lhds was assessed through five measures: 1) whether the lhd is governed by a local board of health (lboh); 2) whether the department is freestanding or part of a larger health and human services (hhs) agency; 3) whether the lhd is a singlecounty jurisdiction or any other type (e.g., city, city-county, multi-county, other complex type); 4) whether the lhd has the statutory authority to set or impose fees for public health; and 5) whether the lhd had a state, local, or shared governance structure. in addition to these measures, lhds were analyzed by state to assess intra-state variation in informatics capacity. due to naccho profile restrictions on identifying respondents and to small sample sizes within each state, state-specific results are not presented individually. lhd leadership was assessed through a measure of whether the department’s executive director has a clinical background (defined as md, do, rn, msn, bsn, lpn, or lvn [23]). the clinical versus non-clinical background of an executive director has been shown to correlate with department strategy and performance [23]. finally, the types of services provided by an lhd were assessed through measures of lhd direct provision of an expansive list of public health services. previous research has revealed that breadth of lhd service provision is positively associated with likelihood of emr usage [18]. this study therefore uses broad range of services, relying on a set of 42 services categorized by bekemeier et al. as being either individualor population-focused, and as basic, expanded, or specialized [32]. analysis analyses focused on lhds with data on informatics use for 2013. from the original module sample of n=505, a total of 46 lhds were excluded due to missing data for at least one informatics functionality; the final sample size was 459 lhds. analysis of lhds with missing data suggests that these 46 lhds did not differ significantly from the 459 lhds with complete data, with the exception that lhds included in the analytic sample were more likely to be singlecounty jurisdictions than those excluded. univariate and bivariate analyses were conducted to examine relationships between the primary outcome of interest (classification on lhd public health informatics typology) versus predictors of interest. sample weights were used in univariate and bivariate analyses to account for survey http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 design and non-response [33]. chi-square and anova tests were used to examine significant associations. pair-wise comparisons of means were used to test for significant differences in predictor variables across the typology’s three categories, with significance levels adjusted using the tukey method [34]. finally, sensitivity of results to outcome coding decisions, survey weights, and other alternative model specifications were explored. overall findings were not sensitive to variable coding or model specification (results available from author upon request). all analyses were performed using stata version 13.1 [35]. institutional review board approval was not required as the research did not involve human subjects or individually-identifiable data. results a total of 459 lhds were included in this study. characteristics and univariate statistics on the sample are summarized in table 1. table 1: analytic sample of local health departments (n=459) lhd characteristic percent or mean (sd) population served: < 50,000 47.7% 50,000 – 499,999 39.4%  500,000 12.9% per capita expenditures ($): total 47.85 (3.60) local sources 11.75 (0.69) state and federal sources 19.43 (2.20) medicare/medicaid sources 9.60 (2.40) other clinical sources 2.52 (0.59) workforce: total ftes per 10,000 5.5 (0.8) employs any information systems personnel 37.4% organization, governance & leadership: has local board of health 70.0% freestanding, not part of hhs agency 27.1% single county jurisdiction 75.4% lhd has authority to set/impose fees 71.6% executive director has clinical background 46.2% governance type: state 19.6% local 72.3% shared 8.1% public health services: number services provided by lhd 18.7 (1.0) http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 the empirical cluster analysis of lhd public health informatics capacity revealed three groups of lhds in terms of informatics capabilities, as shown in table 2. one cluster of lhds reported the lowest use in five of the six informatics functionalities and are referred to as “low” informatics capacity lhds. a second cluster of lhds had the second highest reported use in four of the six informatics functionalities and are referred to as “mid” informatics capacity lhds. a third cluster of lhds had the highest reported use in five of the six of the informatics functionalities and are referred to as “high” informatics capacity lhds. overall levels of lhd informatics capacity varied significantly across states (chi-square with 94 degrees of freedom = 247.2, p-value < .001, data not shown in tables). in addition, there was significant state-level variation in capacity for each of the six individual informatics functionalities. table 2: clusters of local health departments by public health informatics capacity (n=459) type of functionality percent with functionality difference between groups total low (n=112) mid (n=92) high (n=255) low vs. mid low vs. high mid vs. high immunization registry 85.8% 49.1% 98.9% 97.3% *** *** electronic disease registry 75.8% 18.8% 93.7% 93.3% ** *** electronic syndromic surveillance system 66.5% 47.3% 60.9% 76.9% *** electronic lab reporting 51.4% 17.9% 0.0% 84.7% *** *** electronic health records 25.1% 17.9% 19.6% 30.2% *** * health information exchange 13.9% 5.4% 6.5% 20.4% *** *** * p < .05 ** p < .01 *** p < .001 usage varied significantly (p < .01) between lowand high-informatics capacity lhds across all six types of functionalities. for the two most common functionalities (immunization registry and electronic disease registry), midand high-informatics capacity lhds had statistically equivalent levels of usage while the low lhds had significantly lower levels of usage. for the three least common functionalities (electronic lab reporting, electronic health records, health information exchange), lowand mid-informatics capacity lhds had statistically equivalent levels of usage while high lhds had significantly higher levels of usage. a complete comparison of lhd awareness, consideration, and implementation of each informatics functionality is shown in the appendix, table a2. informatics capacity by lhd characteristics public health informatics capacities were compared across the broad categories of lhd characteristics as described above. findings are shown below in table 3. six of the 14 variable categories revealed significant differences between any of the three informatics categories. table 3: lhd characteristics versus public health informatics capacity (low, mid, high) http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 lhd characteristic percent or mean (sd) difference between groups total low mid high low vs. mid low vs. high mid vs. high public health informatics capacity -24.4% 20.4% 55.6% population served: < 50,000 47.7% 47.3% 57.6% 44.3% ** 50,000 – 499,999 39.4% 42.9% 38.0% 38.4%  500,000 12.9% 9.8% 4.4% 17.3% per capita expenditures ($): total 47.85 45.17 44.90 48.81 local sources 11.75 12.85 11.58 11.07 state and federal sources 19.43 18.83 18.67 19.99 medicare/medicaid sources 9.60 6.13 3.21 12.18 ** other clinical sources 2.52 2.68 3.29 2.21 workforce: total ftes per 10,000 5.5 5.0 5.4 5.9 employs any information systems personnel 37.4% 32.2% 20.0% 45.7% *** organization, governance & leadership: has local board of health 70.0% 68.5% 73.9% 69.0% freestanding, not part of hhs agency 27.1% 29.3% 23.0% 28.1% single county jurisdiction 75.4% 58.9% 79.4% 81.2% ** *** lhd has authority to set/impose fees 71.6% 79.5% 76.5% 66.3% executive director has clinical background 46.2% 32.1% 50.0% 51.0% * ** governance type: state 19.6% 25.0% 6.52% 22.0% *** local 72.3% 66.1% 90.2% 68.6% shared 8.1% 8.9% 3.3% 9.4% * p < .05 ** p < .01 *** p < .001 informatics capacity by types of services provided by lhd public health informatics capacities were compared across 42 public health services as described above. findings are shown below in table 4. significant differences were found in provision levels across at least one of the three informatics capacities for 25 of the 42 services. comparison of lhd informatics capacity versus types of public health service provided revealed several notable and statistically significant patterns. first, lhds with the lowest informatics capacity provided significantly fewer public health services than lhds with mid-or high-levels of informatics capacity (p < .01). second, for 25 of the 42 services, high-informatics capacity lhds had significantly higher levels of provision than low-informatics capacity lhds. third, none of the 42 services saw lowor mid-informatics capacity lhds with significantly higher levels of provision than high-informatics capacity lhds. http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 all of the six categories of services shown in table 4 saw at least one service with significantly different provision levels according to informatics capacity. differences were most pronounced for services classified as basic population focused (9 of 9 services had significantly different levels of provision across levels of lhds informatics capacity) and were least pronounced for services classified as specialized individual focused (1 of 5 services had significantly different levels of provision across levels of lhds informatics capacity). table 4: types of public health services provided by public health informatics capacity (low, mid, high) service percent providing service difference between groups total low mid high low vs. mid low vs. high mid vs. high individual focused – basic adult immunizations 92.8% 86.6% 92.4% 95.7% ** childhood immunizations 91.9% 83.0% 94.6% 94.9% ** *** epsdt 31.8% 33.0% 23.9% 34.1% family planning 52.1% 36.6% 47.8% 60.4% *** mch home visits 58.4% 50.0% 53.3% 63.9% * wic 65.6% 58.0% 60.9% 70.6% individual focused – expanded cancer screening 34.9% 23.2% 26.1% 43.1% *** ** cardiovascular disease screening 30.3% 27.7% 28.3% 32.2% diabetes screening 36.8% 28.6% 32.6% 42.0% * high blood pressure screening 53.4% 47.3% 59.8% 53.7% home health care 16.1% 19.6% 8.7% 17.3% oral health 24.8% 17.9% 18.5% 30.2% * prenatal care 27.2% 21.4% 26.1% 30.2% primary care services 11.8% 8.0% 5.4% 15.7% * school health 35.1% 39.4% 28.3% 36.1% well-child clinic 29.9% 25.9% 26.1% 32.9% individual focused – specialized behavioral/mental health services 13.1% 12.5% 10.9% 14.1% hiv/aids treatment 25.5% 22.3% 17.4% 29.8% obstetrical care 6.3% 0.9% 3.3% 9.8% ** school-based clinics 21.8% 20.5% 21.7% 22.4% substance abuse 31.2% 26.8% 23.9% 35.7% population focused – basic blood lead screening 59.0% 45.5% 57.6% 65.5% *** communicable/infectious disease 92.8% 84.8% 94.6% 95.7% * *** hiv/aids screening 62.1% 47.3% 44.6% 74.9% *** *** nutrition 71.2% 61.6% 65.2% 77.7% ** http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 std screening 66.2% 45.5% 55.4% 79.2% *** *** tuberculosis screening 83.9% 72.3% 82.6% 89.4% *** tuberculosis treatment 76.7% 62.5% 75.0% 83.5% *** tobacco prevention 69.1% 60.7% 59.8% 76.1% ** ** unintended pregnancy 50.3% 30.4% 43.5% 61.6% *** ** population focused – expanded behavioral risk factors 39.9% 27.7% 37.0% 46.3% ** chronic disease programs 52.7% 42.9% 45.7% 59.7% ** maternal & child health surveillance 62.1% 50.9% 56.5% 69.0% ** physical activity 56.2% 50.0% 48.9% 61.6% std treatment 61.7% 42.0% 53.3% 73.3% *** ** population focused – specialized chronic disease epidemiology 46.8% 40.2% 42.4% 51.4% injury surveillance 27.5% 19.6% 23.9% 32.2% * injury prevention 40.1% 25.9% 33.7% 48.6% *** * mental illness 16.1% 12.5% 9.8% 20.0% substance abuse services 9.2% 11.6% 2.2% 10.6% * syndromic surveillance 53.6% 39.3% 43.5% 63.5% *** ** violence prevention 23.3% 19.6% 13.0% 28.6% ** mean number of services provided (out of 42) 18.7 15.8 17.0 21.3 *** *** * p < .05 ** p < .01 *** p < .001 discussion public health informatics includes a broad range of systems and capacities aimed at strengthening public health practice, research, and learning [2]. previous research has shown that while lhds are known to use a wide range of it systems and programs to meet their informatics needs, very little is known about the specific patterns of informatics use among lhds [14]. this study presents a novel empirically-derived typology of lhd informatics usage patterns. according to this study’s empirically-derived typology, lhds are clustered in three categories of informatics usage: low (24.4%), mid (20.4%), and high (55.6%). the lhds with the lowest level of informatics usage had significantly lower levels of usage for all six functionalities assessed. rather than informatics capacity being somewhat evenly dispersed across the spectrum of lhds, there is clear evidence of a substantial difference between the lowestand highest-informatics capacity lhds that is consistent across functionality types. for many functionalities, the differences were not only statistically significant but substantively important. for example, while 93% of high-informatics capacity lhds utilized an electronic disease registry as of 2010, only 19% of low-informatics capacity lhds did so—a nearly five-fold difference. while highand low-capacity lhds differed across all six of the informatics capacities examined, a notable pattern emerged for mid-capacity lhds. namely, these lhds were http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 indistinguishable from high-capacity lhds for the three most common informatics functionalities (immunization registries, electronic disease registries, and electronic syndromic surveillance systems) as both groups had relatively high levels of usage compared to lowinformatics capacity lhds. it is perhaps notable that immunization registries, electronic disease registries, and syndromic surveillance systems are frequently state-supported functionalities. however, for the three least common informatics functionalities (electronic lab reporting, electronic health records, health information exchange), mid-capacity lhds had levels of usage on par with low-capacity lhds. a clear pattern of three levels of informatics capacities emerged: 1) lhds that had consistently low levels of use across all forms of informatics, 2) lhds that had consistently high levels of use across all forms of informatics, and 3) lhds that had high levels of use of relatively common forms of informatics and low levels of use of relatively uncommon forms of informatics. one potential reason for these differences may relate to responsibility for system creation and or operation that can vary by state. for example, in some states the state public health department or its equivalent may be responsible for lab reporting, so lhds may not have the authority to adopt an electronic lab reporting system, or it may appear seamless to the lhd because it is directly incorporated into their communicable disease or disease registry surveillance systems. this may help explain the relative abundance of lhds with mid-informatics capacities reporting “no activity” in the area of electronic lab reporting. however, significant differences in informatics capacity were observed between midand high-informatics capacity lhds across all functionalities, including those for which the lhd would have fuller control over adoption decisions, such as electronic health records. applications that are frequently state-supported (i.e., immunization registries, electronic disease registries, and electronic syndromic surveillance systems) may be somewhat simpler for lhds to implement if these systems are operated at the state level and an lhd therefore operates more akin to information consumers than information brokers. for applications that are less-frequently state-supported, specific initiatives would require an lhd to have the capacity and leadership support to pursue. thus cluster of lhds with a mid-capacity in public health informatics may be distinguished, at least in part, due to the nature of the functionalities explored and the presence or absence of state-level activity in these areas. informatics adoption and use is complicated by factors both internal to an agency (e.g., public procurement processes) and external to an lhd (e.g., reliance on stateor national-level systems and actors). thus, promoting robust lhd informatics capacity may require coordination of local, state, and national leadership. given the autonomy that many states have in designing and implementing certain informatics systems, lhd leadership and information specialists may not always have the flexibility to custom-fit stateor nationally-run information systems to their local needs. yet input from all levels of informatics system users can be essential for ensuring system success. for example the new national biosense 2.0 system for electronic surveillance was designed after seeking input from state and local practitioners over a two-year period, with the specific intention of maximizing value for every level of the public health system [36]. state and national public health informatics leaders may want to specifically consider how best to coordinate their efforts to target lhds with low-informatics capacity across the range of functionalities explored and lhds with mid-informatics capacity in need of additional support for specific functionalities. http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 in considering where such support might be targeted, we identified multiple lhd characteristics that were significantly associated with informatics capacity. lhds with the highest levels of public health informatics capacity tended to serve larger jurisdictions and have larger per capita amounts of medicare and medicaid expenditures. lhds with the lowest levels of public health informatics capacity were less likely to be a single-county jurisdiction (and thus more likely to be multi-county, city-county, city, or another type) and were less likely to have an executive director with a clinical background. lhds in a regional structure may not always have methods for cost or personnel sharing which would be essential to developing shared informatics capacity. previous surveys have shown an extremely strong correlation between lhd jurisdiction size and informatics-related activities [16], our study found that lhds serving jurisdictions with fewer than 50,000 persons comprised roughly 45% of both the lowand high-informatics capacity clusters, suggesting that jurisdiction size alone does not differentiate lowversus highinformatics capacity. rather, the observed patterns speak to the diverse impacts that an lhd’s setting, finances, governance, and leadership may have on informatics capacity. an lhd’s state was associated with significantly different levels of capacity overall and for all six individual functionalities and is potentially related to previous comments about state vs. local responsibility for information systems. future national profiles of lhd informatics, such as those conducted by naccho, may provide additional contextual information in this area. public health service provision was strongly associated with informatics capacity. public health service provision was almost invariably positively associated with informatics capacity. statistically, there were two instances where a service had higher levels of provision at lower levels of informatics capacity (substance abuse services and violence prevention). there were 36 instances where a service had higher levels of provision at higher levels of informatics capacity. this provides strong evidence that informatics capacity and service provision are positively associated. whether expanded service provision leads to expanded informatics capacity or vice versa remains to be shown. associations between service provision and informatics capacity were especially prevalent among certain categories of public health services (as categorized by bekemier et al. [32]). for example, basic population-focused services showed stronger differences in service provision across the three levels of informatics capacity than specialized individual-focused services. indeed, the three levels of informatics capacity saw starkly different service provision levels across many population-focused services. this relationship was not as strong for services oriented towards the individual. given the nature of the informatics functionalities explored, this relationship—and in particular the strength of the relationship observed for population-focused service provision—is not unexpected. it emphasizes the role that informatics plays for specific public health services and the symbiotic nature of broad-based capacity for public health informatics and broad-based provision of population-focused services. this may mean that current informatics capacity at lhds is oriented towards direct service provision rather than towards the collection, analysis, and reporting of data from external organizations [20]. limitations this study’s findings should be viewed in light of its limitations. first, the typology of public health informatics capacity was based on a discrete set of six functionalities and their selfreported levels of use by lhds. it is possible that lhds may have systematically overor underreported informatics use, though given that several previous studies have found logical and http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 longitudinally consistent patterns in the naccho profile assessment of lhd informatics usage [14,18], this misreporting is not hypothesized to severely bias findings. in addition, the sensitivity of typology classification to the specific services was explored partitioning the data into training and validation sets, with sample characteristics remaining consistent across both [31]. second, since only one year of data were available, the extent to which informatics capacity changes or remains static over time is unknown and the causal nature of the relationships examined cannot be proven. future study with subsequent waves of naccho data may shed additional light on this issue. third, imperfect data were available to capture stateversus localauthority pertaining to each of the service areas relevant to the six functionalities (e.g., state-level authority for lab reporting). while a thorough review of the state-by-state authorities for services pertaining to all six of the informatics functionalities was outside of the scope of this study, we did explore state-level variation for functionalities not hypothesized to be determined by statelevel authority or availability (e.g., electronic medical records) and found consistent variation across all informatics functionalities examined. fourth, the measures employed for both informatics use and service provision do not capture information about effectiveness, merely the presence or absence of a given functionality/service. conclusion national patterns in the use of public health informatics have been poorly understood to date. with the growing importance of lhd ability to receive, analyze, and report out electronic data in concert with a range of partner organizations, a better understanding of the current capacity for and patterns in the use of public health informatics is essential. this study’s empirically-derived typology represents a novel conceptualization of department-wide informatics capacity. findings showed strong evidence that informatics usage is clustered within lhds, with some departments demonstrating consistently high levels of use of all six informatics functionalities explored. other departments have lower capacity across all six functionalities. a third group demonstrated high levels of use for relatively common informatics functionalities and low levels of use for relatively uncommon informatics functionalities. a major distinguishing factor for lowversus high-informatics capacity lhds is breadth of public health services provided by the department. provision of population-focused services are especially highly correlated with higher informatics capacity. this may suggest two broad areas of need if we are to strengthen informatics capacity on a national level. first, lhds with low levels of informatics capacity may need support or technical assistance that is cross-cutting across multiple informatics areas. special consideration may also be necessary for how these lhds can maximize the value of informatics to their work and the communities they serve, given their lower levels of service provision relative to high-informatics capacity lhds. second, for the group of lhds with mid-level informatics capacity, special consideration of strategies to promote adoption and use of less common technologies (e.g., electronic health records or health information exchange) may be most beneficial. these departments already have strong capacity for the more common informatics capacities, but targeted work is likely to be more beneficial than broad-based approaches. consideration to state-level factors may be especially important for these lhds. the findings suggest that lhds with high levels of informatics capacity also have relatively higher levels of service provision. future research to establish the direction of this relationship or studies to explore the linkages between lhd informatics applications and community partners http://ojphi.org/ ojphi patterns and correlates of public health informatics capacity among local health departments: an empirical typology 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e199, 2014 may help build upon the empirical finding that lhds cluster into three distinct patterns of informatics capacity. developing solutions 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20377759&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12542838&dopt=abstract http://dx.doi.org/10.1186/1472-6947-3-2 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11297884&dopt=abstract http://dx.doi.org/10.1016/s0895-4356(00)00314-0 http://dx.doi.org/10.2105/ajph.2013.301426 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24228663&dopt=abstract 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts evaluating cost and performance for improved meningitis disease surveillance in chad ngozi erondu* and ulla k. griffiths london school of hygiene and tropical medicine, london, united kingdom objective this presentation shares findings of a cost and performance evaluation of the meningitis surveillance system in chad. we will also present methods used to design an operational standard for meningitis surveillance in chad and a cost extrapolation model for other meningitis affected countries in sub-saharan africa. introduction the costs of addressing specific needs to improve surveillance systems in sub-saharan africa are often unknown. for centuries meningococcal meningitis epidemics have occurred every few years in the sahelian and sub-sahel regions of africa also referred to as the “african meningitis belt”. a serogroup a meningococcal conjugate vaccine, menafrivac®, was licensed in 2009 and introduced in phases through mass immunization campaigns for all 1-29 year olds. the long term health impact of menafrivac® can only be determined if strong disease surveillance is in place. the objective was to estimate the costs and assess the performance of meningitis surveillance in chad to determine resources needed for implementing a district casebased surveillance strategy. methods performance and cost data were obtained through structured interviews with laboratory and clinical staff in seven districts and at the national level. in each district, three primary care facilities and one district laboratory was included. surveillance officials at intermediate and national levels were also interviewed. resource quantities and respective unit costs were collected and categorized according to core surveillance functions (detect, report, analysis, feedback, investigation, and response), and support activities (training, supervision, communication, and co-ordination). unit costs were collected from numerous sources, including financial records of the ministry of health and international partners. based on the data, an upgraded surveillance standard was developed by 1) defining the gaps in the meningitis surveillance system, 2) identifying the needs at each jurisdictional level, 3) constructing a feasible operational standard and 4) estimating the cost of implementing the standard. results optimal surveillance was severely hampered by limited resources, including missing laboratory materials and inadequate staff. in 14 of the facilities, confusion and misinterpretation of a national policy translated into missed opportunities for staff to perform lumbar punctures on patients presenting with suspected meningitis; patients were referred to district hospitals in the remaining seven. missing and unreliable data affected case detection and reporting; in three of the districts, no meningitis cases were reported during 2012. in the other four districts, reported cases varied between 43 and 232, equivalent to between 11 and 89 per 100,000 populations. 9% of specimens were sent to the national laboratory for confirmation and 4% of probable meningitis cases had a known outcome reported. in facilities with no detected cases, resources spent on surveillance were marginal. costs per detected cases amounted to us$ 49. costs of lumbar puncture comprised 43% and laboratory analysis 41% of total costs. several features were included in the upgraded surveillance model such as, periodic surveillance training for relevant staff, motorbikes for district surveillance officers, resources for patient transfer, and a courier system for specimen transport from district to national laboratories. conclusions the study findings provide cost estimates that were used to recommend a feasible and effective meningitis surveillance strategy for chad. while investments are needed across the system in many african meningitis belt countries, a systematic approach that assesses performance gaps and highlights areas of optimization can provide a more sustainable solution for integrated disease surveillance. keywords costing; monitoring and evaluation; meningitis; menafrivac; chad acknowledgments the authors would like to acknowledge and thank our chadian collaborator, centre de support en santé internationale. we also are grateful for the contributions by the world health organization, agence de médecine préventive, and our funder, the bill and melinda gates foundation. references daugla dm, gami jp, gamougam k, naibei n, mbainadji l, narbe m, et al. effect of a serogroup a meningococcal conjugate vaccine (psa-tt) on serogroup a meningococcal meningitis and carriage in chad: a community study [corrected]. lancet. 2014;383(9911):40-7. who. epidemic meningitis surveillance in the african meningitis belt: deciding on the most appropriate approach. geneva: 2014. kasolo f, roungou j.b., perry, h. technical guidelines for integrated disease surveillance and response in the african region. who and cdc, october 2010. *ngozi erondu e-mail: ngozierondu@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e72, 2015 ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 supporting the diffusion of healthy public policy in canada: the prevention policies directory christopher e. politis 1 , michelle h. halligan 2 , deb keen 3 , jon f. kerner 4 1. canadian partnership against cancer, policy analyst, prevention, on 2. canadian partnership against cancer, program manager, prevention, on 3. canadian partnership against cancer, director, prevention & research, on 4. canadian partnership against cancer, senior scientific lead for population health and knowledge management, on abstract healthy public policy plays an essential role in a comprehensive public health approach to preventing cancer and chronic disease. public policies spread through the ‘policy diffusion’ process, enabling governments to learn from another’s enacted policy solutions. the prevention policies directory (the directory), an online database of municipal, provincial/territorial, and federal cancer and chronic disease prevention policies from across canada, was developed to facilitate the diffusion of healthy public policies and support the work of prevention researchers, practitioners, and policy specialists. this information technology solution was implemented, through a participatory engagement approach, as a communication channel or policy knowledge transfer tool. it also addressed the intrinsic shortcomings of environmental scanning for policy surveillance and monitoring. a combination of quantitative web metrics and qualitative anecdotal evidence have illustrated that the directory is becoming an important tool for healthy public policy surveillance and policy diffusion in canada. keywords: healthy public policy, policy informatics, knowledge transfer and exchange, chronic disease prevention, cancer prevention, policy diffusion correspondence: christopher.politis@partnershipagainstcancer.ca doi: 10.5210/ojphi.v6i2.5372 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction regulation through healthy public policy is a fundamental component of a comprehensive public health approach to addressing the mounting global burden of chronic diseases. the role of healthy public policies in health promotion gained momentum in 1986 through the ottawa charter where incorporating health into the mandate of policymakers across all sectors and at all jurisdictional levels was recognized as paramount to the prevention of chronic diseases [1]. healthy public policies play a role in targeting and addressing the modifiable risk factors of chronic diseases, such as cancer, including alcohol consumption, exposure to occupational and environmental carcinogens, infectious agents, unhealthy eating, physical inactivity, tobacco use, and ultraviolet/ionizing radiation [2]. while the importance of policy action for health promotion http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 and cancer and chronic disease prevention is clearly demonstrated through successes seen in areas such as tobacco control [3], policy adoption and non-adoption is characterized by a complex interplay of factors dependent on the unique characteristics in each different jurisdiction. diffusion of health policies policies are rarely adopted by governments based solely on internal considerations; instead policy decisions are often influenced by the choices made by other jurisdictions – a process referred to as policy diffusion [4]. the occurrence of policy diffusion has been demonstrated in a multitude of healthy public policy cases, such as wellness policies moving between school district and state levels [5] and through the spread of smoke-free spaces bylaws amongst municipalities in alberta and ontario [6]. beyond an elementary understanding that policy diffusion exists, a growing collection of literature on the impetus behind the movement of policies has shed light on why policies diffuse. initially, the study of policy diffusion identified and focused on geographic proximity, with policies moving and being adopted amongst regional players in clusters or webs, as the primary causative agent behind the process [4,7]. while sometimes applicable – for instance jurisdictions in close proximity can share common population and environmental characteristics thus making policies appealing to neighbouring governments – there are numerous examples (described below) where policies have disseminated beyond neighbouring governments [4,7]. more recent theories have put forward policy diffusion drivers arguably more powerful than locality to explain the variability in diffusion patterns. a modest number of studies have focused on the policies themselves and how characteristics such as the: relative advantage over existing policies; compatibility with current values, experiences and needs; complexity; ability to observe results; possibility of implementation on a trial basis; and perceived success in other jurisdictions, affect the movement of policies between jurisdictions [8,9]. another school of policy diffusion thought examines the role political actors, individuals and associations, play in advocating for policy change and shifting the agendas of government as ‘policy entrepreneurs’ [10,11]. these theories that narrowly focus in on components of policy diffusion (e.g., the policy, stakeholders) as driving forces have largely been overshadowed by emerging work on the interrelationships between jurisdictions and how those relationships act as mechanisms for policy diffusion. four relationships (learning, imitation, coercion and competition) have been identified as motivators for governments to base their policy decisions on the choices made by other jurisdictions. the relational mechanism of learning is grounded in the concept that the successes and failures of adopted policies in one jurisdiction provide additional information about the consequences of similar policy action or inaction in other jurisdictions [12]. this has been demonstrated across diverse policy arenas, from criminal justice to the liberalization of emerging market economies [12,13]. a second mechanism based on the interplay between governments is imitation. imitation occurs when a jurisdiction ‘copies’ the policy choice of an external body regardless of any observed or learned outcomes often due to the similarities among the political and ideological environments of the jurisdictions in question or as a result of an overwhelming perception that the policy is a ‘social norm’ or the ‘right thing to do’ [7,14]. the third policy diffusion motivator based on governmental relationships, coercion, involves a concerted effort by one agent (e.g., government, financial institution, international organizations, etc.) to directly http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 influence the policy decisions of another through the employment of threats or incentives [4,15]. competition, the final relational driver, captures the struggle between governments in contending for economic advantages through taxes and attracting business [16-18]. policies that bestow a benefit on one jurisdiction, for example through establishing a lottery system and thus a new revenue stream that has the capacity to poach money from neighbours without a lottery, are thought to quickly diffuse amongst competitors in a race between governments for larger tax bases and financial gains [16-18]. these four relational mechanisms, in conjunction with theories describing the impact of policy entrepreneurs and the attributes of a policy, all have the capacity to affect the process of policy diffusion. but what does the policy diffusion process itself look like? many scholars subscribe to variations of the diffusion of innovations (di) theory to model how the policy diffusion mechanisms outlined above alter the relative effectiveness and payoffs of a policy choice in the minds of decision-makers [19-21]. the result is key actors in the policy process are influenced to adopt or reject a policy alternative and ultimately decide whether a policy successfully diffuses. beyond describing the interaction between policy diffusion mechanisms and decision-makers, the di theory also explains the diffusion of an innovation process through the combination of four key components: (1) the innovation, (2) communicated via an assortment of channels, (3) over time, (4) amongst a social system [22]. thus, in the context of policy diffusion the di theory would see policy decision-makers (the social system) learn about an innovation (the policy) from another jurisdiction. this learning would occur through one or more communication channels (e.g., newspapers, colleagues). the decisionmakers weigh the relative effectiveness and payoffs of the policy, determined by mechanisms such as policy attributes, policy entrepreneurs and relational factors between jurisdictions (i.e., competition, learning, coercion, or imitation), and after a period of time decide whether to adopt the policy. while all four components are required for policy diffusion, the communication channel component is particularly important because it makes the previously unaware social system of policy decision-makers within a jurisdiction conscious of possible external policy options and therefore sets policy diffusion in motion [22]. furthermore, the internet has revolutionized communication channels with the enablement of quick access to vast amounts of specialized information, resulting in reduced decision times around policy adoption [22]. the concept of a communication channel can be explained through the lens of knowledge management as well. knowledge transfer (kt) is described as the movement of knowledge between a source and a receiver, with the receiver accumulating new knowledge [23]. explained this way, a policy option is a unit of knowledge that can be transferred from a source – a colleague or newspaper for instance – to the receiving decision-makers in other jurisdictions. in this example, the source plays the role of the communication channel outlined in the di theory. considering that communication channels, in a way, play a gateway role in the policy diffusion process, a communication channel centered on healthy public policy would support the diffusion of such policies. the prevention policies directory (the directory), an online kt tool, was constructed to fulfill this role as a specialized communication channel facilitating the diffusion of healthy public policies amongst canadian jurisdictions. the prevention policies directory recognizing the importance of policy action on cancer prevention, in 2009 the world cancer research fund (wcrf) and the american institute for cancer research (aicr) published an http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 extensive document entitled policy and action for cancer prevention: food, nutrition, and physical activity, containing 48 cancer prevention policy recommendations across nine different sectors [24]. the canadian partnership against cancer (the partnership), funded by the canadian federal government to accelerate action on cancer control and to implement canada’s national cancer control strategy, commissioned a series of environmental scans to understand the canadian cancer and chronic disease prevention policy landscape in an effort to translate the wcrf/aicr recommendations into a canadian context. the environmental scans, focusing on the modifiable risk factors (e.g., nutrition, physical activity, environmental and occupational exposures, etc.) for cancer and chronic disease, yielded 771 policy initiatives from federal, provincial/territorial, municipal, and school governing bodies from across canada between 1997 and 2007 (and as far back as the 1980s for toxic use reduction policies) [25-27]. results of the environmental scans highlighted areas where canadian policy makers had a burgeoning appetite to act; however, a key finding was the need for expanded monitoring and surveillance of canadian cancer and chronic disease prevention policies to build capacity for tracking progress as well as identifying new policy approaches [25,26]. essentially, this recommendation called for a mechanism, or communication channel, for canadian research, practice, and policy specialists to learn of up-to-date policy options outside their jurisdiction. it was apparent that further environmental scans were not the solution to address this required need around policy surveillance. despite the valuable snapshot of the canadian cancer and chronic disease prevention policy landscape provided by the environmental scans, these scans were time-consuming, resource intensive and ultimately possessed a very limited shelf life. due to the ever-changing nature of policy development, environmental scans are often out-of-date by the time they are published. in addition, the scans did not provide a communication channel among research, practice, and policy specialists in different jurisdictions. environmental scan reports are usually disseminated through traditional knowledge dissemination means such as professional presentations and interactions, and therefore would reasonably be expected to have a more limited effect on the diffusion of the collected policy information to new jurisdictions. with these challenges of collecting and communicating policy information in mind, a new canadian conduit for the transfer and exchange of cancer and chronic disease prevention policy information, with capacity for regular updating, was envisioned. the national cancer institute’s state cancer legislative database (scld), a repository of state-level cancer-related health policies established in 1989 in the united states [28], provided a model for the creation of a similar canadian resource: the prevention policies directory (the directory). although the scope of the scld, which encompassed state policy information addressing topics such as access to cancer treatment, health disparities, genetics, cancer registries, tobacco use and a range of specific cancer disease sites, differed from the prevention of cancer and chronic disease via modifiable risk factor approach conceived for the directory, the scld was an appropriate model for the development of an online database of canadian healthy public policy alternatives. in order to further ensure the utility of the directory, research, practice, and policy specialists were involved throughout the development in a participatory engagement approach. numerous methods, from across the social sciences, have been identified in which participants are purposefully engaged throughout a research, planning, or evaluation process and all share the objective of a final product with the greatest value to participants and creators [29]. this is accomplished via participant engagement methodologies by including the perspectives and experiences of future participants to ensure the needs of the target audience are met, in turn http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 resulting in greater uptake, impact, and success [30,31]. research, practice, and policy specialists helped shape the directory, through early and frequent engagement, explained in greater detail later in this paper, into a useful healthy public policy tool. the directory was conceived and created through the combination of four key elements that included a foundation in policy diffusion theory, findings from several environmental scans calling for increased policy surveillance and tracking, the precedent set by the scld in the united states, and the participatory engagement of research, practice and policy specialists to meet the needs of the intended target audiences. the result is a policy diffusion communication channel, or kt tool, that regularly collects and updates prevention policies from jurisdictions across canada related to the modifiable risk factors associated with cancer and chronic diseases and indexes them within an online, bilingual, and searchable database. methods directory design in designing the directory, the web-based scld and the shortcomings of the policy environmental scans were instrumental. internal it specialists were responsible for developing the online user interface for the directory (figure 1) that would enable research, practice, and policy specialists to explore the database by any combination of user-defined keywords and four search fields: risk factor, policy type, jurisdiction, and geographic location. with the directory positioned as an online resource, following the scld model, there was an opportunity to address limitations encountered through the environmental scanning approach associated with policy monitoring, primarily the large investment of time required and the quick obsolescence, through a custom web crawling it solution. built by an external it firm with unique legal and policy expertise, the custom web crawler automated the policy scanning operation by applying a set of search criteria to find and capture relevant documents on, currently fifty, prescribed websites. this allowed policy scanning to occur twenty-four hours a day and seven days a week with no human resources required, alleviating some of the resource-intensive burden associated with environmental scanning. moreover, the continuous nature of the web crawler allowed for regular updating of the directory to prevent content from becoming outdated. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 figure 1. the prevention policies directory website landing page, which includes the search fields to access over 1,700 canadian cancer and chronic disease prevention policies. part and parcel of having a web crawler conduct automated policy scanning was the requirement of a robust quality assurance procedure to ensure the directory was populated with credible and relevant policy information for the directory. a set of inclusion criteria (figure 2) defined the scope of the directory and provided a checklist for a quality assurance analyst to cross-reference against policies captured by the web crawler to determine relevancy. the inclusion criteria ensured that directory content was solely composed of seven policy document types and were adopted within canadian jurisdictions. it also required that content was related to prevention, and specifically the prevention of cancer and/or chronic diseases. finally, content had to be related to one of eight modifiable risk factors associated with cancer and chronic disease, or be aimed at more broadly impacting public health and health promotion, such as public health acts. when captured policies met the inclusion criteria they were considered relevant and published to the online directory. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 figure 2. prevention policies directory inclusion criteria an important challenge in constructing a resource aimed at knowledge transfer and exchange between jurisdictions across canada is being inclusive of both official languages: english and french. this was addressed through the directory design in a two-step fashion. first, the web crawler was able to employ both english and french search terms and therefore was able to capture documents in both languages. secondly, a bilingual analyst was responsible for implementing the quality assurance criteria against captured french content. while bilingual human resources and skills were still necessary for the quality assurance procedure, the bilingual nature of the web crawler provided an innovative way to readily scan for policies in more than one language. criteria 1 •must address cancer and/or chronic disease through one or more of the modifiable risk factors (alcohol consumption, built environment, infectious agents, nutrition, physical activity, occupational and environmental exposures, tobacco use, and/or uv/ionizing radiation) • alternatively, can contribute generally to the cancer and/or chronic disease prevention capacity of a jurisdiction (i.e., public health act) criteria 2 •must be cancer and/or chronic disease prevention-oriented, rather than dealing with management or treatment criteria 3 •must be enacted in a canadian jurisdiction (federal, provincial/territorial, or municipal) criteria 4 •must be one of the following policy or legal instruments: bill, bylaw, code, policy, regulation, or statute • alternatively, can be a policy evaluation document http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 target audiences the directory is positioned as a tool for research, practice, and policy specialists working in the area of chronic disease prevention and the related modifiable risk factors. these target audiences were engaged continuously to inform the scope and direction of the directory, primarily via two mechanisms: formal external advisory structures and ad hoc expert consultations. a steering committee was formed at the outset of the development work with a mandate to provide advice regarding the implementation of the web crawler as an automated monitoring mechanism for chronic disease and cancer policies and relevant knowledge transfer and exchange strategies. membership on the pan-canadian steering committee consisted of twelve individual experts in cancer and chronic disease prevention and the modifiable risk factors. the members also represented the research, practice, and policy communities and came from a variety of academic, non-governmental organizations, and government organizations. these experts played a significant role in brainstorming search terms sufficient for finding cancer and chronic disease prevention policies, as well as a list of relevant canadian web sites where those policy documents were hosted. furthermore, the group of advisors was consulted on issues of scope, for instance the decision to include municipal content (e.g., bylaws and policies) beginning with a selection of canadian municipalities and the resolution to exclude school board level policies due in large part to web accessibility difficulties. the steering committee was also provided an opportunity to have regular updates with members of the target audience to ensure the directory’s development remained aligned with their needs. apart from the formal steering committee, several decision points arose where additional advice was solicited from the target audiences on an as-needed basis. in the case of usability testing, a mix of research, practice, and policy specialists, ranging in familiarity with the directory, were utilized to evaluate the user experience in working with the tool. when building or changing the search criteria, in addition to guidance from the steering committee, experts from the cancer and chronic disease modifiable risk factor areas were enlisted to help with the process. by engaging cancer and chronic disease prevention experts from the research, practice, and policy communities other than those present on the steering committee, fresh and different perspectives were incorporated into directory-development decision-making. this participatory engagement approach attuned the development of the directory to the intended target audiences and at the same time built a network of champions comprised of invested research, practice, and policy specialists knowledgeable with the resource. phase 1: initial launch the development of the prevention policies directory can be separated into three phases: the initial launch phase, the search methodology refresh phase, and the expansion to municipalities phase. the setup phase consisted of building the required information technology foundation for the new tool – previously described in the directory design section – as well as determining the directory’s scope and search methodology to be employed by the web crawler. the involvement of expert advisors in the development of the search terms and relevant web site list through the participatory engagement approach, as previously described produced a search methodology consisting of 138 keywords to be applied to 280 web sites, representing federal, provincial and territorial policy sources. the final step in preparing the search methodology was the french cultural translation of the keywords. with these two critical pieces in place, the directory was launched in the spring of 2010. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 phase 2: search methodology refresh approximately two years into the directory’s operation, and based on continuous evaluation of the web crawler monitoring platform, the search methodology was refreshed. the change in search methodology applied by the web crawler consisted of a shift from simple keyword searches to search strings incorporating boolean operators. boolean operators function by relating individual search terms to each other to influence the retrieved results, either by reducing, expanding or improving the precision of those results [32]. for example, figure 3 shows how a search string for active transportation (under the built environment risk factor) was created by combining a risk factor search term prefix with a policy search term suffix. the “or” boolean operator linked affiliated risk factor and policy terms to form a prefix and suffix respectively, while the “and” boolean operator combined the two into a search string – requiring that at least one term from the prefix and one from the suffix would need to be identified in a document for it to be deemed relevant. the combination of risk factor terms with policy terms in boolean search strings equipped the web crawler to not only locate documents pertaining to the modifiable risk factors of cancer and chronic disease, which the previous keyword methodology accomplished, but to specifically recognize policy documents dealing with the cancer and chronic disease risk factors. also of note in the construction of the search strings, the boolean “and not” operator enabled the exclusion of common terms found on the internet, such as “privacy policy”. expert advice was leveraged to create 101 search strings which in turn underwent the french cultural translation process applied to the original keywords, and in 2012 the directory was re-launched with a new search methodology. phase 3: expansion to municipalities the latest evolution in the directory’s development was an expansion of the scope of policy content. after the launch, through ongoing discussions with steering committee experts and feedback from the directory’s target audience, the need for access to municipal policy content by research, practice, and policy specialists was identified. the importance of municipal content to research, practice and policy specialists was succinctly put by u.s. supreme court justice louis brandeis when he described how a local government could act as a, “laboratory; and try novel social and economic experiments” [33]. variability in the policies adopted at the local level provide provincial, territorial and federal governments with more sources of evidence to inform their own decisions [34]. in fact, state and provincial governments have routinely looked to local level policies as trial runs and used their success or failure to inform their own political decision in a vertical policy diffusion process [35]. this upward diffusion has been demonstrated in smoke-free spaces policies in the united states and in the restriction of cosmetic pesticide use in canada [34,36]. by expanding the directory’s scope to include municipal content, emerging policy directions not yet on the provincial, territorial or federal radar would be captured and available to research, practice, and policy specialists. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 figure 3. exemplar boolean search string for active transportation as of 2011, there were 5,253 municipalities in canada [37]. scanning all of these jurisdictions for municipal bylaws and policies was not feasible for a variety of reasons. for instance, the time required by the web crawler to search all of the relevant web sites and web pages, the time required by the analyst who would review captured policies, and the simple fact that many canadian municipalities lack up-to-date, online archives of their bylaws were barriers to collecting municipal content for the directory. in consultation with the urban public health network (uphn), a group of chief medical officers of health from the eighteen largest canadian cities, a collection of thirty-one municipalities were initially selected for inclusion (table). the thirty-one municipalities constituted the eighteen uphn member municipalities plus thirteen additional municipalities to ensure each province and territory was represented by at least two municipalities. the municipalities selected for inclusion in the directory’s scope showcase a wide range of municipal environments present in canada, from large urban centres with populations in the millions (e.g., calgary, montreal, and toronto) to small provincial cities/towns with populations under thirty thousand (e.g., conception bay south and summerside) to remote northern communities with less than five thousand inhabitants (e.g., arviat and hay river). with a pan-canadian range, the selected municipalities also provide a policy suffix ("active design" or "active transportation" or "active school transportation" or "active communit*" or "walkable" or "walkability" or "walkable communit*" or "walkable cit*" or "walkable neighbour*" or "livable communit*" or "bicycle trail" or "bicycle path" or "bike path" or "bike trail" or "bikeability" or "cycling infrastructure" or "greenway" or "greenbelt" or "pedestrian area" or "walking" or ("cycling" and not "recycling") or "human locomotion" or "transportation system" or "traffic calming" or "safe routes to school" or "pedestrian zone" or "neighbour* safety" or "stairs" or "car registration fees" or "30km zones" or "collective transportation" or "provincial parks" or "park lands" or "park-lands" or "parklands" or "play area" or "playground" or "activity friendliness" or "hiking trail" or "hiking path" or "recreation* area" or "open space" or "recreation* infrastructure" or "recreation* activit*") (("policy" and not "privacy policy") or ("code" and not ("comput*" or "program*")) or "bylaw" or "legislation" or "in force since" or "royal assent" or "statute" or "enforcement" or "regulation" or "health impact assessment" or "tax incentive" or "tax credit" or "tax rebate" or "sales tax" or "excise tax" or "tax benefit" or "policy evaluation") and risk factor prefix http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 diverse representation of demographics, socioeconomic status, industries, natural environments, political considerations, and more. the expansion in scope to include municipal content, following the shift of the search methodology to boolean search strings, signaled the final chapter in the directory’s development. table 1. prevention policies directory’s 31 municipalities province/territory municipality alberta • calgary • edmonton british columbia • surrey • vancouver • victoria manitoba • brandon • winnipeg new brunswick • fredericton • moncton • saint john newfoundland and labrador • conception bay south • st. john’s northwest territories • hay river • yellowknife nova scotia • halifax • region of queens municipality nunavut • arviat • iqaluit ontario • hamilton • london • ottawa • regional municipality of peel • toronto prince edward island • charlottetown • summerside quebec • longueuil • montreal • quebec city saskatchewan • regina http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 • saskatoon yukon* • whitehorse note: italicized municipalities are uphn members. * technical difficulties with website architecture on the websites of the largest municipalities in yukon have prevented the inclusion of more than one municipality for that territory. results content with development complete, a significant amount of policy content has already been reviewed and published to the directory. by december 2013, 1,742 policies had been published to the directory, 50 of which were unilingual french policies and the other 1,692 a mix of english and bilingual policy documents. the directory’s policies are categorized according to four dimensions: risk factor, policy type, geographic location, and jurisdiction (previously discussed as search options for directory users). figure 4 shows the breakdown of policies in the directory by the risk factors they act upon to prevent cancer and chronic disease. policies can address multiple risk factors, therefore when examining the number of policies in each risk factor the total will sum to more than the number of policies indexed in the directory – this is the only dimension where this is the case. also of note is the ‘general’ risk factor, which is not a modifiable risk factor of cancer or chronic disease, but a category where broad policies affecting health promotion and cancer and chronic disease prevention capacity and authority can be found, such as provincial and territorial public health acts. occupational and environmental exposures was the risk factor with the largest number of policies (n=623, 29%) at the time of publication, with built environment policies the second largest category (n=355, 16%). cancer-causing infectious agents (e.g., hepatitis c, human papillomavirus, etc.) prevention policies number the fewest in the directory with only forty-seven (2%). however, the prevalence of policies by risk factor, seen in figure 4, is not consistent across each level of government in canada. figure 5 shows the 146 federal policies in the directory according to the risk factor they address. figure 5 illustrates that at the federal level, there are no built environment policies, while nearly half (n=70, 48%) of the federal policies deal with occupational and environmental exposures. while the occupational and environmental exposures category remains the largest, as in the cross-section including all jurisdictions combined (figure 4), the federal level is more skewed with 80% of the policies addressing occupational and environmental exposures, nutrition (n=20, 14%), and tobacco use (n=26, 18%) categories. the spread of the 1,376 policies by risk factor at the provincial/territorial level (figure 6) closely resembles the cross-section of all jurisdictions (figure 4). again, the occupational and environmental exposure group is the largest (n=395, 29%) and cancer-causing infectious agents prevention policies make-up the smallest group (n=39, 3%). however, the remaining seven risk factors are relatively uniform in that they each account for between 9-12%. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 figure 4. prevention policies directory content policies by risk factor figure 5. prevention policies directory content policies by risk factor (federal) note: there are no built environment policies at the federal jurisdiction level. 143, 7% 355, 16% 47, 2% 180, 8% 623, 29% 140, 7% 292, 13% 249, 11% 157, 7% alcohol consumption built environment infectious agents nutrition occupation + environmental exposures general physical activity tobacco use uv and ionizing radiation 9, 6% 1, 0% 20, 14% 70, 48% 4, 3% 6, 4% 26, 18% 10, 7% alcohol consumption built environment infectious agents nutrition occupation + environmental exposures general physical activity tobacco use uv and ionizing radiation http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 figure 6. prevention policies directory content policies by risk factor (provincial/territorial) the final jurisdictional slice of 664 municipal policies by risk factor is shown in figure 7. the variation of policies by risk factor differs from the federal level and at this level the built environment risk factor is the largest group of policies (n=233, 35%). similar to the federal level, three risk factors, built environment (n= 233, 3%), occupational and environmental exposures (n=158, 24%), and physical activity (n=122, 18%) account for the majority of municipal policies within the directory. the infectious agents and general categories are the smallest at the municipal level each representing 1% of the policies. figure 7. prevention policies directory content policies by risk factor (municipal) 119, 9% 122, 9% 39, 3% 139, 10% 395, 29% 132, 9% 164, 12% 157, 11% 109, 8% alcohol consumption built environment infectious agents nutrition occupation + environmental exposures general physical activity tobacco use uv and ionizing radiation 15, 2% 233, 35% 7, 1% 21, 3% 158, 24% 4, 1% 122, 18% 66, 10% 38, 6% alcohol consumption built environment infectious agents nutrition occupation + environmental exposures general physical activity tobacco use uv and ionizing radiation http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 seven different types of policy documents are within the scope of the directory; the definitions of each can be found in table 2. the types of included policy documents run the gamut of legal instruments (e.g., statutes, regulations, and bylaws), to so-called ‘soft’ policies that act to guide and standardize decision-making, to evaluations of implemented policies. figure 8 shows that the majority of the directory is composed of the three legal instruments (79%) with federal, provincial and territorial statutes and regulations responsible for 58% of the policy content. codes account for only 1% of the directory’s make-up. table 2. types of policy documents indexed by the prevention policies directory legal instruments bylaw a subordinate legislation made by any authority subordinated to a legislature. the most frequently referenced bylaws are those made by municipalities. regulation a subordinate legislation adopted by a government according to lawmaking powers conferred by a statute. regulations clarify information that is found in the statute by providing more details or definitions. statute an act of a legislature adopted pursuant to constitutional authority. these are written laws that are also referred to as acts or legislation. statutes usually permit the enactment of regulations. in terms of hierarchy, these have more legal authority than regulations, bylaws, or bills (in that order). other policies bill a document submitted to a legislature for its consideration and/or enactment. it is simply a proposed piece of legislation and is subject to change before it is enacted as a statute. unlike statutes, regulations, or bylaws, they are not binding legal instruments. code any set of standards put forth and enforced by government for the protection of public safety and health (i.e., building codes for ventilation or sanitary requirements). policy a set of organizational rules intended to promote health. these can be in the form of a plan or a course of action. other evaluation any document that provides a progress or final report on an implemented policy with the aim of describing its efficacy and achievement in reaching its prescribed goals and objectives, excluding academic publications. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 figure 8. prevention policies directory content policies by policy type evaluation findings the directory is evaluated both as an it tool and as a kt vehicle. tracking the usage of the directory with web metrics gives a sense of whether the tool is being used and the size of the audience. since the search refresh and municipal policy addition phases, from april 2012 to march 2013, there have been 3,815 unique visits to the directory (google analytics an average of 318 unique visits per month) as illustrated in table 3. when compared to the web traffic experienced by the scld (2,200 unique visits per month 1 ),the directory’s web traffic is in line with the level of usage in the united states as canada has a population that is approximately 10% the size of the united states population. the total number of visitors to the website (n = 5,374) was also split into first-time visitors to the directory (n = 1,266) and repeat visitors (n = 4,108) who access the website at least one more time after their initial visit. over the same time period, a total of 2,945 searches of the directory were completed (table 3). among all visitors to the directory, 54.8% conducted a search. table 3. prevention policies directory web metrics web metrics (2012-2013) unique visitors 3,815 unique visitors per month 318 180, 10% 127, 7% 362, 21% 596, 34% 407, 24% 13, 1% 57, 3% policy bill bylaw regulation statute code evaluation http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 new visitors 1266 (23.56%) repeat visitors 4108 (76.44%) total visitors 5374 total searches 2945 web metric data provides insight into the usage of the prevention policies directory, but of equal, if not greater importance, is the impact the directory has on policy decision-making. one useful indicator for measuring the impact of the directory is tracking when it is cited in a report or document from another organization. for example, the directory has been cited on numerous occasions by several publications, including the journal healthcare quarterly, as a useful tool and credible source of policy information [38-40]. in fact, the directory has also been cited as a resource used in healthy public policy analyses [41,42]. further to documented citations, anecdotal evidence that the directory has played a role in policy diffusion and political decisionmaking is also valuable. one example from the eastern coast of canada involved a municipal jurisdiction using the directory to find a policy restricting alcohol advertising on public transit property from a central canadian municipality and used this policy to inform their own alcohol advertising policy development and council debates. through a variety of citations and qualitative evidence, the directory is increasingly being recognized as a valuable resource in policy-making and is demonstrating its influence on canadian healthy public policy diffusion. limitations while the utility of the directory lies in its ability to describe the canadian policy landscape compiled through examples of cancer prevention policies, the absolute counts of policies should not be interpreted as reflecting the real prevalence of policies in a given jurisdiction or for a given risk factor area. the directory’s content is populated with a convenience sample of policies, in part determined by the search methodology that includes prescribed websites for the web crawler to search. the selected websites are pre-vetted to ensure that the website contains policy content and that the website architecture is accessible by the web crawler. there remains the likelihood that additional relevant policies in a given jurisdiction or risk factor area have not been captured due to this web-crawling method of monitoring and surveillance. this limitation will continue to be mitigated in the future through the addition of new websites to search as the directory continues to mature. nonetheless, the relative proportions and examples of policies in a given jurisdiction or risk factor area provide a basis for informing comparative policy analysis and policy decision-making. there also remains the obstacle, as mentioned earlier in the description of the municipalities expansion phase of the directory’s development, that it is not feasible to systematically collect policies from the websites of 5,243 canadian municipalities (pre-supposing that they all have websites with up-to-date policies). with municipalities acting as important sources for emerging public health policy solutions, this is an important gap in the positioning of the directory as a credible and valuable resource. to this end, a mechanism allowing user contributed submissions from any municipality in canada to a complementary database and plotted on a map was developed. this approach is described in the ‘further developments’ section below. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 another limitation, with respect to the evaluation of the directory, is understanding the role it plays in the policy diffusion and policy development process. using web metric data in conjunction with tracking citations and qualitative anecdotes provides clear evidence of the tool’s reach and impact; however, the value the directory adds to specific policy decisionmaking processes is currently undocumented. conducting key informant interviews with members of the directory’s target audience who have indicated they have used the resource may provide a way to collect qualitative case studies on the role this resource plays in the policy decision-making process. discussion and conclusion challenges and opportunities in creating a tool to facilitate the diffusion of healthy public policies in canada and act as a policy surveillance platform, challenges included: the resource burden of scanning for policy documents, accommodating a bilingual and national audience, and ensuring that the directory is valuable to research, practice, and policy specialists whose work impacts chronic disease prevention (e.g., urban planners, transportation engineers, environmentalists, etc.). the custom web crawler was a unique it solution aimed at reducing the required time and human resources associated with environmental scanning. the web crawler also addressed finding french policies in addition to english, through the easy application of a bilingual search methodology, enabling the directory to meet the needs of a national canadian audience. however, an it approach was not without its own set of obstacles, chief among them is the differences between public health and it vocabulary. to mitigate the effects of a language barrier between the external it firm and internal public health specialists, in-house expertise was mobilized to ensure effective communication. it was crucial to have expertise in public health, but it was equally as important to have expertise in it to comprehend technical terminology and translate expectations to the vendor. in addition, internal it specialists were heavily relied upon for their expertise in dealing with the external it firm, through reviewing technical documents and consulting prior to and after updates to the directory’s infrastructure. recognizing the differences in the languages of it and public health was a key step that allowed internal resources to be leveraged to minimize this barrier. there were additional human resources and financial considerations associated with the web crawler and database it solutions that may impact the feasibility of utilizing this approach in different contexts. for instance, while the web crawler automated the search methodology and the database facilitates the organization of a large volume of documents, human resources were still necessary for reviewing content. a full-time analyst was required to implement the quality assurance process and manage the database; french documents were handled by a part-time bilingual analyst. in the same vein, unless an organization has existing internal it capacity for web crawling, financial resources could be a barrier to replicating the implementation of this custom it solution. costs are not only connected with the development of a web crawler, but also the ongoing hosting, maintenance and support agreements required to continue its operation. these human and financial resource considerations must be made when determining whether an it option is the correct solution. opportunities stem from the value of the directory which lies in part in the credibility and comprehensiveness of the information it contains. with over 1,700 policies from three levels of http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 government in jurisdictions across canada, the directory provides a large cross-section of the healthy public policy landscape in the country. already it contains nearly 1,000 more cancer and chronic disease prevention policies than were captured in the three environmental scans used to inform the directory’s development. the breakdown of the policies within the directory by risk factor showcases the ability of the resource to provide a summary of the canadian policy landscape. from the policy cross-sections provided in figures 5, 6, and 7, it can be surmised which areas of cancer prevention each jurisdictional level is, or has been, active within. for instance, nearly half of the federal policies in the directory are related to occupational and environmental exposures which reflects the role this level of government plays in regulating chemicals, products, and environmental pollutants. at the same time, the provincial/territorial level is characterized by greater uniformity across the risk factors and suggests this jurisdiction acts in each, but does not necessarily focus specifically on any particular cancer prevention domain. likewise, the municipal level, which is the level of government closest to local infrastructure planning and development, is the only jurisdiction to have a majority of built environment policies. on the other hand, risk factor areas with fewer policies indicate untapped policy realms, potential areas for action. in the case of alcohol policy at the municipal level, a relatively small number of policies (n = 15, 2%) were adopted in the 31 municipalities scanned by the directory at the time of this analysis, which suggests that alcohol control is a policy arena that municipalities have entered, but are not necessarily fully engaged in as of yet [43]. however, one caveat with this analysis is that hundreds more policies have been captured by the directory’s web crawler and await rigorous quality assurance review. for instance, at the time this article was written, 875 federal government documents have been flagged for assessment. the fact that 7% of the total number of policies in the directory are from the federal government, at the time of this article’s publication, affirms that the backlog of policy content is the leading opportunity to improve the resource’s integrity. it is also indicative of the leading challenge – efficient human resources able to keep up with the policies being identified by the regular web crawls. with the development of the directory completed, the focus has shifted to aggressively adding and updating content. at the time of writing, all relevant policies for the 31 included municipalities and eight provinces and territories have been added to the directory. it is anticipated that the quality assurance process for the four remaining provinces and territories and federal jurisdictions will be complete by march 2014. a participatory engagement approach to the development of the directory proved particularly fruitful in the uptake of the resource by researchers, practitioners, and policy specialists. by engaging members of the target audience at all stages of the construction of the tool, the directory has been shaped by its eventual users and made as utilitarian for that group as possible. this is evidenced, in part, by the large number of repeat visitors. the “unique visitors” indicator provides a measure of the number of individuals aware of the directory and the size of the audience it has reached, while the “new visits” indicator demonstrates how the audience is growing. perhaps the most important indicator is “repeat visitors”, which represents the core users who value, and return to use, the tool. over 75% of the 5,374 user visits to the directory were repeat visitors suggesting it resonates with the target audience and addresses a need amongst the user base. furthermore, citations and mixed methods qualitative feedback marking the directory as a credible and reliable source of prevention policy information lends credence to the notion that participatory engagement in the design and implementation of the directory has helped create a resource of value to its target audience. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 further developments with development complete and the directory positioned as an important resource for canadian researchers, practitioners and policy specialists, the focus has shifted to knowledge transfer and exchange (kte) efforts. kte activities will play a significant role in adding value to, and repackaging, the wealth of existing policy information in the directory to increase its utility amongst the target audience. from consultations with, and feedback from, the research, practice, and policy communities, evidence related to healthy public policy was identified as a clear priority. to address this need, two pathways forward were devised; one would see policies in the directory linked directly to sources of evaluation evidence of the need for the policy or its impact, and the other was a mechanism to collect contextual policy process evidence to shed light on policy development challenges and successes. a partnership was formed with carex canada 2 , a national surveillance project funded by cpac’s prevention program that estimates the exposure of canadians to carcinogenic substances in the workplace and community environments, to explore the feasibility of linking directory policies to a source of exposure evidence. carex canada provides profiles and estimates for 85 carcinogens that detail carcinogenic evidence, uses of the substances, and potential for exposure to canadians. many of the 85 carcinogens carex canada monitors are regulated to some degree by the occupational and environmental exposure policies in the directory. as a first step in linking directory policies to sources of evidence, the occupational and environmental exposure policies have been cross-linked with the carex canada profiles and estimates. in practice this entailed adding hyper linkages between occupational and environmental exposure policies that address one or more of the 85 carcinogens and the corresponding carex canada carcinogen profiles and vice-versa. linkages between the directory and carex canada will provide users of the carex canada carcinogen profiles with examples of regulatory action taken across canada, while directory users will have access to carcinogen exposure evidence providing some context for why regulation is, or is not, occurring. the second kte activity will provide a mechanism to capture qualitative evidence around the policy development and implementation processes involved in enacting healthy public policies as well as provide a space for collaboration amongst researchers, practitioners, and policy specialists. using google maps, directory policies are being plotted across the country to create policy maps on specific risk factor subjects. in addition to providing a visually enhanced interface to view policies in the directory, functionality for users to submit their own policies will address scoping limitations of the directory (e.g., 31 municipalities out of over 5,000 in canada). user contributions will also enable context-specific, policy development and implementation process evidence related to healthy public policies to be captured. while policyrelated evidence was a priority identified through exchanges with the research, practice, and policy communities, the experiences of colleagues with the policy process were viewed as particularly valuable. by enabling users to contribute their own policy development and implementation information to a map, they can include, context-specific information around the challenges faced and the solutions employed through the policy process. the first policy map was recently launched and focused on municipal-level active transportation policies. future maps may target other aspects of the built environment, specific carcinogens addressed by environmental and occupational exposure policies, or other policy areas related to the cancer and http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 chronic disease modifiable risk factors where the demand from users for this type of information is high. conclusion the directory, as a centralized source of existing canadian healthy public policies, has proven an important tool for cancer and chronic disease prevention researchers, practitioners, and policy specialists. a higher proportion of directory searches amongst return users coupled with numerous citations and user-reported anecdotal evidence supports this conclusion. additional developments have focused on expanding the credible content in the directory, as well as leveraging that information through the production of knowledge products, such as crosslinkages with sources of evidence and policy maps. the multifaceted approach that informed the development of the directory, consisting of combining policy diffusion theory, addressing gaps in conventional environmental scanning and policy surveillance, and utilizing participatory engagement to position the tool to best serve the needs of its target audience, played a key role in this robust outlook for the directory moving forward. key requirements for a web-based policy scanning and document management tool identify the extent to which relevant policy documents are available online develop custom it solution to web crawl for policy documents develop effective search methodology for web crawler to find relevant policy documents secure human resources for monitoring and quality assurance, and financial resources for ongoing hosting, maintenance, and support of custom it solution engage target audience/knowledge users early and regularly to align tool with audience needs evaluate impact of tool through combination of quantitative web metrics and qualitative indicators (e.g., citations, anecdotal user statements, etc.) box 1. key requirements for a web-based policy scanning and document management tool availability the prevention policies directory can be found at: http://www.cancerview.ca/preventionpolicies. acknowledgements production of the prevention policies directory and this article has been made possible through a financial contribution from health canada to the canadian partnership against cancer. the views expressed herein represent the views of the authors and not necessarily the views of the canadian partnership against cancer. financial disclosure none. http://ojphi.org/ ojphi supporting the diffusion of healthy public policy in canada: the prevention policies directory 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e177, 2014 competing interests none. references 1. world health organization, health and welfare canada, and canadian public health association. ottawa charter for health promotion: an international conference on health promotion the move towards a new public health, nov. 17-21, ottawa. geneva; 1986. 2. world cancer research fund / american institute for cancer research. food, nutrition, physical activity, and the prevention of cancer: a global perspective. washington, dc; 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http://dx.doi.org/10.12927/hcq.2011.22655 41. timmings c, savvaidis s, drennan-scace m. addressing healthy eating and active living: a community level policy scan, 2nd edn. toronto, on: program training and consultation centre; 2011. 42. canadian partnership against cancer. population health in canada's largest cities: a cancer system performance spotlight report. toronto, on; 2013. 43. camh health promotion resource centre and ontario agency for health protection and promotion (public health ontario). making the case: tools for supporting local alcohol policy in ontario. toronto, on; 2013. 1 scld web metrics acquired from the mayatech corporation (http://www.mayatech.com) on april 7 th , 2011. 2 http://www.carexcanada.ca http://ojphi.org/ http://dx.doi.org/10.12927/hcq.2011.22655 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license 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����� ��+��� ���� =�% �����+�� ��� ��9���%<�g #���!;�0�� ��� ��� ����� ���� ����� � �� �� ���������������������� �� ���� ���� ������ � ��� ����� ���� � �����f�!�� ���� ���� ��� �� �!����0 �����-.(-�,��>(3�2�@3.5�((� *reshma bhattacharjee e-mail: rbhattacharjee@inovalon.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e76, 2014 the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi the effects of mobile phone use in clinical practice in cape coast teaching hospital emmanuel kusi achampong1, gabriel keney 2, nathaniel ofori attah sr.3 1. department of medical education & it, school of medical sciences, university of cape coast, cape coast, ghana 2. directorate of academic planning and quality assurance, university of cape coast, cape coast, ghana 3. school of medical sciences, university of cape coast, cape coast, ghana abstract background: information technology has become an inevitable, constitutive element of the healthcare institution as well as health education. this study investigates the effects of mobile phone use in clinical practice at the cape coast teaching hospital. the use of mobile phones to access health information by health professionals has the potential to improve the provision of health service to the population. in addition, primary care physicians can use mobile phones to communicate with their patients before and after they are discharged, thereby improving the health of individual patients and the population at large. method: the study adopted the cross-section survey design and obtained data using questionnaire from 100 medical students (medical, nursing and midwifery students) through purposive sampling procedure. descriptive statistics and pearson chi-square were used for the analysis. results: the results show that 98% of the respondents owned smartphones, thus, they are receptive to and can confidently use their phones to access medical information (65%). it also emerged that, respondents can render effective and continuous service to clients (90%) with assistance from mobile medical apps. respondents dispelled fears that it was unethical to always depend on mobile apps for medical information. however, there was no significant relationship between using mobile apps to access medical information and ensuring effective and continuous service to clients (p≤ 0.937). conclusion: in spite of high patronage of mobile phone, respondents maintained that accessing mobile phones during medical practice does not interfere with the service delivery, rather it facilitates effective and continuous service, speed up access to healthcare information and helps to increase knowledge as well as improve care giving skills. mobile phone use can ensure quick communication between health facilities and health professionals which can help control diseases of public health concern thereby improving the health of the population. keywords: apps, smartphones, medical information, health professionals, medical practice the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi abbreviations: applications (apps), deloitte global center for health solutions (dgcfhs), cape coast teaching hospital (ccth), university of cape coast (ucc), clinical teaching centre (ctc), college of health and allied sciences (cohas)correspondence: gabriel.keney@ucc.edu.gh doi: 10.5210/ojphi.v10i2.9333 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the third millennium saw the advent of paradigmatic shift in information technology and data use. there has been an increase in global internet usage in households and among individuals. for example, ghana saw an increase in internet usage penetration from 25.5% in 2015 to 28.4% in 2016 [1]. this increase includes the use of downloaded applications (apps) on mobile devices, such as smartphones, tablets and ipads. to date, smartphones remain a prevalent mobile device of choice in ghana because of their capability to interface mobile telephony with advanced computing features, offering users ready access to apps [2,3]. there are currently more than 10,000 apps within the apple app store’s “medical, health care & fitness” category alone [4]. some of these apps are specifically designed for healthcare professionals such as medical calculators [5], logbooks [6], medical reference tools [7], medical guidelines such as resuscitation algorithms5 and drug guidelines [6]. the use of apps, as well as other functions of smartphones, such as viewing patients’ radiological images [8,9], and communicating with colleagues [10], permit healthcare professionals to perform numerous tasks at point of care centers. apps usage in clinical practices has burgeoned in africa in recent years; however, there is limited research of the efficacy of apps usage in clinical practices in the ghanaian healthcare sector. a recent study by the deloitte global center for health solutions (dgcfhs), though from the standpoint of economics, drives the relevancy of engaging apps in clinical practices in african countries. the delloitte report asserts that apps “can strengthen and improve the current health care system and they have the potential to deliver healthcare to patients in the most remote areas” [11]. the work of flanigan and mcaloon, though not about apps usage in africa, is indicative of the apps usage in clinical practices. flanigan and mcaloon found that drug dosage calculator apps increase doctors’ and medical students’ accuracy and confidence in drug dosage calculations [12]. in another study, low et al. found that a specifically designed app improves healthcare professionals’ performance in a simulated cardiac arrest emergency scenario [13]. whereas zanner et al. found that non-medically trained individuals’ performance of cardiopulmonary resuscitation in a hypothetical emergency scenario was slower in those using a specifically designed app compared to those without an app [14]. furthermore, the use of a specifically designed app also impeded the speed at which healthcare professionals assessed an ischemic stroke [12]. thus, apps need to be rigorously evaluated prior to implementation within clinical practices or as a first aid tool to the public. although some medical apps appear to be efficacious because they assist healthcare professionals to provide adequate patient care, the use of mobile phones within clinical practice does come with mailto:gabriel.keney@ucc.edu.gh the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi concerns. one such concern that has been raised is the risk of pathogen transfer [3,15,16]. it can be argued that mobile phones should be treated like all medical equipment (e.g., stethoscopes, pulse oximeters) in terms of cleaning them. however, using the same disinfectants to those used to wipe down standard medical equipment may damage mobile phones [3]. placing a protective cover over mobile phones that can withstand the usual disinfectants used on medical equipment has been suggested but this would only be suitable for touch screen devices and not mobile phones with a built-in keyboard [3,16]. smartphones interfering with medical equipment, especially in critical care settings, is another concern [17]. one study found that smartphones placed within 3 centimeters of critical care equipment produced interference [17]. however, this problem can be overcome by ensuring that smartphones are kept at a safe distance, one meter away from a critical care bed [17]. another concern associated with using smartphones within clinical practice, i.e., treating patient information in a secure manner, is the problem of data or security breach should a smartphone be lost or stolen [2,18]. however, this concern can be mitigated if data is stored within programs that can be erased remotely when a smartphone is reported lost or stolen [19]. closely related to this topic is that of patient confidentiality, which could be breached when in message or data transmission between and among medical colleagues [3,18]. another issue of concern is whether privately owned mobile phones should be used for clinical purposes (e.g., taking photographs of patients’ wounds), given that these devices are also used privately. unless patient information can be securely separated from non-clinical information and erased remotely, the likelihood that patients’ information may be compromised is a potential danger. despite the availability of thousands of medical apps [4], efficacy of data, and the awareness of potential concerns associated with using smartphones in clinical practice remains a major concern [16,19]. an underlying factor that propels this danger is the fact that there is no known evidence about healthcare professionals’ use of and attitudes towards using smartphones in clinical practice. low et al., found that after using an app in a simulated emergency resuscitation scenario, participants who were medical and healthcare professionals did not think that using such an app reflects poor training or that it appears unprofessional [13]. however, it must be kept in mind that low et al.’s study involved a simulated environment, as opposed to real clinical situations, and did not obtain extensive information pertaining to healthcare professionals’ attitudes towards using mobile phones in clinical practice setting [15]. the aim of the present study was to enumerate the number of healthcare professionals that use mobile/smart phones and apps during clinical practices. pertaining to the issue of confidentiality, this study was particularly interested in establishing who owns the mobile phone, how it is used during a clinical practice and the purposes for which it is used (e.g., taking photographs of patients’ wounds). this study limited the apps usage to healthcare professionals’, exploring their attitudes towards using mobiles devices, especially smartphones within clinical practice. furthermore, by way of comparison, the study seeks interest in healthcare professionals’ attitudes toward internet use in clinical practice. the was guided by the following research questions: 1. what are the perceptions of healthcare trainees on the use of mobile phones in clinical practice? 2. how does the use of mobile phone affect the continuity of service? the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi 3. how does the use of mobile phone affect access to healthcare? methods ethics respondents were assured that the information gathered is solely for academic purpose and were assured of the confidentiality of the information they will provide and therefore were instructed not to write their names or any identification on the questionnaire. design and sample the study used the cross-sectional survey design [20] and obtained its data using structured questionnaires. the target population included healthcare students (medical, nursing and midwifery students) and house officers. purposive sampling technique was used to sample 100 participants for the study. study area participants for the study were recruited from the clinical teaching centre (ctc) of the college of health and allied sciences (cohas), university of cape coast (ucc). participants included medical and nursing students in the final year, and doctors and nurses on the staff of cape coast teaching hospital (ccth). results table 1: demographic characteristics of respondents (n = 100) item frequency percentage gender male 39 39 female 61 61 age below 20 years 14 14 20 to 24 years 39 39 25 to 29 years 29 29 30 to 34 years 11 11 35 to 39 years 7 7 practical duration eight months 21 21 nine months 25 25 twelve months 54 54 the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi own phone with internet access yes 98 98 no 2 2 own simple phone yes 76 76 no 24 24 the results in table reveals that majority (61%) of the respondents were females and fell within the typical tertiary students’ age range between 20-24 years and another 29% within the age group of 25 to 29 years. the mean age was 20 years. again, it emerged that respondents had various duration of clinical practice at the time the data was collected for the study. the longest clinical practice involved the house officers and final year students who represented 54% of the respondents. clinical practice for nurses was shorter compared to the other healthcare professionals. ninety-eight percent (98%) of the respondents at the cape coast teaching hospital, who participated in the study had smartphones which had access to the internet, however, another 76% of the respondents also indicated that they had simple phones, in addition to the smartphones, that can be used for calling and texting only. simple phones, here refers to phones which cannot access information on the internet and basically with calling and texting capabilities. the respondents asserted that the simple phones were useful since it helped them make calls when the smartphones’ battery runs down. it was observed that, clinical year medical students used their phones to watch videos on youtube, chat on whatsapp and facebook platforms during service delivery. table 2: perception on the use of mobile phones in clinical practice items strongly disagree (%) disagree (%) uncertain (%) agree (%) strongly agree (%) i can effectively use phones to access the information i need 6.0 12.0 17.0 26.0 39.0 i can easily troubleshoot problems in using mobile phones to access the internet 28.0 45.0 6.0 5.0 16.0 mobile phone use in clinical practice is not a problem for me 3.0 12.0 4.0 56.0 25.0 i am concerned about the authenticity of clinical information available on the internet 8.0 11.0 6.0 49.0 26.0 the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi i enjoy using mobile phones to find new information 8.0 16.0 13.0 37.0 26.0 using mobile phones to get information is time consuming 18.0 21.0 7.0 32.0 22.0 mobile phones can be used to access medical information anytime you need it 5.0 14.0 6.0 63.0 12.0 majority of the healthcare workers at the clinical sessions have knowledge of how to use mobile phones to access information as shown in table 2. twenty-six percent (26.0%) and 39.0% of the respondents can effectively use mobile phones to access information on the internet. similarly, 56.0% and 25.0% of the respondents specified that the use of mobile phones in clinical practice is not a problem to them. however, 73% reported difficulty troubleshooting problems related to internet access on their smartphones. these respondents usually relied on their colleagues to access information even though they have their own smartphones, they are not privy to solving it. in addition, majority of the respondents (75.0% comprising; 49.0% agreed and 26.0% disagreed) were skeptical about the authenticity of the clinical information available on the internet. almost everything that one is looking for is available online and so getting information from credible sources were of interest to the respondents. the 75.0% respondents usually used medscape, oxford handbook of clinical medicine and medical dictionary to find information using their phones furthermore, it was realized that using mobile phones to access information online is time consuming but it is worthwhile than making a mistake during service delivery. thirty-seven percent of the healthcare workers agree that they enjoy using mobile phones to find new information. this is backed by 26.0% of the respondents who strongly agreed to the same item. the respondents (75.0%) might be enjoying this service because 63.0% agreed and 12.0% strongly agreed that mobile phones can be used to access medical information anytime when needed. table 3: perceive effects of mobile phone usage on continuity of service items strongly disagree (%) disagree (%) uncertain (%) agree (%) strongly agree (%) constant use of mobile apps does not help to internalize information 33.0 10.0 20.0 8.0 29.0 mobile apps can always be used as a source of information on medical issues 10.0 29.0 10.0 38.0 13.0 in the absence of mobile apps, delivery of healthcare service becomes a problem 64.0 10.0 1.0 16.0 9.0 the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi without mobile medical apps, there cannot be effective and continuous service to the clients 90.0 8.0 2.0 0.0 0.0 thirty-seven percent (37.0%); 29.0% strongly agree and 8.0% respondents asserted that mobile apps do not help to internalize information. twenty percent (20.0%) of the respondents were uncertain on the use of mobile apps to internalize information. thirty-three percent (33.0%) strongly disagreed that mobile apps do not help to internalize information. sixty-four percent (64.0%) of the respondents strongly disagreed that without mobile applications delivery of healthcare services would be a problem and supported by another 10.0% of the respondents. ninety percent of the respondents strongly affirmed that it is possible to deliver effective and continuous service to the clients without mobile application assistance. again, continuity of care was achieved through specified review days that are prescribed for patients in addition to special clinic days that patients are aware of when coming to see their doctors or follow up on a workup case of concern. these service deliveries are effective and ensure continuity even when mobile applications are not patronized. table 4-effect of mobile phone usage on access to care (n = 100) items strongly disagree (%) disagree (%) uncertain (%) agree (%) strongly agree (%) always depending on mobile apps for information is not ethical and can lead to low self-confidence 80 6 1 1 12 mobile apps help to speed-up access to healthcare information 23 20 50 1 6 errors in mobile apps can lead to fatal condition of patients 50 30 5 5 10 without information from mobile apps and the internet, it will be difficult to serve patients better 90 8 2 0.0 0.0 mobile apps and technology help healthcare professionals to improve upon their care giving skills and knowledge 79 10 5 3 3 mobile apps help in getting quick access to healthcare when patients book for appointment 50 20 1 25 4 the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi from table 4, it evident that majority (80.0%) of the respondents strongly disagreed that it is unethical to always depend on mobile apps for medical information. these respondents believed that frequent checking of information on their smart phones does not lead to low self-confidence. meanwhile, 50% of the respondents were uncertain that accessing mobile applications help to speed-up access to healthcare. the same statement was strongly disagreed by 23.0% with 20.0% also disagreeing it was realized that mobile apps help in getting quick access to healthcare when patients book for appointment. however, 70% respondents did not approve to this. they did not feel comfortable adding their clients to their medical calendars since the special clinic days were the official period to assess healthcare. table 5: correlation between mobile phones use to access medical information anytime against effective use of phones to access the information variable n df p i can effectively use phones to access the information i need 100 16 0.001 mobile phones can be used to access medical information anytime you need it table 5 indicates that there is a significant relationship between the effective use of mobile phones to access information needed and mobile phones can be used to access medical information anytime you need it (p ≤ 0.001). this result indicates that the respondents agree to the use of mobile phones to access medical information anytime the need arises. table 6: correlation between using mobile apps to access medical information and ensuring effective and continuous service to clients variable n df p mobile apps can always be used as a source of information on medical issues 100 8 0.937 without mobile medical apps, there cannot be effective and continuous service to the clients from table 6, there is no significant relationship between using mobile apps to access medical information and ensuring effective and continuous service to clients (p≤ of 0.937). therefore, the statement that “mobile apps can always be used as a source of information on medical issues” did not agree with the question “without mobile medical apps, there cannot be effective and continuous service to the clients”. discussion the results reveal that even though age is not a constraint when it comes to tertiary education, majority (68%) of the respondents in the study could be identified within this age groups (20-29 the effects of mobile phone use in clinical practice in cape coast teaching hospitala online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e210, 2018 ojphi years) of most students in tertiary institutions [21] and have positive attitudes towards smartphone [2,3]. these medical students were young, technology savvy and comfortable using contemporary technology in medical practice, especially to access needed medical information. the high proportion of respondents who owned smartphones reemphasize the findings by [1-7]. the results also showed that respondents expressed mixed feelings about the effects of mobile usage on continuity of service delivery. for instance, respondents were divided as to whether constant use of mobile apps help to internalize information. a section of the respondents asserted that it is better to use recommended text books to study and internalize the information therein than to use information from apps which can be debunked during a presentation. others also believed that information, either received from mobile applications or in text books, can be studied or internalized. while some researchers such as [3] argue that mobile phones should be treated like all medical equipment (e.g., stethoscopes, pulse oximeters), in tandem with these findings [3,15,16], noted that although some medical apps appear to be efficacious because they assist healthcare professionals to provide adequate patient care, the use of mobile phones within clinical practice does come with concerns. to another group of the respondents, mobile apps can come in handy and you enjoy the luxury having all your notes on ipad or smartphones [16,17,19]. it also emerged that, respondents dispelled fears that service delivery will be disrupted in the absence of medical information on mobile apps. these respondents have acquired sufficient knowledge, training and skills to deliver service to patients. they also work as a team during service delivery which is not dependent on the use of smartphones. the study results reinforce conclusions by [11] that mobile apps “can strengthen and improve the current health care system and they have the potential to deliver healthcare to patients even in most remote locations.” to this end, respondents in the study dispelled fears that using mobile apps to access medical information was unethical and can lead to low self-confidence. the findings again coincide with [12] who found that “drug dosage calculator apps increase doctors’ and medical students’ accuracy and confidence in drug dosage calculation” and improves healthcare professionals’ performance [14]. the use of apps, as well as other functions of smartphones, such as viewing patients’ radiological images [8,9], and communicating with colleagues [10], permit healthcare professionals to perform numerous tasks at point of care centers. conclusion the use of mobile 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discussion conclusion references 5018-37912-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 46 (page number not for citation purposes) isds 2013 conference abstracts accrued – an r package for visualizing data quality for aggregate surveillance data julie eaton1, ian painter*2 and william lober2 1university of washington tacoma, tacoma, wa, usa; 2university of washington, seattle, wa, usa � �� �� �� � � �� �� �� � introduction ������� �� ���� �������� ����� ������������� ����� ����������������� � �� ������� ������������������������ ��������� ������ �������� �������� ����� ������ �� ���� ������������ ��� ������� �� ������������ � ������� ������������������ ����� ���������������� � 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����#���3(=��-��8��"����8��������c��������%&&'����%&&d�2�8+��� c������ <�(�� ���)��%&����!���%./<�33b�%9�������<@@ ������� �� �������@������@%�ed7%9f /��8�������(��-�����>��4 ���#��3������#��+�����0��%&�����2�g� �� �)� ������������ �� <���� �������� 2�-��������?�� ��������� �>����� � &�@%&��.�7<���77��#4(<�&�/7&%@���"��7�&����77 7��3�#��� �������c����������%&&d���3<�!� ���������������������������� ���� ���� ������������3�$������������������� ���� �c����������g������� !� ������(�=b�/�d&&&9��&f�&��h3+�����<@@ �3����"�������� 9������<@@����������"�������@ ��@���*��� @�������@ *ian painter e-mail: ipainter@uw.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e8, 2014 consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi consumer health informatics: the application of ict in improving patient-provider partnership for a better health care benjamin abaidoo1, benjamin teye larweh2 1 university of ghana medical school korle bu, department of surgery, eye unit 2 ghana cocoa board, public affairs department. abstract background: there is a growing interest concerning the potential of ict solutions that are customized to consumers. this emerging discipline referred to as consumer health informatics (chi) plays a major role in providing information to patients and the public, and facilitates the promotion of self-management. the concept of chi has emerged out of the desire of most patients to shoulder responsibilities regarding their health and a growing desire of health practitioners to fully appreciate the potential of the patient. aim: to describe the role of ict in improving the patient-provider partnership in consumer health informatics. methods: systematic reviewing of literature, identification of reference sources and formulation of search strategies and manual search regarding the significance of developed chi applications in healthcare delivery. results: new consumer health it applications have been developed to be used on a variety of different platforms, including the web, messaging systems, pdas, and cell phones. these applications assists patients with self-management through reminders and prompts, delivery of real-time data on a patient’s health condition to patients and providers, web-based communication and personal electronic health information. conclusion: new tools are being developed for the purposes of providing information to patients and the public which has enhanced decision making in health matters and an avenue for clinicians and consumers to exchange health information for personal and public use. this calls for corroboration among healthcare organizations, governments and the ict industry to develop new research and it innovations which are tailored to the health needs of the consumer. keywords: consumer health informatics, information communication technology, patient, provider, clients and internet. correspondence: benjamin_abaidoo@yahoo.com doi: 10.5210/ojphi.v6i2.4903 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction there is a growing awareness concerning the potential of information and communication technology and e-health solutions that are modified to meet the health needs of the consumer of http://ojphi.org/ consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi healthcare services. this emerging field is referred to as consumer health informatics (chi). consumer health informatics (chi) plays a major role in providing information to patients and the public, which facilities the promotion of self-care, enabling informed decision-making, promoting healthy behaviors and peer information exchange. eysenbach defines chi as a medical informatics that analyzes consumer’s needs for information, studies and implements methods of making information accessible to consumers, and models and integrates consumer’s preferences into medical information systems [1]. the american medical informatics association (amia) identifies chi as a subspecialty in medical informatics which studies from a patient/consumer perspective the use of electronic information and communication to improve medical outcomes and the healthcare decisionmaking process [2]. the advent of consumer health related web sites, online support groups and electronic patientcentered communications, presents new challenges for clinical practice. healthcare givers have an important task of helping their clients and the public locate, assess, and interpret health information [3]. this study adopts the definition of chi by gibbons mc. et al [4] who defines chi as any electronic tool, technology, or system: (a) primarily designed to interact with health information consumers (anyone who seeks or uses healthcare information for non-professional work); (b) that interacts directly with the consumer who provides personalized health information to the to the chi system and receives personalizes health information from the tool or system and (c) in which the data, information, recommendations or other benefits provided to the consumer may be used with a healthcare professional, but is not dependent on a healthcare professional. this study describes the consumer of healthcare as any individual who assesses healthcare services (both patients and individuals who are not ill) or information for his/her personal health needs and the provider as the healthcare professional or facility providing health needs to the consumer. the desire of most clients (patients) to assume more responsibility for their health and the growing desire of health practitioners to fully appreciate the potential of the patient and their families correspond with the emergence of interactive information technology solutions available to clients. these developments are further fueled by cost-saving pressures in health systems and the hope that giving consumers more information has the potential of increasing quality of care and save costs by fully realizing the self-help potential of the patient and their family. there is an increasing pressure on healthcare consumers to actively participate in health care resulting in a thirst for health information. further, there is also an increasing need for health information technologies to assist patients in participating in their own health care [5]. meke et al [6] surveyed health services providers in namibia to find out how they use icts in the delivery of healthcare. twenty-one (21) health workers completed a set of structured questionnaire in two regions of namibia (one urban, one rural). icts were seen as very important tool for medical services (100.00%). ninety-one percent (91.00%) of health service providers viewed ict as helping them interact with colleagues. the telephone was the most commonly used ict tool, which was used in the admission of patients (36.00%). personal computer was the next most commonly ict (23%). http://ojphi.org/ consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi consumers are now ready to become partners in their own healthcare. a new breed of empowered, computer-literature consumers is slowly redefining the physician-patient relationship [7]. one of the most radical steps towards consumer empowerment is the creation of electronic health records accessible via the internet. this will enable consumers to practice online-doctoring as in the case of e-commerce and e-banking. a recent study by the pew internet survey revealed that 72% of internet users in the usa say they looked online for health information. in this study, the internet became the second most trusted source of information after a personal doctor. most consumers visit google before going to esteemed sources for health information and most don’t check the origin of the information obtained [5]. tele-health has great potential of advancing access to quality healthcare. however, its adoption in routine healthcare has been slow and consequently, the lack of clarity about the value of telehealth implementation is a concern for the slow adoption [8]. lack of face-to-face meetings or privacy did not appear to be a problem for consumers in their use of ict in communicating with providers [9]. consumer health informatics developments offer further opportunities and an increasing potential to grant consumers access to relevant information. integration of personal data with explanatory information may allow elderly consumers access to their electronic medication lists via the internet. such systems may be integrated with drug information [10]. this study therefore describes the role of ict in improving the patient-provider partnership in consumer health informatics by looking at current trends and future directions in consumer health informatics, examining issues surrounding consumers’ use of health information technology and discussing current applications of ict in consumer health informatics. methods systematic reviewing and blending of available literature and evidence from 1999 to 2013 regarding the impact of developed chi tools on healthcare delivery. searching for literature involved identifying reference sources and formulating a search strategy for each source. for the searching of electronic databases, medical subject heading (mesh) terms were used. to identify articles that are potentially relevant to the topic and the objectives, terms relevant to the definition of chi applications were used including key word search such as consumer, ict use, patient-provider partnership and informatics. eligible studies were also reviewed by reviewing the references in pertinent reviews. data sources included searched literature of peer reviewed literature databases and grey literature databases as well as hand searching from the medline, cochrane library, nursing and allied health literature (cinahl) databases and google scholar. results findings reveal that information technology and consumer health informatics is becoming an important aspect of modern public health practice. http://ojphi.org/ consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi currently, existing systems designed for health professionals are being adapted to be used at home by patients. new consumer health it applications are being developed to be used on different platforms such as the internet, messaging systems, pdas, and cell phones. low-cost communication systems (skype, handled radios) have been developed to assist patients in communicating with physicians and obtaining vital information pertaining to their health care. smartphone applications, self-management systems, electronic personal health records, patient portals, peer interaction systems, telemedicine and elearning, mobile health are all ict tools which facilitates the delivery of quality healthcare to consumers. e-mail and text-messaging are all vital ict tools developed to assist the patient and the provider in the quest for good health and quality health care delivery. discussion information technology and consumerism are synergistic forces that promote an information age healthcare system in which consumers are able to apply ict in self-care management and accessing of health information thereby utilizing healthcare resources more efficiently. ict applications have now become a conduit in channeling health information to consumers. there are softwares designed to assist consumers in making informed decisions with care givers tailoring interventions appropriately. several ict tools are being developed to enhance quality health information on the internet and also to facilitate the education of consumers. consumer health informatics tools and their use can benefit both patients and providers [11]. these applications have various purposes including assisting with self-management through reminders and educational prompts, delivering real-time data on a patient’s health condition to both patients and caregivers and storing personal health information in an easily accessible format. one example of the potential benefits of these kinds of applications is illustrated by the use of messaging capabilities available in certain consumer health it applications that enable timely communications between patients and their providers [12]. moreover, consumer health it applications that allow gathering and integrating data from various health care sources can serve as a comprehensive resource for patients and their providers. in addition to convenience, consumer health it applications are very significant in emergency situations in the provision of critical health information to medical staff. applications of smart phones smart phones adoption has paved way for the retrieval of a wealth of health information quickly and efficiently. users are able to accessed information through the internet regularly through their smart phones, including materials relating to health. users apply smart phones in looking for health information before deciding to consult a doctor. they use it in searching for healthy recipes to cook at home. users expressed interest in apps that gave information and advice that they could access on the go [13]. http://ojphi.org/ consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi self-management systems this includes systems with different combinations of functionalities utilizing multiple platforms with some providing effective and real-time response to information about the current status of the user. some of them allow for monitoring and transmission of information, such as blood pressure or blood glucose. depending on system design, feedback to a patient regarding his/her health status can be received from the system directly or from the provider who receives information from the system. a transfer of care activities from the healthcare providers to the patients could be achieved by equipping patients with adequate tailored tools (in particular, dedicated for self-management). in this way, patients become real partners in the care process, and healthcare providers can give further attention to the treatment of more serious health conditions [13]. electronic personal health records and patients portals electronic personal health records (phrs) are defined as an electronic record of health-related information concerning an individual that conforms to nationally recognized interoperability standards and that can be drawn from multiple sources while being managed, shared and controlled by the individual [14]. an electronic phr can exist as a stand-alone application that allows information to be exported to or imported from other sources or applications or as a tethered application that is linked to a specific health care organizations information system. tethered phrs, also referred to as patient portals, typically allow patients to view, but not modify, data from the providers electronic health record (her). relevant information that is often retained in a phr includes personal identifiers, contact information, health provider information, problem list, medication history [15]. some applications such as the electronic health records also allow patients to communicate electronically with their providers. peer interaction systems the internet has become a very resourceful avenue for clinicians and clients as well in problem solving and emotional support about their health concerns. the internet is not a replacement for doctors, but they can help you exchange information about treatment options and experiences, disabling conditions, care facilities, dealing with complications, current research and many other topics. peer interaction can take the form of stand-alone applications or can sometimes be a part of multi-component applications. these applications can increase the perceived peer support and improve personal and social outcomes [13]. telemedicine and elearning telemedicine is the use of medical information exchanged from one site to another via electronic communications to improve a patient’s clinical health status [16]. telemedicine helps to improve healthcare services, promote healthy lifestyles and education, where the patients or clients are geographically separated. telemedicine facilities for clinical consultation include patient assessment, diagnosis and treatment, continuing professional education, health promotion and healthcare management. its application is very significant in rural healthcare delivery, inmate http://ojphi.org/ consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi correction facilities, schools, industrial health services, disaster relief organizations, mobile health facilities among others. low-cost communication systems these include skype and handheld radios for improved medical communication among health practitioners and their clients. health applications web sites these web sites can help you track information, analyze it and even save it in a form you can share with your doctor. mobile health mobile health applications can help one to collect, analyze and use the same information. some mobile health applications even the sensors in ones phone to enrich the information collected such as how far one climbed or how fast one ran. other mobile health applications help you keep track of critical information such as your medication schedule and can provide alerts to keep you on track. in a recent world health organization global health survey, 60% of high-income countries and 30% of low and middle-income countries admitted using sms messages and other mobile health communications tools for improving treatment compliance. the related programs in low and middle-income countries address a variety of priority health concerns, including h1n1 influenza virus infection, hiv infection, vaccination, reproductive health and management of chronic illness [17,18]. e-mail and text messages e-mail and text messages are very useful in reminding patients about their appointments and other important information which may be needful in managing a chronic illness. literature reviews indicate that sms messages and other tools for communicating with patients between medical visits can improve health behaviors and physiological outcomes [19,20]. a review of seven interventional studies and four randomized trials revealed text messaging as a significant application for improving adherence rates [19]. studies on the effectiveness of sms messages for health promotion have also shown improvements in the outcomes of care [21,22]. in a trial of smoking cessation involving 5800 participants, the percentage of participants who had quit smoking (verified biochemically) had more than double 6 months after a txt2stop intervention [22] conclusion: information technology and consumer health informatics are becoming an integral part of modern concepts of public health and national health care policies communication is far from being realized. the internet has both the consumer who want to know the status of their health http://ojphi.org/ consumer health informatics: the application of ict in improving patient-provider partnership for a better health care 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e188, 2014 ojphi and the providers whose responsibility is focused on providing better information for a better healthcare and decision making. as more interactive, consumer-facing applications appear patients are now playing a more active role both in their own health management and in the shaping of services. the shift if responsibility and power from institution to individual has huge implications for healthcare systems worldwide. a key trend is towards integration of services, technologies, media, data, knowledge and communities. changes in the organization and funding of care will both influence and be influenced by patient-centered innovations. application of ict in chi can provide information to patients and the public, promote self-care, enable informed decision-making, promote healthy behaviors and promote peer information exchange and social support. with ict resources, consumers will feel more confident and empowered with increased knowledge and improved health status. new consumer health information technology (health it) applications are being developed that allow patients to manage, share and control their health information electronically and to assume a more active role in the management of their health. quality, research methodology and accessibility must all be increased to ensure that chi achieves its potential to improve the healthcare system. further, there is the need for corroboration among healthcare organizations, governments and the ict industry to develop new research and it innovations which are tailored to the health needs of the consumer. acknowledgment we are very grateful for the support accorded us by mrs. janet esi abaidoo and ms. benedicta kwagyir entsie. conflicts of interest none. funding sources none. references: 1. eysenbach g. 2000. consumer health informatics. bmj. 320, 1713-16. pubmed http://dx.doi.org/10.1136/bmj.320.7251.1713 2. american medical information association consumer health informatics working group. available at: http://www.amia.org/programs/working-group/programs/consumer-health informatics. 3. houston, t. & ehrenberger, h.. the potential of consumer health informatics. seminars in oncology nursing.17 (1): 41:47. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10864552&dopt=abstract http://dx.doi.org/10.1136/bmj.320.7251.1713 consumer health informatics: the application of ict in improving 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(retrieved on 8/5/2013). 22. free c, knight r, robertson s, whittaker r, edwards p, zhou w.. smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomized trial, lancet 2011; 378: 49-55. doi: 10.1016/s0140-6736(11)60701-0 pmid: 21722952. http://ojphi.org/ ojphi-06-e164.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 182 (page number not for citation purposes) isds 2013 conference abstracts surveillance of human papilloma virus in the united states to evaluate vaccine impact andrew wilson*1, ryan welch2 and rosemary she3 1department of family and preventive medicine, university of utah, salt lake city, ut, usa; 2arup institute for clinical and 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���;��5�!������ ������ �� ��� ����� ������������ ����������!���������������� ��������� ��������� �� %��� ����+&"�. �����-�$��� �� ��������$�������������������$���������� ����������� � ��� ��������������!����������������� �" a��� ��������������������������� ��������� ����������� ������ �� ����������� ��%0�� *���������&��������� ��������������!�� ����� �!������������ !���������� ��0"�b�����������$���������������������� %,*�����*���������-����� �� ��� �������&-�$�������!� ������� � �� ��� ����� �������� ������� ���� ������������������ ����� ������������� �� ��� � ������2������������� ������� �� ��� ����������������� �" keywords ���� ����� ���������c������ � ��c������� � ��c������ �c�=������ acknowledgments ?�d�� ���������� ��1 ��=���� ��-�(��� �������-�� ��7� ���!�7���� �?� � �� ������������ ���" references '"�7�$!�63-����� ���<�"������! ���������� ����� �������������� ��"�<� 8 � �. ����"�a�!����+c''+%;&e''+)�'')0" �"�8� ���������6�������8-������ ��� "����� ����� �������������������� �� �������� �����������-���� ����,"�aa?3"�a�� ����!�� ������� ��!� $��� !�������"�1��������'�c+'e�;,��+'" 0"� ������� <�-� =�� ���� =�� � �� f�����!� f"� ���� ���������� � ���� �� ��� �7��� �.����������������! ���������� ����� ��������������� �"� g������� ��!�����c'0e+0'h+05"� "�61i�#g"�g������� ����������� � ����������2�������� ���� ������� �� ��� �"�(���������"�6���� ���'-�'5+5�'5+5c;+%0&e +0� ) " ;"��������(�-�<����� �(3"�. ���������������������� � ������!���� ����� � ������������� �������� ��$����� ��2��� ��������� ���� $�������� �� � ������ �����������-����0����5"�<��� � ��������� ��!�� ���������"� ��'�c'e�+" *andrew wilson e-mail: wilson.stats@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e164, 2014 2010 accelerating public health situational awareness through health information exchanges: an annotated bibliography 1 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 accelerating public health situational awareness through health information exchanges: an annotated bibliography debra revere 1 , kevin c. stevens 2 1 center for public health informatics, university of washington, seattle, wa 2 marion county health department, indiana abstract in 2008, the centers for disease control and prevention awarded contracts to health information exchanges in indiana, new york and washington/idaho to accelerate public health situational awareness. awardees in each state have disseminated their findings and lessons at professional conferences and in peer-reviewed journals. the dissemination formats ranged from papers, oral presentations, posters, panels and demonstrations at interoperability showcases. with a focus on health information exchange and public health, topics included biosurveillance, electronic laboratory reporting, broadcast messaging, and notifiable disease surveillance. each presentation is summarized in this bibliography, and the authors affiliated with each site are highlighted. keywords: biosurveillance, situational awareness, electronic laboratory reporting, health information exchange, notifiable diseases introduction with funding from the centers for disease control and prevention (cdc) in february 2008, the “accelerating public health situational awareness through health information exchanges” project was initiated in three locations: indiana, new york and washington/idaho. this paper is an annotated bibliography of the peer-reviewed and grey literature publications and presentations by the three health information exchanges (hies). these hies implemented the capability to electronically move clinical information between disparate health care and public health information systems while maintaining the meaning of the information being exchanged. for this project, hies have developed real-time, nationwide public health event-monitoring capability to assist with and improve early event detection, public health situational awareness, outbreak management, and countermeasure and response administration. in addition, the hies are collaborating on a consortia level to cooperatively develop specifications for and trial implementations of the emerging and interoperable “network of networks” that is the nationwide health information network (nhin). each hie represents a wealth of innovative patientand population-based experience from public health practice, academia, business, and computing and information technology fields. furthermore, each hie has contributed to improving the operation of information technology systems and secure exchange of data and information to enhance early detection and a rapid accelerating public health situational awareness through health information exchanges: an annotated bibliography 2 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 response to and management of public health emergencies. this annotated bibliography represents the material outputs of each hie’s participation in the accelerating public health situation awareness initiative funded by the cdc. acronyms used: ahic american health information community cste council for state and territorial epidemiologists ehr electronic health record elr electronic laboratory reporting emr electronic medical record hie health information exchange himss health information management and systems society hitsp healthcare information technology standards panel ihe integrating the healthcare enterprise mbds minimum biosurveillance data set nedss national electronic disease surveillance system nhin nationwide health information network onc office of the national coordinator for health information technology phin public health information network rhio regional health information organization presentations summaries of the presentations by awardees of the cdc contract are listed in descending chronological order. authors affiliated with each site are highlighted in bold. 2010 stevens kc, grannis s, gibson pj, merriwether r. local public health and health information exchange business model. amia now! may 2010. summary: this poster describes the interaction between local public health departments and health information exchanges in the development of a business model. the business model incorporates the indiana health information exchange services: clinical repository (reportable laboratory results), clinical messaging (public health alerts) and clinical quality services (chronic disease management). http://amianow2010.amia.org/files/2010-amia-now-final-program.pdf (p. 26) stevens kc. the role of a public health informatician at a health information exchange. amia now! may 2010. summary: this poster describes how a public health informatician (phi) at a hie identifies and evaluates the quality and quantity of clinical data that is sent to public health. the role of a phi at a hie is compared to the cdc “competencies for public health informatician” and future implications for public health in the context of the nhin. http://amianow2010.amia.org/files/2010-amia-now-final-program.pdf (p. 26) http://amianow2010.amia.org/files/2010-amia-now-final-program.pdf http://amianow2010.amia.org/files/2010-amia-now-final-program.pdf accelerating public health situational awareness through health information exchanges: an annotated bibliography 3 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 karras b, gibson j, johnson g. public health and health information exchange: lessons learned a centers for disease control and prevention sponsored initiative in new york, indiana and washington/idaho. 2010 cste annual conference, june 2010. summary: a presentation demonstrating how collaborative efforts between public health and hie to enable electronic information exchange between clinical and public health settings can improve and transform public health capabilities. representatives from the three regions presented lessons learned and the organizational /technological infrastructure from the work that has been done to date. http://www.cste.org/conference/agendaandevents/tabid/383/default.aspx painter i, bugni p, trebatoski m, revere d, karras b, dobbs d, lober w. potential to estimate vaccine uptake in an emerging epidemic using data from a health information exchange. 2010 cste annual conference, june 2010. summary: a report regarding the ability of hies to provide realor near real-time vaccination information during an epidemic when obtaining vaccination uptake information is critical for decision-making about targeted vaccination efforts. using data obtained through the northwest public health information exchange (nwphie), the authors reported that sufficient selfreported vaccination data is present in the hie data to make it usable for vaccination surveillance. in addition, in comparison to other methods for obtaining this data, nwphie provides nearly real-time provision of relevant data for surveillance by washington state department of health. http://www.cste.org/conference/agendaandevents/tabid/383/default.aspx karras b. public health: case reporting, biosurveillance and immunization tracking. himss10 annual conference interoperability showcase, march 2010. summary: this presentation describes the use of hitsp standards and ihe profiles to support public health functions such as biosurveillance, case reporting and immunization tracking. also discussed are the current practices and an overview of the demonstrations taking place in a model hie represented at the himss interoperability showcase. http://himssconference.org/education/sessiondetail.aspx?eventid=4116 dixon b. bi-directional communication: enhancing situational awareness in public health and clinical practice. himss10 annual conference interoperability showcase, march 2010. summary: this presentation described how a health information exchange could facilitate efficient and effective dissemination of information to clinicians to keep them informed of emerging public health threats. a new component of the indiana network for patient care that delivers information in real-time from local public health agencies and integrated into existing clinical workflow was described, and a real-world example from the 2009 h1n1 outbreak was demonstrated. http://himssconference.org/education/sessiondetail.aspx?eventid=4124 2009 magruder c, johnson g, grannis s, karras b. enhancing public health practice through health information exchange – new york, indiana, and washington/idaho perspectives. 2009 cste annual conference, june 2009. http://www.cste.org/conference/agendaandevents/tabid/383/default.aspx http://www.cste.org/conference/agendaandevents/tabid/383/default.aspx http://himssconference.org/education/sessiondetail.aspx?eventid=4116 http://himssconference.org/education/sessiondetail.aspx?eventid=4124 accelerating public health situational awareness through health information exchanges: an annotated bibliography 4 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 summary: this panel presentation demonstrates how organizational and technological infrastructure developed for clinical data exchange can enhance public health activities. an interoperable network of systems to share information among medical providers, hies, and public health at the local, state, and federal levels is being implemented through innovative technologies and development of business, policy, and governance rules. http://www.cste.org/dnn/annualconference/2009annualconferencearchive/tabid/321/default.a spx safran c, garrett n, dente m, le l, grannis s, karras b. panel: translating public health information into clinical action: a national demonstration project / connecting public health to clinical care through health information exchange implementation of ahic biosurveillance. amia spring congress, may 2009. summary: panel presentation of two projects. the first presentation reported on a publicprivate partnership that is extending the capability of public health agencies to communicate with emr systems, using a standard messaging format and other data standards (e.g. hitsp) to create actionable and consumable alerts for providers. the second presentation reported on the hie collaboration for improving public health situational awareness through establishing and enhancing public health services in existing hies. http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_onsite_program.pdf dixon be, grannis s. enhancing public health surveillance and communication through health information exchange. amia spring congress, may 2009. summary: the authors present recent technologies developed to improve detection and reporting of disease outbreaks, including elr that enhances public health monitoring, communication, and workflow. also presented are mechanisms for sending mbds data to public health in real-time, and how public health can increase the timeliness of information it disseminates to clinicians. http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_onsite_program.pdf (information sharing presentations, s27) shepherd d, friedlin j, grannis s, hui s, kho a. a comparison of automated methicillinresistant staphylococcus aureus identification with current infection control practice. amia annu symp proc; 2009:594-8. summary: using data from infection control providers (icps), the microbiology lab, and a regional healthcare information exchange, the authors compared the accuracy of automated identification of mrsa using hl7 lab result messages to current manual infection control practices at a local hospital during july-september 2008. the study concluded that an automated processing of hl7 lab report messages is a more sensitive method of capturing mrsa cases than current standard infection control practice, with minimal loss of specificity. pubmed abstract: http://www.ncbi.nlm.nih.gov/pubmed/20351924 free full text: http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2815488/?tool=pubmed grannis sj, dixon be, gibson j, smiley a, dearth s, stevens k, barnes m. design and deployment of an integrated, standards-based public health alerting system. 2009 phin annual conference, august 2009. http://www.cste.org/dnn/annualconference/2009annualconferencearchive/tabid/321/default.aspx http://www.cste.org/dnn/annualconference/2009annualconferencearchive/tabid/321/default.aspx http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_on-site_program.pdf http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_on-site_program.pdf http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_on-site_program.pdf http://2009springcongress.amia.org/files/congress2009/amia_spring_congress_on-site_program.pdf http://www.ncbi.nlm.nih.gov/pubmed/20351924 http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2815488/?tool=pubmed accelerating public health situational awareness through health information exchanges: an annotated bibliography 5 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 summary: a presentation of the system deployed by the marion county health department to deliver public-health alerts to directly to the clinical inbox of clinicians. the system was first used during the h1n1 influenza outbreak. in addition to broadcasting to all physicians within the system the system is capable of targeting customized combinations of physician specialties and geographic regions. abstract: http://cdc.confex.com/cdc/phin2009/webprogram/paper21186.html hills ra, lober wb, dixon b, grannis s, lombardo j, revere d. demonstrating interoperability: biosurveillance in a model health information exchange. 2009 phin annual conference, august 2009. summary: a summary of the himss ihe demonstration that explored the feasibility of enhancing biosurveillance using interoperability specifications within a model hie. the scenario focused on a salmonella outbreak and demonstrated the ihe infrastructure that enhanced communication between public health agencies as they monitored the outbreak and initiated alerts to providers. the project successfully illustrated the feasibility of utilizing an hie infrastructure for outbreak detection, alerting, and other public health functions. http://cdc.confex.com/cdc/phin2009/webprogram/paper21087.html hills ra, lober wb, baseman jg, sibley j. xforms for public health: notifiable condition case reporting and public health alerting using ihe's rfd profile. 2009 phin annual conference, august 2009. summary: public health alerting and notifable condition reporting rely on bi-directional communication. this presentation reviewed the himss ihe 2009 demonstration that successfully used of retrieve forms for data capture (rfd) and its underlying xforms standard for two public health use-cases. http://cdc.confex.com/cdc/phin2009/webprogram/paper20771.html lober wb, magruder c, grannis s, soulakis n. sharing a summarized public health surveillance data set (sphsds). 2009 phin annual conference, august 2009. summary: presentation of a model for using a summarized public health surveillance data set (sphsds) to support public health situational awareness that delivers summarized biosurveillance data before more detailed patient-level data is revealed when public health events of interest are identified. various approaches for using a sphsds are discussed, including plans for using a sphsds by the cdc’s three “situational awareness through hie” awardees in ny, in and wa state / idaho. http://cdc.confex.com/cdc/phin2009/webprogram/paper21110.html lober wb, hills r, revere d, kirnack a. towards a unified framework of public health knowledge for clinical decision support. 2009 phin annual conference, august 2009. summary: while alerting is one of the traditional responsibilities of public health agencies, real-time computerized decision support; those developing clinical systems have, primarily addressed including immunization decision support. this presentation covered commonalities between the himss ihe 2009 alerting and immunization scenarios and highlighted the way standards supported tailored decision support in both cases. the authors suggest that these commonalities represent a unified approach to knowledge management and retrieval. http://cdc.confex.com/cdc/phin2009/webprogram/paper21165.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21186.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21087.html http://cdc.confex.com/cdc/phin2009/webprogram/paper20771.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21110.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21165.html accelerating public health situational awareness through health information exchanges: an annotated bibliography 6 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 magruder c, dobbs d, karras b, blake pa, grannis s, johnson gs. public health and health information exchanges: developing a common roadmap to future success. 2009 phin annual conference, 2009. summary: a presentation that examined the benefits of and best practices for sharing health information electronically between hies and public health. in particular, the following issues are highlighted: collaboration and data sharing agreements; nhin standards and gateway services; innovative information-sharing practices; extending existing technical standards to support summarized data exchange; rresponding to the h1n1 outbreak using hie data and processes; and evaluation plans for assessing the utility and effectiveness of hie and public health interactions. http://cdc.confex.com/cdc/phin2009/webprogram/paper21174.html johnson gs, wu w, davissson m, grannis s, abellera j, magruder c. health information exchange and notifiable disease surveillance. 2009 phin annual conference, august 2009. summary: review of the approach to assessing the utility of hies to support surveillance and notifiable disease case reporting as focused on acute hepatitis b virus (hbv) infection. assessments will build upon established exchange of the ahic mbds between hies and their respective public health agencies. approaches to evaluating the mbds in the context of notifiable disease case reporting, identifying and assessing the availability of additional data elements necessary to conduct acute hbv surveillance, comparing results with existing surveillance mechanisms, and assessing the impact in public health jurisdictions with and without existing elr mechanisms is covered. http://cdc.confex.com/cdc/phin2009/webprogram/paper21148.html grannis s, friedlin j, overhage jm, merriwether r. practical aspects of an operational open source notifiable condition detection system. 2009 phin annual conference, august 2009. summary: building on standards for message structure and content (hl7 and loinc ® ), the indiana network for patient care hie has implemented and maintained an automated notifiable condition reporting system that receives real-time hl7 clinical results from a variety of hie stakeholders, translates these disparate proprietary codes into loinc codes, determines whether the results carried by the message indicates a notifiable condition by checking the abnormal flag sometimes contained in the message, or by comparing the test results with criteria in the phin notifiable conditions mapping table. this presentation covered expansion of the notifiable condition processor infrastructure to create a modular notifiable condition processor, the--notifiable condition detector (ncd)--which is re-usable by other hies and public health stakeholders. http://cdc.confex.com/cdc/phin2009/webprogram/paper21179.html 2008 doctor jn, baseman jg, lober wb, davies j, kobayashi j, karras bt, fuller s. timetradeoff utilities for identifying and evaluating a minimum data set for time-critical biosurveillance. med decis making 2008; 28(3):351-8. epub 2008 may 13. summary: the authors establish and evaluate a method for measuring the utility of biosurveillance data using multi-attribute utility theory (maut). maut is used to assess and http://cdc.confex.com/cdc/phin2009/webprogram/paper21174.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21148.html http://cdc.confex.com/cdc/phin2009/webprogram/paper21179.html accelerating public health situational awareness through health information exchanges: an annotated bibliography 7 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 rank the risks of a decision-maker's preferences. in this paper, the authors apply this theory to measure the utility of biosurveillance data using a time-tradeoff exercise. empirically, the method shows initial promise for evaluating a minimum data set for biosurveillance. the authors suggest that future applications of this approach may prove useful in disease surveillance planning and evaluation. pubmed abstract: http://www.ncbi.nlm.nih.gov/pubmed/18480039 gotham ij, le lh, sottolano dl, schmit kj. public health preparedness informatics infrastructure. a case study in integrated surveillance and response: 2004-2005 national influenza vaccine shortage. lecture notes in bioinformatics, vol 5354 (proceedings of the 2008 international workshop on biosurveillance and biosecurity). berlin: springer, pp. 42-55. summary: integrating information systems within an informatics framework supporting a community of information trading partners engaged in routine hie can support key public health emergency preparedness (phep) activities. the authors describe how this framework supported and enhanced the efficacy of new york's response to the announcement of significant influenza vaccine shortfalls in 2004. this phep event required a full array of integrated and heightened activities at state and local levels. the authors describe how the presence of the hie framework supported advanced preparedness and just-in-time response, and detail related performance metrics and lessons learned from the response. abstract: http://portal.acm.org/citation.cfm?id=1485119.1485125 hills ra, lober wb, painter is. biosurveillance, case reporting, and decision support: public health interactions with a health information exchange. lecture notes in bioinformatics, vol 5354 (proceedings of the 2008 international workshop on biosurveillance and biosecurity). berlin: springer, pp. 10-21. summary: this paper describes support for three public health practice domains— biosurveillance, case reporting, and communication from public health to providers through integrated decision support—in demonstrations of a model hie. the model hie implements interoperability through the use integration profiles standards that support specific data transfer use cases. these methods were validated for each public health domain in a distributed environment in national showcase demonstrations. the authors believe that this work has implications for the integration of public health functions into any hie, regardless of architecture and may be extended to strengthen development of the public health grid. abstract: http://www.springerlink.com/content/k4701h773010v367/ overhage jm, grannis s, mcdonald c. a comparison of the completeness and timeliness of automated elr and spontaneous reporting of notifiable conditions. am j public health 2008; 98(2): 344-350. summary: in a comparison of traditional spontaneous paper-based reporting to automated elr through the hie, the authors found that automated elr of notifiable-diseases results in information being delivered to public health departments more completely and quickly than is the case with spontaneous, paper-based reporting. pubmed abstract: http://www.ncbi.nlm.nih.gov/pubmed/18172157 free full text at http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2376898/ http://www.ncbi.nlm.nih.gov/pubmed/18480039 http://portal.acm.org/citation.cfm?id=1485119.1485125 http://www.springerlink.com/content/k4701h773010v367/ http://www.ncbi.nlm.nih.gov/pubmed/18172157 http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2376898/ accelerating public health situational awareness through health information exchanges: an annotated bibliography 8 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 painter i, hills ra, lober wb, randels km, sibley j, webster e. extending functionality of and demonstrating integrated surveillance for public health within a prototype regional health information exchange. amia annu symp proc. 2008:969. summary: a poster describing the himss 2008 conference demonstration of how multijurisdictional public health surveillance and monitoring processes could be supported and expedited through integration with a prototype hie. pubmed abstract: http://www.ncbi.nlm.nih.gov/pubmed/18999244 friedlin j, grannis s, overhage jm. using natural language processing to improve accuracy of automated notifiable disease reporting. amia annu symp proc. 2008:207-11. summary: the authors report on the results of applying a natural language processing (nlp) system to automated elr, focusing on methicillin-resistant staphylococcus aureus (mrsa). the nlp system improved accuracy and completeness for mrsa, and achieved high sensitivity, specificity, positive predicted value and f-measure. the authors suggest that using nlp can improve the completeness and accuracy of automated elr. pubmed abstract: http://www.ncbi.nlm.nih.gov/pubmed/18999177 free full text at http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2656046/ magruder c, brady j, dobbs d, grannis s, le lh. enhancing public health capabilities through health information exchanges a new five-year initiative in new york, indiana and washington/idaho. 2008 phin annual conference, august 2008. summary: a presentation by each hie of the individual and collaborative vision of enabling bidirectional sharing of clinically relevant information to improve situational awareness and case reporting. the nihn backbone for information sharing is described. also covered are plans for completing an evaluation of the minimum dataset to assess its use for responding to and managing high-risk morbidity and mortality events such as seasonal or pandemic influenza, and its adherence to the hipaa minimum necessary requirement. http://cdc.confex.com/cdc/phin2008/webprogram/session9221.html grannis s, biondich p, downs s, shelley m, anand v, egg j. leveraging open-source matching tools and health information exchange to improve newborn screening follow-up. 2008 phin annual conference, august 2008. summary: the value of sharing data among hies and public health is demonstrated by improving newborn screening follow-up by identifying infants who may lack screening. this use case requires a robust matching process for linking vital records data with newborn screening results to identify unscreened infants. the indiana network for patient care has implemented a configurable, generalized probabilistic method to perform the matching using an open-source probabilistic matching tool. the design and implementation of the system is described and results for matching infant data across multiple organizations is presented. http://cdc.confex.com/cdc/phin2008/webprogram/paper17722.html grannis s, overhage jm, friedlin j. architectural and operational components of a real world operational automated notifiable condition processor. 2008 phin annual conference, august 2008. summary: presentation of the indiana network for patient care, an operational hie built on standards for message structure and content (hl7 and loinc ® ), that has proven reliable in http://www.ncbi.nlm.nih.gov/pubmed/18999244 http://www.ncbi.nlm.nih.gov/pubmed/18999177 http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2656046/ http://cdc.confex.com/cdc/phin2008/webprogram/session9221.html http://cdc.confex.com/cdc/phin2008/webprogram/paper17722.html accelerating public health situational awareness through health information exchanges: an annotated bibliography 9 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 delivering results and has scaled to multiple clinical data sources over several years of use. the presentation covered implementation of a notifiable condition processor in the context of an hie rather than at local hospitals, including the technical architecture, and operational aspects of the system. http://cdc.confex.com/cdc/phin2008/webprogram/paper17733.html le lh, gotham ij, evans lm, mostashari f, morse d, soulakis n, fuhrman j, brady j, ciampa m. universal public health node: an evolution of health information exchange for public health in new york. 2008 phin annual conference, august 2008. summary: a presentation of the vision and architecture of the universal public health node (uphn). the example of new york state is used to demonstrate how the uphn will address nationwide situational awareness by connecting public health in the hie with a clinical-based rhio partners. this connection is accomplished through a common set of standards and services for the bi-directional exchange of data while ensuring appropriate privacy protection. also presented were four novel functionalities of the uphn that support multiple public health scenarios and are needed to implement and extend the biosurveillance use case and the minimum dataset. abstract: http://cdc.confex.com/cdc/phin2008/webprogram/paper17377.html tokars j, karras bt, dobbs d, blake p, stephens cm, fraser j, howell jf, stevens kc, dixon be, morse d, le lh, magruder c. biosurveillance: coordinating a nationwide approach. 5 th nationwide health information network forum, december 2008 summary: presentation demonstrating how the ahic biosurveillance use case can be implemented within the nhin. the presentation further describes the roles that the cdc biosense program, hies, and state and local health departments are playing in the situational awareness contracts funded by the cdc. http://healthit.hhs.gov/portal/server.pt?open=18&objid=849959&parentname=communitypage &parentid=6&mode=2&in_hi_userid=10731&cached=true (requires authentication) turner am, stavri z, revere d, altamore r. from the ground up: determining the information needs and resources of public health nurses in an oregon county health department. j med libr assoc 2008; 96(4): 335-42. summary: the authors identified and assessed information needs and resources of public health nurses in a local health department using the qualitative approach of constant comparative method on semi-structured in-depth interviews with seventeen public health nurses at a local health department in rural oregon. major themes that emerged included: (1) differences in information needs depending on position and role; (2) colleagues as the most efficient and trusted source of information; (3) limitations of existing knowledge-based resources; (4) need for up-to-date and pertinent information; and (5) need for personal computers, basic communications software, and expanded internet access. free full text: http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2568844/ pubmed abstract: http://www.ncbi.nlm.nih.gov/pubmed/18974810 conflicts of interest the authors have no conflicts of interest to report at this time. http://cdc.confex.com/cdc/phin2008/webprogram/paper17733.html http://cdc.confex.com/cdc/phin2008/webprogram/paper17377.html http://healthit.hhs.gov/portal/server.pt?open=18&objid=849959&parentname=communitypage&parentid=6&mode=2&in_hi_userid=10731&cached=true http://healthit.hhs.gov/portal/server.pt?open=18&objid=849959&parentname=communitypage&parentid=6&mode=2&in_hi_userid=10731&cached=true http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2568844/ http://www.ncbi.nlm.nih.gov/pubmed/18974810 accelerating public health situational awareness through health information exchanges: an annotated bibliography 10 journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.2, no. 2, 2010 acknowledgement the authors would like to thank brian dixon for his comments on this manuscript. this project was funded by the centers for disease control & prevention under contract 200-2008-24368. correspondence debra revere: drevere@u.washington.edu mailto:kstevens@hhcorp.org ojphi-07-e9.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an ecological analysis of the impact of temperature inversion on emergency department visits for respiratory syndromes and subsyndromes using biosense 2.0 frontend data anne burke*, david jackson and allyn k. nakashima bureau of epidemiology, utah department of health, salt lake city, ut, usa objective to determine the association between emergency department (ed) visits for the respiratory syndrome and subsyndromes and air quality indices (aqi) for fine particle pollution in salt lake county, ut using frontend biosense 2.0 data. introduction during the winter months, utah experiences a temperature inversion which traps pollutants, such as fine particle pollution (pm 2.5), in the salt lake valley. a previous study determined the impact of inversion on ed visits for asthma,1 however similar phenomena have yet to be examined using the biosense 2.0 syndromic surveillance system. while similar studies utilize a time-stratified case-crossover design, the absence of individually identifiable information on the dashboard precludes the utilization of this methodology. using biosense 2.0 frontend data, an ecological study design may allow for analyses to determine the impact of inversion on ed visits for respiratory syndrome and subsyndromes from submitting facilities in salt lake county, ut. methods numbers and proportions of visits were tabulated for the biosense 2.0 respiratory syndrome and subsyndromes (asthma, bronchitis and bronchiolitis, chest pain, cough, cyanosis and hypoxemia, dyspnea, hemoptysis, influenza-like illness (ili), otitis media, pleurisy, pneumonia and lung abscess, respiratory failure, respiratory syncytial virus (rsv), and upper respiratory infections) for january 2012 to february 2014. particulate concentration data for pm2.5 in salt lake county, ut were be obtained from the epa website and converted to aqi for the same dates. voluntary and mandatory action days were identified by pm2.5 (15-25 and >25 g/m3, respectively) and analysis of variance (anova) models were performed to determine if the proportion of ed visits differed significantly by action day status. the cdc’s c2 aberration detection methodology was used to determine dates when the number of ed visits for respiratory events exceeded the alarm threshold. these days were examined in relation to aqi using anova models in order to determine if there was a significant association between days exceeding the threshold and air quality. a linear regression analysis was conducted to determine the association between biosense 2.0 respiratory syndrome and subsyndromes and aqi when controlling for seasonality. results a total of 80 voluntary and 88 mandatory action days occurred during this time period. the mean proportion of total visits for all respiratory syndromes and subsyndromes except for respiratory failure and otitis media increased significantly with more severe action day status (p<0.05). overall, 85 alarms were triggered for respiratory syndromes and subsyndromes during this time period. the mean aqi was significantly greater on days in which the proportion of ed visits exceeded the alarm threshold for ili, pleurisy, and respiratory failure (p<0.05). after adjusting for seasonality, the aqi was significantly associated with the proportion of ed visits for rsv, ili, and the respiratory syndrome (p<0.05). conclusions regardless of analytical technique, the proportion of ed visits for the biosense 2.0 respiratory syndrome and ili were significantly associated with aqi; however, the association of other subsyndomes differed by method of analysis and the inclusion of control variables. these data suggest that voluntary and mandatory action days were associated with higher proportions of respiratory visits for most respiratory conditions. alarm events, on the other hand, were most likely to be associated with aqi for ili, pleurisy, and respiratory failure; this data may be useful to ed practices in salt lake county. rsv and ili remained significantly associated with aqi when controlling for seasonality. the association between these infectious conditions and aqi may inform the use of syndromic data to conduct disease surveillance during the influenza and rsv seasons. overall, these data suggest that frontend biosense 2.0 data may serve useful to ecological analyses for air quality issues, such inversions in salt lake county, ut. because of its scope and differing characteristics of respiratory subsyndromes, breaking the respiratory syndrome down into its various subsyndromes for analysis may provide more a more accurate representation of its association with air quality. keywords biosense 2.0; respiratory syndrome; air pollution references 1. beard jd, beck c, graham r, packham sc, traphagan m, giles rt, morgan jg. winter temperature inversions and emergency department visits for asthma in salt lake county, ut, 2003-2008. environmental health perspectives 2012;120(10):1385-1390. *anne burke e-mail: aburke@utah.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e , 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts keeping public health surveillance practice up to speed: a training strategy to build capacity kathleen laberge*, lisa jensen, dr. philip abdelmalik, katie rutledge-taylor and lena shah public health agency of canada, ottawa, on, canada objective to enhance the knowledge and ability of public health practitioners to integrate and apply surveillance concepts, principles, and emerging tools into their practice. introduction public health surveillance practice is evolving rapidly. in the past decade we have witnessed the globalization of health threats, the emergence and re-emergence of infectious diseases, and an explosion of easily accessible new technologies. this fluid environment challenges the public health community, but also provides it with a unique and fertile environment to innovate and improve its practice. as surveillance is a core function of public health practice, public health practitioners need to be well equipped to achieve this function and address present and future public health challenges. we developed a five day training course that focused on the practical use of surveillance concepts and principles in public health. we are sharing findings on the development of the course and learner outcomes. methods the course included three modules covering surveillance concepts and principles, surveillance data assessment and analysis, and innovative surveillance tools. adult learning principles were applied to the course’s instructional design. a range of instructional methods (interactive lectures, group discussions and exercises, case studies, and games) were used. technical content was presented by national and internation experts. we administered preand post-tests to measure participants’ learning and to assess the applicability of the training to their current practice. canadian public health practitioners meeting pre-requisites were the target audience. results thirty public health practitioners participated in the course. participants designed, in small groups, a lyme disease surveillance system. they learned how to critically appraise a surveillance system, to define and apply steps in surveillance data analysis. participants used innovative surveillance tools such as gphin, promed, healthmap, and hoodris, and described participatory surveillance tools. they articulated the links to traditional surveillance and explored concepts to evaluate innovative surveillance approaches. participants described examples of innovative surveillance in canada and internationally, and discussed strengths and weaknesses of both innovative and traditional surveillance tools. participants recognized how public health may change in the future in order to integrate innovative surveillance tools. the preand post-tests results indicated an increase in learning. participants found the training relevant, interactive and practical. they appreciated the balance between theory and practical applications. conclusions public health practice is evolving swiftly. it is paramount that knowledge of basic surveillance concepts and principles, and most current methodologies and tools be translated into public health best practice. to maximise the application of learning, the training format needs to be appropriately adapted to the adult learners. training for public health practitioners needs to be interactive, practical, and include peer learning opportunities. content experts should be involved in the delivery of training as sharing their knowledge and experience also improves public health surveillance practice. this training was a unique initiative that allowed public health practitioners to improve and update their surveillance practice with the application of innovative learning strategies and tools. it builds capacity and equips the public health community to better face current and future public health challenges that result from our complex and evolving world. keywords public health surveillance; training; public health practice acknowledgments public health agency of canada *kathleen laberge e-mail: kathleen.laberge@phac-aspc.gc.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e33, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts surveillance for opioid overdose in the veterans health administration, 2004-2014 carla winston* and mark holodniy u.s. department of veterans affairs, palo alto, ca, usa objective to examine inpatient admissions for opioid overdose among u.s. veterans using national-level surveillance data. introduction drug poisoning, or overdose, is an epidemic problem in the united states1,2. in keeping with national trends, a recent study combining u.s. veterans health administration (vha) data with the national death index showed increases in opioid overdose mortality from 2001 to 20093. one of the challenges in monitoring the overdose epidemic is that collecting cohort data to analyze overdose rates can be laborintensive. moreover, analysts are often unable to collect real-time data on overdose events. to explore solutions to these challenges, we examined opioid overdose by using veteran healthcare data already being collected for syndromic surveillance. methods we analyzed data from the vha electronic surveillance system for early notification of community-based epidemics (essence) platform for january 2004 through june 2014. after evaluating both inpatient and outpatient data, we restricted the analysis to admissions with a principal diagnosis of opioid poisoning in order to reflect severe acute events. we included any admissions with a principal diagnosis of 965.00 (opium alkaloids unspecified), 965.01 (heroin), 965.02 (methadone), or 965.09 (other opiates and related narcotics). we calculated opioid poisoning rates per 1,000 inpatient admissions by age group and u.s. census region. results a total of 6,317 admissions with a principal diagnosis of opioid poisoning were reported out of 5,459,815 admissions. death was the final outcome for 64 (1.0%) opioid admissions. rates of opioid poisoning were highest among younger veterans, and lowest among veterans age 65 years and older (table). trends over time showed increases for all except 45–54 year olds (p=0.16; p <=0.02 for all other groups), for whom the opioid poisoning rate was initially highest during the study period. overdose rates were highest in the western united states, where trends were stable over time (p=0.46) compared with increases in other regions (p<0.01). total rates appear to have peaked in 2010 and subsequently plateaued (figure). conclusions we found that surveillance data could effectively be mined for evaluating opioid poisoning among veterans. although we focused on principal diagnosis, trends in all opioid poisoning icd-9 codes and supplemental e codes indicating causes of injuries paralleled our current findings. as a proof of concept similar to assessing suicidal ideation using telephone triage data4, we have used routine vha surveillance data as a timely way to assess the opioid epidemic among veterans. vha is committed to reducing opioid morbidity through treatment and education5. opioid poisoning admissions, icd-9 codes 965.00-965.09, by age keywords opioids; overdose; trends; regional variation acknowledgments the findings in this report are those of the authors and do not reflect the official position of the u.s. department of veterans affairs. references 1. office of national drug control policy. white house summit on the opioid epidemic. http://www.whitehouse.gov/blog/2014/06/19/ white-house-summit-opioid-epidemic accessed august 14, 2014. 2. centers for disease control and prevention. vital signs: overdoses of prescription opioid pain relievers—united states, 1999-2008. mmwr. 2011 nov 4;60(43):1487-1492. 3. bohnert as, ilgen ma, trafton ja, et al. trends and regional variation in opioid overdose mortality among veterans health administration patients, fiscal year 2001 to 2009. clin j pain. 2014 jul 1; 30(7):605612. 4. ludwig a, lucero-obusan c, schirmer p, holodniy m. characteristics of veterans accessing the veterans affairs telephone triage who have depression or suicidal ideation: opportunities for intervention. online j public health inform. 2013; 5(1): e136. 5. veterans health administration. implementation of opioid overdose education and naloxone distribution to reduce risk of opioid-related death. under secretary for health’s information letter, il 10-2014. may 13, 2014. *carla winston e-mail: carla.winston@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e60, 201 ojphi-06-e94.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 141 (page number not for citation purposes) isds 2013 conference abstracts electronic epidemiologic biosurveillance in the cameroon military karen saylors*, matthew lebreton, essisima foe, aboudem bavou clement martial, nancy ortiz, christopher perdue and ubald tamoufe 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(��'������"'��<���� ����=$��'����� �.&��.�>��%�1&��"%����#<���������� #"��=��9���0'��"& �"?�&�" �������<������&���������"��������+����� ����������� ���0�������" ������� ���� �1��� ����'����� �"���� ���� �'�"�� ��5)@*��,a((� ,��"������+ ��<�� ��"��+������������ ������������� ����������� �� �� �� �� � ������������� ������� ����//.1�" �������" ���,a(,:�5(?�,a�,-� *karen saylors e-mail: ksaylors@metabiota.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e94, 2014 health information seeking and social media use on the internet among people with diabetes 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 health information seeking and social media use on the internet among people with diabetes ryan j shaw 1 , constance m johnson 1 1 duke university school of nursing, durham, nc abstract patients who are active and involved in their self-management and care are more likely to manage chronic conditions effectively (6, 26). with a 5-fold increase in the incidence of chronic illness over the past 20 years, access to information can provide patients the tools and support to self-manage their chronic illness. new media technologies can serve as tools to engage and involve patients in their health care. due to the increasing ubiquity of the internet and the availability of health information, patients are more easily able to seek and find information about their health.. thus, the internet can serve as a mechanism of empowerment (4, 5). this is especially important for people with diabetes mellitus where intensive selfmanagement is critical. introduction diabetes is the sixth leading cause of death in the us (31). over 24 million people in the us have diabetes, with over 1.6 million new cases diagnosed in 2007 in people over the age of 20 (29). in particular, minorities are disproportionately affected by diabetes. african american and native american adults are twice more likely to have diabetes than white adults (33). if this trend continues, one third of all americans will develop diabetes and will lose an average of 1015 years of life (29). the most common form of diabetes is type 2, which is linked to obesity and physical inactivity, and accounts for 90%-95% of diabetes cases (29). diabetes causes serious complications and can lead to poor quality of life (1). people with diabetes are more susceptible to other illnesses and often have a worse prognosis from them. yet, people with diabetes can reduce the occurrence of these potential complications through lifestyle management. there are many secondary prevention (glucose control and blood pressure control) and tertiary prevention (screening for eye, foot and kidney abnormalities) measures available to prevent the onset of diabetes and decrease the severity of diabetes complications (12). strategies that would decrease the burden of diabetes are not used regularly, resulting in increased morbidity, http://ojphi.org health information seeking and social media use on the internet among people with diabetes 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 complications and expenses (34). people with diabetes who are compliant with their regimen and maintain strict glycemic control have lower rates of complications (30). training and education to help people self-manage their diabetes helps prevent unnecessary health care utilization and hospitalization (8). people who have increased morbidity and mortality risk, particularly those with a disability or chronic illness such as diabetes, are more likely to engage intensely with online resources (18). searching for health information is the third most popular use of internet technology (17). it is estimated that in the us, health information is sought online by 81% of internet users and 66% of all adults (22). the internet brings to the general population the ability to perform in-depth information searches and can assist health consumers with treatment decisions, in determining when to see a provider, and to help prepare them to actively participate in their care (20). research demonstrated that online social support programs targeting chronic illness have been shown to decrease symptoms, improve health behaviors and reduce utilization of health care resources (28). through the internet, online programs can serve as an interactive medium for providing health information and enhancing social support. research suggests that health communication is more effective when it reaches people on an emotional as well as a rational level, relates to people‟s social or „life‟ contexts, is a combination of interpersonal communication and mass media, is tailored, and is interactive (32). online social media such as facebook, one of many online social networking sites, has these communication elements in its design. since 2004, it has become increasingly popular with over 350 million users worldwide, and over 100 million users in the u.s. alone (14). more than 25% of us adults over 50 stay connected using online social networking sites (2010). other online social media outlets such as twitter, youtube, and blogs are also popular mediums for communication. twitter is a social networking and micro blogging site that allows users to post and read short messages, “tweets”, of up to 140 characters. these messages are posted to the author‟s website and sent out to subscribers. twitter has over 175 million users and over 95 million tweets are written per day (37). users are on average between the ages of 18-55 and are equally male and female (37). youtube is the world‟s most popular online video community and allows users to share original-created videos. there are over 3 million youtube subscribers and over 2 billion videos a day are watched (39). over 100 million blogs are estimated to exist with the average user being between 21-35 years old with an equal balance of males and females (36). popular online social media sites are platforms that can potentially be used to reach out to large numbers of people to deliver health education and support. however, evidence of how popular online social media and networking sites (i.e. facebook, myspace, habbo, twitter, youtube, etc.) can be used as a milieu for delivering chronic illness education and support is lacking. in addition, we do not know how well we can leverage these mediums to reach minority populations where diabetes in particular is especially prevalent. by understanding the online health-seeking behaviors of people with diabetes and their use of online social media, in particular online social networking, we may be able to understand how to reach people to deliver diabetes education and promote social support through these new media. the purpose of this study was to examine the online health-seeking behaviors of people with a chronic illness. the researchers asked the following questions: (1) what are the online social http://ojphi.org health information seeking and social media use on the internet among people with diabetes 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 media use behaviors of people with diabetes? (2) are people with diabetes willing to use online social media to discuss their health status and health information? (3) are these mediums appropriate for reaching minority and rural populations? methods design and sample this study used a cross-sectional survey design with a convenience sample of people with diabetes (n=57) recruited from the sub-urban and rural southeastern, us between june and october 2009. flyers were placed in primary care clinics and libraries across two counties. participants were asked to complete an online web-based survey tool (surveymonkey.com llc, palo alto, ca). due to the length of the web address url, a shortened url was created through tinyurl.com and placed on the flyers and online classified ads. flyers were posted on bulletin boards near computer stations to help maximize recruitment. flyers were reposted each month to replenish tear off tags and to comply with the 30 day flyer limit at many locations. in addition, on two separate occasions, an online classified advertisement was placed on a free online local classified service. participants who responded to the online advertisement clicked a link that sent them directly to the online survey. eligibility criteria were aged 21 and over, diagnosis of type 2 diabetes, able to read and write in english, and able to give consent. at the end of the survey, participants entered their name and mailing address to receive a $10 compensation for completing the survey. the duke university medical center irb approved the study protocol. measures participants met the inclusion criteria by completing a demographic survey that determined eligibility and proceeded to the second section of the survey. the second part of the survey asked for information about participants‟ diabetes, and their internet and online social networking use. questions related to internet use, types of health information sought online, and online health seeking behaviors were adapted (23). the author developed specific questions about online social networking, youtube, and twitter use. these questions asked about frequency of use and willingness to use these venues to discuss health information. analytic strategy descriptive statistics and correlation analyses were using jmp (sas inc. cary, nc) were employed to describe the sample, to determine access to health information online, willingness to discuss health information online, use of online social media and their correlations by race. results fifty-seven participants completed the online questionnaire. the majority of the sample was female (75.4%) with nearly half identifying as white non-hispanic; the respondents reported membership in a wide range of race/ethnic groups (see table 1). participants were highly educated and the majority had used the internet for 4 years or more. the mean time of diagnosis with diabetes was 7 years. http://ojphi.org health information seeking and social media use on the internet among people with diabetes 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 the majority of participants used the internet to search for health information (see table 2). there was a non-significant correlation between searching health information online and race (df = 1, r = .2, p > .1). this indicates that health information seeking online was sought equally among participants regardless of race. of the participants (n=8) who do not seek health information online, half (n=4) indicated they are not comfortable seeking health information online. among the participants that seek health information online, 36 (78.5%) indicated that the health information they found online has changed the way they think about health. a variety of diabetes-related information is sought online. many participants reported using popular online social networking sites such as myspace or facebook, accessing them frequently two or more times per week. there was a non-significant correlation between using a popular online social networking site and race (df = 1, r = -.13, p > .1). this indicates that popular online social networking sites were used regardless of race. the majority of participants read online blogs. approximately half of participants watched youtube and only a few used twitter. many participants would be willing to discuss health information online in chat rooms, discussion groups, or online support groups (see table 3). there was a non-significant correlation between willing to discuss health information online and being a person of color (df = 1, r = -.1, p > .1). this indicates that people were willing to discuss health information online regardless of race. table 1. demographics (n=57) female 43 (75.4%) race/ethnicity white non-hispanic 28 (48.3%) african american 23 (39.7%) hispanic 2 (3.4%) asian-pacific islander 3 (5.2%) native american 4 (6.9%) other 1 (1.7%) education less then high school 3 (5.2%) high school/ged 4 (6.9%) some college 13 (22.4%) associates 15 (25.9%) bachelors 15 (25.9%) masters 7 (12.1%) doctoral/professional 1 (1.7%) length of internet use < 6 months 2 (3.5%) 6-12 months 1 (1.8%) 1-3 years 6 (10.5%) 4-6 years 12 (21.1%) 6-10 years 15 (26.3%) > 10 years 21 (36.8%) table 2. health information sought online http://ojphi.org health information seeking and social media use on the internet among people with diabetes 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 health information sought online (n=57) all participants 86% white non-hispanic 92% african american 78% native american 100% information about diabetes is sought online 82.1% diabetes information obtained from the internet general information 69.9% treatment options 60.9% ways to cope 54.3% alternative or complementary therapies 28.3% nutrition 69.9% journal articles 23.9% chat rooms, discussion groups, or online support groups 19.6% financial assistance 19.6% table 3. online social media created a profile online that others can see (i.e. myspace, facebook, or linkedin.com) all participants 59.6% white (non-hispanic) 52% african american 67% native american 100% frequency of visiting online social networking sites daily 41.9% almost daily 11.6% 5-6 time per week 4.7% 2-4 times per week 16.3% 1 or less times per week 25.6% frequency of reading online blogs daily 15.7% almost daily 5.9% 5-6 time per week 3.9% 2-4 times per week 11.8% 1 or less times per week 27.5% frequency of watching youtube daily 13.7% almost daily 3.9% 5-6 time per week 3.9% 2-4 times per week 29.4% 1 or less times per week 23.5% never 25.5% http://ojphi.org health information seeking and social media use on the internet among people with diabetes 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 frequency of twitter use daily 2% almost daily 2% 5-6 time per week 2% 2-4 times per week 2% 1 or less times per week 9.8% never 82.4% used an online patient portal to access medical records and health information 19.2% willing to discuss health information online in chat rooms, discussion groups or online support groups all participants 65.4% white (non-hispanic) 60% african american 67% native american 100% discussion these results suggest that a significant percentage of people with diabetes seek health information online and in particular information about diabetes. this is consistent with the literature that states that over 61% of american adults seek health information online (19). the majority of participants from this study frequent popular online social networking sites (i.e., facebook, myspace, etc.) and would be willing to discuss health information on these venues. approximately half of the survey respondents were non-white with no significant difference between race in searching for health information online, utilizing popular online social networks, and willingness to discuss health information online. in addition, participants were recruited from a rural and suburban location in the southeastern, us. this may indicate that using these online venues could reach diverse and non-urban populations. these results compliment findings that people with diabetes use online resources and diabetes specific online social networks such as diabetesfriends.net with over 1,000 members (13) and tudiabetes.org with over 16,000 members (11). other online social networking sites include dlife (27), diabetessisters (2),and the diabetesoc (7). a search on facebook revealed over 500 existing diabetes related groups such as the heart of diabetes (15) and talk diabetes (16). web-based venues can be beneficial in promoting positive health behavior change (38) and serve as a setting to deliver and improve diabetes social support (3, 35). our results suggest that online social media, and in particular popular online social network sites (i.e., facebook, myspace, etc.), may be an appropriate way to reach people to deliver diabetes education and to implement social support networking. though diabetes specific online social networking sites already exist, they do not contain the immense number of subscribers, as do the mainstream popular online social networking sites such as facebook. limitations http://ojphi.org health information seeking and social media use on the internet among people with diabetes 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 the limitations of this pilot study are that this study only represents participants from one urban setting in the southeastern us and is not representative of the us general population. this study has a small sample size. recruiting from the online classified service craig‟s list may have skewed the sample to more computer literate participants. implications for future research further research is needed to understand how we can make use of these popular online social networking sites to reach people directly where they are and utilize features of these sites for delivering diabetes education and support. specifically further investigation of how to engage people to join diabetes social networks within popular online social networks. there are many features of these sites that may serve as valuable tools for communication for social support and education. these communication tools include private or public messages, instant chat features, news feeds, blogging, fan groups, and video and photo sharing. in particular, these sites are imbedded with applications that can potentially deliver and generate tailored health information and outreach to people through analysis of information on individuals‟ social networking page. as a pedagogical platform, interactive applications can be created that deliver diabetes and social support education. these can exist in forms such as surveys, tests or games. needed are randomized controlled interventions that evaluate the mechanisms of these sites, their impact on physiological and psychological diabetes outcomes, and how nurses can be integrated to serve as deliverers or moderators of care. additionally, researchers must carefully take into account the issues of privacy and security with regard to health information and consider the implications of the hipaa privacy rule on social networking websites. consideration to protect people‟s health information and how to prevent a false sense of privacy is needed (21). conclusion social networks can impact behavior change (10). health improvements in one person may spread to another (christakis & fowler, 2008), through a “viral” process. popular online social networks may have a similar impact and have been shown to change perceptions of social support (3). with the advent of web 2.0, internet users are now able to dynamically communicate with each other through video, wikis, blogs, virtual environments, and social networking technologies. these in many ways allow similar social networking behaviors to exist online as they do in real life with the added advantage of being able to easily connect to anyone in the world. by integrating aspects of the health care delivery system into these venues, health care providers may be able to help people become increasingly involved in their care leading to informed and activated patients. this in turn could potentially lead to improved health outcomes. with over 100 million active users in the us on facebook alone (14),popular online social networks and online social media have the potential to serve as important platforms for nursing and public health interventions and to reach diverse populations (24). references http://ojphi.org health information seeking and social media use on the internet among people with diabetes 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 1, 2011 1. american diabetes association. type 2 diabetes complications. retrieved may 11, 2009, from http://www.diabetes.org/type-2-diabetes/complications.jsp 2. barnes b. diabetes sisters. retrieved june 25, 2010, from, 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editorial: ojphi volume 4 number 2 (2012) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 editorial: ojphi vol 4, no 2 (2012) edward mensah, phd 1 1 university of illinois at chicago, school of public health, chicago, il usa recent world health organization data show that while cigarette consumption has been decreasing in the advanced countries over the last decade the worldwide number of smokers is still increasing. the rate of tobacco use has not declined among the youth and young adults in the lower to middle income countries mainly due to aggressive tobacco industry marketing practices. smoking is a major risk factor for cancer. if the current trend is not curtailed smoking is expected to contribute to 8 million deaths worldwide by 2030. in order to design effective smoking cessation strategies it is important to provide policy makers and epidemiologists with quality and reliable data. portable handheld computers and electronic data management systems have been used to develop and implement surveys in advanced countries. these mobile health applications have not been widely used in developing countries due to infrastructural and financial constraints. in a paper titled ‘electronic data collection and management system for global adult tobacco survey” pujari, et. al., describe the development and implementation of a global adult tobacco survey aimed at collecting data to monitor and measure global adult tobacco use. the survey was implemented in 14 countries using an electronic data collection system. the authors demonstrate than an electronic data collection and management system can be used effectively in conducting representative surveys in lowto middle-income countries. evidence-based research has demonstrated that obesity increases the risk of health conditions such as coronary heart disease, type 2 diabetes, hypertension, dyslipidemia, stroke, and certain types of cancer. overweight and obese workers impose financial burden on employers through reduced productivity, absenteeism, and increased health care costs. effective health promotion program can improve the health of workers and reduce health care costs. in a paper titled ‘metabolic risk factor reduction through a worksite health campaign’ daubert et. al., demonstrate the effectiveness of a web-based, heart-health educational program in reducing risk factors linked to overweight and obesity among workers. while school-based computer-assisted hiv/aids intervention models are known to be effective in improving the hiv knowledge, skills, and attitudes of the youth, such models are ineffective in improving the sexual behaviors of the youth. in a paper titled ‘contextual mediators influencing the effectiveness of behavioural change interventions’ angella musiimenta demonstrated that the incorporation of contextual mediators such as relationship characteristics, familial influences, peer factors, gender-based social norms, economic factors and religious http://ojphi.org/ editorial: ojphi volume 4 number 2 (2012) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 beliefs in computer-assisted hiv interventions could improve the sexual behaviours of the youth in uganda. an integrated laboratory response system capable of responding to threats in a timely manner is a requirement for effective control of public health emergencies. the global laboratory directory mapping tool (gladmap) is a technology that facilitates connectivity between laboratory networks and thereby makes it easier for the laboratories to provide a coordinated response to pandemics. mukhi et. al., evaluated the effectiveness of the gladmap search tool, the descriptive content of the networks and the profiles of the laboratories (function, location, expertise) in the gladmap database. the evaluation revealed a very low participation rate and information sharing in gladmap. the authors suggest that the addition of an optional functionality to protect the privacy and security of health information would improve the participation of laboratories in gladmap. there is an information and communication technology (ict) revolution currently going on in usa and advanced countries. the idea is to deliver quality care cost-effectively, expand access to care, improve care coordination among providers, and address the escalating cost of care. the health ict revolution is yet to penetrate developing countries due to lack of capital, expertise, and other complementary infrastructures. emmanuel achampong reviews the state of ict investment and adoption in the healthcare sector in ghana in comparison with other african countries. his research also identifies some of the major constraints to health ict adoption and makes suggestions for improving the adoption rates. the meaningful use requirements of the health information technology for economic and clinical health act of 2009 provide public health agencies the opportunity to be integrated into the healthcare sector. to participate effectively in the health information exchanges public health departments must be capable of exchanging data and information among themselves and with other providers. in a paper titled “quality and integration of public health information systems: a systematic review” vest et.al. review the barriers and enablers of effective utilization of public health information systems, focusing on immunization information systems and vital records information systems. the researchers conclude that the problems presented by public health agencies’ reliance on a large number of autonomous data providers using different technologies must be addressed in order to improve data sharing among these agencies. an important objective of this journal is to provide a forum for master’s of public health informatics students to share their capstone reports with the general health informatics community. in this issue osama chaundhary, a recent mph graduate of the public health informatics program at the school of public health, the university of illinois at chicago, explores the foundations for integrating syndromic surveillance development into the implementation of health information exchange at yolo county health department, california. the report provides useful insights for other financially constrained health departments striving to implement health information exchanges and syndromic surveillance systems. http://ojphi.org/ editorial: ojphi volume 4 number 2 (2012) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 edward mensah, phd editor-in-chief online journal of public health informatics 1603 w taylor st, rm 757 chicago. il. 60612 email: dehasnem@uic.edu office: (312) 996-3001 http://ojphi.org/ mailto:dehasnem@uic.edu 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the public health community platform: shared resources for enterprise solutions marcus rennick*1, scott gordon1, monica huang1, anita samuel1, paula soper1 and laura conn2 1informatics, association of state and territorial health officials, arlington, va, usa; 2centers for disease control and prevention, atlanta, ga, usa objective to update the public health practice community on the continuing development of the public health community platform (phcp). introduction public health is at a precipice of increasing demand for the consumption and analysis of large amounts of disparate data, the centralization of local and state it offices, and the compartmentalization of programmatic technology solutions. public health informatics needs differ across programmatic areas, but may have commonalities across jurisdictions. initial development of the phcp was launched with the goal of providing a shared infrastructure for state and local jurisdictions enabling the development of interoperable systems and distributed analytical methods with common sources of data. the phcp is being designed to leverage recent successes with cloud-based technology in public health. success of the phcp is dependent on the involvement of state and local public health jurisdictions in the transparent development and future direction of the platform. equally critical to success is the selection of appropriate technology, consideration of various governance structures, and full understanding of the legal implications of a shared platform model. methods the association of state and territorial health officials (astho), under a cooperative agreement with the centers for disease control and prevention (cdc), is coordinating an ongoing national effort to create the framework of the phcp. a steering committee (an interim advisory board) was initially formed to provide an overarching advisory role in designing various assessments and identifying use cases for the proposed phcp. the steering committee met regularly, both in-person and virtually, throughout the initial phase of development to refine the direction of the phcp and help guide the work of the assessment teams (technical, governance, communication, and use cases). results technical assessment: the mitlincoln laboratory (mit-ll) provided a roadmap for development, an assessment of existing technology, and an expansive analysis of risk and mitigation for various modes of data sharing. the suggested roadmap focused on the premise that users adopt a system that serves an immediate need and improves workflow efficiency. this immediate need necessitates seeding any platform development with explicit functionality to benefit the individual user. sharing is secondary. governance and sustainability: astho contracted with clinovations to identify several options for ownership, governance, and sustainability. each ownership option (federally owned, incubated by a non-profit, or separately incorporated) can institute a form of governance with varying levels of control and community driven consensus. advantages and disadvantages of several funding models were also enumerated by the contractor. communication: porter novelli, a communications firm, was contracted to provide branding and marketing materials for the phcp. generally, porter novelli worked with the steering committee to formulate how to express the concept of the phcp. use cases: two use cases were identified by the steering committee: an elr reportable disease decision trigger (developed by aphl) and a clinical decision support for immunization forecasting (conducted by phii). phii also provided a gap analysis between available tools for community health assessments and current needs. conclusions the assessment teams provided the necessary framework to build a successful community-owned platform for innovative and collaborative public health practice. the phcp has the potential to provide a single source for public health decision support solutions, reducing the number of redundant systems. additionally, the platform could be instrumental in providing the implementation space for national pilot informatics projects. the next year of development will involve implementing steering committee decisions, prioritizing use cases that can be advanced as pilots, and building the technical architecture and community to support those pilots. figure 1. phcp development roadmap keywords platform; informatics; enterprise; solutions; interoperability acknowledgments we would like to acknowledge the tremendous amount of effort from our steering committee members. *marcus rennick e-mail: mrennick@astho.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e2, 2015 5128-38121-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 38 (page number not for citation purposes) isds 2013 conference abstracts modeling of hepatitis b epidemic process by the risk factors analysis tetyana chumachenko*1 and olga radyvonenko2 1epidemiology department, kharkiv national medical university, kharkiv, ukraine; 2national aerospace university “kharkiv aviation institute”, kharkiv, ukraine � �� �� �� � � �� �� �� � objective ��������� � ���������� ���������������������� ��������������������� ������������� ������ ���������������������������������� introduction �������� ������������������� ������������ ����� ����������� 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1947-2579 * http://ojphi.org * 6(1):e51, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 76 isds 2014 conference abstracts modelling based estimates for severe pneumonia and pneumonia deaths in indian states habib h. farooqui*1, david heymann2 and sanjay zodpey1 1public health foundation of india, gurgaon, india; 2london school of hygiene and tropical medicine, london, united kingdom objective this presentation highlights the use of mathematical model to estimate burden of disease in absence surveillance data. we estimated the burden of severe pneumonia, pneumococcal pneumonia and pneumonia deaths in indian states using a mathematical model through application of vaccine probe methodology and attributable fraction. introduction the child health epidemiology reference group (cherg) has predicted around 43 million pneumonia cases in india. it is recognized that for huge nation like india, which accounts for 23% of global pneumonia burden, the national estimates may hide regional disparities(1). in this context, we have generated indian state specific burden of severe pneumonia, pneumococcal pneumonia and pneumonia deaths through use of mathematical model. methods we developed a microsoft excel-based model to estimate number of new episodes of severe pneumonia for each indian state. this model is based on the epidemiological concept of potential impact fraction(1) as follows: n e/cy = (pop <1yrs) x (incind) x {1 + (rf=1 ->n) [(prevrfn prevrfnind) x (rrrfn 1)]}, where ne/cy is the number of new episodes of clinical pneumonia per year in selected indian state, pop < 1yrs is the population of children less than 1 years in each state, incind is the estimated incidence of severe clinical pneumonia at all india level, prevrfn is the prevalence of exposure to n-th risk factor among under-fives in the indian state of interest, prevrfnind is the prevalence of exposure to n-th risk factor among under-fives at all india level and rrrfn is the relative risk for developing clinical pneumonia associated with the n-th risk factor. we then estimated the number of pneumococcal pneumonia cases by applying the vaccine probe methodology to an existing philippines trial. the study reported 19.8% efficacy against radiologic pneumonia ((95% ci: -8.8, 40.8) in children age less than 1 year(2). the 11 serotypes contained in the vaccine were estimated to account for 65.33% of ipd in india. with vaccine efficacy against vaccinetype pneumococcal disease of 83%(3), we observed that 23.8% of radiologic pneumonia cases are due to vaccine serotypes and 36.51% due to any pneumococcal serotypes. the mortality rate in hospitalized cases of severe pneumonia (1.95%) and pneumococcal pneumonia (16.7%) in children age less than 5 years was estimated from multicentric studies. results we estimated that in 2010, 3.57 million severe pneumonia cases and 0.35 million all cause pneumonia deaths occurred in children age less than 5 years in india. three states requires specific mention, uttar pradesh contributed (24% of severe pneumonia cases and 26% of pneumonia deaths), bihar (16% cases, 22% deaths) and mp (9% cases, 12% deaths) to the national figures. they were also top contributors pneumococcal pneumonia burden, i.e. up (1,33,160 cases, 27,285 deaths), bihar (91,574 cases, 23,202 deaths) and mp (52,247 cases, 13,043 deaths). the total numbers of severe pneumococcal pneumonia cases and deaths in 2010 were estimated to be 0.56 million and 95 thousand respectively. the contribution of pneumococcal pneumonia was 15.8% to all cause pneumonia cases and 20.8% to all cause pneumonia deaths. the in age specific analysis, we observed that pneumonia related morbidity was highest in 0-1 year age group (51 %) followed by 1-2 age group (22%), 2-3 years (11%), 3-4 years (9%) and 4-5 years (7%). conclusions to summarize, the state-specific estimates are key for identification of states with high burden of pneumonia related morbidity and mortality and to target interventions for pneumonia prevention and control especially pneumococcal conjugate vaccines to achieve maximum impact. keywords mathematical modelling; pneumonia; vaccine probe; pneumococcal conjugate vaccine; pneumococcal pneumonia acknowledgments this work was supported by a wellcome trust capacity strengthening strategic award to the public health foundation of india and a consortium of uk universities. references 1. rudan i, boschi-pinto c, biloglav z, mulholland k, campbell h. epidemiology and etiology of childhood pneumonia. bull world health organ. 2008;86(5):408-16. 2. lucero mg, nohynek h, williams g, tallo v, simoes ea, lupisan s, et al. efficacy of an 11-valent pneumococcal conjugate vaccine against radiologically confirmed pneumonia among children less than 2 years of age in the philippines: a randomized, double-blind, placebo-controlled trial. the pediatric infectious disease journal. 2009;28(6):455-62. 3. klugman kp, madhi sa, huebner re, kohberger r, mbelle n, pierce n, et al. a trial of a 9-valent pneumococcal conjugate vaccine in children with and those without hiv infection. the new england journal of medicine. 2003;349(14):1341-8. *habib h. farooqui e-mail: drhabibhasan@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e23, 2015 leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 leveraging cloud computing to address public health disparities: an analysis of the sphps arash jalali, 1,3,4 olusegun a. olabode, 1,2 and christopher m. bell 3,4 1 university of illinois at chicago, school of public health 2 u. s. department of veterans affairs and the department of defense (dod), captain james a. lovell federal health care center 3 university of illinois hospital & health sciences system 4 university of illinois at chicago, biomedical and health information sciences abstract as the use of certified electronic health record technology (cehrt) has continued to gain prominence in hospitals and physician practices, public health agencies and health professionals have the ability to access health data through health information exchanges (hie). with such knowledge health providers are well positioned to positively affect population health, and enhance health status or quality-of-life outcomes in at-risk populations. through big data analytics, predictive analytics and cloud computing, public health agencies have the opportunity to observe emerging public health threats in real-time and provide more effective interventions addressing health disparities in our communities. the smarter public health prevention system (sphps) provides real-time reporting of potential public health threats to public health leaders through the use of a simple and efficient dashboard and links people with needed personal health services through mobile platforms for smartphones and tablets to promote and encourage healthy behaviors in our communities. the purpose of this working paper is to evaluate how a secure virtual private cloud (vpc) solution could facilitate the implementation of the sphps in order to address public health disparities. keywords: public health informatics, smarter public health prevention system, cloud computing, virtual private cloud, big data analytics, health disparities background with the emergence of big data analytics, predictive analytics, and cloud computing, an information technology revolution is occurring in the field of public health informatics (phi). phi “is the science of applying information-age technology to serve the specialized needs of public health,” 1 and is “the systematic application of information and computer science and technology to public health practice, research, and learning.” 2 phi utilizes an interdisciplinary approach and methods from various disciplines, including: information science; computer science; management; organizational theory; psychology; communications; political science; law; and public health fields, to make information-driven decisions and present newknowledge. 1, 3, 4 a comprehensive public health program should include a “broad social enterprise http://ojphi.org/ leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 that seeks to extend the benefits of current knowledge in ways that will have the maximum impact on the health status of a population.” 3 a comprehensive public health program should also include strategic agility. strategic agility is “the ability to capitalize on opportunities and dodge threats with speed and assurance.” 5 most cloud architectures are defined as “public” or “private.” a vpc is neither and has the characteristics of both:  “public” clouds are open to the general internet and are available to the public. these clouds are suited for services designed for wide usage by many people, and security in these clouds is usually dependent on the application and web servers installed in the cloud space, vs. the cloud space itself. in contrast, a vpc places security walls around the cloud space, restricting access to specific users, roles or machines, separate and predicate to the servers in the cloud address space.  “private” clouds are not open to the general internet, and are usually available only to private users within the firewalls of an organization. generally they refer to infrastructure already internal to an organization, which is organized and managed as a cloud. this is done to gain efficiencies similar to engaging external cloud vendors, yet keep control and security with familiar boundaries. in contrast, a vpc is cloud space outside of the firewall of an organization, but is often directly connected to internal networks via network connections. the field of phi is also affected by external rules and regulations changes occurring at the federal level. 6 the health information technology for economic and clinical health act (hitech act) was enacted as part of the of the american recovery and reinvestment act of 2009 (arra) to promote health information technology (hit) including health information exchanges (hies) and support the electronic use and exchange of health information, in addition to improving health care quality, safety, and efficiency. 7 arra establishes hit adoption as a national priority from patient-centric care to population wide health initiatives, which has the potential to improve population and public health outcomes. 8, 9 it is the goal of arra to promote electronic health record (ehr) adoption through meaningful use of cehrt which fundamentally transforms the way public health professionals “monitor health threats and respond to injury, disease, and disability among the population.” 8 one of the key concepts of ehr meaningful use is to “improve the health of the population” and public health. 10 according to the national priorities partnership’s national priorities and goals: population health 10 “envision communities that foster health and wellness as well as national, state, and local systems of care fully invested in the prevention of disease, injury, and disability—reliable, effective, and proactive in helping all people reduce the risk and burden of disease.” additionally, the mission of national prevention council is to "increase the number of americans who are healthy at every stage of life.” 11 the national prevention strategy will improve american health through four pillars which are “create healthy and safe communities, expand clinical and community-based preventive services, empower people to make healthy choices, and eliminate health disparities.” 11 http://ojphi.org/ leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the field of phi is also affected by rapid speed of change of the globalized social enterprise and increasing technological innovations. 5 internet searches like google’s flu trends detect outbreaks of influenza in accelerated fashion. 12, 13 conventional public health influenza surveillance systems are lacking behind google flu trends. 12, 13 without the adoption of cloud computing, public health programs cannot aggregate the data they need in other to get an actual picture of population health. methodology public health professionals will need to align public health information technology (phit) with national polices including the national prevention council’s national priorities strategy. this alignment requires a more agile hardware and software computing infrastructure to support “stronger partnerships and coordination of care between the public health and health care delivery systems.” 6, 10, 11 a cloud computing infrastructure will help public health professionals to access real-time or near real-time data to respond to emerging public health threats including improving population health and elimination of health disparities in our communities. 6, 10, 11 what is a virtual private cloud (vpc)? according to mcdonald and kapashi, vpc is a cloud space dedicated to a single project/entity. it usually implies that everything from “network-in” is unique to a specific entity or “tenant.” this means that many resources associated with cloud deployments are defined, dedicated and are not shared with other cloud consumers/tenants, including:  virtual machines  databases  application servers  repositories  directories  network address spaces figure 1. vpc architecture for public health agencies http://ojphi.org/ leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 it is essentially the opposite of the most common type of cloud infrastructures, called “multitenant.” in a multi-tenant architecture, multiple consumers or “tenants” share the same resources, which can lead to increased security risks and data isolation issues. in short, a vpc has isolation and security characteristics similar to private clouds, but retains the flexibility of usage and connection common to public clouds. what are the advantages of a vpc? the vpc architecture provides several significant advantages: security: the architecture is well suited to meet the compliance, privacy and security requirements imposed on health care organizations to safeguard individually identifiable health information (iihi) by the health insurance portability and accountability act of 1996 (hipaa) as modified by the hitech act and its implementing regulations. isolation: data within vpcs are separated from other vpcs at the network layer. this eliminates the opportunity for data or information from one cloud infrastructure mingling with those of another cloud infrastructure. flexibility: the vpc architecture would allow for ehr exchange among physician clinics, hospitals, electronic clearinghouses, insurance providers and promote potential researchers at universities to collaborate on the data for innovation to improve health care while reducing medical costs. will vpc transform phi? public health agencies are a hub for population health big data. as eligible professionals and hospitals adopt cehrt and climb the meaningful use ladder, public health agencies are required to increase their technical capacities to send and receive health data securely. 7, 9 utilizing the captured data from various sources, including hies, to create a population health record (pophr) for serving communities, phi will be the trusted source of pophr. 14 with the introduction of vpc, as shown in figure 1, public health agencies can remove information technology obstacles including, hardware and software interoperability, and network or bandwidth capacity barriers. by moving all computing resources into a vpc environment, public health agencies will have the capability to analyze the increasing data streams. converted data stored in the vpc environment allows for the extraction of real-time knowledge. the sphps converts a patient centric view of health data into population centric view of health data, as shown in figure 2. figure 2. sphps centric views http://ojphi.org/ leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the case use scenario perspective as the implementation of the sphps allows for the exchange of data, illustrating the sphps through the lens of a use case scenario can show how the data will be shared among various stakeholders. the sphps can estimate “population prevalence of specific conditions” by analyzing various data sources, which includes cehrt. 14 figure 3 shows how a patient’s interaction with a provider is translated into data that is then stored in the patient’s ehr. next, the patient ehr data is captured in a cehrt; health information captured within cehrt is securely exchanged among providers through beacon communities, regional extension centers (rec), state health information exchanges (state hie), and etc. sphps accesses the patientcentric cehrt data and removes the iihi information to create a pophr. moreover, data from various sources like public health agencies, national center for health statistics (nchs), and data.gov, is added to create what we call big data contextual awareness within pophr. 14, 15 finally, the pophr information is made accessible through a dashboard and mobile platforms. public health professional can utilize the pophr to create community-based interventions, and a clinicians can use this data to reduce contextual error, which are created when “a physician does not identify elements of a patient's environment or behavior, such as access to care, that must be addressed to appropriately plan care.” 14, 15 conclusion http://ojphi.org/ leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 cloud computing solutions can bring tremendous benefits to public health agencies and health care organizations as well as help them enhance health status or quality-of-life outcomes in atrisk populations, eliminate health disparities, and reduce overall health care costs. 3, 10, 11 vpc offers a flexible, isolated, and secure environment for public health informaticists to gather patient centric data that can be used to reduce disparities in population health. cloud computing can foster sharing information between public health and health care organizations, which are stored across disparate information systems, for real-time analytics and shared decision making. it can also free up health care staff to attend to more critical tasks in an efficient, secure, and cost-effective manner. sphps will securely incorporate population centric view and patient centric view to form a cloud-knowledge discovery environment. vpc will solve the hipaa compliance issues for public health agencies and health care organizations to drive knowledge based decisions. sphps can be a hub of pophr for reducing health disparities in our communities, promotion and encouragement of healthy behaviors in our communities, prevention of epidemics and spread of disease, and primary source of secondary usage of health data in the new era of hies. 3, 14, 16 thus, the over-arching goal of sphps extends the current phi knowledge in ways that will have the maximum impact on population health. 3 we can achieve this goal by accelerating our knowledge discovery and preventive strategies through cloud computing. sphps offers the possibility of promoting healthy and safe communities. 11 sphps can eliminate health disparities while reducing escalating health care costs and services. acknowledgments the authors would like to thank john mcdonald, chief executive officer, cloudone and vishal kapashi, chief architectural officer, cloudone. corresponding author arash jalali, mph phi jalali@uic.edu references 1. friede a, blum h, mcdonald m. public-health informatics how information-age technology can strengthen public-health. annu rev public health. 1995;16:239-52. 2. yasnoff wa, o'carroll pw, koo d, linkins rw, kilbourne em. public health informatics: improving and transforming public health in the information age j public health manag pract. 2000 nov;6(6):67-75. 3. turnock, bj. public health : what it is and how it works. 5th ed. burlington, ma: jones & bartlett learning; 2012 http://ojphi.org/ leveraging cloud computing to address public health disparities: an analysis of the sphps online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 4. o’carroll p. introduction to public health informatics. o’carroll pw, yasnoff wa, ward me, ripp lh, martin el, editors. springer new york; 2003. 3 p doi: 10.1007/0-387-227458_1 5. kotter jp. accelerate! (cover story). harv bus rev. 2012 11;90(11):43-58. available from: http://search.ebscohost.com/login.aspx?direct=true&db=buh&an=82532450&site=ehostlive 6. sittig df, singh h. a new sociotechnical model for studying health information technology in complex adaptive healthcare systems. qual saf health care. 2010 oct;19:i68-74. 7. american recovery and reinvestment act of 2009, h.r. 1, 111th cong. 8. burke t. the health information technology provisions in the american recovery and reinvestment act of 2009: implications for public health policy and practice. public health rep. 2010 jan-feb 2010;125(1) 9. centers for disease control and prevention. cdc: meaningful use stage 1: introduction. available from: http://www.cdc.gov/ehrmeaningfuluse/introduction.html 10. national priorities partnership. national priorities and goals: aligning our efforts to transform america’s healthcare. washington, dc: national quality forum; 2008. 11. national prevention council, national prevention strategy, washington, dc: u.s. department of health and human services, office of the surgeon general, 2011. 12. polgreen pm, chen y, pennock dm, nelson fd. using internet searches for influenza surveillance. clin infect dis. 2008 dec 1;47(11):1443-8. 13. carneiro ha, mylonakis e. google trends: a web-based tool for real-time surveillance of disease outbreaks. clin infect dis. 2009 nov 15;49(10):1557-64. 14. friedman dj, parrish,r.gibson,,ii. the population health record: concepts, definition, design, and implementation. j am med inf assoc. 2010 jul;17(4):359-66. 15. schwartz a, weiner sj, harris ib, binns-calvey a. an educational intervention for contextualizing patient care and medical students' abilities to probe for contextual issues in simulated patients jama. 2010 sep 15;304(11):1191-7. 16. safran c, bloomrosen m, hammond we, labkoff s, markel-fox s, tanc pc, detmer de. toward a national framework for the secondary use of health data: an american medical informatics association white paper. j am med inf assoc. 2007 jan-feb;14(1):1-9. http://ojphi.org/ ojphi-06-e85.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 77 (page number not for citation purposes) isds 2013 conference abstracts redefining syndromic surveillance amy ising*1, larissa may2 and charlie ishikawa3 1emergency medicine, unc chapel hill, chapel hill, nc, usa; 2the george washington university department of emergency medicine, washington, dc, usa; 3international society for disease surveillance, boston, ma, usa � �� �� �� � � �� �� �� � objective �������� ������ ���������� ��� ������������� ��� ������������� ������� �� ������ �� � ������� �������� ���� ��� ����� � ��������� ����� ���� ��� ��� ������ � ���� �� ��� ������� ���������������� ������� �� ������ ����� ����� ������� ��� ��� ����������������� �� ����� ������ �� ������ �������� � introduction �� ��� � ������� ������� �� ������ ������ � �� ����� �� ���� �� ������� ���� ������ �� ������� ��������������� �������� �� �� � � � ������� ��� ����� � �������� � �������� ����������� �� ���� ��� ���� ������ �� � ��������� �������������� �� ������� ������ �� �������� ������� �� ������ ��� � ����� � ���� ������ ������� ����� ����!"� ��� ������ ���� ����� ��� � ����� ������ �������������� ������� �� ��� ���� �� ����� �������� ����� �#���� ���� ��������������� ������� �� ������� �� ������ �� ����� �� �� ������ ����������� ��$��%��&� ����� �� � �� ���� ������������� ������� �� ������ ���������� ��������� ��� ���� ��������� ��� ���� ��� �� ������ ��� ������� �������� �� � ������ '��(��� ��������� �) ������ ��*� � ��� �+����� �� ��������� ������� �� ������ ����� ��������� � ������������ � ���������� ���� � � ������ ��� ������ ������� ����� ���������� �� ������ �� ��� � description (��������� � ���� ��� ��������������� ������� ������������ � ����� � �� ������� ����� ��� � ��������������� ������� �� ������ ��,��� � �������������� ��� �� ��� ������������������ ��������� ������������� � ��������� � �� ���� � ��������������� �� � ����� � �� ������ audience engagement �� ������������������������ �������������������� � � �������������� ��� ������� �� ������ ������� �������� ���� ��� � ��� �� ���� ���� � ������ � ���� �������������������� ����& ��� ���� �������� �� ��� �� �� ����� � � �����������,������� ���������������� ������� ��� ���������� � ��������� ������� ��� �� ��� ��� � ��� ���������� ��, �������� �� � ������� ��� ������ ����� ����������� ������� �� ����������(���� � � ������ �� ������������� ��� � ���� �� ����������� ��� ������ ������ ����� �� ���� ���� ������� �� ������ ��� � ��� ��� �� ��� �� ����� �� ������� ���� ����� ������� �� ������ ��� �� � ����������� �� ��� � � ���� ������ �� � �� � �������� keywords ��� ������� �� ������ .� ���������.�� ������� ��� .�� � � � �� ������ references %��&�/��)����0��1�� ��2��� ���3��/ ���������� ������� �� ������ ��2� �� �������3�� ���������� �4������5 �����+6''.�'��+'�!'� *amy ising e-mail: ising@ad.unc.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e85, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts chikungunya epidemic in the french overseas territories using syndromic surveillance anne fouillet*1, jacques rosine2, vanina bousquet1, sylvie cassadou3, luisiane carvalho4, martine ledrans2, audrey andrieu2, thierry cardoso1 and céline caserioschönemann1 1french institute for public health surveillance, saint maurice, france; 2french institute for public health surveillance, fort-defrance, martinique; 3french institute for public health surveillance, pointe-à-pitre, guadeloupe; 4french institute for public health surveillance, cayenne, french guiana objective description of the temporal pattern of the chikungunya epidemic and the characteristics of patients in the french overseas territories of americas using the french syndromic surveillance system sursaud. introduction in december 2013, an emergence of chikungunya was observed in the french caribbean region. starting on the saint-martin island, the epidemic of chikungunya spread in martinique on december 2013. the first cases were then observed in guadeloupe in december 2013 and in january 2014 in the french guyana [1]. a specific surveillance system has been implemented based on a sentinel general practitioners’ network enabling the estimation of the number of cases clinically suggestive [2]. severity of this arbovirus is assessed using the number of hospitalized cases. the syndromic surveillance system sursaud, based on the daily collection of two complementary morbidity data sources, is also implemented in these territories and has contributed to the surveillance of this outbreak. methods individual data collected in two emergency departments (ed) in guadeloupe and one ed in french guyana are daily transmitted and analyzed by the french institute for public health surveillance (invs). in martinique, the emergency departments are not still included in the syndromic surveillance network but data from the emergency general practitioners’ association sos médecins are daily collected and analysed by invs. in both data sources, individual demographic (age, gender, place of residence), administrative (date of visit) and medical information (chief complaints, medical diagnosis, hospitalization after the consultation) are available. the weekly number of ed attendances with the medical diagnosis coded “a92.0” (icd10) in guadeloupe and french guyana and the weekly number of consultations in the gp’s association sos médecins with a clinical diagnosis of chikungunya in martinique have been compared with the dynamic of the estimated number of cases clinically suggestive provided by the specific surveillance system. the outbreak has also been described by age group and gender. results in guadeloupe and in french guyana, the temporal evolution of the number of attendances with a chikungunya diagnosis is correlated with the evolution of the epidemic followed by the specific surveillance system. in guadeloupe, patients who visited the ed are mainly children aged less than 15. this diagnosis is the third pathology recorded in the ed for this age group from january to august 2014 (after trauma and ent infection). the distribution of patients by age group is different between the two ed. because of the location of the gp’s association sos médecins in a part of martinique, the pattern of the number of consultations is highly correlated with the outbreak followed with the specific surveillance system during the first step of the outbreak but is less correlated when the epidemic spread into the other parts of the island. in this data source, the patients with a chikungunya diagnosis are older (50 years old in average) than patients with another diagnosis (44 years old). conclusions the specific and syndromic surveillance systems ensure a complementary surveillance of chikungunya outbreak. the added values of syndromic surveillance data sources are the reactivity (daily collection) and the availability of individual information enabling description of patients and identification of the most vulnerable population groups. the specific surveillance system based on sentinel gp’s network allows a representative surveillance of cases in the population, enabling an estimation of the number of cases in the population. keywords chikungunya; caribbean area; france; emergency department; gp’s association acknowledgments the authors thank the emergency departments and the association sos médecins for providing data and their contribution to the surveillance. references [1] van bortel w, dorleans f, rosine j, et al. chikungunya outbreak in the caribbean region, december 2013 to march 2014, and the significance for europe. euro surveill.2014;19(13):pii=20759. [2] ledrans m, cassadou s, boucau s, et al. émergence du chikungunya dans les départements français d’amérique : organisation et résultats de la surveillance épidémiologique, avril 2014. bull epidémiol hebd. 2014 (21-22):368-79. *anne fouillet e-mail: a.fouillet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e73, 2015 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka office of health protection, illinois department of public health, chicago, il, usa objective our objective was to measure the incidence and prevalence of intentional violent injury and death using illinois’ syndromic surveillance system. introduction violence is now clearly recognized as a public health problem1. intentional injuries ranked among the top six leading causes of death for illinois residents aged 1-44 in 20132. the illinois department of public health currently collects data on violent injuries and deaths from emergency medical services reports, death certificates, coroner/ medical examiner reports, law enforcement reports, and crime lab reports. however, syndromic surveillance provides near real-time data on violence-related emergency department visits that would increase the timeliness and quality of data available for public health interventions. methods we developed violence syndromes using text and icd-9 codes in the chief complaint and diagnosis code data from illinois emergency departments. these were defined as weapon-related (gunshots and stabbings) and other physical assaults. legal interventions and emotional/psychological abuse were excluded from the analysis. the violence syndromes were used to describe the number and distribution of cases of injuries treated at illinois hospitals in 2014 that were attributable to physical assaults, gunshots, and stabbings. the data was stratified by demographic (gender, race, age group), temporal (since january 2014), and spatial (county, regional cd) characteristics. these results were compared with prior-year data reported through the emergency medical service data system and the national vital statistics system, as an initial estimate of data quality. results the violence query retrieved 13,179 reports for the year 2014. of these reports, whites (60.2%), females (51.8%), and the 25-49 year old age group (33.8%) had the majority of incidents. compared to the syndromic surveillance reports, emergency medical service data system data contained a lower proportion of females (35.4%) and whites (36.5%), and a higher proportion of incidents in northeastern illinois (70.8% vs. 42.0%). there was a similar proportion of 25-49 year olds (36.4%). data from the national vital statistics system indicated a higher incidence of violent death in whites (68.1%) and victims aged 25-49 (43.7%), and lower incidence in females (20.4%), compared to syndromic surveillance reports. conclusions results obtained from the syndromic surveillance system were comparable to the data in emergency medical service and violent death reporting system for demographic and spatial characteristics. it was not possible to differentiate between new visits of violence-related injuries and repeat visits to eds for conditions treated previously, but we assumed the rate of duplicacy was similar across emergency medical service and syndromic surveillance data. we also assumed that morbidity and mortality trends in illinois would be similar, and this assumption may limit the generalizability of our analysis. syndromic surveillance has the potential to provide real-time data to inform timely and data-driven public health responses to violence. keywords violence; syndromic surveillance; data quality references 1. dahlberg l, mercy j. history of violence as a public health issue. ama virtual mentor. 2009 february; 11(2). 2. cdc injury-wisqars. [internet]. atlanta: centers for disease control and prevention; c2015. fatal injury reports, 1999-2013, for national, regional, and states (restricted); 2015 january 2015 [cited 2015 september 2]; [about 2 screens]. available from: http:// webappa.cdc.gov/sasweb/ncipc/datarestriction_inj.html *jennifer vahora e-mail: jennifer.vahora@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e76, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency 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influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a ojphi-06-e56.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 144 (page number not for citation purposes) isds 2013 conference abstracts outcomes of allogeneic stem cell transplant (allo-sct) recipients in the era of newer antifungal agents nishi shah*2, 1, ajay k. nooka1, sagar lonial1, hannah j. khoury1, edmund waller1 and amelia langston1 1winship cancer institute, emory university, atlanta, ga, usa; 2rollins school of public health, emory university, atlanta, ga, usa � �� �� �� � � �� �� �� � introduction 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citation purposes) isds 2013 conference abstracts evaluation of chlamydia case report data: completeness of key variables—united states, 2012 elizabeth torrone*, hillard weinstock, julie garon and robert nelson division of std prevention, centers for disease control and prevention, atlanta, ga, usa � �� �� �� � � �� �� �� � objective ����������� �� ��� ��������������������� ����� ��������������� � ��� ����� ��� �������� ������������������������������ �������������� �������������� ������������������������������� �! introduction � ������"�����#�� ������������������ �����$ � � ��� ������� ��������������� ����� �� � ���� �����������������"����� ������� ��% ��� ������������������ ��������� �������������� ��&������'�����!( )� ���� ���������� ����������� � "�������� � "��������������%�� ����� ����������������������������������"���� ��������� ���������������� ���� ����������"�������������*������"������������������� ���������������� � ��������! methods +�������$���� � �������� 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������������������������������������������ ��� ��������!�c����� ��� $ � ��������������� ���������������������������������� ������� � �� ����������������������������d�������*�� ���������������!�e����% �� �������� �������������� ������������������� �����$�� � ��� ��������� �� ������� ������������ ������ ��*���������#������ ��!�b��� �"� ��������� ���� ������������ ��������!�!"�� ��� ����������������������� ������������ ����� ����� ������� ���������������! keywords ������?������� �� ����� ������?��������� ��������� references !����!�'������������������ �����������%�&������'�����"��� !�99+4� 9����9���� �+� ��4��!��� >�e� �,?8��,>�; % 7! *elizabeth torrone e-mail: igf0@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e96, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts preparing disease surveillance systems for icd10 alimelu jonnagadla*2, wayne loschen2, julia gunn1 and jennifer evans1 1boston public health commission, boston, ma, usa; 2johns hopkins university applied physics laboratory, laurel, md, usa objective to help users seamlessly query and analyze data in disease surveillance systems using both icd9 and icd10 codes during the transition period. additionally, the mappings between icd9 and icd10 codes must be flexible enough to support locally required changes based upon a user’s needs. introduction the compliance date for the icd9-icd10 transition is october 1, 2015. the hospitals have started the icd9-icd10 transition. however, not all data providers will transition the data at the same time. in order to facilitate some coherence to the data during this transition period, user interface and data processing functionalities have been developed in essence to allow usage of both classification systems simultaneously. this capability will allow users to perform icd10based queries across all the hospitals in their system, irrespective of the exact number of hospitals that have completed the icd10 transition. methods the boston public health commission utilizes an essence system to perform disease surveillance in their community. this project enhanced their existing essence system to support icd10based queries. using the centers for medicare & medicaid services (cms) and the centers for disease control and prevention’s (cdc) general equivalence mappings (gems) as a starting point, processes were built to convert the icd9 codes in both existing and incoming data to the equivalent icd10 codes. this newly derived information is stored in a new field to allow the system to query against it, while keeping the original information for users to view as well. the user interface to perform queries in essence was updated to support icd10-based filtering. additionally, the interface provides an icd10 lookup mechanism using codes and keywords to help users learn or remember the icd10 codes they may need for a query. also, a user interface and a backend process were built to provide administrators the ability to modify the icd9-icd10 mappings as needed. this gives the administrators the mechanism to adjust the mappings based upon their needs or to respond to coding patterns of their specific hospitals. finally, the system provides a mechanism to reprocess the entire historical dataset using the modified mappings when required. results the above described essence functionality has been implemented and deployed to the boston public health commission system. the described features have enabled the public health professionals to query the data using icd10 codes without any concern of which code classification the data source originally included. this new capability also provides users with the ability to perform yearly reporting even during a transition year. this has allowed a hospital that has 9 months of icd-9-cm data and three months of icd-10-cm/pcs data in same year to still be queried on and provide usable results. conclusions public health entities will face many challenges due to the transition of the icd code set. adding the ability to convert to icd10 codes and provide a single query function to essence well in advance of the compliance date will ease the transition and allow users to still utilize the hospital’s data during the change. providing the flexibility for a system to modify the mappings will allow for local behaviors in coding practices to be accounted for that otherwise may be lost by using a single mapping system. keywords icd10; disease surveillance; essence; icd9-icd10 transition acknowledgments this work was funded by boston public health commission *alimelu jonnagadla e-mail: jonnaas1@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e85, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using hospital ed data to identify mental illness trends after hurricane sandy ursula lauper*1, cristian pantea1, jian-hua chen2, hwa-gan chang2 and shao lin1 1bureau of environmental and occupational epidemiology, new york state department of health, albany, ny, usa; 2division of epidemiology, new york state department of health, albany, ny, usa objective 1) to define mental health keywords using daily hospital emergency department chief complaint (edcc) data during and after hurricane sandy 2) to track shortand long-term trends in mental health edccs. 3) to compare mental health edccs in affected counties to the rest of the new york state population. introduction edcc data provides an opportunity for capturing the early mental health impact of disaster events at the community level, and to track their impact over time. however, while rapid mental health assessment can facilitate a better understanding of the acute post-disaster period and aid early identification of persons at long-term risk,1 determining how wide a net to effectively capture the critical range of mental health sub-categories has not yet been clearly defined. this project creates a comprehensive set of mental health sub-category keywords, and applies them to evaluate shortand long-term trends in posthurricane sandy mental health outcomes in new york state. methods mental health keyword lists were generated through a literature search, consultation with subject matter experts, and collaboration with new york city and new jersey health department syndromic surveillance staff. these lists were used to collect mental health edccs before, during, and after hurricane sandy. we conducted statistical analyses to compare the number of mental health chief complaints in three affected counties (nassau, suffolk, and westchester) with a non-affected area (the rest of new york state, excluding new york city). three time periods were tested: the 12-day sandy period, the three-month period post-sandy, and one year post-sandy. to control for seasonality, these periods were also compared to the average of the same date ranges for the previous fiveyear periods. data were analyzed using sas 9.3. chi-square tests and negative binomial regression models were used to test associations with statistical significance at 0.05. results eight sub-categories of mental health related keywords were generated. compared with the unaffected area, the relative risk of edccs in the affected counties was significantly higher during the 12-day sandy period (rr 1.16, 95% ci 1.03-1.31), the 3-month postsandy period (rr 1.18, 95% ci 1.12-1.24), and the 1-year post-sandy period (rr 1.15, 95% ci 1.13-1.18). the increase in the 3-month period post-sandy was not significant (rr 1.12, 95% ci 0.99-1.26). analysis of population vulnerability to sandy is still ongoing. conclusions this project provides a sound model for utilizing edcc data. tracking mental health trends during and after disaster events can provide valuable insight into the impact and trends of mental health edcc on a community for the purposes of mitigation and future disaster planning. keywords syndromic surveillance; mental health; disaster references 1. meewisse, ml, et al. the course of mental health disorders after a disaster: predictors and comorbidity. journal of traumatic stress. 2011 august; 24. *ursula lauper e-mail: ursula.lauper@health.ny.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e34, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an evaluation of the biosense 2.0 “poisoning by medicines” syndrome using chief-complaint data in utah anne burke*, david jackson and allyn k. nakashima bureau of epidemiology, utah department of health, salt lake city, ut, usa objective to evaluate the biosense 2.0 “poisoning by medicines” syndrome by determining chief complaint terms for inclusion and exclusion based upon pre-defined icd-9 codes and a comparison of binned and unbinned chief complaint data. introduction biosense 2.0 uses predetermined syndromes based upon icd-9 codes and chief complaint data to allow users to view and analyze data from emergency department (ed) visits, yet further validations of these syndromes are needed. previous studies have validated syndromic surveillance syndromes by comparing chief complaint data to discharge diagnosis;1,2 however, these efforts are not possible for jurisdictions in which facilities do not submit icd-9 code data. currently in utah, the syndromic surveillance data submitted includes only chief complaint information. thus, efforts to validate biosense syndromes, such the “poisoning by medicines” syndrome, can be informed by but not analyzed in accordance with icd-9 code and discharge diagnosis data in utah. methods icd-9 codes included in the biosense 2.0 syndrome were examined to create a working definition for the “poisoning by medicines” syndrome. biosense 2.0 was utilized to generate a list of all-cause ed visits for one year prior to the day of study initiation (july 1, 2014) from participating utah hospitals (n=20). terms for inclusion and exclusion were defined using iterative processes. groups of reviewers consisting of the local and state health department epidemiologists and biosense users convened to discuss potential search terms. searches of the all-cause ed visits were completed using these terms while new terms continued to be determined. the cycle of determination of terms for inclusion continued until no new terms were established. all visits binned to the biosense 2.0 “poisoning by medicines” syndrome were reviewed for validity and terms for exclusion were determined. a paired t-test was conducted to compare the number of ed visits binned to the “poisoning by medicines” syndrome using the initial and revised definitions. results review of the icd-9 codes binned to the “poisoning by medicines” syndrome suggested that chief complaints for poisoning by drugs, medicinials, and biological substances should be included for visits which are accidental, intentional, assault, or of undetermined intention. important terms for inclusion not previously binned to the syndrome included “od,” “overmedicated,” and “ingested.” important terms for exclusion were identified which pertained to ed visits for alcohol, carbon monoxide, and food poisoning; swallowing a foreign body; and contact dermatitis, but not ed visits for drug, medicinals, and biological substance overdose. overall, 909 and 3,073 visits were excluded and included from binning by these new criteria, respectively. the mean number of visits for “poisoning by medicines” per day increased significantly with the revised definition (t=27.1149; p<0.0001). conclusions the results of this study suggest that in the absence of icd-9 code data, review of binned and unbinned chief complaint data may suggest terms for inclusion and exclusion for biosense 2.0 syndromes. overall, the initial biosense 2.0 “poisoning by medicines” syndrome underestimated the amount of visits for this chief complaint. the results of this analysis are specific to hospitals reporting syndromic surveillance in utah. efforts to on-board facilities in utah are ongoing. other locations may use different chief complaint terminology to describe ed visits for “poisoning by medicines” and these results may not be generalizable to other facilities and locations. reviewing binned and unbinned chief complaint data may help to ensure data quality and allow for greater accuracy when using syndromic surveillance data to monitor the health of the population. table 1. chief complaint terms for inclusion and exclusion in the “poisoning by medicines” syndrome keywords biosense 2.0; syndrome definition; poisoning by medicines; chief complaint acknowledgments the authors would like to thank mary hill, melanie spencer, jenny robertson, sarah willardson, and anna fondario for their thoughtful review. references 1. oyong k, kajita e, araki p, luarca m, hwang b. validation of los angeles county departmet of public health respiratory syndrome using electronic health records. online journal of public health informatics 2014;6(1):e81. 2. patel m, hoferka s. an evaluation of heat-related emergency department visits based on differences in heat syndrome definitions in northern illinois. online journal of public health informatics 2014;6(1):e88. *anne burke e-mail: aburke@utah.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e10, 2015 do it now.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 118 (page number not for citation purposes) isds 2013 conference abstracts impact of staff qualification and training on performance of the core function activities (cfa) of the communicable disease surveillance system, gazera state, sudan, 2009 magda a. nogodalla* and ahmed i. abdelghani epidemiology department, ministry of health, khartoum, sudan � �� �� �� � � �� �� �� � objective �������� �������� ������� ������������������� ���� �� ��� ����� �� ������������� ��������� ������� �� ���� ���� �������������������� � � �� ���������������� ����������� ���������!�"���� ����#� ���$ introduction ������� ������������������� ������� ������ ��� ���� ��������% ����������� ������ �� ���������������� ���������������������� ���� ������� ����&����������!�"���� ����$����� ���������������� � ��������� ������������������ �� ������������������ �����'(()%'((*$� ��������������� �������� �������������������� ����� �� ��&���������� �� ���� ������������ ���� ������+��� �������� ��������������������% �� ����������+������������������ �� � ����� ����������,�$ � �� ��������������������!�"���� ������ ���� ��� ������������% ������ ��� �� ������������� ���������'�$����� �����+��� ��� ����� 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������������������������������������� ���������������$ conclusions ������ ���� ��� ���������������+��������� ������������ ������������ ���������������������������� ���������������#������� ����� ����� �� !�"���� ����� ���������������������� ����������� ������ ����� � ����� ��������$������������������������3���� ������������������ �� ��� ��������� ����������� #����������� ������� ���������������� ������������ ����������#� ����"��� �� ���������� ���#���� ������� ��������� ������ ���� ��� ���#������� ������ �������� �����@��� ���� ! �������������������������������$����� ������#��������� �� ������� �� �������� ���������� ��� ����� �������� +��������� ������ �����% ������������#������� ������� ������������� keywords �������� ����������>�� �� ������������������ ����������>�������% ����>� ��� acknowledgments .��������������6�������&��������� �������������� ������ �� ����������� �&������������� �#����� ������������$���3�a�������.��� ��a������#����� @��� ����� ������ �� ����8�� ����������#�a&�������8� ����� ��7�% � ��"��� �#� ������� �� �������� ������������ � ��������������������� ��������������������� �������������+��� ��+����������������������+ ��� � ������������� ������$ references ,$��� ���������� � ��������� ��#������� �����!������� �8����������.���% ����#�a������ � �������������#���������� ����� �� ���#���������.��% ������ ����������.�2�#� ���$'((1$ '$��������������$�������������� ������ ���� �������������������� ����� �% � ����������������� ������������������� �������������� ���#�'((,% '(('#�b���� �� �����.�������� �������#�!������������� ����� �� ��������#�/��������������� ��$:���� ���/��������@�+��c������#���% � ����'((= *magda a. nogodalla e-mail: sarajuja@yahoo.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e46, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 213 isds 2014 conference abstracts an innovative approach to surveillance training in the african region man kai wong*2, 1, monique tuyisenge-onyegbula2, peter gaturuku3 and helen perry2 1oak ridge institute for science and education (orise), oak ridge, tn, usa; 2centers for disease control and prevention, atlanta, ga, usa; 3world health organization regional office for africa, brazzaville, congo objective this presentation addresses the challenges of expanding district level surveillance training in africa. we developed an e-learning course and field tested the modules using an innovative approach to assess the feasibility of delivering electronic surveillance training. introduction integrated disease surveillance and response (idsr) is a world health organization (who) regional office for africa (afro) strategy for strengthening national public health surveillance and response systems in african countries. the strategy incorporates the international health regulations (2005) core capacities for public health surveillance and response systems1. since 2010, more than 30 countries have conducted at least one idsr training workshop2. limited resources preclude conducting workshops in each of the 4,500 districts in all who-afro region. one solution is to implement an electronic version for idsr training. in collaboration with who-afro, we conducted a literature search to identify e-learning best practices, and transformed the idsr workshop training materials into electronic modules using measurable performance objectives, realistic examples, meaningful practices, and real time feedback to the learner. we also utilized an online learning management platform that lets course managers track learner progress and share supporting materials. the idsr e-learning course, available in english, french and portuguese, aims to increase access to skills that support the prevent-detect-respond goal areas of the global health security agenda. methods in 2014, we field tested a pilot version of the e-learning course in two modalities: one onsite and the other remote. the three-day onsite field test took place at the zimbabwe who country office with participants from national, provincial and district levels. we observed learner performance and discussed challenges with the participants each day. the remote field test took place during the same time in six african countries where participants took the course from their work stations or homes using their own computers and internet connection. they submitted real-time feedback using an online course review tool. all participants were sent a post-course survey to rate the feasibility of the delivery medium, the relevance of the content, and the functionality of the course. results a total of 74 participants (15 onsite and 59 remote) from various health levels and roles took part in the field test, and 57 participants (14 onsite and 43 remote) completed the course. participants provided 261 comments, most of which were about local adaptations for the content and functionality of some learning screens. we observed that computer literacy for onsite participants ranged from competent to very basic. remote participants reported that time and internet connectivity were barriers to completing the course. participants indicated on their surveys that the course content is very relevant to their jobs. they also suggested distributing usb modems or cd-roms in areas with insufficient internet connectivity, and integrating the course into pre-service training programs to ensure the continuity of an idsr-trained workforce. conclusions as the use of technology in the african region advances, e-learning tools and platforms provide innovative opportunities for strengthening health systems. the unique design of this field test allowed us to gain insights from a range of health professionals on their experience using technology. the rich data collected is used to devise a dissemination strategy that takes into account local adaptations and accommodates different computer literacy levels. we anticipate that this e-learning course will contribute to building skills for preventing, detecting, and responding to health threats in all health levels. keywords e-learning; africa; integrated disease surveillance and response (idsr) acknowledgments the collaboration on idsr between cdc and who-afro is supported by the united states agency for international development africa bureau. technical assistance in creating the electronic course was provided by instructional designers at cdc’s center for surveillance, epidemiology and laboratory services. we also want to gratefully acknowledge the contributions from the zimbabwe ministry of health and child welfare and the who zimbabwe country office. references who and cdc. technical guidelines for integrated disease surveillance and response in the african region. brazzaville, republic of congo and atlanta, usa; 2010. who and cdc. idsr district level training course. facilitator guide and participant modules. brazzaville, republic of congo and atlanta, usa; 2011. *man kai wong e-mail: wyu9@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e61, 201 ojphi-06-e30.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 102 (page number not for citation purposes) isds 2013 conference abstracts detecting public health impacts associated with air pollution events in the uk using syndromic surveillance yolande macklin*1, 2, andrew kibble3, alex elliot4 and gillian smith4 1school of health and population sciences, university of birmingham, birmingham, united kingdom; 2centre for radiation chemical and environmental hazards, public health england, birmingham, united kingdom; 3centre for radiation chemical and environmental hazards, public health england, cardiff, united kingdom; 4real-time syndromic surveillance team, public health england, birmingham, united kingdom � �� �� �� � � �� �� �� � objective �������� ��� ���� �������������������������� �������� � ������ �������� ����� ������� �� ��������������� ������� ���� ����� �� ������ ������������������������� �������� � � ������� ������������������ ��� introduction ���������������������� �������� ����� ���� �������� ��������������� ��������� ��������� ��������� � ����������� ����� ����� ��� ����� ��� �� ��� �� ���������������� ����� �� ������� ������ �������� �������� �����!�� ��������������� ��� ������������� �������� ��� �� ������� "#$��%� ���� ����� 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attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 148 (page number not for citation purposes) isds 2013 conference abstracts validity of the surveillance quality indicators timeliness and completeness in surveillance systems with variable data quality 1public health foundation of india, indian institute of public health hyderabad, hyderabad, india; 2directorate of health, department of health, medical & family welfare, government of andhra pradesh, hyderabad, india; 3public health foundation of india, new delhi, india; 4institute for health metrics and evaluation, university of washington, seattle, wa, usa; 5london school of hygiene and tropical medicine, london, united kingdom � �� �� �� � � �� �� �� � objective ����������� �� ��� �������� ����� ������������������� ���������������� ������������� ������������������ 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�� ���� ,������� ��� ��"���� ���� %� 277)� d�� ���a62�3�c'34 *3%�+�"<� �+<�&c�'(')()36% 3%�d� ���d��&�� ����e��� �����@+��8������<%����������!�� �����!����� ����������� ������������� ��%�e�����%�27''�d���'6a3ff�(f4'�c262 4(%� +�"<� �+<�&c�2'22f677% *%�-��� �.���� �/�!���0�����c����������"��� ���������� ����������� � ���������������� �!�� ��������������!��� �� �������!�-��� �.���� � /�!���0�������2774% 6%�<�=�""��d��� ��!��!����g����<��-� �"����=��"�!��+��1�����-����� ��%���������������������������"���� ���� ���� ����������� ���������� � ������������������� ���� ���������������><����"���� ���� %�2772a2c2%� +�"<� �+<�&c�'')*4))(%�+�"�� ���������+<��&c�466()% 4%� .������� &e�� g� ���� ?ga� -./� /����������� �������� ����� ��� � �� ?��"���/��"�����1������� �g��������=������%�.������������������ � ���� c�-./���� ����������������!��!��� ��� ����!��!������������ �� �����%�� ��e�����������������&���������277'��'c3*6h363%� ��c'7%'7'4i �'*f3 37((�7'�77'*) f���� c'')f')7f *vivek singh e-mail: vivek.singh@iiphh.org� � � � vivek singh*¹, jagan mohan², u prasada rao², lalit dandona³,⁴ and david heymann⁵ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e176, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts military real-time syndromic surveillance system for biosurveillance portal in korea chulwoo rhee*1, howard burkom2, changgyo yoon1, sangwoo tak3, aaron katz2 and miles stewart2 1armed forces medical command, seoul, korea (the republic of); 2johns hopkins applied physics laboratory, laurel, md, usa; 3joint program executvie office for chemical and biological defense, edgewood, md, usa objective this presentation aims to elaborate our experiences from initiating a syndromic surveillance system as a part of current biosurveillance developments in korea. we developed military active realtime syndromic surveillance (marss) system with data from all of 19 korean military hospitals as a part of the us-rok joint biosurveillance project. introduction biosurveillance portal (bsp) is a web-based enterprise environment that is aimed to facilitate international collaboration, communication, and information-sharing in support of the detection, management, and mitigation of biological events in korea. in oct 2013, republic of korea (rok) ministry of national defense has made the project agreement with united states (us) department of defense joint program executive office of chemical and biological defense to develop biosurveillance portal which will provide tools and capabilities to facilitate timely identification and detection of biological events to minimize operational impacts on rok-us forces. as a part of this project, armed forces medical command (afmc) undertook the initiative to develop the military active realtime syndromic surveillance system. methods afmc currently operates 19 military hospitals across the country, and all electronic health records including patient information, icd-10 diagnoses and medical prescriptions are collected daily to a centralized server. all the relevant data from jan 1st 2012 to may 31st 2014 were retrieved, including total patient counts and counts of patients with specific diagnoses. seven syndromes were chosen for surveillance based on clinical symptoms and manifestations likely resulting from the suspected use of weaponized pathogens. the selected syndromes are respiratory, gastrointestinal, botulism, dermatologic, neurologic, hemorrhagic, and fever. the syndrome definitions for diseases associated with critical bioterrorismassociated agents were developed by center for disease control and prevention (cdc) in oct, 2003 [1]. all icd-9 codes were converted to icd-10 codes based on the general equivalency mapping (gem) files published by center for medicare & medicaid services [2]. for development of the syndromic surveillance system, afmc collaborated with johns hopkins university applied physics laboratory (jhuapl) to modify the suite for automated global electronic biosurveillance (sages). the underlying statistical methodology of the electronic surveillance system for the early notification of community-based epidemics (essence) was modified for this purpose. for each syndrome, we performed sensitivity analysis to calculate to obtain practical background alert rates. we estimated positive predictive values (ppv), defined as the proportion of true alert counts during outbreaks to all alert counts, corresponding to these thresholds using effects of known events in the data. results corresponding alerting thresholds and other algorithm settings were derived based on the characteristics of marss time series using daily counts of icd-10 codes for each syndrome. for example, series based on the respiratory and fever syndromes clearly showed seasonal variation while series based on the other syndromes did not. lower algorithm thresholds produced shorter detection delays but also reduced ppv. we chose thresholds with ppv above 30% to avoid excessive alerting. for the rarer syndromes such as botulism and hemorrhagic illness, true outbreaks are very rare, and algorithm settings were chosen to avoid frequent nuisance alerting. conclusions despite the shared surveillance purpose of different syndromic groups, the frequency and seasonality of endemic illness underlying each group should be used for separate algorithm adjustment. for deciding alert thresholds, we have considered not only statistical aspects but also the capacity for alert investigation. more efforts are required to validate the system-generated alerts, and standard operating procedures to investigate these alerts are being developed. keywords biosurveillance; syndromic surveillance; bio-threats; sages; essence acknowledgments this study was funded by armed forces medical command. (afmc 2014-024) references 1. cdc. syndrome definitions for diseases associated with critical bioterrorism-associated agents. atlanta, ga: us department of health and human services, cdc, 2003. available at http://www. bt.cdc.gov/surveillance/syndromedef/index.asp. 2. cms. official industry resources for the icd-10 transition. baltimore, md: us department of health and human services, cms, 2014. available at http://www.cms.gov/medicare/coding/icd10/index. html. *chulwoo rhee e-mail: rhee275@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e48, 2015 model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) osaro mgbere, phd, ms, mph 1, 2* and salma khuwaja, md, mph, dr.ph 1 1 disease prevention and control division, houston health department, houston, texas, usa 2 institute of community health, university of houston college of pharmacy, texas medical center, houston, texas, usa abstract background during the 2009 h1n1 influenza pandemic (ph1n1), the proportion of outpatient visits to emergency departments, clinics and hospitals became elevated especially during the early months of the pandemic due to surges in sick, ‘worried well’ or returning patients seeking care. we determined the prevalence of return visits to a multispecialty clinic during the 2009 h1n1 influenza pandemic and identify subgroups at risk for return visits using model-based recursive partitioning technique. methods this study was a retrospective analysis of ili-related medical care visits to multispecialty clinic in houston, texas obtained as part of the houston health department influenza sentinel surveillance project (issp) during the 2009 h1n1 pandemic influenza (april 2009 – march 2010). the data comprised of 2680 individuals who made a total of 2960 clinic visits. return visit was defined as any visit following the index visit after the wash-out phase prior to the study period. we applied nominal logistic regression and recursive partitioning models to determine the independent predictors and the response probabilities of return visits. the sensitivity and specificity of the outcomes probabilities were determined using receiver operating characteristic (roc) curve. results overall, 4.56% (prob. 0.0%-17.5%) of the cohort had return visits with significant variations observed attributed to age group (76.0%), type of vaccine received by patients (18.4%) and influenza a (ph1n1) test result (5.6%). patients in age group 0-4 years were 9 times (aor: 8.77, 95%ci: 3.39-29.95, p<0.0001) more likely than those who were 50+ years to have return visits. similarly, patients who received either seasonal flu (aor: 1.59, 95% ci 1.01-2.50, p=0.047) or ph1n1 (aor: 1.74, 95%ci: 1.09-2.75, p=0.022) vaccines were about twice more likely to have return visits compared to those with no vaccination history. model-based recursive partitioning yielded 19 splits with patients in subgroup i (patients of age group 0-4 years, who tested positive for ph1n1, and received both seasonal flu and ph1n1 vaccines) having the highest risk of return visits (prob.=17.5%). the area under the curve (auc) for both return and non-return visits was 72.9%, indicating a fairly accurate classification of the two groups. conclusions return visits in our cohort were more prevalent among children and young adults, and those that received either seasonal flu or ph1n1 or both vaccines. understanding the dynamics in care-seeking https://orcid.org/0000-0002-2863-6284 model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi introduction in the spring of 2009, a novel influenza a (h1n1) virus emerged and was detected first in the united states [1-3] and spread quickly across the united states and the world. from april 12, 2009 to april 10, 2010, cdc estimated there were 60.8 million cases, 274,304 hospitalizations, and 12,469 deaths in the united states due to the (h1n1) pdm09 virus [4,5]. despite the enormous health consequences, the pandemic provided an important test of our nation’s preparedness activities and our ability to respond and adapt to a large-scale, protracted public health emergency [5]. during the 2009 h1n1 influenza pandemic (ph1n1), the proportion of outpatient visits to emergency departments (ed), clinics and hospitals became elevated around the united states especially during the early months of the pandemic due to surges in sick and ‘worried well’ individuals or returning patients seeking care. this occurred regardless of the availability of “h1n1 flu self-evaluation,” designed to help individuals decide whether to seek medical care or stay home, if they had symptoms consistent with 2009 h1n1 [5]. surges in patient volumes can compromise the healthcare systems’ ability to deliver care, as revealed by the 2009 h1n1 influenza pandemic [6]. to counter the impact, some healthcare providers and institutions developed novel approaches for emergency care delivery such as triage tents and drive-through examination areas [7,8], to help alleviate surge volumes and potentially prevent transmission of h1n1 influenza. despite the surges experienced at some facilities, the 2009 h1n1 retrospective summary produced by the us department of health and human services (hhs) concluded that the ‘‘2009 h1n1 pandemic did not fully test the health care system’s ability to meet a surge in demand for care’’ [5]. the media reports are major sources of health information and they play key roles in health behavior change. previous researches suggest that intense news media coverage of novel communicable diseases coupled with community attitudes can create public concern, and amplify risk perception [5,8-11]. these have been reported to increase the number of outpatient visits and return visits, influence physicians’ practices and behaviors (example, increase awareness and reporting of communicable diseases), and increase demand for clinical and diagnostic health services [5,11,12]. however, some factors have been identified as motivators for return visits including previous experience with ili [13], personal beliefs about vaccination [14,15], and previous error in the diagnosis of an illness or the progression of an illness [16-18], and widespread behavior during pandemic would assist policymakers with appropriate resource allocation, and in the design of initiatives aimed at mitigating surges and recurrent utilization of the healthcare system. keywords: model-based recursive partitioning, decision tree, subgroup analysis, influenza-like-illness, h1n1, influenza pandemic, care-seeking behavior, return visit * correspondence: osaro.mgbere@houstontx.gov doi: 10.5210/ojphi.v12i1.10576 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:osaro.mgbere@houstontx.gov model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi report of morbidity and mortality during the pandemic [19]. in contrast, researchers have also noted that up to 73% of patients that showed up at ed for h1n1 influenza fear were the “worried well” and that over 95% of the presenting concerns were minor or nonexistent [20]. the houston health department (hhd) uses several syndromic surveillance systems to monitor influenza-like-illness (ili) activity in houston, texas [21] including the sentinel provider network, which was instrumental in monitoring the novel 2009 h1n1 pandemic in houston [22]. although the 2009 ph1n1 influenza has been cataloged by cdc with respect to the timing of the outbreak, geographic distribution, characteristics of cases, and epidemiologic parameters [23], and retrospective summary of events [5], much still remains to be learned from the pandemic, which continue to circulate seasonally in the u.s., and throughout the world [24]. in our most recent study, we explored the dynamics of care-seeking behaviors between the ili phases (pre-ph1n1, ph1n1 and post-ph1n1) using facility-level data rather than patients’ self-reported survey data and found that 90% of the return visits to clinics occurred during the ph1n1 phase [25]. this finding prompted the need for further research to explore the demographic and clinical factors associated with return visits to clinics during the ph1n1. in recent years, considerable efforts have been dedicated to mathematical modeling studies to complement disease surveillance efforts in the planning of interventions against emerging pandemics including the 2009 h1n1 influenza pandemic [25-34]. the efficient prediction of the expected impact of an emerging pandemic would allow appropriate preparation to be made without diversion of excess resources, and thus, have the potential to reduce pandemicand non-pandemic related illness and death [32]. several studies have assessed return visits to emergency departments [16,35-38], but only one known study had assessed return visits for ili-related conditions over the disease phases using facility-level data from a multispecialty clinic [25]. illuminating the trend and risk factors for return visits during the ph1n1 may enable clinicians and public health authorities to identify individuals at highest risk for return visit, develop strategies for preventing it and assist policymakers with appropriate resource allocations during future pandemics. therefore, the objectives of this study were to determine the prevalence of return visits, identify the associated risk factors and subgroups at high risk for return visits during the 2009 h1n1 influenza pandemic using a model-based recursive partitioning technique. methods data source data used for this study was obtained from the influenza sentinel surveillance program (issp) at the houston health department (hhd), houston, texas, and represented a subset of data from a large multispecialty clinic. the issp was aimed initially at providing a system to help detect ongoing local ili activity, monitor trends and morbidity, and provide information that may assist providers in patient care management [22]. when the ph1n1 arose in 2009, the existence of issp provided an opportunity for hhd to jump start and continue to work with the local partners to monitor and evaluate the local ili activities including the impact of the pandemic on related healthcare-seeking behaviors [25]. the multispecialty clinic was chosen because it provided the largest pool of data with the most complete information covering the period of interest. model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi study population we used a cohort of 2680 individual patients who received ili-related medical care at a multispecialty clinic in houston, texas during the ph1n1 period (17 april 2009 through 1 march 2010) as defined locally by mgbere et al. [25]. the patients made a total of 2960 healthcare visits to the clinic for ili that also served as the qualifying index visits in our cohort, and subsequently, yielded 135 return visits during the period under review. analytic measures dependent measure the main outcome variable of interest in this study was return visit. using data from the hhd issp, we created a metric to capture aggregate changes in patient visit behavior and care-utilization over time [25]. this metric was designed to reduce bias from an increase in sheer volume of onetime patients, as would be expected during a typical pandemic period. the resulting metric, ‘return visits’ included only non-initial visits (non-index visit) made by a given patient within the ph1n1 phase including those that may have resulted in hospitalization [25]. all return visits for ili-related conditions prior to and post-ph1n1 period were excluded from the current study since our main objective was to assess the proportion of visits and return visits during the ph1n1 period only. we adopted a 30-day visit-free period prior to study phase to serve as wash-out period that allowed for accurate definition of the index visit, thus avoiding the need to characterize the factors as well as the trajectory of prior visits. if a patient had more than one qualifying visits over the study period, we considered the earliest visit as their index visit [25]. return visits associated with any other diagnosis outside ili were also excluded. healthcare visit to clinic was represented as “0” for nonreturn visit (index visit) and “1” for return visit. independent measures the independent measures used in this study include gender (female, male), age group (0-4, 5-24, 25-49 and 50+ years), vaccine type (seasonal flu, ph1n1 and no history), influenza a (ph1n1) test result (negative, positive) and hospitalization (no, yes). the laboratory-confirmed 2009 pandemic influenza a (h1n1) infection was defined as a positive test result at the local and state public health laboratories or at cdc laboratory using real-time reverse transcription-polymerase chain (rrt-pcr) and viral culture protocols, probes, primers, and reagents approved by cdc [39,40]. the independent associations of these measures with the outcome variable were determined, and subsequently, the independent measures were used as covariates in the prediction and recursive partition model analyses. data analysis the demographic and clinical characteristics of the study cohort were summarized using frequencies and proportions, as appropriate. the associations between the independent factors (gender, age group, vaccine type, influenza a (h1n1) test result and hospitalization) and the study outcome (return visit) were determined using chi-square test (χ2) and the fisher’s exact test, where expected cell size was <5. furthermore, we conducted nominal logistic regression model for the outcome variable (return visit) incorporating all the independent factors, except hospitalization model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi (p>0.05), as predictor variables. this produced the model parameter estimates, standard errors, and the associated hypothesis tests. we also carried out the effect likelihood ratio tests of the independent factors and produced estimates of the adjusted odds ratios (aors), 95% confidence intervals (95% cis) and p-values. the model diagnostics and fit statistics were determined using the -loglikelihood test, uncertainty coefficient of determination, corrected akaike information criterion and bayesian information criterion. finally, we conducted a recursive partition model (also refer to as decision tree or classification and regression trees (cart) model) analysis to determine the relationships between the predictors (age group, vaccine type, influenza a (ph1n1) test result) and the dependent factor (return visit), forming a tree of decision rules until the desired fit was reached. although influenza a (ph1n1) test result variable was not statistically significant (p>0.05) in the univariate analysis, its epidemiologic and clinical importance warranted its inclusion in the model. this tree-based method predicts the value of a response variable by forming subgroups of patients within which the response is relatively homogeneous based on the values of a set of predictor variables. the decision tree model was chosen because it has the advantage of being very intuitive and flexible, does not require scaling or normalization of data and the outcome can easily be interpreted by stakeholders. the splitting criteria is based on the likelihood-ratio chi-square (g2) [2*entropy]. a candidate g2 chosen is given by the formula below: g2 test = g 2 parent (g 2 left + g 2 right) when partition calculates a g2 or r2 on excluded data for a categorical response, it uses the rate value 0.25/m when it encounters a zero rate in a group with m rows. otherwise, a missing statistic would be reported, since the logarithm of zero is undefined. the predicted probabilities for the decision tree method used were calculated using the probability statistics. rate is the proportion of observations at the node for each response level while prob is the predicted probability for that node of the tree. the method for calculating prob for the ith response level at a given node is as follows: where the summation is across all response levels, ni is the number of observations at the node for the ith response level, and priori is the prior probability for the i th response level, calculated as: priori = λ*pi+ (1-λ)pi where pi is the priori from the parent node, pi is the probi from the parent node, and λ is a weighting factor currently set at 0.9. the method used for calculating prob assures that the predicted probabilities are always non-zero. the decision tree model fit was assessed using the following measures: entropy r-square, generalized r-square, mean -log p, root mean square error (rmse) and mean absolute model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi deviation. also, the misclassification rate was used to determine how many observations were correctly and incorrectly classified for each value of the response variable, thus, indicating the model fitness to the data. to avoid the partitioning overfitting the model, we applied the k-fold cross validation method by using part of a data set to estimate model parameters and using the other part to assess the predictive ability of the model. the final model was selected based on the cross-validation r-square. furthermore, we used the receiver operating characteristic (roc) curve to determine the “goodness of fit” and measure the sorting efficiency of the model’s fitted probabilities for the response levels. thus, the true positive y-axis is labeled “sensitivity” (the probability that a given x value (measure) correctly predicts non-return and return visits) and the false positive x-axis is labeled “1-specificity” (the probability of incorrect prediction of non-return and return visits based on a given x is 1–sensitivity). the area under the curve (auc) statistic, defined as the percentage of the space ‘under the curve’ (100% represents perfect classification) was used as the basis for determining the extent to which our model successfully classifies the responses. all tests were two-tailed, with a probability value of α = 0·05 used as the threshold for declaring statistical significance level. all data management and statistical analyses were conducted using jmp statistical software version 14.1 (sas institute, cary, north carolina, usa). human subject protection the data used for this study was originally collected for public health surveillance purposes and therefore, was not considered to be human subjects research in accordance with federal human subjects’ protection regulations. the dataset used contained no individual identifiers, thus, maintaining anonymity of subjects. hence, this study received an exempt status approval from the houston health department investigative review committee. results participants’ characteristics and medical visits table 1 show the distribution of the characteristics of study subjects and their associations with the ili-related visits to multispecialty clinics during 2009 h1n1 influenza pandemic period. the overall mean age of the cohort was 22.89±0.35 years, while the mean age of patients who had return visits was 10.70±1.11 years. majority of the cohort were females (56.1%), of age 5-24 years (40.6%) with approximately 61% of them having no documented vaccination history. of the 2,960 subjects who had ili-related medical visits, 84.9% were negative for ph1n1 and only 16.1% of them tested positive for ph1n1 while less than 1% of the reported hospitalizations were related to ili. we recorded only 4.56% (n=135) of return visits in the cohort following the index visit during the ph1n1 period (table 1). the number of patients who had return and non-return visits to the multispecialty clinic during the study period by morbidity and mortality weekly report (mmwr) weeks is depicted in figure 1. the proportional variations in return visits were mainly associated with patients’ age group (χ2=66.74, p<0.001) and vaccine type (χ2=37.33, p<0.001) administered prior to ili-related visits and/or diagnoses. about 87.5% of the return visits occurred among model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi patients who were aged 0-24 years and subsequently decreased significantly with increase age. patients who received ph1n1 vaccine had 0.71% less return visits (p<0.0001) than those who either received seasonal flu vaccine or had no vaccination history. gender, ph1n1 test result and hospitalization were not significantly (p>0.05) associated with return visits in our cohort. table 1: characteristics of study populations’ ili-related visits to multispecialty clinic during 2009 ph1n1 characteristic ili-related visits to clinic test statistics total n (%) non-return visits n (%) return visits n (%) χ2 (df) p-value overall 2960 (100) 2825 (95.44) 135 (4.56) 384.99 (1) <0.0001**** gender female male 1659 (56.05) 1301 (43.95) 1586 (53.58) 1239 (41.86) 73 (2.47) 62 (2.09) 0.22 (1) 0.636ns age group (years) 0 – 4 5 – 24 25 – 49 50+ mean ± sem 529 (17.87) 1203 (40.64) 860 (29.05) 368 (12.43) 22.89 ± 0.35 476 (16.08) 1138 (38.45) 847 (28.61) 364 (12.30) 23.47 ± 0.36 53 (1.79) 65 (2.20) 13 (0.44) 4 (0.14) 10.70 ± 1.11 66.74 (3) <0.0001**** vaccine type seasonal flu ph1n1 no history 590 (19.93) 565 (19.09) 1805 (60.98) 538 (18.18) 534 (18.04) 1753 (59.22) 52 (1.76) 31 (1.05) 52 (1.76) 37.33 (2) <0.0001**** influenza a (ph1n1) test negative positive 2483 (83.91) 476 (16.09) 2376 (80.30) 448 (15.14) 107 (3.62) 28 (0.95) 2.27 (1) 0.132ns hospitalization no yes 2942 (99.39) 18 (0.61) 2809 (94.90) 16 (0.54) 133 (4.49) 2 (0.07) ----f 0.197ns abbreviations: χ2 (df): chi-square (degree of freedom); sem: standard error of mean. within a given characteristic, the percentages may not add up to exactly 100 due to rounding. f fisher’s exact test was used for 2 x 2 table involving cell size less than 5 cases. significance level: ****=p<0.0001, ns=not significant (p>0.05). model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi figure 1: return and non-return visits to multispecialty clinic by mmwr week return visits the logistic model parameter estimates, and adjusted odds of patients’ return visits to the multispecialty clinic is presented in table 2 and indicate that return visits were significantly (p<0.05) associated with age group and vaccine type received by patients. return visits were generally more likely to occur among young individuals compared to older individuals. for instance, patients who were of age 0-4 and 5-24 years old were about 9 (aor: 8.768, 95%ci: 3.388-29.945, p<0.0001) and 5 (aor: 4.884, 95%ci: 1.978-16.246, p<0.001) times more likely to have return visits to the clinic compared to those who were 50 years and above. similarly, patients who received ph1n1 and seasonal flu vaccinations were 74% (aor: 1.741, 95%ci: 1.0852.750, p=0.022) and 59% (aor: 1.586, 95%ci: 1.007-2.500, p=0.047) more likely to have return visits to the clinic compared to patients who had no history of vaccinations on record. in contrast, some patients who had no vaccination history on record tended to be 43% (aor: 0.575, 95%ci: 0.364-0.922, p=0.022) less likely to have return visits to clinic compared to those who received ph1n1 vaccination (table 2). model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi table 2: logistic model parameters estimates and adjusted odds ratios (aor) for patients’ return visits to multispecialty clinic β level 1 /level 2 (ref) aor 95% confidence interval p-value lower upper gender [l-r χ2 (1) = 0.195, p=0.659ns] € male female 0.923 0.647 1.314 0.659ns female male 1.083 0.761 1.546 0.659ns age group (years) [l-r χ2 (3) = 40.260, p<.0001****] € 5-24 0-4 0.557 0.364 0.855 0.008** 25-49 0-4 0.176 0.086 0.340 <.0001**** 50+ 0-4 0.114 0.033 0.295 <.0001**** 0-4 5-24 1.795 1.169 2.749 0.008** 25-49 5-24 0.316 0.163 0.570 <.0001**** 50+ 5-24 0.205 0.062 0.506 0.001*** 0-4 25-49 5.674 2.941 11.574 <.0001**** 5-24 25-49 3.160 1.753 6.124 <.0001**** 50+ 25-49 0.647 0.181 1.852 0.435ns 0-4 50+ 8.768 3.388 29.945 <.0001**** 5-24 50+ 4.884 1.978 16.246 0.001*** 25-49 50+ 1.545 0.540 5.535 0.435ns vaccine type [l-r χ2 (2) = 6.949, p=0.031*] € ph1n1 no history 1.741 1.085 2.750 0.022* seasonal flu no history 1.586 1.007 2.500 0.047* seasonal flu ph1n1 0.911 0.550 1.525 0.720ns no history ph1n1 0.575 0.364 0.922 0.022* no history seasonal flu 0.631 0.400 0.994 0.047* ph1n1 seasonal flu 1.098 0.656 1.818 0.720ns influenza a (ph1n1) test [l-r χ2 (1) = 0.467, p=0.495ns] € positive negative 1.169 0.739 1.795 0.495 ns negative positive 0.856 0.557 1.353 0.495 ns abbreviations: ref: referent, aor: adjusted odds ratio. β tests and confidence intervals of odds ratios are likelihood ratio based. € based on effect likelihood ratio test (l-r χ2 (df)). significance level: *=p<0.05. **=p<0.01, ***=p<0.001, ****=p<0.0001, ns=not significant (p>0.05). model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi recursive partition model the recursive partition model analysis based on the best fit of the independent factors levels produced a decision tree with 19 splits (figure 2) and variational contributions of the factors as follows: age group 3 splits (g^2=68.33, 76.0%), vaccine type 11 splits (g^2=16.54, 18.4%), and influenza a (ph1n1) test result 5 splits (g^2=5.08, 5.6%), and resultant model entropy coefficient of determination (r2) of 0.0818 and a misclassification rate of 0.0456 (table 3). table 4 displays the terminal nodes, the various combinations of independent factors levels and their associated response probabilities and counts for return and non-return visits. the recursive partition model showed significant variations across the terminal nodes as defined by the leaf labels with the probability of return visits to the multispecialty clinic ranging from 0.0% to 17.5%. the highest risk of return visits to the multispecialty clinic during the 2009 ph1n1 was among patients in terminal node tn01, who were of age group 0-4 years, tested positive for ph1n1, and received both seasonal flu and ph1n1 vaccines (subgroup i, prob.=17.5%). this was followed by those in terminal node tn02 who were of the same age group (0-4 years), tested positive for ph1n1 and had no history of vaccination (subgroup ii, prob.=13.3%) and patients in terminal node tn03 who were of age group 5-24 years, tested positive for ph1n1 and received only seasonal flu vaccine (subgroup iii, prob.=12.2%). the fourth subgroup was those of age group 04 years, who tested negative for ph1n1 and received ph1n1 vaccine (subgroup iv, prob.=12.1%). in general, patients who were 50 years and above and tested positive or negative for ph1n1 had a zero probability of return visits to the clinic. the tradeoff between sensitivity and specificity of the probabilities for false-positive and true-positive rates for the clinic visits are depicted in the receiver operating characteristic (roc) curve in figure 3. conceptually, the roc curve displays the efficiency of a model’s fitted probabilities in sorting out the response levels. the area under the curve (auc) for both return and non-return was 0.729. the auc measures discrimination level, that is, the ability of the recursive partitioning model to correctly classify the return and non-return visits. a summary table for the roc parameters estimates can be accessed in the supplementary materials section (appendix a). model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi figure 2: classification decision tree for patients’ return visits to multispecialty clinic model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi table 3: column contributions and recursive partition model fit term number of splits g^2 plot proportion age group 3 68.329 0.760 vaccine type 11 16.537 0.184 influenza a (ph1n1) test 5 5.076 0.056 measure value definition entropy r-square 0.0818 1-loglike(model)/loglike(0) generalized r-square 0.0965 (1-(l(0)/l(model))^(2/n))/(1-l(0)^(2/n)) mean -log p 0.1702 ∑ -log(ρ[j])/n rmse 0.2052 √ ∑(y[j]-ρ[j])2/n mean abs dev 0.0842 ∑ |y[j]-ρ[j]|/n misclassification rate 0.0456 ∑ (ρ[j]≠ρmax)/n n 2960 n model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi table 4: recursive partition model leaf report showing predicted response probabilities and counts for patients’ return and non-return visits to multispecialty clinic terminal node leaf label response probability response counts return visits non-return visits return visits non-return visits tn01 ^&age group(0-4)&influenza a (ph1n1) test(positive)&vaccine type(seasonal flu, ph1n1) 0.1753 0.8247 8 37 tn02 ^&age group(0-4)&influenza a (ph1n1) test(positive)&vaccine type(no history) 0.1332 0.8668 3 19 tn03 ^&age group(5-24)^&influenza a (ph1n1) test(positive)&vaccine type(seasonal flu) 0.1216 0.8784 7 50 tn04 ^&age group(0-4)&influenza a (ph1n1) test(negative)^&vaccine type(ph1n1) 0.1212 0.8788 6 43 tn05 ^&age group(0-4)&influenza a (ph1n1) test(negative)^&vaccine type(seasonal flu) 0.0936 0.9064 27 261 tn06 ^&age group(5-24)^&influenza a (ph1n1) test(negative)&vaccine type(ph1n1) 0.0879 0.9121 17 176 tn07 ^&age group(25-49)&influenza a (ph1n1) test(positive)&vaccine type(ph1n1) 0.0866 0.9134 1 10 tn08 ^&age group(0-4)&influenza a (ph1n1) test(negative)&vaccine type(no history) 0.0719 0.9281 9 116 tn09 ^&age group(25-49)&influenza a (ph1n1) test(negative)&vaccine type(seasonal flu) 0.0648 0.9352 1 14 tn10 ^&age group(5-24)^&influenza a (ph1n1) test(negative)&vaccine type(seasonal flu) 0.0614 0.9386 11 168 tn11 ^&age group(5-24)&vaccine type(no history)&influenza a (ph1n1) test(negative) 0.0426 0.9574 22 495 tn12 ^&age group(5-24)^&influenza a (ph1n1) test(positive)&vaccine type(ph1n1) 0.0355 0.9645 3 82 model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi terminal node leaf label response probability response counts return visits non-return visits return visits non-return visits tn13 ^&age group(5-24)&vaccine type(no history)&influenza a (ph1n1) test(positive) 0.0292 0.9708 5 167 tn14 ^&age group(25-49)&influenza a (ph1n1) test(positive)&vaccine type(no history) 0.0173 0.9827 1 58 tn15 ^&age group(25-49)&influenza a (ph1n1) test(negative)^&vaccine type(no history) 0.0136 0.9864 9 655 tn16 ^&age group(50+)&influenza a (ph1n1) test(negative)^&vaccine type(no history) 0.0134 0.9866 3 223 tn17 ^&age group(50+)&influenza a (ph1n1) test(negative)^&vaccine type(ph1n1) 0.0095 0.9905 1 107 tn18 ^&age group(25-49)&influenza a (ph1n1) test(negative)^&vaccine type(ph1n1) 0.0092 0.9908 1 110 tn19 ^&age group(50+)&influenza a (ph1n1) test(negative)&vaccine type(seasonal flu) 0.0037 0.9963 0 9 tn20 ^&age group(50+)&influenza a (ph1n1) test(positive) 0.0015 0.9985 0 25 leaf label or terminal node (tn) showing the independent factors level subgroups. model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi figure 3: receiver operating characteristic (roc) curve showing the model’s fitted probabilities for false-positive and true-positive rates [ discussion although the 2009 h1n1 pandemic tested some aspects of the nation’s response capabilities, it did not fully address others including testing the health care system’s ability to meet a surge in demand for care [4]. for this reason, acting now on lessons learned is imperative especially as the occurrence does not reduced the risk of a future, severe pandemic. our study assessed the prevalence of return visits during the 2009 h1n1 pandemic and used model-based recursive partitioning to identify patients’ subgroups that may be at high risk of return visits. the prevalence of return visits in our cohort was 4.56% with significant variations noted across age groups and vaccine types received. this rate is similar to the range of 2.0-5.2% reported for return visits in other studies [16,17,41,42]. the majority of the return visits (87.5%) in our cohort occurred among patients who were of age group 0-24 years. patients of age group 0-4 years were about 9 times more likely to have return visits compared to patients who were 50 years and above. an earlier model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi study reported more return visits during the ph1n1 than in the preand post ph1n1 phases [25]. our study noted that patients who received ph1n1 and seasonal flu vaccines were about 2 times more likely to have return visits than those with no history of vaccination. since the (h1n1)pdm09 virus was very different from circulating h1n1 viruses, vaccination with seasonal flu vaccines offered little cross-protection against (h1n1)pdm09 virus infection [4]. it is possible that some patients may have been vaccinated against seasonal flu and/or ph1n1, but their historical records were not available or reported to the surveillance team, causing the misnomer of the patients being protective. however, the reported vaccine effectiveness (ve) for influenza a (ph1n1) during the study period was 56% (95%ci: 23-75%) [40,43]. the uptake rates for both seasonal influenza and ph1n1 pandemic vaccines in our cohort prior to their return visits were generally very low (38.5% vs. 23.0%). although the vaccination patterns in our study mirrored the national trends [44,45], it is possible, in part, to attribute the low uptake to the late availability of ph1n1 vaccine in us in october 2019 [4]. it has been reported that the introduction of a vaccine four months after the pandemic virus arrival limited the use and effectiveness [36]. despite the low ve, influenza vaccination remains the primary strategy to prevent influenza illness and its complications. therefore, improvements in both vaccine effectiveness and coverage are needed to help reduce avoidable medical visits. several factors such as previous experience with ili [13], personal beliefs about vaccination [14,15], and previous error in the diagnosis of an illness or the progression of an illness [16-18], and widespread report of morbidity and mortality during the pandemic [19], have been identified as motivators for return visits. it is likely that the single most important non-medical cause of surge in health care visits was the media coverage of the pandemic, which created unnecessary public concerns, and amplify the risk perceptions [5,8-11]. unfortunately, our interpretation is limited by the absence of data on the reasons for the patients’ return visits to the clinic including the roles that media coverage may have played. however, previous research noted that up to 73% of patients that showed up at ed for h1n1 influenza fear were the “worried well” and that over 95% of the presenting concerns were minor or nonexistent [20]. using the model-based recursive partitioning model, we created a decision tree that classified our cohort into 19 subgroups with the probability of return visits to the multispecialty clinic ranging from 0.0% to 17.5%. the procedure allowed for the automated detection of patient subgroups that are linked to predictive factors by means of a decision tree, and thus, splitting the study population into more homogeneous subgroups [46]. among the predictor variables, age group was identified as the most important factor influencing patients’ return visits followed by vaccine type and ph1n1 test outcome and their respective interaction effects. identifying subgroups in this way can help inform decision making thus improving individualized patient care by targeting treatment accordingly [46,47]. return visits in our cohort were more common among younger patients, which tended to mirror the national trends in us, where approximately 60% of the reported cases of ph1n1 occurred in persons 18 years of age or younger [43]. our results support the finding that some older adults may be less likely to develop influenza a (h1n1) infection [48], and consequently, leading to a smaller number of return visits among the older individuals’ subgroups. nearly one-third of people over 60 years old had antibodies against this virus, likely from exposure to an older strain of h1n1 virus earlier in their lives [4]. nevertheless, given concerns about the potential scope of future pandemic influenza outbreaks, efforts are needed to prepare for rapid increases in health care-seeking behaviors and to develop effective communication strategies that model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi would encourage behaviors that help slowdown the spread of the virus and minimize unnecessary health care visits to reduce health care surge [49]. although the proportion of variance explained in the model was about 8.18%, the auc for both return and non-return visits was 72.9%, indicating a fairly accurate classification of the two groups. the resulting performance statistics derived from the model (sensitivity/specificity) indicate that the proposed model may be useful in practice as a screener for patients at high risk of return visits. accurate and timely forecasts could aid public health response by informing key preparation and mitigation efforts that could help reduce the overall health and socio-economic impact of pandemics. limitations and strength of study the findings of this study should be interpreted with several important limitations in mind. first, the study was based on data originally collected by hhd for surveillance purposes, and not research. therefore, the extent to which we can interpret the current findings without supplementary information is limited. for instance, certain important variables of interest such as patients’ reasons for returning to the clinic and/or whether physician recommended the return visits were not available. it is possible that some patients were initially misdiagnosed or mistreated, or experienced progression or some complications in their illness leading to their return visits to the clinic. also, some patients may have been ill because of another pathogen causing ili-related symptoms, or that some patients may have been exposed to ph1n1 during the initial clinic visit. availability of this information would have been beneficial in providing contexts to our results. second, information on socioeconomic status, race/ethnicity, and health insurance coverage of the patients visiting the clinics were not available for this study. such data would have enabled identification of more demographic-specific healthcare utilization patterns in the context of ph1n1. however, being a private clinic, we assumed that our cohort represented mostly insured population that may experience different and fewer socioeconomic stressors than uninsured or underinsured populations. in addition, some variables such as vaccination history of the patients were either incomplete or unavailable. third, while the definition of ili is well understood, the decision to administer the test is at the discretion of the individual healthcare worker. individual variation may also have existed between healthcare workers who administered the rapid influenza tests. fourth, we used data from a single multispecialty clinic because it provided the largest pool of data with the most complete information covering the period of interest. this implies that caution should be exercise in interpreting the outcomes as they may not be representative of the ili activities in the houston metropolitan area during the 2009 influenza pandemic. fifth, the surveillance data did not capture whether patients’ levels of exposure to information about influenza or ph1n1 from traditional or social media sources had any influence on their decision to seek care. it is also possible that the return visits may have been due to clustering effects associated with common factors such as shared living space and similar access to information [25], which may have had a direct impact on both the disease transmissions and beliefs regarding medical care utilization. finally, we acknowledge that there could be some biases toward predictors with more variance or levels or due to a small change in the learning data with possible direct impact on the structure of the decision tree. however, our model diagnostics generally indicated that our current model was stable. model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi despite the constraints that these limitations may have on generalizability, application to practice, and/or utility of our findings, we relied on the best information available at the time to identify the patients’ subgroups that are likely to be at high risk of encountering return visits, if similar pandemic were to occur. the use of actual facility-level data (instead of self-reported survey data) and the segmentation procedure of the recursive partition model allowed for the display of reliable terminal nodes’ discriminatory ability of the associated predictor factors resulting in homogeneous risk strata. in addition, we conducted a series of model diagnostics including cross-validation and sensitivity analyses to support the base-case estimations. consequently, the tree-based approach applied empowered predictive model with high accuracy and stability, and enhanced ease of interpretation and understanding by the medical community and other stakeholders. conclusions return visits accounted for a small proportion of the medical visits, with majority of those being associated with children and young adult patients. our study helped to empirically identify and rank the subgroups at high risk of return visits, and consequently, patients who could benefit from intense outpatient referral or intervention program to prevent unnecessary return visits. furthermore, findings from our study could be used by policymakers as part of a decision support system to create awareness and understanding of surges due to recurrent utilization of the healthcare system during pandemics and emergency preparedness. understanding the dynamics in care-seeking behavior during the 2009 h1n1 pandemic is important to help prepare for future outbreaks of pandemic influenza viruses. this would assist policymakers with appropriate resource allocation, and in the design of policy initiatives aimed at mitigating surges and recurrent utilization of the healthcare system. it is recommended that future studies should take into consideration the limitations identified in the present study including patients’ perspectives on the social and medical factors that may lead to patients’ return visits and health care surge capacity. summary points what was already known? • during the 2009 h1n1 influenza pandemic (ph1n1), the proportion of outpatient visits to emergency departments, hospitals and clinics became elevated. • the 2009 ph1n1 did not fully test the health care system’s ability to meet a surge in demand for care. • considerable efforts in recent years have been dedicated to mathematical modeling studies to complement disease surveillance efforts in the planning of interventions against emerging pandemics. what this study added to our knowledge • this study used model-based recursive partitioning to predict return visits to multispecialty clinic during the 2009 ph1n1. • this study empirically identified and ranked patients at high risk of return visits for 2009 ph1n1 into subgroups that could benefit from intense outpatient referral or intervention program to prevent avoidable return visits. model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi • this study brings about understanding of the dynamics of care-seeking behaviors during 2009 ph1n1 and the associated predictive factors that could help the medical community prepare for future outbreaks of pandemic influenza virus. authors’ contributions om conceived and designed the study, conducted the data analysis and results interpretation, prepared the initial draft of the manuscript, and participated in the critical review and revision of the article. sk supervised the acquisition of data, participated in results interpretation, and critical review and revision of the article. both authors read and approved the final version of the article for publication. acknowledgements we thank the physicians and nurses at the clinics in houston, tx that participated in the hhd issp for their support. the generous contributions and assistance of the houston health department staff at the bureaus of epidemiology and laboratory services to the issp are also greatly appreciated. the findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the houston health department. funding the authors received no financial support for the research, authorship, and/or publication of this article. conflict of interest disclosure the authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. corresponding author dr. osaro mgbere, disease prevention and control division, houston health department, 8000 north stadium drive, houston, texas, 77054, usa. email: osaro.mgbere@houstontx.gov; tel: 1-832-393-4593. appendix a: supplementary materials supplementary materials consist of data provided by the authors that are published to benefit the reader. the contents of this supplementary data are the sole responsibility of the authors. all questions should be directed to the corresponding author dr. osaro mgbere at osaro.mgbere@houstontx.gov. model-based recursive partitioning of patients’ return visits to multispecialty clinic during the 2009 h1n1 pandemic influenza (ph1n1) 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi supplementary material appendix a: receiver operating characteristic curve parameters estimates for return and non-return visits to multispecialty clinic probability 1-specificity sensitivity sens-(1-spec) true positive true negative false positive false negative . 0.0000 0.0000 0.0000 0 2825 0 135 0.1341 0.0007 0.0148 0.0141 2 2823 2 133 0.1233 0.0131 0.0593 0.0462 8 2788 37 127 0.1173 0.0283 0.1037 0.0754 14 2745 80 121 0.1076 0.1207 0.3037 0.1830 41 2484 341 94 0.0817 0.1274 0.3259 0.1985 44 2465 360 91 0.0798 0.1565 0.3481 0.1917 47 2383 442 88 0.0730 0.1742 0.4000 0.2258 54 2333 492 81 0.0709 0.2152 0.4667 0.2514 63 2217 608 72 0.0693 0.2775 0.5926 0.3151 80 2041 784 55 0.0633 0.3370 0.6741 0.3371* 91 1873 952 44 0.0475 0.3961 0.7111 0.3150 96 1706 1119 39 0.0410 0.5713 0.8741 0.3027 118 1211 1614 17 0.0268 0.5749 0.8815 0.3066 119 1201 1624 16 0.0231 0.6138 0.8889 0.2751 120 1091 1734 15 0.0210 0.6188 0.8963 0.2775 121 1077 1748 14 0.0175 0.6202 0.8963 0.2761 121 1073 1752 14 0.0159 0.6205 0.8963 0.2758 121 1072 1753 14 0.0156 0.6411 0.9037 0.2626 122 1014 1811 13 0.0150 0.6789 0.9111 0.2322 123 907 1918 12 0.0137 0.6821 0.9111 0.2290 123 898 1927 12 0.0134 0.9140 0.9778 0.0638 132 243 2582 3 0.0101 0.9211 0.9778 0.0567 132 223 2602 3 0.0087 1.0000 1.0000 0.0000 135 0 2825 0 0.0087 1.0000 1.0000 0.0000 135 0 2825 0 the row with the highest 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of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e4, 2020 ojphi 46. seibold h, zeileis a, hothorn t. 2016. model-based recursive partitioning for subgroup analyses. int j biostat. 12(1), 45-63. doi:https://doi.org/10.1515/ijb-2015-0032. pubmed 47. mistry d, stallard n, underwood m. 2018. a recursive partitioning approach for subgroup identification in individual patient data meta-analysis. stat med. 37(9), 1550-61. doi:https://doi.org/10.1002/sim.7609. pubmed 48. centers for disease control and prevention (cdc). 2009. serum cross-reactive antibody response to a novel influenza a (h1n1) virus after vaccination with seasonal influenza vaccine. mmwr morb mortal wkly rep. 58(19), 521-24. pubmed 49. iuliano ad, reed c, guh a, et al. 2009. notes from the field: outbreak of 2009 pandemic influenza a (h1n1) virus at a large public university in delaware, april-may 2009. clin infect dis. 49(12), 1811-20. doi:https://doi.org/10.1086/649555. pubmed https://doi.org/10.1515/ijb-2015-0032 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27227717&dopt=abstract https://doi.org/10.1002/sim.7609 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29383818&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19478718&dopt=abstract https://doi.org/10.1086/649555 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19911964&dopt=abstract convenience stores are associated with early childhood obesity among children in low-income households understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda maria koleilat 1 , shannon e. whaley 1 , abdelmonem a. afifi 2 , leobardo estrada 3 , gail g. harrison 4 1 dept of research and evaluation, phfe wic program, irvindale, ca 2 dept of biostatistics, ucla school of public health, los angeles, ca 3 dept of urban planning, ucla school of public affairs, los angeles, ca 4 dept of community health sciences, ucla school of public health, los angeles, ca abstract the aim of this study was to examine the association between the local food environment and obesity proportions among 3to 4-year-old children who were participants in the wic program in los angeles county using spatial analyses techniques. arcgis, spatial analysis software, was used to compute the retail food environment index (rfei) per zip code. geoda, spatial statistics software was employed to check for spatial autocorrelation and to control for permeability of the boundaries. linear regression and anova were used to examine the impact of the food environment on childhood obesity. fast-food restaurants represented 30% and convenience stores represented 40% of the sum of food outlets in areas where wic participants reside. although there was no statistically significant association between rfei and 3to 4-year-old obesity proportions among wic children, analysis of variance (anova) tests demonstrated statistically significant positive associations between obesity and the number of convenience stores and the number of supermarkets. our findings suggest that rfei, as currently constructed, may not be the optimal way to capture the food environment. this study suggests that convenience stores and supermarkets are a likely source of excess calories for children in low-income households. given the ubiquity of convenience stores in low-income neighborhoods, interventions to improve availability of healthy food in these stores should be part of the many approaches to addressing childhood obesity. this study adds to the literature by examining the validity of the rfei and by demonstrating the need and illustrating the use of spatial analyses, using geoda, in the environment/obesity studies. key words: spatial analysis; geoda; retail food environment index; convenience stores; fastfood restaurants; supermarkets; produce vendors; childhood obesity; wic http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 introduction high rates of obesity in early childhood are a major public health problem in the united states. 1 the high prevalence of obesity in early childhood is of concern because of its associated complications and its link with adult obesity. 2 obese children are already experiencing predominately adult illnesses such as hypertension, hyperlipidemia, abnormal glucose tolerance, 3 and type 2 diabetes. 4 children from minority communities, and particularly latino children, are disproportionately affected by the epidemic of obesity. 5 previous studies have hypothesized that the communities in which low-income and minority families reside may contribute to the increased risk of obesity. 6 this study aims to examine the impact of food environments, including fast food outlets, convenience stores, grocery stores and farmer’s markets, on the prevalence of obesity among 3to 4-year old participants of the special supplemental nutrition program for women, infants and children (wic). the social ecological model for obesity prevention provides the context for this work, specifically the idea that health is a function of the built environment in which one lives. 7, 8 understanding the complex interrelationships between individual factors, social environmental factors and the built environment can help define obesity prevention strategies. the media and the scientific literature have frequently blamed fast-food restaurants for higher caloric intake, lower vegetable consumption, rising obesity rates and health disparities in the us. 9,10 recently, the role of small convenience stores/corner stores has been highlighted. 11-13 a recent study showed that calories from snack foods were a likely culprit for higher obesity rates in south los angeles. 12 south los angeles had a dramatically higher concentration, compared to other sections of los angeles, of the type of small convenience stores that sell high calorie snacks. in another study examining the shopping patterns of schoolchildren in urban philadelphia, borradaile and colleagues found that half of the 800 students in their sample reported shopping at a corner store at least once a day, five times a week. 11 almost one third visited a store both before and after school; students spent about $1 and purchased 356 calories of snack foods including chips, candy, gum and sugary beverages during each visit. conversely, a study exploring the role of supermarkets on obesity and its related chronic diseases has documented that the presence of supermarkets is related to lower rates of obesity. 14 access to supermarkets has been associated with more frequent fruit and vegetable consumption. 15 fullsize supermarkets were found to be less prevalent in racially mixed 16-20 and low-income 17, 18-20 neighborhoods compared to higher-income and white neighborhoods. there has been speculation that this unequal distribution of healthy and unhealthy retail food outlets among communities is a likely contributor to the disparities in the risk of obesity. 20, 21 the current research explores the association between the proportions of childhood obesity among 3to 4-year old wic participants and the relative availability/distribution of various types of food outlets. we examine the distribution of fast-food restaurants, convenience stores, supermarkets, and produce vendors in areas where wic participants reside, hypothesizing that more fast-food restaurants and convenience stores and fewer supermarkets and farmers’ markets in close proximity to households are associated with higher obesity prevalence among 3to 4year-old children in those households. this study adds to the literature of the retail food environment and obesity by examining the very young 3to 4-year-old wic children in los angeles county who are predominately latino, a http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 group so far ignored in studies of the retail food environment and obesity. in los angeles county, the majority of children under five participating in wic are latino and nearly 40% have bmi levels greater than the 85 th percentile. 22 given the broad reach of the wic program in los angeles county to reach over 67% of all births in the county, 23 linkages of wic administrative data with retail food environment data have great potential to illustrate the role of community level factors on early childhood obesity. this study adds significantly to the literature by evaluating the validity of the rfei, a widely used measure that has not been validated before. in addition, this study illustrates how to use spatial analyses to account for spatial correlation and permeability of geographic boundaries, two analytical issues so far ignored in the food environment/obesity studies. methods data sources wic data: extensive information about wic families is collected regularly in the california integrated statewide information system (isis), including socio-demographic and health information. while isis has historically enabled the analysis of wic administrative data at either the state level or local wic agency level, it was not electronically aggregated for los angeles county, where seven local agency wic programs provide services. funded by first 5 los angeles (f5la), the data mining research partnership addressed the critical gap in information about low-income families in los angeles county. this partnership created an electronic system that supported the aggregation of wic administrative data (isis) across the county, with an annual data download in march of each year. the 2008 wic data included information on 538,555 wic participants in los angeles county including women, infants and children. zip code level data were drawn from this dataset for this analysis. obesity status of 3to 4-year-old children: the most recent weight and height measurements for each child downloaded in march 2008 were analyzed. children ages 2-5 are considered obese if their body mass index (bmi) is greater than or equal to the 95th percentile with regard to the standard reference population. 24 as part of the wic eligibility requirements, children must be weighed and measured every 6 months at a wic site. all heights and weights are captured in isis. the child’s height and weight measurements are taken as part of the wic standard protocol. this protocol involves removing shoes and heavy outwear and measuring height using a stadiometer (model pe-wm-60-76) and weight with a scale (health-o-meter 402lb) that is calibrated every 6 months. recent work by crespi et al (in press: personal communication) 25 has validated the use of height/weight measurements taken by phfe wic staff. demographic measures: income, race/ethnicity and education are potential covariates of any association between residential environments and obesity. income was represented by proportions of wic participants per zip code with income less than 100% of federal poverty level vs. proportions of wic participants with incomes at or above 100% of poverty level. race was represented by proportions of latino wic participants per zip code vs. non-latino wic participants. education was represented by proportions of wic participants per zip code with less than 12 years of education vs. proportions of wic participants with 12 years of education or more. http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 food retailer data: the infousa business file from esri (redlands, ca) was utilized to assess the retail food environment (spring 2009). the database included 80,047 observations. from this dataset, we selected all fast-food restaurants; convenience stores; supermarkets and other grocery stores; and produces vendors according to the north american industry classification system (naics) code for restaurants. the naics is an industry classification system used to classify economic units that have similar processes in the same industry and specific codes used in this analysis are listed below. fast-food restaurants: as in a study published by the california center for public health advocacy (ccpha), 26 fast-food restaurants were defined following the national restaurant association’s distinction between “table service” and “quick service (fast-food)” restaurants. in addition to counter service, fast-food restaurants are characterized by meal service (vs. snacks, dessert, and coffee) and low price (less than $7/meal). we did not limit the inclusion of fast-food outlets to the ones with five or more locations with the same names. businesses with the following naics codes for restaurants were included: 72211002, 72211011, 72211012, 72211013, 72211016, 72211020, 72221101, 72221103, 72221104, and 72221105. convenience stores. businesses with naics code 44512001, such as 7-elevens and other chains that do not sell gasoline or other fuel, were included as convenience stores. we also included supermarkets and grocery stores that had two or fewer employees (naics codes: 44511001, 44511002, 44511003, 44511004, and 44511005). supermarkets and other grocery stores: establishments with more than two employees engaged in retailing canned and frozen food, fresh fruits and vegetables, fresh and prepared meats, fish, and poultry were included as supermarkets and grocery stores (naics codes included 44511001, 44511002, 44511003, 44511004, and 44511005). produce vendors: produce vendors included produce stores (naics codes 44523001 and 44523003), and 2009 farmers’markets. 27 information regarding the actual physical locations of farmers’ markets was verified using google maps. statistical analysis sas version 9.2 (2008, sas institute, inc, cary, nc) was used to describe the sample. the number of fast-food restaurants, convenience stores, supermarkets and other grocery stores and produce vendors was determined for each zip code in los angeles county. arcgis version 9.3.1 (2009, esri, redlands, ca) was used to compute a retail food environment index (rfei) for every zip code in los angeles county. rfei measures the relative availability of different types of food retailers in zip codes where wic participants reside. 26, 28 it is defined as follows: rfei = ______fast-food restaurants + convenience stores____________ supermarkets + other grocery stores + produce vendors http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 the index was determined for each zip code in los angeles county where wic participants resided. a high rfei indicated that a zip code had a large number of fast-food restaurants and convenience stores compared to supermarkets, other grocery stores and produce vendors. for example, a zip code with an rfei of 2.0 had twice as many fast-food restaurants and convenience stores as supermarkets, other grocery stores and produce vendors. 26 linear regression and anova were used to examine the impact of the food environment on childhood obesity. first, three linear regressions were conducted to examine the association between rfei and obesity proportions. model 1 included only rfei as a predictor, model 2 included the spatially lagged rfei as a predictor and model 3 included rfei, income, race and education as predictors. zip codes were then divided into quartiles based on the proportions of 3to 4-year old children who were obese (lowest, 2 nd , 3 rd and highest quartile). anova was used to compare the mean number of each type of food outlet by level of obesity in that zip code. spatial data analysis why spatial data analysis? most studies investigating the influence of the built environment on obesity use nonspatial methods such as multilevel modeling 29 or general linear models, 30 ignoring two analytical issues that can bias results in studies of geographic places: spatial autocorrelation and permeability of the boundaries. 31 legacy geoda 0.95i (2003, arizona state university), a spatial statistics software, was used to help account for these analytical issues and better model the problem of childhood obesity among wic participants. descriptive spatial techniques exploratory spatial data analysis (esda) was used to examine whether data collected from different areas exhibited complete randomness or spatial dependence, 32 in other words to identify spatial autocorrelation and spatial outliers. 33,34 as part of esda, the moran scatter plot was constructed and the moran’s i coefficient was computed. the latter allows quantification of the extent to which spatial association is present. in general, a significant moran coefficient indicates that the rates of obesity across neighborhood areas are correlated so spatial statistics may be necessary. weight matrices it is necessary to identify the “adjacencies” matrix or connection matrix, meaning identify which units are neighbors to one another prior to computing the moran’s i coefficient. there are two types of weight matrices; the contiguity-based spatial weight matrix and the distance-based spatial weight matrix. 31,33 for this study, based on the hypothesis that people might shop or have a meal at food outlets in neighboring zip codes, the contiguity-based matrix (queen’s matrix) was selected. this identifies neighboring areas based on shared borders and vertices. spatially lagged rfei the same queen’s weight matrix was employed to compute the spatially lagged rfei. the spatially lagged rfei is the sum of spatial weights multiplied by values for rfeis at neighboring locations. including the spatially lagged rfei as an independent variable in the http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 regression model examines whether a relationship exists between the type and distribution of food outlets in neighboring areas and the outcome measured within each target area. results descriptive summary statistics sample characteristics are shown in table i. the study area comprised 266 zip codes in los angeles county. almost 20 % of the children were obese. more than 68% of the participants were latino and almost 14% were white. the majority of wic participants in this sample, almost 63%, resided in households with incomes below 100% of the us federal poverty level and almost 40% of the participants resided in households where the highest level of education achieved was less than12 years. a total of 6,436 retail food outlets were identified in los angeles county in 2009, 40% of which were convenience stores, 30% were fast-food restaurants, 24% were supermarkets, and 6% were produce vendors. the average rfei for areas in los angeles county where wic participants resided was 2.68, meaning that neighborhoods where wic participants lived had more than twice as many fast-food restaurants and convenience stores as they did grocery stores and produce vendors. more than 85% of wic participants resided in areas with an rfei ranging between 1 and 5. exploratory spatial data analysis (esda) global spatial pattern: moran’s i the global moran‘s i test statistic was used to quantify the degree to which data were clustered or uniformly distributed to check whether spatial autocorrelation existed. the results of the moran’s i scatter plot (moran’s i = 0.0475 and p = 0.072) did not suggest a strong spatial autocorrelation. therefore, there was no need to run a spatial regression model, an ordinary least squares regression was appropriate. regression analyses and anova the results of the simple linear regression models 1 and 2 showed no significant differences in the proportion of obese 3to 4-year-old wic children by rfei (p = 0.69) and no statistically significant association between the proportion of 3to 4-year-old obese wic children and the spatially lagged rfei (p = 0.95). results of the model 3 multiple regression analysis showed that when the proportion of latino wic participants increased by 1%, the proportion of obese children increased by 0.14%, holding all other variables constant (p = 0.04) (table ii). no other predictor variables were significantly related to childhood obesity (table ii). given the nonsignificant statistical association between rfei and proportions of obese 3to 4year-old wic children, we examined the association between the number of stores for each type of food outlets and proportions of obese 3to 4-year-old wic children. anova showed that while the number of fast-food restaurants, produce stores and farmers’ markets did not vary much across quartiles of obesity for 3and 4-year-old children, the number of convenience stores and supermarkets increased significantly across quartiles of obesity for 3to 4-year-old children http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 (table 3). rates of childhood obesity were highest in communities with more convenience stores and in communities with more supermarkets. table 1. summary statistics of wic variables on average across zip codes, los angeles county, 2008 variable “mean” or “percent” bmi ≥ 95th percentile 19.6% race asian 8.4% black 8.2% latino 67.9% native american 0.5% pacific islander 0.4% other 0.6% white 13.8% poverty level below 100% federal poverty level 62.5% 100-133% federal poverty level 19.3% 133-185% federal poverty level 16.8% above 185% federal poverty level 1.4% highest level of education completed in the household 0-4 years 3.1% 5-8 years 12.4% 9-11 years 24.1% 12 years 38.6% 13-15 years 15.2% 16 years and up 6.7% wic population per zip code (mean ±sd) ~2010 ± 2461 number of zip codes 266 table 2. multivariate regression results regressor variable coefficient se p value rfei 0.00 0.00 0.679 proportion of wic children residing in households with incomes below 100% of the us federal poverty level vs. those with incomes above this level 0.09 0.06 0.145 proportions of hispanics wic participants compared to proportions of other races 0.15 0.04 0.045 http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 proportions of wic households where the highest level of education completed is less than 12 years compared to those with 12 years or more 0.04 0.05 0.510 table 3. summary statistics of food store outlets per quartiles of obesity among 3to 4-yearold children, los angeles county, 2008 (full sample of zip codes n = 266) number of outlets per zip code (mean (sd)) quartiles of obesity fast-food restaurants convenience stores* supermarkets* produce stores farmers’ markets lowest quartile: 0.0016.67% (n = 65) 6.8 (5.1) 4.1 (5.2) 4.0 (3.2) 0.5 (1.1) 0.5 (0.7) 2nd quartile: 16.6820.16% (n = 68) 7.0 (4.2) 7.1 (6.4) 5.2 (3.3) 0.5 (0.7) 0.4 (0.6) 3rd quartile: 20.17%22.74% (n = 67) 8.0 (4.4) 13.8 (10.3) 7.5 (4.0) 1.1 (2.0) 0.4 (0.7) highest quartile: 22.75%52.63% (n = 66) 6.4 (3.8) 12.8 (14.1) 6.8 (4.7) 1.8 (10.4) 0.4 (0.6) *p <0.0001, as indicated by analysis of variance discussion we found no statistically significant association between the proportion of 3to 4-year-old obese wic children and the relative availability of different types of food retailers as represented by rfei. also, the spatially lagged rfei did not turn out to be statistically significant, implying a lack of a spatial lag effect for our main independent variable, rfei. these findings are consistent with the few studies that have looked at the environmental determinants of childhood obesity, 35 suggesting that the built-environment-childhood obesity relationship is still poorly understood. our findings also suggest that the rfei may obscure the effects of the different components of the index on childhood obesity. supermarkets are in the denominator in the rfei, and are thus considered protective. in fact, examination of availability of each type of food outlet individually showed that the number of convenience stores and the number of supermarkets increased significantly as obesity rates of 3to 4-year old children went up, thus calling into question the protective effect of supermarkets. until we know more about the effects of supermarkets on childhood obesity, results based on the rfei should be interpreted with caution. while in past years the tendency has been to focus on fast food outlets as contributing to the rising obesity epidemic, findings from this study suggest that the ubiquitous availability of convenience stores should not be overlooked. descriptive statistics demonstrated that while fastfood restaurants represented 30% of the total number of food outlets in areas where wic http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 participants resided, convenience stores totaled 40%, despite excluding convenience stores associated with gas stations from these analyses. rates of childhood obesity were highest in communities with more convenience stores and in communities with more supermarkets. these findings are not surprising given the limited availability and high price of fresh produce and widespread availability of high calorie foods in convenience stores. these small stores sell items such as soft-drinks, candy, ice-cream, and occasionally grocery items. most of these stores are open 24 hours, making them a source of readily available inexpensive calorie-dense food. findings are consistent with recent studies that have examined the link between convenience stores/corner stores and obesity. 11-13, 36 more qualitative research is necessary if we are to understand the obesogenic factors within these stores and the associations between food environment, food retail usage and food consumption patterns. findings regarding supermarkets and childhood obesity were not consistent with the majority of literature as supermarket availability was associated with higher obesity rates among 3to 4year-old children. studies have found that full-size supermarkets are less prevalent in predominately racially mixed and low-income neighborhoods compared to higher income and white neighborhoods. 16-20 however, living near a supermarket has been found to be associated with lower prevalence of obesity and overweight, presumably due to greater availability of healthy food options. 14 these data suggest that the impact of supermarkets on childhood overweight merits more study. it is likely that supermarkets carry both healthy and unhealthy food items and the decision regarding which items to buy depends on factors other than availability alone, such as price, preference, and location in the store. in fact, some studies have found that supermarkets in census tracts with predominately black residents generally carry fewer healthy food options than supermarkets in other neighborhoods. 37 another study found that the ratio of energy-dense snack foods to produce in full-size supermarkets is greater than that of corner stores and medium sized grocers and only exceeded by convenience stores. 38 as this was not a qualitative study, we cannot describe the foods carried by supermarkets in lowincome community. moreover, the significant correlation (p < .0001) between the number of supermarkets and number of convenience stores could very well be another potential explanation for the unexpected association between child obesity rates and the number of supermarkets. future research is needed to better understand the important role of supermarkets and access to healthy and unhealthy foods. conclusion in conclusion, the current study suggests that convenience stores and supermarkets are associated with early childhood overweight and may be a source of excess calories. healthy food availability and accessibility is a matter of equity and social justice and healthy food must be made available and affordable in every community. more studies are needed if we are to understand how to make healthy food more available and accessible in low-income communities. improvements to this line of investigation should be made and include evaluating different environmental factors and using spatial analyses techniques to control for spatial autocorrelation and permeability of the boundaries. meanwhile, given the ubiquity of convenience stores, these food outlets should be regarded as a potential asset for improving diet quality of residents in lowincome neighborhoods. interventions providing incentives to store owners to improve http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 availability of healthy foods in their stores should be part of the many approaches to addressing childhood obesity. study limitations our study has limitations that need to be acknowledged. first, due to confidentiality reasons, we were not able to obtain the participants’ individual-level data and addresses; therefore this study incorporated only group level data. lack of individual-level data meant that we were not able to compute the simultaneous effects of group-level variables and individual-level variables on the outcomes of interest; and lack of addresses meant that we were not able to investigate measures of distance. however, although the use of multilevel modeling is a great advancement, these models often fail to examine model fit from a spatial perspective and to recognize that there is additional information to be gained by using spatial statistics tools, such as examining models for spatial autocorrelation in the unexplained variance. that is why our results concerning the relationship between rfei and early childhood obesity using spatial analyses techniques does make an original contribution to the literature in the area of obesity and the built environment. our second limitation concerns causality and generalization of our findings. like the majority of studies in this field, this is a cross-sectional study and causality cannot be determined. moreover, due to the racial and geographic distribution of this population, we cannot generalize our findings beyond our study population: predominantly 3to 4-year-old wic children in los angeles county. insomuch as this is a group so far ignored in studies of the retail food environment and obesity, we feel this study fills an important gap in the literature. finally, the use of infousa data has been criticized for having many missing outlets. 39 it is unlikely, however, that this bias would vary as a function of our childhood obesity rates and thus should not have influenced our findings. despite these limitations, our study has unique strengths that include examination of the validity of the rfei index that was previously used in public policy papers but never validated, use of spatial analyses to account for the analytical issues that arise from studying neighboring areas, and reliance on measured height and weight data, thus avoiding a potential significant misclassification bias that can arrive from self-reported heights and weights. 40 acknowledgements this work was supported by usda/csrees#2005-35215-16075 and first 5 la. corresponding author maria koleilat postdoctoral research analyst department of research and evaluation phfe-wic program irwindale, ca email: mariak@phfewic.org mailto:mariak@phfewic.org http://ojphi.org understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 references 1. ogden, c.l., carroll, m.d., curtin, l.r., lamb, m.m., flegal, k.m. 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(2003). geoda™ 0.9 user’s guide. urbana: spatial analysis laboratory, department of agricultural and consumer economics, university of illinois, urbana champaign and center for spatially integrated social science. http://www.healthycity.org/ http://www.phfewic.org/projects/docs/datamining/survey/wicdatareport2003-2009.pdf http://www.cdc.gov/obesity/childhood/ http://www.acwn.co.la.ca.us/scripts/cfm_print.cfm.%20%20%20accessed%20july%202009 http://www.acwn.co.la.ca.us/scripts/cfm_print.cfm.%20%20%20accessed%20july%202009 http://www.publichealthadvocacy.org/pdfs/rfei%20policy%20brief_finalweb.pdf http://ojphi.org http://dx.doi.org/10.1017/s136898000700007919 http://dx.doi.org/10.1017/s136898000700007919 http://dx.doi.org/10.1017/s136898000700007919 http://dx.doi.org/10.2105/ajph.92.11.176120 http://dx.doi.org/10.2105/ajph.92.11.176120 http://dx.doi.org/10.1016/j.amepre.2008.09.02522 http://dx.doi.org/10.1016/j.amepre.2008.09.02522 http://dx.doi.org/10.1016/j.amepre.2008.09.02522 http://www.healthycity.org/ http://www.phfewic.org/projects/docs/datamining/survey/wicdatareport2003-2009.pdf http://www.phfewic.org/projects/docs/datamining/survey/wicdatareport2003-2009.pdf http://www.cdc.gov/obesity/childhood/ http://www.publichealthadvocacy.org/searchingforhealthyfood.html http://www.publichealthadvocacy.org/searchingforhealthyfood.html http://www.acwn.co.la.ca.us/scripts/cfm_print.cfm http://www.publichealthadvocacy.org/pdfs/rfei%20policy%20brief_finalweb.pdf http://www.publichealthadvocacy.org/pdfs/rfei%20policy%20brief_finalweb.pdf http://www.publichealthadvocacy.org/pdfs/rfei%20policy%20brief_finalweb.pdf http://dx.doi.org/10.2105/ajph.2004.06095430 http://dx.doi.org/10.2105/ajph.2004.06095430 http://dx.doi.org/10.1016/j.ypmed.2003.09.02931 http://dx.doi.org/10.1016/j.ypmed.2003.09.02931 http://dx.doi.org/10.1093/swr/30.4.19832 http://dx.doi.org/10.1093/swr/30.4.19832 understanding the relationship between the retail food environment index and early childhood obesity among wic participants in los angeles county using geoda 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 34. anselin l. 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colabianchi n, lamichhane ap, barnes tl, hibbert jd, et al. 2010. validation of 3 food outlet databases: completeness and geospatial accuracy in rural and urban food environments. am j epidemiol. 172, 1-10. http://dx.doi.org/10.1093/aje/kwq292 40. akinbami lj, ogden cl. 2009. childhood overweight prevalence in the united states: the impact of parent-reported height and weight. obesity (silver spring). 17(8), 1574-80. http:// dx.doi.org/10.1038/oby.2009.1 http://www.ncbi.nlm.nih.gov/pubmed?term=%22galvez%20mp%22%5bauthor%5d http://www.ncbi.nlm.nih.gov/pubmed?term=%22hong%20l%22%5bauthor%5d http://www.ncbi.nlm.nih.gov/pubmed?term=%22choi%20e%22%5bauthor%5d http://www.ncbi.nlm.nih.gov/pubmed?term=%22liao%20l%22%5bauthor%5d http://www.ncbi.nlm.nih.gov/pubmed?term=%22godbold%20j%22%5bauthor%5d http://www.ncbi.nlm.nih.gov/pubmed?term=%22brenner%20b%22%5bauthor%5d http://ojphi.org http://dx.doi.org/10.1093/epirev/mxm00936 http://dx.doi.org/10.1093/epirev/mxm00936 http://dx.doi.org/10.1016/j.acap.2009.05.00337 http://dx.doi.org/10.1016/j.acap.2009.05.00337 http://dx.doi.org/10.1007/s11524-009-9390-339 http://dx.doi.org/10.1007/s11524-009-9390-339 http://dx.doi.org/10.1093/aje/kwq29240 http://dx.doi.org/10.1093/aje/kwq29240 ojphi-06-e87.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 50 (page number not for citation purposes) isds 2013 conference abstracts accuracy of eidss software prognosis on cchf natural foci activity in kazakhstan aizhan s. esmagambetova1, stanislav v. kazakov2, alexey v. burdakov*3, kenes s. ospanov4, kakimzhan k. kyraubaev4, erlan b. sansyzbaev2, veronika p. sadovskaya2 and andrey o. ukharov3 1committee of state sanitary and epidemiological surveillance of the ministry of health of kazakhstan, astana, kazakhstan; 2kazakh scientific center of quarantine and zoonotic infections, almaty, kazakhstan; 3black & veatch, overland park, ks, usa; 4scientific center of sanitary and epidemiological expertise and monitoring, almaty, kazakhstan � �� �� �� � � �� �� �� � objective ������� �� ��� ��� �� ������� ����� ���� ��� �� ������ �� � �� � ������� ��������������� � ���� ������ �� ���� ����� ������� ����!� ������"���#� � ��$�%��������& ������� �� �� ����� �� � � '����������( ������)���� ���� ��)������ �'())!����� ��*+ introduction , � ����������������� �&������� ����� ����� ���������� ��� �� $�%�������+�-��������� ���������� �������� � ������� . � �� ��+� ���������������� ����� �����������/� ��� � �� ������� ����� �� # ���������������� ����#� �� ��� �+�0��� � �� ������������� ��� � � � ���� ���� �� ���.��������� �� ��������� ������ ���� ���������� ����� ����� � ��������� �� ���� �� �� �� ���� ������ ������� ������ 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� � :�������� ������� ��� ��������"##()�&3�"�c"#(�"&? *ta-chien chan e-mail: dachianpig@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e77, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts syndromic surveillance of motor vehicle crash related injuries in nebraska sandra gonzalez*1, 2, ashley newmyer1, guangming han1, ming qu1 and thomas safranek1 1division of public health, epidemiology & informatics unit, nebraska department of health and human services, lincoln, ne, usa; 2university of nebraska-lincoln, lincoln, ne, usa objective the objective of this pilot study is to demonstrate the value of emergency department (ed) syndromic surveillance (ss) data to aid the surveillance of motor vehicle crash (mvc) related injuries in nebraska. introduction motor vehicle crashes (mvc) are a significant public health problem in nebraska. these events cost nebraska $1.6 billion a year, are the leading cause of injury death, and the fourth leading cause of injury hospital treatment in the state. speeding, driving under the influence, distracted driving, and adverse weather are the main causes of mvc in nebraska. effective prevention efforts to reduce mvc related deaths and injuries depend greatly on a surveillance system that monitors the frequency of these events so stakeholders may ascertain the mvc related causes and impact on the state. currently, the nebraska department of health and human services (ndhhs) crash outcome data evaluation system (codes) monitors mvc related death and injuries by linking the following databases statewide crash data, hospital discharge data (hdd), trauma registry, emergency medical system (ems) data and death certificate data1. although this system has been effective in identifying the causes of mvc-ralated injuries and supporting community based highway safety programs, it is limited by the lack of immediate availability of data. ‘an ed based ss system could potentially be used to enhance mvc injury surveillance by allowing the timely detection of clusters, anomalies and trends. therefore, and ed ss system could be incorporated to support an efficient and rapid prevention response to mvc-related injuries. methods the reporting consistency of the ss ed data was assessed by comparing 2011-2012 ss ed data to ed hdd from hospital a, located in douglas county, ne. syndromic surveillance ed 20112014 data and ed hdd 2011-2012 data were queried for icd9-cm codes associated with mvc-related injuries (e810-e819). pierson correlation coefficients were calculated to determine the reporting consistency between the two databases. the mean time in hours between patient visit and receipt of ed ss record at ndhhs was also calculated. time series graphs of weekly mvc-related injury ed visits were created for years 2011-2014. climate data was analyzed for snow depth, precipitation and severe weather. dates corresponding to relevant climate, sports and entertainment events were identified. results for the time period of 2011 and 2012, significant correlations (> 0.70, p< 0.001) were detected between ss ed data and ed hdd. the mean time of receipt of ed ss records was 12 hours. each year, between 2011 and 2014 a spike of mvc injury related ed visits occurred during significant climate, sports, and entertainment events. the analysis of the 2011-2014 ss ed data indicates a possible temporal trend in the incidence of mvc-related injuries in ne. during winter, mvc-related injuries were more common during weeks with higher snow depth or freezing rain. moreover, higher proportionsof mvc-related injuries were also observed during summer weeks that corresponded to relevant entertainment events. results suggest that there is a strong correlation between weather, sports and entertainment events and spikes in mvc-related injuries. conclusions the pilot study indicates that mvc injury related icd9-cm codes in ss ed data are highly correlated with ed hdd. results also suggest that ss ed data can be used for the timely identification of mvc-related injuries in nebraska. thus allowing the rapid identification of mvc hot spots and the timely deployment of accident prevention measures. keywords syndromic surveillance; emergency department; injury; motor vehicle crash acknowledgments authors: sandra gonzalez, phd; ashley newmyer, mph; guangming han, phd; ming qu, phd; thomas safranek, md other contributors: bryan buss, dvm, mph; deborah hastings, md; qiao ma, phd; ran gu; gary white, david loyall, and jesse clarke references 1. newmyer a. crash outcome data evaluation system (codes) injury surveillance: vehicle crash and safety reports [internet]. lincoln (ne): nebraska department of health and human services, division of public health; [updated 2014 mar 18; cited 2014 sep 2] available from:http://dhhs.ne.gov/publichealth/pages/codes_trafficsafety.aspx *sandra gonzalez e-mail: sandra.gonzalez@nebraska.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e24, 2015 ojphi-06-e165.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 99 (page number not for citation purposes) isds 2013 conference abstracts surveillance of overdose-related emergency department visits in rhode island matthew lozier*1, colleen martin2 and daniel chaput3 1epidemic intelligence service, centers for disease control and prevention, atlanta, ga, usa; 2national center for environmental health, centers for disease control and prevention, atlanta, ga, usa; 3rhode island department of health, providence, ri, usa � �� �� �� � � �� �� �� � objective ������� ��� ������� ������� ��������������������������� ������ �������������� ���������������������� �� ������������� ����������� ��� �� ����� �� �������� � ���� �������� ����� � ��������� ��������� ����� ���� ����������� ����� ������������ �������� �����!����� �� �"��� ���� �������������� ������#$�� ���!��� ��!�� ��� �� ��� ����� � ������� � ��������% introduction ���� ��&����'&���()#*+�#,� ���� ���������� ��������� ��������� ���������������������� � �� ��+��� ������ ������� !� ����� ��� ���� ������� ���! �� ����� ���� � #%�-� � ������������������������������ � &����+������ ������� � ��� ��� ������� ���������� �� �� ������������� ����� ���� ����������� ���#%������ ������������������������������ ���� ��� �� ����� .�� � 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&�����()#*% ����� �����! ����� ���������� ��!��� ��%�"�� ��� �������� ��� ������������ ��������!������ ��� � ������� ����� ���� ���� ������� ������ �������� �� ���� ���"��������%��� �� ������������� ����������� !� ������ ��.���� ��!��� ��������� ������������� ��������������� ���� � ���"���������� ��! �� �������!� ����������������� ����� �� ���� ��� ��������� ������� ������� ���� ��� ��������% keywords �� �� ���� ��������� ��>� ����� ���� ��� ��������� ��>� ���� ��� ������� references #%�1�1%�d ���� � ������9����8�a������9� �� �������� ���9����������e� �� ������� �+�&����'&���()#*%�&&0��()#*>:(8@)*�,% *matthew lozier e-mail: mlozier@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e165, 2014 ojphi-06-e162.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 135 (page number not for citation purposes) isds 2013 conference abstracts results from the biosurveillance technical opportunity prioritization survey tera reynolds*1, bill storm2, andrew fine3, howard burkom4, todd stout5 and victor del rio vilas6 1international society for disease surveillance, boston, ma, usa; 2ohio department of health, columbus, oh, usa; 3boston children’s hospital, boston, ma, usa; 4johns hopkins applied physics laboratory, laurel, md, usa; 5firstwatch solutions, inc., encinitas, ca, usa; 6pan american health organization, rio de janeiro, brazil � �� �� �� � � �� �� �� � objective �������� ����� � ���������������������� ���� ��� �������� ��� �� ������ ��� ���������� �!������"���� ��#� �������� � ��$� ���%�&�� �$' $��� ����� ��� ����� ����(� ������(������� ��� ����� �(� ������ �����(� ����� � ������������� ��� ���(�� �������������� �) introduction ����%� ���� �������� �� �� ���&����������������� ��!%�&�"� �$' $��� �� �$������������#�������� �#�������*��� ���)�+���� ���� �� ��� %�&��$�������� ��(��� ��#� ���� ��� ����(���� � ��� �(����������' ������� �������(��� ��(��#���(��� ��#������� ���� ��������� ����(���� � ��� � ����������� ����� ��$��� ����#)���� ������(,����%�&��-��.#����� (�������(������������������ ���(�� � ����(������� ���� ��� � ��� �����(� ���� ���������� � ���%�&�� �$$��� �)������#����������,� ���������� -�����(�� � ��������(�� � ���� �� ������ ������ ��� ������� ��������� � ����� � ���$� ��(���#���) methods /� �$���,� �� � (������� #����� � � ����� � $� ��� �*��� �� (�������(� ���������� ����#������ ��� ������� ���)����� ������������-�������(�' �����(����������(� ����#���'$���� �� ���%�&�� �$$��� ������ ����� ����)���������� ��-��������������(������� �� ���%�&��0�� ���� �)� ���������� �1��� ����� �������(�� � � � ����� �������,���(� ��������' ��� ��� ���,����-������� ������� � ����� �-��#� �� ������ �(� � ��� ��� �� ��������� ���� ���� ��$��� �� �� ��������������� �) results 2���� �,��� �� ��1��� ����� �������������(������� ��� ���,� ������� ������(�� ����(����� �� ��(����(��#����(�$�#����� ����������,�-���� �������������(����#� ��������� � ���) conclusions ��������� ��� � �����������-���������(���� ��������� ��$� ���� �� ����� � ��� ��(��� ��(��#� ��(� �� ��#� ������ ���� ��� ������ �� ��(� ��� � ��� � ����������� ����� ��$��� ����#) keywords ������ ��� ���3�0�$$��� ��������$�� 3�������������� ������� ��� ��� acknowledgments �����-��.���������� �(���� ���%� ���� �������� �� �� ���&��������������' ��� �) *tera reynolds e-mail: treynolds@syndromic.org� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e162, 2014 a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi a causally naïve and rigid population model of disease occurrence given two non-independent risk factors olaf dammann,1,2 kenneth chui, 1 and anselm blumer, 2 1. department of public health and community medicine, tufts university school of medicine, boston, ma 2. department of gynecology and obstetrics, hannover medical school, 30623 hannover, germany 3. department of computer science, tufts university school of engineering, tufts university, medford, ma abstract we describe a computational population model with two risk factors and one outcome variable in which the prevalence (%) of all three variables, the association between each risk factor and the disease, as well as the association between the two risk factors is the input. we briefly describe three examples: retinopathy of prematurity, diabetes in panama, and smoking and obesity as risk factors for diabetes. we describe and discuss the simulation results in these three scenarios including how the published information is used as input and how changes in risk factor prevalence changes outcome prevalence. doi: 10.5210/ojphi.v10i2.9357 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. 1. introduction in epidemiology, the concept of multi-causality holds that the occurrence of any disease depends on a set of risk factors, not just one. the generation of virtual databases that reflect the properties of populations is called micro-simulation [1]. in their simplest form, such models require as input two risk factors and their association with one outcome variable. one example is synthea, a virtual population of individuals and their electronic health records (ehrs) [2]. the algorithm could simulate individuals with, say, three characteristics: a binary disease outcome (coded as yes/no) and two binary risk factors (yes/no). the algorithm uses as input parameters the population prevalence of the two risk factors and the outcome variable; the allocation of “yes” or “no” for each variable is done by applying a monte-carlo simulation that uses random numbers and the population prevalence as a threshold. this ensures that, for instance, a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi on average 37% of the virtual population will have a certain disease if the real population prevalence of that disease is 37% and the threshold for “disease = yes” is set at 0.37. these microsimulations have one particular disadvantage: if the presence or absence of each variable in the final database is based on separate yes/no attribution processes, the variables will be independent. this, of course, is highly unlikely in reality, because the very definition of a risk factor is that it is associated with the disease under investigation. moreover, the two risk factors will be independent of each other, which is also rarely the case in real life situations. this way of performing microsimulations will lead to populations that look like their real-life counterparts only with regard to the population average of risk factors and outcome. however, these datasets cannot be utilized to simulate population-wide changes in risk factors with the goal to study populationwide changes in the outcome (disease). therefore, we wanted to design a model that requires as input the population prevalence of the outcome of interest and of two risk factors, as well their three associations (figure 1). figure 1. the associations among two non-independent risk factors and one outcome are quantified by three odds ratios. in what follows, we describe a population model with two risk factors and one outcome variable in which the prevalence (%) of all three variables, the association between each risk factor and the disease, as well as the association between the two risk factors is the input. we briefly describe three examples: (#1) retinopathy of prematurity; (#2) diabetes in panama, and (#3) smoking and obesity as risk factors for diabetes. next, we describe the simulation results in these three scenarios including how the published information is used as input (step 1) and how changes in risk factor prevalence changes outcome prevalence (step 2). a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi 2. methods 2.1 the model suppose we have a standard 2 x 2 table for an outcome against a risk factor (figure 2). label the cells a, b, c, d where a is the percent of the population for which both the risk factor and the outcome are positive, b is the percent where the risk factor is positive but the outcome is negative, c is the percent where the risk factor is negative but the outcome is positive, and d is the percent where both are negative. then if rf is the percent of the population with positive risk factor and out is the percent of the population with positive outcome, we have (1) b = rf a (2) c = out a (3) d = 100 a b c the equation for the odds ratio is based on the quantities depicted in figure 2: (4) or = ad/bc. we can substitute for b, c, and d using the first three equations, giving a quadratic equation for a with coefficients in terms of rf, out, and or: (5) (or-1)a2 + (100+(or-1)(rf+out))a + or • rf • out = 0 figure 2. fourfold table depicting the four entities defined by the presence (+) or absence (-) of a binary risk factor and an outcome. a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi solving this will give a 2 x 2 table that matches the given population values for rf and out and has the desired odds ratio. this much is calculated in "step 1" in the javascript implementation of the model (available at http://www.cs.tufts.edu/~ablumer/popstat.html). we can also use this equation to model the effect of keeping the odds ratio fixed and changing the percentage of the population that has the risk factor. this can be done by replacing a and rf in the above equation with r*a and r*rf and solving for the value of out that keeps the odds ratio constant. this assumes that relative percentages of the population with positive and negative outcome within positive risk factor (a relative to b) stay the same when the positive risk factor population is changed. since we have two risk factors, we can do identical calculations relating risk factor 1 to the outcome and relating risk factor 2 to the outcome. similarly, we can find the entries for the 2 x 2 table relating risk factor 1 to risk factor 2. 2.2 examples 2.2.1 example #1: retinopathy of prematurity we previously analyzed a data set of 617 very preterm newborns [3]. in that project, we found that 47% of all babies developed retinopathy of prematurity (rop), a serious eye disorder among extremely preterm infants [4]. systemic inflammation [5] and oxygen exposure data [6] are competing pathogenetic mechanisms that interfere with normal vasculogenesis [7]. the capability to simulate interventions on one or both of these pathomechanisms in order to study changes in rop occurrence would be a groundbreaking step towards the prevention. in our data analysis, we also found that 32% of the infants had sepsis and 75% had been exposed to high levels of oxygen. the association between sepsis and oxygen on the one hand and rop on the other (measured as an odds ratio, or) were 2.8 and 3.6, respectively. the or for the association between sepsis and oxygen was 2.6. in figure 3 we clarify how these data were then entered into the model. 2.2.2 example #2: diabetes in panama a second example is a study on diabetes in panama (5.4%) [8] with female sex (rf1: 60%) and age 50+ years (rf2: 31%) as risk factor exemplars. female sex was associated with diabetes with an or=1.4, age 50+ had an or=5.1. the or for the association between female sex and age 50+ was 0.85 (see figure 4). obviously, in this case, the risk factors are not to be modified to simulate a population intervention as in the previous example. instead, we are interested in the effect on diabetes prevalence due to the discrepancy between the observed age distribution described in [8] (50+ years = 31%) compared to national data published by the united nations (20%) [9]. a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi figure 3. simulation results of step 1 in example #1, retinopathy of prematurity. 2.2.3 example #3: smoking, bmi, and diabetes a randomized controlled trial (rct) of estrogen plus progestin (ep) versus placebo was conducted in the 1990s to explore the effect of ep on subsequent development of coronary heart disease (chd) in postmenopausal women [10]. we wanted to use the publicly available data from this rct to explore the influence of smoking and body mass on diabetes, and use these data as input for a simulation of the effect of two interventions, smoking cessation weight reduction, on diabetes occurrence. 3. results 3.1 example #1 in step 1, we entered the population percentages for both risk factors and the outcome, as well as the three associations among them. the estimated four-fold tables provided by the model are depicted in figure 3. in step 2, we proceeded to the simulation of risk factor modification. first, we reduced rf1 incrementally down from 32% to 0% (table 1). this resulted in a drop of rf2 from 75% down to 70% and a reduction in outcome occurrence from 47% down to 39%. a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi table 1. example #1. risk factor (rf)2 and outcome (out) changes when rf1 declines (%). rf1 (sepsis) rf2 (oxygen) outcome (retinopathy of prematurity) 32 75 47 30 75 46 25 74 45 20 73 44 15 72 43 10 72 41 5 71 40 0 70 39 second, we reduced rf2 incrementally down from 75% to 0%. this resulted in a drop of rf1 from 32% down to 18% and a reduction in outcome occurrence from 47% down to 25%. third, we calculated that even if both rf were reduced to 0, we are still left with a 21% outcome rate, which is probably attributable to other risk factors. it is also possible that the odds ratios change as the population statistics approach the extremes. 3.2 example #2 the estimated four-fold tables provided by the model after step 1 are depicted in figure 4. 3.3 example #3 in the publicly available hers dataset (http://www.biostat.ucsf.edu/vgsm/data.html), we looked at diabetes (on oral medication or insulin) as the outcome, and at smoking and overweight/obesity as risk factors (table 3). in an exploratory data analysis we found that in this cohort of postmenopausal women with an average age of 67 years, 26% had diabetes, 13% were smokers, and 34% were obese (defined as a bmi ≥30). smoking was associated with a reduced risk for diabetes (or 0.5, 95%ci 0.4, 0.7), obesity with a strong risk increase (3.3; 2.7, 3.9), and smoking had an inverse association with obesity (0.6; 0.4, 0.7)(table 3). a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi figure 4. simulation results of step 1 in example #2, diabetes in panama. in step 2, risk factor modification simulation for age 50+ from the observed 31% down to the 20% estimated by the un in a population prevalence decrease for diabetes from 5.4% to 4.4% (data not shown). table 2. example #1. risk factor (rf)1 and outcome (out) changes when rf2 declines (%). rf1 (sepsis) rf2 (oxygen) outcome (retinopathy of prematurity) 32 75 47 31 70 46 29 60 43 27 50 40 26 40 37 24 30 34 22 20 31 20 10 28 18 0 25 a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi table 3. diabetes among 2758 postmenopausal women, the association between risk factors (smoking and overweight/obese) and diabetes, and the association between risk factors. these data served as input for example #3. diabetes yes no or (95%c.i.) n (row %) 728 (26) 2030 (74) smoking, n (col %) 60 (8) 299 (15) 0.5 (0.4, 0.7) obese, n (col %) 397 (55) 545 (27) 3.3 (2.7, 3.9) association rf1/rf2 smoking n (row %) obese (bmi ≥30), n (col %) yes 359 85 (24) no 2399 857 (36) 0.6 (0.4, 0.7) we then simulated two interventions, smoking cessation and weight reduction. we have to keep in mind that while obesity is associated with a risk increase, smoking is associated with a decreased risk for diabetes. the fact that the two risk factors are negatively associated (less obesity among smokers) might explain this “protective effect of smoking”. reducing smoking to zero in this population led to a minuscule increase of diabetes occurrence from 18 to 19%, which we confirmed in a stratified analysis excluding smokers (table 4). among non-smokers, diabetes prevalence was 19.2%. reducing obesity was associated with a prominent risk reduction for diabetes, from 18% down to 10%. at the same time, smoking increased from 13 to 17% (table 5). table 4. example #3. risk factor (rf)2 and outcome (out) changes when rf1 declines (%), simulating smoking cessation intervention. rf1 (smoking) rf2 (overweight/obesity) outcome (diabetes) 13 56 18 10 57 18 8 57 18 6 57 19 4 58 19 2 58 19 0 58 19 a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi table 5. example #3. risk factor (rf)1 and outcome (out) changes when rf2 declines (%), simulating weight reduction intervention. rf1 (smoking) rf2 (overweight/obesity) outcome (diabetes) 13 56 18 13 50 17 14 40 16 15 30 14 16 20 13 17 10 11 17 0 10 5. discussion 5.1 advantages our model has three prominent advantages. first, it is novel. to our knowledge, no other population model exists that appreciates the association between risk factors. second, the model is relatively simple. with only one outcome and two risk factors, the complexity of inputs is limited to their population prevalence and associations between each other. we are currently developing a tool is that includes a third risk factor and that can be used for microsimulations, i.e., it outputs a data file of a virtual population, which can be used in further simulations. third, the model is freely available online for the community to use and explore. 5.2 drawbacks the model is currently limited to two-level exposures and outcomes. it is also limited to only two risk factors. we are currently developing a similar model for three predictors and their interrelations. perhaps the most prominent limitation of the model is that it is causally naïve and rigid. much of the complex methodology toolbox of modern epidemiology is geared towards the identification of causal risk factors [11]. our model is not helpful in this regard. the association between risk factors and outcomes is modeled as odds ratios, which are simple measures of strength of association without implying causality or causal direction. the model is also rigid in that the input is reduced to population prevalence and association measures (odds ratios). within the constraints of these values, the output is not probabilistic, but determined. however, the model can be run multiple times with different values for odds ratios as input that come from within the range of odds ratios defined by the observed confidence interval. a causally naïve and rigid population model of disease occurrence given two non-independent risk factors online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e216, 2018 ojphi 5.3. conclusion in this paper, we present a simple model of disease occurrence in populations. based on the prevalence of a disease and of two risk factors, and of their association with the disease and between each other, the model calculates fourfold tables for these associations (step 1). thereafter, the population prevalence of either risk factor can be modified to simulate population risk factor increases or decreases, and changes in disease occurrence can be observed (step 2). we will now develop this model further to include three risk factors and microsimulation capabilities. in the meantime, we hope it will be helpful to others and would appreciate feedback, preferably in the form of constructive criticism. acknowledgements the following colleagues have contributed to the development of earlier versions of this model: benjamin hescott, inbar fried, sadchla mathieu, ryan durgham, yaa konama pokuaa, and eva chege. we acknowledge internal support from the tufts-collaborates! initiative 2014 and tufts university school of medicine chairs’ initiative for strategic research collaborations 2016 references 1. rutter cm, zaslavsky am, feuer ej. 2011. dynamic microsimulation models for health outcomes: a review. med decis making. 31(1), 10-18. pubmed https://doi.org/10.1177/0272989x10369005 2. walonoski j, et al. 2017. synthea: an approach, method, and software mechanism for generating synthetic patients and the 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280(7), 605-13. pubmed https://doi.org/10.1001/jama.280.7.605 11. glass ta, et al. 2013. causal inference in public health. annu rev public health. 34, 61-75. pubmed https://doi.org/10.1146/annurev-publhealth-031811-124606 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9718051&dopt=abstract https://doi.org/10.1001/jama.280.7.605 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23297653&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23297653&dopt=abstract https://doi.org/10.1146/annurev-publhealth-031811-124606 a causally naïve and rigid population model of disease occurrence given two non-independent risk factors abstract 1. introduction 2. methods 2.1 the model 2.2 examples 2.2.1 example #1: retinopathy of prematurity 2.2.2 example #2: diabetes in panama 2.2.3 example #3: smoking, bmi, and diabetes 3. results 3.1 example #1 3.2 example #2 3.3 example #3 5. discussion 5.1 advantages 5.2 drawbacks 5.3. conclusion acknowledgements references crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 (page number not for citation purposes) isds 2013 conference abstracts electronic infectious disease surveillance system during humanitarian crises in yemen kamran ahmed*, mohammad dauod altaf and fekri dureab world health organization, sana’a, yemen � �� �� �� � � �� �� �� � objective ��������� � � ��� ����� ��������� �� �� ����������������������� ������������������� ����������� ��� ������ �� �������� ���� � �� ���� ����� ����������� ���� ��� �� �� �� �������� ������!����������� ��������� ���������������������� ����� ������� � � �������������� �� 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��� ����$������ ���� ���� ��� ������" methods ���������� � �� ��$������� � � ����'�� ��()*+�� �� ������!� ��� �� ���� �� ������ ������������� �������� �$��!������ �� �� � ���� ��� �� �� ������������� � ������ ���� ���*))���������� �������� ���,�� ���� ���������%����� �� �� �������"����������� ���� ���$��� ���� � �� � � ����� ������� �� �� ����� �� �� ������ �� � ��� � � ��� �������� ���������������������� ����� ����� ������� ����� ������������� ��� �� ��� ����� ����� �� ��� ���� ����������� ������ ��� ���������� � ������-./������� �0�$������������������ �������� ��"�1����� ��� ��������� �� ������� ���� ���� �� �� ����������� ���� ���������� �� ������ � ��� �������� �� ����� ����� ��� ���������!��� � ���"�2������'��� ���� �� �� ���������� ���� ������������� �� ������ �������� ���#����� � �� ����� � �������� ����� ��������� ������ ��� ��� �&"�3����������� � �'��������� ���� ��� ������� ��� ��� ���������������" results 2�� ��+)�2������()*+#����������� ��� �� �4,5���������� �,� ��� 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��� �"����!���� � ��� � �� ���/� � �45#�*f:" ���� �#�g"��"#�'"�-������� �'"�2"�e �� �����())4�"�h� � ��� �������� �������� ��������"i����������1��� �� ������������*+�*�j�*�5" *kamran ahmed e-mail: drkamranrajput@hotmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e134, 2014 ojphi-06-e52.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 52 (page number not for citation purposes) isds 2013 conference abstracts monitoring risk among injection drug users in new orleans—findings from new orleans national hiv behavioral surveillance 2005-2012 kiva fisher*1, narquis barak1, meagan c. brown3 and william t. robinson2, 3 1no/aids task force, new 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online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e52, 2014 a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi a model for sustaining and investing in immunization information systems michael popovich*, ceo, masters of science systems engineering (msse) todd watkins, president, bachelors systems and industrial engineering (bsie) belinda baker, senior public health specialist, former immunization information systems manager washington state department of health scientific technologies corporation, 411 s. 1st street, phoenix, az 85004 abstract in the past three years, scientific technologies corporation electronically sent one-hundred fifty million retail pharmacy patient immunization events to state and community public health immunization information systems. today, as a conservative estimate, over 85% of the u.s. population has an immunization record in an electronic health information system. health technology, data exchange and increasing online patient health records offer consumers, providers and the immunization community new platforms to proactively identify vaccine coverage gaps. as the value of online immunization information increases, the cost to sustain and leverage these new technologies escalates. online immunization records and integrated decision support tools are being used extensively from the pharmacy to the emergency room. they are moving from health data vaults with few users to more ubiquitous point of care services and direct consumer engagement. the data and the supporting technology infrastructure empower the community within the immunization ecosystem. to use this opportunity to reduce the impact of vaccine preventable disease on populations, investment in sustaining and modernizing existing immunization health technology systems suggest models to articulate their value and return on investment. this paper illustrates cost and technology drivers that impact sustainability and modernization of the immunization information system infrastructure. it provides a model to support investment priority decisions and estimate costs. it reviews the technical evolution of public health immunization registries and their current legacy state providing a pathway to migrate to opportunistic third generation technology platforms. it will answer: how much should be budgeted? what can this budget achieve over the next five years? what investments should be prioritized? is there opportunity for public-private partnerships to support sustainment cost sharing? it shows that an investment of fifty million will modernize a quarter of the current second generation immunization systems and support the remainder over the next five years. keywords: immunization information system, iis, software as a service, immunization ecosystem, vaccine preventable disease, public health data systems, return on investment, decision support and economic modeling. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi background in the early 1990s national efforts were initiated to develop strategic plans with the goal of designing and developing childhood immunization tracking systems. the centers for disease control and prevention (cdc) and a number of national organizations including robert woods johnson, the flinn foundation, and the annie b. casey foundation provided strategic planning grants for the design of these data systems. the objective was to ensure family physicians and pediatricians had the most current immunization histories of their patients. a subsequent national strategy in 1994 expanded the concept from a registry to an immunization information system (iis) [1]. the computer technology of early systems used desktop applications and client server software operating over land line telephone connections. early relational databases resided on government computers with few users. immunization coverage, provider and patient reports were generated through custom software. the evolution of internet services and supporting technology in the later part of the 1990s allowed systems to move to second generation applications. these were browserbased with access to the data through internet connections. over the past two decades data queries for information access and larger and faster hardware served as the architecture for maintaining and evolving these systems. today there are sixty-four iiss built in this framework. of these, approximately a quarter are moving toward the end of their life cycle legacy. cdc and the iis community have identified these as most in need of modernization overhauls. to remain relevant to the national user community however, all iis over the course of the next decade have to undergo significant modernization efforts. as a prelude to this, in 2017 cdc leadership presented four strategic priorities for the iis community: (1) enhance iis performance, (2) sustain the iis community, (3) influence and monitor the health it environment, and (4) promote adherence to standards [2]. in june of 2017 a public-private immunization registry summit convened at the hatch in indianapolis, indiana [3]. participants from the immunization technology, retail pharmacy, public health, and payer communities met to consider leveraging data value in the iis to identify, promote and ensure continued sustainment investment from the user community. the vision of a public– private immunization ecosystem created by these stakeholders contemplated potential beneficiaries of a robust and modernized iis data asset. the value of immunization data and public–private financial contribution opportunities were the areas of focus. recommendations included increasing iis data value, documenting return on investment, expanding partnerships corresponding author: *michael_popovich@stchome.com doi: 10.5210/ojphi.v11i2.10243 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi between public and private immunization programs and estimating future costs to modernize and sustain systems. in march of 2018 the result of the summit was shared with cdc iis management [4]. cdc leadership highlighted the financial and resource pressures for maintaining the existing allocations for public health to support and evolve these systems. two primary concerns were raised. the first concern was the increasing costs to sustain the existing legacy systems. the second concern was determining a prioritized schedule of investments with a limited budget. they noted many of the iis were overdue for technical upgrades. these upgrades are required to ensure ongoing compliance with increasing iis functional requirements, expanding electronic data exchanges with the private sector and the expectations the information in these public health systems would be used to influence individuals, thereby decreasing the impact of vaccine preventable disease. additional conversations with the iis technology and user advocacy communities emphasized individual concerns about increasing costs to maintain web-based systems, as well as an indication that tighter federal and state budgets were becoming a limiting factor [5]. increasing costs for servers, infrastructure modernization, application support, user support from electronic data exchange onboarding, and quality control were all identified as areas needing investment. labor resources and costs are increasing each year as the need to maintain each of their aging, customized systems grows. another “elephant in the room” is that government (state and federal) has difficulty in attracting significant numbers of the best developers and seems to resolve they will not have enough resources to do the work well. in addition, securing the investment to completely overhaul a stateowned and operated system from legacy to the next generation is very challenging. the response from the legislature is usually, “well it’s still working so let’s leave it another year.” the conclusion: iis 2nd generation systems are now a bona fide legacy that struggles with finding qualified personnel and adequate financial resources to work on antiquated platforms [6]. over the past two decades of ongoing maintenance each has trended to custom features and components, making cost sharing difficult. the value and use of iis data is increasing. this was recognized by public and private participants in the hatch leadership summit. the rapid change of technology, a move toward cloud-based web services, mobile technology, new development, testing and delivery tools along with innovative health it products is creating opportunity to re-architect and re-design systems to exponentially empower providers, payers and patients. these new frameworks will eventually replace the immunization legacy systems and create a new model for an iis platform. skilled technical talent entering the market will be a major work force to deliver these new platforms. the question is… should funding for the existing iis to upgrade these legacies be replaced with encouraging and investing in modernization and third generation platform development? in 2017 a technical framework for a third generation iis was conceptualized [7]. the architecture was a move from fixed on-premises server applications to scalable redundant secure cloud servers. moving from existing iis architectures, built with integrated components tightly coupled to their underlying databases, requires de-coupling and modernization to be independent web services tested and maintained once and available for consumption by all iiss across the country. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi as an example, a vaccine forecaster update would no longer be integrated into a specific iis requiring independent integration and testing. under the iis 3.0 model it would be updated and tested as a single web service. the new iis architectures would allow user communities to see the updates in the same instance the revised forecaster was deployed to all. when the time and cost is calculated to deploy a forecasting update independently into 64 second generation iiss versus a single update that is served immediately to all systems it becomes evident where cost savings occurs. to move all iis strategically to third generation systems requires an understanding of the potential economic savings compared to traditional costs to support existing systems. a model for this economic assessment was tested with a sample of state iis. iis cost and decision support modeling to determine the return on investment (roi) of the immunization system investment model, an iis readiness score and an investment strategy roadmap was developed. the readiness score establishes the likelihood that an investment will return value. the investment strategy roadmap estimates a total cost to modernize and sustain the iis. the model was developed using historical and present immunization costs, assessments of successful implementations and the current technology trends of existing high performing systems [8]. it was applied to existing iis environments to validate the reasonableness of the approach. likelihood of success – a readiness score the roi includes a metric which measures the likelihood of success when considering investing to modernize an immunization system. when each iis being considered for modernization is assessed similarly, the result will be a ranked order with the higher scoring iiss providing a prioritization roadmap. these early adopters of a third generation iis platform will help define the effort and investment required to support the transition and play a pivotal role in monitoring progress and overall accountability. technology and systems are only as good as the environments in which they operate. a commitment of leaders, influencers and an empowered user community will drive technology conversions and implementations to expectations of success and value. the following table identifies the seven key iis readiness indicators that were determined to increase the likelihood of a successful technology update: a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi table 1: leading indicators for iis investment success measurement policy/environment/politics: iis investment readiness score wght success category criteria importance scale notes readiness score max points = readiness score x weight 5 lifespan iis maintain immunization histories birth to death? 0 or 5 5 25 5 exemption s exemptions allowed? 3+ = 1, 2 = 3, 1 = 5 1, 3 or 5 1 25 3 rx friendliness age limits, vaccine restrictions, public health perception. 1-5 3 15 4 provider perception of iis mandatory versus voluntary, willingness to participate, iis reputation. 1-5 5 20 3 public/ private coalition is there one? how often does it meet? well represented across ecosystem? 1-5 3 15 4 opt-out or opt-in is the iis opt-out or optin? 1 or 5 5 20 3 consumer friendliness does iis community allow for or support allowing consumer access? 1-5 5 15 total readiness score possible (weight x score) = f (∑for each category) 135 weights are subjective for each success category and can be set by the user of the model as long as all iis environments are evaluated under the same rules. these success categories were agreed upon by a team of immunization program and iis experts with applied experience implementing successful systems throughout the u.s [9]. the scales established use the lower number to identify gaps and limitations. a high score indicates this success factor is optimal for advancing the utilization of information within the iis and its community. accumulating the scores across all areas equates to a single readiness success metric. the higher the overall score the more likely the stakeholders in that state’s ecosystem should expect to see a faster and more optimal return. the theory is that the higher community ownership, accountability a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi and proactive partnerships will drive more rapid updates and data utilization. ownership by the state, the legislature, the provider community, and the consumers is what makes or breaks a system, and ultimately makes or breaks a major transformation of that system. the total score when compared to the readiness score card, illustrated in table 2, provides a likelihood of a more rapid uptake by the community and more tangible immunization-related results. use of this score creates a scale to compare investment priorities. table 2: readiness score card iis readiness score readiness for modernization 100+ excellent the environment is near optimal for a consideration in investment. 75-99 good with a few environmental changes recommended to occur simultaneous with any investment opportunity for success is high 50-74 average a number of readiness factors are low and should be worked in parallel to a scaled investment that increases as the environment improves. <50 poor the environment supporting the iis is not at a sound readiness baseline to recommend significant investment. improving the readiness areas is recommended prior to future funding. this score card does not guarantee overall success. this would include other factors such as the technology and approach to move to the third generation platform, resources available (both the amount of investment dollars and staff), and the mindset and willingness of the community to invest time and energy in the change and process. an example of the readiness scale eight states were assessed with this approach. they ranged in population from a half million to over eleven million people. their iis contained immunization events from eight million to over ninety-nine million patients. each of the eight currently requires a level of investment to modernize and move to a third generation immunization platform. table 3 illustrates the results. funding priorities would suggest higher score card results are optimal. equally as important are suggestions a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi offered to improve the readiness score which would improve the likelihood of securing funding if implemented. table 3: readiness score card for an eight state review table 3 readiness score card for an eight state review readiness scores state 1 state 2 state 3 state 4 state 5 state 6 state 7 state 8 wght category description scale score * wght score * wght score * wght score * wght score * wght score * wght score * wght score * wght 5 lifespan is your iis birth to death? 0 or 5 25 25 25 25 25 25 25 25 5 exemptions what exempti ons do you allow? 3+ = 1, 2 = 3, 1 = 5 1, 3 or 5 5 5 25 25 25 25 15 25 3 rx friendlines s age limits, vaccine restrictio ns, ph percepti on. 1-5 9 9 6 9 6 9 6 6 4 provider perception of iis mandato ry vs voluntar y, willingn ess to participa te, iis reputatio n, etc… 1-5 20 16 8 8 16 16 12 12 3 public/priv ate coalition is there one? how often does it meet? well represent ed across ecosyste m? 1-5 9 3 3 12 9 3 3 3 a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi table 3 readiness score card for an eight state review readiness scores state 1 state 2 state 3 state 4 state 5 state 6 state 7 state 8 wght category description scale score * wght score * wght score * wght score * wght score * wght score * wght score * wght score * wght 4 opt-out or opt-in is your iis optout or opt-in? 1 or 5 20 20 12 12 12 12 12 4 3 consumer friendliness does your state allow for or support allowing for consume r access to iis? 1-5 15 9 3 9 9 9 3 6 total readiness score (max = 135) 103 87 82 100 102 99 76 81 100+ excellent 75-99 good 50-74 average < 50 poor in this example states 1, 4 and 5 would be considered as first tier investment opportunities. state 7 a recommended third tier investment and would be advised of their indicator scores gaps and encouraged to accelerate improvements in these areas before funding. there are a variety of strategies that could be applied to the actual decision but in all cases an underlying common evaluation approach was used to provide decision guidance. the cost to modernize and sustain iis the cost to modernize a legacy iis and evolve to a third generation platform and sustain going forward is part two of the model. the practicalities of budget processes and allocation of funding creates investment challenges. each year there are limited funds allocated to iis modernization and ongoing support. for example, if there was $40 million a year available to allocate to all 64 iiss, those making the allocation decisions would be better positioned to make optimal allocations if they understood a readiness to succeed with each investment. funding authorities would gain decision support knowledge if there were costs identified to modernize all iis estimates. budget requests driven by a model of this type are likely to establish a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi a process that produces more cost-effective modernization efforts, accountability and monitoring, versus a bottom-up approach where each iis estimates its funding independently. iis budgets are not static each year. labor and technology costs increase required allocations. the following table is the current annual technical support costs of eleven second generation legacy state immunization systems. current budgets support minor ongoing application updates, application bug fixes, and user support. table 4: sample of iis annual technical support costs population / million annual technical support costs immunization events in iis – millions 1 < 2 $ 250,000 12 2 5-10 $ 289,000 78 3 5-10 $ 350,000 81 4 2-5 $ 678,000 61 5 2-5 $ 323,000 39 6 <2 $ 172,000 10 7 >10 $ 415,000 100 8 5-10 $ 648,000 64 9 5-10 $ 500,000 99 10 <2 $ 318,000 17 11 <2 $ 343,000 8 total >50 $ 4,286,000 569 source: stc health iis survey 2018 the costs do not include hardware and supporting application license costs. for example, oracle or sql licenses, user training, ongoing efforts to onboard provider electronic data exchanges and major iis customizations are all areas where additional funding is often requested. from the above table, the average annual application support cost for these 11 systems is just under $390,000. this is a labor cost for supporting new releases, application administration, and problem resolution. a bell curve using $300k as a lower limit and $500k as an upper limit results in 5 states that fall within the center of the curve. it suggests current annual application support costs for all 64 iis to be just under $30 million. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi table 5: total iis sustainment cost 2019 sustainment per year iis annual $ 400,000 25% 16 $ 6,400,000 $ 450,000 50% 32 $ 14,400,000 $ 500,000 25% 16 $ 8,000,000 64 basic support $ 28,800,000 these costs do not include software customization, cost for hardware, software licenses or service labor hours. based on the eleven states surveyed for this paper, the additional budget requirements for it servers, licenses, labor to support efforts such as electronic data exchanges (onboarding), and training will double the application support. increasing the $28.8 million by a factor of two ($57.6m) moves closer to the true cost of ownership for system and support sustainment. time to modernize a key challenge for all technology and application solutions is to remain current with features and functions. iis have been in existence for well over two decades. as vaccine policy and procedure changes occur, new compliance requirements are implemented, forecasts are updated and electronic data exchanges accelerated. it can be a challenge for public health to maintain the technology change velocity required to keep pace with the program demands to effectively operate a state’s immunization program. second generation iis are robust, complex and highly integrated systems. these legacy environments require greater attention to maintain as time passes. there is a realization in the industry that, of the 64 iis environments, a portion of these is in need of a “fresh start.” moreover, there is the understanding that over the next five years they all will require increased funding to move forward. sustaining costs estimated above do not account for modernizing these current systems through a migration to a third generation immunization platform. within the next five years it is estimated that 25% of the iis environments will require a significant investment to move from their current environments. in a recent cdc grant opportunity $1 million was used to establish an investment budget for modernizing older legacy systems [10]. table 6 incorporates legacy migrations with ongoing sustainment costs to estimate a total investment budget to modernize the first 25% of the country’s iis infrastructure. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi table 6: total iis sustainment and modernization estimates annual sustainment invest $1m to modernize with 10% support increases (increases apply to those being modernized allowing for a 12-18 month process to migrate) per year iis percent annual $ 400,000 16 25% $ 6,400,000 16 $ 23,040,000 $ 450,000 32 50% $ 14,400,000 32 $ 15,840,000 $ 500,000 16 25% $ 8,000,000 16 $ 8,800,000 64 100% $ 28,800,000 $ 47,680,000 the cost model illustrated in table 6 utilizes a $1m allocation to modernize 16 systems. it allows a window of 12-18 months before the new system can replace existing systems, requiring ongoing support for this 25% and the remainder 75% as well. since labor and technology costs are expected to continue to rise, this model includes a 10% annual increase in the basic budget. a projection using these assumptions provides a $47.68 million estimate to sustain and modernize one fourth of today’s iis. however, this amount reflects only application support and modernization. it does not include supporting technology and programmatic technical resources. therefore, the model incorporated: total cost = application & modernization ($47.68m) + hardware ($120k/year each) + software licenses ($50k/year) + technical support ($200k/year ~ 1-2 ftes) these ancillary support services add $23.7m, bringing the annual budget to support and modernize 64 iis environments to $71.3m. modernization strategy government investment limitations mean 16 of the iis requiring a re-launch would not be undertaken in a single year. budget and resource limits, time to publish formal requests for new systems and the likelihood that each of the 16 locations have created a recommended environment that would lead to success suggests the evolution of the 16 would likely occur over the next 3-5 years. table 7 incorporates this phased approach and the model calculates investment over a fiveyear window. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi a 5% increase in the $1m each budget each year was used along with the previous 10% annual increase in sustainment costs. a baseline of $400k annually was used for each of the systems not modernized. annual sustainment costs for systems modernized are assumed to begin in 2021 with a $500k base and a 5% increase each year to cover increasing it support through currently available modernized resources such as cloud computing. table 7: iis budget estimates over a five-year time frame costs to modernize and replace the first 25% of iis year replace remainder replacement cost sustain remainder sustain replaced total 2020 3 13 $ 3,000,000 $ 5,200,000 $ $ 8,200,000 2021 3 10 $ 3,150,000 $ 4,400,000 $ 1,575,000 $ 9,125,000 2022 3 7 $ 3,307,500 $ 3,388,000 $ 3,307,500 $ 10,003,000 2023 3 4 $ 3,472,875 $ 2,129,600 $ 5,209,313 $ 10,811,788 2024 4 $ 4,862,025 $ $ 7,293,038 $ 12,155,063 16 $ 17,792,400 $ 15,117,600 $ 17,384,850 $ 50,294,850 the modernization cost over the course of the 5 years is $50.3m versus the $47.6m if funding could be allocated in one year. although this is not a major difference in total cost, the likelihood of success is increased significantly as the feasibility of 16 modernization efforts being successful and occurring at the same time is low. a total cost to modernize one-fourth of the 64 iis over the next five years and continue to sustain the other three-fourths with a 5% increase in costs per year with ongoing it, licensing and labor allocations illustrated above is included in table 8 below. table 8: total annual project budgets for 64 iis with 25% modernized over five years year total in million 2020 $49.96 2021 $52.97 2022 $56.04 2023 $59.15 2024 $62.91 total $281.04 a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi summary investments in population-based public health immunization data assets require a national commitment. traditionally this commitment is through federal and state government budgets. the annual ask each year for investments in these data systems should be expected to increase as they have consistently done for more than two decades of their existence. labor and technology costs, growth in system data, quality and utilizations increase each and every year. the value and importance of these health data assets to effect the change these systems are designed to support encourage their use across the entire immunization ecosystem. investments to execute the important evolution of iis that involves modernization, staying current with technology changes, fully leveraging the value of the data contained in the iis, and securing the phi from unauthorized access and use collectively create the demand for systems and support that require the best platforms and solutions the industry has to offer. this paper illustrates a model that supports ongoing sustainment and the modernization of public health immunization information systems over the next five years. it extended the cost model to support an approach to determine the likelihood of success based upon the supporting jurisdiction’s iis ecosystem and environment. implementing technology to simply have the latest and greatest is not an acceptable budget justification. iis technology updates and support must be justified on the value of the systems and data to the end users they are designed to support. high quality data, real time information and tools to empower all users from patients and consumers to clinical providers, pharmacists, payers and the supporting communicable disease networks is essential. if a migration from legacy iis to currently available third generation platforms is initiated, in the next 5 years adequate and continued funding will be essential. if this process is based on the proposed theory that the data represents value to the entire ecosystem, it is time to also consider funding solutions from all stakeholders in the ecosystem who stand to benefit from a national initiative of modernized iis infrastructure. government funding to support these assets should be but one of those investments. if national commitments across the ecosystem were supported, the final question to be answered to round out this model could be, “what’s my share?” table 9 is a hypothetical example of a cost sharing approach to investing in the ongoing development, modernization and sustainment of the iis. once migrated to third generation platforms the use of these already valuable public health data assets will provide even more benefit to everyone across the ecosystem. more importantly, the use of these assets by all the stakeholders will undoubtedly result in closing the immunization care gaps. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi table 9: an example cost sharing approach total cost $ 49,960,000 $ 52,973,000 $ 56,043,400 $ 59,154,208 $ 62,914,604 investment share % of total 2020 2021 2022 2023 2024 public health (local, state & federal) 30% $ 14,988,000 $ 15,891,900 $ 16,813,020 $ 17,746,262 $ 18,874,381.05 payers 20% $ 9,992,000 $ 10,594,600 $ 11,208,680 $ 11,830,842 $ 12,582,920.70 providers 4% $ 1,998,400 $ 2,118,920 $ 2,241,736 $ 2,366,168 $ 2,516,584.14 pharmacy 14% $ 6,994,400 $ 7,416,220 $ 7,846,076 $ 8,281,589 $ 8,808,044.49 pharmaceutical 30% $ 14,988,000 $ 15,891,900 $ 16,813,020 $ 17,746,262 $ 18,874,381.05 immz ngos 1% $ 499,600 $ 529,730 $ 560,434 $ 591,542 $ 629,146.04 vaccine research 1% $ 499,600 $ 529,730 $ 560,434 $ 591,542 $ 629,146.04 total 100% $ 49,960,000 $ 52,973,000 $ 56,043,400 $ 59,154,208 $ 62,914,604 although the cost sharing model is the topic of another paper, it should be noted that the investments in these public health assets, no matter the cost model utilized, should be open for discussions centering on cost sharing versus value received. limitations the paper is not a definitive economic model to establish costs and thus a resulting budget to replace and sustain the next generation of immunization systems. the paper is a conceptual model to establish actual estimates. it utilizes actual cost data from a percent of state registries and projects these costs to the total national iis assets. the cost models utilize an annual escalation estimate representative of expected technology and labor increases. the application of the iis readiness measures is based upon subjective assessments and is not statistically validated through a larger sample set. the model is intended to establish a decision framework and a general cost magnitude that establishes a first look at the future sustainability investments. as additional data is collected and the sample set increases improved cost estimates are possible. the cost sharing approach assumes a reasonable ask for stakeholder investment and is based upon the authors’ evaluation of the investment value to each sector. a model for sustaining and investing in immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e20, 2019 ojphi references 1. centers for disease control and prevention – draft / preliminary technical plan for the state immunization information system (siis), june 1994, by nise west department of the navy telecommunications and engineering division. 2. april 12, 2017 warren williams, aira conference, chicago, illinois. 3. hatch leadership summit, indianapolis, indiana, june 20, 2017, unpublished summit notes. 4. conversations with warren williams, former director of cdc/ncird/iissb, march 2, 2017, atlanta, georgia. 5. outcomes and action plan notes, scientific technologies corporation, iis user group summit, phoenix, arizona, june 21-24, 2018. 6. https://en.wikipedia.org/wiki/legacy_system. 7. scientific technologies corporation. july 2017, scottsdale, arizona, technical workshop, architecture, tools and migration iis modernization planning, design notes. 8. examples include amazon.com, google, social medial frameworks, and gamming technologies. 9. independent interviews with public health professionals, iis support teams and leadership; “ideas start here day,” february 2019, stc 411 s. 1st ave, phoenix, arizona. 10. cdc 2019 grant opportunity cdc-rfa-ip19-1901. editorial: ojphi vol 4, no 3 (2012) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 editorial: ojphi vol 4, no 3 (2012) welcome to the last issue of the 4th volume of the online journal of public health informatics. i am sure you are all aware that the national library of medicine has approved our application for indexing and archiving in pubmed central. the next step is to deliver all past issues of the journal to nlm in the required xml dtd format. the nlm will provide links to other related biomedical informatics journals. this process will be completed in february 2013. we can all proudly say that we now have a scholarly biomedical informatics journal dedicated to the exciting field of public health informatics. this issue contains nine original articles, two review articles, a technology review, and a commentary. the topics covered in this issue include the identification of some fundamental concepts and factors that must be understood if public heath informatics applications are to support high-level cognitive activities; the development of computerized decision support systems to capture structured clinical data from providers using office-based fax machines and the delivery of just-in-time alerts to pcps statewide; identification of information requirements and barriers to information exchange by public health workers; development of a methodology to establish and maintain successful electronic health record exchanges between clinical practices and public health agencies; the use of data from the behavioral risk factor surveillance systems surveys to study dental health disparities; the development of an develop an agent-based model to simulate the spread of sexually transmitted infections in a population; the use of institutional logics perspective to analyze the implementation of health information systems in a developing county; the development and use a modified health belief model to improve the predictive power of factors that influence healthy eating behavior; an analysis of the history, issues, and potentials for successful implementation of electronic prescribing in the current health reform environment; improvement of hiv/aids knowledge management using electronic health records; the development of a methodology to facilitate communication between open source and propriety systems using interoperability principles; and a technology review on leveraging cloud computing to develop smarter public health prevention systems (sphs) to provide real-time reports of potential public health threats; and a commentary on identification of successes and challenges in the use of mobile phone applications in health and demographic surveillance systems in a developing country. it has long been noted by researchers that human and non-technical factors, rather than technological defects, contribute significantly to failures in implementation of public health informatics (phi) applications. in order to improve the success rate of phi tools it is important to pay more attention to human-centric design issues rather than completely focusing on technology issues. in the paper titled “beyond information access: support for complex cognitive activities in public health informatics tools” the authors draw on research from the general area of human-information interaction in complex cognitive activities to identify some of the extant research needs of public health informatics tools. they also discuss a number of considerations for the design and evaluation of phi tools, and demonstrate how an integration of such considerations facilitates the design and evaluation of successful phi tools. in order to improve the successful adoption of electronic health records and health information exchanges by health http://ojphi.org/ editorial: ojphi vol 4, no 3 (2012) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 departments in the current hitech era it is important to encourage the participation of public health professionals in the design discourse. the secure sharing of protected health information is a fundamental determinant of the success of the hitech ac. an insignificant percentage of hospitals currently share structured data via the health information exchanges. the participation of majority of practices in the hies consists of using the office-based faxes to receive results or to log onto the hie portal, where one exists, to receive health information. in the paper titled “real time alert system: a disease management system leveraging health information exchange”, the authors developed a computerized decision support system (cdss) using existing hie infrastructure. the cdss captures structured clinical data from providers using existing office-based fax machines and delivers real time alerts for recommended services to pcps in the statewide indiana network for patient care when their patients visit. the technology was evaluated for emergency room visits anywhere in the state of indiana. the results showed that the cdss successfully delivered the just-in-time alerts to the pcps across the state. physicians who reported finding the information helpful also reported making a follow-up phone call or seeing the patient for a follow-up care. this study demonstrates that, even before the implementation of health information exchanges on a national level, most states can use such a cdss to coordinate care, improve outcomes, and reduce costs of care. an understanding of the information requirements and barriers to information exchange by public health workers should precede information systems design efforts. in the paper titled “public health practice within a health information exchange: information needs and barriers to disease surveillance”, reeder, revere, hills, et.al, investigated information usage by public health professionals working on disease surveillance activities at a medium-sized health department. their results indicated barriers in information systems usability; data timeliness, accuracy, and completeness; and social interaction with clients. in orders to improve the adoption of information systems and implementation of information exchanges by public health departments systems designers must address these barriers. in order for clinicians to make the best possible clinical and economically responsible vaccination decisions it is important to have access to information about the earliest possible intervals that are safe to administer vaccinations, especially to children at highest risk of vaccine preventable diseases. the required data are contained in state immunization information systems (iis) and registries. the meaningful use stage 1 standards require providers and hospital ehrs to demonstrate the ability to send immunization data to an iis while the meaningful use stage 2 standards include the requirement to send clinically correct and complete immunization records from the provider’s ehr to an iis. in a paper titled “a threestep approach for creating successful electronic immunization record exchanges between clinical practice and public health”, janet balog presents a methodology to establish and maintain electronic health record exchanges; demonstrates the value of clinical and technical testing before implementation of data exchanges; and discusses how the meaningful use requirement to advance data exchange with public health agencies achieves mutual health outcomes for providers and public health programs. monitoring health disparities is a major activity of the healthy people 2020 initiative. research shows that the most important determinants of oral health are poverty, race and ethnicity. in the paper titled “overcoming data challenges examining oral health disparities in appalachia”, http://ojphi.org/ editorial: ojphi vol 4, no 3 (2012) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the authors use the behavioral risk factor surveillance systems survey data to study dental health disparities. using beale codes to define metropolitan and non-metropolitan statistical areas and gis maps the authors provide informative sub-state results to assist health planners in targeting oral health intervention strategies. while public health professionals devote significant amount of time and money to screen and treat stiss annually this cluster of diseases remain a major public health challenge. estimates from cdc show that approximately 19 million new cases of stis occur in the u.s. each year, costing the healthcare system $12 billion to $20 billion annually. in recent decades researchers are increasingly employing agent-based models to simulate the spread of sexually transmitted infectious diseases as a complementary approach to the traditional statistical or differential equation-based models. in the paper titled “an agent-based model for simulating the spread of sexually transmitted infections”, rutherford, friesen, et.al, develop an agent-based model to simulate the spread of sexually transmitted infections in a population of 1000 agents over a 10year period. the model allows the effects of various mitigating and control policies and behaviors to be analyzed. the results show that changes in individual behaviors can reduce the risk of exposure. however, population-wide behavior modifications through public health activities have can have more dramatic impacts on the transmission of stis. the healthcare sector is one of the most information-intensive enterprises and health information systems (his) are important components of health reform. well-developed and implemented health information systems can improve the coordination of care, reduce duplications and errors, improve access, quality, and reduce costs. however, even in the u.s., less than 50 percent of health information systems are outright successes, delivering the expected functionalities on time and within budget. this significant failure has been attributed to the complexities of the technologies and the difficulty in satisfying the multiple conflicting objectives of the many stakeholders involved. the situation is even worse in developing countries. in a paper titled “understanding hmis implementation in a developing country health country” ime asangansi employs the institutional logics perspective to analyze hmis implementation in a state government ministry in northern nigeria. the author recognizes that certain institutional logics may be conflicting and lead to increase in the risk of failure. the following institutional logics were identified: a) the logic that sustainability can only be achieved through local ownership or the local control logic, b) the logic of universal coverage, and c) the logic of network-centric organizations where network technologies disrupt institutional power structures. it is proposed that the resolution of the conflicting logics must be addressed within the context of deinstitutionalization, changeover management, and by balancing the competing interests. a major recommendation from the study is that, in order to improve the implementation of hmis in developing countries, policy makers must understand and resolve the conflicting logics the increase in lifestyle-related health problems and meaningful use requirements provide incentives to providers to shift their practices from the current treatment-and-prescription centric models to patient-and population centric prevention and health promotion models. when meaningful use stage 2 and stage 3 requirements are enforced in future healthcare providers will be expected to provide patients with information that will help them make lifestyle changes. health application and game designers are already developing behavior modification programs to assist patients in making healthy choices. behavior modification models based on intuition are http://ojphi.org/ editorial: ojphi vol 4, no 3 (2012) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 less successful or sustainable than those informed by evidence-based research. one of the most widely used theories for behavior modification is the health belief model (hbm) which was developed to investigate why people fail to undertake preventive health measures. the main limitation of this model is that it has very low predictive power. in a paper titled “towards an effective health intervention design: an extension of the health belief model”, the authors extended the hbm to include new lifestyle changing factors and tested the validity of the extended hbm and the original model on healthy eating behavior. the results showed a significant increase in the predictive power of the extended model over the original model. as the health information exchanges mature and generate individual and population level databases researchers will use these data to create new evidence for lifestyle modifications in order to achieve the objectives of the accountable care act. this is the final issue of the online journal of public health informatics in 2012.many thanks to the editors, the journal manager, and all the volunteers who have contributed to the success of this project. through your hard work the field of public health informatics can now boast of a scholarly journal dedicated to the dissemination of evidence-based information to our stakeholders. i look forward to your continued support in 2013 and beyond. happy new year! edward mensah, phd editor-in-chief online journal of public health informatics 1603 west taylor street, room 759 chicago, illinois, 60612 email:dehasnem@uic.edu office: (312) 996-3001 http://ojphi.org/ visual analytics of tuberculosis detection rat performance 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi visual analytics of tuberculosis detection rat performance joan jonathan1, camilius sanga2, magesa mwita1, georgies mgode3, 4 1 centre for information and communication technology, sokoine university of agriculture, p.o box 3218, chuo kikuu, morogoro, tanzania 2sokoine national agricultural library (snal), sokoine university of agriculture, p.o.box 3022, morogoro, tanzania 3pest management centre, sokoine university of agriculture, p.o box 3110, chuo kikuu, morogoro, tanzania 4apopo tb project, sokoine university of agriculture, morogoro, tanzania abstract the diagnosis of tuberculosis (tb) disease remains a global challenge, and the need for innovative diagnostic approaches is inevitable. trained african giant pouched rats are the scent tb detection technology for operational research. the adoption of this technology is beneficial to countries with a high tb burden due to its cost-effectiveness and speed than microscopy. however, rats with some factors perform better. thus, more insights on factors that may affect performance is important to increase rats’ tb detection performance. this paper intends to provide understanding on the factors that influence rats tb detection performance using visual analytics approach. visual analytics provide insight of data through the combination of computational predictive models and interactive visualizations. three algorithms such as decision tree, random forest and naive bayes were used to predict the factors that influence rats tb detection performance. hence, our study found that age is the most significant factor, and rats of ages between 3.1 to 6 years portrayed potentiality. the algorithms were validated using the same test data to check their prediction accuracy. the accuracy check showed that the random forest outperforms with an accuracy of 78.82% than the two. however, their accuracies difference is small. the study findings may help rats tb trainers, researchers in rats tb and information systems, and decision makers to improve detection performance. this study recommends further research that incorporates gender factors and a large sample size. keywords: data mining in healthcare, african giant pouched rats, classification technique in tuberculosis diagnosis *correspondence: joan jonathan (joanjonathan@sua.ac.tz) doi: 10.5210/ojphi.v13i2.11465 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:joanjonathan@sua.ac.tz visual analytics of tuberculosis detection rat performance 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 1 introduction tuberculosis (tb) is one of the life-threatening infectious diseases causing death worldwide [1]. the who report [2] shows that 10 million people are infected with tb each year. microscopy is the widely used tb diagnostic tool in developing countries despite its lower sensitivity [3, 4]. nucleic acid-based test such as genexpert mtb/rif is now in use with higher sensitivity and specificity than microscopy. however, its full roll-out and utility is limited to some areas. there is a need for new cheap and rapid diagnostic approaches to enhance tb case detection in countries with a high tb burden. since 2007, antipersonnel landmines detection product development (apopo) and sokoine university of agriculture (sua) have been exploring the potential application of the trained african giant pouched rats (herorats) for detection of pulmonary tb in sputum samples [7]. trained rats retest heat inactivated sputum samples after smear microscope and other hospital tests to detect missed tb cases. the study conducted by poling et al. [6] evaluated sputum 21,600 from tanzanians and 9,048 from mozambicans which was already screened by the microscope. however, after the evaluation by rats it was revealed that there were 1,412 new patients with active tb in tanzania and 645 new patients in mozambique. the new detected cases increase the detection rate by 39% in tanzania and 53% in mozambique when compared to smear microscopy, the standard diagnostic for tb. furthermore, trained rats increase pediatric tb detection by 68% as the additional of 23 children patients who tested tb positive from 982 children sputum samples [7]. the endorsed conventional tests such as concentrated smear microscopy offer higher sensitivity than the direct microscopy and thus are used to confirm detection rat results before patients start treatment [1]. these scent detection rats detect the specific volatile organic compounds produced by mycobacterium tuberculosis bacterium that causes tb [5]. the usefulness of this scent detection technology is also due to the rats’ rapid diagnostic speed in which rats can test up to 100 samples in 20 minutes that will take a laboratory technician about four days when using the microscopy to examine the recommended 30 samples per day [3]. the detection performance of trained rats may depend on rats’ characteristics, which include age, sex, time of day, bacteria count, and weight [8]. the study conducted by ellis et al. [3] identified that older rats do better than less old rats also time of day of training influences the detection performance. however, there were no significant differences between male and female detection performance. in another study of mgode et al. [7] rats can detect tb in samples with a lower number of bacteria count likely to be missed by microscope. based on the experience older rats and weighty rats have low detection performance. and as such, there is no empirical evidence on the main influencing factors, and the trend of their impact is not clear. therefore, this study intends to use data mining techniques to predict factors that influence tb detection rats’ performance. the scent detection technology at apopo produces massive data that need an indepth look to obtain insights on various valuable information using data mining techniques. data mining is a useful field for discovering interesting patterns and information from multidimensional data. in healthcare, data mining techniques such as classification, clustering, and association are most used to solve health problems [9]. most of the studies [10, 11] used classification technique in the diagnosis of tuberculosis to categorize and find the relationships among the manipulated variables. furthermore, the study of asha et al. [12] propose that the classification technique helps the health sectors to have better decision toward their operations. visual analytics of tuberculosis detection rat performance 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 1.1 objective of the study the objective of the study was to use data mining techniques to predict factors associated with tb detection performance of the rats. the goal is to provide a deeper understanding of the main factors influencing detection performance as well support decision making, improving human health, and scaling up of the detection technology. to further contribute to this body of knowledge the study focused on the following three different hypotheses: null hypothesis: i) there is no measurable accuracy difference between the three algorithms of a classification technique in predicting the factors associated with tb detection performance in rats ii) there is no measurable difference between different predicted factors of rats that affect tb detection performance iii) there is no measurable difference between the ability of rats in tb detection performance alternate hypothesis: i) there is a measurable accuracy difference between the three algorithms of a classification technique in predicting the factors associated with tb detection performance in rats ii) there is a measurable difference between different predicted factors of rats that affect tb detection performance iii) there is a measurable difference between the ability of rats in tb detection performance 2 methods this study used the cross-industry standard process for data mining (crisp-dm) as an analytical framework for knowledge discovery. crisp-dm involves a systematic and organized approach in the data mining process [9]. crisp-dm consists of six phases, namely: (1) business understanding (2) data understanding (3) data preparation (4) model building (5) testing and evaluation (6) deployment. these phases are explained in detail underneath. 2.1 business understanding phase this phase dealt with what apopo tb center needs from a business perspective. apopo is a belgian non-government organization (ngo) based in morogoro, tanzania, which aims at using rat odor detection technology to solve humanitarian problems. extracting knowledge of the application domain was useful to create an understanding of the aim, requirements, and constraints of the center. 2.2 data understanding phase this phase focused on the access, description, and identification of the relevant data from the apopo tb center. the given rats’ detection performance data comprised of two datasets: detection rats data and rat_weight. detection rats data dataset composed of 18 detection visual analytics of tuberculosis detection rat performance 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi performance variables (17 independent and 1 dependent) and 471,133 observations from 2011 to 2019 years. meanwhile, the rat_weight dataset contained four (4) independent detection performance variables and 1438 records from 2012 to 2019. this dataset also contained the five female rats. however, the fifth rat had no corresponding detection performance variables and thus disqualified. hence, this study used the four (4) female rats’ data from 2014 to 2018 years. table 1 shows the two datasets with their respective variables. table 1: rats datasets description detectionratsdatadataset description number variable name data type description variable type 1 dots_name string name of the dots center independent 2 dots_patients_number integer number of patients from dots center independent 3 entry_year integer year when patient attend dots center independent 4 id_sample integer identification of the sample independent 5 id_bl_dots integer identification of bacteria level from dots center independent 6 hit boolean tb detection rat performance (categorical variable) dependent 7 id_bl_apopo integer identification of bacteria level from apopo center independent 8 id_configuration integer identification of the cage during training independent 9 id_bl_fm integer identification of bacteria level by fluorescence microscope independent 10 id_evaluation_session integer identification of evaluation session independent visual analytics of tuberculosis detection rat performance 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 11 session_date date date when a session performed independent 12 id_rat integer identification of the rat independent 13 rat_name string name of rat independent 14 gender string sex of rat independent 15 age integer age of rat independent 16 start_time datetime date and time when detection task started independent 17 end_time datetime date and time when detection task ended independent 18 dob date date when rat was born independent rat_weight dataset description number variable name data type description variable type 1 id_rat integer identification of rat independent 2 rat_name string name of rat independent 3 weight_date date date when weight of the rat was measured independent 4 weight integer weight of the rat independent 2.3 data preparation following the data understanding, this phase prepared the data into the well-formed data using the four main steps [9]. these steps were data consolidation, data cleaning, data transformation, and data reduction. data consolidation step, data were accessed from apopo tb center based in morogoro, tanzania. the data were integrated into a single file to ease the data mining process. data cleaning step, irrelevant variables, and empty rows were removed to prevent inconsistencies and outliers. as a result, the prepared data had 365,843 observations from 471,133 observations. data transformation step, the new three (3) detection performance variables were created. these variables are rat_av_weight_per_year, session_start_time, and session_completion_time visual analytics of tuberculosis detection rat performance 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi as shown in table 2. it is important to note that all variables were converted to the required data type. data reduction step, the prepared observations were reduced from 365,843 to 200,000 to ease the analysis by using random sampling method. the study conducted by czarnowski et al. [13] shows that data reduction focused on reducing the volume of dataset while maintaining the integrity of data since the reduced dataset has the same acceptable amount of information as the original dataset. the main four steps were governed by r-language and rstudio. rstudio is a data mining tool and an integrated development environment for r, is a free programming language with extensive modeling and quality graphs resources [14]. considering the given four (4) and three (3) new created detection performance variables, seven (7) prepared detection performance variables and 200,000 observations were used for analysis as shown in table 2. where six (6) are independent variables and one (1) is the dependent variable. table 2 and table 3 show description and descriptive statistical summary information of the variables used to build predictive models respectively. table 2: description of the variables used to build predictive models variable description data type variable type values dots_name name of dots center factor independent dots centre name rat_name name of rat factor independent rat 1, rat 2, rat 3, rat 4 rat_age age of rat in years numeric independent 0.79, 2.04, 3.22 rat_av_weight_per_year average weight of rat per year numeric independent 846.35, 866.80 session_start_time time of day when detection session started in 24 hours integer independent 12,13,14 session_completion_time differences in minutes between session start time and session end time numeric independent 1,2,3 performance performance of rat during the session factor dependent true, false visual analytics of tuberculosis detection rat performance 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi table 3: descriptive statistical summary information of the independent variables used to build predictive models age av_weight_per_ye ar (g) session_start_time (hours) session_completion_ti me (in min) min 0.79 843.7 8.00 1.0 max 7.95 1054.8 18.00 129.0 mean 3.83 899.4 12.16 10.5 median 3.71 866.8 12.00 10.0 range 7.16 211.16 10.00 128.0 sd 1.72 84.44 1.67 4.89 ci 0.0056 0.027 0.0041 0.016 table 3 depicts statistical summary information of the independent variables where the younger and older rats have ages of 0.79 and 7.95 years respectively with the mean, median, range, sd, and ci age of 3.83, 3.71, 7.16, 1.72, and 0.0056 years. moreover, the rats’ lowest and highest average weight per year are 843.7g and 1054.8g respectively, with the mean, median, range, sd, and ci age of 899.4, 866.8g, 211.16kg, 84.44 kg, and 0.027 kg. besides, the table shows that their lowest and highest session start time are 8:00 and 18:00 hours, with the mean, median, range, sd, and ci age of 12:16, 12:00, 10:00,1.67, and 0.0041. furthermore, the minimum and maximum session completion time is 1 and 129 minutes, with the mean, median, range, sd, and ci age of 10.5, 10.0, 128.0, 4.89, and 0.016 minutes. since the mean and median are not equal, it manifests that the data used for this analysis lack normal distribution. table 4 shows number of named rats, and the associated observations by factor of interest. table 4: summary for number of rats, and the associated observations by factor of interest rat_name gender age av_weight_per _year (g) session_start _time (hours) session_completion _time (min) no of observations sofia f 0.76.3 846-877 11:00-14:00 4.0-14.0 50448 catia f 1-6 846-877 10:00-15:00 8.2-13.0 50271 happy f 1-8 844-1055 10:00-15:00 6.4-23.0 50035 mkuta f 1-6 844-1055 9:00-16:00 4.5-15.8 49246 visual analytics of tuberculosis detection rat performance 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi the data from table 4 depicts four female rats used in the analysis with their observations completed during the detection tasks. sofia completed many numbers of observations compared to all since it started detection tasks early to the age of 0.7 years. besides, mkuta has few numbers of observations whereas its large average weight of 1055g may have caused this performance compared to catia from which both started and end detection tasks at the age of 1-6 years, respectively. moreover, happy is older and has a few observations than sofia and catia. the large average weight of 1055g and the removal of irrelevant data from 2011 to 2013 and 2019 could have led this since there is a possibility that happy had many observations in the irrelevant years. furthermore, the table shows that there is sex inequality in the data given from the dots center, since all rats are female. table 5 shows the dependent variable, and the associated observations used in this analysis. table 5: summary for dependent variable (performance) and the number of observations detected performance no_samples false 157686 true 42314 from table 5, there is performance inequality in the distribution of rats’ detected observations. true observations are far less by 21.2% than false of about 78.8% for all observations. and as such, this analysis used more false than true. it was also important to examine/measure the association of continuous independent variables with a dichotomous dependent variable (performance). thus, the logistic regression analysis was used to describe data and explain the relationship between the dependent variable and independent variables as shown in table 6. table 6: association between dependent and independent variables variable pr(>|z|) age < 2e-16 session_completion_time < 2e-16 session_start_time 2.53e-09 av_weight_per_year 2.98e-07 with regard to the table 6, the pr (>|z|) column indicates the p-value corresponding to the zstatistics. the p-values for the independent variables are below 0.05, and implies that there is a relationship between independent variables and the dichotomous dependent variable, and variables are statistically significant. however, the data assumption of normality was not achieved since the corresponding values are less than 0.05. the normality was examined by prediction analysis (logistic regression) using the kolmogorov smirnov (ks) test under the z test statistic. visual analytics of tuberculosis detection rat performance 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 2.4 model building after obtaining the data with the required format, this phase used to select and apply the data mining technique and algorithms based on the nature of the data. this phase applied classification technique to build predictive models that assigned a class for each rat in the given data and predicted the factors that influence rats tb detection performance. not only that but also the predictive models might be useful to place and predict the new instances (rats) with unknown labels into their respective classes. classification is a supervised learning technique in data mining and machine learning that learn the relationship or patterns between independent variables (input) and the dependent variable (output) from the past data and classify each data item into a predefined class label. before presenting the prepared data to the algorithms, r was entirely used to partition the data (200,000 observations) using a simple split estimation method. the simple split estimation is the most popular method, which divided two-thirds of the data (134,000 observations) in the training data and one-third in the test data (64,000 observations) [9] as shown in table 7. therefore, the training data was used to build a predictive model while the test data used to assess the predictive model classification accuracy. table 7: summary of a simple random data splitting type of data number of observations training data 134000 testing data 66000 the data from table 7 indicate that this analysis consisted of 67% training data and 33% test data. the training data were given many observations to build the predictive model while test data were used only to assess the performance of the model generated. despite many classification algorithms used for prediction, this study used decision tree, random forest, and naïve bayes for prediction. a decision tree algorithm is a supervised classification algorithm which generates the decision tree automatically by examining the weight of each variable used to the extent that each leaf node has the same class [11]. also, it generates rules that are easy to interpret and understand [16]. the decision tree is a tree-shaped diagram comprises many input variables that may have an impact on classifying different patterns. additionally, it is known as a decision support algorithm which depends on the input to show the possible outcomes [12]. the decision tree was generated by recursively dividing the training data until each division consisted of the variables of the same class or values based on conditions. following this, a split point used in each node to test the manipulated variables and decide the way to divide the data. the split decision focused on the amount of information a computed variable offered in the class (information gain) and its randomness (entropy). as a result, the variable with the highest information gain and the lowest entropy split and tested. the information gain and entropy determined the decision on the split of data and construction of the decision tree. however, the growth of the decision tree influenced deep learning. control on the parameters used to overcome this problem through pruning [9]. pruning is the process of reducing the size of decision trees by removing sections of the tree that provide little power to classify instances. visual analytics of tuberculosis detection rat performance 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi the generated decision tree consists of a root node, branches, and leaf nodes. the root node is the node at the top of the tree which implies the most important factor responsible for classifying the observations. the branches represent the pattern classification outcome of a test using one of the variables based on conditions. the leaf nodes placed either before or at the end of the decision tree imply the nodes without children. and as such, they identify the last class choice for a pattern. moreover, the decision tree formed rules (if-then statements) from the root node to the leaf nodes which are easy to interpret and understand. as a result, they enhanced the discovery of exploratory knowledge on the factors that influence rats tb detection performance. furthermore, the random forest algorithm was also applied to predict the influencing factors and compare the prediction accuracy of the models. a random forest algorithm is a supervised classification algorithm used to build multiple decision trees called forest in random during the training process. the choice of most of the trees determined the final decision of the algorithm based on the given manipulated variables. there is a relationship between the number of trees in the forest and the results. thus, many trees, the more accurate the result. the motives behind this algorithm are that it can be used for both classification and regression problems and lowers the risk of overfitting [11]. overfitting is a modeling error which occurs when the outcome of the analysis is limited only to specific data. as a result, instead of predicting the whole manipulated data, the model predicts only for that set of data [16]. in the random forest algorithm, the process of determining the root node and the splitting of the variable nodes were performed randomly from the training data. during the training, no control of parameter (pruning) involved preventing the decrease of the relationship between trees. however, pruning is of importance for the reduction of complexity in variable computation during the training. as a result, the algorithm handled about 500 trees in the ensemble and identified the error rate based on the training data. following this, the random forest algorithm predicted the factors for detection by pinpointing the mean decrease in gini values for each variable. furthermore, the naive bayes algorithm was applied to compare their predictive accuracy by using the same test data, and finding the best algorithm with high classification accuracy rates for the given data. naive bayes is the supervised classification algorithm that uses a probability theory (bayesian theorem) to generate the classification model. moreover, to place an instance in the desired class. this theory supported to calculate a set of probabilities by counting the frequency and values of the manipulated variables from the given data [11]. the naive bayes algorithm is a well-performed algorithm owing to its simplicity in execution time. and as such, it can build a final model that can learn rapidly different classification problems [17]. however, this algorithm assumed that all variables were independent of the given data while few real-world applications may agree with this [12]. the main advantages of the naive bayes algorithm compared to the other two algorithms are the run-time speed on large and complex datasets. hence, most healthcare field researchers across the world use this algorithm due to its better speed and accuracy. this algorithm identified a priori probability for the dependent variable and conditional probabilities for every independent variable based on the manipulated data. the naive bayes algorithm does not show the weights of each variable included in the classification, but it has been used purposely to compare its prediction performance with the results generated from the decision tree and random forest algorithms [17]. visual analytics of tuberculosis detection rat performance 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 2.5 testing and evaluation this phase was used to test and assess the classification performance of the three generated predictive models. the assessment was based on the accuracy metric to show the predictive accuracy of the model from the confusion matrix. however, the confusion matrix has several assessment measures such as sensitivity and specificity. the confusion matrix is a table used to describe a classification model performance based on the test data. the accuracy measure was used to assess the ability of the models to accurately predict the class label of the test data. the accuracy entailed the matching between actual class labels of the test data and the class labels of the predicted models. the accuracy measurement focused on the accuracy rate, the percentage of test instances that were accurately classified by the predictive model as shown in table 8. the accuracy (acc) and error rate (err) values of the classification matrix rated the predictive model performance. the error rate (err) implies the fraction of the sum of false positives and false negatives and the sum of the total number of all the predictions made. table 8 presents the comparison of predictive models classification accuracy rate and error rate between training data and test data for all three algorithms. since the predictive models learned to classify the rats tb detection performance into true or false, the positive class is a false value since it has many observations of about 157,686 samples as reported in table 5. therefore, the following formulas measured the percentage accuracy rate and error rate respectively for the positive class. accuracy (acc) = tp + tn tp + fp + tn + fn error rate (err) = fp + fn tp + fp + tn + fn tp, tn, fp, fn mean true positive, true negative, false positive and false negative respectively. visual analytics of tuberculosis detection rat performance 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi table 8: comparison of predictive models’ classification accuracy rate between training data and test data for all algorithms from table 8, during the building of the predictive model, the decision tree algorithm correctly classified 105573 observations equal to the accuracy rate of 78.74% and incorrectly classified 28227 observations equal to the error rate of 21.06%. in other hands, random forest correctly classified 105878 observations equal to the accuracy rate of 79.00% and incorrectly classified 28142 observations equal to the error rate of 21.00%. furthermore, the naïve bayes correctly classified 105674 observations equal to the accuracy rate of 78.86% and incorrectly classified 28326 observations equal to the error rate of 21.14%. with regards to the test data, the decision tree algorithm correctly classified 51997 observations equal to the accuracy rate of 78.78% and incorrectly classified 14003 observations equal to the error rate of 21.22%. additionally, random forest correctly classified 52019 observations equal to the accuracy rate of 78.82% and incorrectly classified 13981 observations equal to the error rate of 21.18%. again, the naïve bayes correctly classified 51946 observations equal to the accuracy rate of 78.71% and incorrectly classified 14054 observations equal to the error rate of 21.29%. thus, the random forest algorithm outperforms both during the building of the predictive model and assessing the classification performance. however, the ability to overcome overtraining problem might have led to this. additionally, data overlapping, and the random nature of the modeling algorithms presumed to affect the overall performance of the three predictive models. training data evaluation criteria predictive model decision tree random forest naive bayes accuracy (%) 78.94% 79.00% 78.86% error rate (%) 21.06% 21.00% 21.14% correctly classified observations (tp) 105573 105878 105674 incorrectly classified observations (fn) 28227 28142 28326 mcnemar's test p-value <2e-16 <2e-16 <2e-16 test data evaluation criteria predictive model decision tree random forest naive bayes accuracy (%) 78.78% 78.82% 78.71% error rate (%) 21.22% 21.18% 21.29% correctly classified observations (tp) 51997 52019 51946 incorrectly classified observations (fn) 14003 13981 14054 mcnemar's test p-value <2e-16 <2e-16 <2e-16 visual analytics of tuberculosis detection rat performance 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 2.6 deployment this is the last phase that was used to organize and present the knowledge gained to the end-user for real application using visualization techniques, such as a plot. the knowledge obtained is explicitly aimed at helping users to predict rats’ factors that influence tb detection performance and the classes of new data instances (where the class label is unknown). table 6 pinpoints the association between a dichotomous variable and independent variables. moreover, figure 2 shows a variable importance plot used for proper interpretation and ease understanding of the knowledge gained. 3 results and analysis the data mining process aimed to elicit knowledge from the given structured data and present it to the end-user for the real application. and as such, this process was managed by the classification technique and algorithms that helped to learn the relationship between the patterns. however, the classification technique used three algorithms which are decision tree, random forest, and naïve bayes to build the predictive models. thus, this section presents results and analysis based on the formulated different three hypotheses as follows: • there is no measurable accuracy difference between data mining algorithms in predicting the factors that associated with tb detection performance in rats with regards to the first hypothesis, our findings show that there is in fact a measurable difference in between the three data mining algorithms. according to mcnemar’s test, the test checked if there was significant difference between the counts in two cells made in both predictive models. by capturing the errors made by both models. hence, table 8 shows that the errors made by both models in the test data are not the same, and thus the result of the test is significant and the null hypothesis is rejected. additionally, the mcnemar's test p-values for both models is <2e-16 which are below the 0.05 leading to the rejection of the null hypothesis that the data mining algorthms are statistically significant. • there is no measurable difference between the predicted factors of rats that affect tb detection performance the classification technique was used to build predictive models that predicted the class for each rat and the factors that influence tb detection performance. however, this technique applied three algorithms to learn the relationship between variables. the independent variables (input) include age, av_weight_per_year, session_start_time, and session_completion_time while the dependent variable (output) is performance. therefore, all three algorithms applied these variables separately to build predictive models on factors that influence rats’ tb detection performance. starting with the decision tree algorithm, it generated a decision tree where the top node (root node) shows the most significant factor that influences tb detection performance. the ability of the algorithm to seek optimal splits in variable values has led to this. moreover, the leaf nodes indicate the class of every instance from the observations. visual analytics of tuberculosis detection rat performance 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi figure 1: decision tree with rats factors that influence tb detection performance figure 1 depicts the hierarchy of variables where the variable with a high correlation (age) with the prediction, split on first. thus, the age of the rat is the most significant factor. however, the other predicted factors are shown on the leaf nodes which indicate the class of every instance. moreover, the decision tree algorithm-generated rules which are easy to interpret and understand. these rules are the result of the if-then statements from the root node to the leaf nodes as reported in table 9. table 9: classification rules generated from decision tree algorithm rule number rule performance decision true false number of observations in % 1 if age >= 6 ⇒ 0.13 0.87 10% 2 if age < 0.88 & session_completion_time>= 7 ⇒ 0.14 0.86 1% 3 if age is 3.1 to 6 0.15 0.85 6% visual analytics of tuberculosis detection rat performance 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi & av_weight_per_year< 845 ⇒ 4 if age is 3.1 to 6 & av_weight_per_year >= 845 & session_start_time >= 14 ⇒ 0.19 0.81 11% 5 if age < 3.1 & session_completion_time< 7 ⇒ 0.20 0.80 10% 6 if age is 3.1 to 6 & av_weight_per_year >= 845 & session_start_time < 14 ⇒ 0.22 0.78 36% 7 if age is 0.88 to 3.1 & session_completion_time >= 7 ⇒ 0.25 0.75 26% from table 9, the first rule implies that older rats (with ages greater or equal to 6 years) had a performance chance of 0.13, true and 0.87, false, and detected fewer observations (10%). considering the second rule, rats with the age of fewer than 0.88 years and at least 7 minutes (session completion time) had a performance chance of 0.14 true, 0.86 false, and detected 1% of the observations. hence, older and less young rats portrayed low performance. the sixth rule has 36% of the detected observations. rats with ages of 3.1 to 6 years, at least 845g of the average weight per year, and the session start time before 14:00 hours had a detection performance chance of 0.22 true, and 0.78, false. this rule is consistent with the fourth one except for the session start time. since the sixth rule had many observations than the fourth, the session starts time before 14:00 hours are the most performed one. furthermore, the fifth rule has 10% detected observations, which imply rats with ages of 3.1 years and session completion time of fewer than 7 minutes had a performance chance of 0.20 true and 0.80 false. when comparing this rule with the second one, rats with a session completion time of fewer than 7 minutes depicted potentiality in detection since this rule had many observations compared to the second one. therefore, the results pinpointed in table 11 manifest that rats with ages of 3.1 to 6 years, at least 845g of the average weight per year, the session start time before 14:00 hours, and fewer than 7 minutes as the session completion time performed well. however, it is of importance to understand the extent to which each factor contributed to the prediction. the random forest algorithm pinpointed the predictor variables that are important in predicting the outcome based on the mean decrease in gini (impurity), as shown in figure 2. mean decrease in gini is the average (mean) of a variable total decrease in the likelihood of incorrect classification of a new instance of a random variable from the data set. visual analytics of tuberculosis detection rat performance 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi figure 2: variable importance generated by random forest algorithm from figure 2, a higher (1791.9167) and lower (233.5753) mean decrease in gini portrays greater and less variable importance, respectively. hence, age and av_weight_per_year are the most and least significant factors. both decision tree and random forest have indeed shown age as the most significant factor. thus, the random forest algorithm and decision tree algorithm have predicted the factors that influence rats’ tb detection performance by using the classification technique. however, the naive bayes algorithm was used to create the model and compare their classification accuracy since it measures the probabilities of the variables and not their weights. therefore, regarding the second hypothesis, as the p-values shown in table 10 are less than 0.05, we reject the null hypothesis and conclude that there is a measurable difference between the predicted factors of rats that affect tb detection performance. hence, the predicted factors are statistically significant. table 10: predicted factors with their corresponding p_values factor p_value age < 2e-16 session_completion_time < 2e-16 session_start_time 2.53e-09 av_weight_per_year 2.98e-07 • there is no measurable difference between the ability of rats in tb detection performance from the given data and the aim of the study, the rats’ performance implies their ability to detect a sample with either tb, true (sensitivity) or without tb, false (specificity). table 3 manifests that the youngest and oldest rats had the ages of 0.79 and 7.95 years respectively with the median age of 3.71 years. meanwhile, the less weighty and weighty rats had the weights of 843.7g and 1054.8g respectively with the median of 866.8g. and as such, rats with ages and visual analytics of tuberculosis detection rat performance 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi weights below and above the median refer to younger, older, less weighty, and weighty rats respectively. additionally, early detection conducted at 8:00 hours while the late detection was done at 18:00 hours with the minimum completion time of 1 minute and maximum completion time of 129 minutes respectively with the median completion time and start time of 10 minutes and 12:00 hours. since the given data had many numbers of observations with false values than true values as shown in table 5, the rats’ high performance in these data had a false value. in this conception, rats’ performance depended on the number of observations accomplished. therefore, the results pinpointed in table 3 manifest that rats with ages of 3.1 to 6 years, at least 845g of the average weight per year, the session start time before 14:00 hours, and fewer than 7 minutes as the session completion time performed well. with regards to these results, it is obvious that there is difference in ability of rats to detect tb samples. this is also evidenced by table 10 which indicated the p_values of each predicted factor which are less than 0.05, and thus makes the results significant and reject the null hypothesis. 4 discussion 4.1 characteristics of data considering data understanding phase, the given data consisted of many variables and observations, but the sample size for characterizing a tb rat is therefore only four female rats. the given number of rats was the ones found with the requested data and was expected to address the aim of the study of finding the influencing factors based on the number of observations as shown in table 4 and not comparing the performance of every rat which would require large sample sizes. moreover, table 4 reported that, there was no gender equality in the given data since all rats were female. however, for the future it is advantageous to analyze data with large sample size and both male and female rats to understand which gender influences detection performance. based on dependent variable performance, table 5 demonstrated that data consisted of many false values than true values. since it was the target class for classification, it is presumed to have an impact on the results. thus, when one value has many samples than the other, its performance is also higher. it was valuable if the data would have an estimation of about an equal number of values of the observations in the detection performance class. and as such, it would reduce the suspicion that the results might rely on one group of the data and limits generalization. furthermore, table 6 shows the logistic regression analysis which examined the association of independent variables with a dichotomous dependent variable (performance). the p-values for the independent variables are below 0.05, and implies that there is a relationship between independent variables and the dichotomous dependent variable. hence, the variables are statistically significant. 4.2 factors influencing rats tb detection performance the results depict the strength of the age factor in the detection performance. figure 1 shows that age split first due to the highest information gain ratio. as a result, it has appeared in all generated rules in table 9. contrary to the other variables that are shown only once in the generated rules. moreover, in the variable importance of random forest depicted in figure 2, the decrease mean gini of age was higher than the other variables. the results manifested that rats visual analytics of tuberculosis detection rat performance 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi between the ages of 3.1 to 6 years positively affected the performance. however, it may limit the generalization of the results since it referred to female rats. the study of brushfield et al. [8] proposes that detection performance may depend on rats’ characteristics such as age. nevertheless, successful training and growth progress might have led to good detection performance. furthermore, the results show that older rats portrayed a low detection performance. and as such, the olfactory deficit might have caused this since detection performance depends on the rats’ olfactory sensitivity [13].moreover, the results provide new insight into the relationship between time differences when the rat starts and ends detection tasks (session completion time). and as such, good performers were the rats that completed the number of observations with less than 7 minutes. since these rats have a high-speed of detecting 100 samples in 20 minutes, good performers were the rats that completed the number of observations with less than 7 minutes. however, the given data might have influenced the session's completion time since the samples contained many values of false (samples without tb bacteria). the study conducted by mgode et al. [7] pinpoints that during the training, rats learn to pause for a long time of about 3 seconds to the sample hole with tb bacteria and take a short time of about 1 second to the sample hole without tb bacteria. not only that but also, the results contribute a clearer understanding of the influence of average weight per year (av_weight_per_year) on detection performance. rats with an average weight per year of greater or equal to 845g performed better. according to the study conducted by beyene et al. [5], the weight range of adult rats’ females ranges from 1 to 1.5kg. therefore, one can argue that young rats with at most 1.05 kg were the most performers. however, presumed the reliability of the results could increase with the corresponding weight rather than the applied average. however, these results may limit generalization since they refer to female rats. therefore, rats tb trainers and decision-makers must consider these results to utilize the usefulness of this technology and should maintain it for sustainability. on the other hand, the results reveal that for the three different algorithms used, the classification accuracy was much more in the random forest (78.82%) than decision tree (78.78%) and naive bayes (78.71%). conversely, the predictive models’ accuracies differences are small. the nature of data and algorithms used might have caused this in the sense that random forest and decision tree algorithms fit in skewed data different from naive bayes which, do better in normally distributed data [9]. moreover, in the random forest, the ability to assembly several trees and make the final decision from several trees might influence this highest classification accuracy [12]. despite the found results based on the dependent and independent variables given from the data, other factors presumed the influence on these results. these factors may include training procedures, trainers or recorders (data recording), experimental setup, and laboratory technicians (quality control) [7, 9, 15]. the study conducted by reither et al. [15] argue that since rats are trained based on the conditioning techniques which support to change their behavior such as learning to recognize sound during the training, it is useful to have the justifiable rules to avoid incorrect results. likewise, mgode et al. [7] demonstrate that rats’ successful and consistent training procedures are most important in tb healthcare centers that apply rat as odor-detection technology. with this regards, it is presumed that rats from the given data succeeded in the training procedures and thus manifested better performance. moreover, observing precision in data recording during detection tasks is highly emphasized to avoid false results. since rats’ trainers and recorders are the ones performing data recording and training, they should have skills in getting consistent records. hence, a well-organized experiment setup may facilitate rats visual analytics of tuberculosis detection rat performance 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi to portray better performance [1]. additionally, before presenting the sample in a cage for detection, a standard heat is applied into it to kill infectious microorganisms and enhance quality control. hence, quality control may determine the effectiveness of rats in detection performance. consequently, despite the outcome of the dependent and independent variables, the mentioned confounding variables might influence the results. therefore, rats’ detection performance depends on the main and confounding factors. 5 conclusion and recommendations this study has focused on the prediction of factors influencing rats’ tb detection performance using data mining techniques. also, building predictive models for predicting the class for every new instance (rat). while this study also concentrated on understanding the relationship of the manipulated variables, the results indicate that age, session_completion_time, session_start_time, and av_weight_per_year are the factors influencing rats tb detection performance. however, the results show that the age of the rat was the most influencing factor. the results also pinpoint that rats with the age of 3.1 to 6 years, at least 845g of the av_weight_per_year, before 14:00 hours as the session start time, and less than 7 minutes as the session completion were the best performers. these results are useful to rats’ trainers and decision-makers in understanding the potential factors that may affect the detection performance and hence increase tb detection performance. ultimately to support decision making, scaling up of the detection technology and improve human health. considering predictive models, the random forest predictive model has the highest classification performance accuracy of 78.82%. followed by the decision tree with 78.78% and naive bayes is the last model with 78.71% and thus makes the random forest predictive model the best model for the study. since this study implemented data mining techniques in a social setting by predicting factors that influence rats in detecting tb disease, it is also helpful to the academic society of information systems. however, confounding factors such as training procedures, trainers or recorders (data recording), experimental setup, and laboratory technicians (quality control) might have an impact on the results. therefore, to maximize the effectiveness and efficiency of these results, several criteria for future research will have to be optimized. first, a dataset with large sample size and many desirable variables for rats tb detection performance is valuable to increase the number of known factors. moreover, to predict significant sex differences, the dataset should balance gender distribution. acknowledgments data for this study was supported by apopo tb training and research center in morogoro, tanzania. many staff from the apopo tb center have provided advice and appreciated suggestions. colleague’s critiques and comments have consistently improved the paper. conflicts of interest on behalf of all authors, the corresponding author states that there is no conflict of interest. visual analytics of tuberculosis detection rat performance 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 6 references 1. poling a, weetjens b, cox c, beyene n, durgin a, et al. 2011. tuberculosis detection by giant african pouched rats. behav anal. 34(1), 47-54. pubmed https://doi.org/10.1007/bf03392234 2. world health organization. global tuberculosis report 2018. new york, united states of america: who; 2018. 3. ellis h, mulder c, valverde e, poling a, edward t. 2017. reproducibility of african giant pouched rats detecting mycobacterium tuberculosis. bmc infect dis. 17, 298. doi:. pubmed https://doi.org/10.1186/s12879-017-2347-3 4. weetjens bj, mgode gf, machang’u rs, kazwala r, mfinanga g, et al. 2009. african pouched rats for the detection of pulmonary tuberculosis in sputum samples. int j tuberc lung dis. 13, 737-43. pubmed 5. beyene, n., mahoney, a., coxi, c., weetjens, b., makingi, g., mgode, g, et al. (2012). apopo’s tuberculosis research agenda: achievements, challenges and prospects. tanzania journal of health research. doi: 10.4314/thrb.v14i2.5 6. poling a, valverde e, beyene n, mulder c, cox c, et al. 2017. active tuberculosis detection by pouched rats in 2014: more than 2,000 new patients found in two countries. j appl behav anal. pubmed https://doi.org/10.1002/jaba.356 7. mgode gf, cox cl, mwimanzi s, mulder c. 2018. pediatric tuberculosis detection using trained african giant pouched rats. pediatr res. 84(1). doi:. pubmed https://doi.org/10.1038/pr.2018.40 8. brushfield a, luu t, callahan b, gilbert p. 2008. a comparison of discrimination and reversal learning for olfactory and visual stimuli in aged rats. behav neurosci. 122(1), 54-62. pubmed https://doi.org/10.1037/0735-7044.122.1.54 9. sharda, delen & turban (2014). business intelligence and analytics (tenth edition). 10.nagabhushanam d, naresh n, raghunath a, praveen kumar k. 2013. prediction of tuberculosis using data mining techniques on indian patient’s data. int j cloth sci technol. 4, 262-65. 11.suresh, n. & arulanandam, d. (2018). a mining approach for detection and classification techniques of tuberculosis diseases. 12.asha, t., natarajan,s., murthy, k.n.b., (2011). effective classification algorithms to predict the accuracy of tuberculosis-a machine learning approach. 13.czarnowski i., jędrzejowicz p. 2018. an approach to data reduction for learning from big datasets: integrating stacking, rotation, and agent population learning techniques. complexity. doi:. https://doi.org/10.1155/2018/7404627 https://pubmed.ncbi.nlm.nih.gov/22532730 https://doi.org/10.1007/bf03392234 https://pubmed.ncbi.nlm.nih.gov/28438117 https://doi.org/10.1186/s12879-017-2347-3 https://pubmed.ncbi.nlm.nih.gov/19460250 https://pubmed.ncbi.nlm.nih.gov/27718224 https://doi.org/10.1002/jaba.356 https://pubmed.ncbi.nlm.nih.gov/29617007 https://doi.org/10.1038/pr.2018.40 https://pubmed.ncbi.nlm.nih.gov/18298249 https://doi.org/10.1037/0735-7044.122.1.54 https://doi.org/10.1155/2018/7404627 visual analytics of tuberculosis detection rat performance 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(2):e12, 2021 ojphi 14.hussain, s. (2015). educational data mining using r programming and r studio. journal of applied and fundamental sciences 15.reither, k., jugheli, l., glass, t.r., sasamalo, m., mhimbira, f.a., weetjens, b.j, et al. (2015). evaluation of giant african pouched rats for detection of pulmonary tuberculosis in patients from a high-endemic setting. 16.chaurasia v, pal s. (2013). data mining approach to detect heart disease. international journal of advanced computer science and information technology volume 2, issue 4, issn: 2296-1739. 17.ameri h, alizadeh s, hadizadeh m. 2014. assessing the effects of infertility treatment drugs using clustering algorithms and data mining techniques [persian]. j mazandaran univ med sci. 24, 26-35. 18.ayas, s. &ekinci, m. (2014). random forest-based tuberculosis bacteria classifications in images of zn-stained sputum smear samples. doi: . 19.kraemer s, apfelbach r. 2014. olfactory sensitivity, learning and cognition in young adult and aged male wistar rats. physiol behav. pubmed 20.mahoney a, edwards tl, weetjens bj, cox c, beyene n, et al. 2013. giant african pouched rats (cricetomysgambianus) as detectors of tuberculosis in human sputum: two operational improvements. psychol rec. 63, 583-94. https://doi.org/10.11133/j.tpr.2013.63.3.012 21.maniya h, hasan mi, patel pk. 2011. comparative study of naïve bayes classifier and knn for tuberculosis [ijca]. int j comput appl. 22.mulder c, mgode gf, reid se. 2017. tuberculosis diagnostic technology: an african solution … think rats. afr j lab med. 6(2), https://doi.org/10.4102/ajlm.v6i2.420 23.prasannadesikan. kuo-wei hsu, srivastava,j. (2011). data mining for healthcare management. siam international conference on data mining. 24.world health organization. make every mother and child count. geneva, switzerland: who; 2005. https://pubmed.ncbi.nlm.nih.gov/15135015 https://doi.org/10.11133/j.tpr.2013.63.3.012 https://doi.org/10.4102/ajlm.v6i2.420 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the french emergency department oscour network: evaluation after a 10-year existence anne fouillet*, vanina bousquet, isabelle pontais, anne gallay and céline caserioschönemann french institute for public health surveillance (invs), saint maurice, france objective implemented 10 years ago, the french emergency department surveillance system (oscour network) has been assessed using four major evaluation criteria in syndromic surveillance: stability, coverage, data quality and utility. introduction after the major impact of the 2003 heat wave, france needed a reactive, permanent and national surveillance system enabling to detect and to follow-up various public health events all over the territory including overseas [1-2]. in june 2004, the french syndromic surveillance system based on the emergency department (ed) has been implemented by the national institute for public health surveillance (invs). beginning with 23 ed in 2004, the network has progressively included new ed and several steps have contributed to accelerate this permanent increase. a first evaluation of this data source was conducted for the specific surveillance of heat wave [3]. methods after 10 years of experience, four evaluation criteria on essential characteristics for a syndromic system are explored: 1/ stability and regularity of data transmission, evaluated by a prospective calculation of the proportion of ed having transmitted their data on time (daily transmission is expected), 2/ the national coverage, calculated by the proportion of ed included in the network and the proportion of the daily ed attendances in the total number of attendances in france, 3/ the data quality, particularly for the demographic and medical information (icd10 codes), and 4/ the utility of the system, evaluated by describing the publichealth situations for which the system has been used, regarding the objectives of the system (detection and situational awareness). results in august 2014, about 600 ed are included in the oscour network for the syndromic surveillance, recording 80% of the national total attendances. the 26 french regions are covered by the system, with a coverage rate ranging from 21% to 100% in 5 regions. the transmission from data providers to invs is very stable and regular: every day, the institute receives about 40,000 attendances recorded the previous day (d-1) by about 85% of the 600 ed participating to the network. the 15% of missing ed generally send their data in the two following days. more than 98% of the demographic information (date of birth and gender) are correctly recorded. 75% of the medical diagnoses coded in icd10 are recorded. in 2013, about 12,800 different icd10 codes have been used by the physicians. this major variety of codes ensures a large surveillance based on various syndromic indicators. surveillance is performed routinely both at regional and national level, based on daily and weekly dashboards monitoring about 60 indicators. during the last decade, few events have been detected by the system, even if that was the initial objective of the system. however, the system has often allowed for situational awareness of both unexpected events (air pollution episodes, floods, cyclone, measles, chikungunya emergence) and usual events (heat/cold waves, flu and winter usual epidemics, dengue outbreak,…). the system has also contributed to the surveillance of the population health during mass gatherings (2012 olympic games, 2014 world equestrian games, g8/g20 summit). conclusions since the beginning of the system, the coverage and data quality have permanently been increasing. the mandatory transmission of ed data set up in july 2013 helped recently to improve the national coverage but with some adverse effects on coding practices which have to be carefully monitored. with a 10-year historical database, robust statistical methods can be implemented for detection. reflexions are ongoing to complete the system with early medical management data from the pre-hospital period and include the french medical rescue units into the syndromic surveillance system. keywords emergency department; evaluation; france acknowledgments to emergency department data providers and invs regional units for their substantial contribution to the system. references [1] josseran l, nicolau j, caillère n, astagneau p, brücker g. syndromic surveillance based on emergency department activity and crude mortality: two examples. euro surveill 2006;11:225-9. [2] caserio-schönemann c, bousquet v, fouillet a, henry v. le système de surveillance syndromique sursaud (r). bull epidémiol hebd 2014;3-4:38-44. [3] josseran l, fouillet a, caillère n, brun-ney d, ilef d, et al. (2010) assessment of a syndromic surveillance system based on morbidity data: results from the oscour® network during a heat wave. plos one 5(8): e11984. doi:10.1371/journal.pone.0011984 *anne fouillet e-mail: a.fouillet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e74, 2015 surveillance of an online social network to assess population-level diabetes health status and healthcare quality 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 surveillance of an online social network to assess population-level diabetes health status and healthcare quality elissa r weitzman, 1,2,4 scd, msc, skyler kelemen, 1 ba, kenneth d mandl, 1,3,4,5 md, mph 1 children’s hospital informatics program at the harvard-mit division of health sciences and technology, children’s hospital boston, boston, ma, usa 2 division of adolescent medicine, children’s hospital boston, boston, ma, usa 3 division of emergency medicine, children’s hospital boston, boston, ma, usa 4 department of pediatrics, harvard medical school, boston, ma, usa 5 manton center for orphan disease research, children’s hospital boston, ma abstract objective: test a novel health monitoring approach by engaging an international online diabetes social network (sn) in consented health surveillance. methods: collection of structured self-reports about preventive and self-care practices and health status using a software application (“app”) that supports sn-mediated health research. comparison of sn measures by diabetes type; and, sn with behavioral risk factor surveillance system (brfss) data, for us-residing insulin dependent respondents, using logistic regression. results: of 2,414 sn app users, 82% (n=1979) provided an a1c and 41% (n=996) completed a care survey of which 931 have diabetes. of these: 65% and 41% were immunized against influenza and pneumonia respectively, 90% had their cholesterol checked, 82% and 66%, had their eyes and feet checked, respectively. type 1/lada respondents were more likely than type 2/pre-diabetic respondents to report all five recommended practices (adjusted or (95% ci) 2.2 (1.5, 3.2)). past year self-care measures were: 58% self-monitored their blood glucose (smbg) ≥ 5 times daily, 37% saw their diabetes nutritionist, 56% saw a diabetes nurse educator, 53% saw a doctor for their diabetes ≥ 4 times. reports of health status did not differ by diabetes type in the sn sample. the sn group was more likely than the brfss comparator group to use all five preventive care practices (adjusted or (95% ci) 1.8 (1.4, 2.1) and smbg ≥ 5 times daily (adjusted or (95% ci) 10.1 (6.8, 14.9). conclusions: rapid assessment of diabetes care practices using a novel, sn-mediated approach can extend the capability of standard health surveillance systems. keywords: diabetes, healthcare quality, social networks, surveillance, social networking, chronic illness http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 introduction traditional health surveillance programs, for example the behavioral risk factor surveillance system (brfss) and the national health and nutrition examination survey (nhanes), depend on increasingly unreliable channels for communication such as landline-based telephone surveys and household interviews. these approaches have enriched our understanding of population patterns of disease and illuminated the increasing morbidity and mortality burdens imposed by chronic illnesses (1, 2). however, standard approaches face challenges to obtain and retain participants, accommodate rapidly shifting patterns of disease, and balance breadth with depth of data collected in a fashion that supports assessment of disease prevalence and also healthcare and self-care practices of important affected subgroups (3). recently, we reported about our efforts to engage participants in an international online diabetes social network (sn) in consented public health monitoring of their disease (4). using a software application (“app”) we launched into their facebook-like community, sn members could report about and share their diabetes health data and participate in research as part of a distributed public health research cohort. the project falls within the rubric of new ‘citizen science’ efforts (5) to engage populations—including online social networks—in advancing public health through contributing data and observational energies to research (6, 7). this snmediated approach to health monitoring may address some of the challenges facing standard surveillance systems by engaging a population of interest in bidirectional communication about important and often overlooked aspects of their disease vital to targeted interventions. for this report, we investigated diabetes health status and adherence with diabetes-specific recommended preventive and self-care practices among sn members using this novel app and approach. a primary focus of the investigation was to characterize care patterns in the sample overall and by diabetes type, an important stratification variable for understanding diabetes—a heterogeneous disorder originating in different biologic and sociologic processes (8-10). type data are not collected in brfss (11) nor nhanes (12), limiting the utility of these systems for informing targeted response. a secondary focus of the investigation was to compare care metrics reported by the sn sample to those reported by respondents from a standard health surveillance system to ascertain the extent to which patterns resemble each other. methods we used the tuanalyze app (4) to survey participating members of the tudiabetes community about their preventive and self-care practices. members report and share their diabetes data and obtain both contextualized views of personal measures of glcyemic control (a1c%) (figure 1), and summary reports about the health status and care patterns of all application users (figure 2). the application is available in english and spanish language versions. its use is voluntary. study activities were reviewed and approved by the children’s hospital boston institutional review board. details about the application design, technology platform, operations and early adoption/use patterns are published elsewhere (4). http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 1. contextualized map view of a1c% data visible in the tuanalyze application. users of the tuanalyze application who self-report measures of glycemic control (a1c%) using the application are able to view near real-time summary data of their a1c% charted against the frequency distribution of other members’ shared a1c% measures, by geographic area (state, province or country). map views of contextualized a1c% measures are delivered on a geographic information system display in which only a given user can see her personal data arrayed against summary data. figure 2. illustration of a tuanalyze blog post and feedback report in which aggregate tuanalyze data were shared with the tudiabetes community and tuanalyze users. in the tuanalyze application, members of the tudiabetes.org host online social network as well as users of the tuanalyze app, can read summary reports generated from data entered into the application by the community of users and shared as a means of communicating “results” and sparking engagement with the technology. comments and questions posted by users are reviewed and incorporated into the study agenda as part of the participatory research approach. http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 tuanalyze sample eligibility eligibility criteria for tuanalyze and the larger tudiabetes social network include being age 18 or older (younger users may join with a parent/guardian), affected by diabetes, ability to read and write english and/or spanish, and having internet access. persons who do not have diabetes and are using the application as a proxy for another person, such as family members (n=65), were not included in these analyses. response rate during the study period, 996 out of 2,414 tuanalyze users (response rate 41.2%) took a survey about their diabetes and associated care patterns, a response rate considered high for web surveys (13). the final sample consisted of 931 users who had completed the survey and have some form of diabetes. 105 users completed the survey but did not enter an a1c measure into the application; these were excluded from all analyses of a1c but otherwise included. survey respondents who had not entered an a1c value were less likely than those who had to be white, to reside in the us, to have health insurance, and to use the english language version of the application. measures analyses draw on two sets of data: self-reported data from tuanalyze users including a1c% values and surveys administered in the application environment; and self-reported health and demographic data from a us national sample surveyed for the 2009 behavioral risk factor surveillance system (brfss). the surveys administered in tuanalyze were constructed from questions adapted to the application and taken from national health surveys, including the brfss (1, 12), and the national health and nutrition examination study (nhanes) (14). tuanalyze users indicated their diabetes type by selecting from a dropdown list in the survey with options for self-identifying as having type 1, type 1.5 (lada), type 2, pre-diabetes and gestational diabetes. we dichotomized diabetes type into two main groups, based on the underlying disease mechanism, as type 1 or lada and type 2 or pre-diabetes. no respondents indicated gestational diabetes and this type is therefore not included. recommended diabetes preventive care practices were defined consistent with the 2010 american diabetes association (ada) clinical practice recommendations, and included any history of a pneumonia vaccination, and past year history of an influenza vaccination, dilated eye exam, foot exam, and lipid profile (cholesterol check) (8). self-care was assessed by reported typical frequency of self-monitoring of blood glucose, dichotomized at the ada minimum recommended threshold of five or more times per day, and by describing reports of seeing a diabetes nurse educator and nutritionist in the past year and having four or more visits with one’s doctor in the past year. tuanalyze respondents were asked to rate their health on a standardized scale. those who reported “excellent”, “very good” or “good” health were classified as having better health, http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 compared to those who reported their health as “fair” or “poor”. respondents who had ever been diagnosed with one or more health problem besides depression from a list of chronic conditions provided in the survey (arthritis, asthma, cancer, cardiovascular disease, depression, stroke and “other”) were categorized as having a chronic illness comorbidity; those who reported having been diagnosed with depression from this list were categorized as having experiencing depression. consistent with ada standards (8), respondents with a most recent a1c value of 7% or higher were classified as “above target” compared to those with lower values. for both the tuanalyze and brfss cohorts, we classified as white respondents who identified themselves solely as caucasian and non-hispanic. age at the time of survey was determined from respondents’ date of birth (for tuanalyze) and reported age at time of survey (for brfss). country location for tuanalyze users was gathered from data entered upon first engaging with the application; all brfss respondents are based in the us. data analyses reported use patterns of diabetes-specific preventive and self-care practices among the tuanalyze sample were estimated in aggregate and across diabetes types. tuanalyze patterns were compared with those found in analyses of the 2009 us brfss (15), the most recent us national health monitoring system for which comparable preventive and self-care measures are available. brfss data were downloaded at no cost from the us centers for disease control and prevention website (15). descriptive statistics characterize the tuanalyze sample and the chisquared test was used to compare demographics, care behaviors and health outcomes across the two major diabetes type groups; this approach was also followed in comparing preventive and self-care practices across the tuanalyze and brfss samples. comparative analyses of tuanalyze and brfss samples were undertaken for restricted samples that included respondents with diabetes, who reside in the us and who take insulin—a proxy for disease type given the absence of type data in the brfss. cross-type and cross-sample comparisons are reported for bivariate and multivariate logistic regression models, the latter control for effects of sex, race (white/other), and age (continuous). analyses were conducted in sas version 9.2. results demographic characteristics of the tuanalyze sample as shown in table 1, of 931 respondents, a majority is white, located in the us, and female. approximately two-thirds (62%) report having type 1 diabetes and another 10% have type 1.5 (lada); 27% have type 2 diabetes and the remaining 1% report pre-diabetes. age of the sample ranged from 13 to 81, with an average and median of 43. http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 table 1. characteristics of the tuanalyze study sample, in aggregate and by type n (%) total sample 931 (100) type 1 & lada 664 (71.3) type 2/ prediabetes 267 (28.7) or for type 1/lada english language site 797 (85.6) 594 (89.5) 203 (76) 2.7 (1.8, 3.9)*** us location 664 (71.3) 505 (76.1) 167 (62.6) 1.6 (1.1, 2.2)** white 745 (80) 564 (84.9) 181 (67.8) 2.7 (1.9, 3.4)*** male 367 (39.4) 235 (35.4) 132 (49.4) .56 (.42, .75)*** over 40 447 (54.1) 311 (46.8) 202 (75.7) .28 (.21, .39)*** *p<.05 **p<.01 ***p<.001 use of recommended diabetes preventive and self care practices use of recommended preventive care practices was high to very high among respondents. upwards of four-fifths of the sample reported having an annual check for cholesterol and a check for retinopathy in the past year. upwards of two-thirds reported having an annual check of their feet for circulatory problems and neuropathy, and an influenza vaccination, in the past year. in contrast, two fifths reported having ever been immunized for pneumonia. despite these prevalence levels for individual care practices, less than one third of respondents reported obtaining all five preventive care practices, a signifier of comprehensive preventive care. use of preventive care practices varied by diabetes type. users reporting type 1 or lada diabetes were more likely than their peers with other diabetes types to report they were ever immunized for pneumonia and to report receiving an influenza vaccine, eye exam and foot exam in the past year. there was no difference in reporting a cholesterol check in the past year by diabetes type. type 1 and lada respondents had twice the odds of obtaining all five recommended preventive care practices as type 2 and pre-diabetes respondents, controlling for age, sex and race (adjusted or 2.2, 95% ci 1.5, 3.2, p <.001). in terms of self-care, a majority of type 1 and lada respondents reported checking their blood glucose five or more times per day; these respondents are far more likely than their peers with other diabetes types to report doing so. slight majorities reported seeing a diabetes nurse educator in the past year and meeting with their diabetes physician in the past year. these patterns did not differ by diabetes type. fewer reported meeting with a nutritionist with no difference across type. http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 table 2. reported use of recommended diabetes preventive care practices, by diabetes type n (%) or for type 1/lada (95% ci) all tuanalyze users n=931 type 1 and lada n=664 (71.3) type 2/prediabetes n=267 (28.7) unadjusted adjusted for age, sex, race pneumonia shot ever 380 (40.8) 275 (41.4) 105 (39.3) 1.1 (.8, 1.5) 1.6 (1.1, 2.3)** flu shot/past year 606 (65.1) 459 (69) 148 (55.4) 1.8 (1.3, 2.4)*** 2.3 (1.6, 3.3)*** cholesterol check/past year 837 (89.9) 595 (89.6) 242 (90.6) .9 (.5, 1.4) 1.7 (1, 3.1) eye exam/past year 766 (82.3) 580 (87.3) 186 (69.7) 3 (2.1, 4.3)*** 4.8 (3.1, 7.5)*** foot exam/past year 611 (65.6) 454 (68.4) 157 (58.8) 1.5 (1.1, 2)** 2.3 (1.6, 3.3)*** all 5 care practices 251 (30) 193 (29.1) 58 (21.7) 1.5 (1.1, 2.1)* 2.2 (1.5, 3.2)*** self-monitors blood glucose (smbg) 5 or more times per day 535 (57.5) 477 (71.8) 58 (21.7) 9.2 (6.6, 12.9)*** 10.1 (6.8, 14.9)*** nutrition visit/past year 343 (36.8) 241 (36.3) 102 (38.2) .9 (.7, 1.2) .7 (.5, 1) dne visit/past year 525 (56.4) 383 (57.7) 142 (53.2) 1.3 (.9, 1.6) 1 (.7, 1.4) 4 or more md visits/past year 489 (52.5) 345 (52) 144 (53.9) .9 (.7. 1.2) 1 (.7, 1.4) *p<.05 **p<.01 ***p<.001 health status a minority of users reported fair or poor health and approximately one-third report a most recent a1c that is above the recommended target of 7%. one-quarter report a history of depression and nearly half report any other comorbidity. no differences in health indicators across type were found in adjusted analyses. type 1 and lada users were less likely to report a chronic comorbid condition and more likely to have an above-target a1c in unadjusted analyses only. health status did not differ in relation to use of recommended preventive care measures in analyses that adjusted for age, sex, race and type. http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 table 3. health status, in aggregate and by type n (%) or for type 1 (95% ci) all users (n=931) type 1 & lada n=664 (71.3) type 2 & prediabetes n=267 (28.7) unadjusted adjusted for age, sex, race poor self-rated health 149 (16) 111 (16.7) 38 (14.2) 1.2 (.8, 1.8) 1.5 (.93, 2.3) depression 216 (23.2) 143 (21.5) 73 (27.3) .7 (.5, 1) .72 (.5, 1) any other comorbidity 411 (44.2) 275 (41.4) 136 (50.9) .7 (.5, .9)** 1.1 (.78, 1.5) a1c>7% (n=826) 277 (33.5) 212 (35.8) 65 (27.8) 1.5 (1.04, 2)* 1.2 (.85, 1.8) comparison of tuanalyze and brfss samples in analyses of us-residing insulin-dependent respondents from both samples, tuanalyze users were more likely to be white and less likely to be over the age of 40 than the brfss sample, confirming the extension of health monitoring into a different demographic. there was no difference in the sex distribution across the two samples. table 4. demographics of the tuanalyze sample and the brfss subsample tuanalyze (n=577) brfss a (n=9,832) or for tua (95% ci) white 510 (88.4) 6,693 (68.1) 3.6 (2.8, 4.6)*** male 222 (38.5) 4,030 (41) .9 (.76, 1.1) over 40 325 (56.3) 9,245 (94) .08 (.07, .1)*** a note that the brfss subsample comprises that portion of a national probability sample that self-reports diabetes and insulin use. in analyses that controlled for age, sex and race, tuanalyze respondents were more likely than brfss respondents to report they received an influenza immunization, eye exam, and all five care practices in the past year. brfss respondents were more likely than the tuanalyze respondents to report they received an annual foot exam. the two samples differed greatly on self-monitoring of blood glucose; nearly three quarters of the tuanalyze sample and less than one tenth of the national sample reported checking their blood sugar five or more times per day. no difference was found in the history of pneumonia vaccination, annual lipid profile, or frequency of doctor visits. comparisons of diabetes educator or nutritionist visits were precluded by the absence of these data in the brfss sample. http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 table 5. comparison of reports of obtaining recommended diabetes preventive care practices between insulin dependent tuanalyze and brfss respondents n (%) or for tuanalyze (95% ci) tuanalyze (n=577) brfss (n=9,832) unadjusted adjusted for age, sex, race pneumonia shot ever 291 (50.4) 6,262 (64.6) .56 (.47, .66)*** 1 (.84, 1.2) influenza immunization/ past year 415 (71.9) 6,537 (66.5) 1.3 (1.1, 1.6)* 1.9 (1.6, 2.3)*** cholesterol check/past year 525 (91) 8,769 (93.1) .75 (.56, 1) 1.1 (.78, 1.5) eye exam/past year 509 (88.2) 7,415 (75.4) 2.4 (1.9, 3.2)*** 3.5 (2.6, 4.5)*** foot exam/past year 420 (72.8) 7,777 (79.7) .68 (.56, .82)** .65 (.52, .79)*** all 5 care practices 208 (36.1) 3,264 (33.2) 1.1 (.95, 1.4) 1.8 (1.4, 2.1)*** smbg 5 or more times per day 422 (73.1) 903 (9.2) 26.9 (22.1, 32.8)*** 13.3 (10.8, 16.5)*** nutrition visit/past year 343 (36.8) --- dne visit/past year 525 (56.4) --- 4+ md visits/past year 314 (54.4) 5,638 (57.3) .89 (.75, 1.05) .98 (.82, 1.2) * p<.01 **p<.001 ***p<.0001 discussion using a novel health monitoring approach, we collected information about preventive and selfcare practices from members of an international online diabetes social network. while a majority of respondents appear to follow practice guidelines for specific preventive care services, less than one third of the sn sample reported all five recommended practices—suggesting substantial room for improvement in quality of care and disease management. patterns vary by diabetes type with higher levels of preventive care reported by respondents with type 1 or lada compared to type 2 or pre-diabetes. a similar pattern was seen for reports of appropriate smbg. results are consistent with reports of type-based differences in service use, adherence and self-care generated from studies of traditionally sampled cohorts (16-19). adherence to select healthcare practices reported by us-residing insulin dependent users of tuanalyze was mixed in relation to patterns found in a restricted comparator sample created from the us national brfss. for the majority of comparison measures, sn application users reported better use of preventive and self-care practices. this finding is not surprising given that users of tuanalyze and its host community tudiabetes may include disproportionate percentages of persons concerned with their health and with managing their diabetes—an artifact http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 of the self-selecting nature of the sample. it is notable however that even in this context there were no differences across samples in levels of adherence to recommended quarterly doctor visits, possibly reflecting insurance eligibility requirements in the us. even allowing for the healthy subject selection effect of the sn sample, there are sizeable gaps in care practice use especially with regard to the composite measure of adherence to the five recommended care practices. stepping back, this report adds to our previous finding that the sn mediated surveillance approach can be used to engage distributed populations in health research by reporting about current healthcare and self-care patterns by diabetes type. these measures are not available through standard reporting systems. learning how to monitor these issues using novel approaches is, we contend, of high importance to public health given the large and growing burden imposed by chronic illness. effective monitoring of chronic as opposed to contagious illness may benefit from tracking care patterns and management in detail and preservation of a bidirectional communication channel with study samples for follow-up. ensuring patient (or sample) engagement may be especially important in this model. patient engagement in disease focused social networking is strong and growing (20). growth reflects the appeal of this organic and grassroots phenomenon, and patients’ need for community (21), information and support (22-24). harnessing this engagement for public health research may be an important new direction for population health monitoring. social networking is increasingly common in the area of diabetes—about which there are many active online communities of varying quality and safety (25). opportunities are manifold to extend health surveillance into these motivated and high value samples. the tuanalyze approach accomplishes this without sacrificing privacy, safety or the autonomy of individuals and their communities. our approach is novel and findings should be read in the context of important limitations. biases in participation and validity of self-reported data are a focus of our research and they are intrinsic to the model. moreover, selection and participation biases are also present in more traditional health monitoring systems. comparisons of tuanalyze and brfss data are novel however they rely on assumptions about the adequacy of using self-reported insulin use as a proxy measure for diabetes type and they reflect fundamentally different approaches to surveillance. we recognize that these approaches are different and likely to yield different results. the social networking medium is inherently open and dynamic and affords a bidirectional communication channel with subjects. application use and survey completion happen on a rolling basis that is in part indexed to the overall growth of the community and changing uptake of the application. given this, it is challenging to ascertain a denominator that describes persons exposed to the site or active during a given time period (4). this is acceptable when the goal is rapid and not representative health surveillance—as befits this complementary monitoring mechanism. conclusion rapid assessment of preventive care practices and diabetes management strategies using a novel, sn-mediated approach is feasible and can be used to fill gaps in traditional public health http://ojphi.org surveillance of an online social network to assess population-level diabetes health status and healthcare quality 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 monitoring of care practices by diabetes type. our ability to harness this engagement without sacrificing privacy or user control may provide an important new direction for public health surveillance. acknowledgements this work was supported by po1hk000088-01 from the centers for disease control and prevention (cdc). corresponding author elissa r weitzman, scd, msc children’s hospital informatics program one autumn street, room 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conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts assessment of national poison data system algorithms to identify public health events royal k. law*1, howard burkom2, alvin bronstein3 and josh schier1 1centers for disease control and prevention, chamblee, ga, usa; 2johns hopkins applied physics laboratory, laurel, md, usa; 3american association of poison control centers, alexandria, va, usa objective to compare the effectiveness of current surveillance algorithms used in the national poison data system (npds) to identify incidents of potential public health significance with 1) new algorithms using expanded npds surveillance capabilities and 2) methods beyond the npds’ generalized historical limits model. introduction npds is a near real-time surveillance system and national database operated by the american association of poison control centers. npds receives records of all calls made to the 55 regional us poison centers (pcs). the centers for disease control and prevention (cdc) use npds to 1) provide public health surveillance for chemical, radiological and biological exposures and illnesses, 2) identify early markers of chemical, radiological, and biological incidents, and 3) find potential cases and enhance situational awareness during a known incident. anomalies are reviewed daily by a distributed team of pc medical and clinical toxicologists for potential incidents of public health significance (iphs). information on anomalies elevated to iphs is promptly relayed to state epidemiologists or other designated officials for situational awareness and public health response. current npds surveillance algorithms utilize the historical limits method, which identifies a data anomaly when call volumes exceed a statistical threshold derived from multiple years of historical data [ref]. alternative analysis tools such as those employed by essence and other computerized data surveillance systems have been sought to enhance npds signal analysis capability. technical improvements have been implemented in 2013 to expand npds surveillance capabilities but have not been thoroughly tested. moreover, other data aberration detection algorithms, such as temporal scan statistics, have not yet been tested on real-time poison center data. methods the data series for this assessment are 7-year historical time series of npds hourly call counts. npds catalogues confirmed phs call clusters each year for current surveillance algorithms. new algorithm strategies will be run on the same data series to compare with current algorithm strategies. these new strategies include the use of the newly expanded npds surveillance capabilities and new methods (e.g. scan statistics) beyond npds’ historical limits model. the same methodology will be used for substance-specific surveillance, including carbon monoxide (co) exposures and food poisonings. to focus on missed cluster-type concerns, scenario-driven call clusters will be injected into the data series. phs identification sensitivity and non-phs alert rates will be balanced to assess the ability to capture historical and injected call clusters of desired exposure types for each type of surveillance algorithm. for example, performance of an algorithm with higher sensitivity and lower nonphs alert rates will be considered superior. results the collected data series includes hourly time series of general poison center call volumes and substance-specific subseries, such as co exposure and food poisoning. selected inject scenarios are under discussion, including a latency model to capture the problem of delayed uploads. r code implementing the generalized algorithm queries is written and will be shared. summaries of npds-specific evaluation measures, such as positive predictive value, defined as counts of phs clusters divided by all clusters, will be presented for method comparison using both historical iphs and injected call clusters. conclusions based on the results, the authors will determine the most effective algorithms for identifying iphs without overburdening the npds surveillance team with high quantities of anomalies. this work will improve surveillance activities using npds and may be helpful for health departments that conduct surveillance using poison center data. keywords toxicosurveillance; poison control center; npds references stroup, d.f., et al., evaluation of a method for detecting aberrations in public health surveillance data. am j epidemiol, 1993. 137(3): p. 373-80 *royal k. law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e35, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a term-based approach to asyndromic determination of significant case clusters howard burkom*, yevgeniy elbert, christine piatko and clay fink johns hopkins applied physics laboratory, laurel, md, usa objective explain and demonstrate the performance of a statistical method for detection of anomalous terms in pooled, contiguous blocks of freetext chief complaints from a health facility with emergent or urgent care capability. introduction biosurveillance systems commonly depend on free-text chief complaints (cc)s for timely situational awareness. however, diagnosis codes may not be available soon enough and may have uncertain value because they are assigned for billing purposes rather than for population monitoring. existing systems use syndrome categories to classify records based on these free-text fields. a syndromic cluster determination method (toa) based on patient arrival times has been implemented in versions of essence and in ncdetect [1]. while effective for finding case clusters whose cc terms are classifiable into syndromes, toa implementations do not find clusters whose cc terms share only uncategorized terms. natural language processing literature is rich with topic-detection methods, many involving medical ontologies and language models. however, identifying clusters of ccs poses several challenges. freetext ccs often contain only a few words, include abbreviations that vary among institutions, and evolve with usage and presentation trends. our approach pools ccs into contiguous time blocks and uses a statistical hypothesis test to seek current terms that are anomalous relative to their occurrence in a large sliding baseline. sets of anomalous terms (if any) are then presented for further investigation. methods our development used a collection of emergency department (ed) patient records with ccs spanning 7 years from 15 hospitals in the national capital region. for pre-processing, spelling correction was applied followed by removal of english stop-words and of the common words “pain”, “left”, and “right”. for anomalous word detection, we applied fisher’s exact test to the count of each word in the tested 8hour block of ccs, based on a 30-day sliding baseline of pooled ccs ending exactly 7 days before the current test block. using fisher’s exact test, anomalous words from the current 8-hr block may be presented to human health monitors up to 3 times each day. we tested this method to detect two anomaly types: a small number of instances of rare words with little or no representation in the sliding baseline, and disproportionately large numbers of common words. multiple alerting thresholds, block sizes, and hypothesis tests were tested to achieve both types of detection with at most a handful of anomalous words expected from each block. for scenario-based detection, sets of records were injected using stochastic draws from distributions of demographic fields, of words associated with chosen outbreak types, and of date/time distributions covering from 6 hours to 3 days and kept consistent with diurnal ed visit patterns. results the methods were tested on ccs from individual hospitals and on pooled data from 15-20 hospitals. individual anomalous word tests were conducted to test the ability to detect excess clusters of rare, moderately common, and very common terms, respectively. these terms were injected into each 8-hour block over 7 years using sliding baselines with no injects. as an alert burden measure for 15 single hospitals, the method returned no anomalous words in 60-90% (depending on the facility) of the tested (>7,600 consecutive) 8-hr blocks for a pvalue threshold of 0.01. in 95-100% of these blocks, fewer than 5 anomalous words were found. multiple roc-like curves showed practical sensitivity. for example, using data from the busiest single ed tested, 3 additional occurrences of “exposure” were detected in >90% of the tested blocks with less than 2 anomalous words identified per block. conclusions the presented method is a practical, understandable way to monitor single care facilities for cc clusters of concern based on unusually high occurrence of rare or common terms that need not be related to syndromes. routine implementation requires a human monitor to inspect ccs containing the anomalous terms and make follow-up decisions. a prototyped combined visualization shows recent blocks of these anomalous cc terms with syndromic time-of-arrival alerts and unusual groupings of demographic strata. keywords chief complaint; fisher’s exact test; asyndromic; cluster references deyneka l, xu z., burkom h., hicks p., benoit s., vaughan-batten h., ising a. finding time-of-arrival clusters of exposure-related visits to emergency departments in contiguous hospital groups. emerging health threats journal. 2011; 4:11702. doi: 10.3402/ehtj.v4i0.11702. *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e11, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts unplanned school closures in the united states: evaluation of economic and social costs and consequences for students’ families yenlik zheteyeva*, margaret coleman, jianrong shi, hongjiang gao, amra uzicanin and jeanette j. rainey dgmq, cdc, atlanta, ga, usa introduction the centers for disease control and prevention (cdc) recommends implementing early targeted school closures as one of the front-line interventions to slow progression of a severe influenza pandemic before appropriate vaccine becomes available. however, prolonged school closures may impose unintended economic and social costs and consequences to students’ families. these costs and consequences have not been carefully evaluated. to better understand this unintended impact, we conducted five investigations of unplanned school closures lasting 4 school days implemented for various reasons from august 2012 through may 2013. each closure was investigated separately as a public health evaluation. school closures implemented for reasons other than pandemic influenza may serve as a proxy to pandemic-related closures. our findings can inform updates to cdc’s pandemic preparedness guidance. methods we investigated unplanned school closures in an urban-rural school district (20 schools, 14,368 students, closed to prepare for anticipated hurricane) in a coastal region of mississippi, a rural school district (3 schools, 524 students, closed for high influenza-related absenteeism) in kentucky, an urban elementary school in maine (427 students, closed due to electric fire), a school district (3 schools, 204 students, closed due to high influenza-related absenteeism) in a suburban community in colorado, and a rural school district (4 schools, 1,534 students, closed to prepare for flooding) in illinois, with overall enrollment of 17,057 students. we distributed paper questionnaires about the unplanned school closure to all parents and guardians via students at each affected school. we requested completion of only one questionnaire per household. we evaluated perceptions of difficulty arising from the school closure, including difficulty finding alternative childcare options, additional expenses incurred during the school closure, and difficulties related to lost access to subsidized school lunches. results questionnaires were completed by 2,700 households (accounting for 4,935 students), with household response rate ranging from 2538% in different locations. median duration of school closures was 5 work days (range: 4-7days). the majority (82.5%) of responding households were those affected by the mississippi unplanned school closure. the median proportion of households reporting that school closure caused them difficulty was 25%, ranging from 18% in the illinois school district to 98% in the elementary school in maine. the most commonly reported difficulty (reported as one of the top two reasons from at least three sites) included uncertainty about the duration of the school closure and difficulty with arranging childcare. the median proportion of households reporting difficulty providing food to families due to lost access to free or reduced price school lunch program during the school closure was quite low, 8% (ranging from 3% in colorado to 18% in mississippi). approximately 25% of all respondents reported incurring additional expenses during the closures, with a median daily amount of $33 (range: $14.3-$53.7). the most frequently reported childcare provider was a non-working adult household member in all survey communities except maine, where a non-household adult was reported as the most frequent provider. households where all adults work outside of the home were significantly more likely to report difficulty arranging childcare (p <0.001 in kentucky). conclusions one of the primary challenges for families during unplanned school closures is making emergency childcare arrangements. while the most frequently childcare is provided by a non-working household member, this option is not available for families where all adults are employed full-time. although disruption of subsidized school lunch program did not appear as a difficulty for the majority of eligible families during these school closures, more research might be needed on closures that last longer than 7 days. keywords school closures; pandemic influenza; economic and social impact *yenlik zheteyeva e-mail: igg0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e62, 201 improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 improving hiv/aids knowledge management using ehrs erik d. malmberg 1 , thao m. phan 2 , glynn harmon 1 , richard f. nauert 3 1 school of information, the university of texas at austin 2 school of public health, the university of texas health science center at houston 3 school of health administration, texas state university-san marcos abstract background: a primary goal for the development of ehrs and ehr-related technologies should be to facilitate greater knowledge management for improving individual and community health outcomes associated with hiv / aids. most of the current developments of ehr have focused on providing data for research, patient care and prioritization of healthcare provider resources in other areas. more attention should be paid to using information from ehrs to assist local, state, national, and international entities engaged in hiv / aids care, research and prevention strategies. unfortunately the technology and standards for hiv-specific reporting modules are still being developed. methods: a literature search and review supplemented by the author’s own experiences with electronic health records and hiv / aids prevention strategies will be used. this data was used to identify both opportunities and challenges for improving public health informatics primarily through the use of latest innovations in ehrs. qualitative analysis and suggestions are offered for how ehrs can support knowledge management and prevention strategies associated with hiv infection. results: ehr information, including demographics, medical history, medication and allergies, immunization status, and other vital statistics can help public health practitioners to more quickly identify at-risk populations or environments; allocate scarce resources in the most efficient way; share information about successful, evidenced-based prevention strategies; and increase longevity and quality of life. conclusion: local, state, and federal entities need to work more collaboratively with ngos, community-based organizations, and the private sector to eliminate barriers to implementation including cost, interoperability, accessibility, and information security. key words: usability of health information, information technology, health promotion / disease prevention mesh headings: hiv, acquired immunodeficiency syndrome, public health informatics, electronic health records, knowledge management http://ojphi.org/ improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 introduction there has been considerable discussion at the local, state, national and international level about the benefits and challenges associated with electronic health records (ehrs). public health information practitioners play an important role in helping control the hiv/aids epidemic by working with community, state, national, and international partners in surveillance, research, and prevention and evaluation activities. the centers for disease control and prevention (cdc) “estimates that about 1.1 million americans are living with hiv, and that 21% of these persons do not know they are infected” [1]. improved information gathering is essential to improving treatment, care, and support for hiv-infected persons as well as building the resources and infrastructure for prevention efforts in the united states and globally. “hiv treatment and research generally carry federal, state, and/or local government reporting obligations, particularly in programs funded by the ryan white comprehensive aids resources emergency (care) act” [2]. support should continue for the creation of bi-directional ehr systems (or add-on modules) in which hiv/aids information can be entered as well as accessed and utilized by public health information practitioners. hiv / aids crisis in america hiv stands for the human immunodeficiency virus. hiv is a serious medical condition that damages a person’s body by destroying blood cells that help the body fight diseases. hiv is spread primarily by:  not using a condom when having sex with a person who has hiv.  having multiple sex partners or the presence of other sexually transmitted diseases (stds) can increase the risk of infection during sex. unprotected oral sex can also be a risk for hiv transmission, but it is a much lower risk than anal or vaginal sex.  sharing needles, syringes, rinse water, or other equipment used to prepare illicit drugs for injection.  being born to an infected mother hiv can be passed from mother to child during pregnancy, birth, or breast-feeding [3]. when certain conditions are met, this virus can lead to acquired immune deficiency syndrome, or aids. it is estimated that more than one million people are living with hiv in the united states and that more than half a million have died after developing aids [4]. yet the cdc reports that there are only about 680,000 cases as of 2008 [5]. this discrepancy is due in part to the lack of:  adoption of confidential name-based reporting of hiv diagnoses in all 50 states;  inclusion of anonymous tests, including home tests, in case reports; and  diagnoses of hiv infection or person’s awareness that they are infected. “hiv statistics reported in the usa are currently only available for 40 states and five u.s. dependent areas with confidential name-based hiv infection reporting (since at least january http://ojphi.org/ improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 2006). aids statistics include all 50 states, the district of columbia and five u.s. dependent areas” [6]. globally, in 2008 there were 33.4 million people living with hiv, and 2.7 million newly infected cases [7]. in the past 30 years, hiv/aids pandemic has caused 60 million infections and 30 million deaths [8]. it is the sixth leading cause of death in the world and is therefore one of the major public health problems [9]. public health is playing a major role in its attempts to control and eradicate this disease. according to the cdc, the earliest known case of hiv infection occurred in an adult male born and living in the congo in 1959. before the virus was identified in 1983, most documentation was based on diagnoses of opportunistic infections not found in health immune systems. it was not until 1999 that a group of researchers determined that a subspecies of chimpanzees native to west equatorial africa were the original source of the virus. tracking of aids cases began in 1982 in the united states. by 1983 public health practitioners began using the term “acquired immunodeficiency syndrome,” or aids, to describe the occurrences of opportunistic infections and even today the lay public continue to view these terms interchangeably (see table 1). this was also the case table 1. acquired immunodeficiency syndrome acquired – means that the disease is not hereditary but develops after birth from contact with a disease-causing agent (in this case, hiv). immunodeficiency – means that the disease is characterized by a weakening of the immune system. syndrome – refers to a group of symptoms that indicate or characterize a disease. in the case of aids, this can include the development of certain infections and/or cancers, as well as a decrease in the number of certain specific blood cells, called cd4+ t cells, which are crucial to helping the body fight disease. because people infected with hiv would be diagnosed with aids in just a few years. advances in research have led to increases in longevity and quality of life. “since the beginning of the epidemic, an estimated 617,025 people with aids have died in the u.s.” [10]. the cdc works with other federal agencies, state and local health departments, nongovernmental organizations (ngos), community-based organizations, and the private sector to reduce the spread of hiv through  behavioral interventions;  educational interventions;  policy interventions;  hiv testing;  linkages to treatment and care; and http://ojphi.org/ improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012  collection of data about transmission rates, incidence, testing behaviors [11] (cdc, 2012). while these are important parts of dealing with the hiv epidemic, a lack of coordination and effective communication, inability to access and utilize information rapidly, and inconsistency in the execution of prevention strategies can negatively impact efforts to curb the spread of hiv infections in the united states. electronic health records (ehrs) and innovations in knowledge management will be a critical component for sustained success in prevention efforts and patient care associated with hiv/aids. knowledge management in public health most public health organizations face common challenges for managing information effectively. these efforts can be compounded even further when dealing with hiv / aids related health issues. the lack of standardized reporting procedures, exclusion of anonymous and home test from reports, and challenges in diagnoses primarily due to patient unawareness at the state and national level is troublesome. for approximately the past eight to ten years, public health officials have been increasingly interested in new knowledge management solutions. knowledge management can generally be described as a process of organizing and analyzing information to make it understandable and applicable to problem solving or decision making. “key reasons for this interest include the following: capturing knowledge to ensure public health preparedness, managing information more effectively, enabling public health professionals to work collaboratively in a virtual environment, and improving effectiveness in the face of dwindling resources” [12]. the challenge is that public health officials may fail to understand that effective knowledge management requires more than just developing policies and standards procedures. it requires commitment of capital and human resources, constant communication with a wide variety of constituencies, supportive leadership, buy-in public health officials at all levels, and technology. the sheer volume of information that is produced at local, state, national, and international levels provides an impetus for using technology for better knowledge management. reporting of hiv/aids cases is required at almost every level. however, there is also the challenge that ten states are still not able to effectively report hiv infection rates. a key question for those entities that are reporting information is whether this knowledge is being put to the best use. the vast amounts of knowledge created every day also raise concerns about information quality. developing successful public health initiatives and practices requires reliance upon quality evidence-based data. knowledge management can help public health officials to organize and classify knowledge with greater confidence. another challenge associated with knowledge in public health is lack of consistency in format. knowledge can come in many combinations including paper v. born-digital, image v. text, and text v. audio. there can also be other differences in what information was collected and for what purpose. lack of standardization also makes it more difficult for systems at various levels – local, state, national, or international – to communicate with each other and promote greater http://ojphi.org/ improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 sharing. a knowledge management system helps create a controlled vocabulary, ease of access and standard processes that eliminate waste. finally public health officials frequently express reservations about embracing knowledge management because of technology. using information technology has risks associated with information security. this can be of particular concern when it comes to patient privacy and confidentiality. great care must be taken and every effort made to reduce or eliminate security risks associated with public health knowledge management systems. using electronic health records an electronic health record (ehr) is generally defined as a systematic collection of electronic health information about individual patients or populations [13]. electronic medical records (emrs) and personal health records (phrs) are important subsets of this overarching term. ehrs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal stats like age and weight, and billing information. “the ehrs is a pivotal instrument in integrating clinical and public health data systems so public health authorities will have reliable, real-time data to support health policy decisions for better and safer care” [14]. the department of health and human services (dhhs) has developed a knowledge management model with a vision “to build a national health information network (nhin) of regional health information organizations (rhio) and exchanges (rhie). the rhio and rhie would in turn be formed of health care providers (hcps) integrated via electronic health record systems (ehrs) to improve patient safety and deliver quality care” [15]. public health knowledge management and consequently, treatment, care, and prevention stand at a threshold for exciting innovations as a result of the automation of in-patient and out-patient clinical data collected via ehrs. “virco lab, an independently operated biotechnology and diagnostics software subsidiary of pharmaceutical manufacturer johnson & johnson, has released what may be the first electronic health records (ehr) system both optimized for hiv and aids care and certified to federal meaningful use standards” [16]. efforts must continue towards the development of holistic ehr systems and/or add-on modules specifically for hiv/aids information with bi-directional or multi-directional communication features. electronic health records (ehrs) are not without their concerns or challenges. one of the major issues associated with use of ehrs, particularly given the structure of the united states health care system, is the cost to develop and implement. healthcare providers and practitioners need the necessary hardware and software as well as the training for how to use it effectively. while there may be greater savings and efficiencies over time, these components have considerable startup costs. in the current economic climate, state and federal governments are being asked to prioritize resources in other areas. in addition, the political climate is such that efforts are being made to continue a decentralized approach to healthcare in the united states. another challenge that public health officials face in developing and using ehrs is accessibility and software quality. healthcare reform in the u.s. has brought increased competition from within the marketplace for ehr systems. each developer attempts to focus on a variety of http://ojphi.org/ improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 aspects including design, content, and accessibility with varying degrees of success. while various systems that adopt the federal meaningful use standards will be able to communicate with each other, the interface for the users will still vary to some degree. the current lack of standardization both in terms of interface and interoperability continue to result in low adoption rates of ehrs among public health practitioners. a third major challenge towards full adoption of an ehr, especially for hiv/aids related information, is security and privacy issues. “threats to health care information can be categorized under three headings:  human threats, such as employees or hackers.  natural and environmental threats, such as earthquakes, hurricanes and fires.  technology failures, such as a system crashing. these threats can either be internal, external, intentional and unintentional” [17]. while there are federal and state laws preventing discrimination on the basis of hiv/aids status, public release of ehr either intentionally or accidentally can have serious repercussions including loss of employment, health insurance coverage, or relationships. both patients and public health officials remain concerned about how to reduce or eliminate these security and privacy risks. while an ehr definitely has its advantages over a paper health record, there still needs to be considerations about archiving practices and recoverability in the event of natural or man-made disasters. given the multiple benefits and costs of ehrs and the complexities and difficulties related to their adoption and implementation, the us and other countries have mandated the phased adoption of ehrs within their respective jurisdictions. in the us several programs (e.g., the american investment and recovery act and the hightech act) call for the phased adoption of ehrs over the next several years among many health care organizations [18]. conclusion the ehr will be a critical tool for public health officials to improve the treatment, care, and prevention of hiv infections and aids diagnoses. ehr information, including demographics, medical history, medication and allergies, immunization status, and other vital statistics can help public health practitioners to more quickly identify at-risk populations or environments; allocate scarce resources in the most efficient way; share information about successful, evidenced-based prevention strategies; and increase longevity and quality of life. “when the components of knowledge management – governance, content, process, and technology – are identified and addressed, organizations can begin to set a course that creates a significant asset from scattered, siloed stores if information” [19]. local, state, and federal entities need to work more collaboratively with ngos, community based organizations, and the private sector to eliminate barriers to implementation including cost, interoperability, accessibility, and information security. http://ojphi.org/ improving hiv/aids knowledge management using ehrs online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 limitations the items listed above have been derived empirically and represent a broad picture of the challenges associated with developing a bidirectional public health knowledge management system. undoubtedly there are other issues that may be equally significant and deserve recognition and attention. although all stakeholders desire robust knowledge management, development of successful infrastructure remains an issue in countries far more advanced in their use health information technology and ehrs (uk, new zealand) than the united states [20]. adoption of ehrs in the united states remains a work in progress with the cdc reporting that 55% of physicians in office-based practices were using an ehr in some fashion in 2011 [21]. our discussion of a bi-directional flow of information to communicate important hiv/aids information is consistent with federal expectations for hit over a five-year period, if all the challenges can be surmounted including development of secure health information exchanges. conflicts of interest the authors declare that they have no conflicts of interests. corresponding author erik d. malmberg jd, phd master’s degree candidate school of information the university of texas at austin austin, tx email: malmberged@utexas.edu references 1. centers for disease control and prevention (2012). hiv/aids, ¶ 1. retrieved march 19, 2012, from http://www.cdc.gov/hiv/. 2. versel, neil (2011). electronic health record eases hiv, aids reporting. informationweek. 3. centers for disease control and prevention (2012). hiv/aids basics. retrieved march 21, 2012, from http://www.cdc.gov/hiv/resources/qa/definitions.htm 4. avert (2011). united states hiv & aids statistics summary. retrieved april 3, 2012, from http://www.avert.org/usa-statistics.htm. 5. centers for disease control and prevention (2012). hiv/aids. retrieved march 19, 2012, from 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(2005). an electronic health record public health (ehr-ph) system prototype for interoperability in 21 st century healthcare systems. amia annual symposium proceedings. retrieved april 15, 2012, from http://www.ncbi.nlm.nih.gov/pmc/articles/pmc1560434/. 15. orlova, anna o., dunnagan, mark dunnagan, finitzo, terese, et al. (2005). an electronic health record public health (ehr-ph) system prototype for interoperability in 21 st century healthcare systems. amia annual symposium proceedings. retrieved april 15, 2012, from http://www.ncbi.nlm.nih.gov/pmc/articles/pmc1560434/. 16. versel, neil (2011). electronic health record eases hiv, aids reporting. information week. retrieved april 11, 2012, from http://www.informationweek.com/news/healthcare/emr/232300871. 17. wikipedia (2012). electronic health records. retrieved may 1, 2012, from http://en.wikipedia.org/wiki/electronic_health_record. 18. hoyt, r. e. and others (2012). health informatics: practical guide for healthcare and information technology professionals. raleigh, nc: lulu.com. 19. liebowitz, jay (2009). knowledge management in public health [electronic resource]. hoboken: crc press. 20. neame, roderick (2012). design principles in the development of (public) health information intrastructures. online journal of public health informatics. retrieved august 17, 2012, from http://ojphi.org/htbin/cgiwrap/bin/ojs/index.php/ojphi/article/view/4022/3222. 21. jamoom e, beatty p, bercovitz a, et al. (2012). physician adoption of electronic health record systems: united states, 2011. retrieved september 15, 2012, from http://www.cdc.gov/nchs/data/databriefs/db98.htm. http://ojphi.org/ http://www.who.int/mediacentre/factsheets/fs310/en/index.html http://www.avert.org/usa-statistics.htm http://www.cdc.gov/hiv/ http://en.wikipedia.org/wiki/electronic_health_record http://www.ncbi.nlm.nih.gov/pmc/articles/pmc1560434/ http://www.ncbi.nlm.nih.gov/pmc/articles/pmc1560434/ http://www.informationweek.com/news/healthcare/emr/232300871 http://en.wikipedia.org/wiki/electronic_health_record http://ojphi.org/htbin/cgiwrap/bin/ojs/index.php/ojphi/article/view/4022/3222 http://www.cdc.gov/nchs/data/databriefs/db98.htm ojphi-06-e57.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, 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� ��� ����5� �� ���������� & ��&���� � ��� � ��� �� ��� ���� �� ����������� ���% ���������� � ������� ������ ����;�����$������� ,-<� ������ ������� �"((*4�"9�9#8*9:&)*� "��. ��.!�3� 2�� �=!�$���� � ���!�> � � ��!�?�����.!�3�� �� ��+��3��& � � ������� & ��&���� � ������ ����� ������ �� ����� �� ����������� ��� �� � � ��&@�%�a� ���� ���33?��"(()4�)9������8�9 &b� *shaofa nie e-mail: sf_nie@mails.tjmu.edu.cn� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e57, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts cdc surveillance strategy – a strategy for improving cdc activities in public health surveillance chesley richards and brian lee* office of public health scientific services, cdc, atlanta, ga, usa objective this presentation aims to share the cdc surveillance strategy’s goals, initiatives and activities. the surveillance strategy describes how cdc will: � enhance accountability, resource use, workforce and innovation for surveillance by establishing a surveillance leadership board, a surveillance workforce plan, and an innovation consortium; � accelerate the utilization of emerging tools and approaches to improve the availability, quality, and timeliness of surveillance data by establishing enhanced hit policy engagement, hit vendor forums, and informatics innovation projects; and � initiate four cross cutting surveillance system initiatives to improve surveillance by addressing data availability, system usability, redundancies, and incorporation of new information technologies introduction public health surveillance guides efforts to detect and monitor disease and injuries, assess the impact of interventions and assist in the management of and recovery from large-scale public health incidents. today’s ever-present, media-hungry environment pressures public health scientists, researchers and frontline practitioners to provide information, on an almost instantaneous basis, responsive to public and policy maker concerns about specific geographies and specific populations. actions informed by surveillance information take many forms, such as policy changes, new program interventions, public communications and investments in research. local, state and federal public health professionals, government leaders, public health partners and the public are dependent on high quality, timely and actionable public health surveillance data. with a charge from the cdc director, this surveillance strategy aims to improve cdc’s overall surveillance capabilities, and by extension those of the public health system at large. the strategy guides efforts to make essential surveillance systems more adaptable to the rapidly changing technology landscape, more versatile in meeting demands for expanding knowledge about evolving threats to health, and more able to meet the demands for timely and populationspecific and geographically specific surveillance information. the strategy will also facilitate work to consolidate systems, eliminate unnecessary redundancies in reporting, and reduce reporting burden. these expectations compel this strategy and argue for cdc to lead the public health system in improving the timeliness and availability, as well as the quality and specificity of surveillance data to cdc programs, stlt agencies, and other stakeholders. methods to improve surveillance efforts at cdc and to make progress on the four strategic goals, cdc will collaborate with principal public health surveillance stakeholders, customers, and partners, and together will focus efforts to achieve three goals to improve cdc health surveillance activities and investments: � goal 1 enhance the accountability, resource use, workforce and innovation for surveillance at cdc and in support of stlt agencies � goal 2 accelerate the utilization of emerging tools and approaches to improve the availability, quality, and timeliness of surveillance data � goal 3 through cross-cutting agency initiatives, improve surveillance by addressing data availability, system usability, redundancies, and incorporation of new information technologies in major systems or activities results to improve surveillance efforts at cdc and to make progress on the strategic goals, in 2014 cdc initiated four cross cutting agency initiatives aimed at large surveillance systems: � initiative 1 national notifiable diseases surveillance system (nndss) modernization initiative � initiative 2 biosense enhancement initiative � initiative 3 accelerating electronic laboratory reporting � initiative 4 mortality surveillance-related initiatives with the national vital statistics system conclusions an essential objective and outcome of the board’s leadership in implementing these action steps and the entire surveillance strategy will be to optimize cdc’s investments in the emerging surveillance systems infrastructure through: � coordination with stlt health departments, health care providers, and other partners; � transparency in decision making for cdc’s surveillance data systems; � harmonization of cdc’s participation in hit standards development and in developing public health reporting functionality in ehr systems; and � assurance that cdc’s future investments in data collection systems — including funding that individual cdc programs provide to stlt health departments — comply with the new surveillance strategy. keywords surveillance; strategy; workforce; informatics; policy *brian lee e-mail: fya1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e49, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts supplementing obesity-related surveillance with persistent health assessment tools meredith keybl*, john henderson, guido zarrella, john gibson and maeve kluchnik mitre corporation, bedford, ma, usa objective we developed persistent health assessment tools, phat, to equip public health policy makers with more precise tools and timely information for measuring the success of obesity prevention programs. phat monitors social media to supplement traditional surveillance by making real-time estimates based on observations of obesity-relevant behaviors. introduction in response to the rise in obesity rates and obesity-related healthcare costs over the past several decades, numerous organizations have implemented obesity prevention programs. the current method for evaluating the success of these programs relies largely on annual surveys such as the centers for disease control and prevention’s behavioral risk factor surveillance system (brfss) which provides state-by-state obesity rates. as a result, public health policy makers lack the fine-grained evaluation data needed to make timely decisions about the success of their obesity prevention programs and to allocate resources more efficiently. methods we developed a practical interface that enables policy makers to leverage social media. specifically, we developed models for predicting obesity rates from sets of tweets and developed a dashboard to provide interactive navigation and time slicing. we isolate tweets of interest and import them into the dashboard where policy analysts can query and browse to observe instances where a program’s outreach was successful and identify ways it can be improved. these tools enable policy makers to study relevant demographics, adjust content and messaging faster, and closely measure their program’s impact. model: traditionally, survey data is imputed onto different populations using multilevel regression with post-stratification1. our approach built models predicting a survey result from nonsurveillance indicator variables namely text in social media posts, location, and time by fitting margin-matching models. those models were then used to predict the missing results on other demographic slices from that same data. we used 2011 brfss data2 state-aligned with a sample of 7.5m tweets (500,000 users) from u.s. users to produce logistic regression models of obesity rate as a function of tweet texts. dashboard: to develop the dashboard, we combined data from social media and the brfss and imported them into an interactive display that allowed the information to be viewed in multiple formats such as timelines or maps. timelines allow policy makers to track the response after a health program is rolled out. color-coded maps allow them to see how obesity rate and program impact vary with location. results we utilized a separately-produced user demographics prediction model to build a database aligning states with twitter users. the model excluded users with irrelevant or low confidence geotags, fake accounts, and spam campaigns. a round-robin cross-validation of the obesity-rate prediction model was performed: 50 models were built, each holding out one of the states, and the result predicted for the testing state was compared with the brfss reference. pearson correlation of the resulting 50 estimates was 0.82. a large portion of the obesity rate was captured in those models. this initial result was promising and encourages us to pursue more complex models based on the tweets. this initial success provided us with the necessary data to build the phat dashboard. the dashboard allows policy makers to view traditional health survey results (such as the brfss) with greater granularity, to query tweets while filtering through demographic variables or health program goals, and to drill down on individual messages. conclusions we demonstrated that signals in social media can supplement existing survey data to provide policy makers with better tools to evaluate the success of their obesity prevention programs. the development of phat, especially the interactive dashboard, provides a more timely understanding of a program’s impact. future works include tailoring phat to evaluate a specific program. we also plan to expand our raw data-tagging to other demographic areas (e.g. veteran status) and apply phat to other health behaviors (e.g. smoking). keywords social media; obesity; surveillance references 1. park dk, gelman a, bafumi j. bayesian multilevel estimation with poststratification: state-level estimates from national polls. political analysis. 2004;12(4):375-85. 2. centers for disease control and prevention (cdc). behavioral risk factor surveillance system survey data. atlanta, ga: u.s. department of health and human services, centers for disease control and prevention, 2011. *meredith keybl e-mail: meredith.keybl@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e86, 201 improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention james studnicki, scd 1 *, david c. reardon, phd 2, donna j. harrison, m.d. 3, john w. fisher, phd1, ingrid skop, m.d. 3 1charlotte lozier institute, arlington, virginia 2elliot institute, springfield, illinois 3american association of pro-life obstetricians and gynecologists, eau claire, michigan abstract background: the current measuring metric and reporting methods for assessing maternal mortality are seriously flawed. evidence-based prevention strategies require consistently reported surveillance data and validated measurement metrics. main body: the denominator of live births used in the maternal mortality ratio reinforces the mistaken notion that all maternal deaths are consequent to a live birth and, at the same time, inappropriately inflates the value of the ratio for subpopulations of women with the highest percentage of pregnancies ending in outcomes other than a live birth. inadequate methods for identifying induced or spontaneous abortion complications assure that most maternal deaths associated with those pregnancy outcomes are unlikely to be attributed. absent the ability to identify all maternal deaths, and without the ability to differentiate those deaths by specific pregnancy outcomes, existing variations in pregnancy outcome-specific maternal deaths are masked by the use of an aggregated (all outcome) numerator. under these circumstances, clear and accurate data is not available to inform evidence-based preventive strategies. as the result, algorithms applied for analyzing maternal mortality data may return distorted results. conclusion: improvement in the effectiveness of maternal mortality surveillance will require: mandatory certification of all fetal losses; linkage of death, birth and all fetal loss (induced and natural) certificates; modification of the structure of the overall maternal mortality ratio to enable pregnancy outcome-specific ratio calculations; development of the appropriate icd codes which are specific to induced and spontaneous abortions; education for providers on identifying and reporting early pregnancy losses; and, flexible information systems and methods which integrate these capabilities and inform users. keywords: maternal mortality, maternal mortality ratio, pregnancy outcomes, induced abortion, natural fetal loss, icd-10 coding improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi background maternal mortality is the death of a woman during her pregnancy or within some fixed time from the termination of her pregnancy. definitions in use differ on the termination-to-death time frame applied (42 days, 365 days, or greater than one year) and whether the pregnancy alone is the sole criterion for a mortality relationship (world health organization/ who) or requires a proximate nexus of the pregnancy to the death (centers for disease control and prevention/cdc) [1,2]. these differing definitions, the practical difficulties involved in linking a maternal death to all pregnancy outcomes, limitations associated with the international classification of diseases (icd10) coding of maternal death causes, and inconsistent and incomplete reporting have handicapped the process of maternal mortality surveillance in the u.s [3]. additionally, fundamental structural and conceptual flaws impair maternal mortality surveillance worldwide. the denominator employed to compute comparative metrics in all of these definitions is the number of live births and not the number of pregnancies, conveying the false impression that all maternal deaths are consequent to a live birth. pregnancy, of course, may end in many ways: a live birth, an induced abortion, or a natural fetal loss (stillbirth, ectopic pregnancy, spontaneous abortion, molar pregnancy) occurring at any time during gestation. a pregnancy that does not result in a live birth will not be included in the denominator. if the outcome of the pregnancy is spontaneous or induced abortion it is unlikely to be included in any numerator due to coding limitations and lack of mandatory reporting. although there are over 2000 obstetric codes in icd-10, only twelve codes cover complications of induced abortion. these codes can be difficult to locate, as most search abbreviations: cdc: centers for disease control and prevention icd: international classification of diseases mmr: maternal mortality ratio nhb: non-hispanic black nhw: non-hispanic white tfr: total fertility rate who: world health organization *corresponding author: jstudnicki@lozierinstitute.org doi: 10.5210/ojphi.v11i2.10012 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi mechanisms will lead to spontaneous abortion codes rather than induced abortion codes, and there are no specific codes for psychologic sequelae, transfusion, and return to operating room or death related to abortion. there are also no codes to specify whether a complication results from a medical versus a surgical abortion. in some sub-populations, non-hispanic blacks (nhb) in the u.s. for example, in which a substantial proportion of pregnancies do not result in a live birth, these definitional and coding limitations introduce significant distortions into “maternal mortality” statistics and, subsequently, the analytical methods applied to them. main text pregnancy outcomes and maternal mortality metrics in the u.s. in calendar year 2009, there were 6,369,000 pregnancies; 64.8% resulted in live births, 18.1% in induced abortions and 17.1% in natural fetal losses [4]. only the live birth number, representing less than two thirds of all pregnancies, may be considered reliable. the numbers of induced abortions and natural fetal losses are crude estimates based on incomplete state reports and infrequently conducted small sample surveys. this data gap is particularly problematic for the category of natural or induced fetal losses for two reasons. first, research has indicated that pregnant women who do not successfully carry to term may be at significantly greater risk of premature mortality, often from emotional or social factors such as suicide or violence whether the fetal loss was by induced abortion or a spontaneous loss [5,6]. second, the loss of fully 1/3 of all pregnancies, nearly half of them for unknown causes, has a significant impact on the total fertility rate (tfr) of the nation. although the u.s. tfr has been consistently below replacement level since 1971, virtually nothing is known of the causes for the vast majority of early fetal losses. the cdc has collected fetal death statistics since 1939 when the first fetal death certificate was promulgated. the cause of death has been included on this certificate from the outset. however, the definition of fetal death is left to the states, although the cdc does post a recommended definition in the model state vital statistics act and regulations (latest revision 1992). “each fetal death of 350 grams or more, or if weight is unknown, of 20 completed weeks gestation or more, calculated from the date last normal menstrual period began to the date of delivery, which occurs in this state shall be reported within 5 days after delivery to the (office of vital statistics) or as otherwise directed by the state registrar. all induced terminations of pregnancy shall be reported in the manner prescribed in section 16 and shall not be reported as fetal deaths.” [7]. as a consequence of this very narrow definition and the voluntary nature of reporting, only about 25,000 fetal deaths are actually reported to the cdc each year, or less than 2.5% of an estimated 1.1 million natural fetal losses. since induced abortions are explicitly excluded from the fetal death reporting requirements, and there is no mechanism in place to report other early pregnancy losses, matching live birth and fetal death certificates to women’s death certificates overlooks nearly 1/3 of all pregnant women who are at the highest risk for pregnancy-related mortality improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi moreover, if we disaggregate the pregnancies into racial/ethnic groupings (table 1), we note significant differences among these sub-populations by the proportional composition of pregnancy outcomes. most notably, less than half of all nhb pregnancies result in a live birth compared to nearly 70% of non-hispanic white (nhw) and hispanic pregnancies. the difference is mostly attributable to an abortionsto –pregnancy rate which is nearly three times higher for nhbs than nhws. also of note is that the percent of natural fetal losses-to-pregnancies is higher for nhws than for hispanic and nhb pregnancies. outcome composition of pregnancies for racial/ethnic sub-populations in the u.s., therefore, demonstrates significant differences. further, these differences are likely to be present in other variously defined sub-populations of comparative research and public policy interest such as individual states or insurance status beneficiary groups. table 1. u.s. pregnancy outcomes (2009) n, (%) pregnancies births abortions fetal losses all 6369000 4131000 (64.8) 1152000 (18.1) 1087000 (17.1) hisp 1474000 1000000 (67.8) 252000 (17.1) 222000 (15.1) nhb 1253000 615000 (49.1) 445000 (35.5) 192000 (15.3) nhw 3207000 222000 (69.6) 383000 (11.9) 591000 (18.4) other 435000 281000 (64.6) 72000 (16.5) 820009 (18.9) for the same calendar year, 2009, a total of 960 maternal deaths (icd-10-cm, a34, o00-o99) were reported. if we allocate all these deaths to the appropriate racial/ethnic sub-populations, we can compare maternal mortality ratios using two different denominators: live births and all pregnancies (table 2). table 2. u.s. maternal deaths (2009) and maternal mortality (mm) ratios deaths, n mm ratio (births) mm ratio (pregnancies) % δ all 960 23.2 15.1 34.9 hisp 180 18.0 12.2 32.2 nhb 305 49.6 24.3 51.0 nhw 425 19.0 13.2 30.5 other 50 17.8 11.5 35.3 improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi the overall maternal mortality ratio (mmr) with live births as the denominator is 23.2 deaths per 100,000 live births. at the racial/ethnic sub-population level, we see a nhb mmr which is 2.6 times that of the nhw mmr. if we calculate a mmr using all pregnancies as the denominator, we see a reduction in the overall mmr to 15.1 because the denominators have increased with the inclusion of abortions and fetal losses without an increase in the numerators. note, however, that mmr reductions are not equal among ethnic groups since the outcome composition differs by racial/ethnic sub-population. with pregnancies as the denominator, the nhb mmr is now only 1.8 times that of the nhw mmr. therefore, we can see that mmrs are proportionately, but not uniformly, affected by an expansion of the denominator-a function of basic mathematics. however, both of these mmr calculations are based upon an assumption that is known to be false: that all pregnancy outcomes have exactly the same mmr. the research literature clearly indicates that there are significant differences in death rates following specific pregnancy outcomes. studies from nations with comprehensive reporting of abortion morbidity and mortality, based upon comparison of death certificates of reproductive age woman with all pregnancy related services rendered via a single payer system, and not constrained by a 365 day post termination time frame, conclude that induced abortion or natural fetal loss significantly increases the likelihood of womens’ mortality [8-10]. therefore, meaningful maternal mortality reporting requires not only that all pregnancies be included, but that the overall metric should be disaggregated, numerators and denominators, by the specific pregnancy outcome categories. when maternal mortality ratios are calculated for each outcome defined sub-population (births, abortions and fetal losses), the results can vary widely, even though the aggregate results will produce the same mmr for all pregnancies irrespective of outcome. table 3 presents three scenarios in which the 425 nhw maternal deaths in 2009, might be allocated among the three pregnancy outcomes and the resultant changes in outcome-specific mmrs. table 3. three alternate scenarios: outcome specific death counts and rates, n (%) total pregnancies births abortions fetal losses #1 3207000 2232000 (69.5) 383000 (11.0) 591000 (18.4) deaths 296 51 78 mmr 13.2 13.2 13.2 #2 projected percent (50) (30) (20) projected deaths 213 127 85 projected mmr 9.5 33.1 14.4 improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi #3 projected percent (30) (40) (30) projected deaths 127 171 127 scenario number one distributes the 425 deaths based on the percent each outcome contributes to total pregnancies. the resultant death allocations are: 296 from births, 51 from abortions and 78 for natural fetal losses. with this allocation the resultant mmrs, calculated with outcome-specific denominators, are exactly the same for each pregnancy outcome, 13.2 deaths per 100,000 births, abortions, or fetal losses. this is the flawed assumption which undermines the interpretation of all maternal mortality statistics. that is, each outcome specific mmr is exactly the same for each defined sub-population. scenarios 2 and 3 demonstrate how changes in the proportional allocation of deaths among outcomes will change the outcome specific mmrs. as the proportion of fetal losses, for example, increases from 18.4% to 30% of all pregnancies, the number of deaths attributed to fetal losses increases from 78 to 127, and the outcome specific mmr increases from 13.2 to 21.5. similar changes are estimated for births and abortions. note that in all three scenarios the total of nhw maternal deaths remains 425 and the overall (all outcome) mmr remains 13.2. conclusions the current measuring metric and reporting methods for calculating maternal mortality exhibit serious inconsistencies, incomplete reporting, incomplete and inaccurate death certificate completion, absence of comprehensive reporting of abortions and natural fetal losses, limitations in the ability to link a maternal death to the appropriate outcome of the pregnancy, and serious limitations and exclusions in the icd-cm-10 coding system. improvement in maternal mortality surveillance in the u.s. and the effectiveness of preventive strategies will benefit from the following actions: 1) require mandatory certification and reporting of all fetal losses including all categories encompassed by the cdc maternal mortality reporting system: live birth, stillbirth, abortion (disaggregating spontaneous and induced) ectopic pregnancy, and molar pregnancy. 2) require the department of health of each state to link death certificates, birth certificates, and fetal loss certificates to publish a report each year tabulating the total number of deaths within 42 days, within one year, and one through five years of the decedents’ most recent pregnancy outcomes. 3) modify the structure of the overall maternal mortality ratio metric by changing the denominator from live births to all pregnancies; and enabling specific outcome maternal mortality ratios for live births, stillbirths, spontaneous abortions, induced abortion, ectopic pregnancies, molar pregnancies, and unknown outcomes of pregnancy. 4) educate providers on where and how to report complications of early pregnancy losses, and work with icd-10 coding specialists to create more specific abortion complication related codes, with special attention to distinguishing and separating medical from surgical abortion complications. improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi declarations ethics approval and consent to participate: not applicable consent for publication: not applicable availability of data and materials: datasets analyzed in this study are publicly available and the source appropriately referenced in the manuscript competing interests: the authors declare that they have no competing interests funding: not applicable authors' contributions: js: conception and design; analysis and interpretation; draft, revise and approve final manuscript; accountable for all aspects of the work dcr: interpretation; revise and approve final manuscript djh: interpretation; revise and approve final manuscript jwf: acquisition of data; analysis and interpretation; draft, revise and approve final manuscript is: interpretation; revise and approve final manuscript acknowledgements: not applicable references 1. world health organization. (2004) icd-10: international statistical classification disease and related health problems: tenth revision. who, geneva. 2. hoyert dl. national center for health statistics (us-nchs-cdc) (2007). maternal mortality and related concepts. department of health and human services, centers for disease control and prevention, national center for health statistics, hyattsville. 3. schaible b. 2014. improving the accuracy of maternal mortality and pregnancy related death. issues law med. 29(2), 231-42. pubmed 4. curtin sc, abma jc, ventura sc. nchs data brief no. 136, december 2013. centers for disease control and prevention, national center for health statistics, atlanta. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25936208&dopt=abstract improving the metrics and data reporting for maternal mortality: a challenge to public health surveillance and effective prevention online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e17, 2019 ojphi 5. gissler m, berg c, bouvier-colle m-h, buekens p. 2005. injury deaths, suicides and homicides associated with pregnancy, finland 1987-2000. eur j public health. 15(5), 45963. doi:https://doi.org/10.1093/eurpub/cki042. pubmed 6. reardon dc, ney pg, scheuren f, cougle j, coleman pk, et al. 2002. deaths associated with pregnancy outcome: a record linkage study of low income women. south med j. 95(8), 83441. pubmed https://doi.org/10.1097/00007611-200295080-00011 7. model state vital statistics act and regulations, 1992 revision, february 1994. centers for disease control and prevention/national center for health statistics (cdc/nchs). 8. reardon dc, thorp jm. 2017. pregnancy associated death in record linkage studies relative to delivery, termination of pregnancy, and natural losses: a systematic review with a narrative synthesis and meta-analysis. sage open med. 5, 1-17. doi:https://doi.org/10.1177/2050312117740490. pubmed 9. reardon dc, coleman pk. 2012. short and long term mortality rates associated with first pregnancy outcome: population register based study for denmark 1980-2004. med sci monit. 18(9), ph71-76. doi:https://doi.org/10.12659/msm.883338. pubmed 10. gissler m, kauppila r. 1997. merilainen d. pregnancy associated deaths in finland 19871994: definition problems and benefits of record linkage. acta obstet gynecol scand. 76, 651-57. pubmed https://doi.org/10.3109/00016349709024605 https://doi.org/10.1093/eurpub/cki042 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16051655&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12190217&dopt=abstract https://doi.org/10.1097/00007611-200295080-00011 https://doi.org/10.1177/2050312117740490 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29163945&dopt=abstract https://doi.org/10.12659/msm.883338 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22936199&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9292639&dopt=abstract https://doi.org/10.3109/00016349709024605 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts preparing for ilinet 2.0 joel greenspan* martin, blanck & associates, alexandria, va, usa objective this paper outlines the current state of ilinet (ilinet 1.0), the accepted national gold standard for outpatient influenza-like illness (ili) surveillance, and demonstrates how ilinet 2.0 could be more automated, timely, and locally representative if it were to utilize increasingly available electronic healthcare data rather than a specific group of recruited sentinel providers. introduction a neolithic transformation is underway in public health, where the ubiquity of digital healthcare (hc) data is changing public health’s traditional role as data hunter-gatherers to one of data farmers harvesting huge reserves of electronic data. ilinet 1.0 is the current u.s. outpatient ili surveillance network dependent on ~2000 volunteer sentinel providers recruited by states to report syndromic ili. ilinet 1.0 began in the 1980s and represents a largely unchanged, ongoing hunter-gatherer approach to ili outpatient surveillance involving the independent efforts of all state health departments. many significant changes have occurred in the u.s. healthcare system since ilinet 1.0 was initiated. for example, ecommerce standards emerged in the 1990s creating ubiquitous amounts of easily accessible electronic healthcare administrative data. since 2001 new public health surveillance approaches and investments have emerged including methods for syndromic surveillance (e.g. biosense). most recently healthcare reform efforts hold great promise (as yet largely unrealized) for public health to access electronic information derived from ehrs/hies (e.g., meaningful use). could and should the current u.s. gold standard for ili outpatient surveillance benefit from these new opportunities, and if so, what approach should be used and who should be responsible? methods data reviewed for this analysis included: 1) each weekly national ilinet 1.0 report from cdc’ fluview interactive site for the 20131014 season; 2) aggregate data from the state of ga representing 85.5m medical and 213.5m pharmacy electronic healthcare reimbursement claims (ehrcs) from a commercial healthcare data warehouse; 3) ilinet 1.0 data from the ga department of community health; and 4) all state influenza surveillance reports available online for week 2014_20. results weaknesses of the ilinet 1.0 model include duplicated costs of provider recruitment and data management, low practice coverage, duplicated efforts in provider practices, inconsistent weekly provider compliance, slow data turn-around, lack of publicly available msalevel ili data, and lack of forecasting capability during the current epi-week. an alternative ilinet 2.0 model shows that 1) ehrcs can generate timely outpatient ili signals without recruiting providers directly, 2) tracking anti-influenza prescription drugs provides a comparable signal to provider-office ili signals; 3) ili trends can be generated at local levels (e.g., msas); and 4) ili trends for the current week can be accurately estimated before the epi-week ends. conclusions big ehealth data can be harvested immediately to begin the evolution towards ilinet 2.0 and faster and more granular ili surveillance for the u.s. public health and national security communities. keywords ilinet; ehealth; ilinet references dixon, b. the neolithic revolution in pubic health. pa times online 2013. http://patimes.org/neolithic-revolution-public-health/ *joel greenspan e-mail: greenspan@comcast.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e2, 2015 public health information and statistics dissemination efforts for indonesia on the internet 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 public health information and statistics dissemination efforts for indonesia on the internet febiana hanani 1 , takashi kobayashi 1 , eitetsu jo 1 , sawako nakajima 1 , hiroshi oyama 1 1 department of clinical information engineering, health services sciences, school of public health, graduate school of medicine, the university of tokyo, japan abstract objectives: to elucidate current issues related to health statistics dissemination efforts on the internet in indonesia and to propose a new dissemination website as a solution. methods: a cross-sectional survey was conducted. sources of statistics were identified using link relationship and google™ search. menu used to locate statistics, mode of presentation and means of access to statistics, and available statistics were assessed for each site. assessment results were used to derive design specification; a prototype system was developed and evaluated with usability test. results: 49 sources were identified on 18 governmental, 8 international and 5 non-government websites. of 49 menus identified, 33% used non-intuitive titles and lead to inefficient search. 69% of them were on government websites. of 31 websites, only 39% and 23% used graph/chart and map for presentation. further, only 32%, 39% and 19% provided query, export and print feature. while >50% sources reported morbidity, risk factor and service provision statistics, <40% sources reported health resource and mortality statistics. statistics portal website was developed using joomla!™ content management system. usability test demonstrated its potential to improve data accessibility. discussion and conclusion: in this study, government’s efforts to disseminate statistics in indonesia are supported by non-governmental and international organizations and existing their information may not be very useful because it is: a) not widely distributed, b) difficult to locate, and c) not effectively communicated. actions are needed to ensure information usability, and one of such actions is the development of statistics portal website. keywords: public health, public information, statistics, web services, usability public health information and statistics dissemination efforts for indonesia on the internet 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 introduction indonesia has achieved significant improvement in health over the past decades as indicated by the decrease in infant mortality rate and the prolonged life expectancy [[1]]. new challenges, however, have emerged as a result of health transition, economic development, globalization and decentralization [[1] [2]]. epidemiological transition, i.e. double burden of disease, imposes a huge pressure on health program and system in indonesia [[1] [2]]. economic development and globalization contribute to the spread of major risk factors for non-communicable diseases such as tobacco and obesity [[1] [2]]. further, decentralization, under which local governments are given larger autonomy, poses challenge for them on how to manage their new autonomy so as to benefit local community [[1]]. the three core functions of public health, assessment, policy development and assurance, as outlined in the future of public health [[3]], emphasize the significance of health statistics for public health decision-making. health statistics are needed to assess population health, plan, implement and evaluate the effectiveness of health program, and allocate health resources [[4]]. nowadays, statistics that include a wider array of health issues from communicable diseases and maternal/child health to non-communicable diseases, disaggregated by socioeconomic and demographic characteristics, are needed to develop effective responses to existing challenges [[5]]. the government of indonesia in its national health information system (nhis) policy and strategy has acknowledged that the provision of accurate, updated and timely presentation of information is a prerequisite for the achievement of healthy indonesia [[6]]. one of the nhis’s missions is the provision of information for various purposes and different information users [[6]]. government’s efforts to provide health statistics for the public have been joined by other public bodies such as international organizations. to date, however, no effort has been made to identify how far along are they in their efforts especially in which they are using websites as dissemination tools. in contrast to healthcare information, there have been few studies of health statistics on the web. raban et al. reviewed the availability of health statistics for india [[7]] while orc macro surveyed us government’s sites used for statistics dissemination [[8]]. public health information and statistics dissemination efforts for indonesia on the internet 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 objectives thus, the objectives of this research are to elucidate current issues related to health statistics dissemination efforts on the internet in indonesia, and to propose a solution when the need arises. methods our review methodology was based on those used in raban’s study[[7]]. the following steps were done subsequently: 1) identification of sources of health statistics available on the web, 2) assessment of menu used to locate statistics on website, 3) assessment of mode of presentation and means of access to statistics, 4) assessment of available statistics, and 5) whenever appropriate, offer solution to issue identified in the assessment. review of the characteristics of websites offering health statistics for indonesia: 1) identification of sources of health statistics available on the web standard search using search engine would be inadequate for this purpose because many of indonesian websites were not indexed or indexed with keyword that could not represent the contents of the sites. having identified government websites well-known for providing statistics (ministry of health, central bureau of statistics and directorate general of population administration) and using the knowledge that government sites often provide links to other sites with related content, we opted to search on websites linked to the well-known sites, and on any link on these websites (figure 1-a). figure 1-a. the diagram of search results of public health statistics websites using link relationship we focused on sites belong to the government, non-governmental and international organizations as they are reliable sources of information. another reason why we did not use public health information and statistics dissemination efforts for indonesia on the internet 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 search engine as our first strategy was because it was difficult to choose the appropriate search keywords. the only keyword that we identified as quite specific and yet not too broad was “health survey” and thus, we used it to identify all surveys that we might have missed on our first search (figure 1-b). figure 1-b. the diagram of search results using google™ search engine 2) assessment of menu used to locate statistics on website information on website was organized by menus. users selected one of the menus on website homepage to get to the page displaying information of interest. in previous study by orc macro [[8]] various menus that lead to the page displaying health statistics were identified and classified into good menus and inappropriate/misleading menus. good menus were those whose titles were intuitive enough for users to guess that they lead to the statistics of interest and that allowed for efficient search i.e. by providing search tool [[8]]. menus titled “health statistics” and “health topics”, as well as menu titled “publications” that provided search tool for searching among various statistical and non-statistical reports were classified as good menus [[8]]. meanwhile, inappropriate/ misleading menus were those whose titles were not intuitive and that required users to search more to find the statistics of interest [[8]]. government agency was often divided into divisions/departments. menu using a department’s name as its title was classified as inappropriate/misleading as it was not intuitive and required users to know what department was responsible for disseminating statistics [[8]]. menu titled “publications” that did not provide search tool required users to search manually among a large number of reports and thus, was classified as inappropriate [[8]]. in this study, various menus used to locate statistics were described and classified into good and inappropriate/misleading menus. 3) assessment of mode of presentation and means of access to statistics it has been suggested that visual displays such as graph, chart and map are likely to be more effective in conveying information [[9]]. additionally, the ability to query and request for specific statistics and to have access beyond reading them online, i.e. to export/print, may assist users in finding, extracting and using desired information [[8],[10],[11]]. in this study, public health information and statistics dissemination efforts for indonesia on the internet 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 we measured the proportion of sites using visual displays for presentation and providing feature to query/export/print statistics. 4) assessment of available statistics available statistics were classified into statistics on mortality and cause of death, morbidity and health status, risk factors, service provision and health resources [[5]]. examples of health statistics assessed were infant mortality rate, hiv prevalence, prevalence of obesity, antenatal care coverage, and number of midwives. statistics from health surveys were further classified according to the administrative level at which the survey was conducted as well as the administrative level at which the statistics were reported on the web: national, regional, province, district and sub-district. 5) proposal for a new dissemination website and its subsequent evaluation assessment results were used to derive design specification, and a prototype health statistics portal website was developed. further, a preliminary usability test was conducted with 10 subjects, composed of health professionals and university of tokyo’s medical students from indonesia, to measure how well users can use the portal site to find health statistics. this study was approved from the ethics committee of graduate school of medicine, the university of tokyo (no.3125). results review of the characteristics of websites offering health statistics for indonesia: 1) identification of sources of health statistics available on the web 49 sources were identified on 18 governmental, 8 international and 5 non-government websites. they were classified into: census, vital registration, survey, estimation/projection/modeling, service/ surveillance and administrative records [[14]]. 31 sources (63%) were found on government sites, and only 13 sources (26%) and 17 sources (35%) were found on non-government and international sites (figure 2). public health information and statistics dissemination efforts for indonesia on the internet 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 2. the difference of sources of public health statistics in the governmental, non-governmental and international websites of 31 sources on government sites, only 12 sources (39%) were distributed to other site: 5 sources were distributed to non-government site, 5 sources were distributed to international site, and 2 sources were distributed to other government site. 2) assessment of menu used to locate statistics on website 49 menus that lead to statistics were identified. titles of the menus were “health statistics”, “health topics”, “news/articles”, “download”, and “publications”. additionally, the name of a survey, data repository/database, country, or divisional part of the organization who owns the website was also often used as menu’s title. in addition to menus titled “health statistics”, “health topics” and “publications” (with search tool), menus using the name of a survey, data repository/ database and country as their titles were classified as good menus. menus using the name of a survey/data repository/database were accompanied by explanation about the survey/data repository/ database and the statistics collected and thus, allowed users to recognize them as the menus to statistics. organizing statistics by countries from which they came from, on the other hand, allowed users to search efficiently for data from a specific country. 15 of 17 menus (88%) on international sites, 7 of 10 menus (70%) on non-government sites, and 11 of 22 menus (50%) on government sites were classified as good menus (figure 3). in addition to menus titled “publications” (without search tool), menus titled “news/articles” and “download” were classified as inappropriate/misleading as they were not intuitive enough for users to guess that they lead to statistics and required more efforts to separate statistics from other (descriptive) information. 16 of 49 menus (33%) were classified as inappropriate/misleading and 11 of them (69%) were found on government websites. public health information and statistics dissemination efforts for indonesia on the internet 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 3) assessment of mode of presentation and means of access to statistics of 31 websites, 26 websites (84%) and 16 websites (52%) used report and table to present statistics. only 7 websites (23%) and 12 websites (39%) used map and graph/chart. further, only 10 websites (32%), 12 websites (39%) and 6 websites (19%) provided feature to query, export and print the statistics. figure 3. the characteristics of public health statistics related websites’ menus in governmental, non-governmental and international websites 4) assessment of available statistics 34 sources (69%), 28 sources (57%) and 25 sources (51%) reported morbidity, risk factors and service provision statistics. only 12 sources (24%) and 18 sources (37%) reported health resources and mortality statistics. 31 sources (63%) were household surveys. 11 surveys (35%) and 2 surveys (6%) were conducted at district and sub-district level – the two lowest levels of administrative division in indonesia. only 8 surveys (26%) were reported at district level and none was reported at sub-district level. others (74%) were reported at higher administrative levels. 5) proposal for a new dissemination website and its subsequent evaluation i) design specification and development of statistics portal website based on the assessment results, there was low data accessibility. internet users often faced difficulty in locating statistics on website. the menus used to locate statistics were often not intuitive enough for users to guess that they lead to statistics. possible solutions include providing guidance on how to locate statistics on website and/or providing one access point that aggregates direct links to report, table, graph, chart, and map containing the statistics. public health information and statistics dissemination efforts for indonesia on the internet 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 statistics portal site that offered both solutions was developed. the portal site was built using joomla!™ content management system [[13]] and consisted of a homepage and 6 other web pages (url: http://www.indopublichealth.comule.com). each web page was dedicated for a health topic and divided into 4 sections. the topics were “communicable diseases”, “non-communicable diseases”, “maternal & child health”, “lifestyle & environmental health”, and “service provision & health resources”. they were chosen by identifying common themes shared by various statistics that we surveyed. figure 4 shows the portal site’s user interface: (a) menus built from health topics, (b) “dataset & statistics” section consisted of various link to statistics, (c) “latest news” section consisted of public health-related news feeds, (d) “search websites” section consisted of google custom search engine for searching on public health-related websites, and (e) “resources” section consisted of various public health-related documents. figure 4. the graphical user interface of the developed portal site’s based on our survey results. (http://indopublichealth.comule.com) public health information and statistics dissemination efforts for indonesia on the internet 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 ii) preliminary usability test a preliminary usability test was conducted with 10 subjects; for subject characteristics, see table 1. table 1. the participants’ characteristics in the usability test (n=10) variable n(%) sex male 4(40) female 6(60) age means ± sd 38.7 ± 2.21 occupation student 5(50) health professional 5(50) education undergraduate 5(50) graduate 5(50) subjects were given 3 questions. each question asked them to search for health statistics using the portal site (a total of 30 searches; 3 for each subject). performance data score and time spent for each question were collected. additionally, a questionnaire was used to collect demographic data and users’ subjective ratings [[14]]. most subjects earned high scores indicating that their success rate was quite high (figure 5). subjects reported being somewhat satisfied or very satisfied with the result obtained from 87% of the searches. the average time spent to find particular statistics was 1 minute and 58 seconds (mean=118.4sec [sd = 33.7]) (figure 5). 0 5 1 0 1 5 2 0 n u m b e r o f s e a rc h e s 0 .2 .4 .6 .8 1 scores 0 5 1 0 1 5 n u m b e r o f s e a rc h e s 60 90 120 150 180 210 time (s) figure 5. the distribution of search scores (left) and time (right) public health information and statistics dissemination efforts for indonesia on the internet 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 subjects gave neutral to favorable ratings on satisfaction with the time spent on 63% of the searches. subjects also gave neutral to favorable ratings for website usefulness on all searches, and for website ease of use on 97% of the searches (figure 6). further, subjects gave neutral to favorable ratings on frustration experience on 53% of the searches (figure 6). figure 6. portal site usefulness (left) and level of frustration (right) the frustration was, however, not caused by the portal site but by the difficulties found on the websites that provided the statistics, and those caused by subjects’ mistakes i.e. mistake in not thoroughly reading the questions. minor usability issues pointed out by subjects included those related to the unavailability of language options and advance search feature. discussion various sources of health statistics have been identified including census, vital registration and survey. most were on government websites. the availability of various sources on and, for some sources, only on these sites emphasizes the role of government as the main producer and distributor of information. the roles of international and non-governmental organizations, meanwhile, were limited to particular sources only. despite of its role, government’s statistics were seldom shared by multiple sites. the availability of information on multiple sites could increase the chance of finding that information. the easier to find the information is, the more useful it is for its users [[8]]. thus, for existing statistics to be more useful, they should be distributed to as many sites as possible. to get to the page displaying health statistics, user needs to select one of the menus on website homepage. the titles of the menus especially on government websites were, however, often not intuitive enough for users to guess that they lead to statistics. in addition to be public health information and statistics dissemination efforts for indonesia on the internet 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 misguided, they were often required to search manually among a large number of information. the more difficult to find the information is, the less useful it is for its users [[8]]. thus, appropriate title should be used for menu and, to allow for efficient search, either a search tool was provided or the statistics were stored separately from other information. despite of the suggestion that visual displays such as graph, chart and map are likely to be more effective in conveying information [[9]], they were infrequently used by the websites surveyed here. additionally, means of access to statistics was limited as users were often unable to query them and could only access them online. due to these reasons, intended information may not reach audiences and consequently, it is not used. addressing these issues could potentially lead to an increased use of information in public health practice. as internet users worldwide continue to increase [[15]], the role of the internet as dissemination tool becomes more significant. information, especially those come from reliable sources such as the government, should be reported on the web. our findings showed that various statistics have been reported on the web. those on mortality and health resources were, however, particularly difficult to find. government and other responsible organizations should be aware of this, and act accordingly to address this information gap. surveys conducted at district/sub-district level should be able to provide detailed statistics on the web. unfortunately, because some were reported at higher administrative levels, the availability of detailed statistics on the web was less than expected. for the information on the web to be useful, especially to local government responsible for the implementation of health program at district or sub-district level, it should be reported in detail enough to be used in practice. reporting statistics at the same level at which the data were collected is one way to achieve this purpose. internet users often faced difficulty in locating statistics on website. the menus used to locate statistics were often not intuitive enough for users to guess that they lead to statistics. to improve data accessibility, statistics portal site was developed. usability test of the portal site demonstrated its potential to assist users in locating statistics and provided us with necessary information to further improve its usability. despite of all efforts that have been taken to make statistics available on the web, information gap still exists and additionally, existing information may not be as useful as it was intended to be. related to information gap, attention should be brought to 2 issues: the scarce availability of health resources and mortality statistics, and the less-than-expected availability of sub-district/district level statistics. appropriate actions should be taken in order to address public health information and statistics dissemination efforts for indonesia on the internet 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 these issues. further, there are several reasons why existing information may not be very useful: a) it is disseminated only on limited websites, b) it is difficult to locate, especially those on government websites, and c) it is not effectively communicated. if the information is to be used as basis for action, users should not have difficulty in finding and using it i.e. they should have access whenever they need to and be able to extract the information without any difficulty. health statistics are reported on the web with the intent of making them useful. we have performed, to the best of our knowledge, the first research that attempted to describe health statistics dissemination efforts for indonesia. this has enabled us to highlight significant issues and suggest potential ways for improvement. although we may have missed some information, due to imperfect search strategy, it is unlikely that this will lead to a different message than the one conveyed here. the bottom line, actions are needed to ensure the usability of health statistics on the web. as one of such actions, we developed statistics portal website to improve the currently low data accessibility. usability test of the portal site showed promising results but subsequent evaluation with more and a wider range of subjects would be needed to further improve its usability. disclosure the authors have no personal financial or institutional interest in any of the drugs, materials, or devices described in this article. acknowledgements the author would also like to thank all of the subjects for their generous participation in this study. corresponding author: hiroshi oyama, m.d. department of clinical information engineering, health services science division, school of public health, graduate school of medicine, the university of tokyo. address: 7-3-1 hongo, bunkyo-ku, tokyo, 113-8655, japan. tel&fax: +81-3-5841-1893 e-mail: hoyama-nsu@umin.ac.jp mailto:hoyama-nsu@umin.ac.jp public health information and statistics dissemination efforts for indonesia on the internet 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[15] internet world stats. usage and population statistics. internet usage statistics. world internet users and population stats. internet world stats; 2010. http://www.internetworldstats.com/stats.htm. accessed may 30, 2010. ojphi-06-e141.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 170 (page number not for citation purposes) isds 2013 conference abstracts evaluation of swiss abattoir data for integration in a syndromic surveillance system flavie vial*1 and martin reist2 1veterinary public health institute, university of bern, bern, switzerland; 2swiss federal veterinary office, bern, switzerland � �� �� �� � � �� �� �� � objective ��������� �� �� ���� ������ �������� ���� ����������� 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�-� ��������� ����������� acknowledgments �������>�����������/��� ��"�� ����0������<����?���� ��� ����� � ����� ���&'(� references ��� /� ���@0��a�����0;��b� �����c@����"����b��b��.��(��&�� �� � � ���� ����� ������������� ��������� ������ � �������������������� ��� ������ ��� ����(� �����%)) �%))*d�������� ��� ����������������� ��������� �������������b�e��� ���������� �������%) )�1��-7d8%�� %��� <������ ������� 1��(fe�������"#��<������/�����4�����0��b�����;�� #����� ���� ����������������� ������ ������������� ���������� ��� ���� ����� ������ ���� ���g������������"�� �����h������������ ����� �� ���� ������ ������� ��� ��� ����� ����������� ������������ �����'� �������� g�������%) %�"���ijk� *flavie vial e-mail: flavie.vial@vetsuisse.unibe.ch� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e141, 2014 ojphi-06-e93.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 90 (page number not for citation purposes) isds 2013 conference abstracts detecting unanticipated increases in emergency department chief complaint keywords ramona lall*, alison levin-rector, robert mathes and don weiss new york city department of health and mental hygiene, queens, ny, usa � �� �� �� � � �� �� �� � objective ���������� �������� �� � ������ ��� �� ��������������� ������ ���� ����������� ��� ������������������������������������� introduction ������������������� ��� ���� � ���������� �������� ���� �� ���� �� �� � ��� ����� !���������� �������������������� ������ ��� ��� ������ � �� � ������ ������������������� ��� ��!� ��� ����������"����������� ��� ��� ��� ������� �� � ������������� ��� ������#$������ ������ ���%����������&������%����������������� ����� � �� ��� ��������������� ��� ��� �� ������ �������������� �������� �� � ���� ��� � ���� � �� �� ��������������������� �� ��� ������� ������ ��� ���� % ������'����������� ��!� ��� ����� ����� ������������������ ��� ������ ������� �� ������� ������� � ������������������������(&��%) *�+#�,��(�"*-$'"',������(+.$/�#,������������������� ���������� ��(���� �����������,� ��� ������ ���� ��� ����� !��������� � ) ��� ��������������� ��������������!��� ������� ����������������� �� �� ��0���� � ��1���� � � ���� ����������)� �!��������������� � ������� ��� ���������������� ���� �� � ������ ��� �� ����������������� ���� � � ��� ���!� ������������2� ������ � �������� ���� methods ��� ������������� � ��������������������� �� ������������� ��� �� �� ��������������������������� �������!� ����������� �� ������������ �� ��� ������ � �������� �����*� ������������� ����� �� ���������� �� ����������!� ����� �� ������!� ��� � �������� ������ ����� ����� ������ � ����������� ���� �������� ���� �345���� ��������������*���� ��� �!�����������(������ � ,��� ����� � ������������������ ������� �� �� ������� ��� ���!� ��������������������� ������������������ �� ) ������������������� ������ ���������� ��������������� ������6)��� � � ������������ ��� ���������� �������7 �������* � ��896:����� �!�� �������� �� ��� ���������������� ������� ������ ������������� �� ����������������� ������65)���� � ����������������������� ���� ������ ) ��������!��� ;�% ������'��������8968������� ��<<� ����� ����� � �8969� results � ��������6)��� � � ��������� ��� ������� ���� ��=>?�999� ��� �� �� � ���������������������� ������� ������� ���������� � ������ ���) �� ����!���������("',��(*#�,��������������� � ����� � ������������) �� �������������� ����7 ��)* � ��896:�������������� � ���� � ����� ��������� ��� � �� ����������(@"a�,��(&+�+b�$�-�,��(�b*c) c"�,��(@��,��(&+'de"�+,��("#'���,��(@"��',������(@"���#,�� "�������� ���������� ���� ��� ��������� �� ����� �� ����������� � � (&�#"#/"�"',�� ����4::������(�.f+'�,�� ����4:8���+��� ��� � ��!� ������������� ��� ��� ������ ��� ��������� ���� � ������������ � !� �������� ������������ ����!��� �������� ����� ����� ���������) �������������������� ��b�� � �����!������� � ������������������ ) �) �������!��� ��� ���� ��������!����� �� � ������ ����� � �������� � ;� (&��%*�+#�,��(&��"�*�"+#,��(�"*-$'"',��(+.$/�#,�� ����(%$f+�%�b&"*,���� �����*���������������� ���� ���� ��� �) � ����������� ������ ������� �� �� ������!�� ����� �� ����'����� �������� �) ���������������� �� �������� ��<<� �� conclusions ��� ��������� �!��� ��� ��������� �������� � ����� �� �������� ��� ��� �������� ���� �!��� ��������� �� ��� ���������� ������� c � ���� 0��������� � ������������������������� ��������� ����!� ) ���������� �������;��6�� ������ � ������� �������������� ��������� ������������ ��������!��������� ����g��8����� �� � ���� � ����� � � �������(%$f+�%�b&"*,���� ���� ����0������� ��������������������) � �� � ��������)� �� ������� � ��������(f*"#,���� ��������� ����� ���� g��:����� �������� ��� �������� � ��������(�.f+'eb�,�� ���� � ������� ����� ������� �� ��� � � ��������(&�#"#/"�"'��.f+) 'eb�,������������� ���� ���������� ���#�!� ����� ����� �������� ����� ������������ ����� ���� ������<������� � ��������� �������� � ���� ����� ���������������� �� �� ���!� ���0��� ������ � �������������� ����������� ) ��������!��� keywords ���������������g��� ��� �� ����g� �� ����������� ��� *ramona lall e-mail: rlall@health.nyc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e93, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts temporal association between ili and the winter holiday break, u.s. 2004-2012 hongjiang gao*, yenlik zheteyeva, karen wong, jianrong shi, amra uzicanin and rainy jeanette us cdc, atlanta, ga, usa objective to explore the relationship between influenza-like illness observed by influenza out-patient network and winter holiday breaks in us. introduction decreasing contact between infectious and susceptible people in community settings may reduce influenza transmission. examining the temporal relationship between the winter holiday break and seasonal influenza activity can provide insight of alternative contact patterns on influenza spread. methods we examined weekly influenza-like illness (ili) rates by hhs region in the united states from 2003–2014. we used time series analyses to compare observed and predicted ili rates for the last week of each year and the first week of each new year using the autoregressive integrated moving average method. results most (72/90; 80%) observed ili rates for the last week of the year were higher than predicted, and 12 among these 72 were even higher than the upper bound of 95% prediction interval. in contrast, most (76/90; 84%) observed ili rates for the first week of the year were lower than predicted, with 14 out of 76 lower than the lower bound of 95% prediction interval. conclusions most frequently, the last week in december had higher observed than predicted ili rates, and the first week of january had lower observed than predicted ili rates. further research is needed to determine whether these observed differences are due to changes in social mixing patterns or in other factors, such as healthcare-seeking behavior. keywords influenza; time series; winter holiday breaks *hongjiang gao e-mail: uxi7@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e75, 2015 ojphi-06-e148.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 (page number not for citation purposes) isds 2013 conference abstracts implementation of a syndromic surveillance pilot program in selected cattle markets in texas, usa judy e. akkina*1, leah estberg1, gary ross1, cynthia johnson1, marta remmenga1, randy munger1 and andy schwartz2 1united states department of agriculture, animal and plant health inspection service, veterinary services, centers for epidemiology and animal health, fort collins, co, usa; 2texas animal health commission, austin, tx, usa � �� �� �� � � �� �� �� � objective ������� ���� ����� ������� ���������� ������������ �� ���� �� �� ������� �� ��� �������������������� ����� ������������� introduction ���� �� ���� �� ����������� ���������� ���������� �������������� � �� ������� ����� ���������� ������ ���� �������� ������� �� ����� ��� ������ ����������� ��� ����� ���� ��������� ������ �������������� ��!������ � ����� ����� �������������� ���� ����� ���� ���� �� ������ "��� ���� ������������ ������� ������ ������������ ����� ����� ��� ����� ���� ����� ������� ��� � ����� �������� ������������������������������ ������� ������� ������������ ��� � ����� �������� ������ ���� ����� ���� ��� ����� �� ���!������������� ����� ������� ���������� � � ��� ����� � ��� ������� ������ ���������� �������� ��� ��� ������������� � ����� ��������� ��������� ������� �� ����� �������������������� � ������� � ����� ���#���� �����$%���&��� ��� �'���� ����(��� ������� �������� ��� � �����!����� ���� �����������)��� ����������� ���� �� ��$��)�&��� ��������*�����+���� �"������ ����� � ���$�*+"�&� ,��� �� ���� � ����$,�&������ ���������� ����+���� �-��� �� ��� $��+-&��� ��� ���������� �������� �� ���� �� �������� ����� �'���� ���� �������� ���� ����� �� ��,�������������������� �� ������ �� � � ������� ���� ���� ��� �� �������������� ������� ����������� ����� ��������� � ����� ��� ��� ��� ���� ���� ��������� �� ��� ��� � ��� ��� ���� �� ��� ������ ������ ��� ������� �.������������ ������������� �� � ��� � ������������ ���� methods �!����������� ������ ������ ����� ����������+-� �� ����!� �� �������������� � � ���������� ������� � � ����!� ������������������ ! � ���� � � ����� ����'���� ������������������� ����� ���� � � ����� � � �� ������� �� ����������!�� �� ������������������������ ��� ����� �� ������! � �� ��� � � ����� ����� ��'���� ��� �� ������ �� ��� ���� ������ ����� ���� ��� � � ����� � ������� ���������! ������� � ����� � ������"������� ������ ������ ������� �� �������! ������� ����/������� ���� � ����� ����� ��� �� ���� ����������� � ��������������������� ��� ���� ��������������� ��� �� ����� ���� �� ��� ��� ��� ����� ��� � �������� ���� �������� � ��"������� ������ ������ �������!�������� � ������ �����!������ ��� ��!���� ������! � �� ���������������������� �������� ���������� ��� �������������������������� ������������������� $����������� � ����������� #����� ����������!���� ��� �����!��������&�� �������� ���������������� ������ ���� ���� ������$��� ��&��� ���� �� 0� ������ �� ���1��� � ����������$0�1�&�-2����� � ���,������ ��+-������� ����!���������� �������� ������� �� ��� ��� results )����������� ������� ��34������������ ��������������(�� �� ��3�� 5635��( ���� ��������� ��� ��������55��5632���� �� ������� �������� �������������278�494� ������������������38����� ������ ��� ����� �� ������� ���� ����� ������� ���� ����� ���� ���� �� �������� �� ���� "���� � ���� �������������� ��!� �������!������� ��� ������� � ��� ���! � ���+-���� � �������� ������� ���(� ������������� ����� ��� ��:����5632� �� ��������� �� ����� ��� �������� �������������� $( �� ��3&��-����� ��� ���! � �� �� ������� ����� ���� ���� ��� � ��� � ��� ������ � ���� ���� ��� ��� �� �����!������ ������������� �� ������ � �� ��� ��� �������� ��������� �!� � ��� ���������� ������ ���� �� �� ������� ������ �� ���� ��� ������ ���� ������!� ������������ �� ������ ����� �� ��������� ����� ���� ����������������� ��� ��� ��� conclusions � �� ���������� ������� ��� �'������������������������� ����� �� ������ ������������� ������� ����� � �� ���� � � ��� ��� ������� ��� ���� ��� �� ���������� �� ��������� � ��� ��� �������� ���� ���� �� ������ � ��� �'���� ����������������� ����� � ���� ��;���������� ���� �� �����/� ��� ������������� � � ��� �������������� ������ ������������� � ������ ������������ ��� ����� ������� � ������� �� ����� ���<������ ������ ���� �������� ������� �� ����������� � ������������ �� �� � ��� ������������ ������ ��������� � � ������� ���<�� ���������� ��� � ��� ������������������������� ��� ��������� ��� � �������� �������� ��� � ������ �� ������� ��� ��� ���������������������� ����� �� ��� � ������ %���#���� �� ���� �� ������� ( �� ��3� keywords �� ������ �� ������<�� ���������� ���<����� �� ���� �� ������ *judy e. akkina e-mail: judy.e.akkina@aphis.usda.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e148, 2014 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 51 (page number not for citation purposes) isds 2013 conference abstracts village doctors’ acceptability to a syndromic surveillance system in rural china yang fei*1, yan ding1, biao xu2, shaofa nie3, weirong yan3, 4, vinod k. diwan4, rainer sauerborn1 and hengjin dong1, 5 1institute of public health, heidelberg university, heidelberg, germany; 2fudan university, shanghai, china; 3tongji medical college of huazhong university of science and technology, wuhan, china; 4karolinska institutet, stockholm, sweden; 5zhejiang university school of medicine, hangzhou, china � �� �� �� � � �� �� �� � objective ������� � �� ����� � �� ������ ����� ��� ������ ������������ � ��� � ����� �� � ������� ������� ������������� ������������� ������� �� ��� � ���� �������������� ����� �� �������������� � ����� ���� ������ � �� ������������� introduction ��������������������������� ������ ������ �� ����� ������ ����� ��������� ������������������������� ������!��� ������������� ���� � ����� ���!� ���� � � ������� ��� �"#$$����%&&$'����� ����� �� ����� � � ������ ������()������%&&*������� ��� ��������� ����������� �������� ��� ����������������� ���� ������������� ������ ������������ ��� ������������������� ������� ������������� ������ �������� �� � ������� �����'� ������ ������� ������ � �������� ���� ������ � ���� ��� ���� ��������+�������� �� � ����������������� ������� ���������� ��� ���������� ��������,���� � ���������� ��� ���� � ������ �������� ���� ����-$.��/ �� ������ ����������� ��� �'������ ����� 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,����������� � �� �!��� ���� � � ���!��� �� ��� � ��� �b�*���������a���� �� �� �������������� � ��� �� ��� ������ ����� �� conclusions 4���������� � �������� ����� ��� �������� �� ��� � �� ���� �c�� � ����������(� !��� ������� � ������������� ����������� ����� ���� ������� �� ����� � �������� ����� �� � ���������������������� � ���(� ���� �������������� ������������������������������������������ ��� ���������'�!����� ���������� ������'�������9����!� ,��������� �������9������� �� �� ���� keywords ���� ������� ���������b�(���� ����� �b�4���������� � �b�)� ���0���� acknowledgments �����!� ,���� ���������������� ��������� �� ����� � ��� � ������d����� e ���!� ,�2 �� ���f��� �c�� ���a�%:$"&&�� references $��(�������� �� � �� ?0 ��� � ������ � ��� ����9����,������������ � ����������� � ������ ����9���� � ��,gd����(� �e� ���(������'�0��� � ���'�%&&"��33+)�3� ��3� ���+,���)��'�%&$$��8&�$*�a����:&8�"� %��h� � ���'�i�'�� ����'� ����� ����� ������� ������� ���������� � � ����� ��� �� �� ����� ��� � ��,������������ � ���� ������������ ����������� ��� h�2������i��� ���/� �'�%&&@��*&�:�a����*f6�@*� *��j����� '�h�+�'�� ����'�e ���!� ,� � ������� ��������������� ���� ����� ��������� ���� � ��� ����� �� ����� ��� � ��,�a� ��������� ����� ��� ���010�+� ,����k �����33+)�)������)��'�%&&:��6*�))�6�a� ���$�$$� *yang fei e-mail: feiy99@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e82, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts assessing the work practices and information needs of disease investigators neil abernethy*, lauren carroll, alan au and tsung-chieh fu biomedical informatics and medical education, university of washington, seattle, wa, usa objective the goal of this work is to identify specific work practices in disease investigation that would be supported by data visualization, such as identifying exposure, contact, and spatiotemporal clustering. introduction investigation of cases, clusters, and outbreaks of infectious disease is a complex process requiring substantial support from protocols, distributed and cooperative work, and information systems. we set out to identify public health information needs, the types of data required to meet these needs, and the potential alignment with visualizations of this data. methods we used mixed methods to identify common high-level themes and low-level data elements required during disease investigation. we first studied disease investigation protocols and contact investigation data forms from 31 jurisdictions in the context of tuberculosis case investigations. to provide further insight into the disease investigation process, we conducted qualitative research with a public health department to identify work practices, protocols, paper forms, electronic tools, visualizations, and information needs encountered during case and outbreak investigations. this research consisted of two focus groups with different divisions of a public health department, attendance of daily meetings, and participant observation of investigators, nurses, and epidemiologists in the communicable disease section. results we obtained 25 data forms and 21 protocols from 31 states representing contact investigation practices. both forms and protocols were available from 15 states, allowing comparison of the data fields in these artifacts. of 82 data elements recorded, only 10 were commonly represented in both data forms and protocols. this suggests that not all work practices were encoded in these protocols, that some protocoldriven activities are not reflected in the forms we analyzed, and that the functions of these artifacts are not well coordinated. disease investigation practices vary widely by jurisdiction, however as expected some data fields (e.g. demographics, transmission risk, and clinical/case management variables) are more common across jurisdictions. similarly, some common functionality is required of information systems, such as patient identification and search, diagnosis, treatment monitoring, tracking trends, and identifying or plotting links between cases and those at risk. we used qualitative methods to obtain a more complete picture of disease investigation work practices that emerged from state protocols. focus groups were used to explore several themes, including the use of information tools and how well they satisfied public health information needs. participants routinely used information tools to record data during the disease investigation process, although existing tools were not as well-suited for data retrieval and analysis. variations in data reporting requirements between local/state systems also affected work practices involving data entry. participants made use of free-text data fields to record case-specific information, but this complicated extraction and analysis of these data. direct observation sessions reiterated the use of information systems for data management in conjunction with paper records. consistent with findings from the data form and protocol analysis, investigators used systems to check for common risk factors that might indicate that cases were related and to track case status. visualization might benefit such data management tasks; however most visualizations observed were not routinely available. information systems were also used for process-related tasks, such as de-duplicating entries, accessing health records from partnering agencies, and conducting workload management tasks. conclusions this research helps establish that 1) potential users of information systems have diverse workflow needs and data requirements that should be considered in system design; 2) gaps exist between the current capacity of health programs and the functions of information tools; 3) greater levels of standardization of public health data are needed within and between jurisdictions; and 4) coordinated development of data standards, infectious disease protocols, and information tools (including user interfaces and visualizations) is needed. coordination of what public health workers should do, what they actually do, and what they could do with case data will improve future public health surveillance and response activities. keywords outbreak management; contact investigation; data standards; qualitative methods; comparison acknowledgments this research was supported by nlm grant #r01 lm011180-02. we are grateful for the participation of staff from public health seattle & king county. *neil abernethy e-mail: neila@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e99, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts public health (ph) preparedness: improving data exchange for monitoring ph threats through content standardization nikolay lipskiy* csels/dhis, cdc, atlanta, ga, usa objective the objective of this presentation is to evaluate progress in developing semantically interoperable content for ph systems that monitor ph threats. also, it highlights potential solutions for improve standardization of those data exchanges. introduction effective ph information exchange depends on standardized data to ensure system-to-system interoperability and is a critical component of preparedness and response. the common ground preparedness framework (cgpf) was developed through a three-year collaboration of eight state and local health departments to define and categorize ph business processes related to preparedness to include prepare, manage, monitor, investigate, intervene and recover. the cgpf may be used to prioritize standardization activities.[1] monitoring, which is the crucial cgpf category for the entire ph preparedness business processes includes assessing population trends, and conducting surveillance.[2]. the author used the ccpf monitoring process as a basis for the comparison to determine those standards that aligned with these processes and identified any gaps in the standards. this assessment may help in better understanding content standardization for preparedness and areas for improvement. methods the following four cgpf monitoring business processes were analyzed in our study: assess population trends and patterns; conduct syndromic surveillance; conduct notifiable disease surveillance and conduct environmental surveillance [1]. the standardization needs for these processes were assessed against public health information network (phin) standards’ repository that contains 62 electronic data exchange standards [3] and 15 data classifications [4]. based on cdc guidance [5] we made an assumption that the phin repositories reflect the landscape of existing ph standards implemented by cdc and partners’ ph systems. we assessed data elements, value sets and classifications that are included in standards against already defined content categories such as, patient information, encounter, health problems etc. [6] and content categories that support cgpf business processes. results we found that the methodology used to assess the standards by specific business process and content categories was helpful for purposes of this study. it demonstrates that phin content standards supports standardization of all four business processes that comprise the cgpg ph threats monitoring category. however, we observed differences in the level of content standardization of these processes as well as level of standardization for exchanging of data on patient and population-levels. specifically, assess population trends and patterns business process appeared to be on the lowest level of standardization. existing content standards for the cgpf monitoring category are primarily oriented for exchange of patient-level data. we found that the phin classifications effectively cover contents standardization needs for the cgpf ph threats monitoring category. however, a level of a codification appeared to be different by content category. for example, existing standards do not provide guidance on mapping of icd-9/10 codes to syndromes for conducting syndromic surveillance. conclusions while progress towards content standardization for exchange of data for monitoring of ph threats exists, there are some gaps that become evident in this study approach. this study demonstrates that content standards for monitoring ph threats at a patient-level are better defined than at population level. results of this study underline the importance of better coordination of data harmonization efforts between and within domains of ph knowledge. keywords . monitoring ph threats; standardization of surveillance; content standards references 1. gibson pj, theadore f, jellison jb. the common ground preparedness framework: acomprehensive description of public health emergency preparedness. am j public health, 2012; 102(4):633-642. http://www. ncbi.nlm.nih.gov/pmc/articles/pmc3489381/ 2. isds. electronic syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record data. http://www. syndromic.org/storage/isds_2012-muse-recommendations.pdf 3. phin. data interchange standards. http://www.cdc.gov/phin/resources/ standards/data_interchange.html assessed on 07/16/2014 4. phin. classifications. http://www.cdc.gov/phin/resources/standards/ classifications.html cdc. public health preparedness capabilities: national standards for state and local planning, 2011. http://www.cdc.gov/phpr/capabilities/ 5. hhs. hhs/onc. standards and interoperability framework. data harmonization profile. version 2.0 http://wiki.siframework.org/ file/view/dataharmonizationprofile_02132018.docx/428760522/ dataharmonizationprofile_02132018.docx *nikolay lipskiy e-mail: dgz1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e36, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 42 isds 2014 conference abstracts digital sources of food purchasing data for the surveillance of dietary patterns katia charland*, hiroshi mamiya and david buckeridge mcgill university, montreal, qc, canada objective to demonstrate the utility of automatically captured store-level (i.e. point-of-sale) food purchasing data for the surveillance of dietary patterns before and after interventions. we assessed the effects of two interventions in montreal, canada that were intended to reduce the consumption of sugary drinks. introduction in canada, the economic impact of unhealthy eating is estimated at $6.3 billion annually and in the us the estimated cost is $87 billion. despite the critical need to identify effective diet-related interventions through empirical evaluation, public health practitioners and researchers lack timely access to representative data sources collected at a fine spatial and temporal resolution. food surveys, for example, are costly, infrequent, delayed, and subject to biases [1]. the nielsen corporation collects data on food purchasing directly from scanners in grocery and convenience stores around the world. these data hold great potential for public health practice. we were interested in using these data to analyze purchases of regular (sugary) soda and water, before and after two interventions aimed at reducing sugary drink consumption. the first intervention, ‘gobes-tu ça’, was a counter-advertising campaign targeting the age group with the highest consumption of soda, 12-17 year olds [2]. the second intervention, ‘sois-futé, bois santé’, targeted elementary school students. both began in the fall of 2011 and ramped up over time. methods using nielsen data, we determined the monthly number of (240ml) servings of soda and water purchased at 180 sampled convenience and grocery stores in montreal, canada, from 2008 to 2013. the outcomes were the number of soda and water servings purchased per month and the proportion of servings (out of water and soda) that were soda servings. we first identified significant breakpoints in both the soda and water series [3]. then, to assess the relationship between the timing of the intervention and the purchasing outcomes, we used log-linear and logistic time series regression. heteroskedasticity and autocorrelation consistent estimators were used to account for temporal correlation [3]. the independent variable representing the timing of the intervention, had a value of zero before the intervention (before september 2011). afterwards, to account for the intervention ramping up, the variable had a value equal to the number of months since the intervention was implemented. covariates were the month, the average monthly maximum temperature, and the ratio of the average price of soda to water. results results the number of soda servings sold and the proportion that were soda dropped in late 2011 (figure). there was one significant breakpoint for the soda series in dec 2011 (breakpoint month: 12/2011; 95% confidence interval 3/2011, 12/2012) and none in the water series. after accounting for the average price ratio of soda to water, the month and the average maximum temperature, from the time of the intervention to the end of the study period there was an average 26% increase in water sales (1.26, 95% ci 1.09, 1.46) and 12% decrease in soda sales (0.88; 95% ci 0.78, 0.97), resulting in an average 30% decrease in the proportion of sales of soda (odds ratio 0.70; 95% ci 0.61, 0.95). conclusions using digital food purchasing data we identified a significant drop in the sales of soda relative to water following the public health interventions. future work will involve examining geographical variations in the effect of the intervention, possibly due to socioeconomic status or demographic factors. figure: monthly sales (/10 million) of soda, water, and the proportion of soda sales. the shaded region indicates the intervention period. keywords evaluation; diet; intervention references 1. diane mcammond and associates. food and nutrition surveillance in canada: an environmental scan. 2000 mar. 2. langlois k, garriguet d. sugar consumption among canadians of all ages. health rep. 2011;22. 3. zeileis a. econometric computing with hc and hac covariance matrix estimators. j stat softw. 2004 nov; 11(10). *katia charland e-mail: charlandk@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e12, 2015 ojphi-06-e63.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 187 (page number not for citation purposes) isds 2013 conference abstracts rickettsia and borrelia prevalence study among ticks in georgia e. zghenti1, r. sukhiashvili1, e. khmaladze1, n. tsertsvadze1, sarah pisarcik2 and p. imnadze1 1national center for disease control and public health, tbilisi, georgia; 2usamriid, frederick, md, usa � �� �� �� � � �� �� �� � objective ���� ��� ��� � �� �� ����� ������ � �� � � ������ ���� ���� ������ �� ��������� � ��� � ����� � ��� ������ � �������� �� � ���������� ���� ��� �� �� ��� � introduction ��������� �� ��� �� � � �� ��� ���� � ��� ���� � �� ����� ������� ��������������� ��� �������� ������ ������������ ���� ������� �� ��� ������ �������� ������ ��������������� ���� ������ ����������� ������ ���� � �� ����� ���� �� ����� ����� ������ ���� ���� ����� � ������ ���� ����� � ����� ����������� �� ��� ��� ���� �������� ���� �� ����������� � � � �������� ����� ������������ ������ ���������� ������ ���������� ���� � �� �� �������� �� ���� ������ � ���� ������������ �����!���� �� ��� �������� ���� ���� ������������������� ��� ���� ���� ������������� ��� ��������� �� ��� ������ ��� �� ��� �� ���� ���� ������ �� � ����� � ��� ������� ��� ���������������� �� �� 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����������� �� ����� �������� ���� ����� �� ��� � �� ���� ������ ��������� ���� ����� ����� ����� �������� ��������� keywords ��������� ��� ��� �� ����� acknowledgments +����������� ��������� ��"�������) ��� ������� � ��� ��<�� �� ���� !� �� * richard obiso e-mail: rik@attimogroup.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e63, 2014 ojphi-06-e53.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 168 (page number not for citation purposes) isds 2013 conference abstracts mortality monitoring in the netherlands liselotte van asten*1, marit de lange1, carel harmsen2, kees van den wijngaard1, mirjam kretzschmar1, 3 and wim van der hoek1 1national institute for public health and the environment, centre for infectious disease control netherlands, bilthoven, netherlands; 2statistics netherlands, department of demographic and socio-economic statistics, the hague, netherlands; 3university medical center utrecht, julius center for health sciences and primary care, utrecht, netherlands � �� �� �� � � �� �� �� � objective �������� ��� ��������� ����� ������������������ ������������������ ���������� � ������ �� �������� � ���� ������ ������� ������������ ��������� ����������������� ��������� ������ �������� ������ ������ ������������������� ����������������� �� ��������� ������������ ������� ��������� �� ��������� introduction ���� ��������� �������� � � �� � �� � ���������� ��� � �� !""#� � ��� ��������� �����$�%&'&������� ����(��� �������������������� ����������)����������(������� �*� �� ��)��������)(�������+���� ��� � '��������� ������ � �� � ������ ����������������������� ������� ����� ����� ������,�&-�.� ����������� 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����������������������������������������������� �� ������ �������� ���� ��� �������������������� �����������������&������ �������� � ����;����� ������������� ���������������� ����� �� ���� ��������� ���������� ����������������� ��� � ��������� ���������� ��� ���2+*��� ��������1+*���� ���� � � ����������������������������� ����� �������������������������������� ������ ������� ������������������� �������� ����������������� �������� ����� ��������������������� �� ����� ���� keywords 9��������� �������� b�6 �����%������+ ����������b�(��� ���� *liselotte van asten e-mail: liselotte.van.asten@rivm.nl� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e53, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts monitoring respiratory syncytial virus regionally in children aged < 5 years old using emergency department and urgent care center chief complaint data in florida’s syndromic surveillance system, week 1, 2010 week 32, 2014 heather rubino*, david atrubin and janet j. hamilton epidemiology, florida department of health, tallahassee, fl, usa objective in florida, pre-approval of prophylactic treatment by insurance companies is tied to seasonality. previous analyses determined that florida’s syndromic surveillance system (electronic surveillance system for the early notification of community-based epidemics [essence-fl]) was capable of monitoring florida’s statewide rsv seasonality. this analysis aims to determine if essence-fl can also be used to describe rsv and rsv-associated hospitalizations in children < 5 years by region and season. introduction national studies estimate that respiratory syncytial virus (rsv) is responsible for one in 38 emergency department (ed) visits for children < 5 years old. the council for state and territorial epidemiologists position statement (13-id-07): “rsv-associated pediatric mortality” advocates for improved rsv surveillance including monitoring of rsv-associated pediatric mortality and hospitalizations. the goal of that data collection is to establish prevaccine baselines to evaluate vaccine effectiveness should one become available. as rsv is not reportable in florida, rsv surveillance relies on a small subset of all florida hospital laboratories to report data in aggregate and calculation of percent positive of all tests for rsv performed. these data assess virus activity, and do not allow for assessment of morbidity or agespecific analysis. moreover, this data is not complete or timely, most often becoming available a minimum of a week after the testing was conducted. florida’s rsv surveillance efforts guide clinical decision making and insurance reimbursements. florida’s rsv seasonality not only differs from the nation but there is strong variation among five distinct regions, as exemplified by southeast florida where the rsv season is year round. in florida, pre-approval of prophylactic treatment by insurance companies is tied to seasonality. methods essence-fl was used to identify the number of visits to eds and urgent care centers (uccs) with rsv listed in the chief complaint or discharge diagnosis between week 1, 2010 and week 32, 2014. from a subset of the participating hospitals, essence-fl also provided information on admission status for children < 5 years old. chief complaint data were available for all 213 facilities participating in essence-fl (180 eds and 30 uccs), admissions data were available for 171 participating facilities, and discharge diagnosis was available for some facilities. all of these data elements were sorted by rsv region. results a total of 28,779 visits to eds and uccs were identified with a discharge diagnosis of rsv; children < 5 years old accounted for 27,153 (94%) of all visits and children < 1 year old accounted for 17,574 (61%) of all visits. ten percent of ed and ucc visits of children < 5 years old with a discharge diagnosis of rsv resulted in hospital admission. children < 1 year old accounted for 77% of those admissions. trend analysis showed that chief complaint and discharge diagnosis data in children < 5 showed a high correlation with laboratory surveillance data from the state’s hospital-based rsv surveillance program. for each region, essence-fl data mirrored the existing knowledge about unique seasonality. conclusions ed chief complaint and discharge diagnosis data accessed through a syndromic surveillance system can be used for effective, timely monitoring of rsv hospitalizations in children < 5 years old and may be a more efficient and complete means of monitoring seasonality of rsv activity by region and statewide compared to hospital-based laboratory data reporting. additionally, this surveillance technique can efficiently monitor rsv activity as well as estimate hospital admissions due to rsv and may be a useful approach for other states with syndromic surveillance systems. keywords respiratory syncytial virus; syndromic; rsv; surveillance; seasonality *heather rubino e-mail: heather.rubino@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e50, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts google flu trends: spatial correlation with influenza emergency department visits joseph klembczyk*1, mehdi jalalpour2, scott levin1, raynard washington3, jesse m. pines4, richard rothman1 and andrea dugas1 1school of medicine, johns hopkins university, baltimore, md, usa; 2cleveland state university, cleveland, oh, usa; 3ahrq, rockville, md, usa; 4george washington university, washington, dc, usa objective to test if google flu trends (gft) is predictive of the volume of influenza and pneumonia emergency department (ed) visits across multiple united states cities. introduction gft is a surveillance tool that gathers data on local internet searches to estimate the emergence of influenza-like illness in a given geographic location in real time.3 previously, gft has been proven to strongly correlate with influenza incidence at the national and regional level.2,3 gft has shown promise as an easily accessed tool to enhance influenza surveillance and forecasting; however, further geographic validation of city-level data is needed. 1,2,6 methods using healthcare cost and utilization project (hcup) data, we collected weekly counts of ed visits for all patients with icd-9 codes for pneumonia or influenza from 2005-2011 at 19 different cities geographically spread throughout the us.5 corresponding gft data for cities and associated states were collected.4 we then evaluated the correlation between gft and the volume of pneumonia and influenza-related ed visits in each city. results correlation coefficients between city-level gft and ed visits for pneumonia and influenza from 19 different cities range from 0.67 to 0.93 with a median of 0.84. coefficients are shown geographically in figure 1. conclusions we demonstrate a strong correlation between city-level gft and ed visits for pneumonia and influenza across numerous us cities. establishing broad geographic generalizability of city-level gft is key to understanding its capabilities and further integration into other surveillance or forecasting models. figure 1: geographic representation of 19 cities and their respective correlation coefficients for city-level gft and influenza and pneumonia-related ed visits. keywords google flu trends; data science; big data; influenza; surveillance acknowledgments this work was done in collaboration with the agency for healthcare research and quality (ahrq). its contents are solely the responsibility of the authors and do not necessarily represent the official views of ahrq. references 1. dugas, a. f., jalalpour, m., gel, y., levin, s., torcaso, f., igusa, t., et al. (2013). influenza forecasting with google flu trends. plos one, 8(2), e56176. 2. dugas, a. f., hsieh, y. h., levin, s. r., pines, j. m., mareiniss, d. p., mohareb, a., et al. (2012). google flu trends: correlation with emergency department influenza rates and crowding metrics. clinical infectious diseases : an official publication of the infectious diseases society of america, 54(4), 463-469. 3. ginsberg, j., mohebbi, m. h., patel, r. s., brammer, l., smolinski, m. s., & brilliant, l. (2008). detecting influenza epidemics using search engine query data. nature, 457(7232), 1012-1014. 4. google. google flu trends. available at: http://www.google.org/ flutrends. accessed 15 june 2014. 5. hcup nationwide emergency department sample (neds). healthcare cost and utilization project (hcup). 2005-2010. agency for healthcare research and quality, rockville, md. www.hcup-us. ahrq.gov/nedsoverview.jsp 6. pervaiz, f., pervaiz, m., abdur rehman, n., & saif, u. (2012). flubreaks: early epidemic detection from google flu trends. journal of medical internet research, 14(5), e125. *joseph klembczyk e-mail: jklembc1@jhmi.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e87, 201 ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 calls to the british columbia drug and poison information centre: a summary of differences by health service areas kathleen e mclean 1 , sarah b henderson 1, 3 , debra kent 2 , tom kosatsky 1 1. environmental health services, bc centre for disease control, vancouver bc canada 2. bc drug and poison information centre, bc centre for disease control, vancouver bc canada 3. school of population and public health, university of british columbia, vancouver bc canada abstract objectives: poison control centres provide information on the management of poisoning incidents. the british columbia (bc) drug and poison information centre recently implemented an electronic database system for recording case information, making it easier to use case data as a potential source of population-based information on health services usage and health status. this descriptive analysis maps poisoning case rates in bc, highlighting differences in patient age, substance type, medical outcome, and caller location. methods: there were 50,621 human exposure cases recorded during 2012 and 2013. postal code or city name was used to assign each case to a health service delivery area (hsda). case rates per 1,000 person-years were calculated, including crude rates, age-standardized rates, age-specific rates, and rates by substance type, medical outcome, and caller location. results: the lowest case rate was observed in richmond, a city where many residents do not speak english as a first language. the highest rate was observed in the northwest region, where the economy is driven by resource extraction. pharmaceutical exposures were elevated in the sparsely populated northern and eastern areas. calls from health care facilities were highest in the northwest region, where there are many remote aboriginal communities. conclusions: case rates were generally highest in the primarily rural northern and eastern areas of the province. considering these results alongside contextual factors informs further investigation and action: addressing cultural and language barriers to accessing poison centre services, and developing a public health surveillance system for severe poisoning events in rural and remote communities. keywords: poison control centers; british columbia; geographic mapping. abbreviations: british columbia (bc), health service delivery area (hsda), poison control centre (pcc), drug and poison information centre (dpic), united states (us), visual dotlab enterprises (vdle). correspondence: kathleen.mclean@bccdc.ca doi: 10.5210/ojphi.v6i2.5376 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health info rmatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mailto:kathleen.mclean@bccdc.ca ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 introduction british columbia (bc) is canada’s westernmost province, covering almost one million square kilometres, and containing 9.5% of canada's total land and freshwater area [1]. the province has a wide range of geographic regions including coastal, mountain, temperate rainforest, semi-arid desert, and boreal forest. in 2011, 60.4% of the 4.4 million residents lived in metropolitan vancouver and victoria in the southern coastal region (figure 1) [2]. the rest of the population is distributed throughout the vast remainder of the province in smaller centres, and in rural and remote communities. the population is ethnically diverse, with 24.8% identifying as visible minorities (the highest percentage of all canadian provinces and territories) [3], and 4.8% identifying as aboriginal [4]. figure 1: population density and health service delivery areas in british columbia (population density data for 2011, visualized using arcgis 10.) health care is publicly funded in bc, and is governed by the bc ministry of health via six independent health authorities. the provincial health services authority oversees province-wide programs and services, and five regional health authorities serve specific geographic areas (figure 1). the vancouver coastal health authority includes the municipalities of vancouver, richmond, north vancouver and west vancouver. it is characterized by an ethnically diverse population with a comparatively high percentage of adults in the 20-39 age range (table 1), and http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 an economy dominated by the financial and service sectors. the fraser health authority includes several commuter suburbs near vancouver, as well as rural communities heavily engaged in dairy, poultry, and berry agriculture. fraser health has the largest population on the smallest land area of all the health authorities [5], including diverse ethnic and aboriginal groups and a high percentage of children (table 1). the vancouver island health authority and the interior health authority are retirement destinations with larger populations of older adults and seniors. services, tourism and forestry are common to the economies of both, while interior health also relies on coal, mining, agriculture, and hydroelectric production. the economy in the northern health authority is predominantly resource-based, including forestry, mining, oil and gas exploration and production, agriculture, hydroelectric production, and tourism. there are many aboriginal communities in the northern health authority, and it has the highest percentage of children 19 years and under in the province. table 1: average population counts and age distributions by health service delivery area for 2012 and 2013 health service delivery area total population <= 5 years (%) 6-12 years (%) 13-19 years (%) 20-39 years (%) 40-69 years (%) 70+ years (%) east kootenay (#1) 76284 6.2 7.0 7.9 21.8 44.5 12.6 kootenay boundary (#2) 77154 5.2 6.8 7.8 20.4 46.2 13.5 okanagan (#3) 347214 5.2 6.5 8.0 22.1 42.4 15.8 thompson cariboo shuswap (#4) 217689 5.6 6.8 8.3 22.4 44.2 12.7 fraser east (#5) 286202 7.2 8.4 9.4 26.1 38.0 10.8 fraser north (#6) 632749 6.0 6.8 8.5 29.3 40.7 8.7 fraser south (#7) 759100 6.8 8.2 9.6 27.2 39.0 9.1 richmond (#8) 200029 5.3 6.6 8.7 27.7 42.4 9.4 vancouver (#9) 652513 4.8 5.2 6.5 35.2 38.7 9.6 north shore/coast garibaldi (#10) 279488 5.5 7.2 8.6 23.4 43.3 11.9 south vancouver island (#11) 369820 5.0 5.8 7.3 26.6 41.8 13.3 central vancouver island (#12) 261936 5.2 6.3 7.8 20.3 44.8 15.4 north vancouver island (#13) 119096 5.6 6.8 8.2 20.0 46.2 13.1 northwest (#14) 72848 7.1 8.9 10.1 24.1 41.9 8.0 northern interior (#15) 141765 7.0 8.0 9.6 25.4 41.8 8.3 northeast (#16) 68758 8.9 9.1 9.6 31.0 35.4 6.2 given its vast geography and diverse population, it is important to evaluate whether health services reach everyone in bc. the 16 health service delivery areas (hsdas) nested within the regional health authorities provide an ideal geographic unit for assessing spatial differences (figure 1). previous work has used days in hospital, physician billings, and pharmaceutical records to assess use of health services, showing considerable disparity across the hsdas [5]. for example, the northern health authority had the highest rates of acute hospital care and http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 general physician billings, but the lowest rate of specialist physician billings and the smallest percentage of high-demand health care users [5]. this type of spatial information is valuable for retrospective evaluation of health services and status, and for prospective planning to support improved access and delivery. poison control centres (pccs) provide information to the general public and healthcare professionals on the management of poisoning incidents, resulting in health care savings and improved outcomes. access to pccs can reduce hospitalizations and emergency room visits [6], and the length of hospital stays [7,8]. a recent economic review found health care savings of up to eight dollars for every dollar spent on pccs [9]. the bc drug and poison information centre (dpic) was established in 1975 as one of the first pccs in canada, and is currently operated by the bc centre for disease control, an agency of the provincial health services authority. current programs include a 24-hour toll-free poison information service for the entire province and a separate toll-free drug information consultation service for healthcare professionals. in october 2011, dpic implemented a system for recording case information using an electronic database called visual dotlab enterprises (vdle). previously, dpic used paper records and limited data were entered for routine reporting. the new system offers many benefits, including access to all case data in near-real-time. when a client calls about a suspected poisoning, the responding dpic specialist (a pharmacist or nurse) records information about the patient, the exposure, evident symptoms, and pertinent context. after assessing the situation and potential toxicity, the dpic specialist provides treatment advice. this may include the recommendation to stay at home with follow-up from dpic if necessary, or it may involve referral to the hospital with additional consultations as needed. in emergency situations, the dpic specialist helps coordinate transport to the hospital, and informs the emergency department of the situation and treatment recommendations. during a call, the highest priority is obtaining information essential for managing the poisoning incident, so non-essential information is usually collected at the end. thus, data may be incomplete if the caller ends the call early, or if other circumstances make it difficult to request non-essential information. data from pccs are a potential source of population-based information on health services usage and health status. several epidemiologic studies have used data from pccs to investigate specific outcomes such as suicide attempts [10], or specific exposures such as pharmaceuticals [11,12], inhalants [13], and pesticides [14-17]. data from pccs are also used for public health surveillance in the united states (us) and europe, but not yet in canada [18]. in the us, the american association of poison control centers and the centers for disease control and prevention have implemented a national surveillance system called the national poison data system. this system automatically aggregates and analyzes data from us poison centers, issuing alerts if anomalies are detected in total call volume, call volume by clinical effect, or for certain defined cases [19]. the vdle software used by dpic conforms with the same data guidelines. although pcc data have proven valuable for evaluating surveillance questions, few studies have leveraged spatial information to assess differences between populations [12,14,17]. until the implementation of vdle, there was no simple, systematic way to map spatial differences in the use of dpic services. this study is the first descriptive analysis of dpic cases in the new database, providing current information about dpic operations, and baseline information for more specific studies in future. the primary objective was to map poisoning case rates by http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 hsda, and to highlight differences in patient age, substance types, medical outcome, and caller location. methods there were 61,131 cases recorded in the dpic database between january 1, 2012 and december 31, 2013. of these, 2,758 were calls to the drug information service for healthcare professionals. the remaining 58,373 were related to poisonings, with 50,621 (86.7%) human exposures, 1,688 (2.9%) animal exposures, and 6,064 (10.4%) information-only calls (figure 2). this work focuses on the human exposure cases. population estimates for 2012 and 2013 from bc stats [20] were used to calculate age-standardized and age-specific case rates. patient age was unknown for 9,748 (19.3%) cases. figure 2: cases recorded in the dpic database between january 1, 2012 and december 31, 2013, showing those selected for analysis in red to obtain geographic information, dpic specialists ask for the caller’s city name and/or postal code. if the dpic specialist enters a postal code into vdle, the city field is auto-populated with the appropriate city. if only a city name is entered, vdle automatically assigns the first available 6-digit postal code as the default for that city unless the dpic specialist enters a code specifying that the postal code is unknown. the 50,621 exposures included 13,441 urban and 551 rural postal codes that were merged with a comprehensive postal code database and assigned to hsdas. cases with no postal code were matched to an hsda by the city name, if provided, using a database of bc communities and health regions. in total, 49,613 exposure cases (98.0 %) were matched to an hsda and the remainder were excluded from the spatial analyses. all further data analysis and visualization was conducted in r 3.0.2 [21]. case counts by call type and age were then aggregated by hsda. crude human exposure case rates per 1,000 person-years were calculated using all available data for 2012 and 2013. directly age-standardized case rates were calculated after removing cases with unknown age. confidence intervals for standardized case rates were calculated using a http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 method by fay and feuer (1997) that approximates exact confidence intervals [22]. for all other rates, 95% confidence intervals were calculated as follows: confidence interval = (1000 person-years / n)*(d +/(1.96*d)), where n is the population of the region and d is the number of cases upon which the rate is based. to compare rates and other measures from different regions in the maps, z-scores were calculated as follows: zi = (mi – mu)/sd, where i is the hsda region (1,2,…16), m is the measure of interest, mu is the mean of the measure of interest for all regions, and sd is the standard deviation of the measure of interest for all regions. human exposure cases were categorized as pharmaceutical or non-pharmaceutical based on the primary substance involved. dpic specialists rank substances based on the relative contribution of each to the observed clinical effects [23]. categorization of the substances occurs in vdle following the us national poison data system guidelines. cases were also categorized by medical outcome, classified as mild or severe for these analyses. the medical outcome is determined by the dpic responder at the conclusion of a case. mild outcomes included the following categories: no effect; minor effect; not followed judged as nontoxic exposure (clinical effects not expected); not followed minimal clinical effects possible (no more than minor effect(s) possible); unrelated effect the exposure was probably not responsible for the effect(s); and confirmed non-exposure. severe outcomes included the following categories: moderate effect; major effect; death; and death indirect report. finally, cases were categorized by the site of the caller. most calls come from a private residence, healthcare facility, or workplace. calls from a public area, school, restaurant, or other site were classified as 'other'. results age-adjusted case rates the crude human exposure case rate per 1,000 person-years for 2012-2013 ranged across the hsdas from 2.6 in the municipality of richmond (hsda #8) to 8.7 in the northwest (#14). the province-wide crude rate over that same time period was 5.4 cases per 1,000 person-years. ageadjusted rates were lowest in richmond at 2.2 and highest in the northwest and east kootenay (#1) at 6.4 cases per 1,000 person-years (table 2). age-specific rates age-specific case rates (figure 3) were expressed as z-scores for children 5 years of age and under, children 6-12 years, teens 13-19 years, adults 20-39 years, adults 40-69 years, and seniors 70 years or older. province-wide case rates for these age categories were 40.0, 3.6, 4.2, 2.3, 1.4, and 2.1 cases per 1,000 person-years, respectively. the highest z-scores for these age groups were 1.08 in east kootenay (#1), 1.72 in north vancouver island (#13), 2.04 in the northern interior (#15), 2.24 in the northwest (#14), 2.56 in the northwest, and 1.95 in east kootenay, respectively. the lowest z-scores were in richmond (#8) for all age categories. rates, confidence intervals, and z-scores for each hsda are included in the supplemental material (table s1a and s1b). http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 table 2: crude and adjusted human exposure case rates per 1,000 person-years for 2012-2013 by health service delivery area health service delivery area human exposure cases crude case rate 95% confidence interval (lower, upper) ageadjusted case rate 95% confidence interval (lower, upper) east kootenay (#1) 1170 7.7 7.2, 8.1 6.4 6.0, 6.8 kootenay boundary (#2) 1055 6.8 6.4, 7.2 6.0 5.6, 6.4 okanagan (#3) 3821 5.5 5.3, 5.7 4.8 4.7, 5.0 thompson cariboo shuswap (#4) 2923 6.7 6.5, 7.0 5.6 5.4, 5.8 fraser east (#5) 3894 6.8 6.6, 7.0 5.0 4.8, 5.2 fraser north (#6) 6098 4.8 4.7, 4.9 3.8 3.7, 3.9 fraser south (#7) 6891 4.5 4.4, 4.6 3.4 3.3, 3.5 richmond (#8) 1050 2.6 2.5, 2.8 2.2 2.0, 2.3 vancouver (#9) 6583 5.0 4.9, 5.2 3.9 3.8, 4.1 north shore/coast garibaldi (#10) 2766 4.9 4.8, 5.1 4.1 3.9, 4.3 south vancouver island (#11) 4118 5.6 5.4, 5.7 4.8 4.6, 5.0 central vancouver island (#12) 3191 6.1 5.9, 6.3 5.4 5.2, 5.6 north vancouver island (#13) 1549 6.5 6.2, 6.8 5.5 5.2, 5.8 northwest (#14) 1266 8.7 8.2, 9.2 6.4 6.0, 6.9 northern interior (#15) 2153 7.6 7.3, 7.9 5.8 5.6, 6.1 northeast (#16) 1085 7.9 7.4, 8.4 5.2 4.8, 5.5 other categorizations there were 22,916 pharmaceutical (e.g. analgesics, muscle relaxants, and street drugs [23]) and 28,489 non-pharmaceutical (e.g. adhesives, tobacco, and cleaning products [23]) exposures during 2012-2013. to compare across the hsdas, we examined pharmaceutical exposures as a percentage of all exposure cases (figure 4). the east kootenay, northwest, and northeast (#16) regions had percentages of pharmaceutical exposure cases that were significantly higher than the provincial average with z-scores of 1.46, 1.70, and 1.61, respectively. the percentage of pharmaceutical exposure cases was significantly lower than the provincial average in kootenay boundary (#2) and south vancouver island (#11). rates, confidence intervals, and z-scores for each hsda are included in the supplemental material (table s2). there were 41,867 cases with mild outcomes and 8,754 severe cases. we used the percentage of all exposure cases with a severe outcome to compare across the hsdas (figure 4). northwest and vancouver (#9) had more severe outcomes, with z-scores greater than 1.0, whereas south http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 vancouver island had fewer severe outcomes. rates, confidence intervals, and z-scores for each hsda are included in the supplemental material (table s3). of the 49,613 exposure cases, 30,525 (61.5%) calls were from a residence, 7,490 (15.1%) were from a health care facility, 640 (1.3%) were from a workplace, and 10,947 (22.1%) were from a site classified as 'other'. northwest had the lowest percentage of calls from a residence (48.0%) and the highest percentage of calls from a health care facility (32.7%) (figure 5). south and central vancouver island (#11 and #12, respectively) had the highest percentages of calls from a residence (67.8% and 65.5%, respectively), and among the lowest percentages of calls from a health care facility (7.4% and 11.8%, respectively). vancouver and richmond had the highest percentages of calls from a workplace at 2.3% and 1.7%, respectively, while some of the more heavily industrialized hsdas such as east kootenay and northeast had the among the lowest percentages (0.7% and 0.9%, respectively). discussion after 35 years of operation, this is the first time that calls to dpic have been mapped across bc. the province-wide crude human exposure case rate was 5.4 per 1,000 person-years for 20122013 and the regional rates ranged from 2.6 to 8.7. these were slightly lower than corresponding non-standardized rates at 52 us poison centres in 2001, which ranged from 4.8 to 17.1 cases per 1,000 person-years [24], with a mean of 8.1 cases per 1,000 population in 2001 and 7.2 in 2012 [25]. our results showing lower rates in the predominantly urban hsdas of vancouver, richmond, and fraser south compared with the predominantly rural hsdas in the north and east of the province highlight the need for this kind of analysis. in particular, richmond had consistently lower rates than other hsdas. one possible explanation is language and/or cultural barriers to accessing dpic services. in 2006, 61% of residents in richmond spoke a language other than english or french as their first language, the highest of all bc hsdas [26]. in addition, 37% of richmond residents in 2006 were immigrants who landed in canada after 1990, compared with 13% for the entire province [26]. dpic currently offers service in languages other than english, but it did not prior to 2013. a challenge with interpreting case rates from pcc data is assessing the extent to which rates reflect true poisoning incidence rather than pcc awareness and use [24], but previous studies suggest that our results reflect true regional differences. regional variation in standardized mortality ratios and rates of hospital separations (deaths and discharges) for unintentional injuries have been shown among children and youth [27] and adults and seniors [28], with rural health regions tending to have higher rates than urban regions. a more recent study reported that the northern health authority had the highest age standardized rate for poisoning-related hospitalizations in bc from february 2001 to june 2005, but the lowest age standardized poisoning mortality rate from 2000 to 2003 [29]. there are several potential factors contributing to higher exposure case rates in rural areas, including more employment in resource-based and industrial occupations and aboriginal communities where substance abuse has been attributed to multi-faceted social and economic challenges [30]. further characterization of the cases in rural areas would provide more insight into these factors. http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 figure 3: age-specific human exposure case rate z-scores by health service delivery area a: children 5 years of age and under; b: children 6-12 years; c: teens 13-19 years; d: adults 2039 years; e: adults 40-69 years; and f: seniors 70 years of age or older. http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 figure 4: the percentage of cases that had a pharmaceutical substance as the primary exposure and the percentage of cases that had a severe medical outcome as z-scores by health service delivery area http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 figure 5: caller site by health service delivery area for 2012 & 2013 higher case rates in rural hsdas could also reflect higher pcc awareness and use. emergency rooms and other health care facilities in remote communities tend to be small, isolated, and resource-limited, making complicated poisoning incidents particularly challenging to manage. our analysis found higher proportions of calls originating from health care facilities in the more sparsely populated hsdas (figure 5), suggesting that staff at rural health care facilities rely on dpic services more often than those in urban areas. furthermore, patients must travel longer distances to access primary care in rural/remote areas, so these patients may be more likely to contact the pcc before seeking care. comparing case rates from dpic data with rates of poisonings derived from current bc administrative health data will help to determine whether rates reflect true incidence versus pcc awareness and utilization. http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 our results show higher percentages of cases involving a pharmaceutical substance in several of the primarily rural northern and eastern hsdas compared with other parts of the province. pharmaceutical exposures are of interest for several reasons. firstly, per capita expenditures for prescription drugs in bc increased by 10-13% each year from 1996-2003, driven mainly by increased use, and two thirds of bc residents filled at least one prescription in 2006 [31]. with increased use, pharmaceuticals become more present in the environment and opportunities for accidents and abuse increase. pharmaceutical poisonings in children continue to be a public health issue despite prevention efforts in recent decades. calls to us pccs regarding pharmaceutical poisonings in children 5 years of age and younger increased from 2001-2008 [32]. human exposure case rates for children 5 years of age and younger in east kootenay were the highest in the province. this hsda also had a higher percentage of pharmaceutical exposures. further characterization of these cases could potentially inform pediatric poisoning prevention policies. finally, pharmaceutical exposures are of interest because they relate to prescription drug abuse. there are minimal statistics on the extent of this issue in bc. a survey of grade 7-12 public school students found a statistically significant increase in the proportion of students who had ever tried prescription pills without a doctor's consent from 9% in 2003 to 15% in 2008 [33]. in the context of the northern and eastern regions of bc, a factor that may be linked to prescription drug abuse is the prevalence of industrial camps, at which workers for remote mining, oil and gas, or forestry operations live and work, and problems with substance abuse among workers are common [34]. the northwest hsda had a higher percentage of cases involving pharmaceutical exposures and a higher percentage of cases with a severe outcome; however, this was not true for other hsdas, suggesting that other factors affect severity of outcome. limitations the use of pcc data is limited by a number of factors. exposures are often self-reported and do not necessarily represent true poisoning incidents. exposures that present directly to a health care facility will not be included unless healthcare staff consult the pcc. dpic case data can be incomplete depending on the nature of the call, the precision of the dpic specialist taking the call, and the willingness of the caller to provide information. there are also some limitations specific to dpic data. for instance, the default behaviour of the vdle system around postal codes may affect data quality. quality assurance and control systems are continuously updated to address these ongoing issues. future work will involve further spatial analysis of the case data, and development of surveillance systems for specific exposures and/or outcomes. as with the us national poison data system [19], a surveillance system using dpic data could detect anomalies by comparing call volumes to defined baseline rates. for example, dpic is exploring methods to automatically extract, analyse, and report on data related to carbon monoxide poisonings. conclusion this study examined spatial differences in the rates of human exposure cases from dpic. rates are generally higher in the rural hsdas in the north and east of the province and lower in the urban hsdas of the southern coastal region. considering these results alongside contextual factors, such as the geography of bc and diversity of its population, informs further investigation and action. we suggest: addressing cultural and/or language barriers to accessing dpic services; http://ojphi.org/ ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e179, 2014 further characterizing cases in the rural health regions to identify factors contributing to higher rates; linking dpic data with other sources of administrative health data to assess the extent to which dpic case rates reflect true poisoning incidence; and developing public health surveillance systems for severe poisoning events in bc. acknowledgements the authors thank dennis leong and victoria wan for accessing and managing the data, and all of the staff at dpic for contributing to data collection and sharing their experiences. we also thank our reviewers for helping to improve this work. financial disclosure none. competing interests none. references 1. land and freshwater area, by province and territory [internet].: statistics canada; 2005 [updated 2005-02-01. available from: http://www.statcan.gc.ca/tables-tableaux/sumsom/l01/cst01/phys01-eng.htm. 2. population and dwelling counts, for canada, provinces and territories, census metropolitan areas and census agglomerations, 2011 and 2006 censuses [internet].: statistics canada; 2013 [updated 2013-01-30. available from: http://www12.statcan.gc.ca/censusrecensement/2011/dp-pd/hlt-fst/pd-pl/select-geochoix.cfm?lang=eng&t=202&gk=pr&rpp=50. 3. visible minority groups, percentage distribution, for canada, provinces and territories 20\% sample data [internet].: 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http://www12.statcan.gc.ca/census-recensement/2006/dp-pd/prof/92-591/index.cfm?lang=e http://www12.statcan.gc.ca/census-recensement/2006/dp-pd/prof/92-591/index.cfm?lang=e ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas 16 supplementary material table s1a: age-specific case rates per 1,000 person-years, 95% confidence intervals, and z-scores for 0-5, 6-12, and 13-19 years for 2012-2013 health service delivery area case rate (0-5 years) 95% confidence interval (lower, upper) z-score case rate (6-12 years) 95% confidence interval (lower, upper) z-score case rate (13-19 years) 95% confidence interval (lower, upper) z-score east kootenay (#1) 57.4 52.6, 62.2 1.08 5.2 3.9, 6.6 1.60 6.2 4.8, 7.6 1.24 kootenay boundary (#2) 55.3 50.1, 60.4 0.89 3.4 2.3, 4.5 -0.60 5.1 3.9, 6.4 0.34 okanagan (#3) 47.1 44.9, 49.4 0.18 4.0 3.4, 4.6 0.13 4.4 3.9, 5.0 -0.22 thompson cariboo shuswap (#4) 53.9 51.0, 56.8 0.77 4.1 3.4, 4.8 0.25 4.6 3.9, 5.3 -0.06 fraser east (#5) 47.0 45.0, 49.1 0.17 4.0 3.4, 4.6 0.13 4.9 4.3, 5.5 0.18 fraser north (#6) 35.5 34.1, 36.8 -0.84 3.1 2.7, 3.5 -0.97 3.2 2.8, 3.5 -1.19 fraser south (#7) 30.1 29.0, 31.1 -1.31 2.7 2.4, 3.0 -1.46 3.6 3.3, 3.9 -0.87 richmond (#8) 18.4 16.6, 20.3 -2.34 2.4 1.8, 2.9 -1.83 2.0 1.5, 2.4 -2.17 vancouver (#9) 30.2 28.9, 31.6 -1.30 3.5 3.0, 3.9 -0.48 5.2 4.7, 5.7 0.43 north shore/coast garibaldi (#10) 37.3 35.2, 39.5 -0.68 4.1 3.5, 4.7 0.25 4.1 3.6, 4.7 -0.47 south vancouver island (#11) 46.9 44.7, 49.1 0.16 4.5 3.9, 5.2 0.74 4.1 3.6, 4.7 -0.47 central vancouver island (#12) 54.2 51.4, 56.9 0.80 4.4 3.7, 5.1 0.62 4.1 3.5, 4.7 -0.47 north vancouver island (#13) 53.9 50.0, 57.9 0.77 5.3 4.2, 6.4 1.72 4.7 3.8, 5.7 0.02 northwest (#14) 52.4 49.7, 56.8 0.64 4.1 3.0, 5.2 0.25 5.7 4.5, 7.0 0.83 northern interior (#15) 54.1 50.9, 57.4 0.79 4.4 3.6, 5.3 0.62 7.2 6.2, 8.2 2.04 northeast (#16) 47.9 44.0, 51.8 0.25 3.1 2.1, 4.0 -0.97 5.7 4.4, 7.0 0.83 ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas 17 table s1b. age-specific case rates per 1,000 person-years, 95% confidence intervals, and z-scores for 20-39, 40-69, and 70 plus years for 2012-2013 health service delivery area case rate (20-39 years) 95% confidence interval (lower, upper) z-score case rate (40-69 years) 95% confidence interval (lower, upper) z-score case rate (70 plus years) 95% confidence interval (lower, upper) z-score east kootenay (#1) 3.9 3.2, 4.6 1.26 1.8 1.5, 2.1 0.84 3.1 2.3, 3.9 1.95 kootenay boundary (#2) 4.1 3.4, 4.8 1.47 1.8 1.5, 2.1 0.84 2.2 1.6, 2.8 -0.13 okanagan (#3) 2.5 2.3, 2.8 -0.27 1.3 1.2, 1.4 -0.38 2.2 1.9, 2.5 -0.13 thompson cariboo shuswap (#4) 3.0 2.6, 3.3 0.27 1.7 1.5, 1.9 0.60 2.3 1.9, 2.7 0.10 fraser east (#5) 2.5 2.2, 2.7 -0.27 1.5 1.3, 1.7 0.11 2.5 2.1, 2.9 0.56 fraser north (#6) 1.9 1.7, 2.0 -0.93 1.2 1.1, 1.3 -0.63 2.2 1.9, 2.4 -0.13 fraser south (#7) 1.8 1.6, 1.9 -1.04 1.2 1.1, 1.3 -0.63 1.6 1.4, 1.8 -1.51 richmond (#8) 1.3 1.1, 1.5 -1.58 0.6 0.5, 0.7 -2.10 1.3 0.9, 1.7 -2.21 vancouver (#9) 2.2 2.1, 2.4 -0.60 1.6 1.5, 1.8 0.35 2.0 1.8, 2.3 -0.59 north shore/coast garibaldi (#10) 2.3 2.1, 2.6 -0.49 1.1 1.0, 1.2 -0.87 2.0 1.6, 2.3 -0.59 south vancouver island (#11) 2.1 1.9, 2.3 -0.71 1.3 1.2, 1.5 -0.38 2.6 2.3, 3.0 0.79 central vancouver island (#12) 2.7 2.4, 3.0 -0.05 1.3 1.2, 1.5 -0.38 2.2 1.9, 2.6 -0.13 north vancouver island (#13) 2.6 2.1, 3.1 -0.16 1.6 1.4, 1.9 0.35 2.1 1.6, 2.6 -0.36 northwest (#14) 4.8 4.1, 5.5 2.24 2.5 2.1, 2.9 2.56 2.7 1.7, 3.6 1.02 northern interior (#15) 3.3 2.9, 3.7 0.60 1.4 1.2, 1.7 -0.14 2.4 1.8, 3.1 0.33 northeast (#16) 3.0 2.5, 3.5 0.27 1.4 1.1, 1.8 -0.14 2.7 1.6, 3.9 1.02 ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas 18 table s2. case rates and 95% confidence intervals per 1,000 person-years for pharmaceutical and non-pharmaceutical exposures for 2012-2013 health service delivery area case rate for pharmaceutical exposures 95% confidence interval (lower, upper) case rate for nonpharmaceutical exposures 95% confidence interval (lower, upper) percentage of cases with a pharmaceutical exposure (%) z-score east kootenay (#1) 3.6 3.3, 3.9 4.0 3.6, 4.3 46.8 1.46 kootenay boundary (#2) 2.8 2.5, 3.0 4.0 3.6, 4.3 40.4 -1.62 okanagan (#3) 2.4 2.3, 2.5 3.0 2.9, 3.1 43.6 -0.08 thompson cariboo shuswap (#4) 2.8 2.6, 2.9 3.8 3.6, 4.0 41.6 -1.04 fraser east (#5) 3.0 2.9, 3.2 3.7 3.5, 3.8 44.3 0.26 fraser north (#6) 2.1 2.1, 2.2 2.6 2.5, 2.7 44.6 0.40 fraser south (#7) 2.0 1.9, 2.1 2.5 2.4, 2.5 44.1 0.16 richmond (#8) 1.1 1.0, 1.2 1.5 1.3, 1.6 43.0 -0.37 vancouver (#9) 2.2 2.2, 2.3 2.7 2.6, 2.8 44.4 0.31 north shore/coast garibaldi (#10) 2.1 2.0, 2.2 2.8 2.6, 2.9 43.0 -0.37 south vancouver island (#11) 2.3 2.2, 2.4 3.2 3.1, 3.3 41.3 -1.19 central vancouver island (#12) 2.6 2.4, 2.7 3.5 3.3, 3.6 41.9 -0.90 north vancouver island (#13) 2.7 2.5, 2.9 3.7 3.4, 3.9 42.1 -0.80 northwest (#14) 4.1 3.8, 4.4 4.4 4.1, 4.8 47.3 1.70 northern interior (#15) 3.4 3.2, 3.6 4.1 3.8, 4.3 44.7 0.45 northeast (#16) 3.7 3.4, 4.0 4.0 3.7, 4.4 47.1 1.61 ojphi calls to the british columbia drug and poison information centre: a summary of the differences by heath service areas 19 table s3. case rates and 95% confidence intervals per 1,000 person-years for cases with severe and mild medical outcomes for 2012-2013 health service delivery area case rate for severe outcome 95% confidence interval (lower, upper) case rate for mild outcome 95% confidence interval (lower, upper) percentage of cases with a severe outcome (%) z-score east kootenay (#1) 1.4 1.2, 1.6 6.3 5.9, 6.7 18.0 0.39 kootenay boundary (#2) 1.2 1.1, 1.4 5.6 5.2, 6.0 18.1 0.45 okanagan (#3) 1.0 0.9, 1.0 4.6 4.4, 4.7 17.3 -0.01 thompson cariboo shuswap (#4) 1.2 1.1, 1.3 5.5 5.3, 5.8 17.3 -0.01 fraser east (#5) 1.1 1.0, 1.2 5.7 5.5, 5.9 16.1 -0.71 fraser north (#6) 0.8 0.7, 0.8 4.1 3.9, 4.2 15.8 -0.88 fraser south (#7) 0.7 0.7, 0.8 3.8 3.7, 3.9 16.5 -0.48 richmond (#8) 0.4 0.4, 0.5 2.2 2.0, 2.3 16.4 -0.53 vancouver (#9) 1.0 1.0, 1.1 4.0 3.9, 4.1 20.3 1.72 north shore/coast garibaldi (#10) 0.8 0.7, 0.9 4.1 4.0, 4.3 16.6 -0.42 south vancouver island (#11) 0.8 0.8, 0.9 4.7 4.6, 4.9 14.7 -1.52 central vancouver island (#12) 1.0 0.9, 1.1 5.1 4.9, 5.3 16.8 -0.30 north vancouver island (#13) 1.2 1.0, 1.3 5.3 5.0, 5.6 17.9 0.33 northwest (#14) 1.9 1.7, 2.1 6.8 6.4, 7.2 21.8 2.59 northern interior (#15) 1.3 1.2, 1.5 6.3 6.0, 6.6 17.5 0.10 northeast (#16) 1.3 1.1, 1.5 6.6 6.2, 7.0 16.1 -0.71 the representation of causality and causation with ontologies: a systematic literature review ojphi the representation of causality and causation with ontologies: a systematic literature review suhila sawesi, phd1,2 *, mohamed rashrash, phd3, olaf dammann, md, sm, phd1,4,5 1. dept. of health informatics and bioinformatics, grand valley state university, school of computing, mi, usa 2. dept. of public health and community medicine, tufts university school of medicine, boston, ma, usa 3. dept. of pharmaceutical and administrative sciences, university of charleston, school of pharmacy, charleston, wv, usa 4. dept. of gynecology and obstetrics, hannover medical school, hannover, germany 5. department of neuromedicine and movement science, ntnu norwegian university of science and technology, trondheim, norway abstract objective: to explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. methods: we conducted a systematic literature search on eight databases (pubmed, institute of electrical and electronic engendering (ieee xplore), association for computing machinery (acm), scopus, web of science databases, ontobee, obo foundry, and bioportal. we included studies published between january 1, 1970, and december 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. further inclusion criteria were publication in english and peer-reviewed journals or conference proceedings. two authors (ss, rm) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. results: the search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. conclusion: no current ontology captures the wealth of available concepts of causality. this provides an opportunity for the development of a formal ontology of causation/causality. keywords: causality, causation, ontology, knowledge representation doi: 10.5210/ojphi.v14i1.12577 * corresponding author, suhila sawesi. 333 michigan st ne, grand rapids, mi 49503. suhilasawesi@gmail.com. copyright ©2022 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes the representation of causality and causation with ontologies: a systematic literature review ojphi introduction the relationship between cause and effect is one of the fundamental problems in the sciences. in the population health sciences, epidemiologists have discussed causality and causal inference to a great extent [1-3]. despite the widely discussion of causes, it is not clear that epidemiologists are sharing the same concept. one way to share a common understanding of the structure of information among people and software agents is through the use of what is called an “ontology.” ontology is a branch of philosophy concerned with the nature of what exists and the relations between entities [4]. in information science, the term has been adopted as the name for formal systems that provide definitions of entities in a given domain and allow informaticians to represent data and their interrelations semantically. in this way, ontologies can be used by computing algorithms to facilitate searching, querying, and/or reasoning about the data [5]. ontologies describe reality using logical expression as well as plain english without the need to rely on human interpretations that are oftentimes very ambiguous. these descriptions allow information sharing between computer systems as well as humans to better understand the intended meaning of classes and properties within the framework, thus enabling knowledge reusability, integrability, and interoperability [6]. in order to clarify how the terms "causality" and "causation" are used in biomedical ontologies, we performed a systematic literature review. while it is not our intention to provide a novel theory or framework for causality representation, we want to provide a general synopsis on how "causality" and "causation" are conceptualized and ontologically represented. thus, our focus will be on the nature of causation rather than on causal inference. this will be helpful in the development of a formal ontology for causality and causation. thus, we wanted to address two questions: (1) how are causality and causation conceptualized (defined, described, and represented) in the health science publications that use ontology as a representational tool? (2) are there well-developed frameworks that could be used to develop an ontology for causality? the paper is divided as follows. in section 2, we explain each step of the methodology in detail. next, we present the results of the retrieved articles in section 3. in section 4, we discuss our results and the limitations of the literature review. finally, we conclude and present ideas for future research in section 5. we use the term "causality" to refer to the general concept of a relationship between entities in which the cause makes a difference to the occurrence of the effect. we use "causation" to refer to the process that connects causes to their effects. we use "cause" to refer to the antecedent event in causal processes and "effect" to denote the event that defines the outcome of causal processes (table 1). the representation of causality and causation with ontologies: a systematic literature review ojphi table 1. glossary of terms term definition ontology a data structure of: (1) unique identifiers representing types and natures of entity, (2) labels and meanings related to these identifiers, and (3) specified relationships between the entities [6] bfo basic formal ontology (bfo) is an upper-level ontology that provides a structure for development by categorizing entities into two groups: continuants and occurrents [6] bioportal repository (portal) for the hosting and maintenance of biomedical-related ontologies [81] ontobee a linked ontology data server for publishing and browsing biomedical ontologies in the open biological ontology (obo) foundry [82] obo foundry repository (portal) for the hosting and maintenance of ‘gold-standard’ ontologies, adhering to clearly defined principles of best practice [9] entity a domain’s object, attribute, or process represented in an ontology domain a specified area of knowledge. for example, the domain of health science and medication adherence domain. in mathematics and computer science domains, it refers to the set of possible input values, and the range is the set of possible output values. triplets an ontology model in which ontology consists of a set of three entities (subject, predicate, object) triplets that codifies a statement about semantic data (e.g., sars-cov-2 causes covid-19 disease) [83]. protégé protégé is a free, open-source ontology editor and a knowledge management tool [84] causality a general concept is that there is such a thing as a cause-effect relationship between entities, in which the cause makes a difference to the occurrence of the effect. causation a process that connects causes to their effects. cause an antecedent event in causal processes. the representation of causality and causation with ontologies: a systematic literature review ojphi term definition effect an event that defines the outcome of causal processes. web ontology language (owl) a language for defining and instantiating web ontologies [85]. materials and methods data source we conducted this systematic review following the procedures for performing systematic reviews [7] and the preferred reporting items for systematic reviews and meta-analyses (prisma) guidelines [8]. two search strategies were used to retrieve the relevant publications. (1) electronic databases: pubmed, institute of electrical and electronic engendering (ieee xplore) digital library, association for computing machinery (acm) digital library, scopus, and web of science databases were searched for items published between january 1, 1970, and december 9, 2020. reference lists from all included studies were searched for potentially relevant studies as well. we included conference papers as some ontology frameworks could only be identified in gray literature. (2) ontology repositories: obo foundry [9], bioportal [10], and ontobee [11] were navigated to retrieve published ontologies. however, for an ontology from ontology repositories to be considered for inclusion, a publication in a peer-reviewed journal and/or conference proceedings was required. search strategy we followed the cochrane handbook for the search strategy to maximize literature retrieval [12]. we utilized "or" to combine our search terms and amended the search command by utilizing the truncation function "*". when used within a search term (e.g., “caus*mod”), this placeholder function allows databases to retrieve words that include the letters before and after the asterisk and any combination of characters in lieu of the asterisk (e.g., cause model, causal model, causation model, and so on). two different research concepts were used: (1) cause: it has many related mesh descriptions in clinical and epidemiology; however, the following terms are semantically selected: “causality,” “causation,” “causal mechanism,” “causal explanation,” “pathogenesis,” “pathogenic mechanism,” “pathogenetic mechanism,” “etiology,” and “etiopathogenesis.” (2) ontology: no mesh terms were detected, but the following terms are used: “ontology,” “ontology-based model,” “ontological approach,” “ontological framework,” “ontology representation,” and “formal representation.” the search strategy is provided in the supplementary material (supplement table 1). the representation of causality and causation with ontologies: a systematic literature review ojphi inclusion criteria we selected papers that were based on research and not merely a report or discussion of an existing model. we further restricted the search to studies that formally represent causality and/or causation in the biomedical domain using ontology as a representational tool. we excluded proposed (future) ontologies, studies on diseases without reference to etiology, publications on causality in areas other than biomedicine, studies that address data mining, gene annotation, and prediction using ontologies, ontologies that were inaccessible, not written in machine-readable languages such as ontology web language (owl), or not published in a peer-reviewed journal and/or conference proceedings. data extraction and synthesis our data extraction and synthesis goal was to record all important data obtained from the primary papers accurately. thus, two authors (s.s. and r.m.) independently screened the title and abstract and reviewed the full text between october 2020 and march 2021, following the inclusion and exclusion criteria. as some information was not explicitly mentioned and reaching the author(s) was impossible, assessing the agreement between measures was essential. thus, a test-retest process [13] was performed to measure intra-rater reliability, in which each researcher performed a second extraction from a random selection of five primary studies at a different point in time to check data extraction consistency and accuracy. the correlation between the two sets of results was calculated using intraclass correlation coefficient (icc), in which values less than 0.5 represents poor reliability, between 0.5 and 0.75 indicates moderate reliability, between 0.75 and 0.9 reveals good reliability, and greater than 0.90 is indicative of excellent reliability [13]. the results unveiled no inconsistencies (icc >0.90). in addition to measuring intra-rater reliability, we calculated the inter-rater reliability (irr)—the agreement between the measurements obtained by the two evaluators (s.s and m.r.). the cohen kappa (κ) statistic was used to test for inter-rater reliability (irr) with values ≤ 0 indicating no agreement, 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement [14]. discrepancies were resolved by discussion until consensus was reached and/or by consulting a third reviewer (od). data were extracted according to a predefined extraction form (supplement table 2). this form allowed us to record full details of the included studies and to be specific about how each of them addressed our research questions. in addition, the reasons for exclusion were recorded. each ontology was downloaded in protégé [15] and checked for logical consistency by performing classification with the hermit reasoner [16]. hermit can determine the consistency of any given ontology and identify the subsumption relationships between its classes [17]. this reasoner is based on a hypertableau calculus which provides more efficient reasoning than previously-known algorithms [16]. the central aspect of this algorithm is that it is less non-deterministic than other existing tableau algorithms and can classify ontologies that no other reasoner can currently process in a fast manner [17]. causality-related terms were represented manually in triplet structure (table 3), as in "subject, predicate, object" [18]. subject and object were further specified whether they were classes (i.e., entities) or individuals (i.e., instances of classes). this structure allowed us to know which part the representation of causality and causation with ontologies: a systematic literature review ojphi represents the class, which part is the relation, and which is the instance. the subject (i.e., domain) can be a cause (e.g., sars-cov-2) or effect (e.g., covid-19 disease). predicate, i.e., property or indicator verb of causation, defines the type of relationship that exists between the subject and object (e.g., caused by, causes). the object (i.e., range) is an entity or value that describes the subject through the relation that connects them. the object is similar to the subject in that it can represent cause or effect. the object of one triplet can become the subject of another triplet or vice versa (figure 1). figure 1: example of graphical representation of a triplet structure with subjects, predicates, and objects due to the vast heterogeneity in study methodology, settings, ontology types, and outcomes, a quantitative data synthesis using a meta-analytic approach is not feasible. therefore, eligible studies were evaluated in a narrative format using some statistical analysis when feasible. quality assessment two authors (ss, rm) independently assessed the likelihood that the selected articles will add value to this review using a checklist of 23 quality criteria. fourteen criteria were adapted and modified from [7,19-24], and the remaining criteria were added by the authors according to this review's topic and scope. the criteria are specified in table 3. we were mainly interested in evaluating the quality of reporting and documenting of each primary study, the degree of reusability of the developed ontologies, the extent of covering methods and evaluation, and the quality of representing the usefulness of the finding to the research community and practice. we devised a three-grade scale to score the quality criteria, either as "yes," "to some extent," or "no." we added the answer "to some extent" to give some credit to the limited information available in some papers that helps answer the assessment questions. we assigned a numerical value to each quality assessment question (yes = 1, no = 0, and to some extent = 0.5). we calculated a "quality assessment score" for each study by summing up the scores for all questions for that study. we then divided the total score for each study by the total number of items and multiplied by 100. to create the overall quality grades, we used the following definitions: high quality for studies scored the representation of causality and causation with ontologies: a systematic literature review ojphi between 80-100%; medium quality for studies scored between 50-80%; low quality for studies scored between 0-50%. the quality assessment of studies was done in parallel with data extraction. the cohen kappa (κ) statistic was used to test for inter-rater reliability (irr) with values ≤ 0 indicating no agreement, 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement [14]. microsoft office excel 2016 was used to test the irr between the two reviewers. we did not plan to exclude any studies based on their quality scores alone, as they might cover essential knowledge related to causality and causation that might be worth considering even if they were poorly scored. results and analysis search results the search returned 8,480 articles via electronic databases and 21 articles from online medical ontology repositories. after removing duplicates, 4,304 remaining titles and abstracts were read by two reviewers (ss, rm). based on the selection criteria, 661 articles were retained for a more detailed review. a full-text assessment of these studies led to the exclusion of 611 studies. the reasons for exclusion are depicted in the flow diagram (figure 2). a total of 50 articles met all of the study inclusion criteria. the kappa statistic for the title and abstract screening was 0.78 (substantial agreement) for the first round and 0.89 (almost perfect agreement) for the second round. for the full-text screening, it was 0.75 (substantial agreement) in the first round and 0.91 (almost perfect agreement) in the second round before the consensus agreement was reached. the prisma process was followed and is outlined in figure 2. the representation of causality and causation with ontologies: a systematic literature review ojphi figure 2. flow diagram of search for and selection of relevant publications the representation of causality and causation with ontologies: a systematic literature review ojphi study characteristics’ results a summary of the general characteristics of included articles is shown in table 2. a full description of the 50 included articles is available in supplement table 2. publication years ranged from 2003 to 2020, with an overall increase in articles published more recently. only slightly more than half of the included publications were peer-reviewed (58%, 29/50). with respect to the different targeted disorders, nervous system (neurological) disorders were most frequently represented (14%, 7/50, e.g., alzheimer's disease). the dominant type of ontology was domain type ontology (76%, 38/50), followed by application type ontology (14%, 7/50), and reference type ontology (10%, 5/50). only 42% (21/50) of ontologies are accessible in one of the ontology repositories. the majority of studies used web ontology language (owl) to formally represent the domain knowledge (88%, 44/50), and 40 publications adopted protégé to encode the domain-specific knowledge (80%, 40/50). table 2. study characteristics category number (percentage) publication type peer-reviewed paper 29(58) conference paper 21(42) publication year 2000-2005 4(8) 2006-2010 9(18) 2011-2015 15(30) 2016-2020 22(44) domain category nervous system (neurological) disorder 7(14) alzheimer's diseases (29%, 2/7) 2(29) epilepsy and seizure 1(14) parkinson's disease 1(14) non-specified mental disease 3(42) infectious diseases 7(12) covid-19 1(14) pneumonia 1(14) dengue fever 1(14) schistosomiasis 1(14) the representation of causality and causation with ontologies: a systematic literature review ojphi non-specified infectious disease 3(42) endocrinology and metabolic disorders 4(8) diabetes mellitus 2(50) lipoprotein dysregulation 1(25) pancreatic lesions 1(25) hypersensitivity and autoimmune diseases 4(8) adverse reaction 2(50) allergy 1(25) rheumatoid disease 1(25) radiology 2(4) clinical pathway 1(2) non-specified chronic disease 9(18) ontology type domain ontology 38(76) application ontology 7(14) reference ontology 5(10) ontology representing language web ontology language (owl) 44(88) dogma 1(2) frame-cg (fcg) 1(2) xml 1(2) ontology editor protégé 40(80) webkb 2(4) neo4j 1(2) hozo 1(2) ns 5(10) upper-level ontology basic formal ontology (bfo) 14(28) yet another more advanced top-level ontology (yamato) 2(4) descriptive ontology for linguistic and cognitive engineering 1(2) the representation of causality and causation with ontologies: a systematic literature review ojphi (dolce) domain upper-level ontology (biotop) 1(2) na 32(64) application dependency application semi-independent 32(64) application knowledge base 10(20) application-independent 8(16) approaches to identify concepts top-down approach (starts by modeling top-level concepts, which are then refined in the next step). 9(18) middle-out approach (starts with the certain middle-level concepts and then applies the bottom-up or the top-down methods appropriately as needed). 4(8) hybrid approach (a combination of top-down and bottom-up strategies). 4(8) bottom-up approach (starts from the most specific concepts and builds a structure by generalization). 1(2) ns 32(64) accessibility in ontology repository accessible 21(42) inaccessible 29(58) using existing ontology ontology integration 26(52) ns 24(48) ontology building method authors' engineering methodology 10(20) principles of the open biomedical ontologies (obo) foundry 4(8) methontology 4(8) seven-step method 6(12) best practices for ontology design 3(6) ns 23(46) ontology evaluation approaches application or task-based evaluation 15(30) the representation of causality and causation with ontologies: a systematic literature review ojphi criteria or used-based evaluation 12(24) gold standard-based evaluation 6(12) data-driven evaluation 5(10) ns 12(24) ns=not specified quality assessment results the quality assessment of the 50 reviewed papers is summarized in table 3. only 16 articles (32%, 16/50) were rated as high quality. inter-rater agreement between the two assessors was almost perfect (k=0.895). all of the included papers clearly articulated their authors’ aims, objectives, rationale, the domain of interest and had a well-written discussion and conclusion (100%, 50/50). the authors of the vast majority of the included papers specified their knowledge representation language (94%, 47/50) and the knowledge management tool (90%, 45/50). twenty-seven out of 50 papers contained information on the methods used for ontology development (54%), only 24 articles had a specific information regarding the ontology metrics (i.e., number of classes, properties, axioms, type of axioms, rules, individuals) (48%), 16 had a well-documented knowledge acquisition strategy (32%, 16/50), 38 had well-reported sources of gathered knowledge (76%, 38/50), and 19 had well-defined approaches to identify concepts (38%, 19/50). only 42% (21/50) of ontologies are open and freely available in one of the online medical repositories. the rest was available only through request. twenty-eight out of 50 ontologies had a well-organized, hierarchical structure (56%), including those developed based on foundation ontology (34%, 17/50). the remainder did not incorporate the properties or relationships between entities (44%, 22/50). only six ontologies (14%, 7/50) included clear and non-circular natural language definitions for most of their entities. most ontologies are built without foundational ontologies (64%, 32/50). however, half of them integrated some parts or full ontologies in their frameworks (52%, 26/50). eight models (16%, 8/50) included a relation ontology (ro) model to represent the relationships among entities. the majority of included ontologies were evaluated (76%, 38/50), either through software applications or use-case scenarios. most of the authors of the included articles (82%, 41/50) offered a special recommendation for future work and improvement. unfortunately, more than 64% (32/50) did not explicitly discuss their models' limitations. however, most ontologies were assessed as having a potential research impact (98%, 49/50). the representation of causality and causation with ontologies: a systematic literature review ojphi table 3. quality assessment of included articles author s q1 [24] q2 [7] q3 [19, 24] q 4 * q5 [24] q 6 [2 0, 24 ] q 7 [2 1, 24 ] q8 [24] q 9* q1 0* q1 1* q1 2* q1 3 [24] q1 4 [24] q1 5 [24] q1 6 [24] q1 7 [20] q1 8* q1 9* q2 0* q2 1* q2 2 [21, 22] q23 [23] qua lity (sco re) [25] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 (23) [26] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 (23) [27] 1 1 1 1 1 1 0 0 0 0 0.5 0 0 0 0 1 1 1 0 0 1 1 0 50 (11. 5) [28] 1 1 1 1 1 1 1 0.5 1 1 0.5 1 0 1 1 1 1 1 1 0 1 1 1 87 (20) [29] 1 1 1 1 1 0 0. 5 0.5 1 0 0.5 0 0 0 0 1 1 1 1 0 0 1 0 54 (12. 5) [30] 1 1 1 1 1 1 0. 5 0.5 0 0 0.5 0 0 0 0 1 1 1 0 0 0 1 0 50 (11. 5) [31] 1 1 1 1 1 1 0. 5 0 1 0 0.5 0 0 1 0 0.5 1 1 1 1 1 1 0 67 (15. 5) [32] 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 1 1 1 1 0 1 1 0 70 (16) [33] 1 1 1 1 1 1 1 0 1 1 0.5 0 0 0 0 0.5 1 1 1 0 0 1 0 61 (14) the representation of causality and causation with ontologies: a systematic literature review ojphi [34] 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0.5 1 1 1 0 1 1 1 72 (16. 5) [35] 1 1 1 1 1 1 1 0 1 0 1 0 0 0 0 0.5 1 1 1 0 1 1 1 67 (15. 5) [36] 1 1 1 1 1 1 1 0.5 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 85 (19. 5) [37] 1 1 1 1 1 1 0. 5 0 0. 5 1 0.5 0 0 0 0.5 1 1 1 1 0 1 1 0 65 (15) [38] 1 1 1 1 1 1 1 0.5 1 0 0.5 0 0 0 0 1 1 1 1 0 1 1 0 65 (15) [39] 1 1 1 1 0 0 1 1 1 0 1 0 0 1 0 1 1 1 0 0 1 1 0 61 (14) [40] 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 87 (20) [41] 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0.5 1 1 1 0 1 1 1 85 (19. 5) [42] 1 1 1 1 1 1 1 0.5 1 0 0 0 0 0 0 0 1 1 1 0 1 1 0 59 (13. 5) [43] 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1 0 78 (18) [44] 1 1 1 1 1 1 0. 5 0 1 1 1 0 1 0 0 0.5 1 1 1 1 1 1 0 74 (17) [45] 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 83 (19) [46] 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 61 (14) the representation of causality and causation with ontologies: a systematic literature review ojphi [47] 1 1 1 1 1 1 0. 5 0 1 0 0 0 0 0 0 0.5 1 1 0 0 1 1 1 57 (13) [48] 1 1 1 1 1 1 0. 5 0 1 0 1 0 0 1 1 0.5 1 1 0 1 1 0 0 65 (15) [49] 1 1 1 1 1 1 1 0 0. 5 1 1 0 1 0 0 1 1 1 1 0 1 1 0 72 (16. 5) [50] 1 1 1 1 1 1 0. 5 0 1 0 0.5 0 0 0 0 1 1 1 1 0 1 1 0 61 (14) [51] 1 1 1 1 1 1 0. 5 0 1 1 1 0.5 1 1 0 1 1 1 1 1 1 1 0 83 (19) [52] 1 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 70 (16) [53] 1 1 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 70 (16) [54] 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 87 (20) [55] 1 1 1 1 1 1 0 0 0 0 1 0 1 0 0 0.5 1 1 1 0 0 1 1 59 (13. 5) [56] 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 1 74 (17) [57] 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0.5 1 1 1 0 1 1 1 72 (16. 5) [58] 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 87 (20) [59] 1 1 1 1 1 1 1 1 1 1 0.5 0 0 0 0 0.5 1 1 0 0 0 1 0 61 (14) [60] 1 1 1 1 1 1 0. 5 0.5 1 0 0.5 0 0 0 0 0.5 1 1 1 0 1 1 0 61 (14) the representation of causality and causation with ontologies: a systematic literature review ojphi [61] 1 1 1 1 1 1 0. 5 0.5 0. 5 0 0.5 0 0 0 0 0.5 1 1 1 0 1 1 0 57 (13) [62] 1 1 1 1 1 0 1 0.5 0 0 1 0 1 0 0 0.5 1 1 1 0 1 1 1 65 (15) [63] 1 1 1 1 1 1 0. 5 0 1 0 0.5 0 0 0 0 0.5 1 1 0 0 1 1 0 54 (12. 5) [64] 1 1 1 1 1 1 1 0 1 0 0.5 0 0 1 1 0.5 1 1 1 1 1 1 0 74 (17) [65] 1 1 1 1 1 1 0. 5 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 80 (18. 5) [66] 1 1 1 1 1 1 0. 5 0 0. 5 0 1 0 1 1 0 1 1 1 0 1 1 1 1 74 (17) [67] 1 1 1 1 1 1 0. 5 0 0. 5 1 1 0 1 0 1 0.5 1 1 1 1 0 1 0 72 (16. 5) [68] 1 1 1 1 1 1 0. 5 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 89 (20. 5) [69] 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0.5 1 1 1 1 1 1 1 85 (19. 5) [70] 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0.5 1 1 1 1 0 1 0 72 (16. 5) [71] 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0.5 1 1 1 1 1 1 0 80 (18. 5) [72] 1 1 1 1 1 1 1 0.5 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 89 (20. 5) the representation of causality and causation with ontologies: a systematic literature review ojphi [73] 1 1 1 1 0 1 0. 5 0.5 1 0 0.5 0 0 1 1 0.5 1 1 0 0 1 1 0 61 (14) 1 1 1 1 1 1 0. 5 0.5 0. 5 0 1 1 0 1 1 1 1 1 0 1 1 1 0 76 (17. 5) no% (n) 0 0 0 0 4 (2) 8 (4) 10 (5) 46 (23 ) 16 (8) 64 (32 ) 10 (5) 84 (42 ) 52 (26 ) 50 (25 ) 64 (32 ) 2 (1) 0 0 24 (12 ) 58 (29 ) 18 (9) 2 (1) 66 (33) yes% (n) 100 (50 ) 100 (50 ) 100 (50 ) 10 0 (5 0) 96 (48 ) 92 (4 6) 54 (2 7) 30 (15 ) 74 (3 7) 36 (18 ) 60 (30 ) 14 (7) 48 (24 ) 50 (25 ) 34 (17 ) 56 (28 ) 100 (50 ) 100 (50 ) 76 (38 ) 42 (21 ) 82 (41 ) 98 (49 ) 34 (17) tse% (n) 0 0 0 0 0 0 36 (1 8) 24 (12 ) 10 (8) 0 30 (15 ) 2 (1) 0 0 2 (1) 42 (21 ) 0 0 0 0 0 0 0 1=yes, 0=no, 0.5=to some extent (i.e., tse); * authors of this paper; q1: is the ontology’s full name, including the acronym reported? q2: are the aims and objectives of the primary study clearly stated? q3: is the rationale explicitly specified? q4: is the domain of the developed ontology mentioned? q5: is the knowledge representation language specified? q6: is the ontology editor or knowledge management tool reported? q7: are the methods and proposed techniques clearly explained? q8: are knowledge acquisition documented? q9: are sources of the gathered knowledge reported? q10: do the approaches use to identify concepts specified? q11: does the ontology provide definitive and exhaustive classification of entities? q12: does the ontology include clear and non-circular natural language definitions for all/most of its entities? q13: are the ontology metrics specified (i.e., number of classes, properties, axioms, type of axioms, rules, individuals)? q14: did the study reuse/incorporate an existing ontology (full or part)? q15: is the model built based on a foundation ontology/upper-level ontology? q16: are the ontology relationships represented? q17: did the study discuss the resulted ontology? q18: is the article presented a conclusion related to the research aims and objectives? q19: is the ontology evaluation carried out? q20: is the ontology open, free, and universally implementable? q21: are the recommendations on future work and improvement specified? q22: are the contributions and potential research impacts specified and reported? q23: are the study’s limitations explicitly discussed? the representation of causality and causation with ontologies: a systematic literature review ojphi causality representation results indicator verb of causality one hundred thirty-one indicator verbs of causality were manually extracted from the selected ontologies and represented in triplet form (supplement table 3). the majority of these relations are asserted between classes to provide a standardized vocabulary for knowledge bases. representing the causality-related terms in triplet form was done to identify how ontologies link causes to their effects. examples of these verbs are "cause", "is responsible for", "associated with", "induces", "is triggered by", "influences", "leads to", "has etiology", "may cause", "can cause", and "may effect". causality also presents in the form of necessary and sufficient causes (e.g., infertility due to extra testicular causes, as represented in [61]. nature of causality authors of most of the included papers did not specify neither the nature (i.e., what type of entity in reality) of the cause nor that of the effect (72%, 36/50). moreover, 86% (43/50) do not provide a clear and non-circular natural language definition of cause and effect. for example, chen and hadzic [27] defined “etiology” as a subtype of the ontology class “lipoprotein,” which obviously does not make biological sense. another example is that “allergen” is categorized as a subclass of “allergy,” which is also nonsensical [33]. among those that defined the nature of cause (28%, 14/50), 85% (12/14) used bfo as an upper-level ontology to define the cause as in [25] in which etiology characterized as a material entity, and as in [72], in which cause (e.g., medical intervention) and effect (e.g., causal adverse event) are defined as processes. types of causality representations we identified seven distinct but not mutually exclusive aspects of how causality was conceived and represented: 1. association. six articles (12%, 6/50) represented causality in the form of "a is associated with b." for example, lin and sakamoto [28] used the relation "associated-with" between genetic factors and disease in order to identify genetic susceptibility to human disease. causal terminology is avoided and replaced by statistical terms (association as in correlation). another interpretation of "association" would be simple co-occurrence without statistical connotation. 2. determinism. thirty articles (60%, 30/50) used an assertion of the form "if a, then b." for example, "radiation causes cancer", "treatment causes side effect" [49] and "herbs treat disease" [56]. the assumption seems to be that causation is like a natural law: whenever the cause occurs, it is safe to infer that the effect will occur. 3. temporal order. five articles (10%, 5/50) used the form "a causes b if a precedes b." temporality is a necessary criterion for a causal association between an exposure and an outcome, in which a cause should precede its effect [46]. 4. disposition. the authors of seven articles (14%, 7/50) seemed to assume that causal relations are not dependency relations between entities (objects or events). instead, causation is reflected in the circumstances in which objects express and generate the representation of causality and causation with ontologies: a systematic literature review ojphi their powers. for example, adverse effects can be conceived as a disposition of a patient to adversely respond to exposure to a drug [25]. 5. causal chain. two articles (4%, 2/50) structured causality as a series of events, each of which is caused by the immediately previous event, e.g., adverse series of events [71] or a pathogenetic process [70]. this view is reminiscent of the way causes of death are listed on death certificates and how causality is conceived in some legal contexts. 6. influence. the authors of two articles (4%, 2/50) conceptualized causality as "process a influences (positively_influences, negatively_influences) the occurrence of b," such as in [31], in which the “nicotine_withdrawal positively_influences smoking_relapse” refers to a scenario in which an increase in withdrawal severity would tend to increase smoking relapse. 7. production. the authors of three papers (6%, 3/50) used a representation in the form "a causes b if a produces b." for example [36,39], used "produces" and "triggers" to represent the concept of causality. discussion in this systematic review on the causality and causation representations using ontology as a representational tool, we reviewed and narrowed over 8480 records to a final set of 50 articles. overall, we found that causality and causation have been conceived and represented in different and incompatible meanings. none of the existed ontologies appeared to provide the detail needed to adequately explain the exact nature of causality and causation in each context. one of the most revealing findings from this systematic review was related to the nature of causation and causality. several ontologies lacked semantic standards and were created de novo to represent this domain. these ontologies are information silos in the same way as some softwares are siloed within enterprise applications. thus, they are prima facie unable to resolve the interoperability dilemma that ontologies are supposed to resolve [6]. most of the terminologies used to represent causation and causal relations are varies based on the purpose of the ontology at hand and its domain. for example, the relation indicated by the verb "infect" may be useful in the medical domain but will have little or no use in the computer domain [74], except perhaps in the context of a computer virus “infecting” a machine. the term "causes" itself is often replaced with causal words, such as "produces" [61], “induces” [72], "influences" [31], "bring about" [75], "is a result of" [37], "is responsible for" [60]. causality is also expressed in the forms of sentence connectives (e.g., because, since), prepositions (e.g., due to, because of), and lexical causatives (e.g., drug-associated with adverse effect (ae)) [54] that jointly encode both cause and effect. remarkably, many studies used the terms "causality," "causation," and "cause and effect" as synonyms. however, those who use them may have different meanings and concepts in mind. many studies appear to define these terms inconsistently or ignore their definition entirely. sometimes, a definition of "cause" provides no more information about the nature of cause than does the term "cause" itself. for example, in [38], “cause” is defined as “the reason why some event happens.” the representation of causality and causation with ontologies: a systematic literature review ojphi another example is that the “etiology of lipoprotein dysregulation” is defined as “a possible cause of lipoprotein dysregulation” [27]. such definitions are similar to the notion that a cause is something that produces or creates an effect [76], without additional explanation of what "produce" and "create" actually mean. we suspect that in most cases, investigators refer to "causal production," where a cause brings about its effect in the sense of being responsible for it. causes simply defined as “a causes b if a precedes b in time" would allow for the inference that the rooster’s crowing makes the sun rise. discussions of the meaning of causality often include the concepts of necessitation and sufficiency. for example, some authors of the papers we reviewed seem to claim that the effect cannot happen without a certain cause [38,77]. this proposition is that of a cause being necessary for the effect to occur. a sufficient cause, on the other hand, does not need to be necessary for the effect to occur (other causes might do the same) but it does not need any other ingredient to be causal. rothman [78] has proposed a model of causation for health research that distinguishes between necessary and sufficient causes. he proposes that sufficient causes are almost always constellations of multiple factors that are causal when acting in concert, but only some of which might be necessary to make such constellations sufficient. most causal information in the medical domain is represented in uncertain or vague statements, the truth value of which cannot be established or inferred in all cases. with the current semantic structures of the ontology languages (e.g., web ontology language (owl)), any statement must be either true or false. this statement should hold for all individuals/instances of that class. thus, it is difficult, and perhaps even impossible, to represent probabilistic causal relations. although, causal relationship between exposure to radiation and breast cancer is firmly established, not every person with breast cancer has been exposed to radiation. therefore, building an ontology-based application, such as a clinical decision support system (cdss) based on this expression, might exclude the diagnosis of breast cancer if the patient hadn't been exposed to radiation. a mismatch between the intended meaning and the formal model would lead to an unreliable automated reasoner. although, several approaches in the reviewed literature aim to overcome this rigidity of the semantic structure of current ontologies, they are however, inconsistent with their representation in a given area, and researchers tend to generate their own model. the authors of [79] for example uses both existential restrictions, such as, "a causes some b" (e.g., radiation causes breast cancer in some cases) and universal restrictions, such as "a causes only b" (e.g., radiation causes only breast cancer). the authors of [30,33,53] minimize the deterministic assumptions in favor of probabilism, where the verbs indicating causality such as "cause" and "effect" are replaced by "may-cause" [53], "can-cause" [30], and "may_affect" [33]. causality does not always require that a must be present if b is present, and it also does not require that b is present if a is present. probabilistic verbs still indicate a statistical association between cause and effect. in essence, a causal ontology should allow for the representation of relationships that denote possibility, not necessity. theoretical underpinnings of such representation could come from disposition accounts of causation [6,80]. on such accounts, radiation and breast cancer are not cause and effect, respectively, but represent a unit of causal change. when placed in the situation of being exposed to radiation, the breast tissue tends to develop cancer. the representation of causality and causation with ontologies: a systematic literature review ojphi review limitations due to the fact that the recommendations for reporting of ontology development [24] came after the development of most of the included ontologies, it is possible that some aspects of development were not reported and hence missing from our review. moreover, the quality assessment of whether the identified ontologies were evaluated or not was restricted only to the information written in the given paper without searching for any subsequent ontology evaluation papers the authors of this paper would have been done, thus it might be not captured in our review. conclusion in this systematic review, we synthesized data from 50 articles on the existing biomedical ontologies that include concepts of causality and causation. in summary, we found that causal relationships are represented in ontologies in very heterogeneous ways, and very little attention is being paid to the need to define the causal terminology and concepts employed. although there are many published medical ontologies that include entities related to causality and causation, we are far from having established an explicit common conceptualization of the causality domain. the diversity and inconsistency in causality representation pose a challenge for the integration and reuse of these existed ontologies. therefore, and in future work, we aim to stand up our approach and bridge the gaps of current works by forming definitions and classifying this heterogeneous information to avoid idiosyncrasies to improve domain knowledge interoperability and provide consistency in data description. we will concentrate on developing an ontology that domain experts and researchers can be used to assist in the detection of the cause of diseases. the potential practical implications and future recommendations based on this review are summarized in table 4. table 4. implication and future recommendation of the study suggestion implications developing a robust ontological framework for representing causality and causation causality representation using ontology as a representational tool can serve as a definitive and comprehensive source of causalityrelated knowledge. it can be utilized in healthcare decisionmaking, intervention development, detection risk factors, capturing current and future findings from different applications and publications, etc. the representation of causality and causation with ontologies: a systematic literature review ojphi gold standard for ontology evaluation it would be valuable to further evaluate the ontologies to form a more coherent picture of their effectiveness in representing causality and causation. a gold standard for ontology evaluation is needed to draw a clear conclusion on these ontologies' efficacy and determine the best ways to guarantee reusing or designing a new interoperable framework for causality and causation. the literature also needs to focus more on reporting accurate information about ontology evaluation to assessing the effectiveness of these frameworks and facilitating their successful adaptation, and implementation (e.g., designing applications) future review due to the lack of information regarding ontologies development and documentation in most studies, especially for those published before issuing the minimum information for reporting an ontology (miro) guidance [24], 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https://doi.org/10.31263/voebm.v69i3.1733 https://doi.org/10.1093/nar/gkw918 https://pubmed.ncbi.nlm.nih.gov/27733503 https://pubmed.ncbi.nlm.nih.gov/27239556 https://doi.org/10.1145/2757001.2757003 the representation of causality and causation with ontologies: a systematic literature review abstract introduction materials and methods data source search strategy inclusion criteria data extraction and synthesis quality assessment results and analysis search results study characteristics’ results quality assessment results causality representation results indicator verb of causality nature of causality types of causality representations discussion review limitations conclusion references crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 85 (page number not for citation purposes) isds 2013 conference abstracts u.s. pneumonia and influenza mortality surveillance: a new era krista kniss*1, bianca malcolm2, paul sutton2 and lynnette brammer1 1centers for disease control and prevention, influenza division, atlanta, ga, usa; 2centers for disease control and prevention, national center for health statistics, division of vital statistics, hyattsville, md, usa � �� �� �� � � �� �� �� � objective ������� � �� � ���� ��������� � � ���� ���� � ������� � � ��� ��� ��� �� � �� �� ����� ��� � ��������������� ����� � �������� �� � ���� ��������� ��� �� ����� ����� introduction � ��������� ������ ����� ������!����� ��"�� �#� ��$ �� �� �������� ����� � �������� �� ����������!����� ���������� �� � �����%� �� �������� ������� ������ ! �� �� ��� ���� �� �������� � ������� ������ � ����� �� �� &���������� � ��$ ��!�� ���� ������ � ���� ����� �������� '���(�� � ���� �� ���� )� ��� ����� 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'��d�� ��� �� ��e���� ���� /�0�3�(��: �'!�����c� ���cdd��������!��d�� �d� � �� �� *krista kniss e-mail: krk9@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e78, 2014 a multi-agent system for reporting suspected adverse drug reactions 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi a multi-agent system for reporting suspected adverse drug reactions yanqing ji1,*, fangyang shen2, see-yan lau3, john tran4 1. department of electrical and computer engineering, gonzaga university, spokane, wa 2. department of computer systems technology, new york city college of technology, brooklyn, ny 3. department of pharmacy, abertsons inc., spokane, wa 4. frontier behavioral health, spokane, wa, usa abstract adverse drug reactions (adrs) represent a serious worldwide public health problem. current postmarketing adr detection approaches largely rely on spontaneous reports filed by various healthcare professionals such as physicians, pharmacists et.al.. underreporting is a serious deficiency of these methods the actually reported adverse events represent less than 10% of all cases. studies show that two important reasons that cause the underreporting are: 1) healthcare professionals are unaware of encountered adrs, especially for those unusual adrs; 2) they are too busy to voluntarily report adrs since it takes a lot of time to fill out the reporting forms. this paper addresses these two issues by developing a multi-agent adr reporting system. the system 1) helps healthcare professionals detect the potential causal relationship between a drug and an adr by analyzing patients’ electronic records via both case-based analysis and statistical data mining approach; 2) allows healthcare professionals to add new rules to signal potential adrs based on their medical expertise; 3) makes the reporting much easier by automatically linking the patients’ electronic data with the reporting form. a functioning prototype of the system has been developed. the proposed data analysis approaches as well as the performance of the system have been tested. the results indicate that this system has a great potential to improve the spontaneous reporting rate of suspected adverse drug reactions. keywords: adverse drug reactions, post-marketing drug surveillance, underreporting, intelligent agents, multi-agent systems. abbreviations: food and drug administration (fda), adverse event reporting system (aers), structured query language (sql), association rule mining (arm), multi-agent system (mas) correspondence: ji@gonzaga.edu* copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction thousands of drugs are currently available on the u.s. market. these drugs can occasionally cause adverse drug reactions (adrs) in patients. an adr is defined by the world health organization (who) as “an effect which is noxious and unintended, and which occurs at doses used in man for either prophylaxis, or diagnosis, or therapy.” [1]. adrs could worsen the patient’s medical problems, place patients in life-threatening situations, lead to increased health http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi care costs, and extend patients’ length of stay in hospitals. for example, a recent study of adrs in u.s. hospitals [2] indicates that about 2 million hospitalized patients per year experience serious adrs and approximately 100,000 die from adrs, which rank the fifth in leading causes of death as well as being one of the more frequent causes of hospitalization. in addition, patients experiencing an adr while hospitalized have a substantially increased risk of death (approximately 20%) and incur significantly higher costs for hospitalization and drug treatments (estimated at $516 million in total costs and $37 million in additional drug costs for medicare patients alone [2]). even though premarketing clinical trials are required for all new drugs before they are approved for marketing, these trials are necessarily limited in size and duration and are not capable of detecting relatively infrequently occurring adverse reactions caused by normal use of drugs. in general, if the occurrence rate of a potential adr is less than 0.1%, it cannot be recognized in randomized and controlled clinical trials due to limitations in trial size [3]. as an example, bromfenac (marketed as duract) was a nonsteroidal antiinflammatory (nsaid) agent that was removed by the food and drug administration (fda) from the market in 1998, less than 1 year after it was introduced, because it caused liver injury in 1 in approximately 20,000 patients taking the drug for longer than 10 days [4]. this type of rare and delayed drug reaction is very difficult to detect during preapproval clinical trials since most new drugs are approved after an average of only 1,500 patient exposures. drug safety depends heavily on postmarketing surveillance the systematic detection and evaluation of medicines once they have been marketed [3]. current postmarketing methods largely rely on voluntary, spontaneous reports of suspected adrs to be filed by various healthcare professionals such as physicians, pharmacists, and others. to ensure quality of patient care, many healthcare facilities (e.g., hospitals) have a drug safety office which collects spontaneous adr reports and monitors the safety of drugs, particularly recently marketed drugs. at the national level, many countries have their own spontaneous reporting systems. for example, the united states has an adr reporting system called medwatch, which is managed by the fda. internationally, the world health organization (who) also maintains a similar reporting system that collects individual case safety reports from healthcare providers in member countries of the who program. because adr reports are filed at the discretion of the users in the reporting system, there is gross underreporting [5-7]. hazell and shakir examined 37 studies from 12 countries and found that the median underreporting rate across these studies was 94% (interquartile range 82-98%) [6]. because of underreporting, long delays occur before a sufficient number of suspect cases (signals) are accumulated to raise concern among clinicians and regulators. consequently, the current post-marketing surveillance approach may require years to identify and withdraw problematic drugs from the market [8,9], and result in unnecessary mortality, morbidity, and cost of healthcare. studies have shown that lack of time and uncertainty of reactions being caused by drugs are two major reasons that hinder reporting [10-12]. as indicated by the study of [10], more than 35% of medical practitioners have the opinion that reporting of adrs takes too much time, and it is too bureaucratic. for instance, the fda’s reporting form has four sections: patient information, adverse event, suspect medication(s), and reporter information. for each section, a lot of specific details are requested. for example, the adverse event section requests outcomes attributed to http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi adverse event, date of event, date of report, description of event, relevant tests/laboratory data including dates, and other relevant history including preexisting medical conditions (e.g. allergies, race, pregnancy, smoking and alcohol use, hepatic/renal dysfunction, et.al.). finding these details and putting them in a reporting form are both tedious and time-consuming. uncertainty as to whether the reaction is caused by a drug is another important factor for underreporting [10]. elucidating causal relationships between a drug and an outcome of interest is often clouded by broad boundaries of uncertainty. reasons for uncertainty include (i) multiple conditions may result in similar outcomes (e.g., a possible adr); (ii) a drug may be essential for producing an outcome or condition of interest; however, it is not a sufficient cause in and of itself. that is, there may be other circumstances such as genetics or other underlying conditions that participate in concert with the drug to produce the adverse outcome. intelligent agents provide a promising approach to ameliorating the underreporting problem because they can act on behalf of human users to find and filter information, automate complex tasks, and collaborate with other software agents to assist complex decision-making. even though intelligent agents have been used for different purposes, they usually share some common characteristics such as autonomy, collaboration, delegation, and communication. a multi-agent system may be defined as “a collection of autonomous agents that communicate between themselves to coordinate their activities in order to be able to solve collectively a problem that could not be tackled by any agent individually” [13]. the multi-agent technology provides a new paradigm for developing artificial intelligence applications in a variety of fields. in the health care domain, exemplary applications include the aadcare (agent architecture for distributed medical care) system for integrating the patient management processes [14], the organ transplant management system for the management of organ and tissue transplants among different medical centers [15], the internal hospital tasks management systems for monitoring the application of medical protocols [16], the adr-monitor system for monitoring potential adrs of interest using electronic patient records [17], and the multi-agent system used for finding similar patients [18]. up to date, the number of biomedical applications is still very limited. in this paper, we develop a functioning prototype of an agent-based spontaneous adr reporting system. the system provides both case-based analysis and statistical data mining approach to help healthcare professionals establish the causal relationship between a drug and a potential adr. in addition, the system has a rule-adding framework that allows healthcare professionals to add new rules to signal potential adrs based on their medical expertise. moreover, once a healthcare professional decides to file an adr report, his/her assistant agent will retrieve the required data from related electronic health databases and automatically fill a significant portion of the reporting form. as a result, filing a report would become much easier and takes significantly less time than existing methods. methods multi-agent system architecture our agent system architecture designed for a healthcare organization is shown in figure 1. note that a healthcare organization can be a hospital, a medical center, a sizable clinic, or any other http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi health organizations in which people would like to file adr reports. there are four types of agents: assistant agent, data mining agent, management agent, and data collection and monitoring agent. the first three types of intelligent agents are accessible by any computer connected to the internet through web pages. all these agents can communicate with each other through agent communication languages. the databases at a healthcare organization contain detailed information of its patients. the data are important since they provide, among other information, direct or indirect evidence of the clinical state, and comorbid conditions. figure 1: multi-agent system architecture physicians or other healthcare professionals have their own intelligent assistant agents, which would help them effectively file adr reports. first, each assistant agent can communicate with various data collection and monitoring agents and obtain the electronic data of the involved patient for a particular adr. more importantly, the agent is powered by a rule-based algorithm which can assess the degree of causal association between a drug and a potential adr based on the retrieved patient data related to a particular patient. second, a healthcare professional can add new rules (through his/her assistant agent) to signal potential adrs based on his/her expertise and experience in a particular class of drugs or adrs. third, an assistant agent can contact the data mining agent to get high-level statistical information regarding the strength of the association of a drug-adr pair. fourth, if a healthcare professional decides to file an adr report, his/her agent would automatically feed the available data to the reporting form. more details regarding the rule-based algorithm, the data mining method, the rule-adding framework, and the auto-filling process will be described in the next section. the data mining agent is maintained by a safety officer/manager who has background in epidemiology or biostatistics. the agent collects statistical data through data collection and monitoring agents and calculates the value of an adopted interestingness measure for suspected drug-adr signal pairs. the value of the measure indicates the strength of the association of a drug-adr pair from a statistical perspective. the agent keeps a list of signal pairs and http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi periodically updates the measure’s value for all pairs. if a healthcare professional wants to look at the statistical strength of a particular signal pair, the data mining agent would search its list and send the information to his/her assistant agent. the management agent provides agent management, a white-page service through which an agent can register and discover other agents, and a yellow-page service through which an agent can publish and search for “services.” an agent is allowed to subscribe services. whenever a service registration or modification is made, the agent will be notified. the data collection and monitoring agents convert information requests from other agents into appropriate sql (structured query language) statements and retrieve the data from various databases. moreover, they help other agents track further development of the suspected patient cases. for example, a physician may be interested in what happens to the patients after the suspected drug is discontinued. in general, the physicians and other healthcare professionals could more easily determine the causal relationship between a potential adr and a drug by monitoring the related cues. system design and functionalities in this section, we present the detailed system design and functionalities. specifically, we discuss how the agent-based system assists healthcare professionals in submitting adr reports, by providing both case-based and statistical decision support, by allowing healthcare professionals to add new rules for evaluating the potential causal association between a drug and an adr, and by automatically filling out most of the reporting form using electronic health data. decision support to help healthcare professionals confirm potential adrs, our reporting system provides two types of decision support. first, each assistant agent can utilize a rule-based algorithm to assess the degree of causal association between a drug and an adr based on the electronic data of a single patient. second, the data mining agent can also do a numerical analysis of the correlation between a drug and adr using a data mining method. 1) single patient evaluation using a rule-based algorithm the physician and pharmacist in the project team developed a rule-based algorithm (table 1) in order to evaluate the potential association between a drug and an adr on the basis of a patient’s electronic data. the design of the algorithm was based on their own expertise and the literature research [19]. the algorithm poses a list of questions that indicate the correlation of a drug-adr pair from different aspects. each question has a set of answers, each with a score or “weight”. for instance, the answer to an example question “is the patient 65 or older?” might be “yes [+1]”, “no [-1]”, or “unknown [+0]”. the number in the bracket is a score assigned to the corresponding answer. one can see that each answer represents an if-then rule. for instance, the answer “yes [+1]” to the above question indicates “if the patient is 65 or older, then the score of this question is +1 for the patient”. the score for a patient case is calculated by summing up the scores of each question. depending on the total number of scores accrued, one of the four labels – “definite”, “probable”, “possible”, or “doubtful” is assigned to the adr. 2) statistical association analysis using a data mining method http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi studies have shown that data mining techniques can be very helpful in analyzing the electronic health data and discovering potential adrs [20-24]. however, mining the association between a drug and an adr is not easy due to the large size of the data. since electronic patient data are generally stored in relational databases, we developed a sql-based association mining algorithm that can deal with large amount of data. the algorithm employs risk ratio to evaluate the strength of association between each drug-adr pair given all the electronic data in a database. risk ratio (rr) provides a measure of association between an exposure and the outcome of interest. in mathematical epidemiology, it is defined as a ratio of the probability of the event occurring in the exposed cohort versus a non-exposed cohort. risk ratio is used widely in the statistical analysis of binary outcomes where the outcome of interest has relatively low probability. the magnitude of risk ratio may vary from 0 to infinity. when the risk ratio is over 1.0, the incidence of the outcome is greater in the exposed cohort than the unexposed cohort. in our study, this method is used to investigate the potential association between the drug of interest (exposure) and a reaction (outcome). specifically, it is employed to compare the risk of developing an adr in people receiving a drug as compared to the people who are not receiving the drug. its calculation is based on a 2x2 contingency table as shown in table 2. the formula is given below: table 1: rule-based algorithm when did the adverse drug reaction occur? • prior to drug administration [-1] • unknown [0] • less than two weeks later [+3] • less than one month later [+2] • less than six months later [+1] • more than six months later [0] is the patient 65 or older? • yes [+1] • unknown [0] • no [-1] did the reaction stop if the drug was no longer administered? • yes, within one week [+2] • yes, within one month [+1] • yes, more than one month later [0] • no [0] if the patient was given the drug again, did the reaction re-occur? • yes, within one month [+2] • yes, more than one month later [0] • no [-3] http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi the total score calculated from the above questions defines the category of an adr as follows: <= 1 [doubtful] 2 – 4 [possible] 5 – 8 [likely] 9 – 10 [definite] however, the risk ratio value alone is insufficient to determine association since we must ask whether an estimate for the risk ratio is statistically significant. can the estimate that is greater than 1.0 be attributed to chance, or is it sufficiently greater to require further scrutiny? for statistical inference in this study, we also calculate the chi-squared ( 2χ ) value as shown below: where n represents the number of cells in the table and it is equal to 4. that is, the summation applies over all four cells of the 2x2 contingency table. oi is the observed frequency and ei is the expected (theoretical) frequency asserted by the null hypothesis. for example, o1 and e1 for the first cell should be calculated as table 2: two-by-two contingency table for reactions patients with drug of interest patients with all other drugs patients with reaction of interest a b patients with all other reactions c d for the remaining cells it takes the value contained in the cell (i.e. b, c, d) in turn. to confirm a drug-reaction signal pair, we also include another factor: the number of patients with the reaction of interest in the database (i.e., a in the table 2). the reason is that, even a fairly small a can possibly produce a high risk ratio. but, in post-marketing surveillance, a signal pair generated by an interestingness measure is generally not considered as valid if only one or two patient cases contain the pair [25]. considering the above three factors, we mark a drug-reaction pair as a signal when it satisfies: 1) 2rr > , 2) 2 4χ > , and 3) 3a ≥ . hence, the association mining task is to find all the drug-reaction pairs that satisfy the three criteria in a relational database. we use 𝐷(𝑑1, 𝑑2, … 𝑑𝑖, … 𝑑𝑚) to represent a list of all drugs in the pharmacy database. similarly, 𝑅�𝑟1, 𝑟2, … 𝑟𝑗, … 𝑟𝑛� is utilized to represent a list of all reactions (or medical conditions) covered by the patient medical record database. each pair< 𝑑𝑖, 𝑟𝑗 >forms a potential association 𝑑𝑖 → 𝑟𝑗whose rr value needs to be http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi computed based on the contingency table shown in table 2. we assume that all the data needed to calculate the rr value are in three tables: demography table containing patient’s demographic information such as age, gender, weight, etc., drug table consisting of drug-related data such as drug name, dose, start date, etc., and reaction table containing reaction-related data such reaction name, start date, and so on. our association mining algorithm is shown in algorithm 1 where the same variables (i.e. a, b, c, and d) in table 2 are used in the description of the algorithm. to compute rr based on eq (1), four values need to be obtained; i.e., a, a+c, b, and b+d. six getxxx() functions are utilized to get all drugs and reactions as well as all the information needed to compute the four values. the two functions getalldrugs() and getallreacs() retrieve all the distinctive drugs and reactions from drug table and reaction table, respectively (line 1 and 2). the function gettotalnumofpatients() is used to obtain the total number of patients (line 3) from demography table. note that each patient is uniquely identified by a patient identification (pid) number that represents the primary link field (primary key) between database tables. in addition, one or more drugs and reactions may appear in a single patient record. for each distinctive drug 𝑑𝑖, the function gettotalnumofpatientsondrug (𝑑𝑖) is used to obtain a+c (from the drug table) which represents the total number of patients who have taken the drug (line 5). since the total number of patients is equal to a+c+b+d, b+d can be obtained by subtracting a and c from the total number of patients (line 6). given a pair < 𝑑𝑖, 𝑟𝑗 >, the function gettotalnumofpatientsondrug&reac(𝑑𝑖,𝑟𝑗) is used to get a (line 8) based on both drug table and reaction table. the value of b is obtained by first getting the total number of patients on the reaction 𝑟𝑗 (which represents a+b) and then subtracting a from this number (line 9). after all the four values for a, a+c, b, and b+d are obtained, rr and 2χ can be computed using eq (1) and (2), respectively. http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi the data mining agent maintains a list of drug-reaction pairs. for each pair, its rr, 2χ , and a are updated periodically (e.g. each week) under the help of data collection and monitoring agents. a pair is marked as a signal when it satisfies the criteria defined above. a higher risk ratio value indicates a stronger signal when the other two criteria are satisfied. if a healthcare professional wants to confirm a potential adr before reporting it, his/her assistant agent can always contact the data mining agent and bring the above three values to him/her. rule-adding framework to make our multi-agent system more flexible and useful, a rule-adding framework is designed to allow healthcare professionals to adjust existing rules or adding new rules in the rule-based algorithm based on their knowledge of a particular class of drugs or adrs. for example, a class of drugs frequently used in a particular medical domain (e.g., psychiatry) may cause an adr only after the drug toxins are accrued to a certain level after patients have taken the drug(s) for a long time. in such a case, healthcare professionals in the domain may want to adjust the scores assigned to each answer for the question “when did the adverse drug reaction occur?” in table 1. in addition, other factors (e.g., abnormal lab results) can indicate the occurrence of adrs caused by a particular class of drugs. in these cases, new questions and rules can be added to the algorithm through the framework. virtually any “question” that can be expressed in english and answered using information from a hospital database can be added to the framework. healthcare professionals have the ability to select which signals would be used to indicate a potential adr and add their medical expertise to our framework using their assistant agents. any rule, whether a part of the rule-based algorithm or a custom rule added by a healthcare professional, is stored in a table in a database. each rule consists of four parts: rule id, descriptions, status, and code. the rule id represents a unique identifier of a rule. the descriptions part provides specific descriptions of a rule in english language. the status (either active or non-active) of a rule indicates whether a rule is used when calculating a patient’s total score. the code portion is critical, as it formally describes what variable is evaluated, the upper or lower limit, and the score if the condition of a rule is satisfied. we developed a custom language to make up the code section of a rule. the custom language is translated into a sql command that is then executed. generally, the code follows this format: first of all, a database table name and a column name within that table are listed. then an operator and a constant is listed, followed by a score. the table/value, the operator, and the constant combine to form the if-condition of a rule. if the condition is answered “yes”, then an appropriate score is assigned to the rule. the code for an example question “is the patient greater than 65 years old?” is expressed as “patient table|age|>|65|1”, where the vertical bars (|) represent the delimiters of different components of the code. the “patient table” refers to a table in the hospital database from which data are retrieved and evaluated. the “age” represents a column name in the “patient table”. the symbol “>” is an operator that represents “greater than”. the two numbers “65” and “1” represent the lower limit of “age” and the score, respectively. table 3 is a list of operators supported by the custom language. note that users do not need to learn the custom language and manually create the code for each rule. an easy-to-use web interface is provided to allow users to select the patient table, column name and operator, http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi and enter the upper and/or lower limit and the score. the system then automatically generate the code based on the inputs. table 3: list of operators in the custom language operator description < less than <= less than or equal to > greater than >= greater than or equal to = equal to != not equal to between between two specified values diff difference between two values auto-filling analysis of various adr reporting forms indicates that the majority of information requested can be obtained from the healthcare organization’s electronic health databases. this includes patient demographics and disease history, reporter identification, and general information regarding medication. by developing a system that automatically retrieves the information and fills them in, we can reduce the amount of work required by the reporters. as a result, the professionals would become more willing to file an adr report. we have implemented the auto-filling property using the fda’s spontaneous reporting form. the user of our prototype system is provided with a web-based interface through which he/she can login the reporting system. after that, the user is taken to a new page and linked with his/her assistance agent. for a new user of the system, he/she needs to apply for an account and then a new unique assistant agent will be created for him/her by the system. note that an agent keeps running in the background unless it is deleted. to report an adr, the user enters two identification numbers which identify the patient and medication suspected (i.e., patient_id and medication_id). from there the assistant agent will retrieve all the information needed to fill out the fda form except the date of event and a description of the event. this is a significant reduction in the amount of information the reporter is required to fill out. the system will then return to the user a form with all of the information filled out so that the user can review the form before submitting it. system implementation the implementation of the adr reporting system follows the model-view-controller (mvc) pattern as shown in figure 2 [26]. the model consists of the intelligent agents of the system. the view defines the presentation layer of an application which consists of web pages in our system. the controller manages the flow of the system, and this work is done by servlets. a servlet is a small java program that receives requests from web clients, usually across the hypertext transfer protocol (http). the requests can be forwarded to the model, which may trigger databased analysis. the analysis results will be returned to the servlet and then be forwarded to the view for presentation. the data store in figure 2 represents the various electronic health http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi databases in our system. since we use a couple of tools that are implemented in java, we build the system using the java programming language. figure 2: block diagram of mvc model web-based interface we implemented our web interface using apache tomcat, an open source software implementation of the java servlet and javaserver pages (jsps) technologies. tomcat is used as both the web server and the servlet and jsp engine for our web-based interface. jsps allow us to embed basic java code in html pages, while servlets do the processing for the dynamic web pages of our system. netbean was used as the development environment for developing the whole system including jsps, servlets, intelligent agents, and interface to databases. intelligent agents agent-related implementation issues include agent creation, communication, interoperability, and service registry. we found that the java agent development environment (jade [27]), one of the agent system development software packages, could readily handle them. we utilized it to implement these aspects of the intelligent agents in our reporting system. jade is an integrated tool suite for constructing, managing, and testing intelligent software agents. it fully complies with the foundation for intelligent physical agents (fipa) specifications and adopts fipa acl as agent communication language. the fipa standards allow agents effectively interoperate with each other. jade provides both white-page and yellow-page services for agent management. these features free us from many of the implementation details of developing and managing agents, and allow us to focus our time and energy on developing their behaviors and interactions. implementation of rule-adding framework as explained earlier, each rule in the rule-based algorithm is formally represented by its code which will be automatically translated into a sql statement. for example, the code for question “is the patient greater than 65 years old?” is “patient table|age|>|65|1”. it would be translated into a sql statement “select pid, age from `demography table` where age>65”. the patient identifier (i.e., pid) is automatically added to the statement and it is known from the context. that is, when a healthcare professional intends to file an adr report, he/she must identify the patient relevant to the report so that his/her assistant agent can analyze the patient’s records using the rule-based algorithm. this sql query would return each row in the database that contains the desired patient’s id and an age value greater than 65. if a row was correctly returned, we know the patient is older than 65 years, so the appropriate score should be assigned. http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi we implemented web pages for the rule-adding framework through which users can edit existing rules or add new ones based on their medical expertise. results experiment data to test our adr reporting system, we created four relational mysql databases (i.e., pharmacy database, administration database, laboratory database, and patient medical record database), each of which contains certain types of data. the creation of the databases were based on real data from the fda’s adverse event reporting system (aers) and our past experience in working on real health datasets retrieved from the veterans affairs medical center in detroit, michigan. the aers is a database that contains approximately 4.5 million adr reports, with approximately 100,000 additional reports released each quarter. the aers data is freely available in plaintext format on the fda’s website. when importing the aers raw data into our databases, we made some changes. for example, we standardized ages by converting from days, weeks, months, or decades into years. we also pruned out patients missing any data regarding drug name, or therapy start/end dates. as a result, we obtained 801,623 patient cases in our databases based on the aers data released in the last two years. experiment results using the above data, we tested the two decision-making strategies (i.e., case-based analysis using the rule-based algorithm and statistical analysis using rr) imbedded in our system, the scalability of the association mining algorithm as well as how indexes affect the performance of the algorithm. the experiments were done on a pc with a 3.4ghz intel i7-3770 processor and 8 gb of ram, running windows 7. we first examined the degrees of agreement between the causality categories (i.e., “definite”, “likely”, “possible”, and “doubtful”) assigned by the rule-based algorithm and those by an experienced physician. to do this, we randomly selected 100 patient cases, within each of which a distinctive drug-adr pair was reported. according to the hipaa (health insurance portability and accountability act) privacy regulations, a healthcare professional can only access the data of his/her own patients. we assume that the selected patients are associated with a particular healthcare professional. these 100 patients were used to validate the assignment of causal link strengths by the proposed rule-based algorithm. since our patient data were derived from fda’s spontaneous reports, the reported drug and potential adr are known for each patient case. the algorithm was used to identify and assign likelihoods of causal associations between the drug (cause) and adr (effect) for the 100 patients. to provide preliminary validation for the capacity of the rule-based algorithm, the physician on our project team participated in the experiment. he was provided a general overview of the objectives of the study and was asked to review each of the 100 patient cases and make a judgment regarding the likelihood of a significant causal association between each drug-adr pair. to make the following agreement assessment easier, both the rule-based algorithm and the physician assigned a numerical number between 1 and 4 based on the strength of the perceived causality, where 4 means “definite causal relationship,” 3 is “likely casual relationship,” 2 indicates “possible causal relationship,” and 1 stands for “doubtful causal relationship. http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi because scoring of the causal association was based on ordinal data, we utilized the weighted kappa statistic [28,29] to estimate the levels of agreement between the rule-based algorithm and the physician. the kappa coefficient is an estimate of the agreement between two raters after chance agreement is controlled. kappa scores range between 1 (complete agreement) and 0. because there are no “absolute” interpretations of the kappa coefficients, experts have offered opinions regarding interpretations of agreement. landis and koch [30] suggest that for values of kappa greater than 0.75, there is excellent agreement. for values less than 0.4, there is poor agreement, and for values between 0.40 and 0.75, there is fair to good agreement. the calculated kappa between the algorithm and the physician is 0.894. this coefficient suggest excellent agreement between the proposed algorithm and the physician. we computed the 95% confidence intervals using both the asyptotic formula and the jackknife method. the results of these two methods agreed to the second decimal as shown in table 4. table 4: 95% confidence intervals for weighted kappa coefficients from asymptotic formula and jackknife method kappa coefficient asymptotic 95% confidence interval jackknife 95% confidence interval physician vs. rule-based algorithm 0.894 (0.837, 0.952) (0.838, 0.954) we also examined the consistence between the case-based analysis using the rule-based algorithm and the statistical analysis using rr. for each drug-adr pair in the 100 patients, its rr, 2χ and a were calculated based on all the 801,623 patient cases. whether a pair represents an adr signal is determined by the criteria ( 2rr > ; 2 4χ > ; 3a ≥ ) established earlier. table 5 gives the number of drug-adr pairs in each causality category based on the rule-based algorithm as well as how many pairs in each category represent adr signals according to the statistical analysis criteria. one can see that, among the 100 drug-adr pairs in the selected patients, 68 of them represent adr signals that are confirmed by the statistical approach. in addition, if a pair exhibits a stronger causality according to the case-based analysis, it more likely represents a signal established by the statistical analysis. table 5: the number of drug-adr pairs in each causality category based on the rule-based algorithm as well as the number pairs (in each category) that represent adr signals according to the statistical analysis criteria “definite” “likely” “possible” “doubtful” total number of pairs in each category based on the rulebased algorithm 18 43 27 12 100 number of pairs that represent adr signals according to the statistical analysis. 17 34 15 2 68 percentage of pairs that represent adr signals 94.4% 79.1% 55.6% 16.7% n/a http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi since our association mining algorithm is sql-based its performance can be greatly affected by database indexes. in addition, the number of database connections can also affect the performance of a sql-based algorithm. to query data, one approach is to create a connection to the related database for each sql statement and then close the connection after the query. another approach is to create a single connection for each database that will be used by all the sql statements in the algorithm. we tested how different combinations of these two factors (i.e., index and number of connections) affect the performance of our algorithm. the experiment data contains 5,281 drugs and 7,943 reactions. since the execution time of the program without indexes is very long we randomly chose 100 drugs and 100 reactions for this experiment. that is, 10,000 (100x100) association rules were evaluated by calculating their 𝑅𝑅, 2χ and a values. the experiment results are provided in table 6. as expected, the results show that indexes dramatically affect the overall performance of our sql-based algorithm. the number of connections has less effect on the performance. however, if indexes are set properly, the time consumed by making a lot of connections are still relatively high (see the last two columns of table 6). note that a user of the system normally checks one drug-reaction pair each time, which takes a very small amount of time (comparable to 1 millisecond with indexes and one connection for each database). in addition, the data mining agent can be configured to automatically run the algorithm after every certain period of time (e.g., one month) after new data are entered into the database. then, a query of a pair would be simply to check a table that contains the 𝑅𝑅, 2χ and a values for each pair. table 6: execution time of the association mining algorithm under different experiment conditions experiment conditions no indexes, one connection for each statement no indexes, one connection for each database with indexes, one connection for each statement with indexes, one connection for each database execution time (seconds) 7935 7793 45 7.2 as indexes are so important for reducing the execution time of the algorithm, we decided to test how different index storage types affect the performance using the same dataset. the mysql utilized in this study supports two index types: hash and btree. note that when a new index type is applied to the database, the first run of the program would take much longer time than the following runs since the computer needs time to build the indexes and store them into a new data structure. we examined the execution time for the first run of the program and the average time for the next 10 runs with each index type. the results are presented in table 7. the table indicates no significant difference of the execution time when using different index types. table 7: execution time of the association mining algorithm under different index storage types index storage type hash btree execution time for the first run 34.1 34.8 http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi average execution time for the next 10 runs 7.3 7.2 as each health organization may have a lot of patient data, the scalability of our sql-based association mining algorithm is very important. we tested the scalability of algorithm 1 in terms of the size of the data. we started with 100,000 patient cases, and then added additional 100,000 cases each time the program was executed. the execution time against the number of patient cases was plotted as shown in figure 3. one can see that our algorithm is scalable in terms of the size of the patient data. figure 3: scalability of the association mining algorithm in terms of the number of patient cases discussion the proposed rule-based algorithm can be used to assess the degree of association between a drug and a potential adr within a single patient case. however, even if the causality category assigned by the algorithm is “likely” or “definite”, that may not represent real causal association due to the complexity of the adr problem. for example, a syndrome or an adverse effect may be caused by various other conditions (e.g., underlying diseases), even though the syndrome and the associated drug satisfy one or more rules in the algorithm given the patient data. in addition, a reaction of a drug may indirectly cause an adverse effect. in this sense, the rule-based algorithm should not be used for the purpose of accurately establishing the causal relationship between a drug and an adr. instead, it is an assistant tool that would encourage healthcare professionals to file spontaneous reports of suspected adrs. the statistical analysis results given by the proposed data mining method provide another perspective for healthcare professionals to examine the drug-adr pair of interest. please note that some false adr signals may be generated due to the nature of the adr problem and the complication of medical data. first, adrs normally occur very infrequently. thus, the adverse effects caused by adrs can be hidden by other high frequency symptoms. second, frequently prescribed drugs are often associated with common symptoms by coincidence. these data noises could reduce the effectiveness of any association mining methods. third, the electronic data may be incomplete and has potential bias and even errors [31]. for example, some potential adverse effects may not be recorded in a health database if the results caused by drug reactions are not serious. please note that, in post-marketing surveillance, data mining is primarily used to generate adr signals that will be subject to further analysis. to establish true causal associations, other labor-intensive approaches such as epidemiology study, case review, and http://ojphi.org/ a multi-agent system for reporting suspected adverse drug reactions 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi interpretation by drug safety professionals experienced in the nuances of pharmacoepidemiology and clinical medicine are often used [32]. the rule-based algorithm and the data mining method are complementary to each other. while the former examines the causal association between a drug and an adr based on the data within a single patient case, the latter assesses the association based on all the data from a statistical perspective. they can be used together to assist healthcare professionals to decide whether a drug-adr pair has causal association. note that even if a signal generated by the data mining method represents true causal association, the relevant adr is not necessary to occur on a patient who has taken the drug of interest due to the variation of human bodies. hence, the rulebased algorithm has to be checked when a healthcare professional decides whether a spontaneous report should be filed for a particular patient case. in addition, when there exist few patient cases in which the drug and adr coexist, the data mining method become less reliable. the auto-filling feature of the proposed multi-agent system can improve the quality and consistency of the submitted adr reports. by looking at fda’s spontaneous reports, we found that there existed a lot of typo-errors and some information such as age, gender, drug dose, and so forth are often missing. since our system can automatically fill out the major portion of the reporting form, these issues can be effectively avoided. our system can also be easily adapted to improve other drug safety-related practices in a health organization. for example, it could help physicians make more appropriate choices in drug prescription and avoid preventable adrs. it could also help make formulary decisions. to ensure quality and cost effectiveness of patient care, it has been a common practice in the past decades that healthcare organizations (e.g., hospitals) establish their formularies [33]. the basic premise is that within a given drug class, there is significant overlap and duplication relative to efficacy and safety. a single drug that is representative of a therapeutic class can meet the needs of the vast majority of patients. selecting only one drug streamlines inventory, purchasing and maintenance of databases, diminishes the likelihood of medication errors, and creates a competitive environment for pricing. a major barrier to effective formulary decision making is the lack of sufficient published data on the safety of drugs, particularly recently marketed drugs. our system could provide continuous, active surveillance and timely identification of potential safety issues following the introduction of a new drug to a formulary. such a system could lead to safer drug use policy, more cost-effective formulary decisions, better healthcare, and earlier detection of adrs. conclusion we have developed an agent-based adr reporting system that can potentially improve the spontaneous reporting rate by providing decision-making assistance and automatically feeding most data into the reporting form. this system will be practically tested in the frontier behavioral health in spokane, washington. references 1. anonymous. 1969. international drug monitoring. the role of the hospital. world health organ tech rep ser. 425, 5-24. pubmed http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=4981079&dopt=abstract a multi-agent system for reporting suspected adverse drug reactions 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e193, 2014 ojphi 2. bond ca, raehl cl. 2006. 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agents implementation of rule-adding framework results experiment data experiment results discussion conclusion references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the global biosurveillance portal: biosurveillance for the department of defense janelle a. anderson*, c. nicole rosenzweig, jason roos and brandon flores joint program executive office for chemical and biological defense, aberdeen proving ground, md, usa objective this presentation aims to describe the development, initial use, and future directions of the biosurveillance portal. introduction the 2012 national strategy for biosurveillance1 calls for improved integration, synchronization, and coordination of national biosurveillance activities and acknowledges the benefits of collective knowledge through sharing and receiving of biosurveillance information via strengthened partnerships amongst international, federal, state, local, tribal, territorial, private sector, nongovernmental, academic and other participants. to assist in meeting these goals and goals specifically called out by united states (u.s.) military and international partners, the joint program executive office for chemical and biological defense began developing a software suite to support biosurveillance needs. in october 2014, the biosurveillance portal (bsp) will be deployed to support biosurveillance operations in the korean theater. in october 2016, the bsp will be available globally. methods system development the bsp is being developed on the ozone widget framework (owf) architecture, which will allow users to organize and use multiple web applications in a single window. the bsp will be a system of systems and will pull in capabilities and data streams that are already available across the department of defense (dod), other government agencies and industry. for example, there is a plan in place for data and information to be ingested from the armed forces health surveillance center, the u.s. army public health command, and the centers for disease control and prevention, as well as from feeds from the world health organization, and the program for monitoring emerging diseases. many other government systems are also being developed on the owf, which allows all of these systems to easily integrate, as needed, and allows the u.s. government to produce multiple types of systems in the most cost efficient manner without duplicating efforts. bsp developers are using an agile development approach with a cycle of one month sprints, and three month capability drops. each capability drop delivery is followed up with a user feedback event where users test and evaluate the capabilities that have been delivered. requirements the u.s. special operations command (ussocom) recognized that no current capability exists to support the national strategy’s intent. in addition, ussocom requires a biosurveillance system to close information gaps that presently delay decision making at the tactical, operational and strategic levels, and inhibit effective planning and response. the korean bsp was recognized by ussocom as the potential tool that following modifications could be used to meet their biosurveillance needs. ussocom biosurveillance data and information requirements as well as system requirements were laid out in a requirements document that was validated in may 2014. for each data requirement potential data providers have been identified from offices within the dod, other federal government agencies, and private organizations. bsp developers will work with these data providers to determine how their data will be ingested by the bsp with data agreements put in place as needed. it is anticipated that other u.s. combatant commands (ccmds) have biosurveillance needs that are similar to those of ussocom. because of this, bsp developers are also engaging key stakeholders at other ccmds in order to document and incorporate their requirements into the bsp. results the bsp was used as the primary communication tool during a trilateral whole of government biological event exercise involving the u.s., republic of korea, and australia. the first operational release will represent the first fully integrated dod network accredited capability set and is planned for deployment in october 2014. conclusions the bsp has already proven itself as an effective communication tool that can be used during biological event exercises. as the bsp matures it will provide information sharing across the dod biosurveillance community of interest, other u.s. government agencies, and foreign government agencies to promote a “whole of government” biosurveillance capability. ultimately, the bsp will facilitate collaboration, communication, information sharing, and provide a centralization of biosurveillance resources in support of the detection, management and mitigation of man-made and naturally occurring events. keywords biosurveillance; department of defense; communication references 1. white house. (2012). national strategy for biosurveillance. retrieved from http://www.whitehouse.gov/sites/default/files/national_ strategy_for_biosurveillance_july_2012.pdf *janelle a. anderson e-mail: janelle.a.anderson.civ@mail.mil online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e63, 201 ojphi-06-e166.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 127 (page number not for citation purposes) isds 2013 conference abstracts syndromic surveillance of acute liver failure in emergency departments (france, 2010-2012) corinne pioche1, christine larsen1, céline caserio-schonemann2 and vanina héraudbousquet*2 1french institute for public health surveillance (invs), 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for the detection of social epidemics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e6, 2020 1 ojphi opportunities and challenges for developing syndromic surveillance systems for the detection of social epidemics david scales1* 1joan and sanford i. weill medical college of cornell university abstract this commentary explores the potential and challenges of developing syndromic surveillance systems with the ability to more rapidly detect epidemics of addiction, poverty, housing instability, food insecurity, social isolation and other social determinants of health (sdoh). epidemiologists tracking sdoh heavily rely on expensive government surveys released annually, delaying for months if not years the timely detection of social epidemics, defined as sudden, rapid or unexpected changes in social determinants of population health. conversely, infectious disease syndromic surveillance is an effective early warning tool for epidemic diseases using various types of non-traditional epidemiological data from emergency room chief complaints to search query data. based on such experience, novel social syndromic surveillance systems for early detection of social epidemics with health implications are not only possible but necessary. challenges to their widespread implementation include incorporating disparate proprietary data sources and database integration. significantly more resources are critically needed to address these barriers to allow for accessing, integrating and rapidly analyzing appropriate data streams to make syndromic surveillance for social determinants of health widely available to public health professionals. key words: syndromic surveillance; social determinants of health; social epidemics; public health informatics *correspondence: das9289@med.cornell.edu doi: 10.5210/ojphi.v12i1.10579 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the past two decades have seen parallel but separate developments of renewed interest in social epidemiology—the social, economic, cultural and political forces that impact, and at times determine, patient health outcomes [1]—and syndromic surveillance for infectious diseases, defined as an investigational approach where staff, assisted by automated data acquisition and statistical alerts, monitor disease indicators in real-time or near real-time to detect outbreaks earlier than would otherwise be possible with traditional methods (adapted from [2]). mailto:das9289@med.cornell.edu opportunities and challenges for developing syndromic surveillance systems for the detection of social epidemics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e6, 2020 2 ojphi decades of research have convincingly demonstrated how the social environments in which patients live are risk factors that predict disease risk and outcomes [3]. increasingly experts recommended incorporating ways to screen for and address social determinants of health (sdoh) within clinical care delivery. a number of health systems functioning in an accountable care model, such as some insurance companies and medicaid in certain states, have embarked on aligning the various community stakeholders required to effectively pursue this path, and a number of these initiatives are actively being rolled out and evaluated [4-6]. despite these welcome advances, there has been less focus on the necessary surveillance systems that can improve epidemiological monitoring of sdoh (for example, see [7]), though leveraging social data to benefit communities does appear to be on the rise [8]. specifically, if we view individual sdoh as contributors to health issues, then we should also invest in epidemiological systems that provide insight into how sdoh prevalence in a given area changes over time plus sentinel systems for early detection of rapid changes as harbingers of socioeconomic crises. we are currently far from this epidemiological ideal. sdoh surveillance data lags months to years behind clinical surveillance data, creating a relatively static retrospective tableau of prevalence that misrepresents the real-time environment. this causes significant delays in both detection and response to rapid changes in sdoh, like epidemics of housing instability or food insecurity. this paper will examine a number of challenges to translating the success of syndromic surveillance to the detection of social epidemics, defined as sudden, rapid or unexpected changes in social determinants of population health (definition derived from [9]). these challenges are surmountable, however, and are likely within reach for teams developing integrated frameworks with local partners for sdoh screening at the point of care. with foresight and planning, such partnerships can help ensure the data infrastructure to make future sdoh syndromic surveillance possible as funding becomes more available. the pressing need for more rapid detection of social epidemics data sources measuring sdoh are not released with sufficient frequency or local granularity to provide much insight into real-time trends. yet, as risk-based payment models increasingly require addressing sdoh issues, such interventions must detect and respond to social epidemics as early as possible for the interventions to be economically viable. there is therefore a pressing need for rapid detection via cheaper mechanisms than costly annual surveys. the maximum release frequency of much sociological, demographic and economic data is yearly, a rate far too infrequent to identify trends or detect rapid changes in local sdoh. this is the case for census american community survey (acs) estimates, the behavioral risk factor surveillance system (brfss), an annual telephone survey collecting state data about u.s. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services, and the national health and nutrition examination survey (nhanes), which assesses health and nutritional status by combining interviews and physical examinations. such annual data releases are further limited by their large geographies, like entire cities or, at best, county-level data. the sub-county, micro-level detail, such as neighborhood, zip code, census tract opportunities and challenges for developing syndromic surveillance systems for the detection of social epidemics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e6, 2020 3 ojphi or block group, that would be required to signal local emerging trends may be lost as geographical data is aggregated to reduce sampling error. acs data releases provide more geographic granularity at the expense of precision over time, as, for example, sub-county-level geographies are only widely released to cover a 5-year timespan. a notable exception is us unemployment estimates, released monthly at various geographic levels as small as counties or cities of 25,000 people, and derived from the costly current population survey done by the bureau of labor and statistics. social syndromic systems offer the potential of early detection through less costly mechanisms. these systems, which have been developed at numerous public health departments in the us [10] and in countries around the world since [11], have proven their utility by detecting epidemics of certain diseases much sooner than traditional reporting methods. syndromic surveillance systems have been successfully developed for various epidemic diseases, relying on varied data sources including emergency department chief complaints [12,13], ambulance dispatch records [14], hospital admission diagnosis [13], outpatient clinic visits [15], school/work absenteeism [16], over the counter medication sales [17] and web search queries [18]. there is high potential to use similar tools to detect social epidemics more quickly and at less cost. social scientists have successfully used syndromic surveillance principles to retrospectively detect behaviors that are otherwise challenging to track, like consumer trends [19], tax avoidance [20], and e-cigarette uptake [21]. others have found links to psychological illness [22] and unemployment [23]. while some of these examples demonstrate success at large geographic scales, such as stateor nationwide, more granular local data has also shown promise that these systems can be developed at smaller scales. indeed, initial studies suggest that social service-related query data can be used for effective syndromic surveillance at local levels. for example, 311 data was found to be a better predictor of local crime rates than the “broken windows” theory of crime [24]. though social scientists continue to debate the challenges and opportunities of 311 data [25], it has been shown to be useful especially when combined with other locally contextualized data [26]. neighborhood disorder has also been found to correlate to geographic-specific data from 911 dispatches [27]. integrating and validating existing social data streams therefore has significant potential to provide real time surveillance of sdoh. such integration is an important complement to scholarship proposing three applications for the integration of sdoh into population health analytics: individual patient care, predictive risk modeling and community engagement [28]. individual patients may be reluctant to offer potentially embarrassing socioeconomic information, like housing insecurity, to providers doing sdoh screening at the point of care [29]. more rapid and localized social syndromic surveillance can help inform sdoh screening by providing a rough pre-test probability in patient encounters where privacy and disclosure issues might inhibit point of care data collection. indeed, localized surveillance data has successfully been shown to accurately revise pre-test probability in group a streptococcus pharyngitis, where higher proportions of local positive throat cultures change the threshold at which a clinician should offer empiric antibiotics [30]. opportunities and challenges for developing syndromic surveillance systems for the detection of social epidemics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e6, 2020 4 ojphi at a community level, an early warning system based on sdoh can assist community advocates and clinical partners in mobilizing for increased resources and response well before such epidemics reveal themselves in data released less frequently. similarly, data aggregated at flexible geographies can be helpful in raising hypotheses at the community level, allowing discussion of socioeconomic issues and to design early interventions. moreover, risk-based payment models will likely push payees toward early detection and intervention of social epidemics related to sdoh to help prevent the accumulation of problems that later become both more difficult and expensive to address. despite the successes in infectious disease and bioterrorism epidemiology, there two key interrelated challenges to developing and deploying syndromic surveillance systems for sdoh: data challenges and associated costs. data challenges there are multiple potential data sources for sdoh syndromic surveillance, a number of which are publicly available. for example, google search query data [31], public transportation or bike share program ridership data [32,33], 311 data and other government administrative data [34] are often publicly available. social service-related query data offers probably the highest potential but presents a significant risk because proprietary data, held by private entities who can change their data access policies at will, already presents a challenge for infectious disease syndromic surveillance [35]. still, communities and clinics are partnering with social resource databases to help link patients to resources during clinical sdoh screening, making these entities lynchpins in the framework to integrate sdoh into population health analytics [28]. as a result, query data from social resource databases, like purple binders, aunt bertha, health leads or 2-1-1 have significant potential if leveraged into a social syndromic surveillance system. for example, the united way’s 2-1-1 program [36] has a mandate to provide financial, domestic, health or disaster-related information to callers free of charge. with over 240 systems in north america, the proliferation of city and state-wide 211s and other similar resource databases has created a wealth of data that can be used to combine the speed of syndromic surveillance with social epidemiology in an effort to provide a much more rapid indication of social epidemics. the community information exchange in san diego, which leverages 2-1-1 services, is an innovative example of the cross-sector collaboration required to leverage sufficient data for a sdoh surveillance system, and one that could, with modest additional effort, leverage their existing system for social epidemic detection [37]. despite the applicability of such query data for detecting or predicting social epidemics, research is lacking. a major challenge has been that many of these data remain hidden in proprietary systems, limiting empirical studies. additionally, different social resource database companies often outsource their database to different management companies with different architecture, making it time consuming and expensive to integrate data from various sources into one system. for example, different 2-1-1 systems contract with a handful of different private database opportunities and challenges for developing syndromic surveillance systems for the detection of social epidemics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e6, 2020 5 ojphi companies, making it challenging to integrate data for joint projects. the alliance of information and referral systems (airs) has set guidelines for resource database structures, which should facilitate data integration, but the current incentives for such private enterprises do not yet facilitate data sharing for public health or research purposes [38]. data quality can also be an issue, especially when working with small geographies. infectious disease syndromic surveillance presents some important examples. for example, google flu trends, a tracking system designed to predict influenza, was initially heralded as a success before it was found to have significant flaws and was suspended [39]. however, these systems are responsive to improvements [40]. google flu trend’s problem may stem from using such generalized query data, known as a ‘feature selection’ problem [41] that requires researchers to assume a query for “influenza” means that someone may be ill rather than something else, like someone is doing a school report. the more specialized users and queries in resource databases avoids the “feature selection” problem, which can improve signal-to-noise ratios, suggesting that syndromic surveillance will be most successful when appropriate data sources are be found [42]. moreover, concerns first raised about screening for sdoh also apply to syndromic surveillance, such as concerns about increasing stigma, the ecological fallacy, and the potential to reinforce inequalities [29,43]. cost syndromic surveillance for sdoh faces at least two cost-related barriers. first, there is the cost to developing the system, including gathering the rights to relevant data and integrating and harmonizing disparate data sources into a unified platform. second, there are potential costs to responding to the signals detected to the system that currently remain unknown. syndromic surveillance systems for infectious diseases often require access to proprietary data, and similar systems for sdoh will likely face the same challenge. for example, data from queries of resource databases like purple binder, aunt bertha, healthleads or the united way’s 2-1-1 has high potential to form a backbone in syndromic surveillance of social epidemics. however, most resource databases charge tens of thousands of dollars per clinic per year for their services, regardless of whether they are private or non-profit businesses. gathering search query or use data from these organizations is likely to be expensive. as medical providers and payers, as well as public health departments, take an increasing interest in sdoh surveillance, the cost of integrating disparate data sources to inform the project has been well documented [28]. integrating data sources with particular relevance for syndromic surveillance will have a cost, but these costs can be kept low if these efforts can dovetail with the current efforts to build sdoh surveillance networks and leverage existing syndromic surveillance systems at public health departments. the costs associated with epidemic response presents a different challenge. the uspstf does not recommend clinical screening for depression unless there are sufficient regional resources to enable treatment [44]. the concern is that it may be unethical for a physician to screen for a disease opportunities and challenges for developing syndromic surveillance systems for the detection of social epidemics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e6, 2020 6 ojphi that they cannot treat. similarly, creating syndromic surveillance systems for social determinants of health presents a similar quandary of who bears the ethical burden of responding to syndromic alerts, and what budgetary pressure will that put on already strained clinical and public health systems [4]. despite the budgetary constraints, concerns are overblown. we do not yet know enough about social epidemics to preclude studying them. much more empirical work needs to be done to set activation thresholds and discern which types of social epidemics are most amenable to detection through these systems and at which geographic levels – limitations that will be particular to the different data sets used to construct the time series. such detection thresholds cannot be discerned in advance but must be empirically tested and revised in real time based on surveillance objectives, practical budgetary constraints, geographic limitations of the data and ethical concerns. but learning lessons from infectious disease syndromic surveillance, such systems were similarly uncertain in their ability to accurately detect epidemic and bioterrorism events, with similar questions about alarm thresholds and response capacity [45]. public health officials overcame these challenges by recognizing that the cost of responding late to an epidemic were likely higher than the cost of setting up early detection and response systems. now such systems are nimble and flexible, allowing for near real-time algorithmic adjustments based on surveillance objectives (i.e. novel disease, bioterrorism, or natural disaster) and signal-to-noise thresholds [46,47]. however, similar analyses have not yet been undertaken for sdoh [48]. now syndromic surveillance is an integral component of public health departments, with constant maintenance and testing to ensure data quality, integration, appropriate alarm thresholds and integration of new data sources – an iterative strategy that should guide the approach for sdoh [10]. the way forward clearly all of these challenges are interrelated. a lack of easily accessible data makes it difficult for researchers to combine sufficient time series data for empirical testing. sufficient funds will likely overcome those barriers, but without promising empirical data, it is difficult to procure sufficient funds to surmount the data interoperability and ownership issues. in these settings, crosssector collaboration has already shown much promise, but it needs much more support [49]. with current financial support, progress is likely to be slow, as effective research can only be carried out in environments with data that can be integrated easily into existing database systems. enough evidence already exists from the syndromic surveillance systems developed for infectious diseases and bioterrorism to suggest the capital required to build syndromic surveillance systems designed to rapidly detect changes in sdoh will be worth the investment. therefore funders with mandates to address sdoh should consider early investments in creating data-sharing infrastructure and collaborations. for screening for sdoh is not enough; we can do more to detect social epidemics as they are 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https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25367992&dopt=abstract https://doi.org/10.1377/hlthaff.2014.0645 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts detecting outbreaks in time-series data with recentmax dave carter* and joel d. martin national research council canada, ottawa, on, canada objective to develop an algorithm for detecting outbreaks of typical transmissible diseases in time series data that offers better sensitivity and specificity than the cdc ears c1/c2/c3 algorithms while offering much better noise handling performance. introduction we implemented the cdc ears algorithms in our dadar (data analysis, detection, and response) situational awareness platform. we encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu. we analyzed several flu outbreaks observed in our data, including the h1n1 outbreaks in 2009, and noted that, using the c1 algorithm, even with our adjustable alerting thresholds, there was an uncomfortable number of false alarms in the noisy steady-state data, when the number of reported cases of flu-like symptoms was less than five per day. we developed an algorithm, recentmax, that could offer better performance in analyzing our flu data. methods we developed the recentmax algorithm based on a simple observation: that a good alert for an outbreak tends to be on days (or, more generally, in time slices) where the number of cases today is noticeably larger than on any recent day. for example, intuitively, if the number of cases of flu today is more than twice the greatest number of cases observed on any single day in the last three weeks, it seems reasonable to generate an alert. recentmax takes two parameters: a window of recent history (e.g., ten days), and a theshold factor (e.g. 1.5 ), and generates alerts when the data observed for the current time slice is greater than the maximum observed in the history by the theshold factor, subject to the confidence interval (a tuneable probability of false positive threshold, typically set to 95% or higher, assuming a gaussian distribution around the threshold value). results our recentmax algorithm does a commendable job alerting at the onset of the observed outbreaks. furthermore, recentmax does a better job handing the noisy steady-state behaviour outside of outbreaks, offering intuitive alerts when the number of reported cases starts increasing meaningfully. the h1n1 outbreak of the fall of 2009 is illustrated using real-world data in the accompanying graphs; red bars highlight days on which cdc ears c1 would generate alerts (at 95% and 99% confidence intervals) and days on which recentmax would generate alerts given a 10-day history window and a threshold factor of 1.5 . an interesting side-benefit of recentmax is that, as an outbreak spreads and the number of cases increases somewhat linearly, the algorithm is less likely to generate alerts; that is to say, once an outbreak has been observed and many alerts have been generated (and presumably validated), the algorithm tends to stop alerting (unless the number of cases is increasing exponentially). thus, once public health personnel have been notified of an outbreak in its early phases, the algorithm tends to not keep alerting, thus not telling recipients what they already know. recentmax avoids problems occasionally observed in algorithms built around a decay series. recentmax examines the recent history without providing extra weight to more recent data, and is thus unaffected by noise in the immediately preceding time slice that can confuse some algorithms unduly (e.g., when data increases steadily for six consecutive days and then drops to zero on the seventh as the facility is closed for a holiday). conclusions recentmax offers a compelling alternative to the cdc ears algorithms for detecting outbreaks of transmissible disease in timeseries data. flu outbreak with alerts generated by cdc ears c1 at 95% confidence threshold 2009 h1h1 outbreak with alerts generated by cdc ears c1 at 99% confidence threshold 2009 h1h1 outbreak with alerts generated by recentmax keywords aberration detection; algorithm; outbreak detection; surveillance; situational awareness *dave carter e-mail: david.carter@cnrc-nrc.gc.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e113, 201 ojphi-06-e58.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 35 (page number not for citation purposes) isds 2013 conference abstracts overview of the biosense 2.0 system architecture kelley chester* rti international, alpharetta, ga, usa � �� �� �� � � �� �� �� � objective �������� ����� � ���������� � ������� ����������������������� � ��� � ��� ����� �� � ����� �������������� introduction ���������������� � �� ������� ��� � �������� ����������������� ����� � ���������� � ��� �� ������� ��� � ������ ���!��� ����!����� ��� �� �������� ���� �������� ��� � �� ����� ������������ �� ���� ���!� � �������"� ���!�� ��� �!���������!����� �������� ��������������������� �������� �� ������ ���������� ����� ���#�� ���� ���$�������#�� ���� ����%����� ����&#$#'����� �������� ����� �� ������ �� �� �� (� ��� ���� ��������������� �� � ������ ����� ����� ��� ��� ���������� �� � ��������� 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�� � ��� �� ��� ���������������� ������� �� �� ��������������� ���������� ������ ������� � ��� �� � �������� � ���� ���� �� �� ��������� ������������������� ������� ��� ���� �� ���� ��������� � ��� ������������� ����������������� �� ������������ ������� ���������� ��� �������������� ���������� ��� ������ �������� � ������ ������ ���� ������ ������� �� ��� ������ � ����������� �� ����� ���� �� ������ ���� conclusions ,���� ������ �-��������� ������������ ��������������� � ������ ���� ��� �� ����������������������� �������.��������� ���//����� ��� �������� ������ ����� ��������������� �������� �������� ����� �� ������ ���������������������� � �������������������� �� ��������� �� ������ ���������� ��������� ���� �� �������� ��� ��0������� �������� � � ������������ ����������� ����� ��������������� ����� �� ������ �� ������������� �� � ��������������� ��� ������������� � ������������ ��������� � ���!����� ���� �� �� ������ �������� ���!� ������ �������� �� ����� ���������� 1����� ���� �� ������ � ��������������������� � ������ ����� ������� � � ����� � ����� �!������������� ��0������� �������� � �� ���2��� ��� ���� ��������������� �2������ ����� ��� ���2�������� �� �� � �� � ��� ��������� ������������������ ��� ������3� ��� � ����� �� ������������� ��� ����������� � ��������� � ���� keywords �������� ����������� �2�%���� �4��� ��1� ���� � �2�%���� �4��� �� %�� � � *kelley chester e-mail: kchester@rti.org� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e58, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts documenting the missed opportunity period for influenza vaccination in office-based settings joel greenspan*1 and silvia valkova2 1martin, blanck & associates, atlanta, ga, usa; 2ims health government solutions, fairfax, va, usa objective this paper describes the results of formative research to develop a new metric for public health officials to use in near-real-time tracking of the weekly participation of office-based providers in community influenza vaccination campaigns. introduction missed opportunities for influenza vaccination in office-based settings occur when patients (who are inclined to accept influenza vaccination if a provider recommends it) remain unvaccinated after a fall/winter healthcare visit. healthcare providers can be very influential in encouraging patients to obtain influenza vaccination, but little is known in real-time during annual campaigns of how many and what type of providers are actually giving vaccinations in office settings. many factors affect the ultimate population coverage including taking advantage of opportunities to vaccinate during medical visits. this suggests that provider vaccination behavior, if leveraged, could result in higher rates of influenza vaccine coverage. “big” healthcare data in the form of high volume streams of electronic healthcare reimbursement claims (ehrcs) can potentially be used to track influenza vaccine administration practices in office-based settings in near real-time, thus empowering public health officials to provide this feedback to practitioners and potentially modify behaviors. methods we used ehrc data from the ims health integrated data warehouse (idw) for 85+ million patients visiting office-based practices for any reason between july 2006-june 2010 in the state georgia and its core based statistical areas. data were aggregated by age group and provider specialty for each geographic area. we identified all current procedural terminology (cpt) codes used in ehrcs indicating that seasonal influenza vaccine was administered during a healthcare visit. using these data we calculated on a weekly basis the number and % of primary care providers (pcps) (fp/gp, im, ped) who vaccinated any patients with influenza vaccine (%mdvax). to examine timeliness and reliability of these measures, for any given week of service we observed the cumulative % of claims arriving at the idw daily from the end of a service week through 28 days of claims accumulation. we compared the %mdvax metric from partially accumulated claims at the end of weekly 7-day service periods with comparable metrics after 28 days of claims accumulation. results during the study period there were ~6354 active patient-care pcps in ga office–based practices that could submit ehrcs. we were able to document a weekly average of 4030 unique ga pcps (a 63% sample) whose claims could be evaluated for influenza vaccine administration (or any other procedure or condition). we determined that for any sunday through saturday service week, 58% of fp/gps, 62% of internists, and 64% of pediatricians submitted some ehrcs that were in the idw by the end of the service week. %mdvax calculated from these early submitters had an r2 = >0.99 compared with %mdvax calculated 28 days after the end of the service period for all three pcp groups. annually, in non-pandemic years, officebased pcps begin to administer seasonal influenza vaccine starting in weeks 32-37. peak weeks for %mdvax range from week 42 to 49. pediatricians have the highest %mdvax at peak and sustain high %mdvax longer than other pcps. in non-pandemic years there was a large missed opportunity window of 14-18 weeks between peak %mdvax and peak %ili among ga pcps that occurred during the winter holidays and early new year. conclusions we propose a novel metric that provides a near-real-time estimate of % of providers who are administering influenza vaccines in office-based settings. we welcome feedback from the public health community on how this metric can be used to encourage more influenza vaccinations each year, especially before influenza becomes widespread in communities. keywords infuenza vaccination; healthcare claims; ili *joel greenspan e-mail: greenspan@comcast.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e26, 2015 ojphi-06-e37.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 76 (page number not for citation purposes) isds 2013 conference abstracts electronic school absenteeism system for multiple disease surveillance in hong kong dennis ip, eric h.y. lau*, teresa so, lai-ming ho, bejamin j. cowling and gabriel leung the university of hong kong, hong kong, china � �� �� �� � � �� �� �� � objective �������� � ����� ����������� �������� 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������ ������������������ ����� ������� ����������� ����� ��������� �������������� ������� ����������� �� �� ������������ �������������� ������������ ������ ������ ����������������� ����� � ��� �� � �������������(�� �������������� ����������� ��� ������� ��� ���������� �� ���������%���� ������ ����� ���������� �������������� ��� ����� ������� ������ ������ ��������� ��������������������� �� ���� ��������������� ���������������������� ����������� ��������� �� ������ ����������� keywords � ������� ��>����� ������>�� �� � ��>��� �������� ��� ������ references .��'�� ��' (�'���� ��b=(�#� �)�(����#2(�#� ��?2(�� �"��3 �� )������ �������������� ��������� ����� ��� �� �� � ���� ������� ��(� �� �� � ���)����� ��" ������ ��3��������+,.+(�.-c--6�5� *eric h.y. lau e-mail: ehylau@hku.hk� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e37, 2014 legacy-free by 2023 a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi a demand for data to improve outcomes creates the why to move to third generation immunization information systems todd watkins1*, president, bachelors systems and industrial engineering (bsie) michael popovich1, ceo, masters of science systems engineering (msse) kristina crane1, director of product strategy and project management office, bachelor of arts (ba) 1scientific technologies corporation, 411 s. 1st phoenix, arizona 85004 abstract investments over the past two decades to collect and store immunization events established a national population health data asset. the ability to track vaccine usage and storage has increased accountability, lowered wastage, protected valuable resources, and provided the correct vaccines at the right time. sixty-four immunization registries support the current immunization ecosystem, yet all investments to date have been through state and federal funding. much of the technology supporting these registries is becoming harder to support, limiting the utilization of the data. for the most part all current systems have legacy 2nd-generation technology and architectures as their foundation. current technology investments in these national assets tend to be for systems that within the next five years will not be cost effectively sustainable with only federal, state and local funding. yet quality data is being reported by immunization providers across the health care network that is increasing exponentially through electronic data exchanges integrated within electronic health records (ehr) and pharmacy management systems (pms). this increase in high-quality patient immunization records creates opportunity to build immunization intelligence from the data. however, 2nd-generation immunization information systems (iis) limit the effective and timely use of this information. considering the increasing value of the data to public and private sectors working to close immunization care gaps in populations, supporting technology must ensure easy access. this is the first of two papers that highlights the power of these national registries and the data they contain to provide opportunity intelligence to the immunization ecosystem user community. paper one illustrates the “why” for change and the need for a truly community collaborative path forward to move from 2ndto 3rd-generation systems through partners that leverages cost sharing and common goals. the end goal is to establish new supporting technology assets that accelerate the use of data to impact vaccine preventable disease (vpd) outcomes which create a new model for public-private investments to sustain the iis national infrastructure. the second a working paper with assumptions to be tested a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi introduction “we choose to go to the moon. we choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win, and the others, too.” [1] in 1962, president kennedy established a national goal. america’s neil armstrong and buzz aldrin landed the lunar module eagle on the moon on july 20, 1969. six hours later, armstrong took his famous step, followed less than thirty minutes later by aldrin [2]. in just seven years the mission had been accomplished; with a first generation lunar lander. in 1990, the centers for disease control established a national goal to create childhood immunization tracking systems [3]. these data systems would allow pediatricians and family physicians to record in a state-based central repository the immunization histories of their patients; allowing any physician to know what a child needed on future visits. within five years this goal had been achieved. first generation childhood immunization tracking systems were being implemented in state public health departments [2]. both the manned space missions and the changes in childhood tracking systems have evolved as technology and missions changed. the u.s. has focused on space shuttle orbital missions. registries for kids have become immunization information systems (iis) for all. technology and applications have been required to keep pace in space and continue to deliver high quality solutions health information to the immunization community. the u.s. today continues to invest in technology and people to diminish the impact of vaccinepreventable disease (vpd) on populations – a public mission equally as challenging as going to into space, the moon and soon beyond. immunization technology (it) systems today are required to support increasing numbers of users. with over two decades of data contained therein they are (“a model for sustaining and investing in immunization information systems”), shares cost and investment strategies to complete the migration and create sustainable immunization systems for the future. *corresponding author: todd_watkins@stchome.com doi: 10.5210/ojphi.v11i2.9412 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:todd_watkins@stchome.com a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi poised to deliver immunization intelligence to ensure population health vaccine programs produce positive outcomes. however, the use of data and system expansion is challenged by existing iis architecture legacy designs and technology environments supporting current systems. the increasing costs to maintain, evolve to meet new vaccine recommendations, increase vaccine utilization accountability, diseases and access data in a timely fashion are leading indicators to suggest a next generation of technology is required. historical context there are sixty-four state, local and territorial iis. each is independent. each supports the immunization programs and community of users within their jurisdictions. many that began in the mid-90s used first generation designs and tools to build and implement. the technology was based on client server technology, local area networks and dial-up telecommunications capturing data content focused exclusively on childhood immunization events. as the internet advanced and became mainstream, 2nd-generation technical iis solutions evolved. public health iis vendors and state it staff moved into the dot com world for connectivity and online data input. this was generation 2 versus desktop applications in provider offices that upload immunization events in batch operations (generation 1). over two decades the nation’s iis have grown in the data that is being collected and in new features to support changing vaccine schedules, reporting requirements and expanding user communities from traditional immunization providers to pharmacists, school nurses, and first responders. from 1st to 2nd generation technology solutions, the software changes required to support these changes continued to be built on a single code base. today, these systems include features to support vaccine inventory management and ordering, electronic data exchange with emrs and pharmacy systems, patient reminder/recall, identification process and complex de-duplication algorithms to match patients and their immunization events. these systems include components for the vaccine for children (vfc) provider enrollment efforts, school nurse and day care tools that allow review of student vaccination records. current acip guidelines providing clinical decision support and enhanced security to protect data are common improvements made though out the year. each state has invested and grown their iis code base to support these features. today it makes ongoing sustainability and data quality an increasing challenge. in short, these systems and their supporting infrastructures are getting old. the public health iis is a cornerstone of population health data and is a significant information asset. the key is the ability to effectively utilize the data. existing 2nd-generation iis architectures, software, and processing technology has become a barrier to this goal. consider the technology environment we all live in today. we have seen the rapid uptake of smartphone technology and the increasing use of voice interaction. cloud environments supported a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi by private technology companies are the new normal for hardware servers. the push for electronic health records and sharing with patients is driving the private sector investments and the demand for ease-of-use, integration of predictive modeling, machine-learning, use of data for decision making, and artificial intelligence in the health care field is accelerating the need for better data. software engineers and developers have watched an even more rapid evolution of programming languages, database architectures, development tools, and engineering protocols. today’s lexicon words such as agile, microservices, serverless architecture, devops, blockchain, internet of things, quantum computing, on-demand apis, actionable analytics, pair programming, ecosystem, collaboration, social media, and full stack development are common. however, these words do not describe today’s technology and the human resources supporting public health iiss, the data contained therein, and the users connected across their networks. comments such as, “our technology is too old”; “the cost to sustain the iis technology is growing faster than budgets allow”; “the quality, reliability, and performance are diminishing”; and “we don’t have the staff needed to use the data to impact behavior” are frequently heard. yet when discussions move toward change, the most common comment heard is, “we are fine as is – our users are happy the way it works now.” when president kennedy put forth his moon mission in 1962, few believed it was possible. it was not uncommon to hear “we are fine the way our space program works now.” but it was the time for leadership. kennedy put forth and invested in a unifying mission. legacy-free by 2023: the challenge changing from legacy systems that have been operational for years to 3rd generation registries will not be economically feasible without a collaborative and common approach. the unifying mission for this change is to recognize that the intelligence derived from the data in iis is the key to influence. influence clinical providers and pharmacists to close patient immunization care gaps. influence individuals to close their vaccine gaps, complete their vaccine series or get their annual flu shots. a proactive mission based upon data in the iis that improves population outcomes will demonstrate investment value and maintain future sustainability. with this as a unifying mission, in june 2017, a collaborative of twelve state immunization program leaders met to address current existing iis challenges. as they shared their stories it became clear that their immunization registries were not cost effective to maintain with the present technology and budgets. in all cases what started as a single commercial-off-the-shelf (cots) solution evolved into 12 custom environments. even though all were built on the same code base, each state program implemented a different release of the system. every iis looked the same, but each ran very differently. there was no consistency for the 150,000 users of these systems. a persistent problem was some states took more than a year to implement the current release of their cots system into production. as a result, product updates and bugs that had long been fixed in the software were still causing issues for the states that lagged behind. state it environments that hosted their own iiss varied greatly from one state to the next and had policies that often a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi resulted in delayed roll-outs of releases or patches. users continued to report the same errors in one state that were no longer an issue in another state. while provider end users were unhappy at the pace of updates to the iis, the health departments’ immunization programs were overwhelmed with the frequency of application releases. immunization registry managers struggled with finding resources to continuously perform user acceptance testing to move a release from their testing environment into production. documentation teams could not keep up with updates. the iis customer support teams required a unique knowledge base for each individual client. the bottom line: to address the immediate complaints, individual concessions had to be made for each state based on the version of the cots system they were on, the way each iis database was uniquely structured, and the it environment upon which the iis was hosted. this june 2017 meeting marked the first time twelve state participants decided to start working toward a new approach [4]. the vision was to create a single release of an iis to be used by all – an iis that would leverage the cloud for hardware and performance, a redesigned architecture that would allow for data optimization, and rapid updates of software. the cloud-hosted iis would be one common version supported by agile teams that include public health leadership. the framework took best practices from the technology industry, including clear partnerships based on the saas business model. this would provide added clarity and accountability to both the technical support teams and the public health departments receiving the service. the challenge created by the collaborative also included the requirement for a common modernized system that improved workflows, data capture and quality, tools to enhance user interaction and patient outreach. the iis functionality challenge was to also take advantage of rapid technology advancements and it tools and platforms that allow for employment of modern software engineering principles, notably as automated testing, continuous deployment (devops), microservices, web services and apis – each of which are developed, revealed, and integrated simultaneously with each code update. the core of this foundation should seamlessly enable interoperability and data sharing, providing powerful incentives for investment by private sector stakeholders. this challenge is illustrated in figure 1. convert integrated “monolithic” immunization systems of today to an architecture and set of functions assessable by all through shared services. and to achieve this legacy conversion by 2023. a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi figure 1 the technical challenge to move from 2nd to 3rd generation immunization information systems the value of this new design approach can be illustrated by an example: decision support which is required by all 64 custom systems today to determine immunization care gaps for individuals. on-going acip updates require states to implement update forecasting tools in their systems. these updates once implemented are now integrated into each individual system. with a web services model an update to a single version of a forecasting tool would be deployed to every 3rd generation iis in minutes without affecting other aspects of the iis. compare that speed and efficiency to today’s model, where a traditional software change request is written, an estimate is given, the forecaster code that is buried somewhere in the iis is modified, the entire iis is tested to make sure the changes do not negatively impact another part of the monolithic iis, and then a patch is applied to the iis and the product is released. in a recent forbes business article, “technology changes at a rapid rate due to competitive innovation, lowered implementation costs, and facilitator technologies—such as the cloud and mobile devices… to integrate new technology at a reasonable pace, many organizations can no longer afford to coddle or work around late majority and laggard thinking, and one visionary leader isn’t enough to push a company into a culture of change. individuals throughout the organization need to demonstrate the innovator, early adopter, and early majority traits in order to execute a leader’s vision.” [5] as noted by forbes, lower implementation costs also extend to lower support costs. the 2nd generation of iiss can no longer be expected to grow and serve the hundreds of thousands of users – who access this information daily – with the current level of funding. they no longer can be expected to support accelerated electronic data exchange with clinical providers, pharmacists, and a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi public health clinics to the extent that they are able to mitigate the impact of an outbreak or the next pandemic. the role of the consortium the distinction between first to 2nd iis evolution and now the 3rd is the process that was used and what is now required to achieve success. technology drove the first phase of evolution. vendors and government it professionals simply continued to adhere to the registry use cases (an old school approach) and standards while taking advantage of a new interconnected communication environment through the internet. in spite of the dramatic reach the internet provided from the first to 2nd generations, registries were still about data collection and storage. product owners were non-existent, and public health did not participate as a community to fully leverage the power the internet and all its emerging technologies provided for the purposes of growing the iis beyond its data gathering roots. today that paradigm has shifted. the drivers of the transition today are (1) demands for data to deliver immunization intelligence across the ecosystem; data used to measure population health outcomes, engaging the provider community and empowering individuals; (2) managing the cost of these systems and creating an environment to encourage private and public investment; and (3) ensuring systems are secure and information is protected through software and framework updates to reduce and close access gaps to the unauthorized. first to 2nd generation migration efforts were typically led by the it organization of state and under the oversight of chief information officers (cios). due to the fundamental shift from gathering data to disseminating the immunization intelligence required of the modernized iis, this move from the 2nd to 3rd generation need to be led by a new type of cio – the chief immunization officer. the 12-state consortium that established the unified mission in 2017 has now assumed community accountability for moving forward creating this new iis framework. the new mission required a common mind shift. it is no longer about the technology but the outcomes and how to achieve real goals long sought after when the vision for the first childhood registry was established. the legacy and the new approach transitioning to an iis consortium support model as illustrated in figure 2. a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi figure 2: creating a 3rd generation iis requires a community and engaged consortium approach step one in the process has been for the consortium to support the move from traditional state government “on premise” servers to private, cloud-based hosting services. this creates the opportunity for devops team to deliver updates faster across each system and in real time add capacity to ensure performance under any user demand. this is only step one. a complete modernization of the architecture and software tools for the iis is required. the role of the collaborative as a shared commitment is to oversee modernizing the user experience, improved work flows and set priorities for feature function removal, updates and delivery. the technical team supporting the collaborative assumes the role of re-architecting the design, integration points and database to decouple software code into the required feature functions and sharing the progress with the collaborative as each update becomes available to each iis. transparency by both the collaborative program and technical teams to communicate with the entire iis community and immunization ecosystem is also important to share in lessons learned, successes, risks and agile delivery techniques if after the first 12 have implemented a 3rd generation platform the remaining 52 wish to move in this direction. a key role of this process is as a community to begin to socialize the concept of cost sharing, demonstrating the immense value of the data in the iis, the ability for rapid access and utilization to support day-to-day coverage challenges, current outbreaks or a pending pandemic. this is taking the message to the public – private immunization community that a 3rd generation iis is capable of supporting your individual immunization care gap goals and which in turn is the beginning of discussions to establish an approach to potential new public private partnerships that include investment contributions. limitations the overall hypothesis is there now is a sense of urgency that in order to sustain the investment that has been made in public health immunization information systems that truly do capture birth to death immunization events from all populations, we have reached a cross roads where the evolution to new technologies is essential. the hypothesis assumes change is necessary to improve the user experience, access to data, security and future integration. it assumes when these new a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi platforms are established and implemented that other than the government will be open to discussions to support their sustainment. it also assumes the government is open to these discussions. however, who pays is 2ndary. the first question is will these health data assets 2nd to none in the u.s. be retained. the only limitation is the will of the community to move forward together versus the traditional silo approaches in the past. this paper suggested only the “why.” a 2nd paper illustrates the total cost to move 64 immunizations systems to 3rd generation products while maintaining existing systems. summary the key requirement to stimulate this change is that public health registries must maintain high quality, secure, and reliable data complete with real-time bi-directional updates as immunization events occur. they must support interoperability and data sharing consistent with hipaa rules and regulations. they must be the trusted source of clinical decision support at the point of care for all immunizing providers in order to close the gap on due / past-due vaccines. they must also support engagement and empowerment through the appropriate dissemination of immunization intelligence to all stakeholders most importantly individuals. there is little doubt that this 3rd-generation iis platform will be the catalyst for achieving full data utilization, resulting in improved immunization coverage across all ages. this platform will provide easy and ready access to crucial encounter-level and aggregate information in support of each user’s role in the ecosystem, thereby reducing the impact of vpds. a strong partnership is required with all members accountable for the success of the whole. new technology will certainly prove to be a significant asset for the next decade or more, but it will be the power of the data in these systems that will have the lasting impact the cdc intended when they established a national mission for iis in the early 1990s. references 1. woodfill j. 2012. john f. kennedy moon speech rice stadium. available from https://er.jsc.nasa.gov/seh/ricetalk.htm. 2. bogo j. 2018. the oral history of the apollo 11 moon landing. available from https://www.popularmechanics.com/space/moon-mars/a4248/oral-history-apollo-11/. 3. centers for disease control and prevention. 2012. immunization information systems (iis). available from https://www.cdc.gov/vaccines/programs/iis/about.html. 4. stc annual user group meeting, phoenix arizona, june 2018. collaborative working sessions to explore the iis go-forward plan. 5. newman d. 2016. why you should align your business transformation to the adoption bell curve. forbes. available from a demand for data to improve outcomes creates the why to move to third generation immunization information systems online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e16, 2019 ojphi https://www.forbes.com/sites/danielnewman/2016/05/31/why-you-should-align-yourbusiness-transformation-to-the-adoption-bell-curve/#2fc4aa721160. 2 one example is the state of arizona who first piloted a childhood tracking system in 1993. this arizona statewide immunization information systems (asiis) was operational statewide in 1995. covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research jonathon s. feit1 and christian c. witt1 1beyond lucid technologies, inc., and the braintrust of fire & ems technologists abstract where there is limited access to covid-19 tests, or where the results of such tests have been delayed or even invalidated (e.g., california and utah), there is a need for scalable alternative approaches— such as a heuristic model or “pregnancy test for covid-19” that can factor in the time denominator (i.e., duration of symptoms). this paper asks whether infection among these public health and safety agencies is a "canary in the coal mine," litmus test, or microcosm (pick your analogy) for the communities in which they operate. can covid-19 infection counts and rates be seen “moving around” communities by examining the virus’s effect on emergency responders themselves? the troubling question of emergency responders becoming “human indicator values” is relevant to maintaining the health of mobile medicine (ems and fire) personnel, as well as police, who are an under-attended population, because without these groups our collective resiliency would crash. it has further implications for policies regarding, and investments in, exposure tracking and contact tracing, ppe acquisition, and mental and physical wellness. design: we aggregated data from four (4) different ems documentation systems across twelve (12) states using the mediview beacon prehospital health information exchange. we then outputted lists of charts containing critical icd-10 values that had been identified by the who, the cdc, and the los angeles county fire department’s ems bureau as inclusion criteria for possible signs, symptoms, and clinical impressions of covid-19 infection. results: three important results emerged from this study: (1) a demonstration of frequent exposure to possible covid-19 infection among mobile medical (ems & fire) care providers in the states whose data were included; (2) a demonstration of the nervousness of the general population, given that calls for help due to possible covid-19 based on symptomology exceeded the number of responses with a correlating “provider impression” after an informed clinical assessment; and (3) the fact that this study was empowered by a public-private partnerships between a technology startup and numerous public health and public safety agencies, offers a template for success in rapidly implementing research and development collaborations. limitations: this study incorporates data from only (a) twelve (12) states, and (b) four (4) mobile medical documentation systems. we sought to combat these limitations by ensuring that our sample crosses agencies types, geographies, population demographics, and municipal environments (i.e., rural vs. urban). conclusions: other studies have noted that ems agencies are tasked with transporting the “sickest of the sick.” we found that ppe is particularly essential where the frequency of encounters between covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi introduction as the covid-19 pandemic mingles with other mimicking syndromes—from the common cold, to seasonal allergies, to influenza—myriad government and healthcare organizations from hospitals to insurers to so-called “startup incubators” have wished for a way to tell these overlapping conditions apart, so that the hospital sector stays safe of collapse under the weight of overutilization due to signs and symptoms that “might” indicate covid-19 but also could be any of the aforementioned. the data presented here show that, despite media buzz for and against masks and social distancing, americans in twelve (12) states are more likely to self-assess their ailments as covid-like than to have medical professionals identify their symptoms as covidrelated. in october 2020, the president and first lady of the united states, and several of their colleagues, were diagnosed with covid-19, thrusting back into the spotlight the value and deficiency of current exposure tracking and contact tracing efforts. shortly before this occurred, lawsuits were announced targeting companies, governments, and schools, accusing each of insufficient efforts to identify and isolate persons with suspected—or even confirmed—covid-19 infection. insurance companies, small business “incubators” and “accelerators”, and universities have launched efforts (sometimes called “challenges”) to identify means of exposure tracking and contact tracing that can be rapidly deployed and quickly succeed in a cost-effective manner, with two key objectives: (1) identify those most likely to have—or carry—the coronavirus, and (2) eliminate, to the maximum degree possible, false negatives. the latter are particularly dangerous, because when individuals are falsely identified as uninfected, they may lower their guard and unwittingly become “super-spreaders.” false positives can be expensive and cause psychological stress, however, false negatives can create public health havoc. unfortunately, covid-19 tests are supply-constrained in many locations. elsewhere, results are delayed [1]. california’s h&hs secretary mark ghaly was cited as referencing “a major reporting potentially—or actually—infected patients is high, because from los angeles county to rural texas, without sufficient protection, public health and public safety agencies have become microcosms of the communities they are meant to protect. indeed, data from the first six months of the declared pandemic in the u.s.a. show that intra-departmental spread is one of (if not the) riskiest sources of infection among mobile medical professionals. correspondence: jonathon.feit@beyondlucid.com* doi: 10.5210/ojphi.v13i1.11484 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:jonathon.feit@beyondlucid.com covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi issue…[such that] state and county health officials no longer have a clear idea of how the state's cases are trending. for days, california hasn't received full counts on the number of tests conducted nor the number that came back positive for covid-19.” according to santa clara county (ca) health officer sara cody: “this lack of data doesn’t allow us to know where this epidemic is heading.” [2] public-private partnerships (“p3”) flourish in struggling, fractured bureaucracies. this paper summarizes the success of one p3 between a startup specializing in data science and several public enterprises. it suggests ways to expand the collaboration and create a repository that can be mined securely, in real-time, while maintaining patient privacy and agency autonomy. methods beyond lucid technologies (“blt”), a health-and-safety technology startup based in concord, california, in san francisco’s east bay, makes software for use by emergency medical service organizations of all types (including fire; public, private, and hospital-affiliated ambulances; and “community paramedic” or “mobile integrated health” organizations). the beneficiaries of blt’s data are these organizations as well as their hospital and public health partners. early in the covid-19 pandemic (i.e., march 2020), cypress creek ems (ccems) near houston conceived of a new way of aggregating data about care providers who were exposed to covid-19, suggesting that blt coopt an approach historically used for managing frequent users of ems. blt modified its software accordingly to track personnel-as-patients, and this method was quickly adopted elsewhere in the houston area (i.e., harris and chambers counties), as well as in greater hartford (connecitcut), in southern arizona, and by the los angeles county fire department (“lacofd”), which augmented the approach by automating exposure reporting and monitoring. in addition to self-reporting, to reduce bias, we analyzed patient care charts captured in both 9-11 and community paramedic contexts—from four different ems patient record systems (namely, zoll rescuenet, stryker healthems, meds4 from the ambulance service american medical response, and beyond lucid technologies’s own mediview epcr). incorporating charts completed outside of a longitudinal monitoring program was key because it shined light of the psychology of residents who suspect that they may have been exposed to, or diagnosed with, covid-19. for example, the degree to which they assumed their signs and symptoms, as conveyed to ems (i.e., “complaint”), were related to covid-19, versus the degree to which the symptoms actually aligned with the disease presentation in a skilled caregiver’s opinion (i.e., “provider impressions”). the relationship between these two criteria (complaint vs. impression) is “of the essence” as jurisdictions seek to craft processes and policies related social distancing, mask wearing, and testing. covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi results / data tables covid-19-related symptoms (state & date) state jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 az 5.94% 4.54% 4.15% 5.30% 6.62% 7.79% 5.18% ca 3.60% 1.32% 1.95% 2.38% 6.37% 1.39% 1.43% co 3.99% 3.91% 3.62% 2.70% 4.29% 3.67% 3.68% ct 3.14% 2.96% 3.62% 3.82% 2.99% 2.42% 3.89% il 6.57% 9.52% 11.36% * * * * ks 11.67% 8.78% 10.65% 12.94% 5.97% 7.92% 9.13% ma 12.35% 12.38% 8.95% 16.06% 11.18% 9.25% 9.52% mn 2.36% 8.96% 9.04% 8.39% 9.09% 8.82% 7.66% mo 14.93% 18.59% 13.86% 16.96% 11.89% 11.05% 10.55% nc 15.79% 0.00% 18.75% 7.14% 0.00% 10.00% 18.75% oh 6.82% 12.70% 12.05% 5.80% 6.74% 4.00% 9.57% ok 10.25% 10.15% 10.58% 11.28% 11.05% 11.27% 12.25% pa 0.94% 5.77% 4.67% 8.22% 4.71% 0.89% 5.13% sc 3.21% 3.16% 5.73% 5.56% 5.13% 5.79% 5.74% tn 10.11% 6.41% 7.91% 8.22% 8.88% 9.79% 4.39% tx * * 2.62% 4.84% 15.15% 58.70% 43.10% covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi covid-19-related provider impressions (state & date) state jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 az 0.17% 0.28% 0.25% 0.42% 2.55% 6.48% 5.26% ca 3.71% 2.57% 3.91% 3.37% 3.17% 3.55% 3.89% co 0.28% 0.00% 0.00% 0.00% 0.33% 0.33% 0.00% ct 3.14% 3.10% 3.41% 4.24% 2.42% 2.19% 2.44% il 4.38% 0.95% 3.41% * * * * ks 2.33% 1.15% 3.04% 1.99% 1.00% 2.26% 1.19% ma 0.41% 0.99% 0.53% 5.84% 1.18% 0.58% 1.06% mn 1.42% 2.36% 0.56% 3.50% 1.07% 1.76% 0.45% mo 3.48% 2.51% 1.98% 1.17% 0.00% 0.00% 0.50% nc 0.00% 0.00% 12.50% 0.00% 9.09% 0.00% 0.00% oh 2.27% 3.17% 1.20% 1.45% 3.37% 1.00% 0.00% ok 1.46% 1.86% 2.41% 1.74% 1.36% 0.84% 0.62% pa 0.00% 0.00% 0.00% 0.00% 4.71% 0.89% 0.00% sc 0.12% 0.13% 0.30% 0.60% 0.56% 0.40% 0.87% tn 1.37% 0.32% 2.26% 0.00% 1.15% 1.78% 1.81% tx * * * * * * * covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00% 9.00% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms pcrs and crew reporting -az 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms pcrs and crew reporting -ca 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 4.00% 4.50% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms pcrs and crew reporting -ct 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% covid-19-related symptoms pcrs and crew reporting -tx covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 4.00% 4.50% 5.00% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms pcrs only -co 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% covid-19-related symptoms pcrs only -ks 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00% covid-19-related symptoms pcrs only -ma 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00% 20.00% covid-19-related symptoms pcrs only -mo covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00% 9.00% 10.00% covid-19-related symptoms pcrs only -mn 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00% 20.00% covid-19-related symptoms pcrs only -nc 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% covid-19-related symptoms pcrs only -oh 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% covid-19-related symptoms pcrs and crew reporting -ok covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00% 9.00% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms pcrs only -pa 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms pcrs only -sc 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% covid-19-related symptoms pcrs only -tn 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00% 20.00% jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 covid-19-related symptoms trendlines / best-fit trajectories covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi inclusion / evaluation criteria (icd-10 codes) medical history impressions symptoms u07.1 j12.89 r10.9 b97.29 j20.8 r19.7 j40 r06.02 j80 r05 j22 r53.83 j12.89 r50.9 j98.8 r51 a41.89 r43.9 z20.828 r43.8 z03.818 r53.81 m79.1 r11.0 r11.2 r11.10 covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi discussions using forms that were secure but captured no personally identifiable information, between march 23 and august 31, 2020, lacofd's health programs office (hpo) processed 23,888 forms, covering 6000+ line and support personnel [3]. the hpo identified 213 confirmed covid-19 positive personnel who were placed in active monitoring. such an expansive body of clinical data facilitated the identification of heuristic patterns that became valuable indicators of expected test results—i.e., “a pregnancy test for covid” —enabling triage of tests where they were in short supply, delayed, or simply proven wrong. after six months, lacofd reported a false negative rate of zero percent. a state-by-state compendium of aggregated data is presented above. chambers county ems in texas reported that the form-based process and algorithms “allowed us to follow up with covid-19 exposures quickly…being we are a small service this has freed up significant time from making phone calls to hospitals, public health and others to gather results. we are also able to receive outcome data on our patients that are transported which has improved our training and allowed us to focus on key factors of improvement and changes to protocols.” in adjacent harris county, esd #48 said: “the screening system is working great…we have way too many to track any other way.” [4] conclusions syndromic surveillance is a math problem: statistics and calculus. it is vital to deduce the rate of change of the infection. however, politics—not math—are keeping america from conquering covid-19, so mobile medicine (fire & emergency medical service agencies) must step up to lead...again. the results of this study show that while mobile medical professionals (mmps) are wearing ppe and taking care to limit patient exposures, the concentration of covid-19 symptoms (i.e., patients’ self-reported concerns), and provider impressions has followed the trajectory of the virus across the united states. this is important, because mmps are like a bellwether species— they are like the proverbial “canaries in the coal mine.” mmps’ rates of infection and recovery offer clues about the virus’s movement and impact for three reasons: 1. unless ppe is available and used properly, essential personnel like fire & ems come into contact with potentially infected persons far more frequently than the average; 2. mobile medical professionals—like other public health and public safety teams— tend to live near the areas they cover. if a community is experiencing an outbreak, everyone who lives or works in the vicinity is by definition at higher risk; 3. among the most dangerous contributors to infection inside mobile medical agencies is intra-departmental spread: ppe is not worn during dinner inside a station house. further research the higher rate at which mobile medical (ems & fire) agencies are called to respond to suspected covid-19 cases underscores their risk as crews are being tasked to care for and transport patients covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi whether or not they are later found to be infected with covid-19. mobile medical professionals therefore are not only critical to community resiliency, but possibly also “canaries in the coal mine”: without universal ppe, responders become microcosms of their localities as infection rates ramp, because they have limited ability to minimize exposure to infected patients. in texas, as much as 30% of one ems agency’s personnel were quarantined—a rate correlated with, but higher than, the rural community it serves. due to an overlap of symptoms between allergy, influenza, and covid-19, the algorithms resulted in over-triage, which cause result in unnecessary cost and isolation of personnel. but lacofd reported that exposure tracking technology provided “a continuous, strategic view of the health of our workforce in the face of covid-19. we now have the ability to track each of our personnel from the onset of exposure or illness, through their return to work.” further research, in additional geographies, will refine the algorithms to reduce false positives and adjust parameters for concurrent diseases with mimicking symptoms. limitations of this study the general modus operandi of studies pertaining to mobile medicine (i.e., emergency medical services, including fire) is to focus either on a local site and offer a detailed case example; or else to consult large deidentified datasets—e.g., the national ems information system (nemsis) or the national fire incident reporting system (nfirs)—but those face challenges of timeliness and accuracy, such as different states using different versions of the reporting standards (i.e., nemsis v3.3.4 and v3.4.0, both of which are presently considered “current”). in some cases, large “indices” containing millions of patient records have been published but their data have invariably been sourced by a single company using its own patient record system, so even a very large sample is confounded by selection bias (i.e., “a sample size of one”). this study’s design was intended to combat each of these historical impediments by ensuring that multiple data systems used by medical medical organizations contributed to the knowledge base. the data incorporated were generated by: (1) four different electronic patient care record (epcr) systems; (2) covering a varied geography including both urban and rural care providers; (3) varied agency types, including public ems, private ems, hospital-based ems, fire-based ems, community paramedic, and critical care; and (4) a defined set of investigable codes rather than a subjective text-based determination (i.e., the icd-10 code was queried as opposed to language featuring the word “covid-19”). despite these safeguards, this study has important limitations: only twelve (12) states are sampled, and while significant population centers are represented, figures from thirty-eight (38) states [plus territories and washington, d.c.] are not included. moreover, though some of most widely used data systems are incorporated into the study, figures were unavailable from some software systems—including some with strong regional concentrations—whose figures are unavailable due to matters of technology or preference. covid-19 exposure tracking within public health & safety enterprises: findings to date & opportunity for further research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e3, 2021 ojphi references 1. beers t, smalley c, gorbett t, fertel b, muir m. “assessing real-world ems operations during a pandemic using retrospective patient acuity and mortality data.” journal of emergency medical services. 5 november 2020. https://www.jems.com/2020/11/05/assessing-real-world-ems-operations-during-a-pandemic 2. frosch d. “covid-19 testing is hampered by shortages of critical ingredient.” wall street journal. 22 september 2020. https://www.wsj.com/articles/covid-19-testing-is-hamperedby-shortages-of-critical-ingredient-11600772400 3. dowd k. “'back to feeling blind': what we know about california's missing covid data.” san francisco chronicle / sfgate.com. 5 august 2020. https://www.sfgate.com/bayarea/article/calredie-covid-data-california-underreported15461291.php 4. mcgregor, t., gupta, p., kashani, s., kazan, c., wells, b., feit, j., witt, c., and saylor, d. “development of a heuristic model for likely positive covid-19 tests, where tests are unavailable.” submitted to the national associated of ems physicians. 8 september 2020. 5. emails: harris county esd 48 (june 29, 2020). chambers county ems (september 2, 2020). 6. data tables: aggregate epcr data from mediview mobile, zoll rescuenet, and stryker healthems, collected via beyond lucid technologies’s mediview beacon prehospital health information exchange, january to july 2020. acknowledgements the authors wish to thank the following individuals and their organizations for their clinical ingenuity, and their assistance in aggregating data from multiple electronic patient care record systems during the covid-19 pandemic’s “first wave”: nancy alvarez, adam amezaga, eric bank, bruce baxter, jeremy carillo, joseph casciotti, margie chidley, patrick ciardullo, jennifer correa, darris clark, christina dubois, sean fiske, natalie gedikli, puneet gupta, jeff hevey, maria izarraraz, richard johnson, sergey karishev, saman kashani, clayton kazan, jim koch, terrence mcgregor, matt miller, alex morisano, wren nealy, josh nultemeier, ron nichols, david saylor, parker stambaugh, richard walls, bryan wells—and each of the beyond lucid technologies’s partner-client agencies across the united states who contributed data that proved critical to this research. 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts hiv surveillance: life skills education programme among in-school adolescents olubunmi adeyemi*1, olufunmilayo fawole2 and adedayo adeyemi3 1public health department, abuja, nigeria; 2department of epidemiology and medical statistics, college of medicine, ibadan, nigeria; 3measure/national agency for the control of aids, abuja, nigeria objective to assess the predictors of life-skills-based hiv/aids education on sexual behaviour among secondary school students in south west, nigeria. introduction reduction in hiv transmission needs continuous, comprehensive and effective communication channels to disseminate messages that will sustain efforts to motivate adolescents to engage in a range of options to reduce the risk of hiv infection. life skills education, an integral part of school-based aids programs that include sexual and reproductive health information, has been a timely prevention effort in schools.1 this has proven to be an effective method in delaying the onset of sexual intercourse, and among sexually experienced youth, in increasing the use of condoms and decreasing the number of sexual partners. life skills are behaviors that enable individuals to adapt to and deal effectively with the demands and challenges of life. the life skills approach is an interactive, educational methodology that not only focuses on transmitting knowledge but also aims at shaping attitudes and developing interpersonal skills. the main goal of the life skills approach is to enhance young people’s ability to take responsibility for making healthier choices, resisting negative pressures, and avoiding risk behaviors. limited studies have been done on assessing life skills of adolescents in schools. findings from this study will provide baseline for programmatic scale up. methods a cross–sectional study design was used. a multi-stage sampling technique was used to select eight schools from four local government areas (lgas) within ibadan metropolis, nigeria. knowledge of life-skills in hiv prevention and associated factors were assessed. univariate analysis was done to generate proportions, tables and charts; bivariate was done with mann-whitney u test, and multivariate analysis was done with multiple linear regressions after transformation. results a total of 1467 students were selected with a mean age of 14.2 1.7 years. about half of the respondents were females (51.4%) and christians (52.6%). majority were in senior secondary class (66.3%), mostly from monogamous family (75.3%) and living with both parents (77.9%). about 14.1% of the total population had been exposed to sexual intercourse. the mean age at initiation was 15.2 1.9 years. only about a quarter of the respondents in both groups rated their risk of having hiv/aids to be high. most agreed that adolescents/ young people need life skills (69.3%), and the reason commonly stated was to enable them to know what is right. about half (50.3%) said they have heard about life building skills and the commonest source of information was from school (72.1%). the three most commonly identified life skills were communication (37.5%), decision making (32.4%), and goal setting (29.2%). low proportion of respondents poorly demonstrated negotiation and communication components of life skills. the life skills improve with exposure to family life and hiv/aids education (b coefficient=26.28, p<0.001); increasing age (b=2.97, p=0.03); living in a monogamous family (b=11.45, p=0.03); having more than one sibling (b=29.54, p=0.03); having a trading mother (b=23.01, p=0.02); and having an artisan mother (b=25.47, p=0.03). however, living with either of the parents/guardian (b=29.71, p=0.001) and being in urban schools (b= -19.86, p<0.001) significantly decreases life skills ability of the respondents. conclusions this study has shown that family life and hiv/aids education with some socio demographic factors might have contributed to the improvement of life skills education program. efforts should be made to sustain the effectiveness of school based education programs especially in urban areas.2 keywords hiv surveillance; life skills; in-school adolescents references 1) liao w, jiang j, yang b, zeng x, liao s. a life-skills-based hiv/ aids prevention education for rural students of primary schools in china: what changed? what have we learned? biomedical and environmental sciences. 2010;23:409-19. 2) fawole io, asuzu mc, oduntan so, brieger wr. a school-based aids education programme for secondary school students in nigeria: a review of effectiveness. health education research. 1999;14 (5):675-83. *olubunmi adeyemi e-mail: bumfaks2002@yahoo.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e100, 201 effective sharing of health records, maintaining privacy: a practical schema 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi effective sharing of health records, maintaining privacy: a practical schema roderick neame 1 1 university of queensland st lucia campus, brisbane qld australia abstract a principal goal of computerisation of medical records is to join up care services for patients, so that their records can follow them wherever they go and thereby reduce delays, duplications, risks and errors, and costs. healthcare records are increasingly being stored electronically, which has created the necessary conditions for them to be readily sharable. however simply driving the implementation of electronic medical records is not sufficient, as recent developments have demonstrated (1): there remain significant obstacles. the three main obstacles relate to (a) record accessibility (knowing where event records are and being able to access them), (b) maintaining privacy (ensuring that only those authorised by the patient can access and extract meaning from the records) and (c) assuring the functionality of the shared information (ensuring that the records can be shared non-proprietorially across platforms without loss of meaning, and that their authenticity and trustworthiness are demonstrable). these constitute a set of issues that need new thinking, since existing systems are struggling to deliver them. the solution to this puzzle lies in three main parts. clearly there is only one environment suited to such widespread sharing, which is the world wide web, so this is the communications basis. part one requires that a sharable synoptic record is created for each care event and stored in standard web-format and in readily accessible locations, on ‘the web’ or in ‘the cloud’. to maintain privacy these publicly-accessible records must be suitably protected either stripped of identifiers (names, addresses, dates, places etc.) and/or encrypted: either way the record must be tagged with a tag that means nothing to anyone, but serves to identify and authenticate a specific record when retrieved. for ease of retrieval patients must hold an index of care events, records and web locations (plus any associated information for each such as encryption keys, context etc.). for added security, as well as for trustworthiness, a method of verifying authenticity, integrity and authorship is required, which can be provided using a public key infrastructure (pki) for cryptography (2). the second part of the solution is to give control over record access and sharing to the patient (or their identified representative), enabling them to authorise access by providing the index and access keys to their records. this can be done using a token (fe.g. smart card) or a secure online index which holds these details: this serves to relieve the formal record keeper of responsibility for external access control and privacy (internal access control and privacy can remain an institutional responsibility). the third part of the solution is to process the content of the stored records such that there is a ‘plain english’ copy, as well as an electronic copy which is coded and marked up using xml tags for each data element to signify ‘type’ (e.g. administrative, financial, operational, clinical etc.) and sub-types (e.g. diagnosis, medication, procedure, effective sharing of health records, maintaining privacy: a practical schema 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi background record keepers are faced with increasing demands for widespread access to stored information, often involving providers who are unknown to them and not part of their system or network. whilst endeavouring to meet these demands, they are obligated to ensure that ethical and legal privacy requirements are met. in addition they must make provision for patients to exert control over their own information and to determine who may see what of their records. finally they must endeavour to ensure that data shared with third parties can readily be understood and displayed meaningfully on their desktop – whatever the nature of their technology. a decade ago mandl et al. (3) published an article with a similar title to this, in which they recognised many of the same issues as outlined below, and proposed some general approaches to their solution. mandl’s work was speculative and raised numerous issues, but did not propose any formed solution. more recently steinbrook (4) in 2008 indicated that personally controlled online health data might be coming, based on centralised commercial web services (e.g. microsoft healthvault, dossia and google health) providing the data protection and storage: he raised issues as to whether the public would have confidence that their confidential and personal records were safe, secure and private in the hands of such third party commercial service providers, and whether these providers would be bound by hipaa, but again suggested no solutions to the issues. steinbrook’s paper has little practical detail as to how sharing with assured privacy might work. another paper seemingly addressing a similar topic, by liu et al. (5) proposes an approach to sharing patient data between hospitals using xml tags associated with each significant element: the tags would allow different systems to identify the data elements sent from remote systems and store them in the equivalent places and formats that they use for data generated locally. this environment in taiwan has been developing slowly, and now has a basic smart health token acting as an index and pointer: however it does not address the need for robust security, nor for record readability across an entire range of possible user workstations. taiwan’s development has been made possible by the fact that there are not significant numbers of stakeholders with investments and positions to protect, and that the government has exerted considerable pressure, political and legal, to force the solution through: observers do not believe that a similar approach would be successful elsewhere. investigation result etc.). this ensures that the recipient can always read the data using a basic browser, but can readily manipulate and re-arrange the data for display and storage if they have a more sophisticated installation. correspondence: roddyneame@taskcare.com copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. effective sharing of health records, maintaining privacy: a practical schema 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi this current paper proposes a practical and workable solution, based on tried and tested technology all of which works in similar environments. it builds upon some of the ideas developed in the above publications. it does not threaten any existing investment in the sector, and is entirely platform independent. whilst it could operate using commercial storage and data protection services (e.g. healthvault), the uncertain level of privacy and security they offer suggests that a decentralised but encrypted system would be wiser and more secure, with the patients holding the index and keys to their own data. the main focus of the paper is to offer a practical solution to the sharing of medical data where privacy and security are robust and where the records can be trusted as being unaltered and unchanged as they pass between providers. outline plan the principal issues are outlined above. in this section we address the proposed solution in operational terms. privacy. one of the more important issues for acceptability (and legality) is that the issue of maintaining confidentiality is fully addressed. maintaining the privacy of patient information is becoming increasingly onerous in the face of changing legal requirements and public expectations, increasing penalties for errors and failures, and changes in information technology, networking and data storage. all data which is associated with a readily recognisable identifier (e.g. name, address and date of birth) is confidential and complex rules have to be observed for keeping it generally secret whilst at the same time making it readily accessible to those who are authorised. the simple step of disassociating the humanmeaningful identifiers (names, clinics, addresses, dates etc. – “the context”) from the remainder of the event data (“the content”) can solve much of the privacy issue, since now the stored content reveals nothing on its own unless associated with the context. however that separation may not always be possible, and it may be preferable in some cases to leave the full record marked up with html, and to encrypt it before web storage. the privacy challenge becomes how to manage the association of context with the stored content as and when required for legitimate purposes, and the management of encryption keys. whilst the stored data may be identifier and context free, it must still have some sort of embedded key or tag that serves to identify that block of data itself and which can be used to confirm the data block identity for linking and retrieval. however such tags will have no other meaning. the literature shows that privacy concerns are not a trivial issue, as sometimes represented: there is a significant section of the community who have concerns about the confidentiality of their records (6) and who will act accordingly – either not seeking care or withholding important personal/health details from their consultations. public concern about privacy in the context of computerised information is high, especially in the context of the almost daily losses of personal information from both private and government records, the statistics of which are quite alarming (7): disclosed reports detail over 120 million records that were affected in 2011, with almost one third of the incidents occurring in the health sector; undisclosed losses are in all probability considerably greater. effective sharing of health records, maintaining privacy: a practical schema 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi trustworthiness: the user of the data, typically a clinician, needs to know that they have the correct block of data (from the embedded key), but also that it has not been changed from when it was written and can be trusted – regardless of how that data makes its way to their desktop. without that there will not be sufficient confidence in the data for it to be useful. therefore the block of data will contain a ‘checksum’ (mathematical computation based on the entire contents of the block), and that checksum will be encrypted by a key: decryption using the relevant key and subsequent comparison with the material will permit an automatic check that the data has not been altered since being written and checksummed. in many environments a public key infrastructure is being implemented (2): this offers an additional verification capability. where the author encrypts the checksum with their private key, decryption with the author’s (web-listed) public key not only confirms that the data is unchanged, but also verifies the identity of the purported author. this confirmation is of medicolegal significance, as well as reducing the risk of misadventure. access control: the issue of who should control patient records has at times been contentious. a paternalistic attitude has prevailed widely amongst health professionals that they knew best who should be privy to the records, but this has been supplanted in recent times with the increasing weight given to ethical issues and privacy legislation. privacy as a concept aligns with the ethical principle of ‘autonomy’(8): this principle holds that an individual has a right to self-determination and to control and make their own decisions about personal matters, including who may know what about them. there are numerous situations in which an individual discloses their secrets to a professional (e.g. doctor, priest, accountant, lawyer). the professional receiving the secrets has a clear ethical responsibility to keep that information private, and in addition has a fiduciary (based on trust, confidence and good faith) duty in common law. in terms of related statutory legislation, there are also in most jurisdictions specific bodies of law requiring personal information to be kept private (e.g. data protection, privacy and hipaa legislation etc.) as well as provisions under human rights legislation for ensuring that no organs of the state interfere with the fundamental right to privacy and family life of the individual. all these ethical and legal constraints make clear that as far as possible the person who should be in control is the data subject (in this instance the patient), or their authorised care providers and/or representative(s). this raises the issue of how to give data sharing/disclosure control to the patient such that they may exercise this right from anywhere at any time leaving a trail that is auditable. again there are alternatives. one solution would involve the use of a unique token (e.g. smart card) and/or biological identifier (e.g. scan of fingerprint, iris, face), and a previous publication by this author (9) detailed such a schema. harrison & booth (10) presented views that are entirely consistent with this schema. both of these publications address the need to manage multiple aliases and identifiers that individuals may be assigned or choose to use in healthcare settings as well as in other contexts. an alternative solution is that individuals could choose to store their records in their own personal vault (or on provided by a commercial service, if they are comfortable with the security it offers). a third solution is for the patients to store just the index to their personal records, leaving them where they are on the web. all of these can comfortably co-exist. effective sharing of health records, maintaining privacy: a practical schema 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the key to management of the patients personal records is an index of care encounters, which comprises a series of dates and event/encounter descriptors: these can simply be text files which are dated and assigned an appropriate descriptor, and can therefore be ordered using any folder/file viewing application. that index can be stored on a smart card, or any other similar secure memory token, or it can be kept in a personal online storage location. whichever technology is used, the index requires several components in addition to the list of care encounters/events and their chronology: it must include the location (url) of the stored sharable record and its embedded tag, any associated data such as encryption methods/keys and checksum verification information, and any stripped context (e.g. people and identifiers). assuring meaningfulness: the remaining obstacle to sharing relates to the usability of the data passed to the recipient, who must be able to access and use the data. the reader will typically be a browser-based, although some institutions may use sophisticated point-of-care systems that manipulate and store the data in different ways. the lowest common denominator is a browser which predicates that the data must be stored as ‘raw’ text in html, which can be read (after decryption) by any generic browser: this follows the scheme recently adopted by the hl7 organisation (11) in their clinical document architecture (hl7v3). however to better support those with more sophisticated systems, a further structure based on xml (extended markup language) can be incorporated, along the lines of the approach adopted by liu et al. (5): this would have data elements tagged according to their nature (e.g. financial, administrative, clinical; diagnostic, medication, investigation etc.). based on the tags, the reading system can process, store and display the data intelligently: however for this to function optimally there would need to be agreements in place about data classification and coding. procedure: operationally the record-related steps involved in a care event/encounter would be: 1. care encounter/event commences, automatically assigned an id; patient activates their smart card token, or opens their online deposit, and authorises clinician access to their records 2. clinician retrieves relevant records from local and remote/web storage using the index to guide access to locations and provide required context, keys etc. 3. notes/records for the present encounter are written and stored locally as normal 4. at the conclusion of the encounter, the desktop software prepares an identifier-free encounter/event abstract and exhibits this summary to the clinician for correction and addition of a descriptive title 5. approved summary is processed into html, data are automatically type-tagged with xml, an encrypted checksum added, and the document is assigned a linktag and uploaded to a selected url: the whole document may be encrypted (see discussion) 6. a text file is generated, named according to the event descriptor, and dated: this file contains url, linktag, encryption keys, context etc. the file can be copied to the patient index, whether on a smart card or on the web, or could be placed into secure storage that can be accessed by the patient using a password (provided): where the patient is not present concurrently (e.g. blood analysis report, image report) this index effective sharing of health records, maintaining privacy: a practical schema 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi file can be forwarded to the requesting clinician to pass to the patient at their next encounter. figure 1: steps involved in care/ event encounter discussion the aim of this work is to outline a method for joining up care services for the patient such that their care records are fully portable and can follow them wherever they go, so reducing delays, duplications, errors and risks of care, and cost. access to care records is currently often difficult or impossible since where records contain personal identifiers they have to be secured behind services that greatly impede ready access except to those who are local (i.e. registered users of the host system). this schema frees them of this impediment and allows them to be stored in readily accessible locations on the web. the user then has to be guided to the url of the relevant record, enabled and authorised to access it, and provided with the context and keys to be able to use it: these are all functions that can be managed by the patient using a token which stores urls, tags, keys and any required context of their records, or a suitable secure web service. the role of the record keeper is to make information available as and when required by a duly authorised person in order to support shared care, to ensure that records can follow patients on their journeys through the healthcare system, but at the same time to keep those records secure from unauthorised users. accessing patient records electronically on demand from a effective sharing of health records, maintaining privacy: a practical schema 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi workstation that is not part of the data keepers network is at present generally impossible, and shared care is thus effectively limited to within a single institution, excepting where external parties are included by prior agreement and records are forwarded (‘pushed’) to them. however the real need is for any professional authorised by the patient to be able to ‘pull’ records from storage to their desk top – and this is exactly how the world wide web works, but brings with it issues of access and privacy. the above scheme is based on technology that is tried, tested and for the most part universally available: being web-based it is built on applications such as generic browsers, and is independent of user computing platforms or applications. infrastructure components such as pki, smart health cards and xml tags are already in widespread use, and progressively being implemented more widely: but the system proposed can work whatever the level of sophistication of the local users. most existing records systems are proprietary, storing data within their own structures and formats: the use of the simple transform of creating html outputs from these renders them universally readable and readily ‘pulled’ from an online repository. the use of encryption ensures they cannot be read other than by those with the keys; and the use of checksums enables the records to be validated and their authorship verified. enabling the patient to control access to their own data makes it possible to greatly simplify existing access control infrastructures and transfers the burden of privacy management from record keepers to patients. the scheme also manages multiple identities. harrison & booth (10) noted that individuals do not have just a single ‘identity’ but many – e.g. for healthcare, tax, finance, driving, insurance and many more – and that there is no requirement that all these ‘identities’ are the same although for certain purposes there may be a requirement to provide evidence to prove an identity (e.g. bank accounts, national insurance etc.). an individual may even choose to have multiple identities or personae for different healthcare encounters, despite the risk that using aliases may incur risks due to fragmentation of their records. for example the patient may choose to keep some records (e.g. psychiatric, sexuality or obstetric care events) separated from their general medical records, just as they may choose to keep their court records under a separate alias from their employment or financial records. controlling the linkage between personal information kept in different folders is a right. however individuals will normally have only one ‘official’ identity at any one time, for ‘public’ purposes (e.g. passport, welfare, tax etc.). this identity provides the authority for claims by care providers for care services from public insurers: another insurance identity may authorise private insurance claims. once these ‘top-level’ identities are established, a patient can create or manage sub-identities as suits their requirements, and distribute their care (and other) records between them as they choose. there is an issue of what happens when the patient loses their card/token or access to their web repository. in the absence of the patient index, providers will have to rely as they do currently on their previous records for that patient, which of course they hold on their own system in addition to having placed a sharing copy on the web, and use their clinical acumen in eliciting a relevant history: the patient is no worse off than at present, except that in the absence of their identifiers for claims purposes they may be required to pay for their care effective sharing of health records, maintaining privacy: a practical schema 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi rather than automatically having it billed to an insurer. this will act as an incentive to take care of their card, as it has real monetary and convenience value. facilities to back-up the index, whether on a card or in a deposit box, can be widely provided using public (e.g. using bank atms) or private services so that data should never be lost. and if all else fails, the patient should know where their recent records were created and be able to re-populate the data onto a new token, albeit at some personal inconvenience. so there really is no downside. an obstacle often raised is that of the costs for implementing and maintaining such an arrangement. having the right information in the right place at the right time has the potential to avoid a substantial proportion of the adverse care events that consume so much of the health budget (12), so providing resources to fund required infrastructure. issuing card tokens is cheap: the task could even be outsourced to an organisation (e.g. bank) that does this all the time. the record identifier stripping, checksum insertion and data type tagging can readily be undertaken automatically and simply by a routine, which then writes the event summary, tag details and url to the patient card. storing data on the web is cheap: such organisations as youtube and facebook store vast quantities of web-based data and have easy upload arrangements in place. without undertaking a detailed financial analysis, the system should quickly pay for itself. conclusion there are issues that make the sharing of relevant information between those caring for the same patient difficult. one issue is that of ensuring records are readily accessible whilst at the same time ensuring personal privacy; a second relates to passing control over access and sharing to the patient; and a third concerns storing these records in a form that can readily be imported (‘pulled’) and displayed flexibly by the reader irrespective of whether they have a simple web browser or a more sophisticated electronic records system. this paper outlines a simple schema whereby healthcare data can be shared flexibly. the data for sharing is encrypted, tagged, and made verifiable and secure against alteration before storage on the web, so creating trustworthiness and assuring privacy. the clinician requiring the data can ‘pull’ it to their workstation, using the linking data provided by the patient, who thus controls access to their personal information. the key to the schema is the use of an index held either on a token (e.g. smart card) or in a secure web location, and controlled by the patient. all the required technology already exists and is in routine and widespread use: it is envisaged that such a system could more than pay for itself through savings arising out of reduced delays and duplications, and avoided adverse events. the system can be implemented without the need for standardisation across electronic records systems, although such agreements would be useful. future work patient identification tokens are in use in some locations and in the process of being distributed in many others: these would need to have an application added (if not already present) to hold an index of care events and any associated data (e.g. keys, locations etc.). effective sharing of health records, maintaining privacy: a practical schema 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi there is no special need for the token to be new: any token with sufficient memory (e.g. a bank card) could be registered and used. card readers are already widely distributed, and where individuals do not have their own at home, they could make use of public terminals or even of the banking system. the application to process the records for posting to the internet is of no great complexity as an add-on to a clinic system. developing and agreeing the record data types and sub-types, and their associated xml tags will be necessary, as well as developing a browser add-on that can import and display flexibly the xml marked-up records. corresponding author roderick neame adjunct professor health informatics, university of queensland st lucia campus, brisbane qld australia email: roddyneame@taskcare.com url: www.health-informatics.co references [1] kellermann al, jones ss. what it will take to achieve the as-yet-unfulfilled promises of health information technology. health affairs, 2013; 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[internet] (cited 17 january 2013) http://en.wikipedia.org/wiki/public-key_cryptography [3] mandl kd, szolovits p, kohane is. public standards and patients' control: how to keep electronic medical records accessible but private (2001). bmj 2001;322:283. available from: http://www.bmj.com/content/322/7281/283 [4] steinbrook r. personally controlled online health data – the next big thing in medical care? nejm 2008 358:16. available from: http://www.ipacohio.org/websites/ipac/images/linksresources/nejmelectronicrecords.p df [5] liu ct, long ag, li yc, tsai kc, kuo hs. sharing patient care records over the world wide web. int j med inform 2001 may: 61 (2-3): 189-205. available from: http://libir.tmu.edu.tw/bitstream/987654321/30231/1/2001sharing+patient+care+records+over+the+world+wide+web.pdf [6] sankar p, mora s, merz j, jones n. patient perspectives of medical confidentiality. j gen intern med. 2003 august; 18(8): 659–669. available from: http://www.ncbi.nlm.nih.gov/pmc/articles/pmc1494903/ [7] roiter n. latest wave of healthcare data breaches symptomatic of sloppy security practices. security bistro, april 28, 2012 . [internet] (cited 26 july 2012) available from: http://www.securitybistro.com/blog/?p=1450 mailto:roddyneame@taskcare.com http://www.health-informatics.co/ http://networkingdefinition.bringthegame.info/153/more-changes-in-health-care-needed-to-fulfill-promise-of-health-information-technology/ http://networkingdefinition.bringthegame.info/153/more-changes-in-health-care-needed-to-fulfill-promise-of-health-information-technology/ http://en.wikipedia.org/wiki/public-key_cryptography http://www.bmj.com/content/322/7281/283 http://www.ipacohio.org/websites/ipac/images/linksresources/nejmelectronicrecords.pdf http://www.ipacohio.org/websites/ipac/images/linksresources/nejmelectronicrecords.pdf http://libir.tmu.edu.tw/bitstream/987654321/30231/1/2001-sharing+patient+care+records+over+the+world+wide+web.pdf http://libir.tmu.edu.tw/bitstream/987654321/30231/1/2001-sharing+patient+care+records+over+the+world+wide+web.pdf http://www.ncbi.nlm.nih.gov/pmc/articles/pmc1494903/ http://www.securitybistro.com/blog/?p=1450 effective sharing of health records, maintaining privacy: a practical schema 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi [8] medindia. an introduction to biomedical ethics. [internet] (cited 17 january 2013) http://www.medindia.net/education/familymedicine/biomedical-ethics-autonomy.htm [9] neame r. privacy and health information: health cards offer a workable solution. inform prim care. 2008;16(4):263-70. available from: http://www.ingentaconnect.com/content/rmp/ipc/2008/00000016/00000004/art00003 [10] harrison, j and booth n. applying new thinking from the linked and emerging fields of digital identity and privacy to information governance in health informatics. inform. prim. care 2003; 11: 223-8 available from: http://www.ingentaconnect.com/content/rmp/ipc/2003/00000011/00000004/art00007 [11] hl7 clinical document architecture. [internet] (cited 28 december 2012) http://www.hl7.org.uk/version3group/cda.asp [12] vincent c. adverse events in british hospitals: preliminary retrospective record review. bmj 2001; 322:517. available from: http://www.bmj.com/content/322/7285/517 http://www.medindia.net/education/familymedicine/biomedical-ethics-autonomy.htm http://www.ingentaconnect.com/content/rmp/ipc/2008/00000016/00000004/art00003 http://www.ingentaconnect.com/content/rmp/ipc/2003/00000011/00000004/art00007 http://www.hl7.org.uk/version3group/cda.asp http://www.bmj.com/content/322/7285/517 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts application of an automated surveillance system to evaluate outpatient care of pneumonia jeneen gifford*1, 2 and sylvain delisle1, 2 1university of maryland school of medicine, baltimore, md, usa; 2veterans affairs maryland health care system, baltimore, md, usa introduction with economic pressures to shift the care of community-acquired pneumonia (cap) to the ambulatory setting, there is a need to ensure safety of outpatients with cap. the use of claims data alone remains the primary strategy for identifying these patients, but billing information often does not match the clinical diagnosis and does not have the ability to find unrecognized cases. in our previous work, an automated pneumonia case detection algorithm (cda) was able to detect cases of cap with positive predictive value of 71%.1 for this study, we begin to illustrate how this type of surveillance system may assist in evaluating the quality of outpatient care for cap. methods in this single-center, retrospective observational study, the pneumonia cda was applied to electronic health record (ehr) documentation of all emergency room and outpatient clinic visits at the baltimore va medical center from 01/01/2004 to 12/31/2011. the pneumonia cda consists of: 1) a cda for acute respiratory infection (ari) that uses icd-9 codes, free text analysis of ehr notes to find symptoms, and records of cough remedy prescriptions to detect ari cases with a sensitivity of 99%, and 2) an automated chest imaging classifier that labels reports as non-negative (could possibly support a diagnosis of pneumonia) or negative.1 a manual chart review was completed for all visits flagged by the pneumonia cda (detected by ari cda and having an associated non-negative chest imaging report). exclusion criteria included non-initial visit, lack of new symptoms, and inpatient admission. “nondismissable” pneumonia was defined as having [2 or more pneumonia symptoms and a nonnegative chest imaging report and (new or increasing chest imaging findings or no prior chest imaging).2 treatment was considered “timely” if prescribed prior to discharge from the emergency room or on the same date of the outpatient clinic visit. “pneumonia in plan” describes the documentation of pneumonia as a possible diagnosis by the prescribing provider. for all visits, provider diagnosis, timeliness of treatment, and antibiotic choice were assessed. results the pneumonia cda flagged 666 visits over the 8-year study period. of the 376 included cases, 271 (71.9%) were found to be nondismissable. providers missed pneumonia as a possible diagnosis in 95 (35%) of 271 nondismissable cases. nondismissable cases with pneumonia in plan (n = 176) received antibiotics, but in two cases, treatment was not timely. thirty (11.1%) of the nondismissable cases had no pneumonia in plan and did not receive timely treatment, while 65 nondismissable cases (24%) had no pneumonia in plan but received antibiotics for non-pneumonia ari diagnoses. fluoroquinolones were the most commonly prescribed antibiotic for pneumonia in plan (122 or 61.6% of 198) and for all visits (149 or 61.6% of 376). macrolide monotherapy and doxycycline, which may be used to treat cap in healthy patients with no risk for drug-resistant infection,2 were prescribed in 51 (25.8%) and 12 (6.1%), respectively, of visits with pneumonia in plan. the cda also discovered cases where the provider suspected pneumonia, but prescribed trimethoprim/ sulfamethoxazole (n=1) or beta-lactam monotherapy (n=4) despite current treatment guidelines. conclusions an automated cda is a novel method to identify outpatient cases of cap and can possibly uncover inadequacies in treatment. future cap studies may use cdas to evaluate guideline adherence, treatment timing, both short-term and long-term outcomes, and overall quality of care. antibiotics prescribed for visits flagged by the pneumonia cda keywords community-acquired pneumonia; quality of care; case detection; surveillance references 1. delisle s, kim b, deepak j, et al. using the electronic medical record to identify community acquired pneumonia: toward a replicable automated strateg. plos one 2013;8;e70944. 2. mandell la, wunderink rg, anzueto a, et al. infectious diseases society of america/american thoracic society consensus guidelines on the management of community-acquired pneumonia in adults. clin infect dis 2007;44 suppl 2:s27-72. *jeneen gifford e-mail: jgifford1@medicine.umaryland.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e76, 2015 development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi development of automated text-message reminder system to improve uptake of child vaccination in ethiopia zeleke abebaw mekonnen1*, fedlu nurhussien hussien2, binyam tilahun1, kassahun alemu gelaye3 and adane mamuye 2 1 department of health informatics, institute of public health, university of gondar, gondar, ethiopia 2 department of computer science, faculty of informatics, university of gondar, gondar, ethiopia 3 department of epidemiology and biostatistics, institute of public health, university of gondar, gondar, ethiopia. abstract introduction: non-attendance and delay for vaccination schedules remains a big challenge to healthcare workers. among the frequently mentioned reasons for missed vaccination in children is forgetfulness of caretakers to show up in vaccination schedules. this necessitates developing an automated reminder system with integration of mobile technologies. objectives: this paper aimed to develop and test an automated mobile text message reminder system in the local context of ethiopia. methods: this system is developed using iterative development process through phases of requirement analysis, design, development, testing and refinement. requirement gathering was done before development of the system. front end application was developed using java technologies while back end applications were developed with oracle database. finally, pilot testing of the automated reminder system was done on 30 participants. results: the automated system has been developed based on requirements. the text message reminder system has two components: 1. web based application for client registration and automatic reminder scheduling; 2. sms application for automatic sms text messaging. in the pilot testing, all the text messages (100%) were dispatched from the automated system to the respective participants. finally, the system has shown a notification that the text messages have been sent successfully. conclusion: text message reminder system has been developed for routine childhood immunization program in ethiopian context. text message based mhealth interventions should be carefully designed, developed, tested and refined before actual implementation. key words: text message, automated reminder, mhealth, vaccination, immunization development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi introduction childhood immunization is one of the most successful strategies to prevent illness and death from vpds (vaccine-preventable diseases) [1,2]. to effectively control vpds, maintaining high vaccination coverage is required with the target of the who to reach 90% coverage [3]. the who initiated the epi program in 1974 [4] and in ethiopia, it was launched in1980 [3]. the total number of antigens in the ethiopian national schedule reached to eleven following introduction of additional vaccines like hib and hep b, pcv, rota and ipv vaccines in 2007, 2011, 2013 and 2015 respectively [3]. the ethiopia immunization program considers a child to abbreviations bcg bacille calmette guérin dpt diphtheria-pertussis-tetanus edhs ethiopian demographic and health survey epi expanded program on immunization fmoh federal ministry of health hib haemophilus influenza type b mhealth mobile health opv oral polio vaccine pcv pneumococcal conjugate vaccine rct randomized controlled trial sms short message service vpds vaccine preventable diseases who world health organization * corresponding author: email: zelekeabebaw7@gmail.com doi: 10.5210/ojphi.v11i2.10244 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:zelekeabebaw7@gmail.com development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi be fully vaccinated if the infant has received bcg, 3 doses of dpt-hepb-hib, 3 doses of pneumococcal, 2 doses of rota virus vaccine, 4 doses of opv, ipv and a dose of measles before the age of one year [3]. table 1: schedule of epi in ethiopia age visits vaccines at birth 1 bcg, opv0 6 weeks 2 dtp-hepb1-hib1,opv1,pcv1,rota1 10 weeks 3 dtp-hepb2-hib2,opv2,pcv2,rota2 14 weeks 4 dtp-hepb3-hb3, opv3, pcv3,ipv 9 months 5 measles in order to successfully control and eliminate vaccine-preventable infectious diseases, timely vaccine coverage has to be achieved and maintained as scheduled [5]. however, substantial proportions of children in many countries still fail to benefit from all basic vaccines and vpds still pose a public health risk [6] with the highest rates of child mortality still in sub-saharan africa [7]. ethiopia has also the second largest number of incompletely vaccinated children from africa, next to nigeria [8]. in ethiopia, the edhs survey report has shown a steady progress in epi coverage where all basic vaccination coverage increased from 14% in 2000 to 39% in 2016. in terms of timeliness, the 2016 edhs report indicated that only 22% of children were vaccinated by the appropriate age [9]. such suboptimal coverage coupled with the untimely vaccination of children has contributed to outbreaks of vaccinepreventable diseases frequently [9-11]. the who also recommends that vaccines must be given before the first birthday within a specified vaccination schedules and intervals [12,13]. among the frequently mentioned reasons for missed vaccination in children is the lack of communication between child caretakers and health workers [14-16]. from previous studies; prior reminder not given (32.9%), mother’s forgetfulness (26.6%), mother being too busy (27%), being unaware of the need to return for subsequent doses (19%) and unknown place of vaccination (16%) were the major contributing factors for missing the vaccination doses and not vaccinating on time [17-20]. this necessitates developing an appropriate vaccine delivery strategy with integration of mobile technologies [21-23]. in order to improve access and quality of care, the federal ministry of health of ethiopia has recognized and positioned ehealth as a key transformation enabler [24]. to this end, mhealth technologies offer opportunities to advance the healthcare delivery and improve attendance to health facilities [25]. mobile communications technology has the potential to enhance adherence to health care services by facilitating interactive and timely access to relevant information [26]. the short development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi message service (sms) is one of the most widely used mobile communication method that is capable of sending and receiving text messages as a means of communication. sms based services are now more attractive to service providers and users as a result of the recent mobile phone use penetration and the large scale adoption of the existing services by users [26]. hence, mobile text message reminders are among the cost effective ways to improve attendance for health programs. different studies in bangladesh [27], beirut [28] and pakistan [14] demonstrated that a mobile phone intervention can improve child health services. although mhealth interventions are promising in health care, little is known about current practice in developing countries, including sub-sahara africa where mhealth is a relatively new concept and questions arise regarding the feasibility of the technology [15,16]. the successful implementation of sms based mhealth interventions also demands development of automated reminder systems with the current knowledge of the local context [29]. therefore, this paper reports the development and testing process of text messaging reminders that could improve timeliness and coverage of routine child hood vaccinations in ethiopia. objective to design, develop and test an automated text messaging reminder system for the routine child immunization program in ethiopia. methods description of the automated reminder system this automated reminder system is a web based application developed as an mhealth intervention package for a randomized controlled trial study that will assess the effectiveness of text message reminders in improving completeness and timeliness of routine immunization program in north-west, ethiopia. study design the following specific methods were considered during design and development of the automated reminder system: • conducting a requirement gathering and analysis • conducting a thorough architectural designs of the application in various platforms • conducting a complete database designs • conducting the necessary user interface designs of the final output • coding different components and integrating them to deliver the first prototype of the system • testing the prototype through various mechanisms • completing the whole system from the prototype and the feedbacks gained development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi development of the -system the system is developed using iterative development process. the development process was conducted through consecutive steps summarized in the table below (table 2). table 2. text message reminder system development steps step 1: requirement gathering step 2: designing the automated sms reminder system step3: development and testing of the automated sms reminder system step 4: pilot testing of the automated reminder system step 5: refinement and finalization of the system requirement gathering two methods were used to gather need assessment. the first method was observing the epi work flow of health facilities and making an interview with relevant stakeholders. the second method was by exploring information from secondary sources of data (figure 1). figure 1: requirement gathering for the text message reminder system development system design and development system design: it illustrates how the vaccination reminder system via text message alert operates (figure 2). first, caregivers who decided to use the system will be registered in nearby health facilities. during the registration, information on the caregivers and child will be stored. this includes data on caregiver’s mobile number and date of birth of the registered child. development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi figure 2: text message reminder system architecture for routine child vaccination automated system development they system is developed using front end and backend technologies. for the front end development java technologies have been used. for the back end, oracle database has been used. the system is developed with prime face technologies so that it have flexible graphical interfaces and it is easy to use. technologies used in this automated system development, the following hardware and technological software products were used: hardwares used for system development the system development needed different hardware materials including: • personal computer (pc) • flash disc • sirreta modem: this modem is used to handle the sim card. sofwares used for system development • glash fish server: we have used this web server for development and deployment. • edraw max: it is a graphics software that makes it simple to create professional looking flowcharts, workflows, program structures, web design diagrams and database diagrams. development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi automatic text message feature design each text message was designed within the length of standard characters limit designed for 160 character. based on the prior assessments text messages were developed both in amharic and english languages. each text message will be dispatched automatically to caregivers from the computerized system. sms messaging components with large scale text messaging programs require specialized software applications and services to handle message content, automatic scheduling, and message routing services to deliver messages via multiple cellular network carriers. in this system, an sms gateway encodes and routes text messages. in case of delayed vaccinations, where a child didn't get vaccines on schedule, re programing will be done to occur after 4 weeks from the previous dose. for example, if a pentavalent vaccination is given later than the scheduled date, then text message reminders for the subsequent pentavalent dose will be reprogrammed to occur at 4 weeks from the date of vaccine receipt as per the national immunization guidelines. results the automated reminder system was developed in two phases. the system design and development was completed on november, 2018 and piloting the automated text message reminder system was completed on march, 2019. design and development of the automated reminder system an automated web based application for client registration, automatic reminder scheduling and automatic text messaging has been designed and developed. based on the requirement gathering, initially the content, frequency and duration of the text-messages were designed. the developed text message contents were discussed with research team, selected health workers and mothers for refinement. second, a computerized distribution system was developed on a desktop server interfaced with the network of a local mobile service provider through a global system for mobile communication modem. the automated reminder system underwent repeated refinement before the pilot testing. pilot testing of the automated reminder system pilot testing of the automated system involved 30 participants. in the pilot testing, all the 30 text messages (100%) were automatically dispatched from the application software to the participants. in addition, the system has shown notifications that the text messages have been sent for respective participants successfully. interface design and features of the reminder system the automated reminder system has two components: 1. web based application for client registration and automatic reminder scheduling; 2.sms application for automatic sms text messaging. the features of the automated reminder system has been presented as follows: development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi 1. login page: this page welcomes the user to the reminder system application. the reminder system has a secured log in page which enables users to access the system based on their access privilege. only authorized users are able to use the system and this is due for security purposes. a user needs to provide a user name and password to log in to the system. if both of the information are correct, then the system will display the main page. on the other hand, if the user name or password is incorrect the system will display an error message (figure 3). figure 3. log in page of the automated reminder system 2. client’s registration and list setup: this is the window where each individual caregivers and their infants socio-demographic as well as their health related information’s will be recorded. the system has validation rules that will detect the double recording of individual clients. in addition, it has a must to enter fields to capture all-important information’s. registration of new clients for child immunization can be made by clicking the “create new” button. this page allows the user to add new contact information or edit existing ones. in order to delete any contact the user just clicks the delete link beside the desired contact in the grid in order to delete that contact (figure 4). development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi figure 4. client’s registration and list setup of the reminder system 3. vaccination scheduling setup: the vaccination scheduling setup manages the automatic scheduling of vaccination visits by considering the date of birth of the infant. in case of delayed vaccinations, the reminder system reschedules the vaccination visits by taking in to account the vaccination dates of the previous doses (figure 5). development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi figure 5. vaccination scheduling setup of the automated reminder system 4. reminder analysis setup: this window helps to see the status of text messages sent for each client. it has a filter option to look for individual records. the system also has a field too record the vaccination status of each infant (figure 6). figure 6. reminder analysis set up of the automated reminder system development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi 5. automated text message setup: the automated text messages were developed both in amharic (local language) and english languages for each of the four vaccination schedules. the system has reminder message setting with which the text messages can be edited accordingly (figure 7). figure 7. text message set up of the automated reminder system 6. export options: the automated reminder system can export the data sets to excel and pdf versions (figure 8). figure 8. export options of the automated reminder system development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi 7. automatic text messaging and sent message notification: the system manages an automated dispatch of text-messages based on pre-determined phone number of caregivers. when a text reminder has been sent to a caregiver, the system will display a notification message that says “text message has been sent to the client”. on the other hand, when the system was unable to send the text messages automatically, it shows sending error messages (figure 9). figure 9. text message sending and error notification of the automated reminder system discussions existing practice on vaccination schedule is via written appointment. nevertheless, such approach may not be sufficient as parents may forget due to a tight work schedule and daily routines. missed appointments are also an avoidable which impact upon the health outcomes of clients. similarly, poor awareness about immunization schedules and forgetfulness were the most cited reason parents gave for not completing their child’s immunization timely [30]. as the use of information and communication technologies (ict) has become the nervous system of all modern economies, making health institutions smarter is usually achieved through the use of ict intensive solutions like mhealth. among the mhealth interventions, sms is one which stands for short message service, which is a communications protocol adopted as a synonym for text messages [26]. mobile sms has become more popular because it is easy and simple to use, and every type of mobile phone has this kind of application. health workers are also increasingly utilizing sms based reminder systems to improve health service uptake and continuity of care. hence, simple and effective sms based mhealth interventions that can be integrated in the existing health systems are required to increase caregiver’s attendance for timely immunization of children in ethiopia [31]. sms based reminder systems effectiveness varies across different settings [26]. in line with this, a randomized controlled trial (rct) study is being conducted to test the effectiveness of text message reminders in improving continuity of vaccination services in north-west, ethiopia. this trial is expected to produce evidence on the effectiveness of text message reminders on the development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi uptake of routine child hood immunization which will then be scaled up into similar geographic areas in the country. therefore, this text message based mhealth intervention package is developed as an intervention package for the rct study. this paper demonstrated how best to develop an automated reminder system for vaccination program in ethiopian context. the study indicated the importance of obtaining feedback about the content of text messages and iteratively testing the developed system before actual deployment. research findings also revealed that tailoring the reminder system based on client preferences and applying multiple modalities results in better system development [32]. evidences also showed that text messages should incorporate methods of ensuring that the text messages are developed and tested in the most appropriate way before they are deployed [29,33]. strengths and limitations: the developed reminder system is secured with a password protected user interface which gives access to the system. the system works offline with desktop application and can be accessed remotely with network connectivity. the application has backup system. the system is easy for updates on different domains. as a challenge, poor network connectivity and bugs within the system affected the sending process of the text messages during testing of the system. this led to repeated testing of the automated system until it successfully sent all the desired messages. since the automated system is modem based, it only confirms the automatically dispatched text messages sent status from the system. however, it could not confirm the actual delivery of the text messages to the mobile phones of each caregiver. conclusions text message reminder system has been designed and developed for routine childhood immunization program in ethiopia. text message interventions should be carefully designed, developed, tested and refined before actual implementation. moreover, the development of automated reminder systems should take in to account the local context and involvement of different stakeholders. acknowledgements the authors would like to thank the university of gondar for supporting this system development. funding this study is supported by the university of gondar. competing interests authors have no competing interests availability of data and materials data sharing is applicable up on request development of automated text-message reminder system to improve uptake of child vaccination in ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e15, 2019 ojphi ethical approval and consent to participate this study obtained ethical approval from university of gondar institutional ethical review board ref. no: o/v/p/rcs/05/060/2018. in addition, study permissions were acquired at all levels of governmental administration systems including health offices and health facilities. for the pilot testing, informed 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children with uncomplicated malaria in western kenya. malar j. 14, 320. pubmed https://doi.org/10.1186/s12936-015-0825-x 34. csa. ethiopia demographic and health survey 2016. addis ababa, ethiopia. 2016. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26283229&dopt=abstract https://doi.org/10.1186/s12936-015-0825-x 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts automated collection of electronic health record healthy weight data for surveillance sean p. mikles1, jennifer l. foltz2, ian painter1 and william b. lober*1 1university of washington schools of medicine, nursing and public health, seattle, wa, usa; 2centers for disease control and prevention, atlanta, ga, usa objective to demonstrate the feasibility of using healthy weight (hw) it standards in public health surveillance through the collection and visualization of patient height, weight and behavioral data. introduction clinical data captured in electronic health records (ehr) for patient health care could be used for chronic disease surveillance, helping to inform and prioritize interventions at a state or community level. while there has been significant progress in the collection of clinical information such as immunizations for public health purposes, greater attention could be paid to the collection of data on chronic illness. obesity is a chronic disease that affects over a third of the us adult population1, making it an important public health concern. both hl7 v.2.5.12 and clinical document architecture (cda) messages3 can be used to facilitate the collection of hw ehr data. these standards include anthropometric and demographic information along with the option to transmit behavioral, continuity of care, community resource identification and care plan information. we worked with vendors participating in the integrating the healthcare enterprise initiative (ihe) in developing, testing and showcasing scenarios to facilitate system development, increase the visibility of hw standards and demonstrate potential usages of obesity-related information. methods the ihe initiative showcases integration of health information systems within and across institutions. integration profiles, which describe the use of standards to implement data transactions that satisfy specific use cases, are developed by committees, implemented and tested in a multi-vendor connectathon, and then demonstrated at national and international meetings. this process was followed to illustrate the feasibility and potential uses of hw data. a system was developed to receive and manage data from v.2.5.1 and cda hw messages sent by clinical systems. potential uses of hw data were conceptualized through project team collaborations and discussions with clinical and public health stakeholders and used to create a hw dashboard. these uses included stratified displays of overall weight metrics by demographics and locations, displays of the distribution of behavioral risk factors such as diet and physical activity, and the display of the metrics of a hypothetical provider’s cohort of patients as compared to the entire community. results the hw surveillance data collection and dashboard visualizations have been demonstrated at the 2014 health information and management systems society (himss, orlando fl) and public health informatics (phic, atlanta ga) conferences. the visualizations were presented as part of an integrated walkthrough with vendor partners, which included the entering of clinical information in an ehr, the transmission of the information to the hw information system, and the display of graphs and visualizations of a hypothetical data set based on metrics from the state of washington. the dashboard and displays for himss and phic may be viewed at http://ihe2014.cirg. washington.edu (accessed sept 7, 2014). conclusions this project demonstrated hw it standards based collection of weight-related data from ehrs and explored data visualizations derived from those data. hw surveillance could identify the need for, shape the design of, and support monitoring and evaluation of public health interventions. future work can expand these demonstrations into limited pilot projects to investigate the efficacy of ehr-based hw data collection. visualizations, like this map of simulated bmi data by zip code, can help public health agencies target interventions. keywords healthy weight; obesity; chronic disease; informatics; surveillance acknowledgments this work was supported by funding from cdc/national center for chronic disease prevention and health promotion and in part by nih nlm training grant nr. t15lm007442. references 1. cdc. (2011). obesity: halting the epidemic by making health easier at a glance 2011. [online]. available: http://www.cdc.gov/ chronicdisease/resources/publications/aag/obesity.htm. [6 sep 2014]. 2. hl7. hl7 version 2.5.1 implementation guide: height and weight report, release 1. [online]. available: http://www.hl7.org/implement/ standards/product_brief.cfm?product_id=315. [6 sep 2014] 3. ihe. ihe quality, research and public health technical framework supplement for healthy weight. [online]. available: http://www.ihe. net/uploadedfiles/documents/qrph/ihe_qrph_suppl_hw.pdf. [6 sep 2014] *william b. lober e-mail: lober@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e88, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 136 (page number not for citation purposes) isds 2013 conference abstracts detection of outbreak signals using r steve e. rigdon*1, george turabelidze2, richard declue1, sarah patrick1, ehsan jahanpour2, rong he2 and jennifer lloyd2 1saint louis university, saint louis, mo, usa; 2missouri department of health and senior services, saint louis, mo, usa � �� �� �� � � �� �� �� � objective �������� � � �� �� ����������� � ���� ������� � ������������ ��� ���� ������ � ��������� � �� � 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������� � �������� � ���������� ��� ����� conclusions �.��.��������� � � ��� ����� ���� ��������� ���������������������� ���������� ��� ������ ��� ������������������������� � �� � �� ������ ����� ������ � �����:���� ���� ��� � � ������� ��������� ��������� ������������ ��� ������ �� �� ������5�� ������������������ �� ������� � �� �� �� ���'������ ��(� � �������� ������� � ��� � � � ��� � ������� �� ���������� ���� ��� � ��� ����������������� � � �� ��� ����� �������������� ������� � ��� � ������ �� ������&������� ������ � ����������� ��� ����.��.��� ���������� ���� ������ ���� ��� � �� ������ ��� � ���� ������� ����� �� � � �������� �������������� � � ���) ��������� � ��� �� �������� keywords 4�� ����� ���������;������������� ������ ���;��� � �� ������� � �;� ������ � references *��1����!�<����� ���1� �!�=����!����������� �5� �� � -�6����5���� ��� � ���2!���������!�9���>���!�,++?� ,��2�1������������������ �!�2-�5�� ��� ��� ������������������� � � �� �� �� ��������!�������� ������ ���� ����,�,�*��2�&���� ��������� �� �� �� ��1��������!�@���� !�5� ��� ��8�a9�7�b+++%*�+0�+!�c2d� ����-ee����2����f� �����!�,++%� 7��2������!������!�����-ee����� ������ ��e ����e!�,+*7� *steve e. rigdon e-mail: srigdon@slu.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e128, 2014 ojphi the challenge of big data in public health: an opportunity for visual analytics 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 the challenge of big data in public health: an opportunity for visual analytics oluwakemi ola1, kamran sedig1 1. insight lab, western university, canada abstract public health (ph) data can generally be characterized as big data. the efficient and effective use of this data determines the extent to which ph stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. as stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision -making, interpreting, and problem solving. performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data’s volume, variety, velocity, and veracity. such being the case, computer-based information tools are needed to support ph stakeholders. unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. this paper presents visual analytics (va) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. historically, ph has lagged behind other sectors in embracing new computational technology. in this paper, we discuss the role that va tools can play in addressing the challenges presented by big data. in doing so, we demonstrate the potential benefit of incorporating va tools into ph practice, in addition to highlighting the need for further systematic and focused research. keywords: big data, analytics, public health informatics, visual analytics, analytical reasoning, interactive visualizations, human-information interaction correspondence: oola@uwo.ca doi: 10.5210/ojphi.v5i3.4933 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. introduction data and information are both currency and product within the field of public health (ph) [1]. ph data is often highly complex because of its high volume, its various sources, its velocity of generation, and sometimes the low degree of veracity of the sources from which it originates. ph data is gathered from heterogeneous sources [2], may be unreliable, encoded in a variety of formats [3], [4], and can be volatile (i.e., changing, and available only for a limited amount of time) [5], all characteristics attributed to big data. these characteristics of ph data pose a challenge to the ph workforce in terms of whether and how effectively the data is used. http://ojphi.org/ mailto:oola@uwo.ca ojphi the challenge of big data in public health: an opportunity for visual analytics 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 the ph workforce is comprised of people trained in a variety of disciplines with daily duties necessitating the extraction of information and construction of knowledge from the mass of available data. in this paper we refer to any individual seeking to use ph data in a profes sional capacity as a stakeholder. as stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, interpreting, decision making, planning, and problem solving [6]. performing these activities with data can involve complex cognition and can pose cognitive challenges for the unaided mind. thus, computer -based information systems and tools may be needed to support the activities in which ph stakeholders engage. in the context of ph, access to data does not necessarily guarantee that the data will be used well—i.e., that cognitive activities will be performed in an effective manner (see [6] for more discussion of this issue). additionally, the ph community acknowledges that decisions and policies are often made in an ad hoc fashion devoid of evidence [7]– [8]. the efficient and effective use of data determines the extent to which ph stakeholders can sufficiently address the health concerns of the community [1], [9]. consider the following scenario in the fictional town of lumcard, louisiana, which demonstrates the critical role of data in addressing public health issues. the day before thanksgiving, the director of lumcard’s health department receives an alert showing an unusually high incidence of complaints of diarrhea, vomiting, high fever, and sore throat. discussion with area doctors reveals that local hospitals have confirmed diagnosis of west nile virus (wnv) in a high number of patients; this helps the director dismiss his first assumption that a food poisoning outbreak exists in lumcard. the regional epidemiologist is made aware of the situation and immediately begins to investigate this unseasonable occurrence. the epidemiologist not only needs to be able to access data, but also must compare health records, filter out irrelevant data, examine environmental influences, and identify relationships among various factors. in addition, she will need to develop, test, and discard hypotheses about the cause of wnv and collaborate with other ph stakeholders in order to determine how best to ensure the health of the citizens of lumcard. while having access to data is critical, the lumcard ph team’s success in addressing the potential health hazard is largely dependent on their ability to effectively use the available data in their reasoning, sensemaking, decision making, and planning activities. under different time constraints, ph stakeholders must perform a myriad of activities, which ultimately have health, social, political, economic, and ethical implications for the community [10]. furthermore, as stakeholders interact with data they encounter a number of obstacles relating to its volume, variety, velocity, and veracity [2]– [5], [11]– [12]. over the course of the last 20 years, computational tools and systems have been developed to support the work activities of ph stakeholders. current tools include data analytics tools such as stata [13], and interactive visualization tools such as malaria atlas project [14]. while these types of tools are beneficial in addressing certain work activities of ph stakeholders, they fall short in supporting cognitive activities that involve the use and working with large, heterogeneous, and complex bodies of data [15]. public health tends to lag behind other sectors in the adoption of new technology (for examples, see england et al.’s [16] examination of ph’s slow rate of information technology adoption, and shortliffe’s [17] comparison of healthcare with other sectors). the recent emergence of a http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 category of computational tools known as visual analytics (va) tools is no exception. these tools are intended to alleviate some of the shortcomings of the aforementioned tools with regard to the complexity of data and support of the visuo-analytical reasoning of their human users1. va tools combine interactive visual representations with advanced analytics techniques to synthesize, analyze, and facilitate visuo-analytical reasoning and other high-level cognitive activities involving data [18], [19]. this is beneficial for data-intensive fields [19] such as ph, finance, insurance, sales, and climatology, to name a few. while many fields, such as finance and sales [20], have seen widespread adoption of va tools, ph has not. in this paper, we discuss the role that va tools can play in assisting ph stak eholders to perform cognitive activities involving big data. we focus on analytical reasoning as an activity that plays an important role in many other activities. through a synthesis of research across multiple fields including cognitive science, data mining, human-computer interaction, and informatics, we explicate the benefits of va tools in addressing the challenge that big data poses to ph practice. the rest of this paper is organized as follows. section 2 discusses foundational concepts—i.e., ph data and information, analytical reasoning, visual representations, and human-information interaction. section 3 describes va tools, their components, and how they facilitate analytical reasoning. section 4 discusses the benefits and role of va tools in ph and highlights current tools in use. through the use of a hypothetical scenario, section 5 further explicates the usefulness of va tools. finally, section 6 provides a summary and briefly outlines limitations and some future areas of investigation. background this section presents necessary background concepts and terminology used in this paper. in order to address the health concerns of the community, ph stakeholders interact with data to perform a variety of work activities. we depict the needs that va tools must address for ph stakeholders, by describing the data they interact with, the nature of their work activities, and the analytical reasoning tasks in which they engage. furthermore, a va tool’s interface influences the stakeholder’s ability to access data and perform visuo-analytical reasoning. therefore, we explain two major components of the interface —namely: visual representations and interactions. ph stakeholders the workforce charged with safeguarding and improving the health of the community through a population focus, characterized in this paper as ph stakeholders, is highly varied. as discussed by o’carroll et al. [5], the ph workforce may be more diverse than any other group of health professionals. ph stakeholders come from a diverse set of backgrounds and are trained in a myriad of disciplines [21]. irrespective of their area of expertise and sub-field of application, stakeholders must interact with data to perform a myriad of work activities. ph data and information to frame our discussion, we characterize data as digitally stored, sensed changes in the environment, and information as processed, organized, and/or analyzed data that depicts its relationships2. ph data can be described by its high volume [11], [22], [23], [12], great variety [2], [24], [25], high velocity [5], and low veracity [9], [23], [26]. these four features of ph data are http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 typical characteristics of big data. as a result, ph data is big data. while synthesis of and access to ph data has been a focus of the ph informatics literature [6], the use of data by stakeholders to create information, particularly as mediated by computational tools, presents a growing challenge. computationally-mediated reasoning requires not only the ability to access relevant data, but the ability to control how data is structured, combined, displayed, and interacted with [27]. in addition, stakeholders must be presented with representations that accurately communicate what is known or unknown, the impact of actions, relationships that exist, and extent of uncertainty and risk that are involved during analysis [22], [28]. the seamless incorporation of user-guided analysis techniques into computational tools is crucial in facilitating the systematic use of data. ph activities ph stakeholders engage in a variety of work activities in an effort to improve and ensure the health of the community [5], [24], [21]. these activities vary by work group (e.g., epidemiologist or nutritionist), by level within a work group (e.g., state, local, federal), and by fu nction [29]. in the united states, these work activities have been grouped by the institute of medicine (iom) into three core functions—namely: 1) assessment, which includes investigating and analyzing the occurrence and causation of health problems and hazards; 2) policy development, which includes priority setting, advocacy, and development of policies; and 3) assurance, which includes managing resources and informing and educating the public about health issues and services [30]. in this paper, we use the iom core functions classification to group ph work activities. regardless of the core function with which the ph stakeholder is tasked, ph work activities are a form of knowledge work [6]. in other words, at a basic level, ph stakeholders are knowledge workers—that is, most of their work is performing information-dependent cognitive activities. knowledge work activities are non-routine and require a combination of convergent, divergent, and creative thinking in order to be completed [31]. as knowledge workers, ph stakeholders engage in a myriad of cognitive activities including analytical reasoning, decision making, sensemaking, and problem solving. analytical reasoning while a comprehensive discussion of high-level cognitive activities is beyond the scope of this paper, to fully appreciate the utility of va tools, we examine ph stakeholders’ cognitive processes as they work with data. to this end, we focus on analytical reasoning and discuss some of its characteristics, explain how it facilitates other high-level cognitive activities, and briefly highlight its impact on ph work activities. analytical reasoning is based on a rational, logical analysis and evaluation of data and information and encompasses different kinds of reasoning such as inductive, deductive, and analogical reasoning [27]. an inference or conclusion is reached based on the systematic analysis of data. as an activity, analytical reasoning emerges from the completion of lower -level tasks. some of the tasks include, but are not limited to, identifying relationships among pieces of data, asserting and testing key assumptions, testing biases, assessing alternatives, developing hypotheses, and supporting conclusions with adequate evidence [18], [32]. although analytical reasoning is a structured and disciplined process, the aforementioned tasks typically occur in an http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 iterative and non-linear fashion [27]. in other words, the order in which low-level tasks occur is not fixed, but varies according to the cognitive needs and overall goals of the stak eholder. analytical reasoning seldom occurs in a vacuum, but instead may occur concurrently with other cognitive activities. in particular, analytical reasoning facilitates problem solving and decision making [33], [34]. analytical reasoning can be viewed as a transformative process in which new information, knowledge, and insight are derived from given data [27], [35]. in some situations, this new information, knowledge, or insight serves as the basis for decision-making and problem solving [33]. to illustrate the interconnectedness of analytical reasoning, decision-making, and problem solving, consider further the situation in lumcard: the epidemiologist, engaged in analytical reasoning, concludes that there is a direct correlation between temperature and incidences of wnv in the city. in addition, from her analysis, she is able to narrow down the list of possible mosquito breeding sites to two local bodies of water. subsequently, the epidemiologist and health director make the decision to restrict access o f the residents to local bodies of water, and also send out an environmental health scientist to collect samples to determine the mosquito infestation levels at the shortlisted locations. due to the complex, dynamic, and interdependent nature of public health issues, a faulty decision or policy can have a negative impact that may not be immediately recognizable. analytical reasoning provides the basis for decisions, plans, and policies and should, therefore, not be overlooked. while the ph community recognizes that information should be used to inform policy-making and program development [26], [36], the reality is that decisions and policies are often made in an ad hoc fashion, mostly based on gut feelings, short-term goals, and/or information satisficing [7], [37], [38]. for this reason, there has been a push to move stakeholders closer to adopting evidence-based approaches in ph practice. this approach advocates the systematic use of information and application of scientific reasoning principles in a contextualized manner while making decisions and creating policies [8], [26]. the success of this approach is contingent on ph stakeholders being able to effectively interact with and use data [22]. visual representations when reasoning is mediated by va tools, data is made accessible to the user of the tool through external visualizations—i.e., visual representations. therefore, it is necessary to discuss the benefits of visual representations and their effect on stakeholders’ activities. visual representations encode data items using visual marks (e.g., lines, dots, shapes) and combine and integrate these into more complex structural forms (e.g., scatter plots, heat maps, bar charts) [ 6]. these representations seek to capitalize on the human visuoperceptual system, which is specifically suited to rapid processing of data and recognition of visual patterns. the benefits of such representations have been discussed by researchers including larkin and simon [ 39], glenberg and langston [40], and card et al. [41]. according to card et al., visual representations can amplify cognition by increasing the memory and processing resources available to users, reducing the search for information, enhancing the detection of patterns, enabling perceptual inference operations, and encoding information in a manipulable medium [41]. the manipulability of a medium is an important factor. while static representations have been historically used by ph stakeholders, from john snow’s use of a map to reason about a cholera outbreak in 1850 [42], to the recent use of atlases for mapping the risk of malaria in africa [43], http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 they put the brunt of the information-processing load (i.e., analytical reasoning and decision making) on the cognitive resources of their users [6], [27], hence negatively affecting their usability. computers, on the other hand, allow visual representations to be interactive and dynamically manipulable. this allows information processing to be shared between the user and the tool [ 27], reducing, and possibly bridging, the gap between the internal (mental) representations of the user and the external (visual) representations of the tool [27], [44], [45]. interactive visual representations can offer users flexibility, support convergent and divergent thinking, and accommodate the user’s perceptual and cognitive needs [18], [27]. furthermore, interactive representations allow stakeholders to control which subset of data is visually displayed while still having access to data latent in the system [27] [46], which is important for fields like ph where large amounts of data cannot be visualized all at once. in addition, interactive visual representations allow stakeholders to choose how things are represented [27], [46], which has an effect on the reasoning tasks in which stakeholders engage. researchers in cognitive science have demonstrated that different representational forms can impact how cognitive activities are performed [39], [47], and even constrain and limit stakeholders as they engage in a particular task [45], [47], [48]. therefore, ph stakeholders stand to benefit from tools that allow users to manipulate visual representations, a capability made possible through interaction. human-information interaction through interaction, the user of a va tool is able to control, not only the form or content of the visual representation, but also the entire dialogue with information [ 27], [45]. interaction moderates the discourse between information and the user and can be conceptualized at different levels. in this paper, we describe interaction in terms of the actions the user performs on the interface of the tool, the consequent changes and reactions in the visual representations, and the user’s perceptions of changes to the representations [27]. in the context of va tools, by performing actions on the visual representations, the user is able to reach into the database and operate upon data. examples of such actions include filtering, annotating, drilling, selecting, and comparing [27]. in response, the reactions visible through changes in the visual representations (i.e., on the interface) ensure that the discourse is not one-sided. equally important are the reactions that are not visually perceptible that occur within the va tool [44]. the user’s perceptions of changes to visual representations complete the interaction loop. together, actions, reactions, and perceptions promote the back-and-forth dialogue between the user and the represented information. the sequence in which actions are performed is sometimes at the discretion of the user. this is beneficial in fields such as ph where software designers are not privy to how various subsets of data will be used in analysis by the stakeholder. the user -guided sequencing of actions and discourse with information is critical in va tools that function to facilitate ph stakeholder’s analytical reasoning tasks. visual analytic tools va is sometimes defined as the “science of analytical reasoning facilitated by interactive visual interfaces” [18]. va tools combine data analytics and interactive visualizations to support users’ reasoning, and create an environment in which users engage in a more involved discourse with data and information [18], [19]. prior to the development of va, various groups of computational http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 tools sought to address the information-based needs of professionals. in ph, two groups are data analytics and interactive visualization tools. this section highlights the limitations of these two groups of tools, describes the components of va tools, and explains how analytical reasoning can be performed using va tools. data analysis or analytics tools incorporate techniques and algorithms from a variety of fields including statistics (e.g., mean and correlation), data mining (e.g., classification and clustering), and machine learning (e.g., artificial neural network and support vector machines) to facilitate the discovery and understanding of patterns in data [49]. current data analytics tools that assist ph stakeholders in analyzing data include stata [13] and epiinfo [50]. while the aforementioned standalone data analytics tools are capable of processing massive amounts of data, they neither deal with noisy and highly heterogeneous data efficiently, nor are capable of handling ill -defined problems that require human judgment [15]. because these tools take over the analysis process and mostly hide the intermediary steps, stakeholders can only be minimally in control of or involved in the analytical reasoning process. complementing data analytics tools, interactive visualization tools represent data in a visual form, allow users to control the flow of data, and let them customize representations to cater to their cognitive and contextual needs. some interactive visualization tools focus on visualizing abstract, nonphysical data such as text and statistical data [41], while others portray physical data such as the human body and molecules [51]. current ph interactive visualization tools include malaria atlas project [14] and spatio-temporal epidemiological modeller [52]. while beneficial, these types of tools prove inadequate when faced with problems requiring advanced computational analysis and big data [15]. components of va tools while data analytics tools with advanced automated analysis and interactive visualization tools aided by human judgment are advantageous in certain situations, their respective limitations create a void, and it is only through va tools that some of today’s most pressing data analysis problems can be addressed [15]. va tools fuse the strengths of both sets of tools to create an environment in which the user engages in a more involved discourse with data. this process is not simply an internal automated analysis with an external visual representation displayed at its completion. instead, it is an integrated human-information dialogue in which data processing is distributed between the user and the main components of the tool—described in this paper as the analytics engine and interactive visualization engine [44], which are described below. analytics engine human cognition displays several limitations when confronted with mental tasks that are data intensive (i.e., they involve the use of bodies of data that are too large or too complex), and as a result computational tools can be used to support such tasks. the analytics engine in va tools is intended for this purpose. it stores, transforms, and performs computational analysis on data. this process, as shown in figure 1, is subdivided into three main stages: 1) data pre-processing, 2) data transformation, and 3) data analysis. in the pre-processing stage, data retrieved from a variety of sources is automatically processed. common tasks in this stage include data cleaning, integration, fusion, and synthesis [49]. in the data transformation stage, the pre-processed data is http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 converted into a form that is more conducive to data analysis. this stage includes tasks such as data normalization and aggregation [49]. figure 1: the analytics engine component of va tools finally, the data analysis stage involves the discovery of patterns and allows for the extraction of valuable information. while historically computational tools have focused on the analysis of one form of data, va tools overcome this limitation and can analyze and discover patterns in multiple forms of data (e.g., text, video, geo-spatial, etc.) together in order to create information. this is done by drawing on the tasks and techniques that originate from a myriad of fields including statistics (e.g., standard deviation, correlation analysis), machine learning (e.g., classification, clustering, dimension reduction), textual analysis (e.g., document summarization, concept extraction), image analysis (e.g., image segmentation, object recognition), video analysis (e.g., motion detection), and geo-spatial analysis (e.g., surface analysis, locational analysis) [53]– [54]. in some va tools, computational analysis is not a system-controlled process but a usercontrolled one. the blue arrows in figure 1 are indicative of the extent of the user’s involvement in the analysis process. this process is a sophisticated discourse that goes beyond simplistic interaction to deep user-guided analysis of data. the interactive visualization engine allows the user to access and control the flow and analysis of data. interactive visualization engine in va tools, the interactive visualization engine is composed of the rendering and mapping component that takes analyzed data and creates interactive visual representations (i.e., information). interactive visual representations allow the user to access, restructure, analyze, and modify amount and form of displayed information [18], [19]. the user’s actions can impact the discourse in many ways, three of which are shown in figure 2. firstly, as shown by blue arrow 1, the user can change how the visualized information is encoded, as, for instance, by replacing a pie chart with a bar graph. secondly, as depicted by blue arrow 2, the user can change the subset of information displayed. thirdly, as depicted by blue arrow 3, the user has the ability to guide the analysis process by selecting and ordering how data analysis tasks occur. this in turn sets off a chain of internal reactions resulting in the execution of additional data processing tasks previously shown in figure 1. http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 figure 2: interactive visualization engine component in va tools discourse mediation by va tools in order to understand how the application of va tools facilitates analytical reasoning in ph contexts, it is necessary to explicate the human-information discourse that occurs when ph stakeholders use va tools. analytical reasoning emerges from the collaboration between the user and the tool [27]. consequently, the internal cognitive processes of the user and the components of the analytics and interactive visualization engines are all involved in the predominantly user-controlled dialogue with information [55], [56]. as shown in figure 3, as the user performs actions on the interface, the va tool’s visible reactions are communicated by changes in the representations, which the user can perceive. analytical reasoning can be conceptualized as the top level of a hierarchical structure of processes. when mediated by va tools, analytical reasoning can be broken down into sub activities (e.g., knowledge discovery, sensemaking), which emerge from tasks (i.e., goal-oriented behaviors such as exploring, organizing). these tasks can also be broken down into sub-tasks, which in turn emerge from the completion of lower level actions performed on the tool (e.g., filtering, annotating) [27]. for instance, as shown in figure 3, the epidemiologist engaged in analytical reasoning about the origin of wnv might first need to discover new knowledge about the situation in lumcard. in order to do this, she might first need to complete the task of exploring the redacted health records of confirmed cases. at this point, it is possible she might choose to filter out unconfirmed cases, drill down into the demographic characteristics of confirmed cases, and then compare the attributes (e.g., age, ethnicity, gender etc.) to determine if a correlation exists. thus, analytical reasoning emerges over time through a back -and-forth cyclic chain of actions, reactions, and perceptions. http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 figure 3: the hierarchical structure of analytical reasoning emerging from lower level processes, adapted from [27]. where visual representations are depicted as vrs, perceptions as p x, and reactions as rx (where x stands for 1, 2, 3, and n-1). factors affecting quality of discourse recent theories of cognition suggest that cognitive processes do not take place solely within an individual’s head, but are distributed across social relationships, the material environment, and time [27], [47], [55]. in other words, analytical reasoning, formerly conceived as a cognitive activity that occurs exclusively in the brain of the ph stakeholder, can in fact be distributed across computational tools and other ph stakeholders. as a result, in the context of va tools, a joint cognitive system is formed between the user and the tool [27], [57]. va tools therefore play an important role in—and depending on their design can either enhance or impede—the humaninformation discourse. some factors affecting the quality of the discourse are: how information is encoded in visual representations, how seamless the coordination is between the user’s internal representations and the tool’s external visual representations, and how information processing is distributed between the components of the joint cognitive system (i.e., user, analytics engine, and interactive visualizations). in va tools, external representations not only convey information, but also guide, constrain, and even determine cognitive behavior of the user [48]. the manner in which interactive visual representations are designed is an important consideration as research has shown that external representations should be appropriate for the task in which the user is engaged (for an in -depth discussion see [58]). as users perform analytical reasoning tasks, they seek to harmonize and coordinate their internal representations and the tool’s external representations [48], [59]. when processing data in such a dynamic manner, a cognitive coupling is formed between the user and the tool [27], [60]. the strength of the coupling between the user’s internal representations and http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 the tool’s external representations is dependent upon a number of factors, including what actions are made available to the user and the quality of these actions [27]. in most situations, interactions should allow the user to select which subset of information to display, to manipulate external representations, and to choose which analysis techniques to perform so that s/he is able to complete the task at hand (for an in-depth discussion see [27]). another consideration relating to the discourse is the quality of interaction (i.e., interactivity) that emerges through the use of va tools. this consideration is important because research suggests that the quality of interactions has important cognitive effects (for an in-depth discussion see [44], [57]). as information processing is distributed across the joint cognitive system, properly designed va tools must take into consideration the strengths and limitations of the components of the system when distributing the requisite load of information processing in any given context (for an indepth discussion see [56]). these four considerations, among others, affect the ability of tools to facilitate reasoning and as a result va tools must not be viewed as a silver bullet to alleviate all the problems facing stakeholders, as the efficacy of the human-information discourse in these tools depends on how well and human-centered their design is. benefits of visual analytic tools in public health va tools are advantageous to numerous fields, including ph, because they combine the benefits of both data analytics and interactive visualizations. in ph, conclusions or inferences drawn may need to be conveyed to different groups of stakeholders including legislators, hospital directors, or community group leaders who were not involved in the analysis process [5]. information, therefore, must be conveyed in a manner commensurate with the cognitive and contextual needs of the ph workforce. because va tools allow users to participate in the data analysis process, and give them partial control over the system’s behavior, these tools can provide the flexibility to accommodate the needs of this diverse workforce. this is beneficial to ph in a number of ways, four of which are described. firstly, through interactive visual representations, stakeholders are able to select the most appropriate visual form from a pre-defined set to perform the task at hand. secondly, through interaction, stakeholders are able to control their dialogue with information. this process as previously discussed is not a linear one, and va tools support the unstructured, non-linear process of thinking and data exploration in which ph stakeholders typically engage. thirdly, va tools can automatically generate tailored reports for different group s of stakeholders. finally, va tools can also adjust and scaffold tasks in order to accommodate the cognitive needs of novice and learned stakeholders alike. the rest of this section is divided into two parts; the first describes how va tools can address the challenge of big data in ph, while the second highlights current va tools that can support ph stakeholders’ analytical reasoning tasks. utility of va tools in addressing challenges of big data in ph while interacting with ph data, stakeholders encounter challenges relating to the volume, variety, velocity, and veracity of data. va tools have accounted for and are addressing these challenges. data volume ph stakeholders are overwhelmed with massive amounts of data on a regular basis, and the ph informatics community has yet to sufficiently address the need for data to be presented in a more tractable form [11], [22], [23], [12]. because of this deficiency, stakeholders find themselves http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 spending more time wading through data, and less time actually addressing the health concerns of their community. as discussed in [2], “data set ‘overload’—the consequence of increasingly large data sets generated by surveys and other data collection tools—has forced many epidemiologists to become data managers, making it more difficult to analyze data from a variety of sources in order to detect disease outbreaks at an early stage.” the user-controlled environment that va tools provide allows the stakeholder to guide the analytics engine on how to manage and analyze data. as a result, the user is still cognizant of the characteristics of the data but cedes its processing to the tool. through the division of information processing labor, va tools relieve stakeholders of the tedious task of managing and analyzing obscure and intractable patterns in data. additionally, through interaction, the user is able to control the flow of data and access latent data as needed. data variety and velocity the great variety and high velocity of ph data can impede stakeholders’ reasoning. in regards to its variety, ph data is stored in different formats such as numerical, textual, geospatial, and multimedia [3] and ranges from structured (e.g., health indicators survey data), to unstructured, which in its original state can only be meaningfully interpreted by the human mind (e.g., freeform paragraph in a policy brief or tweets about medical symptoms) [4], [14]. in terms of its velocity, ph data is updated at varying time frames and in some situations is made available for a transient period of time [5]. va tools do not merely synthesize federated data originating from a variety of sources. through the analytics engine, stakeholders can also process various forms and structures of data, and with the interactive visualization engine, these different forms of data can be presented in a manner that is conducive to reasoning. for example, in the wnv scenario, va tools can help the epidemiologist reason more efficiently with tweets related to health, relevant redacted ehr, related national health policy documents, and parish climate data, without having to worry about the original form or source of the information. data veracity as ph policies and decisions have implications that affect the very fabric of society, the veracity of ph data cannot be overemphasized. ph data is often incomplete and inaccurate [9], [23], [26]. as a result, stakeholders are faced with the challenge of dealing with incomplete and discrepant data during reasoning. while some of these challenges require a more efficient hea lth information exchange system, in comparison to data analytics and interactive visualization tools, va tools are more equipped to support stakeholders. through the inclusion of models that describe scientific uncertainty and visual representations that highlight outliers and anomalies, stakeholders are able to better understand the integrity of the data and the ramifications of possible decisions. furthermore, as humans are better able to use incomplete data to make decisions (in comparison with computers), tools that allow for a user-guided analysis process enable users to incorporate their previous knowledge into reasoning tasks. va tools not only address the challenges arising from existing data repositories, but have the potential to enable the use of new sources of data (such as edge data) into ph practice. edge data, which refers to peripheral data that exists in the immediate, surrounding environment, can provide significant information on health events and their impact—example of these include water utility data that can help make sense of how cholera spread within a city, cell tower data http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 can facilitate understanding nurses’ practices during night shift, or traffic data of the intersection in front of a hospital. current application of va tools in ph even though ph has been slow to adopt va tools, other fields, including finance and sales, have aggressively incorporated these tools into their practice. this section highlights current va tools both within and beyond the field of ph, and how these tools can facilitate the work activities with which ph stakeholders are charged. it is subdivided based on the core functions of ph. health assessment work activities in this area include investigating the occurrence of health issues, analyzing the origins and contributing factors to health hazards, and identifying health trends [38]. stakeholders engaged in these activities seek answers to a myriad of questions including what causes disease or injury, what current risks are, what trends exist, and who is at risk. as analytical reasoning emerges from the human-information discourse mediated by va tools, the user is able to address these questions by applying a variety of analysis techniques. in addition, va tools can provide an environment in which hypotheses can be systematically developed, supported, or refuted. one such va tool that does this is nspace which allows stakeholders to rapidly scan and triage thousands of search results in one display [61]. furthermore, nspace provides an environment that supports the generation of hypotheses and evaluation of relevant evidence [61]. epidemic intelligence involves the early identification, assessment, and verification of potential public health hazards [62] and is essential to safeguarding the health of the community. to this end, there has been an increase in the use of social media data to gain insight into the condition of populations, as, for example, garnering information from twitter to estimate flu activity faster than traditional systems [63], and to gauge adverse public reaction to certain drugs [64]. epidemic intelligence stands to benefit from advances in textual analysis techniques, which, when incorporated into the analytics engine of va tools, can support ph stakeholders’ analytical reasoning tasks. policy development ph work activities in this area include prioritizing criteria, finding corroborating evidence, comparing possible policy options, and selecting the best option. by incorporating decision analysis frameworks, va tools can help ph stakeholders explore the complex implications of various policy options in an interactive fashion thus facilitating the use of evidence in policy development. commercial va tools such as tableau [65] and spotfire [66] are being used by business and finance professionals to create interactive dashboards useful for ph stakeholders. one such application in ph involves the analysis of foodborne vibriosis in the united states [ 67]. these visualizations present various options and potential outcomes to enable decision makers to select a course of action. va tools also allow stakeholders to rapidly access and search through available research from relevant studies. uncertainty is inherent in policy making [68] and exists in situations that have complex dynamics and interdependencies [69]. for instance, because of the rapid spread of chloroquine-resistant vectors in east africa, it is difficult to predict the effectiveness of malaria policy in that region [70]. va tools modeling scientific uncertainty in policy simulations can provide policy makers with more information on possible outcomes. http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 assurance in ph, after health issues have been identified and analyzed, and after policy has been developed, it falls on those stakeholders involved with assurance to ensure public awareness o f preventative measures and access to health services. this includes work activities such as enforcing health laws and policies, communicating with the public, managing health resources, educating the health workforce, and evaluating the effectiveness and accessibility of health services [38]. stakeholders engaged in these activities inquire into the services being delivered, the impact programs have, the capacity of ph stakeholders to deal with outbreaks, and the supply of resources for potential epidemics. once again, commercial va tools such as tableau can be utilized to ensure health resources are managed and dispensed properly. va tools create an environment that incorporates predictive models to support ph stakeholders’ reasoning. panviz [71] is an interactive predictive decision support environment that allows stakeholders to explore epidemic models and understand the effect certain response measures could have on the spread of an epidemic. it has also been used to educate indiana ph stakeholders in designing optimal response strategies [71]. a similar tool is epinome, which, in addition to allowing epidemiologists to explore outbreaks, tracks the user’s interactions for post analysis [72]. hypothetical scenario va tools can meet specific challenges facing ph stakeholders as they reason with big data. we will illustrate, through the lumcard scenario, how va tools can potentially support analytical reasoning tasks of ph stakeholders. in this section, we demonstrate how stakeholders are able to control the flow of data, choose representations that are applicable to the task, and use various interactions to perform analytical reasoning tasks. in the process, we show how analytical reasoning emerges from lower level interactions and how information processing is distributed between the user and the tool. in our scenario, we focus on the analytical reasoning tasks of a regional epidemiologist, who, in late november, received a phone call from the lumcard health director about a potential outbreak of wnv. to investigate the situation, the epidemiologist will need access to various forms of data including: 1) surveillance data which includes tweets from twitter and redacted ehr from local hospitals3; 2) geographical data which includes the landscape of the city, and places of high volume interaction including relevant environmental places (e.g., lakes, other local bodies of water, schools, and hospitals); 3) weather data for the city, state, and nation spanning the last ten years; 4) public epidemiological data on wnv for recent years; and 5) journal articles relating to the emergence of vector borne diseases in north america, to name a few. possible cognitive sub-activities may include sensemaking to determine if there is in fact a wnv outbreak in lumcard and knowledge discovery to determine the origins of this unseasonable occurrence. in order to make sense of the situation in lumcard, the epidemiologist will engage in a variety of analytical reasoning tasks that may include exploring the demographic attributes of confirmed wnv cases, comparing the situation in lumcard to the rest of the nation, triaging documents to discover relevant literature on wnv, and gathering evidence to present to other ph stakeholders. using epiprobe (i.e., a hypothetical va tool) to explore the available data, the epidemiologist examines the ehrs to discover collective properties of confirmed cases. with the use of visual http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 representations, the epidemiologist immediately notices a disproportionate number of adolescents (i.e., individuals between the ages of 10 – 20) with the disease. she annotates the visual representation and saves it in the evidence box. next, to contrast the situation in lumcard with cities across the usa, the epidemiologist visualizes the total number of confirmed cases by parish for the current year. then she interacts with the visualization to arrange cities based on the number of confirmed wnv cases. epiprobe calculates the 25 th , 50 th , and 75 th percentile of cases, and further groups the cities accordingly. using the box-plot graph the epidemiologist assesses lumcard is one of three cities with an unusually high number of cases that are in fact outliers. she concludes from this exploration that there is indeed an outbreak in lumcard. next, she decides to triage the literature to find ph documents for the three cities over the last year. her initial query results in over 125 articles. she instructs epiprobe to narrow the list by displaying only articles with the word ‘mosquito’. this reduces the list to seven articles, and epiprobe provides a short narrative of each of the articles by using the summarization textual analysis technique. each of the articles indicates higher infestation of mosquito in the three cities as a result of climate change. with the annotate feature in epiprobe, the epidemiologist writes a brief summary of her thoughts and adds these articles to the evidence box. she then directs the tool to calculate and visualize the least mean square for the dependent variable (i.e., number of confirmed cases) and the independent variable (i.e., temperature) for the three cities. she observes a positive correlation between temperature and confirmed cases in all of the cities. the corresponding scatter plot is added to the evidence box as well. in order to discover knowledge about the cause of the outbreak, she returns to her initial observation of the prevalence of the disease among adolescents. at this point in time, she decides to compare the age, time, and number of cases in the three cities. she selects the image plot representation shown in figure 4 and immediately notices three things. the first is the cyclic nature of the cases; the second is that over the course of the last 5 years all three cities have seen a steady increase in cases. this observation provides further proof that climate change has had an impact on the prevalence of the disease. the third detail she observes is that lumcard is the only city with a high percentage of adolescents with the disease as shown by the three small rectangular shapes in the last column. she describes her findings and adds snapshots of the visual representations as evidence. figure 4: image plots of wnv cases for the 3 selected cities from 2008 2013 http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 wnv is transmitted by mosquitoes to humans. thus, using a textual analysis technique, the epidemiologist filters and searches for relationships to determine if there is any correlation between the tweets by lumcard adolescents and any references to mosquitoes or local bodies of water. as depicted in figure 5, after a series of additional tasks, she identifies a subset of tweets referencing two parties at east lake and west bayou two weeks prior. at this point in time, she accesses gis coordinates for the location and instructs the local environmental scientist to collect samples from these locations. further instruction is given to place a warning at the sites until further investigation is completed. as the scenario shows, all of the aforementioned tasks can be completed with the va tool, epiprobe. the epidemiologist is able to explore ehrs, contrast cases across the country, pay closer attention to cities that defied the norm, perform statistical analysis to determine correlation, use textual analysis techniques to search through published journal articles, and ultimately make sense of twitter data to find a possible lake location potentially contributing to the outbreak amongst adolescents. additionally, the epidemiologist is able to delegate computationally intensive tasks to the tool, such as searching through existing literature and finding relationships between tweets. this delegation of tasks then allows the epidemiologist to focus on the overall task of determining the causation of the outbreak in lumcard. the benefit of va tools is portrayed in this simple example illustrating their critical application in the ph field. figure 5: visual representation depicting spatial relationships between most frequent words in tweets and local bodies of water in lumcard. summary and conclusion the success of an evidence-based approach to ph practice is contingent on stakeholders being able to efficiently use and reason with synthesized, federated sets of big data. as such, http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 computational tools that support analytical reasoning can be beneficial to ph stakeholders. through an examination of visual analytics (va) tools and a discussion of challenges facing ph stakeholders, this paper has shown how va tools can address the big data concerns of ph stakeholders. through the combination of interactive visual representations and advanced data analysis algorithms, va tools create a user-guided environment in which ph stakeholders can interact and reason with data. the data analytics engine in va tools allows for the storage, synthesis, and analysis of, as well as the discovery of patterns within, different forms of digitally stored data. seeking to exploit the human visuoperceptual system, in an effort to enhance cognition, the interactive visualization engine creates representations that display information in a predominantly non-textual manner. these interactive visual representations allow the user to access and control the flow and analysis of data. as cognitive processes do not take place solely in the brain of the ph stakeholder, va tools, that allow the user to access, structure, analyze, and modify the amount and form of displayed information, help to bridge the gap between the internal representations of the user and the external representations, thus facilitating analytical reasoning. we have shown that va tools can facilitate collaboration, coordinate internal representations with external representations, and efficiently provide comprehensible assessments to stakeholders. furthermore, va tools provide flexibility which allows for the customization of the tool to cater to the cognitive, perceptual, and contextual needs of the diverse ph workforce, and ultimately facilitates stakeholder reasoning and decision making. the features of va tools make them suitable to address the challenge of big data in ph that arise from the data’s high volume, great variety, high velocity, and low veracity. because of these reasons, as well as the existing evidence of the success and proliferation of va tools in other domains, we conclude that the use of va tools can be advantageous in ph, where stakeholders must use big data to address the concerns of the populace. limitations va, being an area of research in its infancy, still faces major challenges in understanding how to develop sophisticated tools. while current va tools for ph show promising initial results, more research is needed to develop this promise into tried and tested solutions. the quality of the human-information discourse that is facilitated by va tools is dependent upon numerous factors including the integration of different sources of data, the distribution of information processing load among components of the joint cognitive system, the design of visual representations, and the operationalization of interaction techniques to support the mental tasks of stakeholders. the manner in which the aforementioned factors are considered in developing va tools will partly determine the utility of these tools in addressing the challenge of big data in ph. there is need for systematic and focused research within the context of ph. in other papers, we have developed preliminary frameworks to guide the development of va tools. further research in these areas will contribute to the development of va tools that effectively support ph stakeholders as they interact and reason with ever more divergent, dynamic, and complex bodies of data. http://ojphi.org/ ojphi the challenge of big data in public health: an opportunity for visual analytics 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e223, 2014 references 1. o’carroll pw. “introduction to public health informatics,” in in public health informatics and information systems, vol. 6, no. 3, p. w. o’carroll, w. a. yasnoff, m. e. ward, l. h. ripp, and e. l. martin, eds. new york, ny, usa: springer-verlag, 2003, pp. 3–15. 2. revere d, turner am, madhavan a, rambo n, bugni pf, et al. 2007. understanding the information needs of public health practitioners: a literature review to inform design of an interactive digital knowledge management system. j biomed inform. 40(4), 410-21. 3. rambo n. 2000. information needs and uses of the public health 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http://dx.doi.org/10.1016/j.jvlc.2011.04.002 http://dx.doi.org/10.1109/mcg.2012.31 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts advancing epidemic prediction and forecasting: a new us government initiative jean-paul chretien*1, david swedlow2, irene eckstrand3, dylan george4, michael johansson2, robert huffman5 and andrew hebbeler6 1armed forces health surveillance center, silver spring, md, usa; 2centers for disease control and prevention, atlanta, ga, usa; 3national institutes of health, bethesda, md, usa; 4biomedical advanced research and development authority, washington, dc, usa; 5department of defense, ft. belvoir, va, usa; 6office of science and technology policy, washington, dc, usa objective to accelerate the development of us federal infectious disease outbreak prediction (i.e., identification of future time and place of a disease event) and forecasting (i.e., disease spread) capabilities. introduction the national science and technology council, within the executive office of the president, established the pandemic prediction and forecasting science and technology (ppfst) working group in 2013. the ppfst working group supports the us predict the next pandemic initiative, and serves as a forum to accelerate the development of federal infectious disease outbreak prediction and forecasting capabilities. priorities include identification, evaluation, and integration of disparate biosurveillance and other data streams for prediction/forecasting; characterization of the decision context for us government use of prediction/forecasting models; and development of a common us government vision for federal prediction/forecasting capabilities. the working group comprises 18 federal departments and agencies, as well as the national security council, office of science and technology policy (ostp), and office of management and budget. ostp, the centers for disease control and prevention, and the department of defense chair the working group. methods in 2014, the working group initiated a pilot project, “integrating prediction and forecasting models for decision-making: dengue epidemic prediction”, to demonstrate the practical application of prediction and forecasting models. the first stage of the pilot project was a white house workshop in september 2014 including ppfst working group members and leading us and international experts in dengue surveillance, public health decision-making, and modelling. the goal of the workshop was to initiate a dengue prediction and forecasting effort using existing dengue surveillance and predictor data. a second workshop, in 2015, will evaluate model performance against datasets assembled and provided to modeling teams following the first workshop. results workshop participants considered public health and national security objectives for dengue epidemiological modelling, promising dengue models, and existing dengue surveillance datasets and other data (e.g., climatological) that could inform models. they developed a plan for evaluating the performance of the various models that will be applied to the data. conclusions through the initial dengue workshop, the ppfst working group identified data and models for the dengue pilot project, as well as technical and organizational needs for dengue prediction/forecasting. the working group believes that much of this knowledge also could be applied to future prediction/forecasting efforts for other pathogens. ppfst working group leaders will present workshop outcomes, and seek feedback on the working group’s direction from the isds community. keywords prediction; forecasting; federal acknowledgments the views expressed are those of the authors and do not necessarily represent the views of any part of the us government. *jean-paul chretien e-mail: jpchretien@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e13, 2015 ojphi-06-e143.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 190 (page number not for citation purposes) isds 2013 conference abstracts features of varicella outbreak in primary schools in an integrated syndromic surveillance system in rural china changming zhou*1, tao tao1, huijian cheng2, qi zhao1, genming zhao1, xue li3, shaofa nie4, weirong yan4, 5 and biao xu1 1fudan university, shanghai, china; 2jiangxi province center for disease prevention and control, nanchang, china; 3future position x, gävle,, sweden; 4department of epidemiology and biostatistics, school of public health, tongji medical college of huazhong university of science and technology, wuhan, china; 5division of global health (ihcar), department of public health sciences, karolinska institutet, stockholm, sweden � �� �� �� � � �� �� �� � objective ��� ���� ��� � �� ����� ��� ��� �� ������ ���� ���� �� � ��� � �� �� �� ���������� �� ������������ ������������ ����� �� ������������ ��� ������ ������ ����� ����� ���������� ���� introduction � ���� �� �� ������ ��� ���� ����� ���� �� �� �� �� ������� ������� !��"#� �������������� � ��� �� ���� ���� � � ����� � ��� �������� $ �� ���� ����� ���%�� � �������� ������& ���' �( �� �����" ��� �#�� � ����)��� ������!��"� ����� ���� �������� �� ���� �� �������% � ������ ��� ����������� � ����� ����� ��������� �� ����� ���� ��������� ��� ���� ���������� ���� �� � ���" ����*� ������ 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�( �� �����" �� keywords *� �����=��� ������������ ��=��� �� ������ acknowledgments .>�?� �; ���%� ��( �� �� references ��@���:��(����a���������!��b�������� �� ������ �� ���� �� ����������$ ������ � ����� ����� ������ �� � ���" ����(a���8�������,����,$��$ ��=9 1#c�d�?1e� ��<�% ��.��:����� �&��5 � �����5��6��-���� ���� �%����� ���� ������ ������b����� ��� ��������� ������ ��� �� ��� ������� ����(�� � ����$ ���� ����!���&�!������6 ������,����,$�?$��=�? ?#c�190$e� ,�" ���583��2�� �����(��� ��3���� �.�� ������!��� ��� ���4��� � ��� ����5�������������� � ��� ���=����0� *changming zhou e-mail: 12111020001@fudan.edu.cn� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e143, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using syndromic surveillance data to describe chronic high frequency ed utilization erin e. austin* division of surveillance and investigation, virginia department of health, richmond, va, usa objective leverage existing syndromic surveillance data to characterize the population of chronic high frequency emergency department (ed) users and to understand the health complaints for which this population utilizes emergent health care services. introduction utilization and overcrowding of eds has been a prominent component of the health care reform debate in the united states for the last several years. in virginia, the ed utilization rate has increased 27.5% between 2000 and 2012 from 34.5 visits to 44.0 visits per 100 persons1. individuals with high frequency utilization of eds account for a disproportionate number of visits, which can place burden on already strained health care resources2. this study aims to use existing syndromic surveillance data received electronically by the virginia department of health (vdh) to describe demographic and utilization characteristics among chronic high frequency ed users in order to better understand the health complaints affecting this population. methods a retrospective study was conducted using syndromic surveillance data received by vdh from 44 acute care hospital eds between january 1, 2012 and december 31, 2013. a chronic high frequency ed user was defined as a person with more than 10 ed visits in 2012 and more than 10 ed visits in 2013 at the same acute care hospital based on unique medical record number (mrn). analyses were conducted at the patient level based on mrn to characterize population demographics and at the visit level based on chief complaint (cc) to characterize the health complaints of chronic high frequency ed users. results the study identified 1,336 chronic high frequency ed users. median and mean number of ed visits per user were 33 and 39, respectively (range 22-392), for a total of 52,006 ed visits over the 2 year period. chronic high frequency users represented 0.1% of the ed patient population served during the two year study period but 1.4% of the total 3.7 million ed visits. nearly two-thirds (64.4%) of the chronic users were female, and female users were significantly younger than males, with a mean age of 41.4 years compared to 45.8 years, t(1334) = 5.51, p <.001 (figure 1). males were more likely to have an alcoholor substance abuse-related cc compared to females (or= 16.9, 95% ci: 9.1-31.3). the most prevalent cc among both female and male chronic high frequency users was “pain”, occurring in 42% of visits (n= 21,993) and among 98% of mrns (n=1,309). one-third (34%) of high frequency users had at least one visit with a cc relating to a mental health disorder while one-quarter (25%) had at least one visit related to a dental complaint. conclusions chronic high frequency users of emergent health care services seek care for a variety of health complaints from chronic and acute illness to mental and oral health. based on this initial assessment a portion of visits by highly frequent users could be directed to other health care services for appropriate treatment. findings in this study support that of previous research which found that male high frequency ed users were older and had a higher rate of alcohol and substance abuse than female high frequency users3. major limitations of this study were 1) health insurance coverage information was not accessible, which could assist to further characterize this health care-seeking population and 2) it was not possible to identify high frequency users that sought care across multiple facilities. future research is needed to determine how chronic high frequency ed users differ from non-chronic users to establish potential focuses for interventions to reduce their burden on emergent health care services. keywords ed utilization; frequent ed users; syndromic surveillance references 1. virginia health information, inc. annual licensure survey data for acute care hospital emergency departments, 2000-2012. 2. castillo e, et al. factors associated with frequent users of emergency department resources. annals of emer med. 2012;60(4s):s32. 3. lacalle e, et al. high-frequency users of emergency department care. j emer med. 2013;44(6):1167-1173. *erin e. austin e-mail: erin.austin@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e64, 201 5115-38095-1-sm.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 186 (page number not for citation purposes) isds 2013 conference abstracts improvement in loss to follow-up of newborn hearing screening: a lesson from louisiana early hearing detection and intervention program xiaoling ye*1, tri tran2, 3, mary jo smith2, jeanette webb2, terri mohren2 and melinda peat2 1tulane school of public health and tropical medicine, department of epidemiology, new orleans, la, usa; 2louisiana dhh oph cshs early hearing detection and intervention program, new orleans, la, usa; 3lsuhsc school of medicine, department of pediatrics, new orleans, la, usa � �� �� �� � � �� �� �� � objective ������� ��� � �������� ����������� ��������������������������������� ����������� ���������������������� ��������������������������������� ������������������ ������������ �������������� ����� ��������� ����� ������������������������� ����������������� ��������! introduction "�������������������������������������������������������� �������� ������������������������������������� � 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������������� ������������������������������������+����� � �������������������� #�$%&'����-�@����� ����������� ������ ���� ��������������������� #�$%&'���������������������! conclusions #�������������� ����� ����������������������������������������������� ���������������������������������������� �������������������������� �������� #�$%&'�(����� ��� ����-���� ��������������������������� ������������� ����������������������������������� ���������� �������� �������� ��>��� ! keywords ���� ����� ���=� ����������������=�"������������������������=�&���� ���-��� *xiaoling ye e-mail: janice.ye268@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e47, 2014 ojphi-06-e64.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 65 (page number not for citation purposes) isds 2013 conference abstracts school daze: capturing chemical exposures in syndromic surveillance — new jersey 2013 teresa hamby*1, victor pomary1, eric adler2, stella tsai1 and andrew walsh3 1new jersey department of health, trenton, nj, usa; 2atlantic county (nj) division of public health, atlantic city, nj, usa; 3health monitoring systems, inc, pittsburgh, pa, usa � �� �� �� � � �� �� �� � objective ������� ���� ������������������������ �� ���������� ������� � � � �������� ���������� �� ���� �� ����������������� ����� � introduction !�� �� ��� "��� ��� #$%&'� ���� � �� ���� �� ���� ���� ��� � � �� ( � �� ����� ��)����� �������� �� ���� �� �����'����� �(�� � �������*� ������ ��� � '� ( � ( ����� ������������ ��(� ��������� ������� �� �� �� �� ������� ��� ��������� ������� ��� ��� � ������ �� � !�� �������� ��� � ������ ���'�������+� �� ���������� �������� � � ������ 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������ ��� ���� � ��� � �� ���*( ����� ������ ��� � �� ���� ������ ��� ��� ������ ��� ������������� ������� (��������� ������ results )����� ������ ������� ��� �� �� �(�� ����������� � � ��� �� ���� ��� ��� "� � � %'� #$%&� � ��( � ����� %$'� #$%&� �������� � ��� ��� ��� �(�'� ���������� ��� ��� ����$,%����������� �� ���������� ���� ���� �)�, � � ������'�� ����������,������������������ ��'� ����� �(�� ��������� � � ��� �������� ������� ��'� ��� ����� �� ( ��������������� ���� � �� ���������� ���������6$'�&7�����&$� ���������� ��'������� � ����� �������� '��� ��� ������������ ���� ���� ������ ��� ��������������� � � ������ ����� ����� �� conclusions � �������,� ����������� � � � ���� ���������� (� �������������� �, ��� ������� � ��� �������������� ��� ���( �������������������� �� �, �� ����� � ����#6, �� �� �������� �(����� �� ( ����� ���� �8 ���� � ������*( �������������� ����� ��������� ��� �����$,%��������� ����'� ������ ��� �� �������#������� ��������������������� � �������( �������(� ������ ���� �� ���������� ��� ������� ��� � � � ����� (�� ���� � ���� �� ��� �(��������� ��� ���( ����� � ������� ������ � ��� ��� �� ���� � �� ���� ������ �� ������'� � � � �������� �� � �� ������������ ��� �� ��������� � � '� ( � ( ��� �� ��� ������������� �(� ����� (�� ��,�(��, � �� �(���� ��� �� ������ � ���� ����� ������������� ���� ���� � � 5�� 9�����:���� � ����� � ���� �* �(�� ��� �� �(����9� ��:�������� �4� , �(���� � ����������� �� keywords � �� ���� ������ �;� 3��� �� �� 3� �� ������;� ����� � ��� ��;� �� ����� ;������� ��� *teresa hamby e-mail: teresa.hamby@doh.state.nj.us� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e64, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts determinants of daily attendances in emergency departments for asthma in the paris area céline caserio-schönemann, alice sanna, vanina bousquet, sylvia medina, mathilde pascal, marie-christine delmas and anne fouillet* french institute for public health surveillance, saint maurice, france objective description of the temporal pattern of the daily number of attendances in emergency departments (ed) for asthma in paris area and identification of the main factors influencing this indicator. introduction in june 2004, the french syndromic surveillance system based on the ed has been implemented by the french institute for public health surveillance (invs), starting with 23 ed. in august 2014, about 600 ed (40,000 daily attendances) are included in the oscour network, recording 80% of the national total attendances. asthma is one of the about 60 syndromic indicators monitored each day and followed all over the year. this indicator presents important fluctuations and can be influenced by several environmental and infectious but also societal factors. particularly factors like air pollution are known to have both short and long term impact on asthma while thunderstorms are associated with acute outbreaks of asthma [1-4]. methods using the daily attendances with a clinical diagnosis of asthma (j45 and j46 icd10 codes) recorded in 39 emergency departments of the paris area from 2006 to 2014, the pattern of asthma has been described. the intrinsic effects (day-of-week, seasonal period, holidays) and external factors have been explored by age group. results in 2014, about 54 attendances for a diagnosis of asthma are daily recorded in the ed of paris region, representing 1.03% of all attendances of this region. the part of asthma attendances is higher for children under 15 years old than in adults (2.14% versus 0.59%). while the temporal pattern of the daily number of attendances for asthma is quite stable in the adults, the fluctuations of the attendances in children present major variations depending on the period of the year: a high increase is more particularly observed each september at the beginning of the school year (figure 1). in 2014 two outbreaks in asthma attendances have been observed (figure 1). the first one occurred in march, between the 13th march and the 12th april. during this period, a significant air pollution episode was observed between the 6th and the 1st april and affected mainly the ile-de-france region. an intense second episode was noticed from 19th to 20th of july, concomitant with a thunderstorm episode occurring the same days. conclusions syndromic surveillance constitutes the only routine system for surveillance of asthma at the population scale, enabling the early detection of outbreaks like those observed in 2014. moreover, this system is included in the national surveillance program of short and long term effects of air pollution, with the objective to detect and follow-up the rapid impact of major air pollution episodes in a near real time. figure 1: daily number of attendances for asthma in the emergency departments of paris area from august 2012 to august 2014 by age group keywords asthma attendances; emergency department; france; influencing factor acknowledgments to emergency department data providers and all invs regional unit for their substantial contribution to the system. references [1] jerrett m1, shankardass k, berhane k, et al. traffic-related air pollution and asthma onset in children: a prospective cohort study with individual exposure measurement. environ health perspect. 2008 oct;116(10):1433-8. doi: 10.1289/ehp.10968. epub 2008 jun 18. [2] winquist a1, kirrane e, klein m, et al. joint effects of ambient air pollutants on pediatric asthma emergency department visits in atlanta, 1998-2004. epidemiology. 2014 sep;25(5):666-73. doi: 10.1097/ede [3] dabrera g, murray v, emberlin j, et al.thunderstorm asthma: an overview of the evidence base and implications for public health advice. qjm. 2013 mar;106(3):207-17. doi: 10.1093/qjmed/hcs234. epub 2012 dec 29. [4] elliot aj, hugues he, hugues tc et al. the impact of thunderstorm asthma on emergency department attendances across london during july 2013. emerg med j. 2014 aug;31(8):675-8. *anne fouillet e-mail: a.fouillet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e114, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts pertussis surveillance in veterans affairs medical centers in western united states – 2010-2014 patricia schirmer*1, renee-claude mercier1, 2, cynthia a. lucero-obusan1, gina oda1 and mark holodniy1, 3 1department of veterans affairs, palo alto, ca, usa; 2university of new mexico, albuquerque, nm, usa; 3stanford university, palo alto, ca, usa objective to perform pertussis surveillance in va facilities in the western us. introduction bordetella pertussis infection (whooping cough) has been on the rise and the most cases in the us since 1955 were reported in 2012 (48,277 or 15.4 per 100,000)1. pertussis is highly infectious and can cause serious illness in infants and children as well as adults, and in general is preventable by vaccination. since 2005, it has been recommended that anyone 19-64 years old should have a onetime booster of the pertussis vaccine (tdap). in 2010, that recommendation was broadened stating people 65 years old and older should also obtain a booster of tdap. given the increased number of pertussis cases in the western us, and that approximately 20% of these cases occurred in patients >20 years of age, we performed pertussis surveillance in veterans in care at va medical facilities. methods pertussis cases from 1/1/2010-7/15/2014 were captured using va’s healthcare associated infection and influenza surveillance system (haiiss) with both qc pathfinder (qcp) and va essence applications for 14 states in the western us. patients were excluded if <20 years old. laboratory tests for pertussis utilized in va included culture, polymerase chain reaction (pcr), and pertussis-specific immunoglobulin tests. using qcp, we defined laboratory confirmed pertussis cases as having a positive culture or pcr test from a respiratory site or a positive bordetella igm result, since the latter test was commonly used by providers. inpatient and outpatient encounters coded with a pertussis icd-9 code (033.0, 033.8 or 033.9) were identified using essence for the same time period. va electronic medical records (emr) were reviewed on all laboratory-confirmed or icd-9 coded cases to confirm the diagnosis and determine tdap vaccination status. results a total of 383 potential pertussis cases were identified by haiiss. emr reviews determined that 108 did not have acute pertussis (98 received a pertussis icd-9 code but there was no evidence of active infection either by laboratory results or documented symptoms; 3 had other bordetella species in non-respiratory specimens; and 7 were under age 20). of these excluded patients, 22 were identified as seeking pertussis prophylaxis for a recent exposure. of the remaining 275 unique patients, 62 were identified with laboratory confirmed pertussis by qcp, 232 with pertussis icd-9 codes in essence and 19 by both systems. the number of cases per year from 2010-2014 was 63, 65, 70, 54, and 23, respectively. 49/275 (18%) were female while 226/275 (82%) were male. median age of patients with acute pertussis was 55 years old (range 24-101). figure 1 shows 5-year cumulative total of all cases by state and laboratory confirmed cases by va facility. 28/275 (10%) required hospitalization. the number of patients tested for pertussis was 119/275 (43%). tdap vaccination rates that were received or documented in va’s emr (regardless of whether vaccine was given prior to encounter) was 148/275 (54%). no patients died due to pertussis. conclusions while pertussis is often thought of as a childhood illness, the va is seeing a consistent number of cases in older adults each year. icd-9 codes or laboratory positive cases alone are not reliable indicators of pertussis cases; surveillance was enhanced when both were used to identify cases. mapping cases by residence zip code and by positive laboratory testing by facility showed that utah has less icd-9 coded visits, but a high number of laboratory confirmed cases while the opposite is seen in california. vaccination and testing for pertussis in va could be improved. keywords pertussis; veterans; essence; biosurveillance; icd-9 references 1. cdc 2012 pertussis surveillance report. august 23, 2013. accessed september 3, 2014. available at: http://www.cdc.gov/pertussis/ downloads/pertuss-surv-report-2012.pdf *patricia schirmer e-mail: patricia.schirmer@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e51, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts challenges in surveillance for chikungunya virus (chikv) infection cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 1stanford university, division of infectious diseases and geographic medicine, palo alto, ca, usa; 2department of veterans affairs, office of public health, washington, dc, usa objective we describe challenges and lessons learned conducting surveillance for chikungunya virus (chikv), an emerging infectious disease in the americas. introduction chikv is transmitted by mosquitoes and often occurs in large outbreaks with high attack rates. common symptoms (which can be severe and disabling) include fever, joint pain/swelling, headache, muscle pain and rash. in december 2013, the world health organization reported local chikv transmission in the caribbean. in july 2014, the first locally-acquired case in the continental u.s. (florida) and increasing cases in puerto rico (pr) were reported. due to the growing outbreak, va office of public health began conducting ongoing surveillance. methods chikv infection surveillance in 2014 was performed using a variety of data sources: (1) va essence for outpatient, emergency room and inpatient encounters; (2) electronic laboratory data from va healthcare associated infection and influenza surveillance system (haiiss) data warehouse and va corporate data warehouse; (3) facility reports (includes issue briefs, communication with local infection preventionists and individual case reports from providers). chart reviews were performed on all potential cases to understand surveillance limitations and identify ways to improve case detection. results as of august 14, 2014, 21 confirmed/probable cases were identified at 10 va hospitals. nine were locally-acquired in pr, 8 in dominican republic (dr), 3 in haiti, and 1 had exposures in both dr and haiti. median age of cases was 63 years (range 22-83), 19 (90%) were male and 8 (38%) required hospitalization. the majority of va cases were initially identified via electronic laboratory queries (13, 62%), followed by facility reports (7, 33%) and lastly, essence (1, 5%). principal challenges with essence were lack of a specific chikv icd-9 code and providers using symptom codes in the initial and subsequent encounters, even after chikv was confirmed. additionally, visits containing proper mosquito-borne fever icd-9 codes (icd-9: 066.3 or 065.4) had low specificity. most of these visits had other diagnoses (e.g. west nile virus or dengue), remote infection histories or had chikv in the differential but it was never confirmed. none received a mosquito-borne fever icd-9 code during initial evaluation, even though many reported mosquito bites, travel to regions where chikv is circulating and/or epidemiologic links to other individuals diagnosed with chikv. four were miscoded as dengue (icd-9: 061). only 7 cases ever received an icd-9 code for mosquito-borne fever, allowing them to be eventually identified in essence. for these, it was an average of 20 days after first presentation (range 1-44 days) to find a follow-up visit which was properly coded. the most common codes assigned on initial evaluation were: fever (icd-9: 780.60, 8), joint pain (icd-9: 719.4, 5) and unspecified viral infection (icd-9: 079.99, 4). limitations with electronic laboratory reports included lack of chikv testing (many suspect cases identified via essence could not be confirmed), long turn-around times for results and lack of uniformity in lab test naming. in some cases, we discovered chikv testing as “dengue”, and “miscellaneous”, or results buried in physician progress notes or scanned reports never entered in the laboratory section. challenges with relying on facility reports highlighted the fact that facilities are not necessarily aware of these cases and infection control was not always informed of suspected or confirmed cases, as chikv is not yet a notifiable disease. conclusions based on our experience, a combination surveillance strategy using multiple electronic and non-electronic data sources is essential for chikv detection. recent improvements include: (1) expansion of our electronic laboratory query to capture additional chikv test names; (2) developing chikv testing capability in our va public health reference laboratory; (3) distribution of educational and surveillance materials to raise awareness, encourage testing and proper coding, and improve chikv identification. no icd-9 code query or essence syndrome group has been useful for early chikv case identification. identification via essence may improve with the switch to icd-10 in 2015, as this system contains specific codes for chikv. keywords department of veterans affairs; chikungunya; icd-9; disease surveillance; electronic laboratory data acknowledgments the authors thank gayathri shankar for assistance with the electronic laboratory data. *cynthia a. lucero-obusan e-mail: cynthia.lucero@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e37, 201 ojphi-06-e169.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 115 (page number not for citation purposes) isds 2013 conference abstracts under-ascertainment of illness due to influenza in administrative databases, a population-based record linkage study david j. muscatello*1, 2, janaki amin2, c. raina macintyre2, anthony t. newall2, william d. rawlinson2, 3, vitali sintchenko4, 5, robin gilmour1 and sarah thackway1 1nsw ministry of health, north sydney, nsw, australia; 2university of nsw, kensington, nsw, australia; 3south east area laboratory service, randwick, nsw, australia; 4sydney medical school, the university of sydney, camperdown, nsw, australia; 5centre for infectious diseases and microbiology, westmead, nsw, australia � �� �� �� � � �� �� �� � objective ������� ��������� ������������������������������������ ������� ���� ����� �������� � ������ ������ � � ������������� ����� ������ ���� �� ����������������� ����� �������������������� �� �� ��������� ���� ��� ������� ����������������������������������� ���������������������� � �� ������ ������ ��� ����������� ��� introduction ������ ����� �� ������������������� �������������� ��������������� � ������ ������ � � ����� ����� ���������� ��������������������� � �� ���������� ������������ � ���������� ������ ������� !"��#���� � $ ���� ��� ���� %� ���� ��&���� �'�� (������� )������� *� � ���� �� � �����+�,-��������� ���� ���������� ������ ��.�///������������ ��� ��������� ��� �������������� ���-������� ������������������ ��� ���� 0�������� 1� 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� ��� ��� �������� ��������� � ������� ����� �������������� �������������� ������������� ����������� ������� � � ������ ��������������������������������� �� ������������ ��������� �������������������� ����������� $� �������b������ � ����� ��� ���������� ���������������5 �� ���������� �� �� � ��������� ��������� �5�� �����������7��������� ��9� keywords �������� c�����������5 �c�� � � ��� acknowledgments ����a����������d� ����%������)��5 �����������=�� ������������������� � = ���� ��d� ���� ���e���� ��%��� ����a�������7=de%a9��� ���� �$��� ��������7;f/"?!�������� �� ��g ����6������d� ����$���������9��a���������� d� ����%������)��5 �� references ���$ ���#��h������� ������ ����� !"��h�+�d� �������=������b�� ���� ��������� )�����+�(�������������> ��� ���h��������c�� .+�ff/�f� *david j. muscatello e-mail: dmusc@doh.health.nsw.gov.au� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e169, 2014 phs 398 (rev. 06/09), continuation page probabilistic, decision-theoretic disease surveillance and control 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 probabilistic, decision-theoretic disease surveillance and control michael wagner, md, phd 1 , fuchiang tsui, phd 1 , gregory cooper, md, phd 1 , jeremy u. espino, md 1 , hendrik harkema, phd 1 , john levander 1 , ricardo villamarin, phd 1 , ronald voorhees, md 2 , nicholas millett 1 , christopher keane, phd 2 , anind dey, phd 4 , manik razdan, dmd 2 , yang hu, ms 3 , ming tsai, ms 1 , shawn brown, phd 2 , bruce y. lee, md, mba 2 , anthony gallagher, phd 1 , margaret potter, jd 2 1 center for advanced study of informatics in public health, department of biomedical informatics, university of pittsburgh 2 graduate school of public health, university of pittsburgh 3 department of computer science, university of pittsburgh 4 human-computer interaction institute, carnegie mellon university abstract the pittsburgh center of excellence in public health informatics has developed a probabilistic, decision-theoretic system for disease surveillance and control for use in allegheny county, pa and later in tarrant county, tx. this paper describes the software components of the system and its knowledge bases. the paper uses influenza surveillance to illustrate how the software components transform data collected by the healthcare system into population level analyses and decision analyses of potential outbreak-control measures. 1 introduction the center for advanced study of informatics in public health (casiph) is developing and integrating software to create a probabilistic, decision-theoretic system for disease surveillance and control and is translating this system into practice at the allegheny county health department (achd). this work represents a new paradigm for disease surveillance, based on probability and decision theory. the approach integrates bayesian diagnosis of individual patients with bayesian “diagnosis” of a population. it is capable of estimating the current incidence of influenza in a population from data in electronic medical records (emrs). it is also able to estimate the set of parameters required to initialize a seir epidemic model. it is thus possible to initialize an epidemic model so as to match the current disease status of a population. the epidemic model is then used in a decision model of available control measures. this unique capability will enable decision makers to use epidemic models more effectively when selecting control measures for influenza and other outbreaks. http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 1 shows the software components (blue rectangles) of the probabilistic, decision-theoretic system and how they combine to form an end-to-end disease surveillance system. this paper describes each software component, how information flows in the system, and the level of integration of the components that we have achieved to date. figure 1. information flow in the probabilistic, decision-theoretic disease surveillance system. legend: light blue rectangles, software components: cds, case detection system; ods, outbreak detection and characterization system; hexagons, knowledge bases; rounded rectangles, data and analyses; thin arrows, user interfaces; large arrows indicate that the components exchange data electronically in the direction indicated; dotted arrow indicates a future connection sending case data collected by nurse investigators from trisano ® to cds. 2 software components this section describes the software components depicted in figure 1. 2.1 phoenix the role of phoenix is to receive patient data from an emr and respond to queries from the case detection system for patient data in standard form. phoenix receives emr data as hl7 messages. it then parses the messages, standardizes the extracted information, and stores it in a database. phoenix comprises:  a database: the database uses multiple entity-attribute-value tables, a representation that we found necessary to meet the real-time processing requirement of the project. our initial implementation of the database used openmrs, which we extended with our own relational database schema. however, openmrs was unable to keep up with the volume of patient data from the upmc health system even after we modified it to run on the oracle database http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 management system to take advantage of oracle’s scalability and database optimizations. even with oracle’s optimizations and scalability, a relational table design was too slow, necessitating the entity-attribute-value representation that we use currently.  hl7 parsers, for microbiology, chemistry, dictation, adt, and radiology hl-7 feeds  a data viewer, which gives a clinical episode (e.g., emergency room visit) view of the data for internal development purposes and serves as a prototype for future end-user interface for a health department.  a data standardization program, which converts local upmc health system terminology to standard codes (umls, snomed-ct, loinc). phoenix is not a primary focus of our work; it can be understood as necessary scaffolding that we had to construct to access emr data and allow for the use of standard terminology in the rest of the components. we intend to discard it when emrs are capable of providing phoenix’s functionality to public health applications. 2.2 bayesian case detection system (cds) the role of cds is to infer a patient’s medical diagnosis from data in an emr. in particular, cds computes a probabilistic differential diagnosis for each disease in a set of monitored diseases for every patient visit to a monitored facility. a probabilistic differential diagnosis is a list of diseases with their posterior probabilities, given the available data. at present, cds processes all emergency department (ed) visits in monitored hospitals in allegheny county, pa. it computes both a posterior probability of influenza and a likelihood, p(patient data|influenza) for each visit. it uses information extracted from ed dictations by a natural language processing algorithm as patient data for this inference. cds also computes a posterior probability for the majority of notifiable diseases in allegheny county from the laboratory test results of ed patients. cds uses bayesian networks to compute a patient’s differential diagnosis. a bayesian network is a directed graph in which the nodes represent variables and probabilities distributions for the variables, given its parents (indicated by the directed arcs in the network). a bayesian network is a compact factorization of the joint probability distribution over the variables. a bayesian inference algorithm can compute any marginal or conditional probability from this factorization. in particular, it can compute the probability that a patient has influenza, given that the patient has fever, but not cough. the influenza bayesian network in cds comprises a node that represents the diagnosis influenza and a set of nodes that represent the symptoms, signs, and laboratory results that may contribute to a diagnosis of influenza. there is a single diagnosis node and 367 finding nodes in the influenza bayesian network. the diagnosis node takes one of two values: true and false, where false means that the patient has an illness other than influenza. the finding nodes take one of three values: present, absent, and unknown, where unknown means that the natural language processing cannot determine a value for the finding from the patient data. the notifiable-disease bayesian networks represent only laboratory tests, at present. collectively, the set of “lab-only” bayesian networks functions as a probabilistic electronic laboratory reporting (elr) system, which has equivalent functionality to the existing elr paradigm when its probabilistic thresholds for reporting are set close to 1.0. ultimately, we intend for cds to contain diagnostic models similar to the influenza bayesian network for all notifiable conditions, syndromes of interest, and emerging diseases. the cds runs once per day, at present, although it can process ed visits in real time. in its current configuration, cds obtains patient data from phoenix for all patient ed visits that occurred in the http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 previous 24 hours. cds then computes both the disease likelihood and the posterior probability of influenza and for the monitored notifiable diseases for each patient visit. only the disease likelihoods for influenza, p(findings | influenza), are sent to the outbreak detection and characterization system (ods) described in section 2.4. the data sent to ods for each visit also include the date of visit, home zip code, patient age in deciles, and patient gender, although they are not used by ods at present. cds has been in production operation in allegheny county since 2009. it sends a daily report by email to the health department. the report plots the cds estimate of the number of influenza cases seen in the monitored eds. to generate the estimated number of influenza cases for a day, cds sums the posterior probabilities of every patient seen in the eds on that day to form an expected number of cases for the day. tsui et al. describe cds in detail in an accompanying paper in this issue of ojphi. 2.3 trisano ® trisano ® community edition (ce) is open source “nedss” software used by the utah department of health (http://www.trisano.com/). trisano ® is representative of case investigation software in use by other health departments. we are extending trisano ® to support bidirectional communication with cds, as depicted in figure 1. in the cds-to-trisano ® “reporting” direction, we have implemented the capability for cds to transmit cases to trisano ® and for a trisano ® end-user to set a disease-specific reporting threshold, td, for a given disease d, within that application. when p(d = present | patient data) > td, the case appears in the trisano ® inbox. in a future trisano ® -to-cds connection, trisano ® will send case data recorded by nurse investigators to cds so that cds can recompute the patient’s probabilistic differential diagnosis and in turn update ods. we also expect that the additional information and updated diagnosis may be of value in the clinical care of patients. 2.4 ods the function of the bayesian outbreak detection and characterization system (ods) is to detect outbreaks and to estimate outbreak characteristics, such as infectious period, latent period, and ro (reproductive rate). ods integrates tightly with cds. as was stated earlier, for each patient who visits a monitored ed, cds determines how likely that patient's findings match each of a set of modeled outbreak diseases (e.g., influenza). such a match is expressed as the probability of the findings given a disease, namely, patient disease likelihoods. ods takes as input these likelihoods, plus the prior probability distribution over the various types of outbreak diseases being modeled. it then samples from distributions representing the input parameters to an epidemic model for a given disease type. when using a seir epidemic model of influenza, for example, ods samples from distributions that are characteristic of influenza for the infectious period, latent period, ro, initial number infected, and start date. it then derives the posterior probability of each sampled model. more formally, let m pop denote an epidemic model of the entire population in a region that is being monitored for an outbreak of disease. in our current application, m pop is a seir model. we would like to infer a distribution over such models given evidence about patients who seek care at emergency departments in the region. let m ed denote the disease states of all the ed patients during the http://ojphi.org http://www.trisano.com/ probabilistic, decision-theoretic disease surveillance and control 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 monitoring period. let e designate the clinical evidence that is available about the ed patients during the monitoring period. at a high level, ods is based on the following equation, which is an instance of bayes’ theorem: ( | ∑ ( | ( | ( ) ∫ ∑ ( | ( | ( ) the sum is taken over all possible disease states of all the ed patients being monitored. the number of terms in the sum is therefore very large; however, we are able to take advantage of some basic mathematical techniques, such as the application of the binomial distribution, to compute the sum efficiently. ods approximates the integral in the equation by sampling m pop , which leads to the integral becoming a sum. the terms p(m ed | m pop ) and p(e | m ed ) represent key modeling components of ods. the term p(m pop ) represents a prior probability distribution over the parameters in a seir model of the population. the independence assumption in the equation is that p(e | m ed , m pop ) = p(e | m ed ), which expresses that in predicting ed patient data, knowledge of the disease status of the population at large is irrelevant, once we have knowledge of the disease status of the ed patients. figure 2 shows the most probable models computed by ods using ed emr data from the upmc health system in allegheny county (ac) through september 7, 2009 (retrospective analysis). on september 7, 2009, the influenza surveillance data were beginning to show influenza activity in ac. figure 3 shows posterior distributions over three seir model parameters—ro, infectious period, and latent period (bottom three graphs). these histograms show the parameters of the 269 most likely epidemic models on september 8, 2009. the set of 269 most likely epidemic models were those whose cumulative posterior probability summed to an arbitrary threshold p > 0.99995. figure 3 also shows distributions for peak date, incidence on peak day, and total number infected for the set of 269 most likely epidemic models (top three graphs). for the h1n1 outbreak, the most probable ods model predicted that the number of infected individuals in ac would peak on november 11, 2009. based on laboratory measurements of influenza reported in (1), the h1n1 epidemic is believed to have peaked in ac between october 24th and november 7th. thus, based on ed patient data that was available about seven weeks prior to the actual peak, the most probable model predicted the actual peak quite accurately. based also on laboratory measurements, by approximately november 23, 2009 the percentage of people in ac who had been infected with h1n1 was estimated to be about 21% (1). again, using data only up through september 8, 2009, the most probable model predicted that about 19% of the ac population would be infected by november 23rd, which is close to the actual percentage. this case study, which involved real data and an actual influenza outbreak, provides support that the basic approach outlined above is promising. however, it is only one influenza outbreak, and thus, additional study of the approach is clearly needed. the above approach could be applied to other diseases and it can be generalized to use other types of epidemic models, including segmented compartment models and agent-based models. ods also provides cds with dynamically updated ed priors for influenza. these priors can be combined with the likelihoods computed by cds to obtain a posterior probability of influenza for each http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 ed patient, based on a real-time estimate of influenza prevalence in the ed; in turn, these posteriors can be used to support clinical decision making for individual patients (e.g., decisions about testing and treating for influenza). figure 2: ods, most probable models. http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 this screen shows the three most probable seir influenza models given the likelihoods of influenza from cds for all allegheny county patients seen in monitored upmc eds from may through september 7, 2009. on the daily incidence curves, day zero is september 8, 2009. figure 3. distributions of total infected, peak date, incidence on peak day, and posterior distributions for ro, latent period, and infectious period. ods used uniform prior probabillity distributions for seir model parameters in this analysis. for example, the prior distribution for ro was uniform over the range 1.1 to 1.9. prior work using bayesian algorithms for disease surveillance has had an emphasis on detection rather than characterization. examples of temporal methods include (28), who extended kulldorff’s spatial scan statistic to produce posterior probabilities of influenza in geographical sub-regions. a multivariate generalization was developed in (14). spatio-temporal approaches include the wsare 3.0 algorithm (15), the panda algorithm for detecting anthrax outbreaks (16), the pcts algorithm for detecting outbreaks of all cdc category a diseases that are of special concern for biosurveillance (17), and a bayesian hierarchical model to detect anomalously high levels of influenza (18). in previous research, we http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 developed bayesian algorithms (16, 17) that employed a data likelihood approach, similar to the method we describe here. however, they were based only on chief complaints as evidence. our approach to outbreak detection and characterization (od&c) has important features not present in previous work. first, instead of analyzing counts of data to estimate an epidemic curve (19, 20), we use a flexible and more general approach that models probabilistically the available evidence, such as the rich set of patient findings in ed reports. the approach reflects the intrinsic synergy between individual patient diagnosis and population od&c. in particular, od&c is derived based on past probabilistic patient diagnoses. in turn, the diagnosis of a newly arriving patient is based on prior probabilities that are derived from probabilistic inference over current od&c models. to our knowledge, no prior research (either bayesian or non-bayesian) has taken such an integrated approach to patient diagnosis and population od&c. second, our approach represents a general bayesian framework for modeling od&c. it can be applied with many different types of disease outbreak models including seir (susceptible, exposed, infectious, and recovered) model (21), agent-based, and outdoor-substance-release (osr) models (22). 2.5 bioecon bioecon is a tool for epidemiologists facing decision about control measures. it automatically generates decision models of control strategies, which can then be compared interactively by a user. we are developing bioecon under funding from the national library of medicine. bioecon generates a decision model from a set of control measures, an epidemic model, and a utility function (figure 4). the square in the decision tree in figure 4 represents a decision among three control measures. the three arcs from the decision node represent the three control measures. the deterministic nodes (double lined yellow circles with the letter ‘e’) represent three seir models for influenza, and the triangles represent the utility function. in figure 4, the three epidemic models use the same ro, infectious period, and incubation period. however, the differential effects of the control measures can result in different initializations of the compartments and transition probabilities in the epidemic models. for example, the vaccination control measure adds direct transitions from the susceptible compartment to the recovered compartment. bioecon obtains the information needed to initialize the epidemic model either from ods or an end user. when used with ods, bioecon computes the expected utility of each control measure (or sequence/combination of control measures) by model averaging over the set of seir models produced by ods. note that figure 4 shows the expected utilities for a single ods model. http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 figure 4. bioecon . an automatically generated decision model for influenza, allegheny county, sept 8, 2009 (retrospective analysis). the upper panel, left, shows the generated decision tree. beneath it is a tabular display of the expected utilities (labelled ‘expected values’) of the decision alternatives. the utility function is: v(-$11) + s(-$7811.41), where v is number of people vaccinated, -$11 is the cost of vaccination, s is number sick, and -$7811.41 is the average cost per sick person, which equals the cost of illness and the loss of productivity. 2.6 apollo web service we developed the apollo web service to enable bioecon to connect to epidemic simulators developed by ourselves and others (figure 5) and to also make out epidemic simulators available to other applications. at present, bioecon can access four epidemic simulators through apollo: a seir compartment model capable of modeling vaccination control measures; an influenza agent-based model capable of modeling vaccination and school closure interventions (among others); an aerosol http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 release compartment model capable of modeling antibiotic prophylaxis control measures; and the bard aerosol release simulator. a simple end-user application that demonstrates the basic functionality of the apollo web service can be found at http://research.rods.pitt.edu/apollo/. figure 5. apollo web service end-user applications like bioecon submit configuration objects to an epidemic simulator and receive output objects containing the results of the simulation (e.g., an epidemic curve). 3 knowledge bases cds, ods, and bioecon are knowledge-based systems. a knowledge base is a component in a decision-support system that contains information that an expert might use to solve a problem in computer-interpretable format. it is standard practice to separate this knowledge from the other parts of a system, such as the inference algorithm that operates on the knowledge, to make this information more easy to maintain and verify (23). the knowledge bases for cds, ods, and bioecon are depicted in figure 1 as hexagons labeled “disease models,” “epidemic models,” and “control measures and costs.” disease models, as previously discussed, use bayesian networks to represent medical diagnostic knowledge— the symptoms, signs, and laboratory tests that a physician would use to diagnose a http://research.rods.pitt.edu/apollo/ http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 disease. the disease models represent the same kind of information that one finds in public health case definitions. a key difference is that these disease models are computer-readable and also include the sensitivity and specificity of each diagnostic finding for its disease. the disease models are also discussed in the accompanying paper in this issue of ojphi. epidemic models represent expert knowledge about outbreaks. for example, a seir model represents influenza dynamics using a state transition network whose compartments represent disease states (e.g., susceptible and infectious) and whose parameters (e.g., infectious period, latent period) specify the rates at which transitions between disease states states over time. control measures and costs: bioecon represents knowledge that expert epidemiologist use when making decisions about control measures. bioecon represents this knowledge using objects, which are nested structures that can inherit characteristics from more general parent classes. the object representing a vaccination control measure, for example, has the following attributes: jurisdiction (e.g., allegheny county), supply schedule, vaccine administration capacity, efficacy, and lists of other control measures that it can run concurrent with, follow, or precede. bioecon can acquire and store this information for multiple jurisdictions, each of which can have different capacities. bioecon also requires cost information, including cost of illness, lost productivity, which is knowledge on which rationale decision-making is based. we have not developed extensive representations of cost information within bioecon to date; instead, we use excel spreadsheets and other tools to develop detailed economic models, and represent the rolled up costs in bioecon as components of its utility functions. 4 an example of information flow and transformation this section shows the flow of information through the components of the probabilistic, decisiontheoretic disease surveillance and control system using influenza as an example. in particular, it shows the information that is passed in the rounded boxes in figure 1 labeled coded visit data, differential diagnosis, population analyses, and decision analyses. we assume the reader is familiar with the patient data stored in electronic form in emrs; therefore, we begin with the input to cds, which are patient findings in standard format. coded visit data. the following table shows the coded patient findings obtained by cds from phoenix for three ed visits on september 1, 2009. approximately 600 patients were seen that day in monitored eds. patient visit umls concept id name of finding value a c0000729 abdominal pain absent c0015967 fever absent c0018681 headache present c0027497 nausea present c0043144 wheezing absent c0085593 chills absent c1883552 weakness absent b c0015967 fever absent c0085593 chills absent http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 c1883552 weakness absent c c0015672 fatigue absent c0015967 fever absent c0085593 chills absent c1883552 weakness absent differential diagnosis of patients. the following table shows the output sent by cds to ods for three patient visits on september 1, 2009 in monitored eds. they are not the same patients visits as in the previous table. patient p(evidence of patient | influenza) p(evidence of patient | not influenza) 1 0.015759230220164264 0.0040469980953649898 2 1.3756216427857736e-008 1.754268236767679e-005 3 0.00047169035934720448 0.020177483460983615 population analyses. the following information represents the output sent by ods to bioecon for sept 1, 2009. we are showing numerical output, however, the results are amenable to graphical display as well. we can plot the posterior probability of an influenza outbreak, epidemic curves, and other quantities of interest. start monitoring date: may 28, 2009 current date: september 1, 2009 prior probability that an ed patient has influenza on the current day: 0.007 number of [seir] models searched: 50,000 total run time: 737.3 seconds // outbreak detection // posterior probability of an influenza outbreak: 0.549 [the number relevant to outbreak detection] //outbreak characterization// [the following output is for one of 50,000 seir models searched—the full output from ods contains all 50,000] model_posterior_probability: 0.000113 s: 1215434 (number of individuals in susceptible compartment on september 1, 2009) e: 601 (exposed) i: 903 (infectious) r: 1652 (recovered) latent_period: 2.6 days infectious_period: 5.9 days ro: 1.855 outbreak start day: 64 days after the start of monitoring on may 28, 2009 initial number infected: 96 decision analysis. figure 4 shows the output of a decision analysis, which is a base case analysis with sensitivity analyses (not shown). for expository purposes, the following table shows the mathematical result of a decision model, which is the set of expected utilities computed for the decision alternatives http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 analyzed by the model. the control measure recommended by the decision model in this example is vaccination, with aggressive and routine administration differing by less than 1%. decision alternative (control measure) expected utility vaccinate 14-day policy, without prioritization -$1.11b vaccinate without prioritization (routine vaccination) -$1.12b no vaccination -$2.12b 5 significance in 1959, ledley and lusted identified probability and decision theory as the mathematical foundations of medical diagnosis (24). this insight has had significant impact on the fields of clinical medicine, medical decision-making, and computerized medical decision support. in 2001, we observed that probability and decision theory were also the mathematical foundations of disease surveillance and control (25). further, the methods developed for probabilistic medical diagnosis could be applied, without modification, to case detection for disease surveillance. we observed that probability and decision-theory were an ideal basis for representation and inference at the population level, and that the domains of medical diagnosis and population diagnosis could be bridged by these formalisms. but despite the potential of a probabilistic decision-theoretic approach, the practice of disease surveillance still rests on a boolean foundation: for analytic purposes, a population is represented by 1’s and 0’s, where 1 denotes that an individual has a disease of interest, and 0 indicates that the disease status of the individual is unknown. this limitation applies not only to conventional disease reporting, but also to electronic laboratory reporting and syndromic surveillance. a fundamental problem with a boolean foundation for disease surveillance is that a yes–no classification of a patient into disease or no disease is an information-losing first step in a process that requires maximum use of information as it becomes available (26). it cannot represent the level of uncertainty about a patient’s diagnosis except by ad hoc extensions such as the diagnoses “suspected sars” and “probable sars.” it cannot integrate data from syndromic surveillance, elr, and other disease surveillance paradigms to form a more confident assessment of a patients disease state. the net effect is case and outbreak detection and characterization are less sensitive, specific, and timely than they could be. effectively integrating bayesian medical diagnosis and population diagnosis will address critical barriers to progress in both fields, and can open up major avenues for new research and real-world applications in clinical medicine and public health. in clinical medicine, for example, the answer to the often-voiced criticism, “where do you get the priors?” will be: “from real-time population level analysis.” this problem is particularly nagging for outbreak diseases, where the prior probability may change quickly. a solution to this problem will make case detection more timely, sensitive, and specific for outbreak diseases, with practical benefits for hospital infection control, quality assurance, and case detection for disease surveillance. http://ojphi.org probabilistic, decision-theoretic disease surveillance and control 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 3, 2011 in public health, the biggest advantage of the probabilistic, decision-theoretic approach is that it is a well-organized and formally sound method for integrating multiple weak signals, with medical knowledge, and with epidemiological knowledge, to provide for early and reliable detection at low false alarm rates. also significant, is our approach to the problem of how to synchronize epidemic models with an actual population, which will make epidemic models more useful during outbreaks. integrating probabilistic disease surveillance and medical diagnosis is a paradigm shift for public health. bayesian diagnosis of individual patients is not yet a component in future envisioned architectures such as the public health information network (27), nor is it factored into cmms “meaningful use,” which is incentivizing the healthcare system to modify their information systems in other ways. the architectures, message types, and data standards currently under development will not be able to support this paradigm, unless they are designed with its requirements in mind. the limitations of the current boolean paradigm will become increasingly problematic as disease surveillance takes fuller advantage of data stored in emrs. 6 future work at present, the probabilistic, decision-theoretic system monitors influenza in allegheny county. the cds component is located in a machine room in the upmc health system. ods, bioecon, and three epidemic models are running on servers in our laboratory as web services. we consider the current implementation an operational prototype. our future work will measure the performance of cds and ods for influenza and test hypotheses about the performance synergies achieved as a result of their integration. we also plan to expand the number of diseases being modeled. acknowledgements the centers for disease control and prevention 1p01hk000086-01 supports the “university of pittsburgh center for advanced study of informatics.” the national library of medicine grant nlm 5r01lm009132-02 “decision making in biosurveillance” supports development of bioecon and the apollo web service. the authors would like to thank lee husting, md for his encouragement and professional dedication. corresponding author michael wagner, md, phd department of biomedical informatics parkvale building, suite m-183, room 139 200 meyran avenue pittsburgh, pa 15260 ph: 412-648-6731 fax: 412-802-6803 email: 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2006. 27. cdc. public health information network (phin) strategic plan (draft version 2.2.1). atlanta: cdc march 17, 2011. 28. neill db, moore aw, cooper gf. 2006. a bayesian spatial scan statistic. adv neural inf process syst. 18, 1003-10. http://ojphi.org http://dx.doi.org/10.1371/journal.pone.001003620 http://dx.doi.org/10.1371/journal.pone.001003620 http://dx.doi.org/10.1002/sim.309323 http://dx.doi.org/10.1002/sim.309323 http://dx.doi.org/10.1002/sim.309323 http://dx.doi.org/10.1146/annurev.cs.03.060188.00032324 http://dx.doi.org/10.1146/annurev.cs.03.060188.00032324 http://dx.doi.org/10.1126/science.130.3366.925 http://dx.doi.org/10.1126/science.130.3366.925 http://dx.doi.org/10.1097/00124784-200107060-0000626 http://dx.doi.org/10.1097/00124784-200107060-0000626 http://dx.doi.org/10.1097/00124784-200107060-0000626 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts game plan: communicable disease surveillance for super bowl xlviii – new jersey, 2014 teresa hamby*1, andrew walsh2, lisa mchugh1, stella tsai1 and edward lifshitz1 1new jersey department of health, trenton, nj, usa; 2health monitoring systems, inc, pittsburgh, pa, usa objective to describe the surveillance planning and activities for a largescale event (super bowl xlviii) using new jersey’s syndromic surveillance system (epicenter) introduction in the summer of 2013, the new jersey department of health (njdoh) began planning for super bowl xlviii to be held on february 2, 2014, in met life stadium, located in the meadowlands of bergen county. surveillance and epidemiology staff in the communicable disease service (cds) provided expertise in planning for disease surveillance activities leading up to, during, and after the game. a principal component of njdoh’s super bowl surveillance activities included the utilization of an existing online syndromic surveillance system, epicenter. epicenter is a system developed by health monitoring systems, inc. (hms) that incorporates statistical management and analytical techniques to process health-related data in real time. as of february, 2014, 75 of new jersey’s 81 acute care and satellite emergency departments (eds) were connected to this system. cds staff primarily used epicenter to monitor ed visits for unusual activity and disease outbreaks during this event. in addition, njdoh and hms implemented enhanced reports and expanded monitoring of visit complaints. methods hms and cds staff collaboratively established enhanced algorithms to improve statewide ed data monitoring around the game-related activities in addition to the ongoing analyses already included in epicenter. new jersey eds were requested, via a han message, to use “super bowl” in their chief complaint fields to flag visits from patients who were involved in game-related activities, and these visits were detected automatically. as well, the implemented enhancements monitored out-of-town registrations, registrations from participating metropolitan areas (seattle & denver), novel word complaints and unlikely complaints. hms developed a report summarizing these enhanced data points for facilities in proximity to the stadium. these reports were sent to cds surveillance staff at 6-hour intervals beginning on january 27, 2014, one week prior to the game, and continued until one week after the game (february 10th). results expected increases in visits to facilities due to the influx of visitors related to the super bowl occurred with no anomalies of concern noted. staff maintained an ongoing comparison of ed visits in 2013 and 2014 to quickly note any dramatic differences (see figure 1). no differences of concern were noted when accounting for day of week changes from year to year. twenty ed visits using the key word “super bowl” were reported in facilities around the stadium with 17 records coming from one location. in this case, 16 records reflected a misuse of the key word for vomiting cases. the 17th record was a vomiting case related to a super bowl event. continued monitoring showed nothing widespread or sustained in connection to the case. otherwise, use of the key word was associated with one assault and 2 injuries. the six-hour interval reports provided an important overview to cds surveillance staff that was summarized in updates to njdoh’s emergency command center. no differences of concern were noted when accounting for day of week changes from year to year. conclusions large-scale events pose a challenge to existing surveillance systems where it is expected that additional populations potentially will impact the volume of alerts generated. implementing enhanced algorithms in an existing system allows better determination between expected anomalies and those that might be of greater concern. the collaboration between njdoh and hms, as well as system flexibility within epicenter, helps minimize unnecessary response efforts by public health and emergency preparedness personnel during a largescale event such as super bowl xlviii. keywords syndromic surveillance; new jersey; epicenter; large scale event *teresa hamby e-mail: teresa.hamby@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e2, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts application of an innovative data analytical approach to assessing the disease situation during crises in somalia, yemen and pakistan kamran ahmed*, antony ajanga, omer a. saleh, mohammad d. altaf and ghulam r. popal world health organization emro, nairobi, kenya objective to assess the epidemic and outbreak situations during emergencies through development and application of a data summarization techniques while launching electronic disease early warning systems (edews) in resource poor countries introduction infectious disease outbreaks during crises can be controlled by detecting epidemics at their earliest possible stages through cost effective and time efficient data analytical approaches. the slow or non reporting is a real gap in existing reporting systems that delays in receiving the disease alerts and outbreaks, and hence delays in response causing high burden of morbidity and mortality, especially during crises situation. as on contrary, the functioning electronic databases for fast and reliable disease early warning and response networks (ewarn) have been found very effective in early detection, confirmation and response to disease outbreaks but launching the implementation of such systems is always time consuming due to resource constraints and other limitations during crises. hence introduction of time efficient data analytical approaches can serve as a fast and reliable alternative for electronic databases during the launching phases, and may facilitate assessment of epidemics and outbreak situation by ensuring immediate, reliable and fully functional disease reporting and analysis until online database becomes fully functional and adopted by authorities. methods here, we describe an innovative and low-cost simple analytical method called data summarization technique (dst) that was developed to assess the disease situation for early detection of epidemics while launching the implementation of online database system during humanitarian crises in resource poor countries. the dst approach is easily implementable in practice and has been developed on advanced time efficient ms excel pivot table technique that require basic excel skills at end user level and is customizable for better integration with other databases (example epi info & access) of vertical health programmes in order to generate semi-automated epidemiological reports on timely and ad-hoc basis, an important approach to perform well during emergencies. local capacity was built to utilize dst tools for complete data analysis using simulation exercises where basic ms.excel knowledge was considered essential for health staff participation in trainings. results the development and implementation of edews system took one month on average to become functional in conflict-hit regions and faced many challenges such as limited skilled human resource capacities and access (internet & electricity issues), and other technical and financial considerations. the dst method was successfully tested as an alternative data analytical approach during initial phase of launching of edews to keep monitoring disease situation until online database becomes fully functional. health staff in all humanitarian settings found dst as a simple and flexible tool for data analysis and epibulletin generation as it requires minimum set of analytical skills, and also that it facilitates linking of various databases of vertical health programmes in order to generate disease situation updates for stakeholders. its multilingual support also facilitated offline production of epidemiological bulletins in different languages at national and regional levels, and allowed rapid sharing of epidemiological information through official web pages of humanitarian agencies. introduction of dst approach has also improved timeliness of sharing of information of public health importance from many weeks to few hours or a day in all three countries. conclusions our results indicate that dst is a very useful analytical approach to monitor health situation without any delay during humanitarian crises where poor capacity and issues with access and resources always cause delays in implementation of launching of electronic tools for disease early warning system. furthermore, this innovative approach can help in improving overall public health information management capacities and capabilities of resource poor settings during emergencies due to its flexibility and simplicity in order to meet the challenges in absence of electronic automated analysis tools and with limited skilled human resource capacities. keywords early outbreak detection; data analysis; humanitarian crises; electronic database; surveillance system acknowledgments we wish to thank our data management and edews surveillance teams, too numerous to list individually, who provided us with prompt help, information and facilitation. references 1. ahmed k, altaf md, dureab f. electronic infectious disease surveillance system during humanitarian crises in yemen. ojphi. 2014. 6(1). *kamran ahmed e-mail: drkamranrajput@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e101, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 33 (page number not for citation purposes) isds 2013 conference abstracts system design model for versatile outbreak surveillance hervé chaudet*1, françois delon2, gaëtan texier3, jean-baptiste meynard2, liliane pellegrin2 and xavier deparis2 1umr 912 inserm/aix-marseille université, marseille, france; 2centre d’epidémiologie et de santé publique des armées, marseille, france; 3institut pasteur du cameroun, yaoundé, cameroon � �� �� �� � � �� �� �� � objective �������� �� � � ��� �� �� ���� ���� ��� ��� � � ��� ����� ��� � ��������������� ������� ����������� ������������� �� ���� ��� � � �� ����������� �� ������ � � introduction �� ����������� ����������� �������������� � �� �� � ���� � �� �� ��� ����� ������������ ��� �� ������ � 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�������� � conclusions ������� � � ��� ���� � ��� ������� ����� ������ ���� �������� �� ������ �������� � � ����� ������� $��� ��������� ���� � ����� ���� ���� ��� '���# �� ������ ��� �� �� ��� �� ����� � �� � ���� �� �� �� �� ��� ������ ����������� � ����� ������� ����������8�������� �� � ������ ���� ����� �� ����'���#����� �������� ����� � ������ ��� � � � ���� ����9 � ��� �������:� �������� ��� ��;������ ����&� � keywords �� � � ��� � ��� <� ���� �� � ����<� �� � � ��� � �� � �<� �� ��� �� �� � � ��� ����� �<������ references (!*�;���� ��=��> � ���?� ��� �7 ��� ��� ����� ������ ������� � � � ��� ��� � � ���� ������������� ������ � � ������= � ���� ���� � -��� � �%@@a<!%4baaa�c! (%*�& ��� ��:"��;���� ��=� ��� ��������� ������ ��������� � ���� � ��� � ��� ����� ��� � � ��� ��� ������ ��� ��� � ��� ���b��� � $� � �� � ����� �� ����� � ���� � � �&� �& � �%@@d�0��<!c45!@6b!@ae�c4 ()*�� ������:��.� f��9� ��� �-�� �� ����� � � ��� ���� ������� �� ���� ��� ��������� � ������� �� �� ����� � �-:>��-�%@@d<%)5c6b!%%)�!%44 *hervé chaudet e-mail: herve.chaudet@univ-amu.fr� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e71, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts enhanced monitoring of antiretroviral resistance in persons living with hiv/aids james t. gomez*, shuang wang, syed rizvi and salma k. khuwaja houston health department, bureau of epidemiology, houston, tx, usa objective the aim of this presentation is to illustrate a public health surveillance method for monitoring antiretroviral drug resistance (adr) in persons diagnosed with human immunodeficiency virus infection (hiv). we developed procedures for examining hiv related electronic laboratory reports (elr) transmitted to our health department surveillance system that link to corresponding clinical and demographic data for patients with varying degrees of antiretroviral drug resistance. introduction the implementation of the health information technology for economic and clinical health (hitech) act of 2009, permits health departments with the authority to establish programs to improve health care quality by the promotion of health it and includes guidelines for receiving and transmitting secure electronic health information. when elr health data has been verified for completeness, it enhances the ability to monitor hiv diagnosed patients for virologic failure that in some cases is associated with adr. in recent years the transmission of hiv drug-resistant strains among persons with hiv has been an on-going concern. when drug-resistant hiv becomes resistance to more than one drug class, the control of viral replication becomes more difficult. consequently, measuring the burden of antiretroviral resistance has risen in importance, ranking alongside other major public health concerns when planning hiv prevention campaigns. the analytical method illustrated here aims to improve monitoring of patients with increased viral replication that may lead to poor clinical outcomes and resistance to antiretroviral medications, leading to significant increases in monetary costs when prescriptions for secondline drug regiments are required [1]. methods the automation of data collection was optimized by uploading elrs into the houston department and human services health (hdhhs) disease surveillance system, a collaborative effort involving health facilities and information technology (it) staff for establishing connectivity using national standards (hl7 format, loinc and snomed coding). a laboratory event package for hiv was customized for populating drug resistance data for different classes of antiretrovirals (e.g., nucleoside and nucleotide rt inhibitors (nnrti), nonnucleoside rt inhibitors (nrti), protease inhibitors (pi), etc.) that includes cd4 and viral load counts. an automated query for accessing this information was developed for linking this file to demographic and clinical data to examine records that exceeded viral load thresholds and resistance across different classes of drugs. in an initial analysis, we selected ten months of laboratory resistance data results (857 records) reported by elr to hdhhs from 1-2013 to 10-2013 on adults receiving hiv outpatient care from clinics affiliated with a large local public hospital system. results a total of 294 patients (34%) were shown to have resistance or possible resistance to at least one class of antiretrovirals. of those with resistance, 113 (38%) had resistance in two or more classes of drugs and 8 (3%) patients were found to have resistance to three classes. resistance was most common for nnrti’s 215 (73%), followed by nrti’s 112 (38%), and pi’s 54 (18%). a higher proportion of males (36%) than females (34%) had resistance to one or more drug classes (rr = 1.04; ci = 85 – 1.27). antiretroviral resistance was weakly linked to substance abuse within one year of resistance testing (rr = 1.07; ci = 90 – 1.25). conclusions hhdhs developed a method for evaluating hiv resistance at the community level. we intend to provide health partners in the community with information to monitor groups in which resistance to antiretrovirals has been detected, especially in those with resistance to multiple drug classes to improve their health outcome. further refinements in surveillance analytical methods are planned to correlate resistance data to other clinical and laboratory data for developing a comprehensive profile of this group. keywords anti-retroviral drug resistance; electronic data collection; hiv acknowledgments 1 staff of the hiv/std surveillance program, hdhhs bureau of epidemiology 2 jilin province, china branch of the center for disease control and prevention 3 this work was supported by cooperative agreements from the centers for disease control and prevention (cdc). its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention. references smith, rd; coast, j; (2012) the economic burden of antimicrobial resistance: why it is more serious than current studies suggest. technical report. london school of hygiene & tropical medicine, london. *james t. gomez e-mail: james.gomez@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e77, 2015 ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 development and implementation of a clinical and business intelligence system for the florida health data warehouse raed h. alhazme 1 , arif m. rana 1 , michael de lucca 2 1. nova southeastern university college of osteopathic medicine, fort lauderdale, florida 1 2. broward regional health planning council, hollywood, florida 2 abstract objective: to develop and implement a clinical and business intelligence (cbi) system for the florida health data warehouse (fhdw) in order to bridge the gap between florida’s healthcare stakeholders and the health data archived in fhwd. materials and methods: a gap analysis study has been conducted to evaluate the technological divide between the relevant users and fhwd health data, which is maintained by the broward regional health planning council (brhpc). the study revealed a gap between the health care data and the decision makers that utilize the fhdw data. to bridge the gap, a cbi system was proposed, developed and implemented by brhpc as a viable solution to address this issue, using the system development life cycle methodology. results: the cbi system was successfully implemented and yielded a number of positive outcomes. in addition to significantly shortening the time required to analyze the health data for decision-making processes, the solution also provided end-users with the ability to automatically track public health parameters. discussion: a large amount of data is collected and stored by various health care organizations at the local, state, and national levels. if utilized properly, such data can go a long way in optimizing health care services. cbi systems provide health care organizations with valuable insights for improving patient care, tracking trends for medical research, and for controlling costs. conclusion: the cbi system has been found quite effective in bridging the gap between florida’s healthcare stake holders and fhdw health data. consequently, the solution has improved in the planning and coordination of health care services for the state of florida. keywords: business intelligence; clinical analytics; data warehouse; health care planning; public health informatics. correspondence: ra556@nova.edu doi: 10.5210/ojphi.v6i2.5249 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health info rmatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. background and significance the project was conducted at the custodian of the florida health data warehouse (fhdw), broward regional health planning council (brhpc), incorporated, based in hollywood, ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 florida. brhpc is a non-profit organization that was established in 1983 under florida statute (408.033), as the legislatively designated broward county local health planning entity. brhpc provides health and human services at the national, state, and local level through planning, direct services, implementation, evaluation, and organizational capacity building. during the last several years, brhpc has led statewide collaborative planning activities in partnership with ten other florida local health-planning councils. brhpc has established several databases under the umbrella of their fhdw that provide community members with access to vital health planning and policy making data. such databases include the hospital utilization, nursing home utilization, florida prevention quality indicators (pqi), and diagnosis related group (drg) data warehouse. the medical facilities utilization reporting system improves upon a manual reporting system that the state local health planning councils had been administering for over 25 years. this system consists of two databases, the hospital utilization and the nursing homes. the hospital utilization database collects detailed inpatient and emergency department data from hospitals across the state. the nursing home utilization database tracks admissions and patient days by payer source. these data sets are accessible online, thus improving program efficiency and overall functionality including utilizing data to make capacity and quality related decisions. the database has the ability to generate 39 exportable and/or ready-to-print reports. it was expanded to become a strategic planning tool for health care administrators to assess variances in utilization. hospital and nursing home utilization reporting is required by state statute and is delivered to the agency for health care administration on a quarterly basis. the pqi provides county-level data that identifies hospitalizations and emergency department visits that may have been preventable with the utilization of high quality primary and preventive care. pediatric quality indicators/avoidable admissions (pdi) provides county level data that identifies pediatric hospitalizations and emergency department visits that may have been preventable with the utilization of high quality primary and preventive care. the drg data warehouse is a decision support tool for health care providers and planners. it allows the user to quickly run customized reports by hospital medical services such as cardiology or orthopedics including drg level detail by selected hospitals in an area using the florida agency for health care administration (ahca) hospital inpatient database. these databases provide health care practitioners, planners, researchers, and policy-makers across the state with valuable community-planning resources to target initiatives, set benchmarks to increase health care access and quality, and identify target areas for quality improve [1]. the overall aim of this project was to evaluate cbi’s capability to bridge the gap between brhpc’s data sources and the various end-users who need the data for analysis and, ultimately, for making informed decisions. materials and methods cbi is a powerful set of tools that has the potential to assist in the planning and coordination of the health care services. the project was designed to reflect the system development life cycle (sdlc) development methodology, which consists of five stages: planning, analysis, detailed system design, implementation, and support. the sdlc describes activities and functions that all systems developers perform, regardless of which approach they use [2]. ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 as part of the planning stage of the sdlc development methodology, brhpc permitted the assessment of the existing informatics set-up of the fhdw, so that a thorough gap analysis could be performed. the analysis yielded major drawbacks in the initial set-up of brhpc’s informatics solution. the database infrastructure (systems and connectivity) prevented the high utilization of the large amount of health care data available for analysis and use. figure 1 illustrates the gap in data utilization between the fhdw and the state’s public health service decision makers, health care planners, hospitals, and the public at large. figure 1: health care data utilization gap between the fhdw and relevant stakeholders the state’s decisions makers, health care planners, hospitals, and other groups have limited access to the data because of technical and practical barriers. these groups require the data to be analyzed and converted to information in order to be suitable for the decision making process. conducting the data analysis offline whenever a decision is needed, or as part of normal supervision, requires technical resources that may not be available for many user groups. in addition, the offline analysis is inefficient because it is typically lengthy, time consuming, and has to be repeated every time the data source is updated. this method also adds a technical layer between the data and the user groups, which in turn increases the complexity of the data utilization process. cbi systems have the ability to bridge the gap between the data and the users. cbi may be defined as a set of mathematical models and analysis methodologies that exploit the available ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 data to generate information and knowledge useful for complex decisions-making process [3].the main components of any typical cbi system include extraction, transformation and loading (etl); the data warehouse that consists of unified dimensional models (udms) and multidimensional data marts; and the analytical tools. this set of components was used in this project to overcome the said issues. the etl component was used to retrieve the data from the existing heterogeneous data sources, transform the structure of the data to suit data analysis and data mining, and archive the data in the data warehouse in a multidimensional format. the execution of this process was configured in a manner to ensure the tight synchronization between the data warehouse and the data sources. the data warehouse was configured to host four developed udms: 1) the hospital utilization, 2) the nursing homes, 3) the drg, and 4) the health indicators. each udm was developed with sets of dimensions and measures to reflect its source. the data warehouse also included the data in the sources, but in multidimensional format. this format is very powerful for data analysis, and it is widely used for analyzing large amount of data [4]. the analytical tools used in this project provided access to the information and knowledge generated by the cbi system as a whole. these tools are generally what end-users interact with as part of the cbi system. the dashboard tool is one of the major tools of cbi systems. it consists of screens that show sets of data analysis widgets. figure 2 show a dashboard of fhdwcbi system. figure 2: high-level summary dashboard of the brhpc cbi system there are also other tools that have been implemented in the fhdw’s cbi system, including analytical reports and udm access utilities. these tools allow end-users access to the udms and the multidimensional database for easy data analysis, without the need for technical skills. the ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 data analysis can be done interactively and can be saved on the system for future retrieval. this is a major advantage as it gives end-users the ability to analyze data directly. upon the approval of the planning stage proposal, a project plan covering all the other stages of the sdlc (analysis, detailed system design, implementation, and support) was developed. the complete project plan consisted of three main parts: 1) project preparation; 2) system development and implementation; and 3) project finalization. in order to simplify the project management and enable task dependences, the three parts were divided into ten phases. the initial phase of the project plan started with the analysis stage of the sdlc methodology. the purpose of the system analysis phase is to build a logical model of the new system [2]. meetings were held with different end-users in order to collect the site’s requirements for the cbi system. in addition, various analytical reports, statistics were studied, access to the existing databases was obtained and analysis conducted in order to compile a clear understanding about the site’s needs. after the data collection process was completed, an analysis was done using the collected data and the results were incorporated into a demo system. the demo system had most of the capabilities that were needed to fulfill the end-user requirements, and the system was built based on more than 30 percent of the data that existed at the fhdw. the analysis phase also involved the allocation of all of the needed resources for the cbi system development and implementation, including the acquisition of the back-end and front-end hardware and software. moreover, the phase involved finalizing the site agreement that was necessary for formalizing the project between nova southeastern university (nsu) and brhpc. once the analysis stage was completed, the system design, development, and implementation stages were initiated. the three stages were implemented for each phase of the project. phase 1, the infrastructure implementation, involves the installation and configuration of the cbi system platform. this includes the back-end operating systems (oss), the databases, and the cbi system components, which are the etl, udm service, analytical reporting service, and the cbi portal. phases 2, 3, 4, and 5 are related to the design, development, and implementation of the cbi system components. after phase 5, which is a comprehensive system testing and enhancement process, phase 6was initiated. it consisted of four rounds, each starting with gathering unresolved issues and/or discrepancies as well as enhancing requests from a site’s super-user, and implementing them accordingly. this step was critically important because it tremendously improved the system’s functionality by removing issues before releasing it to the production environment for end-user use. after the four rounds of system testing and enhancement phase were completed, the system was released into the production environment. in accordance with the fifth and final stage in the sdlc development methodology, a support plan was created to maintain the uptime and performance of the system. this was accomplished by compiling a complete system documentation—the technical system architecture (tsa). the tsa document describes the various components of the new system and the technologies used in the development and implementation [5]. it includes technical diagrams that describe the system design, as well as detailed information about the system servers, applications, services, databases, and user accounts. it is anticipated that the project will continue to grow in the years to come. as such, the system will eventually need to be scaled out in order to maintain the targeted system performance. the expansion plan will cover the system’s two main components, the microsoft sql server and the microsoft sharepoint. in addition, the brhpc’s information technology (it) team has been ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 prepared to handle the system maintenance and administration activities. a technical training was delivered to the team. the training covered the tsa document and detailed steps for monitoring and maintaining system operation, user accounts, and backups. discussion vast amount of data is collected and stored by various health care organizations (hco) throughout the country. this data is often underutilized as hcos lack the clinical analytics tools necessary to turn the raw data into meaningful information in real time. cbi systems have the potential to offers hco with valuable insights for improving patient care, tracking trends for medical research, and better controlling costs. in order for this to happen, the components of the cbi system must be designed properly. the portal component of the cbi system brhpc is based on microsoft sharepoint 2010. the application is web-based, which allows end-users to access it from any computer connected to the internet using standard web browsing applications. once valid credentials are provided, the portal will display the main screen. regardless of the user access level, there are three main zones in all screens of the portal: the top control and navigation zone, the side navigation zone, and the analytics zone, as shown in figure 3. figure 3: main screen of the brhpc cbi portal the top control and navigation zone includes a menu for navigation through the different dashboards within the system. it also includes links for controlling tags and notes about the dashboards, in addition to allowing the end-user to logout from the system. the side navigation ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 zone also has a menu for navigation through the different dashboards. additionally, it has a recycle bin link that allows restoring of deleted custom analytics by the user. the analytics zone encapsulates dashboards that display the analytical graphs, analytical maps, scorecards, and analytical reports. in some dashboards, there are tools that allow the end-user to access the udms, analyze, and save the result within the system or on the end-user’s computer. the main screen of the cbi portal displays a high-level summary of all analytics in the system. seven analytical graphs and one analytical map are part of the main screen of the portal. the analytical graphs are interactive and allow the end-user to analyze the information beyond the layout that was developed by default. for example, if an end-user is interested in viewing the details of a year in the total hospital admissions graph, he or she only need to click on the year and the portal will show the admission data by month. the graph can also be enlarged to a full screen size when the title is clicked. to analyze the data of the graph by the available dimensions, the end-user can right-click on the data bar of interest and then select the decomposition tree tool. this tool enables the end-user to drill down through the data easily and interactively, as shown in figure 4. figure 4: drilling-down through the health care data the tool sorts the dimension attributes based on their measure values. graphical and numerical indicators are also some of the useful tool features, as they indicate the share of each dimension in comparison to the overall measure. a number of the web pages in the cbi portal have a section at the bottom called data cubes. this section has a number of tools that can be used to access the udms in the cbi system. the first option is powerpivot, which is a tool that allows ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 the end-user to access udms and analyze them in an easy manner. the look and feel of this tool is similar to the popular office software, microsoft excel, which shortens the learning curve for the new end-users. nonetheless, if the layout of the model needs to be modified, the tool allows end-users that have the necessary permissions to open the model in microsoft excel, modify it, and save it to their personal folder. figure 5: sample analytical graph on some modules of the cbi, such as the hospital utilization, there is an additional section at the bottom of the screen called analytical reports. a number of pre-designed reports can be found in this section. when one of the analytical reports is opened, default parameters are used to run the report. however, the reports allow modifying of certain parameters and re-running the report based on the new parameter configuration. one of the useful features is the ability to print the report or export it in different common formats. analytical reports also have a function called data alerts, which is a data driven notification solution that helps the end-user to be informed about the report’s data that is of interest or importance at any given relevant moment. by using data alerts, the end-user no longer has to seek out information as it gets automatically delivered based on user specifications. data alert messages can be sent by email or through short message service (sms), i.e. text messages. depending on the importance of the information, the end-user can choose to send messages more or less frequently, and only when results change. the end-user has the option to specify multiple email recipients to keep others informed or to enhance efficiency and collaboration among ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 various stakeholders and participants. the configuration screen of data alerts is shown in figure 6. figure 6: configuration screen of data alerts as a data alert owner, the end-user can view information, delete, and edit data alert definitions. an alert has only one owner, the end-user who created it. cbi system administrators can manage data alerts at the site level. they can view lists of alerts by each site user and delete alerts as needed. report subscription is another function in analytical reports that allow reports to be emailed to end-users based on schedule. this function is different because it is triggered only by time, not by changes in the data. with report subscription, the end-user can configure the report to be emailed to one or more email addresses. the report can be emailed in different formats, including comma-delimited (csv), pdf, and tiff image. results although the cbi system has just been released to the fhdw environment, it has already yielded a number of outcomes. the system provides much of the information needed to develop county health plans, which typically consists of hundreds of pages and requires months to compile. unlike the county health plan, the cbi data is up-to-date and can be compiled and formatted in minutes. in addition, end-users can configure certain reports to be emailed to them based on a schedule or based on certain changes in the data. ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 the system also has shortened the time needed to analyze the data, or transform it to information, and prepare it for decision making processes. hospital planners are now able to get the data transformed to information on demand whenever they display one of the cbi portal dashboards. they even have the ability to modify/adjust the information to further fit the situation on hand. typically, a number of data analysis professionals were needed to perform such processes and make decisions. the cbi system has helped eliminate this layer, which was not only costly but also time consuming. in addition to the immediate outcomes just described, there are a number of anticipated advantages to brhpc specifically, and the state of florida generally. the advanced data analysis capabilities of the system are expected to improve the coordination and distribution of health care resources across the state. the quality of health service is also expected to be enhanced, as the system provides the ability to automate tracking issues in the delivery of health care services and reporting them to the relevant personnel. the system is also expected to enhance the health care planning for hospitals, health planning agencies and the state. the data mining component of the system has a number of prediction models that can assist in the planning process. conclusion vast amount of health care data is being collected and maintained nationwide, statewide, and within counties in the united states. however, there is a typical technological gap that exists between the data and users who need access to the data in order plan and coordinate health care services in the area. in the state of florida, cbi has been developed and implemented in order to bridge the divide, and soon after, the solution yielded a number of positive outcomes. based on these results, we suggest cbi as a solution for similar situations in other set-ups. limitations of the study it is important to highlight that the study has not been extended to evaluate the end-users’ experience and the skill levels with the implemented solution. without the end-users’ acceptance and familiarity with the system use, the value of the solution can be significantly compromised [6]. nonetheless, qualitative research methods such as focus groups, interviews and surveys can be used to collect information about the end-users’ impressions toward the solution as well as their levels of ability to use the system. the outcomes of such research can determine the overall impact of the system and also assist in customizing it to meet the end-users’ needs. another limitation was related to the changes of the coding system requirements. the cbi system was built based on standard coding systems such as drg, the international classification of diseases 9 th revision (icd-9) and the current procedural terminology (cpt). however, starting from october 1, 2014, healthcare providers will be required by the centers of medicare and medicaid (cms) to submit their claims using icd-10 [7], which is the newer revision of the coding system that is used by the cbi. this study did not cover how the cbi system will handle the difference in coding between the archived data and the new data that fulfill the new coding requirements. ojphi development and implementation of a clinical and business intelligence system for the florida data warehouse online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e182, 2014 acknowledgements the product of this research work would have not been possible without the help and support from the broward regional health planning council and nova southeastern university. the authors are grateful for the opportunity provided and assistance received to help make this project a reality. references 1. broward regional health planning council. about brhpc. n.d. [cited nov 2012]; available from: http://www.brhpc.org 2. shelly gb, rosenblatt hj. systems analysis and design. 2012, boston, ma: course technology. 3. vercellis c. business intelligence: data mining and optimization for decision making. 2011, west sussex, uk: john wiley & sons ltd. 4. jensen c, pedersen t, thomsen c. multidimensional databases and data warehousing. 2010, san rafael, ca: morgan & claypool. 5. ccta. database and physical process design. 2000, norwich, uk: crown. 6. geddes dr. quality management intensity during is development: does it influence enduser satisfaction? 2007, ann arber, mi: proquest information and learning company. 7. centers for medicare and medicaid. faqs: icd-10 transition basics. 2013 [cited mar 2014]; available from: http://www.cms.gov/medicare/coding/icd10/downloads/icd10faqs2013.pdf ojphi-06-e101.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 121 (page number not for citation purposes) isds 2013 conference abstracts moving digital disease detection from research to action: findings from a survey of isds membership jennifer olsen* university of north carolina-chapel hill, chapel hill, nc, usa � �� �� �� � � �� �� �� � objective ������������ �� �� ��� ��������������������������� �� �������� �������������� �� ����������������� ������������� ����� ��������� �� ������ ��������� introduction �� ���������� ������������������������������������������������ �� �� � ��������� ������������ ���� ���� ��� ��������������� ������������ ������ �� �� � ������ ��������������������������������� �� ������� ��������� �� ��� ������������������������ ��������������� ���� ����� ������ � ��� �� ��� ���������������� ������ �� ������ ��� �������� 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��);;"����>" '!f6='�'6�� 6�����3�� ��9���$�4�dd�����������*��9 �������* ���������� �2������ ������������������������������� � ������������������.���h���f�3�� 9���� ��* ������ >�);;:� *jennifer olsen e-mail: jenolsen.drph@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e101, 2014 abstract-page174 isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 174 (page number not for citation purposes) isds 2013 conference abstracts patterns of emergency care utilization by chronically ill yue wang*, artur dubrawski, lujie chen and ryan mcdermitt carnegie mellon universtiy, autonlab, pittsburgh, pa, usa � �� �� �� � � �� �� �� � objective ������� � ���� 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(http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 175 (page number not for citation purposes) isds 2013 conference abstracts �������� ���� ��� ������������ keywords ���� ���� ��������� ������ ������������������ ������� acknowledgments ����������������� ������������ ���������������� ������� ��� � ����� � 0�������!"##!$%!�#$%!$&'1� *yue wang e-mail: yuewangapply@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e59, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts tracking hiv post-exposure prophylaxis using syndromic surveillance in nyc emergency departments stephanie ngai*1, zoe edelstein2, julie myers2 and don weiss1 1new york city department of health and mental hygiene, bureau of communicable disease, queens, ny, usa; 2new york city department of health and mental hygiene, bureau of hiv/aids prevention and control, queens, ny, usa objective to describe trends in hiv post-exposure prophylaxis uptake in new york city (nyc) emergency departments (eds). introduction hiv post-exposure prophylaxis (pep) involves taking antiretroviral medication after potential exposure to hiv to reduce the probability of becoming infected. new york state recommends pep following certain occupational (e.g., needle sticks by healthcare workers) and non-occupational (e.g., sexual and needle-sharing activities) exposures.1 little information exists on the uptake of pep for hiv in the united states, particularly with regard to nonoccupational exposures.2 ed data have been used previously to identify occupational pep visits3 but have not been used extensively to describe trends in pep visits overall. we aimed to identify hivrelated pep visits in nyc eds to track uptake and inform outreach efforts. methods ed visits in nyc reported to the nyc department of health and mental hygiene from january 1, 2002 through december 31, 2013 were analyzed. a primary case definition for a pep-related visit was developed to search chief complaint and discharge diagnosis fields, containing combinations and alternate spellings of the following keywords and icd-9 codes: ‘hiv’, ‘post-exposure prophylaxis’, ‘pep’, ‘npep’, ‘exposed’, ‘needle’, ‘blood’, ‘fluid’, ‘rape’, ‘sexual assault’, v01.6, v01.7, and e920.5. icd-9 codes were not available in the dataset until 2008. an alternative, more inclusive case definition was also developed that included terms for hiv testing, std-related visits, high-risk sexual behavior, sexual assault, needle exposures, and blood or body fluid. pep visits as a proportion of total ed visits by year were used for analysis, and tests for trend were performed using logistic regression. tests for trend were stratified by sex, and descriptive analyses were stratified by five-year age groups. results using the primary case definition, we identified 2573 pep-related visits in nyc eds from 2002-2013. chief complaint was used to identify 86% (2223) of visits; only these visits were used to assess trends. pep-related visits increased from 0.003% to 0.011% of all reported ed visits from 2002-2013 (p<0.0001). the alternate case definition identified an additional 82,176 visits. when stratified by age group, the highest proportion of visits was among persons ages 25-29 (25%), followed by ages 30-34 (20%). males accounted for 73% of pep visits overall; this proportion increased from 64% to 82% from 2002-2013 (p<0.0001). conclusions pep-related visits as a proportion of all nyc ed visits increased over threefold during the past decade. this may reflect increases in pep use generally and/or increases in pep prescribing in eds. pep awareness among patients may also be increasing given that results were primarily based on the chief complaint field. pep-related visits were more common in men and younger adults, possibly reflecting greater pep use among populations at higher risk for hiv, such as men who have sex with men. incorporation of the primary case definition into routine surveillance could help monitor citywide usage and uptake of pep and inform efforts to educate providers and nyc residents. keywords hiv; post-exposure prophylaxis; syndromic surveillance; pep; npep references 1. hiv prophylaxis following non-occupational exposure. new york state department of health aids institute. updated july 2013. 2. jain s, mayer kh. practical guidance for nonoccupational postexposure prophylaxis to prevent hiv infection: an editorial review. aids 2014;28:1545-54. 3. merchant rc, chee kj, liu t, mayer kh. incidence of visits for health care worker blood or body fluid exposures and hiv postexposure prophylaxis provision at rhode island emergency departments. jaids 2008;47(3):358-68. *stephanie ngai e-mail: sngai1@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e89, 201 development of a process and infrastructure to outreach stakeholders and capture healthcare system stress in emergency response situations development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations taylor read,1 elizabeth white,2 j perren cobb,3 perry mar,4,5 mahesh shanmugam,6 roberto a. rocha,4,5 sarah collins rossetti7* 1 northwestern university, chicago, il, 2 yale school of public health, new haven, ct, 3 university of southern california, los angeles, ca, 4 brigham and women’s hospital, boston, ma, 5 harvard medical school, boston, ma, 6 partners healthcare system, boston, ma, 7 columbia university, new york, ny abstract real time data provided by frontline clinicians could be used to direct immediate resources during a public health emergency and inform increased preparedness for future events. the united states critical illness and injury trials group program for emergency preparedness (usciit-prep), a group of expert critical care and emergency medicine physicians at various academic medical centers across the us, aims to enhance the national capability of rapid electronic data collection, along with analysis and dissemination of findings. to achieve these aims, usciit-prep created a process for real-time data capture that relies on a curated and engaged network of clinical providers from various geographical regions to respond to short online “pulse” queries about healthcare system stress. during a period of three years, five queries were created and distributed. the first two queries were used to develop and validate the data collection infrastructure. results are reported for the last three queries between june 2015 and march 2016. response rates consistently ranged from 39% to 42%. our team demonstrated that our system and processes were ready for creation and rapid dissemination of episodic queries for rapid data collection, transmittal, and analysis through a curated national network of clinician responders during a public health emergency. usciit-prep aims to further increase the response rate through additional engagement efforts within the network, to continue to grow the clinician responder database, and to optimize additional query content. keywords: public health informatics, emergency preparedness, electronic data capture, real-time data capture abbreviations: united states critical illness and injury trials group program for emergency preparedness (usciit-prep); office for the assistant secretary for preparedness and response (aspr); world health organization (who); research electronic data capture (redcap) correspondence: * roberto a. rocha, r.rocha@computer.org. * sarah collins rossetti, sac2125@cumc.columbia.edu doi: 10.5210/ojphi.v11i2.10048 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ r.rocha@computer.org mailto:sac2125@cumc.columbia.edu development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi introduction the united states lacks a technical infrastructure that allows “frontline” clinical providers to collect and report real-time clinical and population data during an emergency. for example, during an influenza outbreak, understanding a data-driven level of stress incurred at healthcare institutions might facilitate provision of resources and guide subsequent planning. real time data could be used to direct immediate resources and inform increased preparedness for future events [1]. stressed institutions are defined as those having to implement alternative policies and procedures to manage day-to-day operations. a public health emergency is a critical period and the process of collecting data during this time should not place undue burden on clinical providers, be delayed in execution, or lack methodological rigor that prevents comparisons over time [2]. as an expedient, scalable, sustainable, and repeatable approach to collect real-time data during a public health emergency, our team has created a network of clinician responders from across the country committed to responding to rapid data collection queries. we aimed to use rapid data collection queries to take the ‘pulse’ of the healthcare system within 24 hours of an identified potential stressor, particularly a public health emergency. the united states critical illness and injury trials group program for emergency preparedness (usciit-prep) is a group of expert critical care and emergency medicine physicians at various academic medical centers across the us. usciit-prep aims to significantly enhance the national capability of rapid electronic data collection, along with analysis and dissemination of findings [1]. the process created by the usciit-prep “pulse project” for real-time data capture relies on a curated and engaged network of clinical providers from various geographical regions to respond to online queries about healthcare system stress [3]. pulse queries are intended to be short and capture only critical data, providing a snapshot into challenges that each site might be experiencing. by collecting data snapshots covering multiple geographical regions, analysis can be rapidly performed and disseminated to different stakeholders. the pulse project was funded through a contract from the office for the assistant secretary for preparedness and response (aspr), whom directs the content and dates of each prospective query. aspr was created after hurricane katrina to focus on prevention, preparation, and response to public health emergencies and disasters [2]. aspr provides operational response capabilities, medical countermeasure research, as well as grants for health care organizations to strengthen readiness procedures [4]. we describe here the first phase of the pulse project, from march 2014 to march 2016, which was primarily focused on process and infrastructure development and validation. background a public health emergency can come from various sources, including but not limited to environmental disasters, pandemics/epidemics, and resource shortages. when these issues arise, it is vital to have a response system in place ready to assess and react with proper protocol and the necessary measures to deescalate the situation. the needs of the healthcare system during a public health emergency should be supported by data-driven solutions, particularly outcomes confirmed by data analysis from prior events [5]. as emergency conditions progress, needs can http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi be anticipated, and countermeasures initiated by evaluating trends from previous emergencies. a delayed response to an outbreak may be attributed to multiple factors, including disease or condition-specific variables, political conditions, economic support, or environmental conditions [6]. despite the challenges associated with real-time data collection, such as rapid configuration and dissemination of electronic data capture tools, it is important to strive for concurrent data collection, as opposed to retrospective, in order to identify opportunities to quickly provide assistance [5]. per the world health organization (who), there is no standardized protocol for data collection and sharing during public health emergencies [7]. these shortcomings have been increasingly apparent during recent public health emergencies, like the 2009 h1n1 pandemic. collection of vital clinical data to synthesize treatments and identify high-risk groups remained challenging [2]. who has started to create methods for public data sharing, but these practices have not been tested or validated in the public health realm. other groups have created data collection tools in response to specific outbreaks, such as ebola, but these were created after the outbreak and therefore lack critical data [8]. during the recent zika outbreak, the who released a data-sharing platform upon the declaration of the public health emergency of international concern in 2016, allowing researchers from around the world to share and access data about the outbreak [9]. in a separate effort, our team created a tool to assess nationwide variability in treatment of influenza for critically ill patients [10]. methods during this first phase of the pulse project, the main goals were to implement and validate a reliable and consistent process for rapid data collection during a real or perceived public health emergency, and to transmit and analyze the resulting data to inform and enable an appropriate response [11]. we focused on three critical infrastructure components needed to adequately prepare for real-time data collection during an emergency: a) an easily accessible electronic data collection tool, b) a network of clinician responders tailored to the specificity of the stressor and area of concern, and c) clinically relevant and validated query instruments. considering the overarching rapid data collection process and these three infrastructure components, we identified four requirements and three associated challenges prior to launching a new query. in terms of requirements, each query had to be (i) concise (i.e. take only a few minutes to respond), (ii) clear (i.e. easily understandable), (iii) remain open for a short period of time (i.e. 48-hour period), and (iv) distributed to an adequate number of clinician responders in multiple regions of the country. challenges in meeting these requirements included (v) enabling the rapid design of a query instrument relevant to the specific public health emergency, (vi) quickly disseminating the query to the representative target audience (i.e. experienced clinical providers available and willing to respond), and (vii) quickly identifying alternate responders for those that were unable to respond. the pulse project aimed to create a configurable data collection infrastructure that could be thoroughly validated before a potential public health emergency. the configurability ensured the rapid creation and dissemination of different data collection instruments to clinical providers http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi using the internet. necessary technical components included a software tool to create and manage queries, a database of clinician responders, as well as web-based analytics tools and geographical mapping software for concurrent data analysis and visualization [12]. the pulse project adopted the ten geographical regions outlined by the u.s. department of health & human services [13]. each region had one or two appointed leads serving as the point person for the clinician responders in that area. the responsibilities of regional leads included outreach and communication to clinician responders. in order to increase the likelihood of a response, prior to each query, our team sent brief requests to clinician responders. these prequery requests included a confirmation of availability to respond to queries, but also the validation of prior collected demographic details, hospital bed count, and academic and clinical (department) affiliation. query implementation pulse queries were implemented using the research electronic data capture (redcap), a secure web-based application developed for electronic data capture [14]. however, redcap had to be extended to meet our data collection and analysis requirements. for example, we configured a mapping application within redcap to generate real-time maps with response rates and geographical distributions, taking into account the location of the various clinician responders. another extension was the ability to prefill query fields with demographic information previously provided by registered clinician responders. prefilling demographic details reduced the effort required from clinician responders, an important feature to reduce the burden of realtime data capture during public health emergencies. our team created and validated a reusable set of standard, generic questions that could apply to several types of public health emergencies. this set of generic questions was validated and optimized with each subsequent query, addressing different aspects of staffing, resources, capacity, and utilization of high-acuity protocols. these same set of generic questions was used for planned quarterly queries, which assessed stress levels at periodic points throughout the year. however, when a specific type of stressor or event is identified, the set of generic query questions was supplemented with additional questions tailored to the specific stressor. these additional stressor-specific questions were rapidly identified and validated, in collaboration with subject matter experts. for example, a query during an influenza outbreak included questions about viral testing procedures, while another query during a saline shortage included questions about alternative practices and products being utilized. while quarterly queries were planned to collect stress data periodically throughout the year, our team was also prepared to send episodic queries when potential stressor events were identified. only a few episodic queries were sent throughout the project’s duration. after the design of a query instrument was finalized, the data capture system was promptly configured, and the online instrument was distributed to the network of clinician responders. in an effort to minimize misinterpretations across different regions and institutions, query instruments only used clear and concise language that conveyed consistent and unambiguous http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi questions as determined during validation sessions with subject matter experts. each query started at 8:00 am eastern standard time, and closed 48 hours later. a period of 48 hours provided adequate time for clinician responders to answer the brief query after determining their local stress at that particular point in time. once a query was initiated, it was crucial to rapidly analyze the data being collected. an initial analysis phase occurred while queries were still running, using descriptive statistics and focusing on response rates by region. response rates were calculated periodically during the 48-hour period, and sent to regional leads to inform the performance of their region when compared to others. regional leads with low response rates were asked to reach out to clinician responders in their region and encourage participation. query data analysis once a query was closed, all collected data were sent to aspr stakeholders who performed the data analysis at a state, regional, and national level. all data collected were considered confidential and promptly transferred to aspr. at its discretion, aspr analyzed and communicated the actual findings to stakeholders. our group utilized the collected data during a query to evaluate the query instrument and the overall data collection process, including specific levels of participation. these details were used to optimize subsequent queries. the post-query analysis evaluated the distribution of responders’ hospitals by size and category, using bed count and the medicare cost report, respectively. each hospital was identified within the american hospital directory to obtain bed count and other details [15]. we also determined the geographical distribution of respondents. “point maps” were created to identify the cities of participating hospitals with different map overlays. one overlay used region boundaries, enabling regional leads to assess areas within their region that lacked representation. another overlay was color-coded with population density from u.s. census data (figure 1). the map overlay with population density helped us prioritize outreach efforts to specific geographical locations, particularly to ensure proper representation of areas with higher population density. these maps were generated in real-time within redcap, during and after each query. http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi figure 1 – example of a map overlay with population density. each dot represents a city with participating clinician responders. results during a period of three years, five queries were created and distributed. we report here the results of the last three queries, which occurred between june 2015 and march 2016 (table 1). the first two queries, which occurred between march 2014 and april 2015, were preliminary efforts used to develop and validate the data collection infrastructure, as well as build the network of clinical responders [2]. pulse query ‘g1’ used the set of generic questions and included two pre-query requests. the first request was used to obtain confirmation that clinical responders were available to participate. the second request was actually a “mock” query sent one week prior to the actual query. pulse query ‘g2’ also included two pre-query requests. the first pre-query request was sent three weeks prior to the query and was used to obtain additional clinician responder details, such as phone number, system name, hospital name, clinical department, and role within the department. this pre-query request also included, for the first time, a section for clinician responders to designate a back-up contact within their department. the intent was to identify another clinical provider that could respond on their behalf if they were ever unavailable to respond. the second ‘g2’ pre-query request was sent one week before the query, with a single question confirming their participation. this participation confirmation also provided an opportunity to validate the alternate contact identified during the previous request. pulse query ‘i1’ was able to reuse the information collected during previous queries and did not include any pre-query requests. instead, while g1 and g2 queries were planned, query ‘i1’ simulated a true emergency and was promptly setup and distributed. http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi table 1 – pulse query details between june 2015 and march 2016 pulse query number date issued type of query (focus) query recipients query respondents response rate g1 jun 2015 general healthcare system stress 344 141 40.99% g2 dec 2015 general healthcare system stress 385 161 41.82% i1 mar 2016 influenzarelated healthcare system stress 354 138 38.98% pre-query requests were analyzed to confirm response rates and correlate to subsequent query response rates. for instance, the pre-query request sent prior to pulse query ‘g2’, 69 out of 385 (17.9%) providers responded with their personal contact information. of those, 32 (46.4%) provided a back-up contact within their department to respond if they were unavailable. the participation confirmation request sent a week before query ‘g2’ generated 103 responses, including 96 positive answers from clinical responders confirming participation. out of the seven negative answers, five clinical responders also included a back-up contact. of the 96 clinician responders who confirmed participation, 85 (88.5%) actually responded to pulse query ‘g2’. of the 282 responders who disregarded the participation confirmation request, 73 (25.9%) responded to pulse query ‘g2’. three (60.0%) clinical providers designated as back-up responded to query ‘g2’, out of the five alternate contacts used. regional leads were asked to oversee the participation of local clinical responders. each lead received a regional analysis during the query to help identify data collection gaps. analysis of regional response rates during pulse query ‘g2’ shows four regions having a greater than 50% response rate, three regions between 30-50%, and three regions below 30% (table 2). we confirmed that regional lead outreach to clinical responders increased response rates, as demonstrated by response rate differences between regions with and without active outreach. http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi table 2 – pulse query ‘g2’ response rates over time by region region after 24 hours after 33 hours final (48 hours) 1 39% (14/36) 56% (20/36) 58% (21/36) 2 29% (10/35) 31% (11/35) 31% (11/35) 3 35% (11/31) 39% (12/31) 48% (15/31) 4 30% (14/47) 51% (24/47) 55% (26/47) 5 25% (14/55) 47% (26/55) 56% (31/55) 6 19% (4/21) 24% (5/21) 24% (5/21) 7 11% (3/27) 15% (4/27) 19% (5/27) 8 13% (3/23) 26% (6/23) 30% (7/23) 9 13% (10/77) 22% (17/77) 29% (22/77) 10 24% (7/29) 41% (12/29) 59% (17/29) the size of the hospitals that responded to pulse queries ‘g1’ and ‘g2’ are summarized in table 3, taking into account total number of beds and number of critical care beds. large hospitals, based on the american hospital directory, are defined as having 400 beds or more, medium with 100 to 399 beds, and small hospitals with 100 beds or less. the distribution by type included 116 academic medical centers, 31 teaching hospitals, 3 critical access hospitals, 9 regional hospitals, 5 county hospitals and 2 corporate hospitals. table 3 – hospitals that responded to pulse queries ‘g1’ and ‘g2’ grouped by size bed count small hospitals medium hospitals large hospitals all beds: range < 100 100-399 > 400 critical care: range 0-36 7-153 28-409 critical care: median 0 35 136 critical care: mean 5 45 145 number of hospitals 10 41 106 discussion our team demonstrated successful implementation and validation of a reliable and consistent process for rapid data collection, transmittal, and analysis during a public health emergency. specifically, we deployed an easily accessible electronic data collection tool to an engaged and http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi curated national network of clinician responders to capture clinically relevant data using validated query instruments. the pulse project lasted three years and executed five different pulses during that time. the principle goals were to be prepared for rapid data accumulation during public health emergencies and to analyze resulting data to inform and enable responses with necessary interventions. to determine the emergency preparedness of the healthcare system, the pulse system tested responses to identified stressors by answering data collection queries. through analysis of response rates, we were able to determine a consistent response rate for the pulse queries, ranging from 39% to 42%. the slight increase in responses between pulse g1 and pulse g2 could be attributed to extensive outreach during the fall of 2015. the slight decline in participation from pulse g2 to pulse i1 (-3%) could be attributed to the lack of pre-query communication for pulse i1. our distribution of responses primarily remained consistent among the three pulses with slight variation between states lacking participation. having the regional leads send reminders during the pulse query appears to positively influence the response rate. this data was shared with regional leads to provide evidence of their work and encourage future outreach to their region. reviewing pre-query request processes allowed us to refine the process for future queries. after initial testing we removed test queries to avoid confusion but continued to send pre-query requests for general healthcare system stress queries. however, we did not send a pre-query request for pulse i1 as it was immediately disseminated upon completion, aligned with an unplanned stress even that did not allow time for a pre-query. we aimed to increase response rates for pre-query requests in order to add additional information for each clinician responder into the database, which in return increases the possible number of pre-populated fields. by obtaining additional information before a query, we are able to determine unit of analysis for each clinician responder with department data as well as send queries to alternative clinicians if back-up clinicians are provided. maintaining an up to date database ensures our response rates were accurate by removing clinicians who were no longer interested and replacing clinicians as they moved to different institutions. using response rate data, we were able to stratify response rates by regions to identify areas of low response. using these data we were able to provide assistance to regional leads directly as well as connecting regional leads to one another for additional bandwidth. targeted outreach initiatives increased geographical representation across each region. our hourly analyses indicated that response rates most likely increased due to the regional lead reminders sent during the queries as indicated by a sharp increase in responses after regional lead contact was made. as a result, we have included this as a formal process during all pulse queries going forward. looking at hospital size distribution, the overall goal was to use this data to create a less skewed distribution of hospitals in the pulse database since the database initially favored large, academic medical centers. this involved seeking out smaller hospitals as well as county and critical access hospitals across the country. this will be a continuous challenge as our current method of recruitment involves networking with current clinician responders and regional leads. review of http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi geographical hospital distribution provided tiered regions for outreach. our first priority is to perform outreach to states with zero responses, then states with one response, followed by states represented by a single metropolitan area. we utilized the network of network approach by reaching out to current responders asking for referrals to clinical providers in the regions of interest. limitations this was a nationwide study lending strength to the sample size and network of clinician responders utilized. however, we acknowledge that a larger and more diverse sample of pulse queries, particularly those in response to an unanticipated event, would strengthen our understanding of the generalizability, scalability and replicability of our results to date. specifically, we note that influenza is an annual occurrence. the results would be strengthened further with data from a pulse query to an unanticipated event that does not allow for pre-query requests and requires substantial supplementation with additional questions tailored to the specific stressor. the time to validate a query instrument requiring significant additions may be substantial and does pose a challenge for executing real-time queries. we believe our approach in using a standard, generic form that can be extended as needed minimizes, but does not completely remove, that effort. further, the curated network is comprised of critical care and emergency medicine physicians and it is possible that an unanticipated event may require development and curation of a network with different clinical expertise. finally, there was lower representation from county and critical access hospitals across the country and variation in participation across states. further work is required to engage these populations for a more representative sample of system stress. conclusion we showed that the process and tools used throughout this project were able to rapidly execute expedient pulse queries through a pre-configured infrastructure and established network of clinician responders. we achieved reliable response rates ranging from 39% to 42% despite the variety in pre-query communication and emergent nature of a query. we determined outreach processes to be successful at maintaining the pulse database of clinician responders due to pre-query requests providing additional useful database fields. distributional analysis by hospital size, type and location was useful for optimization of future outreach to create a more representative database of clinician responders. with several pulse queries completed successfully, the process is ready for future queries. the i1 query that was configured in a time sensitive setting, demonstrated that our system and processes were ready for creation and rapid dissemination of an episodic query if a public health emergency were to arise and ongoing work has confirmed this continued success [16]. usciit-prep aims to further increase the response rate by engaging the regional leads to send personalized reminders to non-responders during the query period as well as establishing prequery communication with all clinician responders. as of august 2016, all pulse related initiatives have transitioned to university of southern california per the aspr contract [16]. http://ojphi.org/ development of a process and infrastructure to outreach stakeholders for capturing healthcare system stress in emergency response situations online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e2, 2019 ojphi usciit-prep will continue to grow the clinician responder database using distribution data such as hospital size, type and geographical location. overtime, we plan to have optimized several sets of query questions that can be reused depending on the type of query. funding acknowledgements: funding provided through a contract from the office for the assistant secretary for preparedness and response (aspr). references 1. blum jm et al. united states critical illness and injury trials group. 2013. chest. 143(3), 808-13. pubmed https://doi.org/10.1378/chest.12-2287 2. lurie n, manolio t, patterson a, collins f, frieden t. 2013. research as a part of public health emergency response. n engl j med. 368(13), 1251-55. pubmed https://doi.org/10.1056/nejmsb1209510 3. read t, white e, karipineni n, ignatov m, shanmugam m, et al. 2016. utilization of cost effective tools for queries on healthcare system stress. amia annu symp proc. 1564. 4. aspr organization. https://www.phe.gov/about/aspr/pages/default.aspx. accessed september 16, 2018. 5. morton m, levy jl. 2011. challenges in disaster data collection during recent disasters. prehosp disaster med. 26(3), 196-201. pubmed https://doi.org/10.1017/s1049023x11006339 6. lee ek, liu y, ferdinand ph. 2017. a compartmental model for zika virus with dynamic human and vector populations. amia annu symp proc. 2016, 743-52. pubmed 7. whitty cj, mundel t, farrar j, heymann d, 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characterize serious illness, injuries, and resource requirements for acute medical responses to public health emergencies. crit care med. 43(11), 2403-08. pubmed https://doi.org/10.1097/ccm.0000000000001274 13. regional offices. https://www.hhs.gov/about/agencies/iea/regional-offices/index.html. accessed august 25, 2018. 14. harris pa, taylor r, thielke r, payne j, gonzalez n, et al. 2009. research electronic data capture (redcap) a metadata-driven methodology and workflow process for providing translational research informatics support. j biomed inform. 42(2), 377-81. pubmed https://doi.org/10.1016/j.jbi.2008.08.010 15. american hospital directory information about hospitals from public and private data sources including medpar, opps, hospital cost reports, and other cms files. https://www.ahd.com/. accessed august 25, 2018. 16. 2018. discovery program for emergency preparedness, assistant secretary for preparedness and response. longitudinal assessment of u.s. health system stress during influenza seasons: pulse program. crit care med. 46(1), 36. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26308434&dopt=abstract https://doi.org/10.1097/ccm.0000000000001274 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18929686&dopt=abstract https://doi.org/10.1016/j.jbi.2008.08.010 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 (page number not for citation purposes) isds 2013 conference abstracts emergency department chief complaint versus discharge diagnosis for tracking disease measures andrea bankoski, anikah salim and zachary faigen* maryland department of health and mental 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zachary.faigen@maryland.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e38, 2014 editorial ojphi vol 11, no 2 (2019) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e1, 2019 ojphi an information technology call to action to support healthy homes kevin g croke, phd (emeritus professor, school of public health) 1, edward k. mensah, phd (associate professor) 1 1 health policy and administration division school of public health university of illinois at chicago 1603 west taylor street (m/c 923) chicago illinois, 60612 doi: 10.5210/ojphi.v11i2.10247 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. advances in information technology over the last decade offer the opportunity to advance the goals of public health advocates to provide safer and healthier home environments. a call to action in public health informatics is needed to realize the benefits of information technology to support healthy home objectives. surgeon general healthy homes call to action in 2009, the surgeon general issued a report, “a call to action,” to promote healthy homes. although there have been tremendous efforts to promote healthy home environments since that time, regrettably, many of the recommendations of that report regarding the need for action are still valid. the report made the case that unhealthy conditions in homes and apartments are a major public health hazard. mold and poor ventilation are responsible for 20% of asthma cases. radon in homes is the second leading cause of lung cancer. fires in homes cause 12,000 injuries and 2,500 deaths per year. according to a report by the centers for disease control and prevention carbon monoxide exposure contributes to approximately 450 deaths and more than 15,000 emergency department visits annually. sixty-four percent of the exposures occurred in homes. research shows that carbon monoxide exposures increase during winter; and a high proportion of the increase is attributed to the following causes: the use of home heating systems, increased use of gasoline-powered generators, indoor use of charcoal grills, portable stoves, and space heaters. over 240,000 children live in dwellings having unsafe exposure to lead. the link between home hazards and public health is indisputable. editorial ojphi vol 11, no 2 (2019) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e1, 2019 ojphi advances in the development of mobile applications, cloud-based computing, online education, and artificial intelligence if applied to the management of healthy home inspection programs could have profound beneficial effects in responding to the surgeon general's call to action. dimensions of information technology support mobile applications using cell phones and tablets transform the collection of home inspection data from the isolated recording of housing conditions to a situation in which reliable data can be collected in a consistent manner. due to public agency budget constraints and the present lack of information technology support, the goal of establishing a continuity of care related to building conditions based on periodic inspections is difficult to achieve. cloud-based data storage allows the analysis of home inspection data from a variety of sources in an efficient and cost-effective manner. of equal importance, advances in information technology allow for progress to be made in developing a profile and prioritization of home hazards. geographic information systems showing areas needing remedial action in public housing could help target resources for inspection. decision support systems that generate actionable alerts from residents to inspectors to trigger priority inspections may also be useful. the level of tenant involvement in healthy homes programs can increase the value of information technology support. for example, organizations, such as the metropolitan tenant organization, receive thousands of calls per year from tenants many of which regard healthy housing problems. these inquiries must presently be answered by personnel manning phones. artificial intelligence applications would now allow many of these inquiries to be handled by computerized chatbots. the wide variety of hazards would underscore the need to involve community and residents in healthy home programs that go beyond simply code enforcement efforts. a “healthy homes for community health workers” report developed by the national center for healthy housing lists the control of dust mites, pest control, operational fire alarms, household chemical hazards, heating and ventilating conditions, carbon monoxide alarms, safety from falls, lead poisoning, asbestos, and radon among the hazards of which residents should be aware. the use of mobile application text based communication vastly increases the ability to customize advice on specific inspected health hazard problems to targeted residents and homeowners. unfortunately, many of the advantages of the employment of information technology have yet to be realized in the existing pen and paper, or even tablet-based isolated home inspection data collection systems. a call to information technology action to support healthy homes at present, there are a number of information technology support systems for home and building inspections, but no clear dominant application. standardized protocols are needed for building inspection activities that can be employed in information inspection applications. little analysis of the effectiveness of information technology in municipal, public housing, or tenant organizations exists. in blunt terms, not enough well-structured experience in the use of information technology support exists to reliably guide in future information technology development. as a general call to action to support healthy homes, the development and funding editorial ojphi vol 11, no 2 (2019) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e1, 2019 ojphi of well-structured pilot projects ideally involving universities, public housing agencies, local communities, and municipalities in partnerships should be initiated to assess alternative development paths. one of the most beneficial areas to introduce pilot programs relates to the housing and urban development (hud) inspection programs. over two million people live in hud supported housing. in 2019 the us government accounting office sent a report to congress which was critical of the hud real estate assessment center (reac) operations relating to their physical inspection program. the report listed a number of weaknesses in the program including misrepresentation of housing conditions, inadequate training of inspectors and a lack of quality control in assuring housing deficiencies are corrected. many of these problems could at least in part be corrected by the support of information technology. initiatives in information technology application by a program as large as hud reac could pave the way for adoption by smaller public health agencies and municipalities. the journal would welcome comments, suggestions or articles regarding how to advance the practice of healthy home informatics. references u.s. government accountability office. (gao). (2019). gao-19-254. real estate assessment center: hud should improve physical inspection process and oversight of inspectors. retrieved march 21, 2019 from https://www.gao.gov/products/gao-19-254 national center for healthy housing – https://nchh.org/resourcelibrary/hh_coalition_case_statement_final_2015.06.pdf national safe and healthy housing coalition, 2015 case statement, june 2015. u.s. department of health and human services, (hhs). (2009). the surgeon general’s call to action to promote healthy homes. u.s. department of health and human services, office of the surgeon general. doi: nbk44192 centers for disease control and prevention, (cdc). (2005). annual smoking-attributable mortality, years of potential life lost, and productivity losses--united states, 1997-2001. mmwr.morbidity and mortality weekly report, 54(25), 625-628. doi:mm5425a1 [pii] centers for disease control and prevention, (cdc). (2005). unintentional non-fire-related carbon monoxide exposures--united states, 2001-2003. mmwr.morbidity and mortality weekly report, 54(2), 36-39. doi:mm5402a2 [pii] daley, w. r., smith, a., paz-argandona, e., malilay, j., & mcgeehin, m. (2000). an outbreak of carbon monoxide poisoning after a major ice storm in maine. the journal of emergency medicine, 18(1), 87-93. doi:s0736-4679(99)00184-5 [pii] 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts overcoming operational differences to attain a national picture for novel threats michael coletta*, achintya n. dey, matthew miller, peter hicks and umed ajani centers for disease control and prevention, atlanta, ga, usa objective demonstrate that information from disparate syndromic surveillance (sys) systems can be acquired and combined to contribute to national-level situational awareness of emergent threats. introduction the may arrival of two cases of middle east respiratory syndrome (mers) in the us offered cdc’s biosense sys program an opportunity to give cdc’s emergency operations center (eoc) and state-and-local jurisdictions an enhanced national picture of mers surveillance. biosense jurisdictions can directly query raw data stored in what is known as “the locker.” however, cdc cannot access these data and critical functions, like creating ad-hoc syndrome definitions within the application are currently not possible. these were obstacles to providing the eoc with mers information. biosense staff developed a plan to 1) rapidly generate query definitions regardless of the locally preferred sys tool and, 2) generate aggregate reports to support the national mers response. methods in consultation with state and local sys epidemiologists, a common-use set of five mers-specific query definitions were developed. the definitions focused on: sensitivity, specificity, travel, symptoms, and icd diagnosis codes. jurisdictions had various operating environments so all definitions were operationalized in sql, r-script, sas, and essence query languages. the definitions and deployment guidance were provided to state and local jurisdictions. also, a webinar was conducted to call for participation in mers-like surveillance activities. cdc answered participants’ questions and setup weekly data reporting, which was collated into reports for the eoc. results call for participation in mers-like surveillance webinar: 192 people attended, representing 60% of registrants. 35% of webinar participants completed an exit survey. 63% of exit survey respondents reported the activity directly applicable to their work. 78% felt they had a better awareness and understanding following the webinar. 42% of exit survey participants reported an anticipated change in behavior, while 68% reported the webinar contained useable ideas and techniques. participation and data sharing among cdc and jurisdictions: of the 11 jurisdictions providing data-sharing feedback, 64% were interested in sharing data and 9% were not. in the end, 15 jurisdictions–6 large cities and 9 states–participated in mers-like surveillance. they represented 822 facilities nationwide. ten of 15 jurisdictions used a local sys system to gather and report data. surveillance activities began may 12 and continued through july 31. participating jurisdictions shared data only with cdc; they did not share with each other. conclusions despite limitations and imperfections, surveillance reports of mers-like visits enhanced the mers national surveillance picture. cdc and partner jurisdictions quickly developed a novel, standardized set of definitions that were widely shared. though limited by the use of differing systems, the community succeeded in providing cdc with mers-like surveillance data. this national activity will inform future development of sys applications. several operational limitations persisted and should be discussed among the community. the 15 participating jurisdictions covered a substantial population but did not approach full or uniform national coverage. despite participants’ fairly quick response, activating the surveillance method was too slow for the mers response. thus, sys data became ancillary to eoc operations. participants never agreed how to share mers-like surveillance data beyond cdc; no data were shared among jurisdictions. however, many stakeholders viewed this attempt to enhance the national mers surveillance picture as a model to expand, and a success that may improve trust among jurisdictions giving hope for providing meaningful national sys data in the future. keywords syndromic surveillance; mers; biosense acknowledgments cdc dhis, cdc eoc, participatory and consultant state and local jurisdictions *michael coletta e-mail: mac0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e14, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the need for address based data: disaggregation of syndromic surveillance systems james n. blackwell, ashley hickson, david heinbaugh, kimberlee mcgee, william stephens and sharefa aria* public health informatics office, tarrant county, fort worth, tx, usa objective the justification for address based syndromic surveillance systems, and building syndrome weighting mechanisms. introduction epidemiological surveillance is used to monitor time trends in diseases and the distribution of the diseases in the population. to streamline the process of identifying outbreaks, and notification of disease, syndromic surveillance has emerged as a method to report and analyze health data. rather than report data by disease status (ie disease/no disease), clinical symptoms are used to detect outbreaks as early as possible.1 currently, only data collected via active surveillance (notifiable disease investigations) are usable for identifying communities that require attention. therefore, any interventions performed using said data is reactive in nature. syndromic surveillance systems must be disaggregated to enable proactive health promotion, and responses. furthermore, a common method must be established to assess the overall impact of syndromes. diseases are not equal; some have a greater impact on health, and life. to address this issue, the world health organization (who) has created disability weights to be used in calculating disability adjusted life years (daly).2 dalys are effective in calculating the overall impact of disease in a community. dalys estimate the burden of disease, not syndromes; therefore, it is reactive tool. to create a more effective syndromic surveillance system, syndromes must be associated with an overall impact weight. methods essence and biosense 2.0 are syndromic surveillance systems used by tarrant county. the data from these systems are aggregated into zipcodes. to demonstrate the necessity of address based data, and syndrome weights, data collected via active surveillance techniques were used as a proxy. the active surveillance data are infectious disease data collected by tarrant county. to demonstrate the utility address data, a heat map with pertussis cases was generated (figure 1). all maps were created in qgis 2.4. results the heat map shows that the highest pertussis densities are smaller than zipcodes. the heat map also give more information on outlying cases, as well. conclusions to perform localized interventions, community based participatory research, and create better syndromic predictors it is necessary to disaggregate health data. address level data will be key in creating more precise outbreak predictions. the tools to assess disease distribution are readily available; it is the structure of syndromic data that is limiting widespread adoption of existing software. keywords gis; disaggregation; spatial weights; tarrant; surveillance references 1. kramer a, kretzschmar m, krickeberg k. modern infectious disease epidemiology. london: springer; 2010. 2. world health organization. metrics: disability adjusted life year. http://www.who.int/healthinfo/global_burden_disease/metrics_daly/ en/. updated 2014. accessed august 08,2014. *sharefa aria e-mail: saaria@tarrantcounty.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e65, 201 ojphi technological and organizational context around immunization reporting and interoperability in minnesota 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 technological and organizational context around immunization reporting and interoperability in minnesota sripriya rajamani 1* , erin roche 2 , karen soderberg 3 , aaron bieringer 4 1. public health informatics program, school of public health, university of minnesota, minneapolis, minnesota 2. minnesota immunization information connection (miic), immunization program, minnesota department of health, st. paul, minnesota 3. office of health information technology (ohit) and minnesota e-health initiative, minnesota department of health, st. paul, minnesota 4. minnesota immunization information connection (miic), immunization program, minnesota department of health, st. paul, minnesota abstract background: immunization information systems (iis) operate in an evolving health care landscape with technology changes driven by initiatives such as the centers for medicare and medicaid services ehr incentive program, promoting adoption and use of electronic health record (ehr) systems, including standards-based public health reporting. there is flux in organizational affiliations to support models such as accountable care organizations (aco). these impact institutional structure of how reporting of immunizations occurs and the methods adopted. objectives: to evaluate the technical and organizational characteristics of healthcare provider reporting of immunizations to public health in minnesota and to assess the adoption of standardized codes, formats and transport. methods: data on organizations and reporting status was obtained from minnesota iis (minnesota immunization information connection: miic) by collating information from existing lists, specialized queries and review of annual reports. ehr adoption data of clinics was obtained in collaboration with informatics office supporting the minnesota e-health initiative. these data from various sources were merged, checked for quality to create a current state assessment of immunization reporting and results validated with subject matter experts. results: standards-based reporting of immunizations to miic increased to 708 sites over the last 3 years. a growth in automated real-time reporting occurred in 2013 with 143 new sites adopting the method. though the uptake of message standards (hl7) has increased, the adoption of current version of hl7 and web services transport remains low. the ehr landscape is dominated by a single vendor (used by 40% of clinics) in the state. there is trend towards centralized reporting of immunizations with an organizational unit reporting for many sites ranging from 4 to 140 sites. conclusion: high ehr adoption in minnesota, predominance of a vendor in the market, and centralized reporting models present opportunities for better interoperability and also adaptation of strategies to fit this landscape. it is essential for iis managers to have a good understanding of their constituent landscape for technical assistance and program planning purposes. keywords: immunization reporting, standards, interoperability, public health informatics, ehrs http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 correspondence: sripriya@umn.edu* doi: 10.52/ojphi.v6.i3.5587 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction immunization information systems (iis) are effective tools in achieving and maintaining adequate vaccination coverage levels to reduce or eliminate the burden of vaccine preventable diseases [1-3]. iis offer related functionalities such as comprehensive vaccination history of a person given across multiple providers and over time, recommendations of vaccine needed through use of vaccine forecasting algorithms and reports of immunizations which are personbased or for a clinic or select population. the minnesota immunization information connection (miic) is minnesota’s statewide, web-based immunization information system, which has been operational since may 2002 [4]. miic is a population-based system with over 66 million immunizations on 6.9 million clients across the lifespan. miic operations are governed by the minnesota data sharing law [5]. though immunizations are reported on voluntary basis, the provider participation in miic is high across health care sectors. ninety-two percent of mnvfc (minnesota vaccines for children) provider sites have submitted data regularly within the past six months. the effectiveness of an iis depends on the robust reporting of immunizations from healthcare providers and community vaccinators. data on immunizations administered are reported to iis by healthcare providers in various formats and mechanisms. although reporting has largely been electronic since 2004, there has been a recent shift from electronic reporting using a proprietary flat file format to reporting using national data exchange standards. this recent change is driven by increased adoption of electronic health record systems (ehrs). the cms ehr incentive program also referred to as “meaningful use” has been a significant driver for adoption of ehrs and effective use of the functionalities to enhance patient care and population health [6]. minnesota has very high ehr adoption rates among hospitals (99%) and ambulatory clinics (94%) [7], reinforced by both meaningful use incentives and a state mandate for interoperable ehrs by january 2015 [8]. this momentum is supported by federal initiatives on standards to propel the use of standardized formats and codes for select public health reporting transactions which include immunizations [9]. in addition to the technology changes affecting the health care sector, reform initiatives are supporting coordinated models of care to support outcomes and payment reforms, including patient-centered medical homes (pcmhs) which are branded as health care homes in minnesota [10] and accountable care organizations (acos). these influence organizational processes and affiliations and have a direct impact on tracking of immunizations at provider level and organizational reporting structures. recognizing the changing health information technology landscape, the iis functional standards were revised and include receipt of submissions by iis and response to queries, based on http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 recommended standards [11]. progress made by iis across the country in meeting functional standards and in pediatric and adult participation rates has been described [12]. the need to utilize the momentum around meaningful use to improve interoperability between ehr and iis has been elaborated [13]. recent studies analyzed the impact on provider reporting of immunizations by comparing the batch and real-time methods [14] and the effects of automated registry reporting from ehrs [15]. access to iis decision support within ehr [16] has been studied and bi-directional communications between clinical sector and public health has been reviewed [17]. white papers on this topic include describing the overlap between ehrs and iis [18] and the developing hie landscape [19] to outline potential strategies and collaborations for iis to consider in this exchange and use of immunization information across clinical sectors and public health. this study attempts to distill the impact of various initiatives on minnesota’s healthcare landscape and to understand the operational context of miic. the objectives of this study are to evaluate the technical and organizational characteristics of healthcare provider reporting of immunizations in minnesota and to assess the uptake and use of standards, including codes, data formats and transport methods. the research seeks to understand the evolution of electronic public health reporting of immunizations to miic from 2010 to 2014, the period of rapid adoption of health information technology by providers in minnesota. methods data were obtained from different programs within the minnesota department of health: miic, the state immunization information system and the mdh office of health information technology. the various data was assimilated and analyzed to understand the changes in adoption of electronic exchange of health information, impact of organizational changes and regulatory requirements and use of various standards over time. this process was used to compile an assessment of immunization reporting and interoperability, as depicted in figure 1. figure 1: conceptual framework of research study http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 miic currently has 4,200 active organizations, some of whom send data as part of public health reporting (e.g. clinics) and some largely have read-only access (e.g. schools). existing lists from miic were collected, organized and updated to reflect current reporting status. queries were run on miic data to get reporting information on some organizations. immunization information system annual report (iisar) for last 3 years was reviewed to gain an understanding of evolution of technical capabilities of miic. the synthesis of reporting status was limited to main healthcare systems as they account for majority of reporting and also to constraint the scope of this study. the second data source is the minnesota ambulatory clinic health information technology survey (clinic survey), an annual online survey designed to uniformly collect and share the progress of minnesota’s providers in adopting and implementing ehr systems, and exchanging electronic health information. the clinic survey is managed by mdh's office of health information technology and has been conducted annually since 2010. the 2014 survey includes responses from 1,206 of 1,404 clinics that have registered with the statewide quality reporting and measurement system (sqrms), for a response rate of 86%. clinics in minnesota are required by law to register with sqrms and respond to the clinic survey. a matrix was created to depict the current state assessment: (a) healthcare provider entities reporting and their technological characteristics such as ehr adoption, type of ehr used, format used for reporting (standards vs. not, type of standards: format, codes, transport), (b) healthcare provider entities reporting, their organizational characteristics such as integrated delivery network and number of sites reported, (c) changing trends around electronic exchange. an organization can be along a spectrum on standards adoption ranging from use of no standards to some set of standards to all recommended standards. if an organization reports using recommended standards for immunization reporting which include hl7 2.3.1/hl7 2.5.1 exchange standard, cvx vaccination codes, mvx manufacturer codes and transport method which supports real-time reporting (e.g. web services), the entity has achieved a high level of interoperability with miic. the study findings were validated with subject matter experts to gain understanding of the context and other factors which facilitate or hinder adoption of standards and hence movement towards interoperability. results reporting of immunizations to miic has evolved over the years since 2004 (refer figure 2). currently, more than ninety percent (91%) of incoming immunizations are reported through electronic modalities (both batch and real-time transfers). this is in contrast to a decade ago when majority of reported immunizations (88.4%) were entered through direct data entry in 2004. of the incoming electronic reporting, two-thirds (60%) of immunizations are reported to miic through batch modes. real-time reporting did not exist during inception of miic, but currently about one third (31%) of immunizations are reported through this automated and instantaneous modality. this transport option had good adoption rate over the last two years, with real-time reporting increasing from 8.9% in 2011 to 31% in 2014. http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 figure 2: trends in immunization reporting to miic: 2004 – 2014 the provider landscape in which miic is operating has undergone dramatic changes as well with more than 90% of clinics having adopted electronic health records in 2014. based on data from minnesota statewide quality reporting and measurement system, as of 2014, about 1,404 clinics representing 235 entities (health systems/medical groups) are operating in minnesota. ninety-three percent of clinics (1,118) had an ehr installed and in use, and another 6% are planning to adopt (see figure 3). these data also present insights into the ehr system used by the clinics and highlight the dominance of a single vendor in the market, which is used by 40% of the clinics. table 1 presents the percent and count of minnesota clinics using each of the common electronic health record systems. figure 3: ehr adoption rate in minnesota clinics, 2014 http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 table 1: ehr systems used by adopting clinics, 2014 ehr vendor percent count epic 40% 449 allscripts (medinotes) 9% 100 eclinicalworks 9% 98 greenway (primesuite) 8% 94 nextgen 6% 67 cerner 5% 53 centricity (ge healthcare) 4% 46 other 18% 205 total 1,118 the format (exchange standard) of immunization reporting to miic has evolved over the years with an increasing reporting based on recommended exchange standards (hl7). from the miic data, the sites reporting using hl7 standards increased from 109 in mid-2011 to 708 sites as of july 2014. current version of hl7 standards (hl7 2.5.1) is being adopted and used by 151 sites. real-time reporting to miic also expanded over the corresponding time period (2011-2014) with a growth from 11 sites in 2011 to 326 sites using real-time methods as of july 2014. a growth in automated reporting occurred in 2013 due to adoption by many providers (143 sites adopted the existing real-time technology) and introduction of new transport option (adopted by one site in that year). figures 4 and 5 depict the adoption of hl7 exchange standard and real-time transport method respectively over the years 20112014. figure 4: standards-based immunizations reporting using hl7 to miic: 2011 – 2014 109 209 250 395 490 520 531 557 0 0 0 0 7 11 12 151 0 100 200 300 400 500 600 700 800 jan-11 jan-12 jan-13 jan-14 hl7 2.3.1/2.4 hl7 2.5.1 http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 figure 5: facilities with real-time data submissions to miic: 2011 – 2014 the next step in the study was to create a current state assessment of reporting. results of compiling of data across programs on ehrs and reporting information (including format, codes and methods) are shown in table 2. with high ehr adoption rates in minnesota, all the main integrated healthcare delivery systems and medical groups reporting to miic are on various ehr platforms. twenty integrated healthcare delivery systems were studied in detail as they report for 874 sites and account for more than two thirds of volume of immunizations submitted to miic. of these, 80% (700 sites) report using hl7 exchange standards. six hundred and ninety sites (79%) report using recommended vocabulary standards for immunizations (cvx) and some of these sites report both cpt and cvx codes. currently, 326 sites submit through real-time reporting and phin-ms is utilized more (241 sites), whereas the newly introduced technology to support real-time reporting via web services/soap has been adopted by 85 sites. to maintain these, 6 interfaces support the phin-ms reporting by main organizational units and 2 interfaces support the web services based reporting. figure 6 depicts the context and centralized reporting in organizations, wherein immunizations given across 874 sites are reported from 20 centralized units in corresponding organizations. this impacts the interfaces required to be maintained by miic and lesser numbers are needed to manage reporting by multiple sites. figure 6: organizational context and reporting through centralized structures http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 table 2: current state assessment of immunization reporting for select systems health system sites / clinics represent ed ehr system in use adoption of standards/recommendations* exchange format and standard vocabulary / codes transport method health system 1 80 epic hl7 2.4 cvx sftp health system 2 87 epic flat file cpt sftp health system 3 33 epic hl7 2.4 cvx phinms health system 4 39 epic hl7 2.4 cvx and cpt phinms health system 5 105 epic hl7 2.4 cvx sftp health system 6 75 epic hl7 2.3.1 cvx sftp health system 7 81 ge centricity hl7 2.4 cvx phinms health system 8 12 cerner hl7 2.4 cvx and cpt sftp health system 9 15 ge centricity flat file cpt sftp health system 10 140 epic hl7 2.5.1 cvx soap health system 11 27 epic flat file cpt sftp health system 12 22 epic hl7 2.5.1 cvx and cpt soap health system 13 13 allscripts hl7 2.4 cpt phinms health system 14 12 allscripts hl7 2.3.1 cpt ui health system 15 73 epic hl7 2.5.1 cvx and cpt phinms health system 16 4 eclinical works data entry in ui cpt user interface (ui) health system 17 15 allscripts hl7 2.4 cvx and cpt phinms health system 18 23 integreat ehr flat cpt sftp health system 19 6 ge centricity flat cpt sftp health system 20 12 eclinical works flat cpt sftp * details on recommended standards for immunization submission are available at http://www.health.state.mn.us/divs/idepc/immunize/registry/hp/data.html. discussion minnesota has high level of ehr adoption with a confluence of integrated healthcare delivery systems and this presents an interesting context in terms of immunizations reporting. incentives, significant e-health policy and programmatic efforts, organizational shifts such as mergers and business affiliate agreements to provide access to ehrs have facilitated ehr adoption. the dominance of a single vendor in the market presents some unique opportunities for collaboration and expansion of technical capabilities and interoperability. http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 miic is a successful immunization information system with over 4,000 registered organizations and this offers venues for collaboration to promote better reporting and iis use. immunization reporting using standards need to comply with all recommendations: hl7 messages for exchange, cvx and mvx for coding immunizations and using appropriate transport protocols (web services recommended). automated standards-based real-time reporting has grown over last couple of years with one third of immunizations being reported through this modality. currently, the hl7 v2.3.1 exchange standard and phin-ms transport are utilized more, but are superseded with new exchange version and transport recommendations. stage 1 meaningful use requirements promoted hl7 v2.3.1 and with grandfathering clause of this format standard in stage 2, there is less interest in upgrades to next version of exchange standard (hl7 v2.5.1). technical assistance to support web services transport and hl7 2.5.1 should be considered to promote adoption. this research has some limitations as well, with current state assessment of interoperability focused on only the clinics. community vaccinators and pharmacies have an expanding role and are an important part of immunization service delivery and their reporting status impacts the overall interoperability and quality of data in iis. similarly, role of hie entities in this space has been evolving. currently, no reporting to miic occurs through hie entities. their clientele needs to be understood to identify opportunities for collaboration [19]. there is variability amongst providers using same ehr vendor in their mode of immunization reporting. understanding its context will help miic to devise strategies in promoting uniformity and recommended standardsbased reporting. the role of emerging models such as acos and pcmhs and impact of those organizational structures on immunization reporting and access to iis services need to be studied. minnesota is a unique position with other drivers in place such as 2015 interoperable electronic health record mandate [8] and certification of health information exchange (hie) entities [20]. the impact of these regulations needs to be evaluated. potential future studies/projects include outreach to large integrated health delivery systems in the state to better understand future ehriis exchange needs. with overlap between some ehr and iis functionalities [18], there is a need for provider education to highlight the iis functions to support improvements in clinical care, population health management and public health assurance. with both technological capabilities and organizational affiliations in flux, there is a need for continued assessment and understanding of trends in healthcare organizations across the state. while suggested format standards exist for exchange, there is a big gap in best practices for data integration and semantic representation. this has implications for clinical decision support, quality measures and use of iis functionality by healthcare providers. these have a significant impact on bi-directional interoperability between clinical sector and public health. conclusion immunization information systems (iis) operate in an evolving health care landscape with technological changes supported through the use of ehr systems by providers, standards-based reporting driven by meaningful use incentive program, presence of hie entities, and emerging models of health care delivery and payment such as pcmhs and acos. this research shows that, even with high ehr adoption rates, utilization of recommended standards for codes, data format and transport methods is not consistent across providers nor ehr vendor systems. it also http://ojphi.org/ ojphi technological and organizational context around immunization reporting and interoperability in minnesota 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 points to emerging organizational structures which impact reporting of immunizations to public health. additional research is required to understand the factors which lead to adoption and use of standards and the context around variability in technological capacity across providers using same ehrs. further studies are also warranted to know more of the utilization of iis functionality by health care organizations which may influence their decisions regarding reporting of immunizations. it is essential for iis managers to understand the technical capabilities and organizational structures of their constituent landscape to support optimal immunization reporting, access and use of iis services and for technical assistance and program planning. acknowledgements the authors would like to thank dr. marty laventure of minnesota department of health and bill brand from public health informatics institute for their guidance. references 1. centers for disease control and prevention. immunization information systems (iis). 2014. available at: http://www.cdc.gov/vaccines/programs/iis/index.html. accessed october 8, 2014. 2. community preventive services task force. 2014. recommendation for use of immunization information systems to increase vaccination rates. j public health manag pract. 2014, 1-4. 3. groom h, hopkins dp, pabst lj, morgan jm, patel m, et al. 2014. immunization information systems to increase vaccination rates: a community guide systematic review. j public health manag pract. epub 06 2014. pubmed http://dx.doi.org/10.1097/phh.0000000000000069 4. minnesota immunization information connection (miic). submitting and exchanging data with miic. 2009. available at: http://www.health.state.mn.us/divs/idepc/immunize/registry/index.html. accessed october 24, 2014. 5. minnesota statutes immunization data. 2014. available at: https://www.revisor.mn.gov/statutes/?id=144.3351. accessed november 4, 2014. 6. centers for medicare and medicaid services. ehr incentive programs. 2010. available at: http://www.cms.gov/ehrincentiveprograms. accessed 13 june, 2014. 7. mdh office of health it. minnesota e-health assessment reports, factsheets and briefs. 2013. available at: http://www.health.state.mn.us/e-health/assessment.html. accessed october 13, 2014. 8. minnesota ehealth initiative. 2015 interoperable electronic health record mandate. 2007. available at: http://www.health.state.mn.us/e-health/abouthome.html. accessed november 4, 2014. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24912082&dopt=abstract http://dx.doi.org/10.1097/phh.0000000000000069 ojphi technological and organizational context around immunization reporting and interoperability in minnesota 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e192, 2014 9. office of the national coordinator for health information technology. standards and interoperability. 2010 available at: http://www.healthit.gov/policy-researchersimplementers/standards-interoperability. accessed june 13, 2014. 10. homes hc. certified health care homes in minnesota. 2010. available at: http://www.health.state.mn.us/healthreform/homes/documents/certhch.pdf. accessed june 14, 2014. 11. centers for disease control and prevention. immunization information systems functional standards, 2013-2017. 2012. 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la, palma jp, pandher kk, longhurst ca. 2013. immunization registries in the emr era. online j public health inform. 5(2), 211. pubmed http://dx.doi.org/10.5210/ojphi.v5i2.4696 17. dixon be, gamache re, grannis sj. 2013. towards public health decision support: a systematic review of bidirectional communication approaches. journal of the american medical informatics association: jamia. 20(3), 577-83. epub 03 2013. pubmed http://dx.doi.org/10.1136/amiajnl-2012-001514 18. hln consulting. iis and ehr feature overlap. 2014. available at: https://www.hln.com/assets/pdf/hln-iis-ehr-overlap-white-paper.pdf. accessed october 8, 2014. 19. hln consulting. iis and hie: is there a future together? 2013. available at: https://www.hln.com/assets/pdf/hln-iis-hie-white-paper.pdf. accessed october 24, 2014. 20. minnesota statutes health information exchange oversight. 2010. available at: http://www.health.state.mn.us/divs/hpsc/ohit/hieoversight.html. accessed november 4, 2014. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24336133&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22424260&dopt=abstract http://dx.doi.org/10.1016/j.amepre.2012.01.009 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25118438&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23874363&dopt=abstract http://dx.doi.org/10.4338/aci-2013-02-cr-0009 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23923096&dopt=abstract http://dx.doi.org/10.5210/ojphi.v5i2.4696 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23467470&dopt=abstract http://dx.doi.org/10.1136/amiajnl-2012-001514 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts chattergrabber: a lightweight easy to use social media surveillance toolkit james t. schlitt*, bryan lewis and stephen eubank virginia bioinformatics institute, virginia polytechnic institute, blacksburg, va, usa objective to formally introduce chattergrabber, an open source, natural language processing based toolset for public health social media surveillance. chattergrabber is designed to collect and categorize a high volume of content at a low cost, providing a readily deployable solution for epidemiologists to track emergent outbreaks in the field and a signal for syndromic surveillance. introduction despite numerous successes in using social media to detect food borne illness and to predict influenza trends, the use of social media as a public health tool has yet to gain widespread adoption. while social media data cannot directly diagnose illness, aggregate trends in symptom proliferation may readily be observed. such trends may allow a health agency to watch for signs and symptoms related to target conditions within its jurisdiction. further, social media surveillance offers a distinct advantage in immediacy and sensitivity as it is not dependent upon infected individuals seeking care for reportable illnesses and as such information is not delayed by the handling, transfer, and processing of reports. these advantages may enable the earlier preparation and initiation of scaled response sequences during public health emergencies. such data may also yield additional evidence through shared symptoms, rumors, and observations crucial to an epidemiological investigation. methods chattergrabber uses the twitter restful api to perform high frequency searches across a local, regional, or international geographic area. after a sufficient volume of posts has been obtained via a keyword search, posts are manually scored and used to train a classifier via the natural language tool kit’s guided machine learning. classifiers and run parameters may be configured by an investigator via an online interface to select n-grams observed, feature frequency limits for training set inclusion, and machine learning methodologies. supported machine learning methods include naivebayes, maximum entropy, decision tree, and multi-category support vector machines. weak classifiers are optimized by a distributed genetic algorithm in which random parameter sets are bred, run, and scored to select for the strongest. classifiers are scored by the unweighted mean of the products of each category’s sensitivity and specificity. this prevents classifiers from succeeding by over-applying to irrelevant posts or by classifying all posts as a common category. once a strong classifier is obtained, posts are processed based upon the classifications chosen. all content is timestamped and located by the google maps api from attached coordinates or profile locations. reports and visualizations are generated nightly and emailed to subscribers, providing an intuitive, distributable summary of area conditions at the start of each day. results the system has been successfully adapted to track norovirus, tick-borne illness, firearm violence, vaccine sentiment, first responder emergencies, regional ebola rumors, and equine herpes virus for state, federal, and local health agencies. output from chattergrabber has been used to help construct ebola models, to monitor rural norovirus epidemics, and incorporated into a health department dashboard system. conclusions chattergrabber provides a cheap, effective, and readily deployable means for epidemiologists to track emergent outbreaks. the combination of natural language processing and geographic region directed searches allows one to surveil any user defined jurisdiction and hasten illness identification. the use of social media data allows for rapid identification of emerging outbreaks and provides a wealth of soft information, quantitative, and qualitative data to aid in an investigation. keywords twitter; surveillance; dashboard; chattergrabber; natural language processing acknowledgments funding for chattergrabber was supported by the national institutes of general medical sciences of the national institutes of health under award number 5u01gm070694-11. *james t. schlitt e-mail: jschlitt@vbi.vt.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e52, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts surveillance on hand, foot, and mouth disease in east asia ta-chien chan*1, chang-chun lee2 and jing-shiang hwang3 1research center for humanities and social sciences, academia sinica, taipei city, taiwan (province of china); 2genomics research center, academia sinica, taipei city, taiwan (province of china); 3institute of statistical science, academia sinica, taipei city, taiwan (province of china) objective enterovirus epidemics, especially affecting young children, have occurred in south-east asia every year. if the epidemic periods are inter-correlated among different areas, early warning signals could be issued to prevent or reduce the severity of the later epidemics in other areas. in this study, we integrated the available surveillance and weather data in east asia to elucidate possible spatio-temporal correlations and weather conditions among different areas from low to high latitude. introduction regional disease surveillance as well as data transparency and sharing are the global trend for mitigating the threat of infectious diseases. the who has already played a leading role in flunet (http:// www.who.int/influenza/gisrs_laboratory/flunet/en/ ) and dengunet (http://www.who.int/csr/disease/dengue/denguenet/en/). however, the enterovirus-related infections which caused a high disease burden for pre-school children in south-east asian regions over the last two decades still lack a comprehensive surveillance system in the region [1]. if the spreading pattern and a possible alert mechanism can be identified and set up, it will be beneficial for controlling hand, foot and mouth disease (hfmd) epidemics in east asia. in some research findings, the transmission of hfmd was correlated with temperature, relative humidity, wind speed, precipitation, population density and the periods in which schools were open [2]. a delayed temporal trend was also found with the increase in latitude [3,4] . in this study, we tried to apply publicly available weekly surveillance data in japan, taiwan and singapore to evaluate the spatio-temporal evolution of hfmd epidemics and how the weather conditions affect the hfmd epidemics. methods hfmd surveillance data are collected online from the national institute of infectious diseases in japan, taiwan cdc and ministry of health in singapore. the study period started from week 31 of 2012 to week 27 of 2014. there were 54 spatial areas including 47 prefectures in japan, six areas in taiwan and one area in singapore. due to different statistical units of the surveillance in different countries, we standardized the hfmd statistics separately by 54 areas. the weather data were collected from the national climatic data center (ncdc) of the national oceanic and atmospheric administration (noaa). the daily mean temperature (fahrenheit), mean dew point (fahrenheit), mean sea level pressure (mb), mean visibility (miles), mean wind speed (knots), precipitation amount (inches), and maximum temperature (fahrenheit) were calculated as the average values for the corresponding weeks. there were 205 weather stations included in this study after excluding the stations located at over 1,000 meters altitude or the stations located on the ocean. latitudes were calculated from the centroid of each area. we used stepwise linear regression and arcgis for the statistical model and visualization, respectively. results there were a total of 101 weeks during the study period. the dew point and latitude were significantly positively correlated with the increase of standardized hfmd values. the mean wind speed and sea level pressure were significantly negatively correlated with the increase of standardized hfmd values. overall, the areas at higher latitudes had later hfmd epidemics than those at lower latitudes. conclusions the weather conditions and the geographic locations could play an important role in affecting hfmd epidemics. regional integrated surveillance of hfmd in east asia is needed for mitigating the disease risk. keywords enterovirus; climate change; communicable diseases surveillance; cross-border surveillance acknowledgments this research was supported by a grant from the ministry of science and technology, taiwan (most 103-2621-m-001 -002). references 1. sabanathan s, tan le v, thwaites l, wills b, qui pt, et al. (2014) enterovirus 71 related severe hand, foot and mouth disease outbreaks in south-east asia: current situation and ongoing challenges. j epidemiol community health 68: 500-502. 2. wang y, feng z, yang y (2012) hand, foot, and mouth disease in china: patterns of spread and transmissibility (vol 22, pg 781, 2011). epidemiology 23: 358-358. 3. chang zr, zhang j, sun jl, zhang wd, wang zj (2011) [epidemiological features of hand, foot and mouth disease in china, 2008 2009]. zhonghua liu xing bing xue za zhi 32: 676-680. 4. chan tc, hwang js, chen rh, king cc, chiang ph (2014) spatiotemporal analysis on enterovirus cases through integrated surveillance in taiwan. bmc public health 14. *ta-chien chan e-mail: dachianpig@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e115, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts patterns of care in michigan emergency departments as insurance coverage expands fatema mamou*, matthew davis, jim collins, jay fiedler, tiffany henderson and kiera wickliffe berger michigan department of community health, lansing, mi, usa objective the purpose of this work is to use the michigan syndromic surveillance system (msss) to assess emergency department (ed) utilization before and after the april 2014 implementation of the healthy michigan plan, an expanded medicaid program. introduction the msss, described elsewhere1, has been in use since 2003 and records ed chief complaint data. as of september 2014, there were 88/136 hospital eds enrolled in msss, capturing 83% of the annual hospital ed visits in michigan. on april 1, 2014 the healthy michigan plan (hmp) was launched. hmp provides healthcare benefits to low-income adult residents who do not qualify for medicaid or medicare. the plan incorporates both federally and state mandated essential health benefits, which includes emergency services. as insurance coverage expands, more people will have the ability to utilize the services of primary care and other providers. in particular, this will affect previously uninsured, low-income populations who are disproportionately affected by chronic disease. we question if access to these services will affect the utilization of emergency services as more people will have a medical home to manage and prevent diseases that may otherwise become an emergent issue. furthermore, this increased access to health care services will expand care options for urgent but not emergent issues beyond eds. conversely, as more people acquire health care benefits the demand for primary care services may exceed the level of access to these services which may lead to an increase of ed utilization for primary care. methods total ed visits were analyzed by month from january 2011 to date. seasonally adjusted monthly ed visits prior to and after the hmp implementation were compared. seasonally adjusted monthly ed visits were stratified by age and the trend of ed visits among those 18-64 years were compared to that among other age groups. michigan behavioral risk factor system data (brfs)2 was used to categorize counties by proportion of self-reported health care coverage. visits were assigned one of four health care coverage categories from low to high based on county of residence. trends in visits for each category were analyzed. select chief complaints that represent illnesses or medical needs that could be addressed by a primary care provider were categorized as primary care sensitive. seasonally adjusted monthly totals for these complaints were analyzed for trends before and after the hmp launch. these visits were further stratified by time of visits to observe the changes in the volume of these visits during times when primary care access is reduced or unavailable. results a total of 76 facilities have been used in the analysis with an average of 330,000 ed visits per month. as of august 2014 over 355,000 michigan residents have enrolled in hmp. analyses are ongoing, but preliminary data as of august 2014 show no significant difference observed in seasonally adjusted total ed visits per month after the launch of the hmp compared to before. similarly, no difference has been observed by age group. a small increasing trend has been observed among visits from people residing in counties that had a low proportion of health insurance coverage based on brfs data. no significant difference in the trend of primary care sensitive chief complaints has been observed. conclusions long term data analyses are needed to evaluate changes in ed utilization as a result of expanded health care coverage. ongoing analyses of this data will be conducted as more and more eligible michigan adults enroll into the hmp. lack of a medical home combined with decreased financial barriers may lead new hmp enrollees to seek medical care at eds. to validate the potential increasing trends observed in counties with previously reported low health insurance coverage2, ed visits will be compared to hmp enrollment data. monthly trends of ed visits among counties with high enrollment into the plan will be compared to ed visits among counties with lower enrollment to assess for differences in ed utilization before and after the launch of the hmp. keywords sydromic surveillance; health insurance coverage; ed utilization references 1. sheline kd. evaluation of the michigan emergency department syndromic surveillance system. advances in disease surveillance. 2007; 4: 265 2. michigan department of community health (mdch). health indicators and risk estimates by community health assessment regions & local health departments, state of michigan, selected tables 2011 – 2013. lansing (mi): michigan behavioral risk factor survey; july 7, 2014. table 7, no health care coverage among those aged 18-64 years; p. 18-19. *fatema mamou e-mail: mamouf@michigan.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e38, 201 a second wave of covid-19 in cook county: what lessons can be applied? 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi a second wave of covid-19 in cook county: what lessons can be applied? gregory w. arling1, matthew blaser2, michael d. cailas3*, john r. canar4, brian cooper4, peter j. geraci5, kristin m. osiecki6, and apostolis sambanis5 1purdue university, school of nursing, college of health and human sciences 2united states environmental protection agency, research associate under an inter-agency agreement with oak ridge institute for science and education 3environmental and occupational health sciences, school of public health, university of illinois chicago 4united states environmental protection agency region v; and health policy and administration, school of public health, university of illinois chicago 5health policy and administration, school of public health, university of illinois chicago 6university of minnesota, rochester, center for learning innovation abstract during the ongoing public health crisis, many agencies are reporting covid-19 health outcome information based on the overall population. this practice can lead to misleading results and underestimation of high risk areas. to gain a better understanding of spatial and temporal distribution of covid-19 deaths; the long term care facility (ltcf) and household population (hp) deaths must be used. this approach allows us to better discern high risk areas and provides policy makers with reliable information for community engagement and mitigation strategies. by focusing on high-risk ltcfs and residential areas, protective measures can be implemented to minimize covid-19 spread and subsequent mortality. these areas should be a high priority target when covid-19 vaccines become available. *corresponding author: mihalis@uic.edu doi: 10.5210/ojphi.v12i2.11506 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged i n the copy and the copy is used for educational, not-for-profit purposes. during the current public health crisis, many agencies and media outlets are reporting covid-19 health outcome information based on the overall population of cook county. as we have demonstrated, overall covid-19 case counts and mortality can be misleading (details in story map 1). moreover, they offer little guidance for delivering public health interventions to high risk populations, a critical need during this second and potentially more devastating wave of the pandemic. the university of illinois chicago school of public health’s public health geographic https://storymaps.arcgis.com/stories/55a419ee24744a7698e9877f73384023 https://storymaps.arcgis.com/stories/55a419ee24744a7698e9877f73384023 a second wave of covid-19 in cook county: what lessons can be applied? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi information system program (uic-sph-phgis) and purdue research team has been examining spatial and temporal patterns of covid-19 mortality with a focus on the significant loss of life from covid-19 among long-term care facility (ltcf) residents in contrast to mortality in the community among residents of private households (non-ltcf; referred to as household population, hp). the goals of the study are: • improve the accuracy of commonly quoted covid-19 mortality indicators; • gain a better understanding of spatial and temporal distribution of covid-19 deaths; • examine the role of race, ethnicity, and socioeconomic status in covid-19 mortality; • identify population and organizational parameters that can inform strategies for public health interventions. prioritizing the allocation of resources based on reliable information is a prerequisite of a successful mitigation strategy and immunization plan. findings from our research have significant practical implications. the state and federal government face a series of policy decisions both due to the recent surge in positive cases and, when the time comes, the need to rationalize distribution of vaccines to high priority groups beyond healthcare workers and nursing home residents in critical areas. the research team seeks to modify prevailing practices in order to derive reliable information that guides policy decisions. at this stage of the study, we identified high-risk ltcfs and residential areas (hp) of cook county from readily available, real-time mortality data. spatial distribution of covid-19 mortality our research has identified distinctly different spatial patterns in covid-19 between ltcfs and community households. figure 1 displays the difference of covid-19 deaths by zip code among ltcf residents compared to residents of households (hp). figure 1. difference of long-term care facility (ltcf) and household population (hp)related mortality rate per zip code (as of july 31, 2020). original figure in story map 3. https://storymaps.arcgis.com/stories/962fe31af7f04a43832d6de375cd6ca7 a second wave of covid-19 in cook county: what lessons can be applied? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi characteristics of ltcfs and neighborhoods related to mortality rates in preliminary analysis we found three neighborhood (zip code) characteristics related to covid19 mortality among persons in the community: high percentage of minority group residents, high density of the population (per square mile operational definition) and low socio-economic status (ses). the mortality rates in neighborhoods with high concentrations of african-american and hispanic populations had the highest adjusted covid-19 death rates for these populations. these findings are in line with other studies finding an elevated risk of covid-19 in minority populations [1-3]. higher density is a well know predictor of disease exposure. the low ses workforce is concentrated in service jobs, including in healthcare, increase the likelihood of covid-19 exposure. racial and ethnic disparities in health and healthcare likely increase risk of severe covid-19 symptoms and mortality. rates of mortality in ltcfs were much more difficult to predict than household mortality. as implied figure 1, the rate of community mortality was unrelated to the rate among ltcfs in the same zip code. the percentage of non-white residents did not seem to be a predictor of ltcf mortality rates overall or for african american/black or hispanic residents. we should caution that our measure of race/ethnicity may be unreliable. we are continuing to explore other characteristics such as medical complexity of residents, rate of post-acute admissions, and measures of overall care quality. in our analysis of covid-19 nursing facility data recorded by the center for medicare and medicaid services (cms) [4], we found a correlation between the number of covid-19 cases among nursing facility staff and cases among residents. also, we found patterns over time in incidence of covid-19 cases among nursing facility staff that paralleled cases among residents. these findings raises the possibility, suggested by other researchers, that the community covid19 incidence is related to ltcf incidence and mortality at a more general level [4,5]. staff of ltcfs living in the wider community, outside the zip code of the ltcf and perhaps in high risk neighborhoods, may be transmitting covid-19 to ltcf residents [6]. simultaneously, ltcf facility staff may be contracting covid-19 in the work setting and transmitting it back into the community. another possible contributor to spread of covid-19 in ltcfs is transfer of individuals between acute care hospitals and the ltcfs. according to cms data, cook county nursing facilities had over 190 weekly covid-19 admissions from the hospital in june, a drop in admissions to 50/week from july – october, and then a sharp upward trend to 150 admissions/week in november. temporal patterns in mortality to demonstrate the benefits of the analytical framework developed by our team, we present results of an early risk detection with the use of moving averages. during the first wave in the spring 2020 (see figure 2), covid-19 cases and mortality in ltcf have lagged behind the covid-19 spreads and deaths in the community (household population, hp). the time lag between the ltcf (green) and hp (red) losses, probably represents the lag in transmission from community to ltcf. as seen in figure 2, the ltcf related losses peaked after surpassing the hp losses reaching an average death toll of 40 per day. a second wave of covid-19 in cook county: what lessons can be applied? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi figure 2. first wave characteristics of ltcf and hp related mortality depicted as a 14days moving average. original figure in story map 3. figure 3. transition and 2nd wave pattern of ltcf and community deaths (hp) depicted as a 14-days moving average. original figure in story map 3. by taking into account the characteristics of the first wave, we performed an early assessment of the second wave pattern. figure 3 displays the 14-day moving average which depicts the hp and ltcf related patterns of mortality. at the start, the almost parallel trajectories signify that the covid-19 transmission pathway to the ltcf residents remained active during the low summer months. however, the latter part of the second wave graph raises serious concerns since after midoctober it is likely that the ltcf related mortality is entering an acceleration phase similar to the one seen during the first wave (see figure 2). on the 8th of november the uic-sph-phgis team raised its concerns about this high risk potential (see story map 2) https://storymaps.arcgis.com/stories/962fe31af7f04a43832d6de375cd6ca7 https://storymaps.arcgis.com/stories/962fe31af7f04a43832d6de375cd6ca7 https://storymaps.arcgis.com/stories/970f29bc1635426abd6cd365b62b1252 a second wave of covid-19 in cook county: what lessons can be applied? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi as of november 25, 52% of ltcf deaths in cook county are located 18 facilities and 10 zip codes. a month ago the losses were 2 per day, currently this loss is close to 10 per day. the risk that the overall death pattern will follow a similar, hopefully less intense, trajectory is further substantiated with the 21-days moving average pattern of the ltcf losses (not shown). without measures, this pattern raises serious concerns since it is likely that within a week from now (12.7.20) the ltcfs death toll will exceed the 10 per day mark and continue its slow upward 14days moving average trajectory. strategies to address a possible second wave of covid-19 from a policy point of view, the above findings raise a number of issues that demand immediate public health action. such action is imperative in order to avoid seeing the losses climb to a 40 per day 5-day moving average seen in the first wave. the first wave put enormous strain on ltcfs under circumstances of staff shortages and limited access to ppe [7]. the ltcfs are probably in a better position now to contend with covid-19 compared to march when the initial covid-19 surge began. according to reports for the week ending november 15 (cms data), all cook county nursing facilities had access to covid-19 testing for residents and staff. yet, only 26% of facilities had testing at the point of care and only 3% of facilities could obtain same-day results. approximately three-fourths of nursing facilities did surveillance covid-19 testing of asymptomatic staff or residents; the other one-fourth only tested when a person exhibited symptoms or had been in contact with a person having covid-19. over 95% of facilities reported having a 7-day supply of masks, eye protectors, and gowns. in addition, 91% of facilities reported no nursing staff shortages the week of november 15. however, it remains to be seen if staff shortages might arise in subsequent weeks with a new surge of covid-19 cases. our findings lead to a two pronged approach aimed at breaking the cycle of transmission from community to ltcfs. public health officials should re-double efforts to prevent a community surge through (1): clearly articulated policies for social distancing, face covering, and restricted access to high-risk setting for spread of covid-19 (2); targeted testing in hot-spot or likely to be hot-spot neighborhoods that are densely populated, low income, and have concentrations of minority groups (3); targeted interventions for vulnerable at-risk populations (with co-morbid conditions, advanced age, and residing in at-risk neighborhoods). when covid-19 vaccines become available, they should be targeted to areas of the city at highest risk. these interventions should combine resources of health departments, area agency on aging, and health systems to reach out to at risk individuals. with regard to ltcfs, health system and public health agencies should devote resources to upstream prevention of covid-19 spread and mortality among ltcf residents before they are hospitalized and strain icu capacity. the cdc [8], illinois department of public health [9], and other sources [10] have urged these actions (1): contact tracing of ltcf staff and residents (2); rapid testing of ltcf residents and staff (3); emergency nursing and medical support for burned out and depleted ltcf staff (4); transitional support between nursing facilities, hospitals, and community (5); vaccinate, as a priority, ltcf residents and all staff when covid-19 vaccines become available (6). the issue of priority vaccinations for high risk communities should also be addressed. ltcfs should receive financial and other resources to step up their mitigation efforts, including carefully controlled visitation, isolation, ppe, and other infection control practices. a second wave of covid-19 in cook county: what lessons can be applied? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi targeting areas of the city the analytical framework developed by our team, provides a better visualization of the areas hard hit during the second wave. this visualization can assist policymakers address the high risk areas which are different than those during the comparable first wave phase. this is seen in figures 4 and 5 where a comparable 1st wave data period is used. for the household population (hp), the majority of high death rate areas are concentrated in the periphery of the city whereas the comparable 1st wave areas where concentrated in the south and south east section. the ltcf distribution is different and the majority high death rate areas are concentrated in the north west part of cook county whereas the comparable 1st wave areas where concentrated in the south west (see figure 5). figure 4. hp-related death rates (per 100,000 population) for the 1st (ending 4.18.20) and 2nd waves (starting 9.27.20). original figure in story map 3. https://storymaps.arcgis.com/stories/962fe31af7f04a43832d6de375cd6ca7 a second wave of covid-19 in cook county: what lessons can be applied? 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi figure 5. ltcf-related death rates (per 100,000 population living in group quarters) for the 1st wave (ending 4.18.20) and 2nd wave (starting 9.27.20). original figure in story map 3. conclusions prevailing practices which rely on overall population numbers are unreliable and can be misleading. we have shown how information on spatial and temporal patterns in covid-19 mortality can guide policies to address high priority areas in cook county. by focusing on highrisk ltcfs and residential areas, protective measures can be implemented to minimize covid19 spread and subsequent mortality. when covid-19 vaccines become available, they should be distributed to the same high-risk populations. at a larger scale concerning the entire state of illinois and its counties, a similar pattern (not shown) has been discerned. references 1. blaser m, cailas md, canar j, cooper b, geraci p, et al. analyzing covid-19 mortality within the chicagoland area: data limitations and solutions. research brief no. 117. policy, practice and prevention research center, university of illinois chicago. chicago, il. july 2020. https://p3rc.uic.edu/wp-content/uploads/sites/561/2020/08/analyzing_covid19_methods_508-1.pdf. 2. selden tm, berdahl ta. 2020. covid-19 and racial/ethnic disparities in health risk, employment, and household composition: study examines potential explanations for racialhttps://storymaps.arcgis.com/stories/962fe31af7f04a43832d6de375cd6ca7 https://storymaps.arcgis.com/stories/962fe31af7f04a43832d6de375cd6ca7 a second wave of covid-19 in cook county: what lessons can be applied? 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e15, 2020 ojphi ethnic disparities in covid-19 hospitalizations and mortality. health aff. 39(9), 1624-32. https://doi.org/10.1377/hlthaff.2020.00897 3. figueroa jf, wadhera rk, lee d, yeh rw, sommers bd. community-level factors associated with racial and ethnic disparities in covid-19 rates in massachusetts: study examines community-level factors associated with racial and ethnic disparities in covid-19 rates in massachusetts. health affairs. 2020:10.1377/hlthaff. 2020.01040. 4. centers for medicare and medicaid services. covid-19 nursing home data. https://data.cms.gov/stories/s/covid-19-nursing-home-data/bkwz-xpvg. published 2020. 5. sugg mm, spaulding tj, lane sj, et al. 2021. mapping community-level determinants of covid-19 transmission in nursing homes: a multi-scale approach. sci total environ. 752, 141946. pubmed https://doi.org/10.1016/j.scitotenv.2020.141946 6. chen mk, chevalier ja, long ef. nursing home staff networks and covid-19. national bureau of economic research;2020. 0898-2937. 7. mcgarry be, grabowski dc, barnett ml. 2020. severe staffing and personal protective equipment shortages faced by nursing homes during the covid-19 pandemic: study examines staffing and personal protective equipment shortages faced by nursing homes during the covid-19 pandemic. health aff. 39(10), 1812-21. https://doi.org/10.1377/hlthaff.2020.01269 8. centers for disease control and prevention. covid-19 nursing homes and long-term care facilities. https://www.cdc.gov/coronavirus/2019-ncov/hcp/nursing-home-long-termcare.html. published 2020. accessed november 22 2020. 9. illinois department of public health. long-term care facilties guidance. https://www.dph.illinois.gov/topics-services/diseases-and-conditions/diseases-a-zlist/coronavirus/long-term-care-guidance. published 2020. accessed november 22 2020. 10. mcgilton ks, escrig-pinol a, gordon a, et al. 2020. uncovering the devaluation of nursing home staff during covid-19: are we fuelling the next health care crisis? j am med dir assoc. 21(7), 962-65. pubmed https://doi.org/10.1016/j.jamda.2020.06.010 11. nguyen lh, drew da, graham ms, et al. 2020. risk of covid-19 among front-line healthcare workers and the general community: a prospective cohort study. lancet public health. 5(9), e475-83. pubmed https://doi.org/10.1016/s2468-2667(20)30164-x https://doi.org/10.1377/hlthaff.2020.00897 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32889290&dopt=abstract https://doi.org/10.1016/j.scitotenv.2020.141946 https://doi.org/10.1377/hlthaff.2020.01269 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32674829&dopt=abstract https://doi.org/10.1016/j.jamda.2020.06.010 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32745512&dopt=abstract https://doi.org/10.1016/s2468-2667(20)30164-x contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours angella musiimenta 1 1 mbarara university of science and technology, and bishop stuart university kakoba abstract background: although uganda had recorded declines in hiv infection rates around 1990’s, it is argued that hiv/aids risk sexual behaviour, especially among the youth, started increasing again from early 2000. school-based computer-assisted hiv interventions can provide interactive ways of improving the youth’s hiv knowledge, attitudes and skills. however, these interventions have long been reported to have limited success in improving the youth’s sexual behaviours, which is always the major aim of implementing such interventions. this could be because the commonly used health promotion theories employed by these interventions have limited application in hiv prevention. these theories tend to lack sufficient attention to contextual mediators that influence ones sexual behaviours. moreover, literature increasingly expresses dissatisfaction with the dominant prevailing descriptive survey-type hiv/aids-related research. objective and methods: the objective of this research was to identify contextual mediators that influence the youth’s decision to adopt and maintain the hiv/aids preventive behaviour advocated by a computer-assisted intervention. to achieve this objective, this research employed qualitative method, which provided in-depth understanding of how different contexts interact to influence the effectiveness of hiv/aids interventions. the research question was: what contextual mediators are influencing the youth’s decision to adopt and maintain the hiv/aids preventive behaviour advocated by a computer-assisted intervention? to answer this research question, 20 youth who had previously completed the wswm intervention when they were still in secondary schools were telephone interviewed between sept.08 and dec.08. the collected data was then analysed, based on grounded theory’s coding scheme. results: findings demonstrate that although often ignored by hiv interventionists and researchers, variety of contextual mediators influence individual uptake of hiv preventives. these include relationship characteristics, familial mediators, peer influence, gender-based social norms, economic factors and religious beliefs. conclusion: to generate concomitant mutual efforts, rather than exclusively focusing on individual level mediators, there is an urgent need to shift to integrative approaches, which http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 combine individual level change strategies with contextual level change approaches in the design and implementation of interventional strategies to fight against hiv/aids. key words: hiv/aids interventions, ict, behavioural change, contextual factors, health promotion, youth. 1. introduction 1.1 applicability of the dominant health promotion theories in hiv/aids prevention there is a variety of models and theories used in health promotion and education. the most commonly used include: social marketing theory [[1]], aids risk reduction model [[2]], the theory of gender and power [[3]], ecological models for health promotion [[4]], procede preceed model [[5]]. social marketing theory [[1]] define social marketing as: “a social change technology involving the design, implementation and control of programs aimed at increasing the acceptability of a social idea in one or more groups of target adopters.” the primary aim of social marketing interventions is to change behaviour. to achieve behavioural change, social marketing employs marketing technologies to analyse, design, implement and evaluate behavioural change interventions. as further demonstrated below, these marketing strategies include consumer analysis, audience segmentation, market analysis, channel analysis, exchange theory and marketing mix [[1]]. given the intensity of health problems in developing countries, social marketing has been extensively used in a variety of health-related interventions aimed at either promoting the rejection of unhealthy behaviours or/and promoting the acceptance of healthy behaviours. for instance: the integration of social marketing principles with community and national level participation was successful in rendering drug abuse socially unacceptable in malaysia [[6]], while the application of social marketing principles integrated with multi-sector involvement, mobilisation of political will and deployment of volunteers (local leaders and health promoters) increased immunisation coverage in colombia [[7]]. in addition, contraceptive social marketing increased the use of contraceptives in the dominican republic [[8]]; a social marketing-based campaign increased the use of kinga condoms in kenya [[9]]; and the application of social marketing campaigns combined with the education of mothers by community leaders and health workers resulted in the adoption of good feeding practice and healthy babies in indonesia [[10]]. despite its potential usefulness, several social marketing and communication scientists [[11][12]]; have noted their concerns about the model’s individual-blaming behavioural change approach that ignores institutional and societal contexts that powerfully influence the individual’s behavioural adoption. in the context of hiv interventions, the importance of this http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 must not be underestimated, since the social context significantly influences ones sexual behaviours [[13]]. aids risk reduction model (arrm) developed by incorporating some constructs of the health belief model, diffusion of innovation model, and social cognitive theory, the aids risk reduction model provides a basis for understanding motivations and inhibitors regarding the adoption of hiv/aids preventive behaviours [[2],[14]-[15]]. the arrm model suggests several mediators that influence the adoption of hiv/aids-related health behaviour, including knowledge of risk behaviours, perceived vulnerability, social norms, sexual partner communication and self-efficacy. generally, arrm tends to take up the victim-blaming ideology by mainly focusing on cognitive individual behavioural change approaches. as argued by [[16]], overemphasis on cognitive individual behavioural change ignores the social, cultural and economic dimensions that are crucial determinants of sexual behaviours. although the arrm may provide a useful frame for hiv prevention, it does not appropriately address contextual determinants of sexual behaviours e.g. it does not address gender-related hiv vulnerabilities and economic constraints. other individual-oriented models of health promotion include social cognitive theory [[15]], and the theory of planned behaviour [[17]]. noteworthy however is that although many of the commonly used health education and promotion models are individual-focused, some theories recognise the role contextual factors in shaping individual behaviours. these theories include the theory of gender and power [[3]]; and ecological models of health promotion [[4]] such as precede preceed model [[5]]. however, these theories have only received small attention by hiv preventive researchers and interventionists. in addition, although connell’s theory of gender and power acknowledges the influence of gender-based social norms, the theory does not cater for determinants of sexual behaviours at an individual level. ecological models for health promotion offer some promise since they advocate considering both individual and contextual mediators. however, the models do not specify particular constructs that should be considered and how such constructs should be investigated. overall, the applicability of the prevailing theories and models/theories of health education and promotions in the context of hiv/aids prevention have long been questioned [[13], [18]]. these theories tend to over-emphasise individual level influences of health behaviour which fails to address contextual dimensions that significantly influence hiv/aids prevention [[19]]. moreover, literature increasingly expresses dissatisfaction with the dominant prevailing descriptive survey-type hiv/aids-related research [[13]]. these descriptive surveys are often ‘force-fitted’ into the prevailing ‘victim-blaming’ individual oriented models of health behaviour. whereas this individualistic conceptualisation of behaviours may be applicable in other health behavioural aspects, sexual behaviours are influenced by interplay of both individual and contextual mediators. this is because, sexual behaviours require commitment from more than one individual [[20]], may not be planned for in advance [[21]], are subjective in nature [[22]], and are influenced by variety of contextual and social-cultural mediators [[23]]. lack of appropriate theories suggests a need for an abductive qualitative research approach where themes and theories emerge from data collection and analysis rather than being pre-determined [[24]]. such an approach can provide in-depth understanding of how different contexts interact to influence the effectiveness of hiv/aids interventions. http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 generally, it is evident that both individual level and contextual level mediators influence hiv/aids prevention decisions. yet, none of the models discussed above effectively accounts for both individual and contextual determinants of sexual behaviours. on one hand, employing individual level models of health behaviour inhibits the understanding of social and environmental drivers of hiv risk-taking behaviours. on the other hand, emphasising social and environmental determinants of health behaviours ignores cognitive mediators that are significant in the actual adoption of hiv preventives. this suggests a shift from behavioural change interventions that address one aspect of behaviour to multilevel approaches that aim not only at understanding and changing individual behaviours, but also focuses on understanding and changing the social structure and environment that can shape individual behaviours. 1.2 contextual factors in hiv/aids connell’s theory of gender and power [[3]] postulates that sexual power imbalances between men and women, the socially condoned sexual norms, and gender-based economic inequalities influence human behaviour; giving men greater power than females in all areas of life, including sexual relationships. cultural expectations of women’s passiveness and ignorance in sexuality constrain their sexual negotiating power, including negotiating for safer sex practices [[25]]. other empirical studies report frequent condom use by young women who can initiate and negotiate condom use with their partner [[26]], who are not constrained financially or subject to sexual abuse [[19]]. [[27]] report women’s perception of being at high risk of getting hiv/aids due to their partners’ risky sexual behaviours. [[28]] reports high prevalence rates of aids among married women who claim to have not had any sexual affair outside their marriages. the persistent disproportionate global increase in feminisation of hiv/aids presents a challenge since women can increase hiv devastation by infecting their unborn babies. out of 11.8 million young people aged 15-24 living with hiv/aids globally, 7.3 of them are young women; in sub-saharan africa, 67% of young women are living with hiv/aids, compared to 33% of their male counterparts [[29]]. in uganda, adolescent girls are 4-6 times more vulnerable to hiv than their male counterparts [[30]], and women are highly infected at younger ages (3034) than men (40-44) [[31]]. hiv/aids prevalence is higher in women (7.5%) than men (5%), and married/cohabiting/widowed people host the majority of new infections—(42%) [[32]], mainly due to men’s extra-marital practices. gender and hiv/aids-related studies (e.g. [[33]]) affirm women’s involvement in unwanted sexual encounters due to their inability to assertively refuse such encounters. women who are more adherent to socially defined sexual norms and beliefs are more likely to experience bad health outcomes [[34]]. despite these startling figures coupled with persistently reported strong relationship between young women’s uptakes of hiv preventive methods and gender constructs, many hiv interventions too often fail to address gender issues in their design and implementation [[22]]. without understanding the gender context surrounding individual sexual behaviour, particularly in an african context where gender-related social norms heavily constrain women’s sexual behaviour, aids will continue to have the “face of a woman” as put by kofi annan, the former secretary-general of the united nations [[35]]. http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 although research on the influence of social norms on hiv risk behaviour has mainly focused on women, social norms do not put women alone at a high risk of hiv/aids; they also increase men’s vulnerability to hiv/aids contraction. gender ideologies shape men’s sexual behaviour. these norms include approval of men having multiple partners [[36]] and associating masculinity and heroism with men’s sexual experience [[37]]. men’s belief in a variety of sexual partners greatly exposes them to being infected with hiv. it is not surprising that in uganda, the married population hosts the highest rate of hiv/aids infection [[32]], which is mainly attributed to men’s infidelity practices [[38]]. norms of masculinity that praise men for their sexual experience drive young men into unsafe sexual experimentation and practice in order to affirm their sexual experience and prove their manhood [[16]]. such norms constrain condom use since condoms are believed to interfere with their sexual performance [[37]]. despite the prevailing evidence about the influence of contextual factors in hiv prevention, such contexts are rarely investigated by hiv/aids researchers or/and targeted by hiv interventionists [[13]]. instead, the focus is normally put on addressing individual level factors such as improving individual knowledge, attitudes, adoption intentions, perceived benefits, behavioural lifestyles and skills necessary to adopt healthy behaviour. this approach alone does not appropriately address mediators beyond the individual level that can interfere with behavioural adoption [[4]]. 1.3 background to hiv/aids in uganda hiv/aids is one of the troubling diseases that have globally consumed many lives especially in sub-saharan africa. in response to the high prevalence of hiv/aids particularly in young people, world leaders are working together globally to prevent hiv/aids. for example, one of the aims of the millennium development goal (mdg) number six is: “…to have halted by 2015 and begun to reverse the spread of hiv including hiv prevalence among population groups aged 15-24 years.” [[39]]. less than two years to the set time for achieving the mdgs, the question of whether or not the spread of hiv will have halted in 2015 is yet to be answered. although uganda had recorded decline in hiv infection rates around 1990’s, it is argued that hiv/aids risk sexual behaviour especially among the youth started increasing again from early 2000 [[40]]. one in four adolescent females aged 15-24 in western uganda admitted having sexual relationships with people whom they knew had other concurrent sexual partners [[41]]. the practice of multiple sexual partnerships is often associated with cross-generational sex where females are engaged in often unprotected sex in exchange for money and other favours [[38]]. in 2001, although 53% of females aged 15-19 knew a source of male condoms; only 36% admitted that they could obtain condoms [[42]]. the wider gap between knowledge of safe sexual behaviour and its actual adoption is of great concern in uganda [[43]]. in response to the high prevalence of hiv/aids in uganda, uganda adopted a comprehensive hiv/aids prevention strategy that acknowledged hiv as a threatening problem. this included involving http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 the government and civil society in advocating abstinence, be faithful, condom use (abc), encouraged hiv testing & status disclosure [[44]]. the increased vulnerability of young people to hiv/aids obliged the government of uganda to consider implementation of hiv/aids interventions in schools. however, many of the implemented school-based hiv/aids intervention did not yield improvement in sexual behaviours [[45]]. surely, there should be factors that account for this persistently reported hiv/aids knowledge-behaviour gap. 1.4 ict for hiv/aids prevention ict (information communication technology) can provide interactive technologies which are instrumental tools in hiv prevention. such interventions can improve individuals’ knowledge of the hiv/aids prevention [[46]], improve individuals’ perception of adopting healthy sexual behaviours as well as equipping them with skills necessary to prevent themselves from contracting hiv/aids [[47]]. however, evidence on changing individuals’ hiv/aids risky behaviours is limited [[48], [49]]. what could be the reasons the limited success in changing behaviour associated with computer-assisted hiv interventions? could there be other factors, other than those normally targeted by the computer-assisted hiv interventionists that influence individual uptake of hiv preventive behaviours? 1.5 objectives of the current study the major objective of this study was to obtain in-depth understanding of contextual mediators influencing the effectiveness of hiv/aids interventions from experiences of the youth who are former students of a computer-assisted intervention. this approach provided an opportunity to investigate how the youth are practically dealing with real world challenges of hiv/aids amidst contextual mediators. the research question was: what contextual mediators are influencing the youth’s decision to adopt and maintain the hiv/aids preventive behaviour advocated by a computer-assisted intervention? 2. methodology 2.1 the computer-assisted hiv intervention: the word starts with me (wswm) intervention the wswm (http://www.theworldstarts.org) is a school-based web-based sexuality and hiv/aids intervention that has been implemented in secondary schools in uganda since 2003. with collaboration with the ministry of education and sports, over 200 secondary schools have since implemented the wswm in uganda. the wswm has also been adapted and implemented other countries including kenya, india, thailand, indonesia, and vietnam. the intervention is sponsored by world population foundation (wpf) and implemented by schoolnet uganda. the overall objective of the intervention is to “improve sexual and reproductive health and rights of young vulnerable populations and to prevent hiv/aids”. the intervention also involves an http://ojphi.org/ http://www.theworldstarts.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 online counselling and support centre. this support centre is available at (http://schoolnetuganda.sc.ug/wswmonlinesupport/). other ict-related components of the intervention include the use of virtual peer educators, interactive safer sex quizzes, and story boards. 2.2 data collection and analysis the intervention teachers in the former schools where the youth completed the intervention assisted in the identification of the initial 10 participants. the identified participants then assisted to identify 10 more telephone contacts of their peers with whom they had completed the intervention. rather than using face-to-face interviews, data collection was conducted using telephone-based interviews due to two major reasons: first, it was not possible to get the youth in a single site since they were attending colleges and universities in different parts of the country. reaching the youth by mobile telephone communication was the only time saving and economically feasible method; second, given the sensitive nature of investigating self-reported sexual behaviours and sexual-related information, compared to face-to-face interviews or selfadministered interviews, telephone interviews can reduce bias and social desirability constraints especially those associated with reporting of sensitive information regarding personal sexual behaviours [[50], [51]]. interview appointments were made by telephone. between sept.08 and dec.08, telephone interviews were employed to collect data from 20 youth who had previously completed the wswm intervention when they were still in many unspecified secondary schools. each interview lasted from 30 to 60 minutes. to stimulate their retrospective thinking, participants were asked how they thought the wswm intervention impacted on hiv/aids-related attitudes, self-efficacy, and sexual behaviours. also, in order to identify influencing contextual mediators for the adoption of hiv/aids preventive measures, participants were asked how easy/hard it is for them to adopt and sustain the sexual behaviours reported to have been obtained from the intervention. the collected data was then analysed, based on grounded theory’s three-stage coding process; open coding, axial coding and selective coding [[52]] to generate the results indicated below. 3. results 3.1 a framework of contextual mediators influencing the effectiveness of hiv/aids interventions relationship characteristics negative partner attitudes towards condom use some youth reported condom use after the intervention. this was due to their improved perceptions of risk of hiv/aids infections from unprotected sex: after the intervention, i realised i was taking a big risk to go live [unprotected]. nowadays, i am a reformed person and i live responsibly, and i make sure i am protected. of course we also use condoms…of course we condomise every time. i don't afford cutting my life shorter with slim [aids] [male youth]. http://ojphi.org/ http://schoolnetuganda.sc.ug/wswmonlinesupport/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 however, some female youth reported not using condoms due to her partner’s bad attitudes towards the use of condoms e.g. associating condom use with cancer, and perceiving them to interfere with sexual pleasure: …but my boy friend calls them paper bags [condoms], and says it is like eating a sweet without removing it from its cover, and he says they can even can cause cancer or even remain in me and cause problems [female youth]. the same participant also reports withdrawn lack of trust in condoms since her partner claims that one can still get hiv even after using condoms due to claims of condoms’ failure to be 100 percent effective in hiv/aids prevention: …though i was like a person who was trusting condoms so much ….but my boy friend, tells me that a condom is not even correct percentage. i know even if you use condoms, you still get aids, so i now don't support them [female youth]. partners’ lack of interest in condom use, coupled with fears of relationship breakdown that can result in insisting on condom use, undermined consistency in using condoms: safer sex would be safe of course…but at times we can fail to agree on it [condom use]. eeh… he gives you all sorts of excuses; he is not interested….. and when you over insist you can break your relationship because he may end up leaving me and go in for girls who are willing [female youth]. perception of low hiv risk in long term relationships the perception of trustworthiness in long term relationships, and perceptions of low hiv risk after both partners have tested for hiv can constrain the use of condoms: yea, the wswm told us the use of condoms for hiv prevention but i really trust her and she trusts me too, we have loved each other for a long time, and we know we don't have aids , we really don’t need to use condoms [male youth]. familial mediators lack of parental-child sexual guidance although timely and reliable sexual health and hiv/aids information from parents can help the youth make appropriate and less risky decisions, also reported was parents’ lack of communication about issues related to hiv and pregnancy prevention. one of the reasons for lack of parental-child sexual education is parents’ lack of confidence in the subject. parents should be open to us in time and tell us to abstain or how protect ourselves from aids and pregnancy. mine weren’t [open]…there are certain traps i could have escaped if they had talked to me… you know like for example getting proper information from them [parents] can help us not fall into traps http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 unknowingly. before the wswm i used to kind of try to learn from friends and others like news papers and friends. yea, sometimes it [information] would be right sometimes you can’t trust it. may be they think we are young and are shy to take to us [male youth]. apparently, due to lack of timely sexual guidance from parents, some youth resort to looking for sexual health information from friends and news papers, yet the reliability of information from these sources is not guaranteed. lack of reliable sexual education could account for some of the hiv-related misconceptions that some youth had before the intervention. for instance, some of the youth perceived themselves to be “too young” to contract hiv/aids. however, after the intervention, the youth perceived themselves to be vulnerable to hiv, as they realised from the intervention that the disease does not discriminate in ages: to some extent yes, unlike before, i now know that i have to be extra careful about whom i move out with. before i joined the wswm, i thought i was too young to get aids or to die from aids; i was completely wrong, these days, i am very much scared of aids [male youth]. the intervention created awareness about the hiv/aids-related dangers of getting involved in risky sexual practices e.g. having more than one sexual partner at a time. the youth felt morally obliged to refrain from multiple sexual practices in order to avoid spreading hiv/aids to innocent partners in a sexual network: a quotation from a youth that was originally sexually undisciplined demonstrates this perception: i was very problematic before… but when i completed the wswm, it emphasised how deadly my habits were…also i learnt from the wswm that having many sexual partners is dangerous; you can get aids and when you get it from one partner, you infect the other partner who is innocent…[male participant]. lack of parental role models the youth reported the need for parents to live as role models of hiv/aids prevention, by practicing fidelity themselves and refraining from enticing the youth with gifts to seduce them into sex: when they [parents] fail to be good examples themselves, then what about children? they [parents] are marrying many wives…, seducing young people into sex… sugar daddies enticing young girls with gifts… [female youth]. family environments environmental conditions surrounding the youth can influence their adoption of hiv preventive measures in the form of protective family environment, and experience of coming from polygamous families. protective family environments reduce the youths’ possibilities of getting involved in risky sexual practices: http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 …for me, i never had a chance to get involved in sexual relationships because my father was very strict and could not allow me get out of the house or give me gap… but when he died, i was like aha i am now free to get involved in sexual relationships. but when i reached school, the wswm warned me of the dangers… [female youth]. peer influence negative peer influence some youth that were already sexually active decided to abstain from sex due to the highlighted role of abstinence in avoiding the hiv/aids-related dangers of sexual activities: …i mean i have opted for abstinence. though i had kind of started getting involved in sex, but from the wswm, i learnt the dangers, and learnt how to control my sexual behaviours. i now have no sexual partner… i would rather wait than do and die [female youth]. however, others reported having been discouraged to adopt sex abstinence by negative peer norms manifested in discriminative actions and bad labels directed to the youth who decide to abstain; such labels included ‘non-starters’ and ‘impotent’. the following narrative illustrates this scenario: …my friends tell me this, they tell me that, they discourage me from sex abstaining. all sorts of things; that i am infertile, they call me “non-starter” and they brag on me about their sexual achievements…trying to persuade me…and now they kind of cut me off from their company [male youth]. positive peer influence personal testimonies of long term abstinence from former students of the wswm intervention acted as exemplary role models that encouraged one of the youth to opt for sex abstinence: …at school, our wswm teachers brought us a former student of the wswm who shared her testimony to us saying she was at the university yet she had no boy friend and she was still virgin. her testimony encouraged me to be like her, so, for the boy friend i have now, we agreed to love each other without sex [female youth]. gender-biased social norms norms interpreting girls’ condom buying/negotiation as prostitution some female participants felt empowered to break the cultural norms and assertively take the lead in condom negotiations including refusing unprotected sex. what seemed to have compelled http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 them to be actively involved in safer sex decision making was the realisation of the disproportionate burden of pregnancy experienced by women as a result of unprotected sex: you know how men used to think that women’s silence means yes, but for me, after being alerted by the wswm, i cannot be silent; if condoms have to be used, i say it out, if not, i refuse. i know some girls still fear and can’t even suggest condom use, and in the end, if anything goes wrong say in case of pregnancy, girls are the ones to suffer most [female youth]. girls’ insistence on condom use seems to be associated with their knowledge of high levels of vulnerability to hiv/aids and pregnancy compared to their boy’s counterparts: …i have one boy friend, and i am not afraid to say no to unsafe sex and stick to it. my no means no, so i can’t fall into problems like that because in any case, apart from aids, it us girls who fall into big problems e.g. carrying unwanted pregnancy [female participant]. despite the above positive impact, the african cultural expectations of women’s passiveness and ignorance in sexuality constrain the capacity of some female youth to negotiate for use of condoms by attaching labels of prostitutes to girls who buy condoms: you are trying to protect yourself but here people will call you a prostitute if they see you're a girl and you are buying or carrying condoms. it is like you know you are sending the message to the whole world that you are a spoilt girl, they report you to your parents and they start saying so and so’s daughter is loose and spoilt [female youth]. norms condoning multiple sexual partners for men there appear to be traditional social-cultural norms that condone and praise men’s sexual experience of having multiple sexual partners: the wswm gave me some guiding principles that i still apply up to now. i see other boys around boosting about their habits of moving out with many girls. for them they think it is prestigious to have many girl friends. …like a young man of course, you know like any other ‘cool’ young man around, i used to have many girls. you know the tradition saying that “omushaija aba owagira abakazi baingi” [a real man should have many women]. but after the wswm, i released the dangers involved in such practices. i now have one girl friend [male youth]. norms associating girls’ virginity with marital social gains it appears that the notation of sex abstinence was more heavily emphasized for girls than boys. this is portrayed by intervention teachers’ stressing of marriage-related social values of virgin girls: http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 …i decided to abstain because our teachers told us that a girl’s greatest gift she can give to her husband is virginity, so if i lose it now, them i will have lost my gift and i won’t have anywhere to go if i lose my virginity [female youth]. however, the practice of emphasising virginity for girls increased their vulnerability to sexual abuse since virgin girls are always perceived to be free from hiv/aids: i used to have more girls, in fact there is a time when me and my friend decided to compete and finish all the girls who we thought were still virgin in my village… i now have one girl friend… [male youth]. economic constraints the boldness and skills gained from the intervention are being applied to assertively confront and challenge old men’s manipulations and attempts to seduce young girls into sexual relationships: before the wswm, i did not know how to answer back in case a sugar-daddy approached me. i know how they sugar-daddies can manipulate us and am very much in position to overcome them. before, i was very shy and to be honest, i wouldn’t even look straight into a man’s eyes. now i can look straight into their eyes and challenge their sexual proposals [female participant]. however, as shown by the following extract, financial difficulties constrained uptake of hiv preventives by driving the youth into unwanted sexual advances, and increases the possibilities of exposure to sexual abuse: …the wswm taught me that abstinence is the only safest hiv prevention method …but sometimes it [abstinence] becomes hard because i don’t have money and another thing is that i am an orphan; when my parents died it’s like i lost hope and i find myself into sex because of survival[female participant] . christian religious beliefs christian condemnations of sex before marriage and infidelity practices coupled with advocacies from the wswm intervention, the youth, particularly the born again christians opted for abstinence and faithfulness in order to fulfil their religious values and obligations: …in the bible, sex before marriage is a sin, so, whether it is protected sex or not, it remains a sin. period. i can [abstain] because the wswm encouraged me to abstain and it also goes against my christian values to engage in sex before marriage…and of course those who are married have to be faithful cause god does not allow unfaithfulness [female youth]. http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 religious condemnation of condom use there were also negative religious perceptions against condom use: …although i am abstaining, i wouldn't support condoms, in fact condoms can lead people to sex, and like i think aids is a curse from god to those who disobey him by getting involved in such acts [male youth]. 4. discussion, implications and conclusion 4.1 discussion: a framework of contextual mediators influencing the effectiveness of hiv/aids interventions relationship characteristic although some youth reported condom use after the wswm intervention, relationship characteristic influenced youth’s non-adoption of condom use as an hiv/aids prevention method. with the notable exception of [[53]], previous research generally concentrates on the influence of individual level negative attitudes towards condom use (e.g. [[54]]). yet, the present study reveals that sexual relationship characteristics (e.g. negative partner beliefs about condoms and perceptions of partner trust in long term relationships) influenced individual decisions to use condoms. some youth could not use condoms due to their partners’ negative beliefs about condoms and perceptions of partner trust.these negative beliefs included perceptions that condoms can cause cancer, and perceiving condoms to interfere with sexual pressure. [[37]] also report inconsistent condom use due to perceptions that condoms reduce the pleasure in sex. unlike other health behaviours, sex behaviours—condom use in particular require consensus and commitment from more than one person [[20]]. it is therefore important that both individuals concerned are equally committed to condom use and have no negative perceptions about its adoption. perceptions of trust and love, particularly in long term relationships, undermined young people’s condom use. suggesting condom use in such trusting relationships may be regarded as lack of love and trust, and evidence of promiscuity [[55]]. yet, such assumptions of lower hiv risks in long-term or loving relationships may not necessarily be true. this is because even if partners knew their hiv status before, one of them can in the long run become infected and bring hiv/aids into the relationship. clearly, consistent condom use requires mutual understanding, consent and commitment from both sexual partners. familial factors although there is a paucity of empirical research about the role of parents and guardians in fighting hiv/aids among their children, this study’s findings are consistent with those of the modest previous research (e.g. [[56], [57]]), in demonstrating the vital role of familial mediators in shaping young people’s sexual behaviours. compared to other contextual mediators, familial mediators significantly influenced young people’s sexual behaviours. although the youth reported improved hiv/aids awareness after the intervention, they stressed the need and importance of child-parent sexual education. compared to other sources, parents were regarded http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 as reliable sources of sexual health information, including information about hiv/aids and pregnancy prevention. yet, young people reported not getting timely sexual guidance from their parents. reasons for lack of parental-child sex communication include misconceptions that sexuality education leads to sex experimentation, and lack of parents’ self-efficacy in parentchild sex communication especially in cases of opposite sex, condom education, and children born with hiv. [[58]] report adults’ feelings of embarrassment and discomfort in discussing sexual-related issues with their children. findings also demonstrate that good parental role models with healthy sexual behaviours encouraged young people’s adoption of hiv preventive behaviours. peer influence although much of the focus in literature has mainly concentrated on the role of negative peer influences in encouraging highly risky sexual behaviours (e.g. [[59]]), this study demonstrates the central role of both negative and positive peer influences. on the one hand, there are negative peer influences that discourage the intervention itself, and the adoption of hiv preventive measures e.g. sex abstinence was discouraged through discriminative actions and giving intimidating labels such as ‘non-starters, ‘impotent’. in the midst of such peer pressures, rejection and intimidating labelling, sex abstinence among the out-of-school young people was constrained in the quest for peer acceptance and desirability. on the other hand, consistent with findings from related studies e.g. [[60], [61]], the present study demonstrates that positive peer influences encourage adoption of hiv risk reduction strategies e.g. peer role frameworks provided exemplary encouragement for young people to abstain from sex. this was enabled by positive encouragement that created obligatory feelings of adopting an hiv preventive method in an attempt to conform to the behaviours of role frameworks. gender-biased social norms although some female youth reported reduced adherence to norms of women’s passiveness in sexual issues, societal norms interpreting girl’s condom buying and suggestion as prostitution constrain girls’ condom use. this is in pursuit of social desirability, good reputation and escape from social accusations. findings demonstrate that unsupportive societal environments undermine girls’ capacity to negotiate for condom use by attaching labels of ‘prostitutes’ to girls who buy condoms or negotiate for condom use. due to such constraining gender-biased societal expectations, some girls refrained from buying condoms, or from proactively and assertively negotiating for condom use, in fear of transgression costs including bad reputations, social accusations and family rejection. the constraints of socially imposed sexual passiveness and culture of silence on women’s condom use is also reported in related literature [[25],[34]]. such unsupportive societal environments also constrain women’s ability to seek hiv risk prevention information or even seek hiv treatment [[62]]. unless such constraining norms are dismantled, women will certainly remain in the midst of the deadliest killer hiv/aids, disproportionately carry the burdens of hiv/aids, and increase hiv devastation by infecting their unborn babies. http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 findings also indicate that social norms associating men’s multiple sexual partnership with heroism and masculinity provide a fertile ground for the spread of hiv/aids and reduce the effectiveness of hiv prevention interventions. some young males felt socially justified in having more than one sexual partner at the same time and were driven into risky sexual experimentation in order to prove their manhood. such norms interfere with condom use since condoms are believed to interfere with their sexual performance [[37]]. adherence to infidelity-related norms also greatly exposes men to hiv infections, which consequently also puts women at risk. in uganda, the highest hiv infection rates are in married couples [[32]]. many women living with hiv report not to have had any sexual affair outside their marriage [[28]]. since hiv is mainly transmitted through sexual practices, men’s infidelity practices could account for the increased hiv infections in married couples in uganda [[38]]. although sex abstinence was generally encouraged for both girls and boys, it was more heavily emphasised for girls compared to boys. the social expectation of girls’ virginity and its related marital gains apparently demonstrates the grass root existence of gender-bias in hiv prevention. such norms of virginity before marriage can increase a girl’s vulnerability to hiv/aids. as indicated in this study, the perceptions of low hiv risk among virgin girls makes them sexual targets for men with risky sexual behaviours. christian religious beliefs christianity can be a practical weapon to dismantle the prevailing social-cultural and religious norms that approve men’s risky polygamous practices, early sex debuts and increased sexual activities. christian young people felt obliged to refrain from sex before marriage and infidelity practices in order to attain spiritual uprightness. religious beliefs about ungodliness associated with sex before marriage motivated some young people to delay sex debut, while religious condemnations of infidelity practices motivated some to remain faithful to their partners. noteworthy also however was religious constraints on hiv/aids prevention through condemnations of condom use. [[63]] also relates religious beliefs with reduction in women’s hiv risk behaviours. however, religion can also be a constraint to condom use. some religious beliefs claim that advocating condom use is an ineffective strategy that only encourages infidelity and moral decay, and increases the spread of hiv/aids [[64]]. economic constraints financial constraints drove some youth into unwanted sexual advances through sex in exchange for money and this increased their possibilities of exposure to sexual abuse. [[19]] report related findings as they record frequent uptake of hiv preventives by women who are not constrained financially or subject to sexual abuse. due to financial constraints, girls’ capacity to refuse unwanted sexual advances or suggest condom use was constrained by the pressing need for survival that was traded off against the long run consequences of hiv/aids infection. http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 4.2 implications for interventionists and researchers 4.2.1 hiv interventionists the identified contextual factors influencing the youth’s sexual behaviour and attitudes have implications for the implementation of hiv interventions. it is important to implement holistic interventions that go beyond influencing individual sexual behaviours, attitudes and skills by supplementing such individual level interventions with contextual level interventions. this is inline with contentions of ecological models for health promotion which posits that effective health promotion interventions should not only intervene at individual level but should also consider interpersonal, organisational, community and public policy contexts [[4]]. the need for broader interventions for hiv/aids prevention is also recognised by the uk’s aids strategy for developing countries, which posits that: “successful hiv prevention is about enabling individuals, couples and communities to make healthy choices about personal aspects of their lives – particularly sexual behaviour. these are not just based on information and rational choice; they are also influenced by complicated drivers of human action, including gender roles, inequality, norms around sexuality...” [[65]]. in particular, interventions at an interpersonal/community level should aim at:  changing negative partner attitudes towards condom use. this can be done by: (1) involving sexual partners of young people in the intervention; (2) implementing parallel community-based interventions targeted at changing the negative attitudes of community members about condom use.  reinforcing positive influences from peers who are role models of hiv prevention, while at the same time addressing negative peer influences that discourage adoption and maintenance of hiv preventive measures. beyond the interpersonal level, there is a need to intervene at social-cultural and religious levels in order to change social-cultural and religious norms and values that encourage and reinforce risky sexual behaviours. interventions at a social-cultural/religious level should aim at changing the norms and values prevailing in an individual’s social networks of peers, families, sexual relationships and religious groups. specifically, this should include:  addressing social norms that tolerate hiv risky practices in men e.g. practices of having multiple sexual partners at the same time. such norms increase males’ vulnerability to hiv by driving them to prove their masculinity, often unprotected as condoms are taken to interfere with their sexual performance. this can be addressed by supplementing school-based interventions by society/community-based interventions. http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012  dealing with norms that condemn females’ condom buying and negotiation by relating such practices to prostitution. these norms constrain girls’ capacity to take active roles in hiv prevention. addressing norms that associate girls’ virginity with marital gains. although such norms encourage girls’ engagement in sex abstinence, they at the same time increase their vulnerability to hiv. as indicated in this study, virgin girls are often innocent targets of men, which can increase their vulnerability to hiv. also, such norms interfere with girls’ confidence to seek sexual health advice in fear of exposing their sexual activity.  dealing with religious incompatibility issues which constrain condom use and condom advocacy. this can be done by involving religious communities in the design and implementation of hiv interventions.  reinforcing religious recommendations of preserving sex for marriage especially for those who are not yet sexually active.  reinforcing religious values of partner faithfulness in young people who are sexually active. finally, interventions at an economic level can aim at economic empowerment of the youth, girls in particular. this will enable them to take active control of their sexual lives, including: (1) avoiding commercial sex; (2) taking an active role in safe sex negotiations; (3) reducing their exposure to sexual abuse. 4.2.2 researchers this qualitative approach has provided rich insights about the contextual mediators that influence the effectiveness of hiv interventions. the devised framework provides insights into varieties of intertwined mediators that influence the applicability of the knowledge and skills gained from hiv interventions to adoption and maintain hiv preventive. however, this framework should be viewed as preliminary and future research should be targeted at fully developing it. this includes: (1) adding more themes/sub-themes, establishing and verifying relationships between themes/sub-themes; (2) determining the relative influence of each theme; (3) developing and validating appropriate scales for each sub-theme. 4.3 conclusion this chapter employed qualitative telephone-based semi-structured interviews aimed at investigating mediators influencing the effectiveness of the computer-assisted sexuality and hiv/aid intervention (wswm) implemented in schools in uganda, from the experiences of the 20 out-of-school young people that completed the intervention while they were still in school. this approach provided an opportunity to investigate how the young people are practically putting to use the hiv knowledge, attitudes and skills gained from the intervention to deal with real world challenges of hiv/aids amidst contextual mediators. previous studies evaluating http://ojphi.org/ contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 school-based hiv interventions have often limited themselves to assessing individual level mediators. the approach sheds light on critical aspects that are germane for sustainable prevention of hiv/aids. this study is the first evaluation to investigate contextual mediators influencing the adoption and maintenance of sexual behaviours promoted by the school-based intervention. similarly to the contentions of ecological frameworks for health [[4]], the findings indicate that sexual behaviours are determined by the interaction of the complex intertwined mediators that go beyond individual characteristics. this implies that exclusive attribution of sexual behaviours to individuals ignores the contextual determinants of sexual behaviours. these mediators include interpersonal characteristics e.g. relationship characteristics, and peer influence. beyond the interpersonal characteristics are socio-cultural and religious mediators that influence sexual behaviours through gender-biased sexual norms and religious beliefs and values. beyond the socio-cultural and religious contexts there are economic mediators that can constrain adoption of hi/aids preventive behaviours. finally, there are religious mediators that influence sexual behaviours through religious norms and values. overall, results from this study appreciate the interrelationships between individual sexual behaviour and the environmental mediators that can shape individual behaviour. mediators beyond the individual level interfere with the practical application of knowledge and skills gained from an hiv intervention. it is evident that the individual’s uptake of risky sexual behaviours is not solely as a personal failure, but rather as a result of intertwined individual and contextual mediators. experiences from this evaluation suggest that to generate concomitant mutual efforts, rather than exclusively focusing on individual level mediators, there is an urgent need to shift to integrative approaches. integrative approaches combine individual level change strategies with contextual level change approaches in the design and implementation of interventional strategies to fight against hiv/aids. this approach will create supportive social-cultural, religious and economic environments and common visions among the hiv community. while more work is needed to further expand and empirically test the formulated framework, this framework provides a systematic step towards this integrative paradigm shift. acknowledgement this work is part a phd research which was financially supported by the commonwealth scholarship commission in the united kingdom, and supervised by dr. donal flynn at the university of manchester. conflicts of interests no conflict of interest to declare. corresponding author angella musiimenta senior lecturer mbarara university of science and technology, and bishop stuart university kakoba email: angellamusiimenta@yahoo.com http://ojphi.org/ mailto:angellamusiimenta@yahoo.com contextual mediators influencing the effectiveness of behavioural change interventions: a case of hiv/aids prevention behaviours 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 references 1. kotler p, roberto e. 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(2003). the abcs of hiv/aids prevention seeking the evidence and telling the truth. the ccih forum 13. 65. dfid. (2008). achieving universal access: the uk's strategy for halting and reversing the spread of hiv in the developing world. fulfilling britain’s hiv commitments contributes to meeting dfid’s departmental strategic objective 1. http://ojphi.org/ http://dx.doi.org/10.1300/j129v05n03_02 http://dx.doi.org/10.1300/j129v05n03_02 http://dx.doi.org/10.1016/ http://dx.doi.org/10.1037/0033-2909.125.1.90 http://dx.doi.org/10.1177/008124639102100303 http://dx.doi.org/10.1080/14681990050109836 http://dx.doi.org/10.1097/00002030-200009080-00019 http://dx.doi.org/10.1097/00002030-200009080-00019 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts preparing for the impact of the icd-9/10 transition on syndromic surveillance peter hicks1, julie a. pavlin2, atar baer3, david swenson4, aaron kite-powell5, achala u. jayatilleke1, brooke evans*6 and laura c. streichert6 1cdc, atlanta, ga, usa; 2armed forces health surveillance center, silver spring, md, usa; 3public health—seattle & king county, seattle, wa, usa; 4state of new hampshire, concord, nh, usa; 5cnts support to afhsc, silver spring, md, usa; 6isds, brighton, ma, usa objective to describe the process undertaken to translate syndromic surveillance syndromes and sub-syndromes from icd-9 diagnostic codes to icd-10 codes and how these translations can be used to improve syndromic surveillance practice. introduction the us department of health and human services has mandated that after october 1, 2015, all hipaa covered entities must transition from using international classification of diseases version 9 (icd9) codes to using version 10 (icd-10) codes (www.cms.gov). this will impact public health surveillance entities that receive, analyze, and report icd-9 encoded data. public health agencies will need to modify existing database structures, extraction rules, and messaging guides, as well as syndrome definitions and underlying analytics, statistical methodologies, and business rules. implementation challenges include resources, funding, workforce capabilities, and time constraints for code translation and syndrome reclassification. to address these challenges, the international society for disease surveillance (isds), in partnership with the cdc and the council of state and territorial epidemiologists (cste), conducted a project to develop consensus-driven syndrome definitions based on icd10 codes. over 130 newly created icd-9 to-icd-10 mappings and corresponding syndromic definitions were fully reviewed and vetted by the syndromic surveillance community, which relies on these codes for routine surveillance and research purposes. the final mappings may be leveraged by other federal, state, and local public health entities to better prepare and improve their surveillance, analytics, and reporting activities impacted by the icd-10 transition. methods translation from icd-9 to icd-10 codes involves a rethinking of how syndromes and sub-syndromes are defined and aligned with practical syndromic surveillance practice. to address this, isds coordinated a multi-stakeholder working group to revisit over 130 existing syndromic surveillance definitions and to compile icd-9 codes that map to these syndromes and sub-syndromes. the individual icd-9 codes were mapped to icd-10 codes using freely available general equivalence mappings (gems). subsequently, we followed a reverse translation validation process to ensure that the appropriate codes were correctly identified. the resulting mapping reference table (mrt) relates syndromic classifications to both code groupings. these syndrome and sub-syndrome base code groupings were then reviewed by the surveillance community and partner agencies, leveraging clinical and epidemiological expertise, to reach consensus on the final syndrome definitions and mappings. results the community vetting process demonstrated that a simple translation using gems is not adequate and underscored the value of the end-user review. in addition to creating a new icd-10 resource, the mrt enables users to address challenges associated with changes in baseline trends as a result of the transition. leveraging the mrt, jurisdictions can quickly map forwards and backwards across the two coding systems to ensure continuity of analytics and reporting during the transition period (figure 1). conclusions the higher level of detail inherent to icd-10 codes will improve the specificity of syndromic surveillance. the development of a consensus-driven mrt will be extremely valuable to entities with the complex task of translating icd-9 to icd-10 codes. the code translations will also help the syndromic surveillance community work towards developing standardized syndrome definitions. future collaborations are under development to re-purpose these code mappings to develop consensus-driven syndromic surveillance definitions that are also vetted though a syndromic surveillance community of practice. fig. 1: forwardand reverse-mapping of icd 9/10 codes can be helpful for creating a time series that spans the transition. (hicks, 2013 isds conference). keywords icd-10; code mapping; syndromic surveillance; biosense acknowledgments we thank the surveillance professionals that assisted with the code set review. this work was supported by the cdc. *brooke evans e-mail: bevans@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e28, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts monitoring acute diarrhea via an electronic surveillance system in the peruvian navy emily alsentzer*1, delphis m. vera2, joan neyra3, luis loayza7, ricardo a. hora2, victor b. osorio5, jose quispe6, sarah-blythe ballard4 and david blazes3 1stanford university, stanford, ca, usa; 2naval medical research unit no. six, lima, peru; 3uniformed services university of the health sciences, bethesda, md, usa; 4johns hopkins bloomberg school of public health, baltimore, md, usa; 5universidad nacional agraria la molina, lima, peru; 6dirección de salud de la marina de guerra del peru, lima, peru; 7marina de guerra del perú, lima, peru objective to use data from the peruvian navy’s electronic syndromic surveillance systems to estimate the baseline incidence of acute diarrheal disease (add) and detect outbreaks among individuals accessing military medical facilities from 2009-13. introduction syndromic surveillance can supplement diagnosis-based surveillance in resource-limited settings with limited laboratory infrastructure. syndromic surveillance allows for early outbreak detection relative to traditional systems and enables community health monitoring during outbreaks1. monitoring and disease diagnosis can be strengthened using pre-diagnostic data and statistical algorithms to detect morbidity trends. alerta (2002-11) and vigila (2011-present) are sequentially implemented electronic disease surveillance systems created by the peruvian navy to improve the detection, prevention, and control of disease outbreaks. the phone-, internet-, and radio-based reporting system now covers over 97.5% of the navy population2, encompassing 169 reporting establishments that treat active and retired service members, dependents, and civilian employees. acute diarrheal disease, respiratory infections, and pneumonias are reported weekly, whereas specific notifiable diseases such as malaria, dengue, and tuberculosis are reported immediately after case detection. methods time series analysis of alerta and vigila reports of add occurring from 2009-13 were analyzed in matlab (mathworks vr2014a). add counts from each reporting location were aggregated for each epidemiological week. add outbreaks were retrospectively identified using exponentially weighted moving average (emwa) control charts. seasonal data variability was eliminated using an 8-week sliding baseline to estimate upper control limits (ucls). ucl calculation distortion was prevented by removing outbreak week data from the baseline. a weight of = 0.3, considered most appropriate for diseases with short incubation periods3, was used with a k value of 3 standard deviations. results from 2009-13, 39,764 non-bloody add cases were reported across all naval bases via alerta and vigila. over 3% of cases were 0-1 years old; 8% were 1-4 years old; and 89% were over 5 years old. non-bloody add prevalence tended to peak from january to march, the summer months (fig 1). ewma control charts detected 25 add outbreaks (fig 2). conclusions real time analysis of syndromic surveillance systems such as alerta and vigila has the potential to promote rapid outbreak response by detecting emerging disease threats and morbidity trends in resourcelimited settings. figure 1. cases of non-bloody add from 2009-2013. outbreaks of disease were most frequent during the summer months of january to march. figure 2. ewma control charts reported 25 weeks with add outbreaks from 2009-2013 (denoted by asterisks). keywords resource-limited setting; acute diarrheal disease; peru; syndromic surveillance; ewma references 1. jajosky ra, groseclose sl. evaluation of reporting timeliness of public health surveillance systems for infectious diseases. bmc public health. 2004 july; 4:29. 2. mundaca cc, moran m, ortiz m, saldarriaga e, quispe j, araujo rv, blazes dl. use of an electronic disease surveillance system in a remote, resource limited setting: alerta disamar in peru. 54th annual meeting of the american society of tropical medicine and hygiene; 2005 dec. 11-15; washington, dc. 3. buckeridge dl, burkom h, campbell m, hogan wr, moore aw. algorithms for rapid outbreak detection: a research synthesis. j biomed inform. 2005 apr; 38(2): 99-113. *emily alsentzer e-mail: ema2016@stanford.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e102, 201 ojphi-06-e65.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 150 (page number not for citation purposes) isds 2013 conference abstracts searching for mers and novel flu with limited resources alan siniscalchi*1 and charlie ishikawa2 1state of ct dept of public health, hartford, ct, usa; 2international society for disease surveillance, boston, ma, usa � �� �� �� � � �� �� �� � objective �������� � ���� � ������������������ � �� � ������������ �������� � �� �� ������������� ����� �������������� ������������������������ � �������������������������� ��������� 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����������������������������� ���� ���� �������� ��������������������� ������ ������������� ���������� � �� � ����������� �������� ��������������� ���� keywords ���� ���� �� ������7�)*+,�-�/��������� ���7��������������� �����7� !"#$���������� ���7�!'#(��������� ��� references 859�,����� �� ��%:��,� �����%��$45"����#�����6���,������� ����;��-��� ������� �� �<�������+��������=�>:3!27?.50@5a(� 8$9�)��� �#��+�����������-����� �)����� ���$45"��,�������� �,��� �� ���� 3������!� �� �*���������@�%�-������ ��������� ��2����� ���� ��,���� ������������ ���,������� �����>:3!27?.$0@5�5$� *alan siniscalchi e-mail: alan.siniscalchi@ct.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e65, 2014 monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi monitoring public health reporting: data tracking in cancer registries abdulrahman m. jabour mhsm, phd 1, brian e. dixon mpa, phd, facmi, fhimss 2, 3, 4 1. department of health informatics, jazan university school of public health and tropical medicine. jazan, saudi arabia. 2. department of epidemiology, indiana university richard m. fairbanks school of public health, indianapolis, indiana, united states. 3. center for biomedical informatics, regenstrief institute, indianapolis, indiana, united states. 4. center for health information and communication, department of veterans affairs, hsr&d service, roudebush va medical center, indianapolis, indiana, united states. abstract: introduction: timeliness of data availability is a key performance measure in cancer reporting. previous studies evaluated timeliness of cancer reporting using a single metric, yet this metric obscures the details within each step of the reporting process. to enhance understanding of cancer reporting processes, we measured the timeliness of discrete cancer reporting steps and examined changes in timeliness across a decade. methods: we analyzed 76,259 cases of breast, colorectal and lung cancer reported to the indiana state cancer registry between 2001 and 2011. we measured timeliness for three fundamental reporting steps: report completion time, report submission time, and report processing time. timeliness was measured as the difference, in days, between timestamps recorded in the cancer registry at each step. we further examined the variation in reporting time among facilities. results: identifying and gathering details about cases (report completion) accounts for the largest proportion of time during the cancer reporting process. although submission time accounts for a lesser proportion of time, there is wide variation among facilities. one-seventh (7 out of 49) facilities accounted for 28.4% of the total cases reported, all of which took more than 100 days to submit the completed cases to the registry. conclusions: measuring timeliness of the individual steps in reporting processes can enable cancer registry programs to target individual facilities as well as tasks that could be improved to reduce overall case reporting times. process improvement could strengthen cancer control programs and enable more rapid discovery in cancer research. keywords: cancer reporting, cancer registry, data timeliness, cancer surveillance, rapid reporting system, public health reporting. *correspondence: ajabour@jazanu.edu.sa doi: 10.5210/ojphi.v10i3.9432 mailto:ajabour@jazanu.edu.sa monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi introduction cancer registries capture and manage tumor-related data to identify incidents, monitor trends, and support cancer-related research. however, data can take months to become available after diagnosis, which limits the utility of cancer registries for population health surveillance. previous work has shown that cancer data take an average of 253–426 days after diagnosis to become available at state cancer registries [1]. timely reporting is an important aspect of cancer surveillance, care delivery, and research. longer reporting times make monitoring the trends of cancer cases more difficult and limit accessibility to timely data for research [2]. as most intervention programs are data-driven, reporting delays hinder timely intervention in cancer care delivery. assessments of prevention, screening, and treatment programs are more valuable if they are presented in a timely manner [3]. the need to improve timeliness in cancer reporting is indicated in a series of national academy of medicine (nam) reports [4-6]. timely data are critical for developing a rapid learning systems (rls) that could enhance the delivery of cancer care [6,7]. although no clear consensus exists on the minimum timeliness required for an rls, the need for improvement is documented [6,8,9]. achieving timely data requires deeper understanding of the status of registries and the quality of their data. prior studies defined timeliness as a single metric, usually as the number of days from the date of diagnosis to the date when data are available for research. for example, hunt (2004) outlines two common approaches to measuring timeliness [10]. the first approach calculates the proportion of cases abstracted during a certain period compared with the number of cases anticipated in that year [10]. the second approach uses date intervals (or date stamps) to calculate the difference between the date of diagnosis and the date of data entry. common data points employed in this approach include date of diagnosis, date of admission or first contact, and date of transferring records to the registry [10,11]. one advantage of the second approach is the ability to analyze long-term trends, including assessment of changes over time. another advantage is the opportunity to examine the different stages within the reporting process. while delays in reporting cancer data are widely recognized, the distribution of reporting timeliness across the spectrum of cancer reporting processes is not fully understood. previous studies often evaluated timeliness from a data quality perspective, in which the emphasis is placed on reporting time as an outcome to be measured and not a process to be understood. therefore, timeliness was evaluated by measuring reporting time as a single process from start to finish, yet the precise duration of each step within the process remains unknown [12-15]. a review of timeliness in reporting of public health data concluded that public health surveillance studies lack detailed descriptions of reporting stages, such as data processing and analyzing [16]. this review also emphasized that both the collection and assessment of time interval data are important elements of any surveillance system or timeliness assessment [16]. copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi separating the cancer registry reporting process into multiple steps will enhance our understanding of reporting timeliness and represent the issue with respect to time at each sub-process as data move from health care facilities to the registry. timestamps are a powerful tool for large scale quality monitoring. in this study, we employ timestamps to evaluate timeliness for the subprocesses within cancer reporting as well as the reporting pattern among facilities. longitudinal data are utilized to assess changes in timeliness for each step and the variation among the different reporting facilities. understanding variation within sub-processes will enable state registry personnel to direct their efforts to the tasks and facilities needed for targeted improvement. 2 methods 2.1 context and data retrieval the study was approved by the institutional review board at indiana university. to examine timeliness of cancer case reporting, we employed a retrospective cohort study using data from the indiana state department of health (isdh) cancer registry. the isdh cancer registry collects data on all malignant cancer cases required for reporting by federal regulation or the national program of cancer registries [17]. the registry contains information about cancer cases needed for performing epidemiological, preventive, and control studies. using the isdh cancer registry, we extracted a cohort comprised of patients diagnosed with breast, colorectal, or lung cancer between 2001 and 2011. we retrieved 76,259 de-identified cases representing one of three cancer types: 28,782 breast cancer cases, 19,530 colorectal cancer cases, and 27,947 lung cancer cases. 2.2 analysis the cancer registry records dates and timestamps when various components of the reporting process occur. to measure timeliness, we calculated the difference, as the number of days, between each reporting process step (depicted in figure-1). descriptive statistics, such as the mean, median and interquartile (box plot), were calculated to assess overall trends as well as variation by reporting source using the statistical package for social sciences (spss) version 21. figure 1: cancer registry case reporting process depicted as a series of steps performed by registrars that occur at different points in time. monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi 2.3 reporting steps cancer reporting involves a series of sequential steps. completion of each step depends on the completion of the previous step; thus, delays in completion of one step will result in delaying the subsequent one and, subsequently, the overall reporting time. the fundamental reporting steps are: case-finding, abstracting, report submission, and report processing and editing. 2.3.1 report completion report completion consists of two steps: case-finding and abstracting. case-finding is the process of identifying new cancer patients for a given time period. the identification includes records associated with any cancer diagnosis, treatment term, or code indicating a reportable cancer condition [18-20]. once a case is reviewed and identified as reportable, it is saved in a temporary database known as a “suspense file” in preparation for abstracting [18]. the “suspense file” serves as a temporary database for incomplete cases waiting for any additional exams or procedures to be done and entered into the ehr. registrars often wait for weeks or months and recheck if the data needed are available before completing the abstracts. abstracting is the process of collecting and compiling information about each reportable patient in preparation for sending reports to the state cancer registry. abstracts are comprehensive reports that include all the cancer-related information required by the receiving registry. the abstract may include data related to demographics, tumor information, staging, diagnostic studies, or treatment. once the abstract is completed it is saved in preparation for reporting. reporting facilities, in general, save completed abstracts in order to report them to registries on a fixed schedule. once all the required information is collected and the abstract meets the receiving registry’s requirements, the abstract is considered complete and ready for reporting. in this study, the report completion time, which includes both case-finding and abstracting time, is defined as the time, measured as number of days, from the “date of first contact 580” in the commission on cancer (coc) standard to the “the date case completed 2090” in the north american association of central cancer registries (naaccr) standard (figure 1). the date of first contact is the date when patients visit the reporting facility for the diagnosis and/or treatment of the tumor. 2.3.2 report submission completed abstracts are sent to the state registry at fixed intervals. state registries require facilities with high volume of cases to report at higher frequencies. the time from abstract completion to when the state registry receives the report was calculated by measuring the time, as number of days, from “the date case completed 2090” in naaccr standard to “date case report received by the registry 2111” in the centers for disease control and prevention (cdc's) national program of cancer registries (npcr) standard (figure 1). 2.3.3 processing state registries receive reports from multiple facilities. reports may need to be checked for duplicates and edited before they are analyzed and reported or made accessible to researchers. the registry processing time was calculated by measuring the time, as number of days, from “date case monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi report received by the registry 2111” in npcr standard to “date tumor record available at the registry 2113” in npcr standard (figure 1). 2.4 variation among facilities to examine variation in reporting time among facilities, we measured the average time taken by individual facilities, annually, for report completion and report submission. since the requirements for report submission differ based on facilities’ annual caseloads, we focused on facilities with higher numbers of cases, due to their greater impact on the overall timeliness of cancer reporting. we included facilities with at least 1,000 cases for the entire study period, which equates to an average of 100 cases per year for the three cancer types included. facilities with an average of 60 to 149 cases a year are expected to report their cases at least every quarter [19]. facilities with higher number of cases are expected to report at a higher frequency. facilities included in this part of the analysis are thereby either expected to report every quarter, every other month, or monthly. 3 results 3.1 reporting steps figure 2 summarizes annual timeliness for cancer reporting in indiana. figures 2-a to 2-c are boxplots showing annual timeliness for the three sub-processes involved in cancer reporting: report completion, report submission, and registry processing. figure 2-d graphs the median reporting times for each process by year. report completion time represents the time taken for case finding and abstracting. the highest report completion time observed was during 2003 and 2004 (figure 2-a). report completion time was more consistent during the last five years (2006–2010) compared with the previous five years (2001–2005). a noticeable reduction in timeliness variability among case was observed after 2005. report submission time represents the time taken from report completion to report receipt by the state registry. with the exception of 2004, the median time was less than 50 days (figure 2-b). however, the box plots show that the data were positively skewed. although most cases (75th percentile) took fewer than 120 days, some cases in the upper whiskers took up to a year. monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi figure 2-a: box plot showing the distribution of time taken by facilities to complete reports. figure 2-b: box plot showing the distribution of time taken by facilities to submit completed reports. figure 2-c: box plot showing the distribution of time taken by the central registry to process received reports. figure 2-d: comparing the median time for each reporting stage. the time taken by the state registry to process submissions into the registry was higher during the first two years (2002 and 2003), with medians of around 30 and 20 days, respectively. a noticeable reduction in processing time was observed in 2004 and beyond. the processing time was lowest between 2005 and 2010 (figure 2-c). when comparing the median reporting time for each reporting stage we observed that report completion time consumes the largest proportion of the total reporting time followed by report submission time and processing time (figure 2-d). the stacked area chart also shows the median reporting time appears to be more consistent after 2005 compared to the first few years of the study period. monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi 3.2 variation among facilities the cases retrieved for the entire 10-year study period were reported from 49 unique facilities. of the 49 facilities, 22 were classified as high volume facilities. the 22 facilities selected accounted for 90.5% of the cases. facility reporting time was investigated by distinguishing report completion time from report submission time. the time taken to complete reports varied among facilities (see figure 3-a); whereas, most facilities’ reported completion time ranged from around 175 to 225 days, a few exceeded 300 days. figure 3-a: reported completion times at high volume facilities. figure 3-b: report submission times at high volume facilities. report submission time is presented in figure 3-b. a noticeable difference among facilities was found in report submission time. whereas most facilities took less than 50 days on average to send completed reports to the state registry, 7 of the 22 facilities took more than twice that time (around 100 to 300 days). those 7 facilities accounted for around 28.4% of the total number of reported cases. 4 discussion the delays in the timely reporting of cancer data have been discussed in many studies and nam reports. previous studies evaluated cancer-reporting time by measuring the total reporting time as a single process [12-15]. to our knowledge, no study has deconstructed the reporting time to examine the time taken per each step within the process. in this study we measured the time taken per each step of the reporting for each facility. one of the advantages of our method is that it provides a total coverage of the reporting sources, which could be utilized for targeted improvement at both the location (facility) and task (stage of reporting). this approach can be very practical for enhancing the quality of registry data and meeting registries certification criteria. moreover, understanding the details of total time distribution can provide guidance for future research by enabling them to focus on the steps with the greater impact. monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi we found that report completion accounts for most of the total reporting time. we also found a large variation in report completion times among facilities. variation can be attributed to many reasons such as the number of registrars working at the facility, or registrars’ access to medical records [18]. prior studies reported more than 50% of the registries and over 70% of the reporting facilities were experiencing shortages in cancer registrars [21-22]. health information technologies can facilitate some of the reporting activities. studies reported that the use of electronic health records (ehr) can improve reporting time and support workflow by improving registrars’ access to patient information, thus minimizing the time needed for data search and retrieval [22]. another example is using the electronic pathology reporting systems (e-path) to automate some of case finding tasks. e-path utilizes text mining to automate the identification and coding of pathology reports [23]. around 90–95% of cases identified at the casefinding stage are identified through pathology reports and the time needed for review during casefinding can be reduced using e-path [23]. studies also indicated that access to health information exchange (hie) can eliminate many of the barriers related information search and retrieval [18]. in most years, the mean report submission time was less than 50 days. however, there was a noticeable variation in the average reports submission time among facilities. although a longer submission time was found only in 7 of the 49 facilities, those 7 facilities accounted for around 28.4% of the total number of reported cases. maintaining timely reporting for all cases is important because cancer registry standards require high completion rates. the surveillance, epidemiology, and end results program (seer) standard, for instance, requires 98% of cases to be reported within 22 months of the date of diagnosis [24]. another example is the naaccr standard that requires 90% of the cases to be reported within 23 months from the date of diagnosis for silver certification and 95% for golden certification [25]. reports are usually transferred electronically from individual facilities to state registries. unlike case-finding and abstracting, the task of submitting reports is easier and less time-consuming. after abstracting, completed reports are stored at a hospital’s database to be submitted at fixed intervals (monthly, bi-monthly, quarterly, etc.). encouraging facilities to submit completed reports more promptly can potentially improve overall registry performance, especially if facilities with longer submission times are targeted. automated reports submission could also be employed to send each report individually upon completion instead of sending them as batches on a fixed schedule. by separating the time taken by facilities to complete and send reports from the time taken to edit and process the reports at the state registry, we found that the registry takes a short amount of time for processing. moreover, a remarkable reduction in registry processing time was found after 2004. in 2004, the time taken by the registry to process and edit cases received began to decrease from 20 to 30 days to fewer than 10 days. additionally, the variation in processing time among cases decreased substantially after 2004 (less than 10 days), whereas in 2002 and 2003 processing time extended up to 75 days. the isdh cancer registry went through some major changes in 2004 [26]. although causation cannot be inferred, changes were concurrent with changes in the median reports processing time at the central registry. one of these changes was the implementation of a registry system that uses file transfer protocol (ftp) (standard for exchanging files online) [26]. it was implemented as an alternative to physically sending discs allowing real-time transfer of data as opposed to postal mail [26]. surprisingly, using ftp did not seem to impact the report submission time. for the most part, report submission time appears to have been consistent over monitoring public health reporting: data tracking in cancer registries online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e220, 2018 ojphi the years. one explanation might be that submission time is constrained by reporting policies and requirements guidelines. reporting facilities are required to submit completed reports at a fixed interval based on their annual caseloads. for instance, hospitals with an average of 1 to 59 cases annually are required to report their cases at least once each year while hospitals with an average of 300 or more cases annually are required to report them monthly [17]. 4.1 limitation one limitation was the inability to distinguish the time needed for case-finding from the time needed for abstracting. distinguishing the timeliness of these steps will allow a focus on the step with the longer reporting time. knowing the timeliness for each step could provide insights into the potential impact of adopting technologies such as e-path. e-path can automate many of the case finding activities but there are some challenges, such as the cost associated with the software implementation and infrastructure [27]. the ability to measure case-finding timeliness can be important for making such an investment decision. 4.2 future research our study showed variation among facilities in both report completion and report submission times. as reporting time can be impacted by many factors such as facility type, resources, technologies, and reporting practice, the variation in these resources across facilities is unknown. for consistent reporting 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decision support application for the ios platform thomas g. savel, brian a. lee, greg ledbetter 2 , sara brown 2 , dale lavalley 1 , julie taylor 3 , pam thompson 3 1 informatics r&d activity, public health surveillance & informatics program office, office of surveillance, epidemiology, and laboratory services, cdc, atlanta, ga, 2 caci international, inc., fairfax, va, 3 office of laboratory science, policy, and practice program office, office of surveillance, epidemiology, and laboratory services, cdc, atlanta, ga abstract objectives: this manuscript describes the development of ptt (partial thromboplastin time) advisor, one of the first of a handful of ios-based mobile applications to be released by the us centers for disease control and prevention (cdc). ptt advisor has been a collaboration between two groups at cdc (informatics r&d and laboratory science), and one partner team (clinical laboratory integration into healthcare collaborative clihc). the application offers clinicians a resource to quickly select the appropriate follow-up tests to evaluate patients with a prolonged ptt and a normal prothrombin time (pt) laboratory result. methods: the application was designed leveraging an agile methodology, and best practices in user experience (ux) design and mobile application development. results: as it is an open-source project, the code to ptt advisor was made available to the public under the apache software license. on july 6, 2012, the free app was approved by apple, and was published to their app store. conclusions: regardless of the complexity of the mobile application, the level of effort required in the development process should not be underestimated. there are several issues that make designing the ui for a mobile phone challenging (not just small screen size): the touchscreen, users' mobile mindset (tasks need to be quick and focused), and the fact that mobile ui conventions/expectations are still being defined and refined (due to the maturity level of the field of mobile application development). keywords: public health, informatics, mobile, clinical decision support, software development correspondence: tsavel@cdc.gov copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:tsavel@cdc.gov ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi introduction from 1984 to 2007, the centers for disease control and prevention (cdc) convened seven institutes on critical issues in clinical laboratory practice.(1) these institutes brought national and international experts together to focus on the role of the clinical laboratory in providing quality testing services for improved patient outcomes. the clinical laboratory integration into healthcare collaborative (clihc) tm , established by cdc’s division of laboratory science and standards (dlss), within the laboratory science, policy, and practice program office (lspppo), has been addressing some of the recommendations from the past institutes by focusing on important gaps to be filled to optimize the ability of practicing clinicians to effectively utilize laboratory services for better patient care.(2) one of the clihc subgroups focused on diagnostic algorithms to address clinicians’ challenges in laboratory test selection by raising awareness of the complexity of choosing the most appropriate laboratory test for evaluating patients in what appears to be a straightforward clinical setting. the output of this group has been diagnostic testing algorithms, as well as the recommendation that value could be found in the use of information technology tools to guide clinicians’ selection of appropriate laboratory tests. thus, while the final touches were being placed on the algorithms for patients with abnormal ptt and normal pt values, cdc staff from lspppo reached out to cdc’s informatics research & development (r&d) laboratory (dry lab) for consultation on the potential implementation of the diagnostic algorithms in a mobile application.(3,4) the r&d team became active collaborators on the project, and began the development of a prototype decision support app for the iphone (ios), ptt advisor. the prototype goal was to leverage the algorithms as documented in flow charts and turn them into an electronic, interactive, decision support tool for clinical provider use. in other words, the mobile application would be designed to be very intuitive for the users by presenting one question / decision point at a time — and automatically walking them through the algorithm. methods the creation of a high-quality mobile app is never a trivial task. to facilitate rapid development, an agile development philosophy was adopted. very early versions of the app were shared in successive iterations with the program staff for feedback, thus allowing for rapid course corrections. in addition, it was crucial that the r&d team leveraged two very different types of expertise: mobile software development, and user experience design. both skills were needed to insure the highest-quality mobile app. user requirements specific to clinical providers were prioritized and implemented in quick iterations using feedback from closely involved pathologists (who created the algorithms), and physicians (who used the functionality). in addition to functional requirements, the design team used industry best practice for core app requirements, such as: intuitive design with easy navigation; rapid, just-in-time error correction; and efficient code design to facilitate multiple changes to the overall algorithm, if needed. finally, while ptt advisor is not considered a “mobile medical app” from an fda regulatory authority standpoint, the design team used the emerging fda draft guidance on mobile medical applications as a source of health and medical-specific requirements for usage, security and privacy.(5) ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the smartphone ecosystem is continuing to mature and evolve at a rapid pace. at the time that this initiative was started, research demonstrated that, within the physician population, use of apple iphone/ios platform was significantly dominant over other platforms, such as google android, rim, blackberry, microsoft windows phone, or hp webos. survey data collected from june 1, 2010 and february 28, 2011 showed iphone/ipad with a 90% share and android at 6%. (6). future versions of the app may support additional devices, such as those running the android operating system, but our initial development efforts were focused on a single mobile device. privacy and security are important aspects of health-related apps. to address potential privacy and security concerns, cdc’s r&d team collaborated with security and legal subject matter experts to review the app and examine how it interacted with user data. this application did not need to collect or transmit any information. as a result, the app clearly communicates to users with an initial splash screen that no data entered into the app is stored nor sent outside of the device. all algorithm definitions are stored locally on the device so the app never uses the network to send or receive data. the application was written in objective-c using xcode 4 as the development environment, and mercurial as the source code management tool.(7,8) ptt advisor was distributed to beta testers, initially using the 3rd party tool, testflight, and then subsequently, using a proprietary mechanism that involved the use of the betabuilder open source software package.(9,10) after having the opportunity to experience the application for 2 weeks, the beta testers submitted feedback to cdc staff via email. as it is an open-source project, the code to ptt advisor was made available to the public under the apache software license.(11,12) to support future revisions to the treatment guidance, as well as additional new algorithms, the application was designed with a modular algorithm navigation engine. the engine does not hard code the algorithms in objective-c, and thus, allows for any type of algorithm to be defined and updated by a non-programmer. discussion the cdc informatics r&d team gained many insights from this mobile app initiative. although the clinical decision support algorithms were very well-defined and designed, in the process of implementing them in a structured mobile application, areas of improvement were quickly brought to light, and the algorithms were subsequently modified. thus, the value of developing a prototype app to obtain valuable insights can not be overstated. in addition, the user experience design aspect of the mobile app was critical. the app was optimized for rapid decision making at the point of care, as compared to a training or educational tool. multiple iterations of the user interface were created and tested to find the most intuitive method for “navigating the algorithm.” figure 1 displays the final version of the central component of the mobile application, and emphasizes simplicity (large buttons, clean color schemes, etc.). as is often the case, an application having a “critical feature” can have a significant impact on user adoption. the application displays a simple, tap-oriented screen. a ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi question or statement is provided in the top-half of the screen, and large buttons are provided in the bottom-half of the screen so decision options are easy to distinguish and tap. additional information, provided in the form of a “footnote” is made available to the user, where applicable, as a button in the upper-right portion of the screen. figure 2 displays the “evaluation review” feature of ptt advisor. this feature is accessible at any time and allows the user to review all the selected steps in the algorithm. the feature also allows the user to change any previously completed step in the algorithm, or if no change is necessary, the user can return to the current step. thus, this innovative feature ensures very efficient use of the clinician’s time, obviating the need to start over if a mistake is made. this feature was refined many times until the optimal experience was achieved. while the app was designed with a goal of a minimal learning curve, a simple help screen was still provided to answer basic questions. figure 3 displays the help screen, which describes how the toolbar (bottom of figure 1) can be used. the button design, again, focuses on clarity and simplicity. after multiple iterations with internal stakeholders, the r&d team ran a beta test with pathologists and practicing physicians to revise the app based on their feedback. the distribution of ptt advisor to beta testers was not trivial, and did require close communication with the beta testers. as apple limits the ability to distribute apps outside of the itunes app store, our team had to work closely with each beta tester to obtain their device id and manually install the app for evaluation and testing. on july 6, 2012, the free app was approved by apple, and was published in their itunes app store. (13) ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 1. welcome screen – ptt advisor application ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 2. decision overview screen – ptt advisor application ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3. help screen – ptt advisor application ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi conclusion regardless of the complexity of the mobile application, the development process is never easy. there are several issues that make designing the ui for a mobile phone challenging (not just small screen size): the touchscreen, users' mobile mindset (tasks need to be quick and focused), and the fact that mobile ui conventions/expectations are still being defined and refined (due to the maturity level of the field of mobile application development). the extra effort allocated to the ui design will significantly improve user experience.(14) the goal of user experience (ux) design is to provide the user with an optimal experience of the application, including ease-of-use, emotional appeal, features provided, and overall value.(15) it is highly recommended that ux expertise be leveraged in the development of any software (or hardware) solution. the r&d lab team found that if the software development process uses short iterations with a direct feedback loop to the end users, consistent with the agile development process, it should result in significant time and resource savings. these tight iterations served to not only refine existing developed clinical guidance algorithms, but to more quickly move these changes from design to practicing hands. with regard to the beta testing and early distribution of a mobile application, is important to note that the complexity of the distribution process will vary, based on the selected distribution tool (e.g., testflight), as well as the specific type of license the developers are using (e.g., single developer vs. enterprise). overall, it is important to factor in adequate time and resources necessary to test the application prior to public launch. although ptt advisor addresses a focused use case, a prolonged ptt and a normal ptt, its design allows for expansion with additional algorithms as they are created, vetted, finalized and released. this is the first in a handful of apps to be released by cdc, in collaboration with its partners, and we look forward to more applications being released in the future. acknowledgements the authors wish to thank the following individuals for their leadership in the development of the diagnostic algorithm: michael laposata, md, phd, marisa b. marques, md, and oxana tcherniantchouk, md statement on conflict of interest no conflicts of interest are noted corresponding author thomas g. savel, md centers for disease control and prevention 1600 clifton road, ne, mail stop e-55, atlanta, ga 30329, usa email: tsavel@cdc.gov mailto:tsavel@cdc.gov ptt advisor: a cdc-supported initiative to develop a mobile clinical laboratory decision support application for the ios platform 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi references [1] cdc’s division of laboratory systems 2007 institutes http://wwwn.cdc.gov/dls/institutes/ (accessed 16 may 2012). 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[7] apple’s xcode 4 for developers https://developer.apple.com/xcode/ (accessed 17 may 2012) [8] mercurial source control managmement tool http://mercurial.selenic.com/ (accessed 16 may 2012). [9] testflight ios beta testing tool https://testflightapp.com/ (accessed 5 may 2012). [10] betabuilder a collection of rake tasks and utilities for managing and publishing adhoc builds of ios apps. https://github.com/lukeredpath/betabuilder (accessed 16 may 2012). [11] code repository for the open source, mobile application, ptt advisor https://bitbucket.org/gledbett/anticoagmon/src (accessed 16 may 2012). [12] apache license version 2.0, january 2004. http://www.apache.org/licenses/license2.0.html (accessed 1 may 2012). [13] ptt advisor app in the apple itunes app store. http://itunes.apple.com/us/app/pttadvisor/id537989131?mt=8 accessed september, 27, 2012. 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�� #����������������� ���������������������) �� �����f0��0���4������!�����0 �#�*993��3(����*� =�'������#�+�!�#�e��#�f�7��1�� ��#� ���!�$��!�����#����� ��������������� ��� �� ���������������������(� $+&:� ������ ��/&/0)� ��������) ��� �����&� ��0��#�*99>��*h�-h�(����359h)*@� *robert mathes e-mail: rmathes@health.nyc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e97, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using big healthcare data to supplement chikungunya surveillance in the u.s. joel greenspan*1 and silvia valkova2 1martin, blanck & associates, atlanta, ga, usa; 2ims health government solutions, fairfax, va, usa objective this paper describes how high-volume electronic healthcare (hc) reimbursement claims (ehrcs) from providers’ offices can be used to supplement chikungunya surveillance in the u.s. introduction chikungunya virus disease (chik) is a mosquito-borne viral infection currently widespread in the caribbean with the potential for emergence and endemicity in the u.s. via infected travelers and local mosquito vectors. chik disease can be severe and disabling with symptoms similar to dengue. chik is not a u.s. nationally notifiable disease and tracking travel-associated and locally acquired cases is currently dependent on voluntary reporting via arbonet. while arbonet cases are laboratory confirmed and highly specific, arbonet is a passive surveillance system where representativeness and timeliness may be lacking. in contrast, submitting an electronic bill following hc services is the most mature and widely used form of ehealth. providers are highly motivated to submit claims for reimbursement and the ehrc process is ubiquitous in the u.s. hc system. hipaa-compliant ehrcs from provider offices can be captured in e-commerce and consolidated into electronic data warehouses and used for many purposes including public health surveillance. ehrcs are standardized and each claim contains pertinent person, place, and time information as well as icd-9 diagnostic codes. ims health (ims) is a global hc information company and maintains one of world’s largest ehealth data warehouses that processes ~1 billion provider office ehrcs annually. ims consolidates ehrcs from >60% of all u.s. office-based providers from all parts of the u.s. the size and predictability of the ehrc flow into the ims data warehouse supports projections of national estimates and time trends of conditions of interest. methods for this study we compared chik cases reported via arbonet from january to august 2014 with ehrc provider claims with an icd-9 code of 066.3 (other mosquito-borne fever) that had been received in the ims data warehouse during the same time period. we classified 066.3 patients as suspect chik cases. ehrc data elements examined included unique patient id, month of provider visit, and patient state and zip-3. for patients with multiple provider 066.3 visits, the earliest visit was used for analysis. results during the observation period arbonet reported 584 chik cases from 42 states and d.c. ims noted 509 suspect cases from 40 states. the distribution of states with any chik cases was similar between arbonet and ims. however, arbonet reported cases in 5 states where ims detected none; ims detected suspect cases in 2 states where arbonet reported none. arbonet reported many more cases in 2 states (fl, tn); ims reported many more suspect cases in 5 states (il, mi, ny, pa, wv). the state with the most arbonet cases was fl (142); the state with the most ims suspect cases was ny (125). ims zip-3 analysis demonstrated a high proportion of suspect cases in several high density urban areas (nyc metro and northern nj, philadelphia, detroit). ims suspect cases in fl were noted in miami and other major population areas. conclusions ehrcs may help overcome some of the limitations of arbonet in documenting timing, occurrence, and spread of chik in the u.s., but not without additional research to verify the predictive value of icd-9 066.3 and whether providers are using this code based on known laboratory values or general awareness of the disease, its symptoms, and patient recent travel history. keywords chikungunya; arbonet; healthcare claims; big data *joel greenspan e-mail: greenspan@comcast.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e78, 201 generation and classification of activity sequences for spatiotemporal modeling of human populations 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi generation and classification of activity sequences for spatiotemporal modeling of human populations albert m lund1, ramkiran gouripeddi 1,2,3 and julio c facelli1,2,3 * 1department of biomedical informatics, 2center for clinical and translational science, 3center of excellence for exposure health informatics the university of utah, salt lake city, utah 84108 abstract human activity encompasses a series of complex spatiotemporal processes that are difficult to model but represent an essential component of human exposure assessment. a significant empirical data source, like the american time use survey (atus), can be leveraged to model human activity. however, tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals, that exhibit similar activity sequences and mobility patterns. using machine learning algorithms, we developed an unsupervised classification and sequence generation method that is capable of generating coherent and stochastic sequences of activity from the atus data. this classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns and records for groups of individuals exhibiting similar activity behaviors. keywords: american time use survey; machine learning; random forests, classification; exposure modeling abbreviations: american time use survey (atus), t-stochastic neighbor embedding (t-sne), densitybased spatial clustering of applications with noise (dbscan), recurrent neural network (rnn) *correspondence: julio.facelli@utah.edu doi: 10.5210/ojphi.v12i1.10588 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction estimating human exposure to airborne and other broadly distributed pollutants presents a significant public health challenge. because humans are mobile and inhabit a variety of microenvironments, it is insufficient to model only the spatiotemporal distribution of pollutants. even for a geographically homogeneous distribution of pollutants, different individuals experience different levels of total individual exposure depending on their activity patterns [1-3]. therefore, any successful model of individual human exposure requires an estimation of the sequences of mailto:julio.facelli@utah.edu generation and classification of activity sequences for spatiotemporal modeling of human populations 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi activities human agents perform, along with their locations and context of those activities. furthermore, a large amount of pollutant emissions in urban environments results directly from human activity. modeling human activities could, therefore, have potential use in estimating pollution distributions directly from mobile sources, like automobile emissions. comprehensive and detailed activity patterns from individuals can be gathered using a variety of tracking devices, including diaries, surveys, and structured observations. but such methods may be cumbersome to implement, prone to privacy concerns, and may fail to capture contextual data [4]. on the other hand, the american time use survey (atus) [5] provides a comprehensive picture of human activities in the united states of america (us) and it can be used to infer human behavioral patterns. the atus dataset is highly complex, with each annual survey containing over 10,000 activity diaries of recorded temporal sequences detailing daily activities of the survey respondents. each activity diary can include up to 80 discrete activities with dozens of auxiliary variables and additional demographic variables embedded in the dataset. the composition and timing of activities can have significant overlap, but also present distinct patterns based on demographics. for example, the majority of the respondents report sleeping, eating, and grooming in most activity diaries, but other activities, such as working, recreation, and child care, will have unequal representation across different demographic categories. the atus has been described in great detail, including a comprehensive descriptive analysis in a recent publication [6]. the degree of complexity in the atus makes expert analysis or the development of a gold standard difficult. its dimensionality and size are above the threshold for effective manual analysis and visualization. the synthesis of activity sequences has been explored with varying levels of success [7-11], but to the authors' knowledge, no attempts to classify atus activities for cohort identification have been reported. classification of individual activity patterns is a critical step for the development of stochastic models of exposure [12]. to address this need, we developed a method for unsupervised classification of the atus data that broadly classifies activity and demographics without relying on human expertise. identification and classification of activity and demographic classes enable us to construct activity sequences, the latter being artificial constructs used to model behavior in our recently published agent-based model [13] for total exposure. we developed a simple approach to construct activity sequences utilizing the concept of starting windows – which are periods where an activity may start. we then show that our method of generating activities results in sequences that are qualitatively indistinguishable from those collected in the atus. methods classification of the atus activity diaries while it can be intuitively conceived that different individuals follow different activity patterns, to our knowledge, there are no studies that have formally organized these activities, recognized common patterns, and classified individuals according to them. the atus activity diaries are organized into multiple tables containing demographic properties of the respondents (age, gender, work status, married status, etc.), activities of each of these respondents (a sequence of records containing activity type, start times, length), and some auxiliary information describing their household composition and activity context. the activities are described using the atus lexicon [5]. variables can be categorical or continuous, possibly censored to protect unique respondents, generation and classification of activity sequences for spatiotemporal modeling of human populations 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi and have hierarchal dependencies based on survey responses. we eliminated variables from the demographic table related to survey questions that had a low response rate and/or low variance, as these would be non-informative and introduce noise in the unsupervised classifiers. our final selection contains 16 demographic variables listed in table 1, all of which can also be inferred from the us census and employment statistics. table 1. list of the 16 demographic variables and activity vectors included in this study. variable names are given as they appear in the atus. the demographic classifier uses only these 16 variables, while the activity classifier used the 16 demographic variables and associated activity vectors as the feature set. feature name & description demographic variables teage age tehrusl1 hours worked at main job telfs labor force status (employed, unemployed, not in labor force) teschenr enrolled in high school, college or university teschft enrolled as full time or part-time student teschlvl school enrollment level (high school, college, or university) tesex gender tespempnot employment status of spouse or unmarried partner tespuhrs hours worked by spouse or unmarried partner trchildnum number of household children under age 18 trdpftpt full time or part-time employment status trhhchild presence of household children under age 18 trsppres presence of spouse or unmarried partner in household tudis2 disability preventing work in the next six months tuelnum number of elderly people cared for this month tuspusft spouse or unmarried partner full time or part-time employment status activity vectors activity count the number of times each type of activity is performed in the activity diary. contains approximately 400 activity counts activity time the main activity performed in each five minutes slice in each activity diary. there are 288 five minute slices in a single day. & the names of the variables in this table may appear somehow cryptic, but we kept the original atus names so anybody interested in reproducing our results know exactly what variables were used. in the second column we give the definition of the variables as described in atus. we transformed the atus activity tables into two separate vectors representing the activities reported by each individual participating in the survey. the first vector with approximately 400 generation and classification of activity sequences for spatiotemporal modeling of human populations 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi dimensions counts the number of instances each unique activity from the atus lexicon [5] is found in the activity diary of each individual. the second vector with 288 dimensions discretizes the 24-hour period of each individual's diary into five-minute intervals, assigning the atus lexicon code [5] of the primary activity reported in each slice to the corresponding slot. together, these activity vectors capture both the categorical and temporal patterns of activities for each respondent. we used these vectors along with the 12 demographic variables to create the feature set for activity classification (table 1). our approach to classifying activities and demographics was as follows (figure 1). first, we generated a random forest with 2,000 truncated trees having a maximum tree depth of five-leaf nodes. we used the random trees embedding method from scikit-learn to generate a random forest-based on random subdivisions of variables in the absence of labels [14]. next, we generated a proximity matrix according to the method proposed by breiman [15], by counting the number of times each pair of feature vectors appear on the same leaf node for each tree in the initial random forest. in our third step, we used this proximity matrix as the input for a two-component tstochastic neighbor embedding (t-sne) [16], which is used for embedding high-dimensional datasets in low dimensional spaces. we normalized the embedded coordinates from t-sne to the interval (-1,1) and performed clustering using density-based spatial clustering of applications with noise (dbscan) [17]. we manually estimated the maximum cluster distance and sample parameters, since these hyperparameters are dependent on the dataset and features used. we used a maximum cluster distance values of 0.03 for a cluster size of 20, and 0.02 for a cluster size of 10 for the demographic and activity feature sets, respectively. using these parameters allowed us to select small and dense clusters and the feature vectors to be non-labeled by the algorithm. figure 1. steps followed in classifying activities and demographics. the dbscan clustering generates a set of labeled and unlabeled feature vectors. in our final step, we used the labeled feature vectors to train a truncated extra random forest [18]. using the generation and classification of activity sequences for spatiotemporal modeling of human populations 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi entropy criterion, which is preferred for categorical data [18], we assigned a maximum tree depth of eight for this forest. we then classified all unlabeled feature vectors using this new random forest. we did this because the initial clustering leaves up to 30% of feature vectors unlabeled, and many of the labeled features are similar enough to be classified the same. as we needed our classes to have some level of statistical power, we generated one additional set of random forests, using the same parameters, but this time without truncation (no maximum tree depth). this set of forests was trained on all classes above a size cutoff of 25 feature vectors, with the remaining small classes being classified by this new classifier. this method produces the final classes for the demographic and activity classes and generates a classifier that can be used in conjunction with the us census as part of our agent-based model [13]. generation of activity sequences using starting windows while the classification by itself is a useful tool for identifying distinct patterns of activity, it is insufficient for predicting or simulating the behavior of an arbitrary agent representing a person in a class. the activity classes generated by our classifier provide a basis for what patterns of activity exist. however, the activity diaries themselves are not suitable for simulation purposes because they are intrinsically tied to the empirical and geographical constraints of the persons interviewed for the atus. instead, we generate synthetic activity sequences from a probabilistic representation of each activity class. generation and classification of activity sequences for spatiotemporal modeling of human populations 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi figure 2. a representation of activity window construction and probabilistic window sorting. the top section of this figure, each dot represents an activity with a start time and length, blue and green represent two different types of activities. although blue represents a single activity type, the circumstances and times of those activities have different contexts. groups of activities can be broken into windows of time where an activity can start. the same can be done with the lengths of activities. creating a grid of starting times and lengths can be used to define contextual starting and length windows and, in turn, in defining types of activities. the lower section of the figure shows probabilities calculated based on the starting windows and probabilistic sorting of activities. trips can be added when activity location change and activity lengths adjusted based on allowed starting times and activity lengths to fill the period of simulation. we generated synthetic activity sequences for each activity class according to the following procedure (figure 2). for each class, we considered each activity present in the cohort separately and collected their starting times. using bayesian gaussian mixtures [19], we generated a set of one-dimensional clusters of activity starting times to create starting windows, which we define as a period of time when an activity can start. for example, if we were to distinguish daytime naps and nighttime sleeping, we would define two separate starting windows for each type of activity based on starting time, even though both instances are classified as sleeping activities utilizing these starting windows, we calculated four different probabilities. first, we calculated the probability that a member of the activity cohort will perform an activity defined by a starting window. this is the probability of a starting window appearing in an arbitrary sequence drawn from the set of activity diaries that contains the starting window of interest. this probability captures the idea that some activities are repeatedly and consistently performed across the population, such as sleeping, eating, and personal grooming, but also allows for exceptions in ordinary behavior. we expected the members of each activity class to follow a schedule, but with potential variations. the second probability we calculated is the joint probability between start windows and activity lengths. we cluster activity lengths into length windows that are generated the same way as to start windows but using activity lengths instead of start times. the reason for using length windows instead of a more common distribution is that activity lengths can exhibit very different scales depending on the context. for example, a nap could last anywhere from twenty minutes to three hours long, whereas a typical night's sleep might vary from four the twelve hours. further, activity lengths can have unusual distributions and cluster in ways that do not approximate to a smooth function. the third probability we calculated is the probability that an activity in one start window is preceded by an activity in another start window. this captures the idea that the order of some activities can be indiscriminate or based on preference, while others have specific causal orders. for example, food preparation always precedes the actual activity of eating. still, the order of reading a book and watching a movie for evening entertainment largely depends on the preference of the participant. estimating this probability allows us to effectively sort activities and insert the necessary stochastic components needed to capture variability in activity order. finally, we calculated the joint probability between the start window and location type. although the atus does not have specific geographic locations in the dataset, it does define the type of location for each activity (e.g., home, workplace, store, etc.). encoding these location types allows generation and classification of activity sequences for spatiotemporal modeling of human populations 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi us to utilize contextual information for assigning precise locations to activities in a synthetic activity sequence. we utilize these four probabilities to generate synthetic activity sequences using monte carlo sampling. for this, we selected a set of start windows, assigned activity lengths, sorted those starting windows stochastically, and then assigned locations types. next, we inserted travel activities between activities that occur at different locations to improve the quality of the sequence. finally, we adjusted activity lengths within the intervals prescribed by the starting windows and minimum or maximum activity lengths to fill the period of the simulation so that there are no gaps in the synthetic sequence. we performed this adjustment using a weighted coefficient based on the selected length of each activity to preserve the relative lengths of activities. the code developed here is available at: https://github.com/uofu-ccts/prisms-comp-model-stham. results and discussion figure 3 shows an example of activity classes derived from the classification process. distinctive patterns of activity can be isolated despite the simplicity of the classification algorithm. significant overlap in activity profiles occurs between some demographic classes, especially in classes where the fundamental activity profiles are essentially the same. still, the timing of activities can be shifted as in cases where shift work is represented. this suggests that the classification method is effective in making distinctions in both temporal and categorical domains. figure 3. examples of activity classes generated by the unsupervised classification method. distinct patterns of activity can be identified from the method. panel a depicts a cohort that primarily participates in recreation activities (watching tv, reading, attending events). in contrast, panel b depicts a cohort that mostly participates in household activities (cleaning, yard work, child care, etc.). panels c and d depict two different shifts of working days. the fact that the algorithm can elucidate temporal patterns is especially useful. generation and classification of activity sequences for spatiotemporal modeling of human populations 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi for these experiments, the demographic classification produced 95 classes with a median class size of 83 and a maximum of 696 individuals, while the activity classification produced 76 classes with a median class size of 82 and a maximum of 1237 individuals; both classifiers have an artificial minimum of 25 records. the number of classes produced by this approach varies due to stochastic elements in the t-sne and random forest algorithms. we attempted to broadly classify the activity classes based on the main category of non-sleep activity that dominated each activity record. roughly 40% of activity classes are dominated by work activities, while recreational activities dominate 25%. the remaining 35% of classes comprise some mixture of household activities, child or elderly care, and school-related activities. figure 4. real and simulated sequences for a single activity class, shown in their sequential form. each row represents a different sequence, while different colors represent different types of activities. generally, the simulated sequences conserve the same relative pattern of activity as the real sequences. deviation from the strict timing of the real sequences is expected since the sequence generation algorithm includes some smearing components. figure 4 shows sets of real activity sequences from the atus and synthetically generated activity sequences for a typical day belonging to a member of the “diurnal working class”. qualitatively the two sets are difficult to distinguish from each other. distinctive temporal boundaries are present between some activities in the real sequences, which are an artifact of the classification algorithm, strongly selecting a subset of temporal features. these temporal boundaries disappear in the synthetic activity sequences due to the length adjustment step and the introduction of randomness from the monte carlo process. despite this variation, the overall profile of activity in the synthetic sequence still visually captures the overall prevalence of activities. we performed a quantitative analysis of our synthetic sequences to validate their similarity to the real sequences. because the temporal sequences are categorical, a detailed temporal analysis of the generation and classification of activity sequences for spatiotemporal modeling of human populations 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi synthetic sequences is complicated. a realistic way to compare categorical temporal sequences is through a binary comparison at the smallest temporal granularity. groups of sequences can be compared through their statistical mode, where the mode similarity is the fraction of minutes where the most frequent activity is the same between synthetic and measured sequences and a measure of dispersion. the later can be calculated with a method like the gini index [20], which is analogous to the mean and standard deviation of a normally distributed continuous variable. we calculated the modes of each activity class by determining the most frequent activity at each minute across all activity sequences in that class. we then made a binary comparison between the modes of the synthetic and the atus reported sequences to obtain a percentage similarity between the two. we obtained the gini index by calculating the frequency of all activities for each minute across all activity probabilities. we compared the synthetic and reported sequences by performing a linear regression of the gini index. figure 5 shows the plot of the r-correlation of the gini indices and mode similarities for all activity sequences. the majority of activity classes (61%) have both gini correlation and mode similarities above 0.8, while 95% of classes are above the 0.6 threshold. this presents strong evidence that our sequence generation algorithm correctly reproduces the majority of the activity classes. in the development of the sequence generation algorithm, we explored several techniques. our results using a simple markov chain ended up being intractable with the generated sequences having little to no resemblance to the atus data and incapable of capturing the structured nature of some activities (especially the home-work-home pattern). we also tried to train a recurrent neural network (rnn) against the atus activity diaries, but we found that the activity sequences were too short to train the rnn reliably. specifically, we believe that the rnn needed to be trained on activity sequences spanning multiple days, which are unavailable from the atus surveys that only cover 24-hour periods. however, we ultimately found that the method we developed was both simpler and easier to implement than an rnn, and required less computational effort to establish and generate sequences. the method we have developed and presented here is also substantially more explainable than an rnn. limitations the results presented here represent the classification of the activities reported in the atus; therefore, they are subject to any limitation in scope and granularity that may exist in the original atus surveys. for instance, the atus does not provide data on school-age children, so their activity patterns have to be inferred from their parents. as discussed above, the methodology is quite general. it could be applied to other activity surveys, but as with any classification method, it is subject to the somehow arbitrary selection cut off values to define the size and number of classes. while the parameters selected here are reasonable, it may be necessary to restrict or increase the number of desired classes depending on the intended use of the classification of activities. generation and classification of activity sequences for spatiotemporal modeling of human populations 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e9, 2020 ojphi figure 5. similarity plot of synthetic and measured activity sequences. for each type of activity sequence, the most frequent activity (the mode) and the gini index is calculated for each minute across the cohort. the mode similarity is the fraction of minutes where the most frequent activity is the same between synthetic and measured sequences. the gini r correlation is from the linear regression of the gini indices for each minute. 61% of activity sequences have both similarities and r-values greater than 0.8. conclusions we successfully developed and demonstrated a generalizable method to classify human activity sequences and generate synthetic spatiotemporal activity sequences. while in this study, we derived activity sequences from the atus activity classes, our method is not specific to this survey. it can be used for any well-structured activity survey data sets. we believe that the application of this approach will enable researchers to make significant inroads into simulating human activity patterns at population levels a first step in generating comprehensive personal exposure profiles records for utilization in translational research. acknowledgments the research reported in this publication was supported in part by nibib/nih under award number 1u54eb021973 and ncats/nih under award number ul1tr001067. computational resources were provided by the utah center for high-performance computing, which has been partially funded by the nih shared instrumentation grant 1s10od021644-01a1. financial disclosure no financial disclosures generation and classification of activity sequences for spatiotemporal modeling of human populations 11 online journal of public health informatics * issn 1947-2579 * 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attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 81 (page number not for citation purposes) isds 2013 conference abstracts detection of some lyssaviruses from fruigivorous and insectivorous bats in nigeria grace s. kia*1, 2, ivan i. kuzmin2, jarlath u. umoh1, jacob k. kwaga1, haruna m. kazeem3, modupe o. osinubi2 and charles e. rupprecht2 1department of veterinary public health and preventive medicine faculty of veterinary medicine, ahmadu bello university (abu), zaria, nigeria; 2centers for disease control and prevention (cdc), atlanta., atlanta, ga, usa; 3department of microbiology, faculty of veterinary medicine, abu., zaria, nigeria � �� �� �� � � �� �� �� � objective ������� ��� �� ���������������������� ���� ���� ���� �������� � � �� ������ ����� ����� �������� ������ ����� � ������ ��� ����� � ��������� 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annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 171 (page number not for citation purposes) isds 2013 conference abstracts improving public health preparedness: strengthening biosurveillance systems for enhanced situational awareness nadja vielot* unc gillings school of global public health, chapel hill, nc, usa � �� �� �� � � �� �� �� � objective �������� � ��� ��� ������� ���� ����� �������������� ��� � ������ ��������� ������� ������� ���� � ��� � ���� ������ � ������������� � ���� ��� ���� �� ���� � introduction ������������� ����������� ���� ������ ��� ����� ��������� �������� �� �������� � ��� ��� �� ����������������������� ����������� ��� ������ ��� ������� ��� ���� �� ���� 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��� � ��9 ������"��� �������) ���������������� ����$��������� �� ������� � ��� � � ����1���������1������� ������-���788d� 1���� ���� #��������� � @��������a�� 788d�� ��� edf/e0�� �� �� ���� ����3� ����3bb ������������ ���������b �����������������g �� ���h.i0f0jik����h���������k����������h � �� �� 0��"����� ����� � � ��"������� ���#�����������" "+ �=� ��������#������ (� ����1������� �����������7.� ��"��������--)%�-��������� ���-��� � �����)��*���%�������78.7� *nadja vielot e-mail: nadjavielot@unc.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e48, 2014 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): 2019 isds 2019 conference abstracts colleagues, i am delighted to present to you the summary abstracts and presentations from the 2019 international society of disease surveillance (isds) conference which was held san diego, ca from january 30th – february 2nd, 2019. over the past several years, the concept of investing in data science and data scientists has been touted as a transformational endeavor for governmental organizations, non-profits, as well as private sector and commercial markets. how “true” data science is harnessed to influence and improve public health surveillance and population health remains to be seen. data science has great potential to provide a new lens to inform and improve public health surveillance and population health. however, this lens needs to focus upon more than just “big data” analytics and information technology. it must also focus on fostering organizational environments and multi-agency collaborations that invigorate curiosity and experimentation and development of cross-disciplinarian partnerships to address multifactorial and multidimensional health and disparity challenges. it also must hone in on producing evidence-based analytic results to improve measurable health outcomes. analysis and summary results are not the end products for surveillance. the concept of data science needs to be leveraged across public health to better communicate the findings of disease surveillance through the “storytelling of illness and disease” to influence public health policy, and ultimately improve population health. this year, with these ideas in mind, and with the support of a dynamic, engaged, and multi-disciplinary scientific planning committee (spc) isds has expanded its conference scope beyond traditional tracks which historically focused on surveillance, informatics, and analysis, to include tracks related to: • one health • non – human health surveillance • ecology • communications, medical rhetoric, visualization, and reporting • chronic disease / mental health • substance abuse • data quality • injury surveillance • substance abuse – opioid surveillance recognizing that public health is a collaborative and multi-disciplinary team sport, we have expanded our outreach efforts to include new partners across academia, the private sector, state, local, and tribal partners, as well as federal agencies. during the 2019 isds conference, we had a significant increase in overall attendance (~375) and abstracts submissions compared to prior years; with 29 countries represented and 130 oral presentations and 95 poster presentations provided over the three-day conference. we held a number of sessions on opioid use and prescribing surveillance as well as medical rhetoric, communications, and visualization that were standing-room only and beyond. our keynote speakers on the intersection of data science and public health included: http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): 2019 isds 2019 conference abstracts william j. kassler, md, mph ibm watson health – deputy chief health officer wilma j. wooten, md, mph public health officer for the county of san diego michael hogarth, md, facp, facmi chief clinical research information officer for university of california san diego health some of the key take-aways from the presentations at the 2019 isds conference were that data science and the act of data collections and analysis are not the end goals of public health surveillance; they are just the beginning. data do not speak for themselves; they require context, curation, interpretation, and ultimately need to effectively communicating findings through the story telling of illness and disease to officials, policy makers, and the public with the objective to inform and influence public health policy, motivate health behavior change, drive public health action, and ultimately improve population health. i encourage you to review the abstracts submitted here in the online journal of public health informatics which were presented at the 2019 international society for disease surveillance 2019 conference and to engage multi-dimensional and multi-disciplinary conversations (reach out directly to authors and presenters) around these important topics, expand your networks and opportunities in the public health community. regards, peter hicks, ma, mph scientific program chair international society for disease surveillance (isds) 2019 centers for disease control and prevention* phicks@cdc.gov *information included in this statement are those of the author and do not represent the official position of the centers for disease control and prevention (cdc) http://ojphi.org/ mailto:phicks@cdc.gov disease outbreak detection system using real time syndromic data in madagascar ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 soatiana rajatonirina 1 , fanjasoa rakotomanana 1 , laurence randrianasolo 1 , norosoa harline razanajatovo 2 , soa fy andriamandimby 2 , lisette ravolomanana 3 , armand eugène randrianarivo-solofoniaina 4 , jean-marc reynes 5 , patrice piola 1 , alyssa finlay-vickers 6 , jean-michel heraud 2 , vincent richard 7 1. unité d’epidémiologie, institut pasteur de madagascar, antananarivo, madagascar 2. unité de virologie, institut pasteur de madagascar, antananarivo, madagascar 3. directions des urgences et de la lutte contre les maladies négligées (dulmn), ministère de la santé publique, antananarivo, madagascar 4. direction de la veille sanitaire et de la surveillance epidémiologique (dvsse), ministère de la santé publique, antananarivo, madagascar 5. unité de virologie, institut pasteur de madagascar. present address: unité de biologie des infections virales emergentes / institut pasteur de lyon 6. malaria branch, division of parasitic diseases us centers for disease control and prevention, president's malaria initiative, madagascar 7. unité d'épidémiologie, institut pasteur de dakar, dakar, sénégal abstract background: epidemics pose major threats in resource-poor countries, and surveillance tools for their early detection and response are often inadequate. in 2007, a sentinel surveillance system was established in madagascar, with the aim of rapidly identifying potential epidemics of febrile or diarrhoeal syndromes and issuing alerts. we present the health and process indicators for the five years during which this system was constructed, showing the spatiotemporal trends, early-warning sign detection capability and process evaluation through timely analyses of high-quality data. methods: the malagasy sentinel surveillance network is currently based on data for fever and diarrhoeal syndromes collected from 34 primary health centres and reported daily via the transmission of short messages from mobile telephones. data are analysed daily at the institut pasteur de madagascar to make it possible to issue alerts more rapidly, and integrated process indicators (timeliness, data quality) are used to monitor the system. results: from 2007 to 2011, 917,798 visits were reported. febrile syndromes accounted for about 11% of visits annually, but the trends observed differed between years and sentinel sites. from 2007 to 2011, 21 epidemic alerts were confirmed. however, delays in data transmission were observed (88% transmitted within 24 hours in 2008; 67% in 2011) and the percentage of forms transmitted each week for validity control decreased from 99.9% in 2007 to 63.5% in 2011. conclusion: a sentinel surveillance scheme should take into account both epidemiological and process indicators. it must also be governed by the main purpose of the surveillance and by local factors, such as the motivation of healthcare workers and telecommunication infrastructure. permanent evaluation indicators are required for regular improvement of the system. keywords: sentinel surveillance, madagascar, early warning, mobile phone. correspondence: vrichard@pasteur.sn http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 doi: 10.5210/ojphi.v6i3.5400 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the concept of surveillance was developed principally for control of the transmission of infections and for the early detection of outbreaks. the main elements of surveillance methods have been described elsewhere. surveillance is a continuous, systematic process of descriptive information collection, validation, analysis, interpretation, and dissemination for use in planning, and in the implementation and evaluation of public health policies and strategies for the prevention and control of diseases or disease outbreaks [1-3]. the public health problems approached in this way, including acute and chronic diseases and environmental hazards, are diverse, necessitating the development of tools for the timely monitoring of disease trends. furthermore, surveillance systems must be evaluated regularly, to ensure that they provide valuable information in an efficient manner [4,5]. efficient disease surveillance systems are the key to the timely detection of early-warning signs potentially signalling the occurrence of disease outbreaks or epidemics. the world health organisation (who) has highlighted the importance of improving national epidemic surveillance capacities [6,7]. recently developed innovative tools, such as mobile telephone technology and electronic systems, have facilitated the improvement of surveillance systems, by reducing data processing [8]. however, these systems are mostly implemented in highincome countries [9], as most developing countries are faced with logistic and budgetary constraints, resulting in low-quality surveillance systems based on pen-and-paper methods. in many cases, these low-tech systems provide health institutions with inadequate support, resulting in frequent “health crises” [10]. moreover, the healthcare infrastructure, laboratory diagnostic capacity, skills and number of physicians in these countries are generally insufficient to deal with emerging diseases likely to cause epidemics. consequently, delays in raising the alarm often limit the possibility of an effective early response to new, emerging public health problems. the need for an efficient sentinel surveillance network in madagascar was highlighted by worldwide infectious disease threats to public health, such as severe acute respiratory syndrome (sars) in 2003, avian influenza a h5n1 in 2005 and the chikungunya epidemics observed in the indian ocean region in 2006. in addition, the 2005 international health regulations stressed the importance of commitment to the goal of global security and asked all member states to establish and implement effective surveillance and response systems, making it possible to detect and contain public health threats of national and international importance. as a result, the government of madagascar, in partnership with the institut pasteur de madagascar, established 13 fever sentinel sites in 2007, expanding the network to 34 sites by 2011, to improve the timely detection and management of febrile disease outbreaks. two key attributes of the sentinel surveillance system are monitored continuously: timeliness and data quality. this system was designed to identify outbreaks for which public health interventions may be required early enough for such interventions to be effective. http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 we report here the indicators, for 2007 to 2011, of the syndromic sentinel surveillance network, presenting spatiotemporal trends, alert detection capability and evaluations of the process on the basis of timeliness and quality data. methods the sentinel surveillance network in madagascar has been described elsewhere [11,12]. briefly, it includes primary healthcare centres (sentinel sites) from across the country (figure 1) and is managed by a national steering committee. the network was expanded from 13 influenza-like illness (ili) sentinel sites in 2007 to 34 sentinel sites in 2011, with the aim of improving geographic coverage and representativeness of the country as a whole (4 sites are located in antananarivo) (figure 1). the sentinel surveillance system makes use of syndromic indicators to monitor the occurrence of selected diseases of importance for the country. the main criterion for the inclusion of cases or patients is fever or diarrhoea. for patients with fever, additional screening criteria (based on syndromic case definitions) are used to identify specific syndromes: malaria, ili, dengue-like syndromes. standard who case definitions are used, to ensure comparability [11,12]. malaria diagnosis requires biologic confirmation with a positive rapid diagnostic test in patients with fever syndromes. cases and patients at the participating sites are identified by trained healthcare personnel participating in the surveillance network on a voluntary basis. one of the key features of the system is the timely transmission of syndromic data, on a daily basis, by coded short message service (sms) messages sent from mobile phones. upon reception at the ipm, the data transmitted in this manner are input daily into a specifically designed ms access® database and analysed as soon as possible after the patients’ initial visit. this results in a turnaround time of 24 hours, from data collection to reception at the ipm, even for data sent from the most remote areas of the country. the data received by sms include: sentinel site code, date of data collection, total number of outpatient consultations, total number of confirmed malaria cases, total number of ili cases, total number of dengue-like cases, total number of diarrhoea cases, and the number of consultations by age group. the age groups were those commonly used by the ministry of health in madagascar: less than 1 year, 1-4 years, 5-14 years, 15-24 years, 25 years and over. surveillance data are analysed and presented in easy-to-interpret tables and graphs providing the number of cases for each syndrome monitored. in addition, daily and weekly baselines (mean number of cases in the corresponding period of previous years) are calculated for each syndrome and plotted against current observations, to identify early signs of outbreaks triggering alerts. the information is disseminated on a weekly and monthly basis to healthcare staff involved in the network and to the staff of the ministry of health (moh) in madagascar. ethics clearance the surveillance protocol was approved by the moh and the national ethics committee of madagascar. http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 figure1: surrounding climate and location of the health centres participating in the sentinel surveillance system in madagascar results description of the epidemiological indicators from january 2007 to december 2011, the data collected on a daily basis corresponded to 917,798 visits (table 1). the age distribution of the patients concerned, as a function of the total numbers of visits and febrile syndromes, is indicated in table 1. in total, 102,200 cases (11.1%) of fever were reported. fever syndromes accounted for 12.1% of visits in 2007, 12.2% in 2008, 11.8% in 2009, 10.8% in 2010 and 10.0% in 2011 (p<0.01, table 2). http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 table 1: annual distribution of visits by age group, according to sms data age group all visits (n=917,798) 2007 2008 2009 2010 2011 n (%) n (%) n (%) n (%) n (%) <1 year 7,663 (9.6) 13,794 (10.0) 22,748 (10.4) 24,405 (10.8) 28,607 (11.2) 1-4 years 12,564 (15.7) 20,967 (15.2) 35,652 (16.3) 38,074 (16.8) 44,382 (17.4) 5-14 years 10,092 (12.6) 17,836 (13.0) 34,911 (15.9) 32,230 (14.2) 37,695 (14.8) 15-24 years 16,096 (20.2) 27,569 (20.1) 39,421 (18.0) 42,254 (18.7) 49,340 (19.3) 25 years 33,456 (41.9) 57,356 (41.7) 86,259 (39.4) 89,196 (39.4) 95,231 (37.3) total 79,871 (8.7) 137,522 (15.0) 218,991 (23.9) 226,159 (24.6) 255,255 (27.8) ili accounted for 14.7% of fever cases in 2007, 8.5% in 2008, 21.3% in 2009, 20.2% in 2010 and 32.8% in 2011 (p<0.01), according to the data transmitted by sms (table 3). dengue-like syndromes (table 3) accounted for 18.6% of fever cases in 2007, 8.7% in 2008, 10.2% in 2009, 11.5% in 2010 and 4.2% in 2011 (p<0.01). confirmed cases of malaria (table 3) accounted for 12.0% of fever cases in 2007, 8.3% in 2008, 10.6% in 2009, 16.8% in 2010 and 12.4% in 2011 (p<0.01). from january 2008 to december 2011, 40,510 cases (4.8%) of diarrhoea were reported in the 837,881 visits (table 3). diarrhoea cases accounted for 3.1% of visits in 2008, 4.9% in 2009, 5.5% in 2010 and 5.1% in 2011 (p<0.01). the epidemiological characteristics of groups with fever-related syndromes, such as those with ili, identified by the sentinel surveillance system, were investigated by the plotting of daily count data on a graph (figure 2). daily and weekly counts, as a function of the regional pattern, were also plotted and analysed for each sentinel centre (data not shown). figure 2 shows a peak in the number of daily visits in november 2009 corresponding to an increase in the number of febrile syndromes and ili cases. a plot of the distribution of febrile and other syndromes over the various years (figures 2-5) showed that ili was the dominant cause of fever in most of the country, from 2009 onwards. a subanalysis of the longitudinal data, using only the first 13 sentinel sites established in 2007-2011, yielded similar trends (figure 4). http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 table 2: process indicators by sentinel site and year 2007 2008 2009 2010 2011 sentinel site opening date fever forms forms / fever % sms delay % fever forms forms / fever % sms delay % fever forms forms / fever % sms delay % fever forms forms / fever % sms delay % fever forms forms / fever % sms delay % ambatondrazaka 2009-05-11 ---- - 297 200 67.3 37 211 185 87.7 38 276 154 55.8 25 ambato boeny 2010-09-01 ---- - ---- 363 53 14.6 51 1094 4 0.4 28 ambovombe 2009-06-02 ---- - 111 111 100.0 34 171 53 31.0 53 190 73 38.4 45 ambositra 2011-08-25 ---- - ---- ---- 212 195 92.0 6 antananarivobhk 2009-01-26 ---- - 412 412 100.0 18 331 180 54.4 22 273 124 45.4 39 antananarivo cda 2009-04-01 ---- - 132 111 84.1 18 240 172 71.7 23 308 198 64.3 43 antananarivo mjr 2009-02-02 ---- - 441 144 32.7 24 292 215 73.6 2 480 235 49.0 7 antananarivo tsl 2009-02-09 ---- - 143 134 93.7 26 44 44 100.0 25 38 28 73.7 10 antsirabe 2008-09-08 ---- 258 256 99.2 20 1304 1304 100.0 7 576 550 95.5 4 1025 653 63.7 4 antsiranana 2007-04-19 1652 1650 99.8 na 2215 2215 100.0 10 2579 2148 83.3 5 1995 1968 98.6 10 1577 1252 79.4 11 antsohihy 2007-05-02 263 263 100.0 na 1172 1172 100.0 19 611 565 92.5 29 585 558 95.4 35 248 180 72.6 23 anjozorobe 2010-07-29 ---- ---- -- - 49 38 77.6 45 158 153 96.8 38 belo sur tsiribina 2010-10-11 ---- ---- -- - 183 182 99.5 48 529 419 79.2 50 ejeda 2007-12-10 5 5 100.0 na 63 63 100.0 12 76 76 100.0 10 113 113 100.0 20 137 137 100.0 24 farafangana 2007-06-07 473 473 100.0 na 929 929 100.0 6 970 961 99.1 8 1102 925 83.9 14 1710 1609 94.1 17 fianarantsoa 2008-08-04 ---- 250 250 100.0 9 427 427 100.0 10 162 145 89.5 11 302 186 61.6 11 ihosy 2007-12-10 71 71 100.0 na 793 793 100.0 9 552 525 95.1 16 350 350 100.0 19 745 538 72.2 11 maevatanana 2007-04-23 1639 1639 100.0 na 1906 1906 100.0 9 2736 2223 81.3 5 3414 3311 97.0 20 2582 1668 64.6 29 mahajanga 2007-04-23 519 518 99.8 na 597 467 78.2 10 851 829 97.4 8 943 922 97.8 11 891 730 81.9 20 maintirano 2010-07-19 ---- ---- ---- 354 311 87.9 25 675 467 69.2 50 mananjara 2010-02-18 ---- ---- ---- 853 822 96.4 27 409 299 73.1 35 mandritsara 2011-09-26 ---- ---- ---- ---- 391 391 100.0 37 maroantsetra 2010-09-02 ---- ---- ---- 158 158 100.0 18 433 240 55.4 28 miandrivaza 2010-05-07 ---- ---- ---- 875 875 100.0 37 582 493 84.7 46 moramanga 2007-04-12 1436 1436 100.0 na 2227 2196 98.6 18 3213 2964 92.3 15 1454 1396 96.0 23 1730 1010 58.4 20 morombe 2011-09-12 ---- ---- ---- ---- 158 130 82.3 21 morondava 2007-04-10 623 623 100.0 na 1163 1163 100.0 5 1182 1182 100.0 8 707 707 100.0 21 617 426 69.0 37 nosy be 2009-06-02 ---- ---- 2402 18 0.7 28 2645 1100 41.6 54 2542 791 31.1 68 sainte marie 2010-03-04 ---- ---- ---- 71 35 49.3 46 61 6 9.8 41 sambava 2009-01-21 ---- ---- 1125 574 51.0 25 1515 279 18.4 40 934 242 25.9 50 taolagnara 2007-04-24 407 407 100.0 na 709 709 100.0 15 742 636 85.7 17 464 427 92.0 23 383 320 83.6 35 toamasina 2007-04-16 1140 1140 100.0 na 2602 2602 100.0 14 3803 2727 71.7 11 2428 2159 88.9 24 2116 1713 81.0 24 tsiroanamandidy 2007-04-30 1056 1056 100.0 na 1152 1152 100.0 13 1199 1199 100.0 36 1093 1048 95.9 48 1024 602 58.8 45 tulear 2007-04-30 352 352 100.0 na 706 706 100.0 15 576 499 86.6 26 714 711 99.6 25 653 494 75.7 33 total 9,636 9,633 99.9 16,742 16,579 99.0 25,884 19,969 77.1 24,455 19,992 81.7 25,483 16,160 63.4 fever= number of febrile syndrome cases declared by sms, forms= number of fever forms received, % forms = percentage of forms for patients with febrile syndromes, na=not available http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e197, 2014 table 3: number of declared syndromes by sentinel site and year 2007 2008 2009 2010 2011 sentinel site opening date ili dls malr diarr ili dls malr diarr ili dls malr diarr ili dls malr diarr ili dls malr diarr ambatondrazaka 2009-05-11 ---- ---- 129 19 7 337 39 4 7 160 122 1 7 234 ambato boeny 2010-09-01 ---- ---- ---- 195 1 156 156 828 18 197 491 ambovombe 2009-06-02 ---- ---- 4 0 7 61 3 2 16 114 6 0 9 137 ambositra 2011-08-25 ---- ---- ---- ---- 68 12 8 118 antananarivobhk 2009-01-26 ---- ---- 381 0 6 1365 160 9 4 1093 193 4 9 826 antananarivo cda 2009-04-01 ---- ---- 34 0 3 298 68 7 3 393 67 6 2 216 antananarivo mjr 2009-02-02 ---- ---- 289 2 2 255 185 4 5 188 239 2 11 175 antananarivo tsl 2009-02-09 ---- ---- 125 0 2 396 41 0 0 569 29 0 2 513 antsirabe 2008-09-08 ---- 150 22 8 288 860 12 7 539 311 1 2 535 765 1 7 467 antsiranana 2007-04-19 236 678 10 - 121 201 3 274 471 180 30 1136 394 210 54 1050 130 115 28 985 antsohihy 2007-05-02 6 22 10 - 0 0 14 1 1 1 40 4 4 9 215 128 64 31 34 179 anjozorobe 2010-07-29 ---- ---- -- - 36 4 4 24 141 0 4 53 belo sur tsiribina 2010-10-11 ---- ---- -- - 52 33 68 82 159 97 63 483 ejeda 2007-12-10 0 0 0 - 5 3 7 43 0 4 3 36 0 0 7 39 0 0 30 56 farafangana 2007-06-07 285 33 23 - 424 45 83 169 289 27 174 322 379 230 59 375 485 37 661 414 fianarantsoa 2008-08-04 ---- 13 0 0 113 37 0 12 212 24 2 1 193 70 0 14 178 ihosy 2007-12-10 0 12 7 - 11 90 63 253 30 63 47 219 72 8 37 131 303 10 33 189 maevatanana 2007-04-23 127 152 628 - 76 60 644 620 472 146 1158 805 269 126 1681 1301 96 11 631 1173 mahajanga 2007-04-23 25 142 50 - 11 56 13 505 98 37 30 285 163 23 56 324 381 41 20 293 maintirano 2010-07-19 ---- ---- ---- 97 0 128 106 346 0 109 248 mananjara 2010-02-18 ---- ---- ---- 143 441 6 888 0 115 67 782 mandritsara 2011-09-26 ---- ---- ---- ---- 90 0 20 212 maroantsetra 2010-09-02 ---- ---- ---- 96 20 18 93 363 2 30 468 miandrivazo 2010-05-07 ---- ---- ---- 266 0 265 142 263 0 39 218 moramanga 2007-04-12 222 12 59 - 2196 9 77 557 2964 63 66 881 203 30 30 667 573 42 55 628 morombe 2011-09-12 ---- ---- ---- ---- 61 0 21 48 morondava 2007-04-10 153 82 139 - 176 15 39 284 117 27 16 575 25 1 62 545 23 0 27 497 nosy be 2009-06-02 ---- ---- 18 205 99 501 783 321 306 991 983 147 135 902 sainte marie 2010-03-04 ---- ---- ---- 32 10 25 5 30 5 25 60 sambava 2009-01-21 ---- ---- 331 16 120 487 306 52 220 272 464 20 44 218 taolagnara 2007-04-24 29 15 8 - 34 32 9 249 93 6 29 331 54 1 90 168 57 2 49 105 toamasina 2007-04-16 53 583 64 - 123 847 315 267 272 1683 782 352 194 1072 468 237 111 260 734 160 tsiroanamandidy 2007-04-30 250 15 150 200 12 112 276 299 99 94 583 249 109 104 693 570 51 42 545 tulear 2007-04-30 34 43 13 17 74 1 395 23 62 3 731 105 80 7 734 266 25 3 838 total 1,418 1,789 1,161 - 1,420 1,466 1,388 4,294 5,503 2,652 2,736 10,711 4,948 2,810 4,104 12,396 8,346 1,055 3,171 13,109 ili= number of influenza-like illness cases, dls=number of dengue-like syndromes, malaria=number of confirmed malaria confirmed cases, diarr=number of diarrhoea cases declared by sms http://ojphi.org/ ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 8 figure 2: mean daily visit counts, by centre, in the sentinel surveillance system in madagascar and daily sentinel surveillance time series plots (%) of fever, total visits and the ili cases among total fever cases, with the moving average (over 10 days – red curve) for daily visit counts, april 14, 2007 – december 31, 2011. figure 3: weekly syndromic data from all sentinel centres in 2011. ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 9 figure 4: weekly syndromic data from the first 13 sentinel centres in 2011 figure 5: annual percentage of fever-related syndromes, by centre, based on data collected from sentinel centres by sms, from 2007 to 2011. ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 10 alerts from 2007 to 2011, 21 alerts resulting from syndromic surveillance were confirmed by biological surveillance and led to a response and epidemiological investigations to assess the risk. in october 2008, in morondava, on the west coast of madagascar, an increase in the percentage of febrile syndromes and the percentage of ili cases was recorded. samples were requested and influenza virus a (h3n2) was detected. in january 2009, an increase in the percentage of febrile syndromes and in the number of confirmed malaria cases was identified, leading to an investigation of factors potentially associated with an increase in malaria transmission. in 2010, excess cases of dengue-like syndromes were declared in mananjary health district, which is located on the southeast coast. the chikungunya virus was identified and the epidemic confirmed. none of these events were detected by the routine surveillance system. however, there was no organised response to any of these outbreaks because the moh lacked the means to deal with these large events. process indicators relevant process indicators have been identified for the monitoring of the network. these indicators are presented in table 2 and concern principally the data transmission and data validation processes. overall, 85% of the data were transmitted within the 24-hour time frame. this indicator was introduced in 2008. the percentage of data for which transmission was delayed increased from 2008 (12.3%) to 2011 (32.6%), and considerable differences between sentinel sites were observed for this indicator (table 3). as previously described [11,12], an individual fever form had to be completed and sent to the ipm for each declared case of febrile syndrome. the fever forms were used to validate the syndrome data transmitted by sms. specific forms relating to fever were completed for 82,333 of the patients presenting fever (80.6%). in 2007, 99.9% of the febrile syndromes were documented on a fever form, but this percentage had fallen to 63.4% by 2011. the sex ratio (male/female) for those with febrile syndromes was 0.88. age was known for 81,981 patients (99.5%), and the mean age of the patients was 12.5 years (ci 95%: [12.412.7]). the age-group distribution is presented in table 4. ili, defined on the basis of the symptoms noted on the fever forms (fever and cough, or fever and sore throat), accounted for 49.4% (40,709) of all cases of febrile illness, but significant differences in these percentages (p<0.01) were found between years: 49.2% (4,739/9,633) in 2007, 53.6% (8,884/16,579) in 2008, 55.6% (11,102/19,969) in 2009, 42.8% (8,563/19,992) in 2010 and 45.9% (7,421/16,160) in 2011. ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 11 table 4: annual distribution of febrile illnesses by age group, according to data from individual fever forms age group febrile syndromes (81,981 data available from 82,222 individuals forms) 2007 2008 2009 2010 2011 n (%) n (%) n (%) n (%) n (%) <1 year 1,601 (16.6) 2,887 (17.1) 2,916 (14.5) 2,923 (14.4) 2,375 (14.7) 1-4 years 3,122 (32.3) 5,391 (31.9) 5,396 (26.9) 6,096 (30.0) 4,864 (30.1) 5-14 years 1,775 (18.4) 3,110 (18.4) 4,905 (24.4) 4,529 (22.3) 3,743 (23.1) 15-24 years 1,156 (12.0) 2,177 (12.9) 3,057 (15.2) 2,983 (14.7) 2,253 (13.9) 25 years 1,837 (19.0) 3,145 (18.6) 3,563 (17.7) 3,542 (17.4) 2,635 (16.3) total 9,491 (11.6) 16,710 (20.4) 19,837 (24.2) 20,073 (24.5) 15870 (19.3) discussion the sentinel surveillance system in madagascar has two key functions: it provides an early warning of potential threats to public health and it can be used to manage public health programmes, by providing data for malaria indicators, for example. it can rapidly detect unexpected increases in the incidence of fever or diarrhoea syndromes and the biological surveillance associated with the syndromic surveillance programme can then identify the causes of these syndromes. this system has been described in terms of the methods used [11] and in relation to aspects of influenza surveillance [12,13], such as the spread of the influenza a(h1n1)pdm09 virus [14,15]. during the influenza a(h1n1)pdm09 pandemic, the circulation of this virus in madagascar was detected and the spread of the virus was followed from october 2009 to march 2010 [14]. we have already highlighted the weaknesses of the routine disease surveillance system in madagascar, which is based on passive collection and limited capacities for diagnosis outside the capital city. none of the early-warning signs was identified by routine surveillance. routine surveillance is useful for monitoring long-term programmes, but inappropriate for the timely detection of aberrant patterns. by contrast, syndrome-based near-real time surveillance can detect unusual events more rapidly [15-18]. this timeliness is a key element of the surveillance system and should be evaluated periodically [19]. the evaluation of surveillance systems should promote the most effective use of public health resources, by ensuring that surveillance systems operate efficiently [20]. the sentinel system in madagascar was clearly simple and rapid, but we found that some process indicators tended to decline over time, due to high staff turnover. the decrease in the number of fever forms received annually, between 2007 and 2011, is one of the weaknesses of this system. the increase in the number of sentinel sites increased the workload of central staff managing the different activities. a lack of co-ordination hindered the training of new healthcare workers entering the network, and changes in practices were discovered only during supervision in the field. challenges resulting from high staff turnover have also been identified in other ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 12 countries [6,8]. the indicators used for the continuous assessment of the sentinel network in madagascar are useful for a rapid, basic internal evaluation, but an external evaluation approach is also required, using cdc guidelines [21], for example, and including economic indicators as an integral part of the surveillance evaluation process [4]. the choice of methods used in the sentinel surveillance system in madagascar was based on the capabilities of the volunteer healthcare providers and the financial resources available. the madagascan network has grown over the years and its expansion is probably now limited by the human resources required to manage the network and data analysis. we have found that progressive step-by-step implementation is best, with assessment of the various processes, evaluations of network management capacity and the training of healthcare workers, to make the processes more acceptable. despite the results obtained to date, the sustainability of this system remains unclear, although data transmission costs amount to only about 2 us dollars per sentinel site per month. the madagascan network has been supported by funding from various sources over the years, focusing on different health topics. self-sustainability is another challenge, as already described [8], and has already been identified as a weakness of this network. we therefore need to focus on the first steps of surveillance system implementation and all system changes. initial funds targeted arbovirosis, because of the spread of chikungunya epidemics in indian ocean countries in 2006, and influenza, due to the threat posed by avian flu. however, the steering committee subsequently decided to include other diseases associated with febrile syndromes. this policy has been tremendously successful, making it possible for the network to provide epidemiological information not only about arboviruses, but also about malaria and influenza, throughout the country. in 2008, the first human case of rift valley fever was detected, by this network, at taolognaro (in the south of the country), a site used for both syndromic and biological surveillance. for malaria, the network has monitored the shift from control to elimination following the strengthening of malaria prevention and control measures. the usefulness of sentinel networks for influenza detection is well documented and was assessed in the last pandemic period in 2009 [15]. funding for work on these diseases has improved geographical coverage and made it possible to extend the network over the last five years. this network has become an additional tool for public health decision-making. the syndromic surveillance has been shown to be an effective approach to surveillance and, thanks to the availability of large mobile phone networks throughout madagascar, the cost of real-time data transmission is low. this surveillance method may also facilitate compliance with the revised international health regulations for low-income countries and the aim of the global outbreak alert and response network (goarn) [22]. limitations however, the rapidity with which the system can identify unexpected events, which is seen as an advantage [23], must be weighed against delays in the response. for instance, the time required to conduct investigations and retrieve diagnostic and epidemiological information might negate the advantage of rapid data acquisition, particularly in developing countries, in which it can be hard to find the resources necessary for investigations. the lack of historical data made it difficult to interpret the syndromic trends at each sentinel centre. one of the challenges in our system is determining epidemiological baselines for each centre, to facilitate the development of better statistical methods and more sensitive alert thresholds, as suggested by several authors [24-28]. indeed, five years after the establishment of this network, large amounts of data are already available and data analysis methods have identified trends for ili, malaria and dengue-like syndromes in areas of madagascar with ojphi early-warning health and process indicators for sentinel surveillance in madagascar 2007-2011 13 different climates. we now need to develop spatiotemporal models to increase the sensitivity of the alert detection process. however, limited geographical coverage and limited resources may prevent the detection of some epidemic events by this network. conclusion it is clear that the greatest advantage of this system is the ease with which it can be implemented, thanks to the availability of mobile phones and mobile phone networks. furthermore the quality of the homogeneous data collected will make it possible to improve the system relative to its principal objective: identifying epidemic events early. we recommend this solution for other african countries, because it performs very well and provides rapid benefits in terms of public health decision-making. financial support this work was made feasible by the setting up of a sentinel network supported by who geneva (apw/ref. od/ap-08-02451), the french ministry of health, the madagascan ministry of health through “projet cresan” (crédit ida – 3302-1-mag (cresan-2)), the us centers for disease control and prevention (cooperative agreement number: u51/ip000327-01), the us department of health and human service (grant number 6 idsep060001-01-01) via the international network of pasteur institutes and the president’s malaria initiative program (usaids). we would like to thank kathleen victoir and marc jouan from the international network of pasteur institutes. competing interests the authors have no competing interests to declare. acknowledgements we thank all the staff from the 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15274746&dopt=abstract http://dx.doi.org/10.1186/1471-2458-4-29 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=3131659&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11871807&dopt=abstract http://dx.doi.org/10.1016/s1473-3099(01)00148-7 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18201388&dopt=abstract http://dx.doi.org/10.1186/1471-2458-8-18 http://dx.doi.org/10.1186/1471-2458-8-18 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17089946&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14607808&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14607808&dopt=abstract http://dx.doi.org/10.1093/aje/kwg239 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=1983924&dopt=abstract http://dx.doi.org/10.2105/ajph.81.1.97 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17937786&dopt=abstract http://dx.doi.org/10.1186/1472-6947-7-29 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18394206&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18394206&dopt=abstract http://dx.doi.org/10.1017/s0950268808000307 beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 beyond information access: support for complex cognitive activities in public health informatics tools kamran sedig 1 , paul parsons 1 , mark dittmer 1 , oluwakemi ola 1 1 western university, canada abstract public health professionals work with a variety of information sources to carry out their everyday activities. in recent years, interactive computational tools have become deeply embedded in such activities. unlike the early days of computational tool use, the potential of tools nowadays is not limited to simply providing access to information; rather, they can act as powerful mediators of human-information discourse, enabling rich interaction with public health information. if public health informatics tools are designed and used properly, they can facilitate, enhance, and support the performance of complex cognitive activities that are essential to public health informatics, such as problem solving, forecasting, sense-making, and planning. however, the effective design and evaluation of public health informatics tools requires an understanding of the cognitive and perceptual issues pertaining to how humans work and think with information to perform such activities. this paper draws on research that has examined some of the relevant issues, including interaction design, complex cognition, and visual representations, to offer some human-centered design and evaluation considerations for public health informatics tools. keywords: public health informatics tools, cognitive science, design, visual representations, interaction introduction public health is an information-intensive field [1]. public health professionals work with a variety of information sources to carry out their everyday activities. for instance, in a recent study of public health systems, merrill and colleagues [2] report professionals’ top ten tasks by time spent; six of these tasks involve working directly with information (e.g., internet use and data reporting), and the remaining four involve information use mediated by interpersonal communication (e.g., telephone use and meeting with clients). furthermore, many of the tasks associated with public health risk analysis and clinician-population relationship management involve extensive information collection, analysis, synthesis, and management [3, 4]. in light of the information-intensive nature of public health work, public health researchers and professionals must carefully consider the efficacy of the tools they use to access and work with information. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 it is through the access and use of information that the health of populations and communities can be assessed, the causes of disease and injury can be reasoned about, immunization policies can be planned, and decisions regarding building codes and food safety measures can be made. the appropriately termed field of public health informatics (phi) has grown in recent years to deal with the information needs of public health professionals. in general, informatics focuses on the acquisition, storage, and use of information in a specific setting or domain [5]. as o’carroll et al. [1] note, it is the principles underlying the respective domains that distinguish various informatics specialty areas from one another. phi, then, can be broadly considered as the discipline focused on the acquisition, storage, and use of information in areas of public health— health promotion, disease and injury prevention, infectious disease surveillance and reporting, and so on. a necessary realization for any field of informatics is that information has primacy over technology. technology, although essential, is only a means by which users “make best use of information” [5]. much of the existing phi literature focuses on the technological structures that facilitate information access [6, 7, 8, 9]. in such contributions, as well as in review papers that consider multiple phi tools (e.g., [10, 3]), criteria for evaluation generally consist of meeting basic information access needs [11]. even the discourse on design principles in the public health literature concerns itself with limited issues such as secure access to varied local, national, and international information sources [12]. although information access is a prerequisite to information use, the nature of public health informatics work demands that researchers go further and study the patterns of human-information interaction prevalent in the field. public health informatics work is a form of knowledge work. as such, this work can be characterized in terms of various complex cognitive activities. for example, public health professionals make sense of large datasets; they reason about relationships between demographics, behaviours, and health outcomes; they plan intervention strategies; and they make decisions that affect public policy [13, 14, 15]. in practice, these cognitive activities tend to be both (a) complex, in that they are co-occurring, mutually reinforcing or embedded within each other, and (b) unstructured/open-ended, in that they do not admit a single correct process or solution (for a more elaborate explanation of complex cognitive activities, see [16, 17, 18]). much of our recent work has been devoted to studying the relationship between such complex cognitive activities and interactive, computer-based tools. one product of this inquiry has been a collection of related frameworks for the design and evaluation of computer-based tools that support complex cognitive activities, elaborated in a series of papers in the fields of humaninformation interaction, visualization, and visual analytics (see, for example, [16, 19, 20, 21, 22]). this paper draws on elements of these frameworks to offer considerations regarding human-centered design of public health informatics tools. more specifically, we are concerned with public health informatics tools that support, facilitate, and/or enhance complex, unstructured, and/or open-ended cognitive activities. this class of phi tools would include highly visual tools for epidemiological simulation, analysis, and decision support such as stem [23], ddss [24], sovat [25], episcangis [26], mdast [27], zeilhofer and colleagues’ component-based tool [28], epinome [29], and panviz [30], but exclude simple medical alert notification systems such as those described by lombardo and colleagues [7] or gesteland and colleagues [31]. throughout the paper we refer to this class of tools as phi tools. http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 when engaging with any computer-based tool, users 1 meet and interact with information through the visually perceptible interface of the tool. perception of the current state of the interface is the primary external aid to users’ cognitive activities. furthermore, users mentally reify information through the visual representations they perceive—that is, from a user’s perspective, the visual representations are the information itself. in this way, the interface is the epistemic locus of any phi tool, and its design warrants careful consideration. interface design involves two closely related components: representation and interaction [16]. representation design involves encoding information in visual forms. for example, consider an epidemiologist using map-based visual representations to reason about incidents of influenza in a region. two possible encoding schemes of sub-region incident rates are: 1) a colour spectrum, and 2) varying saturation levels of the same colour. in this case, the latter encoding is better suited to support users’ reasoning. a colour spectrum requires users to memorize the mapping of individual colour’s various incident rates, whereas varied saturation naturally expresses a numerical value where higher saturation represents a higher value. this simple example illustrates how different encoding schemes entail different perceptual and cognitive effects that may either enhance or hinder users in accomplishing their goals. interaction design is concerned with at least the following: 1) what users can and should do with the represented information, 2) what actions should be made available to users to work and think with the represented information, and 3) what reactions should result from users’ actions [16]. returning to the epidemiology example presented above, let us suppose that individual incidents of influenza are represented as markers on the map. in areas with high incident rates, a dense, difficult-to-understand representation may result. in this case, an interaction that allows filtering out some of the markers could assist the epidemiologist to reason about the situation. furthermore, a dynamic, adjustable filter that operates on particular sub-regions of the map may be more appropriate than a static, non-adjustable filter that operates on the map globally. this simple example is intended to illustrate that interactive features of a tool may affect how users perform complex cognitive activities and whether their goals are accomplished effectively. if public health informatics researchers are to support professionals using new information visualization tools, they must consider the perceptual and cognitive issues involved in interface design. researchers interested in the intersection of health informatics and cognitive science have noted that many failures arise due to a lack of understanding of, and consideration for, human issues in design—that is, lack of understanding of human-centric design. for instance, zhang [32] notes that most failures “are not due to flawed technology, but rather due to the lack of systematic considerations of human and other non-technology issues in the design and implementation processes.” such non-technology, human-centered design issues are the primary focus of this paper. the structure of this paper is as follows. first, we describe some typical complex cognitive activities that users of phi tools perform. second, we describe the nature of public health information spaces. third, we present some considerations for how this information may be represented. fourth, we discuss interaction design for phi tools. fifth, we discuss interactivity and its role in the effectiveness of tools. sixth, we present a brief scenario in which ideas from 1 in this paper, ‘users’ is a blanket term that can refer to public health researchers, practitioners, scientists, decision makers, policy makers, and so on. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 previous sections are applied to the design of a phi tool. finally, we present some concluding remarks. complex cognitive activities in public health informatics consider a public health professional using a phi tool to investigate the factors that contribute to child obesity in a region. after the user gains access to the pertinent information, he or she must (a) plan which factors to investigate and in what order, (b) make sense of information from a variety of sources, (c) analyze and reason about interaction effects between various factors that may vary over time and across the region, and finally (d) decide upon intervention strategies and policy recommendations in light of new findings. in other words, the user must perform a number of different complex cognitive activities. as the investigation unfolds, the user may move fluidly between these activities, perform tasks that serve more than one activity at once, or perform one activity as a part of completing another. for example, the user may periodically return to planning as new goals or tasks come to light. building statistical models may assist the user in making sense of data, analyzing correlations, and reasoning about cause and effect. finally, reasoning and analysis may also be embedded within decision-making, insofar as the purpose of the former is to provide evidence for the latter. each of these activities will potentially involve several tasks and sub-tasks that are completed through interactions with the phi tool at the level of the interface [16]. effective design of a specific phi tool, therefore, depends upon an accurate model of how the tool should support the complex cognitive activities at hand. such a model may emerge from experience in the field, empirical studies, and/or participatory design. in many situations, patterns of cognitive behaviour across users are diverse, and flexibility is required to accommodate user characteristics, needs, and preferences. designers must offer appropriate visual representations and interaction abilities for users to carry out meaningful discourse with information in a way that best supports their pertinent tasks and activities. public health information spaces before approaching issues of visual representation and interaction, researchers and designers must consider what information is relevant to their users. in this paper, we regard information as an external and objective entity as described in [33], not as a subjective construct (i.e., knowledge in one’s head) or as a process (i.e., the act of informing) as described in [34]). we refer to the sum total of information relevant to the user as the information space—for a more thorough discussion of the concept, see [16, 20]. public health information spaces are often heterogeneous [35]. users access and interact with different types of information from a variety of sources to perform complex cognitive activities. for instance, consider a public health state official dealing with a local influenza outbreak. this official may begin by analyzing regional hospital records or state vaccine supply information. as the official’s task unfolds, he or she may review school-based surveillance systems and recent federal recommendations on handling outbreaks before selecting an appropriate course of action. a decision support tool for this official must be designed with due consideration for the scope and content of this information space. http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 of course, researchers and designers may not have a priori knowledge of the full extent of information that is relevant to their users. as the above example illustrates, information spaces are context-dependent, and can change over time. this challenge can be overcome by employing flexible methods of information access, or by considering how a particular phi tool may be situated alongside other phi tools at the user’s disposal. in the above example, a designer may provide explicit access to each information source within a single phi tool, or integrate their phi tool with other tools that provide such access. in any case, a thorough characterization of the user’s information space should preface visual representation and interaction design. visual representations of information once the user’s information space is well understood, the designer must determine how information will be represented. information only becomes accessible to users when it is given form through the visually-perceptible interface of a tool. 2 visual representations play a variety of roles in supporting users to accomplish their tasks; they can anchor and structure thought processes, provide a medium for offloading memory and mental operations, reduce the cognitive effort required by changing the nature of the task, and provide explicit encoding of information for collaboration [36, 37, 38]. the utility of visual representations, however, depends on the designer’s ability to select representations that are appropriate to user tasks and activities. visual representations combine and integrate low-level visual marks, such as lines, dots, and other shapes, into more complex structural forms. these forms, along with visual variables that pre-attentively influence perception, such as colour, orientation, texture, and size, represent and encode information items (e.g., entities, properties, relationships, processes) that exist within an information space. it is possible that different representations of the same information can have very different cognitive effects [39]. furthermore, different representational forms can significantly impact how complex cognitive activities are performed (e.g., [36, 40]). consider a public health professional studying access to healthcare facilities. this professional uses a tool that displays information about local hospitals, including (a) whether the hospital contains an emergency room or urgent care center, (b) the number of beds available in various departments, and (c) the distribution of patient wait times. figures 1 and 2 illustrate two visual representations of this information. figure 1 is a map-based visual representation with repeated symbols to represent beds in different departments and a histogram indicating patient wait-time distribution. figure 2 contains a list of hospitals with a bar graph representation of bed counts and a time-varying plot of individual patient wait-times. both figures encode hospitals with emergency rooms in red, and hospitals with urgent care centers in blue. overall, the map is probably more useful because it facilitates tasks related to the spatial distribution of hospitals. the histogram emphasizes the overall distribution of patient wait times, whereas the timevarying plot emphasizes peak times; the most appropriate representation is dependent on the context and the tasks that must be performed to meet the needs and goals of the user. 2 although information may be made accessible to any one of the senses, this paper is concerned with only visual representations. the terms ‘representation’ and ‘visual representation’ are used interchangeably throughout the paper. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 1. map-based representation of hospital facilities figure 2. list-based representation of hospital facilities note that none of the above visual representations encodes the entire information space. to assess access to healthcare facilities, the user may wish to see population density, areas with high concentration of vulnerable individuals, transit routes, and walk-in clinic locations, among other information. all of this information is unencoded—it remains latent within the information space and is inaccessible to the user. encoding all of this additional information, however, would likely result in clutter and confusion. the tension between unencoded information and information overload is problematic. public health professionals are inundated with massive amounts of information. as a result, practitioners are beginning to demand that information be presented in a compact form that can be readily absorbed [41, 42, 43]. the size of the public health information space means that only a small fraction of it can be encoded into representations at any given time. the only way for the user to access latent, unencoded information is through interaction. interaction print-like, static representations, though beneficial, do not allow the user to actively manipulate representations in ways that may reveal latent information [44], thereby requiring users to bear the brunt of the information-processing load. representations that are interactive can help bridge the gap between the internal mental representations of the user and external visual representations of the tool [16, 19]. such dynamic, interactive representations offer users http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 flexibility by supporting convergent and divergent thinking, and accommodate evolutionary and iterative patterns of thought [45, 46, 47]. patel and kushniruk point out that some computer-based tools in the field of health, when implemented as “short term fixes”, tend to disrupt (rather than enhance) users’ complex cognitive activities [48]. a deeper understanding of interaction and its benefits is crucial for creating tools that support, rather than hinder, cognition [44, 45, 46, 49]. calls for a more comprehensive understanding of the cognitive issues surrounding interaction have been ongoing for the past decade, and span various fields including health informatics, information visualization, human-computer interaction, visual analytics, and information science (e.g., see [32, 44, 45, 46, 50, 51, 52]). much of our recent work has been devoted to characterizing various facets of interaction. interaction is a complex phenomenon, and it is difficult to capture all of its rich characteristics. to mitigate this problem, phi tool designers may find it helpful to think of user activity in terms of several levels of interaction. in our work, we have categorized interaction into four levels: (1) complex cognitive activities as they are presented above, (2) tasks and sub-tasks such as locating or categorizing, (3) individual interactions such as filtering or transforming, and (4) userinterface events such as clicking or swiping [16, 20]. phi researchers and designers may approach these levels from the top down, viewing low-level patterns as embedded within highlevel patterns, or from the bottom up, viewing high-level patterns as emerging from the combination of low-level patterns. in either case, identifying the prevalent combinations of activities, tasks, and interactions that users will perform allows designers to combine interaction techniques that are appropriate to a series of anticipated user contexts. even a cursory survey of the literature in interaction design, information visualization, visual analytics, and related disciplines uncovers an overwhelming abundance of interaction techniques. for phi researchers and practitioners, interaction techniques that have a potentially infinite variety of implementations and are scattered throughout numerous disciplines are of little help in supporting the systematic design and evaluation of phi tools. furthermore, as new technologies are developed, old techniques may fall out of use as techniques more appropriate to the new technology arise. what is needed, then, is the development of frameworks and taxonomies that identify and explicate underlying patterns that are platform-, technique-, and technologyindependent. interaction patterns one aspect of interaction to which we have devoted considerable research effort is the identification and explication of fundamental patterns that are used to interact with information during the performance of complex cognitive activities (also referred to as action patterns [16]). such research stems in part from the observation that interaction techniques are generally operationalized at the level of individual interactions, that is, user-action/system-reaction pairs. since there are potentially infinite interaction techniques, interaction patterns allow designers to situate techniques within general categories according to their function. some of the patterns from [16] are arranging—changing the ordering of visual representations, scoping— dynamically working forwards and backwards to view compositional development and growth of information, selecting—focusing on or choosing particular visual representations, translating— converting visual representations into alternative informationallyor conceptually-equivalent http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 forms, and transforming—changing the geometric form of visual representations. each pattern supports the performance of complex cognitive activities in different ways, and there are innumerable interaction techniques that fall under each pattern. categorizing techniques according to the patterns that they express has numerous benefits. in designing phi tools, considering a manageable variety of interaction patterns and their cognitive effects allows designers to systematize their interaction design process and justify their design decisions in a clear manner. additionally, in evaluating phi tools, interaction patterns offer a common vocabulary for discussing interaction at a low level that is resilient to technological change. consider the phi exploration tool illustrated in figures 3 8 using [53]. the user is exploring the relationship between low birth weight and various external factors. in figure 3, the user has an unordered list of average low birth-weight prevalence. by clicking a menu option, the user arranges the items in descending order (as shown in figure 4). this example is one way of implementing an arranging action pattern; of course, there are many other ways, but all of them achieve the same purpose for the user. that is, the user changes the ordering of the represented information to facilitate tasks such as ranking, classifying, and identifying information items. figures 5 8 illustrate how a sequence of other action patterns—selecting items (figure 5), translating from one visual form to another (figure 6), and scoping through changes over time (figures 7 and 8)—reveals previously undetectable patterns in the information space. for a more comprehensive discussion of these and other interaction patterns, see [16]. figure 3. initial state of bar graph representation figure 4. ordered bar graph representation http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 5. bar graph representation with selection figure 6. bar graph translated into bubble chart figure 7. bubble chart encoding correlation with female head of household (%) and infant mortality rate figure 8. bubble chart scoped from 2002 to 2005 interactivity in addition to interaction considerations, phi tool designers must also consider the quality of interaction, or interactivity, that emerges through use of their tool. this consideration is important because research has shown that factors that affect the quality of interaction have significant cognitive effects (e.g., see [54]). one of our lines of research identifies facets of interaction that affect interactivity. we have recently devised a framework that outlines a number of such facets and discusses how they influence the quality of interaction, and ultimately, the performance of complex cognitive activities (see [19]). just as interaction can be characterized at many different levels, so can interactivity. at the level of individual interactions, a number of elements can give structure to an interaction (see [19]). for example, one such element, called presence, is illustrated in figures 9 and 10. presence is concerned with the existence and advertisement of an action. in other words, it is about the cue or signal from the interface used to prompt the user or to advertise the existence of an interaction. this structural element can be operationalized in one of two ways: explicitly or implicitly. if presence of an action is explicit, the availability, existence, or provision of the interaction is clearly advertised by the tool. when presence is implicit, the interaction exists, but http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 its availability is either not easily perceptible by the user, or it is not visible at the level of the interface. in this case, the user must have previous knowledge of the existence of the interaction. figures 9 and 10 illustrate the two operational forms of presence. the figures depict two implementations of the drilling interaction pattern which, in this case, causes region-specific statistics to be displayed. in figure 9, the presence of the interaction is operationalized in an implicit manner, and the user must hover the mouse cursor over a region to discover that doing so displays more information. in figure 10, the presence of the interaction is operationalized in an explicit manner, advertised as the “info tool” in a toolbar on the left. the obvious design trade-off in this case is weighing the cost of screen space for the toolbar against the risk that the user will not discover unadvertised functionality. however, there is a more subtle cognitive effect at play. the implicit implementation has no “drilling mode” alongside other modes of interaction; hovering the cursor over a region always drills the region, just as clicking a region or clicking a statistic always performs the same action. in the explicit case, the toolbar forces users to think about their actions in terms of the currently selected modality; clicking a region with the “info tool” will not have the same effect as clicking with the “zoom tool”. as a result, the implicit implementation is more predictable whereas the explicit implementation is more flexible. the framework in [19] identifies 11 other elements that collectively give structure to an interaction. similar to presence, each element has different operational forms, each affecting cognitive processes in different ways. if researchers and designers are aware of these elements of interactivity and have an idea of how they affect the quality of interaction, phi tools can be designed in a manner that more effectively facilitates the performance of complex cognitive activities. figure 9. drilling by district requires mouse hover and is unadvertised: implicit presence figure 10. drilling with the info tool is advertised in a toolbar: explicit presence the quality of human-information interaction at a higher level can also be examined according to [19]. at this level, the concern is not the structure of an individual interaction, but the combination and sequencing of several interactions and how they affect the performance of tasks and activities. the framework in [19] has identified a number of factors that influence interactivity at a high level, one of which is flexibility. flexibility is concerned with the degree to http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 which the user can adjust the properties of the interface to suit his/her needs, preferences, characteristics, and goals. flexibility is particularly relevant to phi because phi users’ goals, characteristics, and needs are diverse [55, 56]. as a result, researchers have called for development of tools that cater to this diversity (e.g., [55, 57]). one way of supporting a diverse audience is to incorporate greater flexibility into phi tools. one facet of flexibility is concerned with whether a tool allows users to adjust the settings of different ontological properties of visual representations. parsons and sedig [21] have identified a number of ontological properties of visual representations, the settings of which influence cognitive and perceptual processes during the performance of complex cognitive activities. for instance, one ontological property of all visual representations is density—the degree to which information items are encoded compactly in the visual representation. depending on the task and the user, different settings (i.e., degrees) of density are most appropriate. therefore, to design phi tools that are human-centered, users should be given the ability to adjust such settings to suit their needs and preferences. other properties of visual representations identified in [21] include appearance—the aesthetic features (e.g., color and texture) by which information items are encoded, dynamism—the degree to which encoded information items exhibit movement, scope— the degree to which the growth and development of information items are encoded, and type— the form of a visual representation in which information items are encoded (see [21] for a full discussion of these and other properties). an awareness of the ontological properties of visual representations and how their settings can and should be made adjustable is vital to any systematic endeavor concerned with the use of visual representations in complex cognitive activities. for example, consider a public health practitioner using a gis tool to analyze cases of chlamydia trachomatis, such as in [8]. depending on the user’s needs, he or she may wish to adjust the type of representation to better suit a particular task. if the user is presented with a bar graph, for instance, such a representational form may not be ideal for performing tasks such as identifying the spatial distribution of infection cases. by allowing the user to adjust the type of representation to a cartogram, for example, the task being performed can become more tractable. a user may need to adjust the type of representation many times to facilitate different tasks during the performance of an overall activity. each of the properties identified in [21] have been shown to affect perceptual and cognitive processes and, therefore, giving users the ability to adjust such properties can enhance the quality of interaction by contributing to the flexibility of a tool. concrete examples of this facet of flexibility are drawn from open access tools dealing with phi datasets and are illustrated in figures 11 14. the google public data explorer tool (figures 11 and 12) provides the user with the ability to adjust the type settings of visual representations, so that the same underlying dataset can take on the following forms: line graph, bar graph, geographic map, or bubble chart [53]. the spatio-temporal epidemiological modeller (stem) visualizes the spread of disease over time (figures 13 and 14) [23]. often times, it is the case that users adjust the settings of multiple properties with the same interaction—a phenomenon that can be deliberately designed when designers understand how adjusting the settings of different properties can facilitate a user’s tasks. in figures 13 and 14, for instance, the user is adjusting both the dynamism and scope settings—by increasing or decreasing the degree of movement and the degree to which the growth and development of the information items are encoded. figures 13 and 14 show the spread of influenza through great britain on day 32 and 54, respectively, of a simulated outbreak. adjusting the settings of each property has distinct benefits for performing http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 certain tasks (see [21]), and combining these adjustability options can provide better support for tasks related to the spread of the disease. figure 11. adjusting type settings (1): a bar graph representation figure 12. adjusting type settings (2): an equivalent map-based representation figure 13. adjusting dynamism and scope settings (1): representations are static figure 14. adjusting dynamism and scope settings (2): representations exhibit movement to depict the development and spread of disease design and evaluation of phi tools can be rendered more systematic through awareness of the many elements and factors that influence interactivity (see [19] for a more complete discussion). since the discussed frameworks do not deal with interaction and interactivity at the implementation level, they can promote both systematicity and creativity in the design process. for instance, if designers consider the aforementioned factor of flexibility, allowing users to adjust the settings of ontological properties of visual representations frees designers from preoccupation with selecting the single “right” representation. users are instead empowered to tailor representations to suit their contextual and cognitive needs, while designers are free to direct their energy toward creating novel interaction techniques and combinations and sequences of interactions. http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the frameworks discussed above have a number of benefits for phi researchers and practitioners: they offer a common vocabulary for discussing interaction and interactivity; they are platform-, tool-, and technology-independent; they facilitate systematic comparison and contrast of phi tools; and, finally, they promote systematic design with an understanding of how numerous design considerations affect the performance of complex cognitive activities. scenario: design of a phi tool this section illustrates how the aforementioned frameworks can be integrated to assist with the design and evaluation of phi tools 3 using the scenario mentioned at the beginning of the second section of this paper, in which a public health professional investigates the factors contributing to child obesity in a region, in this case the region being the usa. with the frameworks discussed above, designers can structure their thinking in a comprehensive and holistic manner. design decisions can be made systematically, with an understanding of how they influence the performance of complex cognitive activities. for instance, as previously mentioned, such a scenario involves planning, sense-making, analytical reasoning, and decision-making. if designers and evaluators are not aware of the characteristics of such activities, and how to best support them with interactive visual representations, phi tools cannot be designed and evaluated effectively. using the ideas discussed in this paper, however, designers can think systematically about such issues as the characteristics of the information space with which they are concerned; how to encode different aspects of the information space with visual representations; how representations emphasize different aspects of information and act as lenses through which users perceive the information space; what actions should be made available to the users to work and think with the represented information; how the different ways of operationalizing interactions influence cognitive processes; which properties of the representations should have adjustable settings to best support the users’ tasks; how interactions should be combined and linked together to facilitate the performance of tasks; and, how the interplay of all of these considerations ultimately influences how complex cognitive activities are performed. some of these considerations will be briefly discussed in the context of the scenario described above. we ground our discussion of these concepts with a feasible design that is presented through a functional description and is accompanied by a series of user interface mock-ups 4 . our intention is to illustrate, in broad strokes, one potential realization of these concepts that is suitable to the scenario. it is important to note that the mock-ups should not be mistaken for a polished tool ready for use by public health professionals. in this scenario, the information space with which users are concerned includes health datasets that may originate from multiple sources. these data include categorical and quantitative variables such as age group, sex, socioeconomic status, prevalence of disease, environmental factors, and so on. some of these data may be instances applied to individuals, but most are aggregated at various levels—e.g., by district, by state, or by nation. the information space also includes past and current policy decisions, information about existing programs to combat 3 this section is concerned with both design and evaluation of phi tools. for ease of reading the terms ‘designer’ and ‘evaluator’ are used interchangeably. 4 mock-ups, here, refer to rough sketches illustrating the user interface layout. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 obesity, and obesity-related research. this information is most readily represented in the form of digitized documents describing policy, programs, and research. during analysis, the user will deal with a multitude of variables. in addition, he or she will be interested in statistical models derived from these variables, from linear correlations between prevalence of obesity and a single other variable, to complex models involving a multitude of variables. these can be represented using many different forms, such as maps, scatter plots, and bar graphs, each of which emphasize different aspects of the information space and have different perceptual and cognitive effects. for example, figure 15 shows a variety of variables represented via colour saturation and bar graphs, while regions with a moderate or strong significant correlation with an obesity measure are hatched or cross-hatched, respectively. such encodings have been shown to have cognitive effects at the pre-attentive level of perceptual processing. the user can pan the map-based representation using the scroll wheel of the mouse; by default, the map pans vertically, and depressing the shift key modifies the scroll wheel behaviour to pan horizontally. figure 15. a typical view of the scenario tool containing a large map-based visual representation (top left), a series of notes containing lists and links (top right), and a listing of variables and statistics along with a set of saved representations (bottom) through a variables-and-statistics dialogue (see figure 16) users can adjust several properties of representations. for example, users can adjust density by selecting the level of aggregation; adjust appearance by selecting features such as colour and shape; and adjust type by creating graph, plot, or map-based representations. when the user interacts with the list of statistics (see right side of the modal dialogue in figure 16), in addition to the above-mentioned controls for http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 controlling the statistic’s representation, the user is presented with a form for selecting multiple dependent variables and a type of statistical model from which the statistic is derived before being represented on the map. the user may either alter an existing statistical model by selecting it in the list or create a new model by clicking the “+” entry in the list (see rightmost list in modal window in figure 16). the variables and statistics dialogue can be opened by double-clicking either (a) a variable or statistic name, or (b) a representation of a variable or statistic. through this dialogue, new visual representations can be added to the view, and existing representations can be translated from one form to another. figure 16. a variable-and-statistics dialogue for modifying representations displayed by the tool the density and complexity properties can also be adjusted through two discrete slider controls along the right of the map view (see figure 15). the top control adjusts the zoom level of the map itself. the bottom control contains two adjustable controls on one scale. one control is a discrete marker, similar to the one used for zoom level, that dictates the level of granularity at which the user may select a region for drilling, such as by district or by state. the other control is a two-ended range that dictates the levels of aggregate granularity that are represented. for example, the user may wish to select and drill by state while viewing map regions, graphs, and plots defined for both district and state levels of granularity. zooming and filtering with controls is an example of operationalizing interactions with an indirect form of focus—one of the structural elements from [19] that affects interactivity. even though the user acts upon the control, what he or she is trying to achieve is a change in the map-based visual representation— i.e., though the user’s focus is on the control, the user indirectly affects the map. the drilling interaction brings into view statistical representations that cannot be overlaid directly onto the map. multiple-selection of regions allows the user to view one or more regions http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 at once to make sense of relationships between variables and between regions. in this way, there is strong complementarity between the selecting and drilling interactions. if the user wishes to make a direct comparison, plots of multiple regions can be superimposed by dragging one plot onto the other in one continuous motion. when the user completes the action, the two plots animate to merge together into a single plot; an example of the end result is shown in the rightmost scatter plot in figure 17. this use of continuous action/reaction flow—another element from [19] that affects interactivity—provides users with an intuitive action pattern for combining plots (through dragging) while aiding comprehension of the final result (through a smooth transition). a cylindrical icon represents each data source used to generate statistics. this representation can be further drilled (not shown here) to expose important details such as sample size, year of data collection, related research goals, and related publications. figure 17. a multi-region drilling example when the user discovers a new pattern or correlation, the visualization state can be saved using the “+v” button (see figures 15 and 17). ad-hoc visual compositions can also be constructed by clicking and dragging one visual representation, such as a map region or bar graph, onto the “+v” button. in both cases, the button expands to create room for the user to enter a title for the view. when the user presses ‘enter’, the view is added to the stack of views next to the “+v” button. once a new view has been stored, other ad-hoc elements can be added to it by clicking and dragging them onto the view’s representation on the stack. the dragging-and-naming method of storing a view is an example of an interaction with composite interaction granularity in which multiple steps performed by the user complete a single logical interaction. this example illustrates how composite interactions allow the user to specify certain parameters that affect the outcome of the interaction, in this case, the name assigned to the stored view. http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 to facilitate transitions between planning, decision making, and analytical reasoning, the user is provided with a note-taking component that supports linking to visual representations (in red), external documents (in blue), and other notes (in yellow); examples are shown in the rightmost panel in figure 15. views that are linked to a note are also annotated with a note icon that can be used to open notes about a view as the user works with the representations (see yellow icons in bottom-right of drilling dialogue in figure 17). this is another example of complementarity between two patterns—linking and annotating. the note-taking component also supports task lists that automatically instantiate a task summary when a task is checked off (see last item in task list in figure 15). the task summary workflow allows the user to describe his/her findings after completing a task and to link to relevant documents and visual representations. clearly documenting plans and outcomes in this way assists users to be systematic in their analysis and locate relevant information in the case of preparing a report. table 1 summarizes the interaction patterns from [16] that can help support thinking about design and evaluation of the tool in this scenario. although brief, this section has attempted to illustrate how the ideas presented in this paper can facilitate the systematic design and evaluation of phi tools. only a few considerations were examined here; however, if the frameworks discussed above are considered together, the result is a comprehensive support structure that facilitates coherent and holistic thinking about how numerous complex cognitive activities can be performed using interactive phi tools. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 table 1. detailed interaction design matrix for scenario tool interaction pattern functional description annotating add personal meta-information to linked visual representations assigning bind characteristics to visual representations in variable/statistics modal dialogue comparing when the drilling dialogue shows data from multiple regions, click one visual representation and drag it onto the corresponding representation for another region to identify their degree of similarity composing / decomposing drag visual representations onto an existing representation icon to compose new ad-hoc representations; drag representations outside the tool to break them apart drilling bring out and encode latent information from map regions and data sources by double-clicking or selecting and pressing the enter key filtering show or hide elements of visual representations with discrete slider linking / unlinking establish a relationship between visual representations by selecting and dragging them into note, or with right-click menu or keyboard shortcut selecting click visual representations or icons; click with the shift key depressed for multiple selection storing / retrieving put aside or bring back visual representations by clicking the “+v” button translating depress the ctrl key and click a visual representation to open the variable/statistics window; the user may choose to convert representations into alternative informationallyor conceptuallyequivalent forms summary for public health professionals to efficiently ensure and promote the health of the general populace, they must engage with a variety of information sources to perform their everyday activities. focusing on only information access is insufficient, however, if phi tools are to become powerful enough to support users in the complex cognitive activities associated with phi practice. what is needed is a clear understanding of how users of phi tools engage in a dynamic discourse with public health information in order to assess the health of populations and communities, reason about causal chains that lead to disease, plan immunization policies, and perform other public-health-related complex cognitive activities. to develop such an understanding, the features of this human-information discourse that influence the performance of such activities must be identified, characterized, and explicated. such features include the encoding and representation of items from information spaces into visual forms; the ontological properties of visual representations and how their settings influence cognitive and perceptual processes; the actions that should be made available to users to work and think with the represented information; the different ways in which such actions and their subsequent reactions should be operationalized; and how all of these considerations ultimately http://ojphi.org/ beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 combine to influence problem solving, sense-making, analytical reasoning, and other complex cognitive activities. such an understanding necessarily involves the integration of research from numerous disciplines, including public health informatics, computer science, cognitive science, information visualization, and human-computer interaction design. furthermore, to ensure that relevant research is not scattered and inconsistent, theoretical models, frameworks, and taxonomies that deal with the fundamentals of the aforementioned issues must be developed and made available to researchers, designers, evaluators, analysts, and users. over the past decade, we have been conducting research to develop a number of inter-related frameworks that bring order and structure to the general area of human-information interaction in complex cognitive activities. we believe that such research can be of great benefit to the area of phi. in this paper, we have identified some of the extant research needs for phi tools, presented some fundamental concepts that must be understood if phi tools are to effectively support complex cognitive activities, discussed a number of the considerations from our frameworks that have implications for the design and evaluation of phi tools, and provided a scenario to demonstrate how the integration of such considerations facilitates deliberate and methodical design and evaluation. as phi moves beyond mere information access and becomes concerned with enabling rich discourse with information in order to support complex cognitive activities, the considerations discussed in this paper become critical to the effective design and evaluation of phi tools. as research in this area is still new and emerging, it is hoped that this paper will make 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http://ojphi.org/ http://dx.doi.org/10.1186/1471-2458-6-89 http://dx.doi.org/10.1057/palgrave.ivs.9500047 http://dx.doi.org/10.1016/j.jbi.2004.11.005 http://dx.doi.org/10.1016/j.lisr.2008.07.001 http://dx.doi.org/10.1179/0308018812z.0000000001 http://www.google.com/ http://dx.doi.org/10.1007/bf02344508 beyond information access: support for complex cognitive activities in public health informatics tools online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 57. turner am, liddy ed, bradley j, wheatley ja. 2005. modeling public health interventions for improved access to the gray literature. j med libr assoc. 93(4), 487-94. http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts syndromic surveillance practice in the united states 2014: results from a nationwide survey tera reynolds*1, scott gordon2, paula soper2, james buehler3, richard hopkins4 and laura streichert1 1isds, boston, ma, usa; 2astho, arlington, va, usa; 3philadelphia department of public health, philadelphia, pa, usa; 4university of florida, tallahassee, fl, usa objective to present the results of a nationwide survey designed to assess the syndromic surveillance (sys) practices and capacity-building assistance (cba) needs of u.s. state public health authorities (phas). introduction spurred by recent advances in ph informatics, the implementation of the medicare and medicaid electronic health records incentive programs (meaningful use), and the opportunities provided by the availability of the redesigned biosense program, sys has become an increasingly important component of the biosurveillance enterprise. knowing how and when jurisdictions use sys, as well as challenges faced, allows isds, astho, cdc, and other partners to provide relevant cba – information transfer, training, and technical assistance – to further biosurveillance practice. methods the survey instrument was developed with input from isds staff and board members, astho, and other stakeholders. after piloting, and revising based on feedback, the survey instrument was sent via email to a primary contact responsible for sys or, for phas without a system, a knowledgeable contact, at 50 state, the district of columbia (dc), and 6 territorial phas. quantitative analyses were performed using r©. results for territories, comparisons with earlier surveys, and mixed-method analyses will be included in the presentation. results we received responses from practitioners in all 50 states and dc. eighty percent of the 51 survey respondents report that they routinely access and review data for sys and, of the 10 not currently practicing sys, 6 plan to in the future. key findings (*note: the ns indicated below vary due to skip patterns in the survey and missing data): • of the 39 respondents conducting sys, 13 are using biosense only, 9 another application only, and 17 both. • among those that conduct sys and do not use biosense only: 100% monitor emergency department (ed) visits, and 52% monitor poison control center calls (n=27). the median percentage of ed facilities within a jurisdiction participating in sys is 70% (range: 2–100%, n=25). 96% report that the field on which their syndrome definition relies is chief complaints–free text, 50% chief complaints–drop-down menu, and 46% diagnosis codes (select all that apply question; n=28). • of the respondents using biosense: 40% report that biosense is their primary sys data acquisition, management, and analysis application (n=30). of those using a local application and biosense (n=17), 6% report that biosense is the primary application. 97% are currently, or planning to within the next year, sharing aggregate data with cdc and/or at least one other jurisdiction through biosense (n=30). 27% are currently sharing data at a visit record level with cdc and/or at least one other jurisdiction through biosense (n=30). of those not currently sharing data at a visit record level through biosense (n=22), 23% plan to in the future, 41% are unsure, and 36% do not intend to. • 69% report that data from hospitals flow to their sys system via direct connection from individual hospitals; 14% report that hospitals connect to biosense, and the health department accesses via biosense; and 11% indicated both of these options (select all that apply question; n=36). • of the respondents that conduct sys and have data quality monitoring processes in place (n=27), 85% report that their jurisdiction monitors data acquisition interface(s) at the transmission level on a routine basis. • of those conducting or using sys: 58% find the methods highly useful for trend analysis (n=36), 51% for situational awareness during a ph event/emergency (n=37), and 61% for influenza surveillance (n=38). the median cost of materials and effort per 100,000 people is about $4,800 (range: $18 $18,900, n=18). *note: respondents were asked, to the extent possible, to exclude one-time development costs. the median number of ftes dedicated to sys (a) analysis and response and (b) technical system maintenance is 0.5 for each category (rangea: 0–7.83, n=36; rangeb: 0–6.83, n=36). conclusions sys practice continues to vary greatly between states in terms of adoption and resources allocated. the results of this survey provide a snapshot of the current sys landscape, and are being used to inform the development of cba activities at the state level. for a more complete picture of the landscape, future work will include a survey of sys practice among local phas. keywords challenges; syndromic surveillance practices; survey results acknowledgments this work supported by cdc through a cooperative agreement to astho. *tera reynolds e-mail: treynolds@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e90, 201 designing a browser extension for reliable online health information retrieval among older adults using design thinking 1 ojphi designing a browser extension for reliable online health information retrieval among older adults using design thinking eden shaveet1, marrissa gallegos1, jonathan castle1, lisa gualtieri1 1. tufts university school of medicine, department of public health and community medicine abstract the pervasiveness of online mis/disinformation escalated during the covid-19 pandemic. to address the proliferation of online mis/disinformation, it is critical to build reliability into the tools older adults use to seek health information. on average, older adult populations demonstrate disproportionate susceptibility to false messages spread under the guise of accuracy and were the most engaged with false information about covid-19 across online platforms when compared to other age-groups. in a design-thinking challenge posed by aarp to graduate students in a digital health course at tufts university school of medicine, students leveraged existing solutions to design a web browser extension that is responsive to both passive and active health information-seeking methods utilized by older adults in the united states. this paper details the design-thinking process employed, insights gained from primary research, an overview of the prototyped solution, and insights relating to the design of effective health information-seeking platforms for older adults. keywords: internet, older adults, inventions, curriculum abbreviations: aht2: aarp health tools 2.0, tusm: tufts university school of medicine doi: 10.5210/ojphi.v14i1.12593 copyright ©2022 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. background adults 50 years and older are the largest consumer population of healthcare in the united states [1]. consequently, the methods older adults use to seek health information that may impact their healthcare decisions is noteworthy for healthcare providers, older adult communities, and public health stakeholders. the number of older adults seeking health information online has increased in recent years, which is consistent with this population’s growing adoption of smartphone, tablet, and social media use [2], [3]. however, increased reliance on online health information seeking has heightened concern around rapid dispersion of health misinformation and disinformation [4]. designing a browser extension for reliable online health information retrieval among older adults using design thinking 2 ojphi online health information seeking during covid-19 disruption of routine healthcare service delivery in the u.s. during the covid-19 pandemic prompted increased patient reliance on internet-enabled technologies for virtual visits with providers, consumer-grade digital health application use, and vaccination appointment scheduling [5], [6]. this abrupt dependence on web-based resources for healthcare purposes coincided with increased adoption of online health information-seeking behaviors [7], [8]. in 2020, over 80% of u.s. adults aged 50 years or older claimed that the internet had been an essential resource to them throughout the covid-19 pandemic; however, older adults were also found to be the most engaged with false information about covid-19 across online platforms when compared to other age-groups [9], [10]. misinformation and disinformation misinformation and disinformation refer to false messaging spread under a guise of accuracy [11]. while misinformation is defined as false information spread regardless of intent, disinformation is the distinct sect of misinformation that is deliberately propagated [11]. in recognition of their mutual relevance and harm, we use the terms collectively throughout this paper. the proliferation of online mis/disinformation in older adult communities has garnered considerable public attention in recent years, as older adult populations who experience digital exclusion are, on average, disproportionately susceptible to mis/disinformation when encountered [4]. while its presence has drastically increased since the early 2010’s, pervasiveness of online health mis/disinformation escalated during the covid-19 pandemic [12]. in response, efforts have been dedicated to reducing online health mis/disinformation on the part of information-sharing platforms like google and facebook, asserting that optimal solutions to mitigating proliferation include both reducing the amount of false content in online circulation and promoting better health and digital literacy skills [13], [14]. these are vital endeavors that require considerable time and resources. proposed in this paper is a concurrent strategy conceptualized using design thinking with few barriers to implementation. design thinking design thinking is an approach to innovation and validation used to develop effective solutions to complex problems [15]. distinct from other development methods, design thinking relies on human-centered design principles to observe how people interact with their environments and iteratively design solutions to a population’s expressed needs. interdisciplinary student teams at tufts university school of medicine (tusm) engaged in a design-thinking process adapted to a 14-week digital health course in collaboration with aarp (a united states member-based interest and advocacy group focusing on issues affecting adults over the age of 50) to address a set of obstacles facing older adults in the u.s. teams engaged in a design-thinking process encompassing review of the literature and existing solutions, primary research, problem explication, ideation, prototyping, and solution validation. additional details on the use of design thinking as a teaching medium at tusm are reported in ref [16]. designing a browser extension for reliable online health information retrieval among older adults using design thinking 3 ojphi academic-industry partnership with aarp an academic-industry partnership between tusm and aarp to teach graduate students about design thinking for healthcare innovation has spanned over eight semesters. while student teams are not required to collaborate closely with aarp to design their solutions, our team determined that building upon aarp’s existing resources would be advantageous and lead to a more feasible and trusted solution prototype. trust is foundational to solution adoption and behavior change in older adult communities [17]. aarp’s tenure in the older adult interest and advocacy space has earned the organization recognition as one of the most notable and respected membership-based organizations in the u.s. at a membership count of over 38 million people, aarp is a household name frequently referenced in pop-culture, academic, and industry spaces [18]. in their efforts to mitigate online mis/disinformation about covid-19, google’s early decision to promote covid-19 information sourced from aarp in 2020 amplified the organization’s credibility as a trusted source for health information [19]. for this reason, along with high utilization of their website by older adults, aarp was an ideal entity with which to prototype a high-impact design-thinking solution to the problem of unreliable health information retrieval among older adults online. intended user population older adults who seek health information online are often prompted by receipt of new diagnoses, progression of symptoms, or prescription of new medications [20], [21]. recognizing that 85% of adults 50 years and older live with at least one chronic condition, are likely to experience changes in their conditions, or be prescribed new medications to manage these conditions [22], [23], we identified this population as the intended user base for a formulated design-thinking solution. additional considerations were given to the 35-48% of older adults aged 55 years and older who experience digital exclusion as digital mistrust and limited digital literacy skills may result in misuse of or aversion to technology [17], [24]. in recognition of family members, caregivers, and healthcare professionals who support older adults with health information seeking, we identified members of these populations as secondary users. under the precept of universal design, when digital solutions are designed to be accessible to members of specific communities, like older adults, they are also likely to be accessible to the population at large [25]. existing solutions the internet is a primary source of health information gaining traction among older adults [1]; however, health and digital literacy skills are often poor among older adults who seek health information online [2]. trusted searches that yield valid health information may be achieved by employing methods that in some way limit or verify the trustworthiness of search results [26]. past work in this area includes the development of specialized search engines and content verification indicators. designing a browser extension for reliable online health information retrieval among older adults using design thinking 4 ojphi specialized search engines search engines are software programs that carry out web search queries [27]. specialized search engines are search engines that specialize in retrieval of web-based information relating to a particular topic or category [28]. while several specialized health search engines have emerged in the last two decades, these search engines have been unsuccessful in reaching wide-audiences when compared to google search or in mitigating the proliferation of health-related mis/disinformation online [29], [30]. additionally, advertisement-based monetization models or use of relevance algorithms which leverage data scraping mechanisms and access user cache and cookies jeopardize user privacy and may disincentivize use among older adults who express digital mistrust and privacy concerns [31]. content certification programs online health content certification programs, like honcode, are programs which use visual indicators to inform users of when they are accessing websites which house valid health information [32]. while widely recognized in academic circles whose interests are in health information reliability [33], it's unknown whether certification indicators like honcode are recognized across the general population. content certification indicators which rely on widespread recognition are effective only if enough online health information seekers recognize and find value in them. aarp health tools in 2021, aarp hosted a suite of 12 web-based health tools on their website including a pill identifier, symptom checker, and health encyclopedia populated by content licensed from healthline’s health reference library [34]. some of these tools have since been removed or modified; however, we will refer to them throughout this paper as they existed between may august 2021. aarp perks™ browser extension the aarp perks™ browser extension notifies members of aarp benefits while browsing online [35]. though not designed for the purpose of health information-seeking, the extension’s development was informed by the online information-seeking habits of older adults, including passive information acquisition while browsing for other information. seeking user perspectives design thinking emphasizes understanding the needs and constraints of an intended user population. in pursuit of designing a solution that fit the needs of our intended user population, we conducted informal key informant interviews. we convened a non-probability sample of key informants (n=4) consisting of three intended user population members and one secondary population member (table 1). informants were asked about their health information seeking habits and experience with aarp. designing a browser extension for reliable online health information retrieval among older adults using design thinking 5 ojphi informants largely preferred seeking health information offline for queries about their own health or the health of others (50-75%). they also expressed low trust in commercial search engines and that privacy while browsing for health information online is of high importance to them (75%). however, despite mistrust in commercial search engines, most informants expressed interest in using online health-information solutions, such as health search engines (75%). half of the informants were aarp members, but none had ever visited the aarp website and had thus never used aarp health tools. additionally, no respondents were familiar with the perks browser extension. these insights suggested a need for health information solutions that preserve privacy and are accessible in both online and offline formats. table 1: key informant interview insights health information seeking behavior (n=4) primary health information source (own health) % primary care provider 75 commercial search engine 25 primary health information source (others’ health) primary care provider 25 library/books 25 commercial search engine 25 health information websites 25 prompts to search for health information new medication 50 change in symptoms 50 new diagnosis 50 last searched for health information < 2 weeks 25 < 6 months 50 >12 months 25 search engines and privacy trust in commercial search engines designing a browser extension for reliable online health information retrieval among older adults using design thinking 6 ojphi low trust 75 medium trust 25 importance of privacy online for general queries highly important 75 fairly important 25 importance of privacy online for health queries highly important 75 fairly important 25 health search engines familiar with health search engines yes 25 no 75 interest in using health search engines yes 75 unsure 25 aarp experience aarp member yes 50 no 50 visited aarp website no 100 familiar with aarp perks browser extension no 100 table 1: proportion of key informants who indicated agreement with each corresponding item platform research the design thinking process emphasizes “learning by doing” [36]. we embodied this principle by pursuing platform research from a user’s perspective. our interest in the perks browser extension designing a browser extension for reliable online health information retrieval among older adults using design thinking 7 ojphi and aarp’s existing partnerships for hosting valid health information online informed the focus of our platform research. following review of the literature and assessment of present solutions, we evaluated these aarp platform features to observe their underlying mechanisms, identify barriers and facilitators of health information access, and identify opportunities to leverage these features in a feasible design thinking solution. we tested how aarp’s main search bar recalled content in response to health queries and how the aarp perks browser extension behaved when accessing credible and non-credible online health information resources. additionally, we assessed the readability of aarp’s existing health content, as well as the click path and scroll depth necessary to access them. search bar evaluation we evaluated aarp’s main website search bar to observe content recall and page rank. over the course of seven days, we utilized three cookie and cache cleared web browsers to submit 126 queries of six distinct health-related terms in the following categories: medical conditions, medications, and health products (table 2). forty-two queries on aarp.org were conducted on a location-tracking disabled web browser (duckduckgo) and 84 queries were conducted on two location-tracking enabled web browsers (microsoft edge, google chrome). different browsers with different tracking permissions were utilized to observe whether health content recall and page rank would be affected by the presence or lack of permissions. other differences in browser operation and performance were not accounted for. page rank remained identical for all queries across testers and browsers. notably, information sourced from aarp health tools (health encyclopedia, symptom checker, etc.) was not included in this content recall. these insights led us to suspect use of a fixed index that excludes validated health content from aarp health tools, but no use of location-based relevance algorithms to modify page rank. we did, however, suspect use of a data-scraping mechanism to utilize user cookies and cache for location-specific advertising and product placement which may deter use in older adults who experience digital mistrust. table 2: search bar evaluation search terms location-tracking enabled web browser location-tracking disabled web browser tester locations: ma, wa (usa) tester location: or (usa) medical conditions medications health products medical conditions medications health products diab etes co pd gerit ol® eliqu is® pulse oxim eter metli fe® diab etes co pd gerit ol® eliqu is® pulse oxim eter metli fe® designing a browser extension for reliable online health information retrieval among older adults using design thinking 8 ojphi # que ries 14 14 14 14 14 14 7 7 7 7 7 7 table 2: number of queries submitted per search term by tester location and browser location permissions browser extension evaluation we analyzed the behavior of the aarp perks™ web browser extension to infer the mechanics of the extension’s search function and search-activated notification tab. though the notification tab was intended to notify aarp members of member benefits while browsing online, we were interested in whether health content was ever supplied through the extension and, if so, how to leverage this in a solution focused on health information access. we tested responsiveness to health and non-health related search terms and quantified instances of tab presence across search contexts to surmise an algorithm flow that could be used in a design thinking solution. six health and six non-health related terms were submitted to the perks web browser extension search bar. health-related search terms returned zero results, while non-health-related terms returned 15-30 results. multi-word non-health related search terms returned 0-2 results. these observations led us to suspect that the parsing mechanism favored single-word search terms and used a fixed index that excluded validated health content from aarp health tools. table 3: browser evaluation search terms health search terms non-health search terms (single word) non-health search terms (multi-word) “diabetes” “copd” “geritol” ® “eliquis” ® “pulse oximeter” “metlife” ® “vacation” “money” “shopping” “going shopping” “saving money” “going on vacation” table 3: search terms submitted by category to the aarp perks™ web browser extension when assessing valid sources for health information about chronic obstructive pulmonary disease (copd), the perks browser extension notification tab appeared upon access to some reliable online sources for health information, including the mayo clinic and u.s. centers for disease control and prevention websites (figure 1). it also appeared upon access to webmd, a popular online health content publisher who employs and collaborates with health care providers to validate their designing a browser extension for reliable online health information retrieval among older adults using design thinking 9 ojphi content [37]. it did not appear when accessing social media platforms or community-maintained general information sharing platforms (ex. reddit). the notification tab did not appear upon access to american lung association or the federal medlineplus websites. the extension notification tab also did not appear upon access to or submission of search terms to web browsers and search engines. the information displayed in the notification tab while accessing health-related websites was not inclusive of validated health information sourced from aarp’s health encyclopedia, however, a link to access the “symptom checker” health tool was sometimes returned, demonstrating a health information-seeking use case for the extension. behavior of the notification tab led us to suspect use of a whitelisting algorithm to prompt appearance of the tab upon accessing whitelisted universal resource locators (urls). we were unsure whether the whitelist was manually populated and maintained or if the algorithm relied on other website performance metrics for whitelist classification. figure 1: aarp perks browser extension presence across accessed copd health websites, august 19, 2021 figure 1: copd webpage screenshots and presence/absence of aarp perks™ web browser extension on august 19, 2021 click path and scroll depth analysis click path and scroll depth are metrics which describe a user’s experience on a website or platform [38]. click path characterizes the number of clicks needed to access information of interest from designing a browser extension for reliable online health information retrieval among older adults using design thinking 10 ojphi a defined start point (often the website homepage), while scroll depth characterizes the amount of scrolling necessary to access content between clicks [39]. to minimize transaction costs which dissuade user retention and limit accessibility, short click path and low scroll depth are desirable [40]. our user experience analyses assessed the shortest possible click path and scroll depth needed to access clinically validated health information hosted on the aarp website. the shortest path to validated health information on the aarp website through health tools contained a total of nine steps: seven clicks and two scroll events comprising 75% total scroll depth (figure 2). notably, this path required that users know and submit exact search terms. these observations led us to suspect that website users who do not use exact search terms are unsuccessful in locating validated health content passively or actively. this conclusion was consistent with the lack of key informants who had ever accessed health tools content. figure 2: “high blood pressure” content click path, august 2021 figure 2: click path diagram of the steps needed to access clinically validated health encyclopedia content about high blood pressure on the aarp website in august, 2021 content readability comparison test access to valid health information is mediated by its readability [41]. since we aimed to design a solution that leveraged health information already available through aarp channels, we decided to examine aarp health content readability. to do this, we used open-access readability scoring software, textcompare [42], to assess online content hosted by aarp pertaining to the covid19 vaccination [43] compared to online anti-vaccination content [44] via six readability indices: flesch kincaid reading ease, flesch kincaid grade level, gunning-fog score, smog index, designing a browser extension for reliable online health information retrieval among older adults using design thinking 11 ojphi coleman liau index, and automated readability index. information about each index and its formula appears below. flesch kincaid reading ease point scale: 100-90 89-80 79-70 69-60 59-50 49-30 29-10 10-0 easiest to read most difficult to read formula: 206.835 − 1.015 ( 𝑤𝑜𝑟𝑑𝑠 𝑠𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠 ) − 84.6 ( 𝑠𝑦𝑙𝑙𝑎𝑏𝑙𝑒𝑠 𝑤𝑜𝑟𝑑𝑠 ) flesch kincaid grade level point scale: < 5.0-5.9 6.0-6.9 7.0-7.9 8.0-9.9 10.0-12.9 13.0-15.9 16.017.9 > 18.0 easiest to read most difficult to read formula: 0.39 ( 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠 𝑡𝑜𝑡𝑎𝑙 𝑠𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠 ) + 11.8 ( 𝑡𝑜𝑡𝑎𝑙 𝑠𝑦𝑙𝑙𝑎𝑏𝑙𝑒𝑠 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠 ) − 15.59 gunning fog score point scale: 0-5 6 7 8 9-12 13-16 17 18-20 easiest to read most difficult to read formula: 0.4 [( 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠 𝑡𝑜𝑡𝑎𝑙 𝑠𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠 ) + 100 ( 𝑐𝑜𝑚𝑝𝑙𝑒𝑥 𝑤𝑜𝑟𝑑𝑠 ∗ 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠 )] designing a browser extension for reliable online health information retrieval among older adults using design thinking 12 ojphi *contains ≥ 3 syllables smog index point scale: 0-6 7-20 21-42 43-90 91-132 133-182 183-210 ≥ 211 easiest to read most difficult to read formula: 3 + √𝑝𝑜𝑙𝑦𝑠𝑦𝑙𝑙𝑎𝑏𝑖𝑐 𝑐𝑜𝑢𝑛𝑡 coleman liau index* point scale: ≤ 4 5 6 7 7-10 11-12 13-16 ≥ 17 easiest to read most difficult to read formula: 0.0558(𝐿) − 0.296(𝑆) − 15.8 *l=avg. number of letters per 100 words, s=avg. number of sentences per 100 words automated readability index point scale: ≤ 1 2 3 4 5 6 7 8 9 10 11 12 13 ≥ 14 easiest to read most difficult to read designing a browser extension for reliable online health information retrieval among older adults using design thinking 13 ojphi formula: 4.71 ( 𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑠 𝑤𝑜𝑟𝑑𝑠 ) + 0.5 ( 𝑤𝑜𝑟𝑑𝑠 𝑠𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠 ) − 21.43 when compared to an online anti-vaccination article, an aarp article about covid-19 vaccination options ranked less readable by all six readability indices: flesch kincaid reading ease (41.74, 65.11), flesch kincaid grade level (12.82, 7.45), gunning fog score (15.75, 10.26) smog index (14.33, 10.45), coleman liau index (13.17, 9.06), and automated readability index (13.65, 6.87) (table 4). table 4: readability index scores readability index article source aarp article anti-vaccination blog flesch kincaid reading ease 41.74 65.11 flesch kincaid grade level 12.82 7.45 gunning fog score 15.75 10.26 smog index 14.33 10.45 coleman liau index 13.17 9.06 automated readability index 13.65 6.87 table 4: readability scores between aarp covid-19 vaccination article and anti-vaccination article solution design insights gained from intended user perspectives and platform research informed our iterative ideation process. knowing that the perks browser extension was designed around the browsing habits of older adults, we recognized that a feasible solution could leverage this existing aarp platform architecture to 1.) directly provide aarp’s existing validated health content and 2.) validate the reliability of other health content platforms while browsing online. aarp health tools 2.0 aarp health tools 2.0 (aht2) is a web browser extension prototype for health information validation and delivery designed to be responsive to both passive and active health informationseeking methods employed by older adults. leveraging the existing perks web browser extension designing a browser extension for reliable online health information retrieval among older adults using design thinking 14 ojphi architecture, aht2 would employ a similar whitelisting algorithm to notify users of trusted health information sources while browsing online. proposed modifications to the existing architecture include 1.) redirection of the content repository path to a new fixed index that houses aarp’s health encyclopedia entries and 2.) a rewrite of whitelisting conditions to prompt notification tab appearance on non-whitelisted urls rather than whitelisted urls. by rewriting the whitelisting conditions, the extension’s function changes from member benefits notifications to content validation and supply. populating a fixed content index with content from the health encyclopedia benefits the existing parsing mechanism which favors single-word search terms as health condition entries in the encyclopedia were stored with keywords. prototype overview once installed, the aht2 browser extension is enabled by a user within the partner browser’s extension manager. aht2 relies on a whitelisting algorithm to validate urls as whitelisted or not whitelisted. if whitelisted, the url is deemed a trusted source for health information. if not whitelisted, the url is deemed an unverified source for health information. the aht2 notification tab, whose index is populated by health encyclopedia content, is prompted upon access to non-whitelisted urls. to aid in intuitive use, along with prompting the notification tab on access of non-whitelisted urls, aht2 leverages prior work on content certification indicators [32] to communicate the perceived “trustworthiness” of a website to a user. to accomplish this, aht2 returns a “checkbox” indicator upon accessing whitelisted urls and returns a “null” indicator upon accessing nonwhitelisted urls coupled with the notification tab. in this design iteration, the whitelist would be manually populated and maintained rather than relying on other website performance metrics to inform the whitelist classifier. figure 3: aarp health tools 2.0 (aht2) extension whitelisting algorithm flow diagram figure 3: flow diagram of proposed aht2 extension whitelisting algorithm designing a browser extension for reliable online health information retrieval among older adults using design thinking 15 ojphi addressing user needs to address the need for online and offline health information access, aht2 may be supplemented with health information-seeking tips available in print media through aarp’s popular print publications. quick response (qr) codes with step-by-step instructions may also provide an opportunity to support older adults in building digital literacy skills and encouraging reader access to reliable health information online through the aht2 extension. additionally, in the interest of privacy-preservation, aht2 departs from perks by not requiring member sign-in prior to installation or use and inhibits health information queries to be tied back to an aarp member profile. finally, to maximize the impact of aht2, focus on content accessibility is vital. it’s suggested that online health information be written at the sixth-grade reading level to accommodate accessibility needs [41]. findings of the readability comparison tests revealed that a published covid-19 vaccination article on the aarp website ranked less readable in six out of six readability indices when compared to online anti-vaccination content. we propose that aht2 adoption be contingent on review and revision of existing health encyclopedia content to the sixthgrade reading level to broaden access. limitations this work has several limitations. first, the aht2 protype described in this paper has not yet been developed, only designed. therefore, we are unable to evaluate hypotheses related to the use of the extension as an intervention. second, our key informants comprised a small convenience sample. thus, the insights gleaned from these interviews are likely not representative of all members of our intended user population. third, while we describe that the extension relies on manual intervention for whitelist population and maintenance, as well as content revision to an appropriate reading level, we did not consider the maintenance workflow required by aarp personnel to undertake this work, nor associated operational costs. finally, we did not consider any manual or automated feedback mechanisms to inform extension performance improvement, which is vital for the continual refinement of any design thinking intervention. conclusion web-based health information seeking is on the rise across older adult populations in the us and beyond [1]. in the absence of health information seeking solutions that accommodate the health information-seeking habits of older adults, the potential for exposure to health mis/disinformation escalates [45], [46]. the covid-19 pandemic provides a case study of an infodemic in which older adults, a population disproportionately susceptible to covid-19, were also the most engaged with mis/disinformation that encouraged actions which enabled its spread [10], [47]. in commercial settings, refinement of existing solutions to meet evolving consumer needs has led to feasible product and service implementation [48]. our work acknowledges this idea by presenting a case in which existing solutions may be used for alternate purposes when modified through a lens of design thinking. designing a browser extension for reliable online health information retrieval among older adults using design thinking 16 ojphi to promote adoption in older adult communities, it is beneficial for solutions to be operated or sponsored by entities that older adults already trust [17]. aarp is a trusted entity offering valid health information solutions and products which appeal to the online browsing habits of older adults; however, these products are disparate and may not be broadly accessible or delivered at the appropriate reading level. by consolidating these tools and making slight modifications to their function, a new solution emerges with a proposed ability to mitigate the spread of mis/disinformation online. acknowledgements the authors express gratitude to ted moffatt, aarp digital analytics architect, and raymond deschenes, aarp senior advisor of enterprise search, for their guidance and consultation following our research and preliminary ideation phase. the authors also thank alison bryant, former senior vice president of research & enterprise lead for technology & digital equity at aarp for her guidance and feedback during the project upon which this paper was based. financial disclosure no funding was received for this work. the authors report no competing financial interests. competing interests the authors declare no conflicts of interest. references 1. turner am, osterhage kp, taylor jo, hartzler al, demiris g. 2018. a closer look at health information seeking by older adults and involved family and friends: design considerations for health information technologies. amia annu symp proc. 2018, 1036-45. pubmed 2. berkowsky rw, czaja sj. “challenges associated with online health information seeking among older 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double the number that was apprehended in fy 2013. the majority of uacs were apprehended and processed in the customs and border protection’s (cbp) rio grande valley (rgv) sector of south texas. the cbp facilities were not designed to house or care for children for extended periods. resources and personnel were significantly strained in the management and care of uacs. methods in collaboration with multiple partners, the national biosurveillance integration center (nbic) provided an integrated analysis of the health issues that uacs experienced before leaving their home country, while in transit to the u.s., and while being housed in u.s. custody. analytical reports on the health security of mexican and central american nations were shared with federal partners. cbp office of intelligence and investigative liaison (oiil), office of health affairs (oha) workforce health, and nbic analysts evaluated apprehension rates, migration patterns, and potential health hazards related to immigration to our southwest border. nbic began reporting on border health issues at the beginning of fy 2014, through briefs to dhs components and oha leadership and through timely situational reports to federal, state, and local partners. the nbic assisted federal preparedness by reporting the occurrence of infectious disease, the major infectious and non-infectious diseases, recent notable epidemics, and other major health issues affecting mexican and central american nations. the nbic assisted in the healthcare response by evaluating health data of apprehended uacs, evaluating current practices through oha and nbic personnel deployments, and by providing situational and epidemiological awareness of the most probable infectious agents afflicting uacs in cbp custody facilities. results many impoverished central americans lack the ability to receive adequate healthcare and organized criminal elements have hindered access to available resources. even though the health security of these nations have been strained, the infectious diseases afflicting central american children largely mirror those are reported in the u.s. the majority of illnesses afflicting uacs are relatively minor and do not pose a risk to the local populations. common health ailments, including lice, scabies, influenza, and chicken pox are treatable and are acute infections of limited duration. conclusions timely identification and early awareness of health hazards is essential for protecting personnel, ensuring appropriate resource allocation, and supporting public health intervention strategies. for many uacs, the travel from their home country to the u.s. border was physically demanding and required optimum health. the health impacts of uacs to local u.s. populations were minimal. keywords unaccompanied alien children; border; dhs; health; risk assessment *tyann blessington e-mail: tyann.blessington@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e66, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 107 (page number not for citation purposes) isds 2013 conference abstracts enhancing respiratory infection surveillance on the us/mexico borderarizona bids program sentinel surveillance data orion mccotter*1, 2, zimy wansuala1, catherine golenko1, natalie whitfield2, mariana casal1, jonathan schouest1, robert guerrero1, nubia hernandez3, sergio o. alba3 and francisco n. galvez3 1office of border health, arizona department of health services, tucson, az, usa; 2university of arizona medical center, tucson, az, usa; 3secretaria de salud de sonora, sonora, mexico � �� �� �� � � �� �� �� � objective �������� � ������ ������������� ���������� ���� ������� ������� ��� �������������������� �� ��� � �� ��������������� ������������� � ��������� �������� � introduction �������� �� �!����� ������� � ������� ���"�� ���� ��� �����#������� ��������� ���$"�#�%���������� ����&� ��$��%� ���� � ���� ��� � �� ������� ������ ���� ����� ������� ���&� ��� �� ��!� ������������ �� ������� ����� ��� �� ��$����%�� � ��������� �������� ��������� ��� ����'()(� �� �������� � methods ������������������� �������� �� � ����"�#��� � �������� ������� ����� ����� � ��������� �������� ��*������ ��� ������� ������� ������� ��� ��� ���� ����� � ��� ��� �� ����� � �� ������������� ��� � �� �� ��������������� ��� � �������� �� ���+���� ������� ���� ������ �!� �� ������$���������� � ����������� ���,-�./�0%�� ��������������� ���� �� 1��������� �������� ��� ���� �� �������������� ����������� ����� ������ �������� ���2 � ����������� ���� ��� �+���3������������� �� ������ ����� � � �� ��� �� � �� ����� ��� ���� � � ���� &�� ��� �� ��� �� � �� $��#�%�� ����� �2���������� ������� ����� ����!������ ��� ������ �� ������� ����������� � results �� � ������)-4��� �� ����� � ������������ ��� � �� ��������� � � ����� �� �"�#���������������� ����������� �� �� �� �� ���� ������ '()(� ������� ����� � '(),�� 5� �� �� !��� !���� 64� ������ � � �� ��� �������� � ��� ������ � ���� ������� 7���� ��� �� �� �� �������� � ��� ������ �����8� � ��� ���� �������� � ����� ��� �����������!���������� ������� ������ ���� � ���� ��� ���� ����� �� ������� ���������� ������������ ��!���9���1��� ���� &�������� ���� ���������$���� ����������2���:���������� ����������%������� ���� &���� ����� � �� �� �������������� ��������� ���������������� �� ��!� ��� �� �� �����#�� � ������������$ ;6<%�� � ��� �� ��� � ������ ��� ������� ��#��!������ ����� ����� �� ���� &������ ��������������� �� ����� ���� ��������������� ����� �� �$���50�%� �� � �� conclusions * �� �� ������� ���� � ������������������� �� ���� ������� � �� �2��� ��������� ���������� �� ���� �� ��� � ���� ������ ��� ���� ��� � � � ��� �������� ��������������� ��� �� ����������� ��� ����� ������� ����� �� � keywords ������� ���3���������� ��3���� ��3�� ���� &�3��� ���� *orion mccotter e-mail: orion.mccotter@azdhs.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e135, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts methodology of epidemic risk management in kazakhstan with open-source eidss aizhan esmagambetova2, alexey burdakov*1, stanislav kazakov3, andrey ukharov1, erlan sansyzbaev3 and vladimir kazakov3 1black & veatch, overland park, ks, usa; 2agency of the republic of kazakhstan on consumer rights protection, astana, kazakhstan; 3kazakh scientific center of quarantine and zoonotic diseases, almaty, kazakhstan objective development and approbation of the epidemic risk estimation and management methodology based on multivariate analysis per administrative clusters of kazakhstan using the electronic integrated disease surveillance system (eidss) technical capabilities. introduction in order to transition the forecasting, estimation and management of epidemic risks to individual administrative areas, the agency for consumer rights protection of kazakhstan has developed a concept for modernizing the existing national system of the epidemiological surveillance (ses). it is proposed that the data from the ses (epidemiology, sanitary and epidemiological background, external environmental objects and database) will be consolidated to generate a new epidemic risk control and management tool called the regional sanitary-epidemiological passport (rsep) for each of kazakhstan’s districts. the rsep will contain infectious incidence rate dynamics according to the primary (marker) infections (7 nosologies) including a forecast for 2-3 years, and natural and soil foci gis maps for especially dangerous pathogens (edp) with a 3-5 year forecast of their activity. the rsep is planned as a new working tool for epidemiologists to aid in making objective estimates, forecasting epidemic risks in particular areas of kazakhstan, and taking preventive steps to lower epidemic risks. methods the rsep methodology is based on area specific data of infectious incidence rate marker indicators, and the environmental conditions based on sanitary-epidemiological and socio-economic spheres. basic nosological forms registered in all regions and determining the overall infectious morbidity are used as the epidemic process markers for districts. the average long-term morbidity data per 100,000 of population for a period of 10+ years is used to determine the marker levels permissible (typical) for a particular area. an auxiliary forecast component is the immune background for the main vaccines in use, including bcg vaccination of children up to 1 year of age in maternity hospital. edp natural and soil foci and their activity of epizootic manifestations determine the risk level for populations living in unfavorable areas. since population sporadic morbidity in these foci hinders the ability to forecast morbidity, rsep also contains gis maps of natural (soil) foci both basic and significant for most of the areas, and their distribution and epizootic activity based on long-term observations. public catering facilities, schools and food industry facilities have the greatest impact on the markers of the epidemic process selected in rsep. based on a set of risk assessment sanitary-epidemiological indicators, an aggregate report is generated, which describes the situation in the region with the control criteria for the markers. the open-source eidss will serve as the main tool of the proposed methodology in addition to its functions of collecting, processing and analyzing epidemiological, clinical and laboratory information on 64 controlled infectious diseases in 269 institutions of the agency as the national ses. in 2012-13 we tested the eidss capability to forecast the disease risk for the kazakhstani population of contracting the crimean-congo hemorrhagic fever. the forecast provided by the application proved to be 81.3/88.9% accurate [1]. results the concept and template methodology of estimation and management of epidemic risks have been developed. its approbation by the cchf has been conducted, and a high level of accuracy was shown [1]. tasks for the detailed design and implementation of the methodology were formulated, including: • adjustment of estimation methods (increasing of the number of factors, use of specific algorithms for individual nosologies) • formation of a reach back data bank 15-20 years deep on 7 infections based on historical data • creation of an electronic archive of gis maps of edp natural foci • application of eidss for implementation of a substantial portion or the whole methodology. conclusions the methodology approbation results (cchf) and availability of the eidss electronic system at the disposal of the agency allow us to conclude the efficiency of the methodology and, upon its further development, to introduce it as a tool available to epidemiologists from national to district levels for effective risk assessment and management. keywords risk management; eidss; marker indicators; surveillance system references 1. esmagambetova a., et.al. accuracy of eidss software prognosis on cchf natural foci activity in kazakhstan. ojhpi. 2014; 6(1). *alexey burdakov e-mail: burdakov@usa.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e127, 2015 ojphi-06-e170.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 96 (page number not for citation purposes) isds 2013 conference abstracts use of severity indicators in a public health surveillance system rhonda a. lizewski*1, howard burkom2, joseph lombardo2, christopher cuellar2, yevgeniy elbert2 and julie pavlin1 1armed forces health surveillance center, silver spring, md, usa; 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2international society for disease surveillance, boston, ma, usa objective to identify and characterize challenges experienced by public health practitioners conducting surveillance for the presence of influenza, novel respiratory diseases, and globally emerging viruses in an era of limited resources. introduction public health practitioners endeavor to expand and refine their syndromic and other advanced surveillance systems that are designed to supplement their existing laboratory testing and disease surveillance toolkit. while much of the development and widespread implementation of these systems had been supported by public health preparedness funding, the reduction of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. the appearance of h3n2v and other novel influenza a viruses required agencies to carefully identify which systems provide the most cost-effective data to support their public health practice. recent enterovirus d68 outbreaks, along with the global emergence of influenza a (h7n9), the global emergence of influenza a (h7n9), middle east respiratory syndrome coronavirus (mers-cov), ebola virus strains, and other viruses associated with high mortality, emphasize the importance of maintaining vigilance for the presence of emerging disease. methods this project included a review of data obtained from a survey of public health practitioners recruited among members of the international society for disease surveillance (isds) public health practice committee (phpc) during 2012 and 2013 (1, 2). in these surveys, questions were selected for discussion and additional responses collected from influenza surveillance coordinators using a web-based survey tool managed by isds staff on behalf of the phpc. during 2014, additional information was requested to assess approaches to identify novel influenza strains, mers-cov, and other emerging viruses. special emphasis was made to obtain information on comparative approaches to cost-effective surveillance in follow-up to an isds policy paper (3). results responses from the initial surveys suggest that most jurisdictions are challenged to continue to utilize a variety of surveillance systems for conducting disease surveillance in an era of diminishing resources. one major challenge is the recruitment and retention of well-trained and experienced public health and informatics staff to maintain these systems. many public health practitioners have been asked to establish new surveillance protocols for an increasing number of diseases associated with novel and emerging viruses including influenza a (h3n2)v infections associated with agricultural fairs and ruling out influenza a (h7n9), mers-cov, and even ebola virus infections. most jurisdictions continue to struggle to determine which surveillance systems are the most cost-effective for providing the most valuable data in the face of decreasing funding. conclusions public health agencies strive to develop and maintain cost-effective disease surveillance systems to better understand the burden of disease within their jurisdiction. the emergence of novel influenza and other respiratory viruses and other emerging diseases offer new challenges to public health practitioners. the importance of maintaining sufficient infrastructure and the trained personnel needed to operate these surveillance systems for optimal disease detection and public health response readiness cannot be understated. expansion of academic training programs and promotion of careers in public health surveillance will provide a pool of competent professionals to staff public health agencies. keywords situational awareness; ebola; mers-cov; novel influenza surveillance; resource limitations references [1] siniscalchi aj, schulte a. 2013. can novel flu surveillance be conducted with limited resources? ojphi;5(1):169. [2] siniscalchi aj, ishikawa c. 2014. searching for mers and novel flu with limited resources. ojphi;6(1):e65. [3] mirza n, reynolds t, coletta m, et al. 2013. steps to a sustainable public health enterprise: a commentary from the international society for disease surveillance. ojphi;5(2):1-12. *alan siniscalchi e-mail: alan.siniscalchi@ct.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e53, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts building a better syndromic surveillance system: the new york city experience robert mathes*, jessica sell, anthony w. tam, alison levin-rector and ramona lall new york city department of health and mental hygiene, queens, ny, usa objective to evaluate temporal and spatial aberration detection methods for implementation in a local syndromic surveillance system. introduction the new york city (nyc) syndromic surveillance system has monitored syndromes from nyc emergency department (ed) visits since 2001, using the temporal and spatial scan statistic in satscan for aberration detection. since our syndromic system was initiated, alternative methods have been proposed for outbreak identification. our goal was to evaluate methods for outbreak detection and apply the best performing method(s) to our daily analysis of syndromic data. methods we tested six temporal aberration detection methods: a modified c2 algorithm [1], a cusum algorithm, the holt-winters (hw) smoothing method, a generalized linear model (glm), a temporal scan statistic in satscan [2], and an autoregressive integrated moving average (arima) model. we tested four spatio-temporal methods: a generalized linear mixed model (glmm), a bayesian model [3], and the spatial scan statistic using the bernoulli probability model and space-time permutation scan statistic in satscan [2]. we analyzed ed visits that occurred in nyc during 2010-2011. to test a method’s ability to detect different outbreak types, we added simulated outbreaks to nyc syndromic data. a total of 180 datasets were created, each containing one spike of varying duration and magnitude, allocated either to a single zip code, a cluster of zip codes, or citywide. to compare the performance of these methods, we estimated sensitivity, specificity, timeliness, and positive predictive value (ppv) of signals. timeliness was estimated by calculating the earliest day of detection as a proportion of the outbreak length before the peak number of excess cases (timeliness ranged from 1 to 0, with detection on the first day of an outbreak resulting in a timeliness measure of 1). to determine how difficult it would be to implement these methods into our system, we recorded programming time, computer run time, and skills (detailed coding or statistical) needed to program the method. results the temporal scan statistic, our current method for temporal outbreak detection, performed the best in regard to sensitivity and timeliness of detection (table 1). specificity was low however, suggesting the method may signal often, even when there is no outbreak. all methods had poor ppv, so when a method did signal, it was unlikely to be an outbreak. among the spatial methods, the glmm had the highest sensitivity (table 2), though it signaled the most often. of note, the methods performed better when the outbreaks were of longer duration (>5 days), both in detecting outbreaks at all and the timeliness of outbreak detection. the easiest method to code was the modified c2 and the most difficult was the bayesian spatial model. conclusions while there was variability in method performance, we feel there is no one best method for outbreak detection, as we have to weigh issues like signal frequency, specificity, and timeliness. we also found certain methods required time and skill to code, which is useful when deciding which methods to implement into a daily system. our poor ppv may be explained in that methods are detecting unlabeled outbreaks given we used actual syndromic data. our next step is to continue comparing results between these methods, and select the most promising methods to run prospectively in our current system. table 1. metrics of the temporal models running at a fixed alert rate of 0.01 (1 alarm/100 days) *alert generated at p<0.01 table 2. metrics of the spatial models keywords evaluation; syndromic surveillance; aberration detection methodologies references 1. tokars, j.i., et al., enhancing time-series detection algorithms for automated biosurveillance. emerg infect dis, 2009. 15(4): p. 533-9. 2. kulldorff, m. and i. information management service, satscan v9.1: software for the spatial and space-time scan statistic http://www. satscan.org/, 2009. 3. corberan-vallet, a. and a.b. lawson, conditional predictive inference for online surveillance of spatial disease incidence. stat med, 2011. 30(26): p. 3095-116. *robert mathes e-mail: rmathes@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e39, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using syndromic surveillance to characterize unintentional ingestions in children alyssa z. chase*1, mansi agarwal1, maria mercurio-zappala2 and mark su2 1syndromic surveillance, nyc department of health, long island city, ny, usa; 2nyc poison control center, new york, ny, usa objective to describe unintentional ingestions (uis) in children <5 years using syndromic data from emergency departments in new york city (nyc) from 2010 to 2014. introduction uis are among the leading causes of injury in children younger than 5 years in nyc1. about 3000 calls are received each year by the nyc poison control center (pcc) for this age group1. common ui exposures include medications, cosmetics, household cleaners, foreign bodies, and pesticides2. we examined uis in nyc from january 2010 to july 2014 for children <5 years to investigate the utility of syndromic surveillance in conjunction with the pcc in capturing real-time pediatric uis over time. methods chief complaint free-text and icd9 codes from the nyc syndromic surveillance system were scanned for keywords related to uis, including “accidental ingest” and “poisoning” in children <5 years. chi-square tests were used to assess weekday vs. weekend, month, and year trends in types of uis from 2010 to 2013. neighborhood poverty level (based on patient zip code) was defined as the percent of residents with incomes below 100% of the federal poverty level (per american community survey [acs] 2008-2012) and grouped into 4 categories: <10%, 10% to <20%, 20% to <30%, or 30% in poverty. using a cochran-armitage trend test, we compared the proportion of the population <5 years-old (per census 2010) with a ui visit across neighborhood poverty levels. a multivariable negative binomial regression model was used to examine the association between the zip code-level number of medicinal uis among <5 yearolds with the proportion of all children <6 living with a grandparent (per acs 2008-20123), adjusting for neighborhood-level poverty; the offset term was the log of the total population of <5 year-olds. results we identified 11,605 uis from over 2 million ed visits for children <5 years. mean age was 1.8 years (median 2 years), and 53% were males. uis involving foreign bodies, notably coins, were the most common visit type (39%), followed by visits for medications (20%). analgesics were the most commonly mentioned medication, though 48% of all ui visits for medications did not specify the medication. these trends did not vary significantly by weekday vs. weekend or by month. there were significant annual differences in types of ui visits (p<0.0001) and in types of medicinal ui visits (p=0.01). between 2010 and 2014, uis of pesticides decreased from 4.6% to 2.6% while coin ui increased from 5.4% to 10.7%. reporting uis as “unknown” also increased from 19.6% to 24.6%. for the medicinal visits, the observed association between year and medicinal type was attenuated after “unknown” and “other” medicine types were removed from the analysis. the proportion of children <5 years with ui visits increased with increasing neighborhood poverty level (p<0.001). we also found a positive association between the rate of medicinal uis per 1,000 children and the proportion of children <6 living with a grandparent in a given zip code (p<0.001). conclusions nyc eds see over 2500 visits for uis per year among children <5 years, similar to the number of calls the pcc receives annually for the same age group. moreover, the pcc also listed analgesics as the most commonly reported medication-related call, and similarly found an association between lower neighborhood income and higher ui rates1. taken together, our results suggest syndromic data are representative of reported nyc uis in children. further work will determine congruence between pcc calls and ed visits, with the ultimate goal of improving the completeness of real-time ui surveillance in nyc. keywords syndromic surveillance; poisoning; poison control center; pediatrics references 1. wheeler k, hoffman r, kass d, leighton j. unintentional poisoning in new york city children. nyc vital signs 2009, 8(2): 1-4. 2. the new york city department of health and mental hygiene. household poisons & kids. accessed 13 august 2014. http://www. nyc.gov/html/doh/html/environmental/poison-home-kids.shtml 3. united states census bureau / american factfinder. “b10001 : grandchildren under 18 years living with a grandparent householder by age of grandchild” 2008 – 2012 american community survey. u.s. census bureau’s american community survey office, 2012. web. 25 august 2014 <http://factfinder2.census.gov>. *alyssa z. chase e-mail: achase@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e116, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 73 (page number not for citation purposes) isds 2013 conference abstracts near real-time monitoring of emergency department syndromic surveillance data during the 2013 super bowl and mardi gras events in new orleans, la jenna iberg johnson*, julie hand and raoult ratard louisiana office of public health infectious disease epidemiology, new orleans, la, usa � �� �� �� � � �� �� �� � objective �������� �� �� ����������� ��������� ������������� ��� ���� �� �������� � �� ������������� �������� �� ������������� � �� �� � �� ������������������������ �������������� ��� ! introduction � ������ ���������������"�#����$��� �%�"$&������� ����������� ����� ��� �%'(&� ��������� ������������� ���� ������ �����'����� '��� �(� �� ������ ���%�''(�&!��''(���� ��� ������������ � � ���� ������� ������������ ����������� � ��� �� ������ ����'(��� � ���) ���� ������� �������� ������� !�� ��*���� ��� �(� �� ��'������������ �� ����%*('��&�����"$�� � ��''(�� ������ �������� ��� ��� �� �� ������+���� ������� ���������� �����������)������� � �� ����� �� � ������������ !�"� ����� ����� ������������� �������� �,� ������-� �� ����* ����� �������.�� 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���� ����!� ������ �������� ��������� �����) � � � ���������������0�)��/������� ���������������� ��� ��� � ��6� �� ����*������ ��!�1231=3c%d&327?)3283! *jenna iberg johnson e-mail: jenna.ibergjohnson@la.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e102, 2014 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 146 (page number not for citation purposes) isds 2013 conference abstracts technical challenges of syndromic surveillance system deployment in a health information exchange julio c. silva1, charles cox2, dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1 and michael j. waddell3 1rush 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� ��������� �� & � �� ����� ��-������ ���-� � � � ������������������������-�� �� ����� ����� ����������������� �� �� �� ������-�� �� ����� �� ������ ����� � ����� �� �� ������������ � �* keywords ������� � ��������� �a�"#%'(�%)a����� ��������� ������ ����� acknowledgments "#%'(�%)� � � ������� ��� ��� #+� (���� ��� � ��� (���� ��� �������& � ���� ���� %���� ��� �� �������� '� ��� �� ��� ���� �-���� ������ � b@6cb�&4d&6&4<<9�����b@6cb�&66&6&4166* *gillian s. gibbs e-mail: gillian_gibbs@rush.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e6, 2014 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e109, 2014 technical description of the distribute project: a community-based syndromic surveillance system implementation 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi technical description of the distribute project: a communitybased syndromic surveillance system implementation william b. lober1, blaine reeder2, ian painter1, debra revere1, kim goldov1, paul f. bugni1 justin mcreynolds1, donald r. olson3 1. university of washington, seattle, wa 2. university of colorado | anschutz medical campus, aurora, co 3. international society for disease surveillance, new york city, ny abstract this paper describes the design of a syndromic surveillance system implemented for communitybased monitoring of influenza-like illness. the system began as collaboration between colleagues from state and large metropolitan area health jurisdictions, academic institutions, and the non-profit, international society for disease surveillance. over the six influenza seasons from 2006 to 2012, the system was automated and enhanced, with new features and infrastructure, and the resulting, reliable, enterprise grade system supported peer comparisons between 44 state and local public health jurisdictions who voluntarily contributed summarized data on influenza-like illness and gastrointestinal syndromes. the system was unusual in that it addressed the needs of a widely distributed, voluntary, community engaged in real-time data integration to support operational public health. keywords: syndromic surveillance, secondary use of health data, internet, public health standards, surveillance practice correspondence: lober@uw.edu doi: 10.5210/ojphi.v5i3.4826 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction and overview in this paper we describe the technical components, architecture and processes of the distribute system implementation as developed for the distributed surveillance taskforce for real -time influenza burden tracking & evaluation (distribute) project. initiated in 2006 by the international society for disease surveillance (isds), and operated by isds with technical support from the university of washington (uw), the project enabled community-based public health syndromic surveillance. syndromic surveillance is the practice of monitoring existing health data sources for early detection and ongoing monitoring of adverse changes in population health [1]. the objective of public health syndromic surveillance is to collect, analyze, and interpret data about health events to achieve early detection of an event of public health interest such as an influenza outbreak and http://ojphi.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi to provide timely dissemination of collected information to decision makers. monitoring for influenza outbreaks is of particular value for syndromic surveillance [2]. this monitoring, typically based on influenza-like illness (ili) chief complaint data extracted from emergency department (ed) or urgent care (uc) facility electronic medical records (emrs), can provide advance warning of an influenza season before laboratories can confirm results of viral isolates [3]. chief complaints from patients that indicate ili include symptoms such as “fever”, “cough” and “sore throat”. greater detail regarding the history, epidemiologic basis, and community participatory nature of the distribute project have been published previously [4-6]. briefly, the distribute project began as a set of methods and procedures developed by the new york city department of mental health and hygiene to monitor age-stratified ed visits for fever and respiratory illness, based on data from the 2001-2001 and 2005-2006 influenza seasons [7]. the distribute project was initiated in 2006, and during the 2007-08 influenza seasons, the system was operated using both manual and automated processes to integrate and visualize data from eight participating state and local health jurisdictions [5,6]. findings from this surveillance activity were disseminated via a public web site, www.syndromic.org. by 2011, 44 state and local public health departments, from 29 states, transmitted summarized daily ed and ambulatory care visit data using locally defined ili syndromes to distribute. six sites also transmit data on visits for illnesses classified as gastrointestinal (gi) syndrome. data sent to the distribute system implementation were validated and normalized before being aggregated into a common data repository. metadata were maintained within the distribute system implementation to provide context for the surveillance data. these metadata gave information about the contributing health jurisdictions, about the methods they use to collect their surveillance data locally, including definitions for different syndrome categories, and performance characteristics of the data transmission from the jurisdiction. both data and metadata were maintained through automated and manual curation processes. aggregated syndromic surveillance data were made available to member jurisdictions and other stakeholders through visualizations, downloads on a private web site and apis. one set of apis was used by isds to maintain a public web site showing a limited view of weekly data in the distribute system implementation, at www.isdsdistribute.org. three perspectives demonstrate signature characteristics of the distribute system implementation in 2012, and these characteristics were reflected in the system’s architecture. from an organizational perspective, the distribute project was a voluntary system. state and local health jurisdictions elect to participate as a way of comparing their data with that of neighboring jurisdictions. from an epidemiologic perspective, the distribute system implementation relied on summarized ed/ambulatory care visit counts and totals, not visit level data, or so-called “line listings”. and, from a technical perspective—the focus of this paper—the system underwent a transition from manual processes used to monitor the 2007-08 influenza season [4-6] to the fully automated processes and infrastructure described herein, which have evolved through the subsequent years of seasonal and pandemic (h1n1) influenza. http://ojphi.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi technical overview figure 1 displays an overview of the process flow of services of the distribute system implementation architecture, grouped as authentication, upload, notification, application and data quality services. all services rely on a central database. when ili data files are uploaded to the system, notification services send a confirmation e-mail to both the uploader and uw cirg technical staff. application services route the data to a central database repository where the data are run through a series of automated data quality (dq) processes. a dq team manually reviews data daily and weekly to detect anomalies. the development (operations) team is on call to handle any aberrations in system function. once data are processed, they are made available in 2 ways: the distribute project public web site (http://www.isdsdistribute.org/) which is open to anyone via the internet and the distribute system implementation restricted web site which requires a member id. components of the distribute system implementation are described in detail in the sections that follow. figure 1. overview of the process flow in the distribute system implementation. system architecture hardware. the distribute system implementation runs in a three-tiered development environment: a development server, production and staging server, and an upload and metadata -editing server. three virtual machines host apache servers and mysql databases running on linux. the central database repository consists of a master database and two slave databases where data are replicated for fault tolerance and to ensure data access for uploads, development and application http://ojphi.org/ http://www.isdsdistribute.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi services. the central database embodies a star schema--well known for data warehouse design [8] --that relies on several dimension tables to describe stratified or distinct observations and facilitates flexibility in managing multiple definitions for the same data set. for example, age groups can be defined on-the-fly versus being hard coded and user-defined syndromes can be accommodated with a minimum of effort. a scheduled periodical scheduled job improves query performance for common elements by generating aggregated tables that contain a smaller number of stratifying elements (jurisdiction id, ed visit date) and synchronizes the master database with the two slave databases to improve performance and support scaling. the system is implemented as a series of integrated virtual machines (vm) on a kvm framework [http://www.linux-kvm.org]. the complete national implementation is distributed among 3 vms hosted on a single commercial-grade server (16 cores, 32 gb, 400 gb raid 10). additional server hardware in alternate locations is available to support high reliability. the vm strategy also supports deployments on commercial “cloud” infrastructure, such as amazon’s elastic computing cloud (ec2). authentication. to accommodate convenient login to the restricted web site (see fig. 2), uw cirg developed and implemented a novel authentication services package, developed as a module for the open source apache2 web server, and called “apache2::authany” (authany) which is part of a codebase that will be open-sourced. authany supports authentication through google, basic authentication, shibboleth (uw and protect network), and ldap (lightweight directory access protocol) by linking different accounts into a single identity figure 2. login page for the distribute system implementation restricted web site http://ojphi.org/ http://www.linux-kvm.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi uploads. new data files can be uploaded manually or automatically via http or sftp protocols. typically, jurisdictions providing data use automated extraction processes and automated uploading locally. these files are added to a queue for processing where they are picked up by data transformation and import processes from application services. data formats currently supported include: 1) a data aggregation file in comma separated value (csv) format; 2) a csv file with gipse-formatted [9] data provided by cdc for biosense [10] data uploads; and 3) a file with gipse-formatted xml data provided by a regional health information exchange. these file formats were developed and adopted as necessary to aggregate data from data providing jurisdictions and to reduce burden for meeting rigid data formatting requirements. while most participating jurisdictions upload files with data for a single jurisdiction, cdc uploads files with data for multiple jurisdictions. data aggregation files contain separate broad and narrow ili syndrome definitions. application services. application services for the distribute system implementation are protected by a restricted web site that requires authany authentication. here members can view metadata about each data providing jurisdiction, see data timeliness trends and run interactive visualizations that show comparison of ili trends for different regions. application services are made possible through import and data transformation processes, two application programming interfaces (apis) and visualization, monitoring and editing tools. when the periodic job for upload services detects a new file it: 1) identifies the file format by checking the metadata for the uploading jurisdiction; 2) calls the transformer process appropriate to the data file type for import and storage to the central database; 3) validates the date format and syndrome definitions of the transformed data; and 4) imports data that are syntactically valid to the central database. the data transformation process generates a human readable report that is logged and displayed in an events log (see fig. 3) along with messages about successful uploads. figure 3. events log in the distribute implementation system restricted web site. http://ojphi.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi apis. the distribute system implementation features two apis: a data api and a metadata api. the data api is used internally to allow authenticated applications to access specific types of data in the system to monitor dq, create visualizations and make comparisons of regional data sets and by the public distribute site to visualize data. the metadata api is designed to work in conjunction with the data api. it is used internally to access the metadata for participating state and local health jurisdictions and by the public distribute site to access background and event information interacting with syndromic surveillance data the distribute system implementation generates three types of visualizations on the web site: interactive "annotated time line" visualizations, time-series graphs, and heat maps. the "annotated time line" graphs use a google javascript library to generate an adobe flash [11] widget based on distribute data api. "time series graphs" are png images created by the google chart api in response to an http request (see fig 4 example comparing ili broad and ili narrow syndromic definitions for a de-identified jurisdiction). these images are generated using by scripts that call our data api, encode the output into query parameters, issue get requests to google, and save the resultant png files on our web server. heat map visualizations are also periodically generated and cached on the server, using code that integrates calls to the open source “r” statistical software. figure 4. view of an ili broad and ili narrow comparison for a de-identified jurisdiction in distribute system implementation web site. jurisdiction metadata. metadata provide the context and background of the data provided by each contributing jurisdiction. a metadata editing tool (see fig. 5) is allows administration and modification of metadata content (for example, number of health care facilities from which data are collected; free-text descriptions of ili syndrome definitions; contact information for each juri sdiction, etc). http://ojphi.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi figure 5. metadata editor in the distribute system implementation restricted web site. another type of metadata is concerned with the behavior of the data, such as upload patterns. fig. 6 is a de-identified visualization of uploads for a number of sites over the last 100 days . a long blue bar indicates a site has uploaded every day. white gaps indicate interruptions to the uploads, and make evident the pattern of activity. in addition to daily and weekly manual data inspection, these views can help determine if there are upload anomalies based on usual upload behavior. figure 6. data timeliness view for uploads in the distribute system implementation restricted web site. public site. data from the system is accessed through an api to produce a site and set of visualizations for public access based on weekly trends for a trailing one-year period, [www.isdsdistribute.org]. that site was developed and is maintained for isds by programmers working at the boston childrens’ hospital informatics program. national trends can be seen through a map display of aggregated data from the ten federal health regions, and site visitors may “drill down” to see the recent trends in the localities of data-providing jurisdictions that allow this broad access to their data. http://ojphi.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi gossamer health platform the distribute system implementation runs on the gossamer health (good open standards system for aggregating, monitoring and reporting health) platform, an open source framework developed by the uw clinical informatics research group (cirg). work on gossamer health began in 2001 with the syndromic surveillance information collection (ssic) project [12,13] and has continued in the development of novel informatics applications and approaches to data collection, integration, storage, aggregation, display, and dissemination, in several domains, including surveillance and clinical research support [12-18]. the distribute system implementation has contributed important advances in notification, authentication, and visualization to the gossamer health platform. innovation and customization the distribute system implementation has been tailored for specific uses in two different ways. some applications of the distribute system implementation have used new syndromes while other implementations of the system have created purposespecific systems with distinct sets of data. six sites are presently providing gastro-intestinal data as a way of exploring the utility of the system for the comparison of public health indicators beyond influenza, using the same community-based model. similarly, a research collaborative within the community of practice uses the distribute system implementation to investigate the utility of a common syndrome across jurisdictions. while at first it seems obvious that a single syndrome definition would make data from multiple jurisdictions more comparable, in fact local variation in health care systems, coding practices, and clinical practice creates variation in the underlying clinical data which may be compensated for by local variation in syndrome definition and “tuning” by public health agencies. it remains an open question whether the best national or regional picture of surveillance comes from combining syndrome data based on a uniform definition, or syndrome definition data based on a “best local” definition. while this question is well outside the scope of this paper, it has been useful for the distribute system implementation to develop the technical capabilities to support this type of inquiry. purpose-specific systems have been created both for demonstration and operational uses. since 2009, the distribute system implementation has been used as part of the integrating the healthcare enterprise (ihe) showcase demonstration of a model health information exchange (hie) at the annual health information management systems society (himss) meeting, and that the 2009 public health information network meeting. in those settings, the system has been used to demonstrate the surveillance role of state and federal public health agencies in demonstrations of standards-based interoperability. the data in those systems was typically derived from real-time monitoring of simulated patient events within the showcase. the distribute system implementation was also used for an operational purpose-specific system, by making aggregate, de-identified syndrome data from states and sub-state regions in the pacific northwest available to canadian agencies immediately before and during the 2010 winter olympics in vancouver, british columbia. http://ojphi.org/ technical description of the distribute project: a community-based syndromic surveillance system implementation 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi conclusion and future work the distribute system implementation has served its purpose in demonstrating the value of a community-based system in facilitating local and state public health agencies access to the surveillance indicators of peer jurisdictions. while parts of this architecture are somewhat routine, and other parts are somewhat novel, the distribute system implementation has given us a sense of the complexity required for a system that serves the need for reliable, elective sharing of a relatively homogeneous set of data among a community of voluntary contributors. going forward, the data handling and visualization capabilities of the system are well aligned with the public health business processes identified by isds as part of their recommendations for the syndromic surveillance meaningful use criteria [19]. the distribute system implementation has the ability to incorporate multiple indicators, and to serve as a comparison platform across jurisdictions, are important basic capabilities, but it is important for the public health community to develop a roadmap for their needs to drive the further development of the system acknowledgements the distribute system implementation is a collaborative activity which has involved support from the markle foundation, and the centers for disease control. a portion of the funding for the distribute system implementation supported the development, maintenance, and operations of the platform described in this paper. the annual meetings of isds have provided an opportunity for the distribute community to engage with, and improve the distribute system implementation, and many community members have been active outside of this formal opportunity. special acknowledgment is due to farzad mostashari, rick h effernan, marc paladini and howard burkom, who conceived, advocated for, and shaped the research agenda that drove the technical development of the distribute system implementation. also, special thanks to the board of directors of isds during the period distribute operated for their support and nurturing of this project: david buckeridge, chair, john brownstein, vicechair, howard burkom, joe lombardo, julia gunn, wendy chapman, john paul chretien, and marc paladini. in the interest of disclosure, william lober was a board member of isds during this period, and donald olson served as research director for isds and scientific director for the distribute project from 2008-2012. references 1. henning k. 2004. overview of syndromic surveillance what is syndromic surveillance? 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17683196&dopt=abstract http://dx.doi.org/10.1371/journal.pmed.0040247 http://www.adobe.com/flashplatform/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14633933&dopt=abstract http://dx.doi.org/10.1197/jamia.m1356 technical description of the distribute project: a community-based syndromic surveillance system implementation 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e224, 2014 ojphi 17. hills r, lober w, painter i. biosurveillance, case reporting, and decision support: public health interactions with a health information exchange. in: zeng d, chen h, rolka h, editors. biosurveillance and biosecurity: springer berlin / heidelberg; 2008. p. 10-21. 18. painter is, hills ra, lober wb, randels km, sibley j, et al. 2008. extending functionality of and demonstrating integrated surveillance for public health within a prototype regional health information exchange. amia annu symp proc. 6(969), 969. . pubmed 19. international society for disease surveillance. final recommendation: core processes and ehr requirements for public health syndromic surveillance: international society for disease surveillance (isds) 2011 january 11, 2011. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18999244&dopt=abstract 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts timeliness of chlamydia laboratory and provider reports: a modern perspective patrick t. lai*1, 3, janae e. johns2, uzay kirbiyik2, 3 and brian e. dixon2, 3 1indiana university, school of informatics and computing, indianapolis, in, usa; 2indiana university, richard m. fairbanks school of public health, indianapolis, in, usa; 3regenstrief institute, center for biomedical informatics, indianapolis, in, usa objective to analyze the time delay between a chlamydia positive test diagnosis and when a laboratory and/or a provider sends a report to a local public health department. introduction timeliness of reports sent by laboratories and providers is a continuous challenge for disease surveillance and management. public health organizations often collect communicable disease reports with various degrees of timeliness raising the concern about the delay in patient information received (1). timely reports are beneficial to accurately evaluate community health needs and investigate disease outbreaks (2). according to indiana state law, chlamydia reports are required to be sent to public health within 3 days after a positive test result confirmation (3). therefore, laboratories and providers must be accountable and comply with regulation to ensure accurate data quality of disease assessment. methods a sample of 2,428 chlamydia laboratory and provider reports were collected during the period from may 2012 through july 2012 from a local health department serving the indianapolis area. due to absence of test confirmation dates, dates that a report is sent to public health, and other missing data, only 1,752 reports were included in this study. the time delay was calculated by determining the difference between when the initial report is sent to public health following positive confirmatory test by the laboratory. reports were differentiated as either a laboratory report or a provider report coming directly from a clinician or a hospital setting. statistical analyses and frequency tables were conducted using sas 9.4. results table 1 displays the counts of chlamydia laboratory and provider reports according to the time delay in days, the percentage of reports sent to public health within 3 days, and the summary statistics for the two types of reports with a graphical representation shown in figure 1. there is a clear lag between a lab test and when a provider report is sent to public health. negative binomial regression result was highly significant with p < 0.001. conclusions this study shows the importance of continued examination of timeliness of disease reporting from both laboratory and provider settings. most lab reports are received electronically and comply with state law. however, reports from providers tend to be fax-based and received later than the 72 hours desired by health officials. with the greater adoption of electronic health records (ehr), it might be possible to further enhance disease surveillance through more timely provider-based reporting which could also reduce the volume of missing data from provider reports like those observed with electronic lab reporting (2). future research should examine ehr capacity and clinical workflows to improve provider-based reporting processes. figure 1 frequency count and time delay of chlamydia reporting for laboratory and provider reports keywords public health reporting; timeliness; chlamydia; data quality; communicable disease control acknowledgments this project was supported by grant number r01hs020909 from the agency for healthcare research and quality. the content is solely the responsibility of the authors and does not necessarily represent the official views of ahrq. references 1) overhage jm, grannis s, mcdonald cj. a comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. american journal of public health. 2008 feb;98(2):344-50. 2) dixon be, mcgowan jj, grannis sj. electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. amia annu symp proc. 2011;322-30. 3) indiana administrative code: title 410 indiana state department of health. indiana general assembly. http://www.in.gov/legislative/iac/ iac_title?iact=410. *patrick t. lai e-mail: ptlai@imail.iu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e141, 201 effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study arun keeppanasserril 1 , anil matthew 1 , sapna muddappa 1 1 amrita school of dentistry, amrita institute of medical sciences (aims), india abstract loss of teeth and resultant resorption of the residual ridges is a major oral health problem in india. the resorption leads to irreversible loss of bone volume of the jaws and seriously undermines retention and stability of future dentures. loss of masticatory efficiency causes nutritional deficiencies and affects quality of life. however, construction of over-dentures (dentures anchored to modified teeth or roots), a sophisticated procedure requiring skills of several dental specialists, can arrest the resorption and provide retentive dentures. dental specialists in india are, however, concentrated in urban areas leaving the rural populace under-serviced. the aim of our study was to find out whether newly graduated dentists, under remote guidance from specialists, can fabricate over-dentures that are functional and improve the oral health related quality of life. two groups of subjects were treated with over-dentures. group 1 consisted of subjects attending a rural dental health clinic (site1) and group 2 at a university teaching hospital (site 2). two dental graduates at each site carried out treatments. operators at site 1 were guided remotely over a telemedicine link, cell phones, and emails while those at site 2 were guided directly. functional assessment of dentures was carried out at the end of the treatment period to determine the technical quality of dentures. subjective evaluation was carried out by subjects completing the oral health impact profile (ohip-edent) questionnaire for edentulous subjects before and after treatment. no statistically significant difference was seen between the functional assessment scores of dentures from the two sites (p=0.08) at 95% confidence interval. both groups also experienced significant improvement in all domains of ohip edent. remotely supervised newly graduated general dentists can provide over-dentures of sufficient quality to rural population. this strategy has the potential to improve access to care and elevate the level of dentistry available to rural population when referral to specialists in not feasible. the results of the study provide pointers for dental public health policy makers and administrators in developing nations on how to leverage information and communication technology infrastructure to enhance access to care in rural areas. key words (mesh): general practice, dental/methods, health services accessibility, humans india, remote consultation, telemedicine, denture, overlay, general practice, dental/education*, quality of life, prosthodontics effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 introduction edentulism is a major oral health problem in rural india. although national statistics are not available, point prevalence studies have reported a prevalence rate of 72%(1). loss of teeth triggers rapid onset of residual ridge resorption (rrr) which severely reduces retention and stability of any subsequent removable dentures(2).the functional and aesthetic sequelae of these events result in nutritional deficiencies and impaired quality of life (qol)(3,4)(3,4). construction of over-dentures anchored on few retained teeth can successfully reduce the rate of rrr and maintain functional efficiency of the masticatory system(5). construction of an over-denture is a sophisticated procedure requiring skills which are usually beyond general dentists (6).the expertise of a prosthodontist complimented by endodontists and oral surgeons is indispensable in fabricating them. unfortunately most of the dental specialists in india are concentrated in urban areas leaving the semi urban and rural areas under served. the effect of geographical distance, socio-economic inequality and ‘rural urban’ developmental imbalance on oral health care in india is profound. less than twenty percent of the existing primary health centers in india have the services of a dentist available. at the moment india has one dentist for 10,000 persons in urban areas and one per 250,000 persons in rural areas. almost three-fourths of the all the dentists are clustered in the urban areas, which is home to only onefourth of the country’s population. it is often difficult for the urban poor and the rural population to get access to dental care (7). telemedicine has proved to be effective in overcoming geographical barriers and bridging the urban rural healthcare divide in many parts of the world. most of the instances of tele-medicine application in dentistry are restricted to diagnostic services and post-op follow-ups(8,9).in the only published instance of active dental treatment delivery via telemedicine linkage berndt and leone provided interceptive orthodontic treatment to underserved children in a rural community via telemedicine(10). there exists considerable scope for developing a similar strategy to extend prosthodontic treatment to rural areas, which do not have the services of dental specialists. no comparable report of extending specialized prosthodontic services via telemedicine link is found in peer-reviewed journals. however, evidence suggests that general dentists, under close supervision, can satisfactorily perform a complex dental treatment like dental implants (6). the present study aims to find out answer to the following question. can minimally experienced general dentists guided remotely by prosthodontists make over-dentures that are functionally efficient and improve the oral health related quality of life of patients when compared with over-dentures made by general dentists of similar experience? methods a cohort design was used for the study. this study was conducted at two sites a rural health centre (site 1) and the teaching hospital of amrita school of dentistry in the city of kochi in southern india. (site 2). site 1 was connected to the university hospital periodically by satellite tele-medicine link and regularly by broadband internet and cellphones. consecutive patients attending the dental clinics at both sites were screened for prosthetic need. effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 those who presented with less than 6 teeth in each arch were educated about over-dentures and approached for participation. a research assistant evaluated those who agreed to participate in the study to determine their eligibility as per the inclusion and exclusion criteria. thirty subjects each were enrolled in the study at both sites. both groups had similar selection criteria, treatment protocols, and outcome measures. two interns each were employed as the primary clinicians at both the sites. all of them had completed the 4-year bachelor of dental surgery training and were undergoing the mandatory yearlong clinical rotation. they were given identical pre-training by the faculty at the university consisting of perusal of the office reference manual, shadowing the specialists, sitting in on lectures, pre-clinical simulation exercises and laboratory skill sessions. operators at site 1 were guided remotely by scheduled video conferencing through satellite telemedicine linkage or broadband internet. in addition, cellphones were used sporadically to provide unscheduled guidance as and when required by the operators. operators at site 2 were guided directly. apart from general medical and dental evaluation they were subjected to abutment evaluation and tentative jaw relation measurements. a blinded rater evaluated the case records to cases to ensure that the two groups were identical in complexity as much as possible. convenience sampling strategy was used for recruiting subjects in the study the subjects were allowed to freely choose where they wanted to be treated according to their place of residence to enhance compliance. apart from general medical and dental evaluation the subjects were subjected to abutment evaluation and tentative jaw relation measurements. table 1: inclusion and exclusion criteria inclusion criteria exclusion criteria stable general health unstable health. healthy oral mucosa poor oral hygiene normal jaw movements. teeth with gross coronal or root caries no pronounced tmj disorders unfavorable axial inclination of abutments availability of adequate denture space poor crown root ratio. availability of suitable abutments less than 7 mm alveolar bone support for abutments the outcomes of interest in this study were 1)functional quality of dentures 2)self-reported quality of life (qol) denture quality was measured using the ten point functional assessment of denture (fad) criteria (11). qol was measured using a 19-point oral health impact profile (ohip-edent) modified for effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 denture related quality of health. although its construct validity and reliability have been validated the scale is not yet validated for indian population. however, we chose the instrument because: 1. ohip −14, a smaller subset of ohip-edent has been validated in indian population(12) 2. absence of an oral health related qol instrument for edentulous subjects validated in indian population. functional evaluation of the dentures was conducted by two expert faculty not involved in the study. subjects were required to complete ohip-edent questionnaire translated in to the local language ‘malayalam’ before the start of the treatment and at least 3 months after insertion of the dentures. the institutional review board of amrita school of dentistry approved the study protocol. express written consent was obtained from all the participants of the study. statistical analysis was performed by using spss v11.0 software. paired sample testing was performed for ohip-edent data. a t test was used to compare the fad scores. results table 2: demographic information site i site ii patients with previous denture experience 8 14 minimum age 45 46 maximum age 67 70 average age 56 57 number of males 86.7% 73.3% number of females 4 8 majority of the patients were males (86.7% and 73.3% at sites 1 and 2 respectively). mean age of subjects at sites 1 and 2 was 56 and 57 respectively. the age range for site 1 was 45 to 67 while that of site 2 was 46 to 70. fad scores of the two groups of the dentures fabricated at both the sites didn’t exhibit statistically relevant difference. (p=0.08)(table 3) effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 3: comparison of ohip –edent scores before and after treatment – group (1) domain subdomain mean s.d p-value functional limitation chewing difficulty 1.7000 1.51202 <0.001 food entrapment 1.7000 1.23596 <0.001 ill-fitting denture -0.7667 1.13512 <0.001 physical pain painful aching in mouth 0.2333 0.43018 0.006 eating comfort 1.7333 1.63861 <0.001 presence of sore spots -2000 1.12648 0.339 uncomfortable dentures -0.4333 0.93526 0.17 psychological discomfort worry due dental problems 0.7667 1.07265 0.001 self-conscious due to dental problems 0.1667 0.69893 0.202 physical disability avoiding some types of food 1.8333 1.26173 <0.001 inability to eat 0.3333 1.68836 0.288 interruption to eating 0.4667 1.56983 0.114 psychological disability upset due to dental problems 1.2333 1.88795 0.001 embarrassed due to dental problems 1.0667 1.70057 0.002 social disability avoid going out 0.4000 1.30252 0.103 less tolerant with friends and family 0.3333 0.92227 0.057 irritable to others 0.1000 0.95953 0.573 handicap unable to enjoy company 0.3333 0.66089 0.01 dissatisfaction with life in general 0.7000 1.57896 0.022 comparison between the ohip -edent scores before and 3 months after treatment revealed that subjects in both groups exhibited significant improvement in all the domains. overall, 11 out of 19 subdomains showed improvement. (table 3 and table 4). effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 table 4: comparison of ohip –edent scores before and after treatment – group 2 subdomain mean s.d pvalue functional limitation chewing difficulty 1.7333 0.2793 <0.001 food entrapment 1.8333 0.20945 <0.001 ill-fitting denture -0.2667 1.17248 0.223 physical pain painful aching in mouth 1.167 0.20945 <0.001 eating comfort 1.700 0.33614 <0.001 presence of sore spots -0.100 0.23659 0.676 uncomfortable dentures -0.4 0.81368 0.012 psychological discomfort worry due dental problem 1.3667 1.24522 <0.001 self-conscious due to dental problem 0.4333 0.77385 0.005 physical disability avoiding some types of food 1.8667 1.45586 <0.001 inability to eat 0.5333 1.71672 0.100 interruption to eating 0.931 1.70987 0.007 psychological disability upset due to dental problem 1.4667 1.85199 <0.001 embarrassed due to dental problem 1.1333 1.52527 <0.001 social disability avoid going out 0.6333 1.15917 0.006 less tolerant with friends and family 0.6 1.30252 0.017 irritable to others 0 0.98261 1.000 handicap unable to enjoy company 0.1333 0.9732 0.459 dissatisfaction with life in general 0.6 1.16264 0.008 discussion we evaluated the two major factors that reflect on success of prosthodontic treatment: 1. functional efficiency of the denture as obtained by clinical evaluation 2. oral health related quality of life (ohrqol) as reported by the subjects. successful oral rehabilitation with dentures depend on both technical factors and patients’ subjective perceptions (12). a sizeable volume of research identifies ohrqol as a predictor of successful dentures (13-15). no statistically significant difference was observed between the fad scores of the two groups of patients. both groups also showed significant improvements in almost all domains of ohrqol as measured by ohip -edent. these results are consistent with those from estafandiari et al and j brendt et al (6,9). teledentistry, the extension of telemedicine to include dental services has the potential to ‘’fundamentally change the way dental care is delivered today” (16). the results of the present study have important implications for specialized dental treatment delivery in rural areas of developing nations. employing new graduates receiving real-time guidance from experts located at an urban center may mitigate the shortage of specialist dental practitioners in such areas. this approach has the potential to improve access to care and the level of dental care available to rural population. training of the general dentists, hardware and software problems, poor network, and patient effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 compliance were few of the problems encountered during our study. a high degree of unreliability in connectivity and the need for on-site information-technology support often presented hurdles and led to several appointments at site 1 being extended or even rescheduled. we postulate that rapid improvements in telecommunication infrastructure coupled with improved hardware availability and reduction in cost will significantly reduce these challenges in future. transportation issues leading to missed appointments, poor oral hygiene maintenance, and the not-so-rare lack of family support for the long treatment sequence also presented problems. limitations the study has several limitations which need to be considered in interpreting the results. a. limitations in the choice of study design – randomized controlled trials (rct) are considered the gold standard for establishing the effectiveness of clinical interventions and hence would have been the ideal study design. randomization was difficult to achieve because of the geographic distance between the two sites. construction of overdentures requires multiple appointments over a series of visits. both these factors made true randomization impractical. patient compliance issues also were considered during the design phase of the study. cross over design was considered but the irreversible nature of tooth preparation ruled out its choice. b. limitation in the sample – number of subjects in the study had to be limited due to financial, technical and logical constraints. no attempt was made to match the two groups of patients with each other, which reduces external validity of the results. absence of an economic evaluation is another drawback of the study from policy and sustainability point of view. policy recommendations innovative use of technology and manpower can aid communities in overcoming traditional shortcomings where there is disparity of healthcare access. the present study used a model in which available communication infrastructure was used to extend quality dental treatment to underserviced areas. the model has the potential for significant impact in developing nations like india with vast tracts of rural and semi urban areas with little or no access to specialty dental care. in several regions of the developing world recent improvements in communication infrastructure has opened up a promising avenue to improve healthcare in previously inaccessible areas. the cost of communication services and required equipment are on a downward trend while the available bandwidth is progressively increasing. it is recommended that until such time as physical extension of facilities and manpower are made public health authorities may consider extending the existing manpower and expertise by using telehealth networks. in situations where the use of telemedicine equipment is not feasible it can be substituted by using available broadband internet connections. at the same time, care must be taken to ensure that these services are integrated with the existing health care system. the ideal strategy would be consider factors like cost, accessibility, social isolation, poverty, and to customize the solution to suit local needs. it is a common trend to consider telehealth just as an add-on service. such an approach might result in under realization of its full potential. it is advised to consider telehealth services such as those described in the present articles as potentially high impact interventions, and accorded sufficient attention. legal, ethical, and confidentiality issues also need to be considered effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 while formulating policies. adequate levels of documentation of care as well as technical logs are vital for continued improvement of the services and hence made mandatory. conclusion despite some limitations, this study provides definite indications that pre-trained new dental graduates can, when supervised remotely by specialists, provide tooth supported over-dentures of satisfactory quality. this suggests that telemedicine has the potential to improve access to quality dental care for rural populace. acknowledgments amrita institute of medical sciences permitted use of telemedicine infrastructure and support personnel. dental materials and equipment were provided by amrita school of dentistry. no conflict of interest. references (1)prabhu n, kumar s, d'souza m. partial edentulousness in a rural population based on kennedy's classification: an epidemiological study. journal of prosthodontic society; 2009 ; 9(6) : 18-23 (2)atwood d. reduction of residual ridges: a major oral disease entity. the journal of prosthetic dentistry. 1971 sep;26(3):266-79 (3)akifusa s, soh i, ansai t, hamasaki t. relationship of number of remaining teeth to health‐related quality of life in community‐dwelling elderly. gerodontology: 2005; jun; 22(2): 91-7 (4) mcgrath c. can dentures improve the quality of life of those who have experienced considerable tooth loss ? journal of dentistry. 2001; may; 29(4):243-6 (5)fenton a.the decade of overdentures: 1970-1980. the journal of prosthetic dentistry.1998; jan; 79(1): 31-36 (6)esfandiari s, lund j, thomason j, dufresne e, kobayashi t, dubois m, et al. can general dentists produce successful implant overdentures with minimal training ? journal of dentistry. 2006 nov.; 34(10):796–801 (7)shobha tandon. challenges to the oral health workforce in india j dent educ 2004 68(7): 28-33 (8)berndt j, leone p. using teledentistry to provide interceptive orthodontic services to disadvantaged children. american journal of orthodontics and dentofacial orthopedics 2008; 2008 nov; 134(5):700-706 (9)anastassiadou v, naka o, heath m. validation of indices for functional assessment of dentures. gerodontology. 2002; jul; 19(1): 46-52 (10) acharya s. oral health-related quality of life and its associated factors in an indian adult population. oral health preventive dentistry. 2008; 6(3):175–184 (11) de lucena sc, gomes sgf, da silva wj, del bel cury aa. patients’ satisfaction and functional assessment of existing complete dentures: correlation with objective masticatory function. journal of oral rehabilitation. 2010 oct. 29; 38(6): 440–446 effectiveness of tele-guided interceptive prosthodontic treatment in rural india: a comparative pilot study 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 (12) heydecke g, locker d, awad m. oral and general health‐related quality of life with conventional and implant dentures. community dentistry and oral epidemiology; 2003 jun; 31(3):161-168 (13) allen p. the impact of tooth loss in a denture wearing population: an assessment using the oral health impact profile. community dental health.1999 sep; 16(3): 176-80 (14) bouma j, boerrigter l, van oort r. psychosocial effects of implant-retained overdentures. international journal of oral & maxillofacial implants.1997; jul-aug; 12(4): 515-22 (15) cooper b. knowledge, attitudes, and confidence levels of dental hygiene students regarding teledentistry: a pilot study. the internet journal of allied health. 2007 oct; 5(4) 14 (16) lewis d. optimized therapy for the edentulous predicament: cost-effectiveness considerations. the journal of prosthetic dentistry. 1998 jan;79(1):93-99 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts field lessons from a zoonotic disease study in the nairobi health surveillance system djesika d. amendah*, peterrock muriuki, nicholas ngomi, martin kavao and nelson muhia african population and health research center (aphrc), nairobi, kenya objective disseminate field lessons from a zoonotic disease study nested on the nairobi urban health and demographic surveillance system (nuhdss). the study investigates the emergence and introduction of zoonoses in urban areas introduction about 60% of nairobi residents live in slums with higher poverty, population density prevalence diseases and lower health access than the city average. some residents own livestock or in are in contact with its products. most slums dwellers work outside slums. thus, health surveillance in slum area is vital because of potential disease outbreaks and spread. yet, little is known on practice/challenges of health surveillance in resource-limited slums. methods the african population and health research center established the nuhdss in two slums (korogocho and viwandani) in 2003 and has since collected routine information on birth, deaths, cause of deaths, migration thrice a year. currently, about 78000 persons living in ~ 25,000 households are in nuhdss. on this nuhdss framework was nested a maternal and child health (mch) longitudinal study collecting socio-economic characteristics, childhood diseases, vaccination status, access to care, anthropometrics and other information among ~3000 mother-child pairs. babies<7 months born to resident mothers are recruited and followed-up till 5 years. the urban zoonoses case-control study was nested on the mch. e.choli is used as examplar of emerging pathogen with multiple hosts. from august 2013 to april 2014, we collected additional information on hygiene practices, and diarrhea risk factors in the mch study. moreover, we collected fecal, food and water samples from about 200 children with diarrhea and 400 randomly selected controls to explore diarrhea-causing pathogens in laboratories. results here are the main lessons sustaining community participation: most residents are poor and highly mobile and do not benefit individually from participation in the surveillance which could contribute to attrition. so we ensure their communities benefit by offering health camps with free care, upgrading health facilities, schools and by disseminating research results locally. we also use the nuhdss platform for intervention research to improve knowledge while benefiting study participants. engaging gatekeepers some heads of local organizations, opinion leaders, etc. may act as gatekeepers blocking access to the community. we get their “buy-in” with sensitization, community leadership meetings to explain methods and objectives prior to, and during studies. hiring local fieldworkers we hire as fieldworkers educated slums residents who know local codes and people. we train and give education benefits for skills improvement and retention. engaging with mothers the main respondents are women so we employ a female-only collection team. when respondents are casual workers with odd hours, interviews are conducted at a time and place convenient for mothers which stresses the importance of local fieldworkers and flexibility. keeping employees and material safe despite employing local residents, “no-go” areas exist. we hire additional security to accompany fieldworkers to collect data there and when bulky equipment is used for anthropometrics. collecting sample obtaining samples was easier from children with diarrhea than from the control group. we dedicated fieldworkers to collect sample among the latter and carry them to the labs. offering clinical care clinical officers using lab results offer treatment to children with diarrhea, and others in all households involved in the zoonotic study and referral when needed. these services increase the community’s acceptance of the study conclusions accounting for local context is vital for a successful population surveillance system and the zoonotic study nested on it. keywords zoonotic disease; health surveillance; low-ressource; kenya acknowledgments medical research council, natural environment research council, economic and social research council, biotechnology and biosciences research council for funds received through the environmental & social ecology of human infectious diseases initiative (esei), grant g1100783/1. william and flora hewlett foundation, bill and melinda gates foundation, and sweden international development cooperation agency references emina j, beguy d, zulu em, ezeh ac, muindi k, et al. (2011) monitoring of health and demographic outcomes in poor urban settlements: evidence from the nairobi urban health and demographic surveillance system. j urban health 88 suppl 2: s200-218. aphrc (2002) population and health dynamics in nairobi’s informal settlements. nairobi, kenya: african population and health research center. *djesika d. amendah e-mail: damendah@aphrc.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e103, 201 ojphi-06-5.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 147 (page number not for citation purposes) isds 2013 conference abstracts post-disaster surveillance among state health departments in the united states erin simms*2, amy wolkin1, ekta choudhary1, robert mathes3, michael heumann2 and sharon watkins4 1centers for disease control and prevention, atlanta, ga, usa; 2council of state and territorial epidemiologists, atlanta, ga, usa; 3new york city department of health and mental hygiene, new york, ny, usa; 4florida department of health, tallahassee, fl, usa � �� �� �� � � �� �� �� � objective �������� � � ��� �� ������������� ���� ������ � ���� ����� ����� ������ ���� ���� ��� ����� �������� ������������� �������������������� � ���� � ������������������� �������������� ����� ����� �� ����������� ��� ��� � ����� ����� ����� introduction ������ �������� ������ ����������������������� ������������������� ������������������������ ��� ����������� ����� � ����������� ���� ���������������� � ��� �������������������������� ����� ������ �� ������ ����� �� ���� � ���������� � �� ������ ���� ������ ����� ����� ��!���� ����� ��� ������������ � ����������������� ���������� � ���� ��������� ������"�������� 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"���������������� �� � ���� $������������������������ ����������� ���������� � conclusions ��� ����� �� ����������������� �������� �� ����������������� � ���� ����� ������������� �������������������������������������� �� �� ��� ������� ����� ����� � ������������ ������� ��� ����������� ���� ������#��� ���������*��� �� ������������������� ����� ����� ������ ��� �������������������������� ��������������������� ���������������������� ������������ ������ ��� � ���� ����� ������������� ������������������ ��������� keywords ������ ����5�%� � ���5�%� � ������������ ��� acknowledgments ���� ������ � �� �� � �� ��� �� �� ����� ���� �� ������ � ������������ � ��������� ��������(��)�%� � ����)������� ������������������(%(� %� � ���� )������� ���� (��������� ��� .��������� ���� ����� � � ����� �� ������� �� ���� ����������������������������������������� � ������� ������ ���� ����� ����� *erin simms e-mail: esimms@cste.org� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e5, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an analysis of the international and domestic health hazards posed by the 2014 west african ebola virus disease outbreak andrew hickey, sarah cheeseman barthel, tyann blessington*, yandace k. brown, diana y. wong, william n. albrecht, mark freese, teresa quitugua national biosurveillance integration center, washington, district of columbia, usa objective to categorize and assess the international and domestic health impacts of the 2014 west african ebola virus disease outbreak introduction an epidemic of ebolavirus in west africa, which was first identified in march 2014, is now the largest ebola virus disease (evd) outbreak on record. on 8 august 2014, the who declared the ebola outbreak in west africa a public health emergency of international concern (pheic). as of 4 september 2014, guinea, liberia, sierra leone, and nigeria have reported 3,707 cases (2,106 confirmed, 1,003 probable, and 598 suspected) of evd with 1,848 deaths (50% case fatality) to the world health organization (who). five u.s. citizens have contracted the viral disease – one liberianamerican and four medical-aid workers working in ebola-afflicted countries. methods in collaboration with multiple partners, the national biosurveillance integration center (nbic) provided an integrated analysis of the international and domestic health impacts of the west african evd outbreak. nbic began reporting on evd on 24 march 2014, after a guinean outbreak of hemorrhagic fever was confirmed to be ebolavirus, through briefs to department of homeland security (dhs) components and oha leadership and through timely situational reports to federal, state, and local partners. situational reports were distributed through multiple dhs web-based portals and were also distributed to key personnel in dhs leadership. informational exchange agreements were developed to share nbic reports with homeland security advisory council staff and, at the request of the us department of transportation, transportation canada. nbic analysts worked with the armed forces health surveillance center (afhsc) to identify and monitored the health status of suspected evd cases having a history of travel from or within the west african region and provided daily updates to oha senior leadership and the department of transportation. nbic analysts evaluated air-travel and migration patterns of west african residents in collaboration with customs and border protection (cbp), office of intelligence and investigative liaison (oiil). an nbic analyst also deployed to the health and human services (hhs) secretary’s operations center (soc) for the initiation and the timely transfer of information regarding this epidemic. nbic requested and shared information with other us government biosurveillance analysts through the wildfire portal governed by the biosurveillance indications and warning analytic community (biwac), and provided or facilitated responses to requests for information (rfis) from interagency agencies and groups. nbic representatives also presented and participated in various ebola working groups and inter-agency collaborative groups within the federal government. additionally, nbic analysts collaborated with partners in an interagency ebola modeling coordination group tasked to assess the current disease status and predicts future trends. these collaborative efforts and extensive information exchanges facilitated the development of guidance documents on screening procedures at airports and the assessment of the translocation rate and spread of the virus from africa to the u.s. for the dhs secretary and component agencies leaders. results the current west africa outbreak is believed to have originated in rural areas but subsequently spread to more urban centers. many health centers in west africa are reported to be underequipped and overwhelmed. limited resources, high patient-provider ratio, poor training of healthcare workers, and a strong mistrust and apprehension of the local population towards health-guidance measures administered by the government create a challenge to maintain proper clinical infection control techniques. in general, the health security capacity to mitigate biological threats is significantly greater in the u.s. when compared to many regions in west africa. many of the west africa evd-afflicted nations are resource-poor and already coping with major health challenges. conclusions the west african epidemic will only be quelled through widespread adherence of public health initiatives promoting barriernursing techniques, health education, and the rapid identification of cases. in contrast to the capabilities in west africa, the u.s. health system is well-equipped to treat and contain trans located cases. the ongoing evd outbreak in west africa is unlikely to affect public health in the u.s. significantly. keywords ebola, west africa, outbreak. *tyann blessington e-mail: tyann.blessington@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e79, 201 covid-19: an alarm to move faster towards “smart hospitals” 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e7, 2021 ojphi covid-19: an alarm to move faster towards “smart hospitals” seyyed mohammad tabatabaei 1,2 , marjan rasoulian kasrineh 1 , nahid sharifzadeh 1 , moslem taheri soodejani 3,* 1 department of medical informatics, faculty of medicine, mashhad university of medical sciences, mashhad, iran 2 clinical research development unit, imam reza hospital, mashhad university of medical sciences, mashhad, iran 3 center for healthcare data modeling, departments of biostatistics and epidemiology, school of public health, shahid sadoughi university of medical sciences, yazd, iran keywords: smart hospital, covid-19, ict *correspondence: moslem taheri soodejani, young.researcher.new@gmail.com doi: 10.5210/ojphi.v13i1.11515 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. while our lifestyle is now influenced by novel technologies, we are challenged by the burden of a mysterious disease called covid-19. it began from wuhan, china, in late 2019 and spread rapidly around the world and has caused unprecedented health, social and economic challenges worldwide so far [1]. this virus is mostly transmitted through person-to-person contact; due to the fact that it is mostly transmitted through respiration, it is very contagious and can spread quickly in society [2]. this is why all researchers around the world are trying to find an appropriate solution to control this pandemic and reduce the losses and damages caused by it until a suitable therapeutic solution is reached. the most important source of the disease are infected people who can easily transmit the disease to other people in the community [3]. it seems that excessive admission of infected patients in hospitals, in addition to infecting the health and medical staff, increases their workload and exhaustion among the staff. the movement of infected people in the community as well as their visits to the hospitals increases the possibility of spreading this disease and may cause other sections of the hospital to be infected. all of these will delay the end of this pandemic. this pandemic appeared in a time called the digital age in which we have seen great advances in ict. so we must use all the existing potentials based on digital and smart technologies in order to help control this disease. we are today experiencing the fourth industrial revolution which has combined physical, digital and biological worlds and caused many improvement in healthcare [4]. mailto:young.researcher.new@gmail.com covid-19: an alarm to move faster towards “smart hospitals” 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e7, 2021 ojphi the role of artificial intelligence (ai) as a major catalyst in the healthcare revolution is undoubtable. it has actually changed caregiving by making it smart. it also has caused the creation of an interesting novel concept called “smart hospital” [5]. a smart hospital is a hospital that focuses on optimized automated processes in an ict-based interconnected environment with the aim of improving patient care. of course, different definitions have been provided for smart hospital, but creating an effective connection between patients, health care providers and the machine has been emphasized in them. according to the literature, four key areas should be considered while developing a smart hospital including patient services and interfaces, care processes and orchestration, logistics and support services and also organization and capability design [6]. in a smart hospital, many technologies and tools such as the internet of things(iot), big data, cloud computing, artificial intelligence (ai), robotics, 3d printing, mobile health (mhealth), rfid, biosensors, integration platforms, wearable devices, dashboards and many others are used [5,7,8]. smart hospital would help to reduce the workload of the staff and increase their efficiency, facilitate hospital activities, improve the quality of processes and increase patient safety [9]. of course, achieving such goals is very difficult and complex and requires the cooperation of experts in various fields. considering the existing pandemic and the way it is transmitted, smart hospital can provide many services for patients at the point of care and reduce hospital visits including tele-medicine services, real-time monitoring of patients and online processing of the big data produced to improve healthcare quality. also, automatic tasks such as deliveries and transports can be provided by robots to lessen the close contacts, and better resource management can be provided using artificial intelligence in order to improve the quality of care and reduce costs. in addition, other technologies such as 3d printing to produce needed instruments for operations, virtual reality for rehabilitation and also entertainment in order to strengthen the spirit alongside with augmented reality which is very useful during operations, rfid to better control of the resources, devices and patients and so many others which can be provided in smart hospitals using novel technologies [10]. as a result, the implementation of a smart hospital will help to manage and control such diseases better. it seems that, unfortunately, we have to deal with this disease for a long time, and facing such dangerous diseases in the future seems totally possible. therefore, it is a filip for healthcare providers, managers, policy makers and also the public. given the acceptance of using new technologies, especially in the current situation, countries that have the appropriate infrastructure to implement smart hospitals should use it to make processes smarter and move faster towards the establishment of smart hospitals. it is important for nations to develop this infrastructure so that they can implement smart hospitals as soon as possible. 1. taheri soodejani m, lotfi mh, tabatabaei sm. 2020. is case fatality rate an appropriate index to represent the status of case-finding process for covid-19 in different countries? infect ecol epidemiol. 10(1), 1773733 pubmedhttps://doi.org/10.1080/20008686.2020.1773733. 2. recalcati s. 2020. cutaneous manifestations in covid‐19: a first perspective. j eur acad dermatol venereol. pubmed https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32922690&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32922690&dopt=abstract https://doi.org/10.1080/20008686.2020.1773733 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32215952&dopt=abstract covid-19: an alarm to move faster towards “smart hospitals” 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e7, 2021 ojphi 3. day m. covid-19: four fifths of cases are asymptomatic, china figures indicate. british medical journal publishing group; 2020. 4. schwab k, davis n. shaping the future of the fourth industrial revolution: currency; 2018. 5. "smart hospitals: security and resilience for smart health service and infrastructures". european union agency for network and information security [internet]. november 2016. available from: https://www.enisa.europa.eu/publications/cyber-security-and-resilience-forsmart-hospitals. 6. ad l. building the smart hospital agenda. available from: http://www.adlittle.com/sites/default/files/viewpoints/adl_smart%20hospital.pdf.</eref> 7. tabatabaei sm. 2019. medical big data analytics: an interesting but challenging interdisciplinary field of study. ec clinical and experimental anatomy eco. 1, 22-23. 8. mann s, arora y, anand s. smart hospitals with the use of'internet of things' and artificial intelligence. available at ssrn 3569591. 2020. 9. moro visconti r, morea d. 2020. healthcare digitalization and pay-for-performance incentives in smart hospital project financing. int j environ res public health. 17(7), 2318 pubmedhttps://doi.org/10.3390/ijerph17072318. 10. ting dsw, carin l, dzau v, wong ty. 2020. digital technology and covid-19. nat med. 26(4), 459-61 pubmedhttps://doi.org/10.1038/s41591-020-0824-5. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32235517&dopt=abstract https://doi.org/10.3390/ijerph17072318 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=32284618&dopt=abstract https://doi.org/10.1038/s41591-020-0824-5 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the impact of weather on influenza-like illness rates in chicago shital shah1, dino rumoro1, gordon trenholme1, gillian gibbs*1, marilyn hallock1 and michael j. waddell2 1rush university medical center, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to develop a statistical model to account for weather variation in influenza-like illness (ili) surveillance. introduction weather events such as a heat wave or a cold snap can cause a change to the number of patients and types of symptoms seen at a healthcare facility. understanding the impact of weather patterns on ili surveillance may be useful for early detection and trend analysis. in addition, weather patterns limit our ability to extrapolate data collected in one region to a different region, which may not share the same weather or periodic trends. by modeling these sources of variation, we can factor out their effects and increase the sensitivity of our overall surveillance system. methods the time frame for the retrospective study was 2 years (training set january 1, 2010 through december 31, 2010 and testing set january 1, 2011 through december 31, 2011). the data included in the study were: daily reading of high and low temperature (of), wind direction, wind gust direction, wind speed (mph), wind gust speed (mph), precipitation (inches) and wind chill (of). the daily and seven day moving average (ma) ili rates for the emergency department (ed) were calculated using the guardian (geographic utilization of artificial intelligence in real-time for disease identification and alert notification) surveillance system. in addition, the 7 day ma ili rate was categorized into widespread (ili 12.5%), imminent (12.5%< ili 10%), and normal (ili rate <10%) levels. we included the day of the week, weekend/weekday status, and holiday status (the six national holidays +/two days) to account for variations in ed utilization due to patient preferences. finally, for all of the weather related data we included a one day lag as well as 3 day ma to smooth out variations. descriptive and correlation analyses were conducted. thereafter, we applied both linear regression (dependent variable = 7 day ma ili rate) as well as data mining algorithm (decision rules with dependent variable = 7 day ma ili rate categories). the generated models were validated with the testing dataset. results prior day weather data related to high temperature (r=-0.76), low temperature (r=-0.76), wind gust speed (r=-0.11) and wind chill (r=0.76) were negatively correlated with the 7 day ma ili rate. for the 3 day ma weather data, high temperature (r=-0.78), low temperature (r=-0.77), wind chill (r=-0.78), wind gust direction (r=-0.17), wind gust speed (r=-0.18), and precipitation (r=-0.12) were negatively correlated with the 7 day ma ili rate. the plot of 3 day ma low temperature and 7 day ma ili rate is presented in figure 1. based on regression analysis, the main significant variables were 3 day mas for low temperature ( =-0.6%), wind chill ( =0.43%), wind speed ( =0.14%), high temperature ( =-0.05%), wind gust speed ( =-0.02%), and wind direction ( =-0.01%). some of the sample significant rules based on a decision rules algorithm were as follows: • if 7 day ma high temperature > 47.96 and yesterday’s high temperature > 46.04 and 7 day ma wind chill > 48.31 then 7 day ma ili rate = normal (confidence =99.7%) • if 7 day ma high temperature 43.34 and holidays = no and 7 day ma wind direction 260 and 7 day ma wind speed > 13.35 en 7 day ma ili rate = imminent (confidence =87.5%) • if 7 day ma high temperature 43.34 and 7 day ma low temperature 7.04 then 7 day ma ili rate = widespread (confidence =86%) the accuracy of detecting the ili category was 76% and 67.4% for training and testing datasets, respectively. conclusions we evaluated multiple weather related parameters for determining ili rates using statistical and data mining approaches. the significant variables affecting ili rates consisted of 3 day ma related weather data (i.e., low temperature and wind chill). thus, these factors should be used in determining ili rates and associated clinical and operational preparations. other factors such as day of the week and holiday status had limited contributions in determining ili rates. keywords influenza-like illness surveillance; guardian; weather patterns acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. *gillian gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e91, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts creating a local geographic influenza-like illness activity report dino rumoro1, shital shah1, gordon trenholme1, gillian gibbs*1, marilyn hallock1 and michael j. waddell2 1emergency medicine, rush university medical center, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to create a local geographic influenza-like illness (ili) activity report. introduction mapping ili surveillance data can be useful in identifying the direction and speed of an outbreak and for focusing control measures for an efficient public health response. the centers for disease control and prevention’s (cdc) ilinet currently displays weekly ili geographic data at a national/regional/state level, but this visual data could also be useful at the local level. methods the data elements that were included in this study were: daily emergency department (ed) ili rate, 7 day moving average ed ili rate, zip code, and date from october 1, 2013 to march 31, 2014. the data region was the catchment area of an urban academic medical center (amc). the data were processed using the guardian (geographic utilization of artificial intelligence in real-time for disease identification and alert notification) surveillance system. we created the following derived variables in percentages: • ili within a zip code = ili positive ed visits within zip code/total ed visits within zip code • proportion of zip code ili with regional ili = ili positive ed visits within zip code/ total ili positive ed visits within the amc region • zip code representation = total ed visits within zip code/total ed visits within the amc region these three variables provide information on the relative importance of the ili rate within a zip code compared to the overall ili rate within the amc region. these trends were utilized to generate rules for the development of a color coded zip code map, as follows: • white represents no data available or 0% of total ed visits within the amc region are in the zip code • gray represents insufficient data or <10% and > 0% of total ed visits within the amc region are in the zip code • green represents within normal limits and requires 10% of total ed visits within the amc region are in the zip code and ili rate is below 10% within the zip code • yellow represents more than 2 standard deviations above the mean and requires 10% of total ed visits within the amc region are in the zip code and ili rate is between 10% and 12.5% within the zip code • orange represents more than 20% of all positive ili cases come from that zip code within the amc region • red represents more than 3 standard deviations above the mean and requires 10% of total ed visits within the amc region are in the zip code and ili rate is above 12.5% within the zip code the thresholds utilized in the rules were either generated through historical statistical analysis or based on the need for an adequate sample size. the rules were translated into the guardian system, which auto-generates daily reports of geographic ili activity at the amc. results each morning, the guardian reporting module generates a map for the amc’s current 7-day moving average ili rate by zip code (figure 1). the resulting map and a summary table are combined into a pdf document and emailed out to the response team at the amc. in addition, an interactive web-based version of the map is made available through the guardian user interface, which allows clinicians to review individual patient’s charts. conclusions the ili activity report provides zip code level geographic analysis of ili prevalence using the developed color scheme. with additional data, such as multiple ed sites and temporal information, the ili activity maps could be further enhanced to capture spatial/temporal changes in ili rates within the broader region (e.g., metropolitan area, state level, or cdc region). these daily auto-generated geographic reports can be utilized by clinical and public health personnel for monitoring ili activity within their regions, as well as initiating appropriate emergency management protocols. figure 1: map of the seven day moving average ili rate by zip code on january 14, 2014. keywords influenza-like illness surveillance; guardian; gis acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. *gillian gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e155, 2015 ojphi-06-e66.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 79 (page number not for citation purposes) isds 2013 conference abstracts seasonal patterns in syndromic surveillance emergency department data due to respiratory illnesses kelly johnson*, alecia alianell and rachel radcliffe south carolina department of health and environmental control, columbia, sc, usa � �� �� �� � � �� �� �� � objective ����������� ���� ���� �������������������������� �������������� � ���� ��������������������� ��������� ������ �������������� ���� ���������� introduction ���������������������������������� ����������������� ��������� � �������������������� ����������� �� ��������������� ��������� � �������� ��� ����� �!���� �������������� ��������"�������������� ���������� ��#������������������ ����������� �������������� ���� ������ ������ ���������$������������ ������������������������������� �� ��� �� � � �����"���������� ��������� �� ������ ��������� ���%�� ���������������������������� ��������� ����������������� ����������� ������ �������������������� ���%�"&������� �������� ������������������ ���� ���������������� �������������������� ������ ���������� ����� � � �!���� 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������������ ���� � ������ )��������������1������������������ �� ���� references &����������>�?���������"�����1�����/"�����+� ��>"��������� �������� ��� ������� ���������/��� �@�� ���%�����/�#���$�����������-���������a� >�� �����>��%b�+/���@����(*&*=c�8�a&�7�� (������������$"�'���;d"��� �e"��������� ��������� ���������� ��������� ��� �!����������������������������������@������a����� ������������ ������ 6���,�� � ������$����(**9=9a&7*�&77� *kelly johnson e-mail: johnsok@dhec.sc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e66, 2014 construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center constant joseph koné 1*, ndri nda anatole mian 1, cataud marius guede 1, man-koumba soumahoro 1 1 pasteur institute of côte d’ivoire, epidemiology unit abstract introduction the transmission of test results by laboratories and their receipt by health facilities are common tasks in the processing of medical information. managing the flow of information generated by these tasks remains a challenge for these centers. we describe a new system that will allow for electronic management of the transmission of results. materials and methods the information system implemented is a client-server system consisting three main components: the server installed in the laboratory, the client distributed in the anti-tuberculosis center and the communication channel represented by a virtual network. the exchange protocol is based on the hl7 standard that used messages of type oru_r01. results during the two months of implementation of this electronic result transmission system between the national tuberculosis reference center in abidjan and the anti-tuberculosis center in adzopé, which is about 110 kilometers away, twenty laboratory results were transmitted as soon as they left the laboratory, an improvement from the previous long turn-around-time of about 1 month. the minimalist interface and ease of use of the system have allowed it to be adopted by users. discussion the use of the hl7 protocol for electronic notifications has proven its effectiveness in making transmissions of results instantaneous. our system specifically addresses the problems related to efficient transmission of results; reduction of transmission time, information loss attributed to the use of paper, and transport costs incurred when transmitting results from remote sites. this system representing the 1rst version use a local codification that limits it to an interoperability with other environment that use a different code system. the use of a code system such as loinc would allow full interoperability between different information systems. keywords: information systems, tuberculosis, health facilities, hl7 corresponding author: constant joseph koné email: koneconstant@pasteur.ci, koneconstant@yahoo.fr* doi: 10.5210/ojphi.v11i3.10255 construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi context and issues the transmission of test results from laboratories and their receipt by health facilities are common tasks in the processing of medical information. unfortunately, they do not always satisfy patients and doctors, especially in terms of time [1]. the integration of information systems involved in the exchange of health data faces many problems due to the variability of the hardware, software, terminology, and nomenclatures used to code the information. over the past two decades, standardized messaging protocols such as hl7 version 2 have helped to overcome the obstacles associated with syntactic interoperability. its widespread adoption in health information systems makes it a standard of choice. nevertheless, there are problems of interfacing between the different medical information systems, each system being specific, requires an adequate interface. the transmission of results in electronic form from laboratories is an approach that goes hand in hand with computerized record systems. several technologies have already proven their worth in this area. direct secure messaging, a standardized protocol for the exchange of clinical messages and attachments, is a successful example of electronic transmission due to its flexibility and ease of implementation [2]. there are also commercial solutions such as interfaceware's iguana, which offers integrated systems for the exchange of health data [3]. the results submitted from laboratories in côte d'ivoire are mostly in the form of printed sheets of paper that must be sent to prescribing physicians. the latter can be subject to hazards such as transmission loss and delays, which have a negative impact on patient care. on the contrary, it has been shown that the transmission of results in the electronic form to the computerized patient record can greatly improve patient management [4]. many systems have been developed for the electronic management of laboratory results. the vast majority of them suffer from their generic type design, a handicap due to the specificity of certain tasks and the limitations of the management of these laboratories. in this article, we describe a new system that, through its architecture and functionalities, will allow electronic management adapted to the transmission of results. thus, its adoption by the laboratory will allow an essential saving of time in diagnosis, a rapid sharing of information and a more secure archiving. ii. material and method 1. system deployment environment: case of cote d'ivoire the national reference center for tuberculosis (nrct) housed at the pasteur institute of cote d'ivoire is responsible for confirming the diagnosis of tuberculosis in samples from antituberculosis center (atc) and health facilities (university hospital center, general hospitals, health center) throughout the country. we are interested in atcs, the main centers for tuberculosis management. there are 27 such centers across the country, while the ncrt is copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi located in abidjan in the south of the country (figure 1). the remoteness and geographical accessibility of several atcs can make it very difficult to transport samples and retrieve analytical results. thus, as a solution, for many of them, the recovery of results is only done during the next sample transport, thus combining the recovery time of the result and the time of the next transmission. these time differences can sometimes reach several weeks for acts that are very far away. electronic transmission of results would, therefore, be an asset inpatient care. figure 1: distribution of acts in côte d'ivoire construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi 2. system architecture and features the information system implemented is a client-server system including three main components: first, the server installed in the laboratory, then the client distributed in the atcs and finally the communication channel represented by a virtual private network (vpn). figure 2 details the overall architecture of the data transmission information system. figure 2: information system architecture 2.1 server description the server software is installed at nrct on a desktop computer. java technology including java standard edition is the main language used for the development of the system. several other technologies are used in this system, including an embedded apache derby database that keeps track of all transactions, jasper soft technology for pdf file generation, hl7 version 2.x standard and the hapi library (hl7 application programing interface) allowing its implementation in java [5-9]. the interface has been structured to be as simple as possible. four actions on the graphical interface allow the transmission of a message: 1) the operator enters his identifier to unlock the graphical interface 2) he then enters the sample number in the relevant field. a query on the database of the existing laboratory information system retrieves all information related to this sample (sociodemographic information and biological results) [10]. 3) six laboratory tests (microscopy, gene expert, line probe assay, culture, classical antibiotic susceptibility test, and extended antibiotic susceptibility test) can be generated by the server software. when the examination is actually performed, the server software displays it in its interface at the operator's request, otherwise, nothing is displayed. construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi 4) finally, clicking on the distribution button allows the result to be encoded in hl7 format and then sent over the internet network through the vpn to the client software. 2.2 description of the customer the client software is also based on java technology, so it can be installed on any platform with a java virtual machine. it implements an embedded database like the server to record all transactions. it also implements jasper soft technology to reconstruct the results received in pdf format. the client also has an embedded jetty server that offers the possibility of having an open port waiting for incoming connections [11]. the graphical interface here is again minimalist, three actions are necessary to obtain the printed result. 1) the client software starts automatically when the operating system is launched and runs in the background. an icon marks its presence in the notification bar. when an incoming message is received, the client software displays a dialog box announcing the receipt of the new message. in terms of it processes, when the message is received, it is decoded, the result of this processing generates a java object that is recorded in the embedded database. an acknowledgment of receipt is then generated and sent to the server sending the message. 2) by clicking on the notification, the client software is displayed, the interface is disabled, only the result can be viewed. the activation of the interface is done by entering its identifier in the relevant field. with the "print" button activated, it is possible to print the current result. 3) a table displays all the receipts of the day, a click on the "display" button of a line of the table, displays the result concerned and it is then possible to print it. another table displays a summary of all transactions made and it is possible to display a previous result to print it. 2.3 the communication channel a virtual private network is used between the server and the clients through the internet network to secure data exchanges. we chose hamachi's logmein solution [12]. the star architecture is used here, it offers stricter control over network members and who is connected to whom. the server is connected to all workstations (clients), while the workstations are only connected to the server. this is a typical choice in a company, where workstations are only connected to servers. this architecture is ideal in the field of health where confidentiality is a necessity. 3. message structure the message structure is based on the hl7 standard version 2.5 [13]. the type of message used is oru_r01, which refers to an unsolicited transmission of an observation message. in other words, the oru message r01 is used to transmit the results of laboratory, clinical or other observations to other systems without the latter having requested it. twenty segments make up a message of type oru_r01, some are optional, others mandatory. five segments were used to implement the six laboratory results that the server software can construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi transmit. these are the msh (message header), pid (patient identification), obr (observation request), obx (observation segment), spm (specimen) segments. figure 3 shows a detailed example of the structure of a microscopy message. the hapi library, implementation of the hl7 standard in java, allowed the coding of messages. the hl7 over http protocol was used for message transfer [9]. figure 3: hl7 message of type oru_r01 4. on-site system installation, user training, and implementation the practical implementation of the system was carried out between the nrct housed at the institut pasteur of cote d'ivoire, located in the city of abidjan, and the atc in the city of adzopé, located about 110 kilometers from abidjan. we configured hamachi's logmein virtual private network on a laboratory workstation and the server was installed there. a password was created for each user. a training session was given on the use of the server at the nrct to two laboratory technicians. the simplicity of use as we describe in server description, made it possible to conduct theoretical and practical training in half a day. at the adzopé atc level, the vpn was configured, and the client software installed on a laptop computer. the medical secretary and the main doctor received theoretical and practical training. the system was used for the transmission of results from 1 november 2018 to 31 december 2018. during these periods all test results were transmitted electronically. iii. results we only had to develop two interfaces for our project, one for the laboratory and one for the atcs (figure 4 and 5). installation in other atcs only required changing parameters such as ip address and interface label. during the 2 months of implementation of this electronic result transmission system between the cnrt in abidjan and the atc in adzopé, twenty laboratory results were transmitted. three main advantages identified were: 1) at the time level: what is currently happening in the laboratory is that the transmission of the results is done manually, meaning that applicants must come and get their results from the nrct, it can take a lot of time if the distance between cat and the nrct is great. thus the electronic transmission of laboratory results undeniably saves time in the recovery of these results 2) at the financial level: this electronic system has resulted in substantial savings in fuel purchases and maintenance of the rolling stock needed to recover results. construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi 3) in terms of patient management: the results available in real-time have had a positive impact on improving the quality of consultations. the archiving of patient results and the possibility of regenerating these results later for consultation makes it more of a results directory and a patient data management tool. figure 4: server interface figure 5: client interface construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi v. discussion the nrct routine generates dozens of laboratory results per day for atcs. we noted an accumulation of laboratory results not removed, probably due to the distance from the nrct. the use of a system of electronic transmission of the results showed its effectiveness during the two months of implementation. rajeev et al. showed that the use of electronic notification with the hl7 v 2.5.1 protocol is much faster and more complete. this observation is in line with our work, which allows laboratory results to be made available instantly [14]. unlike direct technology, which has the ability to transmit the results as an hl7 message that can be directly integrated into the medical file or as an attached pdf file, our system only transmits hl7 messages, which after decoding and saving are used to generate the results in pdf format in the receiving system. our approach seemed to us to be the best in the context of countries with limited resources where very little funding is allocated to the computerization of the health system. the purchase of licenses for commercial solutions for such projects can be a barrier in terms of their cost. this approach was also a factor in the adoption of the system. users in the nrc or atc were reluctant to have the data stored online (cloud). the minimalist interface, ease of learning and use are the positive elements reported by users at the laboratory and atc level that have facilitated its adoption. this is in agreement with kavuma, which showed that ease of learning is the most important element in the adoption of electronic health record systems [15]. 1. limits two types of limits were found: the first during the test phase; a bug was found, it was manifested by an impossibility to transmit the gene expert type result. this was due to the software design defect, specifically an hl7 encoding error of the gene expert message. this problem had been controlled. however, concerning the second type, our work has a limit in terms of semantic interoperability management. that can be explain that this application was primarily done for a specific institution. by so, the server is able to transmit messages to all information systems using the same protocol, but they cannot be interpreted by the receiving system. indeed, the coding of the analyses uses a local code that would not be understood by a health information system that does not share the same code system. in subsequent versions, we plan to use the loinc (logical observation identifier names and codes) coding system, which would then allow the results to be transmitted to other electronic systems such as computerized medical records using this terminology [16]. conclusion the implementation of this information system has shown satisfactory results in terms of financial savings, time savings, and improvement in the quality of care. its minimalist interface, ease of learning and use have also enabled its adoption by different categories of users. construction of an exchange interface for the transmission of laboratory results: a case of the national tuberculosis center online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(3):e21, 2019 ojphi the use of an international coding system for analysis coding is a requirement to address the challenges of semantic interoperability in order to deliver messages to other information systems such as electronic health records. the electronic transmission system for the transmission of results is an opportunity for countries with limited resources. 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https://doi.org/10.2196/humanfactors.9317 https://doi.org/10.1504/ijfipm.2010.040211 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22899966&dopt=abstract community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi community based research network: opportunities for coordination of care, public health surveillance, and farmworker research sharon p. cooper 1 , nicholas heyer 2 , eva m. shipp 3 , e. roberta ryder 4 , edward hendrikson 5 , christina m socias 1 , deborah j. del junco 6 , melissa valerio 1 , sylvia partida 4 1. the university of texas school of public health, san antonio regional campus, san antonio, tx 2. battelle, seattle, wa 3. texas a&m school of public health, college station, tx 4. national center for farmworker health, buda, tx 5. salud family health center, ft. lupton, co 6. the university of texas health science center, houston, tx abstract introduction: the lack of aggregated longitudinal health data on farmworkers has severely limited opportunities to conduct research to improve their health status. to correct this problem, we have created the infrastructure necessary to develop and maintain a national research data repository of migrant and seasonal farmworker patients and other community members receiving medical care from community and migrant health centers (c/mhcs). project specific research databases can be easily extracted from this repository. methods: the community based research network (cbrn) has securely imported and merged electronic health records (ehrs) data from five geographically dispersed c/mhcs. to demonstrate the effectiveness of our data aggregation methodologies, we also conducted a small pilot study using clinical, laboratory and demographic data from the cbrn data repository from two initial c/mhcs to evaluate hba1c management. results: overall, there were 67,878 total patients (2,858 farmworkers) that were seen by two c/mhcs from january to august 2013. a total of 94,189 encounters were captured and all could be linked to a unique patient. hba1c values decreased as the number of tests or intensity of testing increased. conclusion: this project will inform the foundation for an expanding collection of c/mhc data for use by clinicians for medical care coordination, by clinics to assess quality of care, by public health agencies for surveillance, and by researchers under institutional review board (irb) oversight to advance understanding of the needs and capacity of the migrant and seasonal farmworker population and the health centers that serve them. approved researchers can request data that constitute a limited data set from the cbrn data repository to establish a specific research database for their project. keywords: farmworkers, electronic health records, limited data set, hba1c correspondence: sharon.p.cooper@uth.tmc.edu doi: 10.5210/ojphi.v6i2.5262 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mailto:sharon.p.cooper@uth.tmc.edu community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi introduction little is known about farmworker health on a national basis and in extensive reviews of the health of farmworkers in the u.s., villarejo noted a particular lack of nationwide clinical health data needed for health care coordination, surveillance, and health outcomes and hypothesisdriven research. u.s. farmworkers experience a disproportionate frequency of injuries and illnesses associated with their work and significant barriers to healthcare access [1]. the lack of accessible medical care data and aggregated longitudinal health data on farmworkers has severely limited provision of optimal health care for this vulnerable and often mobile population. this includes limitations on continuity in health care, including needed follow up care; as well as systematic inclusion of farmworkers in reportable disease surveillance systems [2] and health services and epidemiologic research. in order to address these issues, we have created a community-academic partnership, establishing the community based research network (cbrn) with initial funding by the national institute of environmental health sciences. cbrn has built the necessary partnerships and infrastructure to securely import and merge electronic health records (ehrs) data from five community and migrant health centers (c/mhcs) across the u.s. into a health information exchange platform using business associate agreements with each health center. methods organization cbrn consists of two community (national center for farmworker health – ncfh and salud family health centers, ft. lupton, co) and three research/academic partners (university of texas, texas a&m, battelle) with a steering committee consisting of one representative from each partner. each research/academic partner secured irb approval from their respective institutions, including approval of a hipaa waiver of authorization based upon cbrn’s status as a research project [3]. ncfh, with support from the steering committee, identified five c/mhcs, one each in colorado, new york, washington, california, and michigan, who meet inclusion criteria based upon their geographic distribution, patient population (including farmworkers), current use and facility with an electronic health record (ehr) system, and willingness to share patient medical records, including personal identifiers. centex support systems services (centex), a health information exchange (hie) capable of providing safe hipaa secure health information exchange services, was brought onboard by contract, and established protocols for collecting data and providing security assurances through business associate agreements (baas) with each of the five c/mhcs. in order to assure representation of the participating health centers in decision making, a representative from each center was selected to participate on the cbrn national advisory committee, which then elected one additional member to serve on the cbrn steering committee. the cbrn steering committee, the main governing body, developed a process for the review and approval of requests by external researchers to use cbrn data. data sharing requires unanimous approval by the steering committee, oversight by an irb, and a data use agreement assuring protection of the data and confidentiality. all shared data must meet the standards of a limited data set as defined by hipaa. http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi data collection centex developed the technology necessary to extract ehr data in coordination with each individual center, extracting data on a quarterly basis to update the prospective longitudinal medical records database called the cbrn data repository. in order to insure the integrity of the data, centex would conduct an initial completeness check, reviewing the numbers of patients and visits in the data maintained by the center with those downloaded by centex. additional quality control measures include: • patient matching and merging: centex maintains a master patient index (mpi) that contains medical record numbers, other patient id numbers, ssns, names, dates of birth, addresses and other identifiers from those providing the data. these data are then processed by an entity identification service (mirth match), which utilizes a data matching algorithm from the oklahoma department of mental health that involves blocking, matching weights, and a threshold of likelihood to minimize data duplication. each patient is then assigned a unique identifier within mpi, allowing associated hie components to find, exchange and reference patient data. • data validation and semantic interoperability: centex uses its proprietary data validation engine (trails) to validate incoming clinical data. this program has three basic validation procedures: dates and source ids. for manual inspection and intervention. where source data (such as sex, race, language, marital status, etc.) are mapped to a standardized code and format used by the warehouse. data sharing we used a pilot study to test and evaluate the data sharing process and the usefulness of the data contained in the research data repository. the steering committee created and approved a data query request to allow centex to release data to two members of the steering committee for analyses. the query included all records dated between 1/1/13-8/9/13 from the two health centers which were initially recruited into the project. data were exported from the repository in 11 comma delimited files, with linkages available through random identifiers. the files all met limited data set requirements, and were transferred using a secure ftp protocol. the structure of the files is provided in figure 1, where linking identifiers are designated in bold. each of the files received were evaluated descriptively. the number of observations for each of these files were: patient (n=67,878), provider (n=242), encounter (n=94,189), procedure (n=289,952), vital signs (n=1,112,559), laboratory (n=909,555), diagnoses (n=165,848), medications (n=674,783), immunizations (n=30,712), allergies (n=15,488), and next of kin (n=4,508). the two investigators then reviewed the data files for consistency, missing values, and invalid values or coding errors to verify the quality and completeness of the data. none of the variables were deemed unusable due to a data quality issue. http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi 1. provider file  providerid  centerid  type  group  group_code  degree  status 2. patient file  patientid  providerid  dob  gender  language  maritalstatus  ethnicity  race  raceethnicity  zip  fips  chronicdiag  homeless  migrant  seasonal  veteran  tobacco 3. encounter file  patientid  encounterid  providerid  centerid  clinicid  clinictype  encounteryrmo nth  startdate  enddate  starttime  endtime  duration  age  visittype  chronicdiag  insurancepay  insuranceplan  insuranceplant ype 4. procedure file  patientid  encounterid  procedureid  procedureseq  cpt4code  cpt4desc 5. vital signs file  patientid  encounterid  vitalitemid  vitalsigncode  vitalsign  vitalsignvalue  vitalsignnum  vitalsignunits 6. laboratory file  patientid  encounterid  reqnum  clinitemid  testgroupid  testgroup  testitemid  testitem  labvalue  labvaluenumb er  labreference  labunits 7. diagnosis file  patientid  encounterid  diagnosisid  diagseq  diagchronic  icd9cd  icd9desc  bodysys  class  ccscat 8. medications file  patientid  encounterid  providerid  medid  meddesc  meddose  rxdate  refillstot  instructions 9. immunization file  patientid  encounterid  providerid  immunedate  immunedesc  immunedose  immunelotnum  immunemanufac  poc  poctype 10. allergies file  patientid  allergyrecid  allergyid  allegyrecdate  allergydesc  allergyreact 11. next of kin file  patientid  nokid  relationship  nokgender  nokzip  nokpatient figure 1: relational database structure for cbrn research database pilot study the pilot study selected to study management of hba1c levels given the large burden of diabetes in the cbrn research population, and its usefulness in demonstrating how prospective linkages between provider visit and laboratory data could provide indicators to assess quality of care and evaluate health care systems targeting low-income community-based patients. hba1c values were examined over time by farmworker status, gender, and ethnicity. effectiveness of hba1c monitoring was evaluated by intensity (number of tests in the evaluation period) and changes in hba1c levels. finally a linear regression was constructed to identify variables associated increasing levels of hba1c. for the pilot study, cbrn proceeded by undertaking various data management procedures to link the patient, encounter, and laboratory files and to create a merged dataset. indicator variables were created next to classify patients as farmworker vs. non-farmworker, and to identify the sequential ordering of laboratory values (e.g., hba1c) over time. creating the http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi farmworker indicator variable was straightforward requiring only two variables with no missing values from the patient file. on the other hand, identifying which laboratory variables to use to identify hba1c results was a bit confusing, as the file used “test group” for ordering labs, and “test item” for returned labs – but partially completed this field when ordering. only a small proportion of the laboratory observations (n=6,000; 0.7%) contained missing values because they had not yet been reported and entered into the health record. the biggest challenge was that not all laboratory entries could be linked to a patient encounter. it turned out that this was a structural problem in the way laboratory orders are entered into the emrs. centex has been able to establish a protocol that largely eliminates this problem for future use. after all data management and editing procedures were completed, descriptive statistics were computed for person-level (e.g., demographics), encounter-level (type of clinic, duration of encounter) and laboratory-level variables (hba1c values, sequential order). results overall, there were 67,878 total patients (2,858 farmworkers) that were seen by two c/mhcs from january to august, 2013. farmworkers (migrant, seasonal, or both) tended to be male, hispanic, and spanish speaking compared to other patients. a total of 94,189 encounters were captured and all could be linked to a unique patient. a description of the pilot patient population is shown by farmworker status in table 1. table 1: pilot test findings: patient population description 1/2013-8/2013 (2 centers) migrant (m) seasonal* (s) total m&s other patients gender female male other/unknown 503 (40.1) 751 (59.9) 0 (0.0) 863 (53.8) 741 (46.2) 0 (0.0) 1366 (47.8) 1492 (52.2) 0 (0.0) 37631 (57.9) 27381 (42.1) 8 (0.0) age < 5 years 5-<18 years 18+-64 years 65+ years 163 (13.0) 216 (17.2) 839 (66.9) 36 (2.9) 129 (8.0) 319 (19.9) 1067 (66.5) 89 (5.6) 292 (10.2) 535 (18.7) 1906 (66.7) 125 (4.4) 8612 (13.2) 14643 (22.5) 38337 (59.0) 3428 (5.3) race white african am/black asian american indian native hawaiian / pacific other/refused/unknown 252 (20.1) 82 (6.5) 3 (0.2) 1 (0.1) 0 (0.0) 916 (73.0) 667 (41.6) 24 (1.5) 0 (0.0) 0 (0.0) 0 (0.0) 913 (56.9) 919 (32.2) 106 (3.7) 3 (0.1) 1 (0.0) 0 (0.0) 1829 (64.0) 41181 (63.3) 1875 (2.9) 725 (1.1) 257 (0.4) 21 (0.0) 20961 (32.2) ethnicity hispanic/latino not hispanic/latino other/refused/unknown 957 (76.3) 174 (13.9) 123 (9.8) 1133 (70.6) 421 (26.2) 50 (3.1) 2090 (73.1) 595 (20.8) 173 (6.0) 33453 (51.5) 28706 (44.1) 2861 (4.4) language english 178 (14.2) 911 (72.7) 525 (32.7) 1036 (64.6) 703 (24.6) 1947 (68.1) 40949 (63.0) 21627 (33.3) http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi spanish unknown/other 165 (13.2) 43 (2.7) 208 (7.3) 2444 (3.8) marital status single married partnered legally separated divorced widowed unknown/other 445 (35.5) 267 (21.3) 5 (0.4) 6 (0.5) 10 (0.8) 7 (0.6) 514 (41.0) 740 (46.1) 598 (37.3) 15 (0.9) 7 (0.4) 32 (2.0) 30 (1.9) 182 (11.3) 1185 (41.5) 865 (30.3) 20 (0.7) 13 (0.5) 42 (1.5) 37 (1.3) 696 (24.4) 39216 (60.3) 16267 (25.0) 186 (0.3) 664 (1.0) 2441 (3.8) 1051 (1.6) 5195 (8.0) homeless yes no 12 (1.0) 1242 (99.0) 0 (0.0) 1604 (100.0) 12 (0.4) 2846 (99.6) 880 (1.4) 64140 (98.7) veteran yes no 3 (0.2) 1251 (99.8) 12 (0.7) 1592 (99.3) 15 (0.5) 2843 (99.5) 511 (0.8) 64509 (99.2) tobacco yes no 2 (0.2) 1252 (99.8) 27 (1.7) 1577 (98.3) 29 (1.0) 2829 (99.0) 475 (0.7) 64545 (99.3) chronic diagnosis yes no unknown/other 315 (25.1) 939 (74.9) 0 (0.0) 497 (31.0) 1107 (69.0) 0 (0.0) 812 (28.4) 2046 (71.6) 0 (0.0) 14274 (22.0) 50739 (78.0) 7 (0.0) *includes 24 records for patients that identified themselves as both m&s. in our pilot laboratory data file, 8,563 hba1c laboratory test results were distributed among 7,158 patients. patients were tested up to a total of five times in the period for which data were collected. the distribution of repeated tests was similar across farmworkers and nonfarmworkers. mean hba1c values and their ranges are displayed by farmworker status, language, and gender and order of observation in table 2. mean values increased for the second and third tests, which was expected as only patients with higher values are likely to be re-tested multiple times. table 2: average hba1c values for the first test by demographic variables. variable 1st test mean (range; n) 2nd test mean (range; n) 3rd test mean (range; n) farmworker status yes no 6.9 (4.5-14.6; n=400) 7.1 (4.2-17.8; n=6,758) 7.9 (5.2-13.0; n=80) 8.0 (4.8-17.4; n=1,160) 8.2 (5.4-14.0; n=18) 8.3 (5.0-16.0; n=131) gender female male 6.9 (4.3-16.2; n=4,249) 7.3 (4.2-17.8; n=2,907) 8.0 (4.8-17.3; n=724) 8.0 (4.8-17.4; n=516) 8.3 (5.0-14.3; n=92) 8.3 (4.7-16.0; n=57) ethnicity not hispanic hispanic 6.9 (4.2-15.8; n=2,839) 7.2 (4.5-17.8; n=4,203) 7.7 (4.8-17.4; n=496) 8.2 (4.8-17.3; n=732) 8.0 (4.7-16.0; n=61) 8.5 (5.0-14.0; n=87) note: observations with missing/unknown demographic values are not shown. the sample size was insufficient to examine the fourth and fifth tests. http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi when the change in hba1c levels from an individual patient’s first to last test was examined, hba1c values decreased as the number of tests or intensity of testing increased (see table 3). this finding is supported by the results from a recently published clinical trial of the effectiveness of patient-centered care in the control of type 2 diabetes [4]. a linear regression model was constructed that included farmworker status, gender, and ethnicity for the first observed hba1c value only. an increasing level of hba1c was associated with not being a farmworker vs. being a farmworker (coef.=0.34; t=3.06; p=0.002), being male vs. being female (coef.=0.38; t=7.49; p=0.00), and being hispanic vs. non-hispanic (coef.=0.40; t=7.67; p=0.00). table 3: average 1st visit hba1c values & change in hba1c between 1st and last visit by # of visits all patients with hba1c labs n minimum maximum mean std. deviation first hba1c lab value (%) 7158 4.2 17.8 7.093 2.1367 diff in hba1c lab value (%) -8.20 8.50 -0.0363 0.67318 time diff in hba1c labs (days) 0 238 22.06 51.821 patients with only 1 hba1c lab n minimum maximum mean std. deviation first hba1c lab value (%) 5918 4.3 17.8 6.863 2.0542 diff in hba1c lab value (%) 0.00 0.00 0.0000 0.00000 time diff in hba1c labs (days) 0 0 0.00 0.000 patients with only 2 hba1c labs n minimum maximum mean std. deviation first hba1c lab value (%) 1091 4.8 17.6 8.138 2.1834 diff in hba1c lab value (%) -8.20 8.50 -0.1889 1.57079 time diff in hba1c labs (days) 0 238 120.74 43.080 patients with only 3 hba1c labs n minimum maximum mean std. deviation first hba1c lab value (%) 136 4.9 13.7 8.510 2.0635 diff in hba1c lab value (%) -6.80 5.60 -0.2963 1.82892 time diff in hba1c labs (days) 24 234 176.40 35.700 patients with only 4 hba1c labs n minimum maximum mean std. deviation first hba1c lab value (%) 10 4.2 14.5 8.970 3.0096 diff in hba1c lab value (%) -5.30 1.00 -0.8300 1.76387 time diff in hba1c labs (days) 119 219 164.10 31.963 patients with 5 hba1c labs n minimum maximum mean std. deviation first hba1c lab value (%) 3 8.6 13.1 10.333 2.4214 diff in hba1c lab value (%) -5.10 0.00 -1.7667 2.88848 time diff in hba1c labs (days) 142 189 173.33 27.135 process for data access cbrn has developed an application process to promote qualified researchers under irb supervision to include data from the cbrn data repository in their research. proposed projects must be unanimously approved by the cbrn steering committee. once approved, the researcher may request a query of the data repository to obtain fully anonymous count data in support their research grant. if the grant is funded, they will be able to request more detailed queries of the data repository to obtain data required for conducting their approved research. http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi however, to obtain these data, they must sign a hipaa compliant data use agreement with centex stating that the data provided will be securely maintained, will be used exclusively for the stated research purpose(s), that the identity of the individual health centers and individual subjects will not be conveyed with the data nor any attempt made to reconstruct these identities by receiving researchers, and that all data will be destroyed or returned once the research is completed. centex will then create a project specific database (psd) by querying the cbrn data repository for the approved data request. the psd will have personal identifiers replaced by random identifiers for each patient and c/mhc, but may include exact dates related to individual patients (birth and service dates) and zip code level personal address information as allowable in a limited data set as defined by hipaa. limitations in addition to the noted limitation of linking laboratory values to encounters, another challenge was how to work with demographic fields and other data associated with an individual rather than a clinic visit (encounter). these fields, including an important risk factor such as smoking status as well as farmworker status, are frequently over-written when updated. cbrn has had preliminary discussions with centex to preserve these ‘historical’ fields in a separate file so that the change in status for patients can be captured over time. this will be implemented as we move into the future and continue to prospectively add data to the research data repository. a more important concern for future research on farmworkers were the relatively few number of farmworkers in this dataset (<5%). we believe this may be an identification issue, where clinics tend to categorize patients with respect to how medical payments are made, rather than on potential research questions. we need to continue to investigate how a farmworker is identified and if it is based on a billing/payor assignment instead of a special population designation category. on a national basis, federally qualified health centers that are designated as a migrant health center receive a portion of their annual grant as phs 330 (g) funding in order to address the cost of care for farmworker patients. the amount of this grant is calculated on the basis of a projection of the number of patients to be seen and historically new start awards are seldom enough to cover the cost of care for more than two or three medical encounters, let alone provision of dental, behavioral, or ancillary services such as outreach, transportation, interpretation, or environmental services, which are essential to serving this population. therefore, if a farmworker qualifies for other public or private third party reimbursement, the clinic staff might not document a patient’s farmworker status; rather may classify the patient according to eligibility for payment. possible misclassification of farmworkers by including them in the ‘other’ category needs to be carefully assessed. additional research should further explore missing or additional information including linkage of family members. of particular interest would be the linkage of mothers to their newborn children to facilitate reproductive studies. despite these remaining challenges, we have documented the methodologies necessary to extract data from the data repository, using two health centers, and demonstrated our ability to meaningfully use the data for research. data from three more participating c/mhcs have been transferred to centex since our pilot study. these c/mhcs use electronic medical records systems developed by three ehr vendors, and further demonstrate the flexibility of the process created for incorporation of multiple health centers into the cbrn research data repository. http://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi discussion and conclusion over the last three years, we built a network with the infrastructure to facilitate future health services research, public health surveillance, and epidemiologic research of farmworkers, in comparison to non-farmworkers in the primary care setting. our pilot results demonstrate that a limited dataset could be generated using ehr data merged from different c/mhcs. further, it was shown that it was feasible to develop a cumulative dataset based on these data and that this dataset could support longitudinal surveillance, prevention, and research studies. interpretation of these results is limited by the fact that we could not tell how many previous tests were conducted on these subjects – a problem that will diminish as ongoing longitudinal data collection includes much longer follow-up periods. thus, our pilot study demonstrated that linkage of longitudinal patient encounter and laboratory data from multiple health centers could be successfully collected and merged to provide useful patient care and research information. future use of this repository will be guided by mutually engaged partners including healthcare providers, community health organizations, and academic researchers. this electronic linkage and resulting data repository provide an initial national source of both clinical, health data and farmworker population demographics upon which c/mhcs can better serve their patients, evaluate their success, participate in disease reporting to public health agencies, and demonstrate need. it will be sustained by improved opportunities for coordination of care, engagement by community-academic partnerships, desire for local and comparative data by c/mhcs, and by future research funding opportunities from federal agencies that have prioritized farmworker research in their vision and goals and current research agendas to improve health outcomes, reduce health disparities, and increase access to health care for underserved populations, including immigrant, latino, and young workers in the agricultural sector. in 2011, there were 1,128 us federally-supported health centers that served over 20 million patients, including 862,808 agricultural workers and their dependents [5]. expansion of this network to other c/mhcs could evolve into a pioneering demonstration of a national health information exchange. acknowledgements this project was supported by the national institute for environmental health sciences, grant no. 1rc4es019405-01, southwest center for agricultural safety and health at the university of texas hsc at tyler from (cdc/niosh cooperative agreement no. u50 oh07541), and southwest center for occupational and environmental health, a niosh education and research center, (grant no. 5t42oh008421). its contents are solely the responsibility of the authors and do not necessarily represent the official views of cdc/niosh or nih. this project could not have been possible without the information technology support of centex systems support services (maurice samuels, bryan white, anthony nelson) and the enthusiastic support of participating community and migrant health centers, especially jerry brasher and maria de jesus diaz-perez from salud family health centers, and mary zelazny and lawreen duel from finger lakes community health. references 1. villarejo d. health-related inequities among hired farm workers and the resurgence of labor-intensive agriculture. kresge foundation, 2012. http://kresge.org/library/healthhttp://ojphi.org/ community based research network: opportunities for coordination of care, public health surveillance, and farmworker research 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e190, 2014 ojphi related-inequities-among-hired-farm-workers-and-resurgence-labor-intensive-agricult-0 accessed 8/17/13. 2. klompas m, mcvetta j, lazarus r, eggleston e, haney g, et al. 2012. integrating clinical practice and public health surveillance using electronic medical record systems. am j prev med. 42(6) (suppl 2), s154-62. pubmed http://dx.doi.org/10.1016/j.amepre.2012.04.005 3. u.s. department of health and human services. office of the secretary. federal registry. 45 cfr parts 160 and 164. [45 cfr 164.512 (i)(1)(i) and 45 cfr 164.512 (i)(2)(ii)] standards for privacy of individually identifiable health information; final rule. http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/privrulepd.pdf accessed 10/8/13. 4. slingerland as, herman wh, redekop wk, dijkstra rf, jukema jw, et al. 2013. stratified patient-centered care in type 2 diabetes: a cluster-randomized, controlled clinical trial of effectiveness and cost-effectiveness. diabetes care.; epub ahead of print. pubmed http://dx.doi.org/10.2337/dc12-1865 5. national association of community health centers (nachc). united states health center fact sheet, 2011. http://www.nachc.com/client/us121.pdf accessed 9/7/13. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22704432&dopt=abstract http://dx.doi.org/10.1016/j.amepre.2012.04.005 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23949558&dopt=abstract http://dx.doi.org/10.2337/dc12-1865 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts potential efficacy of pregnancy status on hiv laboratory reports elliott brannon*, jessica fridge and joseph foxhood std/hiv program, office of public health, louisiana department of health and hospitals, new orleans, la, usa objective quantify the opportunities for reducing perinatal hiv transmission risk if pregnancy status was available on electronic laboratory reporting in louisiana. introduction in louisiana, information contained on electronic laboratory reports is not able to identify the pregnancy status for the majority of hiv-infected women.1 laboratories have access to icd9/icd10 codes which could provide information about pregnancy status, but few laboratories provide these codes to health departments. in some areas, such as new york city, the reporting of pregnancy status, if available, is required.2 this study quantifies the opportunities for reducing perinatal hiv transmission if pregnancy status was available on laboratory reports and determines if this information would have been useful for targeting these pregnancies for follow up from disease intervention specialists (dis). if pregnancy status is found to be useful, states should require pregnancy status in their laboratory reporting guidelines. methods all hiv-infected women who gave birth in louisiana between 2008 and 2013 were identified. the mothers were divided into three groups: ‘hiv status known at/after delivery’, ‘hiv status known during pregnancy’, ‘hiv status known before pregnancy’. if a mother’s hiv status was known at/after delivery, no laboratory report would be available for pregnancy identification and if a mother’s hiv status was known during pregnancy, the mother would have been identified as ‘newly infected’ and contacted by dis without additional identification of pregnancy status. the laboratory reports during pregnancy (between four and 36 weeks before delivery) for mothers whose hiv status was identified before pregnancy were used to further divide these mothers into two groups: ‘no viral load test during pregnancy’ and ‘viral load test during pregnancy’. the viral load test is an indication of infectivity and perinatal transmission risk increases with a higher viral load. the mothers with a viral load test were divided into two groups, based on the first viral load test during their pregnancy: ‘low viral load’ (less than 1,000 copies/ml) and ‘high viral load’ (greater than or equal to 1,000 copies/ml). the mothers with no viral load test were also divided into two groups: ‘hiv test during pregnancy’ and ‘no hiv test during pregnancy’. if a mother had no hiv test (including western blot, cd4 counts, etc.) a laboratory report could not be used to identify pregnancy status. the number of perinatal hiv transmissions in each group was also determined. results a total of 977 hiv-infected women gave birth in louisiana between 2008 and 2013. the hiv status for 22 of these mothers was known at/after delivery, resulting in seven perinatal hiv transmissions. the hiv status for 265 of these mothers was known during pregnancy, resulting in five perinatal hiv transmissions. the hiv status for 690 of these mothers was known before pregnancy. two cases of perinatal hiv transmission resulted from mothers with a high viral load (a total of 325 mothers), one case of perinatal hiv transmission resulted from mothers with a low viral load (a total of 270 mothers), and three cases of perinatal hiv transmission resulted from mothers who did have an hiv test during pregnancy but did not have a viral load test (a total of 43 mothers). conclusions for mothers whose hiv status was known before pregnancy and who had an hiv test during pregnancy, the highest transmission rate occurred in those without a viral load test (7.0%) as opposed to mothers with a low viral load during pregnancy (0.4%) or mothers with a high viral load test during pregnancy (0.6%). the viral load test may be an indication of a woman’s hiv care during pregnancy and the viral load of these mothers may have decreased after their initial viral load test during pregnancy (the first viral load test during pregnancy was used for this analysis). this analysis suggests pregnancy status on laboratory reports would be useful for targeting women who have an hiv test during pregnancy but no viral load test, due to the high rate of transmission and low number of cases. health departments should continue to work on the identification of pregnancy status on hiv laboratory reports and should require the reporting of this information in their laboratory reporting guidelines. keywords perinatal hiv; pregnancy identification; electronic laboratory reporting references 1. brannon, elliott, et al. identifying pregnancy status. online journal of public health informatics. 2014; 6(1). 2. rules of the city of new york, title 24, section 13.o3(a)(1) *elliott brannon e-mail: elliott.brannon@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e67, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts wikipedia usage estimates prevalence of influenza-like illness in near real-time david mciver*1, 2 and john s. brownstein1, 2 1boston children’s hospital, boston, ma, usa; 2harvard medical school, boston, ma, usa objective the purpose of this work was to develop a novel method of estimating the amount of influenza-like illness (ili) in a population, in near-real time, by using a source of information that is completely open to the public and free to access. we investigated the usefulness of data gathered from wikipedia to estimate the prevalence of ili in the united states, using data from the centers for disease control and prevention (cdc) as well as google flu trends. introduction each year, there are an estimated 250,000–500,000 deaths worldwide that are attributed to seasonal influenza, with anywhere between 3,000–50,000 deaths occurring in the united states of america (us). in the us, the centers for disease control and prevention (cdc) continuously monitors the level of influenza-like illness (ili) circulating in the population. while the cdc ili data is considered to be a useful indicator of influenza activity, its availability has a known lag-time of between 7–14 days. to appropriately distribute vaccines, staff, and other healthcare commodities, it is critical to have up-to-date information about the prevalence of ili in a population. to this end, we have created a method of estimating current ili activity in the us by gathering information on the number of times particular wikipedia articles have been viewed. not only is the information held within wikipedia articles very useful on its own, but statistics and trends surrounding the amount of usage of particular articles, frequency of article edits, region specific statistics, and countless other factors make the wikipedia environment an area of interest for researchers. furthermore, wikipedia makes all of this information public and freely available, greatly increasing and expediting any potential research studies that aim to make use of their data. methods data was collected on how often, with results up to the hour, selected wikipedia articles were viewed. this information was compared to both official cdc ili data and google flu trends gft data. a poisson model was created which estimated ili prevalence using all of the selected wikipedia articles, and a separate model was produced using lasso regression that automatically and dynamically selects the most appropriate wikipedia articles to fit the data. a splitsample analysis was used to test the reliability of the lasso model, comparing half of the data representing the 2007-2010 flu seasons to the second half representing the 2011-2013 flu seasons. results our wikipedia-based poisson model accurately estimates the level of ili activity in the american population, up to two weeks ahead of the cdc, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. wikipedia-derived ili models performed well through both abnormally high media coverage events (such as during the 2009 h1n1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). wikipedia usage accurately estimated the week of peak ili activity 17% more often than google flu trends data and was often more accurate in its measure of ili intensity. conclusions this study is unique in that it is the first scientific investigation into the harnessing of wikipedia usage data over time to estimate the burden of disease in a population. the application of wikipedia article view data has been demonstrated to be effective at estimating the level of ili activity in the us, when compared to cdc data. wikipedia article view data is available daily (and hourly, if necessary), and can provide a reliable estimate of ili activity up to 2 weeks in advance of traditional ili reporting. this study exemplifies how non-traditional data sources may be tapped to provide valuable public health related insights and, with further improvement and validation, could potentially be implemented as an automatic sentinel surveillance system for any number of disease or conditions of interest as a supplement to more traditional surveillance systems. keywords surveillance; influenza; digital disease detection *david mciver e-mail: david.mciver@childrens.harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e40, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an early warning influenza model using alberta realtime syndromic data (artssn) paul smetanin1, rita k. biel*2, david stiff1, douglas mcneil1, lawrence svenson3, hussain r. usman2, david p. meurer2, jane huang2, vanessa nardelli2, christopher sikora2 and james talbot3 1riskanalytica, toronto, on, canada; 2ahs, calgary, ab, canada; 3ah, edmonton, ab, canada objective we developed early warning algorithms using data from artssn and used them to detect signatures of potential pandemics and provide regular weekly forecasts on influenza trends in alberta during 2012-2014. introduction standardized electronic pre-diagnostic information is routinely collected in alberta, canada. artssn is an automated real-time surveillance data repository able to rapidly refresh data that include school absenteeism information, calls about health concerns from health link alberta (hla); a provincial telephone service for health advice and information, and emergency department (ed) visits categorized by standardized chief complaint (cc)1. until recently, real-time artssn data for public health surveillance and decision making has been underutilized. methods a two-part alberta influenza model was constructed using a bayesian approach and historic hla and edmonton ed data from artssn: an agent-based, event-driven infectious disease model simulating the spread of influenza in the province and a simulation module to model the events that would be recorded in artssn due to influenza circulating in the population. during 2012-2013 and 2013-2014, the algorithms were used to provide weekly updates of the predicted attack rate and influenza season peak time to a provincial surveillance team. syndromic indicators of influenza-like-illness (ili) were selected from hla protocols and ed ccs, compared against the alberta health influenza case definition2, and included in the model. in 2013-2014, additional ccs were included if an ili+ screen was recorded. the model was implemented to detect abnormal events, such as higher than normal attack rates or atypical peak times. a prior distribution assumption based on historical data was used in the analysis; attack rate of 12.5%, peak time of february 1 and a pandemic probability of once every 30 years. results a test of the model using simulated and historical h1n1 pandemic observations showed that the early warning algorithms effectively distinguished pandemics from seasonal influenza well before the peak. no pandemic triggering occurred during the two influenza seasons 2012-2014, suggesting that this tool can be effective in pandemic preparedness. based on lab confirmed cases, the influenza peak in alberta actually occurred during influenza reporting week 1 (dec 30-jan 5) in 2012-2013 and week 2 (jan 5-10) in 2013-2014. during the 2012-2013 pilot season, the model predicted the peak time within 1 week of the true peak as early as october 25. during 20132014, the model predicted the peak within 2 weeks of the true peak as early as november 4. the final model estimates showed that 20122013 was a typical influenza season with an expected attack rate of 11.8% ( 3.2%) and a peak in early january (jan 6 27 days). 20132014 was similar with a final attack rate of 11.5% ( 3.1%) and a peak time of mid-january (jan 10 27 days). the early estimates of peak time were in line with what other trending tools such as fluwatch and google flu trends revealed3,4. the forecasts of influenza attack rates and peak times were used by decision makers to guide allocation and efficient use of resources, such as acquisition of additional vaccine or decisions about opening a rapid assessment centre, and public communications. the modelling and forecasts heightened awareness and discussion among medical officers, surveillance and public health staff, as health resource management decisions were made. conclusions the predictive model developed using real-time artssn data, used prospectively, is a promising tool for influenza planning and preparedness. keywords influenza; syndromic surveillance; predictive model; pandemic; influenza peak time acknowledgments we thank bonita lee, kevin fonseca, albert de villiers, sandra marini, richard golonka, linda duffley, steven probert, elizabeth henderson, evan jones, marcia johnson, gerry predy, carla briante, lynette katsivo, alysha visram, annette lemire and bryan wicentowich. references 1. fan s, blair c, brown a, et al. a multi-function public health surveillance system and the lessons learned in its development: the alberta real-time syndromic surveillance net. can j pub health 2010; 101(6):454-8. 2. alberta health influenza case definition. alberta health public health notifiable disease management guidelines, october 2012. http:// www.health.alberta.ca/documents/guidelines-influenza-2012.pdf. 3. fluwatch http://www.phac-aspc.gc.ca/fluwatch/13-14/w02_14/indexeng.php. 4. google flu trends http://www.google.org/flutrends/ca/#ca-ab. *rita k. biel e-mail: rita.biel@albertahealthservices.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e54, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 69 (page number not for citation purposes) isds 2013 conference abstracts evaluation of the influenza sentinel surveillance system in singapore 2011-2012 pengiran hishamuddin* ministry of health, singapore, singapore � �� �� �� � � �� �� �� � objective ��������� � � ���� ��������� � � ���������� ������� ��������� �� ��������� ������� ���������� ������� ��� ���� � � ������ �� ������� � ����� ������������������� �� ��� �� ���������� ��� ������� !"!#������ � ���������� ��� ������������ � ����� ��� �� �������������� ����� �� � ��� ��$%&&�&$' introduction �(������ �������� ��������� ��������������������� ��������� �� ��� ���� �����(� � ���� �������)��� ����)��� ������ ��������� ����� �� � �� � ��������������� ������ ���������� ��� ����� �� ����� ����� ������ �����'�* 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���'�$%&&'�7����� ������� ����������!������������ �������� �� ��������$%&&'�2������ � ������� � ����� � � ���������� ���� ���� ����' *pengiran hishamuddin e-mail: hishamuddin_badaruddin@moh.gov.sg� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e42, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts school health: a novel school nurse clinic surveillance project in coastal georgia amanda feldpausch*1, 2, wendy smith3, karl soetebier4, cherie drenzek5 and marsha cornell6 1cdc/cste applied epidemiology fellowship program, atlanta, ga, usa; 2acute disease epidemiology section, georgia department of public health, atlanta, ga, usa; 3syndromic surveillance project, georgia department of public health, atlanta, ga, usa; 4health information systems section, georgia department of public health, atlanta, ga, usa; 5epidemiology section, georgia department of public health, atlanta, ga, usa; 6school health services, effingham county, springfield, ga, usa objective this project was designed to demonstrate the feasibility of schoolbased nurse clinic visit syndromic surveillance. additional objectives include using clinic visit data to identify opportunities for health interventions at participating schools and to characterize the type and number of student visits to the school nurses. an electronic module was developed in the state electronic notifiable disease surveillance system (sendss) to facilitate data entry by participating school nurses and data management by the georgia department of public health. introduction the syndromic surveillance program (ssp) of the georgia department of public health collects chief complaint data from hospitals to characterize health trends in near real time. these data were critical for situational awareness during the 2009 h1n1 pandemic. in 2012, ssp and the effingham county schools began a project to collect syndromic surveillance data from school clinics. the hypothesis was that these data may be used to inform interventions during a pandemic, guide school health programs, elucidate health priorities in school-age populations, and quantify nursing staff needs in schools. analysis of data from the first two pilot years has provided a novel look at the disparate burden of disease among students across schools in the county. methods every day during the 2012-2013 and 2013-2014 school years, 12 nurses from 12 schools in effingham county entered data into a web-based module for clinic visits that met the following syndrome definitions: influenza-like illness, rash/fever, gastrointestinal, injury, asthma, diabetes, and total daily visits. at the end of the 2012-2013 school year, the school nurses were asked to review the syndromes. as a result, “asthma” and “diabetes” were modified to include “acute asthma” and “acute diabetes” counts. these new categories captured urgent visits while “asthma” and “diabetes” captured routine management. additionally, “oral health” and “mental health” were added to the syndrome list. given the modification of syndromes, for this abstract, only data from the 20132014 school year were analyzed using microsoft excel and sas. results during the 2013-2014 school year, effingham county school nurses provided care for 79,906 total student visits; 24,301 (30.4%) met at least one of the syndrome case definitions. injury (6,469, 26.6%) was the most common chief complaint, followed by diabetes management (4,477, 18.4%). visits for asthma ranged from 10.6% of surveillance related visits in elementary schools to 2.5% of visits in high schools. the proportion of visits related to asthma varied from 0.1% at one elementary school to 18.8% at another elementary school. similarly, one middle school reported 21.3% of visits were related to diabetes, while the other two middle schools had less than 2% of visits related to diabetes. conclusions this project demonstrated that school clinics are a significant resource for primary healthcare among school-aged children and that clinic syndromic surveillance is valuable for preparedness and education programs. baseline burdens of both infectious and chronic diseases may be established, which can be used to identify trends or outbreaks in the future. these data also show that priority targets for school and community programs include injury prevention, asthma, and diabetes control. evidence of poor chronic disease management among some students provided an opportunity for collaboration with chronic disease programs at dph, perhaps leading to future funding for school-based intervention programs. further analysis will be done to look at environmental factors and socioeconomic status of each school’s population to determine the possible effect on disease burden and management of chronic disease. this information may be used to inform decisions for school inclusion in intervention programs. keywords school health; syndromic surveillance; state and local collaboration; novel surveillance system; student health surveillance acknowledgments we would like to thank the school nurses and marsha cornell of effingham county for their dedication and contribution to the success of this project. *amanda feldpausch e-mail: amanda.feldpausch@dph.ga.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e128, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts combining text mining and data visualization techniques to understand consumer experiences of electronic cigarettes and hookah in online forums annie t. chen2, shu-hong zhu1 and mike conway*1 1department of family and preventative medicine, university of california san diego, la jolla, ca, usa; 2school of information and liberty science, unc, chapel hill, nc, usa objective our aim in this work is to apply text mining and novel visualization techniques to textual data derived from online health discussion forums in order to better understand consumers’ experiences and perceptions of electronic cigarettes and hookah. introduction since their introduction to the us market in 2007, electronic cigarettes (e-cigarettes) have posed considerable challenges to both public health authorities and government regulators, especially given the debate – in both the scientific world and the community at large – regarding the potential advantages (e.g. helping individuals quit smoking) and disadvantages (e.g. renormalizing smoking) associated with the product1. similarly, hookah – a kind of waterpipe used to smoke flavored tobacco – has increased in popularity in recent years, is known to be particularly popular among younger people, and has prompted a range of regulatory responses2. one important – and currently largely unexplored – area of research involves exploring consumer perceptions and experiences of these emerging tobacco products. in this work, we use online health discussion forums in conjunction with text mining and novel data visualization techniques to investigate consumer perceptions and experiences of e-cigarettes and hookah, focusing on the automatic identification of symptoms associated with each product, and consumer motivations for product use. previous related research has focused on using text-mining to analyze e-cigarette or hookah related twitter posts3,4 and on the qualitative identification of e-cigarette related symptoms from online discussion forums5. the research reported in this abstract is – to the best of our knowledge – the first time that text mining techniques have been used with online health forums to understand e-cigarette or hookah use. methods data were automatically crawled from three different sources: vaportalk (www.vaportalk.com – a forum devoted to e-cigarettes), hookah forum (www.hookahforum.com – a hookah discussion forum), and reddit (www.reddit.com – a popular general forum with stopsmoking, e-cig, and hookah “subreddits”). we used two broad approaches to text mining the data. first, we iteratively developed bespoke lexicons representing dimensions of health behavior – e.g. symptoms, cost, quitting – and calculated the proportion of posts in which words from a particular category occurred, allowing us to compare across forums. second, we used topic modeling6 – a set of techniques drawn from the natural language processing and machine learning communities that allow for the automatic identification of topics, as represented by popular key words – to analyze the text. as part of this work, we have developed a novel, interactive visualization system (implemented in python and d3) for the analysis and summarization of forum data. results several unique findings indicate the usefulness of text mining online forum data in conjunction with the use of sophisticated visualization techniques. for example, our analysis indicates that e-cigarette users have a tendency to focus on e-cigarette equipment (vaporizers, liquid types, etc.) hookah users often discuss the sensory experience of smoking (e.g. optimizing “buzz”). further, our analysis of vaportalk health & safety forum, and the reddit stopsmoking subreddit indicates key differences in symptom reporting between the two forums, with vapor|talk concentrating overwhelmingly on physical symptoms associated with e-cigarette use (e.g. headache, coughing), and the reddit stopsmoking forum more focused on psychological symptoms (e.g. craving, anxiety). keywords natural language processing; text mining; informatics acknowledgments this work was supported by a grant from the national cancer institute u01 ca154280. references 1. who framework convention on tobacco control. electronic nicotine delivery systems. 2014 2. grekin e, ayna d. waterpipe smoking among college students in the united states: a review of the literature. j am coll health 2012;60:244-9 3. myslín m, zhu sh, chapman w, conway m.using twitter to examine smoking behavior and perceptions of emerging tobacco products. j med internet res 2013;15(8):e174 4. huang j, kornfield r, szczypka g, emery s. a cross-sectional examination of marketing of electronic cigarettes on twitter. tob control 2014;23:iii26-iii30 5. hua m, alfi m, talbot p. health-related effects reported by electronic cigarette users in online forums. j med internet res 2013;15(4):e59 6. blei d, ng a, jordan m. latent dirichlet allocation. journal of machine learning research. 2003;3:993–1022. *mike conway e-mail: mconway@ucsd.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e117, 201 crappdf1.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 177 (page number not for citation purposes) isds 2013 conference abstracts developing a social media system for biosurveillance julie waters*, kristina howard, heather baker and caroline brown national biosurveillance integration center (nbic), department of homeland security (dhs), washington, dc, usa � �� �� �� � � �� �� �� � objective ��������� �� ��� �� ������������������� 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conference abstracts increased uptake of voluntary medical male circumcision (vmmc) services among older men following mobile technology demand creation in shinyanga and simiyu, tanzania godfrey j. kundi1, lucy mphuru1, james mcmahan1, paul mwakipesile1, salli mwanasalli2, daimon simbeye2, peter masika3 and lorie l. broomhall*1 1monitoring, evaluation and research, intrahealth international, chapel hill, nc, usa; 2centers for disease control, atlanta, ga, usa; 34. tanzania youth alliance (tayoa), dar es salaam, united republic of tanzania � �� �� �� � � �� �� �� � objective ������� ���� ���������������� �������������� ������������� ��� ���� � ��� ������ � � ������ �� ������ ������� �������������� ������ ������������ ���������� � ���� �������� �� introduction ������ !�� ��"� ����#�� ���� �����������$ �� � ����������� ������ ����% � � � �&���$ ���������������%� �������������� ���' � � ���(��)��� �� ���������� ���%������������ ����� �������� ��� ��*� ��!�% � �������� ������ ������� 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access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 105 (page number not for citation purposes) isds 2013 conference abstracts usefulness of syndromic surveillance during ultraendurance running races: example with the “grand raid de la réunion” ultra trail aurélie martin1, pascal vilain*1, arnaud bourdé2, xavier combes2, pierre-jean marianne dit cassou3, yves jacques-antoine4, katia mougin damour5 and laurent filleul1 1regional office of french institute for public health surveillance in indian ocean, saint-denis, reunion; 2university hospital, saintdenis, reunion; 3university hospital, saint-pierre, reunion; 4hospital center, saint-benoit, reunion; 5hospital center, saint-paul, reunion � �� �� �� � � �� �� �� � objective ������� ��������� ����� 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���� ���. ������ �������������.����$�#��%�����.��������� ����>���������������� ������+���� �������������� � �������������� �� ����� �� � �������������� ���� ���� ���������$ *�������$�4� .������ !�+���������������"����������������������'�9";����9<;'� ��������#�� ��'�*� ���'��������?�;@� keywords ���� ��� ��a������� ������+���� ���a���������#�� �� acknowledgments ���� �������%��������� �)�����������.���������� ������� ������ ���.����� ��� ������������� ������+���� ��������� ������������#�� ��$ references 1�3�b ����c'�d ����e9'�b������6 '�8.������$�,������� ��� ���������� ����������0�����$������:f2�c������ ��"9��>������$�8 �e�5 �����b��$� �:ff�b �"e��a��@�(�g�f&h�:2$ 1�3�i������b8$�b���� ��� ��� ������������� � � ������������g��.���+ " ��������� ���>�� ���� �)�� ��$�c��e�5 �����b��$��:f2�b �a�f���g22h2&$ 1(3�j� . )�ce'�k ����c'�5������b8$�5����������0���� ������������ ���� ��� ����� �� ���� � ����� �������$� b��� 5��� 5 ����� >���$� ����� !��a2(����g�(�2"��$ 123�* �����j $�b�������)���� ����0����������������� " � ����g������::�� k���������5���������b��.���������$�c��e�5 �����b��$��::@a(�g(�:h (�($ *pascal vilain e-mail: pascal.vilain@ars.sante.fr� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e79, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts follow-up of breast cancer patients in ghana: challenges to community-based surveillance dennis o. laryea*1, 2, baffour awuah1, 2, yaw a. amoako1, 2, samuel mensah1 and fred k. awittor2 1public health, komfo anokye teaching hospital, kumasi, ghana; 2kumasi cancer registry, kumasi, ghana objective to identify challenges to community-based surveillance and follow up of breast cancer cases in ghana introduction cancers are among the leading causes of deaths globally. in subsaharan africa, cancer-related deaths have been projected to increase significantly in the next few decades. information on cancer is essential in planning and implementing cancer control and prevention activities. registration and follow-up of cancer cases to estimate survival are useful tools in cancer control programmes. in ghana, despite the existence of a national cancer prevention and control strategy, not much attention has been given to the problem. cancer survival has been found to be poor in most developing countries due to late reporting. while late reporting may be a significant factor in cancer survival, the ability of clinical and community health staff to follow-up on cases can help and provide accurate information on cancer survival. methods we set out to follow-up 136 breast cancer cases diagnosed from 2006 to 2008 among residents in the city of kumasi. we reviewed the case notes to determine their places of residence and other relevant demographic information. cases were contacted via telephone calls or visits to the stated residential addresses. the status of the case on contact (dead or alive) was noted. results a total of 51(37.5%) out of the 136 cases could be contacted either personally or by contact with relations where death had occurred. forty one of those contacted were by phone (41) and the remaining (10) by residential address. all remaining 85(62.5%) cases could not be contacted by telephone. twenty six of these contacts did not have a phone number indicated in their folders. of the remaining 59 cases with phone numbers in the folders, attempts to call the numbers resulted in the following responses: ‘wrong phone numbers’ (n=16), ‘phone number switched off’ (n=13) or ‘number not reachable’ (n=30). examination of clinical records for the residential addresses, of all 85 cases who could not be reached by phone revealed 3 cases with no residential addresses 21 with incomplete addresses, 33 with addresses that could not be located and 28 were unknown to current occupants of the residential address given. one (1) case had neither an address nor a telephone number indicated in the folder. among those for whom contact was established, 23 representing 45.1% were alive at the time of contact. conclusions this study has highlighted challenges associated with following up patients in low resourced settings such as ghana. such situations may present a public health risk in cases of communicable disease. there is the need to ensure that demographic data captured for patients are verified in order to allow for easy tracing when necessary. while mobile telephony may be useful in surveillance, multiple mobile phone numbers including those of relatives may facilitate easier follow-up of patients. there is a general need to ensure a more robust addressing system in ghana to ensure that residences can be easily traced. keywords cancer; follow-up; noncommunicable disease surveillance acknowledgments we wish to acknowledge the support of the african cancer registry network (afrcn), the komfo anokye teaching hospital and registrars at the kumasi cancer registry references bah e, carrieri mp, hainaut p, bah y, nyan o, taal m. 20-years of population-based cancer registration in hepatitis b and liver cancer prevention in the gambia, west africa. plos one [internet]. 2013 sep 30 [cited 2013 dec 4];8(9). available from: http://www.ncbi.nlm. nih.gov/pmc/articles/pmc3787012/ bosu wk. a comprehensive review of the policy and programmatic response to chronic non-communicable disease in ghana. ghana med j. 2012 jun; 46(2 suppl):69–78. hadji m, nahvijou a, seddighi z, beiki o, mohagheghi ma, mosavijarrahi a, et al. challenges to promoting population-based cancer registration in iran: a workshop report. asian pac j cancer prev apjcp. 2013;14(10):6189–93. hanna tp, kangolle ac. cancer control in developing countries: using health data and health services research to measure and improve access, quality and efficiency. bmc int heal hum rights. 2010 oct 13; 10:24. o’brien ks, soliman as, awuah b, jiggae e, osei-bonsu e, quayson s, et al. establishing effective registration systems in resource-limited settings: cancer registration in kumasi, ghana. j regist manag. 2013; 40(2):70–7. laryea do, awuah b, amoako ya, osei-bonsu e, dogbe j, larsenreindorf r, et al. cancer incidence in ghana, 2012: evidence from a population-based cancer registry. bmc cancer. 2014 may 23; 14(1):362. *dennis o. laryea e-mail: dlaryea@kathhsp.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e142, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 (page number not for citation purposes) isds 2013 conference abstracts enhancing situational awareness by ground truthing with historical outbreaks lauren castro*, kirsten taylor-mccabe, eric generous, joseph longo, kristen margevicius, reid priedhorsky and alina deshpande los alamos national laboratory, los alamos, nm, usa � �� �� �� � � �� �� �� � objective ������� �� ���� ���������� �������� �������� �� �� ������������� ��������� ��� ������������ ��������������������� ������ ��� �� ������ �������� �������� ������ ������������������� �� ��� ��� ����� ����� ��������������������������� ����������� ��������������� �� ����� � ������ ��� �������������������������� �� ����� �������� ���������� � ������� ���� ���������������� ��������� ���������� ���� ������������ ����������������� ��������������������� ������� ��� �� �� introduction � �! ��� �"������ � ����������# !" $��� 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�������� ������ ������-��� �� �� ������ ������� ������� ������ ��� �� ����� ��������� �������������������������� ������� �������������� ������ � ���� ��� �������������������� ��'�������������� ������������� ����������������� ������������������ ������������������������� ������ �� ����� ������������ �������� ������������������������ ������� ����� ����������.���������������� ������� ����� ����������������������� �������� �� ������� ����� ��������������� �� �������������� �������� ����� ����������������� ������ ������ ����������������� ��� �� �������� � � �� ������������� ���������� �� ���� ������������������ � �������� ��� �������������� �������������������� � ���� ��� ���������� ����������� �������������������� ���������� ������������������ ����� ������-��������� ������ ������ ����������� ������������������ �� ���� ����������������� ������ ��������������� ����� ����������� ������ ������ ������ ������� ������������� �������� ������� ��������� �� �������� ������ ��������� ������������/����� ����� �������� �� ����� � ��������������������������� ��������� ���������� ����������0��������� ���� �������� ���������� ���������� � ��� ������������� ������ � ������ ������ ������ ��� ������ ������� results &� � � ������� ��� ������������� ������ ����������� �������� ����� ('!)�������������� ���� ������ �����-������ ��� �� �������� � � � � �12��� ��������� ������������ ������ �������������� ��� ������� ��� ����������������� ��� �� ����� ����������������������� ��� � ���� ��� � ���� ������������ ��'����� ������,3��� ��������������������� �� � /����� ���� �������������� ������ ��������� ������� ������ �� � ������ ���4��� ������� ��������������������� ������������� ���������� � �� ������������������������ ������� �� ����5������ ����� ������� ���� �������� �� ����� � ��� ��� �� ��������� �� ��������������� ����������� ��� ����� ��������� ���������� ����������� ���� ������ �������������� ����� ��-��������� ���� �������� ��������� ������ ������ ������������� ���� ����� ��������� ��������� conclusions %����������������� � ����������� ��� ���� ������������ �����������!�� ���������������� ���� ����� ������ ��� �� ����������� ������ ��� ��� ����������������������������� ������ ��� �� ���� ���� �������� � ����� �������� ������('!)�������� ������������ ������� ��������� ����� �� ���� � ���������� ����������� ��������������� �������������������� �� �� �������� +������,4���������� ��������������������� �������� �����('!) keywords (����� ����� '�����6� *�� ����� ����6� 7� ������ � -������� 6� (��������� �!������ acknowledgments ��� ���������� � ����������������5������ �����*�� ����� ������� ���� � %�����������8�����(����������������� ��������#8(�-$��%� �� ��������� &���������!������#%�&!$� *lauren castro e-mail: lcastro@lanl.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e136, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts leveraging the master patient index in public health surveillance through collaboration between illinois department of public health and the illinois health information exchange stacey hoferka*1, ivan handler2, steven linthicum2, dejan jovanov1, william trick3 and judy kauerauf1 1illinois dept. of public health, chicago, il, usa; 2illinois office of health information technology authority, chicago, il, usa; 3cook county health & hospital system, chicago, il, usa objective this presentation will describe public health efforts to improve data collection by utilizing technology that supports record linkage through the implementation of the master patient index (mpi). the initial use case will be applied to ambulatory syndromic surveillance at illinois department of public health (idph). it will include applications for incorporating the mpi into currently existing public health surveillance data and benefits to data integration and bidirectional information exchange. introduction meaningful use (mu) stage 2 public health reporting for eligible professionals (eps) included a menu option for ambulatory syndromic surveillance. review of currently existing models lead to a collaboration between the illinois health information exchange (ilhie) and idph to build services that would support the use of the mpi, a database that can uniquely match records across systems. the mpi providers a mechanism for public health to manage multiple data streams, while maintaining confidentiality of health information and supporting the mission of public health to identify patterns of illness, apply effective interventions and conduct program evaluation. this initiative will allow idph to extend the use of the mpi to other surveillance domains, including hospital discharge, communicable disease, cancer and extensively drug resistance organism reporting. methods to fully support the national goals of mu and providers’ needs in our community to achieve mu, idph declared it would accept ambulatory syndromic surveillance on october 1, 2013. the process for implementing the use of the mpi in the health department will be described within the context of specific domains and required services. the exchange of data between providers and the health department, accepted formats, certification message translation services, required data elements to support mpi and the state implementation guidance for syndromic surveillance will be demonstrated with the ambulatory use case. the extension of the mpi to applications within existing surveillance systems will be delineated along with relevant public health applications. results the ilhie services for providers to facilitate exchange of electronic health records across health systems have generated 7.9 million unique mpi identifiers. the sources that will contribute data to the ambulatory surveillance system represent a diverse range of providers and patient populations from large health systems to federally qualified health centers, and is actively under expansion. for meaningful use, over 4,500 eps registered their intent to submit data to idph for ambulatory syndromic surveillance as of september, 2014. by the end of the year, data from at least 1400 providers will be on-boarded by idph and evaluated for completeness and quality of data elements. conclusions there are many advantage to a standardized patient identifier incorporated into public health surveillance that will intergrate independent data collection systems and improve population health analysis. this is important for turning large data sets into actionable information. utilizing the ilhie’s mpi services allows idph to continue to expand its support for public health reporting through the ilhie to eligible providers. this approach will position idph to better implement specialized registries and prepare for stage 3 mu. keywords syndromic surveillance; hie; meaningful use; public health; mpi *stacey hoferka e-mail: stacey.hoferka@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e80, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts adjustment for baseline level of dengue cases due to increased testing in singapore li wei ang*, cindy thiow koon lim, stefan ma, joanne tay and jeffery cutter ministry of health, singapore objective to make adjustment of historical trends to accurately reflect the baseline level of dengue cases in singapore, in view of increased testing for dengue in 2013. introduction dengue is endemic in singapore, with epidemics of increasing magnitude occurring on a six-year cycle in 1986/7, 1992, 1998, 2004/5, 2007 and 2013. the incidence per 100,000 population ranged from 87.2 to 105.6 in 2009-20121, and surged to 410.6 in 2013. the mean weekly number of dengue cases over a five-year period provides an indication of the baseline level. we illustrate an adjustment that has been made to the computation of the baseline level due to increased testing for dengue in 2013. methods dengue is a legally notifiable disease in singapore. in any given year, a subset of dengue infected cases is laboratory-confirmed and notified to the ministry of health, singapore. the remaining cases may not be tested as the medical practitioner has clinically diagnosed them to have dengue based on their presenting signs and symptoms, or they may have mild symptoms or are asymptomatic and are not tested for dengue. in 2013, some of these mild, asymptomatic or clinically diagnosed cases may have been “uncovered” and laboratoryconfirmed, due to increased testing for dengue in that year (fig. 1). adjustment factor we assumed that the proportion of hospitalizations among laboratory-confirmed dengue cases remained constant between 2012 and 2013. the proportion hospitalized was 0.40 in 2012. assuming there was no increased testing and no change in dengue severity, the proportion hospitalized in 2013 should remain the same at 0.40. hence, the expected number of notified laboratory-confirmed dengue cases in each week could be derived by dividing the weekly number of hospital admissions by 0.40. any increase in observed numbers over the expected numbers would represent an adjustment due to increased testing. as such, the weekly adjustment factor due to increased testing for each week could be derived by dividing the observed weekly number of cases by the expected weekly number. we also assumed that dengue testing practices had stabilized since epidemiological week (e-week) 29, from 14 to 20 july 2013. the average 4-weekly moving adjustment factor from e-week 29 to e-week 52 in 2013 was 1.54. removal of outliers weekly number of cases which exceeded mean + 2 standard deviations (sd) for each of the four years from 2009 to 2012 were deemed as outliers and removed. for 2013, outliers exceeding 1.54 x (mean + 2 sd) were removed. computation of baseline levels for 2014 the adjustment factor of 1.54 was applied to the remaining weekly data from 2009 to 2012 after outliers had been removed. for 2014, the mean was then computed using the adjusted weekly numbers from 2009 to 2012 and remaining data points in 2013. results for 2013, the previous mean reflecting the baseline level of dengue was 97, which was computed based on weekly numbers of dengue cases over a five-year period from 2008 to 2012. for 2014, the baseline level of dengue with adjustment for increased laboratory testing was higher at 143 cases per week. conclusions when there are changes to existing public health surveillance such as increased testing for dengue in 2013, there is a need to make adjustment to the computation of baseline level so as to better reflect the underlying disease trends. the mean for 2014 would be lower if the adjustment factor to inflate the weekly numbers from 2009 to 2012 was not incorporated, which would then result in an underestimation of the baseline level for 2014. keywords dengue; endemic; epidemic; laboratory-confirmed; baseline references 1. ministry of health, singapore. communicable diseases surveillance in singapore 2012. singapore, 2013. *li wei ang e-mail: ang_li_wei@moh.gov.sg online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e104, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 131 (page number not for citation purposes) isds 2013 conference abstracts the alert algorithm: how to simply define a period of elevated disease incidence nicholas g. reich*1, derek cummings2, martha zorn1, ann-christine nyquist4, trish m. perl5 and lew j. radonovich3 1division of biostatistics and epidemiology, university of massachusetts-amherst, amherst, ma, usa; 2johns hopkins bloomberg school of public health, baltimore, md, usa; 3veterans health administration, gainesville, fl, usa; 4children’s hospital colorado, aurora, co, usa; 5johns hopkins university school of medicine, baltimore, md, usa � �� �� �� � � �� �� �� � objective ������� �� ��������� ����������� �� ��������� �������������� �� �� ���� ���� �������� �������� � �� ������� ������ �� � ������ ��� ���������������� ����� ������ introduction � ���� ��� ����� �������� ������������������������������ ���������� ����� ���� � ��� ����� ��������� �������������� �� ���� ����������� �� � ������ ��������� ���� �� ���� �� ���� �� �������� ������ ����� � �� � ����� ����� �� ����������� � ���� ����������� �� ������� ���� �� ������ �������� � �� ������� ������ �� � ������ ����������! �"��� � �������������� ������ ��� ���������������� ���� �����"��� �� ��� �� ����� ������ �������� �� �������� ������ ���� ��������� ���������� �� �������� ����� ������ ��� ���� ���������� � �� ����� �� ��� �� � ������ �� ���� �������! � ���������� ������� �#�� �$� ���%� �� ��& ���� ����������� ���'�� ������(#$%&')������������'� �#$%&'����������� ���������� �� ����������� ��� ����� �������� � �� ����� �� ��� �� � � ����� ������������������������������ ���� ����� ��� ������ ����� � ��������������� �������� �� � methods #��� ������� ����� ����� ���������������� ��� ����������������� 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����������� ���������� �� �./00�0.�����./0.�01������ ����� �������! �� �� ���� ����������� ���� ������ ������������� �� ����������������� ��� �������� � ���� �� ������ �� ����������� �-��� ��2��� �� conclusions 3�� � ����������������� ���������� ���������� � �� �������� �� � #$%&'��������������������������������������������� �� ������� �� ��� �������� ����� ���� � ����������� �������� ���� �� �� ��������4�� �� � ��� � ������������ ������������ ��� + ��� �� �������� �� ��'� � #$%&'���������������� �������������� �,�� ����� �� ���������� � ��� ���� ���#�� ����� ������� ��������������� � ������ �#$%&'� ������������� ��� �������� ��� ���� ��� keywords ����� ���5������ ���� � ����5������ �� �������� �� 5���� ����� acknowledgments 67&� ���� ���� �� ��� �� � & � %8'� ������ ( ���� ����������� �� 4�9� 68'/0.:;<.=)��������������� ��� � ����� � ���� �� ���� �8 �� ��� ������� �� �8������������� �-��� ��2��� ��� ����� ������> � �����#������� (8�8�4##�/;*%�;/=?@<)��'� �������������� �� ��������������������� � ������� ����� ������ ��������������������� ��������� �� � ����� � � ��� ����� �8 �� ���������� �� �8����������� � �������'� ����� ���������� ��� ����������� ����������� ��� �������������������� ��������������������� �� �������������� ������ ����� *nicholas g. reich e-mail: nick@schoolph.umass.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e72, 2014 clinical decision support for immunization uptake and use in immunization health information systems 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi clinical decision support for immunization uptake and use in immunization health information systems lauren shrader1*, stuart myerburg2, eric larson1 1 northrop grumman corporation, ga 2 centers for disease control and prevention (cdc), ga abstract context: in the united states, immunization recommendations and their associated schedules are developed by the advisory committee on immunization practices (acip). to assist with the translation process and better harmonize the outcomes of existing clinical decision support tools, the centers for disease control and prevention (cdc) created clinical decision support for immunization (cdsi) resources for each set of acip recommendations. these resources are continually updated and refined as new vaccine recommendations and clarifications become available and will be available to health information systems for a coronavirus disease 2019 (covid-19) vaccine when one becomes available for use in the united states. objectives: to assess awareness of cdsi resources, whether cdsi resources were being used by immunization-related health information systems, and perceived impact of cdsi resources on stakeholders’ work. design: online surveys conducted from 2015–2019 including qualitative and quantitative questions. participants: the main and technical contact from each of the 64 cdc-funded immunization information system (iis) awardees, iis vendors, and electronic health record vendors. results: awareness of at least one resource increased from 75% of respondents in 2015 to 100% in 2019. use of at least one cdsi resource also increased from 47% in 2015 to 78% in 2019. about 80% or more of users of cdsi are somewhat or very highly satisfied with the resources and report a somewhat or very positive impact from using them. conclusion: as awareness and use of cdsi resources increases, the likelihood that patients receive recommended immunizations at the right time will also increase. rapid and precise integration of vaccine recommendations into health information systems will be particularly important when a covid-19 vaccine becomes available to help facilitate vaccine implementation. keywords: cdsi, clinical decision support, immunizations, guideline-base care, immunization schedule, immunization information system *correspondence: ytl7@cdc.gov doi: 10.5210/ojphi.v12i1.10602 copyright ©2020 the author(s) mailto:ytl7@cdc.gov clinical decision support for immunization uptake and use in immunization health information systems 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. introduction in the united states, immunization recommendations and their associated schedules are developed by the advisory committee on immunization practices (acip) [1]. new acip schedule changes are communicated through clinical language in peer-reviewed publications, primarily the centers for disease control and prevention’s (cdc) morbidity and mortality weekly report (mmwr). acip recommendations include age for vaccine administration, number of doses, dosing interval(s), and precautions and contraindications [1]. this same procedure will be used when a coronavirus disease 2019 (covid-19) vaccine becomes available. clinical decision support (cds) helps those involved with health and healthcare make decisions by providing knowledge and individualized patient information. benefits of clinical decision support are increases in quality of care, fewer errors and adverse events, improved efficiency, and provider and patient satisfaction [2]. to implement acip recommendations in clinical settings, technical and clinical subject matter experts must translate the clinical language provided in the acip recommendations into technical logic for processing with cds tools. this is a timeconsuming and complex process that in the past often happened independently within different health information systems (hiss), leading to variability in recommendations to providers because there was no centralized approach to how recommendations were received, implemented, and updated. hiss provide healthcare providers with immunization evaluation and forecasting tools designed to automatically determine the recommended immunizations needed when a patient presents for vaccination or routine visit. examples of his include but are not limited to health information exchanges (hies), immunization information systems (iiss), electronic health records (ehrs), and pharmacy systems. iiss record all immunization doses administered by participating providers to persons residing within a given jurisdiction [3]. in 2017, approximately 95% of u.s. children under the age of six participated in an iis [4]. given this widespread iis participation, it is important that each patient’s immunization record is consistent and up-to-date within an iis. to assist with the translation process and better harmonize the outcomes of existing or new cds tools, cdc initiated the clinical decision support for immunization (cdsi) project. beginning in 2010, the cdsi project began to create new clinical decision support resources to support development and maintenance of immunization cds tools. cdsi was first published in november 2012 and continues to be updated as new acip recommendations are published [5]. cdsi resources are intended to make it easier to develop and maintain immunization evaluation and forecasting/cds engines; increase the accuracy and consistency of immunization evaluation and forecasting; and improve timeliness in accommodating new and updated acip recommendations. if stakeholders are aware of and use these tools they will be prepared to quickly incorporate new immunizations into their immunization evaluation and forecasting/cds engines. during times of a pandemic such as covid-19, facilitating implementation is key. clinical decision support for immunization uptake and use in immunization health information systems 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi cdc understood from the start that the recommendations are complex, the his environment is constantly changing, and cds implementations vary amongst users. because of this variability, cdsi was designed to be technology-neutral and to serve as guidance for developers, rather than as a specific piece of software. the cdsi resources consist of three main components. the logic specification describes the rules required to evaluate and forecast a patient’s immunization(s) against the acip recommendations, considering a patient’s immunization history and other relevant medical, behavioral, and environmental observations. it uses defined vocabulary and domain models to build business rules, decision tables, and a processing model which can be implemented by a cds engine. the supporting data is a representation of the acip vaccine schedule that describes, by antigen, various factors and their accompanying sets of values to be considered when implementing acip recommendations as described in the logic specification. supporting data can be thought of as a set of configuration files used as input to a cds engine. it is published both in microsoft excel spreadsheet and xml formats. the test cases provide a representative set of scenarios and expected outcomes that can be processed against an immunization cds engine to validate its algorithm. they are published as excel spreadsheets, in xml, and as part of an online test case management tool. the intended audience for the cdsi resources includes business and technical implementers of immunization cds engines. these implementers may support any system with an immunization evaluation and forecasting engine. stakeholders included in this assessment were iis awardees, iis vendors, and ehr vendors. the purpose of the cdsi online assessment was to assess stakeholder awareness of cdsi resources, whether cdsi resources were being used by immunization-related his stakeholders, and what impact the cdsi resources have had on stakeholders’ work. after they were released, clinical decision support for immunization uptake and use in immunization health information systems 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi the resources were promoted by program managers immunization registry work group email distributions, american immunization registry association email distributions, the cdc website, at conferences, through a cdc immunization works newsletter article, and via emails from the cdsi project team. the cdsi project has also created infographics, guides, and videos to increase awareness and use of the cdsi resources. these resources are available on the cdc website, distributed at conferences, and used in presentations on cdsi [5]. the goal of the cdsi project evaluation was to understand the impact and use of the resources, as well as inform continued development and support of the cdsi project. in addition to assessing awareness of cdsi resources and whether cdsi resources were being used by stakeholders, we were interested in stakeholder satisfaction with the resources. evaluating the uptake and use of the cdsi resources helps us understand the preparedness for implementing new vaccines for novel viruses, such as sars-cov-2, the virus that causes covid-19, across iiss. methods to assess the uptake and impact of cdsi resources on stakeholders’ work, we designed an online survey assessment. participants cdc funds 64 iis awardees from public health agencies; the main and technical contacts for each iis awardee, identified from the cdc published list of main and technical iis contacts, were invited to participate in the survey. iis vendors provide iis systems and support to awardees. some of these vendors use all or part of cdsi resources, while others develop their own system. iis and ehr vendors were identified by subject matter experts as well as those who had contacted the cdsi support team with questions. the respondents from the iis awardees or vendors could have a variety of roles within their organization including, programmatic, technical, and/or clinical. all stakeholders who are candidates for using the cdsi resources are part of the healthcare ecosystem. this ecosystem can change over time, and additional stakeholders may come into play or drop out. however, the main goal of the cdsi project is for healthcare providers to receive accurate and consistent recommendations across any systems they access. the cdsi resources are product-neutral and the guidance is exactly the same for all stakeholders. for organizational reasons, we split respondents out by iis awardees and vendors, mostly for tracking purposes. however, given the similar responses across respondent types and roles, we combined all responses within our results. the grouping was mainly for follow-up and tracking purposes, since the iis awardees contact information was known and available, while the vendors’ information was not. total organizations invited to participate by type: • round 1 (2015): 64 iis awardees and 17 vendors • round 2 (2016): 64 iis awardees and 19 vendors • round 3 (2018): 64 iis awardees and 15 vendors • round 4 (2019): 64 iis awardees and 18 vendors clinical decision support for immunization uptake and use in immunization health information systems 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi survey design the survey was constructed and launched in surveymethods online software (surveymethods software, dallas, texas). the data were collected and stored through the surveymethods survey website. respondents were asked to provide their name and organization for tracking of response rates, though this was not required. the survey was deployed four times: january 2015 (round 1), january 2016 (round 2), january 2018 (round 3), and january 2019 (round 4). the questionnaire was reviewed and updated before each round to ensure all survey questions were useful and relevant. the questionnaire was programmed with skip logic so that respondents only were asked questions that were applicable to them. first, all respondents were asked about awareness of the cdsi resources, then those who were aware were asked about use of the resources. the users were then asked questions about each resource that they used. some examples of questions for the users included how they use and plan to use the resources; if they felt they had detailed knowledge about the resources; how they learned to use the resources; and their perceptions of the available training materials. the survey was estimated to take 15 minutes or less to complete. in the first two rounds, a pre-notification email was sent to respondents one week prior to the survey launch. the pre-notification email was not sent in third and fourth rounds since the respondents were familiar with the survey from past rounds. all respondents were invited via an email sent to each organization. in addition, a broadcast email with a link to the survey was sent to all iis awardee program managers. one week after the email invitation was sent, a reminder email was sent to those who had not yet responded. the data collection period lasted one month, and a final reminder email was sent a few days before the survey closed. the invitation included a description of the goal of the survey to help each organization identify the person with the correct role to respond. data analysis was conducted using sas version 9.4 (cary, nc) to produce descriptive statistics including frequencies. this project was reviewed by the human subjects coordinator of cdc’s national center for immunization and respiratory diseases and was determined to be public health practice evaluation; therefore, institutional review board review was not required. results response rate overall response rates and total organizations responding by type by year were as follows: • round 1: 75% of stakeholder organizations invited: 52 awardees and 9 vendors • round 2: 69% of stakeholder organizations invited: 48 awardees and 9 vendors • round 3: 82% of stakeholder organizations invited: 57 awardees and 8 vendors • round 4: 78% of stakeholder organizations invited: 55 awardees and 9 vendors awareness first, all respondents were asked which cdsi resources (logic specification, supporting data, and test cases) they had heard about. awareness of at least one resource increased from 75% of respondents in round 1 to 100% in round 4 (figure 1). clinical decision support for immunization uptake and use in immunization health information systems 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi cdsi resource use those who indicated that they were aware of at least one resource were then asked multiple questions about each resource. first, they were given a brief one-sentence description of each of the resources and asked if they used each resource. in round 1, 47% of the respondents indicated that they used at least one of the resources; by round 4, use had continued to increase for each resource, with 78% reporting they used at least one cdsi resource (figure 1). figure 1. awareness and use of cdsi resources abbreviation: cdsi = clinical decision support for immunization. since there are three different cdsi resources available for stakeholders to use, we looked at how use of all three had changed over time. in round 1, only 11% of respondents indicated they used all three of the cdsi resources, compared to 57% in round 4. the percentage of respondents reporting they had either not heard of the resources or not used any of the resources fell from 67% in round 1 to 22% in round 4. clinical decision support for immunization uptake and use in immunization health information systems 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi figure 2. cdsi resources use from round 1 to round 4 abbreviation: cdsi = clinical decision support for immunization. overall satisfaction for cdsi resource users respondents who reported that they used a resource were asked questions about their use of, satisfaction with, and perceptions on impact of the resources. overall, users of each resource reported high levels of satisfaction and positive impact with the cdsi resources. the satisfaction questions were asked in round 1 (2015), round 2 (2016), and round 4 (2019). the questions were not asked in round 3 because similarly high levels of satisfaction in round 1 and 2 were found, and the project team felt that repeating the questions was not a good use of respondents’ time. the questions were added back in round 4 to continue to monitor satisfaction levels as the number of users increased. overall satisfaction (“somewhat satisfied” and “very satisfied” responses) ranged from 77% (logic specification, round 2) to 94% (supporting data, round 1). overall satisfaction increased from round 2 to round 4 by more than 10% for the logic specification and test cases, while supporting data stayed about the same, demonstrating that even as use increased, satisfaction with cdsi resources remained high. overall impact for cdsi resource users the users of each resource were asked about the impact of the resources on their work. over 80% of respondents reported a positive impact from using the cdsi resources. the impact questions were asked in round 1 (2015) and round 2 (2016). across all of the resources, none of the respondents reported a somewhat negative or very negative impact. at the end of the survey three additional impact questions were asked. respondents were asked to rate on a scale of 1 (much better) to 7 (much worse) the overall impact of cdsi on their overall process, the process of accommodating new or changed acip recommendations, and the impact on developing immunization evaluation and forecasting products. in round 4 most respondents clinical decision support for immunization uptake and use in immunization health information systems 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi reported a positive impact. between 68% and 72% of respondents rated each question between a 1 and a 3. figure 3. overall impact results round 4 abbreviation: cdsi = clinical decision support for immunization. discussion since 2015, awareness and use of the cdsi resources has increased. cdsi resource users are highly satisfied and report a positive impact. about 80% or more of respondents were very or somewhat satisfied with the resources, and >80% of respondents reported a very or somewhat positive impact. overall, we observed a trend of increasing awareness of all three resources. there was a larger increase in awareness between round 1 and round 2. in addition to the promotional activities around the cdsi resources between these rounds, the survey and evaluation activities also may have increased awareness of the cdsi resources between all rounds, especially between round 1 and round 2. by round 4, all respondents reported awareness of at least one of the cdsi resources. this is likely a combination of targeting the correct respondent from each stakeholder who had a role in using the cdsi resources, in addition to increased awareness of cdsi resources in general. tracking cdsi resources awareness allows the project team to decide if they need additional promotional activities to focus on awareness. at this point, with awareness at high levels, the project team can focus more on promoting use of cdsi resources rather than awareness. users of the cdsi resources were asked additional questions across the different rounds of the survey. the results of these questions were reviewed and used to help understand the cdsi users and improve the resources. for example, to inform revamping of training materials, users were asked if they felt they had detailed knowledge about the resources, how they learned to use the clinical decision support for immunization uptake and use in immunization health information systems 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi resources, and about the available training materials. questions about how they have used and plan to use the resources were asked to guide development and changes to the resources and new releases. one change made after reviewing feedback from the respondents was adding a yearly flu prerelease. in an effort to improve the time it takes to incorporate new acip recommendations, the cdsi project began providing pre-release materials for the yearly flu recommendation in 2019. the pre-release resources are based on the information provided to the cdsi project team by the cdc subject matter experts after an acip meeting vote, but prior to the official mmwr publication of the acip recommendation. once the recommendation is published, the pre-release material will be removed, appropriately updated, and released as part of an official cdsi version. this model of cdsi pre-release could be applied to allow implementation development work to begin in advance of the official publication of a covid-19 vaccine recommendation. misinterpretation in a cds engine could lead to healthcare providers under-vaccinating and overvaccinating many patients. a review of 2014 immunization records from six iis sites found minimum and maximum age violations and concluded that “minimization of errors reduces wastage, excess cost, and inconvenience for parents and patients.” [6] kirtland et al. examined live vaccine interval errors among children aged 12 months through six years, finding nearly $1 million in revaccination costs associated with errors across two separate one-year time periods and six iis sentinel sites [7]. use of cdsi resources within iis and ehrs is one way to reduce frequency and cost of vaccine errors. correct interpretation of guidance is especially important in systems with a high volume of vaccination queries. for example, epic, an ehr vendor, reported that more than 200 million vaccination queries are sent per year to their vaccination query interface [8]. resources such as cdsi are key to facilitating correct interpretation of clinical guidance (logic specification/supporting data) and extensive testing (test cases) in systems impacting such a large number of individuals. given the number of americans served via hiss, and the extent to which acip recommendations inform policy and practice, it is important to understand user perspectives on cdsi resources provided. utilization-focused evaluations place the emphasis on designing evaluations to ensure their usefulness [9]. this evaluation used a strong utilization-focused approach both in evaluation design, dissemination of findings, and iterative revision of resources informed by the results. there were three key limitations to our survey. one was the sample pool: invited respondents might not include all users of cdsi resources. while we had a known list and current contact information for the iis awardees, we did not have this information for the iis and ehr vendors. the vendors were identified by previous contact with the cdsi team with questions or by subject matter expert identification. there may have been others using the cdsi resources who we were unaware of and did not include in our survey. in addition, we may not have identified the correct contact at organizations who use the cdsi resources, which would lead to lower reports of awareness and use. finally, while we can evaluate uptake and impact of the cdsi resources on the respondents and their organization, we only have anecdotal evidence of impact at the provider and patient level. clinical decision support for immunization uptake and use in immunization health information systems 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi conclusion the cdsi resources provide clarity, consistency, and computability of ongoing childhood, adolescent, and adult immunization evaluation and forecasting to stakeholders. as awareness of the resources increased and use increased, they have become a vital part of the evaluation and forecasting of immunization through cds engines. before stakeholders were aware of and using cdsi resources, they faced the time-consuming and complex task of interpreting clinically written acip recommendations on their own. this increased the risk of misinterpretation, which could lead to cds engine outputs that did not match acip recommendations, and ultimately provide inaccurate decision support to providers/clinicians. reducing the barriers of individually interpreting and implementing acip recommendations results in more rapid adoption of new vaccines which is critical during pandemic situations, such as covid-19. we conducted the cdsi online assessment to assess awareness of cdsi resources, whether cdsi resources were being used by stakeholders, and stakeholders’ perceived impact of cdsi resources on their work. stakeholders have wide and increasing awareness and use of the cdsi resources, coupled with high satisfaction and positive perceived impact. users of the cdsi resources report a positive impact to develop and maintain immunization forecast and evaluation products, including improved timeliness in accommodating new and updated acip recommendations. this leads to more robust immunization clinical decision support engines, with increased accuracy and consistency. timeliness is critical for iiss preparing to implement new vaccines, such as a potential vaccine for covid-19. successful implementation of cdsi tools is likely to facilitate a record of patients’ immunization status that is current, accurate, consistent, and readily available. ultimately, as awareness and use of cdsi resources increases, the likelihood that patients receive proper immunizations at the right time also increases. this will likely reduce frequency and cost of vaccination errors, which in turn will reduce time, cost, and risks associated with revaccination and/or overvaccination. our brief online assessment is an example other projects could learn from and implement to evaluate technical guidance, resources, or initiatives. while we found it important to measure awareness and use levels each round, ratings on questions regarding satisfaction and impact were found to be consistently high. observing this, we edited the set of questions asked of respondents in each round, mindful of respondent burden. a mix of quantitative questions with options for respondents to provide additional comments allowed us to quantify responses as well as obtain deeper understanding when respondents provided clarifying or supporting detail in the additional comments section. these methods and lessons could be applied to evaluate many different types of technical resources or initiatives. acknowledgements the authors thank mary ann kirkconnell hall for her help reviewing and editing the manuscript. clinical decision support for immunization uptake and use in immunization health information systems 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e10, 2020 ojphi financial disclosure this work was supported by ncird – support for immunization technical standards and resources task order 0002 #: 200-2015-87992/0002 references 1. centers for disease prevention and control. acip recommendations. centers for disease prevention and control website. https://www.cdc.gov/vaccines/acip/recommendations.html. reviewed march 24, 2020. accessed june 1, 2020. 2. clinical decision support. office of the national coordinator for health information technology (onc). https://www.healthit.gov/topic/safety/clinical-decision-support. reviewed april 10, 2018. accessed june 5, 2020. 3. centers for disease prevention and control. about immunization information systems. centers for disease prevention and control website. www.cdc.gov/vaccines/programs/iis/about.html. reviewed june 7, 2019. accessed january 7, 2020. 4. centers for disease prevention and control. 2017 iisar data participation rates. centers for disease prevention and control website. https://www.cdc.gov/vaccines/programs/iis/annualreport-iisar/2017-data.html#child. updated september 22, 2017. accessed april 21, 2019. 5. centers for disease prevention and control. clinical decision support for immunization (cdsi). centers for disease prevention and control website. https://www.cdc.gov/vaccines/programs/iis/cdsi.html. reviewed june 7, 2019. accessed october 17, 2018. 6. rodgers l, shaw l, strikas r, et al. 2018. frequency and cost of vaccinations administered outside minimum and maximum recommended ages–2014 data from 6 sentinel sites of immunization information systems. j pediatr. 193, 164-71. doi:https://doi.org/10.1016/j.jpeds.2017.09.057. pubmed. 7. kirtland ka, lin x, kroger at, myerburg s, rodgers l. 2019. frequency and cost of live vaccines administered too soon after prior live vaccine in children aged 12 months through 6 years, 2014–2017. vaccine. 37(46), 6868-73. doi:https://doi.org/10.1016/j.vaccine.2019.09.058. pubmed 8. metroka a, sull m, faber g, aponte a, bunker n. electronic health record query-response: building on our success. presented at the 2018 american immunization registry association national meeting; august 16, 2018; salt lake city, ut. https://repository.immregistries.org/files/resources/5b9bdded9dfb5/c8__a_metroka_ehr_qr.pdf. accessed october 1, 2019. 9. patton mq. utilization-focused evaluation. 4th ed. saint paul, mn: sage publications; 2008. https://www.healthit.gov/topic/safety/clinical-decision-support https://doi.org/10.1016/j.jpeds.2017.09.057 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29249524&dopt=abstract https://doi.org/10.1016/j.vaccine.2019.09.058 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=31563283&dopt=abstract 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts inconsistency of timeliness in a chief complaint-based syndromic surveillance system during two influenza epidemic seasons tao tao*1, qi zhao1, jun zong2, xue li3, vinod diwan4 and biao xu1 1school of public health, fudan university, shanghai, china; 2jiangxi cdc, nanchang, china; 3future position x, gävle, sweden; 4department of public health sciences, karolinska institutet, stockholm, sweden objective to study and compare the timeliness of syndromic surveillance system for the early warning of infectious diseases among different epidemic seasons. introduction syndromic surveillance system has been developed and implemented all over the world, and many studies showed that syndromic data sources had improved timeliness towards traditional surveillance method in the early warning of some infectious disease epidemics. however, owing to the uncertainties of disease epidemic features, clinical manifestations and population behaviors, the early warning timeliness of syndromic data sources might change across time and population, and few studies had explored their consistency in different epidemic periods of infectious diseases. methods we evaluated the timeliness of a chief complaint-based syndromic surveillance system established in two rural counties of jiangxi province, china, towards seasonal influenza epidemics. weekly number of influenza-like illness (ili) patients counted from syndromic surveillance system was compared with the weekly influenza virus positive rate (vpr) data collected from national influenza surveillance network in jiangxi province using cross correlation method. timeliness was defined as the lead weeks of ili data towards vpr data when two data sources reached maximum correlation. daily data check and quarterly field data quality control was conducted to ensure the correctness and completeness of data in syndromic surveillance system. results from 18th week 2012 to 52nd week 2013, there were two influenza epidemic seasons in surveillance areas according to the vpr data from national influenza surveillance network. apparent correlation could be observed between ili and vpr curves, and in some periods the ili peaks occurred earlier than vpr peaks (figure 1). cross correlation study showed that in epidemic season 1, the subgroups of ili data had 0~5 weeks’timeliness towards vpr data, with correlation coefficients between 0.52~0.84. ili reported from upper-level health facilities (county and township hospitals), younger patient groups (3-17 years) had higher timeliness than ili reported from village health stations, elder patient groups (18years) (table 1). the situation in epidemic season 2 were quite different that the correlations between ili subgroups and vpr data were generally weaker than that in epidemic season 1, and some ili subgroups even presented 1~5 weeks’delays towards vpr data. ili reported from village health station, elder patient groups (18years) had better timeliness than that from upper-level health facilities, younger patient groups (0-17 years) (table 2). lab data showed that the dominant influenza strains were different in two epidemic seasons. conclusions the inconsistency of timeliness in syndromic surveillance system might be attributed to the differences in dominant strains, clinical manifestations of influenza, population age groups affected, and health seeking behaviors of influenza patients in two epidemic seasons. further exploring the causes of these phenomena could help public health professionals select most timely data sources for the early warning of influenza epidemics in rural china. keywords timeliness; syndromic surveillance; epidemic season *tao tao e-mail: ttsuper2000@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e92, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a syndrome definition validation approach for ebola virus disease dino rumoro1, shital shah1, marilyn hallock1, gillian gibbs*1, gordon trenholme1 and michael j. waddell2 1emergency medicine, rush university medical center, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to develop and validate an ebola virus disease syndrome definition within the guardian (geographic utilization of artificial intelligence in real-time for disease identification and alert notification) surveillance system. introduction the 2014 ebola outbreak in west africa is one of the largest ebola outbreaks in history. early detection is critical for rapid initiation of treatment, infection control and emergency response plans. to facilitate clinicians’ ability to detect ebola, various syndrome definitions have been developed. methods to create and validate a detailed syndrome definition for ebola, we utilized the literature based methodology developed and documented by guardian researchers.1,2 the syndrome definition utilized clinical signs and symptoms that were documented in historical ebola cases. a testing sample of 800 randomly selected emergency department cases (i.e., true negative cases) and 200 synthetically generated cases (i.e., true positive cases) was created. these 1,000 sample cases were evaluated by the guardian surveillance system to determine the probability of matching the ebola syndrome definition. a probability of 90% was utilized to designate positive ebola cases. we identified the main signs and symptoms contributing to the identification of ebola cases and conducted statistical performance metrics. clinical review of the false positive and false negative cases along with a sample of true positive and true negative cases was conducted by a board certified emergency physician and an infectious diseases expert. results the ebola syndrome definition was developed with 14 articles (10 used for developing the syndrome definition, and 4 used for testing the definition). the sample size for these articles was between 1 and 217 positive ebola cases, with a total of 678 cases across the 14 articles. the publication timeframe for the articles was from 1977 to 2005. some of the main signs and symptoms from the historical cases that contribute to the ebola syndrome definition are presented in table 1. the initial results for the sample testing data showed accuracy, sensitivity, and specificity were 96%, 89%, and 97%, respectively. there were a total of 22 false negative and 27 false positive cases. conclusions the initial ebola syndrome definition utilized by the guardian surveillance system contains similar signs and symptoms to the current cdc case definition, but also includes additional signs and symptoms such as abdominal tenderness, thrombocytopenia, nausea, malaise, weakness, and loss of appetite. in addition, the guardian system provides the relative importance of identified signs and symptoms and allows for proactive surveillance of emergency department patients in real-time. though we didn’t include epidemiologic risk factors, such as travel to an infected region or contact with an infected person in the syndrome definition, guardian has above 89% sensitivity and specificity. thus, inclusion of epidemiologic risk factors would further enhance the early detection of ebola and possibly other viral hemorrhagic fevers when used with the appropriate high risk population. table 1. main signs and symptoms of the ebola syndrome definition. * signs and symptoms that are included in the centers for disease control and prevention (cdc) ebola case definition. keywords ebola; guardian; syndrome definition acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. references 1. silva j, rumoro d, hallock m, shah s, gibbs g, waddell m, thomas k. disease profile development methodology for syndromic surveillance of biological threat agents. emerging health threats journal. 2011; 4(11129). 2. silva j, shah s, rumoro d, hallock m, gibbs g, waddell m. a novel syndrome definition validation approach for rarely occurring diseases. online journal of public health informatics. 2013; 5(1). *gillian gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e156, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 59 (page number not for citation purposes) isds 2013 conference abstracts near-real–time assessment of cardiovascular disease risk factors in nebraska by using essence sandra gonzalez*1, 2, gary white1, 2 and tom safranek1 1nebraska department of health and human services, lincoln, ne, usa; 2university of nebraska-lincoln, 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'�"�������$ �'�b����� ������!�������� ��1�����!�������c�&�� ��:� ���� �� $� ���� � �&����7 ���d����'e�(((# � #$ �f"����&�$ �f��� �f2*,5�*8��������$ �#���'�� ������&���������9'�2*,5� .2/� ! �77g+�!� ������2,'�2*,2#�b ! �h�� ��+ � ��:�����7��� �� �1������� ��������'e�(((# � #$ �f��(�f�!if(�f��>,9*#���'� � ������&���������9'�2*,5� .5/� � ������ ��7������0��2*,2��7�� � $����%���g��� ���&������b!��� ���� $�������(�i� ����� ��&� �� �� �&�������� �:�4�7�� � $���� %���!��� $��� ��������������� ��g���� $� �������� ���� �e'� �&!&'�����:ff���� # �f5),)*)8*'�� ������&���������9'�2*,5 *sandra gonzalez e-mail: sandra.gonzalez@nebraska.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e103, 2014 introduction metabolic risk factor reduction through a worksite health campaign: a case study design 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 metabolic risk factor reduction through a worksite health campaign: a case study design hayley daubert 1 , denice ferko-adams 2 , david rheinheimer 3 , christina brecht 3 1 nutrition counseling services, wescosville, pa 18106 2 wellness press, www.wellnesspress.com 3 east stroudsburg university, east stroudsburg, pa 18301 abstract the purpose of this intervention study was to measure the impact of an onsite and online 12week worksite heart-health campaign designed to reduce metabolic risk factors for employees at bmw of north america, llc. all participants received three coaching sessions by a registered dietitian (rd), participated in eight educational sessions led by an rd, viewed their pre, midpoint and final biometric data online, and had access to other web-based tools and educational booklets. the program used team-based competition. at baseline and week 12, blood pressure, anthropometric and hematologic parameters were measured, including changes in weight, blood pressure, fasting blood glucose, waist circumference, total cholesterol, ldl cholesterol, hdl cholesterol, triglycerides, and smoking habits. of the 100 individuals that enrolled, 95 completed the program, and 87 met criteria to be eligible for data analysis. paired t tests demonstrated significant reductions in weight (p<.0001), body mass index (p=.0047), waist circumference (p <.0001), diastolic blood pressure (p=.0018), and systolic blood pressure (p=.0012). paired t tests for total cholesterol, ldl cholesterol, hdl cholesterol, triglycerides, and fasting blood glucose did not indicate any significant improvements. there was an improvement in body mass index and blood pressure classifications after completion of the program. a friedman’s test of blood pressure classification demonstrated significant improvements in participants’ blood pressure classification from pre-program to midpoint, midpoint to end, and pre-program to end. these results support the effectiveness of a dietitian-led, team-based, worksite heart-health campaign with web-based education to reduce risk factors for metabolic syndrome. mesh keywords: nutrition, worksite, online, registered dietitian, heart health, metabolic syndrome. introduction the 2003-2004 national health and nutrition examination survey (nhanes) found that approximately 66% of us adults are considered overweight or obese, with almost 33% being classified as obese [1]. obesity increases the risk of many health conditions, including coronary heart disease, type 2 diabetes, hypertension, dyslipidemia, stroke and certain types of cancer. as http://ojphi.org/ www.wellnesspress.com metabolic risk factor reduction through a worksite health campaign: a case study design 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 per the national heart lung and blood institute, the following group of risk factors, that are linked to overweight and obesity, increase an individual’s risk of heart disease, stroke and diabetes: abdominal obesity, elevated triglycerides (tg), lower hdl cholesterol, high blood pressure (bp), and high fasting blood glucose (fbg) levels [2]. a person is considered to have diagnosis of metabolic syndrome when three out of five of these risk factors are present. almost 25%, or an estimated that 47 million adults, in the united states have this condition [2]. metabolic syndrome may negatively impact the working adult’s health and increase health care costs. burton, et al., studied the prevalence of metabolic syndrome in a financial services company employing over 5,500 people, and found that the 22.6% with metabolic syndrome were more likely to have increased days absent from work due to illness, poorer self-reported health perception, and report more lifestyle health risks, such as smoking, a sedentary lifestyle, and stress [3]. effectiveness of worksite wellness programs to address the financial burden of unhealthy workers, effective health promotion programs are ideal for a company’s budget. reviews of recent peer-reviewed studies show the effectiveness of worksite wellness programs that focus on nutrition and weight control. touger-decker, et al., measured the impact of a twelve-week workplace intervention focusing on weight management at an academic health sciences university [4]. individual and group sessions were held by an rd, and results of the 117 individuals were evaluated. significant improvements were documented in weight, body mass index (bmi), waist circumference (wc), waist-hip ratio (whr), cholesterol, and bp. furthermore, weight loss was significantly correlated with reduction in bp, percentage body fat, and cholesterol [4]. in another study, changes in bp and weight in a control group of 94 employees were compared with an experimental group of 47 employees who participated in a one-year program led by nurses using the five e’s intervention strategy: evidence, engage, educate, environment, and evaluate [5]. there was no statistically significant difference in baseline demographics, bmi or bp between the control and experimental groups. the quasi-experimental study design showed significant improvements in bmi and systolic bp in the experimental group versus the control group, and 38.3% of the experimental group reported vigorous physical activity (at least 3 or more times per week) at one year, a 100% increase compared to baseline [5]. another successful worksite wellness program was the “healthyroads” program, an obesity management worksite program led by rds, certified health education specialists, and other health professionals was implemented in 119 companies [6]. a pre-test/post-test was used to evaluate changes in health risks of 890 employees with a bmi of 30 or greater, or a bmi between 25-30 with a co-morbid condition, such as type 2 diabetes, hypertension, or cardiovascular disease. after a year’s participation in the program, statistically significant reductions were seen in poor eating and exercise behaviors, cholesterol, bp, and bmi [6]. http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 the cost of obesity and roi (return on investment) in 2009, the centers for disease control and prevention (cdc) indicated that the annual health cost of obesity in the united states is 147 billion [7]. the study also found that obese individuals spent 42% more on medical care than normal weight people in 2006 [7]. as healthcare costs continue to rise, and while most companies’ desire continued financial growth, worksite wellness programs provide a way to lower an organization’s health related costs. for example, the highmark employee wellness programs offered health promotion services, including on-line nutrition, stress management, and tobacco cessation programs, and various health classes, coaching sessions, screenings, and campaigns. after a 4-year study period, return on investment (roi) was $1.65 for every dollar spent [8]. not only can worksite wellness programs decrease a company’s healthcare costs, but they can also improve worker productivity, and so further decrease organizational costs. baker et al., found a $1.17 to $1.00 roi from an obesity management worksite program, and of the total projected financial savings, 59% were from decreased healthcare costs, while 41% resulted from “productivity improvements.” [6]. a 2008 meta-analysis reviewed 46 studies and showed that work health promotion increased work ability, job and mental well-being, and decreased sickness absenteeism [9]. worksite wellness programs appear to benefit both the employee and the employer, as they contribute positively to employees’ health and the company’s financial health. williams, et al., states, “a recent systematic review concluded that multi-component worksite interventions were the only population-based obesity prevention programs with sufficient evidence of effectiveness to warrant recommendation.” [10]. health first sm : a worksite wellness program intervention the health first sm program is a worksite wellness campaign that was provided to employees at the headquarters of bmw of north america, llc. the program is a lifestyle and weight management program designed and delivered by rds using evidence-based guidelines from the national cancer institute [11], national heart lung and blood institute [12], american heart association [13], and american association of diabetes educators [14]. the program incorporates teaching points of the health belief model to conditions including obesity, hypertension, diabetes and heart disease, and the respective biometric data. questionnaires are used to discover where individuals are at in the transtheoretical model, and the cognitive behavior theory is used to make individuals aware of habits, with ideas to change behavior to healthier habits provided in educational sessions and rd counseling. the bmw worksite offered an online tool: participants had the choice to attend live sessions or access powerpoint sessions online. also, personal health records, team and individual points and weekly recipes were available online. the goal of health first sm is to decrease metabolic risk factors, regardless of weight status. depending on the needs of the participant, the targeted program objective may be losing weight (or maintaining a healthy weight), decreasing bp, improving blood lipid and glucose levels, http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 smoking cessation, and increasing awareness of how lifestyle choices impact health and disease (such as increasing activity level and encouraging increased fiber intake. study design a pre-post design was used to evaluate results of the program for participants’ improvement in metabolic risk factors, including measurements for weight, bmi, wc (waist circumference), hip circumference (hc), total cholesterol, hdl cholesterol (high-density lipoprotein cholesterol, ldl cholesterol (low-density lipoprotein cholesterol), tg levels, fasting blood glucose (fbg), non-fasting blood glucose (nfbg), and blood pressure (bp). participants’ self-reported data on smoking habits, cardiovascular and diabetic prescription medication use, and exercise participation. the primary outcome measures were changes in bp, bmi, weight, wc, total cholesterol, hdl cholesterol, ldl cholesterol, fbg, non-fbg, and tg levels. the program implementers’ hypotheses were that participants’ in the health first sm program would experience a decrease in metabolic risk factors. methods a rally was held to inform employees about the health first sm program; flyers and posters were used to recruit participants. enrollment was open to all current employees and retirees. this study was a prospective intervention pilot study; participants served as their own control and volunteered for the program. ninety-eight current employees, and two retirees enrolled in the program, and a total of 95 completed the program. reasons such as retirement, disability leave, and being transferred accounted for the five people who did not finish health first sm . initial baseline measurements were taken. two graduate dietetic students were trained to perform weight, wc, hc and height measurements using a stadiometer, while registered nurses performed the bp measurement and another vendor, impact health, provided the finger-stick blood draw for hematological analysis of total cholesterol, hdl and ldl cholesterol, tg levels and fbg. if fbg was elevated, a hga1c was performed. employees were encouraged to fast, and all blood levels were tracked as fasting or non-fasting. once blood test results were available (within eight minutes), rds provided health coaching, and reviewed results with each participant, setting midpoint and final goals. counseling sessions were based on the participants’ needs, and included use of appropriate health behavior theories. during counseling sessions, the rds would ask questions about participants’ readiness to change, and target messages to move individuals through the transtheoretical model. based on biometric data, rds would also use constructs of the health belief model to educate participants on their potential risk for disease and its consequences, and geared individuals toward overcoming barriers to behavior change, while assessing the benefits of healthier habits. rds helped participants set goals and encouraged participants to work on health issues important to them. a pre-evaluation questionnaire was also completed online at baseline, and reviewed by the rd. the survey gathered information including self-reported smoking and exercise habits, and use of medication (cardiovascular and diabetic). after enrolling in the program, participants were encouraged to become a member of a team, ranging from two to six people. however, individuals were also permitted. each team chose a http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 team name and team captain. team captains logged team points in the web-based program. throughout the 12 week program, team standings were posted online. emphasis was placed on providing a team atmosphere promoted friendly competition, improved communication, and built a new “team” health support network. all team members were able to receive points for weight loss, attendance at the weekly sessions, for meeting exercise goals, achieving health goals, and for completion of pre and post-evaluations. all program participants had access to the health first sm website, where they could view each team’s total points, download education materials, and view power-point presentations from the live sessions. the custom website allowed participants to view his/her personal health records, and any respective changes to these levels throughout the program. in addition to the web-based program components, participants were also given educational booklets that complemented the live sessions. the booklets contained information on nutrition, exercise, metabolic syndrome, and sample eating plans. all of the program components were customized in a theme that matched the client’s workplace. over the first four weeks, informational sessions were held for employees during the lunch hour. led by rds, the interactive sessions covered topics including proper nutrition and healthy eating, portion control, snacking, exercise, metabolic syndrome, and optional food plans such as the dash diet and mediterranean food plan. at the program’s midpoint, a rd measured weight, and a registered nurse took blood pressure, and participants again received individual coaching from a rd. then, four more weekly lunch sessions were held. at week 12 biometric measurements were repeated: blood was drawn for another hematological analysis, and participants received a final coaching session from a rd. an award ceremony was held to recognize winning teams and provide incentives to those who completed the program. of the 95 employees who finished the program, 87 met criteria for data analysis and receiving an incentive prize. these criteria are as follows: completed all initial, midpoint, and final biometric and hematological measurements, completed pre/post evaluations and assessments, attended at least six of eight weekly sessions, exercised at least six weeks, and received three individual coaching sessions. the incentive prize, a fitness bag, was provided to all 87 meeting these criteria, and a raffle was done for a weekend trip to new york city, an additional reward to motivate participants to complete the program. the top three teams scoring the most points throughout the program were also given monetary gift certificates. results eighty-seven participants revealed their age and family medical history for conditions including hypertension, heart disease, diabetes and cancer on the pre-evaluation form. thirteen (14.9%) participants were older than age 60, 15 (17.2%) were ages 51-60, 30 (34.5%) were ages 41-50, 20 (23%) were ages 31-40, and 9 (10.3%) were under age 30. forty-three (49.4%) indicated a family history of hypertension, 35 (40.2%) for heart disease, 31 (35.6%) for diabetes, 30 (34.5%) for cancer, and 16 (23.9%) indicated no family history for the above conditions (e.g. figure 1). http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 figure 1: family history of participants one hundred employees enrolled in the program, 95 completed the program (44 males and 51 females), and 87 met criteria for data analysis (39 males and 48 females). measured weight, height, wc, hc, bp, cholesterol, hdl and ldl cholesterol, fbg, and tg were collected. selfreported data gathered included exercise and smoking habits, and medication use. the health first sm software automatically calculated bmi and bp categories. using the statistical analysis software (sas) version 9.1 for data entry, analyses were performed to determine whether there were significant improvements in anthropometrical and hematological after the completion of the health first sm program. a paired samples t test was used to evaluate the significance of changes in the dependent variables from baseline to completion of the program. the independent variable is the program itself, while dependent variables are weight, wc, bp, cholesterol, hdl and ldl cholesterol, fasting glucose, and tg. the bonnferroni correction equation was used to protect against type 1 error rate, thus setting the level of significance at p<.005. there were significant reductions in outcome measures including wc (t=5.68, p<.0001), weight (t=4.25, p<.0001), bmi (t=2.90, p=.0047), diastolic bp (t=3.24, p=.0018), and systolic bp (t=3.36, p=.0012). changes in fbg (t=0.28, p=0.78), nonfbg (t=0.18, p=0.86), tg (t=1.53, p=0.13), hdl cholesterol (t=-2.05, p=0.04), ldl cholesterol (t=-0.09, p=0.93), and total cholesterol (t=-0.91, p=0.37) were not significant (e.g. table 1). 49.40% 40.20% 35.60% 34.50% 23.90% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% p e rc e n ta g e disease family history of participants http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 table 1: paired samples t-tests for eleven dependent variables variable n mean diff sd t-score 95% ci fbg 20 -0.80 12.69 0.28 (-6.74, 5.14) non-fbg 41 1.10 38.84 0.18 (-11.16, 13.36) tg 72 9.64 53.49 1.53 (-2.93, 22.21) wc 72 0.74** 1.10 5.68 (0.48, 0.99) weight 87 1.90** 4.17 4.25 (1.01, 2.79) bp dia 74 3.16** 8.39 3.24 (1.22, 5.11) bp sys 74 3.84** 9.82 3.36 (1.56, 6.11) hdl chol 74 -2.01 8.44 -2.05 (-3.97, -0.06) ldl chol 72 -0.25 24.73 -0.09 (-6.06, 24.73) total chol 74 -2.54 24.1 -0.91 (-8.12, 3.04) bmi 86 0.17** 0.56 2.90 (0.06, 0.29) *p < .005. **p < .001. frequency procedures were also performed in bmi and bp categories to demonstrate improvement in each classification. as per the national heart, lung, and blood institute standards, bmi classification standards are as follows: underweight bmi: <18.5, normal body weight bmi: 18.5-24.9, pre-obese bmi: 25-29.9, class i obese bmi: 30-34.9, class ii obese bmi: 35-39.9, and class iii obese bmi: 40 and above [15]. initially, 4.21% of individuals were class iii obese, 7.37% were class ii obese, 25.26% were class i obese, 38.95% were preobese, 24.21% were normal, and 0% were underweight. at the conclusion of the program, improvement in bmi categories can be seen with 2.30% class iii obese, 5.75% class ii obese, 21,84% class i obese, 42.53% pre-obese, 27.59% normal, and 0% underweight (e.g. table 2). table 2: frequency procedures for bmi bmi category frequency and percent initial (n=95) final (n=87) normal 23 (24.2%) 24 (27.59%) preobese 37 (38.95%) 37 (42.53%) class i 24 (25.26%) 19 (21.84%) class ii 7 (7.37%) 5 (5.75%) class iii 4 (4.21%) 2 (2.30%) blood pressure classification standards are: normal: <120 mmhg (millimeters of mercury) systolic and <80mmhg diastolic, prehypertension: 120-139 mmhg systolic or 80-89 mmhg diastolic, stage 1 hypertension: 140-159 mmhg systolic or 90-99 mmhg diastolic, and stage 2 hypertension: 160 mmhg or higher systolic, and 100 mmhg or higher diastolic [16]. initially, http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 24.2% fell in the normal bp classification, 54.9% had prehypertension, 18.7% had stage 1 hypertension, and 2.2% had stage 2 hypertension. at program’s end improvements in bp classification can be seen as follows: 50% had normal blood pressure, 36.8% had prehypertension, 11.8% stage 1 hypertension, and 1.3% had stage 2 hypertension (e.g. table 3). table 3: frequency procedures for bp bp category frequency and percent initial (n=91) final (n=76) normal 22 (24.2%) 38 (50.0%) prehypertension 50 (54.9%) 28 (36.8%) stage 1 17 (18.7%) 9 (11.8%) stage 2 2 (2.2%) 1 (1.3%) the blood pressure classifications had to be coded for completion of the friedman’s test. this was done as follows: normal was coded as one, prehypertension as two, stage 1 hypertension as three, and stage 2 hypertension as four. thus, a low average ranking favors a more normal level, and the higher the ranking, the poorer the prognosis. the three time periods for the blood pressure classification data-pre-program, midpoint, and post-were compared with a friedman’s test. the test statistic for this test is the cochran-mantelhaenszel (cmh) statistic, and the results of this test showed a significant difference across the three time periods (e.g. table 4). table 4: friedman test for the three time periods of bp classification ________________________________________________________ cochran-mantel-haenszel statistic (based on rank scores) ________________________________________________________ alternative hypothesis df value ________________________________________________________ row mean scores differ 2 17.78*** mean rankings post hoc comparisons r pre = 46.0 ( r pre – r mid) = 5.0*** r mid = 41.0 ( r mid – r post) = 2.5*** r post = 38.5 ( r pre – r post) = 7.5*** ________________________________________________________ note. post hoc comparisons were computed on mean rank score differences for pairs of time periods. ***p < .001. http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 a post hoc test, described by siegel and castellan [17], was performed on the three time periods to determine which time periods were significantly different. from table 4, it can be seen that for all three-time periods differences were significant, with the order of the mean rankings getting increasingly smaller from the pre to post time periods. two of the five factors that contribute to metabolic syndrome, wc and bp, were significant improvements. of the 14 individuals who met criteria for metabolic syndrome at baseline, only eight of those participants had metabolic syndrome at the program’s end. six (43%) were no longer at risk for metabolic syndrome. other information gathered included self-reported data on smoking habits and prescription medication use. seven individuals reported smoking on the pre-evaluation. on the postevaluation, four quit smoking, two reduced how much they smoked, and only one had no change. fifteen participants reported being on bp medication at the program’s start. at the end of health first sm , four participants stated they reduced their bp medication use, and three reported they stopped taking bp medication, as instructed by their physician. a total of ten participants reported being on medication to control cholesterol at the beginning of the program, and one individual reported decreased use of his or her cholesterol-lowering medication after the program. and, five participants reported taking medication to control diabetes initially, and after the program two reported decreased use, while one reported discontinued use of diabetic medication. sixty-seven participants responded to post-evaluation questions about improved eating and exercise habits compared to before the program. the remainder of participants did not complete the post-evaluation. sixty-one (91%) participants self reported improved eating habits, five (7.5%) were unsure if eating habits changed, and one (1.5%) indicated no change in eating habits after the health first sm program (e.g. figure 2). fifty-six (83.6%) of participants stated they improved exercise habits as compared to before the program, seven (10.4%) were unsure if physical activity level changed, and four (6%) indicated no change in exercise after the program (e.g. figure 3). figure 2: health first’s influence on food choices 91% 7.50% 1.50% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% improved unsure no change p e rc e n ta g e change in eating behaviors health first's influence on food choices n=67 http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 figure 3: health first’s influence on physical activity discussion the data analysis showed statistically significant improvements in weight, bmi, wc and bp of participants enrolled in the health first sm program. bmi classification and bp categories of participants improved, and bp classification of participants got significantly better across the three measured time periods. the findings showed significant improvements from each data collection period, demonstrating that some attrition was noted in the frequency procedures for bmi and bp, which is a limitation. regardless of attrition, the percentages are a reflection of the proportion of change. although the number of smokers in the program was small (seven initially), four smokers quit, and two reduced the amount of cigarettes smoked. quitting smoking has major health benefits, including lower risk of heart disease, stroke, respiratory illness, lung cancer, and other cancers [18]. two unique components of this program is the use of teams to foster support among its members and the ability to view personal health data and aggregate team data online. these aspects may increase participation and completion rates, and also provides a source of fun and safe competition with rival teams. rules are in place to reward safe health behaviors, such as weight loss of no more than 2 pounds per week. the team dynamics and the online information are likely to be strong forces behind program participants’ motivation to change behaviors and to complete the program. study limitations the outcomes are notably positive, yet there are some limitations to this study. no control group threatens internal validity. the lack of a control group makes it difficult to determine how history, maturation, or other influences may have impacted the results. future research needs to ascertain opportunities for a controlled study using a delayed control design. 83.60% 10.40% 6% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% improved unsure no change p e rc e n ta g e change in exercise behavior health first's influence on physical activity n=67 http://ojphi.org/ metabolic risk factor reduction through a worksite health campaign: a case study design 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 conclusion the health first sm worksite wellness program can significantly improve employee health and potentially decrease health care costs. the benefits of using a program with online components may include cost and convenience, and it should be noted that weight loss in web-based programs is comparable to that of traditional programs [19,20]. as cited by university of michigan faculty member dee edington, phd, “…there is a tremendous danger in following a ‘do nothing’ strategy that allows individuals to flow into high risk, high cost categories.” [21]. wellness programs that reach the total population, not excluding those in good health, will help to lower healthcare costs by delaying onset of disease and metabolic risk factors. in summary, a well-designed program, use of knowledgeable healthcare professionals such as rds to provide employees with sound nutrition coaching, health education based on proven theories, thorough and carefully planned evaluation, use of team dynamics, and offering incentives are all steps in the development and execution of a successful program that will reduce both employer costs and employee health risk factors. acknowledgments the author wishes to acknowledge dr. steve godin, carol ireton-jones, donna israel, and amy virus for assistance with the editing of this manuscript. conflict of interest the authors hayley daubert, david rheinheimer, and christina brecht disclose no significant financial relationships or affiliations. denice ferko-adams developed the health first sm program and provided information on the content of the program for this paper. raw data was also provided to east stroudsburg for the independent analysis. correspondence hayley daubert, mph, rd, ldn 2041 fieldview drive nazareth, pa 18064 phone: 610-360-0486 fax: 610-746-2469 email: hayleydaubertrd@gmail.com http://ojphi.org/ mailto:hayleydaubertrd@gmail.com metabolic risk factor reduction through a worksite health campaign: a case study design 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 references 1. national center for health statistics. centers for disease control and prevention. http:// www.cdc.gov/nchs/products/pubs/pubd/hestats/overweight/overwght_adult_03.htm. accessed february 26, 2009. 2. national heart, lung, and blood institute. us department of health and human services. http://www.nhlbi.nih.gov/health/dci/diseases/ms/ms_whatis.html. accessed february 26, 2009. 3. burton wn, chen cy, edington dw. 2008. the prevalence of metabolic syndrome in an employed population and the impact on health and productivity. j occup environ med. 50, 1139-48. http://dx.doi.org/10.1097/jom.0b013e318188b8eb 4. trouger-decker r, o’sullivan-maillet j, 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http://www.diabeteseducator.org/export/sites/aade/_resources/pdf/practiceguidelines2009.pdf http://www.diabeteseducator.org/export/sites/aade/_resources/pdf/practiceguidelines2009.pdf http://www.nhlbi.nih.gov/guidelines/obesity/e_txtbk/txgd/414.htm http://www.cdc.gov/nchs/products/pubs/pubd/hestats/overweight/overwght_adult_03.htm http://www.cdc.gov/nchs/products/pubs/pubd/hestats/overweight/overwght_adult_03.htm http://dx.doi.org/10.1097/jom.0b013e318188b8eb http://dx.doi.org/10.1097/01.tin.0000333557.28325.df http://dx.doi.org/10.1097/jom.0b013e318162f628 http://dx.doi.org/10.1097/jom.0b013e318184a489 http://dx.doi.org/10.1038/oby.2007.384 http://www.cancer.gov/ http://www.nhlbi.nih.gov/health/public/heart/hbp/dash/new_dash.pdf http://dx.doi.org/10.1161/circulationaha.106.176158 http://www.diabeteseducator.org/export/sites/aade/_resources/pdf/ http://www.nhlbi.nih.gov/guidelines/obesity/e_txtbk/txgd/414.htm http://www.nhlbi.nih.gov/health/dci/diseases/hbp/hbp_whatis.html metabolic risk factor reduction through a worksite health campaign: a case study design 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 2, 2012 16. field a. discovering statistics using spss. 2005; 2nd ed. london: sage publications. 17. american cancer society. http://www.cancer.org/docroot/ped/content/ ped_10_13x_guide_for_quitting_smoking.as p?from=fast accessed august 13, 2009. 18. mico n, gold b, buzzell p, leonard h, et al. 2007. minimal in-person support as an adjunct to internet obesity treatment. ann behav med. 33(1), 49-56. http://dx.doi.org/10.1207/ s15324796abm3301_6 19. gold bc, burke s, pintauro s, et al. 2007. weight loss on the web: a pilot study comparing structured behavioral intervention to a commercial program. obesity (silver spring). 15(1), 155-64. http://dx.doi.org/10.1038/oby.2007.520 20. national business coalition on health. national health leadership council. http:// www.nbch.org/documents/nhlcwhitepaperjune2008.pdf accessed july 1, 2009. http://ojphi.org/ http://www.nhlbi.nih.gov/health/dci/diseases/hbp/hbp_whatis.html http://www.cancer.org/docroot/ped/content/ped_10_13x_guide_for_quitting_smoking.asp?from=fast http://www.cancer.org/docroot/ped/content/ped_10_13x_guide_for_quitting_smoking.asp?from=fast http://www.nbch.org/documents/nhlcwhitepaperjune2008.pdf http://www.cancer.org/docroot/ped/content/ http://dx.doi.org/10.1038/oby.2007.520 http://www.nbch.org/documents/nhlcwhitepaperjune2008.pdf http://www.nbch.org/documents/nhlcwhitepaperjune2008.pdf ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact andrew r wilson 1,* , ryan j. welch 2 , mia hashibe 1 , jessica greenwood 1 , brian jackson 2 , rosemary c. she 3 1. university of utah department of family and preventive medicine, salt lake city, utah 2. arup institute for clinical and experimental pathology, salt lake city, utah 3. keck school of medicine of the university of southern california, los angeles, california introduction numerous studies have demonstrated a clear causal relationship between human papillomavirus (hpv) and cervical cancer, with hpv considered necessary but not sufficient to cause cervical cancer [1-7]. hpv also plays a causative role in vaginal, anal, head, and neck cancers [2,8-10]. abstract nationwide positivity rates of high-risk human papillomavirus for the united states before and since the introduction of a human papillomavirus (hpv) vaccine in 2006 would provide insight into the population impact of hpv vaccination. data for high-risk hpv testing results from january 1, 2004 to june 1, 2013 at a national reference laboratory were retrospectively analyzed to produce 757,761 patient records of women between the ages of 14 and 59. generalized linear models and finite mixture models were utilized to eliminate sources of bias and establish a population undergoing standard gynecological screening. unadjusted positivity rates for high-risk hpv were 27.2% for all age groups combined. highest rates occurred in women aged 14 to 19. while the positivity rates decreased for all age groups from 2004 to 2013, the higher age categories showed less downward trend following vaccine introduction, and the two age categories 20 to 24 and 25 to 29 showed a significantly different downward trend between preand post-vaccine time periods (-0.1% per year to -1.5% per year, and 0.4% per year to -1.5% per year, respectively). all other age groups had rates of change that became less negative, indicating a slower rate of decline. keywords: human papillomavirus, surveillance, vaccine abbreviations: hpv, human papillomavirus; us, united states; fda, food and drug administration; acip, advisory committee on immunization practices; pap, papanicolaou; glm, generalized linear models; fmm, finite mixture models; ci, confidence interval; cdc, centers for disease control and prevention correspondence: email: arw2@utah.edu* doi: 10.5210/ojphi.v6i3.5593 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 hpv includes over 100 subtypes and is divided into highand low-risk groups according to oncogenic risk. in 2006, a quadrivalent vaccine (gardasil®, merck) offering protection against high-risk types 16 and 18, and low-risk types 6 and 11, was approved by the food and drug administration (fda). additionally, a bivalent vaccine (cervarix®, glaxosmithkline) protecting against high-risk types 16 and 18, was approved in 2009 [11]. both vaccines have shown close to 100% efficacy against hpv types 16 and 18, the cause of 70% of all cervical cancers [12]. in june 2006, the advisory committee on immunization practices (acip) recommended vaccination for females 9 to 26 years old [13]. it is hypothesized that with good vaccination coverage, the prevalence of hpv and hpv-associated cancers will decline [14]. although comprehensive surveillance for genital hpv positivity and prevalence data are considered difficult to estimate, several approximations exist [15]. a 2007 report showed that the overall prevalence in the united states of any hpv infection prior to vaccine introduction (20032004) was approximately 27% [16]. a follow-up study published in 2013 showed the overall rate from 2007-2010 was 40% [17]. both of these studies estimate national level prevalence, but were conducted on limited sample sizes (less than 5000 per time period), and had conflicting rates, with the 2013 study reporting a prevalence during the years 2003-2006 of 43% [17]. additional prevalence estimates exist, but these are often targeted at specific populations, lack sufficient sample size, are geographically isolated and often non-u.s. based [18-24]. therefore, a need exists for surveillance with complete u.s. coverage to establish overall positivity and prevalence rates as well as trends in these rates [12,25]. the goal of this study is to estimate the positivity of high-risk hpv in the united states from 2004 to 2013 using retrospective data from hpv testing conducted at a national reference laboratory. these positivity rates and trends over time should reflect underlying prevalence rates in the population of women who undergo regular gynecological testing and should be useful in supplementing other nationwide estimates of hpv vaccine impact. this study will illustrate that using such data can overcome the limitations of previous studies because the number of unique patients tested is large and account for a wide geographical spread. additionally, since hpv testing is typically performed in conjunction with routine papanicolaou (pap) testing, and rates of routine testing are above 80% in u.s. women over 18 years of age, the data represent generalizable rates free from selection bias associated with testing typically performed to support clinical suspicion of disease [26]. methods study population and analysis datasets this study was approved by an institutional review board. samples are submitted to and tested by a national reference laboratory for high-risk hpv (genotypes 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68). data from patient test results archived in an electronic data warehouse were extracted for hpv results from january 1, 2004 to june 1, 2013 to produce 757,761 female patient records with conclusive positive or negative results. attached to each record were test results and demographic data including age, sex, and client information. using only the first observation for each patient per calendar year between 2004 and 2013, a longitudinal dataset was created, consisting of 735,437 total high-risk hpv results from 590,036 unique patients at 692 unique client sites in 48 u.s. states. http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 hpv testing liquid-based endocervical samples were collected and submitted to the national reference laboratory for hpv testing. acceptable sample types include digene® cervical brushes (qiagen, hilden, germany), thinprep® preservcyt® media (hologic, inc., marlborough, ma), and surepath™ preservative (becton-dickinson, franklin lakes, nj). testing for hpv was performed according to manufacturer’s instructions by the digene® hc2 hpv dna test, which utilized hybrid capture 2 technology. thinprep® preservcyt® samples were prepared using the digene® hc2 sample conversion kit. statistical analysis positivity rates were analyzed by age category and year (both individual year and year categories: 2004-2007, 2007-2013) and compared. frequency tables and exact binomial confidence intervals were constructed to present positivity rates (with 95% confidence intervals [ci]) by age category and year. generalized linear models (glm) were created to assess differences in positivity between age categories and assess changes over time [27]. twoand three-way interaction models were used to assess both how rates have changed in the preand post-vaccine eras and also how age category affects this preand post-vaccine era effect on rate, respectively. to account for potential bias associated with differences in ordering, several methods were employed. first, it was hypothesized that a positive bias might be associated with physicians who submit specimens for hpv testing based on abnormal cytology results [28]. therefore, finite mixture models were then used to investigate patterns within the distribution of time between sample collection and time of final result release for all patients (figure 1) [29,30]. positivity rates between these different testing patterns were compared. additionally, sensitivity analyses were performed using glm to assess the effects of time between visit, client size, and client consistency on positivity rates [27]. time between visits was calculated for patients with more than one visit as the average time between visits. client size was calculated as number of tests ordered overall and for each year. client consistency was an indicator variable representing whether or not a client had ordered tests both at the beginning and end of the study period (tests ordered in 2004 and 2013). these derived variables were compared for both their main effect on positivity rate and interaction effects with time on positivity rates. all calculations were performed using sas software (v9.3, sas institute inc., cary, nc, usa). results were considered statistically significant if p<0.05. results raw positivity rates: high-risk hpv positivity rates for high-risk hpv were separated and compared across time and by age groups (table 1). 2004-2013 2004-2006 2007-2010 2011-2013 age, years no. positivity, % no. positivity, % no. positivity, % no. positivity, % overall 256,683 19.4 51,080 31.6 119,896 18.6 85,707 13.2 14-19 10,979 50.6 4,692 55.5 5,224 47.8 1,063 43.0 http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 20-24 25,725 50.2 9,276 54.1 11,525 49.5 4,924 44.4 25-29 24,363 36.8 7,545 41.2 11,219 36.8 5,599 30.8 30-39 77,928 15.7 12,439 24.4 36,104 15.1 29,385 12.7 40-49 67,841 9.5 10,824 14.7 31,955 9.0 25,062 8.0 50-59 49,847 7.4 6,304 12.4 23,869 7.1 19,674 6.2 table 1. positivity of high-risk hpv according to age and year group in pattern 1 individuals from a national reference laboratory. overall the positivity rate in women aged 14 to 59 years from 2004 to 2013 (n=735,437) was 27.2% (95% confidence interval [ci], 27.1 to 27.3). when separated by time period, the positivity rates decreased over time, with the pre-vaccine introduction period (2004 to 2006) having an overall positivity rate of 35.3% (95% ci, 35.1 to 35.5) and the final time period (2011 to 2013) having an overall positivity rate of 19.7% (95% ci, 19.5 to 19.9). when separated by age group, each showed a significant decline in overall positivity over time. the largest absolute decrease was in the 30 to 39 year old age group, with a decrease from 27.2% (95% ci, 26.7 to 27.6), in the years 2004 to 2006, to 16.3% (95% ci, 16.1 to 16.6), in the years 2011 to 2013. the smallest absolute decrease was in the 25 to 29 year old age group, with a decrease from 44.0% (95% ci, 43.4 to 44.6), in the years 2004 to 2006, to 42.7% (95% ci, 41.9 to 43.4), in the years 2011 to 2013 (table 1). overall, current rates were highest in both the 14 to 19 year old and 20 to 24 year old age categories, with the positivity rate being 54.5% (95% ci, 52.9 to 56.2), and 54.7% (95% ci, 54.0 to 55.5), respectively in the years 2011 to 2013. factors and significance of bias to establish if physician ordering practices influenced positivity, it needed to be determined if an indication for ordering hpv testing was present prior to sample submission. this was accomplished by analysis of the difference in collection time (reported by the client) and the result time (time at which results are reported from the national reference laboratory). finite mixture models were used and established two distinct populations of patient samples submitted; a population with a peak resulting time minus collection time at three days, and another at eight days (figure 1). a nadir (antimode) in the mixture model was observed at five days, which was then used as the cutoff between pattern 1 datasets (<5 days) and pattern 2 datasets (>5 days). this is consistent with previous studies conducted at the national reference laboratory and matches data that the majority of cytological results on pap specimens are completed within five days [28,31]. we also examined the subset of cases with both hpv test and pap smear results to validate our assumption that hpv testing delayed beyond five days was likely the result of abnormal cytology results. this subset (n = 9,347) showed a bimodal peak, with normal cytology results associated with hpv test results on average six days later, whereas abnormal cytology results had hpv results reported on average eight days later. therefore, it is hypothesized that samples in the pattern 2 dataset were submitted with suspicion of hpv as a result of abnormal cytological findings and should be excluded from our estimates of hpv prevalence. positivity rates support this, as they are significantly higher in pattern 2 compared to pattern 1 in all age groups across all years (tables 1, 2). additionally, sensitivity analyses performed using general linear models showed no statistically significant effect of time between visit, client size, and client ordering consistency on positivity rates. http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 figure 1: finite mixture modeling (fmm) of time between collection and final result indicating distinct distributions. rate of positive change per year, % age, years 2004-2006 2007-2013 difference 14-19 -2.2 -1.5 0.7 20-24 -0.1 -1.5 -1.4* 25-29 0.4 -1.5 -1.9* 30-39 -3.0 -0.8 2.2* 40-49 -1.7 -0.4 1.3* 50-59 -1.4 -0.3 1.1* table 2. average rate of change is positivity per year comparing preand post-vaccine periods. a p<0.05 trends: high-risk hpv following investigation of potential influences on the positivity rate and determining if they had significance or not, it was estimated that the positivity rates in pattern 1 should provide a useful indicator of underlying population prevalence of sexually active women getting regular http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 gynecological screening. overall positivity in this pattern 1 group of high-risk hpv for women aged 14 to 59 years from 2004 to 2013 was 19.4% (95% ci, 19.3 to 19.6). over time, the positivity rates decreased from 31.6% (95% ci, 31.2 to 32.0) during the years 2004 to 2006, to 13.2% (95% ci, 13.0 to 13.4) during the years 2011 to 2013. women aged 14 to 19 years showed the largest absolute decrease in the pattern 1 group in positivity from 55.5% (95% ci, 54.0 to 56.9) during the years 2004 to 2006, to 43.0% (95% ci, 40.0 to 45.9) during the years 2011 to 2013. women aged 50 to 59 showed the largest percent decrease, dropping 49.7% in prevalence from 12.4% (95% ci, 11.6 to 13.2) during the years 2004 to 2006, to 6.2% (95% ci, 5.9 to 6.6) during the years 2011 to 2013. the smallest percent decrease was seen in women aged 20 to 24, with only an 18.0% reduction in positivity from 54.1% (95% ci, 53.1 to 55.1) during the years 2004 to 2006, to 44.4% (95% ci, 43.0 to 45.8) during the years 2011 to 2013. rates of change in high-risk hpv positivity per year were calculated and compared between the pre-vaccination (2004-2006) and post-vaccination periods (2007-2013) in all age groups. generalized linear models showed that age category had a significant effect on rates when these time periods were compared. in the pattern 1 group, all age categories showed positivity decreases in the post-vaccine period; however, only women aged 20 to 24 and 25 to 29 showed negative differences (-1.4% per year and -1.9% per year, respectively) when preand postvaccine period rates of change were compared. all other age categories had rates of change that were less negative, and closer to zero (figure 2, table 2). discussion the goal of this study was to establish and evaluate a tool to estimate the current positivity of and trends in high-risk hpv in women in the united states from 2004 to 2013; specifically, in women who undergo regular gynecological screenings. these data indicate that the overall positivity of hpv is declining, especially in young women; however, the rate at which positivity is declining is slower than other studies indicate in certain age categories [17]. this may be a combination of insufficient vaccination coverage as well as these data being a mixture of vaccine-preventable and other high-risk types collectively. mathematical models have predicted that the introduction of the vaccine should have a strong impact on hpv positivity rates in the types covered by the vaccine [14]. while this data could not be separated entirely by type, a reduction of 18 to 25% in positivity in all high-risk hpv was seen in women aged 14 to 29 when comparing rates prior to the vaccine introduction (2004 to 2006) to current rates (2011 to 2013). while most studies performed to determine hpv positivity or prevalence rely on survey-based methods, this study has several strengths that allow for the generalizability of the results to women who undergo regular gynecological screening in the us. first, the data were retrospective data from a large national reference laboratory, which created a large dataset of more than 700,000 high-risk hpv results that accurately reflected at-risk and vaccine-targeted population. second, the data were filtered in several ways: only the first visit per calendar year of each patient was used to reduce redundancy that may occur as a result of repeat confirmatory testing, and potential ordering bias was reduced by separating results from patient samples believed to be submitted because of abnormal cytology results. to account for ordering bias, finite mixture modeling was used to determine where, if any, a separation may exist between the time the samples were collected and the time that results were entered [29]. the separation http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 observed is likely the result of samples being immediately sent for hpv testing versus samples that had been figure 2: high-risk hpv positivity in pattern 1 individuals by year and age category from a national reference laboratory. screened for abnormal cytology before being sent for hpv testing [28]. the cutoff between pattern 1 and 2 of five days can be further supported by a 2013 survey by the college of american pathologists (cap) that showed that 83.9% of papanicolaou testing took less than five days to completion, with the majority taking 3 to 4 days [31]. additionally, the positivity rates in testing pattern 2 were significantly higher than in testing pattern 1, suggesting physicians had an http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 indication for ordering hpv testing, such as abnormal pap smear results. lastly, sensitivity analyses were performed to determine if differences in clients might influence the positivity rates. since these data included many individual client sites, it was theorized that differences in client size might account for a bias in positivity: clients with fewer sample submissions may be targeting at-risk individuals with a higher likelihood of disease. using generalized linear models, it was shown that there were no differences in positivity rates when adjusting for client size, location, or ordering trends, further supporting that the positivity rates truly are indicative of overall prevalence. based on retrospective data from a national reference lab from 2004 to 2013, the overall positivity of high-risk hpv in all age groups was 27.2%. looking at only pattern 1, which was established as an estimate of unbiased prevalence, the rate drops to 19.4%. our estimate of prevaccine high-risk hpv positivity in women 14 to 59 years old was 31.6%, compared to the rates reported from other studies of 15 – 29% [16,17]. for the current time period (2010-2013), the positivity rate decreases to 13.2% for all women aged 14 to 59. by age group, our positivity estimates are also higher, particularly in the 14-19 year old group (55-57%) when compared to cdc data (15-20%). this could be due to the fact that hpv testing is generally only performed for sexually active women; so positivity estimates in this study likely reflect the positivity rates in the sexually active population, which has been shown to be close to 50% in young adult females, similar to the present study [16,17]. furthermore, the method of specimen collection differed from other studies in that provider-collected cervical swabs were used as opposed to self-collected cervicovaginal swabs. provider collection could not feasibly be standardized in this study, but overall performance of hpv testing has been shown to be similar for both collection methods [32]. it is important to note that despite a decline in high-risk hpv positivity in all age categories, the rate of positivity change per year is not consistent. hpv vaccination is only recommended in women up to the age of 26; therefore, this group would likely have a faster decrease in positivity. it could further be postulated that, since the vaccinated population is increasing in age, an inverse relationship between age and change in prevalence per year would exist. this was observed, with the post-vaccine introduction period rates of positivity change per year being consistent in the vaccine target group (age groups 14 to 19, 20 to 24, and 25 to 29) at a -1.5% and decreasing with each increase in age category to a low of -0.3% in the 50 to 59 year old group. the decreasing rates that were seen in the pre-vaccine introduction period are likely due to inconsistent testing practices among physicians and an increasingly heterogeneous population being screened; however, these rates still provide a baseline to compare post-vaccine period rates. crucially, the difference in the preand post-vaccine era rates of positivity change were only more negative in the age groups that had overlap in vaccine and screening guidelines (20 to 24 and 25 to 29-yearold women). this suggests that the decreases in the rates of prevalence change per year seen in the younger population age categories could be due to increasing vaccination rates. for comparison, 618,261 chlamydia tests were analyzed for females stratified by the same age categories as for hpv and covering years 2004-2013 (supplemental online figure). across all age categories, there is no significant downward trend in positivity rates and, in fact, most age categories show an upward trend (especially since 2008). these results further support the hypothesis that downward trends observed in hpv are likely attributable to vaccine uptake. http://ojphi.org/ ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 limitations of study several limitations to this study exist. current guidelines recommend screening every three years in all sexually active women over 21 years of age; therefore, and as a result of the data being collected at a national reference lab, the population represented is likely a sexually active population with access to healthcare. furthermore, cervical cancer screening guidelines have changed over the duration of data collection, with the introduction of co-testing hpv and pap screening in women older than 30 years and recommendation against screening in women less than 21 years regardless of first sexual contact [33,34]. despite these changes, these data and several studies indicate that the guidelines are not being followed as testing is still frequently performed on an annual basis, in women under 21 years of age, and in women following hysterectomy [28,35,36]. another final limitation is that the hybrid capture 2 method does not differentiate genotypes and differentiation of specific high-risk vaccine preventable hpv strain was not possible [37,38]. we attempted to mitigate some of the potential biases by focusing on collection pattern 1 (results within five days of collection) and only including one visit within the calendar year, but future studies of this kind will certainly be needed to both validate and improve upon methodology. additional studies that have access to multiple testing sites may be able to refine analyses by using characteristics of the different client sites: for example, differences in ordering volumes, within-center positivity rates, or information collected outside of the analytic framework, such as clinical indications for testing in their population. our study demonstrates the potential for using hpv test data from large national reference laboratories to supplement the ongoing and planned efforts to monitor hpv vaccine impact in the us [39]. conclusion in evaluating the surveillance tool, we find that it is important to consider many sources of heterogeneity, e.g., age, type of test, location, and type of testing center, and also consider quantitative methods of adjustment and distribution assessment to construct a useful surveillance tool. further studies should expand on this methodology. the results of the surveillance tool indicate a downward trend in vaccine-appropriate age groups consistent with uptake of the hpv vaccine. the pre-post rate changes were in direct contrast between the age-appropriate groups and the groups too old for the hpv vaccine, further indicating the surveillance tool may be detecting the impact of the hpv vaccine over time. after refinement, this surveillance tool should remain in place to observe the future impact of the hpv vaccine. acknowledgements the authors would like to thank david davis of arup laboratories for his help acquiring the data used in this study. the authors would also like to thank lindsay larkin wilson for her help in editing the manuscript. references 1. lowy dr, schiller jt. 2006. prophylactic human papillomavirus 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papanicolaou testing among older women and among women without a cervix. jama intern med. 174(2), 293-6. pubmed http://dx.doi.org/10.1001/jamainternmed.2013.12607 37. castle pe, solomon d, wheeler cm, gravitt pe, wacholder s, et al. 2008. human papillomavirus genotype specificity of hybrid capture 2. j clin microbiol. 46(8), 2595-604. pubmed http://dx.doi.org/10.1128/jcm.00824-08 38. sargent a, bailey a, turner a, almonte m, gilham c, et al. 2010. optimal threshold for a positive hybrid capture 2 test for detection of human papillomavirus: data from the artistic trial. j clin microbiol. 48(2), 554-58. pubmed http://dx.doi.org/10.1128/jcm.00896-09 39. markowitz le, hariri s, unger er, saraiya m, datta sd, et al. 2010. post-licensure monitoring of hpv vaccine in the united states. vaccine. 28(30), 4731-37. pubmed http://dx.doi.org/10.1016/j.vaccine.2010.02.019 http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21189840&dopt=abstract http://dx.doi.org/10.4103/2153-3539.73504 http://dx.doi.org/10.1093/biomet/56.3.463 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23368858&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23368858&dopt=abstract http://dx.doi.org/10.5858/arpa.2012-0120-cc http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22907569&dopt=abstract http://dx.doi.org/10.1002/ijc.27790 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22422631&dopt=abstract http://dx.doi.org/10.3322/caac.21139 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23090560&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23355601&dopt=abstract http://dx.doi.org/10.1158/1055-9965.epi-12-1266 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24276745&dopt=abstract http://dx.doi.org/10.1001/jamainternmed.2013.12607 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18579716&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18579716&dopt=abstract http://dx.doi.org/10.1128/jcm.00824-08 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20007387&dopt=abstract http://dx.doi.org/10.1128/jcm.00896-09 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20188681&dopt=abstract http://dx.doi.org/10.1016/j.vaccine.2010.02.019 ojphi surveillance of human papilloma virus using reference laboratory data for the purpose of evaluating vaccine impact online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(3):e194, 2014 supplementary material supplemental figure: comparison of positivity rates between high-risk hpv (pattern 1 individuals) and chlamydia by year and age category from a national reference laboratory. http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts starscan: a novel scan statistic for irregularly-shaped spatial clusters sriram somanchi*, david choi and daniel b. neill carnegie mellon university, event and pattern detection laboratory, pittsburgh, pa, usa objective we present starscan, a novel scan statistic for accurately detecting irregularly-shaped disease outbreaks. starscan maximizes a penalized log-likelihood ratio statistic, allowing the radius around a central location to vary as a function of the angle and applying a penalty proportional to the total change in radius. introduction kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. the fast localized subset scan2 enables scalable detection of proximity-constrained subsets and increases power to detect irregularly-shaped clusters, however, unconstrained subset scanning within each circular neighborhood2, may not necessarily capture the pattern of interest, and is too under-constrained for use with case/control point data. thus we propose the star-shaped scan statistic (starscan), a novel method that efficiently maximizes the loglikelihood ratio over irregularly-shaped clusters, while incorporating soft constraints on smoothness. more precisely, we allow the radius of the cluster around a center location to vary along with angle, and penalize proportional to the total change in radius. methods we propose a dynamic programming based solution to find optimal clusters, with penalty terms introduced to control smoothness in the radius of the cluster. our computationally efficient starscan algorithm uses the key observation3 that the log-likelihood ratio score may be written as an additive function, summing over all data elements, when conditioning on the relative risk value q. given a region s, the loglikelihood ratio score f(s) is given by maximizing over the whole range of relative risk values. let the score of a region for a given relative risk be given by f(s | q), and let r(s) be its total change in the radius, for a given center location, to form the cluster. we use dynamic programming to find the optimal region s that maximizes the penalized score f’(s | q) = f(s | q) – r(s), where the constant represents the amount of penalization for a given change in radius. we find the optimal penalized score f’(s*) and corresponding optimal subset s*, by either grid search (evaluating a range of possible values of q) or using branch and bound techniques in order to find the optimal value of q. results starscan was compared to the circular scan1 and fast subset scan2 on simulated respiratory outbreaks and bioterrorist anthrax attacks injected into real-world emergency department data from allegheny county, pa. given a small amount of labeled training data, starscan learns appropriate penalties for both compact and elongated clusters, resulting in improved detection performance. for irregularly shaped injects, starscan improves performance both in terms of increasing the spatial overlap between true and detected regions, and increasing detection power as measured by the average number of days to detection at a fixed false positive rate. finally, we show that starscan generalizes both circular scan (for large ) and fast localized subset scan (for ç0). conclusions starscan generalizes the traditional, circular spatial scan statistic1 by allowing the radius of the cluster around a center location to vary continuously with the angle, but penalizes the log-likelihood ratio score proportional to the total change in radius. this penalization allows starscan to find irregularly-shaped clusters more accurately than either the circular scan or unconstrained fast subset scan, both of which are shown to be special cases of starscan with appropriate choices of penalty. comparison of detection performance keywords biosurveillance; scan statistics; dynamic programming acknowledgments this work was partially supported by nsf grants iis-0916345, iis0911032, and iis-0953330. references 1. kulldorff m. a spatial scan statistic, communications in statistics, theory and methods, 1997. 2. neill db. fast subset scan for spatial pattern detection. journal of the royal statistical society (series b: statistical methodology) 74(2): 337-360, 2012. 3. speakman s, somanchi s, mcfowland e, neill db. penalized fast subset scanning. under review, 2014. *sriram somanchi e-mail: somanchi@cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e55, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts tuberculosis unseen missed opportunities in diagnosis aaron c. miller*, linnea a. polgreen and philip m. polgreen university of iowa, iowa city, ia, usa objective to estimate the potential number of tuberculosis (tb) cases that occur in inpatient and emergency department settings that are missed, diagnosed as something else, go untreated and return to the community, prior to receiving a correct diagnosis of tb. we analyze inpatient and emergency department records from the state of california from 2005-2011. introduction historically, patients with tb have often been diagnosed late1 or after death2. this delay in diagnosis often occurs because tb is misdiagnosed as an alternative respiratory illness (ri), such as pneumonia1. tb infected patients that are not correctly diagnosed when initial symptoms occur may spread infection to others in both healthcare settings and the community. methods hcup state inpatient and emergency department records for the state of california were extracted for patients with multiple stays between 2005 and 2011. records containing an icd-9 diagnosis code beginning with 010, 011, 012, and 018 were used to identify patients with a tb diagnosis. next, we searched for visits that preceded the patient’s tb diagnosis. patient records following the first tb diagnosis and those containing a prior alternative tb diagnosis (e.g. icd-9 codes starting with 015 – tb of the bones and joints) were excluded. we defined a potential missed diagnosis (pmd) to have occurred if: (1) a ri was indicated in the previous visit and (2) that visit occurred in an event window of 5-90 days directly prior to the admission containing the initial tb diagnosis. if the previous visit occurred less than 5 days prior to the tb associated visit, it was excluded because there is a time lag between tb testing and diagnosis. finally, in order to determine if the ri’s in a visit preceding a tb diagnosis represent a pmd rather than a correct but coincident ri diagnoses, the occurrence of such events was compared between patients with and without an initial tb diagnosis. odds ratios were computed for the probability of a ri occurring in a previous stay falling in the same event window between patients with and without a tb diagnosis. multivariate logistic regression was also used to estimate this odds ratio while controlling for patient characteristics. results a total of 6,664 initial cases of tb were identified, 3,164 of which had a ri in the visit directly preceding their initial tb diagnosis. of the initial tb cases identified, approximately 23.33% had a pmd, defined by a ri. the rate of pmds increased with larger time windows: pde rates were 19.75%, 25.33%, 28.45%, to 32.5%, when 60, 120, 270 and 360 day time windows were used, respectively. in comparison to the non-tb patients, those with an initial tb diagnosis were far more likely to experience a ri in a previous visit (or 3.86, ci 3.58-4.16), and only 8.27% of the previous visits in the non-tb population contained a ri. after controlling for patient characteristics, the odds of an initial tb diagnosis having a previous visit containing a ri was 2.77 (ci 2.55-3.00) times the odds of a non-tb visit. conclusions our results indicated that misdiagnosis of tb may be occurring in more than 20% of tb patients diagnosed in inpatient and emergency department settings. while some of these events likely represent coincident ris rather than pmds, patients with an initial tb diagnosis were far more likely to experience a ri in the months leading up to diagnosis than non-tb patients. moreover, given that ris are often diagnosed and treated in outpatient settings, even more misdiagnoses may be occurring than can be observed in the data used here. these findings call for an increased awareness in tb surveillance, especially to prevent healthcare associated spread of tb. keywords tuberculosis; misdiagnosis; disease surveillance references 1. wang j, hsueh p, jan i, liaw y, yang p, luh k. empirical treatment with a fluoroquinolone delays the treatment for tuberculosis and is associated with a poor prognosis in endemic areas. thorax. 2006; 61(10)903-8. 2. bobrowitz i. active tuberculosis undiagnosed until autopsy. am. j. med. 1982; 72(4): 650-8. *aaron c. miller e-mail: aaron-miller@uiowa.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e41, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts cipars: a one-health approach to antimicrobial resistance surveillance anne deckert*1, agnes agunos1, brent avery1, carolee carson1, danielle daignault2, rita finley3, sheryl gow4, david léger1, michael mulvey5, jane parmley1, richard reid-smith1 and rebecca irwin1 1laboratory for foodborne zoonoses, public health agency of canada, guelph, on, canada; 2laboratory for foodborne zoonoses, public health agency of canada, st hyacinthe, qc, canada; 3centre for foodborne, environmental, and zoonotic infectious diseases, public health agency of canada, guelph, on, canada; 4laboratory for foodborne zoonoses, public health agency of canada, saskatoon, sk, canada; 5national microbiology laboratory, public health agency of canada, winnipeg, mb, canada objective the objective of the canadian integrated program for antimicrobial resistance surveillance (cipars) is to provide a unified approach to monitor national trends in antimicrobial resistance (amr) and antimicrobial use (amu) in humans and animals and to facilitate the assessment of the public health impact of antimicrobial use. introduction amr has been identified as a global threat to public health. resistant bacteria and associated genes can move within and between populations of people and animals, making amr a very complex and contentious issue. credible, multi-sectoral surveillance data provide information to promote prudent amu in hospitals, the community, and agriculture. methods cipars was created through agreements with organizations with useful existing data/isolate sources and the development of active surveillance to fill critical gaps. cipars includes active sampling of beef, pork and chicken at farm, abattoir and retail and the submission of clinical salmonella isolates from human and veterinary cases for amr testing. amu data are collected from physicians, hospitals, and pharmacies, veterinary pharmaceutical companies, and pig and chicken producers. a central data repository was developed for laboratory and epidemiological amr data to standardize and facilitate analysis. amu data are stored in flat and relational databases. molecular characterization of targeted isolates provides critical information about the relationship between amr in human and animal populations. broad stakeholder engagement, ensuring the confidentiality of results, and responsiveness to stakeholder feedback were critical to the development and ongoing sustainability of the program. private industry stakeholders were involved in the development of the program and in ongoing data/isolate collection but are not involved in data analysis or report production. notification of results prior to publication is essential for both government and private sector participants. therefore, cipars is coordinated by the public health agency of canada (phac) but is based on collaborations with governments (health and agriculture, federal, provincial, local), private industry (veterinarians, livestock producers, and abattoirs), and academia. results direct stakeholder engagement has led to action based on surveillance results as well as an increased awareness of amr. examples include: the voluntary ban on ceftiofur use in chicken hatcheries in one province based on cipars surveillance results from that province and the more recent ban by the chicken industry on the use of very important human antimicrobials for the prevention of disease in broiler chickens. cipars maximizes the use of surveillance data by collaborating with other public health initiatives such as the enteric surveillance program foodnet canada. cipars data are also used in outbreak investigations, post-approval monitoring of veterinary drugs, research, and source attribution. conclusions by standardizing methods of sample collection, laboratory testing, data analysis and reporting, but creating a flexible system of stakeholder involvement unique to each component of cipars, the system meets stakeholder needs while providing a holistic understanding of amr in canada. by taking a one health approach and generating data at various points in the food chain, it is possible to investigate potential intervention points as well as to evaluate interventions undertaken by both government and/or private industry. keywords surveillance; antimicrobial resistance; antimicrobial use; one-health references government of canada. canadian integrated program for antimicrobial resistance surveillance. [internet]. ottawa (on): [updated: 2007 jul 26; cited: 2014 sep 3]. available from: http://www.phac-aspc.gc.ca/ cipars-picra/index-eng.php government of canada. foodnet canada. [internet]. ottawa (on): [updated: 2013 nov 15; cited: 2014 sep 3]. available from: http:// www.phac-aspc.gc.ca/foodnetcanada/index-eng.php léger d, deckert,a, gow s, agunos a, reid-smith r. cipars: an approach to building collaboration for a voluntary farm surveillance framework. epidémiol. et santé anim. 2011; 59: 348-51 *anne deckert e-mail: anne.deckert@phac-aspc.gc.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e68, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts use of administrative health care data for sexually transmitted disease surveillance elaine w. flagg* and hillard weinstock centers for disease control and prevention, atlanta, ga, usa objective to evaluate the potential usefulness of 3 sources of administrative health care data for sexually transmitted disease (std) surveillance. introduction surveillance for std in the united states (us) relies primarily on case reports from clinicians and laboratories and sentinel surveillance; however, nationwide reporting is not required for viral std and clinical sequelae of std. methods the marketscan commercial claims and encounters (ccae) data from truven health analytics contains annual beneficiary enrollment information and inpatient admissions, outpatient encounters, and outpatient pharmacy dispensing claims from approximately 100 employee-sponsored private insurance plans. the 2012 ccae contains 1.1 billion inpatient and outpatient claims records for 53 million employees and their dependents. the healthcare cost and utilization project kids’ inpatient database (kid) from the agency for healthcare research and quality is the largest publicly-available all-payer pediatric inpatient database in the us. kid data have been available every 3 years since 1997, and are weighted to provide national estimates. the 2012 kid contains 3.2 million birth and hospitalization discharge records for children and adolescents. national medicaid analytic extract (max) data from the centers for medicare and medicaid services contain claims records for enrollees in medicaid, a joint state and federal health insurance program for low-income individuals in the us. max data are structured similarly to the ccae. the 2009 max contains 2.3 billion inpatient and outpatient claims records for almost 68 million child and adult beneficiaries. results ccae data were used to examine trends in genital wart (gw) prevalence in enrollees aged 10-39 years. for gw, a combination of diagnosis, procedure, and prescription drug codes were utilized to create a case definition based on either (1) diagnosis codes specific for gw; or (2) less-specific diagnosis codes for viral warts or prescription drug codes for >1 gw medications, combined with either a procedure code for destruction or excision of a genital lesion, or a diagnosis code for a benign genital neoplasm, within 30 days of the viral warts diagnosis or prescription. kid data were used to estimate national and regional incidence of neonatal herpes simplex virus (nhsv) infection. laboratory test results are not included in inpatient discharge data and procedure codes specific to nhsv treatment do not exist. the case definition initially used hospital admissions within the first 60 days of life with 1 diagnosis codes for herpes simplex infection. however, because the recommended course of treatment for nhsv is >7 days, we further restricted cases to patients hospitalized at least that long or who died within 7 days, to help eliminate ruled-out diagnoses. max data were used to estimate incidence of congenital syphilis (cs) in a cohort of infants continuously enrolled during the first year of life. procedure codes for syphilis tests exist, but codes specific to treatment of this infection do not. several other diagnostic procedures may be used, and follow-up tests of cure should be conducted. all continuously enrolled infants with 1 syphilis diagnosis codes were first identified; possible cs cases were then defined as those with a syphilis test or diagnosis >2 months after the first diagnosis. probable cases were identified as the subset of possible cases which also had a syphilis test or other diagnostic procedure within 30 days of the first diagnosis. strengths of all these sources include availability of diagnosis and procedure codes for large numbers of records. kid data are nationally representative, while max data represent the entire population of medicaid enrollees. all data sources offer standardized data values. however, none of the sources include information on laboratory test results or inpatient medications, although ccae and max do contain outpatient prescription claims. codes may not exist for tests, procedures, or treatments sufficient to construct a plausible case definition for some std. access to these data is not timely; currently, kid and ccae are available through 2012 and max through 2009. race/ethnicity information is available in only kid and max, and these data are incomplete; 8% of race/ethnicity values are missing for kid 2012 and for max 2009. conclusions administrative health care data provide new opportunities to monitor std among large numbers of health care consumers. these data may be particularly useful for assessing non-reportable std and std clinical sequelae, but their delayed availability may limit their utility for public health response. keywords sexually transmitted disease (std); public health surveillance; claims data *elaine w. flagg e-mail: ewf2@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e129, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts validation of new jersey emergency department (ed) registration data in biosense 2.0 feiyan chen1, elizabeth kostial2, teresa hamby1 and stella tsai*1 1new jersey department of health, trenton, nj, usa; 2health monitoring systems, inc, pittsburgh, pa, usa objective to assess and validate new jersey’s ed registration data feed from epicenter to biosense 2.0. introduction biosense 2.0, a redesigned national syndromic surveillance system, provides users with timely regional and national data classified into disease syndromes, with views of health outcomes and trends for use in situational awareness. as of july 2014, there are 60 jurisdictions nationwide feeding data into biosense 2.0. in new jersey, the state’s syndromic surveillance system, epicenter, receives registration data from 75 of nj’s 80 acute care and satellite emergency departments. epicenter is a system developed by health monitoring systems, inc. (hms) that incorporates statistical management and analytical techniques to process health-related data in real time. to participate in biosense 2.0, new jersey worked with hms to connect existing data to biosense. in may, 2013, hms established a single data feed of new jersey’s facility data to biosense 2.0. this transfer from hms servers occurs twice daily via sftp. the average daily visit volume in the transfer is around 10,000 records. this data validation project was initiated by the new jersey department of health (njdoh) in 2013 to assure that the registration records are delivered successfully to biosense 2.0. methods for this assessment, njdoh searches and exports weekly ed visit counts by facility and date from epicenter using built-in export functions and from biosense 2.0 via sql query scripts and then compares them using the data validation tools developed by sas. figure1 shows the procedure of data validation. a summary report of this comparison is generated by sas ods, which includes total number of facilities feeding data, the list of problematic facilities not reporting data, and unmatched visit counts by facility and date between epicenter and biosense 2.0. this sas tool imports, cleans and manipulates these two data sets exported from epicenter and biosense 2.0 using the merge and subgroup functions, and applies the sql procedure to obtain the timeframe of the validation in the final report. a sas macro automatically generates a directory to store the report file. results using the validation tools described, project staff investigate, identify and resolve the data issues. for data discrepancies (count difference > 10) for a specific facility on a specific date, the missing records are identified to compare the individual ed visit records exported from epicenter to those from biosense 2.0. a common reason for discrepancy is programmatic. epicenter is able to process messages without a visit number while biosense cannot do so. other reasons for discrepancies found during validation include: missing data in biosense 2.0 due to processing issues upon receipt, messages from newly added facilities where ids have not yet been fully processed by hms, njdoh, and biosense, an identifier is changed by a facility in the message that is then unrecognized, facilities delay sending records for some period after visit date. in addition, a facility may stop sending data due to system maintenance or upgrade resulting in gaps in data. where applicable for these discrepancies, hms resends the missing data to biosense 2.0. conclusions the data validation tools and procedures used by njdoh are useful to assess the ed registration data sent to the back end of biosense 2.0. most missing data and discrepancies can be detected by these tools. njdoh and hms will continue to improve the tools to validate the data between the back end and the front end of biosense 2.0 based on the knowledge of how data is processed in biosense 2.0. in addition, work on developing data quality tools to evaluate and report the data status will continue. keywords data validation; data quality, data assessment; sas, sql; epicenter, biosense; new jersey *stella tsai e-mail: stella.tsai@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e118, 201 visualizing central line-associated blood stream infection (clabsi) outcome data 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi visualizing central line –associated blood stream infection (clabsi) outcome data for decision making by health care consumers and practitioners—an evaluation study yair g. rajwan 1 , pamela w. barclay 2 , theressa lee 2 , i-fong sun 3 , catherine passaretti 4 , harold lehmann 5 1 visual science informatics, analytics visualization, burke, va, 2 maryland health care commission, baltimore, md, 3 johns hopkins university, baltimore, md, 4 johns hopkins bayview hospital, baltimore, md, 5 division of health sciences informatics, jhu school of medicine, baltimore, md introduction in 2002, the centers for disease control and prevention (cdc) released a public report estimating that 1.7 million healthcare-associated infections (hais) result in 99,000 deaths abstract the purpose of this study was to evaluate information visualization of publicly-reported central line-associated blood stream infection (clabsi) outcome data for decision making by diverse target audiences – health care consumers and practitioners. we describe the challenges in publicly reporting of healthcare-associated infections (hais) data and the interpretation of an evaluation metric. several options for visualization of clabsi data were designed and evaluated employing exploratory working group, two confirmatory focus groups’ observations, and experts’ committee validation of the final designs. survey-data collection and evaluation criteria results, collected from the two focus groups, are presented and are used to develop the final recommendations for how to visualize publicly-reported clabsi data from maryland acute care hospitals. both health care consumer and practitioner’s perspectives are highlighted and categorized based on the visualizations’ dimensions framework. finally, a recommended format for visualizing clabsi outcome data based on the evaluation study is summarized. key words: usability of health information; public health informatics; information graphics; information visualization; sense making; visual communication. mesh headings: catheter-related infections; cross infection; blood-borne pathogens; infectious disease transmission, professional-to-patient; infection control; public health practice. correspondence: yair_rajwan@visualscienceinformatics.com copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. visualizing central line-associated blood stream infection (clabsi) outcome data 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi annually within hospitals across the united states. over the past decade a number of states, including maryland, to drive improvement and increase transparency have enacted legislation that requires hospitals to publicly report hais. central line-associated blood stream infections (clabsis) are one of the more common hais that result in substantial morbidity and mortality as well as increased medical costs. as such, the hai advisory committee of the maryland health care commission, (mhcc), an “independent regulatory agency whose mission is to increase accountability and promote informed decision-making,” chose clabsis in intensive care units (icus) as the first hai outcome measure to be reported in maryland [1]. in determining how best to publicly report clabsi outcome data, mhcc considered the goals and challenges of public reporting. the goals of public reporting are to inform the public about hospital performance, to increase transparency and trust between hospitals and consumers, and to drive best practices and improvement to eliminate healthcare-associated infections [2,3]. the purpose of this study was to help mhcc in choosing the best way of communicating these data that would address needs and concerns of consumers, hospitals, and the state. challenges in public reporting of hai data there are a number of challenges that must be faced when deciding how to publicly report hai or clabsi data. first, one must consider the audience viewing the data. each individual viewing the data may have different objectives and goals ranging from a patient trying to choose a hospital for a procedure to hospital administration utilizing it for performance improvement. guidance in the medical and public health literature related to public reporting of health careassociated infections data is currently limited and there are no articles directly pertaining to how to effectively present hai data to consumers. extrapolating from literature on public reporting of other measures, however, several major principles are apparent. to make informed choices and navigate the health care system, consumers need to have easily available, accurate, understandable, and timely information. consumers likely represent a range of perspectives because they have varying levels of education, different backgrounds and different needs with regards to the data presented. for example, only about 50% of americans have the minimal mathematical skills necessary to understand numbers presented in printed materials [4]. the primary challenge in designing a system for public reporting of health quality data is that quality measures are often difficult to understand or are not meaningful to consumers. cognitive interviews of health care consumers have revealed that consumers prefer information that can be reviewed quickly and that is clear at first review. participants in these interviews frequently felt inundated by the amount of information listed [5,6]. the data must be framed clearly to a broad audience providing neither too much nor too little information [7,8]. the agency for healthcare research and quality (ahrq) recommends that information should be made relevant to what consumers care about, that metrics should be consistent, and that data on sponsors and methods should be included to help legitimize the data for consumers [9,10]. more importantly, however, to ensure that the broadest possible audience utilizes and understands the publicly-reported data, the information presented should be summarized and interpreted for consumers to the greatest extent possible. simple language should be used and guidance on how to read graphs and understand measures should be provided. familiarity with health vocabulary by the public is an important factor in consumer understanding of health related reporting [11]. employing consumers’ vocabulary, in health literacy, can reduce the gap between a vocabulary that is used by health care professionals and the consumers’ visualizing central line-associated blood stream infection (clabsi) outcome data 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi understandings [12]. visual communication using visualization of the information can improve learning and communication [13]. strategies that narrow options and highlight differences are the most useful to consumers [14,15]. display strategies that seem to be particularly effective include rank ordering providers by performance, labeling performance (i.e., excellent, fair, poor or above average, average or below average), or using symbols (i.e., stars or symbols that incorporate the interpretive label as part of the symbol) [16,17]. ahrq recommends against presenting confidence interval (ci) when presenting comparative performance data given that consumers often don’t understand statistics and that research has shown that consumers tend to discount information when the report suggests uncertainty regarding the data [18]. the primary objective of our study was to determine the most effective manner to publicly report hospital clabsi data to both consumers and professionals, based on current standards of data presentation. methods the study method, governance framework, had three phases—exploratory, confirmatory, and validation [19]. exploratory phase the purpose of this phase was to perform four activities: 1) thorough literature review, 2) panel study, 3) experts’ evaluation, and 4) iterative information visualization design. the exploratory working group that participated in the panel study of the exploratory phase included five (5) health care professionals: 1) the director of center for hospital services at the maryland health care commission, 2) the chief of hospital quality initiatives at the maryland health care commission, 3) an assistant professor and hospital epidemiologist at the johns hopkins bayview hospital, 4) a program manager at the center for innovation in quality patient care of johns hopkins university, and 5) a postdoctoral research fellow of the national library of medicine (nlm) in the division of health sciences informatics at the johns hopkins university school of medicine. three (3) experts that were solicited for their expertise and evaluation included: 1) hospital epidemiologist – and professor of epidemiology, medicine, and pathology, 2) anesthesiologist and critical-care specialist, and 3) professor of pediatrics, health policy and management, and health sciences informatics. thirteen (13) members of the mhcc hai advisory committee were solicited for their expertise prior to health care consumers and practitioners’ focus groups. the expert-opinion solicitation employed recurring brainstorm and interview sessions and the hai advisory committee solicitation employed two panel discussion and collection of word-type data that were analyzed for identifying themes. confirmatory phase in order to garner perceptions, on the proposed display formats during the confirmatory phase from both of the intended audience groups, a structured interview tool was developed. the purpose was to capture participants’ opinions and attitudes on the various information visualization alternatives. the structured interview was administered to consumer and health care professional, with expertise in hospital epidemiology and infection control, focus groups to test visualizing central line-associated blood stream infection (clabsi) outcome data 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi the usability and understanding of the alternative display presentations. the focus-groups-based study included structured elicitation of responses of custom-built alternative display formats. volunteer participants in the focus groups, listed in table 1, were identified and recruited to provide feedback on the alternative displays. thirteen (13) study subjects were included in the health care consumer focus group based on chain-referral sampling. the chain-referral sampling was initiated from the exploratory working group members with the aim to recruit study subjects with personal hais’ experience, study subjects with no personal hais’ experience, and representatives from the community and other health care domains. health care practitioners were excluded from participation in the health care consumer focus group. seven (7) study subjects were included in the health care professional focus group, including one (1) hospital’s chief executive officer (ceo), based on a chainreferral sampling. the chain-referral sampling was initiated from members of the exploratory working group and the maryland hai advisory committee with the aim to recruit study subjects that were certify as a health care practitioner. members of the exploratory working group and maryland hai advisory committee were excluded from participation in either group. validation phase summarization of the focus groups comments and the evaluation survey regarding alternative formats were captured and submitted to the hai advisory committee, during the validation phase. following review by the commission staff, the alternative displays for reporting clabsi data for consumer and professional audiences were presented to the hai advisory committee, maryland's panel of hospital epidemiology and infection control subject matter experts, who selected the final format. subsequently, a webinar was held for maryland hospital infection preventionists, performance-improvement, quality -measure, and public-relations staff on the format of public reporting of central line-associated blood stream infection (clabsi) data in icus. capturing consumers and practitioner’s perspective was an important and critical aspect of recruiting diverse composition of participants. table 1: focus groups composition visualizing central line-associated blood stream infection (clabsi) outcome data 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi data collection we captured participants’ comments during all the phases on the various information visualization alternatives. to provide the focus groups with simulated data formats, we presented mocked-up representations of how the data would be visualized. we used the custom-built structured interview tool on four dimensions, see figure 1. at the end of each focus-group discussion the consumer and professional audiences were provided a paper-based custom survey that asked them to rate each display format in terms of four criteria, depicted in table 2, to consider when visualizing complex medical information [20].  clarity – is the information provided in a clear and understandable format?  functionality – does this visualization provide the information and data elements you are looking for?  usefulness – is this visualization useful? (i.e., does this visualization help you make a decision?)  effectiveness – to what extent does the visualization portray the intended information? (i.e., are you able to tell which hospitals perform better or worse easily with this visualization?) evaluation criteria and scale used a likert scale of 1 (very poor) to 5 (very good) to select the evaluation level. participants ranked their top three visualization options according to their overall preference. furthermore, the survey included additional overall ranking of visualization symbols and options, the quality interpretations of the standardized infection ratio (sir) using different symbols, such as stars (full, half, empty), colors, and shapes. figure 1: evaluation of reporting dimensions visualizing central line-associated blood stream infection (clabsi) outcome data 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi consistent metric the standardized infection ratio (sir) is a summary measure used to compare the infection rate of one group of patients to that of a standard population [21]. it is the observed number of infections divided by the predicted number of infections. the predicted infection rate is the number of infections that we would expect if the hospital had the same infection rate as a comparison group, in this case the national average [22]. a sir of 1 means the hospital infection rate and that of the comparison group are the same. a sir > 1 means the hospital has a higher rate (i.e., more infections) than the comparison group. a sir < 1 means that the hospital has a lower infection rate (i.e., fewer infections) than the comparison group. figure 2 illustrates an example of estimated sir for three hospitals. for example, if a hospital’s medical intensive care unit (micu) has five (5) bloodstream infections and based on the national average for that type of icu one would expect only four (4) infections the sir would equal 5/4 = 1.25 (e.g., hospital c). table 2: evaluation criteria and scale figure 2: visualization of standardized infection ratio (sir) visualizing central line-associated blood stream infection (clabsi) outcome data 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi information visualization mockups and options presented to focus groups during the exploration phase six (6) distinct visualization mockups were developed, illustrated in figures 3a to 3f. these six (6) mockups were based on the following information visualizations techniques.  comparative table  box plot  quality graph  analysis table  heat map  tree map after an initial evaluation of the six (6) mockups with the commission, based on their clarity, functionality, usefulness, and effectiveness, the commission requested that only four (4) options be presented to the focus groups for discussion, as depicted in figures 3a to 3d. figure 3a: option 1 (comparative table) figure 3b: option 2 (analysis table) visualizing central line-associated blood stream infection (clabsi) outcome data 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3c: option 3 (box plot) figure 3d: option 4 (heat map) visualizing central line-associated blood stream infection (clabsi) outcome data 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 3e: option 5 (quality graph) figure 3f: option 6 (tree map) visualizing central line-associated blood stream infection (clabsi) outcome data 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi during the focus groups study, the four (4) selected visualizations were labeled as options 1 to 4 without specifying the visualization technique. the intention was to reduce selection bias, which might have been influenced by a preconception of a visualization category. the information visualization layouts included a combination of standards information graphics used in public health and public-oriented visualization. differences in preferences across groups were analyzed by kruskal–wallis non-parametric tests, appropriate for the ordinal data we collected. results the focus group evaluation results, as summarized in table 3, were collected from the maryland health care commission, public reporting of maryland hai outcome data, consumer and health care professional focus groups, which were conducted on august 10-11, 2010. table 3: survey statistics results summary table 4: kruskal-wallis equality-of-populations rank test (with ties) within a group within each group, the evaluation ratings indicate varied preferences of the ratings for each visualization and criteria as depicted in figure 4. testing for kruskal-wallis equalityof-populations rank [23] (with ties), which indicates the degree of dispersion (spread) in the data within a group, in table 4, shows that there are statistically significant differences within the groups. the consumers’ group had a statistically significant difference for ratings across visualization methods. the practitioners’ group had statistically significant difference for most of the ratings visualization mean median low high std err. 95% conf. interval mean median low high std err. 95% conf. interval heat map 4.6 5 2 5 0.0 4.4 4.8 1.9 1 1 5 0.2 1.4 2.4 box plot 3.8 4 1 5 0.1 3.5 4.1 4.3 4 3 5 0.1 4.1 4.6 comparative table 3.1 3 2 5 0.1 2.8 3.4 3.3 3 2 5 0.2 2.9 3.7 analysis table 2.5 2 1 5 0.1 2.2 2.7 3.2 3 1 4 0.1 2.9 3.5 consumer practitioner visualization chi-squared d.f. p-value chi-squared d.f. p-value chi-squared d.f. p-value chi-squared d.f. p-value chi-squared d.f. p-value chi-squared d.f. p-value heat map 22.1 1 0.0001 47.6 1 0.0001 64.7 1 0.0001 29.0 1 0.0001 15.7 1 0.0001 14.4 1 0.0001 box plot 13.0 1 0.0006 33.8 1 0.0001 14.2 1 0.0002 22.4 1 0.0001 comparative table 8.1 1 0.006 0.0 1 0.8353 consumer practitioner box plot comparative table analysis table box plot comparative table analysis table figure 4: visualizations’ evaluation rating visualizing central line-associated blood stream infection (clabsi) outcome data 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi across visualization methods except between “analysis table” and “comparative table.” between consumers and practitioners there were differences in the favorites’ ranking with no consensus on most of the visualization ranking, as depicted in figure 5. overall, consumers preferred the “heat map” and the practitioners preferred “box plot,” as indicated in figure 6. although consumers preferred the “heat map” and practitioners preferred “box plot”, there was also a statistically significant difference of preferences between the two groups for the “analysis table,” as indicated in table 5. in contrast, there was no difference of preferences between the two groups for “comparative table.” favorite 2nd favorite 3rd favorite favorite 2nd favorite 3rd favorite heat map 92% 8% 15% 14% 14% 14% box plot 8% 62% 23% 86% 14% comparative table 15% 54% 57% 29% analysis table 8% 29% 43% no response 8% 8% consumer practitioner heat map 67% 14% box plot 21% 60% comparative table 8% 15% analysis table 2% 10% consumer practitioner figure 5: overall opinion of visualization option figure 6: weighted opinion of visualization options visualizing central line-associated blood stream infection (clabsi) outcome data 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi colors 50% 69% shapes 30% 18% stars 17% 10% consumer practitioner table 5: kruskal-wallis equality-of-populations rank test between groups in evaluating the overall opinion of consumers and practitioners on the use of visualization symbols (stars, colors, or shapes), consumers and practitioners ranked them at the same order, as depicted in figure 7, and selected colors as the overall preferred symbols, as depicted in figure 8, which indicates a consensus among the groups. consumer and health care professional survey sentiments consumers indicated a preference to obtain one overall aggregated clabsi measure supplemented with a symbol for quality interpretation, to make a decision and drive improvement, and then to have an overall hospital clabsi rate by specific units. the consumers group discussed the meaning of the term “expected” and “significantly”. the consumers preferred to view hospitals overall quality and then to drill down to the details. visualization rating obs rank sum rating obs rank sum chi-squared d.f. p-value chi-squared with ties d.f p-value heat map 52 2733 28 507 40.0 1 0.0001 45.7 1 0.0001 analysis table 52 1797 28 1443 9.7 1 0.001 10.6 1 0.001 box plot 52 1905 28 1335 4.1 1 0.04 4.8 1 0.02 comparative table 52 2032 28 1207 0.6 1 0.45 0.6 1 0.43 consumer practitioner favorite 2nd favorite 3rd favorite favorite 2nd favorite 3rd favorite colors 62% 31% 8% 100% shapes 23% 54% 15% 71% 14% stars 15% 8% 62% 14% 86% no response 8% 15% 14% consumer practitioner figure 8: weighted opinion of visualization symbols figure 7: overall opinion of visualization symbols visualizing central line-associated blood stream infection (clabsi) outcome data 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi practitioners indicated to see an overall clabsi performance, number of infections, number of central line days, and sir confidence interval at 95 percent without any additional interpretation symbols. conclusions from this rigorous elicitation of data-visualization preferences for a key state and national measures, our data suggest that consumers prefer “heat map” and that it was desired by them to focus on a meaningful level of aggregation beneath total overall and to employ colors for quality interpretation and grouping. on the other hand, our data suggest that practitioners prefer “box plot” augmented with numerical data. one interpretation of these preferences is that it was desired by the practitioners to focus on the details and relative comparison. one of the methods used to address both groups’ preferences for aggregation and interpretation is by constructing three ordinal categories of performance and by combining symbols and colors that indicate “better than national experience” as green circle, “no different than national experience” as a yellow triangle, and “worse than national experience” as a red diamond. the visual display of quantitative information clarifies data [24] for consumers and practitioners for making decision. the objective of visual design, to organize the data for communicating a message effectively, can be accomplished by prioritizing, grouping, and sequencing the data correctly [25]. but the long-term challenge, in the evaluation of visual communication, is its demonstration of adaptation and utility [26]. hence, the robust triangulation of mixed study methods, as in our study, is necessary because it uses theoretical and applied constructs of usability studies and controlled experiments in all its phases—exploratory, confirmatory, and validation. the deployment and usage of our final formats, on the mhcc website [27,28], are the demonstration of their adaptation and utility. discussion based on the comments and data analysis of the focus groups, two formats were selected for presentation on the mhcc website. these displays are depicted in figure 9 and figure 10. these displays had been designed based on consumers and health care practitioners’ perspectives and the focus groups analysis results. additionally, they encompassed standard information visualization techniques that were employed in multi-dimensional case studies [29]. subsequently, they were validated by subject matter experts. improvement in quality and safety performance over time is important to consider (i.e., what is the current performance and what is the goal in three years). as we demonstrated in our results, in the visualization’s evaluation rating of the “analysis table”, sir and ci were difficult concepts to explain to consumers. visualizing central line-associated blood stream infection (clabsi) outcome data 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi figure 9: post focus group – consumers’ visualization recommendation figure 10: post focus group health care practitioners’ visualization recommendation visualizing central line-associated blood stream infection (clabsi) outcome data 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi however, they were also more interested in seeing the absolute number of infections. it was challenging to explain to consumers the meaning of large confidence intervals as it was related to small number of cases. moreover, combining data was difficult and may result in reaching the wrong conclusions, which is ultimately unfair to patients. overall, operationally, hospitals focus on zero harm to patients (i.e., no infections). the goal is to create meaningful data aggregation (e.g., overall adult, overall pediatric, and specialized units). avoiding priority ranking is important to prevent the unintended consequence of hospitals avoiding high risk patients. thus, we chose to group hospitals alphabetically within the broad categories. as well, we chose to comment that within the broad categories all hospitals have approximately equivalent performance. limitations compared with population surveys, our sample size, based on number of focus group participants, was small. however, these numbers are in line with what is required for assessing user-interface preferences [30]. furthermore, we used multiple formative methods with multiple groups to confirm the preferences that we did elicit of the participants, and we employed a broad cross section of the targeted user populations. future directions visual communication can provide effective evidenced-based information to consumers for decision making and to practitioners for improving patient safety outcomes and processes. additional hais outcomes can be presented and evaluated for surgical site infections (ssi), nosocomial transmission of multi-drug resistant organisms (e.g., mrsa and vre), ventilatorassociated pneumonia (vap) and nosocomial respiratory syncytial virus (rsv). process oriented measurements can also be presented for surgical antimicrobial prophylaxis, hand hygiene compliance, health care worker influenza, and compliance with active surveillance testing for mrsa in icus. moreover, public reporting should focus on reporting: 1) the overall picture, 2) where individual hospitals are, 3) where hospitals should be, and 4) the direction of change toward a target improvement goal. to address these temporal and multivariate dimensions in preand post intervention evaluation, outcomes can be displayed in a run chart, a trend graph, or a statistical process control diagram. visualization capabilities can be employed for understanding an intervention efficacy, providing insight on trends improvement, and acting as a public social influencer. providing those tools for comparing and monitoring performance should influence consumers’ decision, assist practitioners in improving patient safety, and inform policy makers. as a result of our study, the validated visualizations were approved and publicly deployed for consumers [27] and practitioners [28] in maryland. acknowledgments we appreciate the effort of both the consumers and practitioners’ focus groups volunteers from the community and acknowledge their genuine passion to contribute to the effort of patient safety visualizing central line-associated blood stream infection (clabsi) outcome data 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol. 5, no. 2, 2013 ojphi improvement. we also thank the maryland health care commission (mhcc) hai advisory committee members for their dedication to patient safety and the goal to eliminate hais within maryland’s hospitals. thanks to dr. anthony harris for reviewing a prior draft. dr. rajwan was funded by the mhcc and nlm t15lm007452. corresponding author dr. yair g. rajwan, dsc, msc cs, msc hsi, pfnlm visual science informatics, usa email: yair_rajwan@visualscienceinformatics.com references 1. technical advisory committee on healthcare-associated infections. developing a system for collecting and publicly reporting data on healthcare-associated infections in maryland [internet]. maryland health care commission; 2008 p. 79. available from: http://mhcc.maryland.gov/healthcare_associated_infections/hai_report_jan2008/hai_cover.h tml 2. pronovost p, holzmueller cg, needham dm, sexton jb, miller m, berenholtz s, wu aw, perl tm, davis r, baker d, winner l, morlock l. how will we know patients are safer? an organization-wide approach to measuring and improving 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provided the original work is properly cited. 143 (page number not for citation purposes) isds 2013 conference abstracts practical gains from access to an integrated disease reporting system loren shaffer* northrop grumman information systems, atlanta, ga, usa � �� �� �� � � �� �� �� � objective �������� ������ ������ ��� ������������ ��������������� ���������� ����� ������ ������ ������� ��������������������������� �� �� ���� � �� ������ ������������ ��������� �������������� ���� ����������� � ���������� ���������������������� ��� ��� �� ������������������� � � �� � ���������������� � � ������ ������� ���� ������������� introduction ����� ������ �������������� ����������� ������������ �����!� ������������� ������ ������������ ������ ��� �������������������� "������� ��� # � �� �� � ����������� $������� ��� ���� %���� �������� �������&����� ���'���������������� ������� �� ���������&��� ��� ���(���� �� ((!�����')�� � *��� '+!�,������������� � ������� ����� ���.�� ������������ 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���� ���� ��� ��� ��� �������� ��� ����� ��9� ���� ������������������������ �� �������������� � �� �������� ����� � � ������������������ ������������� ������� ������������� ����������� �� ��� � �������� ������ keywords ?�������@� � ������ ����� ����@� ;�� ���3� ������ ��@� ;������ ��@� " ������������ �� references �������-9�;��� ��a9�&� ��, �)�$��a �3����� ��,������ �� ����������� ��� ������� �� �������#-"�4���� �����&��������/001@6 56!� 7��� �� ���3�9���9����"��9����� ��'����� ���3���������������� ����� �� � ���������'���5������������ ���51<>@/0 /�b!c�/06�/0<� *loren shaffer e-mail: loren.shaffer@ngc.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e158, 2014 willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia yared tadesse1, abebaw addis gelagay2, binyam tilahun3, berhanu fikadie endehabtu3,*, zeleke abebaw mekonnen3, kassahun dessie gashu3 1. university of gondar specialized hospital, gondar, ethiopia; 2. department of reproductive health, institute of public health, college of medicine and health sciences, university of gondar, ethiopia; 3. department of health informatics, institute of public health, college of medicine and health sciences, university of gondar, ethiopia abstract background: the proper use of partograph supports to capture key maternal and fetal data. paperbased partograph are prone to error, incompleteness, delayed decisions and loss of clients’ information. electronic (e-partograph) enables to easily retain and retrieve client data to ensure quality of care. mobile technologies found an opportunity for resource-limited countries to improve access and quality of health care. evidences were lacking on end users’ acceptance to e-partograph. objective: this study aimed to assess obstetric care providers’ willingness to use mobile based epartograph and its associated factors. methods: institutional based cross-sectional study was conducted from december 30, 2016 to january 21, 2017. a total 466 obstetric care providers were selected using multistage sampling technique in north gondar zone, northwest ethiopia. a structured self-administered questionnaire was used to collect the data. the data were entered in to epi info version 7 and analyzed by using spss version 20. cronbach’s alpha test was calculated to evaluate the reliability of data. a multivariable logistic regression analysis were used to identify factors associated with dependent variable. adjusted odds ratio with 95%ci was used to determine the presence of association. results: the study found that 460(99.6%) of care providers owned mobile phone. smartphone owners accounted only 102(22%). of them, 205(46%) were willing to use mobile-phone for e-partograph. care providers aged >30 years (aor=2.85, 95% c.i: 1.34-6.05), medical doctors and higher level clinicians (aor=8.35, 95% c.i: 2.07-33.63), health center (aor=4.41, 95% c.i:0.10-9.26), favorable attitude towards partograph (aor=2.76, 95% c.i: 1.49-5.09) and related in-service trainings (aor=7.63, 95% c.i: 3.96-14.69) were enabling factors for willingness to use mobile phone. conclusions: almost all obstetric care providers had access to mobile phone, however; smartphone ownership is still low. willingness to use mobile-phone for e-partograph was low. younger aged, lower willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi background in the sub-saharan africa region, maternal mortality accounted for 62% (179,000)the global maternal deaths [1]. maternal mortality ratio was 412 per 100,000 live births in ethiopia [2]. the majority (70%) of deaths attributed to obstructed labor and ruptured uterus [3]. the ultimate concerns of delivery care are to achieve a healthy mother and baby with the least possible level of intervention, early detection and management of complications. thus; prevention of prolonged and obstructed labor by using partograph during labor is a key intervention in the reduction of maternal and perinatal morbidity and mortality [4]. partograph is one of the strongest and cost-effective tools to prevent unnecessary delay and serve as frontrunner for obstetric caregivers [5]. the world health organization (who) recommends using the partograph to follow labour and delivery with the objective to reduce maternal and fetal morbidity and mortality [6]. partograph is less expensive tool designed to offer a continuous pictorial view of labour progress and to analyze cervix dilation, uterine contraction and fetal heart rate in relation to time. it helps to predict deviation from normal progress of labour, and supports timely and proven intervention. key measurements, including cervical dilation, fetal heart rate, duration of labour and vital signs are recorded on the partograph in graph format. it also helps to facilitate responsibility to the person attending labour [7]. evidences showed that partograph is underutilized and misused in health facilities which reflect poor monitoring of mothers in labour and/or poor pregnancy outcome due to poor knowledge and attitude towards the tools [8-10]. paper-based partograph are prone to error, incompleteness, delay and loss of client information in the health care system [11]. despite long years of trainings and investments on partograph in low-resource settings, the paper-based partograph is still underutilized [12,13]. to have an intended pregnancy outcome proper use of a partograph to record level clinicians, hospital based workers, unfavorable attitude on partograph and lack of in-service trainings were main factors for non-willingness. hence awareness creation on partograph use and digital capacity building are crucial for effective e-partograph management. key words: e-partograph, ethiopia, obstetric care provider list of abbreviations: cpd: cephalo pelvic disproportion, edhs: ethiopian demographic health survey, fhr: fetal heart rate, ieos: integrated emergency obstetrics and surgery, ohcp: obstetrics health care providers *corresponding author: berhanufikadie@gmail.com doi: 10.5210/ojphi.v11i2.9468 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:berhanufikadie@gmail.com willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi key maternal and fetal data is critical. a digital partograph will make it easy to retain patient information and to access client data [14]. it also reduces bulk of paperwork and helps to improve timely decision [14]. high penetration of mobile technologies has become an opportunity for resource-limited countries to improve access and quality of health care delivery [15-18]. the use of e-partograph exhibits limitations due to unreliable connectivity, power sources, low levels of technical training, maintenance and scalability costs [19]. this study was intended to assess willingness of obstetric care providers’ to use mobile phone as electronic (e-partograph) and associated factors. therefore, the findings of this was aimed to give insight for researchers in formulation of appropriate interventional studies to curb existed problems. methods study design and setting institutional based cross-sectional study was conducted from december 30, 2016 to january 21, 2017 in north gondar zone. this zone has a total population of 2,929,628, of whom 1,486,040 were men and 1,443,588 women [2]. in the zone, 23 woredas (districts), around 1,107 obstetrics care providers in 9 hospitals and 126 health centers were serving the catchment population. in this study the source population was all obstetric care providers who were working in public hospitals and health centers in north gondar zone. obstetric care providers were a certified midwife, nurse, health officers, integrated emergency obstetrics and surgery & medical doctors who provide care for the woman during labor and delivery. considering unknown magnitude of willingness to use e-partograph(p=50%) and finite population correction formula and design effect of 1.5; a total of 466 participants were included in this study. care providers who were working in delivery units either on regular basis, by rotation or at night duty time were included in the study. four hospitals and 56 health centers were randomly selected from 9 hospitals and 126 health centers. finally, the study participants were proportionally selected from each selected facility using multistage sampling technique. data collection tools and techniques the dependent variable of the study was willingness to use mobile phone for e-partograph. independent variable includes: socio-demographic characteristics (age, sex, marital status, religion and residence), mobile phone access (accessibility, type of mobile phone); professional characteristics (type of profession, year of experience, qualification level, pre-service training, and in-service training on partograph) and facility related factors (type of facility and working unit). providers’ knowledge and attitude on partograph were also addressed as independent variables. obstetric care providers who scored mean value and above to knowledge related to partograph questions were determined to have good knowledge [20]. obstetric care providers who scored mean value and above to attitude related to partograph questions were also determined to have favorable attitude [21]. willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi a self-administrated questionnaire was used to collect the data using a structured english questionnaire. the questionnaire was adopted from previously published articles. the questionnaire consists of sociodemographic characteristics, qualification, current working department, knowledge and attitude about partograph, and previous obstetric care training. before the actual data collection, the questionnaire was pre-tested. training was given for data collectors for two days. data processing and analysis data clean up and cross-checking was done before analysis. all the questionnaires were checked visually, coded and entered into epi-info7 and exported to spss version 20 software package for analysis. furthermore, the cronbach’s alpha test was calculated to evaluate the reliability of the tool. both bivariate and multivariable logistic regression were used to identify any association between the dependent and independent variables. association and strength were presented using crude and adjusted odd’s ratios, 0.05 level of significance and 95% confidence intervals. results characteristics of study participants a total of 462(258 male and 204 female) obstetric care providers were participated in the study, with the response rate of 99.14%. the mean age of the respondent was 29.1 years with a standard deviation of ± 5.74 years. more than half 302 (65.4%) of participants were single. about 368(79.7%) of respondents were from health centers. regarding their profession, 205(44.55%) were nurses and 194(42.0%) were midwives. the study participants’ experience ranged between 1 and 30 years. half of, 232(50.2%) of obstetric care providers had a maximum of four years clinical experience. two hundred three (43.9%) of the obstetric caregivers were working at delivery ward regularly. among all, 223 (48.3%) of them received partograph utilization training as shown in table1. table 1: characteristics of study participants in north gondar zone, northwest ethiopia, 2017 (n=462). variable frequency percentage sex male 258 55.8 female 204 44.2 age in years <24 149 32.2 25-29 143 31.0 >30 170 36.8 willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi marital status single 302 65.4 married 145 31.4 divorced 9 1.9 widowed 6 1.3 health facility health center 368 79.7 hospital 94 20.3 qualification level diploma 258 55.8 bsc. 185 40.0 medical doctor 6 1.3 msc. 13 2.8 profession nurse 205 44.4 midwife 194 42.0 health officer 45 9.7 ieos 12 2.6 physician 6 1.3 working department delivery ward 203 43.9 anc 91 19.7 pnc 49 10.6 family planning 72 15.6 opd 47 10.2 year of experience <4 232 50.2 5-9 114 24.7 >10 116 25.1 willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi obstetric care providers’ access to mobile phone almost all, 460(99.6%) of the participants have owned mobile phone. based on the type of mobile phone, participants who owned smartphone accounted only 102(22%) and the rest 358 (78%) owned cellphone. younger aged 44(29.5%) and male care providers 62(24.0%) have better smart phone ownerships, as shown in table 2. table 2: type of mobile phone owned by study participants in north gondar zone, northwest ethiopia, 2017 (n=460). variable type of mobile phone owned cellphone smartphone sex male 194(75.2%) 62(24.0%) female 164(80.4%) 40(19.6%) age <25 years 105(70.5%) 44(29.5%) 25-30 years 115(80.4%) 27(18.9%) >30 years 138(81.2%) 31(18.2%) years of experience <5 years 171(73.7%) 60(25.9%) 5-9 years 96(84.2%) 181(5.8%) >9 years 91(78.4%) 24(20.7%) marital status single 229(75.8%) 71(23.5%) married 114(78.6%) 31(21.4%) divorced 9(100.0%) 0(.0%) widowed 6(100.0%) 0(.0%) profession nurse 154(75.1%) 50(24.4%) midwife 150(77.3%) 43(22.2%) health officer 37(82.2%) 8(17.8%) md and higher clinicians 17(94.4%) 1(5.6%) type of health facility health center 280(76.1%) 86(23.4%) hospital 78(83.0%) 16(17.0%) total 358(77.5%) 102(22.1%) willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi willingness to use mobile phone for e-partograph among all obstetric care providers, 205(44%) of them were willing but 257(56%) were not willing to use mobile-phone for e-partograph, as shown in figure 1. figure 1: willingness to use mobile phone for e-partograph in north gondar zone, northwest ethiopia, 2017 (n=462). knowledge on partograph regarding knowledge about partograph, more than half, 258 (55.8%) [95% ci: 51.0-60.0], of the obstetric caregivers had good knowledge about partograph. two hundred sixty five (57.4%), of the obstetric providers knew the definition of partograph and 305(66.0%) of them knew when to start plotting on the partograph. only 221(47.8%) and 203 (43.9%) of obstetric care providers knew about function of action line and satisfactory of progress of labor respectively. half of participants 233 (50.3%) knew the correct function of the alert line on the partograph, as indicated in figure 2. willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi figure 2: knowledge of obstetric care providers on partograph, north gondar zone, northwest ethiopia; october, 2017(n=462). attitude towards partograph about 272(58.9%) [95% ci: 53.9-63.3], of the obstetric providers in this study had favorable attitude towards partograph. majority 281 (62.1%) of the caregivers agreed to use partograph and 245 (53.0%) of them agreed that they have difficulties in using partograph. meanwhile, 276(59.7%) agreed to received training on partograph, as indicated in table 3. table 3: attitude of obstetric caregivers on partograph and its use, north gondar zone, northwest ethiopia, 2017 (𝑛 = 462). variables agreed (𝑛) % 1. attitude related questions 1.1 like to use partograph 287 62.1 1.2 partograph should be used in all normal labor 270 58.4 1.3 partograph reduces risk of maternal/ neonatal morbidity and mortality 267 57.8 1.4 want to received training on partograph 276 59.7 willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi 1.5 wish to use partograph routinely 304 65.8 1.6 not all normal labors need partograph 239 51.7 1.7 all team members must be trained on partograph 328 71.0 1.8 using partograph is the responsibility of the physician only 151 32.7 1.9 partograph is not effective to monitor laboring mother 128 27.7 1.10 using partograph is time consuming 212 45.9 1.11 have difficulties in using partograph 245 53.0 2. overall attitude 2.1 favorable attitude 272 58.9 2.2 unfavorable attitude 190 41.1 factors associated with willingness to use mobile phone for e-partograph factors affecting willingness of obstetric care providers to use mobile phone for e-partograph were assessed using bivariate and multivariable analysis. in bivariate analysis; age, marital status, profession, type of mobile phone owned, knowledge, and attitude on partograph and in-service trainings received on partograph were found to be significantly associated with willingness to use mobile phone for electronic partograph. based on multivariable logistic regression analysis, care provider’s age of >30 years (aor=2.85, 95% c.i: 1.34-6.05), type of profession of medical doctor and above (aor=8.35, 95% c.i: 2.0733.63), type of facility of health center (aor=4.41, 95% c.i:.10-9.26), smart mobile phone owners (aor=.39, 95% c.i:.21-.74), favorable attitude towards partograph (aor=2.76, 95% c. i: 1.495.09) and in-service trainings received on partograph (aor=7.63, 95% c.i: 3.96-14.69), as shown in table 4. willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi table 4: factors associated with willingness to use mobile phone for e-partograph, north gondar zone, northwest ethiopia, 2017 (𝑛 = 462). variables willingness to use mobile based e-partograph cor (95%c.i) aor (95% c.i) no yes age <24 128 21 1 1 25-29 81 62 4.67(2.64-8.23)* 1.65 (.81-3.38) >30 48 122 15.49(8.77-27.38)* 2.85(1.34-6.05)* sex male 148 110 1 1 female 109 95 1.17(.81-1.70) 1.17(.69-1.98) marital status single 204 98 1 1 married 51 94 3.84(2.53-5.82)* 1.29(.73-2.26) divorced 2 7 7.29(1.49-35.72)* 1.89(.29-12.24) widowed 0 6 .000 .000 profession nurse 147 58 1 1 midwife 90 104 2.93(1.94-4.43)* .77(.42-1.43) health officer 16 29 4.59(2.32-9.08)* 2.37(.97-5.80) md and above 4 14 8.87(2.803-28.07)* 8.35(2.07-33.63)* type of facility health center 199 169 1.36(.86-2.18) 4.41(2.10-9.26)* hospital 58 36 1 1 type of mobile phone cellphone 181 177 1 1 smartphone 75 27 .37(.23-.60)* .390(.206-.740)* willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi knowledge on partograph poor 139 65 1 1 good 118 140 2.54(1.73-3.72)* 1.07(.60-1.90) attitude on partograph unfavorable 147 43 1 1 favorable 110 162 5.04(3.32_7.64)* 2.76(1.49-5.09)* in-service training on partograph not received 200 39 1 1 received 57 166 14.94(9.46-23.57)* 7.63(3.96-14.69)* *𝑝 value < 0.05 discussion in low income settings, the ubiquity and penetration of mobile devices provided a potential opportunity for using m-health applications in hard-to-reach areas. access and type of mobile phone technology are precondition among other required infrastructures to enable use of m-health technology in the health care delivery. in this study, 460(99.6%) of the obstetric care providers in public health facilities were accessed to mobile phone. evidences also showed that mobile computing devices were increasingly being used by health care professionals and soon will become ubiquitous in clinical environments [22]. in this study, however, smartphone ownership was low which accounted only 102(22%) and the rest 358 (78%) were basic cellphone owners. this evidence couldn’t be in line with the prospect of smartphone applications on massive development globally to be widely used by health professionals for health care services [23]. the growing penetration rate of the mobile technology still gives optimism to reach the envisaged visions. studies in health information technologies often focused on the design and implementations; little attention is payed for end users’ acceptance and reaction to the technologies [24]. evidences showed that underuse, resistance, and overrides, sabotage, and abandonment of the technologies were some of the scenarios [25,26]. this study tried to assess willingness of end users (obstetric care providers) to use mobile based e-partograph in their routine care. the finding shown that 205(44%) of the participants were willing to use mobile based e-partograph. it indicates that care providers’ acceptance to e-partograph were lower as of the envisioned penetration. it also known that in a hospital and clinical settings, the deployment of new digital data recording technologies often faces poor acceptance by clinicians [27]. willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi the multivariable logistic regression analysis identified that age of care provider, level of profession, working place, type of mobile phone owned and received in-service training related to partograph were main factors associated with willingness to use mobile phone for e-partograph. care providers aged >30 years were 3 times (aor=2.85, 95% c.i: 1.34-6.05) higher likely to be willing to use mobile based e-partograph than younger aged care providers. medical doctors and higher level clinicians were more (aor=8.35, 95% c.i: 2.07-33.63) likely to be willing than nurses. those care providers working at health centers were more (aor=4.41, 95% c.i:.109.26) likely willing than hospital based workers. this finding seems inconsistent with general truth in which hospitals consists of higher level care providers, better infrastructure and budget that would tend the care providers to be willing to use mobile, while the finding was inversely happened. likewise, smartphone owners were 61% less (aor=.39, 95% c.i: .21-.74) likely willing to use mobile based e-partograph. this finding was also deviated from the general truth that smartphone owners tends to be willing for e-partograph. provision of in-service training related to partograph was enabling factor (aor=7.63, 95% c.i: 3.96-14.69) for willingness to use mobile phone for e-partograph. similarly, studies conducted in low income countries showed that lack of training was a barriers to proper partograph use [10,28,29]. according to evidences, even primary health care workers with little or no formal education can be effectively trained to use the partograph [30]. care providers with favorable attitude towards partograph were more (aor=2.76, 95% c.i: 1.49-5.09) likely willing to use mobile than the counter parts. the findings supported each other in which attitude is largely a result of extensive training. limitation the study did not include obstetric care providers in private health facilities; so, this finding may not be generalizable to all obstetric care providers north gondar zone. conclusion almost all obstetric care providers had access to mobile phone, however; smartphone ownership is still low. willingness to use mobile-phone for e-partograph was low. younger aged, lower level clinicians, hospital based workers, unfavorable attitude on partograph and lack of in-service trainings were main factors for non-willingness. hence awareness creation and providing inservice training on partograph use and digital capacity building are crucial for effective epartograph management. ethics approval and consent to participate before commencement of the study, ethical clearance was obtained from the institutional ethical review board of university of gondar. communication with the respective official administrators was made through formal letter obtained from the university of gondar. verbal and written consent was obtained from each respondent after explaining the purpose and objectives of the study. name, personal identifiers would not include in the study. the respondents may notify that they were the right to refuse to participate on the study. willingness to use mobile based e-partograph and associated factors among care providers in north gondar zone, northwest ethiopia online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e10, 2019 ojphi availability of data and material all the required data have been presented in the study. competing interests the authors declare that they have no competing interests. funding source of fund for this study was covered by the authors. authors' contributions initiation of study and design was done by yt, aag and kdg, analysis, interpretation and writeup of data was done by yt, kdg, aag, bt, bfe and zam. verification of article was done by yt, kg, ag, bt, be and zm. all authors read and approved the final manuscript. acknowledgements we would like to thank health care providers in the selected health facilities under north gondar zonal health department for their cooperation during data collection of the study. references 1. organization, w.h. and unicef, trends in maternal mortality: 1990 to 2013: estimates by who, unicef, unfpa, the world bank and the united nations population division: executive summary: http://apps.who.int/iris/bitstream/handle/10665/112682/9789241507 226_eng.pdf;jsessionid=886f29434ef59e99c748091549a06a4b?sequence=2. accessed 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the partogram. int j gynaecol obstet. 100(1), 41-44. pubmed https://doi.org/10.1016/j.ijgo.2007.07.020 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17904145&dopt=abstract https://doi.org/10.1016/j.ijgo.2007.07.020 an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster peter shin 1,2 , feygele jacobs 2 1 george washington university 2 rchn community health foundation abstract since taking office, president obama has made substantial investments in promoting the diffusion of health information technology (it). the objective of the national health it program is, generally, to enable health care providers to better manage patient care through secure use and sharing of health information. through the use of technologies including electronic health records, providers can better maintain patient care information and facilitate communication, often improving care outcomes. the recent tornado in joplin, mo highlights the importance of health information technology in the health center context, and illustrates the importance of secure electronic health information systems as a crucial element of disaster and business continuity planning. this article examines the experience of a community health center in the aftermath of the major tornado that swept through the american midwest in the spring of 2011, and provides insight into the planning for disaster survival and recovery as it relates to patient records and health center data. key words: health information technology; public health; emergency preparedness; data recovery; health centers; low-income communities introduction the devastating impact on health care facilities of 2005’s hurricanes katrina and rita served to underscore the importance of protecting critical patient information, and maintaining patient records in a manner consistent with ensuring continuity of care. the horrific events of september 11, 2001, exposed the limitations of existing reporting and communication systems, and highlighted the need to strengthen population surveillance and community health measurement mechanisms to facilitate both rapid detection and expedited response to public health emergencies and bioterrorism. similarly, the tremendous loss of paper patient records http://ojphi.org an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 during the catastrophic 2005 storms showed that many health systems were ill-prepared for disaster situations.[1] faced with a natural disaster, many providers of all types including local hospitals, health centers and physician offices, lacked the infrastructure necessary to ensure ongoing access to patient profiles, medication histories, lab results, and other vital patient information. community health centers (chcs), initiated as a federal demonstration program approximately 45 years ago, have evolved into a core of the health care safety net for low-income and medically underserved populations. today, over 1,200 health centers, with 8,000 locations, serve approximately 20 million low-income patients across the country in urban inner-city and isolated, rural communities.[2] chcs are located in high-need areas with high rates of poverty, poor health indicators and limited provider resources, offer essential services in accessible locations including public housing projects and schools, and serve as critical source of care for designated special populations and transient families, such as homeless and migrant and seasonal farmworkers.[3] additionally, more than 90 percent of health center patients nationally are lowincome, approximately two-third of patients are non-white and one-quarter of health center patients prefer services in languages other than english, and may require translation and language support.[4] over the course of the last several years, community health centers have embraced the use of information technology to manage patient records, facilitate interand intra-facility referrals, expedite public health and grant-related reporting, and measure health care and operational outcomes.[5] since taking office in january 2009, president obama has prioritized the diffusion of health information technology (hit) and funded substantial hit investments in the general health care provider community and in community health centers in particular.[6] the american recovery and reinvestment act of 2009 (arra) and affordable care act of 2010 (aca) financially incentivize health centers to adopt and use health information technology, especially electronic health records. a recent survey conducted by the national association of community health centers shows that between 2006 and 2010, the proportion of all chcs that transitioned from paper to fully electronic medical records increased from 13 percent to 45 percent.[7] still, electronic capacity remains uneven, as does infrastructure agility to withstand disaster and ensure continuity for patient care. based on discussions with health center leadership and staff of a multi-site community health center with administrative offices in neosho, missouri we describe the strategies used by the center to protect access to clinical and operational information systems, and identify the lessons learned and ongoing vulnerabilities that are important in evaluating and planning for any disaster scenario. joplin, mo/access family care on sunday, may 22, 2011, a category ef-5 tornado (with at least 200 mile per hour winds) destroyed or damaged thousands of buildings and homes over a 15-17 mile long and threequarter mile area in joplin, mo, resulting in an estimated 162 deaths and hundreds of non-fatal http://ojphi.org an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 injuries.[8] the storm devastated the city, including its largest physician complex, regional community hospital, and many physician and dental offices, and affected the operations of all providers locally, including the area’s established chc. the u.s. department of health and human services (hhs) declared the area a public health disaster area and dispatched staff from the national disaster medical system (ndms) to help assess the extent of the damage and begin an immediate assistance and recovery planning process.[9] access family care (afc), founded in the mid -1990s as ozark tri-county health care, is a federally qualified health center serving the communities of southwest missouri, including the joplin area. afc operates 4 sites, including two in the city of joplin, and serves approximately 15,000 patients annually. the center provides a full range of comprehensive primary care medical and dental services for adults and children, as well as some specialty care. approximately 98 percent of afc’s patients are low-income (i.e., have incomes at or below 200 percent of the federal poverty level); 46 percent have health insurance coverage through medicaid and 40 percent are uninsured.[10] while the tornado destroyed afc’s smallest site, a primary care and behavioral health satellite located in joplin proper, the center was able to provide continuity of care for their registered patients across all sites as well as support community emergency relief efforts, as a result of their preparation, experience, and, in part, good fortune. afc’s ability to withstand the storm was determined in large part by early and effective information technology planning. as afc planned for the transition from paper to electronic medical records (emrs), they sought a mobile and accessible system that would render minimal disruption to an efficient workflow, while providing a secure operational platform. afc concluded that to optimize efficiency, they would need to adopt a solution that would permit their providers to access the system from remote locations and use the emr and other clinical application(s) from any secure computer. finally, the health center wanted the flexibility to use both fixed and mobile devices, including ipads and pcs. after reviewing and evaluating various options, the center selected neotech solutions, an experienced local it consulting firm based in joplin, mo, which offered a hosted solution including a “virtual desktop” environment to assist them with the hit implementation process. neotech was able to offer creative and cost-effective options for the center, including smart-card technology, which allows providers to access software and applications from any terminal or device at the point they left off, without needing a repeat log-in. neotech was integral to the center’s emr adoption process and from the inception of the project three years ago, afc’s core it systems and applications, including its emr (ge centricity) and electronic dental record (dentrix), have been hosted through neotech and maintained at a secure off-site data center. thus, the ability for the center to function smoothly depends not only on its internal capacity and infrastructure, but on that of its key hit partner. neotech’s hosting capability was tested twice: first, by a fire in march 2010 that originated in a restaurant next door to their facility and later, by the may 2011 tornado. while neotech’s physical facility was compromised during the fire, http://ojphi.org an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 they successfully relocated the data center to a temporary location and were back in operation in only 48 hours; there was no data loss and afc was able to regain full data access the following day. after the fire, neotech rebuilt their hosting facility and made significant structural improvements to the data center. however, when the tornado struck on sunday may 22, just 14 months later, neotech’s building was largely destroyed; only the north section of the building, which was reinforced to accommodate the data center, withstood the storm. miraculously, no data was lost despite the destruction of the premises and surrounding property. neotech again relocated to temporary quarters in order to ensure that client service was not disrupted, and was able to resume operations in just 12 hours. this proved critical for the health center. when they reopened in the days immediately following the storm afc became the focal service delivery point for their own patients, as well as an emergency access point for others in the community, providing emergency medical triage and medication refills for community residents in need, while continuing to manage direct care and assist their own staff and staff families who were affected by the storm. neotech helped the health center establish on-site intranet connectivity so that there was minimal disruption; loss of internet in the local area, however, lasted a week and prevented connecting to external resources. afc leadership noted that the city and county performed effectively with respect to emergency preparedness and response activities, clearing roads within a few days and fully restoring power to the city within a week. however, with few health care providers in the area, and the local hospital destroyed, afc became an essential part of the response and recovery team. from afc’s perspective, the return on the investment of adopting electronic records and secure offsite hosting was invaluable, and allowed them to function optimally in a disaster scenario. the one-time emr acquisition and installation cost was approximately 4 percent of the center’s operating budget; an additional 1 percent was allocated for initial implementation and support of emr adoption. the initial emr installation lacked some key features, such as document scanning and e-prescribing software, which were subsequently added at a nominal cost. afc also later incurred some additional costs to connect the emr with a third-party lab. two years after the initial installation, afc added eight more emr licenses. while the center incurred some additional costs after the initial installation, its operating budget grew and today, the proportion of the center’s operating budget dedicated to annual emr licensing and support remains at 1%. the center also budgeted 1% for off-site hosting, plus edi services and eprescribing at approximately 0.6% of the total budget. the center reports that the value associated with the cost is immeasurable; it has allowed afc to manage patient care effectively across multiple sites; improve efficiency, and withstand the effects of the tornado that devastated much of the community. neotech, for its part, elected not to rebuild an independent facility after the tornado, and has since moved to a secure co-location site that offers considerable resilience and ability to expand capacity. http://ojphi.org an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 discussion while chcs are major providers of primary health care to individuals, they also provide a critical link that connects lower income and vulnerable communities with initiatives aimed at promoting community-wide health improvement and reducing the incidence of disease and disability.[11] because of their strategic locations, chcs can play a critical role in communitywide surveillance, act as first responders in the event of a public health emergency, and provide continuous physical and mental health services in the event of a major disaster.[12] indeed, chcs have engaged in precisely this type of activity over the years, serving as part of the public health surveillance infrastructure in low-income communities. 1 additionally, many chcs participate in city and county emergency readiness planning.[13] health centers provide hospital back-up and non-critical care, offer vaccinations during outbreaks, serve as surveillance and detection sites for possible outbreaks, and furnish essential information and education to the community. the centers for disease control and prevention (cdc-p) have identified several major categories of natural disasters and other catastrophic events for communityand provider-based emergency planning and response, and chcs have an essential role in disaster preparedness. in reality, no single health center is equipped or staffed to independently address a large-magnitude natural or man-made disaster. however, as the backbone of the nation’s primary care safety net system, the adoption by chcs of a secure health information technology platform serves to better link lower-income and vulnerable communities with other providers to promote continuity of care and helps reduce the incidence of disease and disability, particularly in the wake of natural disasters. the experience of access family care in joplin, as well as of other community health centers at the epicenter of recent disasters, underscores the importance of investment in hit at both the health center and community level. the operating costs incurred by the center are a manageable share of the overall budget and allow for operational and programmatic efficiencies while supporting the delivery of care. in the wake of the recent tornado, these dollars are clearly money well spent. recommendations afc’s ability to quickly recover patient medical records underscores both the significance of federal investments to bolster adoption of health information technology, and the importance of 1 for example, through a cooperative agreement with the cdc, the national association of community health centers, inc. launched the adolescent and school health initiative in 1993 to significantly enhance the capacity of health centers to expand and improve preventive and primary health programs targeted to youth at high risk for hiv/aids, sexually transmitted infections, and other health problems through developing school-based or school-linked comprehensive services in cooperation with schools in their communities. http://ojphi.org an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 continued investments. afc was able to resume operations quickly because they had appropriate it capacity in place, but their experience is not necessarily the norm. today, hit initiatives are focused largely on electronic capture of meaningful clinical data, the use of data to track and improve quality, and the exchange of patient information in a structured format. however, there is relatively little attention to how chcs and other providers should better secure their data and prepare for possible interruptions to care and information access. therefore, the bureau of primary health care, which oversees the health centers program, should develop clear and detailed guidance that can be employed by chcs to help plan for and overcome disaster situations. such guidance should include alternatives and recommendations, based on industry standards, for physical security, data back-up, redundancy planning, staff training, and examples of effective strategies used by chcs. health centers should also be active partners in their community’s emergency preparedness efforts. although most communities rely on hospital resources in the event of disasters, chcs should seek to be active partners in local preparedness planning, train staff in emergency response and prepare their facilities to serve as critical access points. because health centers are often located in isolated communities and serve hard-to-reach populations, state, county, and local agencies must help to equip chcs to participate as emergency responders, and facilitate the inclusion of chcs in preparedness planning. finally, data security should be identified as a specific priority for funding support. federal hit funding presently supports acquisition and implementation, and offer quality incentives, but does not provide direct support for securing data. many chcs may be unable to fund optimal data or hosting solutions without specific support, especially as local and state health center funding continues to decline due to economic downturn. afc’s experience demonstrates that investment in a local, secure hosting center is not only cost effective, but essential for the continued provision of quality care, especially in areas at high risk for natural disasters. corresponding author peter shin associate professor of health policy research director, rchn/ geiger gibson email: pshin@gwu.edu references 1. national association of community health centers, legacy of disaster health centers and hurricane katrina. one year later, 2006. available at: http://www.nachc.com/client/documents/ issues-advocacy/policy-library/research-data/research-reports/katrinareport.pdf (accessed september 16, 2011). http://www.nachc.com/client/documents/issues-advocacy/policy-library/research-data/research-reports/katrinareport.pdf http://www.nachc.com/client/documents/issues-advocacy/policy-library/research-data/research-reports/katrinareport.pdf http://ojphi.org mailto:pshin@gwu.edu http://www.nachc.com/client/documents/issues-advocacy/policy-library/research-data/research-reports/katrinareport.pdf http://www.nachc.com/client/documents/issues-advocacy/policy-library/research-data/research-reports/katrinareport.pdf an hit solution for clinical care and disaster planning: how one health center in joplin, mo survived a tornado and avoided a health information disaster 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 2. 2010 uniform data system (uds), health resources and services administration (hrsa) - all federally-funded health centers are required to submit annual reports on their financial and clinical performance along with patient profiles. 3. hrsa. 2011. health centers: where to go for care you can afford. available at http:// www.hrsa.gov/ourstories/healthcenter/healthcenterweek.html. (accessed september 16, 2011) 4. hawkins d, groves d. 2011. the future role of community health centers in a changing health care landscape. j ambul care manage. 34(1), 90-99. http://dx.doi.org/10.1097/ jac.0b013e3182047e87 5. association of state and territorial health officials, national association of county and city health officials, and national association of community health centers, collaborating with community health centers for preparedness. 2008. available at http://www.nachc.com/client/ collaborating_with_community_health_centers_for_preparedness.pdf (accessed september 16, 2011) 6. blumenthal d. 2009. stimulating the adoption of health information technology. n engl j med. 360, 1477-79. http://dx.doi.org/10.1056/nejmp0901592 7. 2006 harvard university, george washington university, and the national association of community health centers survey and the 2010-11 gw geiger gibson/rchn research collaborative survey of health centers. 8. 2010 uds, hrsa, provided by access family care. 9. office of the assistant secretary for preparedness and response, u.s. department of health and human services. (may 27, 2011). situation reports: 2011 joplin, missouri tornadoes. available at: http://www.phe.gov/emergency/news/sitreps/pages/missouri-tornado2011.aspx (accessed september 16, 2011) 10. kevin murphy (2011, september 14). joplin tornado death toll rises to 162. reuters. 11. national association of community health centers. partnerships between federal qualified health centers and local health departments for engaging in the development of a communitybased system of care, october 2010. available at: http://www.nachc.com/client/documents/ partnershipsbetweenfederallyqualifiedhealthcentersandlocalhealthdepartmentsforengagingi nthedevelopmentofacommunitybasedsystemofcarenachcoctober2010.pdf (accessed september 16, 2011) 12. wood, km., community health centers: the untapped resource for public health and medical preparedness, homeland security affairs, january 2009; v(1): 1-39. 13. hrsa pin 2007-15: health center emergency management program expectations. available at http://bphc.hrsa.gov/policiesregulations/policies/pin200715expectations.html (accessed september 16, 2011) http://www.nachc.com/client/collaborating_with_community_health_centers_for_preparedness.pdf http://www.nachc.com/client/collaborating_with_community_health_centers_for_preparedness.pdf http://www.phe.gov/emergency/news/sitreps/pages/missouri-tornado2011.aspx http://www.nachc.com/client/documents/partnershipsbetweenfederallyqualifiedhealthcentersandlocalhealthdepartmentsforengaginginthedevelopmentofacommunitybasedsystemofcarenachcoctober2010.pdf http://www.nachc.com/client/documents/partnershipsbetweenfederallyqualifiedhealthcentersandlocalhealthdepartmentsforengaginginthedevelopmentofacommunitybasedsystemofcarenachcoctober2010.pdf http://www.nachc.com/client/documents/partnershipsbetweenfederallyqualifiedhealthcentersandlocalhealthdepartmentsforengaginginthedevelopmentofacommunitybasedsystemofcarenachcoctober2010.pdf http://bphc.hrsa.gov/policiesregulations/policies/pin200715expectations.html http://ojphi.org http://www.hrsa.gov/ourstories/healthcenter/healthcenterweek.html http://www.hrsa.gov/ourstories/healthcenter/healthcenterweek.html http://dx.doi.org/10.1097/jac.0b013e3182047e875 http://dx.doi.org/10.1097/jac.0b013e3182047e875 http://dx.doi.org/10.1097/jac.0b013e3182047e875 http://www.nachc.com/client/collaborating_with_community_health_centers_for_preparedness.pdf http://www.nachc.com/client/collaborating_with_community_health_centers_for_preparedness.pdf http://dx.doi.org/10.1056/nejmp09015927 http://dx.doi.org/10.1056/nejmp09015927 http://www.phe.gov/emergency/news/sitreps/pages/missouri-tornado2011.aspx http://bphc.hrsa.gov/policiesregulations/policies/pin200715expectations.html now.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 1 (page number not for citation purposes) isds 2013 conference abstracts 2013 international society for disease surveillance conference translating research and surveillance into action �������� � ���� ����������� ����� ����� ��� ������������� � �� ��������� ���� ��� ������ ������������� � ������������� ������ ���� ��!��"� #�$�����������������%���������������& ����&�&� ����� �� ������ �� ����'������������� ���& ������������� ����� ��� ���!� ��������� ������ ��'����'���� � ��� ��� �� ���& �������� �����&�� ��� �� ��!��&������ �'�!��� ���&� ���!������ �������� ���� ���� ��� � ���� ��'!����� � ����!����&��� ��������!� ��� � ������� ����������� �������� ��� ���� ������ '��'��� ��'������&�� ����� ���& �����#� �������������������� %������� ����!�� �� ���'�(��� ��� ����� ) ��� ���������*�����!�& ����������� 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�������'���� � &� ���������������#�*������ �� ������������&���� �� �� + � ����� ������ ���!������ ���� & ������������ � 9��'�&��& ��������� ������� �������������� ����� +����� ���� � ������<� wayne loschen =�����>�&+����?���� �����*&& ����6�������$ �� �� � �"� �����������������6 �' ��5���������5� � �� online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e1, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts epidemiological inferences using public information, influenza h7n9 epidemic in china eric h.y. lau*1, jiandong zheng2, tim k. tsang1, qiaohong liao2, bryan lewis3, john s. brownstein4, 5, sharon sanders6, sumiko r. mekaru4, caitlin rivers3, gabriel m. leung1, luzhao feng2, benjamin j. cowling1 and hongjie yu2 1the university of hong kong, hong kong, hong kong; 2chinese center for disease control and prevention, beijing, china; 3virginia tech, blacksburg, va, usa; 4boston children’s hospital, boston, ma, usa; 5harvard medical school, boston, ma, usa; 6flutrackers international charity, winter park, fl, usa objective this study described the strength and limitation of using line lists that built on publicly available data in various types of epidemiological inferences during the h7n9 epidemic in china, 2013. introduction the influenza a(h7n9) virus emerged in early 2013 in china, with more than 130 laboratory-confirmed cases identified within a short period of about three months. evidence-based public health response is essential for effective control of the disease, which relies on epidemiological and clinical data with good quality and timeliness. publicly available information from sources such as official health website, online news, blogs or social media has the potential of rapid sharing of data to a wide community of experts for more comprehensive analyses. in our study we described the strength and limitation of these data for various types of epidemiological inferences. methods we obtained historical archives of five line lists for influenza a(h7n9) cases based on publicly available information, created by boston children’s hospital (healthmap), virginia polytechnic institute and state university, flutrackers, bloomberg news and school of public health, university of hong kong, and also the official line lists from chinese center for disease control and prevention, covering the period from early april to end of may. demographic and epidemiological variables such as sex, age, province, city, date of illness onset, hospital admission, discharge, death and health status were available in most of the line lists. we described the potential types of epidemiological inference that could be made for real-time severity and transmissibility assessment of the influenza a(h7n9) epidemic. distributions of basic demographic and epidemiological variables were compared. we also compared the estimated onset-to-admission, onset-to-discharge and onset-to-death distributions and hospital fatality risk (hfr) inferred from different line lists, comparing to the official line list. impact of live poultry market closure on h7n9 infection was also estimated in cities which implemented the intervention. results estimated age and sex distributions, epidemic curves, geographical spread, onset-to-hospitalization and onset-to-death distributions from line lists using public information were similar to those estimated from the official line list, at different time points over the course of the h7n9 epidemic. live poultry market closure was found to be significantly associated with a lower h7n9 incidence in shanghai, nanjing and hangzhou. we estimated a shorter onset-to-discharge period from the line lists based on public information, compared to the official line list. the estimated hfrs from these line lists did not converge to the final estimate towards the end of the epidemic, when the outcomes of most hospitalized patients were available. conclusions the results suggest that public information is able to provide accurate information on demographic characteristics and case counts. they also tend to report more detailed information for severe and death cases. however, information on patient status, hospitalization or discharge was less reliable. publicly available information is likely to provide reliable information for inference of transmissibility or geographical spread but limited information on disease severity. it will be beneficial to public health by developing a protocol to setup an open-access minimum dataset based on publicly available data, with key epidemiological variables of standardized format and definition, along with a time stamp for each variable. this will allow a larger group of experts to carry out more timely and comprehensive epidemiological analyses when a novel disease emerges. keywords epidemiological inference; severity; transmissibility; line list; influenza h7n9 *eric h.y. lau e-mail: ehylau@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e143, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a comparison of fever classified chief complaints and diagnoses with recorded body temperatures patricia araki*, emily kajita, kelsey oyong, monica z. luarca, bessie hwang and laurene mascola county of los angeles, department of public health, los angeles, ca, usa objective the los angeles county (lac) emergency department (ed) syndromic surveillance system (sss) classifies patients into syndrome categories based on stated chief complaints. in an effort to evaluate the accuracy of patientstated chief complaints and final diagnoses, both “fever” chief complaints and diagnoses were compared with patient body temperature readings. introduction the lac sss has been in existence since 2004. currently, the system collects data from over 50 hospitals daily and performs a chief complaint-based syndrome classification analysis of all ed visits. the keyword “fever” is of special interest due to its inclusion within several syndrome category definitions such as influenza, meningitis, etc. however, inclusion of such terms in syndrome definitions may be a disadvantage as such keyword searches would depend upon the consistency in which the term “fever” is reported. in 2014, several lac syndromic surveillance hospital data connections were upgraded to include notes recording patient body temperature. to evaluate the newly added temperature information, analyses were conducted on those observations that included body temperature, chief complaint, and diagnosis information. methods for this study, emergency department admitting data from 9 hospitals were reviewed. a total of 24,402 observations from a five month period were categorized into groups by patient body temperature, those with fever classified chief complaints (n=1441) that included the terms “fever” or “febrile”, or patients with a similarly defined fever classified diagnosis (n=970) and/or corresponding icd9 code. binary classification tests were conducted on this population to observe fever classified chief complaint and diagnosis outcomes against selected body temperature ranges. results the sensitivities for fever classified chief complaints were 28.5%, 59.8%, and 73.9% for body temperatures 99°f, 100°f, and 101°f, respectively. corresponding positive predictive values (ppv) were 60.7%, 43.2%, and 27.8% for the same groups. for fever classified diagnoses, sensitivities were found to be 22.2%, 47.5%, and 56.9% while ppvs were at 70.3%, 51.0%, and 31.8%, respectively, for the same temperature groups. the majority of fever classified chief complaints (81.4%) and diagnoses (75.7%) were categorized into the respiratory syndrome category with 54.3% of all fever classified chief complaints resulting in a consistent diagnosis. furthermore, 55% of observations with both a fever classified chief complaint and diagnosis also recorded body temperatures at or above 100°f. conclusions these preliminary findings have provided a basic understanding of the utilization of the term “fever” within the lac sss. in addition, the evaluation of body temperature versus fever classified chief complaints and diagnoses will be utilized to determine whether, and how, the body temperature variable may be included in future iterations of syndrome classification algorithms. keywords fever; syndromic surveillance; temperature; chief complaint *patricia araki e-mail: paraki@ph.lacounty.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e105, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts syndromic surveillance of respiratory pathogens using routine ed data in england helen hughes*1, roger morbey1, thomas hughes2, thomas locker2, gillian smith1 and alex elliot1 1public health england, birmingham, united kingdom; 2college of emergency medicine, london, united kingdom objective can syndromic surveillance using standard emergency department data collected using automated daily extraction be used to describe and alert the onset of the seasonal activity of named respiratory pathogens within the community? introduction within the uk, previous syndromic surveillance studies have used statistical estimation to describe the activity of respiratory pathogens.1 the emergency department syndromic surveillance system (edsss) was initially developed in preparation of the london 2012 olympic and paralympic games and has continued as a standard surveillance system, with expanding coverage across england and northern ireland.2 all reporting to this system is completely passive, with no extra work required within the ed. the data collection includes the diagnosis for each attendance, where available, using the coding system in use locally. the coding varies by ed with icd10, snomed-ct and the less detailed nhs accident and emergency diagnosis tables all in use. the use of diagnosis coding systems with differing levels of detail creates the need to have a variety of syndromic indicators to make best use of the data received. we aim to describe the trends in respiratory attendances, and their comparison to the known circulating pathogens identified though laboratory surveillance to establish if any single syndromic indicator may be attributed to any one pathogen in particular. we also aim to describe the flexibility in the development of edsss syndromic indicators to best fit the data received. methods using the diagnosis coding received, respiratory indicators were developed, ranging from the most generic ‘all respiratory conditions’ which all eds were able to provide, to more specific named conditions such as pneumonia and acute bronchitis which only those eds using icd10 or snomed ct were able to report on. time series for these indicators were constructed and described. multiple linear regression analysis was used to identify those indicators most sensitive to named pathogen activity as described by laboratory data. results as expected, the edsss indicators for acute respiratory infection and acute bronchitis, in patients aged less than 4 years in particular were found to be most sensitive to rsv activity. the less detailed ‘all respiratory disease’ indicator in this younger age group was also found to be associated with rsv activity within the community. conclusions the surveillance of named respiratory pathogens is possible using automated data extraction from eds. although there are differences in the diagnosis coding systems in use in eds, with some providing more detail than others, this system is able to identify and report on rsv activity in particular using a combination of even the most basic diagnosis coding of ‘all respiratory diseases’ and patient age. the use of details in addition to the diagnosis of each patient allows for a flexible system, better able to provide both early detection and situational awareness for public health surveillance. weekly edsss attendances for ‘all respiratory conditions’ and acute bronchitis in infants aged 0-4 years during winter 2012/13 keywords syndromic surveillance; emergency department; respiratory infection; respiratory syncytial virus references 1. cooper dl, smith ge, edmunds wj, et al. the contribution of respiratory pathogens to the seasonality of nhs direct calls. j infect 2007;55:240-8. 2. elliot aj, hughes he, hughes tc, et al. establishing an emergency department syndromic surveillance system to support the london 2012 olympic and paralympic games. emerg med j 2012;29:954-60. *helen hughes e-mail: helen.hughes@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e81, 201 ojphi-06-e32.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 39 (page number not for citation purposes) isds 2013 conference abstracts developing a tool to cost gaps in implementation of ihr (2005) core capacities stella chungong1, jun xing1, rajesh sreedharan1, stephane de la rocque1, rebecca katz2, julie e. fischer2, mahomed patel3, lisa d. ferland*4, meeyoung park4, ngozi erondu4, william macwright4 and scott j. mcnabb4 1world health organization, geneva, switzerland; 2george washington university, washington, dc, dc, usa; 3australian national university, canberra, act, australia; 4emory university, atlanta, ga, usa � �� �� �� � � �� �� �� � objective ��������� � � �� � �������� ��������� ��� ��� � �� ������ ������ � ��� ����� ������������ ���� � �� ��� � �� � ������������ ����� �� �� ��������� ����� ���� �� � �� � ���� ������ � �� ����������� !"#� � � ��� ��� �� $���� ������� � ����� �% introduction ����� �� � �� � ���� ������ � �� ��&������'������ 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>�?#�=�� � � �)#�8�������@2%�6�� � �������&��������� $ �� � �� � ���� ������ � �� ������ "%�2������ ��� �a��������� �� � ��� � �� � !%� ��-�� @� � ���� 3��� ��-.!%� � �;bb�:%���%���b-�%.��-b ���-<�c%-��-d*lisa d. ferland e-mail: lferland@publichealthpractice.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e32, 2014 ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce brian e. dixon1,2,3, p. joseph gibson4, shaun j. grannis2,5 1. school of informatics and computing, indiana university, indianapolis, in 2. center for biomedical informatics, regenstrief institute, indianapolis, in, usa 3. center for health information and communication, department of veterans affairs, veterans health administration, health services research and development service cin 13-416, richard l. roudebush va medical center, indianapolis, in, usa 4. health and hospital corporation of marion county, indianapolis, in, usa 5. school of medicine, indiana university, indianapolis, in abstract objective: to provide formulas for estimating notifiable disease reporting volume from ‘meaningful use’ electronic laboratory reporting (elr). methods: we analyzed two years of comprehensive elr reporting data from 15 metropolitan hospitals and laboratories. report volumes were divided by population counts to derive generalizable estimators. results: observed volume of notifiable disease reports in a metropolitan area were more than twice national averages. elr volumes varied by institution type, bed count, and by the level of effort required of health department staff. conclusions: health departments may experience a significant increase in notifiable disease reporting following efforts to fulfill meaningful use requirements, resulting in increases in workload that may further strain public health resources. volume estimators provide a method for predicting elr transaction volumes, which may support administrative planning in health departments. keywords: clinical laboratory information systems, computerized medical record systems, disease notification, health information exchange, health manpower, public health informatics doi: 10.5210/ojphi.v5i3.4939 correspondence: bedixon@iupui.edu copyright ©2013 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. introduction electronic reporting of reportable diseases may increase significantly [1,2], and estimates of that increase will help prepare public health agencies. the health information tech nology for economic and clinical health (hitech) act authorized the centers for medicare and medicaid http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 services (cms) to incentivize the adoption of electronic health records (ehrs) [3], a program known generally as ‘meaningful use’ (mu) [4,5]. stage 2 mu criteria require electronic submission of laboratory data for reportable disease cases from eligible hospitals to health departments. case reporting from eligible providers may also increase, as stage 3 criteria may require providers to use elr for submission of data to public health [6]. public health receives case information to monitor and contain disease transmission [7]. responses to reported cases include simply logging the incident, contacting the clinician, verifying treatment, and full case investigation involving direct communication with patients and individuals having contact with the patient. despite efforts to improve notifiable disease reporting completeness through efforts including electronic laboratory reporting (elr) [8], health department processes still depend on manual, provider-initiated submission of information [9,10]. while some health departments receive portions of their cases electronically [8], stage 2 meaningful use requirements may increase electronic report volumes [1]. consequently, health department workloads may increase since electronic methods can mitigate human barriers to improved reporting [11] leading to increased case reporting, an aim of meaningful use policies [12]. estimating the volume of elr submissions resulting from meaningful use can enable health departments to predict workload for epidemiologists, case investigators, and others processing case reports. however, few health departments have experience with high volume elr, making estimation difficult. methods the indiana network for patient care (inpc), a regional health information exchange, has been processing high volumes of elr for over a decade [13-15]. the inpc processes over 500,000 daily transactions, representing 90% of laboratory results for the indianapolis-carmel metropolitan statistical area (msa). when a case meets reporting criteria, the inpc forwards the elr information to state and local health departments. similar reporting models are likely to be adopted by health departments around the nation. to support health department estimation of elr volume, we examined current reporting rates per population and provider. the inpc uses the notifiable condition detector (ncd), an automated case-detection system developed at the regenstrief institute [16], to process clinical transactions from more than 40 inpc hospitals, laboratories and local ancillary service organizations. we previously described the ncd and its ability to detect and report suspected cases of notifiable disease to public health [11,17-19]. a convenience set of data between january 1, 2010 and december 15, 2011 for inpc institutions were extracted from the ncd for analysis [20]. cases with laboratory results associated with reportable conditions as defined by indiana law [21] were included in the analysis. we excluded duplicates of the same disease incidence for the same individual using the open source probabilistic linkage software package utilized by the inpc [22-24]. this analysis was approved by the indiana university institutional review board. http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 unique notifiable disease cases were divided by the 2010 u.s. census population data for the indianapolis-carmel msa to obtain a general estimate of elr rates. because the number of patient days is readily available to other health departments wishing to leverage these results, we stratified notifiable disease cases from each facility using number of patient days. hospitals report number of patient days to the cms healthcare cost report information system (hcris) through approved medicare administrative contractors. further, case investigators at the marion county public health department (mcphd) used a consensus process to assign an expected level of effort for case investigation and follow-up associated with each reportable disease (see table 1). mcphd staff verify that patients receive treatment, initiate prophylaxis for patients’ contacts (e.g., for meningitis or whooping cough), and monitor disease spread patterns; each disease requires varying combinations of these work processes. mcphd staff enumerated their work processes and self-reported their level of effort for specific disease investigation tasks. variation in reported workflow and levels of effort were resolved through discussion with study investigators. study investigators first grouped elr messages into disease classes using the u.s. centers for disease control and prevention (cdc) reportable conditions mapping table [25], then they assigned each disease class to levels based on the associated work processes and levels of effort reported by mcphd staff. level 1 represents cases requiring under 2 hours’ work with minimal paperwork and rare contact investigation. level 2 cases require approximately 3 hours’ work with limited paperwork, patient interviews, and contact investigation. level 3 cases require over 3 hours’ work, detailed patient interviews, and some contact investigation. level 4 cases require extensive contact investigation, patient interviews and provider follow-up. results estimation using overall elr counts and population the inpc reported 71,742 unique cases of suspected notifiable disease in the indianapolis carmel msa during the two year time period. according to 2010 u.s. census data, the indianapolis-carmel msa population is 1,834,672. dividing the notifiable disease case count by the population produces a ratio of 1,955 elr cases per 100,000 population per year. estimation using number of patient days we paired notifiable disease reports with the corresponding hospital or network of hospitals in the indianapolis-carmel msa. a total of 11 hospitals or hospital networks accounted for the 71,742 elr reports. dividing elr report counts by the number of patient days for each year in the study period produced the ratios depicted in figure 1. the average number of patient days was 89,012 with a minimum of 11,014, a maximum of 357,985, and a standard deviation of 107,712. the average ratio of elr reports to number of patient days per year was 0.028 with a minimum of 0.004, a maximum of 0.094, and a standard deviation of 0.029. the county hospital, the first data point in figure 1, reported the highest rate; all other hospitals are non-profit community hospitals. http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 figure 1: comparison of elr reports to patient days per hospital per year elr messaging by disease classification we classified 60,094 elr messages based on the disease-associated workload of mcphd staff who perform follow-up tasks for reported cases, depicted in table 1. while the majority of messages represent level 2 conditions (49.6%), level 3 cases accounted for 34.9%, and level 4 totaled 11.2%. level 1 cases, which require minimal follow-up, represented just 4.3% of all reports. table 1 – elr messages grouped by disease and estimated level of effort to perform local health department follow-up procedures. disease and effort count (%) rate per 100,000 per year mmwr rate per 100,000 2010 mmwr rate per 100,000 2011 level 4 effort: substantial contact investigation, patient interview and provider follow-up required 6,726 (11.2%) 183.30 human immunodeficiency virus 3,452 94.08 11.64 11.41 measles 3,177 86.58 0.02 0.07 tuberculosis 90 2.45 3.64 3.41 typhoid fever 7 0.19 0.15 0.13 level 3 effort over 3 hours: increased paperwork required, patient interviews required, 20,979 (34.9%) 571.74 http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 moderate contact investigation diphtheria 124 3.38 n/a n/a escherichia coli o157 h7 infection 1,306 35.59 1.78 1.96 hantavirus 10 0.27 0.01 0.01 hepatitis a 2,435 66.36 0.54 0.45 hepatitis e 10 0.27 n/a n/a lead exposure 11,208 305.45 n/a n/a listeriosis 7 0.19 0.27 0.28 meningitis (fungal) 1 0.03 n/a n/a meningococcal disease 104 2.83 0.27 0.25 mumps 1,318 35.92 0.85 0.13 mycobacterium non-tb 528 14.93 n/a n/a pertussis 314 8.56 8.97 6.06 poliomyelitis 4 0.11 n/a n/a q fever 1 0.03 0.04 0.04 rubella 2,045 55.73 0.00 0.00 shigellosis 265 7.22 4.82 4.32 syphilis 1,298 35.37 14.93 14.90 trichinosis 1 0.03 0.00 0.00 level 2 effort, up to 3 hours: minimal paperwork and patient interview, minimal contact investigation 29,810 (49.6%) 812.41 arbovirus 11 0.30 0.37 0.28 campylobacteriosis 296 8.07 n/a n/a chickenpox 6,009 163.76 5.03 4.70 chlamydia infection 8,229 224.26 426.01 457.14 cryptosporidiosis 27 1.20 2.91 2.99 dengue fever 6 0.16 0.22 0.08 ehrlichiosis 5 0.14 0.85 0.83 giardiasis 54 1.47 6.45 5.42 gonorrhea 3,224 87.86 100.76 104.14 http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 haemophilus influenzae 210 5.72 1.03 1.15 hepatitis b 7,033 191.67 1.10 0.94 hepatitis c 3,071 83.69 0.28 0.40 hepatitis d 3 0.08 n/a n/a histoplasmosis 164 4.47 n/a n/a legionellosis 446 12.15 1.09 1.36 malaria 1 0.03 0.58 0.56 rickettsial infection 2 0.05 n/a n/a salmonellosis non-typhoid 631 17.20 17.73 16.79 streptococcus pneumoniae 299 8.15 5.40 5.55 tetanus 86 2.34 0.01 0.01 yersiniosis non-plague 3 0.08 n/a n/a level 1 effort, < 2 hours: little paperwork involved, rare contact investigation 2,579 (4.3%) 70.29 cryptococcosis 44 1.20 0.06 0.05 influenza 1,579 43.03 n/a n/a lyme disease 139 3.79 9.82 10.71 streptococcus group b 817 22.27 n/a n/a grand total 60,094 1,637.73 discussion meaningful use elr requirements will likely boost notifiable disease surveillance efforts, which may significantly increase the volume of reports. this is in turn can further burden the public health workforce [26]. we used data from an advanced health information ecosystem to impute population-based elr-based reporting rates. these rates can inform future report volume projections in jurisdictions across the nation. further, our case management workl oad model can enhance health department estimates of future case management workforce capacity needs. the first estimator, a measure of total cases reported using elr from multiple systems, reveals that approximately 20 unique cases of suspected notifiable disease were reported per 1,000 persons each year. this elr estimator is notable given that it is the first such figure reported and more than double the national average of confirmed cases reported by the cdc [27]. rates in other jurisdictions could be higher or lower given differences in regional disease burden as well as reporting laws. regenstrief investigators previously found that compared with traditional, paper-based reporting methods, elr may quadruple the public health case report volume [17]. http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 in addition to using a population-based approach, we matched each elr case to the hospital and hospital network that performed the laboratory test. this approach revealed variance in the number of case reports from each facility or network. according to the agency for healthcare research and quality’s healthcare cost and utilization project (hcup) nationwide inpatient sample, there were 38.6 million inpatient stays in 2011 with a mean length of stay of 4.6 days [28]. using the observed median elr cases per patient day in this study, we estimate that approximately 5.1 million cases of suspected notifiable disease should be reported annually to u.s. health departments. this is nearly double the number of confirmed cases reported by cdc for 2010 [29] and on par with the two-fold increase originally observed by effler et al. [30] following the introduction of elr. we further categorized elr cases based on the perceived level of effort associated with reportable disease case investigation activities as shown in table 1. using this table, individual health departments can estimate impact based on their own division of labor. for example, mcphd has separate teams for the management of hiv/aids, sexually transmitted infections, and all other infectious diseases. each team possesses different training and experience. health departments can extract estimated rates from this table to generate figures more meaningful to their specific approach to managing various notifiable diseases. an increase in suspected notifiable disease reports would significantly impact local and state health departments’ workload. recent downsizing and budget cuts in health departments across the nation [31,32] implies that an increase in reports would likely place pressure on departments to do more with less. the data in table 1 suggests that even a modest increase in overall reporting will necessitate a substantial increase in health department staff effort to validate, investigate, confirm and close cases. thus the potential increased reporting from meaningful use may not only translate into an increase in overall case volume, but an increase in di sease reports for which health departments currently allocate few resources. and while the hitech legislation provided billions of dollars for health care providers to adopt ehrs, it provided only $30 million for public health agencies to enhance their infrastructure to receive and analyze data from ehrs [33]. the anticipated increase in volume includes three types of notifiable disease cases. first are true positive reports involving new cases of notifiable disease that must be investigated with potential follow-up involving providers and patients as well as their contacts. the second type are false positive reports in which the ncd or similar automated algorithms inadvertently submits an elr message to public health who later concludes the report does not meet the case definition for a notifiable disease. for example, the ncd leverages a modified version of the negex information retrieval methodology [34] to identify instances where reportable conditions are mentioned in a negated context, e.g., “no evidence of mrsa”. occasionally the system fails to identify particularly complex negations such as, “positive evidence for mrsa is identified inconclusively.” we previously reported very good sensitivity, specificity, and positive predictive values for the ncd [16]. therefore we do not suspect such false positives may have artificially increased our reported volumes. finally, there exists what we label as “true-false positive” reports in which the ncd or similar algorithm correctly submitted an elr message to public health that meets local or state case definitions but is not ultimately reported to cdc as a new confirmed case. note that in table 1 there are over 7,000 cases of hepatitis reported during a http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 two-year time period. this is an elevated rate, yet the ncd correctly reported these messages to local health departments. many of these cases involved repeat positive cases for patients who are already known to public health. the cases are repeatedly transmitted to public health in compliance with indiana administrative code, which states positive hepatitis tests must be reported to public health. therefore the ncd correctly flagged the elr messages as positive and reported them to local health departments, requiring public health to devote resources to adjudicating duplicate results. to manage the impending increase in suspected case volume via elr, public health agencies should consider a range of strategies. some departments may revise their investigation protocols, de-emphasizing certain conditions or classifications of certain diseases to streamline their workload. others may seek to obtain additional personnel or shift personnel from other program areas. policymakers, agency heads, and epidemiologists should further consider revisions to administrative codes and public laws that currently allow for true-false positive reports. refining local case definitions to increase specificity could help both elr senders and receivers reduce the report volume that must be triaged by local health departments. the estimators and data described in this analysis can help frame discussions regarding which strategies might be best given local policies, infrastructure and processes. public health informatics competencies, including analytics and data management methods, are likely to become increasingly important as they can support epidemiologists’ need to incorporate automated methods for validating, classifying, and prioritizing reports to focus limited staff time on high-value case follow-up and data use; such prioritization of cases is currently a manual process in most agencies. these competencies are further important to implement and optimize receipt of elr data from various clinical informatics systems. for example, the inpc facilitates elr in a consistent way while other states receive elr directly from providers [35]. receipt of elr messages from a variety of sources with distinct methods for identifying patients would require health departments to maintain a master person index to identify and process duplicate reports of notifiable disease. public health informatics professionals should explore methods for helping agencies to improve reporting processes and capacity for elr; the nation should continue to support initiatives that increase informatics training for the public health workforce [36]. limitations our estimates of notifiable disease report volume were derived from elr data produced by hospitals and other clinical data sources in a single state and represented principally mediumto large-sized hospitals. the estimation techniques described herein have yet to be validated by comparison with data from other jurisdictions. actual increases in notifiable disease report volumes may differ in other states based on hospital size as well as technical capacity within state and local health departments. the impact of elr on reporting may also vary due to state and local policies that govern disease reporting processes. however, almost all jurisdictions adhere to a nationally recommended list of reportable diseases; variation is relatively small [37]. elr rates in this analysis were dependent on the regenstrief ncd, which has been described previously [16]. other health departments, providers, or informatics solutions may use different http://ojphi.org/ ojphi estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e225, 2014 algorithms to identify suspected cases of notifiable disease, which could significantly alter the volume reported to public health authorities. conclusion adoption and use of health information technologies continues to increase in clinical organizations. meaningful use incentives aim to further connect clinical systems to public health departments to improve reporting of notifiable disease. health departments must not only prepare technically for the receipt of elr information, but they must also prepare their operations and workforce capacity to manage a potentially significant increase in report volume. we described estimates suggesting that automated elr methods could at least double the volume of suspected notifiable disease case reports. together public health and informatics professionals should work to strengthen the public health infrastructure, developing and evaluating automated methods for assisting health departments with the anticipated reporting volume increases. acknowledgements the authors would like to thank bill brand, mph, of the public health informatics institute and the anonymous reviewers at the journal for feedback on early drafts of this article. competing interests the authors declare they have no real or perceived conflicts of interest. funding this study was supported, in part, by a grant award (5r01hs020209) from the u.s. agency for healthcare research and quality. the article was further supported, in part, by the department of veterans affairs, veterans health administration, health services research and development service cin 13-416. dr. dixon is a health research scientist at the richard l. roudebush veterans affairs medical center in indianapolis, indiana. the views expressed in this article are those of the authors and do not necessarily reflect the position or 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22395299&dopt=abstract http://dx.doi.org/10.1136/amiajnl-2011-000507 ojphi-06-e163.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 55 (page number not for citation purposes) isds 2013 conference abstracts selecting essential information for biosurveillance—a multi-criteria decision analysis nicholas generous*, kristen margevicius, kirsten taylor-mccabe, mac brown, w. brent daniel, lauren castro, andrea hengartner and alina deshpande los alamos national laboratory, los alamos, nm, usa � �� �� �� � � �� �� �� � objective ������� ����� ����� �� �� ����� � ��������� ������������� ������ ���� �� ��� ������ ��� � ��� 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������������ ���� ��������� � ���� ����� ����� �� ���� � � � ��� ��� ���� ������ ��� ��� ����� �� ����� �� � ���� � �� � ��� �� ��������������������� � �����!� �������������� ��� � � ���� � ������ ��� ����� ���������� ��������������������!��.��� � ��������� � ����� ������� � ���� keywords � ��� �� ������;� 0������ ��;� 1���� �� ���;� %��� �� �� �� ��� � ��� ������ �;� ���� � ��� �� acknowledgments �� ��� �.���� ������� �����������2��� ��������� ���� ������������� �� 1 ���� ����<� ����� ��������������������+�� ���(<��+) �1��������� ��� 5����� ���&������(1�5&) *nicholas generous e-mail: generous@lanl.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e163, 2014 cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 cursor on target: research for a sensor network stevenson g 1 , naiman m 2 , valenta al 1 , boyd ad 1 1 college of applied health sciences, university of illinois at chicago 2 center for advanced design, research and exploration, university of illinois at chicago summary terrorism, epidemics, natural, and man-made disasters have increased over the last decade, prompting ongoing evaluation and incremental rebuilding of the american public health system (chan, killeen, griswold, & lenert, 2004a; yu, brock, mecozzi, tran, & kost, 2010). in february 2002, the center for disease control (cdc) identified six focus areas to generate response capacities to bioterrorism and public emergencies. according to one focus area, information sharing and alert notifications between systems and public health agencies must be continuous and automatic (popovich, henderson, & stinn, 2002) advancements in technology set the stage for this uninterrupted data-sharing requirement to be met; for example, “smart devices” can digitally record and transmit information and text messages from remote disaster sites using wireless ad hoc networks. in this context, medical systems and personnel can provide enhanced patient support from the extraction point to the hospital, even when normal landline infrastructure has been damaged. however, care may be restricted due to the limited recognition of proprietary information and the distance between the transmitter and collector system. this article suggests that overcoming these limitations necessitates the adoption of interoperability by basic operations and illustrates how an internet protocol called cursor-on-target can facilitate communication between open source and propriety systems. interoperability is the exchange and processing of data between systems via a structured format and a common vocabulary. disaster and emergency response support is comprised of three cyclical phases: 1) pre-response analysis and dissemination; 2) disaster and emergency support; 3) post-deployment and re-initiation of the pre-response. in order to ensure rapid response and vigilance to any change in chemical, biological, natural, and/or nuclear conditions, unmanned sensors at all public health sites must send updated data around-the-clock to an integrated disease surveillance system (idss) -a public health data repository -for analysis and dissemination (popovich et al., 2002; see figure 1). information from multiple sources is required to sustain a disaster and emergency response capability. the sensor network must be supported by information feeds from all systems available to the combined public health organization. studies have indicated that electronic automated disease reporting systems, which use extensible markup language (xml) format, are not only more efficient but also demonstrate the same accuracy as manual reporting procedures (popovich, et al., 2002; wurtz & cameron, 2005). http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 to achieve interoperability, systems with a common output/interface can employ a multi-site licensed off-the-shelf translator purchased by a federal or state agency in order to decrease the overall cost of changing from one output format to xml. hipaa (health information portability and accountability act) electronic data interchange x12 (edi x.12), and health level 7 (hl7) interface are commonly utilized for public health electronic disease information reporting (popovich, et al., 2002; wurtz & cameron, 2005). off-the-shelf software is also available to translate and map both hipaa edi x.12 and hl7 interfaces to extensible markup language. a foundation for the communications methodology is required to support safe data transportation, whether in the form of radio, internet, or wi-fi. however, regulations, laws, policy, and protocol demand that the data be protected while in transit. cursor-on-target can provide that foundation using a machine language schema with the flexibility of a built in internet document writer, beyond the existing patient centric interfaces mentioned above. http://ojphi.org/ file:///c:/documents%20and%20settings/gsteve1/local%20settings/temp/l%20%22_enref_1%22%20/o%20%22popovich,%202002%20%22%20/l%20%22149%22 cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 figure 1: a vision for an integrated disease surveillance system (popovich et al., 2002) cursor-on-target (cot) is an internet protocol and an xml based machine-to-machine schema that can be read and understood by any system, enabling proprietary and open source systems to communicate with each other. the schema can be used to transmit data from unmanned sensors to collector databases or be displayed on arcgis systems to indicate, for example, contamination expansion during a chemical disaster. during deployments, sensors can collect continuous data from a surrounding disaster area. the information can alert commanders to any chemical, biological, and/or natural contaminants. therefore, commanders can be proactive in protecting their personnel instead of reactive once the symptoms emerge due to this enhanced situational awareness. cot will allow personnel to use available communication signals to crosscommunicate with each other and command, especially when the normal infrastructure is damaged or overloaded; all cot transmissions are traced and displayed on routers. however, public health policies must advocate and provide credibility to the principle of interoperability to support data sharing, meet capacity goals, and enhance safety of the responders. this article presents an expansion on an earlier vision to implement interoperability across the entire public health system that remains compliant with hipaa mandates. interoperability must start with the full data sharing at the basic system level and continue through the command and control level during joint disaster response actions depending upon the agreements reached during interagency policy meetings. background recent terrorism incidents coupled with natural and man-made disasters have forced the public health system into a perpetual capacity building mode(harrison & harrison, 2008; popovich, et al., 2002; yu, et al., 2010). yet, advances in technology and changes in the role of information technology in disaster responses have resulted in a proliferation of sophisticated systems that aim to enhance the survivability of disaster patients (chan, et al., 2004a; harrison & harrison, 2008; popovich, et al., 2002). the american telecommunications system has undergone three upgrades in the last ten years and the wireless network protocol was upgraded to 802.11n in 2009. the new protocol contains provisions for advanced encryption standards, multiple-in multiple-out channels, and quality of service standards during transmissions. internet protocol (ip) version 4 is upgrading to version 6, making it more attuned to mobile networks(camp & knightly, 2008; chong shen & kwong, 2010; choudhary & sekelsky, 2010; force, 2011; n. w. group, 2006a, 2006b, 2007, 2009a, 2009b; hiertz et al., 2008; koskiahde, 2002; nikander, arkko, aura, & montenegro, 2003; transformation, 2004; villavicencio, lu, zhu, & kota, 2007; walke, mangold, & berlemann, 2006; williams, cansever, & islam, 2006; yang, 2005). advancements in communications and computer systems technology have allowed medical systems to enhance patient recognition, medical record selection. thus, remedial systems are no longer restricted to hospitals. immediate remedial medical assistance can be identified at the point of an accident or disaster and given by a first responder until the patient is released from a recovery site(chan, et al., 2004a). although technological advancements have supported medical system enhancements in the field, researchers have identified inter-connective errors in some of the remedial systems(chan, et al., http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 2004a). although programs were tested and deemed operational under simulated disaster criteria in a closed system, these conditions do not reflect the real-world environment (chan, et al., 2004a). thus, any errors were identified as network-related oversights such as problems of coordination and real-time data acquisition. when more realistic situations or actual usage tests were performed, the systems experienced some connectivity failures, especially in cases where transmission point and receipt site were distant from one another and where transmission path was obscured. consequently, the full disaster contingency plan was not activated. in real world disaster responses, public health personnel rely on multiple health agencies and medical responders for help with wounded patients. like the general population, all such agencies utilize airwaves, cellular phones, and wi-fi. system overload eventually causes the communications systems to crash, as happened during katrina and 9/11(harrison & harrison, 2008; popovich, et al., 2002). a communication methodology or platform that optimizes joint usage for more throughput of the same bandwidth (while minimizing the overall impact on the bandwidth) can address these challenges. for example, a rescue helicopter could act as a router for an internet protocol address message if the aircraft was closer than the next known router. interoperability ensures that data in a common language or format flows smoothly through the system. interoperability interoperability is “the ability of different systems to exchange information so that it can be processed meaningfully by all systems involved, e.g., between different software products or sub-systems elements, hardware devices, or multiple networks” (hargrave, 2001). challenges to interoperability have been studied from many perspectives, including health care, information technology, and military. it has been noted that some military systems do not have the capability to exchange information in a meaningful manner (hatzipapafotiou & kreisler, 2005; hernandez, 2007; suri et al., 2009; transformation, 2004; white, 2001). as a result, nato is encouraging enforcement of communication interoperability for any aircraft and communication system coming online (kenyon, 2008). interoperability would allow personnel and equipment to communicate with and through each other while cutting down on actual bandwidth use. all authorized command sites would have a full, consolidated functional picture that incorporates data from a range of participants in a disaster response, including first responders with an iphone application providing air evacuation departure schedules. interoperability would allow timely joint response to any situation while enhancing working communication control. the system must meet one criterion only: it must be communication interoperable with all other systems in the inventory. concurrently, message routing and flow would be optimized vertically and horizontally within any singularly contained or mixed response group. the optimization is based on a common thread that exist between all the data and communication systems currently in use and any that will be allowed to be put into the inventory in the future. all systems must be able to converse with each other using the common machine language that the extend markup language is based upon(harold, 1999). when speaking of communications between or within an interdisciplinary group like a disaster response team or even the public health system, interoperability must cover proprietary data, sensory, and communication systems. interoperability, however, is only part of the answer to the communications problem. policy must guide interoperability, control security and technology, http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 and the formatting and sharing of information. further, interoperability must be regularly tested, as is the case for any form of health technology, and documented in accordance with applicable federal, state, and local laws, regulations, and policies. without regular testing the systems may not work when needed. policy federal, state, and local agencies need explicit intergovernmental roles and responsibilities around interoperability in public health policy. this policy should provide a method for putting interoperability into action, and provide guidelines or limitations on data sharing and group interaction. the guidelines should stipulate when data is available to outside agencies, the responsibility of each agency, and the chain of command during declared emergencies. because disaster and bioterrorism scenarios are not stable and technological advancements occur rapidly, this policy must be flexible and be comprised of standards that can be adjusted to suit diverse and evolving emergencies (gao, 2003). finally, policy should implement guidelines for collecting “baseline” data from laboratory-based reporting systems, hospitals, and federal, state, and local health agencies during pre-disaster deployment phases. all agencies need to train with the available data before encountering it for the first time in the disaster. translators protected health information (phi) must be electronically transmitted during disasters, bioterrorism events, and public health emergencies. patient information must be secured and transmitted to the receiving data sites, preferably in a format that will be recognized by installed applications. with the emergence of regional health information networks, data about the patients at a disaster site could be made available to responders for triage purposes. to access such information, a data translation tool for hl7 and edi x12 are necessary. edi x12 can be translated using java or multiple other off-the-shelf translators that convert all known x12 hipaa formats to extensible markup language (xml) format. these translators support edi for hipaa 5010 and international classification for disease -10. hl7 interfaces can use default encoding in a delimiter-based format called hl7 version 2.2 or 2.3(international, 2012). additionally, level 7 can also use a xml format, and some entities have been migrating to the xml format. however, not all data relevant to a disaster response will contain phi. for example, panackal et al (panackal et al., 2002) and wurtz and cameron (wurtz & cameron, 2005) indicate that electronic laboratory reporting (elr) for infectious diseases is faster, more complete, and just as accurate as reporting done by hand. because the elr uses hl7 format and an oracle database, de-identified data can be easily translated to an xml format and displayed on a viewer for tracking purposes. elr is growing rapidly. an arc-gis interface has already been built for the u.s. air force (odom, 2008) and there are interfaces to global positioning systems (gps), google earth, and falcon view. these display systems utilize the xml tracking mechanism and the input from gps to place moving assets such as aircraft, meteorological cloud boundaries, and personnel accurately (general dynamics, 2011). http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 cursor-on-target the proposed foundation for transmission interoperability is a machine-to-machine based schema called curser-on-target (cot) built by the mitre corporation and the air force. cot is a methodology that reduces interoperability problems while enhancing information technology using basic machine capabilities. “cursor-on-target” was coined when an air force general called for integration of the multiple systems under his control. “the sum of all wisdom is a cursor over the target,” his catchphrase, became the namesake for cot. the schema transmits a target’s “what (type), where (coordinates), and when (time)” without risk of human error or delay. cot is transport agnostic, as the appropriate choice of transport depends on the data that is being moved (e.g., file transfer protocol (ftp), transfer control protocol (tcp), hypertext transfer protocol (http), and so on) (c. o. t. u. group, 2010). the format does not prevent developers from using fourth-generation languages to interact with proprietary systems as translators or information carriers to update and share cross-platforms. cot can update one or many sites at once depending on the parameters set within the schema. xml is open source, and easy to read and write, and the language's tag properties enhance its versatility. one or many entity details can be transported in a single cot message under a singular identification, and each detail’s identifying tag marks it as a new detail record. this ability, together with the message’s normal small bandwidth use, minimizes network usage. the cot router’s user guide suggests that the schema is defined in “event.xsd,” and is registered in the defense information systems agency (disa) department of defense (dod) xml registry (kristan, hamalainen, robbins, & newell, 2009). the schema is available at mitre’s website: http://cot.mitre.org. the schema is terse (concise) and effectively describes “what, when, and where.” an example of the schema is expressed in figure 2. figure 2: basic cot xml message (kristan et al., 2009) http://ojphi.org/ http://cot.mitre.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 the schema has twelve mandatory attributes and can hold multiple sub-schemas through the manipulation of other xml tags (c. o. t. u. group, 2010). as shown in figure 2, cot utilizes three main types of tags. the first tag is the event tag and provides information with unique identification (uid): hierarchical data on the object of interest (type); time the message was generated (time); time the event became of interest (start); time the event is no longer of interest (stale). the “a” in the type line indicates the “atoms branch of the tree” defining detailed types given in military standards 2525b specification. military standards 2525b is the document that gives the symbolic representation and corresponding unique identifier of assets and threats and guarantees quick interpretation by anyone who is familiar with the specification. the second tag is the point tag, which gives the geo-localized position and elevation of the item of interest. lastly, the detail tag enables users to transmit data and/or records to applications or systems using the structured xml format. xml schema accepts extensible style language tags that generate reports, structure web screens, and change reports based on input based on data received from unmanned sensors (c. o. t. u. group, 2010; kristan, et al., 2009). given the wide range of unmanned sensors being developed, the flexibility of this standard and the ability to integrate with gis systems provides a robust interface beyond the existing capabilities. these capabilities enable and strengthen communication to and between proprietary systems. as long as there is a network connection, cot can expand the combined situational knowledge of a disaster area and data transmittal distance by sharing broadcast power and bandwidth. at the third annual cursor-on-target (cot) users group conference convened in 2011, the group demonstrated fifteen disparate systems continuously transmitting and receiving messages from each other. while overall routing was accomplished through a main router, multiple routers directed the message through various vendor system sessions as a test of connectivity. security security measures are considered more reliable when the measures are layered. thus, if one layer fails, the subsequent layers can intercede and prevail where the preceding layer failed(tipton, 2010). security for cursor-on-target is controlled through the basic schema given in the user’s guide (kristan, et al., 2009) as shown in figure 2. the transmission must meet a valid format, but it also must include an authorized uid, symbolic type code, latitude, and longitude or the transmission will be ignored. the uid must be registered with the controlling internet protocol controller before the uid will be recognized on the channel, which is controlled via ip v6. internet protocol version 6 is more secure than version 4 and uses a more sophisticated risk adaptive access control and dynamic security response to changing operational situation(choudhary & sekelsky, 2010). the xml format allows encryption within the format tags(harold, 1999). thus, when used in conjunction with the flexibility built into the user’s guide and the xml structure, encryption provides cot transmission a third and fourth layer of security. http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 discussion in the last two decades, there have been three million deaths due to terrorist attacks, floods, earthquakes, and disease outbreak. health care shoulders a tremendous degradation in its emergency medical response ability amid personnel and budget reductions. yet, crisis response cannot be reduced as disasters require enhanced mobility tasking. secondary response capability to an active disaster-recovery area is decreased by infrastructure damage at the event site. a constant but undetermined drain on the medical supply stockpile used to replenish remote site(s) continues. depending on the level of infrastructure damage at the disaster site, central control may not be aware of the situation on the ground (chan, et al., 2004a; harrison & harrison, 2008; popovich, et al., 2002). field commanders make quick decisions, sometimes basing them on an incomplete tactical situational picture. enhanced interoperability complemented by the communication power of cot can provide commanders the comprehensive picture, vertically and horizontally, they require to make informed decisions. the vertical communication flow between responders and the horizontal flow to site commanders provides an effective chain of command for efficient data and communication channels. interoperability must be used in conjunction with an on-site command structure. as inability to communicate effectively leads to lost lives (panackal, et al., 2002), planning and cooperation can and should address barriers to communication and interoperability. harrison and harrison (2008) advocate a disaster response view based on regional, national, and international efforts to coordinate an effective reaction methodology that provides for local, state, and federal programs established on technological architecture. this architecture would be the foundation for communications, management, and administrative control and interoperability. their proposal aims to ensure optimal use of medical records, geographic information systems and tracking systems and, most importantly, to generate improved medical response capabilities. chan et al. (chan, et al., 2004a) suggest that overcoming potential infrastructure damage by way of layered communication systems is a major disaster response challenge. ip-based cursor-ontarget is flexible and able to multi-cast information collected from satellite, landline, mobile, and wi-fi channels. such multi-cast capability enables coordinated medical responses by broadcasting alerts or request messages to numerous on and off-site control centers(william o jenkins, 2003). the us joint command tested the capabilities of cot xml during an exercise that combined the competencies of three distinctive systems to control intelligence, surveillance, and reconnaissance assets. typically, information from one system is displayed only on its individual screen. to address the lack of communication between systems, four small translation programs -one per system -transformed the data to cot’s xml format. three systems updated a fourth, which in turn updated all systems, allowing information from all four systems to appear on all screens (including a laptop). the joint command is scaling up their use of cot xml to include radio transmissions and chat protocols (lawlor, 2005). public health systems should leverage the capabilities of cot (i.e., using xml to update separate, but disparate proprietary systems) to meet the cdc’s capacity goal for data sharing. http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 data sharing and interoperability transcend national borders and flow intrinsically with the medical, public, and military health personnel whether they are deployed to third world areas or within us borders. interoperability, for example, could have prevented the overloaded communication systems and miscommunicated verbal orders that caused critical patients at the world trade center (wtc) in 2001 to be routed to non-critical care hospitals (chan, killeen, griswold, & lenert, 2004b). cot can link the idss with the remote chemical sensors and a geographic information system during preand postdeployment periods or allow sensors to be placed strategically to provide data during deployments. furthermore, cot has a stale time to indicate when the sensor information is no longer valid. finally, cot ensures data security by encrypting phi. cot uses codes to designate the “what” of the message, and the phi could be encapsulated within the detail sub-schema. the sub-schema can be encrypted within the detail tag format; thus, the transmission will meet the requirements of hipaa. the ability of cot to transmit the geo-position of a patient or track the movement of the boundary of chemicals through tracing when used with arcgis or other applications can visually alert responders to impeding danger, which would dramatically expand the current capabilities of any health care data interchange overseas joint humanitarian relief responses have been hampered by the lack of evidence-based medical assistance and care. healthcare reporting during and after any disaster, bioterrorism, or emergency response action necessary for future evidence-based medical procedures were incomplete due to minimal communications. information is manually captured then lost or never transferred to a database for analysis. most health operations following the initial rescue entail delivery of medical assistance to the injured and efforts to contain or slow the spread of contagions due to unsanitary conditions and close living quarters. evidence-based information is critical to lower the projected morbidity rate during initial triage stages of a disaster (banatvala & zwi, 2000; brattberg & sundelius, 2011; fernando & hilhorst, 2006; jr, mayer, mason, jr, & mahoney, 1985; posen, 1996; queen & pugh, 1992). the reports and information from teams on the front lines provide data to maximize humanitarian evidence-based care while minimizing diseases due to destroyed infrastructure and diminished living conditions. these conditions continue as the responders leave the affected area after an initial ninety or hundred-eighty day window without repairing the underlying communication infrastructure. for example, an ineffective communication infrastructure in haiti still degrades the point-of-care testing and evidence-based care giving and data gathering in some urban and most rural areas. thus, diseases and morbidity rates continue to climb and patients are being tracked via mobile phones during the disaster recovery and rebuild period (bengtsson, xin, thorson, garfield, & von schreeb, 2011). cot or another xml based communication schema that enhances interoperability and is approved for release to third world countries could increase the speed and quality of evidence-based care and data exchange during and after disasters. it would also ensure the quality of research data for future evidence-based procedures while increasing the quality of health in the host community. cot allows captured data to be manipulated using extensible style language (xsl) (harold, 1999). xsl can be used to show visual ranges from sensor output in print format. xsl is simple to use, machine language based, and can be used in a hierarchical (class) manner. reports can be formatted in any way that is needed and delivered straight to a printer in a graphical http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 presentation or to a display in text form. of course, the data can be formatted using xml or xsl and imported directly into a database for analysis at a later date with current needs being met by mobile applications. the versatility of the sub-schemas allows on-the-go modifications with minimal problems and downtime. it also has the add benefit of multicasting to different commands and allowing all levels of personnel to converse in a secure and quick manner. these latter components are great assets if the health units operate with other task forces that use cot or another xml interoperable schema; thus, the cross communication between forces and vertical command and control communication lines is increased. data can be sent to collectors for analysis to assist public and military health personnel with building an evidence-based response. the transport schema assists in providing cross-communications while public and military health personnel treat the affected population for injuries and diseases prevalent in overcrowded populated areas under great stress. limitations cot must be built for each interface with a device and tags delimit fields with the field for comments being set by the remark tags. the cot router routes all messages from one host to another host and holds all the pertinent rules and destination addresses. cot must contain twelve schema tags and cannot deviate from that structure. the schema requires an initial setup of the hierarchy and privileges to utilize the structure. however, these minor stipulations will open the door to interoperability. the xml format and tag usage provides flexibility to each agency and or/unit to determine how they want the output to look at the final destination while meeting the mandated interoperability requirements for data sharing and bandwidth transmission capabilities. conclusion economically, cot is the most efficient and effective methodology to join old and new technology with policy and strategy to gain interoperability. it can dampen technological problems while enhancing communications during a disaster response. cot’s ability to interact with any cross-platform system as it did during the joint command exercise keeps personnel “in the loop” where they can be proactive instead of reactive (lawlor, 2005). it also provides quicker and easier interagency communication, when public health responders are deployed with military health units. as “smart devices” become ubiquitous, cot enables health or other personnel to accurately position themselves via maps or directives displayed on an iphone, preempting errors that often arise in utilizing radio for such communication, for example. of course, data can be formatted using xml or xsl and be imported directly into databases for analysis at a later date with current needs being met by mobile applications. for the american global disaster response policy to work at maximum capability, the interdiction should be based on rapid deployment of evidence-base care to displaced inhabitants together with evacuation of those listed as missing (brattberg & sundelius, 2011; chan, et al., 2004a; fernando & hilhorst, 2006; jr, et al., 1985; posen, 1996; queen & pugh, 1992; yu, et al., 2010). http://ojphi.org/ file:///c:/users/dehasnem/appdata/local/microsoft/windows/temporary%20internet%20files/content.outlook/95vno2de/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 evidence-based medicine requires an available comprehensive database that will provide and accept data from on-site triage facilities. physicians should be able to share data with and train in-county public health agencies and clinicians in an effort to stem long-term morbidities and disease outbreaks. a network-centric communication foundation based on cot would provide the capability for a joint global civilian/military public and medical health task force to share medical information collection equipment and data rapidly and securely with each other (popovich, et al., 2002; shulstad, 2011; transformation, 2004). cot would also allow the medical task force to converse and share information with the american military forces sent to secure the area and patch information back to the united states via satellite connections. homeland security, justice department, military, and members of the medical and public health field, to include the cdc, are already calling for interoperability and rapid disaster response capabilities that are built on sharing data before, during, and after a disaster (banatvala & zwi, 2000; bengtsson, et al., 2011; brattberg & sundelius, 2011; brown, griswold, & lenert, 2005; chan, et al., 2004a; fernando & hilhorst, 2006; gao, 2003; gurss, 2011; harrison & harrison, 2008; jr, et al., 1985; kenyon, 2008; lawlor, 2005; lenert, palmer, chan, & rao, 2005; popovich, et al., 2002; programs, 2005; sharp, burkle, vaughn, chotani, & brennan, 2002; terry, 2012; william o jenkins, 2003; yu, et al., 2010). however, cursor-on-target entails some initial work to build the hierarchy. currently, military standard 2525b does not contain codes related to health as the standards relate to symbolic codes for war fighting vehicles and structures (fletcher, arnold, & cockshell, 2011) . however, the cot users group is glad to assist with setting up the necessary codes. building the system hierarchy should be done in tandem with policy decisions as the code will affect everyone, unless there will be two hierarchies, one each for public and military health. two codes would defeat the purpose of interoperability and slow the process of communicating and saving lives during a joint disaster operation. all organizations have found it is best to cut costs and share data while maintaining the cutting edge readiness necessary to protect people and property from natural or man-made disasters, terrorism, and emergencies. the justice department and homeland security are building their shared foundation using xml (programs, 2005). cot can help ensure that human life is protected proactively instead of reactively. corresponding author greer stevenson clinical assistant professor college of applied health sciences university of illinois at chicago email: gsteve1@uic.edu http://ojphi.org/ cursor on target: research for a sensor network online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 references [1] banatvala, n., & zwi, a. b. 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(2010). future connectivity for disaster and emergency point of care. point care point of care, 9(4), 185-192. http://ojphi.org/ http://it.ojp.gov/jxdm/ http://www.informationweek.com/news/healthcare/emr/232601824 http://www.gao.gov/new.items/d04231t.pdf crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 172 (page number not for citation purposes) isds 2013 conference abstracts using a syndromic approach to study health impact and risk factors of alcohol intoxication in reunion island pascal vilain*1, sophie larrieu1, xavier combes2, arnaud bourdé2, pierre-jean marianne dit cassou3, katia mougin damour4, yves jacques-antoine5 and laurent filleul1 1regional office of french institute 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in any medium, provided the original work is properly cited. isds 2014 conference abstracts a semantic web platform for online vaccine sentiment surveillance arash shaban-nejad*1, 2, sonia menon2 and david buckeridge2 1university of california, berkeley, ca, usa; 2clinical and health informatics research group, mcgill university, montreal, qc, canada objective this paper presents our approach on design and development of an integrated semantic platform to capture the domain knowledge on vaccine sentiments, beliefs, and behaviours using ontologies. the vaccine sentiment ontology (vason) provides more structure around the vast amount of unstructured data scattered over blog posts to facilitate blog content analysis, and discovering patterns of words or phrases in blogs text (e.g. specifying topics, themes, sentiment, beliefs and so on). it also assists in revealing opinionated claims and assertions in blogs and specifying the authors, forms, functions, geographical locations, audiences of blogs, as well as bloggers’ motives. introduction real-time monitoring and analysis of vaccine concerns over time and location could help immunisation programmes to tailor more effective and timely strategies to address specific public health concerns. in recent years attempts [1, 2] are being made to develop a more systematic monitoring of broader public vaccine concerns resulting in vaccine refusals and potential disease outbreaks. automated sentiment analysis software applications are being developed to detect and track the emergence and spread, geographically and temporally, of online social media reports on vaccines by developing a new application for opinion mining and sentiment analysis. although many of the current approaches for automated sentiment analysis provide a timely method to assess the sentiment of a population towards vaccination, they do not assess beliefs, perceptions and behaviours. incorporating semantic approach by using ontologies captures the domain knowledge and supports automated extraction and analysis of text in blog posts related to vaccination. methods the vaccine attitude surveillance using semantic analysis (vassa) framework [3] is intended to support the automated extraction and analysis of text in blog posts related to vaccination. the framework consists of a natural language processing (nlp) module, which is used for text analysis and concept extraction, and the vason ontology, which models existing knowledge about the relationships between vaccination beliefs and behaviours. vason is operating at the overlapping borders between three kingdoms, namely health, online social networks, and human behavioural model. the health part deals with concepts such as vaccine, vaccination, disease, health states, and adverse event. concepts such as blogs, blog posts, bloggers, comments, forum, web sites and media are grouped under social networking category, while we study human perception, awareness, belief, behaviour and sentiment under the behavioural classification. the interaction between these three major components are analysed with the human factor at the centre at both individual and population levels. the data for creating the vason conceptual model comes from the literature, databases and some of the existing vocabularies and biomedical ontologies. results the vason ontology can be used to facilitate concept extraction and analysis of the extracted concepts using an nlp module. the development of the ontology is currently in progress and we are now performing several experiments for our text extraction using the vason sub-taxonomies adapted/imported from the vaccine ontology(vo) and the disease ontology. conclusions vason aims to provide knowledge on the factors driving vaccine refusal and to identify potential interventions for increasing coverage. the work on the ontology is in progress, and the effort on integrating the ontological knowledge with existing relevant public health resources and services is still underway. also we are enriching the semantic model to perform more sophisticated queries on the relations between vaccine beliefs, vaccine adverse events, and the risk factors. keywords vaccine sentiment analysis; online social media; vaccine refusal; semantic web; ontologies acknowledgments the project is funded by the public health agency/canadian institutes of health research influenza research network (pcirn). references 1. salathé m., and khandelwal s.: assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control . plos comput biol. oct 2011; 7(10): e1002199. 2. larson, h.j., smith d.m.,paterson p, et al. measuring vaccine confidence. lancet infectious diseases 2013; 13: 606 – 613. infect dis 2013 . doi.org/10.1016/s1473-3099(13)70108-7 3. brien s., naderi n., shaban-nejad a., mondor l., kroemker d., buckeridge dl.: vaccine attitude surveillance using semantic analysis: constructing a semantically annotated corpus. www (companion volume), acm press, 2013: 683-686. *arash shaban-nejad e-mail: arash.shaban-nejad@mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e157, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 64 (page number not for citation purposes) isds 2013 conference abstracts examining the human element in lab biosafety betiel h. haile1, mark wade2, patricia blevins2, andrew cannons3, richard france3, lisa d. ferland1, affan shaikh1, meeyoung park*1, ngozi erondu1, sean g. kaufman4, heather meeks5 and scott j. mcnabb1, 4 1public health practice, llc, atlanta, ga, usa; 2san antonio metropolitan health district, san antonio, tx, usa; 3bureau of public health laboratories-tampa, tampa, fl, usa; 4emory university, atlanta, ga, usa; 5defense threat reduction agency, ft. belvoir, va, usa � �� �� �� � � �� �� �� � objective ��� ���� ����� � �� ���������� ��� � ��� ���� ��� �� ��� ������ ���� ��� ���������� � introduction ������ ��� ����� ������� �� ���������� �� ������������ � ��� �� ���� ����� ������������� ��� �� ���������� ������ ����������� ��� ��� � ��� ����� ��� �������������� ������ �� ��� ��!"#�� ��$������������ "������ ���� %���� � ������ � ������ �$"%��� �� � ���� ��� ����� � � ��&� ����� ��� � � ��� ���� ��� �� �� � ������ � ��� ���� ������� �� � � ���������� � �� �� �� ��'���� ��� �(� �� ��'��� �������� �&������� �'('������� ��)�������� � ������ ����%���� ��) %��*+,�� ����������#� �-�������&�� ���� � ���� �-�� �� ��� �� � � ����� ��� $"%����� �&��� ��� ������ ����� � �� ��������� ��.��������� ��� ����� � �*/,��� �� '('0) %1 ���� ���������2�� ������������������������������� ���� �� �*�2��,�� ��� ��� ��� ����������� ��� ���� #����������3� � ���� ���� �� ��� �������������� ������� ������� �*4,��5� � �������� ��� �� ��� ������ �6�� � �&��������7����� ��� �� ��� ������������6�������� �������� ��� #���&������������������� ��� ������� � � #����� ����� �� ��������������� ������������ ������� ����� � ��$� ��&������������ ��� ����� �� ���� ������ ���������� �� ������������������� ������ ����������� methods 8������������������ ��� �� �&��-������� ���������������3����&� #� ���� #������� ��� ������ ��9������ �-� ��� ���������������������������� � � ��� �� ������������ �����#���� � ���#�� ������ #�� � ����� ���� ���#����� ������� ��� ���#������ �������� ������� �-� ���������������� �.� ��������� ��� �������#�'('� � ��������' ��:�� ���� ��"��/#��2��� &;��� ���&���������������������� ��������������*4#�<,� 9������� � ����� ���&��������������-� ���������������� ��"�������� ����2�� ���������%���� �(� � �����������%���� ����� ��� ������� �� �� ��������������%���� ����� ��� �� ���������#�9�� �����5��&��� ����#� ������������������ ���� #�������� ������ #�������� ������ � #��������� ��� ������ #�-� ����� &��-������� ��"����������� ������-�������&�� ����#����������� ��� &� � #���� �������� � #��������� ������ � #����� ��� ������ #�&� ����� � #� � ����� �#����������� � ���&�� ����#�-� ���� �� &��-������� �������� �����8���&������������ ��� ��� ������ ������ ��&�� ���"���+#�/�����4������� � ��� � results � �� ����� ��������� +4=� �������� � ������ �� ��� ��� � ��� �� ��� >� � ��&� ���6�� ������� ���;?�#���� ������ � &������� ��:�� ���;@�#����� ��������� &��-��+@���� ��&� ���6�� ���� �������� ������ � &����� ���� �� ������� ������ � ��� ��������������������������"��������� &��� ����������� �������� � ��� � ����� ����������������� ������� �������� ! �����&� �������:� �� ����� ����� #���� � ��� �-� ����� ��� ���a&� �� �������b������� ������ ������-�� �������������� ��� �����a&� �������� ��&�b���� ��������� ����� �����������������.�� � � ��� ���"��4� ����� -� � �����a&� �����������b����a�.� ��������������������������� � � �� ��� �������&�:� ��� ��c�� �� � �������� ���-� � ����� ���������� ��������� ����-�� ����� ����������� ������ ������6�������#����:���� �� �������������� ������� ������#����������������0 ������ � ����� ��� �������� ��� ������� ���a�������� ��� ����b�� conclusions ���������� ��� �#���� ��� ������ ���� ������������������������ �� �� ��� ��������)���� ��� #� ������������������ ����� ��� �� ������������ ��� ������� ������ ������������� ��-� :�� ��������� ������� ������� �&� ���:������#�� � ��� �#����� ������� ������&������ ���6�� ������ ������� ����������� ��� �� ���.� ��������������� ������.� ������"����� -�������������� ���&� ��� �� ��������� ��� ������#� ��� ���#� ����� � ������������������������� ������� ����� ���� ������������� �� � � � � ������ �� �� ������������� ����� �������� ������ ������������ � ������ ������� ����� ��� ���� ������ � :�� �� ��9������#���� � ���� ��#� � ��������������� � ������������ ��� �� ����#�� ����� � ������������� ����� ���������� ���������������������������� ����� &������ � keywords ��� �����7����� ��� �7�� � ���� acknowledgments "��������:� ����%�� ' � �2���������%�� references +��$"%������� ��� ��"������d��������* ��� ���,��/=++�*������/=+4�"���� /,��&��������� ��� ��� >00---�� ����&0����������� 0���� ��� �0$"� %�4<=<���� ��� �� ������������������ /��$"%���"�����$������������"����������%���� ����� �* ��� ���,��8� � ������>�!"�(��� ������������� 7������*������/=+4�"����/,���&�������� � ��� ��� >00---�� ����&0�� �0� �0����.� ��� 4��'('#�) %����� ���������2�� ������������������������������� ��� �� �� ;� ���������� �>�' � �-�����#�8�� ���(����� ����8� ������>�!"�d�&� � ������� �������$�����7�/==e� <����% "0'('�"������������� �� ���� � ��������' ��:�� ���� ��"��/� ���� ��� �� ��8� ������7�/=++� *meeyoung park e-mail: mpark@publichealthpractice.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e43, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts classifying supporting, refuting, or uncertain evidence for pneumonia case review brett r. south*1, 2, heidi s. kramer1, 2, barbara jones1, 2, melissa tharp1 and wendy chapman1, 2 1biomedical informatics, university of utah, salt lake city, ut, usa; 2va salt lake city health care system, salt lake city, ut, usa objective we sought to identify relevant evidence that supports, refutes or contributes uncertainty when reviewing cases of suspected pneumonia and characterize their interaction with uncertainty phenomena found in clinical texts. introduction characterizing mentions found in clinical texts that support, refute, or represent uncertainty for suspected pneumonia is one area where automated natural language processing (nlp) screening algorithms could be improved. mentions of uncertainty and negation commonly occur in clinical texts, and opportunities exist to extend existing algorithms [1] and taxonomies [2]. in general there are three main sources of uncertainty found in healthcare: 1) probability or risk; 2) ambiguity – lack of reliability, credibility or adequacy of the information; and, 3) complexity – aspects of the phenomenon that make it difficult to comprehend [3]. methods we conducted an automated screening of all outpatient encounters occurring at the va salt lake city health care system before 01/01/2012 to identify a cohort of suspected cases of pneumonia. screening criteria included: a) presence of icd-9 code for pneumonia and; b) presence of an electronic physician note and/or same day chest imaging report. from this larger cohort, we selected a random sample of 25 cases containing 58 documents. all cases were reviewed by a pulmonologist, an internist and five allied health professionals. using criteria based on the cdc pneumonia case definition, and the available clinical documentation, each case was classified as “suspected”, “unlikely”, or “cannot be determined”. reviewers classified evidence into three semantic classes: a) words or phrases that support; b) refute; or c) are uncertain for pneumonia diagnosis. to accomplish this task we used an open source annotation tool called ehost [4] and an annotation approach that focused on identifying and characterizing relevant spans of clinical text that support, refute or represent uncertainty for pneumonia. we report entire ranges of pair-wise inter-annotator agreement and the prevalence of annotations in each semantic class. for those annotations marked as uncertain we categorize the information according to the three general sources of uncertainty. results seven annotators generated a total of 2,042 annotations for supports (1,302, 63%), refutes (470, 23%), and uncertain (268, 13%). average agreement for case level classification was 0.60. range for pair-wise inter-annotator agreement across all semantic classes was (0.34-0.61) and individually for supports (0.25-0.67), refutes (0.37-0.47), uncertain (0.36-0.45). errors where one or more reviewer identified a span of text and others did not were more common than classification errors. the majority (70%) of annotations reviewers marked as uncertain were found in chest imaging reports. for annotated mentions marked as uncertain, (159 59%) represented information where linguistic cues implied ambiguity, (29 11%), where data was unavailable, and only (10 4%) where the data quality was questionable. opportunities exist to incorporate more formal linguistic analyses and extend uncertainty taxonomies. conclusions we found substantial annotator variability in identifying supporting, refuting, or uncertain evidence for the diagnosis of pneumonia in clinical text. future work will expand these methods to a larger case sample and incorporate a more formal linguistic analysis to identify specific lexical cues thereby extending existing taxonomies of uncertainty and improving automated nlp algorithms keywords natural language processing; chart review; annotation acknowledgments this study was carried out using resources and support from the va informatics and computing infrastructure (vinci) project id: hir 08204. references 1. chapman, w.w., chu, d., dowling, j.n. context: an algorithm for identifying contextual features from clinical text. in: acl-07 2007. 2.mowery, d., velupillai, s., chapman, w.w. medical diagnosis lost in translation – analysis of uncertainty and negation expressions in english and swedish clinical texts. proceedings of the 2012 workshop on biomedical natural language processing. association for computational linguistics. 2012. 3. han, paul k.j., klein, william m.p., arora, n.k. varieties of uncertainty in health care a conceptual taxonomy. medical decision making 31.6 (2011): 828-838. 4. south, b., shen, s., leng, j., forbush, t., duvall, s., chapman, w.w. a prototype tool set to support machine-assisted annotation. proceedings of the 2012 workshop on biomedical natural language processing. bionlp ‘12, stroudsburg, pa, usa, association for computational linguistics. 2012. 130-139. *brett r. south e-mail: brett.south@hsc.utah.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e56, 201 ojphi-06-e149.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 17 (page number not for citation purposes) isds 2013 conference abstracts increasing outbreak detection power by data transformations tom andersson*1, 2, pär bjelkmar3 and joanna tyrcha2 1swedish civil contingencies agency, karlstad, sweden; 2stockholm university mathematical statistics, stockholm, sweden; 3swedish institute for communicable disease control, stockholm, sweden � �� �� �� � � �� �� �� � objective �������� � �������� � � � �������� �������� � ����� ����� ������� ������ ����������� ���� ���� ����������� ������������������������������� ������ ����������� �� � ������������ ��������������� ����� �� ������ � ��� ������ � ������ �� �������� ����� ������� �������������� �������� ������������������������ ������������ ���������� ����������������� ��� ����������� � �� ���� !��� ��� "�� ����� ���� ����� #������ !��� ��� $$%%&� introduction !������ �� � ��� ������� �������� ���� ������ �� ��� � ��� ��� ��� ��� ������� �� ���������� ���� ����������� ��� ������ �������������� ������� ���� �� ��������� ��� �� ���������� ����������� ���������� �� ����!� ���� ������ ����� �� ���������������������� 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����a��!��������!��2 ������e����� !������ ��� ��� ������������������ ������������� ����������������5� ���� �� ���� �������� ����������� �������������� � �� ������ ������� ����� ����������c �� ������������������ ����� ���������������f ��� ��� ����g�h����� ����+,$1-�$i5$�$$ (�����2��"�)� �0#��* �����#!��.�� � ���������������� ��������� �� � ��� ��������!��� �� ��� ����� � ����+,$$-�1,-1, $<&5$jji�%%�� .� ��*k��/������h!��������/>��0��� ��� ���� ���������������������� � ������� ������������� ����(d�!���� � ����+,,%-< j&5�+$,� *tom andersson e-mail: tom.andersson@msb.se� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e149, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts transmission dynamics of seasonal influenza in abidjan: epidemiology and modeling anderson k. n’gattia*1, 2, daouda coulibaly1, djibril cherif1, hervé kadjo3 and issaka tiembré1, 2 1epidemiologic surveillance service, institut national d’hygiène publique, abidjan, côte d’ivoire; 2université félix houphouët-boigny, abidjan, côte d’ivoire; 3institut pasteur de cote d’ivoire, abidjan, côte d’ivoire objective this study aims to determine the epidemiological and clinical profiles of influenza infections related to different strains and the effect of climatological parameters on the temporal distribution of the disease for the prediction. introduction influenza poses a global health threat. the disease affects all ages, often with variable clinical features. abidjan, where this study took place, has a long rainy season apriljuly with a shorter less intense rainy season october-november. temperatures vary very little during the year. in temperate areas, children and adults aged 65 years are risk groups. in these countries the seasonality of influenza is clearly defined, with seasonal epidemics in cold weather periods (1). but in the tropics, the risk groups of influenza are not as well defined. also, the dynamics of influenza transmission and climatological parameters that influence it are specific to the tropical region and not as thoroughly studied. methods cote d’ivoire’s institut national d’hygiène publique (inhp) operates sentinel influenza surveillance sites throughout the country, including abidjan, following who guidelines. staff at each site obtain specimens for influenza testing from a sample of the people treated each day for influenza-like illness (ili) or severe acute respiratory infection (sari). data used in this report include all persons with laboratory-confirmed influenza from the sites located in abidjan during 2007 to 2012, and climatological data from the sodexam (national weather service). the 2 test was used to compare proportions of cases by influenza type and also by month and season. sequential logistic regression models were used to predict the epidemiological and clinical profiles, and multiple linear regression was used to assess the association between the number of weekly influenza cases and rainfall, relative humidity and ambient temperature. explanatory models of weekly influenza cases based on climatological parameters used data from 2007 to 2010. including data from 2011 to 2012 allowed us to test the prediction models. modeling through the box-jenkins method was used with arima process. data were analyzed using stata mp 12.0 software, statacorp lp, college station, texas. results a total of 921 influenza cases were identified over the period 2007-2010. of these, 663 (72%) were influenza a and 258 (28%) were influenza b. among the influenza a, 60 (9.1%) were h1n1, 177 (26.7%) seasonal h3n2, 34 (5.1%) ph1n1 (2009 pandemic strain), and 392 (59.1%) unsubtyped. the largest number of positive specimens was from young children aged 0-4 years (472; 52.9%). individuals with ph1n1 were more likely than those with other h1n1 to have cough (or=10.52; 95%ci=1.71 to 64.57) and more likely than those with influenza b to be from the age group of 5-14 years (or=3.10; 95%ci=1.32 to 7.3). for patients with the h3n2 influenza, cough (or=6.3; 95%ci=2.466 to 16.347) and arthralgia/myalgia (or =2.54; 95%ci=1.08 to 5.95) were the best predictors versus influenza h1n1. the highest monthly and seasonally proportions of influenza viruses were observed the months corresponding to the long rainy season and the short rainy season in which respectively 48.4% and 20.2% of influenza viruses were isolated. the multiple linear regression showed association between the number of weekly influenza cases and rainfall ( =0,025; p=0.008) but not with relative humidity (p=0.108) and ambient temperature (p=0.786). arimax models perform best only with the introduction of rainfall at lag (epidemiological weeks: ew) ew0(0.187) and ew-5 (0.175). the arimax(2,0,0)rf, was the best model (performance: aic=1257.7; bic=1277.6). the prediction of the weekly incidence of influenza from ew1(2011) to ew52 (2012) was implemented by arimax(2,0,0)rf and fitted best values compared with observed values. conclusions the specific epidemiological and clinical profiles of influenza were clearly identified in our study. weekly rainfall was a good predictor of the transmission of seasonal influenza in abidjan, in a setting with little change in temperature through the year. this should allow physicians to detect influenza cases and strengthen surveillance and preventive measures at the approach of rainy seasons. keywords transmission dynamics; seasonal influenza; epidemiological and clinical profiles; climatological parameters; cote d’ivoire references 1. oms | grippe (saisonnière) [internet]. who:www.who.int/ mediacentre/factsheets/fs211/fr/index.html *anderson k. n’gattia e-mail: jeanandersonk@yahoo.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e42, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts impact of patient self-registration in emergency departments on syndromic surveillance data melinda c. thomas*, david atrubin and janet j. hamilton florida department of health, tallahassee, fl, usa objective to assess the effect of patient self-registration methods in hospital emergency departments on data in a syndromic surveillance (ss) system and provide suggestions for analysis of these data. introduction the florida department of health electronically receives hospital emergency department (ed) data from 180 eds located in 54 of its 67 counties through its electronic surveillance system for the early notification of community-based epidemics (essence-fl). florida eds have begun to offer self-registration options to patients, which include ed self check-in kiosks, and pre-visit registration smartphone applications and websites. essence-fl receives ed data from multiple hospitals that use these patient self-registration methods. to date, limited investigation has been carried out to determine the impact of these self-registration methods on the data submitted to essence-fl. this project investigates and describes how ss data are affected by these options and provides possible best practices for identifying and analyzing these data. methods the essence-fl system was queried by hospital with a weekly time resolution for chief complaints (ccs) containing the term “i” for the period of week 1, 2006 to week 31, 2014. this query assessed the ccs of visits for all hospitals reporting to essence-fl to target potential patient-entered ccs. hospitals with a dramatic and sustained increase of visits with “i” in the cc over the time period were identified as hospitals of interest that may be using patient selfregistration. cc and discharge diagnosis (dd) data were analyzed for each hospital of interest to investigate potential disparities in the data for the time periods before and after implementation of selfregistration, including changes to ed visits flagged by the system as visits of interest (voi) and to visits binned in the influenza-like illness (ili) syndrome category. ed visits are binned in the voi category when a term of epidemiological significance is found in the concatenated cc-dd field. efforts were made to contact hospitals to confirm their use of self-registration methods. the results for one hospital using the inotify by itriage smartphone application and website are given below. results data from one of the 16 hospitals of interest were compared for time periods before and after the estimated date the hospital ed implemented the inotify self-registration feature. during the period before implementation (week 1, 2007 to week 8, 2011), 0.60% of all ed visit ccs contained the term “i” (median 2 visits per week, range 0-12). during the period after implementation (week 9, 2011 to week 31, 2014), 1.18% of all ed visit ccs contained the term “i” (median 4 visits per week, range 0-31). for ed visits binned in the ili syndrome category, 0.76% of ed visit ccs contained the term “i” before implementation compared to 0.89% of ed visit ccs after implementation. there was a negligible difference in ed visits in the voi category when comparing the before and after implementation periods (0.20% vs. 0.23%, respectively). a qualitative analysis of the cc field before and after implementation of patient self-registration will also be discussed. conclusions preliminary analysis indicates that, although not exhaustive, the simple query used to find increases in visits with ccs containing “i” proved a sufficient method to find hospitals potentially using patient self-registration methods with minimal resource investment. there is a discernable difference in the use of cc terms after the implementation of a patient self-registration method, but the change does not appear to negatively affect the ability to successfully query the data for voi. how a hospital decides to store and use the patient self-entered responses directly impacts the content of the cc data submitted to a ss system. understanding the internal business processes of a hospital is essential to providing context for the data received in a ss system, which sometimes necessitates communication with the hospital. implementing a rigorous data field documentation process for each hospital submitting data to a ss system could greatly ease the burden of both hospital and public health staff in performing such data field investigations and analyses in the future. keywords essence-fl; emergency department data; patient self-registration; syndromic surveillance acknowledgments johns hopkins university applied physics laboratory applied public health informatics fellowship program *melinda c. thomas e-mail: melinda.thomas2@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e93, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts development and piloting of ascariasis surveillance system of children in sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2 1department of parasitology, faculty of medicine, university of peradeniya, peradeniya, sri lanka; 2department of community medicine, faculty of medicine, university of peradeniya, peradeniya, sri lanka objective designing, developing and piloting an ascariasis surveillance system of children to determine factors associated with their variations in sri lanka. introduction ascariasis is one of the most common intestinal nematode infections caused by ascaris lumbricoides, especially in the tropics and subtropics where warm, wet climates favor year-round transmission of infection(1). humans become infected by ingesting infective ascaris eggs in contaminated food, water or from hands that have become faecally contaminated and can cause reduced physical fitness, growth retardation, and respiratory and gastrointestinal problems(2). the highest morbidity is found in children, especially in those with a high worm burden(3). to identify high risk areas for intervention, it is necessary to understand the effects of climatic, environmental and socio-demographic conditions on a. lumbricoides infection(4). in sri lanka, although ascariasis was the commonest intestinal parasitic infection among children, information about associated factors and current health impact is insufficient. therefore, this study was designed to develop and pilot an ascariasis surveillance system among children in sri lanka. methods cross-sectional survey data of 547 study participants in the central province in sri lanka were used to analyze associations between socio environmental data and a. lumbricoides infection, from june 2012 to april 2013. single-stool samples were collected from each and every child to investigate a. lumbricoides infection and anthropometric measurements were taken to calculate heightforage (haz), weight-for-age (waz) and weight-for-height (whz) to determine stunting, underweight and thinness respectively. results 547 children with a mean age 6.0 (sd 3.2) years was examined. multivariate logistic regression module identified shared toilet facilities, live in attached houses, de-worming before 6 12 months period and before 12 months as the most important independent risk variables of all independent variables considered ascariasis. drinking treated water, eating unclean fruit, hand washing with soap after defecation and before a meal was not statistically significant in the analysis. there was no statistically significant association between nutritional status and ascariasis. conclusions the identified factors will be used in the establishment of the ascariasis surveillance system among children in sri lanka. risk factors associated with ascariasis in sri lanka in 2012-2013 (a) reference category p<0.05 significant or = odds ratio ci = confidence interval p = p value keywords ascariasis surveillance; children; sri lanka acknowledgments we are thankful to medical authorities, welfare officers and all the children and their parents/guardians who have voluntarily participated in this study. references 1.bethony j, brooker s, albonico m, geiger sm, loukas a. soiltransmitted helminth infections: ascariasis, trichuriasis, and hookworm. lancet 2006 may 6;367:1521–32. 2. de silva nr, chan ms, bundy da. morbidity and mortality due to ascariasis: re-estimation and sensitivity analysis of global numbers at risk. trop med int health. 1997 jun;2(6):519-28. 3. xu lq, yu sh, jiang zx, jyang jl, lai cq, zhang xj, zheng cq. soil-transmitted helminthiases: nationwide survey in china. bull world health organ. 1995;73(4):507-13. 4. brooker s, clements ac, bundy da. global epidemiology, ecology and control of soil-transmitted helminth infections. adv parasitol. 2006;62:221-61. *lahiru s. galgamuwa e-mail: lahiruahs@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e130, 2015 layout 1 isds annual conference proceedings 2012. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2012 conference abstracts collaboration for improved disease surveillance literature review tera reynolds*1, katie suda2, blaine reeder3, william storm4, al ozonoff5 and howard burkom6 1international society for disease surveillance, brighton, ma, usa; 2university of tennessee, memphis, tn, usa; 3university of washington, seattle, wa, usa; 4ohio department of health, columbus, oh, usa; 5boston children’s hospital, boston, ma, usa; 6johns hopkins university applied physics laboratory, laurel, md, usa objective to improve the method of automated retrieval of surveillance-related literature from a wide range of indexed repositories. introduction the isds research committee (rc) is an interdisciplinary group of researchers interested in various topics related to disease surveillance. the rc hosts a literature review process with a permanent repository of relevant journal articles and bimonthly calls that provide a forum for discussion and author engagement. the calls have led to workgroups and society-wide events, boosted interest in the isds conference, and fostered networking among participants. since 2007, the rc has identified and classified published articles using an automated search method with the aim of progressing isds’s mission of advancing the science and practice of disease surveillance by fostering collaboration and increasing awareness of innovations in the field of surveillance. the rc literature review efforts have provided an opportunity for interprofessional collaboration and have resulted in a repository of over 1,000 articles, but feedback from isds members indicated relevant articles were not captured by the existing methodology. the method of automated literature retrieval was thus refined to improve efficiency and inclusiveness of stakeholder interests. methods the earlier literature review method was implemented from march 2007 to march 2012. pubcrawler [1] (articles indexed in medline) and google scholar [2] search results were sent to the rc via automated e-mail. to refine this method, the rc developed search strings in pubmed [3], embase [4], and scopus [5], consisting of over 100 terms suggested by members. after evaluating these methods, we found that the scopus search is the most comprehensive and improved the cross-disciplinary scope. scopus results allowed filtering of 50-100 titles and abstracts in fewer than 30 minutes each week for the identification of relevant articles (figure). journal titles were categorized to assess the increased range of fields covered; categories include epidemiology, agriculture, economics, and medicine (51 categories total). results since implementing the new method, potentially relevant articles identified per month increased from an average of 19 (sd: 13; n= 31) to 159 (sd: 63; n= 3). both methods identified articles in the health sciences, but the new search also captured articles in the life, physical, and social sciences. between march 2007 and march 2012, articles selected were classified into an average of 10 different categories per literature review (sd: 4; n= 31) versus an average of 33 categories (sd: 5; n= 3) with the updated process. conclusions the new search method improves upon the previous method – it captures relevant articles indexed in health science and other secondary databases beyond medline. the new method has resulted in a greater number of relevant literature articles, from a broader range of disciplines, and in reduced amount of preparation time as compared to the results of the previous search method. this improvement may increase multi-disciplinary discussions and partnerships, but changes in online publishing pose challenges to continued access of the new range of articles. figure. overview of 2012 literature review process including, scopus search [6]; zotero [7], a freely available web application that streamlines content management; and summarized article archive on isds wiki [8]. keywords disease surveillance literature; isds research committee; literature search acknowledgments the isds rc would like to thank past contributors and guest authors to the literature review. references 1. pubcrawler. http://www.webcitation.org/6aziisidh. 2. google scholar. http://www.webcitation.org/mainframe.php. 3. pubmed. http://www.webcitation.org/6azils732. 4. embase. http://www.webcitation.org/6azj0rxhu. 5. scopus. http://www.scopus.com/home.url. 6. isds literature review search (scopus). http://www.webcitation. org/6aziwfc6c. 7. zotero. http://www.webcitation.org/6azia0yed. 8. isds literature summaries archive. wiki. http://www.webcitation. org/6azkit9xq. *tera reynolds e-mail: treynolds@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(1):e49, 2013 author template for journal articles syndromic surveillance data for accidental fall injury 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi syndromic surveillance data for accidental fall injury donald e. brannen*, melissa howell, ashley steveley, jeff webb, and deidre owsley greene county public health, xenia, ohio introduction the 2019 community health needs assessment for southwest ohio had the rate of injury deaths as the top health priority [1]. there was wide variability among the 25 counties assessed, abstract background: fall injuries (fi) are a priority for public health planning. syndromic surveillance (ss) is used to detect outbreaks, environmental exposures, and bioterrorism in real time. since information is gathered on patients, the utility of using this system for fi should be evaluated. methods: strategies to integrate fi medical and ss data were compared using a cohort versus case control (cc) study design. results: the cc study was accurate 77.7% (57.7-91.3) of the time versus 100% for a cohort design. the cc study design found fi increased for older age groups, female gender, november, and december months. dates with any freezing temperature had a higher case fatality rate. repeat acute care visits increased the risk of fi diagnosis by over 6% and trended upward with each visit (r=.333, p<.001). conclusions: the cc diagnostic quality of fi were better for age and gender than for area. the cc study found the indicators of increased risk of fi including freezing temperature, repeat acute care visits, older age groups, female gender, november, and december months. a gradient of increasing odds of fi with the number of acute care visits provides proof that community fall prevention programs should focus on those most likely to fall. a cc design of ss data can quickly identify indicators of fi with a lower accuracy but with less cost than a full cohort study, thus providing a method to focus local public health interventions. key words: accidental falls, public health surveillance, case control, risk factors *corresponding author: donald e. brannen • 360 wilson drive, xenia, ohio 45385 • 937 374-5600 • dbrannen@gcph.info doi: 10.5210/ojphi.v13i3.10264 copyright ©2021 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:dbrannen@gcph.info syndromic surveillance data for accidental fall injury 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi with injury deaths ranging from 45.5 up to 97.6 per 100,000, higher than the national rate of 45.3. the greene county community health assessment (cha) reported that elderly residents and 12% of the county’s population was at higher risk for injuries, emergency room visits, hospitalizations, and mortality associated with accidental falls than ohio [2]. based on these findings, the greene county community health improvement plan (chip) 2017 had injury prevention as a top priority [3]. the chip prioritized root causes of death and morbidity from falls as chronic disease, inactivity, and living alone. one of the chip’s cross-cutting strategies to address fall related morbidity and mortality is to integrate public health data with the health care system’s data. this strategy is consistent with research findings that found syndromic surveillance data for injuries, including fall related injuries to be strongly correlated to hospital discharge diagnoses [4]. in ohio, traumatic injury surveillance, including injuries from accidental falls, is conducted using an automated syndromic surveillance system called epicenter [5]. mortality data became ‘routinely’ available through ohio department of health’s (odh) secure data warehouse to local health departments in 2018 [6]. the risk of falls during hospitalizations has been 2.08 times higher risk for persons with a history of falls [7]. evaluations of falls due to weather have found that time of day and snow was a predictor of emergency department care for fall related injuries [8]. several issues arise when conducting small area analysis on surveillance data. the resident’s zip code provides a key to geocoding the health events observed. using population denominators for zip codes is a viable strategy but methodological issues arise with cases within a zip code area that crosses jurisdictional boundaries. the census bureau does provide rate estimates within jurisdiction population counts, for example, by county, for the decennial census. these rate estimates can be applied to the population estimates to estimate denominators. this requires time that may make interventions irrelevant if the intervention is delayed in its implementation. a case control study of accidental falls that draws controls from all other traumatic injuries would alleviate the need for these extra computational steps that a cohort study would otherwise require. the denominators in a case control study could be all the traumatic injuries extracted matched by area and a time variable in order to calculate the odds of accidental falls. otherwise, population counts would have to be estimated for each area studied in order to determine risk. using the traumatic injury data from syndromic surveillance data to conduct an unmatched case control study versus having to calculate risk of injury data from acute care interactions using population level denominators would enhance efficiency if the results were comparable. persons requiring emergency department treatment for falls compared to hospital inpatient and outpatient discharge data have been done [7], but there has not been a cohort study design that compared the risk of injuries from falls within the population versus a more efficient casecontrol of fall injuries using all other traumatic injuries requiring acute care. a case control study that is matched by time of injury would allow for identification of the effects of the type of weather at the time of injury, estimation of the risk of repeated visits on acute care, and identification of indicators for increased risk for traumatic injury from falls. the goal of this current study was to evaluate the diagnostic efficacy of a more efficient case control study compared to a cohort analysis for county level surveillance of accidental fall injuries. the specificity, sensitivity, and accuracy of the case-control odds ratio of injury from accidental falls was compared to the population risk ratio to determine if traumatic injury discharge diagnosis can adequately provide population level surveillance without population syndromic surveillance data for accidental fall injury 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi level census data. other goals are to estimate the risk of repeat acute care interactions on accidental fall injuries and provide an estimate the impact of the weather on the risk of injuries from falls. methods setting: acute care interactions for traumatic injuries and all fall related deaths among all residents of greene county for the 2018 year. all the traumatic injuries that occurred within greene county, ohio residents that sought acute care within ohio during 2018 were included in the study design. design: an observational comparison of case control and cohort study design. participants (inclusion, exclusion criteria), recruitment process: participants were all residents of greene county, ohio. cases were any fall related injury requiring acute care treatment. controls were persons with a non-fall associated traumatic injury. for the cohort analysis, the controls were all residents that were not diagnosed with a traumatic injury or had a fall injury as an underlying cause of death. persons were excluded as cases if they had acute care for their injury outside of ohio. cases were defined as those traumatic injuries that had discharge icd 10 codes w00 through w19 for ‘accidental fall’. indicator variables were created for each code as ‘1’ if the code was found and ‘0’ if the code was not found. for the case-control study, those coded as zero were identified as controls. for the cohort study, population denominators were identified from census data for zip code areas, sex, and age groupings. procedures: no clinical procedures were conducted. measures/outcomes: odds and risk ratios along with specificity, sensitivity, and accuracy of the case control versus the cohort study designs. statistical analysis: the daily climate data was downloaded from the national centers for environmental information in order to assess the risk of injury from falls [9]. this was used to calculate the odds of fall injury during any day with a minimum daily temperature at or below freezing 0 °c, 32 °f. the odds ratio was calculated after matching cases and controls by month and date. the case fatality ratio was calculated as the rate per 100,000 of deaths divided by the rate per 100,000 of injuries from accidental falls multiplied by 100. validation of the syndromic surveillance system’s capability to identify traumatic injuries is outside the scope of this evaluation, the agreement between the free text search for accidental fall injury and the icd 10 diagnostic code provides an indication of internal validity of the result. cohen's kappa was calculated to measure the agreement between the presence of the free text and the presence of any ‘accidental fall’ icd 10 diagnostic code at discharge for the same visit. a value of 1 indicates perfect agreement. a value of 0 indicates that agreement is no better than chance. kappa is based on a square table in which row and column values represent the same scale. any cell that has observed values for one variable but not the other is assigned a count of 0. cohen’s kappa was calculated to determine the level of agreement between the free text field in the medical record and the discharge diagnosis. any mention of ‘fall’,’fell’, ‘fell’, or ‘fall’ was searched for. the injury rates from accidental falls per 100,000 was compared to the risk and odd ratios to determine if there are congruencies among the results. this is to determine if traumatic injury syndromic surveillance data for accidental fall injury 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi discharge diagnosis can adequately provide population level surveillance in the absence of population level census data. the level of agreement was the cutoff rate per 100,000 where the rank per predictor variable is not identified as a risk factor for fall injury. the odds ratio versus the risk ratio for the total population was evaluated for the diagnostic adequacy of syndromic surveillance data of traumatic injuries as controls versus population estimates of the risk factor variables. sensitivity, specificity and accuracy of the odds ratio to predict risk was assessed. protective ratios (those not including one) was coded as negative test results, no difference (those ratios with confidence intervals including one) was coded as ‘negative’, and those ratios with confidence limits above one was coded as ‘positive’. the following definitions was used: sensitivity: probability that a test result was positive when the disease is present (true positive rate) = a / (a+b); specificity: probability that a test result was negative when the disease is not present (true negative rate) = d / (c+d); accuracy: overall probability that a patient was correctly classified = (a+d) / (a+b+d+c). the impact of repeat injury trauma requiring acute care on the risk associated accidental fall injury was assessed by regression of the number of repeats with the dependent variable being the present or absence of a diagnosis at discharge for accidental fall with the constant set to zero. the accidental falls by gender was assessed while controlling for age. risk ratios was calculated for each age strata by gender. to assess the impact of repeat visits for acute care on accidental fall morbidity, any repeat visit for acute care was coded as 1 and the odds ratio was assessed. any other person seeking acute care for traumatic injury was used as a control to assess the impact of acute care on accidental fall morbidity. institutional review board approval was not required as it was conducted under the duties of health departments in ohio to study the prevalence of disease and health conditions within its district, which may be released in summary, statistical, or aggregate form (ohio revised codes 3709.22 and 3701.23). results cohen’s kappa results the syndromic surveillance data was searched for any icd 10 code that indicated an accidental fall among the discharge diagnoses with subjects drawn from those with any traumatic injury during 2018. given that the algorithm for traumatic injury may and should include other types of injury than accidental injury a separate check of the extracted data’s free text primary complaint free text field for any mention of the terms of ‘fall’ or ‘fell’ and their plural or punctuation derivatives. the cross tabulation of the concordance or discordance of diagnostic codes with free text reasons for the acute care interaction associated with any falls with the icd 10 codes for accidental fall w00-w19 was tabulated. the counts for any fall injury and any free text mention was 11,634 (no fall injury-no free text), 3,097 (no fall injury-free text), 372 (fall injury-no free text reason), and 1,403 (fall injury-free text). public health surveillance of acute care interactions uses algorithms to search the plain language primary reason for the health care visit and classify the visit according syndromes. the syndrome of interest in this study is the traumatic injury syndrome. the free text reason for the acute care interaction that mentioned falls was highly associated with any icd 10 indicating an accidental fall with a cohen’s kappa measure of agreement value of 0.346 (p<.001). repeat acute care interactions effect on risk of accidental fall injuries the frequency of acute care interactions in the study population is shown in figure 1. the regression analysis revealed that repeat acute care visits explained 33.3% of the variance of all traumatic injury visits (r=.333 for linear regression through the origin, p<.001). with every syndromic surveillance data for accidental fall injury 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi injury trauma requiring acute care, the risk that the trauma has an associated accidental fall injury increased by 6.47% (95% confidence interval 6.2-6.8). figure 1. odds of accidental fall diagnosis among all traumatic injuries seeking acute care by number of visits for acute care during 2018 among greene county residents. impact of weather on the risk of injuries from falls when injuries are matched by month of occurrence, november and december are significant (see table odds of fall injury by month). while the unadjusted odds ratio ‘day of year with any freezing temperature’ was not significant, the fall injury rate during days with freezing weather was elevated at 315 per 100,000. the deaths from fall injuries during days with freezing weather was 10 per 100,000. the case fatality rate for days with freezing weather was 3.2%. syndromic surveillance data for accidental fall injury 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi figure 2 with data table: odds of fall injury with 95% confidence intervals by month of injury and by day of freezing temperature. fall injury from syndromic surveillance discharge data with icd10 code of w00-w19. risk factor injured expose d no injuryexpose d injured unexpo sed no injuryunexpo sed odds ratio lower cl upper cl january 71 1192 1704 13539 0.47 0.37 0.6 february 63 1044 1712 13687 0.48 0.37 0.63 march 121 1111 1654 13620 0.9 0.74 1.09 april 140 1288 1635 13443 0.89 0.75 1.07 may 167 1551 1608 13180 0.88 0.75 1.04 june 163 1345 1612 13386 1.01 0.85 1.19 july 146 1375 1629 13356 0.87 0.73 1.04 august 152 1283 1623 13448 0.98 0.82 1.17 september 178 1299 1597 13432 1.15 0.98 1.36 october 160 1306 1615 13425 1.02 0.86 1.21 november 199 1010 1576 13721 1.72 1.46 2.01 december 215 927 1560 13804 2.05 1.75 2.4 freezing temperature 506 4040 1269 10691 1.06 0.95 1.18 syndromic surveillance data for accidental fall injury 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi diagnostic efficacy of case-control compared to cohort analysis using count data for each indicator variable from those with accidental fall injuries and those without, 9 of the 14 geocoded zip code areas were true predictors of increases when the more efficient case control study was used. with gender and age predictors were examined the preponderance of the risk ratios (12 out of the 13) were true predictors of increased risk. the sensitivity (the true positive rate) of the ‘fall injuries icd10 w00-w19’ syndromic surveillance data in an unmatched case control study in lieu of population data will identify a risk factor in the population 62.5% (95% ci 24.5-91.5) of the time when the risk factor is present. the specificity (the true negative rate) of the fall injuries icd10 w00-w19 syndromic surveillance data to be used in an unmatched case control study in lieu of population data was 84.2% (60.496.6). the accuracy was 77.7% (57.7-91.3). table 1: fall injuries identified by syndromic surveillance icd10 codes w00-w19 in greene county residents during 2018 by risk factor using population estimates for risk calculations or patients with traumatic injuries as controls for odds ratio calculations. risk factor fall injury exposed [1] no fall exposed fall injury unexpose d no fall unexpose d controls no fall exposed controls no fall unexpose d zip 45305 61 10930 1714 147919 690 14041 zip 45307 1 321 1775 158528 38 14693 zip 45314 16 5853 1759 152996 234 14497 zip 45324 445 38407 1330 120442 2735 11996 zip 45335 31 6652 1744 152197 894 13837 zip 45370 16 2537 1759 156312 160 14571 zip 45385 491 37724 1284 121125 3916 10815 zip 45387 56 5057 1719 153792 339 14392 zip 45430 73 7143 1702 151706 544 14187 zip 45431 181 13370 1594 145479 1973 12758 zip 45432 134 7977 1641 150872 1124 13607 zip 45433 3 2460 1772 156389 45 14686 zip 45434 75 11718 1700 147131 645 14086 zip 45440 193 8700 1582 150149 1394 13337 female gender 1185 81148 590 77701 7531 7200 male gender 590 77701 1185 81148 7200 7531 age group =<1 8 1866 1767 156983 131 14600 age group 1-4 26 7469 1749 151380 1134 13597 age group 5-14 43 18622 1732 140227 2294 12437 syndromic surveillance data for accidental fall injury 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi age group 1524 59 24248 1716 134601 1974 12757 age group 2534 85 21370 1690 137479 1609 13122 age group 3544 79 17732 1696 141117 1402 13329 age group 4554 105 20611 1670 138238 1358 13373 age group 5564 234 21850 1541 136999 1499 13232 age group 6574 296 14849 1479 144000 1302 13429 age group 7584 394 7343 1381 151506 1128 13603 age group 85 plus 446 2889 1329 155960 900 13831 1. exposed to the risk factor. figure 3 with data table evaluation of odds ratio to predict risk of fall injuries among greene county residents fall injuries during year 2018. syndromic surveillance data for accidental fall injury 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi risk factor odds ratio or lcl or ucl risk ratio rr lcl rr ucl diagnostic evaluation of odds ratio zip 45305 0.72 0.55 0.95 0.48 0.38 0.63 true negative zip 45307 0.22 0.01 3.6 0 true negative zip 45314 0.56 0.34 0.94 0.24 0.15 0.39 true negative zip 45324 1.47 1.31 1.65 1.05 0.94 1.17 false positive zip 45335 0.28 0.19 0.39 0.41 0.29 0.58 true negative zip 45370 0.83 0.49 1.39 0.56 0.35 0.92 true negative zip 45385 1.06 0.95 1.18 1.23 1.11 1.36 false negative zip 45387 1.38 1.04 1.84 0.99 0.76 1.29 false positive zip 45430 1.12 0.87 1.44 0.91 0.72 1.15 true negative zip 45431 0.73 0.63 0.86 1.23 1.06 1.44 false negative zip 45432 0.99 0.82 1.19 1.54 1.29 1.83 false negative zip 45433 0.55 0.17 1.78 0.11 0.04 0.34 true negative zip 45434 0.96 0.75 1.23 0.56 0.44 0.7 true negative zip 45440 1.17 1 1.37 2.08 1.8 2.41 true positive female gender 1.92 1.73 2.13 1.91 1.73 2.11 true positive male gender 0.52 0.47 0.58 0.52 0.48 0.58 true negative age group =<1 0.5 0.25 1.03 0.38 0.19 0.77 true negative age group 1-4 0.18 0.12 0.26 0.3 0.21 0.45 true negative age group 5-14 0.13 0.1 0.18 0.19 0.14 0.26 true negative age group 15-24 0.22 0.17 0.29 0.19 0.15 0.25 true negative age group 25-34 0.41 0.33 0.51 0.33 0.26 0.41 true negative age group 35-44 0.44 0.35 0.56 0.37 0.3 0.47 true negative age group 45-54 0.62 0.5 0.76 0.43 0.35 0.52 true negative age group 55-64 1.34 1.16 1.55 0.95 0.83 1.09 false positive age group 65-74 2.06 1.8 2.37 1.92 1.7 2.18 true positive age group 75-84 3.44 3.03 3.91 5.64 5.05 6.29 true positive age group 85 plus 5.16 4.54 5.85 15.83 14.3 17.52 true positive figure 3 data table evaluation of odds ratio to predict risk of fall injuries among greene county residents fall injuries during year 2018. discussion an advantage of the case-control design is that the controls can be matched to the cases by date, sex, zip code area, and age. while population risk factors can be matched on zip code area this syndromic surveillance data for accidental fall injury 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi is done using the american community survey’s annual population estimates and applying the decennial census’ zip code population’s proportion within the jurisdiction of interest. this estimate of risk would be subject to the margin of error of those estimates and widened by the application of the earlier decennial zip code population proportion factor. the log of two risk estimates and their confidence interval should be taken to evaluate if two separate and independent risk ratios are different [10]. we did not compare risk ratios to each other rather, we compared two methods and their overall results using sensitivity and specificity analysis. while our research found higher risk in females and a protective effect among males (figure 3), earlier research identified inpatient males having higher risk for injurious falls (odds 2.08) [7]. we evaluated the risk of falls for the entire community of over 160,000 persons not just inpatients. if both findings are to be believed, then a logical explanation is that most fall injuries among females occur before admission, leaving a surplus of male inpatients to subsequently fall after admission for other injuries. the interplay of treatment effects on stability and the hospital staff’s perception of risk and initiation of fall prevention precautions are potential issues to explore regarding the quality of care among inpatient males. these other issues provide for both these findings to be valid. also, our findings are consistent with a study of persons 65 years and older age that found female sex was associated with an increased risk of falls and fear of falling [11]. our study also revealed that january and february fall injuries were much lower than other months. indeed, these two months were revealed as having a protective odds of fall injury requiring acute care. one explanation is regression to the mean. with so many injuries occurring during the holiday months of november and december, perhaps the pool of person who are susceptible to fall injury have already occurred early during the start of the winter months. of interest in that study is that most falls occurred while walking. this is supportive of our findings that those persons in the later three age groups starting at 65 years and older are at elevated risk. this is consistent with an etiology of the elderly out walking while visiting friends and relatives during the holidays in november and december during a time when icy conditions are variable increase susceptibility to falls. the higher case fatality rate during days with freezing temperature is consistent with this proposed etiology. specifically, this study found that during any freezing temperature have a higher case fatality rate among all those with fall injury. essentially all those who were residents of the community who were injured enough to seek acute care were included in our study. only those who sought care outside of the entire state would have been missed. we assume this number was near zero. we analyzed data for all the county residents no matter where they were treated within ohio. our findings of a low predictive value of odds ratios is consistent with fall assessment tools applied to hospitalized patients [7]. we did not have information on a person’s history of falls, but we did have information on the number of repeat acute care interactions per year. this value was congruent with the odds seen in hospitalized patients. others have studied the rate of return for fall injury from date of discharge for 1 month (2.6%), 3 months (6.3%), 12 months (9.1%), and 2 years (13.4%) [12]. we studied the entire population, not just those who were discharged, these separate and distinct finding are consistent with the concept that increasing need for health care services are associated with rising risks of injury from falls. our finding of rising risk of falls with repeat acute care interactions suggests a biological gradient and a strength of association that supports a causal association. a 2014 study had analogous results of a categorical gradient syndromic surveillance data for accidental fall injury 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi with the use of more than one benzodiazepine or sedative hypnotic medication found to increase the risk of falls [13]. other research found an increase in odds of fall injury with increasing the number of drugs increased the odds for fall injury after discharge, 1.03 (1.021.04) [12]. public health implications the risk of falling has been shown to be able to be reduced through application of interventions based on data from risk calculations [12,14]. diagnosis of fall injuries agreed with free text reasons for acute care. a case control design can provide efficiencies through ease of matching indicator variables of date and area for public health surveillance of fall injuries but at a relative cost of the resulting specificity (84.2%), sensitivity (62.5%), and accuracy (77.7%). the value of a case control design is the provision of valuable data on the odds from exposure to a risk factor and it can do so quickly. community fall injury risks differed from inpatient studies for gender. the case control diagnostic quality of fall injuries was better for age and gender than for residential zip code. freezing temperature increased the fall injury case fatality rate. community risk factors for fall injuries included repeat visits for acute care (6.47% for every additional visits, trending r=.333, p<.001), month of year, age group 65-74 years (or 1.9,1.72.18), 75-84 years (or 5.6, 5.05-6.29), and 85 plus years (or 15.8, 14.3-17.52), and female gender (or 1.9, 1.73-2.11). the use of epicenter for fall injury surveillance provides a useful source of data for local public health agencies. the finding that a gradient of increasing odds of fall injuries with the number of visits for acute care is a strong indicator for causality, suggesting community fall prevention programs should also focus on those most likely to fall as well as other targeted population segments. financial disclosures the authors have no financial interests to disclose. references 1. the health collaborative, greater data hospital association. (2019). sw ohio, n kentucky, se indiana chna community health needs assessment. 2019 report. 2. dockery j, murray c, sullivan m, gill j, hartley b, et al. (2017). greene county community health assessment, 2017. a. steveley, (ed). greene county public health and the wright state university, applied policy research institute, august 10, 2017, xenia, oh. doi: https://doi.org/10.13140/rg.2.2.27794.91845 3. greene county public health. (2017). community health improvement plan 2017. a. steveley, (ed). greene county community health improvement steering committee and work group members. plan adoption date november 2, 2017. 4. seil k, marcum j, lall r, stayton c. 2015. utility of a near real-time emergency department syndromic surveillance system to track injuries in new york city. inj epidemiol. 2(1), 11. doi:https://doi.org/10.1186/s40621-015-0044-5. pubmed 5. health monitoring systems. 2019. epicenter. march 1, 2019. https://www.healthmonitoring.com/syndromic-surveillance-epicenter/ https://doi.org/10.13140/rg.2.2.27794.91845 https://doi.org/10.1186/s40621-015-0044-5 https://pubmed.ncbi.nlm.nih.gov/27747743 syndromic surveillance data for accidental fall injury 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(3):e18, 2021 ojphi 6. odh. (2019). ohio public health information warehouse. secure ohio public health information warehouse version 1.1 ohio resident mortality data, 2018. these data were provided by the ohio department of health. the department specifically disclaims responsibility for any analyses, interpretations or conclusions. http://publicapps.odh.ohio.gov/edw/datacatalog 7. aryee e, james sl, hunt gm, ryder hf. 2017. identifying protective and risk factors for injurious falls in patients hospitalized for acute care: a retrospective case-control study. bmc geriatr. 17(1), 260. doi:https://doi.org/10.1186/s12877-017-0627-9. pubmed 8. gevitz k, madera r, newbern c, lojo j, johnson cc. 2017. risk of fall-related injury due to adverse weather events, philadelphia, pennsylvania, 2006-2011. public health rep. 132(1) (suppl), 53s-58s. doi:https://doi.org/10.1177/0033354917706968. pubmed 9. noaa. national oceanic and atmospheric administration, national centers for environmental information. location details greene county, oh, id fips 39057, period of record, 2018-01-01 to 2018-12-31. https://www.ncdc.noaa.gov/cdo-web/results 10. altman dg, bland jm. 2003. interaction revisited: the difference between two estimates. bmj. 326(7382). pubmed https://doi.org/10.1136/bmj.326.7382.219 11. gazibara t, kurtagic i, kisic-tepavcevic d, nurkovic s, kovacevic n, et al. 2017. falls, risk factors and fear of falling among persons older than 65 years of age. psychogeriatrics. 17(4), 215-23. epub jan 2017. doi:https://doi.org/10.1111/psyg.12217. pubmed 12. castro vm, mccoy th, cagan a, et al. stratification of risk for hospital admissions for injury related to fall: cohort study. bmj. 2014;349:g5863. published 2014 oct 24. doi:https://doi.org/10.1136/bmj.g5863 13. helgadóttir b, laflamme l, monárrez-espino j, möller j.medication and fall injury in the elderly population; do individual demographics, health status and lifestyle matter? bmc geriatr. 2014 aug 23;14:92. doi: https://doi.org/10.1186/1471-2318-14-92. pmcid: pmc4150120 free pmc article pmid: 25151122 14. yokota s, tomotaki a, mohri o, endo m, ohe k. 2016. evaluation of a fall risk prediction tool using large-scale data. stud health technol inform. 225, 800-01. pubmed https://doi.org/10.1186/s12877-017-0627-9 https://pubmed.ncbi.nlm.nih.gov/29115921 https://doi.org/10.1177/0033354917706968 https://pubmed.ncbi.nlm.nih.gov/28692393 https://pubmed.ncbi.nlm.nih.gov/12543843 https://doi.org/10.1136/bmj.326.7382.219 https://doi.org/10.1111/psyg.12217 https://pubmed.ncbi.nlm.nih.gov/28130862 https://doi.org/10.1136/bmj.g5863 https://doi.org/10.1186/1471-2318-14-92 https://pubmed.ncbi.nlm.nih.gov/27332348 https://pubmed.ncbi.nlm.nih.gov/27332348 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts use of syndromic data for enhanced surveillance: mers like-syndrome achintya n. dey*1, matthew miller2, michael coletta1 and umed ajani1 1dhis, cdc, atlanta, ga, usa; 2mcking consulting, atlanta, ga, usa objective to identify and monitor middle east respiratory syndrome (mers) like syndromes cases in the syndromic surveillance system. introduction centers for disease control and prevention’s (cdc) biosense system receives near real-time health care utilization data from number of sources, including dod and va outpatient facilities, and nonfederal hospital eds in the us to support all-hazards surveillance and situational awareness. however, the biosense system lacks some critical functions such as creating ad hoc definition of syndrome or ad hoc query tool development. this limits cdc emergency operations center’s (eoc) ability to monitor new health events such as mers a viral respiratory illness first reported in saudi arabia in 2012. in may 2014, cdc confirmed two unlinked imported cases of mers in the us one in indiana, the other in florida. upon report of a mers case in indiana, staff initiated joint efforts with eoc and several affected jurisdictions to enhance the surveillance of mers irrespective of jurisdictions’ preferred surveillance system. methods in consultation with the state and local jurisdictions, five case definitions were developed to monitor mers like syndromes: (1) was designed to search chief complaint: any mention of mers or any mention of cov or any mention of oman or any mention of camel or middleeast or arabian or abudhabi or egypt or jordan or kuwait or qatar or uae or united arab emirates or bahrain or iraq or iran or israel or lebanon or palestin or saudi or syria or yemen; (2) was designed to focus solely on the travel terms: any mention of oman or any mention of middleeast or arabian or abudhabi or egypt or jordan or kuwait or qatar or uae or united arab emirates or bahrain or iraq or iran or israel or lebanon or palestin or saudi or syria or yemen; (3) was meant to be more specific, requiring the mention of both mers and travel: part a search term included (mers and travel): any mention of mers and any mention of oman or any mention of camel or middleeast or arabian or abudhabi or egypt or jordan or kuwait or qatar or uae or united arab emirates or bahrain or iraq or iran or israel or lebanon or palestin or saudi or syria or yemen) or part b search term included (cov and travel): any mention of cov and any mention of oman or any mention of camel or middleeast or arabian or abudhabi or egypt or jordan or kuwait or qatar or uae or united arab emirates or bahrain or iraq or iran or israel or lebanon or palestin or saudi or syria or yemen; (4) was focused on secondary spread: any mention of fever and pneumonia or any mention of fever and acute respiratory distress; (5) used icd-9-cm, and icd-10-cm: 079.82, 480.3, v01.82, b34.2, b97.21, j12.81, u00-u49, u04.9. these five case definitions were then operationalized in sql, r-script, sas, and essence languages and sent out to each participating state and local jurisdictions to run the weekly query on their syndromic surveillance systems for sharing the results with the cdc. biosense staff compiled the results and set up a weekly reporting system on mers like syndrome cases to eoc. results from may through july, 2014, fifteen reporting jurisdictions participated in mers enhanced surveillance. these 15 reporting jurisdictions reported from 822 civilian hospitals ed facilities. biosense staff created weekly reports for the cdc’s eoc. reports included five case definitions and a table reporting jurisdiction name, date, number of mers case investigated and number of mers cases ruled out. during this enhanced surveillance time period 171 probable mers cases were identified and all of them were ruled out. conclusions in spite of limitations such as low participation and lack of agreement among the jurisdictions in sharing data, cdc biosense staff was able to provide meaning full data to eoc for enhanced surveillance on mers like syndrome. similar collaborative efforts between biosense programs, cdc subject matter experts and jurisdictions will help develop more comprehensive definitions to conduct enhanced surveillance at the national level using multiple syndromic surveillance systems. keywords mers; syndromic surveillance; ed facilities acknowledgments participating state and jurisdictions *achintya n. dey e-mail: aad2@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e69, 2015 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 47 (page number not for citation purposes) isds 2013 conference abstracts environmental public health tracking: success stories from a collaborative surveillance system carrie eggers* and patrick wall centers for disease control and prevention, atlanta, ga, usa � �� �� �� � � �� �� �� � objective ������� ���� �� � ������������ �������������� ����� �������� � � ����������� ������ ����� � �������� �� ���� ��� �������� ��� ���� ��� ��������� ������ ������� ������������������������ ��������� ����� ��������������������� � ����������� �� ���� ��� ������ ��� ��� ���������� �������� ������������ ��� ���� ���������� ��� ���� ��� � ������������� ����� ������������������� ������� ������� ����� ������ � ���������� ������� ������ ����������������� introduction ��������� ���� ���!� ������ ���"����� �� ������ � ������#� �$� �����$�� ���������� ��� ���%��������� ���������� ���� � ����� ���� �������� ���������� 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���� � �������� ��� ������� �� � � ��$� ����� ��������� ���� ���� ����������� ������� ����������� ���� �� ���� � ������� ���� ��� ��� ���� ��������� conclusions /� �������� ����������������� ��� ����������� � ������������ ��� ����������� ����$���������� ���� ������� ���� ��� �� ���� �� ��� ����������������$� �������� � �� �������������"�����!� ��������� ������������������� ���� ���� � ����,�� �� ���� �������������� �� �� � ��������� � ������������������� ��� ����������������������� � ��� � ��!�� �������� � � ������� ��� ����� �����"����$��!� ���������� �������� ���6������������� ������� ������������������ �� ��� ���� ���� ��������������� ����� �� � �������������������� � ��������� ������� ������������� ������������������ �� � � � ���� ���!���� ������ �!�� ��� ���3������� ���� ��� ������� ���� �� ����� �����������$� ����� � ���������� ����� ������������� ����������� ���� ��� ���� � � ����� �������$����� ��� � ������� ��� �������� � ��� ����������� ��� ���� ���� keywords 4���� ������:� �� � ��������������:������ �� *carrie eggers e-mail: ceggers@cdc.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e137, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a novel zoonotic disease outbreak course to improve surveillance and response for the usda julianna b. lenoch* office of health informatics, wisconsin dept of health services, madison, wi, usa objective the united states department of agriculture (usda) created a new course in 2013 to meet needs of the veterinary epidemiologists within the veterinary services (vs) division. the objective of this training was to provide a standard framework to investigate animal disease outbreaks, and apply practical solutions. vs has expertise in animal surveillance, this training course demonstrated how to incorporate the fundamentals of surveillance into an outbreak scenario. the goal of course was to give epidemiologist skills and practice to more quickly and efficiently conduct an investigation, allow rapid identification of a cause, apply control measures, and limit economic and health impacts of a disease. introduction outbreak investigations course consisted to a series of 8 online modules, covering: •confirm that an outbreak is occurring and confirm diagnosis •case definition •descriptive epidemiology •hypothesis generation •analytic epidemiology •preliminary control and prevention •communication of findings •establishing disease surveillance and monitoring the online modules were followed by live webinars, taught by the instructors who wrote each unit of the online webinar. finally, select epidemiologists from veterinary services were invited to a 3-day classroom course, to collaborate with other team members and practice outbreak detection, surveillance and control. three scenarios were created to highlight economic impact, severe consequences of an uncontrolled outbreak, onehealth approach and the collaboration needed during a zoonotic disease outbreak. methods the epidemiologists that attended were required to complete a self-assessment before and after the course to determine their comfort level, understanding of basic and advanced epidemiology processes, and ability to formulate and test hypothesis during an outbreak situation. these questions required answers in a 1-10 ordinal scale. participants were also asked baseline information on their years of service for usda-vs and education level. a separate standard evaluation report was completed by the professional development service of vs. participants were required to evaluate the 3-day live course within a standard questionnaire and allowed to assess 1-5 scoring in several measurement areas. results results of the pooled data from pre and post course evaluations were compared using a t-test for equality of means. results of the standard evaluation by professional development were pooled, and responses were reported with mean, median and standard deviation. knowledge in all nine areas improved after the training, statistically significant improvement in knowledge base was observed in five of the nine measured areas. conclusions this training method was highly successful in meeting the goals of veterinary services, and will be offered again to selected team members. participants valued the knowledge and expertise of the scenario facilitators, and the quality and realism of the scenarios presented. the participants appreciated the ability to work through scenarios in stages and to work in teams. keywords training; surveillance; zoonotic disease; public health workforce; preparation acknowledgments the usdavs epidemiology team at the western region office in fort collins colorado was heavily involved in producing this outbreak investigation course. special thanks to brian mccluskey, jason lombard, jason baldwin, tracey linn and alicia humlicek. *julianna b. lenoch e-mail: julielenoch@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e144, 201 crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 49 (page number not for citation purposes) isds 2013 conference abstracts the effect of training of school physicians on their knowledge regarding surveillance in alexandria nessrin el-nimr* and iman wahdan epidemiology department, high 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louisiana, 2007-2011 xiaoling ye*1, tri tran2, 3, mary jo smith2, jeanette webb2, terri mohren2 and melinda peat2 1tulane university school of public health and tropical medicine, department of epidemiology, new orleans, la, usa; 2louisiana dhh oph cshs early hearing detection and intervention program, new orleans, la, usa; 3lsuhsc school of medicine, department of pediatrics, new orleans, la, usa � �� �� �� � � �� �� �� � objective �������� ����� � ����� �������� �������������������� ������������ � ����� �� ���������������������������� ��������� ������������� ����� ���� �� �������� ������� ������ ��������������������� �������� ����� ��!!" introduction #���������������� �������������� ������� ������ ��������������� ��� ����������� � ������������$"%"� ���� �����"�&���������������� ���� ������������ �������������� �������� �� ��������� ���� � �� ���� �� �� �� �� ��������������� ����������������������������������'���� ������� ��� �������� ���� ����"������ �� ���������� ���������� 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8+7�!"!��!".�:'�������������23���'�517�!"..'�8+7�!"!!�!"9�=�����-'� 517�!".�'�8+7�!"!.�!",-:�������������������� ������������������ ���2� !������������������ �'�517�!".;'�8+7�!"�9�!",!=�>�!������������������ �'� 517�!"; '�8+7�!" ���"�!:"����������� ������ �� ���������������� ����� �� � �� ����� ���� ��� ������������� ��� ������� ��� � �" conclusions ?�� ������� ��������������� ������� ������������������'����� ��� � ��'��������������������� �'������<��� �������� �����������'� �������������������� ������������������ ��'������������ ���� �'����� ������������������������ ���� ������� ����� ����� ����� ���� ���������� ������ � ���������� ���������� ��������������������" keywords )���������������������� �������=������ �� ��������=���������� �=� @�� �� �� � � � � *xiaoling ye e-mail: janice.ye268@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e54, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts carbon monoxide poisoning during hurricane sandy in affected new york state counties jian-hua chen*, ursula lauper, cristian pantea, shao lin and hwa-gan chang division of epidemiology, new york state department of health, albany, ny, usa objective 1) to identify carbon monoxide (co) poisoning in three most affected new york state (nys) counties (nassau, suffolk, and westchester) during and immediately after hurricane sandy with hospital emergency department (ed) chief complaint data reported daily to the new york state department of health (nysdoh). 2) to explore the relationship between power outage and the numbers of co-related ed visits (co-eds). introduction co poisoning is a leading cause of mortality and morbidity in disaster and post-disaster situations, when widespread power outages most likely occur (1, 2). the nysdoh syndromic surveillance system receives daily ed visit chief complaint data from 140 nys (excluding new york city) hospitals. daily power outage data are available from the nys department of public service (nysdps). these data can be used to estimate the risk of co-eds and provide useful information for public health situational awareness and emergency response management during disaster events. methods this was a retrospective study in which the daily co-eds were identified by searching co-related keywords in chief complaints of the ed visits from the affected three counties during the sandy period (october 29 – november 27, 2012). data on daily maximum number of households without power during the hurricane in each of the three affected counties were obtained from nysdps. the correlation between co-eds and maximum number of households without power was estimated with a log-transformed linear regression model. a negative binomial model was used to examine the risk of co-eds by the maximum households affected by power outages each day during the month following hurricane sandy. data were analyzed using sas 9.3 and the p-value for testing statistical significance was set at 0.05. results a total of 188 co-eds were identified in the three affected counties during the study period. overall, the number of co-eds increased during the hurricane period. coinciding with the power outage in the areas, the co-eds increase peaked at 3-4 days after the hurricane made landfall in nys on october 29 (figure 1). statistical analysis showed that there was a significant correlation between the maximum number of households without power and the number of coeds such that 53% of the variance of co-eds can be explained by the power outage (p<0.0001). the risk ratio of co poisoning was more than three times as great on days of significant power outage (i.e. when more than 200,000 households were without power), compared with days of insignificant power outage defined as 10,000 or fewer households (table 1). the higher the number of households without power, the higher the risk ratio of co poisoning. conclusions co exposures can be identified with ed chief complaint data, and the risk of co-eds can be estimated using power outage data. if ed data and/or power outage data are available in a timely base, they can provide valuable co exposure information for situational awareness and emergency management during and after disasters. the study results also support that power outage is an underlying cause that indirectly leads to co exposures. education on proper use of alternative power and heating sources should be part of public programs designed to prevent future disaster-associated co poisoning. keywords co poisoning; event syndromic surveillance; disaster situational awareness; power outage; chief complaint references 1. hampson nb, stock al,. storm-related carbon monoxide poisonings: lessons learned from recent epidemics. undersea hyperb med 2006; 33:257-63. 2. iqbak s, clower jh, hernandez sa, damon sa, yip fy. a review of disaster-related carbon monoxide poisoning: surveillance, epidemiology, and opportunities fro pervention. am j public health 2012;102:1957-63. *jian-hua chen e-mail: jianhua.chen@health.ny.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e119, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts validation of multiplex pcr for detection and differentiation of salmonellas prof. borys stegniy, dr iryna gerilovych, prof. andrii zavgorodniy, anton gerilovych*, prof. vasyl’ vlizlo and vasyl’ arefiev nsc iecvm, kharkiv, ukraine objective this study aimed to perform interlaboratory testing and clarification of the pcr-based test for its implementation in ukraine. introduction salmonellosis is the zoonotic disease caused by salmonella bacteria. these are food-borne pathogens, which require improvement of diagnostics and surveillance measures. prior to implementation of a pcr-based system for monitoring salmonella, presence and differentiation of the agent was validated under office international epizootical (o.i.e.) requirements. methods the pcr-based detection technique for five salmonella species was tested under o.i.e. requirements, to determine specificity, sensitivity, and repeatability. primers were calculated by oligo software and produced with hplc purification. amplification was conducted by a t3000 biometra pcr-machine. testing was performed on a panel of five basic agents: salmonella enterica ser. enteritidis, salmonella ser. typhimurium, salmonella ser. typhi, salmonella ser. dublin, salmonella ser. gallinarum-pullorum results the testing panel included positive samples as controls, containing virus at a concentration of 10-1-107 pfu/ml (n = 5 for each species). artificially contaminated clinical materials were also used as positive controls (n = 5). uninfected clinical materials (n = 20) were used as negative controls. samples contaminated with e.coli, mycobacteria spp. were used to test the platform’s ability to differentiate between bacterial genus. testing determined the overall pcr protocol as 89 % sensitive, 98 % specific (artificial reactions were observed) and repeatable. these results allowed for the recommendation that the testing technique be implemented in laboratory practice in ukraine. furthermore, specific guidelines for performing the test have been developed conclusions we have validated the pcr-based protocol for indication of salmonella genus agents and identification of its basic agents s. enteritidis, s. typhimurium, s. typhi, s. dublin, s. gallinarumpullorumon that recognized it to be sensitive, specific, and repeatable and able to be implemented in the laboratory practice for food safety control. keywords multiplex pcr; salmonella; detection *anton gerilovych e-mail: antger@rambler.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e162, 2015 lessons and implementation challenges of community health information system in lmics: a scoping review of literature 1 ojphi lessons and implementation challenges of community health information system in lmics: a scoping review of literature zeleke abebaw mekonnen1,2#*, moges asressie chanyalew3#, binyam tilahun2, monika knudsen gullslett4, shegaw anagaw mengiste5 1health system strengthening directorate, ministry of health, addis ababa, ethiopia 2department of health informatics, institute of public health, college of medicine and health sciences, university of gondar, gondar, ethiopia 3policy plan directorate, amhara regional health bureau, bahirdar, ethiopia 4faculty of health & social sciences, science center health & technology, university of southeastern norway, norway 5school of business, institute of business, history & social sciences, university of southeastern norway, norway abstract background: accurate and timely information on health intervention coverage, quality, and equity is the foundation of public health practice. to achieve this, countries have made efforts to improve the quality and availability of community health data by implementing the community health information system that is used to collect data in the field generated by community health workers and other communityfacing providers. despite all the efforts, evidence on the current state is scant in low middle income countries (lmics). objective: to summarize the available evidence on the current implementation status, lessons learned and implementation challenges of community health information system (chis) in lmics. methods: we conducted a scoping review that included studies searched using electronic databases like pubmed/medline, world health organization (who) library, science direct, cochrane library. we also searched google and google scholar using different combinations of search strategies. studies that applied any study design, data collection and analysis methods related to chis were included. the review included all studies published until february 30, 2022. two authors extracted the data and resolved disagreements by discussion consulting a third author. results: a total of 1,552 potentially relevant articles/reports were generated from the initial search, of which 21 were considered for the final review. the review found that chis is implemented in various structures using various tools across different lmics. for the chis implementation majority used registers, family folder/card, mobile technologies and chalk/white board. community level information was fragmented, incomplete and in most cases flowed only one way, with a bottom-up approach. the review also indicated that, technology particularly electronic community health information system (echis) and mobile applications plays a role in strengthening chis implementation in most lmics. many challenges remain for effective implementation of chis with unintegrated systems including existence of lessons and implementation challenges of community health information system in lmics: a scoping review of literature 2 ojphi parallel recording & reporting tools. besides, lack of resources, low technical capacity, shortage of human resource and poor information communication technology (ict) infrastructure were reported as barriers for effective implementation of chis in lmics. conclusion: generally, community health information system implementation in lmics is in its early stage. there was not a universal or standard chis design and implementation modality across countries. there are also promising practices on digitalizing the community health information systems. different organizational, technical, behavioural and economic barriers exist for effective implementation of chis. hence, greater collaboration, coordination, and joint action are needed to address these challenges. strong leadership, motivation, capacity building and regular feedback are also important to strengthen the chis in lmics. moreover, chis should be transformed in to echis with integration of different technology solutions. local ownership is also critical to the long-term sustainability of chis implementation. keywords: chis, community health workers, his, lmics, scoping review abbreviations: cha: community health agent; chis: community health information system; chv: community health volunteer; chw: community health worker; echis: electronic community health information system; ehmis: electronic health management information system; ff: family folder; hc: health center; hew: health extension worker; his: health information system; hmis: health management information system; hp: health post; ict: information communication technology; lmic: low middle income countries; who: world health organization doi: 10.5210/ojphi.v14i1.12731 *corresponding author: zelekeabebaw7@gmail.com; # equal contributors copyright ©2022 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes background a health information system (his) refers to a system designed to manage healthcare data. now, the significance of his has become a common knowledge and it is acknowledged by all stakeholders as an essential public health tool that provides both historical and real-time data to support healthcare delivery [1]. a well-functioning his is an integrated effort to collect, process, report and use health information to influence policy and program decision-making [2]. the main goal of his in any health care organization setting is to contribute to an efficient and high quality healthcare with basic health informatics competencies [3]. besides, it supports the health workers to organize and coordinate treatment processes, improve patient safety, enhance patient care, transfigure clinical procedures, circumvent medical errors, minimize operational expenses, save time and increase satisfaction [4-6]. lessons and implementation challenges of community health information system in lmics: a scoping review of literature 3 ojphi the community health information system (chis) is a type of health information system that links all community stakeholders and healthcare providers in a given community [7]. it is a grass root and family-centered health information management system designed for community workers to manage and monitor their work in educating households and delivering an integrated package of promotive, preventive, and basic curative health service to families [8]. it has also strengthened the decision-making process at the community level by providing the data needed by each decision-maker and making the process explicit [9]. the chis has multiple functions in terms of generating evidence to improve program performance and inform decisions. it is essential for promoting community engagement in health, identifying people in need of services, supporting case management and care coordination, and ensuring accountability [10]. the data collected in a chis can be used to inform programming and policy, identify populations in need, monitor the continuum of care, and address equity, access, and accountability. when community members have access to information in a chis, they have the potential to define and prioritize the community’s needs; set objectives and targets for meeting those needs; and participate in planning, implementing, and monitoring programs. a well-functioning chis can also support civil registration and vital statistics, by providing information on births and deaths. information technology plays an important role in the health care sector with its final goal, improving health care [11]. delivering good quality care is a complex endeavor that is highly dependent on patient information [12]. previously household data were collected using family cards and aggregated using tally sheets. thus, data were not being utilized by program implementers adequately [13]. following that, tremendous efforts were exerted globally to harness the ict development to digitalize the chis. with the availability of phone and tablets, some countries have begun to move away from manual systems and digitize the data collection process at the community level. thus, countries have collected client data using tablets and phones instead of family folders and paper recordings. then, data were transferred to the national server that would be ready for use by program implementers, and decision-makers at different levels [14,15]. accurate and timely information on health intervention coverage, quality, and equity is the foundation of public health practice [16]. as a result, countries have made efforts to improve the quality and availability of community health data by implementing the chis [13]. however, evidence showed that it has been fragmented and does not have standardized data elements, which has hampered efforts to harmonize the health systems [17]. moreover, the implementation modality, the type of information collected, the education and certification of health workers for chis and the commitment of governments in the implementation of chis varied from place to place. despite all the efforts, information on the current state, lessons and implementation challenges in the developing countries is scant. therefore, this paper aimed to review the lessons learned and implementation challenges of the chis across lmics. lessons and implementation challenges of community health information system in lmics: a scoping review of literature 4 ojphi objective our review aimed to summarize the available evidence on the current implementation status, lessons learned and implementation challenges of community health information system in low middle-income countries methods data sources and searching strategy we searched electronic databases; pubmed/medline, who library, science direct, cochrane /wiley library. we also searched specific journals, including the journal of health informatics in developing countries, international journal of medical informatics, and electronic journal of information systems in developing countries, as well as google and google scholar. the review included those papers published until february 30, 2022. we used different combinations of keywords and texts to build the search strategy and identify relevant articles. the searching techniques considered boolean operators with the following search terms. (((((((community-based information) or (community health services information systems)) or (community-based information systems)) or (community based health information management system)) or (community based health information system)) or (cbhi)) and (((((((implementation) or (evaluation)) or (status)) or (case study)) or (lessons learned)) or (lessons)) or (challenge))) and (((((developing countries [mesh terms]) or (developing countries)) or (lmics)) or (low middle income countries)) studies eligibility criteria inclusion criteria • studies on community health information system (manual and electronic) in lmics • reports which highlighted lessons learned or implementation challenges of chis in lmics • studies that applied any study design, data collection and analysis methods related to chis • both published and unpublished studies/reports that focused on implementation of chis • studies/reports in english language exclusion criteria • off topic studies that do not relate to chis implementation • studies with no accessible full text lessons and implementation challenges of community health information system in lmics: a scoping review of literature 5 ojphi study selection and data extraction we performed initial searches by two review authors with extensive experience in reviews. screening of titles, abstracts, and full texts was conducted independently by two review authors (ma & za). in addition, we developed a data extraction form to summarize the study findings. data extraction and critical appraisal were carried out for each included study/report. the data extraction template included study characteristics such as: author/organization, year, country, setting/population, study design, lesson learned and identified implementation challenges. data extraction was done by the two authors (moges asressie and zeleke abebaw) independently. the two authors resolved disagreements by discussion consulting a third author (shegaw mengistie) for any persistent disagreements. analysis and reporting we were able to present our narrative account of findings in two ways. first, attention was given to basic analysis of the extent and distribution of the studies included in the review. we produced the distribution of geographical locations, publication years, study designs and types of publications using tables and graphs. second the study findings from the existing literature were presented based on themes. our narrative literature was then structured around the themes derived from the study results or outcomes. the themes emerged from the study included current status of chis implementation, lessons learned and implementation challenges. results flow of the searching process and study characteristics a total of 1,552 potentially relevant studies were generated from the initial search. after, duplicates were excluded 1,298 studies were remained. then, we excluded 1,270 records based on title and abstract review. overall, 28 studies were eligible for full-text screening. afterwards, 7 studies were excluded by reading the full text due to scope not directly related to chis (n=3), reviews (n=2), setting not in lmics (n=2). eventually, 21 articles were retained for the final review (figure 1). lessons and implementation challenges of community health information system in lmics: a scoping review of literature 6 ojphi figure 1: flow diagram for the scoping review process adapted from the prisma statement characteristics of included studies in this review, six studies were from ethiopia [18] [19] [20] [21] [22] [23], two from tanzania [24,25], six from kenya [26] [27] [28] [29] [30] [31], one from south africa [32], one from rwanda [33], one from nigeria [34], two from zambia [35,36], one west and central africa [37] and one from malawi [38] (table 1). lessons and implementation challenges of community health information system in lmics: a scoping review of literature 7 ojphi table 1: characteristics of included studies for the review author country year study state of chis and lessons learnt challenges identified damtew et al ethiopia 2013 qualitative case study • health extension workers (hews) use different data collection tools to document and report the health data • offering extra data collection and reporting tools for hews, other than the family folder creating additional burden to their regular work • hews are spending time in collecting and reporting redundant data instead of serving the community • absence of simple and unified data collection tools at community level • the repetitive nature of the data registration and reporting processes to satisfy the information demand of different health partners • problems related to data collection tools, basically manual and characterized by high fragmentation and cumbersome data elements with duplication of effort kare et al ethiopia 2013 qualitative • the vast majority of the health posts (hps) in the region are implementing chis using the family folders • various registers and reporting formats are still seen in many hps • the use of tally sheet, with recording of the household number against the services provided by the hew, is proving very valuable in assuring data quality • the ehmis in southern nations, nationalities and peoples region (snnpr) has proved to be very handy in allowing the program managers to access monthly data of every individual hp • remoteness of some health posts with subsequent difficulty in ensuring regular supervisory visits and support • lack of skills of hews on how to properly record data on family folders and use the chis • difficult access to some health posts and the noncontinuous supply of printed tally sheets • continuation of parallel recording and reporting requirements godfrey et al zambia 2017 pilot study • paper-based chis is not fully functional due to the challenges of sustaining availability of reporting forms and delivering completed forms to the hcs • using mobile technology (simple-feature phones) was feasible and viable for the provision of real-time community-based health information to all levels of the health care system in zambia • but, smartphones, laptops, or desktop computers are needed to perform data analysis and visualization • fragmentation and disjointed efforts to strengthen community-based health information systems • need for ongoing technical support to troubleshoot challenges with mobile phones and software • limited mobile network and internet connectivity • recurring costs for data bundles and financial sustainability might be a limitation in the future lessons and implementation challenges of community health information system in lmics: a scoping review of literature 8 ojphi elizabet h etal malawi 2017 process evaluative • use of simple wall charts by community health workers to collect and visualize data helped inform data-based decision making for community health education activities, tracking stock-outs and staffing decisions • provision of wall charts to community and facility workers to organize and view monthly data leads to more data-based decision making • continued use of the wall charts will require additional investments in supervision and reinforcement • turnover may limit the potential effect of any data improvement program scott et al west and central africa 2019 cross sectional • the development and implementation of a chis is still largely nascent • the data shows that these governments are severely under resourced to support robust community health information systems • general appetite for a government owned chis that reflects all community data integrated in to the national hmis is strong • countries face significant budget limitations to the development, deploy, and sustain a chis • chis governance and system design problems • still major limitations exist in engaging with community stakeholders themselves in chis development kalle et al ethiopia 2020 cross sectional • the records kept at the health post in rural ethiopia are incomplete • significant improvements could be made by making sure that family folder system are used at the health posts • shifting to more efficient record keeping systems like electronic or mobile phone-based applications could result in further improvements • limited access to reliable electricity • poor mobile phone network at many health posts are major barriers for scaling up hilina et al ethiopia 2017 report • chis as part of the broader hmis was designed and implemented within the framework of the health extension program • chis implemented via unified data collection tool called family folder • implemented in 90% of health posts • introduce and scale up digitizing chis for timely and quality data reporting • completeness and timeliness problems • high staff turn over • inadequate supply of recording forms • inadequate mentoring and supportive supervision • low coverage of electric power supply • poor internet connectivity for e-chis george et al kenya 2005 implementa tion science research • the current status of chmis is worse than facility-based hmis • the method of data collection for chws was a family or household card • this chmis empowered the local communities through provision • inadequacy of qualified and dedicated community volunteers to run the chmis • lack of incentives and supervisors for community health workers and lessons and implementation challenges of community health information system in lmics: a scoping review of literature 9 ojphi of accurate and timely information • there is need to harmonize district hmis and chis in order for each one of them to complement the other • inadequate financing • issues of data validity, reliability, accuracy and completeness asangan si et al nigeria 2013 implementa tion science research • mobile technology can facilitate the reporting of community data • two android mobile applications were developed to supplement the odk • the mobile data collection was easy, friendly and efficient • mhealth can reduce the complexity of community data collection • the mobile solution helped in significantly improving the information quality by ensuring consistency, completeness and timeliness of data collection • poor network coverage can delay data transfer • irregular power supply • weak local organizational capacity • data security & privacy issues • issues of integration with existing system mesaud etal ethiopia 2016 process evaluation • the implementation status of chis is very good • the availability of manual chis tools was 88.7%, compliance 92.54%, completeness 95.8% & consistency is 68.16% • 30% of the hews were using field book as a replacement of ff • the consistency of the data between family folder and master family index was 97.7% and between family folder and households was 80% • in some health posts there was difference between expected and actual households registered in family folder • parallel registration books & reports were their main burdensome in their daily recording & reporting activities tsedeke et al ethiopia 2015 cross sectional • availability of chis training manual, tally sheets and supportive supervision had significant association with chmis performance • the chmis is good in report completeness (87.9%) content completeness of report (87.3%), calculating indicators (92.2%) and data display (95%) • but low in data accuracy (32.7%) discussions on performance (40.6%) and decisions made (24.3%) • majority of the hp are using both family folder and registers 75.8% • 66.7% hew has adequate confidence on chmis tasks • the study findings show there is critical shortage of tally sheet and integrated maternal and child health card (imch) • organizational technical and behavioral determinants affect chis implementation • parallel data recording tool that is creating additional burden on the hew and also has contributed for poor data quality lessons and implementation challenges of community health information system in lmics: a scoping review of literature 10 ojphi michael et al kenya 2015 qualitative • designed and piloted a simple sms-based reporting system for the community health volunteers (chvs) • the system improved chvs reporting rate by 16% for 3-month period and eliminate inaccurate manual aggregation • potential of mobile technology in enhancing chis process flow in low resource settings if chvs are empowered with mobile phones • the research showed that technology offers an alternative approach that’s cheaper to exclusive paper-based tools • challenges with data collection tools, data quality and reporting rates • making the chvs stick to the sms format when many indicators are being reported • limited gsm network connection at times was also seen as a challenge towards making the system achieve its objectives bimere w et al south africa 2019 qualitative • the current practices of recording patient information and processing are done manually and to a lesser degree electronic means • client information was fragmented, incomplete and in most cases flowed only one way, with a bottom-up approach • the study demonstrated that there was poor verification of community mental health information processing and lack of feedback on mental health analyzed data to all users at mental health services level • the way forward is computer technology, to keep accurate patient recording and communicating patient information with health facilities • many health facilities didn’t have information infrastructure for processing health information • there were challenges with validation of information • lack of skills in collecting, processing and utilization of health information • most health facilities have financial constraints in terms of buying computers nzanzu et al kenya 2015 qualitative • the community-based health information reflected on the ministry of health tool, the chalkboard • the study found out the need for targeted information to households by chws, based on evidence from registers to guide their discussions • chws need access to the household registers periodically to help them to determine dialogue topics during household visits • visual display prompts joint discussions towards consensus for action • unavailable data collection tools • poor information flow • inadequate support of the chws by the chcs • lack of coordination between the chws and chews for referrals • decisions by communities on health actions are not always based on the chalkboard evidence as experienced by chws otieno et al kenya 2019 qualitative • sources of data for chis included the chw log-book, the household register and the assistant chief's register • lack of resources and harmonized tools • weak linkages and coordination between the lessons and implementation challenges of community health information system in lmics: a scoping review of literature 11 ojphi • the chws are the wheels that drive information use at community levels • dialogue was the main way of information utilization in the community • clearly the use of information for planning and decision-making was not a culture among community level respondents • the information-seeking behaviour and use of information was poor facility and community and with other partner organizations • the chws mentioned poor coordination between them and the chews, especially in referrals. • a major challenge was the lack of knowledge on data analysis and interpretation kristen et al tanzania 2017 landscape assessment • chws collect data using household registers, stock management forms, and other simplified health management information system tools designed for the program • chws complete monthly reports and send them to health facility in-charges • each level of the system is expected to share data for feedback purposes. for instance, chws are to share health information with communities and households during regular formal sessions • different cbhp documents were developed and guidance sometimes varies • cbhp implementation design reflects slight inconsistencies from earlier documents on the roles and responsibilities of some health system actors, including community groups • often uncoordinated programs wanjala et al kenya 2016 crosssectional • community health workers are volunteers with responsibilities and accountability was not definite • information was regularly shared during community dialogues • most community units (95%) analyzed their data using the chew summaries (79.5%) and provided feedback through monthly review meetings (38.6%) using chalkboards (20.5%) • a third 14(31.7%) of the units had neither trained nor were able to analyze their data promptly • the availability of data collection and reporting tools was inadequate • majority of the community units did not have mechanisms for institutionalizing chis • the technical capacities for data management was weak • the chis system was not resourced, uncoordinated, lacked structured information to be shared regularly and mechanisms for sustainability. lessons and implementation challenges of community health information system in lmics: a scoping review of literature 12 ojphi kimberl y et al rwanda 2017 landscape assessment • two main cadres of volunteer community health providers operate for community health service in rwanda • chws collect data using home visit registers and stock cards. • chws consolidate data into a monthly village paper-based reporting form • data collected are interpreted locally and used to inform decisions • poor service integration and inadequate coordination between health facilities and the local community • limited financial and geographic access to healthcare • insufficient local human and financial resources mutale et al tanzania 2013 pilot study • used simplified paper registries to collect data on community service • the connect project links community data collected by community health agents (chas) with the district and national hmis through its aggregate data from community registers • to facilitate the health facility communication, community health agents and supervisors have been provided closed-user phone groups to communicate without incurring costs • there are notable challenges in collecting and using community-based health information • variation in cha supervisor leadership qualities and motivation • problems concerning the uniformity and proper use of the registers furth et al zambia 2012 implementa tion science research • chis programs tended to keep data on numbers of clients served, but not on how many visits or counseling sessions were provided to each client or on how much work each chw was undertaking • organizational investments in chw programs varied among sites • paying chws and providing them the information, skills and environment they require to spend the necessary time with each client will improve performance • data on clients served by chws often do not accurately reflect the workload of chws • sustaining community involvement, and how to troubleshoot problems with community support of chws • convincing communities to invest time or resources to support chws is challenging nzanzu et al kenya 2014 cross sectional • chis has been very helpful in providing information which they use in the health facility to improve quality of health care • the system enables the community to follow up on the progress of implementation of planned activities and to determine their successes • the sources of information included the chalk board, maintained by chws • this study revealed that utilization of data at community level remain an important pillar of community primary health care • lack of integration among the many parallel data collection systems and inadequate coordination • the aspect of financial commitment from the government has been a great challenge sources of information at the grass root level are many and varied, likewise the subject matters relating to health are many lessons and implementation challenges of community health information system in lmics: a scoping review of literature 13 ojphi the review comprised of five cross-sectional studies, two pilot studies, two qualitative studies, three implementation science researches, two process evaluations, two landscape assessments and one national report (figure2). figure 2. types of studies included for this review the included studies in this review were published between 2005 [26] and 2020 [23]. among the included studies for this review most were published by the year 2017 (figure 3). figure. 3 number of published studies on chis per year lessons and implementation challenges of community health information system in lmics: a scoping review of literature 14 ojphi implementation status of chis in lmics the review indicated that lmics have developed community-based health services, which are delivered by community health workers in close connection to primary care facilities. according to this review, chis come in various structures and cover diverse health areas with different types of health workers across different countries. in lmics, chiss are managed by trained health extension workers, community health workers, community health agents and community volunteers who have different educational background with different duration of education & training including different certification approaches across countries. the implementation modality, the type of information collected, and the tools used for chis also varied from place to place. for the chis implementation, majority used registers, family folder/card, mobile technologies, chalk/white board and multiple data collection and reporting tools at the community level (figure 4). figure 4: types of tools used for chis implementation in lmics most of the community information systems in lmics were still manual and data could not be shared easily for evidence-based decision making. according to this review, the current practices of recording information and processing are done manually and to a lesser degree electronic means. community level information was fragmented, incomplete and in most cases flowed only one way, with a bottom-up approach [19] [21] [30] [32] [39]. in ethiopia, chis as part of the broader hmis was designed and implemented within the framework of the health extension program implemented via unified data collection tool called family folder [22]. a family folder is a pouch, which is the main part of chis, a data collection tool designed by the federal ministry of health (fmoh) for the hews to document both individual and household level data to be utilized as a source of information at the grass root level. alongside this, chis was designed to standardize data collection and integrate data systems to provide relevant information for decision-making at the health posts and to feed the lessons and implementation challenges of community health information system in lmics: a scoping review of literature 15 ojphi hmis on a regular basis. hews report the data they collect to the nearest health centers monthly [19] [21]. process evaluation done in 2016 in ethiopia also showed that the implementation status of chis was good. the availability of chis tools was 88.7%, compliance 92.54% and completeness 95.8%. the consistency of the data between family folder and master family index was 97.7% and between family folder and households was 80% [20]. another cross-sectional study in ethiopia showed that availability of chmis training manual, tally sheets and supportive supervision had significant association with chmis performance. the chmis was good in report completeness (87.9%) content completeness of report (87.3%), calculating indicators (92.2%) and data display (95%). but low in data accuracy (32.7%) discussions on performance (40.6%) and decisions made (24.3%). majority of the hp were using both family folder and registers 75.8% [18]. the connect project, in tanzania, links community data collected by community health agents (chas) with the district and national hmis through its aggregate data from community registers. cha supervisors at the facility level were responsible for meeting with chas to create action plans, and for reporting data collected by chas to the district level. there, a council health management team develops comprehensive council health management plans and reports to the national level [39]. another study in tanzania also showed that chws collect data using household registers, stock management forms, and other simplified health management information system tools designed for the program [24]. in kenya the current status of chmis was reported as worse than facility-based hmis. according to this study, the simplest method of data collection for chws and their supervisors was a family or household card [26]. in rwanda, two main cadres of volunteer community health providers operate for community health service. chws collect data using home visit registers and stock cards. in the village, chws consolidate data into a monthly village paper-based reporting form and submit it to their cell coordinators & health center [33]. moreover, this review demonstrated that mobile technology can facilitate the reporting of community data. the nigeria evidence-based health system initiative (nehsi) used commcare to connect its chis with the provincial and national hmis for planning. community field workers were given android mobile devices, which they used to register people, automatically synchronizes and link that data to the larger information systems [34]. it was also reported that using mobile technology (simple-feature phones) was feasible and viable for the provision of real-time community-based health information to all levels of the health care system in zambia [35]. similarly in kenya, a mobile technology designed with simple sms-based reporting system for the chvs facilitates the reporting of community data to higher levels [27]. this review showed that use of information for evidence-based decision making and community engagement is still weak amongst this group of lmics. two studies from kenya revealed that the use of information for planning and decision-making was not a culture at the community level [29] [30]. on the other hand, data-driven participatory action planning by the community and health centers helped to improve services in some settings. for example, in kenya community health workers inform community health committees on key community health lessons and implementation challenges of community health information system in lmics: a scoping review of literature 16 ojphi indicators, whereupon data collection is planned. data collected are then fed back to the facility to identify health utilization gaps and outbreaks, and ultimately to improve services [28]. in rwanda, data collected at the cell and sector levels are interpreted locally and used to inform decisions [33]. in malawi use of simple wall charts by community health workers to collect and visualize data helped inform data-based decision making for community health education activities, tracking stock-outs and staffing decisions [38]. lessons learnt from chis implementation effective community health management information systems (chmis) are important in lowresource countries that rely heavily on community-based health care providers. the introduction of the chmis was an innovative idea aimed at enriching the data and improving the collection and use of health information at community level. a study from kenya revealed that utilization of data at community level remain an important pillar of community primary health care [28]. in lmics, substantial progress has been made to strengthen community health information systems, with most efforts focusing on digitization, improving data quality and analysis. however, the review indicated that that continuation of parallel recording and reporting requirements with subsequent over burden on community health workers imposed by various departments and partners is threatening the sustainability of chis [19] [20]. in one of the studies, it was indicated that absence of simple and unified data collection tools at community level and offering extra data collection and reporting tools for hews, other than the family folder creates additional burden to their regular work [21]. moreover, in two separate chis efforts in ethiopia, the need for a streamlined physical system is highlighted [19] [21]. the family folders were bulky and vulnerable to damage from rain when carried from house to house, so hews ended up recording in registers instead and transferring the data to folders later on [21]. in the southern nations, nationalities and people’s region (snnpr) of ethiopia, each health post generated a large amount of data, which became manually unmanageable [19]. largely implemented by donors and ngos, these community based tools have been observed as forming community data silos that rarely feed into the national health management information system (hmis). ultimately, discordant and fragmented chw reporting systems result in little in stitutional buy-in and low community data use.there is a clear need for community-based reporting systems to feed into a single centralized, government owned information systems like the dhis2. in kenya, it was suggested to harmonize district hmis and chis in order for each one of them to complement the other [26]. the review also emphasized that, technology plays a role in strengthening chis implementation in most lmics. alongside this, echis and mobile applications have been increasingly developed and deployed to quantify and support the services delivered by community health workers. multiple studies included for this review have reported the use of digital health technology to strengthen chis implementation in lmics [27] [34] [35]. the pilot studies revealed that integration of digital technologies to existing chis were feasible and effective in resource limited setting. in nigeria, the mobile data collection was easy, friendly and efficient. mhealth can reduce the complexity of community data collection. the mobile solution helped in lessons and implementation challenges of community health information system in lmics: a scoping review of literature 17 ojphi significantly improving the information quality by ensuring consistency, completeness and timeliness of data collection and submission [34]. the review also showed that in kenya, technology offers an alternative approach that’s cheaper to exclusive paper-based tools. the research showed the potential of rapid user acceptance to any mhealth system if it matches the user and infrastructural requirements of the given context and there is user involvement [27]. in studies from ethiopia, authors proposed shifting to more efficient record keeping systems, for example, to electronic or mobile phone-based applications that could result in further improvements for chis implementation for timely and quality data reporting [22] [23]. the ehmis in snnpr of ethiopia has also proved to be very handy in allowing the program managers to access monthly data of every individual health post [19]. challenges of chis implementation as with any system, chis implementation is not without challenges. the review showed that organizational, technical, behavioural, economic and capacity building barriers exist for effective implementation of chis in lmics. the need for human resources and capacity building remains as one of the major challenges for successful implementation of chis in lmics. in rwanda, insufficient local human resources [33], in kenya, inadequacy of qualified and dedicated community volunteers to run the chis [26] [28] and in ethiopia lack of technical capacity of community health workers on how to properly record and report chis data and high staff turnover [19] [32] [22] were reported as barriers. additionally, difficulty in ensuring regular supervisory visits and continuous support were reported as barriers of chis implementation in ethiopia [19] [22] and kenya [26] [31]. there are notable challenges in collecting and using community-based health information. among these, problems concerning the uniformity and proper use of the registers were mentioned as barriers in tanzania [25] and kenya [28]. the availability of data collection and reporting tools was also inadequate in kenya [30] and ethiopia [19] [22]. besides, data quality, timeliness and accessibility of the minimum data sets were reported as barriers to utilization of chis data [30]. the repetitive nature of the data registration and reporting processes to satisfy the information demand of different health partners was mentioned as a major challenge in ethiopia [19] [21] [23]. in south africa, many health facilities didn’t have information infrastructure for processing health information [32]. in kenya a major challenge was the lack of knowledge on data analysis and interpretation. similarly, there was a lack of timely use of information which may have led to haphazard planning and health interventions that were unrelated to household health needs [29]. moreover, issues of data validity, reliability, accuracy, completeness and poor information flow were reported in kenya [26] [31] [27]. likewise, in ethiopia, completeness and timeliness problems has been identified as chis implementation problems [22]. still major limitations exist in engaging with community stakeholders themselves in chis development and sustaining community involvement [36] [37]. along this, weak local organizational capacity for institutionalizing chis and issues of integration with existing system lessons and implementation challenges of community health information system in lmics: a scoping review of literature 18 ojphi has been reported from studies in nigeria [34], kenya [28] [29] [30] [31],tanzania [24] and rwanda [33]. the lack of capacity for adapting and implementing software solutions was also reported as a challenge in most of the studies. poor ict infrastructure, access to cell phones, access to reliable electrical power supply, and mobile network issues were mentioned as major barriers for digitalization of chis activities. according to this review, mobile network and internet connectivity limited the use of the ict technology for chis implementation and further scale up in lmics [22] [23] [27] [34] [35] [37]. countries also face significant budget limitations to develop, deploy and sustain a chis and the aspect of financial commitment from the government has been a great challenge [26] [28] [33] [37]. discussion community health workers play a crucial role in providing primary health care to communities and a chis generates information through sources at the community level. as this review demonstrates chis designs vary across countries and can be used for different goals. a review of health information systems also reported that interventions to improve routine health information system in lowand middle-income countries differ in design, methods, and scope [40]. our review finding showed that the community-based reporting systems in lmics has been observed to be inadequate in supplying community stakeholders and governments with the information they desire. as increased healthcare coverage and health equity become more important to countries, the role of community health systems and their information systems will continue to increase [37] [41]. lippeveld et al also argued that the availability of information on health services performance can empower community health workers to improve the quality of community-based health services [42]. it is also indicated that chis should enable information to be shared among community-based services and they should feed into national health management information systems [10] [43] [44] [45] [46] [47]. a chis must collect only relevant information needed by the community for their own use and should avoid gathering too much unnecessary information which is not of immediate use. one potential solution to cumbersome physical data is use of electronic data collection systems, but this is not always possible in low-resource settings, and priority should be placed on collecting the right data [43]. key to such efforts is the development and strengthening of chiss as an integral part of national health information systems to improve the availability, accessibility, qualityand use of community health data [42] [48] [49] [50]. with this regard, technology plays a role in strengthening chis in most lmics. this has been also observed in other studies in lmics where the value of digital health technologies in delivering vital health care interventions, especially in low resource settings is increasingly appreciated [48] [51] [52]. the advent of mobile technology with its increasing penetration into the rural areas has permitted a re-envisioning and redesign of chmis data collection. however, this should be supported by enhancing a culture of information use. lessons and implementation challenges of community health information system in lmics: a scoping review of literature 19 ojphi moreover, this review demonstrated that mobile technology can facilitate the reporting of community data. benefits of the mhealth system were ease of use, savings both in material and human resources and improved data quality [34]. evidence also showed the potential of rapid user acceptance to any mhealth system if it matches the user and infrastructural requirements of the given context and there is user involvement [27] [47]. designing or adapting technologies to the limited infrastructures of lmics are needed to circumvent the lack of certain resources [51]. in the development and deployment of digital health solutions, continuous support is required at all levels from the development of user-friendly and easy-to-use applications to implementation [52]. to take advantage of the maximum benefit provides by digital health information systems and technologies, there is a need to adopt an interoperability standard for echis implementation. if any technology is to be of use it should be able to be suitably integrated into the workflow and social environment of the users [41] [53]. another review also showed that combinations of technology enhancement along with capacity building activities, and data quality assessment and feedback system were found useful in improving data quality [40]. according to this review, many barriers to chis implementation are linked to organizational, technical and behavioral factors which is in accordance with reports from multiple evidence [42] [46] [51] [54]. among the challenges, there has been fragmentation and disjointed efforts to strengthen community-based health information systems in lmics. without complete integration, there are duplicative efforts in data collection, analysis, and reporting [21] [29]. in ethiopia, the parallel data recording tool is creating additional burden on the hew and also has contributed for poor data quality [18] [20]. on another study, it has been reported that no population-based community health system can successfully stand alone [37]. lippeveld et al indicated that there is a need to link the health information generated by chws to the facilitybased routine health information systems. yet, in most countries, this vital information on health services provided by chws is not routinely captured and integrated [42]. this calls for the need for more community-friendly methods of collecting health information and closer coordination among various information systems should be encouraged [44] [51]. the challenge of lack of capacity for adapting and implementing software solutions persists in lmics. hence, efforts at establishing community-based information systems are beset by challenges with supporting infrastructure such as erratic power supply and poor telecommunication [35]. in zambia, ongoing technical support to troubleshoot challenges with mobile phones and software were suggested to strengthen the chis implementation [35]. although the connectivity is poor in many rural areas, the networks are improving [52]. there is also a need for ongoing technical support to address the hardware and software challenges faced by the community health workers [42]. lack of clear policy frameworks for the implementation and use of community health information systems is also another challenge. asangansi et al addresses challenges unique to those chis using mobile technology, including the potential for lost or stolen phones and lack of existing policy around electronic data privacy and security [34]. in this review, the technical capacities for data management were weak and inadequate to collect, analyze and share comprehensive information that may be required for decision-making at community level. a common challenge experienced was lack of technical capacity of community health workers [19] [32], burden of new data collection responsibilities on chws lessons and implementation challenges of community health information system in lmics: a scoping review of literature 20 ojphi and redundant data collection [21] and cumbersome paper-based data chis system [19] [21]. time spent collecting and harmonizing redundant or nontransferable data is time that could otherwise be spent serving the community [21]. another challenge regarding the cws or volunteers is the added workload of data collection and associated activities. there was a lack of timely use of information which may have led to haphazard planning and health interventions that were unrelated to household health needs [29] [42] [55] [47]. in other studies, gaps in the hmis are linked to lack of training, inactive supervision, staff workload pressure, and the lengthy and laborious nature of the system [51] [56]. an evaluation of a chis in kenya also highlights the need for intensive training with periodic refresher courses for chws involved in data collection [28]. findings from another studies demonstrate the importance of resourcing, management of teams, and attitudinal change among community health workers [47] [49]. in terms of financing chis implementation, this review showed that countries are severely under resourced to support robust community health information systems which is in line with other reports which showed that chis system was not resourced and lacked mechanisms for sustainability [30] [37] [51]. other challenges mentioned include lack of data collection supplies in remote areas [19]. with regard to digitalization, recurring costs for data bundles and financial sustainability might be a limitation in the future [35]. the sustainability of chis faces many challenges, which could be addressed through systems’ technical design, stakeholder coordination, and the building of organizational capacity to maintain and enhance such systems [42] [57]. greater collaboration, coordination, and joint action are needed at global and particularly country levels to address these challenges, accelerate progress, and achieve national health priorities. an integrated chis requires a long-term, highlevel focus on good hmis governance, capacity building for data management and information use, and strong commitment to change by leadership across stakeholder groups. chis has lot of potentials; however, it needs to be properly scaled-up, owned and used for realizing its potentials and for its sustainability. limitation this review is not without limitations. it primary included only published articles and gray literature. it is likely that other chis exist that have not yet been discussed in the literature or presented on a website. conclusion and recommendations generally, community health information system implementation in lmics is in its early stage. this review showed that use of community level information for evidence-based decision making is still very weak in lmics. most of the chis in lmics were still manual and data could not be shared easily for evidence based-decision making. there was not a universal or standard chis design and implementation modality across countries that vary depending on what services are provided at the community level. while chis are relatively nascent, this lessons and implementation challenges of community health information system in lmics: a scoping review of literature 21 ojphi review has shown that there are promising practices on digitalizing the community health information systems with technology playing a role in strengthening chis in most lmics. in lmics, many challenges remain for effective implementation of chis with uncoordinated and unintegrated systems including existence of parallel recording & reporting tools. besides, lack of resources, low technical capacity and shortage of human resource were the main challenges in lmics. low data quality and poor culture of information use at local levels were also the problems reported as challenges for chis implementation. poor ict infrastructure including electricity and network related barriers were also the main obstacles for digitalization of chis in lmics. hence, greater collaboration, coordination, and joint action are needed to address these challenges. strong leadership, motivation, capacity building and regular feedback are also important to strengthen the chis in lmics. moreover, chis should be transformed in to e chis with integration of different technology solutions. design of the chis should also include a clear policy, strategy and guidelines for improved performance. there is clearly a need for governments and implementers to invest from domestic resources and to advocate donors to refocus their funding to core chis funding. local ownership is also critical to the long-term sustainability of chis implementation in lmics. availability of data and materials the datasets supporting the conclusions of this article are included in the review competing interests none funding this review received no funding authors’contributions zam, mac and sam designed the study, reviewed literature, selected and appraised the articles, extracted and analyzed data, interpreted results and drafted the manuscript. bt and mk guided the study and reviewed the manuscript for its scientific content. all authors have read and approved the manuscript. acknowledgments we would like to thank the authors of the original articles. references 1. studnicki j, berndt dj, fisher jw. using information systems for public health administration. public heal adm princ popul manag. 2007; lessons and implementation challenges of community health information system in lmics: a scoping review of literature 22 ojphi 2. greenes ra. health information systems. healthc inf manag syst cases, strateg solut fourth ed. 2015. 3. almunawar mn, anshari m. health information systems (his): concept and 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https://pubmed.ncbi.nlm.nih.gov/33499838 https://doi.org/10.1186/s12911-021-01400-5 lessons and implementation challenges of community health information system in lmics: a scoping review of literature abstract background objective methods data sources and searching strategy studies eligibility criteria inclusion criteria exclusion criteria study selection and data extraction analysis and reporting results flow of the searching process and study characteristics implementation status of chis in lmics lessons learnt from chis implementation challenges of chis implementation discussion limitation conclusion and recommendations availability of data and materials competing interests funding authors’contributions acknowledgments references crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 (page number not for citation purposes) isds 2013 conference abstracts predicting levels of influenza incidence anna l. buczak*, liane ramac-thomas, erhan guven, yevgeniy elbert, steven babin, benjamin baugher and sheri lewis jhu apl, laurel, md, usa � �� �� �� � � �� �� �� � objective ������ �� ���� � ������� ��������������� ��� ���� ��� �� ������ ��� ��� ������ ���� ��� �� ���� ��� �� � ����������� ��������� �� �� �� ����� ������������ �������������� ��� ����������������� �� ��� ��� �������� �� ������ ����������� ��� ��� ��� ����� ���� �� introduction ����� ���� ��� ������������ ����������� ��� ����� �� ������������ � ��������� �������� �� ���������� ������ ��� �� ���� � �������� ��������������!"#$��� �� �� �� ���� ����� � ���� ����� �� ������� �� ��� ��������� �� ��� �� ����!%#��&�� ���� ����!'#� ���� ����� � ����� ���� ��� ��������������� �� � � � ����������������� �� 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!"#�1������&$�=��� ��&$�&��� ��4����� ���������� ���� ��� ��������� ���� ����-������� ���� �� � ��� �� �������� �����*:"5",��&�1�� �� ���� �%66>$�?-'6� !%#�5��������:��@ ��� �� ���� ��� ���������� ��� ����������������� � �� � � ����� ������ �-������ �� ����������� ��������� ����*:"5"�%66>,�� &���� ��/����a�0�� �%6""$�"6-"8� !'#�&�� �����$�7 �������5��(� � �� � ������������ �� ��� ���� ����� � � ">"b������ �������� �������;4��� � ���95�4�%66?$�"6+-?8bb� !+#�(�� �����c$�4������c$��������=$�&�����7 �������d$�; .���1$�1��� �� ��$�3������1��/�������� � ����� ��� �������� ������������ ���� ��� � ������������ �� �� ����� ������������� ���90�4�9� ��� ���%6"'$� >"66'">+� !8#�&�������$�d���� �9$�&�����4$�7 ���� ��&$�0 ����4������ ������ �� ��� ������������� ��� ����� ��������� ��� ��� �� ���������������� ����� ��� �� �������� ���&�1�� �����������) ������������%6"%$� "%-"%+� *anna l. buczak e-mail: anna.buczak@jhuapl.edu� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e105, 2014 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e310, 2019 isds 2019 conference abstracts keyword surveillance and the development of evolving reporting in austin, texas ashley n. hawes austin public health, austin, texas, united states objective austin public health's public health emergency preparedness program utilizes a variety of tools and resources to create informative, event-specific, and engaging syndromic surveillance reports to share 1) internally within austin public health; 2) with city of austin and travis county partners; 3) local health care coalition members; and 4) the public during events that affect the austin, texas metropolitan area. introduction austin public health creates a variety of syndromic surveillance reports for events throughout the austin, texas metropolitan area. these events range from responses to major disasters such as the 2017 hurricane harvey sheltering to ongoing special event monitoring such as university of texas football games and the austin city limits music festival. partnerships within the aust in metropolitan region are crucial to ensuring the information-sharing necessary to create robust reports, as well as during the followup process of requesting feedback from partners on the usefulness of the reports. austin public health's public health emerge ncy preparedness program utilizes a variety of tools and resources to create informative, event-specific, and engaging reports, fulfilling multiple reporting needs for all partners. methods the process of generating syndromic surveillance reports begins by keyword surveillance of hospital emergency room chief complaint data. keywords are keyed into the austin metropolitan area's hospital free-text chief complaints via the capital area public health and medical coalition. the searchable keywords are queried to create a baseline picture of an evolving event. data are also requested and gathered from multiple partners including local news stations, the national weather service, the city of austin’s office of vital records (birth and death certificates), social media platforms, austin 3-1-1, and austin/travis county emergency medical services. all data are then analyzed, visualized and displayed in reports that are distributed via multiple platforms including email, social media, governmental websites, geographic information system (gis) storymaps, and we beoc. reports are then combined into event end summaries. accompanying the final summary report are feedback surveys. results the ability to request keywords in an open communication pathway between hospitals, the capital area public health and medical coalition, and the local health department has bolstered area partnerships. previous surveillance reports have been reported to be both useful and beneficial to departmental, community and health coalition partners. for example, the 2017 report following hurricane harvey was used by local hospitals for planning staffing and surge needs, and the 2018 heat report is being used to determine the placement of future cooling stations at special events. a 2019 surveillance report on dockless scooter injuries will be used to inform risk factors and trauma injury severity. requested changes from partners have included: the addition of graphs , keyword-specific changes, inclusion of social media and broadcast media data, and the use of information from other partners to create a final event or year-end summary report. conclusions keyword surveillance of hospital chief complaint data and of other local real-time data are innovative tools to creating meaningful syndromic surveillance reports that provide situational awareness and are adaptable to the needs of events and situations in the area. the development and evolution of these syndromic surveillance reports has helped to build a rapidly deployable syndromic surveillance system that can provide key data for preparing for and responding to future disaster events. by engaging local and regional partners in an iterative process for developing these reports, aph ensures ongoing improvement, thereby providing more powerful and useful reports to all partners involved. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e310, 2019 isds 2019 conference abstracts acknowledgement austin public health, city of austin, travis county, capital area public health and medical preparedness coalition figure 1. example of page 1 of 2018 heat report sent out by austin public health to partners http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts design and implementation of an emergency department (ed) based rapid hiv screening program fredric hustey*, michael phelan, sharon o’keefe and tracy barbour cleveland clinic, cleveland, oh, usa objective to design and implement an ed based rapid hiv screening program targeting high risk patients presenting with signs, symptoms, or concerns for sexually transmitted diseases; to determine the prevalence rate of hiv infection in the tested population; to determine the proportion of hiv-positive patients with successful linkage to outpatient care after ed discharge. introduction in 2003, the centers for disease control and prevention (cdc) in atlanta, georgia estimated that approximately 1 million people in the united states were living with hiv/aids, and that approximately 25% of these were undiagnosed and unaware of their hiv infection. for many such patients the ed may be the only part of the health care system that is utilized. in 2006, the cdc revised their recommendations for hiv testing in a variety of care settings including the ed. in spite of this change, most eds throughout the united states still do not offer routine hiv testing. implementing successful ed based testing models may lead to greater acceptance of ed based testing, earlier detection, and further reduction in the transmission of hiv in the united states (1,2,3,4,5). methods this was a prospective cohort study targeting ed patients presenting with signs, symptoms, or concerns for sexually transmitted diseases. the emergency department is part of a large urban tertiary care center with approximately 55,000 ed patient visits per year. in november of 2011, an ed based poc hiv testing program was initiated using the clearview hiv stat-pack (blood sample) targeted towards patients presenting with signs, symptoms, or concerns for sexually transmitted diseases. patients were recruited and consented for testing by ed clinical staff. all records regarding testing were recorded in the ed point of care lab at the time of the test (per standard lab operations) and then subsequently entered into a secure electronic database by trained research personnel. this database was used to determine the prevalence of hiv positivity in the tested population. for patients testing positive in the emergency department, liaison social worker followed up via patient telephone interview at one week and one month intervals (if there was no linkage established at the initial one week interview) to establish whether there was linkage to care after ed discharge. successful linkage to care was defined as followup in infectious disease clinic within one month. proportions with 95% confidence intervals (ci) are reported. results between november 29th, 2011 and august 18th, 2014 a total of 1090 patients underwent ed based poc hiv testing. 43% (467/1090) were male, 74%(804/1090) african american, and 0.4% (4/1090) hispanic. mean age was 27 years. 32 patients tested positive of which 31 were confirmed to be hiv positive by confirmatory testing (3%; 95%ci, 2-4%). 1/32 was considered to be false positive based on follow up western blot and viral load testing. 27/31 patients were previously undiagnosed (87%; 95%ci, 71-95%), while 4 patients had reported prior positive results performed at other sites.100% (32/32: 95%ci, 87-100%) of poc hiv positive patients were linked to outpatient care. conclusions a targeted poc hiv testing program is feasible in the emergency department setting. targeting high risk populations such as those presenting with signs, symptoms, or concerns for sexually transmitted diseases yielded much higher hiv prevalence rates than previously reported among ed populations. hiv positive patients can routinely be linked to initial outpatient care from the ed setting with acceptable rates of follow up. keywords hiv; detection; prevalence acknowledgments this project was supported in part by grant from the ohio department of health and centers for disease control. references 1. glynn m, rhodes p. estimated hiv prevalence in the united states at the end of 2003. national hiv prevention conference; june 2005; atlanta. abstract t1-b1101. available at: http://www.aegis.com/ conferences/ nhivpc/2005/t1-b1101.html. accessed february 11, 2008. 2. cdc mmwr, august 8, 2008 / 57(31);845-849 http://www.cdc.gov/ mmwr/preview/mmwrhtml/mm5731a1.htm, accessed june 25, 2013 3. branson bm. revised recommendations for hiv testing in healthca re s ettings. available at: http://www.cdc.gov/hiv/topics/testing/ resources/slidesets/pdf/ 4. uspstf recommendations for sti screening, http://www. uspreventiveservicestaskforce. org/uspstf08/methods/stinfections.htm 5. cdc 2010 std treatment guidelines, http://www.cdc.gov/std/ treatment/ 201 *fredric hustey e-mail: husteyf@ccf.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e82, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts application of syndromic surveillance to describe gunshot-related injuries in houston ryan m. arnold*, wesley mcneely, kasimu muhetaer, biru yang and raouf r. arafat houston department of health and human services, office of surveillance and public health preparedness, houston, tx, usa objective to introduce a model to track gunshot-related injuries, describe gun-related injuries in houston, and investigate the association between gun-related injuries and social determinants of health using syndromic surveillance data. introduction in 2011, injury by firearms accounted for 32,351 deaths (10.4 deaths per 100,000 population) in the united states1. this rate was higher than any infectious or parasitic disease (the highest being 2.5 for both viral hepatitis and hiv disease)1. furthermore, death by gunshots accounted for over half of all suicides and over two-thirds of all homicides in the us1. despite the disproportionate media coverage of mass shootings and assault weapon violence, the vast majority of these deaths are attributable to non-mass shootings and to handguns2. though a contentious issue in the united states, understanding this cause of death is vital to confronting the issue locally and nationally. traditionally, death certificates, crime data, cross-sectional studies, and retrospective studies have most commonly been utilized in this endeavor; however, the collection of real-time emergency department (ed) visit information presents a unique opportunity to track gunrelated injuries to supplement our current understanding of this issue. the houston department of health and human services (hdhhs) has been receiving this information for over a decade from eds in the greater-houston area, and the department is currently connected to 32 of the largest eds in the area. the current study aims to enhance the understanding of gunshot-related injuries in the houston area and present a model for utilizing rods information for this purpose. methods gun-related injury data was collected by real-time outbreak disease surveillance (rods) v.4.2, an electronic database of ed visits in the houston area, between september 2012 and august 2014. this data was cleaned, described, and analyzed for associations with social determinants of health, using 2010 united states census data. moreover, the data was compared with national cause of death data provided by the cdc1. oracle sql developer v.3.2 was used to extract and clean data from the rods system. arcgis v.10.1 was used for geo-coding and geographic analysis. sas v.9.3 was used to conduct the descriptive and statistical analyses. results in the past two years, more than 900 gunshot-related ed visits were collected in rods. the data was 84.7% complete for age and 58.4% complete for zip code. using the zip code data, arcgis identified zip codes and areas of houston most common for gunshot-related ed visits (figure 1). most patients were male (86.3%), between the ages of 18 and 34 (64.7%), and received at two hospitals in the texas medical center (69.2%). no temporal or seasonal trends were identified in the past two years of information; however, annual trends may be able to be identified over larger time periods or in larger datasets. conclusions the use of syndromic surveillance data provided a useful tool for understanding gunshot-related injuries in the greater-houston area. despite incomplete demographic information for all the ed visits collected for the present study, this data was able to characterize the demographic trends of these visits and identify populations most affected by gunshot-related injuries. in addition to current sources of information on gun-related violence and deaths, this source supplements our understanding of this issue by providing information that may not be collected through traditional means. additional conclusions are pending the completion of further analysis of this data. figure 1. density of gunshot-related ed visits in the greater-houston area, 09/2012-08/2014. keywords syndromic surveillance; gun-related injuries; geographic analysis; determinant of health acknowledgments this work was supported by the houston department of health and human services office of surveillance and public health preparedness through a grant from the department of homeland security urban area security initiative. references 1. hoyert, dl, xu, j. deaths: preliminary data for 2011. hyattsville (md): national center for health statistics (us). october 2012. 52 p. report no.: (phs) 2013-1120. 2. united states department of justice, federal bureau of investigation. crime in the united states, 2012. clarksburg (wv): united states department of justice, federal bureau of investigation. 2013. *ryan m. arnold e-mail: ryan.arnold@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e106, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts computational method for epidemic detection in multiple populations ekaterina shatskikh* and michael ludkovski statistics, uc santa barbara, goleta, ca, usa objective detect epidemics over multiple populations using computational methods introduction currently centers for disease control and prevention (cdc) employ threshold rules to declare epidemic outbreaks, such as influenza, separately in each population. however each year influenza starts in one population and spreads population-to-population throughout the country. therefore there is a need for an algorithm to declare the epidemic that uses information from multiple populations. methods the standard stochastic sir model can be used to simulate the epidemic behavior in one population1. we use the extended model by coupling populations with each other, where the infection can spread from one population to another. we apply the quickest detection method2 to announce the epidemic. to qualify the detection accuracy, we balance the costs of false alarms and detection delays. thus, we will announce an epidemic if the future cost is greater than the immediate cost. since epidemic evolution is stochastic, we generate epidemic scenarios and estimate the expected costs. we use sequential regression monte carlo3 (srmc) to efficiently approximate the decision rules. this consists of judiciously picking epidemic scenarios using sequential design and then applying regression techniques to construct the entire surface of expected future costs. moreover srmc allows the algorithm to self-correct by simulating additional epidemic scenarios for the model states where the algorithm is not sure about announcing the epidemic. results the figure below illustrates our approach on a simulated dataset involving two populations of size 2000. we constructed a detection map based on the number of infected individuals in the first population it (1) and posterior probability of epidemic in the second population pt. the blue region corresponds to the regions where the epidemic should be announced; while the red region corresponds to the regions where the epidemic should not be announced. the small white regions (which can be seen on the boundary of the previous two regions) represent the regions where the algorithm is not sure about announcing the epidemic. our algorithm is better than the cdc approach because it uses the additional information about the number of infected individuals in other populations. this additional information helps the algorithm to see the whole picture about the state of epidemic. conclusions we developed an algorithm for detecting an epidemic in a multiple population scenario. we are working on an extension of this algorithm to cover the case where we observe only partial information (for example, only diagnosed infected individuals) and we use particle filtering to estimate it (1) and pt. figure: detection map for the second population in a two-population model constructed based on 2000 adaptively generated scenarios. decisions are based on a loess metamodel. keywords simulation; stochastic compartmental models; early outbreak detection acknowledgments this material is based upon work partially supported by the national science foundation under grant no. atd 1222262. references 1. andersson h., britton, t. stochastic epidemic models and their statistical analysis. lecture notes in statistics. springer, 2000. 2. poor h.v., hadjiliadis o. quickest detection. cambridge university press, 2008 3. gramacy r. ludkovski m. sequential regression for optimal stopping problems. arxiv:1309.3832, 2013. *ekaterina shatskikh e-mail: shatskikh@umail.ucsb.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e158, 2015 developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi developing evidence-based population health informatics curriculum: integrating competency based model and job analysis ashish joshi phd, mbbs, mph1*, irene bruce, mph2, chioma amadi, phd3, jaya amatya, mph4, 1cuny graduate school of public health and health policy, ny 2cuny graduate school of public health and health policy, ny 3adjunct faculty, cuny graduate school of public health and health policy, ny 4montefiore medical center, ny abstract with the rapid pace of technological advancements, public health professions require a core set of informatics skills. the objective of the study is to integrate informatics competencies and job analysis to guide development of an evidence-based curriculum framework and apply it towards creation of a population health informatics program. we conducted content analysis of the population health informatics related job postings in the state of new york between june and july 2019 using the indeed job board. the search terms included “health informatics” and “population health informatics.” the initial search yielded 496 job postings. after removal of duplicates, inactive postings and that did not include details of the positions’ responsibilities resulted in 306 jobs. information recorded from the publicly available job postings included job categories, type of hiring organization, educational degree preferred and required, work experience preferred and required, salary information, job type, job location, associated knowledge, skills and expertise and software skills. most common job title was that of an analyst (21%, n=65) while more than one-third of the hiring organizations were health systems (35%, n=106). 95% (n=291) of the jobs were fulltime and nearly half of these jobs were in new york city (47%, n=143). data/statistical analysis (68%, n=207), working in multidisciplinary teams (35%, n=108), and biomedical/clinical experience (30%, n=93) were the common skills needed. structured query language (sql), python, and r language were common programming language skills. a broad framework of integrating informatics competencies, combined with analysis of the skills the jobs needed, and knowledge acquisition based on global health informatics projects guided the development of an online population health informatics curriculum in a rapidly changing technological environment. key words: population health informatics, workforce, competencies, public health, training, skills correspondence: *ashish.joshi@sph.cuny.edu doi: 10.5210/ojphi.v13i1.11517 copyright ©2021 the author(s) mailto:ashish.joshi@sph.cuny.edu developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi 1.0 introduction there is a growing need for evidence-driven population health interventions. the emerging science of population health informatics has continually demonstrated significant promise in revolutionizing the delivery of population health interventions. population health informatics tools and technologies are rapidly enhancing the capacity to translate growing population data into meaningful information that can inform actionable insights. these population health technologies have already shown promise in improving the effectiveness of core public health services such as outbreak detection and control, immunizations and real-time information on health impacts of emergencies [1]. expertise in population health informatics is critical in supporting the activities of public health agencies toward building and sustaining information capabilities that meet evolving population health needs. owing to the wealth of public health functions that rely on data assessments and the rapid pace of technological advancements, public health professions require a core set of informatics skills [1]. in addition, health agencies need health informaticians who work at junior and senior organizational levels to oversee the development and implementation of successful it systems [1]. there are numerous training programs in health informatics in the united states. more than 90 universities provide training in biomedical informatics at certificate, masters and doctoral levels [2]. a plethora of training programs in the field of public health informatics has emerged in the past decade. compared to the public health informatics programs offered, population health informatics is a relatively recent advancement. studies have shown that despite the growing volume of training programs in informatics as well as public health, gaps exist in the competencies of public health graduates in meeting the requisite skills and demands of the dynamic public health workforce [2-4]. in particular, existing workforce assessments have shown that public health professionals across many local health departments lack adequate health informatics skills [5]. a gap analysis of biomedical informatics graduate education competencies identified key topics that were missing from the existing graduate curricula including community health, translational and clinical research, knowledge representation, data mining, communication and evidence-based practice [2]. an initial assessment of existing public/population health informatics programs that offered certificate, masters and doctoral levels, was conducted [6]. the study explored informatics-training programs currently available to meet the growing demand for a trained workforce in global settings. these programs then matched against the 14 competencies determined by the centers for disease control and prevention (cdc). these competencies included workforce supporting development of strategic direction for public health informatics within the enterprise, participating in development of knowledge management tools for the enterprise, use informatics standards, ensure that knowledge, information, and data needs of project or program users and stakeholders are met, support information system development, procurement, and implementation that meet public health program needs, manage it operations related to project or program (for public health this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi agencies with internal it operations), monitor it operations managed by external organizations, communicate with cross-disciplinary leaders and team members, evaluate information systems and applications, participate in applied public health informatics research for new insights and innovative solutions to health problems, contribute to development of public health information systems that are interoperable with other relevant information systems, support use of informatics to integrate clinical health, environmental risk, and population health, implement solutions that ensure confidentiality, security, and integrity while maximizing availability of information for public health and conduct education and training in public health informatics. our findings revealed that all the programs addressed at least some of the competencies for public health informatics. the predominant competencies covered included strategy development, interoperability, integration of clinical and population health, knowledge and project management. one of the key findings; however was that none of the public health informatics programs addressed all the cdc competencies [6]. curriculum is critical to foster effectiveness in higher education [7] both in the short and longterm [8]. results of previous study has demonstrated the need for curricular change in existing programs to facilitate critical contributions toward assuring clinical, social, and population health [9,10]. variety of approaches to curriculum development predominantly focus on medical school curricula. six-step approach to curriculum development in medical schools include problem identification and general needs assessment, targeted needs assessment, goals and objectives, educational strategies, and implementation [11]. another study suggested a 5-step theoretical framework to curriculum development that includes environmental analysis, graduate competencies, curriculum development, pedagogical strategies, and implementation, evaluation, feedback [8]. informatics curricula defined using educational competencies and requirements based on literature reviews, internet searches, needs assessments and assessments of hit workforce such as local health departments [12-14]. the international medical informatics association (imia) has extended a global perspective on educational competencies with a threedimensional framework of educational needs and recommended course content [15]. other approaches to curricula development have included a combination of literature reviews, focus groups, and expert panel reviews/decision making by subject-matter experts (including researchers, practitioners and policy makers) to define specific competencies that course evaluations can address [16-19]. while the variety of approaches to curriculum development utilize existing evidence and educational strategies, the majority of these approaches fail to incorporate a workforce skills assessment and integration into curriculum development. several informatics workforce needs assessments and agendas repeatedly recommend appropriate training and integration of ehealth in existing curricula as methods for addressing the skills gaps in the informatics workforce [5,20-24]. evaluating health informatics programs will foster effective curricula development that will meet the diverse needs of the informatics workforce [25]. an integrated approach of mapping curriculum competencies to workforce demands can help guide development of curriculum that can specifically address demands of the emerging, fast changing job market and make it possible to review and refine the existing health informatics training program competencies. the objective of our study is to integrate program specific competencies with analysis of available positions on job search engine “indeed” to identify population health informatics workforce needs, developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi skills and expertise in the state of new york. findings of the study inform development of an evidence based graduate population health informatics curriculum at cuny graduate school of public health and health policy. methods we conducted content analysis of the population health informatics related job postings in the state of new york between june and july 2019 using the indeed job board. indeed.com is one of the leading job sites. it curates millions of job listings across company career sites, job boards, associations, newspaper classifieds and other online sources [26].the aim of the search was to characterize the knowledge, skills and expertise required by the employers of the informatics workforce. the search terms included “health informatics” and “population health informatics.” the initial search yielded 496 job postings. after removal of duplicates and inactive postings 321 job postings remained. excluding postings that did not include details of the positions’ responsibilities resulted in 306 jobs. content analysis on the remaining final 306 jobs done. the content analysis process involved an inductive and deductive approach [27]. an inductive approach helped to identify and categorize core job posting information followed by a deductive approach that helped to evaluate the skills and expertise required for specific job categories. steps include; 1. identification and reclassification of job titles: individual reviews of all 306-job postings conducted. some examples of the job postings included clinical informatics analyst, application analyst, health informatics specialist, and healthcare analytics manager. the 306 job positions were subsequently classified into 14 unique job functions including administrator, analyst, consultant, coordinator, data governance, designer/developer, director, engineer, faculty/researcher, informatics specialist, manager, quality informatics, specialist, and others (representing job functions that did not fit into the listed roles). this involved an iterative deductive process in which related job positions were combined based on job titles and job descriptions. the degrees required/preferred, and experience required/preferred abstracted from the newly created job categories. 2. extraction and categorization of technical skills: review of the existing literature on informatics workforce needs identified 44 skills across various informatics jobs in diverse settings [28]. these skills used as a template to identify the skills listed across the job postings in the current study. seventy-eight skills extracted from the current study. the process of identifying skills involved an extensive and iterative review of each job description and summary details. the final skills (n=78) identified in our study included both new skills from the current postings in addition to skills that had been previously identified (n=44) in the literature. developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi figure 1. flow chart describing the job assessment process. variable extraction following variables extracted from each job posting and included; • job categories: information recorded from the publicly available job postings and characterized into different categories as analyst, director, faculty/researcher, manager, informatics specialist, designer or developer. • hiring organization type: information recorded from publicly available job postings on the types of organizations where these jobs were available including health system, hospital, academia, pharmacy, and biotechnology/consulting or other. developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi • degree (required/preferred): information was extracted on both required and preferred degree requirements and was characterized into bachelors/ or masters, phd and above. information also recorded if the degree requirements not listed. the required degree requirement reflected the minimal education that the applicant should possess to submit the application for the job. • work experience (required/preferred): information recorded on both required and preferred work experience. the work experience characterized as either 1-2 years, 3 to 4 years, 5 to 7 years, 8 to 10 years and 11 to 15 years. in addition, there were job postings that reported as either commensurate with degree or reported as not listed. • salary: information was recorded on salary and income categories included an annual salary of <$40,000, followed by several other salary categories including $40,000 $49,999, $50,000$60,099, $60,100$70,099, $70,100 $80,099, $81,000 $90,199, $90,200 $123,499, $110,000 $123,549, $123,500+ and $145,400 and above. there were jobs whose salary could not be determined. • job type: information recorded on job types including whether the job postings were contract, full time, internship, art-time or temporary. • location: information recorded on geographic distribution of the population health informatics jobs. • knowledge, skills and expertise: variable information extracted from the job descriptions so that specific knowledge, skills and expertise needed identified. • software skills: analysis performed to determine the most common software skills listed with each job description. 2.1 statistical analysis descriptive statistics computed for the job posting characteristics and skills identified. the results presented as percentages and frequencies. the job postings reclassified into broader categories representing unique job functions. cross tabulations conducted to examine the degrees required/preferred and experience required/preferred across each of the job categories. cross tabulations conducted to examine the distribution of skills across the various job categories. sas text mining used to identify the key terms that used in job description and terms characterized into knowledge, skills and expertise categories. analysis performed in sas v 9.4 and microsoft excel. results 3.1. characteristics of the job postings a total 306 job postings evaluated. the job title of the various positions were characterized as analyst (21%, n=65), director (15%, n=46) and faculty/researcher (14%, n=44) (table 1). these were followed by other titles such as manager (10%, n=32), informatics specialist (9%, n=26), and designer/developers (8%, n=23). more than one-third of the hiring organizations for these jobs were health systems (35%, n=106) followed by hospitals (19%, n=59), and academia (17%, n=51). almost 100% of the jobs were fulltime (n=291). around half the jobs were based in new york city (47%, n=143) and 11% of them were based in westchester in ny state (n=33). more than half of the jobs listed required either a bachelor’s degree as the minimum degree requirement (63%, developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi n=192), 13% masters (n=41) and 9% doctoral degree (n=26). only 19% of the listings preferred a master’s degree (n=58). however, 71% of them did not list the degree preferred. almost half of the job listings required that candidates possess 3 to 7 years of experience (44%, n=132). thirteen percent required 1 to 2 years of experience (n=40). the most common annual salary range for the jobs were $70,100 to $80,099 (22%, n=68), and $110,000 to $123,549 (19%, n=58). twelve percent of the jobs had a salary range of $81,000 to $90,199. table 1 indicates the majority of jobs were available in new york. table 1. descriptive analysis of the job postings variables assessed n=306 job categories analyst 21% (n=65) consultant 2% (n=7) specialist 6% (n=17) quality informatics 4% (n=12) coordinator 4% (n=9) data governance 2% (n=5) director 15% (n=46) faculty/researcher 14% (n=44) manager 10% (n=32) informatics specialist 9% (n=26) designer/developer 7% (n=23) other 6% (n=20) hiring organization type health system 35% (n=106) hospital 19% (n=59) academia 17% (n=51) insurance 3% (n=8) pharmacy 9% (n=27) biotechnology 4% (n=13) consulting 4% (n=13) other 9% (n=29) degree required developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi bachelor's and above 63% (n=192) master's and above 13% (n=41) phd and md or equivalent 9% (n=26) advanced degree 3% (n=10) not listed 12% (n=37) degree preferred bachelor's and above 2% (n=7) master's and above 19% (n=58) phd and md or equivalent 6% (n=19) advanced degree 1% (n=4) not listed 71% (218) experience required (years) 1 to 2 years 13% (n=40) 3 to 4 1% (n=2) 5 to 7 22% (n=66) 8 to 10 4% (n=13) commensurate with degree 3% (n=10) not listed 36% (n=109) experience preferred (years) 1 to 2 years 2% (n=6) 3 to 4 2% (n=5) 5 to 7 2% (n=5) 8 to 10 2% (n=4) not listed 92% (n=286) salary (based on indeed estimation) < $40,000 1% (n=3) $40,000 $49,999 3% (n=7) $50,000$60,099 7% (n=22) $60,100$70,099 8% (n=23) $70,100 $80,099 22% (n=68) $81,000 $90,199 12% (n=36) developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi $90,200 $123,499 3% (n=40) $110,000 $123,549 19% (n=58) $123,500+ 3% (n=9) $145,400+ 1% (n=2) na 12% (n=38) job type contract 1% (n=3) full-time 95% (n=291) internship 1% (n=2) part-time 3% (n=7) temporary 1% (n=3) location of jobs: analysis of the job locations showed that majority of the jobs were available in new york county (48%; n=145) followed by westchester (11%; n=33), nassau (5%; n=15) and suffolk (4%; n=13) (figure 2). ninety –five percent (n=291) of the jobs were full time. of the 145 jobs available in ny, majority of them were full time (95%; n=138). similar trends seen across other counties in the ny state. it was found that of the total jobs available in new york county, majority of them (31%; n=45/145) were offering salary in the range of $110,000 $123,549; 16% (n=23) offered salary between $70,100 $80,099 and 15% offered $90,200 $123,499. 145 33 15 13 12 12 12 12 9 8 6 5 0 20 40 60 80 100 120 140 160 new york westchester nassau suffolk albany bronx erie monroe queens onondaga kings rockland distribution of number of population health informatics jobs in new york state using indeed search engine figure 2. distribution of population health informatics jobs in new york state developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi 3.2. stratified descriptive analysis of the job postings 3.2.1. degree qualifications required across the job categories eighty-seven percent (n=57/65) of the analyst job category postings mentioned the degree requirements required. a hundred percent of coordinator and data governance jobs indicated degree requirements required in their job postings. less number of consultant and quality informatics jobs had indicated required degree requirements as compared to other job categories (figure 2). figure 3. percentage distribution of job categories reporting degree requirements eighty four percent of the postings in the analyst job category required individuals to have bachelors and above degree requirements while 25% (n=15) of them preferred master’s degree. more than half of the positions in the job category of specialist indicated bachelors and above degree requirements (69%; n=11/16) and 31% (n=5) preferred master’s. doctoral degree requirements were primarily required for faculty/researcher, director and manager job categories. nearly 37% of the director positions preferred masters. table 2. distribution of degrees required for each of the job categories job category role total jobs (n) reporte d degree (n) required degree degree preferred advance d degree (n) bachelor's and above (n) master’s degree and above (n) phd and md or equivalent (n) masters analyst 65 57 3 48 6 15 consultant 7 5 4 1 1 developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi specialist 17 16 11 5 quality informatics 12 9 7 1 1 2 coordinator 9 9 1 7 1 2 data governance 5 5 5 3 director 46 43 1 25 9 8 16 faculty/res earcher 44 39 17 10 12 4 manager 32 27 20 3 4 6 informatics specialist 26 22 3 17 2 4 designer/de veloper 23 20 1 15 4 3 other 20 17 1 16 2 total 306 3.2.3. knowledge, skills and expertise required across the job postings data/statistical analysis (68%, n=207), collaboration/working in multidisciplinary teams (35%, n=108), biomedical/clinical experience (30%, n=93), electronic health record/electronic medical record (28%, n=85), and communicating findings/delivering actionable results (275, n=-82) were the most common skills required across various job postings (table 3). around 20% of the jobs required skills in management/organization (23%, n=71), consumer-centric/patient outcome knowledge (23%, n=71), data governance/standards/quality skills (22%, n=67), performance improvement (22%, n=66), data manipulation (21%, n=64), and quality measures (21%, n=65) (figure 4) developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi figure 4. percentage distribution of knowledge, skills and expertise required for various job categories other knowledge, skills and expertise included familiarity with r language (10%; n=32), ms office (10%; n=32), sas (9%; n=28) and familiarity with hl7 knowledge (8%; n=23). some of the other skills included digital health knowledge (8%; n=23), innovative skills (8%; n=23), identifying business problem skills (7%; n=21), and data management platforms (7%; n=22). other technology skills included java script (8%; n=21), c++ (5%; n=16), dashboard skills such tableau (5%; n=14), machine learning and requirement analysis skills (4%n=12). 3.2.4. distribution of skills required across the job categories data/statistical analysis skills were mostly required by analysts (26%, n=51), directors (15%, n=31) and faculty/researchers (12%, n=25) (figure 4). skill in collaboration/working in multidisciplinary teams was most commonly required among analysts (23%, n=25) and directors (21%, n=23). biomedical/clinical experience and ehr/emr knowledge were most commonly required among analysts (22%, n=20; 22%, n=19) and informatics specialists (18%, n=17; 18%, n=15). management/organizational skills were mostly required among directors (24%, n=17), analysts (18%, n=13), and managers (14%, n=10). the least common skills included dashboards (4%, n=13), machine learning (4%, n=12), developing algorithms (2%, n=6), prioritization/resource planning (1%, n=4), and population health skills (1%, n=3) were mostly required by analysts, faculty/researchers, managers, and quality informatics professionals. developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi figure 5. percentage distribution of skills required across the job categories 3.2.5. software skills required across the job categories structured query language (sql) (15%, n=46), python (14%, n=44), and r language (11%, n=33) were the most common programming language requirements. ms office (10%, n=31) and tableau (5%, n=15) were the most common data visualization tool skills required. less than ten percent of the job postings mentioned experience with sas (9%, n=27), and java (8%, n=24). the most common relational database management skills required across the postings included microsoft sql server (5%, n=14) and oracle (4%, n=11). sql was most common software skill mentioned among the 65 positions characterized as analyst (35%, n=23), followed by sas (22%; n=14) and r (22%; n=14). skills in python (15%; n=10), ms office (15%; n=10) and tableau were additional skills reported for the job category of analyst. similarly, python (17%; n=2), r (17%; n=2), sql (17%; n=2) and ms office (17%; n=2) were commonly reported skills for the job category quality informatics. python skills were most commonly reported also for the job categories such as data governance (40%; n=2) and designer/developer (43%; n=10), coordinator (22%; n=2) and informatics specialist (15%; n=4) (figure 6). developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi figure 6. software skill requirements across job postings. 3.2.6. time commitment required across the job categories, and by location. almost all the job postings required a full-time commitment (95%, n=291 out of 306) (table 2). analysts (21%, n=64), director (15%, n=46), faculty/researcher (13%, n=39), and manager (10%, n=31) were the most common full-time jobs. other less common full-time positions included informatics specialists (8%, n=26), designer/developer (7%, n=22), and specialists (5%, n=16). around half of the full-time jobs (48%, n=138) were based in new york. eleven percent (n=32) of the full time jobs were based in westchester. additional full-time jobs were available in nassau (5%; n=15), followed by albany (4%; n=12), bronx (4%; n=12), monroe (4%; n=12), suffolk (4%; n=12), erie (4%; n=12) and queens (3%; n=8) (table 3). table 3. time commitment by job location total (n=306) contract fulltime internship parttime temporary albany n=12 n=12 bronx n=12 n=12 brooklyn n=6 n=6 broome n=2 n=2 developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi clinton n=1 n=1 columbia n=3 n=3 dutchess n=3 n=3 erie n=12 n=1 n=10 n=1 herkimer n=1 n=1 monroe n=12 n=12 nassau n=15 n=15 new york n=145 n=1 n=138 n=2 n=2 n=2 oneida n=1 n=1 onondaga n=6 n=5 n=1 queens n=9 n=1 n=8 rensselaer n=1 n=1 rockland n=5 n=4 n=1 saratoga n=2 n=2 schenectady n=3 n=3 st. lawrence n=2 n=2 suffolk n=13 n=12 n=1 tompkins n=2 n=2 ulster n=1 n=1 warren n=2 n=2 westchester n=33 n=32 n=1 yates n=1 n=1 3.2.7. expected salaries across the job categories, and by location the annual salary range for majority of the analyst job categories was $70,100 $80,099 (31%; n=20) followed by 17% (n=11) having a salary range of $60,100$70,099 (figure 7). of the seven consultant job positions, most of them were in a salary range of $90,200 $123,499 (43%; n=3). of the seventeen positions in the specialist job category, 35% (n=6) of them had a salary range of $70,100 $80,099 while 23% (n=4) had a salary range of $50,000$60,099. similarly, for other positions advertised across different job categories such as quality informatics (33%; n=4), coordinator (33%; n=3), faculty/researcher (25%; n=11), and informatics specialist (35%; n=9), the salary range was $70,100 $80,099. however, majority of the positions in the job category of director (48%; n=22) or designer/developer (26%; n=6) had an annual salary range of $110,000 $123,549. developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi figure 7. percentage distribution of most common salary distribution among various job categories thirty-two percent (n=45) of the jobs in the new york county were in the salary range of $110,000 $123,549, 17% (n=23) in the salary range of $70,100 $80,099, 16% (n=22) in the salary range of $90,200 $123,499. similarly, in westchester county, 31% (n=10) of the jobs had a salary range of $70,100 $80,099. study findings demonstrate skills that are essential to fulfill the population health informatics job needs. table 4 demonstrates how skills often listed as required or desired for positions in population health informatics. findings of this study utilized to inform the design and development of a population heath informatics curriculum. table 4. skills prioritized by employers mapped to competencies and incorporated into the curriculum example of skills identified in job postings informatics competency public health competency working in multidisciplinary teams assess stakeholder data, information, and knowledge needs perform effectively on interprofessional teams requirement skills design, develop, and implement user-centered population health information systems effectively propose strategies to identify stakeholders and build coalitions and partnerships for influencing public health outcomes consumer-centric establish frameworks for evaluating the implementation process of information systems and applications, and make developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi recommendations to improve user satisfaction and outcomes data management assess stakeholder data, information, and knowledge needs select quantitative & qualitative data collection methods appropriate for a given public health context data/statistical analysis establish frameworks for evaluating the implementation process of information systems and applications, and make recommendations to improve user satisfaction and outcomes select quantitative & qualitative data collection methods appropriate for a given public health context interpret results of data analysis for public health research, policy or practice. electronic health record design, develop, and implement user-centered population health information systems effectively design a population-based policy, program, project or intervention communicating findings design, develop, and implement usercentered population health information systems effectively select communication strategies for different audiences and sectors describe the importance of cultural competence in communicating public health content communicate audience-appropriate public health content, both in writing and through oral presentation management/organization skills manage and direct health informatics planning for projects related to public health and information technology apply principles of leadership, governance and management, which include creating a vision, empowering others, fostering collaboration and guiding decision making data governance recommend strategies and solutions that integrate informatics knowledge within organizations and communities and ensure confidentiality, security, and integrity discuss multiple dimensions of the policy-making process, including the roles of ethics and evidence standards apply informatics standards appropriately and contribute to standards development efforts familiarity with hl7 knowledge apply informatics standards appropriately and contribute to standards development efforts developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi innovation manage and direct health informatics planning for projects related to public health and information technology recommend strategies and solutions that integrate informatics knowledge within organizations and communities apply systems thinking tools to a public health issue assess population needs, assets and capacities that affect communities’ health dashboard/data visualization design, develop, and implement usercentered population health information systems effectively communicate audience-appropriate public health content, both in writing and through oral presentation tableau, python and ms sql design, develop, and implement user-centered population health information systems effectively analyze quantitative and qualitative data using biostatistics, informatics, computer-based programming and software, as appropriate job analysis combined with competencies drawn from various sources such as council on education for public health (2016 ceph) [29] criteria, amia working group pre-symposium [30] and public health informatics competencies created by the center for disease control [31] and knowledge acquisition through global implementation of informatics projects utilized to design and develop the ms population health informatics curriculum (figure 8). core competencies included both public health and population health informatics. public health competencies [31] were addressed through the required mph curriculum courses and integrated to informatics competencies through a wide range of course offerings including fundamentals of population health informatics, design and development of population health information systems, surveillance systems and applications of population health informatics, mobile health, population health dashboards, and monitoring and evaluation. figure 8. an integrated approach to academic program development: the graduate school of public health and health policy (sph) of the city university of new york established first of its kind, master of science (ms) in population health informatics, effective fall 2019. the program emphasis on how to design, develop, implement, and evaluate developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi technological interventions and innovations using data driven, evidence-based approaches to guide policy making for the improvement of population health outcomes. while most programs, take a hospital-centric approach, the program primarily focuses on how to operationalize informatics solutions to address important public health challenges influencing individuals, families, communities, and the environment in which they live. the ms program in population health informatics is an online, 39 graduate-level credits program including 15 credits of public health coursework, 18 credits of coursework in health informatics, and 6 credits of a dual practice/culminating experience. the purpose of the master of science in population health informatics is to equip students with an in-depth understanding of informatics that supports the development of interventions addressing 21st century public health challenges. students will acquire the skills necessary to apply technology at the intersection of clinical care and health sciences, strengthening preventive care at the population level. the five public-health specific courses will cover the public health competencies, adopted from the council on education for public health (ceph). the three credit each six-concentration courses include fundamentals of population health informatics, principles of consumer health informatics, applications of population health informatics, population health dashboards, design, development and evaluation of health technologies and mobile health interventions. the program also offers practicum/culminating experience. the practicum involves workshop approach where students not only work on their informatics projects but also acquire skills in the areas of ms sql, python, data visualization tools such as excel and tableau, api, html and android toolkit as these skills broadly identified to be critical in the job analysis that conducted. through this project and the portfolio, students demonstrate their ability to integrate and synthesize the knowledge and skills they have attained in the program. discussion education continues to advance, driven by a combination of changes in the practice domain, theoretical developments, and workplace demands [32]. curricula evolve over time, both because of the newness of the field and in response to the job market. there is an increased focus on public health information and knowledge management systems highlighting need for public health professionals skilled in designing and implementing these systems. prior study has shown that four competency sets adequately represented in public health informatics curriculum including leadership and system thinking skills, followed by financial planning and management skills, community dimensions of practice skills, and policy development/program planning skills [33]. in another prior study, the public health informatics design focused on public health core competencies and organization/systems/information sciences related to analyzing, designing, implementing, and disseminating effective information systems in public health [34]. though many theoretical frameworks and competency guidelines proposed for informatics education, less attention paid to informatics education using analysis of the jobs available today. results of our study helped identify knowledge, skills and expertise that match employer requirements and ensuring that curriculum meet those requirements in this rapidly changing job market. our study combined competencies and analyzed job skills as an integrated approach to design and develop our curriculum. this is a perspective that has been lacking in previous literature developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi on this topic. there are a growing number of ‘informatics’ programs, but they vary substantially by academic level and focus [32]. this suggests studying existing educational programs and their curricula, and examining how such knowledge can contribute to defining the informatics professions. results of our study show that analyst was the most common job category, followed by director and faculty/researcher. population health informatics professionals work in practice, research, or academia and whose primary work function is to use informatics to improve the health of populations. results of our study showed that most common hiring organizations were health system and hospitals followed by academic institutes. recent studies highlight the need for informatics training in the public health workforce [35]. similarly, the council on education for public health (ceph) includes informatics as a foundational competency for accreditation of public health programs. the need for public health education programs informed by current public health needs and employment opportunities long noted. more than half of the jobs required bachelors and above. however, 12% of the jobs did not list the degree required for the particular job. nineteen percent of the jobs listed indicated masters as the preferred while more than half of the jobs did not list the degree preferred. however, there are still significant gaps in workforce skills training. studies examining students’ perspectives on required skill sets are limited. academicians, students, employers, and people working in the health care industry have different perspectives and priorities for required informatics skills. identifying health informatics skill sets for graduate students has always been a challenge. health informatics programs should consider specialized tracks that include specific skills to meet the complex health care delivery and market demand, and specific training components defined for different specialties. the specific skill sets required by different employers vary owing to the increasing rate of technological developments. results of our study showed python, ms sql and tableau as one of the most common technical skills desired in these job openings. in addition, the skills needed for health informaticians vary significantly depending on the position, and health informatics students need skills pertinent to their professional experience for their future career paths [32]. requirements use cases, and business process definitions produced by systems analysis and require expertise in the system development life cycles. our study also showed that various job categories required requirement analysis, business processes and working in multidisciplinary teams skills. international medical informatics association (imia) determined 3 domains and 12 learning outcomes that related to data training and skills. these learning outcomes focus on health data management principles; structure and design principles of health records; principles of data representation and analysis; ethical and security issues; nomenclatures, vocabularies, terminologies, ontologies, and taxonomies; health administration and economics; basic informatics terminology; ability to communicate electronically; and methods of practical and theoretical informatics, mathematics, biometry, and epidemiology. there are several international and national initiatives developing health informatics competencies, determining the standard comprehensive health information skill sets has always been a challenging task because of the continually evolving technology. new job opportunities for health informatics professionals require specific skill sets to utilize new cuttingedge, patient-focused delivery tools. developing evidence-based population health informatics curriculum: integrating competency based model and job analysis 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 13(1):e10, 2021 ojphi the major limitation of this study is its specificity to one geographical area. while it is probable that similar results found for other urban areas, with similar demographics, other areas may have different employee needs. a larger study including wider and more representative geographical areas needed to generalize findings to broader population areas. another limitation is that the position listings examined were from one source such as indeed. these may not be representative of all the positions available to people with degrees in population health informatics. we are expanding the current framework by conducting analysis of the additional jobs available globally across other platforms that will help to formulate various skills needed in this rapidly evolving field of population health informatics. population health informatics curriculum designed and developed took into account various available program specific competencies and integrated knowledge, skills and expertise based on the analysis of the job positions. we will assess the skills of the students using a wide range of methods including case study scenarios, evaluation of existing health information systems, designing and developing health information systems, and project management skills through independent project practicum. in addition, feedback from student preceptors obtained. academicians have also been discussing the integration of skills training into the health informatics curriculum for a long time. faculty members who work in academic institutions, health informatics departments, and programs expected to follow up on the changing requirements and update the content of their curriculum continuously. results of this pilot study can serve as a framework to guide the development of a curriculum by the various academic units, and program directors based on this integrated approach. the framework can also guide evaluation of academic programs on an ongoing basis to identify skills that would be essential for students to acquire and make them job ready in this rapidly changing environment. references 1. miller c, ishikawa c, deleon m, huang m, ising a, et al. 2015. joint recommendations for the 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(cdc) and university of washington school of public health and community medicine’s center for public health informatics. competencies for public health informaticians 2009. atlanta: u.s. department of health and human services, centers for disease control and prevention; (2009) 32. kampov-polevoi j, hemminger bm. 2011. a curricula-based comparison of biomedical and health informatics programs in the usa. j am med inform assoc. 18(2), 195-202. pubmed https://doi.org/10.1136/jamia.2010.004259 33. wholey dr, laventure m, rajamani s, kreiger r, hedberg c, et al. 2018. developing workforce capacity in public health informatics: core competencies and curriculum design. front public health. 6, 124. pubmed https://doi.org/10.3389/fpubh.2018.00124 34. hsu ce, dunn k, juo hh, danko r, johnson d, et al. 2012. understanding public health informatics competencies for mid-tier public health practitioners—a web-based survey. health informatics j. 18(1), 66-76. pubmed https://doi.org/10.1177/1460458211424000 35. sapci ah, sapci ha. 2020. teaching hands-on informatics skills to future health informaticians: a competency framework proposal and analysis of health care informatics curricula. jmir med inform. 8(1), e15748. pubmed https://doi.org/10.2196/15748 https://pubmed.ncbi.nlm.nih.gov/30157524 https://doi.org/10.1055/s-0038-1667081 https://pubmed.ncbi.nlm.nih.gov/21292707 https://doi.org/10.1136/jamia.2010.004259 https://pubmed.ncbi.nlm.nih.gov/29770321 https://doi.org/10.3389/fpubh.2018.00124 https://pubmed.ncbi.nlm.nih.gov/22447878 https://doi.org/10.1177/1460458211424000 https://pubmed.ncbi.nlm.nih.gov/31961328 https://doi.org/10.2196/15748 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an analysis of the challenges and possible solutions for dog bite injury surveillance felicia trembath* 1purdue university, west lafayette, in, usa; 2cdc, atlanta, ga, usa; 3maricopa county department of public health, phoenix, az, usa objective to give an overview of the challenges facing dog bite injury surveillance as well as identify some potential solutions for improving surveillance mechanisms. introduction injuries from dog bites affect approximately 4.7 million americans per year,1 causing significant societal impact. currently dog bites are the third leading cause of homeowner insurance claims, and are estimated to cost the insurance industry $489 million annually.2 when insurance costs are coupled with hospitalizations and lost productivity, dog bites are estimated to cost the united states $2 billion/year.3 however, the true impact of dog bite injuries remains unknown since discrepancies exist in the number of dog bite injuries being found by various mechanisms,4 and many bites may actually go unreported.5 in order to evaluate the true impact of dog bite injuries, the limitations of current surveillance methods must first be delineated and understood. methods a review was conducted of the various surveillance methods for analyzing dog bite injuries. these methods include using the national electronic injury surveillance system (neiss), hospital discharge data, insurance claims data, and bite data collected by local jurisdictions. the techniques were categorized according to data type and a conceptual map was created to determine areas where surveillance might fail to capture cases as well as mechanisms for how discrepancies in data reporting might occur. results improvements have been made in the surveillance of bite injuries, such as better data collection in cases of dog bite related fatalities and wider utilization of hospital discharge data to evaluate injuries. however, these mechanisms only capture a portion of dog bite injuries and by themselves are inadequate to provide the data necessary to ascertain the characteristics and impact of dog bite injuries. furthermore these two methods may not correlate since victims of dog bite related fatalities who never receive medical services would not be captured in hospital discharge data. additionally cases who do not access medical services in a traditional way can fail to be captured by these methods. according to public health regulations, dog bite injuries are required to be reported to the local health department. however, there is no nationwide standard reporting form for collecting information nor are there guidelines on the utilization of the data that is collected. the majority of jurisdictions have developed their own reporting form, and the type of information collected as well as what is done with the data can vary widely. thus although dog bite report forms have the potential to be ripe for data analysis, they are underutilized due to inexistent or inaccessible information. conclusions current surveillance methods are inadequate to ascertain the true impact of dog bite injuries. better surveillance systems which are able to capture a larger breadth of reports while collecting pertinent information are needed. possible solutions include a nationwide standardized form, a repository for the data, inclusion of dog bite injuries in current or developing disease surveillance systems, and an increased focus on reporting dog bite injuries. keywords dog bite injuries; surveillance; disease surveillance systems; dog bites references 1. centers for disease control and prevention (cdc). “nonfatal dog bite-related injuries treated in hospital emergency departments— united states, 2001.” mmwr morb mortal wkly rep. 2003 jul 4;52(26):605-10. 2. insurance information institute. average number of dog bite claims falls in 2012; claims costs still on the rise. press release. [internet]. 2012 may 21 [cited 2014 sept 9]. available from: http://www.iii. org/press-release/average-number-of-dog-bite-claims-falls-in-2012claims-costs-still-on-the-rise-increasing-by-more 3. voelker, r., “dog bites recognized as public health problem.” jama 1997 january 22;277(4): 278-280 4. lindsay, sr. handbook of applied dog behaviour and training vol 1, adaptation and learning. ames, iowa: iowa state university; 2001. 410 p. 5. beck, am., jones ba. “unreported dog bites in children.”public health rep. 1985 may-jun;100(3):315-21. *felicia trembath e-mail: ftrembat@purdue.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e94, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 161 isds 2014 conference abstracts surveillance system evaluations provide evidence to improve public health practice beverley j. paterson*1 and david n. durrheim2, 1 1hunter medical research unit, university of newcastle, newcastle, nsw, australia; 2hunter new england population health, newcastle, nsw, australia objective surveillance evaluations should not only describe surveillance systems but provide evidence to improve public health practice. this presentation documents how knowledge gathered through a syndromic surveillance evaluation in pacific island countries and territories (picts) with local health personnel was translated into action, in collaboration with global health partners. introduction a simplified syndromic surveillance system, based on early detection and reporting of four core syndromes and immediate reporting of unusual events, was established across picts in 2010. an early evaluation of the system was undertaken to make recommendations on improvements(1). the evaluation examined whether the system was meeting its objective of serving as an early warning system and its capacity to investigate and respond to outbreaks. metrics included system acceptability, data quality, timeliness and level of compliance. the evaluation identified a critical need to better equip local public health officials with the knowledge and skills to rapidly and appropriately respond to suspected infectious disease outbreaks across the pacific. in response to the evaluation findings, the rapid (response and analysis for pacific infectious diseases) project was implemented to strengthen capacity in surveillance, epidemiology and outbreak response across the picts. principally funded by australian aid and developed in partnership with the world health organization (who), the secretariat of the pacific community (spc) and the pacific public health surveillance network (pphsn), rapid is an example of a multi-organisational approach to swiftly address identified surveillance issues and strengthen regional surveillance capacity. methods following identification of critical surveillance needs, implementation of the rapid project involved public health epidemiologists and physicians facilitating core project components across the picts. the core components were as follows: component 1: improved understanding and skills among pict syndromic surveillance focal points to ensure strengthened detection, investigation and response to outbreaks in picts -sub-regional/national structured training workshops -pacific outbreak manual component 2: strengthened institutional capacity of the pacific international health regulations national focal points and national outbreak response (epinet) teams on-site capacity building, review and lessons learned training -mentoring -work exchanges -outbreak support results the rapid partners have cooperatively developed and conducted training programs, mentored local students, and provided in-country outbreak support. in partnership with fnu and pphsn, an earlier course ‘data for decision-making (ddm)’ was revitalised as an accredited training programme for the pacific. in the first year, over 150 participants from 13 countries have been trained in ‘outbreak surveillance and response’ and ‘basic applied epidemiology and data analysis’. the centers for disease control and prevention and the pacific island health officers’ association, partners in the pphsn network, have collaboratively contributed to the successful running of the ddm course modules, further fostering strong linkages between the key public health organisations across the pacific. rapid facilitators, working collaboratively with who, cdc, and local ministry of health officials, have also engaged with the papua new guinea (png) field epidemiology training program, offering training and mentoring to png students. through the rapid project, the who and the fijian ministry of health, were supported to rapidly establish and implement a fijian dengue-like-illness surveillance system during a major dengue outbreak. the pacific outbreak manual was also further developed as an infectious disease training manual and a guide for appropriate outbreak response (http://www.spc.int/ phs/pphsn/publications/pacific_outbreak_manual_feb2014.pdf). conclusions the rapid project is a notable example of how evidence gathered through a surveillance evaluation can be used to improve public health surveillance practice. the project showcases how gains in surveillance capacity in lower and middle income countries can rapidly be achieved through cooperative partnerships and flexible approaches. keywords surveillance; training; outbreak response; capacity strengthening acknowledgments the authors would like to acknowledge rapid partners and all facilitators who have provided so much inspiration and training across the pacific. references 1. paterson bj, kool jl, durrheim dn, pavlin b. sustaining surveillance: evaluating syndromic surveillance in the pacific. glob public health 2012;7(7):682-94. *beverley j. paterson e-mail: beverley.paterson@hnehealth.nsw.gov.au online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e202, 201 the title of the article ojphi sculpting the umls refined semantic network 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 sculpting the umls refined semantic network zhe he1, c. paul morrey2, yehoshua perl3, gai elhanan4, ling chen5, yan chen5, james geller3 1 department of biomedical informatics, columbia university, new york, ny, usa 2 department of information systems and technology, utah valley university, orem, ut, usa 3 department of computer science, new jersey institute of technology, newark, nj, usa 4 halfpenny technologies, inc., blue bell, pa 5 bmcc, city university of new york, new york, ny abstract background: the refined semantic network (rsn) for the umls was previously introduced to complement the umls semantic network (sn). the rsn partitions the umls metathesaurus (meta) into disjoint groups of concepts. each such group is semantically uniform. however, the rsn was initially an order of magnitude larger than the sn, which is undesirable since to be useful, a semantic network should be compact. most semantic types in the rsn represent combinations of semantic types in the umls sn. such a “combination semantic type” is called intersection semantic type (ist). many ists are assigned to very few concepts. moreover, when reviewing those concepts, many semantic type assignment inconsistencies were found. after correcting those inconsistencies many ists, among them some that contradicted umls rules, disappeared, which made the rsn smaller. objective: the authors performed a longitudinal study with the goal of reducing the size of the rsn to become compact. this goal was achieved by correcting inconsistencies and errors in the ist assignments in the umls, which additionally helped identify and correct ambiguities, inconsistencies, and errors in source terminologies widely used in the realm of public health. methods: in this paper, we discuss the process and steps employed in this longitudinal study and the intermediate results for different stages. the sculpting process includes removing redundant semantic type assignments, expanding semantic type assignments, and removing illegitimate ists by auditing ists of small extents. however, the emphasis of this paper is not on the auditing methodologies employed during the process, since they were introduced in earlier publications, but on the strategy of employing them in order to transform the rsn into a compact network. for this paper we also performed a comprehensive audit of 168 “small ists” in the 2013aa version of the umls to finalize the longitudinal study. results: over the years it was found that the editors of the umls introduced some new inconsistencies that resulted in the reintroduction of unwarranted ists that had already been eliminated as a result of their previous corrections. because of that, the transformation of the rsn into a compact network covering all necessary categories for the umls was slowed down. the corrections suggested by an audit of the 2013aa version of the umls achieve a compact rsn of equal magnitude as the umls sn. the number of ists has been reduced to 336. we also demonstrate how auditing the semantic type assignments of umls concepts can expose other modeling errors in the umls source terminologies, e.g., snomed ct, loinc, and rxnorm that are important for health informatics. such errors would otherwise stay hidden. conclusions: it is hoped that the umls curators will implement all required corrections and use the rsn along with the sn when maintaining and extending the umls. when used correctly, the rsn will ojphi sculpting the umls refined semantic network 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 support the prevention of the accidental introduction of inconsistent semantic type assignments into the umls. furthermore, this way the rsn will support the exposure of other hidden errors and inconsistencies in health informatics terminologies, which are sources of the umls. notably, the development of the rsn materializes the deeper, more refined semantic network for the umls that its designers envisioned originally but had not implemented. keywords: umls; semantic network; refined semantic network; abstraction network; refined semantic types; intersection semantic types; correction of inconsistencies correspondence: zh2132@columbia.edu doi: 10.5210/ojphi.v6i2.5412 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the unified medical language system (umls) [1,2], is derived from about 168 source terminologies. its metathesaurus (meta) [3,4], contains over 2.9 million concepts. the umls semantic network (sn) [5-7] consists of 133 high-level, broad categories, called semantics types (sts) summarizing the content of meta. one or more semantic types of the sn are assigned to each of the meta concepts, describing the semantics of the concept by identifying its broad category or categories. for example, the semantics of dental fistula 1 is described by its assigned semantic type anatomical abnormality 2 . the sn supports the ongoing integration of new and revised source terminologies into the umls [5]. we view the semantic network as an abstraction network of meta. an abstraction network supports summarization of a repository of concepts by categorizing many concepts into a few broad categories. in [8,9] we introduced an alternative abstraction network for the umls, called the refined semantic network (rsn). this introduction was motivated by two deficiencies of the sn, one implying the other. to explain these deficiencies, we first need to define the extent of a semantic type. the extent of a semantic type (st) of the sn is defined as the set of meta concepts that are assigned this semantic type. for the sn, the extents of the semantic types are not necessarily disjoint. for example, there are 912 concepts that are assigned both disease or symptom and anatomical abnormality. thus these 912 concepts are in two extents. however, an abstraction network is less effective in functioning as a summary if the extents of its semantic types are not disjoint, since it does not provide knowledge about the proportion of the overlaps of the extents of various semantic types. ojphi sculpting the umls refined semantic network 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 figure 1. example of a concept assigned two semantic types. the second deficiency of the sn, implied by the first, is that the extents of many sts are semantically not uniform. for example, as shown in figure 1, the concept abdominal fistula is assigned only anatomical abnormality while the concept fistula of lip is assigned both anatomical abnormality and disease or syndrome. hence, the extent of the st anatomical abnormality is not semantically uniform, since some of its concepts are categorized only as anatomical abnormality, while others are categorized as anatomical abnormality and as disease or syndrome. in figure 1, the overlapping part of the extents of anatomical abnormality and disease or syndrome is highlighted in yellow. an abstraction network is more effective in its summarization if each of its semantic types represents a semantically uniform set of concepts. the refined semantic network (rsn) [8,9] was introduced to overcome these two deficiencies of the sn. it has two kinds of refined semantic types derived from the sn and meta. (1) a pure semantic type (pst) is assigned to concepts that were originally assigned only one semantic type. the name of a pure semantic type is identical to the name of the original semantic type in the sn. the semantics of a pure semantic type is the exclusive semantics of the corresponding original st, whereby “exclusive semantics” means that the concepts assigned this semantic type are not assigned any other semantic type. (2) an intersection semantic type (ist) represents a fixed combination of several sts that are all assigned to one or more concepts. an ist is not created for a combination of semantic types for which no concept appears in the umls. the compound semantics of an ist [8] is defined as the conjunction (and) of the semantics of the combined sts. for example, an ist will be assigned to the concepts that are assigned both diseases or syndrome and anatomical abnormality from the sn. the name of this ist is disease or syndrome  anatomical abnormality. the symbol  is the mathematical intersection symbol and should be read as “intersected with.” for example, the concept fistula of lip is assigned this ist. an ist is semantically uniform, since all concepts of its extent share the same compound semantics. the notion of intersection semantic type (ist) is the most important theoretical construct in this research program. figure 2 shows the ist disease or syndrome  anatomical abnormality (as a yellow box), with its parents anatomical abnormality and disease or syndrome from the original semantic network. anatomical abnormality abdominal fistula anatomical abnormality ∩ disease or syndrome fistula of lip disease or syndrome eyelid diseases ojphi sculpting the umls refined semantic network 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 figure 2. example of an ist of two semantic types. to summarize, the extent of any refined semantic type is semantically uniform and the extents of all refined sts of the rsn are disjoint. thus, the rsn is an abstraction network that provides a better summarization of the content of meta than the sn. for example, in the 2013aa release of the umls, the rsn shows that there are 2543 concepts that are anatomical abnormalities, 90,691 concepts that are diseases or syndromes and 989 concepts that are both anatomical abnormalities and diseases or syndromes. the sn does not make this kind of sharp distinction explicit, but the rsn does. one more definition is required that pulls together ists and the sizes of extents. whenever an ist has a small extent, i.e., this ist is assigned to at most six concepts, we will refer to the ist itself as a small ist. (the choice of “six” will be explained later.) the utility of the rsn for auditing the umls was manifested in enabling several auditing methodologies. in [10-13] the utility of small ists to expose inconsistent or erroneous st assignments was demonstrated. group auditing techniques for large extents of refined semantic types were described in [14,15]. finally, improved categorization for conjugate and complex chemicals was explored in [16]. however, the first version of the rsn from 1998 had a major deficiency as an abstraction network. an abstraction network needs to be small to be effective, but for the 1998 release of the umls, the rsn had 1163 ists and thus was an order of magnitude bigger than the sn with its 132 sts for the 1998 release. this deficiency made the rsn a less attractive supplement for the sn as a umls abstraction network. in [8] we conjectured that many of the small ists were erroneous and should not exist in the rsn. for example, a review of 100 out of 422 ists, assigned to only a single concept each, found 89 erroneous assignments. (reminder: these 422 concepts are distinct!) furthermore 77 of the 1163 ists represented cases of redundant st assignments. an assignment of an st a to a concept c is defined as redundant if c is also assigned another st b, when b isa a, i.e., a is a generalization or parent of b. redundant assignments are forbidden in the umls [5] since they are implied. for example, in the 2011aa release of the umls, subungual swelling is assigned both finding and sign or symptom. the assignment of finding is redundant since sign or symptom is-a finding in the sn. this assignment was removed in the next umls release. anatomical abnormality disease or syndrome anatomical abnormality ∩ disease or syndrome ojphi sculpting the umls refined semantic network 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 our plan at the time was that by an effort of removing of redundant st assignments and other erroneous or inconsistent combinations of sts from the umls, only ists that stand for legitimate combinations of sts would remain, making the rsn considerably smaller. definition: an ist is considered not legitimate if its combination of sts satisfies any of the following: (1) the combination of semantic types is forbidden by the definitions or usage notes in the documentation of the semantic types of the semantic network. for example, the combination of anatomical abnormality and neoplastic process is forbidden. (2) the combination is a redundant semantic type assignment. for example, if a concept is assigned both finding and sign or symptom, the assignment of finding is redundant, since sign or symptom is-a finding in the semantic network, and thus the assignment of finding to such concepts is redundant. (3) the semantic types of the ist are mutually exclusive in the real world, e.g. for sibling semantic types in the subhierarchy of organism. for example, no real world concept is both a fish and a bird at the same time. (4) the semantic types of the ist do not refer to the same concept, but to two (or more) concepts with different real world semantics. an example for this case was presented in our 2003 paper [9] regarding the concept video recording and its child videotape recording, which (in the 2008ab release of the umls) were (still) assigned both manufactured object and human-caused phenomenon or process. this is a semantically impossible combination since an object cannot be a process. in our analysis [9] we realized that the manufactured object semantics referred to the product of the recording while the human-caused phenomenon or process semantics referred to the recording process involved in producing this product. indeed, in the current umls, both above concepts are assigned only manufactured object, similar to the 2008 assignments of video recording’s two other children videodisk recording and videotape/videodisc. definition: an ist is considered legitimate if it is not illegitimate. the legitimate ists deserve to be elevated to first class citizens in the rsn. our assumption was that not too many legitimate ists will remain in the rsn after all the illegitimate ists have been removed. the legitimate ists occur mostly for chemical concepts where both a structurally viewed chemical st, and at least one functionally viewed chemical st, are expected, according to the definition of the chemical st [17]. after 1998 we embarked on a longitudinal study to achieve the goal of eliminating illegitimate ists from the umls in order to obtain a compact rsn. naturally this was difficult, being outside of the national library of medicine (nlm), the curator organization of the umls, we have very limited influence on the development of the umls. this paper is dedicated to describing the process and steps used to “sculpt” a compact rsn out of its 1998 version and the results obtained. the term “sculpting” is used metaphorically, because a sculpture is created by ojphi sculpting the umls refined semantic network 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 removing the excess material from a shapeless block of raw material. in the same way, the “correct” rsn with only legitimate ists should emerge from its initial version. as will be reported, the goal of obtaining a compact rsn was achieved to a substantial degree, but it required a multiyear process. the process was slowed down by the phenomenon of ists that had been removed from the rsn being reintroduced by the nlm due to new, erroneous st assignments in new umls releases. in [18], we introduced the adviseeditor system, which can help the umls team with preventing the reintroduction of erroneous ists in the future, which would preserve the rsn as a compact abstraction network. we stress again that the purpose of this paper is not to introduce new methods for auditing the umls, but to describe various techniques previously employed to transform the rsn into a compact abstraction network. these techniques were at the time published for their own sake, but are reviewed here for their role in sculpting the rsn (and not as novel research.) specifically for this paper, we performed a comprehensive audit of 168 small ists in the 2013aa version of the umls. various terminologies are used in health informatics to support various needs. for example, snomed ct [19] serves for coding electronic health records (ehr). loinc [20] serves for reporting laboratory test results, and rxnorm [21] serves for prescription drugs. the accuracy of such terminologies is important for their proper use, especially in the realm of public health. for example, as a supplement for traditional public health surveillance, syndromic surveillance is being used in numerous states and localities to detect a potential large-scale biologic attack [22]. hripcsak et al. demonstrated the feasibility of using electronic health record data for syndromic surveillance, in which terminologies are used to encode the narrative clinical notes by natural language processing techniques [23]. however, this task is challenging due to complex grammar in free text as well as ambiguous concepts in the terminologies. therefore, identifying and correcting ambiguities, inconsistencies, and errors in the terminologies may accelerate the adoption of standard terminologies in the syndromic surveillance system, which would improve public health. however, due to their size and complex modeling, errors and inconsistencies are unavoidable and quality assurance (qa) for each terminology is required. however, qa of terminologies is difficult, requires experts with multiple training and requires extensive budgets. to facilitate effective qa of a terminology, it is preferred to apply computational techniques that automatically find concepts with high likelihood of errors. such techniques will improve the yield of qa resources available, where the yield is expressed in terms of number of errors found and corrected per effort spent. one approach for effective qa of terminologies used in health informatics utilizes the fact that many of them are source terminologies for the umls. that is, a specific concept of the umls may be mapped into several concepts of various source terminologies. as it happens, the modeling of the concept in the various sources may not be consistent with one another and some modeling may even be outright wrong. while such cases are very difficult to detect by just performing qa on a specific terminology, the inconsistency or error may be manifested by the assignment of multiple semantic types for this concept. for example, one semantic type may follow the meaning of the concept in one source terminology, while another semantic type may ojphi sculpting the umls refined semantic network 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 reflect the meaning of the same concept in the other terminology, or one of the semantic types may have been assigned by mistake. furthermore, concepts with erroneous or inconsistent semantic types assignment may indicate other errors or modeling problems for that concept. however, the erroneous modeling of such concept came from one or more of the source terminologies. in this way, a hidden error or problem in the modeling of a concept in one of the umls source terminologies, is discovered and can be corrected due to improper combination of the semantic types in the umls. examples of such phenomenon are demonstrated in the result section. methods this paper describes the techniques, process, and results that enabled us to reshape the rsn into a compact abstraction network, materializing the vision defined more than a decade ago. the methodology framework of “sculpting” the rsn is illustrated in figure 3. we will describe the techniques employed in the sculpting process in detail, pointing out the longitudinal history of the techniques invented in this framework. figure 3. the methodology framework of “sculpting” the rsn for the umls. generating database tables for the refined semantic network to enable this longitudinal study of sculpting the rsn, we generated two database tables for the refined semantic network for each release of the umls, starting with version 2006ac. we first used the metamorphosys tool developed by the nlm to generate the oracle loading scripts of the umls rich release format (rrf) tables. then we loaded the rrf tables into our oracle database system. in the original rrf schema, the “mrsty” table stores a single semantic type assignment to a concept in one row. therefore, if a concept is assigned multiple semantic types, there would be multiple rows in “mrsty” for this concept. based on “mrsty,” we generated an rsn table called “combos” to store the compound semantic type assignments to a concept. in this way, multiple rows for a concept assigned multiple semantic types (in multiple rows in the “mrsty” table) are combined into a single row in the “combos” table. in other words, the “combos” table stores the assignment of a refined semantic type (either a pst if a concept is ojphi sculpting the umls refined semantic network 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 assigned a single semantic type or an ist if a concept is assigned multiple semantic types) to a concept in the rsn. we further generated another rsn table “cptcntbyst” (concept count by semantic type) to store the number of concepts assigned each refined semantic type with its description. leveraging the database system’s capability of handing a wide range of queries, we can use sql queries to retrieve ists of certain extents for human auditing in the sculpting process. the sql scripts for generating the database tables “combos” and “cptcntbyst” for the rsn are provided as supplementary files. note that the scripts can be executed only after the umls rrf tables have been loaded. removing redundant semantic type assignments in the framework of sculpting the rsn, we employed a fully automated algorithm to detect redundant st assignments. as mentioned before, there were 77 ists in the 1998 umls release implementing forbidden redundant assignments [5]. in 2002, we designed an algorithm for detecting all meta concepts with redundant st assignments [24]. in 1998 there were 8622 such concepts, which we reported to national library of medicine. from that time on, we periodically monitored the umls for redundant st assignments, reporting systematically to the nlm on our findings. seemingly influenced by our publication [24] and repeated reports, the nlm implemented an automatic procedure that removes redundant st assignments before each release of the umls [25]. extending as new form of sculpting to reiterate, we call this kind of action of eliminating erroneous ists from the rsn “sculpting.” the sculpting of the rsn was continued by extending some ist extents [14,15], which was done after detecting concepts missing appropriate semantic type assignments. that is, sculpting does not always involve removing erroneous ists, but always involves correcting st assignments. in other words, sometimes, concepts are missing a necessary second st assignment, and correcting this may increase the size of an ist extent that was not small to begin with. this phenomenon was demonstrated for the ist experimental model of disease ∩ neoplastic process which was enlarged from 33 to 948 concepts by chen et al. [15], and was further expanded to 1397 concepts using another technique in work of chen et al. [26]. similarly, the ist governmental or regulatory activity ∩ intellectual product was expanded from 22 to 32 concepts [15]. the extent of the ist environmental effect of humans ∩ hazardous or poisonous substance was enlarged from three to nine concepts, i.e., it was no longer a small ist [14]. removing illegitimate ists through auditing ists of small extents in our more than 15 years of research in qa of medical terminologies, we identified two recurring themes, regarding concentration of errors in medical terminologies [27]. errors typically appear in complex concepts or in unusual concepts. the following rationale is offered. modeling of complex concepts is more difficult than modeling of simple concepts, and thus they have a higher likelihood of (human) errors. for “unusual” concepts, the reason for the “uncommon” modeling may be the unique nature of these concepts, but there is also a high likelihood that the modeling is wrong, and this is why these concepts appear to be unusual. the interpretation of “complex” or “unusual” varies from one terminology to another according to the different nature of various terminologies. wang et al. have shown that complex concepts ojphi sculpting the umls refined semantic network 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 in overlapping partial areas [28] have a high likelihood of errors in snomed ct [29,30]. (space does not allow an explanation of partial areas.) if a partial area is small, i.e., it contains few concepts, we can label these concepts as being unusual. it has been shown that small partial areas contain relatively more errors in snomed ct and ncit [27,31]. an ist consisting of multiple sts is more complex than a single st, because of compound semantics. in [14,15,26], chen has found many errors in the extents of ists, e.g., in experimental model of disease  neoplastic process. a small ist is unusual, since out of 2.9 million concepts in the meta, only a few concepts are assigned its st combination. thus, we hypothesized that ists assigned to only a few concepts are more likely to have concepts with inconsistent or erroneous st assignments, since the concepts assigned such ists are both complex and unusual. therefore, we conducted a study for auditing concepts of ists with small extents [10]. our finding was that for ists with up to six concepts there is a higher likelihood of wrong st assignments compared to concepts assigned an ist with a larger extent. if all the concepts assigned a specific small ist have an erroneous st assignment, this ist disappears from the rsn, after the appropriate corrections have been made. this makes the rsn smaller, as desired. over the years, we have conducted several studies, e.g. [11-13], where a team of domain experts audited samples of small ists. we forwarded the consensus reached by our auditors to the umls editors for review. in some cases, the umls editors chose an alternative correction rather than the one suggested by our auditors, but the “erroneous” ists still disappeared from the rsn, whenever no concept was left with the combination of sts of this ist. for this paper, we performed a new audit of all ists with small extents (1-6 concepts) left in the 2013aa umls release, removing inconsistent or erroneous semantic type assignments. the resulting rsn, with a smaller number of ists, is an outcome of this paper. the java program used to generate a sample of concepts (concepts assigned small ists in this paper) is provided as supplementary file. given a list of umls concept unique identifiers (cuis) as input, the program will generate a sample of corresponding concepts, including concept names, refined semantic types, definitions, and contextual information of concepts, such as their parents, children, and siblings. the contextual information helps expose erroneous and inconsistent modeling of a concept. note that the java program can be executed only after the two rsn tables “combos” and “cptcntbyst” have been generated. results first, we will report on the progress of sculpting the rsn over multiple releases of the umls. table 1 presents the information we monitored, including the number of concepts, number of sts and ists, number of concepts with redundant assignments and their ists, as well as the number of small ists with their extent sizes, the combined number of ists with extent sizes 1-6, and finally their numbers of concepts, for different umls releases. some of this information is also illustrated in figure 4. information was regularly collected starting with umls version 2006ac. during 2006-2007 our research group submitted reports of redundant and wrong st assignments for small ists to the nlm. for example, for the 2006ac version, we submitted 42 erroneous, small extent ist assignments, 39 of which had one concept and three had two concepts each. the nlm implemented most of our corrections, causing many small ists to disappear. note that we never ojphi sculpting the umls refined semantic network 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 received feedback from the nlm regarding our error reports, but by reviewing the next releases of the umls we could track the changes, presumably caused by our reports. of these 42 small ists, 38 disappeared by the 2007aa version. one of these ists was mammal  experimental model of disease assigned to the concept knock-in mouse, with erroneous compound semantics; of course a mammal cannot be a disease. another ist that disappeared, congenital abnormality  neoplastic process, which was assigned to port-wine stain, was a forbidden combination of sts according to the umls usage note of the st neoplastic process [17]. no change was made only for one ist gene or genome  enzyme. in three cases, the concept assignments were changed, but the ist remained in the rsn, because a new concept was simultaneously assigned the same ist by the umls editors. (more about such occurrences will be discussed later.) in other words, in some cases new errors were introduced while old errors were being corrected. we reiterate that the nlm did not always make the corrections that we suggested. however, the changes they made to the st assignments still frequently resulted in the deletion of small ists. nevertheless, the total number of ists between 2006ac and 2007aa was only reduced from 559 to 555. while some erroneous small ists disappeared, new ones were created due to the assignment of multiple sts to new concepts coming from new sources added to the umls or from new releases of existing umls sources. a systematic decrease in the number of ists is evident in table 1 from 2007ac till 2008ab including 2008aa. the number of ists went down from 532 in 2007ac to 397 in 2008ab, a reduction of 135 ists, 110 of which were small ists with a total of 235 concepts, including in particular 78 ists with one or two concepts each. the removal of such ists from the rsn is consistent with the finding of gu et al. [10] that concepts assigned ists with extents of up to six concepts have a higher likelihood of erroneous st assignments than concepts assigned larger extent ists. many erroneous assignments have been removed either due to our reports (e.g., [11]) or independently by the umls team. furthermore, as mentioned in the previous section, the nlm implemented an automatic procedure for detecting all redundant assignments in the umls, which has been applied before any new umls release starting in 2008 [25]. as can be seen in table 1, no redundant st assignments were detected from the 2008aa to the 2013aa release, except for one case in 2011aa (reason unknown) that was subsequently corrected in 2011ab. during 2009 – 2013 a plateau was reached, with about 400 ists, of which about 170 are small ists, containing a total of about 410-420 concepts. one may think that the rsn had reached a stable state during these years. however, the impression created by the numbers of ists and small ists is misleading. ojphi sculpting the umls refined semantic network 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 table 1. progress of rsn over time umls release #cpts #sts #ists #cpts w/ redundant sts #ists w/ redundant assign #ists w/ 1 cpt #ists w/ 2 cpts #ists w/ 3 cpts #ists w/ 4 cpts #ists w/ 5 cpts #ists w/ 6 cpts #ists w ≤ 6 cpts #cpts in ist w ≤ 6 cpts 1998 476k 132 1163 8622 77 422 n/a n/a n/a n/a n/a n/a n/a 2001 800k 134 874 12161 40 322 113 64 35 28 25 587 1170 2006ac 1.4m 135 559 91 7 124 68 37 32 26 18 305 737 2007aa 1.4m 135 555 598 11 111 65 40 33 23 17 289 710 2007ac 1.5m 135 532 0 0 116 56 35 34 20 15 276 659 2008aa 1.6m 135 464 3 2 105 44 25 25 15 14 228 499 2008ab 1.9m 135 397 0 0 64 30 29 14 17 12 166 424 2009aa 2.1m 135 381 0 0 59 32 24 13 16 11 155 393 2009ab 2.2m 135 385 0 0 61 30 25 15 14 13 158 404 2010aa 2.2m 133 384 0 0 58 32 24 15 16 9 154 388 2010ab 2.4m 133 392 0 0 66 35 19 16 16 8 160 385 2011aa 2.4m 133 409 1 1 75 38 24 16 17 6 176 408 2011ab 2.6m 133 406 0 0 72 34 25 16 19 8 174 422 2012aa 2.6m 133 407 0 0 73 33 26 16 17 7 172 408 2012ab 2.8m 133 402 0 0 61 37 26 14 18 9 165 413 2013aa 2.9m 133 401 0 0 63 33 27 18 16 11 168 428 2013 audit 2.9m 133 336 0 0 48 28 10 3 8 6 103 222 ojphi sculpting the umls refined semantic network 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 figure 4. progress of the semantic network, ists and ists with small extents. blue bars show the number of semantic types in the umls semantic network. red bars show the number of ists in the rsn. green bars show the number of ists with small extents. ojphi sculpting the umls refined semantic network 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 during the period from 2009 to 2013 two ongoing phenomena have been observed that have counteracting effects on the numbers of ists. from one side, erroneous st assignments were detected by the umls team and as a result 69 erroneous ists of typically small extents disappeared (see table 2). from the other side, new umls concepts were assigned semantic types and for 78 of them, new combinations of sts were created (see table 2), leading to the addition of new ists of typically small extents. many times those newly created ists are the same ones that had been removed from the rsn in earlier releases, because erroneous assignments of such ists were corrected. according to table 2, there are 35 such ists over the five releases 2011aa – 2013aa. furthermore, 11 of these ists were added and deleted more than once during this period. these “oscillations” could have been avoided if the nlm would have adopted the rsn as an additional abstraction network for monitoring the umls. besides our publications about the rsn and its use, the rsn was also presented at the nlm-sponsored workshop on “future directions of the semantic network” [32]. a recommendation how to avoid “oscillations” appears in the discussion section. table 2. progress of ist removal in the past five releases 2011aa 2011ab 2012aa 2012ab 2013aa total ists 409 406 407 402 401 small ists 176 174 172 165 168 new ists 23 17 13 14 11 78 appeared before 12 6 4 6 7 35 repeated previously 3 1 3 1 3 11 number of deleted ists 6 20 12 19 12 69 when we reviewed the new ists in the 2013aa and 2012aa releases of the umls, we found that most of them are illegitimate. for example, in table 3 for the 11 new ists in the 2013aa release, the ist mental or behavioral dysfunction  steroid  pharmacologic substance is illegitimate, because a dysfunction cannot be a chemical. amino acid, peptide, or protein  pharmacologic substance  indicator, reagent, or diagnostic aid  element, ion, or isotope is assigned to only one concept fluciclatide f18, which is used as radioactive probe in pet imaging according to the definition of this concept. however, the umls usage note of ‘indicator, reagent, or diagnostic aid’ [33] states: “radioactive imaging agents should be assigned to this type and not to the type ‘pharmacologic substance’ unless they are also being used therapeutically.” thus, the assignment of ‘pharmacologic substance’ is deemed wrong. in 2012aa, the ist carbohydrate  chemical viewed functionally was assigned to the concept viridaphin a(1) glucoside (see table 4). it is surprising that a general semantic type such as chemical viewed functionally is assigned to this concept. according to the rules of the umls [5], each concept should be assigned the most specific applicable st. our team member performing this audit proposed to change this semantic type assignment to a grandchild of chemical viewed functionally, namely antibiotic. finally, we report the results of an audit of the 428 concepts of the small ists of the 2013aa version. they were divided into two sets, 98 non-chemical concepts and 330 chemical concepts. the first set was reviewed by two domain experts, an md, trained in medical terminologies ojphi sculpting the umls refined semantic network 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 (g.e.) and a phd who specialized in techniques for auditing medical terminologies after receiving training in sports medicine (y.c.). the second set was audited by a chemistry professor (l.c.), experienced in auditing chemical concepts. all three auditors are co-authors. they used the neighborhood auditing tool (nat) [12] designed at njit and have previously audited umls st assignments to concepts. table 3. new ists in umls release 2013aa. new ists in 2013aa extent appeared also in years bacterium ∩ pharmacologic substance 1 2012aa 2011ab 2011aa 2010ab 2010aa 2009ab 2008aa 2007ac congenital abnormality ∩ finding 1 2011aa 2007ac 2007ab laboratory or test result ∩ laboratory procedure 1 2008aa 2007ac 2007ab 2007aa pathologic function ∩ anatomical abnormality 1 2007ac 2007ab 2007aa mental or behavioral dysfunction ∩ steroid ∩ pharmacologic substance 1 medical device ∩ indicator, reagent, or diagnostic aid 4 2012aa 2008aa 2007ac 2007ab 2007aa amino acid, peptide, or protein ∩ pharmacologic substance ∩ indicator, reagent, or diagnostic aid ∩ element, ion, or isotope 1 carbohydrate ∩ pharmacologic substance ∩ food 2 lipid ∩ pharmacologic substance ∩ food 5 biomedical or dental material ∩ food 2 2008aa biomedical or dental material ∩ element, ion, or isotope 1 2007aa legend ist removed once ist removed twice ist appeared the first time ist appeared the second time ojphi sculpting the umls refined semantic network 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 table 4. new ists in 2012aa new ists in 2012aa extent appeared also in years bacterium ∩ eukaryote 1 therapeutic or preventive procedure∩ biomedical or dental material 4 natural phenomenon or process ∩ indicator, reagent, or diagnostic aid 1 medical device ∩ indicator, reagent, or diagnostic aid 1 2008aa 2007ab 2007aa medical device ∩ clinical drug 1 2010ab qualitative concept ∩ clinical attribute 1 amino acid, peptide, or protein ∩ biomedical or dental material ∩ inorganic chemical 1 carbohydrate ∩ chemical viewed functionally 1 chemical viewed functionally ∩ inorganic chemical 1 pharmacologic substance ∩ vitamin ∩ indicator, reagent, or diagnostic aid 2 pharmacologic substance ∩ vitamin ∩ inorganic chemical 2 2008aa 2007ab 2007aa pharmacologic substance ∩ food 1 2008aa 2007ab 2007aa vitamin ∩ element, ion, or isotope 1 legend ist removed once ist removed twice ist appeared the first time ist appeared the second time table 5 summarizes the results of auditing 29 small non-chemical ists from the 2013aa release. if all audit results were implemented in the 2013aa release, 16 out of 29 small nonchemical ists would disappear and 2 new non-chemical ists would be added, resulting in 15 such ists. table 5. auditing impact on 2013aa non-chemical ists of the sculpted rsn extent size of ist starting # of non chemical ists 2011aa # of non chemical ists deleted by audit percentage of such ists deleted # of non chemical ists added by audit percentage of non ists added # of non chemical ists after audit net reduction 1 7 5 71.4% 1 14.3% 3 57.1% 2 3 2 66.7% 0 0% 1 66.7% 3 5 3 60% 1 33.3% 3 60% 4 6 4 66.7% 0 0% 2 33.3% 5 2 1 50% 0 0% 1 50% 6 6 1 16.7% 0 0% 5 16.7% total 29 16 55.2% 2 6.9% 15 48.3% ojphi sculpting the umls refined semantic network 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 for example, the ist congenital abnormality  finding is only assigned to congenital abnormality of systemic artery. however, the umls usage note of finding [33] states that “only in rare circumstances will findings be double-typed with either ‘pathologic function’ or ‘anatomical abnormality’.” congenital abnormality has an is-a relationship to anatomical abnormality. thus, the assignment of finding should be removed. consequently, this ist should disappear from the rsn. table 6 summarizes the results of auditing 139 small chemical ists from the 2013aa version. we see that 30 (= 139 109) small chemical ists were found correct and remained in the rsn. also 58 new chemical ists were created in the auditing process, leaving a balance of 88 small chemical ists. table 6. auditing impact on 2013aa chemical ists of the sculpted rsn extent size of ist starting # of chemical ists 2011aa # of chemical ists deleted by audit percentage of ists deleted # of chemical ists added by audit percentage of ists added # of chemical ists after audit net reduction 1 56 44 78.5% 33 58.9% 45 19.6% 2 30 19 63.3% 16 53.3% 27 10% 3 22 21 95.5% 6 27.3% 7 68.2% 4 12 11 91.7% 0 0% 1 91.7% 5 14 10 71.4% 3 21.4% 7 50% 6 5 4 80% 0 0% 1 80% total 139 109 78.4% 58 41.7% 88 36.7% in some cases, an audit resulted in an st combination which added a concept to the extent of an existing ist, which may have been large or small. for example, the concept triomatrix is the only concept assigned amino acid, peptide or protein  biomedical or dental material  inorganic chemical. this is an implantable orthopedic device, namely, a surgical bone implant, composed of living or natural materials. because amino acid, peptide, or protein is an organic chemical, it should not be assigned together with inorganic chemical. with the assignment of inorganic chemical removed, this concept is reassigned the very large ist amino acid, peptide or protein  biomedical or dental material, while the previous ist disappears. the results of the audit of version 2013aa appear in table 1. the last row in table 1 shows the impact of this audit on the size of the rsn. only 15 small non-chemical ists and 88 small chemical ists are left in the rsn. the total number of ists (small and large) decreases to 336 (fourth column, table 1). the audit reports of both samples were submitted to the nlm for review. based on past experience, we expect the recommendation to be at least partially incorporated into the umls, which will reduce the size of the rsn. figure 5 shows an excerpt of the rsn after the sculpting effort. all the ists are displayed as yellow boxes. chemical semantic types are shown as red text. the part above the dashed blue line consists of the original semantic types from the semantic network. the part of figure 5 below the dashed blue line shows the ists with at least one non-chemical intersecting st and their parent ists even if all the sts of the parent ists are chemical, e.g., carbohydrate  ojphi sculpting the umls refined semantic network 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 entity event laboratory or test result laboratory procedure nucleic acid, nucleoside, or nucleotide organ or tissue function genetic function molecular biology research technique therapeutic or preventive procedure virus physical object substance manufactured object foodchemical chemical viewed functionally chemical viewed structurally pharmacologic substance hazardous or poisonous substance indicator, reagent, or diagnostic aid organic chemical carbohydrate lipid anatomical structure fully formed anatomical structure cell component medical device gene or genome organism activity occupational activity health care activity diagnostic procedure research activity phenomenon or process natural phenomenon or process biologic function physiologic function molecular function conceptual entity finding laboratory or test result ∩ laboratory procedure carbohydrate ∩ pharmacologic substance ∩ food carbohydrate ∩ food manufactured object ∩ hazardous or poisonous substance organ or tissue function ∩ diagnostic procedure cell component ∩ lipid gene or genome ∩ nucleic acid, nucleoside, or nucleotide medical device ∩ indicator, reagent, or diagnostic aid lipid ∩ pharmacologic substance ∩ food genetic function ∩ molecular biology research technique therapeutic or preventive procedure ∩ molecular biology research technique virus ∩ pharmacologic substance pharmacologic substance ∩ food manufactured object ∩ lipid carbohydrate ∩ pharmacologic substance lipid ∩ pharmacologic substance lipid ∩ food bacterium bacterium ∩ pharmacologic substance ... ... ... ... … ... … ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... … ... … ... ... ... ... ... … ... … ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... figure 5. an excerpt of the rsn after sculpting. this figure shows all the ists with at least one non-chemical st and their ancestors. all the chemical sts are marked in red. all the ists are shown as yellow boxes. ojphi sculpting the umls refined semantic network 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 pharmacologic substance. as can be seen in the figure, the parents of the ists combining two sts are their corresponding semantic types in the original semantic network. all those ists are in two rows immediately below the dashed blue line. the ists that combine three sts are located in the third row below them. the parents of those ists contribute the three constituent sts. omitted parts of the sn are hinted at by dots. in this paper, we advance in two ways beyond the auditing of small ists reported on in our previous publications [10,11]. one new feature is “group auditing” of small ists, that is, auditing a small group of semantically similar concepts as one unit, as opposed to auditing concepts one by one. group auditing of small ists is expected to be more accurate and easier than auditing a list of concepts in random order. this is distinct from group auditing of large ists [14]. for example, the small ist human-caused phenomenon or process  natural phenomenon or process was assigned to four similar concepts chemical hazard release; biohazard release; incidents, biological and accidents, biological from msh. the first two are children of the concept accidents, assigned phenomenon or process (in 2010aa), and assigned injury or poisoning (starting in 2010ab). the other two are concepts without parents or children. the definitions of the first two are almost perfectly parallel, “uncontrolled release of a chemical (biological material) from its containment that either threatens to, or does, cause exposure to a chemical (biological) hazard, such an incident may occur accidentally or deliberately.” following the definitions and children listed (e.g. bhopal accidental release assigned human-caused phenomenon or process) these four concepts should be assigned only human-caused phenomenon or process. the second advanced feature is an important side effect of the group auditing of concepts of small ists, the discovery of other inconsistencies in such concepts or their neighbors. typically, an erroneous st assignment indicates a misconception or ambiguity of the concept, which may be manifested in other inconsistencies. a concept belonging to a small ist is algorithmically detectable, initiating a manual review of such a concept. however, there may be no known automatic method to detect the other inconsistencies found during this review. their discovery is a byproduct of the review of small ists. we illustrate several such inconsistencies found during the manual review of the ists in the previous example. like the previous two concepts, accidents, biological, should have a parent accidents, which in turn has a wrong parent injury. the other isolated concept in the group, incidents, biological should have the concept incident (from hl7v3.0 [34]) as a parent. such a hierarchical relationship between concepts from two sources can be added by the nlm into the mth source. incident, by its definition, should be assigned phenomenon or process rather than idea or concept. the audit of st assignments of these four concepts as a group suggested the exploration of other neighboring concepts, finding these other inconsistencies. at the same time, those errors suggest the correction of the modeling of concepts in individual health informatics terminologies, by e.g., adding is_a relationships or a missing concept. these corrections were discovered only due to inconsistent multiple st assignments in the umls. another example of group auditing appears with the ist manufactured object (mo)  selfhelp or relief organization (sho). this ist is assigned only three concepts: night shelter, ojphi sculpting the umls refined semantic network 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 social service facility, and community resource center. the assignment of mo to these three concepts is puzzling. all three concepts are from the alcohol and other drug thesaurus (aod) [35]. upon reviewing the context of this set, we see that night shelter has three siblings: day shelter, dry shelter, and web shelter, all children of shelter homeless. all of them are from aod and assigned only sho. shelter homeless in turn has a sibling community resource center and a parent social service facility both assigned mo  sho. finally social welfare assigned only sho is the parent of social service facility. reviewing the context, the auditor suggested removing the mo st from the assignment of these three first concepts for consistency. however, this case of inconsistent st assignment can be a trigger to review the aod modeling. it seems that the assignment of mo st was due to the use of the words “facility” and “center”, in two of these concepts, interpreting them to refer to the building hosting the self help organization. this interpretation exposes an ambiguity in the aod modeling between the organization and the building hosting it. our suggestion with regard to aod modeling is to disambiguate by creating two concepts social service center with the sho semantics that will be the parent of shelter homeless and community resource center and the child of social welfare and a concept social service facility with mo semantics referring to the building hosting it. this way an inconsistency in umls semantic type assignment exposes a modeling problem in the aod source terminology which otherwise would be hidden. discussion in the paper of mccray and hole [7], which introduces the umls semantic network, the authors stated “the current scope of the network is quite broad, yet the depth is fairly shallow. we expect to make future refinements and enhancements to the network based on actual use and experimentation.” this plan for further development of the sn was never executed, in spite of obvious needs. for example, describing the integration of the gene ontology (go) [36] into the umls, lomax and mccray [37] point to deficiencies of the sn in covering the genomics field. while the umls meta grew to be about 96-fold larger than in its first release [38], the sn changed very little, with a few semantic types being added or deleted over the years (see, for example, the third column in table 1). proposed extensions of genomics coverage in the sn [39,40] were not implemented. one may consider the rsn as a step towards fulfilling the above original vision of the designers of the umls semantic network, since it adds to the network depth by adding ists in a way that extends the sn downwards. another important observation is that the rsn is derived from the sn and the st assignments to meta concepts in an intrinsic way without using any knowledge sources that are external to the umls. the extension provided by the rsn follows the same approach and is thus in line with the vision for the umls expressed at its founding. the rsn helps identifying ists with proper compound semantics and treating them as legitimate first class citizens, while removing all the semantically invalid st combinations. for example, in the 2013aa release of the umls, 85 ists are assigned to at least 100 concepts, 36 ists are ojphi sculpting the umls refined semantic network 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 assigned to at least 500 concepts and 21 of these ists are assigned to at least 1000 concepts, demonstrating their validity as legitimate broad categories for meta concepts. only 29 small non-chemical ists exist in the 2013aa release. according to our hypothesis [10], concepts assigned such small ists have a high likelihood of wrong or inconsistent st assignments. indeed, many such ists have already disappeared in past releases. we applaud the efforts of the nlm editorial and qa teams achieving the current situation, by preventing redundant st assignments and eliminating many erroneous small ists. furthermore, even for the current (2013aa) small, non-chemical ists, the hypothesis of gu et al. [10] was found true in our recent audit report (see table 5), according to which only 15 (about half) of the small nonchemical ists are legitimate, i.e., are meaningful in the real world. the situation is different for small chemical ists. as mentioned earlier, ists are expected to exist for chemical concepts, due to their multiple structural and functional views. as a result there are 28 ists which represent combinations of four chemical sts. for example, 118 concepts are assigned amino acid, peptide, or protein  pharmacologic substance  immunologic factor  indicator, reagent, or diagnostic aid. while many of the small chemical ists are legitimate, table 6 indicates that a large portion of them, (109/139) = 78% are erroneous. however, many (58) small chemical ists were added during the audit, when the concepts of the deleted ists were assigned correct semantic types. as a result, 88 small chemical ists were left in the rsn after our audit (see table 6). the concepts of the other 51 (109-58) small chemical ists were typically reassigned existing ists with larger extents, as shown in the example above. the contrast between the 88 small chemical and the 15 small non-chemical ists reflects the high frequency of categorizing chemical concepts by both structural and functional chemical sts, as documented in the usage note for the chemical st of the umls [33]. in this paper, we stressed the success of group auditing of small ists in exposing other errors (besides semantic type assignments) as well. such errors may not otherwise be detectable algorithmically. we recommend the auditing of concepts that were assigned small non-chemical ists in past umls releases, and of their neighboring concepts, for exposing other errors which may be hard to discover by a program. the storage of previous releases of the umls, can enable exposing such errors. furthermore, these errors may expose errors in individual umls source terminologies, which otherwise, would be hard to expose. interestingly, once all erroneous ists will have been eliminated from the rsn, the hypothesis of [10], i.e., ists with small extents contain concepts with a relatively high likelihood of erroneous st assignments, will not be true anymore. this is based on the expectation that the current nlm practice of re-assigning erroneous ists to new umls concepts will cease. this practice has turned the effort of sculpting the rsn into a sisyphean task, since once an erroneous ist has been eliminated by correcting the erroneous st assignments of its concepts, this ist often reappears in a future release, due to new erroneous semantic type assignments. we recommend that the rsn should be used as a support tool for preventing re-assignment of illegitimate ists without hurting the efficiency of the umls team. this issue was the subject of another line of research of some of the authors [18]. in that work we analyzed the various reasons why some st combinations should not be assigned to new umls concepts. these reasons include redundant st assignments, detectable algorithmically [24] and conditions listed in the umls usage notes [33], as illustrated earlier. among the reasons is also the mutual exclusion ojphi sculpting the umls refined semantic network 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 between sibling sts in certain subtrees of the sn, e.g., in the subtree of organism describing the animal kingdom. furthermore, an interactive, web-based system adviseeditor was developed, which accepts as input a pair of sts, and determines whether this pair is legitimate or illegitimate (or whether more research is required for this pair). adviseeditor can also process triples, quadruples and quintuples of semantic types in interactive mode and in batch mode [18]. we recommend that the umls team of the nlm will take advantage of the adviseeditor tool to preserve the rsn as an additional compact abstraction network for the umls (in addition to the sn). working this way will prevent many categorization errors in the future. furthermore, preventing these errors will save the umls team the effort currently spent on meticulously correcting them. limitations some limitations are noteworthy in interpreting this study. first, the auditing of small ists was conducted by human experts. thus, some suggestions might be subjective and arguable. nevertheless, in this study, we tried to reduce the subjectivity by having multiple domain experts review the ists of small extents. second, as we mentioned earlier, the nlm, as the curator organization of the umls, has the full control over its development. therefore, we have limited influence on its development. according to the findings in this study and our past experience, even if the nlm did not adopt all of our suggestions to correct ambiguities, inconsistencies, and modeling errors in the umls, our auditing reports still played a positive role for its qa. from a qa perspective, external auditing can be considered as a necessary task and an ethical advantage, because the nlm team cannot influence what external auditors want to investigate. otherwise, there would be the appearance of a conflict of interest, which diminishes the credibility and integrity of the qa process. third, we performed the auditing of source terminologies in the context of the umls, it might be difficult to make suggested changes in individual source terminologies in their own models, e.g. description logic. in the recent years, numerous domain ontologies are emerging for health informatics applications. due the heterogeneous development models and domain knowledge of their curators, the quality issue has been recognized as one of a factor that has slowed down their adoption [41]. we suggest that a rigorous auditing methodology framework should be incorporated in the life cycle of domain ontologies. as a final note, we would like to stress the importance of longitudinal studies in medical informatics. in medicine, studies extending over 5 or more years are not uncommon. in medical informatics we have seen few such studies. the present paper shows that longitudinal studies are possible and fruitful in medical informatics. conclusions we reported on a longitudinal study of the process of improving the umls as a result of auditing its semantic type assignments. the main instrument used in this sculpting is the auditing of small ists containing concepts with a high likelihood of erroneous or inconsistent st assignments. over the years, the external auditing of the umls has been shown to complement the internal auditing at the nlm. numerous audit reports were submitted and reviewed by nlm team ojphi sculpting the umls refined semantic network 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e181, 2014 members, who also performed their own auditing. the nlm also adopted automatic testing for redundant st assignments 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18693816&dopt=abstract http://www.ncbi.nlm.nih.gov/pubmed/24551360?dopt=abstract 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts spatial clustering of ili in yunnan province, china, based on a geographical information system xia xiao*1, 4, chunrui luo2, xiaoxiao song1, 3, 4, wei liu1, 4, le cai1, 4, yan li1, 4, lin lu4, 2 and qiongfen li2, 4 1school of public health, kunming medical university, kunming, china; 2yunnan province center for disease control and prevention, kunming, china; 3fudan university, shanghai, china; 4yunnan provincial collaborative innovation center for public health and disease prevention and control, yunnan, china objective the purpose of the study was to determine spatial clustering of the spread of influenza like illness (ili) epidemic in yunnan province, china with the aim of producing useful information for prevention and control measures. introduction influenza is a highly contagious, acute respiratory disease that causes periodic seasonal epidemics and global pandemics[1]. yunnan province is characterized by poverty, multi-ethnic, and cross-border movement, which maybe be susceptible of influenza (fig-1). finding from spatial patter of ili will promote to control and prevent the respiratory diseases epidemic. methods data was obtained from the sentinel surveillance of illness like influenza (ili) in yunnan center of disease control and prevention from 2009 to 2013. the characteristics of the ili clustering will be assessed by ‘global’ and ‘local’ moran’s i using monte carlo simulation by geoda. the spatial weights methods based on queencontiguity (polygons are adjacent if they share a border or corner)[2]. results a total of 49139 ili cases were reported from sentinel surveillance data, which accounted for 3.35% of the total outpatient visit. two incidence peaks occurred in spring and autumn. among the positive samples, the top was victoria (accounted for 31.98%), and the follow rank was influenza a (h1n1) (accounted for 26.03%). from the fig-2, we got the global moran’s i=0.256(p<0.05). it indicated clustering was actually apparent throughout yunnan province. the four quadrants in the scatter plot correspond to different types of spatial correlation. however, the global of moran’s i assume that the spatial process under investigation is stationary and fail to know ‘where was cluster of disease’[3]. so we turn to look the local measures of spatial autocorrelation (lisa, local moran’s i). examination of the lisa map showed that most of the counties were no statistical significant differences in 0.05, except only 4 counties. you can add the results of the lisa analysis to the lisa map (fig3). from the above studying, we concluded that the areas susceptible to influenza featured mostly in poorer surrounding districts, or be neighboring with vietnam or/and laos. conclusions general spatial autocorrelation indicated that influenza incidence was aggregated at the provincial level, and local spatial autocorrelation analyses found that borderarea with poorer living-level were evidence for hotspots of high incidence of ili. an approach base on moran’s i statistic complemented with geoda for visualization facilitates decision-making regarding various options such as isolation according to districts and months, and implement specific control measures in high risk districts to control the spread of ili. fig-1 the map of research site -yunnan provincein china fig-2 the moran’s i scatter plot of virus-strain positive rates fig-3 lisa cluster map of influenza virus-strain positive rates for p<0.05 keywords influenza like illness; geographical information systems; spatial autocorrelation references 1. kimura y, saito r, tsujimoto y, ono y, nakaya t, et al. (2011) geodemographics profiling of influenza a and b virus infections in community neighborhoods in japan. bmc infectious diseases 11: 36. 2. anselin l (2005) exploring spatial data with geoda: a work book. spatial analysis laboratory, university of illinois. center for spatially integrated social science. 3. stevenson m, stevens k, rogers d, clements a, pfeiffer d, et al. (2008) spatial analysis in epidemiology. oxford university press, new york. *xia xiao e-mail: xxkmyn@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e176, 201 ojphi-06-e144.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 (page number not for citation purposes) isds 2013 conference abstracts federal interagency interactions during outbreaks of h7n9 influenza and mers-cov steve bennett*, deborah carr, mark freese, janet hendricks, michael stephens, erik pedersen, kandis brown, christopher grant, jamie hobson, jessica ruble, william albrecht, tajah blackburn, todd boddenhamer and teresa quitugua national biosurveillance integration center (nbic), department of homeland security (dhs), washington, dc, usa � �� �� �� � � �� �� �� � objective �������� �� �� ���� ������� ���������������������� � ���������� �� ���� ��������������������������������� �������� �������� ��� � ���� ����!" #��������$������%&'����(� introduction ���� ������)� ���*�������� ����'��������� ��� ���� ���#+,,� ����� ����-� ����.//"0�12������3�4�,,/�5678� ��� � ���������� �������������� ���� ������� ���1 ���7�� ���� ���4� ���� ���9���� � ��� ����!�������������� )�������� ������� ����� � �������������)� 4����������� ������ � �� �������4����� ������� ����������������� � ���� ����� ��� ������� ��� ��)��������������)���� ��� ����� �������� ��� ���� � �8������8����� ��������������� ��� ���� � �� � ������� ��� ���������� ����� ���8������� ���$� ���8�������$� ���8����� ��� ���������� ������������� �4����4����14�� ���� ��)���������� ��������8�������8� ���� 8�������������� �������� 7� �� ���)�������������� ���� �������� ��������� 9������ ��� ���������./,6� 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�������������� ����������������������� ��� ��� 8� ������ ������������������) ������� �� ����������������� ���� ����� ����� ���������!" #������������ ������4�� �4� � ��4�� ���� � ������������������8�������������8���������������������� �� � ���!����)�2� ��������-������������$��!5 ,� results ?����� � ������������� ��������4��� ������ � �� �������4����� ����� ������������� ������ �����)� ������������ ���������������������� � conclusions ������������ ���4� ��� ��� �������)���� ��� � �������������� �������������� ������� ����4� ������������� �����������4���� ��� � �� ����� �� ��� ��������������������������$��-����� �1!" #7����� %&'����(� keywords ��� �����������@� 9���� ��� � ��� !�������� ������ )� 19!�7@� ��������� ���@�!" #@�%&'����( acknowledgments �9��1'-9%�� ����'���8�a�9�a98�9���� ����������$���������� ���� ��� @�9���9������4�����48� ��9�a98�%&'���%78���9-�-2!���(�� 19���b� ����?��-������8�9���� ��8�a���!��� ���������� ����a�����7 references ,��2������3�4�,,/�568�*�������� ����'��������� ��� ���� ���#�,,� ����� ����-� ����.//"0 *steve bennett e-mail: steve.bennett@hq.dhs.gov� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e144, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts development of the multiplex pcr for detection of the dna-contained emergent diseases agents in pigs (african swine fever, aujeszky disease, circoviral disease) anton gerilovych*, prof. borys stegniy, prof. valeriy golovko, dr oleksii solodiankin, dr iryna gerilovych, yevgenia smolyaninova and natalya rudova nsc iecvm, kharkiv, ukraine objective the objective was to develope the multiplex pcr for detection of the dna-contained emergent diseases agents in pigs (african swine fever, aujeszky disease, circoviral disease) for diseases surveilance in pig farms introduction african swine fever virus (asfv), pseudorabies virus (aujrszky’s disease virus, adv) and porcine circovirus type 2 (pcv-2) are some of the most dangerous dna viruses causing high amounts of morbidity and mortality in commercial and backyard pig farms. traditional diagnosis of porcine viral infections requires complex and lengthy combinations of virological and serological tests. this study aimed to develop a method for rapid detection of the dna-containing viral pathogens of pig in clinical materials using conventional multiplex pcr platform. methods a pcr-based detection technique for asfv, adv and pcv-2 detection has been developed and tested under office international epizootical (o.i.e.) requirements, using determination of specificity, sensitivity and repeatability. results the optimal conditions for amplification of asfv, adv and pcv-2 genome in p72, ge and cap genes, respectively, were determined. the developed protocol allowed to detect asfv dna by 276 bp fragment, aujeszky disease virus and pcv-2 – 230 and 421 bp, respectively. validation testing was performed on the panel selected agents: asfv recombinant dna, cloned in puc19, adv strain undiev18v, and pcv isolates 1-02, 6-12, and 4-12. the protocol demonstrated specificity in its ability to distinguishbetween porcine parvovirus, classical swine fever virus, and porcine respiratory and reproductive syndrome virus. the protocol was 90 % sensitive, 100 % specific and repeatable. it was recommended for validation and implementation in the laboratory practice in ukraine. conclusions a multiplex pcr platform for detection of the emergent disease agents in pigs (african swine fever, aujeszky disease, circoviral disease) has been developed for preliminary diagnostics of these diseases. keywords multiplex pcr; porcine viroses; african swine fever *anton gerilovych e-mail: antger@rambler.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e131, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using a syndromic surveillance system to evaluate the impact of a change in alcohol law julia a. dilley*1, atar baer2, jeff duchin2 and julie e. maher1 1program design & evaluation services, multnomah county health department and oregon public health division, portland, or, usa; 2public health seattle & king county, seattle, wa, usa objective to determine whether there were changes in alcohol-related emergency department (ed) visits in washington state associated with statewide alcohol system deregulation. introduction in november 2011, washington state voters passed initiative 1183 (i-1183) which closed state-owned and contracted liquor stores and opened the market for “hard liquor” sales in the private sector. the change in law was implemented on june 1, 2012. increases in alcoholrelated ed visits were postulated as one potential impact if there was increased alcohol use or excessive consumption associated with the change in law. methods we examined weekly counts of ed visits for alcohol-related conditions from all but one hospital in king county, washington, for the period january 2010 – september 2013 (16 months after implementation of the law). king county includes approximately one-third of the state population and ed data are provided to the county as part of ongoing syndromic surveillance. outcomes were ed visits with an alcohol-related chief complaint or diagnosis code. chief complaint search strings included terms such as: alcohol, drunk, names of alcohol types (e.g., beer, whiskey, vodka), alcohol on breath. diagnosis codes included icd-9 codes previously described by the centers for disease control and prevention (cdc) for acute impacts of excessive alcohol consumption (see http://apps.nccd.cdc. gov/dach_ardi/info/icdcodes.aspx). we used a linear regression model with a spline at june 1, 2012, to determine if implementation of i-1183 was associated with changes in weekly ed visits. we fitted a series of regression models, stratified by both age groups (<21, 21-39, 40+) and gender. the main covariates of interest were a binary “policy” coefficient with values of “0” prior to june 2012 and “1” including and after june 2012, and an interaction term (policy coefficient x week). the dependent variables in the regression models were log-transformed to achieve homogeneity in the error structure. the model also included covariates for background trend (time variable for week), seasonality (calendar quarters), and general healthcare access (total ed visits each week for any reason). our models took into account autocorrelated error terms by using an autoregressive modeling approach with newey-west standard errors and a lag of 12 in stata v. 13.0. results statistically significant (p<.05) increases in alcohol-related ed visits post-law were observed among minors (age <21) and adults ages 40 and older. the initial increases among minors, however, appeared to decline over time. no significant changes in alcoholrelated ed visits were observed among adults ages 21-39. findings may be limited by ecological bias as well as the choice of search terms included in the alcohol syndrome definition. future validation of the syndrome definition may improve the sensitivity/specificity of the results. both alcohol-related ed visits as a percentage of total visits and also counts of alcohol-related ed visits (with adjustment for total visits) were independently explored as the outcomes of interest. patterns of significance were similar. trend analysis must incorporate adjustment for known data quality issues in syndromic surveillance, such as data drop-offs or new hospitals being added during the evaluation period. conclusions alcohol-related ed visits increased among minors (age younger than 21) and adults ages 40 and older after changes in washington state law increased availability of “hard liquor.” syndromic surveillance data were useful for describing public health impact of a change in law. keywords alcohol; emergency department data; public health law acknowledgments this study was supported by public health law research, a national program of the robert wood johnson foundation. *julia a. dilley e-mail: julia.dilley@state.or.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e70, 2015 improving accuracy for diabetes mellitus prediction by using deepnet 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi improving accuracy for diabetes mellitus prediction by using deepnet riyad alshammari1*, noorah atiyah2, tahani daghistani1, abdulwahhab alshammari1 1health informatics department, college of public health and health informatics king saud bin abdulaziz university for health sciences (ksau-hs) king abdullah international medical research center (kaimrc) ministry of national guard health affairs, riyadh, ksa 2 faculty of health sciences, simon fraser university, burnaby british columbia, canada abstract: diabetes is a salient issue and a significant health care concern for many nations. the forecast for the prevalence of diabetes is on the rise. hence, building a prediction machine learning model to assist in the identification of diabetic patients is of great interest. this study aims to create a machine learning model that is capable of predicting diabetes with high performance. the following study used the bigml platform to train four machine learning algorithms, namely, deepnet, models (decision tree), ensemble and logistic regression, on data sets collected from the ministry of national guard hospital affairs (mngha) in saudi arabia between the years of 2013 and 2015. the comparative evaluation criteria for the four algorithms examined included; accuracy, precision, recall, f-measure and phicoefficient. results show that the deepnet algorithm achieved higher performance compared to other machine learning algorithms based on various evaluation matrices. keywords: diabetes, artificial intelligence, deep learning *corresponding author: riyadalshamamri@gmail.com doi: 10.5210/ojphi.v12i1.10611 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction: diabetes is a severe disease that affects all genders and ages [1]. diabetes is a metabolic and systemic disease in which a disruption in the metabolism of carbohydrates occurs because of insufficient insulin production for the body's metabolic needs [2]. there are two main types of diabetes; type 1, or insulin-dependent diabetes, which is a result of the elimination of insulinproducing pancreatic cells [2]. type 2, or non-insulin-dependent diabetes, correlates to obesity and mailto:riyadalshamamri@gmail.com improving accuracy for diabetes mellitus prediction by using deepnet 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi results from a relative lack of insulin [2]. this disease is a result of an individual's carbohydrate intake exceeding the capacity of their pancreas's production of insulin [2]. the gravity of the condition is evident in the form of complications [3]. common complications of diabetes include, but are not limited to, heart disease, stroke, and kidney disease, which can result in higher mortality [3]. at the patient level, an individual may fail to recognize they have the disease or fail to receive prompt appropriate care resulting in poor prognosis [3]. the global prevalence of diabetes for adults aged more than 18 years old was 8.5% in 2014 per the world health organization (who) report [4]. of the population impacted by diabetes, 80% of the people lived in low-income and middle-income countries, with the highest diagnosis being type 2 diabetes; however, there is an alarming rise in the prevalence of both type 1 and 2 diabetes [1]. parallel to the increasing prevalence rate of the disease, there is an increase in associated consequences due to the complications of diabetes, i.e. increase in heart disease, stroke, and poor health [3]. therefore, mortality rates as a result of diabetes and its comorbid health problems are rising proportionally [5]. in 2015, there was an estimate of 1.6 million deaths as a direct cause of diabetes [1]. the international diabetes federation reported that the disease affects one in 11 adults worldwide, with one person dying of the disease every six seconds [1]. in 2030, who anticipates that diabetes will be the seventh leading cause of death [4]. in saudi arabia, there is an excessive prevalence of diabetes, with an estimated rise of more than 2.5 million patients by 2030 having the disease [6]. early prediction of type 2 diabetes is a prominent health research topic in saudi arabia. diabetes risk score was the most convenient tool for diabetes prediction [7]. however, this method needs human intervention in decision-making. nowadays, computational models to predict the risk of diabetes can significantly support healthcare providers with decision-making and assist selfdisease management, which, in turn, can potentially decrease the diseases associated mortality rates [8]. therefore, machine learning is gaining attention in the health field as these techniques produce high performance in predicting diabetes. specifically, these models can help identify those who are at high risk of having diabetes, and for which early prevention and control programs can improve health outcomes [7,9]. at the same time, these techniques reduce the human error in necessary healthcare decisions. thus, decreasing health burden and utilizing health service resources [5]. ideally, further development of models that incorporate prior knowledge would be promising for diabetes prediction [10]. the availability of a patient's health data could help to extract meaningful information and hidden knowledge to better the prognosis of the individuals affected by this disease. background the biology of diabetes type 1 diabetes is an abnormal immune reaction controlled by a portion of the hla-d region genes and works directly against molecules expressed only on the β-cells [11]. the pathway for immunological response systems is complex but involves mounting a response towards foreign antigens [11]. in type 1 diabetes, similar attacks occur on certain pancreatic β-cells resulting in an insulin deficiency [11]. improving accuracy for diabetes mellitus prediction by using deepnet 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi type 2 diabetes is one of the most frequent metabolic disorders, and it is a heterogeneous disease distinguishable by its deficient production of insulin secretion via the pancreatic islet β-cells [12]. insulin deficiency results in insulin resistance or impaired insulin sensitivity, which leads to a decline in patient health [12]. genome-wide association studies found for islet function; more than 400 type 2 diabetesassociated gene variations influenced secretion [12] [13]. however, genetic roles in the individual genes explain less than 20% of overall diabetic disease risk [12] [13]. in contrast, the literature on lifestyle modification indicates sedentary lifestyles, poor diet, and a myriad of social determinants of health (such as; low socio-economic status, psychological conditions, poor environment) all play a predominant role in the development of type 2 diabetes [12] [14] [15] [16]. furthermore, parental lifestyle has longitudinal impacts on the life course of an individual. within utero programming and early postnatal metabolic transformation correlates to the risk of diabetes due to dna methylation [12] [17] [18]. type 2 diabetes results from a variety of factors but is often mitigated through lifestyle changes and preventative measures such as diet change, increased exercise, and overall holistic integration of health. in summary, over the past 50 years, diabetes mellitus, or diabetes in layman's terms, continues to increase, with individuals in western, western pacific, asian and african countries all experiencing an increase in disease prevalence [12]. cho and colleagues [19] predict globally for years 2017 to 2045, a diabetes rate increase of at least 50%, meaning approximately 693 million people will be affected by the disease creating an estimated healthcare cost of us$850 billion per year. diabetes in saudi arabia in the past four decades, saudi arabia has undergone significant socio-economic change [20]. specifically, saudi arabia has seen an increase in an ageing population, progressive urbanization, decreased infant mortality rates and increased life expectancy [20]. the changes in population demographics also couples with a rapid change in lifestyles, where individuals are moving towards westernized patterns of consumption, shown in changes in nutrition, less physical activity, higher rates of obesity, and increases in smoking—all resulting in a dramatic rise in the prevalence of diabetes [21] [22] [23] [24]. the who reported in 2016 [25], the prevalence of diabetes in saudi arabia was 14.7% for males and 13% of females. the who [25] also found high prevalence of overweight individuals (67.5% males; 69.2% females), obesity (29.5% males; 39.5% females), and inactivity (52.1% males; 67.7% females). the who [25] further reported high mortality rates attributed to diabetes with 1070 males and 500 females (aged 30-69) and 1460 males and 1020 females (aged 70+) dying due to the disease. overall, diabetes is an important health concern for the citizens of saudi arabia. integrating early detection and prediction models would have both national and global benefits. improving accuracy for diabetes mellitus prediction by using deepnet 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi ai and diabetes dalakleidi et al. [26] applied evolving artificial neural networks (eanns), bayesian‐based algorithm, decision trees and logistic regression to predict the progress of diabetes and its complications related to cardiovascular disease. they achieved an accuracy of 80% with the eanns algorithm. for the difficulties, they produced an accuracy of 92.86%. meng et al. [27] compared three different techniques, namely logistic regression, decision tree and artificial neural networks, to predict diabetes and prediabetes. they achieved the best performance with a decision tree with an accuracy of 77.87%, a sensitivity of 80.68% and specificity of 75.13%. wang et al. [28] built a classification model to recognize people of developing type 2 diabetes. they compared artificial neural networks (anns) and multivariate logistic regression (mlr). they showed that ann outperformed mlp. research is promising and demonstrates that ai has the potential to help in the diagnostic framework of diabetic or prediabetic patients. methods the following section discusses the methodology of this research article. furthermore, this section describes the gathering of the data-set and feature information; while also explaining the algorithms used in the following research and evaluation criteria. a. data-set and features the collection of health data-sets were between the years 2013 and 2015. the health-data was from the electronic health record of the ministry of national guard health affairs databases for all adult patients who had tested for hemoglobin a1c (hgba1c). the process of labelling patients as diabetic relied on the results of the hgba1c. if the value of hgba1c was higher or equal to seven, patients were classified as diabetic. if the value of hgba1c was less than seven, patients were classified as non-diabetic. after the pre-processing of the data-sets, the exclusion criteria (exempting participants from further analysis) included those with a missing value of 40% and higher. the usage of the manual inspection and domain knowledge technique allowed researchers to remove implausible values. furthermore, to check the quality of data, this study used r to analyze the given information. table 1 shows a descriptive analysis of the attributes. the data sets have 17 attributes organized into three categories: 1) demographic attributes such as gender, age, and region; 2) measurement attributes such as the body mass index (bmi) and blood pressure; 3) lab tests. table 1: descriptive statistics of diabetes risk factors risk factors data cities riyadh 54141 (81.63%) dammam 11085 (16.71%) jeddah 1099 (1.66%) improving accuracy for diabetes mellitus prediction by using deepnet 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi sex male 36811 (55.50%) female 29514 (44.50%) age groups 13-19 578 (0.87%) 20-34 4067 (6.13%) 35-44 4486 (6.76%) 45-64 23949 (36.11%) 65-84 29049 (43.80%) >85 4196 (6.33%) body mass index (bmi) 30.77 ± 8.92 blood pressure high blood pressure 128.74 ± 18.225 low blood pressure 67.71 ± 11.154 lab test egfr 78.33 ± 40.83 mean corpuscular volume (mcv) 86.954 ± 7.589 mean corpuscular hemoglobin (mch) 28.03 ± 2.91036 mean corpuscular hemoglobin concentration (mchc) 317.55 ± 38.99 red cell volume distribution width (rdw) 15.23 ± 2.43 platelet count (plt) 273.70 ± 125 mean platelet volume (mpv) 8.55 ± 1.38 white blood cell count (wbc) 9.35 ± 5.81 red blood cell count (rbc) 4.17 ± 0.84 hemoglobin (hgb) 114.56 ± 26.72 hematocrit (hct) 0.91 ± 4.44 values are mean ± sd and n (%). males represented 55.50% of the data-set, while females represented 44.50%. most of the data belonged to patients aged 45 to 84 years old. the percentage of diabetic patients in the data-set was 64.47%. the incidence of diabetes for both genders was higher in those aged 65-84 years, with males at 47.83%, and females at 48.6%. comparatively, those with an age range of 45-64 years demonstrated the following; 37.89% male and 38.03% female. the results show that the improving accuracy for diabetes mellitus prediction by using deepnet 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi female patients in the data-sets had a higher body mass index (bmi) and blood pressure measurement compared to the male patients, see table1. b. algorithms and evaluation criteria bigml [29] is a cloud computing service that provides machine learning as a service (mlaas). the bigml offers a collection of state-of-the-art machine learning algorithms and demonstrates the ability to solve real-world challenges in various domains. the bigml was used to build four machine learning-based algorithms, namely ensemble, models (decision trees), deepnet, and logistic regression. a model in bigml is similar to decision tree representation. each node represents one of the input attributes (predictors), with the first node being the root. each node except the root has two branches (leaves) that represent a value of an attribute. a leaf represents the outcome of the class (objective field) in the chain of branches, starting from the root to the leaf end. an ensemble in the bigml is a group of machine learning algorithms joined together to make a more reliable model. logistic regression is a supervised machine learning algorithm that uses a logistic function with the input values to build a learning model. deepnet in the bigml is an optimized form of deep neural networks that is suitable for classification problems. the deepnet is a supervised machine learning model that simulates the human brain neural circuitry. a 10-fold cross-validation technique was applied to evaluate the performance of each machine learning algorithms. it works by dividing the data set into ten equal folds. the training of the machine learning model utilized a one-fold test on the reaming folds, with an iteration of ten. at the end of the tenth iteration, the result shows the average of all the ten folds [30]. the application of the following matrices selected the best model in predicting the label classes (diabetic vs. non-diabetic); true positive rate, false positive rate, precision, recall, area under the curve and f-measure. the calculated metrics were: ● accuracy: which represents the number of correctly classified records over the total number of evaluated records, calculated based on equation 1: accuracy = (tp+ tn) / (tp + tn + fp + fn) (1) ● precision: which represents the number of true positives correctly identified as diabetic patients over the total number of positive predictions, calculated based on equation 2: precision = tp/(tp+fp) (2) ● recall: which represents the number of true positives correctly identified as diabetic patients over the total number of positive records, calculated based on equation 3: recall = tp/(tp+fn) (3) ● f-measure: which represents the harmonic mean of precision and recall, calculated based on equation 4: 2 * (precision*recall) / (precision+ recall) (4) ● phicoefficient: represents the matthews correlation coefficient, calculated based on equation 5: improving accuracy for diabetes mellitus prediction by using deepnet 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi phicoefficient = (tp * tn fp * fn) / (√((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))) (5) results and discussion: the aim of this study was the following: to build a machine learning model(s) that can predict diabetes with high accuracy. therefore, the usage of the bigml machine learning platform helped in the creation of the four machine learning models, namely ensemble, models (decision tree), deepnet and logistic regression. each machine learning algorithm has different machine learning techniques. the overall goal of this study was to find the best performance model and apply its technique to predict diabetes. the performance of the deepnet model was better than models (decision tree), ensemble and logistic regression on all of the evaluation criteria, table 2. table 2: evaluation of predicting diabetes using ai techniques. ensemble models deepnet logistic regression accuracy 88.1 87.8 88.48 88.19 precision 87.9 87.7 88.29 88.38 recall 87.8 87.6 88.36 87.63 f-measure 0.8783 0.8761 0.88 0.88 phicoefficient 0.7566 0.7522 0.77 0.76 the prediction of diabetic patients who may not know they have the disease is a crucial challenge in the healthcare domain. the machine learning technique demonstrates the ability to predict diabetes with high accuracy using only 17 attributes. furthermore, an offered perk of this method is information collection can occur from routine checkups at a healthcare clinic. this process will allow the integration of up-to-date information into the system expediting medical care and easing the burden on healthcare workers and patients. changing the healthcare workflow can enhance the early healthcare assessments of those with diabetes. as a result, this can decrease the prevalence of the disease and improve initial management practices. furthermore, this will increase patients' satisfaction and overall quality of care. a comprehensive diagnostic framework has the potential to streamline medical services and empower patients. machine learning based on algorithms offers a unique tool for healthcare professionals to utilize, from both an epidemiological and treatment perspective. from a systems standpoint, the ability to centralize medical data and predict trends in population health would allow resource allocation to the identified gaps, which in turn, strengthens the population's health. moreover, another meaningful impact of integrating machine learning is the benefit to the patients. the who [31] defines empowerment as "a process through which people gain greater control over decisions and actions affecting their health" and affects both individual and community levels. to empower the population, they must get access to their information and have it delivered it in improving accuracy for diabetes mellitus prediction by using deepnet 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi an understandable format, transparent, and overall—user-friendly. ease of use is paramount for patient engagement. literature indicates that within the 21st century, mobile health technologies resulted in increases in connecting users on a community, population, and global level [32]. mobile health addresses the rising burden of chronic diseases while encouraging health systems to shift towards patientcentric designs [32]. mobile health consists of medical practice supported by a portable diagnostic device [32]. the use of these devices at the point-of-care resulted in not only a change in healthcare delivery, but an increase in patient engagement, a reduction in healthcare costs, and improved patient prognosis [32]. model learning has the potential to increase patient empowerment via mobile health. the compatibility for our connected world through accessibility from a smartphone, desktop or other personal electronic devices, in the way of an app, is potentially highly useful in capitalizing on our mobile interconnectedness. however, before the implementation of mobile health, guidelines to manage these machine learning models are essential for healthcare. systematically developed statements based on research, best practices, best scientific evidence, and experience act as guidelines [31,33]. guidelines support healthcare providers with an outline for patient care to ensure that individuals receive the same or similar patient care across healthcare facilities. standardizing guidelines across healthcare facilities can aide authorities in bridging the gap between research and practices within these facilities, which helps to foster consistent services. additionally, standardizing guidelines across healthcare facilities helps healthcare providers to identify the what, where, when, and how of the patient's health; while collecting, sharing, and reporting data improves and streamlines the process. the collected and reported data based on clinical guidelines assists healthcare and public health authorities in identifying the age groups or individual patients at high risk of having diabetes (type 1 or type 2). the collected and reported data assists healthcare authorities in planning prevention and treatment plans. the flexibility of the process allows healthcare authorities to navigate the ever-changing healthcare landscape. gaps, limitations, and needs of population health are dynamic, and to avoid steep healthcare costs, the allocation of resources must have a basis in evidence to resolve pressing issues best. conclusion: in this paper, the building of a machine learning model for early prediction of diabetes had a basis on real health data collected from the ministry of national guard health affairs, saudi arabia. the comparison of four machine learning algorithms, namely deepnet, models (decision tree), ensemble and logistic regression, used 17 attributes. under assessment, deepnet achieves the best result using the four different evaluation criteria. this paper demonstrates that machine learning-based algorithms have excellent potential in predicting diabetes with high accuracy. future work is to evaluate the model on a larger data-set and use the model with the internet of things devices. improving accuracy for diabetes mellitus prediction by using deepnet 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e11, 2020 ojphi 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ili situational awareness in georgia joel greenspan*1 and silvia valkova2 1martin, blanck & associates, alexandria, va, usa; 2ims government solutions, falls church, va, usa � �� �� �� � � �� �� �� � objective �������� �� �� � ���������������� � � �� ������ ������ ������ �� � � � ����������� ������� ���� ���� �������� ������ ����� ��� ���� ������ ��� ������ ���� ���� ����������� �� ������ � ����! � ���� ����"#"����������������� � ���������� ������$%&�� � ��' introduction �� �()*(�+��������%� �� ����� �$���� � ������ ��$%,�� ����� �� �������� ������ � �� �����������$%,� �� � �� ������� ���� � �� ������ ���� ��������� � �� �� ���������!������������ � ��'�%� ����������� � �� ����� ������������������ ��� ������� ���������� �������� ��� �� ���� ����� � ����'��"-&&����������� ���������� ����� ����� � � ����� ����������� ������� ������������ �� �������� ���� ������ �� ��� ������������$%,'� ������ ������� �� ������ ���������� ���������� ��� ���� ���.����� .�������� ����� ���������������� � � � 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�������� �� � ����� ���� !.���� ��� � �� ������ �������� ����� ������ ��� ���� ���������������� � �� ����� ��� ������ �����"#"�� � �������� ��������� ��������� ��� ����������������������� ������������a������ ��� �� ���'� ����� ������������������� ����� � ����� ��� �������������������������� �� ���d���'�"������ �������d�������.� ���� ��� � �� � � �������� �� � �� ��� ��������3'%'���� ������ ��� � ������������ � ������� � �� ������ ��� ����� ���������$%,���������� �.� �� ���������������� �� ����� �����e� ������������� � ��� � �������� �� ���������!���� � ���� ��� � ��' keywords �������f�� ������ �������f�"#"f���������������� � ��f��������� ���� � ���� *joel greenspan e-mail: greenspan@comcast.net� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e80, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts social mobilization dengue hemorrhagic vector control and sustainability in indonesia rizanda machmud* faculty of medicine andalas university, departement of community medicine, padang, indonesia objective this presentation aims to developed comprehensive dengue hemorrhagic fever vector control by new approach social mobilization of city residents in padang, west sumatera, indonesia and to monitor its sustainability of the program for 4 years. introduction dengue hemorrhagic fever is the most important mosquito-borne viral disease in indonesia and the most rapidly spreading over the past 40 years. it is a major cause of morbidity and mortality especially among children. beside of that, larval habitats are increasing rapidly in padang city as urban areas. it refers to poor populations lacking basic health services. effective bottom-up community participation increasingly is recognized as an important component of environmentallysustainable control programs. but community based health service action become weak recently in padang indonesia. it needs some new energy to strengthen2. adoption of social capital concept into the program could be a way out. an action research through communitybased approaches is developed to reduce disease transmission and environmental management for control of dengue hemorrhagic fever. methods the research was implemented through 3 steps; mapping the determinant of health’s problem with 400 respondents including key container pupal survey in the jati village, padang; brainstorming with the community about their problems; and find the solution to solve the problem together. furthermore, the action health model based on 4 pillars, there are; empowerment, capacity building, equipment & attractive activity. the project promotes community health condition by developing personal skills, house hold waste managements, environmental management for control of dengue hemorrhagic fever, workshop for income generating, as well as empowerment of health cadre, health expert, facility and funding, all aimed at strengthening community action to develop health service model. we monitor the sustainable of the program 4 years afterward. results result of the pupal survey in pre and post intervention within 6 month showed that the positive containers decreased from 33.3% into 4%. the incident of dengue hemorrhagic fever in jati village decrease within 4 years became null. the output of the implementations are not only succeed to environmental management for control of dengue hemorrhagic fever and initiate and drive processes of social change aiming at the improvement of living conditions conducive to health but also had a multiplayer effect. even it could initiate of growing another community based action in education, income generating activity. after 4 years of monitoring the program is still continuously and sustainable afterward. conclusions adoption of social capital concept into the comprehensive dengue hemorrhagic fever vector control program, with bottom-up community participation modelbased on 3 steps and 4 pillars social mobilization, had shown an effective intervention. community in padang city of these relations demonstrated trust and confidence in each other, which helps enabling them as a social group to become successful in social, cultural, and health terms. this project proved that behind the concept of social capital lies the idea of a well-balanced social system, which favors mutual collaboration between social agencies and sectors for the sake of the sustainability of this system itself. keywords dengue hemorrhagic fever; social capital; community based approach; strengthening community; determinant of health acknowledgments thank you very much for all participant, lecturers and students who participated in community action project. special thanks are given to all people in jati village, who have very strong motivation and enthusiasm to make an improvement of living conditions conducive to health. references world health organization, 2012. global strategy for dengue prevention and control 2012-2020. isbn 978 92 4 1504033, page5. gubler,dj. 2002. epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. trends in microbiology vol.10 no.2 february 2002 fukuyama, francis. 2001. ‘social capital, civil society and development.’ third world quarterly 22: 7-20. *rizanda machmud e-mail: rizanda_machmud@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e145, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts development of mental health classification related to severe weather events alvin f. chu*1, stella tsai1, teresa hamby1, elizabeth kostial2 and jerald fagliano1 1communicable disease service, new jersey department of health, trenton, nj, usa; 2health monitoring system, pittsburgh, pa, usa objective to describe the development and validation of a mental health classification to track emergency department visits for potential needed public health response during severe weather events. introduction real-time emergency department (ed) data are currently received from 78 of 80 new jersey acute care and satellite eds by health monitoring systems inc.’s (hms) epicenter system. epicenter collects, manages and analyzes ed registration data for syndromic surveillance, and provides alerts to state and local health departments for surveillance anomalies. after the 2012 superstorm sandy devastated parts of new jersey, njdoh initiated a plan to develop severe weather surveillance using epicenter to provide the department with the ability to track both health and mental health concerns during adverse weather conditions to alert the public about emerging health hazards. methods the severe weather classifier consists of the following classifications: carbon monoxide, heat related illness, hypothermia/ cold related illness, disrupted outpatient medical care, cardiovascular, tree-related injury, motor vehicle accident, gastrointestinal illness, respiratory illness, and mental health related illnesses. in collaboration with new york city department of health and mental hygiene (nycdohmh) and new york state department of health (nysdoh), a group of specific mental health concerns including anxiety/adjustment, mood, psychotic disorders, suicide/self-inflicted injury, alcohol, and methadone/opiate/heroin use are evaluated. the evaluation process consists of two steps: 1) using cases where icd codes are available that meet case definition to find possible inclusion keywords and text patterns not already included and 2) evaluating cases with keywords of interest but not containing icds meeting case definition to determine text patterns for exclusion. the sensitivity and positive predictive value (ppv) statistical measures are computed for each classification. results during december 1, 2013 to march 31, 2014, eleven emergency departments frequently provided icd diagnosis codes were used for this study. based on this validation project, the anxiety/adjustment, mood, psychotic disorders and alcohol use classifications achieved a moderate-high sensitivity of 58.6% (ppv: 77.6%), 57.1% (ppv: 88.6%), 63.2% (ppv: 52.6%) and 93.7% (ppv: 66.6%), respectively. suicide/self-inflicted injury had a low sensitivity of 37.6% (ppv: 51.2%). methadone/opiate/heroin use achieved a moderate sensitivity of 64.7% but a low positive predictive value of 14.4%. based on the validated keywords, seen in figure 1, the anxiety/adjustment disorders showed slightly higher ed visits during the first week of sandy; mood disorders also increased slightly several days after the storm; alcohol related ed visits followed the weekday/weekend pattern; suicide/ self-inflicted ed visits rates were elevated after approximately one week post-storm; ed visits for methadone/opiate/heroin use increased slightly during several days in the storm’s aftermath. conclusions validation results indicated a moderately-high sensitivity and ppv for anxiety/adjustment, mood, psychotic, and alcohol classifications and a low sensitivity for suicide/self-inflicted ed visits; methadone/ opiate/heroin ed visits had a low positive predictive value. validated keywords from the chief complaint field of ed visits can provide meaningful information for njdoh syndromic surveillance staff to track mental health related illnesses (e.g. anxiety/adjustment, mood, psychotic, and alcohol) before, during and after a severe weather event. for those two classifications with low sensitivity and ppv, additional validations are needed. currently not all facilities provide icd-9 codes in epicenter for keyword validation. however, njdoh continues to recruit hospitals to provide icd information for future validation and evaluation. keywords syndromic surveillance; classification; severe weather event; epicenter; new jersey acknowledgments new york city department of health and mental hygiene; new york state department of health; drs. petros levounis and yvonne farnacio (new jersey medical school) *alvin f. chu e-mail: alvin.chu@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e120, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts disaster public health surveillance response systems in yogyakarta, indonesia bella donna1, endang suparniati2, madelina ariani*1 and rossi sanusi1 1center for health policy and management faculty of medicine ugm, sleman, indonesia; 2dr sardjito hospital, regional referral hospital, moh, sleman, indonesia objective to examine whether the danger zone district health office (dho) and sub-district health centers (hcs) were employing an inter-disaster public health surveillance-response (ph s-r) system after the october 2010 mt merapi eruption and a pre-disaster ph s-r system during the july 2013 mt merapi eruption. introduction the october 2010 eruption of mt merapi (the most active volcano in the indonesia that erupts at 5-years intervals) claimed 141 lives, injured 453 people and displaced at least 278,000 people. this geological event became a disaster as national and international agencies had to step in to assist the yogyakarta province and sleman district administrations in dealing with the devastation caused by the pyroclastic flows. because of its cyclic nature the task of the local governments is to improve the hazard mitigation system and to increase the resiliency of the population. on 22 july 2013 the volcano spewed ash clouds and people of two villages of the cangkringan sub-district evacuated themselves to the local village halls. the hazards posed by the ash clouds of the volcano and by the displacement of vulnerable populations, did cause certain physical and emotional sufferings, but could be controlled by the local administration. methods in-depth interviews regarding the s-r systems used were conducted with the heads and staff of the cangkringan hc and the sleman dho. disease distribution reports and disaster management guidelines were requested from the respective surveillance and disaster management staff. identification of diseases directly caused by the eruption and diseases that cause the population to become more vulnerable were based on data of patients from the danger zone villages who were admitted to dr sardjito hospital during october-december 2010. results during the 2010 mt merapi eruption hcs of the sleman district surrounding the volcano (cangkringan, pakem, ngemplak, and turi) erected health posts at the evacuation points. these health posts provided medical care and sent daily reports to their respective hcs which were forwarded to the sleman dho. the data were not processed and analyzed at the hcs as well as at the dho. data of the dr sardjito hospital showed that 286 victims were admitted during 26 october until 10 december 2010, 114 (40%) from cangkringan, 96 (34%) from pakem, 33 (11%) from ngemplak, and 43 (15%) from turi. forty-two percent of the cases were pyroclastic flow injuries (e.g., burns, asphyxia, fractures) and the remainders were vulnerable population cases (e.g., chronic diseases, problem pregnancies, and births). during the july 2013 eruption the health post of the affected villages reported 350 evacuees and 7 cases. conclusions the danger zone hcs and the dho were conducting routine case recording and reporting during the quiet phase and eminent danger phase. to increase the resiliency of the population during the interdisaster phase it was suggested that the hcs and dho adopt a ph s-r system that focuses on basic, primary, secondary, and tertiary preventions of priority diseases/conditions. to lessen physical and emotional sufferings during the pre-disaster phase it was proposed that the hcs and the dho use a ph s-r system that is aimed at reducing the exposure and susceptibility of the stricken population to biological, physical-chemical, and psychological pathogens. keywords disaster surveillance; volcano hazards; displaced populations; surveillance-response systems references church world service indonesia. 278,000 people evacuated as mt. merapi continues to rumble. accessed 13 august 2013 from http:// www.cwsindonesia.or.id/en/news/ seach, j. merapi volcano. accessed 13 august 2013 from http://www. volcanolive.com/merapi.html *madelina ariani e-mail: madel_ariani@mail.ugm.ac.id online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e189, 201 a review of automatic patient identification options for public health care centers with restricted budgets 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 a review of automatic patient identification options for public health care centers with restricted budgets rebeca i. garcía-betances 1 , mónica k. huerta 1 1 networks and applied telematics group (greta), simón bolívar university, caracas, venezuela. abstract a comparative review is presented of available technologies suitable for automatic reading of patient identification bracelet tags. existing technologies’ backgrounds, characteristics, advantages and disadvantages, are described in relation to their possible use by public health care centers with budgetary limitations. a comparative assessment is presented of suitable automatic identification systems based on graphic codes, both one (1d) and two-dimensional (2d), printed on labels, as well as those based on radio frequency identification (rfid) tags. the analysis looks at the tradeoffs of these technologies to provide guidance to hospital administrator looking to deploy patient identification technology. the results suggest that affordable automatic patient identification systems can be easily and inexpensively implemented using 2d code printed on low cost bracelet labels, which can then be read and automatically decoded by ordinary mobile smart phones. because of mobile smart phones’ present versatility and ubiquity, the implantation and operation of 2d code, and especially quick response® (qr) code, technology emerges as a very attractive alternative to automate the patients' identification processes in low-budget situations. keywords— patient identification, automatic identification, 2d codes, rfid, id tags, ehr. introduction hen patients are admitted at public healthcare centers they are registered by staff who collect their most important data and record it on either printed or electronic forms. personal data, contact information, admission date and time, reason for admission, referring physician, public health identification number, etc. are recorded at this stage. at the same time at least one additional identification number is assigned to the admitted patient, which is also used in the patient´s medical record. this number, along with other basic personal information, is either directly printed on the wrist bracelet that is usually generated and fitted to the patient, or printed on a label which is attached to it. later, the treating physician, with the assistance of other health care personnel, is w a review of automatic patient identification options for public health care centers with restricted budgets 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 responsible for filling out the patient’s medical history in accordance with the particular model used in the public health system or the particular health care center [1]. subsequent patient identification is still today generally performed manually by human observers in many public health care centers. such human interaction in the course of patient identification is highly undesirable because it is inevitably prone to human error. the ensuing identification errors frequently result in serious mistakes when carrying out vital activities such as medication, blood transfusions, clinical trials, surgery, and a variety of other medical procedures. the use of automated patient identification systems for patient identification is a practical means to reduce these risks [2]. such systems can provide quick and reliable patient identification as well as remote instant access and management of patient medical history (ehr: "electronic health record"). although the main purpose of any system for this purpose is to improve the reliability of patient identification, providing a speedy access to clinical information is also a desirable feature. additionally, the system must also include aspects of security and confidentiality of the medical data to be handled. several research groups, as well as commercial health technology solutions providers, have been developing automated patient identification systems. two main technologies are generally used: graphic one-dimensional (1d) codes, normally known as bar codes, which are printed on labels, and radio frequency tags (rfid, "radio frequency identification"). these two technologies are today the most widely used by health care centers where automated id systems are in place [3], [4]. other technologies have begun to emerge recently. most are based on the evolution of the traditional bar code into two-dimensional (2d) graphic codes [5]. these new 2d codes are a notable improvement with respect to bar codes, because they are as easily generated and printed as 1d code, but allow storing greater amounts information [6], [7]. they additionally incorporate other desirable features such as error correction. the present assessment of available technologies was undertaken in the context of its possible deployment by a municipal public health agency that operates a network of local clinics. one of the most important aspects to be considered in deciding what system can be used for automatic identification and access to patient data is the issue of the tag reading device to be used by the health care center’s personnel. whereas health care centers with abundant budgets may opt for providing their staff with dedicated id tag readers, many public health care facilities, especially in developing countries, cannot generally afford the purchase of such dedicated equipment, at least in sufficient numbers [8]. a very effective alternative to the dedicated id tag reader for scarcely budgeted public health care centers is offered by the ubiquitous mobile smart phone. these devices could be provided by the center to its personnel for this purpose, or alternatively the hospital personnel may prefer to use their own devices. of course, in this case the willingness of the personnel to do so must be fully ascertained. the following sections describe the most relevant technologies available today to implement systems for automatic patient identification and access to patient data using the smart phone as an id tag reader. specifically, we consider graphic 1d codes (bar codes), various types of 2d codes, and active and passive rfid technologies. their characteristics, a review of automatic patient identification options for public health care centers with restricted budgets 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 advantages, disadvantages, and some background on selected pertinent applications are analyzed and compared. attention is drawn to those features that would be most important in guiding the selection of a particular technology. the overall determining factor being the suitability of the system to cost-effectively accomplish the specific task of performing positive patient identification (ppi) and remote access to their ehrs by medical personnel within a public health care center environment. one-dimensional graphic codes this vintage form of graphical encoding uses a combination of black and white parallel and adjacent areas of different thicknesses to represent information. its design allows it to be automatically decoded by special reading devices. usually these codes are not descriptive, but store a reference code associated with a database containing relevant information. these codes, sometimes also called "upc symbols" [6] were originally developed by ibm and used for the first time in 1967 [9]. as shown in figure 1, which schematically presents the general structure, the code begins with a "start character" and ends with an "end character," that identify the beginning and end of information. the actual data is contained between these two starting and ending characters. two "quiet zones," at least ¼ inch long, are placed before and after the start and end characters, respectively, for ease of reading. in some types of 1d codes a control digit, or "checksum," is included to add security to the data contained in the code, as it ensures that the reading’s decoding is correct. this check digit is obtained by performing logicalmathematical operations on the other characters of the code. illustrative examples of 1d code use in health care there are numerous precedents of this technology’s use in health care centers. most of them are based on bar codes to control the hospitalized patient’s medication process, blood transfusions, and in laboratory tests (blood, urine, etc.) to identify the test/patient pair. two examples illustrative of id code usage in patient identification are listed below:  houston’s "methodist hospital system," one of the largest in the state of texas (usa), uses a patient data electronic verification system, designated kbma, based on the reading of bar codes. it is connected online to the medical documentation system "method" (medical records database) [10].  the joint commission on accreditation of healthcare organizations (jcaho) has been proposing since 2007, along with its hospital accreditation program, a patient safety plan using bar codes. the jcaho plan expects hospitals to instrument systems requiring that each patient wear a bracelet with a printed barcode that must be scanned to verify the correct patient identity before administering any medications or blood transfusions [11]. a review of automatic patient identification options for public health care centers with restricted budgets 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 figure 1. general structure of 1d codes. types of 1d code there are various types of 1-d bar codes, whose characteristics may not always differentiate them from each other. most of them were originally developed to meet the needs of specific industries and were then standardized for use in different areas. among the most relevant types available are: upc (universal product code), ean (european article numbering), code 39, code 128, and the interleaved 2 of 5. some characteristics of 1d code the main advantages of 1-d technology can be summarized as follows:  fast data capture.  reliability due to the very low level of errors in the capture and decoding of data.  immediate integration of the decoded data into the system or database.  low cost of printing the codes. their main disadvantage compared to other alternatives such as 2d codes and rfid is that they have a relatively low storage capacity of approximately 20 to 30 digits. typical current applications of 1d code one-dimensional codes were used initially in commercial environments for product identification. at present the use of automated identification 1d codes has spread extensively to many other areas such as manufacturing, consumer trade, mail, transport, the health sector, and so on. some examples of specific applications are:  inventory control.  tracking of moving objects such as cars, baggage, mail, packages, medicines, laboratory test samples, etc.  access control to transportation, open-air events, buildings, offices, theaters, etc.  ppi in hospitals. two-dimensional graphic codes two-dimensional codes are in general capable of storing alphanumeric characters including a review of automatic patient identification options for public health care centers with restricted budgets 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 letters, numbers and punctuation. nonalphanumeric characters, such as kanji, can also be stored [6], [9]. the way to store information is to represent it two-dimensionally by means of planar distributed graphic patterns (dots, squares, circles, triangles, hexagons, etc.). the two-dimensionality of these codes allows a greater data storage density than is possible with traditional 1-d bar codes. thanks to this feature, 2d codes use about a tenth of the area required by a 1-d code to store a similar amount of data. figure 2 shows how graphic codes have evolved from the original bar codes to the development of 2-d codes in the 90's. the recent use of 2d code technology for patient identification applications within the hospital environment provides a significant improvement of all health care related identification applications [12]. the implementation of this technology allows to quickly and effectively verify a patient’s identity and data before administering medications, performing medical procedures, and even permits to monitor patients’ location and movement to different hospital areas or other centers within the public health care system. figure 2. historic evolution of graphic codes [6]. the most significant types of 2d code are: code 49, pdf417, and 2d matrix [9]. the matrix code may be divided into several sub-types, including: data matrix [13], semacode [14], maxicode [15], hccb [16], qr code [17], sparqcode [18], among others. table 1 presents a comparison of some characteristics of three of the major 2d code types available today. a review of automatic patient identification options for public health care centers with restricted budgets 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 table 1. comparison of relevant characteristics of three 2d code types [9] name: pdf417 data matrix qr code appearance: source: symbol technol. (usa) rvsi acuity cimatrix (usa) denso (japan) type: stacked 1d bars 2d matrix 2d matrix storage capacity numer. 2710 3116 7089 ˗num. 1850 2355 4296 binary 1018 1556 2953 kanji 554 778 1817 advantages higher capacity than bar code. high capacity, small size. high capacity, small size, high scanning speed, error correction. illustrative examples of 2d code use in health care there are several instances of the use of this technology in medical settings, some of which are outlined below:  some medical institutions, mainly in the asia-pacific region, have adopted this technology for patient identification, and access and control of patient data. health care centers in japan and singapore [6], and more recently in hong kong, have implemented a system known as upi (unique patient identification), which in 2008 achieved full technology transition from 1d to 2d codes [7].  addenbrooke's hospital in cambridge uses 2d codes as part of its patient safety policy [12]. a bracelet on which a 2d code is printed, in addition to other basic personal data, is attached on each patients’ wrist. at first the system was used only to track blood transfusions and to check coincidence between patient and blood type. currently the use of 2d codes has been extended to other areas to reduce the occurrence of some other types of medical errors [12].  an update of houston’s methodist hospital kbma 1d code-based id system has been proposed, consisting of migrating its old 1d code-based patient identification to a 2d code-based system [10]. some relevant characteristics of 2d code two-dimensional code requires a more complex processing and a longer processing time to decode the data than its predecessor the 1d code [6]. this complexity is due to its larger capacity to store information. the required more complex processing calls for more a review of automatic patient identification options for public health care centers with restricted budgets 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 sophisticated, and therefore more expensive, reading devices. however, the significance of the problem has been declining lately because of the considerable progress recently made by image recognition and processing technology [9]. the impact of the cost factor has been further reduced nowadays because of the increasing multimedia capabilities that are being incorporated into modern camera-equipped mobile smart phones and pdas, such processing capabilities enable these relatively inexpensive devices to replace the otherwise costly dedicated 2d code readers/decoders [19]. there are currently available a variety of special programs for this purpose that run in camera-equipped mobile smart phones [20]. these programs can decode the information stored in the 2d codes printed on the patient’s bracelet, display it on the phone’s screen, and even activate other functions, such as dialing a telephone number, or using the smart phone’s internet connection to send email and to remotely link to ehr (electronic health records) data bases to retrieve, or input, patient’s specific medical information. the main advantage of the use of 2d codes for patient identification over other more complex types of identification comes from its simpler technology. 2d code-based technology is much more accessible to users mainly because it does not require the use of any special tags (such as the rf tags discussed in the next section). instead, 2d codes can be very easily generated and printed on a variety of paper or plastic labels, or on any other surface, without the use of specialized equipment. reading and decoding 2d codes, especially with a smart phone, is likewise technologically simpler than other more complex technology-based systems. additionally, in general the camera-based optical capture of 2d code-based readers, including smart phones, inherently has a much narrower capture field than radio frequency-based reading devices. thus, they are less prone to erroneous positive identification, since it is practically impossible for these readers to mistakenly acquire signals from unintended nearby sources. in this respect, 2d code reading is unambiguous since it requires close proximity of the reader device to the patient’s bracelet to capture the code. typical general applications of 2d code nowadays 2d codes are increasingly showing up in a variety of application areas, such as for example:  boarding passes in transportation.  advertising in newspapers, magazines, posters and billboards.  inventory management.  tickets for public events.  personal contact cards.  health services. the use of 2d codes is also gradually growing within the hospital environment, typically in patients’ bracelets, medical equipment, laboratory samples and drugs, administration of medicines, medical procedures, and tracking of internal and external transfer of patients [12]. a review of automatic patient identification options for public health care centers with restricted budgets 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 radio frequency identifiers radio frequency identification (rfid) technology can automatically identify people and objects in the healthcare environment by placing identifier rfid tags on the subjects and then remotely reading them using a specialized reader devices [21]. this technology has already been used for a variety of healthcare applications, such as hospital assets, staff, and patient tracking; positive patient identification (ppi); biosensing; medical procedures alerts; medication control; etc. [22], [23], [24]. the system typically consists of four elements: a label (tag) located on the subject to be identified (patient, medication, lab sample, etc.); the reader or coupler device that reads or writes information on the label; the transmitting antenna; and the database or computer system that handles and processes the information captured from the labels. each tag contains a unique identification number (uin) which is transmitted to the reader whenever required [25]. apart from its identification purpose, the uin may also serve as a reference command to interface with a database that containing additional information. in addition to reading the information stored in an rfid tag, the reader device, if necessary, may be able to write into the tag’s memory. some applications of rfid the most significant applications were rfid technology is currently used are:  access control.  inventory management.  baggage identification and screening.  industrial production chains.  library book input and output.  identification and location of animals.  healthcare. illustrative examples of rfid use in health care there are a growing number of proposals and projects that are being considered or are currently being implemented in health care centers for the use of automated identification, access, management and control of patient data based on rfid technology. below are three representative examples:  taichung hospital in taiwan implemented an rfid-based system, integrated with the hospital information system (his), to improve the efficiency of patient safety during the medication process of hospitalized patients [21].  the orthopaedic institute of palm beach, florida, put into operation a system known as "surgichip" that uses rfid patient identification to help prevent surgical errors [21]. at the time of admission a smart tag is generated containing patient data, place of surgery and specific surgical information and instructions. the rfid tag is read by a dedicated scanner and placed before surgery at the location on the patient's body where the surgical procedure is to be performed. the information obtained from the label is communicated to and corroborated by the patient before being sedated. later the tag is read again in the operating room and the surgeon verifies that the information corresponds to the id number and to the clinical data printed on the patient’s bracelet. only when all the information matches the surgeon proceeds to perform the surgical procedure [26]. a review of automatic patient identification options for public health care centers with restricted budgets 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012  a system for patient identification using a bracelet with a customized rfid tag was proposed by o'halloran and glavin [27]. unlike other rfid systems, which store data in the tag itself, rfid tags are used in this case only as a means to identify the patient and to gain access to the patient’s her which is stored in a centralized database. hers containing medical data, including medical images, may be modified only by authorized medical staff. types of rfid tags labels or "tags" may be classified in various ways. one classification of rfid tags functionally separates them into six classes according to their ability to read and write stored data. this scheme was proposed by epcglobals, an organization that develops standards for epc use (electronic product code) in rfid networks [25]. in general rfid tags can be divided into three main classes: active, passive, and semipassive. passive tags must receive their operating energy from the reader device. to that end, the reading device´s emitted rf electromagnetic field, when placed in close proximity to the rfid tag, is coupled to the tag’s antenna. the rfid tag then rectifies the received rf signal and harvests the energy necessary for its operation. on the other hand, active rf tags can initiate communications with the reader device by themselves, because they contain their own internal energy sources. passive tags are used for patient identification, real-time location service of medical assets, and drug inventory control and monitoring [28]. they are most adequate rfid type for placing on patients’ bracelets in patient identification applications. operating frequencies the operating frequency defines the speed of data transfer. the lower the frequency is, the shorter the data transfer rate [21]. the range is also affected by the operating frequency. maximum reading range has important consequences for rfid-based ppi applications. a large capture range facilitates the unintentional acquisition of signals originating from nearby sources other than the subject’s bracelet, leading to possible failed or incorrect identification. rfid tags typically operate at frequencies within one of three bands: one at low frequencies up to 135 khz, and two around 13.56 mhz and 2.45 ghz [21]. however recently there is an increasing interest in using 900 mhz for these applications. passive tags commonly operate at frequencies such as 128khz, 13.6mhz, 915mhz and 2.5ghz [1]. active rfid tags, on the other hand, usually operate at high frequencies, depending on the distance and memory requirements of the application for which they are used. some relevant characteristics of rfid this technology presents certain advantages compared to other alternatives. rfid tags can store an amount of information in general larger than any graphic code, and considerable more when compared to traditional 1d code (bar codes). furthermore, it does not require the existence of a short line of sight link between the tag and the reader, as graphic codes require. therefore information can be generally accessed at distances up to about 9 meters a review of automatic patient identification options for public health care centers with restricted budgets 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 or more, depending on the configuration and operating frequency [25]. however for many applications, including patient identification, this apparent advantage is rather a disadvantage, because of the inherent reduced target selectivity, as compared to the highly selective short line-of-sight distances used for graphic code capture. perhaps the single main disadvantage, with respect to graphic code technology, that persists today for the use of rfid technology in patient identification applications is its relatively higher cost, both of the reading devices and, especially, of the labels. additionally the need for batteries represents an issue for active rfid tags. another important disadvantage is the greater complexity of implementing and operating rfid technology [22]. near field communications tags an emerging technology closely related to rfid is near field communication (nfc). nfc is a wireless connectivity technology that uses magnetic field induction to establish a communication link between electronic devices placed in close proximity to each other [29]. the use of nfc tags in conjunction with nfc-enabled mobile phones could be an attractive technology for unambiguous and secure automatic patient identification [30]. although still incipient in its use and not sufficiently proven in patient identification applications, this technology may soon show its potential in avoiding some of the inherent shortcomings that standard rfid technology has for this purpose. suitability of the available technologies table 2 provides an organized general comparison between the three main available technologies that can be used readily today for automatic patient identification and access to patient data purposes [9], [17], [21], [25], [31]. a major cause for concern regarding the use of rfid technology use in the healthcare environment is its associated positional uncertainties [32][la10]. rfid technology’s inherent longer range, excluding nfc, is prone to identification ambiguity, which is naturally avoided by graphic code technology that requires close proximity between the tag and the reader device. table 2. comparison of three major technologies suitable for automatic patient identification features 1d codes 2d codes rfid encoding method: printed variablewidth parallel bars. printed twodimensional geometric patterns. radio frequency tag. type of decoder: dedicated optical scanner. cameraequipped smart phone or pda, dedicated scanner. special dedicated rf reader device. descriptive (general unique a review of automatic patient identification options for public health care centers with restricted budgets 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 stored data: reference number. data, web links, contact, etc.) identification number (uin) type of data: numeric, alphanumeric, ascii, control numeric, alphanumeric, binary, kanji. numeric, alphanumeric, binary. data security / error correction method: although optional in some types, most use checksum. reed-solomon coding: adds redundancy. partially corrupted code can be read. no inherent error correction, but frequently include error correction codes capacity (characters): up to 30 numeric: 138 to 7089 alphanumeric: 93 to 4296 binary: 1556 to 2953 kanji: 778 to 1817. from 512 bits to 512 kbytes. active tags have greater capacity than passive ones. advantages: high speed data capture. reliability. integration with database. easily printed low-cost labels. short range*. high storage capacity, small size. stores diverse kinds of data. easily printed lowcost labels. inherent error correction. smart phone readable. high-storage capacity. read and write allowed. can be automatically scanned. can act as biosensor. disadvantages: low-storage capacity. limitations on the types of data they can store. must be manually scanned. long range*. complex use and set up. -needs special tags and dedicated readers. -security issues. (*) referred to unambiguous patient id applications. another limitation of rfid technology is its effect on the healthcare environment. whereas graphic code technology is totally innocuous to it, rfid technology, on the contrary, poses non trivial risks regarding its potential electromagnetic interference with the other medical devices in the health care center [33]. finally, the issue of the availability and cost of the reading device must be given serious a review of automatic patient identification options for public health care centers with restricted budgets 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 attention, especially when the patient identification technology is to be set up in a low budget public health care center, as would probably be the case in developing countries. in this respect, the possibility of using mobile smart phones as the reading devices represents a very strong asset to consider when making a technology choice decision. a full installation and operation cost analysis and evaluation, which should precede the adoption of any particular patient identification technology, is not included in the present brief survey. however, the features, advantages, disadvantages and other peculiarities of the available technologies that have been described here clearly point towards 2d graphic tag technology as the least complex and most cost-effective choice (see table 3). discussion among the several 2d code options for patient identification, qr code technology emerges as the attractive alternative. qr code-based applications intended for the healthcare sector are constantly increasing. the distinct characteristics of qr code-based technology, such as high data storage capacity, low implementation cost, technical simplicity, widespread use, and the ample availability of free programs for reading and decoding it by camera-equipped smart phones, makes this technology outstandingly attractive for unambiguous patient identification, especially for low budget applications and in developing countries in particular. qr code (see figure 3) is one of the latest 2d codes to be developed. it presents several advantages compared to other 2d codes since its characteristics surpass those of the other types [6]: a higher data capacity, printing in smaller areas or sizes, and fast reading. two of the most distinctive characteristics of qr code [17] are summarized below:  an error correction ability to recover up to 30% of the "codeword" (1 codeword = 8 bits).  a capacity to be read or tracked in any direction, and to tolerate bending distortion. this is because the code contains position detection patterns in three of the corners (see figure 3). this allows the reader device to know the code’s position and correctly decode it, without any reader device misalignment affecting the reading speed. table 3. cost comparison of 2d and rfid technologies 2d codes rfid labels / tags optional $10 / 100 400 reading device $150 400 $1500 3000 printer optional $3000 (smart labels) a review of automatic patient identification options for public health care centers with restricted budgets 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 figure 3. qr code characteristic patterns conclusion decreasing medication errors, improving patient safety and increasing the accuracy of clinical procedures are important contributing factors to reduce public health care costs and enhance the quality of public health care. the use of automatic patient identification systems positively impacts these factors and significantly improves the access to and delivery of public health care services. we have presented an assessment of automatic tag reading technologies that are presently suitable for patient identification purposes. special attention was given to cost-effectiveness and technology availability in a framework of public health care centers with restricted budgets. in this context, mobile smart phones are recognized as the best choice for reader devices, considering their ubiquity and widespread use. the smart phone has the advantage over other reading devices of additionally acting as a network terminal through which the patients’ ehrs, and other pertinent medical information, may be remotely and interactively accessed by the health care center’s authorized personnel. the main conclusion of this comparative assessment is that the use of 2d codes, and qr codes in particular, presently embodies the best choice for setting up automatic patient identification capabilities in low-budget public health care centers. the use of qr codebased tag technology, when combined with mobile smart phones as code reading and decoding devices, seems to be the most practical and cost-effective alternative available today for automatic patient identification, as well as for quick remote health record access, by medical personnel in public health care systems with limited budgets. the present assessment did not focus on the issue of mobile smart phone usage as reading devices in the clinical environment. however, this is an important aspect that needs to be addressed. the capacity of the public health system administration to supply mobile smart phones to its medical personnel for patient identification purposes, or alternatively, their willingness to use their personal devices, must be carefully ascertained before making a decision. a review of automatic patient identification options for public health care centers with restricted budgets 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 1, 2012 acknowledgements this work was funded by the office of the dean of research, simón bolívar university caracas, venezuela. corresponding author rebeca i. garcía-betances networks and applied telematics group (greta) simón bolívar university apdo postal 89000 caracas 1080, 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[33] censi f, calcagnini g, mattei e, triventi m, bartolini p, rfid in healthcare environment: electromagnetic compatibility regulatory issues, 32nd annual int. conf. ieee engineering in medicine and biology society (embs), 2010:352–355. http://www.adams1.com/ ojphi-06-e33.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 78 (page number not for citation purposes) isds 2013 conference abstracts development and piloting of national injury surveillance system of sri lanka achala u. jayatilleke*1, diana samarakkody2, achini jayatilleke3 and samantha wimalaratne2 1postgraduate institute of medicine, university of colombo, colombo, sri lanka; 2ministry of health, colombo, sri lanka; 3university of tokyo, tokyo, japan � �� �� �� � � �� �� �� � objective ��������� � ���� ������� ���� ��������������� ���������������� �������������� ������������ introduction ������������ ������ �� �������������������������� ������ ������� �� � ������ ������������ �������� �����! ���������� ��� ������� ����"##$ � ���������%��!��������������������� �� ������������������������������� &���'����� ��� ���������! ���� ������ � ���� ������(������������� � ����� ������ ��� ��&!�������������������)�� � � ��!�)'��� ������ �� ���� � ����� ���������������� ���!�)���� � � ��*��+#�* ������,�,� � ��������� �� �� �� ���� ���� ���������� ����� � �� ����� � �� ��* ��� ����,,�� ��������������� ��-�������������� ��� ��� ����� �� ��� �� �� ������������ ��� ���� ����������������� ���! ��� ���������������� �� ������������������ ������������ � � ������ ���� �� ���� �� ��������� � � ������ ���� ����� � ���� ���������� ���� ����! ���������������� ������ �� �������� �� ��� ������� ������� �� ��� ��.�������� ��/���� � &. 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������* ������� ��� ������ �������� ������ ������ ���������� ��� ����� ����� � �� ������ �������������������� �������� ��! ��!����1 ����� ��?� ����/���� ����� � !<4/�><! � ��� ��*�*� *achala u. jayatilleke e-mail: achala@sltnet.lk� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e33, 2014 food safety informatics: a public health imperative 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 food safety informatics: a public health imperative cynthia a. tucker, phd., mba., rd., ldn., 1 stephanie n. larkin, b.s., mbac, 2 timothy a. akers, m.s., ph.d. 3 1 nutritional science program, school of community health and policy, morgan state university 2 school of business, morgan state university 3 school of computer, mathematical and natural sciences, and center for health informatics, planning and policy (chipp), morgan state university abstract to date, little has been written about the implementation of utilizing food safety informatics as a technological tool to protect consumers, in real-time, against foodborne illnesses. food safety outbreaks have become a major public health problem, causing an estimated 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths in the u.s. each year. yet, government inspectors/regulators that monitor foodservice operations struggle with how to collect, organize, and analyze data; implement, monitor, and enforce safe food systems. currently, standardized technologies have not been implemented to efficiently establish “near-in-time” or “just-in-time” electronic awareness to enhance early detection of public health threats regarding food safety. to address the potential impact of collection, organization and analyses of data in a foodservice operation, a wireless food safety informatics (fsi) tool was pilot tested at a university student foodservice center. the technological platform in this test collected data every six minutes over a 24 hour period, across two primary domains: time and temperatures within freezers, walk-in refrigerators and dry storage areas. the results of this pilot study briefly illustrated how technology can assist in food safety surveillance and monitoring by efficiently detecting food safety abnormalities related to time and temperatures so that efficient and proper response in “real time” can be addressed to prevent potential foodborne illnesses. key words: foodborne illness, surveillance, technology food safety informatics: a public health imperative 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 introduction foodborne illness sees no boundaries. the world has witnessed, first hand, the devastating public health impact of foodborne illnesses that have occurred in europe. the foods that were impacted through this contamination spread quickly and with fatal outcomes affecting nearly 4,023 who became ill, 48 deaths and 885 diagnosed with hemolytic uremic syndrome [4]. results from this outbreak has made it is clear that food safety has advanced to become a world public health problem. food safety in the united states has been a major concern for decades. consequently, congress passed the “fda food safety modernization act of 2010” and it was signed into law by president obama on january 4, 2011. the passing of this act is the first major overhaul of the nation’s food safety systems in over 70 years. this legislation represents a major reform of the original food safety requirements of the federal food, drug, and cosmetic act (fd&c act) of 1938, by expanding the powers of the food and drug administration (fda) with respect to food safety and food security. every year, the u.s. centers for disease control and prevention (cdc) estimates that tens of millions of americans fall ill, hundreds of thousands are hospitalized, and thousands die from foodborne illnesses [10]. according to the cdc, unsafe foods cause an estimated 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths each year in the united states [3]. the financial impact of foodborne illness in the united states is estimated at a staggering $152 billion per year due to sickness, lost of productivity, loss of human life and destruction to entire industries and regional economies [11]. for example, food safety has been at the forefront of media publicity in recent years. many high-profile outbreaks of contaminated food include peanuts and peanut-related products, tomatoes, peppers, spinach, and other foods [15, 16, 17]. in fact, in the summer and fall of 2010, over 32 million cartons of eggs were recalled due to salmonella contamination [5-6]. as a result of these incidences, food safety has become a growing concern for consumers, the food industry and government. in theory, to address the potential impact of further foodborne outbreaks, the federal food regulators are aiming to change the focus of food safety and contamination from a reactive approach to a more rapid response, prevention, and detection strategy. the impact of the recent legislation transcends policy, public health, industry, and technology. it is anticipated that policy-makers, public health analysts, and technologists will soon be left to consider how best to collect, organize, implement, monitor, enforce and analyze these new legislative mandates. however, to date, there does not exist a working definition, description, or processes regarding “food safety informatics” as a conceptual and practical framework or paradigm. creating an appreciation for food safety informatics can help to develop and shift our analytical ability to better understand how to integrate epidemiologic investigations with foodborne outbreak and contamination analyses [18]. our working definition of “food safety informatics” blends human factors with technology through the use of standardization of data elements when collecting data electronically for risk tracking, surveillance, and analysis. food safety informatics: a public health imperative 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 while these new legislative and technological changes are essential for national regulatory food safety monitoring of tainted foods from the producers, this only addresses half of the risk. there still exist a critical public health and food safety gap that is often overlooked and goes unanswered between the producers of food products and the consumers: the eating establishments where the public consumes over 70 billion meals annually at schools, restaurants, retirement homes, universities, businesses, government cafeterias, and other public eating venues [5-6]. it is at the local, community level where food safety is the greatest public health imperative. foodborne incidences have followed a “reactive state of response” by looking back-in-time to determine what could have been the cause of an outbreak. though what have been needed are “real-time” alerts and responsive public health and food safety systems at the local level. currently, governmental inspectors, primarily through local health departments, depend primarily on paper-based documentation provided by businesses or food preparers to verify that the foods to be served are free of contamination. traditionally, recurring violations have been handled through re-inspections or in some cases, fines, suspension of permits, or closures have been enforced. however, there are far too many foodservice businesses, such as restaurants or cafeterias that do not implement proactive systems of controls to prevent violations from reoccurring, thereby, consistently repeating food safety hazards. this is critical because the various methods employed across the food industry have been unsustainable and inconsistent when implementing preventive controls (including critical control points), active monitoring, verification, corrective actions, and accurate and reliable recordkeeping [14]. to date, little has been written about the implementation of utilizing food safety informatics as a technological tool to protect consumers, in real-time, against foodborne illnesses. widespread standardized technologies have not been implemented to efficiently establish near-in-time, or “just-in-time,” electronic situational awareness to enhance early detection of, rapid response to, and management of public health threats toward food safety [9]. moreover, the lack of a broad public health preventative strategy has resulted in the limited ability or failure to collect, analyze and disseminate systematic information related to food safety issues across organizational and jurisdictional boundaries. institutional risk: a public health imperative school cafeterias, restaurants and food service facilities serve millions of meals every day, and usually these meals are safe to consume. however, when food safety problems occur, they are especially dangerous to children, [1, 2, 7, 8, 10, 12, 13, 15] and people with compromised immune systems due to disease (i.e., hiv/aids, cancer, cystic fibrosis, etc.), [8] or patients receiving pharmacologic therapy (i.e., chemotherapy, radiation), [12]. everyday consumers attending public eateries are subjected to the risk of foodborne contaminants, ranging from spoiled food products to the negligent washing of hands. through these experiences public health is confronted with a myriad of challenges, a complex taxonomy of risks that can go unexamined without standardized data collection systems. the food safety informatics: a public health imperative 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 most significant challenge to establish a public health imperative has been educating policy makers and federal, state, and local regulators about the hazards of contaminated foods that are consumed by people in both public and private foodservice facilities. in contrast, if mandated, industry adapts quickly when evidence-based policy helps to guide industry down a path of efficiency. given that significant food safety gaps exist within the current public health monitoring systems, government regulators struggle to monitor the most fundamental aspects of food supply systems, maintenance, and operations. response times in tracing tainted food or non-sanitized environments place the public in serious risk of foodborne illness outbreaks. this has been an ongoing challenge facing the food industry, public health and regulatory agencies. the complexity of these issues increase exponentially when considering the various dimensions to the problem, along with the lack of more sophisticated and state-of-the-art technologies that have the capabilities and capacity to captures real-time food safety data, leading to a rapid and timely interdiction, if a problem is detected. to better understand these shortcomings, a systematic review of the literature was conducted. however, what was achieve was an awareness that significant gaps and deficiencies in the context of public health and food safety technology and their relationship to new advances in monitoring and reporting exist throughout the literature. very little has been written on this interrelationship. methods to address the research deficit and begin to build a body of public health knowledge, the authors of this paper propose to introduce new food safety surveillance and monitoring technology through a pilot study. in the initial phase of identifying a food safety informatics tool, we worked closely with the technology industry to identify a leading food safety data acquisition technology. to identify a particular type of technology, we presented a particular set of detailed criteria to the technology industry. the technology had to be automated, able to automatically collect and transmit its data wirelessly; acquire real-time temperature data, both through automation and manually; and obtain standardized facility’s and maintenance checklist data at regular time intervals. lastly, the data output had to be in graphic form with real-time notification capabilities. the food safety data acquisition technology identified was termed touchblock tm (figure 1). setting in the summer of 2010, to test the food safety informatics tool, the authors of this study developed research collaborations with an industry leader in advanced food safety technology, and with a highly respected national food service caterer who provided services to a historically black college and university (hbcu), located in the eastern united states. food safety informatics: a public health imperative 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 study design this collaboration included a pilot study test of monitoring time and temperatures utilizing the food safety informatics tool touchblock tm . during this pilot, no human subjects were included in this study nor were any existing operations or technologies changed or altered. there was no outside interference whatsoever. over the course of a 24-hour period of time, automated and remote, wireless monitoring and transmitting technology were installed in industrial grade, walkin refrigerators, freezers, and dry storage. the sensor monitors were designed to collect data every 6 minutes throughout the 24 hour period; however to see the data easier, and clearer, 2hour time intervals are reported over the course of 12 hours. the wireless remote sensors were strategically place in all three of the test locations (freezer, walk-in refrigerator and dry storage). where the food was stored served as our unit of analysis and sample. the food monitoring technology chosen, referred to as touchblock tm was designed to transmit real-time and temperature data every 6-minutes. the touchblock tm technological platform in this beta-test collected data across three primary domains: 1) time, 2) temperature, and 3) quality control checklists. however, for purposes of this study, only time and temperature data are reported. instrument touchblock tm was the instrument chosen for this study because it met select criteria for inclusion (see figure 1). the researchers of this study developed particular criteria that included technology with the capability of transmitting real-time temperature and time data. the technology had to be automated, able to automatically collect and transmit its data wirelessly; acquire real-time temperature data, both through automation and manually; and obtain standardized facility’s and maintenance checklist data at regular time intervals. the data output had to be in graphic form with real-time notification capabilities and assessable for surveillance 24 hours a day. lastly, overall data integrity should allow users the ability to conduct hazard analysis critical control point type analysis whereby, root cause analysis and corrective action plans can be developed using quantifiable data that is built upon evidenced-based scientific theory and outcomes. figure 1. touchblock handheld food monitoring data, remote, and manual sensors food safety informatics: a public health imperative 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 results the data collected over the 24 hour period illustrates how reporting food safety data can serve as a real-time food safety monitoring surveillance system for foodservice establishments and public health inspectors. figure 2a represents the surveillance of freezer temperature changes over a 12 hour period. the freezer normal operating range was established from minus 15 to 15 degree fahrenheit. as illustrated in figure 2a, this particular freezer experienced peaks far exceeding its acceptable range, to a peak of approximately 26 degrees fahrenheit. we further investigated this time frame to determine if a delivery had been received and consequently was being stored or was it a physical inventory that could explain the increase in temperature. neither was the case. figure 2a—automatic freezer monitor 1 color code: yellow = normal range, red = above normal range figure 2b show that the acceptable temperature ranges for refrigerator monitor 2 for the foodservice business was between 35 and 41 degrees. however, it is clear that figure 2b shows that there were significant time intervals in which the temperatures far exceeded the acceptable range for prolonged periods of time, peaking at 48 degrees. based on the time, it is clear that these peak changes were most likely due to foodservice workers entering and leaving the coolers. food safety informatics: a public health imperative 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 figure 2b--automatic refrigerator monitor 2. color code: yellow = normal range, red = above normal range normal range for dry storage should have been from 70 to 75 degrees. however, figure 2c shows over the course of the night and throughout the day, the dry storage monitoring sensors indicate that the temperatures exhibit a peak of 100 degrees and never dropping below 84 degrees fahrenheit, which far exceeded the standard temperature range for the industry. figure 2c—dry storage room monitor 3. color code: red = above normal range in summary, the results of the above 24 hour pilot study briefly illustrates how technology can assist in food safety surveillance and monitoring by efficiently detecting food safety abnormalities related to time and temperatures. through the use of touchblock, temperatures outside the accepted range can be quantified, over time, so that efficient and proper response in food safety informatics: a public health imperative 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 “real time” can be addressed to prevent microbial growth, and/or spoilage of products resulting in loss of product inventory and potential foodborne illnesses. discussion since foodborne illness sees no boundaries, it is imperative that those who track it embrace the overall technological concept of food safety informatics surveillance. based on our analysis of this food safety informatics pilot study, the automated food safety data acquisition technology served to validate the food safety informatics tool. with data being collected, in real-time, through automated wireless sensors, the ability of the industry and food protection regulators to monitor the potential impact is significantly enhanced. collecting accurate time and temperature data, to include facility maintenance data of their normal business checklist, far exceeds paper-based data reporting. current normal procedures utilizing paper-based data systems are highly suspect, subject to manipulation, and unreliable. however, the use of date and time-stamped temperature monitoring technology can serve to more readily protect the public’s health against foodborne contaminants due to spoiled foods or other risk issues. in addition, using food safety informatics enables a foodservice operation to download the collected surveillance data into a centralize repository where data can be stored for review, analyzed, interpreted and utilized for reporting purposes. this innovative method of collecting data substantially increases the organization’s creditability with government food safety inspectors thereby, exemplifying their committed efforts to protect the consumer from foodborne illnesses. food safety informatics surveillance systems will soon be found essential in dealing with the myriad of challenges that the foodservice industry face. users of the current “paper” log recordkeeping methods will find that the food safety informatics system will be more straightforward and consistent with a high degree of integrity, accuracy, and reliability. currently, foodservice operations are required to keep a “paper” log of daily time and temperatures. this log is done by hand using a handheld thermometer probe or reading the temperature gauge on freezers or refrigerators. in many instances the temperatures recorded are fraudulently or artificially made up to ensure health code compliance. food safety informatics will minimize the variables of human error in temperature gathering, recording, and transposing data from one source to another and most importantly, allow for faster response time to potential food safety issues or events. an important key advantage of utilizing a food safety informatics surveillance system is that time and temperature data are continuously collected 24 hours a day, 7 days a week. at any given time, a proper authorized user can access the collected surveillance data of food service cooling, freezing and refrigeration equipment to make sure proper holding temperature ranges are maintained and if for some reason they are not, efficient and proper response can be coordinated and applied. other uses of the food safety informatics surveillance system go beyond monitoring equipment and storage areas for food. the touchblock tm informatics technological tool is also designed to replace all other “paper” logs that are used for basic facility check list maintenance reports (e.g., food safety informatics: a public health imperative 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 time lavatory cleaned, countertops sanitized, food rotations, etc.) and virtually all other temperature logs of prepared foods that are expected to be done by hand using a hand-held thermometer probe. figure 3 illustrates how data are collected for the purposes of food safety informatics, as opposed to the paper-based copies. figure 3. automatic handheld probes for temperature collection and transmission manual probes allow for verifiable temperature monitoring of prepared foods outside refrigerated areas to ensure proper holding temperatures that are maintained in accordance with health code requirements. critical products, containing temperature sensitive and biological components (i.e., meats, poultry, seafood, cream dishes, gravies etc.) can be checked with the manual probe to ensure that proper temperatures are either maintained or achieved. the automated surveillance informatics tool will also be time stamped and recorded to guarantee data accuracy and integrity. in summary, today’s food safety technology is sorely underutilized or not used at all and must be developed and introduced into all segments of the food industry. the benefits of a food safety informatics surveillance system are the collection of automated data that can be used as a proactive agent rather than a reactive agent in the prevention of foodborne illnesses. in addition, the use of automation for data collection minimizes human reporting errors and increases the integrity of required recordkeeping. technology cannot totally stop intentional or unintentional food related outbreaks from occurring, however technological advances can help to make the overall food system “proactive” instead of “reactive” in protecting the public in the event of an outbreak or super-spread of a foodborne illness. future direction: a proposed food safety informatics model based on our proof of concept, it is clear that the public’s health is weighty at risk unless new models are introduced to help identify food safety anomalies in the food service industry, food safety informatics: a public health imperative 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 ranging from restaurants to school cafeterias to elderly dining facilities. whatever the venue, it is clear that public health has not delved into this body of research significantly, and a gap in literature clearly exists for the field and practice of public health in this area. for example, the evolution of “health informatics” is, in many respects, not significantly different than in the area of food safety informatics. evidence-based food safety data can serve to identify best practices in the food industry when it comes to food preparation for immediate consumption. figure 4 is a diagrammatical model to illustrate how a historically black university is developing a national model to introduce food safety informatics as a public health imperative. figure 4 illustrates that core surveillance should, ideally, be accessible to critical federal reporting agencies when it comes to food safety. figure 4: food safety informatics model: a new paradigm through our use of the touchblock tm system and key informant discussions with industry, academia, and government, we have been able to develop this conceptual model of the salient issues that need to be considered when bridging this gap between food safety informatics and public health. data collection systems, such as touchblock, tm have served as a single purpose centralized monitoring portal suitable for use in commercially and industrialized environments. the goal of any food safety informatics system should be to gather critical data and verify in real-time that adequate food, facility equipment and conditions are maintained consistently and not for any one snapshot period of time, as is the industry standard convention with paper-based systems that are currently in use. food safety informatics: a public health imperative 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 to develop this national model, two overarching thematic issues were identified as being critical to develop a national food safety informatics model: 1) develop core competencies in food safety informatics and 2) develop curricula. figure 4 illustrates four key areas of development: theory, application, evidence-based analysis, and interdisciplinary relevance. we will summarize the model to briefly describe their role in developing a food safety informatics model. theory the theory courses encompass scientific background of foodborne pathogens, importance of personal hygiene, cross contamination and other conditions that impact food safety. in addition, examining the theory of the use of technology, a critical component in training, can be used to demonstrate its efficiency in operational procedures as it pertains to food safety and the theoretical reasons and rational why technology should evolve, starting with the replacement of existing paper-based systems. application courses that relate to application are sensitive to the foodservice workforce. training is designed and tailored to learners of different generational ages, cultural background, literacy and technological ability with respect to different occupational applications in industry. evidence-based analysis – courses related to evidence based analyses are data driven. these courses provide “real” situational awareness modules that promote effective data gathering through mathematical and epidemiological algorithms which can help in teaching and developing predictive models thereby assisting in timely responses that best meet the challenges of food safety. interdisciplinary informatics relevance – these courses incorporate multiple academic disciplines (i.e., engineering, mathematics, computer sciences, public health, biology, etc.) to collaborate in research to solve food safety issues through innovative technology and science by utilizing technological applications that capture time series data. the importance of interdisciplinary informatics input can help to enhance subject matter expertise that often goes overlooked and neglected by any one discipline, such as public health. in summary, the theoretical model describes food safety informatics as a public health imperative. by today’s public health standards, there exist significant gaps in the nation’s overall ability to monitor, teach, and standardize many of the most fundamental aspects that relate to food supply systems, maintenance, and operations. the use of technology cannot totally stop food related outbreaks from happening. however, technological advances can help to make the overall system of prevention, detection, reporting, and coordinated response more efficient in the event of a small or catastrophic foodborne illness outbreak. conclusion food safety informatics is the first line of defense for eateries to prevent foodborne outbreaks. federal legislators and regulators have an obligation to protect consumers against unanticipated foodborne risks. the hazard analysis critical control point system (haccp) has served to address part of the food safety problem by developing principles used in some food production food safety informatics: a public health imperative 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.3, no. 2, 2011 industries. the pairing of a quality haccp program and a food safety informatics system should lessen the possibility of mass foodborne illnesses. the touchblock system has served to demonstrate that technology can efficiently and reliably become a critical proactive tool in the battle to ensure food safety. federal food regulations within the united states have significantly improved over the last few years, but efficacy of safe food practices across the food industry is woefully lacking. in order to overcome these deficiencies and protect the general public, especially the immune compromised, the young and elderly, the need for bringing about a call to action in food safety through the utilization of advanced technological systems needs to be elevated to the level of a public health imperative, especially at the local levels where foods are produced and consumed. conflicts of interest the authors have no conflicts of interest to report at this time. correspondence cynthia a. tucker, ph.d., mba., rd, ldn cynthia.tucker@morgan.edu morgan state university school of community health and policy nutritional science program jenkins building, room 329 1700 e. cold spring lane baltimore, maryland 21251 443.885.3561 (office) 410.235.1737 (fax) references [1] american academy of pediatrics. children and foodborne illness. pew health group & center for foodborne illness research & prevention. http://www.makeourfoodsafe.org/tools/children-and-fbi-factsheet-11-09.pdf. accessed 2010, june 3. 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[15] stamey, j. 2006. protecting residents from foodborne illnesses: a dietary perspective on infection control. long-term living http://www.ltlmagizine.com/me2?segments/publications. accessed 2010, june 1. [16] us department of health and human services, center for disease control and prevention. 2008. prevention, investigation of outbreak of infections caused by salmonella saintpaul. www.cdc.gov/salmonells/saintpaul/jalapeno/2008. accessed 2010, june 13. [17] us department of health and human services, center for disease control and prevention (2006). update on multi-state outbreak of e. coli 0157:h7 infections from fresh spinach www.cdc.gov/foodborne.edolispinach/100606.htm. accessed 2010, june 13. [18] greis, n.p., nlogueira, m., mcdonald, p., wilfert, r. (2011, may). the ncfodsafet project: ncfeda north carolina foodborne events data integration and analysis tool: a new informatics tool for food safety in north carolina. institute for homeland security solutions. north carolina center for public health preparedness http://www.fda.gov/downloads/food/guidecomplianceregulatory http://www.ext.colostate.edu/safefood/newsltr/v8n3s05.html http://www.ltlmagizine.com/me2?segments/publications http://www.cdc.gov/salmonells/saintpaul/jalapeno/2008 http://www.cdc.gov/foodborne.edolispinach/100606.htm crappdf.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 (page number not for citation purposes) isds 2013 conference abstracts influenza sentinel surveillance in lagos state nigeria, 2009 – 2011 abimbola aman-oloniyo*1, christianah alake2 and ayoade adedokun2 1lagos state ministry of health, lagos, nigeria; 2lagos state university teaching hospital, lagos, nigeria � �� �� �� � � �� �� �� � objective �������� ��� � �������� ������ ��������������� � ��� ���� ������ � ������ ������������ ���������� � ���� ��� ������ ��� �������� ����� ����� � ���� ����� �� ���� �������� � � �!��� �" ������������� ������ ��!"��# introduction $���� � �����!���������� � ���!��������������% � �������� ���� ����� ��� �������&''(��)�#��� ��������������� �%���� ���� �����&''*��)�#� �� � �� ����������!��������������������%������������������ +� ��� ��� ����������� �������������� ���� �� ������ � �������� � ������� �� %�������� ������� �������������� ��������� ����������� � ��� ���� �� ���� ������ ����������� �#������������������ �� ��������������� ��� � ��� ���������������� ������ ������������������ � �,���������� � ������������������������ ������ ��������� 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terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts where do you find interesting biosurveillance publications? katie j. suda*1, tera reynolds2, judy akkina3, sylvia halasz4 and howard burkom5 1va/uic, chicago, il, usa; 2isds, boston, ma, usa; 3usda, fort collins, co, usa; 4yp, glendale, ca, usa; 5jhu/apl, laurel, md, usa objective to identify the disciplines and journal titles of surveillance-related publications from a wide range of indexed repositories and to draw attention to the publication repository created by the isds research committee. introduction the isds research committee (rc) is an interdisciplinary group of researchers interested in a wide range of topics related to disease surveillance. the rc hosts a literature review process that results in a permanent repository1 of relevant journal articles; some of which are presented in bi-monthly calls/webinars that provide a forum for discussion and author engagement.2 the webinars have led to workgroups and society-wide events, boosted interest in isds, the annual conference, and fostered networking among members and guests. since 2007, the rc has identified and classified published articles using an automated search method with the aim of progressing isds’s mission of advancing the science and practice of disease surveillance by fostering collaboration and increasing awareness of current advances in the field of surveillance. in 2012 the rc refined the method of automated literature retrieval resulting in increases in relevant articles identified. the rc literature review efforts have provided an opportunity for interdisciplinary collaboration and have resulted in a repository of 1920 articles from march 2012-august 2014 (2012=37.4% of articles in the repository, 2013=35.1%, 2014=27.5%). methods a search query was developed in scopus,3 consisting of over 100 terms suggested by members. we found that the scopus search is the most comprehensive and improved the cross-disciplinary scope. scopus results allowed filtering of 50-100 titles and abstracts in fewer than 30 minutes each week for the identification of articles of interest to isds members. journal titles were categorized to identify the primary discipline covered by the journal; categories include health sciences, mathematics/physics/statistics (“math”), computer science, animal/ plant, geography/environment, engineering, business/operations/ organizational research (“business”), and other science (e.g., biology). conference abstracts were categorized separately. journals with a primary focus of surveillance were also indicated in the dataset. results the majority (66.9%) of articles in the repository were published by journals with a focus in health science, followed by math (11.0%), geography/environment (6.5%), computer science (5.7%), other science (4.9%), and animal/plant (3.8%). although few articles were published in engineering (1.6%) and business (0.3%), these are emerging areas in surveillance and should continue to be monitored for work impacting the field. only 0.03% (n=14) articles in the repository could not be categorized. journals with the highest frequency of surveillance articles included plos one (21.4% of articles in the repository), influenza and other respiratory viruses (6.1%), vaccine (3.3%), and preventive veterinary medicine (3.1%). journals with a primary focus of surveillance included eurosurveillance, mmwr surveillance summaries, and surveillance and society; publishing 1.7% (n=33) of articles in the repository. conclusions although the majority of articles are published in health science journals, one-third of articles relevant to surveillance are published in other disciplines not indexed by traditional secondary databases (e.g., medline). researchers and public health professionals working in the area of surveillance should use the article repository to identify articles relevant to their work and avoid missing studies outside of the health sciences. keywords biosurveillance; publications; literature search; literature query acknowledgments the isds rc would like to thank many past contributors and guest authors to the literature review process. references 1. zotero. http://www.webcitation.org/6azia0yed. 2. isds literature review summaries archive. isds wiki. http://www. webcitation.org/6azkit9xq. 3. isds literature review search string (scopus). http://www. webcitation.org/6aziwfc6c. *katie j. suda e-mail: katiesuda@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e163, 2015 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e311, 2019 isds 2019 conference abstracts lessons learned from an extreme heat event using aces for situational awareness, ontario, canada nancy vanstone, adam van dijk, paul belanger, kieran moore kfl&a public health, canada objective to describe the lessons learned for public health decision-makers from an analysis of acute care enhanced surveillance (aces) data for the heatwaves experienced in ontario, canada in the summer of 2018. introduction the acute care enhanced surveillance (aces) system provides syndromic surveillance for ontario’s acute care hospitals. aces receives over 99% of acute care records for emergency department (ed) visits; mean daily volume is 17,500 visits. aces uses a maximum entropy classifier and generates more than 80 standard syndromes, fifteen of which are actively monitored for aberrational activity and are considered of higher public health relevance, including resp (respiratory infection, non-croup), ili (influenza-like illness), tox (toxicological, chemical/drug exposure), ast (asthma), opi (opioid exposure), cell (cellulitis), gastro (gastroenteritis), enviro (environmental, heat/cold exposure), mh (mental health), eoh (alcohol intoxication), derm (rash), and sep (bacteremia, sepsis). syndromic surveillance provides a salient source of public health surveillance during extreme heat events; monitoring real-time ed visits can inform local public health authorities of health impacts, provide situation awareness to initiate and/or inform public health response, and help decision-makers allocate resources according to geographic (or demographic) vulnerability. while the use of syndromic surveillance has been well-characterized to monitor infectious disease outbreaks, its use to monitor the heathealth impacts is relatively novel for aces users, specifically local public health authorities. this report describes the data collected during an extended extreme heat event in ontario, canada, to highlight the value of syndromic surveillance during extreme heat events and make recommendations regarding incorporating aces data into routine workflows. methods temperature data were retrieved from environment canada historical databases for mid-june to mid-july 2018. aggregate counts per day for total ed visits and and for individual syndromes were retrieved from aces databases. descriptive statistics were used to analyze all datasets. results an extreme heat event occurred in the southern region of ontario in early summer, 2018. environment canada issues heat warnings for regions throughout canada according to region-specific criteria; for southern ontario, heat warnings are issued when 2 or more consecutive days of daytime maximum temperatures are expected to reach 31°c or when 2 or more consecutive days of humidex values are expected to reach 40. extended heat warnings are issued when the event lasts beyond 2 days. an extended heat event occurred june 29 to july 5, 2018. although the region is large, temperature data from environment canada’s climate monitoring station at toronto’s pearson airport are shown (figure 1) as an example of the temperatures observed for this time period in the region. conclusions lessons learned from an analysis of aces data during an extreme heat event: 1. the enviro syndrome provides real-time monitoring of the health impacts during a heat event and may provide proxy for estimating the indirect effects of heat (e.g., impacts on chronic conditions). public health authorities can monitor local health impacts during an extreme heat event. 2. patients seeking help at the ed do not appear to be skewed in acuity, sex nor age. this does not necessarily reflect the population that experiences the greatest impact from extreme heat, but rather those that are seeking help at the ed for the direct effects of heat. that said, an increase in enviro counts does not indicate whether the http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e311, 2019 isds 2019 conference abstracts increase is due to greater exposure to the heat (or sun), engaging in vigorous outdoor activity during the event (recreational or occupational), or lack of access to air conditioning. 3. ed visits for enviro can be geolocated to determine areas experiencing greater health impacts. this may allow allocation of resources to specifically address vulnerabilities. aces has built-in mapping capabilities that allows a geovisualization of the home addresses for patients. furthermore, aggregate counts for relevant syndromes are available for registered users on the public health information management system (phims), a web-accessible gis tool for situational awareness that gives public health decision-makers access to real time health impacts in concert with demographics, weather, and other emergency management information. acknowledgements aces receives ongoing support from the government of ontario's ministry of health and long-term care. figure 1 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e311, 2019 isds 2019 conference abstracts http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts validation of emergency department and outpatient data using ili syndrome classifiers jenna iberg johnson*2 and komal brown1 1lphi, new orleans, la, usa; 2office of public health, new orleans, la, usa objective the goal of this analysis is to compare the results of influenza-likeillness (ili) text and international classification of diseases (icd) code classifiers applied to the louisiana office of public health’s (oph) syndromic surveillance data reported by new orleans area emergency departments and the greater new orleans health information exchange’s (gnohie) data reported by new orleans area outpatient clinics. introduction the louisiana office of public health conducts ed syndromic surveillance using the louisiana early event detection system (leeds). using outpatient data for syndromic surveillance is a relatively new concept, brought about due to the increasing use of ehrs and hies making such data readily available. previously, there has been limited means of syndrome classification validation for the leeds data and the gnohie data has not been studied widely as a population sample, so this analysis and comparison is valuable on both fronts. due to differences in the types of data (adt messages from eds and ccd from outpatient clinics), as well as different patient populations and site visit capability, the percentages of patients classified as ili between data sets are unequal. the main focus of this analysis is determining whether the ili classifiers applied to both data sets detect similar syndrome trends. each indicator used in the study represents the percentage of total patients seen that week who are classified as ili cases. the study period covered the 13-14 influenza season, cdc week 1340 through 1420 (9/29/2013-5/17/2014). two ili classifiers were applied to both the gnohie and leeds data:the first classifier consisted of icd-9 influenza codes and the second classifier consisted of keywords applied to encounter notes(gnohie data) and chief complaint, admit reason and diagnoses (leeds data). a graph of the data, below, shows the four data sets. methods in order to compare data trends, the first step of analysis is basic correlation coefficients, for overall data comparison. then lagged cross-correlation was applied to examine if the two data sources recognize rises and falls in syndrome trends during the same time period. the ears c2 method was applied to examine simultaneous alerts between data sets. lastly, the trend of the differences between the data sets was analyzed by scaling the gnohie data to have the same mean and standard deviation as the leeds data, and creating control charts of the difference data. results the correlation coefficients for the gnohie data compared with the leeds data, are shown in the table below. ears c2 method generated alarms for weeks 1349 through 1401 for leeds icd9, gnohie icd9, and leeds text classifiers and for weeks 1350 through 1401 for gnohie text classifier. using lagged cross correlation, evidence that the two data sets correspond to one another by time of fluctuation would be a maximized correlation value at t=0. both data sets displayed maximum correlation at t=0. a control chart was created of the value by week of leeds percentage of ili minus the scaled value of gnohie percentage of ili, for both data sets, where the gnohie mean and standard deviation were adjusted to match the leeds data. for the icd-9 data, only two observations at weeks 1351 and 1401 were below and above the 3 standard deviation limits, respectively. for text data, one outlier existed below the limit at week 1420. conclusions the gnohie data and leeds data show similar trends in timing and severity of seasonal ili fluctuations. the correlation coefficients indicated strong correlations, the ears c2 method and lagged cross correlation show a consistency in timeliness of aberration detection, and the control charts maintained a moderate level of statistical control. overall this indicates that the gnohie clinical outpatient data is a good candidate to supplement the leeds emergency department syndromic surveillance data. this is important not just for ili surveillance but for surveillance of other syndromes with a lower incidence rate that may need larger sample sizes for aberration detection. as well, clinical data is becoming more readily available with emergent health information technologies, so outpatient encounter data will be widely available for study in the coming years. keywords influenza-like illness; surveillance; outpatient data *jenna iberg johnson e-mail: jenna.ibergjohnson@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e83, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using essence-fl for situational awareness after national reports of increased enterovirus d68 (ev-d68) infections with severe outcomes, september 2014 david atrubin* florida department of health, tampa, fl, usa objective to provide situational awareness using florida’s syndromic surveillance system during a 2014 outbreak of ev-d68 in other regions of the country introduction the electronic surveillance system for the early notification of community-based epidemics (essence-fl) receives daily (or bi-hourly) data from 184 emergency departments (ed) from around florida. additionally, 30 urgent care centers submit daily data to the system. these 214 facilities are grouped together in an acute care data source category. five to six days after the start of each school year in florida, essence-fl shows increased respiratory illness visits in the school aged population. previous analyses of these data have shown that this increase is a result of increased transmission of the common cold among school children. in early september 2014, during this sustained yearly increase in respiratory visits, reports of more severe infection caused by enterovirus d68 (ev-d68) in children in other parts of the country began circulating. public health officials in florida, as well as the media, questioned whether children in the state were being infected by this virus capable of causing more severe illness, especially among asthmatics. as is the case with many incipient outbreaks, syndromic surveillance played an integral role in early efforts to detect the presence of this illness. the task of providing situational awareness during this period was complicated by this outbreak coinciding with the start of the school year. methods on september 8, 2014, essence-fl staff were made aware of clusters of ev-d68 occurring in kansas city, missouri and chicago. an early release edition of the centers for disease control and prevention’s morbidity and mortality weekly report (september 8, 2014) was reviewed and provided critical information about disease presentation and the characteristics of the population infected with ev-d68.1 using the 184 hospitals only, a time series graph was created to look for statistical aberrations in the percentage of 0-16 year olds being admitted to the hospital. the discharge disposition category field provides an indication of the severity of the illness in the ed patient. essence-fl subsyndromes were analyzed to look for visits in excess of normal levels for the categories of difficultybreathing, wheezing and asthma. specific time series graphs were created in an effort to detect an increase in 0-16 year old asthmatics being admitted to hospitals. results as of september 9, 2014, no increase in admit percentage was observed in children 0-16 years of age. likewise, no levels of respiratory syndrome or difficultybreathing, wheezing, or asthma subsyndromes were seen in excess of typical levels for this time of year for this age group. additionally, the florida department of health received no reports of increased hospital admissions related to this seasonal increase in respiratory visits typically seen at this time of year. conclusions no evidence of increased illness due to ev-d68 was observed in florida’s syndromic surveillance ed data at the time of this writing. syndromic surveillance systems often prove their worth as much by what is not found, rather than what is found. while these systems do not replace open communication with hospitals and other providers, there is a necessity to have near real-time surveillance when outbreaks are occurring even elsewhere in the country or world. understanding the periodicity in your data also facilitates the ability to provide accurate situational awareness. having a specific data field (the discharge disposition) that allowed epidemiologists to gauge the level of severity of illness in an ed patient proved to be very beneficial in this circumstance. keywords enterovirus; respiratory illness; school year; emergency department data; admit data acknowledgments johns hopkins university applied physics laboratory references 1. cdc. severe respiratory illness associated with enterovirus d68 – missouri and illinois, 2014. mmwr sept 8, 2014;63:early release. 2. cdc clusters of acute respiratory illness associated with human enterovirus 68 —-asia, europe, and united states, 2008-2010. mmwr 2011;60:1301-4. *david atrubin e-mail: david.atrubin@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e107, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 162 isds 2014 conference abstracts burden of infectious disease in a state of india: a comparative analysis divya persai* public health foundation of india, new delhi, india objective the present study aims to analyze data from the integrated disease surveillance project (isdp) to estimate the rates of selected infectious diseases in the state of maharashtra in comparison to the rest of india. introduction in india the range and burden of infectious diseases is enormous. to address this burden effectively, an estimate of the burden of infectious diseases is essential. the present study aims to analyze data from the integrated disease surveillance project (isdp) to estimate the rates of selected infectious diseases in the state of maharashtra in comparison to the rest of india. methods district-wise and disease-wise disaggregated data from 20052012 was collated. annual rates was calculated and analysis of epidemiological profile, including spatial trends, rural–urban distributions and annual case rates was done. a comparison of maharashtra idsp case rates with case rates from the national data was made. data was analysed using spss 17. results findings indicate that the existing surveillance system in the state is predominantly reporting urban cases. there are wide variations among reported cases within the state with reports of enteric fever and measles, cholera, viral hepatitis and dengue. conclusions the present study shows that the existing surveillance system in the state is predominantly reporting urban cases. there is a need to strengthen idsp into a comprehensive surveillance system in order to reverse the endemicity of these diseases. keywords disease survelliance; india; infectious disease references 1.john j, dandona l, sharma v, kakkar m. continuing challenge of infectious diseases in india. the lancet, 377: 252 – 269; 2011. *divya persai e-mail: dpersai@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e204, 201 ojphi-06-e167.pdf isds annual conference proceedings 2013. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 116 (page number 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�:��%%%*����*�������������� ��� #�� � ������/ ������������ � ��(�6c214>8��>1c&�'(&/�������%*��# *desiree mustaquim e-mail: dmustaquim@gmail.com� � � � online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(1):e167, 2014 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts geo-based social media analytics and smart dashboard for tracking influenza outbreaks ming-hsiang tsou1, 3, 4, michael peddecord*1, 2, 3, jeffrey johnson5, 6 and chin-te jung1, 3, 4 1graduate school public health, san diego state university, san diego, ca, usa; 2quality health analytics, san diego, ca, usa; 3center for human dynamics in the mobile age, san diego, ca, usa; 4geography department, san diego, ca, usa; 5epidemiology & immunization branch, san diego, ca, usa; 6san diego county health and human services agency, san diego, ca, usa objective we developed geo-targeted social media application program interfaces (apis) for twitter and a web-based social media analytics and research testbed (smart) dashboard to analyze “flu” related tweets. during the 2013-14 flu season, for 10 cities with active surveillance for influenza (ili), we correlated weekly tweeting rates and visual patterns of flu tweeting rates. to facilitate widespread use and testing of this system, we developed an interactive webbased dashboard “smart” that allows practitioners to monitor and visualize daily changes of flu trends and related flu news. introduction active surveillance for influenza is a useful but costly endeavor. in recent years infoveillance tools have been developed to track and analyze data available on the internet and social media (eysenbach 2011). while infoveillance tools have been developed, few tools focus on geo-targeted data collection at a local level combined with geographic information systems (gis) capability. methods tweets are collected continuously with the keyword “flu” using customized geo-targeted twitter search apis from 31 u.s. cities based on a 17 mile radius from week 39, 2013 to week 10, 2014. our model is based on improvement of previous methods (nagel et al., 2013). we compared the temporal patterns of weekly flu tweeting rates in each city against the cdc regional ili data using 2010 census data and gis methods (tsou et al. 2013). the correlation (r) between flu tweeting rates and the regional cdc ili records were calculated and trends were represented graphically. our online smart dashboard was built by using python®, javascript®, and the node. js® platform (figure 1). results we used regional cdc ili data to compare each city’s flu tweeting rate within each region. correlation (r-value) between weekly aggregated flu tweeting rates and disease occurrence (cdc regional ili rates) were very high in many cities (examples: atlanta vs. region-4 ili r= 0.79, nashville vs. region-4 ili r= 0.93, chicago vs. region-5 ili r= 0.75, detroit vs. region-5 ili r= 0.84, los angeles vs. region-9 ili r= 0.88, seattle vs. region-10 ili r= 0.78). we also compare weekly confirmed influenza cases from the san diego county public health laboratory with weekly flu tweeting rates in san diego. the r of 0.93 indicates strong correlation of temporal patterns between confirmed influenza cases and flu tweeting rates (figure 2). conclusions geo-targeted social media analytics with gis methods are feasible to track and monitor flu outbreaks at the local level. a key advantage of our tool is the ability to track flu outbreaks automatically every day. capabilities of the smart dashboard have great potential to assist local public health agencies by providing real-time information to investigate and make appropriate responses to large-scale disease outbreaks. figure 1. smart dashboard — realtime monitoring of “flu” keywords influenza; social media; surveillance; infoveillance; gis acknowledgments this study is partially supported by the national science foundation grant # 1028177, “mapping cyberspace to realspace”. we especially thank anoshe aslam for her data analysis. references tsou, mh et al (2013) mapping social activities and concepts with social media (twitter) and web search engines (yahoo and bing): a case study in 2012 us presidential election. cartography and geo info sci, 40(4). nagel, ac et al. (2013) the complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets. j med internet res, 15(10). eysenbach, g. (2011). infodemiology and infoveillance tracking online health information and cyberbehavior for public health. am j prev med, 40(5). 154-8. *michael peddecord e-mail: mpeddeco@mail.sdsu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e95, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts modeling spatial heterogeneity with excess zeroes from school absenteeism dsata xiaoxiao song*1, 2, qi zhao1, changming zhou1, tao tao1, lars palm3, vinod diwan4, hui yuan5 and biao xu1 1dept. of epidemiology, school of public health, fudan university, shanghai, china; 2school of public health, kunming medical university, kunming, china; 3future position x, gavle, sweden; 4ichar, karolinska instituet, stockholm, sweden; 5jiangxi provincial center for disease prevention and control, nanchang, china objective to describe and explore the spatial heterogeneity via random effects zero-inflated poisson model (re-zip) for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural china. introduction absenteeism has been considered as a potential indicator for the early detection of infectious disease outbreaks in population, especially in primary schools. however, in practice this data are often characterized by an excess of zeros and spatial heterogeneity. in a project on integrated syndromic surveillance system (issc) in rural china(1), random effect zero-inflated poisson (re-zip) model was applied to simultaneously quantify the spatial heterogeneity for “occurrence” and “intensity” on school absenteeism data. methods daily health-related school absenteeism at 62 primary schools in two counties of jiangxi province were reported to the web-based platform of issc during 24 continuous months (from april 1th, 2012 to june 31st, 2014). the re-zip has two components that correspond to two zero generating processes with two random intercept ( 1 and 2). the first process is governed by a binary distribution that generates structural zeros. the second process is governed by a poisson distribution that generates counts, some of which may be zero (named sampling zero) (2), see eqation.1-2. firstly, artificial data was generated to illustrate that the two random effects could appropriately describe the spatial heterogeneity for “occurrence” and “intensity” of school absenteeism (figure_1). then, the spatial heterogeneity for “occurrence” and “intensity” in real absenteeism data from the 62 primary schools were assessed and visualized through quantifying the two random effects in each individual school. all analyses were processed using sas nlmixed(3). results in total, 32283 health-related absenteeism records were reported from the 62 primary schools in the study period. figure_2 displayed clearly the spatial heterogeneity for both “occurrence” and “intensity” in these primary schools indicated by 1 and 2 of the two random effects. the spatial variation in these two counties were also visualized graphically these on map in figure_3 and figure_4. conclusions in infectious disease surveillance, the key questions are “whether an outbreak will happen” or/and “how large the epidemic will be”, which could be answered through observing and monitoring the spatial pattern of health related school absenteeism. findings from this study suggest that re-zip model is an effective tool in the surveillance of health-related school absenteeism data with high spatial heterogeneity. keywords school absenteeism; spatial heterogeneity; random effects zeroinflated poisson model; occurrence; intensity acknowledgments this study was funded by [european union’s] [european atomic energy community’s] seventh framework programmer ([fp7/2007-2013] [fp7/2007-2011]) under grant agreement no. [241900]. references 1. yan w-r, nie s-f, xu b, dong h-j, palm l, diwan vk. establishing a web-based integrated surveillance system for early detection of infectious disease epidemic in rural china: a field experimental study. bmc medical informatics and decision making2012;12(1):4. 2. hu m-c, pavlicova m, nunes ev. zero-inflated and hurdle models of count data with extra zeros: examples from an hiv-risk reduction intervention trial. the american journal of drug and alcohol abuse2011;37(5):367-75. 3. wang j, xie h, fisher jf. multilevel models: applications using sas®: walter de gruyter; 2011. *xiaoxiao song e-mail: chinasxx@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e159, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts improving local non-communicable disease surveillance within a changing data environment allison young*1, mike d. fliss1 and amy ising2 1orange county health department, hillsborough, nc, usa; 2university of north carolina at chapel hill, chapel hill, nc, usa objective this project aims to fill a growing county-level health data gap through the development of a low-cost, excel-based surveillance tool. this prototype utilizes emergency department data (ed) collected by nc detect, a state-wide syndromic surveillance system, in order to visualize, monitor, and compare annual local health indicators for use in local decision making. in this way, the project aims to increase noncommunicable disease surveillance capacity and improve situational awareness within north carolina local health departments (lhds). introduction lhds are operating in a changing data environment. as household telephone use declines, national surveys are not sampling large enough populations to report representative local health statistics.1 as a result, reliable indicators from surveys such as the behavioral risk factors surveillance survey (brfss) are becoming scarce (figure). soon, these indicators may not be sufficient for county assessments. nc detect primarily uses data from emergency departments, the carolinas poison center, and the pre-hospital medical information system (premis) to identify outbreaks and facilitate emergency response.2 however, while built to aggregate “real-time” data, nc detect also provides a source for rich, long-term indicators. the challenge for lhds is that they may not have the knowledge, training, or technical assistance needed to fully utilize nc detect services.1 this project capitalizes on available human, organizational, and technical resources to increase lhd situational awareness3 and to demonstrate the usefulness of both “real-time” surveillance data as aggregate indicators of county health, and of low-cost prototyping using excel’s more advanced business intelligence (bi) features. methods this initiative leverages nc detect’s surveillance system as both a tool for collecting data and a platform for disseminating reports. lhds may already register for access to nc detect and request customized reports. however, visualizing indicators in a webbased system directly can be difficult and costly. an excel template is in development that will provide users with the ability to select common health indicators, pull annual trend data, and visualize data through meaningful reports. a preliminary list of available indicators includes annual ed visits for the following: asthma; diabetes; stds; drug overdose; acute alcohol poisoning; other poisonings; motorcycle, pedal-cyclist, and pedestrian injuries; other injuries; cancer; stroke; cardiovascular disease; obesity; substance abuse; and mental health. results this tool requires low epidemiological expertise and is easily shared. because nc detect currently houses 166 registered lhd users, this tool has broad reach and the potential to impact the overall noncommunicable disease surveillance capacity of north carolina at the local level. conclusions this tool models one solution to a changing data environment. the reports created by this tool can be used by lhds to track indicators that are challenging to find in other places. in this way, the tool will improve local situational awareness and equip lhds with the ability to make informed policy and programmatic decisions. keywords noncommunicable disease surveillance; local health departments; dashboards; situational awareness; low cost prototyping acknowledgments this study was supported in part by an appointment to the applied public health informatics fellowship program administered by cste and funded by the centers for disease control and prevention (cdc) cooperative agreement 3u38-ot000143-01s1. nc detect is funded with federal funds by the north carolina division of public health, public health emergency preparedness grant (phep), and managed through a collaboration between nc dph and the unc department of emergency medicine carolina center for health informatics. references 1. luck j, chang c, brown r, and lumpkin j. using local health information to promote public health. health affairs. 2006; 25(4):97991. 2. north carolina disease event tracking and epidemiologic collection tool [internet]. chapel hill (nc); c2004-2014; [cited 2014 sept 2]. available from http://ncdetect.org 3. endsley m r, and garland d j, editors. situational awareness analysis and measurement. mahwah, nj: lawrence erlbaum associates: 2000. 408p. *allison young e-mail: ayoung@orangecountync.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e177, 201 ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 development of a master health facility list in nigeria authors: olusesan ayodeji makinde 1 , aderemi azeez 2 , samson bamidele 1 , akin oyemakinde 2 , kolawole azeez oyediran 3 , wura adebayo 2 , bolaji fapohunda 3 , abimbola abioye 2 , stephanie mullen 3 1. measure evaluation/ john snow inc., 90 nelson mandela street, asokoro, abuja nigeria 2. federal ministry of health, federal secretariat complex, shehu shagari way, abuja, nigeria. 3. measure evaluation/ john snow inc. 1616 n fort myer dr, arlington, va, united states 22209 abstract introduction: routine health information systems (rhis) are increasingly transitioning to electronic platforms in several developing countries. establishment of a master facility list (mfl) to standardize the allocation of unique identifiers for health facilities can overcome identification issues and support health facility management. the nigerian federal ministry of health (fmoh) recently developed a mfl, and we present the process and outcome. methods: the mfl was developed from the ground up, and includes a state code, a local government area (lga) code, health facility ownership (public or private), the level of care, and an exclusive lga level health facility serial number, as part of the unique identifier system in nigeria. to develop the mfl, the lgas sent the list of all health facilities in their jurisdiction to the state, which in turn collated for all lgas under them before sending to the fmoh. at the fmoh, a group of rhis experts verified the list and identifiers for each state. results: the national mfl consists of 34,423 health facilities uniquely identified. the list has been published and is available for worldwide access; it is currently used for planning and management of health services in nigeria. discussion: unique identifiers are a basic component of any information system. however, poor planning and execution of implementing this key standard can diminish the success of the rhis. conclusion: development and adherence to standards is the hallmark for a national health information infrastructure. explicit processes and multi-level stakeholder engagement is necessary to ensuring the success of the effort. keywords: health facilities, health information systems, master facility list, registries, standards, unique identifiers correspondence: sesmak@gmail.com doi: 10.5210/ojphi.v6i2.5287 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. mailto:sesmak@gmail.com ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 introduction information and communications technology (ict) has promising potential to improve routine health data management in developing countries [1,2]. however, lack of political commitment, uncoordinated efforts, and poor planning of a health information system (his) can cause setbacks, waste resources, and hinder and/or eventually lead to abandonment of the system [3]. realizing the benefits of ict in the management of routine health data will require a systems thinking approach and mental models not previously thought of [4-6]. over the last decade, several routine health information systems (rhis) in developing countries have been moved to electronic platforms [7-9]. however, evaluations of these electronic systems have resulted in mixed findings; e.g. some findings provided evidence for the promise of the system, while others suggested failure of the system [3]. adoption of and subsequent migration from paper-based systems to electronic platforms will, however, present new challenges [10,11]. for instance, an identification crisis in the information system may occur. thus, it is necessary to establish processes that will ensure system integrity and scalability are maintained and fostered. a coordinated effort should incorporate a systems thinking approach, an enterprise-wide view and broad-based planning, which includes the selection and incorporation of standards in developing the electronic information system, called for by the world health organization in the 66 th assembly resolution on “e-health” [12]. a required standard is a unique identification process of health facilities that will report into the information system. identification processes may be complex; for example, in nigeria, the governance structure delegates responsibility to 37 state-level registries, which are further supported by 774 local government institutions. if efforts are uncoordinated and each level of government develops its own process for identifying health facilities which may be similar to another state and potentially result in the same identifier issued to more than one health facility, or a starkly different process, such could make an enterprise-wide system not viable (for instance, if one state uses text characters and the other uses integers to classify health facilities). while these are potential challenges to creating an electronic platform, early planning is necessary to develop a successful electronic rhis. therefore, the role of unique identification within an information system cannot be underestimated [13-16]. in terms of identifiers, different ideologies argue either for or against using intelligent unique identifiers [17]. using intelligent identifiers, the codes allotted convey information, while non-intelligent unique identifiers are system-generated numbers/text characters which reference another table for the meaning and do not convey information in its face value [17]. in nigeria, the federal ministry of health (fmoh) has begun adopting electronic applications for the management of routine health data and applies best practices to ensuring success of the effort [18,19]. for this transition, the fmoh has ensured standards are met; in which one of the essential standards was developing an identification process that assigns unique national provider identifiers (npis) to health facilities nationwide. this process ultimately resulted in documenting a comprehensive master facility list (mfl). various facility lists have independently existed for decades at the fmoh, state ministry of health (smoh), and local government area (lga) offices. however, in recent years, there has been an effort to centralize and coordinate these registries. emphasis for a significant shift has ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 stemmed from growing adoption of information technology (it) in routine health information management in nigeria, and the need to address numerous challenges in order to realize the benefits of e-health applications. therefore, in 2010, the fmoh initiated coordination efforts to update the national health facility list, which was completed in 2013. this improved version incorporates a new dimension to the health facility identification process. the mfl was used as a platform to allocate unique identifiers to each health facility, following an intelligent coding system applicable across all states and lgas. in nigeria, there are 36 states and the federal capital territory (fct). each state is further divided into lgas. the governance structure in nigeria (table 1) delegates responsibility of health facility registration to the smoh. thus, each state and the federal capital territory had independent processes for registering and assigning identifiers to the facilities; in which each state had authority to develop the processes for assigning and updating health facility identifiers. under the system, development of a national electronic rhis was a difficult task. in some states, though the lists of the health facilities were available, there were actually no systematic processes for identification. to create the mfl, these 37 registries had to be used by the fmoh to cumulate the current health facility lists and to develop a systematic process to allocate new unique identifiers and eliminate any duplication. table 1: levels of governance in nigeria governance level oversight function in his federal government leads the development of policies, tools, standards and guidelines, and provide technical support to the sub-national government and audit the quality of data from each state. state governments lead state-level coordination efforts; print and distribute tools to lgas; collates and analyzes data for the state; provide technical support to the lgas; audit the quality of data emanating from the lgas; and solicit state interests on the national rhis. house the hospitals management board which accredits and registers new health facilities. local government areas lead lga level coordination effort; distribute tools to the health facilities; train and provide technical support to health facility records staff; and audit the quality of data emanating from the health facilities. nigeria has 774 lgas. mfls are essential components of a his; which, according to some experts, are considered the foundation of public health [20]. a responsive his is composed of several sub-systems which are more meaningful when linked, rather than as stand-alone systems. the mfl provides an opportunity to link the sub-systems in a national his architecture. for example, a human resource information system maintains data on the number of healthcare providers in a health facility, while the district health information system (dhis) contains routine encounter data for the health facility. the linkage between these two sub-systems using the npis can provide critical data on health facilities; for instance, where the physician/ patient or nurse/ patient ratio is low which would require resource redistribution. if coordinated, a comparison can feasibly be conducted to identify areas of over-allocation and under-allocation by state or nationally, which supports evidence-based resource distribution. overburdened healthcare providers can significantly impact the quality of care at health facilities [21,22], therefore this should be monitored and if needed, changes made to ensure quality of health care is maintained. these ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 challenges, among others, can be addressed through coordinated his at national and sub-national levels. establishing a financial information system can provide the necessary resource data, which has been repeatedly echoed to often be unreliable in several developing countries [23]. these systems can be modeled to mirror the national health accounts (nha) framework, which is regarded as the international standard for health resource tracking [24]. the potential linking of resource data, routine encounter data, and human resource data using the npi can provide an objective analysis of resource expenditure and the associated effect in populations, in addition to informing health resource allocation and policy decisions. furthermore, since the npis will remain permanently with a health facility, it provides the ability to track the health facility data in the event of a change in name. for longitudinal studies, this is significantly important to ensure linking of data collected over time. longitudinal studies are the hallmark for identifying causality [25], and the mfl will be an essential resource for studies on health facilities or their immediate environment. additionally, the mfl proposes the inclusion of geographic coordinates as part of the record for each health facility. geographic coordinates are needed to answer the “where” question for epidemiologists investigating disease outbreaks. john snow, an influential epidemiologist, in his landmark research in london in 1854 determined that cholera cases were linked to water supply by demonstrating the proximity of reported cases and associated mortalities to specific water pumps after developing maps to show clustering of cases [26,27]. applying this principle to health facility data analysis can help identify areas of an outbreak and take measures to control them. in event of an undesirable clinically associated occurrence like high early neonatal mortality in a geographic region observed from routine health data, such can spur further investigation on the quality of obstetric and immediate newborn care in that area. triangulation of data such as this can help drive change and improve the quality of care in health facilities as part of an established review of health outcomes.”. the availability of geographic coordinates and the type of health services offered in each health facility in the mfl can also inform objective investment in the building or upgrading of facilities. the proximity of facilities providing needed services can be analyzed, and the need for additional health facilities may be determined based on the target population. availability of routine health data in an electronic database enables the development and incorporation of decision support systems (dss) that can alert investigators whenever there is a deviation from the norm [28]. this can help improve the speed of response to outbreaks and adverse events, and in addition, improve the detection rates when variations are mild, which could otherwise be overlooked. swift response can reduce the impact of these outbreaks through the reduction in case fatality and associated morbidities. the mfl can also be used for other needs, which are summarized below: • application in the health insurance sector nigeria has a fledgling national health insurance program, in which application of the npi will be a significant standard. the npis will facilitate the integration and interoperability of proprietary software used for financial data management in health facilities, with the application the insurance program uses in claims ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 management. such connections can improve efficiency in the processes and the speed of claims management. • utilization in research with access to the comprehensive list of health facilities, researchers and program managers can use the mfl as a sampling frame for heath facility assessment and research. • unique patient identifier system further development of the npi for patient-level identification may provide a unique patient identification system. while maintaining patient confidentiality, this could provide access to health records in a nationally, interoperable electronic health record system. in this paper, we describe how this mfl documentation was conducted; the limitations of the mfl; and what we hoped to achieve with the development of the mfl. we believe that the basic processes and standards described will provide evidence and lessons learned for other countries adopting electronic routine health information systems. methods several consultations were held among stakeholders to determine how to optimally generate the unique npi. it was agreed that the npi follow a systematic coding process which embeds various characteristics in the generation of the intelligent identifiers (described in table 2). a npi for each facility was developed by concatenating a state code, lga code, health facility type code, health facility ownership status code, and a unique health facility serial number for the lga. this ensured that no two health facilities have the same codes across states. each unique identifier was made of 10 characters (written as “aabbcdeeee”). an intelligent identification system was chosen over a non-intelligent system so that the identifiers convey basic information to users. since several processes in the transmission of routine health data in nigeria remain paper-based, it was anticipated that an intelligent identifier system will also convey important information to his specialists using the paper-based data system. though the intelligent system was selected for use, concerns were raised on the sustainability of the mfl, should additional states or lgas be created. however, no new lga or states have been established for over 17 years, and hopefully this will remain. it is also expected that if a health facility changes its level of care, or there is a change in ownership class, a new npi would be obtained according to the guidelines. in this situation, the original npi would be discontinued. yet, these may be limitations of this intelligent system if facilities frequently change their statuses. the process for the collation of the health facility list followed a bottom-top approach. states were requested to compile and send a comprehensive list of health facilities within their locality to the fmoh (inclusive of the criteria described in table 2). the fmoh created a criteria template in microsoft excel and state-specific workbooks. these state workbooks were prepopulated with state and lga standard codes previously developed and utilized by the national bureau of statistics (nbs) and national population commission (npc) before distribution to the states (refer to table 3). for quality assurance, multiple columns were created to collect data which included the actual status of a facility before generating the corresponding code; for example, it was necessary to indicate that a health facility is a primary health facility before ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 entering “1” in another column). states in turn contacted and liaised with the lgas within their geographic coverage to collate this list inclusive of the criteria from the fmoh. the step-by-step processes of data collection and submission to the fmoh are described in figure 1. figure 1: collation process for the master facility list ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 table 2: parameters in the unique npi code area description character length state 01-37 2 characters (aa) lga 01-44* 2 characters (bb) health facility ownership (1) public [2] private 1 character (c) health facility type (1) primary [2] secondary [3] tertiary 1 character (d) unique lga level serial number 0001-9999 4 characters (eeee) * the upper limit varied in each state by the number of lgas in that state. table 3: an illustrative populated template used for data collection ekiti state health facility list lga ward names of health facility facility type ownership code s t a t e l g a f a c il it y t y p e o w n e r s h ip f a c il it y n o ado ekiti okesa prison clinic primary public 13 0 1 1 1 0001 ado ekiti police clinic primary public 13 0 1 1 1 0002 ado ekiti xts sch clinic primary public 13 0 1 1 1 0003 ado ekiti xts girls clinic primary public 13 0 1 1 1 0004 ado ekiti joe jane medical center secondary private 13 0 1 2 2 0005 ado ekiti adedoyin hospital secondary private 13 0 1 2 2 0006 ado ekiti odoado odo ado health center primary public 13 0 1 1 1 0007 ado ekiti grace of hope hospital secondary private 13 0 1 2 2 0008 the nbs and npc developed the state and lga codes, which were adopted nationally and ranged from ‘01’ to ‘37’ representing the 36 states and the federal capital territory. the health facility ownership is allocated a ‘1’ for public health facilities and ‘2’ for private health facilities. in addition, the unique identifier includes a number for the type/level of care, where ‘1’ is ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 indicated for primary health facilities, ‘2’ for secondary health facilities, and ‘3’ for tertiary health facilities. the last four characters are for the lga-level health facility serial number. since lgas are relatively small, it is unlikely that an lga will exhaust the 9,999 health facilities that the four characters have the ability to accommodate. the lga officers assigned serial numbers to the health facilities in his/her territory and the appropriate code based on the new guidelines. upon completion, the templates were submitted to the smoh, which in turn collated all the lgas within their geographic area before submitting to the fmoh. at the fmoh, quality assurance was implemented to ensure that there were no duplicate unique identifiers. in addition, the quality checks verified each of the assigned codes for the state, lga, health facility ownership, and the level of care for each facility was correctly allocated based on the criteria. results over 34,000 health facilities were issued unique identifiers during this process. there were 11,395 (33%) private health facilities and 23,028 (67%) government health facilities. in total, 30,345 (88%); 3,993 (11.6%); and 85 (0.25%) facilities were primary, secondary, and tertiary health facilities, respectively. the distribution of the health facilities by the state and level of care is described in table 4. discussion the future of the master facility list with a vision for a national his that is equipped with accessible and available information necessary for planning and monitoring the health system in nigeria, the mfl has a promising future. an enterprise approach to improving the national his is recommended to maximize the gains of applying ict to health data management [2,5]. the npis will serve as the unique key for identifying health facilities across the different hiss envisioned by the fmoh. the subsystems that will collectively comprise the nigerian his include: the routine his, human resources information system, logistics management information system, and the health finance information system among others. these components can be developed as standalone information systems, with the expectation of an eventual integration. this integration capacity is important with the increasing availability of open source applications that can address the different his aspects. leveraging the growing number of open-source applications created by software developers and international donors, such as the human resources information system (ihris) created by the u.s. agency for international development-funded capacity plus project and dhis developed by the university of oslo, development and adoption of standards (such as the npi) that will facilitate the integration and interoperability of these applications is critical. this could significantly reduce the cost of deploying a national his [29], which could be considerable. standards must be incorporated to facilitate the scalability of the system in a phased manner. furthermore, nigerian health facilities are increasingly adopting electronic medical records (emr) for routine patient information management. the ability of these emrs to export aggregate data into the national his will further strengthen it. thus, development of the system to identify and classify health facilities across multiple information systems will lay the foundation for a national his revolution and the success of a scalable platform. ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 table 4: distribution of health facilities across nigerian states type of facility state primary secondary tertiary grand total abia 519 95 1 615 abuja (fct) 559 90 7 656 adamawa 998 28 1 1027 akwa ibom 356 186 1 543 anambra 1360 123 2 1485 bauchi 1010 22 2 1034 bayelsa 172 58 1 231 benue 1111 94 1 1206 borno 421 52 1 474 cross river 597 135 2 734 delta 805 102 2 909 ebonyi 516 48 3 567 edo 870 48 6 924 ekiti 395 62 2 459 enugu 524 342 2 868 gombe 508 22 1 531 imo 808 527 2 1337 jigawa 595 16 2 613 kaduna 1524 33 4 1561 kano 1142 39 2 1183 katsina 1463 32 1 1497 kebbi 380 31 1 412 kogi 869 208 1 1078 kwara 567 172 1 740 lagos 1785 460 7 2252 nasarawa 874 33 2 909 niger 1567 16 2 1585 ogun 1372 145 3 1520 ondo 769 39 3 811 osun 1030 61 4 1095 oyo 763 470 4 1236 plateau 835 47 1 883 rivers 417 54 5 476 sokoto 668 43 2 713 taraba 1030 14 1 1045 yobe 466 28 1 495 zamfara 698 20 1 719 grand total 30345 3993 85 34423 ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 the collaborative effort between the states and lgas made the documentation of the mfl possible. the need for this standard was jointly agreed as a step towards improving routine health information management. additionally, several assessments highlighted the need for improved routine health data management and coordinated efforts [30-32]. one solution to improve the rhis is to use an electronic platform in the transmission of the data. lessons from countries that implemented similar activities emphasize the need for standardization and proper coordination of activities [18,33,34]. to promote sustainability and continued registration and generation of identifiers for new health facilities, training and development of guidelines for creating npis and updates to the mfl are needed. state and lga officers need to be empowered and have clear understanding on the processes for continuously updating the mfl. though the first level of development of the mfl has been for health facilities (i.e. hospitals and clinics), extending the list to laboratories and pharmacies is necessary to link them into the national his infrastructure. these are important health institutions that provide essential information for the planning and monitoring of population health. planning for their inclusion in the national his at an early stage is necessary. information system development since there can be a leading “0” for the first character in the npi, information system designers/ developers are advised to create this variable to store string or text in order to maintain the 10 character length. this field should be created as an indexed field without duplicates and possibly made the “primary key” of the database. it will be a fundamental field in the different information systems that are envisioned to activate the health information revolution in nigeria. health programs also storing facility-level data are advised to use the npi as the primary key in their program information systems, in order for the databases to be feasibly integrated. limitations of the mfl since the idea and development of the mfl, evolution of the activities has shown some limitations in its initial design and implementation. these are important lessons for other countries preparing for similar interventions. the activity was designed as a “snapshot”, without processes for continuously updating the list. as a result, the mfl has been static since it was compiled. thus, the fmoh has identified the need for the development of guidelines that will facilitate updates, as needed, of the registers at the lgaand state-levels. the guidelines will encompass processes to be followed when health facility changes; for instance, if a health facility upgrades from a primary to a secondary health facility, or a secondary to tertiary health facility; and if a health facility changes ownership from private to public (or vice versa). another key challenge identified was that an electronic application had been previously deployed for the management of routine health facility data before the development of the standardized mfl. this electronic application utilized system-generated unique identifiers that were nonintelligent and not desirable to all stakeholders. matching health facilities to the unique codes developed has posed challenges as there is no exact common field in the two databases, which resulted in a manual matching process. frequently, spelling errors in one database or name changes made the matching process more difficult. therefore, it is important to create the ojphi development of a master health facility list in nigeria online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e184, 2014 standard prior to implementation of an electronic application. additionally, it is necessary to develop processes for updating the mfl at the onset of the project as facilities continue to be built, change status, or close. conclusion unique identifiers are necessary for any database architecture. a well-designed process for their generation and maintenance must be incorporated in the planning stages of national his development efforts. countries adopting electronic routine information systems must develop and prioritize standards, such as the mfl, before system roll out. in addition to health facilities, other health institutions, such as laboratories and pharmacies, need to be considered when issuing npis, as they provide useful health data. the increasing availability of open-source electronic applications creates new channels for routine health information management in developing countries. these will help improve public health practices by increased data availability and transparency that, when utilized, can improve evidence-based decision making. adopting standards that will facilitate the integration and interoperability of different subsystems of a national his, as available, will ensure the relevance and sustainability of the information system. acknowledgements we appreciate the effort of all federal, state and lga officers who contributed to the completion of this mfl effort. without them, the mfl would not have been possible. we also appreciate the effort of ms. heather pitorak who helped to review an earlier draft of the manuscript and provided feedback for its improvement. financial disclosure authors working for measure evaluation were funded to support the federal ministry of health by the united states agency for international development (usaid) under cooperative agreement number: gha-a-00-08-00003-00. competing interests none references 1. braa j, kanter as, lesh n, crichton r, jolliffe b, et al. comprehensive yet scalable health information systems for low resource settings: a collaborative effort in sierra leone. amia annual symposium proceedings [internet]. 2010 [cited 2013 dec 1]. p. 372. available from: 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phd2,3,4, chester l. schmaltz, phd2,3 1department of mathematics, science, and informatics, college of professional advancement, mercer university 2missouri cancer registry and research center (mcr-arc), university of missouri-columbia (mu), 3department of health management and informatics (hmi), school of medicine, university of missouri-columbia (mu) 4mu informatics institute (muii), university of missouri-columbia (mu) abstract background: health-related data’s users have trouble understanding and interpreting combined statistical and mapping information. this is the second round of a usability study conducted after we modified and simplified our tested maps based on the first round’s results. objective: to explore if the tested maps’ usability improved by modifying the maps according to the first round’s results methods: we recruited 13 cancer professionals from national american central cancer registries (naccr) 2016 conference. the study involved three phases per participant: a pretest questionnaire, the multi-task usability test, and the system usability scale (sus). software was used to record the computer screen during the trial and the users’ spoken comments. we measured several qualitative and quantitative usability metrics. the study’s data was analyzed using spreadsheet software. results: in the current study, unlike the previous round, there was no significant statistical relationship between the subjects’ performance on the study test and the experience in gis tools (p = .17 previously was .03). three out of the four (75%) of our subjects with a bachelor’s degree or less accomplished the given tasks effectively and efficiently. this study developed a comparable satisfaction results to the first round study, despite that the previous round’s participants were highly educated and more experienced with gis. conclusion: by considering the round one’s results and by updating our maps, we made the tested maps simpler to be used by subjects who have little experience in using gis technology, and have little spatial and statistical knowledge. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi introduction there are enormous geographic information system (gis) technologies that have been created and designed to visualize different kinds of health and health-related data. these tools should be designed and modified to meet the perceptions and needs of these technology’s possible users [1]. software developers and designers, as well as the gis technology innovators should concentrate on how to make this technology effective and efficient for the targeted users [2,3]. keywords: geographic information systems, interactive maps, missouri cancer registry, naaccr, usability abbreviations: gis: geographic information system hmi: health management and informatics irb: institutional review board mcr-arc: missouri cancer registry and research center mph: master of public health naaccr: north america association of central cancer registries ore: overall relative efficiency sus: system usability scale tbe: time-based efficiency tcr: task completion rate correspondence: 3001 mercer university drive., aacc building atlanta, georgia, 30341 benramadan_aa@mercer.edu office phone: (678) 547-6240 doi: 10.5210/ojphi.v11i2.9483 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi public health professionals have started using specific advanced software to examine and illustrate population-based databases. illustrating and visualizing this kind of information becomes essential and important to make a measurable impact on the public health problems in any community. this type of technology has been influential on public health research, as well as on development of new effective public health policy. therefore, we have to be sure that the population-based databases are would be analyzed intelligently and examined appropriately to yield reliable outcomes, and that the results do not mislead the targeted audiences [4]. the previous usability literature pointed out that most of any new numerical technology users face difficulties in interpreting the combined and multiple sources data [5-7]. the same difficulties have challenged new gis tools because of the combined geographical and statistical data of these tools. the scientifically proved interpretation was: insufficient knowledge and inadequate hand-on experience on using gis tools, ignorance and a decline to practice this technology between its prospective users, and because there were usability issues and complexity perception among the possible users towards this kind of technology [8]. as previous scientific research revealed, regular and interactive mapping reports can produce knowledge, create evidence, and augment strategies [9]. therefore, every interactive mapping report should carry a clear aim and convey a definite message to the targeted audiences [10]. these interactive maps must include citations and details of used information resources and the detailed methodology which followed the production of visualized results. the gis technology related literature revealed that the mapping reports should undergo strong scrutiny and evaluation, using representative samples of the users, to assess the usability and make this technology fit the users’ needs and preferences [4] the collaboration between public health experts and the other specialists of the same research and practice interests has been proven scientifically and this relation’s positive impact on the public health problems and disparities has been confirmed [11]. using highly advanced and sophisticated gis tools will help public health experts to plan and create cost-effective health-related strategies and policies [11]. during the third millennium, the health related maps have changed from being static to be interactive [12]. the scientific literature discovered that the gis technology users prefer dynamic interactive maps [4]. the same literature concluded that the atlas creators need to take in their account the potential users’ preferences and perceptions. the map developers should consider the users’ needs starting from the planning and designing processes, and ending by evaluating the maps before and after releasing them to the actual users [13]. many cancer registries have established interactively visualizing their data’ outcomes but small number of them are evaluating the usability of their maps [14-16]. by conducting this two-round study we seek to fill this breach and give a standard model in evaluating cancer registries’ maps and help in tailoring these tools according to the potential users’ insight and favorites. in the first round of the study, the investigators conducted a usability testing trial to assess the usability of the two published missouri cancer registry and research center (mcr-arc)’s interactive reports. the first round’s participants were faculty and staff of master of public health usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi program (mph) and the department of health management and informatics (hmi) at university of missouri-columbia [17]. in the current study, we conducted a second round of the same first round’s methodology to measure effectiveness and efficiency of the published instantatlas reports of mcr-arc after we modified them according to the first round results [17]. in this round, we selected a convenience sample of cancer professionals who attended the north america association of central cancer registries (naaccr) conference-june, 2016. the second round of study also aims to evaluate if and for how extent the users’ action will be influenced by the users demographic information, experience, education level, and the work type. methods study design the investigators selected a mixed methodology tactic. the tested mapping reports are already published on the mcr-arc (see multimedia appendices 1 and 2) [17-19]. the trial elements were the same round one’s components: a pretest questionnaire, the multi-task usability test, and the system usability scale (sus) per every participant respectively [17,20]. the pretest questionnaire the questionnaire involved inquiries on every subject’s age, race, work type, education level, total experience in public health field, and experience in gis tools use (see multimedia appendix 3). this step was followed by the multi-task test. multi-task usability test this stage included ten independently ranked tasks, which cover most of the maps’ functionality. these tasks were handled by the study subjects individually to test the usability of the tested instantatlas reports. the multi-task scenario was constructed by the study’s investigators grounded on the anticipated functionality of the tested maps. this phase aimed to precisely estimate the efficiency and effectiveness in terms of task completion success rate and task completion time [21]. all the tasks were in the same classification and context for all subjects (see multimedia appendix 4). the system usability scale (sus) this phase was composed of a manufacturing and simple ten-item scale to assess the subjects’ independent assessment of the experienced mapping reports’ usability. this phase was immediately conducted after the multi-task scenario phase. the sus score range is between 0 and 100. sixty-eight or above has been considered as satisfactory based on previous usability literature and upper score up to 100 is the optimum to finest score [20]. scores above 68 points are acceptable according to usability scholars and higher scores represent the optimal to best score [20]. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi participants the study’s protocol was accepted by the health sciences institutional review board (irb) of the university of missouri-columbia. the current round’s primary investigator attended the naaccr-2016 conference and she convinced group of attendees to participate in the study and conduct the study’s trial. the convenience sampling method was selected to collect the current study’s participants. the investigators conducted the study’s experiment on the first reacted thirteen participants. as the usability scholars confirmed, five was the smallest number of subjects to run a fruitful usability study; a five subjects study enables revealing from 55 to 100% of the usability issues of any experienced material [22]. the investigators increased the number to thirteen subjects to expose as many as probable of the usability issues of our refined published mapping reports [23]. study procedure each subject tried ten tasks in a secure place for an average of 30 minutes per participant. a specific computer laptop was used to conduct the study. the researchers installed a microsoft windows-7 software, windows media player, to audio-video record the laptop’s screen while the subjects performing the experiment. task on time and task completion success rates were analyzed manually based on the recordings. the subsequent usability metrics were also measured: performance metrics some metrics were measured to evaluate the effectiveness and the efficiency of the tested maps and to explore the maps’ usability issues. the critical error is when the participant required assistance from the test viewer to finish a task [21]. the investigators measured the following metrics: effectiveness, task completion rate (tcr): tcr is a measure of tasks that were completed without critical errors, and the outputs of the task were correct [25]. tcr equals to the effectiveness of the tested maps and was represented in two different means: by participant and by task. tcr per participant: the percentage of tasks that were successfully completed by a participant [24]. tcr per participant = number of tasks completed successfully total number of tasks undertaken ∙ 100% tcr per task: the percentage of participants who successfully completed a given task [24]. tcr per task = number of participants who completed successfully total number of participants ∙ 100% efficiency: the efficiency is defined according to the iso-9241 as: “resources spent by user in order to ensure accurate and complete achievement of the goals” [25]. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi the investigators calculated the efficiency and the productivity of the tested mapping reports using the following couple of formulas time based efficiency (tbe) [24] �̅�𝑡,𝑗 = 1 𝑁 ∑ 𝑛𝑖𝑗 𝑡𝑖𝑗 𝑁 𝑖=1 𝑁 = total number of tasks 𝑛𝑖𝑗 = result of task 𝑖 by user 𝑗 = { 1 if the user successfully completes the task 0 otherwise 𝑡𝑖𝑗 = the time spent by user 𝑗 on task 𝑖. overall relative efficiency (ore) [24] �̅�𝑗 = ∑ 𝑛𝑖𝑗𝑡𝑖𝑗 𝑁 𝑖=1 ∑ 𝑡𝑖𝑗 𝑁 𝑖=1 ∙ 100 user satisfaction sus scale was used to assess the satisfaction per study subject [20]. see the detailed sus mechanism under the study design section. factors affecting the participants’ performance the current study researchers measured some factors those they expected might impact the participants’ performance and satisfaction during the trial [26]. those factors were: the participants’ education level, work type, experience in healthcare field, and previous experience with mapping reports and gis tools. the investigators chose different statistical measures, as needed, to assess the chosen factors’ relationships (wilcoxon-mann-whitney test, pearson correlation test, and/or simple regression test) [27]. the intended sample size of this study was small since we primarily wished to uncover major usability problems; post-hoc power calculations for simple linear regression with the observed sample data indicates that the power for testing the relationships between the participants’ factors and the tcr or sus ranged between 7% and 32% [28]. we used a type i error rate (α) of 0.05 for the hypothesis tests conducted in this project. results 1. participant demographics the study researchers interviewed thirteen cancer health professionals, four males and nine females. their ages ranged from 29-56 years old (mean=40.85 years old, median=40 years old). all of the thirteen participants were cancer professionals who attended naaccr-2016 conference. the race of our participants was as following: seven whites, four blacks, one asian, and one native hawaiian or other pacific islander. three of the subjects hold phd degrees, six hold master degrees, one bachelor holder, one associate degree holder, and only one got some college level education. the associate degree and the some college holders were working as certified cancer registrars in two separate central cancer registries. at the time of data collection, usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi the phd holder subjects’ work roles were: a cancer researcher, an epidemiologist, and a director and associate professor. the master degree holders’ work roles were: a program director and epidemiologist, a statistical consultant, a research coordinator, a software engineer, a cancer surveillance and epidemiologist, and an epidemiologist. the bachelor degree holders were functioning as: a research analyst and public health epidemiologist, and an abstractor and quality control manager. the associate degree holder was functioning as a certified cancer registrar and the subject who holds some college education was working as a data manager in a central cancer registry. the total experience in public health for the all subjects ranged between 2 years to 19 years (average= 10.38 years, median= 10 years). the subjects’ total experience in using gis tools in work was between null experience to 10 years (mean= 2 years, median= 0 years). 2. reports’ effectiveness and efficiency the mapping reports’ effectiveness effectiveness per participant one of the participants could not complete three successive tasks #4, 5, & 6. the subject held a bachelor degree and she did not have any previous experience in using gis tools and interactive mapping reports. another subject could not get the assigned results for two successive tasks #4 & 5. this subject holds a phd degree and have had an experience in public health for 15 years, an experience in using gis tools at work, and work as a director at one of the central cancer registries. two of the subjects could not complete two non-successive tasks: one of these subjects holds master’s degree and could not finish the tasks #5 & 8 successfully and another subject holds just some college education missed the tasks #4 & 8. the master’s degree subject had experience in public health field for 15 years and five years of using gis tools and software, and the some college degree holder had experience of 19 years in public health and no previous experience in using or reading gis tools. four of our subjects could not accomplish just one task and the missed tasks were #5, 6, &9. two of these participants were phd holders, one master degree holder, and a bachelor degree holder. the two phd holders had experience of 8 and 10 years in public health field and experience of 4 and 5 years of using gis tools in their daily work. the master degree holder had 17 years of public health experience and no previous experience in gis tools. the bachelor degree holder had experience in public health field of 10 years with null experience in using gis tools. only four of our 13 participants (30.76%) achieved the tcr of 100%. three of these participants were master’s degree holders and one of them held an associate degree in science. these four subjects hold experience in public health field between two to ten years, and all of them carried no previous experience in using gis tools and interactive mapping reports. twelve of our 13 participants (92.30%) were able to accomplish the tasks with tcr of 79% or above. the only one subject who got tcr of <79% was a phd holder and a director of a central cancer registry with 12 years public health experience and null gis experience. the results were ranged from 70% to 100% as figure (1) shows. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi figure (1). task completion rate for all tasks per participant. blue bars indicates participants who finished the trial with > 78% tcr; purple bar indicates a participant who finished the trial with <78% tcr. effectiveness per single task the above task completion formula were also used to calculate the task completion rate per task of the study’s ten tasks. the results are presented in figure (2) figure (2). task completion rate per task for all the participants. blue indicates tasks involving the area health profile (tasks 1–6); red indicates tasks involving the double map (tasks 7–10) usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi as figure (2) shows, four tasks (38.46%) reached the 100% completion success rate, one task (7.69%) got 90% of the completion success rate, one task (7.69%) got 85% or higher completion rate and three tasks (23.08%) got less than 80% of completion success rate. the tasks #1, 2, 3, 7, and 10 got tcr of 100%. as the multi-task scenario shows (see the appendix), the tasks #1, 2, & 10 had very simple context and functionality. the mentioned tasks do not inquire to catch or interpret difficult epidemiology or statistical outcomes. task # 3 was ranked as a one of the complicated tasks, but it was successfully accomplished by all the subjects. task #6 was ranked as one of the complicated tasks, but was accomplished effectively in both study rounds. task #6 was effectively accomplished by the study subjects in the two rounds of the study, with completion rate of 100% in the first round and just 85% in the second round. the tasks #4, 5 & 8, had the lowermost tcrs. these tasks were not effectively conducted because the tasks’ tcrs were less than 78%. these tasks were ranked as difficult tasks and require certain abilities and understanding to be completed successfully. some of these tasks are multi-stepping tasks and needs special knowledge to be figured out. from the first round of the study, the same tasks in addition to the task # 3 got the lowest completion rates [17]. mapping reports’ efficiency and productivity the investigators used the audio-video records to measure the time per task which was measured from starting the examined task to the time of beginning the next task. the median of task #8 was the highest followed by # 4, 5, 6, and 3. this was relatively connected to the task’s difficulty. table (1) shows the times which our subjects spent on the study tasks individually. table (1). time on the study tasks task # task time range task time mean task time median 1 6-17 seconds 9.77 seconds 7 seconds 2 6-300 seconds 44.92 seconds 16 seconds 3 6-114 seconds 28.69 seconds 19 seconds 4 9-166 seconds 83.38 seconds 113 seconds 5 12-245 seconds 85 seconds 51 seconds 6 15-196 seconds 82.62 seconds 78 seconds 7 6-43 seconds 11.62 seconds 8 seconds 8 50-429 seconds 182.77 seconds 179 seconds 9 9-26 seconds 13.75 seconds 12 seconds 10 3-7 seconds 5.54 seconds 6 seconds usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi time based efficiency (tbe) and overall relative efficiency (ore) the time based efficiency rates were ranged between 0 goals/second for task #8 and 0.12 goals/second for task #1. figures (3) and (4) show the time-based efficiency and the overall relative efficiency for every individual performed tasks. figure (3). time-based efficiency (tbe) per task from figure (3), the tbe per task was different for the all the tasks. task #10 gained the maximum tbe (.35 goals/second) and this result was associated with ease of the task (close the map), followed by the tasks #7, 9 and 1 and all of them ranked as simple and straight forward tasks (proceed to the “double map” link on the desktop, open the “area profile” map link in the desktop, and check the sources of our mapping report data). tasks #5, 6, and 8 got the lowest tbe levels, 0 goals/second for the task #8 and .02 goals/second for the tasks # 5 & 6. tasks #5,6,&8 were ranked as the most complicated tasks. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi figure (4). overall relative efficiency per task. orange indicates tasks with 100% ore per task, blue indicates task with less than 100% ore per task figure (4) shows that the highest ore rates were for the tasks #1, 2, 3, 9& 10 and they were categorized as easy tasks except the task #3, which is ranked as a complex task. task #8 got an ote of 93% in comparison to almost ote of 90% in the first round [17]. task #6, a ranked complicated task, followed with an ote of 88% ore rate, in comparison to the ote of about 95% in the first round [17]. tasks # 4, 5, & 7 had the lowest ore per task. 3. users satisfaction the scale was given to every study subject to be completed at the end of the study experiment. this tool was constructed to assess the expectations and the insights of the users regarding the tested systems [20]. figure (5) presented the study subjects’ sus scores. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi figure (5). system usability scale (sus) scores of the study’s participants. brown color indicates sus score of > 68 points, and blue color indicates sus score of < 68 points as figure (5) illustrates, the sus score range between 0 and 100. the sus scores for all the current study’s subjects stretched from 25 to 82.5 with mean of 58.85 points and median of 65 points. four of our 13 (30.77%) participants got a sus score of more than 68 points, and the remaining nine subjects (69.23%) did not reach the accepted satisfaction point, as in the first round of the study [17]. 4. factors affect the participants’ performance education level and work type factors the researchers conducted wilcoxon-mann-whitney test to explore the statistical association among the education level of the participants from one part and the task completion rate and the sus scale for the study subjects. there was no significant statistical difference between the performance of the graduate school degrees holder participants and the undergraduate degree or less holder participants (p = .72). the results show that there was no significant statistical relationship between the participants’ education level and the satisfaction score of the sus (p = .21). the same inferential test was also used to assess the statistical relationship between the participants’ performance on the test in terms of the task completion rate and the work type of the same participants as cancer researchers and epidemiologists from one part, and participants who do not do research and do not have previous experience in cancer epidemiology or surveillance on the other part. the relationship between the studied two factors was statistically insignificant (p = .63). usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi all the above results were comparable to the first round’s findings [17]. experience in healthcare field and experience with mapping reports and gis tools factors a simple regression test was used to search if there is a significant statistical relationship between the performances of the study participants in the current usability trial and between both: the experience in healthcare field and the previous experience with mapping reports and other gis tools. the statistical relation between the performances of the participants in the trial and between the experience in healthcare field was insignificant (p = .51). in the current round, there was no significant statistical relationship between the subjects’ performance on the study test and the experience in gis tools (p = .17), while in the previous round the relationship was significant [17]. after we conducted a simple linear regression test, there was no significant statistical relationship between the sus levels and both the experiment completion rate and the previous experience with gis tools for the study participants. the p values for these results were (p = .67) and (p = .61) respectively. table 2. demographic and previous expertise factors of the study participants versus the trial’s tcr and the participants’ sus scores the studied factors p education level vs tcr .72 education level vs sus score .21 work type vs tcr .63 previous experience in healthcare field vs tcr .51 previous experience in gis use vs tcr .17 previous experience in gis use vs sus score .61 5. correlation between the studied usability elements (effectiveness, efficiency, and satisfaction) we measured the correlation between the major three usability elements, effectiveness, efficiency, and the satisfaction of the users. the current study revealed the following findings: there was a very weak correlation between the task completion rates and the sus scores for the participants (r = .31, p = .31), while the correlation was strongly high between these factors in the first round of the study (r = .7, p = .08). the researchers studied the relation between the task completion rates by task from one side and both the tbe and the ore factors. the current study discovered that there were strong correlation usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi between the effectiveness and the efficiency usability elements of the studied maps and the correlation was respectively as following (r = .39, p = .18) and (r = 81, p = .0007). the correlation is weaker than the correlation between these factors in the first round of the study (r = .50, p = .25), and (r = .92, p = .003). the current research also discovered a very strong correlation between the time spent by the participants on the given tasks and the study subjects’ satisfaction (r = .92, p = .000009). this correlation is stronger than the correlation between the same factors in the first round of the study (r = .70, p = 0.07) [17]. table 3. correlation between the studied usability elements (effectiveness, efficiency, and satisfaction) the studied factors correlation coefficient p tcr vs sus score .31 .31 tcr vs tbe .39 .18 tcr vs ore .81 .0007 efficiency per participant* vs sus score .92 .000009 *: the total time in seconds of the whole trial per participant discussion main findings the current round was conducted to assess the usability of the published mapping report of mcrarc using cancer professional participants. this multi-approach usability testing methods might aid map creators to design friendly-used mapping reports and help them to access the maps’ prospective users’ anticipations. the study findings support using usability testing studies before and after releasing the mcr-arc maps to the potential users, and support extensive examination of the mapping reports to improve their usability. participant demographics the researchers conducted this study as a second round of the previous pilot usability study on the same tested mapping reports which are published by mcr-arc. previous study includes seven convenient academic health professionals [17]. the investigators refined the tested maps and the usability study’s multi-tasks scenario considering all the recommendations and preferences from the first round subjects. the investigators assumed that the first round’s results might be tightly connected to the favorites and the insights of the academic health professionals who did not handling cancer data registration and/or analysis, did not directly make and advocate for cancer policy, and did not use cancer mapping reports in their daily practical life [17]. that is why the investigators tried to apply the experiment on the cancer officials who were attending naaccrusability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi 2016 conference aiming to explore the usability of the refined tested maps using a convenience sample of cancer officials. effectiveness and efficiency effectiveness per participant in the current round, we are aiming to attain 100% of completion success rate per participant, but the usability specialists allocated that for any usability study it is ok to consider 78% as the average tcr per participant [24]. according to the results in figure (1), the trial was completed effectively by twelve out of our 13 participants (92.30%). the trial was conducted effectively despite the diversity in the education, public health field experience, or gis experience of the study subjects. a phd holder subject with a heavy cancer and public field experience could not accomplish the minimum border of the accepted tcr, while the other lower educated and less experienced subjects could handle the test effectively. effectiveness per task as the results section shows, the ranked easy tasks were accomplished effectively than the tasks were ranked as complicated. these findings supported the previous study’s round and a previous scientific study’s results, which revealed that tasks accomplishments are influenced by the task context’s complexity [17,29]. surprisingly, task #6 was graded as one of the complex tasks but was conducted successfully in both study rounds [17]. in this study round, it could be explained that because the task is very linked to the prior tasks and it was easy to handle after the subject solve the former tasks. the variance in tcrs per task between the two study’s rounds might be interpreted as following: the second round’s participants’ educational backgrounds were very diverse and not all of them had solid epidemiological and/or statistical background that the first round participants hold. efficiency as in the first round of the study, there was difference in the time per task even with the tcr of 100%. the study subjects in the two study rounds spent various time to accomplish the given tasks. as we revealed from the first round of the study, these findings were relatively related to the complexity of the tasks, where the complex tasks toke longer time than the easy ranked ones [17]. also as we revealed from the first round, this round of study discovered that even the simply ranked tasks, some of the subjects consumed longer time to accomplish the tasks effectively than the other subjects [17]. these results are unpredictable because, as we discussed previously, the study investigators adjusted the tested maps according to the first round subjects’ comments. we were looking for making the tasks performed by all the users within comparable times. based on the tbe results from the previous study round and the current one, the investigators expected that in addition to the intellect and knowledge which are essential to achieve these tasks the usability usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi problems also could even make the given tasks more difficult than the study researchers assumed before they started the study [17]. for both study rounds, we revealed that otes for most tasks were fairly related to the difficulty of these tasks’ context, and this supports the previous usability literature findings [17,22]. but surprisingly, this is inapplicable for the previously ranked complicated tasks, #3, 6 & 8. after close scrutiny of the recordings, we discovered that the repeatability and re-doing of the preceding tasks profoundly influenced the tasks #3, 6 and 8 completion by the study subjects in both study rounds. participants satisfaction sixty eight points or more has been considered satisfactory according to usability literature and advanced marks through 100 points exemplifies the optimal to greatest sus score. in comparison to the first round of the study, we found that both rounds have an averages and the medians measures of less than 68 points and were closed to each other [17]. our explanation to the comparability of the satisfaction results between both study rounds is that in the second round, we tested cancer professionals of more varied races and with mixed graduate and undergraduate levels and most of these participants had null previous experience in using gis. these participants developed a comparable satisfaction results as the first round study’s subjects, who were holding graduate degrees and had rich experience in statistical and epidemiological knowledge, as well as previous experience in using gis tools [17]. we assumed that, when we updated our maps according to the first round’s results, we simplified our tested maps to fit the needs of our potential users of different biographic and experience levels. usability scholars revealed that the users’ insights and anticipations are critical to building faultless technology. considering all the users’ commentaries are essential to make the technology more satisfactory and useful [30]. we considered the participants’ texts which were taken at the end of every trial, in addition to the audio-visual records of all the participants to explore more usability issues of the tested maps. the revealed usability issues helped us to explain and find out why some of the assigned study tasks were hard to be handled by the highly educated and knowledgeable subjects. the study researchers considered all of the discovered usability problems, and accordingly, will refine our published mapping reports and publicize them on the registry’s website. factors affect the participants’ performance there weren’t significant statistical relationships between all the studied factors. while there was a significant statistical relationship between tcr rates and the previous experience in using gis tools in the first round of the study, in this round, there is no significant statistical relationship between these two factors. the current study’s finding might be explained as following: by considering the round one’s results and by updating our maps, we made the tested maps simpler to be used by the subjects who had null experience in using gis technology. both study rounds revealed that there is no dependency of the sus score on the both the tcr and the previous experience with gis tools for the study participants as the investigators assumed [17]. usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi the correlation between the studied usability elements (effectiveness, efficiency, and satisfaction) the results revealed positive correlation between the three usability elements which range from very weak to very strong positive correlations. the strong correlation between the sus and the efficiency is very reasonable, and this supported the findings of the previous round [17]. this could be explained that by updating the maps, we made the maps more usable by the users and the participants conducted the trial more efficiently and they were satisfied about the entire experience. strength of the study this study is the second round of the usability research conducted on the published mcr-arc instantatlas mapping reports to evaluate the quantitative and qualitative measurements using a larger sample than we used for the first round [17]. the investigators used a sample of 13 participants who are professionals in cancer epidemiology, cancer surveillance and/or cancer research. we are assuming that the second round professional participants will be the potential users of the interactive mapping reports than the academia people who had been selected for the first round trial. larger sample of cancer professionals and testing the modified reports based on the first round’s findings, make this round more reasonable and the results tend to be more applicable [17]. these results might be generalized to assess the usability and the functionality of all the mcr-arc’s mapping reports. conclusion current round of the study measured the three main components of the refined tested mapping reports: effectiveness, efficiency, and user satisfaction. the study results supported the first round’s findings that the three usability elements are correlated positively to each other. as we revealed from the previous round, the examined reports’ effectiveness results were superior in comparison to the efficiency and satisfaction results. as we pointed out from the first round, we revealed that the effectiveness and efficiency metrics were strongly associated with the trial tasks context’s complexity. as we concluded previously in the previous study, the graded simple tasks were achieved effectively and efficiently easier than the graded complicated tasks. the sus scores of the current study round were comparable to the previous round study’s sus scores with a poor average and median while the sus score ranges of the both studies were ranged from very poor to excellent scores [17]. the current study, as the opposite of the previous round, showed that there was no significant statistical relationship between the subjects’ performance on the study test and having previous experience in using the gis tools [17]. updating the tested maps and tasks, made the reports simpler to be used even by users who do not have previous gis experience. in a nutshell, the mapping reports must be extensively refined and modified to correct all the revealed usability concerns and to meet the perceptions and requirements of the maps’ potential users. also, the two round study methodology could be applied on other mcr-arc atlases, and usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi might be served to improve the usability of these maps. including the gis tools’ users should be considered at the initial phases of scheming and creating the gis reports. limitations of the study there are several limitations for the study. the sampling technique was the convenience sampling method. the methodology might not determine all the detailed performance and behavioral usability metrics of the participants. the audio-video records were analyzed manually by the primary investigator. we might also think of introducing more sophisticated methodology using advanced usability software, for example, advanced usability software and/or eye tracking software to record and analyze our usability data. future directions and recommendations we are recommending conducting usability testing pilots on all current and the future designed mapping reports by the mcr-arc. also, we are targeting that usability assessment should begin from the planning procedure, map release, and after dissemination the mapping reports by using a representative sample of these maps’ probable users to precisely assess the usability of these maps. additionally, we are hoping that we could use more advanced usability tools in future to evaluate our released maps. acknowledgements core activities of the missouri cancer registry is supported in part by a cooperative agreement between the centers for disease control and prevention (cdc) and the missouri department of health and senior services (dhss) (5nu58dp003924-04/05) and a surveillance contract between dhss and the university of missouri. the publication’s content is exclusively the responsibility of the authors and does not necessarily reflect the views of the funders. conflicts of interest none declared usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi multimedia appendix 1 usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi multimedia appendix 2 multimedia appendix 3 usability assessment of the missouri cancer registry’s published interactive mapping reports: round two online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e3, 2019 ojphi multimedia appendix 4 references 1. haklay m, tobón c. 2003. usability evaluation and ppgis: towards a user-centred design approach. int j geogr inf sci. 17(6), 577-92. doi:https://doi.org/10.1080/1365881031000114107. 2. brooks jr fp. the mythical man-month: essays on software engineering, anniversary edition, 2/e. pearson education india; 1995 sep 1. 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30. el-abed m, giot r, hemery b, rosenberger c. a study of users' acceptance and satisfaction of biometric systems. insecurity technology (iccst), 2010 ieee international carnahan conference on 2010 oct 5 (pp. 170-178). doi: 10.1109/ccst.2010.5678678 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14587545&dopt=abstract https://doi.org/10.3758/bf03195514 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25470668&dopt=abstract https://doi.org/10.1111/jep.12277 https://doi.org/10.1016/j.gpb.2015.11.004 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26829645&dopt=abstract 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts user-customizable health pattern detector framework: twitter analysis example lianna m. hall*, kevin k. nam, jason thornton, marianne deangelus and timothy j. dasey informatics and decision support, mit lincoln laboratory, lexington, ma, usa objective to demonstrate a framework for user-customizable text processing that can improve the efficiency and effectiveness of mining text for biosurveillance, with initial application to twitter. introduction early detection of a disease outbreak using pre-diagnostic textual data is available in biosurveillance systems with the integration of data such as chief complaints. social media has been identified as an additional pre-diagnostic data source of interest1. textual data analysis in public health is usually based on a keyword search and often involves a complex boolean combination of terms that produce results with many false alarms. epidemiologists may wish to query the data differently based on the event of interest, yet the process is laborious to weed out uninteresting content. specialized detectors that decide on the topical relevance of keyword search usually require developers to adapt methods to new uses, which is a timeand costprohibitive activity. users need the ability to rapidly build text content detectors on their own. methods a generalizable detector framework called customizable pattern analytics (cpa) was adapted and tested with the twitter biosurveillance data mining application. cpa was originally developed for detecting features in videos2, but has a general purpose mathematical framework that allows migration to other data discrimination problems. cpa automatically reconfigures multiple stages of a detector processing chain (e.g. feature selection and classification) based on binary feedback from the user on the utility of returned results. it does so by computing a wide range of features about the data, and adjusting the feature weighting and the decision boundary on the combined features based on user feedback. the result is a user-built detector that can be specific to a situation. for twitter processing, cpa analyzes many characteristics of each tweet that is returned from a keyword search, including term frequencies, common word combinations, content flags, and metadata (e.g. location). a user interface transparently shows ranked examples of the returned tweets of suspected relevance to the user. the user can select examples as either relevant or irrelevant, and the interface progressively displays a new set of options based on an underlying reengineering of the detector by cpa. results the figure shows an example user interface screenshot. the application is available for demonstration. performance curves (i.e. true vs. false positive rate) show that cpa achieves superior performance than that of keyword search alone or from one specific type of text analysis. importantly, the type of text processing to apply is varied based on the particular keywords used in the search. cpa can select the most productive combination of text processing methods to apply that best match the user-supplied yes/no labels. empirical trials have demonstrated that there is a significant accuracy boost with as few as 5 yes/no labels, and that this accuracy approaches its upper limit after several dozen feedback labels. conclusions application of cpa to twitter data analysis was demonstrated with superior performance to simpler text analysis and with the versatility to be applied to a wide range of microblog processing tasks. these methods should be directly applicable to similar text processing tasks (e.g., chief complaints). automatic or user-guided keyword expansion methods are being investigated to extend the capability. extensions to other text processing problems are imagined, such as for electronic health record analysis. example user interface keywords visual analytics; user adaptable search; social media acknowledgments this work is sponsored by the assistant secretary of defense for research and engineering (asd(r&e)) under air force contract #fa872105-c-0002. opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the united states government. references 1. cdc. health department use of social media to identify foodborne illness — chicago, illinois, 2013–2014. mmwr 2014; 63(32); 681685. 2. thornton j, deangelus m, chan m. online customization of video detection capabilities. ieee int’l conf. on security technology; 2014 oct 13-16; rome, italy. accepted for presentation. *lianna m. hall e-mail: lianna.hall@ll.mit.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e132, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts surveillance of cardio vascular risk factors among patients undergoing coronary artery bypass surgery shumaila furnaz* and hasanat sharif surgery, aga khan university and hospital, karachi, pakistan objective to investigate the prevalence of cardiovascular risk factors among patients undergoing elective coronary artery bypass graft surgery (cabg) in karachi, pakistan. introduction according to world health organization report 2011, coronary artery diseases are the number one cause of death globally: more people die annually from coronary artery diseases than from any other cause. an estimated 17.3 million people died from coronary artery diseases in 2008, representing 30% of all global deaths. of these deaths, an estimated 7.3 million were due to coronary heart disease and 6.2 million were due to stroke. lowand middle-income countries are disproportionally affected: over 80% of coronary artery diseases deaths take place in lowand middle-income countries and occur almost equally in men and women. populations living in low and middle income countries are exposed to more risk factors associated with coronary artery disease as well as other non-communicable diseases and are less exposed to prevention efforts than people in high income countries. methods cardiothoracic surgery quality improvement is a core value of healthcare provision. in order to improve quality of care, information on key indicators needs to be systematically collected and maintained. in 2006, the cardiothoracic department at aga khan university developed an infrastructure that would enable us to answer the more challenging research queries in cardiac surgery practice. the resulting electronic cardiothoracic database is based on the european association of cardiothoracic surgeons database and the society of thoracic surgeons database. we chose the following aspects of patient care to be included in the database form: pre-surgery patient condition and medications, anesthesia information, perfusion information, surgery information, recovery information, status of the patient atdischarge,30-days and 365-days post-surgery follow-up information . information was collected through structured questionnaire by trained data abstractor and entered into microsoft access software and analyzed in spss(statistical package of social sciences) software. results in this prospective study 2073 undergoing elective cabg were included. mean age of the patients was 54.85 9.7 years, 14.7% of patients were females. prevalence of risk factors among the study population, included: 47.10% were overweight, 14.7% were obese, 47.7% were diabetic, 69.50% were hypertensive, 50.20% were dyslipidemia, 47.7% were as smokers, 9.2% were in renal failure, addition, 53% of patients had family history of coronary artery disease, 46.7% had a history of myocardial infarction (mi). the,operative mortality rate was 0.3%.,post surgery complications included renal failure in 14.8% of patients, arrhythmias in 7%, reoperation in 2.3%, prolonged ventilation in 3.1%. conclusions there is a high prevalence of risk factors like dyslipidemia, hypertension, diabetes and smoking for ischemic heart disease in our population. once we establish this fact we will work to control the risk factors and reduce the burden of disease so that’s why this study is being done keywords coronary artery bypass grafting; risk factors; surveillance acknowledgments mubashir khan, hasanat sharif *shumaila furnaz e-mail: shumaila.furnaz@aku.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e190, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts comparison of ilinet and essence for influenza surveillance at the local level sarah connolly1, 3 and gregory danyluk*1, 2 1florida department of health, seminole county, sanford, fl, usa; 2epidemiology, florida department of health, polk county, bartow, fl, usa; 3emory university, laney graduate school, atlanta, ga, usa objective to compare influenza-like illness (ili) data reported to the centers for disease control and prevention (cdc) u.s. outpatient influenza-like illness surveillance network (ilinet) with discharge diagnosis data for influenza from the same reporting source obtained through the electronic surveillance system for the early notification of community-based epidemics (essence) in seminole county, florida. introduction ilinet is used nationwide by sentinel healthcare providers for reporting weekly outpatient visit numbers for influenza-like illness to cdc. the florida department of health receives urgent care center (ucc) data through essence from participating facilities. seminole county is unique in that its four sentinel providers located in separate uccs report into both systems, and all their discharge diagnoses are available through essence. however, the reported number of patients being discharged from those providers with diagnoses of influenza is not equivalent to the number of cases reported into ilinet. data from the two systems were therefore compared both among and between the individual sentinel providers in order to determine the extent of the variation over four influenza seasons. methods influenza-like illness is defined by the centers for disease control and prevention (cdc) for surveillance purposes as “fever (temperature of 100°f [37.8°c] or greater) and a cough and/or a sore throat without a known cause other than influenza.” [1] ilinet data from sentinel providers for percent ili visits were extracted for each of the influenza seasons (reporting weeks 40 through 20 of the following year) from 2009 through 2013. essence data for patients with a discharge diagnosis of influenza were extracted from the same providers for the same seasons, and the percentage of influenza discharge diagnoses was calculcated based on total visit counts. pearson correlation coefficients were calculated to compare ilinet and essence data for each influenza season and provider location, both for the entirety of the season as well as identified peak weeks (table 1); for the few instances where ilinet data had not been reported, those weeks were not included in the calculations. results when data from all four providers were aggregated for each respective surveillance system, the correlation between ilinet and essence discharge diagnoses of influenza ranged from 0.53 to 0.89 over the entirety of each season, and from 0.45 to 0.86 for peak weeks (table 2). by contrast, the correlation by individual provider between the two systems ranged from 0.02 to 0.77 over each season, and from -0.15 to 0.92 for peak weeks (table 3). conclusions although the correlations between the surveillance systems ilinet and essence were positive when viewed in aggregate, individual sites demonstrated inconsistency in reporting. this study demonstrates a need to identify the sources of differences that occur between making and reporting a determination of ili versus those involved in a discharge diagnosis of influenza table 1 table 2 table 3 keywords essence; ilinet; influenza surveillance; influenza-like illness surveillance; influenza acknowledgments we wish to thank the epidemiology staff of the florida department of health in seminole county, their counterparts at the florida department of health in orange county, and the staff at the bureau of epidemiology for the florida department of health in tallahassee, in particular david atrubin for essence assistance, and colin malone for his comments regarding statewide influenza surveillance. we also extend a deep appreciation to the ilinet sentinel healthcare providers for their continued participation. references 1) centers for disease control and prevention (us). overview of influenza surveillance in the united states [internet]. atlanta. [updated 2013 october 24; cited 2014 august 29]. available from: http://www.cdc.gov/flu/weekly/overview.htm *gregory danyluk e-mail: gregory.danyluk@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e121, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts reciprocal data sharing: sending monthly summary reports of syndromic data to ed anthony w. tam*, amita toprani and robert mathes new york city department of health and mental hygiene, long island city, ny, usa objective to share monthly summary reports of syndromic data to participating eds in nyc. introduction over several months in 2012, nyc dohmh syndromic surveillance staff met with directors of all 49 participating eds in our syndromic system to collect information on their health information systems coding practices [1]. during these interviews, ed directors expressed interest in receiving summary reports of the data they send to the syndromic unit, such as number of ed visits, most common complaints, and temporal and spatial trends. this effort was done to increase communication and cooperation between the syndromic unit and the eds that provide data to the syndromic system. methods we contacted a sample of ed directors to inquire about the variable types and visualization formats they would find most helpful in a summary report, as well as the frequency they would like to receive reports. variables for analysis include date and time of visit, chief complaint, age, sex, residential zip code of the patient, disposition, and mode of arrival. reports were generated using r and latex. results the most requested variables included total number of ed visits, top ten chief complaints (stratified by age group, 17 years and 18 years), average number of visits by day of week and shift, and geographic distribution of visits by zip code. optimal frequency was determined to be once a month. we developed a two page report summarizing these major variables, to be created on a monthly basis, compiling and analyzing data from the previous month. chief complaints were categorized into general categories by a medical epidemiologist, including: injury or accident, cardiac, respiratory, abdominal, complaints involving the head or face, urinary, genital, medication refill or laboratory test, respiratory infections, nonspecific symptoms, and missing or uninformative information. the top ten chief complaints were binned into two tables, one for adults and the other for children. temporal trends were summarized by average number of visits by 8-hour shift and day of week. a heat map was created to show the geographical distribution of ed patients’ residence. conclusions for health departments that receive syndromic data directly from hospitals, it is critical to establish and keep close relationships with participating eds. sharing summary reports with the eds in our syndromic system is one way to maintain contact with key ed staff, which is important during outbreaks or when we observe changes in data or data quality. furthermore, regularly sharing data analyses engages eds with how health departments use their data, making collaborations more likely and improving the success of future reciprocal data sharing. keywords syndromic; surveillance; emergency; department; communication references 1. sell, j. and a. wong, a survey of data recording procedures at new york city emergency departments. online j public health inform, 2013. 5(1): p. e114. *anthony w. tam e-mail: atam@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e164, 201 characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi characterizing the access of clinical decision support offered by immunization information system in minnesota sripriya rajamani1*, aaron bieringer2, miriam muscoplat2 1 public health informatics program, school of public health, university of minnesota, minneapolis, minnesota 2 minnesota immunization information connection (miic), immunization program, minnesota department of health, st. paul, minnesota abstract background: healthy people 2020 aims to improve population health by increasing immunization rates to decrease vaccine-preventable infectious diseases. amongst the many strategies, role of immunization information systems (iis) are recognized by studies and taskforce reports. iis are unique in their offering of clinical decision support for immunizations (cdsi) which are utilized by healthcare providers. federal initiatives such as meaningful use (mu) and affordable care act (aca) aim to improve immunization rates through use of technology and expanding access to immunization services respectively. mu, the electronic health record (ehr) incentive program includes use of iis cdsi functionality as part of stage 3. it is essential to understand access and use patterns of iis cdsi, so as to utilize it better to improve immunization services. objectives: to understand the utilization of clinical decision support for immunizations (cdsi) offered by immunization information system in minnesota and to analyze the variability of its use across providers and ehr implementations. methods: iis in minnesota (minnesota immunization information connection: miic) offers cdsi that is accessed through ehrs and branded as alternate access (aa). data from miic and technical documents were reviewed to create details on organizations which implemented aa functionality. data on ehr adoption in clinics and local health departments was obtained from minnesota ehealth assessment reports. data on access were tracked from january 2015 through mid-october 2015 through weekly specialized reports to track the queries by organization, volume and day of the week. data were analyzed, findings were synthesized and reviewed with subject matter experts. conclusion: high ehr adoption offers a great opportunity to promote use of iis cdsi at point of care. analysis did not track use at individual clinic level and how the queries were being generated. additional research is needed to understand the provider level use of this cdsi and other organizational and technical factors which influence access to iis. this is essential for iis as they execute projects to improve population-level immunization rates, plan provider outreach and prioritize their system enhancements to meet federal requirements. keywords: clinical decision support, immunizations, public health informatics, bidirectional, immunization information systems (iis), electronic health records (ehrs) http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi introduction immunizations are proven to be the key success factor for the decline of many infectious diseases and are recognized as a highly cost-effective clinical preventive service [1]. one of the goals of the healthy people 2020 which seeks to improve population health is to increase immunization rates and improve proper immunization series compliance to reduce preventable infectious diseases [1]. amongst the many strategies to improve immunization compliance, adoption of immunization information systems (iis) is increasingly recognized by various studies and taskforce reports as a key and effective tactic [2]. iis are population-based, confidential computerized systems present in most us states and territories [3]. iis have the unique advantage of holding immunization data across various providers over time, therefore offering comprehensive vaccination histories. the vision for management of immunization information is availability of real-time, consolidated data and services for all ages, to clinical, public health and other stakeholders within their workflow [3]. one of the modalities for achieving this vision is through immunization information system (iis). the iis in minnesota, known as minnesota immunization information connection (miic) [4] has been in operation for more than 12 years. miic contains 75,855,190 million immunization records for 7,675,687 people, as of october 2015. minnesota’s healthcare landscape is unique with high electronic health record (ehr) adoption supported by state initiatives such as interoperable ehr mandate [5], state health reform [6], state innovation model grant efforts [7] and federal incentives for ehrs, referred to as meaningful use [8]. recommendations on vaccines from the advisory committee on immunization practices (acip) [9] are disseminated through various modalities including iis. iis contain vaccine forecasting algorithms to predict immunizations, also known as clinical decision support for immunizations (cdsi). this evaluation and forecasting is complex, including factors like age for vaccine administration, number of doses, their intervals, precautions, and contraindications. with increase in use of ehrs, some of these complex cdsi rules have been built into ehrs as cds modules and/or accessed from iis (through ehrs or directly via iis interface). due to immunization schedule complexity, the need for comprehensive vaccination history for accurate predictions, and the variability of cdsi across provider groups and across ehr implementations, it is recommended to access and use the cdsi from iis. iis serve as a powerful tool for improving vaccination rates [2] and cdsi in iis play an important role. bi-directional communication between clinical and public health is an emerging field with previous work focusing primarily on clinician alerts for diseases [10,11]. study on access to iis decision support within ehr [12] concluded that visual integration of external registries into an correspondence: sripriya@umn.edu doi: 10.5210/ojphi.v7i3.6282 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mailto:sripriya@umn.edu characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi ehr was feasible with improvement in provider satisfaction and registry reporting. study by rajamani et al. [13] on submission of data to miic highlighted uptake of standards-based reporting in the last three years. these studies point to increasing technical capacities of provider organizations, and serve as a barometer to their capabilities for implementing new functionalities. research by stockwell et al. [14] evaluated immunization data exchange between ehr and iis for informing stage 3 mu and outlined its benefits. bi-directionality across iis and ehrs has been advocated wherein benefits of integration supersede the challenges posed by variability in ehr technologies and various iis [15]. the overlap of functionalities between ehrs and iis have been outlined with recommendations for guiding their integration [16]. miic currently offers an option branded as ‘alternate access’ (aa) [4] to access miic and the cdsi from within the provider ehr. this solution offers the ability to generate a query to miic for vaccination history and forecasting and is based on demographics of the record in ehr. this option addresses the issue of repeat data entry for the query and also does not require logging into the miic system separately. with this aa functionality, the history and forecast are presented to user within their ehr. this data can either be displayed as ‘read-only’ view or can be ‘integrated’ and most have implemented ‘read-only’ option. the current response for queries to access miic cdsi is custom-built, but will be updated to recommended standards. meaningful use (mu), the electronic health record (ehr) incentive program recognizes public and population health as one of the priority areas [8]. it focuses on tactics using health information technology to achieve public health objectives. stage 1 mu recommended reporting to public health including iis, based on nationally recognized standards. stage 3 mu [17] has drawn to the cdsi functionality of iis by recommending bi-directional communications between certified ehr technology and iis which enables submission of immunization data and receiving immunization forecasts and histories from iis. affordable care act (aca) expands and strengthens access to immunization services for children and adults [18]. aca also fastens the time for availability and coverage of acip recommended vaccines. iis cdsi which incorporates acip recommendations and has vaccine forecasting based on comprehensive vaccination history in iis is a great tool to promote immunizations at point of care. there is a need to understand the current status of adoption of iis cdsi functionality, its access and use patterns (profile of users, their volume of queries and the number of records found), so as to utilize it better to improve immunization services. the study objectives are to understand the utilization of iis cdsi through ehr access option, a feature offered by iis in minnesota (miic) and to analyze the variability of its use across providers and ehr implementations. methods minnesota immunization information connection (miic), the iis for the state of minnesota has 4,852 organizations which participate in the iis currently. some of these organizations submit data for reporting purposes (e.g., health systems including clinics, hospitals) and some have access to data which are read-only (e.g., schools). all organizations can use the iis interface to access the clinical decision support (cdsi) functionality offered by miic. a sub-set of submitting organizations have the ability to access miic from their ehrs through their implementations of alternate access functionality. data from various sources and customized queries were compiled, analyzed and reviewed with subject matter experts. the study framework is depicted in figure 1 and outlines the sources and approach to characterize the overall utilization of miic cdsi. http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi figure 1: study framework the framework to characterize miic cdsi access comprised of: (1) healthcare organizations, number of affiliated sites, ehr platform; (2) trend in queries for january through september 2015 and stratified by healthcare system vs. local public health; and (3) detailed analysis of records found for healthcare system queries by organizations and for one week period october 12 -18, 2015. results were discussed with subject matter experts to validate findings and to gather contextual information about the organization and any additional data on miic cdsi use. results minnesota has high rates of ehr adoption in clinics with nearly all (97%) ambulatory clinics with ehrs, representing 1,146 clinics in 2014. epic is the dominant ehr vendor system and is in use by 49% of minnesota’s clinics which have adopted ehrs (table 1). table 2 presents the ehr adoption and vendor data for local public health for the year 2014 and all reported use of ehrs with the exception of two local public health departments. ph-doc product was the prevalent ehr, used by 28 out of 50 (56%) local health departments. data from miic and technical documents data from surveys of clinics and local public health departments customized weekly reports from miic queries details on organizations with alternate access (miic cdsi) implementations details on access of miic cdsi by organization, volume of queries, day of week details on adoption and use of electronic health records analysis of data synthesis of findings review by subject matter experts http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi table 1: ehr system adoption and vendors in minnesota clinics, 2014 electronic health record systems percent count epic 49% 559 nextgen 6% 71 allscripts 6% 70 eclinicalworks 6% 67 cerner 4% 50 centricity 4% 45 greenway 3% 40 meditech 2% 24 other 19% 220 total 1,146 table 2: ehr adoption and vendors in minnesota local public health departments*, 2014 electronic health record systems percent count ph-doc 56% 28 champ 36% 18 carefacts 12% 6 custom-built local system 8% 4 digital health department 4% 2 decade 2% 1 other 10% 5 total 50* *assessed at community health board (chb) level; more than 1 system may be used in table 3 presents the organizations that have implemented the aa functionality and number of affiliated sites with access and currently this comprises of 25 healthcare systems/organizations representing 599 individual provider sites. the number of sites accessing an aa installation varied from 1 to as much as 110 affiliated sites. healthcare systems have the most sites (559 out of 599) with access to this functionality. with the exception of one local public health system with 23 affiliated sites, most were single installations. http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi table 3: organizations with miic cdsi implementations: affiliated sites, ehr systems organization number of affiliated sites using functionality ehr system healthcare systems health system a 110 epic health system b 62 epic health system c 12 cerner health system d 83 epic health system e 93 epic health system f 4 epic health system g 84 epic health system h 28 epic health system i 83 ge centricity (transitioning to epic) local public health departments local public health a 1 ph-doc local public health b 1 ph-doc local public health c 1 ph-doc local public health d 2 ph-doc local public health e 1 ph-doc local public health f 1 ph-doc local public health g 1 ph-doc local public health h 23 ph-doc local public health i 1 ph-doc local public health j 2 ph-doc local public health k 1 ph-doc local public health l 1 ph-doc local public health m 1 ph-doc local public health n 1 ph-doc local public health o 1 ph-doc local public health p 1 ph-doc http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi figure 2 displays the analysis of weekly alternate access reports to understand the volume of queries over a nine-month period from january to september 2015. the query count of health care systems (labeled health care system a – h) ranged from 1000 to 12,000 per week whereas health system i had query count in ranges of 30,000 – 40,000 per week. this health system i was an outlier in terms of its access of aa functionality with exceedingly large number of queries. this query volume could not be explained with current metric tracking and requires additional information to understand pattern of access and use. the overall access volume didn’t vary across weeks/months. health system i had high volume in july which exceeded 300,000 queries to miic. figure 2: queries to access miic cdsi by health systems, january-september 2015 figures 3 and 4 present the details of analysis of queries in week of 10/12/15 to 10/18/15 and is stratified by health system and local public health respectively. the queries in this week range from 11,625 (health system a), 3,621 (health system b), 1,776 (health system c), 3,395 (health system d), 5,829 (health system e), 995 (health system f), 4,336 (health system g) and 3,076 (health system h). figure 3 excludes health system i which was an outlier with 73,659 queries. for the week of 10/12/15 to 10/18/15, the query volume by local public health was in the range of 50 or less, with the exception of one system i which had 581 queries to access miic cdsi. local public health systems p and f were next highest users with queries in range of 60. local public health system i has implemented aa functionality for many years and involved in public health information exchange projects and that could attribute to the increased volume of queries. http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi figure 3: queries to access miic cdsi by health systems*, week of 10/12/15 10/18/15 *graph does not include data from health system i, an outlier with much higher volume of queries figure 4: queries to access miic cdsi by local public health, 10/12/15 10/18/15 the number of records located through alternate access was analyzed in detail for the week of 10/12/15 through 10/18/15. figure 5 depicts that query was able to successfully locate a single matching record for 59% of searches, could not find a record for 5% and resulted in blank/error for 31% of queries. figure 6 presents the varying match rates across health systems with some searches yielding no records to some searches resulting in 13 records. similar variability was found across local public health systems as well (table 4), but the maximum records found in a search was six. figure 7 shows that the volume of queries to access cdsi was less on week-ends (saturday and sunday), when compared to week days (monday through friday). http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi figure 5: miic cdsi query results by records found, week of 10/12/15 10/18/15 figure 6: miic cdsi query results by health systems*, week of 10/12/15 10/18/15 *graph does not include data from health system i, an outlier with much higher volume of queries http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi figure 7: miic cdsi query results by day, week of 10/12/15 10/18/15 table 4: miic cdsi query results by local public health, week of 10/12/15 10/18/15 queries by local public health number of records found total 0 1 2 3 4 5 6 error / blank local public health a 4 4 local public health b 18 5 23 local public health c 6 6 local public health d 11 1 12 local public health e 2 1 3 local public health f 56 4 60 local public health g local public health h 2 2 4 local public health i 30 424 91 1 3 32 581 local public health j 35 4 3 42 local public health k local public health l 27 3 1 31 local public health m local public health n 5 5 local public health o local public health p 1 43 8 9 61 http://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi discussion the organizations that had implemented the alternate access functionality are using ehr technology which are considered mature products in the marketplace. the two ehrs with aa functionality are also products with high adoption rates in minnesota and reflective of current ehr product landscape in minnesota. the prevalence of affiliated sites with as much as 110 sites accessing via one aa installation poses both opportunities and challenges. it poses a perfect platform for rapid adoption of technology, but presents a complex scenario to track miic cdsi access by individual clinical sites. the queries to access miic were evenly distributed across the study time period with volume ranging from 1000 to 12,000 per week depending on size of the organization. the large volume of queries by a single organization needs additional research to understand the usage. access is more on week days (monday-friday) and corresponds to access of miic for immunizations (one of preventive services) by primary care and local public health clinics. the access volume on week-ends, though less as compared to week days, may be an indicator for querying miic during urgent and emergency care visits. the number of records located through access/queries is an indicator of the quality of data in iis (miic in this study) and robustness of query parameters. the query yielded a single record for two-thirds of access and represents good quality data for 59% of searches. but one third resulted in error or blank response, which could be due to technical issues or lack of matching records and requires additional research. five percent of searches did not yield any match and points to missing records in miic. similarly, the numerous matches (close to 13 for some searches) may be an indicator of not so robust search criteria or the need to improve quality of data. the variability of records found across healthcare systems could be based on population they serve with healthcare spread across organizations and an indirect indicator of quality of various streams of immunization reporting. conclusion this study analyzed usage patterns over a time period of 9 months (january-september 2015) and described use in depth for 1 week (october 12-18, 2015). there may be variability in other time segments, but review of data shows similar patterns across weeks, with the exception of an outlier organization. follow-up study could yield insights into variability (if any) across organizations that present ‘read-only’ data view as compared to those which integrate data into their ehrs. this research presents important aspects on access of miic cdsi, in terms of organizations, their affiliated sites, ehr technology in use, volume of queries, and records located. these offer insights into bi-directional exchange across iis and ehrs. in addition, it highlights the number of records found through this access which is an indicator of quality of data, in particular comprehensiveness and accuracy. high ehr adoption in minnesota and market dominance of select products offers an opportunity to promote use of miic cdsi through ehrs with better workflow integration. outreach to users/stakeholders with varying organizational roles (clinical, managerial and technical) will be needed to understand the numerous factors which impact iis cdsi access and its use. understanding the context of this use is critical to improve its access at point of clinical care and further studies are needed. these are much needed next steps for miic and overall iis community as they plan provider outreach, prioritize their system enhancements to support bihttp://ojphi.org/ characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi directional communications for stage 3 mu and other initiatives, and execute projects to improve population-level immunization rates. study limitations. study has some limitations, one of which is focus on a single iis. follow-up research needs to be conducted which looks into adoption and use of similar functionality supported by other iis. analysis did not track use at individual clinic level and how the queries were being generated. this could explain the volume of queries by site, day and outliers. provider access and use at point of care is essential to impact immunization services, which may be influenced by organizational processes and technical factors. additional research is needed to understand provider use, organizational and technical factors which impact use of iis cdsi. acknowledgements the authors would like to thank erin roche, manager of minnesota immunization information connection (miic) and karen soderberg, assessment lead at mdh office of health information technology for their support of this project. this project was supported by on the horizon grant from the university of minnesota informatics institute (umii). references 1. u.s. department of health and human services. office of disease prevention and health promotion. healthy people 2020. 2010. available at: http://www.healthypeople.gov/2020/topics-objectives/topic/immunization-and-infectiousdiseases. accessed november 30, 2015. 2. community preventive services task force. 2015. recommendation for use of immunization information systems to increase vaccination rates. j public health manag pract. (21)3, 249-52. pubmed 3. centers for disease control and prevention. immunization information systems (iis). 2014. available at: http://www.cdc.gov/vaccines/programs/iis/index.html. accessed october 15, 2015. 4. minnesota immunization information connection. miic resources. 2009. available at: http://www.health.state.mn.us/divs/idepc/immunize/registry/index.html. accessed october 25, 2015. 5. minnesota ehealth initiative. 2015 interoperable electronic health record mandate. 2007. available at: http://www.health.state.mn.us/e-health/hitimp/index.html accessed october 28, 2015. 6. minnesota department of health. minnesota's health reform law. 2008. available at: http://www.health.state.mn.us/healthreform/about/. accessed september 29, 2015. 7. minnesota dhs and mdh. minnesota accountable health model state innovation model grant. partnership by the minnesota department of human services and minnesota department of health. 2013. available at: http://www.dhs.state.mn.us/sim. accessed october 25, 2015. 8. centers for medicare and medicaid services. ehr incentive programs. 2010. available at: http://www.cms.gov/ehrincentiveprograms. accessed october 27, 2015. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24912083&dopt=abstract characterizing the access of clinical decision support offered by immunization information system in minnesota online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e227, 2015 ojphi 9. advisory committee on immunization practices (acip). immunization schedules. 2012. available at: http://www.cdc.gov/vaccines/schedules/hcp/index.html. accessed november 30, 2015. 10. dixon be, gamache re, grannis sj. 2013. towards public health decision support: a systematic review of bidirectional communication approaches. journal of the american medical informatics association: jamia. 20(3), 577-583. epub 03 2013. pubmed http://dx.doi.org/10.1136/amiajnl-2012-001514 11. gamache r, stevens kc, merriwether r, dixon be, grannis s. 2010. development and assessment of a public health alert delivered through a community health information exchange. online j public health inform. 2(2). epub 01 2010. pubmed http://dx.doi.org/10.5210/ojphi.v2i2.3214 12. stevens la, palma jp, pandher kk, longhurst ca. 2013. immunization registries in the emr era. online j public health inform. 5(2), 211-211. pubmed http://dx.doi.org/10.5210/ojphi.v5i2.4696 13. rajamani s, roche e, soderberg k, bieringer a. 2014. technological and organizational context around immunization reporting and interoperability in minnesota. online j public health inform. 6(3). pubmed http://dx.doi.org/10.5210/ojphi.v6i3.5587 14. agency for healthcare research and quality (ahrq). devise: data exchange of vaccine information between an immunization information system and electronic health record (new york). 2013. available at: http://healthit.ahrq.gov/ahrq-funded-projects/devise-dataexchange-vaccine-information-between-immunization-information. accessed 15. dombkowski kjc. 2012. s. j. redefining meaningful use: achieving interoperability with immunization registries. am j prev med. 42(4), e33-35. epub 03 2012. pubmed http://dx.doi.org/10.1016/j.amepre.2012.01.009 16. consulting hln. iis and ehr feature overlap. 2014. available at: https://www.hln.com/assets/pdf/hln-iis-ehr-overlap-white-paper.pdf. accessed october 8, 2014. 17. u.s. department of health & human services. medicare and medicaid programs; electronic health record incentive program—stage 3 and modifications to meaningful use in 2015 through 2017; final rule 2015. available at: http://www.gpo.gov/fdsys/pkg/fr-2015-1016/pdf/2015-25595.pdf. accessed november 30, 2015. 18. u.s. department of health & human services. the affordable care act and immunization. 2012. available at: http://www.hhs.gov/healthcare/facts-and-features/fact-sheets/aca-andimmunization/index.html. accessed november 30, 2015. 19. mdh office of health information technology. clinics: adoption and use of ehrs and exchange of health information, 2014. available at: http://www.health.state.mn.us/ehealth/summaries/reportclinic2014.pdf. accessed september 9, 2015. 20. mdh office of health information technology. local public health: e-health capacity, capability, and challenges, 2014. available at: http://www.health.state.mn.us/ehealth/summaries/reportlph2014.pdf. accessed september 9, 2015. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23467470&dopt=abstract http://dx.doi.org/10.1136/amiajnl-2012-001514 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23569583&dopt=abstract http://dx.doi.org/10.5210/ojphi.v2i2.3214 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23923096&dopt=abstract http://dx.doi.org/10.5210/ojphi.v5i2.4696 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25598866&dopt=abstract http://dx.doi.org/10.5210/ojphi.v6i3.5587 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22424260&dopt=abstract http://dx.doi.org/10.1016/j.amepre.2012.01.009 characterizing the access of clinical decision support offered by immunization information system in minnesota introduction methods results discussion conclusion acknowledgements references public health practice within a health information exchange: information needs and barriers to disease surveillance public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 public health practice within a health information exchange: information needs and barriers to disease surveillance blaine reeder 1 , debra revere 2 , rebecca a hills 2 , janet g baseman 2 , william b lober 1 1 school of nursing, university of washington, seattle 2 school of public health, university of washington, seattle abstract introduction: public health professionals engage in frequent exchange of health information while pursuing the objectives of protecting and improving population health. yet, there has been little study of the information work of public health workers with regard to information exchange. our objective was to gain a better understanding of information work at a local health jurisdiction before and during the early stages of participation in a regional health information exchange. methods: we investigated the information work of public health workers engaged in disease surveillance activities at a medium-sized local health jurisdiction by conducting semistructured interviews and thematically analyzing interview transcripts. results: analysis of the information work of public health workers revealed barriers in the following areas: information system usability; data timeliness, accuracy and completeness; and social interaction with clients. we illustrate these barriers by focusing on the work of epidemiologists. conclusion: characterizing information work and barriers to information exchange for public health workers should be part of early system design efforts. a comprehensive understanding of the information practice of public health workers will inform the design of systems that better support public health work. mesh keywords: public health informatics, public health practice, disease notification, communication barriers, information services, health information technology introduction public health professionals work with numerous stakeholders to fulfill requirements for notifiable conditions reporting, disease surveillance and immunizations to safeguard and improve population health (1). however, many local health jurisdictions (lhjs) lack the information and communications infrastructure to effectively engage with technologies and systems that can support access to and/or use of population-level health data (2). a health information exchange (hie) provides a secure, interoperable infrastructure for electronically moving clinical data between heterogeneous health information systems and its stakeholders, including public health. http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 participation in a hie presents the opportunity to support public health workers engaged in disease surveillance (3-6); however, it is unclear how the public health practice need for hie data can best be understood and expressed to hie organizations and stakeholders. by providing a framework for integrated care management and coordination across the health care setting, hie efforts are a means for overcoming a fragmented healthcare system in the u.s. hies hold great promise for addressing many of the barriers to effective care management by providing complete clinical information at the point of care. by including public health, the information exchange is broadened to population-level oversight, collaboration and coordination, facilitating the real-time ability of local, state, regional, and federal entities to share clinical and facility-based resource utilization information to enhance rapid response to, and management of, potentially catastrophic infectious disease outbreaks and other public health emergencies. in their 2010 annual survey of hies initiative in the united states, the ehealth initiative reported continued increases in the number of initiatives and a significant increase in the number of public health organizations involved in these initiatives (7). there is a recognized need to better understand health information exchange at the individual, group, organization, and network levels to facilitate system design improvements and successful adoption by stakeholders (8). while the information needs and work of public health practitioners have been the subject of several studies (9-12), existing studies of hies are primarily physicianand hospital-focused (13-17). little work has been done to characterize the information practice of public health workers with regard to the exchange of health information within public health organizations or with external stakeholders. in particular, a comprehensive view of epidemiologists’ information practice and communication is important and warranted given the epidemiologist’s central role in disease investigation and community health assessment (18, 19). this study explores the public health practice need for hie data in the context of a lhj in the early stages of interactions with a hie organization. methods semi-structured interviews were conducted in february 2009 with 9 participants at a mediumsized lhj in washington state. the lhj has approximately 250 employees and serves a population of more than 400,000. participants were drawn from a convenience sample of lhj employees with a range of job titles and disease surveillance responsibilities. study participation was voluntary; all participants were consented into the study. interview questions solicited descriptions of information uses, information needs and exchange of health information related to outbreak investigation and disease surveillance. study procedures were approved by the university of washington institutional review board. interviews were recorded using a digital audio-recorder. recordings were transcribed verbatim and imported into qsr nvivo 8 qualitative data analysis software that facilitates coding, analysis, and text searches of documents (20). three coders (br, rh, dr) thematically coded the transcripts (21, 22) to describe the characteristics of information exchange between external stakeholders and public health practitioners at a lhj in the early stages of participation in a hie. http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 codes were created for work activities related to programs and services. the terms “information practice” and “information work” were used interchangeably to refer to routine and impromptu activities and processes involving some form of information and information processing (23, 24) in an organizational setting or community of practice (25). given that there are numerous conflicting definitions of communication (26), for simplicity’s sake, we used a conventional definition of “communication”: transmission of information from a sender to a receiver (27). two coders (br and rh) independently coded and reconciled three transcripts by discussing discrepancies to ensure consistency in the application of codes (21). both coders met to compare results and differences were resolved through discussion until agreement was reached. the codebook was reviewed by a third coder (dr) for face validity and consistency. two coders (rh and dr) partially coded and reconciled a fourth transcript as an additional test of inter-rater reliability. one coder (br) coded the remaining interviews. results nine individuals with the following job roles participated in the study: epidemiologist (4), program manager (2), public health nurse (1), health program specialist (1) and administrative assistant (1). see table 1 for participant job roles and responsibilities. table 1. participant job roles and responsibilities participant job role responsibilities participant 1 public health nurse track and manage cases of tuberculosis through interaction with internal and external stakeholders participant 2 health program specialist conduct surveillance and partner notification for sexually transmitted illness (stis) participant 3 program manager coordinate community health assessments based on hospital data and other data sources participant 4 program manager manage communicable disease epidemiology and disaster preparedness efforts participant 5 epidemiologist conduct communicable disease surveillance and investigation participant 6 epidemiologist conduct communicable disease surveillance and investigation participant 7 epidemiologist conduct communicable disease surveillance and investigation participant 8 epidemiologist conduct chronic disease surveillance and community health assessments participant 9 administrative assistant receive, verify and route sti reports from health care providers to internal or external investigators all participants reported exchanging information with a number of external stakeholders. table 2 shows references to unique external information exchange partners made during interviews, grouped by role and exchange partner type. external information exchange partners were http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 grouped into three types based on their characteristics: healthcare system partners, community and government partners and public stakeholders. epidemiologists referenced the greatest numbers of unique external information exchange partners. we note that epidemiologists were represented in the greatest numbers in the participant sample. table 2. number of external information exchange partners grouped by role and type participant role healthcare system partners* community and government partners** public stakeholders*** epidemiologist 19 5 8 public health nurse 10 2 1 health program specialist 8 1 2 program manager 8 1 1 administrative assistant 5 1 2 *healthcare system partners include: local health jurisdictions, the local board of health, the u.s. centers for disease control and prevention (cdc), private laboratories, medicaid, medicare, local plasma centers, providers (hospital, non-hospital and unspecified), washington state department of health (doh) and the u.s. department of veterans affairs (va) **community and government stakeholders include: other states, community and non-profit organizations, municipal courts and universities ***public stakeholders include: clients, their partners and family members, the media, private citizens (“the public”) and local schools public health work is complex, non-linear and dependent on information exchange and data that must be interpreted in context. documenting public health information practice is important to the design of future information systems that will interoperate to exchange health information. common characteristics and barriers to the information work of public health workers are described below, with a focus on epidemiologists as frequent exchangers of information. the four epidemiologists who participated in this study all worked in the disease prevention division of the lhj with different responsibilities related to communicable disease investigation and community health assessment. they were frequent exchangers of health information with external stakeholders in their work of responding to communicable disease reports and requests for population health information from the public and community partners. data require context public health workers must trust the source, provenance, collection methods and processing of the data they use. the visualization of these data and relationships among them must be clear and understood. “i think one of the things that’s really hard for people, sometimes, with data sets, is understanding kind of how the data is structured – so what are these variables? and what does this actually mean? and, you know, just really having a codebook http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 for people that are going to be using whatever sort of database or interface you have .” (participant eight, epidemiologist) information practice is non-linear information work is non-linear in nature, as described in this narrative of note-taking during investigations. “i take notes on paper, the reason i like that is because then i can organize my notes based on the conversation so i can see if i’m missing things i want to ask, but if all i’m doing is typing in a single straight line whatever’s coming in to me... they’re bouncing all over the place, so that way i can start here and then move over here because there’s something else, go back over there, come back here…that makes it easier. and then stuff that’s superfluous to the interview i don’t have to input, and – cause it’s not relative to the risk factors… i need a pad that just throws it in.” (participant seven, epidemiologist) disease investigations vary the variable nature of disease investigation is illustrated by this description of investigation details. “bare minimum for an interview is usually… 15 minutes, sometimes they go 10 if it’s very uncomplicated… some of the enteric disease issues, vaccine preventable disease issues are usually a multi-stage interview… we let them know that they can expect a second interview… with the vaccine preventables we always try to contact within the next day… to identify more contacts. so, a lot of those are usually a couple of phone calls of direct contact. but, with the viral hepatitis… if you’re looking at an interview, you’re looking at probably 30-40 minutes by the time you’re doing all the calls.” (participant six, epidemiologist) publicly available tools are useful web-based tools that have become available in recent years are sometimes used to enhance situational awareness through information exchange. “it’s not really active surveillance, but we have employed survey monkey quite a few times over the last 3 or 4 years… we’re trying to collect some basic information for a large group, that kind of tool is great for us… people sitting at their desks at work can complete it the survey in 10 minutes… we don’t have to interview all [of] them, and so that’s been a great help to us”. (participant five, epidemiologist) assessing community health is a complex endeavor http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 assessing disease burden can be difficult even if data are available to public health workers through information exchange. “you know the thing that is super hard for us to talk about is the disease burden in our community, because we look at things that are chronic conditions… for example, diabetes, ‘oh, well, we’ve got deaths, and we’ve got in-patient hospitalizations, and recently we were able to access er data’. but does that really tell us how many people have diabetes in our community?” (participant eight, epidemiologist) quality improvement efforts rely on good data quality improvement efforts are informed by information exchange and involve complex processes based on available data. “we go by disease and by investigator looking at time from when we received the report to time the investigation starts, so we want to see how timely we are in terms of beginning an investigation, then we look at time from beginning an investigation to the time of completing and investigation… what are the percentage of cases by disease that are completed…? it’s interesting to kind of see, by disease, the difference in terms of how easy it is to complete an investigation, and how long it takes.” (participant six, epidemiologist) barriers to information exchange public health workers experience barriers specific to their work processes with regard to usability, data timeliness, data accuracy, data completeness, and social interactions while collecting data during case investigations (table 3). these barriers were common to all participant roles. table 3. barriers to information exchange for public health workers type barrier usability information systems do not match work processes usability data streams may not be in a usable format timeliness lag in time for reports to reach public health workers accuracy information from clients and medical workers may vary completeness lack of information exchange with other states completeness client demographic information from labs is often insufficient for investigations completeness poor denominator data completeness incomplete vaccination records in the state immunization registry social interaction clients may not want to talk to public health workers during investigations social interaction investigation questions may be sensitive to clients and public health workers social interaction clients may not speak the same language as public health workers barrier type: usability http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 usability: information systems do not match work processes given the non-linear, complex and variable nature of their work, information systems often are not congruent with or supportive of public health work processes. participants identified issues such as the following: data errors resulting from faulty system logic; inability to record necessary data, such as complete information about food-borne illnesses during investigations, in the information system; inability to track information from phone calls; and information systems that do not capture and show information that is available in other formats, such as vital statistics data from death and birth certificates that are available in hard copy formats. usability issues related to information systems that do not match work processes can influence overall data timeliness, accuracy and completeness as well as impact the information systems of external stakeholders during case investigations. “the hassle… when you call a provider is the demographic information. you know they may have the name and date of birth right there, but getting their phone number and name of parents or guardians and the address, and occupation… they’re having to wait… to go to a different screen… to the billing screen to get the address and phone number because it’s not in the medical screen. it seems very strange that it’s so dislocated.” (participant seven, epidemiologist) usability: data streams may not be in a usable format clinical data that come to public health workers through the regional hie may not arrive in a usable format. “there’s no algorithms applied to it, it’s just raw…you can see diagnosis and icd9 data without anything applied to it to tell you whether the data is usable or not and some of the numbers are so small that it wouldn’t really matter anyhow.” (participant seven, epidemiologist) barrier type: data timeliness timeliness: reporting lags participants reported receiving case reports more quickly when they are reported through public health reporting of electronic data (phred), the washington state laboratory reporting system (28). however, since phred is not consistently used across all labs required to report to the lhj, this timeliness benefit is not universal. in addition, the local lab does not report through phred which delays receipt of notifiable conditions lab reports to public health workers. for those labs that do report through phred, not all cases are reported through the system. cases reported in a less timely fashion are less useful to investigations. http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 “just going back to giardia, for some reason, we will get, at the end of the month, every month… a listing off of cases that came in through [the local lab], and some were reported and some were not, so i’m not sure why that happens, and at that point, it’s maybe moot? you know, if we get the report on february 2nd, and the case was diagnosed on january 2nd, well that’s not that helpful.” (participant five, epidemiologist) barrier type: data accuracy accuracy: information from clients and medical workers may vary public health workers receive information from a variety of sources that must be verified because of conflicts. for example, onset dates as reported by clinicians and clients during investigations may differ by several days. barrier type: data completeness completeness: lack of information exchange with other states participants reported that the lack of formal information exchange agreements and technology between other states and regions presents a barrier to the information exchange that would support interstate and international collaboration for disease investigations and population health assessments. completeness: client demographic information from labs is insufficient for investigations patient name and age are usually provided on lab reports; patient date-of-birth, provider’s name, patient phone number, patient address, patient occupation and parent/guardian information are often missing. when an investigation is warranted and patient demographic data is missing from lab reports, public health workers must contact the provider before calling the client. completeness: poor denominator data incomplete data with regard to the number of tests ordered and the number of positive tests results is a barrier to understanding the complete picture of disease in the community. “we don’t have the number of tests ordered for a particular issue, as a result we have no denominator, so we have no idea…so what we have 10 of this…? it doesn’t tell us anything. it doesn’t tell us that there are… physicians who are ordering this like crazy, because then we could do a provider thing saying… ‘if you have a positivity rate of 1 out of 1000 tests that are done, why are you doing all those tests? why are you thinking this, when you’re never getting a positive test?’” (participant seven, epidemiologist) http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 completeness: incomplete vaccination records in the state immunization registry incomplete immunization data can be a barrier to disease investigations and limit comprehensiveness of community health assessments. “what’s our immunization rate? well, we know how many kids exempt when they’re in kindergarten. ok, well, that doesn’t really tell us if the 2 ½ year olds are up-to-date on their immunizations. so, i think that’s one other piece that’s really lacking.” (participant eight, epidemiologist) barrier type: social interaction social interaction: clients may not want to talk to public health workers during investigations the nature of the disease under investigation may dictate the willingness of clients to interact with public health workers. “if someone has an acute diarrheal illness, they are usually pretty eager to talk to you. if someone has chronic hepatitis c, you know, they figure they’ve had that for years, maybe they had a period of time where they were or are using intravenous drugs…it’s: ‘what are you telling me, you’re not telling me anything new.’ …some people will hang up on you with those or don’t really want to talk about specific issues about their disease.” (participant six, epidemiologist) social interaction: investigation questions may be sensitive to clients and public health workers questions regarding race and ethnicity can be sensitive issues for both clients and public health workers during investigations. “the two most important pieces of information that you have to ask a client, which i don’t want to ask the client, is what is your race, and what is your ethnicity, and your ethnicity is either ‘unknown’, ‘hispanic’, or ‘not hispanic or latino’… [the client says:]‘what do you think? i have an accent.’… i can’t do that, you know, i have to ask you the question…” (participant seven, epidemiologist) social interaction: clients may not speak the same language as public health workers lack of common language between clients and public health workers can be a social interaction barrier during investigations. limitations this is an exploratory study and does not capture an all-inclusive view of information practice in the study setting. although all lhjs provide variations of the same services, results may not http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 generalize to other health agencies nationally or internationally due to local differences related to organization, culture and population. discussion our objective was to better understand information practice at one lhj before and during the early stages of participation in a regional hie. we conducted and qualitatively analyzed interviews to identify themes that describe information exchange and information work. information practice within a medium-sized lhj is a complex endeavor that is characterized by multiple, non-linear information processes. public health information work is data-intensive and requires data from a variety of sources. disease investigations vary but follow a basic set of steps, the order of which is determined by specific disease instance, circumstances of the investigation and the work load of individual public health workers. community health assessments require context and effort even when data are readily available and, as a result, are difficult to automate. public health workers do not rely on a single, well-designed information system to get their data but employ a wide variety of technologies, including in-house and state information systems, free web-based survey tools and, often, the telephone. public health workers face usability barriers when information systems do not match their information work, data are delivered in formats that are not ready to use and data are of poor quality. data quality barriers of timeliness, accuracy and completeness are common in data originating outside the lhj. at times, data that are needed to assess community health or conduct investigations are simply unavailable. in addition, lack of data sharing agreements block the exchange of information with external stakeholders. while public health workers have much to gain from better support of their information practice through well-designed technology, other barriers are independent of information systems. however, although public health workers rely on technology tools to support their information practice, much of their work is accomplished through interactions with people. it is unlikely that social interaction barriers will ever be overcome by technology. transforming workflows is challenging in the face of disparate information systems, cultures, organizational structures and budget constraints within and external to public health agencies. building or transitioning to better designs that overcome the usability barriers we identified in this exploratory study may appear daunting but, even in the face of limited funding and short delivery timeframes, these challenges must be met. human-centered design and methods to improve the usability of information systems are an integral part of public health informatics that have yet to be fully utilized in organizational contexts. identifying the barriers and facilitators of the information work of public health practitioners would help ensure that public health workers do not reject new systems, duplicate work or develop work-arounds that cause preventable delays and inefficiencies in disease surveillance, outbreak investigation, community health assessment, and other public health work. although public health processes and work are often characterized as unique to each lhj context, understanding public health workers' information needs and workflow will not only provide a clearly defined roadmap for improved system design but participatory design of new systems can increase the possibility that these systems will be widely adopted and championed. http://ojphi.org/ public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 conclusion characterizing public health information practice is essential for the design of systems that minimize the investigation of low risk alerts for infectious disease outbreaks and improve targeted and timely surveillance for seasonal outbreaks and other disease events. public health workers must trust the data they use to make decisions during investigations and assessments of population health. hies, as formal organizations, have the potential to improve population health by linking public health practitioners and other stakeholders through information access. though additional studies are required to describe the full range of information work for all roles in different types of public health organizations, this study has characterized the information practice of one group of public health workers to inform design of information systems that support health information exchange for public health practitioners. as part of future hie design efforts, we advocate the engagement of public health workers early in the design process. acknowledgements the authors would like to thank melissa clarkson, ma, mdes for data mockups and visualizations that facilitated analysis. thank you also to the university washington center for public health informatics, science applications international corporation (saic) health solutions and the participants of this study. this work was supported by national library of medicine (nlm) training grant t15lm007442, national institute of nursing research (ninr) training grant t32nr007106 and saic proposal no. 06-6617-71-2008-533 entitled “accelerating public health situational awareness through health information exchanges”. conflicts of interest none. corresponding author blaine reeder, phd biobehavioral nursing and health systems school of nursing university of washington box 357260 seattle, wa 98195 (206) 616-3065 breeder@uw.edu http://ojphi.org/ mailto:breeder@uw.edu public health practice within a health information exchange: information needs and barriers to disease surveillance online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.4, no. 3, 2012 references 1. public health informatics institute, national association of county and city health officials. taking care of 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medium, provided the original work is properly cited. isds 2014 conference abstracts anthropology and ecohealth research in control of diseases for pastorals in tanzania peter e. mangesho*1, 2, esron karimuribo1, 3, james e. mlangwa3, leonard e. mboera4, jonathan rushton5, richard kock5, angwara kiwara6 and mark rweyemamu1 1department of veterinary medicine and public health, sokoine university of agriculture, morogoro,, southern african center for disease infectious surveillance, morogoro, united republic of tanzania; 2national institute for medical researchamani medical research center, muheza, united republic of tanzania; 3department of veterinary medicine and public health, sokoine university of agriculture, morogoro, tanzania, morogoro, united republic of tanzania; 4national institute for medical research, dar es salaam, united republic of tanzania; 5royal veterinary college, university of london, london, united kingdom; 6muhimbili university of health and allied sciences, dar es salaam, united republic of tanzania objective to collect and assess indigenous knowledge and practices to manage diseases of food security as well as create opportunities to disseminate results for improving self-help. introduction the burdens of poverty and disease continue to affect the livelihoods of pastoralists in tanzania. their knowledge of seasons and the ecosystems has evolved over years to manage human and animal health problems, including food insecurity. but, both local and global factors are putting pressure on their knowledge base and their capacity to manage health issues, this conflict has not been adequately explored nor have the synergies between indigenous and exotic knowledge. methods a situational analysis using focus group discussions, interviews and observations was employed to collect preliminary data on perceived human and animal disease syndromes with high impact on food insecurity. results preliminary findings show that the changing institutional setting of land management has a big impact on pastoralist’s capacity to address human and animal diseases. they had high knowledge on animal compared to human diseases. pastoralists, for grounded reasons, still rely on herd maximization to abate poverty in the face of widespread diseases. modern medical and veterinary systems exist but fail to well accommodate diseases amidst challenging terrains and prevalent poverty. low knowledge on disease transmission mechanisms compel people to implement decisions relying on disease syndromic knowledge alone. perceived risks to diseases and consequently health seeking options seem not to match disease control possibilities. conclusions more disease focused ethnographic and socio-economic studies are still needed to further investigate the broader dynamics on how pastoralists and make decisions to identify and address health issues within their broader ecology 1). keywords health; poverty; pastoralism; ecology; tanzania acknowledgments 1. sacids 2. idrc canada grant no. 107030-001 for funding this study. references [1] young a.g. current research on health among tanzanian pastoralists, and future directions for improving pastoral health in east africa. east africa journal of research 1[1], 71-78. 2009. *peter e. mangesho e-mail: peter.mangesho@sacids.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e146, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen communicable disease, cook county department of public health, forest park, il, usa objective to identify geographic clustering of elevated emergency room (er) usage rates for incorporation into community health assessments (cha) in suburban cook county and to validate this metric as a potential sub-county level community health indicator. introduction community health assessments are a foundation of public health practice and a prerequisite to achieving public health accreditation. best practice dictates that chas must incorporate qualitative and quantitative data and utilize a number of indicators to create a detailed picture of a community’s health. metrics may describe demographics, social and economic factors, health behaviors, health outcomes, and healthcare access and utilization. commonly used indicators facilitate cross-jurisdiction comparisons and simplify decisionmaking. however, while many readily available indicators exist on a county level, few have been made available on the sub-county level1. syndromic surveillance messages, typically emergency room visit records, contain sub-county level data on patient residence, such as zip code or municipality. as hospitals progress towards meeting stage 2 meaningful use requirements, transmission of syndromic surveillance data to public health entities will become standard. analysis of emergency room visit data, either in aggregate or by specific syndromes, may be a valuable sub-county level indicator of community health status and access to care that can be standardized across jurisdictions. methods emergency room visits in 2014 for suburban cook county residents were extracted from a syndromic surveillance system, essence, containing records from 45 hospitals in northern illinois. all hospitals in suburban cook county report to the system. crude and age-adjusted rates of er usage by zip code were calculated using population estimates from the 2013 american community survey. rates were further stratified by sex and age. arcgis 10.1 was used to visualize and analyze spatial clustering of rates, including identification of hot spots using getis-ord gi* hot spot analysis. results in 2014, 767,282 residents of suburban cook county visited emergency rooms reporting to essence for an age-adjusted rate of 281 visits per 1,000 persons. rates varied by zip code of residence, with higher rates in the south district. the aggregate rate of zip codes in the south district was 377 visits per 1,000 persons compared to 240 in the north district, 273 in the west district, and 288 in the southwest district. a similar trend was seen across all age groups and genders, with the exception of persons 65 years and older who exhibited higher rates in the north and southwest districts. hot spot analysis of age-adjusted rates identified a large cluster of 27 zip codes, spanning the south and southwest districts, with significantly elevated er usage (figure 1, p < .01). median age-adjusted rate of this cluster was 382 visits per 1,000 persons with a range of 204 to 807. areas with elevated er visit rates are consistent with areas of need identified by other community health indicators, such as high infant mortality or low socioeconomic position. conclusions spatial analysis of emergency room visit rates by zip code can identify neighborhoods that may have greater public health or medical needs. syndromic surveillance data has the potential to provide public health departments with a useful and widely available community health indicator at the sub-county level. figure 1. hot spot analysis of age adjusted emergency room visit rates, suburban cook county, 2014 keywords syndromic surveillance; disparities; gis; community health assessment references 1. community health assessment for population health improvement: resource of most frequently recommended health outcomes and determinants. atlanta, ga: centers for disease control and prevention; 2013. *kelley bemis e-mail: kbemis@cookcountyhhs.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 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population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic 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syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts development of an infectious disease surveillance framework at public health ontario tina badiani* and brenda lee communicable disease prevention and control, public health ontario, toronto, on, canada objective this presentation will outline the development process for public health ontario’s (pho’s) first infectious disease surveillance framework (the framework), highlight key elements of the framework, and identify examples of infectious disease (id) surveillance activities and projects that align with the framework. introduction since its inception in 2008, pho has grown through new funding to establish the agency, as well as a series of program transfers from the government of ontario, including id surveillance.1 pho’s current role in id surveillance in ontario is to support the public health and health care systems with surveillance information, tools, and resources for the prevention and control of ids. pho also provides scientific and technical expertise for ids, including different aspects of surveillance (e.g., data entry requirements, statistical algorithms, provincial surveillance reports). the overarching aim of the framework is to establish pho’s key priorities, strategies, and actions to guide id surveillance over the next five years and will help advance id surveillance across ontario. this is pho’s first step towards a strategic and coordinated approach to id surveillance. methods the development process for the framework began with an environmental scan to identify: (i) surveillance strategies in other jurisdictions, including the key themes, and (ii) approaches taken to strategic planning for surveillance. pho then formed an internal steering committee and a working group, with representatives from key client groups, including public health units and the health care sector. they directed the completion of other preliminary work and identified stakeholder consultations as a key step in the development process. internal consultations were held the summer of 2013, and were comprised of a broad range of pho staff and senior management representatives. the external consultations included representatives from local public health units (e.g., epidemiologists, program managers, and medical officers of health); government representatives (e.g., ontario’s ministry of health and long-term care, public health agency of canada); and health care providers (e.g., infection control specialists). during the same period, the steering committee also met with the provincial infectious diseases advisory committees to elicit their recommendations for the framework. two members of the steering committee collated and analyzed the information collected from the consultations to determine key themes, which were reviewed by the steering committee and working groups. once drafted, the framework was further validated and refined through an iterative review process with our stakeholders. results during the consultation process, four cross-cutting themes repeatedly emerged: 1) improve data quality; 2) enhance data integration; 3) develop accessible and timely products and tools; and 4) strengthen collaboration and capacity building. these four themes became the priorities of the framework, from which the supporting strategies and actions were developed. some examples of future projects that pho will complete to support the framework include: develop standardized questionnaires and case reporting forms; set benchmarks for data quality; and evaluate routine id surveillance reports. conclusions with the guidance of staff, management, and clients, pho has developed its first infectious disease surveillance framework that will strategically direct pho’s id surveillance initiatives over the next five years. keywords communicable diseases surveillance; plan of action; data quality; collaboration; data integration acknowledgments pho: camille achonu, tina badiani, ellen chan, mark coulter, dr. natasha crowcroft, lisa fortuna, dr. gary garber, corey green, dr. frances jamieson, dr. ian johnson, brenda lee, dr. liane macdonald, keisha mair, dr. anna majury, alex marchand-austin, sean marshall, dr. robert stirling grey bruce health unit/apheo: alanna leffley mohltc: dr. erika bontovics, melissa helferty sunnybrook hospital: sandra callery toronto public health/comoh: dr. michael finkelstein references 1. ontario agency for health protection and promotion (public health ontario). annual report: 2011-2012. toronto (on): queen’s printer for ontario; 2012. 44. *tina badiani e-mail: tina.badiani@oahpp.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e108, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 163 isds 2014 conference abstracts real-time forecasting of the 2014 dengue fever season in thailand nicholas g. reich1, 2, krzysztof sakrejda1, stephen a. lauer*1, derek a. cummings2, paphanij suangtho3, soawapak hinjoy2, 3, sopon iamsirithaworn3, hannah clapham2, henrik salje2 and justin lessler2 1biostatistics, university of massachusetts, amherst, ma, usa; 2johns hopkins bloomberg school of public health, baltimore, md, usa; 3thai ministry of public health and bureau of epidemiology, nonthaburi, thailand objective to develop a statistical model for dengue fever surveillance that uses data from across thailand to give early warning of developing epidemics. introduction dengue is a major cause of morbidity in thailand. annual outbreaks of varying sizes provide a particular challenge to the public health system because treatment of severe cases requires significant resources. advanced warning of increases in incidence could help public health authorities allocate resources more effectively and mitigate the impact of epidemics. methods starting in november of 2013, we received biweekly dengue fever count data from the thai ministry of public health and bureau of epidemiology. after cleaning, this data was combined with historical case counts dating back to 1968 and seasonal trends for each province to build a series of statistical models, ranging from smoothed regression-based models to gaussian process models. once case counts are completed for a given biweek which can take up to six months in rural provinces each model can be cross-validated and compared, using methods such as mean average squared error (mase). results thus far we have generated forecasts at 22 time points for a suite of different models. since cases continue to be reported throughout 2014, the evaluation of the prediction performance is ongoing. conclusions one of the largest challenges from real-time reporting is dealing with incomplete case count data. we have found that we do not receive a full case count for a province in a given biweek for at least two months and often up to six months. since this is the first year of collected data, we have found it difficult to decipher whether case count differences are attributable to reporting rates, seasonality, climate change, or other population factors. through working on this project, we have learned that models built on historical data are not necessarily applicable in real-time and that novel methods must be employed to improve infectious disease predictions, especially in developing countries with partial reporting data. keywords infectious disease surveillance; real-time forecasts; statistical model selection; dengue fever *stephen a. lauer e-mail: stephenalauer@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e205, 201 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e312, 2019 isds 2019 conference abstracts making syndromic surveillance relevant and valuable for emergency managers william e. smith1, charlie ishikawa2 1 office of epidemiology, maricopa county department of public health, phoenix, arizona, united states 2 kahuina consulting, roslindale, massachusetts, united states objective identify and document strategies that enhance the value of syndromic surveillance (sys) data and information for the response, recovery, mitigation and preparedness needs of local and state emergency management professionals in the u.s. introduction intense stress can severely degrade one’s ability to process and utilize new kinds of information [1]. this psychological phenomena may partially explain why epidemiologist are challenged to communicate and establish the value of sys information with emergency management professionals (emps). despite the timely and useful insights that sys data and methods can provide, it is very difficult to convey what these data are when emps and epidemiologists are working to make intense, highly-scrutinized and high-consequence emergency decisions. if state and local authorities want emergency plans and responses that benefit from the powerful insights that sys can provide, epidemiologists need to learn how to best report information and establish a strong rapport before emergencies strike. over the past ten months, isds’s nssp’s syndromic surveillance and public health emergency preparedness, response and recovery (spherr) committee has worked to identify gaps, potential best practices, document use cases, and identify tools for integration of sys data in em activities. during spherr practice exchange meetings, sys professionals have consistently cited effective communication between sys staff and emergency preparedness staff as a top priority in integrating sys more fully into all phases of emergencies. methods participants will engage in an interactive and guided discussion that identifies and documents effective strategies and tools to communicate sys information in ways that provide emps with useful, actionable and valuable insights. as a prompt and further framing device, examples or use cases will be gathered from participants based on health conditions of interest; i.e., infectious disease, environmental exposures, injury, mental health conditions, health care utilization, and exacerbations of chronic disease conditions [4]. examples presented or discussed by spherr will also be used as prompts. the authors will use grouping and appreciative inquiry techniques to facilitate this round table discussion, and document the lessons learned. the discussion will inquire and analyze communication methods that participants use, or plan to use for conveying relevant sys insights to emps during each phase of the emergency management cycle. examples by preparedness phase are included below. during the preparedness phase, establishing sys/emergency management relationships can identify ways in which sys information can address gaps in emergency management capabilities. ongoing relationships and inclusion of sys information in exercises helps ensure that this information is incorporated and effectively utilized in emergency management. during the response, sys data can be used to monitor changes in the number of emergency department (ed) visits, increases in emergency-related syndromes, timing of impacts to eds, and relative impact by geographical location of eds. displacement of populations during mass-care events can also be examined. conducting surveillance for emergency-related key-words in ed reports can facilitate targeted surveillance for outcomes of interest. sys data can also be used to screen for potential cases of disease, so that interventions can be targeted effectively. example use cases of how sys information has informed event responses will be discussed. during recovery from the emergency, sys data can be used to track population displacement, as populations return to the area affected by the emergency. it can also be used to track ed visits, to determine when/if they return to pre-event levels. secondary effects of the emergencies (such as carbon monoxide poisoning, flood-water contaminated food, hazmat events or suicidal ideation/attempts) can also be examined. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e312, 2019 isds 2019 conference abstracts sys data can help in mitigation activities to prevent emergencies, reduce the chance of their occurrence, or reduce their damaging effects by monitoring ed data for patterns of syndrome presentations, or clusters of syndromes which could indicate a potential outbreak or event of public health significance. for diseases with typical seasonal patterns, sys data can be used as an indicator of the beginning of the season, so that public health disease prevention messages and other interventions can be timed more effectively. historical sys data can also be examined to identify patterns of presentations that occurred before an outbreak is recognized, to increase the index of suspicion for these patterns in future surveillance. results at the end of the discussion, roundtable participants will possess a matrix of strategies and tools that they can customize to better utilize sys in have tools and templates customized to communicate the value of sys information in addressing hazards, vulnerabilities and threats faced by their communities. conclusions integration of sys data into a highly functioning surveillance system facilitates rapid identification and characterization of potential threats, enhances health and medical situational awareness and increases the evidence base for making emergency management decisions.the importance of integrating surveillance data into emergency management and of effective and timely communication of this data to enhance situational awareness and share surveillance information with emergency managers has been repeatedly cited in both cdc guidance and in after-action reports for real-world events. this roundtable will help ensure that participants have the knowledge to effectively communicate sys to em personnel and ensure that this potentially life-saving information is integrated into all phases of emergency management. acknowledgement thank you to the membership of nssp’s syndromic surveillance and public health emergency preparedness, response and recovery (spherr) community of practice. references 1. bourne, l, yaroush, r. stress and cognition: a cognitive psychological perspective. moffett field (ca): national aeronautics and space administration; september 2003 155 p. report number (nasa/cr-2003212282), p.6. 2. arroyo-barrantes s. rodriguez,m, perez, r, editors (pan american health organization). information management and communication in emergencies and disasters; manual for disaster response teams. 2009 washington, (dc): area on emergency preparedness and disaster relief. 138 p. report number nlm hv553. 3. kahn, a., kosmos, c, singleton, c. public health preparedness capabilities: national standards for state and local planning. atlanta (ga); march 2011 252 p. office of public health preparedness and response, centers for disease control and prevention. 4. final recommendation: core processes and ehr requirements for public health syndromic surveillance. international society for disease surveillance; jan 2011 69 p. 5. a primer for understanding the principles and practices of disaster surveillance in the united states (1st ed.). centers for disease control and prevention (cdc). atlanta (ga): cdc; 2016. 6. uscher-pines, l, farrell, c, babin, s, cattani, j, gaydos, c., hsieh, y, rothman, r, framework for the development of response protocols for public health syndromic surveillance systems: case studies of 8 u.s. states, disaster med health prep. 2009 jun 3 (s1), s29-36. 7. monitoring health effects of wildfires using the biosense system—san diego county. 2008. california, october 2007, centers for disease control and prevention. atlanta (ga). mmwr. 57(27), 741-47. 8. 2006. morbidity surveillance after hurricane katrina---arkansas, louisiana, mississippi, and texas, september 2005. centers for disease control and prevention. atlanta (ga). mmwr. 55, 727-31. http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts development of genomic surveillance bioinformatics modules eishita tyagi1, r. c. hopkins*1, dan baker1, king jordan2 and shiyuyun tang1, 2 1booz allen hamilton, atlanta, ga, usa; 2georgia institute of technology, atlanta, ga, usa objective to develop a modular approach to infectious disease genomic analysis that can easily integrate with public health analytics systems. using dynamic approaches to genomic sequence analysis, relevant whole genome data can be quickly and accurately visualized and correlated, using a minimum of computational resources. we propose to develop visualization modules that integrate disparate data sources including integrate geospatial location metadata with associated epidemiological factors to enable faster outbreak identification and enhance surveillance. introduction whole-genome sequencing of disease-causing organisms provides an unabridged examination of the genetic content of individual pathogen isolates, enabling public health laboratories to benefit from comparative analyses of total genetic content. combining this information with sample metadata such as temporal, geospatial, morbidity, and mortality can greatly increase the efficacy of genomics analysis. however, with the vast amount of data generated by such techniques, meaningful, rapid, and accurate analysis that interprets and correlates nucleotide polymorphisms for public health practice presents many challenges. to this end we have created a modular genomics analysis toolkit that can easily integrate diverse data streams and couple analysis with an array of visualization platforms. methods using open source tools we have assembled an analysis package that automatically processes next generation sequencing (ngs) data from the ubiquitous illumina miseq. fastq files are uploaded, filtered, trimmed and assembled. the largest contiguous dna assemblies are blasted (basic local alignment search tool) to determine closest reference genome match in refseq to identify the species of any isolate sequenced. after reference determination, a custom gene by gene typing algorithm calculates the core genome alignment required for phylogenetic evolutionary analysis. this approach is based on the whole genome multiple sequence typing (wgmlst) approach that was developed to define a rapid universal identification and typing scheme for pathogens. alternative genomic methods used to process ngs data for evolutionary analysis rely on first calculating high quality single nucleotide polymorphisms (snps) for all sequenced isolates with respect to the reference genome and then creating a phylogeny. these approaches however can be computationally expensive as the number of sequenced isolates increases. our algorithm attempts to overcome these computational bottlenecks through the more efficient gene by gene typing approach. additionally, a key component of our algorithm is a rapid tree construction module where we calculate the minimal set of genes that can effectively recreate the ideal (core genome) phylogeny at a user accepted threshold of consensus identity. results this toolkit provides an automated analysis suite for processing isolate sequencing data directly from the illumina miseq. utilizing a minimal core genome algorithm simplifies the data sets and reduces overall compute time for even large data sets. additional modules being developed utilize open source tools and common sequence formats to integrate evolutionary analysis results from quality scored whole genome sequences with geographical data in order to provide geospatial visualization of distinct and related isolates in an outbreak. output data from the perl modules is seamlessly integrated into open source c++ qt libraries prepackaged to perform geospatial visualization and relatedness clustering using multidimensional scaling (mds) approaches. platform independent qt libraries provide a cross-platform application framework for easy integration of these “genomic surveillance” modules into existing surveillance applications. the virtual overlay of phylogenetic relationships onto isolate maps provides population structure in epidemiological studies and provides a mechanism for rapid real time analysis of transmission chains and effective retrospective analysis of pathogen evolutionary trends. conclusions utilizing and analyzing raw whole genome sequence data directly from the illumina miseq moves current capabilities one step closer to real-time infectious disease characterization. minimal core gene alignment analysis allows for computation on systems commonly available to infectious disease laboratories, circumventing the need for computationally expensive analysis. these genomic methods, if implemented within existing public health laboratory response programs, promise to revolutionize the ability of the laboratory to provide information and evidence on the evolution, transmission and virulence for pathogenic organisms. keywords bioinformatics; epidemiology; next generation sequencing *r. c. hopkins e-mail: hopkins_robert@bah.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e96, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts assessing the potential impact of the biosense 24-hour rule using nc detect ed data amy ising*1, clifton barnett1, dennis falls1, anna e. waller1, john wallace2 and lana deyneka2 1emergency medicine, unc chapel hill, chapel hill, nc, usa; 2north carolina division of public health, nc dhhs, raleigh, nc, usa objective nc detect emergency department (ed) data were analyzed to assess the impact of applying the biosense “24-hour rule” that combines ed visits into a single visit if the patient id and facility id are the same and the earliest recorded dates occur within the same 24-hour time frame. [1] introduction per a frequently asked questions document on the isds website [1], approximately two thirds of hl7 records received in biosense do not provide a visit id. as a result, biosense data processing rules use the patient id, facility id and earliest date in the record to identify a unique visit. if the earliest dates in records with the same patient id and facility id occur within the same 24-hour time frame, those two visits are combined into one visit and the earliest date will be stored. the ed data sent by hospitals to nc detect include unique visit ids and these are used to identify unique visits in nc detect. these data are also sent twice daily to biosense. in order to assess the potential differences between the nc detect ed data in nc detect and the nc detect ed data in biosense, an initial analysis of the 24-hour rule was performed. methods 4,822,347 unique ed visits from 2013 nc detect data were included in this analysis. the 24-hour rule was applied to these visits; if ed visit records met the 24-hour rule inclusion criteria, the data from only the first ed visit was kept. counts were compared to the data based on unique visit id to the data based on the 24-hour rule by ed disposition, age group, syndrome, payor source, transport mode, county of residence and patient sex. the percentage of ed visits “lost” to the 24-hour rule was calculated. results after applying the 24-hour rule, the 4,822,437 ed visits in 2013 were reduced to 4,740,250, a 1.7% reduction in ed visits. table 1 shows the lowest and highest % loss of ed visits by data element. conclusions combining ed visits by the same patient that occur at the same facility within 24 hours reduces the overall ed visit count in nc by approximately 1.7%. the percentage of ed visits lost is higher for certain types of visits, e.g. behavioral health, healthcare utilization, gi illness and varies significantly across counties in nc. additional research and documentation are needed to determine if this initial analysis is an accurate understanding of the biosense 24-hour rule, and the potential impact this has on timely surveillance for select indicators. table 1: percentage of ed visits lost after applying the 24-hour rule, by select data elements keywords data quality; biosense; data processing; emergency department data acknowledgments nc detect is funded with federal funds by the north carolina division of public health, public health emergency preparedness grant (phep), and managed through a collaboration between nc dph and the unc department of emergency medicine carolina center for health informatics. references 1. biosense v2.0 webinar: data processing [internet]. boston: isds [cited 2014 sept 9]. available from http://www.syndromic.org/ resources/isds-webinars/upcoming-recent/742-biosense2-webinardata-processing. *amy ising e-mail: ising@ad.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e84, 201 ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 provision of telemedicine services by community health centers peter shin 1 , jessica sharac 1 , and feygele jacobs 2 1. department of health policy, the milken school of public health, george washington university, washington, dc 2. the rchn community health foundation, new york city, new york abstract the objective of this study was to assess the use of telemedicine services at community health centers. a national survey was distributed to all federally qualified health centers to gather data on their use of health information technology, including telemedicine services. over a third of responding health centers (37%) provided some type of telemedicine service while 63% provided no telemedicine services. a further analysis that employed anova and chi-square tests to assess differences by the provision of telemedicine services (provided no telemedicine services, provided one telemedicine service, and provided two or more telemedicine services) found that the groups differed by meaningful use compliance, location, percentage of elderly patients, mid-level provider, medical, and mental health staffing ratios, the percentage of patients with diabetes with good blood sugar control, and state and local funds per patient and per uninsured patient. this article presents the first national estimate of the use of telemedicine services at community health centers. further study is needed to determine how to address factors, such as reimbursement and provider shortages, that may serve as obstacles to further expansion of telemedicine services use by community health centers. keywords: community health centers, telemedicine, medically underserved area abbreviations: community health centers (chcs), health information technology (hit), electronic health record (ehr), bureau of primary health care (bphc), health resources and services administration (hrsa), uniform data system (uds), meaningful use (mu), patient centered medical home (pcmh), full-time equivalent (fte) correspondence: pshin@gwu.edu doi: 10.5210/ojphi.v6i2.5421 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. introduction community health centers (chcs) are a vital source of care for medically underserved populations. in 2012, 1,198 federally qualified chcs served over 21.1 million patients and 93 look-alike chcs served an additional 951,242 patients [1]. the patient population at chcs is ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 largely low-income and over one in three patients is uninsured, which illustrates the extent to which chcs fulfill their statutory requirements to provide comprehensive primary care services to all patients in need, regardless of insurance status, and to charge uninsured patients on an income-based, sliding scale basis. data on the use of health information technology (hit) at community health centers indicates that its use has rapidly expanded in the past few years. while only 26% of surveyed chcs had an electronic health record (ehr) system in 2006, this had increased to 48% in 2008 and 69% in 2010/2011 [2]. the bureau of primary health care (bphc) of the health resources and services administration (hrsa) began reporting the use of ehr systems at chcs for the year 2011 in its annual report on data from the uniform data system (uds), to which all federally qualified health centers are required to submit annually data on patients served and services provided as well as financial, staffing, and quality of care data. in 2011, 80% of 1,128 chcs reported that they had a full or partial ehr system in use and this percentage increased to 90% in 2012 [3]. increasingly, chcs have added telemedicine services to the array of hit services offered, with the objective of reducing inequities in health care access while improving the cost-effectiveness and quality of health care [4]. telemedicine may incorporate both synchronous and asynchronous clinical consults, remote monitoring, and various forms of mobile communication; what each of these applications has in common is the exchange of clinical information across locations and between multiple providers, or between providers and patients. there is some evidence that telemedicine can increase access to specialist care and decrease referral wait times [5]. yet obstacles to widespread implementation of telemedicine remain. research indicates that barriers to the use of telemedicine include reimbursement and licensing issues as well as problems with applying quality of care measures that may require in-person, face-to-face encounters to the practice of telemedicine [6]. the objective of this study was to gather data on the use of telemedicine services at federally qualified health centers and to determine if health center characteristics varied according to the extent of telemedicine services use. while telemedicine has been in use for more than a decade, most notably by the department of defense and in the veteran’s administration system, there are relatively few studies documenting its application, benefits, or value. the cochrane collaboration reviewed seven studies comparing telemedicine with face-to-face patient care and concluded that although no studies reported detrimental effects of telemedicine, neither were the reported benefits unequivocal [7]. a systematic review of patient satisfaction with telemedicine found that although all the studies on the subject had methodological issues, they also were unanimous in finding good levels of patient satisfaction [8]. two systematic reviews conducted a decade apart, in 2002 and 2012, both assessed the cost-effectiveness of telemedicine and found limited evidence that telemedicine is more cost-effective than practice-based care [9]. chc-based research provides some evidence that telemedicine can improve health outcomes while providing care with which both patients and providers are satisfied. a comparison of telemedicine-based and practice-based collaborative care at rural chcs for patients who screened positive for depression found that the telemedicine-based group had significantly better responses to treatment, rates of remission, and reductions in depression severity compared to the practice-based group, although the authors concluded that the significant differences were largely due to better adherence to the collaborative care model in the telemedicine group [10]. a study ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 on the use of telemedicine in maine, which has one of the largest state-wide telemedicine systems, reported high patient and provider satisfaction rates at chcs and savings of providers’ time and travel [11]. methods the readiness for meaningful use (mu) [12] of health information technology and patient centered medical home (pcmh) recognition survey was conducted from december 2010 to february 2011 by researchers from the milken institute school of public health at the george washington university’s department of health policy in conjunction with the national association of community health centers. all federally qualified health centers in the united states were invited to participate. results from the readiness survey were combined with data from the 2009 uniform data system (uds) and analyzed using anova and chi-squared (x 2 ) tests to determine which center-level characteristics were associated with the provision of telemedicine services. in the survey, telemedicine was defined as: the exchange of clinical information from one location to another through electronic audiovisual media to improve patients' health status. the exchange may either be between providers or between provider and patient. this exchange may be rendered by using audio-visual technology such as webinars or video-conferencing that is interactive in real time (synchronous) or by transmission of clinical information using technology such as email with document and image transfer that is not real-time interactive (asynchronous), i.e. send a message or question and wait for a response. results of the 714 health centers that completed the readiness survey, 625 health centers answered questions on the provision of telemedicine services (the results for those who responded that they were “not sure” whether telemedicine was offered were not included in the total number of 625). of those 625 health centers, 396 (63%) provided no telemedicine services, while 229 (37%) provided some type of telemedicine services. this included 147 chcs that provided one service and 82 that offered two or more services. table 1 shows the distribution of telemedicine services provided by type of service. the most commonly offered telemedicine service was “consults offsite providers without patients present” (16% of all respondents and 43% of all centers offering some telemedicine) and the least common was “receives information from home monitoring” (4% of respondents and 11% of those offering telemedicine services). table 2 presents the results of anova and x 2 tests for differences between chcs that offered no telemedicine services with those that provided at least one telemedicine service and with health centers that provided two or more telemedicine services with respect to the use of health information technology (hit), health center location and patient population, and quality variables. a review of significant findings follows. meaningful use compliance in 2011, cms began to offer incentives through the medicaid program to health care practices that demonstrated that their providers had achieved “meaningful use” (mu) of hit. to qualify for these incentives, providers must comply with a series of defined functional objectives and quality measures, including 15 core functional measures and 10 additional “menu set” ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 measures. for stage 1, these measures focus on the electronic capture of patient information in a standardized format, data tracking, and initiating communication. centers that provided two or more telemedicine services were more likely to have met core-mu and menu-mu requirements and to have achieved stage 1 mu compliance at the time of the survey. table 1: number and percentage of health centers offering each telemedicine service health care services other locations consults offsite providers with patients present consults offsite providers without patients present receives information from home monitoring mobile health communication via mobile devices other telemedicine services number 65 93 99 25 36 41 percent (of 625 total responses) 10% 15% 16% 4% 6% 7% percent (of 229 chcs that offer telemedicine services) 28% 41% 43% 11% 16% 18% location health centers that provide no telemedicine services were more likely to serve urban communities while chcs that provided two or more services were significantly more likely to serve rural areas. the survey found that among chcs that provided two or more telemedicine services, a higher proportion was located in rural communities (55%), while 28% percent was located in urban communities and 17% served both urban and rural areas. conversely, health centers that offered no telemedicine services were more likely to be located in urban areas (47%), while 34.9% were situated in rural areas and 18.2% in both urban and rural settings. health center population characteristics and staffing chcs that provided two or more telemedicine services had a higher percentage of elderly patients (8.7% compared to 7.1% for chcs that provided no telemedicine services). health centers that offered two or more telemedicine services also had higher staffing ratios based on full-time equivalent (fte) staff per 10,000 patients for mid-level providers, such as physician assistants or nurse practitioners (5.2 ftes per 10,000 patients), and medical personnel 1 (25.9 per 10,000 patients), while chcs that offered one telemedicine service had the highest ratio of mental health providers (2.6 per 10,000 patients). quality measures analysis of seven key quality of care measures reported in the uds related to diabetes management, control of hypertension, childhood immunization rates, cervical cancer screening, birth weight, and trimester of entry into prenatal care, found a significant difference only with 1 this designation includes physicians, mid-level providers, nurses, laboratory personnel, x-ray personnel, and other medical personnel. ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 respect to “percentage of diabetic patients with hba1c levels less than 7%” (a measure of good control of diabetes), with centers with one telemedicine service reporting the highest percentage (42.2%). funding variables the health centers differed significantly with respect to funding characteristics, with chcs that offered two or more telemedicine services receiving substantially higher state and local funds per patient and per uninsured patient than those centers that provided no telemedicine services and centers that provide only one telemedicine service. table 2: comparison of selected indicators by health centers’ provision of telemedicine services variables provided no telemedicine services provided one telemedicine service provided two or more telemedicine services anova or x 2 significance distribution (n) 396 147 82 distribution (% out of 625) 63.4% 23.5% 13.1% meaningful use (mu) compliance core mu compliance now 10.5% 10.2% 23.2% 0.005 menu mu compliance now 25.4% 23.8% 40.2% 0.014 stage 1 mu compliance now 6.2% 4.1% 14.6% 0.007 ehr operation full 45.6% 42.2% 51.2% 0.650 partial 23.6% 23.8% 23.2% none 30.8% 34.0% 25.6% duration of ehr operation less than a year ago 30.7% 28.9% 30.0% 0.419 1-2 years ago 30.0% 38.1% 25.0% 3+ years ago 39.3% 33.0% 45.0% has received pcmh recognition 6.8% 7.5% 2.4% 0.280 received technical assistance from a rec or sub-contractor 32.3% 40.8% 36.6% 0.172 location rural 34.8% 48.3% 54.9% 0.000 urban 47.0% 30.6% 28.0% 0.000 both 18.2% 21.1% 17.1% 0.683 health center patient population variables mean total patients 17,285 19,769 21,077 .214 mean percentage medicaid patients 33.8% 31.1% 30.4% .082 mean percentage uninsured patients 40.8% 39.8% 40.2% .877 mean percentage elderly patients 7.1% 8.2% 8.7% .012 mean percentage medicare patients 7.7% 8.9% 8.7% .061 2 see for example http://www.cms.gov/outreach-and-education/medicare-learning-networkmln/mlnproducts/downloads/telehealthsrvcsfctsht.pdf ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 variables provided no telemedicine services provided one telemedicine service provided two or more telemedicine services anova or x 2 significance mean percentage minority patients 48.6% 46.0% 46.8% .693 mean percentage patients requiring translation services 20.9% 20.3% 21.3% .960 health center staffing variables physician ftes per 10,000 patients 4.7 4.5 4.9 0.703 mid-level provider ftes per 10,000 patients 3.5 4.0 5.2 0.000 medical ftes per 10,000 patients 23.2 23.7 25.9 0.035 dental ftes per 10,000 patients 4.5 4.7 5.2 0.491 mental health ftes per 10,000 patients 2.0 2.6 2.4 0.030 substance abuse ftes per 10,000 patients 0.8 0.7 0.7 0.919 enabling services providers ftes per 10,000 patients 7.1 8.0 7.0 0.596 quality measures percentage of diabetic patients with hba1c levels <7% 38.6% 42.2% 40.6% .007 percentage of diabetic patients with hba1c levels <9% 70.4% 73.5% 71.0% .053 bp control among hypertensive patients 62.8% 61.7% 60.3% .337 childhood immunization rate 63.9% 63.3% 64.9% .885 low or very low birth weight births rate 8.7% 8.6% 7.6% .778 pap test rate 55.4% 51.9% 53.4% .203 percentage of pregnant women with first prenatal visit in the first trimester 69.1% 71.8% 73.3% .093 funding variables percentage of total revenue from medicaid 30.5% 28.7% 27.9% .303 mean medicaid dollars per patient $555 $593 $604 .364 received arra funding 70.7% 74.7% 81.7% .110 mean american recovery and reinvestment act (arra) new access point (nap) and increased demand for services (ids) funds $154,794 $128,041 $135,722 .207 mean arra capital improvement project funds (cip) and facility investment program (fip) $146,088 $173,186 $192,444 .195 ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 variables provided no telemedicine services provided one telemedicine service provided two or more telemedicine services anova or x 2 significance percentage of total revenue from arra funds 5.7% 4.1% 4.3% .086 mean arra funds per patient $41 $24 $28 .469 mean arra funds per uninsured patient $100 $77 $98 .537 mean state and local funds $1,312,620 $1,272,824 $1,501,310 .780 percentage of total revenue from state and local funds 10.6% 9.6% 12.1% .341 mean state and local funds per patient $77 $72 $152 .002 mean state and local funds per uninsured patient $223 $217 $1,587 .024 percentage of total revenue from state and local funds 10.6% 9.6% 12.1% .341 discussion the results of this survey indicate that over one in three surveyed health centers provides at least one telemedicine service. health centers that offer telemedicine services are more likely to be located in rural areas and chcs that offered two or more telemedicine services have more generous state and local funding. the locational finding seems intuitive because reimbursement streams support the provision of telemedicine in rural areas, while limiting the extent to which urban health centers can obtain reimbursement. while these data may reflect the perceived and real value that telemedicine provides in non-urban locations, where access to certain services and specialties may be particularly challenging, it is also likely a reflection of reimbursement rules which, in the case of medicare, for example, restrict coverage to services rendered in rural health professional shortage areas or outside of metropolitan statistical areas [2], limiting the extent to which urban health centers might offer such services. implications for health policy and research research indicates that telemedicine services garner high patient and provider satisfaction and can offer access to specialty services, including behavioral health care, that are not available locally. despite having demonstrated successful telemedicine experiences at chcs in new york, california, and south dakota, among other states, the expansion of telemedicine services at chcs is limited by the availability of key trained personnel and reimbursement for services [13]. medicaid reimbursement for telemedicine services is based on medicare’s definition of telehealth services and is covered at the option of states; according to a recent report, 42 states offer medicaid reimbursement for telehealth services and 22 states provide reimbursement for telemedicine services offered by health centers [14]. although telemedicine services can be of great benefit to rural and remote populations by providing access to services that are geographically remote, the value of telemedicine in urban settings should also be considered. urban health centers also benefit from the use of telemedicine given the general challenges in ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 maintaining capacity for clinical [15], dental [16], and behavioral [17] services in underserved communities. given the potential of telemedicine services to improve health centers’ ability to served medically underserved populations, further study is needed to determine the extent to which chcs’ provision of telemedicine services is limited by reimbursement constraints and a shortage of consulting specialists and trained local providers who can facilitate the provision of telemedicine services. limitations this survey provides the first and, to the best of our knowledge, only national estimate of the use and scope of telemedicine in community health center settings. although the study findings are limited to the survey period of 2010-2011 and the survey did not specifically ask about barriers to the use of telemedicine services, they provide significant insight about some of the internal, organizational, and financial factors that likely influence health center adoption and use of telemedicine. we have also tried to minimize reporting errors by providing health centers with a standard definition of telemedicine services. we also believe misreporting is minimal due to health centers’ regular self-reporting of uds data, in which all grantees must submit information on adoption and use of electronic health records to hrsa, so health centers would be accustomed to providing detailed data on their use of health information technology. financial disclosure the authors have no financial or other interests related to the research or resulting policy implications. the research was funded by rchn community health foundation. competing interests the authors report no competing interests related to this publication. references 1. hrsa. 2012 health center data national program grantee data http://bphc.hrsa.gov/uds/datacenter.aspx?year=2012 hrsa. 2012 health center data national look-alikes data http://bphc.hrsa.gov/uds/lookalikes.aspx?state=national&year=2012 2. cunningham m, lara a, shin p. (2011). results from the 2010–11 readiness for meaningful use of hit and patient centered medical home recognition survey. policy research brief #27 geiger gibson/rchn community health foundation research collaborative, issue no. 27. retrieved from http://www.rchnfoundation.org/?p=905 lardiere m. (2009). a national survey of health information technology (hit) adoption in federally qualified health centers. national association of community health centers. retrieved from http://www.nachc.com/client/nachc%202008%20hit%20survey%20analysis_final_6 _9_091.pdf shields, a. e., shin, p., leu, m. g., levy, d. e., betancourt, r. m., hawkins, d., & proser, m. (2007). adoption of health information technology in community health centers: results of a national survey. health affairs, 26(5), 1373–1383. pubmed http://dx.doi.org/10.1377/hlthaff.26.5.1373 ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 3. bureau of primary health care. (2012). uniform data system (uds) report 2011.washington, dc: health resources and services administration, us department of health and human services. retrieved from http://bphc.hrsa.gov/uds/doc/2011/national_universal.pdf bureau of primary health care. 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(2012). community health forum, 22-27. retrieved from http://www.rchnfoundation.org/wp-content/uploads/2013/02/1706_001.pdf.pdf http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19534591&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19534591&dopt=abstract http://dx.doi.org/10.1089/tmj.2009.9960 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24493760&dopt=abstract http://dx.doi.org/10.1377/hlthaff.2013.0992 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24205836&dopt=abstract http://dx.doi.org/10.1089/tmj.2013.0167 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10796678&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10834899&dopt=abstract http://dx.doi.org/10.1136/bmj.320.7248.1517 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23429924&dopt=abstract http://dx.doi.org/10.1176/appi.ajp.2012.12050696 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12699605&dopt=abstract http://dx.doi.org/10.1089/153056203763317620 ojphi provision of telemedicine services by community health centers online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e185, 2014 14. national association of community health centers. (2013). fqhc reimbursement for telemedicine services in medicaid. state policy report #48. retrieved from http://www.nachc.com/client/telemedicine%20%20spr48.pdf 15. u.s. department of health and human services, health resources and services administration, national center for health workforce analysis. projecting the supply and demand of primary care practioners through 2020. retrieved from http://bhpr.hrsa.gov/healthworkforce/supplydemand/usworkforce/primarycare/projectingpri marycare.pdf 16. institute of medicine. (2012) improving access to oral health care for vulnerable and underserved populations. retrieved from http://www.iom.edu/reports/2011/improvingaccess-to-oral-health-care-for-vulnerable-and-underserved-populations.aspx 17. u.s. department of health and human services, health resources and services administration. (2013) increasing access to behavioral health care through technology. retrieved from http://www.hrsa.gov/publichealth/guidelines/behavioralhealth/behavioralhealthcareaccess.pd f 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an integrated system for enteric disease surveillance and outbreak detection kristen soto*1, quyen phan1, meghan maloney1, jessica brockmeyer1, paula clogher2 and therese rabatsky-ehr1 1epidemiology, connecticut department of public health, hartford, ct, usa; 2yale emerging infections program, new haven, ct, usa objective to develop an integrated system for routine enteric disease surveillance, cluster detection and monitioring, information sharing among key stakeholders, and documentation. introduction the connecticut department of public health (dph), in collaboration with yale emerging infections program (eip), receives funding to particpate in the foodborne diseases active surveillance network (foodnet) and foodborne disease centers for outbreak response enhancement (foodcore). foodnet is an active population-based surveillance network that monitors trends for ten enteric diseases and conducts special studies to better understand the causes of foodborne illness.1 foodcore develops best practices related to the detection, investigation, and control or disease outbreaks, particularly those due to to salmonella, shiga toxin-producing e. coli, and listeria (ssl).2 foodborne disease surveillance and response is a collaborative effort requiring real-time data sharing between key stakeholders including: dph epidemiology, dph laboratory, dph food protection program, yale eip, and local health department (lhd) staff. methods prior to 2011, dph and yale eip maintained several separate access and epi-info databases to manage foodborne surveillance, disease follow-up, and outbreak management activities. surveillance and laboaratory data were routinely reviewed to identify clusters based on demographic information or spatial/temporal clusttering. during 2011, foodborne disease surveillance and follow-up activities were migrated to maven, connecticut’s electronic disease surveillance system (cedss)3. all cases of physician or laboratory reportable foodborne illness are initiated as events in maven. each event contains demographic, laboratory, and follow-up information for the case. foodborne clusters and outbreaks are also initiated as unique events in maven; each outbreak events contains basic summary information, a repository of relevant documentation, and outcome measures data. outbreak events are linked with associated case events. workflows and reports have been developed to facilitate data management and analysis, and to generate automated alerts. dph epidemiology, dph laboratory, dph food protection program, yale eip, and lhd staff can access records in maven to update data and to facilitate information sharing. results during january 2012-june 2014 3238 enteric disease cases and 152 ssl clusters, including 16 identified outbreak events, were initiated and managed in cedss. during 2012 a foodcore student interview team was formed resulting in increased completeness of salmonella follow-up from 51% (2009-2011) to 82% (2012); data management and information sharing were facilitated through cedss. conclusions the use of a centralized electronic disease surveillance system has improved foodborne disease surveillance and outbreak detection by streamlining data management needs and improving communication and data sharing among key stakeholders. future areas of development involve implementing electronic laboratory reporting and integrating information from the dph food protection program’s foodborne complaint and restaurant inspection systems. ongoing system evaluation is needed to continue to identify areas for increased streamlining and data sharing of foodborne disease information. keywords foodborne diseases; foodborne outbreaks; surveillance system; information sharing; integrated disease surveillance references 1. foodborne disease active surveillance network (foodnet) [internet]. atlanta: centers for disease control and prevention.; updated 2014 jul 18; cited 2014 sep 8]. available from: http://www.cdc.gov/ foodnet. 2. foodborne diseases centers for outbreak response enhancement (foodcore) [internet]. atlanta: centers for disease control and prevention.; updated 2014 aug 14; cited 2014 sep 8]. available from: http://www.cdc.gov/foodcore. 3. consilience software. maven electronic disease surveillance system, version 4.0. austin, tx. *kristen soto e-mail: kristen.soto@ct.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e160, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using emergency department data for detection of a synthetic marijuana outbreak lourdes w. yun*1, robert beum1, michele askenazi2, yushiuan chen2, dean mcewen1, moises maravi1 and arthur davidson1 1denver health and hospitals, denver, co, usa; 2tri-county health department, greenwood village, co, usa objective the aims of this presentation is to use ed chief complaint data, to test biosense 2.0 for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the biosense syndromic surveillance library. introduction timely access to emergency department (ed) chief complaint (cc) data, before the definitive diagnosis is established, allows for early outbreak detection and prompt response by public health officials.biosense 2.0 is a cloud-based application that securely collects, tracks, and shares ed data from participating hospitals around the country. denver health (dh) is one of several colorado hospitals contributing ed chief complaint data to biosense 2.0. in august 2013, ed clinicians reported an increase in patients presenting with excited delirium, possibly related to synthetic marijuana (sm). we used this event to test the use of cc field of ed data for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the biosense syndromic surveillance library. methods daily routines retrieved dh hospital data from biosense 2.0 to local public health servers: 1. ‘r’ programs access biosense servers that contain the raw, binned and exceptions syndromic data; 2. unix cron jobs were created to schedule these ‘r’ jobs to run on a regular daily basis and pull the results down to the dh servers; 3. scheduled sas batch job that appends the retrieved data into one sas dataset. using sas index functions, we searched for specific keywords likely related to sm-usage in the cc field. for example, “if (index(cc, “blueberry”) > 0 or index(cc, “boogie”) > 0 or index(cc, “cannabinoid”) > 0 or index(cc, “cannabis”) > 0 or ….the ‘if’ logic grew quickly as the list of possible keywords/exclusions and the criteria became unmaintainable. to develop a maintainable code solution, a custom built library containing 89 sm-related keywords and exclusion words was developed. sm-related keywords were provided by ed clinicians. exclusion words were words in the cc field that came back as “hits” but were not sm-related. for example, “spice” is a keyword but “hospice” is an exclusion word. not all 89 keywords were found in the cc data; only 20 keywords provided significant numbers of hits for possible sm-use. because of the sheer volume of records returned and lack of specificity of some keywords, 15 of the 20 keywords were further excluded. to assess the sensitivity and specificity of this method, we compared the patients seen at dh ed with a known sm-related diagnosis (gold standard) during the event period (september 4-19, 2013) with the patients with a cc where a sm-related keyword was observed. results between august 2013-april 2014, using the 5 keywords (mamba, marijuana, mj, spice, thc), 106 smrelated records were identified in the analysis. during the outbreak period (september 4-19, 2013), 21 cases of sm-related illness presented to the dh emergency department and a total of 27 of sm-syndrome associated records were identified. sensitivity and specificity analysis was based on the sample of these 27 records. using this method to detect sm-related events was highly specific (99.6%) but only moderately sensitive (44%). conclusions keywords and exclusions were used to identify a novel synthetic marijuana outbreak with adverse health effects through analysis of ed record cc field. keyword data were difficult to analyze due to many acronyms, abbreviations, misspellings, and truncation of words. as triage personnel may be unfamiliar with sm-related terms and enter the target keyword efforts may be required to educate ed clinicians during the event to improve sensitivity. keywords syndromic surveillance; synthetic marijuana; emergency department *lourdes w. yun e-mail: lourdes.yun@dhha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e178, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 111 (page number not for citation purposes) isds 2015 conference abstracts building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 1the ohio state university, columbus, oh, usa; 2panaftosa (paho/who), rio de janeiro, brazil objective to take the first step in identifying how american countries can strengthen their capacities to manage zoonoses risks by capturing information regarding their national zoonoses programs and priorities. introduction zoonoses account for over 70% of emerging infectious diseases in humans1. in recent years, global public health security has been threatened by zoonotic disease emergence as exemplified by outbreaks of h5n1 and h1n1 influenza, sars, and most recently ebola. the occurrence of a number of these zoonoses, and their spread to new areas, is related to globalization, environmental changes, and marginalization of populations. this phenomenon holds true for latin american and the caribbean countries (lac), where 70% of the events public health emergencies reported to the who from 2007 to 2008 in the americas were classified as zoonoses or communicable diseases common to humans and animals2. despite this record, there are no national or regional disease burden estimates in lac for many zoonoses3. to start filling this void, the pan american health organization (paho) conducted a survey of lac countries to collect information on priority emerging and endemic zoonoses, countries prioritization criteria and methodologies, and suggestions to strengthen countries capacities and regional approaches to zoonoses control. methods an online questionnaire was sent to the zoonoses program managers of the ministries of health (moh) and ministries of agriculture (mag) of all 33 lac countries from january to april of 2015. the questionnaire comprised 36 single, multiple choice and open-ended questions to inform the objectives of the survey. a descriptive exploratory analysis was completed in r (i386 3.1.2). results fifty-four ministries (26 moh, 25 mag, and 3 combined responses) in 31 lac countries responded to the survey. within the ministries, 22 (85%) moh, 5 (20%) mag, and 2 (67%) combined entities indicated they had specialized zoonoses units. for endemic zoonoses, 32 ministries responded that they conduct formal prioritization exercises, most of them annually (69%). the three priority endemic zoonoses for the mohs were leptospirosis, rabies, and brucellosis while the three priorities for the mags were brucellosis, rabies, and tuberculosis. diagnosis for rabies and leptospirosis were cited as the capacities most in need of development. the most wanting cross-cutting capacity was coordination between stakeholders. for emerging zoonoses, 28 ministries performed formal prioritization exercises. the top prioritization criteria were probability of introduction into the country and impact. the three priority emerging zoonoses for the mohs were ebola, avian influenza, and chikungunya while for the mags were avian influenza, bovine spongiform encephalopathy (bse) and west nile virus disease. surveillance for avian influenza and ebola, and diagnosis for bse were quoted as the capacities most wanting. for all zoonoses, the majority of respondents (68%) ranked their relationship with the other ministry as productive or very productive, and 31% minimally productive. many countries demanded greater regional coordination, the constitution of a regional group to handle emerging zoonoses, and recommended the dissemination of reports on zoonoses occurrence and capacities across the region. conclusions the survey is the first comprehensive effort to date to inform the status of zoonoses programs in lac, and provides the evidence to build a regional strategy and identify capacity needs. a number of improvements appear evident: i) standardization of prioritization approaches, surveillance definitions and evaluation processes to support comparisons, ii) greater communication and coordination between countries, and iii) a platform to inform zoonoses occurrence in the region and the status of the region’s capacities. keywords zoonoses; prioritization; capacity building; survey; pan american acknowledgments boren fellowship. references 1.wang, lf, crameri, g. emerging zoonotic viral diseases. rev sci tech off int epiz. 2014; 33(569–81). 2.schneider m.c. et al. importance of animal/human health interface in potential public health emergencies of international concern in the americas. rev panam salud publica. 2011; 29(371–9). 3.hotez pj. et al. the neglected tropical diseases of latin america and the caribbean: a review of disease burden and distribution and a roadmap for control and elimination. plos negl trop dis. 2008; 2:e300. *melody j. maxwell e-mail: 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judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira 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burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le 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cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in 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to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts evaluation of praedico™, a next generation big data biosurveillance application mark holodniy*1, carla winston1, cynthia a. lucero-obusan1, gina oda1, anosh mostaghimi1, julie a. pavlin3, payam etminani2, chris lee2 and farshid sedghi2 1office of public health, department of veterans affairs, palo alto, ca, usa; 2bitscopic inc., redwood city, ca, usa; 3armed forces health surveillance center, silver spring, md, usa objective the purpose of our study was to conduct an initial assessment of the biosurveillance capabilities of a new software application called praedico™ and compare results obtained from previous queries with the electronic surveillance system for the early notification of community-based epidemics (essence). introduction the national strategy for biosurveillance promotes a national effort to improve early detection and enable ongoing situational awareness of all-hazards threats. implicit in the strategy’s implementation plan is the need to upgrade capabilities and integrate multiple disparate data sources, including more complete electronic health record (ehr) data into future biosurveillance capabilities. thus, new biosurveillance applications are clearly needed. praedico™ is a next generation biosurveillance application that incorporates cloud computing technology, a big data platform utilizing mongodb as a data management system, machine-learning algorithms, geospatial and advanced graphical tools, multiple ehr domains, and customizable social media streaming from public health-related sources, all within a user friendly interface. methods initially, 1 million va patient records were analyzed as a training set for validation of the extract-transform-load (etl) layer into a designated biomart within the va healthcare-associated infection and influenza surveillance system (haiiss) and analyzed for utilization, processivity, searchability, display functions, timing and accuracy. in addition, a validation set of combined va and department of defense (dod) biosurveillance data comprising 17 million dod and 25 million va records respectively, was used to assess the performance of praedico™ using icd-9 encounter codes from known influenza-like illness (ili) outpatient visits previously analyzed using essence alone1. results utilizing a big data nosql database backend, praedico™ required significantly less data storage and utilization, while providing the same analytical capabilities. praedico™ was flexible in terms of hardware and software requirements using a modular design that could run on commodity hardware and any major operating system including linux, windows and os x. using a single operation in praedico™, the number of ili encounters in va and dod as a proportion of the total number of cases during that same 2007-2008 time period (figure 1) could be queried and displayed in seconds. praedico™ dashboard also geographically displayed the results for both va and dod on a single map using a geospatial tool embedded directly in the application. both applications displayed the same number of va ili cases. additionally, new ili queries could be run within the praedico™ dashboard and updated results displayed in real time. conclusions our analysis demonstrates improved resource utilization and response time for praedico™, and results were comparable to va essence. figure 1. praedico dashboard displaying va and dod ili data keywords biosurveillance; veterans; big data; system architecture; essence acknowledgments the findings and conclusions in this report are those of the authors and do not reflect the official position of the u.s. departments of defense and veterans affairs references 1. pavlin ja, et al. combining surveillance systems: effective merging of u.s. veteran and military health data. plos one 2013 epub dec 26. *mark holodniy e-mail: mark.holodniy@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e133, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts new ways to disseminate the us national notifiable disease provisional data anna grigoryan* cdc, atlanta, ga, usa objective the purpose of this study was to identify ideas for an enhanced dissemination of the us national notifiable diseases surveillance system (nndss) provisional data. introduction the nndss is a nationwide collaboration that enables all levels of public health (local, state, territorial, federal and international) to monitor, control, and prevent the occurrence and spread of statereportable and nationally notifiable diseases and conditions. the nndss data are a critical source of information for monitoring disease trends, effectiveness of prevention and control programs, and policy development. to provide timely nndss data, state and territorial health departments voluntarily report notifiable disease incidence data to cdc when they become aware of these cases and as per recommended national notification timeframes. these provisional data are published each week in morbidity and mortality weekly report (mmwr). great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data (1). however, there have been fewer efforts in the area of online dissemination of surveillance data. appropriate dissemination of surveillance data is important to maximize the utility of collected data (2). methods the author conducted a search of all us state health department websites looking for on-line data display tables and tools for either “reportable” or “notifiable” diseases. in addition, the scope of the search of websites was expanded to include notifiable diseases of several countries, organizations and institutions. several articles relevant to the topic of the study were reviewed as well. there were few examples of ways to display data that may be more effective and useful to data users than the current static nndss data display. particular emphasis was placed on the display of reportable or notifiable disease provisional data. provisional nndss data in mmwr table ii are a running count of selected nationally notifiable infectious disease cases that have not been adjusted for variations in reporting procedures across different states or for delays in reporting. as a result, provisional data are subject to change based on the outcome of further case investigation. results current nndss provisional data display problems can be summarized as an overall unsatisfactory user experience due to the following factors: information: static mmwr table format, cluttered presentation, data not easily accessible for further analysis; technology: not user-friendly; location: information separated into different places (nndss website, different notifiable diseases websites maintained by different cdc programs, etc.); and service: not catered to users individual needs and overall not helpful. as a result of this study the author proposes enhancement of the current notifiable disease data display from a static format to a more interactive dashboard design. proposed nndss dashboard can help users to overcome information overload, saves users time through quick access to a database and provides opportunity to present data in a more effective way. users from states can also compare their epidemiological findings with other states in order to plan and collaborate on important public health issues. several currently nonexisting display options need to be added as multiple area comparisons to facilitate geographical comparison across different us states, etc. conclusions advances in infectious disease informatics research in recent years has allowed significant improvements not only in data collection, sharing, reporting, and analyzing, but also in data dissemination and visualization maximizing the data usage. the findings of this study are subject to a few limitations: 1) only english public websites and/ or databases were reviewed based on the search term strategy; 2) this review focused on the dissemination of surveillance data, and has not reviewed the underlying surveillance systems. dissemination of the us national notifiable disease provisional data using interactive dashboard design can take advantage of what technology can offer nowadays. keywords public health surveillance; data display; public health informatics; united states references 1. teutsch sm, churchill re, eds. principles and practice of public health surveillance. 2nd edition. oxford university press; 2000 2. cheng ck, lau eh, ip dk, et al. a profile of the online dissemination of national influenza surveillance data. bmc public health. 2009; 9: 339 *anna grigoryan e-mail: ffg7@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e191, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts spatial cusum chart based method for rapid detection of outbreaks sesha k. dassanayaka* mathematical and statistical sciences, university of colorado denver, denver, co, usa objective to develop a computationally simple and fast algorithm for rapid detection of outbreaks producing easily interpretable results. introduction since the release of anthrax in october of 2001, there has been increased interest in developing efficient prospective disease surveillance schemes. poisson cusum is a control chart-based method that has been widely used to detect aberrations in disease counts in a single region collected over fixed time intervals. over the past few years, different methods have been proposed to extend poisson cusum charts to capture the spatial association among several regions simultaneously. in the proposed method, we extend an algorithm2 in industrial process control using multiple poisson cusum charts to the spatial setting. the spatial association among regions is captured using the method proposed by raubertas3, which has been successfully applied in several prospective surveillance schemes. also, to improve the power of the traditional multiple poisson cusum charts, poisson cusum charts were used along with fault discovery rate (fdr) control techniques2. methods we start by defining the overall the false discovery rate which allows the user to define an acceptable false alarm rate. then for each region m, we sum up the counts of all the immediate neighbors, creating m spatially correlated neighborhoods. a poisson cusum statistic is then calculated for each neighborhood. to calculate the poisson cusum statistic, the in-control poisson parameter needs to be calculated using past data and a tolerable out-of-control parameter needs to be specified by the user. a computationally simple random walk method is used to find p-values for each regional poisson cusum statistic2 instead of the computationally intensive markov chain-based method while producing comparable results. the use of p-values to identify alarms makes the procedure easy to interpret for a wider audience in comparison to the traditional average run length (arl) and cut-off thresholds that are only used in an industrial process control setting. the use of fdr to control multiple testing allows the utilization of popular multiple testing techniques such as benjamini-yekulieli1 procedure. once the p-values are calculated, we use the general version of the benjamini-yukatieli procedure for multiple testing of the p-values to identify neighborhoods with unusually high disease counts; currently, we are considering alternative resampling based procedures to gain even more power. results we developed a grid of neighboring regions and simulated poisson counts with constant mean during the first half of the time period. during the second half, we introduced a step-increase in mean at varying degrees of intensity to simulate an outbreak in neighboring regions. then we ran the simulation to calculated an independent poisson cusum statistic for each region. fixing the fault discovery rate at 0.05, we used the benjaminhochberg1 procedure to determine the speed at which the procedure identifies the simulated outbreak. next, for the same simulated data, we cumulated the possion counts of the neighboring regions and used benjamini-yekutieli procedure to detect alarms from the spatially correlated neighborhood statistics. the results provide convincing evidence that using the neighborhoods instead of independent regions significantly reduces the time for detection while increasing the correct identification of time periods of the simulated outbreak. the method proposed by raubertas acknowledges spatial association but does not account for it, as it simply cumulates neighboring counts without using a measure of spatial correlation to calculate neighborhood statistics. to further improve, we are currently investigating spatial statistical methods to account for spatial correlation. finally, we plan to apply this procedure to lower respiratory infection data during 1996-1999 from multiple boston clinics. conclusions we have developed a simple, efficient algorithm for prospective disease surveillance that is relatively easy to interpret without a high level of statistical expertise. our preliminary results from simulation studies provide evidence of the strengths of the method proposed. keywords spatial cusum charts; biosurveillance; disease surveillance; multiple control charts; false discovery rate references 1. benjamini y, yekutieli d. the control of the false discovery rate in multiple testing under dependency. the annals of statistics 2001; 29(4):1165–1188. 2. li y, tsung f. multiple attribute control charts with false discovery rate control, qual. reliab. eng. int. 2012; 28(8):857–871. 3. raubertas, rf. an analysis of disease surveillance data that uses the geographical locations of the reporting units. statist. med.1989; 8:267–271. *sesha k. dassanayaka e-mail: sesha.dassanayaka@ucdenver.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e122, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts evaluating the utility of healthmap as a supplementary surveillance tool melinda c. thomas*, aaron kite-powell, david atrubin and janet j. hamilton florida department of health, tallahassee, fl, usa objective to assess the outbreak detection utility of healthmap, a publically available event-based biosurveillance system utilizing various internet-based media resources to identify outbreaks, at the state and local level. results may help determine whether healthmap should be monitored more closely as a supplementary surveillance tool. introduction healthmap collects and aggregates information from online sources to generate outbreak alerts based on disease and geographic location. this project will assess the timeliness and sensitivity of healthmap based on outbreak posts from epicom, the florida department of health’s disease outbreak and health incident alert network. methods this project compared epicom posts and healthmap alerts in florida for similarities in timing for outbreaks from january 1, 2009 to august 31, 2013. the project assessed sensitivity and timeliness of healthmap, whether both sources captured the outbreak, and which source’s post was earlier. healthmap alert date was compared to epicom’s post date and the date the outbreak was reported to the county or state health department. outbreaks of legionellosis, dengue, measles, and influenza and influenza-like illness (ili) were assessed. results for measles are described below. the variation of epicom post timeliness by county size was also investigated. results during the study period, epicom reported on 13 confirmed measles outbreaks in florida. eight of these outbreaks were also found in healthmap, giving healthmap a sensitivity of 61.5% for measles. two healthmap measles posts based on non-u.s. media sources had no match to any epicom post. based on post date, healthmap was timelier than epicom for 2 of the outbreaks and just as timely for 3 of the outbreaks. however, based on the date the county health department was notified in the epicom posts, regular state and local surveillance detection practices are timelier for 100% of the outbreaks. conclusions preliminary analysis suggests that healthmap is useful for surveillance activity but not for initial outbreak detection by the state and local health department due to its relatively low sensitivity and timeliness of detection. however, due to its worldwide focus, it may be useful in providing a better international view of disease activity, which could be especially valuable for florida for situational awareness and surveillance activity due to the large amount of international travelers who visit. additionally, there was evidence suggesting public health is being too conservative in its utilization of epicom. healthmap can also be a means of informing the general public of health concerns in their area and assisting public health in recognizing what events are in the news. keywords disease outbreak detection; surveillance and alerting; event-based biosurveillance; healthmap acknowledgments this project was supported in part by an appointment to the applied public health informatics fellowship program administered by cste and funded by a cdc cooperative agreement. references freifeld cc, mandl kd, reis by, brownstein js. healthmap [internet]. 2014. available from: http://www.healthmap.org. *melinda c. thomas e-mail: melinda.thomas2@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e165, 201 towards unified data exchange formats for reporting molecular drug susceptibility testing 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi towards unified data exchange formats for reporting molecular drug susceptibility testing wilfred bonney, phd1,2*, sandy f. price, pmp1, swapna abhyankar, md3, riki merrick, mph4, varsha hampole, mph5, tanya a. halse, bs6, charles didonato, ms7, tracy dalton, phd1, beverly metchock, drph1, angela m. starks, phd1, roque miramontes, mph1 1division of tuberculosis elimination, national center for hiv/aids, viral hepatitis, std, and tb prevention, centers for disease control and prevention, atlanta, ga, usa 2public health informatics fellowship program, division of scientific education and professional development, center for surveillance, epidemiology, and laboratory services, centers for disease control and prevention, atlanta, ga, usa 3loinc and health data standards, regenstrief institute, inc., indianapolis, in, usa 4association of public health laboratories, silver spring, md, usa 5california department of public health, richmond, ca, usa 6wadsworth center, new york state department of health, albany, ny, usa 7new york state office of information technology services, albany, ny, usa abstract background: with the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. keeping up with specialized standard coding for these immense datasets will be extremely challenging. this challenge prompted our effort to create a common molecular resistance logical observation identifiers names and codes (loinc) panel that can be used to report any identified antimicrobial resistance pattern. objective: to develop and utilize a common molecular resistance loinc panel for molecular drug susceptibility testing (dst) data exchange in the u.s. national tuberculosis surveillance system using california department of public health (cdph) and new york state department of health as pilot sites. methods: we developed an interface and mapped incoming molecular dst data to the common molecular resistance loinc panel using health level seven (hl7) v2.5.1 electronic laboratory reporting (elr) message specifications through the orion health™ rhapsody integration engine v6.3.1. results: both pilot sites were able to process and upload/import the standardized hl7 v2.5.1 elr messages into their respective systems; albeit cdph identified areas for system improvements and has focused efforts to streamline the message importation process. specifically, cdph is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the u.s. centers for disease control and prevention. towards unified data exchange formats for reporting molecular drug susceptibility testing 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi introduction background with the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. keeping up with specialized standard coding for these immense datasets will be extremely challenging. even though health level seven (hl7) international has developed and published implementation guides for electronic laboratory reporting (elr) involving the use of the hl7 v2.5.1 messaging standard [1], many implementers of the standard have difficulty representing molecular drug susceptibility testing (dst) results in the hl7 v2.5.1 elr message segments to achieve meaningful health information exchange. molecular dst results refer to a single genetic testing result or a large panel of multiple results for different genes/loci and their implied susceptibility interpretations. hl7 has developed an implementation guide known as “hl7 version 2.5.1 implementation guide: electronic laboratory reporting to public health, release 1 (us realm)” (represented as hl7 v2.5.1 elr r1) [2] that is named in the meaningful use (mu) standards for electronic health record (ehr) technology among healthcare providers [3,4]. the hl7 v2.5.1 elr r1 messaging standard is a national-level profile that is further constrained to accommodate agency-specific differences for the purposes of submitting laboratory test results to public health agencies. test tools are available to validate conformance of implementations to the standard [5]. the optionality property of the hl7 v2 messaging standard (i.e., an inherent property that indicates whether a field within a message segment is required, optional, or conditional [1]) supports implementers’ systems that are using different levels of precoordinated or postcoordinated expressions, subsequently contributing to different ways of reporting the same battery of laboratory test results. discussion: the common molecular resistance loinc panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains. conclusion: the study demonstrates that it is possible to exchange molecular dst data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance loinc panel. keywords: data exchange formats, electronic laboratory reporting, health information exchange, loinc, health level seven, public health surveillance. *correspondence: nto5@cdc.gov doi: 10.5210/ojphi.v12i2.10644 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. towards unified data exchange formats for reporting molecular drug susceptibility testing 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi likewise, elr implementations existed in the united states, before the hl7 v2.5.1 elr r1 messaging standard became adopted in the mu as the standard for nationally notifiable laboratory test results, and not all state and local systems have been upgraded. this adds to the variations in hl7 elr message formats received at the public health agencies. these variations of hl7 elr messages, coupled with fragmented, voluminous, and heterogeneous data sources from the public healthcare environment, make it difficult to transmit and translate notifiable disease cases to the u.s. centers for disease control and prevention (cdc) and other public health agencies. the objective of this paper is to describe the development and use of a common molecular resistance logical observation identifiers names and codes (loinc) panel, developed in collaboration with cdc, association of public of health laboratories (aphl), california department of public health (cdph), new york state department of health (nysdoh), and regenstrief institute, to pilot molecular dst data exchange in the u.s. national tuberculosis surveillance system (ntss) using cdph and nysdoh as external partners. the ntss contains reported tuberculosis (tb) cases provided by all 50 states, the district of columbia (dc), puerto rico, and other u.s.-affiliated island jurisdictions [6,7]. related work the health informatics literature contains limited research pertaining to data exchange formats for reporting molecular dst results. in a study conducted to formally define a comprehensive and minimal sets of data elements for molecular dst, tornheim et al. [8] developed a framework for standardizing clinical laboratory reporting of next generation sequencing (ngs) data for resistance-associated mutations in mycobacterium tuberculosis complex (mtbc). the standardized reports were developed in consultation with participants from workshops and dozens of stakeholders worldwide [8]. the focus of the standardization efforts by tornheim et al. [8], however, was not elr-based but was primarily meant to be used in a paper-based format for reporting of ngs-derived molecular dst results for mtbc. in 2019, we reported our first attempt to standardize molecular dst data elements in an elrbased format [9]. we utilized a combination of standardization protocols to develop data exchange formats to capture, store, and monitor molecular dst results for mtbc. those formats, however, were limited in their ability to support health information exchange for molecular dst results in other disease domains [9]. moreover, most of the standardized vocabulary and value sets used in the study were restricted to the specifications published in the implementation guides by the hl7 clinical genomics workgroup [10-12], which are limited to human genetic testing. hence, the need to develop a unified template for reporting molecular dst results for all microorganisms in different disease domains with generic vocabulary and value sets is of great importance. methods use of implementation guides and reference terminology standards the study utilized standardization protocols that involved many published implementation guides and reference terminology standards. molecular dst data elements were standardized in alignment with the hl7 v2.5.1 elr message specifications [13]. while the hl7 v2.5.1 elr message specifications were used as the preferred data format for the molecular dst data towards unified data exchange formats for reporting molecular drug susceptibility testing 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi elements, the common molecular resistance loinc panel was used as the preferred terminology for identifying the laboratory tests or laboratory test results. other preferred terminologies used for the study include: systematized nomenclature of medicine clinical terms (snomed ct) for microbiology related test results [13]; and clinical genomics coding systems [10-12] (i.e., 2.16.840.1.113883.6.340 (genecodencbi) for the mycobacterium tuberculosis resistance genes’ identifiers [14]; 2.16.840.1.113883.6.328 (hgvs.c) for the nucleic acid changes; and 2.16.840.1.113883.6.329 (hgvs.p) for the amino acid changes [15]). panel design and considerations the current study used the common molecular resistance loinc panel (i.e., 92254-2: microorganism identification and resistance pattern determination panel by molecular genetics method) developed as an extension of our previously described study [9]. loinc is a freely available universal standard for identifying clinical and laboratory observations [16,17], with its panel being defined by a collector term that contains an enumerated set of discrete child elements [17]. briefly, the goal of the panel design is to build on our previous work [9] and make it more generalizable. this will allow for the establishment of a common template to report sequence, resistance gene and gene mutation detection, and implied susceptibility interpretations across microorganisms, similar to the clinical genomics panel developed by the hl7 clinical genomics workgroup [10-12]. the common molecular resistance loinc panel supports advanced molecular microorganism resistance testing and includes “terms to identify: 1) the microorganism identified; 2) the resistance genes, target sequences and/or mutations tested for and identified; and 3) the predicted susceptibility or resistance to one or more antimicrobials based on the genotyping results” [18]. more specifically, the panel includes a “representative set of loinc terms for various antimicrobials, but any loinc term for antimicrobial susceptibility by genotyping may be used as appropriate depending on genes and/or mutations tested for” [18]. the panel was first released in trial status in the june 2019 loinc version 2.66, pending feedback from public health and clinical laboratories regarding this approach for reporting the results of molecular resistance testing. several design considerations were made during the development of the panel. notable among them was consideration of the preferred data format availability in laboratory information management systems (lims) and state/local epidemiologic data systems as well as how the data flow from lims to state/local epidemiologic data systems and then to cdc. it was also important that the incoming data fit cdc data requirements. more importantly, the panel is designed to be generalizable across other resistance genes and ideally also applicable to diseases other than tb. the common molecular resistance loinc panel is intended to be used by public health and clinical laboratories to report the results of molecular dst. the design approach used in the panel is also “intended to be more sustainable over time compared to creating new loinc codes for every microorganism + gene + antibiotic combination” [18]. the panel also includes vendor device information that is entirely optional. the panel is focused on microorganisms, not on human clinical genomics. for the purposes of this study, the panel was used to represent two types of molecular dst results: probe-based and sequence-based methods [19]. whereas the cepheid xpert® mtb/rif assay was used to illustrate results from a probe-based method, results provided towards unified data exchange formats for reporting molecular drug susceptibility testing 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi back to states using cdc’s molecular detection of drug resistance (mddr) service were used to illustrate specific genetic mutations detected using sequence-based methods [9]. use of the panel to pilot molecular dst data exchange the common molecular resistance loinc panel was used to pilot molecular dst data exchange within the ntss. the piloting process consisted of (a) developing an interface to receive hl7 v2.5.1 elr messages from the ntss case reporting (ntsscr) application via orion health™ rhapsody integration engine v6.3.1 (rhapsody) [20]; (b) creating data mapping routines using the common molecular resistance loinc panel in rhapsody to aid in transforming and constructing structured query language to load molecular dst data elements into the ntss; and (c) executing test (plans) to verify that the interface satisfies the design requirements. the pilot was conducted using sample molecular dst data from cdph and nysdoh. whereas the cdph data exchange involved the use of sample cepheid xpert® mtb/rif test results from california reportable disease information exchange (calredie), the nysdoh data exchange involved the use of sample whole genome sequencing test results received from the nysdoh laboratory. essentially, all the sample molecular dst results from the two pilot sites were manually entered into the cdc ntsscr application and then mapped to the common molecular resistance loinc panel using the hl7 v2.5.1 elr message specifications in rhapsody. results data representation of rifampin resistant results from cepheid xpert® mtb/rif the cepheid xpert® mtb/rif is a u.s. food and drug administration (fda) market-authorized method that uses a cartridge-based, fully automated nucleic acid amplification test (naat) for simultaneous detection of mtbc and mutations associated with rifampin resistance [9,21,22]. keeping in alignment with our previous study [9], the key molecular dst data elements of interest for xpert® mtb/rif are mtbc identification; and if mtbc is identified, whether a mutation in the rifampin resistance determining region of the rpob gene has been detected indicating resistance. table 1 depicts the key codes from the common molecular resistance loinc panel for xpert® mtb/rif and their associated minimum data representations in hl7 v2.5.1 elr. towards unified data exchange formats for reporting molecular drug susceptibility testing 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi table 1. key codes from the common molecular resistance loinc panel for xpert® mtb/rif and their associated minimum data representations in hl7 v2.5.1 elr data representation of specific genetic mutations detected from dna sequencing results public health and clinical laboratories performing sequencing-based assays report the identity of specific mutations in the gene [19,21]. for the purposes of this study, results from the cdc’s mddr service were used as a guide to generalize the common molecular resistance loinc panel for results from sequence-based assays. the cdc’s national mddr service examines dna from mtbc isolates or naat-positive sediments for mtbc at specific targets to determine the presence of mutations known to be associated with drug resistance [9]. the key molecular dst data elements of interest for the cdc’s mddr service, therefore, focus on capturing and storing data elements relating to antituberculosis drug names, mtbc gene names, nucleic acid changes, amino acid changes, indels (nucleic acid change types), and results indicating if genetic mutation is detected or not at each relevant target gene [9]. table 2 shows the key codes from the common molecular resistance loinc panel for dna sequencing results and their associated minimum data representations in hl7 v2.5.1 elr. towards unified data exchange formats for reporting molecular drug susceptibility testing 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi table 2. key codes from the common molecular resistance loinc panel for dna sequencing results and their associated minimum data representations in hl7 v2.5.1 elr. towards unified data exchange formats for reporting molecular drug susceptibility testing 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi pilot results the common molecular resistance loinc panel was piloted to assess its usability and impact in supporting molecular genetic data exchange within the ntss. the piloting process resulted in the generation of four sample hl7 v2.5.1 elr messages from ntsscr that are compatible with the common molecular resistance loinc panel. while two hl7 v2.5.1 elr messages representing cepheid xpert® mtb/rif test results were sent to cdph for subsequent importing into calredie, two hl7 v2.5.1 elr messages representing whole genome sequencing test results were sent to nysdoh for subsequent importing into that agency’s electronic clinical laboratory reporting system (eclrs). both pilot sites were able to process and upload/import the standardized hl7 v2.5.1 elr messages into their respective systems; albeit cdph identified areas for system improvements and has focused efforts to streamline the message importation process. specifically, cdph is enhancing the current drug susceptibility result module within calredie to better capture parent-child elements and ensure that the data collected can be accessed by the messaging module for seamless message creation for cdc. nysdoh, on the other hand, did not identify any major areas for system improvements. discussion no standard guideline for reporting molecular dst results exists within the united states. reporting of molecular dst results is often unique to the public health laboratory performing the test. different laboratories are using different loinc codes to report the same battery of tests. for example, results of cepheid xpert® mtb/rif are currently being reported with five different loinc codes (i.e., 38379-4; 48176-2; 46244-0; 33634-7; and 48174-7) to calredie by laboratory facilities in the state of california. the common molecular resistance loinc panel harmonizes across all five different loinc codes for the cepheid xpert® mtb/rif results to support efficient reporting of molecular dst. the panel is also extended to include all molecular dst results generated by dna sequencing services similar to those provided by cdc and nysdoh. promoting broad adoption of the common molecular resistance loinc panel remains a challenge. the testing infrastructure services and certification programs developed by the national institute of science and technology (nist) for sending nationally notifiable laboratory test results to public health agencies does not address the multiple competing approaches that many healthcare providers and health information technology (hit) vendors are using to represent laboratory tests and their associated results. integrating these fragmented, heterogeneous, and voluminous data into a single common format for use by the public health surveillance community is proving to be difficult. nist has a suite of hl7 v2 elr testing tools [23] that test conformance of implementations and compliance with the hl7 v2.5.1 elr r1 message specifications. the nist elr testing tools validate elr messages created by healthcare providers and hit vendors for the purposes of satisfying the 2014 and 2015 edition certification testing criteria for ehr technology mandated by the office of the national coordinator for hit [23]. the nist suite of validation tools also includes test cases to test the implementation guide entitled “hl7 version 2.5.1 implementation towards unified data exchange formats for reporting molecular drug susceptibility testing 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi guide: s&i framework lab results interface, release 1, dstu release 2 – us realm” (referred to as lri ig), which provides guidance on how to communicate laboratory test results from a laboratory information system to a recipient system (e.g., ehr, public health, other laboratory) [12]. however, since the lri ig was not named in the mu standards, it is not widely implemented, thus contributing to many variations of existing hl7 interfaces between laboratories and healthcare providers. efforts are underway to ensure that the common molecular resistance loinc panel is widely understood by leading public health and clinical laboratories across the nation. in order to create awareness, representatives from aphl are providing education and technical assistance to industry stakeholders, such as fda and manufacturers of laboratory instruments/toolkits, regarding the availability, flexibility and usability of the panel. for example, if this panel were to be included in the fda’s shield (systemic harmonization and interoperability enhancement for lab data) program, the loinc codes could be harmonized across manufacturers and across coded results [24]. the study is limited to the data elements and formats of molecular dst results received from cdph and nysdoh. majority of molecular dst results are currently reported by public health and clinical laboratories in pure narrative text formats with no computer accessible coding and processing of the results [9]. the common molecular resistance loinc panel is, however, designed to provide a unified template for reporting machine-processable molecular dst results. also, considering the fragmented and heterogeneous nature of voluminous data coming from the various laboratories and public health agencies, one cannot ignore the problems associated with the practicality of integrating them into a single common format for use by the public health surveillance community. to alleviate these burdens, early adopters of the panel may acquire and maintain functional interface/integration engine (i.e., rhapsody, mirth connect, cloverleaf, etc.) to support seamless data mapping routines and health information exchanges. conclusion this study demonstrates that it is possible to exchange molecular dst data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance loinc panel. even though this paper has focused on the usage of the panel in the tb domain, the common molecular resistance loinc panel is designed to be generalizable across other areas of antimicrobial resistance (e.g., staphylococcus aureus resistance to vancomycin) and ideally also applicable to other disease domains. moreover, the common molecular resistance loinc panel is also intended to be more sustainable over time compared to creating new loinc codes for every microorganism + gene + antibiotic combination. acknowledgments this work was supported in part by an appointment to the public health informatics fellowship program at the centers for disease control and prevention (cdc) with funding from cdc’s combating antimicrobial resistant bacteria initiative. towards unified data exchange formats for reporting molecular drug susceptibility testing 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi disclaimer the views expressed in this manuscript are those of the authors and do not necessarily represent the views of the centers for disease control and prevention (cdc). references in this manuscript to any specific commercial products, process, service, manufacturer, or company does not constitute its endorsement or recommendation by the u.s. government or cdc. financial disclosure no financial disclosures. competing interests no competing interests. references 1. hl7 international. hl7 messaging standard version 2.5.1: an application protocol for electronic data exchange in healthcare environments. 2007 [cited 2020 march 12]. available from: https://www.hl7.org/implement/standards/product_brief.cfm?product_id=144. 2. hl7 international. hl7 version 2.5.1 implementation guide: electronic laboratory reporting to public health, release 1 (us realm). 2010 [cited 2020 march 12]. available from: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=98. 3. u.s. department of health and human services. health information technology: initial set of standards, implementation specifications, and 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(march), 2019. supplementary material illustration a: sample supported segments for cepheid xpert® mtb/rif obr|1|||92254-2^microorganism identification and resistance pattern determination panel by molecular genetics method^ln|||20180220140000|||||||||||||||20180222133406|||f||||||10220^tuberculosis^nnd| obx|1|cwe|92253-4^microorganism identified in isolate or specimen by molecular genetics method^ln||113858008^mycobacterium tuberculosis complex^sct| obx|2|cwe|92249-2^microorganism gene tested for [identifier] by molecular method^ln|1|888164^rpob^genecodencbi| obx|3|cwe|92246-8^microorganism resistance mutation detected [presence] by molecular method^ln|1|260373001^detected^sct| obx|4|cwe|89489-9^rifampin [susceptibility] by genotype method^ln|1|30714006^resistant^sct| obx|5|cwe|85069-3^lab test method [type]^ln|1|702675006^probe with target amplification^sct| obx|6|st|67099-2^vendor device name^ln||genexpert| obx|7|st|67715-3^vendor device model type^ln||genexpert system| obx|8|st|67716-1^vendor device model^ln||xpert mtb/rif ultra assay| obx|9|st|74715-4^vendor serial number^ln||301-5987| obx|10|st|74716-2^vendor firmware version^ln||rev. d may 2017| obx|11|st|74717-0^vendor model number^ln||123456789| spm|1|||119334006^sputum specimen^sct|||||||||||||20180220140000|20180221141100| optional block towards unified data exchange formats for reporting molecular drug susceptibility testing 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(2):e14, 2020 ojphi illustration b: sample supported segments for dna sequencing results obr|1|||92254-2^microorganism identification and resistance pattern determination panel by molecular genetics method^ln|||20180220144700|||||||||||||||20180221130016|||f||||||10220^tuberculosis^nnd| obx|1|cwe|92253-4^microorganism identified in isolate or specimen by molecular genetics method^ln||113858008^mycobacterium tuberculosis complex^sct| obx|2|cwe|92249-2^micr oorganism gene tested for [identifier ] by molecular method^ln|1|888164^rpob^genecodencbi| obx|3|st|92250-0^microor ganism gene tar get r egion [type] by molecular method^ln|1|rrdr| obx|4|cwe|92246-8^microorganism resistance mutation detected [presence] by molecular method^ln|1|260373001^detected^sct| obx|5|cwe|92245-0^microorganism resistance mutation nucleic acid change by molecular method^ln|1|tcg>ttg^tcg>ttg^hgvs.c| obx|6|cwe|92244-3^microorganism resistance mutation nucleic acid change type by molecular method^ln|1|la6690-7^substitution^ln| obx|7|cwe|92248-4^microorganism resistance mutation amino acid change by molecular method^ln|1|ser531leu^ser531leu^hgvs.p| obx|8|cwe|89489-9^rifampin [susceptibility] by genotype method^ln|1|30714006^resistant^sct| obx|9|cwe|85069-3^lab test method [type]^ln|1|444287002^analysis using pyr osequencing^sct^^^^^^pyrosequencing| obx|10|cwe|92249-2^microor ganism gene tested for [identifier ] by molecular method̂ ln|2|885638^katg^genecodencbi| obx|11|st|92250-0^microorganism gene target region [type] by molecular method^ln|2|ser 315 codon| obx|12|cwe|92246-8^microor ganism r esistance mutation detected [pr esence] by molecular method̂ ln|2|260373001^detected^sct| obx|13|cwe|92245-0^microor ganism r esistance mutation nucleic acid change by molecular method̂ ln|2|agc>acc^agc>acc^hgvs.c| obx|14|cwe|92244-3^microor ganism r esistance mutation nucleic acid change type by molecular method̂ ln|2|la6690-7^substitution^ln| obx|15|cwe|92248-4^microor ganism r esistance mutation amino acid change by molecular method̂ ln|2|ser315thr^ser 315thr^hgvs.p| obx|16|cwe|89488-1^isoniazid [susceptibility] by genotype method^ln|2|30714006^resistant^sct| obx|17|cwe|85069-3^lab test method [type]^ln|2|444287002^analysis using pyrosequencing^sct^^^^^^pyrosequencing| obx|18|st|67099-2^vendor device name^ln||pyromark q96 id system| obx|19|st|67715-3^vendor device model type^ln||pyrosequencing device| obx|20|st|67716-1^vendor device model^ln||pyromark q96| obx|21|st|74715-4^vendor ser ial number ^ln||sn123589-453368-78| obx|22|st|74716-2^vendor fir mware ver sion^ln||pyromark identifire software 1.0| obx|23|st|74717-0^vendor model number ^ln||123456789| spm|1|3000654137||258589002^lymph node sample^sct|||||||||||||20180220144700|20180220152200| rpob gene block katg gene block optional block using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 1 ojphi using a machine learning algorithm to predict online patient portal utilization: a patient engagement study ahmed u. otokiti,1 colleen m. farrelly,2 leyla warsame,3 angie li4 1. icahn school of medicine at mount sinai hospital, internal medicine and informatics department, new york, ny 10029, usa 2. staticlysm, llc, palmetto bay, fl 33157, usa 3. geisinger health systems, internal medicine and clinical informatics department, danville, pa 17821, usa 4. university at buffalo, department of biomedical informatics, buffalo, ny 14203, usa abstract objective: there is a low rate of online patient portal utilization in the u.s. this study aimed to utilize a machine learning approach to predict access to online medical records through a patient portal. methods: this is a cross-sectional predictive machine learning algorithm-based study of health information national trends datasets (cycles 1 and 2; 2017-2018 samples). survey respondents were u.s. adults (≥18 years old). the primary outcome was a binary variable indicating that the patient had or had not accessed online medical records in the previous 12 months. we analyzed a subset of independent variables using k-means clustering with replicate samples. a cross-validated random forestbased algorithm was utilized to select features for a cycle 1 split training sample. a logistic regression and an evolved decision tree were trained on the rest of the cycle 1 training sample. the cycle 1 test sample and cycle 2 data were used to benchmark algorithm performance. results: lack of access to online systems was less of a barrier to online medical records in 2018 (14%) compared to 2017 (26%). patients accessed medical records to refill medicines and message primary care providers more frequently in 2018 (45%) than in 2017 (25%). discussion: privacy concerns, portal knowledge, and conversations between primary care providers and patients predict portal access. conclusion: methods described here may be employed to personalize methods of patient engagement during new patient registration. abbreviations: american medical informatics association (amia), area under the curve (auc), body mass index (bmi), electronic health record (ehr), health information national trends survey (hints), information technology (it), national cancer institute (nci), veteran health (va) correspondence: ahmedotoks@yahoo.com* doi: 10.5210/ojphi.v14i1.12851 copyright ©2022 the author(s) mailto:ahmedotoks@yahoo.com* using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 2 ojphi this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction patient engagement is a set of behaviors that foster active patient involvement in care, thereby increasing motivation and self-determination to become an active player in the healthcare journey [1]. these behaviors increase compliance, improve health outcomes, and overall public health and reduce cost [1-3]. health it solutions can serve as a means to increase patient engagement, as online patient portals have been shown to increase patient engagement and personalized care [4,5]. online patient portals are web-based applications tethered to a patient’s ehr that allow secure access to health data. through this portal, patients can view lab results, medication history, and discharge summaries, and they can securely message their physicians, request prescription refills, and schedule appointments [6]. the meaningful use stage 2 incentive mandated by the health information technology for economic and clinical health act in 2009 was a significant driver for increase in patient portal offerings by healthcare institutions across the nation [7]. despite the significant investment in online portals, these sites continue to experience a low rate of adoption/use by patients, which hinders the potential benefits of patient engagement and its public health impact [8-10]. the most significant positive factors associated with higher portal use include higher education level, female gender, caucasian ethnicity, internet access, higher income, patients not on medicaid insurance, and patient trust in the healthcare provider and system [7,8,11]. the most significant negative factors associated with lower patient portal use include privacy and security concerns and user friendliness [12,13]. machine learning is gaining popularity in healthcare due to the ability of this method to process complex nonlinear relationships between predictors and yield stable predictions [14]. this approach has been used to predict outbreaks, suicide risk among army personnel, and intrusion detection within ehr systems [15,16]. several prior studies have analyzed patient behavior regarding health technology usage and its impact on patient health [17-21]. one study which employed the random forest algorithm found that health-related internet searches predicted patient healthcare utilization [22]. these findings suggest that understanding patient interactions with medical technology may help providers offer better care and be proactive in making decisions about online patient engagement tools. this study aimed to determine which patients are most likely to utilize online portals at patient registration and to build a predictive model that could be used to create a short survey to support real-time decision support. as interaction terms likely exist between factors and because the model is high dimensional, we choose to use machine learning models to parse out factors and groups of factors associated with online portal utilization. patients who opt for a technology-based platform may benefit from other types of engagement with technology beyond patient portals, including text messaging or automated calls. machine learning algorithms can identify important predictors of portal usage, as well as provide robust predictions to flag those most likely to benefit from portal using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 3 ojphi usage versus those who may engage better with alternative channels. to our knowledge, no previous studies have utilized a machine learning algorithm to predict patients to utilize patient portals as a patient engagement tool. data from hints was used for our analysis. hints is a nationally representative survey that has been administered by the nci since 2003 [23]. the hints survey and data collection program was set up to monitor changes in the rapidly evolving field of health communication. it collects nationally representative data about the public's use of cancer-related information and serves as a test bed for researchers to evaluate new theories in health information and communication. the data can also be used to help understand how adults use different communication channels to obtain health information [23,24]. two cycles of hints data were utilized in our analysis: hints-5 cycle 1 (2017) and hints-5 cycle 2 (2018). although hints is funded by the nci with the primary goal of evaluating health communication theories in cancer patients, only 504 individuals out of 3,285 survey participants (15.3%) in hints 5 cycle 1 were diagnosed with cancer [25]. materials and methods study design and setting this was a predictive analytic study using data from two iterations of the hints survey. the survey for both hints cycles utilized in this study was disseminated via mail to the participants. more information on the survey mailing protocol, data collection, data cleaning/editing, and handling of incomplete/invalid data can be found on the nci hints website [23]. study participants survey respondents were sampled from the u.s. population (≥ 18 years old). a two-stage sampling method was utilized: stage one was a stratified sample of residential addresses and stage two sampling was the selection of one adult from each sampled residential address. the same sampling methodology was utilized for hints 5 cycle 1 and 2. more information about sampling methodology of the hints survey can be found on the nci hints website [23]. the sample sizes of both iterations were as follows: hints 5 cycle 1: of the 3,285 respondents, 97% of the surveys were completely filled out (november 2017); hints 5 cycle 2: of the 3,504 total respondents, 98% of the surveys were completely filled out (november 2018). these two iterations were chosen because they were the most recent at the time of our analysis, had uniformity of survey collection, and had captured similar variables of interest. study variables target variable/outcome variable access to online medical records or patient portals was the target or outcome variable. the survey question was, “how many times did you access your medical record in the past 12 months?” (hints 5 cycle 1: question d4; hints 5 cycle 2: question d6). we recoded the response as >1 for “accessed online medical record” and <1 for “did not access online medical record.” using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 4 ojphi labels/predictor variables a total of 51 initial predictor variables were added based on domain knowledge and a literature search of previously identified significant determinants of online portal use (table 1) [5,7,13,26,27]. the following variables were re-coded: a. bmi re-coded to an ordinal variable from a continuous variable for clinical significance (bmi: <25, 25-30, 30-40, >40) b. chronic medical condition: any one of the following: hypertension, heart condition, lung disease, and arthritis c. anxiety/depression: re-coded as an independent variable a random forest-based boruta method was used for variable selection after initial variable inclusion based on domain knowledge and a literature review; a total of 39 variables were selected (table 1). table 1: variables initial variables before the boruta algorithm final variables analyzed after the boruta algorithm demographics 1. age 2. education level 3. race/ethnicity 4. marital status 5. occupation status 6. english language proficiency 7. sexual orientation 8. total persons in the household 9. gender 10. rent or own a house 11. annual household income looking for health information 12. trust health information from newspapers/magazines 13. trust health information from the internet 14. trust health information from charitable organizations demographics 1. age 2. education level 3. race/ethnicity 4. occupation status 5. english language proficiency 6. sexual orientation 7. annual household income looking for health information 8. trust health information from newspapers/magazines 9. trust health information from the internet 10. trust health information from charitable organizations 11. trust health information from religious organizations 12. if there were a strong need to get information about your health, where would you go first? using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 5 ojphi 15. trust health information from television 16. trust health information from religious organizations 17. if there were a strong need to get information about your health, where would you go first? overall health 18. in general, what is your state of health? 19. body mass index 20. chronic medical conditions: diabetes mellitus, hypertension, heart disease, lung disease, rheumatologic 21. chronic medical condition: depression/anxiety your healthcare 22. health insurance from employer? 23. health insurance bought directly from insurance company? 24. medicare 25. medicaid 26. military healthcare/tricare 27. va 28. indian health services 29. health insurance other medical research and records 30. who offered you online access to your medical records: healthcare provider? 31. who offered you online access to your medical records: insurance company? 32. how many times have you accessed online medical record in the last 12 months? your healthcare 13. health insurance from employer? 14. health insurance bought directly from insurance company? 15. medicare 16. medicaid 17. military health care/tricare 18. indian health services medical research and records 19. who offered you online access to your medical records: healthcare provider? 20. who offered you online access to your medical records: insurance company? 21. how many times have you accessed your online medical record in the last 12 months? 22. how confident are you about the safety and confidentiality of your electronic medical record? 23. have you ever kept information from your health care provider because of privacy concerns? internet use 24. internet use through broadband 25. internet use through a cellular network 26. internet use through a wireless network 27. internet use through a computer at home 28. internet use through a computer at work 29. internet use on a mobile device (cell phones, tablet, etc.) 30. in the past 12 months, have you looked for medical information for yourself? 31. in the past 12 months, have you used the internet to communicate with a healthcare provider’s office? using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 6 ojphi 33. how confident are you about safety and confidentiality of your electronic medical record? 34. have you ever kept information from your healthcare provider because of privacy concerns? internet use 35. internet use through broadband 36. internet use through a cellular network 37. internet use through a wireless network 38. internet use through; computer at home 39. internet use through a computer at work 40. internet use on a mobile device (cell phones, tablet, etc.) 41. in the past 12 months, have you looked for medical information for yourself? 42. in the past 12 months, have you used the internet to communicate with a health care provider’s office? 43. in the past 12 months, have you used the internet to view your test results? 44. do you have a tablet? 45. do you have a smart phone? 46. do you have a wellness app on your phone or tablet? 47. has your tablet or smartphone helped you make health decisions? 48. in the last 12 months, have you used other electronic devices to monitor your health? 32. in the past 12 months, have you used the internet to view your test results? 33. do you have a tablet? 34. do you have a smart phone? 35. do you have a wellness app on your phone or tablet? 36. has your tablet or smartphone helped you make health decisions? 37. have you visited a social networking site in the last 12 months? 38. have you watched a health-related video on youtube in the last 12 months? 39. have you sent or received a text message from a health care provider in the last 12 months? using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 7 ojphi 49. have you visited a social networking site in the last 12 months? 50. have you watched a healthrelated video on youtube in the last 12 months? 51. have you sent or received a text message from a health care provider in the last 12 months? machine learning approach/statistical analysis since there are known limitations for some statistical algorithms and notable issues with the reproduction or generalization of clinical and social science study results, we decided to use more robust methodologies, including multiple supervised machine learning approaches; we also used cycle 2 as a replication population upon which to compare our initial cycle 1 results to ensure replicability across populations [28,29]. thus, cycle 1 was partitioned for use in variable selection, model training, and initial testing of the trained models, and cycle 2 was saved for replication of cycle 1 test sample results. often, especially with linear regression, either only one data collection step is used to validate a model, leading to generalization problems on other sets of data collected on similar populations, or the model is trained on one population and tested on another. both are statistically problematic methods in creating a model [29]. one study applied multiple-sampling approaches with pooling (how this study set up the methodology) and was able to replicate >90% of the problematic samples noted in one of the prominent replication studies suggesting that most clinical paper results do not generalize properly [29]. unsupervised learning to determine which subgroups of patients did not choose to access online health records, we clustered two samples of patients who did not access online health records (notaccessed_concernedprivacy and notaccessed_nointernet) using k-means clustering on the data from cycle 1 and cycle 2. the number of clusters was determined using the elbow on both cycles [30]. results were compared between cycles to understand how behaviors changed over time. to identify the types of records accessed by patients who did choose to access online health records, we clustered four variables (recordsonline_refillmeds, recordsonline_requestcorrection, recordsonline_messagehcp, and recordsonline_addhealthinfo) on record-accessing patients from cycles 1 and 2. the main using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 8 ojphi groups that appeared in both clustering results were compared across cycle 1 and cycle 2 to understand how usage changed over time. supervised learning we used stratified sampling to split cycle 1 data into three parts: variable selection training sample, model training sample, and model test sample. to select variables, we used the boruta algorithm, which statistically tests a random forest model to select statistically significant variables (set to p<0.05). this allowed us to identify main effects as well as interaction terms related to our outcome. to identify main effects and interaction terms separately for clinical evaluation, we fit two supervised learning models in r (logistic regression for main effects and evolved tree model for complex interaction effects) [32,33]. the evolved tree model, fit using evtree in r, allowed us to visualize complex interaction terms that are common in medical data. we then evaluated our logistic regression model and evolved tree model on the cycle 1 test set by measuring the auc, false positive and negative rates, and accuracy. for the logistic regression model only, we used the akaike information criterion, which measures the goodness of fit balanced with the number of variables included in the logistic regression model [34]. evaluation was replicated on the cycle 2 sample to assess reproducibility of model performance across time periods. results variable selection after the five runs of the boruta algorithm on our first cycle 1 sample, we looked at which variables were not selected by any of the selection runs and identified the following: maritalstatus, totalhousehold, selfgender, rentorown, trusttelevision, generalhealth, bmiover25, chronicmedicalflag, medconditions_depression, healthins_va, healthins_other, and otherdevtrackhealth. these were discarded from the subsequent training and test sets (figure 1). using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 9 ojphi figure 1: variable output of the boruta algorithm unsupervised learning results for patients who did not access their medical records, the k-means model for both cycle 1 and cycle 2 selected the optimum number of clusters as 4 (all possible combinations of the two variables, giving 100% of the variance accounted for in the k-means models). access issues generally decreased between cycle 1 and cycle 2, suggesting that access to the internet declined as a significant barrier to usage over time (table 2). table 2: unsupervised learning results: k-means model for both cycle 1 and cycle 2 for those who did not access their medical records not accessed subgroup cycle 1 percent cycle 2 percent privacy only 17% 13% access and privacy 10% 4% other 57% 74% access only 16% 10% for patients who accessed their online records, the optimal clustering for cycle 1 included 5 clusters (~60% of variance accounted for), with major groups including a large subset of patients who mainly refilled medication and messaged primary care providers, a small subset who performed every task online, and a large subset that rarely used online portals for any tasks. the best k-means model for cycle 2 comprised six cluster groups, including three groups of interest from cycle 1 results. the number of patients who refilled medications and messaged primary care providers increased dramatically between cycles, suggesting a common use of online medical records (table 3). table 3. unsupervised learning results: k-means model for both cycle 1 and cycle 2 for those who accessed their online medical records main accessed subgroup cycle 1 percent cycle 2 percent rare usage 49% 35% refill meds and message primary care 25% 45% using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 10 ojphi every task online 3% 4% supervised learning results for the logistic regression model, we found that the model selected in cycle 1 training data did not generalize to cycle 2 data (with the test set auc falling from 84% to 55% between cycles). thus, we discarded our results as not reproducible or useful as a clinical decision model. however, the evolved tree model (figure 2) was reproducible between cycles with auc falling marginally from 85% to 81% between cycles, see auc of cycle 1 data in figure 3 and auc of cycle 2 data in figure 4. significant predictors of online portal usage, according to this model, included privacy concerns, a proactive offering of access to online portals by primary care providers, and prior use of the portal to check test results (figure 2). figure 2: decision tree diagram of the supervised learning method using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 11 ojphi figure 3: evolved tree model auc for cycle 1 figure 4: evolved tree model auc for cycle 2 using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 12 ojphi discussion this study sought to identify predictive factors that determine online patient portal use using machine learning methodology. we found that previous use of online portals is a positive predictor of online portal usage, as well as the offering of online portals by primary care providers. the 2009 health information technology for economic and clinical health act and meaningful use facilitated the creation and availability of online patient portals; however, there has been a low adoption rate among patients. studies have shown that, although organizations have created portals and provided patients with log-in information, patients did not utilize the portals. however, providers that encourage portal use by tasking patients with items to complete or helping patients with the initial log in improves usage [9,35]. irizarry et al. (2015) found that provider endorsement and engagement with patient portals positively affected patient portal utilization [5,7]. privacy concerns are a negative a predictor of patient portal use [12,13]. news of recent data breaches does little to instill confidence in how institutions protect health information and how accessible it is to unauthorized entities [36]. communicating institutional safety measures to secure patient privacy could improve patient trust [37]. anthony et al. (2018) recommended that providers play a role in improving trust in portals by addressing privacy concerns directly with patients [9]. our study indicates that access to the internet is not as significant of a barrier as described in previous studies. the amia released a statement in 2018 that “broadband access is or will become a social determinant of health;” [38] however, with greater access to smartphones, a socioeconomic divide in internet access is no longer a strong predictor of portal use [8,39]. additionally, other populations, such as seniors, now have improved internet access [8]. nambisan (2017) postulated that use of the internet for health information seeking is a better predictor of portal use rather than access to the internet [40]. however, even with the minimal digital divide, health literacy, computer literacy, and care preferences may continue to represent barriers to patient portal utilization [7,39]. according to our study, online portals are most commonly used to refill medications and message primary care providers. patel et al. showed that more than half of patients who access their online portals use it to perform health-related tasks and to communicate with their healthcare providers [9]. the institute of medicine identified patient-provider communication as a core focus in improving patient outcomes. secure messaging augments clinical encounters by providing asynchronous communication between providers and patients [41]. our study has shown that the prospect of utilizing a machine learning model to predict patient engagement via patient portals is promising. this technique may be scaled up to a clinical decision support tool as a user-friendly web interface or app to predict it engagement patterns for clinic registration of new patients. further research and validation of the model in a real ambulatory setting is necessary prior to implementation of such a tool. limitations although this study is a novel attempt to implement a machine learning approach for patient portal utilization, including the clustering method that provided additional insight, it is not without using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 13 ojphi limitations. first, the cross-sectional design of the hints survey does not allow inferences of causality. secondly, the variables in the survey are subject to individual interpretations of the survey questions by the respondents in addition to any response bias that may be present. limitations of k-means clustering include assumptions about outliers (that groups are even-sized and non-overlapping). most real-world data will violate this to some extent. in addition, generally, evolved trees are not the most stable learners; therefore, it is possible that other tree models can be used. however, our results were consistent across partitions of data, and statistical testing on the validation sample confirmed that the model was robust. conclusions the tree model produced more consistent prediction accuracy across cycles than the regression model. it also identified privacy and data protection concerns (negative predictors) and proactive patient portal access offering by physicians (positive predictors) as the most significant determinants of patient portal use. our unsupervised learning algorithm identified a fairly consistent cluster of patients who did not use online portals due to privacy concerns across both cycles of data. among patients who used online portals, there was a consistent cluster of patients across cycles that used the online portal for medication refills and to message their primary care provider. our results showed that machine learning algorithms can be used to identify factors associated with online portal use. these methods may be employed in a clinical decision support tool during new patient registration to personalize methods of patient engagement. the variables identified by our model corresponded with the characteristics of online portal users identified by previous studies [5,8]. we recommend asking patients about privacy concerns and proactively offering patients a way to access their records online or providing an alternative (text messaging, automated call, etc.) based on their response to questions asked during registration. financial disclosure no financial disclosures. competing interests no competing interests. data availability the data set used and analyzed for the study are available for free on the u.s. department of health and national cancer institute website: https://hints.cancer.gov/ references 1. graffigna g, barello s, bonanomi a, riva g. 2017. factors affecting patients’ online health information-seeking behaviours: the role of the patient health engagement (phe). model. using a machine learning algorithm to predict online patient portal utilization: a patient engagement study 14 ojphi patient educ couns. 100(10), 1918-27. epub may 2017. doi:https://doi.org/10.1016/j.pec.2017.05.033. pubmed 2. laurance j, henderson s, howitt pj, matar m, al kuwari h, et al. 2014. patient engagement: four case studies that highlight the potential for improved health outcomes and reduced costs. health aff (millwood). 33(9), 1627-34. doi:https://doi.org/10.1377/hlthaff.2014.0375. pubmed 3. james j. 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https://pubmed.ncbi.nlm.nih.gov/19590030 using a machine learning algorithm to predict online patient portal utilization: a patient engagement study abstract introduction materials and methods study design and setting study participants study variables target variable/outcome variable labels/predictor variables machine learning approach/statistical analysis unsupervised learning supervised learning results variable selection unsupervised learning results supervised learning results discussion limitations conclusions financial disclosure competing interests data availability references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using twitter to detect and investigate disease outbreaks david marchette* and elizabeth hohman naval surface warfare center, dahlgren, va, usa objective in this work we investigate the extent to which social media, in particular twitter, can be used to detect an outbreak of a disease or illness. we term these outbreaks “events”, and we will describe methodologies for detecting events. introduction social media is of considerable interest as a sensor into the thoughts, interests and health of a population. we consider three types of health events that an analyst may wish to be made aware of: given a known disease, such as mers, sars, measles, etc., an event corresponds to individuals contracting the disease. given a set of symptoms (fever, stomach pain, etc.), an event is an unusual number of individuals1 complaining of the symptoms. most generally: an event is an unusually large group of individuals who can be identified as being effected by some personal illness. note that to detect an “unusual number” of something, we need to count the indicators of the event, and we need to compare the current count with past counts. further, we are generally interested in geographically constrained events, and so for this work we will focus on county-based counts. we will count the number of items (tweets or individuals) expressing the event indicator (a disease name, symptom, or classified as “personal health related” as indicated by our classifier). our approach to detecting health related events is: filter -> classify -> detect. we first filter out tweets that contain no “health related” terms, then apply a classifier to each tweet. this classifier is designed to flag a tweet as being about “personal health” or not. we then aggregate the positive instances per day at the county level and detect as an event any county/day pair with an unusually high count (as compared to the recent past). methods we collect all tweets with latitude and longitude within the continental united states. these are then filtered with a set of phrases which are designed to retain tweets that might be related to some disease, sickness, or other health event. these phrases are symptoms (fever, headache, feel sick, etc.), disease or pathogen names (flu, a cold, salmonella) and remedies (cold meds, ibuprofen, nyquil). a random forest, trained on 13k hand tagged tweets, is then applied to the matching tweets to classify them as about “personal health” (class 1) or not (class 0). finally, we count the number of class 1 users within a county per day, and compare this to the past using a sliding window z-score approach. any county/day pair with an unusual count (over 3 standard deviations) is flagged as an event and is then assessed by the analyst as to its nature and importance. results we illustrate the methods through detections of the boston marathon bombing and several events related to new year’s festivities. we also show how discussion of the ebola outbreak in west africa changes over time. conclusions the approach of filter -> classify -> detect, wherein we first consider only those tweets matching certain “health related” terms, then classify the tweets as being about “personal health” or not, and finally detect anomalies based on localized counts, is a very powerful method for processing these data. keywords biosurveillance; social media; twitter; disease outbreak; text analysis acknowledgments this work was funded through an interagency agreement with the department of homeland security national biosurveillance integration center (nbic). *david marchette e-mail: david.marchette@navy.mil online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e147, 201 role of pharmacovigilance in india: an overview ojphi role of pharmacovigilance in india: an overview sanvidhan g suke 1*, prabhat kosta 2, harsh negi 3 1. department of biotechnology, priyadarshini institute of engineering and technology, nagpur440 019, india 2. division of pharmacovigilance, accenture services pvt. ltd. bannerghatta main road, bangalore560 076, india. 3. clinical research & medical services, fresenius kabi oncology ltd, gurgaon122 001, india abstract pharmacovigilance (pv) plays a key role in the healthcare system through assessment, monitoring and discovery of interactions amongst drugs and their effects in human. pharmaceutical and biotechnological medicines are designed to cure, prevent or treat diseases; however, there are also risks particularly adverse drug reactions (adrs) can cause serious harm to patients. thus, for safety medication adrs monitoring required for each medicine throughout its life cycle, during development of drug such as pre-marketing including early stages of drug design, clinical trials, and post-marketing surveillance. pv is concerns with the detection, assessment, understanding and prevention of adrs. pharmacogenetics and pharmacogenomics are an indispensable part of the clinical research. variation in the human genome is a cause of variable response to drugs and susceptibility to diseases are determined, which is important for early drug discovery to pv. moreover, pv has traditionally involved in mining spontaneous reports submitted to national surveillance systems. the research focus is shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records, biomedical literature, and patient-reported data in health forums. the emerging trend in pv is to link premarketing data with human safety information observed in the post-marketing phase. the pv system team obtains valuable additional information, building up the scientific data contained in the original report and making it more informative. this necessitates an utmost requirement for effective regulations of the drug approval process and conscious pre and post approval vigilance of the undesired effects, especially in india. adverse events reported by pv system potentially benefit to the community due to their proximity to both population and public health practitioners, in terms of language and knowledge, enables easy contact with reporters by electronically. hence, pv helps to the patients get well and to manage optimally or ideally, avoid illness is a collective responsibility of industry, drug regulators, clinicians and other healthcare professionals to enhance their contribution to public health. this review summarized objectives and methodologies used in pv with critical overview of existing pv in india, challenges to overcome and future prospects with respect to indian context. keywords: pharmacovigilance; adverse drug reaction; clinical trials; pharmacogenomics; data mining; indian pharmacopoeia commission correspondence: sgsuke@hotmail.com doi: 10.5210/ojphi.v7i2.5595 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi 1. introduction pharmacovigilance (pv) was officially introduced in december 1961 with the publication of a letter (case report) in the lancet by w. mcbride, the australian doctor who first suspected a causal link between serious fetal deformities (phocomelia) and thalidomide, a drug used during pregnancy: thalidomide was used as an antiemetic and sedative agent in pregnant women [1]. in 1968, the world health organization (who) promoted the “programme for international drug monitoring”, a pilot project aimed to centralize world data on adverse drug reactions (adrs). in particular, the main aim of the “who programme” was to identify the earliest possible pv signals. the term pv was proposed in the mid-70s by a french group of pharmacologists and toxicologists to define the activities promoting “the assessment of the risks of side effects potentially associated with drug treatment” [2]. pv is the science of collecting, monitoring, researching, assessing and evaluating information from healthcare providers and patients on the adverse effects of medications, biological products, blood products, herbals, vaccines, medical device, traditional and complementary medicines with a view to identifying new information about hazards associated with products and preventing harm to patients. the challenge of maximizing drug safety and maintaining public confidence has become increasingly complex. pharmaceutical and biotechnology companies must not only monitor, but also proactively estimate and manage drug risk throughout a product’s lifecycle, from development to post-market [3]. pv is particularly concerned with adrs, which are drug responses that are noxious and unintended, and which occur at doses normally used for the prophylaxis, diagnosis or therapy of disease, or for the modification of physiological function [4]. continuous monitoring of drug effects, side effects, contraindications and outright harmful effects which could result in a high degree of morbidity, and in some cases, even mortality, are essential to maximize benefits and minimize risks. no degree of care and caution at the pre-clinical and clinical testing stages can guarantee absolute safety, when a drug is marketed and prescribed to large populations across the country and outside. because clinical trials involve several thousands of patients at most, less common side effects and adrs are often unknown at the time a drug enters the market. post marketing pv uses tools such as data mining and investigation of case reports to identify the relationships between drugs and adrs. the drug regulatory agencies have the responsibility of having a well-established pv system to monitor adrs during the drug development phase and later during the life time of a marketed drug [5]. a complex and vital relationship exists between wide ranges of partners in the practice of drug safety monitoring such as government, industry, health care centers, hospitals, academia, medical and pharmaceutical associations, poisons information centers, health professionals, patients, consumers and media [6-8]. sustained collaboration and commitment are vital if future challenges in pv are to be met in order to develop and flourish. since very few new drugs were discovered in india and hardly any new drug was launched for the first time in india in the past, there was no major compulsion to have a strong pv system to detect adrs of marketed products. the experience from the markets where the drug was in use for several years before its introduction in india, was used by the companies and the regulatory agencies to assess the safety parameters and take corrective actions, such as the withdrawal or banning of the drug in question. the evolution of a new patent regime in the indian pharmaceutical and biotechnology industries as a trade related intellectual property rights and services (trips) makes it incumbent upon india to no longer copy patented products and market 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi them without license from the innovator company. the leading indian companies, realizing the compulsions of the new regime, have already initiated investments of substantial resources for the discovery and development of new drugs needed for both indian and international markets. this in turn means that during the coming year, research and development by the indian pharmaceutical and biotech companies will hopefully lead to new drugs based on pre-clinical and clinical data generated mostly in india. in such cases, the indian regulatory agencies cannot count on the experience of other markets to assess the incidence and prevalence of importance of a properly designed pv system in india. with the indian companies’ capacity to develop and market new drugs out of their own research efforts, it is important that adequate pv standards are introduced to monitor adrs of products first launched in india. 1.1 scope of pv the discipline of pv has developed considerably since the 1972 who technical report, and it remains a dynamic clinical and scientific discipline. it has been essential to meet the challenges of the increasing range and potency of pharmaceutical and biological medicines including vaccines, which carry with them an inevitable and sometimes unpredictable potential for harm. the risk of harm, however, is less when medicines are used by an informed health profession and by patients who themselves understand and share responsibility for their drugs. when adverse effects and toxicity appear, particularly when previously unknown in association with the medicine, it is essential that they are analyzed and communicated effectively to an audience that has the knowledge to interpret the information. this is the role of pv, of which much has already been achieved. but more is required for the integration of the discipline into clinical practice and public policy. to fulfill the pv obligations for its marketed products as per regulations, a pharmaceutical company in india has to essentially carry out activities such as collection, and expedited reporting of serious unexpected adrs [9]. a typical setup for pv studies, including people involved on various levels, organizational units and their functions are shown in figure 1. 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi figure 1. a typical pharmacovigilance setup 1.2 vaccines and biological medicines vaccines and biological medicines require modified systems of safety monitoring. they are often administered to healthy children. this applies particularly to vaccines used within a national immunization program. in many countries, those exposed to a particular vaccine represent the entire birth cohort and therefore a sizeable part of the entire population. people’s expectations of safety are high, and they are reluctant to countenance even a small risk of adverse events. concerns regarding vaccine safety, real or imagined, may result in loss of confidence in the entire vaccine programs. this can result in poor compliance and a consequent resurgence in morbidity and mortality of vaccine-preventable disease. the difficulties in monitoring and dealing with vaccine safety are complicated by the problems inherent in determining the causal link between an adverse event following immunization and a vaccine [10,11]. for example, information on dechallenge and rechallenge is often missing, and vaccines are given to most of the country’s birth cohort at an age when coincidental disease is likely. several vaccines are likely to be administered concurrently. the possibility of programmatic errors should never be overlooked. a programmatic error is a medical incident that is caused by 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi errors in the transportation, storage, handling or administration of vaccines. however, the responsibility of the regulatory authority is by no means limited to the safety of vaccines used in immunization programs. several biological products are used in specific patient populations as preventive or curative measures. the efficient regulation of these products is crucial in order to avoid potential harm to the public as a result of substandard manufacture or improper transportation and storage of imported vaccines and biological medicines. in recent years, the safety of biological products and blood products has come under public scrutiny. concerns about the safety of medicinal products of animal origin have been raised in connection with variant creutzfeldt-jacob disease (vcjd) and with contamination of blood and blood products by infectious organisms such as hiv and hepatitis b [12]. the quality of screening and sterilization procedures and appropriate selection of donors are linked to the risks of contamination. such safety issues related to the use of plasma-derived medicinal products should fall under the aegis of pv programs. for that to happen, pv centers would have to consider the special issues related to safety of these products. expertise in biological products, virology and medical microbiology would be required. clinical trials in large patient populations are being considered for testing the efficacy and safety of biological medicine. 2. clinical trials in india global pharmaceutical companies have found india to be a preferred destination for clinical trials because india's clinical research space and opportunities are very attractive [13]. some of the advantages for clinical trials that india has as are as follows: • high degree of compliance to international guidelines such as the international conference on harmonisation (ich) / who good clinical practice (ich-gcp) and the regulations lay down by the us food and drug administration. • availability of well qualified, english speaking research professionals including physicians. • ongoing support and cooperation from the government. • lower cost compared to the west [14]. • increasing prevalence of illnesses common to both developed and developing countries. • availability of good infrastructure. • changes in patent laws since january 2005. as per a recent report from federation of indian chambers of commerce and industry (ficci), scientific feasibility, medical infrastructure, clinical trial experience, regulations, commercialization potential and cost competitiveness are some of the growth drivers responsible for the metamorphosis of indian clinical research in the recent past [15]. indian-born contract research organizations (cros) were able to offer the advantages of understanding the indian scenario better, provide services at more competitive costs, and having better knowledge of investigator sites in the country compared to the newer entrants in the market. india’s existing favorable regulatory framework and regulations with international standards, increasing awareness of good clinical practice guidelines and its implementation by clinicians are some of 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi the main reasons propelling the growth of clinical research in india [16,17]. the therapeutic area wise distribution of clinical trials and availability of diverse patient population across major therapeutic segments in india is shown in figure 2 [18]. figure 2. therapeutic area wise distribution of clinical trials outsourced to india. 2.1. swot analysis of indian clinical trial sector strengths • large population of over 1.2 billion, about 16% of the world’s population. • huge pharmaceutical and biotech industry base with availability of skilled persons. • third largest players in the world with 500 different active pharmaceutical ingredients [19]. • currently accounts for 8% of global pharmaceutical production, being fourth in the world [20]. • conducive initiatives to harness india’s innovative capability by government. • huge data mining related to safety profile of drugs due to large population [21]. weaknesses • as per 2009-10 estimates, expenditure on health sector was 2.1% of the total budget and 0.35% of the gross domestic product (gdp) of india [22]. • developed countries like united states, france, switzerland and germany, spent around 16%, 11%, 10.8% and 10.4% of their gdp respectively. 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi • less funding available for implementation of programs and issues of national importance such as pv [23]. opportunities • the indian population is the largest source of human biodiversity. • consists of 4635 culturally and anthropologically well-defined populations, representing a perfect model to study efficacy, disease susceptibility, etiology, molecular pathology, and safety profile of drugs with respect to genetic diversity. • excellent potential for skilled human resources required for an effective pv system due to >300 medical, >230 dental, >830 pharmacy and >650 recognized nursing colleges in india [24]. threats • under reporting of adrs. • low availability of funds. • less adrs monitoring centers. 2.2. agencies involved for clinical research regulation in india various regulatory agencies of india and their prominent roles in overseeing clinical trial along with ethics committee are shown in table 1 [25]. 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi table 1. roles of various regulatory agencies 3. role of pharmacogenomics in pv pharmacogenomics (pgx) combines traditional pharmaceutical sciences such as biochemistry with annotated knowledge of genes, proteins, and single nucleotide polymorphisms (snp). it is the technology that deals with the influence of genetic variation on drug response in patients by correlating gene expression or single-nucleotide polymorphisms (snp) with a drug's efficacy or toxicity. by doing so, (pgx) aims to develop rational means to optimize drug therapy, with respect to the patients' genotype, to ensure maximum efficacy with minimal adverse effects [26]. such approaches promise the advent of "personalized medicine"; in which drugs and drug combinations are optimized for each individual's unique genetic makeup. the science of 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi pharmacogenetics (pg) originated from the analysis of a few rare and sometimes serendipitously found extreme reactions (phenotypes) observed in some humans. these phenotypes were either inherited diseases or abnormal reactions to drugs or other environmental factors [27]. pg and pgx research remain iterative processes and there is more room for opportunities for improvement in each of the approaches. figure 3 shows that multiple approaches have to be combined to obtain pgx knowledge that is of value for the development of new therapeutics or for the improvement of existing therapies. figure 3. the pathways of pharmacogenomic research in clinical 3.1. find the gene for disease risk pg primarily deals with the therapeutic effects and the adverse effects of humans to drugs, poisons and other types of chemicals and environmental factors. however, soon after the field of pg emerged, this scope was broadened and the genetic polymorphisms were extensively studied, not only in relation to known specified exposures but also as susceptibility factors for diseases in general. in many of these studies the disease initiating conditions were not known. in parallel, the functional nature of the genes considered in pg research broadened from drug-metabolizing enzymes to almost all other classes, such as drug transport, dna-repair, cell-cycle regulation 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi and signal transduction [28]. the overall number of publications studying pg polymorphisms in relation to disease risk exceeds, several-fold, those studying the polymorphisms in relation to response to drugs, and it is not possible to give a meaningful summary of that research on the hundreds of candidate genes as disease susceptibility factors. all recent genome-wide screens did not focus on the response to drugs but on the identification of genotypes predisposing to certain multi factorial and polygenic diseases. some of the basic techniques of the wet and dry laboratories methods of pg and genomics are shown in table 2. table 2. techniques for genotype analysis in pharmacogenetics and genomics 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi 3.2 statistics, bioinformatics and systems biology previously reported by brockmöller and tzvetkov, it is mentioned that there are almost 12 million snps in the human genome [exactly 11,811,594 snps, of which 5,689,286 are validated snps, according to the last release of the database dbsnp, build 127 (accessed march 2007): http://www.ncbi. nlm.nih.gov/snp/notes/build127_announce.txt]. in the near future, a clinical scientist will have to deal not only with clinical and laboratory data on his/her volunteers or patients but also with data on the patient’s 500,000 or 1 million snps. snps are dna sequence variations that occur when a single nucleotide (a, t, c, or g) in the genome sequence is altered. snps occur every 100 to 300 bases along the 3-billion-base human genome, therefore millions of snps must be identified and analyzed to determine their involvement (if any) in drug response. further complicating the process is our limited knowledge of which genes are involved with each drug response. since many genes are likely to influence responses, obtaining the big picture on the impact of gene variations is highly time-consuming and complicated. knowing one's genetic code will allow a person to make adequate lifestyle and environmental changes at an early age so as to avoid or lessen the severity of a genetic disease. likewise, advance knowledge of particular disease susceptibility will allow careful monitoring, and treatments can be introduced at the most appropriate stage to maximize their therapy. table 3. bioinformatics databases and software tools for pharmacogenetics and genomics 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi the cytochrome p450 (cyp) family of liver enzymes is responsible for breaking down more than 30 different classes of drugs. dna variations in genes that code for these enzymes can influence their ability to metabolize certain drugs. less active or inactive forms of cyp enzymes that are unable to break down and efficiently eliminate drugs from the body can cause drug overdose in patients. today, clinical trials researchers use genetic tests for variations in cytochrome p450 genes to screen and monitor patients. in addition, many pharmaceutical companies screen their chemical compounds to see how well they are broken down by variant forms of cyp enzymes [29]. thus, the development of bioinformatics and genetic statistics plays a crucial role in the further development of pg and genomics (table 3). since the first interspecies comparisons of haemoglobin, protein sequences homology search has been an important tool to identify those segments in the genome that are particularly crucial for the biological function of a certain protein. linkage of genetic variants on the same chromosome is a central basis of methods in genetic statistics. if a person has a certain variant at a certain position, the same person will probably also have other linked variants 10,000 or 50,000 bp nearby, and the same constellation will be found in several relatives of this person. hence, pg and pgx are the most important reasons behind interethnic differences in drug effects which are studied in worldwide marketing of drugs and in pv shown in the table 4. it is immediately evident that disease risk and response to drugs may depend on the combinations of several genes, and years ago scientists and companies emerged with the concept to sell predictive marker combinations. however, it is all too easy to identify in each study predictive marker combinations which are more predictive than the single markers alone. the aim, however, is to identify predictive marker combinations which remain predictive beyond the study in which they were identified. predictive marker combinations may be identified as interaction terms in cross-tabulations, analyses of variance or logistic regression analyses, and data mining tools, such as recursive partitioning, are particularly helpful for this. the situations in which such recursive partitioning should be performed in whole genome scans is not yet clear: in the entire data sets or, as some authors have performed, in subsets identified in a monofactorial analysis as possible risk factors [30]. 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi table 4. summarizing the role of pgx in pv 4. data mining for pv pv, also known as drug safety surveillance, is the science of enhancing patient care and patient safety regarding the use of medicines by collecting, monitoring, assessing, and evaluating information from healthcare providers and patients. in that view, pv can be divided into two stages such as premarketing surveillance – information regarding adrs is collected from preclinical screening and phases i to iii clinical trials; and post-marketing surveillance – data accumulated in the post approval stage and throughout a drug’s market life shown in figure 4 [31]. 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi figure 4. pharmacovigilance at different stages of drug development pv has relied on biological experiments or manual review of case reports; however, due to the vast quantities and complexity of data to be analyzed, computational methods that can accurately detect adrs in a timely fashion have become a critical component in pv. large-scale compound databases containing structure, bioassay, and genomic information, as well as comprehensive clinical data sets such as electronic medical record (emr) databases, have become the enabling resources for computerized adr detection methods [32]. 4.1 premarketing surveillance pv at the pre-marketing stage has been devoted to predict or assess potential adrs early in the drug development pipeline. one of the fundamental methods is the application of preclinical in vitro safety pharmacology profiling (spp) by testing compounds with biochemical and cellular assays. the hypothesis is that if a compound binds to a certain target, then its effect may translate into possible occurrence of an adr in humans. however, experimental detection of adrs remains challenging in terms of cost and efficiency. there have been numerous research activities devoted to developing computational approaches to predict potential adrs using preclinical characteristics of the compounds or screening data. most of the existing research can be categorized into protein target-based and chemical structure-based approaches. others have also explored integrative approach [33]. 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi 4.2 post-marketing surveillance although a drug undergoes extensive screening before its approval by the food and drug administration (fda), many adrs may still be missed because the clinical trials are often small, short, and biased by excluding patients with comorbid diseases. premarketing trials do not mirror actual clinical use situations for diverse (e.g. inpatient) populations, thus it is important to continue the surveillance postmarket. pv plays an essential role in the post-market analysis of newly developed drugs [34,35]. pharmaceutical companies' competition along with rigorous regulatory evaluation procedures empowers a complex research and development process before launching a new drug into the market. several unique data sources are available for postmarketing pv [36]. pv research is based on the analysis of "signals". the world health organization (who) defines signals as undisclosed assertions on direct relationships between effects on the human organism and a drug to induce adverse events [37]. to generate comprehensive signal datasets, clinicians and researchers use spontaneous reporting systems (srs). electronic srss are already in place throughout some european countries and the united states. likewise, other solutions, such as general practitioners’ databases analysis, post market studies or prescription monitoring, among others, are being thoroughly explored. nevertheless, the majority of data is not publicly available for researchers, which, jointly with other barriers, severely limits signal detection [3840]. although drug companies are required to track and manage adverse events reported by clinicians, lawyers or patients, the detection process relies mostly on the physician's ability to recognize a given trait as a drug adverse event. whereas the problem for collecting and filtering adr data from multiple distributed nodes has already been studied in the past, researchers continue to pursue the best strategies to delve into the wealth of collected data in conjunction with other post drug administration inputs [41]. with data and text-mining techniques scavenging millions of electronic medical records, pv researchers are now faced with the problem of delivering knowledge-oriented tools and services that exploit the scope of collected data. ultimately, the adequate exploration of these data will pave the way for improved drug evaluations, critical for pharmaceutical companies, regulatory entities and researchers [42]. 4.3 spontaneous reports a spontaneous report is an unsolicited communication by health care professionals or consumers to a company, regulatory authority, or other organization that describes one or more adrs in a patient who was given one or more medicinal products and that does not derive from a study or any organized data collection scheme. spontaneous reports play a major role in the identification of safety signals once a drug is marketed. in many instances, a company can be alerted to rare adverse events that were not detected in earlier clinical trials or other pre-marketing studies [43,44]. spontaneous reporting of adrs and adverse events is an important tool for gathering the safety information for early detection. case reports collected by such a system represent the source of information providing the lowest level of evidence and highest level of uncertainty regarding causality. spontaneous reporting has advantages in that it is available immediately after a new product is marketed, continues indefinitely and covers all patients receiving the drug. it is the most likely method of detecting new, rare adrs and frequently generates safety signals which need to be examined further [45]. the main limitations are the difficulty in recognizing previously unknown reactions, particularly events that are not usually thought of as being adrs, 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi and under-reporting, which is variable and sensitive to reporting stimuli and difficult to quantify. it usually does not confirm hypotheses; although situations exist where spontaneous reporting data alone allow conclusions that a signal indeed represents a true adr [46]. 4.4. yellow card scheme yellow card schemes (ycs) were applied to spontaneous reporting systems. it was established in 1964 as a result of the thalidomide tragedy. since then, the system has become one of the major international pv resources [47]. the yellow cards are classified into seven priorities by a member of the scientific staff according to the drugs and the nature of the adr shown in figure 5. figure 5. adverse drug reactions online information tracking and yellow card system sources of data the ycs is run jointly by the medicines control agency (mca) which is the regulatory agency and the committee on safety of medicines (csm) which is the experts committee. since 1991, the ycs has been enhanced by a new computer system, the adroit (adverse drug reaction online information tracking) system. adroit is different from other databases. not only does it store the details of the report, but also the image of the yellow card in the optical system. multiple users can view any yellow card on screen at the same time. the reports are prioritized so that serious adverse drug reactions receive early attention [48]. 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi figure 6. adverse drug reaction reporting form 4.5 detection and reporting a healthcare professional or marketing authorization holder reports suspected adrs related to one or more medicinal products, to a pv centre. reports are made on writing report forms, by telephone, electronically, or by any other approved way [49]. reports are collected and validated by the pv centre and are usually entered into a database. serious reactions should be handled 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi with the highest priority. the database is used to identify potential signals and analyze data in order to clarify risk factors, apparent changes in reporting profiles etc [50]. a typical adr reporting form is shown figure 6. systematic methods for the detection of safety signals from spontaneous reports have been used. these methods include the calculation of the proportional reporting ratio, as well as the use of bayesian and other techniques for signal detection. data mining techniques have also been used to examine drug-drug interactions [51]. data mining techniques should always be used in conjunction with, and not in place of, analyses of single case reports. data mining techniques facilitate the evaluation of spontaneous reports by using statistical methods to detect potential signals for further evaluation (figure 7). figure 7. pharmacovigilance systematic methods for the evaluation of spontaneous reports collected from different data sources. 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi this tool does not quantify the magnitude of risk, and caution should be exercised when comparing drugs [52]. furthermore, when using data mining techniques, consideration should be given to the threshold established for detecting signals, since this will have implications for the sensitivity and specificity of the method (a high threshold is associated with high specificity and low sensitivity). confounding factors that influence spontaneous adverse event reporting are not removed by data mining. results of data mining should be interpreted with the knowledge of the weaknesses of the spontaneous reporting system and, more specifically, the large differences in the adr reporting rate among different drugs and the many potential biases inherent in spontaneous reporting. all signals should be evaluated recognizing the possibility of false positives. in addition, the absence of a signal does not mean that a problem does not exist. 5. pv in india in india, consideration for the surveillance of adrs developed relatively late, as traditionally there was no concept of surveillance of medicines in the country. even though pv is still in its infancy, it is not new to india. it was not until 1986 when a few physicians, mainly from academic institutions, called for greater attention to be devoted to the potential adverse effects of prescription medicines and rational prescribing of medicines. this led to the formation of the first adr monitoring program consisting of 12 regional centers, each covering a population of 50 million, but was unsuccessful [53]. nothing much happened until a decade later when india joined the who adverse drug reaction monitoring programme based in uppsala, sweden in 1997. three centers for adr monitoring were identified, mainly based in the teaching hospitals: a national pharmacovigilance center located in the department of pharmacology, all india institute of medical sciences (aiims), new delhi and two who special centers in mumbai (kem hospital) and aligarh (jln hospital, aligarh). these centers were to report adrs to the drug regulatory authority of india. the major role of these centers was to monitor adrs to medicines marketed in india. however, they were non-functional as information about the need to report adrs and about the functions of these monitoring centers never reached the prescribers and there was lack of funding from the government. this attempt was unsuccessful, and hence, again from 1 january 2005, the who-sponsored and world bank-funded national pharmacovigilance program (npvp) for india was formulated [54]. npvp structure is shown in figure 8. the npvp, established in january 2005, was to be overseen by the national pharmacovigilance advisory committee based at the central drugs standard control organization (cdsco). two zonal centers, the south-west (sw) zonal center (located in the department of clinical pharmacology, seth gs medical college and kem hospital, mumbai) and the north-east (ne) zonal center (located in the department of pharmacology, aiims, new delhi) were to collect the information from all over the country and send it to the committee as well as to the uppsala monitoring centre (umc) in sweden [55]. three regional centers would report to the mumbai center and two to the new delhi one. each regional center, in turn, would have several peripheral centers (24 in total) reporting to it. the program had three broad objectives. the shortterm objective was to foster a reporting culture, the intermediate objective was to involve large number of healthcare professionals in the system in information dissemination, and the long-term objective was for the program to be a benchmark for global drug monitoring. however, this program also failed [56]. 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi figure 8. national pharmacovigilance program zone structure 5.1 current pv program in india recognizing the need to restart the npvp, in a brainstorming workshop jointly organized by the department of pharmacology, aiims and cdsco in late 2009, the framework of the new and current program was formulated. the program, now rechristened as the pharmacovigilance programme of india (pvpi) was initiated by the government of india on 14th july 2010 with the aiims, new delhi as the national coordination centre (ncc) for monitoring adrs in the country for safe-guarding public health. in the year 2010, 22 adr monitoring centers including aiims, new delhi was set up under this programme [57]. to ensure implementation of this programme in a more effective way, the ncc was shifted from the aiims, new delhi to the indian pharmacopoeia commission (ipc), ghaziabad, uttar pradesh on 15th april 2011 [58]. the main aim of the ncc at ipc is to generate an independent data on the safety of medicines, which will be at par with global drug safety monitoring standards. year-wise target phases of pvpi is shown in figure 9. 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi figure 9. targets for the pharmacovigilance program in india the recent report of the amc’s under the pvpi has been reported based on the entry of adr’s in the vigiflow. in total, ncc received 3,537 individual case safety reports (icsrs) and 1,948 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi adverse events following immunization (aefi) from the amcs in the month of may 2014. seven more centres have been given access to vigiflow by umc, sweden. out of 97 amc’s where the vigiflow is functional, 82 centres have provided the adr’s reports via vigiflow. it was observed that pgimer, chandigarh entered the highest number of adr’s reports (311 reports in may 2014) followed by 225 reports from mmc, chennai; 216 reports from jss, mysore; 184 reports from ucms-gtbh, delhi; 167 reports from lhmc, new delhi. these reports are under (quality and medical) assessment at ncc [59]. current status of ncc-pvpi published on may 31, 2014 by ipc that is shown in table 5. table 5. current status of ncc-pvpi 5.2 framework of the new program the center at ipc focuses on developing india’s own database on drug information and adrs, so that india will not have to be dependent on data from other countries to take decisions relating to the banning and suspension of drugs. at present, india does not have a strong database on adrs and has to depend on information from western countries. so far, only 2823 adrs have been reported since september 2010 under the current pvpi, which is very small to draw any meaningful conclusion implicated for any particular signal [60]. table 6. chronological developments in pv sector with special reference to india 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi it is being envisaged that all the medical institutions, hospitals, colleges, and public health programs in the country, both government as well as private, will take part in the pvpi and report adrs to ipc, so that all the data generated will be collated and analyzed at one place. the chronological developments in the field of pv with special reference to india are given in table 6 [61]. pvpi administered governing body and monitored centers are shown in figure 10. figure 10. pharmacovigilance program in india and responsibilities. 23 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi the program was envisaged to be rolled out in three phases. phase i would include 40 adr monitoring centers (amc) to be rolled out in 2010. the program would be expanded in phase ii to include up to 140 mci recognized medical colleges by 2011. until the end of 2011, a total of only 60 amcs have been included. phase iii would ultimately cover the entire healthcare system by 2013 [62]. the amcs get operational and logistic support from the respective zonal cdsco centers situated at ghaziabad, kolkata, mumbai, and chennai. the zonal cdsco centers will be under administrative control of the cdsco headquarters at new delhi. organizational structure of pvpi and respective responsibilities of are shown in figure 11 [63]. figure 11. governance structure 5.3 adr data flow adr reports were collected at the amc by the pv staff who checked for validity of the report and conducted provisional causality assessment. the adr forms are then dispatched to the coordinating center. the amc staff maintains a log of all the activities of the center and the selected amcs also carry out focused adr monitoring of drugs as per the watch list. the coordinating centers are conducting causality assessment and upload the reports into the pv database. the coordinating center also prepare a consolidated report of adrs collected at defined time intervals and then implement and integrate pv activities into public health 24 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi programs involving mass usage of drugs [64]. finally, the integrated adr data is then transmitted through vigiflow interface into the umc adverse reaction database where signal processing can be carried out [65] (lindquist 2008). programme communication of adr data flow is shown in the figure 12. there is a quality review panel which ensures the quality of adr data that has been constituted for maintaining quality assurance in the program. all the centers will be assessed based on performance metrics criteria, completeness of reports, training imparted, and other parameters mentioned in the pv program protocol. figure 12. adr data program communications 5.4 haemovigilance programme haemovigilance is a continuous process of data collection and analysis of transfusion-related adverse reactions in order to investigate their causes and outcomes and prevent their occurrence or recurrence. ipc, in collaboration with the national institute of biologicals (nib) at noida, has launched a haemovigilance programme of india (hvpi) across the country under its pvpi with two main objectives. first to track adverse reactions/events and incidence associated with blood transfusion and blood product administration, and second to help identify trends, recommend best practices and interventions required to improve patient care and safety, while reducing overall cost of the healthcare system [66]. 25 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi the recognition and management of transfusion reactions (trs) are critical to ensure patient safety during and after a blood transfusion. transfusion reactions are classified into acute transfusion reactions (atrs) or delayed transfusion reactions, and each category includes different subtypes. different atrs share common signs and symptoms which can make categorization difficult at the beginning of the reaction. moreover, trs are often underrecognized and under-reported. to ensure uniform practice and safety, it is necessary to implement a national haemovigilance system and a set of national guidelines establishing policies for blood transfusion and for the detection and management of trs [67]. in this context the haemovigilance programme was launched on 10th dec 2012 and has already enrolled 90 medical colleges of india under pvpi as an integral part of pvpi. nib is the coordinating centre for hvpi to collect and collate data pertaining to haemovigilance coming from medical institutions all over the country. a software “haemo-vigil” has been developed by it division of the nib [68]. 5.5 visibility, communication, and feedback of pvpi a website created by cdsco is dedicated to pvpi. in phase ii of the program, there is a provision for online reporting of adrs by healthcare professionals who are not covered under the program. the cdsco headquarters, in collaboration with ncc, published a quarterly “medicine safety newsletter” comprising 4-16 pages. approximately 3000 copies will be printed for circulation to healthcare institutions across the nation. a medicine safety card has been included in the medicine safety newsletter, and in national medical journals, to ensure that healthcare professionals not covered under the program can report adrs directly to any of the centers. these are creating awareness about the program and ensure reporters get adequate feedback and remain motivated. in addition, to enhance the awareness and visibility of the programme, focused workshops, symposia, and group meetings on adr reporting and causality assessment are carried out at regular intervals by all the centers [69]. 5.6 guideline of pvpi globally, many countries have formulated their own pv guidelines with the aim of having a systematic process of safety reporting. the ich has six guidelines pertaining to various aspects of drug safety [70,71]. e2aclinical safety data management: definitions and standards for expedited reporting, e2bclinical safety data management: data elements for transmission of individual case safety reports, e2cclinical safety data management: periodic safety update reports for marketed drugs, e2dpost-approval safety data management: definitions and standards for expedited reporting, e2e-pharmacovigilanve planning, and e2fdevelopment safety update report. hence, legislative requirements of pv in india are guided by specifications of schedule y of the drugs and cosmetics act 1945. schedule y also deals with regulations relating to pre-clinical and clinical studies for development of a new drug, as well as clinical trial requirements for import, manufacture, and obtaining marketing approval for a new drug in india. schedule y was revised and amended on 20 january 2005 as a continued commitment of the drugs controller general of india (dcgi) to ensure adequate compliance of pv obligations of pharmaceutical companies. an attempt has been made in the amended schedule y to better define the roles and responsibilities of pharmaceutical companies for their products, as well as relating to reporting of aes from clinical trials [72]. 26 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi india has only a small section of schedule y dedicated to drug safety, which when viewed in light of contemporary global practice, seems to have many lacunae. there is thus a felt need that cdsco must formulate detailed pv guidelines. such guidelines shall incorporate all relevant areas of pre and post marketing safety, address the current lacunae and bring about clarity on issues as discussed above. most importantly, the guidelines shall be in tune with the current international norms, so as to support india’s growth like any participant in multinational clinical trials [73]. 5.7 the challenges of pv in india the biggest challenge facing the pvpi is the gross underreporting of adverse effects. there are many reasons for this, including lack of medical expertise in drug administration and adequate skilled resources in pv, and inadequate nationwide awareness of pv. the other challenges are infrastructure which are still conservative, wide time interval between guidelines and laws, orthodox attitude to new drug research, and pv and regulatory inspections that are almost nonexistent. the system needs to be refined with the help of pv experts in collaboration with information technology (it) because india boasts of a highly developed it sector. since pv deals with large numbers of adrs, it would be wise for pv experts to collaborate with software professionals to develop and build a robust system. software programs developed can be used for collection and analyses of data sets, determining trends of drug usage in various disease areas, compliance, medication errors and drug interactions leading to adrs. moreover, with more clinical research and pv outsourcing work now being conducted in india, it has been worthwhile for the dcgi to invest in a robust pv system to enable assessors and decision makers to analyze safety data and take regulatory decisions without the need to depend on other countries [74]. however, sometimes adrs are not recognized by the physicians on admission and adrs may be responsible for the death of many patients. furthermore, the financial cost of adrs to the healthcare system is also vast [75]. in the market, when new medicines are launched without long term safety studies by the regulatory authorities, patients self-medicate and switch from prescription-only medicines (pom) to over-the-counter (otc) more widely, and this is the main reason of exposing itself to adrs. in the earlier period, india's regulatory agencies and drug companies based their safety assessments on experiences derived from long-term use [76]. in recent years, many indian companies are increasing their investment in research and development and are enhancing their capacity to develop and market new drugs with their own research efforts. once a product is marketed, new information will be generated, which may have an impact on the benefit-risk profile of the product. the detailed evaluation of the new information generated through pv activities is important for all products to ensure their safe use. hence, dcgi should take some tough decisions and make commitments to make pv mandatory and start the culture of pv inspections. 5.8 future prospects as future prospects increase, pv systems capable to detect new adrs, and taking regulatory actions are needed to protect public health. little emphasis has been put into generating information that can assist a healthcare professional or a patient in the decision-making process. the gathering and communication of this information is an important goal of pv [77]. information about the safety of drug active surveillance is necessary. when develop new 27 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi methods for active post-marketing surveillance, one has to keep in mind that the important to collect complete and accurate data on every serious reported event. spontaneous reporting is a useful tool in generating signals, but the relatively low number of reports received for a specific association makes it less useful in identifying patient characteristics and risk factors [78]. pv methods must also be able to describe which patients are at risk of developing an adr. as a source of information, the pv approach would be consistent with the growing patient involvement in drug safety. the pg could play a role in identifying individual risk factors for the occurrence of certain adrs. in the future, pv has to concentrate on the patients as a source of information in addition to the more traditional groups, such as the health professionals. at present, the dcgi should act quickly to improve pv so as to integrate good pharmacovigilance practice (gpp) into the processes and procedures to help ensure regulatory compliance and enhance clinical trial safety and post marketing surveillance. an appropriately working pv system is essential if medicines are to be used carefully. it will benefit healthcare professionals, regulatory authorities, pharmaceutical companies and the consumers. it helps pharmaceutical companies to monitor their medicines for risk [79]. post-marketing pv is currently a challenging and laborious process, not only industry-wide, but also for regulatory agencies. the aim of the pv is to receive the information, documentation of the work and knowledge online while giving priority to the new and important safety issues. non-serious events have less priority than serious events but important in comparing the changes in health, although they are also screened routinely [80]. in present time, glaxosmithkline has created a powerful new approach to pv, integrating traditional, case-based pv methods with disproportionality and data visualization tools. these tools exist within a system framework that facilitates in-stream review, tracking of safety issues and knowledge management [81]. this very innovative tool and the processes will help to advance pv by improving efficiency and providing new analytical capabilities. similar approach may be adopted by pharmaceutical companies for prompt detection and analysis of adrs. transparency and communication would strengthen consumer reporting, which are positive steps towards involving consumers more in pv. 6. conclusion the pv in india has become an important public health issue as regulators, drug manufacturers, consumers, and healthcare professionals are faced with a number of challenges. the pv in india continues to grow, evolve, and improve. india is the largest producer of pharmaceuticals and now emerging as an important clinical trial hub in the world. apparently, the requirements for professional specialization, a combined view on pgx and clinical requirements are needed. that helps to identify factors that increase the risk of unwanted outcomes from drug therapy and prior to commencing drug treatment and in tailoring drug treatment for individual patients. the pv has also involved in data mining technology in spontaneous reports submit to the national surveillance systems. the pvpi is coordinated at ipc through ncc under the control of indian government to generate an independent data on safety of medicines, which will be at par with global drug safety monitoring standards. national and regional pv systems are well-adapted bodies, attuned to the intricate collection and analysis of adr data that leads to timely alerts and interventions to protect population health. furthermore, it is responsible in india of 28 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e223, 2015 role of pharmacovigilance in india: an overview ojphi entire campaign to improve pv knowledge and increase the number of adrs reports up to the gold standard level established by the who. the adverse events reported by pv system will potentially benefit to the community due to their proximity to both the population and public health practitioners, in terms of language and knowledge of the lifestyle and habits of patients, enables easy contact with reporters, for example by telephone, email, text massages by mobile phones. the development of new and effective medicinal products makes a positive contribution to the health and well being of individuals. however, there is a need to improve pv systems to more effectively monitor and take action on safety issues associated with medicines to enhance their contribution to public health. hence, 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11651973&dopt=abstract http://dx.doi.org/10.2307/3563683 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21731854&dopt=abstract http://dx.doi.org/10.4103/2229-3485.80366 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15994364&dopt=abstract http://dx.doi.org/10.1136/jme.2004.010447 http://dx.doi.org/10.3109/10601333.2012.692688 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16007244&dopt=abstract http://dx.doi.org/10.1172/jci25694 role of pharmacovigilance in india: an overview 1. introduction 1.1 scope of pv 1.2 vaccines and biological medicines 2. clinical trials in india 2.1. swot analysis of indian clinical trial sector 2.2. agencies involved for clinical research regulation in india 3. role of pharmacogenomics in pv 3.1. find the gene for disease risk 3.2 statistics, bioinformatics and systems biology 4. data mining for pv 4.1 premarketing surveillance 4.2 post-marketing surveillance 4.3 spontaneous reports 4.4. yellow card scheme 4.5 detection and reporting 5. pv in india 5.1 current pv program in india 5.2 framework of the new program 5.3 adr data flow 5.4 haemovigilance programme 5.5 visibility, communication, and feedback of pvpi 5.6 guideline of pvpi 5.7 the challenges of pv in india 5.8 future prospects the aim of the pv is to receive the information, documentation of the work and knowledge online while giving priority to the new and important safety issues. non-serious events have less priority than serious events but important in comparing the chan... 6. conclusion the pv in india has become an important public health issue as regulators, drug manufacturers, consumers, and healthcare professionals are faced with a number of challenges. the pv in india continues to grow, evolve, and improve. india is the largest ... the adverse events reported by pv system will potentially benefit to the community due to their proximity to both the population and public health practitioners, in terms of language and knowledge of the lifestyle and habits of patients, enables ... conflicts of interest authors’ contribution references ojphi-07-e109.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the need for address based data: disaggregation of syndromic surveillance systems james n. blackwell, ashley hickson, david heinbaugh, kimberlee mcgee, william stephens and sharefa aria* public health informatics office, tarrant county, fort worth, tx, usa objective the justification for address based syndromic surveillance systems, and building syndrome weighting mechanisms. introduction epidemiological surveillance is used to monitor time trends in diseases and the distribution of the diseases in the population. to streamline the process of identifying outbreaks, and notification of disease, syndromic surveillance has emerged as a method to report and analyze health data. rather than report data by disease status (ie disease/no disease), clinical symptoms are used to detect outbreaks as early as possible.1 currently, only data collected via active surveillance (notifiable disease investigations) are usable for identifying communities that require attention. therefore, any interventions performed using said data is reactive in nature. syndromic surveillance systems must be disaggregated to enable proactive health promotion, and responses. furthermore, a common method must be established to assess the overall impact of syndromes. diseases are not equal; some have a greater impact on health, and life. to address this issue, the world health organization (who) has created disability weights to be used in calculating disability adjusted life years (daly).2 dalys are effective in calculating the overall impact of disease in a community. dalys estimate the burden of disease, not syndromes; therefore, it is reactive tool. to create a more effective syndromic surveillance system, syndromes must be associated with an overall impact weight. methods essence and biosense 2.0 are syndromic surveillance systems used by tarrant county. the data from these systems are aggregated into zipcodes. to demonstrate the necessity of address based data, and syndrome weights, data collected via active surveillance techniques were used as a proxy. the active surveillance data are infectious disease data collected by tarrant county. to demonstrate the utility address data, a heat map with pertussis cases was generated (figure 1). all maps were created in qgis 2.4. results the heat map shows that the highest pertussis densities are smaller than zipcodes. the heat map also give more information on outlying cases, as well. conclusions to perform localized interventions, community based participatory research, and create better syndromic predictors it is necessary to disaggregate health data. address level data will be key in creating more precise outbreak predictions. the tools to assess disease distribution are readily available; it is the structure of syndromic data that is limiting widespread adoption of existing software. keywords gis; disaggregation; spatial weights; tarrant; surveillance references 1. kramer a, kretzschmar m, krickeberg k. modern infectious disease epidemiology. london: springer; 2010. 2. world health organization. metrics: disability adjusted life year. http://www.who.int/healthinfo/global_burden_disease/metrics_daly/ en/. updated 2014. accessed august 08,2014. *sharefa aria e-mail: saaria@tarrantcounty.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e , 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 1santa fe institute, santa fe, nm, usa; 2the university of texas at austin, austin, tx, usa objective improve situational awareness for influenza by combining multiple data sources to predict influenza outbreaks in at-risk populations. introduction evidence from over 100 years of epidemiological study demonstrates a consistent, negative association between health and economic prosperity (farmer 2001; marmot 2005). in many settings, it is clear that causal links exist between lower socioeconomic status and both reduced access to healthcare and increased disease burden (shi et al. 1999; liao et al. 2004). however, our study is the first to demonstrate that the increased disease burden in at-risk populations interacts with their reduced access to healthcare to hinder surveillance. despite overwhelming evidence for a causative relationship between poverty and disease, critical gaps exist in our understanding of how to design surveillance systems for these at-risk communities. past work on infectious disease surveillance has focused at the statelevel (polgreen et al. 2009; scarpino et al. 2012) or assumed that risk was evenly spread across well-mixed populations (pelat et al. 2014). surveillance studies focused on broader definitions of health and on chronic diseases have found similar disparities to the ones presented here (liao et al. 2004; kandula et al. 2007). methods as a measure of situational awareness, we focus on a surveillance system’s ability to predict hospitalizations. to achieve this goal, we constructed generalized additive models. first, zip codes are partitioned into poverty quartiles. we then expanded each predictor in a third-order b-spline basis with six degrees of freedom to allow for non-linear effects. to avoid overfitting, we regularize the spline coefficients using a lasso penalty, with the regularization parameter chosen by cross-validation. we also evaluated out-of-sample poisson log-likelihoods and performed least squares regressions. to test for the coherence of each poverty quartile, we calculated the pairwise correlation coefficient between all zip codes within a grouping. we confirmed these results using a principle component analysis. to determine significance, both for the correlation analysis and predictive performance, we randomly assigned zip codes to poverty groups 5000 times and repeated the analyses. results we analyzed the effectiveness of an integrated surveillance system—-one that combines data from biosense 2.0, ilinet, hospital discharge records, and google flu trends. at higher levels of aggregation—-e.g at the state-level, or multiple counties within a state-level—-we find that these data sources correlate well with seasonal influenza. we find strong evidence that these data sources work significantly better for affluent populations than for less affluent, at-risk populations. furthermore, we find that these most atrisk zip codes are more synchronous with each other and have higher hospitalization rates for influenza. conclusions populations with lower socioeconomic status often experience higher hospitalization rates across a range of diseases. one causative mechanism for this increased burden is reduced access to healthcare (shi et al. 1999). our results suggest that this reduced access may also have profound public health consequences, by impairing situational awareness. specifically, we find that an integrated, data-driven surveillance system can accurately predict one-week ahead inpatient influenza hospitalizations in wealthier, but not poorer, more at-risk zip codes. this indicates that the high-tech, integrated surveillance systems of recent focus in the literature have a data blindspot—-these technology-driven systems miss at-risk populations. keywords disease surveillance; poverty; influenza; forecasting references farmer (2001). infections and inequalities: the modern plagues. kandula et al. 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(1999). income inequality, primary care, and health indicators. the journal of family practice. *samuel v. scarpino e-mail: scarpino@santafe.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e37, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control 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french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, 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lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud marion county public health, indianapolis, in, usa objective an interactive, point-and-click application was developed to facilitate the routine assessment of known data quality factors that compromise the integrity and timeliness of data sets used at the marion county public health department (mcphd). the code (and associated documentation) for this application is being made available for other surveillance practitioners to adopt. introduction data sets from disparate sources widely vary in the number and type of factors which most hamper integrity and timeliness of the data. to maintain high quality data, data sets must be regularly assessed, particularly for those vulnerabilities that each is especially prone to due to the methods involved in collecting the data. for surveillance practitioners charged with monitoring data from multiple data sources, keeping track of the issues that each data set is susceptible to, and quickly identifying any inconsistencies or deviations from normal trends, may be a challenge. an application that can track all those issues, and trigger alerts when patterns diverge from what is expected, could help to enhance the efficiency and effectiveness of the surveillance efforts. methods mcphd data sets that have experienced recurrent and welldefined issues were identified and subsequently prioritized based on the impact to public health surveillance and response efforts. our electronic lab report data, with its pattern of consistent delays and missing positive cases, was selected as the highest priority. originally the intention was for our solution to be utilized by personnel whom are not expected to be literate in a programming language; therefore, the potential solution needed to be user-friendly and easy-to-use. r is a statistical computing application, known for its versatility and ability to create powerful visualizations. shiny is an r package that facilitates the creation of interactive, easy-to-use point-and-click applications. for end users, shiny applications eliminate the need to be familiar with the r programming language and retain the ability to harness r’s analytic tools. we looked to r and its shiny package extension as a candidate solution. results the r-shiny application was developed and is presently in use at mcphd. the application in its current state allows the user to select 1) the time frame for analysis, 2) the sliding window size used by the analysis as the number of days, and 3) the alert threshold as the number of standard deviations. upon selection of any of these parameters, the program will automatically recalculate alerts for the selected time frame and plots them as red dots on the disease-specific trend lines. a dynamic table is also displayed in which the user can sort variables (date, disease name, count, alert status). conclusions in the past, mcphd has had a significant period of time go by without realizing that certain electronic lab reports were not being received. the alert-enabled r-shiny application that was developed in this project allows us to check report trends on a daily basis in order to confirm whether the expected number of reports is being submitted. further metrics and visualizations will be added to the application in order to monitor other aspects of the data that have been problematic. we are next looking to integrate two other mcphd data sets (hiv and vital records) into the application. based on our initial success, we are also beginning to create an online library of code in order to facilitate the adoption of this r-shiny tool. figure 1 figure 2 keywords data quality; r; shiny *harold gil e-mail: hgil@marionhealth.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e115, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 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kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of 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lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for 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annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 1centers for disease control and prevention, chamblee, ga, usa; 2american association of poison control centers, alexandria, va, usa objective the centers for disease control and prevention analyzed all calls to poison centers about synthetic cannabinoid use from january to may 2015 to identify risk factors and adverse health effects related to this emerging public health threat. introduction synthetic cannabinoids include various psychoactive chemicals that are sprayed onto plant material, which is then smoked or ingested to achieve a “high.” these products are sold under a variety of names (e.g., synthetic marijuana, spice, k2, black mamba, and crazy clown) and are sold in retail outlets as herbal products and are often labeled not for human consumption. law enforcement agencies regulate many of these substances; however, manufacturers may frequently change the formulation and mask their intended purpose to avoid detection and regulation. on april 6, 2015, automated surveillance algorithms via surveillance through the national poison data system (npds), a web-based surveillance system of all calls to united states (us) poison centers (pcs), identified an increase in calls to pcs related to synthetic cannabinoid use. to identify risk factors and adverse health effects, cdc analyzed all calls to pcs about synthetic cannabinoid use from january to may, 2015. methods we defined a synthetic cannabinoid call as any human exposure to a pc where use of a synthetic cannabinoid product was known or suggested during january to may 2015. we identified calls using npds and compared total and monthly call volumes during the study period to the same months in the previous year. descriptive statistics for sex, age, most frequent signs and symptoms, medical outcome, and route of exposure were calculated. npds medical outcome designations utilized for this study included one of the following: 1) death, 2) major (signs or symptoms that are life-threatening or result in substantial residual disability or disfigurement), 3) moderate (signs and symptoms which are not likely to be life-threatening or result in severe disability), 4) minor (signs or symptoms that are minimally bothersome and generally resolve rapidly with no residual disability or disfigurement), or 5) not followed (the patient likely exhibited only minimal toxicity based on clinical judgment). we identified the most common drugs used when multiple substance use was reported. we compared demographic characteristics of calls reporting more severe medical outcomes (major effect and death) to calls reporting less severe outcomes (moderate effect, minor effect, no effect, not followed). results during the 2015 study period, pcs reported 3,572 calls related to synthetic cannabinoid use, a 229% increase from the 1,085 calls during the same january–may period in 2014. the number of monthly calls spiked notably in april (1,501 [42.0%]) before decreasing nearly to 2014 levels by the end of may. most calls concerned use among males (2,882 [80.7%]). among 3,442 (96.4%) calls where age of the user was recorded, the median age was 26 years (range = 7 months–72 years). agitation was the most frequently reported health effect (1,262 [35.3%]), followed by tachycardia (1,035 [29.0%]), drowsiness or lethargy (939 [26.3%]), vomiting (585 [16.4%]), and confusion (506, [4.2%]). among 2,961 calls for which a medical outcome was reported, the majority had either moderate (1407 [47.5%]) or minor outcomes (1,095), [37.0%]). there were 15 (0.5%) reported deaths. inhalation by smoking was the most common route of exposure (2,870 [80.3%]), followed by ingestion (698 [19.5%]). most reported use was intentional (3,310 [92.7%]). among 626 calls reporting use of synthetic cannabinoids in combination with one or more other substances, alcohol was most commonly reported (144 [23.0%]), followed by plant-derived marijuana (103 [16.5%]), and benzodiazepines (69 [11.0%]). one of the 15 deaths included reports of multiple substance use. males were significantly more likely to have a severe outcome (88.6%) than a less severe outcome (80.1%) (p<0.001). age group and severity were significantly associated with each other (p<0.001); persons aged 30–39 years and aged >40 years were significantly more likely than those aged 10–19 years to report a severe outcome (p = 0.001 and p<0.001, respectively). conclusions increased calls related to synthetic cannabinoid use likely related to availability of new variants suggest that synthetic cannabinoids pose an emerging public health threat. the increase in calls described here suggest a need for greater public health surveillance and awareness and targeted public health messaging. keywords synthetic; marijuana; cannabinoid; surveillance; poison center *royal k. law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e65, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 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vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a understanding discussions of health issues on twitter: a visual analytic study 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi understanding discussions of health issues on twitter: a visual analytic study oluwakemi ola1*, kamran sedig2 1university of british columbia, 2western university abstract social media allows for the exploration of online discussions of health issues outside of traditional health spaces. twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). the unrestricted access to opinions and a large user base makes twitter a major source for collection and quick dissemination of some health information. health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on twitter. however, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. for instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. for twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. the purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on twitter. visual analytics enhances the understanding of data by combining computational models with interactive visualizations. our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on twitter. in this paper, we report on the process of data collection, analysis of data, and representation of results. we present our findings and discuss the implications of this work to support the use of twitter for health promotion. keywords: twitter; social media; online discussion analysis; health issues; visual analytics; interactive visualizations; machine learning; health sentiments correspondence: *kemiola@cs.ubc.ca doi: 10.5210/ojphi.v12i1.10321 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. 1 introduction health information can be gathered from diverse media, including social media. using social media allows stakeholders to explore online discussions occurring outside of traditional health spaces in a rapid fashion [1], [2]. twitter is one of the largest social media platforms, with over 320 million active accounts [3]. this platform allows users to post short comments (i.e., tweets) that contain 280 characters or less. tweets may also contain pictures, videos, or links to webpages. users can like, retweet (i.e., repost a tweet), and reply to tweets. unregistered users can only read understanding discussions of health issues on twitter: a visual analytic study 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi tweets. the unrestricted access to opinions and a large user base has made twitter a source for the collection and dissemination of information for various domains including health [4-7]. currently, health organizations are using twitter to promote healthy lifestyle choices, identify disease outbreaks, explore human behavior, and assess the public’s perception of health issues [2], [8-11]. these organizations also use twitter for health promotion. the department of health and human services in the united states is one such organization that uses twitter to provide the public with actionable health information [12]. in addition to health organizations, individuals, news organizations, businesses, interest groups, and a host of other entities discuss health issues on twitter. on any given day, over 500 million tweets are posted [3]. the voluminous number of tweets presents many challenges to those who seek to use twitter to improve their knowledge of a wide variety of health issues and understand ongoing discussions. observational studies on specific health issues on twitter show many formal and informal conversations taking place [13]. while following a health organization’s twitter account may be beneficial for learning about a specific health hazard, for stakeholders who want to obtain a high-level understanding of the social discourse on a wide variety of health issues, challenges abound. currently, it is difficult for stakeholders to understand the overall sentiment on a health issue, the types of users involved in the discourse, and the content of their tweets. the platform’s open participatory nature and the brevity of a given tweet message can result in the distortion of information [14], [15]. in addition, the quality of the information varies and the identity of the individual tweeting, which is helpful in evaluating the tweet’s credibility, is not always known [13], [15]. for twitter to be an effective tool for health promotion, stakeholders need to be equipped to understand and appraise health information on the platform [16]. a high-level understanding can help address misinformation and equip individuals with a better conceptual model to assess how health issues are discussed. in addition to supporting the information-seeking tasks of the public, an analysis of the health discourse on twitter can benefit health professionals and social scientists by providing them with a lens through which they can better understand the public’s perception of these issues and effectively utilize twitter for health promotion [17], [18]. manual content annotation and computational models have been used to analyze the discourse of health on twitter. studies that utilize manual content analysis have looked at health issues such as swine flu, dental pain, concussions, breast cancer, and marijuana use [19-23]. these studies typically involve content analysis of a small set of tweets (e.g., 1,000 to 10,000). manual content analysis studies are typically time-consuming because they require the manual coding of tweets by individuals. on the other hand, computational models have been employed to analyze large samples of twitter data promptly. some of the work has focused on sentiment analysis. sentiment analysis involves using natural language processing and computational linguistics to characterize sentiment, opinion, attitudes, and emotion from written language [24]. salathé and khandelwal [10] applied sentiment analysis to understand the perception of the h1n1 vaccine on twitter. myslin et al. [25] used machine learning classifiers to deduce sentiment for tweets related to tobacco usage. in addition to sentiment, cole-lewis et al. [1] used machine learning techniques to classify tweets based on user description, genre, theme, and relevance to the topic of e-cigarettes. existing research has focused predominantly on understanding one or two health topics on twitter. understanding discussions of health issues on twitter: a visual analytic study 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi the goal of this paper is to build on this research and provide insight into a variety of health issues through a visual analytic study. visual analytics enhances the understanding of data by combining computational models with interactive visualizations [26-28]. a recent survey on visual analytics highlights the need for more research in supporting the use of social media data in public health practice [29]. our study is meant to demonstrate how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on twitter. to this end, we retrieved over half a million health-related tweets, and randomly selected a sample of 3000 on which we conducted manual content analysis. we used the sample to create models that classified tweets based on their content and user category. these models were then applied to the larger tweet dataset. finally, we created a visualization that supports the exploration of the discourse of health issues in the tweet corpus. in this paper, we report our findings and discuss their implications. the rest of the paper is organized as follows. section 2 presents the research method. section 3 discusses the results. section 4 highlights the limitations of the work. section 5 covers the implications of this work. the final section, section 6, presents general conclusions. 2 method in this study, supervised machine learning was used to build classification models that assess themes of tweets and categories of twitter users. for our analysis, we are more concerned about what is being said about certain health issues as opposed to their frequency or popularity. in addition to the tweet text, twitter allows developers to access relevant metadata about the user who posted the tweet. user information includes username, description of the account, the number of followers, the number of people the user is following, and the number of tweets the user has posted [30]. in this section, we describe how the data was collected and processed. 2.1 data collection in the past, hashtags and search terms have been used to search for health-related tweets [31-34]. we opted to use search terms. our initial list of search terms comprised causes of death that have been identified by the institute for health metrics and evaluation (ihme) [35]. we utilized these causes as search terms primarily because this work is part of a larger research plan to facilitate sensemaking of health data and we wanted to have a consistent terminology. ihme classifies causes into 21 cause-clusters, which are aggregated into three main groups: 1) non-communicable, 2) injury-based, and 3) communicable, maternal, neonatal, and nutritional. to get a better understanding of the ability of these terms to provide relevant tweets, we collected a sample of over 50,000 tweets. we utilized tweepy [36]—a twitter application programming interface—to search for and retrieve the tweets. iteratively, for each search term, we retrieved up to 200 recent tweets to determine whether the search terms predominantly retrieved health-related tweets. in certain situations, search terms were modified to improve results. for instance, the forces of nature search term was expanded to include earthquake death, tsunami death, flood death, and hurricane death. appendix a includes the final list of the 117 search terms used. over a one-month period, we retrieved tweets using the search terms. the total number of english language tweets retrieved during this period was 547,921. the tweets were stored in a mongodb database. understanding discussions of health issues on twitter: a visual analytic study 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi 2.2 analysis 2.2.1 sentiment analysis similar to existing research practices, we measured sentiment as being either negative, positive, or neutral [1], [10], [32]. for our study, we utilized alchemyapi’s sentiment analysis tool to assign polarity and sentiment value to our tweets. alchemyapi, a company acquired by ibm, was a text mining platform that extracted metadata such as keywords and sentiment from text-based documents [37]. we selected alchemyapi because, at the time of this study, it was one of the leading free sentiment analysis tools with a high accuracy rate [38-40]. for a text fragment, alchemyapi returns a sentiment category and score. the sentiment score is in the range -1 to +1 and expresses the strength of the sentiment. the category is based on the score value. for a score less than 0, the category is negative, for a score over 0, the category is positive, and for a score of 0, the category is neutral. table 1 includes some of the tweets and the corresponding sentiment score and category it was assigned. table 1. sample of alchemyapi sentiment analysis of health tweets tweet score category involved lymph nodes in hpv positive oropharyngeal cancer regional control is preserved after dose de excavated 0.0000 neutral ambulance came in hospital with atrial flutter on like this -0.2296 negative share the love via candygram amp support to feed people affected by hiv aids valentinesday 0.4615 positive 2.2.2 manual annotation to obtain a better understanding of who was tweeting and the content of each tweet, we analyzed 500 tweets that were randomly selected from the corpus. based on previous research [1] and our analysis, five content themes and six categories of users were established. the five identified content themes are as follows: • educational: post about relevant health-related news, factoid, resource, research, or public health announcement. tweet that contains general health information, research, or information to raise awareness on a health issue. for example, o “brain cancer two essential amino acids might hold key to better outcome cancer news” o “preparation and characterization of irinotecan loaded cross linked bovine serum albumin heads for liver cancer” • fundraising: post that seeks to raise funds or solicit money or services for a health organization, cause, or individual needing medical treatment. for example, o “that dollar goes to the measles and rubella initiative to buy a vaccine for a child against measles and rubella” o “lets save a life baron has suffered with throat cancer for 5 years and lung cancer for eyes your contribution matters” understanding discussions of health issues on twitter: a visual analytic study 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi • personal: post in which the user is giving an opinion on a health issue, reporting on their own personal health status, or asking health-related questions. for example, o “his bronchitis has my chest feeling heavyyyyyy” o “i am wheeling like an old man with asthma after a joy thank you of” • promotional: post promoting or advertising a for-profit health event or product. for example, o “find out how you can prevent and reverse diabetes won the at real good health summit” o “or lane vishnubala will be teaching our coming of obesity and diabetes specialist instructor course” • unrelated: post that contains search terms but is unrelated to health. for example, o “i feel like i am drowning without your loooooveeeeeeeeee” o “nationalism is an infantile disease it is the measles of mankind” the user categories are as follows: • businesses: for-profit organizations, e.g., retailers, pharmaceutical companies, fitness companies. • celebrities: famous people in pop culture, politics, sports and news media. • interest groups: unofficial organizations for specific health interests, e.g., school groups, health food groups, anti-vaccination groups. • media: reputable news source such as new york times, washington post, wall street journal, associated press and reputable journals that publish health research. • official agencies: government agencies and large non-government health agencies, e.g., national institutes of health, centers for disease control and prevention, american heart association. • public: general public that does not fall into one of the aforementioned categories. after establishing the categories, three thousand tweets were coded. table 2 shows the categorization of these tweets. overall, 74.3% of the tweets were found to be health-related tweets. the predominant user category is the general public which accounts for 75.5% of the tweets. for the content category, the predominant theme is education with 45.7%. these tweets served as the test and training data for our classification models. in the next section, we describe how the classification models were constructed. table 2. categorization of tweets by user and content (n = 3000) user content category frequency theme frequency public 2264 (75.5%) educational 1370 (45.7%) interest groups 227 (7.6%) personal 770 (25.7%) media 227 (7.6%) unrelated 761 (25.3%) understanding discussions of health issues on twitter: a visual analytic study 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi businesses 215 (7.2%) promotional 66 (2.2%) celebrities 40 (1.3%) fundraising 33 (1.1%) official agencies 27 (0.9%) 2.2.3 model construction our models were constructed with the scikit learn library [41] for python. we used the bag-ofwords approach, which is a 3-step process that involves transforming the text into numerical features, which are then analyzed. the first step is tokenization, which involves splitting each document (i.e., tweet or text) into words based on whitespace and punctuation. next, the occurrences of each word are counted and stored in a matrix. the last step involves normalizing and weighting the occurrences. normalization is important because when dealing with a large corpus, common words like ‘a’ and ‘the’, which frequently appear, typically convey little meaningful information about the content of the document. re-weighting was done with the frequency-inverse document frequency (tf-idf) transform, which helps to measure how important a word is to a document in a collection by taking into consideration the number of times a word appears in a document and the frequency of the word across the entire corpus [42]. in the following subsections, we discuss how models were constructed for user categories and content themes. user category we utilized support vector machine (svm) models for classification as previous research points to the benefits of using svm for short text (e.g., tweets) [1], [25]. models were created based on the following attributes: • user description: a user-provided string that describes their account (e.g., “united nations development programme helps empower lives & build resilient nations. to learn more, follow @asteiner & visit: http://www.undp.org”). • user verification status: indicates whether the account has been deemed authentic by twitter. twitter authenticates an account so that the public is aware that the account holder’s identity has been verified. this is typically done for individuals in the entertainment, government, religious, media, business, or sports spheres. • user screen name: unique user name or handle name that is used to identify the tweeter, typically preceded by the @ symbol in tweets (e.g., @undp, @who, @unicef). • influence score: this attribute helps determine how influential an account is on twitter. past research notes that influence is not solely based on the number of people that follow an account on twitter but is also affected by the number of people the account follows [43]. the score is calculated by dividing the number of followers by the number of people that the account follows. for instance, for @undp the number of followers is 1.13 million while the following is 4656. the influence score is 242.70. understanding discussions of health issues on twitter: a visual analytic study 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi table 3 shows the average accuracy rate for 100 runs for four different models. accuracy rate is defined as the percentage of observations that were correctly classified in the test dataset. for each run, 80% of the coded tweets (i.e., 2400 tweets) were used to train the model, while the remaining 20% (i.e., 600 tweets) were used to test the model. the experiment was run 100 times for each of the models created. the model with the highest accuracy rate was model a1, which used the user description alone. subsequent models that incorporate the username, influence score, and user verification status of the account, resulted in lower accuracy rates. table 3. accuracy rate for user category model construction (n = 600) model average accuracy rate (%) a1: description 86.86 b1: description and screen name 79.83 c1: description, screen name, and influence score 79.84 d1: description, screen name, influence score, and user verification status 79.75 tweet theme machine learning models were built for the tweet theme. we used a bag-of-words approach and support vector machine technique for our models. the first model uses the tweet, the second model uses the tweet text as well as the number of reserved news words (e.g., newspaper, news, official), the third model uses the tweet and the verification status of the tweeter’s account and the last model uses the tweet, the verification status, and the number of reserved news keywords. table 4 shows the average accuracy rate for the tweet themes for the four models. the experiment was run 100 times for each of the models created. for each run, 2400 tweets (i.e., 80% of the coded tweets) were used to train the model, while the remaining 600 tweets (i.e., 20%) were used to test the model. table 4. accuracy rate for tweet theme model construction (n = 600) model average accuracy rate (%) a2: tweet 80.99 b2: tweet and number of reserved keywords 81.09 c2: tweet and user verification status 81.14 d2: tweet, number of reserved keywords and user verification status 81.44 based on the experimental analysis of model construction, we used model a1 to classify the user categories and model d2 to classify the tweet themes. 24% of the tweets were classified as unrelated and were removed. in the next section, we discuss the results of the remaining tweets. understanding discussions of health issues on twitter: a visual analytic study 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi 3 results a total of 416,900 tweets remained in our corpus after unrelated tweets were removed; these tweets represent over 100 different causes that contribute to mortality. each tweet has a sentiment score and type, category for the user who sent the tweet, and content theme. in this section, we first present a brief overview of the results, describe the design of a visualization we created to facilitate understanding discussions of health on twitter, and then highlight results for certain causeclusters. table 5 shows the frequency of tweets categorized by sentiment, theme, and user group. 73% of the tweets were deemed negative, while 27% of the tweets were either positive or neutral. similar to the manually coded data, the majority of tweets in our corpus were tweeted by the general public (84 %). the tweets by the media and official agencies made up less than 5% of the corpus. this is important to note because individuals may assume that a significant portion of health-related tweets are from reputable sources, which is not the case. in terms of the content, 66% of the tweets were educational tweets, while personal themed tweets made up 34% of the corpus. combined, fundraising and promotional tweets were less than 1 percent. table 5. frequency of tweets by sentiment, theme, and user categories (n = 416,900) sentiment percent (%) theme percent (%) user percent (%) negative 72.85 educational 65.99 businesses 4.98 neutral 14.47 fundraising 0.16 celebrities 0.01 positive 12.68 personal 33.62 interest group 6.71 promotional 0.23 media 4.73 official agency 0.04 public 83.52 the visualization described in this section includes prevalent words (i.e., non-search terms that frequently appear in the corpus) and the net sentiment rate for causes as well as clusters of causes. in the context of tweets, net sentiment rate is defined as the subtraction of the number of negative tweets from the number of positive tweets, divided by the total number of tweets. net sentiment rate = number of positive tweets − number of negative tweets total number of tweets 3.1 description of sentiment visualization using javascript and the d3.js visualization library [44], we created a visualization to facilitate the exploration of the results. figure 1 shows the default configuration of the visualization. the visualization has three main parts. the first part is comprised of circular arcs that frame the rest of the visualization. these arcs represent the top 50 words across the entire corpus. the size and location of each arc depict its prevalence. the larger the arc, the more times the word appeared in understanding discussions of health issues on twitter: a visual analytic study 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi the corpus. by hovering over the arc (i.e., a word), the number of occurrences appears. the arcs are arranged from left to right in descending order based on prevalence. as shown, the words get, health, like, women, may, type, and new are frequent words in the corpus. some of the screenshots used in the figures only include partial representations of the entire visualization; this is done to aid in the reading of the textual content in the visualizations. the central portion of the visualization (see figure 1), is a variation of a visualization developed by bremer [45]. it depicts the breakdown of tweets by cause-clusters, user category, and tweet theme. in the center of the visualization is a list of the 21 cause-clusters arranged in descending order according to the number of tweets. the diabetes, urogenital, blood/endocrine cluster has the highest number of tweets in the corpus, while the transport injuries cluster has the least. figure 1: default configuration of the sentiment visualization on the left side of the cluster list is a sub-visualization of the tweets by content themes. the links that branch out of each theme represent the presence of tweets for a cause-cluster. for instance, figure 2 shows a partial screenshot of the visualization when the promotional theme is selected. understanding discussions of health issues on twitter: a visual analytic study 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi there are 13 links for the promotional theme because there are no promotional tweets for the other eight cause-clusters. the clusters that do not have promotional-themed tweets are greyed out. figure 2: screenshot of sentiment visualization with the promotional theme selected the right sub-visualization shows the breakdown of user categories, and is encoded in a similar fashion as the left sub-visualization. for instance, figure 3 shows the state of the visualization when the celebrities user category is selected. figure 3: screenshot of sentiment visualization with the celebrities user category selected understanding discussions of health issues on twitter: a visual analytic study 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi it is worth mentioning that the content themes and user categories are arranged based on the number of tweets. we use both size and location to encode quantity so that it is easier for users to determine which group is bigger. for example, for the user categories (see figure 1), the media (4.73%) and businesses (4.98%) arcs appear to be the same size but because the arcs are ordered, users of the tool can deduce that the businesses category has more tweets. the lower portion of the visualization has two alternating views. the first view is shown in figure 1 and it depicts the net sentiment rate for cause-clusters. the second sub-visualization (see figure 4) depicts sentiment for the causes that make up a specific cluster. this sub-visualization contains curved heatmaps and is divided into two parts. the first part shows the breakdown of sentiment by the user categories and the second by the theme of the tweets. the sections of the heatmap are encoded with color, where red is used to indicate negative sentiment, green for positive, and grey is used to depict the absence of data. for instance, as shown in figure 4 when the cardiovascular & circulatory diseases cluster is selected, the visualization shows that there are no tweets from official agencies or celebrities for all the causes that make up the cluster. also, the atrial flutter, hemorrhagic stroke, cardiomyopathy, and peripheral arterial disease causes have a net sentiment score that is positive for certain themes and user categories. with this visualization, users can explore the sentiment for different causes and cause-clusters, learn about the different user groups, and understand the general themes of the tweets. figure 4: screenshot of sentiment visualization with the cardiovascular & circulatory diseases cluster selected understanding discussions of health issues on twitter: a visual analytic study 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi 3.2 exploration of tweet corpus with visualization now that the visualization has been described, let us take a close look at how it aids in the understanding of the discussions on hiv/aids&tb, mental and behavioral disorders, and neglected tropical diseases. figure 5a depicts the breakdown of tweets for the hiv/aids&tb cluster by user category and content theme. this cluster is one of the clusters in which tweets on all four content themes are present in the corpus. in addition, all user categories are tweeting on at least one cause in this cluster. figure 5b depicts the sentiment across the various categories. with this sub-visualization, one can notice that the tweet corpus does not include any tweets from celebrities on tuberculosis, but the discussion on hiv/aids includes all user groups. another observation is that for promotional and fundraising tweets, the sentiment is positive for both hiv/aids and tuberculosis. it may seem intuitive that promotional and fundraising tweets are more positive than other themes, but the same pattern is not observed for other cause-clusters. figure 5: (a-b) screenshots of sentiment visualization with the hiv/aids & tb cluster selected figure 6a shows the lower portion of the visualization when the mental and behavioral causecluster is selected. for the mental and behavioral cause-cluster, the tweets in the corpus do not include fundraising and promotional-themed tweets. furthermore, official agencies are not understanding discussions of health issues on twitter: a visual analytic study 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi tweeting on alcohol use disorders, but they are tweeting on drug use disorders. another observation worth highlighting is the positive net sentiment of alcohol use tweets and the negative sentiment of drug use tweets by personal accounts. the discussion on tropical diseases such as malaria, dengue, ebola, and chikungunya is highly varied. figure 6b depicts the net sentiment rate for tropical diseases. the sentiment for the discussion of ebola is mostly positive. this may seem erroneous, given that the 2014-15 outbreak resulted in thousands of deaths. our data collection coincided with the release of a statement by the world health organization in which they discussed the successful containment of the disease [46]. this observation emphasizes the importance of context when using visualizations for data exploration. figure 6: (a-b) screenshots of sentiment visualization with the mental & behavioral cluster and the neglected tropical diseases & malaria cluster selected this work provides a cross-sectional analysis of the discussion on twitter for a broad range of health issues for a limited time frame. subsequent steps would be to provide real-time analysis that includes historical data so that users can better understand the discussion of health issues and how it changes over time. understanding discussions of health issues on twitter: a visual analytic study 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi 4 limitations this paper has presented a visual analytic study that contributes to the growing body of literature on understanding how health issues are portrayed on social media platforms. one of the contributions of the study is a demonstration of how supervised machine learning methods can be combined with interactive visualizations to help with an understanding of health issues on twitter. in this study, we analyzed over half a million tweets that were retrieved from twitter. although we tried to apply as much rigor as possible, certain limitations exist. first, our data collection occurred over one month, which may have resulted in certain health issues being oversampled and others being under sampled. future studies can examine the discourse for more extended periods or explore real-time analysis of tweets. secondly, we only retrieved english-language tweets. as a result, our findings cannot be generalized to other languages. despite this limitation, we did not specify a geographical location, and consequently, our analysis may be relevant in countries in which english is widely used. our use of search terms to retrieve tweets and machine learning models to classify data resulted in some non-health related tweets being included in the analysis. in addition, while alchemyapi was a sentiment analysis tool, its veracity at categorizing health tweets remains largely untested. furthermore, our categorization of twitter accounts did not include the verification of whether individual accounts are managed by twitter bots or trolls. in addition, our analysis is of the discussions on twitter, and as twitter is not widely used across all demographics, our study cannot be generalized to the entire public discourse on health issues. lastly, our constructed classification models are based on manual content analysis, which may be subject to bias. 5 discussion despite the limitations mentioned above, valuable findings emerge from this study. on twitter, the discussions of health topics are primarily mediated by the general public. though 66% of the tweet corpus is educational, most of these tweets come from the general public, and not reputable health organizations. the discussions mainly revolve around topics such as treatment options and news reports on health ailments. for public health stakeholders, the fact that the public plays a significant role, and that the majority of the content is educational, presents both an opportunity and challenge for health promotion efforts. the use of twitter to spread misinformation on zika and ebola outbreaks across the globe highlights one challenge [47]. while the burden of stemming misinformation may rest on social media organizations, an awareness of these issues can help inform policy on proper social media engagement. more research is needed to determine the influence (i.e., reach of tweets) for different types of users (e.g., see [7]). while efforts exist to use social media platforms for health education, our research highlights that there is still more work to be done. official health and news agencies, which typically provide reputable information, are largely underrepresented in the discussion. these findings corroborate research that suggests that public health organizations may not yet effectively use twitter to educate or engage in dialogue with the general public [4]. while our paper has primarily focused on the development of analytic models and how interactive visualizations can be used to synthesize the results, there is a need to discuss the implications beyond the methods and results. one area of future research is user categories. in this work, we broadly categorized users into six groups. while this categorization is beneficial for a high-level understanding discussions of health issues on twitter: a visual analytic study 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi understanding of participants in the discussion, there is a need to explore the interplay and communication between subsets that may exist within a category. network analysis can support the identification of such communities and help professionals better understand the interaction between them. recent work highlights that on twitter, the communication between pro-vaccine and anti-vaccine communities was minimal when compared to the communication within each of the communities [48]. for public health stakeholders, understanding the structure of the communities that exist within a category is important, especially if the goal is to educate the populace on polarizing issues. furthermore, more work is needed to understand how bots and trolls are used to spread health misinformation on twitter. for example, one study on the discussion of vaccines on twitter, observed that russian trolls posted messages on both sides of the discussion that were divisive and political [49]. this work suggests that visual analytic tools may be beneficial in supporting public health stakeholders charged with developing targeted and effective health campaigns that debunk misinformation. however, before such tools can be adopted, user studies must be conducted to understand how the tools will fit into public health practice. such studies are critical for the successful deployment of visual analytic tools because they will help developers understand the workflow processes of public health stakeholders, as well as their expectations. a survey of visualization and analytics tools highlights barriers that exist to the successful adoption of such tools in public health practice [50]. one such barrier is the risk of misinterpreting the encoded information [50], [51]. in addition, when analytic models are employed, there is an additional risk of not understanding how the data was analyzed. to address these challenges, there is a need for exploring approaches in which computer scientists and public health stakeholders work together to design visual analytic solutions [29]. our work highlights how interactive visualizations that allow for the rapid exploration of data can support hypothesis generation. for instance, with our sentiment visualization, one is able to observe that the overwhelming categorization of health issues on twitter is negative. some may postulate that the very nature of health issues and the challenges they present may influence the sentiment of tweets and seek to explore whether there is a difference between the sentiment of health and non-health issues. other researchers may choose to explore why the overall sentiment for one health issue is more positive than another. research in this area may avoid using prepackaged sentiment analysis tools, in order to better understand how sentiment is calculated. unlike surveys, which have been crafted for a specific objective, the analysis of the public’s discourse on twitter is not mediated. as a result, the findings of analyzing the discourse tend to serve as a useful starting point. for instance, let us consider a situation in which every adult in a local government uses twitter and that the data has been analyzed and visualized. knowing that the sentiment is overwhelmingly positive for a specific health issue does not answer the question, “why is the sentiment positive?” therefore, for practitioners, there is a need for policies that offer guidance on how the results of studies, such as ours, can be used to inform health practice [52]. as social media becomes more embedded in society, and the data it generates increasingly valued, we need to not only develop tools to facilitate the quick analysis and exploration of data, but also create guidelines on how to effectively use both the tools and social media for health promotion. understanding discussions of health issues on twitter: a visual analytic study 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi 6 conclusions this work demonstrates how combining machine learning methods with interactive visualizations can help with an understanding of health discussions on twitter. findings from this work highlight the need for studies to understand the reach of content by the various user categories and how visual analytics tools can be incorporated into public health practice. furthermore, it provides a foundation on which further research that involves the real-time analysis of twitter data can be built upon. it also provides a way to understand which topics are being discussed and by whom, which has implications for health literacy. this research provides a reference point for public health officials engaged in using social media to promote health policies. while our focus has 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hiv/aids paralytic ileus aortic aneurysm hurricane death parkinsons disease asthma hypertensive heart disease peptic ulcer atrial fibrillation influenza peripheral arterial disease atrial flutter interpersonal violence peripheral vascular disease bile duct disease intestinal ischemic syndrome pharyngeal cancer biliary tract cancer intestinal obstruction pneumoconiosis bladder cancer iron-deficiency anemia pneumonia https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=31300292&dopt=abstract https://doi.org/10.1016/j.vaccine.2019.06.086 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24747356&dopt=abstract https://doi.org/10.1016/j.jbi.2014.04.006 https://ajph.aphapublications.org/doi/10.2105/ajph.2018.304497 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29927648&dopt=abstract https://doi.org/10.2105/ajph.2018.304497 understanding discussions of health issues on twitter: a visual analytic study 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi brain cancer ischemic heart poisonings breast cancer ischemic stroke pregnancy hypertensive bronchitis kidney cancer preterm birth complications cardiomyopathy kidney disease prostate cancer cervical cancer laryngeal cancer protein-energy malnutrition chagas leukemia pulmonary sarcoidosis chikungunya liver cancer rheumatic heart chronic obstructive pulmonary disease liver cirrhosis rheumatoid arthritis colon cancer low back pain road injury congenital anomalies lung cancer self-harm dengue malaria sepsis diabetes male infertility skin disease diarrhea diseases maternal hemorrhage skin melanoma diffuse parenchymal lung disease measles stds drowning medical treatment adverse effect stomach cancer drug overdose meningitis subcutaneous disease earthquake death migraine syphilis ebola mouth cancer tetanus encephalitis multiple myeloma tornado death endocarditis multiple sclerosis trachea cancer epilepsy myocarditis transport injury esophageal cancer nasopharyngeal cancer tsunami death falls neck pain tuberculosis fire death neonatal encephalopathy typhoid gall bladder nervous system cancer typhoon death gallbladder cancer non-hodgkin lymphoma urinary disease glomerulonephritis oropharyngeal cancer urinary organ cancer understanding discussions of health issues on twitter: a visual analytic study 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e2, 2020 ojphi gout osteoarthritis uterine cancer heat death ovarian cancer whooping cough isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 1office of border health, arizona department of health services, tucson, az, usa; 2binational border infectious disease surveillance program, tucson, az, usa; 3santa cruz county health services, nogales, az, usa; 4university of arizona, tucson, az, usa; 5centers for disease control and prevention, office for state, local, territorial, and tribal support, atlanta, ga, usa; 6national center for atmospheric research, fort collins, co, usa objective the objective of this work is to develop an efficient communitybased strategy to enhance mosquito surveillance for aedes spp., vector for chikungunya and dengue viruses, in santa cruz county on the u.s.-mexico border. we aim to determine vector presence, distribution, and seasonality by using ovitraps maintained by community members. introduction since 2003 some arizona counties have followed mosquito surveillance protocols to trap the west nile virus vector, culex spp., using co2 traps. despite low sensitivity of these traps to detect aedes spp., one out of seven co2 traps deployed in santa cruz county detected aedes aegypti in 2014. enhancing surveillance for aedes spp. in this region is critical, given that local transmission of dengue has occured across the border in nogales, sonora. limited resources in santa cruz county have previously inhibited efforts to enhance mosquito surveillance 1. to broaden the reach of county surveillance, we implemented a community participatory project by engaging residents to conduct ovitrapping, a non-technical trap that attracts aedes spp. 2 methods multiple strategies were employed to recruit community members to conduct the ovitrapping. key groups approached included high school science clubs, international baccalaureate programs, and senior citizen groups. during the sessions, program staff reviewed the project and the importance of enhancing aedes spp. surveillance. volunteers were instructed on the ovitrap use including how to make a hay infusion, put together the ovitrap, identify eggs, and store and transport specimens for identification. protocols for ovitrapping were developed jointly by the bids program, the university of arizona, and national center for atmospheric research. recruitment of participants began in march 2015. traps were placed as individuals were recruited and data was collected on a weekly basis. tracking of the frequency of reporting from the community participants was conducted. as samples were sent in, the county reported positive traps to the state and eggs were reared and identified to species. all trap locations were mapped and the presence of aedes spp. was noted for each collection period. results as of august 3, 2015 a total of 43 trapping sites were set and maintained by community members. these 43 sites supplemented 11 county-maintained sites, seven of which were housed at fire stations. participants included community members from senior citizen groups (n=24), local high schools (n=12), and other local residents (n=7). the geographic trap distribution included 17 sites in the nogales area closest to the border, two in the central patagonia area, and 24 in the tubac area approximately 25 miles north of the border. traps were kept in participants’ backyards. of the 43 community participants, 72% (n=31) collected data systematically and according to the protocol. participation rates varied among community members. while senior citizen groups had regular participation rates, a barrier to retaining high school participation was the overlap of summer vacation with the surveillance season. conclusions by recruiting community members to maintain ovitraps, we tripled the county’s surveillance capacity for aedes spp. citizen science projects are challenging, but strong protocols and an engaging educational component can harness community labor and gather baseline vector data within a county. data acquired through this project will allow epidemiologists to anticipate and prevent possible outbreaks at the border. ovitrap distribution in santa cruz county keywords aedes spp. surveillance; ovitraps; citizen science; mosquito references 1. kampen h, medlock jm, vaux ag, et al. approaches to passive mosquito surveillance in the eu. ;(9). doi: 10.1186/s13071-0140604-5. 2. polson ka, curtis c, seng cm, et al. the use of ovitraps baited with hay infusion as a surveillance tool for aedes aegypti mosquitoes in cambodia. dengue bulletin 2002; 26:178-184. *mariana g. casal e-mail: mariana.casal@azdhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e51, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e313, 2019 isds 2019 conference abstracts mapping pps: a case study of story map journals for interactive health reporting susan rauch english and media studies, massey university, w est end, palmerston north, new zealand objective a case study on the visual-material components of story map journals as visual, new media interactive health reporting used in population health surveillance. the story map journal is demonstrated an effective tool that visually reports, maps and tracks global support networks and health resources for postpolio (pps) survivors. introduction how are interactive story map journals situated within the genre of interactive, health science reporting? how can reporting information to public audiences be theorized through traditional and contemporary understandings of new media genres in technical, health and science communication [1-7]. although the polio vaccine has eradicated the disease in the united states, and 99% worldwide [8], pps has emerged as a present-day condition that continues to affect many polio survivors years after the initial onset and recovery. since the symptoms of pps are oftentimes mis-identified as other illnesses, the diagnosis and management of disease is especially challenging for pps survivors due to the limited knowledge of and access to pps resources and support networks [9-11]. in 2011, esri created the arcgis story map initiative to meet a need for public audiences who sought how to critically think, better understand, communicate, and interact with world news events. arcgis is a geospatially-driven, new media platform that enables audiences to engage with interactive storytelling of news events. public health and news reporting agencies are now turning t o esri and similar interactive, geospatially driven new media platforms for health and disease surveillance [12-14]. esri’s arcgis mobile and web technology platform visually reports, maps and tracks population health data information. with the emergence of such new media applications, it is therefore important to recognize multimodal, visualization strategies that investigate how interaction design choices within the story map journal influence and engage public health audiences. in the field of technical and professional communication [15], applied concept of visual-material rhetorics is a useful mode of inquiry in the study of interactive story map journals. propen’s concept presents a new understanding of how researchers in disease and public health surveillance can anal yze the effectiveness of text and new media technology in relationship to space, place, and geospatial mapping. more specifically, propen’s concept situates the visual-material as the applied use of text with visual, interactive multimodal components inclusive of images, video, and gps/gis mapping technologies. methods this presentation includes a discussion of genre analysis as applied to visual-material components used to study the genre of new media-driven story map journals for the reporting of public and population health resources. post-polio syndrome (pps) is presented as a case study of how story map journals in population health can be used to create information about global support networks and resources for pps survivors. results the story map journal is an effective genre of new media, interactive reporting in health and disease surveillance. the analysis alongside propen’s mode of inquiry demonstrates the effectiveness of visual-material components of story maps, and how pps survivors and medical clinicians can use the story map journal to easily access, visualize, and interact with information about diagnosis and disease management, as well as find connections to local and global support networks. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e313, 2019 isds 2019 conference abstracts conclusions story map journals as visual, interactive reporting should be considered when analyzing the accessibility and surveillance of health data for public audiences. the case study of pps global networks and resources, provides one example of how story map journals can assist public audiences who experience difficulties finding support networks and public health resources references 1. andersen j. genre theory in information studies: emerald group publishing; 2015. 2. caquard s, cartwright w. narrative cartography: from mapping stories to the narrative of maps and mapping. taylor & francis; 2014. 3. geisler c, bazerman c, doheny-farina s, gurak l, haas c, et al. 2001. itext: future directions for research on the relationship between information technology and writing. j bus tech commun. 15(3), 269-308. https://doi.org/10.1177/105065190101500302 4. propen a. 2007. visual communication and the map: how maps as visual objects convey meaning in specific contexts. tech commun q. 16(2), 233-54. https://doi.org/10.1080/10572250709336561 5. propen ad. 2006. critical gps: toward a new politics of location. acme int e-j crit. 4(1), 131-44. 6. propen ad. cartographic representation and the construction of lived worlds: understanding cartographic practice as embodied knowledge. rethinking maps: routledge; 2011. p. 131-48. 7. villanueva ls, dolom mac, belen js. genre analysis of the “about us” sections of asian association of open universities websites. asian association of open universities journal. 2018. 8. who. 10 facts on polio eradication: who; 2017 [cited 2018]. available from: http://www.who.int/features/factfiles/polio/en/. 9. cope g. 2017. post-polio syndrome: the legacy of a long-forgotten problem. independent nurse. 2017(4), 2124. https://doi.org/10.12968/indn.2017.4.21 10. duncan a, batliwalla z. 2018. growing older with post-polio syndrome: social and quality-of-life implications. sage open med. 6, 2050312118793563. pubmed https://doi.org/10.1177/2050312118793563 11. muñoz fc, morales ms, faz mg, ariza mg, salazar ja, et al. 2018. polio and post-polio syndrome, viewed by patients and health professionals in primary care. rev esp salud publica. 92. pubmed 12. partee rp, lindsay jm. 2017. esri’s arcgis for desktop basic with spatial analysis: a review for medical libraries. j electron resour med libr. 14(1), 17-22. https://doi.org/10.1080/15424065.2016.1179153 13. rosner s, hackl-herrwerth a, leucht s, vecchi s, srisurapanont m, et al., eds. opioid antagonists for alcohol dependence. society for social medicine annual scientific meeting & european congress of epidemiology; 2018. 14. fornace km, surendra h, abidin tr, reyes r, macalinao ml, et al. 2018. use of mobile technology-based participatory mapping approaches to geolocate health facility attendees for disease surveillance in low resource settings. int j health geogr. 17(1), 21. pubmed https://doi.org/10.1186/s12942-018-0141-0 15. propen ad. locating visual-material rhetorics: the map, the mill, and the gps: parlor press; 2012. http://ojphi.org/ https://doi.org/10.1177/105065190101500302 https://doi.org/10.1080/10572250709336561 https://doi.org/10.12968/indn.2017.4.21 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=30202523&dopt=abstract https://doi.org/10.1177/2050312118793563 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29938691&dopt=abstract https://doi.org/10.1080/15424065.2016.1179153 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29914506&dopt=abstract https://doi.org/10.1186/s12942-018-0141-0 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts unintentional drug overdoses in virginia: analysis of syndromic and death data amanda wahnich*, kathrin hobron, erin e. austin and tim powell vdh, richmond, va, usa objective determine if syndromic surveillance data can be used to provide a real-time picture of the drug using population by analyzing trends of emergency department (ed) visits for unintentional drug overdose (overdose visits) in conjunction with unintentional deaths that prescription or illicit opiates contributed to or caused (overdose deaths). introduction drug overdoses and related deaths have been escalating nationally since 19701. in virginia, the rate of drug overdose deaths increased 36% from 5.0 to 6.8 deaths per 100,000 population between 1999 and 20102. while initiated for bioterrorism event detection, syndromic surveillance has shown utility when extended to other health issues. ed visits may complement information from overdose deaths investigated by the office of the chief medical examiner (ocme) in describing drug overdose trends. due to its real-time nature, syndromic surveillance data could act as an early indicator for emerging drug problems in the community, serving as an alert to public health. methods virginia department of health (vdh) receives syndromic surveillance data comprised of ed visit chief complaints to monitor and detect public health events. overdose visits were compared to overdose deaths among virginia residents from 2012-2013. relevant text strings within chief complaints from 82 eds were identified using structured query language (sql). descriptive and geospatial analyses were performed to compare the data sources and describe the burden of unintentional drug overdoses in virginia. results the analysis found 13,443 (0.28%) overdose visits among 4,790,060 total ed visits for 2012-2013, for an estimated rate of 82 visits per 100,000 population. women (7,972 visits, 59.3%) and individuals age 15-24 (3,937 visits, 29.3%) represented the greatest proportion. of virginia’s 134 counties, 12 had rates more than double the statewide drug overdose visit rate. these counties are predominately located in northwest and southwest virginia, corresponding to central and south central appalachia3, supporting previous findings of high drug use throughout the appalachian region4. the ocme identified 1,151 (9.9%) overdose deaths among 11,576 total deaths investigated for 2012-2013. overdose deaths are predominately male (730 cases, 63.4%). the highest proportion of deaths was among individuals age 25-34 (309 cases, 26.8%), closely followed by those age 35-44 (281 cases, 24.4%). the mortality rate was 14.2 overdose deaths per 100,000 population, with the highest rates in southwest virginia. overdose visits and overdose deaths were not significantly correlated (correlation coefficient of -0.08). conclusions both overdose deaths and overdose visits corroborate previously identified trends showing distribution of drug activity throughout the appalachian region; however, the demographics of the population that experienced overdose visits differ from those with overdose deaths. virginia’s overdose deaths are consistent with nationallyobserved trends showing increased drug and prescription overdose death rates driven by middle-aged individuals and males. overdose visits, however, are comprised of younger individuals and females. a simple predictive model using overdose visits was not pursued due to a lack of significant correlation with overdose deaths. further study is ongoing to explore whether overdose visits represent an opportunity to implement public health or clinical interventions to prevent future overdose deaths. keywords drug overdose; syndromic surveillance; drug overdose surveillance references 1. centers for disease control and prevention. unintentional drug poisoning in the united states. 2010. http://www.cdc.gov/ homeandrecreationalsafety/pdf/poison-issue-brief.pdf 2. trust for america’s health. prescription drug abuse: strategies to stop the epidemic. 2013. http://healthyamericans.org/reports/ drugabuse2013/ 3. appalachian regional commission. subregions in appalachia. 2009. http://www.arc.gov/research/mapsofappalachia.asp?map_id=31 4. appalachian regional commission. disproportionately high rates of substance abuse in appalachia. 2008. http://www.arc.gov/news/ article.asp?article_id=113 *amanda wahnich e-mail: amanda.wahnich@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e97, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 1black & veatch, overland park, ks, usa; 2committee on consumer rights protection of the ministry of national economics, astana, kazakhstan; 3scientific-practical center of sanitary-epidemiological expertise and monitoring, almaty, kazakhstan objective development of a concept for sanitary-epidemiological monitoring system reinforcement in kazakhstan based on the deployed electronic surveillance system, risk assessment and management approach, and establishment of a situational center. introduction the sanitary-epidemiological monitoring system in kazakhstan is passive and statistical in nature. due to the modern threats, activation and emergence of new and recurring diseases (corona virus, ebola, etc.) it is vital to transition from current epidemiological surveillance approaches to new prognostic, epi risk probability assessment, and bio risk management technologies, and in addition to urgent response develop preventive measures procedures to minimize the consequences of potential epi outbreaks. methods a set of measures for epi surveillance system improvement and modernization was developed based on the following initiatives: 1) implementation of a unified open-source electronic integrated disease surveillance system (eidss) (eidss.codeplex.com) in the epi surveillance and monitoring service [1] 2) development of regional sanitary-epidemiological passports (rsep) [2, 3] 3) creation of a republican situational center (sc) the concept of sc creation is new for epidemiology in kazakhstan. the sc will be a 24/7 working department with special staff collecting and aggregating epi information from all available internal and external sources. the external threat assessment is based on the current situation in bordering territories, diseases in other countries that have international proliferation potential (confidential ihr 2005 site through the network of national who coordinators), and infectious diseases in other countries (promed, cdc networks and ministry of health websites). the internal threat assessment is based on the currently used surveillance system sanitary-epidemiological surveillance-1987, the implemented unified epi program eidss for 64 diseases, the developed rsep, natural especially dangerous disease foci in kazakhstan (available through maps and electronic geo-information systems), and the current state and capabilities of the national infection diagnostic laboratories. results the following tasks will be executed based on the collected data: 1) situational monitoring: information collection and analysis on the epi situation in real-time 2) improvement and development of new risk prognosis methodology based on multi-factor analysis of internal and external sources [3] 3) coordination of territorial units on emergency response: assessment of the situation regarding foci and in the country at large, readiness preparation monitoring of response units, coordination of services and units, and international cooperation 4) state authorities information brief (immediate access to emergency situations information, available through a website) conclusions active surveillance, risk assessment and prediction with a unified monitoring system within a situational center (sc) will improve epi situation control level. the sc will become the main source of reliable information on the epi situation in kazakhstan as well as for trans-boundary epi risks to the republic. the sc can become the foundation for mutli-sectoral one health collaboration on zoonotic diseases, as well the base element for regional cooperative systems within global health security agenda. keywords risk assessment; real-time monitoring; situational center references 1. burdakov a. et al. strengthening national one health disease surveillance with open-source eidss // 16th international congress on infectious diseases (icid), cape town, south africa, april 2-5, 2014. 2. esmagambetova a. s., et al. accuracy of eidss software prognosis on cchf natural foci activity in kazakhstan // international society for disease surveillance (isds) 2013 annual conference, new orleans, la, usa. 3. kazakov s.v. et.al. methodology of epidemic risk management and prevention in natural foci of especially dangerous pathogens with opensource eidss in kazakhstan // iohc 2015, amsterdam, netherlands, march 15-18, 2015. *alexry burdakov e-mail: burdakov@usa.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e92, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of 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andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset 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radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra 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essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 1public health england, birmingham, united kingdom; 2health and social care information centre, leeds, united kingdom; 3nhs england, birmingham, united kingdom objective we compared weekly laboratory reports for a number of seasonal respiratory pathogens with telehealth calls (nhs 111) to assess the burden of seasonal pathogens on this syndromic surveillance system and investigate any potential for providing additional early warning of seasonal outbreaks. introduction seasonal rises in respiratory illnesses are a major burden on primary care services. public health england (phe), in collaboration with nhs 111, coordinate a national surveillance system based upon the daily calls received at the nhs 111 telehealth service. daily calls are categorized according to the clinical ‘pathway’ used by the call handler to assess the presenting complaints of the caller e.g. cold/flu, diarrhoea, rash. methods multiple linear regression models were used to identify significant contributions from respiratory pathogens to seasonal variation in nhs 111 calls for respiratory symptoms, including cold/flu, cough, difficulty breathing and sore throat. children under 5, aged 5-14 and adults 65+ were examined separately and time lags of up to four weeks introduced in the models to investigate any potential early warning provided. results respiratory pathogens explained over 47% of the variation in calls for cold/flu, cough and difficulty breathing. the most sensitive signal for influenza virus was nhs 111 cold/flu calls; whilst for rsv the most sensitive signal was cough calls. the models illustrated that nhs 111 calls for cold/flu and cough peaked a week before the specimen date of laboratory reports for rsv and influenza. conclusions daily surveillance of nhs 111 telephone calls can provide early warning of seasonal rises in influenza and rsv compared with traditional laboratory surveillance methods. selected regression models where significant positive correlations with pathogens were found the estimated burden of calls due to the pathogen are shown (sept 2013 july 2015). proportion of nhs 111 cold/flu calls. stacked bars show proportion due to specific pathogens under regression model. keywords syndromic surveillance; respiratory; regression acknowledgments the authors would like to acknowledge support from: nhs 111 and hscic. also the help of all the resst team, including sue smith, paul loveridge, helen hughes and leandro carrilho. ram, aje and ges receive support from the national institute for health research health protection research unit in emergency preparedness and response. the views expressed are those of the authors and not necessarily those of the nhs, the nihr, the department of health 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estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. 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olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 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interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french mathematical and statistical sciences, university of colorado denver, denver, co, usa objective to propose a computationally simple and a fast algorithm to detect disease outbreaks in multiple regions introduction emerging disease clusters must be detected in a timely manner so that necessary remedial action can be taken to prevent the spread of an outbreak. the exponentially weighted moving average method (ewma) is a particularly popular method, and has been utilized for disease surveillance in the united states [1]. a spatio-temporal ewma statistic is proposed for on-line disease surveillance over multiple geographic regions. to capture spatial association, disease counts of neighboring regions are pooled together, similar to a method originally proposed by raubertas [2] for a different control chart. also to increase statistical power in testing multiple ewma statistics simultaneously, false discovery rate (fdr) is used instead of the traditional family-wise error rate (fwer). methods first, an acceptable false alarm rate is set by the user to define the false discovery rate. then, at each time t, disease counts for each of the m regions y 1t ,y 2t ,...,y mt are collected; the weighted counts of immediate neighbors are pooled to form regional neighborhoods with counts y’ 1t ,y’ 2t ,...,y’ mt . then, the corresponding ewma statistics for the regional neighborhoods e’ 1t ,e’ 2t ,...,e’ mt are calculated. to construct empirical in-control distributions for each region, b bootstrap samples are drawn with replacement, respecting spatial order, from an initial time period with no outbreaks. for the bootstrap samples, the corresponding ewma statistics are computed for each region to determine the empirical in-control distributions from which the corresponding p-values p’ 1t ,p’ 2t ,...,p’ mt are calculated. finally, a state-of-the-art multiple comparison procedure is used to determine the alarms with the pooled model. this model is compared to a baseline model using independent regional counts using a standard multiple comparison procedure. simulation studies provide strong evidence that the pooled model using the more powerful and current multiple testing procedure detects outbreaks faster than the independent model using the standard multiple testing procedure. results the proposed method was applied to a data set of salmonella newport cases reported weekly from 16 german federal states between years 2004-2014. the first two years of data (2004-2005) were used to estimate the in-control distribution in each state since there were no unusually high disease counts reported from any of the states during this period. plots of the disease counts (a), the ewma statistics (b), and the corresponding alarms (c) are shown for two states bavaria (figure 1) and bremen (figure 2);the blue lines depict the independent model and the red lines show the pooled model. both plots illustrate the rapid detection ability of the proposed method. conclusions the proposed method of pooling regional neighborhood counts increases the speed of detection compared to the baseline model using independent regional counts. more statistical power can be gained using a more innovative multiple testing procedure. keywords spatio-temporal ewma charts; biosurveillance; prospective disease surveillance; control charts; false discovery rate references 1. elbert y, burkom, h. s. development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data. statistics in medicine. 2009 november; 28(26) 2. raubertas r.f. an analysis of disease surveillance data 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j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, 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guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 181 isds 2014 conference abstracts involvement of local communities and households in the implementation of the “one health” initiative through the east african integrated disease surveillance network (eaidsnet) stanley s. sonoiya* health, east african community secretariat, arusha, united republic of tanzania objective the main objectives of the network are to promote cross-border integrated diseases prevention and control through “one health” approaches and joint action focusing on innovative human, animal and ecosystem health interventions, to among others:1) enhance and strengthen cross-country and cross-institutional collaboration through regional coordination of activities and local community participation, 2) promote exchange and dissemination of appropriate information on integrated disease surveillance (ids) and other disease control activities, 3) harmonize integrated disease surveillance systems in the region, 4) strengthen capacity for implementing integrated disease surveillance and control activities, and 5) ensure continuous exchange of expertise and best practices for integrated disease surveillance and control of pandemics and epidemics of communicable and vector-borne diseases in the east african community partner states (burundi, kenya, rwanda, uganda and tanzania). introduction the east african community (eac) is the regional intergovernmental organization of the republics of burundi, kenya, rwanda, the united republic of tanzania, and the republic of uganda, with its headquarters in arusha, tanzania. the east african community (www.eac.int) is implementing the “one health” initiative through the “east african integrated disease surveillance network (eaidsnet)” which is a regional collaborative effort of the national ministries responsible for human and animal health as well as the national health research and academic institutions of the five (5) eac partner states. description methodology: the “east african integrated disease surveillance network (eaidsnet)” is implementing the “community-based early warning integrated disease surveillance and response” for the prevention and control of various human and animal diseases, parasites and pests, including involvement of local communities inter-facing with wildlife under the “one health” initiative in the cross-border areas. conclusion: involvement of local communities and households through participatory approaches in “one health initiative” leads to sustainable “community-based early warning integrated disease surveillance and response” for the prevention and control of various human and animal diseases, parasites and pests audience engagement through oral presentations real life situations and experiences in the implementation of the east african regional transboundary community-based integrated one health disease prevention and control initiative in the kagera river basin ecosytem involving the republic of uganda, the republic of burundi, the republic of rwanda and the united republic of tanzania keywords one health; eaidsnet; communities; disease; surveillance acknowledgments local communities and volunteer community health workers in the kagera river basin ecosystem in the east african transboundary region of rwanda, burundi, uganda and tanzania *stanley s. sonoiya e-mail: stanleysonoiya@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e206, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts comparison between hl7 and legacy syndromic surveillance data in new york city janette yung*, promise nkwocha, anthony w. tam, ramona lall and robert mathes bureau of communicable diseases, nyc department of health and mental hygiene, queens, ny, usa objective to evaluate potential changes in emergency department (ed) syndromic surveillance data quality, as hospitals shift from sending data as flat file format (legacy data) to real-time/batch hl7 messaging standard version 2.5.1, in compliance with meaningful use requirements. introduction data from the emergency departments (eds) of 49 hospitals in new york city (nyc) is sent to the department of health and mental hygiene (dohmh) daily as part of the syndromic surveillance system. currently, thirty-four of the eds transmit data as flat files. as part of the center for medicare and medicaid services electronic health record incentive program, otherwise known as meaningful use, many eds in our system have switched or are in the process of switching to hl7 messaging standard version 2.5.1. given there may be differences in data completeness, quality, and content between the new hl7 data and legacy data, we evaluated data sent in both formats in parallel by several eds. methods we compared the total number of daily visits, syndrome counts, percent completeness of variables, and number of words in the chief complaint field from four hospitals sending parallel data from july 15 2014 to august 15 2014. syndromes tested include influenza-like illness (ili), fever-flu, respiratory, diarrhea, and vomit. variables analyzed for completeness included age, chief complaint, gender, zip code, discharge disposition, and discharge diagnosis (icd9 or icd10 code). results overall, for the four hospitals tested, the total number of daily visits was greater in the new hl7 data (a 10% increase). however, this varied by hospital, with a single hospital accounting for the majority of this increase. there was an overall increase in syndrome counts of ili (5%), fever-flu (23%), respiratory (24%), diarrhea (35%), and vomit (97%). these changes in syndrome counts also varied by hospital. we observed no difference in percent completeness of the variables age, chief complaint, gender, and zip code between the hl7 data and legacy data. however, the percent completeness for discharge disposition and discharge diagnosis variables in the hl7 data did increase from 53% to 78% and 23% to 87%, respectively. the average word count for chief complaint field increased from 4 to 8. conclusions as hospitals shift from sending syndromic data as flat files to real time/batch hl7 messaging, parallel testing will become a critical step in maintaining data integrity. discrepancies in the total visit counts between the parallel data streams and other issues with data quality (e.g., completeness) are reported back to individual hospitals for an explanation or correction, and testing in parallel is discontinued once all issues are resolved. we will continue assisting hospitals as they go through this process, to maintain or improve ed data quality. keywords syndromic surveillance; meaningful use; hl7; hl7 messages; data quality, data assessment acknowledgments the alfred p. sloan foundation (grant number: 2010-12-14) *janette yung e-mail: jyung@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e179, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang los angeles county department of public health, los angeles, ca, usa objective to describe how syndromic surveillance was used to monitor health outcomes in near real-time during the 2015 special olympics in los angeles county (lac), california. introduction lac hosted the 2015 special olympics (so) which welcomed approximately 6,500 athletes from 165 countries, as well as 30,000 volunteers and 500,000 spectators from july 25 to august 2, 2015. international athletes were not required to show proof of vaccinations and were housed in dormitories for nine days, creating potential for infectious disease outbreaks. in response to these unique public health challenges, we describe how lac’s syndromic surveillance system (sss), which captures over 65% of all emergency department (ed) visits, was used to detect potential emerging health events congruent with so games and pre-game events. methods the sss team queried ed visit databases for key terms within chief complaint, triage notes and diagnosis fields during the surveillance period of july 20 to august 7, 2015. in addition to monitoring events of particular interest in the so setting such as category a bioterrorism agents, meningitis and measles, one additional query was created to identify so attendees. in order to increase response rates, we requested that ed staff proactively tag within an so attendee’s chief complaint record the phrase “special olympics.” upon request, we worked with hospital staff to pinpoint where in the ed registration software keywords could be entered for capture within the syndromic feed. line lists were reviewed daily. for syndromes with common or multifactorial symptoms such as gastrointestinal, rash, neurological, respiratory, heat, syncope and seizure, we performed 120 day trend analyses. we focused the analyses geographically by determining the eds that served the most patients in the five regions where dormitories and competition venues were located. we calculated signal thresholds using modified cumulative sum algorithms. all surveillance results were created using sas and summarized in a customized so syndromic surveillance report generated in microsoft word, consisting of manually reported short synopses of case detection results as well as graphs and signal summary tables that automatically updated as source files were output by sas. results the so syndromic surveillance report was created daily for a three week time period (one week before, during and after the so). no ed visits due to measles or category a bioterrorism agents were detected during the surveillance period. there were 37 visits with mention of meningitis, however they were unrelated to the so. four hospitals were able to provide screenshots and test patient validation, from which 16 so labeled ed patients were detected. of these, two had symptoms consistent with possible gastrointestinal illness, and the rest had physical injury or symptoms such as syncope, seizure and chest pain with nonspecific causes. of syndromes that we monitored for regional trends, there were cumulatively eleven signals over three regions. none were sustained for over one day, and counts were close to thresholds and comparable to intermittent peaks in the past 120 days. conclusions although the sss did not detect increases in any syndromes routinely monitored nor in overall ed visits per hospital during the surveillance period, the customized report was a useful tool for summarizing surveillance results for multi-day special events. relatively low response rates for so tagged ed visits may be explained by patients visiting non-syndromic participating eds, or athletes receiving medical care through so organized poly clinics and medical stations. hospitals may also have had experienced barriers to tagging ed records such as the lack of free text based chief complaint data fields or not having enough implementation time in the approximately two weeks given. others may have tagged patient records but used data fields not captured by the syndromic feed. in the future, we would select fewer but strategically important hospitals to enable more time with each ed to determine eligibility, validate test patients and provide instruction specific to their systems. in the case of the so, this would have meant soliciting only hospitals closest to the competition venues and dormitories. although time consuming to implement, proactive tagging of keywords will increase the capture of data specific to special event monitoring. keywords emergency department; mass gathering; case detection; trend analysis acknowledgments many thanks to acdc’s hospital outreach unit nurses for their assistance with hospital outreach in this project and beyond.. *emily kajita e-mail: ekajita@ph.lacounty.gov 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parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using sages openessence for mass gathering events damian hoy2, alize mercier2, paul white2, salanieta t. saketa2, adam roth2, yvan souares2, christelle lepers2, richard wojcik*1, aaron katz1, timothy campbell1, shraddha patel1, brian feighner1 and sheri lewis1 1jhu/apl, laurel, md, usa; 2spc int, suva, fiji objective present how a surveillance tool such as sages was used for disease surveillance for mass gathering activities. introduction the sages (suite for automated global electronic biosurveillance) team at the john hopkins university applied physics laboratory was approached by the public health division of the research, evidence and information programme of the secretariat of the pacific community (spc) to explore the feasibility of using the sages disease surveillance toolkit for two mass gathering events, the 8th micronesian games held from 19-31 july in pohnpei federated states of micronesia and the 3rd international conference on small island developing states (sids) held from 1-4 september 2014 in apia samoa. mass gatherings are congregations of large numbers of people in a specific location(s) for a defined period of time such as major sporting events, concerts and festivals. a downside of these gatherings is the potential for communicable/infectious diseases to spread efficiently and rapidly. infected individuals may subsequently return home and disseminate these infections in their local populations. the pacific syndromic surveillance system commenced in 2010. enhanced syndromic surveillance is increasingly being used in mass gatherings. this involves inclusion of more syndromes and more regular reporting than routine syndromic surveillance. while enhanced syndromic surveillance is an important mechanism at mass gatherings, also, and somewhat opportunistically, mass gatherings can provide a unique chance to initiate or strengthen existing surveillance systems. methods sages is a collection of modular, open source software tools designed to meet the challenges of electronic disease surveillance in resource-limited settings. sages uses mobile and web-based methods to collect structured data from sms, wi-fi, and internet connected devices. the primary data analysis tool, openessence (oe), provides a web-based interface with data analysis, visualization, and reporting. these tools may be used in concert with existing surveillance applications or be used en masse for an end-to-end biosurveillance capability. sages oe can augment enhanced syndromic surveillance by providing a web-based data entry and analysis environment, which enables multiple data entry points, and more timely processing of data. which, ultimately, enables the preparation of daily situation reports during and around the mass gathering. for the games, oe was hosted remotely because of a very short deployment and testing window. it also afforded the sages and spc teams access to the web application to customize data entry forms and visualizations. for this event, the spc explored the feasibility of wireless tablet data entry at the point of care event aide stations. however, due to connectivity coverage issues at several remote sites, paper forms were used instead for all locations. the forms were collected and the data entered daily into the oe web app at a central location. following the games, a request to deploy oe for the sids conference was made. the information collected was similar to the games and required minimal changes to the entry forms to reflect the conference’s reportable conditions and point of care locations. however, for this event, the samoan ministry of health decided to host an oe web site on their it infrastructure. results the sages oe tool was used for disease surveillance in two different mass gathering events. oe was adapted in less than a couple of weeks to fit the needs of the games and the conference. visualizations used during the games and for daily sitrap reports and can be found at the following url http://www.spc.int/phs/pphsn/ surveillance/mass_gathering.htm similar visualizations are planned for the conference. oe was generally very well received and provided a highly useful tool for assisting with detecting and containing the spread of communicable/infectious diseases. sustainable improvements with syndromic surveillance systems are likely with longer lead (6 months) in trial periods. conclusions the sages electronic disease surveillance tools are designed with an eye toward ease of installation and use, with an ultimate goal of creating a self-sustaining sages users community. we believe that tools such as sages tools can be rapidly deployed (although longer lead in times are preferable) to monitor mass gathering events as well as facilitate local and regional electronic disease surveillance and increased who ihr 2005 compliance. keywords mass gatherings; disease surveillance; biosurveillance acknowledgments federated states of micronesia, pohnpei department of health services samoa ministry of health *richard wojcik e-mail: richard.wojcik@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e134, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts standardising syndromic classification in animal health data fernanda c. dórea*1, céline dupuy2, flavie vial3, crawford revie4 and ann lindberg1 1department of epidemiology and disease control, national veterinary institute, uppsala, sweden; 2french agency for food, environmental and occupational health & safety (anses), lyon, france; 3veterinary public health institute, bern, switzerland; 4centre for veterinary epidemiological research, charlottetown, pe, canada objective to develop an ontology for the classification of animal health data into syndromes with application to syndromic surveillance. introduction veterinary syndromic surveillance (vss) is a fast growing field, but development has been limited by the limited use of standards in recording animal health events and thus their categorization into syndromes. the adoption of syndromic classification standards would allow comparability of outputs from systems using a variety of animal health data sources (clinical data, laboratory tests, slaughterhouse records, rendering plants data, etc), in addition to improving the ability to compare outputs among countries. the project “standardising syndromic classification in animal health data” (ssyncahd) aims to standardize the classification of animal health records into syndromes. methods ssyncahd will make use of current technologies from information management, which aim to promote intelligent access to data. in particular, ssyncahd will be informed by the latest thinking relating to the semantic web, which supports the development of frameworks to maximize the potential for data sharing and reuse. the use of an ontology allows the knowledge base used for syndromic classification to be structured into a formal representation. formally, “an ontology defines a common vocabulary for researchers who need to share information in a domain. it includes machineinterpretable definitions of basic concepts in the domain and relations among them” [1]. the use of ontologies in human syndromic surveillance has been previously explored by conway and collaborators [2]. in animal health surveillance, further challenges are posed by the number of species monitored, the variety of data sources explored, and the lack of homogeneity in the syndromes defined in different systems. in order to investigate the potential for developing an ontology for animal health syndromic classification, a workshop was held in may 2014, gathering interested experts with experience in veterinary syndromic surveillance and/or animal health data recording. results the results of the workshop indicated strong interest in the field. it was proposed that development should be based on one syndrome at a time, and be data-driven; that is, collaborators would grow the ontology, for each syndrome, based on an analysis of the data sources available to them. the syndromes “reproductive” and “respiratory” were chosen as a starting point. the data-driven development will be intercalated with rounds of expert elicitation. workshops and digital elicitation will serve to resolve conflicts, take decisions when needed, and ensure completeness of the concepts included in the ontology. the project will also promote the use of existing controlled vocabularies and classification standards in animal health. a follow-up workshop is planned for march 2015 in connection with the annual conference of the society for veterinary epidemiology and preventive medicine. the development work to be carried out prior to the 2015 workshop will consist of: an inventory of veterinary surveillance systems and the syndrome definitions used; an inventory of the use of ontologies in health surveillance; and development of a template of collaboration for the data-driven steps in ontology construction. conclusions ssyncahd proposes to harmonise syndromic surveillance data use rather than data recording. this will be achieved by standardising the classification of records into syndromes. potential advantages include: an ability to achieve syndromic classification from different sources of data which are recorded using an institution’s own vocabulary; the ability to compare vss system outputs within and between countries; and the more timely development of vss systems. keywords animal health; syndromic surveillance; ontology references [1] ontology development 101: a guide to creating your first ontology 2001. available at http://protege.stanford.edu/publications/ontology_ development/ontology101.pdf. [2] conway m, dowling j, chapman w. developing an application ontology for mining free text clinical reports: the extended syndromic surveillance ontology. in: 3rd international workshop on health document text mining and information analysis (louhi 2011); 2011. p. 75–82. *fernanda c. dórea e-mail: fernanda.dorea@sva.se online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e123, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts establishing an epidemiosurveillance centre in a resource-constrained setting: a zambian experience raymond hamoonga*1, 2, nadi songolo1, 2 and chimuka musako2 1national livestock epidemiological and information centre (naleic), lusaka, zambia; 2department of veterinary and livestock development, ministry of agriculture and livestock, mansa, zambia objective this presentation outlines the steps and challenges involved in setting up a regional epidemio-surveillance centre in a resource constrained setting. while this zambian experience is unique, the challenges encountered are typical of most developing countries and therefore the lessons learned can be applied to much of the developing world. introduction livestock diseases in most developing countries are often widely distributed. the wide distribution of diseases often renders whole countries ineligible to participate in international trade of meat and meat products 1. these trade restrictions serve as a continuous primary source of revenue loss. the world organisation for animal health (oie) now promotes establishment of disease free zones to lessen the impact of trade restrictions. these “islands” of disease freedom not only serve as a first step in total disease freedom, but for many countries they also serve as a beacon of hope to ever returning to international trade. the selection of a region within a country to be designated as a disease free zone is likely to be based more on the fact that a particular region is yet to experience cases of a given disease than it is on the nation’s veterinary department ability to keep the region disease free. as such, geographical regions that seem to have inherent protection against disease incursions usually due to geospatial features such as mountains, rivers, remoteness etc. serve as the best candidates for creation of disease free zones. because the process of disease free zone creation is slow, it is important to ensure that disease surveillance is these regions is enhanced so that disease control authorities may serve as agile responders to disease incursions. this current presentation outlines the creation of a provincial epidemiological and information centre (peic) in zambia’s luapula province. this is only the second epidemiosurveillance centre in the country. luapula province in the northern part of zambia being one of only 3 provinces out of a total of 10 provinces that are free of theilleriosis in zambia 2 has the potential of being zambia’s largest disease free zone. the challenges as well as lessons learnt from setting up this epidemiosurveillance centre are presented methods the first task was to create a reliable data management system capable of providing data on disease reports received in the past as well as those that were yet to be received. the second task was to train district staffs in basic epidemiological measures of disease association and measures of health. the recently completed national livestock census provided a basis for establishing base species specific population at risk figures. this enabled the staff to take into account the population at risk when reporting disease occurrence thereby making interpretation of disease incidence reports more meaningful. the third task was to incorporate gis and spatial epidemiology in disease surveillance. currently, basic epidemiology formulae are being incorporated into the disease reporting spreadsheets to enable district staff to calculate measures of health and disease association. results disease surveillance has now been enhanced in the province. using microsoft excel, a database has been created and is still undergoing improvement. convention of veterinary camps to gis point data is underway. district staffs continue to receive training when resources are available. the long term plan is to have these trainings conducted on a regular basis. conclusions the luapula province epidemiosurveillance centre was successfully set up. however, challenges continue to be present and are mainly in the form of lack of funding to acquire modern and better data management systems. similarly, resources to conduct trainings of end users who serve as primary data entry officers are lacking. keywords surveillance; epidemiology; zambia; developing country acknowledgments the authors wish to thank the zambian national livestock epidemiological and information centre (naleic) directorate and the ministry of agriculture and livestock for availing the data presented here references 1. hamoonga r, stevenson ma, allepuz a, carpenter te, sinkala y. risk factors for foot-and-mouth disease in zambia, 1981–2012. preventive veterinary medicine. 2014;114(1):64-71. 2. makungu c, mwacalimba kk. a quantitative risk assessment of bovine theileriosis entering luapula province from central province in zambia via live cattle imports from traditional and commercial production sectors. preventive veterinary medicine. 2014;116(1– 2):63-74. *raymond hamoonga e-mail: geminivet@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e192, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts influenza surveillance in mozambique: results and challenges from the first year of implementation almiro r. tivane*, noorbebi adamo, neuza nguenha, sadia a. ali, edma d. dimbane, diocreciano m. bero, jorfélia j. chilaúle, gabriela d. pinto and tufaria mussa virus isolation laboratory, national institute of health, maputo, mozambique objective analyse challenges of the first year of surveillance implementation in mozambique, according to samples income, hospital staff performance and available tools compare two influenza surveillance approaches introduction in mozambique about 10% of deaths in children are due to ari 1. although influenza (flu) virus may be implicated in these infections, little is known about the circulation of this virus in the country. thus, mozambique implemented the influenza surveillance based on sentinel sites, facing a great challenge due to several factors. one of them is the proper influenza case definition along with others challenges since its international standardization is difficult. in order to get insights to the epidemiology of flu we reviewed the first year of surveillance implementation monitoring data to improve procedures methods three hospitals in maputo (four sentinel sites) were selected and trained for flu surveillance. initially, all patients meeting 2012 world health organization (who) case definitions for ili and sari2 were eligible. this approach was used during 40 epidemiological weeks (27/2013 to 13/2014). a systematic monitoring of each site was performed to evaluate the inputs. the flow and health staff (their perception and respective roles and commitment) at sentinel site level, the available tools, the case definitions criteria and adequate sentinel sites were reviewed. thus, other approach had been implemented from 20/2014 switching to sari cases (in the same hospitals) defining as eligible all patients admitted or in medical observation presenting any respiratory infection/disease symptom (not preditive for, with onset within 10 days. naso and oropharingeal swabs were collected and sent to the national institute of health (nhi) for testing using one-step real time rt-pcr. all specimen data were entered in an access database and the laboratory results were timely reported to the sentinel sites and to who results using the first approach, the laboratory received and tested 91 specimens from four sites (two specimens/week vs 56 specimens expected), of which 47.3% were from children under 5 years-old. the influenza virus was detected in 16/91 (17.58%) specimens, in which 10/16 (62.5%) were flu a(h3) and 8/16 (50%) were flu b. a codetection of flu a(h3) and flu b was observed in 12.5% (2/16) from patients of two and 23 years. most flu cases were adults (15-50 years) (56.3%). about 60% (57/91) of all the cases and 50% (8/16) of the flu positive cases met all the 2012 who case definition. with the second approach, the specimens were collected from three sentinel and the number of specimens increased considerably (nine specimens/week versus 45 specimens). a total of 148 specimens were received within 15 weeks (20 34/2014). more than 60% of the specimens were collected from children under five years-old. only one specimen was positive for flu a. with this new approach, less than 40% of the cases met the 2012 who flu case definition. individual perception, high turnover, motivation and commitment of the hospital staff, adequate staff (nurse/clinicians) and work overload influenced both approaches conclusions although flu surveillance is at very early stage, these findings corroborate with previous researches reporting the circulation of flu virus in mozambique. the first approach seemed to be more specific for influenza virus and should be appropriate for influenza surveillance and the second approach has contributed to increase the number of specimens. however, it seem to have reduced the specifity for influenza and this strategy may have become costly, since the routine testing is only for flu. additionally, other respiratory virus should be considered. it is still a big challenge to find out adequate methods, case definition and stuff with awareness and commitment with ari surveillance in mozambique, thus further analysis are necessary keywords surveillance system; sari; awarness/commitment; influenza; specificity/sensivity acknowledgments the authors are grateful to the cdc-atlanta, nicd (south africa) and to the who for their input in the design of the surveillance protocol and material support. acknowledgement is also extended to the ministry of health including the health centres where the activity is being implemented references 1. national institute of health (mz). national study about infant mortality – summary. press. maputo (mozambique). 2009. portuguese. 2. world health organization. who interim global epidemiological surveillance standards for influenza. press. geneva (switzerland). who press. 2012 july. english. *almiro r. tivane e-mail: artivane@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e166, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts epidemic situation in ukraine related to the quality of drinking water iryna rudenko* department of european integration of the state sanitary epidemiological service of ukraine, state sanitary and epidemiological service of ukraine, kiev, ukraine introduction the world health organization (who) puts a great emphasis on the study of diseases related to using or consuming poor-quality water and the absence of proper hygiene. unfortunately, consumption of poor-quality water causes certain diseases in ukraine. methods the data of the official reports for 1997-2014 were used as the information basis for our study. we used theoretical and statistical methods of analysis. the purpose of the study was to obtain summary data regarding the outbreaks that occurred as a result of water consumption, to evaluate their scope, and the main causes of disease. results 70 outbreaks of severe enteric infections were registered among the population in 19 administrative territories in ukraine over the past 17 years. the source of infection was poor-quality water. a total of 8265 people acquired an infection, 4140 of them were children. clinical manifestations of disease in the outbreaks can be divided into: viral hepatitis – 33 outbreaks (2947 people infected), dysentery – 18 outbreaks (1217 people infected), typhoid – 9 outbreaks (189 people infected), enteroviruses – 4 outbreaks (457 people infected), rotaviruses – 3 outbreaks (3353 people infected), opportunistic pathogenic microflora – 2 outbreaks (70 people infected) and yersiniosis – 1 outbreak (32 people infected). most patients (80%) had clinical manifestations of gastroenteritis of rotavirus etiology. it was verified by clinical and viral data. the age group of the disease included children of 1-6 years old (60% specific gravity). the largest number of sick persons was registered in 2001during the outbreak of rotavirus infection in odesa. 3143 people were infected, 2277of them were children. conclusions most outbreaks were related to the piped water contaminated by rotaviruses, as a result of emergencies in the water supply and sanitation systems. the poor condition of the water supply in ukraine is caused by the following factors: contamination of water supply reservoirs by untreated sewage; non-observance or absence of sanitary control zones of the water supply reservoirs; usage of old and ineffective techniques for water purification and decontamination; poor technical conditions of water supply plants and systems; and cutting-off water supply plants from the power supply system. the absence of the power for a long time allows bacterial contamination of the water in water supply systems. keywords quality of drinking water; infectious disease; outbreak *iryna rudenko e-mail: dok7tor@ukr.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e157, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives olusesan ayodeji makinde1,2 , aderemi azeez3, wura adebayo3 1. viable knowledge masters, abuja, nigeria 2. demography and population studies program, schools of public health and social sciences, university of the witwatersrand, johannesburg, south africa 3. department of health planning, research and statistics, federal ministry of health, abuja, nigeria abstract background: master facility lists (mfl) maintain an important standard (unique identifier) in country health information systems that will aid integration and interoperability of multiple health facility based data sources. however, this standard is not readily available in several low and middle income countries where reliable data is most needed for efficient planning. the world health organization in 2012 drew up guidelines for the creation of mfls in countries but this guideline still requires domestication and process modeling for each country adopting it. nigeria in 2013 published a paper-based mfl directory which it hopes to migrate to an electronic mfl registry for use across the country. objective: to identify the use cases of importance in the development of an electronic health facility registry to manage the mfl compiled in nigeria. methods: potential use cases for the health facility registry were identified through consultations with key informants at the federal ministry of health. these will serve as input into an electronic mfl registry development effort. results: the use cases identified include: new health facility is created, update of status of health facility, close-out, relocation, new information available, delete and management of multi-branch health facility. conclusion: development of an application for the management of mfls requires proper architectural analysis of the manifestations that can befall a health facility through its lifecycle. a mfl electronic registry will be invaluable to manage health facility data and will aid the integration and interoperability of health facility information systems. keywords: health facilities; health information exchange; master facility list; pubic health informatics; registries; standards correspondence: emailsesmak@gmail.com doi: 10.5210/ojphi.v8i2.6350 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:sesmak@gmail.com potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi introduction health information systems (his) have been widely described as the foundation of public health, responsible for driving evidence-based decisions (1). however, their ability to drive the health system has been sub-optimal in several countries (2–4). this deficiency has been more prominent in low and middle income countries (lmic) where evidence-based resource allocation is most needed but reliable data is hardly available. the unavailability of reliable health data arises as a result of lack of processes and systems, poor human resource capacity and the huge cost attached to data management (3,5,6). also, the structure of paper-based his that characterize health systems in lmic are inefficient (5). to address these shortfalls, several developing countries have begun deploying electronic applications for the management of their routine health data (7). however, these deployments are challenged by the unavailability of standards that will facilitate data exchange (8,9). one major benefit of health information technology (it) is the ability to gather data on a single health facility from multiple points of generation and use these data for multi-level decision making (10). unfortunately, this is only possible when these information systems can be linked and exchange data. identification of health facilities across multiple information systems can pose a big challenge to the success of this endeavor (10). this arises as health facilities can change names or there could be more than one health facility with the same name, thus making the name of the health facility undesirable as a unique identifier across systems, a necessity for integration and interoperability of different information systems which house health facility data. several other technical, motivational, economic, political ethical and legal barriers have been implicated as contributing to the inability of his to exchange data (11,12). to address the identity challenge for health facilities, the world health organization (who) in 2012, developed guidelines for the creation of master facility lists (mfl) for countries (13). according to who, “a mfl is a complete listing of health facilities in a country (both public and private) and is comprised of a set of identification items for each facility (signature domain) and basic information on the service capacity of each facility (service domain)” (13). the country mfls will serve as a repository for the allocation and maintenance of unique identifiers, the standard that will facilitate linking of health facility data sources thereby aiding integration and interoperability of these systems (14). since health facilities are continuously built and some close out, the processes to update the mfl are a necessary step in making the mfl continuously relevant and useful. in an earlier paper from nigeria, absence of processes and an information system to manage the mfl were identified as major limitations which threatened the success and usefulness of the mfl compiled (10). handling this gap requires extensive analysis of the actions that can befall a health facility in the mfl over time and setting up a detailed registry to respond to all the scenarios. the possibility of implementing a mfl management registry via paper-based processes is complex and it is not guaranteed that it will be achievable when there are several players involved across large geographic areas. this has affected the ability of the mfl developed in nigeria to be kept up-to-date since established. in addition, since routine health information systems (rhis) are being moved to electronic platforms (10), it will be cumbersome to ensure that paper based steps are continuously synchronized with applications using the mfl products electronically. the chance for moving the management of the mfl to an electronic registry necessitates detailed planning to ensure its success. potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi in this paper, we describe the potential scenarios that can befall a health facility within a mfl which will serve as inputs when developing an electronic registry to manage the mfl. our focus is on nigeria where this planning activity took place. methods in 2013, nigeria published a mfl booklet directory which allocated unique identifiers to all the health facilities within the country (15). the process for achieving this and the parameters captured in the mfl have been described by the federal ministry of health (fmoh) and partners elsewhere (10). nigeria is a federation and the governance structure in the country is three tiered: the federal government is the national government and gives policy directions, the state governments (36 states and the federal capital territory) oversee affairs at the states and localize and implement policies at a lower level, while the local government is the closest level of governance to the people and implements activities and policies at a lower level than the states. the compilation of the mfl had been carried out by the fmoh working with the states to collate the data on all the hospitals and clinics in their various domains to come up with the national mfl following the allocation of a unique identifier to each health facility. health facility registration is carried out at the state ministries of health and thus, 37 different registries are required to manage the national health facility records. the parameters captured in this directory include the name of the health facility, the state of location, the local government area of location within the state, the ward of location within the local government, the ownership of the health facility (private or public), the level of care provided (primary, secondary or tertiary) and a unique provider identifier. this unique identifier was generated through an intelligent coding system that concatenates values allocated based on the described parameters. consultations were held with key personnel in the fmoh in nigeria between 2013 and 2014 to document the potential scenarios that can befall a health facility through its life cycle. this information is important for planning when developing an electronic registry application to manage the mfl. these key personnel were the people who initiated the compilation of the mfl earlier, and were the most knowledgeable about the national mfl within the country. the consultations were both one-on-one and in groups to jointly outline the important processes necessary for an electronic health facility registry. the group discussions served as an opportunity to validate and agree on the most important first steps in developing the system. documentation was completed by the lead author during these consultative sessions. the identified pathways were presented and further discussed in follow up sessions before a list of processes was arrived at. these scenarios have not been scored or arranged by any level of importance. potential use cases/ results the scenarios presented in this paper are a first step which though, not believed to be exhaustive, are needed to be planned for in the development and roll out of an electronic registry for the management of the mfl. this can subsequently be built upon as new scenarios which were initially unplanned for emerge as the application is put to use. as a base, the mfl compiled will be uploaded into the system and will be the starting point for any follow up updates. potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi the identified scenarios are new health facility creation, update information on an already listed health facility/ change of status, close out, relocation of health facility, additional information available, delete a health facility and management of multi-branch health facility. these are further elaborated upon in the next section. new health facility continuously, health facilities are being established in the country and once established need to be issued unique identifiers and listed in the mfl. also, other necessary information on the health facility needs to be captured and archived. the registry must be able to accept the creation of a new health facility in the system and maintain a unique identifier for the facility. though not yet appropriately defined, the need for this step to be completed by an accreditation agency that has certified the health facility, properly equipped and ready to provide services in a specific category (primary, secondary or tertiary) was identified. a process must be developed to facilitate information transfer from the accreditation agency to the mfl managers or a portal which allows this accreditation agency to log this information directly into the electronic registry. the information stored should include a date of approval and the authority that granted the health facility an operational right. this is particularly important if there are multiple organizations that can grant operational rights within the country. update information on a health facility/ change of status a health facility may be upgraded from primary to secondary, secondary to tertiary or downgraded if the criteria for accreditation change or the health facility fails to meet the status for accreditation during a re-evaluation exercise. this re-evaluation exercise might need to be conducted at a specified interval to continuously check that health facilities are maintaining agreed standards and to assure the quality of care provided. also, the ownership of a health facility can change from private to public or vice-versa, if it is bought over or bequeathed by a former private owner to the government. the mfl registry must permit the change of status of a health facility and must be able to provide the status by any date queried in case the status has changed. this must be factored into the design of the mfl registry application. close out health facilities can close out for various reasons. for example, a one-man practice may have to shut down if the proprietor dies and there is no one else licensed to maintain the health facility accreditation. however, the details of the health facility must remain archived including when the close-out status was achieved and possibly, the circumstances that led to this close-out. in this situation, the system must be designed such that the health facility does not contribute any further, to the statistics of health facilities while its information is still retrievable on a need basis. the status of health facilities (active or dormant) might be determined during an annual licensing routine or when evaluators visit the health facility for quality monitoring. thus, the process for which information from these follow up assessments feed into the mfl registry should be properly established. potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi relocation of health facility in nigeria, the standard process for the generation of a unique health facility identifier incorporates the local government area and state of location of the health facility (10,15). thus, if a health facility moves to a new local government area or state, it nullifies the existing unique identifier and a new one must be allotted. if the relocation is still within the same local government, the process for updating the location information should be properly designed into the system with comments on the reason for the change. in this situation, the health facility retains its unique identifier. additional information available at the baseline of the collation of a mfl, it is unlikely that all the important data on each health facility will be available. this is particularly important in nigeria as the compilation of the mfl recently completed had fewer than 10 parameters available for each health facility as previously described (10). as such, the mfl registry should be scalable and there should be opportunity to continuously add data on each health facility so that the records can be built up over time. several health facility assessments are conducted in nigeria for various reasons and by different parties in the country and these efforts can be leveraged to improve the completeness of the data in the national mfl. delete a health facility deleting will be a restricted action which would be sanctioned only when there has been an approval for this process to move ahead by the authorities that govern the management of the mfl. situations for which the deletion of a health facility can be sanctioned will include when a health facility has been wrongly created in the system and there is no associated data to the health facility or the data has been migrated elsewhere. in this case, the system should still maintain a deleted files log that can be retrieved. multi-branch health facility health facilities may operate in more than one physical location and this raises issues on the management of these affiliated health facilities in the mfl registry. each site will need to be accredited independently and granted an independent unique identifier in the mfl. nigeria uses an intelligent coding system in the generation of the unique identifier which carries some information on where the health facility is located. attempting to use the same unique identifier for more than one health facility site will result in an error of attribution in the system. as such, health facilities affiliated with a parent will be treated as independent health facilities and will need to be issued independent unique identifiers. while handling multi-facility identification is important, this is not a specific process that needs to be programmed into the system but is an important governance issue that was repeatedly echoed during the consultative meetings. potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi discussion the continued usefulness of a mfl requires that it is updated and used in the knowledge generation process. the mfl registry will maintain the unique identifier of health facilities which is a technical standard in enhancing integration and interoperability of his, unavailability of which can limit the gains of a country his. it is a necessary standard for health information exchanges to function and link different health facility based data sources that are useful in health planning. the interconnection of these different data sources is a significant first step to routine data use for public health surveillance (16). a recent systematic review identified poor availability and use of technical standards as one of the barriers to data sharing across the world (11). metadata and standards are lifelines that can always help to achieve information system continuity. their absence or unreliability can be a limitation to the success of integration and interoperability of sub-systems. thus, the mfl registry as the harbor of a technical standard will be a major hub for linking multiple health facility data sources in nigeria as the country continues to adopt it in the management of health facility data. recognizing the complexity of mfl management and the importance of keeping an up-to-date mfl registry for developing countries, two prominent international initiatives (facility registry and the open health information exchange) have been launched which intend providing open source applications for managing health facility registries and enhancing the exchange of health data between information systems that house health facility data (17,18). however, these efforts need to be fed with the processes that are important for managing mfls. most mfls in different countries will have some basic similar processes. however, there will be some specific considerations that are necessary for different countries which need to be customized to suit the country’s specific needs. since the status of a health facility can change over time, the system must be able to store the information longitudinally. this will facilitate the ease of determining the status of a health facility at any point retrospectively. to ascertain that the status of a health facility is always up to date in the mfl electronic registry, it might be necessary to periodically assess the status of health facilities in a local government or state. an opportunity to achieve this can be through an annual license or recertification exercise. this, besides making the mfl reliable, will further ensure an accurate denominator statistic when the number of active health facilities is required for calculating routine health indicators. the responsibility to carry out these routine assessments must be incorporated in the health facility accreditation organization or unit in the country, with the outcome of the assessment fed to the registry. the recently assented nigerian national health act of 2015 in part ii (health establishments and technologies), sections 12-19 further provides legal credence for the better coordination of health facility registries and accreditation organizations in states (19). this section of the act necessitates proper classification of all health establishments, with definition of their role within the national health system along with their installed capacity. based on an assessment, a certificate of establishment will be issued to the health facility that will specify the category of services for which the establishment is licensed to operate. in event that the establishment is interested in scaling services, a reevaluation will be necessary based on a new set of criteria. this section of the act also prescribes penalties for defaulters. as this new national health act begins potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi to be implemented, the role of the mfl registry will become of greater importance for managing these important data and providing the knowledge to the government and the public. beside the processes in the information system, the human steps for managing the data generation process are equally important. this will include outlining the organizations involved and detailed step by step processes to ensure quality is maintained at each registration point which are in nigeria’s 36 states and the federal capital territory. limitations with the rise in the use of computers and information systems in healthcare delivery and the concomitant expansion of global health indicators, there has been undue emphasis on hardware and software in health information systems without similar effort on the people and processes that will make the systems work effectively (20). this is a major threat to the success of the mfl registry development endeavor in nigeria and thus there is an increased need to educate and advocate to major stakeholders and decision makers on the need to address the his holistically including the establishment and empowerment of governance systems. furthermore, the procedures identified herein are not exhaustive and will require additional investigation and scale up as the application is put to use. the challenge of sustainable funding for health information exchanges (hies) which has been identified as a major threat to the sustainability of hies in developed countries is also a threat in developing countries (12). as such, models that will provide for sustainable financing of the systems should be considered as developing countries continue to adopt applications that will facilitate integration and interoperability of their his. conclusion the mfl registry is an important platform for managing and maintaining the unique identifier for health facilities, a necessary standard that will aid the integration and interoperability of several health facility data sources. development of an application to manage the mfl must take into consideration several potential scenarios that can befall a health facility through its life course. the mfl registry will provide an appropriate platform for managing the pronouncements of the nigerian national health act of 2015 on health establishments. the mfl registry to be developed should be scalable to capture new use cases as new requirements emerge. acknowledgements the efforts of the staff of the monitoring and evaluation division of the federal ministry of health and other participants during the consultations are appreciated. conflicts of interest oam worked for measure evaluation between april 2013 and february 2015 which received funding from the united states agency for international development to support the government of nigeria on health information systems strengthening. potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e191, 2016 ojphi references 1. abouzahr c, boerma t. health information systems: the foundations of public health. bull world health organ [internet]. 2005 aug [cited 2013 nov 30];83(8):578–83. available from: http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2626318/ 2. amouzou a, kachaka w, banda b, chimzimu m, hill k, et al. monitoring child survival in “real time” using routine health facility records: results from malawi. trop med int health [internet]. 2013 oct [cited 2013 dec 2];18(10):1231–9. available from: http://www.ncbi.nlm.nih.gov/pmc/articles/pmc3787785/ 3. aqil a, lippeveld t, hozumi d. 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community wiki [internet]. [cited 2014 nov 13]. available from: https://wiki.ohie.org/display/documents/home 18. facility registry api [internet]. [cited 2014 nov 13]. available from: http://facilityregistry.org/#about 19. federal government of nigeria. national health act. federal government of nigeria; 2015. 20. thomas jc, silvestre e, salentine s, reynolds h, smith j. 2016. what systems are essential to achieving the sustainable development goals and what will it take to marshal them? health policy plan. pubmed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27296063&dopt=abstract potential use cases for the development of an electronic health facility registry in nigeria: key informant’s perspectives introduction methods potential use cases/ results new health facility update information on a health facility/ change of status close out relocation of health facility additional information available delete a health facility multi-branch health facility discussion limitations conclusion acknowledgements conflicts of interest references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the application of a novel statistical method for syndromic surveillance in england roger morbey1, helen hughes*1, alex elliot1, neville verlander2, nick andrews2, andre charlett2 and gillian smith1 1public health england, birmingham, united kingdom; 2public health england, london, united kingdom objective this paper describes the design and application of a new statistical method for real-time syndromic surveillance, used by public health england. introduction syndromic surveillance is the real-time collection and interpretation of data to allow the early identification of public health threats and their impact, enabling public health action.1 statistical methods are used in syndromic surveillance to identify when the activity of indicator ‘signals’ have significantly increased. a wide range of techniques have been applied to syndromic data internationally. as part of the preparation for the 2012 olympics public health england expanded its syndromic surveillance service.2 as new syndromic systems were introduced, statistical methods were developed and applied for each system, tailored to the particular system challenges at the time, e.g. a lack of historical data, and regular changes to geographical coverage.3 methods the ‘rising activity, multi-level mixed effects, indicator emphasis’ (rammie) statistical method was developed in spring 2013 and tested alongside existing methods before being applied to telehealth, ed and general practitioner (gp) syndromic surveillance systems. in august 2013 the method began to be used as the sole method for these systems and was later extended to a new gp ‘in hours’ system. in october 2013 the rammie method was applied to a new national telehealth service available across england, nhs 111. results separate models were created for each syndromic indicator within each system because the seasonal pattern across the year and even across the week varied between indicators. a poisson or a negative binominal model was used because the data was in the form of ‘counts’; a total count was used as an offset to allow for changes in coverage and data volume. a poisson model was used for the multi-level sub-national models as it was computationally quicker to converge, whilst the negative binominal model was preferred at the national level because it allows for potential over-dispersion. the method has been applied successfully to syndromic systems in england providing realistic models for baseline activity and utilising prioritisation rules to ensure a manageable number of ‘alarms’ each day. the method is shown here to have a high sensitivity (92%) and specificity (99%) compared to previous methods, whilst halving the time taken to detect increased activity to 1.3 days. conclusions the rammie method has proved to be a reliable, effective method for generating automated alarms for syndromic surveillance. the multi-level models have enabled local models to be created for the first time across all systems and these have proved themselves to be robust across all the signals. the method is able to cope with the wide range of data volume and temporal cycles seen across the large number of signals. the prioritisation rules that reduce duplication by emphasising the most important signals help to keep alarms to manageable levels. new syndromic surveillance data sources have been incorporated into rammie with a minimum of extra development work. keywords syndromic surveillance; statistical methods; risk assessment; public health action references 1. triple s project. assessment of syndromic surveillance in europe. lancet 2012;378:1833-4. 2. elliot aj, morbey ra, hughes he, et al. syndromic surveillance a public health legacy of the london 2012 olympic and paralympic games. public health 2013;127:777-81. 3. morbey ra, elliot aj, charlett a, et al. development and refinement of new statistical methods for enhanced syndromic surveillance during the 2012 olympic and paralympic games. health informatics j 2014: doi 10.1177/1460458213517577. *helen hughes e-mail: helen.hughes@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e148, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts identifying congenital syphilis cases through a birth registry match elliott brannon*, jessica fridge and jeffrey hitt std/hiv program, office of public health, louisiana department of health and hospitals, new orleans, la, usa objective to identify infants perinatally exposed to syphilis in louisiana that were missed by routine surveillance activities and to ensure that all infants perinatally exposed to syphilis are investigated. introduction in 2012, louisiana’s case rate for congenital syphilis was 49.2 cases per 100,000 live births, the highest in the nation and over six times the national average1. in louisiana, case investigations for infants perinatally exposed to syphilis are initiated through two primary methods: shp may receive a positive syphilis test on an infant or a syphilis-infected woman may be contacted and identified by partner services during pregnancy. this identification process is similar to perinatal hiv surveillance in louisiana with one major exception: for perinatal hiv surveillance an annual birth match is completed. through this match women known to be hiv-infected are matched to women who gave birth during the previous year. over 90% of perinatal hiv exposures are identified prior to this match, but the match ensures that louisiana’s hiv surveillance system identifies all infants perinatally exposed to hiv. methods a syphilis birth registry match was completed for the first time in louisiana. compared to hiv, a syphilis birth match is more complex due to the fact that a woman with a history of syphilis can eliminate perinatal transmission risk with appropriate treatment whereas perinatal transmission risk is always present for an hiv-infected woman. for 2013, all women in louisiana’s std database (prism) who ever had syphilis, were linked to all women in louisiana’s vital records birth registry. the matching algorithm was locally developed and deterministic based on first name, last name, date of birth, and social security number of the mother. matches that had already been investigated as possible congenital syphilis cases were removed. matches were then divided into two groups: mothers who received treatment and mothers who did not receive treatment. shp identified useful constraints for each group to remove unproductive investigations. for mothers with treatment the constraints included the following: treatment less than 30 days before delivery (or after delivery) or late/unknown latent syphilis without appropriate treatment.2 for mothers without treatment the constraints included the following: last titer within three years and positive treponemal test and no treatment in prism. each potential case was reviewed in prism and those unlikely to become cases were not investigated (for example, women who were falsely positive for syphilis). a similar match was also completed with the stillbirth registry. results the birth/stillbirth match identified 18 potential cases of congenital syphilis. ten of these have been investigated and eight of the investigations identified cases of congenital syphilis. the remaining eight investigations are currently underway. these cases will significantly increase louisiana’s congenital syphilis case rate. conclusions the birth match prompted the investigation of many infants exposed to syphilis perinatally. many of these investigations turned out to be cases and these cases will substantially increase louisiana’s congenital syphilis case rate. the birth match also identified the need of guidelines for the initiation of congenital syphilis investigations. shp does not receive adequate resources to investigate all the potential cases and had to deprioritize investigations which were considered ‘low risk’. take for example a woman who gave birth in 2013, had late latent syphilis in 2008, was treated with one set of bicillin in 2008, and had no rise in titer during pregnancy. if investigated, this would likely have been identified as a congenital syphilis case due to the mother’s inadequate treatment. shp, however, did not investigate cases similar to this that were identified because of inadequate resources and the low risk of infection based on kassowitz’s law. although states may wish to conduct a similar birth match to ensure complete surveillance, initiation guidelines should be developed to prioritize potential cases that are productive in identifying health care systems issues. keywords congenital syphilis; birth match; registry linkage references 1. centers for disease control and prevention. 2012 sexually transmitted diseases surveillance. available at http://www.cdc.gov/std/stats12/ default.htm. retrieved july 15, 2014. 2. centers for disease control and prevention. congenital syphilis case definition. available at http://wwwn.cdc.gov/nndss/. retrieved july 15, 2014. *elliott brannon e-mail: elliott.brannon@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e110, 201 managing aboriginal and torres strait islander data for public health research. 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi managing aboriginal and torres strait islander data for public health research. d. van gaans 1, s. ahmed 1, k. d’onise 1, s. m. taylor 2, r. mcdermott 3 1. centre for research excellence in the prevention of chronic conditions in rural and remote populations, university of south australia, south australia 2. centre for chronic disease prevention, australian institute of tropical health and medicine, college of public health, medical and veterinary sciences, james cook university, queensland 3. college of public health, medical and veterinary sciences, james cook university, queensland abstract good quality data on aboriginal and torres strait islander peoples are needed to assess the effectiveness of programs and interventions, and to evaluate policies that are designed to improve the status of, and service delivery to, aboriginal and torres strait islander peoples. due to the lack of longitudinal data it is difficult to gain knowledge on the specific causes or consequences of changes in indigenous outcomes. variables such as name, date of birth and address for aboriginal and torres strait islanders may be subject to more variation and be less consistently reported than other australians. improving the collection and management of key identifying variables for aboriginal and torres strait islanders are key to providing more quality information on this population group. key words: aboriginal, research, data management, population health, informatics correspondence: deborah.vangaans@unisa.edu.au doi: 10.5210/ojphi.v8i3.7055 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the council of australian governments (coag) closing the gap commitments aim is to overcome key health disparities between indigenous and non-indigenous australians [1]. another fundamental aspect of achieving the coag commitments is the capacity to measure health gaps between indigenous and non-indigenous people, and to monitor progress in closing those gaps [1]. data or information collected and organised for analysis and interpretation, can shape, debate and guide policy decisions [2]. data can be used to identify areas and populations with the greatest need and direct resources accordingly, monitor change and show where http://ojphi.org/ mailto:deborah.vangaans@unisa.edu.au managing aboriginal and torres strait islander data for public health research. 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi outcomes are improving, stagnating or worsening. most importantly, data can be used to evaluate policies and programs for their efficacy and cost effectiveness, thereby improving individual and collective wellbeing [2]. accurate data about aboriginal and torres strait islander people are needed to guide policy formulation, program development and service delivery, as well as to monitor and evaluate the success of government and community programs in reaching the ‘closing the gap’ targets [3]. due to the lack of longitudinal information, we know little about the specific causes or consequences of changes in indigenous outcomes [2]. lee [4] states that data collected in the past have typically been inadequate to inform service planning. biddle [2] highlights a consistent lack of information and data on the specific policies and programs that will lead to sustained improvement in indigenous wellbeing or what some of the unintended consequences of particular programs might be. studies such as those by li and mcdermott [5] highlight the use and importance of linked data for indigenous public health research. li and mcdermott [5] collected baseline data from 2787 adults in 19 rural indigenous communities across three health districts in far north queensland. they linked baseline data to hospital separation data using probabilistic linkage to quantify the risk of hospitalization for infections in indigenous australian adults with diabetes. this research showed that there was an extremely high background rate of community-acquired infection plus high prevalence of type 2 diabetes among indigenous australians leads to excess hospitalization for infections [5]. population health research such as this are reliant on the ability to link individual patient records. evidence-based approaches to inform policy and program development continue to be of paramount importance [6]. the quality of indigenous status data across key health data sets can be undermined by a range of issues including misclassification, structural limitations and high proportions of ‘unknown’ indigenous status [1]. there is evidence that name, date of birth and address variables may be subject to more variation and be less consistently reported among aboriginal and torres strait islander australians than among other australians [3]. objectives: to improve the collection and management of key identifying variables (name, address, and date of birth) for aboriginal and torres strait islanders for the creation of longitudinal quality information. methods: clinical data was collected from 285 aboriginal and torres strait islanders from 1/1/2012 to 31/12/2015 as part of the project titled: “primary health care models and the prevention and management of chronic conditions in rural and remote high risk populations: a collaboration between university of south australia, the aboriginal health council of south australia, queensland aboriginal and islander health council, the royal flying doctor service and james cook university”. data was collected from aboriginal and torres strait islanders from the following islands: mabuiag island, st. pauls, murray island, boigu island, stephen island, darnley island, yorke island, saibai island, and badu island. data from the project was stored in the public health research data management system at the university of south australia (phredms) [7]. using the australian institute of health and welfare, meteor metadata online registry, national health data dictionary (nhdd) http://ojphi.org/ managing aboriginal and torres strait islander data for public health research. 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi version 16 as a guide for the project, participant’s name, date of birth, and address were captured within the phredms (table 1). table 1: format of person-name, persondate of birth, address-purpose of address from meteor metadata online registry, national health data dictionary (nhdd) version 16 [8] meteor variable name representation class data type format maximum character length permissible values person – name code string aaa 3 lgl aka bth mdn new prv prf stg trb oth legal name also known as name at birth maiden name new born identification name previous name preferred name stage name tribal name non-specific name usage type person date of birth date date/t ime mmyy yy 6 address – purpose of address code string aa[a] 3 pr sec tem bus ovs del pos oth unk primary property address secondary property address temporary accommodation business address address when overseas delivery address postal/correspondence address other address not stated/unknown http://ojphi.org/ managing aboriginal and torres strait islander data for public health research. 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi results name within the phredms the participant’s name has been managed the same way as described in the meteor metadata online registry, national health data dictionary (nhdd) version 16 [8]. the participant’s name is stored using the following structure: name_title prefix to the participants name. name_firstname the participants first name. name_middlename the participants middle or second name. name_lastname the participants surname, family or last name. name_nametype the usage type of a person’s family name that enables differentiation between each recorded name change. name type included the following: legal name, also known as, name at birth, maiden name, new born identification name, previous name, stage name, tribal name, and non-specific name usage type. for example using the first authors name from this paper she could have 3 different name types associated with her name: deborah van gaans legal name deb van gaans also known as deborah duncombe-wall maiden name using this structure for a participant’s name, the phredms has been able to manage 54 participants with two name types and 10 participants with three name types. the use of name type can be seen in figure 1, which shows that all participants had a “legal name”, 11 of those also had “also known as” name type, which consisted mainly of nicknames, or shortened versions of their “legal name”. thirty one of the participants also had a “non-specific name usage type”, which captured the variations in the spelling of their names. figure 1: number of participants with different name types (n= 285). 285 11 22 31 0 50 100 150 200 250 300 350 legal name also known as maiden name non-specific name usage type n um be r name type http://ojphi.org/ managing aboriginal and torres strait islander data for public health research. 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi date of birth within the phredms the participant’s date of birth was managed so that it captured the following: bd_birthdate date of birth of the participant bd_preferredbirthdateflag preferred date of birth bd_source the origin of the date of birth bd_source included the following: primary health care centre, pharmaceutical benefits scheme, medicare, hospital, laboratory, royal flying doctor, database derived, australian bureau of statistics, births deaths and marriages, unknown, and hospital admission. handling the date of birth with this structure has allowed multiple birth dates to be recorded for each participant, with 3 participants having a second date of birth. address within the phredms the participant’s address was managed so that it captured the following: add_line1 property name add_line2 street number add_line3 street name add_suburb the suburb and postcode add_addresstype the type of a person’s address that enables differentiation between each address change. add_addresstype included the following: mailing address, postal address, and residential address. using this address structure the phredms was able to capture the 5 participants that had two addresses. discussion the australian institute of health and welfare and bureau of statistics [9] identify that first and last names are critical linkage variables in both probabilistic and deterministic data linkage. variations in the spelling of names can affect the quality of the linkage particularly for the spelling of traditional aboriginal and torres strait islander names which may have a different structure to european-type names, with its own set of nicknames, aliases and diminutions [3]. lawrance et. al. [10] examined an aboriginal birth cohort of mobile subjects belonging to diverse cultural and language groups in the northern territory and found that aboriginal children had multiple names relating to kinship, clans and relationships with family groups, and that name changes often occurred following the death of another community member. lawrance et al. [10] found that, out of a sample of 686 aboriginal mother-child pairs living in the top end of the northern territory, by the age of four years: 30% of children had changed their names at least once, 18% had changed address once, 2% had had three different name changes, and that 2% had had four different addresses. using a data structure to be able to capture the name changes is critical if we are intending to build longitudinal datasets on aboriginal and torres strait islander populations. there is evidence that some older aboriginal and torres strait islander people, particularly those living in remote communities, have had difficulties providing date of birth information. in the northern territory, for instance, date of birth was not recorded on death registration forms until 1994; only age at death was recorded in prior years [11]. a high http://ojphi.org/ managing aboriginal and torres strait islander data for public health research. 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi proportion of older indigenous people in the northern territory do not know their exact age and have only an approximate year of birth [11]. therefore date of birth information on some data sets may be incomplete (it may only include year of birth), or be inaccurate (it may only include approximate year of birth or be approximate date of birth calculated from an approximate age), these quality issues affect the quality of data linkage [3]. by capturing multiple dates of birth and their source it may be able to improve data linkage in the future by providing multiple fields to match the participant with. according to memmott et al. [12], there is strong evidence in remote aboriginal communities of linked households or clustered households that are characterised by an extended family group dispersed across a number of places of residence. people may move between several residences and not regard themselves as having a single usual place of residence [9]. mobility also affects the stability and completeness of reporting of the address variable, thereby limiting its use as a stable variable for linkage [9]. memmott et. al. [12] found high levels of mobility among remote aboriginal and torres strait islander communities, which may result in different levels of reporting for the address variable. for instance, address may be recorded as: town, suburb, community region. consequently, in some datasets the full address may be recorded, while in others, community name may be the only address information available [3]. capturing multiple addresses and allowing for different levels of address quality appears to capture aboriginal and torres strait islander addresses in a culturally appropriate way. conclusions variables such as name, date of birth and address variables for aboriginal and torres strait islanders may be subject to more variation and be less consistently reported among aboriginal and torres strait islander australians than among other australians. improving the collection and management of key identifying variables for aboriginal and torres strait islanders are key to providing more quality information on this population group. conflict of interest the authors declare that there are no conflicts of interest. human subjects protections the data for this project forms part of the project titled : “primary health care models and the prevention and management of chronic conditions in rural and remote high risk populations: a collaboration between university of south australia, the aboriginal health council of south australia, queensland aboriginal and islander health council, the royal flying doctor service and james cook university”. ethics approval for this project was granted by the far north queensland human research ethics committee. acknowledgements the research reported in this paper is a project of the australian primary health care research institute, which is supported by a grant from the commonwealth of australia as represented by the department of health. the information and opinions contained in it do not necessarily reflect the views or policy of the australian primary health care research institute or the australian government department of health. http://ojphi.org/ managing aboriginal and torres strait islander data for public health research. 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi references 1. australian institute of health and welfare. towards better indigenous health data. cat. no. ihw 93, 2013, canberra: aihw. 2. biddle n. data about and for aboriginal and torres strait islander australians. issues paper no. 10 prepared for the closing the gap clearinghouse. australian government, australian institute of health and welfare, australian institute of family studies. july 2014 3. australian institute of health and welfare and australian bureau of statistics. national best practice guidelines for data linkage activities relating to aboriginal and torres strait islander people. aihw cat. no. ihw 74, 2012, canberra: aihw. 4. lee ksk. better methods to collect self-reported alcohol and other drug use data from aboriginal and torres strait islander australians.drug and alcohol review (september 2014), 33, 466–472 doi: 10.1111/dar.12159 5. li m. and mcdermott r. high absolute risk of severe infections among indigenous adults in rural northern australia is amplified by diabetes – a 7 year follow up study. journal of diabetes and its complications. 30 (2016) 1069-1073. 6. australian institute of health and welfare & australian bureau of statistics. recent developments in the collection of aboriginal and torres strait islander health and welfare statistics 2005. aihw cat. no. ihw 15; abs cat. no. 4704.0.55.001, 2006, canberra: aihw & abs. 7. van gaans d, d’onise k, cardone t, mcdermott r. “the development of the public health research data management system.” electronic journal of health informatics (2015) vol. 9(1): e10 8. australian institute of health and welfare, meteor metadata online registry, national health data dictionary (nhdd) version 16. accessed 25th october 2013, http://meteor.aihw.gov.au/content/index.phtml/itemid/274816 9. australian bureau of statistics. population characteristics, aboriginal and torres strait islander australians, 2006 abs cat. no. 4713.0. 2010; canberra: abs. 10. lawrance m, sayers sm, singh gr. challenges and strategies for cohort retention and data collection in an indigenous population: australian aboriginal birth cohort. bmc medical research methodology 2014; 14:31 doi:10.1186/1471-2288-14-31 11. condon j, barnes a, cunningham j, smith l. demographic characteristics http://ojphi.org/ http://meteor.aihw.gov.au/content/index.phtml/itemid/274816 managing aboriginal and torres strait islander data for public health research. 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org *8(3):e202, 2016 ojphi and trends of the northern territory indigenous population 1966 to 2001. darwin: cooperative research centre for aboriginal health, 2004 12. memmott p, long s, bell m, taylor j, brown d. between places: indigenous mobility in remote and rural australia. ahuri positioning paper no. 81, 2004; melbourne: australian housing and urban research institute, queensland research centre, rmit university. http://ojphi.org/ managing aboriginal and torres strait islander data for public health research. introduction methods: results discussion conclusions conflict of interest references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a novel method for defining health facility catchment areas in a low income country kate zinszer*2, ruth kigozi3, katia charland1, dr. grant dorsey4, dr. moses kamya5 and david buckeridge1 1mcgill university, montreal, qc, canada; 2harvard medical school, boston, ma, usa; 3uganda malaria surveillance project, kampala, uganda; 4university of california san francisco, san francisco, ca, usa; 5makerere university, kampala, uganda objective we propose a simple statistical method, the cumulative case ratio, for defining a catchment area using surveillance data. introduction the catchment area of a health-care facility is used to assess health service utilization and calculate population-based rates of disease. current approaches for catchment definition have significant limitations such as being based solely on distance from the facility or using an arbitrary threshold for inclusion. methods the catchment areas of six health-care facilities in uganda were determined using the cumulative case ratio: the ratio of the observed to expected utilization of a facility for a particular condition by patients from small administrative areas. the cumulative case ratio for malaria-related visits to these facilities was determined using data from the uganda malaria surveillance project. catchment areas were also derived other methods such as the straight line and road network distances from the facility. subsequently, the 1-year cumulative malaria case rate (the total number of cases during one year) was calculated for each catchment area, with at-risk populations estimate from catchment areas determined using the cumulative case ratio, the straight-line distance, and the road network distance. results the 1-year cumulative malaria case rate varied considerably with the method used to define the catchment areas. with the cumulative case ratio approach, the catchment area could include noncontiguous areas. with the distance approaches, the denominator increased substantially with distance, whereas the numerator increased only slightly. the largest cumulative case rate per 1000 population was for the kamwezi facility: 234.9 (95% confidence interval, ci: 226.2– 243.8) for a straight-line distance of 5 km, 193.1 (95% ci: 186.8– 199.6) for the cumulative case ratio approach and 156.1 (95% ci: 150.9–161.4) for a road network distance of 5 km. conclusions the variation in estimates of the cumulative rate of confirmed malaria cases was attributable to the differential change in the numerator and denominator of the case rate calculation as a function of the distance used to define the catchment area. an erroneous view of the catchment area can lead to inefficient and inadequate services, misspecification of the catchment population and potentially flawed decision-making on other facilities, such as deciding where to locate a new facility. our approach is simple, reproducible, and is based on a statistical measure to decide which administrative units should be included in catchment areas. keywords catchment; surveillance; uganda *kate zinszer e-mail: kate.zinszer@mail.mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e98, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul french institute for public health surveillance, saint-denis cedex 9, réunion objective to present the implementation and the first results of a prospective spatio-temporal analysis from emergency department (ed) data in reunion island. introduction many syndromic surveillance systems use spatio-temporal analysis to detect local outbreaks such as gastrointestinal illnesses and lower respiratory infections [1, 2]. in reunion island, the syndromic surveillance system is based mainly on ed visits. spatial analysis was first used in 2013 to validate retrospectively a cluster of viral meningitis [3]. at the end of 2014, the regional office of french institute for public health surveillance implemented a prospective computer-automated space-time analysis in order to launch daily analyses of ed visits. methods to realize the prospective space-time analysis, a r program generate the parameter files of satscan™ and then run the software in batch mode. the results are displayed in a web interface. the space-time permutation model is used with the following parameters: the upper limit on the geographical size of the outbreak is a circle with a 5-km radius, the maximum temporal length is set at 1 and 7 days, the number of days included in the calculation of the expected number is 60 days and the number of replications is set at 999. if the recurrence interval of a signal is 2.7 years for a temporal window of 1 day (p<0.001) or 19.1 years for a temporal window of 7 days (p<0.001) then an investigation is carried out. results between january 1st and may 31 2015, the prospective spatiotemporal analyses from ed data generated 13 cluster-signals for a temporal window of 7 days and 3 cluster-signals for a temporal of 1 day (table 1). from 25 to 31 january 2015, several consecutive signals of gastroenteritis were generated. the epidemiological investigation allowed to confirm the outbreak nevertheless the source of contamination has not been identified. at the end of january 2015, the surveillance system detected a significant increase of conjunctivitis cases. during the investigation, the general practitioners (gps) of the area confirmed the signal and laboratories identified the coxsackievirus a24 as the agent of this epidemic that spread throughout the island. on february 24, 2015 an ear, nose and throat (ent) infections signal was generated. the gps of the area have confirmed this heath event that remained localized. on april 2, 2015 a significant increase of bronchiolitis was detected but was not confirmed by gps. in consequent the signal has been invalidated. conclusions the implementation of a prospective computer-automated spacetime analysis based on ed data allowed the early detection of several infectious diseases outbreak. though the source of contamination is not always identified, it makes possible early implementation of control measures. table 1: results of space times analyses from ed data, january 1 to may 31, 2015, reunion island keywords syndromic surveillance; emergency department; spatial analysis acknowledgments all emergency departements of the reunion island references [1] van den wijngaard cc, van asten l, van pelt w, doornbos g, nagelkerke nj, donker ga, van der hoek w, koopmans mp. syndromic surveillance for local outbreaks of lower-respiratory infections: would it work? plos one. 2010. 29;5(4):e10406. 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[3] vilain p, ernould s, caillère n, larrieu s, belmonte o, mougin damour k, et al. intérêt du réseau oscour® pour la validation d’un signalement de méningite virale dans l’ouest de la réunion [usefulness of oscour® network in validation of viral meningitis report in the western part of réunion island]. bull epidémiol hebd. 2014;(3-4):53-7. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e171, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts sero-prevalence of rubella virus among pregnant women in kaduna state nigeria 2015 aishatu b. gubio*, steve olonitola, edward jattau and maryam a. mukhtar ahmadu bello university, zaria, nigeria objective to determine the igm and igg antibodies of rubella virus circulating among pregnant women in kaduna state nigeria. introduction rubella virus causes -“german measles,” also known as “three-day measles.” this is usually a milder disease than red measles. red/hard measles or just measles is caused by rubeola virus. the result of acute infection of the virus is a benign systematic rash which is significantly pathogenic to humans. this virus is a, positive-strand rna virus that replicates in the cytoplasm of the infected cell. (brooks et al., 2007). if placental infection of the virus spread during 8-10 weeks gestation it causes a chronic infection of the fetus leading to the development of congenital rubella syndrome (crs) (matthews et al., 2011) the effect of the infection of the several organ systems which include the eyes, ears, heart, brain, and endocrine system is known as congenital rubella infection (cri) (chantler et, al.,2001) rubella is endemic in nigeria. studies among women of child bearing age in nigeria put seroprevalence at 66.6% in imo, 77% in lagos and 93.5% in oyo (8-10). thus as part of the control measure, the availability of an effective vaccine to prevent rubella infection and therefore crs, is necessary to evaluate the burden of disease in a country where mmr vaccine is not covered in the immunization schedule or in vaccination strategy methods a cross-sectional study carried out on pregnant women attending ante-natal clinic from the three different senatorial district in kaduna state. blood samples were screened for rubella igm & igg antibody using commercially produced enzyme linked immunosorbent assay (elisa), questionnaires were administered to obtain demographic information and possible risk factors associated with rubella virus. data was analzyed using epi info 6 version 3.5.3. results of the 900 pregnant women screened, 572(63.3%) were positive for rubella igg. the prevalence of rubella igg was highest among the age group 21-25 with 198(34.6%) and igm was highest among the age group 21-25(51.3%). the igg test results shows that 317 (66.0%) pregnant women tested positive for their first trimester, while the igm positive results shows 17(33.3%) for their first trimester. although the southern senatorial district had the highest seroprevalence 14(35.9%) among the three centres, the differences were not statistically significant (p>0.05). only 3 people claimed to have been vaccinated against rubella virus. acquisition of primary education and being a house wife were insignificantly associated with raised titres. (p>0.05). conclusions the serological evidence of rubella virus found in pregnant women among age group & their first trimester in this study is an indication that rubella is prevalent in nigeria. it is however still necessary to immunize seronegative women against rubella before they get pregnant. table 1: sero-prevalence of rubella antibodies among pregnant women attending antenatal clinic in kaduna state based on age table 2: sero-prevalence of rubella antibodies among pregnant women attending ante-natal clinic in kaduna state based on trimesters ssd-southern senatorial district, nsd-northern senatorial district, csd-central senatorial district. keywords rubella; igg; igm acknowledgments i would like to acknowledge and express my sincere gratitudes to prof o.s olonitola, prof e.d. jattau, and dr. m. aminumukhtar who mentored me. for their immense contributions i simply say thank you. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e185, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts references brooks, f. g., carroll, c. k., butel, s. j. and morse, a. s. (2007). jawetz, melnick, & adelberg’s medical microbiology. 24th ed. u.s.a.: mcgraw-hill companies. pp 562-564. bukbuk, d. n., el nafaty a.u. and obed, j.y. (2002). prevalence of rubella – specific igg antibody in non – immunised pregnant women in maiduguri, north-eastern nigeria. central europe journal public health, 10(1-2): 21 – 23. eleazu, c. o., chinedum, e. k., john, a. esther, a. (2012). “survey of the sero-prevalence of igm antibodies in pregnant women infected with rubella virus plateau state specialist hospital in jos” e3. journal of biotechnology and pharmaceutical research 3 (1): 10-14. *aishatu b. gubio e-mail: yabintu@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e185, 2017 isds16_abstracts-final 74 isds16_abstracts-final 75 birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? eduard poltavskiy1, j. david spence2, jeehyoung kim3, heejung bang1,4 1. graduate group in epidemiology, university of california, davis, ca, usa 2. stroke prevention & atherosclerosis research centre, robarts research institute, western university, london, ontario, canada 3. department of orthopedic surgery, seoul sacred heart general hospital, seoul, korea 4. division of biostatistics, department of public health sciences, university of california, davis, ca, usa abstract in the modern era, with high-throughput technology and large data size, associational studies are actively being generated. some have statistical and clinical validity and utility, or at least have biologically plausible relationships, while others may not. recently, the potential effect of birth month on lifetime disease risks has been studied in a phenome -wide model. we evaluated the associations between birth month and 5 cardiovascular disease-related outcomes in an independent registry of 8,346 patients from ontario, canada in 1977-2014. we used descriptive statistics and logistic regression, along with model-fit and discrimination statistics. hypertension and coronary heart disease (of primary interest) were most prevalent in those who were born in january and april, respectively, as observed in the previous study. other outcomes showed weak or opposite associations. ancillary analyses (based on raw blood pressures and subgroup analyses by sex) demonstrated inconsistent patterns and high randomness. our study was based on a high risk population and could not provide scientific explanations. as scientific values and clinical implications can be different, readers are encouraged to read the original and our papers together for more objective interpretations of the potential impact of birth month on individual and public health as well as toward cumulative/total evidence in general. key words: birth month; cardiovascular disease; electronic medical record. correspondence: hbang@ucdavis.edu doi: 10.5210/ojphi.v8i2.6643 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi introduction possibly beginning with hippocrates, the environment, including air, water and place has been suggested to influence human health [1]. numerous researchers hypothesized that pre or peri-natal or early life conditions can have impacts on the occurrence of various diseases over a lifetime. non-ignorable evidence has been found in some respiratory illness (e.g., asthma) and mental illnesses (e.g., attention deficit hyperactivity disorder, schizophrenia). for example, in the 1970 s there was evidence that being born in the winter increased the risk of schizophrenia by 10% [2-6]. on the other hand, some findings have been claimed and refuted by re-analysis of the same data, and are being used as example in statistical education [7-9]. nowadays, the volume of related literature is growing and emergence of large, convenient and easily gathered datasets facilitates the analysis of a number of events and potential determinants. so far, the track record has been mixed [10,11]. a recent study assessed whether birth month affects lifetime disease risk of 1,688 conditions in a phenome-wide model based on an electronic medical record (emr) database, including about 1.75 million individuals, from an institution in new york city from 1900-2000 [12]. the interesting findings generated from the statistical analyses of a huge database received great attention from the scientific community and the media. the authors reported that cardiovascular disease (cvd) was significantly dependent on birth month, asserting that this association was newly discovered in their study. an earlier review of 246 suggested coronary risk factors, including constitutional, demographic and environmental factors, did not include birth month [13]. in contrast, it has been reported that a general tendency for people born in the first half of the year to die at younger age, more from heart disease and cerebrovascular disease, than those born in the second half of the year in austria [14]. the authors also performed an extensive literature review – 19 out of the 55 identified diseases are supported by the literature – and used rigorous methodologies, including widely accepted statistical adjustment of multiplicity and quality control, which have been common issues in similar studies based on convenient, tertiary datasets that were not collected for research or policy making purposes. in this paper, we attempt to evaluate the validity and generalizability of their findings in an independent, external patient registry. we focused on 5 cvd -related outcomes: hypertension, coronary heart disease (chd), stroke, diabetes, and chronic kidney disease (ckd) [15,16]. other outcomes (such as respiratory and reproductive diseases highlighted in the original paper) were not available to us. materials and methods study population and sample the study was conducted using the emr of the stroke prevention & atherosclerosis research centre, robarts research institute, london, ontario, with patient visits occurring in 1977 -2014. before 1995, patients were referred to the hypertension clinic at victoria hospital, london, canada. since 1995, they were referred to one of several clinics at university hospital: a stroke prevention clinic, an urgent tia clinic, and a premature atherosclerosis clinic. western university health science research ethics board approved this study. http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi exposures and outcomes we analyzed age and birth month (both in integer), without date or more details. hypertension was defined as antihypertensive medication usage or systolic blood pressure (bp)>140 mmhg or diastolic bp>90 mmhg, where the higher value was selected from measurements in the left and right arms. we defined chd as present if myocardial infarction or vascular surgery was recorded. a cerebrovascular disorder was defined if stroke or transient ischemic attack (tia) was present; these were combined as stroke. diabetes was restricted to type-2 diabetes. ckd was defined using glomerular filtration rate (gfr)<60, estimated from the ckd -epi formula [17]. data analysis we used summary statistics to describe patient characteristics, such as mean, standard deviation and interquartile range for continuous variables, and frequency and percentage for categorical variables. we computed the frequency and (row and column) percentage for each health outco me by birth month. we indicated the highest and lowest percentages, and tested the equality of the proportions over different months by the chi-square test. since we utilized emr data, missing data were common. in all analyses, we included all available data, without imputations, in the included variables in each analysis, and we indicated the sample size. we fitted simple logistic regression for each health outcome with each predictor separately. we considered 3 demographics as predictors or independent variables: month, sex, and age, where age was analyzed both as a continuous and dichotomized variable (>50 vs. ≤50 years) and we did not treat age and sex as confounders in regression. to compare the different models, we employed standard measures for evaluating models and prediction [18-20]: area under the receiver-operatingcharacteristic curve (auc) and akaike and bayesian information criteria (aic/bic). auc is a discrimination statistic; 0.5 means random and 1 means perfect discrimination between cases vs. non-cases. aic is a measure of the relative quality of a statistical model for given data, and bic might be considered a bayesian extension. a lower value of aic/bic indicates improved model fit. some interpret that aic addresses explanation and bic addr esses prediction [21]. of note, aic/bic do not have a simple range, unlike p-value, correlation or auc; they should be compared within the same outcome, not across outcomes due to different sample sizes. as ancillary analyses, we computed the distribution of 4 raw bp measurements (left vs. right, systolic vs. diastolic) over months to examine time-trends, and to check if these measurements support the ‘january peak’ and ‘october trough’ for hypertension that were reported in the original study. also, we fitted the event rate by penalized b-splines by sex in order to see if patterns are similar for men vs. women. sas 9.3 was used for analysis (sas institute, cary, nc). p-values and confidence intervals (cis) are 2-sided and unadjusted for multiplicity. results table 1 describes the characteristics of the 8,346 patients included in our study. patients tended to be older (with mean=63 and range=9-99 years at the first visit to the clinic) and 52% were male. http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi hypertension was highly prevalent (66%), compared to other outcomes (<25%). birth months were quite evenly distributed with the null value of 8.3% (=100/12) overall (7.5 -8.9%, p=0.06). figure 1 presents the event rate of individual health outcomes for each birth month. january (69%), april (22%), july (25%), november (20%), and march (27%) showed the highest proportions for hypertension, chd, stroke, diabetes, and ckd, respectively, and the lowest proportions were in october (63%), september (13%), september (20%), and march (18%). when we computed the percentage of different birth months among those who had the outcome (i.e., using column percent in place of row percent), the same highest months were observed. when we modeled different demographic factors as independent variable and different health outcomes as dependent variable by regression, birth month was associated with chd, diabetes and ckd (mostly for post-hoc selection of the highest month) with p=0.02-0.05. in contrast, sex was highly significant with these 3 outcomes (p≤0.003). months (without post -hoc dichotomization) yielded slightly higher auc than sex for hypertension, stroke and ckd, which may imply enhanced discrimination, but aic/bic tended to indicate the reversed performance in model fit/quality; see table 2. in the ancillary analysis with raw variables for hypertension, the key findings (january as highest and october as lowest) from the original study were confirmed. on the other hand, we observed that right arm bps were highest among people who were born in january, whereas left arm bps were highest in july, which are opposite seasons. event rate plots by sex revealed less systematic, substantially different trends among men vs. women; see figure 2. table 1. patient characteristics variable n of complete data mean (standard deviation) [interquartile range] or percentage age, years 8346 62.6 (14.7) [52.0-74.0] male 8346 51.5% height, cm 6876 168.6 (10.2) [160.0176.0] weight, kg 7197 78.8 (17.7) [66.0-89.1] serum creatinine, mmol/l 4002 87.5 (39.0) [69.0-96.0] current smoker 8217 18.3% hypertension 6663 66.2% diabetes (type 2) 7971 16.4% myocardial infarction 6541 11.2% vascular surgery 6566 9.6% http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi stroke 6855 14.4% transient ischemic attack 6751 12.3% chronic kidney disease* 4002 22.5% birth month 8346 1 8.4% 2 8.1% 3 8.8% 4 8.5% 5 8.6% 6 8.9% 7 8.9% 8 8.2% 9 8.2% 10 8.0% 11 8.2% 12 7.5% *the ckd-epi formula was used to estimate glomerular filtration rate; the threshold used to define chronic kidney disease is an egfr<60 ml/min/1.73 m2. http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi event rate for each month hypertension (p=0.58) coronary heart disease (p=0.05) stroke (p=0.65) diabetes (p=0.59) the size of each area reflects the percentage of persons who had the specified outcome among those who were born in each month. p-values are for testing the null hypothesis that the proportions are equal for 12 months. chronic kidney disease (p=0.56) figure 1. nightingale plots of the distribution of birth month for health outcomes january 69.2%february 67.9% march 68.6% april 66.1% may 66.6% june 64.2% july 65.0% august 66.8% september 64.5% october 62.8% november 66.0% december 66.5% january 17.8%february 20.5% march 16.8% april 21.6% may 17.9% june 18.4% july 18.9% august 18.5% september 13.0% october 17.1% november 19.9% december 15.7% january 21.6%february 24.9% march 24.1% april 21.3% may 23.0% june 23.8% july 25.2% august 24.6% september 20.1% october 24.5% november 23.0% december 23.7% january 15.0%february 17.8% march 15.0% april 16.9% may 17.0% june 15.7% july 16.3% august 15.5% september 15.9% october 15.3% november 19.6% december 16.4% january 17.9%february 22.4% march 26.9% april 22.3% may 21.8% june 23.3% july 22.0% august 22.5% september 22.0% october 20.7% november 24.0% december 24.2% http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi a) time-trends of four blood pressure measurements over month b) penalized b-spline plot by sex for hypertension and heart disease figure 2. ancillary analyses jan feb mar apr may jun jul aug sep oc t nov dec month 60.0 62.5 65.0 67.5 70.0 72.5 e v e n t r a te ( p e r 1 0 0 ) mf hypertension jan feb mar apr may jun jul aug sep oc t nov dec month 10 15 20 25 30 e v e n t r a te ( p e r 1 0 0 ) mf cad http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi table 2. discrimination and model-fit statistics from simple logistic regression health outcome predictor p-value auc aic bic hypertension (total n=6663) birth months 0.58 0.522 8539 8621 highest month (jan vs. the rest)* 0.12 0.506 8526 8540 sex 0.60 0.503 8528 8542 age (continuous) <0.0001 0.643 8124 8138 age >50 <0.0001 0.585 8285 8299 coronary heart disease (total n=6472) birth months 0.05 0.539 6020 6102 highest month (april vs. the rest)* 0.03 0.510 6016 6029 sex <0.0001 0.598 5873 5886 age (continuous) <0.0001 0.621 5846 5859 age >50 <0.0001 0.572 5889 5902 stroke (total n=6845) birth months 0.66 0.523 7452 7534 highest month (july vs. the rest)* 0.25 0.505 7440 7453 sex 0.94 0.509+ 7441 7455 age (continuous) <0.0001 0.598 7300 7313 age >50 <0.0001 0.556 7344 7358 diabetes (total n=7971) birth months 0.59 0.525 7118 7202 highest month (nov vs. the rest)* 0.02 0.510 7102 7116 sex <0.0001 0.530 7092 7106 age (continuous) <0.0001 0.597 6974 6988 age >50 <0.0001 0.566 6977 6991 chronic kidney disease (total n=4002) birth months 0.57 0.530 4286 4361 highest month (march vs. the rest)* 0.04 0.511 4271 4284 sex 0.003 0.528 4267 4279 age (continuous) <0.0001 0.796 3457 3470 age >50 <0.0001 0.602 3991 4003 each predictor is separately modeled as a univariate covariate in simple logistic regression. http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi birth month (1-12) is included as a categorical covariate (via 11 dummies); sex is binary; and age (in years) is included as a continuous or binary covariate (>50 vs. ≤ 50 years old). *highest month (vs. rest as binary variable) is selected post-hoc, so results may suffer optimism bias. p-value is computed from wald chi-square test; degrees of freedom=11 for birth month and 1 for all others. auc, area under the roc curve, is a discrimination statistic; 0.5 means random discrimination and 1 means perfect discrimination. aic, akaike information criteria, is a measure of the relative quality of a statistical model for a given set of data: a lower value means a better model fit. bic, bayesian information criteria, is a bayesian extension of aic: a lower value means a better model fit. aic and bic should be compared within the same outcome due to different ns and amount of information. +estimation issue so we fitted the model with y=stroke or tia, and averaged the auc of 0.511 and 0.507. discussion in the bigdata era, with advanced, fancy statistics and informatics tools and highly educated minds, many things that have been impossible are becoming possible. many small and previously unidentified effects or associations and rare cases are being discovered and repor ted on a daily basis. at the same time, high standards in data quality and statistical analyses are being emphasized, similarly to deming’s 6-sigma that has been a gold standard in industry and quality control for decades [22,23]. yet, two different issues are never answered by large sample size, statistical analysis and computing software: 1) clinical or practical meaningfulness (e.g., is the effect size large enough to be clinically meaningful or lead to any action?) and 2) biological plausibility (why does this happen? is an association of insect bite and birth month with adjusted p=0.001 scientifically explainable?) [12]. our findings support some of the authors’ claims (e.g., hypertension-january and cvd-april with the highest, and september-october with the lowest), which may be regarded as external validation, particularly because london, ontario and new york city are not very different in climate. but we also found conflicting evidence in related diseases (e.g., january, april, july, november with the highest); so coherence, consistency and plausibility in causal viewpoints might be weakened [24]. our analysis demonstrates high randomness going on, which may be natural. for example, the phenomena of ‘right arm bp highest in january and left arm bp highest in july’ and of the differential effects of birth month for males vs. females are not biologically plausible. can sub diseases/conditions within the same disease category be qualitatively different and be associated with different months? small but real differences or being ‘fooled by randomness’ cannot be excluded [25]. http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi the observed aucs, a key measure in prediction, are tantalizing, accepting that the role of age is fully known. for hypertension and stroke, month may offer better discrimination than sex, but model-fit seems to show that sex could be better. birth month did not increase discrimination ability for all outcomes once age and sex are included in the model; auc increase=0.001 for hypertension and 0 for others (results not shown). large data sets cause impressive p-values with minor differences in biology. are they clinically relevant [13,22,26,27]? despite substantially different study populations and sample sizes, dramatically different p-values for two validated outcomes (chd and hypertension) are noteworthy: p-values<0.001 adjusted for 1,688 comparisons (or p-value ~10-22 unadjusted using our best guess from the manhattan plot) in the original study vs. unadjusted p-values=0.03-0.58 in our study [28,29]. when a number of p-values  probably the most popular statistical measure in research  are computed, a simple ‘p-value plot’ together with auc could be helpful for assessing overall randomness in associations [8,30,31]. related to the recent ‘bad luck-cancer controversy’, the validity and proper interpretation of another popular statistic, r2, for aggregated data have been discussed [32]. the limitations of our study and caveats for readers should be noted. first, we utilized retrospective data from a high-risk sample in a single geographical region, which could make already small associations even smaller. very large population-based cohort or census would be ideal. second, we are unable to explain some findings and to identify causes or underlying mechanisms; yet these issues are shared by the original, our and many other non-experimental studies. third, the common goal of a long history of birth month research could be different from ours; its goal is generally a basic or pure science one to find diseases that may be related to developmental effects of environmental exposures, which presumably would later be investigated to elucidate the mechanism of that association. in contrast, our goal is closer to a clinical practice one, which may be better addressed by a statistical or prediction measure such as auc, in addition to or place of p-value. for example, should physicians or patients be more suspicious of and investigate more closely for certain conditions based on birth month? (e.g., screening); should parent planning a pregnancy aim to have their child born in a certain month? indeed, significant seasonality but different seasons/months for the best outcomes with high randomness have been demonstrated in infertility, autism and mortality-related research as well [2,14,33-37]. the main strengths are: a relatively large sample size covering a long term from multiple hospitals; multiple cvd-related outcomes; use of clinical data (e.g., multiple raw bps in place of coded data where underreporting can be severe) and emr with continuous quality checks [38-42]; and statistical measures that address different aspects of model and association, beyond p-value. since our cohort mostly consists of older adults, the ‘lifetime risk’ of cvd that the original study intended to address might be well-captured although representativeness is weaker. scientists and readers’ efforts to confirm important findings and attitudes to wait for more evidence should be valued more, in addition to discovery, innovation and productivity that are currently emphasized [22]. it is well documented that the framingham risk score ─ a landmark in cvd research ─ does not perform well in asian populations or hiv patients [43]. we do not think this is a major weakness of the method/finding as no method is perfect and virtually no finding is universal. also, for every finding, we need to determine whether it is real vs. not (e.g., random), http://ojphi.org/ birth month and cardiovascular disease risk association: is meaningfulness in the eye of the beholder? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e186, 2016 ojphi and if real, the next step might be to assess biological mechanisms as well as practical value and clinical implications. conclusions we could validate the associations between birth month and the two primary cvd -related outcomes, but also found randomness was high. until a definitive or ultimate answer, which may be provided from very large, representative samples with accurate outcomes data covering different climates, physicians and patients need not be much concerned about birth month; modifiable factors are a more appropriate focus. when faced with reports of novel discoveries, healthy skepticism and waiting for validations and explanations in similar and different settings are crucial for citizens in the information age. finally, we still believe that emr offers invaluable resources and opened a new chapter in research and data science. the following quotation, often attributed to galileo galilei, is apt: measure what is measurable; make measurable what is not so [44]. perhaps clinical and lab data are more suited to the first task, while administrative or selfreport data try to do the latter (as the next best option). acknowledgements we thank drs. robert elston, dmitri zaykin, stan young, and allan jaffe for providing useful comments. conflict of interests: none declared. funding: h. bang was partly supported by the national center for advancing translational sciences, national institutes of health, through grant number ul1 tr000002. references 1. hippocrates. on airs, waters, and places. accessed on 09/2016; available from: http://classics.mit.edu/hippocrates/airwatpl.mb.txt. 2. zerbo o, et al. 2011. month of conception and risk of autism. epidemiology. 22(4), 469-75. http://dx.doi.org/10.1097/ede.0b013e31821d0b53 3. kauhanen l, et al. 2006. social disadvantages in childhood and risk of all-cause death and cardiovascular disease in later life: a comparison of historical and retrospective childhood information. int j epidemiol. 35(4), 962-68. http://dx.doi.org/10.1093/ije/dyl046 4. torrey ef, et al. 1997. seasonality of births in schizophrenia and bipolar disorder: a review of the literature. schizophr res. 28(1), 1-38. http://dx.doi.org/10.1016/s0920-9964(97)000923 http://ojphi.org/ http://dx.doi.org/10.1097/ede.0b013e31821d0b53 http://dx.doi.org/10.1093/ije/dyl046 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http://dx.doi.org/10.1016/s0006-3223(00)00837-4 43. myerson m, et al. 2014. prevalence, treatment, and control of dyslipidemia and hypertension in 4278 hiv outpatients. j acquir immune defic syndr. 66, 370-77. http://dx.doi.org/10.1097/qai.0000000000000168 44. kleinert a. 2009. der messende luchs. zwei verbreitete fehler in der galilei-literatur. ntm zeitschrift für geschichte der wissenschaften. technik und medizin. 17(2), 199-206. doi:10.1007/s00048-009-0335-4 http://ojphi.org/ http://dx.doi.org/10.1176/ajp.152.5.798 http://dx.doi.org/10.1093/oxfordjournals.aje.a009067 http://dx.doi.org/10.1161/strokeaha.111.640789 http://dx.doi.org/10.1161/01.str.0000110981.96204.64 http://dx.doi.org/10.1016/j.cjca.2015.03.005 http://dx.doi.org/10.1016/j.jpeds.2008.09.022 http://dx.doi.org/10.1016/s0006-3223(00)00837-4 http://dx.doi.org/10.1097/qai.0000000000000168 data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi data, information, evidence, and knowledge: a proposal for health informatics and data science olaf dammann, md, sm dept. of public health and community medicine, tufts university school of medicine, boston, ma, united states abstract in this commentary, i revisit and modify ackoff’s data-information-knowledge-wisdom (dikw) hierarchy. i suggest to de-emphasize the wisdom part and to insert evidence between information and knowledge (diek). this framework defines data as raw symbols, which become information when they are contextualized. information achieves the status of evidence in comparison to relevant standards. evidence is used to test hypotheses and is transformed into knowledge by success and consensus. as checkpoints for the transition from evidence to knowledge i suggest relevance, robustness, repeatability, and reproducibility. keywords: data, information, evidence, knowledge correspondence: olaf.dammann@tufts.edu doi: 10.5210/ojphi.v10i3.9631 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged i n the copy and the copy is used for educational, not-for-profit purposes. introduction data, information, and knowledge are central concepts in health informatics and data science. it is not always clear how authors define these entities and how they envision the transition from data to knowledge to work. in this commentary, i first review the knowledge/wisdom hierarchy proposed by organizational theorist russell a. ackoff in 1989 [1]. second, i outline a modification of ackoff’s framework that does away with his notion of wisdom and makes room for evidence. i also discuss the transition process of from data to knowledge, with a focus on the transition from evidence to knowledge. i hope that the ideas summarized here will prove helpful to those in charge of knowledge generation in health informatics and data science. ackoff’s knowledge hierarchy russell l. ackoff (1919-2009) introduced what is now known as the knowledge hierarchy or knowledge pyramid (fig. 1, left) in his presidential address to the international society for general systems research (isgsr) in 1988 [1]. he starts with the notion that wisdom is situated at the top of a hierarchy of types of content in the mind, followed by understanding, knowledge, information, mailto:olaf.dammann@tufts.edu data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi and data (fig. 1; of note, ackoff’s original article does not have a figure, nor does it refer to pryamids.) he defines data as symbols that are properties of observables, and information as descriptions. the difference between the two is not structural, but functional, and information is inferred from data. ackoff discusses management needs in terms of information availability. he states that managers are usually confronted with an information overload and do not necessarily need more relevant information but less irrelevant information, a truism then and now. he defines knowledge as knowhow that comes from learning, i.e., by instruction or from experience, and adaptation, i.e., the correction of the learned in accordance with new circumstances. this process requires understanding what error is, why error occurs, and how to correct it. ackoff thinks that (1) information systems can be automated and generate information out of data, (2) that computerbased knowledge systems require higher-order mental faculties; “they do not develop knowledge, but apply knowledge developed by people”, and (3) that wisdom adds value, endures forever, and will probably never be generated by machines. figure 1. ackoff’s knowledge (dikw) hierarchy (left) and the diek modification proposed in this commentary (right)(reprinted from [3]). ackoff’s hierarchy is often depicted as a pyramid (as in fig. 1 in this article) with data at the bottom, information and knowledge above, and wisdom at the top. probably for this reason, jennifer rowley uses the term “wisdom hierarchy“. [2] although she seems more interested in the wisdom part than in other components of the pyramid, the bulk of her 2007 paper on ackoff’s work is a summary of terminological definitions, of data, information, knowledge, and wisdom, as pulled from major textbooks used in information system and knowledge management education. her review reiterates two opinions; first, her view that data, information, and knowledge are connected, one helping define the other, and second, her view about the organization of the hierarchy as such. the ways how the individual items in the hierarchy are converted and elevated to the next level is less well defined. data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi data, information, evidence, knowledge: diek although the ackoff hierarchy has received much attention over the years, i strongly believe that in our current evidence-based environment some modifications are in order. first, in a book coauthored with philosopher of science ben smart, i suggest dropping the notion of wisdom because, first, the term is fraught with too much baggage from non-scientific context [3]. second, ackoff’s definition of wisdom (the addition of value to knowledge that requires judgement) ignores the fact that judgement is needed at all levels of the hierarchy. more importantly, i do not think that wisdom adds much to the decision-making based on the hierarchy. instead, i hold that knowledge deserves the position at the pinnacle of the hierarchy. knowledge can be defined, in the context of medical and public health informatics and data science, as predictive, testable, consistently successful belief, if there is a causal connection between the facts represented by the data, information, and evidence on the one hand, and our beliefs on the other. data in the context of public health informatics, mensah and goderre define “data” as raw facts, statistics, context-free numbers [4]. i’d like to suggest that data are symbols as retrieved, collected, or simulated (table 1). these include numbers resulting from measurements or from text-mining, images, sound recordings, survey results, simulations, and so on. they can usually be tabulated and depicted as graphs, or displayed as figures. more formally speaking, data are quantitative or qualitative values of variables. figure 2 displays a framework for transitions from data to knowledge, and what the arrival at each new stage is good for. data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi table 1. explanations of what data, information, evidence, and knowledge are, and how they are produced, by whom, and why (modified from [3]). concept what is it? how produced? by whom? goal? data numbers, symbols, text, images, sound recordings, unit values collected from field research, database, measurements in experiments, from individuals, populations data collector use as raw data or for information generation storage, curation, retrieval information data in context contextualization by making data useful, and using them, for specific tasks informatician, informaticist, statistician, data scientist use as source for answering questions storage, curation, retrieval evidence useful, contextualized information comparison with standards, reference values, reference information scientist, theoretician, philosopher interventionist, policy maker use for analysis and hypothesistesting to support claims/hypotheses and decisionmaking knowledge evidencebased, (predictive, testable, consistently successful) belief consensus based on reasoning and discussion justification data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi figure 2. framework for the transition from data to knowledge (left) and what each level is good for (right) (reprinted from [3]). information is data contextualized mensah & goderre further suggest that “information is the collection, aggregation, analysis, and presentation of data that provides understanding”. [4] although this definition describes how we arrive at information based on data, it does not tell us what information is. i think that information is data in context. information is data that have been processed so it is clear what they are about. once they are collected and contextualized, data are information. according to this view, all information is data, but not all data are information. evidence is information compared information thus conceived can give rise to evidence, which has been defined as “information bearing on the truth or falsity of a proposition”. [5] evidence is information that can be used to support a hypothesis by testing it. thus, all evidence is information, but not all information is evidence. the comparison of information in support of competing conjectures helps define what counts as evidence that, in turn, generates the knowledge that a certain overarching claim is true. data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi evidence is generated by comparing information to reference values or standards, which prepares the information for further analysis. in the context of public health, brownson and colleagues have argued that (f)or a public health professional, evidence is some form of data— including epidemiologic (quantitative) data, results of program or policy evaluations, and qualitative data—for uses in making judgments or decisions” [6] they describe three kinds of evidence in public health contexts: (1) the causes of illness and the magnitude of risk factors, (2) the relative impact of specific interventions, and (3) how and under which contextual conditions interventions were implemented [6]. we discuss the interventionrelated part of these kinds of evidence in more detail elsewhere [7]. in general, evidence is information that bears on the truth of a proposition compared to a standard. according this definition, information becomes evidence only if it bears on the truth or falsity of the proposition that the gardener was indeed the murderer. only if we can find good evidence that is coherent with this claim can we say that we have knowledge that he really is the culprit. actionable knowledge is usually generated from coherent evidence from multiple independent sources of information [8]. if we refer to evidence as information that supports a specific proposition by bearing on its truth, evidence is context-dependent, because it becomes evidence only by virtue of being relevant as support for a specific proposition, and relevance is, by definition, a contextual concept. knowledge from evidence the traditional tripartite concept of knowledge as justified, true belief goes all the way back to plato [9]. gettier argued in 1963 that the tripartite definition is not sufficient to constitute knowledge, in essence by offering two counterexamples in which some justified, true beliefs clearly do not count as knowledge [10]. multiple strategies to defeat gettier have been suggested [11]. in our present context, i think that knowledge consists of beliefs that 1. turn out to be predictive: predictions that are based on such beliefs turn out to be correct; 2. generate hypotheses that can be tested, and 3. ideas that lead to interventions that are successful, 4. for a long time. in other words, i suggest that beliefs qualify as knowledge if they predict outcomes with satisfactory precision, if they can be translated into scenarios that put the belief to the test, and if actions based on such beliefs are consistently successful. in short, knowledge is predictive, testable, consistently successful belief. indeed, this is exactly what we refer to some belief as being evidence-based. this is why evidence-based medicine and public health should actually be considered knowledge-based once the evidence has turned out to be predictive, is tested, and interventions have been designed and are consistently successful. of course, the decision when data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi that point has been reached is not made by any one person, but by consensus [12,13]. thus, all knowledge is evidence, but not all evidence is knowledge. are there checkpoints that support the decision to promote evidence to the level of good before we have seen the quality of its predictions, witnessed its testability, and received the good news that interventions based on such evidence are being consistently successful? here is a collection of candidate checkpoints that i think allow us to proceed from evidence to knowledge. since we ask this question with an intervention in mind, our query is not really what makes evidence so good that it is knowledge, but rather what makes evidence so good that it is useful in our context. usefulness, in turn, is simply the possibility to use this knowledge in ways that turn out to help improve the health of individuals and populations. we need knowledge to justify action. first, although this should go without saying, good evidence is relevant to the problem at hand. consider this quote from the annual review of public health: legislators and their scientific beneficiaries express growing concerns that the fruits of their investment in health research are not reaching the public, policy makers, and practitioners with evidence-based practices. practitioners and the public lament the lack of relevance and fit of evidence that reaches them and barriers to their implementation of it [14] if evidence is irrelevant, it isn’t useful. the focus on usefulness is, yet again, motivated by the goal of health informatics efforts to inform decision making which leads to effective action. second, good evidence is robust. this is what broadbent has called the stability of a result, i.e., the characteristic of a theory or piece of evidence that it is (a) not soon contradicted by good scientific evidence, and (b) unlikely that it will soon be contradicted by good scientific evidence, if good research were to be done on the topic [15]. third, good evidence is repeatable in the sense that similar data gathering and integration efforts lead to similar evidence repeatedly: “repeatability concerns the exact repetition of an experiment, using the same experimental apparatus, and under the same conditions”. [16] fourth, good evidence is reproducible: “reproducibility is … implementing the same general idea, in a similar setting, with newly created appropriate experimental apparatus”. [16] conclusion my version of the ackoff hierarchy is based on what is being done to make such transitions possible, not what transitions represent or what happens when moving from one level to another, such as changes of meaning and value [17] or the physical, cognitive, and belief structuring when constructing data, information, and knowledge, respectively [18]. as rowley’s focus is on the relative paucity of explications of wisdom, mine is instead on the fact that the concept of knowledge, now at the top of the hierarchy, is not well defined either. a similar model has been proposed by richard heller. in his model, accessing data yields information, appraisal of which yields knowledge. what is missing in heller’s model is the distinct role that evidence plays between information and knowledge. neither in his book [19] nor in the data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi underlying paper [20] does he define evidence. however, in their 2002 publication, heller and page offer a list of statistical and implementation characteristics they see as methods with an appropriate population focus that can be aligned with the methods used in evidence-based medicine because the authors consider the entire process from data via information to knowledge to be evidence-generating. i should stress that knowledge isn’t something out there for us to discover. instead, knowledge is made. in this commentary, i have outlined a framework that builds on ackoff’s knowledgehierarchy, in which data give rise to information, which leads to knowledge and finally wisdom. my version of the model drops the notion of wisdom, because it is too imprecise a notion to be useful in a health science context. instead, i suggest to insert the notion of evidence into the inferential sequence between information and knowledge. data are used mainly as raw material for information generation. when these data are put into context, they yield information that may be useful as evidence. based on such evidence, knowledge is generated. knowledge is evidencebased belief that is predictive, testable, and consistently successful, as judged by consensus among stakeholders. i hope that this proposed modification of ackoff’s framework will contribute to the progress of health informatics and data science. references 1. ackoff rl. 1989. from data to wisdom. j appl syst anal. 16, 3-9. 2. rowley j. 2007. the wisdom hierarchy: representations of the dikw hierarchy. j inf sci. 33(2), 163-80. https://doi.org/10.1177/0165551506070706 3. dammann o, smart b. making population health knowledge, in causation in population health informatics and data science. 2019, springer nature: cham, switzerland. p. 63-77. 4. mensah e, goderre jl. data sources and data tools, in public health informatics and information systems, j.a. magnuson and p.c. fu, editors. 2014, springer: london. p. 107131. 5. audi r. the cambridge dictionary of philosophy. 2nd ed. 1999, cambridge; new york: cambridge university press. xxxv, 1001 p. 6. brownson rc, fielding je, maylahn cm. 2009. evidence-based public health: a fundamental concept for public health practice. annu rev public health. 30, 175-201. pubmed https://doi.org/10.1146/annurev.publhealth.031308.100134 7. dammann o, smart b. integrating evidence, in causation in population health informatics and data science. 2019, springer nature cham, switzerland. p. 99-115. 8. dammann o. 2018. hill’s heuristics and explanatory coherentism in epidemiology. am j epidemiol. 187(1), 1-6. pubmed https://doi.org/10.1093/aje/kwx216 https://doi.org/10.1177/0165551506070706 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19296775&dopt=abstract https://doi.org/10.1146/annurev.publhealth.031308.100134 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29121224&dopt=abstract https://doi.org/10.1093/aje/kwx216 data, information, evidence, and knowledge: a proposal for health informatics and data science online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e224, 2018 ojphi 9. ichikawa jj, steup m. the analysis of knowledge. the stanford encyclopedia of philosophy 2013 03/07/2014]; 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new york: oxford university press. xii, 126 p. 20. heller rf, page j. 2002. a population perspective to evidence based medicine: "evidence for population health. j epidemiol community health. 56(1), 45-47. pubmed https://doi.org/10.1136/jech.56.1.45 https://doi.org/10.1093/analys/23.6.121 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19705558&dopt=abstract https://doi.org/10.1146/annurev.publhealth.031308.100049 https://doi.org/10.1145/2723872.2723875 https://doi.org/10.1016/0268-4012(96)00020-5 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11801619&dopt=abstract https://doi.org/10.1136/jech.56.1.45 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 188 isds 2014 conference abstracts validation of the michigan’s public health syndromic system using electronic medical records sandhya swarnavel*1, jim collins2 and corrine miller3 1health system information fellow (hsip), michigan department of community health, lansing, mi, usa; 2director, communicable diseases division, michigan department of community health, lansing, mi, usa; 3state epidemiologist and director, bureau of disease control, prevention and epidemiology, michigan department of community health, lansing, mi, usa objective validation of the syndromic system by comparing the chief complaint data to the electronic medical records (emr) of a tertiary hospital. introduction michigan has been collecting chief complaint data from emergency departments statewide to support situational awareness activities related to communicable disease since 2004. we validated the syndromic system by comparing the chief complaint data to the electronic medical records (1,2,3) of a tertiary hospital in southeast michigan to better understand the utility of the system for noncommunicable disease situations. methods we examined the michigan syndromic surveillance system (msss) free text chief complaint data that were submitted over a 3-month period from december 2013 to february 2014. for a pilot test, we extracted a subset of hl7 messages (4) with unique identifiers and linked the msss data to the medical records of hospital a. we compared the agreement of the msss data to icd codes in the hospital emr. results a total of 22,336 hl7 message transactions were received during the three months. of 144 hl7 messages in the pilot, 33 (22.9%) contained incomplete data and could not be linked to the emr . of the remaining 111 records that could be linked to the emr, 5 self-reported chief complaints did not correlate with the icd codes. the percent positive agreement was 94.34%.the results of the 400 randomized syndromic chief complaints will be presented, with further analysis of data quality, data completeness and accuracy. conclusions findings of this study will help determine the accuracy of the automated classification of data based on chief complaints. this study can add confidence in planning for public health preparedness activities and situational awareness. keywords validation; syndromic system; electronic medical records; hl7 messages acknowledgments this report was supported by an appointment to the health system integration fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevent (cdc) cooperative agreement number 5u38hm000414. the other partners in this project are astho, naccho and phii. references 1. lawrence jm, black mh, zhang jl, slezak jm, takhar hs, koebnick c, mayer-davis ej, zhong vw, dabelea d, hamman rf, reynolds k. validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.am j epidemiol. 2014 jan 1; 179(1):27-38 2. bobo wv, cooper wo, stein cm, olfson m, mounsey j, daugherty j, ray wa. positive predictive value of a case definition for diabetes mellitus using automated administrative health data in children and youth exposed to antipsychotic drugs or control medications: a tennessee medicaid study.bmc medical research methodology 2012, 12:128. http://www.biomedcentral.com/1471-2288/12/128 3. cunningham a1, stein cm, chung cp, daugherty jr, smalley we, ray wa. an automated database case definition for serious bleeding related to oral anticoagulant use.pharmacoepidemiol drug saf. 2011 jun;20(6):560-6. 4. majeed rw1, röhrig r. identifying patients for clinical trials using fuzzy ternary logic expressions on hl7 messages. stud health technol inform. 2011; 169:170-4. *sandhya swarnavel e-mail: swarnavels@michigan.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e207, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts fever in children an assessment of validity by a shewhart model in a syndromic surveillance system in china changming zhou*1, huijian cheng2, genming zhao1, qi zhao1, biao xu1, vinod diwan3 and xue li4 1public health, fudan university, shanghai, china; 2jiangxi province center for disease prevention and control, nanchang, china; 3karolinska institutet, stockholm, sweden; 4future position x, gavle, sweden objective to evaluate the validity of a syndromic surveillance system in health facilities of rural china, signals generated by shewhart charts from the reported febrile patients in children were compared with that from the common infectious disease patients reported to the conventional case report system (cisdcp, china information system for disease control and prevention) introduction since april 2012, an integrated syndromic surveillance system in rural china (issc) has been established in health facilities in two rural counties of jiangxi province, china [1]. the objective of issc is to integrate syndromic surveillance with conventional case report system for the early detection of infectious disease outbreak in rural china. methods a total of 167 health facilities including 2 county hospitals, 15 township hospitals and 150 village health stations were sampled as surveillance sites in 2 counties in jiangxi province, china. health facility visits of patients with ten major symptoms including fever, cough, sore throat, diarrhea, and nausea/vomiting, together with their age and gender were reported to a web-based platform during april 2012 and march 2014. data on children patients with cid common infectious diseases (cid) in these two counties reported to the cisdcp during the same period were retrieved including measles, hand foot mouth disease (hfmd), chickenpox, rubella, influenza, and mump. the time trend and alert signals in both issc and cisdcp were generated by shewhart model [3] (baseline=15 days, lag= 2 days, =3). sensitivity and ppv were used to compare the signals generated in issc with that in cisdcp (using the signals in cisdcp as reference). a matched signal was defined as a signal generated in issc having at least one corresponding signal occurred in cisdcp within a duration of 7 days. results there were 28,049 and 42,029 reports respectively for febrile patients in children from health facilities in these two counties during the two-year period. according to the cisdcp, there were 511 and 1779 selected cid cases reported respectively (table 1). the time trend of febrile patients and cid patients with shewhart signals were illustrated in figure 1 and figure 2. the time trend of the two datasets generally matched to each other. however in jan. 2014 there was a peak in febrile patients in issc with no observed changes in cids in cisdcp. the sensitivity were 29.03% and 34.78%. the ppvs were 64.29% and 53.33% in the two counties respectively. (table 2.) conclusions conclusion: the sensitivity of signals in the syndromic surveillance is relatively low using the shewhart model. this might be the result of using the non-specific symptom fever. howeverppv was relatively high in fever in issc for detecting cid within children using shewhart model. these results suggested that this system had potential ability to supplement conventional case report system in detecting common infectious disease outbreaks in children, under the condition that every signal can be verified with high quality by local disease control workers. table1. records of children febrile patients in issc and cid in cisdcp table2. numbers of signals generated by shewhart model and sensitivity & ppv keywords syndromic surveillance; shewhart model; children acknowledgments this study was funded by european union’s seventh framework programmer ([fp7/2007-2013] [fp7/2007-2011]) under grant agreement no. [241900]. we thank all the data collectors and participants in issc project in jiangxi sites. references [1]. yan, w., et al., iss—an electronic syndromic surveillance system for infectious disease in rural china. plos one, 2013. 8(4): p. e62749. [2]. the tenth national people’s congress, law of the people’s republic of china on prevention and treatment of infectious diseases. 2004. [3]. babcock, g.d., et al., use of cusum and shewhart charts to monitor regional trends of birth defect reports in new york state. birth defects res a clin mol teratol, 2005. 73(10): p. 669-78. *changming zhou e-mail: 12111020001@fudan.edu.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e180, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen communicable disease, cook county department of public health, forest park, il, usa objective to determine whether unreported cases of potential human exposure to rabies can be detected using an emergency department (ed) syndromic surveillance system and to assess both reporting completeness and compliance with clinical guidelines related to rabies exposures in suburban cook county. introduction rabies post-exposure prophylaxis (pep) can prevent fatal encephalitis associated with exposure to the rabies virus. however, overuse and inappropriate administration of rabies pep are common.1 mandatory reporting of potential rabies exposures provides opportunities for public health practitioners to monitor the appropriateness of pep administration and offer recommendations. in illinois, potential human exposure to rabies, including any person started on pep and any person with contact to a bat, must be reported to the local health authority. previous investigations into the completeness of rabies reporting have concluded that active surveillance in addition to mandatory reporting may be useful.2 as rabies pep is often given in an emergency department setting, syndromic surveillance records may provide a basis for estimating completeness of reporting and identifying candidates for active surveillance follow up. methods emergency department records from 45 local hospitals between 1/1/2013 and 6/30/2015 were queried for chief complaints or discharge diagnoses pertaining to rabies, pep, or contact with a bat. exclusionary terms and manual record review eliminated unrelated visits. cases of potential human exposure to rabies reported to the cook county department of public health (ccdph) during the same time period were extracted from the illinois national electronic disease surveillance system. cases were matched to ed records based on provider, visit date, age, sex, and zip code. the remaining unmatched individuals with ≥2 visits were considered probable unreported instances of pep initiation. demographics of unreported individuals were compared to reported individuals using chi square. results between 1/1/2013 and 6/30/2015, 241 individuals visited local eds with a chief complaint or discharge diagnosis related to bat contact or rabies pep. of these 241, 63 (26%) were previously reported to ccdph. of the remaining 178, 80 (45%) had ≥2 visits suggesting a true instance of unreported pep initiation. reporting of these individuals was less common in winter compared to spring, summer, and fall (18% versus 64%, 48%, 54%, respectively, p=.03). region of patient residence also exhibited an association with reporting ranging from 92% in the south district to 28% in the north district (p<.01). regional trends likely reflect differential reporting behaviors among hospitals in the area, ranging from 100% to 0%. of note, the 63 previously reported individuals identified by syndromic surveillance queries represented only 54% of the individuals reported to ccdph during the same time period, suggesting that underreporting as measured here remains a significant underestimate. based on these results, ccdph instituted a new active surveillance policy for individuals visiting local eds with a chief complaint or discharge diagnosis related to bat contact or rabies pep, retroactive to 1/1/2015. individuals identified through active surveillance will be compared with individuals reported through passive surveillance to assess differences in whether pep was given in accordance with recommendations and administered correctly. conclusions a large proportion of potential human exposures to rabies in suburban cook county are not reported. analysis of syndromic surveillance records is an effective tool for evaluating reporting completeness, identifying targets for active surveillance, and ensuring compliance with clinical best practice and reporting requirements. keywords syndromic surveillance; rabies; post-exposure prophylaxis; evaluation references 1. moran gj, talan da, mower w, et al. appropriateness of rabies postexposure prophylaxis treatment for animal exposures. jama. 2000 aug 23;284(8):1001–7. 2. thiede h, close ns, koepsell j, baer a, duchin js. completeness of reporting of rabies postexposure prophylaxis in king county, washington. j public health manag pract. 2008 oct;14(5):448–53. *kelley bemis e-mail: kbemis@cookcountyhhs.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e10, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, 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bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and 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decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika 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storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e281, 2019 isds 2019 conference abstracts a machine-learning algorithm to identify hepatitis c in health insurance claims data mohammed a. khan2, 1, jae eui soh2, 1, matthew maenner2, william w. thompson2, noele p. nelson2 1 emory university, atlanta, georgia, united states, 2 centers for disease control and prevention, atlanta, georgia, united states objective we developed a machine learning-based algorithm to identify patients with chronic hepatitis c infection in health insurance claims data. introduction hepatitis c virus (hcv) infection is a leading cause of liver disease-related morbidity and mortality in the united states. monitoring the burden of chronic hcv infection requires robust methods to identify patients with infection. insurance claims data are a potentially rich source of information about disease burden, but often lack the laboratory results necessary to define chronic hcv infection. we developed a machine learning-based algorithm to identify patients with chronic hcv infection using health insurance claims alone and compared it a previously developed icd-9 code-based algorithm. methods we obtained insurance claims, demographics, enrollment information, and hepatitis c laboratory results from the ibm marketscan® commercial claims and encounters databases. we defined chronic hcv infection cases as a patient with one or more positive hcv rna result and required controls to have a negative hcv antibody result and no positive hcv rna or antibody results. patients were required to be continuously enrolled in a health insurance plan during the si x months before and after the first positive or negative test result (index date). outpatient and inpatient insurance claims for the six months b efore and after the index date were included in the analyses. the study period spanned from 2011 to 2014. subjects were randomly divided into a training sample (80%) and test (20%) sample. we trained a random forest classifier using age, sex, region, charlson comorbidity index, and variables defining the presence and frequency of 67 icd-9 diagnosis codes and cpt procedure codes related to hcv and liver disease. we up-weighted cases to account for the low prevalence of infection in our sample. we generated forests of 1,000 trees for all models. the initial model included all variables. permutation -based variable importance scores from this initial model were used to select variables for the final model. the previously developed algorithm defined chronic hcv infection as either two claims with codes for chronic hepatitis infection > 60 days apart after an hcv rn a test claim or three claims with codes for chronic hcv infection on different dates after an hcv rna test claim. we compared the predicted classification to hcv laboratory resultdefined classification and calculated percent agreement, kappa, sensitivity, specificity, positive predictive value, and negative predictive value. we then applied the final classifier to all individuals continuously enrolled in commercial and/or medicare supplemental insurance to estimate the prevalence of chronic hcv infection in this population in 2014. analyses were performed in sas version 9.4. results we identified 5,780 (5.6%) cases with chronic hcv infection and 97,831 controls with negative hcv test results. the training dataset consisted of 82,888 individuals with approximately six million inpatient and outpatient claims. the final model included 23 variables related to hepatitis c (e.g., number of hcv rna test claims), liver disease (e.g., cirrhosis diagnosis code), and comorbidities. in the training dataset, percent agreement, kappa, sensitivity, specificity, positive predictive value, and negative predictive value were 99.2%, 0.92, 92.3%, 99.6%, 93.2%, and 99.5%, respectively. the presence of a cpt code for hcv rna testing had the highest variable importance score. the test dataset included 20,723 individuals with approximately 1.5 million inpatient and outpatient claims. in the test dataset, percent agreement, kappa, sensitivity, specificity, positive predictive value, and negative predictive value for the final classifier were 98.9%, 0.89, 89.9%, 99.4%, 89.0%, and 99.4%, respectively. percent http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e281, 2019 isds 2019 conference abstracts agreement, kappa, sensitivity, specificity, positive predictive value, and negative predictive value for the previously devel oped algorithm were 96.3%, 0.50, 35.0%, 99.9%, 96.7%, 96.3%, respectively. among the 35.6 million individuals with continuous commercial and/or medicare supplemental insurance in 2014, 317,932 (0.9%) were classified as having chronic hcv infection. conclusions our machine learning-based algorithm was able to identify chronic hepatitis c cases in commercial health insurance claims data with relatively high estimates for percent agreement, kappa, sensitivity, specificity, positive predictive value, and negative predictive. future analyses and models will explore the ability of the algorithm to estimate the prevalence of hcv infection in different populations covered by different health plan types (e.g., commercial, medicaid, medicare, or no insurance) and for populations where laboratory testing data is not available or collected. http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a meaningful journey to onboard syndromic data through a local hie jeffrey johnson*, jessica yen, brit colanter and eric mcdonald county of san diego public health services, san diego, ca, usa objective this presentation aims to highlight technical approaches, validation activities, outcomes, and lessons learned while onboarding local hospitals through a local health information exchange (hie) for meaningful use stage 2 syndromic surveillance. introduction the federal meaningful use initiative is a major driver to the establishment of expanded electronic syndromic surveillance capacity across the united states. much has been documented about the background and requirements for eligible hospitals to achieve the syndromic meaningful use objectives. however, the role and efforts by public health agencies in the syndromic onboarding process, which varies by jurisdiction, is a significant component of the success of meaningful use. this presentation will highlight the onboarding process developed by the county of san diego, public health services, for working with the local hospitals and the local health information exchange. included will be a review of the technical requirements, project management tools, resources used, challenges encountered, validation techniques, data storage models, the development of performance metrics, and the establishment of protocols for hospital follow-up when detected events using meaningful use. lastly, a plan to discover the meaning within the syndromic information during the upcoming stage 2 year 2 period and the integration of these data into the existing syndromic surveillance system will be described. methods in the process of onboarding, county of san diego, public health services, actively engaged with the hospitals, their electronic health record (ehr) vendors, and the local hie. public health services was actively involved with hl7 message structure validation, connectivity, hl7 message content validation, use case driven testing, and quality assurance. through these steps, a several lessons have been learned that have led to optimized technical approaches to onboarding future hospitals, as well as gaining insight into how the data can be used in a meaningful way. going forward, a dedicated meaningful data discovery phase process is being planned for the first 180 days of stage 2 year 2. this will enable insight into the public health value that can be obtained from the meaningful use data. lastly, the process for integrating the meaningful use syndromic data into the existing syndromic system will be described. results the local health information exchange in san diego incorporated as a 501c3 nonprofit in 2013. in 2014, hospitals began to pursue syndromic meaningful use stage 2 activities. many hospitals have achieved or are on track to achieve ongoing syndromic data submission capacity by the end of 2014. several tools, resources, technical approaches, and data management models have been developed as a result of working with the local hospitals. this includes adherence to specification documents, use of the nist validation tool, and in collaboration with the local hie. preliminary outcomes have shown that it is valuable to have public health involved in the onboarding process and, more importantly, during validation activities. a significant challenge identified is that many hospitals have different information workflows that manifest outwardly in the submitted data. these include differing classification of inpatient and ambulatory related messages, message segment values and how the patient’s treating location is coded. conclusions public health agencies across the united states vary in their capacity to onboard meaningful use syndromic data. at the same time, various technology approaches are currently in place or are in development to collaborate with the hospitals pursuit of stage 2 year 2 attestation. following a collaborative onboarding process with the public health agency as a key stakeholder is critical to understanding and using meaningful use syndromic information. keywords meaningful use; syndromic surveillance; onboarding; health information exchange; validation *jeffrey johnson e-mail: jeffrey.johnson@sdcounty.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e193, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts evaluation of an electronic smart-card based school absenteeism surveillance system dennis ip, eric h.y. lau*, yat-hung tam, teresa so, chi-kin lam, benjamin j. cowling and gabriel m. leung the university of hong kong, hong kong, hong kong objective this study evaluated the performance of an electronic smart-card based school absenteeism system in hong kong, 2008-2014. introduction an electronic smart-card based school absenteeism surveillance system was introduced to hong kong since 2008. the pilot surveillance system initially began with 18 schools in 2008, and expanded to 107 schools in the current academic year of 2013-14. data on all-cause absenteeism were collected from all participating schools and absenteeism due to sickness such as influenza-like illness, gastroenteritis and hand-foot-and-mouth disease were collected from 39 (36.4%) schools. data collected were aggregated for the whole territory on a weekly basis for analysis. temporal trend of influenza activity was disseminated with simple public health advice to each participating schools and the general public through a web-based dashboard [1]. these steps of data aggregation, analysis, and feedback report generation were automated by scripts in the software r which enhanced the timeliness and minimized workload required for maintaining the system. methods we evaluated the school absenteeism system in the period from 1 january 2011 to 31 december 2013 after the system has become more stable. we examined its performance on several aspects, such as acceptability, simplicity, stability, resources requirement and data quality in terms of completeness, representativeness, timeliness and performance in reflecting elevated disease activity. a questionnaire survey was conducted in january 2014 in the participating schools which have joined the surveillance system and contributed for at least 3 years of data. 4 weeks of data were also randomly selected for assessment of data completeness. results 47 of the 72 (65%) eligible participating schools responded to our survey. 80% of the responded schools considered that the system was easy to operate and can be easily learnt by other colleagues when needed, while 63.8% opined that it should be more widely used in local schools. while 44.7% of the schools reported their overall workload of staffs remained unchanged, 36.2% and 19.1% reported increased and decreased workload respectively by the use of this surveillance system. the initial capital cost to install the eclass electronic school management system varied from as low as hk$0 – 1,000 (usd 0-128) to >$50,000 (usd 6,410). this had been considered as “very/ relatively expensive” or “very/relatively inexpensive” by the same proportion of responded schools (38.3%). the main maintenance cost in the school of public health was manpower which cost for about $79,000 (usd 10,128) annually. data reporting from four randomly selected weeks in 2013 (week 5, 10, 23 and 40) were examined which involved a total of 1602 data reporting files from 107 schools. the reporting of two data fields (“number of all-cause absentees” and “total number of enrolled student”) were 100% complete from all participating schools. however, the reporting of data field “number of ili-specific absentees” was only 36.4% from all participating schools. the system took around 4-5 working days from the data capturing to information dissemination. the longest time lag was about 2-3 days from data submission to data analysis, mainly to wait for data submission from the majority of the schools. disease trends reflected by both the all-cause and ili specific absenteeism rate closely followed the general rise, peak, and fall of influenza epidemic as measured by the pre-existing system. 3 of the 5 epidemic peaks matched exactly, and the remaining 2 peaks showed the school-based surveillance occurring 1-3 weeks before the peak shown by other systems. conclusions the result demonstrated the feasibility and potential benefit of employing electronic school absenteeism data as captured automatically by a smart card system as an alternative data stream for monitoring influenza activities, and flexibility in establishing surveillance for emerging diseases. the increasing popularity of usage of smart card technology in various community settings might also represent potentially timely and cost-effective opportunities for innovative surveillance systems. keywords school absenteeism; surveillance; electronic; evaluation references [1] cheng ck, ip dk, cowling bj, ho lm, leung gm, lau eh. digital dashboard design using multiple data streams for diseasesurveillance with influenza surveillance as an example. j med internet res. 2011 oct 14;13(4):e85. *eric h.y. lau e-mail: ehylau@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e135, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts dry climate as a predictor of chagas’ disease irregular clusters: a covariate study luiz h. duczmal*1, gladston j. moreira2, luís paquete3, david menotti2, ricardo takahashi1 and denise burgarelli1 1statistics, universidade federal de minas gerais, belo horizonte, brazil; 2universidade federal de ouro preto, ouro preto, brazil; 3universidade de coimbra, coimbra, portugal objective we employ climate information to assess the possible spatial dependence on the occurrence of chagas’ disease irregular clusters in central brazil, using a variant of the spatial scan statistic [1], the geo-dynamic scan (gdscan) [4]. introduction chagas’ disease, caused by the protozoan trypanosoma cruzi, is spread mostly by triatominae bugs. high carbon dioxide emission and strong infra-red (ir) radiation are indicative of their presence. periods of low atmospheric water saturation favor their dispersal, when the bugs’ ir perception is high [2]. the fast subset scan (fsscan) [3] is very efficient for the detection of the most likely geographic cluster. covariate studies associating the presence of regular clusters with environmental factors are routinely done using the circular scan, the simplest version of the spatial scan statistic. however, if the study employs irregular clusters instead, accurate results depend on the generation of a rich family of variants of the primary cluster. methods gdscan’s bi-objective optimization approach simultaneously minimizes the population and maximizes the number of cases of each candidate solution. the efficient solution set of the bi-objective problem is formed by several candidate solutions, as opposed to the (generally) unique solution provided by other methods, such as the circular scan or the fsscan. each candidate solution z is a nondominated solution, in the sense that any other solution w with more cases than z and smaller population than z cannot exist. the idea is to penalize regions which have humid climates, since the bugs prefer dry places. figure 1 shows minas gerais state in central brazil, indicating chagas’ disease rates (figure 1a) and dryness levels during the driest season, from june to august (figure 1b). the efficient solution set returned by gdscan is then evaluated according to dryness value. the cluster solutions located in drier regions, considered as more plausible, should receive the higher values. for clusters with very similar likelihood ratio scores, the most plausible solution is the one with maximum dryness. results figure 1c shows gdscan’s primary cluster variant with the lowest precipitation level, corresponding to the best solution found which incorporate the driest regions. figure 1d presents variants of the primary cluster provided by gdscan (open circles) and fsscan (crosses); the “better” solutions (more cases within less populated clusters) are located in the lower-left part of the graph. as we can see, primary cluster variants generated by gdscan are better situated in the graph than those generated by fsscan. conclusions the cluster of figure 1c is nearly identical with the most likely cluster found by both gdscan and fsscan (not shown). this is an indication of a strong correlation between dryness and chagas’ disease incidence, supporting previous findings [2]. this information could be useful in chagas’ disease surveillance and prevention. gdscan is computationally more expensive; however, it finds more potentially useful variants of the primary cluster as compared to fsscan (e.g., those variants with more desirable covariate values). chagas’ disease rates (a), precipitation levels with darker shades indicating dryer areas (b), gdscan’s primary cluster variant with lowest precipitation level (c), and variants of the primary cluster, provided by gdscan (open circles) and fsscan (crosses) (d). keywords irregular cluster; spatial scan statistic; chagas’ disease; geo dynamic scan; fast subset scan acknowledgments the authors were supported by cnpq, fapemig and capes. references 1. kulldorff m (1997) a spatial scan statistic. comm. stat: th. meth. 26:1481-1496. 2. catalá ss (2011) the infra-red (ir) landscape of triatoma infestans. an hypothesis about the role of ir radiation as a cue for triatominae dispersal. infection, genetics and evolution 11(8):1891-1898. 3. neill db (2012) fast subset scan for spatial pattern detection. jrssb. 74:337-360. 4. moreira gjp, paquete l, duczmal lh, menotti d, takahashi rhc (2014). multi-objective dynamic programming for spatial cluster detection. env. ecol. stat. (to appear) *luiz h. duczmal e-mail: duczmal@ufmg.br online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e124, 201 study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan dr. nitin k. joshi1*, dr. pankaj bhardwaj2, dr. praveen suthar3, dr. vibha joshi4 1 phd scholar, department of community medicine and family medicine and demonstrator, school of public health, all india institute of medical sciences, jodhpur 2 additional professor, department of community medicine and family medicine and coordinator, school of public health, all india institute of medical sciences, jodhpur 3 research associate, school of public health, all india institute of medical sciences, jodhpur 4 scientist “c”, resource centre, health technology assessment, all india institute of medical sciences, jodhpur abstract objective: asha-soft is the pioneer e-health program which was launched to manage online payment and for monitoring performance of asha workers in rajasthan. there is a paucity of studies which documents the feasibility and effectiveness of this program with aim to assess the feasibility and effectiveness of asha-soft program. methods: study was conducted in jodhpur using quantitative and qualitative method. primary and secondary data approach was used to assess feasibility and effectiveness of asha-soft. purposive sampling was done to recruit 150 asha workers having experience of more than 5 years to capture the perception before and after implementation of asha-soft. qualitative data was also obtained from asha workers and key stakeholders. to assess the effectiveness secondary data was obtained from various sources was analyzed. results: mean age of participants were 35.51 + 6.7 years. most of ashas agreed that asha-soft mediated timely payment (68%) and payment according to their performance (81%). it also increased their motivational level (96%).there were no significant difference in different work experience of ashas and perception towards asha-soft regarding timely payment (p=0.99), improving quality of life (p=0.66) and motivation level (p=0.40). this program has provided standard online procedure of online payment and monitoring for ashas. incentives received by ashas increased to 77%, performance increased by 7% and 9% for maternal health and child health respectively within one year of its initial implementation. conclusions: study finding demonstrate that asha-soft program is acceptable to the users and is effective in terms of meeting organizational requirement. keywords: feasibility study, program evaluation, online system, employee performance appraisal, community health workers study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi introduction: asha-soft is one of the pioneer e-health program which was implemented in rajasthan for the very first time in india [1]. this program was developed by national informatics center, rajasthan for helping medical health and family welfare department, government of rajasthan in managing online payment and performance monitoring of asha (accredited social health activist) workers [2]. ashas are the grass-root-level community workers appointed by ministry of health and family welfare government of india under nrhm (national rural health mission) to function as a primary healthcare facilitators and health service provider [3]. the objective of launching this program were to develop timely and transparent payment system, monitoring performance of ashas, avoid human interference and also to build friendly work relationship between the nrhm and asha workers of rajasthan state [4]. this e-health program like other program has been launched based on the perception that digital health program improve and facilitate the functioning of health system [5]. there is a paucity of published studies which documents the feasibility and effectiveness of this e-health program. understanding feasibility and effectiveness of program allow the decision makers to guide further implementation and replication of the intervention [6]. in this context the current study is planned to highlight the feasibility and effectiveness of asha-soft program. methods: primary and secondary data approach was used to assess feasibility and effectiveness of ashasoft. this study was carried out during november 2019 to february 2020. purposive sampling was done to recruit 150 asha workers of jodhpur who were having experience of working as asha sahyogini for more than 5 years. work experience more than 5 years was considered for this study because asha-soft was implemented in december 2014 and we wanted to capture the perception of those ashas who worked in both scenarios i.e. before and after implementation of asha-soft [2]. to collect quantitative data from asha workers perception based interview schedule on 5 point likert scale was developed using extensive literature search. qualitative data was also obtained from ashas and key stakeholders to meet the desired objective of the study. to assess the effectiveness of asha-soft secondary data obtained from various articles, documents and reports were analyzed. *correspondence: drjoshinitin30@gmail.com doi: 10.5210/ojphi.v12i1.10662 copyright ©2020 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. mailto:drjoshinitin30@gmail.com* study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi result: feasibility of asha-soft and perception of asha workers towards asha-soft program: a total 150 asha workers were interviewed. mean age of ashas were 35.51 +6.7 years. for the study purpose ashas were categorized by experience into three groups 6-8, 9-11 and ˃12 years. around 35% and 38% ashas strongly agreed that asha-soft provide full payment on time and according to their performance respectively. most of the ashas feel that this program has improved their quality of life (77.33%) and has provided feeling of job security in them (52.67%). there was strong agreement among 68% ashas that asha-soft program is a positive change brought by nrhm. table 1 provide the detailed perception of ashas towards asha-soft program. table 1: perception of asha workers towards asha-soft. perception of asha workers in context to asha-soft no. of respondents in % (n=150) mean strongly agree (5) agree (4) undecide d (3) disagree (2) strongly disagree (1) timely payment of incentives 35.33% 32.67% 10.00% 14.67% 7.33% 3.7 appropriateness of program for measuring performance 44.00% 37.33% 10.67% 2.67% 5.33% 4.1 full payment based on work performance 38.00% 26.67% 16.67% 12.66% 6.00% 3.7 creating a good work environment 52.67% 30.67% 4.00% 1.33% 11.33% 4.1 providing improved quality of work life 77.33% 16.67% 3.33% 2.00% 0.67% 4.6 reduced corruption at various levels 46.67% 28.00% 4.00% 10.00% 11.33% 3.8 improving motivational level 61.33% 34.67% 3.33% 0.67% 0.00% 4.5 opportunities for solving complaints 45.33% 28.00% 6.00% 4.00% 16.67% 3.8 study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi consider this program as an optimistic program of the national health mission 68.00% 18.67% 4.00% 1.33% 8.00% 4.3 it was found that there is no significant difference in different work experience of ashas and perception towards asha-soft regarding timely payment (p = 0.9937); improving quality of life (p = 0.6614) and motivation level (p = 0.4063). significant difference (p = 0.0162) was seen for perception regarding addressing complaint for solving payment issues (p-value ˂ 0.05 considered statistically significant). table 2 illustrate difference in work experience of ashas and related perceptions. table 2: different work experiences of asha workers and related perception towards asha-soft program. perception of ashas work experience (years) sum of rank x2* df** pvalue*** timely payment of incentives 6-8 4332.5 0.013 2 0.9937 9-11 4137 12 and above 2855.5 appropriateness of program for measuring performance 6-8 4450.5 3.261 2 0.1959 9-11 4418.5 12 and above 2456 full payment based on work performance 6-8 3939.5 2.377 2 0.3046 9-11 4239 12 and above 3146.5 creating a good work environment 6-8 4146.5 0.761 2 0.6835 9-11 4116.5 12 and above 3062 providing improved quality of work life 6-8 4074.5 0.827 2 0.6614 9-11 4329.5 12 and 2921 study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi above reduced corruption at various levels 6-8 3881.5 3.100 2 0.2123 9-11 4267 12 and above 3176.5 improving motivational level 6-8 4071 1.801 2 0.4063 9-11 4495 12 and above 2759 opportunities for solving complaints 6-8 3713.5 8.247 2 0.0162 9-11 4143 12 and above 3468.5 consider this program as an optimistic program of the national health mission 6-8 4230.5 1.006 2 0.6048 9-11 3997.5 12 and above 3097 x2* = chi square value, df** = degree of freedom, p-value*** = level of significance additionally to gain the qualitative perception interviews with ashas and key stakeholders were conducted. while seeking qualitative perception we also documented challenges and recommendation to improve service delivery through asha-soft program. majority of ashas seems to be satisfied with this program and perceived that it has streamlined their work. one of the asha said that: “we are happy with this initiative and we hope govt. should come up with these type of more programs in future”. when we interviewed data operators they provided a positive perspective toward asha-soft program. one of the data operator who was interviewed mention that: “asha-soft is a user friendly program and it provides convenience for retrieving the relevant information needed at any point of time.” during interview few challenges and suggestions were also provided by the interviewees. an enthusiastic asha recommended that: “we should be provided a copy of submitted claim form so that we can assess our claim status in case of any rejection of incentive”. study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi one of asha supervisor provided a practical challenge and recommendation in context to asha-soft program: “process of updating new details in central server such as change of asha’s catchment area take some time which causes error while providing incentives”. “if a dashboard has separate tab for providing information of all rejected payments then it will be more-easier for us to track the desired information sought by the concerned”. effectiveness of asha-soft: the effectiveness of this program was judged using the guidance provided by who on monitoring and evaluating digital health interventions under the following criteria [7]: changes in process brought by asha-soft: under this criteria we tried to find out that weather and how this intervention has changed processes. for this we compared work process scenario before and after implementation of asha-soft. before implementation of asha-soft, there was non-existing of systematic standard procedures for providing payment to ashas and for monitoring and assessing their work performance [8]. payment was provided using multiple payments points and there was requirement of cash to be maintained at relevant healthcare facilities [8]. there was complex payment mode for ashas as they were provided incentives for various activities under major health services at different time through various channels [9]. there was no designated way time frame for providing payments. this complexity lead to corruption, exploitations and delay in payments. these factors were posing negative impact on ashas and they were de-motivated in providing health services to the communities [10]. monitoring and assessment of ashas were carried out during bi-monthly meetings at phcs, where axillary nurse midwifery was provided information from ashas regarding the progress made and the report was summarized to be provided to medical officer [8]. this process was prone to bias and manual error in analysis and reporting [8], [9]. this program has benefited state health department in many ways [11]. it has provided standard online procedure for capturing beneficiary wise details of services delivered by an asha to the community, providing online payment of asha in their bank accounts based on the details and generating various kinds of reports to aid in monitoring the progress of various programs [12]. implementation of this program made the process of payment and monitoring transparent and eliminated the manual error and bias, hence reducing the corruption and complexities [12], [13]. the time period for providing the payment has been reduced significantly from about 67 days to 12 days [14]. variety of monitoring and analytical reports are generated through the system, which are available and accessible at any point of time [14], [15]. ashas receives payment for their work between 5th to 7th of the every month and additionally a sms is sent to asha’s mobile number as soon as the payment is transferred in their bank account [13], [15], [16], [17]. study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi changes in outcome brought by asha-soft: under this criteria we tried to find out that weather and how this intervention has changed outcomes. we documented the changes in the selected health indicators of rajasthan (related to tasks of asha workers), incentives paid and performance transpired after one year of implementation of asha-soft. asha workers have important role in mobilizing the community and facilitating them for accessing healthcare services such as ante natal check-up (anc), institutional delivery, immunization, family planning, integrated child development services and other public healthcare services [18]. the anc registration in the first trimester is an important indicator which shows the effectiveness of a health service delivery system [19]. in rajasthan proportion of anc registered within first trimester against total registrations of pregnant women increased from 60.7% to 62.8% from 2015-16 to 2017-18. similarly for the same year institutional deliveries increased from 73.85% to 74.83%, full immunization increased from 78.06% to 87.59% and under five child mortality per 1,000 births decreased from 50 to 45 [19], [20], [21]. through asha-soft, asha workers are provided incentives for various activities performed under six major categories of health services viz. maternal health, child health, immunization, family planning, monthly meeting of asha’s, and national health programs [18]. incentives received by asha workers increased to 77% within one year of initial implementation of asha-soft program [8]. [[10], data revealed that there was huge surge in the incentives (42% increased for immunization, 35% for routine monthly activities) provided to asha workers for the same period [8], [9], [10]. after the implementation of asha-soft within one year performance of asha workers increased by 7% and 9%, for maternal health and child health [8]. discussion: this study was conducted to assess feasibility and effectiveness of asha-soft. for assessing feasibility we focused on operational and technical practicality of this program. interview with end users showed that this program is highly acceptable to the users. perception of ashas were exceedingly positive and most of them were in agreement that asha-soft is a proper tool for monitoring performance (mean 4.1), it has improved their quality of work life (mean 4.6) and motivational level (mean 4.5). both ashas and other users perceived asha-soft program as an optimistic initiative taken by medical health and family welfare department, nrhm, government of rajasthan [2], [4], [8]. since experiences can shape perception, we also analyzed whether perception towards asha-soft program depends on work experience of ashas [22]. we found that there was no significant difference in the perception of asha workers with different experiences which indicates that technically asha-soft program is a feasible program and even ashas with less experience can adopt to this program easily. similar findings were seen in study conducted by rathore s. and rai m. (2018) in barmer district of rajasthan [15]. study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi though all the interviewed users were of consensus that this program is a user friendly and technically sound program but they also mentioned few areas of improvement which could be easily adopted such as providing copy of submitted claim forms and creation of separate tab for line listing of rejected claims forms. change in process was evident by transformation which was brought by introduction of ashasoft in context to creating a standardized procedure for performance monitoring and online payment to ashas. it helped drastically in reducing cumbersome clerical process and heavy pen-paper work involved at various level which caused delay in payment by two to three months [8]. to assess the change in outcome we compare the health indicators related to the work profile within one year implantation of asha-soft [8], [10], [17]. we analyzed that most of the indicators were on improving track, additionally we also documented that performance and incentive paid to ashas were upraised [8], [10]. this study indicates that asha-soft program which has made entire health system available on dashboard and has created a well-accepted timely payment and monitoring system is a feasible and effective program [8], [10], [11], [16]. conclusion: the findings of this study demonstrate that asha-soft program is acceptable to the users and is able to meet the organizational requirement. it is a pioneer initiative which has helped in improving health services in rajasthan. references: 1. national health mission. department of medical, health and family welfare, govt. of rajasthan. schemes/programs [internet]. nrhmrajasthan.nic.in. 2015 [cited 22 november 2019]. available from: http://nrhmrajasthan.nic.in/ithmis%20projects%20dt%2002.04.2018.htm 2. national health mission. department of medical, health and family welfare, govt. of rajasthan. asha soft (the online payment and monitoring system) [internet]. nrhmrajasthan.nic.in. 2015 [cited 22 november 2019]. available from: http://nrhmrajasthan.nic.in/asha%20soft.htm 3. mohfw. govt. of india. accredited social health activist (asha): national health mission [internet]. nhm.gov.in. 2019 [cited 22 october 2019]. available from: https://nhm.gov.in/index1.php?lang=1&level=1&sublinkid=150&lid=226 4. national health mission. department of medical, health and family welfare, govt. of rajasthan. asha soft [internet]. nrhmrajasthan.nic.in. [cited 29 november 2019]. available from: http://nrhmrajasthan.nic.in/award/ashasoft%20brochore.pdf study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi 5. who guideline recommendations on digital interventions for health system strengthening. geneva: world health organization; 2019. 1, introduction. available from: https://www.ncbi.nlm.nih.gov/books/nbk541905/ 6. duerdenm, wittp. assessing program implementation: what it is, why it's important, and how to do it. journal of extension [internet]. 2012 [cited 25 december 2020];50(1/v501a4). available from: https://www.joe.org/joe/2012february/a4.php 7. monitoring and evaluating digital health interventions: a practical guide to conducting research and assessment. geneva: world health organization; 2016. licence: cc by-ncsa 3.0 igo.. 8. jainn, toshniwalt, mittala. asha soft online payment & performance monitoring system for ashas in rajasthan [internet]. informatics.nic.in. 2015 [cited 25 january 2020]. available from: https://informatics.nic.in/uploads/pdfs/2ce74d3b_asha.pdf 9. jainn. asha soft – the online payment and monitoring system for ashas ehealth magazine [internet]. ehealth.eletsonline.com. 2015 [cited 25 january 2020]. available from: https://ehealth.eletsonline.com/2015/03/asha-soft-online-payment-monitoring-system-ashas/ 10. jainml, chauhanm, chhabrar, et al.2016. review of online payment system asha soft. int j health sci res. 6(6), 314-19. 11. joshink, bhardwajp, sutharp, jainyk, joshiv, et al.2020. assessment of monitoring and online payment system (asha soft) in rajasthan using benefit evaluation (be) framework. j family med prim care. 9, 2405-10. https://doi.org/10.4103/jfmpc.jfmpc_48_20 12. state institute of health and family welfare. govt. of rajasthan. e-news letter. [internet]. sihfwrajasthan.com. 2015 [cited 27 october 2019]. available from: http://sihfwrajasthan.com/oct%20to%20dec%2015.pdf 13. cips. database of innovative practices [internet]. cips.org.in. [cited 23 october 2019]. available from: http://www.cips.org.in/dbinnovativepractices?id=435&category=health 14. asha-soft. rajasthan received e-health healthcare leader’s award 2015 | national informatics centre [internet]. nic.in. [cited 25 february 2020]. available from: https://www.nic.in/awards/asha-soft-rajasthan-received-e-health-healthcare-leadersaward-2015/ 15. rathores, raim. 2018. an empirical study on performance monitoring and online payment system. sjcc management research review.8(1), 33-45. 16. department of medical, health and family welfare, govt. of rajasthan. an initiative asha soft (the online payment and monitoring system), rajasthan. pwc; 2016. study of feasibility and effectiveness of asha-soft (online payment and performance monitoring system) in rajasthan 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 12(1):e12, 2020 ojphi 17. national informatics centre and national health mission. rajasthan. asha soft: the online payment and monitoring system. presentation presented at; 2015; jaipur, rajasthan. 18. update on asha programme [internet]. nhsrcindia.org. 2019 [cited 25 february 2020]. available from: http://nhsrcindia.org/sites/default/files/update%20on%20asha%20programme_2019_f or%20web.pdf 19. healthy states progressive india: report on the ranks of states and union territories [internet]. social.niti.gov.in. 2019 [cited 18 january 2020]. available from: http://social.niti.gov.in/uploads/sample/health_index_report.pdf 20. standard reports [internet]. nrhm-mis.nic.in. [cited 10 january 2020]. available from: https://nrhm-mis.nic.in/hmisreports/frmstandard_reports.aspx 21. national family health survey-4 2015 -16, state fact sheet rajasthan [internet]. rchiips.org. 2017 [cited 14 january 2020]. available from: http://rchiips.org/nfhs/pdf/nfhs4/rj_factsheet.pdf 22. odendaalw, goudgej, griffithsf, tomlinsonm, leonn, danielsk. healthcare workers' perceptions and experiences on using mhealth technologies to deliver primary healthcare services: a qualitative evidence synthesis. cochrane database syst rev. 2015 nov;2015(11):cd011942. doi: 10.1002/14651858.cd011942. epub 2015 nov 10. update in: cochrane database syst rev. 2020 mar 26;3:cd011942. pmid: 27478408; pmcid: pmc4966615. 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts public health surveillance: challenges and solutions for the road ahead lipika nanda1, 2 and sandeep mahapatra*1 1indian institute of public health bhubaneswar (iiphb), bhubaneswar, india; 2public health foundation of india (phfi), delhi, india objective aim to setup a nodal agency called the centre for public health preparedness (cphp) dedicated towards public health surveillance during disasters in the state of odisha, india. for better public health preparedness in disaster management the objectives of cphp will be to: 1. to strengthen public health surveillance and preparedness for disaster management, through research, capacity building and action. 2. to act as a comprehensive technical support unit and resource centre for state government of odisha, india on public health preparedness for disaster management and to eventually provide similar support to other states of india. 3. to collaborate with national disaster management authority to provide support in surveillance, preparedness and capacity building during public health emergencies. 4. to become a national centre of excellence in the area, in due course of time. introduction the vulnerability of mankind to disasters of various types has increased considerably all over the world1. the situation in india is not better since 55 per cent of india’s landmass is prone to earthquakes; 68 per cent is vulnerable to drought; 12 per cent to floods; and 8 per cent to cyclones apart from the heat waves, and severe storms2. odisha, one of the developing coastal states of india, with a coastline of around 495 km and a population of about 43 million, is most prone to natural disasters like super cyclones and major floods every year. it has witnessed and suffered a number of super cyclones and floods in the last ten years. such conditions give rise to various health hazards and diseases. such an environment demands for a nodal agency which is dedicated towards public health surveillance during disasters in the state of odisha, india. this paper tries to put forth the goals, requirement and role in setting up a centre for disaster preparedness for preparedness and surveillance during disaster. the paper also discusses various challenges and roadblocks in establishing such an institute. methods public health foundation of india (phfi) with collaboration and able guidance from national disaster management authority (ndma), india proposes to set up an organization called the centre for public health preparedness (cphp), targeting public health surveillance during disasters. the initiative in the state of odisha, india will be implemented by indian institute of public health, bhubaneswar (iiphb). indian institute of public health, bhubaneswar is one of the four institutes set up by public health foundation of india (phfi) as a part of its charter to build public health capacity in india. suggested structure of the centre for public health preparedness the proposed centre of excellence/technical support unit would have a team of multi-disciplinary professionals as a strategic unit shown in figure 1. results expected results the cphp for disaster management will bring together experts from across the state of odisha, country and internationally, to set standards for data collection, sharing, and reporting during and after a public health disaster. concretely, this means investing time and resources also in the informal networks that can detect rumours, other alerts, the laboratory network and transport of samples for verification of immediately notifiable diseases more commonly in place. information will be shared regularly with partners to strengthen the early warning component of disease surveillance in crises. through the work of the cphp, morbidity and mortality surveillance tools and training materials can also be developed. conclusions the various perspectives of cphp should be considered to develop strong health surveillance systems during disasters. there is potential for a better surveillance system to provide new capabilities in responding to public health emergencies. keywords disaster preparedness; surveillance; disaster management references 1. kesavan p.c and swaminathan m.s 2006. managing extreme natural disasters in coastal areas.phil.trans. r.soc.a, 364. 2. public policy towards natural disasters in india, centre for budget and government accountability available at http://www.cbgaindia.org/files/working_papers/public%20 policy%20towards%20natural%20disasters%20in%20india. pdf, accessed on july 03, 2014. *sandeep mahapatra e-mail: sandeep@iiphb.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e149, 2015 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts what did syndromic surveillance show during london 2012? lessons for mass gatherings dan todkill1, 2, helen hughes*1, alex elliot1, roger morbey1, obaghe edeghere1, sally harcourt1, brian mccloskey2 and gillian smith1 1public health england, birmingham, united kingdom; 2public health england, london, united kingdom objective we assessed the impact of the london 2012 olympic and paralympic games on syndromic surveillance systems including the incidence of syndromic indictors and total contacts with health care. introduction mass gatherings can impact on the health of the public including importation of infectious diseases, exposure of international visitors to endemic diseases in the host country and the increased risk of bioterrorist activity.1 public health surveillance during mass gatherings therefore affords an opportunity to identify, and quantify any impact (or reassure on the absence of impact) on public health in a timely manner. in preparation for the games, public health england undertook a programme of work to expand the existing suite of syndromic surveillance systems to include daily general practitioner out of hours (gpooh) consultations and emergency department (ed) attendances at sentinel sites.2 these new systems complemented existing syndromic surveillance systems offering the opportunity to monitor trends in patient contacts with gps outside of normal day time opening hours, as well as potentially the more severe end of the disease spectrum which would present at eds. we assessed the impact of the 2012 olympics on national surveillance systems, comparing to periods before and after the games and in previous years and also the impact of specific events during the games. methods the daily syndromic surveillance data gathered during the olympic period 2012 were compared to the comparable surveillance data collected throughout the period preceding and following, and equivalent time period in 2013. results the principal finding was that very few differences were found between years, within both the ed and gpooh systems, nationally or within london. some exceptions were noted; insect bite and sting related indicators were at higher levels during and immediately after the olympic period in 2012 than the equivalent time period in 2013 and an increase in attendances for acute alcohol consumption related diagnoses were observed in london eds around the olympic opening ceremony. a reduction in total daily ed attendances and gpooh contacts during summer 2012 was observed, but a similar pattern was observed in the following year and this appears to be a secular trend coinciding with school holidays. conclusions the 2012 london olympic games had very little obvious impact on the daily number of ed attendances and gpooh contacts both nationally and within london, with what has now been observed to be a normal summer reduction observed across both systems. this highlights the importance for any surveillance system to be used for a mass gathering to have a sufficient amount of baseline information from the same time period in at least one previous year to aid in the identification of any departures from what is considered to be ‘normal’ within each system, at each location at that time of the year. the ed and gpooh systems provided valuable reassurance of no emerging public health issues during the games, with the two items found to have been at higher levels than 2013 being those related to non-infectious causes (i.e. bites and stings) and only one of these having a potential link to the games itself (acute alcohol related ed attendances around the timing of the opening ceremony). keywords syndromic surveillance; mass gathering; olympic games; baseline references 1. tsouros ad, efstathiou pa. mass gatherings and public health the experience of the athens 2004 olympic games. 2007. http://www. euro.who.int/document/e90712.pdf (accessed 13 february 2013). 2. elliot aj, morbey ra, hughes he, et al. syndromic surveillance a public health legacy of the london 2012 olympic and paralympic games. public health 2013;127:777-81. *helen hughes e-mail: helen.hughes@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e167, 201 ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi ehealth literacy and general interest in using online health information: a survey among patients with dental diseases saeideh valizadeh-haghi 1, shahabedin rahmatizadeh 2* 1. department of medical library and information sciences, school of allied medical sciences, shahid beheshti university of medical sciences, tehran, iran 2. department of health information technology and management, school of allied medical sciences, shahid beheshti university of medical sciences, tehran, iran abstract: objectives: the aim of the study is to explore the ehealth literacy and general interest in using ehealth information among patients with dental diseases. methods: a total of 171 patients with dental diseases completed the survey including the eheals. the effect of participants' age, gender and education on ehealth literacy was assessed. spearman’s correlation coefficient was also used to assess the correlation between the importance of access to health information and the usefulness of the internet for decision-making. results: the mean score of ehealth literacy in the participants was 30.55 (sd=4.069). the participants' age has significant effect on ehealth literacy level (t=3.573, p-value=0.002). moreover, there was a significant correlation between the total score of ehealth literacy and the importance of access to ehealth information (r=0.33, n=171, p<0.s001). the difference in ehealth literacy in terms of educational background showed no statistically significant differences (f=1.179, p-value=0.322). discussion: the participants had a high level of ehealth literacy. determining ehealth literacy among dental patients leads to a better understanding of their problems in health decision-making. conclusion: dental institutions efforts should aim to raise awareness on online health information quality and to encourage patients to use evaluation tools, especially among low electronic health literate patients. keywords: ehealth literacy; eheals; dentistry; online health information; consumer health information; patient education abbreviations: electronic health(ehealth) *correspondence: shahabedin rahmatizadeh, email: shahab.rahmatizadeh@gmail.com doi: 10.5210/ojphi.v10i3.9487 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi introduction the internet is regarded as an important resource for obtaining health information (1) and a valuable tool that assists in dealing with all health concerns (2). the world wide web and other technology-based applications have turned into a routine part of health care and public health settings (3), and rather than consulting with health professionals, people are increasingly using these tools as their main resource for seeking information and accessing medical advice (1,4,5). the widespread availability of the internet has facilitated access to information that was previously only available through health professionals (6,7). moreover, using the internet has increased people's knowledge and awareness about medical and health issues and has helped them have greater participation in their own health care through informed decision-making (8,9). obtaining information from the internet is different from gathering information from traditional resources such as books (10,11). due to the complexity of the web, people with no experience of searching the internet for health information will find locating information difficult (4). health information consumers should therefore be able to effectively process and understand online health information as they locate them. in fact, a set of skills is required to enable the use of online health information and informed participation in health care decision-making (12,13). this matter is highly important regarding health issues because of the poor-quality, incorrect and confusing information available on the internet that can be harmful to the patients (2,14,15). in addition, for the proper and correct use of health information obtained from the internet, consumers require a minimum knowledge about relevant sciences and the means of identifying scientific documents and recognizing reliable resources (4). in most cases, the information retrieved from the internet is not properly understood by all groups of people. moreover, to find ehealth information and use it for the purpose of self-care, people need proper techniques for simple and advanced information locating (4). in truth, people need ehealth literacy in order to properly use health information. electronic health literacy (ehealth literacy) is defined as "the ability to search, locate, understand and use health information through electronic resources and use this knowledge to resolve healthrelated problems" (3). it aims to help people make informed decisions about healthcare using ehealth resources (3) and is considered a tool for improving health outcomes and reducing health inequities (16). health professionals should have a proper understanding of the patients' ability to use electronic resources before recommending the use of online health information (12), and it is thus necessary to assess people's ehealth literacy. oral hygiene is regarded as one of the main components of physical health and well-being (17). dental services have an excessive cost throughout the world. from dental plaque to extensive infections, oral diseases can be easily prevented through access to dental care and the effective training of the patients (18). one way of reducing dental costs is to train the public on how to prevent oral and dental diseases. in the past, providing instructions about prevention, diagnosis and treatment was exclusive to the dental team members, but the emergence of the internet made this information largely available to the general public and removed professionals' monopoly on it (19). the internet has helped patients’ self-care by providing information about oral and dental health services and has a potential role in their education and empowerment. the potential benefits of informing and educating patients through the internet include the improved quality of oral and ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi dental care and encouraging the acceptance of healthy behaviors, better compliance with the recommendations and the proper use of the medications. in addition, the internet does and will continue to have a central role in doctor-patient relationships (18). in dentistry, however, there is little information about patients' ehealth literacy. the present study was therefore conducted to: (a) describe the importance and benefits of access to ehealth information resources from the perspective of dental patients; (b) determine the ehealth literacy of dental patients; and (c) identify the factors affecting ehealth literacy. this study hypothesizes that age and education affect ehealth literacy level. methods study samples and setting the study setting was a private dental clinic in the west of tehran that provided a variety of dentistry services, including oral and dental surgery, periodontics, endodontics, general dentistry, orthodontics, pediatric dentistry, restorative dentistry and prosthodontics. this descriptiveanalytical study was conducted using convenience sampling. the participants consisted of all the patients presenting to the clinic from june to september 2017. the patients younger than 18 years old and those with no experience of using the internet for obtaining health information were excluded from the study. a total of 171 adults aged 18 to 60 years participated in the study and 171 questionnaires were completed over three months. many researchers divide adults into three age ranges, including young adults (under 40), middle adults (over 40) and older adults(over 65) (20). therefore, the participants in this study were divided by age into over 40 age group (middle adults) and a ≤40 age group (young adults). the participants were fully briefed on the study and then completed the relevant consent form. this study has been approved by ethics committee of sbmu (ethics code: ir.sbmu.retech.rec.1397.329) data collection tools to measure ehealth literacy in the study population, the ehealth literacy scale (eheals) was used (12). this scale assesses participants' ability to properly search, locate, understand and use ehealth information. the eheals contains eight items scored based on a 5-point likert scale (from ‘totally disagree’ =1 to ‘totally agree’ =5), with the total score ranging from 8 to 40 and higher scores indicating a higher level of ehealth literacy (12). this scale contains two supplementary items designed to assess the study population’s general interest in using ehealth information. the items include the importance of access to the health information available on the internet and the usefulness of the internet in health decision-making, and are scored based on a 5point likert scale (from ‘totally useless’ =1 to ‘totally useful’ =5), with the total score ranging from 2 to 10 (12). the internal consistency of the eheals was assessed and confirmed with a cronbach's alpha of 0.82, which agrees with those reported in other studies (11,12,21). data analysis data were analyzed in spss-16. the effect of participants' age, gender and education was assessed on ehealth literacy. the qualitative variables were described using absolute and relative frequencies and the quantitative variables using the mean and standard deviation. the mean scores of ehealth literacy were compared by age and gender using the independent t-test and by education ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi using the anova. the difference in the scores of "usefulness of the internet for decisionmaking", "the importance of access to ehealth information", "ability to differentiate between quality and reliable online health information resources and poor-quality and unreliable ones", and "adequate self-confidence in using online information for medical and health decision-making" was measured by age and gender using mann-whitney’s test and by education using the kruskalwallis test. spearman’s correlation coefficient was also used to assess the correlation between the importance of access to health information and the usefulness of the internet for decision-making. results: as shown in table 1, the majority of the participants were female (73.7%) and had university education (63.2%). also, most of them (87.7%) were younger than age 40 (table 1). table 1. sociodemographic characteristics of study participants demographics n (%) gender female male no response 126 (73.7) 44 (25.7) 1 (0.6) education high school graduate associates degree bachelor degree master degree phd no response 63 (36.8) 22 (12.9) 53 (31.0) 21 (12.3) 10 (5.8) 2 (1.2) age group 18-40 (young adults) >40 (middle adults) no response 150 (87.7) 20 (11.7) 1 (0.6) the mean score of ehealth literacy in the participants was 30.55 (sd=4.069). given that the maximum score obtainable for this questionnaire is 40, it can be argued that the participants had a high level of ehealth literacy. most of the participants (84.5%) believed that the internet is useful or very useful for health decision-making and access to health information was also important or very important to most of them (89%). regarding the eheals items, figure 1 shows the frequency of the answers to each item. most of the participants (80.7%) stated that they knew how to use the information retrieved from the internet to find the answer to their health questions. the majority (77.2%) also said that they were able to find useful health information resources on the internet (figure 1). ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi figure 1. responses frequencies to eheals items "i can tell high quality from low quality health resources on the internet" received the lowest score (0.908±3.56), indicating that the participants had the greatest difficulty in distinguishing between reliable and unreliable resources (table 2). table 2. mean and standard deviation of eheals items ehealth literacy scale mean std. deviation i know what health resources are available on the internet 3.87 .684 i know where to find helpful health resources on the internet 3.84 .749 i know how to find helpful health resources on the internet 3.83 .696 i know how to use the internet to answer my questions about health 3.96 .685 i know how to use the health information i find on the internet to help me 3.97 .627 i have the skills i need to evaluate the health resources i find on the internet 3.80 .764 i can tell high quality health resources from low quality health resources on the internet 3.56 .908 i feel confident in using information from the internet to make health decisions 3.65 .945 the difference in ehealth literacy between the genders was also assessed, and the mean score of ehealth literacy was higher in women (30.81±4.039) than in men (29.84±4.157), but according to the independent t-test, the difference between them was not significant (t=1.357, p-value=0.177). mann-whitney’s test was used to measure the difference between the genders in the importance ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi of access to ehealth information, the usefulness of the internet in decision-making and also the ability to differentiate between reliable and unreliable online health information resources. the results showed that although the mean scores of the importance of access to ehealth information, the usefulness of the internet in decision-making and the ability to differentiate between reliable and unreliable online health information resources were reportedly lower in men than in women, the difference between them was not statistically significant. moreover, although the mean scores of self-confidences in using information from the internet for medical and health decision-making were reportedly higher in men than in women, the difference between them was not statistically significant (table 3). table 3. the difference in variables score between the genders p-value mean score variable male female 0.541 4.05±0.776 4.14±0.629 the importance of access to ehealth information 0.140 3.82±0.870 4.04±0.592 the usefulness of the internet in decision-making 0.239 3.41±1.106 3.61±0.829 the ability to differentiate between reliable and unreliable online health information resources 0.551 3.72±1.031 3.63±0.919 self-confidence in using information from the internet for medical and health decision-making the difference in ehealth literacy was also examined in terms of education background. the results of the one-way anova for ehealth literacy score between the five different education groups, including the high school graduates (30.46±3.986), the associate degree holders (29.45±4.778), the bachelor's degree holders, the master's degree holders (32.00±3.715) and the phd holders (30.20±4.686), showed no statistically significant differences (f=1.179, pvalue=0.322). the results of the kruskal-wallis test also showed no significant differences between the education groups in terms of the importance of access to ehealth information, the usefulness of the internet for decision-making, the ability to differentiate between reliable and unreliable online health information resources and self-confidence in using information from the internet for medical and health decision-making (table 4). ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi table 4. the difference in variables score between the educational groups pvalue mean score variable phd master degree bachelor degree associates degree high school graduate 0.136 3.90±0.876 4.24±0.436 4.19±0.557 4.32±0.780 3.98±0.729 the importance of access to ehealth information 0.472 4.00±0.471 3.95±0.498 4.11±0.543 4.00±0.882 3.87±0.793 the usefulness of the internet in decision-making 0.301 3.80±1.033 3.71±0.902 3.68±0.872 3.23±1.110 3.51±0.821 the ability to differentiate between reliable and unreliable online health information resources 0.70 4.10±0.994 3.95±0.973 3.65±0.926 3.23±1.193 3.65±0.786 self-confidence in using information from the internet for medical and health decision-making the mean score of ehealth literacy in the over-40 and 40-and-below age groups was measured using the independent t-test. the results showed that the mean score of ehealth literacy was significantly higher in the patients younger than 40 (31.03±3.736) compared to the patients older than 40 (27.05±4.785); (t=3.573, p-value=0.002). based on the results of mann-whitney’s test, the mean score of the importance of access to ehealth information was also significantly higher in the patients younger than 40 (3.80±0.616) compared to the patients older than 40 (4.17±0.642). also, the mean scores of the ability to differentiate between reliable and unreliable online health information resources was significantly higher in the patients younger than 40 (3.61±0.918) compared to the patients older than 40 (3.2±0.696), but no significant differences were observed between the two age groups in terms of the usefulness of the internet for decision-making. although the mean scores of self-confidence in using information from the internet for decision-making was higher in the patients younger than 40 compared to those older than 40, the difference between them was not statistically significant (table 5). ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi table 5. the difference in variables score between the age groups p-value mean score variable <40 18-40 0.013 4.17±0.642 3.80±0.616 the importance of access to ehealth information 0.368 3.99±0.675 3.89±0.737 the usefulness of the internet in decision-making 0.147 3.35±0.933 3.69±0.942 the ability to differentiate between reliable and unreliable online health information resources 0.036 3.20±0.696 3.61±0.918 self-confidence in using information from the internet for medical and health decisionmaking spearman’s correlation test showed a significant correlation between the total score of ehealth literacy and the importance of access to ehealth information (r=0.33, n=171, p<0.001), such that a greater importance of access to ehealth information correlated with a higher ehealth literacy. discussion health promotion through self-care management is regarded as a patient empowerment tool throughout the world (22-24). healthcare organizations and health policy-makers are both interested in involving the patients in their medical decision-making (25). healthcare managers also consider using health information by individuals as a key factor for limiting the costs (26). given that the internet and other new media are used by the general public for dealing with health care concerns, acquiring ehealth literacy skills appears necessary (12). many studies have shown the effect of ehealth literacy on health outcomes. to our knowledge, the present study is the first research to address the dental patients' ehealth literacy and also their understanding of the usefulness of ehealth information. previous studies have shown that more knowledgeable people use more appropriate healthcare facilities and have fewer demands for unnecessary medical tests (27). people with a high level of ehealth literacy are likely to have a high level of medical knowledge and make greater efforts to learn of and carry out screening tests compared to people with a lower literacy (28). in addition, ehealth literacy skills are important for the prevention of diseases and can help patients have a more active role in healthcare decision-making and disease management (29), since online health information empowers patients (30,31). the present findings showed that dental patients have a high level of ehealth literacy. due to their high ehealth literacy and good knowledge about preventing oral and dental diseases, the patients in this study appeared to often make greater efforts ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi to preserve their oral and dental health, and in this way, they could better manage their dentistry costs. nonetheless, regarding the patients' success rate in preventing diseases and maintaining oral and dental health, seeking the views of dentists was also mandatory. the present study showed that access to ehealth information was important to most oral and dental patients, and they considered this information important for decision-making. health information consumers are faced with challenges such as locating, evaluating and effectively using ehealth information for maintaining their health. most of the participants (68%) stated that they had adequate skills for finding useful health information resources on the internet and adequate selfconfidence for using information obtained from the internet for health decision-making (61.4%). nonetheless, only about half of the patients (54.9%) stated that they had the ability to differentiate between reliable and unreliable online health information resources. people should be critical of online health information resources and should not easily accept every information they retrieve on the web (32). it is therefore essential that the patients receive support and training for ehealth literacy from healthcare professionals, especially regarding the quality of health information, so that they can properly evaluate the retrieved information. the present study showed that access to ehealth information and the usefulness of the internet for decision-making had greater importance for patients with higher ehealth literacy. this finding may be due to the fact that people with high ehealth literacy have the skills needed for obtaining health information from the internet and therefore consider using this medium as one of the most important and useful resources for obtaining health information. most of the participants (69%) stated that they knew how to use ehealth information to their benefit. the results of a study conducted by park (2015) on nursing students in south korea were consistent with this finding (29). in a study on the effect of the internet on dentistry, all the dentists believed that the internet is a useful resource for the patients to obtain oral and dental health information, but the patients are likely to wrongly interpret this information (33). dentists' active support to help patients properly understand online oral and dental information therefore seems absolutely necessary. conversations that patients have with the doctors about their health information-seeking behaviors can help prevent the harmful and costly consequences of delayed treatment or risks caused by wrong actions or wrong interpretations of information by the patients (34,35). most of the participants (54.9%) stated that they were confident about recognizing the quality of online health information. a study conducted by the pew internet and american life project on people who use the internet to find information showed that 72% trust a large part of the information they retrieve online (9). patients should be familiar with the limitations of information resources in order to be able to critically assess online health information and effectively cooperate with health professionals in health decision-making (36,37). some studies have shown that patients trust the information they retrieve online and only half of them consult with their doctors about the information obtained this way (38-40). in their search for health information on the internet, people may come across wrong, faulty and even misleading information, and it is necessary for them to recognize the accuracy and credibility of this information. given that the participants of this study believed that they were able to differentiate between reliable and unreliable content, it is necessary to know on what basis they place their analysis of the accuracy and rigor of this information. since ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi the initial assessment of online information is affected by the website design, such that more amateur people trust more attractive websites and consider unappealing and poorly-designed websites as unreliable (41,42), it is important for dental patients to know that the proper understanding of virtual health information requires professional knowledge and a thorough consultation with the doctor (43). dentists should also be prepared to discuss the issues raised by their patients about the information they have retrieved online. one problem is that the lay public (i.e. the non-experts) may not be able to properly assess the quality and accuracy of online health information due to their lack of training and skills. the present study showed that "the ability to differentiate between quality and reliable online health information resources and unreliable ones" was the biggest problem for dental patients. this finding agrees with the results obtained in a study on ehealth literacy in copd patients (44). it is therefore necessary for healthcare professionals to warn their patients about the risks of the improper assessment of the validity of online health information to reduce the destructive effects of using incorrect information by the patients. the assessment of ehealth literacy-related variables these findings present the factors associated with dental patients' ehealth literacy. in this study, the sociological variables including gender and education had no significant relationship with ehealth literacy in oral and dental patients. as for age, the participants were divided into a 18-40 and an over-40 age group, and the ehealth literacy of the younger dental patients was assumed and later proven to be higher, and age was found to be a factor affecting ehealth literacy (t=3.573, pvalue=0.002), as consistent with the results of other studies (6,28,45,46). also, the middle adult patients reported less ability in recognizing the quality of information retrieved online compared to the younger adult patients, which agrees with the results of other studies (6,47). this finding may be due to the fact that young adults have more experience using the internet compared to middle adults and have better access to digital media (7). dentists should therefore be aware that not all patients have the same knowledge for using ehealth resources and younger patients have higher ehealth literacy. it is therefore important to take note of the patients' age when advising them to use the internet and to encourage them to consult with their dentists about ehealth information. another study conducted on dental patients' information-seeking behavior showed that those with higher education display greater motivation for using online information resources (48). people with higher education levels were thus expected to be more capable of recognizing quality and reliable health information resources from poor-quality and unreliable ones, but the results of the kruskal-wallis test showed no significant differences between the different education groups in terms of this ability (p-value=0.301). this issue may be due to the higher knowledge of educated people about the difficulty of recognizing the quality of online health information resources, which must have made them report less ability. education level affects people's internet access and subsequently online search for health information (49). the present study therefore assumed the dental patients' ehealth literacy to be related to their education level, but in line with the results of other studies (28,48) no significant relationships were observed between education level and the score of ehealth literacy. a study measuring ehealth literacy in cardiovascular patients showed that patients with lower education ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi have lower ehealth literacy (50). further studies are required to find a more definitive relationship between ehealth literacy and education level. in the present study, greater confidence in using the internet to get health information for decisionmaking was associated with a higher ehealth literacy score. it should be noted, however, that people who search the web for medical information are mainly those who seek further (or alternative) information about the treatment of a particular disease that answers their health-related questions and helps them make informed decisions (51). nonetheless, this objective is not always easily accomplished, since people who search the web for medical information often encounter contradictory evidence (52). health professionals are concerned about people's use of the internet to identify and treat diseases, because, in the absence of consultation with doctors, patients may interpret or use online information wrongly (53,54). patients also search for further information about their doctors' advices (55); however, they should accept the fact that there are various alternative treatments and interpretations of the symptoms of diseases and even different interpretations of pathophysiologic test results (56). some studies suggest that women use the internet for obtaining health information more than men (57) and often decide to visit a doctor after they have retrieved some health information online (55). given this tendency and the fact that obtaining and properly using ehealth information requires ehealth literacy, women are expected to have greater ehealth literacy than men in the current research. the present study therefore assumed female dental patients to have greater ehealth literacy than male patients, but the results showed no significant differences between the genders in terms of the ehealth literacy level, which agrees with the results of previous studies (6,45,58). a study conducted by aponte (2017), however, showed that men's health literacy is much lower than women's (59). further studies are therefore required on the health literacy of men and women. in addition, the present findings showed that access to online health information is more important to women. it is therefore necessary to warn female patients about the risks of information retrieved from the internet to enable them to use this information in an informed and correct manner. doctors also use the internet as a supplemental tool to search for medical information (32). electronic health literacy is therefore also needed by health professionals, including dentists, to enable them to help their patients obtain more updated, reliable and quality information highlighted. conclusions this study showed that dental patients have a high level of ehealth literacy. it is imperative though to assess the actual ehealth literacy of these patients to reach a definitive understanding. using online health information was important to most oral and dental patients. dental clinics can use the results of this study to publish information on their websites about different oral and dental diseases and the means of their prevention and treatment, so that people can easily access the information they need about the services provided by these centers and decide about their use. ehealth literacy and general interest in using online health information: a survey among patients with dental diseases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(3):e219, 2018 ojphi women had a greater interest in obtaining health information and found it more important compared to men. since women have a key role in maintaining their own and their family's health, this issue may be interesting and beneficial to institutions such as women's health awareness campaigns, especially oral and dental health campaigns. by publishing health information on their own and other reliable websites, these institutions can have a key role in maintaining community’s health, particularly women's health. determining ehealth literacy in oral and dental patients leads to a better understanding of their problems in health decision-making. further empowering oral and dental patients requires more comprehensive studies on the use of the internet and medical services. limitations our findings are hampered by some limitations. at first, our study measured the ehealth literacy of patients based on their self-reports and not actual performance or record of internet use. therefore, their actual online information usage or their actual performance has not been identified. thus, more studies that measure actual use and skill are required. the other limitation is related to the sampling frame. in this study convenience sampling was used because of ease of access to patients. other patients who referred in other period of time may have been less or more ehealth literate. moreover, the study of ehealth literacy in different contexts can get different results. acknowledgements the authors would like to thank professor yasser khazaal and appreciate his kind supports for this study. financial disclosure this article is extracted from the research project (code 14598) which is funded by the school of allied medical sciences, shahid beheshti university of medical sciences. 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porcine epidemic diarrhea virus in the united states yandace k. brown* and tyann blessington national biosurveillance integration center, office of health affairs, department of homeland security, washington d.c., dc, usa objective nbic utilized information from various sources to communicate pertinent information on the emergence of porcine epidemic diarrhea virus (pedv) in the united states in written products to be distributed to its federal partners. introduction the united states department of agriculture (usda) animal and plant inspection service (aphis) announced the first diagnosed case of pedv in u.s. swine in iowa on may 17, 2013. pedv subsequently spread rapidly among the domestic swine herds throughout the united states. as of august 20, 2014, pedv had been detected in 30 u.s. states affecting more than seven million pigs since the epidemic began. in the first year of emergence, pedv reporting was voluntary, but on june 5, 2014, the secretary of agriculture issued a federal order that all cases of swine enteric coronavirus diseases (secds), including pedv, were required to be reported to the usda aphis. pedv only affects swine and is not a public health concern. however, nbic was interested in reporting on this disease due to the economic impact. swine and pork products are valuable contributors to the domestic and international markets. methods on june, 4 2013, pedv first appeared as an item on the national biosurveillance integration system (nbis) reporting list. the daily nbis reporting list is distributed to federal as well as state and local partners that contains brief summaries of the events being monitored. information regarding the progression and milestones of pedv emergence, research, and other developments were obtained from open source media reports and updates collected on the american association of swine veterinarians (aasv) website. recognizing usda aphis as the lead federal agency and one of its partners, nbic coordinated a brief conference call with representatives from usda aphis on december 16, 2013 to discuss the disease emergence and current response to the disease event. as pedv continued to spread within and between u.s. states, the event was elevated to the level of a biosurveillance event report (ber), which was first written on january 24, 2014. results in the beginning, experts had predicted that the economic impact would be rather costly considering the importance of the pork industry to agriculture and the observed high mortality of the virus. while the swine industry and pork prices are still suffering an impact due to pedv, the economic decline is not as drastic as predicted. over the course of the virus’ emergence, six countries have imposed import restriction on u.s. pigs and pork products. at least two of those countries, china and japan, have since lifted their restrictions after the federal order was put into place. the epidemic peaked in the winter of 2013 – 2014. open source reports suggest that this pattern may possibly be due to a harsh winter contributing to viral spread in frozen manure on trucks used to haul pigs between farms and other production locations. animal health experts anticipate pedv in the u.s. to increase again in the fall of 2014 as temperatures grow cooler and outdoor conditions become damper. though seasonality should be considered, the lack of a coordinated response from a single authoritative agency may have been an important factor in the rapid spread of the virus across the country. industry organizations and academic institutions exerted a strong response to the emergence of pedv; however, there was no single agency ensuring coordination between or within states. conclusions without a formal response protocol by a single coordinating body, pedv spread rapidly between states. several sources have proclaimed that the emergence and spread of pedv has awakened the u.s. livestock industry and governments to lapses in current biosecurity systems and the need to increase vigilance to avoid having other dangerous foreign animal diseases (fads) enter the country. though aphis has been actively involved from the beginning of the pedv emergence, the federal order has marked a more formalized and coordinated response, which nbic predicts will exert greater control over the epidemic despite the predicted viral surge in the colder months. nbic is mandated to serve as a federal coordinating center and does not take the lead on any one event. events such as the emergence of pedv present opportunities for collaboration with federal partners. keywords pedv; swine; coordination; biosecurity *yandace k. brown e-mail: yandace.brown@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e111, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 1queen’s university, kingston, on, canada; 2kingston, frontenac, and lennox & addington public health unit, kingston, on, canada; 3public health ontario laboratories, toronto & kingston, on, canada objective explore the use and feasibility of self-swabbing mediated by a telephone health helpline (thhl) as a complementary tool for surveillance of influenza and other common respiratory viruses in ontario, canada. introduction currently, three main sources of data are used to monitor the prevalence of influenza in ontario: public health agency of canada’s (phac) fluwatch, ontario’s acute care enhanced surveillance (aces) data and public health ontario’s (pho) traditional laboratory data. however, a limitation of these data sources is that it typically underestimates the burden of infection in populations living in remote communities and/or populations with less severe symptoms. this study describes a self-swabbing surveillance system mediated by a thhl that uses syndromic surveillance tools to recruit and monitor participants with influenza-like illness. the intent of this system is not to replace, but rather to complement other surveillance systems and clinical based testing for influenza, thereby extending the reach of surveillance through the use of self-swabbing. an additional rationale for this type of surveillance system is that it can reduce transmission of infection by limiting the number of visits to emergency departments or doctors’ offices, thereby reducing contact with the young and elderly populations, who are at most risk for infection. methods recruitment and system operation: participants were recruited through a thhl available to all residents of ontario. callers were triaged based on assessment by a registered nurse and deemed eligible to partake in the study if classified under the “referral” or “self-care” categories. participants must also be at least 2 years old, and have one of the following symptoms: fever, cough, sore throat or coryza. upon agreeing to participate, participant information was collected and a self-swabbing package, including a swabbing kit, consent form, questionnaire, and description of the study with instructions for participation, was sent out the next business day. the participant completed the documentation, used the nasal swab to obtain a specimen, and returned them to the laboratory. data was then collected and the swab was tested for influenza viruses. analyses and evaluation: the questionnaire data and laboratory results were used to evaluate the feasibility of the surveillance system. evaluation included a descriptive analysis of the population captured by these methods and a basic assessment of the operational aspect of the system. detection of respiratory viruses using the self-swabbing methodology was reported based on the molecular test results. furthermore, the timely detection of influenza emerging in ontario was compared to other surveillance systems that are routinely used in the province via peak comparison methods. results a total of 2431 participants were successfully sent a package and 666 (27.40%) returned a package with consent, thus their samples were tested. the mean and median number of days between the time of call to the thhl and the time a package was received at the laboratory was approximately 10.4 and 8.6 days, respectively. the time between swab collection and package reception was 4.9 days on average, or a median of 4 days. given these timelines, selfswabbing proved to be a viable method of detecting influenza and other respiratory viruses as 279 (42%) specimens tested positive for a virus by molecular methods. in terms of early detection, this surveillance system adequately captured the 2014 influenza b season in a timely manner when compared to data generated by fluwatch, aces, and pho’s traditional laboratory data; however, it did not necessarily detect the emergence of influenza b any earlier than alternative sources. surveillance of influenza a by our system was also evaluated; however, the number of cases of influenza a peaked approximately two weeks after that reported by pho’s data. conclusions this study demonstrated the potential a thhl-mediated selfswabbing surveillance tool has for capturing data on a population that is not typically included in existing surveillance methods, and for the timely detection of influenza for surveillance purposes. certain limitations of this study made for a challenging evaluation of the system. despite these challenges, this surveillance system was able to obtain viable specimens for laboratory testing and capture seasonal patterns of influenza b in time with the aforementioned alternative surveillance systems. keywords influenza; self-swabbing; surveillance; respiratory viruses; health helpline *danielle mcgolrick e-mail: 8dcm3@queensu.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e24, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala 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swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a an informatics solution for informing care delivery of immediate public health risks to their patients an informatics solution for informing care delivery of immediate public health risks to their patients 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 an informatics solution for informing care delivery of immediate public health risks to their patients joseph s lombardo 1 , nedra garrett 2 , wayne loschen 1 , richard seagraves 1 , barbara nichols 2 , steven babin 1 1 the johns hopkins university applied physics laboratory 2 the national centers for disease control and prevention abstract: this paper describes a public health alerting approach that has the potential to improve patient care during a public health outbreak and reduce healthcare costs, streamline the process of public health alert management and dissemination, and heighten the crucial feedback loop between public health officials and clinicians. the approach ties public health alerts into the diagnostic process and allows clinicians to more easily determine when an observed medical condition may be related to a more widespread disease outbreak. a prototype alert knowledge repository (akr) service using this approach was demonstrated within the health information and management systems society (himss) and the public health information network (phin) interoperability showcases in april and september 2009, respectively. 1. introduction over the past several years, new public health risks have become increasingly visible in the news media. when the west nile virus was introduced to the united states in 1999, there was little experience in u.s. healthcare communities with this virus, which can cause a severe neuroinvasive disease in about 20 percent of those infected [1]. beginning in february 2003, a communicable respiratory disease known as severe acute respiratory syndrome (sars), later found to be caused by a previously unknown coronavirus, rapidly spread from china to 37 countries [2] in a matter of months, resulting in 8,098 known cases and 774 deaths. in the summer of 2008, peppers containing salmonella serotype saintpaul caused the largest recorded food borne outbreak in the united states [3]. in 2009, the world health organization (who) declared that the spread of a novel h1n1 variety of influenza had reached phase 6 pandemic levels [4]. these new and emerging diseases pose widespread public health risks and highlight the need for closer communications between health care providers and public health officials responsible for monitoring health risks. this need is greater in an environment where additional demands are being placed on limited public health resources. in the fall of 2008, the u.s. centers for disease control and prevention (cdc) convened a meeting to discuss an informatics solution linking the health care community to the immediate concerns of public health. the meeting resulted in collaboration among the cdc, general an informatics solution for informing care delivery of immediate public health risks to their patients 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 electric (ge) healthcare, the university of utah (ut), and the johns hopkins university applied physics laboratory (jhu/apl). in particular, the participants determined that electronic medical records (emrs) may potentially enhance the delivery of care by having the most recent public health alert information available for the clinician during the encounter with the patient. an emerging technical specification for emrs, known as the t.81 or the retrieval of medical knowledge transaction construct [5], uses the context-aware information retrieval (infobutton) as a mechanism to obtain additional health information. using the t.81 emr transaction, jhu/apl created an alert knowledge repository (akr) that permits clinicians to view relevant high priority public health alerts that correspond to the patient they are currently seeing. this article describes a prototype akr that was demonstrated within the interoperability showcases of two national conferences. 2. background an akr is an electronic collection of all active public health alerts of importance to the health care of the population. for example, mcdonald et al. [6] described a local health information network established in indiana that included medical record information from fifteen hospitals, and served public health functions for county and state health departments. maro et al. [7] discussed potential uses of a more general distributed health data network and described how such a network might serve the needs of multiple users, including the u.s. food and drug administration. mcmurry et al. [8] discussed the feasibility of an architectural model of a national health data network that uses the shared pathology informatics network [9] as a template for protecting patient privacy and providing authorized access. hills et al. [10] described demonstrations of a model health information exchange (hie) that uses existing semantic and syntactic standards to support data transfer in three types of scenarios: biosurveillance, case reporting, and vaccination. the cdc established a nationwide health alert network (han) that provides communications, information, distance-learning, and organizational infrastructure to link local and state health departments to each other and to community first-responders, hospitals and private laboratories. the cdc han messaging system is currently capable of directly and indirectly transmitting health alerts, advisories, and updates. however, transmission is unidirectional and occurs when the state health department transmits an alert, instead of being viewed on demand by the recipient [11]. the concept presented in this article discusses the functions of a prototype akr that supports the bidirectional exchange of timely public health information using information technology standards, the internet, and the emerging nationwide health information network (nhin). the objectives of nhin include the establishment of a secure, interoperable network for exchanging health information among state and local governments, community health centers, laboratories, pharmacies, and clinicians. this network will support requests for deidentified health information, with the possibility of case follow up in the event of a public health emergency. among the public health benefits are the early detection of and rapid crossjurisdictional response to infectious disease outbreak and improved disease management by clinicians treating patients affected by these outbreaks. the jhu/apl center of excellence in public health informatics working with the cdc defined the initial specifications for an akr. the akr uses the t.81 construct to communicate with the ge healthcare centricity emr. the akr concept of operations is shown in figure 1. an informatics solution for informing care delivery of immediate public health risks to their patients 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 public health departments can author, view, and edit health alerts with descriptions of case definitions in the akr. using the patient data entered in an emr and the infobutton feature of the system, a clinician can trigger an automated query of the akr that includes a de-identified patient profile. the akr then matches all health alerts relevant to the chief complaint and demographics of the patient, and replies to this query by sending all active public health alerts pertaining to the current health conditions of the patient. public health alerts retrieved this way can contain the information on the public health event (e.g., case counts, location; disease details; diagnostic procedures; prevention and treatment options; and reporting information). the emr system can record the alerts viewed by the clinician for further reference and provide a mechanism to allow the clinician to give feedback on the relevancy of the alerts. this feedback may be conveyed back to the akr, providing public health officials with data on diagnostic relevance of the generated alerts, alert usage, and potential new cases. by intimately tying the public health alerts into the diagnostic process, clinicians may be able to more easily determine when a given medical condition they see in their patients may be related to a more widespread disease outbreak. the public health alerting approach described herein has the potential to improve patient treatment and care in the face of developing disease outbreaks, and may reduce healthcare costs, streamline the process of public health alert management and dissemination, and heighten the crucial feedback loop between public health officials and clinicians. 3. design objectives the objective of this research is to demonstrate an easy-to-use technical architecture and prototype system to improve communication between public health officials and clinicians. to evaluate the concept and architecture, a prototype akr was developed that met the functional requirements described below: 1) the akr must provide an alert management web interface (amwi) for public health officials to author, monitor, and maintain alerts within the repository. the interface must be user friendly, web-based, and rely on standard computer messaging protocols. the amwi must have security features that limit access to only those public health officials responsible for entering and maintaining alerts, while access at another level will permit public health and clinical care workers to view but not modify existing alerts. a desired feature of the authoring function is to provide easy-to-use templates for public health users to create public health alerts. these templates would ease the authoring of alerts, and when entering a disease would search available guidance on the recognition and management of the disease to pre-populate the alert with relevant information needed by the care delivery personnel. 2) the akr must leverage the increasing presence of emr systems and the standardized interfaces for those systems. the system should allow clinicians to request public health alerts based on current data in the patient’s emr. the clinicians should be able to receive public health guidelines and protocols for their patients with symptoms that match the symptoms of the health condition specified in an informatics solution for informing care delivery of immediate public health risks to their patients 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 the public health alert database. the system should also provide clinical providers with up-to-date information on the event, number of cases, any particular population at risk, as well as information on diagnostic evaluation, treatment, prevention, and reporting. 3) the akr must include a correlating function that matches the characteristics of a patient’s present illness with elements of the case definition in the public health alert. to perform this function, the akr should include a natural language processor for categorizing free-text entries of a patient’s chief complaint. figure 1 alert knowledge repository interfaces the prototype akr system was developed and demonstrated on the basis of the functional requirement described above. in the future, the akr may be used to:  demonstrate the transmission and integration of public health information into the clinical workflow;  determine if public health alerting systems can be leveraged to identify specific patients with risk factors related to the health condition identified in the alert;  evaluate the impact using qualitative approaches based on clinicians’ behaviors; an informatics solution for informing care delivery of immediate public health risks to their patients 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009  determine if this functionality should be considered as a criterion for electronic health record certification. 4. system description to create the akr, jhu/apl software developers modified a structured messaging software application called infoshare [12], which was previously developed to share information among public health agencies within the electronic surveillance system for the early notification of community-based epidemics (essence) disease surveillance system [13]. infoshare was developed in close collaboration with local public health agencies as part of a public health translational research effort [14]. infoshare protects the information from unauthorized access, while allowing surveillance monitors in one public health agency to interpret their own data and share their analyses with surveillance monitors in other public health agencies. to mitigate personal health information privacy concerns, the information messages being shared adequately describe the public health situation without requiring identifiable data to be exposed. by using a grid-enabled web-services interface, a modified standalone version of the infoshare application was created to share information with users of the national center of public health informatics (ncphi) public health research grid [15]. the web-interface component of the service provides a user-friendly means of data entry and promotes the entry of information into structured fields to enable computer readability. these structured fields permit the information to be used by other visualization, algorithm, decision support, and messaging tools. the infoshare application was originally designed to provide a simple and structured method for sharing public health information among public health jurisdictions. for the akr, the application was modified to provide for the posting of public health alerts derived from epidemiological investigations or from automated syndromic surveillance system alerts. when such investigations are determined by public health officials to be considered of epidemiological and clinical significance, the public health officials may then automatically upload the alert information via the amwi interface. the archive of alerts provides a repository of immediate public health concerns that may be viewed by public health agencies and also queried by clinicians requesting alert information through their emr. software developers from ge healthcare made modifications to ge’s centricity emr to enable it to connect to the prototype akr. this concept was then demonstrated using the jhu/apl akr as a web service, the ge centricity emr with the t.81 construct implemented, and the internet. figure 2 illustrates the internal components of the akr, including the amwi user interface, a chief complaint text processor, an alert/patient relevance processor, a t.81 message request receiver, a hyper text markup language (html) alert explorer, and the alert database. an informatics solution for informing care delivery of immediate public health risks to their patients 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 health care encounters public health officials akr archive w e b u i in te rf a ce alert viewer alert authoring e m r i n te rf a ce alert /patient relevance processor html alert exporter t-81 request rcvr web services interface templates chief complaint processor figure 2 components of the alert knowledge repository 4.1 the alert management web interface (amwi) the amwi allows the public health official to create an alert. the amwi contains authoring tools and a viewer. if the public health entity uses the essence disease surveillance system [13], then alert parameters can be transferred to akr automatically after review by the public health official. if the official does not have a disease surveillance system that has this feature, the authoring tool shown in figure 3 can be used to create an alert within akr. akr public health alerts contain information including alert date, disease/condition, affected geographic region, relevant case data, as well as recommendations on diagnostic evaluation, treatment, prevention, and reporting. the public health official may enter text comments and color code them according to their level of concern, ranking from highest to lowest as responding, investigating, monitoring, not concerned, and information. these comments may describe the individual public health agency’s interpretation of their data. only a limited number of the fields in the alert message are shown in figure 3. an informatics solution for informing care delivery of immediate public health risks to their patients 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 figure 3 alert management web interface authoring tool a library of disease templates may be used so that once the disease is entered by the public health official, much of the remaining form can be pre-populated with relevant disease, treatment, and outbreak response information. health alerts residing in the akr database are available for retrieval by both public health and healthcare users. figures 4 and 5 present alerts within the akr as viewed from the amwi interface. the upper portion of the amwi display (see figure 4) provides a list of the current alerts. data elements presented include concern level, the title of the alert, the author of the alert, the beginning and end of the alert, and the date the alert was last modified in the akr. the last data element is an attempt to place the alert into a syndrome group. the lower portion of the amwi display (see figure 5) contains the details of the alert selected. these details include the alert identifier, the author, the urgency, the disease extent of the outbreak, locations where it has been identified, symptoms, information regarding the disease and its management, and any other information public health agencies would like to pass to the clinician. an informatics solution for informing care delivery of immediate public health risks to their patients 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 figure 4 upper portion of the alert management web interface an informatics solution for informing care delivery of immediate public health risks to their patients 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 figure 5 lower portion of the alert management web interface an informatics solution for informing care delivery of immediate public health risks to their patients 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 the amwi interface permits users to quickly search for alerts within the akr. the search criteria at the top of the amwi display provides a filter for disease categories and concern levels for alerts that are contained within the akr. as shown in figure 6, selecting the salmonella category displays only active salmonella alerts. figure 6 filtering alerts within the akr using the search criteria function 4.2 the emr interface the akr has an emr interface that permits a user of an emr system to view patient-specific alerts contained within the akr. when the physician’s office receives the patient, the patient information and current condition is entered into an emr. the proposed infobutton function in the emr generates a query to the akr to see if patient-specific information matches an active public health alert. the emr initiates a t.81 transaction query [5] to the akr. first, the query generates a de-identified profile that includes basic demographic data and symptom information, then sends the profile as a healthcare information technology standards panel (hitsp) t.81 request to the akr. this transaction enables the request and receipt of emr health information based on specific context parameters using health level 7 (hl7) context-aware information retrieval, which is an american national standards institute (ansi) standard. using any hitsp t.81 compliant third party emr, authorized users can query for public health alerts relevant to their own particular patient’s chief complaint, demographics, and geographical location. this query returns a report of matching health alerts that may include diagnostic and treatment information. an informatics solution for informing care delivery of immediate public health risks to their patients 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 the alerts in the akr are matched with the clinician’s emr-based query (figure 7). the de-identified patient profile is produced by the emr as shown on the right hand side of the figure 7. figure 7 matching an alert to an emr query the chief complaint is a brief description of the patient’s stated or primary reason for the health care visit. chief complaints are recorded as free text and may contain misspellings, acronyms, and abbreviations. included in the akr query logic is jhu/apl’s chief complaint parser [16]. the chief complaint parser is a natural language processing system that can parse a chief complaint text into disease-related syndrome categories. in the akr, these categories are mapped via a lookup table to a set of diseases/conditions that are matched in the public health alert. an alert/patient relevance processor matches the emr query with alerts in the akr database. to facilitate matching the health alert with the patient’s data, the chief complaint text processor [16] expands abbreviations and acronyms and performs a parsing of the text (e.g., “diarrhea” and “abdominal cramps”). in the example shown in figure 7, the akr contains an alert that closely matches the patient’s parameters. matches are made on chief complaint, patient demographics (age, race, and gender), and jurisdiction (zip code) in the first instance of the akr. future enhancements are planned that provide a more extensive set of matching parameters. an informatics solution for informing care delivery of immediate public health risks to their patients 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 the query from the emr system to the akr returns the results of the match to clinicians in the form of a web page using a hypertext transfer protocol (http). the returned alerts are viewed within the emr and have the potential to aid in forming a diagnosis and suggesting relevant treatment options. a sample web page returned by the akr in response to a t.81 query is shown in figure 8. figure 8 http formatted results for a health alert query each alert message is a structured collection of fixed-value and free-form data fields. the structured message approach was selected to represent public health alerts. the authors in partnership with public health experts defined a message structure to support the development of a fully configurable repository with fields that sufficiently describe the public health alert. the structure also supported the ability to add special instructions to the alert in the akr to prompt clinicians to ask specific questions or perform more specific laboratory or radiology tests to confirm or exclude their patient from the public health risk. additionally, other information that may impact the treatment decisions of the clinician for their patients may be provided. an informatics solution for informing care delivery of immediate public health risks to their patients 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 5. results the akr prototype was successfully demonstrated within the interoperability showcase at the healthcare information and management systems society (himss) 2009 conference in chicago, il, in april 2009, and again at the seventh annual public health information network (phin) conference in atlanta, ga, on august 30 through september 3, 2009. these demonstrations were conducted in collaboration with the cdc, ge healthcare, and the regenstrief institute. cdc personnel populated the akr with several public health alerts, ge healthcare and regenstrief institute accessed the akr through their emr systems to demonstrate interoperability, and jhu/apl provided the akr. as a result of these live demonstrations, a number of previously unidentified requirements were discovered, as well as gaps in existing hitsp standards. for example, the t.81 requests standard returns query results in an unspecified format and it was found that requiring html retrieval allowed the query results to be viewable in the ge emr software. additional requirements for the full akr were initiated based on the results obtained from these demonstrations. these include the ability to provide feedback to the health departments based on the number of alert matches. 6. discussion to better communicate public health alerts to clinicians and improve disease incidence reporting to public health officials, the akr concept was developed by collaboration among jhu/apl, cdc, and ge healthcare. the feasibility of this concept and the corresponding technical architecture were successfully demonstrated by a functional prototype of an akr service at two major national public health informatics conferences. this akr service utilizes a web-based secure interface to allow authorized public health officials and clinicians to view and query a database of public health alerts created by public health officials. these public health alerts contain information that may include free text descriptions and interpretations, links to web sites with additional text and graphical information, and color-coded levels of concern provided by the public health official. these alerts are processed and archived so that they may be shared among public health agencies and queried by clinicians. clinicians can use the patient’s emr to perform a query to find a match between a de-identified profile describing their patient’s symptoms and any public health alerts that may be of concern. therefore, the public health alerts are available to the physician at the time and point of care in lieu of an e-mail or fax distributed via an alerting system. in the future, the collaborators plan to promote this akr in the nhin environment [15]. still to be determined are whether akr instances should be local, regional, or national, along with issues such as the logistics of how to synchronize local information with regional or national federated systems. however, the akr service already has the potential to allow bidirectional communication between clinicians and public health officials with minimal risk to patient privacy. the public health officials would be able to obtain improved disease incidence information and contact information of the physician or hospital seeing the relevant patients so that they may assist in follow-up and public health intervention. to reduce the burden placed on clinicians and prevent “alert fatigue,” the emr system could be used to mark alerts as “acknowledged” to ensure that previously viewed alerts are not constantly re-transmitted for a an informatics solution for informing care delivery of immediate public health risks to their patients 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 given patient profile. the clinician is immediately able to determine if the patient they are seeing is potentially part of a larger public health concern and may obtain advice on how to address relevant public health concerns and possible case management. by improving the communication between public health officials and clinicians, the akr potentially may help improve patient care and provide for more effective and efficient case identification, public health alert management, and disease outbreak control. acknowledgements this work was supported by grant number p01-hk000028-02 from the u.s. centers for disease control and prevention (cdc). its contents are solely the responsibility of the authors and do not necessarily represent the official views of cdc. references 1) hayes eb, komar n, nasci rs, montgomery sp, o’leary dr, campbell gl, 2005. epidemiology and transmission dynamics of west nile virus disease. emerging infectious diseases 11(8): 1167-1173. http://dx.doi.org/10.3201/eid1108.050289a 2) smith rd, 2006. responding to global infectious disease outbreaks: lessons from sars on the role of risk perception, communication and management. journal of social science and medicine, 63, 3113-3123. http://dx.doi.org/10.1016/j.socscimed.2006.08.004 3) jungk j, baumbach j, landen, m, gaul lk, alaniz l, dang t, miller ea, weiss j, hedican e, smith k, grant f, beauregard t, bergmire-sweat d, griffin d, engel j, cosgrove s, gossack s, roanhorse a, shorty h, cheek j, redd j, vigil i, 2008. outbreak of salmonella serotype saintpaul infections associated with multiple raw produce items – united states, 2008. mmwr 57(34):929-934. 4) world health organizations, 2005. who pandemic phase descriptions and main actions by phase, can be found at: http://www.who.int/csr/disease/swineflu/frequently_asked_questions/levels_pandemic_alert/en/i ndex.html (accessed 23 october 2009). 5) healthcare information technology standards panel, 2009. hitsp retrieval of medical knowledge transaction: hitsp/t81. version 1.1, july 8, 2009. american national standards institute, washington, dc. available on-line at http://www.hitsp.org/constructset_details.aspx?&prefixalpha=3&prefixnumeric=81 (accessed 23 october 2009). 6) mcdonald cj, overhage jm, barnes m, schadow g, blevins l, dexter pr, mamlin b, inpc management committee, 2005. the indiana network for patient care: a working local health information infrastructure. health affairs 24(5): 1214-1220. http://dx.doi.org/10.1377/hlthaff.24.5.1214 7) maro jc, platt r, holmes jh, strom bl, hennessey s, lazarus r, brown js, 2009. design of a national distributed health data network. annals of internal medicine 151:341-344. http://dx.doi.org/10.7326/0003-4819-151-5-200909010-00139 http://www.who.int/csr/disease/swineflu/frequently_asked_questions/levels_pandemic_alert/en/index.html http://www.who.int/csr/disease/swineflu/frequently_asked_questions/levels_pandemic_alert/en/index.html http://www.hitsp.org/constructset_details.aspx?&prefixalpha=3&prefixnumeric=81 an informatics solution for informing care delivery of immediate public health risks to their patients 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * vol.1, no. 1, 2009 8) mcmurry aj, gilbert ca, reis by, chueh hc, kohane is, mandl kd, 2007. a self-scaling, distributed information architecture for public health, research, and 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and the framework for evaluating syndromic surveillance systems. mmwr sep 24, 2004. 53(suppl):159-165. 14) westfall jm, mold j, fagnan l, 2007. practice-based research "blue highways" on the nih roadmap. jama 297(4):403-406. http://dx.doi.org/10.1001/jama.297.4.403 15) u.s. department of health and human services, 2008. hhs enterprise transition strategy 2008, version 1.0, february 2008, us dhhs enterprise architecture program management office. us government printing office, washington, dc. available at http://www.hhs.gov/ocio/ea/documents/proplans.html (accessed 23 october 2009). 16) sniegoski ca, 2004. automated syndromic classification of chief complaint records. johns hopkins apl technical digest 25(1): 68-75. http://www2a.cdc.gov/han/index.asp http://www.hhs.gov/ocio/ea/documents/proplans.html isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts development of syndrome definitions for acute unintentional drug and heroin overdose em stephens* virginia department of health, richmond, va, usa objective to develop and evaluate syndrome definitions for the identification of acute unintentional drug overdose events including opioid, heroin, and unspecified substances among emergency department (ed) visits in virginia. introduction nationally, deaths due to opioid overdose have continually increased for the past 15 years1. deaths specifically related to heroin increased more than four-fold between 2002 and 20142. hospital inpatient discharge data provide information on non-fatal overdoses, but include a significant lag in reporting time3. syndromic ed visit data provide near real-time identification of public health issues and can be leveraged to inform public health actions on the emerging threat of drug overdose. methods virginia department of health (vdh) developed two syndrome definitions in 2014 to capture acute unintentional drug overdose events among syndromic ed visit data. syndrome 1 captured visits for overdose, whether or not a specific substance was mentioned. syndrome 2 captured only visits for heroin overdose. definitions were based on free-text terms found within the chief complaint and standardized text or international classification of diseases (icd) codes within the diagnosis field. in 2016, both definitions were revised to identify additional inclusion and exclusion criteria according to cdc guidance documentation and syndrome definitions used by other state jurisdictions. microsoft sql was used to modify both definitions based on the newly identified chief complaint and diagnosis criteria. record level data were analyzed for their adherence to established criteria using an iterative evaluation process. the scope of syndrome 1 (2016) was narrowed from the 2014 version by excluding visits for non-opioid substances, heroin, and non-acute indicators. it included chief complaint and diagnosis terms related to opioids, unspecified substance overdose, narcotics, and narcan or naloxone, and excluded terms related to suicide, alcohol overdose alone, withdrawal, detoxification, rehab, addiction, constipation, chronic pain, and any specified non-opioid drug or medication. syndrome 2 (2016) included chief complaint or diagnosis terms mentioning heroin overdose and excluded suicide, withdrawal, detoxification, rehab, and addiction. visits with mention of suicide, rehab, or addiction were identified during the evaluation process, resulting in the exclusion of these terms in the revised query. from january 1, 2015 to july 31, 2016, the number of visits captured by the revised syndrome definitions was compared to the number captured by the 2014 definitions. correlation coefficients were calculated using sas 9.3. results the revised syndrome 1 found 4296 fewer ed visits (29% decrease) for acute unintentional drug overdose between january 1, 2015 and july 31, 2016 compared to the 2014 definition. despite the drop in volume, the monthly trends were similar for the 2014 and 2016 definitions (correlation coefficient = 0.95, p < 0.001). for the same time period, the revised syndrome 2 definition returned 108 fewer visits (6% decrease) for acute unintentional heroin overdose. the monthly trends were also similar for the 2014 and 2016 definitions (correlation coefficient = 0.98, p < 0.001). conclusions both revised syndrome definitions improved specificity in capturing overdose visits as syndrome 1 (2016) identified 29% fewer visits and syndrome 2 (2016) identified 6% fewer visits found to be unrelated to the desired overdose criteria. when developing the revised syndrome definitions, vdh decided to exclude non-acute drug-related visits. terms such as addiction, detoxification, rehab, withdrawal, chronic pain, and constipation were indicative of habitual drug use or abuse instead of acute overdose and were thus excluded. in narrowing the scope of syndrome 1, vdh also identified and excluded visits for specified drug and medication overdose. together, these expanded exclusion criteria resulted in greater specificity with both updated syndromes. these revised syndrome definitions enable vdh to better track opioid and heroin overdose trends in near real-time and over extended time periods which can be used to inform public health actions. limitations include the inconsistency of diagnosis coding among syndromic data submitters, which may lead to geographic underrepresentation of unintentional drug overdose visits based on the location of health care systems. vdh will continue to evaluate and refine these overdose syndrome definitions as this emerging health issue evolves. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e15, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords drug overdose; opioids; syndrome definition; syndromic surveillance acknowledgments erin e. austin references 1. cdc.org [internet]. atlanta: injury prevention and control: opioid overdose; [updated 2016 mar 14; cited 2016 sept 2]. available from: https://www.cdc.gov/drugoverdose/opioids/heroin.html 2. cdc.org [internet]. atlanta: wide-ranging online data for epidemiologic research (wonder); [updated 2016 jul 12; cited 2016 sept 2]. available from: http://wonder.cdc.gov 3. cdc.org [internet]. atlanta: injury prevention and control: data &statistics (wisqars); [updated 2016 may 10; cited 2016 sept 2]. available from: http://www.cdc.gov/injury/wisqars/facts.html. *em stephens e-mail: emily.stephens@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e15, 2017 isds16_abstracts-final 185 isds16_abstracts-final 186 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e303, 2019 isds 2019 conference abstracts an analysis of risk communication surrounding increases in a polio-like condition in the u.s. amie nisler northrop grumman, decatur, georgia, united states objective to assess the type, tone, consistency, and accuracy of communications surrounding a rare polio-like condition called acute flaccid myelitis between 2014-2017 from from cdc, other health agencies, researchers, news media outlets, and the public. introduction in 2014, cdc started receiving an increase in reports of children in the united states with unexplained limb weakness or paralysis (120 total cases). these children were later confirmed by neurology experts to have a rare condition called acute flaccid m yelitis (afm). the council of state and territorial epidemiologists created a standardized case definition for afm in 2015, allowing cdc to establish standardized surveillance to monitor afm, determine possible causes and risk factors, and attempt to estim ate the baseline incidence. through this surveillance, cdc identified another increase in afm cases in 2016 (149 total cases), and obtained valuable information on the clinical presentation to help characterize this illness and the epidemiology of afm. however, despite the ongoing investigation, many questions still remain about afm, including why the increases occurred and what has caused most of the afm cases. the lack of afm knowledge has made preventing afm, finding effective treatments for patients, and developing communication messages challenging. methods we compiled a timeline of events surrounding afm and the investigation from 2014 to 2017, and across this timeframe, we analyzed communications from cdc, other health agencies, researchers, news media outlets, and the public. we reviewed scientific articles, press releases, websites, social media, educational materials, and news stories. we assessed the type, tone, consis tency, and accuracy of the afm information based on the principles in cdc’s crisis and emergency risk communication manual. results the afm communications included information about possible causes, symptoms, severity, transmission, risk, prevention, prognosis, and the possibility of future increases. several materials included stories about patients. information from the different sources evolved along with the investigation and was overall consistent, but especially differed on whether afm was associate d with enterovirus d68. the amount of information released from the different sources was also variable, with some sources releasing more information than others. conclusions emerging diseases, like afm, pose threats to the public’s health, requiring credible and timely risk communication so people can make decisions about their well-being. cdc has played a critical role in relaying the best available scientific information about afm in a timely manner to healthcare professionals and the public, as the situation has evolved since 2014 and there have bee n many unknowns. the messages we communicate during these times, using risk communication principles and the valuable data collected from our afm surveillance and investigation, and their timing affects our level of involvement in the national conversation about afm. working closely with health departments, healthcare providers, researchers, and other partners is important for consistent communication messaging and release of information. http://ojphi.org/ isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts 2016 international society for disease surveillance conference new frontiers in surveillance: data science and health security the international society for disease surveillance (isds) held its fifteenth annual conference in atlanta, ga, from december 6-8, 2016. since 2001, individuals interested in sharing and learning emerging trends in surveillance research and practice have found the isds annual conference a unique forum to advance their knowledge in the discipline of disease surveillance. the 15th isds conference received a total of 233 abstracts from 23 countries. from the submissions, 189 (81%) were accepted for presentation at the conference as an oral presentation (n=96) or poster (n=93). the theme for the 15th annual conference was new frontiers in surveillance: data science and health security. the theme united two dominant trends in public health surveillance: 1) a growing desire to extract knowledge from increasing volumes of structured and unstructured data available from health information systems; and 2) increased pressure on nations to strengthen their capacity for disease surveillance and response to outbreaks when and where they occur across the globe. in addition to the major themes of the conference, abstracts were accepted in additional tracks that remain important to the practice of public health around the world: one health uniting animal and human health; methodological advances in applied epidemiology; public health informatics; public health policy; and biosurveillance practice. as usual, accepted abstracts for the 2016 isds conference span the breadth of surveillance practice around the globe. there are timely abstracts on the detection and response to vector-borne diseases such as zika virus and chikungunya across the americas, as well as abstracts on the surveillance of opioid abuse observed in many parts of the u.s. other abstracts cover the surveillance of non-communicable diseases that are now the leading causes of death globally. additionally, some abstracts focus on capacity building within low resource settings on multiple continents to enhance global health security. while other abstracts describe the impact of health information technology (or ehealth) policies on surveillance practice at local, national, or regional levels. and still other abstracts contain emerging, novel methods that advance our understanding of how to analyze “big” data or reduce the messiness associated with realworld surveillance data. together these abstracts represent the broad, diverse and interesting nature of surveillance practice. furthermore, the abstracts represent important work being done in high income countries like the u.s., canada and the u.k. as well as critical work being done in low-and-middle income nations such as nigeria, pakistan, and sierra leone. i wish to thank the dedicated members of the scientific programming committee (spc) and isds staff who helped to manage the process of selecting this year’s abstracts for presentation. these individuals are domain experts across the spectrum of tracks and themes represented in the program, and their service is much appreciated. the spc helped to recruit dozens of public health researchers and practitioners who also spent time reviewing abstracts. i also thank these volunteers for contributing to the richness and diversity of this year’s program. finally, i wish to thank the track chairs who reviewed abstracts and recruited peers to perform reviews, and whom helped me organize presentations into meaningful sessions for the final conference program. their names are listed in the proceedings to recognize their selfless service to isds and the field of public health surveillance. i hope that these proceedings help to advance scientific understanding and the practice of surveillance in public health. please use the knowledge herein to improve how you practice or evaluate surveillance in your jurisdiction. or you may find ways to apply the knowledge elsewhere in population health. however you use it, i ask that you document your lessons or findings and submit to isds in the future to share the outcomes with others. together we can reduce the burden of disease and improve health outcomes for populations globally. brian e. dixon, mpa, phd, fhimss indiana university and the regenstrief institute, inc. 2016 isds scientific program committee chair track chairs for the 2016 isds conference eric lau, phd school of public health, the university of hong kong chair for methodologies and analytics julie pavlin, md, phd, mph infectious disease clinical research program chair for public health policy peter hicks, ma, mph centers for disease control and prevention (cdc) chair for informatics and data science vivek singh, mph, mbbs indian institute of public health (iiph) – hyderabad chair for public/population health practice online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 192 isds 2014 conference abstracts establishing prospective road traffic injury surveillance in india: challenges and solutions shailaja tetali*1, lakshmi josyula1, shivam gupta2, shirin wadhwaniya2, g gururaj3 and kent stevens2 1indian institute of public healthhyderabad, hyderabad, india; 2iiru, baltimore, md, usa; 3nimhans, bangalore, india objective to describe the challenges and lessons learned in establishing road traffic injury surveillance in two large government teaching hospitals in two states of south india, with solutions that eventually helped streamline the process. introduction lowand middle-income countries (lmic) disproportionately bear the high burden of injuries, with 90% of all deaths due to injury occurring in these countries1. in india, data on pre-hospital care of the injured is either absent or incomplete. the bloomberg philanthropies global road safety program is a five-year (20102015) project implemented by a consortium of partners to improve road safety in 10 lmics2. the risk factors being addressed in india are reduction of drink driving and increasing helmet use3. up to 16 months of data from two large hospitals in hyderabad (population 8 million) were retrospectively reviewed to examine the burden of road traffic injuries. unfortunately, key information on the following was incomplete: alcohol use; location and activity of patient at the time of injury; nature of collision; vehicle in which patient was traveling; striking vehicle and type of road user. information on the presence of safety equipment like helmet and seatbelt, and occurrence of prehospital care was uniformly absent. to overcome the information gap, round-the-clock injury surveillance was established in july 2013 in hyderabad and in june 2014 in visakhapatnam. methods hospital surveillance data were collected round-the-clock using a validated paper form. however, this abstract only describes the challenges encountered while establishing the surveillance system in the two states. problems and concerns were identified through personal discussions of data-collection experiences through field notes, review of data-collection forms, and field observation of data-collection process by the investigators. corrective action was undertaken when indicated. results the challenges encountered and solutions employed are described below: conclusions there are many challenges in establishing surveillance systems for road injuries in india, predominantly rapid staff turnover, heavy workload and the absence of already existing data recording and management in hospitals. pending administrative solutions such as improved staffing and posting, the chief measures to address these challenges were sustained dialogue and rapport-building with hospital administrators, training of data collectors, and enlisting the aid of bridge personnel, such as interns. reiterating the value of surveillance data to negotiate for hospital resources commensurate with the high burden of road injuries may help convince hospital administrators to sustain such surveillance initiatives. challenges and solutions keywords injury surveillance; establishing systems; india; challenges, solutions; public health practice acknowledgments this work was conducted as part of the global road safety program, funded by bloomberg philanthropies. references 1.stevens et al. establishing hospital-based trauma registry systems: lessons from kenya. injury, int. j. care injured 44 s4 (2013) s70–s74 2. editorial: road safety in 10 countries: a global opportunity a a. hyder, d bishai 3. qualitative study to explore stakeholder perceptions related to road safety in hyderabad, india. tetali s, lakshmi jk, gupta s, gururaj g, wadhwaniya s, hyder aa *shailaja tetali e-mail: shailaja.t@iiphh.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e208, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* national institute of standards and technology, gaithersburg, md, usa objective describe how the 2015 edition of the national institute of standards and technology’s (nist) syndromic surveillance messaging validation suite continues to support federal efforts to increase healthcare information technology interoperability for timelier public health surveillance in the us; and show how this tool is used to validate messages. introduction speed, reliability, and uniformity of data collection enable syndromic surveillance (sys) systems to provide public health authorities (phas) with timely information about community health threats and trends. increasingly, healthcare information technology (hit) is being used to accelerate and automate data collection for more real-time surveillance, reducing irregularity in how sys data are packaged and sent by healthcare providers. continuing to focus on patient and population health outcomes, the on-going us federal program that certifies hit to promote interoperability has mandated broader use of an updated standard for communication of sys data. under the edition 2015 federal rule tied to medicare and medicaid reimbursement, hospitals, in addition to emergency departments and urgent care centers, are now required to provide sys data to phas using hl7 2.5.1 messages that are in conformance with release 2.0 of the cdc’s public health information network (phin) guide for sys. to facilitate the intended application of this updated standard, a new version of conformance testing tools is being published, which will enable hit developers to increase their probability of meeting the requirements outlined in the standard and lead to enhanced product interoperability and reliability. methods to advance conformance to the standard and promote interoperability of hit, the office of the national coordinator for health information technology (onc) has continued to maintain a voluntary hit certification program. onc certification testing provides the means to evaluate sys data messages created by hit against a mandated standard through use of a context-based validation method composed of a suite of publicly accessible, web-based tools developed by subject matter experts from nist, isds, and the cdc. the updated edition 2015 sys messaging validation suite (sysmvs) will support testing sys data messages based on release 2.0 of the phin guide and includes test stories that set the clinical context, test sys data, testing guidance, automated sys message validation, and a forum for testers to ask questions or provide feedback about the suite. onc-accredited testing laboratories (atls) will use the suite to test hit modules for certification, and hit vendors can use them while developing their products. healthcare providers and phas can download a version of the validation suite and use it locally to verify conformance of sys messages containing real patient data for implementing sys data exchange. results as of august, 2015, the updated version of the sys-mvs is being developed in anticipation of the publication of the onc edition 2015 hit certification final rule due in q3 2015. several hit vendors have been recruited to pilot test the draft sys-mvs prior to release of the final version in december 2015. the atls are to begin using the sys-mvs in january 2016, and local phas are anticipated to begin using the validation suite as part of their on-boarding process with their healthcare providers at that time. feedback about the 2014 edition of these tools was positive, indicating that the users: (1) were motivated to think through the steps for capturing/reporting syndromic data in more detail than before; (2) appreciated the subject matter expertise that generated the tools; and (3) used these tools locally to promote collection and reporting of standardized sys data in a timely manner. conclusions the 2015 edition sys-mvs, to be used nationally by the atls to test hit for certification and locally by healthcare providers to validate electronic messages before sending their sys data to phas, will help move the industry further toward true interoperability for efficient reporting and use of syndrome-based public health surveillance information. keywords certification; healthcare information technology; interoperability; conformance; standards *sheryl l. taylor e-mail: sheryl.taylor@nist.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e38, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: 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moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts integrated disease surveillance to reduce data fragmentation – an application to malaria control kate zinszer1, 2, arash shaban-nejad1, sonia menon1, anya okhmatovskaia1, lauren carroll2, ian painter2, neil abernethy2 and david buckeridge*1 1clinical and health informatics research group, mcgill university, montreal, qc, canada; 2department of biomedical informatics and medical education university of washington, seattle, wa, usa objective driven by the need to bring malaria surveillance data from different sources together to support evidence-based decision making, we are conducting the “scalable data integration for disease surveillance” (sdids) project. this project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria control and demonstrate a model applicable to other diseases. central to this initiative is collaboration between academia, governmental and ngo sectors. introduction there is growing recognition that an inability to access timely health indicators can hamper both the design and the effective implementation of infectious diseases control interventions. in malaria control, the global use of standard interventions has driven down the burden of disease in many regions. further gains in hightransmission areas and elimination in lower transmission settings, however, will require an enhanced understanding of malaria epidemiology, population characteristics, and efficacy of clinical and public health programs at the local level. currently, there is a dearth of information available to fine-tune malaria control interventions at the local level. a key obstacle is the fragmentation of data into silos, as existing data cannot be brought together to estimate accurate and timely health metrics. methods under this initiative, our overarching aims are to demonstrate an approach to integrating existing data collected by organizations for malaria surveillance in uganda and the gambia and to make the resulting information available to guide malaria control activities. in the first phase, we developed a catalogue of data sources for malaria control, describing how malaria control programs and sponsors analyze data, and use information to make programmatic decisions. in the second phase, we aligned these data with indicators and descriptive and analytical epidemiological methods required to guide the delivery and evaluation of disease control interventions. in the third and final phase, we are developing and evaluating software tools to interact with the aligned data sources and calculate and analyze indicators that are of value to malaria control programs and funding agencies. results at the completion of our project, we will have developed an openaccess prototype system that will support sharing of comparable surveillance data within and across countries. conclusions a cornerstone of our approach is its ability to employ a common knowledge platform to scale-up and extend structural and semantic mapping across existing data sources to other geographical regions and global health priority diseases in resource-constrained settings. our multi-sectorial partnership also aims at facilitating the up-stream flow of information from the national to the international level, to effectively contribute to designing evidence-based disease control policies. keywords integrated disease surveillance; malaria control; data fragmentation; open-access prototype system acknowledgments the bill and melinda gates foundation *david buckeridge e-mail: david.buckeridge@mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e181, 201 feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 1 ojphi feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study priya nambisan, phd1, donna lamkin, ma2, carrie delong, rn3 1. assistant professor | dept. of health informatics & administration, college of health sciences, university of wisconsin – milwaukee, wi 53201-0413 2. chief program officer | center for disability services, albany, new york 12208 3. assistant director of health care services, center for disability services | albany, new york 12208 abstract telemonitoring is being increasingly used to provide services to patients with developmental disabilities in residential community settings. the objective of this study is to assess the feasibility, benefits and challenges of using telemonitoring for aging patients with developmental disabilities. we also assess the benefits and challenges of telemonitoring for the caregivers of these patients. focus groups and questionnaire-based surveys were used to collect data from patients and caregivers. the study found that telemonitoring was feasible and beneficial for the aging with developmental disabilities, albeit for those who are moderate to high functioning. it was not beneficial or feasible for those with very low functional capabilities. the study found that telemonitoring was beneficial towards providing more independence, more self-confidence in carrying out daily activities, and more knowledge regarding their disease. the study also found that telemonitoring was useful for caregivers to better understand their patients and their needs, better coordinate the services delivered, and to enhance the satisfaction of caregiving. the discussions include limitations of using quantitative methods in this type of setting. correspondence: nambisap@uwm.edu, lamkin@cfdsny.org doi: 10.5210/ojphi.v6i2.5460 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction it is estimated that there are around 641,000 aging people with developmental disabilities (dd) across the country and this number is set to double by 2030 [1]. developmental disabilities typically occur during childhood (before the age of 18) and include cerebral palsy, mental retardation, learning disorders, autism, and epilepsy; these conditions have been found to cause significant functional impairment in areas such as independent living, self-care, learning, language skills, and economic self-sufficiency. however, this is not a homogenous population as disabilities vary and the same disability can affect different individuals at varying degrees. as treatments for such childhood diseases are becoming better, people are living longer with such disabilities along with the associated functional impairments. indeed, a sizable population ends up living to old age. although this is an improvement in the treatment of dds, this population http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 2 ojphi faces severe disparities in healthcare accessibility [2,3]. there is a higher prevalence of adverse conditions, inadequate and timely attention to healthcare needs [2], lack of health promotion and access to quality healthcare services among this population [3]. one of the main reasons for not getting adequate healthcare, apart from poverty and lack of health insurance, is the functional impairment that is characteristic of this population [4]. some individuals with dd age prematurely, leading to an onset of age related disorders common in the general population like diabetes and heart disease. in addition, there is also an increased incidence of osteoporosis and dementia with the associated psychiatric problems [5]. all this, along with higher intake of anticonvulsant medications, increases the rate of acute care hospitalizations, emergency care, need for short-term nursing home for rehabilitation and need for community based group home for longer care. studies have shown that small group settings or group homes that provided these individuals with more focused care and more opportunities for leisure and recreation were better than large facilities for people with developmental disabilities [6,7]. as the life expectancy of this population (currently averaging ~66.1) as well as the number of individuals who belong to this vulnerable population both steadily increase, technological innovations would be needed to deliver proper care for this population. it has been noted in prior studies that adequate and timely preventive care and a multidisciplinary approach to tackle issues for this population would be needed in order to make healthcare provisions both time and cost effective [8-10]. as many of these individuals have communication problems along with many functional disabilities, healthcare workers need to be trained in surveillance methods that are efficient in identifying changing physical needs and use subjective and objective methods to review their medical status. many researchers and health care providers in this area call for a research agenda that would aggressively examine the relationship between different care models and successful aging in this population [8,11]. as a result, many providers are turning to new technologies such as telemonitoring to provide effective solutions in this regard. telemonitoring for the aging with developmental disabilities telemonitoring is defined as “an automated process for the transmission of data on a patient’s health status from home to the respective health care setting.” [12] telemonitoring differs from telemedicine in that telemonitoring is limited to support provision for patients who need regular monitoring using various telecommunication technologies, whereas telemedicine is the provision of clinical care in the form of diagnosis, treatment and consultation by a provider using various telecommunication technologies. telemonitoring allows providers to remotely monitor patient status for long periods of time. for example, a patient diagnosed with heart disease can be monitored for bp at the patient’s home. a bp monitor that is connected to a telemonitoring device can be used to record the readings and to transmit them to the healthcare organization. telemonitoring has been found to be useful in the treatment of chronic diseases among the general population [12-16], however, there hasn’t been any attempt to examine whether such technologies can be effectively used for the chronically ill and aging patients with developmental disabilities [17]. this set of population is unlike any other as the range of disabilities and health issues is quite high in addition to functional impairments and communication difficulties. we predict that the perceivable benefits could be similar to that evidenced in the case of the general population: cost-effectiveness, better health outcomes, reduced hospital utilization, and enhanced caregiver satisfaction. this technology would be particularly useful for the aging with dd http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 3 ojphi population due to the following factors: (1) transportation difficulties – more difficult than for general aging population; (2) need for constant and regular monitoring – functional impairments make it difficult for any amount of self care and hence need outside help for regular monitoring; (3) higher need for preventive care – higher number of co-morbidities make it extremely necessary for preventive care as complications are higher than when there is only one chronic disease to deal with; (4) need for cost-effective solutions – the constant attention and care needed for this population can overwhelm any provider economically; (5) lack of understanding of own disease/s – a telemonitoring device that gives immediate feedback might be useful in helping them better understand the associated causal relationships for e.g. high bp would mean you get a call from a caregiver; and (6) need for care in a home setting. telemonitoring and caregiver satisfaction despite these varied and important potential benefits, deploying telemonitoring for this particular population can be quite challenging, especially for professional caregivers. first, training them to use the telemonitoring devices could be more difficult compared to the general population. many patients in this target population may not be able to fully utilize all the features in the telemonitoring device, leading to caregiver frustration and provider dissatisfaction. lack of understanding on how to use the equipment could create doubts among caregivers as to whether the data they are receiving are reliable enough. in addition, improper use of the telemonitoring equipment could lead to damage of the equipment and financial loss. in this context, the caregivers are not only taking the complete burden of technology adoption on their part they are also expected to understand the challenges of training a set of patients with dd use this technology and adapt themselves. however, once implemented telemonitoring has the potential to reduce time spent with each patient, reduce traveling to each patient location, reduce stress and burnout and overall improve satisfaction with caregiving as caregivers for this population are known to have higher burnout rate and higher level of stress [17] compared to the rest of the population. hence, the study is also focusing on the benefits, challenges and satisfaction of caregivers when providing care using telemonitoring. a better understanding of the feasibility and benefits of telemonitoring technology for this population as well as the challenges that the technology may pose for both the patients and their caregivers [18] would provide us with a foundation for further studies in this area. the current study is aimed at developing such an understanding by examining the use of telemonitoring in a healthcare center that primarily provides disability services in the northeastern region in the us. data and method study subjects: data was collected from the center for disability services (cfds) at albany, ny. we used both quantitative (survey) and qualitative (focus groups) to collect data. the study focused on patients who can be classified as ‘aging with developmental disabilities’. patients who are above the age of 45 were classified as aging given that people with dd age earlier than the general population – and we also did individual evaluation of each case to determine whether a patient can be classified as ‘aging with developmental disabilities’. patients with different levels of functionality were enrolled in the study. the enrollment criteria also included different patient settings where care was provided: individualized residential alternative (ira); center independent living facilities, (a set of cfds supported apartments designated as ira); and http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 4 ojphi private homes. these settings were selected with the objective of representing an array of patient living options in rural, suburban, and urban areas. a total of 21 patients participated in survey part of the study and a total of 6 patients participated in the focus group. all study subjects had co-morbidities. in addition to dd, these subjects have been diagnosed with one or few of the following diseases seizure disorders, schizophrenia, parkinson’s, physical disabilities, mental retardation, autism, mental health issues like anxiety disorder and psychoses, hyperlipidemia, gout, poor circulation, insulin-dependent and noninsulin dependent diabetes, hypertension, prostate issues, peripheral vascular disease, recurrent utis, severe osteoporosis, recurrent skin breakdown, cp, degenerative joint disease, asthma, peripheral neuropathy, colitis, breast cancer, dyslipidemia, gerd, irritable bowel syndrome, gastritis, hyperlipidemia, barret’s esophagus, marfan’s syndrome, l ventricular hypertrophy, aortic valve stenosis, arthritis, recurrent asthmatic bronchitis and chronic bronchiectasis. some subjects have communication difficulties, which is characteristic of dd and the caregiver usually collects medical data from subjective and objective observation. table 1 provides more details on the patient sample. the patients enrolled in this study who had some level of functional independence were trained to use the telemonitoring device (e.g. for measuring blood pressure, pulse, weight, glucose level etc.). patients with limited functional independence were assisted by professional caregivers from cfds. table 1: description of the patient sample characteristic n % sex male 11 52% female 10 48% age 35 45 1 4.7% 45 55 6 28.6% 55 65 8 38.1% 65 75 4 19% 75 85 2 9.5% race non hispanic black 2 9.5% non hispanic white 19 90.5% geographic location urban 6 28.6% rural 12 57.1% suburban 3 14.3% living arrangement with family 0 0 alone 3 14.3% with friend 0 0 residential service 18 85.7% primary diagnosis mild intellectual disability 8 38.1% moderate intellectual disability 1 4.7% http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 5 ojphi severe intellectual disability 4 19% profound intellectual disability 8 38.1% chronic disorders psychiatric disorder 12 57.1% seizure disorder 2 9.5% obesity 2 9.5% diabetes 2 9.5% bowel dysfunction 7 33.3% cardiovascular disease 9 42.9% pulmonary disease 5 23.8% neurological disorder 3 14.3% urological disorder 9 42.9% cerebral palsy 9 42.9% functional independence high 6 28.6% moderate 7 33.3% low 7 33.3% we also collected data from caregivers – nurses and care staff at cfds who used the telemonitoring system or helped patients to use the telemonitoring system. similar to the patient subjects, we used both quantitative (questionnaire) and qualitative (focus group) methods to collect data from caregiving staff. the objective was to understand the feasibility, benefits and challenges of using telemonitoring system for this subset of population from a caregiver’s perspective. while the patients are the end users, for this population group, caregivers play an important role in the continued use of the technology. telemonitoring system: the telemonitoring system included web-based genesis dm monitors, card readers and glucose cables from honeywell homemed lifestream telehealth platform. the genesis dm unit collects data on bp, pulse, oxygen saturation, weight and glucose. to utilize the genesis dm for multiple people, an id card must be used with an id card reader. caregivers had access to the lifestream platform (ge honeywell's software program), which records all of the readings and places alerts on results that are out of the parameters designated for each person individually. the clinical staff can then respond to these alerts and then add response notes for that session. the telemonitoring unit also can query the user with regard to subjective data (e.g. is the individual experiencing a headache?), and objective data (e.g., the presence of a bruise). patients were provided with these telemonitoring devices as well as the training needed to use the devices based on their individual needs. the training was provided by the care staff and a residence rn at cfds. each residential program has an assigned residence rn for a specific number of hours weekly who is typically responsible for overseeing current health related data (e.g., vital signs, prn usage, bowel habits, current health concerns, medical or other appointments). this residence rn was assigned to provide training to the residents themselves when appropriate and/or the caregivers. the training started before the telemonitoring device was implemented and continued through out the time the study was conducted. the residence rns are usually on site once weekly, depending on the needs of the patients, and available to care staff during regular hours. a 24/7 on-call rn is available for concerns that arise outside of regular hours. http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 6 ojphi data collection we employed both focus groups and questionnaire-based survey to collect data from the study subjects. a total of 21 patients participated in survey part of the study. questionnaire-based surveys were administered to the study subjects (patients), pre and post intervention. the survey was used to collect data on the quality of life of individual patients, an important health outcome in the use of telemonitoring system. to measure quality of life, we used a short 8-item questionnaire adapted from an existing, validated scale [19]. the questionnaire comprised of two sets of questions – the first set had 2 items that asked directly about their quality of life and their satisfaction with their health and the second set had 6 items that were associated with general quality of life from the cdc scale [19]. the two sub-scales were both equal interval likert type scale and were tested for reliability. cronbach alpha scores were .828 and .886 respectively. data on specific health outcomes were taken from individual patient record maintained by the healthcare facility. pre and post health data was compared (6 months before the start of telemonitoring usage was taken for the pre data and 6 months during the telemonitoring usage was taken as the post data). at the end of the post survey, we also conducted a focus group with 6 patients who were mostly ‘moderate to high functioning’ (given the fact that they needed the ability to respond to our questions). we also administered questionnaire-based survey to 25 caregivers (associated with the patients in our subject pool), pre and post intervention. the survey was used to collect data on caregiver satisfaction and to evaluate differences in pre and post attitudes towards the new technology. in addition, at the end of the study, we also conducted a focus group with 19 caregivers to collect data on perceived benefits and challenges and feasibility in using the telemonitoring system among the target population. all the study tools including the survey and the focus group questions were reviewed and approved by the first authors’ academic institutional review board (irb) as well as by the irb at cfds. focus group method for patients and caregivers the focus group was conducted by the first author and it was recorded in writing by the author and a nursing staff from cfds. the entire focus group was also audio recorded with permission from the participants. the first author, who is not affiliated in anyway with the participants (patients or caregivers), developed the questions, administered and moderated the focus group. the focus group participants for the patient group were recruited based on their functionality and ability to respond to questions and also their interest to participate. all the caregivers were invited to participate in the focus group; 19 out of the 25 subjects participated. there were 2 sessions for the caregivers with 12 participants in the first session and 7 participants in the second session. we employed the exploratory method for conducting the focus groups. this involved using open-ended questions. this method is deemed appropriate for new areas of study with limited prior theoretical understanding such as the current study context [20]. the focus group study questions for patients related to 1) their change in quality of life, 2) satisfaction with the telemonitoring technology, 3) perceived benefits of the new technology, 4) perceived problems and disadvantages of the technology, 5) whether they will continue using the technology, and 6) suggestions for improvement. the focus group study questions for caregivers were intended to capture 1) caregivers’ technology related satisfaction – usability issues, http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 7 ojphi convenience and other benefits from the technology itself; 2) caregivers’ satisfaction with care provision – self-efficacy, job satisfaction; 3) caregivers’ willingness to continue using the telemonitoring device; and 4) other concerns, issues, and suggestions for improvement. each of the focus group sessions took about an hour or so. data analysis data collection and analysis for the quantitative part of the study was quite challenging (due to low ‘n’) and the results were not conclusive. however, we report that as we intend to discuss the comparative benefits of using qualitative studies for this population subgroup. with the quantitative data from patients, we conducted a paired sample t test to compare pre and post data. the quantitative part with caregivers was also challenging due to the high turnover rate of nursing staff and caregivers. data from the focus group were analyzed by a combination of summarizing the discussion and detailed content analysis. the summary of discussion was also partially produced during the focus group discussion by periodically summarizing the groups’ answers for each section starting with the prepared open-ended questions and taking their feedback and modifying it until consensus is reached – this is a model out-lined by richard krueger [21] for in-session summarizing. the transcript from each focus group was carefully reviewed and detailed content analysis was conducted to breakdown the discussion into individual concepts and then related concepts were grouped together. we also analyzed the most frequently mentioned factors for each focus group discussion section. results results from patient data the results with regard to the impact of telemonitoring on quality of life while positive were not conclusive. the mean for quality of life was slightly higher for all the indicators in the post data, but not all of them were statistically significant. two indicators – “to what extent do you feel that physical pain prevents you from doing what you need to do?” and “how healthy is your physical environment?” showed a higher statistically significant mean during the study period (post data) than before telemonitoring usage (pre data). the results for “to what extent do you feel that physical pain prevents you from doing what you need to do?” were the following – mean for pre test was 4.45 with a s.d of .510 and mean for post test was 3.90 with a s.d of .912; t (19) =2.604; p<.05). the results for “how healthy is your physical environment?” were the following – mean for pre test was 3.77 with a s.d of .922 and the mean for post test was 4.23 with a s.d of .752; t(21)= -2.215, p<.05). with regard to the specific health outcomes, each individual’s health record was carefully analyzed by an rn for changes in their health conditions, number of hospital/doc visits, emergency room visits and for anything that could be attributed to the daily monitoring using the new technology. we did not find any major changes in health conditions or in number of hospital/doc/emergency room visits and if there were any, it could not be attributed to the use of the telemonitoring system. the relatively short study period (6 months) and the low number of patients in this study might explain the lack of stronger findings with regard to the impact on quality of life. however, the findings from the quantitative analysis were generally positive regarding the impact on health outcomes among the target population. http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 8 ojphi the results from the focus group with patients strongly support that telemonitoring is beneficial for aging people with dd. for the open-ended question about quality of life, four patients responded. all the four study subjects said that their quality of life has become better; when asked to elaborate on it, they mostly described how it is benefiting their health – at least two people mentioned that their weight has been going down as a result of the regular monitoring. from the analysis, the terms that did repeat at least 5 times was ‘i like it’. however, two people mentioned that ‘sometimes it gets on my nerve’ which points to some of the usability issues of the system. at least 2 people mentioned the terms ‘more independent’ and the terms ‘doing it myself/on my own’. two people mentioned that ‘it was good because it reminds me to take my medication’. the next section about benefits seemed to resonate the same themes about how it was helping them be aware of their health, better understanding of their disease and the treatments, feelings of safety, security and self-efficacy. in addition, participants also repeated some practical benefits such as self-health monitoring and medication reminders. table 2 provides a sample of statements captured from the focus group, which are illustrative of (and provide evidence for) the ‘benefits from telemonitoring’ and improved ‘quality of life’. table 2: focus group statements from patients “very useful to know my bp and weight on a daily basis”. “if my weight is going up and my bp reading is not good, i know that i have to eat better tomorrow and exercise”. “when i exercise and eat better the reading is better next day, and ever since we got the machine, my weight has been going down and my bp is 120/80”. “it reminds me of my medications”. “it is terrific because i can do it on my own and i can get the values directly”. “before, nobody used to tell me the numbers, they would just say it is not good – now i understand why it is not good”. “it energizes me, because i have to get up and go to take the reading, something i look forward to everyday”. “i feel safe and secure that the nurses are also getting the readings and i know if something is awfully wrong, i will get a call from them”. in summary, the focus group revealed high satisfaction rate and there were no disagreements regarding the benefits or improved quality of life from the use of telemonitoring. for the open-ended question on issues and challenges of the telemonitoring system, one had difficulty with getting the blood sugar readings, one complained about time settings (that it was different from her home clock), another wished that his doctors would have access to the regular monitoring (he sees a doctor outside of cfds). for the open-ended question on suggestions for improvement – one said that staff should be able to help more. other than that there were no suggestions for improvement. overall, the focus group results indicate that telemonitoring helps to improve patient literacy/knowledge regarding own disease. this in turn could also explain the increased level of self-confidence and self-efficacy as well as the greater motivation for self-care and prevention. thus, the study findings (from the survey and the focus group) together indicate a stronger level of satisfaction with the new technology (and the associated benefits) among the higher functioning patients. http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 9 ojphi results from caregiver data on the caregivers’ side, we encountered an unanticipated problem – as high turnover rate of the caregiving staff (associated with the patients in our subject pool). this is not unusual in this context as typically there is high turnover rate among nursing staff in these type of contexts [22,23]. however, due to this turnover, while we had administered the pre questionnaire for caregivers, the post questionnaire could not be administered as only one staff remained from the original study group. thus, here, we report only the results from the focus group that we conducted for the caregivers. the questions for the focus groups were similar to that of the patients’ focus groups -their satisfaction with the new technology, perceived benefits of the new technology, perceived problems and disadvantages of the technology and suggestions for any improvement. the caregiver focus group revealed a strong support for the main study theses regarding the benefits from using the telemonitoring technology among aging patients with dd. the main benefit from regular telemonitoring from the caregivers’ perspective was getting daily values on their patient. caregivers found that this provided them with better understanding of the patient and more control on what is going on. caregivers also noticed that some of their patients had a better understanding of their disease and were asking more questions regarding their disease. many commented on how happy the patients were when they started using it and how they seemed to be looking forward to using the telemonitoring device. everyone agreed that the above 3 factors improved caregivers’ relationship with patients. table 3 provides a sample of statements from the caregivers in the focus group, which are illustrative of the benefits of telemonitoring. table 3: focus group statements from caregivers “very beneficial to get daily values instead of once a week”. provides better control and an understanding of what is going on – “i am more aware of the patient”. provides better understanding of what is a normal value for that patient “i know what is normal for a patient”. “this method was less intrusive as the patients did this, on their own, privately”. when asked about their concerns with the new technology, the caregivers in the focus group noted that the main concern was that this technology was not quite useful for patients with very low functionality. they did not have any doubts on whether this technology would benefit their higher functioning patients. there were issues associated with specific physical disabilities such as spasticity (which is common among patient with cerebral palsy), where the bp readings were inaccurate due to their movements during blood pressure checks and the staff had to manually administer it. the other main usability issue was associated with the card reader – in some facilities it worked fine, whereas in others, it didn’t. another issue or concern was associated with the questions posed by the telemonitor with regard to the mental health of the patient. some thought that the ‘yes’ or ‘no’ questions did not capture the actual mental health state of the patients. for example, a question on ‘are you feeling happy today’ is very difficult to answer in just ‘yes’ or ‘no’. one caregiver found that some patients were not answering these questions truthfully – “i have a patient who says she answers all the questions positively as she did not want anyone to find out that she is not in a good mental health state and when i asked her why she is doing that, she said it was because she didn’t want to get anymore drugs” http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 10 ojphi when asked about any technology-related improvements they would like to see, some caregivers suggested that the technology should enable adding weight scales for weighing patients in wheelchairs. some caregivers suggested that they were helping patients use the telemonitoring device but that they themselves did not have access to the recordings (i.e. the data). typically, the data is sent to the facility where the medical staff has the primary access to the data. the caregivers said that it would be highly beneficial if they also got access to the data. overall, the caregiver focus group indicated a unanimous and strong support that the telemonitoring technology is useful and allowed more independent living for higher functioning patients, but that it was not that much useful for low functioning patients. this conclusion in turn reinforces the findings from the patient survey and focus group and in turn enhances the confidence associated with the support found for our broader study theses regarding the benefit and feasibility of employing telemonitoring technology among aging patients with dd. next, we discuss the limitation and then implications of these findings. limitations as mentioned before the low number of subjects was one of the major limitations on the quantitative part of this study. the other limitation is the short duration of the study. it is important to conduct long term studies to better understand the feasibility and benefits of telemonitoring for aging people with developmental studies. another limitation is that we were able to conduct only 1 session of patient focus group. more participants and more sessions would help in gathering more richer and wider range of information. however, this is an exploratory research and this study may have produced results sufficiently indicative of the benefits of using telemonitoring for this population group. discussion & implications the study’s main finding is that it is feasible and beneficial to use telemonitoring for this set of population. at the same time, there are some challenges that we will need to address as practical measures for enhancing the effective use of this technology. the first challenge relates to the nature of the patients – aging people with dd. these patients require much more training and continued evaluation compared to regular population, however, this study has proven that it is feasible to get them to use a telemonitoring device. further, the caregivers for this population have a higher level of work stress and burnout than that for the general population [17]. there could also be staff and other workers who are undertrained or under educated, as shortage of caregiving staff for this population is higher than for the rest of the population [22,23]. hence, training and continued evaluation during the course of the study is highly essential. in addition, studies show that providing care staff with better support and internal locus of control would reduce burnout rate [17,24]. this study showed that the caregivers became more in touch with their patients and expressed more satisfaction with their jobs. hence, future studies could also focus on whether using telemonitoring system would improve job satisfaction and there by enhance the retention rate of caregiving staff. another important implication of the current study is with regard to the need to employ multiple research methods. some of the research methods typically used in studies focused on the general population are not likely to be appropriate for the focus population (aging people with dd). a combination of varied qualitative and quantitative methods would likely be the best approach http://ojphi.org/ feasibility, benefits and challenges of using telemonitoring for the aging with developmental disabilities (dd): an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e186, 2014 11 ojphi especially when using telemonitoring for a new set of population group. as our study and another study [25] in this area indicate quantitative methods may not always provide conclusive evidence regarding various health outcomes. however, the focus group from the current study indicates that future studies should focus on outcomes such as feeling of independence, empowerment, better understanding of their own disease and feelings of security. it may also indicate the need for future quantitative studies to narrow the focus to study the impact of telemonitoring in a specific disease context i.e. telemonitoring for developmentally disabled aging population with diabetes. furthermore, we may also need longitudinal cohort group studies to understand the cost effectiveness, hospital utilization and health outcomes of using telemonitoring. such longitudinal studies may offer insights that are not derived from the crosssectional studies that are typically done in this context. there also needs to be more studies to understand the unique needs of this population subset and design more robust telemonitoring systems that include features that can measure the bp of patients with spasticity or design a peripheral scale that can interface with telemonitoring systems that will allow patients in wheel chairs to take their weight. other features such as wearable monitors that can automatically detect vitals as part of the telemonitoring system can be useful for patients with very low functionality. 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this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* national center for disease control, delhi, india objective the specific objective was to evaluate the vpd surveillance system of delhi, focusing on measles and diphtheria. introduction the vaccine preventable diseases (vpds) of measles and diphtheria in india were responsible for 47% of global measles mortality and 20% of global diphtheria mortality in 2010. we evaluated the vpd surveillance system of delhi, focusing on measles and diphtheria. methods we evaluated the health management information system (hmis) and reviewed the available data for measles and diphtheria surveillance from north delhi district from 2012 to 2013. primary data were collected by interviewing key state and district level stakeholders using a semi-structured questionnaire. documents like protocols, operational manuals, training documents and hospital and dispensary records were also reviewed. results hmis is a web-based reporting system started in delhi in april 2008. data are collected through passive surveillance. the system uses standard definitions and reporting mechanisms. data validation is standardized and documented. the system is useful and simple to use; the system has shown flexibility in adapting to needed changes over time. the system is acceptable due to involvement of field staff in the process. we observed completeness in reporting of forms (93.8% [845/900]) and systemic support (manpower, infrastructure, funds) for effective functioning of hmis. the surveillance system is sensitive enough to see trends but data are not available at the population level to know the true burden of disease. the data quality is good for case data but poor for mortality data. the system provides incomplete representation for private sector [captures only 3.3% of reporting units (15/450)]. only 42% (38/90) of reporting units reported on time for january 2013. conclusions the overall quality of the surveillance system is good, and it is meeting its objectives. however timeliness of reporting and representativeness needs further improvement. keywords measles; diphtheria; surveillance; vaccine; system acknowledgments none *kapil goel e-mail: drkapil123@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e116, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic 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to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 1french institute for public health surveillance, saint maurice, france; 2french institute for public health surveillance, rhône-alpes régional office, lyon, france; 3emergency department, university hospital of grenoble, grenoble, france; 4sos médecins france, paris, france; 5resuval, vienne, france objective to timely assess the potential health impact on the population living or working in a terrorist attack area using syndromic surveillance introduction since the terrorist attacks against the satirical newspaper charlie hebdo in january 2015, france has activated the highest level of its national anti-terrorist security plan. a new terrorist attack occurred the 26th of june at 9:50 am in a gas production plant located in the industrial area of saint quentin fallavier nearby lyon (east -south-of france). the plant produces several different chemical products like gas and plastics and employed 40 people. the attack resulted in an explosion followed by fire. the french institute for public health surveillance (invs) was alerted at 11 am and decided to implement with its rhône-alpes regional office a protocol to timely assess the potential health impact on the population living or working around the attack area on emergency health care facilities (ehcf). methods the french sursaud® system is national syndromic surveillance system led by invs and based on the daily collection of data from emergency departments (ed) network oscour® and general practitioner’s emergency associations sos medecins. individual data including medical diagnoses are analyzed by invs through syndromic groups of interest for public health surveillance, including groups related with potential cbrne exposure. in the 26th of june attack, the analysis focused on cbrne groups related with potential respiratory and cutaneous exposure or psychological effects in selected structures in the perimeter of the attack. at the same time, a labelling protocol already tested in a situation of natural disaster was implemented. for each visit identified as related to the attack, it was requested to emergency physicians to use a specific labelling code as associated diagnosis in addition to the main medical diagnosis. the code was different for ed (icd10 code w40 “explosion”) and for sos médecins association (specific code dedicated to exceptional events e99). the analysis began the 27th of june (dday+1) and was performed up to a week afterwards. results main health threats (industrial chemical or toxic release, air pollution) were rapidly excluded and the health impact assessment focused on psychological impact. nine ed involved in the oscour® network and located in the area of interest were contacted directly or by invs regional partners to implement the labelling protocol. the lyon sos médecins association and the national sos médecins federation were contacted for the same task. the labelling procedure was completely implemented at 4 pm. no significant increase in the global indicators (numbers of allcauses ed attendances and sos médecins visits) nor in the specific indicators (numbers of attendances/visits for conjunctivitis, burns, malaise, dyspnea, anxious troubles, stress) was observed in the selected structures. a few sos médecins visits (n=11) with the specific labelling code were recorded from the 26th of june to the 2nd of july. however since early 2015 we found out that this code was already used by sos médecins in other circumstances. it was not possible, retrospectively, to distinguish visits associated to the event. conclusions the surveillance implemented during the 26th of june terrorist attack in france is in favor of no significant impact on the ehcf. this study shows that a labelling procedure to assess a potential impact of an intentional event like a terrorist attack can be implemented fairly rapidly. however, the attack was limited in terms of modus operandi, geographical area and population concerned. one limitation was the non-specificity of the labelling code chosen for sos médecins, which has been used in other circumstances. the use of this specific code has to be evaluated with the partners. some other aspects should be assessed, particularly the acceptability of ed and sos médecins physicians to implement the labelling protocol and its feasibility in a most severe situation. keywords impact assessment; intentionnal event; emergency data; terrorist attack acknowledgments to the emergency departments, the resuval regional network, the emergency medical service of grenoble (samu 38) and the lyon sos médecins association for providing data and for their contribution to the surveillance. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e52, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, 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with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts analytics, machine learning & nlp – use in biosurveillance and public health practice mujitha b. k b*1, ajil jalal2, vishnuprasad v1 and nishad k a1 1informatics, longriver infotech, bangalore, india; 2iit madras, chennai, india objective to summarize ways in which analytics, machine learning (ml) and natural language processing (nlp) can improve accuracy and efficiency in bio surveillance and public health practices. we also discuss the use of this framework in typical surveillance applications (integration with devices/sensors, web/mobile, clinical records, internet queries, social/news media). introduction currently, there is an abundance of data coming from most of the surveillance environments and applications. identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today’s applications. use of analytics has revolutionized many areas. at longriver infotech, we have used various machine learning techniques (regression, classification, text analytics, decision trees, clustering etc.) in different types of applications. these methodologies are abstracted in a generic platform, which can be put to use in many public health and surveillance applications, which are enumerated here. methods in this generic ml platform, we brought together modules covering each of the ml and nlp areas. this platform was then evaluated in a simulated environment – interfacing with a web/mobile surveillance data capture application, medical devices/sensors over rfid, and social feeds. surveillance data in both streaming and batch modes have been used for this test environment. ‘r’ was used for ml algorithms and infrastructure tools like nosql database (apache spark), map reduce (spark/hadoop) and visual tools (r/tableau) were integrated in this pilot study. results each of the independent modules included in this environment had been evaluated in separate projects (precision, recall rates, r-squared values, auc etc. for respective algorithms). scaling capabilities (input data, ml processing) of the platform was evaluated in an apache spark cluster. conclusions this framework can be plugged into any surveillance application, which has the required it infrastructure in place – for efficient and scalable distributed processing and big data handling. from our evaluation so far, there is an increased interest from various stakeholders in using these machine learning algorithms and nlp technology on surveillance data. further enhancements in nlp include: 1) speech recognition, which enables users to tell their problems (which can first be converted to text and then nlp can act upon it) 2) support for multiple languages (which enables public to tell in their own local language) 3) question-answering (which enables machine processing of user stories and responding with the findings/solutions) a primary motivation for presentation at this conference is to solicit feedback from public health practitioners on this idea and its potential / challenges for use in existing surveillance systems. analytics use in bio surveillance analytics use in public health practice keywords responsive messages; clustering; classification; machine learning algorithm; text analytics *mujitha b. k b e-mail: mujitha@longriverinfotech.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e194, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts assessment of several algorithms for outbreak detection using bovine meat inspection data for syndromic surveillance: a pilot study on whole carcass condemnation rate céline dupuy*1, 3, eric morignat1, fernanda c. dórea2, christian ducrot3, didier calavas1 and emilie gay1 1anses, lyon, france; 2swedish zoonosis centre, uppsala, sweden; 3inra, saint genès champanelle, france objective the objective of the work was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnation introduction the majority of farmed animals are sent to slaughterhouses, making them a focal point for potential collection of health data. however, these data are not always available to health officials, and remain under-used for cattle health monitoring. meat inspection data are mainly non-diagnostic (condemned portion and reasons for condemnation) and cover a large population. these characteristics make them a good candidate for syndromic surveillance. whole carcass condemnation rate is linked to acute infections which reduces the dilution bias due to the variable period of time between cattle infection and the detection of lesions at the slaughterhouse. methods data from 177,098 cattle slaughtered in one french slaughterhouse from 2005 to 2009 were used (proportion of whole carcass condemnations: 0.97%). the method for outbreak detection covered three steps as previously explored by dórea et al. on laboratory test data [1]: i) preparation of an outbreak-free historical baseline over five years, ii) simulation of over 100 years of baseline time series with injection of artificial outbreak signals with several shapes, durations and magnitudes and iii) assessment of the performance (sensitivity, specificity and precocity) of several algorithms to detect these artificial outbreak signals. the tested algorithms were the shewart p-chart, onesided confidence interval of a negative binomial regression model, and ewma and cusum control charts on residuals of a negative binomial model. age and sex were taken into account because of their known effect on whole carcass condemnation [2]. results the highest sensitivity was obtained using negative binomial regression and the highest specificity using cusum or ewma (table 1). ewma sensitivity was too low to select this algorithm for efficient outbreak detection. cusum showed complementary performance to negative binomial regression. conclusions the use of whole carcass condemnation data for syndromic surveillance is more complex than monitoring counts because we need to take into account the denominator (number of cattle slaughtered) as well as age and sex. the recent deployment of a national meat inspection database in france will enable prospective investigation of this indicator on real data. the shewart control chart could be used as a first step considering its high sensitivity and simplicity of implementation followed by the negative binomial model and cusum on residuals of the negative binomial model when historical data becomes available. summary statistical values of performance indicators for all age-sex categories for each indicator the median (minimum-maximum) values for each age sex category, each outbreak duration (2, 4 and 8 weeks) and magnitude (1 to 4) are presented by outbreak shape and outbreak detection algorithm. parameters for each algorithm were: for shewart: k=1.3; for cusum: h=2; for ewma: lambda=0.4 and l=1.3; for negative binomial regression: 80% confidence interval. keywords syndromic surveillance; animal health surveillance; early outbreak detection; time series references 1.dórea fc, et al. syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation. j r soc interface 2013; 10(83). 2.dupuy c, et al. factors associated with offal, partial and whole carcass condemnation in ten french cattle slaughterhouses. meat sci 2014; 97(2): 262-269 *céline dupuy e-mail: celine.dupuy@agriculture.gouv.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e125, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using nc detect for comprehensive morbidity surveillance on poisoning and overdose amy ising*1, katherine j. harmon1, anna e. waller1, scott proescholdbell2 and lana deyneka2 1unc chapel hill, chapel hill, nc, usa; 2north carolina division of public health, nc dhhs, raleigh, nc, usa objective twelve new case definitions were added to the nc detect web application to facilitate timely surveillance for poisoning and overdose. the process for developing these case definitions and the most recent outputs are described. introduction a retrospective analysis of emergency department data in nc for drug and opioid overdoses has been explained previously [1]. we built on this initial work to develop new poisoning and surveillance reports to facilitate near real time surveillance by health department and hospital users. in north carolina, the availability for mortality and hospital discharge data are approximately one and two years after the event date, respectively. nc detect data are near real time and over 75% of ed visits receive at least one icd-9-cm final diagnosis code within two weeks of the initial record receipt. methods the case definitions were developed with input from the north carolina division of public health, the injury prevention research center at unc chapel hill, the carolina center for health informatics in the unc department of emergency medicine, as well as feedback from end users. case definitions from the centers for disease control and prevention, the substance abuse and mental health services administration, and the safe states injury surveillance workgroup were reviewed. given the wide variation among the case definitions among these national groups, the nc expert group decided to develop case definitions for use in north carolina, with the expectation that they may be revised over time and may eventually inform surveillance approaches in other states. the case definitions cover acute alcohol poisoning, poisoning, unintentional poisoning, heroin overdose, medication or drug overdose, methadone overdose, opioid overdose, prescription opioid analgesic overdose, narcan/naloxone, and unintentional medication or drug overdose. all but the naloxone report search in the first six of up to 11 icd-9-cm final diagnosis codes received for each ed visit in nc detect. some of the reports include keyword searches as well and the narcan/naloxone report is entirely keyword-based. the case definitions are available on the nc detect website at http://ncdetect.org/images/pdf/fact_sheets/ nc_detect_odpoisoning_definitions_20140115.pdf results the new case definitions were added to nc detect in may and june 2014. authorized users can access detailed line listing information for all case definitions back to 2009. local health department users can compare their counties at the aggregate level to other counties and the state. figure 1 compares heroin-related ed visits by week from january to july 2013 and 2014. figure 2 compares weekly ed visits for medication or drug overdose from january to july 2013 and 2014. conclusions establishing poisoning and overdose reports in a near real time surveillance system allows local health departments to gather timely feedback on these issues to inform interventions and the work of partner agencies. additional efforts are ongoing to improve the systematic dissemination of this information to those who are not nc detect users but are involved in overdose prevention efforts. figure 1 figure 2 keywords poisoning; overdose; timely surveillance acknowledgments nc detect is funded with federal funds by the north carolina division of public health, public health emergency preparedness grant (phep), and managed through a collaboration between nc dph and the unc department of emergency medicine carolina center for health informatics. references 1. harmon kj, proescholdbell s, marshall s, waller ae. utilization of emergency department data for drug overdose surveillance in north carolina (abstract). online j public health inform. 2014;6(1):e174. *amy ising e-mail: ising@ad.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e136, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande a1, los alamos national lab, los alamos, nm, usa objective evaluate utility of point of need diagnostic tests in relationship to current standard influenza detection methods. introduction each year several thousands contract the seasonal flu, and it is estimated that these viruses are responsible for the deaths of over six thousand individuals [1]. further, when a new strain is detected (e.g. 2009), the result can be substantially more dramatic [2]. because of the potential threats flu viruses pose, the united states, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data [3]. currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. as new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. for data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. two types of multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in california. methods data acquisition percent influenza like illness data was obtained for california from the cdc website as well as local california public health websites from 2014 to 2015. point of need (pon) data was obtained by the biosurveillance ecosystem and included data about tests run, and results of tests. outbreak identification flu data from california was split into discrete outbreaks based on the cdc’s current flu outbreak definition [3]. data stream analysis for each outbreak, the date that point of need diagnostic data is available was compared to the cdc’s flu surveillance data. further analyses will examine outbreak trends between pon data and the cdc’s data to determine if pon data is capable of detecting outbreaks earlier than standard methods. results figure 1 shows one comparison of point of need (pon) data to the current public health standard in san diego. the green line shows the weekly percent ili in san diego during the 2014 to 2015 flu season. the orange line show the total number of pon records (or number of pon tests conducted) during the same outbreak, and the purple line shows the number of those records that detected the flu (i.e. were positive). similar analyses will be conducted for the upcoming 2015-2016 flu season, and will be conducted for other locations in california, both retrospectively and prospectively. conclusions this data will be used to evaluate point of need data streams in influenza outbreaks in california. we will be able to determine if they provide additional useful data that can be used to identify outbreaks earlier, or if they do not add beneficial data to the influenza surveillance system. figure 1: shows a comparison of influenza like illness data in san diego from 2014 to 2015 (green), corresponding point of need records/ tests run (orange), and the number of records that were positive (purple) keywords point of need diagnostic; influenza; surveillance; california acknowledgments this project is funded by the defense threat reduction agency (dtra). the data was provided as a part of the biosurveillance ecosystem (bsve). references 1. centers for disease control and prevention (cdc). estimates of deaths associated with seasonal influenza —united states, 1976-2007. mmwr morb. mortal. wkly. rep. 59, 1057–1062 (2010). 2. fineberg, h. v. pandemic preparedness and response — lessons from the h1n1 influenza of 2009. new england journal of medicine 370, 1335–1342 (2014). 3. overview of influenza surveillance in the united states. (2007). at <http://www.cdc.gov/flu/weekly/pdf/flu-surveillance-overview.pdf> *ashlynn daughton e-mail: adaughton@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e102, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh 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article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 1biomedical informatics, university of utah, salt lake city, ut, usa; 2psychology, university of utah, salt lake city, ut, usa objective we aim to develop an annotation scheme and corpus of depressionrelated tweets to serve as a test-bed for the development of natural language processing algorithms capable of automatically identifying depression-related symptoms from twitter feeds. introduction major depressive disorder has a lifetime prevalence of 16.6% in the united states. social media platforms – e.g. twitter, facebook, reddit – are potential resources for better understanding and monitoring population-level mental health status over time. based on dsm-5 [1] diagnostic criteria, our research aims to develop a natural language processing-based system for monitoring major depressive disorder at the population-level using public social media data. methods in this pilot study, three annotators two psychology undergraduates (a1, a2) and a postdoctoral biomedical informatics researcher (a3) annotated 900 tweets using a linguistic annotation scheme based on dsm-5 depression criteria (e.g. anhedonia – “i don’t enjoy singing anymore”) [2]. we report agreement between annotator pairs computing f-score, a surrogate for kappa. finally, we trained and tested three machine learning classifiers – support vector machine, naïve bayes, and decision tree – for predicting two depressionrelated classes: 1) whether a tweet represents clinical evidence of depression or not and 2) if the tweet is depression-related, whether it is classed as low mood, fatigue or loss of energy, or problems with social environment. we trained and tested each classifier using the weka toolkit (v.3.6.8) with 10-fold cross validation using unigram features, and then reported classifier performance compared against 884 adjudicated tweets with sensitivity and positive predictive value. results we observed high agreement between annotator pairs: a1/a2: 81%, a1/a3: 76%, and a2/a3: 78%. of the 884 adjudicated tweets, the majority of tweets represented no clinical evidence of depression (n=635; 72%) then clinical evidence of depression (n=249; 28%). the skewed distribution of the most frequent 3 clinical evidence of depression symptoms/stressors ranged from low mood (n=114; 13%), fatigue or loss of energy (n=51; 6%), and problems with social environment (n=36; 4%). overall, we observed comparable sensitivities and positive predictive values among classifiers for discerning whether a tweet represented clinical evidence of depression or not (table 1). no clinical evidence of depression and fatigue or loss of energy can be more accurately identified than low mood, problems with social environment, and other types of clinical evidence of depression by all three classifiers. no one classifier performs with both superior sensitivity and positive predictive value for any one symptom/stressor, suggesting that different approaches may be necessary for reliable classification. conclusions automatically identifying depression-tweets with a moderate degree of accuracy is feasible. we are actively improving our training models leveraging a corpus of ~10,000 tweets, expanding symptoms/ stressors being detected, and experimenting with richer features for symptom/stressor detection (e.g. gender, age). table 1. classification performance using unigrams. svm=support vector machine; nb=naïve bayes; dt=decision tree; sens=sensitivity; ppv=positive predictive value keywords social media; mental health; twitter; public health; natural language processing acknowledgments this work was funded by a grant from the national library of medicine (r00lm011393) and was granted an exemption from review by the university of utah institutional review board (irb 00076188). references 1. american psychiatric association. diagnostic and statistical manual of mental disorders, fifth edition (dsm-5). american psychiatric publishing. 2013. 2. mowery dl, bryan c, conway m. toward developing an annotation scheme for depressive disorder symptoms: a preliminary study using twitter data. in: mitchell m, coppersmith g, hollingshead k, editors. proceeding of 2nd workshop on computational linguistics and clinical psychology from linguistic signal to clinical reality; 2015 june 5; denver, co. association for computational linguistics; c2015. p. 89-98 *danielle mowery e-mail: danielle.mowery@utah.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e144, 2016 assessing the 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indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e314, 2019 isds 2019 conference abstracts pocketaid: the pocket atlas of infectious diseases mobile application bonnie gale, hamid mansoor, chen-yeou yu, lauren e. charles pacific northwest national laboratory, richland, w ashington, united states objective the pocket atlas of infectious diseases (pocketaid) mobile application developed at pacific northwest national laboratory (pnnl) provides infectious disease education and decision support offline for an enhanced personal situational risk assessment anywhere in the world. the app integrates a user’s location, demographic information, and infectious disease data to present the user with important information including personalized, calculated risk level. pocketaid features a global disease distribution map and epidemiological curve of country-based case counts by year. filter options allow users to customize disease lists available to aid in situational awareness. pocketaid, first of its kind, is being developed for offline decision support use by department of defense’s defense threat reduction agency (dtra). introduction there are a wide variety of available web-based apps, such as cdc’s epidemic information exchange, that provide infectious disease information and disease distribution [1]. publicly available, online data can be used to inform a user of general risks based on disease distribution maps and case count data. unfortunately, each app contains different aspects of t he data, which is often represented in different ways and incompatible formats. this heterogeneity can overwhelm a user with confusing information making it difficult to interpret or gain valuable insight into their own situational risk in a specified loca tion. in addition, online resources do not filter information based on the user’s current location or situational needs and, therefore, reduces the value of information a user may be interpreting. however, information formatted and represented appropriately in a single app could be used to better understand an individual’s situational infectious disease risk. in addition, this information may further educate a user based on a situation or incident to prevent disease spread, especially in higher risk populations. to accomplish these goals, pnnl has developed an offline, android app that provides the user with simple, easy to understand filterable global infectious disease information integrated with their loca tion to provide personalized situational health risk and decision support in the field. methods this prototype mobile app was a product of pnnl’s biosurveillance application competition, sponsored by dtra. our implementation of this prototype consisted of two parallel efforts: data collection and android app development. data. infectious disease information was collected from cdc, who, biosurveillance resource directory, and analytics for investigation of disease outbreaks websites [1-4]. visualization feature data for global disease distribution and the case count curves was collected from cdc, who, and ecdc websites [1,2,5]. data used for the disease filter and risk level warning features were associated to the collected infectious disease information and user inputted demographic information. application. the prototype app was built using android operating system. information about diseases, e.g., transmission mode, symptoms, properties, was stored in sqlite database that was imported into the phone at install time to provide offline information access. we used osmdroid, an open source project, for map and location services. downloaded map tiles made zoomable, interactive maps available offline. results pocketaid biosurveillance android app was targeted for active duty service members, although deemed useful to a much broader audience. given the various challenges that service members can face during deployment, such as no connectivity in remote are as, http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e314, 2019 isds 2019 conference abstracts the app provides full functionality offline. the general purpose of pocketaid is to provide a user with infectious disease situational awareness and decision support, not be used as an analytic tool to test, treat, or diagnose disease. upon launch, the user is shown their location on a zoomable, interactive map and a list of diseases that are known to be pres ent in their current country (detected automatically using the device’s gps). the user can change their location by selecting a coun try from the location dropdown menu, filtering the populated list of diseases. the user can further filter diseases by diseas e attributes: symptoms, transmission, and properties. clicking on a disease redirects the user to a page with more details about the diseas e, an interactive map of global disease distribution, and epidemiological curve displaying case counts by year for selected disease in selected country. the user can input basic demographic information (i.e., age, gender, occupation, and pregnancy status) in the settings page of the app, which then enables an automated assessment of disease risk. since specific diseases pose an increased risk to certain groups of people, the app can personalize the user’s risk level. in other words, if a user’s demographic information matches a disea se’s risk groups, the user is shown a warning alert. the app was awarded second prize in the competition by judges from across the government for its perceived benefit to biosurveillance, innovation and originality, quality of user experience, and long-term value and sustainability. conclusions the pocketaid provides global disease distribution on a zoomable map, infectious disease background information, disease case counts, offline capabilities, and diseases filtered by the location. this educational app offers a situational health risk assessment for the user through accessing infectious disease information with a disease attribute filter, personalized risk level warning, and user’s gps or selected location to help improve decision support and reduce situational risk. the app was vetted by domain experts across the us government, who found it to be useful and valuable. acknowledgement this work was funded by the defense threat reduction agency (project number cb10190). references 1. centers for disease control and prevention [internet]. atlanta (ga): u.s. department of health & human services; [cited 2018 aug 17]. available from: https://www.cdc.gov/. 2. world health organization [internet]. geneva (switzerland): world health organization; c2018 [cited 2018 aug 17]. available from: http://www.who.int/gho/en/. 3. margevicius kj, generous n, taylor-mccabe kj, et al. 2014. advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance. plos one. 9(1), e83730. doi:https://doi.org/10.1371/journal.pone.0083730. pubmed 4. analytics for investigation of disease outbreaks [internet]. los alamos (nm): los alamos national security, llc for the u.s dept. of energy's nnsa; c2018 [cited 2018 aug 17]. available from: https://aido.bsvgateway.org/. 5. surveillance atlas of infectious diseases [internet]. solna (sweden): european centre for disease prevention & control; c2018 [cited 2018 aug 17]. available from: http://atlas.ecdc.europa.eu/public/index.aspx. http://ojphi.org/ https://doi.org/10.1371/journal.pone.0083730 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24392093&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e314, 2019 isds 2019 conference abstracts figure 1. application prototype main page (left) and disease information page (right). the main page includes filtered diseases by the user’s current location or a selected location from a drop-down menu and further filtering by disease transmission, properties, or symptoms. disease information pages include a risk level warning dialogue box, global disease distribution on a map, epidemiological curves, and disease information. http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* national wildlife health center, madison, wi, usa objective the usgs national wildlife health center in conjunction with federal, state, tribal partners proposed an event reporting system with current and historic information on wildlife morbidity and mortality events in north america. the vehicle to accomplish this goal is whispers, the wildlife health information sharing partnership event reporting system. this system depicts laboratory confirmed wildlife mortality events using geospatial mapping capability. data are collected by multiple partners to collectively enhance the understanding of disease in wildlife populations. introduction although national surveillance systems are maintained for human health (cdc) and for livestock disease (usda); there is no network or data repository in the area of wildlife disease surveillance. because emerging and re-emerging diseases severely affect wildlife populations, impact domestic and agricultural animals, and are a reservoir for zoonotic transmission, it is crucial to have early notification and recognition of disease patterns in wildlife populations. due to fragmented systems of wildlife management, inconsistent investigation into sudden mortality events, and limited laboratory availability, there is not a single entity that is responsible for reporting disease events in north american wildlife populations. methods in effort to create a readily available data repository for wildlife disease events, nwhc created a partner-driven online database for recording on-going and historical wildlife morbidity and mortality events. although the system was initially populated with nwhc’s 30+ year database on us wildlife morbidity and mortality events, a portal for data entry by other wildlife professionals is currently underway. the software system continuously updates reported events. the database can be used to improve recognition of seasonally or cyclically epidemic diseases. many diseases are quite predictable, occurring in the same locations at similar times each year. by chronicling these “typical” events, we can develop a better picture of disease impacts on wildlife across north america. knowledge of “typical” events also make it easier to identify new diseases as they emerge and potentially spread across the landscape. a centralized repository of this information promotes better awareness of wildlife disease and augments opportunity for both proactive and timely reactive response by natural resource managers. results the data informatics team at the usgs national wildlife health center created whispers to meet the national need for a singular reporting system for wildlife disease events. confirmed events that meet these guidelines: 1) 5 or more animals with illness or death in a defined geographic area 2) laboratory confirmed cases 3) reportable at the county level to protect sensitive information and landowner privacy 4) diagnosis is categorically attributed to infectious origin (viral, bacterial, fungal, parasitic), traumatic, nutritional, toxic, or other sick or dead animals must be observed, collected, submitted to a diagnostic laboratory, and the information shared in whispers in order to appear on the map and in the associated database. whispers may be used to report morbidity events, mortality events from infectious disease (like avian cholera) and sudden die-offs following environmental toxicity (harmful algal blooms). the collected data in whispers is available publically and free of charge. this information may be used by wildlife scientists, researchers, and natural resource managers. the data may also serve to alert of events that may affect agricultural animal or human populations, supportive the concept of onehealth surveillance. conclusions technological challenges prevent wildlife agencies, diagnostic laboratories, and other organizations from sharing knowledge of mortality events. the introduction of whispers should improve the access to data for wildlife mortality events in the united states. a long-term goal for whispers is to grow the numbers of contributing partners and make it easier for data to be shared. improved usage and submission to whispers by partners at the federal, state, tribal and local level will help improve accuracy and completeness of the surveillance system. keywords wildlife; surveillance; management; detection; zoonotic acknowledgments the concept for whispers was developed by the usgs national wildlife health center, in partnership with federal, state, tribal, non-governmental, and academic partners. system software was developed by the usgs national wildlife health center. references https://www.nwhc.usgs.gov/whispers/ *julianna b. lenoch e-mail: julielenoch@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e66, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts identification of sufferers of rare diseases using medical claims data jieshi chen* and artur dubrawski auton lab, carnegie mellon university, pittsburgh, pa, usa objective to identify sufferers of a rare and hard to diagnose diseases by detecting sequential patterns in historical medical claims. introduction patients who suffer from rare diseases can be hard to diagnose for prolonged periods of time. in the process, they are often subjected to tentative treatments for ailments they do not have, risking an escalation of their actual condition and side effects from therapies they do not need. an early and accurate detection of these cases would enable follow-ups for precise diagnoses, mitigating the costs of unnecessary care and improving patients’ outcomes. methods a sequential rule learning algorithm1 was applied to a medical claim dataset of about 1,700 patients, who are pre-selected to have medical histories indicative of gaucher disease (gd) but only 25 of these patients were confirmed positives. about 168,000 medical claims and 142,000 pharmaceutical claims were featurized into sequences of asynchronous events and regularly sampled time series as inputs for the model, such that an occurrence of a certain diagnosis code in a medical claim was counted as one event along the timeline of the patient’s medical history. similar method was applied to other key attributes of claims data including procedure codes, national drug codes, diagnosis related groupers, etc. these types of events as well as their temporal statistics, e.g. moving frequencies, peaks, change points, etc., formed the input feature space for the algorithm which was trained to adjudicate each test case and estimate their likelihood of having gd. a random forest algorithm was also applied to the same feature set to comparatively evaluate the utility of sequential aspects of data. the models were evaluated with 10-fold cross-validation. results figure 1 shows the receiver operating characteristic (roc) curves of the temporal rule model with area under the curve score exceeding 81% and significantly outperforming the random forest and default models. considering the practical costs to perform follow-up genetic tests, we prefer a model achieving high positive recall at low risk of false detection. our model correctly identifies more than 25% of known positive cases well within 0.1% of the false positive rate, while the performance of a more popular alternative is indistinguishable from random. this demonstrates the utility of sequential structure of medical claims in identifying patients who suffer from rare diseases. our algorithm infers from data highly interpretable rules it uses in case adjudication. figure 2 illustrates one of them. the root node of the case adjudication tree (event.7969) reflects the icd-9 diagnosis code of “other nonspecific abnormal findings”. among the 14 patients that have this particular icd-9 code present in their claim history, 36% are confirmed gd sufferers. compared to default prevalence in our pre-selected data set of 1.47%, this rule lifts the estimated likelihood of gd 25 times. the rule further develops into two children nodes. the left child node adds the condition of having any outpatient claim observed within 43 claims recorded nearby the occurrence of the root node event. it isolates 5 patients all of whom are gd-positive. the right child shows that 3 patients without event.7969 in their claim history but prescribed ndc 62756-0137-02 (gabapentin by sun pharmaceutical industries ltd.) are all gd-positive. this is just one example of a simple and easy to implement business rule that is capable of identifying previously undiagnosed sufferers of rare diseases. conclusions our model successfully utilizes sequential relationships among events recorded in medical claims data and reveals interpretable patterns that can identify sufferers of rare diseases with high confidence. the algorithm scales well to large volumes of medical claims data and it remains sensitive in despite of a very low prevalence of target cases in data. roc diagrams of models trained to identify gd patients shown with decimal logarithmic scale of the false positive rate axis. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e29, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts example rule used to adjudicate gd cases. keywords sequential patterns; medical history; rare diseases acknowledgments this work has been partially supported by nsf (1320347) and cmu disruptive health technology institute. references 1. guillame-bert m, dubrawski a. classification of time sequences using graphs of temporal constraints. journal of machine learning research, 2016 (under review). *jieshi chen e-mail: jieshic@andrew.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e29, 2017 isds16_lig_identification of sufferers_chen isds16_abstracts-final 39 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 1advocate health care, rolling meadows, il, usa; 2valence health, chicago, il, usa; 3university of illinois at chicago, chicago, il, usa; 4nova southeastern university, fort lauderdale, fl, usa introduction population health relies on tracking patients through a continuum of care with data from disparate sources. an assumption is made that all records of a patient from all the sources are connected1. as was realized during the process of operationalizing algorithms for population health, not all patient records are connected2. disconnected records negatively impact results: from individual patient care management through population health’s predictive analytics3,4. an enterprise master patient index (empi) system can be employed to connect a patient’s records across disparate systems5, but it requires comprehensive tuning to maximize the number of connected records. this presentation describes how one large healthcare integrated delivery network tuned their empi system to maximize the number of connected patient records across all sources. methods several methods were employed to reduce the number of disconnected records. the 5 sources containing the most disconnected records were chosen from the 32 sources of data in the system that represented 10.5 million records. retention rules were developed for removing records from the empi database that did not meet the criteria for retention and those records were removed. using sampled data, the weighting factors applied to the data elements used to determine a score to allow the empi system to link records together (autolink), and the score at which an autolink occurs were reconfigured to allow the empi system to link more records. the matching algorithm was enhanced for combining the patient records into a single entity for sources that were sending a high rate of duplicate patient records with differing patient ids and identical demographics. a crossmatching function was executed to force the re-evaluation of all the linkages between all the records within the empi database. the data stewardship team used the delphi method to determine false positive and false negative rates. results the number of disconnected records was reduced by 99.8% (tables 1, 2, 3). conclusions an idn can employ several tactics to address unmatched patient records across multiple sources without manually reviewing all patient records for possible matches. this project represented the first pass of data standardization and reconciliation. during project execution, additional data quality issues were discovered. this led to a number of follow-on interventions, such as the development of a new source onboarding policy, development of a go-live data validation checklist, inclusion of third party software to validate addresses, and developing guidelines for reducing data errors and the number of duplicate patient records sent to the empi system at patient intake. keywords enterprise master patient index; patient data linkage; data governance references [1] goth g. running on empi. health information exchanges and the onc keep trying to find the secret sauce of patient matching. health data manag 2014 feb;22(2):52, 54, 56 passim. [2] anderson hj. curing an integration headache. health data manag 2009 jul;17(7):38. [3] albright b. the power of empi. health systems are tapping the value of empis to eliminate duplicate patient records. healthc inform 2008 apr;25(4):28-30. [4] harron k, wade a, gilbert r, mueller-peabody b, goldstein h, evaluating bias due to data linkage error in electronic health records. bmc med res methodol 2014;14:36 [5] martin z. a new application for biometrics. health data manag 2007;15:46-48. *annamarie hendrickx e-mail: annamarie.hendrickx@advocatehealth.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e130, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts cholera public health surveillance system in cameroon moise c. ngwa*1, song liang1, leonard mbam2, mouhaman arabi3, andrew teboh4, kaousseri brekmo5, onana mevoula6 and john glenn morris1 1emerging pathogens institute, gainesville, fl, usa; 2who country office, yaoundé, cameroon; 3his, university of maroua, maroua, cameroon; 4fmbs, u of yaoundé i, yaoundé, cameroon; 5rdph, far north, maroua, cameroon; 6who country office, far north, maroua, cameroon objective to describe cholera public health surveillance systems in cameroon within its hierarchical health system introduction effective infectious disease public health surveillance systems are often lacking in resource poor settings. in response, the world health organization (who) put forword recommnded standards for public health surveillence.[1] following the recommendations, the who regional office for africa (afro) in 1998 proposed the integrated dieases surveillance and response (idsr) strategy for the prompt detection and response to key communicable diseases in the african region.[2,3] in 2003, cameroon adopted the idsr-strategy to fortify surveillance in the country. we describe cholera surveillance within idsr-strategy, and assess whether its goal of data analysis and rapid response at the district level have been met. methods semi-structured key informant interviews besides record reviews were conducted in the far north and centre regions of cameroon in 2013. in the far north, we interviewed cholera surveillance officials at the regional delegation of public health (rdph), and the cholera command and control center (c4). in the centre region, we met surveillance officials at the ministry of health (moh), c4 for the centre, and who-cameroon office. interviews lasted about an hourand-a-half. about 20 officials in both regions commented on activities at all levels. results the health system hierarchy inlcudes the peripheral (community, health facility, and health district), intermediate (rdph), central (moh), and who levels (fig.1). the surveillance system is passive and surveillance core activities, response core activities, and support functions have been integrated at all hierarchical levels. surveillance core activities case definition: suspected case is any patient 5-y or older with severe dehydration or diarrhea with or without vomiting or death from acute watery diarrhea. confirmed case is suspected case plus isolation of v. cholerae o1 or o139 in stool. the epidemic threshold is one lab confirmed case. data flow: cholera data is captured at health facility, forwarded to the district that compiles and directs them to the rdph in paper format (fig.1). rdph de-identifies the data and sends them to the central level via internet and from there to the who (fig.1). there is no data analysis and rapid response at the district level. reponses core activities vary across levels. support functions: training in public health surveillance is weak at the community, health facility, district, and regional levels. general administrators and nurses perform surveillance activites at district and health facility levels, respectively. central level performs supervisory visits while it is only partially executed at both the regional and district levels. health facility labs are ill equipped to confirm v. cholerae. mobile phone fleet at districts, laptops at the rdphs, and c4 in all ten regions of cameroon are major milestones in the surveillance system. conclusions the surveillance system is passive with neither data analysis nor rapid response at health district level. thus the goal of idsr strategy has not been met yet. both human (trained surveillance officers) and material (computers) resources are needed at the district level to achieve this goal. keywords cholera; surveillance; cameroon; outbreak acknowledgments we are thankful to the cholera command and control centre staff, joseph koona and hans m. cacharel, for their valuable insights during interviews. we appreciate funding by supplement to nih grant #ro1ai097405 without which this project would not have been realized. references 1. world health organization: who strategic action plan for pandemicinfluenza 2006–2007 (who/cds/epr/gip/2006.2). 2006. 2. world health organization, regional office for africa: integrated disease surveillance and response: a regional strategy for communicable diseases 1999–2003 (afr/rc/48.8). harare: who. 1999. 3. kaboré, a., mcdonnell, b., perkins. technical guidelines for integrated disease surveillance and response in the african region. who, regional office for africa, division of communicable disease prevention and control. 2001 *moise c. ngwa e-mail: ngwam@epi.ufl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e150, 2015 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts leveraging the niche of open data for disease surveillance and health education ta-chien chan*, yung-chu teng, chiao-ling kuo, yao-hsien yeh and bo-cheng lin academia sinica, taipei city, taiwan objective to visualize the incidence of notifiable infectious diseases spatially and interactively, we aimed to provide a friendly interface to access local epidemic information based on open data for health professionals and the public. introduction transparency of information on infectious disease epidemics is crucial for not only public health workers but also the residents in the communities. traditionally, disease control departments created official websites for displaying disease maps or epi-curves with the confirmed case counts. the websites were usually very formal and static, without interaction, animation, or even the aid of spatial statistics. therefore, we tried to take advantage of open data and use a lightweight programming language, javascript, to create an interactive website, named “taiwan infectious disease map (http://ide.geohealth.tw/)“. with the website, we expect to provide real-time incidence information and related epidemiological features using interactive maps and charts. methods this study used infectious-disease-related open data from taiwan’s open data platform (http://data.gov.tw) maintained by the taiwan cdc. it covers 70 types of infectious diseases starting from 2004, and the latest status is updated every day. we then automatically bridge this data into our database and calculate the age-adjusted incidence rate by annual census data and 2000 wh0 standard population. the spatial resolution is mostly at the township level, except that resolution for sexually-transmitted infectious diseases is at the city level. the temporal resolution is month and year, except for dengue fever, which is by week. we used r software to automatically compute incidence every day, and also used its package named “spdep” to compute the spatial clusters of the selected infectious diseases online. in addition, we used javascript language, php, openlayers 3 and highcharts to implement interactive maps and charts. all the data and graphical figures from the charts viewed in this website can be downloaded freely. the temporal animation slider can be played and paused at any time point. the health education button can directly link to an introduction to the selected infectious disease maintained by the taiwan cdc. results the website of the taiwan infectious disease map is displayed in figure 1. the users can select the temporal precision, types of infectious diseases, spatial precision and the gender at the beginning. in this case, the left map is the spatial distribution of the cumulative incidence of tuberculosis (tb) in 2016. the darker red color represents higher incidence. the right top panel is the ranking of tb incidence among 368 townships. the right middle panel is the ranking of tb incidence among 22 cities or counties. the right bottom panel is the annual tb incidence from 2004 to the current date. the highest tb incidence was 67.47 per 100,000 in 2004, and this declined sharply to 15.92 per 100,000 in 2015. conclusions with this user-friendly web application, the public and local public health workers can easily understand the current risk for their townships. the application can provide relevant health education for the public to understand diseases and how to protect themselves. the spatial clusters, gender distribution, age distribution, epi-curve and top ten infectious diseases are all practical and important information provided from this website to assist in preventing and mitigating next epidemic. keywords infectious disease; standardized incidence; spatial visualization; spatial clustering acknowledgments this research was supported by a grant from academia sinica (multidisciplinary health cloud research project: technology development and application of big health data). *ta-chien chan e-mail: dachianpig@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e43, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts evaluation of redcap as a tool for outbreak data management, illinois, 2013-2014 jennifer vahora* and m. allison arwady office of health protection, illinois department of public health, chicago, il, usa objective to evaluate the use of the research electronic data capture (redcap) application to manage outbreak data at the local, state, and multi-jurisdictional level introduction the research electronic data capture (redcap) application has been used to build and manage online surveys and databases in academic research settings. public health agencies have begun to use redcap to manage disease outbreak data. in addition to survey and database development, and data management and analysis, redcap allows users to track data manipulation and user activity, automate export procedures for data downloads, and use ad hoc reporting tools and advanced features, such as branching logic, file uploading, and calculated fields. redcap supports hipaa compliance through userbased permissions and audit trails. these additional capabilities may provide an advantage over commonly used outbreak management tools such as epi info and microsoft access. the illinois department of public health (idph) has not used redcap to date. prior to adopting this web-based application, an evaluation was conducted to assess how redcap may facilitate outbreak data management. methods we conducted a retrospective review of four different types of outbreaks that recently occurred in illinois: a restaurant-associated foodborne illness outbreak; the introduction of middle east respiratory syndrome (mers cov) to the united states; a large rash outbreak; and a healthcare-associated cluster of new delhi metallobeta-lactamase (ndm). using these four case studies, we evaluated how redcap may have impacted the response to each outbreak using six criteria: 1) magnitude of cases and contacts across jurisdictions; 2) self-reporting of symptoms and exposures; 3) storage and multisite access to lab reports; 4) reuse of templates for future outbreaks; 5) repeated measurements; and 6) ability to perform long-term case follow-up. results redcap would have improved data management capabilities for all four types of outbreaks. for the mers cov and rash outbreaks, redcap would have assisted with the management of large-scale outbreaks with hundreds of contacts and multi-jurisdiction response. for all four types of outbreaks, redcap would have facilitated self-reporting of symptoms and exposures through the design and administration of online surveys to cases and contacts. redcap’s document upload functionality would have facilitated storage and access of lab reports for foodborne illness, mers cov, and ndm outbreaks. redcap also would allow public health responders to perform long-term monitoring of symptoms and disease incidence in ndm outbreaks. conclusions in illinois, public health agencies currently lack a secure, hipaacompliant outbreak management system that facilitates survey development, online data entry, data management tools such as automated exports, contact tracing, and coordination across jurisdictions. the evaluation of recent outbreaks shows that redcap provides these desired capabilities. table 1. recent illinois outbreaks in which redcap may have improved data management keywords redcap; outbreak data management; evaluation references 1. arwady, a., gibson, c., et al. role of social media in investigating rash in mud race obstacle course participants — illinois, july 2013. from a paper presented at the meeting of the council of state and territorial epidemiologists, nashville, tn, june 2014. 2. centers for disease control and prevention. notes from the field: new delhi metallo-lactamase–producing escherichia coli associated with endoscopic retrograde cholangiopancreatography — illinois, 2013. mmwr. 2014 jan 3 [cited 2014 sep 9]. available from: http:// www.cdc.gov/mmwr/preview/mmwrhtml/mm6251a4.htm 3. centers for disease control and prevention. first confirmed cases of middle east respiratory syndrome coronavirus (merscov) infection in the united states, updated information on the epidemiology of mers-cov infection, and guidance for the public, clinicians, and public health authorities — may 2014. mmwr. 2014 may 16 [cited 2014 sep 9]. available from: http://www.cdc. gov/mmwr/preview/mmwrhtml/mm6319a4.htm?s_cid=mm6319a4_w *jennifer vahora e-mail: jennifer.vahora@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e168, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 1defense health agency, department of defense, falls church, va, usa; 2department of veteran’s affairs, palo alto, ca, usa; 3henry m jackson foundation and uniform services university, bethesda, md, usa objective determine an optimal course of action for achieving a more mission and cost-effective model for implementing combined or collaborative biosurveillance across the departments of veterans affairs (va) and defense (dod). introduction the joint incentive fund (jif) authorization creates innovative dod/va sharing initiatives. in 2009, dod and va commenced a biosurveillance jif project whose principle objectives include improved situational awareness of combined va/ dod populations 1 and determining the optimal business model allowing both agency biosurveillance programs to operate more efficiently by: 1) consolidating information technology assets; 2) targeting enhanced collaboration for improved public health outcomes; and 3) improving buying power, and return on investment. we analyzed various interoperability models aimed at biosurveillance data sharing, asset consolidation and enhanced collaboration. potential end states to be evaluated include maintaining separate departmental systems, bidirectional exchange of data to separately managed systems, consolidation of data within one department and shared access to a common system, consolidation of data in a neutral repository accessed by separately run legacy systems, or a custom developed biosurveillance solution utilizing a common data repository. methods our analysis complied with us office of management and budget’s circular a-94, which promotes efficient resource allocation through well-informed decision-making by providing guidance for conducting cost-benefit and cost-effectiveness analyses. our analysis utilized the analytical hierarchy process (ahp), which is a decision support methodology for solving complex decision problems using a multi-level hierarchical structure of objectives, criteria, sub-criteria, and alternatives. pertinent data are derived using a set of pairwise comparisons to obtain weights of importance for decision criteria, and relative performance measures of alternatives for individual decision criterion. additionally, we used the delphi technique to solicit input from dod/va program leadership, current essence users, and an independent executive leadership team who formerly served in key positions across both agencies. to assess the merits of current information technology assets, we included a combination of standardized questionnaires as well as “hands on” evaluations and interviews. a wide array of biosurveillance program information was collected from both agencies. results we identified 6 top level decision criteria and 26 sub-criteria and determined relative importance weighting among this criterion, which formed the basis of an ahp model. five alternative courses of action as well as the current state were evaluated and scored by public health subject matter experts. these experts scored the alternatives within three discrete timeframes, representing: 1)fy2015-2016; 2) fy2017-2018; 3)fy2019-2024. in addition, an overall score was computed for the entire timeframe spanning from fy2015 through 2024. dual one-way data feeds was the highest scoring alternative for the overall timeframe, however however scores were also very high for the period fy2019-2024, involving the creation of a a cloud based joint data repository. the dod and va will endeavor to pursue these alternatives to improve syndromic biosurveillance efforts in the future. conclusions this project identified six possible end states for va and dod future biosurveillance activities. subject matter experts have determined the criteria which are most important in evaluating alternative scenarios. the two highest scoring solution set’s will be pursued in the future in order to improve syndromic biosurveillance across the agencies. keywords essence; biosurveillance; decision support references 1.pavlin ja, burkom hs, elbert y, lucero-obusan c, winston ca, et al. 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surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store 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mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya 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lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for 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da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts evaluating ascertainment of hepatitis c cases and deaths by electronically linking surveillance and vital statistics data in utah anne burke*, jeffrey eason, david jackson and allyn k. nakashima bureau of epidemiology, utah department of health, salt lake city, ut, usa objective to evaluate the ascertainment of deaths among hepatitis c virus (hcv)-infected persons reported to public health and to identify additional hcv cases not reported to public health in utah through review of death certificate data. introduction while hcv infections are associated with substantial morbidity and mortality in the united states, deaths due to hcv may not be detected well in utah’s surveillance system. new interferon-free drugs for hcv can result in virologic cure with limited side effects, but treatment is expensive. it will therefore be increasingly important that public health accurately document the prevalence of hcv and outcomes, such as death, to inform policy makers and others who are responsible for allocating resources. a previous analysis conducted in utah determined that a two-step methodology electronically linking death certificate data to hiv surveillance data was effective at ascertaining previously unreported deaths and cases in the hivinfected population.1 similarly, linkage to death certificate records may also provide an important avenue to identify deaths among the chronic hcv cases included in surveillance data and identify cases of hcv not previously reported to public health in utah. methods a spreadsheet of all chronic hcv infections with investigations initiated by the utah department of health from was generated using trisano®, utah’s electronic surveillance system. electronic death certificate data were obtained from utah vital statistics for 20092012. electronic record linkage was utilized to match surveillance and death certificate data using first and last name, gender, and date of birth. a subsequent manual search of death certificate records was conducted to identify persons with an underlying or contributing cause of death attributed to chronic hcv infection (icd-10 code b182). persons with causes of death including hcv infection who had not previously been reported to public health were identified. date of birth was determined for all persons who had died of hcv during the time frame and were identified in this study. results overall, 600 deaths among hcv-infected individuals were identified between 2009 and 2012 in utah. using electronic record linking, 346 deaths among persons with chronic hcv infection were identified. of these, only 34 (9.8%) deaths had been reported to public health. manual search of death certificate records identified 350 individuals with hcv-associated underlying or contributing codes. among these, the proportion previously documented in trisano® as hcv cases and as deceased hcv cases was 27% (n=96) and 6% (n=21), respectively. seventy-three percent (254/350) of individuals with death certificate codes associated with hcv were newly identified as cases not previously reported to the public health. the majority (76%) of deaths in this study occurred in persons born between 1945-1965. conclusions the results of this analysis suggest that death certificate record linkage may provide a mechanism to more accurately measure the prevalence of people living with chronic conditions, such as hcv infection, in utah. despite this fact, the majority of deaths due to hcv infection in utah were in persons who had never previously been reported to public health. these data support the need for extended hcv testing in utah, particularly for those with risk factors or within the age cohort born 1945-1965 population. additionally, they support the creation of a hepatitis c registry in utah in order to gain better understanding of the infected population and support interventions to prevent the morbidity and mortality associated with the disease. keywords data linkage; communicable diseases surveillance; death ascertainment; hepatitis c virus acknowledgments the data for this analysis was provided by the utah department of health and mylitta barrett from the office of vital records and statistics. references 1. burke a, mietchen m, jackson d, nakashima a, a quality improvement analysis of death ascertainment among utah hiv/aids cases. paper presented at: annual conferences of the council for state and territorial epidemiologist; 2014 june 22-26; nashville, tn. *anne burke e-mail: aburke@utah.gov evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) celestine a. ameh*1, muawiyyah b. sufiyan2, matthew jacob3, ndadilnasiya e. waziri1 and adebola t. olayinka2 1. nigerian field epidemiology and laboratory training program, abuja, nigeria 2. ahmadu bello university zaria, nigeria 3. kaduna state primary health care agency, kaduna, nigeria abstract objective: to evaluate the case-based measles surveillance system in kaduna state of nigeria and identify gaps in its operation. introduction: in africa, approximately 13 million cases, 650,000 deaths due to measles occur annually, with sub-saharan africa having the highest morbidity and mortality. measles infection is endemic in nigeria and has been documented to occur all year round, despite high measles routine and supplemental immunization coverage. the frequent outbreaks of measles in kaduna state prompted the need for the evaluation of the measles case-based surveillance system. methods: we interviewed stakeholders and conducted a retrospective record review of the measles case-based surveillance data from 2010 – 2012 and adapted the 2001 cdc guidelines on surveillance evaluation and the framework for evaluating public health surveillance systems for early detection of outbreaks, to assess the systems usefulness, representativeness, timeliness, stability, acceptability and data quality. we calculated the annualized detection rate of measles and non-measles febrile rash, proportion of available results, proportion of lgas (districts) that investigated at least one case with blood, proportion of cases that were igm positive and the incidence of measles. we compared the results with who(2004) recommended performance indicators to determine the quality and effectiveness of measles surveillance system. results: according to the stakeholders, the case-based surveillance system was useful and acceptable. median interval between specimen collection and release of result was 7days (1 – 25 days) in 2010, 38 days (range: 16 – 109 days) in 2011 and 11 days (range: 1 – 105 days) in 2012. the annualized detection rate of measles rash in 2010 was 2.1 (target: 32), 1.0 (target: 32) in 2011 and 1.4 (target: 32) in 2012. the annualized detection rate of non-measles febrile rash in 2010 was 2.1 (target: 32), 0.6 (target: 32) in 2011 and 0.8 (target: 32) in 2012. case definitions are simple and understood by all the operators. conclusion: this evaluation showed that the surveillance system was still useful. also, the efficiency and effectiveness of the laboratory component as captured by the “median interval between specimen collection and the release of results improved in 2010 and 2012 compared to 2011. however, there was a progressive decline in the timeliness and completeness of weekly reports in the years under review. keywords: measles; case-based; surveillance; evaluation; nigeria http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi correspondence: celestine attah ameh, nigeria field epidemiology and laboratory training program, no. 50 haile selassie street, asokoro, abuja, nigeria. e-mail: cameh1085@gmail.com, phone: +2348035992494 doi: 10.5210/ojphi.v8i3.7089 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction measles is an acute viral infectious disease and an important cause of childhood morbidity and mortality [1-5]. in 2010, the estimated annual measles incidence aggregated across all countries was 1.6 cases per 1000, resulting in about 139,000 deaths globally [6]. in africa, about 13 million cases, 650,000 deaths occur annually, with sub-saharan africa having the highest morbidity and mortality [7,8]. measles is endemic in nigeria and exhibits a seasonal pattern with increasing incidence during the dry season (november to may). the severity of this disease is higher in the northern part of nigeria [9], due to the inadequate utilization of measles control strategies [6]. outbreaks occur due to the high birth cohort, sub-optimal immunization coverage and the wide interval between follow-up campaigns, resulting in the accumulation of large numbers of susceptibles [10,11]. the burden of measles in nigeria prior to the introduction of the accelerated measles control strategy in 2005 was high. for instance, in 1985, 3.6 million cases resulting in 108, 000 deaths were reported [12]. following the implementation of the measles catch-up supplementary immunization activities (sias) in 2005, there was a significant reduction in measles morbidity and mortality [10]. however, the post implementation phase of this strategy witnessed a non-linear increase in measles incidence [13]. thus in 2006, 383 measles cases were reported in children less than 15 years (annual incidence rate [air]: 0.6 cases /100,000 children). in 2007, it increased to 2,542 cases (air: 3.6 cases/100,000 children). a breakdown of cases in 2007, showed 62% to be aged between 1-4 years, and 23% aged between 5-14 years. in 2008, there was a further increase to 9,510 cases (air: 13.4 cases/100,000 children). by 2011, the number of measles cases had risen to 17,248 (air: 18.2 cases/100,000 children) [8]. most of the cases occurred predominantly among younger children [6]. despite high administrative estimates of measles vaccine coverage (>99%) at first (routine) dose and during sias in nigeria, outbreaks continue to occur [6,8,14]. about 30, 194, 254 and 169 measles outbreaks were reported in 2006, 2007, 2008 and 2009 respectively [11]. furthermore, between epidemiological week 1 to 43 of 2013, 643 outbreaks were confirmed in 83% of the 774 lgas of this country [15] with kebbi and kaduna states in northern nigeria recording a significant proportion of these outbreaks [8,10]. inadequate surveillance and response capacity in any country can endanger its population. unfortunately, developing countries, where there is the greatest risk for outbreaks, often lack the capacity to promptly detect and adequately respond to these outbreaks [16]. in nigeria, findings had shown that most of the measles outbreaks were detected too late resulting in either no or late response with minimal impact [17]. the frequent measles outbreaks in kaduna state prompted the need to evaluate the measles case-based surveillance system attributes and identify gaps in its operation. http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi methods area covered by the surveillance system kaduna state (province) is made up of 23 local government areas (lgas)/districts and 255 political wards. it occupies a land area of 46,053km2 and a population of 7.1 million inhabitants (2011 census estimate) [18] made up of 281,047 children less than 1 year of age, 1.2 million between the ages of 1-4 years, 1.8 million between 5-14 years, 458,800 between 15-18 years and ages > 19years have a population of about 3.3 million with a crude birth rate of 21.5/1000. there are a total of 1,720 health facilities, out of which 188 report using the integrated disease surveillance and response strategy (idsr). surveillance evaluation methods we adapted the 2001 updated cdc guidelines on surveillance evaluation [19] and the framework for evaluating public health surveillance systems for early detection of outbreaks [20] and employed both qualitative and quantitative methods to describe and evaluate the system. stakeholders from the kaduna state primary health care agency (ksphca) and local government disease surveillance notification officers (ldsno) were interviewed to assess their views on the usefulness and acceptability of the system. a retrospective record review of the measles case-based surveillance data from 2010– 2012 was carried out and data abstracted and analysed to determine the positive predictive value (ppv) of confirmed cases [calculated as the proportion of laboratory confirmed cases {true measles cases} among clinically diagnosed cases]. we assessed data quality by determining the proportion of complete weekly reports that got to the state by close of work (4.00pm) of tuesday of the reporting week. the representativeness of the system was assessed by calculating the proportion of health facilities that actually report. the date of specimen collection, date of arrival at the laboratory, specimen condition on arrival, date of release of results, and measles and rubella igm results were reviewed. the proportions and rates calculated were compared with the who (2004) [21] recommended performance indicators to determine the quality and effectiveness of measles surveillance system. case and laboratory based weekly surveillance data from all the reporting focal sites (188 health facilities) were used to assess some of the system attributes. the standard case definition of measles was adopted to include: suspected measles case: any person with fever (≥380c) and maculopapular (non-vesicular) generalized rash with cough, coryza or conjunctivitis (red eyes) or any person in whom a clinician suspects measles. epidemiologically linked case: a case whose blood was not collected for laboratory investigation but had contact with another case who was laboratory confirmed and is either a family member/school mate/neighbor/playmate or who had in the preceding 2weeks being to an area where a measles outbreak had been declared. confirmed measles case: a suspected case with positive igm antibody or who is epidemiologically linked to a confirmed case in an outbreak. http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi ethical consideration: permission to conduct the study was obtained from the executive secretary ksphca. names of patients and addresses were omitted from the analysis. results surveillance system description the measles surveillance system is a passive, case-based system that is operated by trained personnel’s; doctors, disease surveillance notification officers {dsnos}, laboratorians, nurses, community health extension workers and environmental health officers. measles, being an epidemic prone disease, is reported within the context of the idsr on a weekly basis. at health facility level, data collection is done using three [3] data collection instruments: case investigation form[cif] (001a), laboratory form (001b) and the linelist form (001c). in kaduna state, case based information on every suspected case is inputted into the cif, blood sample collected and laboratory information inputted into form 001b and sent to the measles reference laboratory at yusuf dansoho memorial hospital for confirmation [using igm antibody elisa]. measles negative samples are further tested for rubella. if the number of confirmed measles cases exceeds three [3] in any catchment area or health facility in one month, an outbreak is declared and all suspected cases are then line-listed at health facility level. in kaduna state, one hundred and eighty eight (188) health facilities from a total of 1,720 health facilities (10.9%) report case based information on epidemic prone diseases on a weekly basis to the lga level. these reports are collated by the ldsno before the close of work on a monday of each week and forwarded to the state epidemiologist before the close of work on tuesday of the same week. the state epidemiologist forwards all aggregated reports from the lgas to the national level (surveillance department) on or before close of work on wednesday of the same week. at the state level, a copy of the surveillance database is shared with the kaduna office of the world health organization (who). feedback on the final case classification is given to the ldsno and the reporting facility through the same channel used for reporting (figure 1). usefulness: the analysis and interpretation of the surveillance data in the years under review showed that the surveillance system was able to detect and confirm cases of measles and nonmeasles febrile rash, however the detection of these conditions fell below the who recommended targets in 2011 and 2012 (table 1). timeliness: timeliness of reporting was assessed by the proportion of weekly reports from the ldsno that gets to the state dsno on or before 4pm (close of work) on tuesday of the reporting week. in 2010 and 2011, nineteen [19] lgas [83%] had timely reports above 80% (who target), while in 2012, 15 lgas (65%) had timely reports above 80%. in 2011 and 2012, kargarko and birnin-gwari lgas had the least proportion for timeliness (75% and 39% respectively). http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi figure 1: measles case-based surveillance flow chart http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi table 1: measles surveillance performance indicators for kaduna state (2010 2012) years under evaluation indicators 2010 2011 2012 target * annualized detection rate of measles per 100,000 4.8 1.0 1.4 32 annualized detection rate of non-measles febrile rash per 100,000 2.1 0.6 0.8 32 available laboratory results (%) 95.0 66.0 94.3 380 lga that investigated at least one case with blood sample (%) 100.0 65.0 100.0 380 measles incidence (per 1000,000) 102.0 16.0 23.0 <6 *who regional office for africa: guideline for measles surveillance, revised in 2004 [21] data quality: data quality was assessed by the proportion of complete weekly reports that got to the state by close of work (4.00pm) of tuesday of the reporting week. in 2010, 80% of the lgas had complete reports. in 2011, three [3] lgas (13%) had proportions lower than the 80% recommended target. while in 2012, five [5] lgas (22%), had proportion lower than the recommended target. representativeness: the measles surveillance system is operated by different cadre of professionals; physicians, nurses, laboratorians, environmental health officers and community health officers. all the twenty three [23] lgas are involved in surveillance. one hundred and eighty eight (188) out of one thousand seven hundred and twenty (1,720) health facilities actually report. positive predictive value: the positive predictive value for the case based measles surveillance system was 53.9% in 2010, 36.6% in 2011 and 40.2% in 2012 (table 2). acceptability: the surveillance system was acceptable to all stakeholders and operators of the surveillance system. this acceptability is reflected in the reporting of suspected cases. http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi stability: the system is donor driven. who provides monthly monetary allowance and logistic support to surveillance officers. in addition, it also provides reagents and laboratory consumables for the measles reference laboratory. table 2: median turn-around times for collected samples and proportion of confirmed measles and rubella cases in kaduna state, 2010 2012 samples turn-around times, proportion of confirmed cases 2010 2011 2012 specimen condition †good †good †good median interval between specimen collection and receipt in the lab {lag time[days]} (range) 1(0 – 29) 2 (0 – 40) 2 (0 – 26) median interval between specimen collection and release of result {turnaround time}/days (range) 7 (1 – 24) 38(16 109) 11(1–105) median interval between onset of rash to specimen collection[days](range) 5(0 – 44) 5 (0 – 43) 4 (0 – 25) total no. of reported cases 625 142 201 confirmed measles cases 337 52 81 ppv (%) 53.9 36.6 40.2 rubella igm confirmed cases (%) 31(4.9) 5(3.5) 39(19.4) † adequate blood sample (5mls), blood specimens not haemolysed discussion the measles case-based surveillance system came into effect in nigeria in 2005 [13] following the successful implementation of the 2005 catch-up supplemental immunization activity (sia) in northern and southern nigeria. this surveillance system forms part of the four pronged strategy for the accelerated control of measles in nigeria. its main objective is the reduction of measles associated morbidity and mortality. periodic evaluation of the surveillance system is important in assessing its efficiency and effectiveness and to ascertain if the system is meeting the objective for which it was established. in the years under review, the surveillance system was associated with a progressive decline in timeliness and completeness of reporting. this same finding had been shown to be consistent in most part of africa especially where paper based reporting is used [22,23]. the major challenge with the decline in timeliness is that most outbreak go undetected and when finally detected would have caused a lot of harm [23]. http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi the surveillance system was also shown to have a low ppv. this is reflected in the low annualized detection rate of measles and non-measles febrile rash. since measles is endemic in nigeria with a high prevalence [24,25], the measles surveillance system having a low ppv implies that the system would not be able to detect cases adequately. the reduced low ppv could be due to low reporting representiveness, as a relatively large number of public health facilities and most private health facilities do not report. furthermore, the under-reporting associated with this system simply means that the surveillance system cannot predict outbreaks and most outbreaks that occur are undetected. the evaluation of the laboratory component of the case-based system showed impressive turnaround times between time of onset of rash and the collection of specimen and also reduced times between sample collection and submission in the laboratory. with the exception of 2011, the proportion of lgas that investigated at least one measles case with blood sample exceeded the who target. also, the proportion of laboratory results made available exceeded who recommended target in 2010 and 2012. this finding is a plus on the measles case-based surveillance system as it will ensure the production of timely results. the system is useful as the data it generated was used to access the performance of the measles control strategies being implemented, for instance, data emanating from the system showed a high incidence of measles in 2010. the incidence rate reduced in 2011 and 2012, but were all above the target limits set for measles elimination [21]. this finding has brought out the need to further strengthen the intervention strategies currently in place to achieve measles control targets. conclusion even though the performance of the surveillance system was not optimal, most of the major stakeholders found the surveillance system to be useful and acceptable as it was able to detect cases despite its low ppv. the surveillance system also played an important role in assessing the effectiveness of the current measles control strategies. however, the system was discovered to be unstable as it was highly donor dependent for funding and technical support. limitation a major limitation of this study was our inability to determine the sensitivity of the surveillance system as there was no gold standard to compare the surveillance system with. recommendation public and private healthcare facilities currently not reporting, should be encouraged to report. since the surveillance system had captured the increased incidence of measles above the recommended target per annum, there is need to further strengthen the accelerated measles control strategies in kaduna state. finally, kaduna state should take complete ownership of the case-based surveillance system and ensure its sustainability by providing funding and logistic support. acknowledgements the authors wish to thank the nigerian field epidemiology and laboratory training program, the surveillance officers at the various levels of surveillance in kaduna state and the laboratory http://ojphi.org/ evaluation of the measles surveillance system in kaduna state, nigeria (2010-2012) 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e206, 2016 ojphi personnel’s at the measles reference laboratory at yusuf dansoho memorial hospital, for giving us the opportunity to carry out this study and also providing the data which was used for the analysis. competing interests: no competing interests declared. references 1. simons e, ferrari m, fricks j, wannemuehler k, anand a, et al. assessment of the 2010 global measles mortality reduction goal: results from a model of surveillance data. lancet [internet]. 2012 jun 9 [cited 2013 apr 15];379(9832):2173–8. available from: http://www.ncbi.nlm.nih.gov/pubmed/22534001. 2. li l, qingfeng li, lee ar, friberg ik, perin j, et al. trends in 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natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 1rush university, chicago, il, usa; 2pangaea information technologies, ltd, chicago, il, usa; 3h-core, llc, chicago, il, usa objective to review the natural language processing (nlp) and technical challenges encountered in an automated influenza-like illness (ili) surveillance system. introduction processing free-text clinical information in an electronic medical record (emr) may enhance surveillance systems for early identification of ili outbreaks. however, processing clinical text using nlp poses a challenge in preserving the semantics of the original information recorded. in this study, we discuss several nlp and technical issues as well as potential solutions for implementation in syndromic surveillance systems. methods this is a retrospective, cross-sectional study conducted at the eds of a large urban academic medical center and community hospital. the study timeframe was october 1, 2014 to june 30, 2015. geographic utilization of artificial intelligence in real-time for disease identification and alert notification (guardian) – a syndromic surveillance program – received and processed hl7 messages in real-time and generated ili surveillance reports. the sophisticated guardian nlp algorithm processed each patient chart component, consistent with a physician’s manual review [1]. a random sample of 10 ili-positive cases detected by guardian was drawn each week for manual review to confirm the positive presence of the ili case definition terms: fever, cough, and sore throat. false ili-positive cases, and associated causes, were documented and categorized as shown in table 1. results of the 519 ili-positive charts reviewed, 56 cases were false positive, mainly due to nlp or programming errors (e.g., incorrect concept parsing due to certain punctuation and word combinations). temporal relationships were found to be a challenge for nlp: examples included when a clinician noted a fever in the past or documented instructions to return for a fever. physician documentation style was also a common and difficult problem: examples include the use of italics or bold text to represent a positive or negative symptom. we identified new terms to add to the negation list and discovered a problem caused by the use of a long negation string. guardian was programmed to acknowledge negation strings up to 16 words since any more words than that decreased accuracy. we found that temperatures were occasionally corrected in the emr, while guardian had no way of knowing the original value was an error. lastly, on occasion, there was also reception of incorrect inpatient vitals and truncated nurse notes. with the modification of our system architecture and nlp engine, we were able to reduce the associated ili false positives from 56 (10.8%) to 32 (6.2%). conclusions the use of nlp can enhance the efficacy of syndromic surveillance systems. however, there are limitations to nlp processing loads. while many nlp errors can be corrected, yielding improved accuracy, some issues cannot be resolved. sharing known technical and nlp issues, and their resolutions, can assist in minimizing errors to acceptable levels (<5%) leading to refinement of existing syndromic surveillance systems. table 1. review of nlp and technical challenges in ili surveillance. note: *issues were resolved by modifying the system architecture and nlp engine. keywords influenza-like illness; natural language processing; lessons learned acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. references silva j, shah s, rumoro d, bayram j, hallock m, gibbs g, waddell m. comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: guardian vs. rods vs. electronic medical record reports. artificial intelligence in medicine. 2013;59:169-174. *gillian s. gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e158, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 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system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and 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markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara 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web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned craig savel1; stan mierzwa1; pamina m. gorbach (dr.p.h.)2; samir souidi1; michelle lally (md)3; gregory zimet (ph.d.)4; adolescent medicine trials network for hiv/aids interventions 1. information technology, population council, new york, ny 2. department of epidemiology, university of california, los angeles (ucla), ca 3. alpert medical school of brown university, lifespan hospital system, and va medical center, providence, ri 4. indiana university school of medicine, indianapolis, in abstract this paper reports on a specific web-based self-report data collection system that was developed for a public health research study in the united states. our focus is on technical outcome results and lessons learned that may be useful to other projects requiring such a solution. the system was accessible from any device that had a browser that supported html5. report findings include: which hardware devices, web browsers, and operating systems were used; the rate of survey completion; and key considerations for employing web-based surveys in a clinical trial setting. keywords: self-report data collection; electronic data collection; casi; avatars; html5; smartphones; web browsers; web-based survey; clinical trials correspondence: smierzwa@popcouncil.org doi: 10.5210/ojphi.v8i2.6719 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction given the challenges associated with collecting accurate self-reported data in research studies, new approaches using customizable avatars and online questionnaires are being developed in an attempt to improve the frequency and accuracy of self-reports. in looking for ways to better collect survey data, we developed a technology solution consisting of a web-based self-report data http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi collection system that used customizable avatars to collect data. participants were instructed to take two surveys at specific time periods. self-created avatars “traveled” with participants such that they would appear during the first and second surveys and also appear if a participant restarted a survey that was not completed on the first attempt. the survey website was html5 compatible, but we elected not to use html5 local storage because of confidentiality concerns and requirements for data security in this public health research study. the survey was designed to work on any html5-compatible browser and on any tablet, smartphone, or computer that had a browser that supported html5. although we recognized that participants who had older browsers (internet explorer 8 or earlier, old versions of firefox and chrome) might not be able to access and complete the self-report survey, it was felt that most of the target audience would have little trouble doing so. to the best of our knowledge, no other study has specifically examined the devices, internet browsers, and operating systems used to complete a web-based self-report survey for a public health research project. methods many self-report electronic data collection systems in hiv and/or other public health research studies use technology that exists in a controlled environment. the study protocol generally dictates the type of computer or device to be used and the method for presenting the study’s survey. this study was an ancillary study to a large clinical trial of pre-exposure prophylaxis use by hivnegative adolescent males 15–17 years of age conducted at 12 sites in the united states through the nichd-funded adolescent trials network. most of these sites were adolescent hiv clinics (atn 110/113). after completing procedures for the clinical trial, adolescent males enrolled in the trial were offered participation in this ancillary study. if they agreed to participate, they were given choices on how to complete the study questionnaire. participants were able to access the webbased survey either from inside a study clinic (using clinic computers) or from a device of the participant’s choice (either inside or outside the clinic setting). this meant that the self-report survey system needed to be built such that participants could access the survey from computers, tablets, or smartphones on a variety of operating systems using many different browsers. the system needed to allow participants to create their own customized avatars that would follow them through the questionnaire. it also had to allow them to edit or use the same avatar in a follow-up survey. the customized avatars would appear on each question screen, and they would move to different locations on the screen in order to present the survey questions within a text bubble [1]. during the web-based self-report survey data collection, information was collected on several technical measures such as which web browser and operating system was used. the method of data collection was made available via log files that are common in web servers. our study used the microsoft internet information server to capture this information. several elements will be reported in the findings section, including the preference of using the interactive questionnaire web-based survey system, recording the amount of time to complete the electronic survey, and the percentage of participants that completed the survey. many of the qualities of the very simple end-user screen design, as well as the elements of start and end time, and computer name were adopted from the population council acasi technology solution [2]. http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi findings (results) the study enrolled its first participants in july 2013 and completed enrollment in july 2015. since each of the 167 study participants should have made at least two visits to the assigned clinic during the study, and may have accessed the survey additional times as needed to complete it, we were able to collect sufficient data. because of potential confidentiality issues, we were not able to use cookies to track visitors, even anonymously. we were therefore also not able to use google analytics. the preferred strategy was to parse the user agent, glean as much as possible, and write that information into a secure database. browser data were sent to the server via a user agent text string with every request; this occurred automatically when the browser on the computer communicated with the device. a user agent string indicates which browser was used, its version number, and details about the user’s system, such as the operating system and version. for various reasons, including incomplete or corrupted user agent strings, some visits to the survey may not have been logged/recorded (meaning communication between the browser and the server). table 1. basic result data on utilized internet browsers and versions: june 2013–july 2015 browser and version # visits % of visits internet explorer 196 34% 7.0 28 …..8.0 19 …..9.0 126 …..10.0 23 safari 149 26% 0.0 (see note) 110 4.0 1 5.0 3 5.1 3 6.0 1 6.1 31 chrome 128 21% 0.0 (see note) 118 18.0 8 27.0 2 firefox 4 < 1% http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi 16.0 1 21.0 3 android 4.0 13 2% other or unknown 89 16% note: version 0.0 for both safari and chrome browsers occurs when the browser version is not part of the string sent to the server. these data show some notable differences from general statistics for us users. the website statcounter (http://gs.statcounter.com/#all-browser-us-monthly-201306-201504-bar) collects statistics on web usage. among participants taking the survey, the most popular browser was internet explorer by a margin of 8%. the second most popular was safari, and the third most popular was chrome. (see table 1.) in contrast, statcounter shows that for the time period the survey was running, chrome was the most popular browser at 31%, internet explorer was second at 24%, and safari was third at 23%. firefox had 11% usage but less than 1% usage for the survey. what can account for this difference? it is impossible to know, but we hypothesize that more users opted to fill out the surveys at the clinic than we had expected. businesses, governments, and social service organizations are often “late adopters” of technology, and if users filled out the surveys at the time of the visit to the clinic that could account for the difference. since survey participants were young people, we expected mobile browsers, especially iphones or ipads, to be factored in. apple mobile products use safari as the default and this can account for the relative greater use of safari in our survey as opposed to general statistics. table 2. basic result data on operating systems usage: atn 123* june 2013–july 2015 operating system # visits % of visits windows 302 53% windows 7 264 windows 8 4 windows xp 31 windows vista 3 mac osx 99 17% unknown 89 16% linux 39 7% ios 24 4% android 20 3% http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi *atn – adolescent medicine trials network striking differences were also found between survey respondents’ operating system usage (see table 2) and the general statistics for us users during that time period. in reviewing statcounter data on operating systems (http://gs.statcounter.com/#all-os-us-monthly-201306-201504-bar), we notice that more than 50% of responses were from a windows-based computer, whereas 17% were from a mac osx-based system. although it is more difficult to determine the device type by webserver log files, one can infer a likely device from a log file. for instance, if a log file entry shows a windows operating system, the device is obviously a windows pc or laptop. if a log file shows ios as the operating system, then it is an apple mobile device. it is much more difficult to determine, however, whether that device is an iphone or an ipad. another problem is that for many versions of mobile operating systems, there is no accurate information in the header file. this is especially true of android devices. how successful was the survey? to gauge effectiveness, some of the questions we might ask are: what percentage of respondents finished the self-report avatar survey? how many finished on the first attempt? how long did it take to finish the survey? the number of respondents who started survey 1 was 154; 96 completed it. the number of respondents who started survey 2 was 106; 89 completed it. there was only one user who attempted both surveys and completed neither. table 3. totals of users who started and completed surveys 1 and 2 started survey 1 started survey 2 completed survey 1 completed survey 2 completed 1; did not complete 2 completed 2; did not complete 1 completed both surveys 154 106 96 89 9 2 87 among those who started the first survey, 62% finished it. almost 84% of those who started the second survey completed it. (see table 3.) participants were allowed multiple attempts to complete the survey. most who completed the survey did so on one try, although a few required multiple tries. we cannot know, of course, why a participant needed more than one attempt to complete a survey, but it is instructive to compare survey attempts. for example, what were the browsers and operating systems used for each attempt? were they the same or different? can we observe patterns? a quick and preliminary look at survey statistics shows that most of those who logged in more than one time for a given survey logged in using the same browser and operating system as they had used previously. there were a few trends though. most users who switched went from a windows machine using internet explorer to either windows using chrome or a mac. the second http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi largest group of switchers went from safari on ios to safari on mac os. those who started on android tended to stay on android. some participants did not complete the surveys. this includes those who did not complete the surveys at all and those who did not complete the surveys in the allotted time but returned to restart from the beginning. for participants who did not complete either survey on the first visit, including those who returned and completed the survey later and those who did not, certain questions were “stoppers.” in other words, many users who stopped the survey stopped at the same questions. the one that caused the most users to stop was a complicated type of question that was presented in a calendar. users were required to answer two questions for each calendar date, accessible via a pop-up. users were not able to advance to the next question until both questions were answered for all dates. eighteen participants left the survey without completing the question that was presented in that format. six users logged in, did not complete any questions, and did not return to complete the survey. other than that, no more than two users were stopped at any other particular question. discussion the web-based self-report survey was made available to participants with the option of taking it in a controlled clinic environment or on their own outside the clinic using whatever device they had, wherever they were. because of confidentiality concerns it is not possible to verify that the surveys were more often completed in a clinic. in a future similarly designed project, it would be beneficial to consider adding logic to the survey to record whether it was actually taken in one of the original clinic sites on a computer belonging to the study. the web-based self-report survey included many of the assumed benefits of electronic surveytaking via the web: consistency in survey presentation, minimization of errors in data collected because of edit and range checks, and the ability to know when surveys are completed and consequently prevent users from taking a survey they had already completed. the number of studies collecting self-reported data via the web continues to increase rapidly [3]. the quality of anthropometric data collected using a web-based questionnaire, with regard to missing and plausible answers, has been shown to be equal to, or better than, that of data collected using a paper version of the questionnaire [4]. to complete the web-based survey, participants were provided with the secure link to the site as well as a user id and password to use when logging in. when surveys were taken in the clinic it was much easier to ensure or validate that the actual participant was taking the survey; when the survey was taken away from the clinic, there is a possibility that the participant is being aided or having the survey done by someone other than themselves. limitations for 16% of the surveys, it was not possible to get information on the browser or operating system. these data come from text strings sent from a user’s browser to a server. a large percentage of http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi data were unclassifiable, and this could have had an impact on the outcome of top browser and operating system used, given that the separation between the top three browsers was small: 34%, 26%, and 21%. it is important to remember that there was no absolute requirement to send the survey and no correction mechanism if the data was incomplete or missing. karl groves writes in online design journal boxesandarrows: “server log files are inappropriate for gathering usability data. they are meant to provide server administrators with data about the behavior of the server, not the behavior of the user. the log file is a flat file containing technical information about requests for files on the server.” [5] since the avatar-based self-report survey was administered in the united states, it was assumed that internet access would be widely available; it was therefore anticipated that, in most cases, participants would perform the survey at home. however, we cannot be sure that the participants had internet access via their mobile devices and/or home computers. the requirement of internet access may lead to a higher rate of completion in clinics if studies such as this are conducted in the developing world where many such public health research projects are likely to take place. in addition, for those who did not have internet access, it was not assessed if this was associated with any sociodemographic factors. if future similar projects are to include self-report web-based surveys in the context of a clinical trial, we would recommend reviewing data on internet broadband access availability. the national broadband map (nbm) is an available resource that is created and maintained by the national telecommunications & information administration in collaboration with the fcc, 50 us states, 5 territories, and the district of columbia (www.broadband.gov). by reviewing the internet access data available to households in the united states, one could do a scan to ensure that adequate coverage is available. household broadband adoption rates have increased dramatically over the past decade, from about 4% in 2000 to nearly 70% in 2011 (6). although this is quite an increase, the latest us census population surveys do suggest there is still a gap in home internet access. current population survey data from 2003 to 2011 demonstrate a persistent 12–13 percentage point gap in broadband adoption rates between metropolitan areas and nonmetropolitan households (6). depending on the demographic characteristics of the survey participants in particular studies, it could also be useful to focus more specifically on the internet access that is available to particular age groups as well as the race and/or ethnic background of the householder. such data is available in the us census american community survey reports. as of 2014, it was reported that for individuals in the age range of 15–34 years, internet access was available to 77.4% of households. in addition, internet access was available to 76.2% of white-only households, 60.6% of black-only households, 86% of asian-only households, and 65.9% of hispanic of any race households [6]. these data could be helpful in scanning a prospective survey participant population to determine the probability of using a web-based self-report survey outside the clinic. in a study that compared adolescent survey completion via telephone versus web-based, overall 41.5% completed the survey online as compared to 59.8% via the telephone interview [7]. this finding also indicates that considering the method for administering an adolescent survey may be valuable, rather than assuming that a web-based approach is optimal. finally, the surveys were conducted at 12 locations across the united states. the sites may have implemented the study differently and it may have been easier for them to manage reimbursement and retention if participants completed the survey in the clinic. this may have had an effect on the survey data discussed above. http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi conclusion public health researchers, particularly in the social science and epidemiology arenas, continue to consider technologies that would aid in obtaining more accurate response options when doing selfreport surveys. there is ample research on conducting web-based surveys, but more knowledge and research into self-report public health surveys is needed. we did find that the majority of our surveys were completed on windows-based computers with a corresponding internet explorer browser. for participants who needed more than one visit to complete a survey, the largest percentage went from a windows-based computer running internet explorer to an apple os (either mac os or ios) running safari. for future projects requiring the use of a self-report survey that allows respondents to use their own personal devices (byod), we would consider adding several new elements to the solution. these changes or additions would include: better logging of the exact browser, version, and operating system used; the time it took to respond to each question; access to the internet and device access away from the clinic for participants; and individual participant demographics. acknowledgments we thank sarah thornton and the atn data and operations center at westat for their collaboration in setting up the operational data collection process at the many sites involved in this research study. we acknowledge the contribution of the investigators and staff at the following sites that participated in this study: university of south florida, tampa (emmanuel, straub, enriquez-bruce), children's hospital of los angeles (belzer, tucker), children's national medical center (d'angelo, trexler), children's hospital of philadelphia (douglas, tanney), john h. stroger jr. hospital of cook county and the ruth m. rothstein core center (martinez, henryreid, bojan), tulane university health sciences center (abdalian, kozina), university of miami school of medicine (friedman, maturo), st. jude's children's research hospital (flynn, dillard), baylor college of medicine, texas children’s hospital (paul, head); wayne state university (secord, outlaw, cromer); johns hopkins university school of medicine (agwu, sanders, anderson); the fenway institute (mayer, dormitzer); and university of colorado (reirden, chambers). we would like to acknowledge irene friedland, at the population council, for the thorough edit of the paper she provided. the comments and views of the authors do not necessarily represent the views of the eunice kennedy shriver national institute of child health and human development. the study was scientifically reviewed by the atn’s community prevention leadership group. network, scientific and logistical support was provided by the atn coordinating center (wilson, partlow) at the university of alabama at birmingham. the investigators are grateful to the members of the local youth community advisory boards for their insight and counsel and are indebted to the youth who participated in this study. this work was supported by the adolescent medicine trials network for hiv/aids interventions (atn) and nih support, bill kapogiannis and with supplemental funding from nida and nimh grant nichd 5 u01 hd 40533 and 5 u01 hd 40474. http://ojphi.org/ avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e189, 2016 ojphi references 1. savel, c., mierzwa, s., gorbach, p., et al. 2014. web-based, mobile-device friendly, selfreport survey system incorporating avatars and gaming console techniques. online j public health inform. 6(2), •••. pubmed 2. mierzwa, s., souidi, s., friedland, i., et al. 2013. “approaches that will yield greater success when implementing self-administered electronic data capture ict systems in the developing world with an illiterate or semi-literate population.” new york: population council. 3. bonn, s.e., trolle lagerros, y., and bälter, k. 2013. how valid are web-based self-reports of weight? j med internet res. 15(4), e52. doi:http://dx.doi.org/10.2196/jmir.2393. pubmed 4. touvier m., méjean, c., kesse-guyot, e., et al. 2010. comparison between web-based and paper versions of a self-administered anthropometric questionnaire. eur j epidemiol. 25(5), 287-96. doi:http://dx.doi.org/10.1007/s10654-010-9433-9. pubmed 5. groves, karl. 2007. “the limitations of server log files for usability analysis,” boxesandarrows. http://boxesandarrows.com/the-limitations-of-server-log-files-for-usabilityanalysis/ 6. file, thom and ryan, camille. 2014. “computer and internet use in the united states: 2013,” american community survey reports. united states census bureau, acs-28. 7. rivara, frederick p., koepsell, thomas d., wang, jin, et al. 2011. comparison of telephone with world wide web-based responses by parents and teens to a follow-up survey after injury. health serv res. doi:10.1111/j.1475-6773.2010.01236.x. 8. whitacre, brian, strover, sharon, and gallardo, roberto. 2015. how much does broadband infrastructure matter? decomposing the metro-non-metro adoption gap with the help of the national broadband map. gov inf q. 32, 261-69. http://dx.doi.org/10.1016/j.giq.2015.03.002 http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25422726&dopt=abstract http://dx.doi.org/10.2196/jmir.2393 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23570956&dopt=abstract http://dx.doi.org/10.1007/s10654-010-9433-9 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20191377&dopt=abstract http://dx.doi.org/10.1016/j.giq.2015.03.002 avatar web-based self-report survey system technology for public health research: technical outcome results and lessons learned introduction methods findings (results) discussion limitations conclusion acknowledgments references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts successful implementation of electronic disease reporting in georgia lia sanodze*1, naile malakmadze2, rusudan chlikadze1, maka tsilosani1, tamar teimurazishvili1, tsira napetvaridze3, natia kartskhia3 and khatuna zakhashvili1 1national center for disease control and public health, tbilisi, georgia; 2northrop grumman, atlanta, ga, usa; 3national food agency of georgia, tbilisi, georgia objective the objective of this study was to evaluate several aspects of the electronic disease reporting system and its abilities used in georgia. also, the study assessed if the system abilities are tailored to the national surveillance requirements. user attitudes (system strength and weaknesses) toward the system was also surveyed. introduction the ministry of health of georgia accepted the electronic integrated disease surveillance system (eidss) as an official disease reporting system in 2012. the georgian government adopted electronic reporting for both veterinary and human diseases in 2015. we conducted a comparative assessment of progress in the implementation of electronic reporting. methods a face-to-face initial survey was conducted in 2012, a follow-up survey (through telephone interviews) was performed in 2016. the initial survey was conducted in regions that had eidss installed and the follow-up survey was conducted in all regions. standardized questionnaires were used and data was analyzed in epi info. results out of 450 trained eidss users, 32% were interviewed in the initial survey and 25% (of 550) eidss trained users were interviewed in the follow-up survey. of 147 respondents in the initial survey and 138 in the follow-up survey, 44% and 79%, believed that they were using eidss effectively, respectively. the follow-up survey showed a 23% increase in respondents who acknowledge an improvement of the electronic reporting; acceptance of eidss increased from 80.3% to 97.8%. of those interviewed in the follow-up survey, 19.7% mentioned that the main success in development of the system is due to improved collaboration between institutes. however, 17.36% of the respondents in the follow-up survey reported non-sufficient quality data. conclusions our study suggests that the acceptance and use of eidss has noticeably improved, indicating the successful implementation of electronic reporting. recommendations have been made to further improve the data quality by conducting regular data cleaning and additional user training. we recommend the continuation of eidss training. keywords eidss; electronic reporting; implementation; assessment; government regulation acknowledgments participation in this conference was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed. *lia sanodze e-mail: lia.sanodze@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e57, 2017 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e304, 2019 isds 2019 conference abstracts communicating the detection capabilities of syndromic surveillance systems roger morbey public health england objective to communicate the detection capabilities of syndromic surveillance systems to public health decision makers. introduction increasingly public health decision-makers are using syndromic surveillance for real-time reassurance and situational awareness in addition to early warning [1]. decision-makers using intelligence, including syndromic data, need to understand what the systems are capable of detecting, what they cannot detect and specifically how much reassurance should be inferred when syndromic systems report ‘nothing detected’. in this study we quantify the detection capabilities of syndromic surveillance systems used by public health england (phe). the key measures for detection capabilities are specificity and sensitivity (although timeliness is also very important for surveillance systems) [2]. however, measuring the specificity and sensitivity of syndromic surveillance systems is not straight forward. firstly, syndromic systems are usually multi-purpose and may be better at identifying certain types of public health threat than others. secondly, whilst it is easy to quantify statistical aberration detection algorithms, surveillance systems involve other stages, including data collection and human decision-making, which also affect detection capabilities. here, we have taken a ‘systems thinking’ approach to understand potential barriers to detection, and summarize what we know about detection capabilities of syndromic surveillance systems in england. methods within the systems thinking approach all stages in surveillance (data collection, automated statistical analysis, expert risk assessment and reporting of any aberrations) were considered. sensitivity and specificity were then calculated for the system as a whole, and the separate impact of each process stage. to communicate these findings to decision-makers, we created an evidence synthesis. evidence was synthesised from research involving phe syndromic surveillance systems and retrospective incidents detected and/or investigated by phe. we then summarized the evidence for different types of incident. results we identified the following stages which influence detection: the proportion of people who become symptomatic; the proportion of symptomatic people who present to different types of health care; the coding of symptomatic patients; coverage of different health care systems by syndromic surveillance; statistical algorithms used to identify unusual clusters within syndromic data; risk assessment process used to determine action resulting following automated statistical alarms [3]. stages 1 to 3 depend on the type of incident that is affecting peoples’ health or healthcare seeking behaviour: stages 3 to 6 depend on the capabilities of the syndromic surveillance system. in general, each stage increases the time until detection, and reduces sensitivity but should improve specificity. our evidence synthesis identified a wide range of threats to public health including: seasonal outbreaks of respiratory infections; allergic rhinitis; insect bites; gastrointestinal outbreaks; air pollution; and heat waves. we ranked the available evidence, giving more weight to actual events detected and validated against independent evidence, and less to purely descriptive epidemiology or modelled simulations. we created different measures for sensitivity, specificity and timeliness depending on the type of evidence http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e304, 2019 isds 2019 conference abstracts available. sensitivity ranged from 100% for seasonal influenza to 0% for seasonal adenovirus. specificity also varied, with high specificity where we had a specific syndromic indicators, e.g. sunstroke, and lower for those associated only with more generic multi-purpose indicators e.g. acute respiratory infections. timeliness varied from being able to provide early warning of up to seven days prior to traditional surveillance methods for some respiratory illnesses, to being able to detect and report on the health impact of air pollution within four days of a period of poor air quality. conclusions this study has shown that a syndromic surveillance systems’ utility depends on more than just an algorithm’s specificity and sensitivity measure. we’ve identified the impact of the different surveillance stages and separately considered different types of incident. thus, we can identify the impact of issues such as local population coverage and an individual investigator’s risk assessment practices. furthermore, the evidence synthesis will provide a summary for decision makers, and help identify gaps in our knowledge where more research is required. references 1. colon-gonzalez fj, lake ir, morbey ra, elliot aj, pebody r, et al. 2018. a methodological framework for the evaluation of syndromic surveillance systems: a case study of england. bmc public health. 18(1), 544. doi:https://doi.org/10.1186/s12889-018-5422-9. pubmed 2. kleinman kp, abrams am. 2006. assessing surveillance using sensitivity, specificity and timeliness. stat methods med res. 15(5), 445-64. pubmed https://doi.org/10.1177/0962280206071641 3. smith ge, elliot aj, ibbotson s, morbey r, edeghere o, et al. 2016. novel public health risk assessment process developed to support syndromic surveillance for the 2012 olympic and paralympic games. j public health. •••. doi:https://doi.org/10.1093/pubmed/fdw054. pubmed http://ojphi.org/ https://doi.org/10.1186/s12889-018-5422-9 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29699520&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17089948&dopt=abstract https://doi.org/10.1177/0962280206071641 https://doi.org/10.1093/pubmed/fdw054 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27451417&dopt=abstract 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 218 isds 2014 conference abstracts managing dengue fever by using the one health approach and electronic surveillance shagufta zareen*1, 2 and syed m. mursalin3 1government of pakistan, pakistan; 2federal training and research, lahore, pakistan; 3national institute of health, islamabad, pakistan objective the objective of this abstract is to share the lessons learned from the dengue epidemic in lahore, pakistan in 2011 and development of a comprehensive electronic surveillance system for dengue prevention and management. introduction various serotypes of df were frequently reported in different regions of pakistan on smaller scale. however, the worst dengue outbreak in pakistan was experienced in 2011 in lahore which is a 2nd most popolous city of pakistan and capital of province punjab. this epidemic erupted during the post monsoon season and claimed 301 lives in just 4 months. to address that health crisis, government adopted a multipronged strategy with a robust dengue fever surveillance program in punjab. methods like many other diseases, there was a weak disease surveillance system for df and dengue haemorrhagic fever (dhf) in punjab. immediately after the outbreak, the punjab prevention and control of dengue (temporary) regulations 2011 was promulgated. the capacity of the hospitals was increased by establishing separate dengue wards with 1500 beds in public hospitals and 500 beds in the private hospitals to accommodate huge influx of dengue patients. moreover, 150 dispensaries were converted into dengue filter clinics along with establishment of 20 new diagnostic centres and recruitment of more medical personnel. a uniform and standardized protocol for treatment of dengue patient was enforced in the province. low cost was fixed for blood test for df. the interdepartmental coordination was ensured to adopt a multidimensional approach ranging from case management and vector control to advocacy and social mobilization campaign. a toll free helpline 08009000 was operative round the clock for patients counselling, receiving requests for solid waste disposal/ fumigation services to advising treatment protocol to the health practitioners. in 2012, the government took an innovative step by launching an it (information technology) based application called ‘satscan’, which generates early warning signs for dengue on the basis of historic data as well as on the cases reported. once the patient arrive in hospital, his data is uploaded on the web. if the diagnosis is proved, he is labelled as “confirmed” patient, otherwise categorized as “suspected” case of dengue. this application “satscan” is uploaded on around 1500 android smartphones, provided to field workers of different government departments who upload geotagged photos of their vector surveillance efforts. more than 680,000 photos have been received through this system till todate. results the focus of dengue disease surveillance efforts was lahore city. during the period of acute dengue outbreak (ie. 2011) more than 300 deaths attributable to dengue were reported. however, after launching this disease surveillance system, no death was reported in 2012 out of 124 confirmed dengue patients. however, in 2013, 13 deaths were reported out of 1512 confirmed patients while only 18 confirmed cases were reported in lahore. this year, the focus of df in punjab was shifted to another city, rawalpindi which was expected to import disease from another adjoining province of khyber-pakhtunkhwa (kpk). in 2013, an outbreak of df was experienced in swat district of kpk with 6376 suspected cases and 23 deaths. in the same year, the epidemic was at its worst in sindh with 15000 patients affected with df leading and 32 human lives were lost. conclusions this was the first successful coordinated government effort to benefit from multidisciplinary ‘one health approach’ coupled with accelerated use of information technology to curtail the menace of df in the province punjab. however, there is still a need to take long term measures/ remedial steps for contrl of this deadly disease on national scale. therefore, the federal government is required to expand effective vector control measures and dengue surveillance programs to other provinces as well. in addition, mass scale community mobilization efforts need to be strengthened. keywords dengue; surveillance; satscan acknowledgments dr.aamir.z.chaudhry *shagufta zareen e-mail: drzareen1@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e209, 201 development of the national health information systems in botswana: pitfalls, prospects, and lessons 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi development of the national health information systems in botswana: pitfalls, prospects and lessons onalenna seitio-kgokgwe1*, robin d. c. gauld2, philip c. hill2, pauline barnett3 1. ministry of health gaborone, botswana 2. department of preventive and social medicine, university of otago school of medicine, dunedin, new zealand 3. health sciences centre, university of canterbury, christchurch, new zealand abstract background: studies evaluating development of health information systems in developing countries are limited. most of the available studies are based on pilot projects or cross-sectional studies. we took a longitudinal approach to analysing the development of botswana’s health information systems. objectives: we aimed to: (i) trace the development of the national health information systems in botswana (ii) identify pitfalls during development and prospects that could be maximized to strengthen the system; and (iii) draw lessons for botswana and other countries working on establishing or improving their health information systems. methods: this article is based on data collected through document analysis and key informant interviews with policy makers, senior managers and staff of the ministry of health and senior officers from various stakeholder organizations. results: lack of central coordination, weak leadership, weak policy and regulatory frameworks, and inadequate resources limited development of the national health information systems in botswana. lack of attention to issues of organizational structure is one of the major pitfalls. conclusion: the ongoing reorganization of the ministry of health provides opportunity to reposition the health information system function. the current efforts including development of the health information management policy and plan could enhance the health information management system. keywords: disease outbreaks, electronic health records/classification, machine learning, natural language processing, public health informatics, public health surveillance/methods correspondence: oseitio@gmail.com doi: 10.5210/ojphi.v7i2.5630 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes introduction generation and effective use of health information is viewed as a central component of the stewardship function in a health system [1-3]. health information is necessary to improve health outcomes, guide identification of health problems and population needs, inform planning and design of health interventions to address public health problems, guide decision making during http://ojphi.org/ mailto:oseitio@gmail.com development of the national health information systems in botswana: pitfalls, prospects, and lessons 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi allocation of scarce resources, and provide opportunity for monitoring and evaluating progress towards achievement of health goals [4-6]. the world health organization (who) views health information systems (his) as one of the building blocks of any health system [3]. although the value of health information is acknowledged world-wide, most developing countries have weak and fragmented his [6]. african countries, in particular, lack effective systems that can ensure availability and use of health information to strengthen and support their health systems [7-9]. having overall responsibility for health in many countries, ministries of health are charged with the responsibility for ensuring availability of health information. in 2008, african ministers of health made a formal commitment to strengthen their countries’ his through the algiers declaration [9,10]. this is a daunting task considering widespread failure of his in developing countries [11,12]. many factors influencing sustainability of information systems in general have been documented in the literature. these include organizational characteristics such as management support, and availability of infrastructure (including computing); and factors inherent in the implementation of information systems projects such as the extent of user involvement, and relationship with stakeholders including developers and donors [13]. in ghana, infrastructure challenges, mainly internet connectivity and unstable power, limited development of his [14]. in tanzania and mozambique, lack of technical capacity, including personnel, and failure to involve end users were some of the challenges that affected development of his [12], while in nigeria, relations and conflict of interests between the ministry of health (moh) and the donor delayed implementation of a his project [15]. studies evaluating development of his in developing countries are limited. in addition, most of the studies reported are based on pilot projects or cross-sectional studies focusing on short-term outcomes [11], limiting the opportunity to learn from past experiences. this article contributes to closing this gap. drawing from a research study assessing the performance of the moh in botswana [16], we take a longitudinal approach to analyzing the development of botswana’s his based on data collected through document analysis and key informant interviews. the article seeks to: (i) trace the development of the national his in botswana (ii) identify pitfalls during development and prospects that could be maximized to strengthen the system; and (iii) draw lessons for botswana and other countries working on establishing or improving their his. methodology setting botswana is a middle income country with a population of 2 million [17] sparsely distributed across a land of about 582 000 km2. more than 40% of the population is estimated to live in rural areas [17]. health services in botswana are delivered through the public and private health sectors. the public health sector is organized into different levels based on the complexity of services provided. at the lowest level are 810 mobile health stops, 340 health posts and 243 clinics [18]. there are 16 primary hospitals and seven district hospitals, while three national referral hospitals represent the highest level of the system [18]. before april 2010, the moh was responsible for all public hospitals while the ministry of local government was responsible for clinics, health posts and mobile stops. since then all services were consolidated under the moh. the private health sector in botswana is poorly understood [19,20] and undocumented. http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi generally, it comprises not-for-profit and for-profit hospitals, clinics, pharmacies, laboratories and medical aid schemes. study design we adopted a case study approach [21] and used a mixed methods research design in assessing performance of the botswana moh [16]. the dominant methodology was qualitative using document analysis, key informants and focus group interviews. the quantitative arm comprised surveys for hospital managers and health workers [16]. data were collected in 2009 through 2010. this article is based on data from the document analysis and key informant interviews. framework the who/health metrics network framework and standards for country health information systems (hmn framework) [6] was adapted and used to guide the analysis. the health metrics network (hmn) is a global partnership established in 2005 that focuses on strengthening the his of low-middle income countries [6]. the network developed the framework and an assessment tool for health information systems that are increasingly guiding countries in development, assessment and management of their his [22]. the hmn framework identifies six components of a functional his as his resources, indicators, data sources, data management, information products, and information use [6]. drawing from these components we identified five assessment domains: structure for coordination and leadership of the national his; policy and regulatory framework for the his; infrastructure, financial and human resources to support the his; availability, adequacy and quality of health information; and health information use in planning, monitoring and evaluation of health services. a set of performance indicators was developed for each of the five assessment domains and used to guide the analysis (table 1). the importance of appropriate structures for coordination and leadership of national his has been well documented [9,23]. a well defined structure delineates the roles and responsibilities of the different players ensuring accountability and efficiency in the management of health information. the central coordination and leadership role is crucial in ensuring that activities of various players and organizations are coordinated to ensure integration and coherence of the system [9]. table 1: assessment domains and indicators assessment domain indicator structure for coordination and leadership of the national his i. availability of a structure for coordinating national his his policy and regulatory framework i. availability of his policy ii. availability of his strategic plan iii. availability of his regulatory framework his resources, financial and human resources to support the health information systems i. availability and his infrastructure ii. availability of his human resources iii. availability of financial resources for his availability, adequacy and quality of health information i. availability of health information ii. adequacy of health information iii. quality of health information http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi health information use in planning, monitoring and evaluation of health services i. extent of health information use in planning, monitoring and evaluation appropriate policies and legislation form the basis of a sound his [5]. policies define priorities and provide a guiding framework within which all stakeholders operate. legislation enhances access to data from all sources including the private and non-governmental health institutions [6]. appropriate his resources such as infrastructure, finance and human resources enhances the system’s capacity to collect, store, retrieve and analyse data, and disseminate information [6]. health information is only valuable to health systems if it is of good quality, available and adequate. an effective his provides timely access to reliable data that meets the needs of different users including policy makers [6]. effective use of health information in policy development, priority setting during planning and designing of health interventions, monitoring and evaluation of health services and programs enhances delivery of quality health services that contribute to improved health outcomes in an equitable and responsive manner [6]. while the indicators based on hmn framework [6] provide detailed and comprehensive assessments of his at national and sub national levels, the assessment in this article is at a more general level while its scope is limited to the public health sector activities at the moh. data collection data were abstracted from published and unpublished documents including national development plans; moh policies, strategic and annual performance plans, and consultancy reports; budget speeches and related reports from government and other agencies; and research reports [16]. websites of major organizations such as the who were explored. we also searched electronic databases such as pubmed, sciencedirect, ovid, scopus, web of knowledge, proquest, and google scholar for published articles. a total of 54 key informants were purposively selected and recruited through personal contact including telephone calls and emails based on the relevance of their positions to the issues under consideration. a snowballing technique was used to identify some of the informants, especially those who had retired from the public service. the participants comprised policy makers, senior managers and staff of the moh (n=40), including a total of nine retired employees who held key positions in the ministry. senior officers from various stakeholder organizations (n=14), including ministry of local government, non-governmental, private and professional organizations were also interviewed. all interviews were audio taped and transcribed. data analysis data from documents and transcripts were analyzed using content and thematic analysis respectively guided by miles and huberman’s approach which consists of data reduction, data display and conclusion drawing/verification [24]. a deductive approach was adopted where the study indicators acted as the organizing framework [16]. http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi results context between 1997 and 2007, there were two major projects designed to improve the status of the his in botswana. one of the projects was part of a larger initiative on strengthening health sector development through a collaborative effort between the government of botswana and the government of norway in what was commonly referred to as the botswana/norway health sector agreement (bnhsa). this agreement ran from 1997 to 2003 [25]. the second project known as beanish (building europe africa collaborative network for applying information society technologies in the healthcare sector), was a european union funded project of the world information technology forum (witfor). among other things, this project focused on strengthening his in africa in order to improve the quality of health care decisions [26]. it started in 2005 and ended in 2007. due to their centrality to his development in botswana, reference will be made to these projects throughout this analysis. structure for coordination and leadership of the national health information systems there are several key players in the management of health information in botswana. the health statistics unit is seconded from the ministry of finance and development planning, and based at the moh headquarters. it is responsible for collection of data from all health facilities and reporting of national health statistics. the various disease control programs in the moh based in the department of public health collect individual program data for purposes of program monitoring and evaluation. the department of civil registration in the ministry of labour and home affairs is responsible for the registration of births and deaths. the national aids coordinating agency has overall responsibility for hiv/aids related information. the department of information technology in the ministry of transport and communication coordinates and oversees the distribution and use of information and communication technology (ict) in government facilities. the health statistics unit (previously the medical statistics unit) was established as early as 1978 [27]. this unit was charged with the responsibility for coordinating collection, collation, analysis and dissemination of health information to support delivery of health services. while this unit could have been the focus for development of the national his, it was limited by human resource capacity due to inability to retain appropriately qualified personnel [28]. in 1983, an organizational review of the moh observed that the overall health information function was weak [29]. the 1984 organizational structure, a product of the 1983 review, however, did not establish a unit dedicated to central coordination of health information. responsibilities for health information were given to both the health statistics unit and the department of primary health care which comprised all the public health programs. there was lack of clarity on how the two structures related with their shared responsibility. with both structures collecting data from health facilities using different data collection tools, standards and procedures, poor coordination and related challenges such as duplication of efforts and poor quality of data were observed [30]. concerted efforts to revamp the system were made in the late 1990s through the bnhsa (agreement between botswana and norway) which attempted to address the coordination issues by giving the health statistics unit more responsibility to coordinate and carry out health http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi information management activities [31]. efforts were also made to establish a health information committee comprising representatives from the various stakeholder organizations including development partners such as who and unicef [32]. the impact of this project was, however, limited. consequently the moh’s 2002 organizational review noted serious gaps in health information management: the moh lacks a comprehensive health management information system (hmis), which would provide timely and reliable information on the performance of the health sector. at this moment in time, the moh only has scanty and fragmented information on the output of the health services...in consequence, ‘burden of disease’ assessments, and attempts to specify priorities...for the public, are absent [33]. based on this observation, the management information systems division, comprising health statistics and information technology (it) units, was established as an integral part of the department of policy planning monitoring and evaluation (dppme) [33] in a new organizational structure adopted in 2005. among its responsibilities, this division was to establish, coordinate and maintain an efficient and effective national his; produce and disseminate national health statistics; provide informatics leadership and expertise; and build a sustainable and reliable it infrastructure and electronic health systems in the ministry and its facilities [34]. the dppme also housed the monitoring and evaluation division. the responsibilities of this division included establishing and coordinating national monitoring and evaluation systems, and developing strategies for monitoring and evaluation of health policies and interventions [34]. the separation of health information and monitoring and evaluation appeared to have created confusion in roles. consequently these two functions were regarded as one by the moh employees and often referred to as health information/monitoring and evaluation. while it was generally understood that the health statistics unit has overall responsibility for health statistics at a national level, its role in overall health information management and its relation to the moh became increasingly blurred: ...the belief is that there are forms used to collect information, but this information is collected for the purposes of the statistics act, not for the purposes of the consumption of the ministry. that is the big difference that people must be aware of. the health statistics unit has its own mandate and the moh has its own mandate… a key limitation for the development of the coordinating structure for health information was human resource capacity. while dppme as a whole suffered capacity challenges, this was particularly so for the health information function. almost five years after the adoption of the 2005 organizational structure, the monitoring and evaluation division was non-functional as suggested by an interviewee: ...almost 3-4 years after restructuring, some of the divisions within some departments are non-functional...for example, the division of monitoring and evaluation in the department of policy, planning, http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi monitoring and evaluation is non-functional because there are no ‘bodies’ there. the health statistics unit lacked the necessary expertise and other resources to meet its obligations. consequently, different programs in the ministry continued to develop and manage their own systems [35] to meet their needs: the current limitation within the health statistics unit is that they have backlog in as far as the information that they have got. you can imagine getting data 3 weeks later and you find that you have an outbreak that you could have addressed earlier had you known…until we are confident that we have the necessary expertise at the central level to address these issues, then we need to have these parallel systems... this lack of coordination also meant that the efforts of other stakeholders in information management are poorly coordinated with multiple data flows contributing to a fragmented and inefficient system. health information systems policy and regulatory framework the national his has always operated within a very weak policy and regulatory framework characterized by absence of health information legislation, national policy and strategic plan. one of the bnhsa his project’s early intents was to establish a national health information policy to guide development of the his in the country [36]. this initiative was abandoned because of lack of ministry management support despite efforts made by different stakeholders [36,37]. however, a major achievement in policy development was made at the national level with the development and adoption of the national ict policy in 2007 [38]. through this policy, the government affirmed its stance of promoting the use of ict as a driver of efficiency to promote the country’s social, economic and cultural development. the policy outlined specific objectives, programs and projects for different sectors. for the health sector, an e-health program was identified with a number of initiatives including establishment of an e-health council to provide leadership and coordination of e-health projects, review and develop appropriate policies, legislation and standards, and identify infrastructure necessary to support effective electronic information management [38]. the e-health council was, however, not pursued and consequently the gap in the policy and regulatory framework continued to date [39]. in the absence of appropriate policies, the roles and responsibilities of the various players remain unclear, including that of the health statistics unit and the moh. the lack of appropriate legislation contributes to the current challenges experienced in collecting data from private health institutions. private hospitals provide very limited information while private practitioners and non-governmental organizations do not report any data [39]. absence of standards or guidelines at a national level [35] still characterise the system. the moh does not have a national health information strategic plan. the absence of policy and strategic plan is reported to have played a major role in the uncoordinated development of the electronic systems at program levels using varying levels of technology and platforms. these are http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi mainly supported by donor funding which influences what departments can do irrespective of other planned activities or the ministry’s overall strategy: …departments that have donor funding are able to do other things...they will just write and tell you that this is what they are moving forward with because some donor is rushing in with a lot of money... health information systems resources infrastructure development of information system infrastructure is coordinated at a national level by the ministry of transport and communication as part of the broader government strategy to promote use of ict in the public sector. while disparities in availability of infrastructure to support the his were previously reported [40], the government computerization strategy enhanced provision of infrastructure such as communication networks and computers. in 2009, the infrastructure was reported as generally adequate [39]. functioning computers and some communication systems such as telephones, email/internet services were widely available [39]. in line with the national ict policy, the moh made considerable strides in promoting the use of ict in health care. in the absence of his strategic plan, this has created a myriad of standalone systems with limited interoperability. some of the existing systems include the integrated patient management system initiated in 2003 and currently implemented in a few hospitals and clinics, patient information management system dedicated to management of patients on antiretroviral treatment and cancer and tb registration systems [39]. other systems for various functions of the ministry including drug procurement and management, and health professions registration also exist. while these are notable achievements, the main challenge is the integration and management of data from all these systems [35]. in the past, the bnhsa project attempted to improve the system’s capacity to integrate and manage information through software called health-net [36,37]. this software was reportedly installed in most of the health districts that by the end of the project were found to have essential infrastructure to support it. however, the system was reported not to be effectively utilized because of lack of appreciation, commitment and ownership [37]. within two years of the end of the bnhsa project, the beanish project aimed to address the issues of data fragmentation through a data warehousing system based in the health districts-district health information system (dhis), with overall coordination at the ministry level [41]. the dhis was to use open source software that was to allow each program to maintain its own data set while providing for integration. by the end of the project, the dhis was reported to have been piloted in four districts. the dhis, however, fell off the ministry’s priorities until national development plan 10. this plan emphasized the need for health information, and has identified rolling out the dhis and integration of the different information systems as some of the key activities [42]. the dhis remains distant to participants from the districts, while it is a window of hope for some participants at the ministry headquarters: the dhis as far as i am concerned is still a concept. yes they have met and done all their it things to come up with that...but nobody can give you anything tangible... http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi dhis can be used, it is a good system, it has been used in other countries such as south africa, but you need to build the structures and you need to integrate these into the business rules... financial resources although the moh identified the need for health information as one of the key priorities during national development plan 6 and national development plan 7 [43,44] the budget to support his development was not delineated. a breakthrough in financial support came as part of the bnhsa project that established a line budget that supported development of the his [45,46]. although the intention was for the moh to carry over financial support of the overall his development [47], the ministry’s budget over the years has been dedicated to building the necessary ict infrastructure in public hospitals [48-50] with limited focus on the overall his. although the national development plan 10 has identified the need for health information, emphasis is still on it infrastructure where participants noted some growth in budget allocations: we have seen quite some growth in terms of the budget that is set up for ndp10 so that we can have all the basic infrastructure in place...even though it is insignificant if you look at the whole ministry budget. human resources the bnhsa project made significant efforts to build human resource capacity for health information management [36,51] particularly at the central level. several officers were trained on different aspects of health information management activities [37,51]. however, high staff turnover undermined the achievements made in this area and limited development of the entire system. although the moh acknowledged the need for people trained in health information management in its national health manpower plan 1997-2003 [52], challenges with external training and recruitment continued to undermine the system’s human resource capacity. the lack of personnel trained in health information management and related fields continued to be felt at all levels of the system [35]. availability, adequacy and quality of health information failure to produce timely annual statistics reports; poor data quality related to fragmentation of data collection using different tools, procedures and standards; inadequate data analysis; limited, unsystematic and non-institutionalized feedback from the ministry to the districts; and generally poor information dissemination are some of the persistent challenges that have characterized the botswana his over the years [28,30,35]. participants from the districts were particularly concerned with the lack of feedback from the ministry headquarters: information generated is meant to be able to serve the user which is the district or the facility itself. they should be able to get feedback to take corrective measures...but there is a feed forward system because we all have to submit reports to the moh but there is no feedback mechanism to ensure this information is shared... i am sitting here as a coordinator of health services. i have very little information...if you ask me “what is the health of your nation http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi in the district?” i would not be able to tell you…actually i go to gaborone in may and november now to see how my district is performing because the reports are going directly there. although huge amounts of data are reported as collected by the various agencies in the health sector, lack of policies guiding access to data contributes to the perceived lack of data or access to it by stakeholders: ...if you look at (name) program, huge chunks of data! but there is paranoia on who should access information and who should analyze it and publish on it. but they are so preoccupied with day to day service delivery. nobody will have the opportunity or even the time to dip in and look at those data and analyze those data sets... the bnhsa project attempted to address data fragmentation and quality issues through development of a national set of core indicators to promote coordination and efficiency of data collection [32]. while it was reported that a set of 44 indicators measuring morbidity and mortality for common health problems was developed through consultation with relevant stakeholders [32], there is no evidence that these indicators were accepted and used as intended, as different programs continue to collect their own data using different tools. health information use limited data use has been reported due to various reasons including lack of timeliness in production of reports, analysis, interpretation, and dissemination [30,32,39]. fragmentation of data and challenges with access discourages potential data users: our data is scattered. very often when we are supposed to prepare reports, it is not readily available in one place. you will find that some information is somewhere in files, some information is in a computer somewhere... but not all the information is such that you can easily access it…it is extremely fragmented and much of it is paper based. past efforts made by the bnhsa and beanish projects had limited impact on making health information available and effectively used by decision makers. both projects made unsuccessful efforts to strengthen the national his technical capacity to manage information through the health-net and dhis which could facilitate data harmonization and the creation of the reports to support decision makers. while fragmentation and inefficient his contributes to poor utilization of information, lack of appreciation of the important role played by health information in managing health services on the part of health managers is also seen to be a major influence: ...i have never heard anybody looking for specific information for planning. they just want numbers to put in tables or when they are going to present somewhere. as to how the information is used, how we are using it for planning, nothing hardly happens... http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi however, in the area of program management, several participants acknowledged that disease specific programs in the department of public health and department of hiv/aids and care adequately use their program specific information to monitor program performance and effectively use such information to improve program designs and implementation. discussion, pitfalls and prospects drawing from the who/health metrics network framework and standards for country health information systems (hmn framework), we identified five assessment areas and developed a set of performance indicators that we used as a guide in tracing the development of botswana’s national his. we identified challenges, pitfalls and opportunities that could provide learning experiences for botswana and other countries in the process of developing their his. coordination and leadership one of the key challenges for botswana’s health information is the weak capacity at the national level for effective coordination and management of the his. issues related to structure had a significant impact on development of the his coordination function. although the health statistics unit has always existed in the moh, albeit as a seconded unit from the ministry of finance and development planning, it was poorly integrated with the moh structures, and lacked leadership support and ownership. as a result, the unit suffered capacity challenges limiting its ability to undertake coordination and leadership responsibilities for health information. the need for a central unit in the moh responsible for health information management is considered a critical success factor in development and management of an efficient his [6,9]. while significant efforts were made to address the issue of health information coordination in the moh 2005 organizational structure, there were several pitfalls in this structure. during the design of the structure, the health statistics unit was combined with the it unit to form the management of information systems division. while the role of technology in enhancing his is acknowledged worldwide [12,53], the mandates of the two units are different. the consequence of this structure was the equating of his with it systems. investment and development therefore focused on providing general ict infrastructure including the various electronic systems with limited focus on the overall national his. these units have since separated with it moved to the department of corporate services, while the health statistics unit remained in the dppme. poorly defined roles and responsibilities was another challenge in the structure. in addition to the management of information systems division, the monitoring and evaluation division was also charged with some health information responsibilities. this lack of clarity of roles between the two divisions created confusion. well delineated roles and functions prevent duplications and fragmentation of work [54]. lack of coherence and integration of function [2,54] was also a challenge. although the health statistics unit and monitoring and evaluation division are in the same department these exist as distinct entities with poor linkages creating inefficiencies. lack of effective national leadership for health information management in botswana was another chronic challenge. over the years, projects designed to strengthen the his could not achieve their goals because of leadership challenges, lack of commitment, support and guidance. the bnhsa project, for example, was not able to facilitate development of a national his policy. similarly the beanish project had minimal impact on addressing the fragmented nature http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi of the his. effective leadership at senior management level is essential for placing his issues in the policy agenda, advocating or motivating for the use of the systems, negotiating for resources and promoting effective use of the system outputs [13,55,56]. the moh started another round of organizational review in 2012. the new organizational structure is going through the approval processes. this reorganization provides an opportunity for the ministry to reposition the health information management function, set up appropriate structures, clearly define roles and responsibilities, and establish well-defined linkages with other related functions. the new structure also provides an opportunity to address the human resource needs for the health information function through recruitment or retraining. both technical and leadership competencies are critical for the success of the system. policy and regulatory framework for health information systems the absence of his policies and legislation in botswana contributed to poor availability and low quality information. since provision of health information is not mandatory, the country receives very little information from the private sector and nongovernmental organizations, which affects the completeness and hence undermines the accuracy of the information. poor reporting from the private sector is, however, not unique to botswana. countries such as uganda, zambia and malawi [57-59] share similar problems although they are due to inadequate enforcement of regulations as opposed to lack of regulations since their policy frameworks are reportedly well established. the lack of policies defining responsibilities and guiding the actions of the different actors creates inefficiencies which have negative impact on the overall his. lack of a coherent national strategy for his contributed to poor coordination of activities promoting duplication of efforts and poor investment. in 2011, the moh revised its 1995 national health policy. the revised policy identified his as one of the priorities and, hence, aimed to establish a coordination mechanism that will facilitate data management and use [60]. subsequent to review of the national health policy, the ministry developed a national health service plan [61] to facilitate its implementation. this plan outlines the strategic plan for health information systems/monitoring and evaluation. strategic actions in this plan include defining coordination structures and roles and responsibilities of key actors; identifying set of core indicators; and strengthening data use. consequently, in 2013, the monitoring and evaluation division facilitated development of the data management policy and monitoring and evaluation plan which are both awaiting further consultation and approval. although these are promising developments, there is need to consider the challenges that often bedevil policy implementation such as failure to adequately plan for implementation, mobilize the necessary resources [62,63], and to provide effective leadership for the implementation process. the his policy and plan will contribute significantly to his development if they are implemented. resources for health information systems as in other developing countries [12,14,15], the his in botswana has suffered from inadequate resources. the moh does not have a line budget to support his activities. the system also lacked appropriately qualified personnel at all levels for the various functions spanning from data collection to data analysis and promotion of effective use of health information. lack of training in health information management for personnel at different levels of the health system can have a significant impact on the availability and quality of data and the extent to which information http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi will be used for planning and decision making [64]. an opportunity to address the human resource needs for the health information function is provided by the local tertiary education sector with some training institution having established training in health informatics and monitoring and evaluation. the merit will be on moh to use these programs to retrain its staff. availability, quality of health information the chronic lack of central coordination of the his in botswana has perpetuated the development of an array of disjointed sub-systems contributing to fragmentation and poor integration of data. consequently, the system is inundated with data from various sources, for example, disease specific programs, health statistics unit and several facility-based electronic systems. the importance of a minimum essential data set to reduce redundancy and promote efficiency in data collection and reporting is considered one of the key factors in developing an effective his [65]. while some countries have benefited from establishing essential data sets of indicators to monitor the overall health system [66], botswana has not been successful in this regard. the collection of data for multiple programs using multiple tools undermines the quality of data and also overburden health care providers [64,67]. as part of the overall government ict strategy, the moh is working on rationalizing the various existing electronic systems. this will call for termination or integration of some systems. other efforts include strengthening the data warehouse at the moh and establishing a minimum set of core indicators, which are some of the tenets of the draft national monitoring and evaluation policy and plan. it is, however, important to note that experience from other countries illustrates that attempts at integration of fragmented his present significant challenges and is difficult to achieve [22,55,68]. sahay, monteiro et al based on their experience working with the health information systems in india, argue that the integration of his should not be viewed only as a technological undertaking, but should also consider the needs and interests of other stakeholders including program managers and their donor agencies [68]. this view is well supported by studies on development of health information systems in other countries [69]. data use as is the case in many countries [64,67], the fragmented his and lack of effective coordination has limited availability of data for planning, decision making and overall support of health system development in botswana. lack of timeliness in production of annual health statistics reports denies planners access to evidence. poor feedback or dissemination of information denies the health districts and local facilities opportunities to use their health information to improve delivery of services. while there might be challenges in the system’s capacity to analyze data related to human and technological capacity [4], the lack of policies to guide access to information plays a key role in denying other stakeholders such as researchers the opportunity to effectively use health information for the greater good of the public [70]. countries developing their his are implored to address the issues of health information uses at the design phases of their his to ensure equity of access and promote effective and resourceful use of health information [70]. in an attempt to improve health information management and enhance data use, the monitoring and evaluation division is promoting the use of dhis. through this system all programs can enter their data, and produce basic reports at user level. these data will also be available real time to program managers and other data users at the ministry level where it could be aggregated http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi at district and national levels. the data management policy, which waits management approval, aims to establish data sharing mechanisms to facilitate access to health information. the merit will be timely approval and implementation of this policy. lessons for botswana and other developing countries establishing his in taking an evolutionary approach in analyzing development of the his in botswana, some key lessons emerge that botswana and other developing countries can learn as they develop their systems. the need for a structure dedicated to coordination of his is vital. roles, responsibilities, and relationships between various structures with health information responsibilities must be clearly defined. the relationship of the health statistics unit and the moh health information unit, in particular, needs to be defined and if possible guided by some form of policy instruments. the lack of his coordinating structure and overlapping roles and responsibilities has undermined development of his in botswana. technical and leadership competencies in the health information systems coordinating structure is an essential ingredient to his success. able leadership could provide strategic direction for the his function, and mobilize resources needed to support the development and functioning of the his. finally, while it is essential as an enabler of effective his, it is important to recognize that the two are not synonymous, and hence investment in both should occur concurrently to have a functional system that could provide health information in a timely manner. botswana invested significantly in it infrastructure, while the overall his was under funded. consequently the his remains poorly developed. conclusion in this article, we traced the development of the his in botswana, identifying pitfalls and prospects in this historical journey and drew important lessons for those working in his development. overall, botswana struggled to establish a functional his over the years. significant challenges existed in all aspects of the system undermining progress. chronic challenges such as lack of central coordination, weak leadership, poor policy and regulatory frameworks, and inadequate resources were observed. some pitfalls included lack of attention to organizational structure designs creating duplication of roles, and poor integration of units and functions. opportunities are provided by the ongoing reorganization of the moh through which the health information function can be repositioned. ongoing efforts to establish the policy framework for health information management could be optimized. training of health information professionals in local institutions can strengthen the human resource capacity of the national his. limitations we adapted and used the hmn framework and standards for country health information systems to guide assessment of the botswana his. while this framework has been used in several countries to evaluate national his, its use in research is still limited. there is need for more studies using this framework to build evidence on its utility in his research. http://ojphi.org/ development of the national health information systems in botswana: pitfalls, prospects, and lessons 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e210, 2015 ojphi acknowledgements the authors would like to thank the ministry of health-botswana management and staff for the support, and study participants for their willingness. the research reported in this article was primarily funded by a university of otago scholarship, for 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prospects and lessons introduction methodology setting study design framework data collection data analysis results context structure for coordination and leadership of the national health information systems health information systems policy and regulatory framework infrastructure financial resources human resources availability, adequacy and quality of health information health information use discussion, pitfalls and prospects coordination and leadership policy and regulatory framework for health information systems resources for health information systems availability, quality of health information data use lessons for botswana and other developing countries establishing his conclusion limitations acknowledgements ethical issues competing interest financial disclosure references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts enhancing epicenter data quality analytics with r andrew walsh* health monitoring, pittsburgh, pa, usa objective to demonstrate the broader analytical capabilities available by making the r language available to epicenter reporting introduction the epicenter syndromic surveillance platform currently uses java libraries for time series analysis. expanding the data quality capabilities of epicenter requires new analysis methods. while the java ecosystem has a number of resources for general software engineering, it has lagged behind on numerical tools. as a result, including additional analytics requires implementing the methods de novo. the r language and ecosystem has emerged as one of the leading platforms for statistical analysis. a wide range of standard time series analysis methods are available in either the base system or contributed packages, and new techniques are regularly implemented in r. previous attempts to integrate r with epicenter were hampered by the limitations of available r/java interfaces, which were not actively developed for a long time. an alternative bridge is via the postgresql database used by epicenter on the backend. an r extension for postgresql exists, which can expose the entire r ecosystem to epicenter with minimal development effort. methods the pl/r extension version 8.3 was installed in postgresql 9.2 using r version 3.2.1. gaussian and poisson regression models were fit using the base glm function. negative binomial regression models were calculated using the r package mass version 7.3.42. regression models were fit using covariates calculated from dates day of week, hour of day, days since start of time series, and periodic variables with an annual period. model fits were compared using root mean squared error (rmse) and median average deviance (mad). out-of-range values were defined as observations outside the 99.9% confidence interval defined by the model distribution. predictions for periods with outages were generated from the datebased covariates and models fit to other data. results a total of 16,028,901 emergency department (ed) registrations from 415 hospitals were collected from july 1, 2014 to june 30, 2015. hospitals were grouped on whether known or obvious data quality issues existed in their data (n=71) or not (n=344). model performance was assessed on data from hospitals without apparent data quality issues. gaussian regression models were fit with no covariates, approximating epicenter’s moving average analysis method. poisson and negative binomial regression models were fit using date-based covariates. the gaussian models had an average rmse of 2.89; for the poisson and negative binomial models it was 2.09. the mad for the gaussian models was 2.26; for the poisson and negative binomial models it was 1.26. out-of-range values were generated comparing observations to the 99.9% confidence intervals calculated from the model fit. all detected issues were assumed to be false alerts, as no known or obvious data quality issues existed in this data. figure 1 shows the number of false alerts relative to ed volume. false alerts from the gaussian model decreased with increasing volume, while false alerts from the other two models showed the opposite trend. detection of known outages in the data from the 71 hospitals with known issues showed a similar performance profile between the modeling options. conclusions the pl/r extension for postgresql provides a convenient option for extending the data quality analytics of epicenter. by adding the resources of the r environment, new techniques can be implemented and deployed flexibly with minimal development effort. future work will focus on integrating these methods into the main epicenter workflows. ed volume data is not always well modeled with a gaussian distribution, particularly at smaller facilities. regression models can account for the structure in the data, such as the day-of-week effect, and also more accurately reflect the true distribution of the data, improving precision in detecting data quality problems. while the analysis presented here makes some over-broad simplifying assumptions (e.g. there are almost certainly unknown and subtle data quality issues in the data which was assumed to be reliable for the purposes of quantifying false alerts), it does demonstrate the advantage of expanded analytical capabilities. figure 1 keywords r; data quality; epicenter; syndromic surveillance acknowledgments we wish to thank the new jersey, ohio, pennsylvania and wyoming departments of health for funding support and data for this work. *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e172, 2016 assessing the usage of dating sites 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equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts community perceptions on integrating animal vaccination and health education by veterinary and public health workers in the prevention of brucellosis among pastoral communities of south western uganda catherine kansiime*1, lynn m. atuyambe1, victor guma1, anthony mugisha1, samuel mugisha1, benon b. asiimwe1, innocent b. rwego2 and elizeus rutebemberwa1 1health policy planning and management, makerere university, kampala, uganda; 2university of minnesota, minnesota, mn, usa objective to explore community perceptions on integration of animal vaccination and health education by veterinary and public health workers in the management of brucellosis. introduction brucellosis is a zoonotic disease of veterinary, public health and economic significance in most developing countries, yet there are few studies that show integrated human and veterinary health care intervention focusing on integration at both activity and actors levels. the aim of our study, therefore, was to explore community perceptions on integration of animal vaccination and health education by veterinary and public health workers in the management of brucellosis. methods this study used a qualitative design where six focus group discussions (fgds) that were homogenous in nature were conducted, two from each sub-county one with the local leaders, and another with pastoralists and farmers. five key informant interviews (kiis) with two public health workers and three veterinary officers from three sub-counties in kiruhura district were conducted. all fgds were conducted in the local language and tape recorded with consent from the participants. kiis were in english and later transcribed and analyzed using latent content data analysis method. results all the groups mentioned that they lacked awareness on brucellosis commonly known as ‘brucella’ and its vaccination in animals. respondents perceived improvement in human resources, facilitation of the necessary activities such as sensitization of the communities about brucellosis, and provision of vaccines and diagnostic kits as important in the integration process in the communities. the focus group discussion participants also believed that community participation was crucial for sustainability and ownership of the integration process. conclusions the study reported a limited understanding of brucellosis and its vaccination in animals. the community members believed that mass animal vaccination in combination with health education about the disease is possible if it involves government and all other stakeholders such as wildlife authorities, community members, local to national political leaders, as well as the technical personnel from both veterinary, medical and public health sectors. keywords brucellosis; perceptions; integration; animal vaccination; health education acknowledgments the authors are grateful for financial support from international development research centre (canada), all the interview respondents who participated in the study, the community leaders and to all the research assistants for their contribution to the success of this research. *catherine kansiime e-mail: cathie.kansiime@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e195, 201 ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use case zachary faigen1, lana deyneka1, amy ising2, daniel neill3, mike conway4, geoffrey fairchild5, julia gunn6, david swenson7, ian painter8, lauren johnson9, chris kiley10, laura streichert9, howard burkom11 1. north carolina department of health and human services 2. university of north carolina at chapel hill, department of emergency medicine 3. carnegie mellon university, event and pattern detection laboratory 4. university of utah, department of biomedical informatics 5. los alamos national laboratory, department of analytics, intelligence, and technology 6. boston public health commission, department of communicable disease control 7. new hampshire department of health and human services, department of public health services 8. university of washington school of public health, department of health services 9. international society for disease surveillance 10. defense threat reduction agency, chemical & biological defense program 11. johns hopkins university applied physics laboratory abstract introduction: we document a funded effort to bridge the gap between constrained scientific challenges of public health surveillance and methodologies from academia and industry. component tasks are the collection of epidemiologists’ use case problems, multidisciplinary consultancies to refine them, and dissemination of problem requirements and shareable datasets. we describe an initial use case and consultancy as a concrete example and challenge to developers. materials and methods: supported by the defense threat reduction agency biosurveillance ecosystem project, the international society for disease surveillance formed an advisory group to select tractable use case problems and convene inter-disciplinary consultancies to translate analytic needs into well-defined problems and to promote development of applicable solution methods. the initial consultancy’s focus was a problem originated by the north carolina department of health and its nc detect surveillance system: derive a method for detection of patient record clusters worthy of follow-up based on free-text chief complaints and without syndromic classification. results: direct communication between public health problem owners and analytic developers was informative to both groups and constructive for the solution development process. the consultancy achieved refinement of the asyndromic detection challenge and of solution requirements. participants summarized and evaluated solution approaches and discussed dissemination and collaboration strategies. practice implications: a solution meeting the specification of the use case described above could ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 improve human monitoring efficiency with expedited warning of events requiring follow-up, including otherwise overlooked events with no syndromic indicators. this approach can remove obstacles to collaboration with efficient, minimal data-sharing and without costly overhead. keywords: asyndromic, case cluster, disease surveillance, chief complaint. abbreviations: international society for disease surveillance (isds), technical conventions committee (tcc), defense threat reduction agency (dtra), biosurveillance ecosystem (bsve), north carolina division of public health (ncdph), carolina center for health informatics (cchi), north carolina disease event tracking and epidemiologic collection tool (nc detect), emergency department (ed) correspondence: howard.burkom@jhuapl.edu doi: 10.5210/ojphi.v7i3.6354 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction fifteen years into the 21st century, after worldwide publication of hundreds of articles, there is no consensus among the global disease surveillance community on preferred technical methods for public health data monitoring. utility of such methods includes various aspects of situational awareness such as risk mapping, predictive modeling, anomaly detection, and transmission tracking. while surveillance epidemiologists frequently lack resources to address analytic needs, solution developers often lack both understanding of public health goals/constraints and the data access necessary to develop the required tools. to bridge the long-standing gap between resource-constrained scientific challenges and analytic expertise, the international society for disease surveillance (isds) launched a technical conventions committee (tcc) in january 2013 [1]. committee activities included collection of surveillance-related use case problems from public health professionals, multidisciplinary meetings to refine these use cases, and formation of standardized requirements templates with benchmark datasets, all to facilitate the development, implementation, and publication of solution methods. in late 2014, isds was awarded a contract by the defense threat reduction agency (dtra) to enhance these activities with in-person consultancies focused on individual use cases. these activities complement the mission of the dtra biosurveillance ecosystem (bsve), an emerging capability to enable real-time biosurveillance for early warning and course-of-action analysis. the aim of bsve is to create an unclassified virtual analyst workbench integrating health and non-health data streams and providing customized data analytics and visualization, in a cloud-based, open-source, self-sustaining web environment [2]. this paper describes the methodology of enabling inter-disciplinary and cross-agency collaboration for the advancement of public health surveillance. we provide a description of the first consultancy with its featured use case as both a concrete example and a challenge to potential developers. previous authors have discussed strategic approaches to cross-disciplinary collaboration with workshops [3], surveys [4], and frameworks [5-7]. many articles have been written on applicability of various statistical methods to biosurveillance [8]. however, the mailto:howard.burkom@jhuapl.edu ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 authors found few articles going beyond theoretical applications to address specific needs, constraints, and operational and data limitations of health-monitoring institutions. examples include the adaptation of the historical limits method by levin-rector et al. for city-level monitoring [9] and adaptation of older regression methods at the national level by noufailly et al. [10] the recognition of the gap between the large body of analytical research and routine technical needs of public health monitors led to the formation of the tcc and the dtra-funded initiative behind the work reported below. the isds-initiated effort describes a tactical approach seeking solution methods that meet welldefined analytic needs from a work environment with known data sources and constraints. the approach is also a call to engagement for innovative, applied technologies. materials and methods: consultancy with dtra support, isds formed an advisory group of epidemiologists, technical analysts from academia and industry, and public health managers to select tractable use cases as subjects of consultancies. the consultancies’ purpose is to translate use case-specific analytic needs into well-defined technical problems with shareable de-identified benchmark datasets, promote development of freely shareable solution methods, and ensure applicability in the end-user environment. use cases considered were technical challenges posed by health departments to the tcc. these challenges were detailed in requirements templates written by health department staff including the surveillance problem description, form of output required, description of available data, and technical constraints restricting possible solutions. advisory group conference calls were conducted to select use cases for the consultancies based on criteria that included the public health importance and technical clarity of the proposed challenge and the likelihood of obtaining a sufficient shareable benchmark dataset. a schematic illustrating the concept of the consultancies and target use cases is shown in figure 1. the first selected use case was posed by the north carolina division of public health (ncdph) and the carolina center for health informatics in the department of emergency medicine at the university of north carolina at chapel hill (cchi) for use in the north carolina disease event tracking and epidemiologic collection tool (nc detect), which receives, processes, and analyzes daily data for ncdph. the challenge, summarized in the initial template provided in appendix a, was to find clusters of emergency department (ed) visits of public health concern using free-text chief complaints in electronic patient records from over 100 hospitals. problem details and the public health work environment are described below. ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 figure 1: conceptual diagram of inter-disciplinary consultancy to refine and disseminate target use case applications results: consultancy the consultancy was held at the unc gillings school of global public health on june 9-10, 2015 with a planning call held on june 3. the 20 attendees included seven epidemiologists and managers from ncdph and cchi, three epidemiologists representing other health departments and cdc, seven analytic solution developers, and staff from isds and dtra. the planning call familiarized participants with the use case, solution constraints, and the consultancy goals. day 1 of the consultancy was devoted to the scope and details of surveillance activities at ncdph; the functionality of the nc detect web application; the use case problem of interest; the formation, composition, and exploratory analysis of the benchmark dataset; and candidate solution ideas. with this background established, day 2 was an in-depth discussion to refine solution requirements and to propose evaluation methods. the final day also included a discussion of how to disseminate the use case problem and benchmark dataset to legitimate interested developers who would sign the ncdph data use agreement. tactics discussed included publicizing at conferences and on professional society forums, and staging focused workshops. the provision of adequate financial incentives for every prospective developer is not feasible, and the purpose of use case development was not to identify a single winner. however, given the number of annual publications on biosurveillance without such incentives and without ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 authentic datasets, expectations of a number of solvers motivated to addresses a known public health use case with data seem reasonable. see appendix b for the consultancy agenda. a post-consultancy survey and related discussions yielded the following lessons learned: 1. preparatory calls should focus on details of the consultancy purpose and use case to ensure that participants come prepared and to allot more time for crossdisciplinary dialogue. multiple respondents suggested a second, more structured preparatory call. 2. the structure of the consultancy was considered effective. of ten responses to the post-event survey, all selected “agree” or “strongly agree” to the statement, “the consultancy resulted in better definition of the use case.” nine of ten responded similarly to: “the consultancy resulted in better understanding of what is needed for a case solution”, with one “neutral” response. 3. sufficient time should be allotted during the in-person meeting to strategize the dissemination of the use case and incentivizing solutions. issues such as case definition and constraints therefore require substantial discussion before a consultancy. to the statement, “the consultancy ended with a clear plan to move forward on the case”, seven respondents chose “neutral”, and three chose “agree”. materials and methods: use case health department problem environment: north carolina’s statewide syndromic surveillance system nc detect provides early event detection and timely public health surveillance to authorized public health and hospital users. it was created by ncdph in 2004 in collaboration with cchi. a partnership of ncdph with the north carolina hospital association promoted the passage of general statute (gs) 130a-480, which became effective january 1, 2005 [11]. this statute mandates that all nc civilian hospitals with 24/7 acute care eds electronically report ed data elements to ncdph at least once every 24 hours. streaming information in nc detect includes near-real-time data feeds from approximately 120 north carolina hospitals. approximately 4.75 million ed visits, 1.3 million ems calls, and 90,000 cpc calls are reported annually. nc detect uses validated syndromes for infectious diseases, injury, chronic diseases, and natural disaster response. syndromes are defined based on icd-9-cm final diagnosis codes and/or keywords in chief complaint and or triage notes. aberration detection algorithms are based on cdc’s early aberration reporting system [12]. signals generated by nc detect are analyzed, investigated, and followed up by authorized users at the state, local health department, and hospital levels. users of nc detect have access to aggregate and line listing information as well as a variety of customizable reports. nc detect has been integral in supporting statewide disease surveillance and providing nearreal-time data for population-based monitoring of illnesses and injuries before, during, and after public health emergencies. asyndromic use case description: nc detect has the flexibility to add and/or modify syndromes as needed. however, users have desired a method for detecting potentially interesting ed visit clusters without pre-classifying ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 records into syndromes. this approach would facilitate identification of clusters linked by nonsymptomatic keywords indicating place names (e.g. “midtown café” or ”stadium”), event names (e.g., “picnic”or “football game”), or other non-medical phrases. ncdph and cchi developed a draft use case for this public health problem in 2014, and the requirements have been fine-tuned in subsequent meetings. in addition, a dataset was created specifically for this use case to be shared with solution developers through a data use agreement with ncdph. benchmark dataset for method development: the dataset includes partial records of approximately 200,000 ed visits from three hospitals in north carolina and includes the following variables: unique visit id, arrival date and time (altered), age group, free-text chief complaint, and non-identifying hospital code a (~31,500 records, 15.9%), b (~46,200 records, 23.3%), or c (~120,800 records, 60.8%). the number of missing values is negligible in all of the fields provided. each chief complaint string was reviewed, and any identifiable information (names of physicians, hospitals, nursing homes, etc.) was removed. chief complaint strings are generally short, averaging 2.7 words per record for all hospitals. the age-group classification was formed from the birth date in the original records, and these groups are shown with record frequencies in table 1. table 1: age groups with distribution of record counts in the nc detect benchmark dataset age group frequency percent of total infant (0-1 yr.) 7857 3.96 toddler/pre-school (2-4) 8076 4.07 elementary school (5-9) 8023 4.04 middle school (10-14) 6827 3.44 high school (15-18) 8517 4.29 college (19-24) 23191 11.68 young adult (25-44) 59380 29.91 middle aged (45-64) 46265 23.31 senior (65+) 30375 15.30 unknown 4 0.00 total 198515 100.00 to ensure some basis for comparison of candidate methods, artificial records with related chief complaint text were added to the dataset to form injected clusters for detection. the injected cluster records were inserted in single or multiple hospitals’ data. natural clusters are also likely to exist in the dataset, derived from authentic nc detect data, but verification of the number and significance of these clusters is impractical. the role of natural clusters in method evaluation will depend on the nature of these unknown clusters in submitted outputs, as discussed below. ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 results: use case solution requirements: solution methods must allow rapid identification of clusters of ed patient records needing public health follow-up. they may leverage patient age group and/or location in the identification of clusters as well as chief complaint and arrival date and time. the primary motivation for the use case is identification of clusters not identified through traditional record classification based on symptom-specific phrases such as “nausea”, “fever”, or “food poisoning”. the traditional approach does not identify clusters linked by non-symptomatic keywords indicating place names (e.g., “midtown café” or “stadium”), event names (e.g. “picnic” or “football game”), or other event-informative non-medical phrases. solution methods will be run twice daily in a windows 2012 server environment enabled with 48 gb ram and 32 virtual processors. methods should provide separate results for current sets of ed records from up to 121 hospitals that send approximately 16,000 records daily, and should also be applicable to records from pooled subsets of hospitals. historical data may be used for training and adaptation; historical data are available back to 2008 for most hospitals. several types of flexibility are required. the requested method is intended for use by epidemiologists at the state level monitoring all hospitals and also by epidemiologists in the field monitoring subsets of 1-10 hospitals. user settings should permit adjustment of the method specificity and sensitivity from default values. the user should also be able to change the default length of the time window for inspected records, such as the previous 12, 24, 72 hours, or week. lastly, the user should be able to influence clustering of concepts, with options to exclude terms or increase their significance. these flexibility requirements are intended to reduce the burden on human epidemiologists determining follow-up and response decisions. the primary method output is any collection of recent ed records whose free-text information indicates some linkage potentially stimulating public health follow-up. within nc detect or other surveillance systems, this output should enable rapid line listings of the included records or other visualizations such as time-series graphs or maps. identified clusters should be ranked according to an objective significance measure for convenience, though determination of significance in practice will depend on current knowledge, concerns, and constraints of the end user. evaluation of solution methods: solution methods for finding asyndromic clusters will be evaluated and compared according to several criteria. resource costs, execution time relative to the requirement of processing records from all hospitals twice a day, and ease and clarity of use will all be considered. in addition to these usability criteria, the detection performance of candidate methods will be evaluated. the benchmark dataset of 200,000 records contains a number of injected clusters of records known to the ncdph and nc detect staffs. the dataset presumably contains additional authentic clusters of interest, but enumeration and verification of these is not feasible. therefore, a twofold performance measure will be applied. in the performance evaluation, a set of clusters produced by each method from the entire benchmark dataset will be submitted along with their significance rankings. the clusters with the highest rankings will be evaluated, for a fixed such as 200 for all methods. each evaluated ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 cluster will be labeled by ncdph analysts according to whether a) its records are sufficiently similar to those of an artificial cluster to call it an inject, b) it is not a result of injects but appears to merit public health follow-up, or c) it does not require follow-up. the evaluation will be blinded to the extent possible depending on the number of solutions and how soon results are available. let , , and be the numbers of clusters in these respective categories so that , and suppose that is the number of known injected clusters. category c) clusters will be treated as false positives. if , then the remaining injects are considered undetected by the method. this labeling allows for two types of evaluation, on the known injected clusters, and on an unknown number of unspecified clusters of interest. for evaluation of performance on the known injects, suppose that are their ranks in descending order according to the solution method, with as the rank of the most significant inject. then for , set the number of false positives ranked above , so detecting the top injects would yield false positives. a plot of against is then formed for each candidate method. these plots then permit direct comparison of methods according to the known injects. in epidemiological terms, gives sensitivity, and gives positive predictive value (ppv). in the eyes of the human monitor, is the number of expected alerts for a method to produce one cluster of interest at sensitivity , so a high ppv means a low alert burden. for performance on the subjectively labeled clusters that occurred without injects, a similar procedure will be applied to the ranked clusters from categories b) and c). the number of true positives is unknown, and true sensitivity cannot be measured. however, the plots of against j may still be used to estimate ppv as a function of the number of authentic clusters detected. considering the authentic clusters found by each method, the ncdph staff will weigh the detection/ppv trade-off for authentic clusters against the results from injected clusters for practical evaluation and comparison. multiple candidate solution methods may be adopted for various purposes and for multiple types of free-text data. technical approaches: general considerations and keyword-based approaches: researchers with free-text analysis experience in other domains should be aware of several challenges posed by this use case. chief complaint strings from most hospitals average 3-4 words in length. the developer may treat these strings as individual documents or pool them into blocks. for the pooled text approach, the choice of block sizes for both testing and training/learning (e.g., 1-hour, 24-hour, fixed or variable) is a key consideration. a potentially important decision is the exclusion of common or syndromic terms from the tested strings and as in solution requirements above, ad hoc inclusions or exclusions may be desired in practice. lastly, solutions are to be used by epidemiologists monitoring one, several, or many facilities. in the benchmark dataset as in proposed practice, patient records have facility ids. thus, proposed solutions may accommodate inter-facility differences in patient schedules, variation in common free-text terms, and patient-base characteristics. multiple solutions or parametric settings may be needed for these scenarios. ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 multiple developers have considered direct, purely statistical keyword-based approaches [13-16]. a purely statistical keyword-based method with limited pre-conditioning of chief complaint text, pooling into 8-hour blocks, and a 30-day sliding baseline, showed promise for use in single facilities’ data when combined with appropriate visualization [17]. for more sophisticated natural language processing or data mining strategies, added detection value should be weighed against clarity and throughput and other resource costs. the next paragraphs outline promising strategies. topic models: another potential solution approach is based on discovering new “topics” in the free-text chief complaint data that emerge over space and time. a topic is a probability distribution over keywords, and recent topic modeling approaches such as latent dirichlet allocation [18] enable automatic discovery of topics from text, grouping related keywords (such as nausea, vomiting, and diarrhea) into a single topic. a recently developed “semantic scan” approach [19] incorporates spatial and subpopulation information (in the benchmark data, hospital, and age group) and can identify emerging patterns of keywords. preliminary evaluation results on the nc detect dataset [20] suggest that semantic scan can identify more relevant clusters than purely keyword-based methods, since it can detect novel or unusual combinations of frequently occurring keywords as well as individual, rarely occurring keywords. these results were achieved using individual chief complaint strings as separate documents. however, topic modeling-based approaches can be computationally expensive and are sensitive to the choice of parameter values, and open questions remain as to how they can be applied most effectively in this use case context. feature-based clustering: this solution type involves two stages. in stage 1, the benchmark dataset is divided into current (from period that is the focus for surveillance) and historical (prior data for reference corpus). surprisingly frequent words are identified in current data using dunning’s log-likelihood based on historical data [21]. chief complaints containing these words will then be used as input for the second stage. in stage 2, the chosen chief complaints are clustered using a feature representation based on character-based n-grams (e.g., “migraine” consists of the following 3 character n-grams: mig, igr, rai, ain, ine). this choice of feature representation is based on the published finding that under certain circumstances, character-based n-grams can better represent morphological and spelling variation than word-based methods [22]. the stage 1 method is widely used in natural language processing and corpus linguistics [23] but can be computationally intensive when combined with the cluster analysis proposed in stage 2. discussion in addition to consultancy survey responses summarized above, free-text responses and followup discussions provided additional feedback. direct communication between public health problem owners and analytic developers was informative to both groups and constructive for the solution development process. the use case originators felt that it was helpful to have a moderator from outside their health department. the participation of staff from other health departments with slightly different goals and requirements enriched the use case definition and ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 practical investigation of case clusters. furthermore, potential competition among solution developer attendees did not hinder the open discussion of solution requirements and approaches. the primary limitation of the consultancy was the brief 1.5-day time available for discussing the use case and health department environment, for refining requirements analysis, for exploring solution approaches, and for strategizing dissemination of the use case and dataset to potential developers. a limitation of the use case generation process is that funding cannot be provided to all who wish to develop solution methods, and effective incentives for solution development may vary with each use case. the only shareable dataset for the first consultancy was a large collection of ed visit records that included free-text chief complaints, age group, masked location and masked date and time. thus, an important limitation of the asyndromic cluster detection use case is that methods that work well for finding case clusters from chief complaints may not work as well for triage notes or other data sources with longer and more complex freetext fields. ensuring de-identification of the free text was a labor-intensive process that might be intensified for other data sources. the goal of the initial consultancy was to stimulate near-term implementation of shared methods to benefit one or a few health departments which would then inspire more generalizable development. methods that meet the ncdph detection requirement would need validation for application to facility data and monitoring practice in other geographic regions. conclusions a solution meeting the specification of the asyndromic detection use case described above could improve human monitoring efficiency with expedited warning of events requiring follow-up, including events that would be otherwise overlooked. however, monitor expertise would remain essential for deciding on a course of action. attendees with health department experience discussed follow-up criteria that would be applied when evaluating a candidate cluster. for some clusters, the indicative chief complaint terms would be inconclusive, and the epidemiologist would consider additional information fields and correlations among them in search of linkages and public health significance. such fields would include: patient age group (accounting for known quality issues such as blank age fields interpreted as 01jan1900 and age > 115), gender, zip code (accounting for locations of longterm care facilities and public assistance centers), and race/ethnicity. other clusters with phrases such as “rule out measles” or “carbon monoxide” would warrant immediate follow-up. recent health concerns in neighboring populations or media reports would also influence perception of candidate clusters. clustering may also identify new terms (e.g. narcan; ebola) indicating changing documentation and/or health care practices. these follow-up/response considerations are external to the use case challenge of initial cluster-finding. subsequent addition of these terms to textual classifiers or triggers used for syndromic surveillance can also help keep other detection methods current. from the above considerations, this use case exemplifies the project concept: enabling useful collaboration between methodology developer and surveillance epidemiologist without costly, long-term business arrangements, and sharing only the minimum datasets necessary for development. anecdotal experience in this project thus far supports the assertion that combinations of data de-identification and, as necessary, truncation, perturbation, and simulation of data fields can be made feasible and acceptable for both public health problem owners and solution developers to enable such collaboration. ojphi cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use cases online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e228, 2015 references 1. coletta m, burkom h, johnson j, chapman w. 2013. an isds-based initiative for conventions for biosurveillance data analysis methods. online j public health inform. 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methods for the statistics of surprise and coincidence. comput linguist. 19(1), 61-74. 22. miao y, kešelj v, milios e. document clustering using character n-grams: a comparative evaluation with term-based and word-based clustering. paper presented at: proceedings of the 14th acm international conference on information and knowledge management; 10/31/2005, 2005. 23. jingjing liu al, stephanie seneff. automatic drug side effect discovery from online patient-submitted reviews: focus on statin drugs. 2015. http://dx.doi.org/10.5210/ojphi.v6i1.5069 http://dx.doi.org/10.5210/ojphi.v6i1.5012 http://dx.doi.org/10.5210/ojphi.v6i1.5111 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts pre-art mortality and its determinants in a tanzania public driven hiv care program bonita k. kilama* epidemiology unit, tanzania national aids control program, dar es salaam, united republic of tanzania objective the aim of this write up was to assess the level of mortality and its determinants among hiv infected adults prior to art initiation. introduction limited information is available on mortality experience of hiv infected patients prior to the start of antiretroviral therapy (art), as monitoring of hiv care services has mainly focused on art initiation and subsequent patient survival. by 2013, tanzania 1,209 health facilities with hiv services, and 800,000 patients accessing art methods a retrospective cohort study of 526,059 hiv infected adults ( 15 years) enrolled in care prior to art initiation from november 2004 to december 2011 in 348 health facilities was conducted. the data was used to analyze mortality and its determinants in pre-art phase, tb events and cd4 testing. results sixty seven percent of patients were female. among the enrolled 429,476 patients had follow-up data and 10,362 deaths. the majority (85%) had working status, 82% cd4 count test done in three months and 91% were screened for tb at first visit. of the 7.2 million visits 93% had tb screening documented and of 397,288 cd4 tests done in the pre art phase 71% (282,936 tests) were done in the first month. the overall mortality rate was 37.6 deaths per 1000 person years (95% ci 36.9 38.3). independent predictors of pre-art mortality were: who stage 3 (ahr=2.37; 95% ci 1.94-2.90), who stage 4 (ahr=4.53; 95% ci 3.64-5.64), female sex (ahr=0.62; 95% ci 0.56 -0.70), cd4 count 200 (ahr=0.17; 95% ci 0.15-0.20) and weighing more than 45kg at ctc enrolment (ahr=0.53; 95% ci 0.46-0.62) was significantly associated with a lower hazard of death. conclusions routinely collected data suggest high mortality among patients in the pre-art phase especially among those with low cd4 counts, tb confirmed, and who stage 3 and 4. from findings tb screening and cd tests are largely done as per national guidelines. there is need to establish effective interventions targeting patients in the pre art phase for patient and program improvement keywords pre-art; mortality; adults acknowledgments 1. dr candida moshiro biostatistician with muhimbili university of health and allied sciences for supervising my masters dissertation tirelessly. 2.jim todd reader at london school of hygiene and tropical medicine for spearheading the various analysis work we do as an institution as well as masters students like myself references 1. burtle d, welfare w, elden s, et al. introduction and evaluation of a “pre-art care” service in swaziland: an operational research study. bmj open. 2012;2(2):e000195. 2. palombi l, marazzi mc, guidotti g, et al. incidence and predictors of death, retention, and switch to secondline regimens in antiretroviraltreated patients in subsaharan african sites with comprehensive monitoring availability. clin. infect. dis. 2009;48(1):115–122. 3. geng eh, bwana mb, muyindike w, glidden d v, bangsberg dr, neilands tb, et al. failure to initiate antiretroviral therapy, loss to follow-up and mortality among hiv-infected patients during the pre-art period in uganda. 2013;63(2):64–71. *bonita k. kilama e-mail: bonitakilama@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e137, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts a syndromic surveillance service supporting environmental public health incidents alex elliot, helen hughes*, roger morbey, paul loveridge, sally harcourt, sue smith, zharain bawa, leandro carrilho and gillian smith public health england, birmingham, united kingdom objective to deliver a national syndromic surveillance service, linking analytical and statistical methods with public health action to provide surveillance support for national public health programmes monitoring the spread of infectious diseases and the health impact of environmental incidents in england. introduction public health england has developed a suite of syndromic surveillance systems, collecting data from a number of health care sources, and linking to public health action to try and improve the public health benefit of the surveillance.1 we aim to describe this national syndromic service, highlighting the flexibility of the systems in responding to a range of environmental incidents. methods syndromic surveillance data from general practitioners (‘in hours’ and ‘out of hours’), sentinel ed attendances and telehealth calls were received on a daily basis using automated and secure data transfer processes. data were analysed using a number of statistical and analytical processes generating statistically significant spikes in data, or increases compared to baselines. results syndromic surveillance reporting routine weekly surveillance bulletins are produced from each syndromic system: these reports present the current indicator trends and encapsulate the main summary findings from the weeks surveillance, providing a number of key messages for health professionals.1 heatwave surveillance syndromic surveillance plays an important part in assessing the health impact of heatwaves and plays an integral role in the heatwave plan for england. during summer 2013 england experienced a heatwave: significant increases in heat/sunstroke were observed during 7-10 july 2013 (figure).2 in addition to the direct impact of heat, syndromic surveillance systems detected a spike in ed asthma attendances during a series of thunderstorms that followed the heatwave.3 cold weather surveillance a cold weather plan for england was launched in 2011 with the aim of reducing preventable mortality and morbidity due to severe cold weather. syndromic surveillance plays a part in this plan: emergency department cold weather indicators have been developed and are used each winter to assess the impact of cold weather on public health.4 air pollution during march/april 2014 an air pollution event affected large parts of the uk; syndromic surveillance was used to assess the public health impact. indicators for the presentation of asthma, difficulty breathing and wheeze all showed marked and significant increases during the air pollution episode, illustrating the impact of the episode. flooding during early 2014 there was severe flooding across several areas of england. syndromic surveillance systems were used, monitoring indicators of gastroenteritis, diarrhoea and vomiting. daily data were compared between affected and unaffected areas to determine whether there had been an impact on health. there were no gross changes in the incidence of syndromic indicators in flooded areas, thus providing reassurance to national and local response teams. conclusions historically, syndromic surveillance systems have been primarily targeted towards supporting national infectious disease surveillance programmes e.g. influenza. however, the flexibility of the syndromic surveillance systems, and in particular the clinical indicators, allow these surveillance systems to monitor the health impact of environmental incidents. syndromic surveillance ‘heatwave’ indicators during summer 2013 keywords syndromic surveillance; heatwave; flooding; air pollution; thunderstorm asthma references 1. public health england. real-time syndromic surveillance. 2014. https://www.gov.uk/government/collections/syndromic-surveillancesystems-and-analyses (accessed 29 aug 2014). 2. elliot aj, bone a, morbey r, et al. using real-time syndromic surveillance to assess the health impact of the 2013 heatwave in england. environ. res. 2014:doi 10.1016/j.envres.2014.08.031. 3. elliot aj, hughes he, hughes tc, et al. the impact of thunderstorm asthma on emergency department attendances across london during july 2013. emerg med j 2014;31:675-8. 4. hughes he, morbey r, hughes tc, et al. using an emergency department syndromic surveillance system to investigate the impact of extreme cold weather events. public health 2014;128:628-35. *helen hughes e-mail: helen.hughes@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e126, 2015 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts how’s the weather? severe weather classifications in syndromic surveillance teresa hamby*, stella tsai and hui gu new jersey department of health, trenton, nj, usa objective to report the results of the application of new jersey’s severe weather classifier in new jersey’s syndromic surveillance system during two extreme weather events. introduction hurricane ‘superstorm’ sandy struck new jersey on october 29, 2012, causing harm to the health of new jersey residents and billions of dollars of damage to businesses, transportation, and infrastructure. monitoring health outcomes for increased illness and injury due to a severe weather event is important in measuring the severity of conditions and the efficacy of state response, as well as in emergency response preparations for future severe weather events. following the experience with hurricane sandy, njdoh initiated a project to develop a suite of 19 indicators, known as the severe weather classifier (swc) in epicenter, an online system which collects emergency department chief complaint data in real-time, to perform syndromic surveillance of extreme weather–related conditions. njdoh has since used these classifiers in more recent events to monitor for weather-related visits to storm-affected area emergency departments (ed’s). in june, 2015, a squall line of damaging thunderstorms, known as a “bow echo,” caused downed wires and multi-day power outages in camden and gloucester counties in southern new jersey. almost exactly seven months later, in january, 2016, winter storm jonas dropped more than a foot of snow over new jersey. these events provided an opportunity to assess the indicators within swc. methods the impact of these storms on ed visits was assessed in epicenter by using the swc sub-classifications for disrupted outpatient medical care (dialysis and oxygen needs, and medication refills). rates per 1,000 ed visits were calculated on two weeks of ed visits by classification for each storm. for the june 2015 bow echo storm, this assessment focused on gloucester and camden counties, the two hardest hit by the storm. for winter storm jonas, rates per 1,000 ed visits were calculated statewide since all counties were impacted. results after the june, 2015 bow echo storm, both camden and gloucester county ed’s experienced increases in disrupted medical care, the most notable being for oxygen needs (figures 1 and 2). during and after winter storm jonas, ed visits for oxygen assistance and medicine refills were the most impacted (figure 3). it is speculated that ed visits for dialysis were not noticeably higher since the storm occurred over a weekend when, generally, treatments take place during weekdays. conclusions while not every classification in the suite that makes up the swc would be relevant in every extreme weather event, having the 19 various elements available provides tools for state and local users to monitor storm impacts both locally and at the state level. keywords classifications; severe weather; syndromic surveillance; epicenter acknowledgments alvin chu, phd, gabrielle goodrow, jessie gleason, msph, and jerald fagliano, phd *teresa hamby e-mail: teresa.hamby@doh.nj.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e30, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts evaluation of dod syndrome mapping and baseline for icd-9-cm to icd-10-cm transition jessica f. deerin*, jean-paul chretien and paul e. lewis armed forces health surveillance branch, arlington, va, usa objective the transition from icd-9-cm to icd-10-cm requires evaluation of syndrome mappings to obtain a baseline for syndromic surveillance purposes. two syndrome mappings are evaluated in this report. introduction the department of defense conducts syndromic surveillance of health encounter visits of military health system (mhs) beneficiaries. providers within the mhs assign up to 10 diagnosis codes to each health encounter visit. the diagnosis codes are grouped into syndrome and sub-syndrome categories. on october 1, 2015, the health and human services-mandated transition from icd9-cm to icd-10-cm required evaluation of the syndrome mappings to establish a baseline of syndrome rates within the dod. the dod data within the biosense system currently utilizes dod essence syndrome mappings. the master mapping reference table (mmrt) was developed by the cdc to translate diagnostic codes across the icd-9-cm and icd-10-cm encoding systems to prepare for the transition. the dod essence and mmrt syndrome definitions are presented in this analysis for comparison. methods dod data was pulled from the biosense platform through a rstudio server on october 11, 2016, querying data from october 1, 2014 to september 30, 2016. this time period provides twelve months of icd-9-cm data and twelve months of icd-10-cm data. the icd codes were binned to both dod essence syndromes and mmrt macro syndromes for comparison. although a patient visit may contain up to 10 icd codes, only the first four were included for this analysis. providers are trained to prioritize diagnosis codes by position. only 2.2% of visits had greater than 4 diagnostic codes. each icd code in a visit is binned to an applicable syndrome. the total number of visits includes visits that binned and did not bin to a syndrome. multiple syndromes may be assigned to one patient’s health encounter visit if multiple icd codes are binned. additionally, more than one code per visit may bin to the same syndrome; however, only unique syndromes are counted in the total syndrome rate. the total syndrome rate was calculated by total unique syndrome visits as the numerator and total number of visits during the icd-9-cm or icd-10-cm time period as the denominator. the rates per 1000 total visits were calculated. results among the dod essence syndromes, the icd-9-cm rate for ili was 36.3 per 1,000 compared to the icd-10-cm rate of 38.6 per 1,000. the icd-9-cm rate for neurological was 18.1 per 1,000 compared to the icd-10-cm rate of 0.2 per 1,000. among the mmrt syndromes, the icd-9-cm rate for ili was 16.7 per 1,000 compared to the icd-10-cm rate of 38.4 per 1,000. the icd-9-cm rate for mental disorders was 73.8 per 1,000 compared to the icd-10-cm rate of 73.2 per 1,000. conclusions this analysis provides baseline rates of mmrt syndromes and sub-syndromes for syndromic surveillance during the icd-9-cm to icd-10-cm transition. these data will serve for future comparison and tracking of syndrome-specific trends for military-relevant health threats. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e16, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts figure 1. frequency of visits of dod essence syndromes by coding system, october 1, 2014 september 30, 2016 keywords syndromic surveillance; syndrome mappings; dod essence; mmrt acknowledgments the authors thank devin hunt for sas coding assistance. *jessica f. deerin e-mail: jessica.deerin@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e16, 2017 isds16_abstracts-final 52 isds16_abstracts-final 53 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e315, 2019 isds 2019 conference abstracts precarious data: crack, opioids, and visualizing a drug abuse epidemic candice welhausen auburn university, auburn, alabama, united states objective i analyze a collection of data visualizations created during the crack and opioid epidemics, respectively, published by mainstream news media using three criteria: genre, subject matter, and language used to describe the graphic. i use precarity as a theoretical framework--that is, “a politically induced condition in which certain populations suffer from failing social and economic networks of support and become differentially exposed to injury, violence, and death” (butler, 2009, p. 35)--to argue that visualizations created during the crack epidemic positioned addicts as criminals whereas opioid addicts have been positioned as patients in need of treatment. introduction in late 2015, two economists studying health-related data inadvertently discovered an alarming trend: death rates for middle-aged, white americans were dramatically increasing from drug overdoses (kolata, 2015), particularly opioids (cdc, 2015). the opioid epidemic has since been widely publicized in the media. however, as critics have argued, the government's response to the crack epidemic differs dramatically from an arguably equally devastating “drug epidemic” that hit many inner us cities thirty years ago—the influx of crack cocaine. more specifically, opioid addicts, who tend to be white, have been positioned as patients, whereas in the 1970s and 80s during the war on drugs, heroin and crack addicts, respectively, who tended to be people of color, were criminalized (hart, 2017; hutchinson, 2017). methods i collected data visualzations created during the crack epidemic for 1/1/86-12/31/92 and for the opioid epidemic from 11/3/15 (the date the nyt covered case and deaton's study)-9/30/18 for opioids from the following mainstream news organizations: newsweek, the chicago tribune, the los angeles (la) times, the new york times (nyt), the washington post (wapo), time magazine, u.s.a today, and u.s. news and world report. i then organized each collection by genre (bar or pie chart, line graph, map, etc), subject matter (crime-related, drug use and abuse related, effects on children, effects on health including deaths and treatment, stds, and trafficking), and also assessed whether the text in the article directly referred to the graphic and discussed the data shown. results seventy three images were included of the crack epidemic and 100 were included for the opioid epidemic. the majority of graphics created during the crack epidemic were bar and line graphs whereas there was far more variation in the genre of graphics created during the opioid epidemic. the majority of graphics created during the crack epidemic also showed crime-related information (defined as crime rates, location of crimes, number of crimes committed, specific types of crimes such as homicides as well as information about arrests and sentencing) whereas very few data visualizations created during the opioid epidemic were related to crime. indeed, the majority of these visuals showed effects on health (more specifically mortality). finally, data visualizations create during the crack epidemic were rarely directed referred in the text of the article, but were usually discussed albeit, along with other visual information. in contrast, data visualizations created during the opioid epidemic were usually directly referenced and overtly discussed. conclusions i suggest that these results illustrates precarity (bulter, 2009) by revealing systemic inequalities that protect some people, but leave others vulnerable through two counter narratives: opioid addiction is a public health issue, but crack addiction is a crime. references butler j. (2009). frames of war: when is life grievable? brooklyn, ny: verso books. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e315, 2019 isds 2019 conference abstracts case a, deaton a. 2015. rising morbidity and mortality in midlife among white non-hispanic americans in the 21st century. proc natl acad sci usa. 112(49), 15078-83. pubmed https://doi.org/10.1073/pnas.1518393112 cdc. (2015). controlled substance prescribing patterns — prescription behavior surveillance system, eight states, 2013. morbidity and mortality weekly report. october 16, 2015 / 64(ss09);1-14. hart cl. (2017, august 18). the real opioid emergency. the new york times. hutchinson, e. o. (2017, june 21). the opioid crisis in black and white. huffington post. kolata g. (2015, november 3). rise in deaths for u.s. whites in middle age. the new york times. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26575631&dopt=abstract https://doi.org/10.1073/pnas.1518393112 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts statistical monitoring of condemnations from slaughterhouses flavie vial*1, sarah thommen2 and leonhard held2 1vetsuisse faculty, veterinary public health institute, bern, switzerland; 2institute of social and preventive medicine, zürich, switzerland objective we evaluate the performance of the improved farrington algorithm for the detection of simulated outbreaks in meat inspection data. introduction meat inspection data are routinely collected over several years providing the possibility to use historical data for constructing a baseline model defining the expected normal behaviour of the indicator monitored. in countries in which the reporting of data is compulsory (e.g. in the eu), coverage of the majority of the slaughtered population is ensured. methods this study focuses on the whole carcass condemnations (wcc) of cattle slaughtered in switzerland between 2007 and 2012 (1). the monthly time series was retrospectively modelled using the function {hhh4} in the r {surveillance} package (2). the mean monthly incidence was decomposed additively into an auto-regressive component; and an endemic component multiplied by the offset to adjust for variation in the number of total animals slaughtered per month. the retrospective model was used to simulate a baseline series to which an outbreak was added at a random starting point ti. outbreak sizes were randomly generated as poisson with mean equal to k times (with k from 2 to 10) the standard deviation of the baseline count at ti (1000 simulations for each k). the improved farrington algorithm (3) was applied for outbreak detection using the {faringtonflexible} function: results algorithm a includes parameters chosen according to the results of the retrospective analysis (no trend and no seasonality). algorithm b includes more historic data (b=3 instead of b=2) and considers monthly seasonal effects (w=0). both algorithms produced less than one false alarm per year. in terms of probability of detection (pod), algorithm a outperforms algorithm b at the detection of larger outbreaks (k=5 to 10), but the latter is more appropriate when looking to detect smaller outbreaks (figure 1). conclusions the improved farrington algorithm led to low false positive rate (fpr) but the pod was low for small outbreaks. furthermore, more than 50% of the outbreak cases had already occurred by the time the outbreak was detected. the low temporal resolution of this dataset currently limits its use for early detection. table 1: outbreaks attributes in wcc data (averaged over 1000 simulations for each k) pod and fpr of algorithms a &b for different outbreak sizes (k) keywords meat inspection; veterinary public health; syndromic surveillance acknowledgments marion zumbrunnen extracted the data & sebastian meyer helped with the r programming. references 1.vial f, reist m. evaluation of swiss slaughterhouse data for integration in a syndromic surveillance system. bmc vet res. 2014 jan;10:33. 2.höhle m. { surveillance} : an r package for the monitoring of infectious diseases. comput stat. 2007 aug;22(4):571–82. 3.noufaily a, enki dg, farrington cp, garthwaite ph, andrews nj, charlett a. an improved algorithm for outbreak detection in multiple surveillance systems. stat med. 2013 mar 30;32(7):1206–22. *flavie vial e-mail: flavie.vial@vetsuisse.unibe.ch online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e169, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 153 isds 2014 conference abstracts tracking communicable disease electronic laboratory data in new york state candace m. noonan-toly*, xiaohong wang and hwa-gan chang epidemiology, nys department of health, albany, ny, usa objective ensure all reportable communicable disease data coming through the electronic clinical laboratory reporting system (eclrs) is reported to the communicable disease electronic surveillance system (cdess) in a timely and complete manner. introduction all positive laboratory tests of reportable conditions on persons residing in new york state (nys) are mandated to be sent to the nys department of health (nysdoh) via eclrs. nys, excluding new york city (nyc), receives over 100,000 eclrs messages on general communicable diseases (cd) and hepatitis (hep), not including lyme disease and influenza, annually. although eclrs is integrated with cdess, the local health departments (lhd) need to review each lab report for proper initiation of a case investigation. once the investigation is created, the lhd may need supportive evidence to create a reportable case or may dismiss it if evidence does not support the case definition. our goal is to follow all eclrs records from official retrieval by the lhd through cdess case creation, to ensure all cases are reported and are done so in a timely manner. cases for diseases that are nationally notifiable are sent to cdc the following week for publication in the morbidity and mortality weekly report. timely reporting to cdc allows for more accurate description of disease occurrence, which is essential for public health planning and response.1 methods all eclrs records transferred to cdess by lhd staff are recorded in an interface table with a key that is unique to the investigation created on cdess. any eclrs records or cdess investigations that are dismissed are recorded with a reason for dismissal which includes: does not meet case definition; duplicate report; negative; out of state/nyc; not a human report; not a sterile site; false positive; not a reportable disease; and case already reported. any reasons deemed unacceptable are followed up on with the lhd. the lhd is given 60 days before the nysdoh contacts them regarding outstanding laboratory reports. routinely, eclrs records are matched by the unique key with cdess and any records that do not match are reviewed. records are de-duplicated by patient name, date of birth, and disease prior to follow-up. timeliness is calculated as the number of days between the eclrs official retrieval date and the cdess investigation creation date and is used as a performance measure. very high priority diseases must have an investigation created on cdess within one day of official retrieval, average priority diseases three business days, low priority diseases five business days. results from january 2012 through may 2014, eclrs received 85,730 cd messages. thirty-four percent were dismissed prior to becoming investigations, the primary reason being duplicate report (27%), followed closely by negative report (26%). sixty-six percent of the eclrs cd messages were transferred to cdess. of the 56,395 transferred messages, 38,236 became unique investigations. the percent of cases created from cd investigations increased from 69.8% in 2012 to 76% in jan-may 2014. accordingly, the percent of dismissed investigations has decreased 30.2% in 2012 to 23.7% in jan-may 2014. the majority of cd investigations were dismissed (>50%) because they did not meet case definition. tickborne diseases accounted for 44% of cd dismissed investigations because they did not meet case definition. of the 187,560 eclrs hep messages received, 12.1% were dismissed from eclrs, of which 55% were dismissed as a duplicate report. yearly, from jan 2012-may 2014, on average 90% of the unique hep investigations became cases or updated existing cases. a performance incentive program was conducted from november 1, 2013 through april 30, 2014 with comparison of 2012 data for timeliness. the percentage of timely reports created went from 83.3% for lowest priority diseases through 88.6% for highest priority diseases in 2012, to 98.5% for low priority and 100% for high priority in 2014. conclusions follow-up of cd messages ensures complete reporting of all diseases. all laboratory reports can be tracked through our systems. the high percentage of duplicate reports needs review as it increases the work of the lhds. the performance measures reports for lhds will continue as incentives for continued timeliness. keywords surveillance; data; communicable; laboratory; tracking references [1] adekoya n, truman b, davies-cole j, ajani u,”comparison of provisional and finalized 2011 data from the weekly national notifiable diseases surveillance system of the united states”.2014. located at:division of health informatics and surveillance,center for surveillance,epidemiology and laboratory services,office of public health scientific services. *candace m. noonan-toly e-mail: candace.noonan-toly@health.ny.gov isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts mapping laboratory reports to illinois’ extensively drug-resistant organism registry emily augustini*1, william trick2 and stacey hoferka1 1office of health protection, division of infectious diseases, illinois department of public health, chicago, il, usa; 2cook county health & hospitals system, cdc prevention epicenters program, chicago, il, usa objective to streamline carbapenem-resistant enterobacteriaceae (cre) surveillance by integrating electronic laboratory reporting (elr) data and electronic case reports (ecr) automatically into illinois’ extensively drug-resistant organism (xdro) registry. introduction cre are drug-resistant bacteria that have a mortality rate of up to 50% in those infected1. several clusters of cre have been detected in illinois, often in long-term acute care hospitals2. in response illinois created the xdro registry, a mandatory reporting system designed to aid inter-facility communication concerning cre. despite being a high priority for control in the us, the case definition for cre has been the subject of debate3. there are over 70 enterobacteriaceae which can have different mechanisms of carbapenem resistance3. criteria for carbapenem resistance include susceptibility results, and phenotypic or genotypic detection. the case definition for the xdro registry is intentionally more exclusive (specific) than that used by cste (table 1). cste utilizes a definition designed to maximize sensitivity. illinois’ xdro registry’s definition is more specific, meant to reduce unnecessary adoption of contact precautions and the negative consequences some patients may experience. currently, case reporting to the xdro registry is a manual data entry process, which has important advantages. however, transitioning to automatic elr integration will streamline the reporting process and minimize data entry effort. unfortunately, the clinical information needed to investigate xdros is often not captured by elr. the ecr is a new message type being piloted in illinois that contains many clinical data elements. we examined the feasibility of combining elr and ecr into reports for the xdro registry. in the construction of these reports we examined the impact of using cre definitions from cste and the xdro registry. methods we obtained sample hl7 cre messages from illinois’ elr database. using these messages and the hl7 implementation guide for electronic laboratory reporting, we mapped elr fields to those in the xdro registry. specific codes corresponding to the registry fields were found though a systematic keyword search of loinc, snomed, and sample messages. when there was no match for an xdro field in elr, we referred to the hl7 cda implementation guide for the electronic initial case report and sample ecr messages. a collection of fields and codes was created to correspond to both the cste and illinois cre case definition. results the xdro registry has 37 unique fields. twenty-six can be populated from elr, four can be found in the ecr, and seven are generated within the system. in sample elr and ecr messages all of the necessary fields were populated with appropriate text and codes. the mapping process was straightforward for demographic and facility information, but more complicated for culture and organism information. some xdro tests do not have corresponding loinc or snomed codes, so we will develop a logic statement to fill these based on free-text. addition of the ecr adds important information to the registry report, notably encounter type and encounter/admission date. we were able to create separate mapping schemas for the cste and xdro registry definitions for cre. using each of these definitions, we will quantify how many elr messages would be committed to the xdro registry. conclusions by combining the data captured in elr and ecr, it is possible to populate the fields of the illinois xdro registry. when this merge is completed it should result in more complete and better quality data on cre in illinois. as intended, the definition of cre used by the registry is less inclusive than that used by cste. future work will show the number of cre lab results captured by each definition. table 1: cre definition keywords cre; data mapping; elr; ecr acknowledgments project shine references 1. centers for disease control and prevention (cdc). (2015). cre toolkit: facility guidance for control of carbapenem-resistant enterobacteriaceae. atlanta, georgia: cdc. 2. trick, w. e., lin, m. y., cheng-leidig, r., driscoll, m., tang, a. s., gao, w., weinstein, r. a. (2015). electronic public health registry of extensively drug-resistant organisms, illinois, usa. emerging infectious diseases. 21(10):1725-1732. 3. council of state and territorial epidemiologists (cste). (2015). standardized definition for carbapenem-resistant enterobacteriaceae (cre) and recommendation for sub-classification and stratified reporting. 15-id-05. *emily augustini e-mail: emily.augustini@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e44, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 1university of bern, bern, switzerland; 2united states department of agriculture, animal and plant health inspection service, veterinary services, fort collins,, co, usa; 3pan american center for foot-and-mouth disease, pan american health organization, rio de janeiro, brazil; 4international society for disease surveillance, boston, ma, usa; 5swedish zoonosis centre, department of disease control and epidemiology, national veterinary institute (sva), uppsala, sweden; 6french ministry of agriculture, lyon, france; 7public health foundation of india (phfi), indian institute of public health (iiph), hyderabad, india objective the primary purpose of this study was to explore the attitudes of surveillance stakeholders from different domains to: -determine whether there is a perceived need for ohs -identify significant surveillance gaps -assess the motivation to change (fill the gaps) a secondary purpose was to gather a group of surveillance stakeholders to identify and prioritize strategies to move one health surveillance forward. introduction as interest in one health (oh) continues to grow, alternative surveillance infrastructure may be needed to support it(1). since most population health surveillance is domain specific; as opposed to oh which crosses multiple domains, changes to surveillance infrastructure may be required to optimize oh practice. for change to occur there must be a strong motivation that propagates from a perceived need. since the purpose of surveillance is to produce information to support decision making, the motivation for change should relate to a lack of surveillance information needed to make oh decisions, or a gap in the surveillance infrastructure required to produce the information (2). methods the study had two parts: 1) an electronic questionnaire emailed to surveillance stakeholders working in different health domains around the world, to assess their attitudes towards integrated ohs. questions included: -would ohs be a benefit in your jurisdiction? -is ohs a challenge in your jurisdiction? -how difficult would it be to make improvements in ohs? -what priority does ohs have in your jurisdiction? 2) a workshop was held at the annual conference of the international society for diseases surveillance (isds) held in philadelphia, pa, on dec 10-11, 2014, where a formal identification and prioritization of solutions to ohs implementation was conducted. results a total of 185 questionnaires were returned by respondents from 44 countries including low income countries (58, 31%) and high income countries (127, 69%). respondents reported working primarily in public health (96, 52%), followed by animal health (36, 19%) and environmental health (4, 2%). forty six (25%) respondents reported working in multiple domains. the majority (158, 85%) of respondents reported that ohs would benefit them in their work. there were no significant differences in the perceived need for ohs across domains, or between low and high income countries. almost half of the respondents (87, 47%) reported that assessing the risk of transmission across domains was a frequent or ever present challenge indicating that a gap in surveillance may exist in some jurisdictions. most (129, 70%) reported that improvements would be somewhat difficult or very difficult. many (120, 65%) reported that making improvements was a medium to high priority indicating that some respondents were motivated to change. there were 61 workshop participants from 6 countries who identified solutions, including: cross domain staff exchanges, tools for data integration, and making diseases that require multi-domain collaborative responses or control programs reportable in multiple domains. conclusions this study provides some support that among surveillance stakeholders, ohs is valued. it also provides support for the existence of surveillance gaps, and the presence of motivation among some stakeholders to improve existing surveillance to meet the information needs of oh. the study population included respondents who worked in public health, animal health and multiple domains, and from many countries. however the sample was not large and it is not known how well this sample represents the biosurveillance community in general. keywords one health surveillance; one health; integrated surveillance acknowledgments this work was supported by the isds: http://www.syndromic.org/ and the skoll global threats fund: http://www.skollglobalthreats.org/ references 1.zinsstag, j., schelling, e., waltner-toews, d., whittaker, m., & tanner, m. (2015). one health: theory and practice of integrated health approaches. oxfordshire, uk: cabi. 2.el allaki, f., bigras-poulin, m., michel, p., & ravel, a. (2012). a population health surveillance theory. epidemiology and health, 34, e2012007. doi:10.4178/epih/e2012007 *john berezowski e-mail: john.berezowski@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e11, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), 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h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara 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electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi the use of electronic medical record data to analyze the association between atrial fibrillation and birth month koji matsuda, md1*, keunsik park, md, phd2, hiroaki tatsumi, md3, ryoko kitada, md3 and minoru yoshiyama, md, phd3 1. matsuda eye clinic, sennan, osaka, japan 2. department of medical informatics, osaka city university hospital, osaka, osaka, japan 3. department of cardiovascular medicine, osaka city university graduate school of medicine, osaka, osaka, japan abstract objectives: cardiovascular disease is a condition of enormous public health concern. recently, a population study newly revealed associations between cardiovascular diseases and birth month. here, we investigated the association between atrial fibrillation in cardiovascular disease and birth month. methods: we retrospectively extracted birth date data from 6,016 patients with atrial fibrillation (3,876 males; 2,140 females) from our electronic medical records. the number of live births in japan fluctuates seasonally. therefore, we corrected the number of patients for each birth month based on a japanese population survey report. then, a test of the significance of the association between atrial fibrillation and birth month was performed using a chi-square test. in addition, we compared the results of an analysis of patient data with that of simulated data that showed no association with birth month. results: the deviations of birth month were not significant (overall: p = 0.631, males: p = 0.842, females: p = 0.333). the number of female patients born in the first quarter of the year was slightly higher than those born in the other quarters of the year (p = 0.030). however, by comparing the magnitudes of dispersion in the simulated data, it seems that this finding was mere coincidence. conclusion: an association between atrial fibrillation and birth month could not be confirmed in our japanese study. however, this might be due to differences in ethnicity. further epidemiologic studies on this topic may result in reduction of disease risk in the general population and contribute to public health. key words: birth month; season of birth; cardiovascular disease; atrial fibrillation; melatonin *correspondence: koji matsuda, md. matsuda eye clinic, 2965 shinge, sennan, osaka 590-0503, japan. matuda@osaka.med.or.jp doi: 10.5210/ojphi.v9i3.7864 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:matuda@osaka.med.or.jp the use of electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi introduction birth month may be associated with several medical conditions [1]. this tends to be a very popular topic among the general public, and thus news media often erroneously report such ideas as scientific fact. however, with few exceptions, these associations have remained tentative. although such associations appear simple, on closer examination they are complex; therefore, additional rigorous research is necessary. associations between birth month and medical conditions can be divided into three types. the first type is observed only in early life. in these associations, the effects of seasonal exposure during fetal life or infancy do not persist because of repeated exposure throughout life, and the association disappears in adulthood. neonatal anthropometric measurements are an example of associations included in this type [2,3]. the second type describes an association between birth month and a special trait. such traits are affected by seasonal exposure only during fetal life or infancy. because there is little change in the trait from subsequent repeated exposure, the influence in early life persists as a vestige throughout life. studies on this type of association include those on hyperopia and corneal curvature, since the theoretical grounds for associations with these conditions have been demonstrated [4-6]. finally, the third type of association concerns the development of a disease in the future being dependent on past seasonal exposure despite the fact that the disease did not develop at the time of the original exposure. this type of association is difficult to explain. well-known diseases in this type are schizophrenia and multiple sclerosis [7,8]. we propose a hypothesis to explain the origin of many of the diseases in this third type of association. although an association between the pineal gland and birth month has not been previously reported, the pineal gland exhibits special traits of the second type of association described above. in the pineal gland, cell differentiation and organ growth are completed during infancy, and the secretory capacity for melatonin is also determined during this time [9-11]. based on this, if light seasonally influences the development of the pineal gland during infancy, then melatonin secretion should exhibit variations dependent on birth month. in addition, melatonin might contribute to the pathogenesis of schizophrenia and multiple sclerosis [12,13]. these findings suggest that the mechanisms underlying the risk of developing diseases of the third type of association could be related to melatonin secretion. a population study systematically explored the relationship between birth month and lifetime disease risk for 1,688 conditions and newly revealed associations between cardiovascular diseases and birth month [1]. moreover, numerous studies have suggested that melatonin plays an important role in various cardiovascular diseases [14]. therefore, according to our hypothesis, it is entirely possible that cardiovascular diseases are linked to birth month. cardiovascular diseases are common diseases compared to schizophrenia or multiple sclerosis, and are responsible for considerable mortality in the population. associations with birth month carry profound meaning and might present new valuable information for public health. here, we attempted a confirmatory study on the association between one cardiovascular disease and birth month. methods our study was based on a retrospective analysis of the electronic medical records of patients at the osaka city university hospital (osaka, japan, northern latitude of 34.7 degrees). we focused on the use of electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi atrial fibrillation [15] because of its rigorous diagnostic criteria among the reported cardiovascular conditions, such as essential hypertension, congestive cardiac failure, angina, cardiac complications of care, mitral valve disorder, pre-infarction syndrome, cardiomyopathy, and chronic myocardial ischemia [1]. the electronic medical record data for other cardiovascular conditions include complications and outcomes that might be dependent upon ambiguous diagnostic criteria. our dataset included 6,016 patients (3,876 males, 2,140 females) ranging in age from 12 to 101 years (average = 68.1±12.0 years) who were diagnosed with atrial fibrillation from may 1986 to august 2016. the number of live births in japan fluctuates seasonally. therefore, to cancel this variation, we corrected the number of patients for each birth month based on a japanese population survey report separated by sex [16]. the formula used to correct the variation was as follows: weighing coefficient = nyear ⁄ (12 × nmonth), where “nyear” is the number of national births during the year a patient was born, and “nmonth” is the number of national births in the month during the year the patient was born. the chi-square test was performed to determine the significance of the association with birth month. knowledge of the inevitable dispersion of birth months is a prerequisite to avoid overestimating the results of analysis. we therefore compared the current results of patient data with simulated data showing no association between atrial fibrillation and birth month. this simulated data was generated using the excel® function ‘=int(rand()*12)+1’. briefly, we threw a dice with 12 pips 6,016 times. we referred to the first 3,876 times as male and the following 2,140 times as female. to show the association pattern of atrial fibrillation with birth month graphically, it is necessary to decrease the potential random disturbances in the time-series data. consequently, we calculated three-point moving averages that also included values for the preceding and following month. for example, the number for january was the average of the data for the three months of december, january, and february. our study received approval from the ethics committee of the osaka city university hospital. results table 1 shows the observed numbers and the corrected numbers of patients with atrial fibrillation for each birth month. the peak birth month was march, regardless of sex. the trough birth month was june for all patients, august for male patients, and september for female patients. however, chi-square analysis showed that the deviations of birth month were not statistically significant (overall: p = 0.631, males: p = 0.842, females: p = 0.333). table 1. number of patients with atrial fibrillation by birth month birth month overall (n = 6,016) male (n = 3,876) female (n = 2,140) n corrected (%) n corrected (%) n corrected (%) january 750 527.6 (8.82) 467 333.5 (8.63) 283 194.1 (9.17) february 573 499.7 (8.35) 362 317.7 (8.22) 211 182.0 (8.60) the use of electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi march 693 △542.0 (9.06) 427 △341.2 (8.83) 266 △200.8 (9.48) april 442 490.6 (8.20) 300 332.6 (8.60) 142 158.0 (7.46) may 406 480.9 (8.04) 264 309.4 (8.00) 142 171.5 (8.10) june 375 ▼479.5 (8.01) 252 320.1 (8.28) 123 159.4 (7.53) july 451 510.8 (8.54) 305 341.9 (8.84) 146 168.9 (7.98) august 453 482.3 (8.06) 288 ▼303.0 (7.84) 165 179.3 (8.47) september 473 488.0 (8.16) 321 330.8 (8.56) 152 ▼157.2 (7.42) october 475 483.3 (8.08) 303 307.5 (7.95) 172 175.8 (8.30) november 488 489.2 (8.18) 310 306.5 (7.93) 178 182.7 (8.63) december 437 509.6 (8.52) 277 322.0 (8.33) 160 187.6 (8.86) average 501.3 498.6 (8.33) 323.0 322.2 (8.33) 178.3 176.4 (8.33) p-value 0.631 0.842 0.333 p-values were computed using the chi-square test, degrees of freedom = 11. △ represents a peak, ▼ represents a trough among all, male, and female patients, respectively. figure 1. association pattern between atrial fibrillation and birth month a: results using corrected numbers of patients in this study b: simulated data with no association between atrial fibrillation and birth month figure 1 shows the pattern of the association between atrial fibrillation and birth month. in figure 1a, the number of patients born in the first quarter of the year among all patients was slightly higher than those born in the other quarters of the year. however chi-square analysis showed that the deviation between quarters was statistically significant only for females (overall: p = 0.156, the use of electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi males: p = 0.623, females: p = 0.030, see table 2). in figure 1b, a simulation result with no association between atrial fibrillation and birth month showed a casual peak in september. by comparing the magnitudes of dispersion in the simulation (see figure 1b), the findings in patients seemed mere coincidence (see figure 1a). table 2. corrected number of patients with atrial fibrillation by birth quarter birth quarter overall male female first quarter 1569.3 992.4 576.9 second quarter 1451.0 962.1 488.9 third quarter 1481.1 975.7 505.4 fourth quarter 1482.1 936.0 546.1 average 1495.9 966.6 529.3 p-value 0.156 0.623 0.030 p-values were computed using the chi-square test, degrees of freedom=3. discussion the number of female patients with atrial fibrillation born in the first quarter of the year was slightly higher than those born in the other quarters. however, there were no statistically significant deviations in birth month and birth quarter for the remaining patients. additionally, an analysis of the simulation results also showed that this finding might be mere coincidence. the birth month with the highest number of patients was march, similar to previous reports [1,17]. it is unclear whether this consistency in peak birth month is due to chance or is a non-negligible finding. the association between atrial fibrillation and birth month could not conclusively be confirmed in the present study of japanese patients. however, differences in ethnicity may account for the lack of significance. north american and european studies demonstrate a relationship between schizophrenia and birth month. however, asian studies, particularly japanese and korean studies, have difficulty finding such associations [18]. in general, any condition that is associated with birth month largely varies with geographical condition, ethnicity, sex, and generation [2,7,18,19]. further studies are required to confirm any association between birth month and cardiovascular diseases. the most well-known association between a disease and birth month is that reported for schizophrenia. recent work has progressed beyond the stage of merely confirming the existence of this association, which was established 40 years ago [20]. currently, the aim of research in this area is to investigate the causes of the association, although designing suitable experiments is challenging because large numbers of participants are needed due to the low prevalence of schizophrenia. in contrast to schizophrenia, the prevalence of cardiovascular diseases is high. moreover, these can be assessed quantitatively by measuring blood pressure and other values [17]. therefore, the details of the association between cardiovascular diseases and the use of electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi birth month can be analyzed much more easily than that of schizophrenia. recently, medical records have been computerized in many facilities, allowing large data sets to be investigated. generally, studies on associations between diseases and birth month, including this study, have the same limitation. specifically, even if a significant association is observed, it is impossible to derive practical information from the study that should be applied to public health practice. the reason for this limitation is that the origin of an observed association is unknown. to overcome this limitation, various association patterns that have been strictly confirmed should be compared among various kinds of diseases. we suspect that associations are not caused by a different origin for each disease. associations can be roughly divided into a group of diseases that are often seen in individuals born in the spring and a group of diseases that are often seen in individuals born in the autumn [1]. the associations observed in these groups may have a common origin. if diseases in the former group are affected by melatonin insufficiency, all infants must be exposed to a light environment that allows for sufficient pineal gland development. accordingly, further investigation into this topic will overcome the limitation and provide practical information for disease prevention. this study has two further limitations. one is that we used diagnostic data only from electronic medical records. using record details introduces the problem of the diagnostic data not always being correct. when considering heart failure or hypertension, the ambiguity of diagnostic data may affect the study results. however, it was impossible to confirm the diagnostic data by checking the details of each medical record in this study. therefore, taking this into consideration, we limited our examination to atrial fibrillation, which has clear diagnostic criteria. the other limitation is that our study method required a correction for the number of patients for each birth month. if we had studied blood pressure, the average values of participants born in january and june could have been compared directly. however, when comparing the number of patients, it is necessary to consider the bias in birth month within the population. in japanese population statistics, the magnitude of that bias differs by birth year [16]. therefore, we corrected the number of patients even by considering the birth year to eliminate the seasonal fluctuation within the population. our hypothesis was that the risk of developing a medical condition in the future is related to birth month via melatonin secretion. it is possible that development of the pineal gland is influenced seasonally by the light environment during the infantile period. if we can show proof of differences in melatonin secretion associated with birth month, our hypothesis would be supported. unfortunately, no evidence of this was found in the present study. however, if our hypothesis is true, an association between cardiovascular diseases and birth month should exist, and improvement of the light environment during the infantile period might lead to prevention of cardiovascular diseases. moreover, an association between birth month and age-related maculopathy, which involves developmental differences in melatonin secretion [21-23], should be also found in the future. age-related maculopathy is the leading cause of blindness in older people in the united states, united kingdom and many other industrialized countries [24]. though associations between birth month and medical conditions have been limited to a few rare diseases thus far, this issue is worthy of study from a public health point of view. the use of electronic medical record data to analyze the association between atrial fibrillation and birth month ojphi conclusions an association between atrial fibrillation and birth month could not be confirmed in our study of japanese patients. however, the lack of statistical significance could be due to differences in ethnicity. previously, even if a significant association were observed, it was impossible to derive practical information that should be applied to public health practice. however, further studies on this topic will carry a profound meaning and present new valuable information on the environment in infancy that can reduce the risk of cardiovascular disease in the general population. consequently, though associations between birth month and medical conditions have been limited to a few rare diseases so far, this issue is worthy of study from 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patients. eur rev med pharmacol sci. 20, 4196-201. pubmed 23. blasiak j, reiter rj, kaarniranta k. melatonin in retinal physiology and pathology: the case of age-related macular degeneration. oxid med cell longev. 2016; 2016: 6819736. doi:10.1155/2016/6819736 24. pascolini d, mariotti sp, pokharel gp, pararajasegaram r, et al. 2004. 2002 global update of available data on visual impairment: a compilation of population-based prevalence studies. ophthalmic epidemiol. 11, 67-115. pubmed https://doi.org/10.1076/opep.11.2.67.28158 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19710945&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27831657&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15255026&dopt=abstract https://doi.org/10.1076/opep.11.2.67.28158 introduction methods results discussion conclusions conflict of interests: references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts advancing ph emergency preparedness informatics to support emergency responses nikolay lipskiy* and james tyson centers for diseases control and prevention (cdc), office of public health preparedness and response’s (ophpr), division of emergency operations (deo), atlanta, ga, usa objective the purpose of this project is to demonstrate progress in developing a scientific and practical approach for public health (ph) emergency preparedness and response informatics (epri) that supports the national health security strategy and global health security agenda (ghsa) objectives. ph emergency operations centers (eoc) contribute to health security objectives because they operationalize response, recovery and mitigation activities during national and international ph events. the primary focus of this presentation is to describe the results of an analysis of cdc’s eoc, and other eocs, in building their epri capabilities. introduction global travel and human migration patterns facilitate the spread of diseases such as influenza a/h1n1, ebola, and zika, increasing pressure to ph systems to protect their constituents against global health threats. effective prevention, detection, and rapid response to threats rely heavily on adequate information sharing. this requires effective information management through ph epri applications such as information systems and tools, knowledge management, and a continuous cycle improvement to maintain system quality. enhancement of ph epri capabilities contributes to improved decision making during emergencies1. it transforms public health practice and improves health outcomes through better surveillance, epidemiology, integrated delivery of services, and other emergency preparedness and response activities. epri activities depend on both technical systems and the people who use them. without adequate training, these systems cannot be effective. cdc’s ph eoc information processes and data flows are a notable use case, utilized by hundreds of emergency responders during large-scale ph events. by analyzing this use case, cdc’s informaticians have identified multiple opportunities for advancing ph epri and advance the objectives of the ghsa. methods ph epri is an interdisciplinary science, incorporating knowledge and techniques from a multiple fields of research and practice. these include epidemiology and surveillance, gathering and distributing information for situational awareness (sa), technology infrastructure development, incident management, and several other disciplines. cdc’s situational awareness branch used three sources for this analysis: direct analysis of cdc’s eoc information systems and sa activities; who’s framework for a public health emergency operations centre2, and hhs’ public health and medical situational awareness strategy3. this assessment also included a comparison of the objectives of ph epri to the objectives of other emerging disciplines, such as ph informatics and emergency preparedness informatics. this helped in avoiding overlap with other disciplines and fixing gaps within ph epri. results the following information flows were identified as part of the cdc’s eoc operations: managing and commanding, operations, planning and intelligence, logistics, and finance/administration. these information flows are standard for ph epris. each information flow is supported by an information structure that consists of hierarchical categories. for example, the operations information flow includes task tracking, event investigation, and controlling. as of august, 2016, cdc’s eoc defined 41 hierarchical categories for ph epri data flows. cdc’s eoc harmonized different information flows by using a consistent vocabulary to describe the hierarchical components of each information flow. two hundred thirty six data elements of this vocabulary were harmonized as of august 2016 to standardize its epri systems. the hierarchy of ph eoc data flows and harmonized data elements were published in the cdc vocabulary and access distribution system, vads4. some information flows were unique to ph epri, and were not covered by other emerging disciplines. examples of these unique information flows include some incident management data, logistics for deployment of ph personnel and resources, and some event mitigation data. conclusions cdc’s eoc has several harmonized information flows that benefit users and cdc emergency activations. understanding these unique ph epri data flows helps improve preparedness of staff for working with information flows during emergency activations. advances in harmonization and standardization helped improve ph epri, optimize staff training. keywords global health security; ph emergency preparedness and response informatics; emergency preparedness and response; data harmonization and interoperability references 1. cdc. why is global public health informatics important to public health? at: http://www.cdc.gov/globalhealth/healthprotection/gphi/ what/objectives.html 2. who. framework for a public health emergency operations centre. interim document. november, 2015. at: http://www.who.int/ihr/ publications/9789241565134_eng/en/ 3. medical situational awareness strategy. hhs, assistant secretary for preparedness and response (aspr), 2014. at: http://www.phe.gov/ preparedness/legal/documents/phms-sas-20140516.pdf 4. cdc. public health information network. vocabulary access and distribution system (phin vads). at: https://phinvads.cdc.gov/ vads/searchvocab.action *nikolay lipskiy e-mail: dgz1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e59, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts localized surveillance: a fresh perspective for regional syndromic surveillance mansi agarwal1, nimi idaikkadar3, josé lojo4, kristen soto2 and robert mathes*1 1bureau of communicable disease, new york city department of health and mental hygiene, long island city, ny, usa; 2connecticut department of public health, hartford, ct, usa; 3bureau of epidemiology services, new york city department of health and mental hygiene, new york, ny, usa; 4philadelphia dept. of public health, philadelphia, pa, usa objective to outline successful strategies for regional data-sharing and discuss how these strategies can be applied to other regions. introduction recent efforts to share syndromic surveillance data have focused on developing national systems, namely biosense 2.01. the problems with creating and implementing national systems, such as legal issues, difficulties in standardizing syndrome definitions, data quality, and different objectives, are well documented1,2. in contrast, several local health departments have successfully shared data and analyses with each other, primarily during emergency events. the benefits of locally-driven data sharing include: (1) faster dissemination of data and analyses that have been created by those who understand the nuances of their own data, (2) easier process of standardizing syndrome definitions, (3) quickly designing appropriate analyses for the event, (4) smaller group of partners for consensus-building, and (5) ultimately improved timeliness in detection of public health events. the strategies used to share data and analyses between local and state health departments during planned and unplanned events may be informative to national systems. description discussion will start by highlighting a successful collaboration between health departments in new york city, new york state, new jersey, philadelphia, and connecticut, including sharing of data through new york city’s epiquery website. the roundtable will then briefly focus on factors that make this collaboration successful. audience engagement the audience will be asked to draw on their own data-sharing experiences, with the following questions: 1) what are successful strategies for data sharing and why did they work? 2) how do successful collaborations overcome general datasharing challenges? 3) can successful methods be adapted to other regions and how? 4) what is the best way to disseminate best practices in localized data sharing across regions? from the discussion at the roundtable, we will create a repository of examples and general strategies that have been successful in regional data sharing to share with the larger public health community. keywords data sharing; syndromic surveillance; emergencies references 1. rennick, marcus, david j. swenson, stacey hoferka, charlie ishikawa, and rebecca zwickl. “check! explore barriers and solutions to data sharing on biosense 2.0.” online journal of public health informatics 6.1 (2014). 2. ross, emma. “perspectives on data sharing in disease surveillance.” centre on global health security (2014). chatham house. 3. ishikawa, charlie, comp. syndromic surveillance regional data sharing workshop: hhs region 5 final report. international society for disease surveillance (2013). *mansi agarwal e-mail: magarwal@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e182, 201 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts predicting acute respiratory infections from participatory data bisakha ray*1 and rumi chunara2 1new york university school of medicine, new york, ny, usa; 2new york university, new york, ny, usa objective to evaluate prediction of laboratory diagnosis of acute respiratory infection (ari) from participatory data using machine learning models. introduction aris have epidemic and pandemic potential. prediction of presence of aris from individual signs and symptoms in existing studies have been based on clinically-sourced data1. clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. thus, the viral information that comes from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms. participatory data — information that individuals today can produce on their own — enabled by the ubiquity of digital tools, can help fill this gap by providing self-reported data from the community. internet-based participatory efforts such as flu near you2 have augmented existing ari surveillance through early and widespread detection of outbreaks and public health trends. methods the goviral platform3 was established to obtain self-reported symptoms and diagnostic specimens from the community (table 1 summarizes participation detail). participants from states with the most data, ma, ny, ct, nh, and ca were included. age, gender, zip code, and vaccination status were requested from each participant. participants submitted saliva and nasal swab specimens and reported symptoms from: fever, cough, sore throat, shortness of breath, chills, fatigue, body aches, headache, nausea, and diarrhea. pathogens were confirmed via rt-pcr on a genmark respiratory panel assay (full virus list reported previously3). observations with missing, invalid or equivocal lab tests were removed. table 2 summarizes the binary features. age categories were: ≤ 20, > 20 and < 40, and ≥ 40 to represent young, middleaged, and old. missing age and gender values were imputed based on overall distributions. three machine learning algorithms—support vector machines (svms)4, random forests (rfs)5, and logistic regression (lr) were considered. both individual features and their combinations were assessed. outcome was the presence (1) or absence (0) of laboratory diagnosis of ari. results ten-fold cross validation was repeated ten times. evaluations metrics used were: positive predictive value (ppv), negative predictive value (npv), sensitivity, and specificity6. lr and svms yielded the best ppv of 0.64 (standard deviation: ±0.08) with cough and fever as predictors. the best sensitivity of 0.59 (±0.14) was from lr using cough, fever, and sore throat. rfs had the best npv and specificity of 0.62 (±0.15) and 0.83 (±0.10) respectively with the cdc ili symptom profile of fever and (cough or sore throat). adding demographics and vaccination status did not improve performance of the classifiers. results are consistent with studies using clinicallysourced data: cough and fever together were found to be the best predictors of flu-like illness1. because our data include mildly infectious and asymptomatic cases, the classifier sensitivity and ppv are low compared to results from clinical data. conclusions evidence of fever and cough together are good predictors of ari in the community, but clinical data may overestimate this due to sampling bias. integration of participatory data can not only improve population health by actively engaging the general public2 but also improve the scope of studies solely based on clinically-sourced surveillance data. table 1. details of included participants. table 2. coding of binary features. keywords ari; machine learning; participatory epidemiology acknowledgments nsf grant 1343968. references 1. monto as, et al. clinical signs and symptoms predicting influenza infection. arch intern med. 2000;160(21):3243-7. 2. chunara r, et al. flu near you: an online self-reported influenza surveillance system in the usa. online journal of public health informatics. 2012;5(1). 3. goff j, et al. surveillance of acute respiratory infections using community-submitted symptoms and specimens for molecular diagnostic testing. plos currents. 2014;7. 4. burges cj. a tutorial on support vector machines for pattern recognition. data mining and knowledge discovery. 1998;2(2):121-67. 5. breiman l. random forests. mach learn. 2001;45(1):5-32. 6. parikh r, et al. understanding and using sensitivity, specificity and predictive values. indian j ophthalmol. 2008;56(1):45. *bisakha ray e-mail: bisakha.ray@nyumc.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e71, 2017 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e305, 2019 isds 2019 conference abstracts data capture and visualization for a canine influenza outbreak — new york city, 2018 katherine whittemore, rachel corrado, marc paladini, alexander davidson, chaorui c. huang, demetre daskalakis, sally slavinski, david e. lucero new york city department of health and mental hygiene, long island city, new york, united states objective the objectives of this project were to rapidly build and deploy a web-based reporting platform in response to a canine influenza h3n2 outbreak in new york city (nyc) and provide aggregate data back to the veterinary community as an interactive dashboard. introduction data-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (qa; e.g., completeness, standardization) is critical [1]. additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manne r. dynamic data visualization is a particularly useful means of sharing data with healthcare providers and the p ublic [2]. during spring 2018, detection of canine influenza h3n2 among dogs in nyc caused concern in the veterinary community. canine influenza is a highly contagious respiratory infection caused by an influenza a virus [3]. however, no central database existed in nyc to monitor the outbreak and no single agency was responsible for data capture. our team at the nyc department of health and mental hygiene (dohmh) partnered with the nyc veterinary medical association (vma) to monitor the canine influenza h3n2 outbreak by building a web-based reporting platform and interactive dashboard. methods the nyc dohmh built and deployed a web-based reporting platform to aid veterinarians in reporting cases of canine influenza. we leveraged redcap cloud, a cloud-based graphical user interface data capture and management software. redcap cloud collected information regarding the provider, owner, dog, residence of dog, illness history, and influenza testing. we levera ged redcap qa functionality in the form of mandatory questions to ensure data completeness. several different field types — including dropdown menus, mutually exclusive radio buttons, and multi-select check boxes — were used to ensure data standardization. skip logic was incorporated to guide users through unique sequences of questions based on the answers they entered. reporting was voluntary. dohmh also created an interactive dashboard in r with flexdashboard, plotly, and leaflet packages to display the report data. an r package is a fundamental unit of reproducible r code that contains functions as well as documentation on how to use them [4]. we chose to use r for its flexibility in creating dynamic data visualizations. reported cases of canine influenza were dis played by date and by borough. flexdashboard was used to create the layout out of the dashboard and to embed interactive widgets. we used plotly to create a dynamic histogram and leaflet to create an interactive map. the dashboard was hosted on the nyc vma website and updated weekly. results after requirements were gathered, the redcap web-based reporting platform was rapidly deployed in approximately two business days. over the course of one week, multiple versions of the dashboard were produced and the final iteration was completed. the entire system was built on server-side software that is available as free or open-source for individual licenses. the dashboard can be found at the following link: http://www.vmanyc.org/canine_influenza_dashboard.html. a total of 28 cases were reported by 6 providers during june–august 2018. all of the 28 cases were reported from 2 of the 5 nyc counties (boroughs); 17/28 (60.7%) were reported from brooklyn and 11/28 (39.3%) were reported from manhattan. we were able to collect mostly complete data by leveraging redcap qa functionality. the reporting facility was listed in all cases, and an owner http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e305, 2019 isds 2019 conference abstracts was listed in all but two cases. all reported cases used a pcr test for the detection of canine influenza h3n2. one reported case indicated polymerase chain reaction (pcr) test results as “not detected” which suggests that one negative case was reported through the system. conclusions using redcap cloud and r, we were able to rapidly build and deploy a web-based reporting platform and dynamic data visualization during an emergency response to an outbreak of canine influenza h3n2. our system was used by veterinarians to report 28 cases of canine influenza. future emergency responses for human disease outbreaks will likely benefit from the experience our team gained during our partnership with the nyc vma. acknowledgement we would like to acknowledge the new york city veterinary medical association for hosting the dashboard online. references 1. centers for disease control and prevention. public health emergency response guide for state, local, and tribal public health directors. https://emergency.cdc.gov/planning/pdf/cdcresponseguide.pdf. 2. meyer m. the rise of healthcare data visualization. http://journal.ahima.org/2017/12/21/the-rise-ofhealthcare-data-visualization/. 3. american veterinary medical association. canine influenza faq. https://www.avma.org/kb/resources/faqs/pages/control-of-canine-influenza-indogs.aspx. 4. wickham h. r packages. http://r-pkgs.had.co.nz/. http://ojphi.org/ editorial online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e229, 2015 ojphi editorial ojphi vol 7, no 3 (2015) edward mensah, phd editor-in-chief online journal of public health informatics correspondence: dehasnem@uic.edu doi: 10.5210/ojphi.v7i3.6373 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. happy holidays to all our readers, reviewers and authors; and welcome to the last issue of ojphi in 2015. i am happy to announce that this is the 7th issue since we started publication in 2009. congratulations to all of you for achieving this milestone of success. this issue contains 5 original articles covering various topics in the application of data sciences to public health practice. these range from development of data visualization platforms to facilitate health workforce planning; the development of visualization methods for utilizing incomplete data for surveillance; development of agent-based model to explore the prevalence of gonococcal infections among those who adhere to the cdc guidelines for the use of pre-exposure prophylactic for the prevention of hiv infections in high risk populations; description of a methodology for facilitating inter-disciplinary collaboration for the advancement of global public health surveillance; and an examination of the use of clinical decision support systems within immunization information systems. health workforce planning is critical to successful implementation of health policy. local health officials typically collect data required to plan and deliver health care services within their jurisdictions and have limited access to what happens in other localities. these fragmented local health data contribute to inefficient planning at both local and state levels. fortunately the affordable care act offers incentives for data sharing and collaboration among stakeholders. in the paper titled “data lakes and data visualization: an approach to addressing the many challenges of health workforce planning” denise et. al developed data visualization platforms to facilitate state-wide health workforce planning in mississippi. public health practitioners quite often must rely on clinical health data for real -time surveillance. the utility of the surveillance models depends on the quality of the data used. clinical data from health care encounters are typically aggregated into data sets and sent to the surveillance system http://ojphi.org/ editorial online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e229, 2015 ojphi at periodic time intervals, leading to lags in the availability of the data for surveillance purposes. to be useful for real-time surveillance the data must be timely, accurate and complete. however, if they have to use incomplete data for surveillance it is necessary to understand the structure of the incompleteness. the two options available to public health practitioners are to wait until sufficient time has elapsed to ensure data completeness or develop tools for using incomplete data. to date, practitioners have developed few methods for utilizing incomplete data in surveillance. in a paper titled “visualizing the quality of partially accruing data for use in decision making” the authors demonstrate that in order to avoid biased conclusions one must account for accrual lag in partially accruing data. the first comprehensive guidelines for the use of a pre-exposure prophylactic for the prevention of hiv infection in high risk populations was recently published by the centers for disease control and prevention. the guidelines include a daily regimen of the pre-exposure prophylactic as well as condom usage during sexual activity. the fear within the medical communit y is that those who adhere to the pre-exposure prophylactic may forgo the use of condoms during sexual activities, thereby exposing them to the risk of other sexually transmitted diseases. in a paper titled “agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines” escobar, et. al., demonstrate that such attitudes of aversion are unfounded. the global disease surveillance community lacks consensus on preferred technical methods for monitoring public health data. the existence of universally acceptable surveillance methodologies can provide benefits in terms of anomaly detection, transmission tracking and risk mapping, among others. in a paper titled “cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use case” faigen z., deyneka l., et al. describe a methodology of enabling inter-disciplinary and cross-disciplinary collaboration for the advancement of public health surveillance. immunization information systems (iis) contain immunization data across various providers over time and offer comprehensive vaccination histories. the iis also contain clinical decision support systems for immunizations (cdsi). the cdsi facilitate bi-directional communications between the immunization information systems and certified electronic health records. in a paper titled “characterizing the access of clinical decision support offered by immunization information in minnesota” rajamani et. al., examine the use of clinical decision support system within the minnesota immunization information system. increased adoption of certified electronic health records required by stage 3 meaningful use will promote the utilization of clinical decision support systems for immunization. http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi iowa state university statistics department, ames, ia, usa objective to use multiple data sources of influenza epidemic severity to inform a model which can estimate and forecast severity for the current influenza epidemic season by accounting for the bias from each source. introduction timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. the cdc’s ilinet system collects data on influenza-like illnesses from over 3,300 health care providers, and uses this data to produce accurate indicators of current influenza epidemic severity. however, ilinet indicators are typically reported at a lag of 1-2 weeks. another source of severity data, google flu trends, is calculated by aggregating google searches for certain influenza related terms. google flu trends data is provided in near-real time, but is a less direct measurement of severity than ilinet indicators, and is likely to suffer from bias. we create a hierarchical model to estimate epidemic severity for the 2014 2015 epidemic season which incorporates current and historical data from both ilinet and google flu trends, allowing our model to benefit both from the recency of google flu trends data and the accuracy of ilinet data. methods to forecast for the 2014 2015 influenza epidemic season, we provide our model with both ilinet and gft data from previous seasons, starting with the 2004 2005 epidemic season, and going through the 2013 2104 epidemic season. our model has a hierarchical structure, which allows ilinet and gft data from previous seasons to inform epidemic severity prediction in the current season. ilinet data is modeled as being an unbiased but noisy estimate of the true, unknown influenza severity. gft severity measurements, on the other hand, are influenced by external factors such as media coverage. these factors could consistently bias gft severity estimates to over or under-estimate the true epidemic severity depending on the intensity of media influenza coverage in a season. to account for this potential bias in gft data, we include an autoregressive error term, which allows over or under-predictions made by gft data in one week to carry over into the next. estimation is performed using the bayesian statistical software stan (http://mc-stan.org/). results we examine the increase in forecast accuracy that gft data provides by comparing the forecasting ability of our model using both gft and ilinet data to that of a model given only ilinet data. the two models are evaluated for their ability to predict the week of maximum epidemic severity using only data from early points in the influenza season. examining 95 % credible intervals for the peak severity week, we find that the model which uses only ilinet data consistently predicts that the peak severity week will occur later than it was observed to occur. the model using ilinet data and gft data, on the other hand, is able to accurately estimate a range for the peak severity week several weeks before it was observed to occur. we also evaluated each model’s ability to forecast epidemic severity one week into the future. figure 1 shows a comparison of the one week ahead forecasting abilities of these models. conclusions combining up-to-date google flu trends data with accurate ilinet data improves epidemic severity forecasting ability significantly. additional data sources, such as data from twitter or wikipedia, could likely further benefit forecasting. the green band shows a 95% credible interval for a forecast of ilinet data in week t, using the ilinet model given data up to week t-1. the blue band shows a 95% credible interval for a forecast of ilinet data in week t, using the combined model given data up to week t-1. keywords influenza; google; model; bayesian; hierarchical references 1. chretien jp, george d, shaman j, chitale ra, mckenzie, fe. influenza forecasting in human populations: a scoping review. plos one. 2014 apr;9(4):e94130 2. ginsberg j, mohebbi mh, patel rs, brammer l, smolinski ms, brilliant l. detecting influenza epidemics using search engine query data. nature. 2009 feb 19;457(7232):1012-4. *nicholas l. michaud e-mail: michaud@iastate.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e25, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts an informatics framework to support surveillance system interoperability in minnesota samuel t. patnoe*1, 2, martin laventure1, rebecca e. johnson1, jennifer fritz1, barbara frohnert1, geoffrey mbinda1, karen soderberg1 and bree allen1 1office of health information technology, minnesota department of health, st. paul, mn, usa; 2council of state and territorial epidemiologists, atlanta, ga, usa objective to create an informatics framework and provide guidance to help minnesota’s public health surveillance systems achieve interoperability and transition to standards-based electronic information exchange with external health care providers using the state’s birth defects registry as an initial pilot program. introduction the minnesota department of health (mdh) needs to be able to collect, use, and share clinical, individual-level health data electronically in secure and standardized ways in order to optimize surveillance capabilities, support public health goals, and ensure proper follow-up and action to public health threats. mdh programs, public health departments, and health care providers across the state are facing increasing demands to receive and submit electronic health data through approaches that are secure, coordinated, and efficient; use appropriate data standards; meet state and federal privacy laws; and align with best practices. this framework builds upon existing informatics models and two past studies assessing health information exchange (hie) conducted by the mdh office of health information technology (ohit) to provide mdh surveillance systems with an outline of the key elements and considerations for transitioning to more secure, standards-based, electronic data exchange. methods development of the informatics framework incorporates information gathered in several phases. the first phase involves additional analysis of data collected from the mdh informatics assessment of interoperability and hie1 that was conducted in 2015 to evaluate the current state of interoperability and hie readiness across the agency. the second phase involves a comprehensive environmental scan and literature review of existing standards, practices, models, toolkits, and other resources related to electronic hie and interoperability. the third phase involves gathering additional information on programmatic needs, workflows, and capabilities through key informant interviews. key informants include program managers, staff, and content-area experts from select mdh programs, the state’s central information technology organization (mn.it), and external health care provider organizations including hospitals. minnesota’s birth defects registry, the birth defects information system (bdis), was selected as the pilot program because it was identified in the 2015 mdh informatics assessment as having a high level of interest in implementing an interoperable and standardsdriven approach to electronic health data exchange. the bdis is also exploring options for being designated as an eligible public health registry for meaningful use. as a pilot program for this project, the bdis assists in the development and implementation of the informatics framework. results the 2015 mdh informatics assessment identified and evaluated 21 mdh programs with information systems that accept and manage clinical, individual-level health information. among these 21 mdh programs, wide variations exist regarding information system size (range, 400 to 10,000,000 individuals), staffing numbers (range, 0.2 to 21 ftes), budgets (range, $20,000 to $1,876,000), and other key characteristics. despite these variations, programs identified similar barriers and needs related to achieving interoperability and electronic hie. areas of need include management and information technology support to make interoperability a priority; policies and governance; additional application functionality to support hie; and additional skills for the workforce. results from the environmental scan and key informant interviews will be incorporated with additional analyses of the 2015 mdh informatics assessment to inform the development of an agency-wide informatics framework to support mdh programs in achieving interoperability. conclusions mdh surveillance systems are calling for practical guidance to help implement and maintain a more efficient and effective way to electronically collect, use, and share health data with external and internal stakeholders. this informatics framework provides an outline of the key elements and considerations for achieving greater interoperability across mdh surveillance systems. additional research is required to assess how system interoperability and hie can improve data quality and advance population health goals. keywords informatics; interoperability; hie acknowledgments this work is supported in part by the mdh and an appointment to the health systems integration program fellowship administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement 3u38-ot000143-01s4. references 1. please contact mn.ehealth@state.mn.us for a copy of the 2015 mdh informatics assessment. *samuel t. patnoe e-mail: sam.patnoe@state.mn.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e31, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts evaluation of exposure-type stratification to improve poison center surveillance royal k. law*1, howard burkom2 and josh schier1 1national center for environmental health, centers for disese control and prevention, chamblee, ga, usa; 2johns hopkins applied physics laboratory, baltimore, md, usa objective our objective was to determine if the detection performance of current surveillance algorithms to detect call clusters is improved by stratifying by exposure category. introduction the centers for disease control and prevention (cdc) uses the national poison data system (npds) to conduct surveillance of calls to united states poison centers (pcs) to identify clusters of reports of hazardous exposures and illnesses. npds stores basic information from pc calls including call type (information request only or call reporting a possible chemical exposure), exposure agent, demographics, clinical, and other variables. cdc looks for anomalies in pc data by using automated algorithms to analyze call and clinical effect volume, and by identifying calls reporting exposures to pre-specified high priority agents. algorithms analyzing call and clinical effect volume identify anomalies when the number of calls exceeds a threshold using the historical limits method (hlm). clinical toxicologists and epidemiologists at the american association of poison control centers and cdc apply standardized criteria to determine if the anomaly is a potential incident of public health significance (iphs) and then notify the respective health departments and pcs as needed. discussions with surveillance system users and analysis of past iphs determined that call volume-based surveillance results in a high proportion of false positive anomalies. a study assessing the positive predictive value (ppv) of this approach determined that fewer than four percent of anomalies over a five-year period were iphs.1 a low ppv can cause an unnecessary waste of staff time and resources. we hypothesized that first stratifying call volume by exposure category would reduce the number of false positives. with the help of medical toxicologists, we created 20 toxicologically-relevant exposure categories to test this hypothesis. methods to compare cluster detection performance between the two approaches, we used a historical testbed of hourly exposure call counts with and without initial stratification by exposure category from 10 selected pcs from jan 1, 2006 jul 31, 2015. we ran the hlm for both non-stratified and stratified testbeds to estimate the monthly number of anomalies triggered (i.e., alert burden). our target signals to assess detection performance consisted of call samples from three large public health events: the 2009 salmonella food poisoning event from contaminated peanut butter, the 2012 hurricane sandyassociated carbon monoxide poisonings in new jersey, and the 2014 elk river contaminated water spill in west virginia (wv). for each event, we chose 30 random calls one thousand times to obtain 1000 random sets of inject clusters. each inject cluster was iteratively added into the testbed with and without initial stratification by exposure category. we then applied the hlm for each iteration to see if the algorithm identified the inject cluster. the sensitivity for each approach for each pc was calculated as the proportion of iterations where the algorithm identified the inject cluster. we reported median sensitivities from the ten pcs for each of the time windows of 1, 2, 4, 8, and 24 hours. results figure 1 summarizes results for the wv event with markers showing anomaly burden (x-axis) and sensitivity (y-axis) using the stratified (δ) and the non-stratified (o) approach by different time windows (hrs). the results from the other two events are not shown but established similar patterns. anomaly burden is shown as the estimated monthly anomaly count for each approach. for example, markers linked by the arrow show that with a 4-hour time window, the stratified approach achieves nearly perfect sensitivity with ~10 anomalies as the monthly anomaly burden while sensitivity of the non-stratified approach is below 20% with ~40 monthly anomalies. the stratified approach gave improved overall sensitivity across all time windows, and reduced anomaly burden for 1-, 2-, and 4-hour time windows. conclusions we found a consistent detection advantage (higher sensitivity and lower anomaly burden) for the stratified vs traditional nonstratified approach for 1-, 2-, and 4-hour time windows. further research should focus on refining the stratified approach and the specific surveillance parameters (such as time windows) that increase algorithm performance. figure 1: detection performance comparison: stratified vs non-stratified approach; 2014 elk river contaminated water spill in west virginia scenario keywords poison center; surveillance; npds references 1. law rk, sheikh s, bronstein a, thomas r, spiller ha, schier jg. incidents of potential public health significance identified using national surveillance of us poison center data (2008-2012). clin toxicol (phila). 2014;52(9):958-963. *royal k. law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e17, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts outbreak prediction: aggregating evidence through multivariate surveillance flavie vial*1, wei wei2 and leonhard held2 1vetsuisse faculty, veterinary public health institute, bern, switzerland; 2institute of social and preventive medecine, zürich, switzerland objective the question of how to aggregate animal health information derived from multiple data streams that vary in their specificity, scale, and behaviour is not trivial. our view is that outbreak detection in a multivariate context should be viewed as a probabilistic prediction problem. introduction production animal health syndromic surveillance (pahsys) data are varied: there may be standardized ratios, proportions, counts of adverse events, categorical data and even qualitative ‘intelligence’ that may need to be aggregated up a hierarchy. pahsys provides some unique challenges for event detection. livestock populations are made up of many subpopulations which are constantly moving around between farms and markets to slaughter. pathogen expression often varies across production types and rearing-intensity levels. the complexity of animal production systems necessitates monitoring many time series (figure 1); and makes the investigation of statistical signals imperative and at the same time difficult and resource intensive. having multivariate surveillance methods that can work across multiple data streams to increase both sensitivity and specificity are much needed. methods although the benefits of ‘model-based’ inference and prediction seem to be generally well accepted in numerous scientific disciplines, this does not seem to have found the same resonance in the context of surveillance data. a potentially promising alternative to traditional outbreak detection algorithms is to fit a model to the surveillance data, allowing for historic outbreaks, and to base outbreak detection on the posterior distribution of suitable model parameters or on the predictive distribution of xn+1, the case counts at tn+1. with this approach, xn is used to fit the model and all available historic information on the disease is taken into account. while both outbreak detection and outbreak prediction have strengths and weaknesses for univariate time series, the benefits of a model-based predictive approach become overwhelming in multivariate surveillance. the challenges of multivariate surveillance, such as different data time lags, different frequencies of observations (e.g. weekly and daily), etc., are most easily met by suitable statistical modelling. we propose to use swiss pahsys data to develop model-based predictive methods based on the two-component model for counts of disease cases described in (1). evaluation of the one-step-ahead predictions can be used to assess if the predictions are well calibrated. in contrast, the quality of traditional outbreak detection methods is usually assessed by simulation based on artificially inserted outbreaks. results the concept will be presented since development has not started yet. conclusions while univariate outbreak detection algorithms can be useful in practice, a potentially promising alternative for multivariate pahsys is based on model-based predictive methods. these methodological advances will provide a concrete step towards the development of operational multivariate pahsys systems which will contribute to disease risk reduction and increased food safety and public health. diversity of health data for production animals available to surveillance system developers at the federal food safety and veterinary office in switzerland. datasets in purple are held by private companies but have been made available. keywords animal production system; forecasts; multivariate health data; statistical challenges; syndromic surveillance acknowledgments f. vial and w. wei are equal contributors on this project. references 1. held l, hofmann m, höhle m, schmid v. a two-component model for counts of infectious diseases. biostatistics. 2006 jul 1;7(3):422–37. *flavie vial e-mail: flavie.vial@vetsuisse.unibe.ch online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e170, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts surveillance of surveillance: inventorying gaps and commonalities across the universe of surveillance systems catherine ordun* and atlanta health informatics alliance strategic innovation group, booz allen hamilton, decatur, ga, usa objective to identify analytic gaps and duplication across u.s. government, international agencies, non-profit and academic health surveillance systems, programs, and initiatives in four areas: analytics, data sources, statistics, and system requirements. introduction health surveillance systems provide important functionalities to detect, monitor, respond, prevent, and report on a variety of conditions across multiple owners. they offer important capabilities, with some of the most fundamental including data warehousing and transfer, descriptive statistics, geographic analysis, and data mining and querying. we observe that while there is significant variety among surveillance systems, many may still report duplicative data sources, use basic forms of analysis, and provide rudimentary functionality. methods we conducted a review of 15 “review articles” revealing 57 biosurveillance systems dedicated to infectious disease and clinical surveillance. in a second review, we identified an additional 179 systems, programs, initiatives, and databases yielding 236 systems, programs, initiatives, and databases that have been labeled as “surveillance systems”, across multiple u.s. government agencies and health conditions. we chose 30 out of 236 systems based on availability of public data, completeness of information about the program, and longevity of the system, for a preliminary inventory analysis. next, we conducted topic modeling using mallet open source software from boston university to identify key terms collected for all 30 systems that revealed common terms across four categories: analytics, data sources, statistics, and system requirements. results thirty systems, programs, and initiatives were identified across 14 owners, with the largest representation from the centers for disease control and prevention (cdc). topic modeling revealed the following key terms and using wordle.net, we visualized the relationship and frequency of the occurrence of these topics by generating word clouds, by each category. common analytics: text, maps, temporal analysis, charts, and some form of trend analysis and algorithms. common data sources: state-based data, use of news, clinical data and laboratory data. common statistics: basic descriptive methods; gaps evident in more advanced analysis. common system requirements: web-based portals; lack of specific it architectures such as open source, or cloud-based systems. conclusions the 30 systems ranged from very basic surveillance collected through surveys via local health departments, to sophisticated natural language processing algorithms to collect open source data. our topic modeling confirmed our understanding that the majority of surveillance systems are still basic web-based portals, general analytics, basic visualizations, some descriptive statistics. we are exploring a more rigorous methodology to conduct a “gaps and commonalities” analysis across all 236 systems and seek to develop a ratings system that scores across each of the four categories we reviewed (analytics, data sources, statistics, and system requirements) as an evaluation framework for future analysis. keywords surveillance system; topic modeling; georgia tech; knowledge gaps; emory university acknowledgments dr. lance waller, professor and chair, department of biostatistics, rollins school of public health, emory university dr. sweta sneha, assistant professor at kennesaw state university in the department of computer science and information systems ms. michelle morgan schmitz, doctoral candidate, global epidemiology, rollins school of public health, emory university dr. dan baker, senior associate, booz allen hamilton, strategic innovation group ms. yusra ahmad, associate, booz allen hamilton, strategic innovation group the atlanta health informatics association (ahia), an academic and private sector consortium consisting of emory university, georgia tech, kennesaw state, and booz allen hamilton, leveraging informatics and big data analytics to fast track public health innovation *catherine ordun e-mail: ordun_catherine@bah.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e152, 2015 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e316, 2019 isds 2019 conference abstracts rhetorical framing and needle exchange in rural indiana: shifting perspectives and policy ryan m. murphy english, purdue university, lafayette, indiana, united states objective this abstract proposes a poster presentation aimed at explaining rhetorical framing as a technique for articulating and identifying cases in healtchare policy. introduction political discourse surrounding matters of public health is exigent because human life is at stake—this is unquestionably the case with respect to widespread opioid addiction. while intravenous drug use itself is described as a health concern, the spread of diseases such as hepatitis c and hiv through the sharing of needles is a disease surveillance emergency. this research centers on municipal-level decision making in the community of greater lafayette, indiana. here, the mayors of sister cities west lafayette and lafayette hold different positions regarding a clean needle exchange program operated by the county health department. methods as scholars of harm reduction note, “harm reduction disputes are the results of participants placing different weight on moral foundations that drive typical public health discourse” (alderman et al., 2010). the methodology of rhetorical framing includes vocabulary and tactics for navigating the spaces of different weights and competing values. articulated by george lakoff as, “mental structures that shape the way we see the world,” framing deliberately selects vocabulary built around a platform of values in order establish the parameters of a discourse (2004). in the case of harm reduction and needle exchange, two different frames compete. the rule of law and order is a frame that prioritizes civil stability through a fair and unyielding adherence to civil and criminal law. this frame corresponds closely to what lakoff calls the “strict father” frame which takes a rigid paternalistic approach to responsibility and a binary approach to morality (2004). for the needle exchange program, this frame asserts that the fundamental issue at hand is criminal drug use and that no conciliatory actions should take place to accommodate lawbreaking of any kind. a different frame can be described as the healthcare and risk reduction frame. this frame seeks to prioritize the healthcare risks associated with drug use and act to abate them. on this view, drug use and addiction is a treatable medical condition. this frame corresponds to what lakoff calls the “nurturant parent model” (2004). here nurturance is taken to include responsibility and empathy. in this light, the healthcare frame recognizes the pain and damage caused by drug use for individuals and communities. it holds people responsible by helping them break drug habits rather than simply punish them for being addicted. this project focuses on a series of radio-broadcast interviews in which the mayor of lafayette expresses his support of the exchange while the mayor of west lafayette voices his concerns. i argue the reason for the difference in opinion, despite both mayors agreeing on the essential facts, rests on the problem of invoking conflicting frames. on one hand, the healthcare and risk reduction frame sees the needle exchange as a form of harm reduction and something that generally improves public health. at the same time, the frame of rule of law and order interprets the needle exchange as a government complicity in illegal drug use. results rhetorical framing in health communication is a strategy for effectively reaching expert and non-expert audiences alike. rhetorically framing disease communication achieves two key functions: it identifies a specific focus and it usually minimizes situational features that are outside of the focus. in this case study, both mayors previously had careers as police officers in the local police department. one mayor adheres to the rule of law and order frame to maintain ties with his colleagues in law enforcement while the other is willing to split and adopt the healthcare and risk reduction frame. this commonality permits another remarkable discussion—what does it take to persuade someone to adopt a different frame? http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e316, 2019 isds 2019 conference abstracts beyond shaping policy opinion toward the needle exchange and disease prevention, rhetorical framing establishes the types of surveillance and data that policy makers consider meaningful in terms of measuring success. whereas the healthcare risk and reduction frame is willing to consider qualitative reports of opioid users who visit the exchange and seek further treatment, the rule of law and order frame is inclined to insist on quantitative data such as the rate of return for needles exchanged or reductions in drug use or the spread of hepatitis c and other diseases transmitted through the use of shared needles. in addition to differences in data types, rhetorical framing also helps to explain differences in methodological approach to data collection and surveillance. conclusions in this case, political support by the mayors depends on which interpretive frame takes precedence and the rhetorical delivery and framing of available data often determines which frame dominates. whereas some data (anecdotal evidence) can be modulated and delivered through appropriate framing, other types of data (the rates of return for needles) cannot be so easily reframed. at minimum, rhetorical framing is capable of determining whether public policy concerning needle exchange is communicated primarily as a public health issue or as a matter of criminal law. for disease surveillance more broadly, rhetorical framing provides not only specialized vocabularies for describing observations, but also helps to identify research perspectives including points of overemphasis and potential blindness. acknowledgement i would like to acknowledge professor richard johnson-sheehan and the interview staff at wbaa for making this project possible. references alderman j, dollar km, kozlowski lt. 2010. commentary: understanding the origins of anger, contempt, and disgust in public health policy disputes: applying moral psychology to harm reduction debates. j public health policy. 31(1), 116. pubmed https://doi.org/10.1057/jphp.2009.52 dennis, john “ask the mayor: west lafayette’s john dennis on leading a disposable city.” ask the mayor, by stan jastrzebski, 2 november 2017. dennis, john “ask the mayor: west lafayette’s john dennis on paths (walking, driving and to recovery).” ask the mayor, by stan jastrzebski, 13 december 2017. harm reduction international. global state of harm reduction 2016, https://www.hri.global/files/2016/11/14/gshr2016_14nov.pdf. accessed 15 february 2018. lakoff, george, don’t think of an elephant: know your values and frame the debate. chelsea green, 2004. paul, joseph. “needle exchange to open its doors friday near downtown lafayette.” journal & courier. 9 august 2017. updated 2:50 p.m. et. roswarski, tony “ask the mayor: lafayette’s tony roswarski on affordable housing and needle numbers.” ask the mayor, by stan jastrzebski, 14 december 2017. roswarski, tony “ask the mayor: lafayette’s tony roswarski on crossings, water bills and softball numbers.” ask the mayor, by stan jastrzebski, 18 september 2017 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20200522&dopt=abstract https://doi.org/10.1057/jphp.2009.52 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts cutaneous anthrax surveillance by person, place, and time in georgia (2008-2013) anna kasradze3, khatuna zakhashvili3, diana echeverria1, 5, nicholos heyer1, david garcia*1, 2, ian kracilik4 and paata imnadze3 1battelle memorial institute, bakersfield, ca, usa; 2university of north carolina, chapel hill, nc, usa; 3national center for disease control and public health, tbilisi, georgia; 4university of florida gainesville, gainesville, fl, usa; 5university of washington, seattle, wa, usa objective to characterize descriptive trends using data collected by the national electronic integrated disease surveillance system (eidss, 2008-2013). introduction cutaneous anthrax is endemic in georgia1. the eidss program captures notifications from 72 municipal public health centers. it links urgent notification, case investigation data, and laboratory data on an online basis. eleven virulent and 4 non-virulent strains of b. anthracis have been isolated. genotype gk 35 and gk 44 are strains found in the turkish-southern caucasian region. it is hypothesized that human rates are caused by increased contact with infected animals. the recent re-introduction of animal vaccination programs in 2013-2014 heightens interest is establishing a defensible 6-year baseline trend in humans. methods data from all investigations of suspect, potential, and laboratory confirmed cases of cutaneous anthrax were obtained from eidss from 01/01/2008 to 12/31/2013. the ncdcph laboratory network confirmed diagnoses. samples were plated on plet and sda media and final confirmation was performed using real-time pcr (roche light cycler 2.0 using b. anthracis target 2 and b. anthracis target 3 idaho technology kits). dna isolation was completed using qiagen dneasy blood & tissue mini kits. a descriptive data analysis was performed by person, place, and time. census data was provided by the national statistics office of georgia. results among 634 notifications, 343 were confirmed cases (54%), 136 were probable cases (21%), and 9 were suspect cases (1%). laboratory testing eliminated 22.0% and 86% were confirmed by bacteriology and pcr, 3.4% by pcr and 10.6 % by bacteriology. number and incidence rates (ir) per 100,000. the 500 cases were examined annually where 82% were male between 20 to 60 years of age suggesting infection maybe occupationally related. also 38% of cases reported contact with animal products, 29% cleaned farms, and 12% had contact with soil. the 6-year mean ir among georgian nationals (n=290) was 1.32 and annual rates were 1.45, 0.79, 0.57, 1.56, 1.69, and 1.86. the rate for azerbaijani nationals were 9.31 which increased from 2.11, 1.76, to 3.16, 7.73, 23.88, and 17.21. the contrast was pronounced in kvemo kartli (gargabani ir= 45.49 vs 14.21; marneuli/tsalka ir=20.10 vs 7.61), in kakheti (sagarejo ir=23.28 vs 4.06), and in samtskhe-javakheti (bojormi and bakuriani ir=3.17 vs 0.51) where a large proportion of azerbaijani pastoralists lives and migrates along sheep and cattle corridors. variation by year and week in the year. cases appeared to increase from 2010 to 2013, but only cases for the azerbaijani minority group met statistical significance. overall statistical differences were also observed for the mean number of cases in 2008-2010 from that in 2011-2013. the 3-year means in weeks 21-29 and in weeks 39-44 increased respectively from 0.79 to 4.39 in spring and from 0.92 to 3.22 in the fall. conclusions the evidence is consistent with the conclusion that cutaneous anthrax cases are secondary to epizootic events more likely occurring in populations that work with unvaccinated sheep and cattle. the recent increase in cases is more related to a true increase in disease rather than improved reporting because incidence rates remained steady in georgian nationals and only increased in azerbaijani minorities that work and live along animal corridors. higher occupational risks are also supported by higher rates observed in 20 to 60 year old males as well as independently computed odds ratios of 6.1 (2.1-17.7 95% ci) for working in animal husbandry2. pending approval, the ncdcph proposes a national state program designed to conduct active surveillance in pastoralists. the evidence also underscores the importance of other one heath recommendations to activate anthrax awareness campaigns in russian and azerbaijani, supervise the destruction of known carcasses, record gis sites and disinfect infected soils, and introduce a participatory health education tool on anthrax. keywords cutaneous anthrax; georgia; azerbaijanis; pastoralists acknowledgments we would like to thank mariam broladze who served as our disease coordinator. references kracalik i, malania l, tsertsvadze n et al. 2014. plos negl trop dis. jul 2014; 8(7): e2985. navdarashvili a. 2013. feltp eis aog meeting. atlanta georgia. *david garcia e-mail: garciadvd77@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e196, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts return to public healthundeliverable letters of communicable disease patients uzay kirbiyik*1, 2, hassan shah4, 2, patrick t. lai4, 2, jennifer l. williams2, brian e. dixon1, 2 and shaun grannis3, 2 1iu richard m. fairbanks school of public health, indianapolis, in, usa; 2regenstrief institute, indianapolis, in, usa; 3indiana university, school of medicine, indianapolis, in, usa; 4school of informatics and computing, indianapolis, in, usa objective explore causes of undelivered letters sent by public health departments to patients with communicable diseases. introduction preventing communicable disease spread is a primary objective for public health (ph). reaching contagious people in a timely manner is essential to limit disease spread. notifying patients of their infectious status also serves as an official reminder of social and legal responsibilities. the marion county public health department (mcphd) sends disease information and notice of privacy practices to patients using the united states postal service (usps). we examined communicable disease cases with undelivered mail to ascertain failure causes and rates. methods we reviewed completed case files for 3 communicable diseases investigated by mcphd. mcphd sends a letter to each of these new positive cases. the addresses are gathered from provider and lab reports and from other sources. when the letters are undeliverable, ph staff logs these mails to the case files. we reviewed all cases with undelivered letters and identified source of address, reason for return, and patient demographics. results the letter return rates among diseases were significantly different (x2 p=0.0006). the age distributions among races were also significantly different (t-test p=0.002); mean age for caucasian and african american were 36 vs. 49 years respectively. the proportions of usps endorsements for the undelivered mail were as follows: attemptednot known 36% not deliverable as addressed 22% no such number 16% moved left no address 10% insufficient address 9% forward time expired 2% no mail receptacle 1% refused 1% vacant 1% analysis of root causes for undeliverable addresses yielded a framework of address information flow (fig. 1). since the communication between the patient and the provider is not documented, it is not possible to accurately discern the source of the initial error. conclusions several factors contributed to undeliverable mail. mchd staff suggests some of these are due to patients giving false information to avoid health bills. we have come across a significant number of institutions (15%), like churches and shelters for homeless, used for address. we also noted many drug abuse and incarceration reports among the undeliverable cases. this is expected due to socio-economic problems and communicable diseases being common with these groups. the differences among racial groups also suggest other cultural factors in play and need further exploration. ph staff resends the notification if they can figure out the reason for the returned mail (e.g. typos, choose different address, check the address with the provider/patient). while this is another burden for ph, the actual delivery rates are likely higher than what initially reported. however, letters may not always be delivered to the person intended. an earlier study found 13.3% of letter sent to fictitious people did not return, but all invalid addresses did [1]. further, we were able to geo-locate 31% of undeliverable addresses labelled as “no such number” by usps which we classified as postal error (5%). due to its fragmented nature, there is no one solution to undeliverable mail problem. technical solutions may include incorporating usps address verification system, sending an initial confirmatory mail by the provider or using phone or email as alternative. our framework allows us to establish a baseline for future research including cost-effectiveness analyses for streaming lining the process for process improvements. keywords return to sender; undeliverable mail; communicable disease notification; patient address acknowledgments we thank the staff at mcphd for their help with this research. references 1. sandler rs, holland kl. fate of incorrectly addressed mailed questionnaires. j clin epidemiol. 1990;43:45–7. *uzay kirbiyik e-mail: ukirbiyi@iupui.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e138, 201 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts public health decisions using point of care data from open source systems in africa burke mamlin*1, 2 and theresa cullen1 1regenstrief institute, indianapolis, in, usa; 2indiana university, indianapolis, in, usa objective we demonstrate an architecture for driving regional public health decisions with automated and semi-automated data collected from open source point of care systems in resource constrained environments. introduction ministries of health in low and middle income countries (lmic) are making or trying to make public health decisions for infectious disease conditions like hiv using data garnered from sentinel events and disease tracking in the community. the process of gathering and aggregating data for these case-based reports for is, in all too often a cumbersome or paper-based process. the center for disease control (cdc) was interested in prototyping and piloting approaches that could improve the efficiency and reliability of case reports in resource-constrained environments. one of their primary goals was to demonstrate how electronic data gathered in the front lines of care could be leveraged to automate and improve the reliability of data within case reports driving public health decisions at regional and country levels. openmrs is an open source medical record system platform often used in resource constrained environments.1 since openmrs is used as an electronic medical record system in several african countries and has been connected to regional or country-level health exchanges, the cdc was interested in building a working solution for electronic case based reporting using openmrs and a health information exchange. methods working closely with the cdc, we developed a case-based reporting (cbr) module for openmrs, using hiv as an initial use case. trigger events were defined based on sentinel events and key clinical monitoring conditions and these were mapped or added to standard terminologies. we use health level 7 (hl7) messaging standards to deliver case reports from openmrs to the health information exchange.2 we used existing manual workflows and epi officers to define the needs for a surveillance officer role and the requirements for the cbr module. the module was developed as open source using agile methodologies. openhie (ohie.org) was selected to demonstrate the ability of openmrs module to submit an electronic case report to a health information exchange. results we have a working, open source case-based surveillance module for openmrs that comes with nine pre-defined hiv-specific triggers: ● new case ● new disease ● new treatment ● evidence of lack of monitoring ● evidence of treatment failure ● switched to second line regimen ● treatment stopped ● lost to follow up ● patient died we have been able to demonstrate the automatic creation of hivbased case reports based on data within an electronic medical record system, placement of these proposed case reports into a work queue for a surveillance officer, and successful electronic submission of these case reports into a health information exchange. conclusions this work demonstrates the ability to develop open source point of care software solutions for lmic that can be used for sentinel awareness as well as longitudinal monitoring of individual patient care. the current scenarios, trigger identification standards, and messaging specifications are easily accessible and published on the openmrs wiki.3 our incorporation of user centered design through epi officer engagement helped ensure that our solution is responsive to the end user. the cdc is able to use this solution to demonstrate the feasibility of incorporating electronic case reporting in lmics and to demonstrate the benefits and promote the adoption of electronic medical record systems and health information exchanges in resource constrained environments. in the next phase of this work, we will be working with the cdc to identify sites within africa for deployment and refinement of the cbr module. keywords open source; hiv; case reporting; africa acknowledgments this work is supported by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention. we would like to acknowledge the guidance, assistance, and hard work of eric-jan manders, lisa murie, wyclif luyima, and jamie thomas. this work was also made possible through the openmrs infrastructure, which is supported by xsede’s jetstream project. references 1. mamlin, b. w., biondich, p. g., wolfe, b. a., fraser, h., jazayeri, d., allen, c., et al. (2006). cooking up an open source emr for developing countries: openmrs a recipe for successful collaboration. amia annual symposium proceedings / amia symposium amia symposium, 529–533. 2. hl7 cda® r2 implementation guide: public health case report, release 2 us realm the electronic initial case report (eicr). available at http://www.hl7.org/implement/standards/product_brief. cfm?product_id=436. 3. openmrs documentation on proof of concept for case based reporting. available at https://wiki.openmrs.org/display/docs/ case+based+reporting+module. *burke mamlin e-mail: bmamlin@iu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e57, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 1national center for immunization and respiratory diseases, centers for disease control and prevention (cdc), atlanta, ga, usa; 2national center on birth defects & developmental disabilities, cdc, atlanta, ga, usa; 3center for global health, cdc, atlanta, ga, usa objective to present the summary results of a literature review pertinent to mental health and psychosocial aspects of ebola virus disease (evd). introduction the 2014 outbreak of evd is the largest and most complex ebola outbreak since 1976 affecting several countries in west africa. the mental health and psychosocial implications of the 2014 ebola outbreak are serious and multifaceted, impacting survivors, families, communities, healthcare providers, and the public health response. in addition, psychosocial support is a key priority to the ebola response. cdc’s ebola mental health team (emht) was activated in september 2014. this study has been conducted to support the cdc’s emht tasks. methods we searched on-line cdc libraries and public websites for “mental health and ebola” and “psychosocial and ebola” word combinations. the scope of the search was expanded to include press releases and newspaper articles due to the urgent nature of the 2014 ebola outbreak. this review was conducted during november 2014-february 2015 and was limited to information published in english. particular emphasis was placed on the mental health and psychosocial issues of evd that might provide further guidance to health care personnel and ebola responders. results a total of 140 documents were identified, including peer-reviewed research, newspaper articles, pamphlets, and guidelines/training manuals. as of march 1, 2015 the review of different library resources and websites showed that there are three broad categories of mental health issues: 1) ebola patients; 2) healthcare providers and ebola responders; 3) ebola survivors. one of the main obstacles in reducing the outbreak has been the widespread ignorance, and potential panic over evd, leading to fear, isolation, and stigmatization. using results of this review the cdc’s emht developed stigma mitigation related messaging, which addressed psychosocial support and stress management for ebola survivors and responders. results of the study can help readers to compare identified mental health and psychosocial aspects of ebola outbreaks to other severe epidemics in order to plan on important public health issues. conclusions the results of this literature review can be used by healthcare personnel and public health professionals to complement cdc’s other guidance documents on evd. this review could assist with further studies and publications, and facilitate intergovernmental collaboration in the areas of ebola mental health and psychosocial support and public health practice. disclaimer: the views expressed are those of the authors and should not be construed to represent the positions of the centers for disease control and prevention. keywords ebola virus disease (evd); mental health; psychosocial support; literature review; population health references 1. “a time of fear”: local, national, and international responses to a large ebola outbreak in uganda. kinsman globalization and health 2012, 8:15 2. ebola: epidemic echoes and the chronicle of a tragedy foretold. mark honigsbaum, the lancet, volume 384, no 9956, p1740-41, 15 nov 2014 *anna grigoryan e-mail: ffg7@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e117, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja 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hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts measuring and improving the quality of data used for syndromic surveillance brian e. dixon*1, 2, 3, jon duke4, 1 and shaun grannis1, 5 1center for biomedical informatics, regenstrief institute, inc., indianapolis, in, usa; 2indiana university richard m. fairbanks school of public health, indianapolis, in, usa; 3department of veterans affairs, health services research & development service, indianapolis, in, usa; 4georgia tech research institute, atlanta, ga, usa; 5indiana university school of medicine, indianapolis, in, usa objective to extend an open source platform for measuring the quality of electronic health data by adding functions useful for syndromic surveillance. introduction nearly all of the myriad activities (or use cases) in clinical and public health (e.g., patient care, surveillance, community health assessment, policy) involve generating, collecting, storing, analyzing, or sharing data about individual patients or populations. effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems, including but not limited to electronic health records. however, the quality of data in electronic health record systems can be poor or “unfit for use.” therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers. methods using the health data stewardship framework1, we will extend automated characterization of health information at large-scale longitudinal evidence systems (achilles), a software package published open-source by the observational health data sciences and informatics collaborative (ohdsi; www.ohdsi.org) to measure the quality of data electronically reported from disparate information systems. our extensions will focus on analysis of data reported electronically to public health agencies for disease surveillance. next we will apply the achilles extensions to explore the quality of data captured from multiple real-world health systems, hospitals, laboratories, and clinics. we will further demonstrate the extended software to public health professionals, gathering feedback on the ability of the methods and software tool to support public health agencies’ efforts to routinely monitor the quality of data received for surveillance of disease prevalence and burden. results to date we have mapped key surveillance data fields into the ohdsi common data model, and we have transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients for importation into achilles. using these data, we are exploring the existing 167 metrics across 16 categories available within achilles, including a person (e.g., number of unique persons); and observation period (e.g., distribution of age at first observation period). syndromic surveillance (ss), however, is driven largely by monitoring patient stated chief complaints (non-standard free text clinical data) in addition to coded diagnoses. consequently, achilles must be extended to maximally support use in analyzing ss datasets. conclusions this work remains a work-in-progress. over the coming year, we will not only explore existing achilles constructs using real-world public health data but also introduce new functionality to explore 1) patient demographics; 2) facility and location (e.g., emergency department where care was delivered); and 3) clinical observations (e.g., chief complaint). the design and methods for examining these aspects of surveillance data will be included on the poster, and they will be made freely available for distribution with a future instance of the achilles software. we ultimately envision these tools being available for use on platforms such as the cdc’s biosense – open to all local and state health agencies as a one-stop portal for surveillance data analysis – or research environments where they can be used to examine and improve the quality of data output from informatics systems. keywords data quality; syndromic surveillance; electronic health records acknowledgments research reported in this abstract was supported by the national library of medicine of the national institutes of health under award number r21lm012219. the content is solely the responsibility of the authors and does not necessarily represent the official views of the national institutes of health. references 1. dixon be, rosenman m, xia y, grannis sj. a vision for the systematic monitoring and improvement of the quality of electronic health data. studies in health technology and informatics. 2013;192:884-8. *brian e. dixon e-mail: bedixon@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e45, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 1usaid, washington, dc, usa; 2oregon state university, corvallis, or, usa; 3pacific northwest national laboratory, richland, wa, usa objective to determine the potential of twitter data as an early warning of a likely communicable disease outbreak following a natural disaster, and if successful, develop an open-source algorithm for use by interested parties. introduction previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. this indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. the philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes). in these situations, evacuation centers are used for safety and medical attention and often house up to 50k people each for 2 or more months, sometimes with unclean water sources and improper sanitation. consequently, these conditions are a perfect venue for communicable disease transmission and have been proposed to cause disease outbreaks weeks after the original disaster occurred. coined the social media capital of the world1, the philippines provides a perfect opportunity to evaluate the potential of social media use in disease surveillance. methods the social media analyzed consists of 50 million geotagged tweets from the philippines between 2012 and 2013. monthly disease outbreak case counts by location were collected from the filipino department of health. disaster data was identified through the national operational assessment of hazards and the international disaster database website, em-dat. all data were split into 17 national regions to improve spatial resolution and decrease social variation within populations. outbreaks of interest were identified if they occurred 1-2 months following a natural disaster. topic modeling and theme identification methods were used to explore and understand filipino twitter use and language. to identify tweets of interest, lexicons were developed in english, tagalog, taglish, and other native dialects. the final disease lexicon, verified by visual confirmation, was used to filter the tweets and create histograms of tweet counts per day per region. this data was modeled by time series analyses to identify change points indicative of disease outbreaks (r breakoutdetection). the data was analyzed on multiple spatial scales and compared to known disease outbreak counts and natural disasters with 4 potential spatiotemporal correlations between disease and disaster identified. autoregressive integrated moving average model was used to forecast spikes and enhance outbreak detection (fig. 1). this tweet model identified 1 potential correlation between tweet, disaster, and outbreak in 2012 and 4 in 2013, potentially illustrating the increase of power in social media as twitter use increases over time. monthly outbreaks were regressed against various predictors including tweets (past and current), disasters (type, time since last occurrence), and region-specific characteristics (population density). conclusions current models using monthly disease outbreak data lack significant correlation, which is most likely due to the loss of information when aggregating data to a monthly scale. in effort to increase outbreak detection, models will be developed using newly acquired continuous weekly case counts by region. in addition, disease-specific outbreaks will be regressed against disease-specific filtered tweets and disaster information to determine the best combination of predictors by region. the final goal is to create a model that integrates historical disaster data with disease-related twitter counts to be used as a disease forecast system for streaming twitter data. this effort, funded by usaid, will be transitioned to regional universities and the government of the philippines. figure 1. histogram of disease tweets and acute gastroenteritis outbreak post typhoon yolanda in the philippines eastern visayas region. keywords health surveillance; disasters; social media; biosurveillance acknowledgments this effort was funded by the usaid. references 1 boaz jl, ybañez m, de leon mm, et al. understanding the behavior of filipino twitter users during disaster. gstf j comput 2013 doi: 10.7603/s40601-013-0007-z *lauren charles-smith e-mail: lauren.charles-smith@pnnl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e53, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen 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epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 1public health informatics group, denver public health, denver, co, usa; 2gilead, denver, co, usa objective to describe denver public health’s model for designing a business intelligence (bi) tool for hiv surveillance and outreach and the impact after implementation. introduction recently signed in denver, the paris declaration demonstrates a collective resolution to end aids by continually monitoring these goals. however siloed data and in/out migration results in poor capacity to track population level care indicators for persons living with hiv (plwh). surveillance should not only enumerate plwh but also support prevention and care programming (1). we designed and implemented the hiv data to care tool to describe the continuum, from case finding to hiv care. this study describes a system to combine data sources to inform local hiv surveillance, outreach, and care. development objectives included targeted community and clinical interventions and evaluation, user defined reports to identify subpopulation disparities, and a persistent data visualization readily available to stakeholders. methods existing local data sources were integrated into a common data infrastructure, including: cdc’s hiv surveillance system from denver and 4 denver-adjacent counties, us census denominators and demographics, state mandated clinical measure reporting (viral load and cd4 counts), and hiv clinic encounter data (e.g., sexually transmitted infections, pharmacy and prevention measures). denver health, an integrated safety-net provider, serves 25% of denver’s residents with electronic health record data for its patients. collaborative requirements gathering defined needs and specifications, including wireframes, a data dictionary, and business needs. the web-based, custom developed business intelligence dashboard was built with ms-sql server reporting services and deployed after testing for quality assurance and usability. results requirements gathering identified six population health use cases (and dashboards) for integrated data analysis, within a bi environment: diagnosis summary, linkage to care, retention in care, care summary, community viral load, and a surveillance summary page. three patient line lists were also developed to meet outreach worker needs. users control content through configurable parameters including demographics, location of plwh residence, care site, and transmission category, allowing custom reporting to identify disparities (i.e., black msm incidence rates), see figure 1. conclusions the tool integrated existing regional data sources and permitted new, near real-time visualization of complex information. utilized locally for program planning and patient management, it provided a more nuanced understanding of the local epidemic and benefited resource allocation. it has improved evaluation, assessment and quality improvement of linkage,, reengagement, and clinical outcomes by subpopulations, including identification of those not in care. additionally reporting has supplemented the denver community health assessment, grant applications, and strengthened regional realtionships through collaborative development. new efforts are focused on extending the tool to serve adjacent counties and integrating new data soruces for more comprehnshive reporting. a jurisdiction may benchmark an hiv-infected individual’s or population’s movement through the care continuum (2), gauge intervention effectiveness, and generate timely, user driven reports. the tool’s development and implementation model have proven reusable and translatable to address other priorities (e.g., chronic disease and immunization coverage). figure 1: screenshot of the hiv data to care tool surveillance summary page keywords informatics; visualization; hiv references 1. sweeney p, gardner l, buchaczk, garland p, mugavero m, bosshart j et al. shifting the paradigm: using hiv surveillance data as a foundation for improving hiv care and preventing hiv infection. milbank quarterly. 2013;91(3):558-603. 2. gardner e, mclees m, steiner j, del rio c, burman w. the spectrum of engagement in hiv care and its relevance to test-and-treat strategies for prevention of hiv infection. clinical infectious diseases. 2011;52(6):793-800. *lauren e. snyder e-mail: lauren.snyder@dhha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e39, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a public health and epidemiological databases for the enhancement of medical education 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi public health and epidemiological databases for the enhancement of medical education qazi mohammad sajid jamal1*, mughees uddin siddiqui1, mohammad abdulrahman alzohairy2 and mohammed abdullah al karaawi2 1. department of health information management, college of applied medical sciences, buraydah colleges, al-qassim, kingdom of saudi arabia. 2. department of clinical laboratory, college of applied medical sciences, buraydah colleges, al-qassim, kingdom of saudi arabia. abstract the collaboration of public health education and information technology has made patient care safer and more reliable than before. nurses and doctors use handheld computers to record a patient's medical history and check that they are administering the correct treatment. fortunately public health informatics (phi) is the intersecting point of technology and public health. therefore, the inclusion of online medical and epidemiology databases in the course curriculum of budding medical professionals and postgraduate students would be beneficial in enhancing the quality of health care, extensive epidemiological research, health education, health policies, health planning and consumer satisfaction as well. the purpose of this article is to discuss and provide introduction of various databases which have huge information and it could be used to enhance the public health education. abbreviations: information technology, public health informatics, public health education, databases correspondence: sajqazi@gmail.com doi: 10.5210/ojphi.v7i2.5853 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes introduction information technology can be an essential tool and important linkage for the current medical education. the article explores the need of information technology in the field of public health education to provide the best knowledge. as we know, through information technology, we can explore, transmit or retrieve the data as a useful resource of our interest. in the current era of modern technology, we can implement the health information technology in our current medical education systems. major fields of health information technology are phi, biomedical informatics, telemedicine, radio-informatics, pharmaco-informatics and bioinformatics. the fastest growing new era of biomedical informatics is building a splash in biomedical education, research and data management. phi is the systemic application of available online information and computer science to public health practice, research and planning. therefore the phi strategies would help the medical mailto:sajqazi@gmail.com public health and epidemiological databases for the enhancement of medical education 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi professionals have complete information related to disease management, treatment etc. the available epidemiology database would help epidemiologists, public health researchers, health managers, health policy makers, health educationist and health philanthropist as well in understanding the disease distribution, its determinants and indicators, morbidity, mortality statistics, disease trend,and its causing factors,aggravating factors and ways of prevention. phi innovations have prepared a roadmap in improving health in the following ways: • tie up between the geographically dispersed health providers and consumers. • delivery of public-health services by strengthening and streamlining data collection. • utilization of electronic health records and improved laboratory systems to support primary and secondary prevention. • data collection of research studies such as drug and vaccine trials. • enhancing medication system informatics through evidence based, rational and cost effective prescribing. • monitoring chronic disease and ensuring that patients stick to their treatments. keeping in view the growing demand and benefits for the collaboration of public health related issues and information technology, it seems mandatory that the public health education during medical education must be well equipped with the latest databases in order to provide the best in class and updated medical knowledge to the budding medical professional. this inclusion would definitely help them to understand the urgency and necessity to gain sound knowledge of latest available medical databases in order to implement their knowledge at the district level and national level as well as serving the mankind devoid of latest medical facilities. in this fast and competitive world where the entire globe is online the latest update of medical databases can also help the medical professional to take quick and correct decision with minimal chances of error, and on the other hand it will also help the community to avail medical benefits at the consumers end. in this era of technology, the information technology is playing a vital role in different spheres of human life. human lives are benefitted with the emergence of information technology by making the use of online available information related to disease, its sign, symptoms, precautionary measures and available contact information of the experts those can provide assistance when needed. furthermore, it has become very easy for the human beings to develop an understanding about their biological system, seasonal diseases their home based treatments and precautions, food and nutrition, etc. many databases and online resources are freely available for the retrieval of the information has also enabled doctors to use e-mail, texts, videos, and conference facilities to consult colleagues from all over the world. this practice, known as telemedicine, is especially useful for doctors and patients in rural and underdeveloped areas. the emergence of robotic surgery, telemedicine, hospital information system, a centralized information system of the hospitals at the district level and state level as well demands the inclusion of biological and epidemiological databases in the medical course curriculum for the enhancement of public health education. but the major drawback is that it’s not included in our education syllabus at the larger level. all the informatics resources have huge utilization at the academic level as well as research level. we can put the information about online biological and epidemiological databases in the syllabus which can definitely prove as strong asset in imparting public health education . public health and epidemiological databases for the enhancement of medical education 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi the tools of public health informatics enable practitioners, researchers obtain a complete picture of the population’s health and risk status and gather information from disparate resources. online epidemiological databases can accurately predict medical trends. now we can decide our self why the biological and epidemiological databases should not be included in the medical course curriculum. therefore, it is a need of an hour to fill the gap between the information technology and current public health education in order to utilize the online available resources at its best and serve the entire mankind. databases for the biomedical information currently huge biomedical information is available through different online resources worldwide. in this section we are going to discuss the major databases. pubmedhealth national center for biotechnology information, us national library of medicine: 8600 rockville pike, bethesda md, 20894. figure 1: home page of pubmed health the emergence of pubmed health in google search engine has been a notable topic among medical librarians and the blogging community. in august 2010, the midcontinental region news [1] announced the new pubmed health resource from the national center for biotechnology information (ncbi), which was quickly followed by a post from the krafty librarian [2]; however, no official announcement was made about this new resource. then, pubmed health appeared as the number one google search result for medications starting in august 2010 and made a bigger appearance in february 2011 [3], creating several questions among the blogging community. with still no official announcement, the blogging community was left to fill in the gaps [4]. the sudden arrival of this new resource created a flurry of public health and epidemiological databases for the enhancement of medical education 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi questions followed by uncertainty until finally ncbi tweeted [5] about the new pubmed health on march 2, 2011, with a link to the newly released home page. pubmed health includes consumer guides summarizing comparative effectiveness research, fact sheets on diseases and conditions, information on drugs and supplements, encyclopedic overviews of health topics, and links to external websites [6]. the content on pubmed health is supplied and updated by the following resources: “comparative effectiveness review summary guides for consumers” from the agency for healthcare research and quality (ahrq); “informedhealthonline: fact sheets and research summaries” from the german institute for quality and efficiency in health care (iqwig); pubmed clinical q&a, ncbi summaries of comparative effectiveness drug reports; a.d.a.m. medical encyclopedia; and american society of health-systems pharmacists consumer medication information [6]. comparing the content and content sources to medlineplus reveals several similarities—even identical information— between the two resources. website: http://www.ncbi.nlm.nih.gov/pubmedhealth/ the visible human project® the visible human project® is an outgrowth of the nlm's 1986 long-range plan. it is the creation of complete, anatomically detailed, three-dimensional representations of the normal male and female human bodies. acquisition of transverse ct, mr and cryosection images of representative male and female cadavers has been completed. the male was sectioned at one millimeter intervals, the female at one-third of millimeter intervals. figure 2: home page of the visible human projects the long-term goal of the visible human project® is to produce a system of knowledge structures that will transparently link visual knowledge forms to symbolic knowledge formats such as the names of body parts [7]. http://www.ncbi.nlm.nih.gov/pubmedhealth/ public health and epidemiological databases for the enhancement of medical education 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi website: http://www.nlm.nih.gov/research/visible/visible_human.html unified medical language system® (umls®) in order to enable interoperability between computer systems the umls, or unified medical language system is a software that brings together numerous health and biomedical vocabularies . figure 3: home page of umls you can use the umls to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators [8]. website: http://www.nlm.nih.gov/research/umls/ web-based medical information retrieval system (webmirs) the web-based medical information retrieval system. webmirs is a graphical java program providing access to the nhanes ii & iii databases of medical survey data and x-ray images [9]. public health and epidemiological databases for the enhancement of medical education 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 4: home page of webmirs website: http://archive.nlm.nih.gov/proj/webmirs/ the a.d.a.m. medical encyclopedia the a.d.a.m. medical encyclopedia includes over 4,000 articles about diseases, tests, symptoms, injuries, and surgeries. it also contains an extensive library of medical photographs and illustrations [10]. figure 5: home page of adam medical encyclopedia website: http://www.nlm.nih.gov/medlineplus/encyclopedia.html http://archive.nlm.nih.gov/proj/webmirs/ http://www.nlm.nih.gov/medlineplus/encyclopedia.html public health and epidemiological databases for the enhancement of medical education 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi the allele frequency database the purpose of designing alfred is to make allele frequency data on human population samples readily available for use by the scientific and educational communities. figure 6: home page of alfred the alelle frequency database (alfred) is designed to store and disseminate frequencies of alleles at human autosomal polymorphic sites for multiple defined population samples, primarily for the population genetics and molecular anthropology communities. the focus is on allele frequencies of normal, common dna variants, i.e., polymorphisms, in samples of anthropologically defined populations. links are provided to molecular databases for precise definitions and locations of the polymorphisms and to anthropological databases for linguistic, ethnographic and demographic information on the populations sampled. references to publications are associated with the frequencies and linked to pubmed, whenever possible. many polymorphisms have links to low-tech protocols suitable for small laboratories engaged in anthropological research. alfred has information on 672 polymorphic sites typed on at least one population sample and 288 populations typed for at least one polymorphism [11]. website: http://alfred.med.yale.edu/alfred/index.asp the tuberculosis database tuberculosis (tb) is a public health challenge of paramount importance. control of tb will require a multifaceted approach integrating efficient public health interventions with the discovery and use of new vaccines and drugs. tbdatabase (tbdb) makes available the tools and resources available at the stanford microarray database and the broad institute. public health and epidemiological databases for the enhancement of medical education 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 7: home page of tuberculosis database anyone is welcome to access the published data available on the tbdb site without signing in. some data in tbdb are unpublished and can therefore only be accessed by the authors and their collaborators after they sign in. the "access polices" page provides more information about tbdb accounts [12]. website: http://www.tbdb.org/ the genetic association databases the genetic association database is an archive of human genetic association studies of complex diseases and disorders. this includes summary data extracted from published papers in peer reviewed journals on candidate gene and gwas studies. http://www.tbdb.org/ public health and epidemiological databases for the enhancement of medical education 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 8: home page of genetic association database the goal of this database is to allow the user to rapidly identify medically relevant polymorphism from the large volume of polymorphism and mutational data, in the context of standardized nomenclature. study data are recorded in the context of official human gene nomenclature with additional molecular reference numbers and links. it is gene centered. that is, each record is a record of a gene or marker. if a study investigated 6 genes for a particular disorder, there will be 6 records. the best feature of it that anyone may view this database, and anyone may submit records [13]. website: http://geneticassociationdb.nih.gov/ omim® online mendelian inheritance in man® welcome to omim®, online mendelian inheritance in man®. omim is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. the full-text, referenced overviews in omim contain information on all known mendelian disorders and over 12,000 genes. omim focuses on the relationship between phenotype and genotype. it is updated daily, and the entries contain copious links to other genetics resources. http://geneticassociationdb.nih.gov/ public health and epidemiological databases for the enhancement of medical education 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 9: home page of omim this database was initiated in the early 1960s by dr. victor a. mckusick as a catalog of mendelian traits and disorders, entitled mendelian inheritance in man (mim). twelve book editions of mim were published between 1966 and 1998. the online version, omim, was created in 1985 by a collaboration between the national library of medicine and the william h. welch medical library at johns hopkins. it was made generally available on the internet starting in 1987. in 1995, omim was developed for the world wide web by ncbi, the national center for biotechnology information [14]. website: http://www.omim.org/ genecards® genecards is a searchable, integrated database of human genes that provides comprehensive, updated, and user-friendly information on all known and predicted human genes. public health and epidemiological databases for the enhancement of medical education 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 10: home page of genecards genecards extracts and integrates gene-related data, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information. this is automatically mined from >100 carefully selected web sources, thereby allowing one-stop access to a very broad information base. genecards overcomes barriers of data format and heterogeneity, and uses standard nomenclature and approved gene symbols. it presents a rich subset of data for each gene, and provides deep links to the original sources for further scrutiny. genecards is widely used, and assists in the understanding of gene-related aspects of biology and medicine [15]. website: http://www.genecards.org/ clinvar clinvar provides a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. public health and epidemiological databases for the enhancement of medical education 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 11: home page of clinvar clinvar facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation. clinvar collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other supporting data. the alleles described in submissions are mapped to reference sequences, and reported according to the hgvs standard. clinvar then presents the data for interactive users as well as those wishing to use clinvar in daily workflows and other local applications. clinvar works in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible [16]. website: http://www.ncbi.nlm.nih.gov/clinvar/ medical subject headings (mesh®) mesh is the national library of medicine's controlled vocabulary thesaurus. it consists of sets of terms naming descriptors in a hierarchical structure that permits searching at various levels of specificity. http://www.ncbi.nlm.nih.gov/clinvar/ public health and epidemiological databases for the enhancement of medical education 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 12: home page of mesh mesh descriptors are arranged in both an alphabetic and a hierarchical structure. at the most general levels of the hierarchical structure are very broad headings such as "anatomy" or "mental disorders." more specific headings are found at more narrow levels of the twelve-level hierarchy, such as "ankle" and "conduct disorder." there are 27,149 descriptors in 2014 mesh. there are also over 218,000 entry terms that assist in finding the most appropriate mesh heading, for example, "vitamin c" is an entry term to "ascorbic acid." in addition to these headings, there are more than 219,000 headings called supplementary concept records (formerly supplementary chemical records) within a separate thesaurus. mesh applications the mesh thesaurus is used by nlm for indexing articles from 5,400 of the world's leading biomedical journals for the medline®/pubmed® database. it is also used for the nlmproduced database that includes cataloging of books, documents, and audiovisuals acquired by the library. each bibliographic reference is associated with a set of mesh terms that describe the content of the item. similarly, search queries use mesh vocabulary to find items on a desired topic [17]. website: http://www.nlm.nih.gov/pubs/factsheets/mesh.html global atlas of infectious diseases in a single electronic platform, the who’s communicable disease global atlas is bringing together for analysis and comparison standardized data and statistics for infectious diseases at country, regional, and global levels. the analysis and interpretation of data are further supported through information on demography, socioeconomic conditions, and environmental factors. in so http://www.nlm.nih.gov/pubs/factsheets/mesh.html public health and epidemiological databases for the enhancement of medical education 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi doing, the atlas specifically acknowledges the broad range of determinants that influence patterns of infectious disease transmission. figure 13: home page of global atlas of infectious diseases over the next year, the system aims to provide a single point of access to data, reports and documents on the major diseases of poverty, including malaria, hiv/aids, tuberculosis, the diseases on their way towards eradication and elimination (such as guinea worm, leprosy, lymphatic filariasis) and epidemic prone and emerging infections for example meningitis, cholera, yellow fever and anti-infective drug resistance. the database will be updated on an ongoing basis and in addition to epidemiological information, the system aims to provide information on essential support services such as the network of communicable diseases collaborating centers, the activities of the global outbreak alert and response network among others. website http://gamapserver.who.int/globalatlas/home.asp cell centered databasetm the ccdb project was started in 1998 under the auspices of the human brain project to provide a venue for sharing and mining, cellular and sub cellular data derived from light and electron microscopy, including correlated imaging. it was one of the first web databases devoted to the then emerging technique of electron tomography [18, 19]. public health and epidemiological databases for the enhancement of medical education 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 14: home page of ccdb the ccdb has been on-line since 2002. the goals of the ccdb project include: • providing access for the biomedical community to primary and derived imaging, 2d, 3d and 4d data from light and electron microscopy • developing advanced database capabilities for storing and mining complex cellular and subcellular imaging data • creating the necessary infrastructure for managing and sharing light and electron microscopic data securely within and between laboratories • developing tools and strategies for integrating data across scales and modalities and federating databases through the use of ontologies and shared spatial frameworks. website: http://ccdb.ucsd.edu/index.shtm the human protein atlas the human protein atlas portal is a publicly available database with millions of high-resolution images showing the spatial distribution of proteins in 44 different normal human tissues and 20 different cancer types, as well as 46 different human cell lines. http://ccdb.ucsd.edu/index.shtm public health and epidemiological databases for the enhancement of medical education 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 15: home page of the human protein atlas the data are released together with application-specific validation performed for each antibody, including immunohistochemisty, western blot analysis and, for a large fraction, a protein array assay and immunofluorescent based confocal microscopy. the database has been developed in a gene-centric manner with the inclusion of all human genes predicted from genome efforts. search functionalities allow for complex queries regarding protein expression profiles, protein classes and chromosome location [20]. website: http://www.proteinatlas.org/ public health and epidemiological databases for the enhancement of medical education 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi national information center on health services research and health care technology (nichsr) figure 16: home page of nichsr the overall goals of the nichsr are: to make the results of health services research, including practice guidelines and technology assessments, readily available to health practitioners, health care administrators, health policy makers, payers, and the information professionals who serve these groups; to improve access to data and information needed by the creators of health services research; and to contribute to the information infrastructure needed to foster patient record systems that can produce useful health services research data as a by-product of providing health care. website: http://www.nlm.nih.gov/nichsr/ cdc wonder welcome to cdc wonder -wide-ranging online data for epidemiologic research -an easy-to-use, menu-driven system that makes the information resources of the centers for disease control and prevention (cdc) available to public health professionals and the public at large. it provides access to a wide array of public health information. public health and epidemiological databases for the enhancement of medical education 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 17: home page of cdc wonder with cdc wonder you can: • access statistical research data published by cdc, as well as reference materials, reports and guidelines on health-related topics; • query numeric data sets on cdc's computers, via "fill-in-the blank" web pages. public-use data sets about mortality (deaths), cancer incidence, hiv and aids, tuberculosis, vaccinations, natality (births), census data and many other topics are available for query, and the requested data are readily summarized and analyzed, with dynamically calculated statistics, charts and maps. website: http://wonder.cdc.gov/ the incidence and prevalence database (ipd) the incidence and prevalence database (ipd) is the most efficient way to look at the world’s epidemiology data. the ipd covers over 4,500 diseases, procedures, symptoms and other health issues for incidence, prevalence, morbidity, mortality, comorbidity, treated or diagnosed rates, cost and much more. public health and epidemiological databases for the enhancement of medical education 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 18: home page of ipd our analysts track and report the epidemiological content for more than 280 medical journals and over 35 government and industry agencies. the global reach of the ipd includes all countries and regions where data are available. ipd combines extensive epidemiological and market research experience with the breadth of life sciences service offerings from thomson reuters, creating a powerful combination for clients. website http://thomsonreuters.com/incidence-and-prevalence-database/ public health and epidemiological databases for the enhancement of medical education 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi alzrisk ad epidemiology database figure 19: home page of alzrisk ad epidemiology database the alzrisk database aims to provide a comprehensive, unbiased, centralized, publicly available and regularly updated collection of epidemiologic reports that evaluate environmental (i.e., nongenetic) risk factors for alzheimer disease (ad) in well-defined study cohorts. eligible publications are identified through contact with each cohort study supplemented by a systematic review of the literature [21]. website http://www.alzrisk.org/ atlas of ms the atlas of ms is the most extensive worldwide study of the disease. public health and epidemiological databases for the enhancement of medical education 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi figure 20: home page of atlas ms the atlas of ms is the most extensive worldwide study of the epidemiology of ms and the global availability and accessibility of resources for people with ms. in september 2008, the ms international federation and the world health organization (who) published atlas: multiple sclerosis resources in the world 2008. as well as the published report, the ms international federation developed the first atlas of ms website, which enabled users to query the data online, and to compare results across different regions and countries. in 2012/2013 the ms international federation carried out a second survey in order to update the information in the atlas and the 2013 data was added to the 2008 information online. website http://www.msif.org/about-us/advocacy/atlas/ conclusion in this era of technology, the information technology is playing a vital role in different spheres of human life. human lives are benefitted with the emergence of information technology by making the use of online available information related to disease, its signs, symptoms, precautionary measures and available contact information of the experts those can provide assistance when needed. furthermore, it has become very easy for the human beings to develop an understanding about their biological system, seasonal diseases their home based treatments and precautions, food and nutrition, etc. some of the databases and online resources are freely available for the retrieval of the information as discussed in the review. the emergence of robotic surgery, telemedicine, hospital information system, a centralized information system of the hospitals at the district level and state level as well demands the inclusion of biological and epidemiological databases in the medical course curriculum for the enhancement of public health education. but http://www.msif.org/about-us/advocacy/atlas/ public health and epidemiological databases for the enhancement of medical education 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi the major drawback is that it’s not included in our education syllabus at the larger level. all the informatics resources have huge utilization at the academic level as well as research level. we can put the information about online public health and epidemiological databases in the syllabus which can definitely prove as strong asset for the public health education. now we can decide our self why these databases should not be included in the medical course curriculum. therefore, it is a need of an hour to fill the gap between the information technology and current public health education in order to utilize the online available resources at its best and serve the entire mankind. acknowledgments we would like to thank the all database curators for the development of informative portals to provide benefits to the scientific community. the reviews cannot be completed without them. conflicts of interest the authors declare no conflicts of interest. references 1. midcontinental region news. pubmed and medlineplus updates [internet] national network of libraries of medicine [rev 17 aug 2009; cited 28 mar 2011. 2. kraft m. pubmed health. the krafty librarian [internet] [rev 19 aug 2010; cited 28 mar 2011]. 3. dettmar n. who's on first? nlm and google health searches. eagledawg [internet] [rev 9 feb 2011; cited 28 mar 2011]. 4. hamilton j. pubmed health who? psychology today [internet] [rev 14 feb 2011; cited 28 mar 2011]. 5. national center for biotechnology information (ncbi) new pubmed health! @ncbi, twitter.com [internet] 2 mar 2011 [cited 28 mar 2011]. 6. national library of medicine. pubmed health about page [internet] the library [cited 4 apr 2011]. 7. venuti j, imielinska c, laino-pepper l, thumann r, molholt p. (6 october 2000). the third visible human project conference proceedings. 8. "unified medical language system® (umls®) news: revised license agreement, new umls terminology services and browser, discontinued umlsks, and api changes". nlm technical bulletin. u.s. national library of medicine. 29 july 2010. 9. rodney long l, stanley r. 1997. pillemer; reva c. lawrence; gin-hua goh; leif neve; george r. thoma; webmirs: web-based medical information retrieval system. proc. spie 3312. storage and retrieval for image and video databases. vi, 392. doi:10.1117/12.298448. 10. national library of medicine taps a.d.a.m. multimedia encyclopedia". wireless news (close-up media). april 1, 2009. public health and epidemiological databases for the enhancement of medical education 23 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e217, 2015 ojphi 11. rajeevan h, osier mv, cheung kh, deng h, druskin l, et al. 2003. alfred – the allele frequency database – update. nucleic acids res. 31(1), 270-71. http://dx.doi.org/10.1093/nar/gkg043. 12. reddy tb, riley r, wymore f, montgomery p, decaprio d, et al. tb database: an integrated platform for tuberculosis research. nucleic acids res. doi:10.1093/nar/gkn652. 13. kevin g. 2004. becker, kathleen c barnes, tiffani j bright & s alex wang,the genetic association database. nat genet. 36, 431-32. doi:10.1038/ng0504-431. 14. hamosh a, scott af, amberger js, bocchini ca, mckusick va. 2004. online mendelian inheritance in man (omim), a knowledgebase of human genes and genetic disorders. nucleic acids res. 33(database issue), d514-17. http://dx.doi.org/10.1093/nar/gki033. 15. rebhan m, chalifa-caspi v, prilusky j. 1997. lancet, d. genecards: integrating information about genes, proteins and diseases. trends genet. 13, 163. http://dx.doi.org/10.1016/s0168-9525(97)01103-7. 16. landrum mj, lee jm, riley gr, jang w, rubinstein ws, et al. 2014. clinvar: public archive of relationships among sequence variation and human phenotype. nucleic acids res. 42(1), d980-85. doi:http://dx.doi.org/10.1093/nar/gkt1113. 17. rogers fb. (jan 1963). "medical subject headings". bull med libr assoc (in eng) 51: 114– 6. issn 0025-7338. pmc 197951. pmid 13982385. 18. martone me, gupta a, qian x, wong m, zhang s, et al. (2002). the cell centered database: an online resource for high resolution cell level data, soc for neurosci abstr. 19. martone me, gupta a, wong m, qian x, sosinsky g, et al. 2002. a cell centered database for electron tomographic data. j struct biol. 138, 145-55.http://dx.doi.org/10.1016/s10478477(02)00006-0. 20. uhlen, et al. 2010. towards a knowledge-based human protein atlas. nat biotechnol. 28(12), 1248-50. doi:http://dx.doi.org/10.1038/nbt1210-1248. 21. weuve j, mcqueen mb, blacker d. the alzrisk database. alzheimer research forum. available at: http://www.alzforum.org. accessed [date of access]*. public health and epidemiological databases for the enhancement of medical education introduction databases for the biomedical information pubmedhealth the visible human project® unified medical language system® (umls®) web-based medical information retrieval system (webmirs) the a.d.a.m. medical encyclopedia the allele frequency database the tuberculosis database the genetic association databases omim® online mendelian inheritance in man® genecards® clinvar medical subject headings (mesh®) mesh applications global atlas of infectious diseases cell centered databasetm the human protein atlas national information center on health services research and health care technology (nichsr) cdc wonder the incidence and prevalence database (ipd) alzrisk ad epidemiology database atlas of ms conclusion acknowledgments conflicts of interest references 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts how should we be conducting routine analysis of traditional emergency department syndromic surveillance data? david atrubin*2 and michael wiese1 1fl doh hillsborough cnty, tampa, fl, usa; 2florida department of health, tampa, fl, usa objective to discuss how various emergency department based syndromic surveillance systems from across the country and world are being used and to develop best practices for moving forward introduction along with commensurate funding, an increased emphasis on syndromic surveillance systems occurred post september 11, 2001 and the subsequent anthrax attacks. since then, many syndromic surveillance systems have evolved and have ever-increasing functionality and visualization tools. as outbreak detection using these systems has demonstrated an equivocal track record, epidemiologists have sought out other interesting and unique uses for these systems. over the numerous years of the international society for disease surveillance (isds) conference, many of these studies have been presented, however, there has been a dearth of discussion related to how these systems should be used on a routine basis. as the initial goal of these systems was to provide a near real-time disease surveillance tool, the question of how to most effectively conduct this type of routine surveillance is paramount. description this roundtable will provide a forum for national, state, and local users of syndromic surveillance systems to discuss how they use these systems routinely. with the increasing number of participating hospitals (due in large part to increased incentives from meaningful use), this question has taken on added significance. an important part of this discussion will focus on the value that syndromic surveillance can provide that other surveillance systems cannot address. audience engagement this roundtable is well suited to audience participation as the discussion will revolve around how different jurisdictions are utilizing their syndromic surveillance data. roundtable participants will be asked about their routine surveillance methods, including the frequency of data analysis and the type of reports that are produced. participants will also be encouraged to share their opinions as to uses and misuses of syndromic surveillance data sets. sample questions: -how does your jurisdiction conduct syndromic surveillance each day/periodically? -does syndromic surveillance need to be conducted locally or is it possible to do it effectively at the state of national level? -when conducting an epidemiologic analysis, how should decisions be made regarding using a syndromic surveillance data set versus a hospital discharge data set? keywords emergency department data; routine analysis; local public health; disease surveillance; outbreak detection *david atrubin e-mail: david.atrubin@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e183, 201 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts utility of nontraditional data sources for early detection of influenza shari barlow2, jonathan l. temte2, yenlik zheteyeva*1, ashley fowlkes3, carrie reed3 and derek cummings4 1dgmq, cdc, atlanta, ga, usa; 2department of family medicine and community health, university of wisconsin, madison, wi, usa; 3influenza division, cdc, atlanta, ga, usa; 4university of florida, department of biology, emerging pathogens institute, gainesville, fl, usa objective this session will provide an overview of the current systems for influenza surveillance; review the role of schools in influenza transmission; discuss relationships between school closures, school absenteeism, and influenza transmission; and explore the usefulness of school absenteeism and unplanned school closure monitoring for early detection of influenza in schools and broader communities. introduction influenza surveillance is conducted through a complex network of laboratory and epidemiologic systems essential for estimating population burden of disease, selecting influenza vaccine viruses, and detecting novel influenza viruses with pandemic potential (1). influenza surveillance faces numerous challenges, such as constantly changing influenza viruses, substantial variability in the number of affected people and the severity of disease, nonspecific symptoms, and need for laboratory testing to confirm diagnosis. exploring additional components that provide morbidity information may enhance current influenza surveillance. school-aged children have the highest influenza incidence rates among all age groups. due to the close interaction of children in schools and subsequent introduction of influenza into households, it is recognized that schools can serve as amplification points of influenza transmission in communities. for this reason, pandemic preparedness recommendations include possible pre-emptive school closures, before transmission is widespread within a school system or broader community, to slow influenza transmission until appropriate vaccines become available. during seasonal influenza epidemics, school closures are usually reactive, implemented in response to high absenteeism of students and staff after the disease is already widespread in the community. reactive closures are often too late to reduce influenza transmission and are ineffective. to enhance timely influenza detection, a variety of nontraditional data sources have been explored. school absenteeism was suggested by several research groups to improve school-based influenza surveillance. a study conducted in japan demonstrated that influenzaassociated absenteeism can predict influenza outbreaks with high sensitivity and specificity (2). another study found the use of allcauses absenteeism to be too nonspecific for utility in influenza surveillance (3). creation of school-based early warning systems for pandemic influenza remains an interest, and further studies are needed. the panel will discuss how school-based surveillance can complement existing influenza surveillance systems. keywords surveillance; absenteeism; influenza; early detection; schools references brammer l, budd a, cox n. seasonal and pandemic influenza surveillance considerations for constructing multicomponent systems. influenza other respi viruses. 2009;3:51-58. sasaki a, hoen ag, ozonoff a, et al. evidence-based tool for triggering school closures during influenza outbreaks, japan. eid. 2009;15: 1841-1843. besculides m, heffernan r, mostashari f, et al. evaluation of school absenteeism data for early outbreak detection, new york city. bmc ph. 2005;5:105. *yenlik zheteyeva e-mail: igg0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e58, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 1impact evaluation, ifakara health institute, morogoro, united republic of tanzania; 2epi results, pietermaritzburg, south africa objective this study aimed to estimate socio-demographic inequalities in hiv testing and prevalence among adults aged 50+ years, living in ifakara town, tanzania, in 2013 introduction like in other african countries, most hiv research in tanzania focuses on adults 15-49 years, ignoring persons aged 50 years and above. in tanzania, the hiv testing rate (ever tested) for 15 49 year olds has increased from 37% to 62% for women and 27% to 47% for men between 2008 and 2012. limited data is available on hiv testing and prevalence among older adults specifically. some studies in sub-saharan africa have, however, reported a high hiv prevalence among older people. methods this is a cross-sectional study using data from the baseline measurement of the ifakara mzima cohort study conducted in 2012/13. consenting participants were interviewed and tested for hiv. information on prior testing behaviour, age, marital status, occupation, gender, ethnicity, religion, education (progress framework indicators) was retrieved from the interviewer administered questionnaires. multivariable logistic regression analysis was used to establish associations between hiv testing and prevalence with the socio-demographic indicators. results among the 1,643 adults 50+ years included in the study, hiv prevalence and the hiv testing rate (ever tested) were 6% and 11.4% respectively. multivariable analysis showed that the hiv testing rate was lower for older people (or=0.19 (95%ci 0.09-0.41 for 75+ versus 50-54); those separated/divorced/widowed had higher odds of testing than those married (or=1.46; 1.02-2.10); and “other christians” had a higher odds than muslims (or=1.95; 1.06-3.58). with respect to hiv prevalence, it is higher for older people (or=0.27; 0.11-0.66 for 75+ versus 50-54); and catholics have a lower odds compared to muslims (or=0.54; 0.34-0.85). conclusions these results are valuable as they provide insight into the sociodemographic inequalities among older adults. the high hiv prevalence among this group and the low hiv testing behaviour call for more efforts on hiv prevention, treatment and care. also the older adults should be included in the national surveillance systems like dhs and thmis as they are also the hiv risk population. furthermore, the government should establish elder friendly services and strengthening the capacity of the health system to deliver quality services for hiv and other diseases in the country.additionally, the findings of this study also warrant further research on the hiv prevalence and testing behaviour of older adults, including studies on the sexual behaviour of older adults keywords hiv; older adults; testing; prevalence; social economic inequalities acknowledgments this study was supported by health information systems grant from global fund round 9; the author would like to acknowledge the support from the ministry of health and social welfare (mohsw), kilombero district, ihi staff, and the local community of ifakara. references 1. davis t, zanjani f. prevention of hiv among older adults: a literature review and recommendations for future research. journal of aging and health. 2012;24(8):1399-420. 2. negin j, mills ej, albone r. continued neglect of ageing of hiv epidemic at un meeting. the lancet. 2011;378(9793):768. 3. mills j, rammohan a, awofeso n. ageing faster with aids in africa. the lancet. 2011;377(9772):1131-3. 4. unaids. the gap report, beginning of the end of the aids epidemic. geneva: unaids, 2014. 5. unaids. hiv and aging, a special supplement to the unaids report on the global aids epidemic 2013. geneva, switzerland, 2013. 6. negin j, cumming r. hiv infection in older adults in sub-saharan africa: extrapolating prevalence from existing data. bulletin of the world health organization. 2010;88:847–53. 7. shisana o, et al. south african national hiv prevalence, incidence and behaviour survey, 2012. cape town: hsrc press;. 2014. 8. gómez-olivé f, et al. prevalence of hiv among those 15 and older in rural south africa. aids care. 2013;25(9):1122-8. 9. negin j, et al. hiv attitudes, awareness and testing among older adults in africa. aids and behavior. 2012;16(1):63-8. 10. who. universal access progress report. geneva: unaids, 2011. 11. tanzania commission for aids (tacaids) zacz, national bureau of statistics (nbs), office of the chief government statistician (ocgs), and icf international. tanzania hiv/aids and malaria indicator survey 2011-12: key findings. dar es salaam, tanzania: 2012. 12. hajizadeh m. socioeconomic inequalities in hiv/aids prevalence in sub-saharan african countries: evidence from the demographic health surveys. international journal for equity in health. 2014;13:1-2. *angelina c. mtowa e-mail: amtowa2001@yahoo.co.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e145, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state 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heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 1university of new mexico, albuquerque, nm, usa; 2los alamos national laboratory (lanl), los alamos, nm, usa objective compare and contrast military and civilian outbreaks for malaria and influenza like illness to identify indicators for early warning and detection. introduction using influenza like illness (ili) data from the repository held by afhsc, and publically available malaria data we characterized similarities and differences between military and civilian outbreaks. pete riley et al. utilized a similar ili dataset to investigate civilian and military outbreaks similarity during the 2009 flu epidemic [1]. they found, overall, high similarity between civilian and military outbreaks, with military peaking roughly one week after civilian. our analysis is meant to extend their analysis temporally, geographically, and to see if such trends hold true for other diseases. methods ili data from january 2000 to december 2014 was obtained from afhsc. this dataset included any records where the ili icd-9 diagnostic code was included in the top 8 diagnostic codes reported by the healthcare facility. malaria data was collected from peer reviewed literature and official public health reports. sources included the cdc’s mmwr, afhsc’s msmr, and public health websites. military and civilian outbreaks with data from the same location (country or state) and the same time were ‘paired’. outbreaks were compared with respect to time of peak. additional comparisons including outbreak duration, and comparisons of similarity to laboratory confirmed data, are planned to be completed shortly. all analyses are done using r. of note, locations were limited to places where the u.s. military travels that additionally report malaria or ili. further, temporal granularity was limited to the highest “timestep” (i.e. if one dataset was reported in months, the ‘pair’ dataset was aggregated to monthly data as well). results ‘paired’ civilian and military malaria and ili data were available for locations listed in table 1. figure one shows data in afghanistan and south korea for 2009 to 2012. there is visual similarity between military and civilian data (see figure 1), but it is not statistically significant. among the 10 malaria pairs, civilian outbreaks peak between 3 months before and 2 months after military, with an average of 0.1 months after. we currently have 23 pairs of ili outbreaks in the united states, japan and south korea. additional data is available and will be analyzed shortly. in the current dataset, there is an average of 5.7 weeks difference in peak, with military peaks typically occurring first. as with malaria, this average has a substantial range (95% ci: 1.8 9.5 weeks). similarity of peak difference depends on location (see figure 2). in south korea, civilian outbreaks peak on average 1.6 weeks after military outbreaks (95% ci: -1.34 – 4.55), while in japan, civilian outbreaks peak an average of 6.4 weeks after military outbreaks (95% ci: 1.53 – 11.22). conclusions this data indicates potential trends among diseases occurring in a particular location, with military outbreaks tending to peak earlier than civilian outbreaks. there is extensive variability between malaria and ili, as well as variability between locations. conclusions, especially for malaria, are limited by a lack of granular temporal data and a general lack of military data. additional data analyses are ongoing to further substantiate these observations and will be completed before the isds meeting. table 1: malaria and ili ‘pairs’ and peak comparison keywords influenza like illness; malaria; military; surveillance acknowledgments we would like to thank afhsc for providing us with outbreak data. we would also like to acknowlege reid priedhorsky for his computational assistance. references 1. riley p, ben-nun m, armenta r et al. multiple estimates of transmissibility for the 2009 influenza pandemic based on influenzalike-illness data from small us military populations. plos comput biol. 2013;9(5):e1003064. doi:10.1371/journal.pcbi.1003064. *ashlynn daughton e-mail: adaughton@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 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syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge epidemiology, biostatistics, and occupational health, mcgill univeristy, montreal, qc, canada objective to assess the influence of in-store price discounts on soda purchasing by neighborhood socio-economic status in montreal, canada using digital grocery store-level sales data. introduction obesity and related chronic diseases cost canadians several billion dollars annually1. dietary intake, and in particular consumption of carbonated sweetened drinks (soda), has a strong effect on the incidence of obesity and other illness2. marketing research suggests that in-store promotion, and more specifically price discounting, has a strong effect on the purchase of energy-dense products such as soda3. attempts by public health authorities to monitor price discounts are currently limited by a lack of data and methods. although rarely used in public health surveillance, electronic retail sales data collected around the world by marketing companies such as the nielsen corporation have an immense potential to measure dietary choices at high geographical resolution. these scanned sales data are recorded in real-time and they include a detailed product description, price, purchased quantity, store location, and product-specific advertising activities. methods we obtained from the nielsen corporation data on weekly storespecific sales and price discounting of non-diet soda items from 83 sampled grocery stores in montreal, canada between january 2008 and december 2013. to account for the correlation within stores and chains, we used linear mixed regression to model the log-transformed weekly sum of soda serving sales as a function of store-level weekly price discounting, which is defined as the average discount fraction over all soda items in each store. to examine the moderating effect of ses on price discounting, we added an interaction term between discount magnitude and area-level measures of ses for the threedigit postal code in which stores were located. factors examined were the proportion of population with post-secondary qualification and the median household income. we used month, year, and statutory holiday indicator variables as covariates. using the fitted model, we plotted predicted percent increase of soda sales in response to price discounting at various levels of the ses indicators. results the regression coefficient of price discounting and area-level education attainment was 10.17 (95% confidence interval [ci]: 9.41 to 10.94) and 1.47 (95% ci:-3.05 to 5.99) respectively, and the coefficient of their interaction term was significant and negative (-10.07, 95% ci: 11.68 to -8.46), indicating that the impact of discounting on soda sales was more pronounced among stores located in areas with lower educational attainment as demonstrated in the figure. area-level income and its interaction term with price discounting showed modest effect on sales (0.18, 95% ci: 0.09 to 0.27 and -0.11, 95% ci: -0.35 to 0.13, respectively). conclusions our analysis of digital scanner data from grocery stores found that consumer sensitivity to price discounting of soda was inversely associated with neighborhood education attainment. this finding demonstrates the utility of electronic point-of-sales data to monitor the influence of in-store price discounting on purchasing of unhealthy foods, an important environmental risk factor for obesity and related illness. figure: predicted percent growth of soda sales from the joint effect of price discounting and neighborhood education (grouped into tertile levels). keywords obesity; soda consumption; digital purchase data; diet; health disparity acknowledgments we 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purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts semantic analysis of open source data for syndromic surveillance erica briscoe2, scott appling2, edward clarkson2, nikolay lipskiy*1, james tyson1 and jacqueline burkholder1 1centers for diseases control and prevention (cdc), office of public health preparedness and response’s (ophpr), division of emergency operations (deo), atlanta, ga, usa; 2georgia tech research institute (gtri), georgia institute of technology, atlanta, ga, usa objective the objective of this analysis is to leverage recent advances in natural language processing (nlp) to develop new methods and system capabilities for processing social media (twitter messages) for situational awareness (sa), syndromic surveillance (ss), and event-based surveillance (ebs). specifically, we evaluated the use of human-in-the-loop semantic analysis to assist public health (ph) sa stakeholders in ss and ebs using massive amounts of publicly available social media data. introduction social media messages are often short, informal, and ungrammatical. they frequently involve text, images, audio, or video, which makes the identification of useful information difficult. this complexity reduces the efficacy of standard information extraction techniques1. however, recent advances in nlp, especially methods tailored to social media2, have shown promise in improving real-time ph surveillance and emergency response3. surveillance data derived from semantic analysis combined with traditional surveillance processes has potential to improve event detection and characterization. the cdc office of public health preparedness and response (ophpr), division of emergency operations (deo) and the georgia tech research institute have collaborated on the advancement of ph sa through development of new approaches in using semantic analysis for social media. methods to understand how computational methods may benefit ss and ebs, we studied an iterative refinement process, in which the data user actively cultivated text-based topics (“semantic culling”) in a semi-automated ss process. this ‘human-in-the-loop’ process was critical for creating accurate and efficient extraction functions in large, dynamic volumes of data. the general process involved identifying a set of expert-supplied keywords, which were used to collect an initial set of social media messages. for purposes of this analysis researchers applied topic modeling to categorize related messages into clusters. topic modeling uses statistical techniques to semantically cluster and automatically determine salient aggregations. a user then semantically culled messages according to their ph relevance. in june 2016, researchers collected 7,489 worldwide englishlanguage twitter messages (tweets) and compared three sampling methods: a baseline random sample (c1, n=2700), a keyword-based sample (c2, n=2689), and one gathered after semantically culling c2 topics of irrelevant messages (c3, n=2100). researchers utilized a software tool, luminoso compass4, to sample and perform topic modeling using its real-time modeling and twitter integration features. for c2 and c3, researchers sampled tweets that the luminoso service matched to both clinical and layman definitions of rash, gastro-intestinal syndromes5, and zika-like symptoms. layman terms were derived from clinical definitions from plain language medical thesauri. anova statistics were calculated using spss software, version. post-hoc pairwise comparisons were completed using anova turkey’s honest significant difference (hsd) test. results an anova was conducted, finding the following mean relevance values: 3% (+/0.01%), 24% (+/6.6%) and 27% (+/9.4%) respectively for c1, c2, and c3. post-hoc pairwise comparison tests showed the percentages of discovered messages related to the event tweets using c2 and c3 methods were significantly higher than for the c1 method (random sampling) (p<0.05). this indicates that the human-in-the-loop approach provides benefits in filtering social media data for ss and esb; notably, this increase is on the basis of a single iteration of semantic culling; subsequent iterations could be expected to increase the benefits. conclusions this work demonstrates the benefits of incorporating nontraditional data sources into ss and ebs. it was shown that an nlpbased extraction method in combination with human-in-the-loop semantic analysis may enhance the potential value of social media (twitter) for ss and ebs. it also supports the claim that advanced analytical tools for processing non-traditional sa, ss, and ebs sources, including social media, have the potential to enhance disease detection, risk assessment, and decision support, by reducing the time it takes to identify public health events. keywords syndromic surveillance; natural language processing; situational awareness; event-based surveillance; twitter acknowledgments this work was supported by cdc award 200-2015-f-87619. references liu, x, zhang, s, wei, f, zhou, m: recognizing named entities in tweets. in proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-vol. 1, 359-367 (2011). dredze, m, paul, mj: natural language processing for health and social media. ieee (2014) hossain, l, kam, d, kong, f, wigand, r, bossomaier, t: social media in ebola outbreak. epidemiology and infection, 1-8 (2016). speer, rh, havasi, c, treadway, kn, lieberman, h: finding your way in a multi-dimensional semantic space with luminoso. in: proceedings of the 15th international conference on intelligent user interfaces. 385-388 (2010). chapman ww et al. developing syndrome definitions based on consensus and current use. j am med inform assoc. 17(5): 595-601 (2010). *nikolay lipskiy e-mail: dgz1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e72, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 1school of environmental sciences, university of east anglia, norwich, united kingdom; 2public health england, birmingham, united kingdom; 3public health england, london, united kingdom objective to devise a methodology for evaluating the effectiveness of syndromic surveillance systems introduction while results from syndromic surveillance systems are commonly presented in the literature, few systems appear to have been thoroughly evaluated to examine which events can and cannot be detected, the time to detection and the efficacy of different syndromic surveillance data streams. such an evaluation framework is presented. methods a number of possible public health scenarios were identified (e.g. outbreak of pandemic influenza, cryptosporidium outbreak and deliberate anthrax release) and deterministic compartmental models were used to simulate the number of disease cases generated for a range of severities. data were used from four national syndromic surveillance systems (a non-emergency medical number, emergency department records, and information from family doctor in and out of hours consultations) coordinated by public health england. for each of these four surveillance systems, simulation data were estimated based upon transmission models. such simulation data were superimposed onto baseline syndromic surveillance data to create a test dataset. random noise was added to these test data to represent expected variability. existing statistical detection algorithms currently used for near-real time syndromic surveillance were used to evaluate these simulations. for each scenario, timeliness was assessed as the number of days between the start of the simulation and extra activity being detected by syndromic surveillance. timeliness was assessed for a range of disease severities. the efficacy of different syndromic data streams and reported syndromes was assessed. results an evaluation methodology was developed enabling the thorough evaluation of syndromic surveillance systems. using the system developed for england this indicated that for an outbreak of pandemic influenza (ah1n1) a national family doctor-based syndromic system would be the first to detect such an event. specific times to detection will be reported as well as results from cryptosporidium outbreaks and anthrax events. the outputs are sensitive to changes in parametrization of the compartmental model and the proportion of people reporting to each data stream. conclusions we have developed an effective methodology for the systematic evaluation of syndromic surveillance systems in terms of their ability to detect events and their timeliness to detection. we argue that this methodology can be widely adopted to provide more empirical analysis of the effectiveness of syndromic surveillance systems worldwide. keywords syndromic surveillance; evaluation; detection acknowledgments we acknowledge support from royal college of emergency medicine, eds participating in the emergency department system (edsss), ascribe ltd and l2s2 ltd; ooh providers submitting data to the gpoohss and advanced heath & care; tpp and participating systmone practices and university of nottingham, clinrisk, emis and emis practices submitting data to the qsurveillance database; and nhs 111 and hscic for assistance and support in providing anonymised call data the underpin the remote health advice syndromic surveillance system. we thank the phe real-time syndromic surveillance team for technical expertise. the authors received support from the national institute for health research health protection research unit in emergency preparedness and response. the views expressed in this abstract are those of the authors and not necessarily those of the nhs, the nihr, the department of health or public health england *felipe j. colón-gonzález e-mail: f.colon@uea.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e131, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding 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deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination karen soderberg1*, sripriya rajamani2, douglas wholey3, martin laventure4 1. office of health information technology, minnesota department of health, st. paul, minnesota 2. public health informatics program, school of public health, university of minnesota, minneapolis, minnesota 3. public health informatics program, school of public health, university of minnesota, minneapolis, minnesota 4. office of health information technology, minnesota department of health, st. paul, minnesota abstract background: minnesota enacted legislation in 2007 that requires all health care providers in the state to implement an interoperable electronic health record (ehr) system by 2015. 100% of hospitals and 98% of clinics had adopted ehr systems by end of 2015. minnesota’s 2008 health reform included a health care home (hch) program, minnesota’s patient centered medical home. by end of 2014, 43% of hch eligible clinics were certified with 335 certified hchs and 430 eligible but not certified clinics. objectives: to study the association between adoption and use of ehrs in primary care clinics and hch certification, including use of clinical decision support tools, patient registries, electronic exchange of patient information, and availability of patient portals. methods: study utilized data from the 2015 minnesota health information technology clinic survey conducted annually by the minnesota department of health. the response rate was 80% with 1,181 of 1,473 minnesota clinics, including 662 hch eligible primary care clinics. the comparative analysis focused on certified hchs (311) and eligible but not certified clinics (351). results: hch clinics utilized the various tools of ehr technology at a higher rate than non-hch clinics. this greater utilization was noted across a range of functionalities: clinical decision support, patient disease registries, ehr to support quality improvement, electronic exchange of summary care records and availability of patient portals. hch certification was significant for clinical decision support tools, registries and quality improvement. conclusions: hch requirements of care management, care coordination and quality improvement can be better supported with ehr technology, which underscores the higher rate of utilization of ehr tools by hch clinics. optimizing electronic exchange of health information remains a challenge for all clinics, including hch certified clinics. this research presents the synergy between complementary initiatives supporting ehr adoption and hch certification. ultimately, improvement in health outcomes depends on effective intersection of people, processes and technology. http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 keywords: informatics; electronic health records; health care homes; health care reform; patient care management correspondence: karen.soderberg@state.mn.us doi: 10.5210/ojphi.v8i3.7094 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction minnesota has a strong health reform environment supported by policies and programs related to e-health and patient centered medical home (pcmh). policy makers in minnesota recognized that more effective use of ehrs, including timely exchange of information is needed to improve quality and safety of care, help control costs, and improve population health. in 2007, minnesota enacted legislation that requires all health care providers in the state to implement an interoperable ehr system by january 1, 2015 (minn. stat. §62j.495) [1]. minnesota’s ehr mandate predates the centers for medicare and medicaid service’s (cms) ehr incentive program [2], commonly known as “meaningful use”. the minnesota ehr mandate also differs in that includes a broad range of providers extending across the continuum of health and care. the state-wide program to support implementation of the ehr mandate is led by the minnesota department of health (mdh) with guidance from the minnesota e-health initiative and its ehealth advisory committee [3]. by 2015 all of minnesota’s hospitals and nearly all ambulatory clinics had adopted ehrs (refer figure 1)[4]. other settings not eligible for the cms ehr incentive program have also advanced in adopting ehr systems. figure 1: trends in ehr adoption in minnesota in 2008 the minnesota legislature enacted comprehensive health reform legislation comprising of elements targeting population health, market transparency, payment reform and consumer engagement and intended to improve the affordability, access and quality of care [5]. an important element of this legislation is the health care homes (hch), which is minnesota’s http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 version of pcmh. the minnesota hch initiative is a joint effort of mdh and the minnesota department of human services (dhs) (minn. stat. §§256b.0751256b.0753) [6]. hch was implemented as “an approach to primary care in which primary care providers, families, and patients work in partnership to improve health outcomes and quality of life for individuals with chronic health conditions and disabilities.”[7]. hch comprises of both redesign of care delivery and payment reform. hch certification requirements included coordination of care, documenting patient conditions and treatments, establishing registries of participating patients and supporting patient engagement. certification is voluntary, but only certified hchs are eligible to receive the care coordination payment. mdh began certifying hchs in 2010 and “eligible” clinics included any that provide primary care services and are located in minnesota. by end of 2014, 43% of hch eligible clinics were certified with 335 certified hchs and 430 eligible but not certified clinics (figure 2). figure 2: certified health care homes in minnesota literature review points to the role of health information technology in facilitating the movement towards patient centered medical care [8, 9], but it’s also been noted that just technology is not adequate and requires needed functionalities (e.g. efficient data exchange and interoperability; notifications on patient status across settings; reporting activities; monitoring patients; better fit with clinical workflow) to impact cost and quality [9-13]. prior studies examining pcmh and ehr have shown variability in use of tools [14] and have pointed to payment reform as a critical element in influencing care processes rather than ehr alone [15]. this research examines synergies between the two programs on ehrs and hch by studying the association between adoption and use of ehrs in primary care clinics and hch certification. in addition, the study presents detailed view at a state level to understand the effect of these programs. specifically, this analysis considers the utilization of ehr systems among http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 minnesota’s hch eligible clinics, including use of clinical decision support tools, patient registries, electronic exchange of patient information, and availability of patient portals. methods we used data from the 2015 minnesota health information technology (hit) clinic survey, a cross-sectional study conducted annually by the minnesota department of health. clinic(s), for the purpose of this study, means any location where ambulatory clinical care services are provided for a fee by one or more physicians in minnesota. the 65-question online survey was administered from february 18 to march 17, 2015, and included questions characterizing the adoption and utilization of technology, as well as health information exchange activity. all physician clinics in minnesota were required to register and complete the survey under the minnesota statewide quality reporting and measurement system (sqrms; minnesota rules, chapter 4654) [16]. the response rate was 80% with 1,181 of 1,473 minnesota clinics responding [17]. eligible primary care clinics are identified through the sqrms registry and matched to the hit survey data, resulting in a sub-set of 662 hch eligible clinics that responded to the survey. the comparative analysis focused on certified hchs (311) and eligible but not certified clinics (351). ehr capabilities related to hch requirements of care management and care coordination were characterized as the use of clinical decision support (cds) tools, utilization of patient disease registries, electronic exchange of summary care records, and availability of patient portals. the hit clinic survey is administered at the health system level, meaning that a single response for a health system is attributed to all clinics within that system when all of those clinics have implemented the same ehr and ehr functionalities. since health systems implement ehrs system-wide, the health system is likely to be an accurate respondent of ehr functionality in its clinics. because not all of the hch eligible clinics within health systems are certified, the comparisons include both certified and not certified clinics within a health system. we compared the difference between certified and not certified clinics using logistic models regressing the presence of a tool (e.g., asthma registry, copd registry, etc.) on an indicator for the type of tool and certification status. the possibility of correlated errors due to clinics nesting in health systems was addressed by including a health system random effect. results hch clinics utilized ehr systems more extensively for the metrics examined in this study: use of clinical decision support functionalities, utilization of patient disease registries, use of ehr data to support quality improvement efforts, electronic exchange of summary care records, and availability of patient portals. the difference between certified and not certified clinics was significant (p<.05) in all models without correcting for nesting within health systems. when correcting for nesting within health systems, hch certification was significant at the .05 level for clinical decision support tools, registries, and quality improvement. differences in patient portals and electronic exchange of patient information were associated with health system differences. http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 utilization of ehr clinical decision support functions clinical decision support (cds) functions offered by ehrs, such as automated alerts, guidelines, care plans and reminders, support the care planning and coordination activities required of certified hchs. all hch eligible clinics had near universal use of medication guides/alerts. certified hchs were stronger utilizers of six cds tools compared to not certified clinics for: medication guides/alerts (100% compared to 95%); preventive care service reminders (98% compared to 84%); patientor condition-specific reminders (97% compared to 82%); automated reminders for missing labs and tests (95% compared to 78%), clinical guidelines based on patient characteristics (95% compared to 83%); and chronic disease care plans and flow sheets (86% compared to 72%) (figure 3). patient registries hchs are required to manage electronic searchable patient registries and related tools to support care coordination. while ehrs are not the only mechanism that can be used for registries, they are an efficient registry tool and certified hchs extensively used this functionality. more than nine in ten certified hchs used electronic registries for patients with asthma (98%), diabetes (96%), depression (95%), hypertension (91%) and vascular disease (90%). these clinics outperformed not certified clinics by an average of 21 percentage points. certified hchs also outperform not certified clinics in maintaining registries for obesity (77% compared to 44%), congestive heart failure (70% compared to 38%) and chronic obstructive pulmonary disease (60% compared to 29%) (figure 4). figure 3: use of electronic clinical decision support tools among hch eligible clinics in minnesota http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 29% 38% 44% 69% 61% 76% 80% 79% 60% 70% 77% 90% 91% 95% 96% 98% 0% 20% 40% 60% 80% 100% copd congestive heart failure obesity vascular disease hypertension depression diabetes asthma percent of mn clinics maintaining electronic registries certified hch not certified clinic figure 4: use of electronic disease registries among hch eligible clinics in minnesota use of ehr data for quality improvement efforts quality improvement efforts relating to care coordination are important aspects of hchs as learning organizations, and ehrs offer information tools to support these efforts. most hch eligible clinics utilized data from the ehr for such efforts. certified hchs were stronger utilizers of quality improvement activities using ehr data (figure 5): sharing data with providers (100% compared to 91%); creating benchmarks or develop clinical priorities (99% compared to 86%); setting goals around clinical guidelines (99% compared to 83%); and supporting professional development activities (71% compared to 50%). electronic exchange of patient information a summary of care record is a standardized machine-readable data packet that includes patient information relevant to care providers, such as procedures, diagnoses, problem lists, medication lists, vital signs, and more. certified hchs used electronic summary care records more than not certified clinics, with 43% of certified hchs using these for 50% or more of patients who required transition of care, compared to 23% of not certified clinics (figure 6). availability of patient portals a patient portal is an internet application maintained by the clinic that allows patients to access their electronic health records and permit two-way communication between patients and their health care providers. many portals also offer health information for patients to view, such as test results, medication lists, and visit summaries. ninety-nine percent of certified hchs offered a patient portal, compared to 91% of not certified clinics. http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 50% 83% 86% 91% 71% 99% 99% 100% 0% 20% 40% 60% 80% 100% to support professional development activities to set goals around clinical guidelines to create benchmarks or develop clinical priorities to share data with providers percent of mn clinics with ehrs using tools certified hch not certified clinic figure 5: use of data from the ehr for internal quality improvement efforts among hch eligible clinics in minnesota 23% 43% 65% 54% 13% 3% 0% 20% 40% 60% 80% 100% not certified clinic certified hch percent of mn hch-eligible clinics with ehrs used for 50% or more of transitions used for 0-50% of transitions figure 6: use of electronic summary of care record transitions of care among hch eligible clinics in minnesota discussion while all primary care clinics in minnesota have high ehr adoption rates, certified hchs are using the tools that ehr systems offer at a higher rate than eligible but not certified clinics. though minnesota’s hch program does not require ehrs, almost all certified clinics had adopted and used this technology extensively, which could be attributed to the ehr mandate in minnesota. these ehr systems support care management, care coordination, and quality improvement efforts that are needed for minnesota’s health care homes to better manage their patients that have chronic health conditions and disabilities. certified hchs have more extensively implemented clinical decision support tools that can result in improved care, improved patient safety, and lower costs. certified hchs also better utilized electronic patient registries, allowing them to better track patients with chronic conditions in order to improve care, observe outcomes, and monitor progress toward care plan goals. technology offers the opportunity for patients to engage in their health. for patients with http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 chronic conditions and/or disabilities, access to their health information, such as through a patient portal, can support patient-centered care by providers and caregivers. patient portals are offered by majority of hch certified clinics. our results differ from other studies of ehr implementation in patient centered medical homes, which found more variable adoption of care coordination tools than our study found [14]. a potential source of this difference is the convergence of complementary health reform initiatives in minnesota, and has provided opportunity to show distinct differences in the utilization of ehrs among clinics that have committed to ehr adoption and hch certification. the differences between certified and not certified clinics were significant for clinical decision support, registries, electronic exchange of health information, quality improvement, and patient portals when not adjusting for the nesting of clinics in health systems. the differences for clinical decision support registries, and quality improvement were significant when adjusting for nesting in health systems. this suggests that the differences between certified and not certified clinics in electronic exchange of health information and patient portals cannot be separated from health systems efforts to implement both ehrs and hchs. while the reason for the association between hchs and ehr implementation in minnesota may differ, the fact remains that certified hchs have utilized ehr-based tools extensively. hch certification in minnesota comprise of five standards: access/communication; patient tracking and registry functions; care coordination; care plans and performance reporting and quality improvement[7]. utilization of ehrs as a tool serves as supporting factor and enhances the meeting of these standards. ultimately, improvement in health outcomes depends on effective intersection of people, processes and technology. another factor which may explain the increased use of ehr tools by hch certified clinics is the maturation of clinics and practices over time. these underscore the need to convey messages that effective utilization of ehrs is critical and not just adoption of the technology. electronic exchange of health information is an essential tool to support coordination of care across varied providers, such as primary care, behavioral health, home care, and social support services. all eligible hchs in minnesota struggle to electronically exchange summary of care records to support coordination, but certified hchs outperformed the not certified clinics. limitations: as with all surveys, the minnesota hit clinic survey is subject to observation error. the survey was completed by clinic administrators/managers with knowledge of ehr implementation at their clinic/health system. lack of understanding of some terms associated with hit may led to misinterpretation of survey questions. furthermore, the respondent may not necessarily have thorough understanding of all items queried in the survey. another limitation is the possibility of correlated errors due to clinics nesting in health systems; this was addressed by including a health system random effect, as described in the methods section. this work does not examine the factors that influence a clinic to seek hch certification and its impact on ehr utilization. the study points to use of various ehr functionalities, but additional research is needed to understand if survey responses adequately capture the level of use in a clinic and if the ehr tools are appropriately used. future directions for this body of research should focus on impact of effective ehr utilization and hch certification on the quality of care. http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 conclusions ehrs and other hit offer promises to advance individual and population health by providing tools and the right information for providers when they need it to support improved health and clinical care. the real value from investing in and implementing an ehr system comes from using it to support efficient workflows and effective health and clinical decisions. the rapid uptake in ehr technology across the nation, combined with health reform efforts that focus on accountability and care coordination, pose challenges and opportunities for clinical care providers. opportunities are availability of tools that support decision making, quality improvement and reporting. challenges remain for clinics in minnesota to optimize health information exchange. this research highlights the higher utilization of various ehr tools in settings influenced by state policy (hch certification) and has implications for policies and programs. the need to meet various care coordination requirements were likely drivers for better ehr utilization by hch clinics. these findings suggest that broader health policy objectives can complement overall health reform initiatives. furthermore, sets of policies can benefit from each other, providing a synergy that each policy alone may not accomplish. acknowledgements this research was supported by the health care home evaluation contract done in collaboration with the minnesota department of health and the minnesota department of human services. the authors would like to thank the health care certification (hch) program at the minnesota department of health for sharing contextual knowledge and relevant data. references 1. minnesota statutes. 62j.495 electronic health record technology. 2007. available at: https://www.revisor.mn.gov/statutes/?id=62j.495. accessed november 1, 2016. 2. centers for medicare and medicaid services (cms). ehr incentive programs. 2009. available at: www.cms.gov/ehrincentiveprograms. accessed october 16, 2016. 3. minnesota ehealth initiative. minnesota interoperable electronic health record mandate. 2007. available at: http://www.health.state.mn.us/e-health/hitimp/index.html. accessed october 19, 2016. 4. minnesota department of health office of health information technology. minnesota ehealth brief. 2016. available at: http://www.health.state.mn.us/ehealth/ehealthdocs/briefehealth.pdf accessed november 2, 2016. 5. minnesota department of health. minnesota's health reform law. 2008. available at: http://www.health.state.mn.us/healthreform/about/. accessed october 29, 2016. 6. minnesota statutes. 256b.0751 health care homes. 2008. available at: https://www.revisor.mn.gov/statutes/?id=256b.0751. accessed november 1, 2016. http://ojphi.org/ ojphi health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e204, 2016 7. minnesota department of health. health care homes. 2008. available at: http://www.health.state.mn.us/healthreform/homes/index.html. accessed october 16, 2016. 8. health information technology. turning the patient-centered medical home from concept to reality. by david meyers, md, matt quinn, mba, and carolyn m clancy, md april 2010 agency for healthcare research and quality, rockville, md https://archiveahrqgov/news/newsroom/commentaries/pcmh-concept-to-realityhtml. 9. adler-milstein j, cohen gr. implementing the it infrastructure for health reform: adoption of health it among patient-centered medical home practices. amia annual symposium proceedings amia symposium. 2013;2013:11-6. epub 2014/02/20. 10. leventhal t, taliaferro jp, wong k, hughes c, mun s. the patient-centered medical home and health information technology. telemedicine journal and e-health: the official journal of the american telemedicine association. 2012;18(2):145-9. epub 2012/02/07. 11. adler-milstein j, cohen gr, markovitz a, paustian m. the impact of hit on cost and quality in patient-centered medical home practices. amia annual symposium proceedings amia symposium. 2014;2014:232-9. epub 2014/01/01. 12. schapira mm, sprague bl, klabunde cn, tosteson an, bitton a, et al. 2016. inadequate systems to support breast and cervical cancer screening in primary care practice. j gen intern med. 31(10), 1148-55. epub 06 2016. pubmed http://dx.doi.org/10.1007/s11606-0163726-y 13. richardson je, vest jr, green cm, kern lm, kaushal r, et al. 2015. a needs assessment of health information technology for improving care coordination in three leading patientcentered medical homes. j am med inform assoc. 22(4), 815-20. pubmed http://dx.doi.org/10.1093/jamia/ocu039 14. morton s, shih sc, winther ch, tinoco a, kessler rs, et al. 2015. health it-enabled care coordination: a national survey of patient-centered medical home clinicians. ann fam med. 13(3), 250-56. epub 05 2015. pubmed http://dx.doi.org/10.1370/afm.1797 15. king j, patel v, jamoom e, desroches c. 2016. the role of health it and delivery system reform in facilitating advanced care delivery. am j manag care. 22(4), 258-65. epub 05 2016. pubmed 16. minnesota department of health. statewide quality reporting and measurement system. 2008. available at: http://www.health.state.mn.us/healthreform/measurement/index.html. accessed october 31, 2016. 17. minnesota department of health office of health information technology. minnesota ehealth report -clinics: adoption and use of ehrs and exchange of health information, 2015. 2015. available at: http://www.health.state.mn.us/ehealth/summaries/reportclinic2015.pdf. accessed november 2, 2016. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27251058&dopt=abstract http://dx.doi.org/10.1007/s11606-016-3726-y http://dx.doi.org/10.1007/s11606-016-3726-y http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25796597&dopt=abstract http://dx.doi.org/10.1093/jamia/ocu039 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25964403&dopt=abstract http://dx.doi.org/10.1370/afm.1797 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27143291&dopt=abstract health reform in minnesota: an analysis of complementary initiatives implementing electronic health record technology and care coordination introduction methods results utilization of ehr clinical decision support functions patient registries use of ehr data for quality improvement efforts electronic exchange of patient information availability of patient portals discussion conclusions acknowledgements references isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e306, 2019 isds 2019 conference abstracts developing mindful and targeted data visualizations for diverse audiences sanura m. latham, charlotte cherry office of informatics & analytics, tennessee department of health, nashville, tennessee, united states objective through opioid overdose surveillance data briefs, we aim to focus on creating meaningful targeted reports that incorporate mindful “data points” and visualizations for diverse audiences. data briefs provide information that is actionable to support decision making across the spectrum of partners involved in responding to tennessee’s opioid epidemic. additionally, visualizations and reporting of opioid overdose surveillance data create pathways and processes for sharing data and opportunities to collaborate with oth ers’ expertise that enrich communication among multi agency collaborators and interdepartmental partners. introduction tennessee has experienced an increase of fatal and non-fatal drug overdoses which has been almost entirely driven by the opioid epidemic [1]. increased awareness by medical professionals, new legislation surrounding prescribing practices, and mandatory use of the state’s prescription drug monitoring program has resulted in a decrease of opioid prescriptions and dosages. paradoxically, emergency department discharges and inpatient hospitalizations due to opioid overdoses have continued to increase. the tennessee department of health, office of informatics and analytics (tdh oia) has developed visualizations and reports for opioid overdose surveillance data to enhance communication and timely response by health partners. through opioid overdose surveillance reporting data briefs we aim to focus not on “big data” analytics, but rather meaningfully targeted data briefs that illustrate mindful “data points” and visualizations. these data briefs provide information that is actionable to support decision making across the spectrum of partners involved in responding to tennessee’s opioid epidemic. methods tdh has partnered with state agencies including the department of mental health and substance abuse services (dmhsas) and the tennessee bureau of investigation (tbi) as well as internal tdh divisions to foster enhanced opioid response communication. in order to provide a comprehensive yet digestible way to share information we have created two sets of data visualizations t hat communicate pertinent weekly and monthly opioid overdose trends. a bi-weekly brief contains information from four data sources: tennessee’s controlled substance monitoring database which is tennessee’s prescription drug monitoring program (pdmp); the drug overdose reporting system which contains non-fatal opioid overdoses captured in hospitals’ emergency departments; vital records information system management which captures fatal drug overdose information; and the tennessee incident based reporting system which includes opioid and heroin related arrest information. the bi-weekly data brief provides a quick yet inclusive layout of data in an easily consumable manner. a one page front and back layout is divided into four sections, representing each of the four data sources. a nonfatal opioid overdose “counter” displays a year-to-date count of non-fatal opioid overdoses as compared to the previous year. the monthly report follows a slightly different layout, as the report hones in on data pertaining only to non -fatal opioid overdoses reported from hospital emergency departments. a monthly year-to-date count of non-fatal opioid overdoses and counts of non-fatal opioid overdoses by race and age are included in the report, as well as a visualization depicting the number of non-fatal opioid overdoses by month by opioid class. the monthly report also includes a choropleth map that displays non -fatal opioid overdoses by zip code for the reporting month and a heat map of non-fatal opioid overdoses for cumulative 2018. initial feedback from partners about the visualizations included requests to add footnotes for readers to distinguish the data sources and data limitations, as well as requests to provide enhanced contextual information such as year to date counts, previous ye ar counts, and previous month comparisons. further visualization discussions included requests to add public health regions as a map overlay, identifying metrics that best illuminate “red flags” or upticks in numbers, and assessing whether to display counts or rates for a given data source. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e306, 2019 isds 2019 conference abstracts results data briefs and reports are disseminated to tdh leadership, the office of the state chief medical examiner, the office of general counsel, tbi, dmhsas, regional epidemiologists and the newly formed opioid overdose coordinating office. these data briefs have been proven to be an effective tool for enhanced communication and responding to the opioid crisis. for example, the tdh commissioner’s office requested additional information about a county that was consistently ranking high in non-fatal overdoses as illustrated on a data report. the dissemination of data briefs has also strengthened internal tdh partnerships including l inking viral hepatitis and hiv programs with oia to develop visualizations that incorporate hcv and hiv data (hepatitis c virus; human immunodeficiency virus) with opioid overdose data to better understand and elucidate the syndemic of opioid overdose, hcv, and hiv in tennessee. the dissemination of data briefs and reports has also been an effective tool for responding to the opioid crisis with our interagency partner, dmhsas. a dmhsas team utilized the data briefs to inform how and where to expand an overdose recovery navigator program within the state—a program that provides individuals who have recently overdosed (and are still in the emergency room) with information for treatment and recovery resources. current work, also in partnership with dmhsas, has been the creation of a data brief specifically on the topic of harm reduction. the data brief will include a map that shows areas of naloxone distribution to law enforcement agencies throughout the state, as well as a map that illustrates naloxone administration locations by law enforcement officers in the field. additionally, information on locations of syringe exchange programs will be included in the brief. members from dmhsas have provided feedback that they anticipate using the harm reduction brief to assess which remaining law enforcement facilities have not yet on-boarded to receive naloxone, to pin point areas where additional distributions of naloxone might be needed, and identify where supplemental naloxone administration trainings for, either law enforcement or the community, might be targeted. conclusions visualizations have illuminated patterns and “red flags” in geographic areas that have helped guide decision makers in making data-driven decisions about opioid response. visualizations and reporting of opioid overdose surveillance data has also importantl y enriched communication among multi agency collaborators and interdepartmental partners that, until now, have been largely sil oed. pathways and processes for sharing data and opportunities to collaborate with others’ expertise have been strengthened by th e dissemination of targeted mindful “data point” briefs over large scale “big data” analytics. acknowledgement the authors thank their partnership with the tennessee department of mental health and substance abuse services and the tennessee bureau of investigation. grant support comes from the us department of justice, office of justice programs, bureau of justice assistance, harold rogers prescription drug monitoring program grant #2016pm-bx-k002. references 1. office of informatics and analytics, tennessee department of health. prescription drug overdose program 2018 report: understanding and responding to the opioid epidemic in tennessee using mortality, morbidity, and prescription data [internet]. tennessee department of health, 2018. available from: https://www.tn.gov/content/dam/tn/health/documents/pdo/pdo_2018_report_02.06.18.pdf. http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia new jersey department of health, trenton, nj, usa objective the new jersey department of health (njdoh), occupational health surveillance (ohs) unit staff proposes to evaluate a realtime surveillance tool to track a variety of occupationally-related emergency room visits throughout the state via epicenter, the njdoh’s existing real-time surveillance system. introduction syndromic surveillance has been used by state agencies to collect real-time information on disease outbreaks but has not been used to collect data in the occupational setting. therefore, ohs staff has begun evaluating a real-time surveillance tool to track a variety of occupationally-related emergency room visits throughout the state via epicenter, the njdoh’s existing real-time surveillance system. this proposal applies established epidemiologic techniques to a different set of circumstances than they have been applied to in the past. incorporating syndromic surveillance data with hospital discharge data will enhance the ability to classify and capture work-related nonfatal injuries and improve efforts of prevention. by employing a realtime, independent data source such as epicenter, the classification of work-related injuries and illnesses could be greatly enhanced, leading to a better understanding of the burden of non-fatal work-related injuries and illnesses, and allowing for quicker intervention. methods a work-related injuries and illness classifier will be developed to trigger epicenter to alert staff of an occupational event. classifiers are specific to certain events such as, heat related illness or chemical exposure; are composed of keywords related to those events; and are searchable in the chief complaint fields. once an initial draft classification is developed, the historic data will be surveyed for the defined keywords or international classification of diseases (icd) codes to refine the inclusion/exclusion keywords. using a modified sas code, keywords and icd codes will be pulled from the historic data set. once the syndrome classification is developed and validated, preliminary alert thresholds for work-related injury or illness events based on counts of emergency department (ed) events which meet the occupational syndromes will be determined. these alert thresholds may be based on absolute number of events or based on the number of cases needed for njdoh public employee occupational health and safety (peosh) to respond to. ohs staff will conduct sensitivity analysis to identify threshold cut-offs and long-term surveillance to detect trends in injuries. results ohs currently receives automatic real-time electronic notifications when three or more cases of chemical exposures are seen in the same ed or among residents in the same county in nj, within a 24hour period. ohs staff quickly reviews the cases using the secure epicenter website and contacts the ed nurse manager to obtain information on the chemical the patients were exposed too, how they were exposed, and if it was work-related. recently, ohs staff began exploring using epicenter as a real-time surveillance system for occupationally related chemical exposures. this real-time surveillance of occupational events has detected diverse exposures and illnesses including: exposure to toluene vapor at a nail polish manufacturing plant; six cases of pesticide exposure at a produce repacking facility; and carbon monoxide poisoning of two police officers due to exhaust problems in their vehicle. conclusions this use of the epicenter chief complaint reporting system has shown that it can yield real-time knowledge of incidents and local conditions for ohs to assist with and identify prevention opportunities. occupational surveillance currently involves collecting data on potential cases on a quarterly or yearly basis, often long after the diagnosis was made. however, epicenter allows staff to identify illnesses early so a rapid response can be initiated, reducing further risk of occupational injuries and illness. the use of multiple data sources can help identify populations, occupations and industries at high risk of a work-place injury and illness in real-time, along with providing data to help monitor trends of work-related injuries and illness over time. keywords occupational; injuries; surveillance acknowledgments this project was funded by niosh cooperative grant #5u60oh00848508. references 1. pransky g, snyder t, dembe a, himmelstein, j. under-reporting of work-related disorders in the workplace: a case study 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in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian 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salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 1university of liverpool, institute of infection and global health, leahurst campus, neston, united kingdom; 2university of lancaster, faculty of health and medicine, furness college, lancaster, united kingdom; 3university of liverpool, institute of infection and global health, waterhouse building (block f), liverpool, united kingdom; 4university of liverpool, school of veterinary science, leahurst campus, neston, united kingdom objective to describe how a real-time surveillance system for early detection of gastrointestinal disease (gi) outbreaks in small animal and human health is being developed by collecting electronic health records (ehrs) from veterinary practitioners and a telephone-based 24-hour medical triage service in the uk. introduction in human and animal health, conventional approaches to preventing and controlling gi have not reduced the overall disease burden. in order to understand and mitigate shared gi aetiologies between humans and animals it is necessary to develop one health surveillance approaches that integrate data-sources contributed to by human and veterinary healthcare. such approach is described here. methods veterinary data were collected electronically in real-time by savsnet, the small animal veterinary surveillance network, from 102 uk veterinary practices (total of 197 premises) using a compatible version of practice management software. the study sampling frame included all dog and cat ehrs recorded within the savsnet database between january 2014 and july 2015. each record included the animal signalment (including species, breed, sex, age, etc.), clinical free text, owner’s postcode, treatment, syndrome information and results from a short questionnaire administered to veterinary surgeons and appended after ~25% of gi consultation. the owner’s postcode data were used to link each animal against uk databases containing human and environmental factors such as indices of multiple deprivation. a bayesian spatio-temporal mixed effects binary regression model was used to model the incidence of gi in dogs and cats as a proportion of all presentations. the model was fitted to data between 01/11/2014 and 15/11/2014 using a bespoke markov chain monte carlo algorithm to generate samples from the predictive distribution of the underlying spatio-temporal incidence surface. these samples were then used to compute predictive probabilities for exceedance of policy-relevant relative risk thresholds; a high predictive probability at a particular time and place gives an early warning of a possible gi outbreak. to test the ability of the model to detect gi outbreaks, a data set it was created with a fictitious premise having an excessive number of fake gi cases. the synthetic data is based on a typical premise in the savsnet dataset to ensure it reflects the characteristics of the genuine data. this premise is placed 1.5km east and 3.9km south of the chosen genuine premise. the outbreak is defined throughout the fictitious premise as the eight days from monday, 03/11/2014 to monday, 10/11/2014 inclusive. we run the model using data from 01/11/2014 to 15/11/2014. electronic data relating to human gi has recently become available by extracting spatial and temporal information from phone calls made to the nhs111 telephone-based 24-hour medical triage service by patients seeking medical advice about acute gi symptoms. the methodology that was initially developed using small animal data, as described above, can be easily adapted to analyse these data. results ehr were obtained from 491,193 consultations (361,203 dogs, 129,990 cats). gi comprised 4.59% of canine and 3.63% of feline consultations, respectively. the final model included as explanatory variables age, species, weekend indicator, a measure of deprivation, animal’s breed classified as purebred or crossbred and longitude / latitude effects. predictive probabilities for a relative risk threshold of 1.2 or more were all comfortably greater than 0.5 identifying the faked outbreak. conclusions this is the first demonstration of the feasibility of real-time syndromic surveillance in uk small animal practices. in future work, we intend to adapt the model to early detection of human gi outbreaks, and to investigate the possible inter-dependence of spatio-temporal variations in gi risk between companion animals and people. keywords one health surveillance; early detection; gastrointestinal disease; uk; savsnet acknowledgments we are grateful to bsava and to wt/hicf-t5-354 project (integrate) for their funding of this work, and also to the many practitioners and pets’ owners without whom these data would not be accessible. *fernando sánchez-vizcaíno e-mail: fsvb@liv.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e159, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household 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felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts using an emergency department syndromic surveillance system to assess the impact of cyclone bejisa, reunion island pascal vilain*1, frédéric pagès1, katia mougin-damour2, xavier combes3, pierre-jean marianne dit cassou4, yves jacques antoine5 and laurent filleul1 1regional office of french institute for public health surveillance in indian ocean, saint-denis, réunion; 2hospital centre, saintpaul, réunion; 3university hospital centre, saint-denis, réunion; 4university hospital centre, saint-pierre, réunion; 5hospital centre, saint-benoît, réunion objective to assess the health impact of cyclone bejisa from data of emergency departments (eds) and emergency medical service (ems) introduction on january 2, 2014 the cyclone bejisa struck reunion island. this storm of category 3 (saffir–simpson scale) disturbed electricity supply and drinking water systems. floods, roof destructions and the threat of landslide led to the evacuation of residents to emergency shleters. in this context, the regional office of french institute for public health surveillance in indian ocean set up an epidemiological surveillance in order to assess the impact in the aftermath of the cyclone. methods short-term health effects were assessed using a syndromic surveillance system including the activity of all eds and the ems of the island1. from these data, several health indicators were collected and monitored. daily indicators were analyzed using the c2 aberration detection algorithm and weekly indicators, the threshold was defined using the method of farringhton. to complete this assessment, a field investigation was carried out in eds. all medical files recorded in the eds of reunion island from january 2 to 5, 2014 were reviewed in order to identify visits related to the cyclone and to determine mechanisms of injuries. results the number of calls to the ems peaked the day of the cyclone and the number of ed visits increased markedly over the next two days. at the same time, a significant increase of visits for trauma, burns, conjunctivitis was observed on all eds (figure 1). six visits for carbon monoxide poisoning were detected in the southern ed. epidemiological investigations allowed to identify an inappropriate use of generator. among 1 748 medical records reviewed, 216 visits were related to the cyclone. injuries represented nearly half of these visits. the main mechanisms of trauma were falls and injuries by machinery or tools during the clean-up and repair works. due to prolonged power outage, several patients were hospitalized: some to assure continuity of care, others to take care of an exacerbation of a chronic disease. conclusions the reactivity and the flexibility of the syndromic surveillance system based on the near real-time transmission of data from eds allowed to rapidly assess the health impact of the cyclone bejisa in reunion island, the syndromic surveillance system appears to be an adapted response in this context2. however an underestimation of this impact was still possible. in the near future several work leads will be planned in order to improve the assessment. figure 1. daily number of ed visits for trauma, burns, conjunctivitis and all causes, december 1, 2013 to january 7, 2014. keywords syndromic surveillance; natural disasters; public health reports acknowledgments we acknowledge all practitioners of emergency departments and emergency medical service. references 1.vilain p, l filleul. la surveillance syndromique à la réunion : un système de surveillance intégré. [syndromic surveillance in reunion island: integrated surveillance system]. bulletin de veille sanitaire. 2013;(21):9-12. http://www.invs.sante.fr/ publications-et-outils/bulletin-de-veille-sanitaire/tous-les-numeros/ ocean-indien-reunion-mayotte/bulletin-de-veille-sanitaire-oceanindien.-n-21-septembre-2013. accessed august 12, 2014. 2.hope k, merritt t, eastwood k, main k, durrheim dn, muscatello d, todd k, zheng w. the public health value of emergency department syndromic surveillance following a natural disaster. commun dis intell q rep. 2008;32(1):92-4. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e171, 201 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts improving detection of call clusters through surveillance of poison center data royal k. law*1, howard burkom2 and josh schier1 1national center for environmental health, centers for disese control and prevention, chamblee, ga, usa; 2johns hopkins applied physics lab, baltimore, md, usa objective our objective was to compare the effectiveness of applying the historical limits method (hlm) to poison center (pc) call volumes with vs without stratifying by exposure type. introduction the centers for disease control and prevention (cdc) uses the national poison data system (npds) to conduct surveillance of calls to united states pcs. pcs provide triage and treatment advice for hazardous exposures through a free national hotline. information on demographics, health effects, implicated substance(s), medical outcome of the patient, and other variables are collected. cdc uses automated algorithms to identify anomalies in both pure call volume and specific clinical effect volume, and to identify calls reporting exposure to high priority agents. pure and clinical effect volume anomalies are identified when an hourly call count exceeds a threshold based on historical data using hlm.1 clinical toxicologists and epidemiologists at the american association of poison control centers and cdc apply standardized criteria to determine if the anomaly identifies a potential incident of public health significance (iphs) and to notify the respective health departments and local pcs as needed. discussions with npds users and analysis of iphs showed that alerting based on pure call volume yielded excessive false positives. a study using a 5-year npds call dataset assessed the positive predictive value (ppv) of the call volume-based approach. this study showed that less than 4% of anomalies were iphs.2 a low ppv can cause unnecessary waste of staff time and resources analyzing false positive anomalies. as an alternative to pure call volume-based detection where all calls to each pc are aggregated for anomaly detection, we considered separating calls by toxicologically-relevant exposure categories for more targeted anomaly detection. we hypothesized that this stratified approach would reduce the number of false positives. methods we derived our exposure categories based on the criteria that the categories must: 1) relate to hazardous exposures of public health importance, 2) reflect categories based on clinical effects and treatment modalities, 3) avoid high priority exposures that may be triggered by single calls, 4) be compatible with exposure substance identification codes currently used by pcs and npds, and 5) include enough calls for meaningful tracking. we queried all calls reporting exposures to the proposed categories between january 1, 2009 and july 31, 2015 for ten pcs. we applied the hlm method after stratifying by exposure category and tabulated the number of alerts triggered for each category during the study period. we then applied the hlm method for the ten pcs on all combined exposure calls to represent the traditional non-stratified approach. we compared the combined alert burden generated by stratifying by exposure category with the alert burden for the non-stratified approach for varying time windows (1-, 2-, 4-, 8and 24-hours). we conducted analysis in r. results we derived a total of 20 exposure categories, including chemicals (n=4), drugs of abuse (n=6), pesticides (n=3), gas/fume/vapors (n=2), contaminated food/water (n=1), and others (n=4). call counts during 2015 for these categories ranged from approximately 5,000 to 90,000. table 1 shows the total number of alerts triggered for each method by time windows. there was a marked reduction of alert burden when first stratifying by exposure category for time windows shorter than eight hours compared to the alert burden for the non-stratified approach. conclusions stratification of call volume by exposure category and time window suggests potential improvement over traditional nonstratified approach by having a lower alert burden. further work should focus on refining the exposure categories, refining the time window for surveillance, and assessing other detection performance metrics, such as sensitivity. table 1: alert burden comparison for the non-stratified vs stratified approach keywords poison center; surveillance; npds references 1. stroup df, wharton m, kafadar k, dean ag. evaluation of a method for detecting aberrations in public health surveillance data. am j epidemiol. 1993;137(3):373-380. 2. law rk, sheikh s, bronstein a, thomas r, spiller ha, schier jg. incidents of potential public health significance identified using national surveillance of us poison center data (2008-2012). clin toxicol (phila). 2014;52(9):958-963. *royal k. law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e18, 2017 emergency medical text classifier: new system improves processing and classification of triage notes 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi emergency medical text classifier: new system improves processing and classification of triage notes stephanie w. haas1, debbie travers2,3, anna waller3, deepika mahalingam2, john crouch3, todd a. schwartz2,4, javed mostafa1,3 1. school of information and library science, university of north carolina at chapel hill, nc 2. school of nursing, university of north carolina at chapel hill, nc 3. school of medicine, university of north carolina at chapel hill, nc 4. school of public health, university of north carolina at chapel hill, nc abstract objective: automated syndrome classification aims to aid near real-time syndromic surveillance to serve as an early warning system for disease outbreaks, using emergency department (ed) data. we present a system that improves the automatic classification of an ed record with triage note into one or more syndrome categories using the vector space model coupled with a ‘learning’ module that employs a pseudo-relevance feedback mechanism. materials and methods: terms from standard syndrome definitions are used to construct an initial reference dictionary for generating the syndrome and triage note vectors. based on cosine similarity between the vectors, each record is classified into a syndrome category. we then take terms from the top-ranked records that belong to the syndrome of interest as feedback. these terms are added to the reference dictionary and the process is repeated to determine the final classification. the system was tested on two different datasets for each of three syndromes: gastro-intestinal (gi), respiratory (resp) and fever-rash (fr). performance was measured in terms of sensitivity (se) and specificity (sp). results: the use of relevance feedback produced high values of sensitivity and specificity for all three syndromes in both test sets: gi: 90% and 71%, resp: 97% and 73%, fr: 100% and 87%, respectively, in test set 1, and gi: 88% and 69%, resp: 87% and 61%, fr: 97% and 71%, respectively, in test set 2. conclusions: the new system for pre-processing and syndromic classification of ed records with triage notes achieved improvements in se and sp. our results also demonstrate that the system can be tuned to achieve different levels of performance based on user requirements. keywords: disease outbreaks, electronic health records/classification, machine learning, natural language processing, public health informatics, public health surveillance/methods abbreviations: centers for disease control and prevention (cdc), chief complaint (cc), emergency department (ed), emergency medical text classifier (emt-c), emergency medical text processor (emt-p), fever-rash (fr), gastro-intestinal (gi), master term list (mtl), north carolina disease event tracking and epidemiologic collection tool (nc detect), respiratory (resp), sensitivity (se), specificity (sp), support vector machines (svm), structured query language (sql), triage note (tn), term frequency-inverse document frequency (tf-idf), unified medical language system (umls) correspondence: shaas@email.unc.edu doi: 10.5210/ojphi.v6i2.5469 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:shaas@email.unc.edu emergency medical text classifier: new system improves processing and classification of triage notes 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi introduction and background the primary purpose of public health syndromic surveillance is to serve as an early warning system for disease outbreaks. in addition, syndromic surveillance systems provide situational awareness, allowing public health officials to monitor ongoing disease events, and identify related factors [1]. syndromic surveillance can be framed as a classification problem: given syndrome definitions and information from the visit record, determine whether the record is likely to represent an instance of one, more than one, or none of the syndromes of interest. the costs of incorrect classification include unnecessary work for the public health official if a record is incorrectly classified as a syndrome instance requiring investigation or other action (a false positive), and not recognizing a potential outbreak in a timely manner if it is not classified (a false negative). syndrome definitions include a text description (e.g., “acute infectious gastrointestinal illness of 7 days or less”) and a list of symptoms that are associated with the syndrome (e.g., diarrhea, fever). therefore, a successful syndromic surveillance system must map symptoms as they are expressed in the visit record against those included in the syndrome definition, and determine if classification as an instance of the syndrome is warranted. early detection of outbreaks by syndromic surveillance systems depends on community-wide health-related data which are: 1) available in a timely manner and 2) quickly and accurately classified. syndromes are typically created by local and state public health jurisdictions who may make use of bioterrorism syndromes defined by centers for disease control and prevention (cdc) (e.g., botulism-like, hemorrhagic illness) as well as other syndromes of interest (e.g., influenza-like illness, gastrointestinal) defined by the syndromic surveillance community through a consensus process [2,3]. traditional surveillance systems use health-related data such as lab reports and final diagnoses to confirm the presence of diseases that meet case definitions such as influenza or mrsa [1,4]. in contrast, syndromic surveillance systems rely on symptom data recorded during emergency department (ed) patient visits. ed records contain timely clinical data which have been shown to act as an early warning system [5]. although the diagnosis data and lab results from the ed record would provide the most accurate information for surveillance, these data elements are not available in a timely manner. in a study of diagnosis data available in a statewide public health surveillance system, the majority of ed diagnoses were not available for days to weeks after the ed visit [6]. for syndromic surveillance, "timeliness" is defined in terms of hours, and action by public health officials may be warranted before there is a definitive test result or diagnosis. pre-diagnostic data used for surveillance include initial vital signs (e.g., measured temperature, heart rate) and chief complaint (cc) [7-9]. researchers have also explored the addition of different portions of the electronic medical record to capture data in addition to chief complaint, such as discharge prescriptions, diagnostic test orders, structured clinical notes, and triage nurses' notes in narrative form [10-13]. non-coded clinical data in unstructured (free) text form provide rich information for syndromic surveillance, but working with these data can be challenging. the triage note (tn) and chief complaint (cc) fields of an ed record capture the very initial interactions of clinicians with a patient. the cc describes the primary reason for the patient's ed visit in a few words or medical terms, while the tn, when present, contains a narrative with more detail about the history of the present illness and sometimes includes the nurse’s observations. both fields may contain terms that represent syndrome-related symptoms, and can thus be used as evidence for the classification decision [14]. because the cc is usually briefer and more focused, it is generally easier to automatically extract symptoms from the cc than from the tn. the tn is more wide emergency medical text classifier: new system improves processing and classification of triage notes 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi ranging in the information it includes, as shown in table 1. all three ed records have a cc of fever. when the triage note is added, each record meets a different syndrome definition, based on the highlighted keywords. many syndromic surveillance systems use only the cc for syndromic classification, but the addition of data from clinical notes has been shown to increase the accuracy of syndromic surveillance [10]. in a pilot study, the addition of information from tns led to improved sensitivity for three syndromes, respiratory, fever-rash and gastrointestinal, from 17% 40% to over 81%, while specificity was maintained above 80% [11]. however, use of tns can also increase the risk of false classification, since they can include syndrome-relevant information in non-standard terms, such as abbreviations, misspellings, negated forms, and other expressions characteristic of free-text notes [15]. table 1: examples of chief complaints and triage notes chief complaint triage note syndrome fever diarhea then febrile x2d now vom gastrointestinal fever pt c/o wheezy cough, chest/throat sore, felt hot, w/shaking chills. worse in last 48 hrs. respiratory fever mom reports child awoke w/fine red rash on trunk.. lethargic, temp up to 103.2.pt listless. fever-rash the development of syndromic surveillance systems using textual data is based on two major research areas: text mining of medical data to identify relevant concepts, and automated classification based on textual features. meystre et al. [16] discuss some established techniques for information extraction from clinical unstructured text, such as incorporating supplemental information sources (e.g., the umls), mapping terms to a standard representation (e.g., icd-9cm codes), and cleaning and normalizing text using pre-processing software (e.g., chief complaint processor (ccp), emergency medical text processor (emt-p), negex, context) [3,7,17-27]. although structured data would be easier to work with, fields such as the triage note are usually recorded in unstructured text. a system that is unable to extract concepts from these fields risks missing valuable information. the north carolina disease event tracking and epidemiologic collection tool (nc detect) is a state public health surveillance system which receives twice daily feeds of ed visit data. during the period covering the data used in this study, the number of hospitals providing data grew from 94 of 113 (83%) in 2006 to 110 of 112 (98%) by the beginning of 2009 [6,28]. all records contain the patient’s chief complaint and, if available, also include initial vital signs and triage notes. once received, cc text is cleaned and normalized using emergency medical text processor (emt-p) [23] to correct misspellings, expand abbreviations, map synonyms to preferred terms, etc. negated concepts are identified using negex [17]. after this pre-processing, nc detect classifies the records as positive or negative for each of several syndromes. the syndrome definitions are represented as structured query language (sql) queries; classification is thus operationalized as a query to the nc detect database of pre-processed ed visit records. approximately 13,000 new ed records are processed each day. this translational research project created a system that can be deployed for near-real time syndromic surveillance. the system employs techniques drawn from natural language processing, text mining, and automatic classification, to extract symptoms from triage notes. the goal was to further increase sensitivity above that found in the pilot study [11], while not sacrificing specificity, in order to reduce manual intervention to identify false positives and emergency medical text classifier: new system improves processing and classification of triage notes 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi increase the overall reliability of the system. negex [24] has been shown to help reduce false positives by identifying negated terms and concepts in unstructured text including tns [29], but we found that it is not possible to compile a comprehensive list of all such terms. with our new system we sought to identify new syndrome-relevant terms automatically from a training set to produce a more robust classification that could be used by surveillance systems. previous research at the time the study data were collected, only 27% of all visits in the nc detect database contain a tn; however that percentage has been growing and the increase is expected to continue as more eds capture the tn electronically. previously, researchers found that the sensitivity for acute respiratory surveillance improved from 13% without a tn to 35% with a tn [14] and that a disproportionate percentage (46%) of visits that are flagged as positive for one or more syndromic surveillance reports include a tn [11]. although the additional terms found in the tn may improve the system's recognition of syndrome-positive records, they also increase the possibility of false positives. nc detect currently uses a rule-based classification approach which is triggered by the presence of specific terms or concepts. an advantage of this approach is that the rule is a relatively straight-forward translation of the expert knowledge in the syndrome definition into the query. for example, the gi symptom "vomiting" is implemented as the inclusion of "vomiting" as a term in the sql query. however, such term-by-term transfer is insufficient: synonyms and other ways of expressing a symptom must also be included; the query must also include "emesis", "threw up" and so on. this type of rule maintenance must be performed by hand [14,30,31]. table 2 shows results of testing nc detect queries against a manually classified sample [14,32].these results illustrate the challenge of syndromic surveillance: previous methods tend to generate low sensitivity and high specificity. increasing sensitivity improves case detection, but is usually accompanied by a decrease in specificity, resulting in an increased burden for public health staff who must review positive signals. table 2: baseline nc detect performance gastrointestinal [32] respiratory [14] fever-rash [32] # records manually classified (weighted to total # records) 3353 (2,418,168) 3699 (956,015) 3640 (2,418,168) sensitivity: 0.28 0.23 0.45 specificity: 0.97 0.99 0.99 classifiers can be developed by training machine learning algorithms, such as decision trees, support vector machines (svm), and bayesian classifiers (usually coupled with a weighting scheme), to identify patterns of relevant features that predict membership of a text file or patient case to a class [8,31,33]. one advantage of machine learning is that the system may learn patterns that are not obvious to a person. features used by the classifier may include terms or sets of related terms (e.g., synonyms), and the features can be weighted since the presence of some features may provide stronger evidence than the presence of others. in machine learning, the classification model built during training is used to predict the correct classification for each incoming record. for syndromic surveillance, an accurate model will correctly identify records that are instances of a syndrome, based on the information available in the record, while minimizing false positives and false negatives. selection of useful features to form the model is emergency medical text classifier: new system improves processing and classification of triage notes 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi critical; preprocessing cc and tn improves the quality of features available for the model. previous efforts on term expansion in medical information retrieval systems have used the unified medical language system (umls) as a knowledge source to add only terms relevant to the context [34,35]. relevance feedback is a strategy used to improve information retrieval performance by adding to and/or re-weighting the initial query [36-38]. the user identifies relevant documents returned by the initial query, which the system then uses as a source for additional query terms (e.g., synonyms) or as a basis for re-weighting existing query terms [39]. in machine learning, this technique may help identify new features or re-weight existing features by incorporating new information from the input stream or the incoming document set [39]. fully automated systems use blind feedback, also known as pseudo-relevance feedback [34,40], which eliminates the need for human intervention. the system described here uses pseudo-relevance feedback to identify new features from classified ed records. study objective the objective of this study was to develop and test an automatic system for syndrome classification. goals for the new system were to draw information from tns as well as the cc, be easily adaptable to include new syndromes, and respond to changes in existing syndrome definitions and new ed data. also, the new system should improve upon the performance of the baseline sql queries currently in use by nc detect. methods our new system for processing and classifying ed records with triage notes was developed using syndrome definitions established by the cdc and expert consensus [2,3] as the basis for the initial query, making it comparable to the existing query or rule-based approach. the pseudorelevance technique was based on the assumption that the top-ranked documents (i.e., ed records) returned by the initial query are relevant, and thus can serve as a source for additional terms. all terms were used as features in our vector space model for syndrome classification [41]. our previous work [41] describes the basic vector space model, along with initial results obtained for gi syndrome. the version of the system described in this paper incorporates additional sources of features, including syndrome exclusion criteria and the umls. we evaluated the new system in our informatics laboratory using data from the nc detect warehouse for ed visits from 2006-2008. this version was tested on 3 syndromes, gastrointestinal severe (gi), respiratory (resp), and fever-rash (fr), as defined by the nc detect syndrome definitions workgroup. system components figure 1 illustrates the architecture of our new system, called emergency medical text classifier (emt-c). emergency medical text classifier: new system improves processing and classification of triage notes 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi figure 1. emt-c architecture there are two inputs to the system. the first is a vector representing the master term list (mtl) for a syndrome; we describe its construction in the next section. the second is a set of preprocessed ed records which have been normalized using emt-p modules [22,23]. these modules normalize terms commonly found in cc and tn (e.g., acronyms, abbreviations, truncations, misspellings and coordinate structures) to standardized terms from the umls. note that although nc detect receives structured data from other fields of the ed record, emt-c uses data only from the cc and tn.) a record's normalized terms are then translated to a binary vector, where 1 represents a term in the mtl also present in the record, and 0 represents a mtl term that is absent from the record. the record's vector is compared to the mtl vector using cosine similarity. a mean similarity value is calculated from similarity values for a set of records, such as all records submitted to nc detect during a 24-hour period, and is used as the threshold for classifying the records in that set. cosine similarity between a tn vector (v1) and syndrome vector (v2) is defined as: records with a similarity value greater or equal to the mean are classified as syndrome positive, and those whose similarity value is below the threshold are classified as syndrome negative. this design decision is based on the assumption that syndromic surveillance is based on identifying emergency medical text classifier: new system improves processing and classification of triage notes 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi surges in the number of records fitting a syndrome definition. the threshold is set to a lower similarity value if most records in the batch are dissimilar to the mtl; conversely if most records are highly similar, a higher threshold is needed to identify a surge. creating the master term list the master term list (mtl) represents the syndrome definition to which each ed record is compared. the initial mtl consisted only of terms in the existing nc detect baseline sql query. this list is pre-processed using the same emt-p modules used for ed records, and represented as a binary vector. this vector contains only 1s, as all mtl terms are present in the mtl vector. training set a training set of ed records was pre-processed with emt-p modules, and then translated into vectors for comparison with the mtl vector as shown in fig. 2. the training set consisted of 259,365 ed records that had been classified as positive by the baseline nc detect sql queries for one or more of three syndromes (gastrointestinal, respiratory, fever-rash) over a period of three years (2006-08). based on the assumption that the n records with the highest similarity values are true positives for the syndrome, terms from these pre-processed records not already in the mtl are added to it. this forms the pseudo-relevance feedback loop. the augmented mtl is translated into a new vector, as are the records, and their vectors are compared again. the feedback loop could iterate multiple times; as new records from the training set rise into the top n they can be harvested for new terms, (although over-fitting would be a risk if more than a couple of iterations were run). for this experiment we iterated once and performance improved. upon a second iteration, performance degraded, so in the production version of emt-c, the system iterated only once. any future revision of the mtl would be triggered by degradation in performance. the pseudo-feedback approach allows emt-c to automatically incorporate terms from actual records that are not in the syndrome definition, thus expanding the evidence it uses for classification. for example, after the initial classification for the respiratory syndrome, highranking records contained the terms "crackle" and "albuterol", which were not in the initial mtl list. "crackle" describes abnormal lung sounds, and "albuterol" is a medication used to treat respiratory conditions. these terms were then added to the mtl list. evaluation we tested emt-c on two sets of ed records from nc detect that were previously manually annotated for syndromic surveillance research [14,22]. each record contained the tn, cc and vital signs from the ed visit. a set of 485 was used for initial pilot testing of emt-c [22]. the system was modified based on these results before final testing on a set of 3,053 [14]. the tn terms from this set were weighted by their term frequency-inverse document frequency (tf-idf) values, a standard information retrieval method used to identify terms that provide good discrimination between relevant and non-relevant documents [42]. a subset of the highly weighted terms was used to construct the master term list for classifying the records in the final testing dataset. emergency medical text classifier: new system improves processing and classification of triage notes 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi figure 2. creation of master term list (mtl) vector gold standard set: records in both test sets [14,22] had been manually classified by three clinicians as part of the previous studies. the clinical experts used the entire ed record (cc, tn, final diagnosis, measured temperature, admitted v. discharged) to retrospectively determine whether the visit conformed to one of the nc detect syndrome definitions (gastrointestinal severe, respiratory, fever-rash). though the 3 classes are not mutually exclusive in the production environment, the samples we used for this study contained records that were positive for no more than one syndrome. the initial agreement between two of the clinicians was measured with the kappa statistic and for the two studies was 0.76 and 0.82 respectively [14,22]. a third clinical expert adjudicated the cases with disagreement, and these final judgments were treated as the gold standard classifications for this study. table 3 lists the number of positive and negative records for each syndrome in both test sets. tns in the emt-c pilot dataset contain an emergency medical text classifier: new system improves processing and classification of triage notes 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi average of 22 words (128 characters) per tn. tns in the final testing dataset contain an average of 25 words (151 characters) per tn. table 3: distribution of records in pilot and final test sets syndrome pilot study dataset n=485 final test dataset n=3053 positive (%) negative (%) positive (%) negative (%) gastrointestinal 80 (16.5%) 405 (83.5%) 23 (0.8%) 3030 (99.2%) respiratory 87 (17.9%) 398 (82.1%) 171 (5.6%) 2882 (94.4%) fever-rash 5 (1.0%) 480 (99.0%) 249 (8.2%) 2804 (91.8%) test plan emt-c performance was measured in terms of sensitivity and specificity values calculated by comparing the system output with gold standard classification. sensitivity is defined as the ratio of correctly classified syndrome positive records to gold standard syndrome positive records, while specificity is the ratio of correctly classified syndrome negative records to gold standard syndrome negative records. weighted versions of sensitivity and specificity were used for the final testing dataset to reflect the unbalanced stratified sampling used to select these records and make the results generalizable to the entire set of records. several configurations of emt-c were tested to assess the trade-off between sensitivity and specificity values. configurations were based on 4 variations. 1. augmentation of the mtl with terms from the training set. in one condition, the mtl contained only terms drawn from the syndrome sql definition; in the other, it was augmented using terms extracted from the training set. these added terms were those with the highest tf-idf rankings. 2. source of terms for record vector. in one condition, the vector was built using terms from both the tn and cc. in the other, terms were drawn only from the tn. in some records, the two fields contain the same terms: use of both fields does not add any additional information, nor does it add any "deceptive" terms. in other records, the two fields contain different terms. as mentioned earlier, this is a double-edged sword, providing stronger evidence of a true positive, or deceptive information leading to a false positive. 3. use of exclusion terms. the nc detect syndromes include standalone exclusion criteria for each of the syndromes [7]. for example, the gastrointestinal syndrome excludes records with “crohn” and “irritable bowel” since these chronic conditions are associated with symptoms that are identical to many of those in the gi syndrome, which could lead to false positive classification. the experiments were run with and without the use of exclusion terms. 4. use of pseudo-relevance feedback. in one condition, the mtl was augmented through the pseudo-relevance feedback method described earlier; in the other, the feedback loop was omitted. emergency medical text classifier: new system improves processing and classification of triage notes 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi results table 4 shows the maximum results of emt-c processing on the final test set. included in the table are the highest levels of sensitivity (se) and specificity (sp) produced for each syndrome, along with the settings for the variations (described in the previous section) for each value. tables 5 and 6 provide the complete results for the pilot and final test sets, respectively. table 4: maximum weighted sensitivity and specificity values, and the variation settings for the configurations that produced them, on final test set (n = 3053 weighted to 1.34 million). gastrointestinal respiratory fever-rash maximum sensitivity 0.97 1. mtl terms: syndrome 2. record terms: tn + cc 3. exclusion terms: either 4. relevance feedback: with 0.91 1. mtl terms: syndrome 2. record terms: tn + cc 3. exclusion terms: either 4. relevance feedback: with >0.99 1. mtl terms: either 2. record terms: tn + cc 3. exclusion terms: either 4. relevance feedback: either maximum specificity 0.94 1. mtl terms: syndrome 2. record terms: tn 3. exclusion terms: with 4. relevance feedback: without 0.91 1. mtl terms: either 2. record terms: tn 3. exclusion terms: either 4. relevance feedback: without 0.93 1. mtl terms: syndrome 2. record terms: tn 3. exclusion terms: either 4. relevance feedback: without in all configurations of emt-c in the final test set (table 6), sensitivity was improved over the baseline nc detect queries (table 2), while specificity decreased. augmenting the mtl with highly ranked (tf-idf) terms from the training set (variation 1) improved sensitivity for the gi syndrome in the absence of pseudo-relevance feedback, but decreased it for respiratory syndrome in the same configurations. the effect of augmentation on specificity was mixed. for the gi syndrome, maximum values of both se and sp were produced without terms from the training set (see table 4), but it decreased specificity in the absence of pseudo-relevance feedback. for the respiratory syndrome, it had the opposite effect. sensitivity was higher in almost all configurations using pseudo-relevance feedback (variation 4) than in those without feedback for the gi and respiratory syndromes. in contrast, specificity was higher in configurations without pseudo-relevance feedback. maximum values of se were produced using pseudo-relevance feedback for the gi and respiratory syndromes; but maximum values of sp were produced without feedback for all three syndromes (see table 4). although drawing mtl terms from both tn and cc (variation 2) improved sensitivity in many configurations, in others, the opposite was true. this variation decreased specificity slightly in some configurations, and seemed to have no effect in others. maximum se values were produced using both tn and cc. in contrast, maximum sp values were produced using tn alone (table 4). emergency medical text classifier: new system improves processing and classification of triage notes 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi table 5: sensitivity and specificity values for all system configurations of emt-c on pilot test set (n = 485). maximum values for sensitivity (se) and specificity (sp) are bolded. configuration settings based on 4 system variations 1) augmentation of master term list, 2) source of terms for record vector, 3) use of exclusion terms, 4) use of pseudo-relevance feedback. master term list configuration gastrointestinal respiratory fever-rash 3. without exclusion terms 4. without relevance feedback 4. with relevance feedback 4. without relevance feedback 4. with relevance feedback 4. without relevance feedback 4. with relevance feedback se sp se sp se sp se sp se sp se sp 1. mtl terms: syndrome 2. record terms: tn+cc 0.89 0.86 0.90 0.71 0.87 0.86 0.97 0.73 1 0.87 1 .086 1. mtl terms: syndrome 2. record terms: tn 0.76 0.88 0.96 0.56 0.83 0.86 0.97 0.73 1 0.87 1 0.87 1. mtl terms: syndrome + training 2. record terms: tn+cc 0.88 0.86 0.94 0.57 0.86 0.86 0.97 0.73 1 0.58 1 0.57 1. mtl terms syndrome + training 2. record terms: tn 0.75 0.88 0.90 0.59 0.82 0.87 0.95 0.73 1 0.59 1 0.58 3. with exclusion terms 1. mtl terms: syndrome 2. record terms: tn+cc 0.83 0.88 0.84 0.78 0.87 0.86 0.97 0.73 1 0.87 1 0.87 1. mtl terms: syndrome 2. record terms: tn 0.71 0.90 0.90 .063 0.83 0.86 0.97 0.73 1 0.87 1 0.87 1. mtl terms: syndrome + training 2. record terms: tn+cc 0.83 0.88 0.88 0.67 0.86 0.86 0.97 0.73 1 0.59 1 0.58 1. mtl terms syndrome + training 2. record terms: tn 0.71 0.90 0.85 0.66 0.82 0.87 0.95 0.73 1 0.60 1 0.58 emergency medical text classifier: new system improves processing and classification of triage notes 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi table 6: weighted sensitivity and specificity for all system configurations of emt-c on final test set (n=3053 weighted to 1.34 million). maximum values for sensitivity (se) and specificity (sp) are bolded. configuration settings based on 4 system variations 1) augmentation of master term list, 2) source of terms for record vector, 3) use of exclusion terms, 4) use of pseudo-relevance feedback. master term list configuration gastrointestinal respiratory fever-rash 3. without exclusion terms 4. without relevance feedback 4. with relevance feedback 4. without relevance feedback 4. with relevance feedback 4. without relevance feedback 4. with relevance feedback se sp se sp se sp se sp se sp se sp 1. mtl terms: syndrome 2. record terms: tn+cc 0.83 0.92 0.97 0.76 0.65 0.89 0.91 0.62 >0.99 0.91 >0.99 0.69 1. mtl terms: syndrome 2. record terms: tn 0.77 0.93 0.86 0.79 0.54 0.91 0.87 0.64 0.96 0.93 0.98 0.79 1. mtl terms: syndrome + training 2. record terms: tn+cc 0.94 0.76 0.94 0.78 0.64 0.90 0.81 0.80 >0.99 0.91 >0.99 0.73 1. mtl terms syndrome + training 2. record terms: tn 0.92 0.80 0.86 0.81 0.54 0.91 0.76 0.80 0.96 0.93 0.98 0.76 3. with exclusion terms 1. mtl terms: syndrome 2. record terms: tn+cc 0.77 0.93 0.91 0.79 0.65 0.89 0.91 0.62 >0.99 0.91 >0.99 0.69 1. mtl terms: syndrome 2. record terms: tn 0.72 .094 0.80 0.81 0.54 0.91 0.87 0.64 0.96 0.93 0.98 0.79 1. mtl terms: syndrome + training 2. record terms: tn+cc 0.89 0.80 0.89 0.81 0.64 0.90 0.81 0.80 >0.99 0.91 >0.99 0.73 1. mtl terms syndrome + training 2. record terms: tn 0.86 0.83 0.83 0.80 0.54 0.91 0.76 0.80 0.96 0.93 0.98 0.76 using exclusion terms (variation 3) decreased sensitivity for the gi syndrome, and had little or no effect for the other two syndromes. there was slight improvement in specificity using exclusion terms for the gi syndrome, and again, little or no effect in the other two. maximum sensitivity values for gi and respiratory were produced using pseudo-relevance feedback without augmentation from the training set. sensitivity scores for the fever-rash syndrome were excellent for all configurations in the final test. this is likely due to the nature of the syndrome itself: its symptoms, and thus terms representing them, are not associated with other syndromes, and thus their presence or absence provides relatively unambiguous evidence for classification. in contrast, other symptoms such as headache and dizziness are associated with more than one syndrome, as well as with other non-syndromic conditions. emergency medical text classifier: new system improves processing and classification of triage notes 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi maximum specificity values, on the other hand, occurred in configurations without pseudorelevance feedback except for the gi syndrome when the mtl was augmented with additional terms. however, the increase was slight. discussion the results indicate that emt-c, a syndromic classification system based on the vector space model with pseudo-relevance feedback, is a promising approach to syndromic surveillance. in a laboratory setting, we achieved improvements in sensitivity with moderate decreases in specificity. terms drawn from a training set of ed records improve performance over terms drawn only from the syndrome definitions. the unstructured text expressions found in ed cc and tn contain synonymous terms and phrases (often more colloquial in nature) for symptoms in the syndrome definitions. in addition, they may contain information related to syndromic symptoms that are not necessarily symptoms, but describe the patient's situation. the risk is that the additional terms may also be associated with false positives, thus, adding them increases the noise in the system. these findings reflect the difficulty of the surveillance task. early warning of an outbreak is crucial to successful management, but the data that are available in near-realtime are often of mixed quality. the ed cc and tn are available in a timely fashion, and are a rich source of information, but are difficult to mine [43,44]. our results also indicate that there may not be a single model or configuration that is ideal for all syndromes. this is apparent in comparing the fever-rash results with those of the other two syndromes, but there were also differences between gastrointestinal and respiratory. one contributing factor is the precision of symptom terms in identifying one and only one syndrome. another factor to consider is the frequency of occurrence of a syndrome in the population (prevalence rate) [44], as well as the relative risk of false positives and false negatives in managing an outbreak. public health officials weigh the relative risks involved in missing a case; their preferences should be reflected in system design. for a syndrome such as fever-rash, the risk of false negatives may be deemed greater than the burden of dealing with false positives, thus an emt-c configuration that maximizes sensitivity may be preferred. the converse may be true for respiratory; thus, maximizing specificity may be the goal [31,32]. these requirements highlight the advantage to a system architecture such as that used in emt-c that supports multiple configurations. limitations this study was carried out in an informatics laboratory setting using data from previous years. in the actual production environment, batched data are uploaded into nc detect twice daily. ed visits are only accepted for upload if they contain several key data elements, including cc, so some visits are added in subsequent batches. also, additional data elements can be added after the initial upload. these production constraints may influence system performance. the training set for this research consisted of ed records from 2006-2008. we do not know if the vocabulary used by triage nurses in cc and tn has changed over time. if it has, retraining would reveal any changes that were significant enough to affect syndromic classification, as well as improving system performance. future work next steps include emt-c testing in the production environment on current ed visit records. in addition to examining the effects of the constraints described above, we also plan to gather user emergency medical text classifier: new system improves processing and classification of triage notes 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi input on how emt-c performs in this environment. for example, while our approach has been to improve the ability to detect signals with the syndromic surveillance system, users may have differing needs for the sensitivity/specificity tradeoff. future work will also focus on leveraging the system architecture to improve system performance in several ways. one advantage of the emt-c model is the relative ease with which it can be trained. the work presented here developed models for 3 syndromes: gastro-intestinal, respiratory, and fever-rash. we plan to expand coverage of emt-c to include more syndromes for acute infectious diseases of interest (influenza, neurological). in principle, the emt-c model should be applicable to other types of classification problems and to other types of unstructured text, although we have not done so at this time. this version of emt-c uses single terms as features, which is common in the literature (e.g., [39]). it may be that using unordered bigrams (pairs of words) as features would improve performance. this would allow the model to represent terms that commonly co-occur (e.g., “vomiting and diarrhea”) or are used as phrases in the cc and tn, such as “rash all over”. emt-c currently uses binary vectors to represent the mtl and the records. that is, a vector represents only the presence or absence of terms. the vector space model also supports weighted vectors, in which some terms are represented by values between 0 and 1 to indicate the strength of evidence they provide. the presence of a stronger term is then represented with a higher weight than that of a weaker term. thus, a small number of strong terms or a larger number of weak terms are equivalent in terms of similarity value to the mtl vector. weights can also be negative, indicating that the presence of a term is evidence against a syndrome classification. we can also experiment with different ways of calculating the similarity threshold, such as averaging similarity values from a larger number of records, or setting a lower bound for the threshold. training could also incorporate ed data produced later in the ed visit, such as diagnostic icd-9-cm codes. although not available for the real-time classification desired for syndromic surveillance, they may be helpful in building the mtl [4]. one purpose of this study was to investigate ways of utilizing the information provided in tns to improve syndromic surveillance. therefore, the records included in the test sets all included tns. in reality, over 50% of ed records sent to nc detect do not include a tn at this time. we expect more eds to include tns in the future, following the trend we have observed over the past several years. however, as emt-c is put into production, we will determine whether it should classify all ed records regardless of the presence or absence of the tn, or whether two processing streams should be established, i.e., emt-c for records with the tn, and the existing nc detect sql query for records without the tn. conclusion we developed a new system for pre-processing and syndromic classification of ed records with triage notes that achieved improvements in sensitivity and specificity over previous results in the laboratory setting. our work also shows that emt-c can be tuned to achieve different levels of performance based on user requirements. after field testing, emt-c could be incorporated into nc detect to augment or substitute for the sql queries currently in use (described in section 1.1). emt-c could also be incorporated into other surveillance systems that receive as inputs text fields containing useful information that is unstructured, uncoded, and/or not easily quantified. emergency medical text classifier: new system improves processing and classification of triage notes 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi human subjects protections this study was approved by the public health-nursing institutional review board in the office of human research ethics at the university of north carolina at chapel hill. acknowledgements data for this study were provided by the nc detect data oversight committee and nc detect. the nc detect 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classification of triage notes 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e178, 2014 ojphi the cdc working group. mmwr cdc. 2004;53:125-129. http://www.cdc.gov/mmwr/pdf/rr/rr5305.pdf 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts an exploration of the h1n1 outbreak in champaign and urbana elementary schools christopher komisarz and ian brooks* ncsa, university of illinois, urbana, il, usa objective the goal of this project is to examine the patterns of school absenteeism during the h1n1 pandemic of 2009 comparing two contiguous school districts with very different enrollment policies. introduction champaign and urbana, illinois are considered twin cities that share the university of illinois. due to different geographic recruitment procedures, champaign and urbana public elementary schools offer a particularly novel opportunity to examine the h1n1 outbreak among students. urbana schools recruit from specific geographic areas (neighborhoods) designated by the school district whereas champaign schools are non-selective in their composition where students residing in champaign can attend any school within the city. methods daily absence data from elementary schools in champaign and urbana school districts were obtained through the ncsa indicator1 database. school population data were obtained from the illinois state board of education annual school report cards2. data were examined as a proportion of students within a school considered absent and mapped utilizing arcmap 10.1 showing a time course of the percentage of students absent at each school for the period between september 8 and december 18 2009. correlation analysis was used to examine relationships between specific demographics and absence patterns. results in early october, both school districts showed an increase in overall absences with urbana schools showing a quicker and more substantial increase in the beginning of the month then leveling off with a slight increase in later october. champaign had a more gradual but substantial increase over the month of october peaking near the middle of the month and remained elevated for about two weeks. both districts showed a drop in absences until late november when the champaign schools saw another jump to nearly 8%. this was again followed by a decrease to pre-october absence percentages throughout the remainder of the semester. on an individual school level correlations between schools range from 0.692 to -.113 with a mean of 0.292 in urbana indicating significant differences in the absence patterns. 9 of the 15 pairwise comparisons were significant at p<0.05. in champaign the range was from to .817 to -.092 with a mean of 0.497. 51 of the 55 pairwise comparisons were significant at p<0.05. correlation analyses were used to determine trends among the variables. the only significant trends noted were among the student composition and the percentage of low income students. there were no significant correlations between the composition, number of students, or percent of low-income students and maximum or average absence proportion among the schools. conclusions there is clear evidence from this small comparison that there is a difference in the pattern of disease spread depending on the geographic composition of the schools catchment area. in urbana which uses traditional neighborhood schools only 60% of the pairwise epidemiological curves were significantly similar compared to 92.7% of the pairwise curves in champaign. this shows that when students from the city are intermingled through a school district a disease epidemic spreads through the schools in a similar timeframe, whereas there is a greater temporal separation with neighborhood schools. the practical implication of this is that once an outbreak has been detected in a neighborhood school there may be time to prevent it spreading to other schools in the district, whereas with geographically mixed school an outbreak essentially hits all of the schools simultaneously. keywords school absenteeism; h1n1 surveillance; geographic analysis acknowledgments the researchers would like to thank the champaign and urbana school districts; and awais vaid, mph, mbbs. references 1. w. edwards, a. vaid, and i. brooks, “indicator: an opensource cyberenvironment for biosurveillance,” in defining crisis management 3.0, seattle, wa, 2010. 2. illinois state board of education. (2010). 2010 report card definitions and sources of data (2010 ed.) [brochure] *ian brooks e-mail: ian@ncsa.illinois.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e139, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts the threat of ebola virus disease: a call to integrate all sectors in surveillance activities in ghana dennis o. laryea*1, yaw amoako1, dan v. armooh2, emmanuel p. abbeyquaye3 and bernice n. amartey3 1public health, komfo anokye teaching hospital, kumasi, ghana; 2health allianz clinic, accra, ghana; 337 military hospital neghelli barracks, accra, ghana objective to describe ghana’s disease surveillance system operation and the potential challenges in the light of the ebola outbreak in west africa introduction disease surveillance particularly surveillance for communicable diseases is essential in identifying cases and preventing the occurrence of an outbreak. surveillance can also contribute to reducing the size of an outbreak. in order to achieve these, surveillance activities must include all possible sites for case detection. the lack of established mechanisms to provide feedback to the surveillance system at all such points can cause a failure of the surveillance system. these are extremely relevant particularly in the current outbreak of ebola in some parts of the west african sub region. ghana, like many countries has established surveillance systems for specific diseases. currently, 44 diseases/public health events including ebola are under surveillance as part of an integrated disease surveillance and response (idsr) system. although the ministry of health (moh) exercises authority over issues of health, the operation of policies and practices on disease surveillance is by the ghana health service (ghs), an agency of the moh despite the existence of other agencies such as the teaching hospitals. methods we examine ghana’s health system in relation to communicable disease surveillance and identify possible challenges to the current system and suggest some ways to improve disease surveillance in ghana in light of the current threat of ebola and other emerging infectious diseases. results disease surveillance in ghana is carried out by the disease surveillance department of the ghs. information on diseases is obtained mostly from health facilities under the ghs. information on suspected or confirmed cases of diseases under surveillance are transmitted to the national headquarters of the ghs through the district and regional health directorates. where required, specimens in addition to the appropriate notification forms are sent to designated public health reference laboratory for confirmation. test results are transmitted via the same channels to the health facility involved. although the system includes all facilities under the ghs, it does not explicitly involve non-ghs facilities. surveillance practices including training of officers have also focussed on only ghs facilities. conclusions operating a surveillance system through a single agency, the ghs and its facilities with no clear guidelines for the integration of other parallel health service providers including private and other nonghs health facilities in the routine disease surveillance structure of ghana is a source of concern. this is despite the idsr guidelines identifying private hospitals and others such as veterinary services as sources of surveillance information. while active disease surveillance in non-ghs facilities has usually taken place during outbreaks when active case finding is undertaken, there is the need to ensure that these occur on a permanent basis to ensure that cases are not missed at the onset of an outbreak should the first cases of communicable disease outbreak report to a non-ghs facilitie. this is critical as a significant proportion of patients access health care in these non-ghs facilities following the introduction of the national health insurance scheme (nhis). ghana’s current practice of disease surveillance may potentially miss cases of public health importance. the surveillance system must move beyond the administrative structure of the ghana health service and extend to include all potential sources of surveillance information. this will ensure that cases are detected and interventions promptly instituted to prevent or reduce the extent of outbreaks keywords surveillance; health system; outbreak; ghana acknowledgments we acknowledge the contribution of staff of the komfo anokye teaching hospital, kumasi, ghana references ghana health service, facts and figures 2010 [online] available from http://www.moh-ghana.org/uploadfiles/publications/ghs%20 facts%20and%20figures%202010_22apr2012.pdf accessed 5th september 2013 ghana health service technical guidelines for integrated disease surveillance and response 2011 2nd edition *dennis o. laryea e-mail: dlaryea@kathhsp.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e197, 201 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts re-emerging infectious disease (red) alert tool maneesha chitanvis*1, ashlynn daughton1, 2, forest m. altherr1, geoffrey fairchild1, william rosenberger1, emily alipio lyon1, attelia hollander1, nileena velappan1, derek aberle1, nidhi parikh1 and alina deshpande1 1biosecurity and public health, los alamos national laboratory, los alamos, nm, usa; 2university of colorado, boulder, boulder, co, usa objective although relying on verbal definitions of “re-emergence”, descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. re-emerging infectious disease alert (red alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. the tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesisgeneration regarding the global scenario. introduction definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. while the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such. methods our tool automatically computes historic disease incidence and performs trend analyses to help elucidate events which a user may considered a true re-emergence in a subset of pertinent infectious diseases (measles, cholera, yellow fever, and dengue). the tool outputs data visualizations that illustrate incidence trends in diverse and informative ways. additionally, we categorize location and incidence-specific indicators for re-emergence to provide users with associated indicators as well as justifications and documentation to guide users’ next steps. additionally, the tool also houses interactive maps to facilitate global hypothesis-generation. results these outputs provide historic trend pattern analyses as well as contextualization of the user’s situation with similar locations. the tool also broadens users’ understanding of the given situation by providing related indicators of the likely re-emergence, as well as the ability to investigate re-emergence factors of global relevance through spatial analysis and data visualization. conclusions the inability to categorically name a re-emergence event as such is due to lack of standardization and/or availability of reproducible, data-based evidence, and hinders timely and effective public health response and planning. while the tool will not explicitly call out a user scenario as categorically re-emergent or not, by providing users with context in both time and space, red alert aims to empower users with data and analytics in order to substantially enhance their contextual awareness; thus, better enabling them to formulate plans of action regarding re-emerging infectious disease threats at both the country and global level. keywords infectious disease; re-emergence; pattern analysis; contextual awareness acknowledgments this work was supported by the defense threat reduction agency’s joint science and technology office for chemical and biological defense under project numbers cb10007. los alamos national laboratory is operated by los alamos national security, llc for the department of energy under contract de-ac52-06na25396. the authors also wish to thank bryan lewis at the biocomplexity institute of virginia tech and ramesh krishnamurthy at the world health organization. la-ur-1721884. *maneesha chitanvis e-mail: mchitanvis@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e58, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts nbic and dtra, an interagency partnership to integrate analyst capabilities wai-ling mui*4, 1, edward p. argenta2, 3, teresa quitugua4, 1 and christopher kiley2, 3 1national biosurveillance integration center, washington, dc, usa; 2department of defense, ft. belvoir, va, usa; 3defense threat reduction agency, ft. belvoir, va, usa; 4department of homeland security, washington, dc, usa objective the national biosurveillance integration center (nbic) and the defense threat reduction agency’s chemical and biological technologies department (dtra j9 cb) have partnered to codevelop the biosurveillance ecosystem (bsve), an emerging capability that aims to provide a virtual, customizable analyst workbench that integrates health and non-health data. this partnership promotes engagement between diverse health surveillance entities to increase awareness and improve decision-making capabilities. introduction nbic collects, analyzes, and shares key biosurveillance information to support the nation’s response to biological events of concern. integration of this information enables early warning and shared situational awareness to inform critical decision making, and direct response and recovery efforts. dtra j9 cb leads dod s&t to anticipate, defend, and safeguard against chemical and biological threats for the warfighter and the nation. these agencies have partnered to meet the evolving needs of the biosurveillance community and address gaps in technology and data sharing capabilities. high-profile events such as the 2009 h1n1 pandemic, the west african ebola outbreak, and the recent emergence of zika virus disease have underscored the need for integration of disparate biosurveillance systems to provide a more functional infrastructure. this allows analysts and others in the community to collect, analyze, and share relevant data across organizations securely and efficiently. leveraging existing biosurveillance efforts provides the federal public health community, and its partners, with a comprehensive interagency platform that enables engagement and data sharing. methods nbic and dtra are leveraging existing biosurveillance projects to share data feeds, work processes, resources, and lessons learned. a multi-stakeholder agile process was implemented to represent the interests of nbic, dtra, and their respective partners. system requirements generated by both agencies were combined to form a single backlog of prioritized needs. functional requirements from nbic support the development of the prototype by refining system capabilities and providing an operational perspective. dtra’s technical expertise and research and development (r&d) portfolio ensures robust analytic applications are embedded within a secure, scalable system architecture. integration of analyst validated data from the nbic biofeeds system serves as a gold-standard to improve analytic development in machine learning and natural language processing. additionally, working groups are formed using nbic and dtra extended partnerships with academia and private industry to expand r&d possibilities. these expansions include leveraging existing ontology efforts for improved system functionality and integrating social media algorithms for improved topic analysis output. results the combined efforts of these two agencies to develop the bsve and improve overall biosurveillance processes across the federal government has enhanced understanding of the needs of the community in a variety of mission spaces. to date, co-creation of products, joint analysis, and sharing of data feeds has become a major priority for both partners to advance biosurveillance outcomes. within the larger efforts of system development, possible coordination with other agencies such as the department of veterans affairs (va) and the us geological survey (usgs) could expand reach of the system to ensure fulfillment of health surveillance requirements as a whole. conclusions the nbic and dtra partnership has demonstrated value in improving biosurveillance capabilities for each agency and their partners. bsve will provide nbic analysts with a collaborative tool that can leverage use of applications that visualize near realtime global epidemic and outbreak data from a range of unique and trusted sources. the continued collaboration means ongoing access to new data streams and analytic processes for all analysts, as well as advanced machine learning algorithms that increase capabilities for joint analysis, rapid product creation, and continuous interagency communication. keywords biosurveillance; dtra; nbic; bsve; analytics *wai-ling mui e-mail: wai-ling.mui@associates.hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e46, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e317, 2019 isds 2019 conference abstracts timely public health intervention and vectorborne response planning megan holderness epidemiology, okc-co health department, oklahoma city, oklahoma, united states objective demonstrate the impact of surveillance and media engagement on public health protection during a vectorborne disease response. identify surveillance and reporting methods for timely response to positive cases steps. explore and apply best practices for collaboration with partners and surrounding municipalities in order to reduce disease impact introduction the okc-co health department deployed a phased vectorborne response plan to address multiple diseases, including zika virus and west nile virus. this plan is scalable and flexible, but must necessarily prepare for the “worst case” scenario. although not currently a local threat in okc-co, zika virus response planning requires early coordination between state, local and federal agencies in order to mitigate risk to the population. the backbone of the vectorborne response planning has been proven successful through west nile virus response in which oklahoma has experienced three outbreak seasons: 2003, 2007 and 2012. (osdh) in 2015, the okc area experienced a greater than 112% increase in the number of vectors and 18 wnv positive test pools were observed. the heightened number of vectors and positive test pools did not translate to the same escalation in human cases, which demonstrates the strength that public health collaboration between surrounding municipalities and community members has on reducing the potential impact of this seasonal epidemic. during the most recent 2017 mosquito season, local code enforcement, city officials and consumer protection deployed a total of 18 cdc gravid and bg sentinel traps. the final day of sorting took place during the last week of october, as consistent with the decrease in mosquito numbers. there were 23 trapping and testing weeks with a total of 43,079 mosquitos trapped and 33, 846 mosquitos tested. an average of 66% of all trapped mosquitos were tested each week. the maximum likelihood estimation (mle) infection rate is calculated each week. methods the okc-co health department implements a multi-level approach to communication, prevention, surveillance and disease abatement vectorborne disease. this approach includes: -hazard assessment: vector activity, disease risk and mitigation strategies are included in the response plan and discussed with partners as needed. -media outreach -vector surveillance: occhd conducts local vector surveillance by trapping, lab species identification and testing for west nile virus. the plans also provide testing capabilities in partnership with state and federal agencies. trapping is accomplished using cdc gravid traps and bg sentinel traps. trapped mosquitos are brought to the laboratory at occhd on a weekly basis and are frozen overnight. the following morning, mosquito genus and species are identified and vector species are tested. -environmental treatments: consumer protection identifies and treats areas with stagnant water following a complaint. -habitat remediation: occhd and its partners have resources and coordination planned for habitat remediation events -municipality partnerships: okcswq sends occhd weekly reports for each of the trap sites that includes information on trap condition, needed repairs, and weekly mosquito count. occhd provides larvicide to the municipalities to treat areas of stagnant water. in the case of a positive result, occhd coordinates site surveys with the respective municipality. the municipalities conduct windshield surveys around the site and data is collected and graphed along with test result dates, in order to identify trends and identify effectiveness of and plan for mitigation procedures. roles and responsibilities of each agency are outlined in the phased response plan. -human disease surveillance: epidemiologists produce maps using geographic information of cases to identify clusters and possible major sources of mosquitos. this data is sent to consumer protection for site surveys and environmental treatments and possible habitat remediation. the epidemiologist also gathers information on travel history and wnv exposure/risk factors of the patient. additionally, the occhd epidemiologist sends out vectorborne monitoring reports to partners providing local, state and national surveillance data. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e317, 2019 isds 2019 conference abstracts results -media: during the most recent seasons, occhd marketing and communications staff developed prevention messaging and earned more than $600,000 in prevention messaging across the metro, to include outdoor billboard exposure, television, radio and print ads, each season. further, occhd experts engage in an average of 30 media interviews each season(television, radio and print). additionally, occd and tyler outdoor collaborated to present the 4d’s on digital billboards across the okc metro area. vital outdoor also provides digital locations to present preventive information for 30 days, totaling $60,000 in messaging. finally, social media (facebook and twitter) outreach efforts were utilized to spread awareness to the community, including education videos reaching nearly 2,000 people. -vector surveillance: trapping sites are identified across the metro, four in edmond and six in oklahoma city and the mosquito season leading to 43,072 mosquitos captured and identified, of which, 33,846 were tested for wnv, a 131% increase from 2016. -municipality partnerships: 110 positive test pools were identified and the respective municipality is notified of the site that correlated with the positive test pool to conduct site surveys. -human disease surveillance: there were 6 confirmed human cases of wnv, 5 classified as neuroinvasive and 1 classified as wnv fever. gis mapping is used in case investigations and habitat remediation. conclusions as a single agency, this timely vectorborne disease response would be impossible to achieve without partnerships with surrounding municipalities. functioning as a cohesive unit, occhd and municipality agencies are able to set, maintain, repair and collect traps at each site, disseminate surveillance reporting information, coordinate treatment applications and investigate harborage areas. additionally, municipalities coordinate with habitat remediation efforts. having the resources and coordination planned for these events is critical to a timely response, especially in during an outbreak season. occhd is unique in its ability to identify and sort vectors by genus and species. this capability provides detailed surveillance data and aids in preparation and planning, such as designing traps to selectively capture the most common vectors in the area. during seasons with heighted activity, the ability to have team members and partners identify geographic clusters is pivotal to timely preventive measures. practice based strategies to mitigate the vectorborne disease risk to humans during an influx of increased mosquito population and positive mosquito test pools include the careful consideration of: collaboration with outside entities in order to sustain program success; technology and gis mapping; and strategic planning prior to the start of each season with evidence pulled form geographical analyses, vector and climate surveillance and municipality engagement. acknowledgement the two municipality agencies collaborating with occhd are oklahoma city storm water quality (okcswq) and edmond code enforcement (ece). http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh health monitoring systems, inc, pittsburgh, pa, usa objective identify any relationship between alcohol-related emergency department visits in franklin county, ohio and ohio state university football games. introduction according to the center for disease control (cdc), binge drinking causes over half of the 88,000 excessive alcohol use deaths and costs approximately $149 billion dollars annually in the united states1. additionally, excessive alcohol use can increase the risk of many other health problems, including injuries and cancer, placing a large burden on public health. in franklin county, ohio, the ohio state university (osu) football games are an occasion of binge drinking for the student body and columbus population alike. the purpose of this study is to determine if the binge drinking population is significantly different during football games. methods this study looked at emergency department (ed) registration data for franklin county, ohio for the past 5 years that was classified as alcohol-related using epicenter. days that had a significantly elevated number of visits were identified as being two standard deviations above the yearly mean, and termed “high volume” (hv) days. these hv days were characterized by whether or not they occurred relative to an osu football game or football-related tradition. comparisons were drawn between total counts, gender distribution, and average age of football vs. non-football hv days. results an average of 15 total hv days were found each year, ranging from 11 in 2011 to 19 in 2012 (table 1). the total percentage of these hv days that can be correlated to osu football games is 37%. this ranges by year, and seems to trend with the regular season football record. in 2011, the record was 6-6, and the percentage of hv days associated with osu football dropped to 18%. by contrast, the record was either 11-1 or 12-0 the other 4 years, corresponding to higher percentages. a comparison of football to non-football hv days by year can be seen in figure 1. the results were then broken down further, as seen in table 2. there was no significant difference found between the number of visits on football vs. non-football hv days at the 95% confidence level (p value 0.054). visits by females were significantly higher for football hv days than baseline (p value 0.002). the average patient age was significantly different between football and non-football hv days, at 36 and 40 years old respectively (p value 0.001). additionally, the first football game of the season accounted for at least 1 hv day in 4 of the 5 years analyzed. conclusions the binge drinking population is significantly different during football games in franklin county. this population is comprised of significantly younger adults and more females. with this information in mind, public health may desire to do seasonal targeted awareness campaigns addressed to this population. additionally, considering the consistency of binge drinking episodes that occur during the first football game of the season, public health may want to emphasize these dates for the campaigns. table 1 table 2 figure 1 keywords alcohol; epicenter; binge drinking; football; health monitoring systems acknowledgments we wish to thank the ohio department of health for the data for this work. references 1. u.s. department of health and human services, centers for disease control and prevention (march 10, 2015). alcohol and public health; overview of cdc’s alcohol program. retrieved from http://www. cdc.gov/alcohol/index.htm 2. cbs interactive (2015). ohio state football. retrieved from http:// www.ohiostatebuckeyes.com/sports/m-footbl/sched/osu-m-footblsched.html *kristen a. weiss e-mail: kristen.weiss@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e173, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a building an ontology for identity resolution in healthcare and public health 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi building an ontology for identity resolution in healthcare and public health jeffrey duncan, ms1*, karen eilbeck, phd1, scott p. narus, phd1,3, stephen clyde2, sidney thornton, phd1,3, catherine staes, phd1 1. department of biomedical informatics, university of utah, salt lake city, ut usa 2. department of computer science, utah state university, logan, ut usa 3. intermountain healthcare, salt lake city, ut usa abstract integration of disparate information from electronic health records, clinical data warehouses, birth certificate registries and other public health information systems offers great potential for clinical care, public health practice, and research. such integration, however, depends on correctly matching patientspecific records using demographic identifiers. without standards for these identifiers, record linkage is complicated by issues of structural and semantic heterogeneity. objectives: our objectives were to develop and validate an ontology to: 1) identify components of identity and events subsequent to birth that result in creation, change, or sharing of identity information; 2) develop an ontology to facilitate data integration from multiple healthcare and public health sources; and 3) validate the ontology’s ability to model identity-changing events over time. methods: we interviewed domain experts in area hospitals and public health programs and developed process models describing the creation and transmission of identity information among various organizations for activities subsequent to a birth event. we searched for existing relevant ontologies. we validated the content of our ontology with simulated identity information conforming to scenarios identified in our process models. results: we chose the simple event model (sem) to describe events in early childhood and integrated the clinical element model (cem) for demographic information. we demonstrated the ability of the combined sem-cem ontology to model identity events over time. conclusion: the use of an ontology can overcome issues of semantic and syntactic heterogeneity to facilitate record linkage. keywords: ontology, medical record linkage, integrated child health information systems abbreviations: health information exchange (hie), u.s. office of the national coordinator for health information technology (onc), universal newborn hearing screening (unhs), newborn metabolic screening (nbs), business process modeling notation (bpmn), electronic health record (ehr), utah statewide immunization information system (usiis), enterprise master person index (empi). correspondence: jeff.duncan@utah.edu doi: 10.5210/ojphi.v7i2.6010 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:jeff.duncan@utah.edu building an ontology for identity resolution in healthcare and public health 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi background and significance many strategies for healthcare improvement rely on integrating patient clinical data from multiple encounters and from multiple provider organizations. the ability to correctly match patient-specific records within and across organizations in healthcare and public health to support health information exchange (hie) has become such a critical need that the u.s. office of the national coordinator for health information technology (onc) launched the patient identification and matching initiative in september, 2013. the goal of this collaborative initiative was to conduct environmental scans and in-depth literature reviews across stakeholder organizations to identify problems in patient matching and to develop recommendations for improvement. the initiative’s final report cited, among other things, the need to standardize both the structure and content of patient identity attributes used to link records to realize improvements in patient matching across the many disparate organizational boundaries [1]. without standards for personal identity attributes, record linkage is complicated by issues of both structural and semantic heterogeneity [2]. structural heterogeneity arises because different information systems vary in quality, completeness, and formats for storing identifying information. semantic heterogeneity arises from differences in the content and meaning of demographic identity fields in disparate information systems. past research has focused on developing and improving methods for record linkage [3-8]. these methods are constrained by the need to attain extremely high degrees of sensitivity while maintaining almost perfect specificity. according to the onc report, patient safety concerns dictate that matching algorithms be adjusted to produce duplicates rather than overlays (false positives), because wrong care could be provided based on an incorrect match [1]. in practice, both probabilistic and deterministic linkage methods typically divide records being linked into three groups: matches, non-matches, and possible matches. possible matches, which are records that match in many but not all respects, require costly human resolution, estimated to be as much as $60 per record [1]. possible matches often arise from the fact that demographic attributes used to link records such as names and addresses may be recorded incorrectly [9] or may change over time. previously, we showed that events such as adoptions, paternity acknowledgments, and amendments result in changes to birth certificate identities for over 6% of children, particularly in their first two years of life [10]. following the birth of a child in a hospital, these events, combined with numerous reports from hospitals to public health, creates unique challenges for integrating information. a hospital birth drives the creation of electronic records in multiple healthcare and public health information systems. the hospital creates administrative and electronic medical records for the newborn child. hospital staff administer a hepatitis b immunization, details of which are sent to an immunization registry in apublic health department [11,12]. universal newborn hearing screening (unhs) test results are reported to the public health department [13-15], as are newborn metabolic screening (nbs) (heelstick) test results [16]. integrated child health information systems [17], such as utah’s child health advanced record management (charm) [18], attempt to link these records using combinations of non-unique demographic identifiers such as name, date of birth, sex, address, and telephone number, and locally unique identifiers such as newborn screening kit numbers and birth certificate state file numbers. in building an ontology for identity resolution in healthcare and public health 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi addition, efforts such as utah’s statewide master person index have attempted to link persons across public health and healthcare master person indices (mpis) [19]. ontologies are formal descriptions of the terms in a domain and the relationships between terms. they have proven useful in overcoming challenges in integrating information due to semantic and structural limitations [2,20]. for example, ontograte [21] is an ontology-based framework that demonstrates the utility of converting relational database schemas to ontologies to solve query translation and data translation problems across heterogeneous relational databases. ontologies have been used in diverse applications such as semantic integration in biomedical experimental protocols [22], and integrating clinical information for oncology research [23]. in addition to promoting data integration, ontologies modeled in languages such as the w3c standard web ontology language (owl) demonstrate the ability to employ description-logic based reasoning [24]. owl’s reasoning capability has been demonstrated in genomics [25], developing clinical practice guidelines [26], and for studying relationships among biological entities [27]. despite the growing use of ontologies for data integration, we were unable to find literature describing their use for identity resolution or record linkage. the goal of this project was to investigate existing ontologies, or to develop a new one, to facilitate linking birth and earlychildhood records in both clinical and public health information systems. our specific objectives were to develop and validate an ontology to: 1) identify concepts in the domain of identity, including the components of identity and the events subsequent to birth that result in creation or change of identity; 2) develop an ontology to facilitate the integration of data from multiple sources such as an electronic health record (ehr), birth certificate registry, immunization registry, and other public health sources; and 3) validate our ontology’s ability to model identitychanging events over time and their resulting changes to individual identity components. methods we adopted the methods of uschold and gruninger [28], progressing along a continuum of formality from informal domain descriptions to rigorously formal structured ontology language. the basic methodology includes: identify the ontology’s purpose and scope; build the ontology through knowledge acquisition, coding, and integration of existing ontologies; and evaluation. identify ontology purpose and scope we defined our ontology’s purpose as describing: a) the sources of identity information, b)events that result in the creation, change, or sharing of identity information, and c) the components of identity that are created, changed or shared among healthcare and public health entities. because our interest is in the integration of early childhood identities, we restricted the ontology’s scope to the events surrounding the birth of a child in a hospital and the subsequent reports to public health. ultimately, however, this ontology of identity may be extended to cover the continuum of life events. knowledge acquisition we conducted interviews with administrative domain experts at three salt lake city-area hospitals, including university of utah health sciences center, intermountain healthcare, and st. mark’s hospital. we also interviewed public health domain experts within the utah building an ontology for identity resolution in healthcare and public health 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi department of health, from the office of vital records and statistics, utah statewide immunization information system (usiis), early hearing detection and intervention program, and newborn screening program. these interviews resulted in the development of process models describing the creation and transmission of identity information among healthcare and public health entities for post-birth activities. we created process models using business process modeling notation (bpmn) [29] with the goal of documenting specific post-birth events and the identity artifacts created and transmitted among various information systems. integration of existing ontologies to promote interoperability and reuse of domain knowledge, uschold and grueninger recommend integration of existing ontologies. we searched for existing ontologies that describe events and their timing, as well as ontologies for identity information, using various online sources including: national center for biomedical ontologies (ncbo) bioportal (http://bioportal.bioontology.org/); protégé ontology library (http://protegewiki.stanford.edu/wiki/protege_ontology_library); obo foundry (http://www.obofoundry.org/); and google scholar (https://scholar.google.com/). ontology coding we represented our ontology using the web ontology language (owl) [24] using the protégé owl editor [30]. we chose protégé because of its status as an open-source application with a significant user community, availability of plug-ins to extend its functionality, support of automated reasoning and consistency checking, and its ability to both create and instantiate our ontology using the same tool. evaluation we evaluated both the content of our ontology and its potential utility for tasks in identity resolution. one author mapped identifiers from public health databases, including birth certificates, death certificates, and immunization information system (iis) to ontology classes to validate the ontology’s content and coverage. independently, a domain expert from usiis mapped iis identity fields to ontology classes, and a vital statistics domain expert did the same for birth and death certificates. we compared the independent mappings and demonstrated concurrence between them. we then simulated identity events and their corresponding attributes in protégé and used sparql queries to demonstrate ontology use cases. we also explored additional benefits of using an ontological approach for storing and searching identity information. results interviews with domain experts within udoh and in various area hospitals revealed marked similarities, with some interesting differences, in administrative events following the birth of a child. figure 1 depicts a high-level process model derived from these interviews. all of the process models created are included as supplemental materials. building an ontology for identity resolution in healthcare and public health 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi figure 1. high-level process model for birth-related events in a hospital using bpmn childbirth results in the creation of a unique record for the child in the hospital's information system and enterprise master person index (empi). in some facilities, this new record creation building an ontology for identity resolution in healthcare and public health 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi may take place as pre-registration, while in other facilities the newborn child’s record is only created after a live birth. regardless of its timing, the name in the new record is usually a placeholder name consisting of a combination of the mother’s first and last names and the sex of the child, such as ‘baby boy jane doe’ as the newborn son of jane doe. before discharge, a newborn child typically undergoes metabolic screening, hearing screening, and a hepatitis b vaccination, each resulting in a report to the state public health department. these records may be transmitted individually or in batches, electronically or on paper, and may contain the child’s real or placeholder name. before the child is discharged, parents of the newborn complete a worksheet that documents parent and child demographic information, including the name of the newborn child. (an example of the national standard birth certificate worksheet can be found at http://www.cdc.gov/nchs/data/dvs/momswkstf_improv.pdf). hospital birth certificate clerks abstract health information for mother and child using another standardized worksheet, called the facility worksheet, which is based on the 2003 u.s. national standard birth certificate [31]. the contents of both the parental and facility worksheets constitute the child’s birth certificate. in utah, this information is submitted to public health using a web-based form. at some point, typically after discharge, hospital staff will replace the placeholder name in the child’s hospital empi record with the birth certificate name. the timing of this update, and the source of the birth certificate name, varied for each of the three hospitals we interviewed. integration of existing ontologies analysis of the birth events and process models suggested that we focus ontology development on two broad categories: events and their associated timing, and the components of personal identity. event ontologies have been used in distributed event-based systems to integrate temporal information from various sources [32]. eventory, which wang x-j et al. developed as an eventbased repository of multimedia artifacts, uses an ontological approach that defines an event as an occurrence that unfolds over time [33]. the ontology behind eventory identifies who, what, when, and where as the characteristics used to describe events. the event ontology [34], developed to describe the domain of music, combines an event ontology with the reasoning capabilities of owl to create a semantic workspace in which new knowledge added to the repository gains semantic value from existing knowledge in the repository. event model f is a comprehensive event model based on the foundational ontology dolce [35] that provides support for representing mereological and causal relationships. the simple event model (sem) was designed as a general-purpose event model with the ability to integrate domain-specific vocabularies [36]. after a review of event models and their characteristics, we chose sem as our event model because of its simplicity and ability to integrate existing domain-specific ontologies. sem allows for different viewpoints of a single event, resulting in the ability to define event-bounded roles, time-bounded validity of facts, and attribution of the authoritative source of a statement. each of these characteristics is potentially important in a cross-enterprise exchange for identity resolution. event-bounded roles are useful for modeling situations where a person may be a child in one event and a parent in another, for example. time-bounded validity of facts can be used to model changes in specific identifiers over time, while attributing a fact to an authoritative source can be used to create a “golden record” of identity facts based on the most current facts from the most authoritative sources. building an ontology for identity resolution in healthcare and public health 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi components of personal identity much work has been completed attempting to standardize both the storage and exchange of patient clinical information to support interoperability and clinical decision support, including the hl7 reference information model (rim) [37], openehr archetypes [38], and clinical element models (cem) [39]. each of these implements its own language for representation: clinical document architecture (cda) for the hl7 rim, archetype definition language (adl) for openehr, and clinical element modeling language (ceml) for cem. because the personal identifiers are similar across all three, and because the cem has been implemented and validated in owl [40], we chose to adopt cem’s to represent identifiers. we integrated the owl representation of the cem core patient class as a domain-specific representation of the sem sem:actortype property. a high-level overview of the relationship between the two ontologies and a subset of classes and relationships is shown in figure 2. we manually mapped public health source database fields to cem attributes for birth certificates, death certificates, and the immunization registry. in protégé, we mapped individual data elements from contributing systems to our ontology using rdf:sameas relationships. the complete cem core patient model and typical value sets for coded values may be obtained at http://clinicalelement.com. our combined sem-cem ontology contains 92 classes, 32 object properties, 4 data properties, and 1404 axioms. figure 2 shows a high-level overview of the combined sem-cem ontologies. each event in sem-cem can be described with multiple actors, places, and times. sem implements a constraint class named role that is used to modify the actor(s) in an event. this feature allows the same actor to appear in multiple events, as is the case in a database such as the birth certificate registry. we used the role class to indicate an actor’s role in an event record. we added an additional property, recordtype, as a link to the cem core patient class, thus providing event-specific identity information. building an ontology for identity resolution in healthcare and public health 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi figure 2. high-level overview of the combined sem-cem ontologies. (classes are represented by ovals and relationships are represented by arrows.) figure 2 shows a high-level overview of the combined sem-cem ontologies. each event in sem-cem can be described with multiple actors, places, and times. sem implements a constraint class named role that is used to modify the actor(s) in an event. this feature allows the same actor to appear in multiple events, as is the case in a database such as the birth certificate registry. we used the role class to indicate an actor’s role in an event record. we added an additional property, recordtype, as a link to the cem core patient class, thus providing event-specific identity information. time is one of the core classes in sem. the advantage of modeling time as an owl class as opposed to a simple data property is that numerous property assertions may be made about a time instance. for example, a sem:time class may have a data property pointing to a timestamp building an ontology for identity resolution in healthcare and public health 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi indicating the time of an event. additionally, an instance of time can be described by a sem:timetype which may be used to classify a time as actual, estimated, or observed. after creating simulated instances in triple stores, we conducted both structural and functional validations of the combined sem-cem ontology [41]. our structural evaluation was completed using the pellet owl2 reasoner in protégé to validate the classes and properties, and individuals [42]. to validate our ontology as a sparql endpoint for queries, we created simulated events and identities in a test birth certificate repository using protégé and the sem-cem ontology. our repository simulated various birth certificate events, including change events such as paternity registration, amendment, and adoption events that we identified in a previous paper [10]. we then developed sparql queries to search for a combination of identifiers and extract all of the resulting information for the given person, including names and associated events. to validate sem-cem as a central integration agent, we implemented sem-cem in a simulated central repository of identity integrating events from various public health and healthcare sources including hospital, birth certificate, immunization information systems (iis), early hearing detection and intervention, and newborn metabolic screening. we then used sparql to query and assemble identity history across time for our simulated persons. we created instances of identity events using the combined sem-cem ontology in protégé. table 1 describes the events, actors and places that were modeled. table 1. information system events, actors, and places modeled in sem-cem ontology event name place actors comments birthregistrationevent birth registry child a birth certificate records information about a child, mother and, optionally, a father mother father addnewpatientevent hospital empi or ehr child immunization recordevent ehr or iis child immunization may be recorded in the ehr or directly entered by hospital staff into iis immunization reportevent child immunization recorded in ehr are reported to iis in real-time messages or in batches ehr iis newbornscreening reportevent1 child birth facility submits blood spot and identifying information to laboratory for analysis. this is typically a manual process. birth facility laboratory newbornscreening results reportevent1 child reporting results back to the source hospital may be done electronically building an ontology for identity resolution in healthcare and public health 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi laboratory or manually with a fax ehr hearingscreening recordevent ehr child hearingscreening reportevent1 child ehdi = early hearing detection and intervention system ehr ehdi paternityevent birth registry child adoptionevent birth registry child the original record is sealed child 2 a new child record is created, using the original child's state file number (unique identifier) deathregistrationevent death registry decedent deathreportevent1 death registry fact of death information, including date, transmitted from death registry to an external system external system(s) birthcertificate amendmentevent birth registry child amendment, may need to only model fields that change dataupdateevent all information system incorrect or missing information is updated in an existing record postdischarge nameupdateevent hospital empi child change event--hospital updates the placeholder name to the legal name on birth certificate birthcertupdateevent child a child's name may be updated in iis or other system recordmergeevent person1 a record repository such as an empi may merge multiple identities into one, or may split one into multiple person2 recordsplitevent person1 person2 1in report events, information systems are modeled as actors, not places. we created a simulated birth-certificate knowledgebase in protégé using the sem-cem ontology. for example, we created a child john richard doe, born on 11/28/2014 to an unmarried mother, jane doe. a voluntary declaration of paternity filed a few days later changes the child’s last name to stagg in the birth-certificate registry. figure 3 illustrates the sparql query and results for the simulated child. the query returns two events, a birth registration event and a paternity event. it is important to note that the actor class, in this case johndoeactornode, is the uri that refers to the same person involved in both events. a subsequent sparql query was used to drill down into the cem identity items associated with each role returned above. that query and its results are shown in figure 4. building an ontology for identity resolution in healthcare and public health 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi figure 3. sparql query returns associated actors, roles, and events for an individual named john doe, born 11/28/2014. figure 4. sparql query returns identity items and their corresponding types for the two cem instances identified in figure 3. additional strengths of model the approach used to model names in cem, as depicted in figure 5, can be effectively used to enable unstructured searches of proper names in our triple-store. in the cem ontology, each component of a person’s name, including names with multiple values such as mary jane, can be modeled as the object of a cem:item property of a ceinstance. each object has a corresponding rdf:type. building an ontology for identity resolution in healthcare and public health 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi this model enables unstructured name queries using sparql against our identity triple-store, resulting in the ability to search on any combination of first, middle, and last name, given in any order. for example a sparql query for the mary jane doe in figure 5 would return the individual record regardless of whether jane is classified as a first or middle name. this is very advantageous when searching for names, which may often be reversed, missing, or incorrectly split between first, middle and last name fields in a traditional database. this can also be useful for modeling informal variations or nicknames used in place of canonical names, such as jim for james or marge for margaret, or for names encoded phonetically using algorithms such as soundex or metaphone [43]. figure 5. example of the modeling of identity properties in sem-cem. discussion identity resolution and record linkage strategies are able to achieve high degrees of accuracy; however there are always possible matches that must be manually linked [44]. manual linkage, in fact, is typically the “gold standard” as a human judge is able to review a record pair and infer the occurrence of a typographical error or an event such as a name change or marriage. human review is time-consuming and costly, but also essential for some records. a semantic repository that models events and their corresponding identities can be valuable in the resolution of questionable identities. the cem ontology by itself, with its comprehensive list of identifiers, is sufficient to solve the issues of semantic heterogeneity in a record-linkage system. the sem model adds context that can be used to automate the manual linkage of questionable identities by reasoning about changes due to specific events. we did not incorporate contextual reasoning into this project. following are two distinct scenarios for using the sem-cem ontology for identity resolution. scenario 1: integration of distributed events. clinical events such as birth, immunization, and clinic visits, result in administrative events such as creating a new patient record, modifying or verifying an existing patient record, or merging or un-merging records in an mpi. the diffuse nature of these events across healthcare organizations or registries within a public health building an ontology for identity resolution in healthcare and public health 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi department suggests the need for a distributed event-based architecture to manage and coordinate identity. for example, mpis in an mpi cluster may subscribe to events and receive notifications when they occur. thus, any mpi in the cluster may be able to keep up to date when an identity is verified, when a name is changed, or when records are merged or un-merged in any other mpi in the cluster. in this example, the ontology can provide semantic information with respect to the source, quality, and provenance of the identity record. scenario 2: ontology as a query model. when an identifier such as a name is changed in an information system, a master record is typically updated while the previous information may be stored in a relational table as a part of change history. a database query typically searches against what is in the master record for a person, not what previously was in the record. querying for ‘what was’ requires an understanding of the relational structure of the database. using an ontology and storing identity information as triples facilitates the use of sparql, allowing users to query against what is and what was without understanding the underlying structure of the data. if the record is for a child and the difference is in surname, the mpi may initiate a query to the birth database and determine if a name change has been registered. similarly, if surnames and dates of birth are the same but the first names are different, the mpi may initiate a query to determine if a child was part of a multiple birth event. this automated function may be particularly useful in the sensitive context of linking records involving children who are adopted, where human review reveals the link between preand post-adoption identities. limitations the primary limitation of this work is that the events and activities we observed and modeled were in three salt lake city facilities and the utah department of health and may not correspond to other settings. however, national standards and routine practices for in-patient registration and other events in early childhood likely result in similar workflows in other facilities and jurisdictions and our model allows for variation. a second limitation is that we used simulated identity events to test common scenarios that occur during hospital birth and early childhood. more formal testing with real data and scenarios, for a variety of facilities and public health jurisdictions, is needed to thoroughly validate this model. conclusions the sem-cem ontology can be used to overcome structural and semantic heterogeneity issues when linking disparate data sources. the ontology also may be used to create a semantic repository that can be used to provide a view of how an individual’s identity evolves over time, or to provide a more complete view of identity when integrating incomplete or partial records. this view can be useful for both manual and automated resolution of possible matches in the record linkage process. further research is needed to explore the potential of the descriptionlogic based reasoning capabilities of owl in identity resolution. acknowledgments this work was conducted using the protégé resource, which is supported by grant gm10331601 from the national institute of general medical sciences of the united states national institutes of health. building an ontology for identity resolution in healthcare and public health 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi the terminology and cem models referenced in this work are modified 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record linkage using phonetic codes. informatics, 2009 bci'09 fourth balkan conference in; 2009: ieee. http://dx.doi.org/10.1017/s0269888900007797 http://dx.doi.org/10.1016/j.websem.2011.03.003 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12810120&dopt=abstract http://dx.doi.org/10.1016/s1386-5056(02)00103-x building an ontology for identity resolution in healthcare and public health 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e219, 2015 ojphi 44. grannis sj, overhage jm, hui s, mcdonald cj, eds. analysis of a probabilistic record linkage technique without human review. amia annual symposium proceedings; 2003: american medical informatics association. supplementary material a. identity process models for births in hospitals b. identity process models in public health building an ontology for identity resolution in healthcare and public health background and significance methods identify ontology purpose and scope knowledge acquisition integration of existing ontologies ontology coding evaluation results integration of existing ontologies components of personal identity additional strengths of model discussion limitations conclusions acknowledgments references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts collecting infectious disease data from lars and improving data quality in taiwan chih-ting yeh*, chia-lin li, chih-jung ke and chi-ming chang epidemic intelligence center, centers for disease control, taipei city, taiwan objective to improve data quality and sustain a good quality data collected by laboratory automated reporting system (lars), we use a threestage data quality correction (3dqc) strategy to ensure data accuracy. introduction to immediately monitor disease outbreaks, the application of laboratory-based surveillance is more popular in recent years. taiwan centers for disease control (tcdc) has developed lars to collect the laboratory-confirmed cases caused by any of 20 pathogens daily via automated submitting of reports from hospital laboratory information system (lis) to lars since 2014 [1]. loinc is used as standardized format for messaging inspection data [1, 2]. there are 37 hospitals have joined lars, coverage rate about 59% of all hospitals in taiwan. recently, more than 10,000 of data are collected weekly and used in monitoring pathogen activity [3]. therefore, it is important to ensure data quality that the data will lead to valuable information for public health surveillance. methods a 3dqc strategy was designed to improve data accuracy and carried out by teamwork among tcdc, taiwan association for medical informatics (tami) and it company (figure 1). in the first stage of 3dqc, it company checked data format. in the second stage, tcdc verified information between hospital inspection reports and data receiving in lars. in the third stage, tami evaluated loinc mapping and tcdc monitored stability of data transmission. after correcting the data, hospitals were approved to join lars. results during the first stage of 3dqc, we observed that some problems with syntax error in data (e.g. incorrect patient identification number, or lack of residence codes). because some data were stored in hospital information system (his) but not in lis, an error may occur while hospital accessed records from his. in the second stage, 50-70% of inspection reports provided by each hospital had problems with semantic information error. for example, a positive result of influenza a on a screening flu test recorded in lis but hospital transferred the wrong result with influenza b positive into lars. in the third stage, we found that 20-30% of terms mismatched to loinc code. this study categorized these terms into two groups (1) the exception codes, which were considered reasonable and (2) the error codes, and also reviewed error codes and made a modified advice for hospitals to improve loinc mapping. through 3dqc strategy, the loinc mapping rate raised from 40 to 80%, exception codes mapping was 20%, and the total mapping rate was near 95-99% (figure 2). so far, most hospitals have maintained a good quality data even they formally participate in lars. conclusions this study suggested that 3dqc can effectively detect problems and reduce errors of data collected from lars, and indicated that effect of 3dqc can be maintained even hospital formally participates in lars. future research will focus on development of automatic programming of 3dqc to ensure high-quality data. figure 1. a three-stage data quality correction strategy figure 2. rate of loinc mapping, 2015-2016 keywords laboratory automated reporting system; three-stage data quality correction; surveillance acknowledgments we thank all the collaborating hospitals for providing data and cooperating with data correction, and also thank mr. chih-yang yeh from tami for providing expert advice for loinc mapping. references 1. chang cm, kuo hw, ye yw, liu yl, chuang jh. implementation of a nationwide automated laboratory reporting system for infectious disease surveillance based on the epidemic information exchange platform and the use of loinc standard. jtami. 2015 mar; 24(1):1-10. 2. mcdonald cj, huff sm, suico jg, hill g, leavelle d, aller r, forrey a, mercer k, demoor g, hook j, williams w, case j, maloney p. loinc, a universal standard for identifying laboratory observations: a 5-year update. clin chem. 2003 apr;49(4):624-33. 3. kuo hw, chang cm, ke cj, liu yl, liu dp. apply laboratory automated reporting system on infectious disease surveullance. jtami. 2016 mar; 25(1):13-22. *chih-ting yeh e-mail: emilyyeh@cdc.gov.tw online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e32, 2017 automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 james m. kariuki 1, eric-jan manders 1, janise richards 1, tom oluoch 2, davies kimanga 3, steve wanyee 4, james o. kwach 2, xenophon santas 1 1. division of global hiv & tb, center for global health, centers for disease control and prevention, atlanta, usa 2. centers for disease control and prevention-kenya, nairobi, kenya 3. national aids & sti control programme (nascop), nairobi, kenya 4. the international training and education center for health (i-tech) kenya, nairobi, kenya abstract introduction: developing countries are increasingly strengthening national health information systems (his) for evidence-based decision-making. however, the inability to report indicator data automatically from electronic medical record systems (emr) hinders this process. data are often printed and manually reentered into aggregate reporting systems. this affects data completeness, accuracy, reporting timeliness, and burdens staff who support routine indicator reporting from patient-level data. method: after conducting a feasibility test to exchange indicator data from open medical records system (openmrs) to district health information system version 2 (dhis2), we conducted a field test at a health facility in kenya. we configured a field-test dhis2 instance, similar to the kenya ministry of health (moh) dhis2, to receive hiv care and treatment indicator data and the kenyaemr, a customized version of openmrs, to generate and transmit the data from a health facility. after training facility staff how to send data using dhis2 reporting module, we compared completeness, accuracy and timeliness of automated indicator reporting with facility monthly reports manually entered into moh dhis2. results: all 45 data values in the automated reporting process were 100% complete and accurate while in manual entry process, data completeness ranged from 66.7% to 100% and accuracy ranged from 33.3% to 95.6% for seven months (july 2013-january 2014). manual tally and entry process required at least one person to perform each of the five reporting activities, generating data from emr and manual entry required at least one person to perform each of the three reporting activities, while automated reporting process had one activity performed by one person. manual tally and entry observed in october 2013 took 375 minutes. average time to generate data and manually enter into dhis2 was over half an hour (m=32.35 mins, sd=0.29) compared to less than a minute for automated submission (m=0.19 mins, sd=0.15). discussion and conclusion: the results indicate that indicator data sent electronically from openmrs-based emr at a health facility to dhis2 improves data completeness, eliminates transcription errors and delays in reporting, and reduces the reporting burden on human resources. this increases availability of quality indicator data using available resources to facilitate monitoring service delivery and measuring progress towards set goals. automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi the findings and conclusions in this report are those of the authors and do not necessarily reflect the views of the us centers for disease control and prevention or the kenyan government. introduction the fight against hiv has played a major role in the implementation and use of health information systems (his) in many low and middle-income countries for management of longitudinal health records. the number of hiv patients enrolled on antiretroviral therapy (art) has increased exponentially over the last ten years due to improved access to hiv testing and revised guidelines requiring early initiation of art among those infected [1-5]. this has accelerated further adoption and scale up of electronic medical records (emr) and monitoring and evaluation (m&e) information systems to monitor patient and program outcomes [6]. international donor organizations, such as the joint united nations programme on hiv/aids’ (unaids) and the united states government’s president’s emergency plan for aids relief (pepfar), have set ambitious goals towards ending the epidemic and developed comprehensive indicators for monitoring progress made through various programs [7-9]. achieving these goals requires a shift to a data-driven approach that uses data from the national level down to the service delivery (health facility) level. ehealth is a key enabler and driver of improved health outcomes and an essential infrastructure to support information exchange between all participants in the health care system [10,11]. the ability to use information for monitoring health service delivery, planning programs, reporting health indicators, measuring achievement, and improving accountability requires timely, reliable, high-quality, and accessible health service data. these are key to realizing global health goals, especially in resourcelimited settings [12-14]. effectively managing health data requires robust his, including both an emr to manage patient records and an aggregate data system for m&e. although some countries have adopted and implemented both emr and aggregate system with national coverage, they tend to be standalone or silo systems [15]. interoperability among his is essential to achieving health goals by facilitating the availability and use of quality health data. despite increased adoption of electronic his, the lack of data exchange remains a challenge to data quality and availability [16]. printing electronic data from one system and re-entering it into another system manually is commonplace. manual data entry is labor intensive and prone to transcription errors. it increases the time from when the indicator data are generated in the emr to its availability in the aggregate data system, and increases the workload for health workers responsible for reporting [17,18]. as data demand increases, limited-resource sites may struggle to hire and sustain the staff keywords: health information systems, electronic medical records, interoperability, indicator reporting, dhis2, openmrs, health information exchange, data aggregation correspondence: emailjkariuki@cdc.gov, phone+ (1)-(404) 718-8349, address1600 clifton road, ms-e77 atlanta, ga 30329, usa doi: 10.5210/ojphi.v8i2.6722 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi needed to support manual data reporting [14,19]. all these factors can potentially affect the ongoing monitoring of health programs, planning and resource allocation for health services, and delivery of quality and efficient healthcare services. in earlier work, we conducted a laboratory-based study examining the feasibility of automating reporting of a subset of pepfar’s next generation indicators from open medical records system (openmrs), an open-source medical records system [20], to district health information system version 2 (dhis2), a tool for collection, validation, analysis, and presentation of aggregate statistical data, tailored to integrated health information management activities [21]. this study demonstrated that data generated from openmrs and sent electronically to dhis2 can maintain the accuracy and completeness needed to develop appropriate indicators [22]. it also indicated that an automated indicator reporting process had the potential to provide timely health information and reduce staff workload. in this work, we extend our findings to conduct an experiment with a field-based study exploring the impact of adopting his interoperability at the facility level. methods field-test study design we designed a mixed-method field test to compare human resources reporting efforts, data accuracy and completeness, and timeliness of submitting data reports from the health facility to the national health management information system (hmis) for manual and automated indicator data reporting processes. we conducted the study in two phases and used four data collection methods. in the first phase, we developed, tested, and implemented the automated software, while in the second phase we examined the automation’s impact. the four data collection methods were: 1. document review of kenya moh hiv facility reporting tools and national aids & sti control programme (nascop) indicator manual to identify indicators to automate and understand how they are calculated; 2. desk review of monthly reports at the facility and national levels to audit data quality and compare reporting time between the two phases for manual and automated reporting; 3. focus group discussion with health facility staff to gain their perspectives on the data collection, aggregation and submission process; and 4. observation of the manual reporting process to determine the data collection and aggregation procedures used by the staff. definitions of variables measured for this study, we defined three variables: 1. human resources are the staff required to support indicator data reporting from the facility emr to the national hmis. 2. data quality focuses on data accuracy and completeness characteristics of indicator data sent from the facility to the national hmis for manual or automated reporting. this is automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi calculated as a percentage of complete data values entered into national hmis accurately out of the expected data values [23]. • data completeness is the degree to which values of all selected indicator data elements in the facility monthly reports, generated from emr or manual tally, are available in the national hmis. • data accuracy is the degree of concordance between indicator data values in the facility monthly reports, generated from emr or manual tally, with data values in the national hmis. 3. reporting time is the time taken to prepare and submit indicator data reports into the national hmis from the facility. study setting we identified kenya for the field-test study because dhis2, a tool for collection, validation, analysis, and presentation of aggregate statistical data, and openmrs, an open-source medical records system, are currently in use and supported by moh. within kenya, we selected kisumu east district hospital comprehensive care clinic (ccc) because it met our three inclusion criteria: 1) large number of electronic patient records (over 3,000 patients enrolled on hiv treatment), 2) kenya moh support, and 3) established emr and data entry processes. kisumu east district hospital ccc is relatively large (approximately 13,185 adult and pediatric patients were receiving hiv care and treatment at the time of the study) and is operated by the kenyan moh. the hospital has used kenyaemr (a customized version of openmrs) for more than one year as a point-of-care system and for retrospective data entry of routine patient data. field-test study data collection we collected data on manual and automated reporting work processes following the steps shown in figure 1. figure 1: indicator data reporting field-test study work process manual reporting work process automated reporting work process analyze and present data collected o n the manual and a uto mate d re porting processes selected hiv indicators to auto mate training and re port s ubmission into the field test dhis2 config uratio n of indicator repo rting auto mation in the facility emr saved a copy of the re port submitted fro m the emr generatio n of re ports fro m field test dhis2 using data submitte d compare d data in repo rts generated fro m field test dhis2 and copies of emr repo rts saved after submission focus group discussion (fgd) observa tio n of the facility reporting process collect mo nthly repo rts stored at the facility generatio n of mo nthly reports fro m moh dhis2 compare d facility re ports generate d fro m moh dhis2 and copies at the facility and recorde d the data automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi manual reporting work process focus group discussion (fgd): we conducted a fgd with seven key staff who use kenyaemr routinely and are responsible for hiv indicator reporting at the facility. these staff included clinical officers, nursing officers, health records and information officer (hrio), and data clerks at the hiv clinic. during the fgd, we collected information about emr use, current indicator data reporting process to moh dhis2, and human resources effort required for reporting (i.e., the number of staff and duration per month). observation of the facility reporting process: we observed the current reporting process by shadowing the facility’s data clerk and hrio while they prepared the routine monthly hiv care and treatment report. we documented the process and recorded the approximate time it took to accomplish each step using a stopwatch. in addition, we produced the monthly indicator data report in kenyaemr and recorded the time to generate a report during the study period. manual reporting data quality desk review: we collected copies of the hiv care and treatment indicator reports from july 2013 to january 2014 at the facility (see appendix 1 for a sample reporting form) and produced the facility’s monthly indicator reports from moh dhis2 for the same timeframe. then, we transferred the data in the monthly reports into the data quality comparison tool, (figure 2) to compare the completeness and accuracy of indicator data submitted to moh dhis2 and the reports at the facility. we counted the number of data elements with accurate values and data elements with transcription errors or missing values in the two reports and summarized the results in the same tool. for manual reporting, we used the report at the facility as reference. figure 2: data quality comparison tool automation implementation selection of hiv indicators to automate: first, we reviewed the moh comprehensive hiv/aids facility reporting form (appendix 1) to identify hiv care and treatment indicators to automate [24]. selected indicators had data routinely collected in kenyaemr at the facility, including all data elements required to generate aggregate data values. indicator data automation configuration in the facility kenyaemr: to ensure that the study did not interrupt the normal facility monthly reporting to moh, we created a separate dhis2 instance with identical data elements in the moh dhis2 using the openmrs to dhis2 indicator automation guide developed in the previous study [25]. next, we generated an xml report definition template with the hiv care and treatment data elements. then we mapped the data elements in the report definition template to the pepfar hiv care and treatment indicators to enable us reuse the sql code created during the feasibility study [22,26]. using an sql editor, we created query statements to generate each automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi data element value from kenyaemr and add them to the xml report definition template. next, we loaded the dhis2 reporting module into the facility kenyaemr and uploaded the xml report definition template embedded with sql queries. finally, we connected the two systems by adding field-test dhis2 login and link details to the kenyaemr. training and report submission into the field-test dhis2: to ensure a successful implementation, we trained facility staff responsible for reporting on sending indicator data electronically from kenyaemr to field-test dhis2 using the dhis2 reporting module. the facility staff generated and transmitted hiv care and treatment indicator data for seven months, july 2013 to january 2014, from the facility kenyaemr to the field-test dhis2. after transmission, a report containing indicator data values and transmission results for each month was saved on a computer. we also recorded the time to generate and transmit each report from the kenyaemr to the field-test dhis2. automated reporting data quality desk review: to verify the completeness and accuracy of the automated indicators, we produced hiv care and treatment indicators data reports from the field-test dhis2 and printed the reports submitted automatically from the kenyaemr for the seven months. then, we compared the data in the two reports for each month. we counted the number of indicator data elements with identical values, transcription errors or missing in the two reports and recorded the results in the data quality comparison tool (figure2, above). in the automated reporting process, we used the report produced in kenyaemr at the facility as reference. analysis we developed a workflow diagram of the current reporting process at the facility using the data captured during the fgd and observation notes. we reviewed the workflow with facility staff to confirm that it captured the current reporting process (see current reporting process in figure 3). for the descriptive data analysis, we reviewed data in the data quality comparison tool and summarized the data on data quality dimensions. then we calculated the percentage of complete and accurate indicator data values entered into the national hmis out of the total indicator data values expected each month to compare data quality between the manual and automated reporting processes. we also reviewed the observation notes and tabulated data on time to produce and submit reports for each month during the study period in microsoft excel. then we calculated the average time required to generate and submit the report for each reporting process. finally, we developed workflow diagrams on human resources, graphs to visualize data completeness and accuracy, and a timeline graphic with mean, median and interquartile range to visualize data reporting time for manual and automated reporting processes. results facility reporting process data were entered into the facility emr by clinicians during patient visit or retrospectively by data clerks. these data were also captured in paper registers. when planning the field-test, we expected that facility staff generated and printed indicator data reports from patient data in the kenyaemr, then manually entered it into moh dhis2. however, the fgd and observations indicated that staff manually tallied hiv indicator data from several registers to compile the moh reporting form before automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi manual entry into moh dhis2, bypassing the kenyaemr entirely (see current reporting process in figure 3). in terms of staffing responsible for hiv reporting, one receptionist/data clerk prepares hiv indicator reports every month, and one of the three health records and information officers (hrios) at the facility reviews and submits it to moh dhis2. the hrios have access to moh dhis2 through the facility’s internet and can manually enter and submit facility’s indicator data reports to moh. human resources burden comparison for manual and automated reporting this study compares the human resources needed for manual and automated indicator reporting activities. the current reporting process has five activities, each requiring at least one person to complete. the data clerk collects registers from various clinics, tallies and aggregate value for each indicator, compiles the monthly indicator report form, and sends it to the hrio office. the facility hrio reviews the report, then manually enters the data into dhis2 (see current reporting process in figure 3). the expected reporting process has three activities, each requiring at least one person to complete. the data clerk generates the monthly indicator data report from kenyaemr, prints it, and sends it to hrio office. the facility hrio reviews the report, then manually enters the data into dhis2 (see expected reporting process in figure 3). the automated reporting process has one activity to generate, review, and submit the indicator report electronically into dhis2. this activity can be completed by one hrio within the emr (see automated reporting process in figure 3). figure 3: comparison of human resources required for the three reporting processes automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi comparison of data completeness and accuracy between manual and automated reporting processes using the selection criteria, we identified eight indicators for automation in the hiv care and treatment section of the moh 731 reporting form. including disaggregates; these eight indicators had 45 data elements. data values for all 45 selected indicator data element submitted electronically from kenyaemr to dhis2 for the seven months were 100% complete and accurate. manually entered indicator data averaged 89% completeness (ranging from 66.7% to 100%) and 71% accuracy (ranging from 33.3% to 95.6%). this indicates that during manual data entry, some indicator data values were not entered and transcription errors were introduced. figure 4 shows the completeness and accuracy for both processes during the seven-month study period. figure 4: comparison of completeness and accuracy between manual and automated data elements comparison of indicator data reporting time between manual and automated reporting figure 5 shows the time required to prepare and get indicator data into dhis2 for each reporting process. while this study assumes of data contained in kenyaemr, it includes time for the current manual reporting process observed in october 2013. of the 375 minutes needed for the current process, approximately 345 minutes (92%) of the total time was used to aggregate and compile indicator data. the 30 minutes required to enter indicator data values manually into dhis2 were comparable to the expected reported process. 55.6% 53.3% 86.7% 84.4% 33.3% 95.6% 88.9% 0 10 20 30 40 50 jul-13 aug-13 sep-13 oct-13 nov-13 dec-13 jan-14 n um be r of d at a el em en ts manual entry of indicator data to dhis 2 data elements with missing data values data elements with transcription errors data elements with accurate data values complete data elements 100% 100% 100% 100% 100% 100% 100% 0 10 20 30 40 50 jul-13 aug-13 sep-13 oct-13 nov-13 dec-13 jan-14 n um be r o f d at a el em en ts automated entry of indicator data to dhis2 data elements with missing data values data elements with transcription errors data elements with accurate data values complete data elements automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi figure 5: timeline comparing time to generate and submit indicator data into dhis2 using manual and automated processes discussion the study findings indicate that it is feasible and beneficial to automate indicator data reporting from emrs to aggregate data systems, and that implementing automated process improves the completeness and accuracy of indicator data reports. in addition, the results validate that automating reporting reduces the facility’s human resource burden by eliminating manual data entry, which is time and labor intensive and prone to human error [18]. the automated reporting process tasks required one staff member to complete one task, while the manual reporting process required at least one staff for each of the three activities. often staff are pulled away from their health care-related responsibilities to prepare reports. as data demand increases, highvolume sites may not be able to sustain the human resources needed to support manual data reporting. with automated reporting, existing staff can generate and send reports from the emr to the national hmis, eliminating the need for additional staff. surprisingly, this study discovered that facility staff were bypassing kenyaemr and spending more than half a workday (5.75 hours) aggregating reporting data from paper records by hand each month. while out of scope for this study, this indicates a need for further investigation. automated entry improved timely availability and quality of indicator data, consistent with studies on automated entry of surveillance data [17,18]. while aggregate data was complete using the automated process, data completeness in the manual system was only about two–thirds. not only was the data accuracy much higher for automated data entry than for manual data entry, there was also a substantial difference in the time required to generate and submit data between the manual and automated reporting processes. this is fundamental to both monitoring progress toward meeting performance targets, planning, and resource allocation, and identifying areas needing additional support to improve health outcomes and impact. automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi while there is a great promise with indicator reporting automation, a number of issues need to be addressed to ensure successful implementation. during the transition from paper-based systems, some patients’ data or records may not be in the emr [27,28]. this requires proper planning to ensure that key data for automated reporting is in the emr or the ability to coordinate reporting using different methods. procedures for routine data quality assurance and audits in the emr are necessary to ensure data exchanged is of acceptable quality. staff will require training and active engagement to adjust to new work practices and workflows. robust information technology infrastructure (including reliable electric power, adequate computers and internet access) and support at the facility are critical to ensure consistent emr availability. in addition, staff should actively identify and share lessons learned and best practices with other facilities, moh, and funders to help improve use of the emr and system interoperability [29]. furthermore, there is a need to assess feasibility of automating indicator data reporting from other emrs used in resource-limited settings to national hmis. this will consolidate information to guide development of standardized indicator data reporting, as well as identify the best approaches to support scale-up of electronic health information systems with available resources. limitations: our study had a few limitations. we were not able to observe and record the time taken to review reports before data entry into dhis2, which would have provided additional information when comparing the manual and automated reporting processes. therefore, we excluded the time to taken to review indicator data reports on all processes to ensure consistency. while quality of data in the emr is important, in this study we defined data quality as the completeness and accuracy of aggregate data transmitted from kenyaemr to dhis2 (e.g., the data sent was the same as the data received.) the quality of the patient-level data used to calculate the indicator was not assessed. we assumed that the study site experienced similar data quality challenges reported in other studies conducted in resource-limited settings [16,30,31]. in addition, only one facility was used in this study, which limits our ability to generalize the results widely. this effect was minimized by selecting a facility that is typical of hiv care and treatment facilities in kenya and uses the same indicator reporting process. the large number of patients enrolled at our study site enabled us make observations that would be expected in other busy, yet understaffed health facilities. conclusion this study demonstrates that sending indicator data automatically from a health facility emr (based on openmrs) to the national-level reporting system (dhis2) is both possible beneficial. it eliminates need for manual data entry that can introduce transcription errors and reduces delays, thus improving indicator data completeness and accuracy for use at the facility, subnational, and national levels. it also reduces the amount of time to prepare and submit indicator data and the number of facility staff required to fulfil reporting requirements at health facilities, which is key to scale up of his without the need for additional human resources. there is potential to increase indicator data completeness, accuracy and availability in the national hmis. additionally, increasing the focus on automated indicator data reporting may facilitate the development of internationally recognized data exchange standards for aggregate data, which is fundamental to monitoring global health outcomes and impact. further studies should be conducted on the effect of the use of data exchange standards for automated reporting using different emrs on data quality and timeliness. automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi acknowledgements thanks to kisumu east district hospital for allowing us to conduct the field-test study. to molly oloo, rinnie juma, and moureen ogonda, thank you for providing information on reporting and your support at the facility during the study. we would like to thank and george owiso, john gitahi, nicholas ingosi, benard otieno, and prisca teka from i-tech kenya for their technical and logistics support; bob jolliffe and thái chương, working with hisp-india, for sharing information on dhis2 reporting module code and insights on how to implement it; and the cdc public health informatics research laboratory, which provided infrastructure. thanks to jan macgregor for her help in reviewing and editing the field-test report. this study was supported by the united states president’s emergency plan for aids relief (pepfar) through the u.s. centers for disease control and prevention (cdc), atlanta. the associate director for science at the center for global health of the cdc approved this study, and permission to conduct the field test was granted by the kenya ministry of health (moh). references 1. hermans sm, van leth f, manabe yc, hoepelman ai, lange jm, et al. 2012. earlier initiation of antiretroviral therapy, increased tuberculosis case finding and reduced mortality in a setting of improved hiv care: a retrospective cohort study. hiv med. 13(6), 337-44. doi:http://dx.doi.org/10.1111/j.1468-1293.2011.00980.x. pubmed 2. ford n, kranzer k, hilderbrand k, jouquet g, goemaere e, et al. 2010. early initiation of antiretroviral therapy and associated 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national scale-up of antiretroviral therapy in malawi. plos med. 7(8). doi:http://dx.doi.org/10.1371/journal.pmed.1000319. pubmed 30. mphatswe w, mate ks, bennett b, ngidi h, reddy j, et al. 2012. improving public health information: a data quality intervention in kwazulu-natal, south africa. bull world health organ. 90(3), 176-82. doi:http://dx.doi.org/10.2471/blt.11.092759. pubmed automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi 31. ledikwe jh, grignon j, lebelonyane r, ludick s, matshediso e, et al. 2014. improving the quality of health information: a qualitative assessment of data management and reporting systems in botswana. health res policy syst. 12, 7. doi:http://dx.doi.org/10.1186/1478-4505-12-7. pubmed automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e188, 2016 ojphi appendices appendix 1: moh 731 hiv/aids facility reporting form note: care and treatment indicators enclosed in the red box automating indicator data reporting from health facility emr to a national aggregate data system in kenya: an interoperability field-test using openmrs and dhis2 introduction methods field-test study design definitions of variables measured study setting field-test study data collection manual reporting work process automation implementation analysis results facility reporting process human resources burden comparison for manual and automated reporting comparison of data completeness and accuracy between manual and automated reporting processes comparison of indicator data reporting time between manual and automated reporting discussion conclusion acknowledgements references appendices appendix 1: moh 731 hiv/aids facility reporting form 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts community engagement among the biosense 2.0 user group stacey hoferka*1, marcus rennick2, erin e. austin3, anne burke4, rosa ergas5, jay fiedler6, laura streichert7 and amanda wahnich3 1il dept public health, chicago, il, usa; 2astho, arlington, va, usa; 3virginia dept. of health, richmond, va, usa; 4utah dept. of health, salt lake city, ut, usa; 5mass. dept. of public health, boston, ma, usa; 6michigan dept. of community health, lansing, mi, usa; 7isds, brighton, ma, usa objective this roundtable will provide a forum for the syndromic surveillance community of practice (cop) to learn about activities of the biosense 2.0 user group (bug) workgroups that address priority issues in syndromic surveillance. it will be an opportunity to discuss key challenges faced by public health jurisdictions in the era of meaningful use and identify further needs and best practices in the areas of data quality, data sharing, onboarding, and developing syndrome definitions. introduction biosense 2.0 has become a platform for technical receipt and analysis of syndromic surveillance data for many jurisdictions nationwide, as well as a collaborative effort that has engaged a larger community of syndromic surveillance practitioners, governance group, and federal agencies and organizations. the potential longterm benefits of biosense 2.0 for resource and data sharing have at times been overshadowed by the short-term limitations of the system and disconnected efforts among the cop. in may 2014, representatives from 41 jurisdictions attended a 2-day, in-person meeting where four workgroups were formed to address on-boarding, data quality, data sharing and syndrome definition in an effort to advance changes that resonate with actual surveillance practice. description representatives from each workgroup will describe their efforts to date and engage the cop in the discussion. the goals of the workgroups are to coordinate efforts nationwide, better inform development of biosense 2.0 to the governance group and cdc, and achieve high-quality outcomes for the practice of syndromic surveillance. as figure 1 illustrates, the biosense 2.0 workgroups create a cooperative environment to address complex system-wide concerns. the workgroups’ focuses are described as follows: data quality workgroup aims to document data processing steps, develop tools to assess data in biosense 2.0, and determine best practices for addressing data quality deficiencies. working toward these aims will give jurisdictions the ability to understand, measure, and improve their data, resulting in increased comparability, utility and trust of data housed in the biosense 2.0 application and output derived from these data. data sharing workgroup is rooted in the fundamental desire to leverage cloud based data storage of the biosense 2.0 architecture toward enhanced data sharing within and between jurisdictions and system participants. the two primary initiatives of this group are to specify data sharing roles and permissions for users in the system and to evaluate the need for data use agreements between jurisdictions to facilitate data sharing activities. on-boarding workgroup aims to create processes for and implement documentation of on-boarding standards and contextualize these data through creation of jurisdictional profiles. these efforts will help to define functionality for automated structural message conformance and content, ensure compliance to implementation guides, and describe best practices through a reference mapping process. syndrome definition workgroup aims to standardize the syndrome definitions available in biosense 2.0 and determine the best processes for syndrome binning. working towards these aims will ensure that data are accurately parsed and searchable, resulting in comparability across jurisdictions. audience engagement activities from each of the four workgroups will be presented to the audience. current status, planned development, and functionality requests to the cdc will be identified.participants will be asked to comment on benefits and additional needs that would improve the current work. each workgroup will spend five minutes presenting a tool or solution and then invite open discussion with the audience. the end result of the roundtable will be a list of functionality and enhancement requirements to be considered in the workgroups’ ongoing efforts. recruitment of additional workgroup participants will also occur. figure 1. biosense 2.0 user group community of practice workgroups keywords biosense 2.0; workgroups; community of practice; syndromic surveillance *stacey hoferka e-mail: stacey.hoferka@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e184, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts public health and mass gathering events: assessing need for surveillance in wales, uk priyanka parmar1 and daniel rh thomas*2 1immunisation technical support unit, public health foundation of india, gurgaon, india; 2public health wales, uk objective to identify the need of surveillance during mass gathering events in wales by identifying a causal relationship between public health and characteristics of a mass gathering event grounded on risk assessment. introduction mass gathering is defined by who as – “more than specified number of persons (which can be as few as 1000 persons to more than 25000) at a specific location for a specific purpose (social function, large public event or sports competition) for a defined period of time”1. mass gatherings are considered to have the potential for public health incidents, outbreaks and casualties attributed to the inevitable overcrowding in a place. because populations are increasingly mobile, and more able to attend large gatherings, the risk for outbreaks of infectious diseases among a susceptible population has increased, and a substantial responsibility is placed on health services if outbreaks occur. currently, there are no existing programmes of surveillance for mass gathering events in wales. although a variety of surveillance system exist, no systematic process is established to gather the information from important large events and collect into a common database for future reference and to study the impact. the number of people attending these gatherings in wales range from few thousands to 250,000. methods an intensive literature review was carried out to identify risk variables in mass gathering events. based on identified risks a ‘risk assessment tool’ was developed for the mass gathering events. the risk variables are given a score according to its known risk level. the maximum total risk level score of an event according to tool can be 27 and lowest 2. all the identified mass gathering events in wales were assessed according to this tool. a web based semi structured survey to identify public health issues during mass gatherings in wales was designed on the nhs website for local public health officials involved in preparedness of mass gatherings. results the assessment tool was observed consistent with the other international events in the world when their risk levels were compared. mass gathering events in wales were identified as low and medium risks. high risk events that were identified by tool were also identified in the survey. alcohol and drug abuse, food safety, accidents, young adults and poor weather (rain) were the factors mainly highlighted as high risk variables in the survey. 75% of the respondents mentioned prior preventive measures undertaken during mass gatherings. follow up of the mass gathering events and surveillance were noticed to be absent. surveillance is a part of ongoing epidemiological surveillance in different health sectors but mass gathering events are only monitored if any serious outbreak occurs otherwise the routine measures are followed. conclusions the risk level depends on the dose of each risk variable in the event on the attending individuals. the need for a continuous surveillance is associated to the risk level of the event: high requires surveillance and action to be taken to reduce consequences; moderate requires regular monitoring and preparedness ; low requires routine measures. thoroughly planned and prepared events usually show fewer chances of any public health issues. various factors have been identified as high risks (weather, age group of attendees and alcohol and drug abuse) which necessitate the need of monitoring the events. most of the studies were found to support enhanced surveillance system during large national and international mass gathering events, which helps them to see the outcome of the surveillance and impact of the mass gathering. enhancing the current surveillance system to establish a collaborated department would require less resources to monitor events and for training in emergency conditions. a detailed planning of deliveries during future events, adequate staffing, defined funding and resources and cost effectiveness should be implemented. keywords mass gatherings; mass gathering events; surveillance; risk assessment references 1. communicable disease alert and response for mass gathering. key considerations. world health organisation. june 2008. 7 p. *daniel rh thomas e-mail: daniel,thomas@wales.nhs.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e153, 2015 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts agent-based investigation of sexually transmitted infection dmytro chumachenko*1 and tetyana chumachenko2 1informatics department, national aerospace university “kharkiv aviation institute”, kharkiv, ukraine; 2kharkiv national medical university, kharkiv, ukraine objective to develop agent-based model of sexually transmitted infections spreading by example of syphilis and its analysis. introduction every year nearly 12 million new cases of syphilis in the world are registered. currently, in many countries of the world the stabilization or even reduction of the incidence of syphilis is marked, but this does not apply to ukraine. the current stage of development of the syphilis problem in ukraine is characterized by not only high morbidity, but also the fact that in the overwhelming number of cases, we are talking about the latent forms and atypical manifestations of the disease and resistance to therapy [1]. preventive and prophylactic measures are important in maintaining the public health. predicting the dynamics of disease spreading allows developing appropriate countermeasures and ensuring rational use of human and material resources. qualitative forecast of syphilis spreading is possible to implement by means of mathematical modeling. methods deterministic analytical models that are most common in epidemiological studies do not take into account the dynamic and stochastic nature of epidemics. agent-based simulation approach to modeling allows fixing these shortcomings. it allows conveying the social structure of simulated system by the most natural and easy way. each agent has individual state variables and rules of behavior that allows detailing the model very deeply. therefore there is no need to describe the complex system of mathematical formulas and probability of the dynamics of the epidemic process is defined parametrically. the netlogo software has been used for the program realization of the developed model. results the model of morbidity by syphilis spreading has been developed by the tradition sir model expansion. thus, agents can be in following states: s (susceptible) for health people, ip (infected primary) for infected people who stay in primary stage and can transmit the infection by direct sexual contact with susceptible person, is (infected secondary) for infected people who stay in secondary stage and have also infectious skin lesions, il (infected latent) for infected people who stay in latent stage and change its contagious rate from early latent syphilis to late latent syphilis, it (infected tertiary) for infected people who stay in tertiary stage and transmit the infection partially, and r (recovered) for people who are recovered from the infection. infecting of agents in the model depends on the number and state of agents and the stage of infected agent’s disease. also, in order to correctly determine the intensity of contacts with other agents different age groups of agents have been highlighted in the model. screen form of developed agent-based model of syphilis spreading is shown in figure. the transmission between agent’s states are defined by probabilistic way and depends on features of particular states as well as different factors, such as coupling tendency, condom use, commitment, test frequency etc. the analysis of experiments under developed model has shown that the most influencing factor in the reduction in the percentage of patients is frequency of checks on the disease and isolation of patients, the second most important factor is constancy of sexual partners, the third is the use of condoms, and finally, the fourth is the number of exchangeable partners. conclusions the agent-based model of syphilis spreading has been developed. the model allows forecasting the morbidity by infection and analyzing the disease by changing the initial data. all data has been checked by the factual statistics on the syphilis incidence in kharkiv region (ukraine) from 1975 to 2015 years. the simulation results allow us determining the direction of prevention of syphilis treatment and the main factors in reducing morbidity. as is evident from the simulation results, social factors take precedence over the health care that gives grounds for advocacy in health policy among the population, especially the youth. developed model can be configured for other sexually transmitted infections by changing the disease transition rules. figure. the main panel of simulation management and graphic visualization. keywords agent-based simulation; public health; syphilis; mathematical modeling; epidemic process references 1. herbert l. j., middleton s. i. an estimate of syphilis incidence in eastern europe. journal of global health, 2012, 2 (1), pp. 1-7. *dmytro chumachenko e-mail: dichumachenko@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e60, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserioschönemann1 1french institute for public health surveillance, st maurice, france; 2louis mourier hospital emergency department, colombes, france; 3french society of emergency medicine, paris, france; 4bastia hospital emergency department, bastia, france; 5federation of regional observatories for emergency activities, paris, france objective to estimate the real burden of influenza epidemic on emergency departments (ed) attendances and hospitalizations among patients over 65 years in order to better understand determinants of overcrowding and mortality excess. introduction while the link between excess winter mortality and winter respiratory diseases in the elderly is well described, the impact of the epidemic of influenza in the elderly is mainly assessed in france through specific surveillance in the general population. syndromic surveillance data enables to monitor ed attendances and hospitalizations for various diagnostic codes groupings throughout the influenza epidemic, some of which often cited as influenza proxies, such as cardiorespiratory diagnostic groups1-2. in mainland france, the 2014-15 season was characterized by an intense influenza epidemic in the community (sub-type a(h3n2) dominant virus). hospital overcrowding was early reported, partly linked to serious clinical presentations among the elderly, and leading to the triggering of a national emergency plan. we hypothesized that ed numbers of clinical influenza cases underestimate the influenza burden among patients aged 65 years and over, especially when a a(h3n2) influenza subtype circulates. methods in october 2014, the french syndromic surveillance system based on the ed (oscour® network) included about 550 ed (45,000 daily attendances), recording 85% of the national total attendances. about 270 ed transmitted daily during the whole study period (20,500 daily attendances). numbers of visits and hospitalizations for influenza proxy variables were extracted from the national database for the period october 2010 to april 2015 and aggregated to a weekly level for patients aged 65 years and over. weekly isolates of influenza viral subtypes were obtained from reference laboratories. diagnostic codes groupings were selected based on their association with either influenza symptoms or complications of an influenza infection. visual examination of times series correlation with influenza positivity rate along with spearman correlation were performed to select principal influenza proxy variables. attendances and hospitalizations of patients aged 65 years and over were modeled separately as a function of each selected proxy variables for influenza, seasonality and trend using a negative binomial regression model2. two surrogate’s measures for influenza activity were tested: clinical influenza attendances and laboratory confirmed influenza identifications (positivity rate). numbers of visits for bronchiolitis among children under 2 years old were used as a proxy of rsv activity. results for the entire study period, visits were significantly associated with influenza positivity rate for acute bronchitis (irr=2.0 95% ci=1.7;2.3), pneumonia (irr=1.4 95% ci=1.2;1.5), chronic obstructive pulmonary disease (copd) (irr=1.4 95% ci=1.3;1.6), dyspnea (irr=1.2 95% ci=1.2;1.3), asthma (irr=1.5 95% ci=1.3;1.8), acute cardiac failure (irr=1.2 95% ci=1.1;1.3), and dehydration (irr=1.2 95% ci=1.1;1.4). similar measures of associations were found for hospitalizations. conclusions our study shows that the burden of influenza is underestimated among the elderly, due to the lack of sensitivity of coding, especially during a(h3n2) seasons, partly explained by the large panel of influenza clinical presentations in this age group. based on our results, a diagnostic codes grouping will be specifically built to monitor influenza epidemic in ed and to estimate its burden among patients aged 65 years and over. keywords influenza; emergency departments; flu proxy variables references 1. schanzer dl, langley jm, tam tws. hospitalization attributable to influenza and other viral respiratory illnesses in canadian children. pediatr infect dis j. sept 2006;25(9):795-800. 2. thompson ww, shay dk, weintraub e, brammer l, bridges cb, cox nj, et al. influenza-associated hospitalizations in the united states. jama. 15 sept 2004;292(11):1333-40. *vanina bousquet e-mail: v.bousquet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e12, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the 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room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi 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an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 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security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s 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heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts towards tracking opium related discussions in social media albert park* and mike conway biomedical informatics, university of utah, salt lake city, ut, usa objective we aim to develop an automated method to track opium related discussions that are made in the social media platform called reddit. as a first step towards this goal, we use a keyword-based approach to track how often reddit members discuss opium related issues. introduction in recent years, the use of social media has increased at an unprecedented rate. for example, the popular social media platform reddit (http://www.reddit.com) had 83 billion page views from over 88,000 active sub-communities (subreddits) in 2015. members of reddit made over 73 million individual posts and over 725 million associated comments in the same year [1]. we use reddit to track opium related discussions, because reddit allows for throwaway and unidentifiable accounts that are suitable for stigmatized discussions that may not be appropriate for identifiable accounts. reddit members exchange conversation via a forum like platform, and members who have achieved a certain status within the community are able to create new topically focused group called subreddits. methods first, we use a dataset archived by one of reddit members who used reddit’s official application programming interface (api) to collect the data (https://www.reddit.com/r/datasets/comments/3bxlg7/i_ have_every_publicly_available_reddit_comment/). the dataset is comprised of 239,772 (including both active and inactive) subreddits, 13,213,173 unique user ids, 114,320,798 posts, and 1,659,361,605 associated comments that are made from oct of 2007 to may of 2015. second, we identify 10 terms that are associated with opium. the terms are ‘opium’, ‘opioid’, ‘morphine’, ‘opiate’,’ hydrocodone’, ‘oxycodone’, ‘fentanyl’, ‘oxy’, ‘heroin’, ‘methadone’. third, we preprocess the entire dataset, which includes structuring the data into monthly time frame, converting text to lower cases, and stemming keywords and text. fourth, we employed a dictionary approach to count and extract timestamps, user ids, posts, and comments containing opium related terms. fifth, we normalized the frequency count by dividing the frequency count by the overall number of the respective variable for that period. results according to our dataset, reddit members discuss opium related topics in social media. the normalized frequency count of posters shows that less than one percent members, on average, talk about opium related topics (figure 1). although the community as a whole does not frequently talk about opium related issues, this still amounts to more than 10,000 members in 2015 (figure 2). moreover, members of reddit created a number of subreddits, such as ‘oxycontin’, ‘opioid’, ‘heroin’, ‘oxycodon’, that explicitly focus on opioids. conclusions we present preliminary findings on developing an automated method to track opium related discussions in reddit. our initial results suggest that on the basis of our analysis of reddit, members of the reddit community discuss opium related issues in social media, although the discussions are contributed by a small fraction of the members. we provide several interesting directions to future work to better track opium related discussions in reddit. first, the automated method needs to be further developed to employ more sophisticated methods like knowledge-based and corpus-based approaches to better extract opium related discussions. second, the automated method needs to be thoroughly evaluated and measure precision, recall, accuracy, and f1-score of the system. third, given how many members use social media to discuss these issues, it will be helpful to investigate the specifics of their discussions. line graphs of normalized frequency counts for posters, comments, and posts that contained opium related terms line graphs of raw frequency counts for posters, comments, and posts that contained opium related terms keywords data mining; surveillance system; social media online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e73, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts acknowledgments we restricted our analysis to publicly available discussion content and the university of utah’s institutional review board (irb) reviewed the study procedure and data (irb 00076188). dr. park was funded by national institute for health-national library of medicine t15 lm007124. dr. conway’s contribution to this research was supported by national library of medicine of the national institutes of health under award numbers r00lm011393 & k99lm011393. the content is solely the responsibility of the authors and does not necessarily represent the official views of the national institutes of health. references 1. reddit. reddit in 2015 [internet]. 2015. available from: http://www.redditblog.com/2015/12/reddit-in-2015.html; archived at: http://www.webcitation.org/6ethn0tfd *albert park e-mail: alpark7@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e73, 2017 isds16_abstracts-final 147 isds16_abstracts-final 148 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e307, 2019 isds 2019 conference abstracts enhancing drug overdose alerts with spatial visualization robert gottlieb, kayley dotson, amanda billman indiana state department of health, indianapolis, indiana, united states objective this poster presentation shares indiana’s approach of alerting local health departments (lhds) with near real-time drug overdose data and how this process has been enhanced through mapping and analysis with a geographic information system (gis). introduction since 2008, drug overdose deaths exceeded the number of motor vehicle traffic-related deaths in indiana, and the gap continues to widen [1]. while federal funding opportunities are available for states, it often takes years for best practices to be developed, shared, and published. similarly, local health departments (lhds) may experience lengthy delays to receive finalized county health statistics. indiana collects and stores syndromic emergency department data in the public health emergency surveillance system (phess) and uses the electronic surveillance system for the early notification of community-based epidemics version 1.21 (essence) to monitor public health events and trends. in july 2017, the indiana overdose surveillance team (iost) developed a standard process for monitoring and alerting local health partners of increases in drug overdoses captured in essence at the county level. isdh is enhancing these alerts by mapping the data in gis and providing spatiotemporal data to lhds to inform more targeted intervention and prevention efforts. methods the iost monitors drug overdoses statewide by analyzing daily queries from essence and sending email alerts to lhds that are experiencing a statistically significant increase in suspected overdose activity at a hospital or county level. the iost then requests that lhds complete an overdose response feedback survey describing their actions after receiving an overdose alert. the iost gis analyst has enhanced overdose alerts by utilizing daily emergency department data queries from the phess database based on chief complaint and diagnosis text. python™ and arcgis™ are used to deduplicate and geocode records, calculate the rate of cases within a hexagonal grid, and calculate the kernel density of case counts to show patterns at the neighborhood l evel. comparisons to previous time periods are also calculated. temporal and spatial scales of analysis are flexible, but 7 days and 30 days are used most often. results are mapped in an html file using an open source python package for dissemination to lhds. results between july 26, 2017, and sept. 4, 2018, the iost sent 89 suspected overdose alerts to lhds. alerts were sent to 45 different lhds, of which 22 received multiple alerts (range: 1-9 repeat alerts). lhds were requested to complete the survey on their initial alert, and a total of 31 jurisdictions completed this survey (31/45 = 69%). the majority of the lhd respondents (27/31 = 87%) wanted to continue receiving overdose alert emails. our enhanced spatial analysis project has mapped more than 500 cases per week. geocoding was successful for approximately 87% of the addresses received through phess. neighborhoods in urban areas with higher counts have been identified, though variability from week to week is high. areas of high overdose rates that cross county boundari es have also been detected, which would not have been possible using essence alone. conclusions notifying lhds of near real-time drug overdose trends is a catalyst for drug overdose planning and response efforts in indiana. gis mapping of the data provides an easy way for lhds to view and share spatial trends with their local planning partners and identify community intervention strategies that can reduce drug overdose rates and improve outcomes for overdose survivors. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e307, 2019 isds 2019 conference abstracts acknowledgement chris waldron, garry raynor, peter krombach, harold gil references 1. overdose prevention [internet]. indianapolis: indiana state department of health; 2017. indiana special emphasis report: drug overdose deaths 1999-2015; august 2017. [cited 2017 sept 25]. available from: http://www.in.gov/isdh/files/2017_ser_drug_deaths_indiana.pdf figure 1 http://ojphi.org/ http://www.in.gov/isdh/files/2017_ser_drug_deaths_indiana.pdf isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e307, 2019 isds 2019 conference abstracts figure 2 http://ojphi.org/ 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts childhood injury in wake county, nc: local use of public health surveillance data anna e. waller*, steven lippmann, amy ising and carolyn crump unc-chapel hill, chapel hill, nc, usa objective to utilize secondary data sources to describe childhood injury and prioritize prevention efforts in wake county, nc. introduction a local foundation commissioned a project to determine the leading causes of childhood injury in wake county, nc. multiple sources of secondary data, including syndromic surveillance data, were used to describe leading causes of childhood injury in the county.1 methods mortality (deaths) were identified in data available online through the nc state center for health statistics (schs) and the nc violent death reporting system. hospital discharges were identified through the nc schs hospital discharge data file and accessed by staff in the injury and violence prevention branch at nc division of public health (nc dph). emergency department visits were identified through nc detect and accessed by project staff under a data use agreement with nc dph. descriptive statistics and crosstabulations were computed using sas version 9.2 and microsoft excel. injury intent and mechanism were categorized based on icd10 (mortality) or icd-9-cm (ed and hospital) external cause codes. rates were computed using estimates for 2010-2012 as the population denominators. results to develop a list of the leading causes of injuries among wake county children ages 0-17, we rank-ordered the top five injury mechanisms in each of the three data sources (table 1) and determined the order of the top ten causes based on their occurring in all three, two, or only one of our major data sources. motor vehicle crashes (mvc) (occupant, pedestrian, all) were one of the five leading causes for all three data sources, thus their placement as the 1st and 3rd leading injury causes. mvc-pedestrian was kept separate as the 3rd leading cause given differences in prevention approaches for occupants versus pedestrians. assault and self-inflicted/self harm were in the top five injury causes for both mortality and hospital discharges, thus their placement as 2nd and 4th leading causes. falls was in the top injury cause for both hospital discharges and ed visits, and the number of fall events was significantly higher than the number of deaths due to unintentional suffocation, thus the placement of falls and suffocation as the 5th and 6th leading causes, respectively. burns, struck by/against, natural/environmental factors, and bicycle injury/crashes were placed in the 7th through 10th positions because they were among the five leading injury causes for hospital discharges or ed visits only. more than half the injury related hospital discharges (58.1%) and ed visits (58.5%) were by male children; this was true for all age groups. children ages 1-4 years had the most injury ed visits, followed by those 10-14 years. children ages 15-17 years had more injury hospitalizations than any other age group, accounting for 27.7% of all injury related hospital discharges. the number of injury-related ed visits increased steadily between 2006 and 2012. nine out of ten injury-related ed visits by wake county children resulted in being discharged to home (91.2%), while 4.5% resulted in being admitted to the hospital or transferred to another hospital. there were 29 deaths in hospital for injured patients (1.0%). conclusions a wealth of information about childhood injury in wake county, nc, was available through secondary data sources, including syndromic surveillance data. timely and comprehensive ed visit data collected in nc detect proved invaluable to assessing local childhood injury causes. the ranking of childhood injury causes varied greatly between data sources, highlighting the importance of including a variety of data in a community health assessment. including icd-9-cm codes in a syndromic surveillance system enables inclusion of these data in health assessments and other retrospective analyses. keywords injury surveillance; childhood injury; community assessment; local public health; secondary data acknowledgments the ed visit data were obtained from nc detect under a data use agreement with the nc division of public health. the nc detect data oversight committee takes no responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. references 1. crump c, page r, letourneau r, waller a, lippman s, ising a (healthy solutions, university of north carolina, chapel hill, nc). a profile of wake county childhood injury & injury prevention. raleigh (nc): john rex endowment; 2014, 102 p. *anna e. waller e-mail: awaller@med.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e172, 201 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts streamlining foodborne disease surveillance with open-source data management software michael judd* and karen wong centers for disease control and prevention, atlanta, ga, usa objective the “ledsmanager”, a data management platform built in r, aims to improve the timeliness and accuracy of national foodborne surveillance data submitted to the laboratory-based enteric disease surveillance (leds) system by automating the data processing, validating, and reporting workflow. introduction the national surveillance team in the enteric diseases epidemiology branch of the centers for disease control and prevention (cdc) collects electronic data from all state and regional public health laboratories on human infections caused by campylobacter, salmonella, shiga toxin-producing e. coli, and shigella in leds. these data inform annual estimates of the burden of illness, assessments of patterns in bacterial subtypes, and can be used to describe trends in incidence. robust digital infrastructure is required to process, validate, and summarize data on approximately 60,000 infections annually while optimizing use of financial and personnel resources. methods we leveraged the robust and extensible programming facilities of the r programming language and the active community of r users to develop a data integration, processing, and reporting pipeline for leds via an internal software package we named “ledsmanager”. we designed all data retrieval, cleaning, and provisioning algorithms using tools from rstudio software packages1–3 and tracked changes to source code and data using cdc’s internal gitlab server. we automated data validation requests to reporting partners by generating customizable emails directly from the r console4. we streamlined the data reconciliation process using openrefine5, a point-andclick tool for cleaning big data. we automated generation of annual reports, a process that was previously manual, using parameterized rmarkdown documents. staff epidemiologists performed design and implementation internally, requiring no external consulting. results developing our free and open-source software platform for national foodborne surveillance data management has saved the enteric diseases epidemiology branch thousands of dollars because we no longer depend on proprietary software requiring annual licensing fees. this transition occurred without any disruption in surveillance operations. partial automation of email-based data validation and annual report generation processes reduced employee time requirement from one full-time position to one part-time position. the modular nature of ledsmanager permitted leds to collect an expanded set of data elements with no changes to the core data processing and reporting workflow. conclusions we developed and implemented a flexible tool that helps maintain the integrity of surveillance data and reduces the need for manual data cleaning, which can be laborious and error-prone. the user-friendly design features of ledsmanager demonstrate that data management can be optimized using programming skills that are increasingly common among epidemiologists. our work on improving the accuracy and efficiency of enteric disease surveillance has served as a proof of concept for plans to streamline data processing for other surveillance systems. keywords r; open source; surveillance references 1. wickham, h. (2017). tidyverse: easily install and load ‘tidyverse’ packages. r package version 1.1.1. https://cran.r-project.org/package=tidyverse 2. wickham, h; hester, j; francois r. (2016). readr: read tabular data. r package version 1.1.1. https://cran.r-project.org/package=readr 3. wickham, h and miller, e. (2017). haven: import and export ‘spss’, ‘stata’ and ‘sas’ files. r package version 1.1.0. https://cran.r-project.org/package=haven 4. premraj, r. (2016). mailr: a utility to send emails from r. r package version 0.6. https://github.com/rpremraj/mailr 5. ham, k. (2013). openrefine (version 2.5). free, open-source tool for cleaning and transforming data. journal of the medical library association: jmla, 101(3), 233. http://openrefine.org. *michael judd e-mail: vuj4@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e60, 2018 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts insight into malaria transmission and control in endemic areas beatty v. maikai*1, jarlath u. umoh1 and victor a. maikai2 1department of veterinary public health and preventive medicine, faculty of veterinary medicine, ahmadu bello university, zaria, nigeria; 2college of agriculture and animal science, ahmadu bello university, kaduna, nigeria objective to examine the likely impact of malaria parasite intervention points for a steady state regional control program in endemic areas introduction the global effort of malaria control is in line with the one world one health concept, but then a globally defined ‘‘one-size-fits-all’’ malaria control strategy would be inefficient in endemic areas. plasmodium falciparum is the type of malaria parasite that most often causes severe and life-threatening malaria. people get malaria by being bitten by an infective female anopheles mosquito. regional malaria elimination campaigns in 1940s followed by the global malaria eradication program in 1955 did not succeed in eliminating malaria from subsaharan africa, which accounts for 80% of today’s burden of malaria (1,2). the basic reproductive number, ro, has played a central role in epidemiological theory for malaria and other infectious diseases because it provides an index of transmission intensity and establishes threshold criteria (3). methods use of systematic literature review to propose a simple model on the likely impact of targeted intervention points on control of malaria parasite. assumptions were varied about two targeted epidemiologic control points on the basic reproductive number, ro, which is affected by different factors and upon which the status of malaria in any community will depend. taking to be expected number of infectious bites per person over a given time period; 1 as the effective contact between susceptible individuals and malaria vectors; 2 as the effective contact between individuals under intervention and malaria vectors; 3 as the effective contact between susceptible malaria vectors and infected individuals; s= susceptible population, v= population under intervention, d= dead mosquitoes and r= immune humans. at any time t in a population, j, vectoral capacity c(t) = mj (t) aj 2 pj n/(-ln pj) ( 4); infected human j=1 population at intervention point, ih(t)=c(t) ( 1sh + (1 – ) 2v) r; infected mosquito population at intervention point, im(t)= (1 – ) 3sm(c(t)). if is the degree of protective intervention, which is also equal to 1. thus 1 is the intervention failure. intervention will reduce the probability of infection when exposed to malaria pathogens and this equals the degree of protection = ih(t)/ im(t); ro 1/ ; ro= b/ where b is infectivity of humans to mosquitoes. results population important in malaria transmission are the susceptible, infected and infectious anopheles mosquitoes and human populations. three factors in this basic model that can affect ro are the infectivity of humans, b, the effective or adequate contact between vector and individuals, , and the vectoral capacity, c(t). increase in ro will inversely decrease . for there to be decrease in ro, control has to be effective. when there is interventions targeted at reducing density of mosquitoes and humans through destruction of breeding sites and prophylaxis/treatment/use of nets respectively over a given time period, number infected will be immuned at which point, ro = 1, more immune individuals wil llead to ro1 until when there is a steady state control program at which point ro=0. this is the point that intervention is very effective. conclusions the two targeted control points should be considered for any effective malaria control and eradication program in endemic areas so that ro can be consistently lowered to a level that is below threshold. keywords malaria; control; endemic references 1.carter, r, mendis kn. evolutionary and historical aspects of the burden of malaria. clin microbiol rev 2002; 15: 564-94. 2.lopez ad, mathers cd, ezzati m, murray chjl. global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. lancet 2006; 367: 1747-57. 3.hadeler kp, and castillo-chavez c. a core group model for disease transmission, mathematical biosciences 1995; 128: 41-55. 4. garrett-jones c. prognosis for interruption of malaria transmission through assessment of the mosquito’s vectorial capacity. nature 1964; 204: 1173–1175 *beatty v. maikai e-mail: beatt18@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e198, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts now trending in your community: social media insights for your public health mission diana m. kushner* department of health and human services, washington, dc, usa objective the goal of the now trending website is to provide a web based tool that pulls out relevant twitter conversations concerning illness and disasters and provides meaningful analytics on how those conversations are trending. the website gives the user the ability to view trends overall and for specific geographic areas. introduction in today’s fast paced world, information is available (and expected) instantaneously. social media has only fueled this expectation as it has permeated all aspects of our lives. more and more of the population is turning to social media outlets to share their thoughts and update their status, especially during disasters. with all these conversations occurring, it is only reasonable to assume that health status is part of the information being shared. in fact, studies by johns hopkins university1 and harvard university2 have shown that social media reporting can serve as an early indicator and warning of emerging health issues within a community. whether people are talking about being sick themselves or fear of illness in the community, there is a wealth of knowledge to be gained by tapping into this information. yet gaining insight and understanding from social media data can be problematic. the unstructured nature of the data, the presence of social media “spam”, and the frequency of reposting information makes social media a noisy data source. being able to harness this data would provide the opportunity to use social media as an effective situational awareness and early warning tool for biosurveillance missions. but how do you accomplish this? there are tens of millions of conversations happening on social media every day that would need to be sifted through to get to the health related topics. no public health entity has the time or staffing for that endeavor. methods utilizing an innovation challenge, the assistant secretary for preparedness and response (aspr) within the department of health and human services held a competition to create a web-based application that would provide federal, state, and local public health authorities the ability to cut through the vast amounts of twitter data and pull out relevant conversations on illness for the national level as well as their geographic area of interest. this information is then available to use in multiple ways such as serving as an indicator of potential health issues emerging in the population, building a baseline of trend data, engaging the public on trending health topics, or crossreferencing other data sources. working with the winning team, aspr transitioned the winning website over to government cloud space to provide consistent and reliable tool for its public health partners. this website, https://nowtrending.hhs.gov, is available free of charge to not only public health practitioners, but all interested parties. results the now trending application is a web-based tool which allows users to review trends in discussions of illness and disaster related topics on twitter in real time. users of now trending have the ability to view analytics of twitter trends by geographic area or by health condition. the analytics are shown as time period trends which range from the previous 2 hours to the previous week. users can click on points in the graph to see the actual tweets that make up the trends in order to gain better context of the twitter conversations. now trending cuts through the noise in twitter by filtering health condition searches using a list of terms for each health condition, including the common terms used by the general population and some of the common symptoms associated with the condition. in addition, now trending uses qualifier terms to further eliminate irrelevant tweets. there are currently 27 health conditions tracked by now trending, but this can be easily expanded to include terms that are relevant to current events. conclusions the now trending website is being presented to ensure all public health personnel are aware of this free tool and the advantages it can provide them in their public health missions. keywords social media; biosurveillance; geolocation references [1] paul, michael and dredze, mark. you are what you tweet: analyzing twitter for public health. baltimore: johns hopkins university, 2011. [2] keller, mikaela, et al. “use od unstructured event-based reports for global infectious disease surveillance.” emerging infectious disease 15, no. 5 (may 2009): 689-695. *diana m. kushner e-mail: diana.kushner@hhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e140, 201 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts interpreting specific and general respiratory indicators in syndromic surveillance roger morbey*, alex j. elliot, maria zambon, richard pebody and gillian e. smith public health england, birmingham, united kingdom objective to improve understanding of the relative burden of different causative respiratory pathogens on respiratory syndromic indicators monitored using syndromic surveillance systems in england. introduction public health england (phe) uses syndromic surveillance systems to monitor for seasonal increases in respiratory illness. respiratory illnesses create a considerable burden on health care services and therefore identifying the timing and intensity of peaks of activity is important for public health decision-making. furthermore, identifying the incidence of specific respiratory pathogens circulating in the community is essential for targeting public health interventions e.g. vaccination. syndromic surveillance can provide early warning of increases, but cannot explicitly identify the pathogens responsible for such increases. phe uses a range of general and specific respiratory syndromic indicators in their syndromic surveillance systems, e.g. “all respiratory disease”, “influenza-like illness”, “bronchitis” and “cough”. previous research has shown that “influenza-like illness” is associated with influenza circulating in the community1 whilst “cough” and “bronchitis” syndromic indicators in children under 5 are associated with respiratory syncytial virus (rsv)2, 3. however, the relative burden of other pathogens, e.g. rhinovirus and parainfluenza is less well understood. we have sought to further understand the relationship between specific pathogens and syndromic indicators and to improve estimates of disease burden. therefore, we modelled the association between pathogen incidence, using laboratory reports and health care presentations, using syndromic data. methods we used positive laboratory reports for the following pathogens as a proxy for community incidence in england: human metapneumovirus (hmpv), rsv, coronavirus, influenza strains, invasive haemophilus influenzae, invasive streptococcus pneumoniae, mycoplasma pneumoniae, parainfluenza and rhinovirus. organisms were chosen that were found to be important in previous work 2 and were available from routine laboratory testing. syndromic data included consultations with family doctors (called general practitioners or gps), calls to a national telephone helpline “nhs 111” and attendances at emergency departments (eds). associations between laboratory reports and syndromic data were examined over four winter seasons (weeks 40 to 20), between 2011 and 2015. multiple linear regression was used to model correlations and to estimate the proportion of syndromic consultations associated with specific pathogens. finally, burden estimates were used to infer the proportion of patients affected by specific pathogens that would be diagnosed with different symptoms. results influenza and rsv exhibited the greatest seasonal variation and were responsible for the strongest associated burden on general respiratory infections. however, associations were found with the other pathogens and the burden of streptococcus pneumoniae was important in adult age groups (25 years and over). the model estimates suggested that only a small proportion of patients with influenza receive a specific diagnosis that is coded to an “influenza-like illness” syndromic indicator, (6% for both gp in-hours consultations and for emergency department attendances), compared to a more general respiratory diagnosis. also, patients with influenza calling nhs 111 were more likely to receive a diagnosis of fever or cough than cold/flu. despite these findings, the specific syndromic indicators remained more sensitive to changes in influenza incidence than the general indicators. conclusions the majority of patients affected by a seasonal respiratory pathogen are likely to receive a non-specific respiratory diagnosis. therefore, estimates of community burden using more specific syndromic indicators such as “influenza-like illness” are likely to be a severe underestimate. however, these specific indicators remain important for detecting changes in incidence and providing added intelligence on likely causative pathogens. specific syndromic indicators were associated with multiple pathogens and we were unable to identify indicators that were good markers for pathogens other than influenza or rsv. however, future work focusing on differences between ages and the relative levels of a range of pathogens may be able to provide estimates for the mix of pathogens present in the community in real-time. keywords respiratory; syndromic; public health acknowledgments the authors are partly funded by the national institute for health research (nihr) health protection research unit in emergency preparedness and response. the views expressed are those of the authors and not necessarily those of the national health service, the nihr, the department of health or public health england. references 1. harcourt se, smith ge, elliot aj, pebody r, charlett a, ibbotson s, et al. use of a large general practice syndromic surveillance system to monitor the progress of the influenza a(h1n1) pandemic 2009 in the uk. epidemiol infect 2011;140:100-5. 2. cooper dl, smith ge, edmunds wj, joseph c, gerard e, george rc. the contribution of respiratory pathogens to the seasonality of nhs direct calls. j infect 2007;55:240-8. 3. hughes he, morbey r, hughes tc, locker te, pebody r, green hk, et al. emergency department syndromic surveillance providing early warning of seasonal respiratory activity in england. epidemiol infect 2015:1-13. *roger morbey e-mail: roger.morbey@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e19, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e318, 2019 isds 2019 conference abstracts university-community partnership to enhance public health communication jamie j. newman3, nicholas bustamante1, kirk st. amant2 1 studio art, louisiana tech university, ruston, louisiana, united states, 2 english, louisiana tech university, ruston, louisiana, united states, 3 school of biological sciences, louisiana tech university, ruston, louisiana, united states objective the context of communicating care communicating health information across diverse populations is critical to improving public health and sustaining health-related practices within a community. in this context, successful collaborations can provide models for effectively sharing essential information in other communities. this panel examines a case where two entities partnered to create visual and written materials for conveying health information to different underserved populations in a rural and economically disadvantaged region (lincoln parish, located in north central louisiana). introduction a case of effective community-based collaborations for this case, the visual integration of science through art (vista) at louisiana tech university (tech) partnered with the nonprofit lincoln health foundation to produce image-intensive communication materials for certain local populations. the specific audience was undereducated, indigent, non-native english speaking communities in the parish – a population that often cannot readily rely on text-based resources for information. for the project, students enrolled in advanced digital painting, studio art internships, or usability and user experience design classes at tech collaborated with representatives from the lincoln health foundation to • conduct research on the communication expectations and preferences of the target audience • identify the best methods for sharing information on sensitive health issues with the members of this community • develop image-based brochures, website content, and illustrations to convey health information to these populations these final products students produced included illustrations depicting the health complications associated with diabetes and informational brochures on preventative practices associated with sexually transmitted diseases. in creating this content for the lincoln health foundation, the participants created a model for how community health organizations, educators, and students can collaborate to develop informational products for specific local communities. this proposed panel presentation examines the dynamics of forming such partnerships and collaborating to address the needs of the community population. description the dynamics of the case in this initial pilot, there are two separate but complementary projects where the community partner identified an area of need and tech students, under supervision of faculty, created either visual images or website and brochure content to improve communication. specifically, students identified and tested material for dissemination in the community by: • identifying prospective solutions/materials that can address this issue • researching the intended audience to determine the solution(s) that would work best • creating materials for addressing this need (and based on research of audience expectations) • testing these materials with members of the related audience • presenting suggested materials/solutions to community partners • discussing mechanisms the community partner could use to disseminate this information to the intended audience • the objective of the model was to provide students with hands-on, real-life experiences in • project planning • project management http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e318, 2019 isds 2019 conference abstracts • user research • product development • product planning • illustration as a form of communication • understanding audience • crossing language and cultural barriers the community partner, in turn, received a needed resource developed specifically to meet the needs of a given population within that community. acknowledgement we would like to thank the lincoln health foundation for their participation in this project. in addition we would like to thank louisiana tech university, the college of liberal art, and the college of applied and natural sciences for supporting this initiative. http://ojphi.org/ isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts nedss base system (nbs): electronic data exchange and workflow decision support jennifer ward*, christi hildebrandt and akshar patel csra, atlanta, ga, usa objective the nedss base system (nbs), an integrated disease surveillance system, implemented extensible functionality to support electronic data exchange for multiple use cases and public health workflow management of incoming messages and documents. introduction the nbs is an integrated disease surveillance system deployed in 22 public health jurisdictions to support receipt, investigation, analysis and reporting, and data exchange for state reportable conditions. the nbs is governed by the centers for disease control and prevention (cdc) and state, local, and territorial users that make up the nbs community. in the early 2000’s, electronic laboratory results reporting (elr) was implemented in an effort to improve timeliness and completeness of disease reporting. as standards-based electronic health records (ehrs) are adopted and more surveillance data become available, modern surveillance systems must consume information in an automated way and provide more functionality to automate key surveillance processes. methods many use cases exist for exchanging data with an integrated public health surveillance system. these can include exchange of electronic case and laboratory reports from healthcare, data sharing between public health entities, data migration from legacy systems, and ongoing exchange with other public health systems (e.g. immunization registries). the nbs implemented an interface specification called the public health document container (phdc). phdc is based on hl7 version 3 clinical document architecture (cda). it allows import of patient (cases and contacts), investigation, treatment, interview, and laboratory information into nbs. cda was chosen as the building block to facilitate data exchange with the healthcare community. through use of data integration tools, incoming data can be mapped from any format to phdc and imported into the system. existing services, such as patient, provider, and organization deduplication are applied. to assist with management of incoming electronic documents, nbs implemented a functionality called workflow decision support (wds). wds uses configurable algorithms to automatically process incoming documents (including case reports, laboratory reports, etc.) into the public health workflow. users can choose to mark an incoming document as reviewed or automatically create an investigation and case notification message to cdc (for nationally notifiable conditions). results through phdc, nbs is able to receive data from healthcare using national standards, such as the hl7 electronic initial case report (eicr). three nbs partners are currently collaborating to pilot eicr functionality. phdc was successfully used to migrate large volumes of data from a legacy surveillance system into the nbs. two nbs states are using phdc to implement ongoing data exchange between separate surveillance systems within their jurisdiction. in several nbs jurisdictions, wds is used to automatically create investigations and case notifications for high-morbidity conditions such as gonorrhea and chlamydia. in other jurisdictions, wds is used to assist with managing high volumes of hepatitis b and c reports. conclusions cda-based phdc does require that public health have knowledge of standards and data integration resources to transform incoming messages to the phdc interface; however, the flexibility provided by this approach ensures the system is able to respond to new and changing standards without system development. additional enhancements are needed to support data exchange with immunization registries. wds functionality does reduce burden on public health staff, especially when dealing with high-volume diseases. future functionalities include the ability to define more criteria (such as age or gender) to drive the actions taken on an incoming lab or case report. keywords data exchange; case reporting; decision support; interoperability; laboratory reporting acknowledgments the nbs is funded by the centers for disease control and prevention and supported by the nbs user group (nug), consisting of state, local, and territorial public health staff and their representatives. *jennifer ward e-mail: jjward33@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e47, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger kfl&a public health, kingston, on, canada objective to describe how the social determinants of health (sdoh) mapper is used by kfl&a public health to enhance real-time situational awareness of vulnerable populations across ontario by facilitating the inclusion of information relating to marginalization and deprivation indices. introduction geographic information system (gis) technology provides visual tools, through the creation of computerized maps, graphs, and tables of geographic data, which can assist with problem solving and inform decision-making. one of the gis tools being developed by kfl&a public health is the social determinants of health (sdoh) mapper. the sdoh mapper consists of layers of information related to deprivation and marginalization indices across ontario. the sdoh mapper facilitates the inclusion of information related to vulnerable populations with the use of both age and social determinants of health data into the gis portal. this is useful for observing trends in marginalization and deprivation across dissemination areas in ontario, and for examining health inequities in an area over time. the sdoh mapper will, in this way, improve knowledge transmission on the effects of poverty and marginalization on outcomes. methods the sdoh mapper allows users to customize maps demonstrating social determinants of health as they apply to specific geographic areas within the province of ontario. the tool allows the user to visualize graphs with access to seven layers related to the marginalization and deprivation levels for a specified population so that they may better understand the health outcomes related to poverty, and thereby make informed decisions relating to health equity. deprivation layers are derived from the deprivation index of health developed by the quebec public health institute (inspq). the deprivation social and material deprivation, as well as material and social deprivation combined. marginalization data is obtained from the ontario marginalization index (on-marg) developed by the centre for research on inner city health. the marginalization layers include residential instability, material deprivation, dependency, and ethnic concentration, which are derived from 18 indicators reflecting inequality and marginalization in canada from both the 2001 and 2006 census of canada. the sdoh mapper encompasses a number of different functions and tools. users can choose from several basemaps that identify different geographic features, such as topography, streets, or public venues, to best meet their needs. users can apply seven deprivation and marginalization layers to the selected basemap, which allows for the appropriate visualization of social determinants of health as they apply to a specific geographic area. a legend is displayed that identifies the colour-coded quintiles for the marginalization and deprivation layers. using the population summary tool, users are able to visualize graphs demonstrating the marginalization and deprivation layers for specific populations. users can also measure the distance between two locations, as well as the perimeter and area of specified geographic locations using the draw and measure function. conclusions the public health informatics team at kfl&a public health is working to develop, in an ongoing systematic manner, a single access point to the gis portal that will visualize multiple environmental and population based data sets in real-time. the single gis portal will include the sdoh mapper along with two other gis applications developed by kfl&a public health: the public health information management system (phims) and the tobacco module. by providing available real-time data from multiple partners into the gis portal, the public health informatics team intends to assist with identifying health events earlier than traditional public health methods. including the sdoh mapper in the gis portal provides an important social determinants of health lens through which environmental data, underlying population based indicators, and health events of interest can be examined and visualized, to provide users with an understanding of the effects of poverty on health outcomes keywords poverty; marginalization; deprivation; map; gis online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e26, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts comparing emergency department gunshot wound data with mass casualty shooting reports andrew walsh* health monitoring, pittsburgh, pa, usa objective to determine whether mass casualty shooting events are captured via syndromic surveillance data. introduction shootings with multiple victims are a concern for public safety and public health. the precise impact of such events and the trends associated with them is dependent on which events are counted. some reports only consider events with multiple deaths, typically four or more, while other reports also include events with multiple victims and at least one death.1 underreporting is also a concern. some commonly cited databases for these events are based on media reports of shootings which may or may not capture the complete set of events that meet whatever criteria are being considered. many gunshot wounds are treated in the emergency department setting. emergency department registrations routinely collected for syndromic surveillance will capture all of those visits. analysis of that data may be useful as a supplement to mass shooting databases by identifying unreported events. in addition, clusters of gunshot wound incidents which are not the result of a single shooting event but still represent significant public safety and public health concerns may also be identified. methods emergency department registration data was collected from hospitals via the epicenter syndromic surveillance system. gunshotrelated visits were identified based on chief complaint contents using epicenter’s regular expression-based classification system. the gunshot wound classifier attempts to exclude patients with preexisting wounds and shooting incidents involving weapon classes that are lesser concerns for public safety, such as nail guns and toy guns. gunshot-related visits were clustered by day of registration and separately by facility, by patient home zip code, and by patient home county. the largest clusters of each type were compared via manual search against media reports of shootings and against the gun violence archive mass shooting database. results a total of 23,132 gunshot-related visits were identified from 635 healthcare facilities from 2013 to 2015. from these, the five largest clusters by facility, by zip code, and by county were identified. the clusters included 112 gunshot wounds in total, ranging in size from 4 to 12 with a median of 7. of the 5 facility clusters, 5 had a corresponding media story and 2 were located in the shooting database. of the 5 zip code clusters, 1 had a corresponding media story and none were located in the shooting database. of the 5 county clusters, 4 had a corresponding media story and 1 was located in the shooting database. conclusions multiple gunshot wound patients being treated on the same day were not necessarily all shot during the same incident or by the same shooter. the information available in a syndromic surveillance feed does not allow for direct identification of the shooter or shooters. given that limitation, a complete correspondence between clusters identified in syndromic surveillance data and mass shootings was not expected. the strong correlation between clusters and media coverage indicates that the news is a reasonable source for shooting data. the smaller overlap with the mass shooting database is likely due to the more stringent criteria required for an incident to qualify as a mass shooting. it is still notable that the majority of gunshot clusters were not associated with any particular mass shooting incident. this serves as a reminder that mass shootings represent only a small portion of the total gun violence in the united states. healthcare data represents a significant additional data source for understanding the complete impact of gun violence on public health and safety. weekly time series of gunshot-related emergency department visits keywords mass shooting; gunshot wound; emergency department acknowledgments we wish to thank our public health customers for funding support and data for this work. references 1. ingraham c. what makes a ‘mass shooting’ in america. washington post (online). 2015 dec 3:wonkblog https://www.washingtonpost. com/news/wonk/wp/2015/12/03/what-makes-a-mass-shooting-inamerica/ *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e33, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts spatial distribution of adolescents with sexually transmitted infections diagnosed in the pediatric emergency departments of washington, dc shilpa patel*1, lisa tuchman2, katie hayes3, gia badolato1, stephen j. teach1 and monika k. goyal1 1division of emergency medicine, children’s national medical center, washington dc, dc, usa; 2division of adolescent medicine, children’s national medical center, washington dc, dc, usa; 3division of emergency medicine, children’s hospital of philadelphia, philadelphia, pa, usa objective (1) to describe the spatial distribution of adolescents with eddiagnosed stis in a large urban area with a high prevalence of sti (2) to compare census block groups and identify “hot spots” of sti. introduction utilization of local surveillance data has been shown to help risk stratify patients presenting to the emergency department presenting with gas pharyngitis or meningitis. (1, 2) adolescents frequently present to the emergency department (ed) with symptoms that may be associated with a sexually transmitted infection (sti). (3) when ed providers perceive high local rates of sti and low rates of follow-up, empiric treatment is considered. this strategy may result in unnecessary treatment. knowledge of the local spatial distribution of adolescents with stis diagnosed in local pediatric emergency departments eds may enhance risk stratification and allow targeted testing and/or treatment among future ed patients in whom sti is considered. methods we performed a retrospective cross-sectional medical record review of all visits made by district residents aged 13-19 years old to the two large urban pediatric eds in washington, dc in 2012 and abstracted demographic and visit data, including sti testing results for gonorrhea and chlamydia. multivariable logistic regression was utilized to compare odds of testing positive for sti between geographic regions. geospatial statistical analyses were performed using arcmap 10.1 tools, hot spot analysis and spatial autocorrelation (moran’s i). results of 1002 adolescents tested for stis, 6.8% tested positive for gonorrhea, 18.6% tested positive for chlamydia, 22.7% tested positive for either gonorrhea or chlamydia and 2.9% tested positive for coinfection. the mean age of patients with stis was 16.9 years (sd 1.5); 78.4% were female. of the 227 patients with 1 positive test, 95% of their addresses were successfully geocoded. hot spot analysis indicated statistically significant clusters of sti cases (figure 1.) spatial autocorrelation (moran’s i) demonstrated that the distribution of cases was not random (z-score 13.3, p value < 0.05). the odds of adolescent patients from dc ward 6 testing positive for chlamydia was twice that of dc ward 7(aor 2.07; 95% ci 1.07 – 3.98). conclusions geographic analyses identified areas with significantly higher cases of ed-diagnosed stis. modeling real time surveillance in conjunction with other clinical information readily available in the electronic medical record may improve prediction of stis in adolescents presenting to the ed, decrease empiric treatment, and allow for targeted public health prevention. incorporation of city-wide data could improve our ability to identify clusters of infection. figure 1. hot spot analysis of ed-diagnosed sti cases by census block group (n=233) keywords sexually transmitted infection; surveillance; emergency department acknowledgments we would like to thank drs. kristen breslin and james chamberlain for their contribution to study conception and design, prof. dante verme of george washington university for assistance with gis and the dc gis program and www.census.gov for providing the base maps. references (1)fine am, nizet v, mandl kd. improved diagnostic accuracy of group a streptococcal pharyngitis with use of real-time biosurveillance. ann intern med. 2011 sep 20;155(6):345-52. (2)fine am, nigrovic le, reis by, cook ef, mandl kd. linking surveillance to action: incorporation of real-time regional data into a medical decision rule. am med inform assoc. 2007 mar-apr; 14(2):206-11. (3)goyal m, hayes k, mollen c. sexually transmitted infection prevalence in symptomatic adolescent emergency department patients. pediatric emerg care. 2010 dec;28(12):1277-80. *shilpa patel e-mail: shilpapateldas@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e154, 2015 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts weather outlook: cloudy with a chance of…— classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano new jersey department of health, trenton, nj, usa objective to introduce and describe methods for evaluating and refining custom classifier keyword lists for syndromic surveillance of several post-severe weather event conditions and to report findings from new jersey’s syndromic surveillance of selected conditions in the aftermath of hurricane sandy. introduction hurricane ‘superstorm’ sandy struck new jersey on october 29, 2012, causing harm to the health of new jersey residents and billions of dollars of damage to businesses, transportation, and infrastructure. monitoring health outcomes for increased illness and injury due to a severe weather event is important in measuring the severity of conditions and the efficacy of state response, as well as in emergency response preparations for future severe weather events. following the experience with hurricane sandy and the foreseeable need to be prepared for future severe weather events, njdoh initiated a project to develop a suite of 20 indicators in epicenter, an online system which collects emergency department chief complaint data in realtime, to perform syndromic surveillance of extreme weather–related conditions. methods the development of 20 severe weather event indicators followed a two-stage evaluation of keyword lists using diagnostic codes. the statistical measures of sensitivity, specificity, and positive predictive value were computed for both the initial keyword list and the final keyword list. application of nine identified severe weather event classifiers was performed by comparing graphs of one-month, three-months, and one-year time periods following hurricane sandy compared against the same time period from the following year. results the updated keyword lists for anxiety/adjustment disorders, disrupted outpatient medical care (dialysis and medication refills), gastrointestinal illness, upper respiratory illness, asthma, and substance abuse resulted in improved accuracy when compared to initial keyword lists and are recommended for use as new customized classifiers when analyzing severe weather events. evaluation did not significantly improve accuracy of the initial epicenter classification for co poisoning, and further research is recommended for the application of disrupted outpatient medical care: oxygen needs. when the time period after hurricane sandy was compared to the same time period during the following year, the impact of the extreme weather event on increases of ed visits for each of the evaluated classifiers became clear. though ed visits for gastrointestinal disease were anticipated to be a post-storm concern, no peak was seen relative to preceding or following months. conclusions overall, this endeavor has provided njdoh with a clearer picture of the effects of hurricane sandy and has yielded valuable information on how the state should prepare to monitor the effects of the next severe weather event. keywords severe weather; weather classification; classification development; superstorm sandy acknowledgments alvin chu, phd, health monitoring systems, inc. *teresa hamby e-mail: teresa.hamby@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e118, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality mary woinarowicz1, ma, molly howell1, mph 1. north dakota department of health, division of disease control abstract objectives: to evaluate the impact of electronic health record (ehr) interoperability on the quality of immunization data in the north dakota immunization information system (ndiis). methods: ndiis doses administered data was evaluated for completeness of the patient and dose-level core data elements for records that belong to interoperable and non-interoperable providers. data was compared at three months prior to electronic health record (ehr) interoperability enhancement to data at three, six, nine and twelve months post-enhancement following the interoperability go live date. doses administered per month and by age group, timeliness of vaccine entry and the number of duplicate clients added to the ndiis was also compared, in addition to, immunization rates for children 19 – 35 months of age and adolescents 11 – 18 years of age. results: doses administered by both interoperable and non-interoperable providers remained fairly consistent from pre-enhancement through twelve months post-enhancement. comparing immunization rates for infants and adolescents, interoperable providers had higher rates both preand post-enhancement than non-interoperable providers for all vaccines and vaccine series assessed. the overall percentage of doses entered into the ndiis within one month of administration varied slightly between interoperable and non-interoperable providers; however, there were significant changes between the percentage of doses entered within one day and within one week with the percentage entered within one day increasing and within one week decreasing with interoperability. the number of duplicate client records created by interoperable providers increased from 94 duplicates pre-enhancement to 10,552 at twelve months postenhancement, while the duplicates from non-interoperable providers only increased from 300 to 637 over the same period. of the 40 core data elements in the ndiis, there was some difference in completeness between the interoperable versus non-interoperable providers. only middle name, sex, county, phone number, mother’s maiden name, vaccine manufacturer, lot number and expiration date were significantly (>=5%) different between the two provider groups. conclusions: interoperability with provider ehrs has had an impact on ndiis data quality. timeliness of data entry has improved and overall doses administered have remained fairly consistent, as have the immunization rates for the providers assessed. there are more technical and non-technical interventions that will need to be accomplished by ndiis staff and vendor to help reduce the negative impact of duplicate record creation, as well as, data completeness. key words: immunization information systems, electronic health records, meaningful use, hl7, data quality correspondence: mary.woinarowicz@nd.gov doi: 10.5210/ojphi.v8i2.6380 http://ojphi.org/ mailto:mary.woinarowicz@nd.gov the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction immunization information systems (iis) are confidential, population based systems that record immunization administration data from participating providers, provide consolidated immunization histories at the point of care and provide aggregate data on vaccinations for use in surveillance to increase immunization rates and reduce vaccine-preventable disease [1]. in 1995, the centers for disease control and prevention (cdc) compiled a list of required and optional core data elements for iis. the national vaccine advisory committee (nvac) reviewed and updated the iis core data elements as part of their initiative on immunization registries [2]. the cdc incorporated the nvac recommendations and again updated the iis core data elements in 2012 to correspond with the iis functional standards and iis strategic plan for 2013-2017 [3,4]. the purpose of the core data elements is to help standardize the capture of data in the iis and to facilitate the consistent exchange of data between the iis, electronic health record (ehr) systems and other iis [2,3]. when the american recovery and reinvestment act (arra) was enacted in 2009, one of its goals was to promote the adoption and increase the “meaningful use” of ehrs [5]. the centers for medicare and medicaid services (cms), in coordination with the office of the national coordinator for health information technology (onc), have set different criteria that must be met by participating providers in order to receive an incentive payment. one of the public health reporting criteria included in all three stages of meaningful use is the electronic exchange of data between an ehr and an iis [6]. this electronic exchange of data is referred to as interoperability. according to the healthcare information and management systems society (himss), “interoperability is the ability of different information technology systems…to…exchange data, and use the information that has been exchanged.” [7] in 2010, the north dakota department of health (nddoh) received arra funding to establish real-time, bidirectional interoperability connections between the north dakota immunization information system (ndiis) and the ehrs of the state’s providers responsible for the highest volume of childhood (< 6 years) immunizations. prior to 2010, the ndiis was not interoperable with any providers. all providers entered immunizations directly into the ndiis, in addition to entry into their ehr. as of august 31, 2013, the ndiis was interoperable with 186 individual provider practices that represented more than half of all doses administered to children 18 and younger in the ndiis. the quality of data in an iis is vital to its ability to determine a patient’s immunization status, calculate immunizations due, assess immunization coverage or generate reminder and recall notices [6]. without timely and accurate data entered into the ndiis, it cannot support its basic http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi functionality or provide a benefit to its users or the people in its jurisdiction [6]. the ndiis has historically maintained high data quality and provider and patient participation by having a number of data quality checks in place that help ensure a high level of data quality. however, these checks are only visible to providers manually entering data directly into the ndiis. providers submitting data electronically (interoperable) do not see these same validation checks or warnings if data is not complete in their ehrs. other iis have evaluated the completeness of immunization records in their iis by comparing the iis immunization records to the records in provider offices and other facilities in order to evaluate the impact of ehr interoperability. they have found that their iis records have had more complete immunization information since it is a consolidated record and not just record of immunizations administered at one provider practice [8,9]. north dakota requires reporting of childhood immunizations to the iis. according to the 2013 iis annual report, the ndiis has 96.8% of north dakota children <6 years of age, 83.4% of adolescents 11-17 years of age and 77.9% of adults 19 years of age and older participating in the ndiis [10]. since the ndiis already has excellent immunization record completeness, the nddoh wanted to examine other areas of data quality related to the immunization record. the objective of this study is to evaluate the impact of ehr interoperability on ndiis data quality by comparing data entry, timeliness and data completeness, as well as, immunization rates for records belonging to providers submitting electronic data (i.e. interoperable) to those for providers manually entering data directly into the ndiis (i.e. non-interoperable). methods in the ndiis, a “provider” refers to a clinic or immunization practice, not to a specific individual practitioner. a client record in the ndiis is assigned to a provider practice if they were the last provider to enter a non-influenza vaccine in that client’s record. data for all clients in the ndiis with doses administered between august 1, 2011 and august 31, 2014 was extracted from the ndiis for evaluation. extracted data elements included client first, middle and last name, birthdate, sex, birth state/country, race, ethnicity, address, phone number, mother’s first, middle and last name, vaccine type, vaccination date (dose date), lot number and vaccine-level vfc eligibility iis core data elements. all data extracted was separated into one of two groups based on assigned provider; records belonging to interoperable providers (ips) and non-interoperable providers (nips). an ip is defined as a provider whose ehr submits data to the ndiis electronically (i.e. without human intervention) using messaging standards set by health level 7 (hl7) international and as defined by the hl7 implementation guide for immunization messaging [11,12]. a nip is defined as a provider who manually enters data directly into the ndiis user interface in addition to documenting in their ehr or paper records. they do not submit data to the ndiis electronically. pre-enhancement (before interoperability) data included doses administered between august 1 and october 31, 2011, prior to the first health system ehr connection to the ndiis. post-enhancement (after interoperability) data included doses administered between september 1, 2013 and august 31, 2014, three through twelve months after the final provider ehrs were connected to the ndiis. http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi the overall data entry analysis evaluated the total number of doses administered by ips and nips per month and by age group for clients five years of age and younger, six to ten years, 11-18 years, 19-59 years and 60 years and older. doses for each month were summed for three months preenhancement and three, six, nine and twelve months post-enhancement. the data entry analysis also evaluated the number of duplicate client records added by ips and nips at three months preenhancement and three, six, nine and twelve months post-enhancement, as well as, the timeliness of all doses entered into the ndiis less than one day after the dose administration date, within one week (i.e. one to seven days) of administration and within one month (i.e. 30 days) for the three months pre-enhancement and three, six, nine and twelve months post-enhancement. completeness of iis core data elements for client and dose records that belong to ips and were entered into the ndiis three months pre-enhancement and at three, six, nine and twelve months post-enhancement were calculated and compared to the completeness of records for nips. immunization rates for children 19 – 35 months of age for the 4:3:1:3:3:1:4 (4 diphtheria, tetanus and acellular pertussis (dtap), 3 polio, 1 measles, mumps and rubella (mmr), 3 hepatitis b, 3 haemophilus influenzae type b (hib), 1 varicella and 4 pneumococcal conjugate (pcv)) vaccine series and for adolescents 11 – 18 years of age for one dose of tetanus, diphtheria and acellular pertussis (tdap) and meningococcal conjugate (mcv4) vaccines, two doses of varicella and three doses of human papillomavirus (hpv) vaccines were calculated and compared for clients that belong to ips versus nips for three months pre-enhancement and three, six, nine and twelve months post-enhancement. sas® 9.3, (sas institute, inc., cary, north carolina) and microsoft® excel® 2010 (microsoft corp., redmond, washington) were used analyze all ndiis data. results doses administered to the 6 – 10 year, 11 – 18 year, 19 – 59 year and 60 years and older age groups by both ips and nips remained fairly consistent from pre-enhancement to three, six, nine and twelve months post-enhancement. nips had a higher percentage of doses administered for all age groups with the exception of the 0 – 5 year age group. pre-enhancement nips entered 53% of the doses in the ndiis compared to 47% for ips. post-enhancement, ips entered 53% of doses at three months, 55% at six months, 56% at nine and twelve months (figure 1). looking at the timeliness of data entry, the overall percentage of doses entered into the ndiis within one month of administration varied slightly (less than 2%) over the three months preenhancement and 4.6% over the twelve months post-enhancement. there were, however, some significant changes between the percentage of doses entered within one day and within one week. the percentage of doses entered within one day increased from 54.6% at the start of the preenhancement period to 79.5% at the end of the twelve months post-enhancement, while the doses entered within one week of administration decreased from 38.6% to 14.9% over the same time period (figure 2). http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi figure 1. comparison of percentage of doses administered to clients in the ndiis by interoperable and non-interoperable providers data obtained from the ndiis for doses added between august 1, 2011 and august 31, 2014. pre-enhancement, the number of duplicate client records created in the ndiis by nips was more than three times the number created by ips (300 vs. 94). post-enhancement, the number of duplicate client records created by nips increased from 377 at three months to 637 at twelve months, whereas the number of duplicate client records created by ips increased to 1,695 at three months post enhancement and continued to increase with 9,883 duplicates created at nine months and 10,552 at twelve months (figure 3). 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 05y rs 610 yr s 11 -1 8y rs 19 -5 9y rs 60 +y rs 05y rs 610 yr s 11 -1 8y rs 19 -5 9y rs 60 +y rs 05y rs 610 yr s 11 -1 8y rs 19 -5 9y rs 60 +y rs 05y rs 610 yr s 11 -1 8y rs 19 -5 9y rs 60 +y rs 05y rs 610 yr s 11 -1 8y rs 19 -5 9y rs 60 +y rs pre-enhancement 3 months postenhancement 6 months postenhancement 9 months postenhancement 12 months postenhancment p er ce nt ag e of d os es ips nips http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi figure 2. overall timeliness of dose data entry by all providers in the ndiis data obtained from the ndiis for dose records added each month between august 1, 2011 and august 31, 2014. of the 40 core data elements in the ndiis, completeness for only three elements, middle name, county and phone number, were significantly (>=5%) higher for nips compared to ips preenhancement. completeness for only two data elements, sex and mother’s maiden name, were significantly higher for ips pre-enhancement. for all other data elements there was no significant difference in completeness between the interoperable versus nips. by twelve months postenhancement, there were five data elements: middle name, phone number, vaccine manufacturer, lot number and expiration date, significantly higher for nips and completeness for mother’s maiden name was significantly higher for ips. additionally, completeness for sex showed almost no difference between ips and nips post-enhancement and the percentage difference in completeness for county was only 2% post-enhancement versus 5% pre-enhancement (table 1). 0% 20% 40% 60% 80% 100% 120% a ug -1 1 se p11 o ct -1 1 se p13 o ct -1 3 n ov -1 3 d ec -1 3 ja n14 f eb -1 4 m ar -1 4 a pr -1 4 m ay -1 4 ju n14 ju l14 a ug -1 4 pre-enhancement post-enhancement p er ce nt ag e of d os es less than 1 day within 1 week within 1 month http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi figure 3. comparison of the number of duplicate client records added to the ndiis by interoperable and non-interoperable providers data obtained from the ndiis for duplicate client records added to the ndiis between august 1, 2011 and august 31, 2014. comparing immunization rates for infants and adolescents, ips had higher rates both preand postenhancement than nips for all vaccines and vaccine series assessed. the rate for the infant 4:3:1:3:3:1:4 series was 82% for ips pre-enhancement and 77% for nips, for a difference of only 5% between the two groups. by twelve months post-enhancement, there was a difference of 12% with the ips still having a higher percentage of their infants up-to-date with the complete series (70%) when compared to the infants of nips (58%) (table 2). 0 2,000 4,000 6,000 8,000 10,000 12,000 3 months 6 months 9 months 12 months pre-enhancement post-enhancement n um be r of d up lic at e cl ie nt r ec or ds ips nips http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi table 1. comparison of the completeness of iis core data elements in the ndiis for interoperable and non-interoperable providers data obtained from the ndiis for records added between august 1, 2011 and august 31, 2014. iis core data element preenhanceme nt ips preenhanceme nt nips differen ce postenhanceme nt ips postenhanceme nt nips differen ce middle name 72.4% 84.1% -11.7% 64.1% 80.3% -16.2% sex 99.9% 94.9% 5.0% 96.9% 96.8% 0.1% mother's maiden name 46.8% 40.0% 6.8% 55.8% 44.1% 11.7% county 93.8% 98.4% -4.6% 96.4% 98.7% -2.3% phone number 73.5% 81.2% -7.7% 69.3% 82.6% -13.3% vaccine manufacture r 71.0% 70.4% 0.6% 68.0% 84.4% -16.4% lot number 70.1% 69.0% 1.1% 64.7% 83.0% -18.3% expiration date 70.1% 69.0% 1.1% 64.7% 83.0% -18.3% the differences in the up-to-date rates for adolescent clients at ips versus nips varied by vaccine and time period. for one dose of tdap, the biggest difference was at twelve months post enhancement with a rate for ips of 79% compared to 66% for nips; for one dose of mcv4 and two doses of varicella, the biggest differences were seen pre-enhancement with a mcv4 rate of 81% and a varicella rate of 50% for ips compared to only 66% and 35% for nips. rates for hpv and two doses of mcv4 had the smallest differences between interoperable and nips with variations of 5% or less at each time interval (table 3). http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi table 2. comparison of the percentage of north dakota infants (19 – 35 months) at interoperable and non-interoperable providers up-to-date with recommended immunizations (4:3:1:3:3:1:4 series) data obtained from the ndiis for client records as of october 31, 2011, november 30, 2013, february 28, 2014, may 31, 2014 and august 31, 2014. interoperability status time period ips nips differenc e pre-enhancement 3 month s 82% 77% 5% post-enhancement 3 month s 78% 72% 6% 6 month s 72% 66% 6% 9 month s 72% 59% 12% 12 month s 70% 58% 13% table 3. comparison of the percentage of north dakota adolescents at interoperable and non-interoperable providers up-to-date with recommended immunizations data obtained from the ndiis for client records in the ndiis as of october 31, 2011, november 30, 2013, february 28, 2014, may 31, 2014 and august 31, 2014. interoperability status and vaccine time period ips nips difference pre-enhancement tdap 3 months 78% 64% 13% post-enhancement tdap 3 months 82% 74% 8% 6 months 71% 63% 8% 9 months 73% 62% 11% 12 months 79% 66% 13% http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi pre-enhancement 1 mcv4 3 months 81% 66% 15% post-enhancement 1 mcv4 3 months 83% 75% 9% 6 months 66% 57% 8% 9 months 68% 57% 11% 12 months 76% 64% 13% pre-enhancement 2 mcv4 3 months 9% 8% 1% post-enhancement 2 mcv4 3 months 32% 27% 5% 6 months 14% 13% 1% 9 months 18% 15% 3% 12 months 29% 22% 7% pre-enhancement varicella 3 months 50% 35% 15% post-enhancement varicella 3 months 65% 58% 7% 6 months 71% 64% 7% 9 months 72% 61% 11% 12 months 72% 61% 11% pre-enhancement hpv 3 months 15% 11% 4% post-enhancement hpv 3 months 23% 18% 4% 6 months 20% 17% 3% 9 months 22% 18% 4% 12 months 25% 20% 5% discussion based on the results of the doses administered analysis pre and post enhancement, ehr interoperability did not have a significant impact, with very little change in the percentage of doses http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi administered between ips and nips for all but one of the age groups assessed. there was a difference with the percentage of doses administered to clients in the 0 – 5 year age group which we would expect to see since the ips administer the highest volume of immunizations to children under six. the overall changes in the percentage of doses administered, however, were not significant. interoperability has had a positive impact on timeliness of doses entered into the ndiis. although the percentage of doses coming into the ndiis within one month has remained fairly consistent, the ndiis is receiving more data entered the same day that the dose was administered, with fewer doses taking one week or more. improved timeliness of entry into the ndiis makes it easier for providers and other ndiis users to make more informed decisions about a patient’s immunization status, current and future immunization needs and to conduct timelier reminder/recall. a significant impact of ndiis interoperability is the increase in the number of duplicate client records being created. the number of duplicate client records being added to the ndiis has increased considerably after ehr interoperability. when electronic messages are sent from an ehr to the ndiis, the client information is matched based on an exact match of first name, last name and birthdate. if an exact match cannot be found, a new record is created in the ndiis. there are a lot of different naming conventions in ehr systems with some systems allowing special characters but not spaces, others only allowing spaces, or users entering patients in an ehr with a nickname instead of their full given name. all of the ehr differences can vary from what the ndiis will allow for names, causing records not to match. additionally, the process for identifying and removing duplicate client records from the ndiis is a manual and time consuming process which involves the manual review of the two or more duplicate records, merging the demographic and immunization data into one complete record and updating the status of the duplicate records to “deleted.” records are not completely removed from the ndiis so that data can be restored if an error occurred and records should not have been merged. in april 2014, the ndiis implemented an automated vaccine de-duplication system that evaluates all incoming dose records for potential duplicate records based on guidance from the american immunization registry association (aira) data quality assurance in immunization information systems: incoming data modeling of immunization registry operations work group (mirow) guide [13]. since being implemented, this system has removed more than 400,000 duplicate dose records. a similar, automated process is needed to help more easily identify and remove duplicate client records. additionally, improvements to the algorithm used when looking for matching records are needed to better match clients from the electronic hl7 message to the ndiis, so that fewer duplicate records are created in the first place. these improvements include searching for a match based on additional data elements, such as middle name and sex and to adjust for potential differences in naming conventions by removing spaces and special characters from the client’s name before searching for a match. overall, completeness of core data elements is one of the biggest data quality challenges. both the sending ehr and the ndiis have to follow the published hl7 guidelines for the electronic transmission of immunization data, but there are some differences between the release of the implementation guide (ig) supported and a data element required in the ndiis [12]. additionally, http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi data being sent from an ehr can still be considered compliant with the hl7 ig, but does not provide quality data to the ndiis. for example, the ndiis requires a client middle name; an ehr may not capture this data element or may automatically send a value of “na.” they are still sending the data element in the hl7 message and in a valid format, but the information is not meaningful in the ndiis. this is also the case with data fields like race and ethnicity and having an “unknown” value sent from an ehr. in early 2014, the ndiis vendor implemented changes to the ndiis interoperability messaging system that would evaluate the source of client demographic information and only make updates where necessary instead of automatically overriding the current ndiis data with data from the ehr. for example, if a client record is already in the ndiis with a valid value for race and the ehr sends an “unknown” value the ndiis data will not be changed to “unknown” as a result. more updates could be done to this algorithm in order to ensure the best data is being kept or added to the ndiis. data completeness for vaccine lot number, manufacturer and expiration date is tied together. the nddoh immunization program requires that providers enrolled in the federal vaccines for children (vfc) program use the ndiis to manage their public vaccine inventory, but they do not have to do the same for their privately purchased vaccine [14]. for providers entering directly into the ndiis, the lot number field in the ndiis dose entry workflow is not a free-text field; it is a drop-down selection tied to a provider’s vaccine inventory. if a provider does not have a specific lot number in their ndiis inventory, the lot number won’t be available for selection and cannot be entered into an individual dose record. because the manufacturer and expiration date are tied to the lot number in the provider’s inventory, if a lot number is not documented, neither are the other two data elements. this also means that when a lot number is electronically sent from an ehr to the ndiis in an hl7 message, there must be an exact matching lot number in the provider’s inventory. if an exact match is not found in the ndiis, the lot number and its corresponding data will not be added to the dose record and a dummy dose will be added instead. in the ndiis, a dummy dose is when the vaccine abbreviation is entered in place of the administered lot number when the lot number is not known. this could lead to a high percentage of dose records without a lot number, manufacturer or expiration date, even if the information was sent to the ndiis. without accurate lot numbers documented, doses can’t be decremented from a provider’s inventory and in the case of a vaccine recall or storage and handling incident; only doses with an accurate lot number could be identified using the ndiis. this would cause individuals who should be notified of the recall and possibly need to be revaccinated to be missed. updates to the matching algorithm that looks for a lot number match between the hl7 message from the ehr and the ndiis provider inventory are needed to try and account for common data entry mistakes, such as replacing a letter “b” with a number “8” or a letter “o” with a number “0.” conclusion interoperability with provider ehrs has had both positive and negative impacts on ndiis data quality. timeliness of data entry has improved and overall doses administered have remained fairly consistent, as have the immunization rates for the providers assessed. there is additional work that will need to be done by ndiis staff and its vendor to help reduce the negative impact of duplicate record creation, as well as, data completeness. http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi moving forward, continuous monitoring of data completeness and overall data quality will continue to be a challenge, but vital in order to ensure that the information being sent from the ehr is complete and that it is being added to the ndiis correctly. additional analysis is needed to understand why the data is not being sent to the ndiis, if the data being sent is incorrect or if the data is not being added to the ndiis correctly. once the issues have been identified, data corrections will need to be made in order to maintain the best possible data quality in the ndiis. as more and more providers submit immunization data electronically, new challenges will arise, making the need for improvements and changes now that much more important. despite the challenges related to data quality and the need for continual data quality monitoring post-interoperability, the benefits of increased timeliness of data entry and the overall increase in data being entered into the iis can be seen based on the results of this study. increased data and improved timeliness will enable better use of the iis for determining a patient’s immunization status, calculating immunizations due, assessing immunization coverage and generating reminder and recall notices as well as for delivering consolidated immunization records and supporting their immunization program efforts to reduce vaccine preventable disease [1,6]. limitations all of the data for this analysis was extracted from the ndiis retrospectively. this means that even though the date ranges used to query data from the ndiis were in keeping with pre and postenhancement time intervals, the data may have been changed since it was originally entered. corrections may have been made to the data that would have improved the data quality even though it was originally incorrect. additionally, any data in records currently marked as “deceased” in the ndiis were excluded from all of the analyses even though the client may not have been deceased at the pre or post-enhancement interval. the ndiis client status for duplicate client records is simply “deleted.” this status is seldom used for any other reason than to remove a duplicate record; however it is possible that a client record has been marked as “deleted” but they were not actually a duplicate record. records for deceased clients may have duplicates that were also marked as “deceased” and would not have been counted as a duplicate record. both of these limitations on client status could have a minor impact on the analysis of duplicate records. conflicts of interest the authors have no financial relationships relevant to this article. disclosure the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the nddoh or the centers for disease control and prevention. this project was supported by the health resources and services administration (hrsa) of the u.s. department of health and human services (hhs) under grant number 5h23ip000784-02 and title http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi for grant amount of $183,440.00. this information or content and conclusions are those of the author inferred by hrsa, hhs or the u.s. government. references 1. immunization information system (iis). (2012, may 15) about immunization information systems: what is iis? retrieved november 23, 2015 from cdc website: http://www.cdc.gov/vaccines/programs/iis/about.html 2. immunization information systems nvac progress report february 2007 3. immunization information system (iis). (2012, may 15) iis recommended core data elements. retrieved november 23, 2015 from cdc website: http://www.cdc.gov/vaccines/programs/iis/core-data-elements.html 4. immunization information system (iis). (2012, may 15) iis functional standards. retrieved november 23, 2015 from cdc website: http://www.cdc.gov/vaccines/programs/iis/funcstds.html 5. electronic health records (ehr) incentive programs. (2015, october 29) about the ehr incentive program. retrieved november 23, 2015 from cms website: https://www.cms.gov/regulations-andguidance/legislation/ehrincentiveprograms/index.html?redirect=/ehrincentiveprograms/ 6. immunization information system (iis). (2012, may 15) meaningful use and immunization information systems. retrieved november 23, 2015 from cdc website: http://www.cdc.gov/vaccines/programs/iis/meaningful-use/index.html 7. what is interoperability? (2013, april 5) himss, transforming health through information technology. retreived june 13, 2016 from himss website: http://www.himss.org/library/interoperability-standards/what-is-interoperability 8. kolasa ms, cherry je, chilkatowsky ap, reyes dp, lutz jp. 2005. practice-based electronic billing systems and their impact on immunization registries. j public health manag pract. 11(6), 493-99. http://dx.doi.org/10.1097/00124784-200511000-00004 9. koepke r, petit ab, ayele ra, jens ce, schauer sl, et al. 2015. completeness and accuracy of the wisconsin immunization registry: an evaluation coinciding with the beginning of meaningful use. j public health manag pract. 21(3), 273-81. http://dx.doi.org/10.1097/phh.0000000000000216 10. immunization information system (iis). (2014, september 9) iis annual report (iisar): 2013 iisar data participation rates retrieved december 22, 2015 from cdc website: http://www.cdc.gov/vaccines/programs/iis/annual-report-iisar/2013-data.html#adult http://ojphi.org/ http://www.cdc.gov/vaccines/programs/iis/about.html http://dx.doi.org/10.1097/00124784-200511000-00004 http://dx.doi.org/10.1097/phh.0000000000000216 the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e184, 2016 ojphi 11. health level seven international. (©2007-2016) introduction to hl7 standards retrieved april 27, 2016 from website: http://www.hl7.org/implement/standards/index.cfm?ref=nav 12. immunization information system (iis). (2012, august 1) iis health level 7 (hl7) implementation guidance. retrieved november 23, 2015 from cdc website: http://www.cdc.gov/vaccines/programs/iis/technical-guidance/hl7.html 13. american immunization registry association. (2008). data quality assurance in immunization information systems: incoming data. retrieved from: http://www.immregistries.org/aira_mirow_chap3_dqa_02112008.pdf 14. vaccines for children program (vfc). (2014, february 14) the vfc program: at a glance. retrieved from http://www.cdc.gov/vaccines/programs/vfc/about/index.html http://ojphi.org/ the impact of electronic health record (ehr) interoperability on immunization information system (iis) data quality introduction methods results discussion conclusion limitations conflicts of interest disclosure references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 1biomedical informatics, university of utah, salt lake city, ut, usa; 2ideas center va slc healthcare system, salt lake city, ut, usa objective we sought to classify evidence that supports, refutes, or contributes uncertainty when reviewing cases of suspected pneumonia. we extend an existing taxonomy of uncertainty to classify these phenomena with the goal of improving existing natural language processing (nlp) algorithms. introduction natural language processing algorithms that accurately screen clinical documents for suspected pneumonia must extract and reason about whether these mentions provide evidence that supports, refutes, or represents uncertainty. our efforts extend existing algorithms [1] and taxonomies [2] that can be leveraged by nlp tools for more accurate handling of uncertainty for suspected pneumonia case review. methods we conducted an automated screening of all outpatient encounters occurring at the va salt lake city health care system between 01/01/2009 and 01/01/2012 to identify a cohort of suspected cases of pneumonia. screening criteria included: a) presence of icd-9 code for pneumonia and b) presence of an electronic physician note and/or same day chest imaging report. we then selected a random sample of 200 cases, 457 documents (216 physician notes and 241 corresponding chest imaging reports). all cases were reviewed by a pulmonologist, an internist, and six allied health professionals. using an annotation tool called ehost [3] and criteria based on the cdc pneumonia case definition, reviewers classified evidence into three semantic classes for words or phrases that a) support, b) refute, or c) are uncertain for suspected pneumonia. three reviewers (a1, a2, a3) conducted a thematic review applying a clinical uncertainty taxonomy to map those snippets marked as uncertain (3,150 unique snippets) into 12 categories of uncertainty (table 1). we report ranges of pair-wise inter-annotator agreement (iaa) and annotations for each semantic class. we also report iaa for mapping uncertain evidence snippets to our uncertainty taxonomy and distribution stratified by document source. results a total of 30,872 annotations were generated for supports (20,477, 66.3%), refutes (6,688, 21.7%), and uncertain (3.707, 12.1%). range for pair-wise iaa across all semantic classes was (0.40-0.73) and individually for supports (0.61-0.81), refutes (0.46-0.65), uncertain (0.19-0.47). we observed substantial iaa between reviewer pairs for mapping the uncertain evidence snippets into 12 categories of uncertainty (table 1): a1/a2: 0.86, a1/a3: 0.89, a2/a3: 0.82. conclusions we have extended an existing taxonomy of uncertainty and applied it to suspected pneumonia case review, deepening understanding of how uncertainty is expressed in clinical texts for suspected pneumonia. despite substantial annotator variability in identifying supporting, refuting or uncertain evidence we observed high agreement for classification of evidence snippets to a taxonomy of uncertainty. table 1. distribution of evidence snippets keywords natural language processing; chart review; pneumonia acknowledgments this study was funded by the va informatics and computing infrastructure (vinci) project id: hir 08-204 and was approved by the university of utah institutional review board (irb 0056785). references 1. chapman, w.w., chu, d., dowling, j.n. context: an algorithm for identifying contextual features from clinical text. in: acl-07 2007. 2. mowery, d., velupillai, s., chapman, w.w. medical diagnosis lost in translation – analysis of uncertainty and negation expressions in english and swedish clinical texts. proceedings of the 2012 workshop on biomedical natural language processing. association for computational linguistics. 2012. 3. south, b., shen, s., leng, j., forbush, t., duvall, s., chapman, w.w. a prototype tool set to support machine-assisted annotation. proceedings of the 2012 workshop on biomedical natural language processing. bionlp ‘12, stroudsburg, pa, usa, association for computational linguistics. 2012. 130-139. *brett r. south e-mail: brett.south@hsc.utah.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e40, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, 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bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts economic modeling of syndromic surveillance systems – a roundtable discussion on association of state and territorial health official’s (astho) investment decision model paula soper1, scott gordon1, marcus rennick1, jane blake*2, victoria adams2, taylor hemby2, walter (chip) jansen2, nasim moghadam2 and kc decker2 1association of state and territorial health officials, arlington, va, usa; 2booz allen hamilton, mclean, va, usa objective provide a demonstration of the recently developed prototype decision analysis model for syndromic surveillance investments. the roundtable will be used to discuss the model, obtain feedback on its usefulness, and brainstorm future uses and improvements. introduction one of astho’s key goals is to help its jurisdictions meet member needs for technical assistance, including making informed decisions about their syndromic surveillance options. to help them make such decisions, astho worked with booz allen to create a decision analysis model, which factors in both a value of information (voi) model and a return on investment (roi) model. the model provides a dashboard of its outputs, which is a simple, easy-to-understand comparative view of multiple syndromic surveillance investment scenarios. description the roundtable will include a demonstration of the decision model, a review of how it can be used in practice, and a facilitated discussion covering its usefulness, applicability in the us and internationally, and potential improvements for the future. the roundtable will be moderated by subject matter expert panel members who participated in the model creation, an astho staff member who facilitated development, and the booz allen project manager. facilitators will include: • jane blake (booz allen) • marcus rennick (astho) • bryant karras (university of washington) • david buckeridge (mcgill university) audience engagement facilitators will begin by providing a demonstration of the model, following by polling the audience to provide scenarios to run the model. the poll will include questions around parameters including cost, functionality, granularity, breadth, and timeliness for an as-is and two potential future scenarios. after the demo, the facilitators will review results, ask for feedback, and discuss potential gaps and additional uses. keywords return on investment; value of information; syndromic surveillance; decision model; analytics acknowledgments timothy andrews, booz allen hamilton atar baer, seattle-king county public health david buckeridge, montreal public health and quebec public health institute jim collins, michigan department of community health julia gunn, boston public health commission bryant karras, washington public health state aaron kite-powell, armed forces health surveillance center joe lombardo, johns hopkins university vishal pachigar, booz allen hamilton *jane blake e-mail: blake_jane@bah.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e185, 201 mobile access to clinical information at the point of care 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi mobile access to clinical information at the point of care fatima m. mncube-barnes1, edd, mph, msis, ben lee2, bs, olumuyiwa esuruoso2, m.d., f.a.c.p., phil n. gona3, phd, mph, msc, stephane daphnis2, mba 1. louis stokes health sciences library, howard university 2. meharry medical college, nashville, tn 3. university of massachusetts, boston abstract objectives: using library subscriptions and accessible on handheld devices, this study sought to promote authoritative health information apps, and evidence-based point-of-care resources. methods: three cohorts of internal medicine residents were issued ipads at the beginning of their second year, and were trained to skillfully access resources from the digital library. preand postintervention surveys were respectively administered at the beginning of the second year and end of the third year of training. the residents’ computer experience and computer knowledge was assessed. additionally, before and after formal introduction to ipads, perceptions on the use of computers to access clinical information were assessed. survey responses were compared using two sample methods and summarized through descriptive statistics. results: sixty-eight residents completed the pre-survey questionnaires and 45 completed the postsurveys. there were significant improvements in the residents’ level of computer experience, and familiarity with medical apps. furthermore, there was increased knowledge obtained in accessing clinical information through electronic medical records. residents positively perceived the potential effects of computers and electronic medical records in medicine. conclusion: study findings suggested that health science libraries can be instrumental in providing search skills to health professionals, especially residents in training. participants showed appreciation of ipads and library support that facilitated successful completion of their related tasks. replicating this study with a larger sample derived from multiple sites is recommended for future studies. participation of mid-level healthcare professionals, such as physician assistants and nurse practitioners is suggested. keywords: active learning; diagnostic reasoning; technology integration; point-of-care resources http://ojphi.org/ mobile access to clinical information at the point of care 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi correspondence: fatima.barnes@howard.edu doi: 10.5210/ojphi.v8i3.7099 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction with the tremendous ongoing improvements in both portable hardware and information connectivity, mobile technology has become an essential and ubiquitous component of modern healthcare [1]. mobile devices coupled with advanced health information technology, either by direct or wireless download, can make vital medical information accessible to medical practitioners at the point of care for medical education and delivery [2]. this makes mobile devices powerful in the clinical settings. mobile access to medical information facilitates “just-in-time” learning at the point of care, where learning “…is timeand place-independent and results in the functional use of information.” [3] mobile access to medical information allows for a novel method of experiential and self-directed learning. furthermore, knowing precisely where specific information is located digitally is likely to save lives at the point-of-care through well-informed decision-making. mobile devices have shown great promise not only for medical students and residents, but also they have the potential to empower medical educators [4-6]. by supplementing the medical information base of students, mobile devices can allow educators to more effectively train and “…assess [student] performance and competence at the highest levels of miller’s pyramid of clinical competence, thereby reflecting real-world practice.” [3] the four sequential stages of miller’s pyramid, from base to apex, include: “information”; “knows how”; “shows how”; and “does.” although mobile access to clinical information is a relatively new field, there exists a wealth of professional medical applications (apps) for mobile devices of varying content, purposes, and delivery, accessible across all mobile device platforms, both freely and commercially available. it can be expected that the medical apps ecosystem will continue to grow and develop substantially [1]. furthermore, the future of healthcare computing is expected to become rooted in mobile technologies [7-9]. the objective of this study was to better understand how the introduction of mobile access to clinical information at the point of care is beneficial to medical resident physicians. methods medical librarians and the internal medicine residency coordinators conducted this study for three successive years (2012-2014). to raise awareness of point of care resources, free ipads were issued http://ojphi.org/ mobile access to clinical information at the point of care 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi as an incentive to promote the use of mobile devices in accessing specific authoritative resources pertinent to medical education. the residents’ computer experience and computer knowledge was assessed, along with computer-related attitudes regarding mobile access to clinical information, before and after formal introduction to ipads and point-of-care resources for clinical use. three cohorts of second year internal medicine residents volunteered to participate and signed a participation contract. they were surveyed prior to intervention and were surveyed again at the end of the third year of training. the intervention comprised of equipping the residents with ipads and also providing them training and continued user support in the basic utilization of the ipad and point-of-care apps and resources. the institutional review board approved the study. to formally introduce mobile access to clinical information, study coordinators gave each participant an ipad with point-of-care resources and apps without any cost to the participants. the medical librarians demonstrated the ipad’s clinical utility for accessing clinical information in the form of authoritative point-of-care resources available through the digital library and medical information apps on the ipad. specifically, medical information apps introduced consisted of point-of-care resources from institutional subscriptions, authoritative public health apps from the national library of medicine, and general healthcare apps that were evaluated and approved by physicians for clinicians through imedicalapps.com. comprehensive technical and resource support was provided to participants throughout the 18 months of the study. manning and gadd’s (2001) survey for evaluating handheld computing in a residency program was modified and customized for this study [10]. the survey questions were created with redcap survey™ software (research electronic data capture), (http://project-redcap.org) [11]. the redcap software provided an intuitive interface using validated data entry; collecting, manipulation and export procedures; automated export procedures for seamless downloads to common statistical packages. using the likert-scale responses, residents were assessed on their familiarity with current computer technologies, opinions regarding the introduction of mobile technology within a clinical setting, and familiarity with medical information apps. composite scores were computed by summing up individual likert-scale item scores in each of the following domains: “computer experience”, “computer knowledge”, “perceived necessary capabilities of computer systems in medicine”, “familiarity with medical information apps”, “potential effects of computers”, and “appreciation for electronic medical record (emr) on medicine.” to assess the effectiveness of the intervention, changes in composite scores were calculated as the post-intervention composite score minus the baseline composite score. a high composite score indicates high information for a domain while a large positive pre-post difference is suggestive of improved information attributable to the intervention. tabulated summary data were stratified by phase, pre-intervention, and post-intervention. for continuous variables the mean difference of composite scores between the two phases was computed. data was summarized using descriptive statistics (i.e., mean and standard deviation for continuous variables and percentages for categorical variables). histograms were also constructed comparing preand postintervention categorical responses. percentage change in composite scores was calculated according to this formula: [(post/pre) -1]*100%. since there were no unique id numbers linking preand post-intervention responses, paired statistical methods could not be used to assess changes in scores preand post-intervention. instead two-sample t-test and chi-squared test were used to compare continuous and categorical variables, respectively. statistical applications software http://ojphi.org/ mobile access to clinical information at the point of care 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi (sas version 9.4) was used to analyze the data. a p-value of 0.10 was considered statistically significant. results sixty-eight participants, (38% women, mean age = 35.1, sd = 5.1 years) completed the baseline survey. forty-five completed the post-intervention survey. post-intervention, there were statistically significant positive increases in composite scores for participant computer experience (percentage change = 9.52%; p = .09) and familiarity with medical information apps for mobile devices (percentage change=50.59%; p < .0001) (table 1). participant appreciation for electronic medical record (emr), (mean percentage change = 4.10%; p = .10) (table 1) increased after intervention. specifically, there were significant increases in the belief that an electronic medical record would be beneficial for time required for documentation (percentage change = 14.29%; p = .04), time required to enter orders (percentage change = 12.90%; p = .03), and patient privacy (percentage change = 25.00%; p = .02). furthermore, in the assessment of the perceived necessary capabilities of computers in medicine, it appeared that the residents value mobility (figure 1-2) and access (figure 3, 4, 5) associated with mobile access to clinical information at the point-of-care both before and after intervention. discussion using the intervention, we observed varying magnitudes of increases of pre-versus postintervention composite scores (table 1). composite score differences for the domains of computer experience, familiarity with medical information apps, and appreciation for emr attained statistical significance. such increases suggest that formal institutional intervention involving access to mobile technology and its utilization in the clinical setting could benefit residents providing patient care. the value of formal institutional intervention became apparent with a closer examination of the large increase of 51% in familiarity with medical information apps among residents. in addition to demonstrating that residents were unfamiliar with medical information apps, it also suggested that valuable digital resources for mobile technology in the clinical setting were possibly underutilized. this study provided evidence that an intervention such as ours would potentially benefit residents in facilitating the process of becoming familiar with digital resources provided by their library. additionally, residents should be made aware of evidence-based subscriptions with mobile apps that require log-ins. familiarity and pre-registration to these resources would allow them quick access to point-of-care information at the bedside. it is unclear if the improvement observed in the domains of resident computer experience, familiarity with medical information apps, and appreciation for emr would translate into enhanced graduate medical education or improved quality of care for patients. such questions would be best addressed in a prospective design and a control group combined with meticulous assessment of patient outcomes before and after implementation. http://ojphi.org/ mobile access to clinical information at the point of care 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi table 1: composite scores composite scores pre post percentage change pvalue* n=68 n=45 mean(sd) mean(sd) computer experience 21 (6.3) 23 (6.0) 9.52% .09 computer knowledge 17.3 (5.7) 17.9 (5.7) 3.47% .58 perceived necessary capabilities of computer systems in medicine 64.6 (15.8) 67.6 (8.8) 4.64% .25 familiarity with medical information apps 8.5 (4.5) 12.8 (3.5) 50.59% <.0001 potential effects of computers 51.9 (9.8) 51.2 (13.9) -1.35% .77 appreciation for emr 70.8 (12.3) 73.7 (15.4) 4.10% .10 *p-value obtained from paired t-test; **percentage change = (mean-post / mean-pre 1)*100 computer experience was derived by summing up 17 items (possible range of values = 0 – 40) computer knowledge was derived by summing up 14 items (possible range of values = 0 – 28) perceived necessary capabilities of computer systems in medicine was derived by summing up 17 items (possible range of values = 0 – 68) familiarity with medical information apps was derived by summing up 13 items (possible range of values = 0 – 26) potential effects of computers was derived by summing up 17 items (possible range of values = 0 – 68) appreciation for emr was derived by summing up 22 items (possible range of values = 0 – 88) further analysis of table 1 revealed the relatively high baseline perception for some composite scores both before and after formal intervention, implied that residents had already deemed, prior to intervention, that a computer system must be highly capable for clinical use, and positively perceived both the potential effects of computers and emr on medicine. with such relatively high baseline perceptions, no room for additional information improvement could be derived from this study. this apparent “ceiling effect” meant that no further intervention effect on information level was possible as a result of a prevalent high baseline information. this finding, in addition to the positive effects of intervention, suggested that the residents generally saw great value and potential in integrating reliable computer systems and emr into the clinical setting. as has been previously reported, such a ceiling effect was often the result of constraints on data-gathering instruments such as the one used in this study. when a ceiling effect occurs in data-gathering, there is a bunching of scores at the upper level reported by an instrument [12]. further analysis of the perceived necessary capabilities of computers in medicine, revealed that residents valued mobility and access afforded by smart phones and ipads/tablets in a clinical setting. for mobility, residents appeared to value, both before and after intervention, the ability to access the computer system at any place (figure 1). this apparent consensus seemingly conflicted http://ojphi.org/ mobile access to clinical information at the point of care 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi with the varied opinions toward interacting with a computer without the need of a keyboard as reported (figure 2). assessing both sets of data simultaneously, suggested that mobility did not necessarily coincide with “keyboardless” interaction with computers in a clinical setting. for access, residents greatly seemed to favor, both before and after intervention, a system that always responded to queries in less than five seconds (figure 3), always displayed x-rays and other images in less than 30 seconds (figure 4), and always functioned without any “down time” (figure 5). such data underscored the idea that speed and reliability were important to mobile access to clinical information. figure 1: figure 2: 70.6 11.8 8.8 1.5 7.4 57.8 33.3 8.9 0.0 0.0 0.0 20.0 40.0 60.0 80.0 vitally necessary generally necessary somewhat necessary not necessary unable to respond pe rc en ta ge "i can access the system at any place in the clinical setting" pre (n=68) post (n=45) 10.3 23.5 30.9 27.9 7.4 15.6 24.4 28.9 26.7 4.4 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 vitally necessary generally necessary somewhat necessary not necessary unable to respond pe rc en ta ge "i can interact with the computer without using a keyboard" pre (n=68) post (n=45) http://ojphi.org/ mobile access to clinical information at the point of care 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi figure 3: figure 4: 39.7 30.9 14.7 7.4 7.4 40.0 35.6 24.4 0.0 0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 vitally necessary generally necessary somewhat necessary not necessary unable to respond pe rc en ta ge "the system always responds to my queries in less than five seconds" pre (n=68) post (n=45) 42.6 30.9 10.3 5.9 10.3 46.7 44.4 6.7 0.0 0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 vitally necessary generally necessary somewhat necessary not necessary unable to respond pe rc en ta ge "the system always displays x-rays and other images in less than 30 seconds" pre (n=68) post (n=45) http://ojphi.org/ mobile access to clinical information at the point of care 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi figure 5: limitations one significant limitation, despite a modest sample size which caused the authors to use a liberal significance level of p<0.10 instead of p<0.05, is that the two-sample statistical analysis used was less optimal for preand post-intervention studies. a paired-sample analysis in which each participant serves as own control between the two waves would have been more ideal. however, due to the anonymous nature in which the study was administered, a unique identifier could not be used to link participant responses in the two waves. with no id variable to link the wave responses for each participant, the characteristics of completers versus the non-completers could not be compared. another limitation was the smaller number of participants who completed both phases. there was no means to determine whether completers represented a high level of enthusiasm relative to non-completers. larger multi-institutional studies are recommended in which participant responses are linked in each wave to allow a paired analysis. furthermore, our small sample size did not allow for multivariable adjustment for potential confounding due to age and gender. it is therefore possible that findings for this study could be attributed to residual confounding that could not be adjusted for multivariable regression analysis. conclusion regardless of the above weaknesses, this 18-month pilot study offered an encouraging glimpse into merits of formal introduction of mobile access to clinical information and how it impacted internal medicine residents’ computer-related attitudes and selected preferences regarding accessing clinical information using ipads. it was apparent that the introduction of mobile access to clinical information was well received by internal medicine residents at our institution because 45.6 22.1 19.1 4.4 8.8 35.6 40.0 15.6 6.7 2.2 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 vitally necessary generally necessary somewhat necessary not necessary unable to respond pe rc en ta ge the system is always functioning. there is never any 'down time' pre (n=68) post (n=45) http://ojphi.org/ mobile access to clinical information at the point of care 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e197, 2016 ojphi it facilitated medical education and direct healthcare delivery. even though participants were technology-literate, our findings suggest that institutions play a critical role by providing technology training to their resident physicians, especially with regard to accessing authoritative clinical information from library subscriptions. depending on the size of institutions and associated allied health programs, health science libraries pay millions of dollars in subscriptions for resources. skillfully searching and mastering different aspects of these resources can be challenging. it is recommended that future studies be conducted at multiple sites and participants should include mid-level healthcare professionals such as physician assistants and advanced nurse practitioners. conflicts of interest the authors have no financial relationships relevant to this article. disclosure the findings and conclusions in this report are those of the authors and do not necessarily represent the official position 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25748271&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25087529&dopt=abstract http://dx.doi.org/10.1186/s12909-015-0356-8 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25889996&dopt=abstract http://dx.doi.org/10.1016/j.jbi.2008.08.010 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18929686&dopt=abstract mobile access to clinical information at the point of care introduction methods results discussion limitations conclusion conflicts of interest disclosure references: isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* division of health informatics and surveillance, centers for disease control and prevention, atlanta, ga, usa objective to describe the results of a pilot project that examined selected biosense 2.0 data processing rules and tested sas and essence products in the biosense platform. introduction biosense was launched in 2003 by cdc with its primary aim to establish an integrated system of nationwide public health surveillance for the early detection and prompt assessment of potential bioterrorism-related syndromes or other public health emergencies. with the release of cdc’s surveillance strategy, biosense evolved into the national syndromic surveillance program (nssp). to overcome the challenges experienced throughout the integration of local and state level data to produce a real-time national all-hazards surveillance, cdc sought input from the national syndromic surveillance community of practice (nssp cop). they requested that cdc provide advanced syndromic surveillance functionalities and analytical applications, such as essence and sas to improve the biosense platform. in response, cdc led this pilot project to: 1) conduct security testing of sas[1] and essence[2] in order to identify vulnerabilities; 2) test and improve a limited set of processes that occur before data are transformed; and 3) conduct testing of essence’s functions to ensure the tool worked as intended, and that it will meet user needs. methods the pilot project was initiated on december 3, 2014 and concluded on may 15, 2015 with a proposed course of action for review by cdc’s division of health informatics and surveillance and the nssp cop. cdc engaged eight jurisdictions and two federal agencies via weekly calls and project activities. each week, jurisdictions provided feedback and recommendations on outputs generated by cdc from analyzing the pre-locker processing, including facility table master clean-up, data element profiling, data landscape and flow, documenting current rules, and collapsing of records into visits. simultaneously, installation and vulnerability scanning of sas and essence took place. once complete, jurisdictions assessed essence’s functions, data sharing and permissions, usability, timeliness, and essence user satisfaction. each jurisdiction self-identified as novice, intermediate, or advanced user based on their essence experience. data views and data sharing permissions were preset by cdc. users were provided with a use case that included real-world tasks to test different system features. users recorded their answers on an online survey and follow-up in-depth interviews were conducted. results there were several major outcomes. essence successfully completed vulnerability scanning, and results indicated that it has many strengths. users, overall, had a good experience using the tool and it provided flexibility to streamline data workflows, customize data views, share analyses with others, and generally meet the functional needs of users. we identified the data processing flow and components of the locker including the rules and codes that send data to different tables, views and transport mechanisms. the users also identified different segments needed to populate a few key data fields. lastly, we identified the specific fields that should be included in the master facility list. while there were many strengths, some weaknesses were also identified. sas had three occurrences of high vulnerability that needed remediation. essence has no data sharing control for local administrators, data sharing and data source identification were not intuitive, the user interface did not explicitly provide information about navigating through screens, and there was a learning curve for new and intermediate users. conclusions overall, the pilot proved successful in providing suggestions for specific actions. these include data clean-up (ensuring all priority data fields are processed and stored properly), developing a new data staging environment, developing a local administration tool for data access control for essence, alpha and beta testing of essence and sas, and technical assistance and training for transitioning jurisdictions. keywords essence; pilot project; biosense platform acknowledgments michael coletta, roseanne english, alan davis, david walker, peter hicks, alejandro perez, paul mcmurray, miguel torres-urquidy, julie zajac-cox, aaron kite-powell, caleb weideman, matthew schwei, carla winston, martha sanchez, amanda wahnich, katie arends, harold gil, atar baer, yushien chen references [1] business analytics and business intelligence software. sas. [internet]. cary (nc). [cited 2015 jul 27]. available from: http:// www.sas.com/ [1] lombardo, j, burkom, h, pavlin, j. essence ii and the framework for evaluating surveillance systems. mmwr. 2004 sept 24; 53(suppl):159-65 *cassandra n. davis e-mail: vts4@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e104, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 1data sciences and analytics group, pacific northwest national laboratory, richland, wa, usa; 2yale school of public health, new haven, ct, usa; 3georgia institute of technology, atlanta, ga, usa objective to integrate existing influenza surveillance data sources and social media data into an accurate and timely outbreak detection model embedded into dashboard biosuveillance analytics for the department of defense. introduction influenza-like illness (ili) remains a significant public health burden to both the general public and the u.s. department of defense. military personnel are especially susceptible to disease outbreaks owing to the often-crowded living quarters, substantial geographic movement, and physical stress placed upon them.1 currently, the military employs syndromic surveillance on electronic reporting of clinical diagnoses. while faster than traditional, biologically-focused monitoring techniques, the military surveillance system proved inadequate at detecting outbreaks quickly enough in a recent study conducted by the cdc.2 recently, research has included novel data sources, like social media, to conduct disease detection in real-time and capture communities not traditionally accounted for in current surveillance systems. data-mining techniques are used to identify influenza-related social media posts and train a model against validated medical data.3 by integrating social media data and a medical dataset of all ili-related laboratory specimens and doctor visits for the entire military cohort, a more comprehensive model than presently exists for disease identification and transmission will be possible. methods for analyses, the armed forces health surveillance center (afhsc) provided about 1000 military health facilities’ defense medical surveillance system data, recorded between december 1999 and 2014. this data included laboratory results and medical clinical visits coded with an international classification of disease, 9th edition (icd-9) code under the afhsc’s syndromic definition of ili. health facilities were mapped in esri arcgis with a 25mile buffer. to determine specific locations of interest for historical twitter data purchase and analyses, facilities within each buffer were condensed into a merged location and areas with substantial medical data, military populations, and social media usage were targeted. from this analysis, 25 u.s. and 6 international condensed locations were chosen as study sites. three additional non-military locations, based on comparative attributes, were identified as control sites. geotagged tweets, from november 2011 to june 2015, were purchased within a 25-mile radius of the centroid for each of the 31 identified locations of interest. descriptive summary statistics for each location, time series analyses, and correlation studies of icd-9 codes and laboratory data against regional cdc ili-net and city-level google flu trends were conducted. social media analytics on military and non-military tweets identified differences in twitter discourse between the 2 cohorts, including common language, sentiment and health-related topics (table 1). conclusions twitter flu-related discourse from military members and electronic medical data will be incorporated into a robust outbreak detection model. this model will continually ingest new health and social media data to nowcast and forecast influenza activity on military bases. a user-friendly application will provide military analysts with tools required to allocate resources efficiently and effectively. table 1. differences in twitter health-related terminology between military and non-military populations. keywords social media; influenza-like illness; icd9; hl7; military acknowledgments this work was funded by dod defense threat reduction agency. references 1. sueker j, et al. 2010. influenza and respiratory disease surveillance: the us military’s global laboratory-based network. influenza and other respiratory viruses. 2. assessment of essence performance for influenza-like illness surveillance after an influenza outbreak—u.s. air force academy, colorado, 2009. mmwr morb mortal wkly rep, 2011. 60(13): 4069. 3. fairchild gc. 2014. improving disease surveillance: sentinel surveillance network design and novel uses of wikipedia, in university of iowa: iowa 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yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and 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an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali 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feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using a collaborative approach to tobacco control efforts in marginalized communities crystal robertson*, hadii m. mamudu, maryann littleton, rafie boghozian, daniel owusu, candice collins, liang wang and sreenivas p. veeranki east tennessee state university, johnson city, tn, usa objective to examine community engagement as a means to strengthen tobacco-related policies and programs use in marginalized populations. introduction although significant progress has been made in tobacco control in the united states (us) over the past 50 years, more than 15% of the population currently use tobacco products.1 tobacco use continues to be the leading cause of preventable death, contributing to over 480,000 deaths and about $300 billion in economic costs each year. to achieve the healthy people 2020 (hp2020) objective of 12% national adult smoking rate by 2020, it is important to focus our tobacco control efforts on surveillance and addressing disparities in tobacco use prevalence and tobacco-induced diseases across different subpopulations and geographic areas.2 utah reported the lowest prevalence rate (9.7% in 2014), while rates as high as 28% were identified in central appalachia. modern epidemiology is limited in its ability to explain patterns of tobacco use and tobacco-related interventions and policies in these highly prevalent, marginalized environments. therefore, a combination of quantitative and community-based participatory research (cbpr), as proposed in public health 3.0, will expand the scope and reach to address all factors of tobacco use, including cross-sector collaboration and multi-level actions.3 this study aimed to comprehensively investigate counties in the northeast tennessee region where tobacco use prevalence is disproportionately highest, and to identify regional and culturally specific evidence-based practices for tobacco control. additionally, the study examined how these practices can be scaled up to address similar high tobacco use and disadvantaged populations elsewhere in the us and worldwide. methods grounded by the cbpr framework, a mixed-methods approach triangulated multiple sources of data using a three-prong assemblage of protection, prevention, and cessation, to develop tobacco control recommendations and goals as part of a population health improvement plan for tennessee. information gained from health council discussions, focus groups, interviews, and stakeholder meetings were combined with quantitative analyses of secondary data from tennessee department of health, school-based surveys, and qualitative analyses conducted for descriptive and inferential statistics. all discussions and interviews involving 222 individuals from 91 organizations were recorded and organized using nvivo 10, thematically coded using grounded theory, and analyzed using descriptive statistics. the results utilized aggregated themes generated from the data. results tobacco use in the northeast tennessee region comprises cigarette smoking and smokeless tobacco, with increasing uptake of electronic cigarettes across all age groups. among others, culture of tobacco use and cultivation was identified as the most salient factor for tobacco use. reducing tobacco use requires a foundation built on informatics, community engagement, and a model for sustainable funding to support infrastructure and program interventions. while state and national policies and programs have received less attention in this region, several effective community-based policies and programs to prevent tobacco use were identified, including incentive programs such as baby and me, voluntary smoke-free campus policies by businesses and colleges, 100% screening programs by hospitals, and nicotine-free employee population. overall, a total of 25 recommendations were identified, with 14 aimed at protection, four at prevention, and seven at cessation. these recommendations culminated into five overarching goals: protect the population from tobacco and secondhand smoke exposure through policy enforcement and implementation and counter-marketing; prevent initiation of tobacco use with comprehensive youth-focused programs that increase knowledge and awareness; expand access to cessation resources and treatment, especially in high risk populations; foster collaboration and partnership; and monitor data for evaluation and validity. conclusions this is one of the few comprehensive attempts to address the social dynamics of tobacco use and identify population and geographic policies and programs in highly prevalent communities. among the myriad issues identified, the expansion of surveillance data to inform tobacco policy and culturally-tailored tobacco policies and programs are essential to reduce tobacco use in population subgroups. combining cbpr with actionable data can spur innovations in local efforts, highlight social determinants of health, and contribute to evidence-based policy. while the results of this study primarily provide in-depth descriptions of central appalachia’s tobacco-related risks and their perceptions of and reactions to tobacco prevention intervention, the policies and programs identified through the process may be more readily adopted and scaled-up to address the disparities in tobacco use and tobacco-induced diseases, particularly pertaining to low-income, disadvantaged, and hard-to-reach populations. keywords tobacco; policy change; surveillance; community engagement; disparities acknowledgments centers for disease control and prevention etsu college of public health tennessee department of health references 1. the health consequences of smoking—50 years of progress: a report of the surgeon general. atlanta: national center for chronic disease prevention and health promotion, office on smoking and health; 2014. 2. healthy people 2020. washington dc: office of disease prevention and health promotion; 2014. tobacco use. available from: https://www.healthypeople.gov/2020/topics-objectives/topic/tobaccouse/objectives. 3. desalvo kb. et al. public health 3.0: time for an upgrade. am j public health. 2016 april;106(4):621-622. *crystal robertson e-mail: crystalr501@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e74, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts improving cattle market syndromic surveillance through electronic data capture leah estberg1, randy munger1, cynthia zepeda1, judy akkina*1, susan rollo2 and andy schwartz2 1usda-aphis-vs, fort collins, co, usa; 2texas animal health commission, austin, tx, usa objective implement a mobile technology platform to capture and transmit syndromic cattle data collected at texas market sales. introduction an active syndromic surveillance system was designed to collect cattle health information from a sample of texas cattle market sales. texas animal health commission livestock inspectors record the total number of animals observed along with the total number displaying clinical signs of interest grouped into body system categories (e.g. respiratory, neurologic, etc.). inspection reports are submitted to the united states department of agriculture veterinary services (vs) risk identification team for monitoring. methods the pilot project started in 2012 with paper-based data collection forms to both 1) gain trust from the inspector supervisors and 2) evaluate the value of the system with minimal early investment. the data collected at each sale on paper-based forms were later entered into spreadsheets at the office. these sale inspection reports were then submitted to the inspector’s supervisor for review prior to forwarding by email to vs. vs staff aggregated data from each spreadsheet in to a centralized database and conducted weekly monitoring. recently, a new reporting system was developed at vs to enable collection and transmission of the data on mobile devices running an android operating system capable of transmitting data to vs via a wi-fi connection. the new system was deployed march 2016 following in-person training, release of a user guide document, and a month of user testing. results between march 2014 and june 2016 a total of 1,330 sale inspection reports from 16 markets were submitted by spreadsheet an average 11 days following the sale (range: 1 day through 141 days following the sale). these reports were tracked for data quality issues that required manual intervention. it was discovered that 64 (4.8%) of the reports required correction. the most common types of data quality issues were market sale date not provided, market alias id not provided, report submitted more than once, and report not submitted as an excel file but as an image, such as a pdf file. between march and june 2016 a total of 160 sale inspection reports from 16 markets were submitted using mobile devices an average 7 days following the sale (range: same day through 47 days following the sale). all data submitted could be directly imported into the centralized database and processed as needed for monitoring without any data correction required. some challenges encountered with deploying the mobile technology system included addressing the vs information technology security requirements for establishing user accounts and implementing direct data upload into vs systems. additionally, wi-fi connectivity can be difficult in some remote areas. some advantages to using the mobile technology included having the option to download and run the application on most mobile devices running the android operating system. there was an improvement in data reporting timeliness of 4 days on average, and the range substantially narrowed. there was also time savings for inspectors who no longer needed to transfer hard copy data to a spreadsheet, and for vs personnel who no longer needed to aggregate data from individual spreadsheets. improvements in data quality included the ability to directly report that sales were canceled or not attended; the ability to provide comments at various levels of detail related to the sale, the pen of animals observed, or specific signs observed; and the requirement to supply essential data elements such as sale date and market id. conclusions the conversion from a paper and spreadsheet-based sale inspection report to a mobile technology platform resulted in significant time savings and data quality improvements that appeared to justify the system development and deployment costs and challenges. these benefits support potential expansion of the system. keywords animal health monitoring; mobile technology; electronic data collection acknowledgments this work would not have been successful without the effort from multiple individuals at the texas animal health commission. this work was also supported by usda vs with funding and personnel resources. *judy akkina e-mail: judy.e.akkina@aphis.usda.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e61, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts facilitating the sharing of patient information between health care providers kieran moore and paul belanger kfl&a public health, kingston, on, canada objective to describe how the south eastern integrated information portal (shiip) will support the health links program with the delivery of care for patients, by facilitating reporting, performance monitoring and quality improvement efforts. the portal-based technology that shiip uses to integrate all of a patient’s clinical care information into summarized data and to provide real-time feedback will also be explained. introduction in the current state of the health care system there is uneven access to primary care, and too many people struggling to navigate the system are receiving care in the hospital for issues that would be better dealt with in the community, and then are being readmitted to the hospital only days after leaving. to address these issues and improve efficient continuity of care, the health links program provides a new model of care at the clinical level in ontario. in this model all of the patient’s health service providers in the community, including primary care, hospital, and community care, work together to create a coordinated care plan for the patient. the initial focus of health links is on highcost users. health links, and primary care as a whole, require comprehensive data analysis to effectively support patients and providers. shiip is a portal-based technology solution that enhances individual patient care while providing real-time feedback and summarized data to help plan care. the primary objective of shiip is to develop an integrated portal with core functionalities that will facilitate the sharing of information and enable person-centred care coordination. shiip aims to assists the success of health links by providing consistent maintenance and sharing of patient records, timely communication and collaboration between a patient’s multiple health care providers, and removing physical barriers through the virtualization of care processes. shiip is designed to identify and assist in the delivery of care for complex/high needs patients, and will facilitate reporting, performance monitoring and quality improvement efforts. some of the anticipated benefits of shiip include: enhanced patient experience, reduced workflow duplication, improved access to information at point of care, more efficient clinical documentation, and improved health outcomes. ultimately, shiip helps to improve access and quality of healthcare, and consequently health equity, especially for complex/high-needs patients. methods shiip provides the technology to enable collaborative, multiagency care processes. the shiip system collects patient demographic and clinical data from hospital databases. most of the data is captured from its sources in real-time or near real-time, with an emphasis on unidirectional flow of information from hospitals to primary care providers. through this process, primary care providers are supplied with timely access to various clinical data. algorithms developed in the system use the information collected to identify complex/high needs patients. shiip also uses the collected data to notify, in-real time, primary care providers, health links coordinators, and other health care providers of patient encounters and transitions, and enables tracking of patients within the health system. in addition, as more data becomes available after patient discharge, the complex patient identification algorithms are updated to continuously enrich the patient’s profile. expansion of shiip to additional health care providers is scheduled for future phases. in order for end-users to access the information collected, shiip has a single centralized web interface for all primary care providers in the selhin. the web-based graphical user interface allows primary care providers to view their patients’ emergency department, acute inpatient, and outpatient clinic visits and related information. the interface highlights key patient characteristics useful to primary care providers, such as: current admissions, visit history, chief complaints, complex/high needs flags, interventions, patient flow and other risk factors. this functionality enhances awareness of patient needs and improves communication among different health care providers to enable better care coordination planning and more efficient delivery of health care. results shiip is currently in use in the selhin (a population of approximately 500,000 people), with an evaluation anticipated in 2 years. conclusions the use of shiip will improve the access to and quality of care, especially for complex/high-needs patients. these improvements, which will enable person-centered care coordination, will be achieved through the consistent maintenance and sharing of patient records between health care providers. keywords portal; primary care; real-time online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e68, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts so long and thanks for all the ears: lessons learned from tennessee’s ongoing syndromic surveillance transition caleb wiedeman*, tonya mckennley, glenn yates and erin holt tennessee department of health, nashville, tn, usa objective to share lessons learned in tennessee during its transition from a jurisdictional syndromic surveillance system to a state-wide, centralized system. introduction syndromic surveillance generally refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. originally conceived as a tool for the early detection of potential bioterrorism outbreaks, syndromic surveillance is also used by health departments as a tool for monitoring seasonal illness, evaluating health interventions, and other health surveillance activities. over the past decade, the tennessee department of health (tdh) has utilized syndromic surveillance at the jurisdictional level. these standalone, jurisdictional systems utilized chief complaint data from local emergency departments (eds) and the early aberration reporting system (ears) developed by cdc. some jurisdictions integrated other local data for analysis in ears including 911 call center data, over the counter drug sales, and other non-traditional data sources. the analyses conducted on the data varied from jurisdiction to jurisdiction. cdc dismantled the ears program in 2011, prompting the need for a complete syndromic surveillance overhaul. tdh decided to implement a centralized, statewide system that would maintain all the capabilities that jurisdictions currently had while allowing for statewide data analysis and aggregation. during this implementation process, tdh has been balancing the short term goal of supporting and maintaining the existing jurisdictional systems while moving forward with acquiring a statewide syndromic surveillance solution and establishing the infrastructure to support it. methods tdh supports existing ears systems when possible. failing ears systems have been replaced with hybrid systems using epi info 7 and sas. when possible, data transport from hospitals to jurisdictions and tdh is enhanced through automation with scripts written for microsoft outlook and winscp (a free sftp client). tdh is acquiring essence to serve as a long term syndromic surveillance solution. the ed component of the system will be supported with standardized, hl7 messages received through tdh’s electronic data interchange. large hospital networks are being targeted to pilot the sending of syndromic surveillance messages until tdh declares readiness to receive syndromic surveillance messages for meaningful use in october of 2015. results maintenance and enhancement of the existing jurisdictional systems has progressed well. although some of the solutions are not ideal, they have allowed for continued surveillance activities in jurisdictions. raw, non-standard data from the hybrid systems will be aggregated using sas and integrated into essence once available. tdh’s acquisition of essence has been an arduous process. administrative delays in the contract and information technology approval process resulted in nearly a year and a half internal review. the hl7 onboarding of hospitals has been a slow process and hospitals and their vendors often require long periods of time to respond to requests for necessary changes. many jurisdictions in tennessee revisited their data use agreements (duas) and memorandums of understanding (mous) with local hospitals to ensure appropriate permissions and data elements were being provided. not all of the existing duas and mous were appropriate for a statewide system and many agreements had to be rewritten. communications with hospitals have become confused in some instances due to disconnect between the technology departments, traditional public health contacts, and members of hospital staff. conclusions moving from a jurisdictional syndromic system to a statewide, centralized syndromic surveillance system is a slow moving and time consuming process. administrative delays have hindered our ability to get a solution in place rapidly and have resulted in additional work to reproduce failed systems. although aggregation and transport of existing non-standard data is possible, we would only recommend it for hospitals that do not have infrastructure for hl7 messaging. centrally received hl7 messaging makes the data transport process much more efficient and removes the technical burden of receiving and constantly validating data files from our jurisdictional health departments and local hospital staff. clear and unified communication among regional and state health departments with hospitals and their corporate leadership is crucial in avoiding confusion and efficiently moving any agreement process forward. keywords surveillance system; tennessee; syndromic surveillance; lessons learned *caleb wiedeman e-mail: caleb.wiedeman@tn.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e173, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 1public health unit, komfo anokye teaching hospital, kumasi, ghana; 2kumasi cancer registry, kumasi, ghana; 3kumasi south regional hospital, kumasi, ghana objective to describe the operations and review data from the kumasi cancer registry for the period 2012 to 2014 introduction cancer registration is the systematic collection of data on cancers and the use of such information for action. population-based cancer registration is not common practice in developing countries. ghana has had no population-based cancer registry till 2012 when the kumasi cancer registry was established. established initially as a hospital-based cancer registry, the kscr has made significant strides in the collection and analysis of data on cancers in kumasi. we describe the operations of the registry and provide information on data analysis from information collected by the registry for the three year period 2012 to 2014. methods data from the kumasi cancer registry for the years 2012, 2013 and 2014 was reviewed. the reference population for the registry is the city of kumasi as designated by the 2010 ghana population and housing census. the registry collects data on cancers seen at the clinical departments of the kumasi south regional, manhyia district, tafo government, suntreso government and the komfo anokye teaching hospitals. the pathology and haematology laboratories of kath were also sources of data as was the kumasi birth and deaths registry. demographic, clinical and laboratory data was abstracted from the folders of all identified cases of cancers. cancer sites were coded using the international classification of diseases for oncology (icd-o) 3rd edition and entered into processed electronically using canreg 5 software. data was analysis involved the use of canreg 5 and epi info version 7.1.4. results a total of 1,078 cases of cancer were recorded among residents of kumasi over the three year period. the majority of cases were among females (64.2%). the mean age at incidence in males was 50.4 years and that for females 51.2 years. breast (35.1%), cervix (23.7%), ovary (7.9%), liver (3.8%) and endometrium (3.2%) were the top five c cancers were (3.4%) and bone marrow (2.9%). out of the total cases recorded, 54 (5%) were in children 14 years and below. bone marrow (18.52%), kidney (14.8%), abdomen (9.3%), head, face or neck (9.3%) and retina (7.4%) were the common cancers in children. the majority of the diagnoses were based on the histology of primary site (58.7%), clinical diagnosis (19.2%) death certificates (11.5%), clinical investigation (8.3%) and cytology or haematology (1.7%). conclusions population-based cancer surveillance targeting a properly defined geographic area provides better opportunity for good quality data on cancers in ghana. this three year data from kumasi provides evidence to support this. there is the need to establish more such registries to improve data quality for planning cancer prevention and control programmes in ghana. keywords cancer registration; surveillance; ghana acknowledgments all staff of he public health unit and the oncology department of the komfo anokye teaching hospital. we wish to also acknowledge the support of health information officers of the kumasi south, manhyia, suntreso, tafo and mch hospitals in kumasi references o’brien ks, soliman as, awuah b, jiggae e, osei-bonsu e, quayson s, et al. establishing effective registration systems in resourcelimited settings: cancer registration in kumasi,ghana. j regist manag. 2013;40(2):70–7. jensen o, parkin dm, maclennan r, muir c, skeet r, editors. cancer registration: principles and methods [internet]. lyon, france: international agency for research on cancer; 1991 [cited 2015 jul 28]. available from: http://www.iarc.fr/en/publications/pdfs-online/ epi/sp95/sp95.pdf laryea do, awuah b, amoako ya, osei-bonsu e, dogbe j, larsenreindorf r, et al. cancer incidence in ghana, 2012: evidence from a population-based cancer registry. bmc cancer. 2014 may 23;14(1):362. fritz a, percy c, jack a, shanmugaratnam k, sobin l, parkin dm, et al., editors. international classification of diseases for oncology. geneva, switzerland: world health organisation; 2000. *dennis o. laryea e-mail: denola@live.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e132, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts nist syndromic surveillance test suite 2015 edition sheryl l. taylor* and robert snelick national institute of standards and technology, gaithersburg, md, usa objective the nist syndromic surveillance test suite for 2015 edition onc certification testing was published in february 2016. key information related to the purpose, development, and use of this conformance test tool is provided via snapshots on a poster. introduction details about the onc 2015 edition certification criteria for syndromic surveillance and the related nist test suite were explained previously. we now provide an overview and key information regarding updates to the test suite and how it is designed to be used. methods snapshots are provided on a poster and are used by the presenter to explain the steps involved in developing the nist syndromic surveillance test suite 2015 edition, to show key features of and updates to the test suite, and to illustrate the relationship of the test suite to various releases of the phin messaging guide for syndromic surveillance. results the nist syndromic surveillance test suite for 2015 edition onc certification testing was published in february 2016. as the target stakeholders began using it and providing feedback, this tool and associated documentation were updated. the test suite is being used by test labs for onc certification testing of health information technologies, by developers in preparation for certification testing, and ultimately by public health jurisdictions for on-boarding of provider organizations that need to submit surveillance data. conclusions automated conformance test tools enable validation of health information technologies’ ability to support the requirements published in the phin messaging guide for syndromic surveillance. having this standard and the means to validate conformance helps drive the industry toward the level of interoperability needed to promote efficient reporting and utilization of syndrome-based public health surveillance information. keywords conformance testing; test tool; interoperability standards acknowledgments the nist syndromic surveillance test suite was developed in collaboration with subject matter experts at the centers for disease control and prevention. *sheryl l. taylor e-mail: sheryl.taylor@nist.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e48, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat department of health, city of houston, houston, tx, usa objective review 5 years of surveillance data post electronic lab reporting (elr) implementation and 8 years of data prior to elr, to evaluate timeliness and completeness of disease surveillance. introduction since 2009, houston health department (hhd) uses an electronic disease surveillance system (maven) to receive elrs from reporting facilities in the houston jurisdiction. currently, two large hospital systems, a blood bank, two large commercial labs, and two public health labs are sending elrs to maven. the overall percentage of disease reports received via elr was over 50%. we hypothesize that the implementation of elr has improved the timeliness and completeness of disease surveillance. methods the data are from two sources, maven and casefile, maven’s predecessor. nearly half of disease reports in maven are manually entered, and thus we group reports in three groups: casefile (all manually entered cases 2000-2008), interactive (manually entered cases in maven 2009-2014) and batch (cases in maven automatically populated by elrs 2009-2014). we select campylobacter infection, hepatitis a infection, legionellosis, bacterial meningitis and salmonellosis to represent reportable conditions with different reporting priorities. variables were selected to evaluate the timeliness and completeness of case reporting and investigation. variables were selected for patient demographics. for case reporting, the timeliness is evaluated using the difference between onset date and reporting date, whereas case investigation is evaluated only for reportable (confirmed or probable) cases by the difference between reporting date and investigation close date. for each selected variable, the completeness is evaluated by the percentage of cases without missing observations. results the annual case volume increased substantially post the elr implementation. prior to elr, on average the hhd received 1167 cases per year, and the number increased to 2797 cases per year post-elr. after elr implementation, the percentage of disease reports received via elr increased rapidly by year, and in 2014 the percentage of elr was around 70% (chart1): post elr, the number of reportable cases conditions also substantially increased. pre elr, on average 400 reportable cases per year were reported to hhd, whereas post-elr approximately 700 reportable cases per year were reported to hhd (chart1). in terms of timeliness of case reporting, on average, batch showed improvement over interactive cases (kruskal-wallis chi-squared= 357.7, p-value < 0.01) and over cases in casefile (24.4,,p<0.01) (table1). by comparing interactive cases with cases in casefile, interactive cases were more complete on reporting variables, and reported in more timely manner than the cases in casefile (76.0,p<0.01). moreover, the overall differences were also statistically significant (405.9,p<0.01): the timeliness of case investigation is only evaluated for reportable cases in houston. cases prior to elr were more complete with case investigation information. in maven, it took longer to close a case investigation (p<0.01 for both casefile vs.batch, and casefile vs. interactive). the interactive cases were closed faster than cases populated by elrs (10.9,1,p<0.01) (tab2): variable level completeness is evaluated for case reporting variables and variables of patient information (detailed in method) (see table3). the overall completeness is obtained by averaging completeness over case reporting variables and over patient information variables. the overall completeness shows that cases prior elr were more complete in terms of reporting. in maven, interactive cases had more complete information on disease reporting. in regards with patient information, interactive cases were as complete as cases prior to elr implementation, and batch cases in maven were slightly less complete than interactive cases (table3). conclusions post the elr implementation the annual number of cases (including reportable cases) in houston jurisdiction increased substantially (chart1); prior to the elr it took longer to receive a case report, and the use of electronic disease surveillance system and the implementation of elr improved the houston disease surveillance system capacity of early case detection (table1); however, post elr implementation, probably due to the increase in case volume, it took longer to complete a case investigation (table2); moreover, for patient information, no substantial differences were found between cases pre and post elr implementation, but cases populated by elrs were less complete with case reporting information (table3). keywords disease surveillance; electronic lab reporting; timeliness; completeness *kasimu muhetaer e-mail: kasimu.muhetaer@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e146, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts the longitudinal record: linking hepatitis a outbreak cases and syndromic hl7 data jeffrey johnson, lauren kearney, robyn matthews, jennifer nelson, marjorie richardson, brit colanter*, eric mcdonald and karen waters-montijo county of san diego, san diego, ca, usa objective to describe how the county of san diego linked information from a communicable disease registry and syndromic surveillance system to further describe cases associated with a large hepatitis a outbreak. specifically, to detail the linkage process which resulted in a longitudinal understanding of individuals’ hospital visits before, during, and after the reported hepatitis a incident. introduction with increasing availability of syndromic meaningful use data, new approaches to disease surveillance utilizing linkages to other data systems are possible. expanded communicable disease information may be valuable during outbreaks or other public health emergencies. san diego county is experiencing a significant and protracted hepatitis a outbreak. the disease has been transmitted person-toperson through close contact or through a fecally-contaminated environment, and has been primarily affecting homeless people and injection and non-injection illicit drug users. as of august 31, 2017, there were nearly 400 cases with 15 deaths. approximately, 70% of the cases were hospitalized. this is one of the nation’s largest hepatitis a outbreaks since the introduction of the hepatitis a vaccine in 1995. additional cases are expected over the next twelve months. the population affected by this outbreak presents some challenges for outbreak response. it is often a difficult population to reach. in addition, many have multiple comorbidities and often have health care seeking behaviors that differ from the general population. using the medical record number (mrn) to link hepatitis a disease cases from the communicable disease registry to syndromic hl7 messages for emergency department visits and hospitalizations enabled the identification of additional hospital encounters the cases may have had before, during, or following their hepatitis a disease incident. this allowed an exploration of the ways in which this unique population interacted with the health care system in the context of a communicable disease outbreak. this presentation will highlight the steps to link information across surveillance systems, the results, the challenges, and the benefits of linked information to public health departments. methods electronic information from a communicable disease registry system and syndromic surveillance hl7 data from participating hospitals were utilized. the patient’s mrn, available in both systems, was used to link the records. the syndromic data for this project included syndromic messages from 90 days prior to the first outbreak-related hepatitis a case in november 2016 through august 31, 2017. records with no mrn present, were unmatchable, or records with an encrypted mrn were excluded. the communicable disease registry data included outbreak-related hepatitis a cases from november 2016 through august 2017. records were excluded if the disease incident was associated with a hospital not currently providing syndromic surveillance information. the linked dataset will continue to be updated as the outbreak progresses. using the linked data, relevant dates and date ranges were determined for each case, including onset of hepatitis a-associated illness, hepatitis a exposure windows, infectious periods, and a 90 day post-illness period allowing for identification of possible relapsing illness patterns. based on these dates, hepatitis a case-patients who had hl7 messages for emergency department or hospitalization visits prior to, during, and following their hepatitis a episode were identified. interactions with the health care system were summarized and case studies were developed. results during the study time period, 396 outbreak-related hepatitis a case reports were received and documented in the communicable disease registry and nearly 18 million syndromic hl7 messages were received. after the exclusions, the mrn from 130 hepatitis a cases were linked to one or more syndromic hl7 messages associated with visits to an emergency department or inpatient hospital admissions. a total of 616 hospital encounters were documented among the 130 linked cases which reflects an overall average of 4.7 visits per case. many of these case-patients had numerous health care visits before, during, and after their hepatitis a episode. among the 130 linked cases, 56% (n=73) of the cases linked to one or more hospital visits other than the visit in which they were diagnosed with hepatitis a. many of these visits were made during their infectious period prior to being treated for hepatitis a. in addition, with the available data to date, 25% (n=33) of the linked cases had additional hospital visits following their hepatitis a diagnosis. these and other findings were used to provide additional outbreak response recommendations and shape additional surveillance and case monitoring approaches. conclusions the use of mrn to link records from a communicable disease registry to syndromic hl7 data is a viable tool for public health departments looking to obtain additional information about communicable disease cases and enhance surveillance and disease control activities. in this study, the linkage yielded a more complete profile of patient outcomes and health care-seeking behaviors of individuals diagnosed with hepatitis a. the county of san diego gained a broader understanding of a unique population’s interactions with the health care system, including the identification of missed opportunities for vaccination and earlier diagnosis. the information was then leveraged to improve vaccination and other outreach and prevention efforts. keywords linkages; informatics; integration; longitudinal record; meaningful use *brit colanter e-mail: brit.colanter@sdcounty.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e61, 2018 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts determining disease load through the national health information system in pakistan syed m. mursalin* national health institute, national health inforamtion/disease surveillance centre, islamabad, pakistan objective purpose of this abstract is to show how launch of a standard national health information system is has become the main national data source, and is, playing a pivotal role in facilitating decision making in health care system in pakistan. introduction before the launch of standard national health information system (nmhis) in 2000, there had been acute paucity of reliable and timely health information in pakistan. health departments had no choice than to resort to estimates or carry expensive community based surveys to determine the disease incidence. after the development and deployment of nhmis, overall health system is reshaping itself based upon the regular and frequent information now available on a good number number of priority health problems. this is system is now offering tremendous opportunities for promoting the cause of evidence based decision making and monitoring of its expanded health care structure. this effort had led to putting in place a standard system of data collection and transmission from roughly 13000 public health services (both urban and rural ). the new system is now able to promptly locate pockets of vulnerable communities reporting high disease incidence. methods use of secondary data gathered through national health information system data collection protocols. results recently published ‘national his report2013 has made some alarming revelations this report is developed after an analysis of estimated 118 million visits made to health services during 2013 . out of 43 reportable diseases, 22 diseases are communicable in nature. this system is providing some useful information not only to the district health managers, but also to national managers of public health programs (eg malaria (dots), epi tuberculosis, hepatitis, aid.hiv, epi.) this analysis has revealed that out of the total visits during 2013, 53 % belonged to 43 priority diseases selected for his reporting. from this total a high percentage of patients belonged to acute respiratory infections (30%). this was followed by gastro intestinal diseases (14.8%), scabies (8%) and oral diseases (3%). other communicable diseases, like measles, meningitis, malaria (10%) comprised the next group. non communicable diseases (selected for his reporting) comprised around 24% of the whole patient load. recording for preventive services show that 3.28 million pregnant women visited health facilities for antenatal care services during the year 2012. unfortunately, a high rate of anemic women that is around 30% of pregnant women were recorded (ie. hg < 10 gm%). whereas, 0.7 million c sections were reported from these facilities. system also gathers information on deaths and cause of deaths with special emphasis on maternal deaths. this system is capturing vital information on some of critical priority diseases, for which no other data collection mechanism is currently available. this include information about roadside accidents, rabies, snake bite, sexually transmitted diseases and typhoid. similarly, nhis is one of the main data source also for some non communicable and other diseases/ accidents including diabetes, ischemic health diseases, liver cirrhosis, epilepsy, dental cares, burns and eye diseases. conclusions there is a strong justification to strengthen and promote already functional routine health information systems by putting in more investment and emphasis in its infrasructure, hards and software support, staff capacity building in data generation and use of information. at this stage of achievement it is now the time for promoting the culture of evidence based decision making specially to address its dismal disease profile. keywords national health information; cost effectiveness; evidence based decision making.; standard national system.; health managers acknowledgments dr. shagufta zareen, data analyst/coordinator, national center for health information/surveillanceislamabad, pakistan references national health information system, ministry of health services, pakistan. *syed m. mursalin e-mail: mursalin831@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e199, 201 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e308, 2019 isds 2019 conference abstracts epidemiology as a practical resource to examine the hype & deliver reliable messages sophia anyatonwu epidemiology and preparedness, texas department of state health services, austin, texas, united states objective epidemiologists will be better prepared to serve as a practical resource within their communities and spheres of influence by taking the time to examine data sources behind and implications of news stories and studies that are being widely circulated. introduction it can be difficult to distinguish between truth, half-truth, fiction, and misinformation as we watch the news, read headlines, and scroll through various social media feeds. fortunately, epidemiologists have the tools needed to serve as a practical resource for colleagues, partners, and communities. the scrutinizer challenge is an opportunity for epidemiologists to tackle at least one news story or study a month that is relevant to public health. the goal is that we would do the research necessary to examine data sources and implications of news stories and studies. this process can help us deliver consistent and reliable messages to share with colleagues, partners, and communities. it also provides an opportunity for epidemiologists that practice in different settings to consolidate resources and develop working relationships that may be needed to more thoroughly examine issues. methods the scrutinizer challenge project was launched in january 2018 and introduced to texas public health association (tpha) epidemiology section members. participants were asked to select a headline or study to scrutinize. they were provided a guidance document with 10-25 questions to help identify and determine the credibility of data sources, compare these sources to claims being made, and assess overall implications of the news story or headline. lastly, participants were asked to submit an actionable summary or end product that could be shared with colleagues, a local partner, or the general public. scrutinizer challenge project submissions were shared in the epidemiology section newsletter or distributed to members as an educational resource. results three scrutinizer challenges were submitted between january 2018 and july 2018. news stories and study topics that were scrutinized addressed maternal mortality and morbidity in the united states, social media and population-level behavior change, and supplemental vitamins and minerals for disease prevention and treatment. the actionable summaries that were submitted were aimed at healthcare providers, researchers, and the general public. limited but positive feedback was provided for each submi ssion. sources were found to be mostly credible for each news story or study, however, 2 out of 3 headlines did not support the claims made in the news story or study. conclusions the scrutinizer challenges that have been submitted so far indicate that headlines can make incomplete or inaccurate claims even when credible sources are provided. this preliminary finding supports the need for epidemiologists to serve as a practical re source in their spheres of influence and communities, so that they can help cut through the hype and share reliable messages. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e319, 2019 isds 2019 conference abstracts using state and national surveillance systems during world equestrian games in nc lana deyneka1, zachary faigen1, anne hakenewerth1, nicole lee1, amy ising2, meichun li2 1 epidemiology, nc dph, raleigh, north carolina, united states, 2 unc, chapel hill, north carolina, united states objective to describe surveillance activities and use of existing state (nc detect) and national (nssp) syndromic surveillance systems during the international federation for equestrian sports (fei) world equestrian games (weg), in mill spring, nc from september 11 to september 23, 2018 introduction north carolina hosted the 2018 fei weg in polk county at the tryon equestrian center in september 2018. polk county, located in the mountain region of western north carolina, is home to 20,357 people, and the population is widely distributed. event organizers expected approximately 300,000 to 500,000 people to visit the area, with 800 horses from 71 countries taking part in the games. providing adequate public health epidemiologic investigations and response for the large scale event in the predominantly rural area presented a challenge. the nc surveillance & response team was created to facilitate enhanced surveillance for significant public health events during the weg, assist local public health agencies with epidemiologic investigations and response, develop public health risk assessments, and implement control measures. surveillance data were collected from the north carolina electronic disease surveillance system (nc edss), north carolina’s and cdc’s national syndromic surveillance systems (nc detect and nssp essence), public health epidemiologists from atrium health and mission hospital, and reports from the on-site medical facility (med-1) at the tryon equestrian center. the data were reviewed and summarized in internal and external situation reports. methods nc detect collects statewide data from hospital emergency department (ed) visits and carolinas poison center (cpc) calls. nc detect also collects data from select urgent care centers (ucc) in the charlotte area. cpc data are updated hourly, while ed data are updated twice a day. nc detect data were monitored daily for census (total ed visits), communicable disease syndromes, injury syndromes, and other occurrences of public health significance related to the event. the geographic areas monitored were polk county (the location of the main event), the counties where the guests were lodging in the western nc region (henderson, transylvania, buncombe, rutherford, mcdowell, and cleveland), the charlotte metropolitan area, and statewide. because of the large number of people from other states and countries who attended, ed surveillance was mainly conducted by hospitals so that visits were captured for all patients and not just nc residents. weg dashboards containing ed data were created prior to the event using nc detect and nssp essence systems, and were accessible to epidemiologists at the state level. nssp syndrome queries were shared with the neighboring state (sc) public health agency. surveillance began two weeks prior to the event to establish baseline levels for all ed visits for hospitals in polk county and the western nc region. surveillance occurred daily before the event, during the event, and for two weeks following the event to account for incubation periods of potential diseases. results the 2018 equestrian games in western nc were affected by heavy rain and heat. the weather led to low attendance and cancellation of a few competitions. during the observation period, ed admissions and most of the mass gathering related syndromes in both nc detect and nssp systems were at baseline. ed admissions for motor vehicle collisions and dehydration syndromes were above baseline for 09/19 and 09/21/18 (figures 3-4). cpc calls and uc admissions for selected uc centers in the charlotte area were also monitored, and were at baseline. conclusions nc detect and nssp dashboards provided effective and timely surveillance for the weg event to assist local public health in the rural nc area with epidemiologic investigations and appropriate response. nc detect’s cpc and uc data provided additional valuable information, and complemented ed surveillance during the mass gathering event. syndromic surveillance http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e319, 2019 isds 2019 conference abstracts became essential during weg, as nc dph deployment plans and resource availability changed when hurricane florence bore down on the region. acknowledgement anna waller, scd, nc detect principal investigator clifton barnett, msis, nc detect data quality manager dennis falls, nc detect etl manager / information security officer bill jones, nc dph, public health program manager references 1. joseph s. lombardo, carol a. sniegoski, wayne a. loschen, matthew westercamp, michael wade, shandy dearth, and guoyan zhang public health surveillance for mass gatherings johns hopkins apl technical digest, volume 27, number 4 (2008) 2. kaiser r, coulombier d. 2006. epidemic intelligence during mass gatherings. euro surveill. 11(12):e061221.3. pubmed 3. ising a, li m, deyneka l, vaughan-batten h, waller a. improving syndromic surveillance for nonpower users: nc detect dashboards. emerging health threats journal 2011, 4: 11702 doi: 10.3402/ehtj.v4i0.11702 figure 1 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17213571&dopt=abstract online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e319, 2019 isds 2019 conference abstracts figure 2 figure 3 http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e319, 2019 isds 2019 conference abstracts figure 4 figure 5 http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristin harden3, dolorence okullo2, 3 and shreyas ramani2, 3 1university of michigan medical school, ann arbor, mi, usa; 2university of michigan school of information, ann arbor, mi, usa; 3university of michigan school of public health, ann arbor, mi, usa objective describe the development process and function of a data dashboard for state hiv surveillance and discuss the benefits of creating interactive data dashboards in the r software environment. introduction state hiv offices routinely produce fact sheets, epidemiologic profiles, and other reports from the ehars (enhanced hiv/aids reporting system) database which was created and is maintained by the cdc.1 the ehars software is used throughout the united states to monitor the hiv epidemic and evaluate hiv prevention programs and policies. due to limited variability of ehars throughout the united states, software developed to analyze and visualize data using the ehars database schema may be useful to many state hiv offices. software developed based on the ehars database schema could reduce the time required for analysis and production of reports. the r software environment for statistical computing is an open source project with a thriving community of users who continue to expand r’s analysis capacity through the addition of packages. a package is “a standardized collection of material extending r, e.g. providing code, data, or documentation”.2 shiny is one example of a user-developed package which easily allows r users to create interactive web applications from analytical software. methods an open source r package is under development for state hiv offices. the package is being developed in the open on the github repository. the eharsdash package will create a data dashboard based on the ehars database schema and will rely on the shiny package to create interactive data visualizations from the data in ehars. the eharsdash package will also contain software to import data from ehars into r for analysis and visualization. once developed, state hiv offices may use the software in several ways including the following: 1) users may install the software locally for individual analyses 2) offices may install the software on an intranet for use by multiple users in the office 3) offices may install the the software on a publicly available website, allowing the public to interact with hiv data developers will work with state hiv offices to determine the visualization needs of the office and will also create plots based on hiv epidemiological profiles available from the websites of state hiv offices. if state hiv offices find the eharsdash package useful, the number of data visualizations can easily be expanded through the continuation of the community-driven process. state hiv offices and the cdc could provide feedback and work together with the developers to create an open-source data visualization package for the ehars database. conclusions the r software environment will used to create a powerful data dashboard for the ehars database schema. the eharsdash package will contain software which imports data from ehars into the r environment and analyzes and visualizes the data. it will also enable reproducibility of analyses. due to this reproducibility, the use of the eharsdash package may reduce the time state hiv offices require to complete analyses for reports. state hiv offices may then have additional resources to pursue research and prevention activities and reduce the burden of hiv in their state. the eharsdash package will also encourage a more open structure for presenting hiv surveillance data while maintaining anonymity. although traditionally state health departments have relied on sas software for analysis and data management, consideration should be given to the r software environment due to its open environment, user community, and cost. data transparency and access are essential to understanding and reducing the health inequalities which exist in diseases such as hiv. keywords ehars; shiny; visualization references 1. hiv open data project: ehars the hiv/aids reporting system, person data (icpsr 34725). http://doi.org/10.3886/icpsr34725.v1. 2. r development core team (2015). an introduction to r. url http://www.r-project.org/manuals.html. 3. 2013 std/hiv program report. state of louisiana department of health and hospitals, office of public health. 4. 2014 std/hiv program report. state of michigan department of health and hospitals, office of public health. *elliott s. brannon e-mail: ebrann@umich.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e95, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts management tool to guide rabies elimination programmes kristyna rysava*2, 1, tamara mancero3, eduardo caldas4, mary carvalho6, veronica gutierrez5, daniel haydon2, paul johnson2, rebecca mancy2, jesus f. gonzalez roldan5, victor d. vilas6 and katie hampson2 1university of warwick, school of life sciences, coventry, united kingdom; 2university of glasgow, institute of biodiversity, animal health & comparative medicine, glasgow, united kingdom; 3pan american health organization (mexico), mexico city, mexico; 4unidade técnica de vigilância de zoonoses, brasilia, brazil; 5cenaprece, mexico city, mexico; 6pan american health organization (brazil), rio de janeiro, brazil objective to provide surveillance tools to support policymakers and practitioners to identify epidemiological situations and inform the progressive implementation of rabies elimination programmes. introduction global targets for elimination of human rabies mediated by dogs have been set for 2030. in the americas countries are progressing towards interruption of transmission and declaration of rabies freedom1. guidance for managing elimination programmes to ensure continued progress during the endgame is critical, yet often limited and lacking in specific recommendations. characteristic spatiotemporal incidence patterns are indicative of progress, and through their identification, tailored guidance can be provided. methods using sirvera, a surveillance database for rabies in the americas2, we developed a classification framework for identification of epidemiological situations at subnational level. each situation exhibits a characteristic pattern identified via a set of objective criteria including trends in case detection, assessment of virus variants, case locations and measures of incursion risk. we refined our framework through application to mexico in consultation with stakeholders. to understand factors predicting incursions we analysed state-level data on vaccination campaigns, populations and socioeconomic indicators employing multivariate regression models. results we were able to classify all states in mexico and provide correspondingly tailored guidance. control efforts have resulted in progress towards elimination; however rabies still circulates endemically in one state chiapas, putting its neighbours at risk of re-emergence. epidemiological and socioeconomic factors associated with incursions were primarily geographic proximity to endemic and highprevalence states, and inconsistent vaccination campaigns associated with a low human development index. conclusions our management tool can support rabies programme managers at subnational levels to identify their epidemiological situation, develop tailored plans to meet targets, and sustainably maintain rabies freedom, as demonstrated for mexico. effective surveillance is critical for disease elimination. control options differ depending on whether disease circulates intermittently through reintroductions or persists focally, but with poor detection these situations might be indistinguishable. our analysis enables identification of at-risk areas and methods to reduce risk. investment in remaining endemic areas, through improved implementation and monitoring of mass dog vaccinations, is expected to provide the most cost-effective approach to elimination whilst preventing re-emergence elsewhere. decision-tree framework. rabies incursions in mexico, 2005-2015. blue circles indicate incursion locations, and resulting outbreak sizes, with darker shading for more recent incursions. red shading indicates the duration of endemic circulation over the ten-year period. keywords rabies elimination; incursion detection; policy guidelines; programme management acknowledgments this research was supported by the wellcome trust. data were provided by the paho and regional government and stakeholders in mexico. references 1. oie. global elimination of dog-mediated human rabies−the time is now! report of the rabies global conference. geneva, switzerland, 2015. 2. vigilato man, clavijo a, knobl t, silva hmt, cosivi o, schneider mc, leanes lf, belotto aj, espinal ma. progress towards eliminating canine rabies: policies and perspectives from latin america and the caribbean. phil trans r soc b, 2013; 368. *kristyna rysava e-mail: istyrysava@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e87, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts multidimensional tensor scan for drug overdose surveillance daniel b. neill* h.j. heinz iii college, carnegie mellon university, pittsburgh, pa, usa objective we present the multidimensional tensor scan (mdts), a new method for identifying emerging patterns in multidimensional spatio-temporal data, and demonstrate the utility of this approach for discovering emerging geographic, demographic, and behavioral trends in fatal drug overdoses. introduction drug overdoses are an increasingly serious problem in the united states and worldwide. the cdc estimates that 47,055 drug overdose deaths occurred in the united states in 2014, 61% of which involved opioids (including heroin, pain relievers such as oxycodone, and synthetics).1 overdose deaths involving opioids increased 3-fold from 2000 to 2014.1 these statistics motivate public health to identify emerging trends in overdoses, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved). early detection can inform prevention and response efforts, as well as quantifying the effects of drug legislation and other policy changes. the fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of data points, and has recently been extended to multidimensional data (md-scan).3 while md-scan is a potentially useful tool for drug overdose surveillance, the high dimensionality and sparsity of the data requires a new approach to estimate and represent baselines (expected counts), maintaining both accuracy and efficient computation when searching over subsets. methods the multidimensional tensor scan (mdts) is a new approach to subset scanning in multidimensional data. in addition to detecting the spatial area (subset of locations) and time window affected by an emerging outbreak, mdts can also identify the affected subset of values for each observed attribute. for example, given the drug overdose surveillance data described below, mdts can identify the affected genders, races, age ranges, and which drugs were involved. mdts finds subsets of the attribute space with higher than expected case counts, first using a novel tensor decomposition approach to estimate the expected counts. mdts then iteratively applies a conditional optimization step, optimizing over all subsets of values for each attribute conditional on the current subsets of values for all other attributes3, and using the linear-time subset scanning property2 to make each conditional optimization step computationally efficient. the resulting approach has high power to detect and characterize emerging trends which may only affect a subset of the monitored population (e.g., specific ages, genders, neighborhoods, or users of particular combinations of drugs). results we used mdts to analyze publicly available data from the allegheny county, pa medical examiner’s office and to detect emerging overdose patterns and trends. the dataset consists of ~2000 fatal accidental drug overdoses between 2008 and 2015. for each overdose victim, we have date, location (zip code), age decile, gender, race, and the presence/absence of 27 commonly abused drugs in their system. the highest-scoring clusters discovered by mdts were shared with allegheny county’s dept. of human services and their feedback obtained. one set of potentially relevant findings from our analysis involved fentanyl, a dangerous and potent opioid which has been a serious problem in western pa. in addition to identifying two wellknown, large clusters of overdoses—14 deaths in january 2014 and 26 deaths in march-april 2015—mdts was able to provide additional information about each cluster. for example, the first cluster was likely due to fentanyl-laced heroin, while the second was more likely due to fentanyl disguised as heroin (only 11 victims had heroin in their system). moreover, the second cluster was initially confined to the pittsburgh suburb of mckeesport and a typical demographic (white males ages 20-49), before spreading across the county. our analysis demonstrated that prospective surveillance using mdts would have identified the cluster as early as march 29th, enabling targeted prevention efforts. mdts also discovered a previously unidentified, highly localized cluster of fentanyl-related overdoses affecting an unusual and underserved demographic (elderly black males near downtown pittsburgh). this cluster occurred in januaryfebruary 2015, and may have been related to the larger cluster of fentanyl-related overdoses that occurred two months later. finally, we identified multiple overdose clusters involving combinations of methadone and xanax between 2008 and 2012, and observed dramatic reductions in these clusters corresponding to the passage of the methadone death and incident review act (october 2012), which increased state oversight of methadone clinics and prescribing physicians. conclusions retrospective analysis of allegheny county overdose data suggests high potential utility for a prospective overdose surveillance system, which would enable public health users to identify emerging patterns of overdoses in their early stages and facilitate targeted and effective health interventions. the mdts approach can also be used for other multidimensional public health surveillance tasks, such as sti surveillance, where the patterns or outbreaks of interest may have demographic, geographic, and behavioral components. keywords event detection; outbreak detection; subset scan; drug overdose surveillance acknowledgments this work was partially supported by nsf grant iis-0953330. the author wishes to thank eric hulsey (allegheny county dhs) for feedback on the discovered overdose clusters. references [1] rudd ra, aleshire n, zibbell je, gladden rm. increases in drug and opioid overdose deaths: united states, 2000–2014. mmwr 2016; 64(40): 1378-1382. [2] neill db. fast subset scan for spatial pattern detection. j royal stat soc b 2012; 74(2): 337-360. [3] neill db, kumar t. fast multidimensional subset scan for outbreak detection and characterization. online j pub health inform 2013; 5(1): e156. *daniel b. neill e-mail: neill@cs.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e20, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts delay between discharge and admit time delay in adt-a03 messages via leeds jose a. serrano* office of public health, louisiana department of health, new orleans, la, usa objective to explore the difference between the reported date of admission and discharge date using discharge messages (a03), from hospital emergency departments participating in the louisiana early event detection system (leeds. introduction the infectious disease epidemiology section (idepi) within the office of public health (laoph) conducts syndromic surveillance of emergency departments by means of the louisiana early event detection system (leeds). leeds accepts adt (admit-dischargetransfer) messages from participating hospitals, predominately a04 (registration) and a03 (discharge), to obtain symptom or syndrome information on patients reporting to hospital emergency departments. capturing the data using discharge messages (a03) only could result in a delay in receipt of data by laoph, considering the variability in the length of stay of a patient in the ed. methods emergency department data from participating hospitals is imported daily to leeds and processed for syndrome classification. idepi syndromic surveillance messages received for the period of cdc week 1632 and 1636 (8/8/16-9/8/16) using ms access and excel to calculate the difference (in days) between the reported admit date and discharge date in a03 messages. results 88.1% of the a03 messages submitted in the 4 week analysis period exhibited no delay (delay=0 days) between the admit date and the reported discharge date, compared to only 10.7% showing a delay of one day (delay = 1 day) and 1.06% showing a delay of 2 days or more (delay ≥ 2 days). less than 0.2% of the messages had missing information regarding discharge date (table 1). conclusions syndromic surveillance systems operate under a constant need for improvement and enhancement. the quality of the data, independent of the quality of the system, should always strive to be of the highest pedigree in order to inform disease-specific programs and detect public health aberrations. in order to identify these potential concerns, it is imperative that the data be submitted to public health agencies in a timely manner. based on this analysis, the lapse in time between admit and discharge results in little to no patient syndromic data delay for those hospital ed’s that exclusively send a03 messages. this statement is supported by the finding that close to 99% of messages demonstrated a delay between admit date and discharge date of one day or less. table 1. delay between reported admit and discharge date in a03 messages submitted to leeds keywords syndromic; surveillance; quality *jose a. serrano e-mail: jose.serrano@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e34, 2017 opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi opportunities and obstacles using a clinical decision support system for maternal care in burkina faso s alphonse zakane1,2*, lars l gustafsson2, ali sie1, göran tomson3,4, svetla loukanova5,, pia bastholm-rahmner4 1. centre de recherche en santé de nouna, bp 02 nouna, burkina faso 2. department of laboratory medicine, division of clinical pharmacology, karolinska institutet at karolinska university hospital, se-141 86, stockholm, sweden 3. health systems and policy, department of public health sciences, karolinska institutet, se17177 stockholm, sweden 4. department of learning, informatics, management and ethics, medical management centre (mmc), karolinska institutet, se-17177 stockholm, sweden 5. department of general practice and health services research, heidelberg university hospital, marsilius-arkaden, imneuenheimer feld 130.3, d-69120 heidelberg abstract objective: maternal and neonatal mortality is high in sub-saharan africa. to support healthcare workers (hcws), a computerized decision support system (cdss) was piloted at six rural maternal care units in burkina faso. during the two years of the study period, it was apparent from reports that the cdss was not used regularly in clinical practice. this study aimed to explore the reasons why hcws failed to use the cdss. methods: a workshop, organized as group discussions and a plenary session, was performed with 13 participants to understand their experience with the cdss and suggest improvements if pertinent. workshop transcripts were analyzed thematically. socio-demographic and usage patterns of the cdss were examined by a questionnaire and analyzed descriptively. results: the participants reported that the contextual basic conditions for using the cdss were not fulfilled. these included unreliable power supply, none user-friendly partograph, the cdss was not integrated with workflow and staff lacked motivational incentives. despite these limitations, the hcws reported learning benefits from guidance and alerts in the cdss. using the cdss enabled them to discover problems earlier as they learned to focus on symptoms to prevent harmful situations. opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi introduction in burkina faso, the neonatal and maternal mortality is as high as 27 per 1000 newborns and 400 per 100 000 pregnancies, respectively (1,2). peripheral healthcare facilities are mainly managed by nurse and midwife assistants with occasional access to midwifes, physicians or obstetricians (3,4). it is well known that skilled and educated healthcare workers (hcws) are needed to provide high quality maternal and neonatal care (5) to help reduce neonatal and maternal mortality. one strategy to assist hcws in rural africa in undertaking skilled decisions is to give easy access to guidelines and information at the “point-of-care” by using a computerized clinical decision support system (cdss) (6,7). unfortunately, cdss systems are commonly designed without a clear strategy for understanding the needs of end-users and their working conditions (3,9). designing a cdss without considering the user perspective fails to provide interest, and can raise resistance to their use in daily practice (7,9–11). the benefits of cdss systems are ensured if they have a specific purpose, are “easy to use” and are adapted to the workflow at the healthcare facility (3,6,7,12,13). to assist hcws in their routine work, a cdss was designed, implemented and evaluated in six healthcare facilities in rural burkina faso (3,14). the cdss content was based on who guidelines for maternal and neonatal care (4,14) with two main sections: i) a checklist ensuring that all relevant patient data are compiled and, ii) alerts and recommendations for care based on algorithms that screened entered information for any suspected pathological, clinical, or laboratory data. furthermore, the cdss aimed to simplify patient management and re-training in prenatal care, delivery and neonatal care. conclusion: the cdss was not tailored to the needs and context of the users. the hcws, defined their needs and suggested how the cdss should be re-designed. this suggests that the successful and regular usage of any cdss in rural settings requires the involvement of users throughout the construction and pilot-testing phases and not only during the early prototype design period. keywords: burkina faso, computerized clinical decision support system, healthcare workers, maternal care, rural area, workshop correspondence: *s alphonse zakane, centre de recherche en santé de nouna, bp 02 nouna, burkina faso, department of laboratory medicine, division of clinical pharmacology, karolinska institutet at karolinska university hospital, se-141 86, stockholm, sweden. e-mail: al_zakane@yahoo.fr doi: 10.5210/ojphi.v9i2.7905 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:al_zakane@yahoo.fr opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi before implementation of the cdss in burkina faso, the hcws at six health facilities were interviewed about their perceived needs and attitudes towards using cdss in daily work (3). the main finding showed high expectations on the cdss as a learning tool in maternal and neonatal care. after two years of pilot-testing, it was found that the cdss was not used on a regular basis. consequently, the aim of this study was to explore the reasons and explanations why the hcws failed to use the cdss as planned. materials and methods study design the log-file of hcws use of the cdss showed that it was used in 24% of all antenatal care visits and decreased over time during the 23 months’ implementation and study period (table 1). the users were invited to an interactive workshop to discuss reasons for use or no-use of the cdss during the study period. such a workshop is defined as a group discussion where the participants explored their own statements in small groups and in a concluding plenary session (15,16). the study design was approved by the nouna institutional review board, burkina faso (deliberation n° 2010-5/cle/crsn) as part of the qualmat project (fp7-health-2007b). table 1: antenatal care (anc) visits managed with the cdss during the implementation and study period from june 2012 to april 2014 2012 (june to dec.) 2013 (jan. to dec.) 2014 (jan. to apr.) total total observations 6536 10304 5765 22605 anc visits managed without cdss 77% 71% 85% 76% anc visits managed with cdss 23% 29% 15% 24% study setting and participants the workshop took place in nouna health district in burkina faso in march 2014. the nouna health district covers a population of 340 000 inhabitants served by 43 peripheral healthcare facilities and one 80-bed district hospital. all peripheral healthcare facilities have equal type of services: one clinic for general care, one maternity unit for family planning counseling, antenatal care, non-complicated deliveries, post delivery care and one pharmacy unit. in accordance to policies in burkina faso, the staff of the opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi peripheral healthcare facilities includes nurses, nurse assistants, midwife assistants (except for big cities where you find midwives at this level) and a community member trained to sell drugs at the pharmacy of the facility. the six healthcare facilities (with the cdss) were situated between 25 to 45 km from the district hospital. three of the facilities had tarred roads and one facility had limited possibility to use cars (ambulance as well) for referral during rainy seasons (2 to 3 months per year). the implementation of cdss started in 2012 at six peripheral healthcare facilities with a total of 25 users (3). after two years, 12 users were still working in the facilities using the cdss and all were invited to take part in the workshop. eleven out of 12 accepted. furthermore, two participants with background in obstetric care from the district medical team in charge of monitoring the healthcare facilities were invited to give their external view on the utilization of cdss during the study period. data collection the objectives of the workshop were to understand how the hcws experienced the cdss in clinical practice. the data collection had three parts: initially, all participants filled in a questionnaire with socio-demographic variables and their usage patterns of the cdss. the 13 participants (11 hcws plus the two members from the district medical office) were divided into three groups with three to five participants from different health facilities in each group. all groups were given guiding questions (table 2) to start the discussion. each group was free to choose a moderator who should write a report about their answers to the questions. the group discussion lasted for 90 minutes. table 2. guiding questions to capture participants’ experiences of the cdss during the group discussions number question 1. what is your overall impression about the cdss? 2. what do you think about the different steps of the cdss (data entry, guideline section and training part)? 3. what is your experience with the partograph? 4. what is your experience with the support equipment (laptop, solar panel and batteries, etc.)? 5. would you like to continue to use the cdss as it is today? if yes, what is the best thing with the cdss? if no, what should be modified? 6. have the cdss improved your performance in clinical practice? if yes, in what way? opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi 7. imagine, how an optimal cdss should look like according to your view? 8. what is your experience about the overall implementation, education and training with the utilization of the cdss? the last part of the workshop was a plenary session lasting for two hours. group reports were projected on a screen allowing each participant to comment. all participants were given time to talk and they were enthusiastic to express their opinions about the cdss. two researchers (saz and mk), with good knowledge of the cdss, were in charge of the plenary session. all participants agreed that the session was audio recorded. the participants were informed that their participation in the study was voluntary and that they could withdraw at any time, without any implications. the whole workshop took approximately four hours. no incentives were given for participants to take part apart from food and transportation from their different work places to the research center where the workshop took place. data analysis questionnaire the answers from the individual questionnaires were entered locally by saz using the data-entry form of epi info windows version 3.5.1 (epi infotm, software in the public domain freely available by centre for disease control (cdc), atlanta us at www.cdc.gov/epiinfo/index.html) and analyzed using descriptive statistics (frequency and central tendency trends). b) group discussions and c) plenary session the recorded material of the plenary session in french was transcribed by one researcher (saz) and together with the written notes from the group discussions translated into english (saz). all material were analyzed with inductive thematic analysis with no predetermined categories in a stepwise manner (17). the transcript, together with the written answers from the group discussions, were read several times to get a sense of the main findings. the analysis of the transcript started by sorting all text into two main themes of strengths and weakness in relation to the use of the cdss. the sections of text in each group were summarized and grouped by content into preliminary categories. the next step was to find related patterns within each preliminary category, which means that sections of the text were moved between categories, and new categories were formed. the categories were finally grouped under two main themes: i) inhibiting factors and ii) learning factors in relation to the use of the cdss. each theme is presented with its consistent category and subcategory in table 3. quotes were selected to illustrate each subcategory. the analysis was performed in a reciprocal way by two of the researchers saz (computer scientist) and pbr (behavioral scientist). opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi table 3: the final two main themes of consistent categories and subcategories theme category subcategory 1. inhibiting factors in the use of the cdss working environment as a basic condition to use the cdss: erratic maintenance and lack of computers hardware and software problems with the computer unreliable supply of electricity shortage of computers work process: the cdss does not fit into the workflow in routine work the cdss does not fit into the workflow in routine work software design: feedback on statistical data for performance and poor user-friendliness feedback of individual performance data for hcws as well as statistical reports for the facility poor user-friendliness of the partograph training and knowledge: lack of training in maternal care and computer use lack of continuous training in maternal care need of more training in handling computers and in cdss use organization and leadership: motivation and communication turnover of users and lack of incentives communication and leadership 2. learning factors in the use of the cdss individual learning: skills in computer use and in obstetrics gaining skills in the usage of computers learn from the cdss registered data, from alerts and incorporate documentation organizational learning: improvement of quality of care and eager to use technology improved quality in maternal care a desire to experiment with new technology results the results cover (a) socio-demographic information and (b) hcw reported experience about the use of cdss from the questionnaire, group discussion and the plenary sessions. opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi (a) socio-demographic information seven of the 13 participants were female and six were midwife-assistants with a mean of 7.5 years of experience in maternal care (table 4). table 4. characteristics of the participants modality number of participants n= 13 sex male female 6 7 education primary secondary first level secondary second level university other 3 5 5 0 0 specialization nurse midwife midwife assistant nurse assistant other (nurse specialized in obstetric care) 2 2 6 2 1 current position general consultation maternity vaccination head of the facility other 0 9 2 2 0 years of experience in 1 to 14 years (mean= 5, mode= 2) opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi current position years of experience in maternal cares 4 to 15 years (mean= 7.5, mode= 5) (b) reported usage patterns and experience about the cdss all 11 participants reported that they had used the cdss and five reported that they used it “more than once a week”. however, on the follow-up question “have you used the cdss this week” only 1 participant responded “yes” (table 5). table 5. reported usage of the computerized clinical decision support system (cdss) modality number of participants n= 11 have you use the cdss yes 11 if use of the cdss frequency more than one a week once a week once a month sometimes infrequently 5 0 0 5 1 have you used the cdss this month yes no 2 9 have you used the cdss this week yes no 1 10 opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi users’ experiences of the cdss are presented under the two mains themes, inhibiting and learning factors. 1. inhibiting factors in the use of the cdss 1.1 working environment as a basic condition to use the cdss: erratic maintenance and lack of computers hardware and software problems with the computer an irritation for the users was the frequent breakdown of the computers due to software bugs (standard programs of the computer as well as the cdss software) or problems to simply start the laptop. when a computer broke down it could take up to three to four weeks to get it repaired. the participants suggested that access to a back-up computer at each facility would avoid disruption of their work. group1: “provide maintenance tools like basic instructions for users and repair occasional breakdown on time when needed. plan to include a backup computer in case one computer is broken down.” unreliable supply of electricity working in remote areas without electricity makes the cdss depending on the solar panel system for power supply. the solar panel system is sensitive and easily broke down for up to one to two months, which make the use of the cdss uncertain. the failures of the solar panel system are essentially due to two factors: dust on the solar cells on the roofs of the facilities, or improper maintenance of the equipment’s that cause break down of equipment such as voltage regulator, light bulbs or voltage convertor. the users reported that the maintenance to check the functionality of the solar power system is scheduled twice a year. they thought this is not enough. group1: “sometimes the voltage convertor or voltage regulator of the solar power system broke down. we thought that maybe provision of new equipment on a regular basis like solar cell and batteries; this will reduce the risk of breakdown of the solar electricity system. also, we suggested that the delay to repair the electricity system when breakdown occurs has to be reduced.” shortage of computers as the work in the facilities often is organized in two or three teams working in different rooms, it becomes difficult with access to only one computer if this is the case. group 3: “when you are on duty for the maternal care, you have to perform different tasks like physical examination, vaccination or counseling for hiv testing or counseling for nutrition and delivery. these tasks are sometimes done in different rooms. therefore, it is difficult to use the cdss and perform all work if you are two or three employees to perform the required tasks. because you may need to move in another room to complete one specific task … we need two opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi or three laptops per facility which are connected with the cdss so we can use the cdss at different place in the same facility”. 1.2 work process: the cdss does not fit into the workflow in routine work the hcws complained that the cdss doesn’t fit into the workflow in routine work and causes double work for them. first, they should fill in all information on paper. this information is required by the healthcare administration for each patient (consultation card and register). following this, they had to input the same information into the cdss. this added approximately 30 to 40 minutes extra workload for each patient. group3: “data entry takes time. it is a double work, as we have to fill in all information on paper while we already have problem with lack of staff” group2: “the time we spent for one patient now, before it was two patients for the same time … now we spent 30 to 40 min for one patient in anc, while it was 15 to 20 minutes before we had the cdss”. 1.3 software design: feedback on statistical data for performance and poor user-friendliness feedback of individual performance data for hcws as well as statistical reports for the facility the participants expressed a desire to have access to data from the cdss to know how they individually performed as well as to obtain statistical reports on the performance of the facility. they wanted the statistical reports as comparisons with other facilities so they know in which area they should perform better. group1: “the cdss has established an electronic document containing information collected from clinical work. we hope that this information may at any time be used for investigation purposes on maternal health quality improvement activities. at this moment, we couldn’t get access to these data.” poor user-friendliness of the partograph the participants reported poor user-friendliness and incorrect appearance of the partograph in the cdss, which make it less interesting to use in clinical work as it is not in accordance with present working procedures. in the cdss, the plot of the partograph always starts at cervix opening at zero centimeter, which is incorrect in clinical practice, and this setting currently cannot be changed by the user. when the graph is not correct, it is difficult to follow the delivery process and to make a decision based on this information. the participants explained that this is one of the reasons why they rarely used the cdss. instead they preferred to use the paper-based partograph. group1: “the partograph in the cdss does not correspond to the delivery progress in real life. when the womb is at 4 cm and we ought to start using the partograph, the plot shows the starting opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi point at 0 cm that is not correct. it is not possible to start the plot at 4 cm. this is the reason why we prefer the paper based graphics instead of the cdss partograph.” 1.4 training and knowledge: lack of training in maternal care and computer use lack of continuous training in maternal care the hcws have different levels of training and experience in maternal care. because of poor skills, some hcws typically seek help from more experienced colleagues. this is not easy in a remote area due to distance between the facilities and the hospital, and mobile phones are generally not an alternative due to poor connectivity. a great deal of expectation was seen with the cdss implementation as a continuous training opportunity in maternal care. unfortunately, currently hcws only got few retraining sessions. group 2: “during the intervention there was insufficient retraining in maternal care. as we have different experience on maternal care we expected to get regular retraining.” need of more training in handling computers and in cdss usage participants perceived the design of the cdss as complex. they pointed out that they received one training session only on computer utilization and on how to use the cdss. this was deemed as insufficient as none of hcws taking part had worked with either computers or cdss previously. group2: “the cdss is complex to use. sometimes we don’t understand how to use the module for pregnancy delivery. when discussing the utilization of the cdss with other colleagues from different health facilities, we have the impression of that we are using different systems (…). the cdss should be easy to use for all workers meaning that it should be self-instructive and not complex to use.” 1.5 organization and leadership: motivation and communication turnover of users and lack of incentives the turnover of users is high due to multiple reasons including professional promotion or family events. the respondents even reported that the cdss has been a reason for some hcws to change workplace. plenary session: “the cdss was a motivating factor for us to stay at our posts in rural health facilities. along the way, this has changed and some asked to move to different health facilities. the cdss was complicated to use and there was a restriction, as we were not allowed to use the computer for other tasks. this was a real disappointment for us”. new hcws are not trained in how to use the cdss, which put a pressure on senior staff to train the newly recruited staff on how the cdss works or to do all the work by them self. opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi group3: “there was a high turnover of users, half of the users moved within one year after the beginning of the cdss project. we received many new potential users without any training and experience to use the cdss. it became difficult for us to train them as we have a lot to do.” the participants reported when they started to use the cdss they were promised different kinds of motivational triggers such as a financial incentive based on a combination of group and individual performance every six months and a non-financial incentive such as a congratulation letter, regular training on maternal care or provision of equipment for work related. these incentives were stated in a memorandum of understanding (mou), signed by the district health officer and the country principal investigator of the project. the hcws also expressed during the computer training before they started to use the cdss and during the needs assessment study (3), their expectations to have 1) access to internet to search information and possibilities to exchange experience with others at work, and 2) possibility to use the computer for other activities than just work related activities. these expectations were not fulfilled as they had projected. group1: “with the project, we were expected to receive financial incentives. we were supposed to have free access to the computer for any purpose for work like reporting of facility activities or exchanging experiences with colleagues using internet or for personal tasks like watching video or social networking (facebook). but it was only possible to use the cdss because the computer was locked for other purposes. furthermore, we received irregularly financial incentives based on our performance.” communication and leadership the participants reported that during the course of the project implementation, they received training to follow a new procedure for the management of hiv prevention in maternal care. still, the cdss was not updated according to this guideline. unless the cdss is updated with new guidelines from the healthcare administration it is impossible for the hcws to use the cdss. group3: “we received a new version of the hiv guidelines for maternal care from the healthcare administration but it was not updated in the cdss accordingly … it would be good to integrate new and up to date documents related to maternal care when needed. the cdss must be updated continuously”. 2. learning factors in the use of the cdss despite a range of prohibitive factors in the utilization of the cdss, participants reported that the learning possibilities stimulated the use of the cdss. 2.1 individual learning: skills in computer use and in obstetrics gaining skills in the usage of computers the participants were pleased to have acquired computer skills by working with the cdss. such skills are important for them both privately and for their professional work. opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi group1: “the cdss allow us to enhance our computer skills. we got basic computer training at the beginning of the project. and twice per month we had an it person visiting the facilities. during these visits, we could ask questions and get answers about computers and cdss usage. we also got this opportunity to ask the it person for help to manage our private laptops and our private works.” learn from the cdss registered data, from alerts and incorporate documentation participants reported that all data, from antenatal care to delivery, are stored in the cdss. this stored information made it possible for hcws to easily follow the progress of the pregnancy and detect problems early. this information makes it safer for mothers, especially in case of emergency. group 1: “the cdss is a guide for monitoring pregnant women during antenatal care and during delivery. it also allows early detection of problems related to pregnancy and provides guidance for decision-making to deal with problems that occurs.” group 2: “the cdss allows us to get early warnings of problems that may occur during pregnancy, delivery and during early first hour’s postnatal care.” participants reported satisfaction with the guidance of the cdss. they reported that the cdss help them in rapid diagnosis and safe treatment of the mother. when the hcws get access to the registered information and alerts from the cdss they got to learn “what symptoms to look for in their patient” which help them to discover problems earlier. furthermore, the users consider that with the cdss they get guidance on answers to routine questions that earlier needed consultations with colleagues. group2: “the cdss creates efficiency in the management of patients, for example the prescribing is scientifically based and not by imitation of the actions by colleagues. we understand what we are doing before making the prescription. the cdss guide us through the diagnosis to prescription.” 2.2 organizational learning: improvement of quality of care and eager to use technology improved quality in maternal care the use of the cdss has improved the care as reported by the two participants from the healthcare administration. the users confirm this statement by expressing that they feel safer and secure in their work when making decisions. group2: “the cdss improved considerably our knowledge in obstetric care: no matter of our profile at the beginning of the project. we all gained skills and we are now comfortable to provide anc or delivery without doubt” plenary session (member of the district management team): “from what i heard and what i have seen, the healthcare facilities under the cdss project were well organized and performed well. the maternal and neonatal care indicators are good in these healthcare facilities, i mean opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi mortality and morbidity. the cdss made hcws more knowledgeable in maternal care. by extending access of the cdss to all healthcare facilities, this will improve maternal care indicators by reducing maternal death, stillbirth and neonatal mortality. this will also reduce delay for referral to hospital in case of complication during pregnancy.” a desire to experiment with new technology the participants expressed a desire to be able to contact colleagues and experts internationally by e-mail and/or video through the cdss. this would be an opportunity to learn more. group1: “the idea is to have the cdss and new technique involving the possibility to have video discussion, email or real-time discussion with experts or specialist on maternal care.” discussion in the beginning of the implementation of the cdss, the hcws were enthusiastic to use the system (3). however, when the project proceeded they reduced their use. our study revealed several reasons that inhibited the use of the cdss. the technical challenges included unreliable power supply, a non-user friendly partograph, the poor integration of the cdss into the workflow leading to high workload, and finally, the failure to adhere to promised motivational incentives combined with the high turn-over of users, didn’t facilitate the use of the cdss. despite these obstacles, the hcws reported opportunities by learning moments from using the cdss in both maternal care and in computer use. there are several technical challenges with the cdss when using it in remote healthcare facilities in africa. to prevent problems with unreliable power supply or computer literacy, all facilities implementing the cdss received a solar panel system to support reliable power supply. nevertheless, the participants in the workshop reported poor maintenance support and unreliable electricity supply, i.e. prime conditions necessary to be able to use the cdss. this was even identified as an important factor to be considered by the participants in interviews before they started to use the cdss (3). the problem with reliable electricity supply is well-known and was reported as a basic need in a study from malawi (18,19) where they maintained successful use of the system by access to a low voltage computer. in addition, a review of ict studies from africa, it was stated that functional solar panels for power supply can ensure reliable access to ictsystems in rural settings (20). using the same cdss-systems as in burkina faso, the tanzanian users reported similar electricity challenges, while there was no distractions in ghana since they had access to the national grid electricity system (21). another technical factor inhibiting the use of the cdss were the design of the partograph. the partograph is a critical decision tool for hcws to follow the course of pregnancy deliveries graphically to know when to intervene (22). the hcws did not use the partograph in the cdss as the graph was not designed according to recommendations (22). when the graphical system does not cover clinical variation in the width of cervix, it is difficult for the hcws to observe the delivery process and make a decision from the information. the participants explained that this is one of the reasons why they rarely used the cdss. instead they preferred to use the paperbased partograph. opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi in burkina faso, all respondents reported that the cdss was not well integrated into the clinical workflow, which also caused limited use of the cdss. the consequence of this is the double documentation of patient data where the hcws had to document all patient information both into the cdss and onto the paper-based medical record. this is time consuming and increases the workload. to overcome the problem with double documentation, one suggestion is to integrate the cdss with the patient medical record as earlier research (23) has showed that hcws are keen to accept and use technologies such as a cdss which follows their working process (12,24). furthermore, a practical issue that takes time and disturbs their workflow is that they only have access to one computer per facility. this is a concern as hcws typically change room to complete the anc or to perform a delivery. this creates unwillingness to use the cdss due to extra efforts this may need. indeed using the same cdss, hcws in ghana and tanzania reported that the use of the cdss multiplied the time spent for anc by 2.6 in ghana (going from 7.4 minutes to 19.2 minutes) and 1.7 in tanzania (going from 15.1 minutes to 25.5 minutes) (13). this is in line with our findings in burkina faso where the hcws reported the time spent for anc is 15 minutes more with the cdss. likewise, a recent study concluded that the usability problem of the cdss can be overcome if the designers increased the link of the cdss with the existing working process [24]. one important factor for the hcws was that they have been promised to receive different kinds of motivational triggers (financial and non-financial incentives) to use the cdss. they expected to have access to internet to search for information and to use the computer for other than just work related purposes. in practice, the hcws got irregularly financial incentives but had no possibility to use the computer for other purposes than work related questions. this led to decrease their motivation to use the cdss. the context of ghana showed that retracted incentives together with a high workload led to less motivation and a high turnover of hcws (25). this is in line with results from this study where the hcws reported that lack of incentives together with the increased workload with the cdss partly caused turnover of hcws. despite flaws and weak points during the implementation process of the cdss, the hcws were eager to learn more and optimize maternal care by using the cdss. the respondents reported that they learnt from the guidance and alerts in the cdss, thereby learning what symptoms to look for in their patients. this information provided by the cdss supports them in discovering problems earlier. with this information, the hcws feel more confident in their decisions regarding anc. furthermore; the hcws considered that they get guidance for routine questions through using the cdss where they usually needed colleagues’ advice. this is in concordance with the users from ghana and tanzania, who also reported that they have learnt by using the cdss (13,21). in ghana (13), the hcws were not asking for information about patients’ history before they started to use the cdss, but started to do so after implementation of the cdss according to who and national health authorities (13). when we know that the conducting anc can prevent complications in pregnancy and detect early the possible need for referral of the mother to a higher level of care such as a hospital (26), the findings from this study showed that the use of a cdss can support hcws to give skilled care. there were three prototypes of the cdss before the final software was available. the first prototype was demonstrated to hcws with low computer literacy in burkina faso, ghana and tanzania in order to have their feedback. later, the two other versions of the prototype opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi respectively were used to train the hcws and to be used as a test by hcws to obtain reported feedback on the errors or bugs. all feedback reports were used to develop the final cdss. meanwhile, before the hcws started testing the last prototype, a study was conducted in burkina faso to capture the needs and attitudes of hcws towards accessing guidelines for maternal and neonatal care through cdss (3). unfortunately, the results from this study were delayed and the final version of the cdss was not based on this information. this may have done that the users perceived the cdss as an expert-driven software (built according to researchers or experts) and not from their point of view. the process to enhance the usefulness of the cdss is to integrate the interest of the end-users along the designing process (24). however, it is not enough to focus on the users’ involvement in the prototyping. to obtain better acceptance from the end-user, it is necessary to ask for their needs in depth (analysis of the context and actual working environment), involve end-users feedback during the design process (programming of the different prototype) and then continuously evaluate the implemented cdss in practice (24). methodological considerations and limitations a workshop was preferred to individual interviews as the workshop methodology promoted group interaction. in group discussions, informants can help each other to explore and clarify their views, which would less easily be possible in individual interviews (26).to facilitate the plenary discussions, and to allow all respondents to express their opinions, we started with small group discussions. in this setting, the respondents were familiar with each other which might have made them avoid talking about important issues that negatively could affect their relations (27). regarding the results of this study, with the critical opinion the hcws have on the usefulness of the cdss in clinical practice, we believe that the familiarity was one of the strengths of the workshop. furthermore, we observed that the discussion during the plenary session was open-minded and should favor open discussions and sharing of experiences. another group dynamic issue to consider was to include two people from the health care administration office to attend the workshop. their participation may have restricted hcw feedback. however, in the analysis of the data with this question in mind, we could not find any differences between the groups including an employee from the healthcare administration office and the one without an employee from the healthcare administration office. when we want to investigate the attitudes of a large group of users towards a new technology such as a cdss, another method such as a questionnaire with quantitative measurements may be preferred (28,29). one of the most used methods in these cases are based on the technology acceptance model (tam) for examining key factors for easiness of use and the perceived usefulness (28). in this study, we used the data collection method with a workshop instead of using the tamquestionnaire because of the small number of participants. since this study is a qualitative study, the findings cannot be generalized. however, the findings might well be transferable to similar contexts and settings. we found in our previous study (3) that both well-trained staff members (nurses and midwives) as well as assistants (nurses and midwives) had joint interests to use a new sophisticated technique such as a cdss and to be reminded about best procedures for optimal maternal care. in addition, they could clearly describe how a cdss should be designed and introduced in rural settings in an interactive workshop. the first aspect of what were reported is close to the “usefulness” perspective and the second is close to the “easiness of use” opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi perspective. this supports the view that an interactive stepwise workshop, combining group and plenary sessions, may help define similar factors that are summarized with the tam model. the present findings should therefore be relevant when developing cdss in remote rural areas. conclusion hcws performance in rural settings in africa depends on motivational issues and contextual factors such as working conditions to retain them. in burkina faso, hcws were keen to use the cdss at the beginning of the project. however, in time this enthusiastic expectation turned to demotivation. this lack of motivation was due to many factors including lack of perceived incentives and none integration of the cdss into the workflow which appreciably increased the workload. technical problems such as unreliable power supply and incorrect partograph also inhibited the regular use of the cdss. despite these deterrents, hcws reported that they have learned in implementation of standards in maternal care provision and the usage of computers. to implement a cdss in a rural area remains challenging but can be overcome if end-users’ needs are put in perspective from the very beginning and during the different stages of the construction and testing the cdss. authors’ contribution saz, pbr, and llg contributed to the conception and design of the study and to interpretation of the data. saz conducted the workshop and analyzed the data in a reciprocal way with pbr and with quality controls and inputs from llg. saz, pbr, llg, as, and gt outlined the disposition of the manuscript. the first draft was written by saz and pbr and completed by pbr, saz and llg with inputs by as, gt and sl. the final manuscript was circulated among all authors that approved the final manuscript. competing interests no competing interests acknowledgements this work was supported by european union through the grants 22982 and 654237 from the 7th eu framework program. this study was performed in burkina faso and nested into the qualmat (quality of prenatal and maternal care: bridging the know-do gap) (www.qualmat.net) the project includes the development and implementation of a system of performance based incentives and a computerhttp://www.qualmat.net/ opportunities and obstacles using a clinical decision support system for maternal care in burkina faso online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e188, 2017 ojphi assisted clinical decision support system (cdss) based on who guidelines for maternal and neonatal care (4,14). the cdss intervention was carried out in burkina faso, in ghana and in tanzania (3,13,14,21) with two main sections (13): a checklist ensuring that all relevant data are carried out and algorithms that screening the entered data for pathological issue and give immediate alerts or comprehensive recommendations. the 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manage infsyst q. 27, 425-78. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18343005&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18343005&dopt=abstract https://doi.org/10.1016/j.socscimed.2008.01.033 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26073076&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26073076&dopt=abstract https://doi.org/10.1016/j.ijmedinf.2015.05.002 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23597239&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23597239&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23216866&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23216866&dopt=abstract https://doi.org/10.1186/1472-6947-12-142 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23684240&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23684240&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25106497&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25106497&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19615467&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19615467&dopt=abstract opportunities and obstacles using a clinical decision support system for maternal care in burkina faso abstract introduction materials and methods study design study setting and participants data collection data analysis questionnaire b) group discussions and c) plenary session results (a) socio-demographic information hardware and software problems with the computer unreliable supply of electricity shortage of computers 1.2 work process: the cdss does not fit into the workflow in routine work 1.3 software design: feedback on statistical data for performance and poor user-friendliness feedback of individual performance data for hcws as well as statistical reports for the facility poor user-friendliness of the partograph discussion methodological considerations and limitations conclusion authors’ contribution competing interests acknowledgements references coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 sanjaya dhakal1*, sherry l. burrer2, carla a. winston3, achintya dey1, umed ajani1, samuel l. groseclose4 1. division of health informatics and surveillance, center for surveillance, epidemiology and laboratory services, office of public health scientific services 2. division of environmental hazards and health effects, injury and environmental health, national center for environmental health, office of non-communicable diseases 3. veterans health administration, office of public health, office of public health surveillance and research, u.s. department of veterans affairs 4. office of science and public health practice, office of public health preparedness and response abstract objective: electronic laboratory reporting has been promoted as a public health priority. the office of the u.s. national coordinator for health information technology has endorsed two coding systems: logical observation identifiers names and codes (loinc) for laboratory test orders and systemized nomenclature of medicine-clinical terms (snomed ct) for test results. materials and methods: we examined loinc and snomed ct code use in electronic laboratory data reported in 2011 by 63 non-federal hospitals to biosense electronic syndromic surveillance system. we analyzed the frequencies, characteristics, and code concepts of test orders and results. results: a total of 14,028,774 laboratory test orders or results were reported. no test orders used snomed ct codes. to describe test orders, 77% used a loinc code, 17% had no value, and 6% had a non-informative value, “oth”. thirty-three percent (33%) of test results had missing or non-informative codes. for test results with at least one informative value, 91.8% had only loinc codes, 0.7% had only snomed codes, and 7.4% had both. of 108 snomed ct codes reported without loinc codes, 45% could be matched to at least one loinc code. conclusion: missing or non-informative codes comprised almost a quarter of laboratory test orders and a third of test results reported to biosense by non-federal hospitals. use of loinc codes for laboratory test results was more common than use of snomed ct. complete and standardized coding could improve the usefulness of laboratory data for public health surveillance and response. keywords: logical observation identifiers names and codes (loinc); systemized nomenclature of medicine clinical terms (snomed ct); electronic laboratory report; computerized medical record systems; biosurveillance correspondence: hgj2@cdc.gov doi: 10.5210/ojphi.v7i2.5859 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi introduction because electronic laboratory reporting (elr) has been seen to be more accurate, timely, and costeffective than reporting by other conventional means (e.g., mail, fax, etc.), elr adoption has been systematically promoted as a public health priority [1-7]. a major deterrent to laboratory data being used in public health research and biosurveillance programs seems to be the lack of interoperability of automated laboratory information management systems [8-12]. standardized, universal coding that records laboratory test order and result information in a structured and systematic fashion is an essential component of interoperable elr systems [9,13,14]. the use of local codes or terminology and unstructured text fields to describe laboratory test orders and results varies widely among laboratories [15,16]. several coding strategies are available to make electronic laboratory data more computational and thus readily interchangeable electronically. long established coding systems such as international classification of diseases (icd) and current procedural terminology (cpt) are used for insurance reimbursement and other administrative purposes. icd codes are primarily designed for classifying diseases and other health conditions while cpt is designed to record a medical service or a procedure. therefore, coding laboratory information is out of scope for icd and cpt coding systems. several coding strategies are available to make electronic laboratory data more computational and thus readily interchangeable electronically; logical observation identifiers names and codes (loinc) and systemized nomenclature of medicine clinical terms (snomed ct) are the two most comprehensive coding systems representing lab test type and result information. therefore, these two information coding systems were specifically recommended for use in coding laboratory information in electronic health records by the u.s. department of health and human services office of national coordinator for health information technology established in 1994, loinc is a coding system designed to identify medical laboratory observations and procedures. loinc covers both laboratory tests and clinical observations enabling coding of test orders and test results. the system has been endorsed by the american clinical laboratory association and the college of american pathologists. a loinc code is composed of six attributes that characterize the details of laboratory test orders and results, namely: component (e.g., escherichia coli, potassium), property (e.g., arbitrary concentration, mass concentration), timing (e.g., point in time, over a span of time), system (e.g., stool, blood), scale (e.g., ordinal, nominal), and method (e.g., culture, microscopy). the most recent version of the loinc database contains more than 70,000 codes [17-22]. loinc codes are updated twice a year to reflect changes in diagnostic practices over time. established in 1965, snomed ct is a coding system designed to identify anatomic and clinical pathology information and laboratory results. the system is a collection of medical terms, codes, findings and procedures. a snomed ct code is composed of the following three attributes; unique concepts (e.g., e. coli), concept descriptions (e.g., is a), and relationships between concepts (e.g., bacteria present in stool). the system is updated twice a year and currently includes over 300,000 unique concepts [23]. snomed ct is the ontological basis of the upcoming international classification of diseases 11th edition (icd-11) revision spearheaded by the world health organization [24,25]. objective we examined the use of loinc and snomed ct codes for coding laboratory test orders and results in laboratory reports transmitted to biosense program from 63 non-federal hospitals in the calendar year 2011. in this report, we present the first national level description of the use of loinc and snomed coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi ct codes from biosurveillance data to characterize laboratory test orders and results reported by hospital-based laboratories. materials and methods biosense is a syndromic surveillance system supported by the u.s. centers for disease control and prevention (cdc), and it receives daily medical encounter data from participating hospitals. during 2011, 11% of participating non-federal hospitals also shared laboratory data with biosense [26,27]. details of the data transmission from hospitals to cdc’s biosense program have been previously described [28-30]. in brief, data reporting standards follow guidance from the public health information network messaging system’s (phinms) syndromic surveillance message guide and are transmitted securely via digital certificates and data encryption [31]. the laboratory reports included in this analysis were transmitted using version 2.3.1 of health level 7 (hl7) message formats. all laboratory reports from non-federal hospitals reporting to biosense from january 1, 2011 through december 31, 2011 were extracted from hl7 messages and converted to sas analytic data files. reports included laboratory test orders only, test orders with results, and results only. data elements in each laboratory message can be broadly categorized into three sections: 1) administrative data elements, 2) laboratory test order related data elements, and 3) laboratory result related data elements. administrative data elements include date of visit, type of service facility, testing laboratory id, and date of hl7 message creation. laboratory test order data elements include order number, order test name, order test codes (local and “standard”), order test coding system, and segments of coding structure. laboratory result data elements are grouped into two categories: observation identifier (obr) and observation value (obx). elements of obr include observation result code, observation result coding system, local observation result code, observation result text (local and “standard”), and observation result type. elements of obx include result coding system, result test code (local and “standard”), result status, result notes, result test date, result test name (local and “standard”), result unit, test interpretation, and details of the sample to be tested (component, property, timing, system, scale, method). additionally, biosense laboratory message data include site and type of the specimen, diagnostic criteria, and test sequence number. each laboratory test result with result code was categorized into one of six result status categories, namely: “final result”, “preliminary result”, “specimen in the laboratory”, “correction”, “deletes obx record”, and “result can’t be obtained for this observation”. since results identified as “preliminary” constituted almost one-third of all laboratory results reported we analyzed all reported results regardless of their status. later we compared our analysis with findings from a separate review limited to “final” results. we analyzed the frequency distribution of laboratory test orders and results to determine characteristics of the test orders and results reported, and to examine the 25 most common test orders and results. unique loinc or snomed ct codes used in the reports were identified, along with the percentage of reports that were missing a standardized code. for each of the snomed ct codes reported without corresponding loinc codes, we used the public health information network vocabulary access and distribution system (https://phinvads.cdc.gov) web sites to obtain the snomed ct concept or descriptive text. then each concept was searched in regenestrief loinc mapping assistant (relma®) software version 5.8, to determine if there was a loinc code that might corresponded to the snomed ct concept. since the value “oth” in hl7 messages does not clearly indicate a specific laboratory test order or results, rather it indicates concepts not represented by the code system; we coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi treated reports coded with “oth” as non-informative data. this analysis was determined to be a public health surveillance activity rather than human subject research requiring institutional board review. results out of 569 non-federal hospitals reporting data to biosense in 2011, 63 (11.1%) hospitals in 14 states submitted 14,028,774 laboratory reports (test orders and/ or results) from 821,108 unique laboratory visits. on average, 38,000 laboratory reports were reported daily. in the reports with at least one result code, the hospital diagnostic service ordering the test was categorized into five groups: microbiology (94.7%), serology (2.8%), outside laboratory (1.7%), virology (0.5%), and immunology (0.4%).the reports consisted of final (68.7%), preliminary (31.0%), specimen in the lab (0.2%) and corrected (0.1%) result status (table 1). table 1. characteristics of hospital-based laboratory data reported to biosense, 2011 characteristics n (%) number of reporting hospital laboratories 63 (100) number of unique laboratory visits 821,108 (100) number of laboratory reports (test orders or test results) loinc order codes non-informative order codes (missing or hl7 null code “oth”) 14,028,774 (100) 10,776,494 (76.8) 3,252,280 (23.2) number of laboratory reports with test results code loinc or snomed ct or both result codes non-informative codes (both missing or one missing and the other is hl7 null code “oth”) 9,347,179 (66.6) 4,681,595 (33.4) number of test results with code by code system loinc codes only snomed ct codes only loinc and snomed ct both 9,347,179 (100) 8,584,826 (91.8) 69,566 (0.7) 692,787 (7.4) number of unique loinc codes for orders 805 (100) number of unique codes for results snomed ct codes loinc codes 608 (100) 1,428 (100) test result status final result preliminary result specimen in lab correction deletes observation value (obx) record* result can’t be obtained for this observation 9,347,179 (100) 6,420,538 (68.7) 2,898,975 (31.0) 18,739 (0.2) 7,011 (0.1) 331 (0.0) 7 (0.0) diagnostic services microbiology serology outside laboratory virology immunology 9,347,179 (100) 8,849,051 (94.7) 258740 (2.8) 161,561 (1.7) 42,974 (0.5) 34,853 (0.4) coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi among the 14,028,774 laboratory test reports, 76.8% had loinc order codes, while for the rest either the codes were missing (16.8%) or had the hl7 null code, “oth” (6.4%). no test orders were reported using a snomed ct code. out of 10,776,494 laboratory test orders with loinc codes, 9,347,179 (86.7%) also had either loinc or snomed ct or both codes for the test result. of all laboratory reports, 9,347,179 (66.6%) had at least one result code (loinc or snomed ct or both); of these, 91.8% had only loinc codes, 0.7% had only snomed ct codes while 7.4% had both codes (table 1). of the remaining 4,681,595 laboratory reports, 63.4% were missing a snomed or loinc result code with “oth” reported as a result value while 36.6% had neither loinc nor snomed codes for the results. laboratory test results with “final” status comprised the majority (68.7%) of all laboratory reports with result codes (table 1) findings from the analysis comparing results with “final” status to all results were comparable except that the number of laboratory test results with only loinc codes differed (91.8% in all messages versus 61.4% in only “final” result messages). this suggests that results reported as “preliminary” were not updated when result status changed to “final”. table 2.twenty five most common hospital-based laboratory order test types reported to biosense, 2011, with logical observation identifiers names and codes (loinc) codes loinc test name loinc code test orders n (%) bacteria identified (blood) 600-7 3770050 (35.0) bacteria identified (urine) 630-4 1637400 (15.2) bacteria identifiedrespiratory culture 32355-0 522495 (4.8) bacteria identifiedwound culture 6462-6 459784 (4.3) antibioticagar diffusion 45187-2 328113 (3.0) fungus identifiedculture 580-1 291370 (2.7) mycobacterium sp identified 543-9 284631 (2.6) bacteria identified (anaerobic+aerobic)culture 21020-3 283577 (2.6) staphylococcus aureus methicillin resistant isolateculture 13317-3 207718 (1.9) bacteria identifiedbody fluid culture 611-4 186589 (1.7) microscopic observationwet preparation 680-9 174113 (1.6) bacteria identifiedstool culture 625-4 159657 (1.5) chlamydia trachomatis + neisseriagonorrhoeaeprobe 36902-5 155606 (1.4) bacteria identifiedaerobic culture (wound) 632-0 145213 (1.3) bacteria identifiedcsf culture 606-4 139418 (1.3) bacteria identified (anaerobic)culture 635-3 112496 (1.0) bacteria identified (aerobic)culture 634-6 92773 (0.9) streptococcus pyogenes ag – eia 6558-1 89173 (0.8) microscopic observation (gram stain) 664-3 81129 (0.8) bacteria identifiedculture (systemxxx) 6463-4 67118 (0.6) antibioticminimum inhibitory concentration 21070-8 58891 (0.5) bacteria identifiedculture 43408-4 57084 (0.5) bacteria identified (sputum)respiratory culture 624-7 56906 (0.5) coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi clostridium difficile toxin a+b (stool) 34713-8 55567 (0.5) bacteria identifiedthroat culture 626-2 50977 (0.5) for test orders, 805 unique loinc codes were used. after excluding “oth” and missing, values, loinc codes to identify bacteria in blood (35.0%), urine (15.2%), respiratory specimens (4.8%), or wounds (4.2%), and to test antibiotic susceptibilities by agar diffusion (3.0%), were the five most frequent laboratory orders reported (table 2). fourteen hundred twenty eight unique loinc and 608 unique snomed ct codes were used to describe laboratory test results. for results with loinc codes, the five most commonly reported tests were: bacteria identified by blood culture (12.2%), microscopic observation of unspecified specimen by gram stain (8.9%), appearance of unspecified specimen (7.6%), microorganism identified in unspecified specimen by culture (7.5%), and bacteria identified by urine culture (6.6%)(table 3a). for results with snomed ct codes, a qualifier for antimicrobial susceptibility (10.4%), escherichia coli (10.4%), a qualifier for bacterial sensitivity (9.9%), staphylococcus aureus (9.7%), and a qualifier for non-reactive status (6.5%) were the five most frequent codes (table 3b). table 3a. twenty five most common hospital-based laboratory result types reported to biosense, 2011, with logical observation identifiers names and codes (loinc) codes loinc long name loinc code test results n (%) bacteria identified in blood by culture 600-7 1128381(12.2) microscopic observation in unspecified specimen by gram stain 664-3 823440 (8.9) appearance of unspecified specimen 33511-7 707650 (7.6) microorganism identified in unspecified specimen by culture 11475-1 696652 (7.5) bacteria identified in urine by culture 630-4 611553 (6.6) specimen source of unspecified specimen 31208-2 532232 (5.7) bacteria identified in unspecified specimen 417410* 271916 (2.9) service comment# 8251-1 176943 (1.9) bacteria identified in unspecified specimen by respiratory culture 32355-0 148069 (1.6) fungus identified in unspecified specimen by culture 580-1 140800 (1.5) bacteria identified in blood by aerobe culture 17928-3 127807 (1.4) microscopic observation in unspecified specimen by other stain 11546-9 115486 (1.2) bacteria identified in wound by aerobe culture 632-0 110500 (1.2) microorganism or agent identified in unspecified specimen 41852-5 107766 (1.2) mycobacterium sp identified in unspecified specimen by culture 543-9 106956 (1.2) bacteria identified in wound by culture 6462-6 103717 (1.1) bacteria identified in blood by anaerobe culture 17934-1 98539 (1.1) microscopic observation in wound by gram stain 10357-2 98288 (1.1) microscopic observation in unspecified specimen by wet preparation 680-9 93901(1.0) gentamycin susceptibility 18928-2 75789 (0.8) microscopic observation in unspecified specimen by acid fast stain 11545-1 70546 (0.8) trimethoprim + sulfamethoxazole susceptibility 18998-5 68972 (0.7) microorganism identified in stool by culture 625-4 64295 (0.7) coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi levofloxacin susceptibility 20629-2 60212 (0.7) ciprofloxacin susceptibility: 18906-8 51448 (0.6) *loinc code 41741-0 has been deprecated and superseded by loinc code 23667-9. #loinc code for service comment terms indicates user-defined text. among the 608 unique snomed ct codes used to report laboratory test results, 498 (81.9%) had corresponding loinc codes reported. for the 108 snomed codes that did not have corresponding loinc codes in the reported laboratory results, we found that 49 (45.4%) of the snomed ct concepts could be associated with at least one loinc code. the majority of snomed ct concepts matched to loinc (47 of 49) were microorganism related, while one was an anatomical structure (urethra) and one was a system concept (blood). among 49 snomed ct concepts that matched to loinc codes, 27 concepts matched to more than one loinc code, depending on other loinc components including property, timing, system, scale, and method. among 59 snomed ct concepts not identified in mapping to loinc codes, 47 were for microorganisms, seven were for qualifiers, three were for anatomical structures, and two were for systems. table 3b. twenty five most common hospital-based laboratory results types reported to biosense, 2011, with systemized nomenclature of medicine clinical terms (snomed ct) codes observation identifier (obs) text snomedct code test results n (%) susceptible 131196009 79465 (10.4) escherichia coli 112283007 79255 (10.4) sensitive 83185005 75142 (9.9) staphylococcus aureus 3092008 74216 (9.7) non-reactive 131194007 49852 (6.5) resistant 30714006 44504 (5.8) none* 260413007 39924 (5.2) pseudomonas aeruginosa 52499004 30555 (4.0) gram-negative bacillus 87172008 18016 (2.4) leukocyte 52501007 17678 (2.3) staphylococcus, coagulase negative 116197008 16094 (2.1) proteus mirabilis 73457008 14522 (1.9) reactive 11214006 13693 (1.8) klebsiella pneumoniae 56415008 13446 (1.8) staphylococcus epidermidis 60875001 13182 (1.7) enterococcus fecalis 78065002 12610 (1.7) klebsiella pneumonia ss. pneumoniae 18400002 9681 (1.3) yeast 62093005 8457 (1.1) enterococcus species 131297007 7733 (1.0) enterobacter cloacae 14385002 6794 (0.9) enterococcus 2785000 5720 (0.8) candida albicans 53326005 5446 (0.7) no organism seen 27863008 5139 (0.7) coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi staphylococcus species 116499001 4362 (0.6) stenotrophomonas maltophilia 113697002 4342 (0.6) streptococcus agalactiae 43492007 4113 (0.5) *“none” is a snomed ct qualifier value for “absence findings” discussion the hospitals included in our study used loinc as the coding system to record laboratory test orders. laboratory test results were coded using both loinc and snomed ct coding systems, though, the use of loinc was much more common. our findings indicate that participating hospital laboratories undertook reporting of laboratory test orders and results as recommended in elr hl7 messaging guidance [32]. however, missing or non-informative codes comprised almost a quarter of laboratory test orders and a third of test results from hospitals reporting to biosense in 2011. by design, the primary objectives of biosense program were to monitor clinical syndromes related to infectious diseases for early outbreak detection and ongoing situational awareness [27-29]. therefore, biosense recommended that participating hospitals preferentially report laboratory information related to infectious diseases. as a result, the majority of the laboratory reports in this analysis were microbiology related. we examined both test orders and preliminary and final test results. monitoring laboratory test orders could provide early warning signals of suspicion of infectious disease, while monitoring results could contribute to biosurveillance by providing increased diagnostic specificity to automated syndromic case definition algorithms based on chief complaint text fields [26,33-35]. laboratory data could also be used to evaluate interventions, and to monitor disease trends and progression, which might result in timely more effective outbreak response and management [1,13]. in elr reporting, the debate over the definition of “questions” (orders) and “answers” (results) is far from over. it has been suggested that loinc is used for coding laboratory “questions” while snomed ct is used for coding the “answers”. [22] laboratory reports from hospitals in our analysis indicate that loinc codes are used to code laboratory orders as well as results, illustrating loinc’s potential to provide codes for “questions” as well as “answers”. on the other hand, there may be unique situations where a snomed ct code is required in combination with a loinc code to fully represent a lab test result. for example, the snomed ct system allows coding for certain clinical structures (cervix, urethra), and for certain conditions (e.g., hyperbilirubinemia), that are currently not accounted for in the loinc system. several earlier reports have suggested different mapping strategies to associate laboratory codes to diseases or health conditions [20,36-41]. our findings suggest that the biosense surveillance program might be able to focus on a small subset of loinc and snomed ct codes related to diseases or syndromes of interest, as evidenced by the use of a relatively small number of unique loinc (n= 1,428) and snomed ct (n= 608) codes in the data we analyzed compared to the number of codes available. to determine if laboratory data improves syndromic surveillance performance for enhanced outbreak detection and improved situational awareness, an evaluation of syndromic case definitions that incorporate laboratory test order or result information is required. some limitations in interpreting our findings should be noted. non-federal hospitals who participated in biosense are not a representative sample of all the u.s. hospitals. use of the convenience sample of facilities that are able and willing to share electronic laboratory data with cdc may limit the generalizability of our findings. furthermore, laboratory data in this analysis were primarily collected coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi for infectious disease syndromic surveillance purposes. therefore our findings may not be representative of the use of loinc and snomed ct codes for elr for all hospital laboratory testing. completeness of elr data in biosense has not been validated prior to this study, which shows that one-third of electronic laboratory records are missing standardized codes or are coded as hl7 null “oth” for result information that would be necessary for interpretation for surveillance. we are unable to report specific accuracy metrics such as specificity or sensitivity related to loinc and snomed ct due to lack of a reference “gold standard”. the current analysis has several important strengths, beginning with computability of the coded laboratory test order and result information in the hl7 messaging format that supports automated categorization for surveillance. consequently, we were able to merge data files from different hospitals to create one large laboratory data repository and apply standard criteria that could potentially augment information from other surveillance data sources. laboratory reports in this analysis derived from local hospitals illustrate the potential value of standardizing laboratory data to provide timely information to hospital based infection preventionists and local health departments for situation awareness and early response, as well as contributing to national biosurveillance. most importantly this study adds value to existing scientific knowledge by describing the use of loinc and snomed ct codes in elr contributed by select u.s. hospitals as healthcare organizations move towards implementing meaningful use and health information exchange [42,43]. conclusion we analyzed more than 14 million laboratory reports from non-federal hospital to assess the use of two structured coding systems, snomed ct and loinc. the loinc system was used more commonly than snomed system in the hospitals studied. missing data and differences in representing laboratory test orders and results may inhibit effective analysis of electronic laboratory data. increased completeness of coded lab data, data management tools that translate locally coded laboratory test information into loinc or snomed ct codes, and increased participation of hospitals laboratories are needed to fully realize the value of laboratory data in public health practice and syndromic surveillance. acknowledgments we are thankful to all non-federal hospitals participating in the biosense program that contributed laboratory data used in this manuscript. contributorship statement: sd, sb, cw and sg contributed equally in conceptualizing, analyzing, and interpreting data. sd drafted the manuscript. all authors including ad, ua contributed in revising it critically for important intellectual content of the paper. all authors have approved the final version of the paper submitted for publication. competing interest: there are no competing interests funding: no external funding was received for this project. coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi there are no additional published or unpublished data available from the study. the findings and conclusions in this article ate those of the authors and do not represent the official position of the centers for disease control and prevention or the department of veterans affairs. references: 1. kite-powell ah. 2008. j j; hopkins, r s. potential effects of electronic laboratory reporting on improving timeliness of infectious disease notification--florida, 2002-2006. mmwr morb mortal wkly rep. 57, 1325-28. pubmed 2. moore km, reddy v, kapell d, balter s. 2008. impact of electronic laboratory reporting on hepatitis a surveillance in new york city. j public health manag pract. 14, 437-41. pubmed http://dx.doi.org/10.1097/01.phh.0000333877.78443.f0 3. overhage jm, grannis s, mcdonald cj. 2008. a 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issn 1947-2579 * http://ojphi.org * 7(2):e220, 2015 ojphi 42. cms. medicare and medicaid programs; electronic health record incentive program--stage 2. final rule. fed regist 2012;77:53967-4162. pubmed 43. 2012. office of the national coordinator for health information t. health information technology: revisions to the 2014 edition electronic health record certification criteria; and medicare and medicaid programs; revisions to the electronic health record incentive program. interim final rule with comment period. fed regist. 77, 72985-91. pubmed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22946138&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23227573&dopt=abstract coding of electronic laboratory reports for biosurveillance, selected united states hospitals, 2011 introduction objective materials and methods results discussion conclusion acknowledgments competing interest: funding: references: isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts extensive surveillance for avian influenza a(h5n6) virus in southern china xin wang* and shisong fang shenzhen center for disease control and prevention, shenzhen, china objective to determine avian influenza a(h5n6) virus infection in human and environment using extensive surveillances. to evaluate the prevalence of h5n6 infection among high risk population. introduction since the emergence of avian influenza a(h7n9) virus in 2013, extensive surveillances have been established to monitor the human infection and environmental contamination with avian influenza virus in southern china. at the end of 2015, human infection with influenza a(h5n6) virus was identified in shenzhen for the first time through these surveillances. these surveillances include severe pneumonia screening, influenza like illness (ili) surveillance, follow-up on close contact of the confirmed case, serological survey among poultry workers, environment surveillance in poultry market. methods severe pneumonia screening was carried out in all hospitals of shenzhen. when a patient with severe pneumonia is suspected for infection with avian influenza virus, after consultation with at least two senior respiratory physicians from the designated expert panel and gaining their approval, the patient will be reported to local cdc, nasal and pharyngeal swabs will be collected and sent for detection of h5n6 virus by rt-pcr. ili surveillance was conducted in 11 sentinel hospitals, 5-20 ili cases were sampled for detection of seasonal influenza virus by rtpcr test every week for one sentinel. if swab sample is tested positive for influenza type a and negative for subtypes of seasonal a(h3n2) and a(h1n1), it will be detected further for influenza a(h5n6) virus. follow-up on close contacts was immediately carried out when human case of infection with h5n6 was identified. all of close contacts were requested to report any signs and symptoms of acute respiratory illness for 10 days, nasal and pharyngeal swabs were collected and tested for influenza a(h5n6) virus by rt-pcr test. in the meantime, environmental samples were collected in the market which was epidemiologically associated with patient and tested for h5n6 virus by rt-pcr test. serological survey among poultry workers was conducted in ten districts of shenzhen. poultry workers were recruited in poultry markets and screened for any signs and symptoms of acute respiratory illness, blood samples were collected to detect haemagglutinationinhibition (hi) antibody for influenza a(h5n6) virus. environment surveillance was conducted twice a month in ten districts of shenzhen. for each district, 10 swab samples were collected at a time. all environmental samples were tested for influenza a(h5n6) virus by rt-pcr test. results from nov 1, 2015 to may 31, 2016, 50 patients with severe pneumonia were reported and detected for h5n6 virus, three patients were confirmed to be infected with h5n6 virus. case 1 was a 26 years old woman and identified on dec 29, 2015. she purchased a duck at a live poultry stall of nearby market, cooked and ate the duck 4 days before symptom onset. after admission to hospital on dec 27, her condition deteriorated rapidly, on dec 30 she died. the case 2 was a 25 years old man and confirmed on jan 7, 2016. he visited a market everyday and had no close contact with poultry, except for passing by live poultry stalls. he recovered and was discharged from hospital on jan 22. the case 3 was is a 31 years old woman and reported on jan 16, 2016, she had no contact with live poultry and died on feb 8. for 60 close contacts of three cases, none of them reported signs or symptoms of acute respiratory illness, all of nasal and pharyngeal swabs were tested negative for influenza a(h5n6) virus by rt-pcr test. of 146 environmental swabs collected in the case’s living places and relevant poultry markets, 38 were tested positive for influenza a(h5n6) virus by rt-pcr test. from nov 1, 2015 to may 31, 2016, 2812 ili cases were sampled and tested for influenza type a and subtypes of seasonal influenza. those samples tested positive for influenza type a could be further subtyped to seasonal a(h3n2) or a(h1n1), therefore no sample from ili case was tested for influenza a(h5n6) virus. serological surveys among poultry workers were conducted twice, for the first survey 186 poultry workers were recruited in oct 2015, for the second survey 195 poultry workers were recruited in jan 2016. blood sample were collected and tested for hi antibody of influenza a(h5n6) virus. 2 individuals had h5n6 hi antibody titer of 1:40, 5 individuals had h5n6 hi antibody titer of 1:20, rest of them had h5n6 hi antibody titer of <1:20. according to the who guideline, hi antibody titer of ≥1:160 against avian influenza virus were considered positive. from nov 1, 2015 to may 31, 2016, of 1234 environmental swabs collected in poultry markets, 339 (27.5%)were tested positive for influenza a(h5n6) virus by rt-pcr test. each of the ten districts had poultry markets which was contaminated by influenza a(h5n6) virus. conclusions in 2015-2016 winter, three cases of infection with influenza a(h5n6) virus were identified in shenzhen, all of them were young individuals with average age of 27.3 years and developed severe pneumonia soon after illness onset, two cases died. for acute and severe disease, early detection and treatment is the key measure for patient’s prognosis. h5n6 virus was identified in poultry market and other places where patient appeared, implying poultry market probably was the source of infection. despite the high contamination rate of h5n6 virus in poultry market, we found that the infection with h5n6 virus among poultry workers was not prevalent, with infection rate being 0/381. human infection with h5n6 virus seemed to be a sporadic occurrence, poultry-human transmission of h5n6 virus might not be very effective. keywords influenza; h5n6; surveillance *xin wang e-mail: szwxin@163.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e101, 2017 google and women’s health-related issues: what does the search engine data reveal? 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi google and women’s health-related issues: what does the search engine data reveal? mazin baazeem, md1, haim abenhaim, md, mph1,2 1 department of obstetrics and gynecology, jewish general hospital, mcgill university 2 centre for clinical epidemiology and community studies, jewish general hospital abstract objectives: identifying the gaps in public knowledge of women’s health related issues has always been difficult. with the increasing number of internet users in the united states, we sought to use the internet as a tool to help us identify such gaps and to estimate women’s most prevalent health concerns by examining commonly searched health-related keywords in google search engine. methods: we collected a large pool of possible search keywords from two independent practicing obstetrician/gynecologists and classified them into five main categories (obstetrics, gynecology, infertility, urogynecology/menopause and oncology), and measured the monthly average search volume within the united states for each keyword with all its possible combinations using google adwords tool. results: we found that pregnancy related keywords were less frequently searched in general compared to other categories with an average of 145,400 hits per month for the top twenty keywords. among the most common pregnancy-related keywords was “pregnancy and sex’ while pregnancy-related diseases were uncommonly searched. hpv alone was searched 305,400 times per month. of the cancers affecting women, breast cancer was the most commonly searched with an average of 247,190 times per month, followed by cervical cancer then ovarian cancer. conclusion: the commonly searched keywords are often issues that are not discussed in our daily practice as well as in public health messages. the search volume is relatively related to disease prevalence with the exception of ovarian cancer which could signify a public fear. keywords: information seeking behavior; internet; women’s health correspondence: haim.abenhaim@gmail.com doi: 10.5210/ojphi.v6i2.5470 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the use of the internet in the united states (us) has been growing in the last ten years. as of september 2012, 81% of us adults reported using the internet, of which 72% stated to have searched for health information online in the past year. information seeking through web search engines such as google, yahoo! and ping is the most common use of the internet. in fact, 77% of online health seekers began their query at a search engine, while 13% began their search at a site specializing in health information, such as webmd [1]. taking into consideration that women are more likely than men to seek health information online, we sought to evaluate women’s most prevalent health concerns in the us by examining the searched volume of healthhttp://ojphi.org/ google and women’s health-related issues: what does the search engine data reveal? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi related keywords in the google search engine. our findings will improve our general understanding of online health queries pertaining to women’s health and help inform health care practitioners of potential issues overlooked in daily practice. background a 2012 health survey sponsored by the pew research center’s internet & american life project found that certain populations in the us, such as young adults and individuals with higher education levels are more likely to report gathering health information online [1]. socioeconomic factors such as age, gender, income and education play an important role in online health information seeking [2]. a study evaluating the demographic profile of online health information seekers found that a digital divide still exists between low and high income populations despite an overall increase in availability of computers and internet access in the us [3]. a similar strong digital divide in terms of access to online health information was also found in a study by murray et al, however once access was achieved socioeconomic status and education was not found to predict whether patients found pertinent information online [4]. smart phone devices are helping bridge the digital divide as mobile health information gains popularity with rising smartphone ownership [3]. fifty two percent of smart phone owners have used their phone to search for health information [5]. a study by jadhav et al analyzed the most frequent health searches initiated from personal computers and smart devices to evaluate how device type influences online health information seeking. choice of device typed used was found to change online health information search behavior. internet users asked more health questions using smart devices than personal computers. health queries initiated by a smart device were also longer, more descriptive and had fewer spelling mistakes compared to those made from personal computers. findings also showed that very few online health seekers searched for preventive health information, indicating a need to promote preventive health care [2]. the observed increasing number of online health seekers in the us can have an impact on the traditional physician–patient relationship. in a report about online health information seeking behavior, 80% of physicians reported patients brought printed health information they obtained online at visits [6]. while health information online can improve health literacy and inform health decisions, inaccurate or misinterpreted information can lead to negative health behaviors and outcomes [7]. in a survey studying physicians' experience of patients seeking health information on the internet, accurate and relevant information obtained online by patients was viewed as benefiting the physician-patient relationship. a minority of physicians felt challenged by patients bringing health information to the visit [7]. online health information seeking is shifting the way physicians practice, placing a greater responsibility on healthcare practitioners to support patients in the interpretation of information sought online. research methods the first step of our study was to create subject categories that are pertinent to women’s health. these included obstetrics; gynecology; urogynecology /menopause; oncology and infertility. these categories were then given to two separate obstetrical care providers working in university teaching hospitals, who created elaborate lists of keywords that may be of interest to women pertaining to the set clinical categories. these lists were then combined and the google tool adwords was used to determine the frequency at which the keywords were used from december http://ojphi.org/ google and women’s health-related issues: what does the search engine data reveal? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi 2012 to december 2013 by internet users in the us. adwords google is an internet tool designed to help corporations identify the search volume of a keyword. the google search engine was selected as it has been dominating the internet in the last 6 years with about 65.9% of market share [8]. a one year data collection time period allowed for the control of artificially increased search volumes of keywords as a result of external influences such as media and publicity [9]. we combined the number of hits of similar keywords for each topic. keywords were included only if 90% or more of the content within the first 5 pages of results was relevant. for example in pregnancy and sex our search included only these two keywords in all possible combinations (“pregnancy sex”, “sex pregnancy”, “pregnant sex” and “sex pregnant”) rendering our search more inclusive and the total number of hits were combined into one category (pregnancy and sex). more than 90 percent of keywords were combined. results were tabulated according to frequency in each category. data was limited to the most commonly searched keywords in each category. ethics approval was not required for this study as the information obtained online is publicly accessible and there is no reasonable expectation of privacy. table 1: average top 25 monthly searched keywords of all categories from december 2012 to december 2013 keywords number of hit pregnancy 502,000 hpv 305,400 abortion 247,600 breast cancer 247,190 ovarian cyst 139,990 iud 94,170 cervical cancer 93,510 ovarian cancer 91,020 miscarriage 90,500 pap smear 80,600 fibroid 75,300 how to get pregnant 74,000 colposcopy 60,500 ivf 60,200 hrt 60,200 pregnancy sex 48,100 iui 47,300 hpv vaccine 40,620 tubal ligation 40,610 uterine cancer 37,760 contraception 27,100 infertility 22,200 endometrial cancer 22,200 pregnancy weight 18,490 vaginal dryness 17,910 hpv: human papilloma virus iud: intrauterine device ivf: in vitro fertilization hrt: hormone replacement therapy iui: intrauterine insemination http://ojphi.org/ google and women’s health-related issues: what does the search engine data reveal? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi results the two most commonly searched keywords were pregnancy, which was searched an average of 502,000 times per month, and hpv which was searched an average of 305,400 times per month (table1). however, apart from “pregnancy” as a keyword, pregnancy-related keywords were less searched in general compared to those of other categories. the top 20 pregnancy-related keywords were averagely searched 145,400 times per month. pregnancy and sex was the most commonly searched pregnancy-related keyword with an average search of 48,100 searches per month, followed by pregnancy and weight which was searched 18,490 times per month. of the ten most searched keywords, only two were associated with possible serious adverse pregnancy outcomes (vaginal discharge and rash-pregnancy) (figure 1). figure 1: average top 10 monthly searched pregnancy related keywords from december 2012 to december 2013 hpv, with average hits of 305,400 per month, was the most commonly searched gynecologyrelated keyword followed by abortion with an average search of 247,600 times per month, and then ovarian cyst. of all the contraception methods, intra uterine device (iud) was the most searched keyword with an average search of 94,170 times per month, followed by tubal ligation at 40,610 times per month (figure 2). within the menopause and urogynecology category, hormone replacement therapy was the most searched keyword, averaging 60,200 hits per month. the question “how to get pregnant” was the most commonly searched keyword string in the fertility category with an average search of 74,000 searches per month followed by in vitro fertilization (ivf) with an average search of 60,200 times per month. breast cancer was the most commonly searched female cancer with an average search of 247,190 times per month. of the gynecological cancers, cervical cancer was most commonly searched, followed by ovarian cancer related keywords, then endometrial cancer. uterine cancer comes in 6th place with an average search of 59,960 times per month (figure 3). http://ojphi.org/ google and women’s health-related issues: what does the search engine data reveal? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi figure 2: average top 10 monthly searched gynecology related keywords from december 2012 to december 2013 figure 3: average top 10 monthly searched oncology related keywords from december 2012 to december 2013 discussion with the increasing number of internet users in the us, we sought to use the internet as a tool to estimate women’s most prevalent health concerns by examining commonly searched healthrelated keywords from five categories most important to women’s health. we found that pregnancy related keywords were less frequently searched in general compared to other http://ojphi.org/ google and women’s health-related issues: what does the search engine data reveal? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi categories. according to the centers for disease control and prevention (cdc) there are about 6 million pregnant women annually in the us [10] which could explain the relatively lower number of hits. culture and behavior differences as well as access to the internet may change the percentage of pregnant women using the internet for health information. 95% of pregnant women in a multicenter italian survey used the internet as a source of information [11] compared to an australian study done by grimes et al, where they found that only 44% of their cohort used the internet as a source of information [12]. there are previous studies supporting the opinion that pregnant women prefer receiving information from a health care provider [13]. women receiving care from midwives are less frequently searching information elsewhere compared to women who are receiving care from a doctor [12]. this difference could be because women who receive midwifery care have more opportunities to discuss their concerns and have their questions answered due to differences in practice style and work load compared to physicians [14]. in recent studies, the internet is beginning to exert a significant impact on the decision process during pregnancy [11,12,15]. some of the top searched pregnancy related issues are often not discussed in daily practice. two of the top six searched pregnancy keywords were purely cosmetic (weight and stretch marks) which points out some of the non-medical concerns for pregnant women which are not often addressed in our daily practice. moreover it suggests the need for better internet web sites addressing these issues. hpv was the most commonly searched term in the gynecology category, which could be related to the age group that is affected as young women are more frequent users of the internet [16]. likewise the incidence of the hpv infection among the young population could play a role in this high search volume. it has been estimated that 75 to 80% of sexually active adults will acquire a genital tract hpv infection before the age of 50 [17]. however, this search volume could suggest a change in the low awareness of hpv among us women that has been described before in the literature [18-20] (figure 3). the internet has been reported as a frequent source of information for infertility patients [21,22], with a growing usage of internet-based support groups by infertile men and women [21,23,24]. the search volume for fertility related terms averaged 204,320 hits per month reflecting an increasing demand of fertility related topics. the most common cancer affecting women is breast cancer which accounts for over 230,000 cases each year [25]. in our study we found it to be the most searched female cancer with about 247,190 average searches per month. although ovarian cancer (approximately 22,000 new cases annually [25]) is less prevalent than endometrial cancer (about 50,000 new cases each year [25]) it was searched more frequently with an average search of 91,020 times per month. this could reflect a public fear as ovarian malignancy carries the worst prognosis, and is the most common cause of gynecologic cancer death [25]. in the menopause and urogynecology category, the low searched frequency of menopause symptoms could be correlated to the age group of the internet users [26]. the most frequently searched keyword was hormone replacement therapy (hrt) with about 60,200 searches per month, followed by vaginal dryness, which is not unique to women going through menopause. a total of 765,651 abortions were reported to the cdc in 2010, with an abortion rate of 14.6 abortions per 1,000 women aged 15–44 years [27]. abortion was the most commonly searched keyword in the gynecology category. there is some evidence suggesting an increasing demand http://ojphi.org/ google and women’s health-related issues: what does the search engine data reveal? 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e187, 2014 ojphi for online information about abortion, highlighting potential legal ambiguities, or concerns regarding accessibility and cost about the legal status [28]. this search volume might also be related to the sensitivity of the subject as patients may not feel comfortable addressing such issues with their physician. ovarian cyst was the second most commonly searched gynecological keyword, although we are uncertain of the reason, this frequency could be explained by the age group that is most commonly affected (25-40), the long treatment course, or the fear of malignancy [26,29]. limitations this study was limited to user search queries about women’s health issues in the us from 2012 to 2013. a major limitation of this study is the inherent search bias associated with using a major search engine such as google. paid advertisers sponsoring search engine companies may influence the ranking of sites in the search results [30]. in addition to online advertising strategies, numerous organizations currently use certain technology to manipulate the results of user search queries in order to achieve higher rankings [31]. google was selected for our study as it is one of the most commonly used and the only search engine that provides the adwords tool [8]. only search queries in english were included, thereby excluding the growing number of non-english speakers residing in the united states. in its latest report, the us department of commerce had estimated that about 60.6 million americans (21% of the population) spoke a language other than english at home. of those 60.6 million only 7% did not speak english at all [32]. conclusion the internet has changed the way people deal with their own health issues, providing them with unlimited access to health-related information. our study attempted to identify those issues reflective of women’s health concerns. physicians may consider introducing subjects of particular interest to patients routinely during visits as well as provide pamphlets to patients in their offices on such issues. this study also points out the need for further research on the gap in knowledge regarding women’s health-related issues between patient and physician. bridging this gap will help inform public health messages and could improve the quality and quantity of information available on the internet. conflicts of interest the authors declare no conflicts of interest. references 1. fox s. duggan m. health online 2013. pew internet & american life project. 2013. available at http://www.pewinternet.org/2013/01/15/health-online-2013/. accessed 29/11/2013. 2. jadhav a, andrews d, fiksdal a, kumbamu a, mccormick jb, et al. 2014. comparative analysis of online health queries 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the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha epidemiology, maricopa county department of public health, phoenix, az, usa objective to evaluate the pre-defined “heat, excessive” query in biosense 2.0 using recent maricopa county, arizona data; quantify the number of cases retrieved by the query due to chief complaint terms rather than clinical diagnosis; and provide a list of terms to be considered for exclusion criteria while developing a custom query. introduction monitoring heat-related illness (hri) is a public health priority in maricopa county, arizona. since 2006, maricopa county department of public health has utilized data from hospital discharges, medical examiner preliminary reports, and death certificates to quantify heat-related morbidity and mortality, but these surveillance methods take time. identifying hri more quickly would improve situational awareness and allow public health officials to launch a more immediate response to extreme heat events. arizona began using biosense 2.0 in july 2014 to collect chief complaint and diagnosis data for syndromic surveillance. the biosense front end application uses a standard query definition for hri (i.e., “heat, excessive”), but this definition may perform differently for each jurisdiction. methods we used biosense 2.0 to produce a line list of patient records between 1/1/15 and 8/15/15 that met criteria in the “heat, excessive” query definition. records with a clinical diagnosis or injury code (992 or e900, respectively) were considered confirmed hri cases. we manually reviewed the chief complaint fields of the remaining records and classified each as probable hri case, hri ruled out, or undetermined hri status. we compiled a list of terms that were common among the ruled out cases and determined how the query would perform if we added these terms to the query as exclusion criteria. as a secondary analysis, we determined whether the exclusion criteria would perform differently depending on season. results the “heat, excessive” query retrieved 539 maricopa county, az patient records between 1/1/15 and 8/15/15. nearly half of the records had a clinical diagnosis for hri, while 271 records (50.3%) required a manual review of the chief complaint data. we classified 148 records (27.5%) as probable hri cases because they had symptoms consistent with hri and 32 records (5.9%) as undetermined because they did not have strong evidence to suggest hri but could not be ruled out. we ruled out 91 records (16.9%) for hri because their chief complaint was not related to environmental heat exposure. for example, these patients mentioned feeling heat, swelling, redness, and/or pain; using heat and ice for therapeutic reasons; having dental sensitivities to hot and cold; misspelling “heart”; and misspelling “head”. we built a list of potential exclusion terms and tested it against the 271 manually reviewed records (table). the potential exclusion terms were identified in 132 records (27 probable hri cases; 17 undetermined records; and 88 hri ruled out). during the cooler months (january – april), these terms were identified in 40 cases, but only one was considered a probable case. during the hotter months (may – august), these terms were identified in 92 records (26 probable hri cases; 17 undetermined records; and 49 hri ruled out). conclusions the pre-defined “heat, excessive” query in biosense 2.0 allowed us to quantify maricopa county’s hri burden in a timely manner. the query retrieved 268 records with a clinical diagnosis for hri and 148 additional cases that had symptoms consistent with hri in their chief complaint data (i.e., probable hri). by manually reviewing the chief complaint data, we found that 17% of the records were not related to environmental heat exposure. the query could be more specific if exclusion criteria were added. our next steps will be to continue evaluating data through 2015, determine whether additional terms should be added as inclusion and exclusion criteria, and validate our proposed query definition against both medical records and finalized hospital discharge data. as we refine our query definition for syndromic surveillance, we will increase our capacity to detect and characterize heat-related morbidity in the county. table. number of maricopa county, arizona records that included one or more of the potential exclusion terms keywords biosense; evaluation; heat-related illness; syndrome; query acknowledgments the authors thank arizona department of health services for implementing biosense 2.0 in arizona. *jessica r. white e-mail: jessicawhite@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e174, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts 2014 international society for disease surveillance conference translating research and surveillance into action the international society for disease surveillance (isds) will hold its thirteenth annual conference in philadelphia on december 10th and 11th, 2014. the society’s mission is to improve population health by advancing the science and practice of disease surveillance, and the annual conference advances this mission by bringing together practitioners and researchers from multiple fields involved in disease surveillance, including public health, epidemiology, health policy, biostatistics and mathematical modeling, informatics and computer science. this year the conference received a record number of abstract submissions (267), from 33 countries. we accepted 102 abstracts for oral presentations, along with 40 lightning talks and 100 posters. the theme for this year’s conference-public health surveillance challenges and solutions for the road ahead will highlight key challenges voiced by the public health surveillance community. while the science and practice of public health surveillance, in particular biosurveillance, has significantly matured since the initial conference in new york city in 2002, practitioners continue to deal with recurring problems, such as the impacts of reduced funding and appropriate data sharing, as well as new ones emerging infectious diseases such as mers-cov, chikungunya, and ebola, as well as novel data sources from meaningful use and various social media. many of the abstracts selected for oral and lightning presentation take a nuanced approach in examining some of these issues. in addition, we are delighted to have five notable plenary speakers who have expertise in addressing some the larger problems facing public health today. dr. ziad memish from saudi arabia will deliver the opening plenary on surveillance for mass gatherings. isds founding member and national coordinator for health information technology, dr. farzad mostashari will present at the awards ceremony. on the second day, dr. don weiss from new york city department of health and mental hygiene and dr. kari yacisin from the cdc epidemic intelligence service will present on their experiences with the current ebola outbreak. finally, dr. linda rae murray from the cook county department of public health will discuss social determinants of public health in the closing plenary. we are excited with the program for the 2014 conference. we believe the isds is a unique society, successfully bringing together individuals from public health practice, industry and academia.this community is the driving force behind the isds, and the annual conference is your meeting. we are looking forward to seeing you there. ian painter, msc, phd university of washington 2014 isds scientific program committee co-chair josé lojo, mph philadelphia department of public health 2014 isds scientific program committee co-chair online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e1, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 1primary care information project, nyc dept of health and mental hygiene, long island city, ny, usa; 2city university of new york, new york, ny, usa objective to compare two clinical surveillance systems in development in new york city, one built on a distributed query network of electronic health records (ehrs) and the other accessing data from a health information exchange (hie). introduction the widespread adoption of electronic health records and the formation of health information exchanges has opened up new possibilities for public health monitoring. since 2009, the new york city (nyc) department of health and mental hygiene (dohmh) has been developing two public health surveillance systems for chronic diseases. the first is the nyc macroscope, which is built on a distributed query network (the hub) of 740 new york city ambulatory practices all using proprietary software from one ehr vendor (eclinicalworks). the second model, query health, still in its initial phase, accesses data collected by healthix, the largest nyc hie. this study compares these two models for potential disease surveillance and public health application. methods in the query health model, the data has already been collected and standardized by healthix and is transmitted in hl7 c32 standards, one document per patient. data is available from 100% of the facilities, but facilities are providing healthix with varying areas of the record. for nyc macroscope, queries are developed and executed nightly on each individual practice’s ehr. because queries are being executed against the live practice database, individual practices may not send a response to every query, but all areas of the record are being accessed for each practice that does return data. poor data documentation at certain practices is a challenge in both systems. inclusion criteria for hub data were applied such that annually each provider minimally has a) seen 10 adult patients, b) documented bmi or bp on >=50% of patients, c) prescribed a medication to >=20% of patients, and d) documented 1+ icd-9 codes for >=80% of patients. individual provider documentation has not yet been assessed for query health. results the hub had 6 million patients who ever had a visit at one of the 740 participating ambulatory practices, representing approximately 9% of the estimated over 8,000 nyc practices. this includes nonnyc residents and has not been de-duplicated for patients visiting multiple practices. ninety-three percent (n=686) of hub practices returned data for the nyc macroscope queries. for 2013, 700,000 adult patients with an eligible visit to a primary care provider that met the inclusion criteria were included in the macroscope. the participating query health hie had 26 of the 54 (48%) nyc hospitals, an additional 15 hospitals on long island, and 61 non-hospital facilities submitting patient demographic data for approximately 9 million patients with both in-patient and ambulatory visits. current data coverage is variable with 94% of facilities submitting encounter data, 80% submitting diagnosis data, 65% submitting lab results, 39% submitting vital signs, and 24% submitting medications. practice documentation will also be a factor for query health and inclusion criteria will be applied for future surveillance efforts. conclusions the nyc macroscope has provided a novel view for chronic disease surveillance on a scale not previously seen for the health of new yorkers. this model allows more complete access to the medical record, but is limited to participating practices returning data nightly. unlike the macroscope, query health has access to data for all participating facilities, but not all areas of the patient record are currently available in query health. the areas in patient records currently available for query health have potential for estimating disease prevalence. as query health facilities have limited vital sign and medication data, the macroscope may be a more complete system for disease treatment and control estimates. as hies expand and receive more areas of the patients’ record, query health has the potential to be a large scale source for public health surveillance. continued work is needed with hies and to support improvement of data completeness and quality, especially for public health use cases, in nyc and beyond. keywords chronic disease surveillance; surveillance system; health information exchange *lauren schreibstein e-mail: lschreibstein@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e160, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study. basema saddik phd, mph1*, norah al-dulaijan msc(hi)1 1. college of public health and health informatics, king saud bin abdulaziz university for health sciences, riyadh, saudi arabia abstract objectives: diabetes mellitus is a public health concern worldwide. telehealth technology may be an effective tool for empowering patients in the self-management of diabetes mellitus. however despite the great impact of diabetes on healthcare in saudi arabia, no research has investigated diabetic patients’ willingness to use this technology. this study investigates diabetic patients’ willingness to use tele-technology as a tool to monitor their disease. methods: data were collected from diabetic patients attending the diabetes education clinic at the ministry of national guard health affairs (mngha) in the eastern region of saudi arabia over a three month period. a survey was developed which measured patients’ willingness to use tele-technology in the self-management of their diabetes as well as their perceived expectations from the technology. results: the study found that the majority of patients were willing to use tele-technology to self monitor their diabetes. however, a minority (11.3%) indicated willingness to use the system daily and only half indicated preference to use it once a week (53.8%). patients who were younger, had higher education levels, were employed, had internet access and had type ii diabetes were significantly more likely to report willingness to use the technology. conclusions: diabetic patients could be ready to play a more active role in their care if given the opportunity. results from this study could serve as a baseline for future studies to develop targeted interventions by trialing tele-technology on a sample of the diabetic population. patients with diabetes need to be in charge of their own care in order to improve health outcomes across the country. keywords: diabetes, self-management, tele-technology, willingness. abbreviations: ministry of national guard health affairs (mngha), kingdom of saud arabia (ksa) correspondence: saddikb@ksau-hs.edu.sa; basema.saddik@gmail.com doi: 10.5210/ojphi.v7i2.6011 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes introduction diabetes mellitus is a public health concern worldwide. according to the world health organization (who), more than 347 million people worldwide have diabetes. in 2014, an estimated 4.9 million people died from diabetes and it is expected to become the 7th leading mailto:saddikb@ksau-hs.edu.sa mailto:basema.saddik@gmail.com diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi cause of death in the world by the year 2030 [1]. this is no different in saudi arabia where diabetes mellitus is also considered a major public health problem with an overall comparative prevalence of 23.9% in the general population [2]. diabetes has become more prevalent in saudi arabia with the change in dietary habits and the adoption of modern and sedentary lifestyles which have also led to the rise in obesity and other chronic diseases [3,4]. effective management of chronic diseases including diabetes mellitus requires close cooperation between healthcare providers and the patient. patients with chronic diseases are often the best to estimate the magnitude of their symptoms, problems and the effectiveness of any treatment [5] and chronic disease selfmanagement is an important step to giving patients a better quality of life and improving patient health outcomes [6-9]. for instance, self-management in diabetes, has shown to prevent short-term complications as well as decrease the risk of long-term complications of diabetes [10]. the american diabetes association recognizes self-management in diabetes as the cornerstone of care for all patients who want to achieve successful health outcomes [11,12]. self-management of diabetes has utilized various forms of technology including mobile health and the internet by providing short messaging services, smartphone apps for glycemic control and feedback through emails [13]. telehealth technology and telemonitoring have also been found particularly useful for empowering patients in remote locations through video conferencing between patients and health care providers, uploading physiological data and receiving health care provider feedback [14-16]. also known as an internet based self management system or the “virtual clinic”, this technology offers the patient several advantages, such as communication with health care provider, access to information, and interaction with peers [9,17]. however, whilst we know that technology in health care can help to empower patients [18], it is important to know if specific patient populations are ready and willing to use the technology. previous studies on tele-monitoring in diabetes have measured patients perceived use of telemedicine in their health; however, these studies have focused on elderly patients [19-21]. to date, no research has investigated diabetic patients’ willingness and acceptance of teletechnology in the management of diabetes despite its great impact on healthcare in saudi arabia. it is still unclear whether or not the general population of saudi arabia is ready for the implementation of tele-technology into their daily routines. the current study seeks to focus on investigating diabetic patients’ willingness to use teletechnology as a tool to monitor their disease. it will also determine the effect of patients’ demographic characteristics, diabetes type and ability to access the internet on their willingness to use teletechnology. the study will also identify patients’ expectations from an internet-based self-management system supported by tele-technology. materials and methods study setting the study was conducted at a 112 bed hospital of the ministry of national guard health affairs (mngha) in the eastern region of the kingdom of saudi arabia (ksa). it is considered one of the leading hospitals in the eastern region due to the international accreditation from the joint commission for international accreditation (jcia). the hospital provides services in general surgery, internal medicine, gastroenterology, pediatrics, obstetrics and gynecology, family medicine, ophthalmology, dentistry, endocrinology, orthopedic surgery, pulmonary and diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi neurology. the division of endocrinology provides inpatient and outpatient services in the general area of endocrinology and metabolic disease. the outpatient services provided by endocrinology include several clinics and in particular weekly diabetic clinics which are run with the help of a dietitian and diabetic educator to promote and assist diabetic patients in selfmanagement of their disease. study participants all patients who attended the diabetes education outpatient clinic at mngha – eastern region over a three month period were invited to participate in the study. a total of one hundred and forty nine patients aged 18 and over were invited to participate. convenience sampling was used due to the accessibility of diabetic patients from within the diabetes education clinics. from the 149 patients invited to participate in the study, data were collected from 102 patients hence giving this study a response rate of 69%. study instrument this cross-sectional exploratory study utilized a questionnaire to collect data on patients’ readiness and willingness to use tele technology in the self-management of their diabetes. several studies on assessing willingness and readiness for self-management of chronic diseases were reviewed and a modified version of the buysse et al questionnaire was used in this study [22]. the final version of the questionnaire consisted of 22 items under 6 domains including patient demographic data, internet accessibility, quality of information received from the physician, self-management habits and patients’ willingness to use tele technology through an internet based self-management system. patients were also asked about their expectations from an online self-management system. the questionnaire was created in english and piloted to ensure clarity and consistency between survey items. to ensure face and content validity of the survey instrument, the survey was sent to a group of experts for commenting. the expert panel which consisted of health professionals, a dietitian and a health informatician reviewed the contents of the survey in terms of content accuracy, clarity and comprehensiveness and whether or not the survey met its objectives. as a result, the phrasing of some questions were modified and format was edited for clarity and comprehensibility. the questionnaire was then translated to arabic and back translated to english to ensure accuracy of translation. the final version of the survey was distributed to the patients attending the diabetic education clinic. the purposes of the study were explained to the participants and certain concepts such as tele-technology, selfmanagement and internet were explained to the patients to ensure complete understanding of specific terms. data analysis data were coded and analyzed using the statistical package for the social sciences (spss) version 17.0 for windows. data were analyzed using descriptive statistics for demographic variables and chi-square and monte carlo exact tests to examine the relationship between certain demographic variables and patients’ willingness to use tele-technology through an online self-management system. all p values quoted are two sided; with an alpha level set at 0.05. diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi research approval research approval was granted by the university scientific research committee in king saud bin abdulaziz university for health sciences in september 2012 and by the national guard health affairs hospital – eastern region in april 2012 results there were more female patients than males in the study with ages ranging between 18 and over 50 years. as shown in table 1, almost all of the participants were saudi nationals and more than half of the participants had a secondary education or higher. nearly half of the patients had type 2 diabetes and the majority of patients were diagnosed with diabetes as adults. figure 1 shows that the majority of patients evaluated the quality of information they received from their doctor highly and reported that their questions were always answered sufficiently. in terms of their diabetes self-management habits, a high percentage reported that they measured their blood sugar levels at home and only half reported doing so regularly (46%). of the patients who reported using a method to document their self-management, almost all (95.3%) reported using a paper diary as their current method for documentation. table 1: demographic characteristics of the study participants characteristic frequency(n) (%) age 18 – 28 years 14 14.7 29 – 39 years 35 36.8 40 – 50 years 20 21.1 51 + years 26 27.4 gender male 43 44.8 female 53 55.2 nationality saudi 90 95.7 non-saudi 4 4.3 marital status single 12 12.5 married 81 84.4 widow 3 3.1 education level read and write 18 18.9 primary 12 12.6 intermediate 10 10.5 secondary 30 31.6 college/university 25 26.3 family income per month (sr) < 5,000 14 17.1 5,000 49 59.8 10,000 14 17.1 15,000 + 5 6.0 diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi occupation student 8 8.3 housewife 37 38.5 employed 36 37.5 retired 11 11.5 others 4 4.2 diabetes type type 1 13 14.6 type 2 41 46.1 gdm^ 22 24.7 i don’t know 13 14.6 when were you diagnosed with diabetes? as a child/teenager 10 11.4 as an adult 78 88.6 do you have access to the internet yes i have easy access yes, but it’s hard to access no, i don’t have access 36 16 41 38.7 17.2 44.1 how often do you use the internet? sometimes, once a month or less regularly, once a week or less often, (daily to few times per week) 22 8 22 42.3 15.4 42.3 ^ gestational diabetes mellitus a high percentage of patients showed willingness to use tele-technology (62%) however only (11.3%) indicated they would use such a system daily and over half indicated possibly using it once a week (53%). patients perceived that tele-technology will improve communication with their doctor (94%), help them understand their diabetes better (94%), and help them communicate with other patients (82.3%). further analysis was performed to explore the relationship between the participants’ demographic characteristics (age, gender, social status, education level, family income, occupation, and diabetes type) and their willingness to use tele-technology to monitor their diabetes as displayed in table 2. a statistically significant difference was found between patients’ willingness to use tele-technology and the patient’s age, educational level, occupation and diabetes type. there was no significant difference found between patients’ willingness to use tele-technology and whether or not they had previously used a method to document their diabetes (p=0.513), however, patients who had access to the internet were significantly more likely to perceive using tele-technology to monitor their diabetes p≤0.001 than patients who did not have internet access. diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi figure 1: patients’ self-management habits and willingness to use tele-technology table 2: relationship between patients demographic characteristics and their willingness to use tele-technology characteristics would you use an internet-based selfmanagement system to monitor your diabetes? total test of significance yes no n % n % n % p value age .006* 18 – 28 years 14 100 0 0 14 100 29 – 39 years 22 66.7 11 33.3 33 100 40 – 50 years 9 45 11 55 20 100 51 + years 13 52 12 48 25 100 education level .000* read and write 3 16.7 15 83.3 18 100 primary 4 33.3 8 66.7 12 100 intermediate 6 66.7 3 33.3 9 100 secondary 23 82.1 5 17.9 28 100 college/university 21 84 4 16 25 100 occupation .003* student 8 100 0 0 8 100 housewife 15 41.7 21 58.3 36 100 employed 27 77.1 8 22.9 35 100 retired 6 60 4 40 10 100 others^ 2 50 2 50 4 100 diabetes type .006* type 1 13 100 0 0 13 100 type 2 25 62.5 15 37.5 40 100 diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi gdm 12 57.1 9 42.9 21 100 i don’t know 4 33.3 8 66.7 12 100  gestational diabetes mellitus *significant at p < 0.05 discussion this descriptive study has explored diabetic patients’ willingness to use tele-technology to selfmonitor and manage their diabetes. the majority of patients in the study were in favor of using such technology whilst only about one third of patients indicated reluctance to self-monitor their diabetes using technology. these results are supported by other studies where asthma patients were very interested in using tele-technology to monitor self-management [7]. in our study, of the patients who were willing to use the technology only a small number were willing to use it daily while more than half were willing to use the technology once a week. effective diabetes self-management is an important element in the overall management of diabetes and is crucial in ensuring optimal health outcomes. the reasons for reporting a willingness to use the technology only once a week in this current study raises new concerns into the lack of awareness for the importance of daily monitoring of blood glucose levels in diabetics especially since our results also showed that although patients measured their blood glucose at home, less than half did it regularly. our results further indicated that certain demographic characteristics determined patients’ willingness to use the technology. patients’ age, level of education, occupation, diabetes type and internet access were all found to be significantly associated with their willingness to use the technology whilst gender, marital status, monthly income and previous use of diabetic care documentation were not. these results are supported by other studies where patients who had higher literacy levels were more likely to self-manage diabetes [23] and were willing to use telemonitoring platforms to monitor and transmit their glycemic data [24]. however our results contradict other study results which showed that age, educational level and socioeconomic status were not associated with patients’ willingness to use technology in self-management of asthma patients [8]. these differences may be due to cultural differences in our population where demographic characteristics may actually influence behavioral changes, motivation and efficacy in the management of chronic illnesses [25,26]. the current study also explored patients’ perceived expectations from using an internet based self-management system. the majority of patients expected the system to help them with diabetes management, enhance their understanding of the disease, and improve communication with healthcare providers and mutual support from their peers. however, other research found that patients were less interested in using the technology for ‘‘mutual support’’ from other patients and were more inclined to use it to contact their healthcare providers only [8]. again, these differences may be due to the cultural differences in this population. limitations this study had several limitations including the descriptive cross-sectional design of the study and the sampling technique used. patients who are willing to use the technology may have been more motivated to participate in the study than those who were not and therefore the results cannot be generalized to the whole diabetes population. in addition, the population of the eastern region of saudi arabia may be culturally different to the rest of the saudi population, as different regions within the kingdom have culturally specific practices. furthermore, patients’ perceived diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e214, 2015 ojphi willingness to use the technology was measured by a self-reported single item survey. however, this study was seen as a preliminary investigation into the potential readiness and willingness to use technology for self-management of diabetes and possible other chronic diseases in saudi arabia and despite these limitations, the results from this preliminary study should not be ignored as they provide us with baseline results which will inform further in-depth qualitative research in this area. further research should also look into different regions within the kingdom to provide a better generalization for the use of tele-technology in saudi arabia. conclusions this study has revealed positive willingness from diabetic patients to use tele technology as a tool to monitor and self-manage their disease. patients’ gender, marital status, monthly income and previous documentation of diabetic care did not influence their willingness to use the technology however factors such as education, occupation, diabetes type and internet access did. certain demographic factors cannot be changed in a population however, this study also revealed that a vast percentage of patients in this population do not access the internet and for the introduction of such technology to be effective, infrastructure needs to be improved and patients educated on how to use it. this is crucial before implementation of any tele-technology. furthermore, particular care should be taken to educate patients and raise awareness on effective self-management of this chronic condition. financial disclosure the authors have no financial disclosures conflict of interest the authors have no competing interests references 1. world health organization website. 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20599161&dopt=abstract http://dx.doi.org/10.1016/j.ijmedinf.2010.05.005 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21462726&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23273770&dopt=abstract http://dx.doi.org/10.1016/j.pcd.2012.10.085 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9952053&dopt=abstract http://dx.doi.org/10.1177/109019819902600107 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21675666&dopt=abstract diabetic patients’ willingness to use tele-technology to manage their disease – a descriptive study. introduction materials and methods study setting study participants study instrument data analysis research approval results discussion limitations conclusions references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts fda’s tracking and analysis of surveillance sampling isolates for outbreak detection tyann blessington*1, jennifer beal1, roblyn clemmons1, alyssa clendenin1, phillip curry1, elisa elliot1, thomas kuntz1, david melka1, david rotstein2, errol strain1 and christopher wynens1 1food and drug administration, college park, md, usa; 2food and drug administration, rockville, md, usa objective to create a forum and database for fda and cdc epidemiologists, laboratorians, and outbreak scientists for tracking recent food and environmental surveillance sampling isolates identified through reportable food registries reports and regulatory inspectional findings, and analyzing them for matches to clinical isolates for early outbreak detection. introduction identifying, solving, and stopping foodborne outbreaks in the u.s. requires the collaboration and coordination of multiple federal agencies and centers as well as state and local authorities. fda’s coordinated outbreak response and evaluation (core) network is responsible for outbreak surveillance, response, and post-response activities related to incidents involving multiple illnesses linked to fda-regulated food. core collaborates with cdc to obtain data on foodborne illnesses and illness clusters and with fda centers and field staff to obtain laboratory and inspectional information related to contaminated foods and foodborne illness outbreaks. core’s signals and surveillance team coordinates isolate tracking activities among several organizations within fda and cdc and the isolate database was developed for timely information sharing and early signal detection. methods the isolate tracking database combines information from established laboratory, inspectional, and regulatory programs; investigators across fda and cdc evaluate the information for early outbreak signals. pulsenet is a national laboratory network that compares the pulsed-field gel electrophoresis (pfge) patterns of clinical and non-clinical bacterial isolates and identifies increases in numbers of isolates with matching pfge patterns as outbreak clusters. foodborne outbreak investigational partners, including the cdc and fda, utilize the cdc/palantir technologies-developed platform, the system for enteric disease response, investigation, and coordination (sedric), to evaluate clinical, food, and environmental isolates. core provides additional firm-identifying metadata for new food and environmental isolates from fda, contract lab, and reportable food registry (rfr)-reported samples and analyzes them for pfge patterns matching those of recent clinical isolates. fda laboratorians provide early information about food and environmental isolates that are in queue for pfge and whole genome sequence analyses, trend analysis for recently completed isolates, and genetic clustering with clinical and other isolates. the rfr is a fda-hosted platform for industries and public health officials to report when there is a reasonable probability that a human or animal food that is regulated by fda will cause serious adverse health consequences. the rfr coordinator tracks patterns of adulteration in food, and gathers information from fda district investigators on the availability of pathogen isolates for fda analysis, from fda inspections of firms, and from investigations into the root-cause of contamination. each pathogen detection is evaluated for associations to current outbreak clusters. results the isolate tracking activities have provided investigators with information for hypothesis development, identified trends in laboratory and inspectional findings, aided in the identification of causal food sources in illness clusters, and provided early laboratory and inspectional information to outbreak investigations. within the past year, isolate tracking activities identified early indicators of the presence of listeria monocytogenes in frozen foods before a multistate outbreak of listeriosis was linked to frozen vegetables; identified early indicators of the presence of salmonella in pistachios before identification of a multistate outbreak of salmonella montevideo and salmonella senftenberg; further characterized the microbial hazards of cucumber and pepper contamination through fda’s enhanced surveillance sampling program; and expanded the forum’s scope to include animal foods and their link to human and animal illnesses. conclusions the database and forum provides a platform for information sharing, and collaboration between agencies, offices, and centers by informing the participating groups about early signals of contamination and emerging food risk trends. keywords foodborne; outbreak; surveillance; regulatory; food acknowledgments we would like to thank the efforts of our partners within fda and cdc. *tyann blessington e-mail: tyblessington@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e75, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts integrated spatiotemporal surveillance system: data, analysis and visualization lennon li*1, 2, reuben pererita2, steven johnson1 and ian johnson1, 2 1public health ontario, toronto, on, canada; 2university of toronto, toronto, on, canada objective to build an open source spatiotemporal system that integrates analysis and visualization for disease surveillance introduction most surveillance methods in the literature focus on temporal aberration detections with data aggregated to certain geographical boundaries. satscan has been widely used for spatiotemporal aberration detection due to its user friendly software interface. however, the software is limited to spatial scan statistics and suffers from location imprecision and heterogeneity of population. r surveillance has a collection of spatiotemporal methods that focus more on research instead of surveillance methods based in ontario, canada, we used postal codes for determining the location of cases of reportable infectious diseases. the variation in geographic sizes and shapes of the case and census geographies created challenges for developing a uniform temporal spatial surveillance system, including: linking case and population data due to misclassification errors, distance based correlations due to irregularly shaped areas (e.g. fsa’s), and visualization bias due to variation in population density, e.g. large area with little population. to overcome these challenges, we developed the ontario hybrid information map (ohim) boundary, which is a combination of public health unit boundaries (rural areas), census subdivisions (rural urban mixed) and regular grid cells (urban). the goal is to capture population details in urban areas without losing information in rural areas. ohim has around 4600 geographies with more than half located in urban centers. population distribution by gender and age group was calculated for each ohim geography. a lookup file was also created to link all ontario postal codes to ohim geography. to create baselines, historical data for influenza a were used to model the seasonality and calculate expected case count for each ohim geography for each week. standardized incident ratios (sir) were calculated as exploratory statistics, and a spatiotemporal besagyork-mollie (bym) model was used to calculate the probability that the risk is higher than a pre-specified threshold. integrated nested laplace approximation (r-inla) was used in r to explore different types of spatiotemporal interactions and for fast bayesian inference. the ability to apply the models was verified by examining previous outbreaks and seeking the opinion of staff that routinely perform surveillance on influenza. to ensure the visualization integrates with the analysis, r package shiny was used to build an interactive spatiotemporal visualization on ohim boundary utilizing open street map and html5. the application not only allows users to pan and zoom in space and time to explore the results and locate high risk areas, it also gives users the flexibility to change algorithm parameters for instant feedback. figure 1 demonstrates a zoomed-in ohim boundary with pointers signal for “high risk” area at user specified statistics exceeds a threshold (e.g., sir > 2). using the algorithms and visualization tools, surveillance experts pick the optimal time and place to be notified based on historical data and therefore the optimal threshold, which will be verified by prospectively running the algorithms. results the ohim boundaries build the foundation for efficient spatial modelling and visualization for public health surveillance in ontario. together with the integrated modelling and visualization system, staff are able to interactively optimize the aberration thresholds and identify potential outbreaks in real time. staff reported preference of sir due to its faster computations and easier interpretation. one major challenge was scalability: the ability to handle high resolutions of spatiotemporal data. when the system was applied on 4600 polygons by 200 weeks, significant delays were encountered in both analysis and visualization. difficulties in computational time, memory requirement and visualization interactivity created delays and freezing, thereby limited user experience. this problem was partially addressed by optimizing parameters for fast computations conclusions this work shows the “proof of concept” for an open source, customizable spatiotemporal surveillance system that overcomes existing data challenges in ontario. however, more work is required to make this fully operational and efficient in production. keywords spatiotemporal; surveillance; interactive visualization; r; shiny *lennon li e-mail: lennon.li@oahpp.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e62, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 154 isds 2014 conference abstracts online reports of foodborne illness capture foods implicated in official foodborne outbreak reports elaine o. nsoesie*1, 2, sheryl a. kluberg1 and john s. brownstein1, 2 1boston children’s hospital, boston, ma, usa; 2harvard medical school, boston, ma, usa objective we assessed whether foodservice reviews on yelp.com (a business review site) can be used to support foodborne illness surveillance efforts. introduction traditional surveillance systems only capture a fraction of the estimated 48 million yearly cases of foodborne illness in the united states due to few affected individuals seeking medical care and lack of reporting to appropriate authorities. non-traditional disease surveillance approaches could be used to supplement foodborne illness surveillance systems. methods we obtained reviews from 2005-2012 of 5824 foodservice businesses closest to 29 colleges. after extracting recent reviews describing episodes of foodborne illness, we compared implicated foods to foods in outbreak reports from the u.s. centers for disease control and prevention (cdc). results broadly, the distribution of implicated foods across five categories was as follows: aquatic (16% yelp, 12% cdc), dairy-eggs (23% yelp, 23% cdc), fruits-nuts (7% yelp, 7% cdc), meat-poultry (32% yelp, 33% cdc), and vegetables (22% yelp, 25% cdc). the distribution of foods across 19 more specific food categories was also similar, with spearman correlations ranging from 0.60 to 0.85 for 2006-2011. the most implicated food categories in both yelp and cdc were beef, dairy, grains-beans, poultry and vine-stalk. conclusions based on observations in this study and the increased usage of social media, we posit that online illness reports could complement traditional surveillance systems by providing near real-time information on foodborne illnesses, implicated foods and locations. keywords foodborne illness; foodborne diseases; disease surveillance; social media; gastroenteritis *elaine o. nsoesie e-mail: elaine.nsoesie@childrens.harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e200, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper 1centre on global health security, chatham house, london, united kingdom; objective to address both the policy and technical issues of sharing public health surveillance data across national borders with the aim of establishing new norms so that data can be shared in an open, transparent and equitable way. introduction the outbreaks of severe acute respiratory syndrome (sars) in 2003, influenza a (h1n1) in 2009 and ebola in 2014 have shown increasingly that infectious diseases can spread globally in a short timeframe, affecting both highand low-income countries. taking action to mitigate the impact of future crises relies on sharing public health surveillance data across national borders in an efficient and effective way. however, data users, particularly in high-income countries, often use surveillance data, particularly from lowand middle-income countries, with little or no benefit to the data generator. as indonesia’s refusal to share influenza virus sequences during the 2006 h5n1 outbreak illustrates, this imbalance increases reluctance to share and jeopardizes the global good that can be achieved. in order to share public health surveillance data internationally in an equitable way, technical, political, ethical, and legal issues need to be addressed. the centre on global health security at chatham house is producing guidance that will address both the policy and technical issues with the aim of establishing new norms so that data can be shared in an open, transparent and equitable way. methods we have developed key principles on the technical, legal, ethical, and political implications of cross-border data sharing. these draw on the published literature and expert advice gained through interviews and a series of thematic roundtables. results open, transparent and equitable public health surveillance data sharing requires the engagement of three groups of stakeholders: those generating the data; those who interpret data generated by others; and those facilitating the data-sharing process. these categories are not mutually exclusive. we have outlined seven key principles that encourage optimal global public health surveillance data sharing and promote the equal distribution of benefits: (i) articulating the value proposition; (ii) planning for data sharing; (iii) ensuring high-quality data production; (iv) collaborating in creating data-sharing agreements; (v) building trust and being consistent; (vi) understanding the global legal landscape; and (vii) monitoring and evaluating progress. in addition, standards, capacity building, and ethical considerations, such as those concerning equity, are themes that span these principles and have to be embedded in each of them. conclusions surveillance is a cornerstone of public health. increasingly, public health surveillance data need to cross national borders to address international public health crises the traditional health security approach of protecting national borders clearly does not work in a globalized world. the ebola crisis in west africa has demonstrated that a local event in a remote location can have widespread consequences, and that global health security is only as strong as its weakest point. cross-border public health surveillance data that are both high quality and timely are essential in supporting efforts to mitigate the impact of such crises. the chatham house guidance, scheduled for launch in october 2016, is expected to play a key role in the global community’s management of future threats. keywords data sharing; surveillance data; equity; international *matthew brack e-mail: mbrack@chathamhouse.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e13, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information 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and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts early effect of validation efforts of massachusetts syndromic surveillance data mark bova* and rosa ergas massachusetts department of public health, boston, ma, usa objective to develop a detailed data validation strategy for facilities sending emergency department data to the massachusetts syndromic surveillance program and to evaluate the validation strategy by comparing data quality metrics before and after implementation of the strategy. introduction as a participant in the national syndromic surveillance program (nssp), the massachusetts department of public health (mdph) has worked closely with our statewide health information exchange (hie) and national syndromic surveillance program (nssp) technical staff to collect and transmit emergency department (ed) data from eligible hospitals (ehs) to the nssp. our goal is to ensure complete and accurate data using a multi-step process beginning with pre-production data and continuing after ehs are sending live data to production. methods we used an iterative process to establish a framework for monitoring data quality during onboarding of ehs into our syndromic surveillance system and kept notes of the process. to evaluate the framework, we compared data received during the month of january 2016 to the most recent full month of data (june 2016) to describe the following primary data quality metrics and their change over time: total and daily average of message and visit volume; percent of visits with a chief complaint or diagnosis code received in the nssp dataset; and percentage of visits with a chief complaint/diagnosis code received within a specified time of admission to the ed. results the strategies for validation we found effective included examination of pre-production test hl7 messages and the execution of r scripts for validation of live data in the staging and production environments. both the staging and production validations are performed at the individual message level as well as the aggregated visit level, and included measures of completeness for required fields (chief complaint, diagnosis codes, discharge dispositions), timeliness, examples of text fields (chief complaint and triage notes), and demographic information. we required ehs to pass validation in the staging environment before granting access to send data to the production environment. from january to june 2016, the number of ehs sending data to the production environment increased from 44 to 48, and the number of messages and visits captured in the production environment increased substantially (see table 1). the percentage of visits with a chief complaint remained consistently high (>99%); however the percentage of visits with a chief complaint within three hours of admission decreased during the study period. both the overall percentage of visits with a diagnosis code and the percentage of visits with a diagnosis code within 24 hours of admission increased. conclusions from january to june 2016, massachusetts syndromic surveillance data improved in the percentage of visits with diagnosis codes and the time from admission to first diagnosis code. this was achieved while the volume of data coming into the system increased. the timeliness of chief complaints decreased slightly during the study period, which may be due to the inclusion of several new facilities that are unable to send real-time data. even with the improvements in the timeliness of the diagnosis code field, and the subsequent decrease in the timeliness of the chief complaint field, chief complaints remained a more timely option for syndromic surveillance. pre-production and ongoing data quality assurance activities are crucial to ensure meaningful data are acquired for secondary analyses. we found that reviewing test hl7 messages and staging data, daily monitoring of production data for key factors such as message volume and percent of visits with a diagnosis code, and monthly full validation in the production environment were and will continue to be essential to ensure ongoing data integrity. table 1: ed data in the production environment keywords syndromic surveillance; data quality; validation; r studio *mark bova e-mail: mark.bova@state.ma.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e35, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts preparing for state health department accreditation: pivotal roles for public health surveillance christopher d. williams* drph program, university of illinois at chicago, baton rouge, la, usa objective to specify the pertinent roles of public health surveillance in meeting documentation requirements for voluntary state health department accreditation. introduction on january 24, 2014, the public health accreditation board (phab) released, to the public, its standards & measures version 1.5 guidance document for voluntary public health department accreditation. 1 specifically, standard 1.2 of the guidance encompasses data collection. two measures of standard 1.2 stipulate the requirements of surveillance and their purposes in the context of phab accreditation. first, according to measure 1.2.1a: 24/7 surveillance system or set of program surveillance systems, the purpose of the measure is to assess the health department’s process for collecting and managing health data for public health surveillance. second, according to measure 1.2.2a: communicate with surveillance sites, the purpose of the measure is to assess the health department’s regular contact with sites who report surveillance data to the health department. health departments that are applying for voluntary accreditation through phab beginning july 1, 2014, must adhere to the current standards & measures version 1.5 guidance document. given the recent effective date of the application period under standards & measures version 1.5, there are very few, if any, examples of required documentation that state health departments (shds) must submit in order to meet each public health surveillancespecific measure. accreditation document libraries provided examples for health department applicants under standards & measures version 1.0. because they are no longer applicable to accreditation, examples pertaining to standards & measures version 1.0 have been removed from many documentation library websites. we specifically aim to highlight one state’s informatics approach to meeting the documentation requirements of phab’s standards & measures version 1.5, in respect to public health surveillance measures. due to the dearth of documentation examples for phab’s standards & measures 1.5, standard 1.2 public health surveillance measures, this perspective could be helpful to other shds seeking voluntary accreditation exemplifying an approach to identification of process(es) and/or protocol(s) for the collection, review, and analysis of comprehensive surveillance data on multiple health conditions from multiple sources. methods when considering which documentation of process, protocol, or set of processes or protocols to include in adherence to the public health surveillance measures, 2the seven ongoing elements of any public health surveillance system can provide a roadmap. the elements are listed as follows: planning and systems design data collection data management and collation analysis interpretation dissemination application to public health programs based on the elements of a public health surveillance system and the documentation requirements of the two relevant phab public health surveillance measures, a documentation selection matrix of elements of a public health surveillance system and phab public health surveillance measure criteria is under development, and will be piloted in early november 2014. examples of internal (within the shd) and external surveillance systems (supportive of shd surveillance) will be assessed using the matrix. results the documentation selection matrix will be pilot tested for two weeks at the shd. subsequently, results will be analyzed and a summary report will be generated by the third week in november 2014. graphical depictions of the results will be produced and made available for review. conclusions an impetus for presentation at the isds conference is to garner feedback from attendees regarding improvement and utility of the matrix in selecting appropriate documentation to meet phab standards & measures version 1.5 public health surveillance measure criteria. the matrix could serve as an asset in providing tangible documentation examples for phab’s public health surveillance measures. keywords surveillance; phab; matrix; accreditation; shd references references public health accreditation board. (2014). phab standards and measures, version 1.5. retrieved from http://www.phaboard.org/wpcontent/uploads/sm-version-1.5-board-adopted-final-01-24-2014. docx.pdf thomas g. savel, md, public health informatics and technology program office, cdc, office of surveillance, epidemiology, and laboratory services, 2500 century center, ms e55, atlanta, ga 30329. retrieved from http://www.cdc.gov/mmwr/preview/ mmwrhtml/su6103a5.htm *christopher d. williams e-mail: cwilli52@uic.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e174, 201 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e309, 2019 isds 2019 conference abstracts hurricane harvey aar: preparedness recommendations based on surveillance data michelle carr, tunde onafowokan, john fleming, tolu olumuyiwa, biru yang, amanda eckert informatics, houston health department, houston, texas, united states objective to provide recommendations for future preparedness response efforts based on an assessment of the post-hurricane harvey afteraction report (aar). introduction on august 25, 2017 hurricane harvey moved onshore near port aransas, texas, eventually overwhelming areas of houston with between 41-60 inches of rain (houston health department [hhd], 2017). as a category 4 storm, with wind speeds as high as 130 mph, harvey broke several rainfall records across the state and ended the prolonged period of twelve years in which no major hurricanes had made landfall in the united states (mersereau, 2017). harvey ambled at a leisurely pace through houston and resulted in devastating flooding that destroyed homes and required the evacuation of approximately 37,000 houstonians to over 78 shelter facilities across the affected area (hhd, 2017). through concerted efforts, the american red cross and the hhd established the shelter at the george r. brown convention center (grb) and “delivered or coordinated social services, medical and mental health services, disease surveillance and food/sanitary inspection services” for the duration of the need for the shelter (hhd, 2017). methods syndromic surveillance data is essential to understanding the health status of affected communities during and after a disaster. for this abstract, we reviewed data collected from different surveillance systems and programs within the houston health department (hhd), namely real-time outbreak and surveillance (rods), houston electronic disease surveillance system (hedss) and other program systems, and reports compiled into the aar. the aar contained an assessment of the data collected daily during shelter surveillance and helped identify gaps in the implementation of preparedness plans, current procedures, and best practices. hhd’s informatics team was responsible for data collection, training of staff and maintaining a cloud based repository of information on surveillance data and resources. a review of the aar indicated a need for resources for the general shelter population to address the need for pharmacy data, enhanced behavioral support for individuals with mental health needs, dialysis treatment plans and pharmaceutical needs for patients with respiratory illness or hypertension. results from august 30, 2017 to september 8, 2017 approximately 3,500 evacuees residing at the grb shelter were assessed for a variety of medical conditions and complaints. patient encounters peaked on september 4, 2017, with 705 patient encounters recorded. data from the aar suggested there were four most prevalent conditions of immediate need; affecting almost 25% of the shelter population were hypertension (10.4%), mental and behavioral issues (7.9%), diabetes (5.7%) and dialysis or renal failure (0.3%). there were challenges with supply of medications and synchronization of data collection by hhd and partner agencies. the department’s continuity of operation plan (coop) was voluminous and was not easily accessible during the disaster response. the findings from the afteraction report indicate that disasters present multidimensional health challenges that can overwhelm advance preparations and more needs to be done to address the problems identified from previous disaster responses to improve on future outcomes. conclusions syndromic surveillance can be strengthened in the following recommended areas for better incorporation into disaster response plans; pharmacy and health related data and data collection. the ingestion of pharmacy data by the syndromic surveillance systems could highlight gaps in the supply of needed medications at pharmacies during and post disaster, data from behavioral health clinics could show whether victims of the disaster who suffer mental health issues are able to access care, and whether dialysis treatment plans were continued. based on the gaps identified, recommendations include integration of pharmacy data into the city’s disease surveillance system “essence” for tracking http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e309, 2019 isds 2019 conference abstracts prescriptions and otc purchases, to ensure adequate preparation for disaster stock levels and identification of sources for reordering when stocks run short. additionally, it is recommended to revise and standardize data collection tools used during shelter surveillance to streamline the data collection process and to align the data tools of partner agencies, particularly dmat and red cross, to prevent unnecessary duplication of efforts. finally, the city’s continuity of operation plan (coop) has been revised since hurricane harvey and is periodically assessed and updated. the revised and updated coop provides a concise and readily accessible document which can be easily reviewed and implemented to support an emergency response. acknowledgement i would like to thank all of my co-authors for their collaboration on this endeavor. references 1. houston health department. (2017). hurricane harvey fast facts. 2. houston health department. (2017). after-action report/improvement plan. retrieved from 3. mersereau d. (2017). hurricane harvey broke multiple weather records. mental floss. retrieved from http://mentalfloss.com/article/556940/pluto-planet-afterall-new-argument-emerges http://ojphi.org/ isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts support vector subset scan for spatial outbreak detection dylan fitzpatrick*, yun ni and daniel b. neill heinz college/machine learning department, carnegie mellon university, pittsburgh, pa, usa objective we present the support vector subset scan (svss), a new method for detecting localized and irregularly shaped patterns in spatial data. svss integrates the penalized fast subset scan3 with a kernel support vector machine classifier to accurately detect disease clusters that are compact and irregular in shape. introduction neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. the penalized fast subset scan (pfss)3 provides a flexible framework for adding soft constraints to the fast subset scan, rewarding or penalizing inclusion of individual points into a cluster with additive point-specific penalty terms. we propose the support vector subset scan (svss), a novel method that iteratively assigns penalties according to distance from the separating hyperplane learned by a kernel support vector machine (svm). svss efficiently detects disease clusters that are geometrically compact and irregular. methods speakman3 observes that for a fixed value of relative risk q, the log-likelihood ratio for the exponential family of expectation-based scan statistics can be written as an additive set function over all data elements. this property enables addition of element-specific penalty terms to the log-likelihood ratio, interpreted as the prior log-odds of including a data point in the cluster. we propose an iterative method for setting the penalty terms which leads to spatially compact clusters, alternately running pfss to obtain an optimal subset and training a kernel svm to maximize the margin between points within and outside of the subset. on each iteration of pfss, penalties are assigned based on distance to the svm decision boundary. we apply random restarts across the penalty space to approach a global optimum in the non-convex svss objective function. results we demonstrate detection of disease clusters in mosquito pools tested for west nile virus (wnv), using data made publicly available by the chicago department of public health through the city of chicago data portal. in comparison to the circular scan1, which detects circular patterns with elevated wnv, svss has improved power to detect disease clusters that are elongated or irregular in shape. for example, the top wnv cluster detected by svss roughly conforms to sections of two major rivers in north chicago, overlapping significant portions of the forest preserves adjacent to these rivers. the unconstrained fast subset scan2 has high detection power for subtle and irregular disease clusters, but finds patterns that are spatially sparse and intermingled with non-anomalous points. svss rewards patterns with spatial coherence, detecting clusters that are compact and separated from non-anomalous points while maintaining power to detect slight but significant increases in detected rates of wnv. conclusions svss introduces soft spatial constraints to the fast subset scan2 in the form of penalties to the log-likelihood ratio statistic, learned iteratively based on distance to a high-dimensional svm decision boundary. these constraints give svss greater power to detect spatially compact and irregular patterns of disease. clusters of west nile virus detected by three scanning algorithms. keywords outbreak detection; cluster detection; spatial analysis; machine learning; subset scan acknowledgments this work was partially supported by nsf grant iis-0953330. references 1. kulldorff m. a spatial scan statistic. commun stat theory methods. 1997; 26(2): 1481-1496. 2. neill db. fast subset scan for spatial pattern detection. j r stat soc series b stat methodol. 2012; 74(2): 337-360. 3. speakman, s, somanchi, s, mcfowland, e iii, neill, db. penalized fast subset scanning. j comp graph stat. 2016; 25(2): 382-404. *dylan fitzpatrick e-mail: djfitzpa@andrew.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e21, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts a novel hotel-based syndromic surveillance system for the caribbean region jonathan edwin* and lisa indar surveillance, disease prevention and control division, caribbean public health agency, port of spain, trinidad and tobago objective to describe the caribbean public health agency’s (carpha) tourism and health information system (this), a web-based syndromic surveillance system to increase the capacity of caribbean countries to monitor the health of visitors and staff in hotels, and detect potential infectious disease outbreaks for early and coordinated public health response. introduction the tourism industry is highly vulnerable to health, safety, and environmental sanitation (hse) threats. the caribbean is the most tourism dependent region in the world, with over 54.2 million stayover and cruise ship arrivals in 2015, generating revenues of $us29.6 billion and contributing to 15% of the gross domestic product (gdp) and 2,255,000 jobs [1]. tourists and staff are at an increased risk of acquiring infectious diseases, given the mass-gathering of individuals with varying levels of susceptibility and often times in close quarters in hotels and cruise ships. to prevent the spread of infectious diseases in these settings, early warning and response to potential public health threats is essential. to increase the capacity of countries in the caribbean monitor and protect the health of tourists and staff in their hotel establishments, this was designed as an early warning system for infectious disease outbreaks. methods carpha launched the regional tourism health information, monitoring and response system in 2016 with donor funding received from the inter-american development bank (idb). the overall objective of thmrs project from 2016-2018 is to improve participating country’s capacity to provide cost-effective and quality health, food safety and environmental solutions to hse threats. as part of the thmrs project, the development of a hotel-based syndromic surveillance system for early warning and response to infectious diseases was developed. this was developed in collaboration with six participating idb countries: barbados, bahamas, belize, guyana, jamaica, trinidad and tobago. the implementation plan (2016-2018) with each country involved three stages: 1) project operations, coordination, management (including advocacy, and endorsement) 2) development of the project outputs: gap analysis and best practices; development of surveillance guidelines and training modules, hse standards 3) implementation in participating countries (i.e. technical visits, ongoing technical coordination): preparation, buy-in, training and launch the web-based design of this enables the collection of realtime data which will inform health service delivery decisions/ policies, strengthen national and regional health monitoring efforts, and trigger a rapid coordinated response to outbreaks, and prevent escalation of tourism hse incidents. the system involves a webbased questionnaire with a series of 11 short questions that ask the user for basic non-identifiable demographic information as well as symptoms. the reported symptoms are used by the system to generate six syndromes: gastroenteritis, undifferentiated fever, hemorrhagic fever, fever with neurologic symptoms. fever with respiratory symptoms, fever with rash. data entry persons include hotel staff, physicians, and the case. access to anlaytic dashboards of the aggregated data is limited to registered hotel staff (i.e. managers), the ministry of health of the country where the hotel reporting is located, and carpha. the limited level of baseline data for syndromes in the caribbean region means that statistical aberration detection mechanisms for most syndromes will not be available until this collects at least one year’s worth of data. however, for acute gastroenteritis, until a more accurate threshold can be generated, a cut-off of 3% ill (staff and guests) will be used for alerting potential outbreaks. this is scheduled to be live and functional beginning in hotel facilities in trinidad and tobago at the beginning of october 2016. by the end of 2016, this will be operating in facilities in all six participating countries, allowing for the collection of baseline data for syndromes occurring among tourists and staff in hotel-settings, and providing a mechanism to detect and response to emerging public health threats early and efficiently. conclusions establishing this system is critical to improving countries’ capacities to support the overall health surveillance system of the tourism-dependent caribbean economies, enabling countries to collect real-time data which will inform health service delivery decisions/policies, strengthen national and regional health monitoring efforts to trigger a rapid coordinated response to outbreaks and other crises and thus prevent tourism hse incidents. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e88, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords syndromic surveillance; hotel-based surveillance; caribbean surveillance; infectious diseases acknowledgments this project would not be possible without the donor funding provided by the inter-american development bank (idb), and collaboration from the caribbean tourism organization and participating jurisdictions: barbados, bahamas, belize, guyana, jamaica, trinidad and tobago references 1. travel and tourism economic impact 2016: caribbean. london, united kingdom: world travel and tourism council (wttc); 2016 [september 9 2016]. available from: http://www.wttc.org/-/media/ files/reports/economic-impact-research/regions-2016/caribbean2016. pdf *jonathan edwin e-mail: indarlis@carpha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e88, 2017 isds16_abstracts-final 60 isds16_abstracts-final 61 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e320, 2019 isds 2019 conference abstracts ux case study: tracking ehr automation, scarcity of attention, and transaction hazards susan rauch english and media studies, massey university, west end, palmerston north, new zealand objective to track and visually assess how automated attention structures within the electronic health record (ehr) compete for clinicians attention during computer physician order entry that could potentially lead to transactions hazards in the clinical narrative. introduction in recent years, studies in health and medicine have shifted toward ehealth communication and the relationships among human interaction, computer literacy, and digital text content in medical discourses [1-6]. clinicians, however, continue to struggle with ehr usability, including how to effectively capture patient data without error [7-9]. usability is especially problematic for clinicians, who must now acquire new skills in electronic documentation [10]. challenges with the ehr occur because of clinicians’ struggle with attention to the non-linear format of clinical content and automated technologies [11]. it is therefore important to understand how attention structures are visually situated within the ehr’s narrative architecture and audience for whom electronic text is written. it is equally important to visualize and track how automated language and design in health information technology (hit) affect users’ attention when documenting clinical narratives [12]. in the study of health information technology, researchers of ehealth platforms need to recognize how the construction of human communication lies within the metaphoric expression, design, and delivery of the ehr’s information architecture [13]. many studies of electronic health records (ehr) examine the design and usability in the development stages. some studies focus on the economic value of the ehr medicare incentive program, which affects providers’ return on investment (roi). few studies, however, identify the communicative value of how attention structures within the ehr’s information architecture compete for users’ attention during the clinical documentation process [9,14]. methods this paper highlights methods from an observed ehr pre-launch testing event that analyzes the visual effects of attention structures within the ehr’s information landscape. the observation was completed in two separate stages, each with one it facilitator and two participant demographics: stage 1. on-site hit clinical application staff testing and, stage 2. twenty-five participants (rn and non-rn clinical staff). during the second stage of the event, one participant’s task performance was screencast-recorded. the length of the testing for the one participant totaled 37 minutes. because the ehr domain is propelled by both the internet and intranet, a contextual-rhetorical analysis of the data collected was performed which incorporated nielsen's 10 usability heuristics for interaction design [15,16] and stuart blythe’s methodological approach to analyzing digital writing and technology to defining rhetorical units of analysis in digital web research [17]. result the ux observation and contextual-rhetorical analysis of ehr design supports a 4-year qualitative study consisting of hospital interviews at two acute-care facilities and an online, national survey of revenue integrity and clinical documentation improvement specialists. the testing event served as an opportunity to observe how a healthcare organization user-experience tests the functionality of the ehr’s design build before launching it live. the testing event also provides an understanding of clinicians’ organizational needs and challenges during the clinical documentation process. the contextual-rhetorical analysis identified how the structure of narrative in the ehr represents rhetorical units of value that might influence how clinicians make decisions about narrative construction. conclusions this ux case study analysis of an ehr testing event identifies how scarcity of attention and clinicians’ reliance on technology affect clinical documentation best practices leading to potential transaction hazards in the clinical narrative.the study is relevant in ehealth data surveillance because it shows how visual cues within the design of the ehr's technological landscape affect clinicians’ decision-making processes while documenting the ehr-generated clinical narrative http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e320, 2019 isds 2019 conference abstracts references 1. black a, car j, majeed a, sheikh a. 2008. strategic considerations for improving the quality of ehealth research: we need to improve the quality and capacity of academia to undertake informatics research. inform prim care. 16(3), 175-77. pubmed https://doi.org/10.14236/jhi.v16i3.690 2. meeks dw, smith mw, taylor l, sittig df, scott jm, et al. 2014. an analysis of electronic health record-related patient safety concerns. j am med inform assoc. 21(6), 1053-59. pubmed https://doi.org/10.1136/amiajnl-2013-002578 3. owens kh. rhetorics of e-health and information age medicine: a risk-benefit analysis. jac. 2011:225-35. 4. petersson j. geographies of ehealth: studies of healthcare at a distance2014. 5. solomon s. how we can end the disconnect in health. health voices. 2014(15):23. 6. subbiah nk. improving usability and adoption of tablet-based electronic health record (ehr) applications: arizona state university; 2018. 7. khairat s, burke g, archambault h, schwartz t, larson j, et al. 2018. perceived burden of ehrs on physicians at different stages of their career. appl clin inform. 9(2), 336-47. pubmed https://doi.org/10.1055/s-0038-1648222 8. staggers n, elias bl, makar e, alexander gl. 2018. the imperative of solving nurses’ usability problems with health information technology. j nurs adm. 48(4), 191-96. pubmed https://doi.org/10.1097/nna.0000000000000598 9. yackel tr, embi pj. 2010. unintended errors with ehr-based result management: a case series. j am med inform assoc. 17(1), 104-07. pubmed https://doi.org/10.1197/jamia.m3294 10. stewart wf, shah nr, selna mj, paulus ra, walker jm. 2007. bridging the inferential gap: the electronic health record and clinical evidence. health aff (millwood). 26(2), w181-91. pubmed https://doi.org/10.1377/hlthaff.26.2.w181 11. johnson sb, bakken s, 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devoss d, editors. digital writing research: technologies, methodologies and ethical issues (new dimensions in computers and composition) cresskill, nj: hampton press; 2007. p. 203-28. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19094402&dopt=abstract https://doi.org/10.14236/jhi.v16i3.690 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24951796&dopt=abstract https://doi.org/10.1136/amiajnl-2013-002578 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29768634&dopt=abstract https://doi.org/10.1055/s-0038-1648222 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29570144&dopt=abstract https://doi.org/10.1097/nna.0000000000000598 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20064810&dopt=abstract https://doi.org/10.1197/jamia.m3294 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17259202&dopt=abstract https://doi.org/10.1377/hlthaff.26.2.w181 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17947628&dopt=abstract https://doi.org/10.1197/jamia.m2131 https://doi.org/10.1177/1050651903258129 ojphi editorial ojphi vol 8, no 3 (2016) 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e194, 2016 editorial ojphi vol 8, no 3 (2016) edward mensah, phd editor-in-chief, the online journal of public health informatics correspondence: dehasnem@uic.edu doi: 10.5210/ojphi.v8i3.7166 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. welcome to the last issue of the 8th volume of the online journal of public health informatics. the current issue consists of interesting topics ranging from the design of visualizations for big health data; formulating an ontological framework for analyzing health policy; development of near real-time surveillance models using social media; asthma exacerbation risk predictions; deployment of analytics in community health centers; using google reviews to assess the risk of elder abuse; the use of electronic health records in health care homes; to a review of data privacy and security requirements for public health research. policy makers and researchers require population health data in order to assess the health of communities, manage resources, educate the public, and develop policies. public health data is often big data due to its high volume, low veracity, great variety, and high velocity. such data presents analytic challenges to scholars and researchers. in order to perceive patterns and create hypotheses users need the capability to perform related tasks and see many facets and elements of the data simultaneously. visualization techniques that encode multiple facets of data simultaneously can support complex health-related tasks such as predicting outbreaks of diseases, developing surveillance systems, development of health policies, and discovering populations at risk of disease or injury. the current off-the-shelf visualization tools can be used to represent only one or two facets of health data, thereby missing relevant but hidden patterns. in the era of big data, it is important to analyze the unknown relationships among the multiple data elements in order to discover patterns and develop relevant hypotheses for research and policy development. in the paper titled “beyond simple charts: design of visualizations for big health data” ola and sedig develop novel visualizations for global big health data. their work demonstrates how designers of health visualization techniques can improve upon the existing simple chart-like visualizations to design new visualizations for analyzing the multiple data elements and facets of big health data in order to discover hidden patterns and extract value. china has implemented waves of healthcare reforms since 1950. between 1950 and 2009 the reforms emphasized the shifting of public finances to private sources by turning public hospitals and clinics into private for-profit enterprises. the health care system was also decentralized. http://ojphi.org/ ojphi editorial ojphi vol 8, no 3 (2016) 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e194, 2016 after 2009 different policies were introduced to provide public health services for all citizens and, among other objectives, to improve primary care delivery systems. since the new set of policies did not completely eliminate the old (pre-2009) reforms the multiple layers of complexities made it difficult to analyze the performance of the policies. in a paper titled “china’s national health policies: an ontological approach” guobin d. et al. mined and mapped all the policies systematically and systemically in order to discover their emphases and biases. the framework, the method developed, and the results can be used by policy makers to assess the strengths and weaknesses of china’s health policies and formulate better policies. access to medical information when and where needed enriches and facilitates the learning experience of medical students and residents. clinicians with access to mobile health devices and applications can retrieve relevant treatment guidelines and other necessary information at the point of care in order to deliver quality care. these technologies also allow clinicians to monitor their patients after discharge. in order to optimize the value of mobile devices and applications it is important for medical students and residents to be very familiar with these technologies during training. a study was carried out by fatima m. et al. at a large urban medical center in which residents were introduced to point-of-care institutional resources and authoritative public health applications using mobile devices. the results demonstrated significant improvements in residents’ familiarity with medical information applications, appreciation of the use of electronic medical records, and familiarity with the relevant electronic technologies compared to baseline measures. influenza epidemics in the united states have been responsible for very high morbidity and mortality cases each year. the centers for disease control and prevention and other agencies develop surveillance systems in order to predict the severity of the impending influenza and prepare for vaccination efforts. several factors such as the attributes of the circulating virus, the timing of the flu season, and the effectiveness of vaccine influence the severity of the disease. in recent years experts have used data from social media to develop near real-time surveillance models to provide healthcare workers with wider situational awareness. since this data is based on conversations by regular social media participants it is necessary to verify the accuracy of the associated surveillance models. in a study titled ‘twitter influenza surveillance: quantifying seasonal misdiagnosis patterns’ jared mowery demonstrated that approximately 40 percent of the flu tweets in the 2015-2016 season reflected misdiagnoses. the study also showed that, in addition to including data from other flu seasons it is important to understand the factors that affect twitter users’ misdiagnoses in order to develop more reliable flu surveillance models using data from social media. it has been estimated that asthma affects approximately 24 million americans and can have significant impact on the socio-economic well-being of those affected and their families. the direct medical of ed visits in 2011 alone was estimated at $50 billion. it is essential to develop strategies aimed at improving the timeliness and targeting of preventive and early intervention activities. quite often the investigation capabilities of the professionals responsible for monitoring population health on a daily basis can be constrained by lack of access to near-term relevant information. in a paper titled cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk’ margaret r. et al. focused on improving the response rate of public health officials to asthma exacerbations in boston. this paper continues an initiative conducted http://ojphi.org/ ojphi editorial ojphi vol 8, no 3 (2016) 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e194, 2016 by the international society for disease surveillance with funding from the defense threat reduction agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. the goal is to enhance investigation capabilities of day-to-day population health monitors (those who monitor population health and respond to significant public health issues on a daily basis). the unanimous consensus among the consultancy collaborators from the boston public health commission was that having access to near-term information from the technical experts was essential to improve the asthma monitoring capabilities of the field based professionals. due to the unpredictability of the occurrence of bio-terrorism attacks it is normal for policy makers to be concerned about the capacity of local public health to protect the public from attacks. while it is relevant to have a preparedness plan before the events it is particularly relevant to prepare for persons with special needs. in a paper titled “medical allocations to persons with special needs during a bioterrorism event” donald b. et al. compared the time it takes to dispense medications to two different cohorts comprising the general population and special needs people. the results showed that the service times at the dispensing stations were not statistically different between the general population cohort and those with any special needs. however, the time in between stations was increased resulting in longer total service time for the special needs individuals. modeling of service times per station and cohort type found significant delays at the medical station among persons in the general population who are pregnant. the authors recommend that in order to expedite the dispensing process it is important to ensure the existence of memorandums of understanding for select agencies involved in providing services to those with special needs. a recent study by the national institute of justice found that the elder population in usa is expected to double from the 2010 level of 35 million to approximately 70 million in 2030. most family members are ill-equipped to take care of the elderly at home resulting in an increase in the number of the elderly institutionalized in nursing homes and elder care facilities. the centers for medicare and medicaid services (cms) inspects these facilities in order to determine the quality of care and the presence of elder abuse. the cms inspections are time consuming and labor intensive. in recent years social media and other organizations have developed online applications for reviewing physicians and healthcare facilities. in a paper titled “assessing quality of care and elder abuse in nursing homes via google reviews” jared m. et al. correlate google reviews of nursing homes with the cms inspection results. the study showed that as the number of online reviews increases the correlation coefficient between the cms results and the google ratings converge (0.65). this demonstrates that, in future, as the online reviews increase they will become a valuable source of information for judging the quality of care and the prevalence of elder abuse in nursing homes. the movement from fee-for-service to value-based payment requires a deep understanding of data as a source of value creation. providers have to treat data as a strategic asset than can be leveraged to support their operations and clinical practices. as data volumes and varieties keep increasing there is the need to employ analytic tools capable of quickly processing large quantities of data of various complexity, scope and scale in order to uncover patterns and value. community health centers constitute the nation’s largest source of primary care for the medically underserved populations and frequently have budget constraints. to generate additional revenue under the new payer models community health centers need to improve their capabilities to http://ojphi.org/ ojphi editorial ojphi vol 8, no 3 (2016) 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e194, 2016 understand and leverage an important strategic asset, data. in a paper titled “deployment of analytics into the healthcare safety net: lessons learned” hartzband and jacobs carried out a study to assist community health centers plan for strategic use of data using hadoop and electronic health records applications. numerous challenges and inconsistences were uncovered and more work is required to assist community health centers improve their awareness of data quality, integrate analytic results into practice, and optimize the value of data. the movement towards patient-centered medical care is aimed at providing a comprehensive, team-based, coordinated, accessible, quality care to patients compared to the previous care models where, quite often, care stops at the point of discharge. the incentive payments for the meaningful use of certified electronic health records, and the implementation and use of health information technologies have facilitated the transition to patient-centered medical care and the adoption of the advanced features of electronic health records. minnesota is one of the leaders in the technological and organizational transformation of the health care industry. in a paper titled “health reform in minnesota” soderberg et al. studied the association between health care home (hch) certification and the adoption and use of the advanced functionalities of electronic health records in primary care clinics. the results showed that certified health care homes used the advanced features of electronic health records to support care management, care coordination, and quality improvement at higher rates than non-hch clinics. secure exchange of health information among authorized stakeholders facilitates care coordination and improve efficiency, quality of care, and ultimately reduce costs. the electronic health record is a core component of the health information exchange system. personal health record (phr), the electronic application that provides individuals with the capability to access, manage, and share their health information in a private secure environment, still plays a minor role in the patient engagement process. while there is ample evidence that the use of phrs enables individuals to be engaged in the treatment process its adoption has been constrained by socio-technical factors. the adoption of phrs in developing countries has been investigated by only a few researchers. in a paper titled “perceived challenges for adopting the personal health record in the ministry of national guard health affairs” al-sahan and saddick showed that education was the main barrier to adopting phrs in saudi arabia. patients with higher levels of education showed higher interest in using phrs compared to those with minimal education. educated individuals also expressed more concern for privacy and confidentiality compared to less educated individuals. the overwhelming majority (78%) were not aware of the existence of phrs and other electronic health services on the website of the ministry of national guard health affairs. in order to manage healthcare resources, assess the effectiveness of interventions, improve outcomes, or minimize disparities in health status between various populations one needs high quality and reliable data. among indigenous populations demographic variable such as names (first, middle, last names) may not be consistently reported. depending upon the circumstances the first name could become the last name resulting in an individual assuming multiple identities. it is important to create key identifiers in order to provide quality information on native or aboriginal populations. in a paper titled “managing aboriginal and torres strait islander data for public health research” gaans d. et al examined the structure of key identifying variables of aboriginal and torres strait islander australians. the study demonstrated the necessity to improve the collection and management of key variables such as name, address and date of birth http://ojphi.org/ ojphi editorial ojphi vol 8, no 3 (2016) 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e194, 2016 which usually assume multiple values, in order to improve the quality of health data needed for research and population health management. most hospitals and clinics have implemented electronic health record systems and health information exchanges to facilitate the documentation and sharing of health data in order to, among other benefits, improve the coordination of care and efficiency of the delivery system. data retrieved from electronic health record systems have become valuable assets for the public health research community. in order to maintain faith in the data it is important to safeguard the privacy of the research participants and the security of the sensitive health information. in the commentary section goldstein and sarwate draw a clear distinction between privacy and security and introduce technologies for protecting the individual research participants. i am sure you will enjoy reading the materials contained in this issue. many thanks to the editors, the journal manager, the publishers, and all the reviewers and readers who have contributed to the success of this project. happy new year! edward mensah, phd editor-in-chief the online journal of public health informatics health policy and administration division school of public health 1603 west taylor street chicago, illinoi 60612 email: dehasnem@uic.edu office: (312) 996-3001 http://ojphi.org/ editorial ojphi vol 8, no 3 (2016) edward mensah, phd editor-in-chief, the online journal of public health informatics influenza epidemics in the united states have been responsible for very high morbidity and mortality cases each year. the centers for disease control and prevention and other agencies develop surveillance systems in order to predict the severity of th... it has been estimated that asthma affects approximately 24 million americans and can have significant impact on the socio-economic well-being of those affected and their families. the direct medical of ed visits in 2011 alone was estimated at $50 bill... due to the unpredictability of the occurrence of bio-terrorism attacks it is normal for policy makers to be concerned about the capacity of local public health to protect the public from attacks. while it is relevant to have a preparedness plan before... a recent study by the national institute of justice found that the elder population in usa is expected to double from the 2010 level of 35 million to approximately 70 million in 2030. most family members are ill-equipped to take care of the elderly at... the movement from fee-for-service to value-based payment requires a deep understanding of data as a source of value creation. providers have to treat data as a strategic asset than can be leveraged to support their operations and clinical practices. ... the movement towards patient-centered medical care is aimed at providing a comprehensive, team-based, coordinated, accessible, quality care to patients compared to the previous care models where, quite often, care stops at the point of discharge. the ... secure exchange of health information among authorized stakeholders facilitates care coordination and improve efficiency, quality of care, and ultimately reduce costs. the electronic health record is a core component of the health information exchange... in order to manage healthcare resources, assess the effectiveness of interventions, improve outcomes, or minimize disparities in health status between various populations one needs high quality and reliable data. among indigenous populations demograph... most hospitals and clinics have implemented electronic health record systems and health information exchanges to facilitate the documentation and sharing of health data in order to, among other benefits, improve the coordination of care and efficiency... i am sure you will enjoy reading the materials contained in this issue. many thanks to the editors, the journal manager, the publishers, and all the reviewers and readers who have contributed to the success of this project. happy new year! edward mensah, phd editor-in-chief the online journal of public health informatics health policy and administration division school of public health 1603 west taylor street chicago, illinoi 60612 email: dehasnem@uic.edu office: (312) 996-3001 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts facilitating public health action through surveillance dashboards adrienne macdonald*, hussain r. usman, deena hinshaw, david p. meurer and christopher sikora alberta health services, edmonton, ab, canada objective to address the limitations of traditional static surveillance reporting by developing in-house infrastructure to create and maintain interactive surveillance dashboards. introduction traditionally, public health surveillance departments collect, analyze, interpret, and package information into static surveillance reports for distribution to stakeholders. this resource-intensive production and dissemination process has major shortcomings that impede end users from optimally utilizing this information for public health action. often, by the time traditional reports are ready for dissemination they are outdated. information can be difficult to find in long static reports and there is no capability to interact with the data by users. instead, ad hoc data requests are made, resulting in inefficiencies and delays. use of electronic dashboards for surveillance reporting is not new. many public health departments have worked with information technology (it) contractors to develop such technically sophisticated products requiring it expertise. the technology and tools now exist to equip the public health workforce to develop in-house surveillance dashboards, which allow for unprecedented speed, flexibility, and cost savings while meeting the needs of stakeholders. at alberta health services (ahs), in-house, end-to-end dashboard development infrastructure has been established that provides epidemiologists and data analysts full capabilities for effective and timely reporting of surveillance information. methods an internal assessment of the available resources and infrastructure within ahs was conducted to iteratively develop a new analytics model that provides a foundation for in-house dashboard development capacity. we acquired sas® and tableau® software and conducted internal training for skills development and to transition staff to the new model. this model is highlighted below using our respiratory virus surveillance (rvs) dashboard as an example. for the rvs dashboard, stakeholder engagements were conducted to understand the end users’ needs. next, data access was improved, where possible, by securing direct access to source data (e.g. emergency department visits for influenza like illness (ili), health link calls, hospital admissions, etc.) on existing database servers. sas® code was written for routinely connecting with multiple data sources, data management and analysis, data quality assurance, and posting summary data on a secure oracle® server. the tableau® dashboard development application was then used to connect to the summary data on the oracle® server, create the interactive dashboards and publish the final products to the ahs tableau server environment. key users were consulted in the iterative development of the interface to optimize usability and relevant content. finally, the product was promoted to stakeholders with a commitment to use their feedback to drive continuous improvement. results in-house generated surveillance dashboards provide more timely access to comprehensive surveillance information for a broad audience of over 108,000 ahs employees; within as little as 3 hours of all data being available. they facilitate user-directed deep dives into the data to understand a more complete surveillance picture as well as stimulating hypothesis generation. additionally they enhance productivity of personnel, by significantly reducing response times for ad hoc request and to generate reports, freeing up more time to respond to other emerging public health issues. looking specifically at the rvs dashboard, its ability to bring all relevant surveillance information to one place facilitates valuable discussions during status update meetings throughout the influenza season. among other things it has allowed medical officers of health, emergency department staff, epidemiologists and others to make informed decisions pertaining to public messaging, the need for reallocating resources, such as staffing and handling the burden of ili patients, as well as determining the necessity of opening influenza assessment centers. conclusions surveillance dashboards can facilitate public health action by assembling comprehensive information in one place in a timely manner so that informed decisions can be made in emerging situations. keywords surveillance; dashboards; respiratory virus surveillance acknowledgments jason scarlett; greg macintyre; surveillance and reporting, ahs; public health leadership, ahs *adrienne macdonald e-mail: adrienne.macdonald@ahs.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e102, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts pregnant women with suspected zika virus infection: a claims data analysis silvia valkova* government solutions, ims health, plymouth meeting, pa, usa objective demonstrate the value of consolidated claims data from community healthcare providers in zika virus disease surveillance at local level. introduction zika virus disease and zika virus congenital infection are nationally notifiable conditions that became prominent recently as a growing number of travel-associated infections have been identified in the united states. the centers for disease control and prevention (cdc) have dedicated significant time and effort on determining and addressing the risks and impact of zika on pregnant women and their babies who are most vulnerable to the disease. cdc relies on two sources of information, reported voluntarily by healthcare providers, to monitor zika virus disease: arbonet and the newly established u.s. zika pregnancy registry. a study by ims health compared u.s. trends of the zika virus disease in general and pregnant women with zika virus disease in particular observed in an ims healthcare claims database and the cdc arbonet and the newly established u.s. zika pregnancy registry. methods ims used for this analysis claims for reimbursement from officebased healthcare providers, which are widely accepted standard business practice records throughout the healthcare industry. ims claims data is collected daily from office-based providers throughout the u.s. and processed, stored and analyzed in a centralized database. the information is available at the patient and visit level, with the ability to characterize deidentified patients by age, gender and zip3 location and to trace a patient’s history of visits, diagnoses, procedures, drugs prescribed and tests performed or ordered. the general ims study sample captured all patients throughout the continental united states covered in claims between october 1, 2016 and may 24, 2016 with icd 10 diagnosis code a92.8, other specified mosquito-borne viral fevers. this sample was compared to the sample of laboratory-confirmed zika virus disease cases reported to arbonet by state or territory from the cdc arboviral disease branch from january 1, 2015 through may 18, 2016. in addition, ims compared the subset of patients with both a zika virus disease diagnosis and any icd 10 pregnancy diagnosis to the cdc sample of patients captured by the u.s. zika pregnancy registry with any laboratory evidence of possible zika virus infection in the united states and territories. results throughout the continental united states, the ims claims-based sample captured 875 patients with a zika virus disease diagnosis compared to 548 travel-associated cases reported by cdc. at the state level, especially in new york, new jersey, illinois and texas, the ims data captured a much larger number of cases that the cdc reported cases. most of these possible zika cases are concentrated in the large metropolitan areas around new york city, chicago and houston. many of them are diagnosed and treated by the same healthcare providers. the ims sample captured 577 pregnant women with a possible zika virus infection compared to the 168 pregnant women with a possible zika virus infection reported in the u.s. zika pregnancy registry as of may 24, 2016. many of the pregnant women in the ims sample had multiple visits, often in consecutive months, associated with the zika virus disease diagnosis. pregnant women are more likely to be tested and diagnosed with a zika virus infection due to the risk of fetal malformations from the disease. as many as 250 of the 577 pregnant women with a possible zika virus infection also had a diagnosis of suspected fetal damage due to a viral disease. of all women with a possible zika virus infection in the ims sample, 120 were in new jersey, 111 in new york, 93 in illinois and 74 in texas, and most were concentrated in the large metropolitan areas around new york city, chicago and houston. conclusions these findings suggest that all-payer claims data can be used succesfully to monitor zika transmission trends at local and state level, especially with a focus on pregnant women. healthcare claims data is fast, granular, relevant at local level and can be used to supplement cdc arbonet data for local and state level surveillance and response to the evolving zika virus infection outbreak. this study is an example of a novel approach to surveillance for zika virus disease and potentially many other infectious diseases. keywords zika; pregnancy; local signal *silvia valkova e-mail: svalkova@us.imshealth.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e49, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts identifying and communicating the importance of the variable nature of sys data heather rubino*, david atrubin and janet j. hamilton epidemiology, florida department of health, tallahassee, fl, usa objective this roundtable will provide a forum for national, state, and local managers of syndromic surveillance systems to discuss how they identify, monitor, and respond to changes in the nature of their data. additionally, this session will focus on the strengths and weakness of the syndromic surveillance systems for supporting program evaluation and trend analysis. this session will also provide a forum where subject matter experts can discuss the ways in which this deep understanding of their data can be leveraged to forge and improve partnerships with academic partners. introduction as syndromic surveillance systems continue to grow, new opportunities have arisen to utilize the data in new or alternative ways for which the system was not initially designed. for example, in many jurisdictions syndromic surveillance has recently become population-based, with 100% coverage of targeted emergency department encounters. this makes the data more valuable for realtime evaluation of public health and prevention programs. there has also been increasing pressure to make more data publicly available – to the media, academic partners, and the general public. keywords informatics; surveillance; syndromic; evaluation *heather rubino e-mail: heather.rubino@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e76, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts monitoring for local transmission of zika virus using emergency department data amanda wahnich*, ramona lall and don weiss bureau of communicable disease, new york city department of health and mental hygiene, queens, ny, usa objective case and cluster identification of emergency department visits related to local transmission of zika virus. introduction the first travel-associated cases of zika virus infection in new york city (nyc) were identified in january 2016. local transmission of zika virus from imported cases is possible due to presence of aedes albopictus mosquitos. timely detection of local zika virus transmission could inform public health interventions and mitigate additional spread of illness. daily emergency department (ed) visit surveillance to detect individual cases and spatio-temporal clusters of locally-acquired zika virus disease was initiated in june 2016. methods ed visits were classified into two zika syndromes based on chief complaint text and the international classification of diseases version 9 and 10 diagnosis codes for patients ≥6 years old: 1) fever and 2) zika-like illness. zika-like illness was defined as visits with mention of zika; symptoms of rash, fever, and either joint pain or conjunctivitis; diagnosis of guillain-barré syndrome; or diagnosis of rare and non-endemic arboviral infection. we applied the prospective space-time permutation scan statistic1 in satscan daily since june 2016 to the fever syndrome, selected as a single representative symptom, to detect clusters by hospital or zip code of patient residence. the maximum spatial cluster size is 20% of observed visits, and the maximum temporal cluster size is 14 days – reflecting the incubation period.2 the study period is 90 days. statistical significance is determined using monte carlo simulations (n=999). any cluster with a recurrence interval ≥365 days is summarized in a map and line-list of contributing visits. the map depicts the zip codes of the cluster with an overlay of census tracts at highest risk for human importation of zika virus, as estimated by a zero-inflated poisson regression model developed at nyc dohmh that is updated regularly to reflect the most recent available data on confirmed cases. zika-like illness syndrome visits are output in a daily line-list. dohmh staff contact the eds that patients visited to determine travel to zika-affected country, clinical suspicion of zika infection, and laboratory testing. results during june 1–august 16, 2016, we observed a mean of 253 (range: 202-299) ed visits for the fever syndrome per day. sixteen spatio-temporal fever syndrome clusters have been detected. of these, 2 clusters were during testing and optimization of scan parameters, 13 were due to data quality issues, and 1 was dismissed due to the large geographic range of the cluster, spanning 3 boroughs. during june 1–august 16, 2016, we observed a mean of 2.7 (range: 0-7) ed visits for the zika-like illness syndrome. daily counts ranged from 0-3 visits from june 1-june 16 and 1-7 visits since june 16. nineteen visits that occurred from july 31-august 4 were further investigated to establish a protocol for follow-up. of those, eleven patients reported recent travel to countries with local transmission, one had travel over 3 months ago and an alternate diagnosis, six had unknown travel history due to incomplete follow-up, and one reported no travel. the one without travel had a diagnosis inconsistent with zika virus disease. subsequently, analysts contacted eds only for the subset of zika-like illness syndrome visits with no indication of travel or without an alternate discharge diagnosis. findings from this effort will be presented. conclusions the fever syndrome provides a means to monitor for clusters using ed data. prospective cluster detection signal volume was manageable and has not identified clusters requiring additional investigation. the zika-like illness syndrome can be used for case finding. contacting eds helps to supplement information missing in the syndromic system, such as travel history as well as zika testing and diagnosis. as zika-like illness syndrome counts are low and disease is emergent, contacting eds is feasible and helpful in ruling out local zika virus transmission. no visits or clusters to-date have indicated local transmission. keywords zika virus; syndromic surveillance; cluster detection acknowledgments sharon greene provided guidance on scan statistic parameter settings. the nyc dohmh syndromic unit conducted visit follow-up. gretchen culp assisted with generating maps. references 1. kulldorff m, et al. a space-time permutation scan statistic for disease outbreak detection. plos medicine. 2005;2:e59. 2. cdc. zika virus symptoms, diagnosis, & treatment. https://www. cdc.gov/zika/symptoms/ *amanda wahnich e-mail: awahnich@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e115, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and i 1informatics office, wa state, dept of health, shoreline,, wa, usa; 2university of washington, institute for health metrics and evaluation, seattle, wa, usa; 3washington state health care authority, olympia, wa, usa; 4university of washington, north west center for public health practice, seattle, wa, usa objective washington is leading the way in especially novel approaches. our goal is to share some of these innovative methods and discuss how these are used in state and local monitoring of health introduction this year’s conference theme is “harnessing data to advance health equity” – and washington state researchers and practitioners at the university, state, and local levels are leading the way in especially novel approaches to visualize health inequity and the effective translation of evidence into surveillance practice. description the panel will engage in a discussion on how the methodologies and data analytic approaches will be utilized in the surveillance and healthcare transformation activities in washington state. the discussion will include best practice to combine different data sources to provide a better picture of health at the local level. local health departments need local data to base their policies and programs on and to monitor the impact of their effort. currently, there are several projects in washington state and king county to produce burden estimates at the census tract level. we will show some of the early results and share lessons learned so far. approaches at the uw including methods of gathering information from ehrs via hie for population health issues such as healthy weight (bmi) work done with cdc and some work with developmental screening/early intervention and hearing screening standards as outlined by integrating the healthcare enterprise (ihe) quality, research and public health committee –with analysis of the problem and an architectural model audience engagement ali mokdad (university of washington) will share information about the institute for health metrics and evaluation (ihme), ian painter (university of washington) will share research from the north west center for public health practice (nwcphp), adam aaseby, (washington state health care authority(hca)), will share progress on collaborations with doh and ihme on the assessment, interoperability and measurement (aim) initiative, to support the healthier washington initiative, bryant thomas karras (washington state department of health (doh)) will share development and planning around the informatics interoperability roadmap and several data visualization and mapping activities. figure 1: male and female life expectancy for king county wa figure 2 female life expectancy for wa counties in 2013 figure 3 male life expectancy 2013 keywords population health; big data analytics; visualization; informatics; health disparity acknowledgments grants and federal matching activities from a variety of sources including cms, debeaumont, cdc, cste and astho *bryant thomas karras e-mail: bryant.karras@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e2, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia 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wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts nbic biofeeds: a digital tool for open source biosurveillance across federal agencies heather baker*1, chandra lesniak1, emily iarocci1, gus calapristi2, scott dowson2, michelle hart2, lauren charles-smith2, yi huang2 and teresa quitugua1 1national biosurveillance integration center, washington, dc, usa; 2pacific northwest national laboratory, richland, wa, usa objective the national biosurveillance integration center (nbic) is developing a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the u.s. department of homeland security (dhs) and its federal interagency partners. introduction the nbic integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. nbic serves as a bridge between federal, state, local, territorial, and tribal entities to conduct biosurveillance across human, animal, plant, and environmental domains. the integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts. to meet its mission objectives, nbic utilizes a variety of data sets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. nbic biofeeds is a digital tool designed to improve the efficiency of reviewing and analyzing large volumes of open source reporting by biosurveillance analysts on a daily basis; moreover, the system provides a mechanism to disseminate tailored feeds allowing nbic to better meet the specific information needs of individual, interagency partners. the tool is currently under development by the department of energy (doe), pacific northwest national laboratory (pnnl) and it is in a testing and evaluation phase supported by nbic biosurveillance subject matter experts. integration with the defense threat reduction agency (dtra), biosurveillance ecosystem (bsve) is also underway. nbic biofeeds version 1 is expected to be fully operational in fiscal year 2017. methods the pnnl is applying agile methodology to streamline the build of nbic biofeeds to specifications required for operational use by nbic and its federal interagency partners. biosurveillance, analytics, and system engineering subject matter experts provide guidance on the implementation of features in the tool to ensure functionality aligns with operational workflows and production support. pnnl is leveraging software from a previous government effort to repurpose the technology to meet nbic needs. nbic biofeeds incorporates the open source, document-orientated mongodb database to capture userand system-generated metadata on hundreds of thousands of records, in part, to establish baselines to aid prospective and retrospective analysis on emerging biological events. nbic biofeeds integrates a biosurveillance taxonomy (uniquely developed by nbic), which includes input from interagency partners to recognize critical characteristics of a biological event. in nbic biofeeds version 1, metadata capture of reported events is done manually by nbic analysts; however, moving forward in version 2, the tool will be further automated to flag significant reporting on biological events with a human remaining in the loop to confirm the validity of the system-generated tags. results to serve as a one-stop tool for open source biosurveillance, nbic biofeeds automatically harvests information from thousands of websites, utilizing third party aggregators, paid subscriptions to data feeds, and scraping of high priority sources. users can develop desired queries for automatic updating, leverage a unique review and curation mechanism, and further analyze data from topical, geographic, and temporal visualization features in the tool. to meet nbic’s information sharing needs, the tool allows for design of tailored rss feeds and electronic message-based delivery of analysis on biological events, intended for recipients in the government with unique missions around human, animal, plant, and environmental health. conclusions through current testing and evaluation – underway by biosurveillance subject matter experts – nbic biofeeds is demonstrating value in supporting open source biosurveillance by the center for more rapid recognition and sharing of key event characteristics. centralizing access and analysis of this dataset into a single system is increasing the efficiency of daily, global biosurveillance, while enhancing the value of information identified through use of the querying, curation, and production support features in the tool. keywords pacific northwest national laboratory; national biosurveillance integration center; mongodb; biosurveillance ecosystem; defense threat reduction agency *heather baker e-mail: heather.baker@associates.hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e63, 2017 case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration stan mierzwa1, samir souidi1, carolyne akello2, juliane etima2, richard ssebagala2, monica nolan2, samuel kabwigu2, clemensia nakablito2 1. information technology, population council, new york, usa 2. makerere university – johns hopkins university (mu-jhu) research collaboration crs, kampala uganda abstract this paper will discuss the integration of electronic case report forms (e-crfs) into an already existing android-based audio computer-assisted self-interview (acasi) software solution that was developed for a public health project in kampala, uganda, the technical outcome results, and lessons learned that may be useful to other projects requiring or considering such a technology solution. the developed product can function without a connection to the internet and allows for synchronizing collected data once connectivity is possible. previously, only paper-based crfs were utilized at the uganda project site. a subset or select group of crfs were targeted for integration with acasi in order to test feasibility and success. survey volume, error rate, and acceptance of the system, as well as the operational and technical design of the solution, will be discussed. keywords: self-report data collection; case report forms; crf; e-crf; tablets; android; clinical trials; feature integration correspondence: smierzwa3@gmail.com doi: 10.5210/ojphi.v9i3.7929 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction an informal literature review of journal articles found in google scholar indicate a lack of samples or experiences in public health or clinical trials research in which paper case report forms were integrated with audio computer-assisted self-interview (acasi) and computer-assisted personal-interview (capi) type of questionnaires. case report forms (crfs) are designed to record data on each trial subject during the course of a clinical trial as defined by the protocol. a crf for each patient in the study must be completed and signed by the investigator and assessor. all of the events that happen in the clinical trial should be fully documented, including adverse reactions. within clinical research the crf can include a questionnaire used to collect data on such mailto:smierzwa3@gmail.com case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi items as adherence and acceptability on participants, which in many cases warrant the use of a more private self-report strategy. electronic data collection in rural settings in sub-saharan africa with mobile devices has been found to be superior to that of a paper-based system, in regards to accuracy and completeness of data. a comparison of paper-based data collection with electronic data collection showed that direct electronic data entry was faster and 25% cheaper. data was more accurate (7% paper-based error rate versus 1% electronic error rate) and omission did not occur with electronic data collection. delayed data turnaround times and late error detections in the paper-based system which made error corrections difficult were avoided using electronic data collection [1]. in many behavioral and adherence and acceptability research studies there could be a need to include acasi survey technology, in order to provide the participants with more confidentiality in answering very sensitive, personal or culturally taboo questions. such sensitive acasi surveys have been performed outside the technology solutions provided for e-crf systems. in the hi-4tu (health improvements-for-teen ugandans) effort the population council’s customizable acasi & capi solution was integrated with the pilot e-crf demographic and screening forms so that all the data collected would be centralized in one electronic data source. the benefit of using one single solution are many, but one obvious advantage is that data integration or merging between acasi, capi and e-crf would not be required, thus eliminating a more complicated data consolidation step which can introduce errors. research project background the specific research project discussed in this paper is called "health improvements-for-teen ugandans" or hi-4-tu study. it includes a study population of 519 currently pregnant adolescents (without diagnosed pregnancy related complications), who are attending antenatal care in kampala, uganda and are aged 15 to 19 at enrollment. the study design is a randomized controlled study with the objective of testing the acceptability and effectiveness of two enhanced peer led, reproductive health interventions as compared with routine health care. the behavioral interventions included enhanced group support and enhanced individual support. the study participants are individually randomized to one of three arms. electronic data collection commenced in march 2016 and is scheduled to be completed in october 2017. the collaboration in the research project was between information technology engineers and specialists at the population council and hiv and sti researchers at the makerere university johns hopkins university (mujhu) site in kampala, uganda. further study information is published on the clinicaltrials.gov web portal. methods the base technology utilized in the project was the population council’s customized acasi/capi solution. the pc acasi/capi, otherwise known as suveyx, solution was programmed and modified to support two individual crfs that were required by the site. at the outset, the research team was interested in integrating the entire set of crfs, which totaled 12, but since this was the first attempt at integrating crfs with the pc acasi/capi solution it was decided to pilot with two existing crfs as a way to test feasibility of such a system. the mujhu site was familiar with case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi implementing the pc acasi solution after several years’ experience participating in research in collaboration with an earlier research effort, for which the population council and mujhu were involved and using the already existing acasi solution. this previous experience and our established collaboration helped move this pilot approach forward. the mujhu site provided the finalized crf and acasi surveys to the information technology staff at pc and this allowed us to start the design, architecture and technical build out of the solution. during the time the electronic instrument was being built out, the mujhu site simultaneously conducted the necessary translations to the bantu family language of luganda and recorded the .mp3 audio files required for acasi. mpeg-1 or mpeg-2 audio layer iii is the audio coding format commonly referred to as .mp3. pre-tests without the use of participants were performed at the site with research staff. the pre-testing uncovered translation and audio file clarity problems which were resolved by working closely with the us based population council staff. after corrections were made to the electronic instrument the uganda research staff was notified to perform the automatic update on each of the tablet devices. the software update process was initiated by pressing an update button on an administrative screen on each device, which in turn automatically connected to a custom developed microsoft azure cloud service to determine the program and database differences between the device and the most recent published technology solution. acasi + crf designed solution – operational each of the android tablet units used for the crf and acasi data collection system is configured identically with the software solution. each unit is however named individually to help distinguish on which device data is collected, and the unique assigned tablet name is saved with the result data as it is collected. in addition, as seen in figure 1, each crf and acasi system administrator that will establish the electronic survey for participants is assigned a unique id that is to be utilized when starting up the application. the unique id is saved with each record created in the solution. fig. 1. entry screen to the hi – 4 – tu acasi/crf solution. case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi fig. 2. (a) administrative choices (b) ability to resume a crf or acasi survey. the administrator may resume incomplete surveys and also view all of the surveys completed on the specific device, as shown in figure 2. fig. 3 survey choices included 3 acasi and 2 crf options. the overall hi-4-tu randomized control trial included more crfs than those that were presented in the electronic tablet solution discussed in this paper. as part of this pilot effort, two crfs were selected by the mujhu site to be part of this electronic tablet data collection system. these forms are included on the list of options in figure 3, namely the demographic form crf and the screening form crf. case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi fig. 4 example e-crf screen integrated into pc acasi. the e-crf form completion was accomplished by utilizing the android devices’ touchscreen capabilities, and each question was displayed individually. pre-built logic was designed and integrated into the e-crf where necessary, and response options were limited and restricted to minimize data entry error. a check list of necessary criteria for participation was provided to the clinic staff at the site, as seen in figure 4 and 5. this allowed the site staff to ensure the data was accurate before guiding the participant inside the clinic for flow for the study. acasi + crf designed solution – technical the base android tablet data collection software solution was written using the java programming language, complemented with the sqlite database. all software development was done using the open source eclipse integrated development environment (ide). the data collection system permits for data to be collected offline with the android tablet devices, and to merge with a source data manager laptop computer, without the need for internet connectivity. when internet connectivity is available to the data manager computer, the solution provides for the ability to upload the data to a secure web site or server [2]. this functionality was built using the microsoft azure cloud and also permitted for software updates to be made to each android device when connected to the internet. case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi fig. 5 e-crf response review and confirmation. discussion automating paper crf processes to an electronic format can introduce certain new issues that should be considered. one new requirement for future like research efforts will be to involve the information technology departments in the study sites to provide technology support. paper-based crf did not directly require technology assistance from either the installation or setup perspective or from the technical support perspective. this can introduce delays if clear goals and needs are not properly communicated to the information technology unit as well as receiving a commitment from the unit [3]. electronic crf’s continue to be the normal process for collecting and managing data in clinical trials. the use of paper crf’s or use of fax and optical character recognition have been used frequently in locations in sub-saharan africa for many years, but are now being replaced with such cloud-enabled tools as medidata solutions, specifically the rave module. these new breed of electronic systems for clinical trial data management are proving effective in sub-saharan africa especially as the internet infrastructure has continued to be more and more reliable and effective. however, these new tools may not include a way to integrate more innovative and novel ways of collecting data with a semi-literate population, traditionally when self-report modality of surveying is required for more sensitive questions of nature. there would be a benefit to introduce acasi, casi, v-acasi into these new electronic systems for clinical trial data management. although the acasi type of module or survey modality may not be needed for all participants or trial efforts, there still remains a need for use as was exhibited in this paper. vendors of validated case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e198, 2017 ojphi clinical trial data collection tools should continue to consider integrating more novel or custom ways of integrating innovative self-report survey techniques, such as acasi into their toolsets. questionnaire design still does remain as much an art as a science, but the evidence base for improving the quality and completeness of data collection in clinical trials is growing [4]. conclusion during the process of developing automated systems, engineers and software developers should consider how to “future proof” the solutions. this would involve considering how to make a solution stay useful over time and continue to be valuable. as of the time of this publication successful data collection has gone on for nineteen months. during this timeframe 694 screening and enrollment acasi surveys have been conducted and 340 follow-up surveys. e-crfs will continue to gain use in the developing world, which makes sense because automating the process helps to minimize errors that can be introduced with completing paper forms or transposing the paper results to an electronic record. differences in data entry methods can affect the trial in terms of data accuracy, cost, and efficiency. for example, when compared with paper case report forms, electronic data capture can reduce the time required for data entry, query resolution, and database release or study data closure by combining data entry with data collection [5] [3]. integrating or adding acasi to the e-crf solution will help to minimize disparate systems and will be particularly valuable when there is a need to capture self-report data, such as measuring adherence and acceptability. cost savings can be obtained if there is an ability to integrate self-report data collection into an already existing e-crf solution. self-report survey data integration with e-crf is not limited to acasi; it can also include other technical solutions such as interactive voice response systems (ivrs), computer-assisted telephone interviews (cati), computer-assisted self-interviews or short message service (sms) where data is collected. limitations the technology solution developed for hi-4-tu was not validated for use in clinical trials. data collection that will be utilized as part of an effort for submission to health regulatory bodies, such as the us food and drug administration (fda), will require that such systems be compliant with 21 cfr part 11. we did not evaluate the costs associated with migrating from paper-based crf to e-crf, but future research would be beneficial to the community to include this complementary information. there are advantages with data quality and monitoring that come with e-crf, but the cost benefit could be a function of the size of the trial – meaning that the larger the number of trial participants as well as the number of sites, the greater the potential savings. acknowledgements the authors would like to thank the project sponsor from johns hopkins university as well as the collaborators from makerere university johns hopkins university (mu-jhu) research collaboration and the population council. we would like to acknowledge irene friedland, at the population council, for her contributions to the thorough edit she has provided to this paper. case study: converting paper-based case report forms to an electronic format (e-crf) with acasi self-report integration 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proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 1state university of new york, buffalo, buffalo, ny, usa; 2naval medical research center, silver spring, md, usa; 3louisiana state university, baton rouge, la, usa; 4uralsk anti-plague station, uralsk, kazakhstan introduction brucella spp., coxiella burnetii, and tick-borne encephalitis virus (tbev) are believed to be enzootic in the republic of kazakhstan, and pose a particular public health risk due to their transmissibility in unpasteurized milk and dairy products. we established a milk surveillance methodology employing immuno and molecular assays to identify these agents, and applied this methodology to milk samples collected in western kazakhstan in winter 2014-2015. methods raw milk was collected from cows in the area around uralsk in western kazakhstan over the winter of 2014-2015. samples were defatted and frozen at -20c, then tested as follows for the presence of infectious agents. for tbev, 65 samples were tested using the vectorbest tbev antigen capture kit. for c. burnetii, 50 samples were assayed using a species-specific qpcr assay. for brucella spp., pcr, elisa and fpa testing was initiated and is ongoing. results for tbev, of 65 samples tested, nine percent were found to be positive, indicating that these milk samples contained the virus at the time of collection and thus that the source animals were infected. source animals of tbev negative samples may or may not have been infected, suggesting a need for blood sample collection for antibody assessment in conjunction with milk sample collection in future studies. this additional activity may also provide valuable information regarding how long infected animals shed the virus. for c. burnetii, all 50 samples tested were found to be negative, though positive controls were consistently positive. because c. burnetii exhibits seasonal increases in bacterial load during parturition, these results may be related to the time of sample collection during winter months and may not be representative of year-round presence of c. burnetii in milk, such that additional samples from other seasons will be tested in future studies. for brucella spp., ongoing testing has yielded some positive results by pcr, elisa and fpa. conclusions our data suggest that consumption of raw cow’s milk in western kazakhstan is a risk factor for tick-borne encephalitis and brucellosis. the risk for q fever seems to be small during winter, but may be present at other times of the year. milk samples will be collected yearround in future work, and may be accompanied by collection of blood samples for comparative analysis. keywords tick-borne encephalitis; q fever; brucellosis; surveillance; milk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e119, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann 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through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay 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boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of 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using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato 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syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease 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dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger kfl&a public health, kingston, on, canada objective to describe how the public health information management system (phims) tool is used by kfl&a public health to enhance real-time situational awareness and assist with evidence informed decision-making to help protect the health of the population. introduction geographic information system (gis) applications are increasingly being used for public health purposes. gis technology provides visual tools – through the creation of computerized maps, graphs, and tables of geographic data – that can assist with problem solving and inform decision-making. phims aims to enable visualization and spatial analysis of environmental data with underlying population based indicators. phims displays many layers of environmental information across ontario, and users can view maps demonstrating environmental or demographic data as they apply to specific geographic areas. this is useful for observing where environmental events are occurring, detecting potential emergency situations, and identifying areas with vulnerable populations. by displaying available, real-time, environmental data from multiple partners through phims, public health events can be identified earlier to better prevent, prepare for, and respond to emergencies. methods phims collects and compiles environmental and demographic data, and uses web-based mapping applications, spatial analytic functionality, and third party libraries to achieve map visualization of the information collected. the data collected by phims is derived from various sources. some of these sources include: statistics canada, environment canada, ministry of the environment, u.s geological survey, ministry of natural resources, canadian nuclear safety commission, the ontario marginalization index, and standard public health service data (e.g. tap into kingston, cool down and warm up centres, immunization sites, etc.). phims encompasses several tools and functions that can be accessed through a web-based user interface. phims users can choose from several basemaps to visualize their map with different geographic features. users have the ability to apply demographic layers related to age, deprivation, and marginalization to the selected basemap, which allows for colour-coded visualization of the social determinants of health as they apply to different geographic locations. these options enable users to easily view where the most vulnerable populations reside, which will help prepare and prioritize resources in the event of a public health emergency. phims also enables map visualization of real-time environmental conditions, because environmental layers related to weather radar data, weather conditions, stream gauges, and heat information can also be added to the basemap. phims includes several layers which visualize other pertinent public health data, such as: forest fires, wildfire smoke forecasts, well water uranium levels, nuclear reactor locations, earthquake information, and various factors relating to the air quality index, wind strength and direction, as well as plume dispersion of pollutants and toxins. conclusions having a gis tool, such as phims, to visualize environmental and population based data in real-time on virtual maps, facilitates identification of emergencies earlier than through traditional public health methods. phims, therefore, enhances public health situational awareness to better predict and prepare for extreme weather events and other environmental emergencies. additionally, phims can provide insight into where vulnerable populations are located, so that resources can be properly allocated in the case of an emergency. keywords gis; map; enviornmental; geographic; demographic online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e27, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 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health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in 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health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral 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beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 155 isds 2014 conference abstracts big data analytics for mass casualty incident (mci) situational awareness catherine ordun*1, timothy davis5, brante goode2, dean ross3 and mike hopmeier4 1strategic innovation group, booz allen hamilton, atlanta, ga, usa; 2office of public health preparedness and response, centers for disease control and prevention, atlanta, ga, usa; 3national park service, washington, dc, usa; 4unconventional concepts, inc., reston, va, usa; 5national disaster medical system, assistant secretary of preparedness and response, washington, dc, usa objective discuss how different big-data analytics, techniques, and tools including open source platforms, cloud analytics, social media, crowdsourcing, and geospatial visualization can be used to quickly achieve situational awareness within seconds of a mci, for use by pre-hospital responders, healthcare workers, and policy makers. introduction a variety of big data analytics, techniques and tools including social media analytics, open source visualizations, statistical anomaly detection, use of application programming interfaces (apis), and geospatial mapping, are used for infectious disease biosurveillance. using these methodologies, policy makers and practitioners detect and monitor outbreaks across the world near real time, in multiple languages, 24/7. the non-infectious disease community, namely critical care, injury, and trauma stakeholders, currently lack this level of sophistication. to respond to most mcis like a terrorist bombing, validated, real-time information is typically available via closed radio channels and limited to a specific set of emergency responders. health care workers, policy makers, and citizens reach for news, radio, and internet sources to characterize casualties and hazards, and increasingly social media. during the boston marathon bombing, witnesses began posting tweets seconds after the bombing and 15 seconds before cnn reported the incident. current trauma data sets are unhelpful for real time response, including trauma registries that are used for hospital performance after an incident, and disaster databases consist of secondary reporting used for academic research purposes. description the purpose of this panel is to discuss how different analytic tools can be used to achieve more rapid and insightful situational awareness during a mci. it will discuss how innovative analytics that are used in biosurveillance such as outbreak detection and monitoring,, could potentially be applied to mcis. the panel will share case studies of their respective agency’s challenges in gathering information during a mci, and highlight some potential ways that data and analytics could be used to improve situational awareness about casualty volume, injuries and trauma, secondary hazards, and population movement during the first moments of a mci. the panel is seeking to solicit feedback about which data, tools, and methods might be most applicable to the syndromic surveillance community in creating a mci analytics tool. audience engagement moderator will begin by polling audience on a) their work with mcis, b) use of analytics in their daily tasks, c) what challenges they have faced dealing with “information blackouts” in mcis. each panelist will discuss a brief example from their agency or organization in dealing with a mci from different points of view: a) medical operations (aspr, dod, dhs), b) epidemiology and public health (cdc/vermont), and c) the mass gathering and population monitoring (nps). keywords mass casualty; analytics; open source; informatics; trauma acknowledgments ms. catherine ordun (moderator), mba, mph, associate, booz allen hamilton, strategic innovations group, atlanta, ga dr. tim davis (panelist), md, mph, assistant secretary of preparedness and response, chief medical officer for national disaster medical system (ndms), washington, dc mr. brant goode (panelist), rn/bsn, mph, captain, u.s. public health service, cdc career epidemiology field officer assigned to vermont dept. of health mr. dean ross (panelist), deputy chief, law enforcement, security, and emergency services, branch chief of emergency services at national park service office of public health, national park service mr. michael hopmeier (panelist), ms, president, unconventional concepts, inc., reston, va mr. kc decker (co-author), asem, senior associate, booz allen hamilton, strategic innovations group, atlanta, ga references http://www.ncbi.nlm.nih.gov/pmc/articles/pmc3706072/: cassa ca, chunara r, mandl k, brownstein js. twitter as a sentinel in emergency situations: lessons from the boston marathon explosions. plos currents disasters. 2013 jul 2. edition 1. doi: 10.1371/currents. dis.ad70cd1c8bc585e9470046cde334ee4b. *catherine ordun e-mail: ordun_catherine@bah.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e201, 201 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts leveraging the laboratory response network: a step toward implementing ihr (2005) tyler wolford*, christopher chadwick and chris mangal public health preparedness and response, association of public health laboratories, silver spring, md, usa objective to promote the laboratory response network (lrn) as a model that supports global health initiatives, strengthens worldwide laboratory systems, and advances international partnerships to prepare for and respond to infectious disease threats. introduction in 1969, the twenty-second world health assembly revised and consolidated the international sanitary regulations into what is known today as the international health regulations (ihr). the ihr promote a global collaboration to prepare for, respond to, and prevent the spread of infectious disease and other public health threats. in 2005, the ihr was once again reviewed and expanded to address the increasing threat of emerging infectious disease due to globalization and urbanization. 195 state parties agreed to adopt and implement ihr (2005); however, as of may 2013, over 100 of these states still had not yet met the eight core capacities outlined in the ihr (2005). in february 2014, the obama administration launched the global health security agenda with the aim of moving toward a world safe and secure from infectious disease threats. the global health security agenda offers a path forward to support countries in achieving the core capacities of the ihr. aphl proposes leveraging the distributed structure of the us managed laboratory response network for biological threats preparedness (lrn-b) to develop the core capacity of laboratory testing and to fulfill the laboratory strengthening component of the global health security agenda. the lrn supports five of the eight core capacities and could serve as a model for state parties lacking resources and an implementation plan. the lrn, founded in 1999 by centers for disease control and prevention (cdc), federal bureau of investigation (fbi) and the association of public health laboratories (aphl), is a specialized network of laboratories that are capable of an all-hazard response to a variety public health threats. leveraging lrn assets internationally would provide a standardized approach toward ihr (2005) implementation and ensure a global collaboration to defend against public health threats. methods we performed a literature review to determine if the mission, vision, infrastructure, and success of the lrn was a biosafety and biosecurity asset for implementation of ihr (2005) in struggling regions. results the vision of the lrn for preparedness and response to biological, chemical, radiological and other public health threats aligns with the objectives outlined in the ihr (2005) and supports five of the eight core requirements. although a plan will need to be further developed in order the meet the remaining three core capacities, the lrn model will provide a strong base for building an implementation plan that supports an all-hazards response to public health threats internationally. conclusions implementation of the ihr (2005) is a complex process, especially for state parties lacking adequate resources and management. the infrastructure of the lrn can be leveraged to assist with meeting the core capacities outlined in the regulations. the lrn model will ensure a standardized approach towards implementation, promote international communication, and provide a link for a connected global response to infectious disease. keywords laboratory response network; international health regulations; lrn; global health security agenda references 1. world health organization, world health assembly. international health regulations (2005). 2nd ed. geneva: the organization; 2008. 2. globalhealth.gov. [internet] washington, dc: global health security agenda: toward a world safe & secure from infectious disease threats. [cited 2014 august 1] available from: http:// www.globalhealth.gov/global-health-topics/global-health-security/ ghsagenda.html. 3. centers for disease control and prevention [internet]. atlanta, ga: the laboratory response network partners in preparedness. may 2013 [cited 2014 august 1] available from: http://www.bt.cdc.gov/lrn/. *tyler wolford e-mail: tyler.wolford@aphl.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e175, 201 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts washington state one health initiative: a model framework to put one health in action wayne clifford* washington state department of health, tumwater, wa, usa objective this presentation describes a model of the process used to form washington state’s one health initiative. we will provide three examples of how the one health model is being applied to three emerging pathogen issues. our objective for this information is to provide this model for others to consider who may be seeking to establish one health initiatives in their own regions. introduction motivated by the global one health movement, the washington state department of health began a strategic effort to form a one health initiative for the state in early 2014. early research on the topic found that many states were working on one health, but we did not find any published models of the processes used to establish it as an initiative. methods the process that we developed has seven basic components: • authorizing environment. the initiative was authorized and supported by senior leadership. • conceptualization. the development team researched, and conceptualized what one health would look like for washington state. • narrowing scope. initial brainstorming resulted in an overwhelming scope that was narrowed down to a manageable size. our structure started with a strategic level steering committee. • personalization. we met individually with senior leadership from each organization that we thought should be on the steering committee to gain buy-in. • visioning. we used an interview tool to help the steering committee create their vision and mission. • committee charter. we developed a committee charter that provided the framework for how the committee would do its work. • meetings. the committee held in-person meetings and worked together to identify issues. • issue prioritization. the committee members engaged in a process that focused on identifying the highest priority issues.the committee selected antimicrobial resistance/stewardship, and one health surveillance and data systems as the two top priorities. • implementation. the committee now has a three year action agenda with two workgroups focused on prioritiy issues. two workgroups were formed to focus on antimicrobial data and stewardship, and one health surveillance data. that work is ongoing. results the results of this effort include: • formation of washington state’s one health steering committee. the committee has a strategic focus that provides guidance and direction. • formation of two workgroups. out of the strategic dircetion of the steering committee, we formed two tactical workgroups: the antimicrobial stewardship workgroup, and the surveillance and data systems workgroup. • increased ineternal collaboration. within the department of health, human communicble disease epidemiology and environmental public health sciences are separated organizationally in different divisions, and geographically by 75 miles of congested freeway. our internal focus on increased collaboration has helped us bring the two divisions closer together. • increased external collaboration. agencies, academic institutions, and organizations are working closer together on projects. • application of the model to other one health issues. awareness of the one health model has increased within the agency resulting in an interest in applying the model to other health issues. conclusions 1) the cornerstone to one health is collaboration. collaboration is a time investment. project managers need to build that time in to projects to make collaboration effective. 2) healthy leadership is critical to forming and maintaining relationships.conflicts occur, and success depends on staying focused on constructive outcomes. 3) as collaboration increases, so do the needs that are being served. collaborators need to be transparent with the team about what their needs are from the project. keywords health; zoonotic; collaboration acknowledgments dr. ron wohrle, washington state department of health maryanne guichard, washington state department of health dr. terry mcelwain, washington state university dr. kathy lofy, washington state department of health dr. peter rabinowitz, university of washington dr. kristin mansfield, washington state department of fish and wildlife dr. joe baker, washington state department of agriculture candace joy, washington state veterinary medical association dr. paul pottinger,university of washington robert duff, office of the governor dr. gordon plotts, kulshan veterinary hospital dr. marisa d’angeli, washington state department of health hanna oltean, washington state department of health melissa kemperman, washington state department of health one health workgroup members *wayne clifford e-mail: wayne.clifford@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e55, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts development and pilot study road traffic injury surveillance, kaduna nigeria obafemi j. babalola* residence cohort vi, nigeria field epidemiology and laboratory training pro, kaduna state, nigeria objective we pilot a rti surveillance system using data from frsc, police motor traffic division and health facilities in kaduna metropolis, nigeria to ascertain its feasibility and generate data needed for action toward achieving sustainable development goals 3.6 target. introduction road traffic injury is common cause of unintentional injury globally and low and middle income countries account for 90% of 1.3 million road traffic injury (rti) deaths. in africa region, nigeria accounts for 25% of rti mortality but has no comprehensive and reliable rti surveillance system. data from federal road safety commissions (frsc) shows gaps in rti reporting with large disparity with estimated value from world health organization. methods kaduna metropolis is the capital of kaduna state with estimated population of 1.96 million. it is a major route between abuja, the national capital and 15 northern nigeria states with high vehicular movement. we adapted who injury surveillance guideline and centers for disease control and prevention surveillance training manual for this study. a case of rti is any person injured or died within 30 days as a result injuries incurred from vehicular collision on a public road in kaduna metropolis. data collected using a pretested questionnaire for rti cases at health facilities, police and frsc. data were linked by deterministic method, cleaned and analysed. frequency and proportion were calculated to characterize the rti. the study was supported by a mini-grant from center for disease control and prevention. results data was collected from february to april 2016. of the 324 crashes reported, 566 people injured and 66 deaths with case fatality rate of 11.7%. male gender accounts for 81.8% and age 20 – 39 years were 64.6%. commercial drivers were 20.7%, pedestrian 21% and passengers were 53.7%. sixty percent of the crash occurred between cars or buses while 21% were without collision with any vehicle or stationary objects. of the 66 deaths reported 61(92.4%) died at crash site. frsc evacuated 21%, 38.6% were evacuated by other road users. no use of seat belt and crash helmets reported and only 5.1% received first aid care before reaching reporting facility. rti incidence peaked between 6:00 pm to 8:59 pm with 26 persons per hour. conclusions essential to sustainable development goal 3, a multisector rti surveillance system that generate data for action in kaduna metropolis, nigeria is feasible and data generated was used for action at different levels to mitigate against the burden of rti keywords road traffic injury; pedestrian; injury; crash; surveillance acknowledgments we acknowledge the united states centre for disease control and prevention, nigeria field epidemiology and laboratory training program, kaduna state ministry of health, kaduna nigeria, nigeria police motor traffic division and federal road safety corps, kaduna sector command, kaduna, nigeria *obafemi j. babalola e-mail: drfemibabs@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e129, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts “that was then, this is now” improving public health syndromic surveillance baselines roger morbey*, alex j. elliot, paul loveridge, helen hughes, sally harcourt and sue smith public health england, birmingham, united kingdom objective to improve the ability of syndromic surveillance systems to detect unusual events. introduction syndromic surveillance systems are used by public health england (phe) to detect changes in health care activity that are indicative of potential threats to public health. by providing early warning and situational awareness, these systems play a key role in supporting infectious disease surveillance programmes, decision making and supporting public health interventions. in order to improve the identification of unusual activity, we created new baselines to model seasonally expected activity in the absence of outbreaks or other incidents. although historical data could be used to model seasonality, changes due to public health interventions or working practices affected comparability. specific examples of these changes included a major change in the way telehealth services were provided in england and the rotavirus vaccination programme introduced in july 2013 that changed the seasonality of gastrointestinal consultations. therefore, we needed to incorporate these temporal changes in our baselines. methods we used negative binominal regression to model daily syndromic surveillance, allowing for day of week and public holiday effects. to account for step changes in data caused by changes in healthcare system working practices or public health interventions we introduced specific independent variables into the models. finally, we smoothed the regression models to provide short term forecasts of expected trends. the new baselines were applied to phe’s four syndromic surveillance systems for daily surveillance and public-facing weekly bulletins. results we replaced traditional surveillance baselines (based on simple averages of historical data) with the regression models for daily surveillance of 53 syndromes across four syndromic surveillance systems. the improved models captured current seasonal trends and more closely reflected actual data outside of outbreaks. conclusions syndromic surveillance baselines provide context for epidemiologists to make decisions about seasonal disease activity and emerging public health threats. the improved baselines developed here showed whether current activity was consistent with expected activity, given all available information, and improved interpretation when trends diverged from expectations. keywords syndromic; surveillance; public health acknowledgments ram, aje & ges are partly funded by the national institute for health research (nihr) health protection research unit in emergency preparedness and response. the views expressed are those of the authors and not necessarily those of the national health service, the nihr, the department of health or public health england. *roger morbey e-mail: roger.morbey@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e22, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts electronic case reporting of stis: assessing ehr generated ccds kim peifer*1, mary stark2, rita altamore1 and julieann simon1 1washington state department of health, shoreline, wa, usa; 2planned parenthood of the great northwest and the hawaiian islands, boise, id, usa objective we reviewed ccds (a type of consolidated clinical data architecture (c-cda) document) shared by our clinical partner, planned parenthood of the great northwest and hawaiian islands (ppgnhi) since october, 2015. analyses focuses on: -completeness -degree to which the ccd matches program area information needs -differences in ehr generation methods -presence and location of triggers (based on the reportable conditions trigger codes) that would initiate ccd generation. introduction under the cdc std surveillance network (ssun) part b grant, wa doh is testing electronic case reporting (ecr) of sexually transmitted infections (sti) from a clinical partner. methods two methods of ccd generation, based on existing ehr capabilities, were used to create ccds that were delivered to wa doh using secure file transport protocol (sftp). ppgnhi uses the nextgen ehr system. the first batch received was extracted using the medical summary utility. random selection of cases (25) from lab positive chlamydia (ct), gonorrhea (gc) or syphilis encounters with a follow-up plan in the ehr (1/1/2015-3/31/2015). each ccd contained manually selected encounters (related to sti case). cases are now extracted directly from a patient chart (file-->generate ccd). two types of ccds can be created: single encounter ccds and longitudinal encounter ccds. the ccds were analyzed for completeness, crossover with the existing paper case report, and with relevant cda and c-cda implementation guide (ig) standards. results this analysis includes four reportable events across 6 ccds. one event is represented by both a longitudinal ccd and 2 single encounter ccds. the ccds contained most of the basic demographic information requested in the paper case report with the exception of “middle initial”. information on the important paper case report components “gender of sex partner” and “partner management plan” are not found in the ccd. the ccd results section contained lab tests and results that include site of infection and could confirm diagnosis. the ordered test (panel) is not coded, though the individual tests performed are loinc coded. the ccd medications section meets sti program needs for information about treatment in a case report. information is represented using rxnorm codes as specified by the c-cda ig. the ccd problems section was not present in documents generated using the msu but was present in documents created using file --> generate ccd from the patient chart. the problems section and coded entries (icd-9-cm and icd-10) are required for ccds. the problems do not include effective dates, which are not required by the ig. pregnancy status, and information about hiv testing (including previous positive), are present in the ccd problems section only if the encounter during which testing occurred is included in the ccd submitted. using the ccd in place of the paper case report requires understanding of the clinical workflow and use of ehr. two instances that require specific attention are the “exposure” status of the case (known/possibly exposed vs. not shared/not known), and the “presentation” of the diagnosis (symptomatic vs asymptomatic). for example, the icd-10 code z11.3 (encounter for screening for infections with a predominantly sexual mode of transmission), cannot be interpreted as a true “screening”, as this diagnosis is recorded for all visits that include sti testing. similarly, a code for exposure to stis is sometimes used, but not consistently enough to allow reliable identification of cases in which the patient was tested due to an exposure or possible exposure. work with our clinical partner to understand what inferences can and should be made is an important part of evaluating the ccd as a replacement to the paper case report. conclusions the ccds submitted to doh show that most information requested in an sti case report can be found in a ccd with some exceptions, notably “gender of sex partners” and “partner management plan”. some information is only inconsistently present, for example, exposure status and presentation. understanding how the ccd could replace the paper case report requires working with the reporter to insure that the information is interpreted on the receiving end in the same way that it is interpreted in the clinical workflow and entered in the ehr. keywords electronic case reporting; informatics; data sharing; electronic health records; sexually transmitted diseases acknowledgments -funding for this project in part provided by the u.s. centers for disease control and prevention, std surveillance network, ps13-1306 -this study/report was supported in part by an appointment to the applied public health informatics fellowship and the informatics training in place program administered by cste and funded by the centers for disease control and prevention (cdc) cooperative agreement 3u38-ot000143-01s3. -washington doh ssun part b team, especially rita altamore, julie simon, and teal bell -planned parenthood of the great northwest and hawaiian islands, especially mary stark and dr. laurel kuehl *kim peifer e-mail: kim.peifer@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e36, 2017 using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status christine elizabeth friesen1, patrick seliske2, andrew papadopoulos3 1. university of guelph, guelph, ontario, canada 2. wellington-dufferin-guelph public health, guelph, ontario, canada 3. university of guelph, guelph, ontario, canada abstract objectives. socioeconomic status (ses) is a comprehensive indicator of health status and is useful in area-level health research and informing public health resource allocation. principal component analysis (pca) is a useful tool for developing ses indices to identify area-level disparities in ses within communities. while ses research in canada has relied on census data, the voluntary nature of the 2011 national household survey challenges the validity of its data, especially income variables. this study sought to determine the appropriateness of replacing census income information with tax filer data in neighbourhood ses index development. methods. census and taxfiler data for guelph, ontario were retrieved for the years 2005, 2006, and 2011. data were extracted for eleven income and non-income ses variables. pca was employed to identify significant principal components from each dataset and weights of each contributing variable. variable-specific factor scores were applied to standardized census and taxfiler data values to produce ses scores. results. the substitution of taxfiler income variables for census income variables yielded ses score distributions and neighbourhood ses classifications that were similar to ses scores calculated using entirely census variables. combining taxfiler income variables with census non-income variables also produced clearer ses level distinctions. internal validation procedures indicated that utilizing multiple principal components produced clearer ses level distinctions than using only the first principal component. conclusion. identifying socioeconomic disparities between neighbourhoods is an important step in assessing the level of disadvantage of communities. the ability to replace census income information with taxfiler data to develop ses indices expands the versatility of public health research and planning in canada, as more data sources can be explored. the apparent usefulness of pca also contributes to the improvement of ses measurement and calculation methods, and the freedom to input areaspecific data allows the present method to be adapted to other locales. keywords: area-level socioeconomic status, principal component analysis, priority neighbourhood, census, national household survey correspondence: cfries02@uoguelph.ca http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi doi: 10.5210/ojphi.v8i2.6733 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction determinants of health income has been used in public health research and practice for many years as an indicator of health status and predictor of health outcomes. low income has been associated with an increased risk of developing chronic conditions like arthritis and diabetes, living with a disability, and experiencing mental health issues [1,2]. pre-existing medical conditions may perpetuate a vicious cycle by restricting exposure to employment opportunities [3]. in canada, being low-income, as opposed to middleor high-income, is associated with increased use of health care resources [1], and restricted access to privately-funded medical procedures, dental coverage, screening services, educational resources, affordable housing, and safe working environments [2,3]. at the population level, a large income disparity between wealthy and poor individuals in a community has been linked to increased rates of disease across the population and high costs to the medical system [1,3]. while income remains a very important determinant of health, other factors like education, employment, and family structure can significantly affect the health of individuals and the community as a whole. socioeconomic status (ses) has been used as a predictor of health outcomes and is more comprehensive than income alone. ses encompasses the conditions experienced by individuals and communities created by complex interactions between income, employment, occupation, education level, and family dynamics [4-11]. the relationship between education and income has been shown in several studies [12-15]. for instance, low motivation to pursue education can be the result of family background, poor family standard of living, parental structure, and low educational aspirations by parents [12]. those who have not completed a high school education may not achieve the level of verbal skills nor be exposed to employment opportunities that are associated with the attainment of high-paying jobs [14,15]. additionally, attaining a high school diploma is becoming more recognized by canadians as a requirement for many training programs as well as the common prerequisite for joining the labour force [13]. as a result, those who do not complete high school may be less able to afford safe housing and healthy food, leaving them at higher risk for negative health outcomes and criminal behaviours [3,13-15]. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi job loss is a stressful event that affects self-esteem and can lead to harmful coping mechanisms, such as substance abuse and engaging in criminal activities [3]. unemployed individuals suffer high mortality rates and more severe health problems than those who are employed [16]. when seeking a new job, these individuals may be more likely to accept lower-paying employment in more dangerous conditions [3]. lone parent status is another important social determinant of health that influences ses. families led by a single parent are more likely to be classified as low income and are among the most impoverished priority populations [1,2]. when the parent does not have a wellpaying occupation, the family is at an increased risk for poor health outcomes resulting from lack of access to health and educational services, good housing, safe working environments, and healthy food [1]. compared to single fathers, single mothers are at even higher risk for poor health for themselves and their families, due to pay inequalities between men and women, workplace discrimination, increased psychosocial pressures, and costly childcare [3,7]. socioeconomic status and principal component analysis the objective definition of and research into ses is relatively new due to its complex nature. historically, social status was measured on simple scales that allocated an equal weight to individual-level factors such as occupation type, occupation of friends, income, and education level [8]. however, it has become apparent that social conditions outside of an individual’s direct control can influence one’s health [5]. socioeconomic indicators of health cluster at the neighbourhood level, which contribute to the understanding of health inequities within communities [7]. principal component analysis (pca) is a statistical technique that has been used to develop area-level ses indices that are often mapped using geographic information systems to produce clear visual boundaries of ses differentials [4,6,10,11,17]. this information informs public health resource allocation, service delivery, and program dissemination as it provides a more comprehensive understanding of communities’ levels of disadvantage in relation to one another. relevant ses variables may be inputted into a pca-capable program to extract multiple underlying dimensions based on the variation produced by these correlated variables. common statistical assumptions of normality and homoscedasticity do not apply to pca, which eliminates the need for data transformations that often result in a loss of original information [18]. pca outputs a list of principal components (pcs) that are independent orthogonal linear combinations of the variables and are listed in decreasing order of proportion of explained variance. the first pc produced when utilizing ses indicator variables (such as income and unemployment) has often been considered the only dimension pertaining to ses, and therefore only the variable loadings pertaining to the first pc have been used for ses calculations previously [5,7,9-11,19]. other researchers, however, have used variable loadings from any components that each represents a sufficient proportion of the overall http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi variation [4,6]. the literature generally supports the notion that the first pc represents the economic system aspect of ses [4-7,9-11,19], while subsequent components may represent other dimensions of ses, such as the social system and marginalization [6], depending on which variables contribute highly to that principal component [4]. the use of pca has been important to the development of indices because it assigns different weights to each variable, as opposed to arbitrarily weighing each variable equally [2]. while it is simpler to assign equal weights to variables in an index, modern understanding of ses requires the exploration of complex relationships between variables that historically simple methods do not support. furthermore, pca can provide insight into which variables have greater influence on the dimension(s) of ses [4,6] when using area-specific data to inform public health policy, interventions, and resource allocation according to the area’s unique needs. changes to the canadian census affecting ses index development ses research by wellington-dufferin-guelph public health [2] has been performed in the past to identify priority neighbourhoods in their service area [2]. this research was reliant on accurate census information pertaining to income and non-income variables contributing to ses. due to privacy-related concerns and decreasing response rates, the canadian order in council decided in june 2010 that the mandatory 2011 canadian census would only collect demographic information pertaining to family structure, spoken language, and farm management practices [20]. a second voluntary national household survey (nhs) was distributed that resembled the 2006 long-form census [21]. the validity of data collected by the 2011 nhs, namely income fields, is questionable due to an increased risk of non-response bias stemming from the voluntary nature of the nhs. taxfiler data may prove to be a viable alternative to current nhs data in the calculation of ses at the neighbourhood level. the federal government acquires taxfiler information annually and the completion of personal tax returns is mandatory by individuals in the labour force. taxfiler data in canada are more precise than census estimates in terms of dollar and cent amounts, and must be completed truthfully to avoid fines and penalties, which is further cross-referenced with submitted employer records. the current report presents a method of developing neighbourhood-level ses indices using pca with income and non-income variables indicative of ses. this study assesses the effectiveness of using taxfiler data as an alternative data source to replace current canadian census income variables typically used in ses calculations. the present study also seeks to descriptively validate the use of multiple pcs in the calculation of ses and support the inclusion of non-income variables. this method can be tailored to other locales by selecting variables appropriate for the local demographics and the results may be utilized to guide the allocation of resources that support the health of priority populations. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi methods data sources census and taxfiler data for the predominantly urban census metropolitan area (cma) of the city of guelph were obtained through wdgph’s membership as part of the community data consortium, which permits access to the canadian council on social development’s community data program (cdp) [22]. data for the current project were obtained at the census tract (ct) level, which geographically divides a cma with a core population of 50,000 or more into smaller areas containing 2,500 to 8,000 persons. cts attempt to contain individuals that are generally homogenous in terms of living conditions and socioeconomic characteristics [23,24]. census profiles for the years 2006 and 2011 were obtained in addition to the 2011 nhs profile. taxfiler family data tables were obtained for the years 2005 and 2011. taxfiler data from 2005 were considered comparable to the 2006 census profile in terms of income fields, since the 2006 census income measurements were based on individuals’ assessment of their 2005 incomes. appendix a presents a description of each dataset. ethics approval was obtained through the university’s research ethics board. data processing census and nhs data were extracted from the original cdp files using beyond 20/20 (beyond 20/20, 2015). taxfiler datasets were provided in microsoft excel (microsoft, 2013) format. all datasets were further processed within microsoft excel, where data were restricted to the cts within the borders of the guelph cma and fields pertinent to ses indicator variables were retained (table 1). twenty-one cts based on 2001 census geographies were retained for the 2005 taxfiler dataset. twenty-seven cts were retained for 2006 census and all 2011 datasets. in order to compare 2005 taxfiler income variables with those from the 2006 census, the additional cts in 2006 were combined and averaged according to the previous 2001 census ct geographies. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi table 1. income and non-income socioeconomic indicator variables derived from five datasets for the years 2005, 2006, and 2011 for the census metropolitan area of guelph. income indicator description source datasets median family income ($) calculated by statistics canada taxfiler family data, 2005 taxfiler family data, 2011 census profile, 2006 median single person income ($) calculated by statistics canada taxfiler family data, 2005 taxfiler family data, 2011 census profile, 2006 low income families, after-tax (%) � li couple fam. + li lone parent fam. couple fam. + lone parent fam. � × 100% taxfiler family data, 2005 � li couple fam. + li lone parent fam. couple fam. + lone parent fam. � × 100% taxfiler family data, 2011 calculated by statistics canada census profile, 2006 low income unattached, after-tax (%) � li single person single person � × 100% taxfiler family data, 2005 � li single person single person � × 100% taxfiler family data, 2011 calculated by statistics canada census profile, 2006 non-income indicator description source datasets lone parent families (%) � lone parent families total families � × 100% census profile, 2006 census profile, 2011 single mothers (%) � female − led lone parent fam. total families � × 100% census profile, 2006 census profile, 2011 unemployment rate, 15 years and over (%) calculated by statistics canada census profile, 2006 nhs profile, 2011 low education, 15 years and over (%) � le over 15 (20% of sample) total population (20% of sample) � × 100% census profile, 2006 nhs profile, 2011 average home value ($) calculated by statistics canada census profile, 2006 nhs profile, 2011 average monthly rent ($) calculated by statistics canada census profile, 2006 nhs profile, 2011 managerial occupation (%) � managerial position total work force � × 100% census profile, 2006 nhs profile, 2011 http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi variable selection eleven variables were retained and calculated from the five source datasets (table 1). the following nine variables were selected based on previous research by wdgph [2]: median family income; median single person income; proportion of low income families; proportion of low income unattached; proportion of lone parents; unemployment rate of those aged 15 or older; proportion of those aged 15 or older with a low education level; average home value; and average monthly rent. households were considered ‘low income’ if they fell into an income threshold in which more than 20% of their income was expended on food, clothing, and shelter [25]. previous wdgph research restricted ‘low education’ to individuals with ‘less than grade 9 education’; however, evidence from the literature suggests that less than a high school education is associated with adverse health and economic outcomes [3,12-15]. the present study therefore utilized the census field ‘no certificate, degree or diploma’ to represent individuals with relatively low education. two additional non-income variables were included in the present analysis: proportion of single mothers [3], and proportion of individuals in a managerial occupation [6,7,9]. statistical analyses descriptive statistics means, ranges, and standard deviations for each of the 11 indicator variables were obtained using stata 13 (statacorp lp, 2013). the results are presented in table 2 stratified by data source. taxfiler income variable validation the first pca was performed in stata 13 using the 11 indicator variables from the 2006 census profile. data were automatically standardized on the correlation matrix using the ‘pca’ function in stata 13. the resulting pcs were selected for further analysis if they first met kaiser’s criterion, where pcs with an eigenvector greater than 1.0 should be retained [6,11]. pcs that met kaiser’s criterion were excluded if they represented less than 10% of the variance from the original variables [4]. for the purposes of later calculations, each selected pc was weighted according to its proportion of the sum of the variance represented by the selected pcs: pcw= pcproportion of total variance proportion of total variance represented by selected pcs (1) in order to interpret influential variables amongst each retained pc, un-rotated eigenvector correlations, or variable loadings, were examined. signs indicated the direction of a variable’s influence on the underlying dimension explained by the pc relative to the http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi influence of the other variables. hair et al. [26] suggested that |0.4| should be the lowest cutoff for relevant variable loadings, while central factors should have eigenvectors of at least |0.6|. however, recent research suggests that data realistically produces minimum relevant variable loadings closer to |0.25| with the central factor(s) having an eigenvector of around |0.4| [27]. the present method considered the suggestions of raubenheimer [27], which better suited the variable loadings produced by this research. eigenvectors greater than the absolute average variable loading (eq. 2) as well as eigenvectors within 0.1 less than the absolute average variable loading were considered influential on each pc: � � 1 # of variables � (2) indicator variable loadings were then multiplied by the corresponding pc weight (pcw; see eq. 1) and summed to create variable-specific factor scores to be applied to the ct-specific data values: factor score = �(indicator variable loading × pcw)1…j (3) where indicator var. loading = eigenvector per selected pc for an indicator variable; and where pcw = pc weight for each selected pc. data from the 2006 census profile were converted to z-scores in stata 13 to standardize measurement units. the standardized data were multiplied by the variable-specific factor scores and values were summed to create ct-specific ses scores. ses scores were then standardized to range from zero to one for easier interpretation [6]: the ses scores (4) standardized ses scores were reversed so that a higher ses score represented a higher socioeconomic status of a given ct. ses scores were plotted using a bar graph and divisions in ses score levels were determined descriptively by visual inspection. in addition to this, cut-offs were used to support the visual aspect by calculating the average numerical increment in ses score needed to produce a constant increase in ses score distribution. differences between subsequent ct ses scores greater than 0.0476 (for 2005 and 2006 data containing 21 cts) or 0.037 (for 2011 data containing 27 cts) were utilized to confirm distinctions in ses scores made upon visual inspection. a second pca was performed and an ses index created by substituting the four income fields from the 2006 census profile with four similar fields from 2005 taxfiler data. validation of the use of taxfiler data involved a descriptive comparison between the two methods of ses score distributions and ct movements between ses level classifications. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi a third ses index was produced according to the above procedure using 2011 census profile, 2011 nhs profile and 2011 taxfiler datasets for the cma of guelph. internal validation two internal validation procedures were performed. first, ses indices were developed for all three data groups (i.e. 2006 census only, 2006 census + 2005 taxfiler, and 2011 census + 2011 nhs + 2011 taxfiler), using only the first pc. the ses score distributions of these were compared to the ses score distributions produced when using all of the components with an eigenvalue greater than 1.0 and a proportion of explained variance greater than 10%. ct movements were also compared between methods. the second validation procedure involved removing the non-income variables from the ses score calculation. these ses score distributions produced were compared to the distributions produced by the ‘first pc method’ and the ‘eigenvalue >1.0 method’ to assess the effect of non-income variables on ses level. ct movements between methods were also compared. results 1) descriptive statistics the population of the city of guelph within its cma increased by 5.6% from 114,943 in the year 2006 to 121,688 in 2011, according to census profile data. the 2011 nhs profile reported a similar population of 120,540. taxfiler datasets from 2005 and 2011 reported 86,120 and 92,650 tax filers, respectively. table 2 describes the characteristics of each ses indicator stratified by data source. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi table 2. descriptive statistics of the 11 indicator variables selected for principal component analysis stratified by source dataset for the census metropolitan area of guelph for the years 2005, 2006, and 2011. indicator source dataset median mean sd range min. max. median family income ($) 2006 census profile 73483.00 73439.20 16947.58 43926.00 108581.00 2005 taxfiler 68600.00 68480.95 15218.79 42600.00 97500.00 2011 taxfiler 83550.00 80653.70 17144.56 48080.00 113350.00 median single person income ($) 2006 census profile 28189.00 29336.07 7080.29 17231.00 48928.50 2005 taxfiler 27000.00 27185.71 3023.95 20100.00 32800.00 2011 taxfiler 28960.00 30394.07 5205.80 22430.00 44370.00 low income families, after-tax (%) 2006 census profile 6.1 6.4 3.8 1.0 15.9 2005 taxfiler 8.4 10.6 6.6 3.8 27.8 2011 taxfiler 8.2 10.3 5.8 3.8 26.5 low income unattached, after-tax (%) 2006 census profile 25.2 24.6 9.0 7.8 43.5 2005 taxfiler 21.7 22.1 5.9 8.8 35.4 2011 taxfiler 23.7 24.1 6.5 7.9 35.3 lone parent families (%) 2006 census profile 17.4 17.1 5.3 7.2 26.8 2011 census profile 16.3 16.7 5.1 6.0 28.8 single mothers (%) 2006 census profile 12.6 13.0 4.4 5.5 21.7 2011 census profile 13.0 13.3 4.3 4.3 24.2 unemployment rate, 15 years and over (%) 2006 census profile 5.1 5.4 1.6 2.3 8.2 2011 nhs profile 6.9 6.9 1.7 2.8 10.2 low education, 15 years and over (%) 2006 census profile 21.6 21.5 6.3 11.4 33.0 2011 nhs profile 15.8 17.6 6.0 9.0 31.2 average home value ($) 2006 census profile 261031.00 256708.80 48921.65 180472.00 388042.00 2011 nhs profile 308219.00 313129.30 52772.46 220035.00 428313.00 average monthly rent ($) 2006 census profile 785.00 813.23 78.04 696.00 1014.00 2011 nhs profile 844.00 903.41 202.77 640.00 1560.00 managerial occupation (%) 2006 census profile 7.8 8.6 2.8 3.9 14.8 2011 nhs profile 10.1 10.0 2.9 3.9 15.5 sd, standard deviation http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi 2) taxfiler income variable validation the results of the first pca using 2006 census profile data are presented in table 3. the first three retained components cumulatively represent 78.5% of the total variance. the first pc represents 48.5% of the total variance and is highly influenced by family income, proportion of low-income families, proportion of lone parent families and single mothers, average home value, and managerial occupation. to a lesser degree, single person income, unemployment rate, and low education also contribute to pc1. median family income could be interpreted as having a negative influence on a community’s level of economic deprivation, while the prevalence of low-income families would contribute positively to the community’s economic deprivation. conversely, taking the reciprocal sign may lead to a more intuitive interpretation using economic status, rather than economic deprivation, as the outcome. the second pc represents an additional 16.4% of the total variance and is strongly influenced by both the proportion of low income unattached individuals and low education; however, these have opposite directional effects on the dimension explained by pc2. less influential variables include unemployment rate, single person income, average home value, and managerial occupation. the third and last retained pc represents 13.7% of the total variance and is mainly influenced by single person income, proportion of single mothers, unemployment rate, and average monthly rent. family income has a less pronounced influence. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi table 3. principal component analysis of 11 socioeconomic status indicator variables from 2006 census profile data only for the census metropolitan area of guelph. principal component eigenvalues component eigenvalue difference proportion cumulative pcw (%) pc1 5.334090 3.535230 0.4849 0.4849 61.74 pc2 1.798860 0.292350 0.1635 0.6484 20.82 pc3 1.506500 0.488332 0.1370 0.7854 17.44 c4 1.018170 0.463475 0.0926 0.8780 ── c5 0.554698 0.224526 0.0504 0.9284 ── c6 0.330171 0.156520 0.0300 0.9584 ── c7 0.173651 0.057958 0.0158 0.9742 ── c8 0.115693 0.025609 0.0105 0.9847 ── c9 0.090084 0.034112 0.0082 0.9929 ── c10 0.055972 0.033863 0.0051 0.9980 ── c11 0.022109 ── 0.0020 1 ── indicator variable loadings (eigenvector correlations) variable pc1 pc2 pc3 factor score median family income ($) *0.3639 0.0882 0.2965 -0.1546 median single person income ($) **0.2535 -0.2387 0.5290 -0.1139 low income families, after-tax (%) 0.3690 0.0991 0.0647 0.2597 low income unattached, after-tax (%) 0.1784 0.6239 0.1382 0.2159 lone parent families (%) 0.4017 0.1023 0.1536 0.2961 single mothers (%) 0.3307 0.0151 0.4462 0.2851 unemployment rate, 15 years and over (%) 0.2139 0.3373 0.3641 0.2658 low education, 15 years and over (%) 0.2076 -0.5391 0.0063 0.0148 average home value ($) -0.372 0.2437 0.1220 -0.1577 average monthly rent ($) 0.0005 0.0060 0.4885 0.0868 managerial occupation (%) -0.3706 0.2481 0.0055 -0.1781 c, component. *bolded if ≥|0.3015|. **italicized if ≥|0.2015| and <|0.3015|. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi the results of the second pca using 2006 census profile data and 2005 taxfiler data are presented in table 4. the first three principal components altogether account for 81.8% of the total variance, which is 3.3% more than the retained pcs in the first pca using 2006 census profile data only. compared to the first pca using 2006 census profile data only, more of the variation has shifted to the first and second pcs (51.0% [+2.5%] and 20.0% [+3.6%], respectively) from the third pc (10.8% [-2.9%]). additionally, pc1 is influenced by most (9) of the 11 variables, while pc3 is influenced mainly by a single variable, average monthly rent, and 2 lesser-weighted variables. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi table 4. principal component analysis of 11 socioeconomic status indicator variables from 2006 census profile and 2005 taxfiler data for the census metropolitan area of guelph. principal component eigenvalues component eigenvalue difference proportion cumulative pcw (%) pc1 5.613710 3.414840 0.5103 0.5103 62.39 pc2 2.198860 1.014040 0.1999 0.7102 24.44 pc3 1.184830 0.298320 0.1077 0.8179 013.17 c4 0.886505 0.414606 0.0806 0.8985 ── c5 0.471899 0.258890 0.0429 0.9414 ── c6 0.213008 0.022701 0.0194 0.9608 ── c7 0.190307 0.074306 0.0173 0.9781 ── c8 0.116001 0.037282 0.0105 0.9886 ── c9 0.078719 0.053057 0.0072 0.9958 ── c10 0.025662 0.005162 0.0023 0.9981 ── c11 0.020500 ── 0.0019 1 ── indicator variable loadings (eigenvector correlations) variable pc1 pc2 pc3 factor score median family income ($) * -0.3687 0.1538 0.1220 -0.1764 median single person income ($) -0.3164 -0.2904 0.2619 -0.2339 low income families, after-tax (%) 0.3697 0.2157 0.1286 0.2664 low income unattached, after-tax (%) ** 0.2059 0.5395 0.0046 0.2609 lone parent families (%) 0.4015 0.0218 0.0437 0.2616 single mothers (%) 0.3367 -0.0199 0.2628 0.2398 unemployment rate, 15 years and over (%) 0.2516 0.2733 0.1483 0.2433 low education, 15 years and over (%) 0.1620 -0.5230 0.1265 -0.0434 average home value ($) -0.3290 0.3365 0.0002 -0.1231 average monthly rent ($) 0.0054 0.0255 0.8784 0.1253 managerial occupation (%) -0.3354 0.3025 0.1395 -0.1537 c, component. *bolded if ≥|0.3015|. **italicized if ≥|0.2015| and <|0.3015|. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi the census data from 2006 produced a standardized ses index that increased consistently across most of the cts. however, extreme ses scores were seen at the lowest and highest levels of ses. visual inspection revealed five distinct ses score levels, which were further supported by mathematical cut-offs (figure 1). the ses index produced from a combination of both 2006 census and 2005 taxfiler data revealed clearer distinctions between ses levels, as well as a less extreme values at the high and low ends of the ses spectrum (figure 2). figure 1. socioeconomic status index distribution produced from the 2006 census profile dataset for the census metropolitan area of guelph using all principal components with eigenvalues greater than 1.0 and that represent more than 10% of the variation. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi figure 2. socioeconomic status index distribution produced from the 2006 census profile and 2005 taxfiler datasets for the census metropolitan area of guelph using all principal components with eigenvalues greater than 1.0 and that represent more than 10% of the variation. ses classifications remained mostly consistent among cts between the two data groups (figure 3). due to the overall consistency between both data groups and the shape of the ses index distribution, substituting taxfiler income variables for census income variables was deemed appropriate for creation of the 2011 ses index. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi figure 3. changes to census tract socioeconomic levels for the census metropolitan area of guelph after substituting 2006 census income variables with 2005 taxfiler income variables using all principal components with eigenvalues greater than 1.0 and that represent more than 10% of the variation. 3) 2011 ses index the results of the third pca using 2011 data from the census, nhs, and taxfiler datasets are presented in table 5. the total variance represented by the pcs is 73.03%, which is less than the two previous pcas simply because only two components were retained in this case. notably, more of the total variance shifted to the first pc compared to the two previous pcas (48.49% to 51.03% to 53.03%). pc1 is mainly influenced by median family income, proportion of low-income families, proportion of lone parent and single mother families, average home value and managerial occupation. median single person income and proportion of low education have a slight influence on this pc. pc2 is highly influenced by proportion of low-income individuals, single person income and unemployment rate, and somewhat influenced by proportion of low educated individuals over the age of 15, average home value, and average monthly rent. the datasets from 2011 produced a well-distributed standardized ses index with the clearest distinctions at the lower and higher ends of the scale (figure 4). ses level census tract ses score census tract ses score ses level low a 0.0000 b 0.0000 low b 0.0233 a 0.0295 low-med c 0.1557 e 0.1697 low-med d 0.1696 c 0.2476 e 0.2190 d 0.2887 medium f 0.3241 g 0.3015 g 0.3369 -1 h 0.4663 medium h 0.3581 i 0.5085 i 0.4105 f 0.5117 j 0.4200 j 0.5139 med-high k 0.4843 l 0.6269 med-high l 0.4941 k 0.6271 m 0.5367 o 0.6288 n 0.5435 n 0.6428 o 0.5904 m 0.6515 p 0.5970 +1 r 0.7226 high high q 0.6562 p 0.7850 r 0.6667 t 0.7992 s 0.7050 q 0.8038 t 0.7082 s 0.8522 u 1.0000 u 1.0000 ses level change 2006 census + 2005 taxfiler data2006 census data only http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi table 5. principal component analysis of 11 socioeconomic status indicator variables from 2011 census profile, 2011 national household survey profile and 2011 taxfiler datasets for the census metropolitan area of guelph. principal component eigenvalues component eigenvalue difference proportion cumulative pcw (%) pc1 5.833060 3.632300 0.5303 0.5303 72.60 pc2 2.200750 1.212010 0.2001 0.7303 27.40 c3 0.988738 0.194348 0.0899 0.8202 ── c4 0.794390 0.338159 0.0722 0.8924 ── c5 0.456231 0.185031 0.0415 0.9339 ── c6 0.271200 0.088362 0.0247 0.9586 ── c7 0.182837 0.046625 0.0166 0.9752 ── c8 0.136213 0.024284 0.0124 0.9876 ── c9 0.111929 0.089500 0.0102 0.9978 ── c10 0.022428 0.020202 0.0020 0.9998 ── c11 0.002227 ── 0.0002 1 ── indicator variable loadings (eigenvector correlations) variable pc1 pc2 factor score median family income ($) * 0.3885 0.0826 -0.2595 median single person income ($) **-0.2841 -0.3261 -0.2956 low income families, after-tax (%) 0.3315 0.2752 0.3161 low income unattached, after-tax (%) 0.1370 0.6027 0.2646 http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi lone parent families (%) 0.3986 -0.0284 0.2817 single mothers (%) 0.3961 -0.0295 0.2795 unemployment rate, 15 years and over (%) 0.0411 0.4944 0.1653 low education, 15 years and over (%) 0.2763 -0.2496 0.1322 average home value ($) -0.3578 0.2293 -0.1970 average monthly rent ($) -0.0643 0.2190 0.0133 managerial occupation (%) -0.3375 0.1973 -0.1910 c, component. *bolded if ≥|0.3015|. **italicized if ≥|0.2015| and <|0.3015|. figure 4. socioeconomic status index distribution produced from the 2011census profile, 2011 national household survey profile and 2011 taxfiler datasets for the census metropolitan area of guelph using all principal components with eigenvalues greater than 1.0 and that represent more than 10% of the variation. 4) internal validation the comparability among ses scores across cts between datasets was assessed by building ses indices for each dataset using only the first pc in the calculation. using this method for the 2006 census dataset produced an ses score distribution with identical ses level distinctions as the original method, and cts remained in the same ses levels. this level of similarity was not found between the ses score distributions when using the first pc from 2005 taxfiler and 2006 census variables (figure 5). six cts decreased in ses, while one ct increased one ses level. the transitory ses levels (‘low-medium’ and ‘medium-high’) were comprised of fewer cts while the lowest ses level (‘low’) included http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi more cts than when the ses score was calculated using three components. additionally, the distinctions between ‘medium’, ‘medium-high’, and ‘high’ ses were less pronounced. figure 5. socioeconomic status scores for the census metropolitan area of guelph produced from the substitution 2006 census income variables with 2005 taxfiler income variables when using all principal components with eigenvalues greater than 1.0 and a proportion of explained variance greater than 10% (‘pcs >1.0’) versus using only the first principal component (‘pc1’). using the first pc resulted in even greater discrepancies in the 2011 ses score distribution (figure 6). six cts moved down one ses level while three cts moved up one ses level. one ct moved down two levels from ‘medium-high’ to ‘low-medium’. this ct, ‘s’, was consistently in the ‘medium-high’ or ‘high’ ses levels during previous calculations regardless of dataset or number of pcs used. the resulting distribution presented a larger distinction between ‘low’ and ‘low-medium’ ses levels, with fewer clear distinctions throughout the higher levels of the scale. more of the cts became classified as ‘low’ or ‘low-medium’, with approximately half (14 or 51.8%) of the 27 cts below the ‘medium’ ses level. ses level census tract ses score census tract ses score ses level low b 0.0000 a 0.0000 low a 0.0295 b 0.1575 low-med e 0.1697 -1 c 0.2182 c 0.2476 -1 e 0.2362 d 0.2887 -1 d 0.2493 g 0.3015 g 0.4024 low-med medium h 0.4663 f 0.4262 i 0.5085 h 0.4891 medium f 0.5117 -1 i 0.4946 j 0.5139 j 0.5010 med-high l 0.6269 -1 k 0.5355 k 0.6271 -1 l 0.5454 o 0.6288 o 0.6014 med-high n 0.6428 +1 m 0.6094 m 0.6515 n 0.6815 high high r 0.7226 p 0.6837 p 0.7850 s 0.6963 t 0.7992 q 0.7190 q 0.8038 r 0.7363 s 0.8522 t 0.8187 u 1.0000 u 1.0000 ses level change 2006 census + 2005 taxfiler data (pcs >1.0) 2006 census + 2005 taxfiler data (pc1) http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi figure 6. socioeconomic status scores for the census metropolitan area of guelph produced from a combination of the three 2011 datasets using all principal components with eigenvalues greater than 1.0 and a proportion of explained variance greater than 10% (‘pcs >1.0’) versus using only the first principal component (‘pc1’). the second validation procedure excluded the seven non-income variables from the pca (table 6). only the component met kaiser’s criterion of an eigenvalue >1.0 for all three datasets, and further consideration of proportion of explained variance suggested by drackley et al. [4] was not pursued. the pc of the 2005 taxfiler data represented the greatest total variance (72.72%) while the pc of the 2006 census data represented 63.25% of the total variance. the pc of the 2011 taxfiler data represented 69.16% of the total variance. the resulting variable loadings were approximately equally distributed amongst all variables for all datasets. ses level census tract ses score census tract ses score ses level low a 0.0000 a 0.0000 low b 0.1079 b 0.1629 c 0.2027 c 0.1665 d 0.2718 d 0.2629 e 0.3208 +1 l 0.3917 low-med a 0.3276 +1 e 0.3932 g 0.3323 +1 g 0.3990 low-med l 0.4166 a 0.4049 f 0.4423 f 0.4266 i 0.4521 i 0.4307 b 0.4546 b 0.4703 medium c 0.5093 -1 s 0.4794 k 0.5281 -1 c 0.4944 h 0.5448 k 0.5031 j 0.5489 j 0.5451 medium p 0.5529 f 0.5621 med-high d 0.5817 -1 h 0.5728 e 0.5961 p 0.5798 s 0.6129 -2 q 0.5915 f 0.6320 -1 d 0.6048 q 0.6386 -1 g 0.6147 g 0.6446 -1 h 0.6629 med-high h 0.6451 e 0.6675 r 0.6550 r 0.7016 high t 0.7533 t 0.7832 high i 0.8027 i 0.8136 j 1.0000 j 1.0000 ses level change 2011 census + nhs + taxfiler data (pcs >1.0) 2011 census + nhs + taxfiler data (pc1) http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi table 6. a comparison of principal component analyses from three separate data groups (2006 census profile, 2005 taxfiler and 2011 taxfiler datasets) pertaining to the census metropolitan area of guelph performed utilizing only the four income variables. source dataset component eigenvalue difference proportion cumulative 2006 census profile pc1 2.530160 1.741550 0.6325 0.6325 c2 0.788604 0.206443 0.1972 0.8297 c3 0.582160 0.483082 0.1455 0.9752 c4 0.099079 ── 0.0248 1.0000 2005 taxfiler pc1 2.908720 2.121430 0.7272 0.7272 c2 0.787294 0.532456 0.1968 0.9240 c3 0.254838 0.205690 0.0637 0.9877 c4 0.049148 ── 0.0123 1.0000 2011 taxfiler pc1 2.766560 1.929580 0.6916 0.6916 c2 0.836980 0.485802 0.2092 0.9009 c3 0.351178 0.305902 0.0878 0.9887 c4 0.045277 ── 0.0113 1.0000 source dataset indicator variable pc1 variable loadings 2006 census profile median family income ($) *0.5257 median single person income ($) 0.538 low income families, after-tax (%) **-0.4864 low income unattached, after-tax (%) -0.4445 2005 taxfiler median family income ($) -0.4477 median single person income ($) -0.5343 low income families, after-tax (%) 0.557 low income unattached, after-tax (%) 0.4516 2011 taxfiler median family income ($) -0.4593 median single person income ($) -0.5295 low income families, after-tax (%) 0.5505 low income unattached, after-tax (%) 0.4535 c, component. *bolded if ≥|0.5|. **italicized if ≥|0.4| and <|0.5|. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi note: 2006 census profile data produced signs opposite to the pcas of the other two datasets. signs were reversed for the 2006 pca during ses score calculations to ensure consistency. the ses indices produced from using only income variables maintained the cts within one ses level across all three data groups as compared to the original calculations for the respective data groups (figures 7, 8, & 9). however, by excluding non-income variables, fewer cts were classified as ‘low’ or ‘low-medium’ ses while more were classified as ‘medium-high’ or ‘high’ ses. additionally, the distinction between ‘low’ ses and the other ses levels was much more pronounced, especially for 2005 taxfiler and 2011 taxfiler data groups. figure 7. socioeconomic status index distribution for the census metropolitan area of guelph produced from the 2006 census profile dataset income variables and calculated using the first principal component only. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi figure 8. socioeconomic status index distribution for the census metropolitan area of guelph produced from the 2005 taxfiler income variables and calculated using the first principal component only. figure 9. socioeconomic status index distribution for the census metropolitan area of guelph produced from the 2011 taxfiler income variables and calculated using the first principal component only. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi discussion internal validation this report presented several ses indices across the cma of guelph produced using pca on non-income variables indicative of ses and income variables derived from a novel data source. internal validation showed that using the first pc for ses calculations resulted in skewed distributions with less pronounced distinctions between the ses levels. the ses index produced by the first pc method using 2011 data resulted in one ct dropping two ses levels, an indication that perhaps this method does not fully capture the dimensions of ses (figure 6). additionally, half of the cts in 2011 using the first pc method were considered below the ‘medium’ ses level, which would have severe implications on resource allocation for the support of public health in these areas. several researchers have explained with confidence that the first pc is sufficient for calculating ses [5,7,9,10,17]. interpreting additional components can be difficult and subjective, since the number of components produced is variable-dependent. furthermore, some researchers deemed it unnecessary and potentially counterproductive to consider further components containing variable loadings that negate one another [9]. vyas & kumaranayake [9] considered a second component in their analysis but determined that it included a subset of variables not specific to a wellexplained dimension and maintained that their first component representing wealth was sufficient for their ses index. however, other researchers have explained that to adequately account for the complexity of ses, more pcs must be taken into consideration if they exceed an eigenvalue of 1.0 [6] or represent more than 10% of the variation [4]. these additional components represent more of the underlying variation and higher-order relationships between the variables used in the analysis. krishnan [6] observed that up to five pcs were vital to represent economic, social, and cultural aspects of ses. the present work further supports the inclusion of non-income variables in calculating ses. using single variable measures like ‘proportion of the population living in low income’ presents a limited indication of an area’s economic, social, cultural and health needs [4]. social and geographical factors can significantly influence single variables, and therefore, a composite index better balances any changes incurred by single factors [6-8,11]. additionally, measuring ses can be difficult in rural areas where measures of income do not consider longterm measures of wealth (e.g. self-subsistence agriculture) or assets that may better represent one’s economic or social standing within their community [5,10]. when excluding nonincome variables in the present analysis (table 6), ‘medium-high’ and ‘high’ ses levels were much more prevalent (figures 7-9), which may be an over-representation of wealthy neighbourhoods. in contrast, the distribution of ses levels and clear distinctions between levels produced after including non-income variables further supports research of the past two decades into ses scales and indices [4-11]. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi substituting taxfiler data the present study has shown the comparability and superiority of tax filer to census income data. including taxfiler data in the pca resulted in a higher proportion of variance explained by the first few components, as well as an ses distribution with clear distinctions between levels and few extreme values. the use of readily available taxfiler data can assist in the confirmation of existing and identification of priority neighbourhoods by local public health units. a comparison to previous work by wdgph revealed that due to differences in geographical level of analyses, some smaller areas of ‘low’ ses in 2006 were suppressed within larger areas of ‘high’ ses in the present work of the same year. this was expected, since sources of income variables and pca methods differed between the two works. the present method classified the majority of ‘low’ areas from wdgph in 2006 as either ‘low’ or ‘low-medium’. in terms of identifying new priority areas in guelph, the present analysis of 2011 data classified two areas as ‘low-medium’ ses that were previously ‘high’ in wdgph’s 2006 research. additionally, a small area became ‘high’ ses in the present study where it was originally ‘low’ in wdgph’s 2006 report. while this may be a result of geographical suppression, the methods presented here can act as part of a program evaluation tool for wdgph as well as identify new communities that have since become disadvantaged and require new services. usefulness to local public health units pca can indicate which individual-level variables contribute the most to various dimensions of ses. this information can help local public health units prioritize existing programs and populations, as well as advise the development of new programs as necessary. the present analysis of the most recent available census and taxfiler data from 2011 produced two relevant pcs (table 5). the first pc represents economic deprivation, as it is negatively influenced by family-related income variables, home value, and managerial occupation, and positively influenced by the proportion of low-income families, lone parent family structure, and the proportion individuals with low education. in other words, as family income increases and the proportion of the population with low education decreases, ses in that area would increase. this is in line with the current literature that identifies the first pc as economic conditions when using similar inputs [4-7,9,10,19]. the high weighting of the proportion of both lone parent families and single mothers indicates the importance of these factors in ses, and calls for further attention to these vulnerable groups by wdgph programs like the triple p initiative: positive parenting program [2]. the second pc is influenced mainly by single-person factors, with a slight contribution by the proportion of low-income families. single person income contributes negatively to the second pc while the proportion of low-income individuals and unemployment rate contribute positively, suggesting that the second pc is a representation of unemployment conditions that have been associated with the ses and health of both individuals and populations [3]. http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi this is similar to the finding by drackley et al. [4], whose pca attributed ‘single renter’ characteristics to the second pc. current wdgph initiatives to transition individuals out of poverty due to economic and employment situations, such as the getting ahead workshops and circles support group, should continue to have a presence in the priority neighbourhoods identified using this method [2]. three slightly influential variables in the second component presented directional effects on unemployment conditions that were counter-intuitive. the weightings of low education, average home value, and average monthly rent (-0.2496, 0.2293, and 0.2190, respectively) were approaching the minimum cut-off value of |0.2015|, which brings into question the significance of their influence on the second pc. according to the pca performed using 2011 data (table 5), an increased proportion of individuals with low education within the population are expected to reflect an improvement in the community’s employment conditions. this association warrants further investigation, perhaps by evaluating careertraining programs aimed at individuals who have not completed high school, as well as performing a similar pca in different locales. the weighting of ‘low education’ was higher in the first principal component and weighted positively, suggesting that the relationship between low education and economic deprivation is stronger than with unemployment conditions. conversely, both average home value and average monthly rent positively contributed to individual-level unemployment conditions. average home value weighted higher and positively in the first component, suggesting that it may be more influential on economic deprivation than unemployment conditions. in context of the second pc, average home value may be an indication of short-term or recent unemployment in which families or individuals are transitioning between ses levels. population demographics may play a role in the positive association between average monthly rent and unemployment conditions. students who rent housing while attending the university of guelph may not be employed during their studies, which would support the connection between monthly rent and unemployment rate. since average monthly rent is weighted very low in both pcs, it may not be an appropriate indicator of ses in the city of guelph, especially in the context of the community’s demographics. limitations there are some limitations to the present study. first, the addition of variables into the pca can be subjective, which affects the quantity and weighting of components. this can be useful when exploring community-specific variables but may not be generalizable to larger areas. second, as seen when comparing to previous work by wdgph, using data at the census tract level may in fact suppress smaller areas of high priority within a classification of ‘medium’ to ‘high’ ses that may deter further adjustments to existing public health programs in those areas. finally, caution must be taken when inferring individual-level ses effects from the present aggregate-level data. community turnover should to be assessed on an ongoing basis http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi using readily available individual-level variables to identify neighbourhoods that are consistently ‘low’ and ‘low-medium’ ses. conclusion socioeconomic status and by extension, individual and population health, are influenced by many inter-related factors. the need for comprehensive approaches to health promotion and disease reduction that go beyond acute health care is becoming increasingly apparent. identifying socioeconomic disparities between neighbourhoods is an important first step in assessing the level of disadvantage of communities, and the method presented here can be adapted to other locales for such a purpose. the methods for developing an ses index presented in this paper support the use of pca in assessing and ranking neighbourhoods using appropriate variables from that community. further, the substitution of census income data with taxfiler data contributes to the current understanding of ses and population health. the present report supports a growing body of evidence that education, among other non-income variables, influences both familial and individual aspects of life, such information that should be used to support models such as the ontario ministry of children and youth services strategic framework [28]. by improving ses measurement methods, a political shift may occur that moves the current system beyond poverty-reduction strategies into greater resource allocation to comprehensive programs targeting disadvantaged communities. declarations the authors declare that they have no competing interests. references 1. health council of canada (hcc). 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http://www.statcan.gc.ca/pub/75f0002m/75f0002m2009002-eng.pdf http://ojphi.org/ http://dx.doi.org/10.1007/s10964-009-9501-1 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20047084&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20047084&dopt=abstract http://dx.doi.org/10.1111/ssqu.12039 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25076799&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11227840&dopt=abstract http://dx.doi.org/10.1016/j.ecolind.2009.04.007 http://dx.doi.org/10.1186/1476-069x-13-39 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24886426&dopt=abstract using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi 26. hair jf. jr., anderson, r.e., tatham, r.l., black, w.c., 1998. multivariate data analysis, (5th edition). prentice hall, upper saddle river, nj. 27. raubenheimer j. 2004. an item selection procedure to maximise scale reliability and validity. sa j ind psychol. 30(4), 59-64. doi:http://dx.doi.org/10.4102/sajip.v30i4.168. 28. ontario ministry of children and youth services (omcys). 2008. realizing potential: our children, our youth, our future. url: http://www.children.gov.on.ca/htdocs/english/documents/about/strategicframework.p df http://ojphi.org/ http://dx.doi.org/10.4102/sajip.v30i4.168 using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e192, 2016 ojphi appendix a datasets retrieved from the canadian council on social development’s community data program [22]. census datasets catalogue number census profile, 2006 data provider: statistics canada survey number: 3901 release date: may 1, 2008 94-581-x2006001, 94-581-x2006002, 94-581-x2006003, 94-581x2006004, 94-581-x2006005, 94-581-x2006008 census profile, 2011 data provider: statistics canada survey number: 3901 release date: october 24, 2012 98-314-x2011006, 98-314-x2011007, 98-314-x2011008, 98-314x2011009, 98-314-x2011010, 98-314-x2011011, 98-314-x2011012, 98-314-x2011013, 98-314-x2011014, 98-314-x2011015, 98-314x2011052 nhs profile, 2011 data provider: statistics canada survey number: 5178 release date: september 11, 2013 99-004-x2011015, 99-004-x2011016, 99-004-x2011017, 99-004x2011018, 99-004-x2011019 taxfiler (t1ff) datasets contents f01: total income summary table data provider: statistics canada years obtained: 2005, 2011 table f-1 family data summary, 2005 table f-1 family data summary, 2011 f04: total income by family type data provider: statistics canada years obtained: 2005, 2011 table f-4a family data distribution of total income of couple families by age of older partner, 2005 table f-4b family data distribution of total income of loneparent families by age of parent, 2005 table f-4c family data distribution of total income of person not in census families by age, 2005 table f-4a family data distribution of total income of couple families by age of older partner, 2011 table f-4b family data distribution of total income of loneparent families by age of parent, 2011 table f-4c family data distribution of total income of person not in census families by age, 2011 f17: before tax low-income data provider: statistics canada years obtained: 2005, 2011 table f-17 family data low income (based on before-tax low income measures, lims), 2005 table f-17 family data low income (based on before-tax low income measures, lims), 2011 f18: after tax low-income data provider: statistics canada years obtained: 2005, 2011 table f-18 family data after-tax low income (based on after-tax low income measures, lims), 2011 table f-18 family data after-tax low income (based on after-tax low income measures, lims), 2005 http://ojphi.org/ using principal component analysis to identify priority neighbourhoods for health services delivery by ranking socioeconomic status introduction determinants of health socioeconomic status and principal component analysis changes to the canadian census affecting ses index development methods data sources data processing variable selection statistical analyses descriptive statistics taxfiler income variable validation internal validation results discussion internal validation substituting taxfiler data usefulness to local public health units limitations conclusion declarations references appendix a datasets retrieved from the canadian council on social development’s community data program [22]. isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova mechnikov i.i. ukrainian research antiplague institute of the ministry of health of ukraine, odessa, ukraine objective comprehensive ecological, epidemiological and microbiological investigations of natural foci of edi in southern ukraine were carried out introduction surveillance of the natural foci of especially dangerous infections (edi) is necessary due to their etiological, epidemiological, and clinical diversity, their global spread and overall negative impact on public health. some edi pathogens with natural foci are also potential agents for biological weapons. study of the edi characteristics is important for development of an effective epidemiological protection system. francisella tularensis is one of the most virulent human microorganisms and a critical category a biological agent. in ukraine, tularemia natural foci are registered in 23 of the 25 regions. we conducted integrated ecological-epidemiological and microbiological research on the edi natural foci for the past 20 years. methods we studied ecological-epidemiological data (using retrospective analyses) and isolated f. tularensis strains by molecular-genetic (pcr, vntr-analysis), bacteriological, immunological, environmentalepidemiological, statistic methods. results we detected favorable conditions for formation and long-term functioning of edi natural foci of various etiologies in ecosystems of southern region of ukrainian. here tularemia natural foci were registered in kherson and odessa regions, and the largest outbreak of tularemia in humans in ukraine occurred – 100 people fell ill in 1998 in the odessa and mykolaiv regions. the sources of infection were small mammals (forest and field mice), and hare. in our serological study of tularemia vectors, positive field data results confirmed the stability of tularemia natural foci. seventeen strains of f. tularensis subsp. holarctica were isolated from different sources (rabbits, rodents, ticks, water). we conducted vntr-analysis of the natural isolates, identified 9 genotypes, and constructed genetic passports of individual f. tularensis strains. in some ukrainian southern regions, habitats containing natural foci revealed circulation of various edi pathogens (tularemia, psittacosis, leptospirosis, arboviruses), which requires a polynozological approach to their monitoring, considering the overlap of vectors. conclusions eco-epidemiological and microbiological research in southern ukraine have established the widespread presence of edi of various etiologies. the presence of overlapping natural foci indicates that the southern region is an area with high risk of epizootic and epidemic complications. use the molecular genetic research methods promote a science-based system for confirming areas containing edi-specific territories and optimize the potential for prevention of infections near natural foci. keywords edi natural foci; strains genotyping; diagnosis; monitoring; prevention *zoya nekhoroshykh e-mail: nm_pri@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e147, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to 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fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial 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bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state 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goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts a regional approach for the influenza surveillance in france marc ruello, camille pelat, céline caserio-schönemann, anne fouillet*, isabelle bonmarin, daniel levy-brühl and yann le strat santé publique france, saint-maurice, france objective to describe the results of the new organization of influenza surveillance in france, based on a regional approach. introduction in france, until winter 2014-2015, management and preventive actions for the control of the flu epidemic were implemented when the national incidence of influenza-like illness (ili) consultations in general practice was over an epidemic threshold. the 2014-2015 influenza epidemic had a major public health impact, particularly in the elderly, and caused a severe overloading of the health care system, in particular emergency departments (ed) [1]. the epidemic alert emitted by the french national public health agency at the national level was too late for the hospitals to prepare themselves in many regions. after a national feedback organized in april 2015 with all partners involved in influenza surveillance and management, it was recommended to improve influenza surveillance in france following 3 axes: 1) regionalize surveillance so that healthcare structures can adapt to the particular situation of their region; 2) use a pre-epidemic alert level for better anticipating the outbreak; 3) use multiple data sources and multiple outbreak detection methods to strengthen the determination of influenza alert level. methods a user-friendly web application was developed to provide common data visualizations and statistical results of outbreak detection methods to all the epidemiologists involved in influenza surveillance at the national level or in the 15 regional units of our agency [2]. it relies on 3 data sources, aggregated on a weekly time step: 1) the proportion of ili among all coded attendances in the ed participating to the oscour network [3] ; 2) the proportion of ili among all coded visits made by emergency general practitioners (gps) working in the sos médecins associations [3]; 3) the incidence rate of ili estimated from a sample of sentinel gps [4]. for each region each week, 3 statistical outbreak detection methods were applied to the 3 data sources, generating 9 results that were combined to obtain a weekly regional influenza alarm level. based on this alarm level and on other information (e.g. virological data), the epidemiologists then determined the epidemiological status of each region as either 1) epidemic-free, 2) in pre/post epidemic or 3) epidemic. the r software was used for programming algorithms and building the web interface (package shiny). results the epidemiological status of influenza at the regional level was communicated through maps published in the weekly influenza reports of the agency throughout the surveillance season [5]. in week 2016-w03, brittany was the first french region to declare the influenza epidemic, with nine other regions in pre-epidemic alert. the epidemic then spread over the whole mainland territory. the peak of the epidemic was declared in week 11, the end in week 16. conclusions this regional multi-source approach has been made possible by the sharing of data visualizations and statistical results through a web application. this application helped detecting early the epidemic start and allowed a reactive communication with the regional health authorities in charge of the organization of health care, the management and the setting up of the appropriate preventive measures. keywords influenza surveillance; r-shiny; sursaud; syndromic surveillance; france acknowledgments the authors thank the emergency departments of the oscour network, sos médecins associations, and the sentinelles network for providing data and for their contribution to the surveillance. the authors also thank the regional sursaud team. references [1] bonmarin i. et al. influenza activity in mainland france: 2014-15 season. bull epidémiol hebd 2015;32-33. [2] pelat c, bonmarin i, ruello m. et al. coordinating regional influenza surveillance through the use of automated outbreak detection methods : the 2015-2016 season in france as illustration. submitted [3] caserio-schönemann c, bousquet v, fouillet a, henry v. the french syndromic surveillance system sursaud (r). bull epidémiol hebd 2014;3-4:38-44. [4] valleron aj, bouvet e, garnerin p, ménarès j, heard i, letrait s, lefaucheux j. a computer network for the surveillance of communicable diseases: the french experiment. am j public health. 1986. 76(11):1289-92[5] http://invs.santepubliquefrance.fr// dossiers-thematiques/maladies-infectieuses/maladies-a-preventionvaccinale/grippe/grippe-generalites/donnees-de-surveillance/ saison-2015-2016 *anne fouillet e-mail: a.fouillet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e89, 2017 user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection ellie andres1, shamir mukhi1* 1. canadian network for public health intelligence, national microbiology laboratory, winnipeg, mb abstract objectives: to review user signal rating activity within the canadian network for public health intelligence’s (cnphi’s) knowledge integration using web-based intelligence (kiwi) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating? methods: kiwi rating data was extracted from the cnphi platform. zoonotic & emerging program signals with first rating occurring between january 1, 2016 and december 31, 2017 were included. krippendorff’s alpha was used to estimate inter-rater reliability between users. a z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating. results: the 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% ci: 66.5-77.9%). five users tended to rate significantly outside of the average community rating. an average user rated 58.4% of the time within the signal’s 95% ci. all users who significantly rated within the average community rating rated outside the 95% ci at least once. discussion: a diverse community of raters participated in rating the signals. krippendorff’s alpha estimate revealed moderate reliability for users who rated ≥10% of signals. it was observed that interrater reliability increased for users with more experience rating signals. conclusions: diversity was observed between user ratings. it is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal’s 95% ci can be used as a proxy for user expertise. the introduction of a weighted rating algorithm within kiwi that takes this into consideration could be beneficial. keywords: public health intelligence, digital disease detection, event monitoring, early warning, data mining, event-based surveillance. *correspondence: shamir.mukhi@canada.ca doi: 10.5210/ojphi.v10i2.8547 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi background the use of novel internet-based public health intelligence monitoring techniques for the purpose of providing early warning and situation awareness of potential health threats to support surveillance activities has grown tremendously over the last few decades [1-9]. technologies have been reviewed and evaluated in the literature and authors have suggested a variety of directions for enhancements and growth opportunities. one of these directions is the need for establishing collaborative networks of public health professionals for the verification and dissemination of early warning signals [3]. to meet this need, the national microbiology laboratory’s canadian network for public health intelligence (cnphi) developed an innovative technology for public health event monitoring and early warning signal detection called knowledge integration using web-based intelligence (kiwi) within the context of its existing platform hosting thousands of public health professionals and health-related communities [10]. the cnphi platform was established in 2003 as an initiative of the public health agency of canada, and it provides a variety of scientific informatics tools within the framework of six focus areas: knowledge management, collaboration, surveillance, alerting, event management, and laboratory systems. the kiwi technology aims to complement and support surveillance activities being performed on the cnphi platform and is made available under knowledge management. kiwi was designed to facilitate event monitoring and early warning signal detection for multiple types of public health related events. the zoonotic & emerging (ze) program, for example, focuses on events related to zoonotic, emerging and re-emerging disease. the ze program was customized for – and in collaboration with – the centre for emerging and zoonotic disease (cezd) community, piloted in 2015 and initiated in 2016. cezd has created an active community of professionals from various authorities using cnphi’s collaboration centre and kiwi technologies. as a quick overview of kiwi, the technology collects individual information pieces (iips) from a selection of program-specific online sources, collated, and then analyzed using a programspecific sense making algorithm (sma), which utilizes three dictionaries (hazards, relevant terms, and significant terms) to identify signals of interest. these three steps are performed through an automated process. iips identified with early warning potential are classified as anticipatory intelligence signals (aiss) and are presented to all users for community rating. users can rate each ais on a likert-type scale from 1 (not relevant) to 5 (extremely relevant). the purpose of user rating is to verify which aiss should become early warning signals (ewss) based on a pre-defined rating value threshold. the resulting ewss can be summarized as reports and used to support public health event monitoring and surveillance activities by providing synthesized intelligence and situational awareness. a key feature of the kiwi technology is that there is a community of program-specific experts involved in the rating of aiss. the cnphi platform is used by a variety of public health professionals from multiple jurisdictions, disciplines, and areas of expertise. the purpose of this analysis is to answer the following questions: (1) who is rating, (2) how are users rating and (3) how well are users rating? these questions will be answered within the context of kiwi’s ze program. user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi methods kiwi rating data was extracted from the cnphi platform on january 13, 2018. the dataset contained observations from june 1, 2015 to january 12, 2018 and included the following variables: program id, signal id, user id, rating, and rating date/time. the dataset was refined in spss using two primary exclusion criteria: (a) data linked to signals with first user rating occurring before 2016 or after 2017, and (b) data linked to programs other than the ze program. who is rating? user organizations for those participating in kiwi signal rating were extracted using kiwi’s analytics feature for the time period of january 1, 2016 to december 31, 2017. two variables “jurisdiction” and “type of organization” were created to broadly describe the organizations represented by these raters. how are users rating? the following variables were derived from the original data (see table 1): • year of first rating per signal: the year in which the first rating occurred for a given signal; • number of signals rated per user: total count of signals rated by a specific user; • grouped proportion of signals rated per user: users were grouped based on their proportion of signals rated: (1) less than 10%, (2) 10% to 90%, and (3) more than 90%; • number of days rated per user: total number of days a specific user rated signals; • rating duration per signal: total duration in days for all ratings for a given signal; • number of signals rated per day per user: count of signals rated by a specific user per day; • day of the week: specific day of the week the rating for a specific signal took place; • number of users rating per signal: total count of users rating specific signals; • average and median rating per signal: mean and median values for all ratings per signal. table 1: derived variables for analysis. derived variable base variable(s) formula/logic spss year of first rating per signal o signal id o date/time sorted data by signal id and date/time. identified first rating per signal and extracted year from date/time. o identify duplicates o xdate.year number of signals rated per user o signal id o user id sorted data by user id and signal id. count of signal id per user.* *note: signals can only be rated once per user. o aggregate user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi grouped proportion of signals rated per user o signal id o user id formula = a/b. (a) numerator: number of signals rated per user.* (b) denominator: total number of signals. *note: signals can only be rated once per user. o aggregate o identify duplicates o compute variable number of days rated per user o user id o date/time extracted date from date/time. sorted data by user id and date. count of rating date/time per user id. o xdate.date o identify duplicates o aggregate rating duration per signal o signal id o date/time extracted date from date/time. sorted data by signal id and date. identified both first and last ratings by date. calculated the difference in days plus one day. formula = (date of last rating – date of first rating) + 1 o xdate.date o identify duplicates o compute variable number of signals rated per day per user o signal id o user id o date/time extracted date from date/time. sorted data by user id, date, and signal id. count of signals per day per user. o xdate.date o aggregate day of the week o date/time extracted day of the week using xdate.wkday command. o xdate.wkd ay number of users rating per signal o signal id o user id sorted data by signal id and user id. count of user id per signal. o aggregate average and median rating per signal o signal id o rating sorted data by signal id. mean and median of rating per signal. o aggregate how well are users rating? inter-rater reliability inter-rater reliability is used to reflect the variation between two or more raters who measure the same group of signals [11]. the intra-class correlation coefficient (icc) is commonly used to assess inter-rater reliability; however, it cannot accommodate datasets with a large number of missing data due to its use of list-wise deletion [12]. for example, our dataset has 37 users, but user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi not all users have rated each signal to produce a fully-crossed design. therefore, a huge proportion of our data would be dropped during the calculation of icc. a more appropriate method, in our case, is the use of krippendorff’s alpha, which is capable of handling missing data [12,13]. in response to the call for a standard reliability measure, andrew f. hayes and klaus krippendorff proposed in 2007 that krippendorff’s alpha meets the criteria for a good index of reliability, can be used for any number of raters, levels of measurement, and sample sizes, and can be used in the presence or absence of missing data [13]. hayes developed a spss macro/code for calculating krippendorff’s alpha called “kalpha” [14]. krippendorff’s alpha was calculated using an ordinal measurement level with all users, and again with users grouped by the proportion of signals rated per user. user rating and the community norm average ratings and 95% confidence intervals (ci) were calculated for each signal with more than one rating. individual user ratings were then compared to the average rating per signal. if the user’s rating was within the 95% ci of the signal’s average rating, then it was grouped as “within” and if it was outside of the 95% ci of the signal’s average rating, then it was grouped as “outside”; thus, creating a dichotomous variable. those “outside” were further identified as either “above” or “below” depending on whether the individual rating was greater than the signal’s average upper limit or less than signal’s average lower limit, respectively (figure 1). figure 1: signal ratings within and outside of 95% ci. a one-sample z-test was used to identify whether the proportion of user ratings classified as “within” (rather than “outside”) was significantly different than 50% (i.e. no difference). the zstatistic was calculated using the formula: z= (p� − po) �po*(1-po) n p̂ is the observed proportion of “within” ratings, po is the expected proportion of “within” ratings, and n is the total number of ratings. the null hypothesis was rejected if 1.645 < z < -1.645, which is to say if the p-value was greater than 0.05. user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi results who is rating? during 2016 and 2017, there were 37 cnphi users participating in kiwi signal rating within the ze program. these 37 users represented 20 unique organizations. user organizations stemmed from government (federal and provincial), industry, academia, and professional groups (figure 2). jurisdictionally, users represented international, national (canada), and provincial authorities. the most highly represented jurisdiction was national with 21 users. of these 21 users, 81.0% (n = 17) represented a government organization and 19.0% (n = 4) represented a professional group. the second leading jurisdiction included the provinces with 15 users. of these 15 users, 60.0% (n = 9) represented a government organization, 13.3% (n = 2) represented industry, and 26.7% (n = 4) represented an academic organization. figure 2: types of organizations represented by users participating in kiwi signal rating within the ze program during 2016-2017. how are users rating? a total of 6988 signals were rated within the ze program during 2016 and 2017. of these signals, 48.7% (n = 3400) were first rated in 2016, and 51.3% (n = 3588) were first rated in 2017. based on grouped proportion of signals rated per user: • less than 10%: 73.0% (n = 27) of users rated less than 10% of all rated signals. the number of signals rated by these 27 users ranged from 1 to 609 signals with an average of 88 signals, median of 12 signals, and mode of 1 signal. the number of days in which rating took place ranged from 1 to 51 days with an average of 7 days, median of 2 days, and mode of 1 day. of the 27 users, 22.2% (n = 6) rated in 2016 only, 29.6% (n = 8) rated in both 2016 and 2017, and 48.1% (n = 13) rated in 2017 only. user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi • 10% to 90%: 21.6% (n = 8) of users rated 10% to 90% of all rated signals. the number of signals rated by these 8 users ranged from 978 to 5698 signals with an average of 3122 signals and median of 2896 signals. the number of days in which rating took place ranged from 33 to 345 days with an average of 149 days and median of 129 days. of the 8 users, 12.5% (n = 1) rated in 2016 only and 87.5% (n = 7) rated in both 2016 and 2017. • more than 90%: 5.4% (n = 2) of users rated more than 90% of all rated signals. the number of signals rated by these 2 users ranged from 6323 to 6325 signals with an average and median of 6324 signals. the number of days in which rating took place ranged from 529 to 587 days with an average and a median of 558 days. both users rated in both 2016 and 2017. in total, the number of signals rated per user ranged from 1 to 6325 signals with an average of 1081 signals, median of 57 signals, and mode of 1 signal. the number of days in which rating took place ranged from 1 to 587 days with an average of 68 days, median of 5 days, and mode of 1 day. of the 37 users, 18.9% (n = 7) rated in 2016 only, 46.0% (n = 17) rated in both 2016 and 2017, and 35.1% (n = 13) rated in 2017 only. a summary of user activity is displayed in figure 3. figure 3: user rating activity during 2016 and 2017 within kiwi’s ze program. as a community, 50% of signals were rated within 1-5 days of being identified by the technology, 95% were rated within 1-8 days, and 99% were rated within 1-19 days. overall, the community rated within 1-180 days with an average of 6 days, median of 5 days, and mode of 7 days. the number of signals rated per day ranged from 1 to 102 with an average of 31, median of 23, and mode of 12. when results were separated by day of the week, the average number of signals was 19 with a median of 17 for saturday and sunday, 64 with a median of 67 on monday, and 28 with a median of 22 for the remaining weekdays (figure 4). the number of users rating per signal was normally distributed (figure 5) with an average, median, and mode of 6 users and range of 1-11 users rating per signal. an average signal was rated as 1.7 with a median rating of 2 (some relevance). an overview of community signal rating is provided in table 2. user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi figure 4: average number of signals rated per day by day of the week. figure 5: distribution of the number of user ratings per signal. table 2: an overview of community signal rating within kiwi’s ze program during 20162017. average rating # of signals % of signals 1 – not relevant x̅ < 1.5 2897 41.5 2 – some relevance 1.5 ≤ x̅ < 2.5 3151 45.1 3 – relevant 2.5 ≤ x̅ < 3.5 923 13.2 4 – very relevant 3.5 ≤ x̅ < 4.5 17 0.2 5 – extremely relevant x̅ ≥ 4.5 0 0.0 total 1-5 6988 100.0 how well are users rating? inter-rater reliability overall, krippendorff’s alpha reliability estimate was 69.6% (95% ci: 64.5-74.4%). when users were grouped by their proportion of signals rated, the reliability estimate increased with an increase in the proportion of signals rated. those who rated less than 10% of signals had an estimate of 42.1% (95% ci: 33.8-49.8%), those who rated 10% to 90% had an estimate of 70.1% (95% ci: 63.9-75.9%), and those who rated more than 90% had an estimate of 77.1% (95% ci: 71.7-82.0%). combining the last two groups, the reliability estimate for users who rated greater than or equal to 10% of signals was 72.4% (95% ci: 66.5-77.9%). user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi user rating and the community norm of the 6988 rated signals, 98.4% (n = 6876) were rated by more than one user. for these 6876 signals, an average user rated 58.5% of the time within the signal’s 95% ci (95% ci: 49.567.2%). comparing the proportion of signals rated within the signal’s 95% ci per user to 50% (i.e. no difference), it was found that the raters can be categorized into three groups as follows (see figure 6): category i: no significant result: 29.7% (11/37) of users did not produce a significant result. category ii: significant result and significantly lower than 50% of signals within 95% ci: 13.5% (5/37) of users produced a significant result and had a negative z-score. these users rated significantly less than 50% of signals within the signal’s 95% ci. for these users, the proportion of signals rated outside and above the 95% ci was compared to 50% (i.e. no difference), and it was found that one user did not produce a significant result. category iii: significant result and significantly higher than 50% of signals within 95% ci: 56.8% (21/37) of users produced a significant result and had a positive z-score. these users rated significantly greater than 50% of signals within the signal’s 95% ci. in summary, there were 5 users who rated outside of the average community rating. of these 5 users, three tend to rate high, one tends to rate low, and one has no significant tendency. each of these 5 users rated less than 10% of signals. important to note that all users who rate significantly greater than 50% of signals within their 95% cis have rated outside of the 95% ci at least once. discussion users participating in rating kiwi signals within the ze program are multi-jurisdictional and represent multiple types of organizations. the community is well represented; however, the majority of users were not regularly rating signals. it appears that approximately one in four users rated greater than or equal to 10% of signals over the past 2 years. another distinction between users is whether the user was newly active or had stopped being active. for instance, 35.1% of users were active in both 2016 and 2017, but 18.9% of users stopped rating in 2016 and 46.0% of users only began rating in 2017. krippendorff’s alpha estimate revealed moderate reliability for users who rated greater than or equal to 10% of signals. a krippendorff’s alpha estimate of ≥80% is considered good reliability, and an estimate of ≤67% is considered low reliability [15]. overall, 5 users were found to rate significantly below average; however, these users rated less than 10% of signals and therefore did not contribute to this particular estimate. since the overall reliability estimate was poor for all users, it could be insightful to review the use of rating weights per user based on the user’s level of expertise in the field relevant to the program. evidence suggests that there is an increase in reliability estimate for users who rated an increased proportion of signals. an additional inquiry is whether the frequency of signal rating per user could be used as a proxy approach for determining user rating weights. a limitation of this proxy method is that the reliability estimate indicates the degree of user agreement or user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi similarity but is not necessarily indicative of ‘truth’. development of user profiles to obtain measures on potential parameters that could be incorporated in a new weighted rating algorithm could be helpful. profile parameters could include: expertise: hazards the user has an expert level of knowledge on, and experience: rating frequency. figure 6: the proportion of signals rated within, outside and above, and outside and below the average 95% ci per signal by user. conclusions a diverse community of users (n = 37) participated in rating kiwi signals within the zoonotic & emerging program over a two-year period (2016-2017). users represented multiple jurisdictions (i.e. provincial, national, and international) and types of organizations (i.e. industry, academia, government, and professional groups). users who rated at least 10% of all rated signals produced a moderate inter-rater reliability estimate; however, poor reliability was found among users when all raters were included in the calculation. results indicate that diversity remains between user ratings. it is hypothesized that differences in user rating may be due to varying levels of interest or expertise among raters regarding the hazard represented within each signal. inter-rater reliability estimates were compared by the proportion of signals rated, and it was observed that estimates improved with an increase in user’s experience in rating signals. an additional hypothesis is that number of times the user rates within and outside (above or below) a signal’s 95% ci can be used as a proxy for user expertise. the introduction and further analysis of a user rating activity within kiwi: a technology for public health event monitoring and early warning signal detection online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e205, 2018 ojphi weighted rating algorithm within the kiwi technology that takes this into consideration could be beneficial. limitations inter-rater reliability is an estimate of rating similarity rather than an estimate of truth. this must be taken into account during result interpretation. sample size related to the number of users participating in rating activities was good overall (n = 37). however, only 27.0% of users (n = 10) rated greater than or equal to 10% of all rated signals, and as a subset of this group, only 2 users rated more than 90% of all rated signals. average and median results for this particular group should be interpreted with caution. inter-rater reliability estimates were not dependent on rater sample size, rather signal sample size – which was very large. with this in mind, estimates became more precise moving from users who rated less than 10% of signals to those who rated more than 90% of signals. acknowledgements we would like to acknowledge that the zoonotic & emerging program was developed in collaboration with the cezd community and enabled through the cezd-iir project, which was funded by the canadian safety and security program, managed through defence research and development canada’s centre for security science, and hosted by the canadian food inspection agency. authors would like to acknowledge the cnphi team, the cezd project team, and the cezd rating community. references 1. hartley dm, nelson np, arthur r, barboza p, collier n, et al. 2013. an 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rating and the community norm results who is rating? how are users rating? how well are users rating? inter-rater reliability user rating and the community norm discussion conclusions limitations acknowledgements references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 1public health practice, llc, belmont, ma, usa; 2african field epidemiology network, kampala, uganda objective 1) to establish one health workgroups and conduct an e-surveillance assessment to inform national strategic planning efforts in pilot countries. 2) to provide evidence for the african surveillance informatics governance board (asigb) to address its mission of establishing e-surveillance. introduction information and communication technology (ict) can enhance public health surveillance (phs) by facilitating the digital exchange of information. electronic surveillance (e-surveillance) is the use of electronic systems to empower the digitization of phs functions of prevention, detection, and response. e-surveillance maximizes compliance with the international health regulations (2005), enables efficient integrated disease surveillance and response, and empowers one health. in africa, e-health is hindered by donor-funded, short-term projects known as “pilotitus.” proactive national leadership is required to establish a sustainable e-surveillance program; an assessment and a strategic plan are the first steps. therefore, the one health e-surveillance initiative (ohsi) was conceived and piloted by public health practice, llc (php) and the african field epidemiology network (afenet), with support by the defense threat reduction agency and the u.s. centers for disease control and prevention (cdc). methods ohsi was piloted in burkina faso, cameroon, kenya, nigeria, and uganda from 2013 – 2015. one health country-level work groups (clwgs) were established and composed of medical epidemiologists, veterinary epidemiologists, laboratory scientists, informaticians, and clinicians. clwg members were current employees of the ministries of health and agriculture/wildlife and were supported by the world health organization regional office for africa country representatives. their scopes of work included conducting a national e-surveillance assessment and advocating for one health e-surveillance within their countries. php, afenet, and cdc provided training and technical support for these efforts. as part of the e-surveillance assessment, clwgs collected, cleaned, and analyzed data. they also interpreted data collected and wrote reports. public health and veterinary surveillance units, health facilities, and laboratories at all administrative levels were assessed. results ohsi stimulated formation of an african surveillance informatics governance board (asigb), chaired by the world health organization regional office for africa (who/afro). ohsi established a framework consisting of the formation and training of one health clwg teams who conducted an e-surveillance assessment. additionally, a transnational e-surveillance assessment tool and protocol capturing the variance of ict capacity for e-surveillance were developed. owned by national ministries, the assessment data collected will inform national strategic planning efforts. clwg members are now advocates for one health e-surveillance. conclusions asigb and clwg creation, engagement, and ownership of the ohsi process and outcomes allowed for an in-depth understanding of the variance of ict capacity to support e-surveillance. the data collected through the assessment will support evidence-based strategic planning, and ohsi created champions of one health e-surveillance who can support this process. challenges included multiple languages, poor internet connectivity, and time constraints of clwg members due to the ebola outbreak. national strategic planning should occur using the assessment data collected. after incorporating feedback on the pilot processes and assessment, the asigb should support similar one health e-surveillance assessments in other african countries. ohsi should be replicated in other regions to support establishment of one health e-surveillance. keywords e-surveillance; one health; africa; strategic planning; evaluation acknowledgments ian watson, dylan jones, stephanie fedrigo, kenneth ofosu-barko, who/afro, burkina faso moh, burkina faso mra, cameroon minsante, cameroon minepia, kenya moh, kenya moa, nigeria fmoh, nigeria fmard, uganda moh, uganda maaif, and cdc. *joy sylvester e-mail: 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kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* state of new mexico, nmdoh, santa fe, nm, usa objective to analyze a homeless population, demographically and by health condition, over a 3-year time period who were admitted to an albuquerque area hospital. introduction it is estimated that there are 1.7 million homeless individuals in the united states; 36% are families with children under the age of 18. due to lack of resources, homeless individuals frequent emergency departments for immediate health care needs. homeless individuals are hospitalized more often, and once hospitalized have longer lengths of stay and incur higher hospital costs compared to non-homeless individuals1-2. methods this study is a retrospective analysis of hospital inpatient and discharge data from 50 non-federal hospitals across the state of new mexico. the primary outcome for this analysis was a 30-day readmission for a homeless individual, counted from the date of hospital discharge to the date of the next hospital admission. a homeless record was defined by the patient’s address; either recorded as ‘homeless’, ‘none’, or an address for a shelter in albuquerque. patient records that had at least one instance of being homeless and an admission to an albuquerque hospital were included in the analysis. once identified as a homeless patient, all records for that patient were included in the readmission analysis. records were analyzed from 2010-2012. the comparison group for this analysis consists of homeless individuals who were admitted to albuquerque area hospitals, but did not have a 30-day readmission during the analysis time period. results in all three years, males were proportionately higher in number. the overall mean age over the three year time period was 43.8 years. the predominant admitting primary diagnosis for homeless patients was neuro-psychiatric conditions, followed by digestive diseases and alcohol and drug related conditions. most readmissions occurred early after discharge, with two-thirds of the readmissions occurring prior to 10 days after discharge (66.3%). roughly, one third of the homeless patients experienced a 30day readmission as an inpatient during the three year time period. approximately 45% of patients had multiple inpatient admissions each year, with some patients being admitted more than 10 times in one calendar year (2% of patients). the average 30-day readmission rate among bernalillo county residents (who did not identify as homeless) was 12.3% over the same time period. in adjusted analyses, factors significantly associated with an increased odds of a 30-day readmission included age, gender, certain primary diagnoses, and the number of admissions per patient (table 2). conclusions in this three year, city centric study, the 30-day inpatient readmission rate among patients who identified as homeless was 30.1 percent. given the high readmission rate observed in this study and the work conducted by prior researchers with a similar population, hospitals need to take appropriate steps to identify this population as they come through their doors and create a suitable discharge plan of action for this population to prevent costly readmissions. 30-day readmission rates by year, bernalillo county, 2010–2012 patient hospitalization characteristics and odds of 30-day readmission, bernalillo county, 2010–2012 keywords homelessness; health inequalities; readmission; hospital stay acknowledgments the authors would like to thank all nm residents and general hospitals. the health systems epidemiology program is partially funded by a grant from the national syndromic surveillance program (cdc). references 1. doran km, shumway m, hoff ra, blackstock 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johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 1 ojphi an innovative web based system for reporting rare diseases in paediatrics shamir n mukhi1, melanie laffin thibodeau2*, barbara szijarto2 1. canadian network for public health intelligence, national microbiology laboratory 2. canadian paediatric society abstract background: surveillance of rare diseases in children is an important aspect of public health. rare diseases affect thousands of children worldwide. the canadian paediatric surveillance program (cpsp) has been in existence since 1996, and provides an innovative means to undertake paediatric surveillance and increase awareness of childhood disorders that are high in disability, morbidity, mortality, and economic costs to society, despite their low frequency. traditionally, cpsp used manual paper-based reporting on a monthly basis, which although had an impressive response rate, it had inherent longer processing times and costs associated with it. objectives: to provide an overview and evaluate an innovative web-based system that enables seamless reporting from participants across the country providing a quick, reliable and simple mechanism for the participants to submit data while yielding better data quality, timeliness and increased efficiencies. methods: in 2011, a proprietary electronic cpsp (ecpsp) system was developed to provide a simple, quick and reliable reporting environment for participants. it supports both the electronic and hardcopy reporting. the analysis presented in this paper was conducted based on usage data of this system. results: the response rates of the new ecpsp were found to be very favorable with adjusted rate of 80%, which equals the baseline. approximately 50% of online participants report the first day they receive the notification e-mail. the response time was also reduced considerably. furthermore, there has been significant reduction in data handling related activities (by almost 70%) from estimated 690 hours per year. finally, the number of cases reported that do not fit the study case criteria has fallen, likely because participants can now immediately access the case definition and protocol via the online system. this has reduced both staff and investigator time for case processing. conclusion: the ecpsp has modernized the cpsp program from paper-based reporting to efficient online technology while maintaining the core principles of the program. this simple and intuitive approach has proven to be an efficient approach cutting response times significantly while maintaining the desired response rates. keywords: informatics, surveillance, paediatrics correspondence: melaniel@cps.ca doi: 10.5210/ojphi.v7i2.6018 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:melaniel@cps.ca an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 2 ojphi introduction the importance of surveillance to the practice of medicine cannot be overstated. through ongoing, systematic collection of data, the burden of disease can be determined, interventions to prevent the occurrence of a disorder can be assessed, and the information collected can be used to develop health policy. surveillance takes research data into action. according to statistics canada, the canadian population on july 1, 2014, was an estimated 35,540,400. there were close to 8 million children and youth under the age of 20, representing approximately 22% of the population [1]. although individually uncommon, rare diseases affect hundreds of thousands of canadian children and youth and typically have lifelong impacts. the incidence of many rare diseases is unknown, and yet is essential for improved clinical care, advocacy and health service planning. this article describes the process followed by the canadian paediatric surveillance program (cpsp) and the canadian network for public health intelligence (cnphi) to design and implement an electronic reporting system for rare disease surveillance. it describes the application, discusses preliminary outcomes of implementation, and presents key lessons learned. canadian paediatric surveillance program (cpsp) the canadian paediatric surveillance program (cpsp) is a joint program between the canadian paediatric society (cps) and the public health agency of canada (phac), established in 1996. the program has since become an innovative epidemiological real-time mechanism [2] to undertake paediatric surveillance and increase awareness of rare childhood disorders that are high in disability, morbidity, mortality, and economic costs to society, despite their low frequency. studies that are executed through the cpsp are each led by a principal investigator(s) and co-investigators across canada, and must have strong scientific and public health importance or could not be undertaken any other way. all studies must conform to high standards of scientific rigour and practicality, and the cpsp assures the confidentiality of all information collected. the program has steadily grown from three surveillance studies in the pilot year to over 45 conditions studied since its inception. topics are varied and span a wide range, from infectious diseases, medical and genetic conditions, preventable injuries, and mental health conditions. because not all research questions warrant a full study, the cpsp is also available to investigators as a cost-effective tool to survey participants on a one-time basis to capture a signal, document a change in preand post-study knowledge of a particular disease, identify the prevalence of a problem or answer a specific question of clinical or public health relevance. cpsp surveillance studies have led to important medical and public health actions over the years. for example: • the wheeled baby walker survey results contributed to the total ban on the sale, import and advertisement of these walkers in canada [3]. an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 3 ojphi • the vitamin d deficiency rickets study confirming the importance of reinforcing the current cps recommendation that exclusively breast-fed infants and children receive vitamin d supplementation [4]. • the medium-chain acyl-coenzyme a dehydrogenase deficiency study documented the efficacy of newborn metabolic screening programs in detecting asymptomatic cases that allow for early preventive measures (the two reported deaths during the course of the study did not occur in jurisdictions with screening programs) [5]. • the one-time survey results on concussion management by paediatricians demonstrated the use of concussion/mild traumatic brain injury guidelines and criteria used in the initiation of return-to-play and management of return-to-play for brain-injured children and youth [6]. the program also offers the opportunity for international collaboration with 12 other national paediatric units worldwide, through the international network of paediatric surveillance units (inopsu). incredibly, many of the units have been collecting data on rare childhood diseases for 20 years or more. over 300 rare conditions have been studied to date, including rare infectious and vaccine-preventable diseases, mental health disorders, child injuries and immunological conditions. the network encompasses approximately10,000 health care providers who voluntarily contribute data on these rare diseases every month [7]. canadian network for public health intelligence (cnphi) the canadian network for public health intelligence (cnphi) is a pre-existing cutting-edge phac resource that has been developed and is managed by the national microbiology lab (nml), and for which many federal, provincial and territorial epidemiologists and laboratory scientists are familiar and often routine users [8]. cnphi provides a suite of online tools that enable laboratory and epidemiologic collaboration on a range of public health topics. cnphi currently supports over 4000 registered users and has successfully developed a suite of web-based resources including a pan-canadian alerting system, collaboration centres in use by various communities of practice, national disease surveillance systems, and laboratory based surveillance systems for human, animal, and environmental health domains. the cpsp surveillance system the cpsp gathers data from over 2,500 clinically active paediatricians and paediatric subspecialists each month, on a voluntary basis. to enhance data capture, the program works collaboratively with other professional groups beyond paediatrics, such as psychiatrists, pathologists/coroners, endocrinologists, and adult infectious disease specialists. data is only requested from participants in active practice. participant status is adjusted on an ongoing basis to account for any absences. the cpsp uses a two-tiered reporting system to ascertain and investigate cases: an initial checkoff form, and a detailed questionnaire. the check-off form, listing the conditions currently under surveillance, is distributed monthly to participants, who are asked to indicate the number of new cases seen in the previous month, or if no new cases were seen, which is very important in active surveillance; the cpsp cannot assume that no reply means no cases. the check-off form and detailed questionnaires are provided in either english or french depending on the preferred language of the participant (see figure 1). an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 4 ojphi figure 1: reporting process development of the e-reporting system: feasibility study for the first 15 years of the program, monthly forms were sent by mail. this represented significant overhead to the program as well as long delays to receive surveillance data. a feasibility survey for implementation of an electronic reporting system was conducted with a response rate of 31% [9]. responses indicated that more than two-thirds of participants (72%) were willing to report electronically, 13% were undecided or would require additional information and 15% preferred to continue reporting by hardcopy through the mail. suggestions from respondents included a simple, quick and reliable system with minimal or no log-in requirements, e-mail reminders to submit their reports with links to web forms, very clear sender and subject line to enable identification when rapidly scanning and deleting emails from the inbox, and the highest levels of security and confidentiality. in 2011, the cpsp partnered with cnphi to develop a proprietary electronic cpsp (ecpsp) application. ecpsp application components the ecpsp application comprises of various innovative components as discussed below. • participant management: the ecpsp application includes the ability to send email notifications to participants for a specific reporting month by interfacing with the an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 5 ojphi cps membership database to gather the participant lists including “absence” data to exclude specific participants who are away during a given reporting month. each participant is known to the ecpsp application through an internal participant administrative number; however, the details of each participant are stored in the resident cps database only for confidentiality purposes. during a fetch cycle, each participant has an indicator as to whether they are participating as an online user or offline user. this indicator is used to differentiate between those participants who receive an email notification versus those who will be sent a paper form. • studies: each monthly report may include multiple studies that the program is conducting. given the dynamic nature of these studies, the ecpsp application includes a tool to manage each study. the monthly report auto configures based on the active studies when the notifications for a specific reporting month are generated. this approach allows the administrators full flexibility of managing the studies as needed. • monthly submissions: the application supports two types of monthly submissions as described before, i.e., online and offline (hardcopy). for online submissions, an email notification is sent to each participant and includes a one-time use link to an online reporting form for the current reporting month including any reminders if required while excluding any absences. for offline submissions, the application generates hardcopy report forms with barcodes, which are used by cpsp staff to expedite data entry upon receipt. • case management: participants may report cases in a given reporting month and may also, optionally, include the gender and month/year of birth of the patient to help them recall the case when asked to complete the follow-up detailed questionnaire. participants can also immediately access the case definition and protocol via the online system, which makes it easier for them to determine if the case meets the study criteria. • detailed questionnaires: when participants identify having seen a case on the monthly form, the system generates a letter to the participant with the clinical detailed questionnaire for the study. detailed questionnaires at present time are mailed out in paper form and the ecpsp system maintains an audit for analysis and follow-up purposes. • surveys: in certain situations, a one-time survey of participants may be conducted to either identify the prevalence of a problem or to answer a specific question. much like the full studies, the one-time surveys vary in topic. the ecpsp application includes an integrated capability to design interactive surveys using cnphi’s webdata technology [10]. the ability to send customized surveys through the online system greatly improves speed of response, important in emergency or potentially high-risk situations. • status board: online participants can view the status of their monthly reports and see if they have any outstanding detailed questionnaires to submit. the status board also provides links to communications such as alerts, quarterly statistics and resource articles. an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 6 ojphi • analysis and reports: the ecpsp application generates various reports for monitoring response rates and overall program activity including follow-ups. preliminary outcomes a number of goals were identified for the new e-reporting system. for participants, the cpsp wanted a fast, easy system of reporting, reliability, security, flexibility, and reduced waste. the program aimed to see 70% of participants convert to online reporting, an average monthly response rate equal or better to the baseline 80%, increased speed of communications, better data access and control, reduced staff time dedicated to mailing production and manual reminders, and reduced materials and postage costs. at 3 months following the launch, 44% of participants were reporting online. help requests were minimal, and feedback about ease of use was very positive. response rates remained consistent with the previous system. as of july 1, 2014, 68% of cpsp participants were enrolled in electronic reporting, very close to the 70% target. the average monthly response rate for participants in 2013 was 84% for online participants and 72% for hard-copy participants. with year-end follow-ups, the overall annual adjusted response rate was 80%, equal to baseline. median response time for hardcopy participants in 2013 was 23 working days (mean 54 working days) compared to 2 working days for online participants (mean 35 days). approximately 50% of online participants report the first day they receive the notification e-mail. before implementing ecpsp, all participants received a mailed item at least 12 times per year. including follow up reminders, this amounted to over 67,000 mailed items each year with associated materials, printing and postage costs. staff hours dedicated to monthly surveillance data handling (mailing, intake, etc.) was estimated at 690 hours per year, or 57 hours per week. this has been reduced by almost 70%. another positive outcome of ecpsp has been increased confidentiality. considering the time delay between reporting a case and receiving the detailed questionnaire in the former mail-based system, approximately 1/3 of participants would hand-write recall notes on the monthly forms the online system does not accommodate these types of entries. moreover, participants now receive the questionnaires much more quickly, reducing the need for such notes. the number of cases reported that do not fit the study case criteria has fallen, likely because participants can now immediately access the case definition and protocol via the online system. this has reduced both staff and investigator time for case processing. cnphi’s ability to provide timely reports and statistics on response data has enabled cpsp administrators to gain a better understanding of participants’ reporting behavior and to identify appropriate solutions or follow-up actions. limitations the main limitation at present time of the existing approach is the co-existence of electronic and paper based reporting. the program has not yet been able to abandon the hybrid online/offline system, which will be maintained for the foreseeable future. this has been important to the continued satisfaction of participants and maintenance of the response rate. an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 7 ojphi specifically, • although staff hours dedicated to the processing of hardcopy monthly forms and reminders have been greatly reduced by use of the electronic reporting, personalized follow up with participants continues to be required to maintain response rate; • reduction in the amount of time needed to launch a new study was among the goals of converting to an online system. however, given the need to maintain a hybrid system with simultaneous launch to both offline and online participants, and the need to prepare the hard copies of the protocols and questionnaires, the program has not been able to achieve this goal. conclusion the ecpsp initiative has modernized the cpsp program from paper-based reporting to efficient online technology while maintaining the core principles of the program. this simple and intuitive approach has proven to be an efficient approach cutting response times significantly while maintaining the desired response rates. some of the key lessons learned include: better documentation to support staff turnover; up-front investment to carefully assess the needs both of participants and the program is very important, as is a firm commitment to base system specifications on this data; systematic prioritization of identified needs (i.e. essential versus desired) takes time and negotiation among stakeholders; pilot testing is of extreme importance; and finally, a strong communications and promotions plan was needed to ensure the successful implementation of the online program and recruitment. going forward, the program will be looking to further engage study investigators to take greater advantage of the application features. this will enable greater accuracy and timeliness of case updates. future plans also include the ability to obtain more detailed reporting trends and look into the possibility of implementing detailed questionnaires into the online system. acknowledgements the authors would like to acknowledge contributions of the public health agency of canada, canadian paediatric surveillance program participants, and the cnphi team with the development of ecpsp. references 1. statistics canada. http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo10aeng.htm 2. ugnat am, grenier d, laffin m, davis ma. 2011. the canadian paediatric surveillance program: celebrating 15 years of successful paediatric surveillance. paediatr child health (oxford). 16(4), 203. 3. baby walker survey: results and next steps. the canadian paediatric surveillance program highlights. http://www.cpsp.cps.ca/uploads/publications/highlights-baby-walkers.pdf 4. ward lm, gaboury i, ladhani m, zlotkin s. 2007. vitamin d-deficiency rickets among children in canada. cmaj. 177(2), 161-66. http://dx.doi.org/10.1503/cmaj.061377 http://dx.doi.org/10.1503/cmaj.061377 an innovative web based system for reporting rare diseases in paediatrics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e215, 2015 8 ojphi 5. prasad c, speechly kn, dyak s, rupar ca, chakraborty p, et al. 2012. incidence of medium-chain acyl-coa dehydrogenase deficiency in canada using the canadian paediatric surveillance program: role of newborn screening. paediatr child health (oxford). 17(4), 185-89. 6. gordon ke, do mt, thompson w, mcfaull s. 2014. concussion management by paediatricians: a national survey of canadian paediatricians. brain inj. 28(3), 311-17. epub dec 2013. doi:. http://dx.doi.org/10.3109/02699052.2013.862740 7. 15 years of international research into rare childhood diseases. international network of paediatric surveillance units (inopsu). http://www.inopsu.com/announcements/inopsu15-year-report 8. mukhi sn, aramini j, kabani a. 2007. contributing to communicable disease intelligence management in canada. can j infect dis med microbiol. 18(6), 353-56. 9. canadian paediatric surveillance program. 2009 results. http://www.cpsp.cps.ca/uploads/publications/results-2009.pdf 10. mukhi sn, stuart-chester tl, klaver-kibria jda, nowicki dl, whitlock ml, et al. innovative technology for web-based data management during an outbreak, online journal of public health informatics, online journal of public health informatics, issn 1947-2579, vol.3, no. 1, 2011. http://dx.doi.org/10.3109/02699052.2013.862740 an innovative web based system for reporting rare diseases in paediatrics introduction canadian paediatric surveillance program (cpsp) canadian network for public health intelligence (cnphi) the cpsp surveillance system development of the e-reporting system: feasibility study ecpsp application components preliminary outcomes limitations conclusion acknowledgements references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin centers for disease control and prevention, atlanta, ga, usa objective evaluate usability of alternative data sources, such as public announcements of unplanned school closures, for additional insight regarding influenza-like illness (ili) activity. introduction school children are the primary introducers and significant transmission sources of influenza virus among their families and surrounding communities [1,2]. therefore, schools play an important role in amplifying influenza transmission in communities. using school-related data sources may be an informative addition to existing influenza surveillance. unplanned school closures (uscs) are common, occur frequently for various reasons, and affect millions of students across the country [3]. information about uscs is publicly available in real-time. for this study, we evaluated usability of applying usc data for ili surveillance. methods from august 1, 2011 through june 30, 2015, we conducted systematic daily searches of publicly available online data (via google, google news, and lexis-nexis) to identify uscs lasting ≥1 school day in the united states. we selected uscs for which infectious diseases, particularly respiratory illnesses, were indicated as the main reason for the school closures. we described these uscs and compared their temporal patterns with ili data from outpatient provider visits (available via ilinet at http://gis.cdc.gov/grasp/ fluview/fluportaldashboard.html). we also evaluated the correlation (at alpha=0.05) between weekly occurrence of ili, reported by ilinet, and respiratory illness-related uscs over the 4 school years, excluding summer breaks (weeks 26-31). results of the 396 uscs related to infectious diseases, 232 (59%) were due to respiratory illnesses; the duration of these closures ranged from 1-4 days (based on 135 usc for which these data were available). the patterns of respiratory illness-related uscs corresponded similarly with those of ili activity observed via ilinet data regardless of the severity of influenza season (figure 1). during the 2012-13 and 2014-15 influenza seasons, when ili activity was high and peaked at around 6%, the number of uscs nationwide peaked as well; combined, these 2 school years accounted for 191 (82.3%) of all respiratory illness-related uscs over the 4-year study period. in 2012-13, ili activity was highest around the winter holiday break (at weeks 51-52). following this winter break, a small increase in ili activity at week 4 corresponded with a peak in uscs: more than 30 uscs were announced. similar patterns characterized the 2014-15 influenza season. in 2011-12 and 2013-14 when the influenza seasons were milder and ili activity lesser, usc patterns still corresponded to those of ili activity with highest increase in uscs on week 6 in 2012 and on week 2 in 2014. overall correlation between usc and ilinet data was significant: r= 0.54 with p-value <0.0001. conclusions our data 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ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts georgia’s rapid expansion of mosquito surveillance in response to zika virus chris rustin*1, deonte martin1 and rosmarie kelly2 1environmental health science, georgia southern university, jiann-ping hsu college of public health, statesboro, ga, usa; 2georgia department of public health, atlanta, ga, usa objective to describe the georgia department of public health’s (dph) mosquito surveillance capacity before and after zika virus was declared a public health emergency, review and compare mosquito surveillance results from 2015 to 2016, and evaluate the risk of autochthonous vector transmission of zika virus based on 2016 surveillance data of aedes aegypti and aedes albopictus mosquitoes. introduction zika virus was declared an international public health emergency by the world health organization on february 1, 2016. with georgia hosting the world’s busiest international airport and a subtropical climate that can support the primary zika virus vector, aedes aegypti, and secondary vector, aedes albopictus, the cdc designated georgia as a high risk state for vector transmission. faced with a lack of mosquito surveillance data to evaluate risk of autochthonous transmission and a few counties statewide that provide comprehensive mosquito control, the dph rapidly scaled up a response. dph updated existing mosquito surveillance and response plans targeted for west nile virus (wnv) and expanded capacity to areas that lacked previous surveillance targeting the zika virus vector. methods mosquito surveillance data provided by dph was analyzed for years 2015 and 2016 to date. the geographical distribution of counties conducting surveillance, total number and percentage by mosquito species collected in 2015 were compared to 2016 data. the distribution of counties conducting surveillance was mapped using arcmap 10.4.1 for pre and post zika response. autochthonous vector transmission risk was evaluated based on the overall numbers and percentages of aedes aegypti and aedes albopictus mosquitoes collected for 2016. results in 2015, georgia had 14 counties conducting mosquito surveillance, with a dph entomologist providing direct surveillance in 4 of these counties. in 2016, dph expanded surveillance capacity to 34 counties, a 142% increase, geographically dispersed across the state in urban and rural areas. a total of 76,052 mosquitoes were trapped and identified in 2015 compared to 91,261 mosquitoes trapped to date in 2016, representing a 20% increase. a total of 37 mosquito species were identified in both years with culex quinquefasciatus, georgia’s primary wnv vector, representing the highest percentage (2015-79.45% and 2016-70.41%) of mosquitoes trapped overall. in addition, aedes aegypti represented only 0.108% and 0.007% of the total mosquitoes trapped respectively each year and was found in one county. aedes albopictus represented only 1.50% and 1.82% of the total mosquitoes trapped respectively each year and was found in a majority of the counties conducting surveillance. conclusions dph was able to rapidly expand its surveillance capacity statewide by maximizing existing grant funds to hire new surveillance staff while also collaborating with academic institutions, military bases, georgia mosquito control association, and local health departments to provide training and funding for surveillance and data sharing. this expanded surveillance network provided a clearer picture of the types of mosquitoes potentially exposing the public to mosquito-borne disease risks. historical data for the primary vector of zika virus, aedes aegypti has been isolated to just two counties in georgia. expanded surveillance in 2016 confirmed a low abundance of aedes aegypti, suggesting the primary vector for zika has been displaced by aedes albopictus. this may suggest a reduced risk of autochthonous transmission of zika virus in georgia due to aedes albopictus’ affinity for feeding on both humans and animals. this should be interpreted with caution due to limitations in the data related to unstandardized reporting techniques for each county. dph is working with all counties to improve the quality of data collected and reported and continues to educate the public on ways they can reduce their individual risk of mosquito bites, which in turn reduces the risk of other mosquito-borne diseases such as wnv. in conclusion, dph’s response to zika virus allowed it to rapidly increase its surveillance footprint and with new data, make sound public health decisions regarding mosquito-borne disease risks. keywords zika; surveillance; autochthonous transmission; aedes aegypti; aedes albopictus acknowledgments the authors thank the georgia department of public health for providing data and consultation on this study. *chris rustin e-mail: rrustin@georgiasouthern.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e103, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 1gyeonggi infectious disease control center, seongnam-si, korea (the republic of); 2department of preventive medicine, dongguk university college of medicine, gyeongju, korea (the republic of) objective this study will determine opportunity of using the national health insurance (nhi) claims data for supplemental notifiable infectious disease surveillance system at national or regional levels. introduction infectious disease surveillance is very important as an element in public health system in the prevention and control of infectious diseases. results of the korean national notifiable disease surveillance system (knndss) has contributed to the reduction of amount of infectious disease. nevertheless, the “reporting rate” is continuously being debated [1]. the knndss classifies 77 infectious diseases into 6 groups: group i for those requiring immediate control measures; group ii for vaccine-preventable diseases; group iii for diseases that need routine monitoring; group iv for emerging diseases in korea; group v for parasitic infections; and group vi for disease that need monitoring outbreaks. group i – vi diseases are monitored by mandatory surveillance system that requires obligatory reporting on infectious diseases ‘without delay’ to a district health center [2]. the using the national health insurance (nhi) claims data is the important source of information for healthcare service research in south korea, since south korea achieves universal coverage of its population. in the aspects of data quality and standard, the sixth revision of the korean classification of disease (kcd-6) has been used in korea since 2011, and 99.9% of healthcare providers use to claims to insurers utilizing electronic data interchange transactions. in this respects, nhi claims data is an opportunity as a supplement for nddss data. in this study, we explored the difference between nndss data and nhi claim data and determined opportunity and challenges using nhi data for estimation the magnitude of national infectious disease incidence. methods cases in nndss data and nhi claim data were aggregated yearly from 2011 to 2014. the trends between two data were compared using spearman’s rank correlation coefficient. and also, we classified infectious diseases into four groups according to appearance or trends: (1) disease that there is no incidence in world and korea, so the use of claims data is difficult, (2) diseases that the trend is coincided or similar between nndss data and nhi claim data, (3) diseases that recently the reporting rate increase and it can be used as evaluation index to improve the reporting rate, (4) etc. results the first, there were claims for small pox, polio, diphtheria, etc. in nhi data, and they were classified type i. for these, process of error checking of nhi claim data is needed. brucellosis(r= 1.000), malaria(r= 0.943), hepatitis a(r= 0.943), typhoid fever(r= 0.886), etc. were classified type ii, but their reporting rate is low, efforts for increase of the reporting rate is required. diseases that recently the reporting rate increase, such as chicken pox, mumps, and scarlet fever were classified type iii, and it can be used as evaluation index to improve the reporting rate. conclusions the nhi claims data is the important source of information for healthcare service research in south korea. diseases classified type ii and iii could be used for supplemental nndss. nevertheless, we suggests that comparison between nndss data and nhi data for type i and iv is not appropriate. keywords korean national notifiable disease surveillance system; notifiable disease; national health insurance claim data *seon-ju yi e-mail: yiseonju@gidcc.or.kr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e133, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a 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thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. 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unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts user-friendly rshiny web applications for supporting syndromic surveillance analysis anne fouillet*, marc ruello, lucie léon, cécile sommen, laurent marie, céline caserio-schönemann, camille pelat and yann le strat santé publique france, saint-maurice, france objective the presentation describes the design and the main functionalities of two user-friendly applications developed using r-shiny to support the statistical analysis of morbidity and mortality data from the french syndromic surveillance system sursaud. introduction the french syndromic surveillance system sursaud® has been set up by santé publique france, the national public health agency (formerly french institute for public health invs) in 2004. in 2016, the system is based on three main data sources: the attendances in about 650 emergency departments (ed), the consultations to 62 emergency general practitioners’ (gps) associations sos médecins and the mortality data from 3,000 civil status offices [1]. daily, about 60,000 attendances in ed (88% of the national attendances), 8,000 visits in sos médecins associations (95% of the national visits) and 1,200 deaths (80% of the national mortality) are recorded all over the territory and transmitted to santé publique france. about 100 syndromic groupings of interest are constructed from the reported diagnostic codes, and monitored daily or weekly, for different age groups and geographical scales, to characterize trends, detect expected or unexpected events (outbreaks) and assess potential impact of both environmental and infectious events. all-causes mortality is also monitored in similar objectives. two user-friendly interactive web applications have been developed using the r shiny package [2] to provide a homogeneous framework for all the epidemiologists involved in the syndromic surveillance at the national and the regional levels. methods the first application, named mass-sursaud, is dedicated to the analysis of the two morbidity data sources in sursaud, along with data provided by a network of sentinel gps [3]. based on pre-aggregated data availaible daily at 10:30 am, r programs create daily, weekly and monthly time series of the proportion of each syndromic grouping among all visits/attendances with a valid code at the national and regional levels. twelve syndromic groupings (mainly infectious and respiratory groups, like ili, gastroenteritis, bronchiolitis, pulmonary diseases) and 13 age groups have been chosen for this application. for ili, 3 statistical methods (periodic regression, robust periodic regression and hidden markov model) have been implemented to identify outbreaks. the results of the 3 methods applied to the 3 data sources are combined with a voting algorithm to compile the influenza alarm level for each region each week: non-epidemic, pre/post epidemic or epidemic. the second application, named mass-euromomo, allows consulting results provided by the model developed by the european project euromomo for the common analysis of mortality in the european countries (www.euromomo.eu). the euromomo model, initially developed using stata software, has been transcripted in r. the model has been adapted to run in france both at a national, regional and other geographical administrative levels, and for 7 age groups. results the two applications, accessible on a web-portal, are similarly designed, with: a dropdown menu and radio buttons on the left hand side to select the data to display (e.g. filter by data source, age group, geographical levels, syndromic grouping and/or time period), several tab panels allowing to consult data and statistical results through tables, static and dynamic charts, statistical alarm matrix, geographical maps,… (figure 1), a “help” tab panel, including documentations and guidelines, links, contact details. the mass-sursaud application has been deployed in december 2015 and used during the 2015-2016 influenza season. masseuromomo application has been deployed in july 2016 for the heatwave surveillance period. positive feedbacks from several users have been reported. conclusions business intelligence tools are generally focused on data visualisation and are not generally tailored for providing advanced statistical analysis. web applications built with the r-shiny package combining user-friendly visualisations and advanced statistics can be rapidly built to support timely epidemiological analyses and outbreak detection. figure 1: screen-shots of a page of the two applications keywords shiny; user-friendly application; france; sursaud references [1] caserio-schönemann c, bousquet v, fouillet a, henry v. the french syndromic surveillance system sursaud. bull epidémiol hebd 2014;3-4:38-44. [2] chang, w., cheng, j., allaire, j., xie, y., & mcpherson, j. (2015). shiny: web application framework for r. r package version 0.11, 1. [3] valleron aj, bouvet e, garnerin p, ménarès j, heard i, letrait s, lefaucheux j. a computer network for the surveillance of communicable diseases: the french experiment. am j public health. 1986. 76(11):1289-92 *anne fouillet e-mail: a.fouillet@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e50, 2017 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e321, 2019 isds 2019 conference abstracts zika pregnancy surveillance: transforming data into educational and clinical tools kara polen, titilope oduyebo, jazmyn moore, sascha ellinton, regina simeone, samantha olson, margaret a. honein, dana meaney-delman centers for disease control and prevention, united states objective to describe how zika virus (zika) surveillance data informs and improves testing guidance, clinical evaluation and management of pregnant women and infants with possible zika infection introduction little was known about the maternal and fetal/infant effects of zika infection before the 2015 outbreak in the americas, which made it challenging for public health practitioners and clinicians to care for pregnant women and infants exposed to zika. in 2016, cdc implemented a rapid surveillance system, the us zika pregnancy and infant registry, to collect information about the impact of zika infection during pregnancy and inform the cdc response and clinical guidance. in partnership with state, tribal, local, and territorial health departments, cdc disseminated information from this surveillance system, which served as the foundation for educational materials and clinical tools for healthcare providers. methods throughout the zika response, cdc worked closely with health officers, epidemiologists, and clinical partners to seek expert input on the interpretation of emerging data and the evaluation and management of these vulnerable populations. in response to requests from clinical and public health partners, cdc created targeted educational materials and tools to facilitate the implementation of clinical guidance. these materials equipped healthcare providers with the information needed to care for pregnant women and infants with zika infection. examples of products developed included: 1) screening tools to identify pregnant women for whom testing is indicated; 2) an interactive web tool to assist with implementation and interpretation of zika testing guidance (pregnancy and zika testing widget); 3) patient counseling scripts; and 4) videos to explain critical clinical concepts (e.g., measurement of infant head circumference). these tools were informally pre-tested with the target audiences prior to dissemination, specifically to assess usefulness in clinical settings. cdc disseminated these tools through the cdc website and through comprehensive outreach (e.g., webinars, calls, email alerts) to various audiences. additionally, several professional organizations incorporated these tools into regular communication with their membership. results the us zika pregnancy and infant registry is currently monitoring infants from approximately 7,300 pregnancies in the us states and territories with laboratory evidence of zika. surveillance data provided valuable information, including clues toward the pattern of defects and other neurologic disabilities associated with congenital zika infection, estimates of the risks associated with congenital infection, and timeframes of greatest risk during pregnancy, to help clinicians counsel pregnant patients with potential zika exposure. cdc used these data to inform their clinical tools, particularly in pretest counseling materials and educational factsheets for healthcare providers to use with pregnant women with potential zika exposure. after informal testing among healthcare providers, the tools received positive feedback regarding usefulness and applicability in clinical settings. collectively, cdc’s zika clinical tools were downloaded more than 300,000 times from cdc’s website. the pregnancy and zika testing widget was accessed and followed to an endpoint (e.g., zika testing recommended) more than 17,000 times, with more than 75% of users self-identifying as clinicians. conclusions rapid implementation of zika surveillance captured evolving data about the impact of zika on pregnant women and their infants. these data informed the development of clinical tools for healthcare providers caring for and counseling patients with zika exposure. these tools ensured pregnant women and infants were adequately monitored during the zika outbreak. health education http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e321, 2019 isds 2019 conference abstracts materials and clinical tools based on surveillance data should be considered in future emergency responses, particularly when knowledge is rapidly evolving. references zika pregnancy website cdc. https://www.cdc.gov/pregnancy/zika/materials/index.html http://ojphi.org/ isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts influenza-associated pediatric deaths in the united states, 2010–2015 mei shang*, lenee blanton, sonja j. olsen, alicia m. fry and lynnette brammer influenza division, centers for disease control and prevention, atlanta, ga, usa objective to characterize and describe influenza-associated pediatric deaths in the united states over five influenza seasons, 2010–11 through 2014–15. introduction community influenza infection rates are highest among children. in children, influenza can cause severe illness and complications including, respiratory failure and death. annual influenza vaccination is recommended for all persons aged ≥ 6 months. in 2004, influenzaassociated deaths in children became a notifiable condition. methods deaths that occurred in children aged <18 years with laboratoryconfirmed influenza virus infection were reported from states and territories to the centers for disease control and prevention on a standard case report form. we used population estimates from the u.s. census bureau, 2011 to 2015, to calculate age group-adjusted incidence. we used wilcoxon-rank-sum test to compare medians and chi-square and mantel-haenszel chi-square to compare differences between proportions of two groups. results from october 2010 through september 2015, 590 influenzaassociated pediatric deaths were reported. the median age at time of death was 6 years (interquartile range, 1–12 years). half of the children (285/572) had at least one underlying medical condition. neurologic conditions (26%) and development delay (21%) were most commonly reported. the average annual incidence rate was 0.16 per 100,000 children (95% confidence interval [ci]: 0.15–0.17) and was highest among children aged <6 months (0.75, 95% ci, 0.60–0.94 per 100,000 children), followed by children aged 6–23 months (0.34, 95% ci, 0.28–0.41 per 100,000 children). only 21% (87/409) of pediatric deaths in children ≥6 months had evidence of full influenza vaccination. vaccination coverage was lower in children aged 6–23 months (15%) and 5–8 years (17%) than with those aged 2–4 years and 9–17 years (25%, p<0.01). the majority of children aged <2 years who died had no underlying medical conditions (63%, 105/167); this proportion was significantly higher than that in children aged ≥2 years (45%, 182/405, p<0.01). overall 65% (383) of pediatric deaths had influenza a virus detected, and 33% had influenza b virus detected. children infected with influenza b virus had a higher frequency of sepsis/shock (41%, 72/174), acute respiratory distress syndrome (ards, 33%, 58/174), and hemorrhagic pneumonia/pneumonitis (8%, 14/174) than children infected with either influenza a(h1n1) pdm09 or influenza a(h3n2) virus (p=0.01, 0.03, 0.03, respectively). overall 81% (421/521) of children had an influenza-associated complication; the most commonly reported were pneumonia (40%), sepsis/shock (31%) and ards (29%). among those with testing reported, invasive bacteria coinfections were identified in 43% (139/322); β-hemolytic streptococcus (20%) and staphylococcus aureus (17%) were reported most frequently. most children (39%, 212/548) died within 3 days of symptom onset, 28% died 4–7 days after onset, and 34% died ≥8 days after onset. the median days from illness onset to death for children with an underlying condition was significantly longer than the time for previously healthy children (7 versus 4 days, p<0.01). conclusions each year, a substantial number of influenza-associated deaths occur among u.s. children, with rates highest among those aged <2 years. while half of the deaths were among children with underlying conditions, the majority of children <2 years who died were previously healthy. vaccination coverage was very low. influenza vaccination among pregnant women, young children and children with high-risk underlying conditions should be encouraged and could reduce influenza-associated mortality among children. keywords influenza; pediatric; deaths; surveillance *mei shang e-mail: keq6@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e77, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts mortality surveillance in the netherlands: winter 2015/2016 of moderate severity liselotte van asten*1, marit de lange1, anne teirlinck1, frederika dijkstra1, lenny stoeldraijer2, carel harmsen2 and wim van der hoek1 1rivm (netherlands institute of public health and the environment, centre for infectious disease control, bilthoven, netherlands; 2statistics netherlands, department of demographic and socio-economic statistics, the hague, netherlands objective weekly numbers of deaths are monitored to increase the capacity to deal with both expected and unusual (disease) events such as pandemic influenza, other infections and non-infectious incidents. the monitoring information can potentially be used to detect, track and estimate the impact of an outbreak or incident on all-cause mortality. introduction the mortality monitoring system (initiated in 2009 during the influenza a(h1n1) pandemic) is a collaboration between the centre for infectious disease control (cib) and statistics netherlands. the system monitors nation-wide reported number of deaths (population size 2014: 16.8 million) from all causes, as cause of death information is not available real-time. data is received from statistics netherlands by weekly emails. methods once a week the number of reported deaths is checked for excess above expected levels at 2 different time-lags: within 1 and 2 weeks after date of death (covering a median 43% and 96% of all deaths respectively). a weekly email bulletin reporting the findings is sent to the infectious disease early warning unit (at cib) and a summary of results is posted on the rivm website (national institute for public health and the environment). any known concurrent and possibly related events are also reported. when excess deaths coincide with hot temperatures, the bulletin is sent to the heat plan team (also at rivm). data are also sent to euromomo which monitors excess mortality at a european level. for the dutch system baselines and prediction limits are calculated using a 5 year historical period (updated each july). a serfling-like algorithm based on regression analysis is used to produce baselines which includes cyclical seasonal trends (models based on historical data in which weeks with extreme underreporting have been removed. also periods with high excess mortality in winter and summer were removed so as not to influence the baseline with previous outbreaks). results increased mortality occurred during the entire influenza epidemic and up to three weeks thereafter (weeks 1-14 of 2016), except for a drop in week 7 (figure1). excess mortality was primarily observed in persons 75 or older. additionally, in several weeks mortality was increased in 65-74 year olds, (weeknr 4-6; peaking in week 4 with 564 deaths, when 468 baseline deaths were predicted). also, in week 4, mortality in the 25-34 year-old age group was significantly increased (25 deaths, while 14 were expected as baseline). cumulative excess mortality was estimated at 3,900 deaths occurring during the 11 weeks of the 2015/2016 influenza epidemic and at 6,085 during the total winter season (44 weeks running from week 40 up to week 20). conclusions in terms of number of deaths during the winter season (weeks 40-20) and during the influenza epidemic (weeks 1-11), the 2015/2016 season in the netherlands was of moderate severity compared with the previous five years (and was of similar magnitude as the 2011/2012 winter). notable was the short three-week time span with a higher peak in mortality in 65-74 year olds than has been observed in recent years. although the influenza epidemic reached its peak in week 7, the mortality data showed a dip in week 7. the reason for the temporary decrease is unknown but there was a partial overlap with a public holiday. figure 1. weekly deaths reported within 2 weeks (black line) against baseline (blue) and prediction limit (red) for 2015-2016. blue shading depicts influenza epidemic weeks (according to sentinel ili surveillance). keywords mortality monitoring; influenza; seasonality *liselotte van asten e-mail: liselotte.van.asten@rivm.nl online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e116, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts impact of the 2015 july heat waves in france on heatrelated causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay french institute for public health surveillance, saint maurice, france objective to present the evolution of heat-related pathologies during heat waves occurring in july 2015 in france introduction two major heat waves occurred in france in july 2015. a first episode characterized by early onset, intensity, large geographical coverage and duration occurred between 29th of june 8th of july. a second episode less intense was localized on the south-east of the country from 15th to 23rd of july. the french heat warning system has been operating by invs since 2004 as part of the french national heat wave plan. warnings are based on meteorological forecasts and on real-time follow-up of specific health indicators to support decision-making. the evolutions in emergency health care facilities during the july heat waves are presented. methods real-time morbidity indicators are produced by the french national syndromic surveillance system sursaud® which is based on the daily collection of data from emergency department (ed) involved in the oscour® network and from general practitioner’s emergency associations sos médecins (gpsm). individual data are automatically recorded and transmitted daily to invs including administrative, demographic and medical information (coded medical diagnosis) [1]. ed attendances and gpsm visits for all-causes and for heatrelated causes are specifically followed during heat waves. heatrelated causes include heat stroke/hyperthermia, dehydration and hyponatremia in ed and heat stroke and dehydration in gpsm. these pathologies only represent a small part of the potential health impact during heat waves. they were selected for their reactivity and to provide indications on the spatio-temporal dynamics of the health impacts. indicators are investigated by age, with a special focus on people aged 75 and over. the proportion of hospital admissions (ha) for heat-related diagnoses in the total of ha was also followed. indicators analyses are performed at local and national levels during heat waves, which are defined at the local level when forecasted biometeorological indicators have a high probability of exceeding warning thresholds [2]. results during both episodes, we observed a sharp increase in the number of ed attendances and gpsm visits for heat-related causes. during the first heat wave there were a total of 3 729 ed attendances and 1 456 gpsm visits for diagnoses related to heat, representing respectively 1% and 2.5% of total national activity (in comparison respectively with 0.3% and 0.6% in june 2015). a peak was recorded on the 4th of july with 497 ed attendances and 205 gpsm visits representing up to 3% and more than 8% of total activity in the most affected regions. all age groups were concerned but the elderly were mostly represented in ed visits while children and adults less than 75 years old were mostly represented in gpsm visits. ed attendances for dehydration and hyponatremia were higher in the elderly while visits for hyperthermia/heat stroke were mostly recorded in children and adults less than 75 years old. heat indicators reached lower levels during the second heat wave, which affected a most restricted geographical area. about 3 000 ed attendances and 610 gpsm visits (respectively 0.6% and 1.2% of total activity) were recorded. the proportion of ha for heat-related diagnoses represented locally up to 8,2% of total ha during the first episode and up to 5,2% during the second episode in the most affected regions. conclusions an increase in all heat-related indicators was observed in all age groups and particularly in the elderly during the july 2015 heatwaves. this confirms that heat-related indicators are specific and sensible. a high proportion of ha for diagnoses related to heat in the total of ha is an indicator of severity and could generate local and occasional situations of tensions in the health care system, as observed during the july heat waves. since 2003, improvements have been made to prevent the health impacts of heat (communication, training of health professionals, access to cool rooms in nursing homes…). however, heat waves remain hazardous for population health with potential impact on ed attendances and hospitalizations. keywords heat wave; impact assessment; emergency departements; healthrelated indicators acknowledgments to oscour® emergency departments and sos médecins associations references 1. caserio-schönemann c, bousquet v, fouillet a, henry v. le système de surveillance syndromique sursaud (r). bull epidémiol hebd 2014;3-4:38-44. 2. laaidi k, ung a, wagner v, beaudeau p, pascal m. the french heat and health watch warning system: principles, fundamentals and assessment. saint-maurice: institut de veille sanitaire; 2013. 17 p. *céline caserio-schönemann e-mail: c.caserio-schonemann@invs.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e96, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious 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singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform 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pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food 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lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts nbs: a community-based approach to developing an integrated surveillance system christi hildebrandt*, jennifer ward and akshar patel csra, chattanooga, tn, usa objective the nedss base system (nbs) is designed and developed using input from cdc programs, public health standards organizations, as well as its expansive user community. this community-based approach to development of an integrated surveillance system is described. introduction the nedss base system (nbs) is a web-based, standardsdriven, integrated disease surveillance system launched in 2001 and is currently in use in twenty-two public health jurisdictions. over the past fifteen years, the nbs has grown into a highly functional, modern application that supports: case management, electronic data exchange, metadata-driven data collection, workflow decision support, and a host of other functionalities, all of which are defined and designed through a community-based approach. methods in order to encourage open communication and collaboration across and among the community, there is a well-publicized, longstanding communication plan in place. further, tools such as an online collaboration and support forum, nbscentral, are made available to any person who requests access. also, the nbs source code is provided in an open source package to anyone interested, along with each release, and a demonstration version of the application can be accessed online by anyone to review the latest release of the application. all of these channels are in place to ensure there are ways for all who have in interest in collaborating to easily participate. the nbs community regularly meets to provide input into further development of the system, as well as discuss topics affecting public health. as a community, members: ■ share best practices, tools, and lessons learned across jurisdictions ■ share innovative local approaches to disease surveillance and reporting ■ access nbscentral for support and collaboration ■ participate in the change control and planning process for each nbs release ■ work collaboratively with cdc to define high-level vision and priorities ■ provide input to create community-defined requirements for system development ■ participate in weekly subject matter expert (sme) calls to discuss development and best practices ■ have the opportunity to participate in beta testing for releases ■ attend a bi-weekly nbs user group (nug) call to discuss the system as well as reach out to colleagues to brainstorm creative solutions to common problems in public health surveillance all meetings with stakeholders are recorded and shared with the larger community to ensure full transparency and for historical reference. results through this inclusive development approach, the nbs has evolved into a highly extensible, configurable system that can meet that needs of twenty-two very different public health jurisdictions; the system can be implemented without the need for custom development in a relatively short timeframe due to the fact that it was designed to meet the needs of many. further, it has encouraged interoperability projects, such as: piloting electronic case reporting use cases between nbs implementation sites and building and sharing electronic case investigation forms for data collection using the nbs page builder module. all nbs sites use the same translation routes for electronic lab report, case report, and nationally notifiable disease message processing – embracing the build once, use many concept. most recently, having this collaboration network in place made it very easy for the nbs community to quickly adapt to the changing needs of zika virus surveillance. conclusions it does require clear definition of processes and communication channels, as well as regular update and transparency into the process for community-based development to work. however, when the proper tools and processes are in place, the benefits of collaboration with all key stakeholders are exponential when realized. developing an application in this way has provided nbs users not only with a much better, integrated surveillance system, but also a forum for understanding how other jurisdictions have solved similar issues; it provides a springboard for sharing and building upon novel ideas and new approaches in public health surveillance. keywords nbs; surveillance; community; integrated; collaborative acknowledgments the nbs is funded by the centers for disease control and prevention and is supported by the nbs user group (nug), which consists of state, local and territorial public health staff and their representatives. *christi hildebrandt e-mail: christi.hildebrandt@csra.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e64, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts monitoring and evaluation mechanism for multicenter capacity building gestational diabetes program for physicians in india megha sharma* centre for control of chronic conditions / training, public health foundation of india, delhi, india objective with implementation of program on all india level aim is to develop a robust monitoring and evaluation system to ensure quality assurance and standardized course delivery on all india level. introduction international diabetes federation (idf) estimates that 21.4 million women in 2013 had some form of hyperglycaemia in pregnancy and in india alone an estimated 4 million women have gdm. recognizing the shortfall of trained manpower; certificate course in gestational diabetes mellitus (ccgdm) was launched in 2012; since then it has spread across 17 states and 39 cities across 55 regional training centers and trained 2400 primary care physicians (pcp) all across india. methods inadequate knowledge among pcps is one of the major obstacles in the prevention and management of gdm therefore we developed a comprehensive 4 modular course to train pcps which is a joint program of public health foundation of india and dr. mohan’s diabetes education academy(who collaborating centre) with final approval of the course curriculum by national expert panel.a cadre of observers who are eminent public health professionals were the backbone. it incorporated on-site monitoring visits every month to regional centers for evaluation on the basis of standardized indicators and formats. baseline survey was employed to assess knowledge attitude practices of gdm among participants,sms real time emonitoring system was used for meticulous and timely follow up, end line evaluation was planned at the end of program; by the means of face to face interviews information was received on structure of curriculum, teaching methods, session environment, knowledge improvisation, value addition. results the analysis of data generated was subsequently published in reports and shared with stakeholders. the impeccable delivery of ccgdm has given it recognition from idf and accreditation from south asian federation of endocrine society. the results of end line evaluation concluded with improved knowledge of pcps (pre-test & post-test score, p<0.001), value addition to knowledge (98.5%), value addition to skills (96.8%), program enhanced routine treatment plan (95%); ideal for learning (96.1%),useful case studies (88.5%);interactive and informative sessions (98%), faculty competency (94%); professional network and referral (67.6%). conclusions the launch of capacity building initiative is just the beginning but final success will depend on how effectively we monitor and evaluate it. keywords gestational diabetes mellitus; monitoring & evaluation; pan india acknowledgments prof. d. prabhakaran, dr. sandeep bhalla, dr. v. mohan, dr. ranjit unnikrishnan references idf altas 6th edition *megha sharma e-mail: meghs211@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e161, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 160 isds 2014 conference abstracts protecting australia from future polio outbreaks beverley j. paterson*1, nicolee martin2 and david n. durrheim1 1hunter medical research unit, university of newcastle, newcastle, nsw, australia; 2australian government department of health and ageing, canberra, act, australia objective few countries have tested the sensitivity of their polio surveillance systems, undertaken a comprehensive risk assessment or questioned whether existing polio surveillance strategies are the optimal surveillance at this stage of the global eradication initiative. to address this issue, a risk assessment and review of australia’s polio surveillance systems were undertaken to assess the potential risk of polio introduction by an infected person, product or specimen. introduction the occurrence of global polio is at its lowest level, yet the goal of eradication remains elusive with the ongoing circulation in the three remaining endemic countries. developed countries have a low index of suspicion for polio – relying on astute clinicians to detect imported cases and high immunisation rates to prevent community outbreaks. australia, like all polio-free countries, remains at risk of a polio importation until polio is eradicated globally. gaps in routine immunisation coverage coupled with weaknesses in surveillance may put developed countries, such as australia, at risk of high impact polio outbreaks. methods methods utilised in the surveillance review and risk assessment included document review and semi-structured key informant interviews. data were sourced from various published and unpublished government reports including immunisation coverage, movements of travellers, population immunity, population settlement statistics and polio surveillance data. interviews were recorded, transcribed and thematically analysed. results vaccination coverage for polio in australia is generally high but varies by geographical area, age group and indigenous status. lower areas of coverage have been identified among the general population. the potential for importation of a wild poliovirus exists in australia with arrivals of travellers from polio endemic or re-infected countries, however serosurvey results indicate that there is sufficient herd immunity to prevent outbreaks of type 1 and type 2 poliovirus. australia meets some but not all of its polio surveillance objectives and there continues to be room for improvement, particularly stool sample surveillance targets. case detection and outbreak mitigation were considered key reasons to undertake polio surveillance. identified issues included: importance of supplemental environmental and enterovirus surveillance systems; potential high outbreak response costs resulting from an importation; continued importation risks; low stool sample collection rates; possible biosecurity inadequacy for imported samples and laboratory storage of biological samples containing poliovirus; and opportunities for improved legislative controls around polio. with most of the arrivals from polio endemic or re-infected countries settling in major cities, supplemental environmental surveillance in at least one major city would be useful. conclusions results from this work demonstrate that, while australia has an impressive polio surveillance system, there are some gaps or risks that needed to be addressed. while the threat of an importation is low, ensuring that travellers and immigrants from areas where polio continues to occur are vaccinated against polio will help to address the risk. polio vaccination coverage in australia is high but there continue to be pockets where immunisation coverage is less than ideal, putting families at risk if an imported case were to be introduced into their area. recommendations from this work have resulted in improvements to surveillance, changes in biosecurity requirements for product importations that may contain wild poliovirus, and amendments to vaccination policies; all helping to protect australia from future polio outbreaks. keywords surveillance; polio; needs assessment; australia acknowledgments the authors would like to acknowledge dr bruce thorley and the team at the national enterovirus reference laboratory at the victorian infectious diseases reference laboratory. *beverley j. paterson e-mail: beverley.paterson@hnehealth.nsw.gov.au online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e203, 201 development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies liora sahar1, guy faler1, emil hristov1, susan hughes1, leslie lee2, caroline westnedge2*, benjamin erickson3, barbara nichols4 1. northrop grumman corporation (ngc), information systems, atlanta, ga 2. sra international, inc., atlanta, ga 3. office of public health preparedness and response (ophpr), centers for disease control and prevention, atlanta, ga 4. office of public health scientific services (ophss), centers for disease control and prevention, atlanta, ga abstract objective: to bridge gaps identified during the 2009 h1n1 influenza pandemic by developing a system that provides public health departments improved capability to manage and track medical countermeasures at the state and local levels and to report their inventory levels to the centers for disease control and prevention (cdc). materials and methods: the cdc countermeasure tracking systems (cts) program designed and implemented the inventory management and tracking system (imats) to manage, track, and report medical countermeasure inventories at the state and local levels. imats was designed by cdc in collaboration with state and local public health departments to ensure a “user-centered design approach.” a survey was completed to assess functionality and user satisfaction. results: imats was deployed in september 2011 and is provided at no cost to public health departments. many state and local public health departments nationwide have adopted imats and use it to track countermeasure inventories during public health emergencies and daily operations. discussion: a successful response to public health emergencies requires efficient, accurate reporting of countermeasure inventory levels. imats is designed to support both emergency operations and everyday activities. future improvements to the system include integrating barcoding technology and streamlining user access. to maintain system readiness, we continue to collect user feedback, improve technology, and enhance its functionality. conclusion: imats satisfies the need for a system for monitoring and reporting health departments’ countermeasure quantities so that decision makers are better informed. the “user-centered design approach” was successful, as evident by the many public health departments that adopted imats. keywords: inventory management, countermeasures, public health emergency response events, pharmaceuticals, user-centered design. correspondence: rie7@cdc.gov doi: 10.5210/ojphi.v7i2.5873 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:rie7@cdc.gov development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi introduction background and significance the department of health and human service’s centers for disease control and prevention (cdc) and state and local public health departments share the responsibility to protect the public from negative health consequences resulting from terrorist attacks, natural disasters, or disease outbreaks. when such public health emergencies occur, it may be necessary for state and local public health departments to provide vaccines, pharmaceuticals and medical equipment (collectively referred to as medical countermeasures) to the public in order to save lives. if such an event were to affect large numbers of people, local supplies of medical countermeasures could be depleted quickly, putting persons at risk for illness or death. accordingly, cdc has a plan to resupply state and local public health departments with medical countermeasures through its strategic national stockpile (sns) [1]. “the sns is a national repository of antibiotics, chemical antidotes, antitoxins, life-support medications, iv-administration, airway maintenance supplies, and medical/surgical items. the sns is designed to supplement and re-supply state and local public health agencies in the event of a national emergency anywhere and at any time within the u.s. or its territories [1]”. in addition, cdc advises state health departments to have primary and backup inventory management systems that can support responses to public health emergencies [2]. during a public health emergency, effective dissemination of medical countermeasures is crucial for a fast, efficient response. supporting such efforts requires advanced planning, preparation and stockpiling [1,3-5], as was evident during the 2009 h1n1 influenza pandemic [6]. during this emergency, cdc and state and local public health departments found that they needed a way to better understand their medical countermeasure inventory quantities. improved inventory awareness was needed all the way down to the local point-of-dispensing level. decision makers felt they needed better countermeasure inventory information to help ensure they made the best choices on allocating and distributing antiviral drugs and personal protective equipment. during the h1n1 response, it was clear that state and local public health departments needed a comprehensive management system so they could accurately track and report their countermeasure inventory levels [7]. as a result, the cdc division of strategic national stockpile (dsns) partnered with the cdc office of public health scientific service’s (ophss) countermeasure tracking systems (cts) program to plan and build the inventory management and tracking system (imats). objective imats is a software application used to manage inventories of materials housed in warehouses or other storage facilities owned by or known to state and local public health departments. these inventories include medical countermeasures poised for distribution by public health departments during an emergency. the vision for imats was to increase the capacity at all levels of public health to accurately manage, track, and report the quantities of medical countermeasures needed to address a specific health threat. imats increases response readiness because it is useful for daily non-emergency inventory management operations. this enables users to become familiar with the system before an emergency response. when confronted by a public health crisis, decision makers need timely and comprehensive information that accurately describes the magnitude of the emergency, such as populations at risk, their location, and the availability of development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi medical countermeasures. imats is designed to provide comprehensive information about medical countermeasure inventory available, locations of distribution facilities (i.e., state or regional warehouses, local storage facilities, etc.) and how much product has been used. in this paper we provide an overview of events leading to the development of imats and a review of the system’s design and implementation processes. we provide detailed descriptions of imats components and the role each plays in improving the capacity of public health officials to track and manage medical countermeasure inventory. the current adoption status of imats and proposed system enhancements also are presented. materials and methods partnerships and collaborations following the 2009 h1n1 influenza response, dsns partnered with the ophss cts team to plan and build imats. in support of the cooperative agreement, strengthen and improve the nation’s public health capacity through national, non-profit, professional public health organizations to increase health protection and health equity, cdc formed the sns inventory management and tracking workgroup. workgroup members represented the public health informatics institute (phii), dsns, the ophss cts team, and 10 state and local public health departments. the health department representatives were selected on the basis of their emergency response and inventory management expertise. applying a user-centered design approach, the cts team gathered user input by hosting online, virtual focus-group meetings, webinars to define technical requirements, system demonstrations, in-person user experience and usability workshops, and conference presentations. input from potential imats users was incorporated throughout each stage of development to ensure that imats met or exceeded user needs, preferences, goals and business objectives. figure 1 depicts key partner-collaboration events occurring throughout the imats development process. figure 1: key imats partner-collaboration events during program development development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi the workgroup’s objective was to develop high-level business processes and identify functional requirements to inform further development of the system. in june 2010, the workgroup kick-off meeting was held in atlanta, georgia. the meeting results included defining nine business processes that imats must support (figure 2). figure 2: high-level public health inventory-management business processes the workgroup reconvened in august 2010 to review, refine, and validate the nine business processes. to develop system requirements, the workgroup met biweekly with the cts team from october 2010 to march 2011. during the 13 workgroup meetings, which were conducted via webinar, the group gathered technical requirements, produced design layouts, and reviewed imats screen mockups. from may 3˗5, 2011, workgroup members participated in an in-person user-experience workshop held in atlanta, georgia. the objectives of this workshop were to refine user requirements, test system usability, and ensure required functionality was present. participants were presented a public health emergency scenario with 18 tasks to complete by using a preproduction version of imats. by working through the task assignments, each user had multiple opportunities for comprehensive interaction with imats functionality. to acquire assessments of the greatest value, the participants were not provided imats training before performing their tasks. after completing the scenario tasks, participants recorded their assessments of imats and joined in productive open-group discussions. when this hands-on workshop ended, the cts team was armed with valuable information that they incorporated into further imats development to help increase acceptance of the system. in addition to the sns inventory management and tracking workgroup, the ophss cts team in collaboration with dsns formed a data exchange focus group. the focus group included some members of the workgroup and representatives from state and local public health departments that purchased or developed their own inventory management systems. the focus group’s objective was to develop an imats capability that would enable exchange of inventory development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi data to cdc by those public health departments wishing to continue using their own inventory management systems. focus group members met with the cts team via webinar on six occasions from april to july 2011. these meetings resulted in the creation of an inventory data exchange (ide) specification document. throughout the imats development cycle, cts team members made multiple presentations and held poster sessions at various public health conferences. those conferences also gave state and local public health representatives opportunities to ask questions of the cts team and to describe their current inventory management needs and the limitations of their present systems. their information proved highly useful for developing further imats refinements. the cts team also launched a quarterly electronic newsletter in august 2010 to keep public health inventory managers nationwide informed about progress on imats development. system requirements the set of requirements and constraints guiding imats development is described below. requirements for inventory management and tracking processes to achieve the primary imats requirement to track and report medical countermeasure inventories during public health emergencies, the workgroup identified nine mandatory highlevel inventory management processes that imats must include. these processes all complied with dsns guidance for receiving and redistributing medical countermeasures and for supporting routine operations at warehouses or other storage facilities. the first step in an emergency response is to mobilize required resources (figure 2). the remaining processes are followed iteratively as necessary until the response effort ends and resources are demobilized. from multiple webinars and other discussions, the workgroup developed detailed requirements for imats that met the specific objective for each of the nine processes. the workgroup further suggested that imats be useful for daily operations in addition to public health emergency response activities. the workgroup determined that imats should provide support for analysis, visualization, and data reporting. the system also should support alternative means of data collection including import/export, bar-coding/scanning and radio frequency identification (rfid) technology. these capabilities would make imats useful for routine inventory management activities. this, in turn, would increase users’ system familiarity and alleviate the need for training during public health emergencies, which could delay starting emergency response operations. during public health emergencies, cdc may ask state and local public health departments to provide the agency with what amounts of specific medical countermeasures are needed to protect or treat people in their jurisdictions. imats was designed to aid fulfilling such requests by enabling users to easily produce the appropriate reports. some workgroup members requested that in addition to its emergency response capabilities, the system would also have the ability to operate as a standalone system. as a result, an easy installer and synchronization mechanism was developed, as was a data exchange capability whereby inventory data can easily be reported to cdc. table 1 provides details about the objectives of each process conducted by state and local public health departments. table 1: high-level public health inventory-management business processes process objective mobilization activate additional people and resources when response development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi to an emergency has exhausted or nearly exhausted existing resources order/request resources accurately and efficiently request needed resources on the basis of meeting the jurisdiction’s justification criteria approve/deny request determine the validity of resource requests in a timely manner receive inventory physically receive, inspect and verify the accuracy of newly received inventory within a specified time store inventory identify appropriate storage locations and accurately inventory all items to identify stock that is readily available pick order accurately pick and stage orders to be shipped to another location ship inventory/order ensure timely delivery of assets regardless of barriers, in accordance with federal, state and local requirements dispense medical countermeasures safely and accurately dispense appropriate medical countermeasures to target populations demobilization secure remaining medical countermeasures and return to normal operations design goals and constraints in order to provide a system at no or little cost to partners and to support the above inventory system requirements, the set of design goals and constraints developed were that imats • should be designed as a web application and must be fully capable of being developed and distributed under an open source licensing model, • must scale upwards to support hundreds or thousands of simultaneous users entering large volumes of data such as during an incident requiring mass prophylaxis, • shall integrate with existing state and local public health inventory management systems, • must support data exchange using public health information network (phin) [8] compliant messaging protocols, • can be simultaneously deployed centrally at cdc and at state and local areas, and • should require few as possible hardware and processing assets and be database independent. system design and development imats is intended to have an easy and intuitive user interface that encourages its use and helps increase system adoption. imats must also adhere to the access and use requirements in section 508 of the rehabilitation act of 1973 (http://www.section508.gov) requiring that when federal agencies “develop, procure, maintain, or use electronic and information technology, federal employees with disabilities have access to and use of information and data that is comparable to development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi the access and use by federal employees who are not individuals with disabilities, unless an undue burden would be imposed on the agency”. the imats security approach was developed with the assistance of cdc business, technical and security stewards to be compliant with federal regulations. when the application is deployed centrally at cdc, user registration and authentication is managed by cdc security processes and tools. imats is a secure web-based application that limits user access to functions and data by means of role-based security authorization. database access control is governed by public health jurisdiction or organization ownership. technology and development tools imats is a typical java 2 platform, enterprise edition (j2ee) web application. it has a 3-tier architecture in which each application tier is composed of one or more layers of code. figure 3 shows the tiers and the layers associated with each tier. development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi figure 3: imats 3-tier architecture imats database design followed the concept of database independence to provide maximum portability. for example, stored procedures were avoided and naming conventions were standardized. the system is currently using sql server 2008 enterprise standard edition for the cdc centrally deployed application, and either sql server express or h2 databases for standalone deployment. development and design process project management, design and development followed scrum and agile methods [9]. agile methods refer to iterative, incremental, near-term development cycles (sprints). these enable short and regular feedback loops from customers who can see regular, tangible results [10]. agile development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi methodology supports “user-centered” development and requires that a customer be involved throughout the life cycle of the project. risk mitigation was accomplished through several methods. one such method was stakeholder identification. because cdc is a large organization, communicating the vision of the project to cdc leadership allowed the team to secure necessary approvals, refine project scope, and identify other stakeholders. the 10 state and local public health departments of the workgroup were selected based on their expertise in preparedness and response but also on characteristics of the jurisdiction they represented including location, population, budget, and technical expertise. the resulting workgroup reflected national needs and established solid requirements for development. further risk management processes included strict adherence to agile methodology principles such as direct communication between users and the development team, daily stand-up meetings to exchange ideas, and recurring sprint reviews with demonstrations to stakeholders and users. the agile methodology and risk mitigation methods provided frequent opportunity for users and stakeholders to change requirements and direction or affirm project development was proceeding according to schedule and cost baselines [11]. during the development cycle, no requests for additional funding were made and staffing levels remained constant throughout. this controlled development process allowed imats to be launched by the scheduled release date. trial period the “user-centered design approach,” which focuses on interleaving design and development efforts, continued even after the initial release of imats. public health partners were encouraged to use the system during trial periods before moving forward with the adoption process to ensure imats met their needs. those participating in a trial period were granted full access to the trial system for 30 days. the trial instance of imats was set up in partnership with the informatics innovation unit at cdc, and an email-based “help desk” was created so users could send questions and suggestions. the development team evaluated requests for enhancements and prioritized those selected for implementation. after public health partners completed the trial period, they received implementation guidance and were asked to complete a web-based survey. the survey consisted of 26 multiple choice and short answer questions. the questions were designed to gather information about the • effectiveness of the communication methods used to promote imats availability; • effectiveness of the imats trial period; • identification of decision criteria for imats adoption; • satisfaction level of current inventory management system (not including imats); • identification of inventory management systems in use; and • perceived ability of imats to meet the user’s needs. the survey was distributed to 96 public health departments on august 20, 2012. forty-one responses (43%) were received by the august 31 deadline, covering 17 stateand 24 local-level public health departments. this activity was not research involving human subjects and did not require institutional review board (irb) approval. development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi results imats system description imats enables public health officials to track medical countermeasure inventory during both everyday operations and public health emergencies. state and local public health department users can track quantities of inventory, monitor reorder thresholds and facilitate warehouse operations such as inventory receiving, staging, and storing. to use imats to perform these and other inventory management functions, users access the system’s web-based interface and enter product, location, quantity and other data. listed in table 2 are major system features accompanied by brief descriptions. table 2: high-level public health inventory management system features imats feature description product information includes prepopulated product and supplier data from the fda approved drug products list (the orange book). enables users to add individual items or perform bulk upload of additional product and supplier data into imats as they receive these products at their warehouses. setup establishes data-import capabilities about users, products, suppliers, product units of measure, locations within a warehouse and current inventory data. manual configuration allows users to key in information about new facilities, custom-user roles and funding sources for countermeasures stored in each warehouse. inventory management supports warehouse activities by enabling users to complete purchase order, back order, receive, put away, pick, ship, and import push package operations and complete other inventory functions as needed at each warehouse. reporting provides public health partners with reports of warehouse operations, available in pdf or excel format. types of reports include: • count inventory report: provides current stock levels plus request and shipment information for selected products • audit trail report: provides detailed information on all transactions in the system • inventory reports: enables public health partners to provide inventory status to cdc during public health emergencies inventory data exchange (ide) provides electronic data exchange interfaces, standards and specifications for public health partners to electronically transmit to cdc data files showing state and local public health jurisdictions’ inventory levels. allows data transfer from local public health departments to the state level and then to cdc regardless of the inventory management system in place. data transfer is performed by conventional import/export functionality using standard protocols (e.g., hl7, xml, etc.). synchronization for local deployments of imats, allows communication and data transfer between two instances of imats. for example, from a local instance of the application to a state level or other centralized instance. development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi the current implementation is based on a client-server paradigm in which client instances of imats can synchronize their data with a preconfigured parent imats server instance. the synchronization mode can be automatic with preconfigured intervals of time, or manual via the imats user interface. trial period and adoption state and local public health partners were encouraged to participate in a 30-day imats trial period before adopting the system. in october 2011, the first month the trial site was available, 31 public health departments (9 state, 22 local) began an imats trial period. by september 30, 2012, a year after imats’ release, 102 public health departments (27 state, 75 local) completed an imats trial period. thirty-six public health departments (16 state, 20 local) adopted imats as their primary or backup system. as of june 2015, 27 state and 38 local public health departments have implemented imats. four of the implementations are statewide (implementation at the state and all local public health departments). by june 2015, the cts help desk at ctshelp@cdc.gov responded to 2804 user inquiries and received 94 enhancement requests, of which 44 are implemented thus far. according to results of the survey, the most effective vehicles of communicating the availability of imats were interaction with the state-level sns coordinators (46%) and publication of the imats quarterly newsletter (56%). about 85% of respondents said the imats trial period was helpful in deciding whether to adopt imats. the top three adoption-decision criteria varied among state and local public health departments. states indicated (in descending order) cost, availability, and incident response suitability as their leading criteria. local public health departments cited cost, ease of use, incident response suitability, and efficiency as the leading factors. whereas cost is a major concern at all levels of government, the need for a system that can be successfully used during an incident response was consistently a leading assessment factor. only 15% of respondents indicated they were not satisfied with the value imats provides as an inventory management system (figure 4a). the majority of respondents indicated imats met their expectations for a medical countermeasure inventory tracking system (figure 4b), and that it has sufficient functionality and features to track inventory during a public health emergency (figure 4c). development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi figure 4: x axis (respondent rating) y axis (percentage of respondents). (a) ratings of imats features to track inventory during an event response. (b) satisfaction with the value of imats. (c) imats meets expectations as an inventory management system the public health preparedness capabilities: national standards for state and local planning states in order to receive medical countermeasures, public health departments should “have or have access to a system (hardware and software) to receive and manage inventory.” in addition, the “system must also have a backup which can be inventory management software, electronic spreadsheets, or paper [2].” public health departments can choose whichever system they wish for inventory tracking; they are not required to use imats. to help the cts team understand the current system status of those public health departments interested in adopting imats, respondents to the survey mentioned earlier were asked to identify both the primary and backup inventory management system (figure 5) they currently use. eight percent of respondents indicated they do not have a primary system. the majority (70%) with a primary system invested in a home-grown electronic inventory management system. only 23% of all respondents indicated they were satisfied with their primary system. regarding a backup system, about 85% of respondents indicated they had no backup system in place or were using access, excel, or pen and paper. only 20% of the respondents indicated they were satisfied with their backup system. figure 5: types of inventory management systems in use. primary (left). backup (right) discussion during a public health emergency, accurately reporting medical countermeasure inventory data provides cdc’s dsns with an understanding of the types and quantities of medical countermeasures available at state and local health departments. the specific countermeasure data reported are unique and vary according to the nature of each emergency. imats is a flexible system that can manage data about any type of inventory. the state and local countermeasure inventory data managed and reported through the use of imats better informs decision makers about medical countermeasure availability and resupply needs. imats is a user-friendly system designed to support public health emergency responses as well as daily operations. daily use fosters in-depth familiarity with the system to help ensure smooth development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi and efficient responses during public health emergencies. imats was developed in partnership with its end users in order to tailor the system to maximally support the needs of public health. imats is a free solution that is maintained, updated, and provided to users at no cost. a high percentage (85%) of respondents indicated they are using access, excel, or pen and paper for their backup system or have no backup system in place. that statistic suggests there is considerable potential for many to adopt imats as their backup system. in addition, since only 23% of respondents were satisfied with their current system, imats is a particularly viable option for public health departments ready to adopt a new inventory management system. accordingly, it is important to keep those public health departments not using imats well informed about improvements to the system and its uses. this can be accomplished through state-level sns coordinators, continuing to publish the quarterly imats-update newsletter and discovering additional effective means of communication. limitations and future work it is important that imats continually improve its functionality so it can adapt to changing user needs and technology advancements. currently, imats lacks the capability to support barcode scanning and system access via mobile devices. the cts team is exploring ways to incorporate these advanced technologies, which may interest jurisdictions that have not yet implemented the system. to comply with security requirements, user identity must be verified before system access is allowed. during a public health emergency, delays in onboarding new users and volunteers could slow the response effort. to help alleviate these delays, imats connect was developed to give jurisdictions full control of the user enrollment process and security of both imats and the data it contains. imats connect is a standalone version of imats available for jurisdictions to download, install, and maintain. alternatives that would accelerate the access process continue to be explored. imats is a comprehensive inventory management system designed to meet the specific needs of state and local public health departments. it does not, however, provide the means to track the administration of countermeasures to people. to capture these data, state and local public health departments often rely on state vaccine registries or other systems. the imats team continues to work closely with stakeholders to improve imats so that it provides increasingly better support to cdc and state and local public health departments. our efforts to continue user engagement and to maintain system readiness increases the public health community’s preparedness for effective responses to future public health emergencies. conclusion imats supports emergency response efforts by enabling rapid and accurate data collection and reporting of public health departments’ medical countermeasure inventory down to the local point-of-dispensing level. the development and release of imats provides state and local public health departments with an effective solution to help bridge the inventory data gaps identified during the h1n1 pandemic. the “user-centered design approach” used to develop imats is successfully providing public health partners nationwide with a system that meets their inventory management and tracking requirements. imats continues to be adopted by both state and local public health jurisdictions. many jurisdictions that have adopted imats now incorporate it into their preparedness development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi exercises. survey results show that a majority of imats adopters believe the system provides sufficient functionality and features to track inventory during public health emergencies. these findings are important, as survey results also indicate that only 23% of respondents were satisfied with their current non-imats primary inventory management system. the combination of survey results showing a high level of user satisfaction and the adoption of imats by 65 public health departments (27 state, 38 local) highlights the system’s usefulness as an effective medical countermeasure inventory management and tracking tool. the proven “user-centered design approach” is still being applied to enhance the system. combined with continued support from dsns as the program sponsor, imats continues to evolve and better support the needs of public health partners. acknowledgements the authors thank the public health informatics institute and members of the sns inventory management and tracking workgroup for their important contributions to developing imats. the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the centers for disease control and prevention. references 1. centers for disease control and prevention. strategic national stockpile (sns). c2012 [updated 2012 oct; cited 2013 aug 26]. available from: http://www.cdc.gov/phpr/stockpile/stockpile.htm. 2. u.s. department of health and human services, centers for disease control and prevention. public health preparedness 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[cited 2014 july]; available from: http://www.astho.org/programs/infectious-disease/antiviral-distribution/managingantiviral-medication-during-the-2009-h1n1-influenza-pandemic/. http://dx.doi.org/10.1007/s00291-011-0237-4 http://dx.doi.org/10.1080/0740817x.2010.540639 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20703736&dopt=abstract http://dx.doi.org/10.1007/s10916-010-9458-3 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20816560&dopt=abstract http://dx.doi.org/10.1016/j.pedn.2010.03.001 development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e212, 2015 ojphi 8. centers for disease control and prevention. phin messaging. c2011 [updated 2011 mar 31; cited 2013 aug 26]. available from: http://www.cdc.gov/phin/activities/messagingtransport.html. 9. abrahamsson p, salo o, ronkainen j, juhani w. agile software development methods – review and analysis. vtt publications; 2002. 10. schuh p. integrating agile development in the real world. thomson; 2005. 11. schwaber k. agile project management with scrum. microsoft press; 2003. development of the inventory management and tracking system (imats) to track the availability of public health department medical countermeasures during public health emergencies introduction background and significance objective materials and methods partnerships and collaborations system requirements requirements for inventory management and tracking processes design goals and constraints system design and development technology and development tools development and design process trial period results imats system description trial period and adoption discussion limitations and future work conclusion acknowledgements references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 1epidemiology, florida department of health hillsborough, tampa, fl, usa; 2florida department of health, tampa, fl, usa objective one of the numerous functions of syndromic data has been the identification of visits of public health interest using customized free text queries. a specific query of syndromic data was created to search for and identify emergency department (ed) and urgent care center (ucc) visits possibly related to the use of synthetic marijuana to describe and quantify this public health issue in florida. introduction illnesses related to synthetic marijuana use have been reported in many states, including florida. because these visits can present with a variety of symptoms, as well as be attributed to numerous diagnosis codes, it can be difficult to identify and quantify these visits. the electronic surveillance system for the early notification of community-based epidemics in florida (essence-fl) receives chief complaint (cc) and discharge diagnosis (dd) data as free text allowing uncommon or new terms to be searched for within each patient visit. the main source of data for essence-fl is emergency department (ed) and urgent care center (ucc) data. there are currently 210 eds and 33 uccs throughout florida that send their data to the essence-fl server. using essence-fl, a free text query of patient ccs and dds was used to identify visits related to synthetic marijuana use. this study is designed to analyze these identified visits for trends over time, geographical distribution and descriptive statistics and demographics. methods news articles, publications and internet searches were used to develop a comprehensive list of all terms and names that could be used for synthetic marijuana (ie. spice, k2, etc.). these terms, and their common misspellings, were used to create the free text query. the query attempted to identify visits related to synthetic marijuana, while minimizing the identification of unrelated visits. all identified visits from january 1, 2010 through june 30, 2015 were analyzed using microsoft excel and mapped using gis. the query used was written as: (,(,^synth^,andnot,^synthroid^,or,^syth^,or,^sint^,or,^fake^,),and, (,^pot^,or,^thc^,or,^weed^,or,^mari^,or,^mara^,or,^herb^,or,^bud^, or,^cannab^,or,^cannib^,or,^cananb^,or,^canab^,),),or,(,(,^spice^, andnot,(,^hospice^,or,^spicey^,),),or,^smoked k ̂ ,or,^smoking k ̂ ,or,^of k ̂ , or,^from k ^,^on k ^,),or, ^scooby^,or,^nice guy^,or,^cloud nine^,or, (,(,^used^,or,^smok^,or,^inhal^,),and,(,^potpo^,or,^herb^,or,^incen^, or,^inscen^,or,^insen^,),),or,^genie^,or,^yucatan^,or,^solar flare^,or, ^fire n ice^,or,^fire and ice^,or,^mamba^,or,^bombay^,or,^bad to the bone^, or,^dark night^,or,^berry blend^,or,^magma^,or,^budz^,or,^sativah^, or,^ultra chronic^,or,^zohai^,or,^funky green^ results this query identified 2545 visits between january 1, 2010 and june 30, 2015. conclusions utilization of this query provided key insights and information into the demographics, geographic distribution and trends of synthetic marijuana visits in florida. the tampa bay area hillsborough, pasco, pinellas, and polk counties had a substantial concentration of visits (37.1% of all identified visits). visits were mostly male (75.2%) and with an average age of 26.8 years old (66% of visits were age 14-28). additionally evident is the recent reemergence of this public health issue, the ability to identify visits related to drugs with numerous names and nicknames, as well as novel synthetic drugs, within specific demographics and geographic areas may be integral in the effective implementation of public health interventions. ed and ucc visits related to synthetic marijuana, florida, january, 2010 june, 2015, by county ed and ucc visits related to synthetic marijuana, florida, january, 2010 june, 2015, by month keywords synthetic; marijuana; query; essence; florida *michael wiese e-mail: michael.wiese@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e175, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, 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policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts use of near-real–time data to inform underage drinking surveillance in nebraska sandra gonzalez*1, 3, david devries2 and ming qu3 1university of nebraska-lincoln, lincoln, ne, usa; 2division of behavioral health, nebraska department of health and human services, lincoln, ne, usa; 3division of public health, nebraska department of health and human services, lincoln, ne, usa objective the objective of this pilot study was to develop and evaluate syndromic definitions for the monitoring of alcohol-related emergency department (ed) visits in near-real–time syndromic surveillance (sys) data. this study also evaluates the utility of sys ed data for the monitoring of underage drinking. introduction underage drinking is a significant public health problem in the united states as well as in nebraska1-2. alcohol consumption among underage youth accounts for approximately 5,000 deaths each year in the united states, including motor vehicle crash related deaths, homicides and suicides1. in nebraska, 23% of 12-20 year olds have reported alcohol use during the past 30 days3. in 2010, the estimated total costs of underage drinking in nebraska were $423 million. these costs included medical care, work loss along with pain and suffering2. the health consequences of underage drinking include alcohol-related motor vehicle crashes and other unintentional injuries, physical and sexual assault, suicide, self-inflicted injury, death from alcohol poisoning, and abuse of other drugs1, 4. the monitoring of near-real–time ed data could help underage drinking prevention efforts by providing timelier actionable public health information. methods nebraska sys data from 32 ed facilities was analyzed for visits of 12 to 20 year olds during october 1, 2015 to august 31, 2016. three syndromic definitions were developed and tested for the monitoring of alcohol-related ed visits in near-real–time sys data by using essence. the first and second definitions were based on querying the chief complaint (cc) field for search terms associated with alcohol use and alcohol abuse or intoxication respectively. the third definition consisted of icd-9-cm and icd-10-cm diagnostic codes associated to alcohol abuse or intoxication. these three definitions were evaluated for internal consistency: reported diagnostic codes were used to evaluate the first and second definition, while text in the cc field was used to evaluate the third definition. records with missing cc or diagnostic codes were excluded from the consistency analysis. in addition, the cc field of records detected by the third definition was evaluated for possible alcohol-related health consequences. results a total of 126 cases were detected by using the first definition (cc search terms for alcohol use); 61% (50/82) of these identified alcohol abuse-related diagnostic codes. on the other hand, a total of 64 cases were detected by using second definition (cc search terms specific for alcohol abuse or intoxication); 89% (33/37) of these identified alcohol abuse-related diagnostic codes. the third definition (diagnostic codes only) detected 111 cases; 49% (51/105) of these identified alcohol-related search terms in records with reported cc. however, keywords associated to alcohol-related health consequences, such as injury, assault, and use of other drugs were found in records with no alcohol-related search terms in the cc field. diagnostic codes associated to alcohol-related health consequences were observed in 93% (50/54) of these records. these results indicate that alcohol use is underreported in the cc field. conclusions a higher internal consistency was observed for the syndromic definition based on cc search terms associated with alcohol abuse or intoxication. however, a syndromic definition based on diagnostic codes is preferred due to the underreporting of alcohol use in the cc field. the detection of underage alcohol use-related cases could be improved by adding alcohol abuse or intoxication cc search terms to a syndromic definition based on diagnostic codes. overall, results of this pilot study suggest that a syndromic definition based on diagnostic codes can potentially enhance the surveillance of underage drinking and alcohol-related health consequences. keywords syndromic surveillance; underage drinking; emergency department; alcohol; injury acknowledgments other contributors: bryan buss, gary white, lianlin zhao, david loyall, kevin cueto, and michelle hood references 1. hingson r, white a. new research findings since the 2007 surgeon general’s call to action to prevent and reduce underage drinking: a review. j stud alcohol drugs. 2014 jan;75(1):158-69. review. 2. bekmuratova s, carritt n, kaldahl t, stimpson jp. underage drinking in nebraska [internet]. omaha (ne): university of nebraska medical center, center for health policy; 2013 august [cited 2016 september 7]. available from: https://www.unmc.edu/publichealth/ chp/research/2013-underage-drinking.pdf. 3. u.s. department of health and human services. the december 2015 report to congress on the prevention and reduction of underage drinking [internet]. 2015 [cited 2016 september 7]. available from: https://www.stopalcoholabuse.gov/media/reporttocongress/2015/ state_reports/nebraska_profile.pdf. 4. cdc. fact sheets underage drinking [internet]. 2015 november [cited 2016 september 7]. available from: http://www.cdc.gov/ alcohol/fact-sheets/underage-drinking.htm. *sandra gonzalez e-mail: sandra.gonzalez@nebraska.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e142, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts the evaluation of triage notes using essence-fl for active case finding of zika allison b. culpepper*, david atrubin and janet j. hamilton bureau of epidemiology, florida department of health, tallahassee, fl, usa objective this study assesses the utilization of triage notes from emergency departments (eds) and urgent care centers (uccs) for active case finding in essence-fl during the zika response. introduction the florida department of health (doh) utilizes the electronic surveillance system for the early notification of community based epidemics (essence-fl) as its statewide syndromic surveillance system. essence-fl comprises of chief complaint data from 231 of 240 eds, representing 96 percent of the total number of eds in florida. historically, syndromic surveillance has categorized patient chief complaint data into syndromes for the purpose of disease surveillance or outbreak detection. triage notes are much longer freetext, pre-diagnostic data that capture the presenting symptoms and complaints of a patient. methods triage notes are being collected from 24 eds, representing ten percent of total reporting eds, and seven uccs, representing 17% of total reporting uccs. triage notes were made a searchable field in essence-fl during zika enhanced surveillance efforts, which facilitated additional case finding of zika. during the period of february 3, 2016 – july 25, 2016, a free-text query was created to run against the concatenated chief complaintdischarge diagnosis (ccdd) and triage note fields: ^zika^,or,^ziki^,or,^zica^,or,^zeeka^,or,^zeeca^,or,^microcep^, or,^zyka^ additional queries were created to detect foreign travel visits of interest within the ccdd and triage note fields. results of these queries were analyzed and communicated to county and regional epidemiologists daily for investigation. results the triage note specific queries identified 18 zika triage note and 11 foreign travel triage note visits of interest. all of these visits were reviewed and investigated by county epidemiologists. these triage note queries identified one case of zika that had not been previously reported to public health. of note, seven additional cases of zika infection were identified using the ccdd field in essence-fl (five of the seven flagged in both the ccdd and triage note field). conclusions results from this analysis provide evidence that triage notes within syndromic surveillance systems play a role in active case finding when emerging diseases arise. however, only 31 out of 272 total reporting facilities are submitting triage note to essence-fl, representing only 11% of reporting facilities. relying on chief complaint and discharge diagnosis data only would have resulted in an undetected case of zika that would have not been captured by our free-text zika query. the increased detection of zika cases allows for public health intervention, including mosquito control response, which in turn reduces the chance of zika spreading locally in florida. triage notes often provide pertinent information for determining when a flagged ccdd needs to be investigated further. making triage notes a required data element for meaningful use compliance would benefit case finding conducted through syndromic surveillance. keywords zika; triage note; outbreak detection *allison b. culpepper e-mail: allison.culpepper@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e23, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts ensuring confidentiality and safety of cancer registry data in kumasi, ghana dennis o. laryea*1 and fred k. awittor2 1komfo anokye teaching hospital, kumasi, ghana; 2kumasi cancer registry, kumasi, ghana objective to discuss the implementation of confidentiality practices at the kumasi cancer registry. introduction cancer registration involves collecting information on patients with cancer. population-based cancer registries in particular are useful in estimating the disease burden and to inform the institution of prevention and control measures. collecting personal information on patients with cancer requires strict adherence to principles of confidentiality to ensure the safety of the collected data. failure may have legal and medical implications. the kumasi cancer registry was established as a population-based cancer registry in 2012. the registry collects data on cases of cancer occurring among residents of the kumasi metropolitan area of ghana. issues bordering on confidentiality were an integral part of the establishment of the registry. we discuss the implementation of confidentiality plans during the four years of existence of the kumasi cancer registry. methods the registry has a designed abstraction form which is used to collect data. data sources for the registry are all major hospitals in kumasi providing cancer treatment services. data sources also include private pathology laboratories and the births and deaths registry. trained research assistants collect data from the folders of patients. this is followed by coding and then entering into the canreg 5 software. coded and entered into the canreg5 software for management and analysis. after data entry, the forms are filed in order of registry numbers as generated by the canreg5 software for easy reference. results confidentiality of kscr data is ensured through the following measures. the signing of a confidentiality agreement by all registry staff. the confidentiality agreement spells out terms for the release of data to third parties in particular but even staff of the various facilities. the agreement also spells out the consequences of a breach of any of the clauses. no direct contact is made with patients during the process of abstraction of data by registrars. the data abstraction forms are kept in a secured safe in the registry office. the computers that house the registry data are password enabled and are changed on a regular basis to ensure security. the canreg5 software used for electronic data management also has individual profiles with passwords for all registrars and supervisors. the scope of access to canreg data is limited by the profile status of the respective staff members. supervisors have full access to all data including summarized reports. registrars have limited access mostly restricted to data entry. access to the registry office is restricted to registry staff and other personnel authorized by the registry manager or director. an established registry advisory board is responsible for assessing requests and approval of data from the registry. where files have to be sent electronically, they are password protected and sent in several parts in separate emails. conclusions despite the potential challenges to maintaining confidentiality of data in developing outcries, evidence from four years of cancer data management in kumasi suggests stringent measure can ensure confidentiality. the use of multiple measures to ensure confidentiality is essential in surveillance data management keywords confidentiality; cancer registration; ghana acknowledgments staff of the komfo anokye teaching hospital, kumasi, ghana references 1. storm h, brewster dh, coleman mp, deapen d, oshima a, threlfall t, et al. guidelines for confidentiality and cancer registration. br j cancer. 2005 jun 6;92(11):2095–6. 2. jensen o, parkin dm, maclennan r, muir c, skeet r, editors. cancer registration: principles and methods [internet]. lyon, france: international agency for research on cancer; 1991 [cited 2015 jul 28]. (iarc scientific publications). available from: http://www.iarc. fr/en/publications/pdfs-online/epi/sp95/sp95.pdf 3. laryea do, awittor fk, sonia c, boadu ko. three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana. online j public health inform [internet]. 2016 mar 24 [cited 2016 apr 14];8(1). available from: http://ojphi.org/ojs/index.php/ojphi/article/view/6548 4. beskow lm, sandler rs, weinberger m. research recruitment through us central cancer registries: balancing privacy and scientific issues. am j public health. 2006 nov;96(11):1920–6. *dennis o. laryea e-mail: dlaryea@kathhsp.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e130, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using a one health approach to build an integrated surveillance data system wayne clifford* department of health, washington state, olympia, wa, usa objective integrate and streamline the collection and analysis of environmental, veterinary, and vector zoonotic data using a one health approach to data system development. introduction environmental public health zoonotic disease surveillance includes veternary, environmental, and vector data. surveillance systems within each sector may appear disparate from each other, although they are actually complimentaly and closely allied. consolidating and integrating data in to one application can be challenging, but there are commonalities shared by all. the goal of the one health integrated data system is to standardize data collection, streamline data entry, and integrate these sectors in to one application. methods data assessment. an assessment of each surveillance function was carried out to evaluate data types and needs. identify commonalities. common data was identified across each of the surveillance areas. identify unique data. data unique to specific surveillance efforts was identified. build data structure. a back-end data structure was developed that reflected the data needs from each surveillance area. build data entry interfaces. data entry interfaces were developed to meet the needs of each surveillance area. build data qc. procedures were developed that run several quality control checks on the data. build data eports. to allow users to carry out more extensive analysis of data, customized data exports were built. results this data integration project resulted in: ● reduced time spent entering and managing data ● improved data entry error rates ● increased visibility through automated program metrics ● improved access to data from data users conclusions integrating data and building a data system that reflects the diversity of environmental, veterinary, and vector surveillance data is doable using off-the-shelf database tools. the process of integrating data and building the data structure results in a more intimate understanding of the data revealing opportunities for improving data quality. keywords one health; surveillance data; informatics; database acknowledgments zoonotic disease program staff at the washington state department of health. *wayne clifford e-mail: wayne.clifford@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e65, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts epi archive: automated data collection of notifiable disease data nicholas generous*, geoffrey fairchild, hari khalsa, byron tasseff and james arnold los alamos national laboratory, los alamos, nm, usa objective lanl has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the biosurveillance ecosystem (bsve) as a new data source. this will improve the prediction and early warning of disease events and other applications. introduction most countries do not report national notifiable disease data in a machine-readable format. data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. this presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the biosurveillance ecosystem (bsve). while most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues [1]. for example, an attempt by lanl to obtain a weekly version of openly available monthly data, reported by the australian government, resulted in an onerous bureaucratic reply. the obstacles to obtaining data included: paperwork to request data from each of the australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. this type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. a survey conducted by lanl of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. within the 42 nations that report in pdf files, 32 report in a structured, tabular format and 10 in a non-structured way. as a result, lanl has developed a tool-epi archive (formerly known as epic)-to automatically and continuously collect global notifiable disease data and make it readily accesible. methods we conducted a survey of the national notifiable disease reporting systems notating how the data is reported in two important dimensions: date standards and case definitions. the development of software to regularly ingests notifiable disease data frand makes this data available involved four main steps scraping, extracting, parsing and persisting. for scraping: we would examine website designs and determine reporting mechanisms for each country/website as well as what varies across the reporting mechanisms. we then designed and wrote code to automate the downloading of report pdf files, for each country. we stored report pdfs along with appropriate metadata for extracting and parsing. for extracting: we developed software that can extract notifiable disease data presented in tabular form from a pdf file. we combined the methodology of figure placement detection with the in-house developed table extraction and annotation heuristics. for parsing: we determined what to extract from each pdf data set from the survey conducted. we then parsed the extracted data into uniform data structures correctly accommodating the dimensions surveyed and the various human languages. this task involved ingesting notifiable disease data in many disparate formats extracted from pdf files and coalescing the data into a standardized format. for persisting: we then store the data in the epi archive postgresql database and make it available through the bsve. results the epiarchive tool currently contains subnational notifiable disease data from 10 nations. when a user accesses the epiarchive site, they are prompted with four fields: country, region, disease, and date duration. these fields allow the user to specify the location (down to the state level), the disease of interest, and the duration of interest. upon form submission, a time series is generated from the users’ specifications. the generated time series can then be downloaded into a csv file if a user is interested in performing personal analysis. additionally, the data from epiarchive can be reached through an api. conclusions lanl as part of a currently funded dtra effort so that it will automatically and continuously collect global notifiable disease data—particularly data stored in pdf files—and make it available and shareable within the biosurveillance ecosystem (bsve) as a new data source. this will provide data to analytics and users that will improve the prediction and early warning of disease events and other applications. keywords notifiable disease; data source; standards; scraping; data sharing acknowledgments this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra). references [1] van panhuis wg, paul p, emerson c, et al. a systematic review of barriers to data sharing in public health. bmc public health. 2014. 14:1144. doi:10.1186/1471-2458-14-1144 *nicholas generous e-mail: generous@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e37, 2017 privacy, security, and the public health researcher in the era of electronic health record research 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e207, 2016 ojphi privacy, security, and the public health researcher in the era of electronic health record research neal d. goldstein1,2, anand d. sarwate3 1. christiana care health system, department of pediatrics, 4745 ogletown-stanton road, map 1, suite 116, newark, de 19713 usa 2. drexel university dornsife school of public health, department of epidemiology and biostatistics, 3215 market street, philadelphia, pa 19104 usa 3. rutgers, the state university of new jersey, department of electrical and computer engineering, 94 brett road, piscataway, nj 08854 usa abstract health data derived from electronic health records are increasingly utilized in large-scale population health analyses. going hand in hand with this increase in data is an increasing number of data breaches. ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. in this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. in order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools. keywords: electronic health record; privacy; data security; analysis abbreviations: ehr, electronic health record; irb, institutional review board; hipaa, health insurance portability and accountability act; mpc, multiparty computation; phi, personal health identifiers correspondence: neal d. goldstein, christiana care health system, department of pediatrics, 4745 ogletown-stanton road, map 1, suite 116, newark, de 19713, ngoldstein@christianacare.org, 1302-733-4200 doi: 10.5210/ojphi.v8i3.7251 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction public health research often requires maintaining the privacy and security of sensitive health information. these data may require strict safeguards to protect both the privacy of the research participant and the security of their information. broadly speaking, privacy ensures that research subjects are not identifiable, whereas security ensures the data remain inaccessible to non-essential personnel. despite best efforts, the scientific literature and popular press are replete with examples of data security breaches and privacy violations [1-3]. combined with http://ojphi.org/ mailto:ngoldstein@christianacare.org privacy, security, and the public health researcher in the era of electronic health record research 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e207, 2016 ojphi the increase in collaborative projects between researchers and institutions, demands upon data privacy and security will only increase over time. information technology groups may place more restrictions to protect data security and privacy, and institutional review board (irb) applications may entail stricter scrutiny regarding protections, yet current technological solutions may be out of reach to the average epidemiologist. the goal of this commentary is to draw a distinction between the requirements of privacy and security, introduce future technologies for protecting the individual, and call for an active dialogue to articulate requirements for such technologies to be useful in epidemiological analyses for data derived from a common and timely source: the electronic health record (ehr). a motivating example consider an epidemiologist collaborating with a hospital who has access to an ehrderived, de-identified dataset representing a retrospective, open cohort of patients. depending on how many years back these data go, this dataset may be quite rich in both the number of observations and variables. even though patients may be not directly identifiable from the data, they may be readily linkable to other data sources (such as public records, social media, or online forums) that could be used for re-identification [1]. how do we ensure the privacy of these patients, while recognizing the analysis may require specific markers at the individual level? how do we ensure the security of the data acknowledging the collaborative nature of public health work, and the possible need to share the data with other investigators? in order to answer these questions, we review legislated requirements to health care data and then discuss the role of the researcher. privacy, security, and secondary data depending upon the location of the researcher, there are various information privacy and security laws and regulations that promulgate the use of health information. the european union established the data protection directive to govern the use of sensitive personal data, including health and demographic markers (e.g., race) within its member states. one example of law to comply with this directive is the united kingdom’s data protection act, although it has been criticized for failing to provide adequate safeguards [4]. this directive will be superseded by the general data protection regulation in 2018, which explicitly includes provisions for using data for secondary purposes in research. in canada, the personal information protection and electronic documents act provides the overarching guidelines for the use of personal information in business, including the health sector, and in the u.s. the health insurance portability and accountability act (hipaa) governs basic protections for using health information, including protected health information or phi. releasing health information for research purposes under these regulatory environments follows prototypical patterns: we illustrate these approaches using hipaa as an example. the data collection process is protected by oversight by a review process involving an irb, human subjects review board, or ethics committee, depending on the source of the data. for example, hipaa requires an ethics board to make determinations about use of phi in research on human subjects. examples of phi include obvious identifiers, such as patient name, birth date, and social security number, but also subtler identifiers, such as admission and discharge dates, postal codes, and internet protocol address numbers [5]. http://ojphi.org/ privacy, security, and the public health researcher in the era of electronic health record research 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e207, 2016 ojphi certain exemptions to informed consent may be possible. through the hipaa privacy rule, patient data from the ehr typically do not require informed consent so long as they are retrospective and reside at the institution. in fact, these data will likely receive an exempt irb review provided subjects are non-identifiable [6]. consequently, secondary analyses of ehr data are becoming quite popular in public health research. these data sets are subject to certain privacy and security requirements, and should not be viewed as onerous to the researcher nor relegated to groups external to the research process. a data set has to be certified as shareable before it can be released. there are two main mechanisms in hipaa for ensuring privacy in research datasets [7]. the less-commonly used “expert determination’’ approach requires a trained individual to declare that there is no reasonable threat of re-identification. the safe harbor provision provides a list of identifiers that must be removed from data prior to use [8]. both approaches have significant drawbacks. there have been several well-publicized works showing how to combine anonymized data with public records or other data to re-identify individuals. examples include data sets on movie ratings [2], internet searches [9], and genomics [3]. although we do not have evidence of “wholesale” application of these re-identification attacks, experts should be more cautious about declaring that a particular data set is sufficiently de-identified [10]. from an epidemiological perspective, the safe harbor clause may remove some features of clinical care, such as admission date, potentially important for answering a pressing research question, and inclusion of these phi will require additional irb scrutiny. privacy policies also require security safeguards for storage and access to private data. for example, the hipaa security rule enumerates administrative, physical, and technical safeguards required of electronic health information that the institution must implement. these safeguards may include strong passwords with two-step authentication (password plus an additional token, such as biometric fingerprint), ensuring software are up-to-date with the latest security patches, and encrypted storage solutions. to ensure compliance with the highly technical security rule, institutions can seek certification by organizations such as hitrust (https://hitrustalliance.net). ultimately it is up to the epidemiologist to abide by these solutions, and not try to circumvent security schemes by copying the research dataset to a usb stick or personal laptop. one positive from recent high profile health data breaches [11] is the movement away from bypassing security. bringing informatics to public health working with secondary data is of course not new, nor are the required safeguards. a commentary from 1996 noted that privacy and security issues were more of a social and policy shortcoming than a technological hurdle [12], and now that ehrs are in use by >75% of u.s.based providers [13], the protections needed are even greater. making this issue particularly salient today are the hosts of recent examples describing large-scale healthcare data breaches affecting 500 or more individuals [11]. among these publicized breaches occurring at the healthcare provider from 2009 to the time of writing (n=1131), the ehr was the source of the breach in 8% (n=86) and a network server in 14% (n=156). meanwhile a personal computer or portable device was the source in 47% (n=528) and email in 11% (n=119), suggesting that the onus is not solely upon irb, or information technology groups, to ensure the safeguards of the data: the researcher must take responsibility for the responsible use and management of the data. our goal is to encourage the public health community to engage with the developers of http://ojphi.org/ https://hitrustalliance.net/ privacy, security, and the public health researcher in the era of electronic health record research 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e207, 2016 ojphi new privacy and security tools — by formalizing and articulating how they use data — and become stakeholders in emerging technological solutions for data sharing across institutions. collaborative research involving retrospective analyses of ehr data can involve many different access models. although an onsite epidemiologist can perform the analysis using a secure computer within the institution, in many cases data must be shared with other researchers. sharing a copy of the data (e.g., by using an encrypted usb drive or internet service) requires the research parties enter an irb-approved data sharing agreement. this all too common paradigm also presents the greatest risk: the institution effectively loses control over the data. the shared data can be stolen and potentially re-identified by linking to external data sources. at the other extreme, data may be available only locally. any individual working on this research project would be sequestered in a proverbial (or literal!) silo: a windowless, locked room without network connectivity. this places a great burden upon the researchers and may not even be realistic given the global collaborations that many researchers undertake. these two approaches for privacy and security strike a different balance between the rights of individuals and the public good. in public health, the balance of having data that are useful for epidemiological analysis while protecting the individual can be paradoxical: regression analyses are often conducted at a level that require individual level information yet the results are generalizable to a population of people. we therefore operate squarely in the gray zone between these two competing interests. separating the data from the analysis to return to the two questions posed earlier in our example scenario, there are several new technologies under active development in the computer science community that offer significant promise for collaborative research on private and sensitive data. in such systems, data live in a secured distributed system and the questions of those data (i.e., the analysis) are asked remotely [14-17]. while this may be attractive from an institutional perspective, the statistical and analytic techniques provided in existing solutions may be insufficient to perform typical epidemiological analyses. we contend that epidemiologists should engage computer scientists to ensure such systems will provide useful functionalities for their work. the simplest model is one in which a researcher develops a statistical method (e.g. in sas, r, stata, etc.) and a secure server evaluates that model on the actual data. end-to-end encryption prevents eavesdropping on the results, but such an approach also implies that the researcher is trusted to not reveal information from the queries. a more complex model may restrict the kinds of operations that can be performed on the data to a prespecified menu. yet without input from researchers, these preprogrammed analyses may not accommodate typical epidemiologic methods. research consortia (for example around specific diseases [18]) exemplify a more complex scenario: several researchers, each with their own data, wish to collaborate. new technologies such as secure multiparty computation (mpc) can perform an encrypted computation such that neither the researcher nor server learns more about each other’s data than the result of the computation [19]. in this setting the communication and computation are encrypted. current mpc implementations suffer from high computational cost, but there has been rapid development of more practical approaches in the literature [20-22]. this approach http://ojphi.org/ privacy, security, and the public health researcher in the era of electronic health record research 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e207, 2016 ojphi could be promising for performing more sophisticated joint studies beyond simple metaanalyses. a different paradigm is that proposed by differential privacy, which gives a statistical privacy guarantee for privacy: the result of a computation should not reveal too much about individual data records [23]. differentially private algorithms guarantee this by randomizing the result of the statistical computation; the simplest instance of this is addition of noise [24]. in a differentially private remote-access system, researchers may have different levels of access to the data. again, a limited menu of analytic techniques can be made available, with more advanced epidemiologic modeling available to vetted researchers. differential privacy involves balancing privacy concerns with the utility of the analyses: too much noise can render results meaningless but very private. prototype systems are evaluating the practicality of differential privacy in a variety of systems from search-engine analytics [25] and mobile devices [26] to neuroimaging [16] to social science [27] and medical informatics [19]. engaging with emerging technologies mpc and differential privacy are technologies under active development today, and several research programs are attempting to bring them into mainstream usage in practical settings. this commentary is a call to the public health community to engage with developers to ensure that researchers’ interests are represented. specifically, we identify four actionable items. first, disclose to the appropriate parties the paradigm under which epidemiological analysis occurs. much of our research and methods are built around individual level data and in order to correlate risk factors with disease require access to these types of data. in other words, individual level analyses require individual level data. the tools that separate the data from the analysis must include these types of methods. second, become stakeholders in the conversation. the groups that are developing these tools are likely different from the groups that will use these tools. the computer science and informatics communities develop technologies in response to known needs from applied researchers. in order for our research paradigm to be incorporated we need to be proactive and engage the appropriate groups. one way to accomplish this is by familiarizing ourselves with technological developments and engaging in joint research projects to design prototype systems, as other communities have done [27-28]. third, lower the barriers to using these tools. at present, there is still too great a level of expertise to installing and using the systems. these modalities are not embedded in our statistical software, and therefore may be inaccessible to the researcher despite being mandated by institutional policy. fourth, call others to action. this can be done by organizing research activities, conference workshops on data privacy and security, and writing white paper requirements for the technologies. another avenue may be to seek funding for seed projects or collaboration on an institutes of medicine big challenges paper to influence the direction of the field. in conclusion, recent data breaches indicate that privacy and security of health data derived from ehrs are continuing concerns, and public health researchers need to proactively participate in the development of new technologies that better ensure data protection. we have identified four actionable items that can help ensure that our methodological requirements 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aggregatable privacypreserving ordinal response. in proceedings of the 2014 acm sigsac conference on computer and communications security (pp. 1054–1067). 2014. 26. evans, j. what apple users need to know about differential privacy. computer world. 2016 jun. 27. harvard school of engineering and applied sciences. privacy tools for sharing research data. http://privacytools.seas.harvard.edu. accessed june 10, 2016. 28. plis sm, sarwate ad, wood d, dieringer c, landis d, reed c, panta sr, turner ja, shoemaker jm, carter kw, thompson p, hutchison k and calhoun vd. coinstac: a privacy enabled model and prototype for leveraging and processing decentralized brain imaging data. front. neurosci. 2016;10:365. http://ojphi.org/ http://www.cdc.gov/nchs/data/databriefs/db236.htm http://fcon_1000.projects.nitrc.org/indi/abide/ http://privacytools.seas.harvard.edu/ privacy, security, and the public health researcher in the era of electronic health record research neal d. goldstein1,2, anand d. sarwate3 introduction a motivating example privacy, security, and secondary data bringing informatics to public health separating the data from the analysis engaging with emerging technologies isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts a syndrome definition validation approach for zika virus dino p. rumoro1, shital c. shah*1, marilyn m. hallock1, gillian s. gibbs1, gordon m. trenholme1 and michael j. waddell2 1emergency medicine, rush university medical center, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to develop and validate a zika virus disease syndrome definition within the guardian (geographic utilization of artificial intelligence in real-time for disease identification and alert notification) surveillance system. introduction in 2016, the world health organization declared zika virus a global public health emergency. zika infection during pregnancy can cause microcephaly and other fetal brain defects. to facilitate clinicians’ ability to detect zika, various syndrome definitions have been developed. methods to create and validate a detailed syndrome definition for zika, we utilized the literature based methodology developed and documented by guardian researchers.1,2 the syndrome definition utilized clinical signs and symptoms that were documented in historical zika cases. a testing sample of 1000 randomly selected emergency department cases (i.e., true negative cases) and 200 synthetically generated cases (i.e., true positive cases) was created. these 1,200 sample cases were evaluated by the guardian surveillance system to determine the probability of matching the zika syndrome definition. a probability of ≥90% was utilized to designate positive zika cases. we identified the main signs and symptoms contributing to the identification of zika cases and conducted statistical performance metrics. clinical review of the false positive and false negative cases along with a sample of true positive and true negative cases was conducted by a board certified emergency physician. results the zika syndrome definition was developed with eleven articles (six used for developing the syndrome definition, and five used for testing the definition). the sample size for these articles was between 1 and 72 positive zika cases, with a total of 139 cases across the 11 articles. the article with the most number of zika cases was based on pregnant women with rash. the publication timeframe for the articles was from 1962 to 2016. some of the main signs and symptoms from the historical cases that contribute to the zika syndrome definition are presented in table 1. the initial results for the sample testing data showed accuracy, sensitivity, and specificity were 94.7%, 93%, and 95% respectively. there were a total of 14 false negative and 50 false positive cases. conclusions the initial zika syndrome definition utilized by the guardian surveillance system contains similar signs and symptoms to the current cdc case definition, but also includes additional signs and symptoms such as pruritus/itching, malaise/fatigue/generalized weakness, headache, retro-orbital pain, myalgia/muscle pain, and lymphadenopathy in addition, the guardian system provides the relative importance of identified signs and symptoms and allows for proactive surveillance of emergency department patients in real-time. though we did not include epidemiologic risk factors, such as travel to an infected region or contact with an infected person in the syndrome definition, guardian has above 90% sensitivity and specificity. thus, inclusion of epidemiologic risk factors would further enhance the early detection of zika, when used with the appropriate high risk population. table 1. main signs and symptoms of zika syndrome definition *signs and symptoms included in the centers for disease control and prevention (cdc)’s zika clinical case definition. keywords zika virus; guardian; syndrome definition acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. references 1. silva j, rumoro d, hallock m, shah s, gibbs g, waddell m, thomas k. disease profile development methodology for syndromic surveillance of biological threat agents. emerging health threats journal. 2011; 4(11129). 2. silva j, shah s, rumoro d, hallock m, gibbs g, waddell m. a novel syndrome definition validation approach for rarely occurring diseases. online journal of public health informatics. 2013; 5(1). *shital c. shah e-mail: shital_shah@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e90, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyulus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 1epidemiology and biostatistics, mcgill university, montreal, qc, canada; 2harvard university, boston, ma, usa; 3stanford university, stanford, ca, usa; 4university of washington, seattle, wa, usa objective to develop a scalable software platform for integrating existing global health surveillance data and to implement the platform for malaria surveillance in uganda. introduction electronic data that could be used for global health surveillance are fragmented across diseases, organizations, and countries. this fragmentation frustrates efforts to analyze data and limits the amount of information available to guide disease control actions. in fields such as biology, semantic or knowledge-based methods are used extensively to integrate a wide range of electronically available data sources, thereby rapidly accelerating the pace of data analysis. recognizing the potential of these semantic methods for global health surveillance, we have developed the scalable data integration for disease surveillance (sdids) software platform. sdids is a knowledge-based system designed to enable the integration and analysis of data across multiple scales to support global health decision-making. a ‘proof of concept’ version of sdids is currently focused on data sources related to malaria surveillance in uganda. methods sdids is a web-based, ontology-driven software platform that automates the integration of heterogeneous data from multiple sources, and supports the discovery, retrieval, visualization, and analysis of these data. a data set is first “mapped” or linked explicitly to the ontologies used within sdids, and then the data are ingested into the system and stored in a manner that supports complex queries based on the concepts and relationships defined in the ontologies. data in the system can be accessed via the sdids application program interface (api) for data visualization and analysis. results annotation and ingestion of data: a software client was created to guide a user through the semi-automated process of mapping a dataset to the sdids ontologies. this mapping process identifies the correspondence of each column: to a domain concept (e.g., ‘age’, ‘symptom’), to a data type (e.g., ‘integer’, ‘categorical), and possibly to a unit of measurement. for some data types, such as categorical variables, each unique value (e.g., ‘female’, ‘male’) must also be explicitly linked to a concept in the ontology. natural language processing methods facilitate the identification of ontology concepts that are likely to correspond to elements of a dataset. once the linkages are identified, mapping rules are automatically generated in the w3c standard language r2rml. when executed, these rules transform the data into rdf triples expressed in terms of the sdids ontologies. the r2rml mapping files are saved so that the provenance of all data is always accessible. retrieval and analysis of data: external applications can connect directly to sdids via an api to request data for further processing or to request the results of analyses applied to the integrated data. within sdids, other server components facilitate the retrieval of data using sparql (a query language for semantic data), the transformation of data (e.g., aggregation, projection, joins, unions), and the calculation of indicators. two software clients have been developed to demonstrate the functionality of sdids. one client addresses the needs of a malaria monitoring and evaluation manager tasked with following indicators of disease and control activities. the second client addresses the need of a funding officer in assessing malaria research activity and disease control measures in different geographical areas. conclusions our vision for global health surveillance is that data are easily shared and analyzed across diseases, countries, organizations, and data sources by a variety of users and client applications. sdids is a scalable platform that offers an initial step towards this vision. it is not a replacement for current systems, but a bridging technology that can help to integrate existing data now and encourage convergence of data models in the future. the next stage of the project will focus on scaling-up sdids to integrate surveillance data for the leading causes of under-5 mortality in africa. keywords surveillance; global health; ontology; data integration; malaria acknowledgments research supported by the bill and melinda gates foundation. *david buckeridge e-mail: david.buckeridge@mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e14, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts houston health department’s response to the threat of zika virus eric v. bakota*, kirstin short and amanda eckert health department, city of houston, houston, tx, usa objective this session will explore the role of the houston health department (hhd) in the city of houston’s response to the threat of zika. the panelists will provide perspective from the roles of bureau chief, informatician, and epidemiologist and provide insight into lessons learned and strategic successes. introduction zika virus spread quickly through south and central america in 2015. the city of houston saw its first travel-related zika cases in december of 2015. on january 29th, the city held the first planning meeting with regional partners from healthcare, blood banks, petrochemical companies, mosquito control, and others. additionally the city activated incident command structure (ics) and designated the public health authority as the incident commander. initial steps taken by hhd included expanding the capability and capacity of the public health laboratory to test for zika virus; expand surveillance efforts; created an educational campaign around the “3ds” of zika defense (drain, dress, deet) which were then disseminated through several means, including a mass mailing with water bills; and provided deet to mothers through the wic program. the houston health department took the lead in authoring the city’s zika action plan. in this 3 goals and 6 strategies were identified. goals included 1) keep houstonians and visitors aware of the threat of zika; 2) minimize the spread of the virus; and 3) protect pregnant women from the virus. the 6 strategies employed were to a) develop preparedness plans; b) implement ics within the city; c) ensure situational awareness through surveillance; d) increase community awareness; e) reduce opportunities for zika mosquito breeding grounds; and f) provide direct intervention to reduce the threat of zika. hhd was responsible for many of the action items within the plan. we conducted several community outreach events, where we disseminated educational materials, t-shirts, deet, and other giveaways. these events allowed frequent engagement with the public for bidrectional communication on how to approach the threat. keywords zika; planning; information sharing; informatics; epidemiology *eric v. bakota e-mail: eric.bakota@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e104, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts monitoring emergent avian influenza viruses subtypes h5 and h7 in wild birds in ukraine prof. borys stegniy1, dr denys muzyka1, dr mary pantin-jackwood2, dr oleksandr rula1, anton stegniy1, anton gerilovych*1, dr serhiy vovk1 and prof. mykola mandygra1 1nsc iecvm, kharkiv, ukraine; 2seprl, athens, ga, usa objective to carry out monitoring studies of circulation of the aiv subtypes h5 and h7 in wild waterfowl and shorebirds around the azov-black sea in ukraine introduction to date, avian influenza virus (aiv) is an unpredictable pathogen affecting both animals, birds and people. the regular emergence of new strains and variants with different properties and pathogenicities requires additional monitoring and careful research of those viruses. it is known that wild birds— especially waterfowl and shorebirds— are the main and primary reservoir of aiv in nature which makes epizootological monitoring of populations of these birds necessary. methods sampling of wild birds was conducted in the azov-black sea region of ukraine. during the period from 2000 to 2011, biological material (cloacal, tracheal swabs, fecal samples) was collected from more than 6000 wild birds of 66 different species of orders anseriformes and charadriiformes. virological investigations were carried out by standard methods recommended by the oie (isolation in chicken embryos with identification by hi-test and pcr). results since year 2000, in the azov-black sea region of ukraine the monitoring of influenza viruses in wild waterfowl and shorebirds was being organized by scientists from the national scientific center institute of experimental and clinical veterinary medicine (nsciecvm). particular attention was paid to the circulation of aiv subtypes h5 and h7, which can potentially be devastating to poultry. during the period of years 2005-2008 highly pathogenic avian influenza (hpai) viruses subtype h5n1 were detected in wild birds in ukraine. five isolates of h5 subtype were isolated from great cormorants (phalacrocorax carbo) in 2006 and 3 viruses from great grebes (podiceps major) in 2008. phylogenic analysis showed that these viruses originated from asia, and western europe respectively. during 2010-2012, 59 different subtypes of influenza viruses were isolated, including a low pathogenic avian influenza (lpai) virus subtype h5n2. seven h7 subtype viruses with different neuraminidase enzymes (h7n3, h7n6, h7n7) were isolated, representing 11.86% of the total number of all aiv isolated from samples. all viruses of subtypes h5 and h7 were isolated from the wild mallards during their autumn migration and wintering in the azovblack sea region. these viruses did not cause disease in chickens after intranasal and intramuscular inoculation. conclusions in summary, our results demonstrated the circulation of lpai viruses subtype h5 and h7 in wild waterfowl populations in the azov-black sea region. these findings support the need for ongoing monitoring of avian influenza for early prevention of hpai viruses that may pose a threat to poultry. keywords avian influenza virus; surveilance; wild birds *anton gerilovych e-mail: antger@rambler.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(1):e161, 2015 a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi a repository of codes of ethics and technical standards in health informatics authors: hamman w. samuel 1 ; osmar r. zaïane 1 1. department of computing science, university of alberta, canada abstract we present a searchable repository of codes of ethics and standards in health informatics. it is built using state-of-the-art search algorithms and technologies. the repository will be potentially beneficial for public health practitioners, researchers, and software developers in finding and comparing ethics topics of interest. public health clinics, clinicians, and researchers can use the repository platform as a one-stop reference for various ethics codes and standards. in addition, the repository interface is built for easy navigation, fast search, and side-by-side comparative reading of documents. our selection criteria for codes and standards are two-fold; firstly, to maintain intellectual property rights, we index only codes and standards freely available on the internet. secondly, major international, regional, and national health informatics bodies across the globe are surveyed with the aim of understanding the landscape in this domain. we also look at prevalent technical standards in health informatics from major bodies such as the international standards organization (iso) and the u. s. food and drug administration (fda). our repository contains codes of ethics from the international medical informatics association (imia), the ihealth coalition (ihc), the american health information management association (ahima), the australasian college of health informatics (achi), the british computer society (bcs), and the uk council for health informatics professions (ukchip), with room for adding more in the future. our major contribution is enhancing the findability of codes and standards related to health informatics ethics by compilation and unified access through the health informatics ethics repository. keywords: health informatics ethics, repository, codes of ethics, technical standards, findability correspondence: hwsamuel@cs.ualberta.ca doi: 10.5210/ojphi.v6i2.5484 copyright ©2014 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction ethics deals with decisions about right versus wrong or good versus bad. these normative and moral questions involve people and how they affect each other (1). in the field of medicine, ethical issues have been well-known due to the nature of the profession in dealing with life-anddeath situations. in the present information age of ubiquitous computing, medicine and technology have become overlapped. the field of health informatics focuses on using computers to enhance the way health information is processed. there is extensive literature on the subject of ethical conduct and principles in medicine. similarly, there are existing bodies of knowledge on ethics in computing and information technology. it can be argued that these two bodies of literature cover ethical issues in health informatics (2,3). however, we point out that information a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi technology and medicine together lead to new and more complicated issues in ethics. for example, the therac-26 incident is one of the many examples of software bugs causing loss of lives. because of a bug in the underlying operating system of the therac-26 x-ray device, patients died or were seriously injured due to over-exposure (4). who was to blame: developers, testers, or operators? medical ethics also does not adequately cover modern innovations in medicine. for instance, the united nations has a document covering the principles of medical ethics, but this has no provision for electronic health records (5). also, the american medical association’s (ama) code of medical ethics identifies physician records, yet does not address electronic transmission of patient records (6). on the other hand, computer ethics can deal with issues with data transmission and software development, but does not consider ethical requirements of the medical profession. ethics issues in health informatics require more than just interdisciplinary cooperation in the fields of ethics, medicine, and computing. these issues are very important due to the vulnerability of people needing care and potential risks of using information technology to provide this care. moreover, while there are various codes of ethics and technical standards covering health informatics, the degree of findability of the documents could be enhanced. these documents are available on the respective websites of international, regional, and national bodies and organizations, and given the number of documents, as well as the diversity of content, synthesizing the viewpoints presented by the different documents can prove challenging. the web pages that host the documents also have limited search capabilities that could be improved. the proposed repository aims to address these issues by compilation and unified access to the various documents, along with state-of-the-art search and comparison tools. the rest of the paper is structured as follows. we firstly present the methods used in carrying out this research, and declare any risks of bias. next, we look at health informatics ethics by reviewing codes of ethics and technical guidelines from leading organizations across the globe. later, we discuss the concept of findability and how it relates to codes and standards in health informatics. finally, we present a repository of codes of ethics informatics and outline the specifications of the state-of-the-art search algorithms and technologies used. methodology we carried out a review of literature, codes of ethics, and technical standards from major policy drivers and stakeholders. firstly, we used the health informatics world wide (hiww) online database to identify health informatics organizations. next, we identified ethical codes within the organizations and common memberships to international and regional bodies using the respective organization's website. based on common threads, we then analyzed the major ethical principles being discussed. we also accessed pertinent literature via online subscription databases. we selected all international, regional and national bodies that were currently actively listed in the hiww online database. we measured their state of activeness by visiting their websites and looking at the published time stamps on their web pages. our selection criteria for the codes of ethics ultimately available in our repository were two-fold; firstly, to maintain intellectual property rights, we indexed only codes and standards freely available on the internet. secondly, major international, regional and national health informatics organizations were surveyed, and representative bodies from the various continents were selected. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi health informatics ethics ethics in health informatics, or health informatics ethics is the application of the principles of ethics to the domain of health informatics. essentially, there are three aspects of health informatics: healthcare, information, software (7). information systems are developed to facilitate dispensation of healthcare or the auxiliary activities involved in healthcare. health informatics also deals with efficiently processing information. large volumes of information about patients’ needs to be stored for future reference, and retrieved when needed. transfer of information between healthcare organizations needs to be handled with proper security. ultimately, information is processed, stored and retrieved by software. indeed, software systems are needed to manage clinics and hospitals, as well as information needed by larger healthcare providers. we can further define health informatics ethics using three hierarchies of ethics: general ethics, informatics ethics, and software engineering ethics, as illustrated in figure 1 (8). figure 1. aspects of health informatics ethics all our social interactions dealing with norms and values are guided by general ethics, which covers six major principles: non-malfeasance, integrity, equality/justice, beneficence, autonomy, and impossibility (8). we have a duty to prevent harm to others without undue harm to ourselves, and a duty to fulfill our obligations to the best of our abilities. we also have the right to be treated equally, while advancing the good of others. we have the right to self-determination, and all our duties are subject to our ability to do them. informatics ethics, on the other hand, is a specific set of ethics principles for informatics. it deals with ethical behavior required of anyone handling data and information. informatics ethics covers seven principles: privacy, openness, security, access, legitimate infringement, least intrusive alternatives, and accountability (8). every person has the right to decide how much information they wish to disclose about themselves, and what information they wish to withhold, how it is stored, and used. the recent “right to be forgotten” ruling against google is an example (9). the person about whom the data is being collected needs to be informed of the intent for collecting data and what the data will be used for. once data is collected, it ought to be safeguarded against unauthorized access. in addition, data needs to be protected against malicious and unintentional manipulation. furthermore, everyone has the right to access and correct their own data. an individual's data should not be kept in isolation and allowed to become outdated. however, access is subject to legitimate data needs of a free society. the individual's right to privacy may be infringed for the larger good of society. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi any legitimate infringement should be done with minimum interference to individual rights. legitimate infringement should be reported to the person affected in due time. this implies that eventually the affected individual should be notified of the infringement. health informatics ethics also involves use of software to store and process information. consequently, activities carried out by software developers have the potential of affecting endusers (10). the software developer has ethical duties and responsibilities to the following six stakeholders: public, client, employer, management, colleagues, and self (11). activities ought to be done with the best interest of the society in mind. developers should be aware of social impacts of software systems. this includes disclosing any dangers or known defects in software. also, activities ought to be done in the best interests of clients and employers, while balancing their duties to the public, including being forthright about personal limitations and qualifications. software products should meet expected professional standards. developers should strive to build products that are not sub-standard by thoroughly testing and documenting unsolved problems. moreover, managers and leaders should subscribe to ethical approaches in software development. realistic and effective costs, schedules, and procedures should be promoted. and continuing further, colleagues should be supported and treated fairly. developers should fully credit their colleagues for their work, including intellectual contributions and code re-use. finally, self-improvement and re-training ought to be pursued by the software developer. in addition, developers should not let prejudices lead to unfair treatment of others. ethical conflicts arise as a result of interactions between various stakeholders involved in the health informatics setting (8). to help resolve ethical conflicts, there are various ethical resources available which can help to determine which course of action to take: codes of ethics, case studies, ethics committees and personnel, and informal discussions (12). codes of ethics are formal documents that list ethical principles and duties. members of the profession or organization are required to adhere to the principles of these codes to guide their ethical conduct. in addition, these codes serve an educational purpose by correcting any wrong notions about ethical principles. on the other hand, case studies are often used as a reference for similar ethical conflicts and situations in the past that may have been resolved in a certain manner. these cases can be applied as jurisprudence. organizations also can have committees and trained staff to discuss and resolve ethics issues. these may include ethics boards or ethics professionals that are contacted for consultation when ethical conflicts occur. chats with friends or colleagues can equally lead to informal advice about how an ethical conflict can be resolved. codes of ethics for this study, we focus on codes of ethics as a means of resolving conflicts. there are various national and international bodies related to health informatics. table 1 gives a summary of health informatics bodies extracted primarily from the health informatics world wide (hiww) database (13). in addition, membership to regional and international bodies is indicated, such as the international medical informatics association (imia) (14), the european federation for medical informatics (efmi), the asia-pacific association for medical informatics (apami), and the health informatics in africa (helina) group (15–17). it can be noted that many bodies do not have their own ethical codes. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi table 1. health informatics organizations. ○ indicates has stated code of ethics on website ♦ imia member ◊ efmi member ■ apami member □ helina member. countries and regions are identified using iso alpha-3 codes (18). international north america europe south & central america africa middle east asia pacific 0 0 1 imia ○ u s a amia ○♦ p r t apim a r g aaim ○ m w i miam □♦ s a u sahi ♦ c h n cmia ♦ 0 0 9 achi ○♦ u s a ahima ○◊ b ih bhsmi ◊♦ c h l achisa ♦ z a f sahia □♦ ir n irmia ♦ pa k ehap ♦ 1 4 2 apami ♦ c a n coach ○ b e l bmia ◊ v e n avis ♦ m a r smims n z l hinz ♦ 1 5 0 efmi ♦ c u b socim ♦ d n k dsmi ◊ b r a sbis ♦ m l i somibs □♦ l k a hissl ■ 0 0 2 helina ♦ n o r fdh ◊ u r y suis ♦ n g a ahin in d iami ♦ 8 2 6 ukchip ○ f in finnshia ◊ m e x amim ♦ c m r cahis ♦ jp n jami ■♦ 0 0 1 ihc ○ d e u gmds ◊ k o r kosmi ■♦ ir l hisi ◊♦ m y s mhia ■♦ r o u rsmi ◊♦ p h l pmis ■♦ s v n sdmi ◊ s g p ambis ■♦ e s p seis ◊ t h a tmi ♦ s w e sfmi ◊ a u s hisa ♦ c h e ssmi ◊ h k g hksmi ♦ t u r turkmia ◊ t w n tami ♦ g b r bcs ♦○ h r v csmi ♦ f r a aim ♦ g r c ghia ♦ it a aiim ♦ a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi international codes within the international codes, the international medical informatics association (imia) code of ethics is comprehensive and covers duties of health informatics professionals from three perspectives: fundamental ethics principles, informatics ethics principles, and rules of ethical conduct in health informatics. we also use the ehealth code of ethics by the ihealth coalition (ihc), which is specifically for health-related websites (19). the ihealth coalition code was also referenced by the world health organization (who). north american codes north american codes of ethics include the “coach high-level ethical principles”, published by canada's health informatics association and an abridged version containing ten aspirational high-level principles is available to the public (20). the american health information management association (ahima) code of ethics contains eleven ethical obligations expected of ahima members (21). another u.s. code, by the american medical informatics association (amia) is a relatively recent publication. the amia code references the ahima and imia codes and focuses more on duties of health informatics professionals expected towards key stakeholders in healthcare (22). european codes in europe, the british computer society (bcs) code of ethics and imia codes have shared authorship by dr. eike-henner w. kluge, and the bcs code contains the same material as the imia code (23). the bcs code is acknowledged by the imia as an accompanying handbook (22). similarly, the uk council for health informatics professions (ukchip) code of conduct is adapted from the bcs code (24). the european federation for medical informatics (efmi) does not explicitly state any code, but is a member of the imia (16). other regional codes other regional codes include the australasian college of health informatics (achi), which provides a code of conduct based on the underlying principles of the imia, but is identical in wording to the ukchip version (25). similarly, the argentine association of medical informatics, aaim provides a code of ethics that is a translation of the imia code (26). bodies in the asia-pacific, middle east and africa region do not provide a separate code of ethics but are regional or international members (15–17). notably, the medical informatics association of portugal, apim does not state any codes and are also not a member of any other body (27,28). figure 2 shows how some of the codes are related in terms of inter-referencing one another. technical standards aside from a set of principles that should be adhered to, health informatics ethics can also be defined as a set of activities. ethical conflicts may be preventable even before they happen if these activities are followed during development of health informatics systems. the u.s. food and drug administration (fda) provides a document that aims to minimize software errors and failures through life cycle and risk management. in addition, the international standards organization (iso) has a large collection of standards on health informatics set forth by its technical committee iso/tc 215. we analyze these technical guidelines to identify how these can be applied to ethical issues. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi figure 2. inter-references between health informatics codes of ethics food and drug administration (fda) the fda document titled “general principles of software validation” was created to reduce software defects and recalls resulting from these defects. the document applies to medical software or devices containing software. its goals include reducing risk to patients and users, and also reduce liability to manufacturers of medical software. the fda document achieves this through providing explicit guidelines on how to validate software during the stages of software development (29). development of health informatics systems involves going through the software development life-cycle (sdlc). in general, the software development life-cycle consists of all the stages during the development of software. there are various sdlc models based on different software engineering paradigms. the fda document does not conform to any one model, but provides the following generic sdlc pathway: quality planning, requirements, design, coding, testing by software developer, user site testing, and maintenance (29). quality planning involves tasks and procedures for reporting risks anomalies in software, assigning them to personnel, and resolving them. requirements and information about the device or software and its intended use are identified, including proper documentation of the software or device functions. these requirements are translated into a logical and physical representation of the software through design activities. in addition, possible usage errors need to have appropriate testing measures. ultimately, software is constructed by either coding or by assembling coded components. this is the culmination of the design into a medical device or software product. depending on the coding guidelines being opted for by the developers, there needs to be verification of this compliance. once software is complete, it needs to be tested. this involves running the software with pre-defined inputs and known outputs in order to compare its accuracy. even though testing cannot be exhaustive, all efforts need to be taken for thorough testing of the code. another level of testing involves testing on-site with the user with realistic data. tests are needed to determine if the user correctly understands usage of the interface. finally, maintenance covers both installation and deployment of the software, as well as future changes to software that will require re-validation. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi moreover, the fda document provides a template of tasks and activities for each stage of the sdlc, depending on the level of risk associated with the device or software. this validation process involves providing documentation of plans and procedures for risk management during each of these stages of the sdlc. needless to say, items that are more risky go through more activities before approval. eventually, the plans, procedures, and documented activities need to be evaluated and approved by the fda. the fda recommends independent or third-party evaluation, and also self-evaluation before final submission to the fda. in the sdlc, the specific tasks recommended by the fda for validation depend on the level of concern, which is an estimate of the danger a device or software poses. there are three levels of concern detailed in the fda supplementary document titled “guidance for the content of premarket submissions for software contained in medical devices”: major, moderate, minor (30). the level of concern for a given software is determined by answering a set of key questions. major concerns involve possibility of death or serious injury to patient or operator. moderate concerns have a possibility of minor, non-fatal injury to patient or operator, while minor concerns are almost unlikely to cause any injury to patient or operator. the emphasis on validation in the fda document facilitates the eventual production of software that reduces software faults. in other words, it leads to the development of software that mitigates harm and upholds the ethical principle of non-malfeasance. however, other principles such as privacy are not addressed. international standards organization (iso) another set of technical standards by the international standards organization (iso), specifically addresses health informatics via the standards and guidelines developed by the iso/tc 215 committee. the iso standards in general are globally recognized and the tc 215 committee has international representation in its membership (31). the iso/tc 215 standards address a wide range of issues in healthcare, and at the time of this publication, we looked at eighty-two active standards on different areas in health informatics. three main categorizations can be presented based on the artifacts addressed by these standards: health software, health devices, and health information. majority of these standards focus on communication protocols between devices and computers. the standards also aim to promote common vocabularies, address ehr harmonization, and facilitate health informatics systems. figure 3 summarizes most of the currently active iso/tc 215 standards categorized by artifacts and sub-themes. in addition, many of these standards are ethics-aware, and specifically address some ethics issues directly or indirectly. we identify four matches to the imia ethics principles that are covered via the iso/tc 215 standards: non-malfeasance, privacy, security, and accountability. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi figure 3. overview of the iso/tc 215 standards there are two main standards that address safety: iso/tr 2 7809 and iso/ts 25238. while similar to the fda approach of risk assessment within the sdlc, the iso/tc 215 standards more comprehensively address risk assessment. while acknowledging and referencing the fda standards, the iso/tc 215 propose five classes for risk assessment instead of the fda’s three categories: catastrophic, major, considerable, significant, and minor (32,33). catastrophic risks could involve multiple deaths, or injuries causing disability, while major risks could include death of an individual patient, or severe injuries to multiple patients. considerable risks include severe injury for an individual patient or minor injuries for multiple patients, while significant risks could include minor injuries for an individual patient, or minor shock for multiple patients. finally, minor risks could induce mild shock for an individual patient. additionally, the iso/tc 215 include an assessment of the likelihood of failure of a device or software: very high, high, medium, low, very low. very high likelihood implies failure most likely will occur, while high likelihood means failure is uncertain but very likely to occur. medium failures are likely to occur, but not certain, while low failures only have a small chance of occurrence. very low categories would most likely not occur. there are various standards in the iso/tc 215 catalog that deal with information, and specifically patient information in the form of electronic health records (ehrs). iso 22857 looks at transfer of personal health information generally, and the consequent privacy risks (34). in addition, the standards on ehr communication, archiving, and transmission such as iso13606-1 and iso/ts 21547 acknowledge the importance of privacy. closely related to the need for privacy is its realization via security and access control mechanisms (35,36). security is addressed for communication, transmission, and storage of data. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi there are various standards for personal health devices and point-of-care health devices that look at communication between these devices and computer systems. additionally, security of ehr communication and storage is also covered. iso/ts 22600-1, iso/ts 22600-2, and iso/ts 22600-3 are stipulations on how security is managed via access control. access control covers 4 main aspects: authentication, access, authorization, and function (37). with authentication, the identity of the user is established. next, there is a policy about what information aspects are allowed access. authorization gives a mapping between what can be accessed and who has access. functionally, the possible actions that can be taken upon authorization are known. in addition to controlling access to data, security also covers data transfer and secure exchange. iso 17090-1, iso 17090-2, iso 17090-3 look at digital certificates, while iso tr 11633-1 and iso tr 11633-2 look at remote access. also, iso tr 21089 looks at the role of trust in access control (31). various standards with the iso/tc 215 address the need for accountability via provisioning for audit trails. iso/ts 13606-4 and 13606-1 address security specifically for ehrs, and include an audit log in its guidelines. in addition, the term “auditability” is introduced in 13606-4, and defined as the ability to examine actions of users (36). iso 22857 stipulates that audit trails should be tamper-proof (34). in general, depending on the nature of the artifact, whether health software, device, or information, the appropriate iso/tc 215 standards can be applied. the risks of ethics problems arising are thereby reduced because these standards implicitly or explicitly incorporate ethics aspects. findability findability is a well-known concept in the area of information architecture, and means the ease of locating information on a website (38). findability quantifies the ease of accessing and finding specific content by an information consumer. there are two viewpoints of findability in general: the visibility of a website from the internet via search engines, or visibility of specific content on the website itself. for the development of the health informatics ethics repository, we focus on the latter of these viewpoints. the motivation behind this focus is because of the limited search capabilities of websites hosting the health informatics ethics documents. also, the documents are hosted independent of each other, and findability of content is even further restricted. for instance, a domain researcher may be interested in getting information and references on privacy within the codes, but would be restricted to finding these references per website. in other words, cross-cutting findability of the content is limited. there is no unified point of access to the vast amount of codes and standards for health informatics ethics, and even existing websites that host these documents have limited search capabilities. all of this makes it cumbersome for researchers in this field to comparatively study the various documents. our contribution is compilation and unified access to these documents through a repository built using state-of-the-art search technologies. harmonization of ethics codes and standards health informatics ethics deals with questions of wrong and right resulting from the use of technology in managing healthcare information. it involves adherence to general, informatics, and software engineering ethics principles. in the preceding sections, we presented health informatics ethics as a set of activities that are incorporated into the sdlc. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi in our definition, health informatics ethics as an activity involves software validation and best practices, in accordance with the iso/tc 215 and fda guidelines. whereas the codes provide guidelines on ‘what’ is ethical, the iso/tc 215 and fda documents define ‘how’ to directly or indirectly make it ethical. figure 4 shows the different ethical principles and which of these are directly or indirectly addressed by activities through the iso/tc 215 and fda technical guidelines. figure 4. ethical codes and technical guidelines in health informatics health informatics ethics repository our survey reiterates a large diversity of literature and sources pertaining to health informatics ethics. these sources may not always be easy to access and comparatively analyze. as our contribution to the research community in this domain, we developed a repository that indexes the major codes of ethics and standards related to health informatics that are freely available on the internet. this repository contains codes of ethics from the imia, bcs, ihc, achi, ukchip, and ahima. the system is built using state-of-the-art search technologies to make it easier for researchers to find and compare items of interest across codes. the repository can be freely accessed online on the world wide web. indexing one important step in the creation of the health informatics ethics repository is indexing, which involves parsing and extracting the contents the codes and standards, and saving the contents to a database designed for fast retrieval. in the field of information retrieval, indexing converts unstructured text to a structured format (39,40). contents in the health informatics ethics repository are stored as an inverted index data structure. the inverted index structure allows storing mappings from words in the codes and standards to the source documents. each word is stored uniquely, but can have multiple mappings to different source documents. for example, the word “privacy” would occur in multiple codes and standards, but would have only one instance in the inverted index. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi content life-cycle in order to properly index the relevant documents, we have guidelines in place for the content life-cycle (41). each indexed document has three meta-data properties to facilitate awareness of the document’s version to the readers, as well as to the maintainers of the repository: publication year, source, and date indexed. publication year is the reported year of the current document’s publication, as reported by the publisher/organization. the source is a link to the original document on the internet, as being hosted by the publisher/organization of the code or standard. the date indexed property is the date the document was last indexed. to prevent outdated documents, the date of publication will be checked semi-annually against the organization’s latest version. if the dates are the same, then no further re-indexing will be done. in the case a new version or edition of the code or standard has been released, that document will be re-indexed. comparative searching our consolidated repository has various enhancements to facilitate comparatively searching across different codes of ethics. researchers can enter a search queries and see its occurrence visually highlighted across all the indexed codes and standards. documents can also be hidden if they are not of interest. researchers can also navigate from one occurrence to the next within a particular document via the search result navigation hyperlinks. moreover, we incorporate standard query expansion techniques such as stemming and fuzzy searching to expand user search queries. these expanded queries enables discovery and comparative analysis using words and corrections related to the search queries. stemming in everyday usage of the english language, words have different forms. for instance, a word has a singular and plural form, or a referential form, and so on. the word ‘privacy’ can have related forms such as ‘private’, while the word ‘publish’ can have other forms such as ‘publishes’, ‘published’, ‘publishing’. the job of stemming and lemmatisation algorithms is to determine the other relevant forms of a given word (39). for instance, one form of the porter stemming algorithm works by stripping all suffix or prefix matches from a word. what remains is an approximation of the etymological root of that word. this can then be iterated to create new forms. as an example, the word ‘private’ leads to ‘priv’ as an approximate root, because ‘-ate’ is a common suffix. consequently, ‘priv-acy’ can be related to ‘private’ by adding other suffices to the root. in this fashion, if a researcher searches for the query ‘private’, the search results would also contain matches for ‘privacy’. fuzzy search this is a technique for finding approximate string matches to a given pattern (42). the most common application for this technique is to detect user spelling errors and suggest corrections. for instance, if the search query is typed in as ‘haert’, a fuzzy search algorithm would be able to suggest ‘heart’ as a possible alternative to use. a simple approach to fuzzy search is the edit distance algorithm, also referred to as the levenshtein distance (42). this distance is calculated based on three main single-character operations that can be performed to convert one string to another, namely, insertion, deletion, and substitution. for example, to change ‘haert’ to ‘heart’ two substitutions are needed. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi more generally, the transformed word needs to be checked for validity against some language dictionary to avoid coming up with meaningless word suggestions. search interface demo figure 5 shows a demonstration of the health informatics repository's search interface. the enduser can control which documents are being shown, and can view various documents side-byside. also, when searching for keywords, the interface highlights them in the documents and provides a navigation panel to go through all occurrences of the search matches. the interface also allows parallel reading of multiple codes and standards. figure 5. comparative and enhanced search available through the health informatics ethics repository based on our selection criteria, the repository contains codes of ethics from the imia, bcs, ihc, achi, ukchip, and ahima. codes of ethics from other organizations were not indexed because these organized either did not have a code or were referring to international or regional codes. also, to maintain intellectual property rights, we indexed documents freely available on the internet. this excluded the iso/tc 215 standards. we focused solely on codes of ethics presently, and will be looking into indexing technical standards in future versions of the repository. conclusion and future work in this study, we comprehensively examined codes of ethics from major international, regional, and national policy drivers and stakeholders such as the imia, bcs, ihc, achi, ukchip, and ahima. we also investigated how the iso and fda guidelines are applied to management of software, devices, and data. ultimately, we presented a searchable repository of codes of ethics and standards in health informatics built using state-of-the-art search algorithms and technologies, which enhanced the findability of codes and standards in health informatics ethics. a repository of codes of ethics and technical standards in health informatics online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 6(2):e189, 2014 ojphi the repository platform will potentially benefit public health practitioners, researchers, and software developers as a one-stop reference for various ethics codes and standards in health informatics. our contributions included a systematic review and comparative analysis of various health informatics ethics codes, affiliations, and practices from different bodies around the world. also, as part of our support for the research community in this domain, we introduced a repository with state-of-the-art comparative searching for freely available codes of ethics and standards related to health informatics. for future work, we will explore new search, comparison and feedback features for the repository and plan to index new health informatics codes, as well as technical standards. limitations of the study the survey of health informatics bodies and organizations is limited by availability of information. organizations that do not have some online portfolio were not included in the survey. we also acknowledge that some online resources might not have been accessible due to temporary server downtime. the study is also limited by language selection, and focuses on english-language ethics codes and standards in health informatics. furthermore, to respect intellectual property rights, only those codes and standards that are freely available on the world wide web are indexed in the health informatics ethics repository. codes that required paid subscriptions or memberships are excluded from the repository. conflicts of interest the authors declare that they are not affiliated of any of the health informatics organizations mentioned in this paper. furthermore, all authors are not affiliated with the food and drug administration (fda) or the international standards organization (iso). however, the authors are members of the association for computing machinery (acm). publications from this professional organization have been used in our research. references 1. thomson aj, schmoldt dl. ethics in computer software design and development. comput electron agric. 2001;30:85–102. 2. trzęsicki k. medical informatics ethics and its subject. ann acad medicae bialostoc. 2005;50(2):3. 3. trzęsicki k. medical informatics ethics (subject and 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trees, and sequences: computer science and computational biology. cambridge university press; 1997 jul 1; isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts predicting virologically confirmed influenza using school absences in pa talia quandelacy*2, shanta zimmer3, 4, chuck vukotich3, rachael bieltz3, kyra grantz1, david galloway3, justin lessler2, yenlik zheteyeva5, amra uzicanin5, hongjiang gao5 and derek cummings1, 2 1biology, university of florida, gainesville, fl, usa; 2johns hopkins bloomberg school of public health, baltimore, md, usa; 3school of medicine, university of pittsburgh, usa, pittsburgh, pa, usa; 4university of colorado school of medicine, department of medicine, denver, co, usa; 5us centers for disease control, atlanta, ga, usa objective to determine if all-cause and cause-specific school absences improve predictions of virologically confirmed influenza in the community. introduction school-based influenza surveillance has been considered for real-time monitoring of influenza, as children 5-17 years old play an important role in community-level transmission. methods the allegheny county department of health provided virologically confirmed influenza data collected from all emergency departments and outpatient providers in the county for 2007 and 2011-2016. all-cause school absence rates were collected from nine school districts within allegheny county for 2010-2015. for a subset of these schools, in addition to all-cause absences, influenza-like illness (ili)-specific absences were collected using a standard protocol: 10 k-5 schools in one school district (2007-2008), nine k-12 schools in two school districts (2012-2013), and nine k-12 schools from three school districts (2015-2016). we used negative binomial regression to predict weekly county-level influenza cases in allegheny county, pennsylvania, during the 2010-2015 influenza seasons. we included the following covariates in candidate models: all-cause school absence rates with different lags (weekly, 1-3 week lags, assessed in separate models using all other covariates) and administrative levels (county, school type, and grade), week and month of the year (assessed in separate models), average weekly temperature, and average weekly relative humidity. separately, for the three districts for which ili-specific and all-cause absences were available, we predicted weekly county-level influenza cases using all-cause and ili-specific absences with all previously stated covariates. we used several crossvalidation approaches to assess models, including leave 20% of weeks out, leave 20% of schools out, and leave 52-weeks out. results overall, 2,395,020 all-cause absences were observed in nine school districts. from the subset of schools that collected ili-specific absences, 14,078 all-cause and 2,617 ili-related absences were reported. a total of 11,946 virologically confirmed influenza cases were reported in allegheny county (figure 1). inclusion of 1-week lagged absence rates in multivariate models improved model fits and predictions of influenza cases over models using week of year and weekly average temperature (change in aic=-4). using grade-specific all-cause absences, absences from lower grades explained data best. for example, kindergarten absences explained 22.1% of model deviance compared to 0.43% using 12th grade absences in validation. multivariate models of week-lagged kindergarten absences, week of year, and weekly average temperature had the best fits over other grade-specific multivariate models (change in aic=-6 comparing k to 12th grade). the utility of ili-specific absences compared to total absences is mixed, performing marginally better, adjusting for other covariates, in 2 years, but markedly worse in 1 year. however, these results were based on a small number of observations. conclusions our findings suggest models including younger student absences improve predictions of virologically confirmed influenza. we found ili-specific absences performed similarly to all-cause absences; however, more observations are needed to assess the relative performances of these two datasets. performance of models including week-lagged kindergarten absences to predict virologically confirmed influenza in allegheny county, pa. figure 1. weekly counts of reported all-cause absences (nine school districts, 2010-2015, a subset of schools in three districts 2007-2008, 2015-2016) (top panel). ili-specific absences from three school districts (2007-2008, 2012-2013, and 2015-2016) (middle panel) and virologically confirmed influenza all of allegheny county from 2007 and 2010-2016 (bottom panel). keywords influenza; surveillance; time; series; school *talia quandelacy e-mail: taliaquandelacy@jhu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e117, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts utility of outpatient syndromic data for monitoring influenza-like illness jill k. baber* and michelle feist division of disease control, north dakota department of health, bismarck, nd, usa objective to explore how outpatient and urgent care syndromic surveillance for influenza-like illness (ili) compare with emergency department syndromic ili and other seasonal ili surveillance indicators. introduction the north dakota department of health (nddoh) collects outpatient ili data through north dakota influenza-like illness network (nd ilinet), providing situational awareness regarding the percent of visits for ili at sentinel sites across the state. because of increased clinic staff time devoted to electronic health initiatives and an expanding population, we have found sentinel sites have been harder to maintain in recent years, and the number of participating sentinel sites has decreased. outpatient sentinel surveillance for influenza is an important component of influenza surveillance because hospital and death surveillance does not capture the full spectrum of influenza illness. syndromic surveillance (sys) is another possible source of information for outpatient ili that can be used for situational awareness during the influenza season; one benefit of sys is that it can provide more timely information than traditional outpatient ili surveillance [1,2]. the nddoh collects sys data from hospitals (emergency department and inpatient visits) and outpatient clinics, including urgent and primary care locations. visits include chief complaint and/or diagnosis code data. this data is sent to the biosense 2.0 sys platform. we compared our outpatient sys ili with our nd ilinet and reported influenza cases, and included hospital and combined sys ili for comparison. methods weekly rates from nd ilinet, sys ili, and counts of reported cases from the influenza season (annual weeks 40 through 20) for the 2014-2015 and 2015-2016 seasons were compiled. syndromic categories for outpatient, hospital (emergency department and inpatient), and combined hospital and outpatient data were created, and the biosense 2.0 definition for ili was used. these included data from 127,050 outpatient and 323,318 hospital visits for 2014-15, and 124,597 outpatient and 424,097 hospital visits for 2015-16. because influenza is a reportable condition in north dakota, case data is routinely used to represent the seasonal influenza trend, and is useful when other respiratory viruses are circulating. a pearson correlation coefficient was calculated on all variables using sas 9.4. alpha was set to 0.05. there was no overlap between the outpatient clinics providing syndromic surveillance data and clinics participating in nd ilinet. results all outpatient, hospital, and combined outpatient and hospital ili rates from sys data were positively and significantly correlated with both nd ilinet rates and influenza case counts (table 1). the correlation between outpatient sys ili rates and traditional influenza indicators was lower than for hospital sys ili rates for both years, with correlation coefficients ranging from 0.38-0.48 and 0.56-0.92, respectively. generally sys data was more highly correlated with case counts than nd ilinet rates. for the 2014-15 season, hospital sys data was the most strongly correlated with traditional influenza indicators. for 2015-16, combined sys data was the most strongly correlated. visual inspection of the chief complaint data for ili visits found a significant number of gastrointestinal visits that included the phrase “flu-like illness” in both outpatient and hospital sys data. conclusions although correlation coefficients were lower for outpatient sys ili rates, they are significant enough to be included in our ongoing influenza surveillance. one possible confounding factor for the relationship between ed surveillance and reported cases is that people with more severe illness may be more likely to be tested for influenza, and may be more likely to seek medical attention at a hospital setting. this may explain why hospital sys data provided the strongest correlation during the 2014-15 season, a season with higher rates of more severe illness than 2015-16. the combination of outpatient data and hospital data provided the strongest correlation for the 2015-16 influenza season, indicating the addition of outpatient data, which may increase representativeness of ili data, may be beneficial to sys ili surveillance. we used an existing ili syndrome from the biosense 2.0 tool, and revising this syndrome may improve correlations between sys ili and nd ilinet and case count data. negation terms to remove visits for gi illness incorrectly referred to as “flu-like” would be one useful change. the nature of visits for influenza at outpatient clinic versus hospitals is different, and it is possible this may account for the difference in the strength of correlations between the two data sources. use of a different ili syndrome definition for outpatient sys data should be investigated. table 1. pearson correlation coefficient values for influenza-like illness in three syndromic surveillance categories compared with nd ilinet and influenza case counts. keywords syndromic surveillance; outpatient data; influenza-like illness references 1. miller b, kassenborg h, dunsmuir w, et al. syndromic surveillance for influenza-like illness in ambulatory care setting. emerg infect dis. 2004 oct;10(10):1806-1811. 2. gren lh, porucznik ca, joy ea, et al. point-of-care testing as an influenza surveillance tool: methodology and lessons learned from implementation. influenza res treat. 2013;2013:242970. *jill k. baber e-mail: jbaber@nd.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e51, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts novel analysis and visualization of chemical events for public health surveillance michael j. henry*, lauren charles-smith, kyungsik han and courtney d. corley visual analytics, pacific northwest national laboratory, richland, wa, usa objective pacific northwest national laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. making up the three teams were three graduate students (n=9) from various us schools majoring in nonpublic health domains, such as computer sicence and user design. the interns successfully developed three applications that demonstrated a value-add to chemical surveillance—chemanalyzer (text analytics), retrospect (retrospective analysis of chemical events), and toxicbusters (geo-based trend analytics). these applications will be the basis for the first chemical surveillance application to be incorporated into the dtra biosurveillance ecosystem (bsve). introduction pacific northwest national laboratory (pnnl), on behalf the defense threat reduction agency (dtra; project number cb10190), hosts an annual internbased web app development contest. previous competitions have focused on mobile biosurveillance applications. the 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. the result of the app will be integrated within the dtra bsve. methods pnnl hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. the challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. the interns developed their own ideas (vetted by pnnl and dtra), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistence outside of that provided in an advisory capacity. the competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. results each team produced a unique application. although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. chemanalyzer is a text-analytics platform designed to facilitate more datadriven decision, given a corpus of text data about a chemical event. their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. the keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. the chemanalyzer team finished in third place. the retrospect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. the retrospect team finished in second place. the toxicbusters team—the winners of the competition—created a geo-based situational awareness tool for tracking chemical events. their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. conclusions each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. the apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. this integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. the apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. the web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. the ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the dtra bsve. keywords chemical surveillance; public health; data visualization; data science *michael j. henry e-mail: michael.j.henry@pnnl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e65, 2017 why do we need pharmacists in pharmacovigilance systems? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e193, 2016 ojphi why do we need pharmacists in pharmacovigilance systems? hale z. toklu1 and edward mensah2 1. university of florida department of pharmacology and therapeutics 2. university of illinois at chicago school of public health abstract pharmacovigilance is the science and activity relating to the collection, detection, assessment, monitoring, and prevention of adverse effects with pharmaceutical products. pharmacovigilance basically targets safety of medicine. pharmacists have crucial role in health systems to maintain the rational and safe use of medicine for they are drug experts who are specifically trained in this field. effective use of pharmacists’ workforce will improve the outcome of the pharmacotherapy as well as decrease global health costs. given their advanced training, pharmacists can utilize pharmacovigilance systems interfaced with electronic health records to monitor the performance of the drugs they fill and also identify adverse drug reactions earlier than non -pharmacists, thereby reducing high healthcare costs. keywords: pharmacovigilance; pharmacy; pharmacist; adverse drug reaction reporting; health system; rational use of medicine; pharmacotherapy correspondence: dr. hale zerrin toklu department of pharmacology and therapeutics college of medicine, university of florida, gainesville, 32610 fl, usa +1 352 392-3395 haletoklu@yahoo.com doi: 10.5210/ojphi.v8i2.6802 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. introduction pharmacovigilance is the science and activity relating to the collection, detection, assessment, monitoring, and prevention of adverse effects with pharmaceutical products [1]. the word "pharmacovigilance" is derived from pharmakon (drug in greek) and vigilare (keep an eye on/ monitor in latin). pharmacovigilance basically targets safety of medicines. ultimately, pharmacovigilance is concerned with identifying the hazards associated with pharmaceutical products and with minimizing the risk of any harm that the patients can face [1,2]. in modern dataintensive era, pharmacovigilance information systems, operating like disease surveillance systems, http://ojphi.org/ why do we need pharmacists in pharmacovigilance systems? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e193, 2016 ojphi can enhance the data collection, detection, assessment, monitoring and prevention of adverse effects. effective and safe pharmacological treatment process requires a team work of the patient and healthcare professionals. pharmaceutical care includes considering these risks on a patient oriented basis by ‘‘identifying and solving (or avoiding)’’ drug therapy problems. althoug h the prescription is written by medical doctors in most countries, pharmacists and nurses have a crucial role in follow-up, since they can monitor and determine drug related problems; thus maintain safe use of medicines [3,4]. traditional pharmacy practice typically focuses on filling orders (prescription/ otc), received from medical practitioners. in this model, pharmacists provide a service focused on the dispensing of medicine, rather than the provision of individualized care to the patient [5,6]. however, evidence suggests that patients often receive inadequate care using this traditional model [7-9]. on the other hand, pharmaceutical care is improved when pharmacists are actively involved in the treatment procedure by fostering the pharmacist–patient relationship and enhancing the value of the clinical outcome of the treatment [10,11]. the role of pharmacists in pharmacovigilance systems is amplified under affordable care act or the current health care reform, because people who otherwise had no insurance, now qualify for insurance; and this could increase the demand for pharmacy services. more pharmacists will be required in delivering health education, including education on drug-drug interaction [12]. also, in developing countries, pharmacists have a distinct role in in the health care system since many patients in prefer going to pharmacies for primary care. in such countries, pharmacists are more involved in the treatment process as well as the patient education [13]. pharmacovigilance information systems, managed by pharmacists, can identify adverse drug reactions in developing countries where quality control of medicines is questionable. the study by akici et al (2007) showed that patients had insufficient knowledge about their prescribed drugs, although they had been using them for a while [14]. another problem is ignorant use of herbs and herbal medicine [15]. since many herbal products contain active ingredients that can interact with prescription medicines, who prepared a guideline on monitoring of herbal medicine in the pharmacovigilance systems [16]. in most countries, pharmacists go through an extensive education on medicinal plants which are used in traditional folk medicine, as well as the natural and synthetic medicines. therefore, pharmacists already have the knowledge to detect safety signals of drugs of any origin [17]. signal detection is important to identify the drug related adverse effects. however, the number of reports sent to national pharmacovigilance centers is also important as well as the quality of reports. the quality of reports is definitely superior when they are filled by health professionals who have pharmacology knowledge, i.e. pharmacists, doctors, nurses, physician assistants, dentists etc. it will be even better if it can be documented and retrieved from pharmacy information systems. http://ojphi.org/ why do we need pharmacists in pharmacovigilance systems? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e193, 2016 ojphi according to the 2012 fip pharmacist workforce report, global sample reveals that, on average, 55% of pharmacists were found to work in community pharmacy environments, 18% in hospitals, 10% in industry, 5% in research and academia and 5% in regulation [18]. roughly, 73% of pharmacists work in hospital or pharmacy settings, where they can face events based on adverse drug reactions or other drug related problems. their involvement in pharmacovigilance systems is crucial. the changing role of the pharmacist from traditional ‘drug dispenser’ concept towards ‘pharmaceutical care provider’ expanded the role of pharmacists [13]. pharmacists’ role has become essential for the management of chronic diseases in patient-centered medical facilities where pharmacists are constituents of primary care [19]. furthermore, the development of electronic information systems has been a milestone in identifying and intervening drug related problems such as dosage, adverse reactions, interactions, compliance or ineffectiveness [20]. such decision support systems in electronic medical records can capture drug-drug interactions or identify other issues (e.g. contraindications) with prescriptions before they are filled. thus, they improve safety. such systems have also been shown to improve productivity of the providers as well as saving time [21]. moreover, these databases provide a valuable source for costeffectiveness/ outcome analysis. because of the above mentioned reasons, pharmacists should be used more effectively in the system. they need to be directly involved in adverse drug reaction reporting using information systems to improve their performance. to reach this goal, regulatory bo dies should make legislations to encourage pharmacists to be actively involved in the system. besides their active participation, their assigned role should have a broader spectrum to obtain the maximum benefit based on their expertise. a sample organization scheme suggested for involving pharmacist in pharmacovigilance systems is given in figure 1. this simple organizational scheme can be adapted in many countries effectively. the performance of this system can be enhanced by the use of information and communication technologies. since rational use of medicines requires that the patients receive “medicines appropriate to their clinical needs, in doses that meet their own individual requirements, for an adequate period of time, at the lowest cost to them and their community”; active/ more effective involvement of pharmacists in pharmacovigilance systems will improve the rational use of medicines. conclusion pharmacovigilance basically targets safety of medicine. pharmacists play crucial roles in health systems in maintaining the rational and safe use of medicines since they are drug experts who are specifically trained in this field. effective use of pharmacists’ workforce will improve the outcome of the pharmacotherapy as well as decrease global health costs. http://ojphi.org/ why do we need pharmacists in pharmacovigilance systems? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e193, 2016 ojphi fig. 1. a sample organizational scheme suggested for involving pharmacist role in the pharmacovigilance system. this simple organization scheme can be adapted in many countries effectively. http://ojphi.org/ why do we need pharmacists in pharmacovigilance systems? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e193, 2016 ojphi references 1. world health organization. safety monitoring of medicinal products: reporting system for the general public. geneva: world health organization; 2012. 2. mann r, andrews e. introduction. pharmacovigilance. mann r, andrews e. 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22998048&dopt=abstract http://dx.doi.org/10.2174/1567201811310010012 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24715745&dopt=abstract http://dx.doi.org/10.1310/hpj4903-253 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24023452&dopt=abstract http://dx.doi.org/10.1016/j.jyp.2012.09.001 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15455466&dopt=abstract http://dx.doi.org/10.1002/pds.1020 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21471485&dopt=abstract http://dx.doi.org/10.1377/hlthaff.2011.0002 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27003549&dopt=abstract http://dx.doi.org/10.18553/jmcp.2016.22.3.204 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts public health surveillance strengthening in the kingdom of swaziland joy sylvester1, siphiwe shongwe3, vusie lokotfwako*2, nhlanhla nhlabatsi2, xolisile dlamini5, tony a. trong4, ruben sahabo3, affan shaikh1, paige ryland1, scott j. mcnabb1 and harriet nuwagaba-biribonwoha3 1public health practice, llc, belmont, ma, usa; 2epidemiology and disease control unit, swaziland ministry of health, mbabane, swaziland; 3icap at columbia university, new york, ny, usa; 4u.s. centers for disease control and prevention, atlanta, ga, usa; 5swaziland ministry of health, mbabane, swaziland objective to enable coordination of swaziland ministry of health units for public health surveillance (phs). introduction in the kingdom of swaziland, a baseline assessment found that multiple functional units within the ministry of health (moh) perform phs activities. there is limited data sharing and coordination between units; roles and responsibilities are unclear. the epidemiology and disease control unit (edcu) is mandated to coordinate efforts and strengthen phs through implementing integrated disease surveillance and response (idsr) to fulfill requirements of international health regulations (2005) (ihr[2005]), and the global health security agenda (ghsa). methods a baseline assessment that included key informant interviews of unit representatives was conducted. data flows were developed. results were disseminated at a facilitated stakeholder workshop with unit representatives. a database was then built containing all distinct activities found within the idsr technical guidelines (2010), ihr [2005], ghsa action packages, the baseline assessment, a previous cdc idsr assessment, and suggestions from the stakeholder workshop. activities were categorized by idsr function (identify, report, analyze, investigate, prepare, respond, provide feedback, and evaluate) and designated as an ongoing “role” or a one-time implementation activity. a document containing all phs roles was presented at a facilitated consensus workshop; unit representatives discussed and designated a lead unit/agency for each role. one-time implementation activities were assigned a lead actor, target completion date, and compiled into a 3-year idsr roadmap to guide implementation. results a roles and responsibilities framework was developed that presents a consensus on lead units for all roles within an idsr-based phs system that fulfills requirements of ihr [2005] and ghsa. this document enables coordination by edcu. the idsr roadmap provides time-bound activities with assigned actors to implement idsr. edcu is using these documents to guide coordination of multiple moh units already performing phs activities. conclusions coordinating well-established programs that already collect epidemiological data increases efficiency and enables more complete epidemiologic analysis. stakeholder engagement and clarity of roles is critical for edcu to coordinate phs. consolidating activities for idsr, ihr [2005], and ghsa in guiding documents enables a streamlined approach for public health surveillance strengthening. future work aims to achieve data sharing through an electronic platform and introduce data standards for interoperability among data sets. keywords idsr; international health regulations; global health security agenda; public health surveillance; africa acknowledgments thanks to william macwright. this work is supported by the president’s emergency plan for aids relief (pepfar) through the centers for disease control and prevention under the terms of cooperative agreement number 1u2ggh001271. its contents are solely the responsibility of the authors and do not necessarily represent the official views of pepfar or the centers for disease control and prevention *vusie lokotfwako e-mail: vusielokza@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e78, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using an open gis framework and epidemiological intelligence for dengue surveillance ta-chien chan*1, bo-cheng lin1, chiao-ling kuo1 and li-hsiang chiang2 1academia sinica, taipei city, taiwan; 2public health bureau, pingtung county government, pingtung county, taiwan objective in this paper we designed one cross-platform surveillance system to assist dengue fever surveillance, outbreak investigation and risk management of dengue fever. introduction in the 2015 dengue outbreak in taiwan, 43,784 people were infected and 228 died, making it the nation’s largest outbreak ever. facing the increasing threat of dengue, the integration of health information for prevention and control of outbreaks becomes very important. based on past epidemics, the areas with higher incidence of dengue fever are located in southern taiwan. without a smart and integrated surveillance system, the information on case distribution, high risk areas, mosquito surveillance, flooding areas and so on is fragmented. the first-line public health workers need to check all this information through different systems manually. when outbreaks occurred, paper-based outbreak investigation forms had to be prepared and filled in by public health workers. then, they needed to enter part of this information into taiwan cdc’s system. duplicated work occurred and cost lots of labor time during the epidemic period. therefore, we choose one rural county, pingtung county, with scarce financial resources, to set up a new dengue surveillance system. methods we designed a web-based cross-platform system based on an open geographical information system (gis) framework including openlayers, javascript, php, mysql and open data from government open data in taiwan. there were seven epidemiological intelligence functions within the system including risk management, outbreak investigation, planning controlled areas, intelligent detection of highrisk areas, useful tools for decision making, historical epidemics, and system management. the website was developed by responsive web design which can let public health workers check information and fill in the investigation form by any devices. results the system was promptly set up in june 2016. with first-line public health workers’ efforts and the help of the surveillance system, there were no indigenous dengue fever cases after the system was implemented. there were sporadic imported cases from southeast asia. the dengue surveillance system achieved three major improvements: integration of all decision support information; digitalization and automation of outbreak investigation; and planning the control areas. the results on outbreak investigation and mosquito surveillance can directly transfer to taiwan cdc’s database by web application programming interface (api). it can avoid duplicated work for disease surveillance. conclusions through introducing the new dengue surveillance system into local health departments, first-line public health workers can update all epidemic information at the same time. during epidemic periods, it can provide demographic, epidemiological, environmental, and entomological information for decision making. during non-epidemic periods, it can highlight the high risk areas for enhanced surveillance to reduce the risk of outbreaks. keywords gis; risk management; outbreak control; open data; geocoding acknowledgments this research was supported by a grant from national health research institute, taiwan. references duncombe j, clements a, hu w, weinstein p, ritchie s, espino fe: geographical information systems for dengue surveillance. the american journal of tropical medicine and hygiene 2012, 86(5):753755. hernández-ávila je, rodríguez m-h, santos-luna r, sánchez-castañeda v, román-pérez s, ríos-salgado vh, salas-sarmiento ja: nationwide, web-based, geographic information system for the integrated surveillance and control of dengue fever in mexico. plos one 2013, 8(8):e70231. *ta-chien chan e-mail: dachianpig@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e66, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using local toxicology data for drug overdose mortality surveillance haylea a. hannah*3, 1, karina arambula3, rochelle ereman3, darrell harris2, alexandra torres2 and matt willis3 2county of marin, sherriff’s office, san rafael, ca, usa; 3county of marin, department of health & human services, san rafael, ca, usa objective to describe the potential impact of using toxicology data to support drug overdose mortality surveillance. introduction although marin county ranks as the healthiest county in california, it ranks poorly in substance abuse indicators, including drug overdose mortality.1 death certificates do not always include specific detail on the substances involved in a drug overdose.2 this lack of specificity makes it difficult to identify public health issues related to specific prescription drugs in our community. we analyzed 2013 drug overdose death toxicology reports to determine if they could improve the description of drug overdose deaths in our community and to describe associated data characteristics. methods toxicology reports were requested from the office of the sheriffcoroner for 37 drug overdose deaths among marin county residents, comprising 95% of the 39 total drug overdose deaths in 2013. the remaining two deaths were excluded as they were associated with inhalation of therapeutic gases. select information from toxicology reports was entered into a database for aggregate analyses. drug overdose deaths were considered “fully detailed” if they included the specific types of drugs involved in the death and did not use any broad language to describe the death (i.e. narcotic, multiple drugs). student’s t-tests (α=0.05) were used to identify significant differences between groups of interest. results of the 37 drug poisoning deaths analyzed, 34 (92%) had available toxicology information. the remaining three (8%) deaths occurred outside of marin county and were thus investigated by another jurisdiction. a basic toxicology panel was ordered on 17 (50%) of the 34 drug overdose deaths, while an expanded toxicology panel was ordered on the remaining 17 (50%). alcohol was identified in the toxicology screen of 15 (44%); amphetamines were identified in 8 (24%); and opiates were identified in 25 (74%) drug overdose deaths. among the 25 deaths with at least one opiate identified on the toxicology screen, the majority (52%, n=13) also had alcohol present. the majority of drug overdose deaths, 18 (53%), did not have full information about the type of drug involved. the average number of drugs identified on the toxicology screen of all 34 drug overdose deaths was 6 (sd: 3). the average number of drugs identified in the toxicology screen significantly differed (p=0.0001) between causes of death that were fully detailed (mean: 4; 95% ci: 3-5) and those that were not fully detailed (mean: 8; 95% ci: 7-10). conclusions data from the sheriff-coroner’s office provided detail on the types of drugs involved in overdose deaths; however, it is difficult for local public health practitioners to make decisions about causality or contributions of these drugs to the death. these data may be useful in understanding the difference between fully detailed and non-detailed drug overdose deaths, and a broader context of drug combinations associated with these deaths. less drugs were identified in the toxicology screen of deaths that were fully detailed, suggesting that overdose deaths that are not fully detailed may be exceedingly complex, making it difficult for medical examiners and coroners to assess causality. approximately three-quarters of 2013 drug overdose deaths contained opiates on the toxicology screen, indicating that opiates may be a significant contributor to overdose deaths in our community. our results are descriptive in nature; therefore, even though alcohol or opiates were identified on the toxicology screen, they may not be responsible for the overdose death. given that over half of our 2013 overdose deaths were not fully detailed with drug type, local jurisdictions should work closely with their corner and/or medical examiner to fully detail death certificates with drugs involved in overdose deaths. keywords drug overdose surveillance; toxicology; prescription drug acknowledgments supported by an appointment to the cste applied epidemiology fellowship program administered by cste and funded by the cdc cooperative agreement number 1u38ot000143-03. references 1. university of wisconsin population health institute. county health rankings 2016. www.countyhealthrankings.org. 2. slavova s et al. drug overdose deaths: let’s get specific. public health reports. jul-aug 2015. vol 130: 339-342 *haylea a. hannah e-mail: hhannah@marincounty.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e143, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts an exploration of public events and alcohol related incidents briana a. holliday* undergraduate, university of illinois at urbana-champagin, kankakee, il, usa objective the objective for this research project was to see if there are predictable patterns for certain annual events in champaign county, illinois. the focus was on how alcohol intoxication effected the population and whether or not its effects were dangerous to the community at an alarming rate. introduction champaign county is one of the largest counties in central illinois with a population of ~207,000 and is home to the university of illinois at urbana-champaign which currently has 44,500 students. in the fall the university hosts big ten football games which have recently been drawing an average attendance of ~45,000 people, many traveling from chicago or other parts of the midwest1. the twin cities host a number of community events and festivals throughout the spring and summer. typically the community festivals have liquor licenses whereas no alcohol is served in the football stadium. despite the lack of alcohol availability in the stadium many fans drink during tailgate parties before and after the game. methods in order to assess the impact of alcohol at champaign-urbana mass gatherings we extracted records of patients seeking alcoholintoxication related treatment at carle foundation hospital from indicator2 between 2011 and 2014. we also obtained police citation data for driving under the influence (dui) and minor in possession (mip) from the urbana, champaign, and university of illinois police departments over the same period. the number of patient visits and citations for home football games were compared using an unmatched t-test to fall weekends without a home game. the number of patient visits and citations for the illinois marathon, taste of champaign, boneyard creek arts festival, urbana sweetcorn festival, and rhythm and brews music festival were compared against the event-less 2 weekends before and after the event. results over the 4 year period of the study there were 29 fall saturdays with a home football game and 24 without. of these the difference in the number of minor in possession citations were highly significant (mean=20.72 v 5.04, p<0.0001) whereas there was no statistical difference in number of dui citations (mean=1.21 v 1.25, p>.1) or hospital emergency department visits (mean=5.24 v 4.33, p>0.1). over the same 4 year period there were 25 spring weekend days with city festivals and 47 weekend days without them. of these the difference in the number of minor in possession citations were significant (mean=8.68 v 4.27, p<0.05) as were the number of dui citations (mean=1.44 v 0.91, p<.05). hospital emergency department visits were not significantly different (mean=3.4 v 3.23, p>0.1). during the summer months when the population of champaignurbana is significantly reduced by the absence of students there were 57 days with city festivals and 123 days without them. again the difference in mip cases was significant (mean=3.72 v 1.53, p<0.0001), whereas there was no statistical difference in number of dui citations (mean=0.631 v 0.642, p>.1) or hospital emergency department visits (mean=2.68 v 2.93, p>0.1). conclusions the results from this research have started looking at how alcohol effects the champaign county community. while minor in possession counts are significantly increased during home football games there does not seemed to be immediate danger to the community, considering driving under the influence and reported acute alcohol intoxication hospital visits were not significantly above background. it is interesting however that on preliminary analysis there is a significant increase in the number of dui citations during spring festivals. further work will seek to confirm the validity of these observations and develop predictive models of the number of alcohol related cases taking into account additional factors such as weather, attendance, and the team’s record. home football games keywords alcohol; mass gatherings; college; street festivals acknowledgments we would like to thank carle foundation, urbana police department, champaign police department, and university of illinois police department for sharing data and a special thanks to i3 for funding this project. references 1. 2015 fighting illini record book 2. w. edwards, a. vaid, and i. brooks, “indicator: an opensource cyberenvironment for biosurveillance,” in defining crisis management 3.0 *briana a. holliday e-mail: hollida2@illinois.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e120, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts evaluation of chlamydia and gonorrhea electronic provider reports data quality marion tseng*, natalie raketich and cristal simmons chicago department of public health, chicago, il, usa objective to describe the evaluation process to assess data quality during development of an electronic case report application, and to describe the evaluation results introduction electronic case reporting (ecr) is defined as the fully or semiautomated generation and electronic transmission of reportable disease case reports from an electronic health record (ehr) system to public health authorities, replacing the historically paper-based process1. ecr has been reported to increase the number, accuracy, completeness and timeliness of surveillance case reports2. chicago department of public health (cdph) collaborated with alliance of chicago (aoc) to develop an application to generate electronic provider reports (epr) for chlamydia (ct) and gonorrhea (gc) cases from the ehr system managed by aoc and send epr records to the illinois national electronic disease surveillance system (i-nedss). this application was tested in the ehr database of health center a in aoc’s network. it is essential to ensure epr data are accurate, so that public health receives correct information to take actions if needed. therefore, evaluation is needed to assess epr records data quality. methods cdph developed a five step evaluation plan to validate epr records data quality. step 1 was to validate the epr file format to ensure all i-nedss required fields are present, required value sets were used, and file format did not vary across files generated. step 2 was to validate the algorithm accuracy. chart review was conducted to ensure the epr records do not include non-reportable cases. step 3 was to review epr records loaded in i-nedss to make sure all values in epr raw files appeared correctly on the i-nedss front end. after the application passed steps 1 to 3, it moved to step 4, parallel validation. the first phase of parallel validation was to review historic cases. test epr records for ct and gc cases diagnosed by health center a in 2015 (n=510) were compared to the same 510 cases’ closed surveillance case reports in i-nedss. the completeness of treatment, race, and ethnicity was examined. the application then moved into testing daily data feed. daily epr records were compared with ehr charts and paper provider reports received by cdph to assess completeness and timeliness. step 5 was to re-evaluate algorithms. epr records were validated against the electronic laboratory reports (elr) records, which were used as gold standards of all reportable ct and gc cases, to find missing cases. results the first three steps of evaluation occurred from january to april 2016. test epr files containing historic cases from health center a were vetted weekly. a total of 14 test epr files were reviewed. this process identified required fields not present (patient address, treatment date, treatment, and race), race value sets not returned correctly, and additional logic statements needed to return correct pregnancy status at the time of diagnosis. these issues were discussed with the project team, and the application was modified accordingly. the historic case review found epr data were more complete than closed surveillance reports. compared to closed surveillance reports in i-nedss, 18% (94/510) of the cases had incomplete treatment information in the epr records compared to 78% (400/510), 0.2% (1/510) of the cases did not have race information in the epr records compared to 47% (240/510), and 0.7% (4/510) of the cases had no ethnicity information in the epr records compared to 50% (253/510). these preliminary evaluation results suggest that ecr improves surveillance case reports data quality. the daily data feed data quality evaluation is still on-going, and epr data quality will be monitored continuously. conclusions evaluation plays an integral role in developing and implementing the ecr process in chicago. the stepwise evaluation process ensures epr data quality meeting public health requirements, so that public health will be able to act on more complete information to improve population health. keywords ecr; data quality; evaluation acknowledgments the authors would like to thank the project team, cdph, aoc, s&f software solutions, and illinois department of public health. references 1.16-si-02: electronic case reporting (ecr). http://c.ymcdn.com/sites/ www.cste.org/resource/resmgr/2016ps/16_si_02.pdf 2.birkhead g, klompas m, shah n. public health surveillance using electronic health records: rising potential to advance public health. front public health serv sys res 2015 sep; 4(5):25–32. *marion tseng e-mail: marion.tseng@cityofchicago.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e38, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku nigeria field epidemiology and laboratory training programme, abuja, nigeria objective to describe the socio-demographic characteristics of ebola virus disease (evd) patients and their contacts, magnitude of the outbreak and factors associated with outcome in patients. introduction west africa recently experienced the most persistent epidemic of evd recorded in history. the reported morbidity and mortality of the highly virulent, emerging zoonotic filovirus infection is far larger than all previous epidemics combined1. its spread to nigeria (africa’s most populous country) and to densely populated lagos (a city in nigeria with approximate combined population of guinea, sierra leone, and liberia) raised significant public health concern2. the federal ministry of health was notified of a suspected case of viral haemorrhagic fever on the 22nd july, 2014. a 40-year old male liberian presented in a private health facility on account of fever, vomiting and diarrhea. on the 23rd july, the index case was confirmed to have evd and on 25th july, he died. methods an emergency operation centre was set up on 25th july, 2014. we adapted case and contact definitions from the world health organization (who) guidelines. active case search in health facilities, communities, religious centers and various ports of entry into the country was done by well-trained epidemiologists and surveillance officers. we traced evd contacts by contact identification, listing and active follow-up; taking daily temperature measurements and monitoring of symptoms to identify suspected cases. laboratory confirmation of cases was done by reverse transcriptase-polymerase chain reaction. prompt case management was commenced for positive cases with strict adherence to infection prevention and control measures. data on socio-demographic characteristics, clinical symptoms and disease outcomes was collected using contact listing, contact follow-up, case listing forms and open data kit (odk) for real time data collation. univariate and bivariate analysis was done. results fourteen out of the 16 cases identified (87.5%) were contacts under follow-up. from these cases, 293 contacts were identified. none of the contacts was lost to follow-up. the mean age of cases and contacts were 39.1 ± 11.9 years and 33.2 ± 13.9 years respectively. majority of the cases (43.8%) and contacts (33.0%) were aged 30 – 39 years. of the 16 cases, 8 (50.0%) were females and 10 (62.5%) were health workers. of the 293 contacts 150 (52.0%) were females and 138(47.0%) were health workers. of the 15 cases traced to the index case, 13 (86.7%) were first generation cases while 2(13.3%) were second generation cases. the case reproduction number for the outbreak was 13 and the secondary attack rate was 4.4%. the most reported symptom was fever (85.7%). case fatality rate (cfr) among all the cases was 37.5% (6/16), while cfr among health workers was 40.0% (4/10). health workers proportional mortality rate was 66.7% (4/6). the epidemic curve revealed a common source pattern at the early phase and a propagated pattern thereafter. average duration of illness from onset of symptoms to survival and death was 16 ± 4.8 days and 11.6 ± 5.0 days respectively. there were no statistically significant association between the duration of illness and the outcome of disease (odds ratio (or) = 1.14, 95% ci = 0.08 16.95), and between occupation of cases (health workers versus non-health workers) and outcome (or = 1.33, ci = 0.16 11.08) conclusions health care workers and the active age group were more affected during the outbreak. the outbreak was contained through effective contact tracing and surveillance. duration of illness and occupation were not significantly associated with outcome figure: epidemic curve of evd in lagos state, july to september, 2014 keywords ebola virus disease; outbreak investigation; disease outcome; lagos state references 1.bruce aylward, philippe barboza, luke bawo, b.pharm, eric bertherat, pepe bilivogui, isobel blake, rick brennan. ebola virus disease in west africa — the first 9 months of the epidemic and forward projections. n engl j med 2014; 371(16):1481-95 2.anthony s. fauci. ebola — underscoring the global disparities in health care resources. n engl j med 371;12 nejm.org september 18, 2014 *folasade f. osundina e-mail: sosundina@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e28, 2016 assessing 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within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts estimating spatial patterning of dietary behaviors using grocery transaction data hiroshi mamiya*, erica moodie and david buckeridge epidemiology, biostatistics, and occupational health, mcgill univeristy, montreal, qc, canada objective to demonstrate a method for estimating neighborhood food selection with secondary use of digital marketing data; grocery transaction records and retail business registry. introduction unhealthy diet is becoming the most important preventable cause of chronic disease burden (1). dietary patterns vary across neighborhoods as a function of policy, marketing, social support, economy, and the commercial food environment (2). assessment of community-specific response to these socio-ecological factors is critical for the development and evaluation policy interventions and identification of nutrition inequality. mass administration of dietary surveys is impractical and prohibitory expensive, and surveys typically fail to address variation of food selection at high geographic resolution. marketing companies such as the nielsen cooperation continuously collect and centralize scanned grocery transaction records from a geographically representative sample of retail food outlets to guide product promotions. these data can be harnessed to develop a model for the demand of specific foods using store and neighborhood attributes, providing a rich and detailed picture of the “foodscape” in an urban environment. in this study, we generated a spatial profile of food selection from estimated sales in food outlets in the census metropolitan area (cma) of montreal, canada, using regular carbonated soft drinks (i.e. non-diet soda) as an initial example. methods from the nielsen cooperation, we obtained weekly grocery transaction data generated by a sample of 86 grocery stores and 42 pharmacies in the montreal cma in 2012. extracted store-specific soda sales were standardized to a single serving size (240ml) and averaged across 52 weeks, resulting in 128 data points. using linear regression, natural log-transformed soda sales were modelled as a function of store type (grocery vs. pharmacies), chain identification code and socio-demographic attributes of store neighborhood, which are median family income, proportion of individuals who received post-secondary diplomas, and population density as measured by the 2011 canadian household survey. selection of the predictors and first-order interaction terms was guided by the minimization of the mean squared error using 10-fold cross-validation. the final model was applied to all operating chain grocery stores and pharmacies in 2012 (n=980) recorded in a comprehensive and commonly available business establishment database. the resulting predicted storespecific weekly average soda sales was spatially interpolated to provide a graphical representation of the soda sales (representing an unhealthy foodscape) across the montreal cma. results figure 2 demonstrates the spatial distribution of the predicted soda sales in the montreal cma. conclusions the current lack of neighborhood-level dietary surveillance impedes effective public health actions aimed at encouraging healthy food selection and subsequent reduction of chronic illness. our method leverages existing grocery transaction data and store location information to address the gap in population monitoring of nutrition status and urban foodscapes. future applications of our methodology to other store types (e.g. convenience stores) and food products across multiple time points (e.g. mouths and years) will permit a comprehensive, timely and automated assessment of dietary trends, identification of neighborhoods in special dietary needs, development of tailored community health promotions, and the measurement of neighbourhood-specific response to nutrition policies and unhealthy food advertising. figure 1: schematic representation of the process generating spatial food selection measure using grocery transaction data and business establishment database figure2: predicted weekly sales of soda in the montreal cma in 2012. spatial interpolation was performed on the point quantities of predicted sales at each store. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e131, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords chronic disease; nutrition; spatial anlaysis; transaction data; prediction acknowledgments we thank dr. luc de montigny for providing location-based business registry. references 1. institute for health metrics and evaluation. global burden of disease (gbd) united states [internet]. global burden of disease (gbd) country profile. [cited 2016 sep 9]. available from: http://www. healthdata.org/united-states 2. richard l, gauvin l, raine k. ecological models revisited: their uses and evolution in health promotion over two decades. annual review of public health. 2011;32(1):307–26. *hiroshi mamiya e-mail: hiroshi.mamiya@mail.mcgill.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e131, 2017 isds16_abstracts-final 120 isds16_abstracts-final 121 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 1cdc contractor, inductivehealth informatics, atlanta, ga, usa; 2arizona department of health services, phoenix, az, usa objective this session will inform the biosense community about data validation advancements implemented this past year as well as future plans to improve the biosense validation process to achieve emergency department representativeness goals. introduction one of the greatest hurdles for biosense onboarding is the process of validating data received to ensure it contains data elements of interest (deoi) needed for syndromic surveillance. efforts to automate this process are critical to meet existing and future demands for facility onboarding requests as well as provide a foundation for data quality assurance efforts. by automating the validation process, biosense hopes to: 1. reduce costs associated with the iterative validation process. 2. improve biosense response times for assistance with onboarding. 3. improve documentation to partners about requirements and communicate changes to deoi. 4. provide a better foundation for data quality initiatives. efforts to improve data validation are being developed in alignment with biosense future initiatives and will apply to both biosense, essence and other biosense program applications. biosense onboarding identified critical success factors by participating in isds workgroup initiatives for onboarding and data quality and soliciting feedback from key jurisdictional partners. these critical success factors include; improved documentation, access to raw data, and faster validation response time. description panelists will review the critical success factors and discuss the improvements and advancements made to the onboarding validation process. issues discussed will include: documentation data access validation response time documentation improvements include the release of the first biosense onboarding implementation guide and a new biosense data validation compliance report and facility approval tracking report. data access is important to jurisdiction administrators so that they can perform initial quality assurance during onboarding, assess continuous facility reporting, and perform continuous data quality initiatives. biosense onboarding has advanced this initiative by working to provide jurisdiction administrators with direct access to raw data within their jurisdictional sftp folders as well as providing access to stage_1 database tables which hold the raw form of preprocessed deoi data values that are extracted from the hl7 file. validation response time improvements include a new helpdesk portal to better track requests and ensure quick response times. additionally, the newly developed data validation compliance report provides jurisdiction administrators and developers with better feedback about deoi compliance rates to enable faster issue resolution. further improvements are being planned to include sets of automated sql validation queries, validation description information, as well as proposed plans to create a portal to perform self-validation utilizing these tools thereby providing immediate validation feedback. audience engagement audience participation will focus on discussion of the continued improvements to biosense data validation process and how they impact jurisdictional onboarding initiatives. feedback will be used to help facilitate the continuous improvement initiatives for the biosense onboarding community. keywords nssp; onboarding; validation; automation references 1. http://www.cdc.gov/phin/phinguides 2. biosense onboarding implementation guide 3. biosense data validation compliance report 4. biosense facility approval tracking report online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e3, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts exploring the distribution of coccidioides immitis in south central washington state wayne clifford* department of health, washington state, olympia, wa, usa objective our objective is to describe the environmental conditions associated with confirmed coccidioides immitis growth and accumulation sites in south central washington in an effort to understand the ecology and identify additional potential sites across this emerging endemic zone. introduction coccidioidomycosis, commonly referred to as valley fever, is caused by the soil-borne saprophytic fungus c. immitis and posadasii. these species have historically been found in the desert southwest and mexico; however, in 2010 there were three coccidioidomycosis cases identified in central washington. colonization of soils by c. immitis has been confirmed at exposure sites associated with these cases1. multiple studies have identified a relationship between environmental conditions and c. immitis growth areas2,3,4, but these relationships have not been evaluated in washington. the washington state department of health has been conducting environmental surveillance in an effort to understand the geographic distribution of c. immitis in central washington and the associated risk to humans and animals. here we describe our environmental surveillance efforts and present preliminary findings related to environmental conditions of c. immitis growth areas in central washington. methods we collected soil samples at potential human exposure sites in central washington, as identified through clinical surveillance and patient interviews. soil samples were also collected from areas not associated with human cases by looking for similar soils in areas of interest soil samples are analyzed by the u.s. centers for disease control and prevention using real-time pcr that detects coccidioides-specific targets. we employed data from the usda soil survey geographic (ssurgo) database to describe environmental conditions associated with positive samples. we used our findings to identify un-sampled regions of central washington that could potentially support c. immitis growth. results we detected coccidioides in 13 soil sampling sites at five locations withing the region. these detections included locations not previously described in central washington. we identified a band stretching across central yakima and benton counties with similar soil characteristics to our positive sample sites, which suggests these regions could potentially support the growth of c. immitis. conclusions coccidioidomycosis is emerging in south central washington, and the ecology and geographic distribution of the pathogen are poorly understood. we found that c. immitis presents a risk to humans and animals across a larger region of central washington than previously described and highlights a need for continued environmental surveillance. the potential growth sites we identified also provide a valuable tool for human and veterinary health care providers and public health practitioners to understand and mitigate disease risk. keywords valley fever; coccidioides; spatial analysis; environmental surveillance acknowledgments centers for disease control and prevention, mycotic disease branch, atlanta, georgia. united states geological survey, st. petersburg, florida references 1. litvintseva, anastasia p, nicola marsden-haug, steven hurst, heather hill, lalitha gade, elizabeth m driebe, cindy ralston, chandler roe, bridget m barker, and marcia goldoft. 2015. “valley fever: finding new places for an old disease: coccidioides immitis found in washington state soil associated with recent human infection.” clinical infectious diseases 60 (1): e1–3.. baptista rosas, raul c, alejandro hinojosa, and meritxell riquelme. 2007. “ecological niche modeling of coccidioides spp. in western north american deserts.” annals of the new york academy of sciences 1111 (1): 35–46. 2. baptista rosas, raul c, alejandro hinojosa, and meritxell riquelme. 2007. “ecological niche modeling of coccidioides spp. in western north american deserts.” annals of the new york academy of sciences 1111 (1): 35–46. 3. fisher, frederick s, mark w bultman, suzanne m johnson, demosthenes pappagianis, and erik zaborsky. 2007. “coccidioides niches and habitat parameters in the southwestern united states.” annals of the new york academy of sciences 1111 (1): 47–72. 4. lauer, antje, jorge talamantes, laura rosío castañón olivares, luis jaime medina, joe daryl hugo baal, kayla casimiro, natasha shroff, and kirt w emery. 2014. “combining forces-the use of landsat tm satellite imagery, soil parameter information, and multiplex pcr to detect coccidioides immitis growth sites in kern county, california.” plos one 9 (11): e111921. *wayne clifford e-mail: wayne.clifford@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e10, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts epicore: crowdsourcing health professionals to verify disease outbreaks jennifer m. olsen* skoll global threats fund, san francisco, ca, usa introduction epicore draws on the knowledge of a global community of human, animal, and environmental health professionals to verify information on disease outbreaks in their geographic regions. by using innovative surveillance techniques and crowdsourcing these experts, epicore enables faster global outbreak detection, verification, and reporting. methods through a secure online platform, members are able to easily and quickly provide local information to expedite outbreak verification. epicore volunteer applications are vetted to ensure that they possess the public health and epidemiologic expertise necessary to contribute to the platform. results epicore currently has over 1600 members that span 135 countries. during the first 8 months of epicore’s launch, 172 requests for information to volunteers have been posted with an average response rate of over 80%. conclusions with its geographical distribution of members and high response rate, epicore is poised to enable the world to verify potential outbreak signals faster. by improving situational awareness, de-escalating rumors or false information, and corroborating using other existing sources, epicore is able to reduce the signal to noise ratio in disease surveillance. hence, by detecting and verifying outbreaks faster, health officials can generate early responses that can curb epidemics and save lives. keywords disease verification; crowdsourcing; online platform acknowledgments epicore was initiated as a partnership between the skoll global threats fund, healthmap, promed, and tephinet. *jennifer m. olsen e-mail: jolsen@skollglobalthreats.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e176, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts the importance of age-specific data in routine syndromic surveillance roger morbey*, alex j. elliot and gillian e. smith public health england, birmingham, united kingdom objective to investigate whether aberration detection methods for syndromic surveillance would be more useful if data were stratified by age band. introduction when monitoring public health incidents using syndromic surveillance systems, public health england (phe) uses the age of the presenting patient as a key indicator to further assess the severity, impact of the incident, and to provide intelligence on the likely cause. however the age distribution of cases is usually not considered until after unusual activity has been identified in the allages population data. we assessed whether monitoring specific age groups contemporaneously could improve the timeliness, specificity and sensitivity of public health surveillance. methods first, we examined a wide range of health indicators from the phe syndromic surveillance systems to identify for further study those with the greatest seasonal variation in the age distribution of cases. secondly, we examined the identified indicators to ascertain whether any age bands consistently lagged behind other age bands. finally, we applied outbreak detection methods retrospectively to age specific data, identifying periods of increased activity that were only detected or detected earlier when age-specific surveillance was used. results seasonal increases in respiratory indicators occurred first in younger age groups, with increases in children under 5 providing early warning of subsequent increases occurring in older age groups. also, we found age specific indicators improved the specificity of surveillance using indicators relating to respiratory and eye problems; identifying unusual activity that was less apparent in the all-ages population. conclusions routine surveillance of respiratory indicators in young children would have provided early warning of increases in older age groups, where the burden on health care usage, e.g. hospital admissions, is greatest. furthermore this cross-correlation between ages occurred consistently even though the age distribution of the burden of respiratory cases varied between seasons. age specific surveillance can improve sensitivity of outbreak detection although all-age surveillance remains more powerful when case numbers are low. keywords syndromic; surveillance; age acknowledgments the authors are partly funded by the national institute for health research (nihr) health protection research unit in emergency preparedness and response. the views expressed are those of the authors and not necessarily those of the national health service, the nihr, the department of health or public health england. *roger morbey e-mail: roger.morbey@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e24, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* tennessee department of health, nashville, tn, usa objective to objectively compare the biosense and essence fever syndromes using recorded temperature as a gold standard. introduction syndromic surveillance refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. tennessee obtains emergency department data for syndromic surveillance in standardized hl7 format following the field and value set standards published by the public health information network. messages contain information previously unavailable to syndromic surveillance systems, including quantitative values such as recorded temperature. data are received daily and processed by a tennessee essence application and the national biosense platform. these systems use chief complaint keywords, icd9 codes, and other algorithms to assign syndromes for each record. the differences between the biosense and essence syndrome assignments have not been well defined. detailed comparisons of syndrome assignment across tools are difficult to perform due to the intensity of the manual review required. however, definitions of fever can be easily confirmed in hl7 messages when the recorded temperature is provided. currently, both the biosense and essence syndrome definitions exclude recorded temperature from consideration when assigning syndromes. to compare the performance of the fever syndromes used by biosense and essence, recorded temperature data was used as the gold standard. methods emergency department data from five memphis area hospitals during 7/1/14 – 6/30/15 were used for this analysis. biosense data were queried and extracted using the system supplied version of rstudio and analyzed using sas 9.4. a “visit” was defined a record with a unique combination of facility and the biosense created variable “analysis visit id”. data lacking a syndrome assignment were excluded from the analysis. temperature was determined by taking the maximum recorded temperature (tmax) across all updates for each record. essence overwrites records with each update, so recorded temperature at the last update was used as tmax for this evaluation. a “true” fever was defined as any record with a tmax of greater than 100 degrees fahrenheit. sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the fever syndrome in biosense and essence. results there were 326,966 records assigned to a syndrome with recorded temperature identified in the biosense data (18,744 observations were missing temperature) and 319,246 records identified in the essence data (27,237 missing temperature). sensitivity and specificity are plotted month by month in figure 1. positive predictive value and negative predictive value were not substantially different. conclusions the biosense fever syndrome and the essence fever syndrome performed similarly. essence had higher sensitivity compared with the biosense fever syndrome throughout the study period. there were some discrepancies in both data sets. the small difference in total visit numbers across both systems is likely due to additional processing rules in the biosense platform. also, essence had a larger number of observations missing temperature, likely due to record updates overwriting existing temperature data with missing data. theoretically, update messages sent via hl7 should contain all information sent previously, but this may not be true for recorded temperature data. additional investigation is needed to see if the overwrites are true corrections by the submitting facility or attributable to another source. recorded temperature adds value to syndromic surveillance practice when sent consistently. including recorded temperature data as a queryable variable would increase the sensitivity of the fever syndromes in both essence and biosense. figure 1: sensitivity and specificity of the biosense and essence fever syndromes from july 2014 through june 2015 keywords biosense; essence; fever; syndrome *caleb wiedeman e-mail: 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olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts wikipedia: a tool to monitor seasonal diseases trends? pascal vilain*1, sophie larrieu1, sébastien cossin3, céline caserio-schönemann2 and laurent filleul1 1french national public health agency, regional unit (cire) océan indien, saint-denis, réunion; 2french national public health agency, paris, france; 3french national institute of health and medical research, unit 1219, bordeaux, france objective to explore the interest of wikipedia as a data source to monitor seasonal diseases trends in metropolitan france. introduction today, internet, especially wikipedia, is an important part of everyday life. people can notably use this popular free online encyclopedia to search health-related information. recent studies showed that wikipedia data can be used to monitor and to forecast influenza-like illnesses in near real time in the united states [1,2]. we carried out a study to explore whether french wikipedia data allow to monitor the trends of five seasonal diseases in metropolitan france: influenza-like illness, gastroenteritis, bronchiolitis, chickenpox and asthma. methods to collect wikipedia data, we used two free web applications (https://stats.grok.se and https://tools.wmflabs.org/pageviews), which aggregate daily views for each french entry of the encyclopedia. as some articles have several entries (redirects), we collected view statistics for all the article entries and added them to make time series from january 1st, 2009 to june 30, 2016 (figure 1). then, we compared these data to those of oscour® network, which is a robust national surveillance system based on the emergency departments. for each disease, we modelized daily variations in wikipedia views according to daily visits in ed using poisson regression models allowing for overdispersion. the following adjustment variables were included in the model: long-term trend, seasonality, day of the week. we tested several lags (day-7 to day+7) in order to explore whether one of the two indicators (wikipedia view or ed visits) varied earlier than the other. results the mean number of daily views was 764 [16-8271] for influenzalike illness, 202 [6-1660] for bronchiolitis, 1228 [59-10030] for gastroenteritis, 475 [21-2729] for asthma and 879 [25-4081] for chickenpox. times series analyses showed a positive association between page views and ed visits for each seasonal disease (figure 2). for each increase in 100 wikipedia views, the number of ed visits the same day increased by 2.9% (95% ci=[2.5-3.3]) for influenza, 1.8 (95% ci=[1.4-2.2]) for bronchiolitis, 2.4% (95% ci=[2.2-2.7]) for gastroenteritis, 1.4% (95% ci=[1.0-1.7]) for asthma and 2.9% (95% ci=[1.7-4.1]) for chickenpox. globally, the highest relative risks were observed for lag-1 (day-1) to lag0. conclusions this study allowed to show that french wikipedia data can be useful to monitor the trends of seasonal diseases. indeed, they were significantly associated with data from a robust surveillance system, with a maximum lag of one day. wikipedia can therefore be considered as an interesting complementary data source, notably when traditional surveillance systems are not available in real time. further works will be necessary to elaborate forecasting models for these seasonal diseases. figure1. daily number of page views and ed visits for seasonal dieases, january 1st, 2009 to june 30, 2016 figure2. relative risk between wikipedia page views and ed visits for seasonal diseases by several lags keywords wikipedia; surveillance; seasonal dieases references [1] mciver dj, brownstein js. wikipedia usage estimates prevalence of influenza-like illness in the united states in near real-time. plos comput biol. 2014;10(4):e1003581. [2] hickmann ks, fairchild g, priedhorsky r, generous n, hyman jm, deshpande a, del valle sy. forecasting the 2013-2014 influenza season using wikipedia. plos comput biol. 2015;11(5):e1004239. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e52, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts activity of natural tularemia foci in west ukraine oksana velychko*, liliia vasiunets, oksana semenyshyn and lesya hasiy laboratory of edp, si “lviv oblast laboratory center of moh of ukraine”, lviv, ukraine objective stady the activity of natural foci of tularemia and identify the main types of reservoirs and vectors of francisella tularensis. introduction annually sporadic cases of tularemia in humans are registered in ukraine and new enzootic areas are found. monitoring of tularemia natural foci is important given the potential significant financial losses in case of tularemia outbreaks and taken into account that this pathogen can be used as a bioterrorist agent. methods 1. light microscopy of smears of organs and tissues of animals, bacterial suspension (gram staining) the study of morphological and tinctorial properties of the pathogen. 2. immunofluorescence method for detection of antibody (ifa) detection of tularemia bacterial cells using specific fluorescent immunoglobulin. 3. biological method subcutaneous infection of laboratory animals (white mice) with material from environmental samples and bacterial suspension (for accumulation of tularemia agent in organs and tissues of laboratory animals). 4. bacteriological method inoculation of samples of wild and laboratory animals in differential diagnostic nutrient media (for isolation of a pure culture of tularemia agent). 5. serological method: indirect reaction of agglutination detection of antibodies to tularemia agent in blood of humans, wild rodents (liquid tularemia antigen erythrocyte diagnostic agent). indirect reaction of agglutination detection of tularemia agent and its antigen in suspensions of organs, swabs of substrate from nests of rodents, pellets of birds (liquid tularemia immunoglobulin erythrocyte diagnostic agent). reaction of agglutination detection of tularemia agent and its antigen (dry tularemia diagnostic serum). results tularemia in lviv oblast has been studied for more than 40 years, 69 enzootic localities in 14 administrative districts have been registered. more than 200 cultures of francisella tularensis have been isolated, mostly from ticks (58.3%) and myomorphic rodents (24.5%), the rest from water, straw, other rodents, and patients. in 2012-2015, 210 suspected patients were studied for tularemia, negative results were obtained. 22,320 ticks, 1,810 myomorphic rodents, 282 water samples, 15 straw samples, and 3 bird nests were tested for tularemia. tularemia cultures have not been isolated bacteriologically over the last few years. pathogen circulation in natural foci was confirmed by immuno-serological studies of field material. antibodies to the pathogen were detected in 6.5% out of 630 samples from myomorphic rodents of seven species studied by indirect hemagglutination test. most of the positive results were obtained from the samples of striped field mouse (46.3%), red-backed vole (17.0%), and common vole (14.6%). francisella tularensis antigen was detected in 32 samples out of 14,600 ticks d. reticulatus collected in natural biotopes and in 8.9% out of 289 samples of pellet. conclusions no incidence registered in lviv oblast and difficulty of isolation of francisella tularensis cultures over the last years in other oblasts (the last one happened in 2006) may indicate the decrease of foci activity under the influence of anthropogenic and environmental factors or changes in parasitic systems. but there are some evidence of agent circulation in the oblast, so some precautions should be taken, especially considering the fact that there have been no specific preventive measures taken over the last years. keywords francisella tularensis; tularemia; natural foci; ticks; rodents acknowledgments 1.state sanitary-epidemiological service of ukraine 2.biological threat reduction program, cooperative biological engagement program (dod treat reduction agency, usa) 3.epidemiology department of o.o.bogomolets national medical university references 1. guidelines for laboratory diagnostic techniques in epizootologic survey of tularemia natural foci (no.28-6 / 23 dated 09/12/1983). 2. guidelines for search and study of the spread of tularemia by detecting antigen of the pathogen in pellets of birds and droppings of carnivorous mammals (1973). 3. practical guide for laboratory diagnosis of infectious diseases, moscow, meditsina shiko publishing house, 2009. 4. ucdcm information sheet as of 07/21/2010 no. 04.4-31/40/868 on epidemic and epizootic situation with zoonotic infections common for humans and animals (tularemia, anthrax, brucellosis, ornithosis, listeriosis) and methods of their prevention in ukraine. *oksana velychko e-mail: lab.oni.lviv@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e91, 2017 applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” arjun kumar bhattarai1, aein zarrin1, joon lee1 1. health data science lab, school of public health and health systems, university of waterloo, waterloo, ontario, canada *joon lee, health data science lab, school of public health and health systems, university of waterloo, waterloo, ontario, canada. joon.lee@uwaterloo.ca abstract objective: to investigate the public health domains, key informatics concepts, and information and communications technologies (icts) applied in articles that are tagged with the mesh term “public health informatics” and primarily focus on applying icts to public health. materials and methods: the mesh term “public health informatics” was searched on medline-pubmed. the results of the search were then screened in two steps in order to only include articles about applying icts to public health problems. first, articles were screened based on their titles and abstracts. second, a full-text review was conducted to ensure the relevance of the included articles. all articles were charted based on public health domain, information technology, article type, and informatics concept. results: 515 articles were included. communicable disease monitoring (n=235), public health policy and research (n=201), and public health awareness (n=85) constituted the majority of the articles. inconsistent results were found regarding the validity of syndromic surveillance and the effectiveness of phi integration within the healthcare systems. discussion: phi articles with an ict focus cover a wide range of themes. collectively, the included articles emphasized the need for further research in interoperability, data quality, appropriate data sources, accessible health information, and communication. the limitations of the study include:1) only one database was searched; 2) by using mesh tags as a selection criterion, phi articles without the “public health informatics” mesh term were excluded. conclusion: due to the multi-disciplinary nature of phi, mesh identifiers were not assigned consistently. current mesh-tagged articles indicate that a comprehensive approach is required to integrate phi into the healthcare system. keywords: public health informatics, population surveillance, health policy, environmental monitoring, public health abbreviations: public health informatics (phi), public health (ph) applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi introduction public health informatics (phi) is a discipline of health informatics that integrates information and communications technology (ict) and public health (ph) [1]. the field of informatics pursues collection, storage, retrieval, and analysis of data [2]; this type of manipulation of data enables informaticians to derive meaningful information regarding certain aspects of the data sources, such as trends and predictive models [2]. moreover, informatics allows users to access informative content more effectively and efficiently [2]. one important corollary of the definition of informatics above is that informatics may not necessarily involve any use of ict, although modern informatics tends to emphasize the role of ict. ph is defined as the organized efforts of society to keep people healthy and prevent injury [3]. ph involves a combination of programs, services, and policies that promote the health of the population [3]. to improve the efficiency and effectiveness of public health practice, as well as to make information-driven decisions, public health practitioners and researchers must be able to gather and analyze information in a timely yet reliable manner; moreover public health research and policy-making relies on tools that utilize such information to disseminate new knowledge [4]. aiming to accommodate both informatics and public health, phi could be a pathway to enhance public health practice. in other words, phi could be considered as a potential tool to facilitate public health goals, such as effective health monitoring and surveillance, enhanced decisionmaking, and improved population health. (2 & 5) although the term phi was first used in literature in 1995 [2], research studies focusing on phi proliferated in early 2000s; this may be attributed to several events in the 21st century, such as anthrax bio-terrorism in 2001 [1], alongside the rapid advent and innovation of modern icts [1]. this increase in the number and effectiveness of phi studies resulted in the creation of mesh term “public health informatics” in 2003 [6]. mesh term is a vocabulary used to facilitate the consistency of health research categorization and aims to enhance the availability of existing literature [6]. followed by a complete review of the titles, contents, and references of research studies, mesh terms are assigned to the group of articles that predominantly focus on the field or topic represented by the corresponding mesh identifier [6]. therefore, it is logical to expect that most phi research articles would be tagged with the mesh term “public health informatics”. however, it cannot be assumed that all phi articles would be tagged with the mesh term “public health informatics”, particularly any phi studies published prior to the introduction of the mesh term in 2003, not to mention that there could be a delay with mesh term tagging in practice. correspondence: joon lee, health data science lab, school of public health and health systems, university of waterloo, waterloo, ontario, canada. joon.lee@uwaterloo.ca doi: 10.5210/ojphi.v9i2.7985 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi dixon et al., have conducted a scoping review limited on the recent trends in global health and phi between 2012 and 2014; however, reviews that scope the field since the creation of its mesh term do not currently exist [7]. additionally, the present review is aiming to specifically focus on phi whereas dixon et al. primarily examined global health informatics. considering the criteria for mesh term assignment and the proliferation of phi in the past two decades, a scoping review of the articles tagged with the mesh term “public health informatics” allows an overview of the studies focused on phi. this overview will be used to map the literature to identify trends, potential areas of research, and knowledge gaps in mesh-tagged phi articles. in order to do so, sub-categories and different domains of phi should be investigated. thus, the objective of the present review study was to investigate the public health domains, key informatics concepts, and information technologies applied in articles tagged with the mesh term “public health informatics”. furthermore, we focused only on phi articles with a strong ict angle, to be in line with the direction of modern phi. methods the mesh term “public health informatics” was utilized for the initial search. as mesh terms are a unique feature of medline, only this database was used for this review. this literature search was followed by a two-step screening process (described below) to apply inclusion and exclusion criteria. the inclusion criteria were any article that applies, investigates, analyzes, describes, or assesses ict within the field of public health and was originally published in english. as a result, articles that did not utilize or discuss a direct application of icts to solve ph issues were excluded from the study. this exclusion criterion removed studies that only applied basic statistics to a health dataset, for example. any article that mainly dealt with other branches of health informatics such as biomedical, clinical, nursing, or consumer health informatics or any article published originally in a non-english language was excluded. initially, two researchers reviewed the title and abstract of each of the 1072 articles independently and labeled them as either “include” or “exclude”. studies that did not seem to fit either of the categories were labeled as “maybe”. moreover, if the title and abstract did not provide sufficient information on the content of the literature, the full text of the article was skimmed. note that those studies that were labeled as “exclude” or “include” by both researchers were excluded or included, respectively, prior to the second round of screening. in the second round of screening, the two reviewers met in person to reach consensus regarding the articles they disagreed on; most of which had multiple focuses in addition to phi. only the ones that primarily examined ict in public health were included, and the purpose of the deliberation between the reviewers was to decide whether this was indeed the case. phi was distinguished from other specialties of informatics, particularly bio-medical informatics, by its focus on wide range of preventive interventions within populations and government-based operative context [1]. following the meeting, the full-text of each included article was studied to ensure their eligibility for the present study. the second step of the literature search was “charting” the data; this method was used by ritchie and spencer to scope the gaps or common themes in certain bodies of knowledge in health research [8]. the full texts of the research articles that were labeled as “include” by both reviewers or discussed to be included in later meetings, were studied, analyzed, and summarized applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi in a chart in terms of their citation data, year of publication, article type, public health domain, ict, and informatics concept. public health domains consisted of 8 categories that would satisfy all the goals of public health [3]: communicable diseases monitoring, non-communicable diseases monitoring, emergency response, injury surveillance, natural disaster management, environmental health, public health awareness, and public health policy and research. the ict categories summarized common information technologies used/discussed in each article. the 10 ict categories include: detection/prediction (dp) algorithm, electronic registry, e-mail, geographic information system (gis), mass media, mobile phone, natural language processing (nlp), analytical software, landline telephone, and websites. the articles were also categorized in terms of the informatics concepts that were applied in the research study. the concepts were data collection, data storage, data analytics, data transmission, data security, information sharing and knowledge management, and interoperability. lastly article types were charted with the following 5 groups: qualitative, quantitative, mixed-studies, op-ed/commentaries, and literature reviews. it is important to note that op-eds/commentaries were included to adequately reflect the experts’ opinions on the make-up of the field. however, it is important to note that due to the subjective nature of op-eds/commentaries, they are substantially different from original methodological articles, such as qualitative, quantitative or mixed design studies. following charting the articles, the total numbers of articles in each category and the intersection between categories were tallied; in addition, research gaps were identified and discussed. since this was a literature review study, no research ethics approval was required. results the mesh term search resulted in 1072 publications on medline/pubmed on january 3rd, 2017. a total of 515 articles met the inclusion criteria and were included in the scoping review. figure 1 illustrates the procedure of the study selection. figure 2, and tables 1, 2, 3, and 4 represent the distributions of the articles in terms of year of publication, ph domain, article type, ict, and informatics concept, respectively. figure 2 indicates that there has been a consistent decline in the number of articles tagged with “public health informatics” mesh term since 2004. it is important to note that mesh terms are occasionally added to articles several years after their publication; therefore, the figure might underestimate the extent of mesh term usage in the recent years. the details of each ph domain in table 1 are described in the ensuing sections. table 2 indicates that about 49% of the reviewed articles are op-ed/commentaries. this large portion of op-eds/commentaries has two implications: 1) the rest of the results of this study should be interpreted with caution since opeds/commentaries do not directly represent research activity; and 2) nevertheless, they reflect the general interest level of the research community in phi, which is important to know. note that there was a substantial overlap between categories; therefore, the sum of the numbers in each figure/table may be larger than the total number of reviewed articles. table 3 illustrates the high usage of electronic registries (68.5% of the articles) and websites (47.6%) in the reviewed articles. applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi figures 3, 4, and 5 show the percentage distribution of the articles with respect to the article type, ict, and informatics concept respectively in each ph domain.. according to figure 3, oped/commentaries constitute the majority of article types in all ph domains except injury surveillance, which overlaps with quantitative studies the most. figure 4, once again emphasizes the importance of websites and electronic registries in all ph domains. moreover, high usage of gis in environmental health could be noted in figure 4. lastly, figure 5 illustrates how data security and knowledge management were not applied as much as other informatics concepts in the reviewed articles. figure 1: flow diagram for article selection applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi figure 2: article distribution in terms of year of publication table 1: distribution of articles across ph domains ph domain number of articles percentage of total number of articles (%) communicable diseases 232 45.0 non-communicable diseases 49 9.5 emergency response 10 1.9 injury surveillance 13 2.5 natural disaster management 9 1.7 environmental health 17 3.3 public health awareness 85 16.5 public health policy and research 201 38.9 applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi table 2: distribution of articles across article types article type number of articles percentage of total number of articles (%) quantitative studies 109 21.2 qualitative studies 131 25.4 mixed studies 12 2.3 op-eds/commentaries 252 48.9 review studies 11 2.1 table 3: distribution of articles across information and communication technologies ict discussed number of articles percentage of total number of articles (%) (%) detection prediction algorithm 118 22.9 electronic registries 353 68.5 email 44 8.5 gis 83 16.1 mass media 13 2.5 mobile phones 19 3.7 nlp 21 4.0 analytical software 67 13.0 landline telephone 45 8.7 websites 245 47.6 table 4: distribution of articles across informatics concepts informatics concept number of articles percentage of total number of articles (%) (%) knowledge management 16 3.1 data collection 229 44.4 applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi figure 3: distribution of articles in the intersection between each article type and ph domain data analytics 80 15.5 data retrieval 194 38.0 data security 15 2.9 data storage 71 13.8 data transmission 279 54.2 interoperability 132 25.6 information sharing 171 33.2 applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi figure 4: distribution of articles in the intersection between each ict and ph domain applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi figure 5: distribution of articles in the intersection between each informatics concept and ph the full reference list of all included articles and their categories are provided in the supplementary material. communicable diseases as the largest public health domain, with 232 articles, this category is divided into two subcategories: infectious disease monitoring and bio-terrorism surveillance. infectious disease monitoring constituted 202 papers. communication between clinical settings and ph units is the major theme in this sub-category. communication can be improved through enhancement of reporting systems, web-based disease monitoring, and implementation of technologies, such as gis and nlp. while syndromic surveillance using electronic repositories is suggested most frequently as a monitoring index, food-source monitoring, defense medical surveillance system (dmss), and web-based data query systems are other mechanisms that could be utilized for communicable disease monitoring. lack of internet-based open source query systems and enhancement of data quality in this area are described as the main limitations. about 40% (n= 94) of disease surveillance articles are focused on detecting outbreaks in a timely manner. interpretation of syndromic data with statistical techniques and dp algorithms applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi has been the dominant approach to detect such outbreaks. however, inconsistent results exist regarding the validity of syndromic surveillance as a detection tool. data from emergency departments (ed) is used as the main data source for outbreak detection; in addition, use of mobile phones, over-the-counter drug sales, and laboratory-based results are among other methods that have been used. about 20% (n=45) of the articles focused on bio-terrorism surveillance; the main difference between these studies and the rest of this ph domain is the military/political focus. 88% of bioterrorism surveillance papers were published before 2010. electronic data reporting is discussed in 45% (n=21) of the articles. while interoperability has been illustrated as a major issue in most of these reporting systems, standardization of information infrastructure through icd-9 and icd10 is described to be an effective solution to integrate bio-terrorism surveillance with health care systems. although estimates indicate that only about 25% of the affected individuals attend emergency units, the data derived from these departments have been the main source for the surveillance. furthermore, dp algorithm has been the main technique to integrate bio-terrorism data (discussed in about 30% of the articles). non-communicable disease monitoring this ph domain focuses on monitoring chronic diseases and non-infectious congenital complications, such as some developmental disabilities. from the 49 articles included in this category, 47% (n=23) highlight a certain aspect of disease reporting and chronic disease surveillance. 78% (n=18) of these studies receive their data from clinical or school settings. mother-child health has been another popular topic in this field. there are 2 articles that evaluate maternal and child health monitoring programs. these programs utilize various information technologies to enhance monitoring, such as implementing landline telephone-based surveillance for timely call response and using electronic registries to track mother and child health. two other studies discuss congenital anomaly surveillance systems [14,15]. low data quality is highlighted as a challenge and standardization of birth registries is proposed as a potential solution. six studies discuss the application of gis in chronic disease monitoring; the importance of mapping chronic diseases in terms of policy making was also discussed in all of these articles. application of mobile phones, media, and national health interview survey (nhis) in noncommunicable disease monitoring is identified as new mechanisms to implement adequate chronic disease reporting and monitoring. emergency response the 10 articles in this category aim to detect health emergencies in order to elicit an effective response in a timely manner. health monitoring through dispatch calls is discussed in three articles, where varying conclusions are made regarding the effectiveness of such health monitoring mechanisms. web-based emergency services are mentioned as a tool to evaluate the available healthcare services for patients, such as hospital beds vacancy. lastly, web-based monitoring is described as an emerging tool for health emergency response and monitoring. however, despite its recent public proliferation, no evaluations are made on the efficiency and accuracy of social media as an emergency response tool. applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi environmental health about half of the 17 articles in this public health domain (n=7) apply gis to create a geo-spatial monitoring system. examples of such systems include solap, internet-based gis, and early warning system. web-based databases, real-time electronic monitoring devices, and wireless communication are the main technologies that are integrated with gis. generally, the majority of the studies in this domain focus on the development of systems that could utilize the detection of environmental contaminations and organize the environmental data in an interoperable manner. current environmental health programs are also assessed in three quantitative studies in which state-level public health and environmental health specialists were surveyed. it was indicated that there is a need to enhance the state-federal interactions in the u.s. and that more environmental health data exchange is required. moreover, the validity of ed-based data as a predictive measure for environmental health complications, such as heat waves, was questioned. it is suggested that further assessments are required to enable environmental health programs to rely on emergency-based data. monitoring food-borne diseases, carbon monoxide detection, real-time surveillance, and implementation of new technologies, such as wearable devices that assess the air quality, are the upcoming topics in environmental health articles. injury surveillance there are a total of 13 articles in this domain. the studies are mostly focused on the development of trauma surveillance systems using injury registries. while the implementation of such injury surveillance systems in trauma units were highly recommended by 3 studies [16-18], another 2 questioned the feasibility of ed-based injury surveillance systems [19,20]. additionally, a mixed study was conducted to evaluate a chinese injury surveillance system based on the injury surveillance guideline developed by who and cdc-us 2001 [9]. it was shown that the accuracy of this system was about 80% [9]. 3 articles discussed occupational health hazard prevention by recommending the development of a reliable database that detects and stores occupational injuries. programs such as the national emergency alerting and response systems (nears) [10] and european union public health information network (euphin) [11] have also proposed integration of different health systems such as bioterrorism, traffic incidents and emergency response to improve the effectiveness of surveillance. natural disaster management 9 articles discussed the application of health informatics in management of natural disasters. among the 9 studies, emphasis on capacity building is the most common topic. as the health care information and management systems society (himss) database indicates, about 2,667 (92%) of the total of 2,877 healthcare organizations have wireless connection; however, the number of portable devices that could be used to manage and monitor natural disasters are less than 25% [12]. moreover, lack of coordination among stakeholders, the need for gis application in healthcare and the significance of cross validation of morbidity and mortality data in natural disasters is discussed by other studies. while gis and integrated data repositories could be useful in tackling natural disasters in developed countries, mobile phones are suggested as a promising tool for management of natural disasters in developing countries. above all, lack of interoperability is portrayed as one of the most significant limitations of current natural disaster management in both developed and developing countries. applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi public health awareness 85 articles are included in this ph domain. 61% (n=52) of these articles are focused on webbased information sharing. about 10 different web-based systems are demonstrated through which websites are used to spread health information in the population. the location of these developments is primarily in europe and north america. for instance, the european public health information and knowledge system (euphix) is a web-based system utilized to share public health-related information at the international, national, and regional level across the european union (eu) [13]. this open-source system is developed to inform politicians, policy makers, academics, media, and the general public regarding the current state of public health by providing measurements for public health indicators or demonstrating the on-going public health activities across the eu [13]. in addition, 4 qualitative studies evaluated the current effectiveness of such health information systems using focus groups and semi-structured interviews with ph staff and policy makers. although some of the content of these websites is thought to be helpful, the access is sub-optimal. it is suggested to implement a united web-portal that could be accessed easily by the public. lastly, integration of mobile devices, email, and mass media are proposed as promising methods to share health information with the general public. public health policy, system, and research all of the 201 studies of this domain have a dominant focus on using informatics to aid public health policy making and/or mechanisms that could assist researchers in their phi research. 49% (n=99) of the articles discuss systematic integration of phi as a significant sector of public health. most common methods for such development are efficient data reporting and monitoring systems (n=17 articles), application of ict in public health research (n=31), coherent phi architectural development (n=8), evaluation of usefulness, data quality, effectiveness of electronic databases (n=11), and implementation of electronic registry in both public health policy and research (n=22). 13% of the articles (n=26) illustrate potential mechanisms to enhance the implementation of phi research in public health policy-making. use of websites, surveying stakeholders’ opinions, building an inter-disciplinary infrastructure for phi research, and translating research to practice are among other significant mechanisms that are discussed in the reviewed articles. health data exchange is another aspect of public health policy and research, which is emphasized in 11% (n=23) of the articles. 7 of the 24 studies highlight the effectiveness of real time data sharing between clinical and public health units. moreover, 16 articles discuss potential data linkage systems that connect various jurisdictions of public health research and policy. lastly, web-based information sharing is another area of data linkage systems that works through open source databases. security and data quality of such systems are thought to be the main challenges to their global implementation. other topics in this public health domain include children health information, gis and public health research, clinical decisions support systems, and users’ knowledge and practice in application of ict in disease prevention. discussion while there are 1072 articles tagged with the mesh term “public health informatics”, only about half of them had a major focus on application of ict to public health (see figure 1). this applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi discrepancy indicates that: 1) mesh terms may not necessarily represent the corresponding field of study, possibly due to a lack of standardization and/or guidelines for mesh term assignment; and 2) phi sometimes does not necessarily imply use of ict. this finding also speaks to the interdisciplinary nature of health informatics. at least partially overlapping definitions of health informatics sub-fields complicate distinguishing various disciplines from each other. disease and injury surveillance, environmental monitoring, health promotion, and research/policy utilization were the main public health themes that were observed in the 515 reviewed papers. there have been numerous approaches for each of these fields and enhancements could be easily observed in many areas of phi. more comprehensive repositories, promotion of open-source databases and real-time monitoring facilities, emphasis on capacity building, quality assurance, and application of new technologies, such as gis and nlp, are some of such improvements. despite these advancements, there are several gaps that need to be filled in order to take full advantage of informatics in public health. the following points illustrate some of the observations made throughout this scoping review: 1. about 70% (n=353) of the articles focused on ehr and electronic repositories, which are becoming the dominant platform for collection, storage, and analysis of health data, particularly in the u.s. these health data have substantial research and public health value and could affect the healthcare system dramatically. however, there is a need to standardize the data in such repositories. considering the large volume of data that will be accumulated in the coming years, it will be virtually impossible to derive any valid interpretation from the repositories if the entered data does not follow a standardized structure. as a result, standardization of health data, such as using consistent icd codes, could enhance the usability of repositories in phi. 2. only 12% (n=5) of the bio-terrorism articles have been published since 2010. this dramatic decline in bio-terrorism studies could indicate a potential area of research in the functionality of current bio-terrorism surveillance systems that have not been assessed in the past 7 years. examples of such systems include biostorm & ears, nears, biosense, nrdm, and national bio-terrorism syndromic surveillance demonstration program. another possible interpretation of this trend could be a decline in tagging bio-terrorism surveillance studies with “public health informatics” mesh term. considering that bio-terrorism is one of the fundamental components of phi, this decline could indicate the lack of inclusivity and consistency of mesh term assignment. overall more research needs to be done to investigate such decline in more depth. 3. inconsistency in the adequacy of surveillance methodologies is highlighted many times in number of articles. for instance, various predictors are used for disease surveillance and emergency monitoring, such as syndromic surveillance, dispatch calls, otc drug sale, and school absenteeism. however, there are inconsistent results regarding the validity of most of these indexes. more research should be conducted to further investigate the validity of such predictors. in addition, data sources seem to be sub-optimal. although a large number of articles in communicable disease monitoring and injury surveillance used ed-based data, the validity of this data source is subpar due to the low attendance of patients to emergency departments. therefore, more research needs to be done on either applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi using a more comprehensive data source or identifying the most optimal combination of data sources. generally, considering the importance of surveillance systems in phi, optimizing data sources and predictor variables could be a significant contribution to the healthcare system. with more valid measurements and accurate surveillance systems, phi could establish a strong foundation in our healthcare system and could be implemented as a tool to optimize public health policy making and practice. 4. electronic health data exchange was emphasized several times throughout this scoping review. data and information sharing could help public health sectors communicate with one another and strengthen public health’s coherence. more commonly done through websites and internet-based reporting, electronic health data exchange has become a promising approach to increase phi’s interoperability. however, data privacy is an issue that needs to be ensured, particularly with large-scale health data. moreover, the quality of the data collected at clinical facilities is another major concern for stakeholders. in most clinical units, clinicians are in charge of entering and editing the health data into electronic repositories. as a result, the secondary use of the data solely depends on the clinicians’ understanding of research and policy-making. consequently, lack of health informatics training in the clinical sector, could lead to low quality data entry for phi research purposes. overall, phi is a multi-disciplinary field with promising ideas and complex challenges. there are a large number of stakeholders involved in public health, such as policy makers, clinicians, non-government organizations, and the public itself. this diversity in stakeholders makes decision-making and communication between the parties difficult. however, through the advantages of informatics, decision-making, disease surveillance, communication, and public health research could be enhanced as long as the stakeholders overcome related challenges, such as funding, interoperability and quality of data sources. limitations relying solely on mesh terms rather than searching keywords was a main limitation of this study. for instance, searching the keyword “public health informatics” in pubmed results in several articles that this study did not cover, such as “health informatics and the h1n1 pandemic [5], “design principles in the development of (public) health informatics infrastructures” [21], “an informatics framework to support surveillance system interoperability in minnesota” [22], “the centers of excellence in public health informatics: improving public health through innovation, collaboration, dissemination, and translation” [23], and “beyond information access: support for complex cognitive activities in public health informatics tools” [24]. hence, for a more comprehensive review study on the entire phi field, it would be necessary to expand search terms and inclusion criteria. another limitation of this scoping review was that only one database was searched. more relevant articles are likely also available in other databases. for example, the online journal of public health informatics (ojphi) has been publishing 3 issues a year since 2009. the ojphi also publishes the proceedings of the international society for disease surveillance (isds). while most of the papers published in ojphi directly address the application of icts to public applications of information and communications technologies to public health: a scoping review using the mesh term “public health informatics” online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e192, 2017 ojphi health practice and research, these papers are indexed in pubmed and are excluded from this study. future researchers must expand the search term to cover public health informatics papers published in other health services journals. lastly, sometimes mesh terms are assigned months to years after the publication of a research article. as a result, there is a chance that several studies were missed due to the fact that they were not assigned with a mesh term yet. conclusions in the past 20 years, phi has evolved to encompass numerous sub-categories, each of which focuses on a particular aspect of public health. ict is increasingly becoming the main focus of phi research and practice. furthermore, this diverse range of subcategories, including both the technological and public health aspects of phi, could be utilized comprehensively to assist policy makers and strengthen public health measures. overcoming the current challenges could facilitate this comprehensive integration of phi into the healthcare system. the current challenges include enhancement of interoperability, maintaining high data quality, appropriate data sources, accessible health information, and communication between stakeholders. further research in these areas could be an adequate starting point to tackle these challenges. acknowledgements aein zarrin and arjun kumar bhattarai both contributed equally to this research study. the authors would like to thank the university of waterloo for general research support. the authors also acknowledge the guidance of jackie stapleton and rebecca hutchinson, librarians at the university of waterloo, provided regarding the literature search. financial disclosure this work was supported by a discovery grant (rgpin-2014-04743) from the natural sciences and engineering research council of canada (nserc). akb was funded by the school of public health and health systems, university of waterloo. az was funded by undergraduate student research award from nserc as well as undergraduate research internship program at the university of 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non-communicable disease monitoring emergency response environmental health injury surveillance natural disaster management public health awareness public health policy, system, and research discussion limitations conclusions acknowledgements financial disclosure competing interests references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts progress towards rabies elimination from pemba island, southern tanzania kennedy s. lushasi*1, 2, sarah cleaveland2, joel j. changalucha1, 2, daniel haydon2, rudovick kazwala3, tiziana lembo2, msanif masoud4, mathew maziku5, geofrey mchau6, zacharia mtema1, kassim omar7, sambo maganga1, 2, kristyna rysava2 and katie hampson1, 2 1environmental health and ecological sciences, ifakara health institute, ifakara, tanzania, united republic of; 2glasgow university, glasgow, united kingdom; 3sokoine university of agriculture, morogoro, tanzania, united republic of; 4ministry of health, zanzibar, pemba, tanzania, united republic of; 5ministry of livestock and fisheries development, dar es salaam, tanzania, united republic of; 6ministry of health and social welfare, dar es salaam, tanzania, united republic of; 7ministry of livestock and fisheries, zanzibar, pemba, tanzania, united republic of objective using active surveillance approaches to investigate the transmission dynamics of rabies on pemba island and across southern tanzania, whilst a large-scale dog vaccination program was underway1, to gain a greater understanding of the dynamics of infection as the disease is driven towards elimination. introduction rabies is endemic in tanzania and has circulated on pemba island since the late 1990s. in 2010, an elimination programme was initiated in southern tanzania to demonstrate that human rabies deaths can be eliminated through mass dog vaccinations. we used active surveillance approaches2 to investigate the dynamics of rabies across the area where this programme was implemented. methods government census data and post-vaccination transects were used to estimate the dog population and coverages achieved by vaccination campaigns. routine surveillance of animal bite injuries using a mobile phone-based surveillance system3 and active contact tracing were used to identify animal rabies cases and human exposures. epidemic trees were constructed using spatiotemporal distances between cases and used to estimate the effective reproduction number (re). we examined factors affecting rabies incidence and transmission using generalized linear mixed models. results we estimated a small dog population of 4095 and low dog:human ratio on pemba (1:105). overall island-wide vaccination coverage increased from 16.8% in 2011 to 68.2% in 2014. we found a further 48 human exposures (343%), who either were not reported or did not obtain post exposure prophylaxes (pep). routine surveillance was found to detect less than 10% (~8.75%). there was a rapid decline in cases detected on pemba, from 42 before mass dog vaccinations were implemented in 2011, to 2 cases in 2014 (figures 1). since may 2014, no rabies cases have been detected. similarly, re declined from 1.02 to 0 and a significant relationship was found with rabies cases decreasing with increasing vaccination coverage (p = 0.013, figure 2). across seven other districts on the tanzanian mainland we also observed major declines in rabies cases with very few cases of rabies in dogs detected in 2016 (figure 3). conclusions we conclude that rabies has been eliminated from domestic dog populations on pemba over the five years since vaccination campaigns have been implemented. continued surveillance and investigations of any bite incidents are therefore needed to ensure any subsequent incursions are controlled and freedom from rabies is maintained. on the tanzanian mainland, it has taken longer to control rabies, however trajectories look promising with several districts close to eliminating the disease. however, detection of some wildlife cases in the last 12 months in these districts indicates the need to further investigate remaining foci and the role of wildlife in maintenance. suspect rabies cases in animals (black) and human exposures (red) on pemba island, from 2010 to 2015 estimated vaccination coverage of the dog population (shading) in villages on pemba, and suspected animal rabies cases (dots) since 2010 following each vaccination campaign from 2011 to date. darker shading corresponds to higher vaccination coverage online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e118, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts trajectories of all bite injuries reported to clinics through routine mobile phone-based surveillance (grey) and of rabies cases in dogs (black) and wildlife (red) detected through contact tracing keywords canine rabies; elimination; vaccination; dogs; pemba island acknowledgments we are grateful for the support provided by the staff of the animal and health departments of pemba and to the tanzania ministries of health and social welfare, and of livestock and fisheries development, who country office, and the bill and melinda gates foundation for support. this work was funded by the wellcome trust and ubs optimus foundation. references 1 who. the sixth meeting of the international coordinating group (icg) of the bill & melinda gates foundation–world health organization project on eliminating human and dog rabies. (durban, south africa, 2014). 2 hampson, k. et al. transmission dynamics and prospects for the elimination of canine rabies. plos biology 7, e1000053, doi:10.1371/ journal.pbio.1000053 (2009). 3 mtema, z. et al. mobile phones as surveillance tools: implementing and evaluating a large-scale intersectoral surveillance system for rabies in tanzania. plos med 13, doi:10.1371/journal.pmed.1002002 (2016). *kennedy s. lushasi e-mail: klushasi@ihi.or.tz online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e118, 2017 isds16_abstracts-final 113 isds16_abstracts-final 114 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e56, 2016 isds 2015 conference abstracts surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran goal, dublin, ireland objective describe the evolution of ebola virus disease (evd) surveillance from a largely reactive system structured primarily around responding to reports of illness and death, to one that was more methodical, proactive and comprehensive. introduction port loko district has had over 1400 confirmed evd cases during this outbreak. however, transmission declined rapidly in early 2015; by mid-april, a few weeks had passed with no known cases. simultaneously, reporting of sick persons had plummeted across the district and the number of deaths reported in some areas was fewer than expected. these circumstances signaled the need for the evd surveillance system to broaden its focus from using district surveillance officers (dsos) to respond to reports of ill and deceased persons (hereafter, “sick and death alerts”) to a more proactive and comprehensive system that relied strongly on community engagement and surveillance through existing structures such as peripheral health units (phus), schools and local authorities. while the involvement of local authorities and the community had been central to reporting suspected evd cases earlier in the outbreak, the decrease in alerts suggested that engagement was diminishing. the reopening of schools and reemergence of the primary healthcare system provided opportunities to decentralize surveillance and strengthen the involvement of these structures. the primary objective was improving evd surveillance, but the effort was also used to bolster routine surveillance, in preparation for implementating integrated disease surveillance and reporting. methods • goal, who and district staff developed a standard operating procedure to guide surveillance activities during periods of low evd transmission. • dsos received 5 sessions of training, in addition to mentoring in the field. • the district has 11 chiefdoms, divided into a total of 162 sections. a qualitative survey was developed for dsos to assess and validate the engagement of key partners such as local authorities, social mobilizers and contact tracers in surveillance-related activities in each section. • tools were used to assess surveillance and provide training at phus, schools and border areas. • expected mortality rates (pre-evd outbreak) were compared to the number of death alerts received. sections with the highest levels of under-reporting were prioritized. • phu staff were trained on the importance of using diagnostic tests for malaria, and following up on patients to ensure that no evd cases were missed due to incorrect diagnosis or co-infection. registers were used to record symptoms for all ill patients, as well as test results, treatment and follow up for those with malaria. weekly reports to the district of reportable diseases were checked for completeness. health screening for arriving clients and basic infection prevention and control (ipc) protocols were verified, as were knowledge of the case definition and reporting expectations for suspected evd. community engagement efforts were discussed. • dsos were trained to review trends in absenteeism at schools as an indicator of potential disease outbreaks. also, to ensure active surveillance and ipc were done correctly. • in villages bordering other districts, enhanced cross-border monitoring was used as an essential tool to minimize risk of evd infiltrating from other districts. results • dsos needed more training than anticipated to implement new practices. on-site mentoring was a critical supplement to formal training. • after initial difficulties adapting to new expectations for using the registers, documentation improved, which permitted monitoring the implementation of the protocol for malaria patients, as well as active case search for numerous conditions. • follow-up actions for symptomatic children were unclear to school staff. concerns that direct follow up by dsos might jeopardize school participation indicated that community-supported alternatives were crucial. • using attendance registers to look for disease trends was difficult since attendance hadn’t yet stabilized due to the outbreak itself. • use of expected vs reported deaths to prioritize surveillance efforts is now routine in the district. conclusions this system has been effective in guiding district surveillance activities. additional technology, data and staff have allowed its implementation to become more targeted. capacity for routine surveillance in the post-ebola period has improved. keywords surveillance; ebola; evd; sierra leone; community; malaria acknowledgments goal acknowledges the support of who, cdc, port loko district health managment team and the district ebola response team. *allison m. connolly e-mail: allison.connolly@thepalladiumgroup.com assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 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electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin 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kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 1kazakh scientific center for quarantine and zoonotic disease, almaty, kazakhstan; 2naval medical research center, silver spring, md, usa introduction flea-borne diseases in kazakhstan have been a significant health risk to inhabitants and visitors for ages, particularly plague. flea-borne rickettsial disease threats are unknown in kazakhstan, we therefore initiated a study to detect and identify flea-borne rickettsiae among fleas collected in the almaty oblast, in southeastern kazakhstan. methods fleas (n=248) were collected by members of the taldykorgan anti-plague station from live captured rodents (i.e. the great gerbilrhombomys opimus) and from the rodent burrows collected at five rayons (districts) within almaty oblast (province) during 2015. fleas were identified morphologically by entomologic keys and then pooled together (1-50 fleas/pool) by species and host/rodent burrow. dna was extracted from triturated fleas (prepman ultra kit) and tested by genus(rickettsia), group(r. felis genogroup), and species(rickettsia typhi, rickettsia felis and candidatus rickettsia asemboensis) specific quantitative real-time pcr (qpcr) assays, rick17b, rfelg, rtyph, and rasemb, respectively. with gps coordinates and gis (arcgis) a distribution map was developed. results of 248 fleas (coptopyslla lamellifer 45, echidnophaga oschanini 1, nosopsyllus laeviceps 10, nosopsyllus tarsus 1, nosopsyllus turkmenikus 1, paradoxophsyllus teretifrons 2, xenopsylla conformis 1, xenopsylla gerbilli 87, xenopsylla hirtipes 26, and xenopsylla skrjabini 74) assessed by qpcr 56 were identified as having: rickettsia spp. only n=20, r. felis genogroup n=8, r. felis n=1, and ca. r. asemboensis n=27. x. gerbilli was the flea most frequently found to be infected with a rickettsiae (44 of 87;50.6%) and 25 of the 44 rickettsia-infected fleas (56.8%) were infected by ca. r. asemboensis. x. hirtipes was the next most commonly infected flea (4 of 26; 15.4%). one flea was infected with r. felis, and none were infected with r. typhi. r. felis and r. typhi cause flea-borne spotted fever and murine typhus, respectively. conclusions fleas captured from r. opimus or at their burrows were infected with rickettsiae. most commonly found rickettsia-infected flea species was x. gerbilli and the most commonly found rickettsia was ca. r. asemboensis. future studies may include testing these and other fleas samples for the presence of other disease agents including bartonella spp. and yersinia pestis. keywords flea-borne disease; rickettsiae; kazakhstan acknowledgments the views expressed in presentation are those of the authors and do not necessarily represent the official policy or position of the department of the navy, department of defense, the u.s. government, or the henry m. jackson foundation. this project was funded by the defense threat reduction agency, work unit number a1266. alr and cmf are employees of the u.s. government and their work was prepared as part of their official duties. title 17 u.s.c. §105 provides that ‘copyright protection under this title is not available for any work of the united states government.’ title 17 u.s.c. §101 defines a u.s. government work as a work prepared by a military service member or employee of the u.s. government as part of that person’s official duties. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e148, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez 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del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new jersey department of health, trenton, nj, usa objective describe the inclusion of triage notes into a syndromic surveillance system to enhance population health surveillance activities. introduction in new jersey, real-time emergency department (ed) data are currently received from eds by health monitoring systems inc.’s (hms) epicenter, which collects, manages and analyzes ed registration data for syndromic surveillance, and provides alerts to state and local health departments for surveillance anomalies. epicenter receives pre-diagnostic chief complaint data from 78 of 80 acute care and satellite eds. the need for more specific information raises the possibility that other data elements from eds such as triage notes can be of utility in detecting outbreaks without a significant delay [1]. this study evaluates the inclusion of triage notes in epicenter to detect a recent increased usage of synthetic cannabinoids. at the time of this evaluation, three new jersey hospitals were providing triage notes in their epicenter data. methods in april 2015, the new jersey poison information and education system (njpies) reported an increase in calls to their center for consultation regarding synthetic cannabinoid reactions in ed patients. this increase in calls resembled an outbreak [2], so the department of health (doh) surveillance staff used the opportunity to compare chief complaints and triage notes to call data provided by njpies. njdoh created a custom classification in epicenter to detect synthetic cannabinoid-related ed visits using chief complaint data. doh staff included the keywords “black magic”, “black mamba”, “cloud 9”, “cloud 10”, “incense”, “k2”, “legal high”, “pot potpourri”, “spice”, “synthetic marijuana”, “voodoo doll”, “wicked x”, and “zombie” which were obtained from the new york city department of health and mental hygiene. staff also included the keywords, “agitation”, “k-2”, ”moon rocks”, “seizure”, ”skunk”, and ”yucatan” to characterize the related event. njdoh performed a text search comparison using the same keywords in the triage notes field from three eds currently providing that data to evaluate the synthetic cannabinoid related ed visits. results using the keywords, out of 50 njpies calls, 18 (36%) were identified via chief complaint data and 32 (64%) of the ed visits were not captured due to the non-specificity of the keywords used. among the 18 visits, the most common keywords were “seizure” and “marijuana”. of the 50 calls, 6 ed visits were admitted to hospitals that submitted triage notes data in epicenter. using the same keywords in a triage notes query, 5 (83 %) out of 6 ed visits were identified. the most common keywords were “k2” and “marijuana”. overall, based on the chief complaints and triage notes from these three eds (figure 1 and table 1), triage notes are able to provide more information about ed visits related to synthetic cannabinoid between march 27 and may 16, 2015. conclusions review of njpies synthetic cannabinoid calls suggests that triage notes in syndromic surveillance systems included more specific keywords than the chief complaints and captured most of the calls related to synthetic cannabinoid. triage notes inclusion has been initiated in new jersey. this new data source will provide vital information to syndromic surveillance, which is expected to lead to earlier detection and response to health events like ebola, enterovirus d68, and drug overdose surveillance. table 1: number of ed visits from 3 hospitals submitted both triage notes and chief complaint data between march 27, 2015 and may 16, 2015. triage notes captured more ed visits compared to chief complaints. figure 1: number of ed visits by data type from three hospitals submitting both triage notes and chief complaint data between march 27, 2016 and may 16, 2015. related to synthetic cannabinoid use, triage notes included more visits most days then cheif complaints. on some dates, cheif complaints included more ed visits. keywords syndromic surveillance; epicenter; new jersey acknowledgments bruce e. ruck, pharmd, rph (njpies), elizabeth kostial (hms), new york city department of health and mental hygiene. references 1. walsh a, fowler b. using triage notes to refine syndromic surveillance: an ebola case study. paper presented at: 2015 cste annual conference; 2015 may 14-18; boston, ma. 2. schwarz a. potent ‘spice’ drug fuels rise in visits to emergency room. the new york times.2015 apr 24.available from http:// www.nytimes.com/2015/04/25/health/surge-in-hospital-visits-linkedto-a-drug-called-spice-alarms-health-officials.html *pinar erdogdu e-mail: pinar.erdogdu@doh.state.nj.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e106, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of hepatitis c surveillance in washington state natalie linton* washington state department of health, shoreline, wa, usa objective to evaluate the surveillance system for hepatitis c virus in washington state using the centers for disease control and prevention guidelines for evaluating public health surveillance systems. based on the findings of the evaluation, recommendations will be made for changes in practice. introduction hepatitis c is a nationally notifiable viral infection that occurs as a result of parenteral contact with infected body fluids. an estimated 3.5 million persons are currently infected with hcv.1 infection status is divided into acute (short-term, onset within 6 month of exposure) and chronic (long-term). for most people (75-85%), acute hcv infection leads to chronic infection.2 those with chronic infection remain relatively asymptomatic until the infection becomes severe enough to be recognized or the infected individual is screened for infection with hepatitis c. major causes of morbidity and mortality associated with hcv are liver cirrhosis and hepatocellular carcinoma. treatment is available, but it is expensive and not recommended for some vulnerable populations, such as those with ongoing injection drug use (idu), who account for the majority of new hcv infections in the united states.3-5 washington state records cases of both acute and chronic hcv infection, but the system is fragmented. methods the evaluation will involve key informant interviews as well as review of data respositories such as the public health issue management system in order to assess the simplicity, flexibility, data quality, acceptability, sensitivity, positive predictive value, representativeness, timeliness, and stability of hcv surveillance in washington state. results the evaluation is currently in progress. preliminary results are expected by november 2015. conclusions the findings of this surveillance evaluation will inform the restructuring of other state and local hcv surveillance systems. improved surveillance and care can lead to reductions in the incidence of cirrhosis, hepatocellular carcinoma, and liver transplantation as well as prevent transmission. keywords surveillance evaluation; hepatitis c; hcv acknowledgments office of communicable disease epidemiology, washington state department of health, shoreline, wa; office of infectious disease, washington state department of health, tumwater, wa; scott lindquist, state epidemiologist, washington state department of health. references 1edlin br. towards a more accurate estimate of the prevalence of hepatitis c in the united states. hepatology. 2015 jul 14; doi: 10.1002/ hep.27978. [epub ahead of print] 2thomas dl, seeff lb. the natural history of hepatitis c. clin liver dis. 2005 aug;9(3):383-98, vi. 3mehta s, genberg b, astemborski j, et al. limited uptake of hepatitis c treatment among injection drug users. j community health 2008;33:126–33. 4hagan h, latka mh, campbell jv, et al. eligibility for treatment of hepatitis c virus infection among young injection drug users in 3 us cities. clin infect dis. 2006;42(5):669-67. 5grebely j, haire b, taylor le, et al. excluding people who use drugs or alcohol from access to hepatitis c treatments—is this fair, given the available data?. j hepatology. 2015 aug 4; doi: 10.1016/j. jhep.2015.06.014. 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surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois 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an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali 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di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david 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laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande 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county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts real-time surveillance for chronic conditions in massachusetts using ehr data noelle cocoros*1, john menchaca1 and michael klompas1, 2 1population medicine, harvard medical school, harvard pilgrim health care institute, boston, ma, usa; 2brigham and women’s hospital, boston, ma, usa objective to assess the feasibility of tracking the prevalence of chronic conditions at the state and community level over time using mdphnet, a distributed network for querying electronic health record systems introduction public health agencies and researchers have traditionally relied on the behavioral risk factor surveillance system (brfss) and similar tools for surveillance of non-reportable conditions. these tools are valuable but the data are delayed by more than a year, limited in scope, and based only on participant self-report. these characteristics limit the utility of traditional surveillance systems for program monitoring and impact assessments. automated surveillance using electronic health record (ehr) data has the potential to increase the efficiency, breadth, accuracy, and timeliness of surveillance. we sought to assess the feasibility and utility of public health surveillance for chronic diseases using ehr data using mdphnet. mdphnet is a distributed data network that allows the massachusetts department of public health to query participating practices’ ehr data for the purposes of public health surveillance (www.esphealth.org). practices retain the ability to approve queries on a case-by-case basis and the network is updated daily. methods we queried the quarterly prevalence of pediatric asthma, smoking, type 2 diabetes, obesity, overweight, and hypertension statewide and in 9 massachusetts communities between january 1, 2012 and july 1, 2016. we selected these 9 communities because they were participating in a state-funded initiative to decrease the prevalence of one or more of these conditions. conditions were defined using algorithms based upon vital signs, diagnosis codes, laboratory measures, prescriptions, and self-reported smoking status. eligible patients were those with at least 1 encounter of any kind within the 2 years preceding the start of each quarter. results were adjusted for age, sex, and race / ethnicity using the 2010 massachusetts census data. results surveillance data were available for 1.2 million people overall, approximately 20% of the state population. coverage varied by community with >28% coverage for 7 of the communities and 11% coverage in the eighth. the ninth community had only 2% coverage and was dropped from further analyses. the race / ethnicity distribution in mdphnet data was comparable to census data statewide and in most communities. queries for all six conditions successfully executed across the network for all time periods of interest. the prevalence of asthma among children under 10 yrs rose from 12% in january 2012 to 13% in july 2016. current smoking in adults age ≥20 rose from 14% in 2013 to 16% in 2016 (we excluded results from 2012 due to changes in documentation propelled by the introduction of meaningful use criteria). this is comparable to the 15% rate of smoking per brfss in 20141. obesity among adults increased slightly from 22% to 24% during the study period, results nearly identical to the most recent brfss results for massachusetts (23% in 2014 and 24% in 2015)2. the prevalence of each condition varied widely across the communities under study. for example, for the third quarter of 2016, the prevalence of asthma among children under 10 ranged from 5% to 23% depending on the community, the prevalence of smoking among adults ranged from 11% to 35%, and the prevalence of type 2 diabetes among adults ranged from 7% to 14%. we also examined differences in disease estimates by race / ethnicity. substantial racial / ethnic differences were evident for type 2 diabetes among adults, with whites having the lowest prevalence at 7% and blacks having the highest at 12% in the third quarter of 2016; this trend was consistent over the study period. conclusions our study demonstrates that mdphnet can provide the massachusetts department of public health with timely populationlevel estimates of chronic diseases for numerous conditions at both the state and community level. mdphnet surveillance provides prevalence estimates that align well with brfss and other traditional surveillance sources but is able to make surveillance more timely and more efficient with more geographical specificity compared to traditional surveillance systems. our ability to generate real-time time-series data supports the use of mdphnet as a source for project/ program evaluation. keywords asthma; smoking; obesity; chronic disease; real-time surveillance acknowledgments thank you to our colleagues at commonwealth informatics. references 1. massachusetts dept. of public health, 2015. a profile of health among massachusetts adults, 2014, results from the behavioral risk factor surveillance system, http://www.mass.gov/eohhs/docs/ dph/behavioral-risk/report-2014.pdf, accessed 9/6/2016. 2. robert wood johnson foundation, 2016. the state of obesity 2016. http://stateofobesity.org/states/ma/, accessed 9/6/2016. *noelle cocoros e-mail: noelle_cocoros@harvardpilgrim.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e66, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts icd-9 code reporting among patients from the minnesota sari surveillance program andrea steffens*1, hannah friedlander2, kathy como-sabetti2, dave boxrud2, sarah bistodeau2, anna strain2, carrie reed1, ruth lynfield2 and ashley fowlkes1 1influenza division, centers for disease control and prevention, atlanta, ga, usa; 2minnesota department of health, saint paul, mn, usa introduction the icd-9 codes for acute respiratory illness (ari) and pneumonia/influenza (p&i) are commonly used in ari surveillance; however, few studies evaluate the accuracy of these codes or the importance of icd-9 position. we reviewed icd-9 codes reported among patients identified through severe acute respiratory infection (sari) surveillance to compare medical record documentation with medical coding and evaluated icd-9 codes assigned to patients with influenza detections. methods the minnesota department of health (mdh) conducted sari surveillance at three hospitals. all hospitalized patients with submission of a physician-ordered upper respiratory specimens (e.g., sputum, throat or nasopharyngeal swabs) were enrolled. a medical chart review was conducted to identify those meeting sari criteria, defined as patients admitted to an inpatient ward with new onset of respiratory symptoms or acute exacerbation of chronic respiratory conditions. enrolled patients who did not meet the sari criteria were categorized as non-sari. residual material from the upper respiratory specimens were submitted to mdh for influenza testing by rt-pcr. demographic and clinical data, including up to eight icd-9 codes, were collected through the medical record review. patients with an icd-9 code indicating ari (460 to 466) or p&i (480 to 488) were defined as having an ari/p&i code. we compared the frequency of ari/p&i codes by sari clinical criteria and influenza detection and evaluated the position of the reported ari/p&i code. results from may 2013 through august 2015, we enrolled 5,950 patients, of which 4,449 (75%) met sari criteria and 1501 did not (non-sari). an ari/p&i code in any position was found in 61% (2705) of sari vs. 16% (241) of non-sari patients (odds ratio [or] 8.1, 95% confidence interval [ci] 7.0-9.4); an ari/p&i code in the first position was found in 40% of sari vs 7% of non-sari patients (or=8.6, 95% ci 7.0-10.5). among sari patients with at least one ari/p&i code, 66% had their first or only ari/p&i code in the 1st position, 25% in the 2nd position, and 6% in the 3rd position. for identification of sari, sensitivity/specificity was 61%/84% for ari/ p&i codes in any position and 40%/93% for ari/p&i codes in the 1st position. among sari patients, codes for pneumonia (486) and acute bronchiolitis (466.11, 466.19) were commonly reported. the most frequent codes among sari patients without an ari/p&i code were fever (780.6), acute respiratory failure (518.81), and asthma (493.92) (table). influenza was detected among 8% (351) of sari patients. an ari/p&i code in any position was more common in influenzapositive vs. influenza-negative sari patients (77% vs 59%, or 2.4, 95% ci 1.8-3.1). an ari/p&i code in the 1st position was slightly more common in influenza-positive vs -negative patients though not significant (44% vs 40%). conclusions among patients from whom a respiratory specimen was collected, administrative data identified those meeting sari with moderate sensitivity and high specificity, and with lower sensitivity but greater specificity when limited to the 1st icd-9 position. pneumonia and acute bronchiolitis icd-9 codes were frequent ari/p&i codes among sari patients. further investigation is needed to determine the value of including additional icd-9 codes, such as respiratory distress and acute asthma exacerbation, in identifying sari. most frequently reported icd-9 codes among sari, non-sari and influenza test-positive patients with no ari/p&i codes. keywords icd-9; influenza; hospitalized; respiratory *andrea steffens e-mail: vqg1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e105, 2017 virtual communities of practice: can they support the prevention agenda in public health? 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi virtual communities of practice: can they support the prevention agenda in public health? jennifer ford1*, helena korjonen1, asha keswani1, emma hughes1 1. uk health forum, fleetbank house abstract background: virtual communities of practice (cops) are flexible communication and knowledge management tools enabling collaboration, sharing of best practice and professional development. there have been few studies that have looked at the use and usefulness of virtual cops in public health. methods: this project sought to gather the evidence and develop recommendations for the value of virtual cops in public health through a literature review, and through piloting two cops in obesity. the research aimed to find out how useful cops are in obesity prevention, what makes a cop successful and what evaluation methods are appropriate. results: cops are composed of observers, passive and active contributors with a small group of ‘super-users’. all users learn through reading and listening, even if they do not post. the cops had higher levels of reading activity as opposed to low levels of posting activity. longer existence of cops usually means more active membership. there are complex reasons why users fail to engage in knowledge sharing. success of a cop is creating an online environment where users feel comfortable. cops need administrative support and facilitation. champions play a vital role. conclusions: evidence shows some encouraging results about the value of cops in enabling collaboration and information sharing. despite low membership numbers of the obesity cops piloted, members see value and suggest improvements. findings suggest that success comes from leadership, champions, and larger networks with more posting activity. mixed methods of quantitative and qualitative research are appropriate in measuring the use and impact of cops. keywords: communities of practice, public health, obesity, online networks, knowledge translation abbreviations: public health england (phe), community of practice (cop), uk health forum (ukhf), obesity learning centre (olc), ncmp – national child measurement programme correspondence: jennifer.ford@ukhealthforum.org.uk doi: 10.5210/ojphi.v7i2.6031 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes implications for practice • users of cops can derive benefit from membership even if not actively posting, as reading and taking in information are also part of learning process • cops need time to grow and develop a user base. research indicates that cops attract more members over time, and that this may have a positive knock-on effect on activity levels • cops are valuable in public health around enabling collaboration and information sharing http://ojphi.org/ mailto:jennifer.ford@ukhealthforum.org.uk virtual communities of practice: can they support the prevention agenda in public health? 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi • successful cops have leadership, champions, and more posting activity background the literature suggests virtual communities of practice (cops) are communication and knowledge management tools enabling collaboration. cops also offer a flexible mechanism for interaction with peers and sharing of information. they also aid professional development, capacity building and support sharing of best practice. despite these promising claims there have been few studies that have looked at the use and usefulness of virtual cops in public health settings. how can cops support those working in policy, strategy and decision making, as well as those carrying out front line roles? this paper reports the findings of a review of reports and evaluations of virtual cops serving various groups of clinical health care professionals, including occupational therapists, paediatric pain specialists, primary care workers and oral health care [1-9], with a proposal that these findings can form the basis for similar cops in public health. for like-minded people to interact and share topic information, cops can be a face-to-face, a virtual group or a combination of both. this paper is concerned with virtual cops, but refers to face-to-face cops where relevant and to highlight their usefulness. this project sought to gather the evidence and develop recommendations for the value of a virtual cop in public health. a literature search was carried out to review published research regarding virtual cops in health, their development, implementation and evaluations in order to summarise what is known about cops already. the project also involved developing virtual cops on the obesity learning centre (olc) website, which were piloted and evaluated for 4 months. the olc was launched in 2009 to support the public health workforce working to promote healthy weight and prevent obesity in england, and was funded by the department of health. the results of both the literature review and the practical testing of cops enabled the ukhf to draw conclusions and make recommendations around how virtual cops can benefit public health, which will be highlighted in this paper. what the literature says about cops the concept of cops was first developed by lave and wenger, and a cop is defined as “a group of people who share a concern, a set of problems or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis” [10]. cops can have a number of different purposes, including promoting the transfer of knowledge into practice, improving knowledge management, encouraging and supporting professional development, promoting learning and information exchange and helping to promote and recognise both tacit and explicit knowledge [11]. all cops are set up to facilitate information sharing and knowledge translation, but they often have multiple aims, including overcoming geographical and professional isolation and improving networking [1-4], sharing best practices [5,8], building professional capacity and capabilities [11] and engaging practitioners from different professions and institutions [7]. reported benefits of cops in health studies of cops have identified both short and long term benefits. short term benefits include: rapid identification of skills set within the workforce, knowledge sharing, provision of safe environments for sharing problems, capture and reuse of existing knowledge, improvements in topical knowledge and the rate of implementation of evidence-based practice [12]. long term benefits include providing a forum for expanding skills and http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi expertise, a network for current awareness and to help foster a sense of professional identity [12]. it is thought that information sharing is a key activity in cops which can contribute to continuing professional development, improvement and innovation, and communication over geographical distances. cops are considered inexpensive methods of individuals ‘meeting’ (compared to face-to-face) and effective avenues to disseminate evidence-based information and exchange information globally, in particular in low resource settings [5]. in health care settings, information sharing is often carried out with the intention of improving or sharing best practice [2,5,12] or to improve health services [1,3,5,11], and there is evidence that a cop offers a learning experience which informs policy [1], improves patient care [3], enables long distance consultations with more experienced professionals [3] and also has a social function [3] in terms of supporting practitioners who may be professionally isolated [4]. evidence indicates virtual cops can also improve collaboration including when paired with meetings held using other methods such as teleconferencing [3]. cops can also improve engagement with peers and people of different professions or institutions [7]. if paired with meetings, cops can improve the structure of meetings and discussions [3]. the users of cops cops have a variety of users, e.g. leaders who are hard-core active members, the active users who contribute and peripheral users who are not actively involved [5,7,12]. evaluations of health cops have reported the existence of inactive users, or ‘lurkers’, e.g. those who visit a cop and view pages but do not formally register to join [4] or post [7]. regardless of the number of active members in a cop who are regular contributors or posters, cops can reach all users including their ‘lurker’ members who do not post messages but may still benefit from being recipients of information through reading content [4,7]. how to make cops work health care cops work when members are self-regulated and are stimulated with questions and proposals for action [5]. cops require external support and leadership [5], and need facilitation [7,8,12] and champions [7,8], and in some cases benefit from the addition of meetings via other methods [7]. members of cops may engage for a number of reasons, including in order to boost their self-esteem [13]. however it is also possible that cliques may form and create barriers to collaboration: these situations need careful management. as the evidence suggests that cops require facilitation, and as set up and maintenance of online cops incurs costs related to technical development, it is apparent that they also require financial investment. without such investment, the ability of a cop to support knowledge translation and capacity building may be compromised. bridging the gap between research and practice it has been suggested that cops play a role in bridging traditional rifts between research and practice in the health sector [14,15], where the cop becomes a learning community, allowing members to create their own understandings of the practice-world through interpreting and producing the knowledge that is held by the community as a whole [14]. in a cancer cop in canada, both explicit and tacit knowledge is shared to interrelate learning, practice and peer input with an aim to enhance individual and organisational performance [14]. knowledge management processes are widely recognised as fundamental to improving policy and health systems [16]. cops are core strategies to bridge evidence, policy-making and implementation http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi linking all actors within the system and providing a platform on which members can transfer tacit and explicit knowledge and collaborate towards a common purpose. recent research into the response to the 2009 h1n1 pandemic in canada found that there was a lack of communication between different sectors of the public health workforce at the time of the pandemic (specifically between mathematical modellers and other public health professionals), resulting in a less efficient response to the pandemic [17]. a cop connecting these different workforce groups to promote communication was recommended as a result of the research [17]. the following sections present a comparison of the findings from the existing research with that of the ukhf’s own evaluation of developing and piloting two cops for obesity and weight management practitioners in public health in england. the paper will end by drawing together the ukhf’s recommendations. methods this project sought to gather the evidence and develop recommendations for the value of virtual cops in public health. the literature review was undertaken by looking for evidence of the use and evaluation of virtual cops in health. the ukhf also developed two virtual cops as a pilot for three months, in the area of obesity, and both were evaluated as part of the project. recommendations from both the literature review and the pilot are discussed in this paper. this research sought to answer the following questions: 1. how useful are cops in obesity prevention? 2. what makes a cop successful? 3. what evaluation methods are most appropriate? ethics statement participation in the cops and in the cops evaluation questionnaire was voluntary. information about the purpose of the study and cop were provided, and this allowed participants, members of the public health workforce interested in obesity, to make an informed decision about whether or not to participate prior to data collection. the study did not collect sensitive personal data, and participant’s identities remained confidential. the virtual obesity cops the cops, named olc communities, were launched in november 2014, and ran for a period of three months, the ‘pilot phase’, and were subsequently evaluated. the olc was launched in 2009 and has hosted an information sharing space for the national child measurement programme (ncmp), which has been meeting regularly between 2011 – 13, sharing information and data. for the purposes of this pilot, the ncmp cop was resurrected. the ukhf also launched an additional community, the literature update cop1, at the end of november 2014. the literature update cop provided a place for sharing obesity resources and literature, e.g. case studies and publications. both cops required registration and login in order to gain access. cop functionality each of the cops had a dedicated web page on the olc website, housing a discussion board. there was also a facility for cop members to subscribe to real-time email updates of new http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi messages, but this was not measured as part of the study. to encourage visits to and activity on the cops, a weekly summary of posts called the ‘olc communities email alert’ was sent out via mailchimp (an email management service) every friday. evaluation method a combination of web metrics and a user survey was used to evaluate the olc communities. measures included number of registered users, number of visits, and data on the most visited discussion board topics. qualitative data was collected through an email questionnaire, which asked users how helpful they had found the online cop, how they might use the cop, and what could be done to improve it. the use of web metrics has been criticised because they do not measure the impact that a cop has on the sharing or application of knowledge [1,18]. this implies that qualitative methods that look at the content of the posts, or ask users how they have benefitted from community membership may be useful evaluative measures. content analysis of posts has been employed during cop evaluations [2], and surveys have also been used to assess the users’ perceptions of the cops, and their satisfaction with them [1]. an invitation email was sent out to 145 registered members of the literature update and ncmp cops in january 2015. a reminder email was sent out to an increased 162 registered members of the cops on 9 february 2015. results qualitative evaluation all the registered members (n=145) were contacted to take part in the evaluation. the response rate to the qualitative evaluation (the email questionnaire) was 4% (n=6). due to the small number of responses, response text was read and grouped loosely into common themes that were detected, instead of a full content analysis involving coding of response text. these themes related to the opportunities to learn about practice, posting behaviour and the need for interaction. learning about practice four out of the six respondents stated that they had visited the cops at some point. when asked how they thought a virtual obesity cop could help them, users referred to the opportunity for learning about what works, and good and bad practice: r1 “to gain more learning on what works and good examples of integrated working between local providers, ncmp and other key partners.” r5 “these are common elsewhere and people use them for a number of purposes...learning about good or bad practice elsewhere, information sharing and idea sharing.” posting behaviour there were no posts from users during the pilot. users were asked whether they would prefer to use the community to get information, or would use it to post information themselves. three respondents indicated that they might post messages in future. r1 “i would consider posting messages such as ncmp questions” http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi r3 “i think i will do both. in the future i will likely be producing some obesity related outputs so i will be looking to the olc as a way of getting information and as a way of posting links/sharing information.” r5 “i would consider posting messages.” another user was specific about not using the cop to post themselves: r2 “i wouldn’t post messages myself. my experience of these types of groups is the same person makes all the comments and no-one else responds. i will use the community to find information on a particular topic when it is relevant to me.” need for interaction two respondents stated that they thought more interaction from other public health professionals would improve the cops: r1 “need more interaction from professionals who work in the field of obesity/weight management in local authorities, health and provider services. i would find ncmp discussions very useful as i am looking for more examples (such as ‘town’ and ‘town’) of how local authority/primary care departments are engaging and channel families into weight management services from ncmp such as potential pitfalls and addressing issues that arise.” r5 “more involvement… there doesn't seem to be a lot there at the moment and it seem to be quite old” web metrics the literature update cop achieved a total of 49 registered members in its first two months. the ncmp cop had a total of 145 registered members by february 2015i, with 12 new members joining since the end of november 2014. it is worth noting that some of these members have been part of the ncmp community since the origin of the group (2011) and therefore may no longer be active members or in the same job role. table 1: unique user visits to cops during evaluation month ncmp cop literature update c p november 2014 91 31 december 2014 28 88 january 2015 72 113 total 191 232 average 64 77 on average, the literature update cop received 9 new registrations per month, and the ncmp cop received 3 new registrations per month during the study period (see table 1). regular posts were made to the literature update cop by staff at the ukhf. website metrics for posts to the discussion forums within the two cops suggest that visitor activity was present in low levels throughout the pilot. the literature update cop saw the most activity, and had the most popular posts, which were research and news digests posted in december http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi 2014, each of which received 15 unique visits during the pilot (see tables 2 and 3). website metrics indicate that the ncmp cop received the most visits (n=91) in november 2014, while the literature update cop received the most visits in december 2014and january 2015 (n=88 and n=113 respectively, see table 4). table 2: most viewed posts in literature update cop december 2014 – 5 february 2015 post title original post date no. replies unique pageviews news digest 10 december 2014 10/12/2014 0 15 research digest food environments 10 december 2014 10/12/2014 0 15 olc literature update digest 19 jan 2015 19/01/2015 0 6 attitudes and perceptions of obesity affecting children and young people 20/01/2015 0 5 obesity news daily digest 5 jan 2015 05/01/2015 1 5 taxation of high fat/sugar foods and obesity: literature update 31/10/2014 0 5 obesity and stigmatisation in health and care settings 28/01/2015 0 4 obesity resources digest 28 january 2015 28/01/2015 0 4 childhood obesity rates levelling off 30/01/2015 1 3 daily digest obesity news 29 january 2015 29/01/2015 0 3 olc literature community update 27 january 27/01/2015 0 3 scottish health survey 2013 results published 08/12/2014 0 3 today's twitter digest 11 december 2014 11/12/2014 0 3 12 minutes more new study from nuffield health 05/12/2014 0 2 case studies in public health 10/12/2014 0 2 new phe report planning healthy weight environments 08/12/2014 0 2 news digest 16 december 2014 16/12/2014 0 2 obesity resources daily digest 8 jan 2015 08/01/2015 0 2 physical activity statistics 2015 30/01/2015 0 2 research digest latest on pubmed 15 december 2014 15/12/2014 0 2 news digest 17 december 2014 17/12/2014 0 1 public health england knowledge library services survey 02/02/2015 0 1 table 3 most viewed posts in ncmp cop december 2014 – 5 february 2015 post title original post date no. replies unique pageviews ncmp practice examples 10/11/2014 0 5 ncmp updates now available from youtube 07/11/2014 0 3 http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi information governance review: dame caldicott 18/06/2012 0 1 ncmp workshops 2012 24/04/2012 1 1 ncmp workshops 2012 other speakers' slides 03/01/2013 0 1 table 4: unique pageviews for ncmp and literature update cops month ncmp cop literature update cop november 2014 91 31 december 2014 28 88 january 2015 72 113 total 191 232 average 64 77 discussion the first research question was to find out if cops are useful in supporting the public health agenda and specifically obesity. ikioda points out that all virtual cops will be composed in varying degree of observers, and passive and active contributors [4]. this evaluation shows that no users were actively posting messages or resources on the cops, however, the web metrics indicate that there was reading activity of posts during the pilot, (ncmp cop had 64 unique visits per month and the literature update cop 77 unique visits per month). there are higher levels of reading activity as opposed to low levels of posting activity. this finding of difference in posting and reading activity is supported by the findings of stewart et al. who noted that there was a small group of members who appeared to post and read a lot, classed as ‘super-users’ by the researchers [7]. it is tempting to conclude from the lack of posting activity on the obesity cops, and from the fact that findings of lower levels of posting compared to reading are seen in other cop studies that users of virtual cops are not deriving any value. however, other researchers have pointed out that this is not necessarily the case, as posting is only one aspect of membership of an online cop [5] [19]. in spain, research into the use of a cop for primary care professionals found that 80% of respondents to a survey stated that discussions on the cop had been useful to them, even though 96% of individuals who registered as members of the cop had not been active participants [5]. as the authors of that research pointed out, learning through reading and listening is an essential part of the learning process even if the learner does not say or write anything [5]. the uptake of user registrations supports the idea that users do see some value in being a member of a cop, and are therefore willing to register to join. the low levels of activity in the obesity cops evaluated may in part be due to the short period of time which the cops had to grow and develop. the qualitative evaluation showed that some of the cop users would have preferred a more active cop. ikioda states that increasing numbers of users in an online cop will in itself over time lead to an increase in interaction between those members [4]. the ukhf speculates that had the obesity cops been evaluated over a longer period of time, and had the membership continued to grow at a similar rate, there would have been a higher level of user activity, including posting activity. the qualitative data also hints at some reluctance among users to engage in posting activity on the cops. some respondents stated that they would post in the future, but still had not http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi done so at the time of writing. there is a chicken and egg situation where some cop users want a more active community, but they are reluctant to be active themselves. one respondent was reluctant to post due to their belief that virtual cops tend to be dominated by a single individual. in fact cop users fail to engage in knowledge sharing activity online due to complex reasons, including fear of criticism, fear that they will mislead other cop members, and worry that their contribution may be inaccurate or unimportant [13]. the ukhf notes that similar behaviour exists in face-to-face meetings, and may be related to confidence and/or existing relationships with others in the group. the second research question in this study was ‘what makes a cop successful?’. this suggests that research involving audiences of cops (virtual and face-to-face) should be carried out in order to find ways to overcome barriers to being ‘active’ and to create an online environment in which users will feel comfortable interacting. this research also shows that in order for members of a cop to derive benefits from that cop, they need more interaction from public health professionals and leadership, in order to share best practices between areas. previous research has shown that cops require considerable time, administrative support and facilitation in order to become established [5,8]. it suggests that champions could play a vital role in the early stages of the development of a cop, by helping to promote cops with a view to increasing membership numbers, and by following up discussion threads on cops and posting messages and replies in order to stimulate activity and interaction. the ukhf’s final research question was ‘what methods exist for evaluating cops?’. results suggest that mixed methods evaluations are most appropriate, with a combination of both quantitative data [2,4,12] (user registrations, content analysis of posts, number of visits etc.) and qualitative data (asking users what impact a cop may have had on their work, in improving collaboration, or improved knowledge and in other potential impacts [1]). limitations the main limitation of this study was the short timescale over which the ukhf were funded to undertake this study and therefore the cops were only piloted for a few months before evaluation. they launched in november 2014, and data were collected and analysed in february 2015. the ukhf acknowledge that the cops were then still in an early development phase with a small membership. this is also likely to have caused the low response rate. the views of the respondents may not be representative of the larger workforce, however, the responses correspond with previous evaluations we have undertaken with similar results and with previous published research. conclusions the results of this study into cops in public health show that they are considered valuable in enabling collaboration and information sharing. evidence shows some encouraging results about the value of cops in supporting public health, despite the low membership of the obesity cops. these correspond with the ukhf’s literature review findings, which suggest success comes from leadership, champions, and larger networks with more posting activity. evaluations into the success of cops show that mixed methods of quantitative and qualitative research are most appropriate in measuring the use of, but also the impact of, cops. availability of supporting data the uk health forum can make research data available at request. please contact the primary author with your request. http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi authors’ contributions hk, jf, ak and eh conceived study and participated in its design and coordination and helped to draft the manuscript. hk, jf, eh and ak wrote up findings of literature search that form the background to this paper. jf carried out analysis of qualitative and quantitative data for evaluation of the online cops. authors’ information helena korjonen director: ncd research and information services helena directs the ukhf’s information services. she has a phd from the department of information studies at university college london, an ma in information studies and a bsc (hons) in international studies with an emphasis on the environment and human geography. jennifer ford information manager jenni assists the director in developing the department, undertaking research and technical support work for the ukhf’s websites and cops. she is a qualified information professional, undertaking doctoral research into the information use in public health at ucl. she also has a postgraduate diploma in public health (informatics) from staffordshire university, an ma in information studies from ucl and a bsc (hons) in anatomy and human biology from the university of liverpool. asha keswani senior information officer asha is senior information officer at the ukhf with significant experience in project management and customer services. she has a graduate certificate in information studies from robert gordon university, aberdeen. asha has overall responsibility for developing and managing our communities of practices found on ncdlinks.org, the strategic partnership website and supports our research projects. emma hughes – senior information officer emma hughes is an information officer responsible for planning and delivering current awareness services, and supporting the research undertaken. emma has an msc in information and library studies. she also has a bsc (hons) in philosophy and psychology. competing interests uk health forum has an interest in the use and usefulness of online communities of practice, and is responsible for hosting and maintaining a number of online communities, including ncdlinks, globalink, panacealink and alcoholhealthlink as well as the online obesity communities that were developed and tested for the present research. financial disclosure funding was received from public health england knowledge & information team for the completion of this research. they had no involvement in the design, analysis or reporting of the results. http://ojphi.org/ virtual communities of practice: can they support the prevention agenda in public health? 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi acknowledgements we would like to thank public health england knowledge and library services for funding this research. references 1. avila m, nallathambi k, richey c, mwaikambo l. six years of lessons learned in monitoring and evaluating online discussion forums. p. 621-43. 2. dieleman c, duncan eas. 2013. investigating the purpose of an online discussion group for health professionals: a case example from forensic occupational therapy. bmc health serv res. 13, 253. pubmed http://dx.doi.org/10.1186/1472-6963-13-253 3. falkman g, gustafsson m, jontell m, torgersson o. somweb: a semantic web-based system for supporting collaboration of distributed medical communities of practice. journal of medical internet research. 2008;10(3):e25-e. 4. ikioda f, kendall s, brooks f, de liddo a, buckingham shum s. 2013. factors that influence healthcare professionals’ online interaction in a virtual community of practice. social networking. 02(04), 174-84. http://dx.doi.org/10.4236/sn.2013.24017 5. abos mendizabal g, nuño-solinís r, zaballa gonzález i. 2013. hobe+, a case study: a virtual community of practice to support innovation in primary care in basque public health service. bmc fam pract. 14(1), 168. pubmed http://dx.doi.org/10.1186/14712296-14-168 6. o'brien m, richey c. knowledge networking for family planning: the potential for virtual communities of practice to move forward the global reproductive health agenda. p. 109-21. 7. stewart sa, abidi ssr. applying social network analysis to understand the knowledge sharing behaviour of practitioners in a clinical online discussion forum. journal of medical internet research. 2012;14(6):e170-e. 8. thomas au, fried gp, johnson p, stilwell bj. sharing best practices through online communities of practice: a case study. human resources for he. 2010;8(25). 9. urquhart cy. a; sharp, s. nelh communities of practice evaluation. aberystwyth, wales: 2002. 10. wenger e, mcdermott r, snyder wm. cultivating communities of practice: a guide to managing knowledge. boston, ma: harvard business school press; 2002. 11. bertone mp, meessen b, clarysse g, hercot d, kelley a, kafando y, et al. assessing communities of practice in health policy: a conceptual framework as a first step towards empirical research. health research policy and systems / biomed central. 2013;11:39-. 12. barwick ma. developing a community of practice model for cancer and chronic disease prevention. 2008. 13. ardichvili apv, wentling t. motivation and barriers to participation in virtual knowledge-sharing communities of practice. oklc 2002 conference; athens, greece2002. 14. bentley c, browman gp, poole b. 2010. conceptual and practical challenges for implementing the communities of practice model on a national scale--a canadian cancer http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23822895&dopt=abstract http://dx.doi.org/10.1186/1472-6963-13-253 http://dx.doi.org/10.4236/sn.2013.24017 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24188617&dopt=abstract http://dx.doi.org/10.1186/1471-2296-14-168 http://dx.doi.org/10.1186/1471-2296-14-168 virtual communities of practice: can they support the prevention agenda in public health? 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e222, 2015 ojphi control initiative. bmc health serv res. 10(1), 3. pubmed http://dx.doi.org/10.1186/1472-6963-10-3 15. bartunek j, trullen j, bonet e, sauquet a. 2003. sharing and expanding academic and practitioner knowledge in health care. j health serv res policy. 8(suppl 2), 62-68. pubmed http://dx.doi.org/10.1258/135581903322405199 16. lave jw. e. situated learning: legitimate peripheral participation. cambridge, uk: cambridge university press; 1991. 17. driedger sm, cooper ej, moghadas sm. 2014. developing model-based public health policy through knowledge translation: the need for a 'communities of practice'. public health. 128(6), 561-67. pubmed http://dx.doi.org/10.1016/j.puhe.2013.10.009 18. mcdermott r. measuring the impact of communities: how to draw meaning from measures of communities of practice. knowledge management review. 2002;5(2). 19. wang xyy. 2012. classify participants in online communities [ijmit]. international journal of managing information technology. 4(1). http://dx.doi.org/10.5121/ijmit.2012.4101 1 http://www.obesitylearningcentre.org.uk/networks/olc-literature-updates/ i many of these members date from the original launch of the ncmp community in 2010/11 and may be inactive. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20051125&dopt=abstract http://dx.doi.org/10.1186/1472-6963-10-3 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14596750&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14596750&dopt=abstract http://dx.doi.org/10.1258/135581903322405199 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24461909&dopt=abstract http://dx.doi.org/10.1016/j.puhe.2013.10.009 http://dx.doi.org/10.5121/ijmit.2012.4101 virtual communities of practice: can they support the prevention agenda in public health? implications for practice background what the literature says about cops reported benefits of cops in health the users of cops how to make cops work bridging the gap between research and practice methods ethics statement the virtual obesity cops cop functionality evaluation method results qualitative evaluation learning about practice posting behaviour need for interaction web metrics discussion limitations conclusions availability of supporting data authors’ contributions authors’ information helena korjonen director: ncd research and information services jennifer ford information manager asha keswani senior information officer emma hughes – senior information officer competing interests financial disclosure acknowledgements references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts regional governance of syndromic surveillance for the texas gulf coast wesley mcneely*, eunice r. santos, biru yang, kiley allred and raouf r. arafat osphp, houston health department, houston, tx, usa objective describe and explain the transition of the syndromic surveillance program at the houston health department (hhd) from being a locally managed and aging system to an essence system governed by a regional consortium of public health agencies and stakeholders in the 13-county area of the southeast texas. introduction syndromic surveillance systems are large and complex technology projects that increasingly require large investments of financial and political capital to be sustainable. what was once a minor surveillance tool in the mid-2000s has evolved into a program that is regarded as valuable to public health yet is increasingly difficult to maintain and operate for local health departments. the houston health department installed a syndromic surveillance system (sys) six years before meaning use became known to healthcare communities. the system chosen at the time was the real-time outbreak disease surveillance system (rods) which, at the time and for its purpose, was a suitable platform for syndromic surveillance. during the past 13 years however, maintaining, operating, and growing a sys by a local health department has become increasingly difficult. inclusion in meaningful use elevated the importance and profile of syndromic surveillance such that network growth, transparency of operations, ease of data sharing, and cooperation with other state systems in texas became program imperatives. methods with support from the informatics group at tarrant county public health (tcph) in the form of mentoring, hhd devised a two prong strategy to re-invigorate the syndromic program. the first was to replace rods with essence from johns hopkins applied physics laboratory (jh/apl). the second was to strengthen the regional network by creating a governance structure that included outside agencies and stakeholders. the product of this second effort was the creation of the syndromic surveillance consortium of southeast texas (sscset) on the communities of practice model1 using parliamentary procedure2. results acquiring essence and forming sscset were necessary steps for the continuing operation of the sys. the consortium includes members from local health jurisdictions, health care providers, health policy advocates, academicians, and data aggregators. created as a democratic society, sscset wrote its constitution and by-laws, voted in officers, formed working groups and has begun developing policies. the consortium is cooperating with the texas department of state health services (dshs) as well as tcph. having essence will ensure the hhd-sys will conform to standards being developed in the state and provide a robust syndromic platform for the partners of the consortium. conclusions syndromic systems operated by local health departments can adapt to regulatory changes by growing their networks and engaging regional stakeholders using the communities of practice model. keywords communities of practice; texas; gulf coast; syndromic surveillance acknowledgments the authors would like to acknowledge the support of bill stephens and dave heinbaugh of tarrant county public health and members of the syndromic surveillance consortium of southeast texas from the following jurisdictions: ●beaumont public health department ●brazoria county health department ●fort bend county department of health and human services ●galveston county health district ●harris county public health and environmental services ●houston health department ●montgomery county public health district ●port arthur health department ●texas department of state health services region 6/5 south references 1 wenger, etienne, mcdermott, richard, snyder, william m. cultivating communities of practice. harvard business school press. boston, ma. 2002. 2. robert, henry m,.et al., robert’s rules of order newly revised (11th ed.). philadelphia, pa, da capo press, 2011. *wesley mcneely e-mail: wesley.mcneely@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e79, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using sydromic surveillance to track e-cigarette related emergency department visits jill k. baber* and tracy miller division of disease control, north dakota department of health, bismarck, nd, usa objective to explore the use of emergency department syndromic surveillance data to identify adverse health events related to electronic cigarettes in order in enhance existing surveillance. introduction the north dakota department of health (nddoh) investigated the feasibility of using syndromic surveillance (sys) data to identify health care visits due to electronic cigarette (e-cigarette) use. e-cigarettes have been associated with injuries and fatalities in all age groups, including young children attracted to the colorful liquid nicotine carriage packaging [1]. previously, poison control data was the only resource available to the nddoh for e-cigarette adverse outcomes surveillance. methods data for all visits from june 28, 2014 to june 28, 2015 were downloaded using the biosense 2.0 sys analytic tool. excel was used to identify visits containing key words related to e-cigarettes in linelevel data. we initially searched for visits using variations of the term “e-cigarette.” after meeting with nddoh subject matter experts, we expanded our search to include other related terms: nicotine, clouding, vaping and variations of “electronic nicotine delivery system (ends)”. no diagnosis codes were used as none refer specifically to e-cigarettes. visits were identified solely through searching free text chief complaint and triage notes fields. not all facilities participating in the nddoh sys program during this time period submitted free text data. results out of 650,069 unique visits, four e-cigarette-related visits were identified in rich-text data fields searching for “e-cig” and “e cig.” an additional visit was identified using the search term “nicotine,” although this search primarily identified visits including references to nicotine patches. of the five visits identified, two were poisonings resulting from small children sucking on liquid nicotine cartridges, one referred to eye irritation as a result of accidentally using liquid nicotine as eye drops, and two referred to cardiac issues (chest pain, heart palpitations) after e-cigarette use. searches including terms “clouding” and “vaping,” street terms related to e-cigarettes, did not result in the identification of any additional visits related to e-cigarettes; nor did searches related to ends. poison control data from the same time period yielded two calls related to e-cigarette adverse events. conclusions it is possible to identify emergency department visits associated with e-cigarette use utilizing sys data. more visits were identified using sys data than poison control data, although neither source identified many occurrences of adverse outcomes related to e-cigarettes. e-cig, e cig and nicotine were the most useful search terms, although a search for “nicotine” must exclude the word “patch” to avoid false identifications. the nddoh receives free-text data for a majority of the visits in our system, but not all facilities submit free-text fields, and the number that did varied over the study period. because no drop-down chief complaints or diagnosis codes related to e-cigarettes exist, data from facilities that did not provide free text data were not helpful in identifying e-cigarette-related visits. this investigation emphasizes the need for free text fields when using sys to investigate emerging issues. keywords syndromic surveillance; electronic cigarettes; injury references 1. hua m, talbot p. potential health effects of electronic cigarettes: a systematic review of case reports. prev med rep; 2016 dec;4: 169-178. *jill k. baber e-mail: jbaber@nd.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e144, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts going beyond chief complaints to identify opioid-related emergency department visits andrew walsh* health monitoring, pittsburgh, pa, usa objective to identify heroinand opioid-related emergency department visits using pre-diagnositc data. to demonstrate the value of clinical notes to public health surveillance and situational awareness. introduction overdoses of heroin and prescription opioids are a growing cause of mortality in the united states. deaths from opioids have contributed to a rise in the overall mortality rate of middle-aged white males during an era when other demographics are experiencing life expectancy gains.1 a successful public health intervention to reverse this mortality trend requires a detailed understanding of which populations are most affected and where those populations live. while mortality is the most relevant metric for this emerging challenge, increased burden on laboratory facilities can create significant delays in obtaining confirmation of which patients died from opioid overdoses. emergency department visits for opioid overdoses can provide a more timely proxy measure of overall opioid use. unfortunately, chief complaints do not always contain an indication of opioid involvement. overdose patients are not always conscious at registration which limits the amount of information they can provide. menu-driven registration systems can lump all overdoses together regardless of substance. a more complete record of the emergency department interaction, such as that provided by triage notes, could provide the information necessary to differentiate opioid-related visits from other overdoses. methods emergency department registration data was collected from hospitals via the epicenter syndromic surveillance system. this data included chief complaints, triage notes, discharge disposition, and preliminary diagnosis codes. data elements were linked across a given visit using patient identifiers and visit numbers as appropriate. heroinand opioid-related indicators were identified in chief complaints and triage notes using regular expressions. these were separated into three categories: visits with an indication of overdose, visits for withdrawal symptoms, and visits where opioids were mentioned in some other context such as history of use. these categories were designed to be mutually exclusive. regular expression classification results were compared to classifications based on opioid-related diagnosis codes. results a total of 2,934,610 ed registrations with triage notes and diagnosis codes were collected from 82 hospitals between january 1, 2015 and august 21, 2016. of these encounters, 24,012 referenced opioid use in some way; 16,718 mentioned heroin specifically; 3,663 mentioned fentanyl specifically; and 5,350 mentioned opioids generically. table 1 shows the distribution of heroin-related ed visits across categories and source of the indicator. column totals are not the sum of individual row amounts; they have been adjusted so that a given registration is only counted once. table 2 shows the overlap of heroin-related ed visits between sources of indicators. triage notes showed the least overlap with the other two sources, while chief complaints showed the most. conclusions while it is possible to find indicators of opioid use or overdose in chief complaint data, that field alone does not provide total information about which ed visits are related to opioids. triage notes in particular indicate opioid involvement in a large number of visits not identified by other data sources. while many of these are simply mentions of opioids, possibly indicating past history of use or even in some cases just that questions about opioid use were asked, a substantial number of visits with overdose indicators were also detected solely from triage note data. these results suggest that triage notes can be a valuable additional data source for more complex health concerns such as opioid drug use. table 1: heroin-related ed visits by indicator source and category table 2: overlap of heroin-related ed visits between indicators keywords opioid; heroin; overdose; triage note; fentanyl acknowledgments we wish to thank our public health customers for funding support and data for this work. references 1. case a, deaton a. rising morbidity and mortality in midlife among white non-hispanic americans in the 21st century. pnas 2015 december; 112 (49). *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e39, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 1institute of epidemiology, disease control & research, dhaka, bangladesh; 2massey university, palmerston north, new zealand objective to evaluate the necessity of a coordinated leptospira surveillance in bangladesh through a systematic review. introduction fever is a top cause of morbidity in all age groups in bangladesh and often classified as fever of unknown origin (fuo) in absence of any pathognomonic sign (1). bangladesh with its long monsoon, frequent flooding, stagnant water, high temperature, humidity and largest density of animal and human population serves as an ideal place for zoonotic transmission of leptospira (2). methods we searched three different data basespubmed, web of science and google scholar using the search term leptospir* or “weil’s disease” and bangladesh which yielded 9, 11 and 2590 articles respectively. we screened the titles first for relevance and later abstracts. two articles were written in russian language and the texts were inaccessible. these two articles were not reviewed along with other articles that did not discuss the evidence of leptospira infection in bangladesh. five articles met our criteria and were finally chosen for review. results leptospirosis was evident in different geographical locations of bangladesh such as south-eastern, central and north-western part with different landscapes such as highland, plain land and flood prone areas. it was distinctly frequent during bridging period of later winter, start of monsoon, and after monsoon (3-6). leptospirosis was an eminent cause of fever in urban and rural bangladesh causing hospitalization (4, 6). a study conducted in two hospitals in dhaka showed 18% of the dengue-negative patients were positive for leptospirosis (5). other studies have showed that 2-44% of febrile outpatients had leptospirosis in bangladesh (4, 6). furthermore, fever or fuo causes an average of 6.3 work days loss among bangladeshis (1). moreover, case fatality rate was higher in leptospirosis (5%) than dengue (1.2%) (5). all these epitomize the necessity of a coordinated leptospirosis surveillance in bangladesh. a battery of serogroups such as sarmin, mini, australis, icterhaemorrhagiae, cynopteri, autumnalis, shermani, javanica, djasiman, pyrogenes, sejroe, celledoni and pamana were found in bangladesh. however, study suggested undifferentiated serovars may be circulating in bangladesh which resulted in underreporting of leptospirosis burden (4). in animal, leptospirosis was common in high yield and pregnant dairy cattle. farmer’s education level, semen source, farm placed at peri-urban area, farm size and employee number were the driving force of leptospirosis at farm level. serovar hardjo was found in dairy cattle which confirms the presence of leptospirosis in animal population (7). the allied evidence of leptospirosis in other species as well as risk factors, dynamics of transmission, human animal interfaces, environmental drivers and disease impact can only be generated through a well-established surveillance system. thus the need of establishment of an immediate surveillance is highlighted to mitigate the risk and disease burden human. conclusions our review shows that current study findings do not provide conclusive evidence regarding leptospirosis transmission, circulating serovars and impact in human. thus a well-coordinated surveillance should be accommodated in the routine surveillance in bangladesh to alleviate disease risk and morbidity. keywords leptospirosis; surveillance; bangladesh references 1. statistics bbo. health and morbidity status survey2012. bangladesh bureau of statistics; 2013. 2. muraduzzaman akm. leptospirosis bangladesh overview. 4th global leptospirosis action network meeting colombo, srilanka2014. 3. morshed m, konishi h, terada y, arimitsu y, nakazawa t. seroprevalence of leptospirosis in a rural flood prone district of bangladesh. epidemiology and infection. 1994;112(03):527-31. 4. kendall ea, larocque rc, bui dm, galloway r, ari md, goswami d, et al. leptospirosis as a cause of fever in urban bangladesh. the american journal of tropical medicine and hygiene. 2010;82(6):112730. 5. larocque rc, breiman rf, ari md, morey re, janan fa, hayes jm, et al. leptospirosis during dengue outbreak, bangladesh. emerg infect dis. 2005;11(5):766-9. 6. swoboda p, fuehrer h-p, ley b, starzengruber p, ley-thriemer k, jung m, et al. evidence of a major reservoir of non-malarial febrile diseases in malaria-endemic regions of bangladesh. the american journal of tropical medicine and hygiene. 2014;90(2):377-82. 7. parvez m, prodhan m, rahman m, faruque m. seroprevalence and associated risk factors of leptospira interrogans serovar hardjo in dairy cattle of chittagong, bangladesh. *sultan mahmood e-mail: dr.sultanmahmood@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e97, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, 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heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat houston health department, city of houston, houston, tx, usa objective to assess the usage of dating sites and social networking sites for finding sexual partners among newly diagnosed hiv positive msms in harris county in 2014 introduction internet based technologies are becoming quite prominent among today’s generation due to its easy accessibility through computer or phone devices [1]. internet’s relative anonymity leads high risk groups to find it easier to meet sexual partners with similar characteristics through dating sites like grindr, jack’d, adams4adams etc. and mainstream social networking sites like facebook, twitter, or instagram. according to various studies, young msms prefer to use dating sites and social networking sites more as a source to meet sexual partners than older msms [2]. methods population-based surveillance data was generated from houston enhanced hiv/aids reporting system (ehars) for all newly diagnosed hiv positive msm’s in 2014. data regarding usage of social networking sites as well as dating sites was gathered from the disease intervention specialist (dis) interviews from sexually transmitted disease management information system (stdmis). descriptive analysis was performed to identify the distribution of dating and social networking sites usage across different age and race/ ethnicity groups. logistic regression analysis was used to examine the association of race with the usage of social networking sites and dating sites after adjusting for age. data was analyzed using stata version 13 software. results the study findings indicate that 207 (32.2%) out of 643 msms used dating sites for finding sexual partners. on the other hand, only 22 (3.4%) out of 643 msms used social networking sites. overall 20 (3%) out of 643 msms were not specific about their choice of internet sites for meeting their partners. among different age groups, young msms (18-24 years old) are most likely to prefer dating sites (38%) compared to mainstream social networking sites (6%). white msms prefer dating sites (33%) over social networking sites (2%), same ratio holds true for the african american msms preference of dating sites (31%) vs. social networking sites (2%) and hispanic msms preference of dating sites (32%) vs. social networking sites (4%). msms preferences of the dating sites are as follows: grindr (15.5%), adams4adams (9.49%), jack’d (8.71%), phone apps like mocospace/ badoo (2.95%), scruff (2.02%) and craigslist (1.87%). grindr was favorite choice of finding partners among hispanic msm (7.15%) followed by white msm (4.04%) and african american msm (3.27%). facebook (2.8%) was the only preference among many social networking sites for finding partners. african american msms had a significantly lower likelihood of using social networking sites in to white msms (or: 0.61; 95% ci: 0.37-0.99). similarly, african american msm (or: 0.54; 95% ci: 0.34-0.87) and hispanic msm (or: 0.51; 95% ci: 0.32-0.82) had significantly lower likelihood of using dating sites compared to white msm. conclusions overall, a trend is seen among msm who are more comfortable communicating and pursuing sexual partners through the dating sites than social networking sites. our study shows the dating sites grindr, adams4adams and jack’d being more popular among hispanic msm, and african american msm than white msm. white msm prefers social networking than hispanics and african americans may be because of the disparity in socio economic status and accessibility to networking sites. popular dating sites like grindr, adams4adams, and jack’d, as well as mainstream social networking sites like facebook could be used for culturally targeted hiv prevention programs among at risk populations by community based organizations. keywords msm; internet; hiv; dating sites; social networking sites acknowledgments houston’s hiv surveillance program references [1] holloway iw, dunlap s, del pino h, hermanstyne k, pulsipher c. online social networking, sexual risk and protective behaviors: considerations for clinicians and researchers. nih public access. 2014 sept; 1 (3):220-228. 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in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in 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beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts firearm injury encounters in the veterans health administration (vha), 2010-2015 cynthia a. lucero-obusan*1, aaron m. wendelboe1, 2, patricia schirmer1, gina oda1 and mark holodniy1, 3 1public health surveillance and research, department of veterans affairs, palo alto, ca, usa; 2university of oklahoma health sciences, oklahoma city, ok, usa; 3stanford university, stanford, ca, usa introduction firearm violence is an issue of public health concern leading to more than 30,000 deaths and 80,000 nonfatal injuries in the united states annually.1 to date, firearm-related studies among veterans have focused primarily on suicide and attempted suicide.2-5 herein, we examine firearm violence among vha enrollees for all manners/ intents, including assault, unintentional, self-inflicted, undetermined and other firearm-related injury encounters in both the inpatient and outpatient settings. methods inpatient and outpatient encounters with one or more icd9-cm firearm external-cause-of-injury codes (e-codes) from 1/1/2010-9/30/2015 were extracted from the vha’s praedico™ public health surveillance system, including demographics, era of service/eligibility, encounter type, and deaths. firearm e-codes were classified for manner/intent based on the cdc’s web-based injury statistics query and reporting system (wisqars™) matrix.6 outpatient/emergency department (ed) data were exclusively from vha facilities (a single pediatric patient seen as a humanitarian emergency was excluded from the dataset). inpatient data included vha facilities and some records received from non-vha facilities. vha rate of hospitalization for firearm-related admissions was calculated using the total vha acute-care admissions for the same time period as the denominator. results during the time frame examined, 5,205 unique individuals were seen with a firearm e-code. of these, 4,221 were seen in the outpatient/ ed setting only, 597 in the inpatient setting only, and the remaining 387 had encounters in both the outpatient/ed and inpatient settings. vha firearm admission rate was 1.63 per 10,000 vha admissions, compared to a national rate of 1.96 per 10,000 in 2010.7 table 1 shows the breakdown of encounters by manner/intent. unintentional was the most common firearm injury manner/intent. overall, the median age at initial encounter was 54 (range 19-100 years), and 96% were male. the highest percentage served in the persian gulf war era (2,136, 41%), followed by vietnam era (1,816, 35%) and post-vietnam era (716, 14%). the greatest number of patients with a firearm-coded encounter resided in texas (453), california (349), florida (326), arizona (214) and ohio (212). conclusions unintentional injuries were the most common form of firearm injury among vha enrollees, representing over half of all outpatient/ed firearm encounters and more than twice the number of firearm hospitalizations compared with any other manner/intent. limitations include that not all u.s. veterans are vha enrollees; miscoding and misclassification of firearm-related injuries may have occurred; and data from non-vha outpatient/ed encounters and some non-vha hospitalizations are not available to our surveillance system for analysis. additional study is needed to further understand the epidemiology of firearm-related injuries among veterans and inform vha leadership and providers. keywords veterans; firearms; injury; surveillance references 1. centers for disease control and prevention. injury prevention and control: data & statistics (wisqars). available at: http://www.cdc. gov/injury/wisqars/index.html 2. mccarten jm, hoffmire ca, bossarte rm. changes in overall and firearm veteran suicide rates by gender, 2001-2010. am j prev med. 2015;48(3):360-4. 3. kaplan ms, mcfarland bh, huguet n. firearm suicide among veterans in the general population: findings from the national violent death reporting system. j trauma. 2009;67:503-7. 4. smith pn, currier j, drescher k. firearm ownership in veterans entering residential ptsd treatment: associations with suicide ideation, attempts, and combat exposure. j. psychiart. res. 2015;229:220-4. 5. walters h, kulkarni m, forman j, roeder k, travis j, valenstein m. feasibility and acceptability of interventions to delay gun access in va mental health settings. j. gen hosp psych. 2012;34:692-8. 6. cdc, national center for injury prevention & control: data & statistics (wisqars), matrix of e-code groupings, 2011. available at: http://www.cdc.gov/injury/wisqars/ecode_matrix.html 7. lee j, quraishi sa, bhatnagar s, zafonte rd, masiakos pt. the economic cost of firearm-related injuries in the united states from 2006-2010. surgery. 2014;894-8. *cynthia a. lucero-obusan e-mail: cynthia.lucero@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e146, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts the test-negative design for estimating influenza vaccination effectiveness benjamin j. cowling*1 and sheena g. sullivan2, 3 1school of public health, the university of hong kong, pokfulam, hong kong; 2who collaborating centre for reference and research on influenza at the peter doherty institute for infection and immunity, melbourne, vic, australia; 3fielding school of public health, university of california, los angeles, ca, usa objective we aimed to describe the theoretical basis and the potential applications of the test-negative design for estimating influenza vaccination effectiveness in sentinel influenza surveillance. introduction the test-negative design is a variation of the case-control study, in which patients are enrolled in outpatient clinics (and/or hospitals) based on a clinical case definition such as influenza-like illness (ili). patients are then tested for influenza virus, and ve is estimated from the odds ratio comparing the odds of vaccination among patients testing positive for influenza versus those testing negative, adjusting for potential confounding factors. the design leverages existing disease surveillance networks and as a result, studies using it are increasingly being reported. methods we sought to examine the theoretical basis for this design using causal analysis including directed acyclic graphs. we reviewed studies that used this design and examined the study populations and settings, the methodologic choices including analytic approaches, and the estimates of influenza ve provided. we conducted simulation studies to examine specific potential biases. results we show how studies using this design can avoid or minimize bias, and where bias may be introduced with particular study design variations. a purported advantage of the test-negative design is to minimise selection bias by health-care seeking behaviour and we demonstrate why residual bias may occur. another purported advantage of the test-negative design is minimization of misclassification of the exposure; however we show how this source of bias may persist and how exposure misclassification may be a greater cause for concern not dealt with by the study design. in our review, we found great variation in estimates, but consistency between interim and final ve estimates from the same locations, and consistency between ve estimates from inpatient and outpatient studies in the same locations, age groups and years. one outstanding issue is the potential bias due to non-collapsibility. conclusions our work provides a starting point for further consideration of the validity of the test-negative design, which is an efficient approach for routine monitoring of influenza ve that can be implemented in existing surveillance systems without substantial additional resources. harmonization of analytic approaches may improve the potential for pooling ve estimates. keywords influenza; ve; sentinel *benjamin j. cowling e-mail: bcowling@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e25, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts socio-environmental and measurement factors drive variation in influenza-like illness elizabeth lee*1 and shweta bansal1, 2 1biology, georgetown university, washington, dc, usa; 2fogarty international center, national institutes of health, bethesda, md, usa objective to assess the use of medical claims records for surveillance and epidemiological inference through a case study that examines how ecological and social determinants and measurement error contribute to spatial heterogeneity in reports of influenza-like illness across the united states. introduction traditional infectious disease epidemiology is built on the foundation of high quality and high accuracy data on disease and behavior. digital infectious disease epidemiology, on the other hand, uses existing digital traces, re-purposing them to identify patterns in health-related processes. medical claims are an emerging digital data source in surveillance; they capture patient-level data across an entire population of healthcare seekers, and have the benefits of medical accuracy through physician diagnoses, and fine spatial and temporal resolution in near real-time. our work harnesses the large volume and high specificity of diagnosis codes in medical claims to improve our understanding of the mechanisms driving spatial variation in reported influenza activity each year. the mechanisms hypothesized to drive these patterns are as varied as: environmental factors affecting transmission or virus survival, travel flows between different populations, population age structure, and socioeconomic factors linked to healthcare access and quality of life. beyond process mechanisms, the nature of surveillance data collection may affect our interpretation of spatial epidemiological patterns [1], particularly since influenza is a non-reportable disease with non-specific symptoms ranging from asymptomatic to severe. considering the ways in which medical claims are generated, biases may arise from healthcare-seeking behavior, insurance coverage, and medical claims database coverage in study populations. methods using aggregated u.s. medical claims for influenza-like illness (ili) from the 2001-2002 through 2008-2009 flu seasons [2], we developed a bayesian hierarchical modeling framework to estimate the importance of both ecological and social determinants and measurement-related factors on observed county-level variation of influenza disease burden across the united states. integrated nested laplace approximation (inla) techniques for bayesian inference were used to render our questions computationally tractable due to the high spatial resolution of our data (figure 1) and the multiplicity of models in our analysis [3]. linking data from a variety of publicly available sources, we determined the strength, directionality, and consistency of these factors over multiple flu seasons. results we found that measurement-related factors – healthcare-seeking behavior, insurance coverage, and medical claims database coverage – were strong predictors of greater ili intensity across seasons. secondarily, poverty and specific humidity were negatively associated with ili intensity for several seasons. finally, by incorporating mechanistic and measurement factors into our model, our model predictions present an improved map of influenza-like illness in the united states for the flu seasons in our study period. conclusions we present a flexible modeling approach that applies to different medical claims diagnosis codes and disease surveillance data and demonstrates the utility of bayesian hierarchical models for largescale ecological analyses. our results increase our knowledge of the spatial distribution of influenza and the underlying processes that drive these patterns, promote finer spatial targeting for different types of interventions, and enable the interpolation of burden in areas difficult to surveil through traditional public health. moreover, they highlight the relative contributions of surveillance data collection and ecological processes to spatial variation in disease, and highlight the importance of considering measurement biases when using surveillance data for epidemiological inference. figure 1. observed seasonal ili intensity for united states counties during the 2007-2008 flu season demonstrates the extent and resolution of medical claims data coverage. greater values indicate larger disease burden and grey areas had no reported data. keywords influenza; medical claims; ecological analysis; bayesian; united states acknowledgments this work was supported by the jayne koskinas ted giovanis foundation for health and policy (dissertation support grant to ecl), the rapidd program of the science & technology directorate, department of homeland security, and the fogarty international center, national institutes of health. references 1. lee ec, asher jm, goldlust s, kraemer jd, lawson ab, bansal s. mind the scales: harnessing spatial big data for infectious disease surveillance and inference. j infect dis. in press. 2. viboud c, charu v, olson d, et al. demonstrating the use of highvolume electronic medical claims data to monitor local and regional influenza activity in the us. plos one. 2014; 9(7):e102429. 3. rue h, martino s, chopin n. approximate bayesian inference for latent gaussian models using integrated nested laplace approximations. j r stat soc ser b. 2009; 71(2):319–392. *elizabeth lee e-mail: ecl48@georgetown.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e11, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts evaluation of the malaria surveillance system in kaduna state, nigeria 2016 baffa s. ibrahim*2, aisha a. abubakar1, ummulkhulthum a. bajoga2 and patrick m. nguku2 1ahmadu bello university, zaria, zaria, nigeria; 2nigeria field epidemiology and laboratory training program, abuja, nigeria objective to describe the process of operation of the system and assess its key attributes, to determine the effectiveness and efficiency of the surveillance system and make appropriate recommendations to stakeholders for its improvement. introduction malaria is a parasitic disease caused by plasmodium falciparum. about 3.2 billion people worldwide are at risk of malaria.1 children and pregnant women are particularly vulnerable to the disease. subsaharan africa carries a high share of the global malaria burden.2 effective malaria surveillance system is essential in the control and elimination of malaria. worldwide, there were an estimated 198 million cases of malaria in 2013 and 584,000 deaths.1,3,4 methods this study was conducted using the “cdc’s updated guidelines for evaluating public health surveillance system, 2001”. key stakeholders and malaria focal persons were interviewed. integrated disease surveillance and response case summary data from january to december 2014 was reviewed. data analysis was done using microsoft excel 2016 and epi-info 7. results the system provides information on malaria trends, morbidity and mortality. case definitions are well understood by participants. all malaria focal persons (mfps) were willing to continue using the system. standardized data collection tools are available in 91% of health facilities (hf). the system was rated flexible by 91% of mfps. the system was however not representative because data were essentially from public health facilities only. the system has an average timeliness of 37.7% and completeness of 59.4%, both parameters were below the state’s 80% target. about 91% mfps had refresher training, while 78% mfps received supportive supervision. main challenges identified were lack of commodities in all hfs, and inadequate mobile facilities in 70% of hfs. conclusions the kaduna state malaria surveillance system is meeting its objectives. however, challenges are observed in its timeliness, representativeness, and data quality. efforts should be made to integrate tertiary and private health facilities into the system. mfps need more training on malaria reporting to improve timeliness and data quality. there is the need to improve on the supply of malaria treatment commodities to all health facilities within kaduna state. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e177, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords malaria; surveillance; evaluation; kaduna acknowledgments the authors gratefully acknowledged the support of the kaduna state malaria control program through its program manager mr. ibrahim sale and its monitoring and evaluation (m&e) officer mrs. sakina maikudi. all malaria focal persons of the state. references 1. organization wh. background brief on the proposed targets and estimated costs of implementation of the draft global technical strategy for malaria (2016-2030): who; 2014 [3/20/2016]. 2. nnebue cc, onwasigwe cn, adogu pou, onyeonoro uu. awareness and knowledge of disease surveillance and notification by healthcare workers and availability of facility records in anambra state, nigeria. nigerian medical journal : journal of the nigeria medical association. 2012;53(4):220-5. 3. german rr, lee l, horan j, milstein r, pertowski c, waller m. updated guidelines for evaluating public health surveillance systems. mmwr recomm rep. 2001;50(1-35). 4. declich s, carter ao. public health surveillance: historical origins, methods and evaluation. bulletin of the world health organization. 1994;72(2):285. *baffa s. ibrahim e-mail: baffasule@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e177, 2017 isds16_abstracts-final 88 isds16_abstracts-final 89 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts validation of syndromic ili data for use in cdc’s ilinet surveillance, pennsylvania sameh w. boktor*1, kirsten waller1, lenee blanton2 and krista kniss2 1epidemiology, pennsylvania department of health, harrisburg, pa, usa; 2centers for disease control and prevention, atlanta, ga, usa objective discuss use of syndromic surveillance as a source for the state’s ili/influenza surveillance discuss reliability of syndromic data and methods to address problems caused by data outliers and inconsistencies. introduction ilinet is a cdc program that has been used for years for influenzalike illness (ili) surveillance, using a network of outpatient providers who volunteer to track and report weekly the number of visits due to ili and the total number of visits to their practice. pennsylvania has a network of 95 providers and urgent care clinics that submit data to ilinet. however, ongoing challenges in recruiting and retaining providers, and inconsistent weekly reporting are barriers to receiving accurate, representative, and timely ili surveillance data year-round. syndromic surveillance data have been used to enhance outpatient ili surveillance in a number of jurisdictions, including pennsylvania. at present, 156 hospitals, or 90% of all pennsylvania hospitals with emergency departments (eds), send chief complaint and other information on their ed visits to the department of health’s (padoh) syndromic surveillance system. padoh evaluated the consistency and reliability of ili syndromic data as compared to ilinet data, to confirm that syndromic data were suitable for use in ilinet. methods pennsylvania ilinet data from the past 6 influenza seasons (20112012 to 2016-2017, or 314 weeks of data) were downloaded from the cdc’s ilinet website. the statewide weekly percent of visits due to ili in ilinet was used as the standard for comparisons. for syndromic surveillance, padoh uses the epicenter platform hosted by health monitoring systems (hms); visit-level data are also stored in sas datasets at padoh, and hms forwards a subset of data to the national syndromic surveillance system program. using syndromic data from the same time period, the proportion of weeks with no syndromic data available was calculated for each facility. a state-developed ili algorithm (very similar to the 2016 algorithm developed by the isds syndrome definitions workgroup) was applied to ed visit chief complaint data to identify visits likely to be due to ili. the algorithm flags the er visit as ili if chief complaint has any combinations of words for flu or fever plus either cough and sore throat or fever and both cough or sore throat. the percent of ed visits due to ili per the syndromic algorithm (ilisyn) was calculated for each week by hospital and state-wide. facility ilisyn trends were compared to the state level percent ili data from ilinet by visually examining plots and by calculating pearson correlation coefficients. facilities that had >=15 weeks where ilisyn differed from percent ili in ilinet by more than 5% were considered to be poorly correlated. results a total of 156 hospitals were evaluated in the study. twenty of the hospitals were excluded because they did not have syndromic data for at least 50% of the weeks in the study period, and an additional 20 were excluded because they had not agreed to have data forwarded to cdc. of the remaining 116 facilities, individual facility correlation coefficients between ilisyn and ilinet trends ranged from 0.03 to 0.82 (examples are in figure 1). twenty-four hospitals (20.7%) were determined to be poorly correlated. when data from the remaining 92 hospitals were combined, the state ilinet and state-wide ilisyn trends were strongly correlated statistically and graphically (r=0.82, p <0.0001, figure 2). syndromic data from these 92 facilities were deemed acceptable for inclusion in ilinet. conclusions syndromic surveillance data are a valuable source for ili surveillance. however, evaluation at the hospital-specific level revealed that useful information is not obtained from all facilities. this project demonstrated that validation of data at the facility level is crucial to obtaining reliable and meaningful information. more work is needed to understand which factors distinguish well-correlated from poorly-correlated facilities, and how to improve the quality of information obtained from poorly-correlated facilities. keywords influenza like illness; syndromic surveillance; data validation methods acknowledgments jonah m. long, pennsylvania department of health *sameh w. boktor e-mail: sboktor@pa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e67, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts wisconsin’s novel approach to creating a health outcomes opioid surveillance system milda aksamitauskas*1, lisa bullard-cawthorne2, ousmane diallo1, crystal gibson2, justin martin1, richard miller1, christine p. muganda1, aman tandias1 and anne l. ziege1 1office of health informatics, wisconsin division of public health, madison, wi, usa; 2bureau of community health promotion, wisconsin division of public health, madison, wi, usa objective wisconsin is leading the way in novel approaches monitoring health outcomes for opioid-related adverse events. this panel will share innovative public health informatics methods that harness various data sources (e.g., prescription drug monitoring data (pdmp), death, birth and hospitalization data) for population health surveillance. discussion will include topics on detection of drug abuse and diversion, identifying potential neonatal abstinence syndrome cases, surveillance of substance-related hospitalizations and overdose deaths, and modeling opioid-related mortality risk factors. figure 1. health outcomes opioids surveillance system diagram figure 2. ratios of mme and length of prescription for select groups (2013-2015) map 1. filled opioids over 90mme, southeast region local heaht departments, wisconsin, 2015. keywords prescription drug monitoring program (pdmp); substance abuse; near real-time surveillance; automation; data linking references pdmp center of excellence [internet]. waltham: brandeis university, pdmp center of excellence; c2010-2013 [cited 2016 august 22]. available from: <a href="http://www.pdmpexcellence.org/">http:// www.pdmpexcellence.org/</a>. *milda aksamitauskas e-mail: milda.aksamitauskas@wi.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e53, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 1international society for disease surveillance, boston, ma, usa; 2state of ct dept of public health, hartford, ct, usa objective to continue efforts in characterizing the challenges experienced by influenza surveillance coordinators and other practitioners conducting surveillance for the presence of avian influenza, novel respiratory diseases, and other globally emerging viruses in an era of limited resources among public health agencies. introduction public health practitioners endeavor to expand and refine their syndromic and other advanced surveillance systems which are designed to supplement their existing laboratory testing and disease surveillance toolkit. while much of the development and widespread implementation of these systems was previously supported by public health preparedness funding, the reduction of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. the appearance of highly-pathogenic avian influenza (hpai) h3n2v, and other novel influenza a viruses required agencies to carefully identify systems which provide the most cost-effective data to support their public health practice. the global emergence of influenza a (h7n9), ebola virus strains, middle east respiratory syndrome coronavirus (mers-cov), and other viruses associated with high mortality, emphasize the importance of maintaining vigilance for the presence of emerging diseases. methods this project included a review of data obtained from a survey of public health practitioners recruited among members of the international society for disease surveillance (isds) public health practice committee (phpc) during 2012 and 2013 (1, 2). in these surveys, questions were selected for discussion and additional responses collected from influenza surveillance coordinators using a web-based survey tool managed by isds staff on behalf of the phpc. during 2014 and 2015, additional information was requested to assess efforts to identify highly-pathogenic avian and other novel influenza strains, mers-cov, and other emerging viruses (3). special emphasis was made to obtain information on comparative approaches to costeffective surveillance in response to an earlier isds policy paper (4). an early fall 2015 follow-up survey in being prepared to obtain latebreaking data. results the 2015 survey received an initial response from the majority (82%) of influenza surveillance coordinators practicing throughout the united states. this latest survey revealed that most jurisdictions continue to be challenged to be able to maintain the variety of surveillance systems needed for conducting emerging disease surveillance. a major challenge continues to be the recruitment and retention of well-trained and experienced public health and informatics staff to maintain these systems in an era of increasingly limited resources. many public health practitioners are tasked with establishing new surveillance protocols for an increasing number of diseases associated with novel and emerging viruses including hpai, influenza a (h3n2)v infections associated with agricultural fairs and ruling out influenza a (h7n9), mers-cov, and even ebola virus infections. most jurisdictions continually struggle to determine which surveillance systems are the most cost-effective for providing the most valuable data in the face of decreasing funding. conclusions public health agencies continue to strive to develop and maintain cost-effective disease surveillance systems to better understand the burden of disease within their jurisdictions. the emergence of novel influenza, other respiratory viruses and other emerging diseases offer new challenges to public health practitioners. the importance of maintaining sufficient infrastructure and the trained personnel needed to operate surveillance systems for optimal disease detection and public health response readiness cannot be understated. expansion of academic training programs and promotion of careers in public health surveillance will provide a pool of competent professionals to properly staff public health agencies. keywords situational awareness; ebola; mers-cov; novel influenza surveillance; resource limitations references [1] siniscalchi aj, schulte a. 2013. can novel flu surveillance be conducted with limited resources? ojphi;5(1):169. [2] siniscalchi aj, ishikawa c. 2014. searching for mers and novel flu with limited resources. ojphi;6(1):e65. [3] siniscalchi aj, evans b. 2015. ebola, enterovirus, mers, novel flu, and other challenges for public health surveillance practitioners. ojphi;7(1):e53. [4] mirza n, reynolds t, coletta m, et al. 2013. steps to a sustainable public health enterprise: a commentary from the international society for disease surveillance. ojphi;5(2):1-12. *brooke evans e-mail: bevans@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e162, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts acute flaccid paralysis surveillance system evaluation, oyo state, nigeria; 2008-2014 maureen o. anyanwu* epidemiology and medical statistics, nigeria field epidemiology and laboratory training programme/university of ibadan, ibadan/abuja, nigeria objective we evaluated the afp surveillance system in oyo state to assess its attributes and determine if it was meeting its set objectives. introduction in september, 2015, nigeria was delisted from the list of polio endemic countries globally. to be certified polio free, the country must attain and maintain certification standard acute flaccid paralysis (afp) surveillance for additional two-years. in oyo state, no case of wild polio virus (wpv) has been reported since february, 2009. methods we used the centre for disease control and prevention updated guidelines for evaluating public health surveillance system. we conducted a retrospective review of afp surveillance data between 1st january, 2008 and 31st december, 2014. we conducted in-depth interviews with identified stakeholders. semi-structured questionnaires were administered to disease surveillance and notification officers (dsnos) and afp focal persons. univariate analysis was performed by calculating frequencies, means and proportions using microsoft excel 2010. results the case definition of afp and the tools for reporting are simple. of the 897 afp cases detected during the period under review (2008-2014), 20 (2.2%) were laboratory confirmed wpv. the sensitivity of the system between 2008 and 2014 measured by the annualized non-polio afp (npafp) rate was consistently above the target. of ≥ 2/100,000 population (mean=3.96, standard deviation (sd): 0.48). the mean npafp rate for underperforming lgas during the review period was 1.6, sd: 0.31. the mean stool adequacy and timeliness were 91.43% (sd: 18.3) and 91.3% (sd: 20.3) above the target of ≥ 80% respectively. the mean data quality was 90% (target is ≥ 90; sd: 3.8). positive predictive value (pvp) was 2% (2008 -2009), and 0% in 2010-2014. conclusions the oyo state afp surveillance system is simple, flexible, sensitive and meeting its set objectives. however, pvp was low and the system’s operating conditions are not stable. all the lgas, at one point during the period under review did not meet the npafp and npent rates. we recommended that more logistic support should be provided for non-performing lgas to improve case reporting, investigation, and response. dsnos should be re -sensitized on reverse cold chain, so as to improve the npent rate keywords surveillance; evaluation; wild polio virus; nigeria acknowledgments nigeria field epidemiology and laboratory training programme african field epidemiology network centre for disease control, atlanta, usa oyo state ministry of health *maureen o. anyanwu e-mail: maureenanyanwu23@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e92, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee surveillance team, gyeonggi infectious disease control center, seongnam-si, korea (the republic of) objective this study will explore the timeliness of the korean national notifiable disease surveillance system (knndss) at provincial level, and suggest a reasonable duration for publication the weekly reports to improve timely feedback of infectious disease surveillance data to physicians and community. introduction in south korea, the nndss is organized at three levels: local, provincial, and central. at the local level, physicians report the cased to the public health center (phc) and phc conduct control measures. at the provincial level, the phc reports the cases to the department of health (doh) of the province and doh obliged to report the cases to the korea centers for disease control and prevention (kcdc) and feedback of the surveillance data to phc and physicians. at the central level, the disease web statistics system (http://is.cdc.go.kr/ dstat/index.jsp) provides real-time data on 54 national notifiable infectious diseases [1]. although there are variations according to the disease and surveillance step, the knndss generally functions well in terms of timeliness and yu et al. reported that 89.7% of mumps, one of the most incident contagious disease in south korea, reported in 15 days after the physician’s notification [2]. to improve the timeliness of feedback at the provincial level, we explored the knndss data and suggested an evidence based duration for publication of the weekly reports, in consideration of reducing the publication schedule. methods we analyzed the reported infectious disease surveillance data(n=23,486) at gyeonggi province in 2014. time points recorded in the knndss data include dates of onset, diagnosis, doctor’s notification to the phc, phc reporting to the doh, and doh reporting to the kcdc [2]. using these dates, we defined the time lags in days from physician’s notification to central appraisal, and the number of cases reported last week was summarized by the days of notification and publication schedule. results the average time lags were 9.6 days (sd = 30.3) from physician’s notification to central appraisal. 21,108(92.73%) of reported cases were finished central appraisal till next friday. 96.82%, 92,17% and 90.86% of cases notified on sunday, wednesday and saturday, respectively, could be covered on next friday. 20,491(90.02%) of reported cases were finished central appraisal till next wednesday. 96.13%, 90.47% and 83.60% of cases notified on sunday, wednesday and saturday, respectively, could be covered on next wednesday. otherwise, only 15,684(68.90%) of reported cases were finished central appraisal till next monday. 95.16%, 80.91% and 4.54% of cases notified on sunday, wednesday and saturday, respectively, could be covered on next monday. conclusions our study suggested the utility of the assessment of time-lag distributions for the feedback strategies to improve surveillance and regional infectious disease controls. keywords korean national notifiable disease surveillance system; notifiable disease; timeliness; weekly report references [1] park, sunhee, and eunhee cho. “national infectious diseases surveillance data of south korea.” epidemiology and health 36 (2014). [2] yoo, hyo-soon, et al. “timeliness of national notifiable diseases surveillance system in korea: a cross-sectional study.” bmc public health 9.1 (2009): 93. *seon-ju yi e-mail: yiseonju@gidcc.or.kr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e176, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance 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surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an 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jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke 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system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and 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markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association 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weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain 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and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a modelling medications for public health research online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e190, 2016 ojphi modelling medications for public health research d. van gaans 1, s. ahmed 1, k. d’onise 1, j. moyon 2, g. caughey 3, r. mcdermott 4 1. centre for research excellence in the prevention of chronic conditions in rural and remote populations, university of south australia, south australia 2. information strategy and technology, university of south australia, south australia 3. pharmacy and medical sciences, university of south australia, south australia 4. college of public health, medical and veterinary sciences, james cook university, queensland abstract most patients with chronic disease are prescribed multiple medications, which are recorded in their personal health records. this is rich information for clinical public health researchers but also a challenge to analyse. this paper describes the method that was undertaken within the public health research data management system (phredms) to map medication data retrieved from individual patient health records for population health researcher’s use. the phredms manages clinical, health service, community and survey research data within a secure web environment that allows for data sharing amongst researchers. the phredms is currently used by researchers to answer a broad range of questions, including monitoring of prescription patterns in different population groups and geographic areas with high incidence/prevalence of chronic renal, cardiovascular, metabolic and mental health issues. in this paper, we present the general notion of abstraction network, a higher level network that sits above a terminology and offers compact and more easily understandable view of its content. we demonstrate the utilisation of abstraction network methodology to examine medication data from electronic medical records to allow a compact and more easily understandable view of its content. keywords: medication, public health, modelling, data mapping doi: 10.5210/ojphi.v8i2.6809 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction population health scientists aim to understand disease patterns and develop approaches for disease prevention, detection, and diagnosis at an early stage to reduce the burden of disease [1]. within the last decade there has been a rapid increase in the availability of health care data and never before have population scientists had the capacity to collect, share, and analyse data as they have today. population health researchers often collect data from patients directly or from their health service records. personal health records (phrs) of patients contain a wealth of information, but can be challenging to analyse [2]. there is a variety of paper and electronic medical records (emrs) in use, despite ongoing attempts to standardise data collection and a variety of clinical coding systems implemented by emr vendors [3]. terminologies and terminological systems play an important online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 role in many medical information processing environments, giving rise to the “big knowledge” challenge, when terminological content comprises of tens of thousands to millions of concepts arranged in a non-relational manner [4]. the science of bioinformatics can provide essential tools and a framework for population scientists to manage this potentially overwhelming amount of data [1]. the challenge for health information technology is to design systems that are powerful enough not only to handle the volume and complexity of medical data, but also to support both patients and professionals, resulting in improved health care, reduced costs and health outcomes for patients [2]. data standardization and harmonization can address some of the obstacles to data sharing and pooling [3] [5] [6] [7]. data harmonization is used when data standardization is not possible to achieve interoperability across systems. this work is laborious and entails a transdisciplinary approach wherein informaticists, measurement and topical experts, biostatisticians, and ethicists combine their knowledge to ensure the integrity and security of harmonized data [5] [6]. one method applied to harmonise terminology is the abstraction network. an abstraction network overlays a terminology’s underlying network structure at a higher level of abstraction. in particular, it provides a more compact view of the terminology’s content, avoiding the display of minutiae [4]. the notion of an abstraction network is presented as a means of facilitating the usability, comprehensibility, visualization, and quality assurance of terminologies [8]. one very important feature of an abstraction network is that it is typically multiple orders of magnitude smaller in size than its underlying terminology. this compact structure makes abstraction networks much more manageable from visualization and comprehension perspectives [9]. the reduction in size of an abstraction network is obtained by structurally dividing a large terminology into smaller parts, each of which is represented by one constituent entity (node) of the abstraction network [4]. there are two main ways to define the set of nodes of an abstraction network, intrinsic and extrinsic. intrinsic abstraction networks derive them from the concepts and relationships of the underlying terminology itself. that is, some terminology concepts are of a general nature and can be used to properly categorize other elements [4]. another alternative for choosing nodes is by a domain expert gleaning broad categories, befitting the terminology’s subject matter, from external sources [4]. for example, the categories might be taken from the general body of literature in a subject area or from a standard reference work. an abstraction network derived in this way from sources external to the terminology itself is called extrinsic. for example, for a terminology in the medical field, broad categories could be disease, laboratory test, and procedure. extrinsic networks do put a burden on the designer in terms of determining the level of refinement [4]. the aim of this study was to map medication data retrieved from individual patient health records for population health researcher’s use through the use of an abstraction network to create a compact and more easily understandable view for public health research. methods the modelling of medication data within phredms was based on the monthly index of medical specialties (mims) [10]. in australia mims is commonly used to define independent medicine information used by australian healthcare professionals. medications within mims are classified by: the body system for which the medicine is used, e.g. cardiovascular system medication class, e.g. beta-adrenergic blockers medication subcategory, e.g. rapid acting (under insulins) http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 route, e.g. inhaled, topical, anorectal -medication brand & generic name medications within patient records are often recorded with both brand and generic names being used interchangeably during the prescribing process. whilst a category of medication may have utility across multiple medical conditions, mims medication subcategories are made up of drugs that are similar in terms of both function and formulation. the formulation of any given medication, in terms of its constituent compounds and their function, remains similar regardless of pharmaceutical producer, brand or generic naming. for example, beta blockers are a subcategory of anti-hypertensive drugs. atenolol (brand names include noten & tenormin) and propranolol (commonly branded as apo-propranolol) are both beta blockers. the active ingredient in atenolol is always a benzacetamide compound named atenolol, and in propranolol it is propranolol hydrochloride. both compounds/ active ingredients exert a therapeutic effect by blocking beta adrenergic receptors. for research purposes, we might want to analyse data at the level of beta receptor blockers, or at anti-hypertensives, or even at a higher level of all drugs that exert an action upon the cardiovascular system. a working group comprising of a database administrator, a three person database development team, two researchers and a principal investigator met on an as needed basis to develop the medications mapping model. the researchers were qualified general medical practioners, who have a number of years of experience with patient medical record information. an electronic extract of medications from a clinical information system was compared to the mims database by the researchers. after primary classification of brand and generic medications by the researchers, the working group utilised an iterative process over multiple meetings to clarify the mapping. medications within the phredms were mapped based on active ingredients/constituent compounds (see table 1 for an example). by basing categorisation on the common constituent compounds (often listed in the generic medication name) instead of nomenclature (brand or generic medication names), the working group were able to map medication into subcategories and categories to enable analysis on multiple levels from a single data collection, as well as aid categorisation of data from varied sources. medications within the phredms are mapped based on active ingredients/constituent compounds (table 1). the mims database was used to link generic and brand names to active ingredients, with generic names often mirroring the active ingredient present. phredms does not store the name of branded drugs, it only stores the ingredients. a custom built sql script extracting as many ingredients as are listed against the branded drug in communicare. this was usually no more than three active ingredients. the phredms attaches an active ingredient to any number of subcategories. subcategory descriptors relate either to function or route of administration. categories can be constituted of multiple subcategories and multiple categories can be used to treat a particular health condition. an exception column exists to detail any medications that deviate in function from the rest of their class. medication categories recorded in phredms were examined, with medications focused to the major medication classes and of interest to the research group. however this model can be scaled to include more categories of medication as required. each medication category contains an other subcategory, to classify any new medications that do not fall into pre-existing subcategories. there is also an overall other category, to classify any medications extracted that do not fall into the limited categories as defined in phredms. new medications which fall into existing subcategories or categories can be added by the researcher forwarding their details to the database administrator. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 table 1: medication mapping model in phredms brand name generic name/ active compound function medication subcategory medication category apo carvedilol carvedilol non selective beta blocker beta blocker anti-hypertensive medication dilatrend carvedilol non selective beta blocker beta blocker anti-hypertensive medication coveram amlodipine calcium channel blocker anti-hypertensive medication perindopril arginine ace inhibitor results initially there were 7416 medications extracted from the communicare from which were derived a list of 601 compounds. these compounds were then mapped into 63 sub-categories and 19 categories. nutritional supplements have been collapsed to just their generic compounds as they are primary data for the researchers using the phredms (table 2). table 2: medication categories recorded in the public health research data management system. category subcategory full name full name abbreviation insulins short acting insulins short long acting insulins long intermediate acting insulins intermediate rapid acting insulins rapid mixed insulins mixed oral hypoglycaemic agents biguanides sulphonylureas thiazolidinediones alpha-glucosidase inhibitors meglitinides dipeptidyl peptidase-4 inhibitors dppis antihypertensives http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 beta blockers b-blockers angiotensin converting enzyme inhibitors ace inhibitors calcium channel blockers ca channel blockers centrally acting antihypertensive agents centrally acting sympatholytics other antihypertensives angiotensin 2 receptor antagonists a2 antagonists vasodilators nitrates other vasodilators diuretics thiazides potassium sparing diuretics k+ other diuretics loop diuretics loop antiarrhythmics class i class ia class ib class ic class ii class iii class iv class v antibiotics penicillins macrolides quinolones rheumatic heart disease rhd other antibiotics nutritional supplements fatty acids http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 vitamins minerals other supplements folates & derivatives folates vaccines urinary acidifiers urinary alkalinisers hypolipidaemics serum cholesterol reductors 3-hydroxy-3-methylglutaryl-coenzyme (hmg-coa) reductase inhibitors hmg coa inhibitors fibrates intestinal absorption inhibitors bile acid sequestrants other hypolipidaemics anticoagulants anti factor xa antiplatelet aggregation antithrombotic thrombin inhibitors coumarins superfiial thromboses other anticoagulants antifungals corticosteroids glucocorticoids mineralcorticoids other corticosteroids alpha blockers anti-asthmatics beta2 agonists anticholinergics inhaled corticosteroids other bronchodilators http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 anaesthetics other discussion one way to confront the “big knowledge” challenge is to provide auxiliary support structures to aid in terminology use and maintenance [4]. highly interactive partially-automated mapping tools that are directed by human input to automate parts of the mapping process, with specific expert input, are a promising alternative to fully automated methods [11]. in this study we were able to successfully use an abstraction network to map medication data from patient electronic medical records. this enables patient information to be easily utilised by population health researchers. patient health information is largely stored in emrs, which are used by most health care providers for the regular documentation of care provided. administrators also use emrs for purposes of reporting upon and monitoring activities in health facilities. whilst some patient data is still stored in paper format, researchers are increasingly asking to access emr data for the purposes of population level research, including the development of clinical decision support tools. the vast amounts and complexity of information within emrs complicates information management for each of these groups and increases the risk of incorrect decisions being made due to the difficulties of sifting through so much information. likewise with paper records, the amount, variability and complexity of information stored within them makes it difficult to collect and analyse this data in a reliable manner. managing medications data is a particular challenge; as the burden of chronic disease increases in the population [12] so do issues related to polypharmacy and its management [13] [14] [15]. clinicians and researchers need new tools to manage medicationrelated information. although there can be an overwhelming number of medications and medicine classes prescribed per patient, pharmacology is relatively structured when it comes to classification of the active ingredients or constituents of medication. additionally, various therapeutic guidelines are available that are already in use in practice to thoroughly document and categorise medication, ensuring relatively easy access to the expert opinions required to inform any medications mapping. for research purposes, data is sourced from various emrs and paper patient files, which capture medications data related to mims in different formats. a research database like phredms needs to be flexible enough to contain medications data; 1. from various sources, which may or may not be inter-relatable 2. across various topics, which are externally set by researchers accessing the database 3. that reflects changes over time limitations the development of the abstraction network to manage medication records for individual patient’s for chronic disease management research has been extremely labour intensive. the abstraction network requires updating before new patient records are added to an individual research project to capture new medications that have been recently released into the population. conclusion mapping in the manner described above has enabled data to be categorised to allow researchers to query and analyse it at multiple levels (disease grouping, category, subcategory, and compound) relevant to their particular research question, without compromising the richness of individual level medications data. it allows all data collected by various researchers from various sources to be http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e190, 2016 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22750536&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25877120&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25877120&dopt=abstract http://dx.doi.org/10.5694/mja13.00172 modelling medications for public health research introduction methods results discussion limitations conclusion acknowledgements references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts relationship between baseline influenza-like illness rates and healthcare settings dino p. rumoro1, shital c. shah*1, gillian s. gibbs1, marilyn m. hallock1, gordon m. trenholme1 and michael j. waddell2 1emergency medicine, rush university medical center, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to examine the baseline influenza-like illness (ili) rates in the emergency departments (ed) of a large academic medical center (amc), community hospital (ch), and neighboring adult and pediatric primary care clinics. introduction the primary goal of syndromic surveillance is early recognition of disease trends, in order to identify and control infectious disease outbreaks, such as influenza. for surveillance of influenza-like illness (ili), public health departments receive data from multiple sources with varying degrees of patient acuity, including outpatient clinics and emergency departments. however, the lack of standardization of these data sources may lead to varying baseline levels of ili activity within a local area. methods geographic utilization of artificial intelligence in real-time for disease identification and alert notification (guardian) – a syndromic surveillance program – was used to automate ili detection using free text chief complaint/reason for visit fields and vital signs for a large amc ed, ch ed, and neighboring outpatient clinics during the summer (june 15, 2016 to august 18, 2016) in order to create a baseline. the guardian system defined ili as fever (temperature ≥ 100°f) and cough and/or sore throat. descriptive analysis of the observed ili rates along with bivariate anova with post hoc bonferroni and t-test were utilized to examine the difference within the settings. results the average ili rate for eds is higher than the clinics by at least 0.39%. the ched had 4.23% baseline ili rate as compared to 1.35% for amc-ed. while the amc – clinics have 0.96% baseline ili rate as compared to 0.25% for ch – clinics. the ched and amc – clinics represented higher variations. based on bivariate test, ch – ed was significantly different than amc – ed, amc clinics, and ch – clinics (f= 10.58, df = 1238, p<0.05). for the amc – clinics, the average ili rate for clinics providing services to adult patients was 0.66% (sd: 4.5%) as compared to 2.03% (sd: 10.81%) for pediatric clinics, which was not statistically significant. conclusions the ch ed has higher baseline ili rates compared to other settings, as well as the cdc region 5’s baseline (1.9% for 20152016). based on previous studies1, this is likely due to providers’ use of chief complaint free text fields. thus, the ch – ed will have higher thresholds for widespread ili activity. in addition, differences in baseline ili rates between amc ed, amc clinics, and ch clinics may result in different thresholds for widespread ili activity (i.e., average + 3 standard deviations). the ch – ed and amc – clinics had higher baseline standard deviations, indicting variations in underlying patient populations. in addition, pediatric clinics have higher baseline ili activity but also higher variations, indicating the unique characteristics of pediatric patients. thus, due to the above findings, there is a need to closely monitor the ili rates at various healthcare sites for both timing of onset, as well as the intensity of ili activity. table 1. baseline influenza-like illness (ili) rates during summer 2016 note: amc = academic medical center and ch = community hospital. anova with post hoc bonferroni results ch ed significantly different than amc ed, amc clinics, and ch clinics keywords guardian; influenza-like illness; baseline ili rates acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. references 1. rumoro, d., shah, s., hallock, m., gibbs, g., trenholme, g., waddell, m., & bernstein, j. the impact of documentation style on influenzalike illness rates in the emergency department [abstract]. online journal of public health informatics. 2016;8(1) e35. *shital c. shah e-mail: shital_shah@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e119, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts adoption of public health readiness guidelines for meaningful use eunice r. santos*, wesley mcneely, biru yang and raouf r. arafat office of surveillance and public health preparedness, houston health department, houston, tx, usa objective to describe the challenges and lessons learned for public health and providers to successfully implement public health meaningful use readiness guidelines and navigate from intent to submission of production data while simultaneously upgrading surveillance systems. introduction the syndromic surveillance consortium of southeast texas (sscset) consists of 13 stakeholders who represent 19 counties or jurisdictions in the texas gulf coast region and receives health data from over 100 providers. the houston health department (hhd) maintains and operates the syndromic surveillance system for the gulf coast region since 2007. in preparation for meaningful use (mu) the hhd has adapted and implemented guidance and recommendations from centers for disease control and prevention, office of national coordinator for health information technology and others. hhds goal is to make it possible for providers meet mu specification by facilitating the transmission of health related data for syndromic surveillance. the timing of the transition into mu overlaps with the change in syndromic surveillance systems. keywords meaningful use; health information technology; syndromic surveillance; local health department acknowledgments houston health department would like to acknowledge the contributions from ali momin (hhd it), kavitha gantha (hhd it), chris meredith (texas dshs/talho), bill stephens(tarrant county public health department), daveheinbaugh (tarrant county public health department), texas s2 (texas dshs syndromic surveillance group), and the syndromic surveillance consortium of southeast texas (sscset), cdc essence. *eunice r. santos e-mail: eunice.santos@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e80, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts streamling syndromic surveillance submission on a dime: oregon’s experience laurel boyd* and michelle barber oregon public health division, portland, or, usa objective to design a low budget process to enroll, track and approve syndromic submitters for ongoing submission of data to the oregon public health division introduction in 2012, the oregon public health division (ophd) took advantage of the opportunity created by meaningful use, a centers for medicare & medicaid services (cms) incentive program, to implement statewide syndromic surveillance. the oregon syndromic surveillance project, or oregon essence, began accepting mucompliant hl7 2.5.1 data in late 2013. early onboarding efforts were labor-intensive and led to the creation of a testing queue. as interest in submitting syndromic data increased, oregon essence streamlined the onboarding process by creating guidance for hl7 message construction, message testing and submitter business process details (collectively referred to as “onboarding documents”). oregon essence also built a project management database to track mu testing statuses and data quality variations. with this system, oregon essence collected, tested and approved all 32 eligible health systems (56 hospitals) for production-level submission by mid-2015. one health system (with four hospitals) continued to send non-mu compliant syndromic data for the duration of the project period. methods initially, oregon essence began onboarding syndromic submitters on a first-come-first-served basis. the lack of a clear process for onboarding, a single fte devoted the endeavor and substantial interest in submitting, led to a testing queue. to streamline the onboarding process and accommodate the testing timelines of all submitters, oregon essence created tools to allow for self-paced testing followed by short duration, intensive testing with the project. oregon essence-branded onboarding documents incorporated available resources such as the cdc’s public health information network messaging guide for syndromic surveillance: emergency department and urgent care data, release 1.1 (august 2012) and the nist 2014 edition onc health it certification hl7v2 syndromic surveillance reporting validation tool. submitters began self-paced testing by testing their own messages using the nist tool and sending successful reports back to oregon essence. they then filled out an oregon essence business process survey which asked for meta-data and project contact information. oregon essence built a project managment database in filemaker v14 (filemaker inc., santa clara, ca usa), used to support the statewide communicable disease database, to store information from the business process survey. after completing self-paced testing, submitters selected a single week for intensive testing with oregon essence. each health system’s project staff (registration staff, technical project lead, hl7 translator and data exchange lead) met daily with oregon essence to test messages. oregon essence used rhapsody integration engine v6.2.1 (orion health, auckland, nz), already in use at ophd for electronic lab reporting, to parse test data into a test database and then generated a report for each testing session using sas v9.4 (sas institute inc., cary, nc, usa). the report indicated whether or not the submitter had achieved production-level syndromic messaging by the end of this week of intensive testing. the project management database stored notes from each testing session along with mu testing dates. results oregon essence developed their onboarding documents between november, 2012 and march, 2013 and achieved 100% syndromic submission from eligible health systems in june, 2015. the average duration of onboarding (from initiation of the testing process to achieving production submission) of a single health system decreased from 23 months in 2012 to 4 months in 2014 (see duration of onboarding syndromic submitters: oregon 2012-2015). as interest in the project grew (number of submitters contacting ophd), the amount of time spent onboarding decreased. oregon essence uses their project management database for ongoing syndromic data quality improvement and to communicate mu dates to submitters (by generating health system-specific emails directly from the database). filemaker, rhapsody and sas are all currently used by ophd and did not require any additional expense for their use in this testing process. oregon essence plans to use this onboarding process to collect urgent care data for stage 3 mu. conclusions the onboarding process created by oregon essence streamlined syndromic data submission without the purchase of additional programs or the hiring of additional project staff. submitting facilities benefited from this process by testing syndromic messages without waiting in a testing queue. the project management database created for the testing process will continue to benefit submitters by storing mu testing dates and information for ongoing quality assurance evaluations. the success of this project took advantage of existing informatics capabilities at ophd and speaks to the importance of those skills in public health practice. oregon essence will use these methods again in 2017 to collect urgent care data for syndromic surveillance. duration of onboarding syndromic submitters: oregon 2012-2015 *not eligible for onboarding: 1 keywords syndromic; onboarding; meaningful use acknowledgments this publication was supported by cooperative agreements, number nu90tp000544 and 5u50oe000068-02, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services. *laurel boyd e-mail: laurel.boyd@state.or.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e67, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 1school of environmental sciences, university of east anglia, norwich, united kingdom; 2institute of food research, norwich, united kingdom; 3public health england, birmingham, united kingdom objective to analyse the use of bayesian network structural learning to identify relations between syndromic indicators which could inform decision-making processes introduction syndromic surveillance systems often produce large numbers of detections due to excess activity (alarms) in their indicators. few alarms are classified as alerts (public health events that may require a response). decision-making in syndromic surveillance as to whether an alarm requires a response (alert) is often entirely based on expert knowledge. these approaches (known as heuristics) may work well and produce faster results than automated processes (known as normative), but usually rely on the expertise of a small group of experts who hold much of their knowledge implicitly. the effectiveness of syndromic surveillance systems could be compromised in the absence of experts, which may significantly impact their response during a public health emergency. also, there may be patterns and relations in the data not recognised by the experts. structural learning provides a mechanism to identify relations between syndromic indicators and the relations between these indicators and alerts. their outputs could be used to help decision makers determine more effectively which alarms are most likely to lead to alerts. a normative approach may reduce the reliance of the decision making process on key individuals methods we used bayesian network structure learning to represent and quantify relations between indicators used by the english realtime national syndromic surveillance system for classifying and investigating public health alerts. summary data were obtained from four national syndromic surveillance systems coordinated by public health england. the network structure was initially learnt from the data using score-based algorithms. temporal tiers were defined to separate variables on a timeline based on expert knowledge. networks were constructed using a greedy search and iteratively adding, deleting, or reversing arcs, assessing the accuracy of the final network. the bayesian information criterion was used to evaluate goodness of fit. joint probability distribution for the data was estimated once the algorithm learnt the network structure. we evaluated how different algorithms and data features influence alert classification. the ability of the learnt networks for classifying alerts of public health importance was assessed retrospectively using 1000 bootstrap samples from the summary dataset for 2014 results provisional results show that our networks effectively evidenced the relations between variables and trends in the data known by the experts. the algorithm identified relations between variables that were unknown. the decision as to whether an alarm leads or not to an epidemiological alert was sensitive to several characteristics of the data stream and the period when such extra activity was detected. different algorithms showed different degrees of specificity but most of them showed similar levels conclusions decision-making methods may be difficult to maintain and replicate if they exclusively rely on expert knowledge. bayesian networks may be useful to explicitly decompose and graphically describe the relationships among variables that are more useful for deciding whether extra activity in a syndromic indicator requires a response. when bayesian networks are able to closely mimic the behaviour of syndromic surveillance variables, they may offer a useful explanation of how variables interact with each other which could assist syndromic surveillance teams for training, diagnostic and predictive purposes keywords syndromic surveillance; bayesian networks; structural learning acknowledgments we acknowledge support from royal college of emergency medicine, eds participating in the emergency department system (edsss), ascribe ltd and l2s2 ltd; ooh providers submitting data to the gpoohss and advanced heath & care; tpp and participating systmone practices and university of nottingham, clinrisk, emis and emis practices submitting data to the qsurveillance database; and nhs 111 and hscic for assistance and support in providing anonymised call data the underpin the remote health advice syndromic surveillance system. we thank the phe real-time syndromic surveillance team for technical expertise. the authors received support from the national institute for health research health protection research unit in emergency preparedness and response. the views expressed in this abstract are those of the authors and not necessarily those of the nhs, the nihr, the department of health or public health england *felipe j. colón-gonzález e-mail: f.colon@uea.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e15, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using health information exchange to improve use of prescription monitoring data dan bolton*, jennifer sabel, chris baumgartner, travis kushner and bryant thomas karras washington state department of health, lacey, wa, usa objective demonstrate that use of the washington state health information exchange (hie) to facilitate access to prescription monitoring program (pmp) data enhances the effectiveness of a pmp. the increased accessibility will lead to improved patient care by giving providers more complete and recent data on patients’ controlled substance prescriptions. introduction washington state experienced a five-fold increase in deaths from unintentional drug overdoses between 1998 and 2014. the pmp collects data on controlled substances prescribed to patients and makes the data available to healthcare providers, giving providers another tool for patient care and safety. optimal impact for the program depends on providers regularly accessing the information to review patients’ dispensing history. we have found through provider surveys and work with stakeholders that the best way to increase use is to make data seamlessly accessible through electronic health record systems (ehrs). this approach does not require a separate login to the pmp portal. this linkage works through the health information exchange (hie) to make pmp data available to providers via ehrs. the hie facilitates electronic communication of patient information among organizations including hospitals and providers. in addition to the pmp, another resource to address the prescription drug abuse problem is the emergency department information exchange (edie), a web-based technology that specifically connects emergency departments statewide to track patients who visit multiple eds. we also developed a connection between edie and pmp data through the hie. methods increased provider utilization of the pmp will be achieved by using the hie to create more seamless access to pmp data through providers’ ehrs and through the edie system. this will be done by completing the build out of a transaction using ncpcp 10.6, piloting the connection with healthcare systems and ehr vendors, and by continuing to promote and encourage the pmp to remain an mu option through recent rule changes being proposed by cms/onc. the pilot with epic was conducted in 2015 from april to october. epic has released an update, available to washington customers, that includes the connection between ehr and pmp. pmp data is also connected to edie. that connection is now live in 80 of 93 acute care hospital emergency departments. results to date the transaction is in production with 80 emergency departments and achieving positive results. in 2015 the pmp received more than 2 million queries from the edie system via the hie, compared to 900,000 queries via the online pmp portal in the year before the link through the hie was available. we have also finished a pilot with a major ehr vendor and are working to on-board their customers. we are also working directly with healthcare systems, and as of september 2016 there are 3 healthcare facilities in testing that are expected to go live by the end of the year. over 90 registrations for meaningful use of the pmp have been received, representing more than 1000 clinics. improved access to pmp data benefits providers by allowing them to check the history of transactions linked to their dea numbers, which can alert them to fraudulent prescriptions. conclusions integration of pmp data with other information systems will greatly enhance the accessibility and impact of the data. making a connection to edie alone more than doubled the number of queries we received from providers in 2015. we anticipate even more inquiries once additional care settings are connected. we hope from this to see a continued decline in unintentional poisonings due to prescription drugs. keywords hie; monitoring; overdose acknowledgments this work was funded in part by an appointment to the applied public health informatics fellowship program administered by cste and funded by the centers for disease control and prevention (cdc) cooperative agreement 3u38-ot000143-01s4, by the office of the national coordinator for health information technology standards & interoperability framework, and by federal financial participation from the center for medicare and medicaid services. *dan bolton e-mail: dan.bolton@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e145, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts identifying key transmission route of avian influenza a(h9n2) in live poultry markets eric h. lau*, shengqiu zhang, connie leung, benjamin j. cowling, joseph t. wu and malik peiris school of public health, the university of hong kong, hong kong, hong kong objective this study assessed the transmission of low pathogenic avian influenza in live poultry market setting, using paired fecal and drinking water samples from a longitudinal surveillance program. the relative contribution of transmission via direct fecal-oral route versus drinking water will be determined. introduction live poultry markets (lpms) continue to operate in many asian countries. low pathogenic avian influenza (lpai) viruses are often endemic in the poultry, and lpm presents the opportunity for human-poultry interactions and potential human infections with avian influenza viruses. as a series of interventions to control avian influenza transmission in hong kong lpms, local health authority implemented market rest days once every month since mid-2001, and an additional rest day every month since 2003, during which all unsold poultry were slaughtered and the stalls cleaned and disinfected. rest days were found to effectively reduce avian influenza a(h9n2) isolation rate to baseline level for a few days following the rest days. however, h9n2 isolation rate was still observed to be increasing between the rest days, indicating the existence of efficient transmission in spite of rapid turnover of poultry. in lpms, poultry are usually stored in cages where drinking water is shared among poultry. this is analogous to environmental contamination in the wild, but transmissibility may even be higher due to the dense environment. the use of drinking water for avian influenza surveillance in lpm setting was suggested to be more sensitive than fecal samples (1). however, the relative contribution of direct fecal-oral versus water transmission routes in the lpm setting was not yet understood. this study aimed to determine their role, which will have implications in the control of avian influenza transmission. methods we analyzed 7,321 paired fecal and drinking water samples from a longitudinal surveillance programme during the period with 2 monthly rest days in the lpms. samples were collected from chicken cages and subsequently cultured. positive isolates were subtyped by hemagglutination-inhibition tests and neuraminidase inhibition test. data were aggregated by sampling occasion and days after the rest days. a compartmental transmission model which incorporated turnover and overnight stay of poultry, virus contamination and decay in drinking water was fitted to the data (figure 1). a 12-hour trading day was assumed. based on the parameterized model, we simulated the scenario that water transmission was prohibited to assess the role of transmission via drinking water. results h9n2 isolation rates ranged from 0-25% for fecal samples and 0-56% for drinking water samples. a clear increasing trend can be seen over days after the rest days (figure 2). the estimated parameter for water transmission is higher than the parameter for direct fecal-oral transmission. simulation results show that transmission via drinking water plays a major role in the amplification of lpai in the lpm setting (figure 2). conclusions our study showed that drinking water has a major role in the transmission and amplification of lpai h9n2 in lpms, comparing to direct fecal-oral transmission route. given the relatively low prevalence of h9n2 in chicken, direct transmission is governed by chance events, while chickens are consistently exposed to viruses in drinking water if contaminated. drinking water could be targeted for intervention to control lpai transmission in lpm. the use of drinking fountain or frequent disinfection of drinking water may be considered. avian influenza viruses (e.g. h5n1) may differ in their pattern of virus shedding via oral versus fecal routes and thus extrapolation of these results to other viruses needs to be done with caution. however, h7n9 viruses are similar to h9n2 viruses by being shed primarily via the respiratory / oral route (2) and it is reasonable to assume that these conclusions would apply to h7n9 virus which is of major public health concern. however, our model could not differentiate the effect of indirect fecal-oral transmission through contamination of drinking water by droppings versus contamination through drinking. framework of the compartmental transmission model online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e106, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts influenza a(h9n2) isolation rates from fecal and water samples keywords avian influenza; influenza a(h9n2); live poultry market; fecal-oral; drinking water acknowledgments this work is supported by the harvard center for communicable disease dynamics from the national institute of general medical sciences, the area of excellence scheme of the hong kong university grants committee, and the health and medical research fund of the food and health bureau, government of the hong kong special administrative region. references 1. leung yhc et al. poultry drinking water used for avian influenza surveillance. emerg infect dis. 2007;13(9):1380-2. 2. luk gs et al. transmission of h7n9 influenza viruses with a polymorphism at pb2 residue 627 in chickens and ferrets. j virol. 2015 oct;89(19):9939-51. *eric h. lau e-mail: ehylau@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e106, 2017 isds16_abstracts-final 101 isds16_abstracts-final 102 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts hai surveillance enhancement within epicenter by utilization of triage notes pinar erdogdu*, stella tsai and teresa hamby new jersey department of health, trenton, nj, usa objective evaluate the usage of triage note data from epicenter, a syndromic surveillance system utilized by new jersey department of health (njdoh), to enhance healthcare-associated infections (hais) surveillance for infections following a surgical procedure. introduction in new jersey, health monitoring systems inc.’s (hms) epicenter collects chief complaint data for syndromic surveillance from 79 of 80 emergency departments (ed). using keyword algorithms, these visits are classified into syndrome categories for monitoring unusual health events. hais are infections that patients acquire while they are receiving treatment for a health condition in a health care setting. following the 2014 ebola outbreak in west africa, the new jersey department of health (njdoh) communicable disease service (cds) started recruiting eds to include triage note data in addition to chief complaint data to enhance surveillance capability for ebola and other hais. research by the university of north carolina suggests triage note data improve the ability to detect illness of interest by fivefold1. currently, there are three nj eds with triage note data in epicenter along with icd 10 codes which can be used for comparison. this pilot study will assess whether infections following a surgical procedure can be captured in triage note data along with icd codes. also, this evaluation will determine if triage note data can be used to create hai custom classifications for syndromic surveillance. these classifications can potentially be used by surveillance and/or preparedness personnel and local health departments, as well as hospitals, to better prepare for detecting and preventing hais that are a significant cause of morbidity and mortality in the u.s.2 methods three nj facilities with triage notes information sending to epicenter were included in this study. ed visits occurred from 10/23/2015 to 10/29/2015 and from 2/2/2016 to 2/10/2016 in these facilities with available icd 10 codes information in epicenter were evaluated. this analysis focused on sepsis and post-surgery infections related icd 10 codes: a400, a401, a402, a403, a408, a409, a410, a411, a412, a414, a4150, a4151, a4152, a4158, a418, a419, r571, r578, r579, t811, t81.43. the keywords tested in triage notes are abdominal pain, redness, fev, fver, pyrexia, temp, elev temp, elevated temp, temp elev, hi temp, high temp, temp hi, temp10, temp 10, feeling hot, feels hot, feel hot, fuo, febr, cloudy fluid, cfluid, drainage, abscess, wound, tenderness, swelling, erythema, red, pain, post surgery, fever. the sensitivity, specificity and positive predictive value (ppv) of selected keywords applied in the triage notes were evaluated by comparing to patient’s icd 10 codes. results there were 2757 ed visits with triage notes and icd 10 codes from 10/23/2015 to 10/29/2015 and from 2/2/2016 to 2/10/2016. during these time frames, one ed visit matched with both selected keywords and icd codes, five matched with icd 10 codes only, 59 matched with keywords only, and 2692 did not match with either keywords or icd 10 codes. in table 1, it indicates that selected keywords have a high specificity (97.9 %) but with a relatively low sensitivity (16.7 %) and ppv (1.7%). conclusions selected keywords and icd 10 codes from facilities sending triage notes were used to evaluate the surveillance system on identifying infections following a surgical procedure through analysis of ed triage note field. we also reviewed all nj ed data during the same study period for other facilities not sending triage notes. it indicated that several key icd codes, e.g. icd code t81.4, infections following a surgical procedure, have been included in many facilities. this analysis will be repeated as more eds participate in epicenter with triage notes and other data fields to refine the keywords and to improve the sensitivity and ppv. table 1: sensitivity, specificity and ppv calculations of selected keywords applied in triage notes based on the icd 10 codes related to infections following a surgical procedure. keywords healthcare-associated infections; surgical site infections; epicenter; new jersey acknowledgments elizabeth kostial, kristen weiss and jason liggett (hms). references 1. travers da, barnett c, ising a, waller a. timeliness of emergency department diagnoses for syndromic surveillance. proceedings of the american medical informatics association; 2006 :769-773. 2. healthy people 2020. washington, dc: u.s. department of health and human services, office of disease prevention and health promotion. date url was accessed july 12 2016. available from: https://www.healthypeople.gov/2020/topics-objectives/topic/ healthcare-associated-infections 3. health quality measures of new zealand. excess length of stay associated with postoperative sepsis. date url was accessed july 12 2016. available from: http://www.hqmnz.org.nz/library/excess_ length_of_stay_associated_with_postoperative_sepsis *pinar erdogdu e-mail: pinar.erdogdu@doh.nj.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e40, 2017 there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi there’s an app for that: a guide for healthcare practitioners and researchers on smartphone technology nasser f bindhim1,2*, lyndal trevena2 1. health informatics department, college of health sciences, saudi electronic university. saudi arabia, riyadh. 2. school of public health, university of sydney, australia, new south wales. abstract smartphone technology is nascent compared to other technologies; however, it has shown an unprecedented uptake amongst lay consumers and professionals. this article presents the history, components, and key features of smartphones, as well as their related concepts and how they work, and it also delineates the process of smartphone applications (apps) development and publishing in the app stores. it also describes and discusses smartphone technology utilisation for health consumers, healthcare professionals, and health researchers, as well as the regulations of health-related apps. correspondence: nbin6641@uni.sydney.edu.au doi: 10.5210/ojphi.v7i2.5522 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes what is a smartphone? there is no universally agreed-upon definition for a ‘smartphone’. however, in one definition, a smartphone is considered ‘a mobile phone handset with advanced hardware and software capabilities that enable it to perform complex functions similar to those of laptop computers’ [1]. there are a few key components necessary for a mobile phone handset to be a smartphone: (a) it should be a novice appliance designed for personal use, rather than designed for business or commercial use. this requirement (being a novice appliance) helps exclude personal digital assistants (pdas), which are often developed for business use and may sometimes be called smartphones [2]. (b) the second component is the ability to connect to the internet in a constant, unrestricted way, which helps the user to exchange and generate data ‘on the go’. (c) third is the ability to install a range of applications (apps) from an external source, such as an ‘app store’. (d) the fourth component (which is optional but increasingly common) is being large, with a high-resolution screen and a high-definition camera to facilitate high-quality video conferencing and advanced use for areas such as ultrasound and telemedicine. the history of smartphones in his article, ‘the history of smartphones: timeline’, arthur (2012) states that the timeline of smartphones began in january 2007, when the apple iphone was revealed for the first time [3]. kim (2011) more accurately divided the history of the smartphone into two main eras: (1) pre-iphone and (2) post-iphone [2]. mailto:nbin6641@uni.sydney.edu.au there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi smartphones in the pre-iphone era were largely conceptualised as office machines and seen as enhanced pdas, with features like the ability to make phone calls, address books, calculators, calendars, and the abilities to send e-mail and faxes [2]. these features usually stood alone and could not communicate with each other. an example of a successful preiphone device was the blackberry, which was introduced in 2002 and incorporated a range of office functions, such as internet and fax functions [2]. another pre-iphone operating system (os) was the windows pocket pc 2000, which later evolved into windows mobile 5 and 6 [4]. thus, smartphones during this era could be summarised as mobile phones with extra advancements that usually came as a package of apps decided by the manufacturer, regardless of the user’s needs, and targeted to professionals, rather than the general population. on the other hand, smartphones in the post-iphone era are widely seen as personal computers with mobile-phone functions. they have transformed from mainly data receivers to data generators, and there is no limit to their capabilities to assist in any situation. smartphones have replaced many things in our lives—people no longer need maps for a new city or timetables for public transport [5]. smartphones can store people’s ‘to-do’ lists amongst all of their organising functions and alarms, as well as gym programs, diet plans, and medication reminders—the list goes on and on [5]. however, unlike the pre-iphone era, the range and types of apps on the devices are decided by the users, based on their own needs. moreover, post-iphone apps are able to communicate and share data between each other. for example, apps can access contact lists or images taken by the built-in camera and transfer them to another app for processing or utilise them to perform a new function. eventually, the success of the post-iphone oss has forced the leading pre-iphone os blackberry to consider stopping production [6,7]. windows mobile was also renovated to cope with the advances of the post-iphone era [8]. key features of smartphone apps smartphone users can download apps from app stores using the advanced functionalities of smartphones and tablets [9]. these apps are comparable to those run on a personal computer (pc), which enables smartphones to replace laptops or pcs in most tasks, such as web browsing, document processing, video and music playing, task managing, and video game playing [9]. portable accessibility: the main advantage of smartphone apps over previous computer technology is its portability. we can claim that any health-related internet website is accessible anywhere and at any time, but the fact is that it is only accessible wherever one has the hardware and an internet connection. in contrast, as users carry their smartphones with them all the time, they are available whenever they need them. this saves time and potentially offers more privacy and anonymity. such proximity to the consumer gives smartphones great potential as a health promotion tool [10]. according to a recent google study, 51% of smartphone users search the internet ‘on the go’ with their device [11]. they are used while multi-tasking with other media (e.g., while watching tv, using the internet on a computer, and listening to music) [12]. storage: apps can store user inputs, organise them, and generate new data that can be stored on the device and retrieved via the app. also, most of the apps’ multimedia content is stored locally and can be accessed anytime and anywhere. apps also have the ability to download more content when an internet connection is available, and in addition to local storage apps can store data on remote web servers for the user to access via other devices or computers. apps can be easily updated with new data downloaded directly onto the app, or the user can update the app via the app store with several clicks. there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi notifications: some of the most novel features of smartphone apps are local notifications and push notifications. notifications are a short message service (sms)–like function that is free of cost and more interactive. according to apple’s technical description, notifications ensure immediate, time-sensitive delivery, even when an app is not running [13]. local notifications are generated from the app itself, usually as reminders or an alarm clock, and are repeatable [13]. however, push notifications are server-generated and can initiate an interactive process inside the app [13]. push notifications use a unique device identification (which is different for each app) that acts as a phone number, so the server can deliver the push-notification message to a specific device that uses a specific app [13]. users can enable or disable the notification feature for a specific app, which gives users the ability to control which apps they want to receive notifications from. in addition, the remote server can also receive information about whether users have enabled the notification function. this feature gives the notification services superior spam control over those of sms. ad-hoc devices: an ‘ad-hoc device’ is an add-on developed to extend the utilisation of the main device in an unintended way. for example, a smartphone is not intended to be used as a medical device; however, ad-hocs can be developed to extend its utilisation as a medical device. smartphone apps can be built to interrupt data from ad-hoc devices, which extend the potential utilisation of apps to an unprecedented level. there are currently various medical ad-hoc devices available for health consumers and healthcare professionals, including blood pressure, heart rate, blood glucose level, and ultrasound scanners [14]. key features of app stores app stores are global, virtual stores that sell apps, books, music, and movies. their power comes from the fact that, although users can modify their phones to download apps from other sources, each app store is the only marketplace for selling and buying apps for a specific smartphone os. for example, the apple app store is the only marketplace where iphone, ipod, and ipad users can get apps for their devices. the app store also comes with the default apps package in these devices. the two largest app stores are apple’s app store and google play (previously called the android market), which is the main source of apps for the android os. the latter is now used by many mobile phone handset manufacturers, such as htc, motorola, and samsung. in 2009, after nine months in business, app store consumers had downloaded one billion apps [15]. in 2013, apple users had downloaded 50 billion apps, up from 15 billion in 2011 [16,17]. android users had also downloaded about 48 billion apps by 2013, up from 10 billion in 2011 [18,19]. how they work: app stores offer developer subscriptions, which are accounts for app publishers allowing them to publish their apps on the app stores [20,21]. if an app publisher decides to sell an app, a percentage of the app’s cost will be given to the app stores [20,21]. a publisher that is ready to submit an application has to provide information about the app, including the app’s name, a description to be used in the advertisement, a price (if not free), and the category in which the publisher wants the app to be published [1]. in the apple app store, the publisher must also provide keywords for users to locate their apps. google play, on the other hand, searches the app description, instead of using keywords. thus, in both stores, developers can intentionally select their keywords so that a user can find his or her app in addition to browsing by category. the publisher can specify the countries in which the app is to be published and can customize the app for a specific language [22,23]. however, the ability to limit an app to a specific there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi country is not entirely accurate. in a previous feasibility study, some users from countries that were not included in an app store’s country list were able to download the study app from both the apple and google play stores [10]. finally, only in apple app store the apps go through a review process—which takes between 1 and 2 weeks, on average—where an app store reviewer will perform a technical compatibility and content verification review [24]. app ranking and exposure: app stores rank the apps based mainly on the number of downloads. app publishers consider their app rank to reflect the number of app downloads [25]. there are now companies that aggregate the daily app rankings data from app stores. for example, appannie [26] allows users to search for an individual app’s current ranking, its lifetime ranking, and the countries and categories in which it has been highly ranked [25]. distimo, an app-tracking company, monitors daily rankings in the app stores on a country-bycountry basis. it found that, to rank amongst the top 10–25 in the usa, an app needed between 45,000 and 80,000 downloads per day [25,27,28]. to rank amongst the top 10 to 25 in australia, the uk, germany, france, or italy, an app only requires 4,000 to 18,000 daily downloads [25,27,28]. in most countries, an app needs approximately 3,000 daily downloads to achieve a top 50 ranking [25,27,28]. although app rankings are mostly influenced by the number of downloads, some app publishers also believe that app reviews contribute to improving the ranking. consequently, many app review companies now provide massive review services to app publishers—they can write positive reviews with 5-star ratings for the publisher’s app or give bad reviews and ratings to competitors’ apps [29,30]. the main criticism of the app ranking process is that the high-ranking apps will get more exposure and downloads and stay at the top of the list, thereby reducing the exposure of new apps. however, app stores also have ranked lists of newly released apps based on the date of release, so that they, too, can get more exposure. app stores can also feature an app by listing it on a special list or improve its ranking in a specific country. however, the criteria for featuring apps are unclear. are app stores the only way to access apps? for both google play and the apple app store, publishers need to release their app in the store if they want it to be downloaded by the app store’s users. however, an app that is intended for a specific group of users (e.g., an organisation’s staff or study participants) can be installed onto the users’ devices directly from a website or the developer computer without the need to release it to the app store. smartphones and health for health consumers: there are growing trends of health apps targeting health consumers. such apps are usually released in the health and fitness category of app stores. health apps have the potential to transform healthcare and health promotion in the community [31]. the number of health-related apps on the apple app store reached 13,000 apps by 2012 [32]. many studies have identified health-related apps targeting consumers in the app stores. the apps cover many topics, including smoking cessation [33], ophthalmology [34], hernias [35], diabetes, obesity and weight management [36], asthma [37], personal health records [38], hiv and other sexually transmitted diseases [39], colorectal diseases [40], cancer [41], palliative care [42], pain management [43], fitness and physical activity [44], medication management and adherence [45], women’s health, and child care [46], and depression [47]. however, although hundreds of health apps target consumers, very few are evidence-based, and many are with low-quality content [37,41,43]. there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi in australia, 28% of smartphone users had used a health-related smartphone app, and 13% used their smartphone for health-related activates at least once a week [48]. in 2012, the pew internet research center found that 53% of adults in the united states (us) owned a smartphone, 31% of whom had used their smartphone to search for health information and 19% had downloaded and used an app to manage and monitor their health [49]. furthermore, health-related apps can efficiently reach people in multiple countries who are searching for assistance to change health behaviours, such as giving up smoking [10]. in addition, healthrelated apps appear to be independent and perceived to be useful, regardless of e-health literacy and health information orientation [50]. finally, in may 2014, apple announced its health app and healthkit for developers. the health app (figure 1) records diagnostics, fitness, lab results, medications, nutrition, and vital signs [51]. the app shares data with other health apps in two directions and collects data from other health apps used by the user. this step by apple suggests that health apps are growing in importance and have the potential to change the healthcare landscape. in addition, including health-related apps with smartphones will increase the awareness of and exposure to these apps and eventually increase their use, as more users will become interested in such apps after recognising their abilities. figure 1: screenshots of the apple ‘health’ app to be released september 2014. for health professionals: there are also growing numbers of apps targeting health consumers that are usually released in the medical or references category of app stores. those apps mostly claim to be developed to assist healthcare professionals in their daily tasks. at the same time, the use of smartphones by healthcare professionals is also increasing. in 2011, 75% of physicians in the us used ipads or iphones [52]. another study in the us also found that 89% of residents and 98% of faculty staff owned smartphones, with 57% of house staff and 28% of attending physicians reporting regular personal use of smartphones during attending rounds [53]. other healthcare professionals also use smartphone apps as references or point-of-care tools, including pharmacists [54,55], nurses [56], and dieticians [57]. in addition, smartphone apps are also used by medical students [58] and for medical there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi training [59]. apps targeting various healthcare professionals are also available in the app stores [55,60,61]. however, although many support the use of smartphones by healthcare professionals, which has the potential to improve patient care, the possible dangers associated with their use (e.g., breaches of patient confidentiality, conflicts of interests, and malfunctioning clinical decision-making apps) need to be investigated [62]. finally, apple has recently announced a partnership with mayo clinic to bring new innovations to patient care and improve the quality of health apps by developing new apps targeting patients and health professionals [63]. for health researchers: the proximity to the user, great computational capabilities, and storage make smartphone apps ideal research tools that are capable of delivering complex interventions. apps can collect, store, and transfer data to a remote research database or export data directly to a local computer. in addition, research participants can be recruited directly from app stores [10]. various studies have explored the use of smartphone apps for health research, including cross-sectional [10], observational [64], and randomised controlled trial [65]. in addition, smartphone apps have been used to conduct cross-country studies [10,65]. however, few studies have been conducted to investigate the efficacy of smartphone app interventions. of those studies, one found that a smartphone medication self-management app improved adherence and helped reduce rates of forgetting and of medication errors in elderly patients [66]. moreover, a weight-loss pilot study found that the trial retention was 93% at 6 months in the smartphone group, compared to 55% in the website group and 53% in the paper diary group [67]. health-related smartphone apps are often hastily developed and may not meet consumers’ or healthcare professionals’ needs or expectations [68,69]. recent studies have searched app stores for specific health-related apps and reviewed their quality [34,37,43,70], finding that there were methodological and quality problems that are common amongst health apps [68]. moreover, other studies have found apps that encourage harmful health behaviours [1,9,25]. thus, health-related research using smartphone apps is likely to rapidly evolve in the years to come. therefore, more research efforts are needed to harness the potential of smartphone technology to improve healthcare. regulation of health apps: the us food and drug administration (fda) has focused on a small subset of mobile apps that meet the regulatory definition of ‘devices’ and that (1) are intended to be used as an accessory for a regulated medical device or (2) transform a mobile platform into a regulated medical device [71]. the fda has also stated that, although many mobile apps carry minimal risks, those that can pose a greater risk to patients will require fda review [71]. in addition, the fda recently released a memorandum of a meeting after discussing glucose monitoring sensors with apple executives [72]. the fda stated that it ‘will regulate based on the intended use of a device. using the glucometer example, the glucometer may be unregulated if the intent is for a user to follow their blood sugar for the purposes of better nutrition. if the glucometer is marketed for diabetics, however, it would more likely be regulated as a medical device’ [72]. however, the lay users may not be able to know the difference between what is intended to be a medical device or not. moreover the app stores currently contain many apps that target healthcare professionals, with functions such as medication dosage calculators, which may cause patients harm if they malfunction. such apps are not currently regulated and seem to be ignored [73]. to conclude, although smartphone technology is nascent compared to other technologies, it has shown unprecedented uptake amongst lay consumers and professionals. fast advances in there’s an app for that; a guide for healthcare practitioners and researchers on smartphone technology 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e218, 2015 ojphi apps and ad-hoc devices have increasing complexity, stability, and reliability. it has also shown a promising role in changing the future healthcare landscape through the increasing applications for health and perceived usefulness amongst health consumers and healthcare professionals. contributors all authors made substantial contributions to editing and revising of the manuscript. nfb was responsible drafting of the manuscript. all authors read and approved the final manuscript. no competing interests. acknowledgment thanks to (mada h basyouni) for her feedback and comments on the first draft. references 1. bindhim nf. 2014. freeman b, trevena l. 2014. pro-smoking apps for smartphones: the latest vehicle for the tobacco industry? tob control. 23(1), e4. pubmed 2. kim p. 2011. the apple iphone shock in korea. inf soc. 27(4), 261-68. http://dx.doi.org/10.1080/01972243.2011.583826 3. arthur c. 2012. the history of smartphone timeline: the guardian; 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research agenda for syndromic surveillance richard hopkins2, julia gunn3, john berezowski4 and howard burkom*1 1johns hopkins applied physics laboratory, laurel, md, usa; 2university of florida, tallahassee, fl, usa; 3department of communicable disease control, boston public health commission, boston, ma, usa; 4veterinary public health institute, university of bern, switzerland, bern, switzerland objective to obtain feedback and seek future directions for an isds initiative to establish and update research questions in informatics, analytics, communications, and systems research with the greatest perceived impact for improving surveillance practice. introduction over the past fifteen years, syndromic surveillance (sys) has evolved from a set of ad hoc methods used mostly in post-disaster settings, then expanded with broad support and development because of bioterrorism concerns, and subsequently evolved to a mature technology that runs continuously to detect and monitor a wide range of health issues. continued enhancements needed to meet the challenges of novel health threats with increasingly complex information sources will require technical advances focused on day-to-day public health needs. since its formation in 2005, the international society for disease surveillance (isds) has sought to clarify and coordinate global priorities in surveillance research. as part of a practitioner-driven initiative to identify current research priorities in sys, isds polled its members about capabilities needed by sys practitioners that could be improved as a result of research efforts. a taskforce of the isds research committee, consisting of national and global subject matter experts (smes) in sys and isds professional staff, carried out the project. this panel will discuss the results and the preferred means to determine and communicate priorities in the future. identified practice-oriented priority areas for surveillance research keywords surveillance research; priorities; public health practice *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e164, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts global health surveillance: innovation and coordination for broad health impact ray l. ransom*1, olga l. henao1, leonard peruski1, ruth kigozi2, david blazes3, william bertrand4 and joel montgomery1 1center for global health, centers for disease control and prevention, atlanta, ga, usa; 2infectious disease research collaboration, kampala, uganda; 3bill and melinda gates foundation, seattle, wa, usa; 4school of public health and tropical medicine, tulane university, new orleans, la, usa objective the session will discuss strategies for outbreak prevention, detection, and response for global health security and explore how these activities inform both domestic and international initiatives. innovations in epidemiology, laboratory, informatics, investment, and coordination for disease surveillance will be discussed. introduction multiple agencies are involved in global disease surveillance and coordination of activities is essential to achieve broad public health impact. multiple examples of effective and collaborative initiatives exist. the who/afro developed integrated disease surveillance and response (idsr) framework, adopted by 43 of the 46 afro member states and applied in other who regions, was the first framework designed to strengthen national disease surveillance and response systems. the who international health regulations (ihr) 2005 are an agreement between 196 countries to prevent, detect and respond to the international spread of disease. in 2013 cdc worked with uganda and vietnam to demonstrate the development of surveillance, laboratory, and emergency response center capacity and link data systems for six outbreak prone diseases. more recently, the global health security agenda (ghsa) was launched with the support of 28 countries, who, oie and fao just as ebola was beginning to emerge in west africa. this panel brings together cdc, local implementing partners, academic technical partners, and international non-government donor to discuss current and evolving strategies for prevention, detection, and response activities needed for global health security. keywords surveillance; informatics; laboratory; data integration; global *ray l. ransom e-mail: rransom@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e178, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using a bayesian method to assess google, twitter, and wikipedia for ili surveillance danielle sharpe*2, 1, richard hopkins2, robert l. cook2 and catherine w. striley2 1emory university, atlanta, ga, usa; 2university of florida, gainesville, fl, usa objective to comparatively analyze google, twitter, and wikipedia by evaluating how well change points detected in each web-based source correspond to change points detected in cdc ili data. introduction traditional influenza surveillance relies on reports of influenzalike illness (ili) by healthcare providers, capturing individuals who seek medical care and missing those who may search, post, and tweet about their illnesses instead. existing research has shown some promise of using data from google, twitter, and wikipedia for influenza surveillance, but with conflicting findings, studies have only evaluated these web-based sources individually or dually without comparing all three of them1-5. a comparative analysis of all three web-based sources is needed to know which of the web-based sources performs best in order to be considered to complement traditional methods. methods we collected publicly available, de-identified data from the cdc ilinet system, google flu trends, healthtweets.org, and wikipedia for the 2012-2015 influenza seasons. bayesian change point analysis was the method used to detect change points, or seasonal changes, in each of the web-data sources for comparison to change points in cdc ili data. all analyses was conducted using the r package ‘bcp’ v4.0.0 in rstudio v0.99.484. sensitivity and positive predictive values (ppv) were then calculated. results during the 2012-2015 influenza seasons, a high sensitivity of 92% was found for google, while the ppv for google was 85%. a low sensitivity of 50% was found for twitter; a low ppv of 43% was found for twitter also. wikipedia had the lowest sensitivity of 33% and lowest ppv of 40%. conclusions google had the best combination of sensitivity and ppv in detecting change points that corresponded with change points found in cdc data. overall, change points in google, twitter, and wikipedia data occasionally aligned well with change points captured in cdc ili data, yet these sources did not detect all changes in cdc data, which could indicate limitations of the web-based data or signify that the bayesian method is not adequately sensitive. these three webbased sources need to be further studied and compared using other statistical methods before being incorporated as surveillance data to complement traditional systems. figure 1. detection of change points, 2012-2013 influenza season figure 2. detection of change points, 2013-2014 influenza season figure 3. detection of change points, 2014-2015 influenza season keywords google; twitter; wikipedia; bayesian change point analysis; influenza surveillance references 1. aramaki e, maskawa s, morita m. twitter catches the flu: detecting influenza epidemics using twitter. proceedings of the 2011 conference on empirical natural language processing conference; 2011:1568-1576. 2. broniatowski da, dredze m, paul mj, dugas a. using social media to perform local influenza surveillance in an inner-city hospital: a retrospective observational study. eysenbach g, ed. jmir public health and surveillance. 2015;1(1):e5. 3. mciver d, brownstein js. wikipedia usage estimates prevalence of influenza-like illness in the united states in near real-time. plos comput biol. 2014;10(4):e1003581. 4. nagar r, yuan q, freifeld cc, santillana m, nojima a, chunara r, brownstein js. a case study of the new york city 2012-2013 influenza season with daily geocoded twitter data from temporal and spatiotemporal perspectives. j med internet res. 2014;16(10):e236. 5. paul mj, dredze m, broniatowski d. twitter improves influenza forecasting. plos currents. 2014;6: ecurrents. outbreaks.90b9ed0f59bae4ccaa683a39865d9117. *danielle sharpe e-mail: danielle.sharpe@emory.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e26, 2017 enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria chigozie jesse uneke phd1,2*, abel ebeh ezeoha phd2,3, henry uro-chukwu mbbs, msc.ph2,4, chinonyelum thecla ezeonu mbbs, fcmpaed2,5, ogbonnaya ogbu phd2,6, friday onwe phd2,7, chima edoga mbbs2,8 1. department of medical microbiology/parasitology, faculty of clinical medicine, ebonyi state university abakaliki nigeria 2. health policy & systems research project (knowledge translation platform), ebonyi state university abakaliki nigeria 3. department of banking & finance, ebonyi state university abakaliki, nigeria 4. national obstetrics fistula centre, abakaliki, nigeria 5. department of paediatrics, ebonyi state university abakaliki, nigeria 6. department of applied microbiology, ebonyi state university abakaliki nigeria 7. department of sociology/anthropology, ebonyi state university abakaliki, nigeria 8. catholic relief services (nigeria program) abakaliki, nigeria abstract background: in nigeria, one of the major challenges associated with evidence-to-policy link in the control of infectious diseases of poverty (idp), is deficient information literacy knowledge and skill among policymakers. there is need for policymakers to acquire the skill to discover relevant information, accurately evaluate retrieved information and to apply it correctly. objectives: to use information literacy tool of international network for availability of scientific publications (inasp) to enhance policymakers’ knowledge and skill for policymaking on control of idp in nigeria. methods: modified “before and after” intervention study design was used in which outcomes were measured on target participants both before the intervention is implemented and after. this study was conducted in ebonyi state, south-eastern nigeria and participants were career health policy makers. a two-day health-policy information literacy training workshop was organized to enhance participants’ information literacy capacity. topics covered included: introduction to information literacy; defining information problem; searching for information online; evaluating information; science information; knowledge sharing interviews; and training skills. results: a total of 52 policymakers attended the workshop. the pre-workshop mean rating (mnr) of knowledge and capacity for information literacy ranged from 2.15-2.97, while the post-workshop mnr ranged from 3.34-3.64 on 4-point scale. the percentage increase in mnr of knowledge and capacity at the end of the workshop ranged from 22.6%-55.3%. conclusion: the results of this study suggest that through information literacy training workshop policy makers can acquire the knowledge and skill to identify, capture and share the right kind of information in the right contexts to influence relevant action or a policy decision. http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi keywords: policymakers, information literacy, knowledge, infectious diseases of poverty, workshop correspondence: unekecj@yahoo.com doi: 10.5210/ojphi.v7i2.5874 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes introduction in many parts of the world, there is an increasing attention on the use of evidence from scientific research for the formulation of health policy. a number of recent reports have indicated that focus on the use of research evidence is not limited to high or middle-income countries, but evidence-informed policy is growing in importance among policy makers in low-income countries [1-4]. an earlier report quoted the director of the tanzanian council for science and technology as saying, “if you are poor, actually you need more evidence before you invest, rather than if you are rich” [5]. however, newman and colleagues [6], noted that this demand for research evidence is not only influenced by policy makers’ incentives or motivations to use research but more importantly by their capacity to access, understand and use research. the internet, online databases, digital publications and other technologies have made the search for scientific information more accessible and simultaneously, more complex [7]. there are however, some initiatives which have been designed to help professionals and practitioners to develop their capacities to navigate the world of scientific information more competently and effectively [8-10]. this has given rise to the concept of information literacy. the term ‘information literacy’ describes a set of skills and knowledge employed to discover relevant information, to accurately evaluate retrieved information and to apply it correctly [9]. information literate individuals are ‘those who know when they need information and are then able to identify, locate, evaluate, organize and effectively use the information to address and help resolve personal, job related, or broader social issues and problems’ [11]. these attributes are recognized as being essential for the successful practice of health professionals, and other critical stakeholders in the health sector [12]. ford and hibberd [12] have noted that the pace of discovery relating to the biological basis of health and disease and the development of new therapies, techniques and materials have been increasing at an exponential rate over past decades. consequently, more than ever, it has been argued that evidence-based practice, critical appraisal skills and the ability to access, evaluate and apply new knowledge appropriately are required if health professionals are to keep abreast of current best practice [13]. it is pertinent to state that information literacy is not a new concept among healthcare practitioners as there are numerous studies reporting its use in the training of doctors, nurses, and pharmacists [13-17]. unfortunately, till date studies reporting the use of information literacy to enhance policymakers’ capacity for evidence-to-policy link are very scarce. the lack of knowledge and skill enhancement programmes for policymakers on information literacy might be one of the factors responsible for the challenges associated with uptake of research evidence into policymaking process. court and young [18] observed in their study that one of the most important obstacles to research and evidence being used to influence policy is that policymakers have limited capacity to use and adapt evidence in policy processes. http://ojphi.org/ mailto:unekecj@yahoo.com enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi in nigeria, one of the major with evidence-to-policy link in the control of infectious diseases of poverty, is the grossly deficient skill among policymakers to access and use research evidence [19]. in ebonyi state south-eastern nigeria, the lack of adequate capacity for information literacy among health policy makers hampers the formulation of evidenced-based policies on the control of malaria, schistosomiasis and lymphatic filariasis. these three diseases constitute major public health concern in ebonyi state and severely affect the poor especially in the rural areas [20-22]. since information literacy training programmes among healthcare practitioners have resulted in significant capacity improvement in evidence-based best practice [15,16,23], there is little doubt that similar outcome might be obtained among policymakers. our objective in this study is to use information literacy tool of the international network for the availability of scientific publications (inasp) to enhance policymakers’ knowledge and skill for policymaking on control of infectious diseases of poverty (idp) in nigeria. methods study design a modified “before and after” intervention study design was used in this investigation as described by purdon and colleagues [24]. the outcomes were measured on the eligible population (target participants) both before the intervention is implemented and after. the difference between the before and after measurements was taken to be the impact of the intervention. (in this instance, the ‘before’ – or ‘baseline’ – measurements served as the control measurements.) [24]. ethical consideration approval for this study was obtained from the directorate of research, innovation & commercialization (dric), ebonyi state university, abakaliki nigeria. the approval was based on the agreement that participation in the research was voluntary following informed consent; that participants’ anonymity would be maintained; and that every finding would be treated with utmost confidentiality and for the purpose of this research. these were adhered to in this study. study area and participants this study was conducted at sub-national level and participants were drawn from ebonyi state in south-eastern nigeria. the target participants were career health policy makers, as described by bammer and colleagues [25] and these included: • health professionals in charge of the health systems; • regional, state and local government directors of the health ministry; •directors of primary health care at the local government level; • health professionals working with specific programmes in the health ministry; • staff and consultants involved in public health issues within the health ministry; • programme/project managers under the health ministry; • chief executive officers of civil society groups, including non-governmental organizations; http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi •leaders of national health-based associations (for example, nigeria medical association; national association of nigeria nurses and midwives; and pharmaceutical association of nigeria); information literacy capacity enhancement workshop a total of 70 policymakers were mapped out for this study. the mapped out participants were divided into two batches of 35 persons each. we organized a two-day intensive health-policy information literacy training workshop for the policymakers at ebonyi state university, elibrary centre abakaliki nigeria. the focus of the information literacy workshop was to enhance the participants’ information literacy knowledge and skill so that they can develop evidence-informed policy on the control of idp in nigeria. all the policymakers were invited to the workshop by invitation letters which were sent 2 weeks before the event and was followedup with a text message reminder to their mobile phones a day before the programme. the first batch of policymakers had their workshop in april 2014, while the second batch had theirs in may 2014. the duration of the workshop each day was five hours from 10am-3pm (with a break between 12:30pm-1pm). the training modules and tools used for the information literacy workshop were downloaded from the inasp website: (http://www.inasp.info/file/4f89119275ee9289abfd95636ebd0e14/information-literacy-forpolicy-makers-and-influencers.html). the workshop covered the following modules with emphasis on idp (malaria, schistosomiasis and lymphatic filariasis): module 1: introduction to information literacy module 2: defining and information problem relevant to idp module 3: searching for information online relevant to idp module 4a: evaluating information relevant to idp (part 1) module 4b: evaluating information relevant to idp (part 2) module 5: science information relevant to idp module 6: knowledge sharing interviews relevant to idp module 7: training skills relevant to idp the objectives of the information literacy training workshop were to enable policymakers to: • recognize information problems related to idp • be able to identify the language associated with a problem (specifically when identifying appropriate online search terms) related to idp • feel more confident handling science/research information related to idp • know of at least three reliable sources of information on science topics relevant to idp • critically evaluate sources of information for quality, credibility, relevance and bias relevant to idp • have enhanced skills in how to make use of science information related to idp http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi • have enhanced skills in small group training methods relevant to idp we developed a pre-workshop and a post-workshop measurement instruments (assessment questionnaires) to assess the level of participants’ knowledge and skill of information literacy. the measurement instruments were adapted from the outcome evaluation questionnaire developed in mcmaster university canada by johnson & lavis [26]. however, unlike the johnson and lavis outcome evaluation questionnaire which was a 7-point likert scale [26], we modified the measurement instruments in this present study into a 4-point likert scale. we had validated and used this type of measuring instrument in our previous study involving a training workshop for policymakers’ capacity enhancement on evidence-to-policy link and it proved very reliable [27]. the pre-workshop assessment questionnaire (developed in a 4-point likert scale according to the degree of adequacy; from 1 = grossly inadequate, to 4 = very adequate), was administered prior to the training session to assess the level of knowledge and capacity of the participants on the specific topics covered during the workshop. after the administration of the pre-workshop questionnaire, the training commenced and was facilitated by four resource persons (three senior researchers from ebonyi state university and one senior director from the health ministry). all teaching sessions lasted 35-60 minutes and were done using power-point presentation and handouts on each topic were produced and distributed to all participants. questions/answers/discussions immediately followed each teaching session. it was made mandatory for all lectures to be delivered in simplified, practical and easily comprehensible patterns, with little or no emphasis on complex mathematical or scientific computations/models for the benefit of non-specialists who constituted the majority of the participants. up to 120minutes practical session was held during the workshop in which each participant was able to use an internet connected computer to practice the acquisition of research evidence from relevant electronic databases relevant to malaria, schistosomiasis and lymphatic filariasis. at the end of the workshop, a post-workshop assessment questionnaire (also developed in a likert scale format) was administered to the participants to evaluate the impact of the workshop. data analysis the data collected via the questionnaire was analyzed using the methods developed at mcmaster university canada by johnson and lavis [26]. the analysis was based on mean rating (mnr), median rating (mdr) and range. for instance the figures represent likert rating scale of 1-4 points, where 1point=grossly inadequate; 2 points=inadequate; 3 points=fairly adequate; and 4 points=very adequate. in terms of analysis, values ranging from 1.00-2.49 points are considered low, whereas values ranging from 2.50-4.00 points considered high. the pre-workshop means were compared to the postworkshop means. the epi-info software was used for the performance of the data analysis. result a total of 52 policymakers out of the 70 individuals invited attended the workshop. the profile of the participants is presented in table 1 and included the following: programme officer/project secretaries (27.9%); managers/heads of departments (30.2%); directors/presidents/chairpersons (39.5%). a total of 27.9% of the participants have direct influence on the policymaking process, with 46.5% and 32.6% possessing bachelors and masters degrees as highest academic qualifications respectively. the outcome of pre-workshop http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi knowledge and skill assessment is presented in table 2, while the post-workshop knowledge and skill assessment is presented in table 3. the outcome of the assessment of impact of the training workshop with comparison of the pre-workshop mean ratings (mnrs) and postworkshop mnrs is presented in table 4. result showed progressive increase in the postworkshop mnrs over the pre-workshop mnrs. in terms of the “introduction to information literacy”, the pre-workshop mnr ranged from 2.46-2.77, while the post-workshop mnr ranged from 3.60-3.63, with the percentage increase ranging from 30%-46.3%. in terms of “defining information problem”, the pre-workshop mnr ranged from 2.46-2.63, while the post-workshop mnr ranged from 3.41-3.51, with the percentage increase ranging from 33.5%-42.3%. in terms of “searching for information online”, the pre-workshop mnr ranged from 2.35-2.48, while the post-workshop mnr ranged from 3.34-3.43, with the percentage increase ranging from 38.3%-42.1%. concerning “evaluating information”, the pre-workshop mnr was 2.29, while the postworkshop mnr was 3.47, with the percentage increase as 51.5%. with regard to “scientific information”, the pre-workshop mnr ranged from 2.15-2.35, while the post-workshop mnr ranged from 3.34-3.41, with percentage increase ranging from 43.4%-55.3%. in terms of “knowledge sharing”, the pre-workshop mnr was 2.55, while the post-workshop mnr was 3.53, with the percentage increase at 38.4%. with regard to “training skills”, the pre-workshop mnr ranged from 2.79-2.97, while the post-workshop mnr ranged from 3.56-3.64, with the percentage increase ranging from 22.6%-27.6% (table 4). discussion in this study, we have defined policy makers as the stakeholders from government, civil society or the private sector who occupy a leadership or decision-making role in relation to policies and programmes relevant to the improvement of the health sector. bammer and colleagues [25] identified three categories of policymakers at a national level to include the elected, appointed and career officials. we targeted the third category (career policymakers) i.e., directors, managers, and departmental heads in the health ministry; hospital administrators; health-based csos/ngos; local government/development centres and health directors/heads of health departments. we focused the information literacy knowledge and skill enhancement on this category of policymakers because of the strategic role they play in the policymaking process as principal policy managers, policy administrators and policy implementers. in our previous studies we identified these individuals as the “engine room” of policymaking process in nigeria [28-31]. many of the target policymakers prior to this study lacked information literacy skill, even though they have several years of experience in the control of idp and have worked in the health sector under up to two different government regimes. consequently some of them are deeply knowledgeable about their areas of responsibility in idp control, but only required information literacy capacity on current globally acceptable evidenceto-policy process. others had more generic and less contextualized policy-making skills requiring some development via training in information literacy. therefore we targeted these individuals because information enlightened policy administrators, managers and implementers are crucial for the effective delivery of public health and disease control programmes [32]. this study highlighted the importance of information literacy knowledge and skill enhancement training among health policymakers. the results of this study showed notable improvement in the knowledge and the capacity of the participants to access and synthesize relevant evidence http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi necessary for policymaking on idp. the mean percentage increase in the knowledge and the capacity of the participants recorded in this study ranged from 22.6% to 55.3%, which can be considered very notable (table 4). to the best of our knowledge there is scarcely any previous published work were this strategy was employed for policymakers in low income setting. most of the available reports on information literacy knowledge and skill enhancement focused on the training of either researchers or health care professionals particularly doctors, nurses and pharmacists [13-17]. furthermore there are numerous reports which showed that most knowledge and skill enhancement activities related to policymaking such as knowledge translation/ management and health policy research have focused on health researchers with little emphasis on policymakers [33-35]. dawad and veenstra [36] observed that without adequate capacity, in knowledge translation/management (including information literacy) and health policy research, policymakers will not have the capacity to access and synthesize sound information on which to base decisions and the potential for shared learning will be lost. they further noted that as researchers strive to develop the means to obtain timely information on health system impacts, policymakers need to become skilled at translating this information into appropriate action, to avoid forfeiting any progress made in developing and reforming the health system. the who added that strengthening capacity for evidence-informed policymaking should involve both policymakers and researchers since capacity strengthening is needed for both researchers to generate better evidence and for policymakers and health-care professionals to better use available evidence [37]. it is interesting to note that the areas of greatest percentage improvement in the knowledge and skill of the policymakers in this study were those principally related to access of evidence from various sources and competency in making use of the evidence appropriately (table 4). these include: knowledge on the different pillars of information literacy (46.3%); knowledge on the different reference resources, databases & internet resources (42.3%); knowledge about evaluating information from the internet (51.5%); knowledge about scientific methods & scientific consensus (55.3%); knowledge on the different scientific publications and academic journals relevant to policymaking (43.4%) and knowledge about systematic reviews and policy briefings (45.7%). knowledge and skill constraints associated with accessing evidence from various sources and competency in making use of the evidence appropriately are among the most important capacity needs of policymakers [38]. campbell and colleagues [39] noted in their study that the most common reasons for not using research in policy were: the absence of appropriate and/or relevant research (29%); a lack of skills to access or acquire relevant research (24%). the notable improvement observed in these areas in the present study, suggests that the knowledge and skill enhancement acquired by the policymakers could facilitate their demand for evidence for policymaking process. green and bennett [37] had argued that to achieve evidence informed policy making in any area of the health improvement, policy-makers and their advisers, need a set of skills to enable them to use research in their decision-making. they noted that in particular, policy-makers need to be able to: identify situations where research can help; articulate research questions for topics of policy-relevant research; and access and assess research findings and incorporate them in decision making. http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi limitation of study we acknowledge that the self assessment technique used to assess knowledge and skill improvement in this study is known to have its shortcomings. according to deans and ademokun [40], being able to critically recognize and understand one’s own gap in skills and knowledge is a difficult process which takes guided thought. furthermore haahr and colleagues [41] critiqued the “survey-based self-assessment” that is “frequently used to measure skills” and noted that “self-assessments are subject to self-esteem bias, may be unreliable, and are difficult to validate”. future studies incorporating more rigorous evaluation mechanisms are advocated. another limitation to this study was the short duration (two days) of the information literacy training workshop. to properly enhance the knowledge and skill of the policymakers on information literacy surely requires more than a two-day workshop. a certificate course of not less than four weeks could have produced a better knowledge and skill enhancement outcome. another limitation with the present study was that no mechanism was been put in place to monitor the post-workshop application of the skill acquired by the policymakers. such monitoring mechanism would have aided proper impact evaluation of the workshop. we advocate for the inclusion of post-workshop monitoring mechanism in future studies. conclusion this is the first study done in nigeria that targeted the policymakers for this kind of capacity enhancement. the enhancement of policymakers’ knowledge and skill in specialized fields such as information literacy is very vital in low income settings. globally, it is well established that policymaking is becoming increasingly challenging, consequently equipping policymakers with the right kind of skill becomes very imperative. the outcome of this study suggests that a training workshop on information literacy can be used to enhance the knowledge and skill of policy makers to access and use evidence from various reliable sources for policymaking. the training administered to the policymakers in this study helped to build the confidence of the participants to know what to do when they need information for policymaking. this is because they can to a reasonable extent be able to identify, locate, evaluate, organize and effectively use relevant scientific evidence and other policy-relevant information to address policy issues. our take is that if through the information literacy training programme we are able to enhance the capacity of the career policymakers to identify, capture and share the right kind of information in the right contexts of place and time to influence relevant action or a decision, then we can expect more effective policies on the control of idp in the state. the procedure we adopted in this study is recommended for developing countries that want to achieve effective evidence-to-policy process. this is because the procedure can easily and with minimal cost be replicated in other low income settings and similar outcomes can be expected. acknowledgement this investigation received financial support from the uncicef/undp/world bank/who special programme for research and training in tropical diseases (who/tdr), (reference 2014/397653-0). references 1. adam t, moat ka, ghaffar a, lavis jn. 2014. towards a better understanding of the nomenclature used in information-packaging efforts to support evidence-informed http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 9 online journal of public health informatics * issn 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literacy training workshop for the research capacity strengthening and knowledge management for policymakers in ebonyi state nigeria participant (respondents) attributes number (%) of participants (respondents) n=43 (1) gender female 29 (70.7) male 12 (29.3) (2) age (years) 25 34 2 (4.7) 35 44 13 (30.2)  45 28 (65.1) (3) institutional affiliation federal teaching hospital 8 (18.6) state ministry of health 13 (30.2) local government service commission 17 (39.5) non-governmental organization 2 (4.7) state house of assembly 1 (2.3) educational institution 1 (2.3) (missing) 1 (2.3) (4) official designation programme officer/project secretaries 12 (27.9) managers/heads of departments 13 (30.2) directors/presidents/chairpersons 17 (39,5) (missing) 1 (2.3) (5) years of experience in current designation < 3 8 (18.6) 3 – 5 18 (41.9) 6 – 10 10 (23.3) > 10 3 (6.9) (missing) 4 (9.3) (6) influence on policy making direct (dipp) 12 (27.9) indirect (iipp) 30 (69.8) (missing) 1 (2.3) (7) highest academic qualification ssce/diploma 3 (7.0) bachelor 20 (46.5) masters 14 (32.6) http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi doctorate 1 (2.3) (missing) 5 (11.6) table 2. the pre-workshop knowledge/skill assessment of participants at the information literacy training workshop for the research capacity strengthening and knowledge management for policymakers in ebonyi state nigeria parameter assessed mean median mode range knowledge about information literacy 2.77 3.00 3 2-4 level of knowledge on the different pillars of information literacy 2.46 2.00 2 a 1-4 knowledge of the competencies of an information literate person 2.51 2.00 2 1-4 rate your ability to recognize information need, searching, evaluating information relevant to policy making 2.71 3.00 3 2-4 how would you describe your knowledge about defining information problem? 2.63 3.00 3 2-4 what is your level of knowledge on the different reference resources, databases & internet resources? 2.46 2.00 2 2-3 how would you describe your knowledge about assessing information need & formulating search strategy? 2.46 2.00 2 2-3 how would you describe your knowledge about the internet & world wide web? 2.48 3.00 3 1-3 what is your level of knowledge on the different search tools, live search engines, & reference resources, databases & internet resources? 2.38 2.00 2 1-4 how would you describe your knowledge about assessing information need & formulating search strategy? 2.35 2.00 2 1-3 how would you describe your knowledge about evaluating information from the internet? 2.29 2.00 2 14 how would you describe your knowledge about scientific methods & scientific consensus? 2.15 2.00 2 1-3 what is your level of knowledge on the different scientific publications and academic journals relevant to policymaking? 2.35 2.00 2 2-3 how would you describe your knowledge about systematic reviews and policy briefings? 2.34 2.00 2 1-4 http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi how would you describe your knowledge about “knowledge sharing strategies” and the relevance to the policymaking process? 2.55 3.00 3 1-4 how would you describe your knowledge about learning styles? 2.85 3.00 3 1-4 what is your level of knowledge on the different adult learning strategies? 2.79 3.00 3 1-4 how would you describe your knowledge about participatory training? 2.97 3.00 3 1-4 table 3. the post-workshop knowledge/skill assessment of participants at the information literacy training workshop for the research capacity strengthening and knowledge management for policymakers in ebonyi state nigeria parameter assessed mean median mode rang e present knowledge about information literacy 3.60 4.00 4 3-4 present level of knowledge on the different pillars of information literacy 3.60 4.00 4 3-4 present knowledge of the competencies of an information literate person 3.63 4.00 4 3-4 rate your present ability to recognize information need, searching, evaluating information relevant to policy making 3.63 4.00 4 3-4 how would you describe your present knowledge about defining information problem? 3.51 4.00 4 3-4 what is your present level of knowledge on the different reference resources, databases & internet resources? 3.50 4.00 4 2-4 how would you describe your present knowledge about assessing information need & formulating search strategy? 3.41 3.00 3 2-4 how would you describe your present knowledge about the internet & world wide web? 3.43 3.00 4 2-4 what is your present level of knowledge on the different search tools, live search engines, & reference resources, databases & internet resources? 3.34 3.00 3 2-4 how would you describe your present knowledge about assessing information need & formulating search strategy? 3.34 3.00 3 2-4 how would you describe your present knowledge about evaluating information from the internet? 3.47 3.50 4 2-4 http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi how would you describe your present knowledge about scientific methods & scientific consensus? 3.34 3.00 4 2-4 what is your present level of knowledge on the different scientific publications and academic journals relevant to policymaking? 3.37 3.00 3 2-4 how would you describe your present knowledge about systematic reviews and policy briefings? 3.41 3.00 3 2-4 how would you describe your present knowledge about “knowledge sharing strategies” and the relevance to the policymaking process? 3.53 4.00 4 2-4 how would you describe your present knowledge about learning styles? 3.56 4.00 4 2-4 what is your present level of knowledge on the different adult learning strategies? 3.56 4.00 4 2-4 how would you describe your present knowledge about participatory training? 3.64 4.00 4 3-4 table 4. comparison of the pre-workshop and post-workshop knowledge/skill assessment of participants at the information literacy training workshop for the research capacity strengthening and knowledge management for policymakers in ebonyi state nigeria parameter assessed pre* mean post** mean mean increase percentage mean increase introduction to information literacy knowledge about information literacy 2.77 3.60 0.83 30.0 level of knowledge on the different pillars of information literacy 2.46 3.60 1.14 46.3 knowledge of the competencies of an information literate person 2.51 3.63 1.12 44.6 rate your ability to recognize information need, searching, evaluating information relevant to policy making 2.71 3.63 0.92 33.9 defining information problem on idp how would you describe your knowledge about defining information problem? 2.63 3.51 0.88 33.5 what is your level of knowledge on the different reference resources, databases & internet resources? 2.46 3.50 1.04 42.3 how would you describe your knowledge about assessing information need & formulating search strategy? 2.46 3.41 0.95 38.6 searching for information online on idp how would you describe your knowledge about the internet & world wide web? 2.48 3.43 0.95 38.3 http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e221, 2015 ojphi what is your level of knowledge on the different search tools, live search engines, & reference resources, databases & internet resources? 2.38 3.34 0.96 40.3 how would you describe your knowledge about assessing information need & formulating search strategy? 2.35 3.34 0.99 42.1 evaluating information on idp how would you describe your knowledge about evaluating information from the internet? 2.29 3.47 1.18 51.5 scientific information on idp how would you describe your knowledge about scientific methods & scientific consensus? 2.15 3.34 1.19 55.3 what is your level of knowledge on the different scientific publications and academic journals relevant to policymaking? 2.35 3.37 1.02 43.4 how would you describe your knowledge about systematic reviews and policy briefings? 2.34 3.41 1.07 45.7 knowledge sharing on idp how would you describe your knowledge about “knowledge sharing strategies” and the relevance to the policymaking process? 2.55 3.53 0.98 38.4 training skills on idp how would you describe your knowledge about learning styles? 2.85 3.56 0.71 24.9 what is your level of knowledge on the different adult learning strategies? 2.79 3.56 0.77 27.6 how would you describe your knowledge about participatory training? 2.97 3.64 0.67 22.6 *preworkshop mean **postworkshop mean idp-infectious diseases of poverty http://ojphi.org/ enhancing health policymakers’ information literacy knowledge and skill for policymaking on control of infectious diseases of poverty in nigeria introduction methods study design ethical consideration study area and participants information literacy capacity enhancement workshop data analysis result discussion limitation of study conclusion acknowledgement references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts jurisdictional usage of the new essence word alert feature wayne loschen*1, howard burkom1 and david atrubin2 1johns hopkins university applied physics laboratory, laurel, md, usa; 2florida department of health, tallahassee, fl, usa objective the objective of this presentation is to describe the new word alert capability in essence and how it has been used by the florida department of health (fdoh). specifically, this presentation will describe how the word alert feature works to find individual chief complaint terms that are occurring at an abnormal rate. it will then provide usage statistics and first-person accounts of how the alerts have impacted public health practice for the users. finally, the presentation will offer future enhancement possibilities and a summary of the benefits and shortcomings of this new feature. introduction syndromic surveillance systems have historically focused on aggregating data into syndromes for analysis and visualization. these syndromes provide users a way to quickly filter large amounts of data into a manageable number of streams to analyze. additionally, essence users have the ability to build their own case definitions to look for records matching particular sets of criteria. those userdefined queries can be stored and analyzed automatically, along with the pre-defined syndromes. aside from these predefined and userdefined syndromic categories, essence did not previously provide alerts based on individual words in the chief complaint text that had not been specified a priori. thus, an interesting cluster of records linked only by non-syndromic keywords would likely not be brought to a user’s attention. methods in the fdoh essence system a new detection feature was developed to trigger alerts based on anomalous occurrence of terms in chief complaints.1 this feature used fisher’s exact test to test frequencies of individual chief complaint terms relative to all terms in a 1-month baseline. the feature used a 7-day guard-band, and automatically switched to an efficient chi-square test for sufficiently large term counts. a term triggered an alert if its p-value ≤ 10e-4. this algorithm was then run on chief complaint sets both by hospital and by region, with region assignment according to patient zip code. results were then displayed in new visualizations showing alerts in word cloud and line listing form. additionally, users were given the option to ignore stop words, syndromic terms, and a user-created list of ignorable words in order to focus on words of greater interest. results the result of using the tool since june 2016 has seen three major benefits. first, the original intent for the system to notify users of abnormal word clusters has proven useful. users have been able to see terms such as disaster, shelter and fireworks which were not part of any prior syndromes and use these notifications to investigate possible issues. the second benefit found by users was the ability to find new misspellings or abbreviations commonly used by hospitals. the terms zyka and glf (ground level fall) are examples of these. finally, the system has helped discover new trends in hospital processes. for example, the tool has helped discover first person and non-english phrases in the chief complaint. this observation led to the discovery that some hospitals are using kiosks or mobile phone apps to allow patients to enter their own chief complaints. conclusions the word alert feature has provided value to the users of fdoh essence. while accomplishing its initial goal of triggering abnormal non-syndromic term usage, the additional ability to find new misspellings and abbreviations may have even larger impact by keeping syndrome and subsyndrome definitions up-to-date over time for traditional syndromic alerting. beyond these current benefits, additional visualization enhancements are under consideration. additionally, the resources required to perform the detection are substantial, and implementation improvements are under development to improve the performance and enable more advanced free-text anomaly detection. keywords essence; chief complaint; analysis; visualization acknowledgments this work was funded by the florida department of health. references 1. burkom h, elbert y, piatko c, fink c. a term-based approach to asyndromic determination of significant case clusters. online journal of public health informatics. 2015;7(1). *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e132, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts an integrated mosquito surveillance module in new york state hwa-gan chang*1, jacqueline griffin2, 1, charlie didonato2, 1, cori tice1 and bryon backenson1 1nysdoh, albany, ny, usa; 2ntt data, albany, ny, usa objective to develop a mosquito surveillance module to collect mosquito information testing for west nile, east equine encephalitis (eee) and zika viruses using national standards. to provide a common set of data for local health departments (lhds) and state users to report and share information. to monitor the type of mosquito species that carry diseases. introduction there were several stand-alone vector surveillance applications being used by the new york state department of health (nysdoh) to support the reporting of mosquito, bird, and mammal surveillance and infection information implemented in early 2000s in response to west nile virus. in subsequent years, the electronic clinical laboratory reporting system (eclrs) and the communicable disease electronic surveillance system (cdess) were developed and integrated to be used for surveillance and investigations of human infectious diseases and management of outbreaks. an integrated vector surveillance system project was proposed to address the migration of the stand-alone vector surveillance applications into a streamlined, consolidated solution to support operational, management, and technical needs by using the national standards with the existing resources and technical environment. methods a mosquito surveillance module was designed to link with cdess, an electronic disease case reporting and investigation system, to allow lhds to enter mosquito trap sites and mosquito pool information obtained from those traps. the mosquito test results are automatically transmitted to eclrs through public health lab clinical laboratory information management system (clims) using elr standards. by utilizing these standards, the eclrs was enhanced to add a new nonhuman specimen table and existing processes were used to obtain mosquito laboratory results and automatically transfer them to the surveillance system the same way that human results are transferred. the new mosquito surveillance module also utilizes the existing cdess reporting module, thereby allowing users the flexibility to query and extract data of their choosing. the minimum infection rate (mir) report calculates the number of infected pools with an arbovirus divided by the total number of specimens tested*1000; a trap report shows number of mosquitoes trapped by species type, location and trap type; and a lab test result report shows the number of pools that tested positive and the percentage of positive pools by disease. results the mosquito surveillance module was rolled out in may 2016 to all 57 lhds. a non-human species lookup table was created to allow public health lab to report the test results using health level seven (hl7) v 2.5.1 standards. as of august 31, 2016 there were 4,545 pools tested. a total of 201 (4.4%) pools were positive for west nile and the mir was 1.2. there were no pools positive for eee or zika virus. various reports have been created for monitoring the surveillance of mosquitoes trapped and tested for mosquito-borne diseases. conclusions the integration of mosquito surveillance module within cdess allows lhds and the state to monitor mosquito-borne disease activity more efficiently. the module also increases nydoh’s ability to provide timely, accurate and consistent information to the local health departments and healthcare practitioners regarding mosquito-borne diseases. keywords information system; mosquito surveillance; national standards *hwa-gan chang e-mail: hwagan.chang@health.ny.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e54, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts linking binge drinking, depression to ses? an age old question with fresh eyes reka sundaram-stukel*2, 1, benjamin wiseman2 and anne l. ziege2 1department of agriculture and applied economics, university of wisconsin-madison, madison, wi, usa; 2wisconsin state department of public health, madison, wi, usa objective our primary goal is to move towards establishing a causal link between binge drinking, mental health, employment and income. introduction one of the key questions in health economics is what is the direction of causality: does poverty cause poor health outcomes; does low education cause poor health outcomes; does poor health result in lack of productivity; does poor health cause poor educational and income outcomes; and how is this all related to mental health if at all. we are used to breaking down data into fragments as researchers: an investigator who is predominantly focused on health outcomes will approach the problem with disease as the dependent variable and income as the conditioning variable. however, if we are interested in income inequality we will reverse the direction and income will be the dependent variable with health status as the conditioning variable. the representation above allows us to visualize data as a function of multiple fragments. for example if we want to understand how depression is related to income, one can look at the figure to observe that with lower income there is a higher likelihood of being depressed. with this simple illustration we can see that establishing causal links can be very tricky, if not incredibly challenging. methods two methods are: applied descriptive analysis and estimation. we approach this without causality in mind, but with an intention to explore how behavior responds to income, education, labor and health. our descriptive approach looks at trends in binge drinking and mental health as it affects key economic outcomes such as education, employment, and income. for each outcome we then run a simple probit model controlling for a variety of characteristics. the key co-variates in these models are income, employment and health. it is very useful to look at these simple probits because often it is hard to separate the effects of income on health, employment on income, health on employment, education on employment, health and income, and finally income, employment, health and education on mental health and substance abuse. results our estimated results are rather interesting. examining the marginal probits, e.g. figures 1.3, and 1.5, we show that there isn’t a significant income effect, nor do we find significant education or employment effects associated with binge drinking. in fact we find that in wisconsin binge drinking is a health burden for those who are eligible to drink irrespective of education and that the effect is significant; we also find that higher levels of education increase the probability of being unemployed but not significantly. the second set of probit estimates, e.g. figure 1.7, show that poor health is indeed associated with outcomes lower employment as compared to other groups, and higher probability of depression. the last set of probits, e.g. figure 1.1, show that retired, self employed and employed are less likely to be depressed but not significantly so, and those who are unable to work have a higher estimated probablilty to be depressed. income doesn’t appear to have a significant estimated effect on depression. conclusions our analysis provide insights into the question of socio-economic status (ses), binge drinking, and depression in three important ways. first, we explore the relationship between ses and binge drinking and we find that binge drinking is ses invariant. second we find that depression is not associated with income it does have a strong relationship with employment status. we are in the process of unpacking the effects of ses, binge drinking and depression to move beyond associational inferences to causal inferences. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e81, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords binge drinking; depression; employment; income; education acknowledgments we acknowledge the staff at the office of health informatics for many useful comments. references cawley john, and ruhm christopher j. (2012). “the economics of risky health behaviors.” handbook of health economics, vol. 2, pages 95-199. *reka sundaram-stukel e-mail: reka.sundaramstukel@dhs.wisconsin.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e81, 2017 isds16_abstracts-final 188 isds16_abstracts-final 189 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 1emerging infections program, yale school of public health, new haven, ct, usa; 2new york city department of health and mental hygiene, queens, ny, usa; 3cook county department of public health, forest park, il, usa; 4connecticut department of public health, hartford, ct, usa objective the panel will describe applying the methods of harvard’s public health disparities geocoding project1 to a diverse collection of infectious disease surveillance data from 14 us states and new york city. this session will demonstrate the feasibility and utility of using us census data to reveal sub-populations vulnerable to infectious diseases. introduction most public health surveillance systems in the united states do not capture individual-level measures of socioeconomic position. without this information, socioeconomic disparities in health outcomes can be hidden. however, us census data can be used to describe neighborhood-level socioeconomic conditions like poverty and crowding. place matters. neighborhood affects health independently of personal characteristics. thus, important trends may be elucidated by linking geocoded public health surveillance data to area-based measures of socioeconomic position, such as the percentage of residents with incomes below the federal poverty level. description panel members will each share their experience using the methods of the public health disparities geocoding project on an infectious disease surveillance dataset. topics covered will include: • working with us census data, both decennial population files and american community survey estimates • accessing online methodological resources from the council of state and territorial epidemiologists and the public health disparities geocoding project at harvard school of public health • identifying health department resources needed to complete similar analyses • establishing interdepartmental partnerships to carry out specialized methods (e.g., geocoding) • consulting subject matter experts to interpret results • communicating findings for use in public health policy and action specifically, the audience will hear from three epidemiologists who conducted similar independent analyses. the discussion will include successes and challenges in analyzing the incidence of 53 reportable communicable diseases by census tract-level poverty in new york city, an area of pronounced income inequality2. from connecticut, analysis of reported campylobacter infections from 1999-2009 will highlight the ability of this methodology to detect socioeconomic disparities within sub-groups and include interpretation of unexpected results. finally, discussion of the analysis of influenza hospitalization data from over 70 counties in 14 states will help to illustrate the obstacles to and ultimate value of sharing data across jurisdictions. audience members will learn how to conduct similar analyses with their own data and where to find detailed guidance. audience engagement audience members will be asked to discuss barriers to implementing routine analysis of surveillance data within their own jurisdictions according to area-based poverty. technical as well as conceptual questions will be answered. audience recommendations for acting on the results of similar analyses in terms of policy and prevention measures to advance health equity will be encouraged. keywords health disparities; geospatial analysis; poverty; socioeconomic status; geocoding acknowledgments the authors wish to acknowledge and thank dr. nancy krieger and dr. james hadler for their inspiration and guidance. references 1. krieger n, chen jt, waterman pd, rehkopf dh, subramanian sv. painting a truer picture of us socioeconomic and racial/ethnic health inequalities: the public health disparities geocoding project. am j public health 2005; 95: 312-323. doi:10.2105/ajph.2003.032482. 2. greene sk, levin-rector a, hadler jl, fine ad. disparities in reportable communicable disease incidence by census tractlevel poverty, new york city, 2006-2013. am j public health, 2015;105(9):e27-34. doi:10.2105/ajph.2015.302741. *kimberly yousey-hindes e-mail: kimberly.yousey-hindes@yale.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e4, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts analysis of daily enhanced syndromic surveillance in hillsborough county, fl, 2015 charles r. clark*2 and michael wiese1 1florida department of health, tampa, fl, usa; 2indiana state department of health, indianapolis, in, usa objective enhanced daily surveillance is used to identify reportable diseases, outbreaks, and clusters and provides situational awareness. this project examines how health care visits requiring additional information are detected using enhanced syndromic surveillance and the resources required from detection through completion. introduction the florida department of health in hillsborough county (fdohhillsborough) conducts enhanced syndromic surveillance on a daily basis. the electronic surveillance system for the early notification of community-based epidemics in florida (essence-fl) is the syndromic surveillance system used by epidemiologists within the florida department of health (fdoh). during the time of this study, essence-fl receives data from 210 of emergency departments (ed) and 33 urgent care centers (ucc) throughout the state of florida, including 12 eds and 3 uccs in hillsborough county. in 2014, the essence-fl system added a feature that delivers an automatic daily email to designated primary essence-fl users in each county containing all visits which have been detected by the state’s visits of interest (voi) query. the email contains all visits which have been detected by the visits of interest (voi) query for each essence-fl users designated county. the voi query utilizes the combined chief complaint and discharge diagnosis (ccdd) field of a visit for keywords related to reportable diseases and exposures of public health interest. in addition to this voi email, hillsborough county analyzes time of arrival alerts, specialized emerging infectious disease queries, poison information center data, and volume levels of syndromes and subsyndromes predetermined by essence-fl. a daily summary report of the enhanced daily surveillance analysis is then provided to area public health officials within fdoh-hillsborough and the surrounding counties. this study examines how visits requiring additional investigation are detected and the resources required to complete the investigation. methods during the study period from july 23 through september 30, 2015, visits identified were recorded along with the time and method of detection. each day this surveillance began with the review of the visits of interest email, facility and syndrome volumes, the voi query, emerging infectious disease queries (mers-cov, ebola virus disease, chikungunya, etc.), time of arrival alerts, and the review of florida poison information center data. a daily summary report of the enhanced surveillance was manually created and provided by email to public health officials. after completion of the daily analysis, facilities were contacted about any visits identified as requiring additional investigation, such as a reportable disease or cluster of public health concern. the time of the information request, receipt of the requested information, and completion of the investigation was recorded. results an average of 1740 visits were made each day in hillsborough county in the month prior to the start of this project. during this same time period the daily voi email identified an average of 5.5 visits per day. during the study period, an average of 7.8 visits were detected each day during the enhanced syndromic surveillance protocol. the voi email detected 6 visits per day. overall 558 total visits were detected from the enhanced daily surveillance and 82 percent of these visits were found in the system generated voi email. of the visits identified 149 required additional investigation and 15 were determined to be associated with a reportable disease, most commonly carbon monoxide poisoning and varicella. an average of 1.3 days elapsed from the time a visit occurred to the time it was detected through surveillance. follow-up was started within 1 day of detection and completed in an average of 1.1 days. overall the daily enhanced syndromic surveillance data analysis required an average of 60 minutes of work time daily with a range of 18-144 minutes. conclusions during the study period, 15 visits were found to be cases of reportable diseases, primarily carbon monoxide poisoning and varicella, which would have otherwise gone unreported to fdohhillsborough. the enhanced surveillance process also allows for the quick detection and evaluation of diseases or conditions requiring immediate action that may not always be reported immediately such as meningitis or an emerging infectious disease. the enhanced daily syndromic surveillance in hillsborough county has been useful in detecting reportable diseases, clusters, and providing situational awareness in a timely manner without an overwhelming burden on staff and resources. keywords essence; syndromic surveillance; county health department; evaluation; reportable disease acknowledgments thanks to warren mcdougle, mph, david atrubin, mph and the entire fdoh-hillsborough epidemiology program for their help and support of this research. *charles r. clark e-mail: crclark@mac.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e93, 2017 geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi geographic information systems: usability, perception, and preferences of public health professionals awatef a. ben ramadan, md, mph, phd1,2,3*, jeannette jackson-thompson, msph, phd1,2,3, suzanne a. boren, mha, phd2,3 1. university of missouri-columbia (mu): missouri cancer registry and research center (mcrarc) 2. university of missouri-columbia (mu): school of medicine, department of health management and informatics (hmi) 3. university of missouri-columbia (mu): mu informatics institute (muii) *awatef ahmed ben ramadan, md, mph, phd, aab365@mail.missouri.edu d abstract background: analyzing and visualizing health-related databases using geographic information systems (giss) becomes essential in controlling many public health problems. objectives: to explore the perception and preferences of public health professionals (phps) about the usability of giss in public health field methods: for this scoping review, the investigators searched medline ovid, pubmed, ieee, scopus, and geobase databases. a total of 105 articles were identified. nine articles met the inclusion criteria. results: iterative evaluations, training, and involvement of gis end users are productive in gis usability. more methodologies are needed to support the validity of gis usability studies. the exchange of gis technology impacts public health policy and research positively. discussion: phps are aware of the use of giss in the public health field, and the exchange of visualized health data in determining inequalities and inaccessibility issues. conclusion: giss are essential to control public health problems, if the related health datasets are analyzed carefully and if the mapping reports are extensively evaluated and interpreted. keywords: geographic information systems, gis, public health, usability mailto:aab365@mail.missouri.edu geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi introduction public health work requires collaboration and effective communication between team members [1]. therefore, the geographic information system (gis) tools should be designed to meet the needs and perspectives of the team members. the problem today is not in creating new giss, but in effective and efficient use of the existing ones [2,3]. analyzing and visualizing health-related databases, using sophisticated statistical software, is essential in helping control many public health problems in any community. this data should be handled carefully, analyzed adequately to get reliable results, and not mislead the target audiences [4]. most of the potential users of the health-related spatial data find difficulties in interpreting statistical and mapping information of most health-related spatial reports [5-7]. the major issues are lack of experience and training to use this technology, lack of acceptance to use gis tools, and complicated design of most existing gis technologies [8]. providing the potential users of gis software with clear explanations on the statistical methodology and results and analogies of the combined diagrams and maps will enhance the users’ understanding and motivate them to use this technology [9]. mapped public health data can create knowledge, produce evidence, and generate policies [10]. every mapping report should carry a specific purpose and carry a clear message to the audience [11]. targeting the public health professionals (phps) and policy makers, the mapping reports should include citations of the used databases’ sources and the methodology of the results. in order to make it user friendly, the usability of the gis tools and reports should be iteratively pilot tested by potential users before and after tools’ release [4]. current literature proves the collaboration between the professionals of the same public health interest by linking health information from different sources and designing portals and applications [12]. this will help guide phps and policy makers to develop cost-effective public health interventions [12]. over the last 20 years, spatial health data are transformed from being static to being interactive and dynamic [13]. gis tools could help communication between experts in different fields. the gis developers and users should consider technical, social, and cultural issues during development, evaluations, and updates of the giss tools to enhance the experts’ connection [14]. the investigators of the current correspondence: awatef ahmed ben ramadan, md, mph, phd, aab365@mail.missouri.edu doi: 10.5210/ojphi.v9i2.7437 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:aab365@mail.missouri.edu geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi scoping review could not find previous literature reviews adopting the same aim, including all of this review’s inclusion criteria (see methods section), covering exactly the time limits of this review, and/or using the same searching strategies and similar keyword terms used in the current review. the current scoping review’s aim is to explore the perceptions and preferences of public health practitioners and policy makers about the use of giss in public health practice, and to search the literature about the usability and utility of giss in public health fields. methods study design and search strategy the study design was a scoping review research. the investigators initially searched for eligible journal articles in the following databases: medline ovid and pubmed databases. the following terms were searched using medline ovid: 1) geographic information system (gis) or gis or mapping software, and 2) public health or public health practitioners, and 3) usability or functionality or utility or perception or preferences. the search resulted in two articles. the pubmed was searched with the same strategy and did not produce any results. the investigators tried to search the same terms differently using pubmed database. by using the strategy: geographic information systems and public health and perception and gis, our search results produced 35 articles, including just one article which is strongly related to the current study aim. the article’s title was “interactive map communication: pilot study of the visual perceptions and preferences of public health practitioners” [15]. the investigators searched the article’s references and the article’s similar articles which were listed on the right side of the article’s abstract pubmed page. from both the medline ovid and the unique pubmed strategy, we identified a total of 103 articles. the investigators searched the institute of electrical and electronics engineers (ieee)® xplore database using the same terms and the search produced two articles. the scopus and the geobase databases also were searched using the same terms without producing any related articles. the collected 105 articles were screened by reading their abstracts and 48 articles were excluded because their objectives did not meet the review’s aim. the investigators assessed the eligibility of the selected 57 strongly related articles by reading the articles’ full text. at the end, nine articles met the review’s inclusion criteria (figure 1). inclusion criteria eligible articles for this review were written in english, published in the years from 2000 to 2016, and included usability interviews or usability testing of public health professionals (phps) and/or decisionor policy-makers. in the usability testing literature, the studies test the usability of: giss, mapping atlases, mapping applications, or spatial or spatial-temporal websites and/or portals. these mentioned tools should display spatial or spatial-temporal public health data. in the usability studies, the inclusion criteria for the users were public health practitioners or professionals, geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi epidemiologists, public health program directors, spatial reports developers and analyzers, and public health policy makers. figure (1): search strategy flow chart according to the study design and the methodology, we divided the eligible articles into: 1. articles based on usability testing of gis tools, applications, and/or websites display of spatial-temporal data: four articles met the study inclusion criteria. geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi 2. articles based on interviewing phps and policy-makers to find out their giss’ perspectives and preferences in public health field: five articles met the study inclusion criteria. results there is variety among the nine eligible articles presented in table 1 and table 2. the participants are different (demographics, experience, work type, and degree of education). different gis software is tested and different research measures are used. see table 1 and table 2. 1. usability testing studies: there are four articles in this category. the important information about the studies was extracted and presented in table1. the first study was conducted by a geography scholar. the study’s objective was to explore how epidemiologists take advantage of the geo-visualized technology, and how they expect this information to help them in practice. the study design was usability testing of the exploratory spatial-temporal analysis toolkit (estat) which visualizes multivariate health data to support cancer epidemiology. the study was user-centered and the researchers conducted iterative evaluation processes to refine estat. the study design was multi-staged. in the first stage, graduate students used card sorting and verbal protocol analysis. after a year, the study investigators shifted to the actual end-users after they had problems with the tool’s interface. in the second stage, the researchers conducted verbal protocol analysis on 12 epidemiologists followed by focus group activities to discuss the testing usability sessions. verbal protocol is defined as: “a psychological research method that elicits verbal reports from research participants” [16]. in the third stage, a case study in collaboration with an academic epidemiologist was conducted to analyze estat. the analysis was a positive addition to the tool’s design. in the fourth stage, five experts in data analysis tested the refined tool using verbal protocol analysis followed by focus groups. a scatter plot was the first analytical measure the epidemiologists used followed by bivariate map tools. the complicated and multivariate tools of the estat were not used commonly with the users. the most interesting finding of this stage was that the users did not face a lot of interaction problems, and this indicated improvement in development and refinement of the tool [17]. geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi t a b le 1 . u sa b il it y t e st in g s tu d ie s b a se d o n t e st in g g is t o o ls b y p a rt ic ip a n ts geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi the second study was conducted by scholars from four different specialties and expertise: public health, geography, clinical medicine, and cancer research. the study objective was to test the usability and utility of the pennsylvania cancer atlas (pa-ca) to refine the software. the study design was multi-staged user-centered evaluations of pa-ca usability using web-based application (delphi application). in the first stage, the investigators tested the pa-ca using two groups of users. the first group included seven gis science graduate students, and the second group formed from four cartography and information visualization experts. the second stage included two groups, seven epidemiologists in the first group, and seven spatial analysts and pennsylvania state public health professionals in the second group. every stage of evaluation had four rounds of testing sessions. the professional participants pointed out that the best spatial reports included tables, maps, and charts. the responses and the using of the tested spatial reports were varied by the difference in expertise. most of the participants stressed the importance of integrating tutorials and help information for the pa-ca end-users. the results of the evaluation processes were totally positive. the testers came up with important recommendations on the paca software: improving user-software interface and motivating new methods of temporal analysis. the other main finding of the study was the ability to distribute web-based tools to access different kinds of experts and recruit them to test the design of gis tools [18]. the third study was conducted by researchers of different scientific backgrounds: geography, medical school, and public health specialties. the study design was multi-staged. first stage: there was a user needs assessment, through meetings with public health stakeholders who described need for injury-related gis tools and reports. second and third stages: these combined stages were named as the map development stage. three map types were created by the researchers; the maps were: static, animated, and interactive maps. the created maps displayed the injury data and its socio-demographic determinants. these maps were uploaded to a developed website. fourth stage: the uploaded maps were tested using a sample of public health officials and injury prevention stakeholders. the usability testing sessions were on-site with an observer to write down the users’ comments and their map-interface. the sessions were followed by a self-filled-out questionnaire and short discussion per participant. all the participants revealed that all map types are useful for different purposes. most of them pointed out that the animated maps are more effective than the static maps, and the best maps to effectively compare the injury data to its socio-economic determinants were the interactive maps. most of the users agreed on the effect of the resources in terms of time, money, expertise on the map development industry, and the availability of right and appropriate data to build successful maps [19]. the fourth study was carried out by three scholars from different specialties and educational institutions: public health science, environmental and engineering science, and geography science. the study aims were to conduct a pilot study on a sample of phps to explore their comprehensive and visualization preferences of the interactive online-based mapping reports and to evaluate the effectiveness of the interactive mapping reports’ formats and measure the actual end-users’ interactions with the tested gis tools. the study design was built on a five-section interview questionnaire. the test was on-site, faceto-face, and a gis-based interview. the interviews were accompanied with direct observation and a think-aloud protocol. the participants were asked to examine the tested visualization material, answer the questions, and write down their preferences, perceptions, and expectations on the tested geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi material. seven academic phps were assigned, according to their expertise of using disease visualization maps, based on their answers on a specific question to novice, intermediate, and expert categories. the interviews included five sections in a well-structured questionnaire: user experience, diverging color schemes, data classification schemes, graphical representation of morbidity data, and interactive mapping usability tasks. the novice participants had problems in exploration of the data classifications, in understanding the supplementary sophisticated statistical graphics, and in linking the interactive tables with the maps of the tested reports. there were differences in the perception of the interactive mapping reports among the participants according to their previous geographic experience [15]. 2. usability studies based on just interviewing the participants: there are five articles under this category. the articles’ methodologies were based on interviewing phps, cartographic scientists, map developers, and/ or public health policy makers. see table (2). the first study was conducted as a collaboration of three geography scientists from three different universities. the aim of the study was to analyze the organizational issues which are important to successfully implement giss within the national health service (nhs) in the uk and compare the results to previous studies that were conducted to analyze the same aim. the study design was mixed-methods, starting with a national questionnaire followed by semi-structured interviews. the national questionnaire was formed on the current use of gis software, future plans for gis use, policy-related uses of gis, barriers to using gis, and enumerates human, environmental, and organizational barriers to implement gis. the questionnaire was answered by health services professionals. an in-depth interview was conducted on 20 selected nhs personnel. the interviews included the potential issues to establish gis software: individual issues, policy issues, data issues, organizational issues, and various resources issues. the national survey revealed an increase of gis use, map production and gis use in analysis, modeling, and data integration are important. the examples of gis uses were in: inequalities, accessibility determination, and environmental sciences. less than 50% of the interviewees stated they did not fully operate their giss. informational technology administration and maintaining systems are influential for gis implementation. both the survey and the interviewees stated a list of the obstacles to gis implementation: lack of digital data, difficult analytical tasks of gis, lack of staff resources to operate gis, lack of gis skills, lack of maintenance systems, lack of wide organizational planning, lack of authority’s awareness, insufficient training of the gis users, lack of central plan and support from the department of health to its organizations, and lack of awareness among clinicians and administrators of gis importance. the respondents were asked about the barriers and issues which restricted the geographic information exchange. responses were varied: licensing arrangement issues between the organizations, presence of gis data in specific formats, lack of interest of gis exchange in other organizations, and hardware and software incompatibilities among different organizations [20]. geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi table 2. usability studies based on interviewing the participants author and year country aim study design methodology significant findings recommendations ghetain et al, 2008 usa to evaluate usability and utility of gis technology this is used in cancer research and policy a phone interview based usability study the participants recruited through email invitations. 49 of 50 states' cancer control directors were interviewed about the use of gis in cancer research and policy according to the participants responses: (1) advantages of using gis in cancer policy: identify services inadequacy, explore accessibility, knowing population at risk, and classifying cancer staging. (2) research advantages: monitoring and surveillance, important statistical and cartographic resources, generate research questions and hypotheses (3) relation between mapped behavioral risks data and perceived advantages: : there were significant relationship between using behavioral risks mapped data and only the determination of at risk population policy. (4) cancer incidence, mortality, and staging there was no significant relation of this mapped data to cancer policy, but it was significantly related to producing etiology hypotheses research advantage. (5) environmental exposure: no significant relation of this mapped data to any policy or research advantages. (6) health care services: there was significant relation of this data to accessibility policy. (7)transportation access: there is significant relation to accessibility policy as well as significant to generating etiology hypotheses. (1) the government must encourage the updating and use of diseases registries’ databases in mapping reports and help connect health research with politics environment (2) the gis use depends on training the potential gis end-users and disseminate the gis technology. (3)policy makers should be involved in health related data development, analysis, and gis use (4) the policy makers should be motivated and educated about the importance of the gis use in practice. rhobinson et al, 2011 usa to test and refine the online tool was developed to join and communicate the geovisualization and map developers experts online needs assessment survey with targeted endusers the sampled public health professional participants were probed about the tested tool's learning artifact current learning habits: most of the participants spend <10 hours learning on new tools. some of them were asked to learn by the employer. the net, journal articles, scientific conferences, and asking others were good ways to learn. searching the net is the common way which is used by them find tools’ learning artifacts. other coworkers are better than employers in offering information about tools. most of them admitted the importance of gis use in practice. preferred learning artifacts: most of participants preferred having extensive tutorials and sessions on gis technology’s content, summary, functionality, and usability. >560% want to know the biosketch and the credentials of the learning artifacts’ developers. the artifact content and the summary are the most important parts, and the metadata was the least required thing. contributing learning artifacts: wikipedia, youtube, and facebook were the most important resources of learning. almost all of the participants agreed on the importance of the learner’s biosketch and credentials in motivating them to apply the artifact in practice. the gis tools end users should be involved in designing and development of the learning artifacts of these tools through needs assessment surveys bhowmick et al, 2008 usa to develop and test the new geographic information technology and tools to analyze and visualize the complicated health datasets in public health field key informant interviews on the use of gis technology followed by a systematic review on the related literature sixteen cancer research experts were recruited through snow ball sampling technique. semistructured phone interviews were conducted using focused discussion and act-based representation of knowledge (ark) techniques. the gis non-users were studied to find the barriers to use the gis technology. the participants had different demographic, experience, and scientific backgrounds. most of them were cancer research experts. most of them pointed the importance of gis in detect dataset features, generate hypotheses, and to discover roles of gis in cancer dataset exploration. most of them did not use sophisticated gis technology, but some of them they mentioned a geospatial rules as routine work. the gis technology use was recognized from moderately to a very useful in cancer research field. usability testing studies are more productive than just interviewing public health practitioners or reviewing literature. in depth interviews of a small size samples are more productive than interviewing large samples in less depth. collaboration between the information domain experts and the implementation experts is very important. joyce, 2009 uk to assess the perception of the public health policy makers about the gis use in public health field, advantages and disadvantages of gis in public health field, and considering gis technology as away of collaboration and exchange information semi structured interviews face to face interviews of 23 phps participants, who were chosen based on specific selection criteria. gis technology a producing knowledge, able to integrate and analyze databases. there are challenges to use giss. gis includes temporal-spatial information could search cause-effect theories. giss are crucial for communication and collaboration between experts of the same interests. the linkage and willingness of sharing gis technologies must be strengthened further. most of the gis end users afraid of the gis complicated functions. gis output impacts realities by relating the findings to real life parameters and could affect policy makers’ points of view. gis information could be manipulated and misinterpreted. gis tools becomes crucial in practice. the users should be helped and trained to adopt use the gis in practice. gis likely to be most effective in decision-making when applied in a multidisciplinary context to facilitate sharing of data, knowledge and expertise across the public health landscape. higgs, 2005 uk to search the types of organizational barriers to the fruitful operation of gis within the national health services (nhs) in uk, and compare the findings to the previous related literature national survey followed by in-depth semi-structured questionnaire, followed with a systematic review grounded on a national questionnaire results, the researchers conducted semistructured interviews of 20 nhs personnel. the interviews including these factors: individual issues, policy issues, data issues, organizational issues, and various resources issues. gis use and implementation: increase of gis use after the survey’s conduction. geospatial reports production and analysis are important in practice. examples gis uses: inequalities, accessibility, environment sciences. about half of the interviewees did not fully run their gis. information technology administration and maintaining systems are essential to enhance gis usability. gis implementation barriers: lack of electronic datasets, complicated gis tasks, scarce trained resources, lack of maintenance systems, lack of clear organizational plans and goals, absence of external and central leadership on gis, lack of alertness among clinicians and administrators of gis technology. levels of geographical information exchange: issues with certifying measures for data, inappropriate datasets, bad marketing of giss, inappropriate interoperability between organizations. the collaborated organizations which are tackling health issues should modify their cultural and organizational policies, to be able to exchange the health-related geospatial data. this needs expert advice and guidence. geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi the second study was conducted by three communication and art scientists and a public health scientist. the aim of the study was to evaluate the use and the utility of gis tools in mapping cancer-related data and their effect on cancer control policies and practice, and to measure the participants’ perception on using such tools in the comprehensive cancer control (ccc) program. the researchers recruited participants through email. forty-nine u.s. ccc program directors out of 50 states were interviewed by phone. the interview questions were to explore the relationship of gis reports to public health policy and research. the identified advantages of using gis on cancer policy were: identify service gap, identify access issues, identify cancer staging, followed by identify risk population. the identified advantages of using gis on cancer research were: multivariate modeling tool, monitoring and surveillance, followed by generate etiologic hypotheses. there was no significant relationship discovered between using behavioral risks mapped reports and research, while there was a significant relationship between behavioral risks mapping data and the policy of identifying the at risk population. the study did not discover any significant relationship of cancer burden mapped reports and cancer control policy, but there was a significant relationship of this kind of mapped reports and the generating etiology hypotheses research advantage. about 51% of the interviewed directors stated that the demographics are an important content of any mapped reports, but the study researchers could not find any significant relation of the demographics to any of the policy and research advantages. there was significant relation of mapped reports of transportation access and the accessibility policy, and the etiology hypotheses research advantages. there were no significant relations between policy and research, and all of these kinds of cancer mapped contents: environmental exposure, multi-layer content maps, and healthcare services [21]. the third study was conducted by scientists from geography, environmental science, and public health fields. the aim of the study was to develop and evaluate the tools and methods that might be used by phps in order to extract knowledge and evidence from health-related databases. the methodology of the study was semi-structured phone interviews with 16 participants who were recruited using snowball sampling. the investigators searched the literature of using gis in cancer research to support the interviews’ results. most of the participants were faculty or senior administrators of different demographic and scientific backgrounds. they varied in experience. most of the participants were involved in the cancer research domain. most of the participants pointed out that the typical goals of data exploration were to detect dataset aspects, to develop hypotheses for further cancer research, and to discover roles of geospatial methods in the exploration process. most of them did not use complex spatial analysis, but 30% of them reported geo-coding, map creation, and gis data analysis as regular research activities. gis analyses were considered from moderate to very useful tools in cancer research, specifically in incidence and mortality cancer data. they pointed to the importance of gis in comparing spatial data of different cancer types, disease clustering, correlation with related spatial indicators, and combining geospatial data from different domains. the participants pointed to the following limitations in gis use: difficulties in geo-coding and data aggregation, lack of support for merging data from different data sources and/or constructed with different gis tools, complexity of gis tools use and functionalities [22]. the aim of the study described in the fourth article was to study public health professionals’ perceiving of the gis in practice and research and understand the impact of gis on data sharing and communication. the methodology was face-to-face semi-structured interviews with 23 geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi participants who were policy decision makers. the participants were recruited purposefully. the article findings are: giss are converting raw data to useful data and knowledge. gis has the ability to integrate and analyze datasets. giss are important in public health practice and decision making but include many implementation and usability challenges. gis could be used to explore causeeffect relationship by including time and space and have epidemiological power. giss are crucial for collaboration between experts of the same interests but there are challenges to that. the linkage and willingness of sharing gis technologies must be strengthened further. most of the low experiences in gis use of public health practitioners are fear of the sophisticated functions of the gis tools. gis output impacts realities and could affect policy makers’ decisions. some participants pointed out that gis tools are not neutral and map makers might manipulate datasets using gis power. metadata and detailed text are very important to interpret the gis data. data quality is very important as well as strict standards during constructing gis data. gis tools are considered user-friendly and easier to relate data tools. time and resource constraints, training skills, and intra-organization environments enhance feelings of insecurity and concern among potential gis end-users [23]. the last article’s aim was to conduct a needs-assessment survey on the potential end users of the geo-visual explication (g-ex) portal, which is an online tool designed to connect researchers in geo-visualization to the end users, to refine the g-xl learn module. the researchers developed a web-based survey using their previous in-depth usability studies. the participants were recruited by sending emails. there were 21 participants from different backgrounds: epidemiologists, health policy specialists, geographers, and research scientists. the results were as follows: most of the participants spend less than ten hours per week learning new tools and 20% of them were required by their employers to keep learning these tools. the ways of learning about new tools were: the internet, journal articles, conference sessions, and asking colleagues. the least likely ways participants learned about these tools were advertisements and employer contribution. the participants’ preferred learning artifacts were comprehensive tutorials followed by hands-on training. most of the participants wanted the artifacts to include expected training duration and summary of learning objects, and they preferred to start using the software before starting the training. 63% of the participants liked to know the biography of the trainers. most of the participants spotted that the artifact’s content, summary, and the instructions are the most important parts of any learning module. the other contributing artifacts for the participants were: wikipedia, youtube, and social media, respectively. fifty two of the participants were interested in development of training material to share with others on the g-ex website [24]. discussion the findings of the reviewed articles are discussed separately as they were classified in the methods and the results sections. 1. usability testing studies it is important to include a representative sample of the actual users in any usability testing of gis tools. a five-participant study can demonstrate most of the usability problems of the tested material [25]. this enables the investigators to measure the actual user-tool interface and helps them successfully design, implement, and refine these tested tools. all the studied usability testing geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi articles used the actual users to test the gis systems. the review also revealed that the usability testing research should extend to explore the content, functionality, and utility of the gis tools. the review stated that any gis tool should be iteratively evaluated using different methodologies. the review discovered that case studies by collaboration with experts were very valuable in development and refinement of gis tools. the review concluded that visualizing the health-related data in an interactive way, including tables, maps and graphs, is considered the best way to present such information. the review revealed the importance of the development of online applications to access more potential users and help them participate in testing the gis tools. the review stated that building successful mapping reports depends on the availability of monetary support, right data, and expertise in map construction. the review pointed out that the level of experience in using data visualization is critical for being willing to use gis software and interpretation and linking of the mapping reports’ information. 2. usability studies based on just interviewing the participants the review revealed that even to assess the phps’ preferences and perspectives the researchers do not have to rely on just interviewing the participants, but they need to search for more methodology to support the validity of their results. in some of our reviewed articles, researchers supplemented their interviews with the results of well-respected national questionnaires and some did systematic reviews to support the study evidence and generalizability. health organizations should assess and overcome the organizational, cultural, technical, and expertise barriers to implement and use gis software to visualize their data. one of our reviewed articles recommended the adoption of policies that support visualization of health-related data on the state level and valued the importance of state encouragement of utilization and presentation of disease registries on geographic bases to connect health research to the political environment. all the reviewed articles pointed to the importance of dissemination of successful gis technology, training the potential users adequately and giving up-to-date information technology administration support and maintenance. most of the reviewed articles recommended involvement of policy makers in using gis tools and in analysis of the gis tools results. the review recommended collaboration between gis software developers and implementers and potential end-users to develop new, and test refined, versions of public health gis tools. the review articles in this section revealed that needs assessment is crucial to know the perspectives of the gis potential users and to develop web learning portals and modules. the review suggested that the learning artifacts of giss tools could be presented in different formats based on users’ preferences. the review recommended that employers offer extensive training for their gis potential users before and after implementation of the gis tools. 3. list of recommendations learned from the review the review’s investigators constructed a list of recommendations they learned from conducting the review. this list could help other researchers conducting similar reviews. it also might help public health practitioners to decide on the type of information and the way they should visualize the health-related data to satisfy potential users. the list might help the gis technology designers, builders, and vendors to develop a user-friendly technology by tailoring the developed technology geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi according to their users’ preferences and insights. figure (2) shows the recommendation list we learned from the current review. figure (2). list of recommendations learned from the review geographic information systems: usability, perception, and preferences of public health professionals online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e191, 2017 ojphi conclusion in general, the review revealed that phps are aware of the importance of using gis software on public health policy and research. in most of the studies, participants pointed to the advantages of using giss on public health practice to determine inequalities and accessibility. they also stated the importance of supplementary roles of other contextual indicators on different public health problems when these indicators are visualized with the health-related data. most of the studies revealed the participants were aware of the collaboration and the exchange of the giss technology and data between experts in the public health field, and the importance of including the end users in the basic stages of design and development of gis tools. the participants were also aware of the importance of extensive evaluations for gis tools before and after releasing them and the essential need for training the potential users of these gis tools. review strengths, limitations and future directions due to the innovative review’s purpose, the review’s authors targeted specific databases which could produce a maximum number of scientific-based articles to match the 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2016 conference abstracts respiratory and circulatory deaths attributable to influenza a & b liselotte van asten*, jan van de kassteele and wim van der hoek rivm (nethelands institute of public health and the environment, centre for infectious disease control), bilthoven, netherlands objective to estimate mortality attributable to influenza adjusted for other common respiratory pathogens, baseline seasonal trends and extreme temperatures. introduction assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in deathcause statistics modeling studies are commonly used to estimate the impact of influenza on mortality. methods using primary cause of death (statistics netherlands) we modeled weekly timeseries of 1) respiratory deaths (icd10 codes j00-j99) and 2) circulatory deaths (icd10 codes i00-i99). we used regression models with an identity link and poisson error to relate mortality to counts of influenza a & b diagnoses. we adjusted for other common respiratory pathogens (all pathogen data was at population level from the national laboratory surveillance), temperature (from the dutch royal meteorological institute), and baseline linear and cyclical (i.e. seasonal) trends. to account for the yearly variation in the severity of the main circulating influenza a strain we used time dependent variables for influenza a (fixed at lag 0 – assuming a direct effect of influenza. for influenza b and the confoundig pathogens we considered a 0 tot -4 time lag (thus allowing infection to precede death for up to 4 weeks). we performed the analyses separately per death cause group and by 3 different age groups (0-64, 65-74,75+ years) over a 14-year timeperiod (mid 1999-mid 2013, thus 14 complete winter seasons). results in the netherlands on average 2,636 all cause deaths occur per week varying by season (lower in summer min: 2,219 and higher in winter max: 3,564) with yearly incidence ranging from 20/10,000 in 0-64 year olds to 885/10,000 in 75-plus year olds. circulatory mortality (31% of total deaths) was higher than respiratory mortality (10% of total deaths) and both showed clear seasonality in all age-groups. overall, 0.14% of all deaths were actually coded as influenza deaths. preliminary model estimates showed that the proportion of respiratory deaths attributable to influenza a were quite similar for 0-64 and 65-74 year olds but higher in 75+ (5.1%, 5.7%, 7.0% respectively) while this proportion was stable across age-groups for circulatory deaths (approximately 1.5% in all agegroups for influenza a). influenza b was significantly associated with respiratory deaths and circulatory deaths in the oldest age group of 75+ years (with proportions of 0.7% and 0.2% respectively) while in the 65-74 year olds it was associated only with circulatory deaths (0.2%). influenza b was not significantly associated with either respiratory or circulatory mortality in the 0-64 year age group. on average, yearly in the 75+ age group 70/10,000 respiratory deaths and 39/10,000 circulatory deaths were attributable to influenza a. for influenza b the incidences were 7 to 10 fold lower (7/10,000 and 6/10,000 respectively). conclusions influenza a was significantly associated with respiratory and circulatory mortality in all age groups while influenza b was significantly associated with respiratory and circulatory mortality in the elderly only. keywords mortality; influenza; respiratory *liselotte van asten e-mail: liselotte.van.asten@rivm.nl online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e120, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 1ecohealth alliance, new york, ny, usa; 2columbia university, new york, ny, usa objective mantle will be an open-source, cloud-compatible platform for storing, studying, and sharing data on infectious diseases across plants, animals, and humans. it will meet the needs of three groups of users: scientists, policymakers, and the general public. for scientists, mantle will make datasets portable and connected. scientists will be able to upload datasets to the mantle website or collect data from the field using a mobile app. users in mantle will be able to easily make datasets entirely private, publicly accessible, or shared with specific users or groups. introduction the one health approach suggests that humans, animals, and the environment are closely tied together. human interaction with wildlife and the environment contributes to increased risk for human, plant, and animal infectious disease outbreaks. since human, animal, and ecosystem health are linked, interdisciplinary and holistic approaches are needed to prevent future infectious disease outbreaks. despite the movement towards one health, the software currently available to manage, analyze, and communicate the vast amount of one health data is grossly inadequate. one health data are continually growing in size and complexity, and new technologies must be developed to address the magnitude of the problem. furthermore, the desire of single entities to control and leverage information for greater personal and organizational wealth and power directly opposes the goals of biosurveillance, one health, and science. open access and open source software are needed to address these complex one health problems, and to improve data accessibility, interoperability, and information communication. methods mantle will handle tabular data, and other widely used spatial data formats. it will visualize and explore data in useful ways, and allow data to be downloaded as the originally uploaded file or in a customizable format for use in analytical software. mantle will store metadata—information about a dataset and its contents— using development standards for linked data (e.g., json-ld and wcsv, part of the overarching resource description framework). tapping into the emerging semantic web enables richer interactions with datasets, streamlining many common data tasks. mantle will natively understand a number of data types common to one health data, including spatial and temporal elements, taxonomic names, and case counts, and associates these with published ontologies. mantle will also work seamlessly with any numeric, categorical, and textual data. mantle will store metadata—information about a dataset and its contents—using development standards for linked data (e.g., json-ld and wcsv, part of the overarching resource description framework). tapping into the emerging semantic web enables richer interactions with datasets, streamlining many common data tasks. mantle will natively understand a number of data types common to one health data, including spatial and temporal elements, taxonomic names, and case counts, and associates these with published ontologies. mantle will also work seamlessly with any numeric, categorical, and textual data. conclusions policymakers and decision makers will be able to view real-time visualizations of mantle data feeds in dashboards. researchers will be able to upload datasets representing the output of models built in other analytical software, which can be shared with policymakers, who can also view and interact with the output of custom-built modeling modules to view timely and meaningful summaries of public health data feeds. potential use cases for the general public include browsing day-to-day textual and syndromic surveillance information, viewing the predictions of a one-time study, and monitoring the latest calculated epidemic curve in an outbreak or ongoing epidemic. mantle will facilitate crosscutting collaborations between disciplines and institutions. users will be able to create, manage, and join organizations and groups. groups of users will access and collaborate on collections of datasets, grouped manually or by specified properties. for instance, users interested in ranavirus can view and contribute to the global ranavirus reporting system, a collaborative effort by scientists worldwide to aggregate observed cases of ranavirus across species and locations (a mantle prototype). keywords metadata; one health surveillance; open source; data sharing; one health research *andrew g. huff e-mail: andrewgeorgehuff@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e121, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts cancer health disparities in southeastern wisconsin yi ou* wisconsin departmen tof health services, madison, wi, usa objective to assesse health disparities in all-site cancer incidence and mortality rates, and stage of specific cancer diagnosis (female breast cancer and colorectal cancer) compared between african american and white populations of southeastern wisconsin during 2007-2011. introduction advanced cancer treatments and research have been helping reduce cancer mortality nationally and in wisconsin. however, chronic health disparities in cancer remain a major public health concern as not all population subgroups have equal accesses to these healthcare benefits [1, 3]. previous cancer studies showed that cancer health disparities persisted among racial populations had primarily focused on the entire state of wisconsin [2]. the southeastern region wisconsin, the greater milwaukee metropolitan area, is home to 83% of wisconsin’s african american population, and includes one of the most segregated metropolitan areas in the united states [1]. because of this, better understanding of cancer trends in the southeastern wisconsin region can assist in targeting a focal point to more effectively use resources to eliminate health disparities in wisconsin. methods cancer incidence data were obtained from the wisconsin cancer reporting system (wcrs) for the period 2007-2011. cancer mortality data was assessed from the national center for health statistics (nchs) for the period 2007-2011. for absolute disparities, trends in cancer incidence, mortality, and the stage of diagnoses for african americans and whites were calculated. ratios of african american rates and white rates were used to measure changes in relative disparities [2]. results during 2000-2011, african americans had higher cancer incidence rates and mortality rates than whites, except for breast cancer incidence rates were lower for african american women than for white women(figures 1, 2, 3, and 4). sex affected the trends in disparities and the magnitude of change for incidence rates(table 1). in 2010, african american female breast cancer incidence rate was 8 cases per 100,000 population fewer than white rates. african american women tend to be diagnosed at a late stage of breast cancer with a rate ratio of 1.03 in 2000 and 1.29 in 2011. for colorectal cancer, african american men were more likely to be diagnosed with an advanced stage than white men with a slight decrease in disparity. the ratio between african american and white rates were 1.44 in 2000 and 1.40 in 2011 conclusions reducing cancer burden and eliminiating cancer health disparities will need further research. but this study has shown that the at-risk population in the southeastern wisconsin is a good starting point for public health profesisonals and policy markers to utilize their resources and priotize effectively to reduce racial health inequity in cancer in wisconsin. keywords cancer; registry; health equipty acknowledgments thank you for the sponsorship by cdc/cste applied public health informatics fellowship to support the project at the wisconsin department of health services. i extend my gratitude to the wisconsin cancer reporting system for giving me access to their data and advice for this project. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e29, 2016 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts references 1. american cancer society. wisconsin cancer facts & 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perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, 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patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew 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childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts machine learning for identifying relevance to biosurveillance in multilingual text qiaochu chen*1 and lauren e. charles2 1tulane university, new orleans, la, usa; 2pacific northwest national laboratory, richland, wa, usa objective the objective is to develop an ensemble of machine learning algorithms to identify multilingual, online articles that are relevant to biosurveillance. language morphology varies widely across languages and must be accounted for when designing algorithms. here, we compare the performance of a word embedding-based approach and a topic modeling approach with machine learning algorithms to determine the best method for chinese, arabic, and french languages. introduction global biosurveillance is an extremely important, yet challenging task. one form of global biosurveillance comes from harvesting open source online data (e.g. news, blogs, reports, rss feeds). the information derived from this data can be used for timely detection and identification of biological threats all over the world. however, the more inclusive the data harvesting procedure is to ensure that all potentially relevant articles are collected, the more data that is irrelevant also gets harvested. this issue can become even more complex when the online data is in a non-native language. foreign language articles not only create language-specific issues for natural language processing (nlp), but also add a significant amount of translation costs. previous work shows success in the use of combinatory monolingual classifiers in specific applications, e.g., legal domain [1]. a critical component for a comprehensive, online harvesting biosurveillance system is the capability to identify relevant foreign language articles from irrelevant ones based on the initial article information collected, without the additional cost of full text retrieval and translation. methods the analysis text dataset contains the title and brief description of 3506 online articles in chinese, arabic, and french languages from the date range of august, 17, 2016 to july 5, 2017. the nlp article pre-processing steps are language-specific tokenization and stop words removal. we compare two different approaches: word embeddings and topic modeling (fig. 1). for word embeddings, we first generate word vectors for the data using a pretrained word2vec (w2v) model [2]. subsequently, the word vectors within a document are averaged to produce a single feature vector for the document. then, we fit a machine learning algorithm (random forest classifier or support vector machine (svm)) to the training vectors and get predictions for the test documents. for topic modelling, we used a latent dirichlet allocation (lda) model to generate five topics for all relevant documents [3]. for each new document, the output is the probability measure for the document belonging to these five topics. here, we classify the new document by comparing the probability measure with a relevancy threshold. results the word2vec model combined with a random forest classifier outperformed the other approaches across the three languages (fig. 2); the chinese model has an 89% f1-score, the arabic model has 86%, and the french model has 94%. to decrease the chance of calling a potentially relevant article irrelevant, high recall was more important than high precision. in the chinese model, the word2vec with a random forest approach had the highest recall at 98% (table 1). conclusions we present research findings on different approaches of relevance to biosurveillance identification on non-english texts and identify the best performing methods for implementation into a biosurveillance online article harvesting system. our initial results suggest that the word embeddings model has an advantage over topic modeling, and the random forest classifier outperforms the svm. directions for future work will aim to further expand the list of languages and methods to be compared, e.g., n-grams and non-negative matrix factorization. in addition, we will fine-tune the arabic and french model for better accuracy results. table 1. results of the chinese model using different methods. abbreviations in text. figure 1: methodology for comparing different methods to identify the best approach to classifying text data as relevant to biosurveillance. figure 2. f1-scores of different methods across languages. abbreviations in text. keywords machine learning; biosurveillance; natural language processing; multilingual; articles isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments this work was supported by the department of homeland security science and technology directorate under doe contract number de-ac05-76rl01830 for the management and operation of pacific northwest national laboratory. references [1] gonalves t, quaresma p. 2010. multilingual text classification through combination of monolingual classifiers. proceedings of the 4th workshop on legal ontologies and artificial intelligence techniques, pp. 29-38 [2] bojanowski p, grave e, joulin a, mikolov t. 2016. enriching word vectors with subword information. arxiv preprint arxiv:1607.04606. [3] blei d, ng a, jordan m. 2003. latent dirichlet allocation. the journal of machine learning research. p.993-1022. *qiaochu chen e-mail: qchen7@tulane.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e54, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using mobile technology to facilitate reactive case detection of malaria gordon cressman*1, michael mckay1, abdul-wahid al-mafazy2, mahdi m. ramsan1, abdullah s. ali2, issa a. garimo1, humphrey mkali2 and jeremiah j. ngondi1 1information and communication technology, rti international, research triangle park, nc, usa; 2zanzibar malaria elimination programme, zanzibar town, tanzania, united republic of objective this presentation will share findings from more than three years of using mobile technology for reactive case detection (racd) to help eliminate malaria in a well-defined geographic area. it will review the concepts of racd, the application of mobile technology, lessons learned from more than three years of application, and considerations in applying this technology in other malaria elimination contexts. introduction zanzibar is comprised primarily of two large islands with a population of 1.3 million. indoor residual spraying (irs) campaigns, distribution of long-lasting insecticide treated bed nets (llins), and use of rapid diagnostic tests (rdts) have reduced malaria prevalence from 39% in 2005 to less than 1% in 2011-2012. as malaria burden decreases, there is an increasing need to track and follow up individual cases to contain transmission that could lead to resurgence. one method being used to achieve these aims is reactive case detection (racd). racd is generally understood to be triggered whenever a case is identified by passive case detection. the response involves visiting the household of the newly reported case and screening family members. depending on program protocol, it may also involve screening neighbors within a defined radius. racd has been used or tested in cambodia, china, india, peru, senegal, swaziland, tanzania, and zambia. racd can be resource intensive. several studies raise questions concerning whether and how racd can be prioritized and targeted effectively as case numbers continue to decline. methods since september 2012 zanzibar malaria elimination programme (zamep) has used racd to limit onward transmission, reduce the local parasite reservoir, and gather data needed improve program effectiveness. zanzibar is one of very few malaria elimination contexts using a mobile technology system to support racd.1 this system, called the malaria case notification system (mcn) uses mobile software called coconut surveillance. coconut surveillance is free and open source software designed for malaria elimination. it includes an interactive sms system for case notification, a mobile software application designed to guide mobile case workers through racd, and an analytics software application designed for surveillance and response program managers. data were collected in the coconut surveillance database for more than three years, beginning in september 2012. reports were monitored in real time and periodically to assess racd response times against protocol targets, case trends, case locations, and other data. geographical information system (gis) software was used to produce detailed maps of case households. three independent assessments were conducted of various aspects of the malaria surveillance system. results from september 2012 to december 2015, coconut surveillance has helped malaria surveillance officers in zanzibar respond to more than 8,617 (84%) reported cases of malaria, complete nearly 10,245 household visits, test more than 36,185 household members, and identify and treat 2,032 previously unknown cases. the average number of racd activities occurring within 48 hours increased from 72% in 2013 to 89% in 2015. the number of household members screened during racd also increased from 7,589 in 2013 to 14,987 in 2015. challenges included incomplete registers at health care facilities, lack of transport, inadequate training for clinicians and surveillance officers, and insufficient communication to the affected communities. conclusions in zanzibar twenty malaria surveillance officers equipped with inexpensive android tablets and motorbikes are keeping malaria prevalence at less than 1%. the effectiveness of the system might be enhanced by improving training for clinicians and surveillance officers, ensuring the availability of transportation for surveillance officers, and improving communications to the affected communities. these results suggest key considerations for applying this and similar systems in other malaria elimination contexts. keywords malaria; surveillance; tanzania acknowledgments rti international and the zanzibar malaria elimination programme gratefully acknowledge the collaboration and support of the u.s. president’s malaria initiative. references 1. ohrt c, roberts kw, sturrock hj, wegbreit j, lee by, gosling rd. information systems to support surveillance for malaria elimination. am j trop med hyg. 2015. doi: 10.4269/ajtmh.14-0257. pubmed pmid: 26013378. *gordon cressman e-mail: gmc@rti.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e68, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui bureau of epidemiology, florida department of health, tallahassee, fl, usa objective to investigate the day of week effect on myocardial infarctions (mi) in the age group of 18 – 64 years using essence-fl emergency department (ed) data. introduction syndromic surveillance ed data has historically shown the highest number of visits on mondays, with decreasing volumes throughout the week. previous studies have shown that increased negative health outcomes have occurred on mondays (barnett and dobson, 2004). a study in the european journal of epidemiology provided evidence that suggests a higher incidence of cardiovascular events on mondays compared to other days of the week (witte et al., 2005). methods the florida department of health (fdoh) utilizes the electronic surveillance system for the early notification of community based epidemics (essence-fl) as its statewide syndromic surveillance system. visits from 210/237 emergency departments and 33 urgent care centers are analyzed by fdoh epidemiologists each day. a query was created in essence-fl to identify mi related chief complaints and discharge diagnoses: mi,or,^mi^,or,^mi,or,mi^,or,^heart attack^,or,^cardiac arrest^,or,^myocardial infarction^,or,^acute myocardial i n f a r c t i o n ^ , o r , ^ c a r d i o p u l m o n a r y a r r e s t ^ , o r , ^ s t e m i alert^,or,^stemi^,or,^nstemi^,or,^subendocardial infarction^ this analysis focused on individuals 18 – 64 years of age. mi related terms were used to query ed visits from the time period of december 1, 2012 to august 10, 2015. the total number of ed visits analyzed in this study was 25,448,785. exponentially weighted moving average (ewma) detection algorithms in essence-fl were used to determine anomalies in the targeted data, which generated alerts signaling a larger than expected number of mi related ed visits for a given day. results the created mi query detected 18 red alerts and 57 yellow alerts for the time period of december 1, 2012 to august 10, 2015. a total of 33% of the red alerts occurred on monday. the total of red and yellow alerts demonstrated that 29% of these flagged days occurred on a monday. in comparison to all other days of the week, monday was shown to have the highest proportion of red and yellow statistical alerts. additionally, the daily mean analysis demonstrated that monday, followed by tuesday, showed the largest positive deviation from the overall daily mean (table 1). saturday, followed by sunday, proved to be the two days with the lowest proportion of red and yellow statistical alerts. conclusions results from this analysis provide evidence that a disproportionate number of individuals aged 18 – 64 years with mi related complaints present to the ed on mondays. this study supports the results that were found by previous researchers (witte et al., 2005). a similar analysis of chest pain visits in essence-fl presented analytic validity of a day of the week effect, with the highest proportion of visits occurring on mondays. efforts to understand the periodicity of fdoh’s syndromic surveillance data have resulted in epidemiology staff that are better able to respond to both chronic and infectious disease public health threats. additionally, this study suggests that there is a reduced incidence of myocardial infarction and chest pain on saturday and sunday. this study assumes that an individual having a myocardial infarction, or believed to be having a myocardial infarction, will present to the ed regardless of the day of the week. many ed patients with lesser morbidities, or those who are using the ed for primary care, appear to preferentially select days to go to the ed (faryar, 2013). one limitation of this study is that it relied on, at least in part, chief complaint data. further study of heart attacks by day of the week using a hospital discharge data set would help confirm this finding. keywords myocardial infarction; heart attack; day of the week effect references 1. barnett a, dobson a. excess in cardiovascular events on mondays: a meta-analysis and prospective study. journal of epidemiology & community health 59(2);109 2. witte d. r., grobbee d. e., bots m. l., hoes a. w. excess cardiac mortality on monday: the importance of gender, age and hospitalisation. european journal of epidemiology 20(5);395-399 3. faryar, k. a. (2013). the effects of weekday, season, federal holidays, and severe weather conditions on emergency department volume in montgomery county, ohio. http://corescholar.libraries. wright.edu/mph/94 *allison b. culpepper e-mail: allison.culpepper@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e57, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 1epidemology-preparedness, denver public health, denver, co, usa; 2colorado department of public health and environment, denver, co, usa objective identify population-based clostridium difficile infection (cdi) incidence stratified by health care facility onset (hcfo), community onset-healthcare facility associated (co-hcfa), and community onset-community associated (co-ca) cdi in denver county from 2011 2013 and describe demographic, health care facility exposure, and medication use risk factors. introduction clostridium difficile (cd), a gram-negative, anaerobic, sporeforming bacterium causes symptoms ranging from mild to severe diarrhea and may result in death.1 approximately 75% of cdi cases have symptom onset outside of health care settings.2 annual us costs of treatment and infection containment have surpassed $4.8 billion.3 risk factors for cdi include recent broad-spectrum antibiotic exposure, advanced age, severe underlying morbidities, immunocompromised status, long-term hospital stays, and residence in long-term-care facilities.4 nationally, co-ca cases have increased from 2.8/100,000 person in 1993 to 14.9/100,000 person in 2005.5 methods a retrospective chart review studied cdi cases from 2011 through 2013 with incident (no positive test in the previous 8 weeks) stool specimen (cdi positive toxin or molecular assay) reported to the state health department, among denver county residents older than 1 year. cases were randomly selected for chart review. cases were stratified by epidemiologic classification (hfco, co-hcfa or co-ca) and analyzed for associations with: age, gender, antibiotic exposure, underlying disease, and exposure to high risk areas (e.g., long-term care facilities and hospitals). rates were calculated using state demography office-department of local affairs information. descriptive statistics (means and frequencies) were used to describe cdi trends by year, demographic group, epidemiologic classification, and risk factors. results between 2011 and 2013, 2503 cdi cases were reported and 892 cases were chart reviewed (22%) and identified as co-ca (339), cohcfa (175), or hcfo (44). denver cdi incidence rate increased from 129/100,000 residents in 2010 to 139/100,000 residents in 2013. incidence rates of cases classified as hcfo and co-hcfa remained stable over the study period. rates of co-ca cases decreased from 50.4/100,000 residents in 2011 to 46/100,000 residents in 2013. the mean age for hcfo cases was 67 years, 49 years for co-ca cases, and 56 years for co-hcfa cases. hcfo was more common among men; there were no gender disparities for co-ca or co-hcfa. during the study period, the presence of underlying morbidities increased in all onset types with over half of co-ca and co-hcfa cases reporting underlying morbidities in 2013 (co-ca: 65%; cohcfa: 69%). antibiotic exposure substantially increased between 2011 and 2013, in cases classified as co-hcfa (29% to 70%), and co-ca (18% to 50%). conclusions rates of co-ca cdi rose during the study period. our study reflects national trends in age and antibiotic exposure by epidemiologic classification.6 the frequency of antibiotic exposure and underlying morbidities increased from 2011 to 2013 in both co-ca and cohcfa cases. denver co-ca cases had more underlying morbidities compared to recent reports.7 keywords clostridium difficile; healthcare associated infections; community acquired infections; infectious disease references 1. halabi wj, nguyen vq, carmichael jc, et al. clostridium difficile colitis in the united states: a decade of trends, outcomes, risk factors for colectomy, and mortality after colectomy. j. am. coll. surg. nov 2013;217(5):802-812. 2. centers for disease control and prevention (cdc). vital signs: preventing clostridium difficile infections. morb. mortal. weekly rep. 2012;61(09):157-162. 3. dubberke er, olsen ma. burden of clostridium difficile on the healthcare system. clin inf dis . aug 2012;55 suppl 2:s88-92. 4. dubberke e. strategies for prevention of clostridium difficile infection. j. hosp. med. mar 2012;7 suppl 3:s14-17. 5. evans c., safdar n. current trends in the epidemiology and outcomes of clostridium difficile infection. clin inf dis 2015 (60): s66-71 6. chitnis as1, holzbauer sm, belflower rm, et al. epidemiology of community-associated 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lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts importance of continued data quality assessment of syndromic production data sophia crossen* public health informatics, kansas department of health and environment, topeka, ks, usa objective to explore the quality of data submitted once a facility is moved into an ongoing submission status and address the importance of continuing data quality assessments. introduction once a facility meets data quality standards and is approved for production, an assumption is made that the quality of data received remains at the same level. when looking at production data quality reports from various states generated using a sas data quality program, a need for production data quality assessment was identified. by implementing a periodic data quality update on all production facilities, data quality has improved for production data as a whole and for individual facility data. through this activity several root causes of data quality degradation have been identified, allowing processes to be implemented in order to mitigate impact on data quality. methods many jurisdictions work with facilities during the onboarding process to improve data quality. once a certain level of data quality is achieved, the facility is moved into production. at this point the jurisdiction generally assumes that the quality of the data being submitted will remain fairly constant. to check this assumption in kansas, a sas production report program was developed specifically to look at production data quality. a legacy data set is downloaded from biosense production servers by earliest date in order to capture all records for visits which occurred within a specified time frame. this data set is then run through a sas data quality program which checks specific fields for completeness and validity and prints a report on counts and percentages of null and invalid values, outdated records, and timeliness of record submission, as well as examples of records from visits containing these errors. a report is created for the state as a whole, each facility, ehr vendor, and hie sending data to the production servers, with examples provided only by facility. the facility, vendor, and hie reports include state percentages of errors for comparison. the production report was initially run on kansas data for the first quarter of 2016 followed by consultations with facilities on the findings. monthly checks were made of data quality before and after facilities implemented changes. an examination of kansas’ results showed a marked decrease in data quality for many facilities. every facility had at least one area in need of improvement. the data quality reports and examples were sent to every facility sending production data during the first quarter attached to an email requesting a 30-60 minute call with each to go over the report. this call was deemed crucial to the process since it had been over a year, and in a few cases over two years, since some of the facilities had looked at data quality and would need a review of the findings and all requirements, new and old. ultimately, over half of all production facilities scheduled a follow-up call. while some facilities expressed some degree of trepidation, most facilities were open to revisiting data quality and to making requested improvements. reasons for data quality degradation included updates to ehr products, change of ehr product, work flow issues, engine updates, new requirements, and personnel turnover. a request was made of other jurisdictions (including arizona, nevada, and illinois) to look at their production data using the same program and compare quality. data was pulled for at least one week of july 2016 by earliest date. results monthly reports have been run on kansas production data both before and after the consultation meetings which indicate a marked improvement in both completeness of required fields and validity of values in those fields. data for these monthly reports was again selected by earliest date. conclusions in order to ensure production data continues to be of value for syndromic surveillance purposes, periodic data quality assessments should continue after a facility reaches ongoing submission status. alterations in process include a review of production data at least twice per year with a follow up data review one month later to confirm adjustments have been correctly implemented. keywords data quality; production; legacy acknowledgments this proposal was supported by cooperative agreement number 1 u50 oe000069-01, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the views of the centers for disease control and prevention or the department of health and human services. *sophia crossen e-mail: scrossen@kdheks.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e41, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts immune response and distribution of antigen in chickens after infection lpaiv (h4n6) denys muzyka*1, hyun lillehoj2, olexandr rula1 and borys stegniy1 1laboratory of avian diseases epizoothology, national scientific center institute of experimental and clinical veterinary medicine, kharkiv, ukraine; 2animal parasitic diseases laboratory, beltsville, md, usa objective to study the immune response in chicken on the administration of lpaiv isolated from the natural reservoir. introduction influenza is a serious problem for the health of people, animals and birds. therefore, comprehensive study of influenza virus, its natural reservoir, pathogenesis and immune response will provide further opportunity to ensure protection for animals, birds and people from this infection. methods four-week-old commercial chickens were intranasally inoculated with a h4n6 lpaiv a/garganey/chervonooskilske/4-11/2009 (h4n6), isolated from the cloacal swab of clinically healthy garganey in 2009 in ukraine. cecum, spleen, lung, and trachea samples were collected from infected chickens on 1 14 dpi and examined by immunohistochemical and virology techniques. on these days, we collected blood samples for serological analysis. detection of antibodies to avian influenza virus subtype h4 was performed with chicken serum samples by hi test and elisa. the studies were done according to iacuc. results upon intravenous and intranasal infection with this virus (a/garganey/chervonooskilske/4-11/2009), no clinical signs were observed in chickens and no pathological changes were found at necropsy. infection of poultry with this virus provoked an antibody response at 10 days after intranasal inoculation which ranged from 1:8 to 1:32 serum antibody titers. only 2 of 5 chickens were positive by the hi test and 3 of 5 were positive by elisa at intranasal inoculation. all 10 chickens were positive both by hi test and by elisa after intravenous inoculation. specific antibodies (hi test) to influenza virus h4 were detected in titer ranges of 1:128 to 1:1024. in immunohistological studies, the respiratory tract organs (lungs and trachea) showed higher level of humoral immunity (igm, igg, iga-expressing cells) in the lung compared to the trachea. also, indicators of cell mediated immunity as measured by the cd4 and macrophage markers were higher in lpaiv-infected chickens in the lungs at 14 days post infection compared to uninfected chickens. lymphocytes expressing cd8 were increased starting 7dpi. the chickens in the infected group showed 2 times higher levels of cd8 cells compared to the control chickens. ifn-γ transcripts were observed in the ai-infected chickens starting at 7dpi that coincides with the increasing level of cd4 cells. the number of lymphocytes which secrete il-2 and il-15 in ai-infected chickens were in general 1.5 to 2 times higher compared to the uninfected chickens. in aiv-infected chickens, the level of cells expressing ifn-γ, il-2, and il-15 increased at 7-days after infection. the peak time coincided with a period of increasing cd8 cells. however, there was no significant difference in these cytokine levels between the aiv-infected and uninfected groups. in the cecum, lower levels of cd4 cells were seen on 5 dpi but levels slowly increased from 7 dpi to 14 dpi following aiv infection. in the ceca, a significant increase in the number of cells expressing igm and igg was found. lpaiv infection induced an increase in macrophages and lymphocytes expressing cd4 and cd8 in the spleen throughout the period examined in this study indicating their role in host response to viral infection. the levels of macrophages in chickens of aivinfected group were 2 times higher than the control after 1 dpi. conclusions although infection with a lpaiv did not cause obvious clinical disease, viral replication was detected in the trachea and spleen and both local and systemic cellular and humoral immune responses were elicited in these lpaiv-infected chickens. our results indicate the potential possibility for infection of poultry with viruses isolated from wild birds. but currently it is not completely known why some viruses from wild birds can cause infection in poultry, while others can not. further study of the immune response will enable us to determine the features of the pathogenesis of low pathogenic avian influenza. keywords avian influenza; immune response; chicken acknowledgments our research was funded by usda projects p382a and p444 through the ukrainian science and technology center. the authors express special gratitude dr. pavlo shutchenko. *denys muzyka e-mail: dmuzyka77@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e107, 2017 ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi denise d. krause, phd 1 1. department of preventive medicine, university of mississippi medical center, jackson, ms usa abstract background: there are a variety of challenges to developing strategies to improve access to health care, but access to data is critical for effective evidence-based decision-making. many agencies and organizations throughout mississippi have been collecting quality health data for many years. however, those data have historically resided in data silos and have not been readily shared. a strategy was developed to build and coordinate infrastructure, capacity, tools, and resources to facilitate health workforce and population health planning throughout the state. objective: realizing data as the foundation upon which to build, the primary objective was to develop the capacity to collect, store, maintain, visualize, and analyze data from a variety of disparate sources -with the ultimate goal of improving access to health care. specific aims were to: 1) build a centralized data repository and scalable informatics platform, 2) develop a data management solution for this platform and then, 3) derive value from this platform by facilitating data visualization and analysis. methods: a managed data lake was designed and constructed for health data from disparate sources throughout the state of mississippi. a data management application was developed to log and track all data sources, maps and geographies, and data marts. with this informatics platform as a foundation, a variety of tools are used to visualize and analyze data. to illustrate, a web mapping application was developed to examine the health workforce geographically and attractive data visualizations and dynamic dashboards were created to facilitate health planning and research. results: samples of data visualizations that aim to inform health planners and policymakers are presented. many agencies and organizations throughout the state benefit from this platform. conclusion: the overarching goal is that by providing timely, reliable information to stakeholders, mississippians in general will experience improved access to quality care. keywords: physician, workforce, data, geographic information systems, data warehouse, visualization correspondence: dkrause@umc.edu doi: 10.5210/ojphi.v7i3.6047 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 introduction mississippi ranks 50th in the nation in overall health status [1]. since it is common knowledge that access to care and provider availability are key factors contributing to overall health status, it is not surprising that mississippi also ranks 50th in physicians per 100,000 residents with only 159 physicians per 100,000 population, compared to the national average of 220 per 100,000 [2]. as in many other states, unequal distribution of health care providers further exacerbates the problems related to access to care. in mississippi, about 60% of primary care physician practices are located in urban areas; whereas, about 60% of the population resides in rural areas [3]. of course, physicians are only one profession in the health care team and supply of health care providers is only one component of the supply/demand/utilization equation. access to care is a multi-faceted and extremely complex issue that academic health sciences centers, other health professional training schools and programs, researchers, and health care planners continue to struggle with. how many providers and what types are needed to provide adequate quality health care to the population -when and where? these remain extremely difficult questions to answer. what should the evolving health care team look like? how many slots are needed in training programs to prepare the workforce of the future? again, there is not a “one size fits all” equation to address these complex questions. models of estimation are continually being developed and tested, but estimates are constantly shifting. not only is there is no right answer, but it’s nearly impossible to get the same answer twice! with that said, how can access to care be improved in mississippi? it became quickly clear from strategic meetings with the office of mississippi physician workforce and other key policy, research, and other healthcare stakeholders across the state that they were grappling with similar issues. each was responsible for a piece of the puzzle, but despite the fact that volumes of quality data relevant to these primary missions had been being collected for years, many were still at a loss because other data needed for informed decision-making were outside of their domains. multiple mini siloes of data had been constructed throughout the state. perhaps historically, some groups had protected their turfs. however, in this new age of data sharing and collaboration, walls seem to be crumbling naturally as stakeholders recognize the benefit of partnerships and become more interested in working together to move our state forward improving population health outcomes. in response to this urgent need and opportunity, a strategy was developed to build and coordinate infrastructure, capacity, tools, and resources to facilitate health workforce planning throughout the state. realizing data as the foundation upon which to build, the primary objective was to develop the capacity to collect, store, maintain, visualize, and analyze data from a variety of disparate sources with the ultimate goal of improving access to health care. specific aims were to: 1) build a centralized data repository and scalable informatics platform, 2) develop a data management solution for this platform and then, 3) derive value from this platform by facilitating data visualization and analysis. the stakeholders are many and varied – among them are the state’s only academic health science center, the university of mississippi medical center; william carey college of osteopathic medicine; schools of nursing and other health professions throughout the state; the office of mississippi physician workforce; the office of nursing workforce; the mississippi state health ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 department; and a host of other state agencies and organizations. the overarching goal is that by providing timely, reliable information to groups such as these, mississippians in general will experience improved access to quality care. while the focus is on mississippi, this approach is highly generalizable and can be readily adopted by any other state. methods data sources we began collecting the most pertinent data needed to answer the most important initial questions. publicly available datasets related to population statistics, socio-demographic data, and health services information, from sources such as the u.s. census bureau and the health resources and services administration, were obtained. data contributors included health professional licensure boards, both medical schools in the state, rural physician and dentist scholarship programs, the mississippi state department of health, the mississippi primary health care association, training programs across the state, officials managing loan repayment programs, and many others. we were quickly inundated with valuable data from a variety of data contributors eager to share their data or, at least, benefit from having their data cleaned, organized, visualized, and analyzed. for the health professions data, current and historical licensure data on physicians were obtained initially, but dentists were soon added, and physician assistants and advanced practice nurses are currently being added. other health professions will be included as they come forward and express interest. in addition to data on health professions, data on population health outcomes are being incorporated in order to establish baseline data to be able to chart the success, or lack thereof, of projects, initiatives, and interventions over time. data lake the usual approach to addressing the challenge of data stored in silos is to build a data warehouse [4]. some states have well-developed data warehouse environments [5], but mississippi does not have a mature data warehouse in production for health planning purposes. efforts to coordinate health care workforce planning is still in its infancy in mississippi, yet there is a heightened sense of urgency to address these issues and a realization that this work is timesensitive and cannot wait for a full-fledged data warehouse solution to be implemented. in order to save time and bypass some of the limitations of traditional data warehouses, it was most expeditious to focus on the creation of a “data lake”. this relatively new term refers to an easily scalable, potentially massive, but accessible, data repository built to store “big data” on (relatively) inexpensive computer hardware. data lakes differ from data marts, which are optimized for data analysis by storing only some of the attributes. in contrast, data lakes are designed to retain all attributes of the raw data, which is especially important when the scope of the data or its uses may not yet be fully known [6]. the data lake solves the age-old problem presented by information silos while cutting costs, improving agility, and increasing efficiency by allowing for increased use of information. it becomes an ideal platform for visualizing and analyzing various sources of disparate data in their native forms. managed data lake there is no perfect data solution and there are pitfalls to avoid in the use of data lakes [7]. because data lakes inherently lack data governance, there is the danger of not being able to account for data quality or the lineage of findings. in order to proactively address these ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 limitations, the data lake concept was extended further to a “managed” data lake. to this end, a custom web-based data management tool for lifecycle management was developed (figure 1), extending the data lake to include not only raw data, but also cleaned or processed files and revision histories. with this data management tool, data sources are logged as they come in, along with all pertinent information about their source and their refresh cycles, while also keeping track of their file processing and revision histories and who made what changes, when, where, and why. the administrative dashboard allows for easy monitoring of data sources that need to be refreshed within the next thirty days and if any data are overdue to be refreshed, thereby overcoming many potential pitfall of data quality issues. figure 1. custom web application developed specifically to manage data files, maps and geographies, custom applications, and data marts. additional risks that data lakes can introduce include a lack of security and access control [7]. in order to proactively address these critical issues, the data lake was not designed as an ungoverned data store, but as a centralized managed data repository. from this platform, a path was incorporated from which to extract, transform, and load (etl) certain data into a traditional data warehouse as warranted to provide the single source of truth inherent in a traditional data warehouse environment [8,9]. when streamlined data marts are needed, the lifecycle management application also allows staff to monitor and track the development and delivery of data marts. so, in effect, the data lake is not replacing a traditional data warehouse, but is being used in addition to the data warehouse for a different purpose -to provide a more agile platform for data analytics and the ability to quickly derive value from the data. this managed data lake allows development to be responsive, quick, and productive, without becoming bogged down by the typical lengthy delays associated with data warehousing. ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 data visualization and analysis methods for collecting, storing, and maintaining data for health workforce planning have been discussed. next we demonstrate how value is being derived from this highly scalable informatics platform consisting of data, hardware, software, and analytic tools by means of visualizing and analyzing data. a variety of tools are used for data visualization and data analysis. first, data visualization software by tableau desktop software ® was used to examine data from different sources and run quality control checks. because it was quickly apparent if data issues were present, feedback could be provided to data contributors along with attractive visualizations, giving them the opportunity to take appropriate action to improve data quality. data contributors have been pleased to finally see answers present in their data and have been highly motivated to make improvements in data quality. next, using health professions licensure data and other supporting data sources, a geographic information system (gis) web application was developed for mobile devices to explore the demographics and distribution of the physician workforce currently licensed in mississippi and over time (figure 2). detailed information about the design and development of the health workforce mapping application can be found elsewhere [10]. the primary function of the gis web application is mapping the health workforce and displaying results of custom queries geographically. to complement this application, we used tableau ® again to build visualizations and interactive dashboards of the various datasets, individually or in combination. these dashboards allow for the interactor to slice and dice the data on the fly, revealing answers that may not have been apparent without visualizations designed for data exploration. figure 2. screenshot from the gis web application developed to visualize and query physicians licensed in mississippi. this visualization shows all physicians currently licensed in mississippi with no query attributes defined. ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 results key to success has been building strong working relationships with the mississippi state board of medical licensure (msbml) and their governing board. the msbml was given a presentation of the physician workforce mapping application and a series of visualizations and interactive, dynamic dashboards created using msbml and related data. board members were very interested in being able to visualize and understand the data that are being collected every year through new licensure applications and renewals. this relationship has been further strengthened by bringing back visualizations from their data and working with msbml staff to improve data collection and data quality moving forward. sample dashboards created for the msbml are shown in figures 3 and 4. each part of the dashboard shown in figure 3 is interactive, meaning that the user can make selections as they choose and the data in all other parts of the dashboard redraw to meet the selected criteria. figure 4 shows demographics of the physician workforce over the course of ten years and the distribution of the workforce. work will continue with the msbml, as well as other health licensure boards, to design and build visualizations that can be made available to the public on their websites or used for administrative purposes with security enabled. if organizations would like to build their own visualizations, assistance can be provided to develop this capability. figure 3. a dynamic dashboard created using current medical licensure data allowing the user to interact with the visualization to explore the demographics and distribution of the health workforce based on their selected criteria. creatively designed dashboards can provide a wealth of information at one’s fingertips. ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 figure 4. this dynamic dashboard displays pertinent information about the demographics and state of practice of physicians licensed in mississippi a year at a glance. this visualization is built on ten years of medical licensure data. this informatics platform also is being used to assist the university of mississippi medical center (ummc), the william carey college of osteopathic medicine (wccom), and other training programs involved with health workforce planning. ummc has been collecting huge volumes of data on a continual basis for many years and wccom, although new to medical training in mississippi, is beginning to accumulate a wealth of data as well. one dataset that both medical schools collect includes information on each graduate and the program in which he/she matched for residency training. an example of a dashboard created for the medical schools to examine their match data is shown in figure 5. designing and deploying real-time visualizations of these types of data to administrators, health planners, researchers, and clinicians has great potential to boost advances in health education, research, and patient care. data governance data governance is an essential component to a successful data lake. our data lake is governed primarily by the data owners. each contributing organization remains the data owner. our team serves as data stewards -not deciding how data will be used, but making recommendations, providing ideas and examples, and facilitating these exchanges. memoranda of agreement are being put in place with data contributors with provisions regarding how data can or cannot be shared. access is not provided to any data contributor’s data without their express permission. this is key in building a trusted informatics platform [9]. without trust and relationships, this initiative would fail before it got started. ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 figure 5. dashboard designed to display results of medical school match data by program, volume, and location. discussion these are but a few examples of how mississippi is leveraging a data lake and data visualization tools and techniques to get information back to health planners so that they can make more informed decisions regarding how to best meet the health care needs of the state. to our knowledge, this is the first use of a data lake to be used for state health planning purposes. many others in the field have been using data warehouses, which are valuable, but costly, slow to evolve, and often bogged down in bureaucracy related to data governance. data lakes provide a centralized data repository, are much less expensive, and make analytics more readily available. however, they are not without limitations. it is important to plan proactively and address the lack of data governance inherent in data lakes, potential issues with security, and concerns about data quality. with proper forethought and planning, the advantages of implementing a data lake and data visualization and analysis tools can be realized without compromising integrity as described -by extending the original data lake concept to a managed data lake with a data management solution and applying data visualization and analysis tools. conclusion healthcare has typically been much slower to realize the value of big data than other sectors. getting data, information, and tools back to the business users who understand how the data should be used is a critical component of deriving value in a timely manner. information silos ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 managed exclusively by it along with heavy data governance structures have inherently been slow to realize such value. next steps are to continue to grow the data lake and to work closely with key stakeholders and data contributors to assist them in improving their data collection and quality and setting up strategies to address their most pressing questions. this may involve building visualizations, dashboards, or applications for them or providing the tools and skillsets needed to build their own. we will continue to push some select data to the data warehouse and assist in developing data marts where those are warranted. assistance to other states interested in efforts to leverage such an approach in their states also can be provided. the over-reaching goal was to bring data out of silos into a centralized accessible informatics platform in order to get information to those who need it when they need it and to provide the tools and techniques that enable health planners to do more – faster. an affordable, practical, and sustainable model was designed that transforms the field of health planning in mississippi and could do so in other states. in addition to trying to provide the “perfect numbers” or “best guesses” of how many health care providers will be needed, information is being provided to decision-makers so they will have the knowledge they need when making those tough decisions. from an education and research perspective, the data lake and this platform is being used to support student research projects and further grow research programs in health workforce, health planning, and population health. acknowledgements thanks to dr. john r. mitchell, director, office of mississippi physician workforce, and our many partners throughout the state, without whom this work would not be possible. conflicts of interest the author has no conflicts of interest to report. references 1. america's health rankings. 2014. http://www.americashealthrankings.org/ms. accessed march 25, 2015. 2. 2011 state physician workforce data book. association of american medical colleges center for workforce studies; 2011: https://www.aamc.org/download/263512/data/statedata2011.pdf. accessed march 25, 2015. 3. king an, krause dd. characteristics of physicians practicing in urban, rural, and isolated areas of mississippi. april 30, 2015, 2015; 11th annual aamc health worforce research conference. 4. shams k, farishta m. 2001. data warehousing: toward knowledge management. top health inf manage. 21(3), 24-32. pubmed 5. chute cg, beck sa, fisk tb, mohr dn. 2010. the enterprise data trust at mayo clinic: a semantically integrated warehouse of biomedical data. journal of the american medical informatics association: jamia. 17(2), 131-35. pubmed http://dx.doi.org/10.1136/jamia.2009.002691 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11234730&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20190054&dopt=abstract http://dx.doi.org/10.1136/jamia.2009.002691 ojphi data lakes and data visualization: an innovative approach to address the challenges of access to health care in mississippi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e225, 2015 6. wiktionary. definition of data lake. 2015. accessed march 25, 2015. retrieved from http://en.wiktionary.org/wiki/data_lake. 7. gartner inc. gartner says beware of the data lake fallacy. newsroom2014. accessed march 25, 2015. retrieved from http://www.gartner.com/newsroom/id/2809117. 8. schreiweis b, schneider g, eichner t, bergh b, heinze o. 2014. health information research platform (hirep)--an architecture pattern. stud health technol inform. 205, 77377. pubmed 9. krause dd. 2013. building a trusted healthcare informatics platform: implemention of the enterprise data warehouse at the university of mississippi medical center. journal of the mississippi academy of sciences. 58(2), 169-75. 10. krause dd. 2015. state health mapper: an interactive, web-based tool for physician workforce planning, recruitment, and health services research. south med j. 108(11), 650-56. doi:http://dx.doi.org/10.14423/smj.0000000000000369. pubmed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25160292&dopt=abstract http://dx.doi.org/10.14423/smj.0000000000000369 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26539941&dopt=abstract interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi interactive visualization of public health indicators to support policymaking: an exploratory study moutasem zakkar1,2, kamran sedig1 1. insight lab, western university, canada 2. school of public health and health systems, university of waterloo, canada * moutasem zakkar, school of public health and health systems, university of waterloo, canada. mzakkar@uwaterloo.ca abstract purpose: the purpose of this study is to examine the use of interactive visualizations to represent data/information related to social determinants of health and public health indicators, and to investigate the benefits of such visualizations for health policymaking. methods: the study developed a prototype for an online interactive visualization tool that represents the social determinants of health. the study participants explored and used the tool. the tool was evaluated using the informal user experience evaluation method. this method involves the prospective users of a tool to use and play with it and their feedback to be collected through interviews. results: using visualizations to represent and interact with health indicators has advantages over traditional representation techniques that do not allow users to interact with the information. communicating healthcare indicators to policymakers is a complex task because of the complexity of the indicators, diversity of audiences, and different audience needs. this complexity can lead to information misinterpretation, which occurs when users of the health data ignore or do not know why, where, and how the data has been produced, or where and how it can be used. conclusions: public health policymaking is a complex process, and data is only one element among others needed in this complex process. researchers and healthcare organizations should conduct a strategic evaluation to assess the usability of interactive visualizations and decision support tools before investing in these tools. such evaluation should take into consideration the cost, ease of use, learnability, and efficiency of those tools, and the factors that influence policymaking. keywords: interactive visualizations, social determinants of health, public health indicators, public health policymaking, exploratory study, informal user experience evaluation correspondence: mzakkar@uwaterloo.ca, kamrans@uwo.ca doi: 10.5210/ojphi.v9i2.8000 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:kamrans@uwo.ca interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi introduction health policymaking is a complex process that aims to control the functions of the health system, including health service organization, financing, payment, and health promotion [1]. to take appropriate actions, health policymakers require various kinds of information about the health system performance and public health problems and needs. there are many sources for this information, including medical records, administrative data, national censuses data, health surveys, and research studies. knowledge translation literature is abundant with studies on information synthesis methods for producing the best available evidence. however, less attention has been paid to dissemination methods—how to make information available to policymakers. policy guidance and policy briefs [2], and online evidence repositories [3] are methods of knowledge dissemination that have been used by leading health knowledge producers, including the world health organization and the agency for healthcare research and quality in the usa. public health indicators public health indicators are measures that reflect the status of specific aspects of public health and the health system. public health indicators can be quantitative or qualitative. there are many types of indicators, including public health status (e.g., mortality rates, prevalence of diseases, the burden of diseases), health risks (e.g., obesity, smoking rates), healthcare programs’ outcomes (cancer screening and vaccination rates), health system performance indicators (e.g., wait times in emergency departments, access rates), and health policy indicators (e.g., the cost of care). organizations at international, national, provincial, and local levels select and use the types of indicators that best serve their planning and evaluation processes. information technology and health policymaking there is a scarcity of research on using information technology as a means for knowledge dissemination in health policymaking despite developments in information technology and the availability of tremendous amounts of data that can be used in health policymaking [4,5]. many organizations, including the world health organization, the world bank, and the centers for disease control and prevention publish health indicators databases online, such as healthcare outcomes, mortality and morbidity indicators, and health service utilization. these databases provide various tools to explore, sort, and extract the published information. xu et al. [6] argue that these databases should be equipped with more sophisticated tools that enable users to analyze the indicators and explore associations and trends in data, rather than just listing the indicators. alper et al. [7] suggest that health indicators are important not only for stakeholders’ decision making but also for their continuous learning and understanding of the healthcare system; therefore, health indicators should be represented in ways that support these mental activities and goals such as decision making and learning. interactive visualization tools interactive visualization tools are external artifacts whose primary aim is to support and enhance users’ exploratory and sense-making processes involving visually represented data [8-10] visualizations encode abstract or concrete data (e.g., geographic, scientific, or health data) in a interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi visual form. visualizations can be static or interactive in nature. from the time john snow used a map to reason about a cholera outbreak in 1850 to recent times, static visualizations have been used in public health. though useful, static visualizations do not effectively support decisionmaking tasks. visualizations can be made interactive. these tools use interactive visual representations to convey information and support decision-making tasks by allowing users to customize visualizations, and, in some cases, to perform analytical tasks [11]. since policymaking requires public health stakeholders to reason with heterogeneous data, interactive visualization tools can play an important role. the effective and efficient use of data determines the extent to which stakeholders can sufficiently address policymaking issues. therefore, tools that allow users to interact with information systematically can support decision and policy-making activities. interactive visualization tools are used by decision makers in many domains, including business, engineering, and urban planning. these tools have a visual interface that enables humancomputer interaction and allows the user to organize and adjust the amount and representation of information. the interactive capabilities of these tools increase their “epistemic utility” and enhance cognitive task performance by increasing the user’s information processing power [10,12]. various visualization techniques can be used to represent data, including choropleth maps, heat maps, bubble charts, scatter plots and bar charts [13]. these techniques can use different visual marks (e.g., color, size, and shape) to encode different dimensions of information. visualizations have increasingly been used to represent and communicate health data. however, according to a systematic review conducted by carroll et al. [14], static graphics are still the main method for health data representation. there is scant literature on using visualization tools in healthcare [15]. further research on how to develop domain-specific visualizations, user experience evaluation, and visualization techniques for heterogeneous health data is needed [16]. purpose of this paper this paper reports an exploratory study whose purpose is threefold: 1) to design an interactive visualization tool that represents public health indicators and allows health policymakers to make sense of these indicators, 2) to examine the usability and utility of this tool, and 3) to investigate the benefits of this type of tool for policymakers. in this paper, we only study the visualization of one type of health indicators: health equity indicators. health equity, an overarching principle in the sustainable development goals [17], refers to “the absence of unfair and avoidable or remediable differences in health among population groups defined socially, economically, demographically or geographically” [18] (p. 12). health equity indicators represent the disparities in health status among population groups. study method interviewing prospective users is an effective research method in human-computer interaction research, which can be conducted in any of the product lifecycle phases [19]. the subjective feedback collected from prospective users enables researchers to understand the requirements, interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi views, preferences and practices of users. therefore, interviews might be as effective as ethnographic studies [19]. this method was used in our study. we developed an online visualization tool that represents a set of health equity indicators in canada. the study participants used and examined the tool. user experience was evaluated using the informal user experience evaluation method. this method involves the prospective users of a tool to use and play with it and their feedback to be collected through interviews. this method has been used to evaluate the usability and utility of computer programs and to identify design and functionality problems [20]. the method consists of three phases: demonstration, exploration, and feedback interviews. these phases are summarized in table 1. participants went into these phases individually. in the demonstration phase, the researchers present the visualization tool to the study participants. in our study, we presented the tool to some participants in person, and for other participants, who did not reside in our city, we created a video demonstration, in which we presented the tool using a brief tutorial. in the exploration phase, study participants explored the tool freely and independently and for an unlimited time. they were not asked to perform any specific tasks using the tool. once study participants felt that they were ready for an interview, the researchers conducted a feedback interview with each participant. using the interview method, we aimed to evaluate three aspects of the user experience: 1) the perceived ease of use, 2) the perceived usefulness of the tool, and 3) user satisfaction. we also wanted to understand users’ needs and explore the tools’ context of use. therefore, we used a semi-structured interview with open-ended questions only. we had created an interview guide with a predefined set of themes before we started data collection, and we had updated this guide after each interview to reflect the emerging themes. table 1: the informal user experience evaluation method phase name description location duration 1 demonstration the researcher will present the tool to the participant. participant workplace one hour 2 exploration a participant explores the tool and performs any tasks on it freely and without the attendance of the researcher. participant workplace open as per the participant’s convenience 3 feedback interview the researcher will interview the participant and ask him/her a set of openended questions to evaluate the perceived utility of the visualization tool. participant workplace one session. expected duration is two hours. interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi setting and participants the informal user experience evaluation method, used in our study, requires that the visualization tool be evaluated by its prospective users, including health policymakers at different organizational levels, such as top-level managers with legal power to participate in decision making, and expert policymakers with technical and policy area expertise to analyze policy problems and suggest solutions. however, in the planning stage of the study, we anticipated that we would not be able to recruit top managers because of the time commitment required to participate in the study. therefore, all the study participants were expert policymakers who work at various health system bodies in ontario, such as local public health units, health research centers in hospitals and universities, local health integration networks, and community care centers. we used a purposeful sampling strategy (e.g., advertisement and snowballing) to target and recruit participants. we had planned to recruit five to ten participants; we succeeded in recruiting seven participants. table 2 provides a summary of participants’ positions and education. the western university research ethics board (protocol #106967) approved this study. table 2: participants’ positions and education i d alias name professional position education 1 participant#1 epidemiologist in a health research centre m.sc. & ph.d. in epidemiology 2 participant#2 researcher in a health research centre r.n., ph.d. 3 participant#3 ph.d. student and researcher r.n. 4 participant#4 program manager in a public health unit m.sc. in epidemiology 5 participant#5 assistant professor and researcher b.sc.n., ph.d. 6 participant#6 health records and business analyst m.sc. in health science 7 participant#7 assistant professor and epidemiologist in a public health unit ph.d. in epidemiology study visualization tool the tool used in this study is a website that includes a set of interactive visualizations. we used several visualization techniques, such as choropleth maps (figure 1), scatter plots, heat maps, and bar charts. to support exploration of these visualizations, we used a number of interaction interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi techniques, such as selecting, filtering, hovering, and zooming. we also used several visual marks, including shape, color, and size (figure 2). visualizations were developed using tableau 9.2. to present the visualizations in an easy to navigate way, we visualized one of the conceptual frameworks of the social determinants of health (figure 3), developed by solar and irwin [18], to represent and communicate the pathway of effects of the social determinants of health. this framework was intended to provide a theoretical lens through which policy action could be analyzed. the visualized health indicators and health outcomes were taken from research studies published in peer-reviewed journals. figure 1: a choropleth map for a set of health indicators. these indicators represent access to healthcare services along the rural-urban continuum in canada. the user can 1) select indicators from a list of indicators, 2) select rural and urban areas to see, and 3) zoom in and out on the data. interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi figure 2: the cause-specific mortality by education in canada. data analysis to qualitatively analyze the data, a data analysis software, qda miner lite, was used. data analysis in qualitative research is inductive and allows the emergence of themes from the data [21]. we used an a priori list, which we developed based on the core principles of software user experience and usability evaluation—namely, ease of use, user satisfaction, learnability, and effectiveness—to code the data. despite using this list, we were still open to emerging themes, which we had not considered before the start of the study. because our data analysis started in parallel with data collection, we were able to explore the emerging themes with other research participants and shed more light on those themes. interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi figure 3: the conceptual framework for the social determinants of health as developed by solar and irwin [18] results in this section, we present the themes that emerged, which represent the stories told by study participants at the interviews. our role was to explicate these stories by objectively interpreting the content of these interviews. usability, utility, and user experience themes participants were satisfied with the tool. the main reason for participant satisfaction was the interaction capabilities of the tool. these enable users to retrieve, filter and adjust the displayed information and were the main reason for participants’ interest and satisfaction. participants believed that the tool could be used for different purposes, such as communication, information dissemination, education, and decision support with regard to healthcare issues. participant#5, an assistant professor, believed that the tool is useful because it helps in presenting complex issues: “i think trying to make a more interactive dynamic way to explain very complex health and social phenomena is a worthy endeavor.” interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi participants felt that the tool was easy to use. some participants found that some visualizations were crowded, while others suggested using simpler visualizations and allowing users to add more layers of information to these visualizations. all participants preferred traditional charts (e.g., the bar charts) to more complex visualizations. participant#1, a data analyst and epidemiologist, felt that the simpler the visualizations, the better they are for the users: “data visualization is trying to facilitate conceptualizing the data. but if you make it more complex, it loses the initial idea of visualizing the data, and if you are presenting it to people like healthcare professionals to help them better understand the relationships between the factors and the outcomes, i would say the simpler, the better.” as for the visual marks, participants felt that colors and tooltips were effective in representing the data. however, using too many visual marks could be overwhelming for the users. for some participants, interpretation of size as a visual mark was not easy. as for the visualization techniques, some participants felt that the choropleth map of health indicators was useful; however, other participants believed that map visualizations, in general, could lead to misinterpretation of the data. filters were effective for controlling the visualizations. participants found that visualizing the conceptual framework for the social determinants of health (figure 3) is useful because it provides users with an overall view of the health determinants and the relationships among the different elements. however, despite the usefulness of having an overview of the health determinants, some participants felt that care should be exercised when including the visualizations of frameworks as these can impose on users a predefined structure which may not be universally acceptable to all users. complexity of knowledge communication participants identified a set of challenges that make knowledge communication in healthcare a complex task, including the inherent complexity of healthcare information and the diversity of audiences. these challenges can cause disconnection between knowledge producers and knowledge users. participant#4, an epidemiologist and a program manager at a health unit, described the difficulties that she faces at her work: “as we roll out health status reports, we end up creating three different kinds of reports for three different kinds of users. when i first made the health and income inequalities report, i focused on the differences between pyll (potential years of life lost) and mortality rates among income groups. however, i realized that no one understood what i was saying, and i tried to explain what pyll was but people stared without a blink, and i knew i lost them.” most participants believed that visualization of information could facilitate or impede healthcare knowledge communication, depending on the usability of visualization tools. additionally, some participants felt also that because of the inherent complexity of healthcare measurements and indicators, health policymakers should also have adequate training to be able to understand those indicators. participant#7, an assistant professor and epidemiologist in a health unit, described this issue: interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi “i think that the education piece is what we need in order to elevate the decision makers’ level of understanding for good epidemiologic analysis rather than simply accepting the results produced by epidemiologists.” some of the participants talked about the possible misinterpretation of the represented information by laypeople. participant#5, an assistant professor and a registered nurse, warned about misinterpreting the information: “if you just give people the data without pushing them back to the theoretical understanding, there is a risk that they make their own conclusion. i think that this is the challenge of visualization because people may start asking different questions from what the data was meant for.” however, participants believed that the possible misinterpretation should not lead to not visualizing healthcare information; rather, we need to add more information about the visualized studies to help users fully understand the visualized data. the validity and credibility of the visualized data were very important to study participants; they emphasized the need to include more information about the source studies, including the research questions, context, conclusions and the limitations of those studies. participants also suggested establishing quality criteria to select the studies that will be included and visualized in the tool. health policymakers' needs participants stated that health policymakers do not have enough time for learning and using decision support tools such as our visualization tool. participants also believed that policymakers prefer health reports that are summarized and include simple graphics. participant#7, an assistant professor and epidemiologist, did not think that the tool can be easily accepted by policymakers: “i found your visualization very understandable, but i don't think that it is easily transferable to decision makers without a lot more background for them. however, my personal experience is that many decision makers don't really want to spend the time to understand these things. “ participants stated that data and decision support tools such as information visualization tools play a secondary role in health policymaking compared to other factors that have a bigger impact, including public and media pressure, financial constraints, and political priorities. participant#5, an assistant professor, believed that information about healthcare issues is less important than the media pressure for action: “the way that data is presented is less important than a couple of newspaper articles that make the government look really bad about the issue, so i think the number one factor is that policymakers have a sense that this is a priority issue, and probably the secondary thing is that they can understand the best way to approach the issue, and that is when they would get into how the data is being presented.” interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi discussion our study focused on representing health equity indicators using interactive visualization tools and the benefits of these tools for health policymaking. from an information technology perspective, there are three related elements that should be analyzed: the tool, the users of the tool, and the visualized data. the tool our study shows that using interactive visualization tools to represent health indicators has advantages over traditional representation techniques that do not allow users to interact with the information. despite this, a systematic review conducted by carroll et al. [14]analyzing the information needs of public health professionals highlights the fact that while interactive visualization boosts users' capabilities and enhances their experience, health indicator visualization still predominantly depends on static graphics. to gain the full benefits of interactive visualizations tools, the developers of those tools should follow the user-centered design approach, which includes understanding users' needs, focusing on their task and work processes, iterative design, and continuous user experience testing [22]. however, many visualization tools are designed to impress users rather than help them perform their work tasks effectively and efficiently. we believe that healthcare informatics tools for policymakers should be designed to fit their tasks and needs. the users of the tool in our interviews with the participants, we find that there are many knowledge gaps on healthcare data communication and more research is required to explore this topic. policymakers there are increasing calls to different sectors, such as business, healthcare, and government, to adopt and use data analytics and visualization software in policymaking. the study in this paper re-emphasizes the fact that health policymaking is a complex process; data is only one element among others needed in this complex process. therefore, we believe that researchers and healthcare organizations should conduct a strategic evaluation to assess the usability of these tools before using them. such evaluation should take into consideration the cost and the traditional usability metrics, including ease of use, learnability, and efficiency; it should also look at the policymaking process and the numerous factors that affect this process, including information needs, political factors, financial constraints, and stakeholders’ values and beliefs. this evaluation will help healthcare organizations in estimating the return on investment that they will have, and whether they should invest in these tools. laypeople and misinterpretation of health outcomes information misinterpretation occurs when users of the information ignore or do not know why, where, and how it was produced, or where and how it can be used [23]. our tool presents interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi research studies that show the association between health disparities and the socioeconomic status of population groups. however, the tool only shows the results of the studies (i.e., health outcomes); it leaves out other important parts of those studies, including the context and limitations of each study. this omission may lead to misinterpretation. misinterpretation can also happen when users of the tool lack enough understanding of the health issues that are represented in the tool. we believe that the complexity of association between social determinants of health and health outcomes, and the multilevel pathway of effects are always ignored, lost, or forgotten whenever those outcomes are summarized (i.e., in blogs), reported in the media, or represented visually. we propose here the following guidelines for the publishers of this data to reduce the possibility of misinterpretation of healthcare indicators by laypeople: 1cite and provide a list of the studies from which the data was obtained. 2mention the context of those studies. 3mention the limitation of those studies. 4as we have done in our tool, provide a conceptual framework that represents the association between the several independent factors on the one hand, and the health outcomes—the dependent factors— on the other hand. this framework helps the users to understand the phenomena that you are representing. 5remind the users of the data that the social determinants of health affect people’s health through complex and multilevel pathways, leading to different healthcare outcomes that can’t be attributed to any single determinant alone. the visualized data knowledge translation literature has traditionally focused on the needs of information users, including healthcare providers and policymakers, and on the types and content of knowledge products that best fit those needs. however, less attention has been paid to the difficulty of producing these products, as suggested in this paper. this shortcoming is partly due to the inherent complexity of some healthcare indicators, requiring users to have a certain level of understanding of statistical and epidemiological data. some healthcare providers and most policymakers lack such understanding, forcing knowledge producers (i.e., researchers and epidemiologists) to exert extra effort to simplify health reports and replace certain indicators with easy-to-understand ones. this practice makes those reports less comprehensive and more generalized and threatens information quality. even when information technology tools are used to communicate and represent health reports and indicators, these reports should be simple [14]. therefore, more research is required to explore this issue and design innovative methods to communicate healthcare information and outcome indicators. conclusions interactive visualization tools can be used to represent and communicate public health indicators. however, in health policymaking, the ultimate value of interactive visualization tools is influenced by diverse factors, including the complexity of health indicators, the usability of the tools, and policymakers’ needs, capabilities and priorities. these factors should be taken into interactive visualization of public health indicators to support policymaking: an exploratory study online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e190, 2017 ojphi consideration before investing in interactive visualization software. effective communication of health indicators requires more research to model the complexity of health indicators, and the needs of knowledge producers and users. finally, our study shows that interactive visualization tools have great potential to address the needs of healthcare knowledge management and policymaking. however, for this potential to be actualized, more research is required to develop standard metrics to assess the usability and utility of these tools in supporting healthcare policymaking in different contexts. limitations of this study usability of interactive visualization tools in health policymaking and health communication are rarely discussed in the literature. while our study sheds light on how to design these tools for these purposes, since it is an exploratory study, it has some limitations. firstly, as we described in the method section, interviewing prospective users was used in this study. this method is a legitimate qualitative method in human-computer interaction research. we used purposeful sampling to recruit study participants. a small sample size was used--one that is sufficient for exploratory such studies. our goal was to recruit participants in various policymaking positions, but ended up recruiting experts in decision-support positions, as the former were difficult to recruit. secondly, we tried to create simple and familiar visualizations to facilitate the evaluation of our visualization tool by study participants. however, it is possible that the tool’s design and features impacted participants’ opinions. finally, our research method and sampling strategy have an impact on the generalizability of our findings. as is the case with all qualitative research, we can only claim “tentative application” of our findings because these findings are context-dependent [24]. it is the responsibility of other researchers, wishing to use these findings, to verify their applicability in similar or different contexts. we recommend that future research studies use field 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ca; 1994. p. 105–17. https://doi.org/10.1007/s00371-013-0892-3 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22144529&dopt=abstract https://doi.org/10.1109/tvcg.2011.279 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts administrative and syndromic surveillance data can enhance public health surveillance tara c. anderson*, hussain yusuf, amanda mccarthy, katrina trivers, peter hicks, michael coletta and violanda grigorescu division of health informatics and surveillance, center for surveillance, epidemiology, and laboratory services, office of public health scientific services, centers for disease control and prevention, atlanta, ga, usa objective this roundtable will address how multiple data sources, including administrative and syndromic surveillance data, can enhance public health surveillance activities at the local, state, regional, and national levels. provisional findings from three studies will be presented to promote discussion about the complementary uses, strengths and limitations, and value of these data sources to address public health priorities and surveillance strategies. introduction healthcare data, including emergency department (ed) and outpatient health visit data, are potentially useful to the public health community for multiple purposes, including programmatic and surveillance activities. these data are collected through several mechanisms, including administrative data sources [e.g., marketscan claims data1; american hospital association (aha) data2] and public health surveillance programs [e.g., the national syndromic surveillance program (nssp)3]. administrative data typically become available months to years after healthcare encounters; however, data collected through nssp provide near real time information not otherwise available to public health. to date, 46 state and 16 local health departments participate in nssp, and the estimated national percentage of ed visits covered by the nssp biosense platform is 54%. nssp’s new data visualization tool, essence, also includes additional types of healthcare visit (e.g., urgent care) data. although nssp is designed to support situational awareness and emergency response, potential expanded use of data collected through nssp (i.e., by additional public health programs) would promote the utility, value, and long-term sustainability of nssp and enhance surveillance at the local, state, regional, and national levels. on the other hand, studies using administrative data may help public health programs better understand how nssp data could enhance their surveillance activities. such studies could also inform the collection and utilization of data reported to nssp. keywords administrative data; syndromic surveillance; public health surveillance references 1truven health analytics. marketscan research databases. truven health analytics website. http://truvenhealth.com/your-healthcarefocus/analytic-research/marketscan-research-databases. accessed september 9, 2016. 2american hospital association (aha). aha data and directories. aha website. http://www.aha.org/research/rc/stat-studies/data-anddirectories.shtml. accessed september 9, 2016. 3centers for disease control and prevention (cdc). national syndromic surveillance program. cdc website. http://www.cdc.gov/nssp/. last updated july 29, 2016. accessed september 9, 2016. *tara c. anderson e-mail: tcanderson1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e165, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts utility of natural language processing for clinical quality measures reporting dino p. rumoro1, shital c. shah*1, gillian s. gibbs1, marilyn m. hallock1, gordon m. trenholme1 and michael j. waddell2 1emergency medicine, rush university medical center, chicago, il, usa; 2pangaea information technologies, chicago, il, usa objective to explain the utility of using an automated syndromic surveillance program with advanced natural language processing (nlp) to improve clinical quality measures reporting for influenza immunization. introduction clinical quality measures (cqms) are tools that help measure and track the quality of health care services. measuring and reporting cqms helps to ensure that our health care system is delivering effective, safe, efficient, patient-centered, equitable, and timely care. the cqm for influenza immunization measures the percentage of patients aged 6 months and older seen for a visit between october 1 and march 31 who received (or reports previous receipt of) an influenza immunization. centers for disease control and prevention recommends that everyone 6 months of age and older receive an influenza immunization every season, which can reduce influenzarelated morbidity and mortality and hospitalizations. methods patients at a large academic medical center who had a visit to an affiliated outpatient clinic during june 1 8, 2016 were initially identified using their electronic medical record (emr). the 2,543 patients who were selected did not have documentation of influenza immunization in a discrete field of the emr. all free text notes for these patients between august 1, 2015 and march 31, 2016 were retrieved and analyzed using the sophisticated nlp built within geographic utilization of artificial intelligence in real-time for disease identification and alert notification (guardian) – a syndromic surveillance program – to identify any mention of influenza immunization. the goal was to identify additional cases that met the cqm measure for influenza immunization and to distinguish documented exceptions. the patients with influenza immunization mentioned were further categorized by guardian nlp into received, recommended, refused, allergic, and unavailable. if more than one category was applicable for a patient, they were independently counted in their respective categories. a descriptive analysis was conducted, along with manual review of a sample of cases per each category. results for the 2,543 patients who did not have influenza immunization documentation in a discrete field of the emr, a total of 78,642 free text notes were processed using guardian. four hundred fifty three (17.8%) patients had some mention of influenza immunization within the notes, which could potentially be utilized to meet the cqm influenza immunization requirement. twenty two percent (n=101) of patients mentioned already having received the immunization while 34.7% (n=157) patients refused it during the study time frame. there were 27 patients with the mention of influenza immunization, who could not be differentiated into a specific category. the number of patients placed into a single category of influenza immunization was 351 (77.5%), while 75 (16.6%) were classified into more than one category. see table 1. conclusions using guardian’s nlp can identify additional patients who may meet the cqm measure for influenza immunization or who may be exempt. this tool can be used to improve cqm reporting and improve overall influenza immunization coverage by using it to alert providers. next steps involve further refinement of influenza immunization categories, automating the process of using the nlp to identify and report additional cases, as well as using the nlp for other cqms. table 1. categorization of influenza immunization documentation within free text notes of 453 patients using nlp keywords guardian; clinical quality measures; influenza immunization; natural language processing acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. *shital c. shah e-mail: shital_shah@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e27, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts hiv bio-behavioral risk study implementation in resource-poor military settings stacy m. endres-dighe*, lauren courtney and tonya farris biostatistics and epidemiology division, rti international, research triangle park, nc, usa objective we present lessons learned from over a decade of hiv biobehavioral risk study implementation and capacity-building in african militaries. introduction circumstances within the military environment may place military personnel at increased risk of contracting sexually transmitted infections (sti) including hiv. hiv bio-behavioral risk studies provide a critical source of data to estimate hiv/sti prevalence and identify risk factors, allowing programs to maximize impact by focusing on the drivers of the epidemic. methods since 2005, rti has provided technical assistance (ta) to support hiv/sti seroprevalence and behavioral epidemiology risk surveys (sabers) in 14 countries across sub-saharan africa and asia. sabers are cross-sectional studies consisting of a survey to assess knowledge, attitudes and behaviors related to hiv, coupled with rapid testing for hiv and other stis. rti tailored each survey instrument to be culturally appropriate in content and methodology, trained military personal to serve as data collection staff, and provided logistical support for study implementation. results key lessons learned are summarized below: data collection mode varied from paper-based to computerassisted surveys, depending on country preference, in-country staff capabilities, and the country’s technological capacity. computerassisted data collection systems were preferable because they improved data quality through the use of programmed skip patterns, range, and consistency checks. by eliminating the need for data entry, computer-assisted systems also saved program resources and enabled faster access to the data for analysis. survey administration method varied from self-administered to interviewer-administered surveys. literacy rates, technological familiarity, and confidentiality concerns were key drivers in determining the best data collection method. self-administered surveys such as computer-assisted self-interview (casi) were preferable due to the high-level of confidentiality they provide, but required a high-level of literacy and computer familiarity. if confidentiality was a big concern in low-literacy settings, audio computer-assisted self-interview (acasi) was used if the population had some computer familiarity. interviewer-administered surveys such as computer-assisted personal interview (capi) were used in most low-literacy settings. tailoring the survey instrument and administration for cultural appropriateness was vital to the acquisition of sound, viable data. sexual behaviors and the definition of “regular sexual partner” and other terms varied according to local custom. the sensitive nature of the survey questions also impacted survey administration operationally. the preference for same-sex or opposite sex interviewers varied by country and military setting. it was imperative to pre-test the survey. a skilled workforce and staff retention are essential to provide high quality data. literacy levels, technological familiarity, hiv knowledge, and time commitments must all be considered when selecting data collection staff. retention of staff throughout the duration of data collection activities can be a major issue especially among military personnel who were often called away from study activities to perform military duties. host military ownership was integral to the success of the sabers program. by engaging military leadership early and involving them in all decision making processes we ensured the partner military was invested in the study and its success and found value in the resulting data and findings. host militaries were actively involved in sabers by providing staff for data collection, leading sensitization activities, and monitoring data collection activities in the field. inclusion of capacity building elements during study implementation led to increased host military buy-in. capacity building included staff trainings and practical experience in survey methodology, use of electronic data collection instruments, study logistics and data monitoring. confidentiality of survey data and hiv test results was of increased concern given that these studies were conducted in a work place environment. for this reason, it was imperative to assure participants that disclosures of drug or alcohol use and positive hiv/sti test results would remain confidential and would not affect their military employment. conclusions based on our experience, the following are required for the successful implementation of an hiv bio-behavioral risk study in resource-poor military settings: (1) selection of a data collection mode and survey administration method that is context-appropriate, (2) utilization of local wording and customs, (3) a skilled workforce, (4) local buy-in/partnership, (5) inclusion of capacity building elements, and (6) assurance of confidentiality. keywords survey; capacity building; militaries; resource poor; africa *stacy m. endres-dighe e-mail: sendres@rti.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e179, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters department of homeland security (dhs), office of health affairs (oha), national biosurveillance integration center (nbic), washington dc, dc, usa objective to evaluate different government and commercial air travel route and volume data sources for utility in determining likely points of arrival and subsequent spread of communicable diseases originating from outbreaks outside the united states. introduction the national biosurveillance integration center (nbic) has the responsibility to integrate, analyze, and share the nation’s biosurveillance information provided from capabilities distributed across public and private sectors. the integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts. understanding travel trends and volumes is essential to managing public health and emerging infectious diseases as travelers move across today’s increasingly globalized world. travel routes, seasonal trends, and general passenger flow volume help determine where communicable diseases are more likely to arrive and spread within the us. this type of data is useful in informing policy decisions, especially with respect to resource allocation and border screening procedures. accuracy in the underlying data is important to build better predictive models methods we conducted extensive research into currently available government and commercial air passenger data sources to evaluate data quality, utility, and accessibility for the purposes of biosurveillance. the goal was to identify the most complete and accurate data source of air passenger counts and flight schedules. the scope was limited to commercial passenger air flights. the commercial data sources were limited to two industry leaders: the international air transport association (iata) and the official airline guide (oag). evaluation of their products included interviews with data users in government, interviews with company representatives and some of their analytics staff, system demonstrations and examination of the results of sample data queries. the search for government sources of data included interviews of personnel within dhs customs and border protection (cbp), the federal aviation administration (faa), the centers for disease control and prevention (cdc), and department of defense (dod). identical queries were made of each data source. examination of the differences between the volume values then led to the exploration of how each data source defined its designated variables and derived its calculations. the purpose and use of air travel data to support decision-making was strongly considered during evaluation. some examples of these concerns include resource allocation of staff, communication or messaging of health advisories, or planning for border screening processes and training staff. this plays a role in determining what kind of questions to ask of the data and whether a data source is flexible in altering its parameters to allow that query. results the following are preliminary findings: commercial data sources differ in multiple ways: • not all commercial sources provide passenger volume data. • oag volume data is based on booked seats as gathered from global distribution systems (gds) and, with data delay, from computer reservation systems (crs). iata volumes are based on the business settlement process (bsp) surrounding ticket purchasing. • comparison of the results of oag and iata system query results is difficult as both draw from different data systems and use proprietary algorithms to calculate results. • most single commercial sources appear to require additional estimations to fill data gaps. government data sources: • government sources are usually limited to flight schedules or volumes of passengers crossing us airspace or borders, unless their mission space is in intelligence. • dhs customs and border protection (cbp) us entry data systems are considered the gold standard as they contain complete arriving passenger data. • the results of government data systems queries differ from that of commercial sources due to differences in parameter definition. conclusions the commercial data often used by academia and private performers to build disease spread models can differ significantly from government data not only due to the methodology to derive the passenger volumes (i.e. tracking passenger arrivals as opposed to ticket sales estimations based on flight routes), but also due to differing parameter definitions. keywords air travel; border screening; commercial and government data; translocation *diana y. wong e-mail: diana.wong@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e43, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts mass gathering surveillance: new essence report and collaboration win gold in or meredith a. jagger*1, selene jaramillo2, laurel boyd1, brian johnson2, kelly r. reed2 and melissa powell1 1oregon public health division, portland, or, usa; 2lane county public health, eugene, or, usa objective to streamline production of a daily epidemiology report including syndromic surveillance, notifiable disease, and outbreak data during a mass gathering introduction the 2016 u.s. olympic track and field team trials were held july 1-10 in eugene, or. this mass gathering included over 1,000 athletes, 1,500 volunteers, and 175,000 spectators. the oregon public health division (phd) and lane county public health (lcph) participated in pre-event planning and collaborated to produce a daily epidemiology report for the incident management team (imt) during the event. the state and county public health agencies had collaborated on surveillance for prior mass gatherings, including the 2012 trials. however, 2016 was the first opportunity to use complete state and county syndromic surveillance data. methods phd staff developed an essence report, highlighting seven priority health outcomes: total emergency department visits; injury, gastrointestinal, respiratory, and fever syndromes; and asthmalike and heat-related illness queries. the report included side-byside comparisons of county and state time series graphs, a table summarizing reportable diseases, and space to narratively describe outbreaks. phd staff did a virtual demonstration and in-person tutorial for lcph staff on how to run the report. essence access permissions had to be modified so that county users could see and produce state time-series graphs but not data details for non-lane county visits. emphasis was placed on interpretation of likely scenarios, i.e., one or two days with a warning that was not indicative of an incident of public health importance. results during the event, lcph staff were able to run the report successfully, i.e., there were no technical glitches. for the first few days, lcph staff consulted with phd staff about epidemiological interpretation. state data were of specific interest since data details were suppressed. additionally, increases were seen in the injury syndrome in the days preceding the july 4 holiday. stratification by key demographic factors and looking at subsyndrome breakdowns on warning and alert days provided the needed information without requiring the use of the detail details. conclusions after the event, there were three main recommendations for improving the process. lcph suggested that the side-by-side visualization of county and state time series graphs was useful to see trends but the relative scale of the number of visits was unclear due to size and placement (see figure 1). solutions for future reports include additional explanatory text, limiting the report to only county data, and alternative visualizations that highlight the differences in visit magnitude. as part of the imt process, the lcph lead felt that her efforts to physically go to the emergency operations center to run the report helped facilitate communication with partners. however, it is not clear if this effort directly translated into imt use of the report, which was posted to the online event management system and not included in the daily situation status reports. while lcph leadership and staff reported anecdotally that they found the report to be very useful, no formal evaluation of use was done with either public health or imt staff. in advance of the next event, state and county staff should prepare evaluation metrics. the report feature in essence is a bit cumbersome to set up, but it allows for easy production of appealing and customizable reports. this template can be modified for future mass gatherings, including athletic competitions and county fairs. phd staff will continue to collaborate with lcph to repurpose and improve the report for use in lane and other counties. fostering local user comfort with interpreting essence data and generating summaries for local use is a priority of the or essence team. figure 1: example of side-by-side time series graphs. keywords mass gathering; surveillance; collaboration; essence acknowledgments thank you to the university of oregon imt and track town usa for inviting public health to participate in the 2016 u.s. olympic track and field team trials. this publication was supported by cooperative agreements, number nu90tp000544 and 5u50oe000068-02, funded by the centers for disease control and prevention (cdc). its contents are solely the responsibility of the authors and do not necessarily represent the official views of the cdc or the department of health and human services. *meredith a. jagger e-mail: meredith.a.jagger@state.or.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e133, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts animal surveillance: use of animal health data to improve global disease surveillance karen l. meidenbauer* johns hopkins university apl, columbia, md, usa objective to identify gaps in current u.s. animal data collection and surveillance systems, describe how surveillance of animal populations can provide important early warnings of emerging threats to human populations from infectious disease epidemics, and explain the benefits of integrating human and animal surveillance data into a common linked system. introduction since the majority of emerging infectious diseases over the past several decades have been zoonotic, animal health surveillance is now recognized as a key element in predicting public health risks. surveillance of animal populations can provide important early warnings of emerging threats to human populations from bioterrorism or naturally occurring infectious disease epidemics. this study investigated current animal data collection and surveillance systems, isolated major gaps in state and national surveillance capabilities, and provided recommendations to fill those gaps. methods initially, an extensive literary review was performed to better understand what is currently available for animal health disease surveillance in the united states and recognize the gaps. after this review meetings were arranged with numerous animal health and public health surveillance experts to isolate their surveillance priorities: department of homeland security (dhs), usda animal plant health inspection service (aphis), u.s. army veterinary corps, university laboratories/veterinary teaching hospitals, the national capitol region (ncr) essence public health steering committee, maryland arbovirus, zoonotic, and vector disease group, and the maryland state veterinarian. a key animal disease surveillance stakeholder group that has been underrepresented in prior requirements assessments is private practitioners. preliminary discussions with key practitioners revealed clearly that there are monumental gaps in animal health surveillance and it frequently limits their ability to rapidly respond to potential disease risks within their animal population of concern. to better understand these gaps and potential ways to improve surveillance in this area, a voluntary survey was developed and sent out to members of the maryland veterinary medical association, virginia veterinary medical association, and the district of columbia academy of veterinary medicine. results through this comprehensive study three current u.s. animal health disease surveillance gaps were isolated: integrated human and animal health surveillance, real-time animal health data collection, and companion animal surveillance. the survey was also well received and had almost 160 participants. key issues addressed in the survey included: animal medical records – availability, capabilities, and concerns, zoonotic disease exposure and reporting, and support for development of integrated humananimal disease surveillance tools. key findings: almost 90% of responding practitioners reported having encountered a zoonotic disease in practice. although less than 50% have reported a zoonotic disease to the state or federal government. almost 70% of veterinarians in the national capital region (ncr) who participated in the survey also reported that they do not have access to a surveillance system. veterinarian’s responses to the question: “what is your opinion of the current status of local, regional, or national zoonotic disease surveillance and the use of animal data for surveillance?”: “i think it is difficult to find up to date local and regional data. email alerts etc. would be nice, rather than having to search for information that frequently isn’t current.” “i feel that many zoonotic diseases go unreported due to the lack of ease of reporting them and there is no communication between the human and veterinary medical communities as far as reportable diseases affecting both people and animals.” “with the proliferation of tick borne disease, closer surveillance of animal cases would benefit human medicine. we knew exactly when lyme hit our area. it was three years later before va dept. of health sent out a letter outlining the prevalence of disease in southwest va human cases.” conclusions linking the systems that report human and animal diseases would enable health professionals to swiftly identify and respond to zoonotic disease outbreaks. since funding for animal health surveillance is limited, integrating animal data into existing, well-established human health surveillance systems would reduce the resources needed while still providing the advanced capabilities that are available for human health surveillance. the need for integrated surveillance has been recognized by regulatory officials, but concerns regarding funding, data acquisition, data confidentiality, and identification of desired stakeholders must still be addressed. the sometimes disparate interests of large industry, private practitioners, and state governments make gaining access to large centralized pools of animal health data a challenge. by using existing human health surveillance systems as a platform to develop integrated human-animal surveillance systems and by working with experts in the human surveillance field, these concerns can be ameliorated. this would lead to more advanced integrated health surveillance capabilities and heighten the nation’s ability to quickly detect and respond to emerging zoonotic diseases. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e147, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords one health; integrated surveillance; animal health; public health acknowledgments virginia-maryland college of veterinary medicine, virginia polytechnic institute and state university, and johns hopkins university applied physics laboratory. *karen l. meidenbauer e-mail: karen.meidenbauer@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e147, 2017 isds16_abstracts-final 123 isds16_abstracts-final 124 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts facile dashboard creation using library of syndromic surveillance visualization tools harold gil* marion county public health, indianapolis, in, usa objective a framework and toolbox for creating point-and-click dashboard applications (at no cost) for monitoring several facets of syndromic surveillance data was created. these tools (and associated documentation) are being made available freely online for other surveillance practitioners to adopt. introduction public health surveillance largely relies on the use of surveillance systems to facilitate the identification and investigation of epidemiologic concerns reflected in data. in order to support public health response, these systems must present relevant information, and be user-friendly, dynamic, and easily-implementable. the abundance of r tools freely-available online for data analysis and visualization presents not only opportunities, but also challenges for adoption in that these tools must be integrated so as to allow a structured workflow. many public health surveillance practitioners do not have the time available to 1) scavenge for tools, 2) align their functions so as to create a relevant set of visuals, and 3) integrate these visuals into a dashboard that allows a streamlined surveillance workflow. an openly-available, structured framework that allows simple integration of analytic capabilities packaged into readilyimplementable modules would simplify the creation of relevant dashboard visuals by surveillance practitioners. methods r is a statistical computing application, known for its versatility and ability to create powerful visualizations. shiny is an r package that allows the creation of interactive, easy-to-use point-and-click applications. we looked to r and its shiny package extension as a candidate solution. however, creating a shiny application from scratch requires knowing enough of the r programming language so as to be able to appropriately design and link several chunks of code that interact with one another to generate the desired output. to address this barrier, we sought to create a structured process by which one can easily browse a library of defined code snippets (each of which enables an analytic tool relevant to syndromic data analysis and visualization) and then integrate snippets of interest into a dashboard application in a way that requisite experience with r is minimized. results we first collected several analytic tools that support syndromic data analysis and have been developed for r; examples include heatmaps, change-point detection, outlier detection, tables, maps, etc. we then packaged them into snippets of code (one for each analytic tool) in a way that facilitates integration of the analytic tool into a dashboard application. a fake syndromic dataset was created as well for inclusion in a demo dashboard application that is available for sharing. conclusions the online community of r users makes new tools for data analysis and visualization available every day. the abundance of options can be overwhelming and the process of integrating pieces of code can be time-consuming. this places a constraint on adoption of these tools by epidemiologists working at all levels of government. the present project alleviates this problem considerably by reducing the tool searching process through the introduction of a library of relevant tools for syndromic data analysis and visualization that can be easily integrated into a dashboard application that allows for streamlined syndromic surveillance activities. our next step is to partner with interested jurisdictions to help them adopt this framework and associated tools. given sufficient interest, we would set up a process for others to add their own modules to this library, perhaps through the online platform for collaborative code development and sharing, github. keywords dashboard; visualization; r *harold gil e-mail: hgil@marionhealth.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e55, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts the role of tobacco surveillance to obtain policy, systems, and environmental changes alina chung*, anne-marie coleman, oluwayomi fabayo, kayla lloyd and emma bicego gadph, atlanta, ga, usa objective to analyze tobacco use in georgia to influence policy, systems and environmental changes as tools to reduce its burden on health outcomes introduction tobacco use is the leading cause of preventable illness and deaths in georgia. about 10.1% of deaths among adults in georgia are linked to smoking related illnesses. most first use of cigarettes occurs by age 18 (87%), with nearly all-first use by 26 years of age (98%). although cigarette smoking has declined significantly since 1964, very large disparities in tobacco use remain across different subgroups of the population. multiple environmental, psychological, and social factors have been associated with tobacco use, including race and ethnicity, age, ses, educational accomplishment, gender, and sexual orientation. these factors within the social environment have a huge influence on motivation to begin and to continue using tobacco products for not just the individual but also certain community group within the population. established in 2000, georgia tobacco use prevention program (gtupp) is a program designed to meet the overall goal of reducing the health and economic burden associated with tobacco use for all members of the community. by working with various partners, gtupp plans, implements and evaluates policy, systems, and environmental changes designed to reduce tobaccorelated illnesses and deaths. best practice strategies focus on the following goals: preventing the initiation of tobacco use among young people; promoting quitting among young people and adults (e.g. georgia tobacco quit line (gtql); eliminating exposure to secondhand tobacco smoke; and identifying and eliminating the disparities related to tobacco use among various population groups. methods the following data collection tools were used to educate community members, local coalition groups and policy decision makers on the burden of tobacco use in georgia: youth tobacco survey (yts), youth risk behavioral survey (yrbs) and behavioral risk factor surveillance system (brfss). these tools allows public health professionals to create messaging needed to reach different stakeholders. the following are examples of key data points that were used to influence policy, systems, and environmental change: 27,000 of middle school students and 79,000 of high school currently use tobacco (cigarettes, smokeless tobacco or cigars). approximately 32,400 of middle school students and 72,900 of high school students say they have tried smoking electronic cigarettes (e-cigarettes). smoking prevalence among adult males 740,000 is significantly higher than among females 510,000, and the overall smoking prevalence is highest among adults’ ages 25-34 years 292,000. results currently, the following policies have been adopted as a result of using surveillance to educate policy decision makers and multisector groups in the community at large: 116 school district are 100% tobacco free, 28 parks and recreation are 100% tobacco/smoke free, 46 colleges/universities are tobacco free, 6 cities in georgia have a comprehensive smoke free air law, 65 multi-unit housing (private/public) are smoke free, and 132 hospitals are tobacco free. between june 2015 and july 2016, over 15,000 georgia tobacco users used the gtql services to make a quit attempt, and healthcare providers through a systems change referral approach referred 13% of the users to the gtql. conclusions working with schools (k-12), parks, colleges/universities, hospitals, worksites, and municipalities to adopt tobacco free policies and promote cessation services provides an opportunity for all members of the community to be tobacco free. as tobacco use is associated with chronic diseases it is imperative to engage all members of the community in tobacco free living. removing avoidable structural and social barriers and equally implementing tobacco use prevention programs and policies is essential. keywords tobacco surveillance; policy, systems and environmental changes; chronic disease prevention acknowledgments this abstract is supported by the cooperative agreement number, 1 u58 dp005977-01, funded by the centers for disease control and prevention (cdc). references tobacco surveillance, policy, systems and environmental changes, chronic disease prevention *alina chung e-mail: alina.chung@dph.ga.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e82, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts burden and deaths associated with vaccine preventable diseases in canada, 2010-2014 laurence caron-poulin*, jenny rotondo, jennifer cutler, shalini desai and susan squires public health agency of canada, ottawa, on, canada objective to describe the recent trends in the burden of disease and mortality associated with vaccine preventable diseases (vpds). introduction vaccination is one of the most successful public health interventions. despite this, there are a variety of reasons that vpds continue to be seen in developed countries such as canada. this analysis describes the recent trends in the burden of disease and mortality associated with vpds for which publicly funded vaccination programs for infants or children are implemented across the country and for which national surveillance data are available. methods surveillance data on vpds were obtained from the canadian notifiable disease surveillance system. population and death data were obtained from statistics canada. death data were only available to 2012. in total, 11 vpds have been included in the analyses namely tetanus, diphtheria, pertussis, polio, haemophilus influenza (hi), measles, mumps, rubella, congenital rubella syndrome (crs), invasive meningococcal disease (imd), invasive pneumococcal disease (ipd). exclusion of non-vaccine preventable serotypes from either data source was not possible. analyses included incidence rate, proportion, mortality rate and risk ratio. results surveillance data indicate that from 2010 to 2014, an average of 6,020 cases of vpds were reported annually, representing an average annual crude incidence rate of 17.3 cases per 100,000 population. vpds accounting for the largest proportion of reported cases include ipd (54.4%) and pertussis (29.6%). age groups most affected include children less than 1 year of age (92.6 cases per 100,000) and children between 1 and 4 years of age (36.0 cases per 100,000). age groups least affected include adults between 20 and 24 years old (6.9 cases per 100,000 population) and between 25 and 29 years old (7.3 cases per 100,000 population). age groups affected differed by vpd. death data indicate that from 2010 to 2012, vpds accounting for the largest proportion of deaths across all ages include ipd (58.2%), hi (16.3%) and imd (15.3%). youth aged 19 years and under accounted for 26.1% of vpds deaths (mortality rate of 0.17 per 100,000 population). children less than one year old have the highest mortality rate due to vpds (2.0 per 100,000 population) and were 26.9 times more likely to die from vpds compared to children between 1 and 19 years of age. adults aged 20 years and older accounted for 73.9% of vpd deaths (mortality rate of 0.14 per 100,000 population). a high mortality rate was also seen in adults 60 year old and over (0.3 per 100,000 population); adults 60 years old and over were more 2.6 times more likely to die from vpds compared to adults between 20 and 59 years old. conclusions the results of routine canadian surveillance data suggest that despite high vaccine coverage rates generally seen in developed countries such as canada, a possible preventable burden of illness due to vpds still occurs across all age groups. consideration of vpds as a whole allows a real appreciation of the burden and deaths associated with vpds in general. the analysis has shown that while the incidence rates are highest among children 4 years old and younger, mortality due to vpds continues to occur and primarily affects infants and elderly. due to the asymptomatic nature of some vpds and data limitations, reported cases are likely underestimates of the true burden. keywords vaccine; disease; epidemiology; mortality; burden *laurence caron-poulin e-mail: laurence.caron-poulin@phac-aspc.gc.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e94, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 1isds, boston, ma, usa; 2cste, atlanta, ga, usa; 3cdc, atlanta, ga, usa; 4infectious disease clinical research program & hjf, usuhs, bethesda, md, usa; 5public health seattle & king county, seattle, wa, usa; 6state of new hampshire, concord, nh, usa; 7armed forces health surveillance center, silver spring, md, usa objective to describe the process undertaken to translate syndromic surveillance syndromes and sub-syndromes consisting of icd-9 cm diagnostic codes to syndromes and sub-syndromes consisting of icd-10-cm codes, and how these translations can be used to improve syndromic surveillance practice. introduction as of october 1, 2015, all hipaa covered entities transition from the use of international classification of diseases version 9 (icd-9-cm) to version 10 (icd-10-cm/pcs). many public health surveillance entities receive, interpret, analyze, and report icd-9 encoded data, which will all be significantly impacted by the transition. public health agencies will need to modify existing database structures, extraction rules, and messaging guides, as well as revise established syndromic surveillance definitions and underlying analytic and business rules to accommodate this transition. implementation challenges include resource, funding, and time constraints for code translation and syndrome classification, and developing statistical methodologies to accommodate changes to coding practices. to address these challenges, the international society for disease surveillance (isds), in consultation with the centers for disease control and prevention (cdc) and the council of state and territorial epidemiologists (cste), has conducted a project to develop consensus-driven syndrome definitions based on icd10-cm codes. the goal was to have the newly created icd-9-cmto-icd-10-cm mappings and corresponding syndromic definitions fully reviewed and vetted by the syndromic surveillance community, which relies on these codes for routine surveillance, as well as for research purposes. the mappings may be leveraged by other federal, state, and local public health entities to better prepare and improve the surveillance, analytics, and reporting activities impacted by the icd-10-cm transition. methods isds coordinated a multi-stakeholder working group to revisit existing syndromic surveillance definitions and compile icd-9-cm codes that originated in biosense that map to these categories. the individual icd-9-cm codes within each category were then mapped to the 2013 icd-10-cm using general equivalence mappings (gems). subsequently, we followed a reverse translation validation process to ensure that the appropriate codes were correctly identified. the resulting master mapping reference table (mmrt) relates syndromic classifications to both code groupings. the code mappings were then reviewed by the surveillance community and partner agencies, leveraging clinical and epidemiological expertise, to reach consensus. results the new mmrt tool, released in august 2015, provides a key resource to public health practitioners that use syndromic surveillance to update their systems and to correctly identify trends over time that span the transition period by using both code sets, as well as surveillance activities using exclusively icd-10 cm. conclusions the development of a consensus-driven mmrt assists entities with the complex task of translating icd-9-cm to icd-10-cm codes. it is anticipated that the higher level of detail inherent to icd10-cm codes will improve the specificity of syndromic surveillance. the code translations will also serve to develop standardized syndrome definitions based on conceptual mappings and a deductive development approach from concept to diagnostic codes to syndromes. finally, the mappings will enable users to address challenges associated with changes in baseline trends (figure 1) as a result of the transition. leveraging the mmrt, jurisdictions can quickly map forwards and backwards across the two coding systems to ensure continuity of analytics and reporting during the transition period. keywords icd-10; syndrome definitions; syndromic surveillance; codemapping; icd-10 transition acknowledgments we thank the surveillance professionals that assisted with the code set review. this work was supported by the cdc. *brooke evans e-mail: bevans@syndromic.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e107, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, 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and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun 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disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison 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annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 1department of community health, university for development studies, tamale, ghana; 2regional health directorate, ghana health service, upper east region, bolgatanga, ghana; 3swiss tropical and public health institute, switzerland, and university of basel, basel, switzerland objective the objective of this study was to assess the evd surveillance and response preparedness among frontline health workers in northern ghana. introduction the recent ebola outbreak has been described as unprecedented and its public health impact in terms of morbidity, mortality and coverage has been far greater than previously experienced [1-3]. this outbreak has revealed many weaknesses and inadequacies for disease surveillance and response systems in africa due to underqualified staff, cultural beliefs and sometimes, lack of trust for formal health care sector performance [4-6]. since 2014, ghana had high risk of seeing evd cases [2]. methods ghana is situated in west africa and bordered by ivory coast to the west, burkina faso to the north, togo to the east, and the atlantic ocean to the south. this was an observational study conducted among 47 frontline health workers in all the thirteen districts of the upper east region representing public, mission and private health services. a semi-structured questionnaire with focus on core and support functions (e.g. detection, confirmation etc.) was administered to the informants. in addition, 34 weekly idsr reports (august 2014 to march 2015) were collated from each district. results clinically diagnosed data revealed that 4 out of the 13 districts reported 9 evd cases in 2014. out of the 9 suspected cases, 8 of them died and the cause of death was unexplained. bawku municipal was the only district that reported a suspected case in 2015. all the ten suspected cases reported, none was confirmed (i.e. positive for the virus antigen). the 47 key informants were (medical officers, district directors, disease control officers and laboratory officers). they had knowledge on evd surveillance as well as the reporting of data. however, there were some challenges affecting surveillance and response preparedness such as delay in reporting, low quality personal protective equipment (e.g. gloves, aprons, infra-red thermometers etc.), inadequate staff and lack of laboratory capacity to test samples at the district or regional levels. over 80% (38/47) of the informants were not satisfied with evd surveillance. the reasons cited include lack of infra-red thermometers, ineffective screening, and lack of isolation centres. conclusions evd surveillance is still insufficient, particularly the inadequate ppes and lack of laboratory capacity to test suspected cases as well as local burial practices. the ebola epidemic is a wake-up call for early case detection and response preparedness. this topic remains a neglected and deprived public health issue in ssa. thus, disease surveillance and prevention activities are urgently needed in the health system. key words: disease surveillance, core and support functions, health information system, ghana keywords disease surveillance; core and support functions; health information system; ghana acknowledgments the authors acknowledge the assistance of the staff of ghana health service. references 1. issah k, nartey k, amoah r, bachan eg, aleeba j, yeetey e, letsa t: assessment of the usefulness of integrated disease surveillance and response on suspected ebola cases in the 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visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi hospital e pronto socorro central, são bernardo do campo, brazil objective report successful experience in fighting dengue fever in the hospital and emergency services in são bernardo do campo, joining the flowchart included, telephone monitoring and electronic patient records. introduction dengue fever is a dynamic infectious disease, allowing the patient to rapidly move from one stage to another during its course. proper management of patients depends on early recognition of warning signs, continuous monitoring and re-staging cases and prompt fluid replacement. the telemedicine and electronic patient records (epr) belong to a series of advances of new features such as decision-making support systems including efforts on health monitoring, in view of the epr as a support tool to allow the association of welfare activities as a database for the management of epidemiological information and monitoring. in addition, telemonitoring systems can be used for the monitoring of patients with chronic diseases in their homes which leads to cost savings in hospitalization and ensures appropriate care and the proper development of these patients. the continuous remote monitoring of these patients decreases the amount of hospital visits for monitoring procedures, also facilitating successful treatment, as in the fever dengue cases. methods this study, descriptive in character, was conducted at the hospital and emergency services, in the municipality of são bernardo do campo, são paulo, which deals with coronary emergency cases, trauma, infectious and chronic diseases with prolonged hospitalization; composed of 150 beds, 10 beds for general adult icu and 05 beds for pediatric icu. suspected cases of dengue fever treated in the months of january to may 2015 formed the study population. validation, training and implementation of a screening flowchart of suspected dengue fever cases treated at hospital was performed using a patient identification form and collection of signs and symptoms including the tourniquet test, related to the disease. for tourniquet test training, posters were made describing the proper technique for performing the test, and the qr code* of the educational video on the technique was placed on the poster so that each employee could view the instructional video on their own smartphone. there was a daily tracking of all records in the epr, tracking the medical and nursing history and laboratory results, and the records selected that contained in their records the words dengue and/or the words fever, retro-orbital pain, headache, myalgia or arthralgia, recording 1,298 cases; of which 38 had signs and symptoms of severity and required hospitalization, progressing satisfactorily. for each selected event, phone monitoring was conducted, with information on the progress of the case, timely collection of general serology and guidelines such as the need for adequate hydration during the course of the disease, in addition to warning patients for them to return to the emergency room if they exhibit signs of worsening. results 1,298 patients were attended with suspected dengue fever. the main symptoms exhibited were fever in 99% of cases, accompanied by mialgia in 89% of cases, headache in 82% and retro-orbital pain in 71%. symptoms more closely related to severe forms were vomiting, and bleeding. the prevalence of positivity was 41% which can be low when compared to other services, but the lack of timely collection of the serology may have inhibited this data. comparing the patients with positive serology with those with negative serology, but who developed a feverish state similar to dengue, it was found that the symptoms of retro-orbital pain, itching and rash exhibited statistically significant differences in dengue fever cases. conclusions the monitoring to guide appropriate measures regarding the disease were decisive, we managed to notify and 100% of the cases in a timely manner, thus avoiding complications and keeping the deaths at zero. the epr use in order to improve the quality of the epidemiological indicators produced as part of the local surveillance of all cases of diseases of compulsory notification, is essential. keywords dengue fever; data record patient; surveillance references 1. tdr/who. dengue: guidelines for diagnosis, 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implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 1new york state department of health, albany, ny, usa; 2ntt data, albany, ny, usa objective to establish the infrastructure to provide a linkage between the immunization registry and disease surveillance system using standard for data exchange. introduction new york state department of health (nysdoh) implemented a communicable disease electronic surveillance system (cdess), a single and secure application used by 57 local health departments (lhds), hospital infection control programs and nysdoh staff to collect, integrate, analyze, and report data for infectious disease surveillance. new york state immunization information system (nysiis) is a mandated application for providers to report all vaccinations of persons < 19 years old residing in new york state (excluding new york city). currently, lhd staff must manually search nysiis for vaccine preventable disease case investigations and re-enter the immunization histories into cdess. nysiis has built a hl7 query functionality which can be used to automate the data exchange between nysiis and cdess. methods the business rules and data specifications for exchange of vaccine histories of reported pertussis cases between nysiis and cdess were developed. a process was established, a daily hl7 query file of reported pertussis cases from cdess was generated and the file was matched against nysiis by patient’s last name, first name, date of birth, gender, and address. nysiis utilized its process of generating a hl7 response file that contains vaccine history on matched cases to send back to cdess. cdess then processes the response file and loads vaccine history into its vaccine table for the matched pertussis cases. results an automated process was developed and implemented in april 2015. between april 1 through july 31, 2015, there were 166 pertussis cases reported through cdess, 32 cases over aged 19 years old were excluded, and 80 (60%) cases were matched in nysiis. all pertussis vaccine related information (such as vaccine date, vaccine type, manufacturer, and vaccine lot number) from nysiis were populated in cdess corresponding data fields for matched cases. fifty-four cases were not found as matches in nysiis due to not matching address, no nysiis record, or no pertussis vaccine. conclusions by using existing nysiis query functionality, this is an easy process to establish an automated linkage for data exchange between nysiis and disease surveillance systems. this process provides a more timely and efficient way to assist lhd staff to get vaccine information for vaccine preventable cases. nysiis does not contain all vaccine information (eg. manufacturer, vaccine lot number) and lhd staff may still be required to contact providers. keywords surveillance system; immunization registry; data exchange acknowledgments this publication was supported by cooperative agreement number u50ck000423 from the centers for disease control and prevention (cdc). its contents are solely the responsibility of the authors and do not necessarily represent the official views of cdc. *hwa-gan chang e-mail: hwagan.chang@health.ny.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e98, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men 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and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the 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eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 1university of guelph, guelph, on, canada; 2public health agency of canada, ottawa, on, canada objective the objective of our study was to determine how demographic and exposure factors related to giardiasis vary between travel (both international and domestic) and endemic cases, for residents of the region of waterloo, ontario. introduction increasing immigration to canada coupled with the increasing number of canadians travelling domestically and abroad is expected to significantly impact the burden of illness due to enteropathogens, including giardia, in canada1. when estimating this burden of illness, international travel cases are considered to be distinct from domestically acquired cases due to differences in control measures in other countries1,2. however, there is no distinction made between domestic travel-related cases and endemic cases. as such, there has been no published literature where domestic travel-related cases (dtrcs) have been analyzed separately from endemic cases (ecs). this represents a considerable knowledge gap, as risk factors for contracting giardiasis via domestic travel may be different from those associated with endemic giardiasis or international travel. in our study, we subsequently demonstrated that grouping dtrcs and ecs together for analysis is likely not appropriate due to differences in exposures to risk factors for giardiasis among these groups. methods public health inspectors gathered exposure and demographic data for giardiasis cases reported in the region of waterloo from 2006 to 2012, inclusive. logistic regression models were fit to assess differences in exposure to risk factors for giardiasis between international travel-related cases and canadian acquired cases while controlling for age and sex. multinomial regression models were also fit to assess the differences in risk profiles between international and domestic travel-related cases and endemic cases. results over the six year study period, 472 giardiasis cases were reported to the region of waterloo, 191 (40%) cases were related to international travel, and 282 (60%) cases were acquired in canada. of the cases acquired in canada, 29 (10%) were related to domestic travel, and the remaining were acquired within the region of waterloo. travelrelated cases (both international and domestic) were more likely to go camping or kayaking, and consume untreated water compared to endemic cases. domestic travel-related cases were more likely to visit a petting zoo or farm compared to ecs, and were more likely to swim in freshwater compared to endemic cases and international travelrelated cases. international travelers were more likely to swim in an ocean compared to both dtrcs and ecs. conclusions traditionally, domestic travel related cases and endemic cases are grouped together into a domestic category (i.e., canadian acquired) in studies that examine the effect of travel on the burden of illness on a population. to the best of the authors’ knowledge, this is the first study to separate canadian cases into ecs and dtrcs, instead of the traditional method. we found significant differences in exposures to various risk factors for giardiasis among the various case groups (international travel, domestic travel and endemic), including significant differences between ecs and dtrcs. therefore, we suggest that in future studies, dtrcs and ecs should not be included together as this may result in missing important associations or risk factors. distinguishing between these two case types will likely have an impact on public health policies. perhaps, of greater significance, our findings are important for creating effective and targeted health promotion campaigns to prevent giardiasis in this region, by targeting activity-specific (e.g., endemic, domestic or international travel) risk activities. keywords giardiasis; travel-related infection; endemic infection; giardia; surveillance acknowledgments this research was supported by an infastructure grant to dl pearl from the canada foundation for innovation and the ontario research fund. the authors would like to thank all of the public health inspectors at the region of waterloo public health for collecting questionnaire data. references 1. ravel a, nesbitt a, marshall b, sittler n, pollari f. description and burden of travel-related cases caused by enteropathogens reported in a canadian community. j travel med. 2010 jan-feb; 15(1). 2. gormley fj, rawal n, little cl. choose your menu wisely: cuisineassociated food-poisoning risks in restaurants in england and wales. epi and infect. 2012 jun; 140(6). *alexandra swirski e-mail: aswirski@uoguelph.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e163, 2016 assessing the usage of dating sites and social networking sites in newly 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pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran goal global, dublin, ireland objective a community-based evd surveillance system with improved symptom recording and follow-up of malaria positive patients at phus was implemented during low evd transmission. the rationale and methodology in implementing a phu-focused approach to strengthen surveillance system sensitivity is described. introduction existing evd surveillance strategies in sierra leone use a centralized live alert system to refer suspect cases from the community to ebola treatment centers. as evd case burden declined in port loko district, so did the number of reported alerts. as evd presents similarly to malaria, the number of alerts reported are expected to remain consistent with malaria prevalence in malaria-endemic areas, irrespective of a reduction in true evd cases. declines in reported suspect cases from the community alluded to the possibility that individuals were returning to healthcare centers to seek treatment for malaria, and that phus were not adequately reporting suspect evd cases. district surveillance officers (dsos) were used to investigate the usage of phus by community members, as well as the mechanisms that health center staff used in recording patient visits. surveillance methods specific to phus were introduced to increase the number of reported evd alerts, as well as establish the foundation for future integrated disease surveillance response strategies. methods phu surveillance methodology focused on 4 primary components: • initial evaluation: phu-specific evaluations were conducted to gauge the level of community use of primary health care structures as evd case-load decreased. “under-five”, opd, and triage registers were reviewed to investigate what disease recording mechanisms were used by healthcare staff. • case definition education: healthcare staff were educated on evd case definition and protocol for reporting all suspect cases through the centralized live alert system. • improved symptom recording: a section in all patient registries was included specifically for recording symptoms of any patient upon arrival at a phu. recording of patient symptoms, as opposed to only presumed diagnosis, was introduced as a tool for surveillance of a greater range of diseases by allowing for easy review of demographicspecific trends in symptomatic presentations. recording symptoms of individuals with a positive rapid diagnostic test (rdt) not only forms the foundation for a malaria patient follow-up system but also allows symptomatic groupings for differential evd diagnosis. • malaria algorithm: a system was introduced to aid healthcare staff in eliminating possibilities of malaria and evd co-infection among patients presenting at phus. as arteminisin combination therapy (act) is fast acting and can reduce parasitemia levels within 1-2 days, an algorithm was proposed for improved surveillance (fig. 1.) results initial phu evaluations showed that the majority of community members weren’t actually returning to phus, and that the decrease in live alerts was a result of poor community reporting, unrelated to receipt of healthcare at phus. as a result, social mobilization and community engagement efforts were adapted to stress the importance of returning phus for primary healthcare needs. the phu evd surveillance system was used to implement several small, but effective changes to the recording of patient symptoms, follow-up of malaria confirmed patients, and contributed to an increase in communityreported live and death alerts in the district (fig. 2). conclusions phu attendance was gauged as an indicator of baseline disease in order to investigate the decrease in live alerts in the evd reporting system. low reporting could be correlated to low phu attendance and indicated a need for social mobilization and community engagement. keywords ebola virus disease surveillance; peripheral health unit; sierra leone; malaria acknowledgments goal would like to acknowledge the contributions of the port loko district health management team, district ebola response center, who, and cdc. *alyssa j. young e-mail: ayoung@sl.goal.ie online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e177, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, 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caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd 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gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts automated classification of alcohol use by text mining of electronic medical records lisa lix*1, 2, sree nihit munakala3 and alexander singer1 1community health sciences, university of manitoba, winnipeg, mb, canada; 2george and fay yee centre for healthcare innovation, winnipeg, mb, canada; 3birla institute of technology and science, pilani hyderabad campus, hyderabad, india objective the research objective was to develop and validate an automated system to extract and classify patient alcohol use based on unstructured (i.e., free) text in primary care electronic medical records (emrs). introduction emrs are a potentially valuable source of information about a patient’s history of health risk behaviors, such as excessive alcohol consumption or smoking. this information is often found in the unstructured (i.e., free) text of physician notes. it may be difficult to classify and analyze health risk behaviors because there are no standardized formats for this type of information1. as well, the completeness of the data may vary across clinics and physicians. the application of automated classification tools for this type of information could be useful for describing patterns within the population and developing disease risk prediction models. natural language processing (nlp) tools are currently used to process emr free text in an automated and systematic way. however, these tools have primarily been applied to classify information about the presence or absence of disease diagnoses2. the application of nlp tools to health risk behaviors, particularly alcohol use information from primary care emrs, has thus far received limited attention. methods study data were from the manitoba regional network of the canadian primary care sentinel surveillance network (cpcssn) for the period from 1998 to 2016. cpcssn is a national primary care surveillance network for chronic diseases comprised of 11 regional networks with publicly funded healthcare systems. currently, a total of 53 clinics and more than 260 physicians provide data to cpcssn in manitoba. we classified each record based on unstructured text from physician notes into the following mutually exclusive categories: current drinker, not a current drinker, and unknown1. a standardized de-identification process was applied to each record prior to applying an nlp tool to the data. text classification used a support vector machine (svm) applied to both unigrams (i.e., single words) and mixed grams (i.e., unigrams, and pairs of words known as bigrams) from a bag-of-words model in which each record is quantified by the relative frequency of occurrence of each word in the record3. the training dataset for the svm was comprised of 2000 records classified by two primary care physicians. these physicians were initially trained using an independent sample of 200 emr text strings containing specific reference to alcohol use. cohen’s kappa statistic, a chance-adjusted measure, was used to estimate agreement. internal validation of the svm was conducted using 10-fold cross-validation techniques. model performance was assessed using recall and precision statistics, as well as the f-measure statistic, which is a function of their average. all analyses were conducted using the r open-source software package. results a total of 57,663 unique records were included in the study. the estimate of the kappa statistic for the physician training phase was 0.98, indicating excellent agreement. subsequent classification of the training dataset by the physicians resulted in 1.7% of records assigned as not a current drinker, 16.8% as current drinker, and 81.5% as unknown. average estimates of recall for the 10 validation folds using unigrams were 0.62 for not current drinkers, 0.86 for current drinkers, and 0.98 for the unknown category. average estimates of recall using mixed grams were 0.48, 0.84, and 0.97, respectively. estimates of precision were higher with mixed grams than unigrams for the not currently drinking category, but the opposite was true for the current drinker category. there was no difference in precision between the two methods for the unknown category. the f-measure statistic was higher for classification of current drinkers using unigrams (0.89) than mixed grams (0.83), although the differences for the unknown category were negligible (0.98 versus 0.97). application of the svm with unigrams to the entire dataset resulted in 15.3% of records classified as current drinkers, 2.0% classified as not current drinkers, and 82.7% as unknown. conclusions this study developed an automated system to classify unstructured text about alcohol consumption into mutually-exclusive alcohol use categories. however, we found that only a small percentage of primary care records contained documentation about alcohol consumption, which limits the utility of the automated tool and the data source for disease risk prediction or alcohol use prevalence estimation1. while our automated approach is useful for processing existing emr data, systematic documentation of alcohol consumption will benefit from standardized entry fields and terms to produce clinically meaningful information that will improve the understanding of health risk behaviors in primary care populations. keywords automated analytics; unstructured text; support vector machine; health risk behaviors; primary care surveillance acknowledgments we acknowledge the mitacs globalink program for providing project funding support for snm and the manitoba regional network of the canadian primary care sentinel surveillance network for providing data access. references 1. torti j, duerksen k, forst b, salvalaggio g, jackson d, manca d. documenting alcohol use in primary care in alberta. can fam physician 2013 oct;59(10):1128. 2. wang y, chen es, pakhomov s, arsoniadis e, carter ew, lindemann e, sarkar in, melton gb. automated extraction of substance use information from clinical texts. amia annu symp proc. 2015 2015:2121–2130. 3. figueroa rl, flores ca. extracting information from electronic medical records to identify obesity status of a patient based on comorbidities and bodyweight measures j med syst. 2016 aug;40(8):191. *lisa lix e-mail: lisa.lix@umanitoba.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e69, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts infectious disease reporting and outbreak management improvement project donald e. brannen*, melissa branum and amy schmitt administration, greene county public health, xenia, oh, usa objective improve disease reporting and outbreak mangement. introduction specific communicable diseases have to be reported by law within a specific time period. in ohio, prior to 2001, most of these disease reports were on paper reports that were reported from providers to local health departments. in turn the communicable disease nurse mailed the hardcopies to the ohio department of health (odh). in 2001 the ohio disease reporting system (odrs) was rolled out to all local public health agencies in ohio.1 odrs is ohio’s portion of the national electronic disease surveillance system. odrs should not be confused with syndromic surveillance systems that are for detecting a disease outbreak before the disease itself is detected.2 chronic disease surveillance system data has been evaluated for long term trends and potential enhancements.3 however, the use of communicable disease reports vary greatly.4 however, the export data has not routinely been used for quality improvement purposes of the disease reporting process itself. in december 2014, greene county public health (gcph) begain a project to improve reporting of communicable diseases and the response to disease outbreaks. methods initial efforts were to understand the current disease reporting process: quantitative management techniques including creating a logic model and process map of the existing process, brainstorming and ranking of issues. the diseases selected to study included: campylobacteriosis, cryptosporidiosis, e. coli o157:h7 & shiga toxin-producing e. coli, giardiasis, influenza-associated hospitalization, legionnaires’ disease, pertussis, salmonellosis, and shigellosis. the next steps included creating a data collection and analysis plan. an updated process map was created and the preand post-process maps were compared to identify areas to improve. the median number of days were compared before and after improvements were implemented. modeling of the impact of the process improvements on the median number of days reported was conducted. estimation of the impact in healthy number of days derived from the reduction in days to report (if any) were calculated. results process improvements identified: ensure all disease reporters use digital reporting methods preferably starting with electronic laboratory reporting directly to the online disease reporting system, with other methods such as direct web data entry into system, faxing lab reports, or secure emailing reports, with no or little hard copy mailing; centralize incoming email and fax reports (eliminating process steps); standardize backup staffing procedures for disease reporting staff; formalize incident command procedures under the authorized person in charge for every incident rather than distribute command between environmental and clinical services; and place communicable disease reporting under that single authority rather than clinical services. the days to report diseases were reduced from a median of 2 to .5 days (p<.001). all the diseases were improved except for crytosporodium due to an outlier report two months late. the estimated societal healthy days saved were valued at $52,779 in the first eight months after implementation of the improvements. conclusions improvements in disease reporting decreased the reporting time from over 2 days to less than 1 day on average. estimated societal healthy days saved by this project during the first 9 months was $52,779. management of early command and control for outbreak response was improved. keywords surveillance; communicable; quality improvement acknowledgments matched funding through ohio development services agency grant control no. sbig20150570 references 1. brannen de. ohio disease reporting system rollout. orientation to individual health departments throughout southwest ohio to the web based ohio disease reporting system. june 1, 2001 through january 15th, 2002. 2. ottaway, m., brannen, d. e., yund. c. b. (2002 sept 23-24). practical evaluation of electronic disease surveillance systems for local public health. national syndromic surveillance conference, new york, new york. new york academy of medicine, nyc department of health and mental hygiene and the centers for disease control & prevention. 3 brannen, d. e. (2013). development of a public health surveillance method to prevent melanoma morbidity. dissertation, 1-237. proquest publication number 3591988. 4. brannen, d. e., schmitt, a., & mcdonnell, m. (2009 apr 18). use of ohio disease reporting system export data by local public health epidemiologists in ohio. ohio academy of science annual meeting wittenberg university. abstract and podium presentation. *donald e. brannen e-mail: dbrannen@gcph.info online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e108, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts spatio-temporal cluster detection for legionellosis using multiple patient addresses eric r. peterson* and sharon k. greene bureau of communicable disease, new york city department of health and mental hygiene, queens, ny, usa objective to improve timeliness and sensitivity of legionellosis cluster detection in new york city (nyc) by using all addresses available for each patient in one analysis. introduction the bureau of communicable disease (bcd) at the nyc department of health and mental hygiene performs daily automated analyses using satscan to detect spatio-temporal clusters for 37 reportable diseases.1 initially, we analyzed one address per patient, prioritizing home address if available. on september 25, 2015, a bcd investigator noticed two legionellosis cases with similar work addresses. a third case was identified in a nearby residential facility, and an investigation was initiated to identify a common exposure source. four days later, after additional cases living nearby were reported, the satscan analysis detected a corresponding cluster. in response to this signaling delay, we implemented a multiple address (ma) analysis to improve upon single address (sa) analyses by using all location data available on possible exposure sites.2 methods positive legionella test results for nyc residents are reported to bcd with patient demographic and address data. bcd interviews all cases to elicit additional locations of potential exposure and enters the addresses into a disease surveillance database (maven). addresses are assigned x/y coordinates in near real-time via integration with a geocoding webservice. we used the prospective space-time permutation scan statistic in satscan,3 enabling the advanced input feature on the spatial neighbors tab to “include location id in the scanning window if at least one set of coordinates is included.” this option considered a case as included in a given cluster if any of the case’s addresses were within the cluster. the case file included: unique case id (as the location id), number of cases, onset date, and day of week. the coordinate file included: case id and x/y coordinates for each address per case, resulting in one or more rows per case. we searched for alive clusters with a temporal range of 2 to 30 days and a maximum spatial size of 50% of observed cases. the study period was 1 year. monte carlo simulations (n=999) were used to determine statistical significance. we mimicked prospective surveillance to determine when the september 2015 cluster would have been detected had this analysis been in place, by performing daily sa and ma analyses from september 21 (when the first outbreak-linked case was reported) to september 29 (when the initial satscan analysis signaled). any cluster with a recurrence interval (ri) ≥100 days was summarized in a map and linelist. prospective, automated analyses were launched in april 2016 and run daily using microsoft task scheduler, sas 9.4, and satscan 9.4.1. signals through july 2016 were summarized. results in mimicked prospective analysis, the sa and ma satscan analyses identified clusters of 13 and 11 cases, respectively, starting september 27, 2015. the ma cluster was more spatially focused (2.11 km vs. 5.42 km) and more unlikely to occur by chance alone (ri of 16,256 days vs. 8,758 days). in prospective analyses, a ma cluster of 6 cases was identified on july 5, 2016 with a radius of 1.69 km (ri=100 days). on july 6, the ma cluster case count increased to 7 and maintained the same radius (ri=685 days), while a cluster of the same 7 cases was identified by the sa analysis with a larger radius (1.97 km) and lower ri (292 days). the ri for both clusters peaked on july 7 (ma: 2348 days, sa: 713 days). conclusions in preliminary evaluation, the ma analysis facilitated cluster detection using non-residential possible exposure sites, such as workplaces. timeliness was slightly improved, but the larger practical benefit was identifying more spatially focused clusters. smaller clusters are useful for more precisely targeting legionellosis infection source identification and remediation activities, especially in urban environments with high population and building densities. keywords legionella; cluster detection; satscan acknowledgments martin kulldorff suggested using multiple coordinates per location in satscan. references 1. greene sk, et al. daily reportable disease spatiotemporal cluster detection, new york city, new york, usa, 2014–2015. emerg infect dis. 2016;22(10). 2. bull m, et al. the application of geographic information systems and spatial data during legionnaires’ disease outbreak responses. euro surveillance. 2012;17(49). 3. kulldorff m, et al. a space-time permutation scan statistic for disease outbreak detection. plos medicine. 2005;2(3):e59. *eric r. peterson e-mail: epeterson@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e121, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 1epidemiologic surveillance department, french armed forces center for epidemiology and public health, marseille, france; 2french national reference center for malaria, paris, france; 3department of clinical epidemiology, european hospital georges pompidou, paris, france; 4french military health insurance, toulon, france objective estimate the accuracy of the french ational ealth nsurance nformation ystem (sniiram) as a support for a nationwide malaria surveillance. introduction the estimated incidence of imported malaria in france is about 4,000 cases per year (1). the epidemiological surveillance o0 f malaria in france is mainly based on a hospital laboratory surveillance network, which captures around 50% of cases. there is no comprehensive population surveillance. the sniiram provides data about hospital stays and outpatient drug reimbursements, procedures, examinations and sickness leaves for almost the whole french population(2). we aimed to evaluate the usefulness of the sniiram for implementing epidemiological surveillance of malaria. methods a case-identification algorithm was built in a two-steps process. firstly, an expert panel defined sensitive inclusion criteria, built using hospital discharge diagnoses, emergency department admissions, outpatient biological procedures and drug deliveries. in case of malaria-nonspecific care events, we considered sequences of events. secondly, we described data to identify clusters of cases and outliers. based on this description, we defined exclusion rules, aiming to reduce the number of false-positive and to increase specificity. results were validated comparing some characteristics of our data with those from the french national reference center for malaria (nrc). results the algorithm we built was reusable and automated. from july 1, 2013 to june 30, 2014, we identified 4,248 malaria cases corresponding to 4,099 distinct patients (figure 1). the sniiram allowed us to collect data about the demographic characteristics of cases, the date and place of cares, the duration of hospital stays, the diagnoses, the medical procedures and drug deliveries of outpatients. hospitalization and outpatient drug deliveries data allowed to capture more than 95% of the cases (table 1). time-lapse from initial cares to data availability into the sniiram was up to six months. our results appeared close to those from nrc: no statistically significant difference was observed in the distribution of age, gender, localization and date at onset. conclusions we elaborated an accurate algorithm to implement an epidemiological surveillance of malaria based on the french national health information system. it allowed to study the population living in france as a whole, including sub-populations not accurately covered by existing surveillance methods. the long latency of the sniiram data availability does not permit early alert. our approach should be thus considered as an addition to the traditional epidemiological surveillance, though a formal validation framework for case-identification algorithms is still necessary. table 1: distribution of care events among identified malaria cases (n=4,248) n: number of cases figure 1: flowchart illustrating malaria cases identification keywords malaria; surveillance; database; health insurance acknowledgments this study was supported by the french military health insurance. references 1. rapport annuel d’activité 2013 [internet]. centre national de référence du paludisme; 2014. available from: http://www.cnrpalu-france.org/ docs/rapport_activites_cnr_paludisme_2013.pdf 2. tuppin p, de roquefeuil l, weill a, ricordeau p, merlière y. french national health insurance information system and the permanent beneficiaries sample. rev epidemiol sante publique. 2010 aug;58(4):286–90. *francois delon e-mail: francois.delon@laposte.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e16, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh al-sahan a msc (hi)1, saddik b phd, mph1. 1. college of public health and health informatics, king saud bin abdulaziz university for health sciences (ksau-hs) abstract background: the personal health record (phr) is an electronic record that allows patients to maintain, manage and access their health information in one secure location. however, despite these potential capabilities, the adoption rate of the phr has been slow due to various challenges. objectives: this study, being the first of its kind in saudi arabia, investigates the perceived barriers and /or challenges for phr adoption in the ministry of national guard health affairs (mngha). the study explored perceived barriers and /or challenges from two different perspectives; a technical perspective and a social perspective. methods: the study was conducted using a mixed methods approach. a cross-sectional study design using a questionnaire was used to measure patients’ perceptions of the phr and a qualitative approach through focus groups was used to capture comments and opinions from technical personnel for perceived technical barriers to phr adoption. result: results from 424 patients revealed a positive perception for phr adoption with almost all of the participants (96.7%) indicating interest in using the phr and the majority (73.3%) expressing no confidentiality concerns for the online accessibility of their health information. patients with higher levels of education indicated higher interest in using the phr and expressed more concern with confidentiality than patients with lower levels of education. however, the majority of patients (78.3%) expressed their lack of awareness of existing patient e-services on the mngha website. the themes that emerged from the focus groups reinforced lack of awareness of e-services as a potential barrier for phr adoption as well as the role of policy in the regulation and business process for phr adoption. conclusion: this study has highlighted the perceived challenges and barriers for adoption of the phr in mngha-riyadh. in order to ensure an efficient phr with a strong adoption rate, effective steps need to be undertaken by building phr awareness as well as setting clear guidelines and regulations from policy makers. keywords: phr adoption, challenges affecting phr adaption, phr challenges, phr barriers, personal health record, phr correspondence: basema.saddik@gmail.com, aljohara_jes@hotmail.com doi: 10.5210/ojphi.v8i3.6845 http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the personal health record (phr) has been used by patients as a tool for continuity of healthcare for decades. in the past, the phr was viewed as a paper-based form retained by patients in the form of laboratory results, prescription notes and other forms of health related documents [1-5]. nowadays with the rapid growth of technology within the healthcare sector, the old concept of the phr has been replaced with an electronic phr. according to markle foundation, the phr has been defined as, “an electronic application through which individuals can access, manage and share their health information, and that of others for whom they are authorized, in a private, secure, and confidential environment” [6-9]. there are different types of phrs: the standalone or the thethered. the standalone phr is through commercial online websites that allow patients to record their health behaviors. on the other hand, the tethered phr is usually linked to a specific organization through its electronic medical record (emr) to ensure patient’s access (as read only) to their health information [2-6, 10-12]. in theory, the phr has the capabilities that allow patients to keep track of their health information, monitor and manage their illness especially in cases of chronic diseases, enhance efficiencies in appointment scheduling, medication refills, and improve the communication with healthcare providers for better quality of care [1-6, 9-13]. however, despite the reported potential capabilities of the phr, the adoption rate has been low [4,8-14] due to a number of reported challenges and barriers such as interoperability, policy, confidentiality, privacy concerns, security issues, lack of awareness of services, inadequate internet access, computer literacy, computer anxiety and health literacy [3, 5, 7-10, 12-18]. policy also plays an important role in the establishment of the phr in terms of business processes, regulations and standards which have also been reported to impact on the adoption rates of the phr [8,9]. in the united states three case studies identified policy barriers in terms of what information the phr should include and under which circumstances the information within the phr should be released and shared [15]. halamka et.al, reports that based on state laws certain restrictions were exercised for releasing certain results to the patients such as in the case of human immunodeficiency virus (hiv) [15]. interoperability has also been reported as a barrier to the effective adoption of the phr for the exchange of health information among multiple healthcare systems [3, 5, 7-9, 13, 15]. moreover, the electronic exchange of health information indicates major concerns regarding the issue of protecting patients’ confidentiality, privacy and phr security during the online transmission of health information [3,5,8,9,12-15,17,18]. according to a us national survey around two-thirds of the participants had concerns about the privacy and the security of their health information [3, 17]. another barrier to adoption of the phr which has been reported in the literature is the lack of awareness of the phr. a study in south africa reported that almost all of the citizens were unaware of the existence of the phr or what it could be used for [13]. educating citizens on the benefits of the phr and promoting it as an effective tool for healthcare, has been found to reduce resistance to change [14] as well as address concerns associated with using computers. elderly and computer http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 illiterate patients are less keen on adopting the phr due to computer anxiety and health literacy concerns [13, 16] and healthcare providers should be able to address these concerns early on in order to effectively manage and enhance the adoption of the phr [16]. although the challenges and barriers to phr adoption have been highlighted in the literature, it is still not clear what these challenges are within the saudi society. the ministry of national guard health affairs (mngha) in saudi arabia is in the implementation phase of the phr with the collaboration of the national e-government initiative. although the phr is expected to enhance patient care, its full implementation is also expected to face various challenges as per previously reported experiences. this study aims to investigate the perceived barriers for phr adoption which may also be challenges for implementing an efficient phr in mngha, as well as measure the extent of patients’ interests towards phr adoption according to their health status, and identify patients’ needs in terms of data and services needed to be available in the phr. methodology study design in order to ensure comprehensiveness, this study used a mixed methods approach to investigate the perceived barriers and challenges to phr adoption. a descriptive quantitative approach was used to measure how patients perceived the phr from a social perspective and a qualitative approach was used to capture comments and opinions from technical personnel for perceived technical barriers to phr adoption. study setting the study was conducted at the ministry of national guard health affairs (mngha) king abdulaziz medical city (kamc). mngha consists of different medical cities comprising of four hospitals and sixty primary and secondary health centers scattered in five regions around the kingdom of saudi arabia (riyadh, jeddah, medina, dammam and al-ahsa)[19]. the primary and the largest medical city is king abdul-aziz medical city (kamc) located in riyadh. this study was conducted at the ambulatory care center (acc) of kamc at king fahd hospital. the acc is one of the central centers in the hospital including pharmacy, employee clinic, biobank laboratory and other specialized clinics such as pediatric, neurology, oncology, hematology and other clinics. selection and description of participants in order to clearly understand the perceived challenges for phr adoption from different perspectives, two groups were selected to participate in the study; a patient group and a technical group. convenience sampling was used for both groups who met the inclusion criteria. for the patient group the inclusion criteria included all patients who attended the mngha acc between the ages of 20-60. this age group was selected because individuals less than 20 are usually under the guardianship of the head of the family whose contact details are used for registration and hence do not have direct access to their medical records. furthermore, it is believed that patients over the age of 60 also have their primary carers’ contact numbers registered and their records would usually be accessed by the carer and not the patient themselves. this age group was also selected based on the criteria highlighted in the study http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 conducted by (hamlaka et al.,) [15] which defined record access into three categories; 1) full access to the parents for those patients who are less than 12 years old. 2) balanced access between the patient from age 12 to 18 and his/her parents but some of the information may be restricted to one of them. 3) full access and control of the record for those who are 18 years old and older [8]. for the technical group, the inclusion criteria for technical staff included staff who were working in the information systems & informatics divisions (isid) and were involved in the development and implementation of the phr project at mngha comprising of managers and a representative from the national e-government initiative who came from different regions within mngha ; riyadh, al-hasa and jeddah. ethical considerations the research study protocol was approved by king abdullah international research center (kaimrc) through the international review board (irb) protocol number (sp14/049). all participants in the research study were provided with information prior to participation and informed consent was obtained. no participant was identified and confidentiality was ensured for all participants. all data were reported in aggregate form and no participant was identified in any way. data collection there were two data collection methods used in this study, a questionnaire and two focus groups. the questionnaire was developed to answer the research question and address the objectives of the study. it comprised of 19 questions in total which covered demographic information, health status, concerns about data confidentiality, computer literacy or internet access and seven specific questions on patients’ perceptions of the phr which were borrowed from another validated survey [20]. the questionnaire was translated to arabic and back translated to english to ensure reliability of translation. furthermore, to test the validity and reliability of the questions and to ensure that the objectives of the study were met, the questionnaire was piloted among 31 participants who were patients in mngha and were eligible to participate according to the above mentioned inclusion criteria. based on the pilot testing, the questionnaire was modified to improve clarity. data collection began in mid-september 2014 until the end of october by visiting the acc three days a week in the afternoon excluding the national day and hajj holiday. data were collected in person by inviting patients in the waiting area at the employees’ clinic and other clinics within the acc. the questionnaire along with the consent form were distributed with a brief explanation about the purpose of the questionnaire and clarification on confidentiality concerns. however, in the case of elderly or illiterate patients who could not complete the survey themselves, the data were collected either through their carers or by asking them directly in a secluded area to ensure their confidentiality. as for the focus groups, these were conducted to illicit participants’ opinions on the perceived challenges for implementing the phr from a technical perspective including technical issues, policies, guidelines and/or other regulations. two focus groups were conducted by the researcher in the presence of a moderator and lasted for one hour per group; the first focus group included four participants and was held in the middle of november 2014 in an office at the convention center in king saud bin abdul-aziz university for health sciences in riyadh. the second focus group contained three participants and was held beginning of december 2014 at one of the http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 participant’s office in riyadh. all participants provided their informed consent and permission for an audio recording of the session. the discussions within both focus groups were guided by a list of open-ended questions which addressed the specific themes of the research objectives. in addition to the focus groups an individual in-depth interview was conducted by the researcher with a member of the technical expert team to confirm a certain point raised in the focus group about integration. sample size the sample size for the patient group was calculated based on the average number of patients who visited the acc in 2013 which was approximately 70644 per month. therefore assuming p <0.05 as maximum variability with 95% confidence interval and 5% precision, the sample size required was calculated at 385 in total 454 patients were included in the study, however, 30 patient responses were excluded due to incomplete responses, inconsistent answers or were not eligible based on the inclusion criteria set for the study. as for the focus groups with technical staff, it was expected that 6-10 participants would be included in each focus group. however due to logistical issues, time constraints and the inability to agree on mutually convenient times for all the invited focus group members, only 7 members of the technical team were able to participate in the focus groups and were divided into two focus group sessions. data analysis data from the 19 questions were coded, entered and analyzed using spss software v.20.0. descriptive data were analyzed using frequencies and percentages and chi-square analysis was used to measure associations and correlations between variables of interest. level of significance was determined by a p value equal to or less than 0.05 (p≤0.05). as for the qualitative data collected from the focus groups, each focus group session was recorded and transcribed verbatim and thematic analysis was done by identifying, coding and reporting recurring themes within the data according to the categories that were highlighted as being specific challenges or barriers for phr adoption. results questionnaire findings demographic information and perceived health status the sample mostly comprised of females and those of arabic origin. the majority perceived themselves to be healthy, where only few reported suffering from a chronic disease. very few reported attaining a low educational level (illiterate, literate and completing elementary school). the age distribution among the study sample was somewhat similar, whereby individuals were predominantly between 20 and 39 years of age (table 1). http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 table 1 – demographic and perceived health status on study participants (n = 424) demographic and perceived health status n (%) nationality arabian 366 (86.3%) non-arabian 58 (13.7%) gender male 135 (31.8%) female 289 (68.2%) age (years) 20-29 140 (33.0%) 30-39 132 (31.1%) 40-49 81 (19.1%) 50-60 71 (16.7%) educational level illiterate 22 (5.2%) literate 11 (2.6%) elementary 9 (2.1%) intermediate 31 (7.3%) high school 81(19.1%) diploma 4 (0.9%) university 205 (48.3%) higher education 61 (14.4%) perceived heath status healthy 289 (68.2%) not healthy with chronic disease 84 (19.8%) not healthy with other medical condition 51 (12.0%) technology use the majority of participants reported using smart devices, using computers and browsing the internet, however only 16% accessed e-services from the mngha website (table 2). the leading and most common reason reported from patients who reported using the internet but not accessing patients’ e-services from the mngha website was their lack of awareness of the http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 services provided (78.3%). moreover, (20.1%) reported not knowing how to access these services and only (11.0%) claimed not to be interested. table 2technology use among study participants (n = 424) technology use n (%) use of internet * yes 377 (95.9%) no 16 (4.1%) use of smart devices yes 391 (92.2) use of computer yes 342 (80.7) accessing patients’ e-services at mngha website ** yes 60 (15.9%) no 317 (84.1%) * use of internet was computed among those reported using computers or using smart devices. ** accessing patients’ services at mngha website was computed among those answering “yes” to use of internet. perceptions about the phr few of the participants reported having copies of their health information (e.g., laboratory results, radiology results or others) and almost all of the participants reported an interest in having an online phr. confidentiality concerns were not perceived a threat for the majority of the participants (table 3). only a minority (26.7%) of participants reported they had confidentiality concerns about the online accessibility of their health information. such concerns attributed to confidentiality and privacy (52.2%) in addition to security concerns (21.2%). open ended responses for these questions retrieved responses such as “belief in patient privacy” or “my information should be accessible to me only and should be confidential” as well as, “it may be hacked” or “someone may play with the access” amongst the participants who reported interest in having an online phr, the main information they felt necessary to be accessed were laboratory results, radiology results, medication lists or other data such as diagnosis, medical history, vital signs or medical reports (91.7%, 82.9%, 72.4% and 6.1% respectively). in addition, in terms of what services participants felt they would like to be available online, the main services reported were appointment scheduling (90.5%), communication with the physician (74.1%) while only (0.5%) reported other services such as determining the physician or awareness about the disease diagnosed. (results not presented in table) http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 table 3 perceptions about phr on study participants (n = 424) participants’ perceptions about phr n (%) having copies of their health information yes 83 (19.0%) no 341 (80.4%) interest in online phr very interested 257 (60. 6%) interested 107 (25.2%) somewhat interested 46 (10.8%) not interested 14 (3.3%) confidentiality’s concerns yes 113 (26.7%) no 311 (73.3%) further analysis using a chi-square test with p value set at 0.05 was conducted to determine the significant differences between participants’ demographics information (e.g., age, gender, educational level,...etc.) and their perceptions on phr confidentiality. a significant difference was found between confidentiality concern, participants’ nationality, educational level, and their health status (table 4). patients who were of arabic origin were more likely to have confidentiality concerns than patients who were non arabs. furthermore, patients who had higher education and those who perceived themselves as healthy had significantly higher concerns with confidentiality than patients who had lower levels of education and perceived themselves as not healthy. in terms of participant’s interest in using the phr, significant differences were found between phr interest and educational level p≤0.001 (table 5). patients with higher education were significantly more interested in using the phr than those with lower education levels. table 4participants’ demographic information and perceived health status with their perceptions about phr confidentiality n (%) p-value concerns about confidentiality 113 (26.7) nationality arabian 76(67.2) <0.001 non-arabian 37(32.7) educational level illiterate 2(1.7) 0.003 http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 literate 1(0.88) elementary 1(0.88) intermediate 3(2.6) high school 17(15) diploma 1(0.88) university 62(54.8) higher education 26(23) perceived health status healthy 90(79.6) 0.009 not healthy with chronic disease 15 (13.3) not health with other medical condition 8 (7) table 5participants’ educational level and phr interest interest in phr n (%) pvalue very interested interested somewhat interested not interested total 257(60.6) 107(25.2 46(10.8) 14(3.3) educational level illiterate 8(36.4) 6(27.3) 5(22.7) 3(13.6) 0.001 literate 6(54.5) 5(45.5) 0(0.0) 0(0.0) elementary 3(33.3) 3(33.3) 0(0.0) 3(33.3) intermediate 17(54.8) 11(35.5) 2(6.5) 1(3.2) high school 46(56.8) 20(24.7) 12(14.8) 3(3.7) diploma 1(25.0) 2(50.0) 1(25.0) 0(0.0) university 135(65.9) 45(22.0) 22(10.7) 3(1.5) higher education 41(67.2) 15(24.6) 4(6.6) 1(1.6) focus group findings discussion within the focus groups revolved around predefined questions used as a guide for discussion which continued until saturation was reached. all participants in both groups http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 highlighted the importance of using the phr as they considered it as a patient’ rights. additionally, when asked about the challenges in implementing phr within mngha, the first group viewed the challenges to be related to lack of patients’ awareness while participants in the other group did not view it as impossible indicating that “almost everyone is now using smart devices, even the elderly patients”. despite the challenges claimed, mngha is still in the initiation phase, it was highlighted that policy plays an important role when it comes to phr content, particularly in terms of data and services. it is still not clear what type of data will be included in the phr and what the regulations are for releasing particular results, as was mentioned “phr content needs a task force that is well collaborated between policy makers”. moreover, it was reported that fully automated services depend on a clear workflow and on the currently used system which is acting as a limitation. when asked about the technical challenges (e.g., security, systems integration) all participants considered that the network within mngha to be secure. moreover, participants added that if patients had access to their health information, the access will not be directly linked to the actual data center. in addition, there will be a multilayer authentication access similar to that used in bank transactions. despite having a high secure authentication access, participants agreed that the actual process during registration does not guarantee confidentiality; one participant stated, “when we talk about the security, the technology is there but this also depends on the business process agreed by the decision makers which can be considered as a challenge”. regarding integration, participants agreed that there are no major challenges within mngha. however, participants felt that format of data played a role in integration. one participant stated, “it depends on the data availability in the systems, whether it is in a paper record or not structured (e.g., physician report) where it acts as a challenge to extract all needed data and change their format”. finally, when asked about the potential challenges that may emerge after the implementation of the phr the participants within the second focus group indicated “the resistance to change from the end users whether healthcare providers or patients could hinder the adoption of the phr, for example any test result should be first approved by the physician before being submitted in the phr. such a process may be delayed, so this may be considered a reason for healthcare providers’ resistance ….. that resistance usually has been faced regardless of the type of the system”. discussion this study, being the first of its kind in saudi arabia, highlights the perceived barriers and /or challenges for phr adoption in mngha. from a social perspective, this study was not different to other studies and found that almost all participants browsed the internet whether they used computers or smart devices. this indicates a potential strength for phr adoption in mngha among frequent users of the internet and has been previously reported as a positive indicator for potential phr adoption [12]. as for the usage of smart devices, our study also found that the majority of participants reported regular use of smart devices which has also been reported as a positive indicator for participants’ willingness towards using mobile phr (mphr) [14]. however, despite the use of internet and smart devices in our study population, the majority of participants did not use mngha patient e-services with the majority reporting that they were not aware such services existed. this lack of awareness impacts negatively on phr adoption and is potentially a significant barrier to http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 adoption as highlighted in previous studies [14] and should be an area of concern and action once implementation of phr is complete at mngha. furthermore, the majority of patients reported not having copies of their health information also indicating difficulty or a lack of awareness for their ability to access their own health information. despite the lack of awareness about patients’ e-services, what is surprising in our study findings is that almost all of the participants regardless of health status reported an interest in having a phr which contradicts findings from other studies which found that patients with chronic conditions tend to be more interested in having a phr than others [8, 14]. even though our study population comprised mainly of young females, an interest in adopting a phr is considered a strength for eventual phr adoption especially with the difficulty of obtaining health information. however, our findings for phr interest overall are consistent with other studies [5, 12, 17] regardless of such studies reporting that despite the major interest in the phr the adoption rate remained slow. the features of a phr and the services accessed should be tailored to specific populations as these may differ among different patient and population groups. patients in the saudi context reported higher interest in services such as viewing test results and appointment scheduling in contrast to patients in the united states where appointment scheduling was not perceived as of much importance among patient groups [12, 17]. in the saudi context, accessing test results and appointment scheduling online would alleviate some of the difficulties patients face with transportation in saudi arabia especially for females who represented the majority of our study population. regarding the participants’ concerns about online accessibility and how this would impact on their privacy, confidentiality and security of their information, several studies in different countries like the united states [5, 8, 12, 13, 15, 17]; south africa [14] and canada [3,18], showed it to be a major challenge for phr adoption. however, this was not the case in our study, where the majority of participants (73.3%) showed no concerns. this could be explained by the culture of saudi society particularly when it comes to dealing with health information and family involvement. family is an important resource in patient care in saudi arabia and it may be very normal to share and view patient information with very limited concerns for privacy and confidentiality standards [21, 22]. from a technical perspective, our study showed that policy played an important role as a regulation or business process in the establishment of the phr. thus, it could be considered as a barrier that could slow the adoption of phr. this finding is supported by other studies conducted in the united states [8-10, 13, 15]. moreover, it was shown that integration at certain circumstances could present some challenges consistent with the literature [3, 5, 7-9, 13, 15] in terms of integrating phr with multiple healthcare systems indicating interoperability as a critical challenge as well as healthcare providers’ readiness to adopt the phr [3, 5] which was also a concern highlighted by the focus group participants. healthcare professionals’ resistance to change was also perceived to hinder phr adoption and it is encouraged that in order for the uptake of the phr to be successful among patients, there needs to be enthusiasm from staff, healthcare professionals and patients [23]. therefore we recommend that healthcare providers, policy makers, it professionals and patients work together during the implementation of the phr to ensure effective uptake and adoption of the phr. http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 limitations this study had several limitations including the cross-sectional design of the study and the sampling technique used. the study was conducted in mngha-riyadh, and therefore inferences and generalizability to the whole population of mngha is limited. given that data collection was done over a one month period, it did not capture a diverse population in terms of gender, age, educational level, health status, and technology usability. almost all the participants were educated, healthy and were utilizing technology. the majority of our population were young females, however statistical data retrieved from mngha data centers [24] showed that the majority of patients who visited the acc in 2014 were females and represented almost 60% of patients, therefore indicating that our study results are a true representation of the mngha acc patients in 2014. furthermore, focus group participants came from different regions within mngha and their views represent challenges all over the mngha. focus group data were collected until saturation was achieved; hence ensuring good representation of participant’s views. the self-reported health status is also considered a limitation of this study. further research should assess health status of patients through their records rather than self-report to fully estimate the burden of disease in this population and assess the use of the phr in chronic disease patients. however, despite the limitations of this study, the results present new knowledge on the adoption of phr in a different setting and the perceived challenges and barriers in saudi arabia which will help inform further in-depth studies in this area. further research is required from the social perspective by including a diverse population in terms of their demographic information and other information from the different regions within mngha to attain generalizability. also future research is recommended to evaluate phr usage and its effectiveness with more emphasis on its impact for managing chronic diseases. conclusion this study has discussed the perceived challenges and barriers for implementation and adoption of the phr at mngha. even though participants indicated an interest in having a phr and had no concerns about online accessibility of their health information, there remained an overall concern amongst participants on perceived challenges/barriers such as patient awareness, policy barriers in terms of phr content, other regulations as a part of the existing business process, and the integration at certain circumstances. moreover, end user readiness to have a phr was considered as a potential challenge after its implementation which could hinder its adoption. therefore it is absolutely crucial in building an efficient phr addressing end user concerns to ensure a strong adoption rate. furthermore, effective methods should be utilized in building phr awareness, setting clear guidelines and regulations for use and involving policy makers prior to implementing the phr acknowledgements this study supported by king abdullah medical research center (kaimrc) for research grant (sp14/049). http://ojphi.org/ ojphi perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 13 online journal of public health informatics * issn 1947-2579 * 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personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e205, 2016 [24] research data management – king abdullah international medical research center (mngha). data retrieved 26th october 2016. http://ojphi.org/ perceived challenges for adopting the personal health record (phr) at ministry of national guard health affairs (mngha) – riyadh introduction methodology study design study setting selection and description of participants ethical considerations data collection sample size data analysis results questionnaire findings demographic information and perceived health status technology use perceptions about the phr focus group findings discussion limitations conclusion acknowledgements conflict of interest references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts civilian-military collaboration: department of defense data in the biosense platform jessica f. deerin*, jean-paul chretien and paul e. lewis armed forces health surveillance branch, arlington, va, usa objective the department of defense data is available to national syndromic surveillance program (nssp) users to conduct syndromic surveillance. this report summarizes the demographic characteristics of dod health encounter visits. introduction the dod provides daily outpatient and emergency room data feeds to the biosense platform within nssp, maintained by the centers for disease control and prevention. this data includes demographic characteristics and diagnosis codes for health encounter visits of military health system beneficiaries, including active duty, active duty family members, retirees, and retiree family members. nssp functions through collaboration with local, state, and federal public health partners utilizing the biosense platform, an electronic health information system. methods dod data was pulled from the biosense platform through a rstudio server on october 11, 2016, querying data from november 1, 2015 to september 30, 2016. appointment type and beneficiary category data was not available in biosense until november 1, 2015. appointment type was categorized into clinic visits and telephone consults. demographic characteristics (age group, gender, beneficiary category) are stratified by appointment type. results during the time period of november 1, 2015 to september 30, 2016, data were received from 452 clinics. there is a military treatment facility located in 45 states and a military treatment facility may have one to 12 clinics. there were a total of 86,840,632 healthcare encounter records. the age group, 25-44 years, accounted for 39.4% of the medical encounters; the mean age was 33.9 (sd=19.1). males accounted for 55.6% of the medical encounters. for the time period from november 1, 2015 to september 30, 2016, 78.9% of medical encounters were clinic visits. the remaining medical encounters were telephone consults. of the clinic visits, 53.7% of the medical encounters were for active duty personnel. conclusions this report highlights the dod data available to nssp users for collaborative syndromic surveillance efforts, promoting a community of practice. it is important to understand the population demographics and limitations to the dod data when conducting syndromic surveillance. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e166, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts figure 1. heat map of counts of clinic visits by state, november 1, 2015 september 30, 2016 keywords syndromic surveillance; nssp; dod; demographics acknowledgments the authors thank mike schoelen for producing the map and devin hunt for sas coding assistance. *jessica f. deerin e-mail: jessica.deerin@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e166, 2017 isds16_abstracts-final 50 isds16_abstracts-final 51 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts malaria prevention, diagnosis and treatment strategies in kaduna state, nigeria obafemi j. babalola*1, 2 1residence cohort vi, nigeria field epidemiology and laboratory training pro, kaduna state, nigeria; 2federal neuropsychiatric hospital, kaduna state, nigeria objective we aim to assess the implementation of malaria prevention, diagnosis and treatment strategies, to assess implementation trends from 2011 to 2014 and if surveillance targets were met. introduction malaria is a preventable disease but 3.4 billion people at risk globally with 207 million cases and 627 deaths reported in 2013. africa accounts for 80% of cases and 90% of all malaria deaths. nigeria accounts for 25% of malaria burden in africa. the goal of malaria control is to reduce malaria –related transmissions, cases and deaths to a level where it is no longer a public health concern methods kaduna state, north western nigeria with estimated population of 7.3 million has 23 districts and 1252 health facilities. of these 461 sent malaria surveillance data to national health management information system monthly. data from january 2011 to december 2014 was analysed. we evaluated variables related to malaria interventions strategies such as malaria diagnosis, malaria treatment, malaria prevention in pregnancy. frequencies, proportions and trend analysis were done and odd ratios for associations between variables were calculated with confidence interval set at 95%. epi info statistical software was used for the analysis. results data completeness was 89.8%. of the 1,008,728 people that visited health facilities, 56.6% presented with fever. among the fever cases, 34.2% was tested with rapid diagnostic test (rdt) and 5.5% with microscopy. artemisinin based combination therapy was given to 361,464 of which 36.4% had confirmed malaria. those aged < 5 years with suspected fever were 1.28 (95% confidential interval (c.i), 1.27-1.29; p<0.01)) more likely not to be tested with either rdt or microscopy and they are 2.62 (95% c.i., 2.63 – 6.67; p<0.01) times more likely to have act for confirmed malaria. act prescription to presumptive malaria increases from 31.8% in 2013 to 200.2% in 2014. there is a progressive increase of long lasting insecticidal net distribution and access to second dose of intermittent preventive therapy (ipt-2) for pregnant women. conclusions generally, progress in malaria control transition to elimination in kaduna state, nigeria is favorable with malaria prevalence at 36.4%. some targets were met within the period and recommend strengthening of these malaria control strategies with focus on vulnerable groups and prevent uncontrolled act prescription for presumptive malaria. keywords malaria surveillance; malaria control strategy; malaria elimination program; malaria in pregnancy,; rapid diagnostic test acknowledgments we acknowledge kaduna state ministry of health, kaduna nigeria, kaduna state malaria elimination program and nigeria field epidemiology and laboratory training program, fct, abuja, nigeria *obafemi j. babalola e-mail: drfemibabs@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e180, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts monitoring child mental health related emergency department visits in new york city don olson, willem van der mei*, sungwoo lim, carol yoon, melissa kull and marivel davila new york city department of health and mental hygiene, long island city, ny, usa objective to assess the use of syndromic surveillance to assess trends in mental health-related emergency department (ed) visits among school-aged children and adolescents in new york city (nyc). introduction from 2001-2011, mental health-related hospitalizations and ed visits increased among united states children nationwide [1]. during this period, mental health-related hospitalizations among nyc children increased nearly 23% [2]. to estimate mental health-related ed visits in nyc and assess the use of syndromic surveillance chief complaint data to monitor these visits, we compared trends from a near real-time syndromic system with those from a less timely, coded ed visit database. methods the nyc ed syndromic surveillance system receives anonymized patient chief complaint and basic demographic data for nearly every ed visit citywide to provide timely surveillance information to health authorities. using nyc ed syndromic surveillance data from 2003-2015, we applied previously developed definitions for general psychiatric syndromes. we aggregated ed visits by age group (5-12 years, 13-17 years, and 18-20 years), geography, and temporality. syndromic data were compared with statewide planning and research collaborative system (sparcs) data from 2006-2014 which reported mental health diagnosis (icd-9), treatment, service, and basic demographics for patients visiting facilities in nyc. using these two data sources, we compared daily visit patterns and annual trends overall as well as stratified by age group, area-based poverty (zip code), and time of visit. results both syndromic surveillance and sparcs data for nyc showed an increasing trend during the period. while both showed relative increases with similar slopes, mental health-related chief complaint data captured fewer overall visits than the icd-9 coded sparcs data. trends in syndromic data during 2003-2015 differed by agegroup and area-based poverty, e.g., among children ages 5-12 years the annual proportion of mental health-related ed visits increased roughly 3-fold from 1.2% to 3.8% in the poorest areas, which was greater than the increase in the richest areas (1.7% to 2.6%). seasonal, day-of-week, and school holiday patterns found far fewer visits during the periods of nyc public school breaks (figure). conclusions we conclude that syndromic surveillance data can provide a reliable indicator of mental health-related ed visit trends. these findings suggest potential benefit of syndromic surveillance data as they may help capture temporal and spatial clustering of events in a much more timely manner than the >1 year delay in availability of ed discharge data. next steps include a qualitative study exploring the causes of these patterns and the role of various factors driving them, as well as use of patient disposition and matched data to better characterize ed visit patient outcomes. keywords emergency department; child mental health; syndromic surveillance acknowledgments hannah gould, bureau of epidemiology services; and the syndromic surveillance unit, bureau of communicable disease, nyc dohmh references [1] simon ae, schoendorf kc. emergency department visits for mental health conditions among us children, 2001-2011. clin pediatr 2014;53(14):1359-1366. [2] mills ce, davila m. psychiatric hospitalizations among children and adolescents in new york city. epi data brief, nyc dohmh, june 2016, no. 70. *willem van der mei e-mail: willem.vandermei@columbia.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e134, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts antibody prevalence to influenza type a in wild boars of northern ukraine ganna kovalenko*, ihor halka and aliona molozhanova research training center for animal disease diagnostics, institute of veterinary medicine of the national academy of agrarian sciences., kyiv, ukraine objective a preliminary serological survey was carried out to assess the likelihood of influenza a (ia) infection in wild boars and begin to characterize the role of wild boars in the epidemiology of the ia virus. introduction domestic swine have been viewed as important for the adaptation and spillover of ia from birds into human populations as they are sensitive to both avian and mammalian (including human) influenza viruses [1]. however, in much of eurasia and north america wild swine are geographically widespread, abundant and often come in close contact with humans in rural and agricultural settings. until recently, little attention has been paid to this as an alternate route for ia transmission to human and domestic populations and its significance is not clear. therefore, the monitoring of the exposure of wild mammals to ia was viewed as essential as potential vectors impacting domestic animals and public health. methods from september to december 2014, wild boar sera were collected by professional hunters in 4 oblasts of ukraine: volyn, rivne, zhytomyr, and chernihiv. blood was collected from jugular veins. sera were collected in eppendorf type tubes, separated from whole blood without centrifugation and stored at -20c until serologically tested. to detect antibodies to ia, a blocking elisa was used. serum samples were tested using commercial test kits “influenza a ab test” (idexx, usa). specific antibodies in wild boar serum samples were detected based on manufacturer’s instructions. briefly, sera were diluted 1:10, and incubated in test wells for 60 minutes at room temperature, followed by three washes. anti-ia horseradish peroxidase hrp conjugate was then added and incubated for 30 minutes at room temperature. following three washes, 3',5,5’-tetramethylbenzidine (tmb), as a substrate, was added and incubated for 15 minutes. absorbencies were measured at 650 a using a imark microplate absorbance reader and data were analyzed using microsoft excel. based on the manufacturer’s instructions, a serum sample was considered positive if the sample/ negative control ratio (s/n) did not exceed a threshold of 0.60. statistical analyses were performed with the program “statistics calculator”. results sera from 120 wild boars that were shot in 2014 were tested. thirty boars from each of 4 oblasts were collected in the north central and northwestern regions of ukraine. antibodies against iav were detected using elisa in 27 samples (22.5 %), (table 1). antibodies to ia virus were detected in at least some of the wild boars from all of the 4 oblasts. the highest percentages of seropositive samples were detected in wild boar from volyn and zhytomyr oblasts (fig. 1). the prevalence differences were statistically significant only between samples from volyn and chernihiv oblasts (p<0.05). the average s/n value of all positive serum samples was 0.36±0.03. conclusions this preliminary survey of ia antibodies in wild boar populations of northern ukraine indicates a substantial presence of exposure to iav throughout the region. infection of wild boar populations provides an alternative or additional route for spillover from wild populations to domestic animals and humans. this potential has received relatively little attention until recently, likely in part because feral swine populations have not been viewed as a serious challenge in most regions of the world where the natural history of ia has received serious study. table 1 seroprevalence of ia virus in wild boars in ukraine figure 1 serological surveillance of wild boars for ia virus in northern ukraine table 1 keywords influenza a virus; seroprevalence; wild boars; ukraine acknowledgments thanks to all employees of the laboratory of the research training center for animal disease diagnostics (ivm, naas) for conscientious cooperation. references 1. runstadler j. et al. connecting the study of wild influenza with the potential for pandemic disease. infection, genetics and evolution, 2013; 17:162–187. *ganna kovalenko e-mail: anna.kovalenko31@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e148, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts value of evidence from syndromic surveillance with delayed reporting rahel struchen*1, 3, flavie vial2 and gunnar andersson4 1federal food safety and veterinary office, bern, switzerland; 2epi-connect, skogas, sweden; 3veterinary public health institute, bern, switzerland; 4national veterinary institute, uppsala, sweden objective we apply an empirical bayesian framework to perform change point analysis on multiple cattle mortality data streams, accounting for delayed reporting of syndromes. introduction taking into account reporting delays in surveillance systems is not methodologically trivial. consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. the main drawback of this approach is the resulting reduction in sensitivity and specificity1. combining syndromic data from multiple data streams (most health events may leave a “signature” in multiple data sources) may be performed in a bayesian framework where the result is presented in the form of a posterior probability for a disease2. methods we used a historical national database on swiss cattle mortality to model daily baseline counts of two syndromic time series3. reporting delay was defined as the number of days between reported occurrence and reporting date. the cumulative probability distribution of the estimated reporting delays was used to calculate for each day the proportion of cases that were reported either on the same day or with a delay of 1 to 14 days. we evaluated outbreak detection performance under three scenarios: (a) delayed data reporting occurs but is not accounted for; (b) delayed data reporting occurs and is accounted for; and (c) absence of delayed data reporting (i.e. an ideal system). outputs are presented as the value of evidence (v) in favour of an ongoing outbreak accumulated over n points in time (30 days in this case). at each time t, v is defined as the ratio between the posterior and prior odds for h1 versus h0: [insert equation 1 here] using sensitivity, time to detection and in-control run length, performance of the (v-based) system on large and small non-specific outbreaks was measured. results the evolution of v based on the information available on the 1st, 5th and 10th day after the onset of an outbreak can be visualised in fig. 1. after 5 days, v shows evidence in favour of an outbreak for both syndromes combined, as well as for on-farm deaths alone, only in the “delay aware” and “no delay” scenarios. the development of v for the perinatal deaths alone highlights the importance of considering multiple syndromic data streams for outbreak detection, as it speaks in favour of an outbreak at a later stage than on-farm deaths alone or both syndromes combined. conclusions our empirical bayes approach is an attractive alternative to multivariate cusum algorithms offering a logical approach to weighting variables and incorporating additional information such as delayed reporting, and a performance on a comparable level to an ideal (no delay) system. outbreaks are detected earlier and with only a marginal loss of specificity compared to a system where reporting delay is present but unaccounted for. we also found that the accumulation of evidence from several days resulted in a significantly better outbreak detection timeliness, for a given specificity; or a similar timeliness, but higher specificity, compared to an algorithm4 that only looks for days with unusual high number of counts. fig. 1: evolution of v over three time points (t) for the three scenarios. outbreak starts at t=651. number of observed perinatal (circle) and on-farm deaths (cross), v for both (solid grey) and individual syndromes (dotted grey and black respectively), prior probability that an outbreak is ongoing (grey dashed) and posterior probability that an outbreak is ongoing given the evidence (black dashed). horizontal grey solid line shows v=1. keywords empirical bayes; reporting delay; multivariate surveillance references 1. farrington c p & andrews n j. monit. heal. popul. 2004. oxford university press. 2. andersson m g, faverjon c, vial f, legrand l & leblond a. using bayes’ rule to define the value of evidence from syndromic surveillance. plos one. 2014, 9, e111335. 3. struchen r, reist m, zinsstag j & vial f. investigating the potential of reported cattle mortality data in switzerland for syndromic surveillance. prev. vet. med. 2015, 121, 1–7. 4. salmon m, schumacher d, stark k & höhle m. bayesian outbreak detection in the presence of reporting delays. biom. j. 2015, 57, 1051–67. *rahel struchen e-mail: rahel.struchen@blv.admin.ch online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e28, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts rapidly adapting flexible surveillance systems for emergent event response laura edison*1, 2, karl soetebier2, hope dishman2, wendy smith2, alex cowell2 and cherie drenzek2 1centers for disease control and prevention, atlanta, ga, usa; 2georgia department of pubic health, atlanta, ga, usa objective to describe how flexible surveillance systems can be rapidly adapted and deployed, and increase the efficiency and accuracy of surveillance, during responses to outbreaks and all hazard emergent events. introduction georgia department of public health (dph) epidemiologists have responded to multiple emergent outbreaks with diverse surveillance needs. during the 2009 h1n1 influenza response, it was necessary to electronically integrate multiple reporting sources and view population-level data, while during the 2014–2015 west african ebola epidemic, it was necessary to easily collect and view individual level data from travelers to facilitate early detection of potential imported ebola disease. dph in-house information technology (it) staff work closely with epidemiologists to understand and accommodate surveillance needs. through this collaboration, it created a robust electronic surveillance and outbreak management system (oms) to accommodate routine reporting of notifiable diseases and outbreak investigations, and surveillance during emergent events. methods oms was created within the state electronic notifiable disease surveillance system (sendss); a secure, hipaa-compliant, oracle and web-based platform which collects data on all notifiable diseases in georgia. this flexible platform has multi-functionality including dynamic web-based surveys that link to case records or outbreaks, online case reporting, electronic laboratory reporting, contact tracing, visual dashboards summarizing outbreak data, electronic alerts, and individual accounts for users with varying privileges to limit access to specific modules. these features can be customized for any emergent event. results sendss and oms are widely used by state and district epidemiologists. individual case and outbreak management activities include but are not limited to: notifiable disease and condition cases; all disease clusters; animal bites surveillance including bite investigation and laboratory results; and syndromic surveillance data automatically collected from 90 emergency facilities. oms has been rapidly modified to facilitate efficient epidemiologic responses to emergent events such as: integrating multiple reporting sources during the h1n1 outbreak; shelter surveillance during hurricanes katrina and rita in 2005; active monitoring of >2,500 travelers in georgia during the ebola response; tracking cases investigations during the zika response, and future monitoring of poultry workers if highlypathogenic avian influenza occurs in georgia. conclusions the flexible and customizable features of sendss and oms accommodate the changing needs of epidemiologists to monitor a variety of diseases. rapid implementation has enabled dph epidemiologists to respond efficiently to emergent events using limited human resources, achieving immediate situational awareness by incorporating multiple data sources into user friendly dashboards and notifications, and easily sharing information among state and federal stakeholders to facilitate rapid risk assessment and response as needed. the success of these systems illustrates the return on dph’s preparedness investment in retaining technical staff to work with epidemiologists to meet urgent surveillance needs. keywords informatics; surveillance; emergency response; outbreak response; surveillance system *laura edison e-mail: laura.edison@dph.ga.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e56, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts a value-driven framework for the evaluation of biosurveillance systems victor del rio vilas*1, m kocaman2, howard burkom4, richard hopkins5, john berezowski6, ian painter7, julia gunn8, g montibeller3, m convertino9, l.c. streichert10 and p.a. honoré11 1paho, rio de janeiro, brazil; 2london school of economics, london, united kingdom; 3school of business and economics, loughborough, united kingdom; 4john hopkins apl, laurel, md, usa; 5university of florida, tallahassee, fl, usa; 6veterinary public health inst., bern, switzerland; 7university of washington, seattle, wa, usa; 8boston public health comm., boston, ma, usa; 9university of minnesota, minneapolis, mn, usa; 10isds, boston, ma, usa; 11lsu school of public health, new orleans, la, usa objective to describe the development of an evaluation framework that allows quantification of surveillance functions and subsequent aggregation towards an overall score for biosurveillance system performance. introduction evaluation and strengthening of biosurveillance systems is a complex process that involves sequential decision steps, numerous stakeholders, and requires accommodating multiple and conflicting objectives. biosurveillance evaluation, the initiating step towards biosurveillance strengthening, is a multi-dimensional decision problem that can be properly addressed via multi-criteria-decision models. existing evaluation frameworks tend to focus on “hard” technical attributes (e.g. sensitivity) while ignoring other “soft” criteria (e.g. transparency) of difficult measurement and aggregation. as a result, biosurveillance value, a multi-dimensional entity, is not properly defined or assessed. not addressing the entire range of criteria leads to partial evaluations that may fail to convene sufficient support across the stakeholders’ base for biosurveillance improvements. we seek to develop a generic and flexible evaluation framework capable of integrating the multiple and conflicting criteria and values of different stakeholders, and which is sufficiently tractable to allow quantification of the value of specific biosurveillance projects towards the overall performance of biosurveillance systems. methods we chose a multi attribute value theory model (mavt) to support the development of the evaluation framework. development of the model was done through online decision conferencing sessions with expert judgement, an indispensable part of mavt modelling, provided by surveillance experts recruited from the member pool of the international society for disease surveillance. the surveillance functions or quality criteria that were considered for the framework were initially gathered from a review of the literature with specific attention to a subset of public health quality criteria (1). group discussions with the experts led to a final list of functions, finally reviewed to comply with the properties for good criteria in decision models. the eleven functions were: sensitivity; timeliness; positive predictive value (ppv); transparency; versatility; multiple utility; representativeness; sustainability; advancing the field and innovation; risk reduction; and actionable information. in addition, 24 different scenarios were developed for sensitivity, ppv, and timeliness since their values may differ with the level of infectiousness of the condition/event of interest, its severity and the availability of treatment and/or prevention measures. four or five levels of performance were also developed for each criterion. macbeth (measuring attractiveness by a category-based evaluation technique) tables were used to elicit values of different levels of performance from the experts using qualitative pairwise comparisons and then convert them into numerical values. results to date, two criteria, sensitivity and transparency, have been assessed by more than one expert working on the same scenario. value functions were generated for each criterion and scenario by calculating the median of the different values produced by the experts. for both sensitivity and transparency, value functions were mostly linear, indicating similar preferences between levels of performance. however, for some scenarios, experts allocated greater value to increases at the higher end of the performance level distribution. conclusions at the time of writing new elicitation sessions are planned to conclude the model. next, we will apply swing weights to support the trade-offs between the different criteria. we will present the baseline model elicitated from the experts and demonstrate how to apply portfolio decision analysis to assess overall performance of biosurveillance systems according to the specific needs of stakeholders and in conjunction with macro-epidemiological models. keywords evaluation; value-driven; quantification references honoré et al. creating a framework for getting quality into the public health system. health affairs, 2011, 30, 4, 737-45. *victor del rio vilas e-mail: amfoza@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e83, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts integrating poison center data into oregon essence using a low-cost solution robert laing* and melissa powell public health division, oregon health authority, portland, or, usa objective to enhance oregon essence’s surveillance capabilities by incorporating data from the oregon poison center using limited resources. introduction oregon public health division (ophd), in collaboration with the johns hopkins university applied physics laboratory, implemented oregon essence in 2012. oregon essence is an automated, electronic syndromic surveillance system that captures emergency department data. to strengthen the capabilities of oregon essence, ophd sought other sources of health-outcome information, including oregon poison center (opc). in the past, oregon’s surveillance staff manually monitored opc data on the national poison data service (npds) website. although functional, it was not integrated into oregon’s syndromic surveillance system and required epidemiologists to assess alerts on individual calls. to achieve data integration, ophd pursued an automated solution to deliver opc data into oregon essence. ophd’s growing interoperability infrastructure fostered development of a low-cost, reliable solution to automate the integration of these data sources. methods opc facilitated ophd’s access to the free-of-charge npds web service with an approval request and a data use agreement. ophd uses the rhapsody integration engine 6.2.1 (orion health, auckland, nz) as its primary data transfer and translation mechanism. ophd leveraged its existing rhapsody installation to automatically request data from the npds web service daily. each request contains custom search parameters that query calls from the previous day (24 hours). the service returns an xml file containing poison center call data with multiple nodes of related data. rhapsody uses a javascript ‘filter’ to parse each call and its related data. the oregon essence backend sql database contains a parent table for the call and child tables for the related data (clinical effects, routes, scenarios, therapies, and generic codes). rhapsody inserts data into each of these backend sql tables. results oregon essence displays opc data through its web interface for interpretation by ophd’s syndromic surveillance epidemiologists. integrating npds data into oregon essence allows ophd staff to timely monitor data in an automated, routine manner. syndromic surveillance staff first assess alerts generated by oregon essence. alerts that require follow-up trigger a call between ophd epidemiologists and opc. oregon is the first state to use the npds web service to upload poison center data into oregon essence. conclusions oregon’s successful integration of the npds web service data into oregon essence is the first known of its kind. it leverages ophd’s growing infrastructure of interoperability software applications and staff expertise to create a cost-effective and sustainable solution that can be easily adapted by other public health agencies. keywords interoperability; essence; poison data; oregon; npds acknowledgments thank you to kyle ryff, mph; melissa powell, mph; michelle barber, ms; and laurel boyd, mph, all of whom spent significant time in the development of ophd’s rhapsody engine and informatics infrastructure. this publication was supported by cooperative agreements, number 1u50oe000068-01, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services. *robert laing e-mail: robert.s.laing@state.or.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e42, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts carbapenem resistant enterobacteriaceae infections in houston, texas: an outline hafeez rehman*, imran shaikh, kasimu muhetaer and salma khuwaja houston health department, houston, tx, usa objective to examine demographic as well as clinical characteristics of the carbapenam resistant enteriobacteriacae (cre) organisms cases in houston, texas, 2015-2016 introduction according to cdc, cre is used to describe bacteria that are nonsusceptible to one or more carbapenems; doripenem, meropenem or imipenem and resistant to third generation cephalosporins like ceftriaxone, cefotaxime and ceftazidime. these organisms cause infections that are associated with high mortality rates and they have the potential to spread widely. antibiotic resistant bacteria cause more than 2 million illnesses and at least 23,000 deaths each year in united states. cres are found in many health care settings like acute care hospitals, long term care facilities, nursing homes, rehabilitation facilities and other health care settings. although cres includes a number of species, reporting in state of texas is limited to creklebsiella species and cre-e.coli. methods population-based surveillance data was generated from houston’s electronic disease surveillance system reported to houston health department (hhd) from october 2015 to july 2016. descriptive analysis was performed to examine demographic and clinical characteristics across different age groups, gender and race/ethnicity. hhd has received a total of 463 cre cases during the time period, out of which 72 were non-reportable and did not meet the case criteria, 187 were out of jurisdiction. the remaining 204 cases were included in this study. results out of a total of 204 cases, males and females were represented equally (50% each). the mean age of the cases was 67 years (age ranges from 22-98). majority of the cases were in the older age group, 70 years and above 53 (26%), followed by 48 (24%) in age group 80 and above years. among the different race/ethnic groups, african-americans comprised of 82 (40%), followed by whites 67 (33%) and hispanics 33 (16%). out of 204 cases, 156 (76%) were hospitalized, which included acute care hospital, long-term acute care or nursing home. out of 156 hospitalized cases, 71 (34%) were in intensive care unit (icu) and 136 (67%) had an invasive or indwelling device. of all the cases, 80% had cre klebsiella pneumoniae, followed by 11% who had cree coli. the cases were distributed evenly across the city when plotted on arcgis with their residential addresses. conclusions cre cases are found to be more common among older age groups, african american population and in hospitalized patients. cre can be a ground for increasing infectious diseases in the community and one of the reason may be unnecessary use of antimicrobial agents. this study provides a glimpse into the number of cre cases reported in houston since cres are classified a separate disease in texas. further studies are needed to explore the occurrence of anti-microbial drug resistance among the specific population groups and how the case investigation efforts can be targeted to enhance prevention. keywords cre; drug resistant; anti-biotics acknowledgments staff of the bureau of epidemiology, houston health department *hafeez rehman e-mail: hafeez.rehman@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e95, 2017 observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes michael popovich, msse; brandy altstadter, bsise; lara hargraves popovich, bs biology scientific technologies corporation abstract the health information technology for economic and clinical health (hitech) act encourages health information exchange between clinical care and public health through meaningful use measures. meaningful use specifically identifies objectives to support a number of public health programs including immunizations, cancer registries, syndromic surveillance, and disease case reports. the objective is to improve public and population health. stage 2 of meaningful use focused on compliance to sending of information to public health. the next phase focuses on bi-directional information exchange to support immunization intelligence and to empower providers, pharmacists, and the consumer. the hitech act stage 2 initiative provided incentive and motivation for healthcare providers to encourage their electronic medical record (emr) vendors to implement data exchanges with public health, with the expected result being timely awareness of health risks. the empowerment nugget in the hitech act is not in the compliance reporting to public health. the nugget is the ability for a provider to receive relevant information on the patient or consumer currently in front of them or to those they will connect to through their outreach efforts. the ability for public health to retain current immunization records of individuals from a variety of providers supports their program goals to increase immunization rates and mitigate the risk of vaccine-preventable disease (vpd). the ability for providers to receive at the point of service more complete immunization histories integrated with decision support enhances their delivery of care, thereby reducing the risk of vpd to their patients. indirectly payers benefit through healthcare cost savings and when the focus is expanded from a health model to a business model, there are significant return on investment (roi) opportunities that exponentially increase the value of a bidirectional immunization data exchange. this paper will provide descriptions of case examples to demonstrate the value of electronic data exchanges when pharmacy immunization providers and public health work together. key words: adult immunizations, meaningful use, immunization information system (iis), electronic health records (ehrs), immunization intelligence, vaccine-preventable disease (vpd), public health informatics, population health outcomes, retail pharmacist, healthcare provider, bi-directional data exchange, advisory committee on immunization practices (acip) correspondence:: michael_popovich@stchome.com observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi doi: 10.5210/ojphi.v8i2.6398 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. introduction retail pharmacies are playing an increasingly important role in immunization practices in the u.s. pharmacists are providing patients with convenient access to immunization services creating opportunities for traditional public health organizations to improve vaccine coverage rates particularly for at-risk and underserved adolescent and adult populations. traditional healthcare providers are characteristically not seeing the adolescent and adult populations on a regular basis. visits to the physician after school age are less oriented to preventative care and more to the treatment of illness. the visit focus is not on an immunization event. to minimize missed opportunities physicians must actively check immunization status of each patient in addition to addressing the primary visit purpose. pay for performance and patient and population health outcomes for medical reimbursement have created the need for health record data sharing, improved medical record systems, and registries that capture information from multiple communities of care. as the information systems and data exchanges are extended to a community of care that includes pharmacists, the opportunity to integrate with state public health immunization registries that add decision support expands the current immunization information ecosystem (iie) for increasing vaccine coverage rates of all ages. in the u.s., immunization registries have been evolving for over two decades. the statewide immunization information system (iis) has become the single resource for recording consolidated patient immunization events. today individuals of all ages are receiving immunizations in non-traditional clinical settings including in pharmacies, at the workplace, in travel clinics, and outside their normal area of residence. as immunization information is recorded in medical record systems the data in many is being electronically sent to an iis for consolidation. the iiss exist in all fifty u.s. states 1 and are becoming an important component of public health programs in the canadian provinces as well. retail-based pharmacist vaccinators have not been active users of the data in the iiss historically, although pharmacists in the u.s. are required by most states through policy to report the immunizations they provide to public health. figure 1 summarizes pharmacy vaccine administration authority by state, illustrating the growing landscape for pharmacist immunizations. 2 observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi figure 1. summary by state showing the growing landscape for pharmacist immunization administration as a pioneer in developing and supporting immunization registries, scientific technologies corporation (stc) began to partner with the pharmacy community during the 2009 u.s. h1n1 outbreak. this was the first real opportunity that empowered pharmacies to demonstrate their effectiveness in providing immunizations. without their broad population reach, vaccinations for h1n1 influenza at that time would have been far less reaching and less effective. it was also at this time that health policy began actively expanding to support making a full list of vaccinations available to the pharmacy. national chain and independent drugstores have begun to recognize a value-added component of their business practice as they implement immunization programs. the fact that a significant percent of the population shops regularly at retail stores that contain a pharmacy represents a major opportunity to improve population health through the immunization programs being offered. the american pharmacists association (apha) has estimated that in a one-week span, 300 million individuals will shop in a retail setting that includes a pharmacy. at the april 2016 national association of chain drug stores (nacds) annual conference, it was reported that in a given year an individual visits a pharmacy on average thirty times and a physician will visit a pharmacy on average twice. the national chain drug stores and major retailers have been the first to recognize the value of an immunization program within their locations. independent and community small chains have engaged equally in these programs to maintain their competitiveness and they all recognize the value-added potential for their customers. given the momentum for pharmacy-based immunization programs, the value to public health is this new opportunity to reach populations, such as adults, outside of the traditional child and school-age individuals on which previous programs have focused. stc has undertaken a series of evaluations in the past few years to determine the potential benefit of immunization programs to both the pharmacy and public health. this paper reviews observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi lessons learned from three pharmacy case studies that provide real world applications of public health information technology in practice, with each demonstrating the power of the hitech act to return value and validate decisions to invest: 1) five pharmacies of a small drugstore chain (with 65 stores) in washington state which volunteered to evaluate the additional immunization opportunities when an individual presented at the pharmacy counter for a flu shot and at that time their records were retrieved from the state immunization registry. 2) a national retail chain with locations in all states illustrates technical and practice scalability. 3) a project that specifically targets adolescents and adults using registry data with added intelligence through decision support at the point of care. literature review in 2014, stc provided a white paper “the value of the state immunization registry towards improving vaccination rates and quality of care in retail pharmacies, achieving patient-centered, quality care through the application of immunization registry information when combined with best practices,” which detailed the five pharmacy case study summarized herein. stc is planning an upcoming publication for the third quarter of 2016 focused on a new initiative on which stc is partnering with the washington state pharmacy association, the washington state department of health, and the american pharmacists association foundation (apha foundation). this new study, with eight pharmacies participating, will conduct a formal study to measure and monitor the increase in additional immunizations during pharmacy flu clinics. methods five-pharmacy case study this study, conducted in early 2014 with bartell drugs, sought to identify the benefits of using the iis from the pharmacist’s perspective, and the potential that iis information has on increasing the number of additional vaccinations administered at this point of care. as pharmacists are not traditional iis users, a component of the study also was to evaluate the workflow impact and the use of information technology, and to identify barriers that could potentially inhibit their use of this data as part of their patient care. in washington state, pharmacists are allowed to both prescribe and administer vaccinations and are given access to the public health statewide iis upon request. it has been estimated that approximately 35-40% of pharmacists used the state iis in 2014, even though many have been administering vaccinations since 2011, with a number of pharmacists having vaccinated for as long as fifteen years. the question to answer was how this provider community can benefit from advances in the information and data environment. observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi little was known about whether patients would choose to receive additional vaccinations in this setting if they were informed of their immunization status and whether they were due or overdue for an immunization. bartell pharmacists were making recommendations to patients based on their immunization histories which align with working toward a patient-centered approach to care, however, almost nothing was known about how pharmacists use the iis or how vaccination information is shared with patients in practice. the study involved five pharmacies and eight pharmacists as they expanded their immunization efforts to include consulting patient information contained within the washington state iis. this health data asset adheres to cdc immunization information system core standards 3 and contained records of over 74 million immunization events for over eight million patients. the stc study team observed the pharmacists administering vaccinations to all patients arriving in the pharmacy who requested a vaccination. when a patient arrived for a vaccination, the pharmacist recorded the information into their existing pharmacy management record system as part of their normal patient enrollment process. the pharmacist would then augment their workflow separately by accessing the iis to search for the patient’s vaccination record. the pharmacist retrieved and viewed vaccination historical events. a patient’s vaccination forecast was available to the pharmacist based on the immunization records contained in the iis. this forecast presented as a list indicating patient-specific vaccinations as either “due” or “overdue.” the forecasting decision support tool used the advisory committee on immunization practices (acip) 4 guidelines to forecast this information based upon age and the patient’s previous immunization events. the pharmacist would use his or her own judgment to decide whether to recommend or offer any additional vaccinations to the patient. the patient had the opportunity to obtain one, all, or none of any recommended vaccinations. observers recorded the vaccinations the pharmacist recommended and the vaccines the patient chose to receive. observers also recorded instances of when the pharmacist gave no recommendations and the reason for not recommending vaccines. if a patient refused recommended vaccinations, the refusal was recorded along with the reason for the patient’s refusal. the entire process is diagrammed in figure 2. the most common overdue vaccinations were varicella for chickenpox and measles, mumps and rubella (mmr). pharmacists using the outcomes from the iis decision support for the patient were most likely to recommend meningococcal (66.7%), pneumococcal (50.0%), hep a (50.0%) and tdap (50.0%) if those vaccinations were shown as overdue. there was a strong association between the pharmacist and whether or not the patient chose an additional needed vaccination. this could indicate that patient choice was influenced by their pharmacist, with certain pharmacists more likely to elicit a patient choice of opting for additional vaccination. this small 2014 study showed a need for developing a best practices model and providing training on how to make the appropriate immunization recommendations. it also demonstrated that significant opportunity was missed to vaccinate individuals and thus reduce the risk of disease. observers noted the impact on the pharmacist’s time when this process was outside of their normal workflow. observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi figure 2. pharmacist registry use, recommendation, and vaccination decision tree. the overall results of the five pharmacies case study are shown in table 1. table 1. results from each step in the pharmacist registry use, recommendation, and vaccination decision tree. proportion of total step in registry use and recommendation decision-tree 100% patients arriving for a vaccination. 95% patients searched in the iis. 74% patients found in the iis. 62% patients due or overdue identified by the iis vaccination forecast. 3.22 average vaccinations due or overdue per patient. 33% patients receiving a direct vaccination offer by the pharmacist. 5% patients that opted-in for additional vaccination. although not significant it was evident that within a pharmacy if the pharmacy management system (pms) was interoperable with the iis and through a single-sign on process the patient’s records were available immediately upon entry in to the pms the interaction with the patient would likely increase. it was also clear that this interoperability should include a bi-directional capability with decision support thereby allowing the immunization(s) given by the pharmacist’s to be electronically sent to the iis in real time and a forecast to be presented at the point of care. observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi and finally a preliminary understanding of the opportunity for added revenue was identified as it was noted “money was walking out the door.” how much money was being lost? the washington state iis reported that 449,495 unique patients received a vaccination at a pharmacy between june 2013 and may 2014. using the 3.22 average identified in this study for immunizations past due per individual, this represents 1,447,374 missed opportunities to vaccinate. to illustrate further the value to a pharmacy and the potential business: if the profit on providing each of these immunizations was $15 each, it would represent a total of more than $21 million in additional revenue for all washington state pharmacies. advances in data availability, decision support, and pharmacy clinical practice was shown to be valuable to this small drugstore chain, to the point that the following year the chain implemented a complete system with electronic data exchange between the state iis and all pharmacies and integrated decision support combined with training of pharmacists to message this information towards the goal of providing more recommended vaccinations to patients. major national chain example the washington state study was an early investigation that demonstrated the potential for significant roi if the process was easily integrated into the daily workflow of the pharmacist. in 2015, stc began to work with a major u.s. retail business with thousands of pharmacies throughout the u.s. the initial effort entailed reporting compliance specifically to ensure that all retail pharmacies were able to electronically report the immunizations provided in each location to the appropriate state or local major metropolitan iis. these links were secure hl7 data exchanges. as the electronic exchange links were established a number of public health registries were able to support two-way data exchanges. bi-directional exchanges, unlike the above washington state example which required the pharmacist to look up a patient record on a separate terminal, offered the advantage of the single sign-on process and interoperable iis connectivity. secure hl7 message traffic from the iis was able to return both the patient immunization events as contained in the public health dataset as well as add the decision support information that was determined automatically by the immunization forecaster. as part of the project rollout, the patient match rate and completeness of the state iis records were evaluated in two states. both states require bi-directional connectivity per state law. the first question was to determine the likelihood that the pharmacy demographic would be found in the iis. the typical pharmacy demographic in this retail setting includes over 50% of patients that are 65 or older (indicated for pneumococcal); between 10-15% that are ages 60 to 64; 30% between the ages of 27 and 59; with the remainder (5-10%) under 27 years old. both states reviewed have well-established lifetime immunization information systems. data showed that the pharmacy patients were found in the iis, on average, 73% of the time (77% in the first state and 69% in the second) which was comparable to the washington state example from the 2014 study. the second question looked at the completeness of the iis records. the graph in figure 3 shows the average number of immunizations returned by age group. observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi figure 3. number of vaccinations returned by age. of the patients searched and found in the iis, 91% were due for at least one additional vaccination. the third key question asked was, of the patients due for vaccinations, how many vaccinations were due for each age group. table 2 details the results. table 2. vaccinations due by age group. age % due for one (1) vaccination % due for two (2) vaccinations % due for three (3) or more vaccinations under 18 13% 22% 65% 19 – 26 34% 36% 30% 27 – 59 94% 5% 1% 60 – 64 46% 53% 1% 65 + 19% 37% 45% the above analysis, also presented at the american immunization registry association (aira) 2015 national meeting, shows a wealth of opportunity for pharmacies to improve adult patient outcomes by identifying patient immunization needs and addressing health needs that otherwise go unserved. a tightly integrated and continuously operating system for viewing immunization history and recommendations results in the opportunity for the pharmacist to engage the patient, at every visit, in a conversation about receiving needed vaccinations. 24.41 17.18 4.08 3.99 4.59 0 5 10 15 20 25 30 18+ 19-26 27-59 60-64 65+ number of vaccinations returned by age average number of historical immunizations observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi a targeted pharmacy study as the roi begins to illustrate the value to retail pharmacies, there is also the value of how pharmacists can play a critical role in increasing the uptake of vaccinations in high-risk populations or hard to reach populations. adolescents and adults are commonly underimmunized after their initial childhood series that carried them through their school years. the 2014-2015 five-store pharmacy study which showed an average of 3.22 vaccinations due or overdue per patient suggested that specific public health programs could be implemented in partnership with local pharmacists to increase the uptake of targeted vaccines. this 2015 retail case example moved the manual workflow to an integrated automatic process which empowers pharmacists by eliminating barriers associated with added time for a manual process. similar roi was demonstrated in each case. this case, a partnership of a state pharmacy association, a number of retail pharmacies, and state public health established an immunization-specific target. the results will be fully evaluated with expected publishing in mid-2016. the project is designed specifically to leverage technology, data exchange, and point of care immunizations at pharmacies to increase specific coverages rates in adults that are under-immunized. the pharmacists are actively engaging with the patient after the forecast is returned to proactively offer the missing immunizations. preliminary results from this project indicate that in a six-week period of time, the eight pharmacies provided flu vaccinations to just fewer than 1,000 individuals. of these, over 78% had an immunization record in the state iis. in this six-week period an additional 300+ immunizations were provided to those requesting a flu shot. when an immunization record was not found in the state iis the pharmacist used age-appropriate calculations to suggest vaccinations that might be due or past due. the secondary endpoints of this study include how often a patient declined to receive an additional immunization beyond flu and the reasons why which will help to identify gaps in both patient and pharmacist education. for example, early results have indicated that patients, at a rate of approximately 30%, have declined both pneumovax and prevnar13. they cite the reason as they believe they have already received it. this indicates a major gap in patient knowledge since the prevnar13 vaccine is only two years old and should therefore have a lower coverage rate and overall higher documentation rate then its counterpart, pneumovax. this data could prove to be invaluable to vaccine manufacturers when they are developing marketing campaigns directed to the consumer and creating provider educational resources. discussion these three case studies show early indication of opportunities and potential benefits of establishing interoperable information systems with public health registries. anecdotal feedback from more than one pharmacist indicated that their ability to view the full patient immunization history and to be provided with the information that clearly illustrates what the individual needs has empowered them to provide facts at the point of care. bi-directional observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi exchange of information increased immunization intelligence for the pharmacist and the patient. immunization intelligence created opportunity to increase vaccine coverage rates in at-risk populations. as a result of the initiatives through the hitech act we should expect population health outcomes that show measurable improvement when private-public partnerships work together. it is more than merely the sending and receiving of data — it is developing program alliances that also include common objectives, consumer education, and measurable performance indicators. conclusion the hitech act created a technology roadmap that supports the objective of improving health outcomes by empowering patients and providers with timely and more complete health information. the foresight of supporting meaningful use objectives including public health reporting creates new opportunities to impact population health. adding pharmacies whose immunization programs are exponentially expanding and pharmacists who are delivering increased numbers of immunizations to a broad range of patients into this environment and it is feasible to assess the value of electronic data exchange between all parties. these studies involved early pharmacy adopters supporting public health immunization reporting and the use of information. in each case applied practice illustrated significant benefits to the patients, the pharmacy, and to public health. the ability to accelerate these connections and to develop messaging and consumer outreach will have a favorable impact on population health. public health will be instrumental in building these public-private partnerships with electronic data exchanges forming the backbone of the effort. limitations the case study examples and this article did not detail the technical frameworks of each case study, the standards used, or the integration into the workflow. all are important to understand applied health informatics in data exchanges and to increase the understanding of the costs to implement and support them. they are the topics of a series of follow-up papers in development by stc staff and partners to include: (1) technical frameworks in an interoperable immunization information ecosystem, emrs, pharmacy management systems and consumers. (expected august 2016.) (2) demonstrated impact on adult immunization rates within pharmacy immunization programs in partnership with public health immunization information exchanges (expected october 2016.) (3) the state of data exchanges with state immunization information systems (first quarter 2017.) the case study examples did not address the data quality or the impact on the pharmacists’ workflow nor did they evaluate the economic models and benefits although numerous observations provided for the next steps. observations illustrating the use of health informatics to link public health immunization registries and pharmacies to increase adult immunization rates and improve population health outcomes online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e185, 2016 ojphi references 1. as of may 26, 2016, new hampshire became the final state in the u.s. to implement an iis. the iis is implemented and supported by stc. http://www.dhhs.nh.gov/media/pr/2016/05262016registry.htm 2. http://www.pharmacist.com/pharmacist-administered-immunizations-what-does-your-stateallow 3. http://www.cdc.gov/vaccines/programs/iis/core-data-elements.html 4. http://www.cdc.gov/vaccines/schedules/hcp/index.html isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos university of guelph, guelph, on, canada introduction norovirus, commonly referred to as the winter vomiting disease, is the most common cause of gastroenteritis worldwide, with the total number of cases reported per year in ontario second only to the common cold. the disease is highly infectious, requires a low infectious dose, and is well-known to cause large outbreaks in closely confined populations. while deaths are rare, hospitalization and longterm sequelae are more likely to occur in at-risk populations, such as the elderly or immunocompromised. action to reduce the number of norovirus infections per year is required due to its health and economic burden. it is estimated that norovirus infections cost the united states 2.5 billion cad and the united kingdom close to 200 million cad per year in health care costs alone. while laboratory surveillance is practiced in ontario to detect norovirus outbreaks, early detection remains a challenge. this project aims to utilize syndromic surveillance with telehealth ontario data in order to develop an early warning system mitigating the impact of norovirus outbreaks. methods telehealth ontario call data, using vomiting as the predominant symptom for calling, will be obtained for september 2009 – august 2015. call volumes preceding the winter vomiting season will be analysed such that “normal” call proportions (vomiting calls/total calls) can be determined, and a threshold can be identified. an alarm will be triggered when the proportion of calls with vomiting as a predominant symptom surpasses this threshold for two consecutive weeks, thus signifying the start of the norovirus winter vomiting season, typically october – april. norovirus laboratory reports in ontario for the same time period will be used as a comparison to identify the confirmed norovirus season. results from this work, an early warning system will be created to detect norovirus outbreaks earlier than the conventional laboratoryconfirmed surveillance methods, as vomiting calls to telehealth ontario will precede provincial norovirus laboratory reports. in creating such a system, public health agencies can notify hospitals, long-term care homes, and other vulnerable populations of impending outbreaks. keywords norovirus; surveillance; telehealth acknowledgments i would like to acknowledge the kindness from public health england, public health ontario, kingston, frontenac and lennox & addington public health, and sykes assistance services corporation in the development and execution of my project. *stephanie l. hughes e-mail: shughes@uoguelph.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e122, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of 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using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational 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dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. onigbogi o.o1, poluyi a.o2, poluyi c.o3, onigbogi m.o4 1. department of community health and primary care, college of medicine, university of lagos, idi araba, lagos, nigeria. 2. college of medicine, university of lagos, idi araba, lagos 3. covenant university medical center, ota, nigeria. 4. department of epidemiology, human genetics and environmental sciences, university of texas health sciences at houston, texas, u.s.a. abstract background: there have been few studies conducted on electronic medical records (emr) among medical doctors who practice in resource limited settings. this study aimed to assess the attitude to and willingness of medical doctors at the lagos university teaching hospital to use emr and to identify the factors that are associated with the willingness to use emr. methods: a stratified sampling method was used to select medical doctors to participate in the study according to their experience and professional cadre. a pretested self-administered questionnaire was used to collect data which were entered and analyzed using the epi-info version 7 software. statistically significant associations were tested using the chi-square and fishers exact tests. results: there were 202 participants in the study. all (100%) had good attitude towards emr. nearly all of them (96.54%) were willing to use emr. there was no significant association between age, gender and willingness to use emr. however, there was a statistically significant association with work duration and it skills (p< 0.05). conclusion: work duration and it skills are significant factors in determining the willingness to use emr. there is therefore a need to include it skills acquisition in medical training so as to increase the chance of use of emr. correspondence: lanreonigbogi@yahoo.com keywords: electronic medical records (emr), attitude, willingness, it, skills, doctors doi: 10.5210/ojphi.v10i2.8416 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi introduction the use of electronic medical records (emr) has been proven to improve the quality of health care worldwide by increasing productivity, reducing workload, minimizing costs and creating a sustainable link between health care providers [1]. this has contributed to its increasing adoption by healthcare organizations globally including nigeria, a country where electronic medical records have been prioritized by its government [2,3]. in some parts of the country, electronic medical records have been implemented however, published research on the pre-implementation stages is scarce [4]. the immediate step after a decision to transition to emrs, is addressing the possible barriers to its successful implementation [5]. previous studies have categorized a number of these barriers into functional, organizational, technical, training, political, ethical and financial; the most common of which is related to the unwillingness of its users to accept the transition. this has contributed to the failure rates of many emr installations [2]. it is therefore necessary to have a roadmap before adopting emrs to prevent wastage of resources. most studies are focused on the implementation and post implementation stages of electronic medical records and have neglected the need neglect the need for an initial assessment of the potential users of these systems which in this case, are the doctors [1,6,7]. materials and methods this study aimed to assess the attitude towards and willingness of doctors at the lagos university teaching hospital to the use of electronic medical records. it also aimed to identify the factors that are significantly associated with the willingness to use electronic medical records. study design this was a cross sectional descriptive study to determine the attitude to, and willingness to the use of electronic medical records by medical doctors at the lagos university teaching hospital. setting our study was conducted at the lagos university teaching hospital (luth), founded in 1962. it is the foremost referral hospital in lagos metropolis located in idi-araba, mushin lga of lagos state, south-west nigeria. the hospital is the largest teaching hospital in nigeria with 761 beds and records approximately 10,000 patient attendances in a month [8]. the hospital has thirteen clinical departments; about one hundred and sixty-eight honorary consultants, forty seven consultants, one hundred and ninety senior registrars, two hundred and eighty nine registrars and about two hundred house officers yearly, giving a total of 894 doctors across the thirteen clinical departments [9]. selection of respondents a stratified sampling method was used to select 202 doctors at the lagos university teaching hospital, idi-araba, lagos by dividing them according to their cadre, i.e house officers, resident doctors and senior resident doctors. four clinical departments were assessed; medicine, pediatrics, obstetrics and gynecology, and surgery. doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi data collection the data was collected through a self-administered questionnaire and was pretested among 20 medical doctors in lagos state university teaching hospital (lasuth). the outcome was used in structuring the questionnaire to avoid ambiguity and include likely options. the questionnaire was stratified into sections on socio-demographic information, attitude and willingness to use electronic medical records respectively. sociodemographic information this contained 7 items which included questions on age as at last birthday, sex: which was recorded as male or female or as observed by the researcher. the marital status refers to discrete options describing a person’s relationship with a significant other and was recorded by the respondent as single, married or separated. the religion of the respondents was classified as christianity, islam, traditional or others. a question was asked on the number of years which respondents had worked at the hospital. respondents were also asked to rate their information technology (it) skills from ‘bad’ to ‘very good’. respondent’s attitude towards the use of electronic medical records this section contained 14 questions adapted from a study conducted at the hormozgan medical sciences university, iran [1]. a 5-point likert scale was used to score the responses ranging from “agree” to “strongly disagree”. thirteen of the questions were positive and respondents that gave positive answers got the appropriate marks for the scale. the question that ‘electronic medical records are not feasible in small practices’ was the only negative question and respondents that gave a negative answer to it were correct. the maximum score obtainable was 70 marks while the minimum score obtainable was 14 marks. a score of 42 (50%) and above suggested good attitude while a score of less than 42, suggested poor attitude. respondent’s willingness to use emrs six questions were asked in this section and respondents were asked to indicate either ‘yes’ or ‘no’. five of these questions were scored. the maximum score obtainable was 5 marks. a score of 3 marks and above out of 5 marks suggested willingness while a score of less than 3 marks suggested unwillingness.. data analysis data collected were immediately coded, verified and analyzed. only fully answered questionnaires were analyzed. epi-info, version 7 was used for this. proportions, means and frequencies were calculated and presented as tables. chi-square was used to test for associations between age, gender, work duration in luth, it skills and willingness to use electronic medical records. association was said to be statistically significant, if p value was ≤0.05. ethical consideration was gotten from the health research and ethics committee of lagos university teaching hospital. participants were informed of the purpose of the study and oral consent was obtained from each of the participants before administering the questionnaires. doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi results socio demographic characteristics the mean age of the respondents was 18.74± 1.88. about half of them were within the age range of 30-39 years (see table 1). of the respondents, 107 (52.97%) were males while 95(47.03%) were females (table 1). more than half of them were married 113(55.94%). one hundred and eight(53.47%) of the study participants had worked at the hospital for less than 12 months while less than half of them 94(46.55%) had worked for over 12 months, (table 1). interestingly, 104(51.49%) and 41(20.30%) of the respondents rated their it skills as ‘good’ and ‘very good’ respectively. awareness and attitude towards emrs all the respondents 202 (100%) had heard the term emr before. more than two-thirds (71.78%) of the respondents who had heard about emrs strongly agreed that it would improve quality of care and reduce medical errors. majority of the study participants 197(97.53%) said it would improve quality of practice. about half of the respondents 106(52.03%) strongly agreed that emrs would increase patients’ satisfaction with the health care provider. nearly all of them 187(92.57%) were of the opinion that the benefits of emrs outweigh its cost. more than half 123(60.89%) of the respondents were certain that emrs would decrease the burden on physicians. regarding the perceived barriers to the implementation of emrs, more than three quarters 178(88.12%) of the respondents said that a major barrier to the implementation of electronic medical records is the structural and administrative rigidity of organizations in accommodating the changes involved. less than half of the respondents 99(49.01%) strongly agreed that emrs cannot be used without the availability of skilled resources and provision of support. about 130(64.36%) of the respondents strongly agreed that proper training would be required before the implementation of emrs and were willing 182(90.10%) to devote time to undergo the training required for its implementation. majority 199(98.51%) were of the opinion that it should be implemented in luth. willingness to use electronic medical records more than two thirds 160(79.21%) of the respondents said they were willing to purchase personal laptops/computers to get familiar with the emr system. nearly all 195(96.53%) of them said they were willing to use emrs if properly trained and if the technical infrastructures in luth are made available 198(98.02%). factors associated with the willingness to use electronic medical records in this study, majority of the respondents in the age groups 20-29 and 30-39 were willing to use electronic medical record, (94.8%, 97.12%) respectively. all the respondents between the ages of 40-49 were willing to use electronic medical records. this association however, was not statistically significant. (p=0.527). even though more males were willing to use emrs than females, majority of both sexes were willing to use emrs (97.20%, 95.79%). gender however, was not said to be statistically significant with willingness to use. (p=0.707) doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi majority of the respondents who had worked for less than 1 year (97.22%), from 1-2 years(93.33%), 3-4 years(93.33%), and 4-5 years(90.91%) were willing to use electronic medical records. all the respondents that had worked for 2-3 years (100%) and greater than 5 years (100%) were willing to use electronic medical records. this association was found to be statistically significant (p=0.003). respondents with good it skills were the most willing (95.12%). all the respondents with bad it skills were unwilling to use electronic medical records. (100%) a statistical significance was found between respondents’ it skills and their willingness to use electronic medical records. (p=0.038). table 1: socio-demographic characteristics of respondents (n = 202) variable frequency percentage (%) age (in years) 20-29 30-39 40-49 77 104 21 38.11 51.49 10.40 sex male female 107 95 52.97 47.03 marital status single married separated 87 113 2 43.07 55.94 0.99 religion christianity islam 165 37 81.68 18.32 duration of work (in years) ≤1 1-2 2-3 3-4 4-5 ≥5 108 30 23 15 11 15 53.47 14.85 11.39 7.43 5.45 7.43 rate your it skills very good good average bad 41 104 56 1 20.30 51.49 27.72 0.50 doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi table 2: awareness and attitude of respondents to the use of electronic medical records (n=202) statements strongly agree (%) agree (%) neutral (%) disagree (%) strongly disagree (%) emrs would improve quality of care and reduce errors 145 (71.78) 52 (25.74) 4 (1.98) 1 (0.50) 0 (0.00) emrs would improve quality of work life 140 (69.31) 57 (28.22) 5 (2.48) 0 (0.00) 0 (0.00) emrs would increase patients’ satisfaction 106 (52.48) 72 (35.64) 22 (10.89) 2 (0.99) 0 (0.00) the benefits would outweigh the cost 100 (49.50) 87 (43.07) 12 (5.94) 2 (0.99) 1 (0.50) emr would decrease burden on physicians 123 (60.89) 60 (29.70) 13 (6.44) 5 (2.48) 1 (0.50) emrs would make patients’ data accessible 152 (75.25) 48 (23.76) 2(0.99) 0 (0.00) 0 (0.00) emrs would increase practice productivity 112 (55.45) 59 (29.21) 24(11.88) 5(2.48) 2 (0.99) emrs used in small practices is not feasible because of high capital investment and risk of insufficient return 26 (12.87) 53 (26.24) 33 (16.34) 73(36.14) 17 (8.42) a possible barrier to the use of emrs is administrative rigidity 102 (50.50) 76 (37.62) 11 (5.45) 13 (6.44) 0 (0.00) emrs cannot be used without the availability of skilled resources and support 99 (49.01) 84 (41.58) 7 (3.47) 10 (4.95) 2 (0.99) users resistance to emrs due to fear of the negative consequences of the technology is a barrier 71 (35.15) 80 (39.60) 29 (14.36) 19 (9.41) 3 (1.49) proper training would be required 130 (64.36) 68 (33.66) 2 (0.99) 2 (0.99) 0 (0.00) i would devote time to undergo training for its implementation 95 (47.03) 87 (43.07) 15 (7.43) 5 (2.48) 0 (0.00) an emr system should be implemented in luth 149 (73.76) 50 (24.75) 3 (1.49) 0 (0.00) 0 (0.00) table 3: willingness of respondents to use electronic medical records (n=202) statements frequency percentage (%) i would be willing to undergo computer training to enable my usage of emrs yes 191 11 94.55 5.45 doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi no i am willing to purchase a personal laptop/computer to familiarize myself with the usage of emrs yes no 160 42 79.21 20.79 i am willing to undergo trainings on emrs & its implementation yes no 195 7 96.53 3.47 i would be willing to use emrs if properly trained yes no 195 7 96.53 3.47 i am willing to use emrs if the technical infrastructures in luth are made available yes no 198 4 98.02 1.98 table 4: factors associated with the willingness of respondents to use electronic medical records (n=202) variable group willingness (%) x2 p value willing not willing age 20-29 30-39 40-49 94.80 97.12 100.00 5.19 2.88 0.00 1.547 *0.527 gender male female 97.20 95.79 2.80 4.21 0.298 0.707 work duration in luth ≤1 1-2 2-3 3-4 4-5 ≥5 97.22 93.33 100.00 93.33 90.91 100.00 2.78 6.67 0.00 6.67 9.09 0.00 18.172 *0.003 it skills very good good average bad 95.12 99.04 94.64 0.00 4.88 0.96 5.36 100.00 7.759 *0.038 *fisher’s exact used doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi discussion our study assessed the attitude of doctors at the lagos university teaching hospital (luth) as well as their willingness to use electronic medical records. all the respondents (100%) had heard about the term electronic medical records while majority of them, 182(90.10%) expressed willingness to devote time for training in order to facilitate their use of emrs. this finding is in contrast with a study conducted in bandar abbas, iran where only 30% of the respondents said they would devote the time required for training on the use of emrs [1]. this may be due to the fact that the degree of specialization in luth is higher than that which obtained in the setting for the iranian study. the doctors in our study may have been motivated to have further training in emr by virtue of the expectations of training position which they held during the period. we can conclude from this study that the doctors at the lagos university teaching hospital were excited about the prospects of an emr installation as 191(94.6%) of them were willing to undergo computer training to facilitate their use of emrs. more than two thirds of the respondents 160(79.21%) were also willing to purchase personal laptops/computers for further training with 104(51.9%) of the respondents having very good it skills. these findings are similar to a study in norway were 72% of participants had good computer skills but contrary to the study conducted in the saudi arabia among 106 physicians where 21% of participants had good it experience [10]. this could be due to the unavailability of supporting infrastructure in those settings. all the respondents in this study had a good attitude to the use of emrs. this is similar to a study conducted in kuwait, eastern arabia where the nurses generally had a positive attitude towards computerized health information systems. a study conducted in ethiopia also showed that more than half of the respondents had good attitude. in nigeria, a study among healthcare workers in ekiti, also indicated that majority (97.58%) of the doctors had positive attitude to the use of technology in healthcare [11-13]. our study showed one hundred percent good attitude to the use of electronic medical records as well as high willingness to use electronic medical records at the lagos university teaching hospital. this is similar to a study done in iran where the overall readiness to use electronic medical records was 52% [1]. majority of the respondents willing to use electronic medical records in this study were between 20 and 39 years old. in a similar study done in a semi-urban center in nigeria, majority of the respondents who were willing were also young [13]. this shows that the younger a population is, the more willing they are to try new things. there was a significant association between the years of experience and willingness to use electronic medical records as all (100%) the respondents above 29 years were willing to use electronic medical records. this is also similar to a study conducted in ido-ekiti [13]. several studies have indicated that there is a relationship between the level of it skills and the willingness to use electronic medical records and this study was no exception. respondents with very good it skills were willing to use emrs. those with good it skills were the most willing (95.12%). all the respondents with bad it skills were not willing to use electronic medical records (100%). this is supported by the study in riyadh and jeddah’s city where a significant relationship doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi between the ease of use and self-efficacy was found [10]. in the study done in ethiopia, 80% of them had good it skills and were ready to use electronic medical records [14]. in a military hospital in riyadh however, only 31.3% of the physicians were willing to use emrs and the level of it experience was low (8.7%) [15]. in conclusion, our result shows that there is a very high positive attitude and willingness to use electronic medical records by the potential users of the system. it also identifies work duration and it skills as significant factors in determining the willingness to use electronic medical records. a more comprehensive study can be done to assess the readiness of the lagos university teaching hospital as a site for the installation of the system creating a clear path for the implementation of electronic medical records. limitations our findings should be interpreted with some caution due to certain limitations which we had. our survey respondents consisted of interns and resident doctors only, a group comprising of persons who had clinical training appointments at the time of the study. the expectation of being it-savvy associated with the training environment might have given rise to some bias in their responses to training-related questions. secondly, the training-related infrastructure that is obtainable in teaching hospitals such as that used for the survey may be associated with bias for it and the use of emr. thirdly, this study may have led to an over-estimation because it did not measure actual use but observed the willingness-to-use which may be exaggerated to demonstrate a leaning towards modern methods by the participants. this probability of over-estimation can be reduced in future surveys by conducting mini it-capacity tests which may be a better reflection of abilities as compared with that which was reported by respondents. references 1. lakbala p, dindarloo k. 2014. physicians’ perception and attitude toward electronic medical record. springerplus. 3, 63. pubmed https://doi.org/10.1186/2193-1801-3-63 2. stone cp. glimpse at ehr implementation around the world: the lessons the us can learn. the health institute for e-health policy. may 2014. available from http://www.ehealthpolicy.org/docs/a_glimpse_at_ehr_implementation_around_the_world 1_chrisstone.pdf (accessed 14th january 2018). 3. amosa b, adepoju t, hameed a, fabiyi a, olatunbosun e. 2016. investigating electronic medical record system of selected healthcare institutions in nigeria. ijarset. 3(12), 3086-91. 4. thompson a, castle e, lubeck p, makarfi p. experience implementing openmrs to support maternal and reproductive health in northern nigeria. inmedinfo 2010 (pp. 332-336). 5. who. (west pacific region). electronic health records: manual for developing countries. revised edition. geneva: world health organisation 2006. available at: http://apps.who.int/iris/bitstream/10665/207504/1/9290612177_eng.pdf (accessed 30th june 2017) https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24516790&dopt=abstract https://doi.org/10.1186/2193-1801-3-63 doctors’ attitude and willingness to use electronic medical records at the lagos university teaching hospital, lagos, nigeria. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e211, 2018 ojphi 6. jennett p, jackson a, healy t, ho k, kazanjian a, et al. 2003. a study of a rural community’s readiness for telehealth. j telemed telecare. 9, 259-63. accessed january 14th, 2018. doi:https://doi.org/10.1258/135763303769211265. pubmed 7. demiris g, oliver dp, kruse rl, wittenberg-lyles e. 2013. telemed j e health. 19(4), 23540. accessed january 14th, 2018. doi:https://doi.org/10.1089/tmj.2012.0185. pubmed 8. luth administrative department, cadre of doctors in lagos university teaching hospital as at may, 2017.personal communication. 18th august 2017. 9. tierney wm, achieng m, baker e, bell a, biondich pg, et al. experience implementing electronic health records in three east african countries. inmedinfo 2010; 20: 371-375. avaialble at https://www.ncbi.nlm.nih.gov/pubmed/20841711 (accessed 30th june 2017). 10. khalifa m. 2013. barriers to health information systems and electronic medical records implementation: a field study of saudi arabian hospitals. procedia comput sci. 21, 335-42. https://doi.org/10.1016/j.procs.2013.09.044 11. baron rj, fabens el, schiffman m, wolf e. 2005. electronic health records: just around the corner? or over the cliff? ann intern med. 143(3), 222-26. https://www.ncbi.nlm.nih.gov/pubmed/16061920. accessed june 30th, 2017. pubmed https://doi.org/10.7326/0003-4819-143-3-200508020-00008 12. malik ma, khan hr. understanding the implementation of an electronic hospital information system in a developing country: a case study from pakistan. in proceedings of the third australasian workshop on health informatics and knowledge management. 2009;97:31-36. 13. olufunmilayo a, idowu a, raji o, gabriel e, onigbogi o. 2017. knowledge, attitude and willingness to use mhealth technology. journal of advances in medicine and medical research. 22(8), 1-10. https://doi.org/10.9734/jammr/2017/33232 14. biruk s, yilma t, andualem m, tilahun b. 2014. health professionals’ readiness to implement electronic medical record system at three hospitals in ethiopia: a cross sectional study. bmc med inform decis mak. 14(1), 115. accessed june 28th, 2017. doi:https://doi.org/10.1186/s12911-014-0115-5. pubmed 15. mohamed ba, el-naif m. 2005. physicians’, nurses’ and patients’perception with hospital medical records at a military hospital in riyadh, saudi arabia. j family community med. 12(1), 49. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc3410137/. accessed july 1st, 2017. pubmed https://doi.org/10.1258/135763303769211265 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14599328&dopt=abstract https://doi.org/10.1089/tmj.2012.0185 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23506328&dopt=abstract https://doi.org/10.1016/j.procs.2013.09.044 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16061920&dopt=abstract https://doi.org/10.7326/0003-4819-143-3-200508020-00008 https://doi.org/10.9734/jammr/2017/33232 https://doi.org/10.1186/s12911-014-0115-5 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25495757&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23012072&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23012072&dopt=abstract isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts integrated surveillance: joint modeling of rodent and human tularemia cases in finland chawarat rotejanaprasert* national center for genetic engineering and biotechnology, bangkok, thailand objective we seek to integrate multiple streams of geo-coded information with the aim to improve public health surveillance accuracy and efficiency. specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution can help early prediction of human incidence. to this end, we develop joint modeling approaches to evaluate the contribution of vector or reservoir information on early prediction of human cases. a case study of spatiotemporal modeling of tularemia human incidence and rodent population data from finnish health care districts during the period 1995-2013 is provided. results suggest that spatial and temporal information of rodent abundance is useful in predicting human cases. introduction an increasing number of geo-coded information streams are available with possible use in disease surveillance applications. in this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. therefore it is appropriate to consider the analysis of their spatial distributions together. specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution could inform incidence in humans. tularemia is an infectious disease endemic in north america and parts of europe. in finland tularemia is typically mosquito-transmitted with rodents serving as a host; however a country-wide understanding of the relationship between rodents and the disease in humans is still lacking. we propose a methodology to help understand the association between human tularemia incidence and rodent population levels. methods data on rodent population levels are collected around the country by the finnish natural resources institute. human tularaemia cases are recorded as laboratory-confirmed and reported to the national infectious disease register (nidr). human cases and rodent data were aggregated to match the 20 finnish health districts over the period 1995-2013 [1]. we develop our methodology in a bayesian setting. the counts of human cases for each health district in a given year are assumed to follow a poisson distribution and the rodent data are assumed to have a categorical likelihood. the linear predictors linked to the human and rodent likelihood functions are then decomposed additively into spatial, temporal, and space-time interaction random effects. we then link the two likelihoods via the interaction term by assuming that the human spatiotemporal variation is dependent on the rodent activity with one-year lag. in the case of the rodent data, we also included two additional spatial and non-spatial contextual terms to better model ecological effects associated with rodent population levels as described before [2]. we then finally develop indicators, on the scale 0 to 1, to quantify the association between human incidence and a rodent vector. results results suggest that spatial and temporal information of rodent abundance is useful in predicting human cases. conclusions future modeling directions are recommended to include environmental and epidemiological factors. to the best of our knowledge, this is the first time that rodent data, captured for nonhealth related purposes, is used to better inform the human risk of tularemia in finland. keywords tularemia; surveillance; multivariate; joint modeling references [1] rossow, h., ollgren, j., hytönen, j., rissanen, h., huitu, o., henttonen, h. & vapalahti, o. (2015). incidence and seroprevalence of tularaemia in finland, 1995 to 2013: regional epidemics with cyclic pattern. euro surveillance: bulletin européen sur les maladies transmissibles= european communicable disease bulletin, 20(33). [2] sane, j., ollgren, j., makary, p., vapalahti, o., kuusi, m. and lyytikainen, o. (2016) ‘regional differences in long-term cycles and seasonality of puumala virus infections, finland, 1995–2014’, epidemiology and infection, pp. 1–6. doi: 10.1017/ s0950268816000765. *chawarat rotejanaprasert e-mail: baantoto@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e109, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts sustaining community event-based surveillance in sierra leone eilidh m. higgins*, erin polich, maitreyi sahu, stacey mearns and ruwan ratnayake international rescue committee, freetown, sierra leone objective to assess whether the change in death swabbing policy in sierra leon has begun to affect community death reporting, we analyzed trends in death reporting before and after the policy change. introduction stemming from the 2014-6 ebola virus disease (evd) outbreak, community event based surveillance (cebs) was implemented in sierra leone using community health workers to generate alerts for trigger events suggestive of evd transmission. through september 30, 2015 (last month of active evd transmission), the majority (86%) of alerts reflected community deaths; this was beneficial as ebolarelated deaths were detected with delay during the epidemic’s peak. the government had implemented a policy of mandatory swabbing and testing of all dead bodies. the policy changed on june 30, 2016 wherein only swabbing of deaths deemed to be high-risk for evd is required. to assess whether this policy change has begun to affect community death reporting, we analyzed trends in death reporting before and after the policy change. methods this analysis was conducted using data from nine districts during period 1 (january-june 2016) and period 2 (july 2016). weekly changes in the reporting of death alerts during the two periods were assessed. an interrupted time series analysis (its) with a segmented linear regression was also used to assess the immediate impact of the policy change. results during period 1, monthly changes in death alerts across districts were variable (-8% to 16%). comparison of the weekly average between periods 1 and 2 showed a 33% reduction in death alerts. during period 1 (before the policy change), there was an overall significant increase of 3.2 death alerts per week (p=0.00) and no immediate impact or changes in the trend afterwards. at the district level, on average 354 death alerts were generated weekly in june, compared to 237 in july (33% reduction); moyamba district experienced the largest drop in death alerts from 46 to 16 (65%). conclusions community death reporting provides early warning of evd transmission by rapidly capturing death alerts where vital registration is not fully functional. although we have one month of data postpolicy change, this preliminary analysis suggests that the change in swabbing policy may have halted an observed increase in death reporting. further community mobilization efforts and training are warranted to prevent a drop in death reporting. keywords ebola; surveillane; community-based acknowledgments many thanks to the uk’s department for international development and the united staes agency for international development. *eilidh m. higgins e-mail: eilidh.higgins@rescue.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e122, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene new york city department of health and mental hygiene, queens, ny, usa objective to prospectively identify serotype-specific clusters of salmonellosis in new york city (nyc). introduction nontyphoidal salmonella, consisting of >2,500 distinct serotypes, is the leading bacterial agent of foodborne illness in the u.s., causing an estimated 1 million infections per year.1 in nyc, interviews of all case-patients (n≈1,100 annually) are attempted to support outbreak investigation and control. salmonella clusters in nyc are typically identified either by notification from pulsenet, cdc, or other health departments or by a weekly analysis using the historical limits method. more systematic and timely cluster detection could inform resource prioritization and improve the effectiveness of public health interventions. we initiated daily analyses in may 2015 to detect spatio-temporal clusters by serotype among cases since february 23. in july 2015, an analysis was added to detect purely temporal clusters among cases since may 1. methods salmonella isolates for nyc residents are serotyped by the city and state public health laboratories and reported to the bureau of communicable disease of the nyc department of health and mental hygiene. an automated process assigned a standardized serotype to each salmonellosis case. each case’s residential address at time of report was geocoded in near real-time and assigned a census tract. each case was also assigned an “event date,” representing the illness onset date. we used the prospective space-time permutation scan statistic to detect and evaluate clusters.2 a spatio-temporal analysis, with a maximum spatial size of 50% of observed episodes, was run for each serotype with a case reported in the past 60 days. a temporal analysis was run for all serotypes, replacing “space” in the space-time permutation scan statistic with “serotype” and setting the maximum spatial size to 0.3 for all analyses, the temporal cluster size range was 2 to 60 days. a 14-day lag was implemented to allow for data accrual. the baseline period was 1.5 years. monte carlo simulations (n=999) were used to determine statistical significance. automated analyses were run each morning using microsoft task scheduler, sas 9.2, and satscan 9.4.1. any cluster with a recurrence interval (ri) ≥45 days was summarized in a map and linelist. foodborne disease epidemiologists assessed clusters to determine if cases were linked by a common exposure and/or by pulsed-field gel electrophoresis (pfge) of isolates. results with event dates february 23–july 29, 2015, 391 salmonella cases of 48 serotypes were reported, and 7 spatio-temporal clusters were identified for 5 serotypes. with event dates may 1–july 29, 254 cases of 42 serotypes were reported, and 5 temporal clusters were identified for 5 serotypes. the most unusual clusters were citywide temporal clusters of s. poona (n=9, ri=14.5 years), s. oranienburg (n=4, ri=4.3 years), and s. berta (n=13, ri=3.0 years), and a spatio-temporal s. heidelberg cluster (n=5, ri=456 days), which at the time of signaling corresponded to clusters already identified and under investigation. a cluster of s. muenchen, first identified by our temporal (ri=67 days), then by our spatio-temporal analyses (ri=59 days) 8 days later, ultimately included 6 cases whose isolates were indistinguishable by pfge. the investigation is ongoing. conclusions in the first few months of prospective, automated cluster detection analyses by serotype, a manageable number of clusters were detected. the purely temporal analysis was sensitive to rare serotypes. all clusters either prompted new investigations or corresponded to ongoing investigations, complementing nyc’s existing enhanced salmonella surveillance system. these methods could be useful for other health departments to adopt for primary or confirmatory cluster detection. keywords salmonella; foodborne illness; outbreak detection acknowledgments deborah kapell and alison levin-rector contributed to the sas code; ana maria fireteanu contributed to standardizing serotyping data; public health laboratory colleagues performed serotyping and pfge; and martin kulldorff provided guidance. references 1. scallan e, et al. foodborne illness acquired in the united states—major pathogens. emerg infect dis. 2011;17:7-15. 2. kulldorff m, et al. a space-time permutation scan statistic for disease outbreak detection. plos medicine. 2005;2:e59. 3. huang ss, et al. automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study. plos medicine. 2010;7:e1000238. *eric r. peterson e-mail: epeterson@health.nyc.gov online journal of public health informatics * issn 1947-2579 * 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opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts notifiable disease reporting among physicians practicing in grenada shantel peters* department of public health preventive medicine, st george’s university, st george's, grenada objective the study was carried out to determine physicians’ knowledge of notifiable reporting and to identify the barriers to reporting in grenada. introduction despite the significance of disease reporting to any health system, grenada like most countries struggle with underreporting of notifiable diseases by physicians. in order to improve the national disease surveillance system in grenada, it is critical understand the reasons for any underreporting. the study was conducted to determine physicians’ knowledge of notifiable reporting and to identify the barriers to reporting. methods the grenada medical and dental council identified a total of 129 registered and practicing physicians. a cross-sectional study design was developed to obtain information from all registered and practicing physician. the survey tool included questions on demographics; training history and medical practice details, as well as knowledge, practice and barriers to reporting notifiable diseases. the survey was administered to physicians in both paper-based and electronic formats. results to date only 13 surveys have been returned. preliminary data show that 61.5% of respondents rated an “average” on their knowledge of which diseases are reportable and of those only 46 % knew where to obtain a list of notifiable diseases (nds). fifty three percent (53%) of respondents said that they have reported nds to the relevant authorities in the past. thirty eight percent (38.5%) believed it should be the responsibility of nurses to report nds and 30.8% stated it should be the physician. the major barriers to reporting, identified by the respondents were being too busy, too much time required, and lack of infrastructure or reporting systems. when asked about ways to improve reporting, 38.5% identified improvements to the reporting form, and 30.8% identified education of physicians on reporting protocol and importance. conclusions while this is still preliminary data, the majority of the physicians surveyed had some knowledge of reporting nds. the barriers to reporting identified were being too busy and lack of infrastructure. future improvements to the reporting system in grenada should focus on making forms electronic and less lengthy, and on educating physicians on the importance and protocol of reporting nds. keywords low resource country; grenada; disease notification; knowlege and practice; barriers *shantel peters e-mail: speters@sgu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e181, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts enhanced syndromic surveillance during the 9th indian ocean island games, 2015 pascal vilain*1, frédéric pages1, guy henrion2, xavier combes2, marc weber3, pierre-jean marianne dit cassou4, katia mougin-damour5 and laurent filleul1 1french national public health agency, regional unit (cire) océan indien, saint-denis, réunion; 2university hospital centre, saint-denis, réunion; 3hospital centre, saint-benoît, réunion; 4univertsy hospital centre, saint-pierre, réunion; 5hospital centre, saint-paul, réunion objective to describe how syndromic surveillance was enhanced to detect health events during the 9th indian ocean island games (ioig) in reunion island. introduction the 9th ioig took place in reunion island from july 31 to august 9, 2015. this sport event gathered approximatively 1 640 athletes, 2 000 volunteers and several thousand spectators from seven islands: comoros, madagascar, maldives, mauritius, mayotte, seychelles and reunion. in response to the import risk of infectious diseases from these countries where some of them are endemics, the syndromic surveillance system, which captures 100% of all emergency department visits, was enhanced in order to detect any health event. methods in reunion island, syndromic surveillance system is based on oscour® network (organisation de la surveillance coordonnée des urgences) that collects data from all emergency departments of the island. data are daily transmitted to the french national public health agency then are available to the regional office. at the regional level, data are integrated into an application that allows the built of predefined syndromic groups according to the health risks related to mass gatherings (table 1, parts 1 to 3) and complemented by specific syndromic groups (table 1, part 4). daily analyses with temporal [1] and spatial-temporal [2] algorithms were performed during the surveillance period of july 27 to august 13, 2015. in addition to this monitoring, ed physicians were requested to proactively tag y33 (icd-10) as secondary diagnosis, each ed visits related to ioig. line lists were reviewed daily. each day, an epidemiological report was send to public health authorities. results from july 31 to august 9, 2015, the activity of eds was in accordance with that expected. no health events were detected by the syndromic surveillance system except for the syndrome “alcohol intoxication” for which consecutive signals were observed from august 6 to 9, 2015. this increase occurs commonly at the beginning of each month (due to the social benefits payday) [3] nevertheless this event has probably been increased by ioig (finals for team sports and games closing ceremony). in total, 8 ed visits were tagged y33 as secondary diagnosis. in over half the cases, visits were related to trauma. conclusions the syndromic surveillance system proved to be useful for the surveillance of mass gathering events due to its capacity to detect health events but also to provide reassurance public health authorities [4]. as described in literature [5], few ed visits were tagged in relation to ioig. indeed, the tag of ed visits was implemented two weeks before the games, and given the shifts of ed physicians, some of them may have not been informed. in the future, preparation meetings with physicians will have to be planned several months before in order to improve the response rate for mass gathering events. table1. syndromic groups monitored by the surveillance syndromic system during ioig, 2015 keywords syndromic surveillance; emergency department; sporting event; mass gathering acknowledgments all emergency departements of reunion island references 1. vilain p, pagès f, mougin-damour k, et al. using an emergency department syndromic surveillance system to assess the impact of cyclone bejisa, reunion island. online journal of public health informatics. 2015;7(1):e171. 2. vilain p, cossin s, filleul l. interest of prospective spatio-temporal analysis from ed data to detect unusual health events. online journal of public health informatics. 2016;8(1):e171. 3. vilain p, larrieu s, combes x, et al. using a syndromic approach to study health impact and risk factors of alcohol intoxication in reunion island. online journal of public health informatics. 2014;6(1):e171. 4. henning kj. what is syndromic surveillance? mmwr suppl. 2004;53:5-11. 5. kajita e, z. luarca m, chiang c, wu h, hwang b. syndromic surveillance of emergency department visits for the 2015 special olympics. online journal of public health informatics. 2016;8(1):e129. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e167, 2017 meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky michael abisa, mhi, mbi, mph university of kentucky, lexington, kentucky, usa abstract: objectives: to explore the challenges health information technology (hit) vendors face to satisfy the requirements for meaningful use (mu) and electronic laboratory reporting (elr) of reportable diseases to the public health departments in kentucky. methodology: a survey was conducted of health information exchange (hie) vendors in kentucky through the kentucky health information exchange (khie). the survey was cross-sectional. data were collected between february and march 2014. participants were recruited from khie vendors. participants received online survey link and by email and asked to submit their responses. vendors’ feedback were summarized and analyzed to identify their challenges. out of the 55 vendors who received the survey, 35(63.64%) responded. results: of the seven transport protocol options for elr, vendors selected virtual private network (vpn) as the most difficult to implement (31.7%). secure file transfer protocol (sftp) was selected as preferred elr transport protocol (31.4%). most of the respondents, 80% responded that they do not have any challenge with the health level 7 (hl7) standard implementation guide required by mu for 2014 elr certification. conclusion: the study found that the most difficult transport protocol to implement for elr is vpn and if vendors have preference, they would use sftp for elr over khie choice of vpn and simple object access protocol (soap). khie vendors do not see any variability in what is reportable by different jurisdiction and also it is not difficult for them to detect what is reportable from one jurisdiction verse the other. keywords: meaningful use; electronic laboratory reporting; health information exchange; health level 7; health information technology; transport protocol correspondence: michael abisa, university of kentucky, 513-828-9165, mab224@uky.edu, michaelabisa@gmail.com doi: 10.5210/ojphi.v9i3.7491 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:mab224@uky.edu mailto:michaelabisa@gmail.com meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi 1. introduction public health departments or agencies rely on hospitals, clinics, and laboratory data to provide timely intervention in a community when there is an outbreak of disease to prevent additional illness. because the conventional methods of reporting via mail, facsimile, or telephone require active participation of laboratory staff, automated reporting from clinical laboratories has been proposed as a means to improve the quality and timeliness of disease notification [1]. to this end, electronic laboratory reporting (elr) has been promoted as being integral to improve disease surveillance [2]. at its simplest, electronic laboratory reporting is the distribution of the results of laboratory testing using electronic transmission systems rather than paper-or-fax based processes [3]. electronic laboratory reports are critical for an effective public health response both for routinely reportable diseases as well as potential bioterrorism (bt) agents [4]. with respect to public health disease surveillance activities, elr is useful for conditions where the diagnosis can be based solely on positive (or negative) results from laboratory testing, such as chlamydia and salmonella infection, among others [3]. it is important to mention here that elr is also useful for conditions that require clinical diagnosis as well. the “usefulness” of elr is largely driven by public health investigation and reporting. over the past decades, public health institutions have made much progress in creating secure systems for electronic data transmission to improve the quality of laboratory reporting. public health agencies require health information technology vendors to provide secure transport protocols capable of meeting the requirements of meaningful use (mu) and elr. meaningful use is using certified electronic health record (ehr) technology to: • improve quality, safety, efficiency and reduce health disparities • engage patients and family • improve care coordination, and population and public health • maintain privacy and security of patient health information [5]. interestingly, these agencies do not know whether vendors encounter any challenges in meeting the requirements of meaningful use. to improve elr, it is important to understand challenges vendors go through, if any, in meeting the requirements for mu. identifying these barriers could create an appropriate dialogue between stakeholders to find ways that can improve the system. to accelerate adoption of elr, the center of disease control and prevention (cdc) advanced standards for vocabulary, format, and messaging; funded the development of software; and conducted an extensive outreach campaign to state and local health departments to increase use of the software [6]. to this end, kentucky state and local public health departments have adopted electronic laboratory reporting for reportable diseases to enable hospitals, clinics, and laboratories to submit lab reports electronically. clinical laboratory reporting (as opposed to reporting by health care providers) has become increasingly valuable in disease surveillance [7,8]. major public health threats in recent years in the united states support the call for public health data sources integration which will yield to the benefits of all stakeholders involve. meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi currently, health information exchange (hie) and public health departments across the country have been working with health information technology (hit) vendors to improve elr for public health. improved reporting can help public health departments better allocate limited resources for targeted investigations and interventions [9]. health information exchange provides the infrastructure for information exchange, including the business model, governance structure, operating principles, legal model, and technology model for the exchange of healthcare information among various organizations [10]. kentucky health information exchange (khie) in collaboration with providers and public health departments in kentucky are working to help state vendors to meet the requirements of elr as envisaged by the cdc. khie acts as a hub between public health departments and providers in transmitting laboratory reports for reportable diseases. the role of health information exchange in providing services to providers and health departments can be seen in three scenarios as provided by the office of the national coordinator. scenario 1: complete electronic health records scenario 2: electronic health records and/or laboratory information systems (mu complaint messages) scenario 3: electronic health records and/or laboratory information systems [11]. in the first and second scenarios, a provider uses fully or modularly certified technology to report directly to the public health department or through and hie. scenario one and two do not require any manipulation of message or content on the part of the hie. the hie primary “brokers” the exchange. however, in the third scenario, the provider is not using certified technology and therefore uses the hie as an extension of their technology to attest to meaningful use. there are various types of transport protocols vendors’ use in transmitting laboratory reports to the public health departments. a panel of subject matter experts (sme) established by cdc in 2011 evaluated and analyzed five transport protocol options (i.e. public health information network messaging system (phinms), simple mail transfer protocol and multipurpose internet mail extensions (smtp+s/mime), secure file transfer protocol (sftp), hypertext transfer protocol secure representational state transfer (https rest) and simple object access protocol (soap) which are currently utilized industry transport protocols. the panel identified soap as the protocol that can meet the current and future needs of immunization information system (iis) data exchange and that also has the best chance for broad adoption across disparate healthcare systems [12] but hit vendors are not mandated to use any particular transport protocol for elr. it is essential to note that khie relies heavily on vpn and soap for transport protocols but the sme from cdc did not include vpn as one of the five transport protocol options. khie does not know whether its vendors encounter any challenges in using vpn or soap though that is what they rely on. understanding challenges related to securely transporting elr by vendors was one of the essential goals to khie to improve their reporting system and reduce barriers. in addition, the hl7 standardized interface implementation guide contains a lot of information. meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi khie wants to know whether vendors clearly understand the requirements in the implementation guide or have challenges with some of the vocabularies. historically, most laboratories developed their own set of local codes to describe their findings [3]. this has never been a good practice but currently elr standards specify the use of logical observation identifiers names and codes (loinc) for reporting the type of test, but many laboratories are not yet capable of specifying results using loinc codes. even when they do have that capability there are substantial differences between versions of loinc [3]. it would interest khie to understand any challenges vendors encounter when using the hl7 implementation guide. harmonizing these values is an important and challenging aspect of implementing the elr [3]. in order to have successful elr implementation, there are three areas that khie needs comprehensive feedback through evaluation and analysis. these are transport protocols, reportable diseases, and semantic interoperability (standardized interface implementation). currently, khie has not explored these areas to find out if vendors have any challenges in meeting the requirements of elr. that notwithstanding, khie vendors have been working hard to meet the requirements for elr certification as expected. however, less attention has been paid to understanding the challenges the vendors go through to meet these requirements. thus understanding vendors’ challenges in meeting the requirements for mu and elr is essential to ensure quality laboratory reporting. therefore, the purpose of this study was to explore the challenges that hit vendors face when satisfying the requirements of meaningful use and electronic laboratory reporting on reportable diseases to the public health departments in kentucky. 2.1. study design and sample the study was cross-sectional. participants were recruited from kentucky health information exchange hit vendors. all of the participants were vendors in kentucky who were involved in meaningful use and electronic laboratory reporting. individuals were selected based on their position as the representative or contact person of their organization. participants were recruited by email through khie vendors’ contact list. the list was comprised of 55 representatives of vendors who work with khie with their company names, product types, certification year and contacts. 2.2. recruitment and data collection an active recruitment method was used to appeal to the current vendors who qualified for the study. vendors who had contact details up to the end of the year 2013 or later with khie were actively recruited to participate in the study. in all, nine (9) questions were included in the survey. out of the 55 vendors who received the survey, 35 (63.64%) responded to the survey. there was fourteen (14) reminder notices and three emails sent to vendors to increase participation. the reminder notices and emails helped to increase participation. the data was collected from february 2014 through march 2014. thus, the sample analysis was comprised of 35 khie meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi vendors’ representatives who were authorized by their organizations to share their challenges with khie. the survey questions were carefully designed and discussed with the khie subject matter expert (sme), an expert in public health, and another in biomedical and health informatics. the survey focused on three important areas to collect data from the vendors: transport protocols (focus a), reportable diseases (focus b) and semantic interoperability (standardized interface implementation) (focus c). online survey software (survey monkey) was used to make it easy for vendors to provide responses. vendors who could not respond to the survey through the survey monkey link also received a copy of the questions attached to their email and could email back after completion. a brief introduction about the study was included as to why they had been contacted to participate in the survey and what the study wanted to achieve. a reminder email was sent to vendors one week after they received the survey link to increase participation. 2.3. measures focus a concerned challenges related to securely transporting elr. understanding challenges that vendors face regarding transport protocols was one of the essential needs of khie for improving their reporting system and reducing barriers. the questions in this section were used to find out which of the transport protocols was difficult to implement and if vendors had preferences regarding which protocol they would adopt. seven (7) examples of transport protocols were used in the survey. these include; secure ftp(sftp), simple object access protocol (soap) web service, ebxml via the public health information network messaging system (phinms), direct protocolsimple mail transfer protocol(smtp), secure/multipurpose internet mail extensions (s/mime), hypertext transfer protocol secure (https post), hypertext transfer protocol secure representational state transfer (https rest), minimum lower layer protocol(mllp), and vpn such as point-to-point tunneling protocol (pptp) and layer 2 tunneling protocol (l2tp). vendors were asked to select the most challenging transport protocols for elr and state if they have a preference, which protocol they would prefer for elr. focus b was about reportable diseases: the questions in this section were used to explore what is reportable by jurisdiction and difficulties in detecting what is reportable in different jurisdictions. the following two questions were asked: 1) do you see any variability in what is reportable by jurisdiction? and -2) is it difficult to detect what is reportable in jurisdiction a versus in b? the purpose of focus c was to identify if there are any challenges in hl7 standard implementation guide required by meaningful use for 2014 elr certification. a continued challenge for broader uptake of elr is the slow rate of adoption of the messaging and vocabulary standards that are intended to make the sharing of information easier and more useful [3]. the hl7 standard implementation guide provides useful guidelines to all users to ensure effective and quality reporting. the health it standards committee, which advises the national coordinator for health it, recently recommended a minimum necessary set of vocabulary meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi standards that will enable interoperable electronic health record data elements [13]. these vocabulary standards specify loinc for laboratory tests and diagnostic studies as well as many other categories of information [14]. with this in mind, questions were asked to explore whether vendors have any challenges with the hl7 standard implementation guide required by meaningful use for 2014 elr certification, why they find a particular standard challenging, vocabularies that they have the greatest challenge with, and from where they get their vocabulary standard for public health elr. 3. data analysis and results the survey was initially sent to 55 khie vendors. though, khie has more than 55 vendors, they did not have updated email addresses for the rest of the vendors at the beginning of the survey. of the 55 potential participants, 35 (63.64%) completed the survey. respondents had the option to skip questions as desired or opt-out entirely. a simple descriptive analysis was conducted to look at the frequencies and percentages of the responses toward each variable. cross tabulation and chi-square analyses were then conducted on four variables. all analyses were conducted using ibm spss statistics for windows version 22.0. in all there were nine (9) questions of interest in the survey. the first question was to determine the most challenging transport protocol to implement. out of the seven (7) transport protocol options, vendors selected vpn (pptp and l2tp) as the most difficult to implement (31.7%). see figure1. a chi-square goodness of fit test was calculated comparing the frequency of occurrences of each value of transport protocol. it was hypothesized that each value would occur an equal number of times. significant deviation from the hypothesized values was found (x [2](5) = 16.94, p<.05). the null hypothesis was rejected. sftp was selected as the preferred elr transport protocol (31.4%). a chi-square goodness of fit test was calculated comparing the frequency of occurrences of each value of transport protocol. it was hypothesized that each value would occur an equal number of times. significant deviation from the hypothesized values was found (x [2](5) = 11.80, p<.05). the null hypothesis was rejected. again, a pearson chi-square test of independence was calculated comparing the frequency of transport protocols in question 1 and question 2. it was hypothesized that vendors have challenges with transport protocols and would prefer to use a particular transport protocols if they have a preference. no significant deviation from the hypothesis was found (x [2](25) = 28.42 p >.05 meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi figure 1: of the following transport protocols, which do you find is most challenging to implement? the next focus area was on reportable diseases. almost 54.3% of the respondents answered no to indicate they do not see any jurisdictional differences in reportable diseases while 45.7% (16 of 35 respondents) answered yes to the effect that they do see variability in what is reportable by different jurisdictions. a chi-square goodness of fit test was calculated comparing the frequency of occurrences of each value of a response. it was hypothesized that each value would occur an equal number of times. no significant deviation from the hypothesized values was found (x [2](1) = .28, p>.05). the null hypothesis was retained. see table 1 below for details. besides, about 62.9% (22 of respondents) answered no to show that it is not difficult to detect what is reportable in different jurisdictions. a chi-square goodness of fit test was calculated comparing the frequency of occurrences of each value of a response. it was hypothesized that each value would occur an equal number of times. no significant deviation from the hypothesized values was found (x [2](5) = 2.31, p>.05). the null hypothesis was retained. in addition, a pearson chi-square test of independence was calculated comparing the frequencies of question 3 and 4. it was hypothesized that vendors do not see any variability in what is reportable by jurisdiction and it is not difficult to detect what is reportable in different jurisdictions. no meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi significant deviation from the hypothesis was found (x [2](1) = 2.08 p >.05. see table 1 below for details. table 1 3. do you see any variability in what is reportable by jurisdiction? frequency percent chi-square no 19 54.3 (x [2](1) = .28, p>.05) yes 16 45.7 total 35 100 4. is it difficult to detect what is reportable in jurisdiction a than in b? frequency percent chi-square no 22 62.9 (x [2](5) = 2.31, p>.05) yes 13 37.1 total 35 100 focus c was on semantic interoperability– standardized interface implementation guide. most of the respondents, 80%, responded that they do not have any challenges with the hl7 standard implementation guide required by meaningful use for 2014 elr certification. a chi-square goodness of fit test was calculated comparing the frequency of occurrences of each value of a response. it was hypothesized that each value would occur an equal number of times. significant deviation from the hypothesized values was found (x [2](1) = 12.60, p<.05). the null hypothesis was rejected. see table 2 for details. table 2 5. do you have any challenge with the hl7 standard implementation guide required by meaningful use (mu) for 2014 elr certification? frequency percent chi-square no 28 80 yes 7 20 (x [2](1) = 12.60, p<.05 total 35 100 question 6 and 9 were open-ended questions and responses are summarized under discussion. meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi with respect to question number eight (8) which was to identify vocabulary standard vendors have challenge with, about 11(31.4%) of the respondents selected unified code for unit of measure (ucum) as vocabulary standard they have difficult challenge while 9(26.7%) skipped or did not answer the questions. a chi-square goodness of fit test was calculated comparing the frequency of occurrences of each value of a response. it was hypothesized that each value would occur an equal number of times. no significant deviation from the hypothesized values was found (x [2](3) = 1.0, p>.05). the null hypothesis was retained. regarding question number seven (7), which was designed to find out why vendors find particular standard challenging, 34(97.14%) did not respond. 4.1. discussion the finding suggests that the most difficult transport protocol to implement for elr is vpn (pptp and l2tp). surprisingly, khie relies heavily on vpn for elr. does this mean khie is increasing the burden on its vendors? again, if vendors have preference, they would use sftp for elr. could this result be used as a direction towards a selection of sftp as third transport protocol options for khie vendors? the finding is very interesting to note because the 2011 cdc panel of subject matter experts (smes) did not select vpn (pptp and l2tp) as one of the five transport protocol options perhaps due to similar observations that vpn is difficult to implement or it does not support the current industry utilized requirements. sftp which was selected as the preferred transport protocol was among the five options selected by the cdc panel. the list of diseases considered reportable varies by state and year. since states and jurisdictions are sovereign entities, reportable conditions are determined by laws and regulations of each state and jurisdiction [15]. to this end, it is possible that some conditions considered naturally reportable might not be reported in certain jurisdictions. the study found the majority of the hit vendors in kentucky do not see any variability in what is reportable by different jurisdiction and that it is not difficult to detect what is reportable from one jurisdiction to another. reporting completeness of notifiable diseases is highly variable and related to the condition or disease being reported [16]. the finding again stipulates that vendors in general do not have any challenges with the hl7 standard implementation guide required by meaningful use elr certification. however, some vendors stated that they do have challenges with some of the vocabularies used in the hl7 implementation guide. in particular, if a vendor uses vocabularies that are not up to date, it will affect its data quality and create confusion in the reporting system. it has been observed that delays in development or distribution of an updated version of the vocabulary may result in some laboratories temporarily reverting to paper reporting systems or mapping new aggregate concepts to existing but inaccurate terms [15] the results from the survey show that vendors have greatest challenge with ucum. previous studies found out that vast expressive power, concept richness, and flexibility of a post coordinated vocabulary seem better suited to public health surveillance requirements and the diverse information system capabilities of laboratories, but the post coordinated vocabulary may meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi require additional guidelines for the composition of appropriate and consistent terms [14]. vendors’ having deep understanding of the vocabulary standard for elr is critical for quality laboratory reporting. according to some vendors “hl7 implementation is time consuming and could take a team of three (3) approximately five (5) years to implement hl7 successfully which is too long for any manager to commit resources for implementation”. no specific reason was assigned to why hl7 implementation is time consuming as stated by some respondents. some vendors also mentioned that new required data fields which are not currently being captured by customers make it difficult to incorporate into workflow. another area of interest to khie was to determine where vendors get their vocabulary standard from. elr reporting may be inaccurate if the source does not have an updated vocabulary standard. vendors revealed the following sources of vocabulary standard: center for disease control and prevention (cdc), national library of medicine, infoguard laboratories inc, emds, medicapaedia, centers for medicare and medicaid services (cms) guidelines, unified medical language system (umls), terminology services and regenstrief loinc mapping assistant (relma). this finding suggests that vendors get their vocabulary standard from various sources. this is a major concern for khie that warrants further investigation. 4.2. recommendation as part of khie aims to reduce barriers and improve the elr system, the following recommendation should be taken into consideration. khie should recommend a maximum of three (3) transport protocols that have the best chance of adoptions across disparate hit vendors to move towards the standardization in elr in kentucky. khie should investigate why vendors prefer sftp over their choice (vpn and soap) further research is needed to explore the difficulties in implementing vpn and soap and what can be done to make it less burdensome since khie heavily relies on these two transport protocol options. there is also a need to zero in on why some vendors see variability and find it difficult to detect what is reportable in different jurisdictions. again, there should be follow up studies to find out why hl7 is time consuming to some vendors. 4.3. limitations some vendors’ representatives did not respond to the survey because some of the questions appeared to be too technical for them to answer. those who could not respond to the survey questions due to the technicalities involved were either project managers or administrators who lack the technical understanding of the questions. they promised to get resources from their organization to answer the questions but could not do so before the end of the data collection. the duration of the data collection might have been too short for some vendors to respond to the survey. the contact list of khie vendors did not comprise all of their vendors which limited the meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi number of vendors who could have participated or responded to the survey and possibly altered the outcome of the results. a follow up phone survey could have increased the participation but khie did not have vendors’ telephone numbers on their contact list. 5. conclusion the result of the study has demonstrated the importance of understanding vendors’ challenges in meeting the requirements for elr to ensure quality laboratory reporting. the study found that the most difficult transport protocol to implement for elr is vpn (pptp and l2tp) and if vendors have preference, they would use sftp for elr over khie choice of vpn and soap. most of khie vendors do not see any variability in what is reportable by different jurisdiction and also it is not difficult for them to detect what is reportable from one jurisdiction versus the other. again, vendors in general do not have any challenge with the hl7 standard implementation guide required by meaningful use elr certification. however, some vendors mentioned that hl7 implementation is time consuming and could take a team of three (3) approximately five (5) years to implement hl7 successfully which is too long for any manager to commit resources for implementation. as part of addressing some of the challenges identified by this study, khie should recommend a maximum of three (3) transport protocols that have the best chance of adoptions across disparate hit vendors to move towards the standardization in elr in kentucky. acknowledgements this research wouldn’t have been possible without the blessing and favor from the almighty god and wonderful support from my family, professors, mentors and friends. i would like to thank those who have helped me during the writing of this paper. i want to express my heartfelt appreciation to dr. martha c. riddell, dr. william g. pfeifle, dr. todd r. johnson from university of kentucky and brennan o’banion from khie for their wonderful support throughout the writing process. glossary of terms and phrases bioterrorism; according to the u.s. centers for disease control and prevention a bioterrorism attack is the deliberate release of viruses, bacteria, toxins or other harmful agents used to cause illness or death in people, animals, or plants data: information. electronic laboratory reporting (elr): electronic laboratory reporting is the electronic transmission from laboratories to public health of laboratory reports which identify reportable conditions. elr has many benefits, including improved timeliness, reduction of manual data entry errors, and reports that are more complete (http://www.cdc.gov/ehrmeaningfuluse/elr.html) e-mds: this is a developer of healthcare software solutions. the company is headquartered in austin, texas and was founded in 1996 by david winn, md. the company remains physician-managed, and actively participates in national health information and interoperability efforts meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi health information exchange (hie): the movement of healthcare information electronically across organizations within a region or community. hie provides the capability to electronically move clinical information between disparate healthcare information systems while maintaining the meaning of the information being exchanged. the goal of hie is to facilitate access to and retrieval of clinical data to provide safe, timely, efficient, effective, equitable, patient-centered care. health information technology (hit): the application of information processing involving both computer hardware and software that deals with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decision making. hl7 (health level seven): one of several accredited standards (specifications or protocols) established by ansi (american national standards institute) for clinical and administrative data. systems which are hl7 ‘compliant’ improve the ability for interoperability and exchange of electronic data. hypertext transfer protocol (http): a language protocol used in communication among web sites. hypertext transfer protocol secure (https): this is a communications protocol for secure communication over a computer network, with especially wide deployment on the internet (wikipedia) interoperability – compatibility: the ability of software and hardware on multiple pieces of equipment made by different companies or manufacturers to communicate and work together national institute of standards and technology (nist): founded in 1901, nist is a non-regulatory federal agency within the u.s. commerce department’s technology administration, promoting u.s. innovation and industrial competitiveness by advancing measurement science, standards, and technology. logical observation identifiers names and codes (loinc): this is a database and universal standard for identifying medical laboratory observations. it was developed and is maintained by the regenstrief institute, a us non-profit medical research organization, in 1994. loinc was created in response to the demand for an electronic database for clinical care and management and is publicly available at no cost (wikipedia) meaningful use (mu): the medicare and medicaid ehr incentive programs which provide financial incentives for the “meaningful use” of certified ehr technology to improve patient care. minimal lower layer protocol (mllp): defines the leading and trailing delimiters for an hl7 message. these delimiters help the receiving application to determine the start and end of an hl7 message that uses internet protocol network as transport multipurpose internet mail extensions (mime): this is an internet standard that extends the format of email to support: text in character sets other than ascii non-text attachments message bodies with multiple parts (wikipedia) national library of medicine (nlm): the world’s largest biomedical library, nlm maintains and makes available a vast print collection and produces electronic information resources on a wide range of topics that are searched billions of times each year by millions of people around the globe. it also supports and conducts research, development, and training in biomedical informatics and health information technology office of the national coordinator (onc): is a government agency (part of hhs) that oversees and encourages the development of a national, interoperable (compatible) health information technology system to improve the quality and efficiency of health care. public health information network messaging system (phinms): the centers for disease control and prevention (cdc)’s public health information network (phin) is a national initiative to increase the capacity of public health agencies to electronically exchange data and information across organizations and jurisdictions (e.g., clinical care to public health, public health to public health and public health to other federal agencies). reportable diseases: reportable diseases are diseases considered to be of great public health importance. local, state, and national agencies (for example, county and state health departments or the u.s. centers for disease control and prevention) require that these diseases be reported when they are diagnosed by doctors or laboratories (http://www.nlm.nih.gov/medlineplus/ency/article/001929.htm) meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi secure file transport protocol (sftp). secure file transfer protocol is an interactive file transfer program which performs high speed file transfer operations over an encrypted secure shell (ssh) transport. simple mail transfer protocol (smtp): simple mail transfer protocol is an internet standard for electronic mail transmission. (wikipedia) simple object access protocol (soap). originally defined as simple object access protocol, is a protocol specification for exchanging structured information in the implementation of web services in computer networks (wikipedia) subject matter expert, or sme a "person with bona fide expert knowledge about what it takes to do a particular job. first-level supervisors are normally good smes. "(delegated examining operations handbook survey monkey: survey monkey is a web survey development cloud based company, founded in 1999 by ryan finley (wikipedia) terminology services and regenstrief loinc mapping assistant (relma): the regenstrief institute provides a windows-based mapping utility called the regenstrief loinc mapping assistant (relma) to facilitate searches through the loinc database and to assist efforts to map local codes to loinc codes (http://loinc.org/relma) transport protocols. in computer networking, a transport layer provides end-to-end or host-to-host communication services for applications within a layered architecture of network components and protocols (wikipedia) unified code for unit of measure (ucum): the unified code for units of measure is a code system intended to include all units of measures being contemporarily used in international science, engineering, and business. the purpose is to facilitate unambiguous electronic communication of quantities together with their units. (http://unitsofmeasure.org/trac/) unified medical language system (umls): the umls, or unified medical language system, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems virtual private network (vpn): a network that uses public connections, such as the internet, to link users but relies on encryption and other security measures to ensure that only authorized users can access the network conflicts of interest none declared. references 1. centers for disease control and prevention. preventing emerging infectious diseases: a strategy for the 21st century. atlanta, ga: us dept of health and human services; 1998. 2. panackal aa, m’ikanatha nm, tsui f-c, et al. 2002. automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. emerg infect dis. 8, 685-91. pubmed https://doi.org/10.3201/eid0807.010493 3. rose d. the value of electronic laboratory reporting for public health: scientific technologies corporation: white paper, 2010 4. new jersey department of health and senior services: electronic laboratory reporting technical manual. may 2007 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12095435&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12095435&dopt=abstract https://doi.org/10.3201/eid0807.010493 meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi 5. medicare & medicaid. ehr incentive program. meaningful use. stage 1 requirements overview. 2010 (http://www.cms.gov/ehrincentiveprograms/) 6. nguyen tq, thorpe l, makki ha, mostashari f. 2007. benefits and barriers to electronic laboratory results reporting for notifiable diseases: the new york city department of health and mental hygiene experience [am j public health]. am j public health. 97(suppl 1), s142-45. pubmed https://doi.org/10.2105/ajph.2006.098996 7. klaucke d, buehler j, thacker s, parrish r, trowbride f, et al. 1988. guidelines for evaluating surveillance systems. mmwr morb mortal wkly rep. 37, 1-15. pubmed 8. effler p, ching-lee m, bogard a, ieong m, nekomoto t, et al. 1999. statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. jama. 282, 1845-50. pubmed https://doi.org/10.1001/jama.282.19.1845 9. us centers for disease control and prevention: automated detection and reporting of notifiable diseases using electronic medical records versus passive surveillance--massachusetts, june 2006-july 2007: mmwr: morbidity & mortality weekly report (mmwr morb mortal wkly rep), 2008 apr 11; 57 (14): 373-6. 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(http://www.cdc.gov/vaccines/programs/iis/interop-proj/ehr.html.) 13. clinical quality measures workgroup and vocabulary task force (health information technology standards committee, washington, dc). letter to: dr. farzad mostashari, national coordinator for health information; 2011 aug 17. <http://healthit.hhs.gov/portal/server.pt/gateway/ptargs_0_12811_955546_0_0_18/hits c_cqmwg_vtf_transmit_090911.pdf> 14. abhyankar s, demner-fushman d, mcdonald cj. 2012. standardizing clinical laboratory data for secondary use. j biomed inform. 45(4), 642-50. pubmed https://doi.org/10.1016/j.jbi.2012.04.012 15. mark d. 1999. white, linda m kolar, steven j steindel: evaluation of vocabularies for electronic laboratory reporting to public health agencies. j am med inform assoc. 6, 185-94. doi:https://doi.org/10.1136/jamia.1999.0060185. pubmed 16. center for disease control and prevention. morbidity and mortality weekly report (mmwr): summary of notifiable diseases. available at: www.cdc.gov/mmwr/mmwr_nd. accessed march 26, 2014. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17413058&dopt=abstract https://doi.org/10.2105/ajph.2006.098996 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=3131659&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10573276&dopt=abstract https://doi.org/10.1001/jama.282.19.1845 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20614703&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22561944&dopt=abstract https://doi.org/10.1016/j.jbi.2012.04.012 https://doi.org/10.1136/jamia.1999.0060185 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10332652&dopt=abstract meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e196, 2017 ojphi 17. revere d; stevens kc: accelerating public health situational awareness through health information exchanges: an annotated bibliography: online journal of public health informatics [online j public health inform]. 2010. vol. 2 (2). date of electronic publication. 2010(oct), 29. meaningful use and electronic laboratory reporting: challenges health information technology vendors face in kentucky abstract: 1. introduction 2.1. study design and sample 2.2. recruitment and data collection 2.3. measures 3. data analysis and results 4.1. discussion 4.2. recommendation 4.3. limitations 5. conclusion acknowledgements glossary of terms and phrases conflicts of interest references isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts comparison of statistical algorithms for syndromic surveillance aberration detection roger morbey1, angela noufaily2, felipe d. colón-gonzález3, alex elliot1, sally harcourt*1 and gillian smith1 1public health england, birmingham, united kingdom; 2warwick university, coventry, united kingdom; 3university of east anglia, norwich, united kingdom objective to investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in england. introduction syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. the algorithm currently in use at public health england (phe) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (rammie) method (morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. this research checks whether the rammie method works across a range of public health scenarios and how it compares to alternative methods. methods for this purpose, we compare rammie to the improved quasipoisson regression-based approach (noufaily et al, 2013), currently implemented at phe for weekly infectious disease laboratory surveillance, and to the early aberration reporting system (ears) method (rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. we model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios. results preliminary results suggest all methods provide high sensitivity and specificity, with the (noufaily et al, 2013) approach having the highest sensitivity and specificity. we showed that for syndromes with long-term increasing trends, rammie required modificaiton to prevent excess false alarms. also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects. conclusions our study will provide recommendations for which algorithm is most effective for phe’s syndromic surveillance for a range of different syndromes. furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in england and elsewhere. synthetic syndromic datasets keywords aberration detection; outbreaks; simulation; syndromic acknowledgments rm, ae, gs and fc-g are supported by the national institute of health research’s (nihr) health protection research unit in emergency preparedness and response. the views expressed are those of the author(s) and not necessarily those of the nhs, the nihr, the department of health or public health england. references noufaily, a., enki, d. g., farrington, c. p., garthwaite, p., andrews, n. and charlett, a. (2013). an improved algorithm for outbreak detection in multiple surveillance systems. statistics in medicine, 32(7), 1206-1222. morbey, r. a., elliot, a. j., charlett, a., verlander, a. q, andrews, n. and smith, g. (2013). the application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (rammie) method for syndromic surveillance in england, bioinformatics, 31(22), 36603665. rossi, g, lampugnani, l, marchi, m. (1999), an approximate cusum procedure for surveillance of health events. statistics in medicine, 18, 2111–2122 *sally harcourt e-mail: sally.harcourt@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e4, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts comparative analysis of methods of molecular detection of avian influenza virus maryna sapachova* molecular and genetic research department, state scientific research institute of laboratory diagnostics and veterinary sanitary expertise, kyiv, ukraine objective the performance of comparative analysis of sensitivity and results of detection of avian influenza virus by real time polymerase chain reaction (pcr-rt) and loop-mediated isothermal amplification of the nucleic acids (lamp) was the main goal of the study. introduction as part of this surveillance study for avian influenza both active and passive surveillance samples were tested using pcr and also utilized to validate the lamp method. active surveillance samples include pathological material and tracheal and cloacal swabs from ill poultry, which were subsequently assessed for avian influenza during diagnosis, and birds collected by hunters. passive surveillance included environmental samples such as sand and bird faeces. active surveillance samples were taken mostly from poultry farms across ukraine, where infected birds are required to be diagnosed by state scientific research institute of laboratory diagnostics and veterinary sanitary expertise (ssrildvse) by ukraine law. passive surveillance samples were taken primarily during the annual bird migration season. development of simple, sensitive, and cheap methods for diagnostics of avian influenza is a very important task for practical veterinary medicine. lamp is one of such methods. the technique is based on isothermal amplification of nucleic acids. it does not require special conditions and equipment (pcr cyclers), therefore it is cheaper in comparison with pcr. accurate diagnosis is necessary for determining the risk associated with avian influenza in ukraine and along the dnipro river during the migratory season. methods for the research, we used pcr-rt commercial kit bird-flu-pcr (ukrzoovetprompostach, ukraine), lamp (the protocol has been optimized and patented by ssrildvse), qiaamp® viral rna mini kit. for the study, we used pathological and biological materials from birds, which were sent to the ssrildvse from all regions of ukraine according to the 2013–2014 state monitoring plan. set up of the real time pcr reactions and parameters of amplifications are indicated in the instruction to the kit. the following protocol was used to set up the rtlamp: 2.5 µl 10 x thermopol buffer, 1 mmol/l betaine, 5 mmol/l mgso4, 1.4 mmol/l bntp, 12.5 µmol/l sybr green, 0.5 mmol/l mncl2, up to 25 µl nuclease-free water, 8 u bsm dna polymerase, 0.1 µm/1 of f3, 0.1 µm/1 of b3, 0.8 µm/1 of fip, 0.8 µm/1 of bip, 0.4 µm/1 of lf, 0.4 of lb, 2 µl cdna. during our work, we used the following optimal temperature and time for the amplification – 59°c and 60 minutes. the sensitivity of diagnostic kit bird-flu-pcr and rtlamp was determined by testing cdna of the reference strain of aiv h5n1, which was provided to us by nsc institute for experimental and clinical veterinary medicine (kharkiv, ukraine). for the standard, we employed concentration in the range of 10.0-0.01 ng/sample. results table 1. this table shows the reproducibility results obtained by both methods. however, taken into account absence of highly pathogenic avian influenza virus circulating in ukraine during the studied period, it was not possible to confirm these results with protocols of positive samples. table 2. it has been established that the sensitivity of pcr-rt kit bird-flupcr is 0.01 ng/sample for gene m and 0.1 ng/sample for subtype h5n1. fig. 1. visual detection of lamp products with different concentrations of cdna of avian influenza virus (ng per sample): 1 – 10; 2 – 5; 3 – 1.0; 4 – 0.1; 5–7 – 0.01; 8–9 – 0.1; 10 – negative. we have examined the lamp results using electrophoresis for the confirmation of visual detection and correct interpretation of the results (fig. 2). fig.2. electrophoresis results for lamp products. m – molecular weight marker; 1 – 10.0; 2 – 5.0; 3 – 1.0; 4 – 0.1; 5–7 – 0.01; 8 negative control. it has been established that the sensitivity of lamp is 0.1 ng/sample. slightly lower sensitivity of lamp in comparison to pcr-rt can be explained by visual detection of the products of the lamp reaction. conclusions 1. sensitivity of both methods is high. 2. lamp is a perspective screening method for the diagnosis of viral infectious diseases supported by confirmation of positive results by pcr-rt. table 1. results of tests of pathological and biological materials from birds by pcr and lamp conducted in 2013-2014 table 2. sensitivity of pcr-rt kit bird-flu-pcr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e149, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords avian influenza virus; pcr; diagnostics; isothermal amplification of the nucleic acid; sensitivity *maryna sapachova e-mail: m_sapacheva@meta.ua online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e149, 2017 isds16_abstracts-final 172 isds16_abstracts-final 173 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts in denial: symptom negation in new york city emergency department chief complaints jessica sell* syndromic surveillance, nyc dohmh, queens, ny, usa objective to describe the effect of symptom negation in emergency department (ed) chief complaint data received by the new york city (nyc) department of health and mental hygiene (dohmh), and to devise a solution to avoid syndrome and symptom misclassification for commonly used negations using sas perl regular expression (prx) functions. introduction in july 2016, 77% of ed data was transmitted daily via health level 7 (hl7) messages, compared to only 27% in july 2015 (figure). during this same period, chief complaint (cc) word count has increased from an average of 3.8 words to 6.0 words, with a twenty-fold increase in the appearance of the word “denies” in the chief complaint (figure). while hl7 messages provide robust chief complaint data, this may also introduce errors that could lead to symptom and syndrome misclassification. methods using sas 9.4 and tableau 9.3, we examined data submissions from 14 eds responsible for 97% of the occurrences of the word ‘denies’ in chief complaints in july 2016. to account for variation in chief complaint format among hospitals, we developed three prx patterns to identify entire phrases in the chief complaint data field that began with conjugations of the word “deny” followed by various combinations of words, punctuation, spaces, and/or characters. pattern 1: '/den(y|i(es|ed|ng))(\s|\w|(\/)|(\+)|,|(\\)){1,} ((\.)|(\|)|($)|(;)|(\))|(-))/’ pattern 2: '/den(y|i(es|ed|ng))(\s|\w|(\/)|(\+)|(\\)){1,} ((\.)|(\|)|($)|(;)|(\))|(-)|(,))/'; pattern 3: '/denies:( |\w|\.|,){1,}/'); we separated the ‘denies’ statement from the chief complaint and identified commonly negated symptoms. we then defined symptoms using keyword searches of the chief complaint and the ‘denies’ statement. we compared symptom classification with and without the consideration of symptom negation. results of the 14 eds analyzed, we applied pattern 1 to 8 of the ed’s, pattern 2 to 5 eds, and patterns 2 and 3 to 1 ed. approximately 98% of denies statements were extracted from chief complaints. only 2% of symptom negation was not captured due to uncommon chief complaint format whose symptom negation didn’t meet one of the previously described prx patterns. the most common words associated with a “denies” statement were: pain, chest, fever, loc, shortness, breath, vomiting, nausea, travel, headache, recent, trauma, history, abdominal, injury, diarrhea, sob (shortness of breath), v (vomit), head, n (nausea), pmh (past medical history), suicidal, dizziness, homicidal and d (diarrhea) (see table). by not taking negation into consideration in symptom definitions, between 3.5% and 16.5% of symptom visits were misclassified. symptom misclassification varied greatly by hospital, ranging from 0% to 55%. conclusions as hospitals in nyc implement hl7 messaging, symptom negation is becoming increasingly common in chief complaint data. current symptom definitions are based on keyword searches that do not take into account symptom negations. this leads to symptom misclassification, and could potentially cause false signals or inflate syndrome baselines, causing true signals to go undetected. sas prx functions can be used to flexibly identify symptom negation patterns and exclude them from syndrome definitions. future studies will quantify the effect symptom negation has had on signal frequency in nyc, and examine symptoms associated with other forms of negation such as “personal medical history”, “no” and “negative.” most common symptoms denied in emergency department chief complaints keywords symptom negation; perl regular expressions; hl7 data *jessica sell e-mail: jsell@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e70, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts malaria risk assessment through remote sensing and multicriteria evaluation in madagascar anthonio rakotoarison*1, mampionona rasamimalala1, jean-marius rakotomanga1, brune ramiranirina2, thierry franchard2, laurent kapesa3, jocelyn razafindrakoto3, laurence baril1, patrice piola4 and fanjasoa rakotomanana1 1epidemiology unit, pasteur institute in madagascar, antananarivo, madagascar; 2national malaria control program, ministry of health, antananarivo, madagascar; 3health population and nutrition office (hpn), usaid madagascar, antananarivo, madagascar; 4epidemiology unit, pasteur institute in cambodia, phnom penh, cambodia objective madagascar is one of the low-income countries with limited resources. in order to minimize the cost of the fight against malaria, the main objective of this study is to identify the priority zone for indoor residual spraying (irs). introduction malaria remains a major public health problem in madagascar. indoor residual spraying (irs) is the adopted strategy for malaria control in the chs and fringe regions of madagascar. remotely sensed data analysis combined with multi-criteria evaluation become crucial to target priority areas for intervention. methods satellite images were used to update land cover information using object based image analysis method, noaa and modis for temperature and rainfall data. multi-criteria evaluation was performed by weighted linear combination to obtain the gradient of malaria transmission risk. factor weights were determined by pairwise comparison based on literature review and expert knowledge. fuzzy set theory was used to perform the factors weighting. to estimate a best fit risk magnitude probability per commune, we used per pixel values for inhabited locations, and chose an adjusted mean. the jenks natural breaks algorithm was used to classify the obtained malaria risk gradient. all the process was compiled in a semiautomatic plugin working in an open source software. comparison of risk magnitude between two consecutive years was performed to assess the environmental change. results three models of malaria risk are available for 2014, 2015 and 2016. the updated land cover map showed suitable breeding sites for mosquito responsible of malaria transmission in chs with an accuracy of 84%. a change of 64.4% and 35.6% unchanged were obtained concerning change detection of malaria risk between 2014 and 2015. between the years 2015 and 2016, 11.2% of the area of interest remains unchanged while 88.8% changed. respectively 26.9% decreased and 61.9% increased. conclusions it is crucial to focus the indoor residual spraying efforts according to the risk gradient. this allows to increase the effectiveness of the intervention targeting areas with the most need, as well as to optimize financial and logistical resource management. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords malaria; remote sensing; multicriteria evaluation acknowledgments this research was supported by the usaid. many thanks to the institut pasteur de madagascar, national malaria control program, surveillance de l’environnement assistée par satellite pour l’ocean indien (seas-oi) references 1. rakotomanana, f, randremanana r, rabarijaona l, et al. determining areas that require indoor insecticide spraying using multi criteria evaluation, a decision-support tool for malaria vector control programmes in the central highlands of madagascar. international journal of health geographics, 2007, 6:2. 10.1186/1476-072x-6-2 2. saaty tl: a scaling method for priorities in hierarchical structures. journal of mathematical psychology. 1977, 15: 234-281. 10.1016/0022-2496(77)90033-5. *anthonio rakotoarison e-mail: anthonio@pasteur.mg online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e16, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts establishing a national syndromic surveillance system among asylum seekers mariette hooiveld*1, 2, madelief mollers2, stephanie van rooden1, robert a. verheij1 and susan j. hahné2 1nivel, utrecht, netherlands; 2rivm, centre for infectious disease control, bilthoven, netherlands objective facing challenges to establish a new national syndromic surveillance system in the netherlands for infectious diseases among asylum seekers. introduction most european countries are facing a continuous increased influx of asylum seekers [1]. poor living conditions in crowded shelters and refugee camps increase the risk for outbreaks of infectious diseases in this vulnerable population. in line with ecdc recommendations [2], we aim to improve information on infectious diseases among asylum seekers by establishing a new syndromic surveillance system in the netherlands. this system will complement the notifiable disease system for infectious diseases. the aim of the syndromic surveillance system is to improve the detecting of outbreaks of infectious diseases in asylum seekers’ centres in an early stage of development to be able to take adequate and timely measures to prevent further spread, and to collect information on the burden of infection within this population. methods primary health care for asylum seekers in the netherlands is organized nationally by the asylum seekers health centre, with general practitioners providing care in each reception centre. general practitioners (gps) act as gatekeepers for specialized, secondary health care and the gp is the first professional to consult for health problems. therefore, electronic health records (ehr) kept by gps provide a complete picture of this population. these ehrs contain data on diagnoses/symptoms and treatment of asylum seekers, using the international classification of primary care (icpc). this data is recorded routinely, as part of the health care process. during summer 2016, about 30,000 asylum seekers were housed in about 60 reception centres across the netherlands. results the governance structure was layed down in a collaboration agreement between the asylum seekers health centre, the national institute of public health rivm and nivel. to ensure privacy of the asylum seekers, a privacy protocol has been drawn, taking into account strict privacy regulations in the netherlands. the information system provider of the health care centre developed an extraction tool that automatically generates weekly data extracts from the electronic health records system to a trusted third party (ttp). before transferring the data to nivel, the ttp removes directly identifying patient information, indirectly identifying information like date of birth is replaced by quarter and year, and the personal identification number is replaced by a pseudonym. at nivel, all data is stored in a relational database, from which weekly research extracts are generated for infectious disease surveillance at rivm after applying a second pseudonymisation step (two-way pseudonimisation) [3]. first data extracts are being expected mid-october 2016, after which data quality will be evaluated. weekly, or daily, consultations rates will be calculated based on the number of cases meeting predefined definitions, stratified by immigration centre, age group, sex and nationality. numerators will be based on the number of population housed in the immigration centres. conclusions with the cooperation of a national health care centre, providing primary care to asylum seekers housed at several locations, and the information system provider of the health care centre, ehrs can be used for syndromic surveillance, taking into account strict privacy regulations. the new surveillance system will be evaluated after one year, focusing on data quality, usefulness, and the added value above to the notification of diseases. keywords asylum seekers; syndromic surveillance; electronic health records; governance references 1. catchpole m, coulombier d. refugee crisis demands european unionwide surveillance! euro surveill 2015,20. 2. european centre for disease prevention and control (ecdc). communicable disease risks associated with the movement of refugees in europe during the winter season. rapid risk assessment. stockholm: ecdc; 2015. 3. kuchinke w, ohmann c, verheij ra, van veen eb, delaney b. development towards a learning health system – experiences with the privacy protection model of the transform project. in: gutwirth s, et al (eds). data protection on the move. springer, dordrecht: 2016. *mariette hooiveld e-mail: m.hooiveld@nivel.nl online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e84, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts increase of scarlet fever in march 2017 in france: right or wrong signal? isabelle pontais*, anne fouillet, cécile forgeot, annie-claude paty and céline caserio-schönemann santé publique france, saint-maurice cédex, france objective describe a case study of validation of a scarlet fever outbreak using syndromic surveillance data sources. introduction since 2004, the french syndromic surveillance system sursaud® [1] coordinated by the french public health agency (sante publique france) daily collects morbidity data from two data sources: the emergency departments (ed) network oscour® and the emergency general practitioners’ associations sos médecins. almost 92% of the french ed attendances are recorded by the system. sos médecins network is a group of 62 associations of general practitioners, dispatched all over the territory. sante publique france received data from 61 out of 62 associations. both data sources collect medical diagnosis, using icd10 codes in the ed network and specific medical thesaurus in sos médecins associations. these data are routinely analyzed to detect and follow-up various expected or unusual public health events all over the territory [2]. the system is also used for reassurance of decision makers. in that framework, in march 2017, the french ministry of health requested santé publique france to validate a potential scarlet fever outbreak in france. methods ed attendances for scarlet fever were identified using the icd10 code “a38”. sos médecins visits with the specific code corresponding to “scarlet fever” were considered. the weekly numbers of ed attendances and sos médecins visits for scarlet fever were analyzed from 02/01/2017 (week 5) to 03/31/2017 (week 13) by age group (all ages and less than 15 years old, scarlet fever affecting mainly children) and were compared to the numbers of attendances and visits registered during the same period of the two previous years. analysis was conducted both at national and regional levels. in order to take into account the improvement of data quality during the study period, we also calculated proportion of attendances and visits for scarlet fever among the overall attendances (respectively visits) with medical coded information. results the number of sos médecins visits for scarlet fever started to increase in week 9 of 2017. almost 95% of visits concerned children aged less than 15 years old. sos médecins visits for scarlet fever represented 0.24% of the overall visits for the 2 age groups for weeks 11, 13 and 14. this proportion was never reached in 2015 and was observed twice in 2016, but later in the year (weeks 25 and 26). the regional analysis showed that all french metropolitan regions contributed to the increase, even if paris region was the most impacted. more specifically, cases were mainly located in the east part of the paris region (in seine-et-marne). in the oscour® network, the analysis of the number of attendances for scarlet fever at the national level shows a limited increase from week 9 to week 12. weekly proportion of ed attendances for scarlet fever among the total coded attendances remained comparable to those observed the two previous years on the same period. the regional analysis also showed that 35% of attendances for scarlet fever during this period were observed in paris area. but, number of attendances for scarlet fever in this region was comparable during this period to numbers observed the two previous years. conclusions the analysis of emergency syndromic data sources enables to confirm an increase of consultations for scarlet fever in sos médecins associations from weeks 9 to 14, mainly for children less than 15 years old. the large implementation of the sos médecins associations on the whole territory allowed us to provide a geographical location of the outbreak: mainly in the east part of paris area. the temporal pattern of scarlet fever visits in this region may be in favor of a small cluster of cases. the availability of data collected routinely during a long period of time by the syndromic surveillance system enables to evaluate that the outbreak occurred earlier than the previous years, but the intensity of the outbreak was similar to those observed previously. scarlet outbreak was not confirmed through the ed network, even if a limited increase was observed during the same period of time. the investigation of this outbreak in ed network revealed a miscoding practice in one ed structure, resulting locally in a larger number of attendances than in the other ed of paris area. finally, this case study led to improve data quality and highlighted the importance of the validation step of alarms by epidemiologists, even in an automatized system. keywords scarlet fever; signal validation; emergency department; emergency general prationers; france acknowledgments to sos médecins associations and ed data providers, and to all santé publique france regional units for their substantial contribution to the system. references [1] caserio-schönemann c, bousquet v, fouillet a, henry v. le système de surveillance syndromique sursaud (r). bull epidémiol hebd 2014;3-4:38-44. [2] josseran l, nicolau j, caillère n, astagneau p, brücker g. syndromic surveillance based on emergency department activity and crude mortality: two examples. euro surveill 2006;11:225-9. *isabelle pontais e-mail: isabelle.pontais@laposte.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e140, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 1nigeria field epidemiology and laboratory training program, abuja, nigeria; 2national influenza sentinel surveillance, abuja, nigeria objective to assess the performance of the surveillance system and identify factors affecting the performance. introduction national influenza sentinel surveillance (niss) was established in nigeria in 2006 to monitor influenza occurrence in humans in nigeria and provide a foundation for detecting outbreaks of novel strains of influenza. surveillance for influenza-like illness (ili) and severe acute respiratory infection (sari) is carried out in 4 sentinel sites. specimens and epidemiological data are collected and transported 4 days a week from the sentinel sites to the national influenza reference laboratory. at the laboratory, they are tested for influenza a and b viruses and further subtyped if positive for influenza a virus. methods surveillance facilitators from the 4 functional sentinel sites were interviewed via telephone while oral interviews were conducted with laboratory staff. information on niss structure and management of data and specimens were collected. niss protocol was reviewed, and surveillance data from january to december 2014 were analysed using epi info software. cdc updated guidelines for evaluating public health surveillance systems was used to guide the evaluation. results niss activities are funded through a cooperative agreement with the us centres for disease control and prevention (cdc). of specimens sent from the sentinel sites, 68% reached the laboratory within 48 hours. specimen processing in the laboratory took an average of 23 days. reasons given for the delay include late distribution of supplies from the laboratory to sentinel sites and stock-out of working materials at the laboratory. surveillance activities were disrupted in the month of july 2014 because of health workers’ strike. the predictive value positive was 8.7%. of submitted case investigation forms, 73.7% were completely filled. the testing algorithm and niss protocol have been reviewed (addition of more variables on co-morbidities, age classification of patients seen daily at the sentinel health facilities, and analysis of more influenza subtypes at the laboratory) over the years. conclusions the niss is useful in characterizing influenza epidemiology in nigeria. it is simple, flexible, and acceptable to the stakeholders. however, there was delay in sending of specimens to the laboratory and timely processing of specimens received. the observed delay may undermine its ability to detect early unusual patterns of morbidity and mortality due to influenza or signal the beginning of influenza season. the overdependence of the surveillance activities on donorfunding is a threat to its sustainability. we recommend improvement of logistics management and encouragement of local partnerships for sustainability. map of nigeria showing national influenza sentinel sites and reference laboratory keywords sentinel surveillance; influenza; nigeria acknowledgments we wish to express our gratitute to the national influenza sentinel surveillance team and other stakeholders who made this evaluation possible references 1. federal ministry of health (ng).protocol for national influenza sentinel surveillance. rev. ed. 2011 2. centers for disease control and prevention (us). updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr 2001;50(no. rr-13) *amaka p. onyiah e-mail: pamelaonyiah@yahoo.co.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e150, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes jacob krive1,2,4,5*, joel s. shoolin3, steven d. zink6 1. population health technology business unit, valence health®, chicago, il. 2. information systems department, advocate health care, oak brook, il. 3. department of family medicine, advocate medical group, glenview, il. 4. department of biomedical and health information sciences, university of illinois, chicago, il. 5. department of biomedical informatics, nova southeastern university, fort lauderdale, fl. 6. administration, nevada system of higher education, las vegas, nv. abstract objective: evidence-based sets of medical orders for the treatment of patients with common conditions have the potential to induce greater efficiency and convenience across the system, along with more consistent health outcomes. despite ongoing utilization of order sets, quantitative evidence of their effectiveness is lacking. in this study, conducted at advocate health care in illinois, we quantitatively analyzed the benefits of community acquired pneumonia order sets as measured by mortality, readmission, and length of stay (los) outcomes. methods: in this study, we examined five years (2007–2011) of computerized physician order entry (cpoe) data from two city and two suburban community care hospitals. mortality and readmissions benefits were analyzed by comparing “order set” and “no order set” groups of adult patients using logistic regression, pearson’s chi-squared, and fisher’s exact methods. los was calculated by applying one-way anova and the mann-whitney u test, supplemented by analysis of comorbidity via the charlson comorbidity index. results: the results indicate that patient treatment orders placed via electronic sets were effective in reducing mortality [or=1.787; 95% cf 1.170-2.730; p=.061], readmissions [or=1.362; 95% cf 1.0151.827; p=.039], and los [f (1,5087)=6.885, p=.009, 4.79 days (no order set group) vs. 4.32 days (order set group)]. conclusion: evidence-based ordering practices have the potential to improve pneumonia outcomes through reduction of mortality, hospital readmissions, and cost of care. however, the practice must be part of a larger strategic effort to reduce variability in patient care processes. further experimental and/or observational studies are required to reduce the barriers to retrospective patient care analyses. keywords: evidence-based medicine, medication order sets, health outcomes research, pneumonia, computerized physician order entry (cpoe). correspondence: krive@uic.edu doi: 10.5210/ojphi.v7i2.5527 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:krive@uic.edu effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi introduction efforts to employ evidence-based medicine to treat well-researched patient conditions, heighten core clinical measures compliance, and improve quality through process standardization have been ongoing for decades. studies have demonstrated successful reductions in the rate of adverse drug effects, which have encouraged further advances in such healthcare information technologies as the automated venous thromboembolism (vte) alerts developed at brigham and women’s hospital in boston [1]. indeed, one of evidence-based medicine’s greatest successes has been medication order sets available to physicians via computerized physician order entry (cpoe) applications. approved by multi-disciplinary professional committees typically consisting of physicians, nurses, and pharmacists, these cpoe applications have enabled treatment standardization for certain patient conditions, i.e. pneumonia, congestive heart failure, acute myocardial infarction, via pre-defined templates. these templates are found within cpoe patient view menus among order options and, depending on specific application, may appear in a searchable and/or categorized drop-down list, with only a few clicks necessary to pull up a set and place an order. order sets typically include medication orders and care giver communications such as nursing and dietitian instructions. sets represent a complete care document that includes comprehensive evidence-based patient care orders for a given medical condition. cpoe instructions are easy to access, decrease delays, reduce errors, and improve inventory control. this resonates in the national debate over slowing the growth of healthcare expenditures through more focused applications of information technology not only in cpoe, but also in electronic medical record (emr) and clinical decision support systems (cdss) to demonstrate meaningful use, as mandated in the 2010 u.s. patient protection and affordable care act. aided by sophisticated information technology, expectations of cpoe benefits have moved beyond efficiency and are now linked to sharpening diagnoses, improving clinical outcomes, and tracking treatment plans through the continuum of patient care. despite differing and evolving expectations of cpoe, and its order set component specifically, standardized ordering practices are widely deployed throughout the healthcare sector. yet, few longitudinal clinical studies have empirically explored and validated order set utilization to treat eligible patients, from the healthcare cost and patient outcomes perspectives. early limited scope studies in treating pneumonia found that order sets were effective in reducing mortality, length of hospital stay, and core measures compliance, without affecting readmissions [2,3]. however, none of these studies analyzed large data sets spanning several years of patient encounters. larger data set analysis of pneumonia patients is needed, as this disease is common in older adults and will increase in magnitude as baby-boomers grow older. the analysis should include tracking the impact of health informatics on a sizable patient population. increasing size of the data set in a study will make results appear more convincing for public health informaticists concerned with the overall impact of biomedical informatics on patient populations’ health outcomes. in this study, we examine the effectiveness of pneumonia order sets in a major community integrated healthcare delivery network using patient history from four advocate health care hospitals for the 2007–2011 period. conducted at advocate health care, one of our goals was to explore quantitatively the effectiveness of pneumonia order sets, as well as what successful implementation of an order set means in the context of a larger process of patient care practices effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi standardization. based in oak brook, illinois, advocate is the largest integrated delivery network in illinois (usa), with 12 hospitals, 3,500+ beds, and over 250 sites of care. background and significance medication allergy checking, dose calculations, and drug interactions are some of the most common physician actions performed when ordering medications. these activities are also some of the most error-prone treatment stages, especially in fast-paced community hospital environments [4]. as ahmad et al. [5] and payne et al. [6] showed, cpoe success is measured by (1) the percentage of orders entered directly into cpoe by providers and (2) the overall utilization of the order sets. cowden et al. [7] conducted an order set quality improvement study with the goal of combining two orders and evaluating a combined chi-squared measure to predict order set correspondence to an ordering pattern. performed at ohio state university medical center, the authors concluded that a large percentage of medication orders qualified for an order set, meaning that evidence-based guidelines were available to consider standardization of ordering practices. dixon and zafar [8] defined the theoretical foundation of order sets and their potential effectiveness in standardized treatment in a study sponsored by the u.s. department of health and human services. ballard et al. [9] conducted a congestive heart failure order set study at baylor health care system and concluded that evidence-based prescription methods decrease mortality, length of hospital stay, and rate of readmissions. best et al. [10] found reduced interval rates in initiation of antibiotic therapy from the time of diagnosis in treating febrile neutropenia, with a potential for greater success by swifter clinical response. chisolm et al. [11] studied 790 pediatric asthma patients at columbus children’s hospital and concluded that those who received medications via order sets were more likely to receive spinal cord stimulators (scs) and pulse oximetry (pulse ox) than patients in the control group, resulting in better health outcomes. in a controlled trial of 179 diabetes mellitus and inpatient hyperglycemia patients at an academic hospital, the primary mean percentage of the glucose readings per patient was reduced to 60-180 mg/dl [12]. the result of this experimental study, based on a custom medication order (control group) and a cpoe admission order set (intervention group), indicated a positive outcome from use of the standard medication order sets. wright et al. [13] analyzed patterns in order set utilization among several leading healthcare facilities in the northeast united states. they found that order set governance and maintenance were costly, and only a handful of available order sets incurred high utilization. even so, the primary reasons for order set use included patient safety and ordering efficiency. through 71 physician interviews, another study found cost concerns to be the top factor in predicting order set utilization [14]. yet physician engagement remains a critical success factor in order set implementation. adventist health system in florida attributed physician engagement as the most important reason for the success of a 2.5-year project to address 80% of the diagnosis-related cases with an order set approach [15]. to address the cost and complexity of order set selection and governance, in 2007 the mayo clinic instituted a comprehensive order set review process that included formal committee evaluation, approval process, and mid-term progress checks on utilization and patient outcomes [16]. the mayo clinic discovered that teamwork and inclusive participation in the formal review process by clinicians increased buy-in, order set utilization, and subsequently had positive impact on patient safety. bekmezian et al. [17] quantitatively measured the perceived benefits of pediatric admission order sets (paos) among 97 medical effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi residents at the university of southern california and children’s hospital los angeles. eightynine percent of residents approved paos; 58% admitted using it all the time. eighty-eight percent of residents reported that paos saved time, 93% believed it to be convenient, and most reported less need for communication and clarification with nurses and secretaries. other than health outcomes, the best measures of treatment and process effectiveness are core compliance data, which serve as proof of the order set implementation’s positive impact in clinical settings. broussard et al. [18] conducted a pediatric sedation drug dosing study at louisiana state university health sciences center in shreveport using paper order sets to compare 26 patient intervention cases to the control group of 42 sedations. fully documented compliance cases increased from 32% to 69% and several medication dosages fell within the recommended range. ballard et al. [2] conducted an observational study of pneumonia patients to examine hospital order set use by discharge month, severity of illness, and risk of mortality. among 3,301 patient cases, order set use increased by 55% and significantly improved inhospital mortality [95% cf: 0.66 (0.45 0.97)] and core measures compliance [95% cf: 1.24 (1.04 1.48)], without affecting the 30-day readmission rate. in a study of 120 patients with a diagnosis of septic shock determined within the emergency department of a 1,200-bed academic medical center [19], patients in the experimental cpoe group were more likely to: (1) receive intravenous fluids, (2) receive fluids of >20 ml/kg body weight before vasopressor administration, and (3) be treated with an appropriate initial antimicrobial regimen, compared to patients in the control group. thiel et al. [20] performed a severe sepsis study at the 1,200-bed academic jewish-barnes hospital. bacteremic patients in the experimental cpoe group received more intravenous fluids in the first 12 hours after onset of the hypotension and were more likely to receive an appropriate dose of antibiotic therapy, compared to patients who received orders via manual practices outside of cpoe. mortality statistically decreased in the experimental cpoe group, along with the hospital stay. fleming et al. [3] measured utilization and health outcomes for 4,454 patients after implementation of a community-acquired pneumonia order set at the baylor health care system. analysis showed significant reduction in length of hospital stay, 30-day mortality, and direct cost, with a 75% increase in compliance. o’connor et al. [21] conducted a deep vein thrombosis (dvt) prophylaxis quality improvement study at the 750-bed community hospital in mississauga, ontario. paper medication order sets were voluntarily used by internists without prior education on the benefits of the evidence-based approaches to medicine. order sets were used to prescribe admission medications to 10.9% of the patients, who were 23.4% more likely to receive dvt prophylaxis than patients in the control group. overall, the literature supports cpoe order set effectiveness in increasing core measures compliance, decreasing mortality and morbidity, and shortening the length of hospital stay. empirical evidence demonstrates beneficial application of evidence-based medication prescribing practices through utilization of electronic cpoe-based order sets, as long as appropriate patient groups are targeted and the theoretical basis for selection of the order sets is justified by evidence and approved by interdisciplinary teams of clinicians involved in patient care. however, the number of studies that examined pneumonia order sets is more limited and involved smaller patient populations, leaving room for exploring the subject of order set effectiveness in a large community-based healthcare system. effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi methods data for the study was obtained via sql queries from the enterprise data warehouse (edw) that receives information stored in emrs, patient registration, clinical and financial decision support, and other patient care and data analytics applications. the edw uses kimball architecture and runs on sql server database. pneumonia order sets researched in this study are available to all physicians with access to cpoe, but are not published externally. only electronic sets available within hospital cpoe were included in this study. paper sets were excluded. while paper sets could have provided additional insight into health outcomes as results of evidencebased standardization of patient care practices, such an element would have introduced manual work to a study based on analysis of aggregate historical data, and some of the necessary fields stored in the edw would not have been available for patient encounters where paper sets were applied. in this causal comparative study, we analyzed pneumonia patient data for the 2007–2011 period. our goal was to determine order set effectiveness as applied to “order set” and “no order set” groups of patients based on health outcomes, 30-day readmissions, length of hospital stay, and supported by comorbidity analysis. we focused on patients with community-acquired pneumonia based on large patient volumes, higher utilization of order sets than other conditions, and inclusion in the current key result areas of quality metrics at the research site. as part of a comparative study of patient history, the order set group represented patient encounters where providers placed pneumonia orders using cpoe sets, while the no order set group represented all other pneumonia treatment orders where physicians chose custom ordering methods and did not employ sets. mortality was confirmed by selecting discharge codes complying with icd-9-cm diagnosis codes listed under ahrq quality indicators (iqi #20) pneumonia mortality definitions. eligible “expired” codes were converted to a binary value of 1, with other codes converted to 0. readmission was a “yes/no” binary field, while length of stay (los) was a calculated field between the date/time values of admission through discharge. we used the total charlson comorbidity index (cci) to represent comorbidity. in this study, cci could play a dual role: (1) it could help us to explain los as an index of pre-existing conditions, thus introducing comorbidity adjustment, or (2) it could serve as a measure of treatment complications. due to the lack of a clear definition of comorbidity as a variable in this study, the results are only applicable to discussion. comorbidity calculations are separate due to the lack of a reliable measure to adjust results by comorbidity, with cci serving in the dual pre/post-treatment complication roles to help explain potential reasons for mortality and los changes. the main independent (cause) variable in this study was utilization of the two order set groups. health outcomes – mortality, readmissions, los, and comorbidities were defined as dependent (effect) variables. race, age, and sex were mediating variables analyzed in conjunction with order set utilization to determine combined significance in predicting health outcomes. deidentified patient encounters were obtained via queries against the enterprise data warehouse containing records from cpoe and patient accounting applications. the data were entered into and manipulated in excel to adhere to the following inclusion and exclusion criteria: • adults over the age of 18; • patients with primary or secondary diagnosis of community-acquired pneumonia; and effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi • exclusion of psychiatric and obstetrics patients. due to the retrospective nature of the study and limited harm to patients, irb approval was obtained under the expedited de-identified category, with a hipaa waiver form requiring no consent from patients. the data obtained from the edw contained no identifying information, such as social security numbers or names. records were identified via medical record number (mrn), which is internal to emr/cpoe applications. pneumonia patients were assigned to the order set and no order set groups based on their diagnosis and physicians’ ordering preferences. data were subsequently loaded into ibm spss statistical software for analysis. binary logistic regression with a chi-squared option was employed to measure mortality and readmission outcomes. the mann-whitney u test was utilized to test the null hypothesis for los, while a one-way anova was employed to compare the mean los scores. the latter two tests were also used to measure comorbidity. fisher’s exact test was employed as a secondary method of measuring statistical significance, due to the small order set group size. in the mortality study, population n of the “order set” group was 362, while n of the “no order set” group was 4,725. in the 30-day readmissions study, population n of the “order set” group was 556, while population of the “no order set” group was 4,531. in the los study, population n of the “order set” group was 362, and 4,725 in the “no order set” group. in the comorbidity/complications study, the numbers were 556 and 4,427 between the two respective groups of patients. the differences in sample group sizes between studies can be explained by the availability of patient records on readmissions and cci data in the enterprise data warehouse. low utilization of the order sets in hospitals (around 4% of all pneumonia medication orders) is a challenge faced in all cpoe studies. this limits the categories in which statistically significant results can be reported. in this study, the outcomes for all participating hospitals needed to be combined, which limited analysis of the order set content variances among hospitals, although such variances were small. similarly, due to the retrospective nature of the study, as well as the lack of a research-oriented cpoe design, limited observations regarding causal relationships among clinical records associated with each patient encounter could be made. in practice, order set use is optional, contributing to the low percentage of orders placed via this route and the disproportionally large size of our no order set group, where custom ordering methods have been typically employed. this indicates the co-existence of multiple pneumonia ordering practices. the choice of method to place pneumonia orders is entirely in the hands of healthcare providers. despite using a combination of different pneumonia data sets, all of our data were evidence based and approved by clinicians to be available in cpoe for the purposes of treating pneumonia. table 1 is an example of the actual content of a pneumonia order set utilized at one of the participating hospitals. entries are grouped and appear in the same order as they do in cpoe, but comments regarding order placement logic (i.e., references to allergy-related medication choices, nursing home patients, smoking status, etc.) have been removed for brevity, leaving the primary focus on the content of a typical pneumonia order set. table 1. pneumonia order set example component order details admission admit order inpatient admission admit order place in observation services effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi activity activity patient complete bed rest activity patient up ad lib activity patient up ad lib, assistance needed diet general diet diabetic 1,800 calorie diet cardiac diet npo medication ceftriaxone (ceftriaxone (rocephin)) 1 gm, ivpb, q24h, give first dose stat azithromycin (azithromycin (zithromax)) 500 mg, ivpb, daily, give first dose stat doxycycline (doxycycline (vibramycin)) 100 mg, ivpb, q12h, give first dose stat moxifloxacin 400 mg, ivpb, infusion, q24h, give first dose stat vancomycin 1,000 mg, ivpb, stat vancomycin pharmacist to dose based on weight and renal function piperacillin – tazobactam (zosyn) infusion med set piperacillin – tazobactam (zosyn) 3.375 mg, ivpb, q8h acetaminophen 325 mg acetaminophen 500 mg acetaminophen 650 mg acetaminophen 1,000 mg iv fluids saline lock insertion routine saline lock care lactated ringer’s 1,000 ml, iv, iv soln sodium chloride 0.9% 1,000 ml, iv, iv soln dextrose 5% 0.45% nacl 1,000 ml, iv, iv soln respiratory blood gas routine, arterial blood pulse ox spot check once pulse ox spot check daily, if patient is on oxygen oxygen labs blood culture (blc) stat draw (draw stat/perform routine) blood culture (blc) stat draw (draw stat/perform routine) gram smear [gras] next draw/specimen collection, lung, sputum respiratory culture/smear (rtcs) next draw/specimen collection, lung, sputum legionella ag urine (legeia) next draw/specimen collection influenza rapid ag (fluag) next draw/specimen collection, nasopharyngeal washing effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi cbc with automated differential (cbca) next draw/specimen collection comprehensive metabolic panel (cpnl) next draw/specimen collection strep pneumonia antigen next draw/specimen collection procalcitonin next draw/specimen collection bronchial alveolar lavage (mini bal) routine radiology xr chest pa, lateral 2v stat, transport mode, portable, reason for exam: pneumonia nursing vital signs per unit routine nursing communication order initiate smoking cessation education nursing communication order oral care protocol nursing communication order elevate head of bed 35 degrees consults discharge planning evaluation adult results mortality one of our goals was to determine, quantitatively, whether utilization of pneumonia order sets helps lower inpatient mortality. all tests were conducted to determine whether the null hypothesis could be rejected. the binary logistic regression method revealed that 6.6% of patients in the order set group (n = 362) died versus 11.3% in the no order set group (n = 4,725), or = 1.787, 95% cf 1.170 – 2.730, χ2 = 7.402 (p = .061). the results approached statistical significance. due to the relatively small size of the order set group, a more accurate and possibly more appropriate two-tailed fisher’s exact test was used as an alternative to pearson’s chisquared method, with a statistically significant outcome of p = 0.05. patients in the order set group, whose medications were ordered via pre-defined sets, were nearly twice as likely to survive compared to patients in the no order set group; thus, the null hypothesis for pneumonia mortality was rejected. mortality outcomes are summarized in table 2. table 2. pneumonia mortality as a total for all patients in the study outcomes and measures no order set group order set group pearson chisquared (χ2) 2-sided fisher’s exact 1-sided fisher’s exact exp (β) – binary logistic regression mortality = yes 532 24 mortality = no 4,193 338 percent (mortality=yes) / total 11.3% 6.6% 7.402 (p = .061 0.050 0.034 1.787 [1.170 and 2.730] given the limitation of fully tracking pneumonia patient encounters from admission to discharge on an aggregate basis, it is likely that other factors may have induced mortality among these effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi patients, so utilization of order sets is likely not the only variable impacting the differences between our two groups. comorbidity could serve as one such variable showing that patients in the order set group were “healthier” than those in the no order set group. the results of the comorbidity study are given in the comorbidities/complications section below. readmissions another study goal was to determine whether placing pneumonia orders via sets helped reduce the rate of 30-day hospital readmissions. as in the mortality examination, statistical manipulations were performed to test the null hypothesis. only 10.8% of patients in the order set group (n = 556) were readmitted within 30 days, compared to 14.7% of patients in the no order set group (n = 4,531). the odds ratio exp(β) was 1.362 at the 95% confidence interval [1.015 – 1.827, χ2 = 4.274], at p < .05. the results were statistically significant. the group sizes necessitated verification of the pearson chi-squared test by determining the value of fisher’s exact test, which at 0.041 was not significantly different from the pearson test. the differences in sample group sizes between studies can be explained by the availability of patient records on readmissions and cci data in the enterprise data warehouse (edw). patients in the no order set group had an approximately one-third higher probability of being readmitted, compared to patients in the order set group; thus, the null hypothesis was rejected. readmission outcomes are summarized in table 3. table 3. 30-day readmissions with pneumonia outcomes and measures no order set order set pearson χ2 2-sided fisher’s exact 1-sided fisher’s exact exp (β) – binary logistic regression readmission = yes 579 54 readmission = no 3,952 502 percent (readmission =yes) / total 14.7% 10.8% 4.274 (p = .039) 0.041 0.020 1.362 [1.015 and 1.827] length of stay one-way anova and a mann-whitney u test were employed to address whether application of order sets in the clinical settings can help reduce the los. the mean los for the order set group (n = 362) was 4.32 days versus 4.79 days for the no order set group (n = 4,725), indicating that pneumonia patients benefitted from utilization of the order sets by spending roughly 0.5 days less in the hospital. the one-way anova test was significant, f(1,5087) = 6.885, p = .009. the mann-whitney u test confirmed rejection of the null hypothesis: [u(1,n = 5,087) = 1,148,309; p = .001], indicating the benefit of a shorter stay among the order set group. in some cases, expired patients may have contributed to shorter los outcomes in this study. in order to adjust for mortality, the same calculations were repeated with expired patient data excluded. the results remained consistent, showing shorter los for the order set group (n = effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi 338) versus the no order set group (n = 4,193): mean 4.26 (order set group) versus 4.71 (no order set group). the one-way anova test was significant, f(1,4531) = 6.545, p = .011. the mannwhitney u test confirmed rejection of the null hypothesis: [u(1,n = 4,531) = 1,010,258; p = .001]. comorbidities/complications the mean cci score among the order set group (n = 556) was 2.13 versus 2.40 for the no order set group (n = 4,427). cci is a total score attached to each patient encounter (where available) that was computed by adding individual weights of complications found in a patient. this score does not indicate whether a patient arrived with some of these conditions or acquired them during the hospital stay. therefore, this score has a dual meaning: patients in the order set group were either healthier, if cci represented pre-existing conditions, or had fewer complications following hospital discharge. the one-way anova results were significant: f(1,4983) = 5.954, p = .015. the mann-whitney u test confirmed rejection of the null hypothesis: u(1,n = 4,983) = 1,153,767.5; p = .014. discussion a retrospective study does not allow for variable control, selection of the order set content, or other attributes of an experimental study. however, such data offered an exceptional opportunity for the analysis of a large volume of patient encounters over time. these studies may identify certain trends in outcomes, something that many organizations cannot track based on information currently stored in their cpoe applications and/or clinical data warehouses. our mortality and readmission outcomes showed that, in our patient sample, the risk of death and the possibility of hospital readmission for the same or related symptoms almost doubled over the sample period due, in part, to the lack of consistency in prescribing standard orders and medications – among other possible factors not examined in this study. the los for pneumonia patients was 0.5 days shorter for the order set group, indicating a potential for lowering healthcare costs in the era of value-based reimbursement and accountable care organizations (aco), as well as higher patient and family satisfaction with being discharged earlier. this does not even take into account other positive outcomes resulting from shorter los, such as a shorter exposure to all the possible pathogens within a hospital environment. lower comorbidity among the order set group may indicate that patients in the no order set group were sicker if the cci was viewed as a total reflection of pre-existing health conditions. however, a lower cci may also indicate fewer complications as the result of advance pneumonia treatment orders and medication prescription standardization. currently, no better method exists by which to assess the effects of evidence-based cpoe ordering practices on complication outcomes, so this comorbidity analysis study may be viewed as a reference study conducted as an attempt to help support and explain mortality, readmissions, and los health outcomes. statistics-based studies of large numbers of patient encounters do not always account for numerous other facets of patient care that influence outcomes. most versions of cpoe and clinical data warehousing applications do not allow researchers to track patient records in their entirety, while tracking all treatments and variables involved with each patient. thus, it is reasonable to assume that order set utilization was not the only factor that influenced positive or negative outcomes of mortality, readmissions, and los in this study. the study also did not effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi include variables for undesired/unexpected side effects from care standardization practices. due to the low utilization of order sets across the inpatient care facilities in this study, all data were combined; we disregarded the minor differences in the order set content between facilities, as they did not appear to influence the results significantly, as evidenced from data analysis pertaining to individual hospitals participating in this study. despite the differences, all order sets were required to be evidence-based and signed off departmentally, following formal review by clinicians. combination of the above factors could have had unknown effects on the validity of the outcomes, yet the findings indicate a possible trend in the positive effects of evidence-based medicine on pneumonia treatment. utilization of order sets in the treatment process for pneumonia is only one of many factors in the overarching variability reduction process aimed at improving the quality of patient care through adherence to evidence-based medical practices. variability reduction may include initiatives such as shared governance among clinicians to target specific quality improvement efforts, selection of the most effective evidence-based medicine applicable to local clinical settings and culture, making decisions on selection and maintenance of the order sets, etc. the general availability of an order set, by itself does not mean application of evidence-based approaches to treating patients. indeed, lack of governance around order sets may cause low buyin, lower core measures compliance, and potentially dangerous side effects from improper bundling of medications and other orders. community-acquired pneumonia is a significant public health concern. the centers for disease control and prevention (cdc) reports that there were 49,597 pneumonia-related deaths in 2010 (the most recent publicly available data set), making the combination of pneumonia and influenza the ninth largest cause of mortality in the united states [22]. the majority of these cases affected people age 65 years and older. there were 1.1 million inpatient pneumonia discharges in the united states in 2010, with an average los of 5.2 days [23]. the reduction of approximately 0.5 days in los attributed to order sets in our study represents about 10% of the national average los for pneumonia patients, as listed in cdc pneumonia statistics [23]. in the era of patient-centered care and value-based reimbursement, a 10% reduction in los means a significant reduction in associated healthcare costs, as well as higher patient satisfaction. our odds ratio finding of 1.362 for hospital readmissions with the diagnosis of community-acquired pneumonia indicates approximately 30% fewer patients leaving hospitals without full recovery and the potential for spreading the disease. in this study with a total of 633 readmitted patients over a five-year period, 30% readmission prevention rate translates into 229 fewer patients carrying a risk of pneumonia transmission into the community. given the total number of pneumonia cases in the u.s., the potential for increased efficacy from order set implementation of evidence-based treatment guidelines is even greater on a more global scale. the implication of our findings may be of even greater importance to the most affected patient population suffering from pneumonia, namely 33,700 nursing home residents, which represented 2.3% of the united states nursing home population in 2010 [23]. thus, our study outcomes may be beneficial to healthcare practitioners and scholars across many disciplines and patient populations. limitations as with any retrospective study based on historical data, we did not have an opportunity to control all variables leading to pneumonia health outcomes. the reader should assume that other clinical factors beyond the scope and coverage of this study played a role in determining effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi mortality, readmissions, and length of stay. however, outcomes of this study provide indication that evidence-based guidelines for treatment implemented via standardized ordering practices have a positive impact on outcomes. order set design assumes physicians’ ability to select and/or deselect elements within order sets, and there is no reliable way of tracking all changes on an aggregate basis without manual review of each patient encounter, so there may have been certain differences between orders placed for patients who participated in this study. the dual role of complications and cci, covered in the discussion section, indicates limited effect of analyzing comorbidity, because it could mean that patients were either healthier/sicker prior to admission or came out healthier/sicker after being treated. lastly, greater access to more granular data could help further categorize patients, their health conditions, and what unit(s) of the hospital in which they received treatment. conclusion despite wide availability and sporadic utilization of order sets across many hospitals, effectiveness of evidence-based cpoe ordering practices has been insufficiently explored in community care settings. this study quantitatively analyzed effectiveness of evidence-based cpoe ordering practices for pneumonia patients, measured by mortality, 30-day readmissions, and length of stay health outcomes. the study demonstrates a potentially strong correlation between evidence-based cpoe ordering practices and health outcomes from treating pneumonia. we find that the utilization of order sets to prescribe medications in these cases is beneficial and serves as a sufficient starting point for warranting physician participation in further studies, increasing utilization of the order sets in hospitals, and initiating more narrow focused studies that allow for greater variable control and more granular data collection. much of the literature indicates relatively little use of the evidence-based medication prescription practices embedded in cpoe design. yet, some 54% of the u.s. hospitals have implemented cpoe by 2014 [24], and it is likely that many of these hospitals have capabilities for utilization of order sets as one of the core cpoe components and/or already employ pneumonia sets in their clinical workflows. a study of this size can serve as a catalyst to encourage further collaboration between physicians and clinical informatics researchers in an effort to identify effective applications for cpoeenabled tools grounded in methodology of evidence-based practices. our findings could positively influence patient safety and lead to healthcare cost reductions in many areas. improvements in these areas are important to healthcare facilities that are reviewing their evidence-based practices, cpoe installation and utilization guidelines, and order set governance efforts. these efforts could lead to establishment of new policies governing application of evidence-based practices via wider utilization of technology-enabled clinical workflows, with eventual support for utilization of standardized cpoe ordering practices as a matter of national healthcare policy. this study could also encourage additional research investigations using more granular data to include such outcome variables as hospital infection rates and mediating variables as source of admission and unit where patients were treated. an observational or an experimental study could also help track the exact elements selected or deselected in the sets to avoid such common conditions as allergies to certain medications. acknowledgements the authors thank tom summerfelt, nancy davis, mary gagen, bruce smith, and laurie gift of advocate health care; ling wang and maxine cohen of nova southeastern university for their effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e211, 2015 ojphi efforts in helping to initiate and support this study. the study was initiated, designed, and conducted at advocate health care. funding sources & conflict of interest disclosures this research received no grant money from any funding agency in the public, commercial, or not-for-profit sectors. authors have no conflicts of interest to report, in relation to this study. competing interests authors have no competing interests to report in relation to this study. ethics approval this study was approved as a de-identified expedited with a hipaa waiver form study by the institutional review board of advocate health care (irb protocol #5038). references 1. kucher n, koo s, quiroz r, cooper jm, paterno md, et al. 2005. electronic alerts to prevent venous 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vital statistics reports. 61(4), 1-118. pubmed 23. pneumonia faststats [internet]. washington, dc: centers for disease control and prevention, 2014. available from: http://www.cdc.gov/nchs/fastats/pneumonia.htm. 24. united states emr adoption model [internet]. chicago, il: himss analytics, 2014. available from: http://www.himssanalytics.org/home/index.aspx. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24979972&dopt=abstract effectiveness of evidence-based pneumonia cpoe order sets measured by health outcomes introduction background and significance methods results mortality readmissions length of stay comorbidities/complications discussion limitations conclusion acknowledgements funding sources & conflict of interest disclosures competing interests references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts coordinated enhanced surveillance with healthcare entities for mass gathering events erin e. austin* division of surveillance and investigation, virginia department of health, richmond, va, usa objective to describe the planning strategies and lessons learned by the virginia department of health (vdh) when conducting enhanced surveillance during mass gathering events and coordinating with healthcare entities to distinguish event-related emergency department (ed) visits from community-related ed visits. introduction mass gatherings can result in morbidity and mortality from communicable and non-communicable diseases, injury, and bioterrorism. therefore, it is important to identify event-related visits as opposed to community-related visits when conducting public health surveillance1. previous mass gatherings in virginia have demonstrated the importance of implementing enhanced surveillance to facilitate early detection of public health issues to allow for timely response2. methods between june 2015 and september 2015, vdh coordinated with two healthcare entities representing six acute care hospitals to conduct enhanced surveillance for the 2015 world police and fire games and 2015 union cycliste internationale (uci) road world championships. vdh established initial communication with each healthcare entity between 1 week to 2 months before the event start date to discuss functional requirements with technical, informatics, and clinical staff. requirements included: 1) health care entity identifying gathering attendees during the ed registration, 2) capturing a standardized mass gathering indicator within the patient’s electronic health record (ehr), and 3) transmitting the gathering indicator to vdh through existing electronic syndromic surveillance reporting processes. ed visit records with the gathering indicator were analyzed by vdh using the virginia electronic surveillance system for the notification community-based epidemics (essence) and findings were incorporated in daily vdh situational reports. this same methodology will be applied for the upcoming u.s. vice presidential debate in october 2016. results the duration of the two gatherings in 2015 ranged from 9 to 10 days and the locations were categorized as urban. the population density of the gathering location ranged from 1,950 to 2,889 population per square mile. the estimated number of attendees ranged from 45,000 to 400,000. attendees were defined as having attended at least one day of the mass gathering event. the mass gathering indicator captured during the ed registration included the gathering acronym or a gathering specific field with a drop down menu containing true/false options. vdh utilized essence to identify 42 ed visits (0.5%) with the gathering acronym out of 8,768 total ed visits during the 2015 world police and fire games and 60 ed visits (2.6%) with the gathering specific field out of 2,296 total visits during the 2015 uci road world championships. the results of the u.s. vice presidential debate in october 2016 are pending. conclusions in 2015, vdh partnered with two healthcare entities to conduct enhanced surveillance during two mass gatherings. although vdh routinely uses syndromic surveillance data to identify issues of public health concern, it has previously lacked the ability to identify ed visits specific to mass gatherings. prior to collaboration with vdh, the healthcare entities did not capture gathering-specific ed visits using their ehr systems. the two healthcare entities successfully modified their business procedures and ehr system to capture and transmit a gathering indicator for ed visits despite some challenges. these challenges include constraints with customization of the ehr and syndromic surveillance systems, lack of standardized training among ed registration staff for interpreting and applying the gathering indicator, and limited functionality testing prior to the event. lessons learned from this coordinated effort are to: 1) initiate the planning phase and identification of requirements as early as possible to ensure they are well defined and understandable, 2) implement frequent communications with the healthcare entity, and 3) customize requirements for the specific gathering as much as possible while balancing the burden and benefit to public health and the healthcare entity. the coordinated enhanced surveillance efforts provided both vdh and the healthcare entities with improved situational awareness and capacity building during mass gathering events. the strategies and lessons learned from these two events will be applied to improve enhanced surveillance of public health issues during future mass gatherings, including the u.s. vice presidential debate in october 2016. keywords mass gatherings; syndromic surveillance; healthcare references 1. steffen, robert et al. non-communicable health risks during mass gatherings. the lancet infectious diseases. 2012;12 (2),142-149. 2. centers for disease control and prevention. “surveillance for early detection of disease outbreaks at an outdoor mass gathering— virginia, 2005,” mmwr morb. mortal. wkly. rep. 2006; 55 (3),71-74. http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5503a3.htm *erin e. austin e-mail: erin.austin@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e96, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* 1environmental and occupational health, saint louis university college for public health and social justice, saint louis, mo, usa; 2firstwatch inc, encinitas, ca, usa objective the objective of this oral presentation is to describe the use of near real time 911 emergency medical services data in looking for suspected cases of ebola and heroin cases in the community. introduction arguably the two most significant public health issues over the past two years have been the outbreak of ebola in west africa and the rising epidemic of heroin use and overdoses. in the case of ebola, the cdc issued guidance for inpatient facilities to screen for potential cases, however, there was little guidance for screening patients that presented to ems workers. the west african pateint aht presented to the emergency department in dallas was transported, unknowingly, by ems, potentially exposing them and others to this deadly disease. likewise, heroin has become an exploding epidemic in the united states with deaths from overdoses skyrocketing across the country. there are few data sources for overdoses that can alert and track real time instances of heroin overdose which are arguably the highest risk patients in the community. this will make it difficult for interventions in the community as expressed recently by the white house. methods this is a descriptive study of using multiple different ems data sources for surveillance of emerging infectious disease and acute opoid overdose. for ebola, data were reviewed over a six month period using search terms that reflected the definition used by health care facilities as issued by the cdc. over 40 different ems agencies agreed to have their data included in the surviellance program. the algorithm for identifying potential ebola cases included data from 911 dispatch, emergency medical dispatch codes, physical symptoms as documented by the treating paramedic and free text searches for countries of iterest. for the surviellance of opiod overdoses, data was reviewed for one month in a high volume urban ems system. all calls were reviewed for key search terms including 911 dispatch data and emergency medical dispatch data for overdose codes. likewise the patient care records were surveyed in real time for paramedic diagnosis of overdose, respiratory rate and glascow coma scale, whether the opiod reversal agent nalaxone was used and a free text search for opiod drugs of abuse. multiple logistic regression was used to identify the most predictive terms. variables included dispatch code, narcan use, free text search, change in respiratory rate, change in glascow coma scale and paramedic impreesion of overdose results for ebola, there were 1,532 unique cases identified from october 2014 – march 2015. none of these tested positive for ebola. the range of cases per ems agency was 1 – 1026 with the majority (42, 95%) having < 30 cases. cases ranged from 107 to 331 cases per month. the most common documented physical complaint, area of travel and dispatch diagnosis were “fever”, “africa” and “sick” respectively. for heroin, there were 165 cases that fit the search criteria. all patient care records were reviewed by medical experts to determine whether there was, in fact, a suspected overdose. 96 cases were identified as true overdoses based on the patinet record. the most predictive model for identifying opiod overdose included naloxone use (or 5.51, p< 0.005), and free text search for opoids ((or 3.890, p< 0.005)) conclusions for multiple different public health challenges, ems data is a rich data source for specific information that can be delivered in near real time. patients with emerging infectious disease and opiod overdose may first present to ems personnel. it is important the ems agencies be involved in surveillance activities. this study describes the ems data used for surveillance to identify potential ebola patients and opiod overdoses. this study shows that it is possible for ems agencies to utilize unique data sources for near real-time identification of patients with an emerging infectious disease and opiod overdoses. this study was limited by the differences in definitions for identification of cases for ebola, however the majority used definitions resembling the cdc. the model used to identify opiod overdose cases was very predictive using search terms including use of naloxone and a free text search. more research is required to better refine the abilities of ems data to serve as a surveillance node for disease status within the population. keywords ebola; heroin; ems acknowledgments mr. todd stout at firstwatch inc *alexander garza e-mail: agarza@slu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e58, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) 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articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, 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complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga 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biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 1university of missouri health management and informatics, columbia, mo, usa; 2missouri cancer registry and research center, columbia, mo, usa; 3mu informatics institute, columbia, mo, usa objective utilize existing data sets and data sources to address health equity and improve the management of chronic disease introduction in 2012, half of all adults in the us had one or more chronic health conditions; at least 25% had two or more chronic health conditions (1). seven of the top ten causes of death in 2010 were chronic diseases; two of the seven chronic diseases, heart disease and cancer, account almost for over 50% of all deaths (2). chronic disease is one of the most costly contributors in healthcare expenditures; once diagnosed many patients must be followed for a lifetime. in lower-income countries chronic disease is now the biggest contributor to mortality (3). socioeconomic inequalities are a major driver of the chronic disease epidemic (4). chronic disease in the us, such as cancer, heart disease, renal end stage disease and diabetes are tracked in national datasets but are not linked. chronic diseases share many risk factors, major risk factors, e.g. tobacco, diet, alcohol, and physical inactivity are already known, their interactions with comorbidities are important and clinical practice indicates that the chronic disease epidemic may be addressed more effectively using a holistic approach. however, this approach has not yet been implemented in disease surveillance activities as data collection is still disease specific. data collection is still one disease at a time, without connecting our disease surveillance efforts to get better, more complete and encompassing data. health inequities result in lower quality of healthcare, worse healthcare outcomes for minority racial/ ethnic populations and people with low socioeconomic status, increased direct and indirect healthcare costs, and decreased productivity (5). methods we identified four chronic diseases that contribute to the majority of death and disease: cancer, diabetes, coronary heart disease, and renal end stage disease. we explored datasets related to each disease to identify comorbidities and other overlapping information and common factors present in all four datasets which allows to look at chronic disease as a whole. we describe the four datasets and the information that we have discovered using this approach and discuss how such information can improve outcomes and potentially reduce inequities. in addition, we demonstrate the advantage to link these data. results there is an overlap in the data that are collected for the four chronic diseases that contribute to morbidity and mortality in the us and worldwide. we demonstrate that the method of combining datasets will not only enhance data completeness and quality but also increase accuracy for all four datasets and allow for additional research that would not be possible with dataset silos. conclusions by looking at the four datasets, we were able to identify variations in different racial/ethnic populations, socioeconomic status and risk factors such as tobacco use, obesity and the presence of one or more than one chronic disease. by utilizing data that are already collected and linking datasets, we can capitalize on existing data to support studies that focus on one or more of the diseases and expand the use of previously isolated datasets associated with chronic conditions that contribute to the majority of disease and mortality in the us and worldwide. this approach also opens the door to explore possible connections between chronic diseases that may lead to better understanding of why they go hand-in-hand and to interventions that are culturally appropriate and action-oriented, and can be embedded in the community. keywords disease surveillance; chronic disease; data linkage references 1. ward bw, schiller js, goodman ra. multiple chronic conditions among us adults: a 2012 update. prev chronic dis. 2014;11:130389. doi: http://dx.doi.org/10.5888/pcd11.130389. 2. centers for disease control and prevention. death and mortality. nchs faststats web site. http://www.cdc.gov/nchs/fastats/deaths. htm. accessed august 25, 2015. 3. making the link: chronic diseases and health equity, fact sheet chronic diseases, 2007 (http://eurohealthnet.eu/sites/eurohealthnet.eu/files/ publications/fact-sheet-chronic%20diseases%20and%20health%20 equity-v2%5b2%5d.pdf). accessed july 10, 2015. 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patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji nigeria field epidemiology and laboratory training programme, abuja, nigeria objective • to determine the percentage and trends of newly diagnosed hiv positive pregnant women • to determine the percentage of pregnant women that are counseled tested with result. • to determine the percentage and trend in the uptake art among hiv positive pregnant women. • to determine the average no of individual that are counseled and tested for hiv. • to determine the average no of individual that are hiv positive • to estimate the average no of individual currently on art, newly started on art and those enrolled into hiv care. introduction as of 2012, 3,400 000 million people (all ages) are living with hiv in nigeria. the estimated new hiv infections is 260,000 and estimated aids death is 240,000.the reported number of adults on art(anti-retroviral treatment) was 459,465 and the art coverage based on who guideline was 36%.the number of pregnant women living with hiv who received antiretroviral for preventing motherto-child –transmission was 33,323 and the percentage coverage was 17%1.enugu state has the highest prevalence(6.5%) of hiv/ aids in the south east and the fourth in nigeria.to implement the commitments in the 2011 united nations political declaration on hiv and aids and increase progress towards universal access to hiv prevention, treatment, care and support, nigeria has developed the president’s comprehensive response plan (pcrp).pcrp aims to bridge the current gap in service provision and funding. it assesses needs and gaps, identifies focus areas, and set targets for prevention of mother to child transmission (pmtct), art and hiv counseling and testing (hct) services. we determined the implementation of these preventive services by health care providers in enugu state. methods we reviewed 2010-2013 hiv/aids surveillance data of enugu state. we conducted descriptive analysis of art utilization, pmtct services and hct services using microsoft excel 2007. results the total number of all individuals that accessed hct services from 2010 to 2013 was 87,000, 104,344, 161,517, and 113,903 respectively. the total number of hiv positive individuals from 2010 to 2013 was 8,965(10.3%), 7695(7.3%), 9233(5.7%), and 6110(5.4%). respectively. the overall total number of individual newly started on art from 2010 to 2013 was 15,629.the percentage of pregnant women counseled, tested with result was 23,100, 41,000, 45,318, and 38,440 respectively for 2010 -2013 and those that tested positive are for 2010-2013 are 1,059(4.6%), 1,363(3.3%), 1,845(4.1%), and 1,036(2.7%).among the pregnant women that tested positive, the number that are receiving art for 2010-2013 are 1,028(92.0%), 1,041(76.4%), 1,640(88.9%), and 719(69.4%) respectively. conclusions the state aids and sti control programme, though has achieved success in the prevention of hiv/aids as evidenced by decreased percentage of hiv/aids positive individuals over the years under study and decreased percentage of newly diagnosed hiv positive cases among pregnant women, the state still need to scale up the art coverage among pregnant women by increasing the number of facilities that renders art services in the state. keywords surveillance; human immuno deficiency virus; acquired immunodeficiency syndrome; anti retroviral therapy; hiv counselling and testing acknowledgments nigeria field epidemiology and laboratory training program references 1. the joint united nations programme on hiv/aids.report on global aids epidemic, 2013. *chinyere c. ezeudu e-mail: chiezeudu@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e108, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response 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and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts monitoring the 2016 la county sand fire with multiple early detection systems rachel viola*, monica z. luarca, emily kajita, michael lim and bessie hwang acute communicable disease control, los angeles county department of public health, los angeles, ca, usa objective to detect increases in health complaints resulting from the july 2016 sand fire near santa clarita, ca using syndromic surveillance and complementary systems. introduction on july 22, 2016, the sand fire began burning in the santa clarita valley of los angeles county (lac), ca. this urban-adjacent wildfire breached the city limits of santa clarita (population 180,000). fueled by record heat and an ongoing exceptional drought, the sand fire burned over 40,000 acres in 13 days1 and caused a large increase in the air concentration of fine particulate matter2. the syndromic surveillance team was tasked with reporting on possible health effects from the fire. fire, asthma, and heat related data were monitored until the fire was reported as 98% contained. the team prepared and distributed a daily special summary report to key stakeholders in the lac department of public health. methods emergency department (ed) data were queried for cases related to fire, asthma, cardiac events, eye irritation, heat, and total volume. these queries consisted of key word searches within chief complaint (cc), diagnosis and triage note data fields. queries were conducted on all participating syndromic eds in lac, and also restricted to nine eds closest to the fire. the resulting line lists were reviewed daily to rule out visits that were unrelated to the sand fire. the fire query was refined periodically with additional exclusion terms. complaints related to asthma were tallied in a second query. in order to assess heat-related ed visits and temperature trends, existing queries and report templates were modified to focus on the nine fire-area eds. local temperatures were taken from the weather underground website. complementary systems were also monitored, including over-the-counter medication sales and nurse hotline call data. trend graphs for hospital admissions and ed visits were produced daily to assess volume from 19 reddinet participating hospitals. in addition to internal data sources, the south coast air quality management district website was checked daily to monitor air quality in the santa clarita valley. results there were 48 syndromic ed patient records with direct mention of the fire in lac’s syndromic hospitals in 13 days. of these, 26 did not include asthma, and 32 came from the nine hospitals in the sand fire region; 32 were identified from the cc, six by diagnosis and ten by triage note. despite an increase in fire-related visits, overall trends in ed data were not affected; no increase was found for cardiac events, eye irritation, heat-related illness or total volume. asthma visits increased at the time of the fire, which correlates with a sharp increase in the concentration of fine particulate matter in the santa clarita valley following the start of the fire2. however, these increases were no higher than other peaks observed in previous months3. no increases in calls to a nurse hotline or over-the-counter medication sales were observed. among reddinet hospitals, admissions increased slightly but ed visits remained unchanged. conclusions for the sand fire, ed volume alone was not enough to estimate the subsequent health effects on residents of lac; instead a specific fire query was needed. several factors could explain why overall trends were not affected. in a region where air quality is already compromised, it is challenging to distinguish between asthma increases from air pollution from those exacerbated by wildfire smoke. it is also likely that residents heeded warnings about air quality during active fires, thus reducing their outdoor exposure. although the majority of cases were identified using the cc field, additional data fields such as triage notes available from some hospitals improve the ability to elicit fire related visits. regardless of the challenges presented in measuring health effects related to wildfires, syndromic surveillance and complementary systems continue to be the primary tools for near real-time assessments in lac. keywords syndromic surveillance; disaster; wildfire; early detection references 1. angeles national forest. sand fire [internet]. [place unknown]: united states forest service [updated 2006 aug 6; cited 2016 aug 19]. available from: http://inciweb.nwcg.gov/incident/4878/. 2. air quality data & studies [internet]. diamond bar, ca: south coast air quality management district [cited 2016 aug 19]. available from: http://www.aqmd.gov/home/library/air-quality-data-studies. 3. barboza t. socal hit with worst smog in years as hot, stagnant weather brings surge in hospital visits. los angeles times [internet]. 2016 aug 11 [cited 2016 aug 19]; l.a. now. available from: http://www. latimes.com/local/lanow/la-me-ln-summer-smog-20160805-snapstory.html. *rachel viola e-mail: rviola@ph.lacounty.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e135, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 1department of veterans affairs, washington, dc, usa; 2stanford university, stanford, ca, usa objective to describe va’s experience developing innovative and alternative uses of a surveillance system and improve the overall value proposition of this tool for the agency. introduction va began using essence as a public health surveillance tool in 2005. the system offered alerting capability for pre-defined syndromes and querying capability for outpatient icd-9 diagnosis codes. herein, we highlight examples of how we have invested in upgrades to analytic capabilities and expanded data sources available to essence in order to augment the overall utility of this system within va. methods we reviewed use cases for new data added since 2009 (additional outpatient, plus inpatient, procedure, surgery, and telephone triage data) as well as enhancements to system analytics, geospatial mapping and general functionality. we also describe novel use cases for the original outpatient data elements and document examples of how we’ve merged query results from essence with data from other va data sources to answer important surveillance questions. results the evaluation was divided into three themes. first, and a top priority, was enhancing influenza surveillance capability. a number of innovative use cases for this theme were reviewed. pulling procedure codes (icd-9 and cpt) provided the ability to track immunizations in outpatient and inpatient settings. the availability of outpatient clinic location details allowed us to calculate %ili specifically for our primary care clinics. telephone triage data gave us more timely insight into the rise and peak of influenza activity compared to outpatient visit data alone. the addition of inpatient data assisted us in characterizing the severity of each season by tracking admitting and discharge diagnosis codes for influenza as well as other severity elements, such as ward details (intensive care stays), overall lengths of stay, mechanical ventilation requirements (via intersecting time series of influenza diagnosis query and mechanical ventilation procedure query) and patient disposition (specifically, deaths) for influenza-coded hospitalizations. new geospatial capabilities enabled us to map hospitalizations and telephone triage calls by va facility and region. finally, we merged our inpatient essence data with pharmacy and laboratory data from other va data sources to assess the proportion of hospitalized veterans who had influenza testing performed and/or received antiviral medications. the second theme was epidemiologic reviews and lookbacks. here we sought to identify veterans with an exposure or procedure of interest. most recently, we conducted a review of veterans with carbapenem-resistant enterobacteriaceae (cre) infections having undergone specific endoscopy procedures (endoscopic retrograde cholangiopancreatography, ercp). we queried for ercp procedure codes in essence then merged this with cre laboratory data to generate a cohort of patients requiring further epidemiologic review. the third theme was infection control activities. here, in partnership with facility infection preventionists, we identified procedures, events or diagnoses of interest for specific surveillance questions. we used procedure codes in essence to identify operative procedures defined by the national healthcare safety network to generate surgical site infection (ssi) surveillance denominators. in another example, we sought to assess whether patients with healthcare-associated pneumonia were being appropriately tested for legionella. we used inpatient diagnosis codes to identify pneumonia hospitalizations in essence and merged that with laboratory data as a starting point for the analysis. finally, at the request of our local accident review committee, we used existing outpatient diagnosis codes in essence to identify needlestick injury visits that may not have been properly reported. conclusions we’ve highlighted new and innovative uses of a public health surveillance tool within va. system evaluations such as these are essential for demonstrating usefulness as well as maintaining funding and support for these applications. through the mechanisms described, we have optimized the functionality of this tool to the greatest extent possible. in order to continue to innovate and harness the power of big data available to us, we have begun transitioning to a new surveillance platform (praedico, bitscopic inc.) which provides additional flexibility and system architecture needed to accommodate the volume of data available from multiple sources and improve our analytic, event detection, geospatial and forecasting capabilities into the future. keywords public health surveillance; veterans; system enhancement; influenza surveillance; epidemiology *cynthia a. lucero-obusan e-mail: cynthia.lucero@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e136, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding 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deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 visualizing the quality of partially accruing data for use in decision making julia eaton *1 , ian painter 2 , don olson 3 , william b lober 4 1 school of interdisciplinary arts & sciences, university of washington tacoma, tacoma, wa 2 school of public health, university of washington, seattle, wa 3 new york city department of health and mental hygiene, long island city, ny 4 schools of nursing, medicine and public health, university of washington, seattle, wa abstract secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance, understanding the structure of the incompleteness is necessary. such data are commonly aggregated due to privacy concerns. the distribute project was a near real-time influenza-like-illness (ili) surveillance system that relied on aggregated secondary clinical health data. the goal of this work is to disseminate the data quality tools developed to gain insight into the data quality problems associated with these data. these tools apply in general to any system where aggregate data are accrued over time and were created through the end-user-as-developer paradigm. each tool was developed during the exploratory analysis to gain insight into structural aspects of data quality. our key finding is that data quality of partially accruing data must be studied in the context of accrual lag—the difference between the time an event occurs and the time data for that event are received, i.e. the time at which data become available to the surveillance system. our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. accounting for accrual lag in partially accruing data is necessary to avoid misleading or biased conclusions about trends in indicator values and data quality. keywords: data quality, partially accruing data, accrual lag, data visualization, secondary-use data, realtime surveillance, incomplete data correspondence: jreaton@uw.edu doi: 10.5210/ojphi.v7i3.6096 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 introduction clinical data that are used for real-time disease surveillance present challenges in the context of public health decision-making and such data can be of marginal utility due to data quality issues. clinical data from health care encounters are typically aggregated into data sets and sent to the surveillance system at periodic time intervals, inherently creating a delay in the availability of the data for surveillance purposes. data for surveillance can consist of encounter-level records or aggregate data counts. encounter-level records can be received in real time or batched over time intervals, whereas aggregate data counts are by definition batched over time intervals. surveillance data may be available only as aggregate counts due to individual or corporate privacy concerns, such as retail monitoring of pharmacy data [1] and school absenteeism data [2]. other data lack sufficient individual level variability, such as bed availability data, for which the individual level is a binary measurement [3]. a further level of aggregation in surveillance systems occurs when the source data is already an aggregate summary of multiple sources, such as total number of visits during a time period within a jurisdiction. data collected from multiple sources, each with its own processes and delays, accrues piecemeal, with inherent trade-offs between timeliness and completion. examples of such systems include vaccine surveillance data [4], where data tend to accrue over a period of weeks, jurisdictional level syndromic surveillance data [5], where data accrue over a period of days, and over-the-counter pharmacy data [1], where data accrue over a period of hours. in its broadest sense, data quality can be defined as the degree to which data provide utility to data consumers [6]. this encompasses both intrinsic data quality (the quality of the data in and of itself) and contextual data quality (the utility of the data for the task at hand). intrinsic data quality typically focuses on accuracy, completeness, and timeliness. in real-time disease surveillance, individual record level data has been assessed in terms of accuracy and completeness [7]. contextual utility for disease surveillance has been examined in terms of the relationship between timeliness and the time it takes to detect outbreaks, and the sensitivity and specificity of outbreak detection algorithms [8] or chief complaint classifiers. given the variety of sources of data delay, surveillance data are often timely or complete, but not both. when acting as a secondary user of data, public health practitioners may have little ability to influence the timeliness of surveillance data, which is often provided on a voluntary basis without remuneration. this leaves two options for dealing with timeliness issues: wait until sufficient time has elapsed to ensure that the data are sufficiently complete (which lessens the usefulness for real-time surveillance), or develop tools for using incomplete data. to date, few methods have been developed for using incomplete data in surveillance. one exception is safety monitoring for influenza vaccinations [9]. in vaccine safety reporting systems, lags occur between the time when a vaccine is administered, the time when a record of that vaccine is reported and the time when an adverse event is reported. green et al [9] use sequential analysis (data are continually re-analyzed as more become available) to assess the presence of an adverse event. the only other example of which we are aware specific to aggregate summary data for real-time surveillance is an analysis of thermometer sales data collected from multiple retail stores [10]. the work presented in this paper was motivated by methods developed for the analysis of data sets for the distribute project for real-time influenza-like-illness (ili) surveillance [5]. this is particularly relevant for surveillance based on aggregate data from medical record systems in developing countries, where internet connectivity and even the availability of power is ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 intermittent, and where systems must be explicitly designed to deal with accruing data [11]. the distribute system was used as part of the effort to monitor the h1n1 influenza pandemic outbreak in 2009 [12]. the data available in the distribute system consisted of daily counts of emergency department (ed) visits within each participating jurisdiction, and the number of those visits in which patients exhibit ili symptoms. these data were aggregated from eds (termed “sources” here) by each jurisdiction (termed “sites” here), and subsequently sent to distribute. typically, different sources within a site upload the data to the site at different times—daily, weekly, or haphazardly. a primary design goal of the system was to make the process of supplying data as simple as possible, both from a technical viewpoint and a policy viewpoint [13,14]. an important feature of the resulting data is that it is partially accruing, that is, data for each time point are accrued piecemeal and become more complete over time. in addition, different sources have different accrual patterns, and accrual patterns from a single source may shift over time. the indicators of primary interest in the distribute system are the total counts of ed visits, the ili counts, and the derived ratio of the ili to total counts for each site. the visualization methods presented in this paper were originally developed as part of an exploratory data analysis of the data quality characteristics of the distribute system [15]. the main focus of the distribute data quality analysis was to understand the structural aspects of data quality. in the process we found that standard data visualization methods did not provide adequate insight into the underlying structural characteristics, and we developed additional visualizations to address these inadequacies, using our collective expertise in statistics, visualization, public health and medical informatics. the analysis was conducted in r (an open source statistical system) version 2.10.1 [16]. the visualization methods we developed were implemented as functions in r and these functions were developed into the r package accrued [17]. the tools developed apply in general to any system where aggregate data is accrued over time. in this paper we utilize the notion of accrual lag—the time elapsed between an event and the date at which data for that event become available. we illustrate how accrual lag can be used to understand the structure of partially accruing data, and in particular, demonstrate the utility of data visualizations that depict accrual lag. methods visualization methods were generated through the end-user-as-developer paradigm [18]. methods were developed in r during the exploratory analysis to gain insight into structural aspects of data quality for individual sites. each method was then applied across sites, and those methods that generalized to provide useful information for more than one site were formally developed into r functions and included in the accrued package using the r package development tools [16,17]. the authors served as of analysts, visualization users and developers. data were extracted from the relational database containing the distribute complete data store using sql statements and stored in an r data frame. the complete data store contained a record of the aggregate emergency department ili, gastrointestinal and total visit counts received on each date from each jurisdiction participating in distribute. this allowed us to reconstruct what was known about aggregate counts for any particular date on each subsequent date. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 results the key realization from this analysis was that data quality of partially accruing data must be studied in the context of accrual lag—the difference between the event time (ed visit date in the context of distribute) and receipt time, which is the time at which the data become available to the system. our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. we found three additional aspects of the data that play an important role in understanding data quality issues for partially accruing data: 1. the ability to define a complete data state—an accrual lag point at which the data (and hence indicator values) can be considered complete. this state is important since without it one cannot observe a relationship between partially accruing data and complete data, nor assess the accuracy of partially accruing data. 2. the presence of ‘record skips'—haphazard times at which no data are received. in the distribute data this primarily occurred due to breakdowns in the data upload process. 3. the presence of long-term changes in the data. we observed multiple long-term step-like changes over time in total counts received for most sites. for any particular site the mix of sources reporting to that site may change over time, resulting in these step-wise changes in the counts. for the distribute data, the time units are days, and “date” and “time” are used in this paper interchangeably. due to the piecemeal accrual of data, the value of an indicator for a particular event date changes in the system until the data for that date are complete. we characterized different notions of the indicator value as follows. • the data-at-hand at a particular date refers to the data that are available at that date. • the current value of an indicator refers to the value of an indicator for a particular event date as of the current date, that is, the value calculated from the data-at-hand as of the current date. • the lagged value of an indicator refers to the value for a particular event date a fixed number of days (the accrual lag) after that date. the number of days lagged is specified so that, for example, the five-day lagged value for an indicator for a particular date is calculated using the data-at-hand five days after that date. • the complete data value of an indicator refers to the value of an indicator for a particular event date once all data for the event date have been received. we characterize the visualizations as follows: (1) tools for understanding the relationship between event date and receipt date, (2) timeliness and completion tools, (3) constant lag tools, (4) accuracy visualizations, and ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 (5) completeness visualizations. (1) tools for understanding the relationship between event date and receipt date the data received on any particular date can contain data from multiple event dates. examining the event dates contained in the data received each day allowed us to detect system failures and systematic changes in the underlying aggregation processes. we created a compact display (called a receipt pattern plot, figure 1) to examine the receipt history for each site by generating an image plot where each value of the x-axis represents a date on which data was received, each value on the y-axis represents an accrual lag, and a point is plotted at coordinates (i,j) if the data received on day i contains any data for the date j days prior to day i. figure 1 illustrates six canonical receipt pattern plots (a-f). these plots illustrate several features, including level of consistency and the occurrence of systematic or sporadic changes. the most consistent receipt pattern appears in the plot (a). for each of the first 70 days, data representing the nine most recent event dates were received. a subtle change occurs around day 70, after which data representing the ten most recent event dates were received each day. plot (b) shows a site which only includes records in the data that it sends if the value for that record has changed since the last time the record was sent, or if the record has not previously been sent. this results in a pattern where after a certain lag data tends to be only received sporadically. plot (c) shows a site where receipts occur sporadically on weekdays and almost never on weekends. plot (d) shows a site with a fairly consistent receipt pattern until a 10-day interruption starting just before day 200. a backfill, indicating a long interval of event dates contained within the data received on a single date, occurs immediately following the interruption. plot (e) exhibits four distinct patterns in the event dates received each day, which indicate multiple changes in the aggregation process at that site. the site in plot (f) sends data sporadically, and no records are ever received for some event dates. this site reports data from a single, very low-count source whose counts may be zero on certain days, and which uses a system that only sends data for event dates on which at least one count occurred (a similar pattern would occur if counts were suppressed when very low due to concerns that patients may be re-identified). a variation on this plot type is presented in the supplementary materials (supplementary materials figure 7). (2) timeliness and completeness tools to understand the relationship between accrual lag, timeliness and completeness we developed a visualization tool to examine the cumulative effect of data accrual on indicator values. for count indicators, which will accrue cumulatively, we found that generating time series of differences of the indicator values for successive accrual lags useful. the time series lagged by s days consists of all indicator values lagged by s days, in order of event date. the time series of the sth difference was computed by subtracting the indicator value at (s-1) days from the indicator value at s days, for each event date. this process was repeated for all consecutive lags. these time series were then stacked into a single plot. we termed this plot a stacklag difference plot. figure 2 shows three examples of these plots (a-c). the x-axis represents the event date, and the sth layer shows the difference in count indicator values between lag s and lag (s-1), for s  1. if no counts have been received for the current or any previous lags no value is shown. the height of layer s is proportional to the maximum over x of the range of the difference in counts between lag s and lag (s-1), and a fixed gap is placed between each layer to make them distinguishable. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 figure 1: upload pattern plots. the horizontal axis represents receipt date and each vertical axis represents accrual lag in days. the stacklag difference plot provides a view of the complete history of changes in counts from lag to lag, and is useful for detecting both sporadic and systematic data quality issues. for the site shown in plot (a), a low volume of counts is regularly sent for most event dates. just before day 500, several counts are added from lags 7 to 10, indicating that a sporadic data quality problem (underreporting for a specific event date) occurred and was subsequently corrected. in plot (b), the data appear to be nearly complete by lag 6, and few changes occur past lag 3. in plot (c), the data appear to be complete by lag 6, except for a striking change from lag 10 to lag 11, where the counts are replaced with smaller values. this pattern is systematic from the beginning of the time series until around day 300. this problem was caused by a data processing error at the data source, which was detected and corrected around day 300. this anomaly was not evident from looking at a time series plot of the data-at-hand. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 figure 2a: stacklag difference plot. the horizontal axis represents the event date and each vertical number on the vertical axis represents accrual lag in days. the time series plotted for each accrual lag represents the change in the total number of counts from the previous accrual lag. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 figure 2b: stacklag difference plot. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 figure 2c: stacklag difference plot. (3) constant lag visualization time series of indicator values are an obvious tool for visualizing surveillance data. because of the accrual lag in partially accruing data, time series of current values for indicators tend to show systematic bias for recent dates. values for count indicators in the distribute system showed a persistent drop-off for recent dates, while for ratio indicators, site-specific current values could be systematically above, below or about the same as the complete data values for recent dates. this systematic bias makes comparing the indicators of recent dates with older dates using dataat-hand difficult and obscures not only real trends in data but trends in data quality. to avoid this issue we examined time series of indicator values with the lag held constant (so for example, a time series of the indicator values calculated from the data at hand two days after the event date). this allowed for direct examination of the effect of receipt patterns on the completeness of ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 indicator values, and to provide a visual indicator of when data are sufficiently complete to observe trends in the data. the supplementary material includes examples of these arrayed lagged time series. because lagged time series do not show the persistent drop off for recent dates, they were more suited for use with anomaly detection methods than time series of the data at hand. however we found that standard cusum statistical process control techniques [19] for detecting anomalies in time series did not perform well for data quality detection, since data quality anomalies tended to manifest as systematic long-term changes in the data. to look for long term changes we applied bayesian change point detection [20,21] and found that this technique could determine both dates at which short-term data quality problems occurred and dates when long-term changes in the source makeup of a site occurred. we used the bayesian change point estimation implemented in the bcp package [22] in r to estimate posterior means and probabilities on variance-stabilized day-of-week corrected time series of counts at specific lag values. figure 3 shows a site with both short-term data quality issues (outliers) and longer term changes in mean counts, indicating changes in the underlying sources aggregated by the site. figure 3: posterior mean from bayesian change point detection method. the horizontal axis represents event date and the vertical axis represents the count. the scale of the vertical axis is intentionally suppressed for publication. (4) accuracy visualizations understanding when data are sufficiently complete to be considered accurate is important. accrual lag can have a major impact on the accuracy of indicator values during the accrual period; this can be characterized in terms of the degree of accuracy as a function of the accrual lag. in particular, we consider the error in an indicator value as a function of lag: ( ) ( ) ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 where f is an error function such as a difference or a ratio. considering this error over all event dates in the time series, we obtain an error distribution for each lag. we summarize the error distribution as a function of lag by plotting fixed quantiles of the distributions for each lag, as in figure 4, which summarizes the error distribution for the ili ratio indicator. this plot provides a summary of both the distribution of errors for an indicator caused by accrual lag as well as any bias generated by the accrual lag. in figure 4 the ili ratio calculated at lag 0 is extremely inaccurate, with both high bias and high variability. the bias reduces considerably by lag 3, and the variability reduces substantially by lag 8 and is negligible by lag 10. figure 4: ili ratio errors with 5th, 10 th , 25 th , 50 th , 75 th , 90 th and 95 th quantiles using the ratio error function. we used the 21 day lagged counts as final counts to avoid the effects of long backfills that sites periodically provided. (5) completeness visualizations completeness is a fundamental intrinsic data quality property. for cumulative indicators such as counts, the completeness of the data generally increases with lag. this can be summarized in terms of the average completeness of the data as a function of lag. a full description of completeness requires the examination of the distribution of completeness at each lag. to visualize this distribution we created stacked histograms of the completeness proportion according to lag (figure 5, a-c). plots (a) and (b) exhibit a "binary" pattern—either no data are available or all data are available, with the second site clearly less timely than the first site. this lag histogram pattern can occur when the data received either contain all or none of the data for an event date, but the lag in receiving the data is variable (this could occur for example if a site consists of a single source that sends data to the site manually on a non-regular schedule). plot (c) shows a more gradual migration of the mass density to the right-hand side, indicative of sites with more than one contributing source. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 (a) (b) (c) figure 5: lag histograms for three sites. the horizontal axis represents the proportion of data received; the vertical axis represents the accrual lag. more succinct summaries of completeness can be visualized using summary completion curves—line plots of the mean completion versus lag. these summary plots can also be used as a tool for comparing completeness. in figure 6, two completion curves are compared. the blue curve is the completion curve over all sites in the distribute data for year 2010, and the red curve is the corresponding completion curve for 2011. that the red curve is higher than the blue curve for all lags demonstrates that for each lag, on average, the 2011 data were more complete than the 2010 data. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 figure 6: summary completion curves for all sites. discussion the tools presented here were used for three main purposes: understanding data quality patterns, detecting data quality problems and summarizing data quality measures, with some tools useful for more than one purpose. the receipt pattern plots, stacklag difference plots, constant lag time series plots and stacked lag histograms were useful for understanding data quality patterns. for detecting data quality problems we used the receipt pattern plots, change point estimation plots, constant lag time series and stacklag difference plots. the summary completion curves were used to summarize the effects of accrual lag on completion and the accuracy visualizations were used to assess the effects of accrual lag on the accuracy of indicators. fundamentally, data collected for one set of consumers, such as clinical data, can be of one quality for the intended purpose, but have different data quality properties for secondary use purposes, such as public health surveillance. for example, data collected from the emergency department (ed) intake record include the chief complaint, a short free-text field containing the reason for the ed visit. clinical decisions made solely on the basis of a chief complaint field would be problematic as the field contains little information of diagnostic value, however, on a population basis, accurate estimates of rates of syndrome occurrences can be made from tests with low sensitivity and specificity if sufficient events are available and no systematic biases are present. systematic biases in a diagnostic test may be desirable in a clinical setting (for example ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 in tests used in the first stage of screening) but render population-level estimates problematic. however, as long as the biases are constant over time, the data may be of sufficient quality for other population health purposes such as estimates of trends. difficulties are likely to occur in any secondary use setting where the downstream users have little influence on the primary data collection. for example, in principle, accrual lag can be eliminated through timely data delivery, but in practice this is difficult to achieve in settings where participation is voluntary and there is no direct control over the reporting sites. in the united states, local public health jurisdictions often conduct ed surveillance using data provided on a voluntary basis. this creates a situation where the public health jurisdiction may be reluctant to push data sources to improve the quality of the data for worry of reducing the likelihood that the source will participate in the surveillance. thus methods to deal with data of less than perfect quality are required. this may also occur in resource-constrained settings. though obvious in retrospect, our key insight was the need to assess temporal patterns in the data in terms of accrual lag. when data accrue over time, data-at-hand for earlier dates are fundamentally different than data-at-hand for very recent dates. data aberration detection algorithms that assume the data-at-hand are complete for recent dates will not perform well when applied to partially accruing data, and evaluations of these algorithms conducted under this assumption will be misleading. given that indicator values for data-at-hand for recent dates may differ fundamentally from indicator values for earlier dates, simple time series displays of the data-at-hand can be misleading. clarifying the relationship between accrual lag and the error distribution can mitigate this. one approach is to use the error distribution to determine how long a delay must be for data to reach sufficient accuracy, which can be defined statistically in terms of the mean square error of the indicator, or in terms of the probability of having an error greater than some threshold. a second approach, explored in [23], is to calculate uncertainty measures from the error distributions, such as prediction intervals for the current value of an indicator, as a function of lag. this approach has the advantage that covariates can be incorporated in the prediction so that the uncertainty will be larger when the data are noisier. another advantage is that, rather than not displaying data until a sufficient delay, the prediction bounds can be directly displayed on a time series graph, allowing the user to see all of the data-at-hand and a measure of confidence of the quality of the data. limitations an important limitation of this work is that we did not have access to individual level or facility level data, and so we could not directly associate the observed patterns and changes in data quality with specific causes that may be apparent in more detailed data. many of the visualization methods developed in this paper apply primarily to aggregate level data; when access to more detailed data is possible, methods that make use of this level of detail would likely provide a more complete picture of the data quality patterns. acknowledgements this work was supported though a subcontract from the isds, with original funding coming from the markle foundation, 2009 to 2010 (grant #101003bp-b), the public health informatics ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 institute (phii) and cdc, 2010-2011. valuable feedback was received from the distribute community throughout the project. financial disclosure no financial disclosures. competing 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21672242&dopt=abstract http://dx.doi.org/10.5210/ojphi.v5i3.4938 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24678377&dopt=abstract http://dx.doi.org/10.3402/ehtj.v4i0.11142 ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 supplementary material diagonal receipt pattern plot figure 7 shows a variation on the receipt pattern plot, which shows the events dates of the data (as opposed to the lag values of the data). this creates a less compact display but allows the display of a greater period of event dates. several important features are revealed in figure 7; dates on which no uploads were received, dates for which no events were received and dates for which large data backfills occurred. figure 7: diagonal receipt pattern plot. the horizontal axis shows the date of data receipt and the vertical axis shows the event date. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 arrayed lagged time series plots in figure 8, arrayed lagged time series plots for three different lags show constant lag value time series for various sites with lags of 1, 3, 5 days and the complete data (lag 20) time series. the running median is shown in blue, and the mad (median absolute deviation) envelope lines are shown in green. the original time series is plotted in gray. site (a) shows a well-behaved site that stabilizes quickly (little change between initial lags and the complete data). site (b) shows a more typical site with the variation around the running median lines decreasing as more data become available with increasing lag. site (c) shows an abnormality that is visible only after the data have apparently stabilized; by five days lag, little change occurs between lags up until the last day data are received from the site, at which point a large decrease in the counts occurred. while this change is clearly visible here, this change is not directly observable from any current value time series plot. figure 8: arrayed lagged time series plots for three sites and three different accrual lags. each horizontal axis is event date; each vertical axis is the number of counts with the scale intentionally suppressed. bar code sparkline plots figure 8 shows bar code sparkline plots for six sites. these plots show a vertical line for each date data are received, and provide a compact way to look at both recent and general patterns of receipt frequency. sites 1, 3 and 4 have very similar receipt patterns. for these three sites, data were frequently but not always received, and there is a gap in the latter half of the dates. the similarity of these sites was due to a common mechanism for the data were generated. site 5 had regular but less frequent data receipts than sites 1, 3, 4 and 6. site 2 shows a very sparse pattern of receipts, suggesting a manual process was used, while site 6 has data recorded nearly every day. ojphi visualizing the quality of partially accruing data for use in decision making online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e226, 2015 figure 8: bar code sparklines for six sites showing 100 receipt dates. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts opioid overdose ambulance runs: how wisconsin uses free text data ashley bergeron1, jennifer broad*1, dr. ousmane diallo1, gayatri raol1, 2 and milda aksamitauskas1 1office of health informatics, wisconsin department of health services, madison, wi, usa; 2council of state and territorial epidemiologists, madison, wi, usa objective 1. develop an understanding of the benefits and challenges of analyzing free text fields on a population level. 2. observe how a complex surveillance definition can be created from free text fields. 3. observe how an ambulance data system can be used to describe the opioid epidemic. introduction in 2016, twelve states received center for disease control and prevention (cdc) enhanced state opioid overdose surveillance grants. the purpose of the grant is to explore enhanced data sources to track nonfatal opioid overdoses. one data source is ambulance runs. wisconsin collects ambulance run information within the wisconsin ambulance runs data system (wards). around 84% of all wisconsin administrative services report into this electronic system. this is a timely, robust data system that has not been used previously to examine drug overdoses and presents an analytical challenge as it contains many free text fields. methods wisconsin’s ambulance data system is robust, well-populated, and includes the majority of emergency medical services (ems) within the state. the analytic challenge with this data is that most of the reported fields are free text, which can be difficult to analyze on a population level. wisconsin created a case definition using sas regular expressions to take advantage of the free text fields. a combination of fields (chief complaint, secondary complaint, medications given, and the ems narrative) were used to determine if the ambulance run was due to an opioid overdose. it was necessary to create a definition that used a diverse combination of phrases as free text fields are prone to spelling errors and there are many phrases used to identify opioid overdoses. it was also necessary to create a definition for unwanted phrases that signal a false positive, for example, “withdrawal”. results wisconsin’s opioid definition uses regular expressions to search for the words “heroin”, “opioid”, “narcan”, or “methadone” (including various spellings). the overdose definition searches for words and phrases like “drug abuse”, “drug use”, “poisoning”, “drug ingestion”, and “overdose”. the medication administered fields are examined for “narcan”. in wisconsin, the medication is listed every time it is used, so it is possible to determine the number of times narcan was administered to a single person as well as how many ambulance runs used at least one dose of narcan. false positives are identified with words and phrases like “withdrawal”, “detox”, and if narcan was given but there is no indication that the ambulance run was due to drugs. from january 2016 – june 2017, wisconsin had over 917,000 ambulance runs for people aged 11 years and older. we excluded nonemergency ambulance runs, like medical transports, and so our final denominator was 627,536 runs (32% of all runs were classified as non-emergencies). suspected opioid overdoses were determined to be 1% of emergency ambulance runs. narcan was administered in a total of 5,900 runs and the false positive flag picked up 10,399 runs that may not have been due to suspected opioid overdoses. applying all of these components together, it was determined that in wisconsin from january 2016 – june 2017, there were 4,041 emergency ambulance runs due to suspected, unintentional opioid overdoses for people 11 years and older (rate of 6 per 1,000 people). conclusions the use of regular expressions enables wisconsin to extend analyses to data systems that contain robust information within free text fields. within wisconsin, this has been utilized to enhance opioid overdose surveillance with the use of a rapid data system previously not examined. ambulance run information is a valuable resource to wisconsin with the opioid epidemic. by creating case definitions with free text fields, we can quantify ambulance runs on a population level and create linkable analytic data sets to provide a more complete picture of the health of wisconsin. keywords opioid; surveillance; ambulance; free text acknowledgments this abstract was supported by the grant or cooperative agreement number, u17 ce924885, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services. *jennifer broad e-mail: jennifer.broad@dhs.wisconsin.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e55, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts key elements of infectious disease syndromic surveillance systems: a scoping review stephanie l. hughes*1, alex j. elliot2, scott a. mcewen1, amy greer1, ian young3 and andrew papadopoulos1 1population medicine, university of guelph, guelph, on, canada; 2public health england, birmingham, united kingdom; 3ryerson university, toronto, on, canada introduction syndromic surveillance is an alternative type of public health surveillance which utilises pre-diagnostic data sources to detect outbreaks earlier than conventional (laboratory) surveillance and monitor the progression of illnesses in populations. these systems are often noted for their ability to detect a wider range of cases in underreported illnesses, utilise existing data sources, and alert public health authorities of emerging crises. in addition, they are highly versatile and can be applied to a wide range of illnesses (communicable and non-communicable) and environmental conditions. as a result, their implementation in public health practice is expanding rapidly. this scoping review aimed to identify all existing literature detailing the necessary components in the defining, creating, implementing, and evaluating stages of human infectious disease syndromic surveillance systems. methods a full scoping review protocol was developed a priori. the research question posed for the review was “what are the essential elements of a fully functional syndromic surveillance system for human infectious disease?” five bibliographic databases (pubmed, scopus, cinahl, web of science, proquest) and eleven websites (google, public health ontario, public health england, public health agency of canada, centers for disease control and prevention, european centre for disease prevention and control, international society for disease surveillance, syndromic surveillance systems in europe, eurosurveillance, kingston frontenac, lennox & addington public health (x2)) were searched for peer-reviewed, government, academic, conference, and book literature. a total of 1237 unique citations were identified from this search and uploaded into the scoping review software covidence. the titles and abstracts were screened for relevance to the subject material, resulting in 142 documents for full-text screening. following this step, 55 documents remained for data extraction and inclusion in the scoping review. two independent reviewers conducted each step. results the scoping review identified many essential elements in the defining, creating, implementing, and evaluating of syndromic surveillance systems. these included the defining of “syndromic surveillance”, classification of syndromes, data quality and completeness, statistical methods, privacy and confidentiality issues, costs, operational challenges, management composition, collaboration with other public health agencies, and evaluation criteria. several benefits and limitations of the systems were also identified, when comparing them to other public health surveillance methods. benefits included the timeliness of analyses and reporting, potential cost savings, complementing traditional surveillance methods, high sensitivity, versatility, ability to perform shortand long-term surveillance, non-specificity of the systems, ability to fill in gaps of under-reported illnesses, and the collaborations which are fostered through its platform; limitations included the potential resources and costs required, inability to replace traditional healthcare and surveillance methods, the false alerts which may occur, nonspecificity of the systems, poor data quality and completeness, time lags in analyses, limited effectiveness at detecting smaller-scale outbreaks, and privacy issues with accessing data. conclusions over the past decade, syndromic surveillance systems have become an integral part of public health practice internationally. their ability to monitor a wide variety of illnesses and conditions, detect illnesses earlier than traditional surveillance methods, and be created using existing data sources make them a valuable public health tool. the results from this scoping review demonstrate the benefits and limitations and overall role of the systems in public health practice. in addition, this study also shows that a complete set of key elements are required in order to properly define, create, implement, and evaluate these systems to ensure their effectiveness and performance. keywords syndromic surveillance; elements; scoping review acknowledgments i would like to acknowledge the kindness from public health england, public health ontario, kingston, frontenac and lennox & addington public health, and sykes assistance services corporation in the development and execution of my project. *stephanie l. hughes e-mail: shughes@uoguelph.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e110, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using syndromic surveillance alert protocols for epidemiologic response in georgia rene borroto*1, bill williamson1, patrick pitcher1, lance ballester2, wendy smith1, karl soetebier1 and cherie drenzek1 1georgia department of public health, atlanta, ga, usa; 2emory university, atlanta, ga, usa objective describe how the georgia department of public health (dph) uses syndromic surveillance to initiate review by district epidemiologists (des) to events that may warrant a public health response (1). introduction dph uses its state electronic notifiable disease surveillance system (sendss) syndromic surveillance (ss) module to collect, analyze and display results of emergency department patient chief complaint data from hospitals throughout georgia. methods dph prepares a daily ss report, based upon the analysis of daily visits to 112 emergency department (eds). the visits are classified in 33 syndromes. queries of chief complaint and discharge diagnosis are done using the internal query capability of sendss-ss and programming in sas/base. charting of the absolute counts or percentage of ed visits by syndromes is done using the internal charting capability of sendss-ss. a daily ss report includes the following sections: statewide emergency department visits by priority syndromes (bioterrorism, bloodyrespiratory, feverrespiratory, feverchest, feverfluadmit, feverfludeaths, veryill, and poxrashfever, botulism, poison, bloodydiarrhea, bloodyvomit, fevergi, ili, feverflu, rashfever, diarrhea, vomit). statewide flag analysis: is intended to detect statewide flags, by using the charts capability in sendss ss. possible cases with presumptive diagnosis of potentially notifiable diseases: is intended to provide early-warning to the des of possible cases that are reportable to public health immediately or within 7 days using queries in the chief complaint and preliminary diagnosis fields of sendss-ss. possible clusters of illness: since any cluster of illness must be reported immediately to dph, this analysis is aimed at querying and identifying possible clusters of patients with similar symptoms (2). possible travel-related illness: is intended to identify patients with symptoms and recent travel history. other events of interest: exposures to ill patients in institutional settings (e.g. chief complaint indicates that other children in the daycare have similar symptoms). trend analysis: weekly analysis of seasonality and trends of 14 syndromes. finally, specific events are notified to and reviewed by the 18 des, who follow up by contacting the infection preventionists of the hospitals to identify the patients using medical records or other hospital-specific identification numbers and follow up on the laboratory test results. results since 05/15/2016, 12 travel-related illnesses, 29 vaccinepreventable diseases, 14 clusters, and 3 chemical exposures have been notified to des. for instance, a cluster of chickenpox in children was identified after the de contacted the infection preventionist of a hospital, who provided the de with the laboratory results and the physician notes about the symptoms of the patients. these actions have resulted in earlier awareness of single cases or cluster of illness, prompt reporting of notifiable diseases, and successful interaction between des and health care providers. in addition, ss continues to track the onset, peak, and decline of seasonal illnesses. conclusions the implementation of ss in the state of georgia is helping with the timely detection and early responses to disease events and could prove useful in reducing the disease burden caused by a bioterrorist attack. keywords syndromic; reports; queries; charts references 1. w cameron, a neu, e murray, k soetebier, s cookson. responding to syndromic surveillance alerts: an adaptable protocol for georgia health districts. in: https://www.researchgate.net/ publication/266093117_responding_to_syndromic_surveillance_ alerts_an_adaptable_protocol_for_georgia_health_districts 2. g zhang, a llau, j suarez, e o’connell, e rico, r borroto, f leguen. using essence to track a gastrointestinal outbreak in a homeless shelter in miami-dade county, 2008. advances in disease surveillance. 2008; 5:139. *rene borroto e-mail: rene.borroto@dph.ga.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e123, 2017 leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi leveraging public health's participation in a health information exchange to improve communicable disease reporting ian painter1*, debra revere1, p. joseph gibson2, janet baseman3 1. department of health services, school of public health, university of washington, seattle, wa usa 2. marion county public health department, indianapolis, in usa 3. department of epidemiology, school of public health, university of washington, seattle, wa usa *ian painter, department of health services, school of public health, university of washington, seattle, wa usa. ipainter@uw.edu abstract background: infectious diseases can appear and spread rapidly. timely information about disease patterns and trends allows public health agencies to quickly investigate and efficiently contain those diseases. but disease case reporting to public health has traditionally been paper-based, resulting in somewhat slow, burdensome processes. fortunately, the expanding use of electronic health records and health information exchanges has created opportunities for more rapid, complete, and easily managed case reporting and investigation. to assess how this new service might impact the efficiency and quality of a public health agency's case investigations, we compared the timeliness of usual case investigation to that of case investigations based on case report forms that were partially pre-populated with electronic data. intervention: between september 2013-march 2014, chlamydia disease report forms for certain clinics in indianapolis were electronically pre-populated with clinical, lab and patient data available through the indiana health information exchange, then provided to the patient’s doctor. doctors could then sign the form and deliver it to public health for investigation and population-level disease tracking. methods: we utilized a novel matched case analysis of timeliness changes in receipt and processing of communicable disease report forms. each chlamydia cases reported with the pre-populated form were matched to cases reported in usual ways. we assessed the time from receipt of the case at the public health agency: 1) inclusion of the case into the public health surveillance system and 2) to close to case. a hierarchical random effects model was used to compare mean difference in each outcome between the target cases and the matched cases, with random intercepts for case. results: twenty-one chlamydia cases were reported to the public health agency using the pre-populated form. sixteen of these pre-populated form cases were matched to at least one other case, with a mean of 23 matches per case. the mean reporting lag for the pre-populated form cases was 2.5 days, which was 2.7 days shorter than the mean reporting lag for the matched controls (p = <0.001). the mean time to close a pre-populated form case was 4.7 days, which was 0.2 days shorter than time to close for the mailto:ipainter@uw.edu leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi introduction the prompt detection of communicable and infectious disease outbreaks or trends relies on collecting, analyzing, and sharing data between the clinical health care system and local and state public health agencies (phas) [1]. phas collect morbidity data on notifiable conditions to inform their fundamental activities in investigation, prevention, control, monitoring and assessment of population-based disease. health data are delivered to phas in the form of communicable disease report (cdr) forms or less formal reports from health care providers (hcps) and laboratories. while hcps are legally mandated to report notifiable conditions to phas, they consistently underreport due to workload, lack of resources or time, poor integration of cdr form completion into clinic workflow, lack of knowledge about reporting requirements, unwillingness to report or because clinics assume laboratories will report to phas [2-7]. when hcps do report, the cdr forms submitted to phas are frequently incomplete, error-prone, and delayed [5,8]. and despite efforts to improve hcp reporting [9,10], most pha case management, investigation and resolution, contact tracing, and cluster identification activities traditionally depend on manual, hcp-initiated reporting [11-13]. electronic laboratory reporting (elr) to phas has been demonstrated to improve timeliness of reporting [4,10] although this is likely to vary by disease [14]. however, lab reports typically do not contain the demographic and other patient information phas require to complete a case matched controls (p = 0.792). conclusions: use of pre-populated forms significantly decreased the time it took for the local public health agency to begin documenting and closing chlamydia case investigations. thoughtful use of electronic health data for case reporting may decrease the per-case workload of public health agencies, and improve the timeliness of information about the pattern and spread of disease. keywords: communicable diseases, disease notification, electronic health records, health information exchange, public health surveillance. abbreviations: communicable disease report (cdr), electronic laboratory reporting (elr), health care provider (hc), health information exchange (hie), indiana network for patient care (inpc), local health department (lhd), marion county public health department (mcphd), public health agency (pha), sexually transmitted infection (sti), statewide information management surveillance system (swimss) correspondence: ian painter, department of health services, school of public health, university of washington, seattle, wa usa. ipainter@uw.edu doi: 10.5210/ojphi.v9i2.8001 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:ipainter@uw.edu leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi report, launch an investigation or close a case [5]. thus, while elr may increase timeliness of case reporting, little improvement may be realized when phas are burdened with collecting patient data that frequently is missing from lab reports. in addition, elr increases the volume of reports [4] which can impose an additional processing burden as phas deal with higher volume [15], duplicate cases and reports [16], and sometimes an increase in the frequency of false positives—all of which can drain pha investigation resources [17]. phas may also struggle to combine different sources, often having to use manual processes to match and merge the information, decreasing the timeliness and accuracy of their analyses of disease trends [18]. over 95% of all us phas employ public health nurses or epidemiologists who comprise 18.3% of all pha employees nationwide [19]. the majority of communicable and infectious disease case reporting and investigation is done by these employees using numerous systems which do not interoperate or exchange data [6,20]. one strategy for overcoming barriers to electronic transfer of clinical data and information between disparate information systems is the use of health information exchanges (hies) [21]. hies have demonstrated savings in health care costs [22,23], improvements in patient safety [24], workflow efficiencies [25], and improved data sharing among information systems and hie participants [26]. hies are also stimulating structural and business process changes for phas [27,28]. an hie has the potential to link ehr and laboratory data, providing phas with integrated, timely, consistently organized information that alleviates many challenges phas face in managing case reports. between september 2013-march 2014, the indiana network for patient care (inpc) hie in indianapolis, in implemented an intervention in which cdr forms were electronically prepopulated with clinical, lab and patient data available through the hie's integrated ehr-elr system. these partially completed cdrs were then delivered to the patient’s clinician for verification, signature, and delivery to the responsible pha. the cdr form intervention was piloted at selected ambulatory care clinics (n=6), representing family medicine, internal medicine, women's health, adolescent health, and ob-gyn medical specialties. by automatically pre-populating cdr forms, this intervention aimed to reduce barriers to clinic reporting, improve timeliness and completeness of cdr forms, and reduce the need for hcps to provide additional information to phas. background and full protocols for implementation and evaluation of the pre-populated cdr form intervention are described elsewhere [29]. the focus of this intervention was to reduce hcp burden and improve their reporting timeliness and cdr form completion. however, it was uncertain whether a pha participating in the inpc hie could also benefit from the intervention. while it is presumed that improved reporting will improve public health surveillance [30], there is little research on how more complete capture of cases might impact the efficiency and/or quality of reporting, burden of case investigation, or assessments of community disease burden. this paper reports an assessment of the impacts of the pre-populated form process on phas, utilizing a novel matched case analysis of timeliness changes in receipt and processing of cdr forms and focusing on chlamydia cases which are the condition most frequently reported to the lhd participating in the inpc hie [31]. leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi methods ethics this study was approved by the indiana university institutional review board with crossinstitutional and concurrent irb deferral from the university of washington. setting the marion county public health department (mcphd) in indianapolis participates in the inpc hie. mcphd utilizes swimss, the statewide information management surveillance system, for recording and investigating sexually-transmitted infections (stis) cases. data from elrs, faxed lab results and cdrs for stis are entered into swimss. traditionally, paper, handwritten cdr forms reporting sti cases are faxed by clinic reporters to mcphd and entered into swimss. baseline (pre-intervention) data to describe pre-intervention reporting data was extracted from the swimss surveillance systems for the time period of 01/01/2012 through 09/15/2013 for four target notifiable conditions: chlamydia, gonorrhea, syphilis and syphilis, reactor. in addition, given chlamydia is the most frequently reported sti [31], a sub-set analysis of chlamydia cases in the swimss baseline dataset was conducted. this allowed us, for each system and specifically for chlamydia, to: compare cdr form receipt, processing and time to close for a 21-month baseline period to the hie cdr form deployment (intervention) period. fields were tabulated to determine any data anomalies; data cleaning and quality review included removal of duplicates and generation of a missing data rate table. we also assessed potential confounders of cdr form receipt or processing, such as day of the week or reporter-specific differences. given the focus on reporting lag and time close a case, outliers and anomalies regarding date were identified. based on this analysis, it was determined that any case in which this time difference was greater than 100 calendar days was excluded as atypical of case processing. the final dataset of 39,737 records included chlamydia (n=28018); gonorrhea (n=7791); syphilis (n=810); and syphilis, reactor (n=3118). due to their small numbers, syphilis disease "categories" of early latent, late latent, neurosyphilis, primary, secondary, unknown latent were combined into the single "syphilis" category. the following factors were analyzed to establish the rationale for case matching criteria: • reporting volume: defined as the number of cases received per month into the surveillance system—by all conditions combined and by individual condition. • "reporting lag": defined as the time difference between the earliest date for activity on a case (for example, the date of a positive lab test) and the date the case was logged into the pha’s surveillance system (swimss)—by number of calendar days; by work days (i.e., mondays through fridays when phas are open, excluding holidays); and by day of the week. leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi • "time to close of investigation": defined as the time difference between earliest date for activity on a case and the date the case was closed by public health by number of calendar days—by work days (i.e., mondays through fridays when phas are open, excluding holidays); and by day of the week. intervention data during the intervention period (09/16/2013―03/01/2014), cdr forms were automatically prepopulated with available patient information and hcp contact information before being faxed from a pilot hie clinic to mcphd. while the pre-populated cdr form intervention was made available to the pilot clinics, clinic. clinic reporters were not required to use this new service and sometimes overlooked the form and used their customary reporting method. during the intervention period 23 pre-populated forms were delivered to the intervention clinics, and 21 of those were then submitted to mcphd for chlamydia (n=21) and gonorrhea (n=2) cases. only the chlamydia pre-populated cdr forms were used for the case matched analysis. intervention period dataset a dataset was pulled from the swimss surveillance system for 08/16/2013―04/01/2014 to cover the intervention period plus and minus 30 days. after cleaning and quality review similar to preparation of the baseline dataset, the intervention period swimss dataset—excluding the submitted intervention cdr forms— included 4,372 cases of which 3,165 (72%) were chlamydia, 769 (18%) gonorrhea and 438 (10%) were syphilis. the chlamydia reports were received from 284 unique providers, with 38% of the providers sending a single report. the 23 pre-populated cdr form swimss cases were identified by comparing details from scans of the pre-populated cdr forms with dataset case information. given only 2 pre-populated cdr forms for gonorrhea, these cases were excluded from further analysis. case matching and analysis to control for potential confounding factors [32], the 21 pre-populated cdr form target cases were matched to one or more other chlamydia cases using the following criteria: • same reporting provider id • same day of the week of receipt • receipt of matched case within 30 days of target case • matching case received within plus or minus one week of the target time frame (i.e., between 11/10/2013—03/22/2014). this latter matching criteria was included as the total number of cases received tended to increase outside this date range. of the 21 cases, 16 could be matched to at least one other case. the number of matches to an individual pre-populated cdr form ranged from 1 to 72, with a mean of 23 matches per case. two outcomes were analyzed: 1) reporting lag and 2) time to close of investigation. both were measured in work days. because different numbers of matches were associated with each pre-populated cdr form target case, a hierarchical random effects model was used to compare leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi mean difference in each outcome between the target cases and the matched cases, with random intercepts for case [33]. results here we report the results that informed the case matching method, including reporting lag, time to close of investigation and demographic information (clinic or clinic reporter) for the swimss baseline dataset. the mean reporting lag by calendar days for chlamydia cases was 5.5 calendar days (see figure 1). figure 1: reporting lag: difference in calendar days between date of earliest case activity and date case added to swimss, chlamydia only (n=28,018). the time difference between earliest date of activity and date the case was closed (see figure 2) was four work days or less for just over one half (53.0%) of chlamydia cases. figure 2: time to close: time to close case in work days, chlamydia only (n=28,018). a systematic difference was observed between reporting lag of chlamydia cases into swimss and day of the week of the earliest date (see figure 3). monday cases took substantially less time to be delivered to public health than any other day of the week. in addition, a statistically leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi significant relationship between day of the week of case receipt by mcphd and time to close was identified (see figure 4). sun mon tue wed thu fri sat 0 5 10 15 20 25 30 da ys figure 3: relationship between day of the week of case receipt and reporting lag, in work days, chlamydia cases (p < 0.01, kruskall-wallis test). figure 4: relationship between day of the week of case receipt and time to close case, in work days, chlamydia cases (p < 0.01, kruskall-wallis test). table 2 shows the estimated mean difference between target cases and matched controls in the number of worked days to receive a case and to close a case. both the mean number of days to receive and case and the mean number of days to close a case were lower for the target cases than their matched controls. the mean reporting time for the pre-populated cdr form cases was 2.7 days shorter than the mean reporting lag for the matched controls in the intervention dataset (p = <0.001). the mean time to close a pre-populated cdr form case was 1.3 days shorter than time to close for the matched controls in the intervention dataset (p = 0.796). table 2. estimated difference in reporting lag and time to close estimated mean difference in days* std. error p-value reporting lag 2.73 0.65 <0.001 time to close case 0.19 0.75 0.798 *time for controls – time for cases discussion the primary objective of our project was to investigate the impact of the new pre-populated form technical service being implemented within a hie on public health reporting processes and operational outcome metrics. by collecting and sharing data across health care organizations, hie networks are significantly transforming the work of both clinical and public health, with increased opportunity for more automated and efficient data capture [12]. the expanded adoption of hies promises to improve access to data and automate its extraction, making it leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi easier to collect required data through electronic request from a single source. the pre-populated cdr form service investigated in this study is an example of an intervention that aims to reduce the burden of information-gathering with consequent quality improvements [34]. as noted earlier, elr systems can improve timeliness of reporting to phas [4,10]; expanded connectivity between health record and elr systems—as possible through hies—might further enhance reportable condition data capture and its delivery to phas. this paper describes an effective analytical technique for investigating the impact of a technical intervention on public health agencies' reporting operations that can be utilized in future studies. a matched analysis allowed for early evaluation of the new pre-populated cdr form system, making efficient use of the cases received prior to widespread implementation. matching to multiple cases (as opposed to matching to a single case) increases the power of the analysis by effectively reducing the variance of the mean times for matched cases, at the expense of increasing complexity in the analysis if a variable number of matches is used. one limitation of matching is that biased estimates can be generated if a factor being matched is the result of the exposure rather than a confounding factors [35]. of the factors matched on in this study, the only factor that could plausibly be a result of the exposure is the day of the week that the cdr form is received (which could occur if reports are batched and sent on specific days of the week); if this were the case it could plausible bias the estimate of the mean difference in time to receive a report. a separate analysis matching on the date that the test result is known rather than the date that the report is received however produced a similar time difference (mean difference 2.7 days, p < 0.001). while constrained to a small number of target cases, we observed an improvement in the reporting lag when using the pre-populated cdr form intervention, and a smaller though not statistically significant improvement in the time to close cases. one possible reason for the improvement in the time to receive cases is that auto-generation of pre-populated forms may allow for processing of reportable conditions by providers as soon as the report is generated, whereas without pre-population or auto-generation it may be more efficient from the providers' view point to, again, process reports in batches every few days. an improvement in the time to close cases might be expected if pre-population of fields on cdr forms improves data quality and/or completeness, thus requiring less public health investigator processing time. however, a high field completion rate that is still less than fully complete may not result in major time savings as missing information in a single important field would still necessitate contacting a provider to collect this information. limitations one limitation of this study is that it was conducted using data from one system in one jurisdiction and examined only a single reportable condition. system factors may limit generalizability to jurisdictions that use similar systems, and differences in processing procedure between reportable conditions may limit generalizability to other conditions. a second limitation is in the ability to match for possible confounding factors. matching is inherently limited to measured factors and it is not possible with an observational study to control the effects of leveraging public health’s participation in a health information exchange to improve communicable disease reporting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e186, 2017 ojphi unmeasured confounding factors. a third limitation is that the small numbers of cases limits the ability to rigorously determine sensitivity of the analysis procedure to the selection of factors to match on. acknowledgements we wish to thank the mcphd public health nurses and epidemiologists who reviewed baseline data analyses and provided input on pha surveillance and investigation workflow. we also wish to thank jennifer williams who facilitated collection of cdr form intervention reports for this study. ip, dr and jb designed the study. algorithms to extract data from the surveillance systems were designed by ip, dr and pjg. ip designed and conducted all data analyses with input from dr, jb and jpg. ip and dr drafted the manuscript with review and contributions provided by jb and pjg. all authors approved the final version of this manuscript. financial disclosure this study was conducted as part of the "leveraging a hie to improve public health disease investigation" research project (rwjf award #70338; pi: j baseman, university of washington). the content is solely the responsibility of the authors and does not necessarily represent the official views of the robert wood johnson foundation. competing interests no competing interests. references 1. dato v, wagner mm, fapohunda a. 2004. how outbreaks of infectious disease are detected: a review of surveillance systems and outbreaks. 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communicable disease reporting abstract introduction methods ethics setting baseline (pre-intervention) data intervention data intervention period dataset case matching and analysis results discussion limitations acknowledgements financial disclosure competing interests references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* epidemiology, virginia department of health, richmond, va, usa objective use syndromic surveillance to identify and monitor adverse health events resulting from synthetic cannabinoid receptor agonists (scras) or marijuana. characterize the current trend of scras and marijuana use among emergency department (ed) and urgent care center (ucc) visits in virginia to determine whether findings align with utilization trends identified by other states from poison control center calls and ed visits. introduction scras are accessible and affordable, sold online, in gas stations, and in “head” shops for $5-30 per package.[1] while marijuana is a schedule 1 narcotic, unavailable for any use, scras navigate the legal landscape with marketing as non-consumable and frequent modifications to the active ingredients that outpace lawmakers’ updates. when consumed, scras bind the same receptor as the active ingredient in marijuana with 10-1000 times the affinity. physical reactions to marijuana use include breathing problems, increased heart rate, hallucinations, paranoia, lower blood pressure, and dizziness. [2] health departments have reported varying clinical presentations in response to scras, including extreme agitation and tachycardia. ongoing reports of scra reactions and rising marijuana legalization emphasize the imperative to leverage syndromic surveillance to monitor trends, detect emerging outbreaks, and observe changes in clinical presentations or user demographics. methods a retrospective study was conducted using ed and ucc chief complaint visit data received by the virginia department of health (vdh). a scra and marijuana query was developed using essence to search for relevant text strings within chief complaints based on nationwide media reports, public health alerts such as epi-x notices, and consultation with syndromic surveillance practitioners in other states. descriptive analyses were conducted on ed and ucc visits identified by chief complaint from january 2010 through july 2015. results from january 2010-july 2015, 733 scra or marijuana related ed and ucc visits were identified in virginia, of which 20% (147) occurred since january 2015. visits peaked in september-october 2013 (43) and may-june 2014 (48), and continued to increase throughout 2015, with a peak in april 2015 (32). of the 733 visits, 481 (66%) further identified adverse health events in the chief complaint. most of the 481 visits indicated a nondescript drug reaction (195), while the remaining were grouped into the following 10 categories: cardiac (57), unresponsive (49), restlessness (43), gastrointestinal (31), fainting (24), weakness (24), mental health (22), dyspnea (18), seizure (11), or injury (7). visits occurred predominantly in males 10-29 years of age (318, 43%), with a median age of 23 years. males accounted for roughly twice as many visits as females, both overall and across adverse health event categories, except dyspnea and gastrointestinal which were distributed equally among males and females. conclusions syndromic surveillance identified scra or marijuana related ed and ucc visits in virginia that corroborate findings from poison control center calls and ed visits in other states. virginia temporal trends align with clusters in june 2014 in washington, dc and april 2015 in alabama, new york and new jersey. of the 10 virginia identified adverse health event categories, 9 are represented in reports from new york and new jersey, dc, new hampshire, and cdc. virginia additionally identified adverse health events relating to injury, specifically motor vehicle accidents. study findings resulted in data sharing with virginia poison control centers, presentations to the state fusion center and local public health, and distribution of a clinician letter for heightened awareness and notification of adverse health events from scra use. continued surveillance will allow detection of fluctuations in trends and demographics resulting from the discontinuation of scra sales and regional marijuana legalization. keywords synthetic cannabinoid; syndromic 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culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts performance of acute flaccid paralysis surveillance in bauchi state, nigeria, 2016 luka m. ibrahim*, adamu ningi and jalal-eddeen saleh disease prevention and control, world health organization, jos, nigeria objective to identify and address gaps in acute flaccid surveillance for polio eradication in buchi state introduction poliomyelitis a disease targeted for eradication since 19881 still pose public health challenge. the eastern mediterranean and african regions out of the six world health organization (who) regions are yet to be certified polio free2. the certification of the who africa region is largely dependent on nigeria, while the who eastern mediterranean is dependent on pakistan and afghanistan. surveillance for acute flaccid paralysis (afp) is one of the critical elements of the polio eradication initiative. it provides the needed information to alert health managers and clinician to timely initiate actions to interrupt transmission of the polio disease and evidence for the absence of the wild polio virus.3,4 one of the core assignments of the certification committee in all regions is to review documentation to verify the absence of wild poliovirus.5 good and complete documentation is the proxy indication of the quality of the system while poor documentation translates to possibilities of missing wild poliovirus in the past. we evaluated the performance of the afp surveillance system in bauchi, which is among the 11 high risks states for wild polio virus in nigeria to identify and address gaps in the surveillance system. methods we conducted a cross-sectional study in bauchi state. we assessed the material and documentations on afp surveillance in eighteen of the twenty local government areas (lgas). we assessed the knowledge of the clinician at focal and non-focal sites on case definition of afp, the number and method of stool specimen collection to investigate a case and types of training received for afp surveillance. we verified afp case investigations for the last three years: the caregivers (mothers) were interviewed to authenticate the reported information of afp cases, the method used for stool specimen collection and feedbacks. community leaders’ knowledge on afp surveillance was also assessed. data was entered and analyzed in excel spread sheet. results review of the expected deliverables of 18 out of the 20 lga disease surveillance and notification officers (dsno) revealed that only 2(11%), 5(28%), 6(33%) and 7(39%) had evidence of polio outbreak investigation, supervisory reports, minutes of meeting and surveillance work plan respectively. of the 31 afp cases investigated, correct and complete information was 39% for birth day, 26% for birth month of the child, 23% for date of onset of paralysis and 23% for date of investigation. contacts of informants, afp 001-3 were deficient in the focal and non-focal sites. the non-focal also lacked guidelines for integrated disease surveillance and response (idsr) and terms of reference for surveillance focal person. knowledge of case definition of afp was 71% and 30% among clinician at the focal and non-focal sites, respectively and 88% and 55% for method of stool collection among clinician at focal and nonfocal sites. among the 38 care givers (mothers) interviewed 16 (42%) did not remember the day or month the investigation for the afp was conducted, 36(95%) gave the correct number of stool samples, 15(40%) mentioned that the stool samples were collected 24 hours apart and only 12 (32%) received feedbacks. majority (79%) of the community leaders interviewed were aware of afp and knew that stool was the specimen for investigation of the afp but 21% did not know whom to report a case of afp in their community conclusions our study revealed knowledge and documentations gaps in afp surveillance for certification of polio-free in nigeria. the state ministry of health and the who consultants in the polio eradication unit should update the knowledge of the health care workers at the operational levels on afp surveillance. the state ministry of health and the who consultants should also provide all essential documents required for quality afp surveillance and ensure their judicious use. keywords poliomyelitis; acute flaccid paralysis; surveillance; eradication, acknowledgments we acknowledge the bauchi state primary health care development agency (nphcda) for supporting the study references 1. world health assembly. global eradication of poliomyelitis by the year 2000. geneva, switzerland: world health organization; 1988. resolution wha 41.28. 2. polio global eradication initiative. 2014. 3. world health assembly. global eradication of poliomyelitis by the year 2000. geneva, switzerland: world health organization; 1988. resolution wha 41.28. 4. chandrakant lahariya global eradication of polio: the case for “finishing the job” bull world health organ vol.85 n.6 genebra jun. 2007. 5. world health assembly. global eradication of poliomvelitis by the year 2000. geneva, switzerland: world health organization; 1988. resolution wha 41.28. *luka m. ibrahim e-mail: lukaimangveep@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e182, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 1usda-aphis-vs, ft collins, co, usa; 2johns hopkins university applied physics laboratory, laurel, md, usa; 3uc davis, davis, ca, usa objective to describe the value of the american society for the prevention of cruelty to animals (aspca) poison control center (apcc) livestock animal calls as a passive data stream for biosurveillance of number of calls, species affected, toxicant exposures, and clinical syndromes. introduction the apcc hotline fields daily calls regarding potential animal intoxications from the us, its territories, and canada. we explored the value of these data for identifying increased occurrences of intoxications related to livestock and poultry species, toxicant product categories, clinical syndromes, and illness severity. these data proved valuable for identifying risks of toxicant exposures by species, product category, and season. in addition to identifying intoxication risks to animal health, these data could be used to monitor for infectious outbreaks that may initially be confused for intoxications. methods the apcc hotline was contracted to provide ongoing de-identified call data from calls starting 01 october 2013 that were related to the following livestock taxa: equine, bovine, caprine, porcine, poultry, ovine, and camelid. the ingredient lists provided were categorized into mutually exclusive product categories (e.g. pesticide, prescription medication). clinical signs were categorized into nonmutually exclusive syndromic categories (e.g. death, respiratory). illness severity was categorized as none, mild/moderate, severe, and death. we used the early aberration reporting system (ears) c3 algorithm to identify counts (signals) for weekly toxicosis, species and syndromic events that occurred at a frequency more than three standard deviations above the expected occurrence. results on average, the apcc hotline took 9 calls a week regarding our species of interest (range: 1-22). calls came from all 50 states, puerto rico, and seven canadian provinces. pesticides were the most frequent intoxication product (36.2% of exposures) and were primarily involved in calls regarding horses or cattle. calls regarding pigs were more likely to involve exposure to a human product (e.g. human medications, human food), likely reflecting that most pigrelated calls were about companion animal breeds (e.g. pot-bellied pigs). horses were the most common species discussed. most animals involved in calls did not have clinical signs. of those that did show specific clinical signs, most were gastrointestinal or dermatological. death was most frequently associated with pesticide intoxication. pesticide intoxication had a markedly seasonal trend, with peaks in pesticide calls seen may-august. figures 1 and 2 show the c3 model for detecting increased incidence of calls about pesticides and select species/syndromes respectively. conclusions analyzing apcc calls related to livestock and poultry species is useful for the purpose of risk identification and animal health monitoring. identification of seasonal trends – such as those with pesticides – can inform policymakers and livestock owners of the increased risk during specific times of year. targeted education preceding summer months could help reduce the risk of pesticide exposure to animals. identifying peaks in specific clinical signs could help identify unusual health trends. use of these types of passive data streams provides a valuable information source for all health professionals interested in biosurveillance, animal health management, and risk identification. keywords risk identification; passive surveillance; biosurveillance; animal health monitoring; toxicoses acknowledgments this work supported by usda-aphis-vs-ceah and the saul t. wilson, jr., scholarship. copyright©2015. the american society for the prevention of cruelty to animals (aspca). all rights reserved. *kristen alldredge e-mail: kristen.l.alldredge@aphis.usda.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e85, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts factors associated with immunization of children in kaduna state, nigeria, 2016 lydia a. taiwo*1, 2, 3, aisha a. abubakar2, endie waziri1, 2, lilian a. okeke1 and suleiman h. idriss2 1field epidemiology, nigeria field epidemiology and laboratory training program, abuja, nigeria; 2ahmadu bello university zaria, nigeria, abuja, nigeria; 3federal ministry of health, abuja, nigeria objective 1. to assess the knowledge, perception, and practices of mothers/ caregivers on vaccine preventable diseases in children aged 12-23 months in kaduna state, nigeria 2. to determine the immunization coverages in kaduna state, nigeria 3. to determine the sources of information on routine immunization among mothers/caregivers of children aged 12-23months in the study area introduction immunization is one of the safest and most effective interventions to prevent disease and early child death1. although, about three quarters of the world’s child population is reached with the required vaccines, only half of the children in sub-saharan africa get access to basic immunization2. a substantial number of children worldwide do not complete immunization schedules because neither health services nor conventional communication mechanisms regularly reach their communities3. separate studies in australia and papua new guinea have shown that knowledge gaps underlie low compliance with vaccination schedules3, 4. mothers are less likely to complete immunization schedules if they are poorly informed about the need for immunization, logistics (which includes time, date, and place of vaccination), and the appropriate series of vaccines to be followed5, 6. although knowledge in itself is insufficient to create demand, poor knowledge about the need for vaccination and when the next vaccination is due is a good indicator of poor compliance7. up-to-date, complete, and scientifically valid information about vaccines can help parents to make informed decisions8. immunity gap created by this low immunization coverage in northern nigeria favors the emergence and transmission of some vaccine preventable diseases (vpds) especially measles and polio9. methods a cross-sectional descriptive study was conducted using multistage sampling technique; 379 mothers/caregivers with children aged 12-23 months were recruited. data collection was done using semi structured interviewer-administered questionnaire and analyzed using epi infotm version 7. descriptive statistics using absolute numbers and proportions and odds ratio/chi2 were determined between variables and p≤0.05 was considered statistically significant. multivariate analysis was conducted using logistic regression. results mean age of respondents was 28.6 (sd= ±6.6), 245(64.7%) practiced islam, 128(33.8%) completed secondary school, 246(64.9%) unemployed, 361(92.3%) were married and 186(49.1%) were from rural settlements. among the children whose mothers/ caregivers were interviewed, 163(43.01%) were between aged 16-19 months old while most 238(62.80%) fell within the birth order of 2nd -5th child. only 59 (15.6%) of these children were found to be fully immunized, evidenced by vaccination card history. majority of respondents 244(64.4%) had unsatisfactory knowledge while 197(55.4%) and 204(54.0%) exhibited poor perception and bad practices respectively, regarding routine immunization. commonest source of information was radio 69(61.61%). educational status [or=1.9 (95%ci:1.1-3.3)] and good perception [or=2.6 (95%ci:1.5-4.5)] of mothers were found to be associated with getting information on routine immunization within 12months prior to this study while polygamous family setting [or=0.6(95%ci:0.2-0.6)], unsatisfactory knowledge [or=0.3(95%ci:0.2-0.7)] and bad practices [or=0.5(95%ci:0.3-0.9)] of mothers were independently associated with lack of information on routine immunization. conclusions there is low immunization coverage in this community. mother’s educational status, family setting, knowledge, perception and practices about immunization are important factors that influence access to information on routine immunization. keywords routine immunization; information; mothers; knowledge acknowledgments 1. nigerian field epidemiology and laboratory training programme 2. state ministry of health, kaduna, nigeria references 1. who/ivb. periodic intensification of routine immunization: lessons learned and implications for action 2009; p3-12. www.who.int/ vaccines-documents accessed 16/7/14. 2. bond l., nolan t., pattison p., and carlin j. vaccine preventable diseases and immunizations: a qualitative study of mothers’ perceptions of severity, susceptibility, benefits and barriers. australian/new zealand journal of public health 1998; 22 (4): 441-6. 3. bukenya, g. b. & freeman, p. a. possible reasons for non-completion of immunization in an urban settlement of papua new guinea. papua new guinea medical journal 1991; 34(1): 22-5. 4. eng e., naimoli j., naimoli g., parker k.a., and lowenthal n. the acceptability of childhood immunization to togolese mothers: a sociobehavioral perspective. health education quarterly 1991; 18 (1): 97-110. 5. khanom, k. and salahuddin, a. k. a study of an educational programme on immunization behavior of parents. bangladesh medical research council bulletin 1983; 9:18-24. 6. waisbord, s. & larson, h. why invest in communication for immunization: evidence and lessons learned. a joint publication of the health communication partnership based at johns hopkins bloomberg school of public health/center for communication programs (baltimore) and the united nations children’s fund (new york), june, 2005. 7. greenough, p. global immunization and culture: compliance and resistance in large-scale public health campaigns. social science & medicine 1995; 41 (5): 605-607. 8. offit p.a. & coffin s.e. communicating science to the public: mmr vaccine and austim. vaccine 2003; 22(1):1-6. 9. clements c.j. & ratzan s. misled and confused? telling the public about mmr vaccine safety; measles, mumps, and rubella. journal of medical ethics 2003; 29(1):22-6. *lydia a. taiwo e-mail: shallisbabe@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e168, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts cryptosporidium in wild frogs (rana spp) consumed by humans in kaduna state nigeria grace s. kia*, blessing i. ukuma, jerome okpanachi and mathias b. odoba veterinary public health and preventive medicine, ahmadu bello university zaria, zaria, nigeria objective to evaluate the occurrence of cryptosporidium species in edible frogs (rana spp) sold at the hanwa frog market zaria, kaduna state, nigeria. introduction since cryptosporidium can be transmitted by ingestion of infected food animals and poorly treated water and by direct contact1 it is possible for infection to occur through ingestion of under cooked frogs and through handling and processing of infected frogs. in burkina faso frogs caught are sold to market-women who treat the frogs by emptying their bowels and frying in oil before selling them, this is not always the case for the nigerian frog markets where frogs are sometimes smoked or dried without necessarily been fried, before consumption2. this may pose a health risk for transmission of cryptosporidiosis from infected frogs. presence of cryptosporidium oocysts in frogs may by implication reveal the cryptosporidium status of water bodies from various sources where the frogs were caught. water management programmes for treatment of cryptosporidium is difficult as the oocyst is resistant to several disinfectants including chlorine1. the consumption of such treated water in urban areas and untreated water in most rural communities may expose a great proportion of nigerians to cryptosporidiosis. owing to the number of hiv/aids patients who commonly suffer from cryptosporidial enteritis and cough, the control of cryptosporidiosis in animals and man is of public health significance. methods a cross-sectional study was conducted between february and april, 2016 using intestinal contents from wild captured rana species of frogs (n=117), sourced from 8 different locations, from the frog central collection, sales and processing point at hanwa in zaria. the intestinal contents from the frogs were examined by staining flotation and sedimentation smears with modified ziehl-neelsen stains followed by microscopy and micrometry of the oocysts. results overall, 35.9% of frogs sampled from the hanwa frog market were positive for cryptosporidium oocysts. there were more cryptosporidium oocysts detected by sedimentation test (28.2%) than flotation test (23.9%). although there was no significant statistical association between sex of frogs and oocyst detection (χ2 =0.5349, p>0.05); sex wise, female frogs (40%) and frogs within the weight range 170-219g were more infected with cryptosporidium (66.7%). oocysts size ranging between 6.10µm -7.00µm, had the highest frequency of 10 (23.8%). by size 28.2% of the oocysts detected suggest infection with c. parvum and c. maleagridis. conclusions we present the first report of cryptosporidium oocysts in wild edible frogs (rana spp) sold at the hanwa frog market zaria, kaduna state, nigeria. frog consumption is on the increase in nigeria, but baseline information on associated zoonoses is rare. a cross-sectional study conducted between february and april, 2016 using intestinal contents from wild captured frogs (n=117), sourced from 8 different locations using the modified ziehl-neelsen stains and micrometry revealed 35.9% were positive for cryptosporidium oocysts. of the oocysts detected 28.2% suggest infection with c. parvum and c. maleagridis, this may constitute a health risk for humans. keywords cryptosporidium oocysts; rana frogs; zaria nigeria; public health acknowledgments we acknowledge the department of vet public health and preventive medicine, ahmadu bello university zaria, for partial sponsorship of this work by gsn.kia abu2016/p18758. references 1. besser-wiek, j.w, forfang j, hedberg c.w, korlath j.a, osterholm, m.t, sterling c.r and garcia l. foodborne outbreak of diarrheal illness associated with cryptosporidium parvum minnesota, 1995. morbidity and mortality weekly report, 1996, 45(36):783. 2. mohneke, m. unsustainable use of frogs in west africa and resulting consequences for the ecosystem tag der mündlichen prüfung. 2011, 31.01.2011; page 22. *grace s. kia e-mail: gracegracekia@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e150, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts influenza sentinel surveillance system in surb astvatsamayr medical center, 2013-15 liana torosyan*, lilit avetisyan and artavazd vanyan department of epidemiology of special dangerous and airborne diseases, national center of diseases control and prevention, ministry of health, yerevan, armenia objective the goal of this study was to identify gaps in the severe acute respiratory infection sentinel surveillance system at surb astvatsamayr medical center. introduction influenza is a priority in armenia. there are two influenza surveillance systems in armenia: population and sentinel. the medical center (mc) has been included in sentinel surveillance since 2012. in 2015 a study was undertaken to identify gaps in severe acute respiratory infection (sari) sentinel surveillance system in surb astvatsamayr mc. methods medical records and reporting forms of sari cases were generated for individuals meeting the case definition and analyzed for age groups, risk factors, sentinel surveillance detection methods, laboratory conformation, number of days hospitalized and reporting. results in 2014, 3016 patients were admitted in the hospital with ari, of whom 2982 were younger than 18 years. during the 2014-2015 influenza season (week 40, 2014-week 20, 2015), 77 swabs have been taken in total, of which five were influenza positive (4 b and 1 ah1n1). also in the 2013-2014 influenza season, five samples tested positive (all influenza a). sixty-one (48%) patients with respiratory disease met the who sari case definition (2011), 84 (66%) of all reviewed patients would have met the sari case definition. the numbers for the icu (25 records reviewed) do not reflect the actual percentage of patients admitted with respiratory symptoms. the 33 additional cases taken from the sampling logbook were mainly hospitalized in the icu. influenza tests were performed on 34 patients (mainly icu), five were positive for influenza (four b--all adults—and one ah1n1), and four tested positives for other respiratory pathogens (two rsv, one rv, one bv). all influenza positives had fever or a history of fever and 80% met the who sari case definition (2011). non-sampled cases generally have fewer reported symptoms, but still 44% of cases fits the who sari case definition (2011). conclusions the percentages of patients meeting the who sari 2011 case definition and the who sari 2014 definition shows that they were mainly caused by the absence of shortness of breath in the sari 2014 definition 52% (2011) vs 66% (2014) in surb asvatsamayr. a large number of children from neonatal and children’s departments fulfil the sari case definition and could potentially be swabbed in addition to icu patients. there are gaps in who sari case definitions. the sentinel surveillance system should be improved. comparison of departments and number of patients meeting the who sari case definition keywords sari; case definition; sentinel surveillance; armenia *liana torosyan e-mail: liana_torosyan@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e141, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts nonparametric models for identifying gaps in message feeds andrew walsh* health monitoring, pittsburgh, pa, usa objective characterize the behavior of nonparametric regression models for message arrival probability as outage detection tools. introduction timely and accurate syndromic surveillance depends on continuous data feeds from healthcare facilities. typical outlier detection methodologies in syndromic surveillance compare predictions of counts for an interval to observed event counts, either to detect increases in volume associated with public health incidents or decreases in volume associated with compromised data transmission. accurate predictions of total facility volume need to account for significant variance associated with the time of day and week; at the extreme are facilities which are only open during limited hours and on select days. models need to account for the cross-product of all hours and days, creating a significant data burden. timely detection of outages may require sub-hour aggregation, increasing this burden by increasing the number of intervals for which parameters need to be estimated. nonparametric models for the probability of message arrival offer an alternative approach to generating predictions. the data requirements are reduced by assuming some time-dependent structure in the data rather than allowing each interval to be independent of all others, allowing for predictions at sub-hour intervals. methods healthcare facility data was collected as hl7 messages via the epicenter syndromic surveillance system from june 1, 2017 through august 31, 2017. 713 facilities sent at least 1,000 messages during this period and were included in the analysis. standard poisson regression models were fit to counts of messages per quarter hour. predictors were indicators for day of week, hour of day, and quarter of hour, along with interaction terms between them. nonparametric logistic regression models were fit to data on the presence or absence of any message for each minute of the first two months of the study period, using the minute within the week as a predictor. the last month of data was scanned for outages at 15-minute intervals and calculating the probability of no messages since the last received message per facility as: p(gap from mlast to mnow) = ∏t 1 pmessage(t) four consecutive intervals with probability below 1-10 were considered outages. results a total of 12,710,275 adt a04 messages were received from 713 facilities from june 1, 2017 through august 31, 2017. estimation of poisson regression models averaged 1 minute, while nonparametric models averaged 1.5 minutes to estimate. poisson models required 672 parameters to specify, whereas nonparametric models required 29. calculating predictions from fitted models averaged 0.2 seconds for poisson models and 2 seconds for nonparametric models. although predictions from the two models are not on identical scales and thus not directly comparable, they did correlate well with each other with an average correlation of 0.8. the nonparametric regression method detected 175 resolved outages and 9 open outages in august, 2017. the resolved outages lasted an average of 1.5 days (1.75 hours to 15 days). the likelihood of these outages averaged 6e-13 (3e-160 to 4e-11). figure 1 illustrates how the nonparametric models can be used in a dashboard for all 713 connections. likelihood of an outage is available for each facility based on how long it has been since the last message was received; this can be updated every minute as needed. figure 2 illustrates the predictions from a nonparametric model for a single facility and a detected outage. conclusions nonparametric regression models of message arrival demonstrated suitable performance for use in detecting connection outages. compared to standard poisson regression models, computation time for nonparametric models was longer but within acceptable ranges for operational needs and storage was significantly reduced. further, storage and computation time for standard models will increase if greater time granularity is desired, whereas the nonparametric models require no additional storage or computation. model predictions were sufficiently similar between both models for the two to give comparable performance in detecting outages. given the greater time flexibility of the nonparametric models and the smaller data requirements for initial model estimation (due to fewer estimated parameters), the nonparametric approach represents a promising new option for monitoring syndromic surveillance data quality. fig. 1: dashboard of facility connection status for all 713 facilities. color scale indicates likelihood of outage (green least likely; red most likely) based on the probability of receiving no messages since last one was received. fig 2: hourly time series of expected (green) and observed (grey) messages, with a red bar indicating a detected outage. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords nonparametric regression; data quality; monitoring; hospital connection; real-time acknowledgments we wish to thank our public health customers for funding support and data for this work. *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e19, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts evaluating the application and utility of essence for early event detection allison k. kunerth*, elizabeth baker, alan zelicoff, michael elliot and kevin syberg saint louis university, saint louis, mo, usa objective a mixed methods study is being conducted on the statewide early notification of community based epidemics (essence) system in missouri to identify factors that can improve the timeliness and identification of outbreaks. this research will provide stakeholders with guidance on how best to implement and improve essence usage statewide, and by sharing this research input can be solicited on the utility of the applied framework as well as future implications from this body of work. introduction in spite of the noted benefits of syndromic surveillance, and more than a decade after it started gaining support, the primary use for syndromic surveillance appears to be largely for seasonal and jurisdictional disease monitoring, event response and situational awareness as opposed to its intended purpose of early event detection. (1-4) research assessing the user characteristics and standards applied at local public health agencies (lpha’s) for syndromic surveillance are scarce, and in national surveys epidemiologists frequently tend to utilize their own syndromic surveillance systems as opposed to a national system such as biosense. while the national syndromic surveillance program (nssp) has addressed many operational concerns from stakeholders, and is in the process of providing access to the cloud based biosense platform-along with essence as a key tool, there is still a paucity of research that exists as to what can be done to improve the utilization of syndromic surveillance systems for its primary purpose of early event detection. methods this research proposes to evaluate the use of essence within missouri and the surrounding areas, to comprehensively identify its strengths and limitations, through an assessment of the user experience. this research will evaluate three key areas: 1) the quality of the data received by the syndromic surveillance system, 2) the characteristics of the individuals and organizations utilizing the system (end-users), 3) the influence and extent of syndromic surveillance data on public health actions. essence data will be evaluated directly with special attention to the top three data quality attributes across the literature, completeness, accuracy and timeliness. (5) a survey will also be administered to essence system users and public health leadership at lpha’s, to gain insight into perspectives, perceptions and general practices, as well as how they interact with data from essence. results the data for this research is primarily being collected throughout the fall of 2016, so the hope is to bring preliminary data to this conference as a means to validate some of the findings, solicit input on the proposed framework and share this research in a timely manner for the nssp roll out of biosense and essence. conclusions through a thorough evaluation, the application and utility of essence for early event detection will be better understood, along with the identification of factors that can be targeted in the future (and across syndromic surveillance platforms) for improvement in the timely identification of outbreaks. keywords essence; syndromic surveillance; data quality acknowledgments the data for this research is supplied by the missouri department of health and senior services. references 1. chu a, savage r, willison d, crowcroft ns, rosella lc, sider d, et al. the use of syndromic surveillance for decision-making during the h1n1 pandemic: a qualitative study. bmc public health. 2012;12(1):1-9. doi: 10.1186/1471-2458-12-929. 2. samoff e, fangman mt, hakenewerth a, ising a, waller ae. use of syndromic surveillance at local health departments: movement toward more effective systems. journal of public health management and practice. 2014;20(4):e25-e30. doi: 10.1097/ phh.0b013e3182a505ac. pubmed pmid: wos:000337137700003. 3. buehler jw, sonricker a, paladini m, soper p, mostashari f. syndromic surveillance practice in the united states: findings from a survey of state, territorial, and selected local health departments. advances in disease surveillance. 2008;6(3):1-20. 4. buehler jw, whitney ea, smith d, prietula mj, stanton sh, isakov ap. situational uses of syndromic surveillance. biosecurity and bioterrorism : biodefense strategy, practice, and science. 2009;7. doi: 10.1089/bsp.2009.0013. 5. chen h, hailey d, wang n, yu p. a review of data quality assessment methods for public health information systems. international journal of environmental research and public health. 2014;11(5):5170-207. doi: 10.3390/ijerph110505170. pubmed pmid: 24830450; pubmed central pmcid: pmcpmc4053886. *allison k. kunerth e-mail: akunerth@slu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e85, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts modeling the risk of heat illness among basic training populations within the dod, 2010–2017 jessica f. deerin*, vivek khatri and paul e. lewis armed forces health surveillance branch, alexandria, va, usa objective to identify predictors of the risk of developing exertional heat illness (ehi) among basic training populations in the department of defense. introduction although effective preventive measures for heat-related illness have been recommended and mandated for military personnel, there continues to be incident cases. in 2016, there were 401 incident cases of heat stroke and 2,135 incident cases of “other heat illness” among all active component service members. current military guidelines utilize the wet bulb globe temperature (wbgt) index to measure heat risk, guiding work/rest and hydration practices. the wbgt requires calibrated instrumentation and is based on fixed cutoff values. we propose using readily available meteorological data inputs and ehi cases to identify and validate an ehi risk prediction model. prior studies have found that combinations of wbgt and the previous day’s wbgt and relative humidity and temperature have predictive value for ehi.1 we build upon prior work by using generalized additive models (gams). methods a case-control study was conducted among active component service members from all basic training installations from january 1, 2010 to may 31, 2017. incident cases of ehi were identified utilizing diagnosis codes extracted from inpatient and outpatient medical encounters and confirmed reportable medical events. an equal number of random controls, matched by installation, were selected. mean weather data during daylight hours from the air force weather squadron were provided for the closest weather station to the installation during the same time period. a gam was used due to the non-linear association between ehi and weather predictors, to develop models for the risk of incident ehi. training (75% of data) and test (25% of data) datasets were generated for model training and model validation. three hundred sets of training and test datasets were randomly generated. for each set, sensitivity and specificity for ehi prediction was calculated. four models with different combinations of predictors were compared: model 1 contains month, day of week, and installation; model 2 contains wbgt, month, day of week, and installation; model 3 contains wbgt, previous day’s wbgt, month, day of week, and installation; and model 4 contains relative humidity, temperature, month, day of week, and installation. each predictor was significantly associated with ehi. the mean differences in sensitivity and specificity between all models and model 1 were compared and 95% confidence intervals were generated by bootstrapping. gams were generated using the mgcv package and odds ratios were generated using the oddsratio package in r. results there were 5,258 incident cases of ehi from 2010-2017 among active component service members stationed at basic training installations. there was not a significant difference in model performance when comparing the four models. the mean differences in sensitivity and specificity of each model compared to model 1 are displayed in table 1. the association between log odds of ehi and wbgt, controlling for month, day of week, and installation (model 2) is displayed in figure 1. there is not a single representative odds ratio generated for gams due to the non-linear relationship between predictors and the log odds of ehi. as an example, the odds ratio between two arbitrary wbgt points is displayed. the odds of ehi among those exposed to a mean wbgt of 85°f is 2.55 (95% ci: 2.45, 2.64) times the odds of ehi among those exposed to a mean wbgt of 80°f. the association between the log odds of ehi and relative humidity, controlling for month, day of week, installation, and temperature (model 4) is displayed in figure 2. the odds of ehi among those exposed to 80% relative humidity is 1.36 (95% ci: 1.33, 1.39) times the odds of ehi among those exposed to 60% relative humidity. conclusions our results provide evidence that there is no significant difference in model prediction of ehi utilizing various combinations of weather predictors. however, there is a significant non-linear association between weather predictors and ehi and examples of these relationships are given using different models. model performance can be improved by including more granular exposure data (i.e. physical activity during ehi episode, biometric and physiological measures). table 1. generalized additive model comparison: mean sensitivity and specificity figure 1. association between log odds of ehi and wbgt, controlling for month, day of week, and installation isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts figure 2. association between log odds of ehi and mean relative humidity, controlling for month, day of week, installation, and temperature keywords prediction; heat illness; weather; military references 1. wallace rf, kriebel d, punnett l, wegman dh, wenger cb, gardner jw, gonzalez rr. the effects of continuous hot weather training on risk of exertional heat illness. med sci sports exerc. 2005 jan; 37(1):84-90. *jessica f. deerin e-mail: jessica.deerin@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e17, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* virology, republican veterinary laboratory, baku, azerbaijan introduction lumpy skin disease (lsd) is a cutaneous disease of livestock caused by a dna-containing virus belonging to poxviridae family called lumpy skin disease virus (lsdv). another name of the virus is neethling. the disease is characterized mainly by fever, and lesions appearing on the skin. the incubation period is 6-9 days. mortality of the disease is about 10% [1, 2], however, secondary infection of lesions can increase the mortality rate. lsd was first recorded in zambia, south africa, then spread to sudan, nigeria and european countries. according to information provided by oie, lsd outbreaks also have spread in middle eastern countries including turkey, where it has been considered endemic since 2007-2008 [1]. an outbreak of lsd was also reported in iran in 2013-2014 [1, 2]. signs of lsd in azerbaijan were recorded for the first time in the region (rayon) of bilasuvar in may of 2014 and reported to oie [3]. lsd was quickly suspected, as bilasuvar neighbors iran which had recently reported lsd. the same clinical signs were subsequently recorded in cattle in the regions of jalilabad, aghdash and udjar. some of the cases resulted in death. a pcr assay was set up in the republican veterinary laboratory in azerbaijan in order to test livestock samples for the disease while an epizootological study was conducted to determine the extent of disease spread within the three affected rayons. methods samples from lungs and abdomen, and head and neck lesions were taken from animals in the regions of aghdash, udjar, jalilabad and bilasuvar were sent to the republican veterinary laboratory (rvl) for testing. suspensions were prepared from the samples, and extraction was performed using a dneasy blood mini kit (qiagen, stanford, ca), following the manufacturer’s instructions. specific forward and reverse capripox primers were used for master mix preparation [4] including forward primer: 5’-tccgag-ctc-ttt-cct-tac-tat-3’, reverse primer: 5’-tatggt-acc-taa-att-ata-tac-gta-aat-aac-3’, and probe: 5’ 6famcaatgggtaaaagatttctamgbnfq 3’. positive template control consisted of 5’ atg gcg atg tcc att ccc tga cca atg ggt aaa aga ttt cta tcg taa cag atg aaa gag caa gct act att cct cac gga aat gaa atg ctt c 3’. taq dna polymerase, 10x pcr buffer, 50 mm mgcl2, and 10 mm dntps were used to prepare the master mix according to the protocol described [4]. samples and negative and positive template controls were added to the prepared master mix, and the reaction was run on a light cycler 2.0 pcr instrument using the thermocycling conditions used by balinsky et al. [4] results the lsdv pcr set up by the virology department of the rvl was able to confirm the presence of lsdv in cases from bilasuvar, jalilabad, udjar and aghdash. from 2822 susceptible cattle with lsd lesions, 33 animals died (1.2%). the 255 animals that tested were positive by pcr. the surviving 2567 animals were treated. lesions of the animals exposed to lsdv were disinfected using detergent iodine. appropriate preventative measures were put in place. the epizootological status is currently stable, and the virus is now considered endemic in the azerbaijan republic. keywords lsd; pcr; lumpy skin diseases acknowledgments the authors are grateful to ketan patel and matthew mcgillicuddy of the naval medical research center at fort detrick, maryland, for generously providing the reagents for the assay and to cooperative biological engagement program (cbep) personnel in azerbaijan: sebastian mcclendon for technical assistance with the capripox assay, and april johnson and saida aliyeva for support with publication development and submission. references 1.lumpy skin disease nb oie terrestrial manual 2010, 1, c h a p t e r 2.4.1 4 http://osp.mans.edu.eg/elsawalhy/inf-dis/lsd.htm 2.scientific opinion scientific opinion on lumpy skin disease1 efsa panel on animal health and welfare (ahaw)2,3 efsa journal 2015;13(1):3986 european food safety authority (efsa), parma, italy http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source= web&cd=1&ved=0cb0qfjaa&url=http%3a%2f%2fwww.efsa. europa.eu%2fde%2fefsajournal%2fdoc%2f3986.pdf&ei=veg8vo d6hjlisqtrsolydg&usg=afqjcne-7uo2wml0ktbxyxzenhl pumkw6w 3.report oie lsdv in azerbaijan 7 july 2014 4.balinsky c.a., delhon g., smoliga g., prarat m., french r.a., geary s.j., et al. rapid preclinical detection of sheeppox virus by a real-time pcr assay // journal of clinical microbiology 46.2 (2008): 438-442 *shalala k. zeynalova e-mail: zeynalova.shelale@rambler.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e178, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts developing and validating a fireworks-related syndrome definition in kansas zachary m. stein* bureau of epidemiology and public health informatics, kansas department of health and environment, topeka, ks, usa objective to develop a syndrome definition and analyze syndromic surveillance data usefulness in surveillance of firework-related emergency department visits in kansas. introduction across the u.s.a., multiple people seek treatment for fireworksrelated injuries around the july 4th holiday. syndromic surveillance in kansas allows for near real-time analysis of the injuries occurring during the firework selling season. during the 2017 july 4th holiday, the kansas syndromic surveillance program (kssp) production data feed received data from 88 eds at excellent quality and timeliness. previous and current firework safety messaging in kansas is dependent on voluntary reporting from hospitals across the state. with widespread coverage of eds by kssp, data can be more complete and timely to better drive analysis and public information. methods kssp data was queried through the electronic surveillance system for the early notification of community-based epidemics (essence) v.1.20 provided by the national syndromic surveillance program. data between june 12, 2017 and august 13, 2017 were queried. the first query (query a, table 1.) searched the discharge diagnosis history field for the “w39” icd-10 diagnosis code, “discharge of firework.” these records were searched for common firework terms contained in the chief complaint history field. these firework-related free text terms (query b, table 1.) were then combined with other potential firework-related terms to create a preliminary free text query (query c, table 1.). this preliminary query was run on the chief complaint history field. data were then searched for false positive cases and appropriate negation terms were included to accommodate this. the new query with negation terms (query d, table 1.) was run on the chief complaint history field, combined with the results from the discharge diagnosis history field, and then combined records were de-duplicated based on a unique visit identifier. the final data set was then classified by the anatomical location of the injury and the gender and age group of the patient. results the initial query (query a, table 1.) for the diagnosis code “w39” returned 101 unique ed visits. of these 101 unique ed visits, the following terms were identified in the chief complaint history field: shell, artillery, bomb, sparkler, grenade, fire cracker, firework, and firework show. these key terms were translated into query b, table 1. other key terms deemed likely to capture specific firework-related exposures were then included into query c, table 1., including roman, candle, lighter, m80, and punk. query c was then used to query the chief complaint history field, returning 144 unique ed visits. cases captured by query c were then reviewed by hand for false positives and the negation terms, lighter fluid, fish, nut, and pistachio, were incorporated the query d, table 1. the previous process for query c was then repeated on query d, leaving a remaining 136 unique cases. query a’s 101 unique ed visits was then combined with the 136 unique ed visits captured by query d and de-duplicated. the de-duplicated data set contained 170 unique ed visits which were then reviewed by hand for false positives. the final removal of false positives from the combined and de-duplicated data set left a remaining 154 unique ed visits for firework-related injuries during this time period. for these data, the most common victims of firework injuries were males, accounting for 65.5% of all firework related ed visits and children ages 0 to 19 accounting for 44.2% of these visits. at every age breakout, male injuries exceeded female injuries. the most common anatomical location of the injury was one or both hands with 38.3% of all injuries mentioned hands as their primary injury. injuries to the eyes, face, and head accounted for the second most injuries (28.6% of all patients). conclusions the selling of fireworks will be a yearly occurrence of a specific exposure that can potentially lead to injuries. utilizing syndromic surveillance to review the holiday firework injuries is a very rapid method to assess the impact of these injuries and may allow for future direction of public information during the holiday. having a syndrome definition that builds on knowledge from previous years will allow for quicker case identification as well. state public information regarding firework safety can be significantly bolstered by accurate and rapid data assessment. developing a firework injury syndrome definition that is accurate and returns information rapidly has allowed for increased buy-in to the kansas syndromic surveillance program from public information offices, fire marshal’s offices, and other program fields. queries: firework-related injuries keywords firework; kansas; injury; syndromic; surveillance acknowledgments data collection was supported by the grant or cooperative agreement number 1 u50 oe000069-01, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services *zachary m. stein e-mail: zstein@kdheks.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e36, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher epidemiology and surveillance, goal global, fairfax, va, usa objective systematically assess and strengthen the capacity of communities and sections in port loko district, sierra leone to detect significant events related to the reporting of ebola virus disease (evd) such as sick persons, secret burials and deaths. the components of the enhanced surveillance system will be described. introduction communities and sections that are consistently underreporting both illness and death pose a significant risk to surveillance and their efficacy is dependent upon the reporting of community structures such as government structures (primary health units (phus), schools), evd response structures (contact tracers, community events based surveillance (cebs), social mobilization), and traditional structures (chiefs, traditional healers, village task forces, religious institutions). all structures are required to report to the district ebola response center (derc) as depicted in figure 1. frequent and protocolized information sharing is central to the reporting efficacy within this structure to ensure early capture of all evd-related incidents. methods underreporting in 162 sections and communities in port loko district was determined by using a proxy of standard mortality ratio (smr = reported deaths/expected deaths). port loko estimated mortality data (17.1 deaths per 1000/year) and population figures from the statistics sierra leone were used to calculate expected deaths. reported deaths were gathered through the national 117 alert system and verified by surveillance teams and burial teams. sections were categorized by reporting efficacy (ranging from 0% to 100+% of expected deaths reported). sections with the lowest reporting efficacies were prioritized first. systematic interviews with each community reporting structures (government, evd response and traditional). interviews were conducted using an open data kit (odk) smartphone based closed questionnaire, designed to cross-check evd reporting statistics and actions recorded under each reporting structure. responses will be compared for levels of consensus (weighted scales and kappa scores) to evaluate the efficacy of the communication network and frequency of information sharing between reporting structures. data are being used to find the barriers to reporting (e.g. leadership structure, awareness campaigns and geographic boundaries) and identify areas of weakness to provide a tailored response to strengthening surveillance. results the described active surveillance model was implemented on june 15, 2015. sick and death alerts were compared 4 weeks before the implementation of the active surveillance model and 4 weeks after implementation. in the 4 week period before implementation, port loko had 136 sick alerts and 749 death alerts, compared with 334 sick alerts and 869 death alerts in the 4 weeks following, a 146% and 16% increase respectively, for an overall increase of 36%. this increase in reporting was particularly pronounced in sections that previously underreported. conclusions focusing active surveillance efforts on silent sections leads to increased reporting of significant evd events and the development of an integrated section-level surveillance system to identify disease triggers. finally, strengthening disease-reporting structures may also support the detection of other rare and rapidly propagating infectious diseases, which may improve the reporting capacity of more common endemic disease and thus have wider benefits on the health system. further analysis of the active surveillance questionnaires to assess consensus among reporting structures is still ongoing and central to maintaining the integrity of evd surveillance. figure 1. relationship of reporting stuctures figure 2. reported deaths vs expected deaths by section keywords surveillance; ebola; epidemiology acknowledgments goal would like to acknowledge the contributions of the port loko district health medical team, district ebola response center, who, cdc, unicef *anhminh a. tran e-mail: aat775@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e164, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a correlation of school absenteeism and laboratory results for flu a in alberta, canada elizabeth birk-urovitz*2, 3, ye li1, 2, steven drews4, 5, christopher sikora4, 5, deena hinshaw4, 5, rita k. biel4, faiza habib4, laura rivera6, hussain usman4, david strong4, 6 and ian johnson1, 2 1public health ontario, toronto, on, canada; 2university of toronto, toronto, on, canada; 3uc berkeley, berkeley, ca, usa; 4alberta health services, edmonton, ab, canada; 5university of alberta, edmonton, ab, canada; 6university of calgary, calgary, ab, canada objective to assess the correlations between weekly rates of elementary school absenteeism due to illness (sai) and percent positivity for influenza a from laboratory testing (ppflua) when conducted at a city level from september to december over multiple years. introduction rates of student absenteeism in schools have been mainly used to detect outbreaks in schools and prompt public health action to stop local transmission 1,2. a report by kim mogto et al. 3 stated that aggregated counts of school absenteeism (sai) were correlated with ppflua, but the sample may have been biased. the purpose of this study was to assess the correlation between aggregated rates of sai and ppflua for two cities, calgary and edmonton, in alberta. in such situations, sai could potentially be used as a proxy for ppflua when there are not enough samples for stable laboratory estimates. methods the alberta real-time syndromic surveillance net (artssn) 4 collects elementary sa data from the two major school boards in two cities in alberta with populations >800,000. since reasons for sa are stated, rates of sai can be calculated. data were obtained for three years, 2012 to 2014, for each city. laboratory data on tests of respiratory agents using a standardized protocol were obtained from alberta’s provincial laboratory for public health for the same time period and locations. the dates of the specimens being received by the laboratory were used in this analysis. for each data source, the relative proportions (sai and ppflua) were calculated. data for the first week of school in september and for the last two weeks of december were removed for each year due to the sai rates being unstable. linear regression models were constructed, with rates of sai predicted by ppflua. separate models were run for each city and for each year, resulting in a total of 6 models. percent positivity for entero-rhinoviruses (pperv) was added to see if it improved the models. the regression models were created using excel and checked in the statistical programs, sas and r. an analysis to assess the influence of a lag period was assessed using r. results for each city, the provincial lab tested between 4,000 and 6,000 specimens each fall and sai rates were based on denominators of between 20,000 and 36,000 children. the r2, betas, and p-values for all 6 regression models are shown in table 1. the minimum correlation value was 0.693 and the maximum was 0.935. due to the strong negative correlations between pperv and ppflua, pperv was not retained in the models. looking at the lag periods, the maximum correlations occurred at a zero week lag in two years (2012 and 2014) and at a -1 week lag in 2013. the two years with a zero lag were both dominated by a h3n2 strain while the year with mainly a h1n1 strain showed a lag of -1. only one year of h1n1 data was available for analysis. conclusions we observed strong correlations between the weekly rates of elementary sai and ppflua at the city level over three years, from september to december. the reasons for the difference in lag times between the h1n1 and h3n2 seasons are being investigated. table 1: results of the linear regression models predicting sai by ppflua * p < 0.001, ** p < 0.01 keywords school absenteeism; influenza a; correlation; syndromic surveillance references 1. mook p, joseph c, gates p, phin n. pilot scheme for monitoring sickness absence in schools during the 2006/07 winter in england: can these data be used as a proxy for influenza activity?. european communicable disease bulletin. 2007 dec;12(12):e11-2. 2. besculides m, heffernan r, mostashari f, weiss d. evaluation of school absenteeism data for early outbreak detection, new york city. bmc public health. 2005;5:105. 3. kom mogto ca, de serres g, douville fradet m, lebel g, toutant s, gilca r, et al. school absenteeism as an adjunct surveillance indicator: experience during the second wave of the 2009 h1n1 pandemic in quebec, canada. plos one [electronic resource]. 2012;7(3):e34084. 4. fan s, blair c, brown a, gabos s, honish l, hughes t, et al. a multi function public health surveillance system and the lessons learned in its development: the alberta real time syndromic surveillance net. can j public health. 2010;101(6):454-8. *elizabeth birk-urovitz e-mail: elizabeth.birk.urovitz@mail.utoronto.ca isds 2016 conference abstracts online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e97, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. mailto:elizabeth.birk.urovitz@mail.utoronto.ca http://creativecommons.org/licenses/by-nc/3.0/) objective introduction methods results conclusions keywords references isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts poison control center data in the nc detect syndromic surveillance system zachary faigen*1, lana deyneka1, anne hakenewerth1 and michael c. beuhler2 1north carolina department of health and human services, raleigh, nc, usa; 2carolinas poison center, charlotte, nc, usa objective to describe carolinas poison control center (cpc) calls data collected in the nc detect syndromic surveillance system. introduction cpc provides the 24/7/365 poison hotline for the entire state of north carolina and currently handles approximately 80,000 calls per year. cpc consultation services that assist callers with poison exposure, diagnosis, optimal patient management, therapy, and patient disposition guidance remain indispensable to the public and health care providers. poison control center data have been used for years in syndromic surveillance practice as a reliable data source for early event detection. this information has been useful for a variety of public health issues, including environmental exposures, foodborne diseases, overdoses, medication errors, drug identification, drug abuse trends and other information needs. the north carolina department of health and human services started formal integration of cpc information into surveillance activities in 2004. cpc call data are uploaded in real time (hourly), 24/7/365, to the nc detect state database. methods cpc calls collected by nc detect from 2009-2015 were analyzed in this descriptive study. counts of cpc calls were examined by year to assess total volume and changes over time, by month to assess seasonality, by geographic location, and call site facility and call originator. cpc calls were also categorized by type of call – exposure calls versus information calls – in order to determine why people call cpc and to assess if any trends exist amongst these categories. results the majority of cpc calls originate from the caller’s own residence (53.40%). the age groups most represented are 0-1 years old, 2-4 years old, and 25-44 years old. calls to cpc were for male and female patients in approximately equal numbers. the region of nc that has the highest number of calls, by a fairly wide margin, is the charlotte metro region. in 2009, the total number of cpc calls was over 120,000. this number decreased monotonically every year following, with the total in 2015 being 80,000. this is a 1/3 reduction in the total number of calls over 7 years. when the calls were analyzed by type of call, an interesting trend emerged. the total number of exposure calls remained relatively constant over the time period, ranging from 64,000 to 68,000 per year. however, the total number of information calls decreased each year going from just over 40,000 to only about 5,000. when examined by month to assess seasonality, the data show an increase in the number of calls beginning in february and peaking in may, and then a steady and slow decline throughout the rest of the year. conclusions our study shows that cpc consultations from callers with exposures have remained stable over time. however, in the absence of exposure, fewer people call cpc for information on various substances. drug identification calls saw a decrease each year during the study time period. in 2009 there were 34,495 drug identification calls and in 2015 there were 5,722. this dramatic decrease in information calls is most likely due to the increased use of the internet and search engines. because people have more access to the internet, especially via mobile devices, they may not feel the need to call cpc to obtain information. keywords syndromic surveillance; poison control center; descriptive study; data evaluation acknowledgments we would like to acknowledge all those involved with the nc detect program at both the north carolian department of health and human services and the university of north carolina at chapel hill, as well as the carolinas poison center. *zachary faigen e-mail: zachary.faigen@dhss.nc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e136, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 1epidemiology & preparedness, denver public health, denver, co, usa; 2tri-county health department, greenwood village, co, usa objective to evaluate methods of measuring marijuana-related emergency department visits at denver metropolitan area hospitals participating in the national syndromic surveillance program (nssp). introduction nssp, a centers for disease control and prevention (cdc) surveillance system, allows timely detection of emergency department (ed) trends by matching chief complaint (cc) text or diagnosis (dx) codes to established syndrome criteria [1, 2]. no cdc syndrome definition currently exists for marijuana-related visits. accidental child ingestions and over-consumption of edible products are an emerging concern [3, 4]. a validated marijuana syndrome will allow health departments with access to ed data to measure relative trends and disparities in marijuana-related ed visits. methods a marijuana syndrome definition which incorporates cc and dx variables was developed and evaluated with data from 15 hospitals in adams, arapahoe, denver, and douglas counties reporting to nssp. preliminary marijuana cases were identified based on dx and cc criteria. cc criteria included terms like “marijuana,” “canna”, and “edible.” the dx code variable was searched for international classification of disease 9 codes regularly used to identify marijuanarelated cases: 969.6, e854.1, 304.3, and 305.2 (excluding 304.33 and 305.23, which indicate conditions in remission) [5]. the sensitivity of cc and dx criteria were compared by evaluating the percent of preliminary cases matched on cc, dx, or both and examining frequency of matches on specific search terms. two reviewers then assessed specificity by examining age-stratified case samples (20 cases per age group or all cases if there were less than 20) and categorizing the suspected relationship of marijuana to the visit: 1) directly related, 2) incidental, 3) unrelated, and 4) unclear. findings were used to refine the case criteria and develop a method to exclude or adjust for incidental, unrelated, and unclear cases. results from january to july 2015, 1717 preliminary marijuana cases (an average of 245 per month) were identified. of these, 73% were identified by dx alone, 19% by cc alone, and 8% by both cc and dx. the dx code 305.2 (“nondependent cannabis abuse”) and cc text “marijuana” and “thc” were the most sensitive, respectively returning 1320, 189, and 113 cases (including ones which matched multiple criteria). among cases with an age reported, 20-29 year-olds represented the greatest share of cases (35%). cases were detected in all age groups. several hospitals were working to consistently report key variables during the evaluation period. age was missing for 749 cases; cc was missing for 1249 cases; and dx was missing for 333 cases. during the month of august 2015, overall completeness of age improved to 99.5% and dx code increased to 75.6%. cc completeness continues to improve as hospitals resolve reporting issues. conclusions the use of cc text and dx code criteria in the marijuana syndrome definition gave sensitivity despite limitations in data completeness. evaluating cases identified after august 2015 will permit more accurate assessment of the syndrome’s specificity. with states around the country exploring or legalizing retail marijuana, a sensitive but specific measurement of temporal and demographic trends in marijuana-related ed visits is needed. applying a validated marijuana syndrome definition to nssp data will help monitor emerging trends and inform policies. keywords marijuana; cannabis; syndromic surveillance references 1. centers for disease control and prevention (cdc). monitoring health effects of wildfires using the biosense system—san diego county, california, october 2007. mmwr morb mortal wkly rep. 2008 jul 11;57(27):741-4. 2. benoit sr, burkom h, mcintyre af, et al. pneumonia in us hospitalized patients with influenza-like illness: biosense, 2007-2010. epidemiol infect. 2013 apr;141(4):805-15. doi: 10.1017/s0950268812001549. epub 2012 jul 17. 3. wang gs, roosevelt g, heard k. pediatric marijuana exposures in a medical marijuana state. jama pediatr. 2013 jul;167(7):630-3. doi: 10.1001/jamapediatrics.2013.140. 4. hancock-allen jb, barker l, vandyke m, holmes db. notes from the field: death following ingestion of an edible marijuana product colorado, march 2014. mmwr morb mortal wkly rep. 2015 jul 24;64(28):771-2. 5. colorado department of public health and environment. monitoring health concerns related to marijuana in colorado: 2014. 2015 mar 4. available from https://drive.google.com/folderview?id=0bxqxhstk 92dbfnnfsurhd0vfzjetrfpsveg3bjm5qujxoed0vwzdoun jsnpwwefvtvdiufu&usp=sharing. *kathryn h. deyoung e-mail: kathryn.deyoung@dhha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e17, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts using syndromic surveillance data to monitor endocarditis and sepsis among drug users lana deyneka*1, anne hakenewerth1, zachary faigen1, amy ising2 and clifton barnett2 1epidemiology, ncdph, raleigh, nc, usa; 2unc, chapel hill, nc, usa objective to describe how the state syndromic surveillance system (nc detect) was used to initiate near real time surveillance for endocarditis, sepsis and skin infection among drug users. introduction recreational drug use is a major problem in the united states and around the world. specifically, drug abuse results in heavy use of emergency department (ed) services, and is a high financial burden to society and to the hospitals due to chronic ill health and multiple injection drug use complications. intravenous drug users are at high risk of developing sepsis and endocarditis due to the use of a dirty or infected needle that is either shared with someone else or re-used. it can also occur when a drug user repeatedly injects into an inflamed and infected site or due to the poor overall health of an injection drug user. the average cost of hospitalization for aortic valve replacement in usa is about $165,000, and in order for the valve replacement to be successful, patients must abstain from using drugs. methods we examined temporal trends of drug-related visits to hospital eds, as well as drug-related related ed admissions complicated with endocarditis, bacteremia and sepsis. results the trends in endocarditis/sepsis and drug-related related admissions appear to echo overdose related ed admissions increase. patients ed return visits and hospitalizations for the same problem are also growing compare to the previous years. we will discuss the nc detect case definition used to monitor drug overdose/dependence and infection, case definition transition from icd-9 to icd-10 codes, and will share surveillance analysis results. conclusions nc detect’s system flexibility has been important in rapidly establishing surveillance of infections among drug users. near real time analysis on hospital, county and state levels can be performed using nc detect system reports to provide state officials, hospitals and lhds with situational awareness. limitations: syndromic surveillance ed data contains less accurate information about the diagnosis codes, procedures, length of stay, and severity comparing to the hospital discharge data. keywords surveillance; drug users; endocarditis; bacteremia; sepsis references 1. ising a, proescholdbell s, harmon kj, sachdeva n, marshall sw, waller ae. use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies. injury prevention. 2016 apr 1;22(suppl 1):i43-9. 2. jennifer a. frontera jeremy d. gradon right-side endocarditis in injection drug users: review of proposed mechanisms of pathogenesis oxford journals medicine & health clinical infectious diseases, volume 30, issue 2 3. hannah l. f. cooper, joanne e. brady, daniel ciccarone, barbara tempalski1 karla gostnell, samuel r. friedman increasing infectious endocarditis admissions among young people who inject drugs open forum infect dis (2016) 3 (3): ofw157 accepted july 18, 2016 *lana deyneka e-mail: lana.deyneka@dhhs.nc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e124, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts kidenga: public engagement for detection and prevention of aedes-borne viral diseases chris schmidt*1, alba phippard3, jennifer m. olsen2, kathy wirt2, andrea rivera1, adam crawley2, steve waterman3 and kacey ernst1 1epidemiology and biostatistics, university of arizona, tucson, az, usa; 2skoll global threats fund, san francisco, ca, usa; 3centers for disease control & prevention (cdc), atlanta, ga, usa objective (1) early detection of aedes-borne arboviral disease; (2) improved data on ae. aegypti and ae. albopictus distribution in the united states (u.s.); and (3) education of clinicians and the public. introduction zika, chikungunya, and dengue have surged in the americas over the past several years and pose serious health threats in regions of the u.s. where ae. aegypti and ae. albopictus mosquito vectors occur. ae. aegypti have been detected up to 6 months of the year or longer in parts of arizona, florida, and texas where mosquito surveillance is regularly conducted. however, many areas in the u.s. lack basic data on vector presence or absence. the zika, dengue, and chikungunya viruses range in pathogenicity, but all include asymptomatic or mild presentations for which individuals may not seek care. traditional passive surveillance systems rely on confirmatory laboratory testing and may not detect emergent disease until there is high morbidity in a community or severe disease presentation. participatory surveillance is an approach to disease detection that allows the public to directly report symptoms electronically and provides rapid visualization of aggregated data to the user and public health agencies. several such systems have been shown to be sensitive, accurate, and timelier than traditional surveillance. we developed kidenga, a mobile phone app and participatory surveillance system, to address some of the challenges in early detection of day-biting mosquitoes and aedes-borne arboviruses and to enhance dissemination of information to at-risk communities. methods kidenga sends a weekly push notification prompting users to report symptoms, travel history, and day-biting mosquito activity. if an individual reports through kidenga that they or a family member have had symptoms consistent with zika, dengue, or chikungunya, they receive an email with educational information about the diseases, prevention strategies, and treatment/testing information for clinicians. upon registration, users can opt in to have additional follow-up via email. at any time, users may also view maps of aggregated user reports, confirmed case counts by county from public health partners (in pilot areas), aedes distribution maps, information about prevention and control strategies, and news on the diseases and vectors from a curated newsfeed. users in select pilot areas may also receive press releases issued by their state or local public health department related to the diseases and their vectors. university of arizona owns and maintains the app and its data. local and state health departments that want more detailed information on user symptoms and mosquito activity may request and monitor the data at no cost. a marketing campaign to recruit a broad user base is being implemented in arizona, texas, and florida. results kidenga was developed with significant input from public health stakeholders and launched in september 2016, accompanied by english and spanish radio public service announcements in select arizona markets, press releases, and a social marketing campaign. a spanish version of the app is under development. we will describe the results of user registration and survey submissions, challenges identified during development and deployment of this novel surveillance system, plans for data use and evaluation, and collaborations with public health partners. conclusions the utility of kidenga as a surveillance system will depend on broad and consistent participation among diverse user populations, particularly in low-risk areas; strategies to integrate health reports for high-risk populations who may not have smartphones; validation of data and development of sensitive and specific algorithms for taking public health action, and buy-in from public health departments to use the data and advocate for this novel surveillance tool. kidenga’s secondary function as an education tool on aedes-borne viruses is less dependent upon a large user base and can be evaluated separately. participatory surveillance systems that specifically monitor aedesborne pathogens are relatively new, and the challenges associated with their early detection may differ from those of other diseases. keywords participatory surveillance; mosquito; arbovirus; m-health; zika acknowledgments we acknowledge the collaboration of the arizona department of state health services, city of laredo health department, city of mcallen health department, and florida department of health. *chris schmidt e-mail: cschmidt@email.arizona.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e111, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts soda pop: a time-series clustering, alarming and disease forecasting application jeremiah rounds*, lauren charles-smith and courtney d. corley applied statistics and computational modeling, pacific northwest national laboratory, richland, wa, usa objective to introduce soda pop, an r/shiny application designed to be a disease agnostic time-series clustering, alarming, and forecasting tool to assist in disease surveillance “triage, analysis and reporting” workflows within the biosurveillance ecosystem (bsve) [1]. in this poster, we highlight the new capabilities that are brought to the bsve by soda pop with an emphasis on the impact of metholodogical decisions. introduction the biosurveillance ecosystem (bsve) is a biological and chemical threat surveillance system sponsored by the defense threat reduction agency (dtra). bsve is intended to be user-friendly, multi-agency, cooperative, modular and threat agnostic platform for biosurveillance [2]. in bsve, a web-based workbench presents the analyst with applications (apps) developed by various dtrafunded researchers, which are deployed on-demand in the cloud (e.g., amazon web services). these apps aim to address emerging needs and refine capabilities to enable early warning of chemical and biological threats for multiple users across local, state, and federal agencies. soda pop is an app developed by pacific northwest national laboratory (pnnl) to meet the current needs of the bsve for early warning and detection of disease outbreaks. aimed for use by a diverse set of analysts, the application is agnostic to data source and spatial scale enabling it to be generalizable across many diseases and locations. to achieve this, we placed a particular emphasis on clustering and alerting of disease signals within soda pop without strong prior assumptions on the nature of observed diseased counts. methods although designed to be agnostic to the data source, soda pop was initially developed and tested on data summarizing influenza-like illness in military hospitals from collaboration with the armed forces health surveillance branch. currently, the data incorporated also includes the cdc’s national notifiable diseases surveillance system (nndss) tables [3] and the who’s influenza a/b influenza data (flunet) [4]. these data sources are now present in bsve’s postgres data storage for direct access. soda pop is designed to automate time-series tasks of data summarization, exploration, clustering, alarming and forecasting. built as an r/shiny application, soda pop is founded on the powerful statistical tool r [5]. where applicable, soda pop facilitates nonparametric seasonal decomposition of time-series; hierarchical agglomerative clustering across reporting areas and between diseases within reporting areas; and a variety of alarming techniques including exponential weighted moving average alarms and early aberration detection [6]. soda pop embeds these techniques within a user-interface designed to enhance an analyst’s understanding of emerging trends in their data and enables the inclusion of its graphical elements into their dossier for further tracking and reporting. the ultimate goal of this software is to facilitate the discovery of unknown disease signals along with increasing the speed of detection of unusual patterns within these signals. conclusions soda pop organizes common statistical disease surveillance tasks in a manner integrated with bsve data source inputs and outputs. the app analyzes time-series disease data and supports a robust set of clustering and alarming routines that avoid strong assumptions on the nature of observed disease counts. this attribute allows for flexibility in the data source, spatial scale, and disease types making it useful to a wide range of analysts soda pop within the bsve. keywords bsve; biosurveillance; r/shiny; clustering; alarming acknowledgments this work was supported by the defense threat reduction agency under contract cb10082 with pacific northwest national laboratory references 1. dasey, timothy, et al. “biosurveillance ecosystem (bsve) workflow analysis.” online journal of public health informatics 5.1 (2013). 2. http://www.defense.gov/news/article/article/681832/dtra-scientistsdevelop-cloud-based-biosurveillance-ecosystem. accessed 9/6/2016. 3. centers for disease control and prevention. “national notifiable diseases surveillance system (nndss).” 4. world health organization. “flunet.” global influenza surveillance and response system (gisrs). 5. r core team (2016). r: a language and environment for statistical computing. r foundation for statistical computing, vienna, austria. 6. salmon, maëlle, et al. “monitoring count time series in r: aberration detection in public health surveillance.” journal of statistical software [online], 70.10 (2016): 1 35. *jeremiah rounds e-mail: jeremiah.rounds@pnnl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e5, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts 2015 international society for disease surveillance conference harnessing data to advance health equity the international society for disease surveillance (isds) will hold its fourteenth annual conference in denver, co on december 10th and 11th, 2015. the society's mission is to improve population health by advancing the science and practice of disease surveillance, and the annual conference advances this mission by bringing together practitioners and researchers from multiple fields involved in disease surveillance, including public health, epidemiology, health policy, biostatistics and mathematical modeling, informatics and computer science. this year the conference received 224 abstract submissions, from 20 countries. we accepted 41 abstracts for oral presentations, along with 36 lightning talks and 98 posters. the last decade has seen our field evolve from a focus on disease surveillance to one on health surveillance. similarly the traditional emphasis on health disparities has given way to an approach which emphasizes health equity, language which stresses the universal human right to health. within this framework, surveillance provides the necessary data to advance the health equity agenda. indeed without these data, we can neither identify where we must do better nor measure our progress towards a more equitable society. with a nod to the tradition and heritage of horses in our host city of denver, the theme for this year’s conference is ‘harnessing data to advance health equity’. our scientific program includes four plenary speakers, each of whom has notable achievements related to the theme of health equity. ali mokdad, phd from the institute for health metrics and evaluation (ihme) will deliver the opening keynote address. dr. mokdad was director of the behavioral risk factor surveillance system at cdc, and now directs the surveillance and survey work at ihme. he will describe his research on the global burden of disease and the dissemination of data and tools to address challenges facing global health equity. the afternoon of the opening day will feature a plenary talk from ned colange, md, phd. dr. colange was chief medical officer of the colorado department of public health and chair of the u.s. preventive services task force before joining the colorado trust as president and ceo. he will speak on his organization’s work to achieve health equity for all coloradans. later in the afternoon, we will assemble a panel of three distinguished experts to discuss surveillance, health equity, and human rights. beth rivkin, md, mph is director of the global health and justice project at the university of washington and will speak on the right to be counted. vivek singh, mbbs, mph is associate professor at the indian institute of public health and coordinator of its field epidemiology training program; he will speak on polio eradication in india. ronald st. john, md, mph was director general of the centre for emergency preparedness and response in public health agency of canada at the time of the sars epidemic. he will offer his perspective on surveillance and health equity. the second day of the conference will begin with a plenary talk by art davidson, md, mph. dr. davidson is director of public health informatics, epidemiology and preparedness at denver public health. he will share perspectives on over two decades of work at the interface of health information technology and public health practice. our closing keynote speaker will be anne marie kimball, md, mph. dr. kimball is senior consultant at chatham house, founder of the apec emerging infections network, a guggenheim scholar, and fellow of the american college of preventive medicine. she will speak on emerging threats to health as an issue of equity and the future of surveillance in the 21st century. as modern concepts of surveillance continue to evolve, we are excited with the program for the 2015 conference and its focus on health equity. the isds brings together professionals from public health practice, industry, health care, and academia. the annual conference is a unique opportunity for this diverse community to gather together, share your knowledge and ideas, and develop a vision for the future of surveillance. we are looking forward to seeing you in denver! al ozonoff, ma, phd boston children's hospital 2015 isds scientific program committee chair online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e1, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts spread of clostridium botulinum in the soils of georgia ekaterine zhorzholiani*, neli chakvetadze, guram katsitadze and paata imnadze national center for disease control and public health, tbilisi, georgia objective the main focus of this study was to study the spread of botulism in georgia and the biological characteristics of the strains of clostridium botulinum isolated from territories in the country. introduction accumulation of c. botulinum in soil occurs through excretion of bacterial spores from the intestines of humans, animals, birds and fish. in georgia, during the winter season, the population consumes homemade vegetable preserves, which are made of locally produced (as well as imported) vegetables. historical surveys confirmed that the presence of c. botulinum in the soil is widespread. some researchers consider c. botulinum a characteristic component of soil flora. methods soil samples were collected from areas, where from 2001-2002 cases of botulism caused by homemade vegetable preserves (produced from vegetables cultivated in those areas) were registered. soil samples were collected from kakheti, shida kartli, kvemo kartli, samtkhe-javakheti, and samegrelo regions. standard bacteriology and pcr were used to confirm the presence of c. botulinum from soil samples. separation of strains and their examination was conducted in accordance with the scheme provided by the cdc atlanta reference laboratory (usa), which was later tested by ncdc. toxigenicity and toxin production of strains were tested using a biotest on white mice. results in total, 258 soil samples were tested, from which, 40 (15.5%) cultures of c. botulinum type b were obtained. toxigenicity and toxin production were confirmed through biotests. these results confirm the presence of c. botulinum in agricultural lands, which causes contamination of vegetables cultivated on those lands, which are used for the preparation of homemade preserves, causing botulism in humans. conclusions for the purpose of finding solutions to botulism, it is essential to verify the ecology of the pathogen through establishing the prevalence of bacteria in different soil types. it was shown that some areas of georgia, where vegetable growing is greatly developed, and which, are the main sources of crops, are highly contaminated with c. botulinum. in georgia, land used for agriculture is contaminated with c. botulinum. c. botulinum type b was isolated from 40 cultures obtained from 258 soil samples, which represents contamination in 15.5% of sampled areas. these results suggest that vegetables and melons may be highly contaminated as well. all cases of c. botulinum in humans that were researched were connected to homemade canned vegetables. keywords c. botulinum; soil; spores; strains acknowledgments this work is supported by the national botulism laboratory team, and cdc atlanta, georgia. participation in this conference was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed. *ekaterine zhorzholiani e-mail: eka.zhorzholiani@ch2m.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e160, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts spatial analysis of ss population coverage based on emergency regional healthcare esra morvan*, bernadou anne, ludivine gautier, yassungo silue and dominique jeannel regional office, sante publique france, orléans, france objective to analyse population coverage of syndromic surveillance(ss) based on emergency care data by studying i)the attractiveness of respectively sos médecins (emergency care general practitioners) and hospital emergency departments in the centre-val de loire region and ii) the contribution of ecological deprivation factors in emergency access to healthcare. introduction sos médecins france (sos med) is the first private and permanent network of general practitioners providing emergency care in france. besides hospital emergency departments (hed), sos med is therefore a major source of data for detecting and measuring nearreal-time health phenomena. the emergency services provided by the sos med have been subject to important changes in the recent years. their services are enriched by a medical consultation center together with extended working hours. besides, the south of the region is markedly affected by a declining number of medical practitioners this study was conducted to analyze the regional population coverage of emergency healthcare data provided by hed and sos med to the french syndromic surveillance system (sursaud®) taking into account distance, health care offer, demographic factors and ecological deprivation factors. methods an analysis of the activities and geographic attraction was carried out based on the data respectively provided by the three regional sos med and three hed (bourges, orléans and tours). quasi-poisson regression modelling was used to identify the factors influencing the attractiveness of each organization. next, the findings were refined through spatial analysis of the attractiveness of hed and sos med and analysis of the contribution of deprivation based on socioeconomical and healthcare facilities ecological indexes. results in terms of age group, children under 2 years required the largest service consultations as well as seniors over 75 who sought more emergency visits at home. the sos med were almost always active in urban areas and at least once in two due to continuity of care. so they are an efficient source of general medical care given present work hours. distance as an influential factor may explain the differences in attraction to the support type. the extent of the attraction appears in 36% sos med bourges and 14% for sos med orleans. add the extent of attraction for sos, remote consultation for sos med associations are a good use of care in general practice in present work hours scheme. in terms of monitoring of epidemics, we note that the sos médecins associations are most active in winter, particularly during the seasonal epidemics of influenza. this can be explained by the fact of patient referrals during calls. the most serious cases are redirected to the ed and cases of general medicine to the sos médecins. it is also important to note that the attraction of ed of chr orléans covers more or less important a large part of the regional territory, which is not visible to the ed of ch bourges. it should nevertheless be noted that the chr orleans a larger bed capacity than the ch bourges. conclusions this research has analysed the changes taking place in the sos médecins associations in the centre-val de loire region. findings shows that these associations help ensure access to general medical care in a context of strongly reduced medical demography although with an uneven, primarily urban, geographical coverage. with better knowledge of the geographic span and sources and types of emergency care provision, further research can be undertaken to further refine and interpret the data. keywords emergency department; spatial analysis; ecological indexes; centre-val de loire; sos médecin (emergency care general practitioners) online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e169, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts acknowledgments to sos médecins associations and all emergency department and ors of the region centre-val de loire for providing data references 1.rey g. les tendances des inégalités de santé en france et en europe séminaire sciences-po “réduire les inégalités de santé”, 11 mai, paris. 2011. 2.bodenreider o, lefebvre o, kohler f. modèle pour l’étude de l’attraction exercée par un établissement hospitalier. informatique et santé. 1993;6. 3.geniteau f, guillouet c, eloy a. inégalités cantonales de santé en région centre : une répartition territoriale des déterminants de santé : juillet 2010. *esra morvan e-mail: esra.morvan@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e169, 2017 isds16_abstracts-final 130 isds16_abstracts-final 131 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts infant fever trends following the launch of the meningococcal b vaccine in the uk sally harcourt*1, roger morbey1, chris bates2, helen carter1, shamez ladhani3, alex j. elliot1 and gillian e. smith1 1real-time syndromic surveillance team, public health england, birmingham, united kingdom; 2the phoenix partnership (tpp), leeds, united kingdom; 3public health england immunisation, hepatitis and blood safety department, london, united kingdom objective to use syndromic surveillance data to assess whether there has been an increase in gp fever consultations since the inclusion of the meningococcal b (menb) vaccine in the uk vaccination schedule. introduction from 1 september 2015, babies in the united kingdom (uk) born on/after 1 july 2015 became eligible to receive the menb vaccine, given at 2 and 4 months of age, with a booster at 12 months.1 early trials found a high prevalence of fever (over 38°c) in babies given the vaccine with other routine vaccines at 2 and 4 months. we used syndromic surveillance2 data to assess whether there had been increased family doctor (general practitioner (gp)) consultations for fever in young infants following the introduction of the vaccine. methods gp consultations for fever in infants aged under 1 year were extracted from the phoenix partnership (tpp) researchone database (400 gp surgeries in england).3 data were stratified by week of age over the period 1 september 2015 to 30 november 2015 and 1 december 2015 to 29 february 2016. fever consultation rates (per 100,000 registered practice population in the database) were compared to the same 3 month periods of the previous 5 years (2010-14) using incident rate ratios (irr). preand post-vaccination consultation rates were applied to the england 0-26 week population to estimate excess fever consultations. results between 1 september and 30 november 2015 the average daily fever consultation rate for infants aged 0-26 weeks was 4.72/100,000; the incident rate ratio was 1.46 (95% ci, 1.09-1.92). in the 7-10 week age group the average daily fever consultation rate was 7.79/100,000. the incidence rate was 2.68 times higher than in previous years (95% ci, 1.42-4.94). between 1 december 2015 and 29 february 2016 the average daily consultation rate for infants aged 0-26 weeks was 6.19/100,000. the incidence rate was 1.49 times higher than in the same 3 month period of previous years (95% ci, 1.16-1.90). in infants aged 7-10 weeks the average daily consultation rate was 8.44/100,000 and the incidence rate was 1.83 times higher than previous years (95% ci 1.03-3.16). between 1 september 2015 and 29 february 2016 there were an estimated additional 959 fever consultations for infants aged 0-26 weeks to english family doctors. conclusions we have demonstrated an innovative use of syndromic surveillance to quickly and easily assess the impact on healthcare seeking behaviour for infants with fever following the introduction of a new vaccination into the routine vaccination programme in england. our study provides reassurance that in infants aged 0-26 weeks there was no marked increase in consultations following the introduction of the new menb vaccination. however, in some age groups below 0-26 weeks there was an increase in healthcare seeking behaviour for fever, in particular, the 7-10 week age group which includes infants aged 8 weeks receiving their first vaccination. other age groups also demonstrated increased fever consultations during these two periods, albeit at less significant levels. we will analyse data for the full year from 1 september 2015 to further explore these findings, investigate potential confounders and assess trends since vaccine introduction. keywords syndromic surveillance; meningococcal b; vaccination acknowledgments the authors would like to thank the tpp researchone project committee for permission to use the researchone database for this study. we also thank mr paul loveridge of the public health england (phe) realtime syndromic surveillance team (resst) for technical expertise. references 1. public health england [internet]. menb vaccination: introduction from september 2015 [updated 22 june 2015; cited 24 august 2016]. available from: https://www.gov.uk/government/publications/menbvaccination-introduction-from-1-september-2015 2. public health england [internet]. syndromic surveillance: systems and analyses [updated 15 january 2015; cited 24 august 2016]. available from: https://www.gov.uk/government/collections/syndromicsurveillance-systems-and-analyses 3. researchone [internet]. transforming data into knowledge. [cited 24 august 2016]. available from: http://www.researchone.org/ *sally harcourt e-mail: sally.harcourt@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e86, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts informing public health prevention in nc using falls surveillance data anna waller1, katherine j. harmon*1, shana geary2, amy ising1, anne hakenewerth2, judith tintinalli1 and scott proescholdbell2 1epidemiology, university of north carolina at chapel hill, durham, nc, usa; 2nc division of public health, raleigh, nc, usa objective to describe how a successful partnership between state public health and a university organization has used epidemiologic data, such as mortality, hospital discharge, and emergency department (ed) visit data, to inform falls prevention activities in north carolina (nc). introduction falls are a leading cause of fatal and nonfatal injury in nc. as the size of the older adult population is predicted to increase over the next few decades, it is likely that the incidence of falls-related morbidity and mortality will increase in tandem.1 in order to address this public health emergency, the injury and violence prevention branch (ivpb) of the nc division of public health has partnered with the carolina center for health informatics (cchi) in the department of emergency medicine at the university of north carolina at chapel hill to perform falls surveillance activities. this abstract describes some of the specific research and surveillance activities currently ongoing in nc. methods ivpb developed the special emphasis report (ser) on fall injuries among older adults, 2005-2014 to describe the demographic characteristics and trends of falls-related mortality and hospitalization among adults 65 and older, associated costs of falls-related injury, and current falls prevention activities in nc. the nc ser was based on the centers for disease control and prevention’s injury ser template, a tool designed to facilitate the dissemination of injury data for public health action. while the ser focused primarily on falls among adults ≥65 years of age, cchi was interested in using ed visit data to identify the age at which falls morbidity begins to increase as a means of informing prevention activities to be implemented before the advent of an injurious fall. therefore, cchi performed a descriptive epidemiologic study using ed visit data collected by nc detect. cchi identified all nc ed visits from january 1, 2010 – december 31, 2014 that met the national center for injury prevention and control definition of a fall of unintentional intent.2 during 2010-2014, nc detect captured ed visit data from all 125 24/7, acute care, hospitalaffiliated, civilian eds and over 99% of all ed visits in the state.3 results analysis for the ser found that falls-related death rates increased by 43.1% from 2005 (47.0 per 100,000) to 2014 (67.3 per 100,000), with the greatest increase among males (61.3%) and adults 85 and older (74.4%). conversely, rates of non-fatal hospitalization remained relatively stable and were 1.6 times higher among females than males in 2014 (84.0 and 56.8 per 100,000 respectively). projected lifetime costs associated with falls among nc older adults was approximately $1.4 billion in 2014. nc detect captured 986,024 ed visits during the period 2010-2014 among adults ≥20 years of age (27.4 ed visits/1,000 person-years; 95% ci: 27.4-27.5). throughout the adult lifespan, fall incidence rates in women (33.0 ed visits/1,000 person-years; 95% ci: 32.9-33.1) exceeded those in men (21.3 ed visits/1,000 personyears; 95% ci: 21.3-21.4). starting at age 45, fall rates in women continued to exceed fall rates in men, climbing each year, while rates in men remained stable until after age 65. these results suggest that the risk of having an injurious fall may increase before age 65, particularly among women. due to the public health implications of the results obtained by ivpb and cchi, both organizations are working closely to ensure that relevant information reaches a wide net of potential partners in the effort to reduce falls morbidity and mortality. to date, ivpb and cchi have collaborated on generating fact sheets and short reports available to the public, syndromic surveillance custom event reports available to authorized users, and presenting information to local, state, and national partners. conclusions falls morbidity and mortality are major concerns for the state of nc and the country as a whole. falls surveillance benefits from the collaboration of governmental and university organizations with community partners. for example, when cchi identified an increase in falls incidence in middle age, and, therefore, the potential need to begin falls risk assessment activities at ages < 65, particularly among women, nc dph had the resources to communicate these results to relevant local and state programs and organizations. keywords public health; surveillance; injury; epidemiology; prevention acknowledgments nc detect is a statewide public health syndromic surveillance system, funded by the nc division of public health (nc dph) federal public health emergency preparedness grant and managed through collaboration between nc dph and unc-ch department of emergency medicine’s carolina center for health informatics. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. references 1. r tippett. population growth in the carolinas: projected vs. observed trends. carolina demography, carolina population center, university of north carolina at chapel hill. http://demography.cpc. unc.edu/2015/12/08/population-growth-in-the-carolinas-projectedvs-observed-trends/. page last reviewed december 8, 2015. accessed october 9, 2017. 2. national center for injury prevention and control (ncipc), centers for disease control and prevention (cdc). matrix of e-code groupings. www.cdc.gov/injury/wisqars/ecode_matrix.html. page last reviewed on august 29, 2014. accessed october 12, 2016. 3. carolina center for health informatics, university of north carolina at chapel hill. nc detect: background. http://ncdetect.org/ background/. page last reviewed january 2017. accessed march 8, 2017. *katherine j. harmon e-mail: kjharmon@email.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e142, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts opioid surveillance using social media: how urls are shared among reddit members albert park* and mike conway biomedical informatics, university of utah, salt lake city, ut, usa objective we aim to understand (1) the frequency of url sharing and (2) types of shared urls among opioid related discussions that take place in the social media platform called reddit. introduction nearly 100 people per day die from opioid overdose in the united states. further, prescription opioid abuse is assumed to be responsible for a 15-year increase in opioid overdose deaths1. however, with increasing use of social media comes increasing opportunity to seek and share information. for instance, 80% of internet users obtain health information online2, including popular social interaction sites like reddit (http://www.reddit.com), which had more than 82.5 billion page views in 20153. in reddit, members often share information, and include urls to supplement the information. understanding the frequency of url sharing and types of shared urls can improve our knowledge of information seeking/sharing behaviors as well as domains of shared information on social media. such knowledge has the potential to provide opportunities to improve public health surveillance practice. we use reddit to track opioid related discussions and then investigate types of shared urls among reddit members in those discussions. methods first, we use a dataset4—made available on reddit—that has been used in several informatics studies5,6. the dataset is comprised of 13,213,173 unique member ids, 114,320,798 posts, and 1,659,361,605 associated comments that are made on 239,772 (including active and inactive) subreddits (i.e., sub-communities) from october 2007 to may 2015. second, we identified 9 terms that are associated with opioids. the terms are ‘opioid’, ‘opium’, ‘morphine’, ‘opiate’, ‘hydrocodone’, ‘oxycodone’, ‘fentanyl’, ‘heroin’, and ‘methadone’. third, we preprocessed the entire dataset (i.e., converting text to lower cases and removing punctuation) and extracted discussions with opioid terms and their metadata (e.g., user id, post id) via a lexiconbased approach. fourth, we extracted urls using python from these discussions, categorized the urls by domain, and then visualized the results in a bubble chart7. results we extracted 1,121,187 posts/comments that were made by 328,179 unique member ids from 8,892 subreddits. of the 1,121,187 posts/comments, 82,639 posts/comments contained urls (7.37%), and these posts consisted of 272,551 individual urls and 138,206 unique urls. the types of shared urls in these opioid related discussions are summarized in figure 1. the color and size represent the type and size respectively of shared urls. the ‘.com’ is in blue; ‘.org’ is in orange; and ‘.gov’ is in green. conclusions we present preliminary findings concerning the types of shared urls in opioid-related discussions among reddit members. our initial results suggest that reddit members openly discuss opioid related issues and url sharing is a part of information sharing. although members share many urls from reliable information sources (e.g., ‘ncbi.nlm.nih.gov’, ‘wikipedia.org, ‘nytimes.com’, ‘sciencedirect.com’), further investigation is needed concerning many of the ‘.com’ urls, which have the potential to contain high and/or low quality information (e.g., ‘youtube.com’, ‘reddit.com’, ‘google. com’, ‘amazon.com’) to fully understand information seeking/ sharing behaviors on social media and to identify opportunities, such as misinformation dissemination for improving public health surveillance practice. keywords social media; surveillance system; data mining; opioid acknowledgments we restricted our analysis to publicly available discussion content and the university of utah’s institutional review board (irb) reviewed the study procedure and data (irb 00076188) from further review. ap was funded by the national library of medicine of the national institutes of health under award number t15 lm007124. mc was funded by the national library of medicine of the national institutes of health under award numbers r00lm011393 & k99lm011393. the content is solely the responsibility of the authors and does not necessarily represent the official views of the national institutes of health. references 1. centers for disease control and prevention, national center for injury prevention and control d of uip. drug overdose deaths in the united states continue to increase in 2015. archived at: http://www. webcitation.org/6rl2f65c2 2. fox s. health topics: 80% of internet users look for health information online. pew internet & american life project. 2011. archived at: http://www.webcitation.org/6gbugjfs5 3. reddit. reddit in 2015. 2015. archived at: http://www.webcitation. org/6ethn0tfd isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts 4. reddit_member. i have every publicly available reddit comment for research. ~ 1.7 billion comments @ 250 gb compressed. any interest in this?. 2015. archived at: http://www.webcitation.org/6kgaunxde 5. park a, conway m. longitudinal changes in psychological states in online health community members: understanding the long-term effects of participating in an online depression community. j med internet res. 2017 mar 20;19(3):e71. 6. park a, conway m. towards tracking opium related discussions in social media. online j public health inform. 2017 may 2;9(1):e73. 7. bostock m, ogievetsky v, heer j. d3: data-driven documents. ieee trans vis comput graph. 2011 dec;17(12):2301–9. *albert park e-mail: alpark1216@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e56, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts opioid misuse in missouri: analyzing emergency department use in urban/rural areas evan mobley*, andrew hunter and whitney coffey bureau of health care analysis and data dissemination, missouri department of health and senior services, jefferson city, mo, usa objective compare rate changes over time for emergency department (ed) visits due to opioid overdose in urban versus rural areas of the state of missouri. introduction like many other states in the u.s., missouri has experienced large increases in opioid abuse resulting in hundreds dying each year and thousands of ed visits due to overdose. missouri has two major urban areas, st. louis and kansas city and a few smaller cities, while the remainder of the state is more rural in nature. the opioid epidemic has impacted all areas in the state but the magnitude of that impact varies as well as the type of opioid used. missouri department of health and senior services (modhss) maintains the patient abstract system (pas) which contains data from hospitals and ambulatory surgical centers throughout the state. pas includes data from ed visits including information on diagnoses, patient demographics, and other information about the visit. modhss also participates in the enhanced state surveillance of opioid-involved morbidity and mortality project (esoos). one major aspect of this surveillance project is the collection of data on non-fatal opioid overdoses from ed visits. through this collection of data, modhss analyzed opioid overdose visits throughout the state, how rates compare across urban and rural areas, and how those rates have changed over time. methods the 115 counties in missouri were organized into the six-level urban-rural classification scheme developed by the national center for health statistics (nchs). the attached table shows the breakout of counties into the six different categories. the data years analyzed were 2012 through 2016. ed visits due to opioid overdose were identified using case definitions supplied by esoos. overdoses were analyzed in three different categories—all opioids, heroin, and non-heroin opioids. the all opioid category combines heroin and non-heroin opioids. non-heroin opioids includes prescription drugs such as oxycodone, hydrocodone, fentanyl, and fentanyl analogues. annual rates per 10,000 were calculated for each county classification using population estimates. confidence intervals (at 95%) were then calculated using either inverse gamma when the number of ed visits was under 500, or poisson when the number was 500 or more. changes over time were calculated using both a year over year method and a 5 year change method. results overall opioid rates have increased in all geographic areas during the 5 year period analyzed. large central metro and large fringe metro counties had the highest rates of ed visits due to opioid overdose. these two classifications also saw the largest increases in rates. the large central metro counties collectively increased over 125%, while the large fringe metro area increased 130%. both areas experienced statistically significant increases year-to-year between 2014 and 2016 in addition to the overall 5 year period of 2012-2016. analysis was also conducted for heroin and non-heroin subsets of opioid abuse. there were important differences in these two groups. for heroin ed visits, the highest rates were found in the large central metro and large fringe metro regions. however, the largest increase in percentage terms were found in the medium metropolitan, micropolitan and noncore regions which all saw increases of over 300%. notably, every region experienced increases of over 150%. the medium metro had two consecutive years (2013/2014 and 2014/2015) where the heroin ed rate more than doubled. in contrast, non-heroin ed visits did not experience such a large increase over time. most areas saw small fluctuations year-to-year with moderate overall increases over the 5-year time period. the exception to this trend is the large fringe metro area, which saw increases every year most notably between 2014 and 2015 and had by far the largest 5 year increase at 82%. conclusions the urban areas in missouri continue to have the highest rates of opioid overdose, however all areas within the state have experienced very large increases in heroin ed visits within the past five years. the increase in heroin ed visits in the rural areas suggests the abuse of heroin has now spread throughout the state, as rates were much lower in 2012. the steady increase in non-heroin opioids unique to the large fringe metro may be due to the availability of fentanyl in urban areas especially the st. louis area. this possible finding would correspond with the increased deaths due to fentanyl experienced in and around the st. louis urban area that has been identified through analysis of death certificate data. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords opioid; urban/rural; emergency department; heroin; fentanyl *evan mobley e-mail: evan.mobley@health.mo.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e20, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 1national biosurveillance integration center, office of health affairs, united states department of homeland security, washington, dc, usa; 2university of wyoming, laramie, wy, usa objective provide a succinct review of potential developmental and commercial viral hemorrhagic fever diagnostic assays published in peer-reviewed literature and open-source platforms. introduction as the lead for coordinating domestic incident management across all federal departments and agencies, department of homeland security is responsible for identifying emergency response resources. the 2014 ebola – zaire outbreak in west africa that became a public health emergency of international concern highlighted the need to understand the current and potential availability of diagnostic assays for a number of viral hemorrhagic fevers that may require similar response actions. a concatenated list of publically reported potential viral hemorrhagic fever diagnostic assays was therefore compiled. etiologic agents in the families of arena-, bunya-, filo-, and flaviviridae were included in the analysis. this list identifies assays in various stages of development that could be submitted, at some point, to the food and drug administration (fda) under an emergency use authorization (eua) should such a need arise. methods boolean logic formatted searches were conducted using pubmed, google scholar, and google to identify open-source reports of diagnostic assays for viral hemorrhagic fevers. the general boolean search term was [(pathogen of interest and diagnostic) (rapid or point of care or “assay development” or validation or poc or evaluation or clinical or deploy* or testing or detect*)]. returned results were screened for data elements that provided adequate information to derive test performance statistics (limit of detection, sensitivity with 95% confidence interval, specificity with 95% confidence interval, positive predictive value, and negative predictive value). assays were qualitatively analyzed based on limit of detection, sensitivity, and specificity estimates. genus level etiologic agents by family are: arenaviridae (chapare, guanarito, junin, lassa, lujo, lymphocytic choriomennigitis, machupo, sabia, and whitewater arroyo), bunyaviridae (crimeancongo, hantavirus, and rift valley fever), filoviridae (ebola and marburg), and flaviviridae (dengue, kyasanur forest disease, omsk, and yellow fever). results through the use of the boolean logic, 312 unique assay data sources were identified and examined. one hundred seventeen sources contained adequate information to derive diagnostic test statistics for examination as part of this review. inadequate information was identified for chapare hemorrhagic fever, whitewater arroyo virus, and omsk hemorrhagic fever, so diagnostic information for these agents was not included in the final table. assays were dichotomized as either academic/government or commercial assays. of the 212 total assays, 136 (64%) resided in academic or government laboratories. of those, the vast majority addressed the arena-, bunya-, and filoviridae families (35%, 34%, and 26% respectively), compared to the commercial assays addressing the filoand bunyaviridae families (43% and 27.6% respectively). the disproportionate weight of the commercial assays on filoviridae is likely a function of the 2014 ebola-zaire outbreak. in the academic/government validated assays, 82% were validated on clinically derived samples, in comparison to 57% from the commercial assays. clinically validated assays are the “gold-standard” validation technique as recommended by the fda. conclusions the potential product landscape for rapid diagnostics of viral hemorrhagic fevers is extensive. however, the vast majority of these assays were developed in academic or government laboratories and have not progressed further toward commercialization or broad public health use. subsequently, should an eua be sought for these diagnostic assays, their approval may be delayed due to the lack of validation and standardization required for deployment as a reliable diagnostic tool. lastly, the validation methods by which each assay was tested varied greatly, resulting in difficultly drawing direct comparisons amongst assays. keywords viral hemorrhagic fever; diagnostic test; product landscape acknowledgments this material is based upon work supported by the united states department of homeland security office of health affairs volunteer scholar program and by the united states department of homeland security under cooperative agreement number dhs 2010-st061-ag002. *noah hull e-mail: nhull@uab.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e123, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and 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pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. 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detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts developing semantically interoperable ph emergency preparedness data exchange nikolay lipskiy*, james tyson and jaqueline burkholder csels/dhis, cdc, atlanta, ga, usa objective the purpose of this project is to demonstrate progress in developing functional data models and semantic definitions (content standards) for data elements and value sets comprising information categories supporting ph emergency preparedness and response (epr). the objective is to explain the concepts and methods used to define core ph emergency management and preparedness and response functions, information exchange requirements (iers), data elements, and value sets to create a ph emergency operations center (eoc) minimum data set specification. the primary focus of this presentation is to describe the value of semantic data interoperability and provide operational examples of the value and return-on-investment gained through building semantically interoperable data exchange through content standardization. introduction effective prevention, detection, and rapid response to ph emergencies rely on sufficient and timely delivered information. ph eoc data flows are based on critical information requirements, addressing needs of eoc staff for timely delivered analytical products that provide situational awareness, event-specific data, event investigation tools, resource management etc1. the ability of ph eoc systems to automatically and accurately interpret meaning of the exchanged data depends on a level of semantic data interoperability and utilization of a common information exchange reference model (cierf) that conforms to established data standards. ph eoc data interoperability requires mutual development and close collaboration with partners to develop a ph epr cierf, common terminology and standardized vocabulary. methods the cdc’s situational awareness branch (sab) facilitates national activities on development ph eoc informatics through participation in the who eoc network (eoc-net) 2, and collaboration with national organizations and cdc partners on content standardization. the following sources were used for this analysis: 1) 26 content standards developed by national and international standard development organization, 2) who’s framework for a public health emergency operations centre2, and 3) ph eoc data requirements3 that were published by cdc’s sab. these data requirements were included into the cdc vocabulary and access distribution system (vads) 4, which serves as the primary vocabulary content browser for ph epr informatics. results in analyzing the ph epr content standards, the cdc’s sab arrived at the following results. the cdc eoc’s process of development and implementation content standards is based on the ph eoc critical information requirements. these requirement became business rules for the ph epr cierf. the current, version 2, of the ph epr cierf consists of 12 information modules including ph eoc minimum data set (mds), patient clinical observations, emergency medical systems (ems), data elements for emergency departments (deeds), who mds for health workforce registry, resource utilization message component (vocabulary for hospital resources), vocabulary for the national trauma standard. these ph epr cierf modules are interoperable and built on existing data standards. these modules were codified by vads and ready for utilization by international and national ph eoc partners. at the stage of this analysis the ph epr cierf codification schema was prepared for adding it into the logical observation identifiers names and codes (loinc) content standard. the current ph eoc mds version was released in september 2017. the common terminology and vocabulary that were included into this version are conformant with existing national and international content standards and specifications. comparatively to the previous version 1, the current ph eoc mds contains more than 60% new and updated terminology and value sets. added to the ph eoc mds version 2 new features are the situational analysis concept model, that also incorporates a nomenclature and structure for the public health eoc situational report (sitrep). also, the managing and commanding conceptual model was updated by adding concepts and vocabulary for the agency internal communication, including standardized knowledge repository for managing standard operating procedures (sop) and reports for leadership. the cdc’s sab directly supports the cdc surveillance data platform (sdp) and national organizations on development of electronic forms and form builders. these efforts will provide additional capabilities for collecting and electronically sharing standardized sa information utilizing web-enabled services and mobile capabilities. conclusions cdc’s eoc and division of emergency operations staff is improving the application of emergency management and ph practice in preparing and responding to emergencies through partnerships and coordinated work with standard development organizations (sdos) to add critical epr vocabularies to national and international standards. this work supports national emergency management organizations and is a reference source for the who eoc-net guiding documents supporting international efforts to strengthen global health security. keywords ph emergency preparedness; semantic interoperability; ph emergency information center informatics references 1. lipskiy n., tyson j. advancing ph emergency preparedness informatics to support emergency responses. online j public health inform. 2017; 9(1): e059. at: http://ojphi.org/ojs/index.php/ojphi/ article/download/7637/6158 2. who. framework for a public health emergency operations centre. interim document. november, 2015. at: http://www.who.int/ihr/ publications/9789241565134_eng/en/ 3. cdc. public health emergency preparedness. at: https://phinvads.cdc. gov/vads/searchvocab.action 4. cdc. public health information network. vocabulary access and distribution system (phin vads). at: https://phinvads.cdc.gov/ vads/searchvocab.action *nikolay lipskiy e-mail: dgz1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e37, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 1drexel university thomas r. kline school of law, philadelphia, pa, usa, philadelphia, pa, usa; 2department of health management & policy, drexel university dornsife school of public health, philadelphia, pa, usa objective explore the impact of law and government policy on the practice of syndromic disease surveillance (sys) in the united states. introduction sys systems have great potential to prevent morbidity, injury, and mortality by monitoring population health and providing realtime data to inform public health department decisions. electronic health information technology and federal, state and local incentives and investments have helped to facilitate their rapid and widespread implementation. as a result, sys systems operate in the context of laws and regulations that determine their success. an understanding of the effects of this legal environment is crucial to insuring that sys systems fulfill their potential. methods we conducted semi-structured, in-depth interviews with 55 sys stakeholders (e.g., state/local health department officials, legal counsel, health care facility personnel) from six jurisdictions and facilitated focus groups with national sys stakeholder organizations. all interviews and focus groups were audio-recorded, transcribed, and analyzed by two coders using thematic content analysis and nvivo software. we also identified and conducted a content analysis of relevant legal documents. results four main findings emerged. first, sys was largely conducted under the same legal authority as ‘traditional’ (e.g., notifiable disease) public health surveillance. in some jurisdictions, statutes and administrative rules have been amended to include “disease clusters” and “syndromes” as notifiable conditions or to mandate reporting of emergency department data. in some jurisdictions, these regulations were promulgated at the request of health care facilities reporting sys data that saw them as a legal safeguard. second, requirements for “meaningful use” of electronic health records promulgated under the health information technology for economic and clinical health act have eased health care facilities’ sys-related legal concerns and injected funding that has substantially increased the number of facilities that send sys data to state public health agencies and generally obviated the need for data use agreements. however, many public health agencies lack the staff to maximize the analytic potential of these data receive data only from emergency department and urgent care settings. some public health professionals explained that meaningful use regulations lack a mechanism to maintain the quality of sys data and that the incentive to satisfy meaningful use requirements has prompted some to outsource sys reporting to third party vendors, weakening inter-personal relationships between health department and hospital personnel and hindering follow-up investigations as a result. third, very few legal concerns were expressed related to the federal biosense program. some jurisdictions participate in biosense because they do not have the resources to conduct their own data collection and analysis, others do so to obtain financial resources for surveillance activities and to enhance the robustness of a national surveillance system. officials in several jurisdictions that do not participate cited the indemnification clause of the biosense data use agreement, which holds states liable in the event of a data breach,. others cited concerns regarding deidentification of the data. finally, we found that the primary barriers to maximizing the public health potential of sys systems were technical issues and limited health department resources, not legal concerns. although public health professionals, health care personnel, and public health legal professionals were aware of the legal context in which sys practice operates, legal barriers were viewed as surmountable and secondary to limited information technology and epidemiology resources. in particular, few concerns were expressed regarding legal requirements for protecting data privacy, including those issued under the health insurance portability and accountability act. conclusions sys has become an integral component of public health surveillance in the united states. federal meaningful use electronic health record incentives and state public health statutes that mandate submission of data have facilitated the growth of these systems. they have also alleviated most of the legal concerns that had been raised when these systems were first established. the jurisdictions that participate in biosense benefit from the financial and technical resources available through this program. keywords meaningful use; biosense; law; policy acknowledgments joe gibson, richard hopkins, jim buehler, laura streichert, rwjf *jonathan purtle e-mail: jpp46@drexel.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e31, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice 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michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline 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o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban 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evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? rahel abiy1, kassahun gashu2,4, tarekegn asemaw1, mebratu mitiku1, berhanu fekadie2,4, zeleke abebaw2,4, adane mamuye4,5 ashenafi tazebew3,4, alemayehu teklu3,4, fedilu nurhussien4,5 mihiretu kebede2,7+, fleur fritz 6+, binyam tilahun2,4*+ 1. university of gondar referral hospital, gondar, ethiopia 2. department of health informatics, university of gondar, gondar, ethiopia 3. department of pediatrics, university of gondar, gondar, ethiopia 4. ehealthlab ethiopia, university of gondar, gondar, ethiopia 5. faculty of informatics, university of gondar, gondar, ethiopia 6. institute of medical informatics, university of münster, münster, germany 7. leibniz institute for prevention research and epidemiology – bips, bremen, germany abstract background: anti-retroviral therapy (art) care is a lifelong treatment, which needs accurate and reliable data collected for long period of time. poor quality of medical records data remains a challenge and is directly related to the quality of care of patients. to improve this, there is an increasing trend to implement electronic medical record (emr) in hospitals. however, there is little evidence on the impact of emr on the quality of health data in lowresource setting hospitals like ethiopia. this comparative study aims to fill this evidence gap by assessing the completeness and reliability of paper-based and electronic medical records and explore the challenges of ensuring data quality at the anti-retroviral therapy (art) clinic at the university of gondar referral hospital in northwest ethiopia. methods: an institution-based comparative cross-sectional study, supplemented with a qualitative approach was conducted from february 1 to march 30, 2017 at the art clinic of the university of gondar hospital. a total of 250 medical records having both electronic and paper-based versions were collected and assessed. a national art registration form which consists of 40 art data elements was used as a checklist to assess completeness and reliability dimensions of data quality on medical records of patients on hiv care. kappa statistics were computed to describe the level of data agreement between paperbased and electronic records across patient characteristics. in-depth interviews were conducted using semi-structured questionnaires with ten key informants to explore the challenges related with the quality of medical records. responses of the key informant interviews were analyzed using thematic analysis. results: the overall completeness of medical records was 78% with 95% ci (70.8% 85.1%) in paper-based and 76% with 95%ci (67.8% 83.2%) emr. the data reliability measured in kappa statistics shows strong agreements on the socio-demographic data such as educational status 0.93 (0.891, 0.963), who staging a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi 0.86 (0.808, 0.906); general appearance 0.83 (0.755, 0.892) and patient referral record 0.87 (0.795, 0.932). the major challenges hindering good data quality was the current side by side dual data documentation practice (the need to document both on the paper and the emr for a single record), patient overload and low data documentation practice of health workers. conclusion: the overall completeness of art medical records was still slightly better in paper-based records than emr. the main reason affecting the emr data quality was the current dual documentation practice both on the paper and electronic for each patient in the hospital and the high load of patients in the clinic. the hospital management need to decide to use either the paper or the electronic system and build the capacity of health workers to improve data quality in the hospital. keywords: art data, emr, data quality, ethiopia, developing countries correspondence: binyam tilahun, phd, department of health informatics and ehealthlab ethiopia (www.ehealthlab.org), college of medicine and health sciences, university of gondar, p.o.box 196, gondar, ethiopia doi: 10.5210/ojphi.v10i2.8309 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. background medical records are a combination of both self-reported patient information and physician’s notes on diagnoses, care and treatment. it is used to store and communicate patients’ medical information among health care providers for continuing delivery of medical care [1]. by facilitating the communication among service providers it supports the quality of healthcare [2]. medical records are also used by government officials for planning, resource allocation, budgeting and other required policy decisions on the health infrastructure [3,4]. documentation and contents of data within an electronic medical record (emr) must be accurate, complete, concise, consistent and universally understood by users of the data, and must support the legal business record of the organization by maintaining the required parameters such as consistency, completeness and accuracy [5]. ensuring high data quality in medical records is fundamental to good clinical practice, program management and ultimately to policy decisions [6]. good quality of data is crucial for patient care and for monitoring the performance of health service delivery [2]. patients could directly or indirectly benefit from improved data quality, which in turn supports patient satisfaction in care and compliance in their art program [7]. the quality of documentation in the medical record is dependent upon the consistency and completeness of information entered into the record by all individuals involved in the patient’s care. increasing interest has been recently demonstrated in the establishment of electronic data systems in resource-limited settings to advance paper-based records [6].however, with the limited resources and capacities, most of the electronic systems are still side by side to the paper documentation which is creating burden on health workers [8]. a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi art care is a lifelong treatment, which needs accurate and reliable data. as the art program expands and the number of patients increases, the paper-based medical record continues to be overwhelmingly large and the need for emr in hiv care clinics is increasing [9]. however, the transformation of the medical record system from paper-based to emr largely relies on the quality of paper-based system. hence, improving and ensuring the quality of paper-based medical data is crucial before moving to emr. it is important to collect data for emr from paper-based medical records and gradually adopt emr to simplify the management of large medical records [10]. a comprehensive art medical record is a cornerstone of good quality and efficient patient care. in subsequent follow-up visits, it can provide a complete and accurate chronology of treatments, patient results and future plans for care [10,11]. investing in emr system has dramatically lowered the error rates of hiv care data and has improved client satisfaction [12] as well as quality of hiv care [13].global funding for the management of hiv has also increased the investment in emr systems for the management of complex data in monitoring the lifelong treatment of hiv infected people [11]. however, many electronic systems still rely on paper-based medical records for their data inputs and those transition, still practice side by side documentation on the paper and electronic system. both paper-based and emr systems is being practiced because of power fluctuation and inability of hospital to run full emr systems (14,15,16). based on generic tools developed by the who, paper-based medical records being used in resource-limited settings generally include longitudinal patient flow charts, longitudinal pre-antiretroviral therapy and art registers, pharmacy registers, appointment books, referral forms and outreach logs that are documented by doctors, nurses, pharmacists, social workers and data clerks [6].providing and monitoring hiv care and art requires complete and accurate documentation of patient visit information and laboratory test results [14]. studies from kenya, malawi, mozambique, cote d’ivoire and rwanda revealed that art medical records were found to have errors in clinical staging and missing or incomplete data about loss to art follow-up information [15]. the error rate often increases when inexperienced clinicians are introduced to a new procedure. studies show that lack of regulations, standards, guidelines, training and education, accreditation, clarity of data elements and data flows as well as health professionals’ and data clerks’ proficiency were determinant factors to the consistency and completeness of art paper-based and emr [6,16]. in lowresource settings, there is substantial investment and interest towards computerization but there is limited evidence on the effect of emrs on the quality of medical records at hiv clinics. because of this evidence gap, this study aims to assess the completeness and reliability of paper-based and electronic medical records and explore the challenges of ensuring data quality at the anti-retroviral therapy (art) clinic of the university of gondar referral hospital, northwest ethiopia. methods study area the study was conducted at the university of gondar referral hospital, the oldest and biggest referral hospitals serving for more than five million people in north west ethiopia. the hospital has many specialized clinics, including for chronic illnesses (cardiac disease, renal disease and diabetes), mental health and hiv care. during the study period, a total of 4,907 hiv patients were enrolled on art at the hiv care clinic of the hospital. eleven health professionals (nurses, health a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi officers and physicians), four data clerks, six case managers and seven treatment supporters were running the clinic. study design and period an institution-based comparative cross-sectional study was conducted to compare completeness and reliability of art records between paper-based and emr systems. both pediatric and adult art records were used. the quantitative study design was supplemented with a qualitative study to explore the challenges related to the quality of medical records in the clinic. data were collected from february 1 to march 30, 2017. all patients on art for at least six months, having complete records both paper-based and electronic (in the local emr system called smartcare) and having a follow-up visit in the art clinic were included in the study. there were a total of 4,907 art clients on follow up at the art clinic of the hospital. of these, 250 patient records were found to have both electronic and paper-based records. all these 250 patient records were included in the study. to measure the completeness and reliability of the records, checklists were developed based on the national art data elements. an overall rate of completeness was calculated by adding up the number of complete data elements per patient visit and dividing it by the total number of expected data elements. this proportion was averaged across all patient visits to get an overall rate of completeness for each visit type. paper-based medical records and emr of patients were reviewed to compare the reliability of specific data variables. qualitative data was obtained by in-depth interviews of key informants that included physicians, nurses, health officers, data clerks and art case managers who were purposively selected from the art clinic. a semi-structured questionnaire was used to conduct the key informant interviews. statistical analysis data was entered into epi info version 7 and then exported to spss version 20 for analysis. descriptive statistics were performed. chi-square test statistics were conducted to check statistically significant differences between emr and paper-based medical records’ data completeness. to declare statistical significance a p-value of less than 0.05 was used. reliability between paper-based and emr both for the enrolment and follow-up visits were determined using cohen’s kappa statistic. the level of agreement was used as defined by cohen’s kappa(poor agreement < 0.2, fair agreement = 0.2-0.40, moderate agreement =0.41-0.60, good agreement = 0.61-0.80 and very good agreement = 0.81-1.00) [14]. p-value of 5% or less was considered as statistically significant. the qualitative data was transcribed, coded and thematically synthesized using open code version 4.02 software. coding was based on predefined themes namely; organizational, provider and patient related factors. finally, a narrative analysis was carried out based on the established themes. a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi results socio-demographic characteristics out of 250 patient records,135 (54%) were from female patients. nearly one-third, 29(12%), of the patients were from pediatric art (aged less than 15 years). half, 51(20%), of the patients included in the study have no formal education and 76(30.4%) of them were from rural residence (table 1). table 1. distribution of socio-demographic characteristics of medical records of art patients (n=250), university of gondar referral hospital, northwest ethiopia variables n(%) sex male 101(40.4) female 135(54) not filled 14(5.6) age (years) 1-15 29(11.6) 16-30 86(34.4) 31-60 134(53.6) not filled 1(0.4) marital status never married 51(20.4) married 95(38) divorced 66(26.4) widowed 12(4.8) not filled 26(10.4) level of education no education 51(20.4) primary 66(26.4) secondary 88(35.2) tertiary 32(12.8) not filled 13(5.2) occupation unemployed 12(4.8) government employee 16(6.4) student 11(4.4) farmer 11(4.4) other 121(48.4) not filled 79(31.6) living area urban 171(68.4) rural 76(30.4) not filled 3(1.2) completeness of medical records in this study the overall completeness of medical records in the art clinic was195(78%) with 95%ci (70.8%-85.1%) on paper-based and 189 (76%) with 95%ci (67.8%-83.2%) on emr, pvalue 0.369 (see figure 1). a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi figure 1: overall completeness of paper-based vs. emr (n=250), university of gondar referral hospital, northwest ethiopia the average completeness of demographic data elements was 234(93.4%) in paper-based and 232(93.0%) in emr. among all data elements, the record completeness of patients’ sex was significantly lower236(94.4%), in paper-based than in emr249 (99.6%), (p-value 0.001). record completeness of patients’ occupation was significantly higher in paper-based than in emr(196(78%) vs 171(68%) (p-value 0.001) (table 2). table 2: completeness of socio-demographic variables in paper-based vs emr (n=250), university of gondar referral hospital, northwest ethiopia data elements completeness of records p-value paper-based electronic n % n % age 249 99.6 246 98.4 0.130 sex 236 94.4 249 99.6 0.001 marital status 224 89.6 226 91.2 0.668 religion 245 98.0 245 98.0 1.000 educational status 237 94.8 235 94.0 0.594 occupation 196 78.4 171 68.4 0.001 patient address 247 98.8 250 100 mean 234 93.4 232 93.0 0.625 regarding clinical data elements, the average completeness was 172 (68.8%) in paper-based and 164 (65.7%) in emr. completeness of patients’ weight data was higher with 194(78%) in paperbased than with 175(70%) in emr (p-value 0.009). similarly, the completeness of pregnancy status was significantly better with 184 (74%) in paper-based than with 155(62%) in emr (p-value <0.001). the completeness of who clinical staging data was significantly higher in paper-based 78% 76% 22% 24% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% paper-based emr d at a c om pl et en es s % complete incomplete a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi (77.6) than in emr (69.6). data regarding eligibility for art, reason for referral, and family hiv history had significantly higher level of completeness in emr than in paper-based medical records (table 3). table 3:completeness of art and hiv related variables in paper-based vs emr(n=250), university of gondar referral hospital, northwest ethiopia. completeness of records p-value data elements paper-based electronic n % n % months on art 170 68.0 171 68.3 0.892 weight 194 77.6 175 70.0 0.009 bmi 179 71.6 169 67.5 0.177 pregnancy status 184 74.0 155 62.0 <0.001 who staging 194 77.6 174 69.6 0.006 cpt dose 169 67.7 170 67.8 0.892 art adherence 116 46.2 129 51.4 0.100 art regimen 176 70.4 173 69.2 0.681 functional status 197 78.8 178 71.2 0.008 tb screen 197 78.8 180 72.0 0.017 cpt adherence 195 78.0 205 82.0 0.100 side effect 191 76.4 177 71.0 0.051 next visit date 194 77.6 173 69.3 0.004 oi screening 147 58.8 63 25.2 <0.001 eligibility for art 135 54.0 186 74.4 0.000 referral 239 95.6 240 96.0 0.747 reason for referral 140 56.0 198 79.2 0.000 disclosure 184 76.3 171 68.4 0.077 family hiv history 113 45.2 132 52.8 0.016 prophylaxis’s for oi 118 47.2 66 26.4 <0.001 mean 172 68.8 164 65.7 0.287 reliability of medical records data from a total of 250 art patients was reviewed for reliability of paper-based and electronic records. strong agreements were observed on socio-demographic data such as marital status 0.95 (95% ci: 0.912, 0.983); religion 0.93(95% ci: 0.853, 0.986) and educational status0.93 (95% ci: a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi 0.891, 0.963). similarly, who staging 0.86 (95% ci: 0.808, 0.906); general appearance 0.83 (95% ci: 0.755, 0.892) and patient referral 0.87 (95% ci: 0.795, 0.932) had strong agreements. patients’ address showed a good 0.74 (95% ci: 0.652, 0.828) level of agreement and hiv disclosure to family member had a moderate 0.68 (95% ci: 0.588, 0.759) level of agreement between paperbased and electronic records. the age of the patient had the lowest level of agreement (27.4. table 4: agreement of selected data elements between electronic and paper-based art records using kappa statistics (n=250), university of gondar referral hospital, northwest ethiopia data elements kappa coefficient (95% ci) percent agreement patient age 0.14 (0.093, 0.193) 27.4% patient sex 0.87 (0.812, 0.927)*** 93.2% marital status 0.95 (0.912, 0.983)*** 96.4% religion 0.93 (0.853, 0.986)*** 98% educational status 0.93 (0.891, 0.963)*** 94.8% occupation 0.50 (0.415, 0.569) 56.8% patient address 0.74 (0.652, 0.828)** 89.6% patient referral 0.87 (0.795, 0.932)*** 94.4% reason for referral 0.53 (0.433, 0.607) 66.8% hiv disclosure to family 0.68 (0.588, 0.759)* 80.8% family hiv data 0.56 (0.465, 0.652) 73.6% patient oi history 0.39 (0.241, 0.426) 64% past prophylaxis 0.46 (0.342, 0.540) 69.6% pregnancy status 0.59 (0.507, 0.675) 76% who staging 0.86 (0.808, 0.906)*** 89.6% general appearance 0.83 (0.755, 0.892)*** 92.8% eligibility criteria to art 0.65 (0.652, 0.741) 78.8% strong agreement k***=0.81-1, good agreement k**= 0.60-0.8, moderate agreement k*=0.400.60, data variables with true agreement between electronic and paper-based records are explained by the percent agreement. kappa statistics was also computed to assess the agreement between paper-based and electronic records through different art visits. the value of kappa agreement for cd4 count decreases from 0.893 on first visit to 0.205 on the sixth visit. the kappa agreement for art regimen data increased from 0.672 to 0.939 on first visit and third visit respectively (figure 2). a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi bmi-body mass index; ois-opportunistic infections; cd4cluster of differentiation 4; tbtuberculosis figure 2: kappa agreement of selected paper-based and electronic data elements on art patient medical records (n=250) on follow-up, university of gondar referral hospital, northwest ethiopia challenges on improving the quality of art records ten key informant interviews were conducted among key informants including five healthcare providers, four data clerks and one case manager. six of the key informants were females. the median (range)age and work experience of the key informants were 28 years (24-58) and 2 years (range 2-7) respectively. the key informants reported the major challenges hindering the quality of medical record data is the current dual documentation practice and the patient load in the hospital. one of the nurse in the art clinic said “we are overwhelmingly busy as we manage more than 100 patients per day. but after handling all this patient, we are expected to write all the patient history on the paper and later into the emr system which is creating a lot of burden on us” the attitude of care providers on the need to properly document data is also varied. one art nurse in the clinic said “isn’t it enough if we give proper care to the clients instead of wasting our time writing on the paper and the system?” updating the data elements as clients came for different visits is also not a usual practice in the hospital. a 29 years old senior physician who has worked for more than 5 years in the art clinic said that “art patient data may not be accurate, for example; bmi data variables recorded during the first visit of the patient are copied to visit1 visit2 visit3 visit4 visit5 visit6 bmi 0.58 0.563 0.594 0.573 0.593 0.555 ois 0.46 0.405 0.42 0.418 0.422 0.338 art regimen 0.672 0.874 0.939 0.91 0.863 0.819 cd4 0.893 0.333 0.271 0.472 0.122 0.205 tb screen 0.509 0.523 0.661 0.802 0.654 0.509 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 k ap pa c oe ff ic ie nt a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi the rest of consecutive visits without updated patient height and weight measurements in the paper-based medical record”. additionally, the technical support given to the art clinic to use the emr system was reported to be not enough. head of the art clinic said: “we know that migrating all the data to the emr will be important. we do not want to stop at 250 but the support we got is very minimal. we need back up power. if power goes, we have to go back to the paper passed records once we have all the data in the computer”. the overall results of the key informants outline that the current dual documentation practice and the daily patient load are the main challenges for health workers for not having quality data in the art clinic. discussion this study aimed to determine the completeness and reliability of paper-based medical record and emr and explore the challenges of ensuring data quality at the art clinic of the university of gondar referral hospital. this study showed the overall completeness of medical records in the paper-based version was still slightly higher than the emr. this indicates that there is no enough attention given in the implementation of the emr in the clinic as the clinic staff are currently documenting both on the paper and electronic systems. it is known that emrs are implemented in hospitals to improve quality of data, quality of care and the quality of the health service to patients. however, in this clinic, the quality of the data was still better in the paper based system. this may be due to the fact that the art clinic is using dual documentation systems (paper and electronic). the data quality on the paper finding is consistent with the study from mozambique [5] which reported 72% completeness in paper-based medical recording and relatively higher than the study conducted in cote d’ivoire [11] which reported 62% completeness on art follow-up. data completeness was also assessed specifically for a variety of hiv care data elements on both paper-based and emr of the art clinic. varying data completeness were observed among the specific data elements. for instance, completeness of patient weight data and pregnancy status were higher in paper-based than in emr. data elements such as who staging, functional status, tb screen, next visit date, opportunistic infections (ois)screening and prophylaxis’s for ois have also significantly better level of completeness in paper-based records than in emr. this result shows that the data on the paper were not properly transcribed in to the emr. as outlined by another study in ethiopia [11], this might be due to the workload introduced by the parallel documentation. a previous study in the study area reported that workload significantly affected documentation of the healthcare provided to patients [17]. in both paper-based medical record and emr, completeness of hiv care and art data elements was generally lower than the completeness of sociodemographic data elements. this might be due to the nature of the data by which sociodemographic data do not need frequent updates while it is important to update clinical data generated from every visit. furthermore, sociodemographic data is easier to obtain than clinical data. the difference might also be due to the attitude of care providers to document data [17]. a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi clinical parameters such as patients’ who clinical staging, general appearance, and patient referral have strong agreements. in addition, the consistency of patient address and hiv status disclosure shows a good and moderate level of agreement between paper-based and electronic records. further analysis on reliability on baseline and follow-up values shows a sharp reduction of reliability from 0.89 on the first visit to 0.21 on the most recent visit. this result reflects that the art follow-up data elements are not being updated. the major evidence in this research is the impact of dual documentation on the quality of the data both in the paper based and electronic systems. different previous evidences [18,19] show dual documentation practice is taking the time of health workers but in this research, we were able to identify that the quality of the data is affected as the result of the dual documentation. many emr implementations in developing countries (22) report dual documentation practice but it will be advisable to either use the paper-based record or to totally transition to electronic documentation systems to avoid double effort on health workers. conclusion the overall art data quality was still slightly better in paper-based records than the electronic medical record system. the main reason affecting the emr data quality was the current dual documentation practice both on the paper and electronic for each patient in the hospital. the hospital management need to decide to use either the paper or the electronic system so that health workers can save time by single documentation practice. trainings and continuous support to health workers is recommended to build the capacity of health workers on data documentation practices limitations one of the limitation of the study is that the number of data elements used to compare the paper and electronic system is small. to mitigate it, we supplement the study with qualitative data using key informants. more robust study using large sample size and advanced statistical modeling approach is recommended and planned by our team in the future. competing interest the authors declare that they have no competing interests. authors’ contributions ra, bt, kg contributed the initiation, design of the study, collection, analysis and interpretation of the data. bt and kg were also her supervisors. ta, mm, bf, za, am, fn, at, at contributed in the qualitative data analysis and in thoroughly revising the manuscript. mk and ff thoroughly revised the draft and substantially contributed in the finalization of the manuscript. all authors read and approved the final manuscript. mk and bt share senior authorship. ethical approval ethical clearances were received from the institute of review board of university of gondar and from the university of gondar referral hospital chief executive officer. de-identified patient charts using the unique art number were used for analysis and names and other patient identifiers were not used. a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e212, 2018 ojphi acknowledgements we would like to acknowledge the institute of public health and department of health informatics at the 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https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25769683&dopt=abstract https://doi.org/10.1093/jamia/ocu038 a comparison of electronic medical record data to paper records in antiretroviral therapy clinic in ethiopia: what is affecting the quality of the data? background methods study area study design and period statistical analysis results socio-demographic characteristics completeness of medical records reliability of medical records challenges on improving the quality of art records discussion conclusion limitations competing interest authors’ contributions ethical approval acknowledgements references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 1public health foundation of india, gurgaon, india; 2university college london, london, united kingdom objective to identify and validate methods and scales measuring determinants of social inequalities in health in context to indian adolescents introduction health inequalities are major global public health problem and varies within and between countries [1]. lmics particularly india, are undergoing a phase of rapid economic development leading to an increase in informal settlements or urban slums [2]. these settlements exhibits extreme poverty and suffers from adverse health outcomes. the worst affected are the adolescents because it is a crucial and most vulnerable age when health behaviours and lifestyle choices are established which affects their current and future health [3]. the current health system in many of the developing countries are outdated and have either rudimentary health statistics or none. there is lack of standardized and reliable questionnaires to capture various behavioural aspects of subjective health of the population in india. thus, we aim to identify various measures of determinants of social inequalities relevant to the indian adolescent population context. methods we adopted scales and questions from internationally validated questionnaires, and conducted reliability and validity tests through a cross sectional study on 1386 adolescents residing in diverse areas of residence (slums, middle class and resettlement colonies) and standardized them to be used on indian adolescent population. questionnaire included important determinants of health: degree of neighbourhood social capital, level of social support, health related behaviours, self-rated health and key socio-demographic of adolescents. the social capital scale was adapted from an adolescent social capital scale used by gage et al (2005) [4] and showed a reasonable internal consistency (cronbach’s alpha = 0.63) when tested on indian adolescents. social support scale was adapted from the adolescent social support scale developed by seidman et al (1995) [5] and showed excellent internal consistency (cronbach’s alpha = 0.86) when tested on study population. questions on health related behaviours were taken from who hbsc survey which is a survey of school children undertaken periodically in more than 40 countries of the world [6]. results a social gradient in health inequalities was observed with a sequentially detrimental health outcome at each lower level of areas of residence. conclusions the questionnaire was observed sensitive to lmics setting and consistent with both international as well as indian adolescents context. studying both clinical and subjective health outcomes in a population can provide important insights about different explanations of various indicators of health, highlighting the complex nature of inequalities. the questionnaire is useful in identifying social inequalities in health to advance health equity among adolescents. conceptual framework of urban health (galea et al., 2005) keywords social inequalities; methods; adolescents acknowledgments this work was supported by a wellcome trust capacity strengthening award to the public health foundation of india and a consortium of uk universities. references 1. marmot m. public health social determinants of health inequalities. lancet. 2005;365:1099–104. 2. vlahov d, freudenberg n, proietti f, ompad d, quinn a, nandi v, et al. urban as a determinant of health. j. urban heal. 2007;84. 3. wiefferink ch, peters l, hoekstra f, ten dam g, buijs gj, paulussen tgwm. clustering of health-related behaviors and their determinants: possible consequences for school health interventions. prev. sci. 2006. p. 127–49. 4. gage jc, overpeck md, nansel tr, kogan md. peer activity in the evenings and 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for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, 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kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant 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sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts prescription opioid abuse: gleaning insights from hospital and vital records data reka sundaram-stukel*2, 1, ousmane diallo2, benjamin wiseman2 and richard e. miller2 1department of agriculture and applied economics, university of wisconsin-madison, madison, wi, usa; 2wisconsin state department of public health, madison, wi, usa objective in this paper we used hospital charges to assess costs incurred due to prescription drug/opioid hospitalizations introduction there is a resurgence in the need to evaluate the economic burden of prescription drug hospitalizations in the united states. we used the wisconsin 2014 hospital discharge data to examine opioid related hospitalization incidence and costs. fentanyl, a powerful synthetic opioid, is frequently being used for as an intraoperative agent in anesthesia, and post-operative recovery in hospitals. according to a 2013 study, synthetic fentanyl is 40 times more potent than heroin and other prescription opioids; the strength of fentanyl leads to substantial hospitalizations risks. since, 1990 it has been available with a prescription in various forms such as transdermal patches or lollipops for treatment of serious chronic pain, most often prescribed for late stage cancer patients. there have been reported fatal overdoses associated with misuse of prescription fentanyl. in wisconsin number of total opioid related deaths increased by 51% from 2010 to 2014 with the number of deaths involving prescription opioids specifically increased by 23% and number of deaths involving heroin increased by 192%. we hypothesized that opioids prescription drugs, as a proxy of fentanyl use, result in excessive health care costs. methods opioid hospitalizations was defined as any mention of the icd9 codes (304,305) in any diagnostic field or the mention of (:e935.09) on the first listed e-code. our analysis used the heckman 2-stage model, a method often used by economists in absence of randomized control trials. in presence of unobserved choice, for example opioid related hospitalizations, there usually is a correlation between error in an underlying function (fentanyl prescription) and an estimated function (hospital charges) that introduces a selection bias. heckman treats this correlation between errors as an omitted variable bias. therefore, we estimate a heckman two step model using hospitalization: where the selection function is the probability of being hospitalized for synthetic opioid via logistic regression. finally, we estimate the hospital charges realized if the patient was given opioids. results male patients are significantly more likely to be hospitalized for opioids than are female patients; while white patients are significantly more likely to be admitted for opioid usage than other racial groups. we also find that comorbid factors, such as mental health, significantly impact hospital charges associated with opioid use. we find that persons with private health insurance are associated with higher rates of opioid use. conclusions using a heckman two step approach we show that comorbid conditions such as mental health, hepatitis c, injuries, etc significantly affect hospital charges associated with hospitalization. we use these findings to explore the impact of the 2013 rule mandating doctors share opioid prescription information on the incidence of opioid related death and hospital charges associated with opioid prescriptions. this work is policy relevant because alternatives to opioid prescription such as meditation, pain management therapies may be relevant. estimates for opioid hospital charge using 2 stage heckman significance levels ***99%, **95%, *90% online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e137, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts figure 3. marginal effects of heckman 2-step opioid use keywords opioid overdose; heckman 2-step; hospital discharge data acknowledgments we thank the office of health informatics and epidemiology conference participants for many useful comments. references rice b., kirson n. shei a. cummings a., bodnar k., birnbaum h., ben-joseph r. (2014). “estimating the costs of opioid abuse and dependence from an employer perspective: a retrospective analysis using administrative claims data. applied health economics health policy 12:435-446 *reka sundaram-stukel e-mail: reka.sundaramstukel@dhs.wisconsin.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e137, 2017 isds16_abstracts-final 190 isds16_abstracts-final 191 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 1division of public health, epidemiology & informatics unit, nebraska department of health and human services, lincoln, ne, usa; 2university of nebraska-lincoln, lincoln, ne, usa objective this pilot study evaluates nebraska department of health and human services (ndhhs) emergency department (ed) syndromic surveillance (sys) data quality by cross-validating reported external cause of injury codes (ecodes) associated to racial/ethnic injury health disparities in nebraska. the percent completeness of core data elements in sys data and hospital discharge data (hdd) was also determined. introduction achieving health equality is a national priority. the surveillance of health disparities in minority populations is key for the advancement of health equality. however, the need for improvement in documentation of race and ethnicity has been identified across various public health data sets. currently, due to the lack of reporting of race and ethnicity in hdd, the ndhhs mainly depends on analyses of the statewide behavioral risk factor surveillance system and vital records data for the surveillance of health disparities among minority populations. an alternative data set that might help inform the surveillance of health disparities is sys data. this near-real–time electronic health record data is characterized by required core data elements that provide information about the date and time of patient encounter, treating facility, clinical information, and patient demographics. previously, we demonstrated statistically significant correlations between the 2011 and 2012 ndhhs ed sys and ed hdd data for icd9-cm ecodes corresponding to motor vehicle crash related injury, which is a relevant cause of health disparities. our new objective was to determine the reporting consistency of icd9-cm ecodes associated with other injury related health disparities between 2013 ndhhs sys and hdd ed data. we also sought to determine if near-real–time ed and ip sys data provide a more complete documentation of race and ethnicity than hdd. methods completeness of core data elements was assessed for 2013 ndhhs ed hdd and ip, and for 2013-2015 ndhhs sys ed and ip data from hospital a. core data elements for the sys and hdd datasets included facility id, patient id, visit id, visit date, patient’s age, patient’s sex, patient’s race, patient’s ethnicity, patient’s zip code of residence, and diagnostic codes. the completeness of chief complaint was also analyzed for sys data. the timeliness of sys data was determined by calculating the mean time in hours between patient visit and receipt of ed ss record at ndhhs. the consistency of icd9-cm reporting was assessed by comparing 2013 ndhhs sys ed to 2013 hdd ed data from hospital a. the cross-validation focussed on 2 causes of racial/ethnic injury related health disparities: assault-related injury, and suicide and self-inflicted injury. the corresponding icd9-cm ecodes were: e960–e969, e979 and e999.1 (assault-related injury), and e950-e959 (suicide and self-inflicted injury). pearson correlation coefficients were used to compare the frequency distribution of monthly counts for the specified diagnostic codes. results for hospital a 2013 ndhhs sys data, the percent of completeness of most core data elements was 100%. the percent of completeness for race and ethnicity was 100% and 92% respectively for the 2013 sys ip data and 100% and 0% for ed data. an improvement in the percent of completeness for ethnicity was observed for the 2015 ip and ed sys data, with 100% for both the 2015 ndhhs ed and ip sys data. on the other hand, for the 2013 ed and ip hdd data, while the completeness of most core data elements was 100%, a 0% of completeness was observed for race and ethnicity. statistically significant correlations were observed between hospital a 2013 ed hdd and ndhhs ed sys data for assault-related injury (r = 0.72, p = 0.008), and suicide and self-inflicted injury (r = 0.76, p = 0.004). the timeliness of reporting was 12 hours for the 2013 ndhhs sys ed data and 2 weeks for the sys ip data. conclusions results suggest that ndhhs sys 2015 data provides more complete documentation of race and ethnicity than hdd. in addition, significant correlations were observed for the conditions analyzed. therefore, the ability to identify and describe injury inequalities can potentially be improved by using sys ed data to complement the surveillance of health disparities. these results also indicate that sys ed data could also be used for the timely identification and monitoring of intentional injuries in nebraska. keywords syndromic surveillance; emergency department; health disparities; injury acknowledgments other contributors: bryan buss, dvm, mph; gary white, and anthony zhang, ma *sandra gonzalez e-mail: sandra.gonzalez@nebraska.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e59, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* 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r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts lead poisoning surveillance: a collaborative effort between epidemiology and wic kaylee hervey*1, christine steward1, dante corimanya1, sandra reichenberger2, whitney crager1 and adrienne byrne-lutz1 1epidemiology, sedgwick county health department, wichita, ks, usa; 2children and family health, sedgwick county health department, wichita, ks, usa introduction there is no safe level of lead in the body, and elevated lead in children can lead to decreased intelligence quotients (iq) and behavioral problems. the american academy of pediatrics recommends lead testing of children with a positive risk assessment. children who live in low socioeconomic areas may be at higher risk for lead exposure. as recent events have shown, having an elevated lead poisoning surveillance system can be critical to ensure that there is not a community-wide lead exposure. to reach the children that may not have been screened by a primary care physician, on march 1, 2016 the sedgwick county health department women, infants, and children (wic) program began offering lead screenings to all children in the wic program and their mothers. per centers for disease control and prevention (cdc) guidelines, the sedgwick county health department epidemiology program (epi) investigates anyone who has an elevated blood lead test (5 µg/dl or greater). there are two types of lead tests – screening (capillary finger stick) and confirmatory (venous blood draw). methods sedgwick county wic clients are offered screening lead testing at their wic appointments. education to reduce lead exposure is provided at the time the test is performed. the filter papers used in this testing are sent to the kansas health and environmental laboratories (khel) for analysis, and the results are reported to epi. epi reports the lead testing results to wic, who track the results in their patient charts. epi receives khel results of <5 µg/dl via fax and results of >= 5 µg/dl via electronic laboratory reporting in the epitrax disease investigation software maintained by the kansas department of health and environment. epi notifies any wic clients with results >= 5 µg/dl, while wic staff notify all other clients about their results. education is provided to the client a second time by epi staff and/or a wic nurse or dietician. for clients with elevated blood lead tests, epi interviews the case or guardian using an enhanced blood lead exposure questionnaire which asks about potential lead exposures, both in the home and at other locations (work, hobbies, etc.). if only a screening test was performed, epi recommends confirmatory testing. wic lead testing program measures, including types of exposures identified, are monitored over time using data obtained from epitrax. results between march 1 and july 21, of the 2,150 wic clients offered lead testing, 89% self-reported never having received a lead test previously. of the 1,427 clients with wic lead screening results, seven cases of elevated blood lead were identified. of the seven, five did not have a previous elevated lead test in epitrax. the average screening test result was 8.6 µg/dl (range 6.8 to 13.4). the average age of the cases was 2 years (range 1-4). of the seven cases, two (29%) were confirmed as 10.0 and 11.0 µg/dl through venous testing at their primary care provider’s office. the remaining five cases have not received confirmatory testing. one of the three cases interviewed reported that their babysitter lived in an old home, which could be the source of lead exposure. while interviewing a child’s guardian about an elevated 2016 test (7.9 µg/dl), epi discussed a previous 2015 elevated lead test (6.0 µg/dl) of which the client’s guardian was unaware. conclusions the ease of access to lead testing in the sedgwick county wic program and the joint effort between wic and epi to implement an enhanced lead poisoning surveillance system identified six children with elevated lead levels whose guardians did not know they had elevated lead levels. this new surveillance program educates wic parents about lead, determines the lead levels in children for guardian knowledge (low level) and further follow-up (elevated level), and identifies lead exposures of wic children with elevated lead tests. keywords lead; surveillance system; wic *kaylee hervey e-mail: kaylee.hervey@sedgwick.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e112, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts zika virus speed and direction: reconstructing zika introduction in brazil kate zinszer2, kathryn morrison1, john s. brownstein2, 4, fatima marinho5, santos f. alexandre5 and elaine o. nsoesie*3 1mcgill university, montreal, qc, canada; 2boston children’s hospital, boston, ma, usa; 3university of washington, seattle, wa, usa; 4harvard medical school, boston, ma, usa; 5ministry of health, brasilia, brazil objective to estimate the velocity of zika virus disease spread in brazil using data on confirmed zika virus disease cases at the municipal-level. introduction local transmission of zika virus has been confirmed in 67 countries worldwide and in 46 countries or territories in the americas (1,2). on february 1, 2016 the world health organization declared a public health emergency of international concern due to the increase in microcephaly cases and other neurological disorders reported in brazil (2). several countries issued travel warnings for pregnant women travelling to zika-affected countries with brazil, colombia, ecuador, and el salvador advising against pregnancy (3-7). the risk of local transmission in unaffected regions is unknown but potentially significant where competent zika vectors are present (8) and also given the additional complexities of sexual transmission and population mobility (9,10). despite the rapid spread of zika virus across the americas and global concerns regarding its effects on fetuses, little is known about the pattern of spread. knowledge of the direction and the speed of movement of disease is invaluable for public health response planning, including the timing and placement of interventions. methods data for this analysis were obtained from the brazil ministry of health and consisted of confirmed cases of zika virus disease. the centroids of the municipalities were taken in meters from the shapefiles and used to perform a surface trend analysis. surface trend is a spatial interpolation method used to estimate continuous surfaces from point data. the continuous surface of time to infection was estimated by regressing it against the x and y coordinates. time was in days and x and y coordinates were meters. parameters were estimated using least squares regression and velocity (in km per day) was obtained by inverting the final magnitude of the slope. results data provided from the brazil ministry of health on may 31, 2016, indicated that zika had been confirmed in 316 of the 5,564 municipalities in brazil representing 26 states, with six additional municipalities identified from other reporting sources. our models indicated a southward pattern of introduction of zika starting from the northeast coast towards the southeastern coastal states of rio de janerio, espírito santo, and são paulo. there was also a pattern of western movement towards bolivia. overall, the average speed of diffusion was 42.1 km/day across all models was 6.9 km/day to a maximum of 634.1 km/day. the municipalities in the northeast and north regions had the slowest speeds whereas the municipalities in the central-west and southeast regions had the highest speeds. this is due to proximity of cases in time and space, with more cases having occurred closer in time and over larger areas in south, southeast, and central-west regions resulting in faster rates of introduction. conclusions the average speed of spread was 42 km per day and it took approximately five to six months for zika to spread from the northeastern coast to the southeastern coast and western border of brazil. the rapid spread of zika can help us understand its possible future directions and the pace at which it travels, which are key for targeted mosquito control interventions, public health messaging, and travel advisories. a multi-country analysis is needed to understand the continental spatial and temporal patterns of dispersion of zika virus. keywords zika virus; spatial; brazil; surface trend analysis; velocity references 1. centers for disease control and prevention. all countries & territories with active zika virus transmission. http://www.cdc.gov/zika/geo/ active-countries.html. 2. world health organization. zika virus and complications. http://www. who.int/emergencies/zika-virus/en/. 3. burke rm, pandya p, nastouli e, gothard p. zika virus infection during pregnancy: what, where, and why? br j gen pract. 2016;66:122-3. 4. government of canada. zika virus infection: global update. http://travel.gc.ca/travelling/health-safety/travel-health-notices/152?_ ga=1.38883366.447391562.1463768601. 5. goeijenbier m, slobbe l, van der eijk a, de mendonça melo m, koopmans mp, reusken cb. zika virus and the current outbreak: an overview. neth j med. 2016;74:104-9 6. world health organization. information for travellers visiting zika affected countries. http://www.who.int/csr/disease/zika/informationfor-travelers/en/. 7. alter c. why latin american women can’t follow the zika advice to avoid pregnancy [internet]. new york, ny: time magazine, january 29, 2016. http://time.com/4197318/zika-virus-latin-america-avoidpregnancy/. 8. messina jp, kraemer mu, brady oj, et al. mapping global environmental suitability for zika virus. elife. 2016;5:pii:e15272. 9. basarab m, bowman c, aarons ej, cropley i. zika virus. bmj. 2016;352:i1049. 10. broutet n, krauer f, riesen m, khalakdina a, almiron m, aldighieri s, et al. zika virus as a cause of neurologic disorders. n engl j med. 2016;374:1506-9. *elaine o. nsoesie e-mail: en22@uw.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e125, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 and elizabeth j. mayer-davis1 1univ. of north carolina, chapel hill, nc, usa; 2medical univ. of south carolina, charleston, sc, usa; 3emory univ., atlanta, ga, usa; 4wake forest univ., winston-salem, nc, usa; 5kaiser permanente southern california, pasadena, ca, usa; 6univ. of colorado, denver, co, usa; 7univ. of washington, seattle, wa, usa; 8cdc, atlanta, ga, usa objective the study goal was to develop an efficient surveillance approach for childhood diabetes across two large southeastern us public academic health care systems, using electronic health record (ehr) data. introduction traditional surveillance methods, such as registries that require manual validation of every diabetes case or questionnaires, are resource intensive and associated with considerable delay in reporting results. an ehr-based surveillance system may be more efficient for sustained monitoring of the incidence and prevalence of childhood diabetes, so as to inform health care needs for this growing population. methods the study population at the medical university of south carolina (musc) included all children <20 years of age as of december 31, 2012 who were seen by a health care provider for any reason between july 1, 2012 and december 31, 2012. at the university of north carolina health care system (unc-hcs), we included all children <20 years of age as of december 31, 2011 who were seen by a health care provider for any reason in 2011. ehr data included demographics, billing codes, outpatient medications prescription lists, laboratory test results and patient problem lists. presumptive diabetes cases were identified as those having ≥1 of the following 5 indicators in the past 3.5 years, including elevated hba1c, elevated blood glucose, diabetes related billing codes, diabetes related patient problem lists or outpatient medications. ehrs of the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined by the presence of a diabetes diagnosis in the ehrs written by health care providers. algorithms for identifying diabetes cases overall and classifying type were either pre-specified or derived from classification and regression tree analysis. surveillance approach was developed based on the best algorithms identified. results we used pre-specified algorithms derived from billing codes only and targeted manual ehrs review to develop a stepwise surveillance approach (figure 1a and 1b). the sensitivity and positive predictive value for this surveillance approach in both health care systems were generally ≥90% for ascertaining diabetes cases overall, and classifying cases with type 1 or type 2 diabetes (table 1). this stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. conclusions ehr data may be used to establish an efficient and accurate approach for large scale surveillance for childhood diabetes, although some manual effort is still needed. table 1. the performance of the stepwise surveillance approach *other diabetes cases included diabetes cases that were neither type 1 nor type 2. keywords childhood diabetes; surveillance; electronic health record online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e45, 2016 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts acknowledgments this study is funded by the cdc (pa numbers 00097, dp-05-069, and dp-10-001) and supported by the national 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goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts updates to the implementation guide for syndromic surveillance peter hicks*1, emilie lamb2 and dave trepanier2 1cdc, atlanta, ga, usa; 2isds, boston, ma, usa objective to describe the process to update the implementation guide (ig) for syndromic surveillance via community and stakeholder engagement and highlight significant modifications as the ig is vetted through the formal hl7 balloting process. introduction in 2011, the cdc released the phin implementation guide (ig) for syndromic surveillance v.1 under the public health information network. in the intervening years, new technological advancements, ehr capabilities as well as epidemiological and meaningful use requirements have led to the periodic update and revision of the ig through informal and semi-structured solicitation and collection of comments from across public health, governmental, academic, and ehr vendor stakeholders. following the ig v.2.0 release in 2015, cdc initiated a multi-year endeavor to update the ig in a more systematic manner and released further updates via an erratum and a technical document developed with nist to clarify validation policies and testing parameters. these documents were consolidated into the message guide v.2.1 release and used to inform the development of the nist syndromic surveillance test suite (http://hl7v2-ss-r2testing.nist.gov/ss-r2/#/home), validation test cases, and develop a new rules-based ig built using nist’s implementation guide authoring and management tool (igamt). as part of a cooperative agreement initiated in 2017, cdc and isds built upon prior activities and renew efforts in engaging the syndromic surveillance community of practice for comment on the ig with the goal of having the final product to become an “hl7 v 2.5.1 implementation guide for syndromic surveillance standard for trial use” following a formal hl7 balloting process in 2018. methods isds coordinated a multi-stakeholder working group to revisit the consolidated ig, v.2.1 and began to collect structured comments via an online portal, which facilitated the documentation, tracking, and prioritization of comments for developing consensus and ultimately reconciliation and resolution when there were errors, conflicts or differing perspectives. 132 comments were received during the initial review period (april – july 2017) with 16 elements captured for each comment which included: subject, request type, clinical venue, name, ig section, priority, working & final resolution (fig. 1). the online portal also allowed for members of the message guide workgroup to provide feedback directly to one another through a ‘conversation tab’, this has been an important feature in teasing out the underlying concerns and issues with a given comment across different local, state, and private sector partners which many have differing institutional perspectives and state or locally derived requirements (fig. 2). some comments were able to be fully described and resolved using this feature. following the initial comment period, isds initiated a weekly webinar-based review process to delve into specific issues in an in-depth manner. in general, approximately 12 comments were addressed on a given call. each week isds staff would lead the webinars structured around similar comment types (e.g. values sets, dg1 segments, in1 segments, conformance statements, etc.). this efficiently leveraged the expertise of individuals and institutions with concerns revolving around a specific domain, messages segment, or specification described within the ig. comments for which consensus and resolution was achieved would be “closed-out’ on the portal inventory and new assignments for review would be disseminated across the message guide workgroup for consideration and discussion during the subsequent review calls. results to date this review process has identified and updated a wide-range of specification and requirements described within the ig v.2.0. these include: specifications for persistent patient id across venues of service, inclusion of the icd-10-cm value set for diagnosis, removal of the icd-9-cm requirement for testing and messages, modification of values such as pregnancy status, travel history, and medication list from “o” to “re”, and the update of phin vads value sets. conclusions the results of this multi-agency comment and review process will be synthesized and compiled by isds. the updated version of the message guide (re-branded to the hl7 v 2.5.1 implementation guide for syndromic surveillance) will be made available to the public health community by november 2017, when a second round of review and commentary will be initiated. this systematic and structured review and documentation process has allowed for the synthetization and reconciliation of a wide range of disparate specifications, historical hold-overs, and requirements via the perspectives of a diverse range of public health partners. as we continue to move through this review process we believe that the final hl7 balloted “standard for trial use” ig 2.5 will be a stronger and more extensible product in supporting syndromic surveillance activities across a wider and more diverse range of clinical venues, ehr implementations, and public health authorities. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords messaging guide; syndromic surveillance; hl7 *peter hicks e-mail: phicks@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e63, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts dengue, chikungunya & zika virus in va caribbean hcs, nov. 2015-aug. 2016 patricia schirmer*1, gina oda1, cynthia a. lucero-obusan1, mark winters1, 2, sonia saavedra3, mirsonia martinez3 and mark holodniy1, 2 1department of veterans affairs, palo alto, ca, usa; 2stanford university, palo alto, ca, usa; 3va caribbean healthcare system, san juan, pr, usa objective we describe surveillance for dengue virus (denv), chikungunya virus (chikv) and zika virus (zikv) in va caribbean healthcare system (vachs) from the start of zikv transmission in puerto rico. introduction denv, chikv and zikv are all transmitted by mosquitoes and have occurred in outbreaks in the caribbean. common symptoms (which can be severe and disabling) are similar among the 3 viruses and include fever, joint pain/swelling, headache, muscle pain and rash. in december 2015, the first endemic case of zikv infection was reported by vachs. since that time, an increasing number of zikv infections have been reported in puerto rico. due to the growing zikv outbreak, we performed ongoing testing and surveillance. methods denv, chikv and zikv infection surveillance from november 2015 august 2016 at vachs was performed from 2 primary data sources: (1) va praedicotm public health surveillance system for laboratory results documented within the electronic medical record (emr) and (2) communications with facility clinicians for laboratory results not entered into the emr. laboratory tests were considered unique tests if they were performed >30 days apart. a positive test was defined as a positive igm or rt-pcr test result. serial infection was defined as infection with chikv and zikv or chikv and denv. potential cross-reaction of assays was defined as positive denv and zikv igm results within 30 days. demographic and clinical data was obtained on all positive zikv cases including cases with serial infection. results for the time period evaluated, 2,218 unique tests were performed for denv (744), chikv (741), and zikv (744). five hundred thirty three positive tests were identified for: denv (34), chikv (55) and zikv (444) (figure 1). demographic and virus breakdown of testing is shown in table 1. percent positive range for denv testing was 0-23%, for chikv was 0-14%, and for zikv 0-73%. temporal timing of positive tests for each virus by percent positive is depicted in figure 2. serial infections were identified in 39 patients (1 chikv igm/zikv igm/pcr+, 7 chikv igm/zikv igm+, 26 chikv igm/ zikv pcr+, 2 chikv igm/zikv pcr/denv igm+, 2 denv igm/ chikv igm+, 1 denv igm/chikv igm/zikv igm+). the average age of patients with serial infection was 63.5 years (range 33-85) and occurred in 4 females and 35 males. 21 patients were identified with positive denv and zikv igm tests, which could represent crossreactivity between the assays or co-infection. confirmatory testing of these specimens is pending. conclusions laboratory surveillance demonstrated co-circulation of all 3 viruses, although zikv was the dominant infection identified during this time period. in addition, laboratory data suggests serial infection with chikv and zikv while also identifying patients with probable cross-reaction between denv and zikv tests. additional investigation is needed to determine whether patients with serial infection have increased severity of symptoms or different clinical outcomes. since number of zikv infections continues to increase and all 3 viruses continue to circulate, continued public health messaging remains important. figure 1 table 1: va caribbean healthcare system dengue virus (denv), chikungunya virus (chikv) and zika virus (zikv) demographics and testing, nov. 2015-aug. 2016 figure 2 keywords department of veterans affairs; dengue; chikungunya; zika; electronic laboratory data *patricia schirmer e-mail: patricia.schirmer@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e98, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts a spatial biosurveillance synthetic data generator in r drew levin* and patrick finley sandia national laboratories, albuquerque, nm, usa objective to develop a spatially accurate biosurveillance synthetic data generator for the testing, evaluation, and comparison of new outbreak detection techniques. introduction development of new methods for the rapid detection of emerging disease outbreaks is a research priority in the field of biosurveillance. because real-world data are often proprietary in nature, scientists must utilize synthetic data generation methods to evaluate new detection methodologies. colizza et. al. have shown that epidemic spread is dependent on the airline transportation network [1], yet current data generators do not operate over network structures. here we present a new spatial data generator that models the spread of contagion across a network of cities connected by airline routes. the generator is developed in the r programming language and produces data compatible with the popular ̀ surveillance’ software package. methods colizza et. al. demonstrate the power-law relationships between city population, air traffic, and degree distribution [1]. we generate a transportation network as a chung-lu random graph [2] that preserves these scale-free relationships (figure 1). first, given a power-law exponent and a desired number of cities, a probability mass function (pmf) is generated that mirrors the expected degree distribution for the given power-law relationship. values are then sampled from this pmf to generate an expected degree (number of connected cities) for each city in the network. edges (airline connections) are added to the network probabilistically as described in [2]. unconnected graph components are each joined to the largest component using linear preferential attachment. finally, city sizes are calculated based on an observed three-quarter powerlaw scaling relationship with the sampled degree distribution. each city is represented as a customizable stochastic compartmental sir model. transportation between cities is modeled similar to [2]. an infection is initialized in a single random city and infection counts are recorded in each city for a fixed period of time. a consistent fraction of the modeled infection cases are recorded as daily clinic visits. these counts are then added onto statically generated baseline data for each city to produce a full synthetic data set. alternatively, data sets can be generated using real-world networks, such as the one maintained by the international air transport association. results dynamics such as the number of cities, degree distribution powerlaw exponent, traffic flow, and disease kinetics can be customized. in the presented example (figure 2) the outbreak spreads over a 20 city transportation network. infection spreads rapidly once the more populated hub cities are infected. cities that are multiple flights away from the initially infected city are infected late in the process. the generator is capable of creating data sets of arbitrary size, length, and connectivity to better mirror a diverse set of observed network types. conclusions new computational methods for outbreak detection and surveillance must be compared to established approaches. outbreak mitigation strategies require a realistic model of human transportation behavior to best evaluate impact. these actions require test data that accurately reflect the complexity of the real-world data they would be applied to. the outbreak data generated here represents the complexity of modern transportation networks and are made to be easily integrated with established software packages to allow for rapid testing and deployment. randomly generated scale-free transportation network with a power-law degree exponent of λ=1.8. city and link sizes are scaled to reflect their weight. an example of observed daily outbreak-related clinic visits across a randomly generated network of 20 cities. each city is colored by the number of flights required to reach the city from the initial infection location. these generated counts are then added onto baseline data to create a synthetic data set for experimentation. keywords simulation; network; spatial; synthetic; data acknowledgments funding provided by sandia national laboratories ldrd program. sandia national laboratories is a multi-mission laboratory managed and operated by sandia corporation, a wholly owned subsidiary of lockheed martin corporation, for the u.s. department of energy’s national nuclear security administration under contract de-ac04-94al85000. references [1] v. colizza, a. barrat, m. barthelemy, and a. vespignani. the role of the airline transportation network in the pre-diction and predictability of global epidemics. proceedings of the national academy of sciences, 103(7):2015–2020, feb 2006. [2] fan chung and linyuan lu. connected components in random graphs with given expected degree sequences. annals of combinatorics, 6(2):125–145, nov 2002 *drew levin e-mail: dlevin@sandia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e6, 2017 what’s past is prologue: a scoping review of recent public health and global health informatics literature 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi what’s past is prologue: a scoping review of recent public health and global health informatics literature brian e. dixon1,2,3*, jamie pina4,5, hadi kharrazi6, fardad gharghabi6, janise richards7 1. richard m. fairbanks school of public health, indiana university, indianapolis, in 2. regenstrief institute, inc., indianapolis, in 3. center for health information and communication, department of veterans affairs, veterans health administration, health services research and development service cin 13-416, richard l. roudebush va medical center, indianapolis, in 4. rti international, research triangle park, nc 5. rollins school of public health, emory university, atlanta, ga 6. johns hopkins bloomberg school of public health, baltimore, md 7. u.s. centers for disease control and prevention, atlanta, ga abstract objective: to categorize and describe the public health informatics (phi) and global health informatics (ghi) literature between 2012 and 2014. methods: we conducted a semi-systematic review of articles published between january 2012 and september 2014 where information and communications technologies (ict) was a primary subject of the study or a main component of the study methodology. additional inclusion and exclusion criteria were used to filter phi and ghi articles from the larger biomedical informatics domain. articles were identified using medline as well as personal bibliographies from members of the american medical informatics association phi and ghi working groups. results: a total of 85 phi articles and 282 ghi articles were identified. while systems in phi continue to support surveillance activities, we identified a shift towards support for prevention, environmental health, and public health care services. furthermore, articles from the u.s. reveal a shift towards phi applications at state and local levels. ghi articles focused on telemedicine, mhealth and ehealth applications. the development of adequate infrastructure to support ict remains a challenge, although we identified a small but growing set of articles that measure the impact of ict on clinical outcomes. discussion: there is evidence of growth with respect to both implementation of information systems within the public health enterprise as well as a widening of scope within each informatics discipline. yet the articles also illuminate the need for more primary research studies on what works and what does not as both searches yielded small numbers of primary, empirical articles. conclusion: while the body of knowledge around phi and ghi continues to mature, additional studies of higher quality are needed to generate the robust evidence base needed to support continued investment in ict by governmental health agencies. correspondence: bedixon@regenstrief.org doi: 10.5210/ojphi.v6i3.5931 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:bedixon@regenstrief.org what’s past is prologue: a scoping review of recent public health and global health informatics literature 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi introduction while they share much in common with other branches within the broader field of biomedical informatics [1], public health and global health informatics differ in their scope and applications. public health informatics (phi; also known as public health & epidemiology informatics in europe) focuses on the application of information and communications technologies (ict) to promote the health of populations rather than individual patients. in addition, phi focuses on disease prevention rather than treatment and typically operates within a governmental rather than private environment [2]. phi is generally organized and conducted by governmental public health authorities, which is most often a nation’s ‘ministry of health’ except in the united states where public health practice is dispersed among a federation of local, state and federal health agencies. in recent years, global health informatics (ghi) emerged out of the broader biomedical informatics discipline as a distinct field focused on applying ict to both public health and health care delivery in the context of low-to-middle income countries (lmics). its scope includes technologies that support the delivery of public and private health services (e.g., electronic health record, telemedicine, mobile health) as well as the management of health services across the care continuum within as well as across nations (e.g., health information exchange, health worker registries, epidemiology). thus phi and ghi share many commonalities with each other, yet each contributes distinctly to the science and practice of informatics, medicine, and public health. beginning in 2013, the phi and ghi working groups within the american medical informatics association (amia) have offered a ‘year in review’ session at the amia annual symposium. these sessions highlight advancements and trends in both the science and practice of phi and ghi. this paper builds upon these year in review sessions by providing a semi-systematic review of the phi and ghi literature over the past three years. our goal is to summarize recent advancements and trends in both fields and suggest directions for future work that will further stimulate growth in the fields and advancements in the science of informatics as well as the practice of public health in all nations. methods we conducted a semi-systematic review of the biomedical literature between 2012 and 2014 in accordance with the prisma guidelines [3]. our review is based on the work conducted by the amia phi and ghi working groups to present a ‘year in review.’ given that our review focuses on a synthesis of multiple mini-reviews performed by the working groups and the heterogeneity of the selected articles, we neither assessed the quality of studies nor aggregated study results, thereby classifying our study as a semi-systematic review instead of a comprehensive systematic review or meta-analysis. data sources and searches multiple searches were conducted between september 2013 and august 2014 using disparate keywords to identify all possible english-language phi and ghi peer-reviewed literature indexed in pubmed or medline that was published between january 1, 2012 and september 30, 2014. supplemental articles were gathered by reviewing the bibliographies of selected articles and by soliciting suggestions from phi and ghi working group members using amia listservs. what’s past is prologue: a scoping review of recent public health and global health informatics literature 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi for phi articles, we used the following medline subject headings (mesh) keywords in various combinations: public health informatics, public health, informatics, and information system. use of the mesh headings was preferred, although articles were also identified using the keywords to look within article titles and abstracts. for ghi articles, we used the following keywords: informatics, telemedicine, information system, developing countries, global, national health programs, low resource, and resource-limited. given the recent emergence of ghi in the literature, mesh headings for the field are limited; only some of these are official mesh headings. furthermore, the searches in 2014 expanded the keyword list to include the name of every lmic as defined by the world bank as well as other terms that better reflected ghi activities happening internationally, such as mobile health (mhealth), electronic health records and electronic medical records. this significantly increased the list of candidate articles, but it was determined by cdc health librarians to be the most sensitive method for identifying informatics articles published in lmic nations. a complete listing of the dates and queries used to identify articles is available in appendix a. study selection we used a variety of inclusion and exclusion criteria to narrow the lists of candidate articles. to be included and classified as a phi article: (a) informatics, information science, or computing had to be the primary subject of the study or a main component of the study methodology; and (b) the article needed to focus on a topic related to public health practice or research. we used a broad lens to assess each article’s relevance to public health practice and research, including: 1) activities conducted by, with, or involving a local, state or federal health agency; 2) assessment and monitoring of disease and health outcomes; 3) primary and secondary prevention of disease; 4) social determinants of health as well as health disparities; and 5) development of the public health workforce including phi education and competencies. articles were excluded from the phi group if the research occurred principally within a lmic nation. we further excluded articles that did not constitute original research such as letters, editorials, perspectives, opinions, whitepapers, comments, and study protocols. for a publication to be included and classified as a ghi article, informatics, information science, or information systems had to be the primary subject of the study or a main component of the study methodology. additionally, the primary research must have focused on activities taking place within an lmic, and also have a focus on a global health topic. global health topics follow the framework of koplan et al. [4] and include: 1) health issues that transcend national boundaries; 2) development and implementation of solutions that often require global cooperation; 3) embraces both prevention of populations and clinical care of individuals; and 4) promote health equity among nations and for all people. articles were excluded from the ghi group if they did not constitute original research, such as letters, editorials, perspectives, opinions, whitepapers, comments, and study protocols. multiple volunteers from the phi and ghi working groups assisted with review of candidate articles. two authors (bed and jp) divided the candidate articles into relatively equal numbers and assigned them to reviewers. reviewers examined titles, abstracts, and other article metadata, recommending inclusion or exclusion based on the defined criteria. each article was reviewed by at least two individuals, and disagreements were resolved after further review by the team leads (bed and jp). what’s past is prologue: a scoping review of recent public health and global health informatics literature 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi data extraction selected articles were abstracted by multiple individuals using a set of criteria established by the phi and ghi teams. for phi, trainees in phi programs volunteered to read through the full text of each selected article and summarize key metadata. they examined the information system implemented or evaluated (e.g., syndromic surveillance system, immunization information system); method of data capture (e.g., electronic health record, mobile device); impact of the technology on practice or research; barriers to adoption; size of the cohort or population; study locations; and jurisdiction of practice (e.g., local health department, ministry of health). summarized information was captured in a spreadsheet and reviewed by the phi team leads (bed and hk). for ghi, volunteers had graduate degrees in public health, informatics or informatics-related field and experience in applying ict in global settings. they reviewed the full text of each selected article published before 2014 (n=83). given the expanded size of the corpus in 2014 (n=199), volunteers were asked to examine the abstract and full text if possible. reviewers categorized and summarized the articles using the following metadata: article type (e.g., review, research, methods, models); main keywords; main objective; principal findings; possible impact on the practice of ghi; and other notes. like phi, these observations from the reviewers were captured in a spreadsheet and reviewed by the ghi team lead (jp) to identify themes and trends. results the selection of phi and ghi articles is summarized in figure 1 and figure 2, respectively. from a potential pool of 526 phi articles, we identified 85 articles that satisfied our inclusion criteria. the majority of the candidate phi articles either (a) did not focus on informatics or an information system; (b) presented research performed within an lmic; or (c) were classified as a commentary or letter. from a pool of 1241 ghi articles, we identified 282 articles that satisfied our inclusion criteria. candidate ghi articles were excluded when they (a) did not focus on informatics or an information system; (b) were performed outside of an lmic; (c) did not focus on a global health topic; or (d) were classified as a commentary or letter. figure 1: prisma diagram for phi articles what’s past is prologue: a scoping review of recent public health and global health informatics literature 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi figure 2: prisma diagram for ghi articles what’s past is prologue: a scoping review of recent public health and global health informatics literature 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi synthesis of phi studies phi articles focus on a wide range of information systems (figure 3), including: measuring population health using electronic health data (pop health / quality indicators; n = 20), electronic laboratory reporting (elr) along with communicable disease case reporting (n=13); syndromic surveillance (n=11), and immunization information system (iis; n=6). examining trends across the three years reveals that articles shifted slightly away from a focus on quality indicators towards elr and case reporting. yet all four systems remained in the ‘top 4’ each year. these are all common information systems or uses of electronic data found in public health practice. they are also specific functions called out in the u.s. centers for medicaid and medicare services’ (cms) meaningful use program, which incentivizes the adoption and use of electronic health record systems in health care delivery systems and organizations [5,6]. other common information systems used by governmental public health agencies but not appearing frequently in the selected articles includes chronic disease registries (e.g., state cancer registry), vital information systems (e.g., death registry), and water quality monitoring systems (e.g., toxicology information system). the year 2012 contained a significant number of ‘other’ articles which is indicative of the broader search strategy employed at the beginning of our project that became more focused over time. articles in the other category included topics such as the application of geographic information systems to public health practice [7]; phi education, training and workforce development [8-10]; online information resources [11,12]; and social networking [13,14]. while relevant to the discipline of phi, we did not classify them as focused on an information system used in routine public health practice. what’s past is prologue: a scoping review of recent public health and global health informatics literature 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi figure 3: count of phi articles based on the type of information system implemented or evaluated, stratified by year of publication. in figure-4, we summarize the methods used in the phi articles. while 43 articles used a quantitative methodology, just nine articles employed a controlled experimental or comparative design such as the examination of a new influenza-like-illness classifier [15] or changes in syndromic surveillance system use following a change [16]. twenty articles present initial findings from a pilot study that measured a system’s usability [12] or use [17,18] among early adopters. twelve articles were even more exploratory in nature, outlining simply the design process for a new system [19-21] or presenting the characterization of a new data source [22,23]. twenty-two articles were systematic reviews, including a review of syndromic surveillance classifiers [24], the use of iis for research [25], the use of social networking sites in public health [13], and information needs of public health practitioners [26]. case studies were also present in 16 articles, summarizing the design or implementation of a phi system within a single health department or group of organizations [27,28]. ten studies were surveys, which employ quantitative methods to analyze respondents’ answers to questions about phi training programs [9]; the role of governance in guiding adoption and use of phi systems [29]; public health engagement in health information exchange [30]; or characterizing the quality of data in an electronic information system [31,32]. what’s past is prologue: a scoping review of recent public health and global health informatics literature 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi figure 4: count of phi articles based on study methodology, stratified by year of publication. in figure-5 we summarize the jurisdictions involved in the implementation or use of the information system described in the selected articles. there is a noticeable trend in the data away from articles that describe systems designed, implemented, or used at the federal level towards systems used at the local or state level. the more recent state and local health department articles tended to focus on information systems implemented in compliance with the cms meaningful use program: elr and case reporting [33,34]; syndromic surveillance [16], and quality reporting [21]. more recent articles also synthesize lessons and implementation strategies at the local level for elr, iis, and other meaningful use related information systems [35,36]. fourteen articles describe information systems deployed or used by hospitals or health systems. for example, three studies examined information systems in u.s. hospitals used by infection preventionists to monitor and control outbreaks within a hospital or health system [30,37,38]. other studies examined the use of oncology information systems, in combination with as well as independent from cancer registries, to create databases for studying cancer epidemiology and outcomes [39,40]. while clinical organizations’ roles in the design, maintenance or use were emphasized in these articles, the studies either used public health data resources like cancer registries, or they otherwise involved governmental public health agencies in the design or implementation of the system. other articles employed surveys or interviews that included governmental public health agencies in combination with hospitals or health systems to examine larger topics within the field of biomedical informatics including but not limited to comparative effectiveness research [41,42]. what’s past is prologue: a scoping review of recent public health and global health informatics literature 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi figure 5: count of phi articles based on the jurisdictions that implemented or used the information systems described in the article, stratified by year. synthesis of ghi studies the ghi literature spans a broad range of technologies, health service delivery areas, and foci. reviewers identified several high-level and recurring themes in the ghi literature through full article review (2013) and abstract review (2014). major themes that spanned both years of the review included: telemedicine, mhealth, and ehealth. emerging themes, especially in the 2014 review, included: surveillance, decision support, and geographical information systems (gis). these themes are summarized in figure-6. furthermore, reviewers noted that the overall volume of articles increased in the 2013-2014 review. although the overall number of articles increased, the relative proportion of each major theme remained similar. figure 6: count of ghi articles based on the type of information system implemented or evaluated, stratified by year of publication (2013 n=82 and 2014 n=199) over the two years of review, the theme of telemedicine remains the largest portion of ghirelated literature. telemedicine applications bridge healthcare delivery needs when providers and specialists are not physically present in a specific region. telemedicine has also been a source for what’s past is prologue: a scoping review of recent public health and global health informatics literature 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi access to specialists in fields, such as dermatology and pediatric cardiology, when a country has limited number of specialists available for consultation. another application of telemedicine technology is to provide continuous medical education to healthcare providers in remote regions. the telemedicine literature in a global context has focused on the development and evaluation of telemedicine programs in various clinical contexts, generally describing implementations of telemedicine and the impact of these tools [43-60]. the literature suggests that when telemedicine solutions are implemented with adequate infrastructure, financial and local clinical support they are found to be effective in meeting healthcare delivery goals and impacting healthcare providers’ ability to learn of new medical practices, although garnering this support is challenging in lmics [52,53,56,58]. as mobile technologies have proliferated throughout lmics, their use in a variety of healthrelated activities has been studied [61-72]. as rapidly as mobile technology use has grown, the mhealth literature also has rapidly increased. much of the mhealth literature explored the effectiveness of mhealth interventions, with emphasis on using mobile technologies to deliver health-promoting messages, to improve clinic visit attendance for culturally sensitive health issues such as breast cancer, and to increase patient retention in care, treatment, and prevention programs. the mhealth literature focused on scaling up and evaluating mhealth interventions increased in 2014, suggesting growth in maturity for this area of ghi research. mhealth interventions increasingly span numerous areas of clinical and public health focus, including psychiatric disorders, total health/wellness, surveillance of rabies, infant feeding, drug adherence, and measuring the impact of perinatal interventions. some studies evaluated mobile survey platforms, generally acknowledging the value of this technology for survey distribution [73-88]. the literature also described mobile applications used to improve specific clinical activities (e.g. waiting time, order entry) [74,83,89-93]. the final major theme, ehealth, focuses on the development, use and impact of electronic health record (ehr) systems on lmics. some studies in this category aimed to demonstrate the impact of ehealth applications on patient and population outcomes within various lmic contexts [47,89,94-97]. other studies explored more foundational informatics aspects, such as data quality in ehealth applications, factors that lead to adoption of ehr systems, use of standardized minimum data sets to assist with the electronic exchange of clinical data, and development and use of open source, standardized ehrs . determining the extent of adoption and use of ehrs in lmics and understanding the level of computer literacy as a barrier to adoption continued in the literature over the two years [98-116]. policy research related to e-health was not highly present in the literature; we identified just one systematic review of policy issues [98]. we further examined the methodology used in each selected article, summarized in figure-7. our methods for conducting the review varied from 2013 to 2014, yet in each year we sought to distinguish evaluation articles from reviews and methods papers. review articles, both robust systematic reviews and weaker review articles spanned several topics, including the impact of mhealth interventions on healthcare quality, a general review of mhealth and its potential, the use of open-source ehr systems, health information systems in sub-saharan africa countries, mobile phone interventions for consumer health, and overcoming shortages of human resources in lmics [74,81,84,89,99,100]. we further identified a group papers that focused on new methods of informatics research. finally we identified model papers, which emphasized the development of new models of evaluation or health information technology (hit) development. what’s past is prologue: a scoping review of recent public health and global health informatics literature 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi figure 7: count of ghi articles based on study methodology, stratified by year of publication. the largest category of articles included a mix of various primary studies. in addition to studies that assessed the impact of ict on patient and population outcomes, themes that arose during our discussion of the articles were assessments of infrastructure needed for system implementation and readiness of an environment, such as a clinical facility or a group of system users, to adopt the system. because access to electricity, telecommunications, hardware and other aspects of infrastructure are sometimes constrained in lmics, we noted another theme aimed to measure the use and viability of ict. specifically in 2014 we observed this theme of enumerating infrastructure challenges and proposing solutions to them; topics included measuring system and power outage, security and privacy enhancements, cloud-based migrations and implementations, measuring a hospital’s ability to outsource ict effectively, and data transformations or mapping to multiple standards [101-107]. emphasis was often placed on open-source applications that can be distributed across multiple communities and nations [52,108-113]. discussions examined different aspects of infrastructure as either facilitator or barriers to adoption [94,114-116]. we further identified a set of articles (n=7) we refer to as “readiness studies,” or studies that assess training; availability of health care workers, health delivery facilities, or ict; phone ownership; and data access in lmics [102,114-119]. these studies specifically identify the extent to which an environment within an lmic is prepared to adopt some form of hit. finally, as we opened our search in 2014 to include all countries designated as lmics according to the world bank, we observed new topics surface in the literature. specifically, we observed the presence of imaging informatics publications released in 2014 (n=4). these publications described the improvement of imaging services by integrating systems, reducing cost, using mobile devices, applying new imaging techniques in lmics, and exploring new methods to identify and compare data in images [120-124]. reviewers also identified an additional theme related to emergency services supported by information systems in lmics [125-127]. discussion we performed a semi-systematic review of the phi and ghi literature to identify trends in both innovation and research. a total of 85 phi articles and 282 ghi articles were identified through a search of the available literature and suggestions from amia working group members. the what’s past is prologue: a scoping review of recent public health and global health informatics literature 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi selected articles suggest growth with respect to both implementation of information systems within the public health enterprise as well as a widening of scope within each informatics discipline. yet the articles also illuminate the need for more primary research studies on what works and what does not as both searches yielded small numbers of primary, empirical articles. each field, while maturing, continues to produce a larger volume of articles that describe either a) the design or recent implementation of an information system; or b) a limited review of early systems deployed by a relatively small number of organizations. this limits the conduct of more robust systematic reviews as well as meta-analyses that could inform public health policy as well as health care delivery. public health informatics at the turn of the twenty-first century, phi efforts around the world were characterized by a focus on the core public health function of monitoring populations: early detection of bioterrorism [128], such as the anthrax attacks in the u.s [129] and the tokyo subway attacks [130], as well as global health threats such as sars [131] and the h1n1 pandemic [132]. while the threat of a large-scale epidemic has not diminished in recent years, as evidenced in 2014 by mers [133,134] and ebola [135], the scope of phi has broadened to support the full range of functions performed by governmental public health authorities. as evidenced in this review, phi today contributes not only to disease detection [15,136] but also to the delivery of public health care services [137-139], the measurement of population health indicators within and across jurisdictions [21,140], investigating environmental hazards [141], and prevention of disease [142-144]. phi research to a large extent, just like in many other areas of biomedical research, follows policy and funding patterns within a nation or region. the last few years have witnessed a global recession with shrinking public health budgets and a sharp decline in funds allocated to public health preparedness. funding patterns explain, in part, what appears to be a more balanced portfolio of phi activities with a strong but smaller emphasis on surveillance. in developed nations with a strong integrated public health system such as canada [145,146], australia [147], taiwan [27] and several european nations [148-150], phi activities include an emphasis on integrating data and supporting bi-directional communication between clinical and public health services. whereas in the us, phi efforts have focused largely on ehr incentive policies that seek to improve how public health agencies gather the data they need to monitor the health of populations [29,33,151]. thus to stimulate new advancements in phi, especially in the u.s., policymakers must encourage the development or broader implementation of information systems that span the clinical and public health continuum to support the full range of public health functions. support for this expanded view of phi can be found in both the council of state and territorial epidemiologists’ ‘blueprint version 2.0’ statement on the future of surveillance [152], as well as the workshop report from the robert wood johnson foundation on the future of phi [153]. global health informatics the focus of ghi research intersects at the crossroads of clinical and public health informatics in the context of lmics. given a global emphasis on the development and implementation of national ehealth strategies, recent ghi articles reflect an increase in the development, implementation and use of telemedicine, mhealth, ehr, and other forms of healthcare ict in lmics on all continents. furthermore, the limited studies assessing outcomes or impact of these systems on patient and population health suggests that adoption of ehr and other ict systems what’s past is prologue: a scoping review of recent public health and global health informatics literature 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi in lmics may be accelerating at a pace that exceeds the rate at which the academic community can evaluate them. despite a change in our search methodology, we observed similar patterns across all publication years. first, there is significant growth in the publication of ghi articles. while this may be partly due to our change in search strategy to include specific lmic country names, we believe there is true growth in the development, implementation and use of ict in lmics across the globe. external forces, including the world health organization (who) ehealth technical advisory group (tag) formed in 2013, are encouraging lmics to adopt and implement national ehealth strategies that will strengthen health systems through the use of ict including telemedicine and ehr systems [154,155]. second, despite growth in articles many studies continue to focus on implementation rather than outcomes. we observed numerous, although sometimes subtle, descriptions of implementation challenges. for example, reviewers noted that organizational culture can create barriers, which impact the adoption and implementation of informational tools, was often mentioned in the literature, although this subject is not treated methodologically as the focus of many publications [81,117]. finally, ghi research is intrinsically linked to public health practice in lmics, creating the link between the use of ict and use of clinical and other data for public health activities such as surveillance, health monitoring and public health program evaluation [156-160]. the literature suggests that the use of ict will continue to expand in lmics. this will likely generate new studies on their impact on patient and population health. as these studies are planned and executed, our review suggests the following. first, we note a general dearth of rigorous study designs. better designed studies that follow the good evaluation practices in health informatics (gep-hi) [161] and are reported using the statement on reporting of evaluation studies in health informatics (stare-hi) guidelines [162,163] will support future efforts that can more rigorously systematically review and synthesize outcomes. more rigorous review and synthesis will provide stronger evidence that many policymakers seek as they contemplate policies and funding beyond initial investments in ehealth systems [164]. in addition, given a rich dialogue in the discussion sections of many articles on the barriers and facilitators of adoption, we suggest that future research more rigorously examine the implementation of ict in lmics. there exists a growing body of implementation science literature in the developed world [165,166], which would be strengthened by contributions from lmics if implementation were more rigorously studied in parallel with outcomes. furthermore, we observed that many studies involve partners from multiple universities, research institutes, and countries with diverse funding streams. finding harmony across fragmented stakeholders appears to be a goal of the applied work taking place in these lmics, yet this is not emphasized as a measurable component of research in the current body of literature. future studies should consider studying this dimension of ghi implementation as a facilitator or barrier. limitations our review of the phi and ghi literature contains several limitations. first, our review of the literature was limited to articles published in english, which limited our ability to identify and read relevant articles published in other languages (the iceberg effect). second, our review was limited to primarily peer-reviewed articles indexed in medline, which limited our ability to identify relevant articles published in newer journals or journals which do not meet the scientific scope and quality metrics established by the medline literature selection technical review committee. ghi article review for 2014 was limited to abstract review of selected articles. what’s past is prologue: a scoping review of recent public health and global health informatics literature 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi future directions although our purpose differs from that of the international medical informatics association’s imia yearbook of medical informatics, many of our methods are quite comparable [167]. and like imia, as we continue to evolve the year in review session for amia’s annual symposium, we will seek to refine our methods. we will consider additional sources beyond medline to identify more articles from related disciplines which are not indexed in pubmed such as the computer, information, and decision sciences. we will further explore tools such as bibreview to enhance our ability to identify, select and summarize recent publications in the disciplines of phi and ghi. conclusion although distinct, the phi and ghi sub-disciplines within the field of biomedical informatics seek to advance health systems’ goals of improving the efficiency, costs and outcomes associated with health care delivery to individual patients as well as populations. studies published over the past three years identify growth in our understanding of ict development, implementation and adoption in clinical as well as public health settings. yet the articles also highlight there is much work yet to be done. namely we need more rigorous studies to generate a robust evidence base demonstrating not only whether certain types of ict support better population outcomes but also which systems and implementation methods lead to success in terms of system usage as well as health outcomes. many nations are in the process of either developing or implementing national ehealth strategies, which will undoubtedly require evaluation and refinement in the years ahead. acknowledgements we graciously thank the following individuals who assisted in the selection and review of the articles included in the review: louis imperiale (amia high school scholar); anne l. turner, mls (university of washington); harold lehman, md (johns hopkins bloomberg school of public health); roland gamache, phd; uzay kirbiyik, md, mph (indiana university fairbanks school of public health); patrick t.s. lai, mph (indiana university school of informatics and computing); adam knotts (indiana university fairbanks school of public health); rebecca hills, phd (university of washington); karnali vyawahare, ms (indiana university school of informatics and computing); glynda doyle (british columbia institute of technology); stephen brown, ms (rti international); james kariuki (cdc); soudabeh khodambashi (norwegian university of science & technology); onyinyechi enyia daniel, mph (university of illinois school of public health); suranga kasthurirathne (indiana university school of informatics and computing); saptarshi purkayastha, phd (indiana university school of informatics and computing); jessica s. ancker, phd (weill cornell college of medicine). we further thank the american medical informatics association for creating a venue at its annual meeting to highlight recent advances in phi and ghi. dr. dixon is a health research scientist at the richard l. roudebush veterans affairs medical center in indianapolis, indiana. the content is solely the responsibility of the authors and does not necessarily represent the official views of the regenstrief institute, robert wood johnson foundation, agency for healthcare research and quality, centers for disease control and prevention, department of veterans affairs, or the u.s. government. what’s past is prologue: a scoping review of recent public health and global health informatics literature 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi financial disclosure bed is supported by a mentored research scientist development award from the robert wood johnson foundation (71596) as well as awards from the u.s. agency for healthcare research and quality (r01hs020209), u.s. centers for disease control and prevention (200-2011-42027 0003), the merck-regenstrief program in personalized health care research and innovation, and the u.s. 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22551498&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22294775&dopt=abstract http://dx.doi.org/10.1093/eurpub/ckr195 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22551495&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23569651&dopt=abstract http://dx.doi.org/10.5210/ojphi.v4i3.4290 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22759985&dopt=abstract http://dx.doi.org/10.1097/phh.0b013e318262906e http://www.ncbi.nlm.nih.gov/pubmed/25848630 http://repository.academyhealth.org/egems/vol2/iss4/3/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24093955&dopt=abstract http://dx.doi.org/10.1089/tmj.2012.0232 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25114567&dopt=abstract 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informatics (phi) in 2013 and 2014 to identify articles in phi, we used the following query (customized for medline): “public health informatics”[mh] or (“exp public health”[mh] and “exp informatics”[mh]) or (“public health”[mp] and “informatics”[mp]) http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24523997&dopt=abstract http://dx.doi.org/10.4258/hir.2013.19.4.314 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25087521&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25000011&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21920809&dopt=abstract http://dx.doi.org/10.1016/j.ijmedinf.2011.08.004 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18930696&dopt=abstract http://dx.doi.org/10.1016/j.ijmedinf.2008.09.002 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23599226&dopt=abstract http://dx.doi.org/10.1136/amiajnl-2013-001684 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19664226&dopt=abstract http://dx.doi.org/10.1186/1748-5908-4-50 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25396220&dopt=abstract http://dx.doi.org/10.3414/me14-01-0031 what’s past is prologue: a scoping review of recent public health and global health informatics literature 29 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi this query in 2013 returned 392 results, after specific inclusion and exclusion criteria were applied 65 articles were fully reviewed. in 2014 the query returned 160 results, with 20 articles meeting the criteria for full review. while this query yielded a reasonable set for the committee to review and discuss within a short timeframe, the group feels the query is not sensitive enough to capture “information system” articles that fail to use the term “informatics.” global health informatics (ghi) in 2013 to identify articles in ghi, we used the following query (customized for medline): (“exp informatics”[mh] or “exp telemedicine”[mh] or “information system”[mp]) and (“developing countries”[mh] or global or ministry or “low resource” or “resourcelimited”[mp]) this query with a search range of 20 months (01/01/2012 – 09/31/2013) returned 442 results. during the review process the ghi reviewers thought this query missed many known articles. to overcome these limitations, we worked closely with an information specialist at the cdc library to develop this more sensitive query for our 2014 medline search: ("mhealth"[title/abstract] or "mobile health"[title/abstract] or "electronic health records"[mesh] or "electronic medical record"[title/abstract] or "electronic medical records"[title/abstract] or "electronic health record"[title/abstract] or "electronic health records"[title/abstract] or "informatics"[mesh] or "telemedicine"[mesh] or "information system"[title/abstract] or "information systems"[title/abstract]) and ("developing country" or developing countries or "developing nation" or "developing nations" or "developing population" or "developing populations" or "developing world" or "less developed country" or "less developed countries" or "less developed nation" or "less developed nations" or "less developed population" or "less developed populations" or "less developed world" or "lesser developed country" or "lesser developed countries" or "lesser developed nation" or "lesser developed nations" or "lesser developed population" or "lesser developed populations" or "lesser developed world" or "under developed country" or "under developed countries" or "under developed nation" or "under developed nations" or "under developed population" or "under developed populations" or "under developed world" or "underdeveloped country" or "underdeveloped countries" or "underdeveloped nation" or "underdeveloped nations" or "underdeveloped population" or "underdeveloped populations" or "underdeveloped world" or "middle income country" or "middle income countries" or "middle income nation" or "middle income nations" or "middle income population" or "middle income populations" or "low income country" or "low income countries" or "low income nation" or "low income nations" or "low income population" or "low income populations" or "lower income country" or "lower income countries" or "lower income nation" or "lower income nations” or "lower income population" or "lower income populations" or "underserved country" or "underserved countries" or "underserved nation" or "underserved nations" or "underserved world" or "under served country" or "under served countries" or "under served nation" or "under served nations" or "under served world" or "deprived country" or "deprived countries" or "deprived nation" or "deprived nations" or "deprived population" or "deprived populations" or "deprived world" or "poor country" or "poor countries" or "poor nation" or "poor nations" or "poor population" or "poor populations" or "poor world" or "poorer country" or "poorer countries" or "poorer nation" or "poorer nations" or "poorer population" or "poorer populations" or "poorer world" or "developing economy" or "developing economies" or "less developed economy" or "less developed economies" or what’s past is prologue: a scoping review of recent public health and global health informatics literature 30 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi "lesser developed economy" or "lesser developed economies" or "under developed economy" or "under developed economies" or "underdeveloped economy" or "underdeveloped economies" or "middle income economy" or "middle income economies" or "low income economy" or "low income economies" or "lower income economy" or "lower income economies" or "low gdp" or "low gnp" or "lower gdp" or "lower gnp" or lmic or lmics or "third world" or "lami country" or "lami countries" or "transitional country" or "transitional countries" or africa or asia or caribbean or west indies or south america or latin america or central america or afghanistan or albania or algeria or angola or antigua or barbuda or argentina or armenia or armenian or aruba or azerbaijan or bahrain or bangladesh or barbados or benin or byelarus or byelorussian or belarus or belorussian or belorussia or belize or bhutan or bolivia or bosnia or herzegovina or hercegovina or botswana or brazil or bulgaria or burkina faso or burkina fasso or upper volta or burundi or urundi or cambodia or khmer republic or kampuchea or cameroon or cameroons or cameron or camerons or cape verde or central african republic or chad or chile or colombia or comoros or comoro islands or comores or mayotte or congo or zaire or costa rica or cote d'ivoire or ivory coast or croatia or cuba or cyprus or czechoslovakia or czech republic or slovakia or slovak republic or djibouti or french somaliland or dominica or dominican republic or east timor or east timur or timor leste or ecuador or egypt or united arab republic or el salvador or eritrea or estonia or ethiopia or fiji or gabon or gabonese republic or gambia or gaza or georgia republic or georgian republic or ghana or gold coast or greece or grenada or guatemala or guinea or guam or guiana or guyana or haiti or honduras or hungary or india or maldives or indonesia or iran or iraq or jamaica or jordan or kazakhstan or kazakh or kenya or kiribati or korea or kosovo or kyrgyzstan or kirghizia or kyrgyz republic or kirghiz or kirgizstan or "lao pdr" or laos or latvia or lebanon or lesotho or basutoland or liberia or libya or lithuania or macedonia or madagascar or malagasy republic or malaysia or malaya or malay or sabah or sarawak or malawi or nyasaland or mali or malta or marshall islands or mauritania or mauritius or agalega islands or mexico or micronesia or middle east or moldova or moldovia or moldovian or mongolia or montenegro or morocco or ifni or mozambique or myanmar or myanma or burma or namibia or nepal or netherlands antilles or new caledonia or nicaragua or niger or nigeria or northern mariana islands or oman or muscat or pakistan or palau or palestine or panama or paraguay or peru or philippines or philipines or phillipines or phillippines or poland or portugal or romania or rumania or roumania or rwanda or ruanda or “saint kitts” or “st kitts” or nevis or saint lucia or st lucia or “saint vincent” or “st vincent” or grenadines or samoa or samoan islands or navigator island or navigator islands or sao tome or saudi arabia or senegal or serbia or montenegro or seychelles or sierra leone or slovenia or sri lanka or ceylon or solomon islands or somalia or sudan or suriname or surinam or swaziland or syria or tajikistan or tadzhikistan or tadjikistan or tadzhik or tanzania or thailand or togo or togolese republic or tonga or trinidad or tobago or tunisia or turkey or turkmenistan or turkmen or uganda or ukraine or uruguay or ussr or soviet union or union of soviet socialist republics or uzbekistan or uzbek or vanuatu or new hebrides or venezuela or vietnam or viet nam or west bank or yemen or yugoslavia or zambia or zimbabwe or rhodesia or western sahara or kuwait or united arab emirates or qatar or nauru or tuvalu or bahamas or south africa) and not ("comment"[publication type] or "letter"[publication type]) what’s past is prologue: a scoping review of recent public health and global health informatics literature 31 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(2):e216, 2015 ojphi this query with a search range of 12 months (11/01/2013-10/31/2014) returned 819 results. after a title and abstract review based on seven inclusion criteria, 199 articles were included in the final analysis. what’s past is prologue: a scoping review of recent public health and global health informatics literature introduction methods data sources and searches study selection data extraction results synthesis of phi studies synthesis of ghi studies discussion public health informatics global health informatics limitations future directions conclusion acknowledgements financial disclosure competing interests references appendix a for what’s past is prologue: a scoping review of recent public health and global health informatics literature search queries details for phi and ghi isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman ahmadu bello university, zaria, nigeria objective to study the effect of socio-cultural and environmental factors on microbial contamination of food and the understanding of foodrelated risks, food safety knowledge and practices introduction in most disadvantaged communities in northern nigeria, adolescent girls engage in economic activities so that they can save money for household items to be bought for them when they are given out for marriage. these girls right from before they reach teenage age hawk items which include ready-to-eat foods (1). various socio-cultural and environmental factors reinforce vulnerability of foods to microorganisms. food safety awareness, knowledge and practices among food vendors can be affected by interplay between individual and outdoor factors. teenagers engage in hawking food without understanding food-related risks for the preservation of their health and the health of others. food hygiene is the conditions and measures necessary to ensure the safety of food from production to consumption. lack of adequate food hygiene can lead to foodborne diseases and death of the consumer (2). mishandling of food can occur during food preparation, handling and storage; and studies show that consumers have inadequate knowledge about measures needed to prevent food-borne illness (3). there are a number of factors which are likely to contribute to outbreaks of food-borne illness in the home, including a raw food supply that may be contaminated, a lack of food safety knowledge among the general public, mistakes in food handling and preparation at home (4). methods fifty-four food samples comprising rice and beans meals as well as local delicacies made from maize, soyabeans and others were collected within samaru in kaduna state, nigeria. the samples were pre-enriched and enriched with buffered peptone water and tetrationate broth respectively before plating on desoxycholate citrate agar. structured questionnaires were also administered to the food vendors in form of an interview. questions covered method of food preparation, reheating prior to sale, food safety measures, awareness of food safety, knowledge and behavioral practices that may enhance foodborne illness. results of the 54 samples, 20 (37%) were non-lactose fermenters. two (10%) out of these 20 suspected organisms showed reactions consistent with that of salmonella species upon characterizing them biochemically. the remaining 18 (90%) that were not salmonella showed reactions typical of proteus 5 (9.3%), citrobacter 8 (14.8%), e. coli and arizona spp 1 (1.9%) each and 3 (5.6%) unidentified spp. for food-related risks, a low level of awareness and bad behavioural practices such as playing around food sale points, exposure of food to dusty and unhygienic environment, not washing of utensils and cutleries with clean water prior to serving consumers, hand contact with served foods and a low level of perceived vulnerability to foodborne illness were observed. particular lack of knowledge was identified regarding the impact of temperature on microorganisms as the food sold were either already cold or lukewarm and ignorance on possible health hazards with unprotected wounds on their hands as was seen on some. though more females 43(79.6%) were seen selling foods than males 11(37.0%), but the foods sold by male vendors were more contaminated probably because factors observed in females were more elaborate in males. conclusions understanding of food safety practices is helpful in reducing food-borne illness. preventive health strategies that make use of good behavioral and hygienic environment should be targeted. food vendors should be given the time, tools and training necessary to facilitate proper food handling practices to know basic food safety measures early in life. keywords food; safety; microorganisms; environment; culture acknowledgments laboratory staff and the department of veterinary public health and preventive medicine, ahmadu bello university, zaria, nigeria for laboratory materials and technical assistance references 1.mercy corps. http://www.mercycorps.org/ sites/default/files/mercy%20 corps%20february %20 2013%20-%20 adolescent%20girls%20 in%20northern%20nigeria. accessed september 2015) 2. andrej o, mojca j, peter r. food safety awareness, knowledge and practices among students in slovenia. food cont 2014; 42: 144–151 3. who report. reducing risks, promoting healthy life. geneva, world health organization (http://www.who.int/whr/2002/, accessed march 2005). 4. kaferstein f. actions to reverse the upward curve of foodborne illness. food cont 2003; 14 (2): 101–109 *beatty v. maikai e-mail: beatt18@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e137, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts justification for collecting urgent care data to broaden syndromic surveillance david j. swenson*2, em stephens1, samuel p. prahlow3 and adejare atanda4 1virginia department of health, richmond, va, usa; 2new hampshire department of health and human services, concord, nh, usa; 3florida department of health, tallahassee, fl, usa; 4maryland department of health, baltimore, md, usa objective provide justification for the collection and reporting of urgent care (uc) data for public health syndromic surveillance. introduction while uc does not have a standard definition, it can generally be described as the delivery of ambulatory medical care outside of a hospital emergency department (ed) on a walk-in basis, without a scheduled appointment, available at extended hours, and providing an array of services comparable to typical primary care offices.1 uc facilities represent a growing sector of the united states healthcare industry, doubling in size between 2008 and 2011.1 the urgent care association of america (ucaoa) estimates that uc facilities had 160 million patient encounters in 2013.2 this compares to 130.4 million patient encounters in eds in 2013, as reported by the national hospital ambulatory medical care survey.3 public health (ph) is actively working to broaden syndromic surveillance to include urgent care data as more individuals use these services.4 ph needs justification when reaching out to healthcare partners to get buy-in for collecting and reporting uc data. keywords urgent care; syndromic surveillance; data collection acknowledgments many thanks to all the isds cop jurisdictions and others who shared their uc knowledge and experiences in the february 24th 2017 cop uc webinar and question and answer session, and to those who have participated in the uc justification workgroup conference calls. special thanks go to rich thomas, anna waller, serena rezny, stacey hoferka, and sophia crossen. references 1. urgent care association of america. industry faqs. retrieved from http://bit.ly/2lezttk 2. urgent care association of america. (2014). the urgent care association of america unveils new accreditation program. pr newswire. retrieved from http://prn.to/2wpcqxh 3. rui p, kang k, albert m. national hospital ambulatory medical care survey: 2013 emergency department summary tables. retrieved from: http://bit.ly/2yhlgog 4. centers for disease control and surveillance. (2015). the national syndromic surveillance program: enhancing syndromic surveillance capacity and practice. foa: cdc-rfa-oe15-1502. retrieved from http://bit.ly/2yfxwra *david j. swenson e-mail: david.swenson@dhhs.nh.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e51, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts lessons learned from a healthcare associated infection tabletop exercise, june 2017 dana elhassani and rachel j. ilic* epidemiology, florida department of health in pinellas county, saint petersburg, fl, usa objective to assess healthcare facilities’ level of readiness to respond to an hai outbreak, the florida department of health in pinellas county (doh-pinellas) conducted an hai outbreak tabletop exercise (ttx) on june 6, 2017. other benefits of this ttx were to provide opportunities for collaborative learning, building community partnerships and evaluation of hai preparedness activities in pinellas county healthcare facilities. introduction one in twenty-five patients in acute care hospitals develop at least one health care associated infection (hai); this resulted in approximately 75,000 preventable deaths in 2011. risk factors associated with developing hais include older patients, serviced at a large hospital, central catheter placement, receiving medical ventilation, and placement in a critical care unit. in pinellas county, individuals 65 years of age and older comprise approximately 24% of the total population. methods a line list of contact information for all long-term care facilities in pinellas county was obtained from the doh–pinellas environmental health program. an invitation to the ttx was sent to 234 health care and assisted living facilities. of those invited, 35 individuals attended the ttx on june 6, 2017. the methods used to facilitate discussion included the four scenarios that addressed different stages of an outbreak investigation and a “force decision-making” framework. following the course, a twelve-question evaluation was distributed. the first seven questions were based on a five-point likert scale assessing the course’s impact on knowledge and tabletop learning environment. the other five were open-ended and asked participants to elaborate on what they learned and provide feedback regarding the strengths and areas for improvement of the ttx. results exercise participants included infection control practitioners, safety officers, nursing supervisors, facility managers and epidemiologists from hospitals, assisted living and skilled nursing facilities, hospice, rehabilitation centers and health departments. of the 35 participants, 30 completed the course evaluation for a completion rate of 86%. for questions addressing hai knowledge, participants strongly agreed that the tabletop exercise enhanced their understanding of infection control guidelines for hais. the question that received the lowest score of 4.3 was the perception that “i was able to develop tools for my agency’s infection control guidelines.” in the open-ended questions, themes regarding hai knowledge, resources, response and policies were frequently mentioned, in addition to confusion over the health department’s role and available resources during an hai outbreak. conclusions participation and feedback during the ttx substantiated the importance of increased collaboration across organizations and opportunities for training on hai outbreak response. participants identified a need for an open forum to discuss best practices for hai control and surveillance methods to help guide preparedness and response efforts. to address this need, doh-pinellas will create a hai coalition which would aim to improve understanding of each facility’s role in responding to an hai outbreak. keywords healthcare associated infection; coalition; pinellas county *rachel j. ilic e-mail: rachel.janssenilic@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e143, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts quantifying model form uncertainty of epidemic forecasting models from incidence data nicolas hengartner* and paul fenimore theoretical biology and biophysics, los alamos national laboratory, los alamos, nm, usa objective we present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. the resulting estimates allow to assess how well simpler models, such as seir, fit the observed time series of incidence data. introduction uncertainty quantification (uq), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. while quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. for the latter, one needs a “universal model” to which more restrictive parametric models are compared too. methods this talk presents a useful modeling framework for time series of incidence data from contagious diseases that enables one to identify and quantify the impact of model form uncertainty. specifically, we propose to focus on estimating the time-dependent force of infection. the latter is a universal parameters for all contagious disease model. using a machine learning technique for estimating monotone functions, i.e., isotonic regression and its variants, one can estimate the force of infection without addtional assumptions. we note that most contagious disease model do satisfy this monotonicity assumption, due to a combination of factors: depletion of susceptibles, implementation of mitigation strategies, behavior change, etc. comparing the resulting “non-parametric” estimate with parametric estimates, obtained by fitting an seir for example, can reveal model deficiencies and help quantify model form uncertainties. finally, we discuss how ideas from “strict bound theory” can be used to develop upper and lower uncertainty bands for force of infection that acknowledge the intrinsic stochasticity in the data. results we demonstrate the application of the methodology to weekly influenza like illness (ili) incidence data from france and compare the results to fitted sir and seir models. this comparison can be seen as a nonparametric goodness of fit test, providing one with tools to do simple model selection. conclusions we present a novel and flexible model to statistically describe the force of infection as a function of time. comparing the fit to incidence data of that model with the fit of simpler parametric models enables the quantification of model form uncertainty and associated model selection. keywords uncertainty quantification; isotonic regression; contagious disease modeling *nicolas hengartner e-mail: nickh@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e21, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts surveillance of influenza a/california/h1n1 in dnipropetrovsk, ukraine oleksandr shtepa*, valentyna rezvykh and maryna bredykhina state institution dnipropetrovsk oblast laboratory center of the ministry of health of ukraine, dnipro, ukraine introduction ukraine’s ability to respond to the spread of viruses that cause pandemics and reduce economic losses from influenza, can be strengthened only in the presence of a developed surveillance network including the monitoring of virus circulation in humans. specialists of dnipropetrovsk oblast have great experience in virological surveillance on the circulation of influenza virus a/california/h1n1 and timely determination of the etiology of outbreaks caused by the virus. methods laboratory diagnostics of influenza was performed using serological methods, pcr, and virological studies in the cell culture. during the last seven epidemic seasons, including the flu pandemic of 2009-2010, most of samples came from four health-care facilities of dnipropetrovsk, which were determined as basic hospitals for the sentinel center. patients with severe acute respiratory infections (sari) were examined. nasopharyngeal washouts and swabs were collected into cryo-tubes with a transport medium. the samples were stored at hospitals in dewar flasks. the delivery of the samples to the laboratory was performed according to cold-chain rules. after sample preparation stage, the samples were tested for the presence of influenza a/b virus rna by pcr using bio-rad cfx-96 cycler and the following commercial test-kits amplisens® influenza virus a/b-fl, amplisens® influenza virus a/h1-swine, and amplisens® influenza virus a-type-fl. all positive samples with detected rna of influenza virus a/h1-swine were tested using mdsk cell cultures (canine kidney epithelial cells). flu viruses caused cytopathic changes in the cell cultures in the form of poppy-sand-like degeneracy not earlier than in 72 hours after the infection of the cells followed by cell monolayer fragmentation. fig. 1 mdsk cell culture fig. 2 mdsk cell culture 72 hours after infection with influenza virus a (h1n1) express immunochromatic tests «cito test influenza a+b» or agglutination test (at) using erythrocyte suspension of human 0 (i) group blood were used for the determination of haemagglutinating agents. results during the seven epidemic seasons, 5,467 people were examined for flu and acute respiratory viral infections. during the swine flu pandemic in 2009-2010, 1,217 severely ill patients were tested. positive results were found in 50% of cases (607 persons). from those, pandemic influenza virus (rna of influenza a/h1-swine virus) was detected in 100% of positive cases. fig.3 data on the determined pandemic flu virus strains (rna of influenza a/h1-swine virus) using pcr during epidemiological seasons from 2009 to 2016 in dnipropetrovsk oblast, ukraine frequency of pandemic influenza virus detection declined to zero in the following epidemic seasons (2010-2011 and 2011-2012). however, incidence of the virus variant (influenza a/h1-swine) began to grow slowly during the last four epidemic flu seasons from separate cases (6 in 2012-2013, 1 in 2012-2013) to 26 cases in 2014-2015. during the last epidemic season (2015-2016), the number of pandemic influenza cases increased dramatically to 166, accounting 29% of all examined persons. fig. 4 results of isolation of pandemic strains of influenza viruses in cell culture mdsk flu epidemic seasons from 2009-2010 to 2015-2016 in dnipropetrovsk oblast, ukraine most of the virus isolates were sent for confirmation and further identification to the ukrainian center for influenza and to the world influenza centers (atlanta, usa and london, uk) in order to support ukraine’s participation in the worldwide pandemic influenza surveillance. the world flu centers confirmed the isolates to be influenza virus strain a/california/(h1n1)/07/2009. conclusions 1. circulation of the pandemic type of influenza virus a/california/ (h1n1)/ 07/2009 among the population of dnipropetrovsk oblast is of sporadic character. 2. the return of the virus a/california/(h1n1)/07/2009 after the 2009-2010 pandemic occurred during the last 2015-2016 epidemic season. 3. application of pcr can significantly shorten the examination of patients with severe course of influenza, but cannot help with virus isolation. 4. the use of express immunoassay tests accelerates the identification of viruses isolates. 5. the employment mdsk cell culture for influenza virus isolation allows obtaining of a spectrum of influenza strains circulating during an epidemic period including the strain a/california/(h1n1)/07/2009. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e161, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords influenza; surveillance; pcr; virology; mdsk *oleksandr shtepa e-mail: shtepaap@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e161, 2017 isds16_pos_surveillance of influenza a_shtepa isds16_abstracts-final 181 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 1nigeria field epidemiology and laboratory training programme, enugu, nigeria; 2department of epidemiology and medical statistics university of ibadan, ibadan, nigeria objective •to determine the public health importance and relevance of the surveillance system. •to describe the process of operation and purpose of the system and assess its key attributes. •to determine the effectiveness and efficiency of the surveillance system. •to make appropriate recommendations to stakeholders for its improvement. introduction evaluation of a public health surveillance system is one of the major outputs of the field attachment of the nigeria field epidemiology and laboratory training programme.to conduct this activity, the hiv/aids surveillance system in enugu state, nigeria was evaluated. the evaluation was conducted from february to march 2014.the objectives of the evaluation were to describe the attributes and process of operation of hiv/aids surveillance system in enugu state, determine if the set objectives for establishing hiv/ aids surveillance are being met or not, determine the efficiency and effectiveness of the hiv/aids surveillance system and to make appropriate recommendations for improving the surveillance system. methods the evaluation was conducted using the “ cdc,s updated guidelines for evaluating public health surveillance system,20011. we qualitatively assessed the surveillance system’s key attributes. we interviewed five key informants at state level and reviewed 20102013 data from the enugu state hiv/aids surveillance system. results the hiv/aids surveillance system is a passive system. reporting mechanism entails data flow from the health facilities hiv/aids monitoring and evaluation officers to the local government area m&e officers, state aids/sti control pogramme(sascp), m&e officer and finally the m&e officer in the national aids and sti control programme. data on hiv counseling and testing (hct), prevention of mother to child transmission (pmtct) and anti retroviral treatment art) are collected, collated and transmitted to the national level. the data generated serve to guide decision making at the state and lga level regarding planning, implementation and coordination of aids/sti control strategies. the system is useful, stable, acceptable, flexible, timely, but not representative, sensitive or simple. data quality is poor and inadequate data for analysis of sexually transmitted disease. there is lack of proper integration with integrated disease surveillance and response(idsr) system at the state and lga level. conclusions the hiv/aids surveillance system is useful, fairly stable, flexible, timely but not representative because not all health facilities are captured for example the military hospitals in the state, rendering all hiv/aids services, don’t send their data to the sascp unit. the system is not sensitive because of the rapid diagnostic test that is used to detect the disease. the system is not simple because the data elements are numerous and some of the stakeholders complained of incomplete filling of forms. the data quality is poor because of missing data. the resources to maintain the system are sometimes inadequate at the state and local government level and they are donor driven.some of the recommendations that was made to the state was the state ministry of health should provide fund for transport and to strengthen data collection at lga level, adequate staffing of the m&e unit at the lga level,emphasis on training and retraining of the m&e officers and data clerks at state and lga level and the training should be periodic due to frequent staff attrition.frequent supportive supervision at the health facility and lga levels for data quality assurance.strengthening data collection from private health facilities and lga level by providing funds for transport and review and analysis of data for decision making. keywords surveillance; evaluation; acquired immunodeficiency syndrome; human immunodeficiency virus; nigeria acknowledgments thanks to nigeria field epidemiology and laboratory training programme and state hiv/aids control programme, enugu state ministry of health references 1 centres for disease control updated guidelines for evaluating surveillance system, 2001. *chinyere c. ezeudu e-mail: chiezeudu@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e109, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts a provincial acute febrile illness surveillance network (gafinet), south korea yeon-hee sung1, seon-ju yi*1, kyoung-ho song2, yang lee kim3, jeong yeon kim4, jieun kim5, hong bin kim2, eu suk kim2, heeyoung lee1, soo-nam jo1, kyung-nam kim1, na-young kim1, eun-jung park1, yu-ra lee1, hye-jin jeong1, sungyong choi6 and won suk choi7 1gidcc(gyeonggi infectious disease control center), seongnam-si, korea (the republic of); 2department of internal medicine, seoul national university bundang hospital, seongnam-si, korea (the republic of); 3the catholic univ. of korea uijeongbu mary’s hospital, uijeongbu-si, korea (the republic of); 4gpmc(gyeonggi provincial medical center, suwon-si, korea (the republic of); 5department of internal medicine, hanyang university college of medicine, seongdong-gu, korea (the republic of); 6icdc(incheon center for infectious diseases control), namdong-gu, korea (the republic of); 7infectious diseases, department of internal medicine, korea university college of medicine, ansan-si, korea (the republic of) objective the objectives are to introduce a provincial level surveillance system, which has been initiated in response to the mers-cov outbreak of south korea, and describe findings from systematic investigation of individual admissions attributed to acute febrile illness for the first year. introduction in may 2015, the mers-cov outbreaks in south korea was sparkled from a hospital of gyeonggi-do province (1). in response to this outbreak, the provincial government and infectious disease control center (gidcc) initiated an emergency department (ed) based gyeonggi-do provincial acute febrile illness (afi) surveillance network (gafinet) to monitor for a subsequent outbreak of emerging or imported infectious diseases since september 2016. gyeonggi-do province is located in the north-west of south korea, surrounds the capital city seoul, and borders north korea (figure 1). considering the geographical coverage, gafinet initiative involves ten hospitals, consisted of four university-affiliated hospitals and six provincial medical centers in gyeonggi-do province. these hospitals participated in this network voluntarily, and most staffs including five infectious diseases specialists had direct or indirect experiences in dealing with mers-cov patients. methods periodic surveillance for finding afi patients in ed of participating hospitals was performed prospectively (figure 2). afi was defined as 1) fever: body temperature ≥38 °c at admission, or 2) chief complaint of febrile or chilling sensation. demography of patients and chief complaints were investigated in this first step (crf #1). cases were classified into six categories based on their clinical diagnoses: 1) respiratory afi [afri], 2) gastroenteric afi [afgi], 3) exanthematic afi [afei], 4) other infectious afi, 5) non-infectious afi, and 6) unclassified afi. participating infectious diseases specialists regularly reviewed and reformed this classification. because the aim of gafinet is primarily monitoring community or aboard-acquired infection, nosocomial afi cases or the patients transferred from another hospital were excluded. when a patient had a history of international travel or he/she were undiagnosed in three days after ed admission, more comprehensive information (crf#2 & #3) including history and final diagnosis were obtained. for a baseline data, ageand sex-stratified ed visits were also gathered weekly. the proportion of afi cases per 1000 visits was determined for one week period and analyzed by febrile diseases categories with age-stratification. characteristics of cases with international travel histories or undiagnosed cases were also described separately. results between 30 september and 3 december 2016, about 6,000 of patients visited ed of ten hospitals a week, and 10% of them were afi cases. the proportion of afri was the largest, 33.64 to 71.96 per 1000 visits/week, and the second-largest was the other infectious afi. the proportion of afri showed the highest rate at the age 1-9 years, while those of afgi and afei were the highest at the age under 19 year and 70-79 years, respectively. 31 cases with international travel history were reported, and the majority of them traveled china and south east asian counties. some of them were suspected cases of zika viral infection, mers-cov, or viral hemorrhagic fever. 3 cases undiagnosed until discharge were also reported. conclusions gyeonggi-do province was the most affected region in the 2015 mers-cov outbreak, 67 of 185 cases were residents of this province. gafinet initiative is a meaningful step for rapid detection of emerging or overseas imported infectious diseases at the provincial level. to validate data and co-analysis with pre-existing surveillance data, we need a more long-term of continuous operation of gafinet. as a next step, we are preparing the additional lab-based surveillance system to detect new or re-emerging pathogens. figure 1. locations of sentinel hospitals participating in gafinet (blue dots: university affiliated hospitals; red dots: provincial medical centers) isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts figure 2. flow diagram showing patient flow and collection of clinical data in gafinet keywords acute febrile illness; south korea; emergency department based surveillance; communicable disease surveillance; syndomic surveillance acknowledgments this study was supported by the korea centers for disease control and prevention (kcdc) and gyeonggi-do provincial government. references 1. park h et al. epidemiological investigation of mers-cov spread in a single hospital in south korea, may to june 2015. euro surveill. 2015;20(25):1-6. *seon-ju yi e-mail: yiseonju@gidcc.or.kr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e88, 2018 building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi building and launching an online quality improvement information exchange for home visiting programs in missouri diana r. kennedy, mha, mshi1,2*, suzanne a. boren, phd, mha1,2, julie m. kapp, mph, phd1, eduardo j. simoes, md, msc, mph1,2 1. department of health management and informatics, university of missouri, columbia, missouri, usa 2. mu informatics institute, university of missouri, columbia, missouri, usa *diana r. kennedy, mha, mshi, university of missouri, department of health management and informatics, one hospital drive, ce707 cs&e building, columbia, mo, 65212, usa. kennedyd@health.missouri.edu abstract continuous quality improvement initiatives (cqii) in home visiting programs have traditionally occurred within a local implementing agency (lia), parent organization, or funding provision. in missouri, certain lias participate in the missouri maternal, infant, and early childhood home visiting program (miechv). their cqii activities and the coordination of cqi efforts across agencies are limited to quarterly meetings to discuss barriers to service delivery and newsletters. their designed cqi process does not include evaluation of program fidelity or assessment nor supports to assist with identifying and prioritizing areas where improvement is needed. therefore, much of lia cqii are often lost to the benefit of external agencies facing similar challenges. we developed a virtual environment, the missouri miechv gateway, for cqii activities. the gateway promotes and supports quality improvement for lias while aligning stakeholders from seven home visiting lias. development of the gateway environment aims to complement the existing miechv cqi framework by: 1) adding cqi elements that are missing or ineffective, 2) adding elements for cqi identification and program evaluation, and 3) offering lias a network to share cqi experiences and collaborate at a distance. this web-based environment allows lia personnel to identify program activities in need of quality improvement, and guides the planning, implementation, and evaluation of cqii. in addition, the gateway standardizes quality improvement training, collates overlapping resources, and supports knowledge translation, thus aimed to improve capacity for measurable change in organizational initiatives. this interactive web-based portal provides the infrastructure to virtually connect and engage lias in cqi and stimulate sharing of ideas and best practices. this article describes the characteristics, development, build, and launch of this quality improvement practice exchange virtual environment and present results of three usability pilot tests and the site launch. briefly, prior to deployment to 58 users, usability pilot testing of the site occurred in three stages, to three defined groups. pilot testing results were overall positive, desirable, and vital to improving the site prior to the full-launch. the majority of reviewers stated they would access and use the learning materials (87%), use the site for completing cqii (80%), and reported that the site will benefit their work teams in addressing agency challenges (66%). the majority of reviewers also approved of the developed fidelity assessment: as, easy to use (79%), having a clear purpose (86%), providing value in selfidentification of cqii (75%), and recommendations were appropriate (79%). the system usability scale (sus) score increased (10%) between pilot groups 2 and 3, with a mean sus score of 71.6, above building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi introduction & implications for policy and practice early childhood home visiting programs date back to the 1880s and deliver a vital public service of providing and connecting families with health, educational, and economic resources to support optimal development [1]. the home visitor service delivery model provides intervention and mediation techniques to families with young children [1]. presently, seven local implementing agencies (lia) in missouri are participating in the missouri department of health and senior services (modhss) maternal, infant, and early childhood home visiting program (miechv). the lias coordinate continuous quality improvement initiatives (cqii) as part of a modhss contract deliverable and, potentially, as required by their accrediting model. lias adhere to a designed continuous quality improvement (cqi) process focused on broad programmatic strategies. the miechv program evaluation found the direction and coordination of cqi efforts across agencies were limited to quarterly meetings and newsletters. the evaluation also found a lack of past or present documented cqii. in addition, the miechv cqi process did not include: an evaluation of program fidelity, an identification and prioritization of problems with program implementation, the development and execution of corrective action plans to address shortfalls, and an avenue to disseminate cqi experiences. without dissemination of cqi experiences, cqii are often lost to the benefit of external agencies facing similar challenges. overall, the program evaluation suggested the need to provide more structured cqi supports to enable lias to self-administer and direct their improvement activities, independently from the broader modhss-driven miechv cqii activities. in addition, the evaluation suggested the need to enhance participating lias’ communication networks to foster connecting, sharing, collaborating, and learning across lias. in response to these program challenges, we developed a quality improvement information exchange web-based environment, the missouri miechv gateway. the gateway aims to enhance cqii by providing an infrastructure to self-assess local program activities in need of quality improvement, and to guide the planning, implementation and evaluation of cqii. in addition, the gateway virtually connects and engages lias in cqii by serving as a portal to share cqii, identify best practices, generate new learning, and network across agencies to virtually align stakeholders from the seven home visiting lias. this web-based environment aims to support current lia cqii activities, while at the same time, adding missing elements that will strengthen it. we the u.s. average of 68. the site launched to 60 invited users; the majority (67%) adopted and used the site. site stability was remarkable (6 total minutes of downtime). the site averaged 29 page views per day. keywords: continuous quality improvement, online, public health, home visiting programs, training, information exchange, capacity building. correspondence: diana r. kennedy, mha, mshi, kennedyd@health.missouri.edu doi: 10.5210/ojphi.v9i2.7520 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:kennedyd@health.missouri.edu building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi describe the characteristics, development, build, and launch of this quality improvement practice exchange virtual environment and present results of three usability pilot tests and the site launch. materials and methods concept quality improvement consists of systematic and continuous actions that lead to measurable improvement in services for targeted groups [2]. a simple web search of “quality improvement education” returns over 114 million results. the abundant search results demonstrate cqi web-based resources both exist and are publically shared. however the quality, applicability, and validity of these web-based resources must be evaluated on an individual basis. sifting through those results requires a time commitment many public health agencies cannot afford and is likely overwhelming to the average user seeking basic cqi knowledge. a search of existing literature and web resources uncovered an additional gap in the implementation of online quality improvement sites. the literature, however limited, provided insight on characteristics of other project sites. cqi resources, historically used in business and industry environments (e.g., juran 1951; ishikawa 1985; deming 1986), were translated to learning materials and tools applicable for the home visiting lias. a national association of county and city health officials project reported the most valuable web-based resources as: public health related cqi resources, training, tools, networking, and one-on-one consultation [3]. in a web-based site designed for home visiting programs in ohio and kentucky, the integration of user access to quality indicator performance reports increased user downloads by 297%, and a centralized data reporting system improved the program’s ability to meet performance indicators and standardize treatment across multiple sites [4]. to enhance cqii and promote sharing across lias, we designed and built a web-based portal to provide lias the infrastructure to self-assess local program activities in need of quality improvement, and to guide the planning, implementation and evaluation of cqii. in addition, the missouri miechv gateway virtually connects and engages lias in cqii by serving as a portal for lias to undertake and share cqii, identify best practices, generate new learning, and network across agencies. knowledge from the literature, paired with expert consultation, substantiated both the usefulness and uniqueness of building and developing the missouri miechv gateway, a proof-of-concept website which represents a passage into a quality improvement network. platform planning, designing, building, and developing a website is a significant undertaking. weekly planning sessions occurred for several months before an online environment existed. the website was developed and hosted in the amazon web service (aws) cloud environment on a standard windows 2012 platform. aws was chosen for several reasons: 1) initial low-volume use of a single virtual server in aws is free for development, allowing for economical and fast initial set-up; 2) as with most cloud hosting services, aws allows for rapid expansion of capacity in response to use demand; given the novelty of the site and uncertainty as to the ultimate volume of traffic it will generate over the long term, this flexibility is key to meeting future needs; 3) aws is the largest and most popular cloud hosting system in the u.s., so should be familiar to the widest range of future developers and maintainers who might be involved in the project; and 4) the cloud nature of aws makes it easy to building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi transfer ownership and administrative duties as necessary throughout the indefinite life of the project. the website itself is built within the wordpress cms platform for several reasons: 1) wordpress is free, open source software; 2) wordpress offers a large library of plugin extensions and one of the largest third-party developer communities in the industry, making it functionally extensible; 3) wordpress is one of the most widely deployed cms platforms in the world, so should be familiar to the widest range of future developers and maintainers who might be involved in the project, and; 4) wordpress has modest and easily accessible language and database middleware requirements (in our case, php and mysql, respectively), allowing the server operating system to acclimate to most specifications and the site to be highly portable. once the site was established on the server, a secure http over ssl domain name was registered. information exchange infrastructure and development krug’s (2014) book don’t make me think, revisited has provided valuable insight on website design and usability. krug encourages adopting expected conventions for web pages including where things are located on a page, how things work, how things look and how primary, secondary, and tertiary menus should be added and arranged [5]. we designed and built the site infrastructure with five main content pages: home; cqi process; discussions; education & training; and resources. the home page includes the following secondary pages: about; getting started; feedback; and technical support. under cqi process, the secondary pages of cqi process overview, current cqi project tracker, stage 1: plan, stage 2: do, stage 3: study, and stage 4: act pages are located. the discussion forum, groups, and members secondary pages are housed under the discussions primary menu. events, glossary, miechv, cqi storyboard library, gateway webinars, and training are located under education and training. the resources primary menu includes the secondary menu external resources and organization directory pages. site features to exclusively limit site access to lia and modhss staff, a plugin was enabled to require a username and password to log into the site. this encourages idea sharing and collaboration between the lias, without input from the general public. gateway administrators established initial accounts with a system-generated strong password. once users enter the site, they are directed to set a unique password which meets or exceeds strong password standards. user profile management affords users an identity beyond their username and promotes social networking. a registered user can upload a profile photo, a cover photo, and update information on their public profile such as their job role, professional interests, and other demographic information viewable by all registered users. to draw users into and around the site, a plan for user engagement was established. this plan incorporated the use of gamification methods. gamification is the application of incentives typically found in gaming applications to a non-gaming environment. the first gamification method incorporated was the integration of a badge system. when a user completes defined activities within the site, a medal shaped icon, referred to as a badge, is awarded to the user and displayed on the user’s profile indefinitely (figure 1). building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi figure 1, badges the second incorporated gamification method was the inclusion of a user activity point system. users who complete activities across the site (e.g., daily login, joining a group, participating in a discussion forum, uploading a storyboard, downloading a tutorial, etc.) receive points based on a defined point system ranging from 1-5 points per activity. there is no maximum number of points a user can accumulate, and users can never lose points. a user’s point sum is highlighted on their profile page and in the running footer of the website in a ranked order. the home page content includes a quick link meta slider with scrolling images and text of site pages, getting started links, a weekly poll, a weekly quote, and a listing of upcoming events. of note, the weekly poll is designed as a simple yes/no or multiple choice question to inform site development, site satisfaction, and user engagement. site features under the home menu are organized in an expected arrangement for the average web user. a user guide and frequently asked questions page exist, along with web forms designed for both submitting feedback and seeking technical assistance. the cqi process tab houses the process designed to enable lia users to complete a self-assessment, design and implement a local quality improvement project, evaluate the project, and finalize the project in the format of a cqi storyboard. w. edwards deming’s plan-do-study-act (pdsa) cycle was integrated as the preferred model to drive continuous small-scale cqi improvements [6]. the storyboard captures all stages of the plan-do-study-act (stages 1-4) cqi project, shares lessons learned, and future directions. these storyboards are stored in a searchable directory under the education and training tab. within each stage of the cqi process, guided questions and training tutorials are available to the user. specifically, within stage 1: planning, users begin the cqi process by completing a program fidelity and cqi assessment, a structured survey where the user self-reports the extent to which theoretical model program activities are implemented within their lia and to the miechv program implementation. this assessment provides fidelity score, recommendation, and a document suggestions areas for related potential quality improvement activities. a point system is assigned to user responses as ‘always implemented’ (4 points), ‘sometimes implemented’ [3], ‘seldom implemented’ [2], ‘never implemented’ [1], and ‘unable to evaluate’ (0). these points are summed and divided by the number of questions, excluding those in which ‘unable to evaluate’ is selected. scores are then paired with the scoring key; 75% and above are considered as operating with a high level of fidelity to the model activities. user assessments within this scoring bracket are encouraged to continue ongoing review of program assessment and quality improvement work, as needed, to maintain fidelity. scores within 30-74% strongly recommend the user begins quality improvement projects to improve fidelity, and scores within the 1-29% range recommend users take immediate action in the form of program assessment and quality improvement work to reach a higher level of fidelity. this assessment also captures additional information including the problems with implementation, barriers to implementation, and history of cqi review. following completion of the assessment, users are guided back to stage 1 where they begin a cqi project and navigate through to stage 4. finally, the cqi process tab hosts the current cqi project tracker page, which includes an interactive table listing of building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi current cqi projects in process (figure 2). users add their projects to the public table, update the project status as they progress, and search for current cqi projects of interest. the discussions tab houses three pages, discussion forum, groups, and members. buddypress, a popular social network software plugin that integrates open discussion forums, group forums (private and public), and member connections. within the discussion and group forums, users post and respond to threaded discussions with the ability to upload documents and insert url hyperlinks. the discussions sub-page sidebars include activity streams to easily guide users to active discussions and group forums. figure 2, current cqi project tracker screenshot the education and training menu tab includes the following sub-pages: events, glossary, miechv, cqi storyboard library, gateway webinars, and training. events includes a menu of offerings, from conferences to awareness weeks with each event tagged to applicable categories. these tags are populated in a word cloud-type format that appears in the footer of all site pages. any tagged category can be clicked by the user which filters the full site for content with the selected tag, affording a quick and accurate search. the glossary page includes a comprehensive and alphabetical listing of key cqi terms, definitions, resources, and site training tutorials of frequently used cqi terminology and building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi concepts, designed as a quick reference for user retrieval. the miechv page includes program specific documents such as cqi meeting minutes, cqi newsletters, forms, and reports. the cqi storyboard library, as previously discussed under the cqi process menu, includes a search-enabled directory of completed cqi project storyboards. the gateway webinars page houses recorded webinar videos, their accompanying slide decks, and announcements for upcoming webinars. lastly, the training page hosts meta slider tutorials on common cqi tools and methodologies (figure 3). figure 3, training page screenshot the resources primary menu includes the secondary menu external resources and organization directory pages. external resources consists of a listing of cqi literature and articles, cqi resources from external sites, and resources surrounding specific miechv home visiting program constructs. to support the exchange of resources among lias, the organization directory page hosts a searchable directory of client resources. users may add agency listings to the directory, edit existing listings, and/or search for listings by one or more of the following criteria: agency name, county, state, service type, and population served. the ability to receive and respond to technical inquiries is vital to the success of the site. along with a technical support web-based form, live chat has been integrated to encourage feedback and inquiry from users. users may select the live chat-expanding box (located on all site pages) to connect with a gateway administrator. messages received during off-peak hours receive an auto-reply, followed up with a response the next business day. building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi the site has been developed using a theme optimized for use with mobile devices, tablets, and desktops. site pages can be saved in pdf format and are printer friendly. the site menu header and footer are both static across all pages while the sidebars vary by page to optimize content navigation. finally, site functionality is continually reviewed, modified, and upgraded as needed to maintain a stable platform. user engagement & social media employee engagement is defined as the extent to which employees are committed to a cause or to a person in their organization, how hard they work, and how long they stay as a result of that commitment [7]. employees hold the key to organizational success in today’s competitive marketplace. however, this competitive edge will not be gained until employees are properly engaged. engagement begins from the time of recruitment and continues throughout the time that the employee commits to the organization. the issue today is not just employing and retaining talented people but in maintaining their attention at each stage of their work lives by engaging them [7]. we are now turning our sights to technology, social media specifically, for the purpose of engaging employees, and specifically lia personnel with the web-based portal. social media sites like twitter, facebook, linkedin and web 2.0 applications like rss newsfeeds and blogs have all been utilized with various degrees of success in an effort to increase engagement among employees. even though social media undoubtedly has the potential to elicit employee engagement, the organizational culture and leadership buy-in are major factors that determine if social media would be implemented for the purposes of employee engagement [8]. social media has the potential for being distracting as well as addictive. organizations may be able to control employees’ excessive use of social media by the use of management tactics [9]. thus, it is key to reaching an optimal balance between utilizing social media as a tool for employee engagement and letting it distract employees to the end that organizational productivity diminishes. a sparse amount of published literature is available on the subject of engaging employees using social media. where literature abounds, they have emanated from case studies. these case studies focus on how certain organizations utilized social media for employee engagement. however, this is not readily generalizable as culture and leadership differ from organization to organization. in adopting the capabilities of social media for employee engagement, a number of assumptions are usually made. one major assumption is that employees are all social media savvy; the lack of capability could hinder its use. conversely, excessive use of social media has the potential to take away from organizational productivity. when balanced, social media, if well aligned with organizational culture, has the potential to add to employee engagement. the miechv gateway site established social media accounts on facebook, linkedin, and twitter and added quick access buttons on the gateway. to limit the audience strictly to gateway users, the facebook and linkedin pages required requesting membership to the group while the twitter page was available for viewing/following by the general public. feed management was optimized through the use of the network management site, hootsuite. cqi-focused articles and latest news were fed to all three sites and gateway webinar and site-specific announcements were fed to the facebook and linkedin pages. building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi monitoring plugin software capabilities have been adopted and integrated to monitor site activity (site visits, discussion posts, points, badges, document downloads/uploads, etc.) and capture analytics on site use. a data analytics reporting dashboard has been created to document these analytics, on a monthly and quarterly basis, and provide insight on social marketing and engagement activity needs. to communicate and standardize software specifications across site administrators, a back-end user manual was created. this manual receives regular review and updates to document all technical specifications, theme consistencies, plugin integration, and a comprehensive listing of activities and timelines to perform site maintenance and updates. pilot testing to optimally understand the human-computer interaction with the site, user testing is widely recognized as the most reliable method [10]. prior to site deployment to the lias, pilot user testing is necessary to assess the usability of the website and provide an opportunity to make identified changes prior to full-scale deployment. to garner both expert and stakeholder feedback, pilot testing occurred in three stages, to three defined groups. the three pilot testing groups consisted of: 1) faculty and staff from the university department; 2) administrators within the state department of health; and 3) supervisors and data managers within the local implementing agencies. results pilot testing results pilot testing of group 1 occurred december 2015-january 2016, group 2 occurred february-march 2016, and group 3 april 2016. to conduct the pilot testing, group reviewers were provided access to the website, the online survey, and instructions of the review purpose and process. the survey for group 1 consisted of 13 questions, the survey from groups 2 and 3 consisted of 19 questions. three casebased scenarios were developed, describing educational and professional work experiences of potential users. the survey instructs reviewers to read each case-based scenario and identify site content pages most useful to the potential user. based on responses, the survey prompts users for narrative responses of their site experience and content suggestions. additional survey questions evaluate how frequently reviewers would visit specific site pages, the amount of content on each page, and potential site impact on coordination, collaboration, and learning of cqi education and initiatives (table 1). building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi table 1, pilot usability testing results survey question n point value (minimum attainable points: 0; maximum: 60) mean score (scale 0-4; 0 strongly disagree… 4 strongly agree) this site improves coordination of quality improvement education and initiatives. 15 46 3.06 this site encourages collaboration of quality improvement education and initiatives. 15 49 3.27 this site fosters the learning of quality improvement education and initiatives. 15 48 3.20 last, reviewers within pilot testing groups 2 and 3 were asked to evaluate their overall site experience and respond to 10 statements to measure site effectiveness, efficiency, and satisfaction as defined within a modified version of the system usability scale (sus) [11] (table 2). the university of missouri institutional review board has reviewed these three pilot testing studies. table 2, modified system usability scale survey question number question 1 i would use this cqi project process frequently. 2 i found this cqi project process unnecessarily complex. 3 i thought this cqi project process was easy to use. 4 i think that i would need the support of a technical person to be able to use this cqi project process. 5 i found the various functions (features) in this cqi project process were well integrated. 6 i thought there was too much inconsistency (ex. information, navigation) in this cqi project process. 7 i would imagine that most people would learn to use this cqi project process very quickly. 8 i found this cqi project process very cumbersome to use. 9 i felt very comfortable using this cqi project process. 10 i needed to learn a lot of things before i could begin using this cqi project process. building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi results of the three pilot testing groups were overall positive, desirable, and vital to improving the site for full-launch implementation. the majority (87%) of reviewers reported they would access/use the learning materials (e.g. cqi project process, training tutorials, resources, etc.), stated they would use the site for completing quality improvement projects (80%), and reported the site would help their work teams address internal quality improvement challenges (66%). reviewers were asked to submit feedback for expansion, modification, or further development of the site content through survey prompts allowing for narrative responses. reviewers submitted a total of 98 narrative responses, with 19 from pilot group 1 (average 2.7 per user), 50 from pilot group 2 (average of 6.3 per user), and 29 from pilot group 3 (average of 4.1 per user). reviewers reported they would “frequently/regularly” (64%) or “occasionally” visit (30%) the primary pages (including home page, cqi storyboard library, discussion forum, current cqi project tracker, and cqi process pages). reviewers reported they would “frequently/regularly” (32%) or “occasionally” (54%) visit learning and resource pages (including external resources, miechv, glossary, organization directory, training, events, and groups). in evaluating the amount of content on 20 individual site pages, 75% reported the site pages included the right amount of content, 15% reported the certain site pages were in need of improvement, and 10% reported too much content on certain site pages. the majority of reviewers reported feeling “comfortable” or “highly comfortable” in sharing experiences, practices, and/or concerns in the following site areas: open discussion forums (88%); closed groups (100%); private messaging (88%); and feedback submission forms (100%). the majority of pilot reviewers approved of the “program fidelity & cqi assessment” site assessment tool. most reported the assessment tool was easy to use (79%), and the purpose of the assessment was clear (86%). reviewers were able to use the assessment to self-identify areas where quality improvement work would be beneficial (75%). the majority of reviewers stated the assessment recommendations were appropriate (79%). reviewers identifying the next step in the cqi process after completing the assessment was the lowest rated survey item (72%). the overall satisfaction and usability of the assessment by reviewers calculated as 3.10 out of a maximum of 4 (78%) (table 3). table 3, program fidelity & cqi assessment evaluation survey results survey question group 2 n group 2 mean score group 3 n* group 3 mean score total n point value (minimum attainable points: 0; maximum: 60) mean score (scale 0-4; 0 strongly disagree… 4 strongly agree) the assessment is easy to use. 8 3.13 6 3.17 14 44 3.14 the purpose of the assessment is clearly stated. 8 3.50 6 3.33 14 48 3.43 the assessment allows users to self-identify areas 8 2.88 6 3.17 14 42 3.00 building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi where quality improvement work may be beneficial. the assessment recommendations appear on target. 8 4.13 6 3.17 14 44 3.14 at the conclusion of the assessment, it was clear what my next step in the cqi process was. 8 2.75 6 3.00 14 40 2.86 the assessment is useful in measuring fidelity to a program model. 8 3.00 6 3.00 14 42 3.00 overall usability of the program fidelity & cqi assessment (measured by individual evaluation of the following six questions: ease of use, clearly stated, recommendations appear on target, allows users to self-identify areas where quality improvement work may be beneficial, clarity of next step, and useful in measuring fidelity to a program model) 8 3.06 6 3.14 14 43 3.10 * group 3 had seven reviewers complete the survey, one reviewer did not respond to the series of questions presented in table 3. site improvements made between pilots 2 and 3 were found to benefit the overall site usability and increased the site’s sus score by 10% (6.9 points). the modified sus score of pilot testing group 2 was 68.4, group 3 was 75.4, and weighted mean score of the two pilot groups calculated as 71.6, ranking above the u.s. average of 68 [12]. site launch & results the site launched to 58 users (47 lias; 11 modhss). prior to launch, lias and modhss managers provided user registration data to establish unique user accounts with appropriate roles for gateway users. new users received an automated email with the site web address, unique username, temporary password, instructions to change their password, and general information on utilizing the site. a 10part live-stream weekly webinar series was offered to users. webinars were recorded and posted, with the accompanying slide deck, on the gateway webinars page. a total of 44 participants, across five agencies, joined the live webinars. building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi site performance and activity were measured through integrated software capabilities. site stability and performance were exceptional. throughout the 12-weeks the site was open to users, the site experienced six minutes of total site downtime. downtime was planned to update plugins. the integrated software capabilities that monitor site activity (e.g. visits, visitors, posts, downloads, activity points, technical support inquiries, etc.) capture analytics on site use. site activity metrics were gathered and reported in monthly “miechv gateway site metrics dashboard” to modhss administrators (figure 4). figure 4, miechv gateway site metrics dashboard, september 2016 at the conclusion of the contract period (09/30/2016), a total of 60 users (46 lias; 14 modhss) were registered with 40 active users (66.7% adoption rate). the site averaged 29 page views per day, awarded 3,178 site activity points, and had 540 document downloads. the training page was most frequently visited by users. in regards to social media engagement, at the end of the contract period, there were no members or followers (other than gateway administrators) to any of the three social media sites. further surveying of lia users is necessary to determine if users utilize social network sites, access social media sites in the workplace, access internet and internet-accessible devices at both work and home, and share opinions on using social media for business/employment purposes. building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi limitations the development, build, and launch of this quality improvement practice exchange virtual environment achieved its overarching aim in developing a widely accepted web-based environment to balance cqi training and practice and increase the capacity for organizational change. still, unavoidable limitations exist. first, significant run-time is essential for adoption and utility of any new technology. the short run (12 weeks) did not allow adequate time for a pilot test where generalizable impact to larger user populations could be assured. further and lengthier pilots are necessary to gather and analyze key trends over time such as fidelity score measurements, average site utilization by user, pdsa submission rates, participation by agency, benchmark and construct performance improvements, and others. second, a lack of prior studies on comparable web-based tools pose challenges in the ability to set baseline measurements of whether the web-based cqi intervention achieved meaningful success. lastly, from conceptualizing a problem for improvement to measurement of current to future-state change to monitoring and maintaining the change, there is a strong reliance on certifiable and accessible program performance data. due to data reporting system barriers, external and independent from the site, shared performance data was unable to be integrated as a site resource. however, program reports remained accessible to users via their designated lia supervisor, yet the convenience benefits of directly accessing data reports from within the site could not be achieved during the time of the study. discussion & conclusion advances in web-based collaborative workplace environments offer tremendous potential to improve dissemination of information, access to standardized educational materials, distance collaborations, and overall quality of program delivery and performance. to our knowledge, a virtual environment aimed to create a culture of quality improvement and foster cqii for home visiting program lias has not been previously reported. the missouri miechv gateway site hosts key characteristics advocated by experts in cqi, website development, and online learning environments. development of the gateway environment aimed to complement the existing miechv cqi framework. we were successful in meeting these aims by: 1) adding cqi elements that are missing or ineffective, such as standardized training tutorials, webinars, and structured cqi project forms, 2) adding elements for cqi identification and program evaluation, such as the “program fidelity and cqi assessment” and 3) offering lias a network to share cqi experiences and collaborate at a distance, through avenues such as the discussion forums and the cqi storyboard library. we built a stable site that successfully: achieved an above average (71.6) usability score, developed an acceptable (78% overall satisfaction) fidelity self-assessment tool to prioritize cqi activities, and concluded with a site adoption rate of 67% averaging 29 page views per day. throughout the process of developing and launching the missouri miechv gateway, many lessons are learned. first, the site design is fluid, and it appears to address required flexibility, creativity, and adaptability [13]. the integration of features within the site is not limited, with the widespread availability of third-party plugins one does not typically require a robust programming background to implement new features. second, encouraging open communication, stakeholder buy-in, and ongoing feedback was a necessary activity in garnering shared vision and ownership of the site [14]. stakeholder feedback remains a vital part of the site design and development. frequent meetings continued to occur with administrators of the state health department, and with lias throughout the contract period. building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi additionally, further pilots are necessary to understand how individuals are motivated to use the site. finally, the systematic approach to cqi of examining performance relative to targets requires the integration of real-time data, dashboards, and reports powered by information technology and informatics frameworks [15,16]. the site adds value to quality improvement beyond this presented scope of work. this value virtually connects users and embeds them within an environment balancing cqi training and practice [17]. expansion of this site has endless opportunities given the focus on cqi priorities. from the addition of expanded training tutorials to the expanded integration of digital tools to the measurement of fidelity and outcomes from cqii, these features and characteristics aim to improve and enhance the site. more longitudinal assessments will be needed to further measure and evaluate the gateway site impact on programs and agencies beyond the built population. acknowledgments we are grateful to the missouri department of health and senior services’ miechv program leadership and project personnel for their diligent efforts in implementing this innovative program. funding: this project is supported by the health resources and services administration (hrsa) of the u.s. department of health and human services (hhs) under grant number d89mc2791501affordable care act (aca) maternal, infant, and early childhood home visiting program in the amount of $886,521 with 0% financed with nongovernmental sources. this information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by hrsa, hhs or the u.s. government. funding acknowledgement this project is supported by the health resources and services administration (hrsa) of the u.s. department of health and human services (hhs) under grant number d89mc2791501-affordable care act (aca) maternal, infant, and early childhood home visiting program in the amount of $886,521 with 0% financed with nongovernmental sources. this information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by hrsa, hhs or the u.s. government. references 1. sweet m, appelbaum m. 2004. is home visiting an effective strategy? a meta‐analytic review of home visiting programs for families with young children. child dev. 75(5), 1435-56. pubmed https://doi.org/10.1111/j.1467-8624.2004.00750.x 2. u.s. department of health and human services health resources and services administration. quality improvement. n.d. available from: http://www.hrsa.gov/quality/toolbox/methodology/qualityimprovement/ 3. davis p, solomon j, gorenflo g. 2010. driving quality improvement in local public health practice. j public health manag pract. 16(1), 67-71. pubmed https://doi.org/10.1097/phh.0b013e3181c2c7f7 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15369524&dopt=abstract https://doi.org/10.1111/j.1467-8624.2004.00750.x http://www.hrsa.gov/quality/toolbox/methodology/qualityimprovement/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20009647&dopt=abstract https://doi.org/10.1097/phh.0b013e3181c2c7f7 building and launching an online quality improvement information exchange for home visiting programs in missouri online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e189, 2017 ojphi 4. ammerman r, putnam f, kopke j, gannon t, short j, et al. 2007. development and implementation of a quality assurance infrastructure in a multisite home visitation program in ohio and kentucky. j prev intervent community. 34(1-2), 89-107. pubmed https://doi.org/10.1300/j005v34n01_05 5. krug s. don't make me think, revisited: a common sense approach to web usability: new riders; 2014. 6. deming w. out of the crisis. cambridge, ma: massachusetts institute of technology centre for advanced engineering study xiii; 1983. 7. lockwood nr. 2007. leveraging employee engagement for competitive advantage. society for human resource management research quarterly. 1, 1-12. 8. parry e, solidoro a. 2013. social media as a mechanism for engagement. advanced series in management. 12, 121-41. https://doi.org/10.1108/s1877-6361(2013)0000012010 9. herlle m, astray-caneda v. the impact of social media in the workplace. 2013:67-73. 10. woolrych a, cockton g, eds. why and when five test users aren't enough. ihm-hci 2001 conference; 2001; toulouse, fr. 11. brooke j. sus: a "quick and dirty" usability scale. usability evaluation in industry. 1996. 12. sauro j, kindlund e, eds. a method to standardize usability metrics into a single score. proceedings of the sigchi conference on human factors in computing systems; 2005: acm. 13. radaideh mda. architecture of reliable web applications software: igi global; 2006. 14. alexander m. lead or lag: linking strategic project management & thought leadership: lead-her-ship group; 2016. 15. ghazisaeidi m, safdari r, torabi m, mirzaee m. 2015. development of performance dashboards in healthcare sector: key practical issues. acta inform med. 23(5), 317. pubmed https://doi.org/10.5455/aim.2015.23.317-321 16. institute of medicine. crossing the quality chasm: a new health system for the 21st century. washington, d.c.: national academy press; 2001. 17. anderson t. the theory and practice of online learning: athabasca university press; 2008. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17890195&dopt=abstract https://doi.org/10.1300/j005v34n01_05 https://doi.org/10.1108/s1877-6361(2013)0000012010 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26635442&dopt=abstract https://doi.org/10.5455/aim.2015.23.317-321 building and launching an online quality improvement information exchange for home visiting programs in missouri abstract introduction & implications for policy and practice materials and methods platform information exchange infrastructure and development site features user engagement & social media monitoring pilot testing results pilot testing results site launch & results limitations discussion & conclusion acknowledgments funding acknowledgement references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 1office of health information technology, minnesota department of health, saint paul, mn, usa; 2council of state and territorial epidemiologists, atlanta, ga, usa objective this project describes the informatics characteristics of clinical data repositories among minnesota health systems and their opportunities and readiness to support public health practice. the focus of the study is the use of these data for public health prevention programs and surveillance, including the opportunities to address health disparities. we examine technical, organization, and process readiness of repositories in support of epidemiology and other key public health programs, and how these data can be used as a statewide public health resource. introduction health care reform and the use of electronic health record systems is dramatically changing the health care landscape creating both challenges and opportunities for public health. high adoption of health information technology among minnesota’s health care providers has created an opportunity to advance e-health by collecting and using these data to improve population health. it has been demonstrated that interoperable clinical data repositories can serve surveillance needs to support both public health and clinical care.1 additionally, health reform is fostering the need for the collection of data to manage population health, compare and share data locally and across states for care coordination, and monitor cohorts and attributed populations. this project will provide a critical understanding of the status, challenges, and opportunities for leveraging the substantial investment in health care data systems to better support public health prevention programs, epidemiology, and surveillance to improve population health, address health disparities, and advance health equity. methods an initial scan of e-health data repositories was conducted using the 2014 minnesota hospital survey. the survey found 96 of minnesota’s 133 non-federal acute care hospitals (72%) maintain a clinical data repository to support patient care management, population health, and/or research. a selection of these health systems are invited as key informants to participate in an individual or group interview to address each health system’s e-health data repository, including the type and sources of data, levels of normalized and structured content, scope of data analytics capabilities, timeliness of data, capability for health information exchange, and readiness for use of hl7 and other standard messaging. moreover, governance, policies, and functionality of the repository are assessed. we employ qualitative methods and examine interview notes in order to identify and code themes; narrative responses are reviewed to identify initial themes, and a second assessment identifies subthemes. themes are used to determine readiness and opportunities for e-health data repositories to support population health. results results highlight first the minnesota health data repositories and provide a profile of data elements and structure, indications of data quality, and geographic distribution to support public health surveillance of chronic and infectious disease. second, preliminary findings from key informant interviews reveal a wide range of models for developing and managing e-health data repositories. a minimal number of health systems use repositories for research, and data sharing agreements are not common across organizations. barriers to sharing data tend to be associated with minnesota’s strict consent management laws and several systems noted challenges in harmonizing large and varied data sources in a timely manner. conclusions e-health data repositories have the opportunity to play a critical role in supporting population health and key epidemiologic surveillance activities.1 however, current minnesota e-health data repositories are not used in public health practice beyond attributed populations. in order to take advantage of available data, more research is needed to help understand the types of available data, quantity, quality, and need for policy considerations to support statewide public health practice, as well as to develop a recommended framework for how these repositories can supplement existing registry programs at the minnesota department of health. keywords clinical data repository; health disparities; informatics; population health; electronic health record acknowledgments this study was supported in part by the minnesota department of health and an appointment to the health systems integration system program fellowship administered by cste and funded by the centers for disease control and prevention (cdc) cooperative agreement 3u38-ot00014301s3. references 1. grannis, s, biondich, p, mamlin, b, wilson, g, jones, l, & overhage, j. how disease surveillance systems can serve as practical building blocks for a health information infrastructure: the indiana experience. amia annu symp proc. 2005:286-90. *bree allen e-mail: bree.allen@state.mn.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e86, 2016 assessing the usage of dating sites and social 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health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 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j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated 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zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines erik escobar 1,2 , ryan durgham 1,3 , olaf dammann 1,4,* , thomas j. stopka 4 1. tufts university: sackler school of graduate biomedical studies 2. st. edward’s university, school of natural sciences 3. university of massachusetts, amherst 4. tufts university school of medicine, department of public health and community medicine abstract recently, the first comprehensive guidelines were published for pre-exposure prophylaxis (prep) for the prevention of hiv infection in populations with substantial risk of infection. guidelines include a daily regimen of emtricitabine/tenofovir disoproxil fumarate (tdf/ftc) as well as condom usage during sexual activity. the relationship between the tdf/ftc intake regimen and condom usage is not yet fully understood. if men who have sex with men (msm,) engage in high-risk sexual activities without using condoms when prescribed tdf/ftc they might be at an increased risk for other sexually transmitted diseases (std). our study focuses on the possible occurrence of behavioral changes among msm in the united states over time with regard to condom usage. in particular, we were interested in creating a model of how increased uptake of tdf/ftc might cause a decline in condom usage, causing significant increases in non-hiv std incidence, using gonococcal infection incidence as a biological endpoint. we used the agent-based modeling software netlogo, building upon an existing model of hiv infection. we found no significant evidence for increased gonorrhea prevalence due to increased prep usage at any level of sample-wide usage, with a range of 0-90% prep usage. however, we did find significant evidence for decreased prevalence of hiv, with a maximal effect being reached when 5% to 10% of the msm population used prep. our findings appear to indicate that attitudes of aversion, within the medical community, toward the promotion of prep due to the potential risk of increased std transmission are unfounded. correspondence: olaf.dammann@tufts.edu doi: 10.5210/ojphi.v7i3.6104 copyright ©2015 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 introduction the centers for disease control and prevention (cdc) and us public health service recently published the first comprehensive guidelines for the use of emtricitabine/tenofovir disoproxil difumate (tdf/ftc; brand name truvada®) as a preexposure prophylactic (prep) for the prevention of hiv infection in populations with substantial risk, such as men who have sex with men (msm) [1]. guidelines for prep include a daily regimen of tdf/ftc as well as condom usage during sexual activity. studies have been conducted to determine patient compliance regarding the drug intake regimen. however, the relationship between the tdf/ftc intake regimen and condom usage is not yet fully understood. it has been suggested that prep usage among some members of high risk populations, such as msm, might engage in high risk sexual activities more frequently if prescribed tdf/ftc, placing prep users who are not wholly compliant with cdc guidelines (i.e., do not use condoms) at an increased risk for sexually transmitted diseases (std) other than hiv. in october of 2013, the cdc estimated that there were 1,144,500 persons aged 13 or older in in the united states (us) living with hiv with an estimated 50,000 new infections occurring per year [2,3]. msm have been identified as having the highest risk of becoming infected, with data from 2011 estimating that 79% of all persons in the us living with hiv were msm [1]. in 2011, msm accounted for 63% of all new hiv infections in the us and from 2008 to 2010 this group saw a 12% increase in new hiv infections [1]. for numerous reasons, msm are at a substantially higher risk for hiv infection. prevalence of hiv in the us is higher among msm than other men, with an estimated prevalence rate of 672 per 100,000 for msm compared to 10.1 per 100,000 for other men [2]. incidence of hiv among msm has also been shown to be higher among young msm (age 13-24) and msm of color [1,2]. analysis of high-risk behaviors among msm in the us has shown that a substantial number of msm engage in sexual activity with casual partners of unknown serostatus [3]. additional factors that might affect an individual’s risk of hiv infection include having a higher number of sexual partners, increased alcohol or recreational drug use, injection drug use, frequency of routine hiv testing and awareness of one’s serostatus, income, and other complex social and behavioral factors [3,4]. however, the greatest behavioral influences of one’s risk are one’s choice of partner, sex act, and condom usage, with one’s risk of hiv infection increasing as one engages in more high-risk sexual activities such as unprotected sex with a partner of unknown or positive serostatus [5]. similarly, msm are also at increased risk of non-hiv stds due to similar modes of disease transmission as well as the increased prevalence of stds within the msm population when compared to the general population [4]. the cdc reports that the prevalence rate of gonococcal infection in the year 2012 for all people in the us was 107.5 cases per 100,000 [6]. a study of std prevalence among msm visiting fenway community health, the largest clinical care provider for msm in new england, found that gonococcal infections had a prevalence of 125.8 cases per 100,000 [7]. ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 as changes in mainstream behaviors began to occur during the 1980’s when public health initiatives promoted condom usage to lower hiv/aids incidence, a similar decline in the incidence of other stds was also seen [4]. however, over time the incidence of stds among msm has increased, possibly due to changes in the mainstream perception of hiv infection and the need for condom usage [4,8]. as prep gains in notoriety and popularity, there has been some speculation within the medical community that it might normalize behavioral disinhibition, and increase the incidence and prevalence of hiv and other stds [5,9]. our study focuses on the possible occurrence of behavioral changes among msm in the us over time with regard to condom usage. in particular, we were interested in creating a model of how increased uptake of tdf/ftc might cause a decline in condom usage, causing significant increases in non-hiv std prevalence, using gonococcal infection prevalence as a biological endpoint. since not all stds are reportable to the cdc, there is some difficulty in measuring and estimating the incidence and prevalence of each disease. we chose to use gonorrhea prevalence as a biological endpoint that might elucidate potential trends in the incidence of other stds should prep become widely implemented due to more readily available reported data. we built an innovative agentbased model to assess the potential impacts of prep administration on std and hiv infection rates [10]. we hypothesized that increased usage of prep would be associated with decreases in condom usage among msm and would correspond with increased prevalence of stds within the overall population. methods we used the agent-based, open-source modeling software netlogo to design a computational epidemiological model of hiv and gonorrhea prevalence attributed to varying degrees of prep usage (and decreased condom usage) among msm in the us. we customized the model from a previously existing model (netlogo: aids) [https://ccl.northwestern.edu/netlogo/download.shtml] to fit the purposes of this study [10]. the model begins by assigning values to four global variables: average relationship commitment (duration in weeks), average coupling tendency (likelihood in percent), average condom use (likelihood in percent), and average test frequency (times per year). values for these characteristics are assigned to each agent (individual member of the simulated population), assuming a normal distribution. the percent of agents living with hiv and/or gonorrhea (10.2% and 16.3%, respectively), based upon known prevalence rates for each disease [4,9], are hard-coded into the program. the likelihood of discontinuing condom usage given prep usage is entered at the beginning of the population generation, using an adjustable slider in the user interface of netlogo, before the simulation starts. the program then generates the population by proceeding in 1-week cycles, until a userdetermined number of weeks has been reached. couples form and break up according to coupling tendency and commitment duration, respectively. for couples with a sexual encounter (on average once per week) hiv/gonorrhea infection occurs with different ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 likelihood according to published values for different types of sex acts, with each sex act conservatively set to be occurring with a 25% likelihood [11,12]. each week, the model also simulates hiv testing and post-exposure prophylaxis. once an agent tests positive for hiv, the weekly likelihood for having a sexual encounter is reduced from 100% down to a reduced percentage as preset using a slider, and the likelihood of condom usage is increased by 10% (hard-coded value). one example of the interface screen in netlogo, with our model parameters, is illustrated in figure 1. we created a model that allows for the manipulation of multiple parameters that may play a role in hiv and gonorrhea transmission using agents acting as human individuals by proxy. the parameters selected were obtained from the scientific literature and all values for each parameter remained the same in all treatments, with the exception of the parameter denoting the percentage of the sample using prep routinely (‘prep uptake’). the independent variable ‘prep uptake’ was varied to create nineteen independent treatment groups. levels of prep uptake for these groups, represented by the percent of usage within the msm population, were: 0.0 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0 percent. figure 1: netlogo interface screen (prep uptake 0.0%). depicted is an example of an interface screen for netlogo when used to conduct simulations of population-wide prep usage at 0.0%. all parameters depicted in this screen remained constant through all simulations, with the exception of “prep-usage,” ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 which was increased incrementally as an independent variable. parameters may be adjusted on a sliding scale, with each parameter adjusting the number of agents used in the simulation (initial people), the length of the simulation in weeks (stopping week), or efficacy of prep or pep at preventing seroconversion (prep efficacy and pep efficacy). additional parameters depict the likelihood of agent coupling with another agent when single (average coupling tendency) and the average length of each mutually monogamous coupling (average commitment). parameters governing sexual behavior include “average condom use” and “average test frequency,” which control the likelihood of condom use per sexual engagement and the annual frequency of an agent being tested for hiv. “prep usage” and “pep usage” control the population-wide percentage of agents that use either prophylactic, respectively. “pep frequency” depicts the likelihood that the subset of msm using pep will use the prophylactic per sexual engagement. “transmission aversion” depicts the reduction in likelihood that a known hivpositive agent will forgo sexual engagement. additional parameters not depicted but modified in the source code, include the frequency of sexual engagement, the likelihood of engaging in any particular sexual act, the infection risk factors for each act, and the initial prevalence of hiv and gonorrhea at week 0. our modeled program began at week 0, generating an independent sample with the initial prevalence of hiv and gonorrhea infections within the population (10.2% and 16.7%, respectively) based upon known prevalence rates for each disease [4,9]. agents within the model exhibit monogamous coupling behaviors governed by parameters ‘length of commitment,’ which designates the average length of time in weeks that the monogamous coupling will last before separation occurs due to a generated maximum commitment time for each partner based on the value assigned to this parameter, and ‘coupling tendency,’ which designates the average likelihood that a noncommittal agent will pair with the nearest unpaired agent. parameters for ‘average commitment’ and ‘coupling tendency’ were set at 20 weeks and 50%, respectively. sexual acts encoded in the program are insertive and receptive anal and oral sexual intercourse. likelihoods for any one act occurring within the time intervals of the model are 25% each [13]. the parameter ‘condom usage’ denotes the likelihood that the agents will choose to use a condom after deciding on whether or not to engage in a sexual act or after choosing the sexual act in which to engage. ‘condom usage’ was set at 50.0%. should a condom be chosen, the likelihood of breakage, delayed application, or improper use while engaging in any act was set at 18.1% [14]. per contact risk likelihoods for hiv infection for multiple combinations of prep and condom use were used in the program (table1). although it has been documented that std infection carries a degree of influence over risk likelihoods for contracting hiv, infection risk likelihoods for hiv were calculated independently of gonorrheal infection, with risk likelihoods for gonorrhea being equal among prep users and non-users [9,12]. hiv testing and agent knowledge of personal serostatus were accounted for within the model by allowing agents to be tested with an average annual frequency for the entire ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 population. the parameter ‘testing frequency’ allows agents to be tested based on an annualized average frequency for the entire sample. agents in the model received an hiv test 1.39 times per year on average [15]. those who knew their serostatus as hiv positive were 10% more likely to use a condom before engaging in sexual activity. in addition, the parameter ‘transmission aversion’ reduces the likelihood that an agent of known hiv positive serostatus will engage in any sexual activity. ‘transmission aversion’ was conservatively set at 10% [16]. post-exposure antiretroviral prophylaxis (pep) was accounted for in the model as well. pep is an antiretroviral prophylactic used shortly after one thinks they may have been exposed to hiv to prevent seroconversion. the parameter ‘pep usage’ denotes the percentage of the sample that will use pep. ‘pep usage’ was conservatively set at 15% in all trials, based on limited surveys of msm awareness and interest in pep [17]. in addition, ‘pep frequency’ denotes how frequently those who may use pep will actually do so. ‘pep frequency’ was set at 10% for all trials, equivalent to pep being used once per 10 sexual acts, among those who may use it. the parameter “pep efficacy” denotes the reduction in likelihood that seroconversion will occur if an hiv negative agent engages with an hiv positive agent. “pep efficacy” was set at 99% [18]. prep usage within the sample was accounted for by ‘prep uptake’ or the percentage of the sample that consistently used prep for the duration of the trial. as stated, ‘prep uptake’ ranged from 0% to 90% creating 19 independent treatment groups. ‘prep efficacy’ denotes the reduction in contact risk that one receives by using prep (table 1). ‘prep efficacy’ was set at 92% [19]. the parameter ‘condom abandonment’ denotes the likelihood that those using prep will forgo condom use due to prep usage. this parameter was set at 40%, as a pessimistic estimate higher than previous studies suggest may occur due to widespread prep use [20-22]. the parameters ‘initial people’ and ‘stopping week’ denote the number of initial agents used in the model for the duration of the test and the length of the trial in weeks, respectively. all trials contained 1,000 agents and had duration of 260 weeks (5 years) in an attempt to limit the number of agents used within our trials and simulate the number of participants that may be found in a realistically possible large cohort study. we chose to limit the length of the model to five years or 260 weeks to elucidate possible short-term trends in hiv and gonococcal prevalence. we collected data by executing multiple simulations with the model for each treatment group (ni=19), with a total of 19 independent treatment groups tested. as stated, all parameters for the model were identical with the exception of ‘prep uptake.’ data for cumulative hiv and gonococcal infection were analyzed at each level of prep uptake. cumulative infection data for each disease was gathered from the final week of each trial and used to create a sampling distribution for all treatment groups. data were then analyzed to determine if they satisfied the assumptions of kruskal-wallis’ nonparametric analysis of variance (anova). data analyzed via non-parametric anova also underwent post-hoc pairwise comparative analysis via dunn’s test if warranted by the initial anova. our decision to use the non-parametric version of this test was motivated by previous pilot studies conducted by our lab finding repetitious violations of ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 the assumptions of the parametric anova, specifically, heteroscedasticity between hiv and gonorrhea data that was not corrected by repeated increases of sample size. table 1: assumed per-contact gonococcal and hiv risk percentages among sexually active msm community members based on prep and condom usage. 1 all per contact risks calculated for sex acts with unknown serostatus partner [12]. 2 prep efficacy assumed to lower per contact risk of hiv transmission by 92% [12,27,28]. 3 all per contact risk percentages for gonococcal infection assumed to be equal to their non-prep hiv counterparts. results after initial analysis of the response data to determine the appropriateness of using the non-parametric anova, we found that both sets of data satisfied the test’s assumptions. data for all treatment groups for each disease was plotted to illustrate any potential trends that may be found statistically (figure 2-3). in analysis of hiv data via non-parametric anova, we found a significant difference between the median infection rate of at least one treatment group and all others (p<0.001), providing substantial evidence to reject the test’s null hypothesis that all medians were equal. we conducted post-hoc analyses of the mean ranks generated by the non-parametric anova via dunn’s test to determine ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 significant differences between treatment groups (table 2). through comparative pairwise analysis, we found multiple significant differences between hiv prevalence, with one percentage representing initial prep uptake, and the corresponding paired percentage representing subsequent prep uptake. the significant differences we identified represented significant decreases in hiv prevalence at week 260 with a maximal effect being reached when 5% to 10% of the msm population used prep. figure 2: line plot of median cumulative gonococcal and hiv infections per percent prep uptake at 260 weeks. prevalence results for all treatment groups of prep uptake at week 260 were aggregated and organized for visual aid. depicted in blue is the prevalence for gonorrhea and depicted in red is hiv. in results from the non-parametric anova for gonorrheal data, we found no significant difference between the median infection rates of all treatment groups (p=0.387). that is to say, we found no significant evidence for increased gonorrhea prevalence due to increased prep usage at any level of sample-wide usage, with a range of 0-90% prep usage. we therefore did not conduct post-hoc analysis. ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 figure 3: boxplot of cumulative gonococcal and hiv infections per percent prep uptake at week 260. the number of gonorrhea and hiv infected agents for all simulations were aggregated at each population-wide prep usage level and organized via boxplot. table 2: post-hoc pairwise comparisons of cumulative hiv infection per percent prep uptake at week 260. numerical values indicate p-values for pairwise comparisons of initial and subsequent prep uptake treatments; values lower than the corrected alpha level indicated were deemed significant. family alpha level was 0.05. subsequent prep uptake indicates population-wide prep usage for the duration of the 5 year simulated period in comparison to the initial level of prep usage. 100 120 140 160 180 200 0 1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 0 1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 in fe c te d a g e n ts gonorrhea prep uptake (%) ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 discussion our findings indicate that daily prep usage may achieve herd immunity at a relatively low threshold among those at risk of infection. though it is not currently known how many msm in the us are using prep, our results may be used to indicate the particular threshold at which herd immunity to hiv may be conferred, and the threshold at which statistically significant decreases in hiv prevalence may be found. additionally, our findings indicate that prep uptake does not cause a significant increase in gonorrheal prevalence. our results appear to concur with the findings of recent studies analyzing the medical history and sexual behaviors of msm using prep, indicating that prep usage does not seem to have an effect on the prevalence of other stds, such as gonorrhea [23]. the annual incidence rate of new hiv infections among all people in the us has been estimated at 22.8 new infections per 100,000 [4]. meta-analysis of the explore, 19992001 study, a randomized trial conducted among msm in the us to test the efficacy of a behavioral intervention to prevent high-risk behaviors related to hiv acquisition, found that 78.2% of all study participants had engaged in some form of unprotected sex in the six month period prior to the baseline interview [3]. through the current study, we attempt to aid in combatting the ongoing hiv epidemic in the us, providing additional findings to inform appropriate target thresholds for prep, and potentially decreasing physicians’ and the public’s aversions to prep usage related to concerns that it might place target populations at increased risk for non-hiv stds. hiv prevention until recently, methods of hiv prophylaxis were limited to behavioral methods of prevention, such as condom usage, sexual abstinence, knowing the serostatus of one’s sexual partners, and avoidance of high-risk behaviors, with effective methods of biochemical prophylaxis limited to post-exposure prophylaxis (pep) used in uncommon circumstances in which a patient might have been exposed to hiv [24]. though condoms have been well documented and long recommended as the best prevention for sexually acquired hiv infection besides abstinence, some msm continue to engage in unprotected sexual activity. metadata analysis of male-to-female transmission estimates the efficacy of regular condom usage at preventing hiv acquisition at 94% [25]. data from this same analysis also suggests that inconsistent condom usage can be 79% effective at preventing hiv transmission compared to using no condom during sexual activity [25]. additional studies on “user failure” and condom breakage among msm have estimated that 7.5% of all condoms will break or fail during intercourse and 10.6% will be used partially, or have a delayed application during sex [26]. as an additional protection against hiv infection, prep is now being recommended to hiv-negative people who are at substantial risk for hiv acquisition. those at substantial risk of sexual acquisition of hiv include: serodiscordant couples, men or women with non-mutually monogamous sexual partners, msm that engage in sexual activity with partners of unknown hiv infection status, men or women who regularly engage in sexual activity without a condom with partners of unknown hiv serostatus or who are also at ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 substantial risk of hiv infection [13]. the comprehensive clinical practice guidelines released by the us public health service for prep usage outline prep as a daily dosing of tdf/ftc and condom use when engaging in sexual activity, as well as regular hiv testing, std testing, and regular clinical visits by the patient to evaluate the continued need for prep, and assess acquired risk behaviors and adherence [13]. multiple trials to test the efficacy of prep have seen that tdf/ftc usage may reduce one’s risk of hiv infection between 44% and 75% with the variation in efficacy believed to arise from differences in dosing adherence and behaviors between the populations under study [10,11]. however, data from the iprex study has shown that given the efficacy of tdf/ftc for msm in the us and the elevated risk of hiv acquisition within this subpopulation, prep may be an effective, targeted solution for slowing the us hiv epidemic [12]. controversy surrounding prep despite the possible benefits that increased knowledge, availability, and usage of prep may bring to msm, there has been some speculation that increased prep usage may place msm at greater risk of contracting other std’s due to behaviors known as “risk compensation”, or the belief that engaging one protective behavior will nullify the risk posed by a separate, risky behavior [14]. since the discovery of hiv in the early 1980’s, there have been numerous health campaigns which helped effectively change or influence the decisions and behaviors of the sexually active to help slow the growth of the hiv epidemic [3,15]. however, since the 1990’s there has been an observed increase in the incidence of hiv infection among msm which might be due in part to changing attitudes towards safer sex and hiv infection [3,15]. observed behavioral trends that may be influencing this increase include “safe sex fatigue” and “treatment optimism”, which might contribute to risk compensation among those who might otherwise engage in safer alternatives to their chosen behaviors in the context of hiv and non-hiv std prevention. [15] “safe sex fatigue” has been defined as an agreement to the phrase, “i find it difficult to maintain sexual safety,” implying difficulty in maintaining consistent condom use [15]. similarly, “treatment optimism” describes the abandonment of safer sexual practices due to the belief that one feels less concerned about acquiring hiv because medical advances, such as antiretroviral therapy, have reduced hiv-related morbidity and mortality [15]. several surveys conducted to identify attitudes toward prep interest and safer sexual practices among msm found that the majority of men surveyed were interested in prep usage depending on variations in reported efficacy, however, a substantial number of those interested also expressed that they would likely decrease their frequency of condom usage if they were to use prep [20], [12,16-18]. previous studies have linked changes in behavior among msm due to prevention fatigue or treatment optimism with increases in hiv and non-hiv std incidence [5,9]. however, these studies are not directly analogous to the current issues surrounding prep usage. a study of the likelihood of prep users to engage in risk compensation based on data gathered in the iprex trial found that users were less likely to engage in risk compensation at the end of the trial than at baseline interviews [19]. it should be noted though that data collected in these interviews were self-reported and possibly biased. ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 additionally, trial participants were provided counseling services to discourage increases in risk compensation behaviors, a service that might not be as widely available to nontrial prep users. our study findings should be considered in light of several limitations. our model assumptions of monogamy, condom usage, agent knowledge of gonorrheal infection status, and correct prep/ pep usage, while based on the scientific literature, may not apply to all msm populations. currently, our model assumes that all agents will engage exclusively in mutually monogamous relationships, which may not hold true in local communities. in addition, due to lack of available data, our model assumes that the likelihood of condom usage is equal for all sex acts, whether an agent is engaging in oral or anal sexual intercourse. adjustments may be made to the model to adjust these limiting assumptions, both by adding additional parameters for simultaneous sexual partners and adjusting the likelihood of engagement in types of sexual acts based on available data. however, implementing these modifications may require additional parameters in the case of multiple sexual partners, ascribing partners as primary or secondary, accounting for frequency of sexual engagement with each partner type, and likelihood of condom usage and type of sexual act with each partner. the model also lacks parameters for how frequently an agent will be tested for gonorrhea and, as such, all cases of gonorrhea used in this version of the model should be regarded as asymptomatic. though we found no significant relationship between prep and gonorrhea prevalence, future versions of this model should account for both testing and treatment of gonorrhea among agents to better determine the cumulative prevalence of gonorrhea to allow for a more accurate result. this model also assumes that all agents use prep/pep correctly, adhering to the daily dosage of medication without any lapses, which has not been supported by initial studies of prep/pep efficacy. parameters may be added for each prophylactic regimen, but would require accurate survey data to determine the number of msm who use each intermittently rather than as prescribed. thus far, we have not been able to test the robustness of the model or the sensitivity of each parameter. this is due to the parameters of the model working both synchronistically and asynchronistically, preventing us from accurately testing the effects of any single parameter on our results. despite these limitations, our model provides a basis for future studies in which the model may be updated and modified as future, more accurate data become available. it should also be noted that although agent-based models are particularly useful in simulating the relationships between social interactions, behaviors, culture, and the spread of disease due to their potential for increased model complexity, the agents presented in such models are merely proxies for real people currently living with disease or the potential for disease. as such, it should be remembered that there are various levels and forms of relationships and commitment that are shared between romantic and sexual partners, and numerous social and behavioral factors that can vary across subgroups and individuals. conclusion though there is increasing evidence to support the use of prep in targeted populations, such as msm, to better control the ongoing hiv epidemic, there has been concern that ojphi agent-based computational model of the prevalence of gonococcal infections after the implementation of hiv pre-exposure prophylaxis guidelines online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 7(3):e224, 2015 widespread prep usage could lead to widespread behavioral disinhibition. our findings do not support such hypotheses, however, and have found that, even with a rate of condom abandonment of 40.0% among prep users, there were no significant differences among gonorrhea prevalence across all prep usage levels. in addition we have found that the threshold of subsequent prep usage at which maximal decreases in hiv prevalence among msm depends primarily on the initial percentage of the population using prep. future studies may build upon our model to better simulate 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22153566&dopt=abstract http://dx.doi.org/10.1016/s0140-6736(11)61852-7 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19118769&dopt=abstract http://dx.doi.org/10.1016/j.jana.2008.09.006 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts electronic case reporting of stis: are non-existent codes the reason for missing information? crystal snare*1, rita altamore1, julie simon1, kim peifer1, mary stark2 and bryant karras1 1dshs std infectious disease assessment unit, washington state department of health, olympia, wa, usa; 2planned parenthood of the great northwest and the hawaiian islands, boise, id, usa objective previous research identified data gaps between traditional paperbased sti notifiable condition reporting and pilot electronic initial case reporting (eicr) relying on continuity of care documents (ccds) exported from our clinical partner’s electronic health record (ehr) software.1 structured data capture is needed for automatic processing of eicr data imported into public health repositories and surveillance systems, similar to electronic laboratory reporting (elr). coding data gaps (between paper and electronic case reports) using standardized vocabularies will allow integration of additional questions into ehr or other data collection systems and may allow creation of standard clinical data architecture (cda) templates, logical observation identifiers names and codes (loinc) panels, or fast healthcare interoperability resources (fhir) resources. furthermore, identifying data gaps can inform improvements to other standards including nationwide standardization efforts for notifiable conditions. introduction under the cdc std surveillance network (ssun) part b grant, wa doh is testing eicr of sexually transmitted infections (sti) with a clinical partner. existing standard vocabulary codes were identified to represent previously-identified information gaps, or the need for new codes or concepts was identified. methods in prior work, ccds were securely received from our clinical partner and then analyzed for gaps compared to the existing paper-based sti case reporting form.1 now, codes associated with gap concepts were identified in standard vocabularies such as loinc and snomed ct. standards were searched using online browsers offered by the standards development organizations (sdo) to identify potential codes, which were reviewed with public health epidemiologists and clinicians. gaps were listed, and wording and intent was compared to standard codes including accessory information found in the sdo browsers and a final table of recommended codes was produced. results acceptable congruity between currently used case reporting questions and coded vocabularies was found for the majority of data gaps previously identified. where data need was incongruous with standard coded vocabularies, new codes or concepts could be proposed to the sdo. pregnancy status is often missing from cda documents but is well-conceptualized in both loinc and snomed ct systems under several codes, including any current laboratory tests for pregnancy. hiv status is similarly well-conceptualized in both loinc and snomed ct both as a problem list item well as thru a variety of laboratory tests. however, problem list ehr models lack standard inclusion of date of last hiv test or dates of current pregnancy as an associated coded data element which is desirable for public health. information about the case patient’s sexual partners, need for partner sti treatment, and partner treatment completed is lacking in standard ccd documents.1 number of current sexual partners [requiring treatment] – has a snomed-ct code, but lacks a match in loinc coding system. this gap identified the need for exploration of how information about sexual partners can best be represented in interoperability artifacts, including the most useful division of information content between the information model and the standard terminology. needed concept could resemble ‘record target’ found in hl7 version 3.0 and would allow data to be provided without specifying additional codes. exploration of information model options or new codes is recommended. many codes are possible for site of infection and specific symptoms but the overall concepts of ‘symptomatic infection’ and ‘site of infection’ as coded elements would need to be added. conclusions this coded element gap analysis found that most information requested in an sti case report can be found in a ccd. gaps can be addressed by using existing standard terms except for concepts about sexual partners that might be better addressed by exploring the information model rather than through the addition of standard codes. standards use will facilitate complete case reporting using cda or fihr, for example, within a ‘blue button’ or other system with functionality for exchanging additional information about notifiable condition case patients. collaboration with clinicians, public health practitioners, informaticians, and ehr vendors in will help determine how these concepts might best be modeled. understanding data gaps involves working closely with a broad range of stakeholders, to understand why gaps exist and how well proposed solutions will meet stakeholder needs. keywords electronic case reporting; public health informatics; electronic health records; data sharing; sexually transmitted diseases acknowledgments funding for this project in part provided by the u.s. centers for disease control and prevention, std surveillance network, ps13-1306 washington doh ssun part b team, including teal bell, tom jaenicke, and travis kushner planned parenthood of the great northwest and hawaiian islands, especially dr. laurel kuehl references 1. peifer, k., stark, m., altamore, r., & simon, j. electronic case reporting of stis: assessing ehr generated ccds. ojphi. 2017 september; 9(1). doi:http://dx.doi.org/10.5210/ojphi.v9i1.7614 *crystal snare e-mail: crystal.snare@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e38, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts surveillance system of severe influenza cases admitted to the regional icu,2009-2015 esra morvan*, joanna parra, gerard roy and dominique jeannel regional office, sante publique france, orléans, france objective the study aimed at: i) analyses the regional characteristics and risk factors of severe influenza, taking into account dominant circulating virus(es) ii) estimate the regional completeness of the surveillance system. introduction every year, circulating influenza viruses generate a significant number of deaths. during the 2009 pandemic influenza a(h1n1), a national non mandatory surveillance system of severe influenza cases admitted to intensive care units(icu) was set up in france. this surveillance is regionally driven by the regional offices (cire) of santé publique france, the french public health agency. this report provides epidemiologic analysis of the recorded data since the implementation of surveillance in the centre-val de loire region over seasons 2009-10 to 2015-16 in regard of influenza epidemics dynamics. methods surveillance was carried out each year from october to april. descriptive and analytic analyses were conducted to compare population characteristics, pre-existing risk factors and the clinical data according to influenza season and dominant circulating influenza virus(es). logistic regressions were performed to identify factors associated with an increased risk of acute respiratory distress syndrome (ards) or death. two capture-recapture analyses were performed to establish the completeness of the surveillance system in the region. the first one was realized on all cases, using two data sources (hospital records/surveillance data) and the second one, only on deaths, using three data sources(additional source: medical death certificates). results from 2009-10 to 2015-16, the outbreak of influenza epidemics was started more and more late. the number of severe influenza cases reported in the loire valley varied from 19 in 2010-11 to 75 in 2014-15. overall, the most affected population was adults, from 41% in 2011-12 to 83% in 2009-10. however seniors (more than 65 years old) represented an important part of patients during three epidemics: 50% in 2011-12 and around 45% during the two last seasons; during these epidemics, men, (60%-68%), were more affected than women. patients’ pre-existing risk factors were mainly: being older than 65 years old and suffering of cardiac or pulmonary diseases. the comparison by dominant viruses over the seasons revealed that when a(h1n1) virus prevailed, severe influenza occurred mainly in adults patients with any type of pre-existing risk factors whereas when a(h3n2) virus prevailed, seniors with pre-existing pulmonary disease were the most affected. more than a third of patients declared an ards. the overall observed lethality was close to 16%. ards occurred more frequently in patients who were middle-aged (45-64 years), immunocompromised or infected with a(h1n1). pre-existing pulmonary disease was a protective factor. risk factors associated with death were being older than 65 years, male and having declared an ards. the completeness of this surveillance system was estimated by capture-recapture methods at 59% for severe influenza cases and 40% for death cases. conclusions the epidemiology of severe influenza and epidemics dynamics in the centre-val de loire follow the national trends. every season is characterized by the same dominant virus at national and regional levels in intensive care units. influenza epidemics 2009-10 and 2014-15 were particularly long and severe, the first dominated by the a(h1n1)pdm09 virus and the second by the a(h3n2). our study has demonstrated that the populations at risk of severe influenza differ according to the circulating virus(es). according to the obtained estimations, the completeness of the surveillance system, based on voluntary report by physicians, can be considered as satisfactory. regarding influenza deaths relatively low percentage of completeness may be explained by the fact that two sources are hospital based whereas the third one, medical death certificates, includes all influenzadeaths with no information on the death place. many patients were not vaccinated or their status was unknown. most cases admitted to icu presented pre-existing risk factors included in eligibility criteria in influenza vaccination policies. this study outlines the importance of vaccination as the first prevention measure. keywords severe influenza; surveillance; risk factors; completeness; centre-val de loire acknowledgments to md isabelle runge (orléans regional hospital), md denis garot, md thierry perez (tours university hospital) data providers references 1.hubert b, loury p, ollivier r. les hospitalisations pour grippe en service de réanimation dans la région des pays-de-la-loire (france), 2006-2011. numéro thématique. surveillance de la grippe, saison 2010-2011 : bilan après la pandémie. bull epidemiol hebd 2011; 37-38:401-4. 2.berger f, parent du châtelet i, bernillon p et gallay a. surveillance des infections invasives à méningocoque en france métropolitaine en 2005 – évaluation quantitative par la méthode de capture-recapture à trois sources. saint-maurice (fra) : institut de veille sanitaire, août 2010, 43 p. disponible sur : www.invs.sante.fr *esra morvan e-mail: esra.morvan@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e170, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising unc-ch, chapel hill, nc, usa objective to describe lessons learned from the transition to icd-10-cm. introduction nc detect receives icd-9-cm codes for emergency department (ed) visits and uses these codes in case definitions for syndromic surveillance (e.g. infectious disease, injury, etc.). hospitals will begin using icd-10-cm codes on october 1, 2015. as a result, preparations have been made to accommodate icd-10-cm codes in nc detect for data transmission, receipt, processing and use in the nc detect web application. methods staff from the carolina center for health informatics (cchi) at the university of nc at chapel hill (unc-ch), the injury prevention research center (iprc) at unc-ch, and the nc division of public health (nc dph) reviewed existing case definitions. while other systems are planning to map icd-10-cm codes back to icd-9-cm codes, the approach in nc was to add icd-10-cm codes to existing definitions.1 a variety of resources have been used for updating case definitions including: mapping resources, cdc input, and input from subject matter experts.2 the prioritization of case definitions was based on perceived need, frequency of usage, and ease with which the definitions could be updated. case definition review has also provided the opportunity to streamline definitions where possible, rename definitions for clarity, add new definitions, and archive those that are no longer useful. in addition, once the transition to icd10-cm has taken place, nc detect ed data will be monitored for consistency in reporting. if needed, case definitions will be revised to meet surveillance goals. results to date, 15 case definitions have been updated with icd-10-cm codes, one new definition has been created, and one definition is awaiting approval from a content expert. table 1 displays the updated case definition for heroin overdose and describes key differences between icd-9-cm and icd-10-cm. the original icd-9-cm case definition contains one diagnosis code and one external cause of injury code (e-code). the icd-10-cm case definition contains eight codes, all of which begin with the letter “t”. in icd-10-cm, the last digit contains an “a”, “d”, or “s”. these letters indicate whether the visit was the initial encounter, subsequent encounter, or sequela, respectively. another difference between icd-9-cm and icd-10-cm, is the magnitude of codes available. while icd-9-cm contains ~14,000 individual codes, icd-10-cm contains ~70,000.3-4 since icd-10-cm codes contain many codes that are not found in icd-9-cm, direct mapping is difficult. using the example displayed in table 1, the icd-10-cm case definition includes the code t40.1x3, “poisoning by heroin, assault.” there is no direct match with this code in icd-9-cm; the closest approximation would be e962.0, “assault by drugs and medicinal substances.” since the transition does not take place until october 1, 2015, results are not available for assessing the utility of the updated case definitions. preliminary results will be discussed during the presentation. conclusions the transition from icd-9-cm to icd-10-cm required extensive preparation prior to the implementation to icd-10-cm on october 1, 2015, and equally extensive monitoring after the date of implementation. it is important that local, state, and national organizations involved in syndromic surveillance share lessons learned from the transition to assist other organizations that may be struggling with how to adapt to the coding changes. table 1. example of a nc detect case definition: heroin overdoses keywords syndromic surveillance; lessons learned; definitions acknowledgments we would like to acknowledge nc dph, iprc, and unc-ch. this project is supported by the cdc as part of its core violence and injury prevention program. references 1. hicks, p, et al. preparing for the impact of the icd-9/10 transition on syndromic surveillance.” online journal of public health informatics; 2014; 7(1). 2. annest jl, et al. proposed framework for presenting injury data using icd-10-cm external cause of injury codes. ncipc & nchs, cdc. atlanta (ga): 2014. 3. cdc. the icd-10 transition and public health surveillance what you need to know. cdc; 2013 [cited 2015 aug 26]. www.cdc.gov/nchs/ icd/data/cdc_icd-10_transition_factsheet_12_2013.pdf. 4. ncipc. recommended actions to improve external-cause ofinjury coding in state-based hospital discharge and emergency department data systems. cdc. atlanta (ga); 2009. *katherine j. harmon e-mail: kjharmon@live.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e78, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts real-time surveillance and response system for ebola and other emerging infections pia d. macdonald*, gordon cressman, michael mckay, stephen loo, elizabeth mcclure and emily macguire center for global health, rti international, research triangle park, nc, usa objective we will describe a real-time mobile surveillance and case management system designed to organize data collected by multiple officers about cases and their contacts. we will discuss this surveillance system and its application for ebola and other infectious diseases in the democratic republic of the congo (drc) and other similar settings. we will review the technology, results, challenges, lessons-learned, and applicability to other contexts. introduction improving surveillance and response is a critical component of the global health security agenda. while it is impossible to predict where the next ebola outbreak will occur, it is very likely that another outbreak will occur in the drc. of the 20 known outbreaks, 7 have occurred in the drc, one as recently as 2014. to rapidly detect and respond to an ebola outbreak, we sought to develop a real-time surveillance and response system for use in drc and similar settings. rti international developed coconut surveillance mobile software, which is currently used for real-time malaria surveillance and response in zanzibar, africa, where malaria elimination efforts are underway. we took this system and adapted it for ebola as a possible tool for surveillance and response to ebola and other (re)emerging diseases. plans include pilot testing functionality at clinical sites in drc, where surveillance infrastructure is limited at the local level. coconut surveillance is a mobile disease surveillance and rapid response system currently used for malaria elimination activities. it receives suspected positive case alerts from the field via mobile phones and uses mobile software to guide surveillance officers through a follow-up process. coconut surveillance runs on android mobile devices that are used to coordinate work in the field as well as provide decision support during data collection and case management. in addition to standard case information, the gps coordinates of the case’s household are captured as well as malaria status of all household members. data are collected and accessed off-line, and are synchronized with a shared database when internet connectivity is available. this tool has been used successfully in zanzibar for more than three years and has been recognized as one of the most advanced applications of its kind. methods we adapted the coconut surveillance system for ebola surveillance and response, and expanded the system for use with other communicable diseases. with a near real-time outbreak detection system for ebola, we may reduce the response time and contain an outbreak faster. using a cloud-based data repository, the modified coconut system, known as coconut plus, also has the added value of case and case-contacts specific information sharing in real-time with the national, provincial, and district level public health authorities, who would have convenient and secure access to case and contact information via the internet. the software modifications to the coconut system have been informed by testing and stakeholder feedback. results we have developed coconut plus around the coconut software architecture, which allows the team to quickly develop specific workflows and applications, such as contact tracing, on top of a solid and well-supported base. additionally, the adaptation was structured to accommodate the build-out of multiple diseases, and is uniquely helpful for diseases that require tracking many contacts. we were granted access in drc to test interoperability with dhis 2, the most widely used health information system software in ebola effected countries. coconut plus is now using the dhis 2 organizational hierarchy definition, which means that organizational hierarchy (including information on administrative units and health care facilities) can be exported directly from dhis 2 to coconut plus. stakeholder feedback on the usability and feasibility of the adapted system has been enthusiastic, and stressed the need for additional resources to make a pilot successful, including mobile phones and improved mobility of surveillance staff in the field. the following screencast provides an overview of the application: https://www. youtube.com/watch?v=jjlt3pllw-u conclusions coconut surveillance plus solves an absence of a real-time mobile decision support disease surveillance and response system that can be used for ebola and other infectious diseases in countries with limited surveillance infrastructure. more broadly, this system could also be used for many communicable diseases that require contact tracing and an urgent outbreak response in environments that require rapid scale up of a distributed surveillance, rapid response, and case management system. keywords surveillance system; global health security; informatics; ebola; malaria *pia d. macdonald e-mail: pmacdonald@rti.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e138, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 1nc department of health and human services, raleigh, nc, usa; 2north carolina center for health informatics, chapel hill, nc, usa objective this roundtable will provide a forum for the isds community to discuss the use of emergency department (ed) triage notes in syndromic surveillance. it will be an opportunity to discuss both the benefits of having this variable included in syndromic surveillance data feeds, as well as the drawbacks and challenges associated with working with such a detailed data field. introduction the advent of meaningful use (mu) has allowed for the expansion of data collected at the hospital level and received by public health for syndromic surveillance. the triage note, a free text expansion on the chief complaint, is one of the many variables that are becoming commonplace in syndromic surveillance data feeds. triage notes are readily available in many ed information systems, including, but not limited to, allscripts, cerner, epic, hms, medhost, meditech, and t-system. north carolina’s syndromic surveillance system, nc detect, currently collects triage notes from 33 out of 122 hospitals in the state (27%), and this number is likely to increase. description representatives from the carolina center for health informatics (cchi) in the university of north carolina department of emergency medicine, and the north carolina division of public health (dph) will describe their experiences with triage notes to date and engage the audience in the discussion. there are unique rewards and challenges for both groups. presenting these issues in an organized manner will allow participants to be part of a robust conversation that covers numerous topics, from informatics to public health surveillance practice. the cchi team will describe current approaches to triage note processing, including the use of open source tools the emergency medical text processor (emt-p) and the negation tool negex. pros and cons of the current approach will be presented with attendee discussion of potential alternative approaches. the epidemiologists at dph, who review the syndromic surveillance data on a daily basis, benefit greatly from the addition of triage notes. they provide a wealth of information that can lead to the identification of disease clusters, outbreaks, cases related to ongoing outbreaks, or other events of public health importance that would otherwise not be detected. they can also aid in the follow-up and investigation process. however, this plethora of text can also cause many issues for the reviewer, such as increasing the amount of time it takes to review records and the alteration in the behavior of established signal/alert and report processes. audience engagement experiences and activities regarding the use of triage notes in north carolina will be presented to the audience from both the data processor and data reviewer perspectives. examples of triage notes and their application in surveillance practice will be provided. current status, successes, and lessons learned will be identified. an open discussion will be encouraged so that the audience will have an opportunity to share their own experiences, practices, and questions regarding the use of triage notes in syndromic surveillance. the outcome of the roundtable will be the identification of best practices and the potential creation of a triage note working group to build upon the current successes that north carolina and other jurisdictions have achieved. example facilitation questions include the following: what are the pros and cons of collecting triage notes? how do you handle negation in triage notes? how did you handle the documentation of ebola screens that often appear in triage notes? keywords informatics; syndromic surveillance; triage notes; emergency department data *zachary faigen e-mail: zachary.faigen@dhhs.nc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e179, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze national center for disease control and public health of georgia, tbilisi, georgia introduction anthrax is a widely distributed endemic infection in georgia, affecting nearly the entire country. many of the human cases that are annually registered are agriculturally acquired. anthrax remains a public health risk due to active, resistant soil foci. more than 2,000 anthrax affected areas are registered in the country; around 10% of them are active. recent reports have indicated an increase in the number of human cases as a result of contact with the environment, this is hypothesized to be due to expansion of affected foci, and this has raised concerns of the disease spreading to new areas. the control of anthrax foci is one of the main goals of the public health and veterinary service’s in georgia. a surveillance program of anthrax foci across pipeline constructions in georgia has been ongoing since 2003. field trips are conducted by national center for disease control and public health mobile teams to investigate each possible affected area across pipeline constructions. methods during 2003-2014, 9,296 soil samples were collected from 19 different regions of the country and screened for the presence of bacillus anthracis using bacteriological and molecular methods. laboratory investigations at ncdc were performed using threat agent detection and response (tadr) algorithms. results the data shows that areas around several construction sites were contaminated, therefore, workers had a potential exposure risk. overall, 40 isolates of b. anthracis were obtained from the soil samples. phenotypic (dtra approved algorithm) and genetic (e.g., snp, mlva) profile studies were conducted for strain characterization. conclusions affected territories were isolated and decontaminated, and material that provided education on how to prevent the spread of anthrax in construction zones was provided to appropriate populations. the data obtained from this study highlights the importance of surveillance programs on especially dangerous pathogens. keywords bacillus anthracis; anthrax; surveillance; pipeline; decontamination online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e165, 2016 assessing the usage of dating sites and social networking sites in newly 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and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts control and cost-benefit analysis of fast spreading diseases: the case of ebola romarie morales rosado*, lauren charles-smith and brent daniel pacific northwest national laboratory, richland, wa, usa introduction mitigating the spread of infectious disease is of great importance for policy makers. taking the recent outbreak of ebola as an example, it was difficult for policy makers to identify the best course of action based on the cost-effectiveness of what was available. in effort to address the needs of policy makers to mitigate the spread of infectious disease before an outbreak becomes uncontrollable, we have devised a cost-benefit disease control model to simulate the effect of various control methods on disease incidence and the cost associated with each of the scenarios. here, we present a case study of ebola used to quantify the cost effectiveness of vaccination and isolation methods to minimize the spread of the disease. we evaluate the impact of changing strategy levels on the incidence of the disease and address the benefits of choosing one strategy over the other with regards to cost of vaccine and isolation. methods disease. we use a general seirj model for disease transmission. here, s-susceptible, eexposed (latent), ia – infected (asymptomatic), im – infected (mild symptoms), is– infected (severe symptoms), jm – isolated (mild symptoms at home), js – isolated (severe symptoms in hospital), and rrecovered individuals. in this model, we consider the dynamics of the system and the effect of the relative transmissibility of isolated individuals (l) compared to other infected individuals1. cost. ebola vaccination and treatment are very expensive and not widely available. some preliminary data shows that it will take $73 million (m) to produce 27 m vaccines2 plus the cost for vaccine delivery and health care professionals (not included here). on the other hand, the treatment for ebola in the u.s. would cost $25,000 dollars a day per person3 to ensure proper isolation and adequate care (treatment, health care professionals, facilities and special equipment). although not included in this research, the proper isolation of ebola patients would also lead to a loss in hospital revenue of $148,000 per day due to reduced patient capacity3. here, we use $27,000 per individual hospitalized per day and $2.70 per person vaccinated. model. to evaluate the cost-effectiveness of control methods on disease transmission, we assessed the affect of different levels of vaccination coverage on the resulting number of infected individuals. then, we calculated the overall estimated cost of vaccination and resulting hospitalization for each scenario to identify the lowest costbenefit ratio. results using a base population of 10 m individuals, we ran scenarios for different levels of vaccination (µ = 0.01, 0.05, 0.1) while varying the relative transmissibility of isolated individuals (l = 0.5, 0.6, 0.65). for each combination, we calculated the incidence, vaccination and hospitalization cost per individual per day (fig 1). we note that an increase in the relative transmissibility of isolated individuals leads to a higher number of infected people and, therefore, a reduced number of candidates for vaccination and an overall increase in cost. since the cost of vaccination is 1 ten-thousandth of the cost of hospitalization, our results clearly show the cost-benefit of vaccinating over hospital treatment. in every scenario studied, we observed a measurable reduction in disease incidence when vaccinating a higher fraction of the population compared to isolating individuals post infection. conclusions given these preliminary results, we plan to extend the framework of our model to a dynamic control system where we consider the cost of vaccination and isolation embedded in the system of differential equations. this approach will allow us see the best available control implementation while minimizing the cost of treatment and vaccination. keywords control; epidemiological modeling; transmission dynamics; cost; ebola references 1. chowell d, castillo-chavez c, krishna s, qiu x, anderson ks. 2015. modeling the effect of early detection of ebola. the lancet infectious diseases, 15(2), 148-149. 2. http://www.forbes.com/sites/danmunro/2014/10/23/head-of-gsk-ebolavaccine-research-can-we-even-consider-doing-a-trial/#3cbd929665db 3. http://www.usatoday.com/story/news/nation/2014/11/25/ebola-costsadd-up/19346913/ *romarie morales rosado e-mail: romarie.morales@pnnl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e7, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts early estimation of the basic reproduction number using minimal outbreak data carl grafe* 1salt lake county health department, salt lake city, ut, usa; 2university of utah, salt lake city, ut, usa objective to present a modification to an established approach to estimating the basic reproduction number to allow estimates to be obtained at any point during an outbreak using only the current case count, number currently ill, and the size of the at-risk population. introduction the basic reproduction number represents the number of secondary infections expected to be caused by an infectious individual introduced into an entirely susceptible population.1 it is a fundamental measure used to characterize infectious disease outbreaks and is essential in developing mathematical models to determine appropriate interventions.2 much work has been done to investigate methods for estimating the basic reproduction number during the early stages of infectious disease outbreaks.3 however, these methods often require data that may not be readily available at the beginning of an outbreak.3 an approach developed by becker4 has been widely used to estimate the basic reproduction number using only the final case count and size of the at-risk population.5 a modification to this approach is proposed that allows estimates to be obtained earlier in an outbreak using only the current case count, number currently ill, and the size of the at-risk population. methods the formula derived by becker includes the number of infected subjects who have subsequently recovered, which is assumed to be known only after the outbreak has concluded. however, the number who are currently ill may also feasibly be known during an outbreak. simply subtracting this number from the current case count yields an estimate of the current number who have recovered, allowing the formula to be applied in the same manner as designed, only using earlier data. a stochastic sir (susceptible, infectious, removed) compartmental mathematical model was developed in order to test the performance of the original approach compared with the proposed modification to the approach. the model was run 1,000 times at each of nine assumed basic reproduction numbers from 1 to 5 at 0.5 intervals, which were randomly sampled from a gamma distribution throughout each outbreak in order to simulate individual variation. the duration of illness was generically allowed to vary between 1 and 5 days. the standard approach was applied after the conclusion of each outbreak, and the modified approach was applied at a randomly selected point between the beginning and end of each outbreak. the mean of these estimates was then taken to obtain an estimate for each selected basic reproduction number. confidence intervals were built around these estimates using the standard error formula developed by becker and a similar subtraction of the number currently ill to calculate the modified standard error. the model was run using r 2.1.3. results the original approach yielded estimates that were not statistically significantly different from the basic reproduction numbers selected for the model from 1 to 4. however, estimates were significantly lower for the model runs with basic reproduction numbers of 4.5 and 5. the modified approach yielded estimates for the basic reproduction number that were not significantly different from any of the basic reproduction numbers from the original approach, though estimates were significantly lower than those selected for the model for the selected basic reproduction numbers of 4.5 and 5. conclusions the modified approach appears to yield valid estimates of the basic reproduction number within a reasonable margin of error, and performs at least as well as the original approach derived by becker. both approaches are vulnerable to the same instability when the case count is low or approaches the size of the at-risk population (as shown in the results for the original approach when applied at higher basic reproduction numbers), but this is less likely to be an issue when taking estimates toward the middle of an outbreak as in the modified approach instead of at the end. keywords mathematical modeling; transmission dynamics; basic reproduction number references 1. vynnycky e, white r. an introduction to infectious disease modelling. oup oxford. 2010 may 13. 2. althaus c. estimating the reproduction number of ebola virus (ebov) during the 2014 outbreak in west africa. plos curr. 2014 september 2; 6. 3. davoudi b, miller j, meza r, meyers l, earn d, pourbohloul b. early real-time estimation of the basic reproduction number of emerging infectious diseases. phys rev x. 2012; 2(3). 4. becker ng. analysis of infectious disease data. chapman and hall. 1989. 5. mossong j, muller cp. estimation of the basic reproduction number of measles during an outbreak in a partially vaccinated population. epidemiol infect. 2000; 124. *carl grafe e-mail: cgrafe@slco.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e18, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts elaboration of diagnostic means for detection antibodies to newcastle disease virus oksana f. blotska* biotechnology and control of quality of viral preparations, the state scientific-control institute of biotechnology and strains of microorganisms, kyiv, ukraine objective a test kit for the detection of antibodies to newcastle disease virus (ndv) based on haemagglutination inhibition (hi) assay has been developed and introduced into practice for the first time in ukraine. introduction newcastle disease (nd) is the most important infectious viral disease of poultry. the world-wide economic loss from it is 2-3 billion usd per year. nd is reportable to the world organization for animal health (oie). nd is caused by virulent strains of avian paramyxoviruses belonging to type 1. industrial poultry farming is rapidly developing in ukraine. ornithological fauna of ukraine includes about four hundred species of birds, 207 of which nest within its borders. the territory of ukraine transits 3 out of 14 transcontinental global migration flows. the wild birds are the main natural reservoir of nd agents. it is necessary to control the intensity of post-vaccination immunity in poultry and the timing of revaccinations. oie recommends enzyme linked immunosorbent assays (elisa) and hi test for these purposes [1]. however, it should be noted that hi test, possessing high specificity and sensitivity, is much cheaper. therefore, it is the excellent means for nd timely surveillance. methods during the development of a new diagnostic kit, we used the reference strain “la-sota”, which was obtained from the national center of microorganism strains of ukraine. we have produced haemagglutinating antigen using embryonated spf fowl eggs and 10-11 day incubation. a dilution of the virus was inoculated in 0.1 ml volumes into the allantoic cavity and incubated at 35-37° c for 80-96 hours. for the purpose of ndv inactivation, we used aminoethyleneimine at the final concentration of 0.1%. positive serum was prepared by immunizing 60-day-old chickens with live virus once and by inactivated virus twice with an interval of 2 weeks. negative serum was obtained from healthy birds that did not contain antibodies to ndv. the investigated blood sera were inactivated by heating (56 c/30 minutes). samples of 1% suspension of chicken erythrocytes in phosphate buffered saline (ph 7.0-7.1) were used in hi tests. results the specific haemagglutination activity of the obtained antigen amounted to 10-11 log2. the test was performed using the 4ha units of the antigen. positive control serum activity was in the range of 7-9 log2. negative control serum did not give results of more than 2 log2. the estimation of the quality indexes of the components of the diagnostic test-kit was performed using harmonized methods. in order to examine sensitivity and specificity of hi test kits, antigens and sera from commercial diagnostic kits were used. also, certified negative control serum and samples of international standard sera were used, which were obtained from reference laboratories, namely against the following pathogens: avian influenza a (h5), avian influenza a (h7), egg drop syndrome'76 virus, paramyxoviruses of 2 and 3 serotypes, reovirus, avian infectious laryngotracheitis, avian infectious bronchitis virus, mycoplasma gallisepticum, and ndv. in order to ensure a high degree of specificity for the antigen, special attention was given to the selection of a stabilizer for freezedrying (the subject of a patent). comparison between the national diagnostic test kit for hi and commercial elisa kit (idexx) in the evaluation of humoral immune response to nd in vaccinated chickens was investigated by examining of serum samples (n=152). statistical analysis of data showed that the correlation coefficient for the results of both tests was 0.92. the relative sensitivity of hi test kit was 93.5% and the relative specificity 91.5%. the developed test kit was successfully used for the examination of field samples. we developed regulatory documents, completed the procedure of validation and registration in ukraine of the commercial hi test kit for the detection of antibodies to ndv. conclusions the use of the national standardized diagnostic test kit based on hi for detection of antibodies to ndv allows assessing the postvaccination antibodies level that helps to maintain the disease-free status of the ukrainian poultry industry with regard to nd. keywords newcastle; disease; haemagglutination; inhibition; surveillance references 1. oie. newcastle disease. in : office international des epizooties – manual of standards for diagnostic tests and vaccines. chapter 2.3.14 http://www.oie.int/ newcastle dis.pdf.2012. *oksana f. blotska e-mail: blotskaya@ua.fm online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e99, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts carbon monoxide poisoning in the veterans health administration, 2010 2016 gina oda*1, russell ryono1, cynthia a. lucero-obusan1, patricia schirmer1 and mark holodniy1, 2 1public health surveillance and research, department of veterans affairs, palo alto, ca, usa; 2stanford university, stanford, ca, usa objective to describe characteristics of veterans health administration (vha) patients with icd 9/10 cm inpatient discharge and/or emergency department (ed)/urgent care outpatient encounter codes for carbon monoxide (co) poisoning. introduction it is estimated that in the united states (us), unintentional non-fire related co poisoning causes an average of 439 deaths annually, and in 2007 confirmed co poisoning cases resulted in 21,304 ed visits and 2,302 hospitalizations (71 per million and 8 per million population, respectively)1. despite the significant risk of morbidity and mortality associated with co poisoning, existing surveillance systems in the united states are limited. this study is the first to focus specifically on co poisoning trends within the vha population. methods queries were performed in va praedicotm public health surveillance system for inpatient discharges and emergency room and urgent care outpatient visits with icd 9/10 cm codes for co poisoning from 1/1/2010 – 6/30/2016. a dataset of unique patient encounters with co poisoning was compiled and further classified as accidental, self-harm or unspecified. patients with carboxyhemoglobin (cohb) blood level measurements ≥ 10%2 for the same timeframe were extracted and merged with the co poisoning dataset. we analyzed for demographic, geographic and seasonal variables. rates were calculated using total unique users of vha care for matching time frame and geographic area as denominators. results there were a total of 671 unique vha patients identified with co poisoning. of these, 298 (44%) were classified as accidental, 104 (15%) self-harm, and 269 (40%) unspecified. a total of 6 patients died within 30 days of their coded diagnosis, however only 1 of these was directly attributable to co poisoning. the overall rate of co poisoning over the study time frame was 18 per million unique users of vha care. co poisoning diagnoses were obtained from 396 (59%) outpatients, 216 (32%) inpatients, and 59 (9%) patients with both and outpatient visit and inpatient admission. patients with self-harm classification were less likely to be seen in the ed (only 24 (6%) unique patients compared to 190 (48%) accidental and 182 (46%) unspecified classifications). of patients seen in the ed and subsequently admitted, patients with the classification of accidental poisoning made up the largest percentage with 36 unique patients (61%). there were 71 (11%) females compared to 600 (89%) males. the highest represented age group was 45-64 with 342 unique patients (51%). rates by us census region were highest in the midwest and northeast (27 and 23 per million unique users, respectively) compared to the west and south (15 and 13 per million unique users, respectively) (figure 1). accidental co poisonings showed a seasonal pattern with peaks occurring in late fall, winter, and early spring months (figure 2). co poisonings classified as unspecified had a similar but less pronounced pattern, while those classified as self-harm were too few to observe any pattern over time. cohb blood levels ≥ 10% were present in 111 (17%) of patients with co poisoning codes. of patients with cohb measures ≥ 10%, those with self-harm classification were least represented with only 7 unique patients (6%). accidental and unspecified classifications were equally represented with 53 (48%) and 51 (46%) unique patients, respectively. conclusions the impact of co poisoning on the vha patient population has not been well studied. the geographic distribution of the majority of cases in the midwest and northeast, and the seasonal distribution of accidental cases in colder months seems to be appropriate with respect to what is known of unintentional co poisoning as often associated with heat-generating sources3. opportunities for further investigation include how potential co poisoning cases are evaluated in vha given the low percentage of cases with cohb blood level measurements. figure 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e126, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts figure 2 keywords carbon monoxide; veterans health administration; non-infectious disease surveillance references 1. iqbal s, clower jh, king m, bell j, yip f. national carbon monoxide poisoning surveillance framework and recent estimates. public health rep. 2012 sep-oct;127:486-496. 2. council of state and territorial epidemiologists. public health reporting and national notification for carbon monoxide poisoning, position statement 13-eh-01 available from: url: http://c.ymcdn. com/sites/www.cste.org/resource/resmgr/ps/13-eh-01.pdf 3. centers for disease control and prevention. unintentional non-fire related carbon monoxide-exposuresunited states, 2001-2003. mmwr 2005;54(02);36-39. *gina oda e-mail: gina.oda@va.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e126, 2017 isds16_abstracts-final 142 isds16_abstracts-final 143 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts monitoring trends of self-diagnosis in new york city emergency departments alyssa z. chase* syndromic surveillance, nyc department of health, long island city, new york, usa objective to monitor self-reported diagnosis from new york city (nyc) emergency department (ed) chief complaints (cc). introduction the number of us adults who use the internet to access health information has increased from about 95 million in 20051 to 220 million in 20142, 3. the public health impact of this trend is unknown; in theory, patients may be able to better help the doctor arrive at the correct diagnosis, but self-diagnosed patients may also inappropriately self-treat or delay going to the doctor. the current study examines trends in self-diagnoses in nyc eds, identifies the demographic characteristics of self-diagnosed patients, and compares hospital admission rates of self-diagnosed patients with those who do not self-diagnose. methods the nyc syndromic surveillance system collects near real-time electronic data from 49 nyc eds. the data include patient cc, which is entered using either a pick list, free text, or both. to identify cases of self-reported diagnosis, we scanned ccs for variations of the words “think,” “believe,” “suspect,” “state,” “say” in combination with the words “has” “have” or “having,” excluding complaints where patients noted a non-specific disease, such as “infection” or “rash.” in addition, we looked for the phrase “as per patient” and a specific diagnosis. to identify specific diagnoses, we limited our search to non-chronic diseases, which we hypothesized were more likely to have been previously diagnosed by a physician than an infectious illness or a major health event like appendicitis or stroke. we monitored changes in the proportion of ed visits with selfdiagnoses from january 2003 to august 2014, sub-setting to ed visits that only included patient language in the cc. ccs with patient language were identified using the methods described above, and ccs that contained quotation marks or pronouns were also included. chi-square tests were used to measure associations between the rate of self-diagnoses and gender, age, and hospital admission status. admission rates were used as a proxy for severity of patient illness and were only examined from january 2010 to august 2014 due to the high rate of missing admission values before this period. results we found that 2.6% (n=1,2031,380) of all ed visits from 20032014 contained patient language in the cc. within the patient language-only subset, the percentage of ccs containing a selfdiagnosis nearly doubled from 2004 to 2014, from 3% to almost 6%. patient demographics and admission rates were examined and found to be fairly consistent over time. females self-reported a diagnosis more frequently than males (54.6% versus 45.4%, p<0.001). the most prominent differences between self-diagnosing and non-self-diagnosing patients were observed for patients 19-29 years (28.0% versus 20.6%) and adults 65+ years (7.1% versus 10.6%). self-diagnosis was also associated with lower risk of hospital admission (9.5% versus 10.4%, p<0.001). conclusions nyc eds have seen an increasing trend in self-diagnosis from 2003-2014 consistent with nation-wide increases in internet usage for health information during the same period. women and people aged 19-29 were most likely to self-diagnose, while patients aged 65+ were least likely to do so. these results are consistent with the results of a recent pew research survey that examined self-diagnosis using the internet4. we also found that individuals who self-diagnosed were less likely to be admitted to the hospital, suggesting that self-diagnosis may have a positive health impact. going forward, we will assess the accuracy of self-diagnosis by comparing patient self-diagnoses with clinician-assigned diagnoses available in hospitalization data from the statewide planning and research cooperative system of the new york state department of health. keywords syndromic surveillance; self-diagnosis; chief complaint references 1. fox, susannah. “health information online.” pew research center, washington d.c. (may 17, 2005). http://www.pewinternet. org/2005/05/17/health-information-online/, accessed on august 20, 2014. 2. pew research center, washington d.c. (2014). http://www. pewinternet.org/data-trend/internet-use/latest-stats/, accessed on august 20, 2014. 3. us census bureau. “census bureau projects u.s. population of 317.3 million on new year’s day” (december 30, 2013). http://www. census.gov/newsroom/releases/archives/population/cb13-tps112.html, accessed on august 20, 2014. *alyssa z. chase e-mail: achase@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e186, 201 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts revitalizing the global public health intelligence network (gphin) dave carter*, marta stojanovic and berry de bruijn national research council canada, ottawa, on, canada objective to rebuild the software that underpins the global public health intelligence network using modern natural language processing techniques to support recent and future improvements in situational awareness capability. introduction the global public health intelligence network is a non-traditional all-hazards multilingual surveillance system introduced in 1997 by the government of canada in collaboration with the world health organization.1 gphin software collects news articles, media releases, and incident reports and analyzes them for information about communicable diseases, natural disasters, product recalls, radiological events and other public health crises. since 2016, the public health agency of canada (phac) and national research council canada (nrc) have collaborated to replace gphin with a modular platform that incorporates modern natural language processing techniques to support more ambitious situational awareness goals. methods the updated gphin platform assembles several natural language processing tools to annotate incoming data in order to support situational awareness; broadly, gphin aims to extract knowledge from data. data are collected in 10 languages and are machine translated to english. several of the machine translation models use high performance neural networks. language models are updated regularly and support external dictionaries for handling emerging domainspecific terms that might not yet exist in the parallel corpora used to train the models. all incoming documents are assigned a relevance score. machine learning models estimate a score based on similarity to sets of known high-relevance and known low-relevance documents. phac analysts provide feedback on the scoring from time to time in the course of their work, and this feedback is used to periodically retrain scoring models. documents are assigned keywords using two ontologies: an allhazards multilingual taxonomy hand-compiled at phac, and the u.s. national library of medicine’s unified medical language system (umls). categories are assigned probabilistically to incoming articles (e.g., human infectious diseases, animal infectious diseases, substance abuse, environmental hazards), largely using affinity scores that correspond to keywords. dates and times are resolved to canonical forms, so that mentions like last tuesday get resolved to specific dates; this makes it possible to sequence data about a single event that are released at varying frequencies and with varying timeliness. cities, states/provinces, and countries are identified in the documents, and gaps in the hierarchical geographic relationships are filled in. locations are disambiguated based on collocations; the system distinguishes between and correctly resolves ottawa, ks vs. ottawa, on, canada, for example. countries are displayed with their socio-economic population statistics (gini coefficient, human development index, median age, and so on). the system attempts to detect and reconcile near-duplicate articles in order to handle instances where one article is released on a newswire and subsequently gets lightly edited and syndicated in dozens or hundreds of local papers; this improves the signal-tonoise ratio of the data in gphin for better productivity. templatebased reports (where the same document may get re-issued with a new number of cases but no other changes, for example) are still a challenge, but whitelisting tools reduce the false positive rate. the system provides tools for constructing arbitrarily detailed searches, tied to colour-coded maps and graphs that update on-the-fly, and offers short extractive summaries of each search result for easy filtering. gphin also generates topical knowledge graphs about sets of articles that seek to reveal surprising correlations in the data; for example, graphically reconciling and highlighting cases where several neighbouring countries all have reports of a mysterious disease and where a particular mosquito is mentioned. next steps in the ongoing rejuvenation involve collating discrete articles and documents into narrative timelines that track an ongoing event: collecting all data about the spread of an infectious disease outbreak or perhaps the aftermath of an earthquake in the developing world. our research is focussing on how to build line lists from such a stream of news articles about an event and how to detect important change points in the ongoing narrative. results the new gphin platform was launched in august 2016 in order to support syndromic surveillance activities for the rio 2016 olympics, and has been updated incrementally since then to offer further capabilities to professional users in 30 countries. its modular construction supports current situational awareness activities as well as further research into advanced natural language processing techniques. conclusions we improved (and continue to improve) gphin with modern natural language processing techniques, including better translations, relevance scoring, categorization, near-duplicate detection, and improved data visualization tools, all towards the goal of more productive and more trustworthy situational awareness. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts gphin search interface with map, configurable graph, and a recent alert. keywords natural language processing; software; data annotation references 1. mawudeku, a, blench, m. global public health intelligence network (gphin). 7th conference of the association for machine translation in the americas. 2006. *dave carter e-mail: david.carter@cnrc-nrc.gc.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e59, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts data-driven computational model to assess the risk of epidemics in global mass gatherings sultanah alshammari*1, 2 and armin mikler1 1university of north texas, denton, tx, usa; 2king abdulaziz university, jeddah, saudi arabia objective to develop a computational model to assess the risk of epidemics in global mass gatherings and evaluate the impact of various measures of prevention and control of infectious diseases. introduction global mass gatherings (mgs) such as olympic games, fifa world cup, and hajj (muslim pilgrimage to makkah), attract millions of people from different countries. the gathering of a large population in a proximity facilitates transmission of infectious diseases [1]. attendees arrive from different geographical areas with diverse disease history and immune responses. the associated travel patterns with global events can contribute to further disease spread affecting a large number of people within a short period and lead to a potential pandemic. global mgs pose serious health threats and challenges to the hosting countries and home countries of the participants [2]. advanced planning and disease surveillance systems are required to control health risks in these events. the success of computational models in different areas of public health and epidemiology motivates using these models in mgs to study transmission of infectious diseases and assess the risk of epidemics. computational models enable simulation and analysis of different disease transmission scenarios in global mgs. epidemic models can be used to evaluate the impact of various measures of prevention and control of infectious diseases. methods the annual event of the hajj is selected to illustrate the main aspects of the proposed model and to address the associated challenges. every year, more than two million pilgrims from over 186 countries arrive in makkah to perform hajj with the majority arriving by air. foreign pilgrims can stay at one of the holy cities of makkah and madinah up to 30-35 days prior the starting date of the hajj. the long duration of the arrival phase of the hajj allows a potential epidemic to proceed in the population of international pilgrims. a stochastic seir (susceptible−exposed−infected−recovered) agent-based model is developed to simulate the disease transmission among pilgrims. the agent-based model is used to simulate pilgrims and their interactions during the various phases of the hajj. each agent represents a pilgrim and maintains a record of demographic data (gender, country of origin, age), health data (infectivity, susceptibility, number of days being exposed or infected), event related data (location, arrival date and time), and precautionary or health-related behaviors. each pilgrim can be either healthy but susceptible to a disease, exposed who are infected but cannot transmit the infection, or infectious (asymptomatic or symptomatic) who are infected and can transmit the disease to other susceptibles. exposed individuals transfer to the infectious compartment after 1/α days, and infectious individuals will recover and gain immunity to that disease after 1/γ days. where α is the latent period and γ is the infectious period. moving susceptible individuals to exposed compartment depends on a successful disease transmission given contact with an infectious individual. the disease transmission rate is determined by the contact rate and the transmission probability per contact. contact rate and mixing patterns are defined by probabilistic weights based on the features of infectious pilgrims and the duration and setting of the stage where contacts are taking place. the initial infections are seeded in the population using two scenarios (figure 1) to measure the effects of changing, the timing for introducing a disease into the population and the likelihood that a particular flight will arrive with one or more infected individuals. results the results showed that the number of initial infections is influenced by increasing the value of λ and selecting starting date within peak arrival days. when starting from the first day, the average size of the initial infectious ranges from 0.05% to 1% of the total arriving pilgrims. using the seir agent-based model, a simulation of the h1n1 influenza epidemic was completed for the 35-days arrival stage of the hajj. the epidemic is initiated with one infectious pilgrim per flight resulting in infected 0.5% of the total arriving pilgrims. as pilgrims spend few hours at the airport, the results obtained from running the epidemic model showed only new cases of susceptible individuals entering the exposed state in a range of 0.20% to 0.35% of total susceptibles. the number of new cases is reduced by almost the same rate of the number of infectious individuals following precautionary behaviors. conclusions a data-driven stochastic seir agent-based model is developed to simulate disease spread at global mass gatherings. the proposed model can provide initial indicators of infectious disease epidemic at these events and evaluate the possible effects of intervention measures and health-related behaviors. the proposed model can be generalized to model the spread of various diseases in different mass gatherings, as it allows different factors to vary and entered as parameters. figure 1: scenarios to determine the initial number of infected or exposed individuals upon arrival. keywords mass gatherings; agent-based modeling; infectious diseases; epidemic; hajj isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. memish za, stephens gm, steffen r, ahmed qa. emergence of medicine for mass gatherings: lessons from the hajj. the lancet infectious diseases. 2012 jan 31;12(1):56-65. 2. chowell g, nishiura h, viboud c. modeling rapidly disseminating infectious disease during mass gatherings. bmc medicine. 2012 dec 7;10(1):159. *sultanah alshammari e-mail: sultanahalshammari@my.unt.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e5, 2018 cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston margaret reid1, julia gunn1, snehal shah1,2, michael donovan1, rosalind eggo3, steven babin4, ivanka stajner5, eric rogers5, katherine b. ensor6, loren raun6, jonathan i. levy7, ian painter8, wanda phipatanakul9, fuyuen yip10, anjali nath1, laura c. streichert11, catherine tong11, howard burkom4 1. boston public health commission, boston, ma 2. boston university school of medicine, dept. of pediatrics, boston, ma 3. london school of hygiene & tropical medicine 4. johns hopkins university applied physics laboratory, laurel, md 5. national oceanographic and atmospheric administration, national weather service 6. department of statistics, rice university, houston, tx 7. boston university school of public health, dept. of environmental health, boston, ma 8. university of washington school of public health, dept. of health services, seattle, wa 9. boston children’s hospital, harvard medical school, boston, ma 10. centers for disease control and prevention, national center for environmental health, division of environmental hazards and health effects, atlanta, ga 11. international society for disease surveillance, boston, ma abstract this paper continues an initiative conducted by the international society for disease surveillance with funding from the defense threat reduction agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. the goal is to enhance investigation capabilities of day-to-day population health monitors. a prior paper described the formation of consultancies for requirements analysis and dialogue regarding costs and benefits of sustainable analytic tools. each funded consultancy targets a use case of near-term concern to practitioners. the consultancy featured here focused on improving predictions of asthma exacerbation risk in demographic and geographic subdivisions of the city of boston, massachusetts, usa based on the combination of known risk factors for which evidence is routinely available. a cross-disciplinary group of 28 stakeholders attended the consultancy on march 30-31, 2016 at the boston public health commission. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi known asthma exacerbation risk factors are upper respiratory virus transmission, particularly in school-age children, harsh or extreme weather conditions, and poor air quality. meteorological subject matter experts described availability and usage of data sources representing these risk factors. modelers presented multiple analytic approaches including mechanistic models, machine learning approaches, simulation techniques, and hybrids. health department staff and local partners discussed surveillance operations, constraints, and operational system requirements. attendees valued the direct exchange of information among public health practitioners, system designers, and modelers. discussion finalized design of an 8-year de-identified dataset of boston ed patient records for modeling partners who sign a standard data use agreement. keywords: asthma exacerbation, predictive model, environmental risk factor, asthma surveillance. abbreviations: international society for disease surveillance (isds), defense threat reduction agency (dtra), biosurveillance ecosystem (bsve), emergency department (ed), boston public health commission (bphc), boston public schools (bps), centers for disease control and prevention (cdc), influenza-like illness (ili), national weather service (nws), numerical weather prediction (nwp), community multi-scale air quality (cmaq), tropical rainfall measuring mission, moderate resolution imaging spectrometer (modis), united states geological survey (usgs), normalized difference vegetation index (ndvi), enhanced vegetation index (evi), national oceanic and atmospheric administration (noaa), hybrid single particle lagrangian integrated trajectory (hysplit), bayesian network (bn), business use agreement (bua), conditional probability tables (cpts), artificial neural network (ann), time lagged recurrent networks (tlrn), physiologically equivalent temperature (pet) correspondence: howard.burkom@jhuapl.edu doi: 10.5210/ojphi.v8i3.6902 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction this paper continues a previously reported initiative to connect near-term analytical needs of public health practice with technical expertise in academia, industry, and government for the purpose of enhancing the awareness and investigation capabilities of those who monitor population health and respond to significant public health issues on a daily basis. this initiative is being conducted by the international society for disease surveillance (isds) with funding from the defense threat reduction agency (dtra) to complement the mission of the biosurveillance ecosystem (bsve) [1]. a prior paper described the formation of consultancies bringing together stakeholders for requirements analysis and dialogue regarding costs and benefits of feasible and sustainable analytic approaches [2]. the first use case, driven by the north carolina department of health, was the detection of clusters of emergency department (ed) visits of potential concern without classifying patient records into preconceived syndrome groups. the current paper reports the efforts and findings of a consultancy focused on the use case of improving public health response to asthma exacerbations. a key objective is to supply enough information to aid other jurisdictions in replicating the legal, data-related, and sociotechnical requirements of building similar collaborations. http://ojphi.org/ mailto:howard.burkom@jhuapl.edu cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi the goal of the consultancy was to assess the feasibility and functional requirements of a predictive tool to discern periods of increased exacerbation risk in order to improve the timeliness and targeting of preventive actions by the boston public health commission (bphc). to facilitate identification and adoption of policy, system or programmatic efforts, bphc and isds invited leaders of key city agencies, massachusetts department of public health, asthma and environmental researchers and clinicians at boston’s pediatric hospitals. discussions were informed by the knowledge that boston’s lower income residents are disproportionately affected, as are residents of color and those in specific neighborhoods. this disparity must inform the nature of the interventions adopted. asthma burden asthma is a chronic inflammatory disease of children and adults in which the airways in the lower respiratory tract are hypersensitive to certain stimuli and react by tissue swelling and bronchial constriction. most people presenting with asthma to health care providers have symptoms of cough, dyspnea, and wheezing. presenting signs can include tachypnea, tachycardia, and diaphoresis. severe asthma exacerbations occur in about 10% of asthma patients, and prevention efforts and early intervention are essential. even in less than severe cases, permanent structural changes in the airways may occur and result in progressive loss of respiratory function over time. asthma affects approximately 24 million americans, including 6.2 million children of ages 17 years and under [3]. controlling asthma requires a combination of medication and avoiding exposures that make asthma worse, often referred to as asthma ‘triggers’. asthma can have significant consequences on the health, and social, and economic well-being of affected individuals and their families. in 2013, asthma was associated with nearly 14 million missed school days nationwide[3]. as a major cause of parental work absenteeism, childhood asthma results in loss of productivity estimated at usd $719.1 million [4]. in 2011, there were 1.8 million ed visits with a primary diagnosis of asthma, and the annual direct health care cost of asthma has been estimated to be $50 billion [5]. racial/ethnic and geographic differences in the prevalence of asthma are well documented. in 2014, asthma prevalence was higher among non-latino black americans (9.9%) than nonlatino white (7.6%) and latino americans (6.7%), which reflects a persistent disparity [3]. in 2010, black americans had higher asthma hospitalizations (29.9 per 10,000) than whites (8.7 per 10,000). while latino youth ages 18 and under have similar rates of asthma to white youth, rates of asthma hospitalizations, ed visits and deaths among latino youth are higher than their white counterpart rates [6]. currently, the northeastern united states, which includes boston, massachusetts, has the highest regional pediatric asthma rate at 10.9% compared with the midwest (8.4%), south (8.9%), and west (6.9%) [7]. from 2008-2012 in boston, the age-specific asthma ed visit and hospitalization rates for children aged 3-5 years was persistently higher for black, latino, and asian children when compared to white children [8]. in 2012, the ed visit rate for black children aged 3-5 years was 3.5 times that of white children, and hospitalization rates for black children aged 3-5 years was 4 times their white counterpart rates [8]. in addition, boston neighborhoods with http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi disproportionately more black and latino residents have higher asthma ed and hospitalization rates [8]. these differences in asthma health care utilization may reflect, in part, differential exposure to determinants of health that may prevent or trigger asthma exacerbations. these determinants include a wide range of factors from access to medical care and medications to environmental exposures related to housing and the built environment. efforts to reduce these disparities must consider the role of these determinants in primary and secondary prevention efforts. this consultancy was designed to identify methods to use existing health care and environmental data to inform the design of collaborative interventions to reduce asthma exacerbations among children in boston. given that potential interventions would require multi-sector collaboration and may include policy, systems, and environmental change beyond the scope of the local public health department, it was critical to include a wide range of stakeholders to initiate a discussion of viable and acceptable surveillance methods and requirements. the consultancy included the local public health agency, clinicians, public health practitioners, academic partners, and other city agencies. while the group recognized the need for broad representation, some stakeholders were unable to participate due to scheduling conflicts. additionally the group size was limited to ensure effective communication, and therefore not all stakeholders could be included. current and potential public health responses boston has had a robust, multi-sector response to pediatric asthma for over 15 years. while taking a comprehensive approach to asthma prevention and control, boston has targeted resources to those residents, populations, and geographic areas at higher risk. many interventions across multiple sectors have been implemented. community health workers who reflect the racial and ethnic make-up of those most affected by asthma provide in-home asthma education and coordination with city infrastructure to improve housing conditions that affect asthma. boston’s major public housing providers have changed the design, construction, and maintenance of affordable and public housing to reduce and eliminate asthma triggers. these housing-related initiatives include the introduction of smoke-free housing policies, modifications in pest control practices, and removal of carpeting which collects dust and dust mites (https://www.hsph.harvard.edu/hphi/). the boston public schools (bps), which served 57,100 students in 2015, of which 78% were low income and 77% were black or hispanic, have policies addressing tobacco use, chemicals use, and pest control practices. they have also retrofitted their school buses to reduce diesel exhaust, and have nurse leaders who are trained to manage asthma [9,10]. the bphc has among the most rigorous regulations addressing youth access to tobacco products and tobacco smoke exposure in the nation. the city’s environmental and energy department and transportation department have decreased traffic-related pollution through anti-idling policies, incentivized low emission vehicles, and continued to promote walking, biking and public transportation as viable alternatives to driving. the bphc works with pediatric hospitals and boston’s community health centers to improve clinical management systems and practices and to create a network of community-based services for asthma, particularly addressing the home environment. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi yet, clearly there is more to be done. the efforts of the extensive partnerships in place regarding asthma in boston position bphc to identify and implement policy, system, and programmatic efforts to reduce environmental risk factors for asthma exacerbation. through this consultancy, boston is exploring the potential public health benefit of developing an advanced warning system for factors that contribute to asthma exacerbations on a population level including respiratory infections, weather, and air quality conditions. through this partnership, boston is seeking to develop an actionable, feasible, acceptable, and cost efficient system that utilizes existing syndromic surveillance data quantifying conditions such as asthma, influenza, and common cold syndromes, and environmental data, such as fine particulate matter (pm2.5) and ozone levels, to predict whether individuals with asthma are at elevated risk for experiencing an exacerbation on a given day. potential responses discussed include: • developing an alert system with guidance to notify individuals with asthma or parents. • developing an alert system to notify clinicians and local pharmacies to ensure that patients have adequate medication and guidance in advance of events. • developing prevention strategies for infection with bps and head start programs, as considered in other settings [11]. • developing notification and response for weather or air quality factors, including increased nursing availability, preventive medication for activity, and reduced outdoor time. • identifying alternative spaces to the home environment, which may be affected by poor indoor environmental quality when air quality or weather conditions require staying indoors. • using geographically specific air quality information to target traffic-related public messaging or other interventions to discourage commuters from driving into the city. as the process moves forward, areas for intervention will be prioritized. these are bold policy and system interventions and have associated costs. full engagement of city and other public agencies, clinical partners, academics and others will be required in the planning of such interventions. health data sources: boston’s syndromic surveillance system boston has a source of near-real-time ed data. in 2004, the bphc, in collaboration with the us centers for disease control and prevention (cdc), developed and implemented an electronic ed-based syndromic surveillance system. the system was developed between december 2003 and june 2004, and was implemented in july 2004. a city reporting regulation [12] was passed by bphc, requiring all emergency departments to submit a limited data record for each patient visit. the regulation was updated in 2011 to include data elements for meaningful use. since 2004, all nine acute care hospitals, which include ten emergency departments, have electronically submitted data to the bphc. data submitted by emergency departments include visit date, age, gender, race/ethnicity, zip code of residence, and chief complaint. the diagnostic codes were excluded from this initiative because they are usually not available within 24 hours http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi of a visit. chief complaints are parsed and mapped to various symptom or syndrome groups. asthma syndrome includes the terms asthma, wheezing, and reactive airway disease and known associated abbreviations and misspellings. influenza-like illness (ili) is defined as flu or (fever and (cough or sore throat)). a common cold syndrome was also recently developed that includes the terms for colds and upper respiratory infections excluding hypothermia and related terms. asthma syndrome identified increased visits primarily in the spring and early autumn. during the summer months, visits associated with asthma decreased. this yearly cycle was seen in all eight years’ data. asthma exacerbation risk factor evidence assessment risk factors for asthma include genetic and environmental variables. increases in asthma incidence over the past few decades have brought increased emphasis on environmental factors [13]. according to the us national institutes of health expert panel report on diagnosis and management of asthma [13], “atopy, the genetic predisposition for the development of an immunoglobulin e (ige)-mediated response to common aeroallergens, is the strongest identifiable predisposing factor for developing asthma.” furthermore, the report states that “viral respiratory infections are one of the most important causes of asthma exacerbation and may also contribute to the development of asthma.” the patient’s atopy may affect their response to viral infections, and viral infections may influence the development of allergic sensitization [13]. accordingly, a review by gern and busse [14] described biological and statistical evidence that asthma exacerbations are often triggered by respiratory viral infections that commonly occur during the fall cold season. asthma exacerbations can further increase as a result of rhinovirus transmission in crowded classrooms in combination with poor respiratory etiquette and hand washing practices. exposure to certain outdoor and indoor air pollutants (e.g., particulate matter, ozone, tobacco smoke) has been consistently found to result in asthma exacerbations (e.g., see the review by fu et al. [15]). environmental exposure to risk factors for asthma exacerbations may be a result of occupation, socioeconomic variables, and urban characteristics. kimes et al. [16] described how socioeconomic variables and urban characteristics could explain 95% of the variation in hospital admission rates for pediatric asthma. kimes et al. [16] further emphasized that any study finding a relationship between asthma exacerbations and socioeconomic, urban, or environmental variables must provide evidence beyond gross statistical tests of significance before a cause and effect relationship can be proposed. for example, some people have misinterpreted the lack of seasonal correlation between air pollution peaks and asthma exacerbations as evidence that air pollution plays little, if any, role in asthma exacerbations. however, this lack of agreement between peaks is due to strong seasonal effects associated with other factors that obscure, but don’t eliminate, the relationship between pollutants and asthma exacerbations [17]. because of this complexity, more advanced statistical analysis is required to uncover the effects of air pollutants, especially ozone and pm2.5 [17, 18]. jamason et al. [19] took a synoptic approach to studying overnight asthma hospitalizations and demonstrated that weather could be considered a primary factor for asthma exacerbations during the fall and winter, while air pollution was a primary factor during the spring and summer. based on these studies and many others (e.g., review by brunekeef and holgate [20]), there is ample evidence to consider air pollution a significant risk factor for asthma exacerbations. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi several studies have suggested that very low or very high ambient temperature, as well as humidity, may be associated with asthma exacerbations. higher temperatures occur when there is more sunlight, and sunlight is necessary for certain emissions to be converted to ground-level ozone [20]. cold, dry air primarily results in exercise-induced asthma exacerbations [19, 21] because the normal physiological response to cold air inhalation (e.g., nasal humidification and heating of inhaled air) may become overwhelmed by the isocapnic hyperventilation during exercise. aeroallergens, such as pollen and mold, may trigger allergy-induced asthma symptoms [13, 22]. seasons for various aeroallergens differ by geography because of variations in species and habitat and weather patterns [19, 23]. changes in these seasons may also be impacted by climate change [e.g., 24]. solomon [25] explained that pollen is only released during the dry phase after a cycle of wetting and drying. pollen transport is also facilitated because wind is often increased following a rain event. most windborne pollen grains travel about 100 meters, but some have been measured at much longer distances [25]. the prevalence of pollen in a particular area depends on the season, the abundance of plants, the wet-dry cycle, and the winds. both aeroallergens and air pollutants are facilitated by certain weather conditions that include sunlight, rainfall, humidity, temperature, and wind. during temperature inversions (warmer temperatures aloft and cooler temperatures below), outdoor air pollutants and aeroallergens may be concentrated closer to the surface and thus increase the exposure of susceptible patients with asthma. weather conditions also impact the presence of upper respiratory viral infections [26], which are primary factors in asthma exacerbations [14, 15]. available data sources for asthma exacerbation risk factor modeling for the boston area, certain environmental data are available to help predict the presence of many of these risk factors. unfortunately, there are no aeroallergen measurement stations in the area certified by the national allergy bureau of the american academy of allergy, asthma, and immunology. thus, aeroallergen data are not readily available. the following sections describe sources of data that are available for the boston area. respiratory virus infection data “common cold” infections are the result of infection by one of a large collection of respiratory viruses, and usually cause a mild syndrome lasting a few days. in asthmatics, infections are not always mild, and have been implicated in up to 80% of exacerbations in children [15]. seasonal common cold and ili syndrome prevalence and incidence data are available from local public health agencies’ biosurveillance data, along with school calendar and holiday schedules. from 2008-2015, a total of 4,240,067 ed visits were reported to the boston syndromic surveillance system. individuals who reported a boston zip code of residence accounted for 2,253,372 emergency visits (53% of total ed visits). of visits by individuals who reported a boston zip code, the asthma syndrome accounted for 37,731 visits, the ili syndrome for 31,966 visits, and the common cold syndrome for 24,208 visits. boston residents 18 years of age or younger accounted for 368,061 of all boston resident visits, 14,965 of asthma syndrome visits, 10,810 of ili syndrome visits and 4,589 of all cold syndrome visits. a dataset was created http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi specifically for this use case to be shared with solution developers through a business use agreement (bua) with bphc. information regarding the data set and bua is available by contacting research@bphc.org. field name value id integer asthma integer 1 (yes) or 0 (no) influenza-like-illness (ili) integer 1 (yes) or 0 (no) common cold integer 1 (yes) or 0 (no) visit date date (yyyy-mm-dd) gender single letter f = female m=male age (years) integer (998=<1 year old) race/ethnicity integer 2=asian 3=black or african american 4=latino 6=white 9=other 10=unknown zip code 5 characters surface and rawinsonde measurements of atmospheric data surface weather measurements are available from airports such as boston’s logan airport. data are collected and recorded according to standards established by the us national weather service (nws). surface data include air temperature, humidity, pressure, wind speed and direction, rainfall amounts, etc. in addition to these hourly surface measurements, rawinsondes (balloons carrying weather sensors) are launched twice daily to obtain vertical profiles of atmospheric variables such as temperature, winds, etc. vertical measurements are important because temperature inversions (i.e., when a higher temperature layer lies over cooler temperatures near the surface) act like a lid and serve to concentrate pollutants and aeroallergens near the surface. also, winds typically change both speed and direction with altitude. both vertical and horizontal atmospheric measurements are also used as input air quality measurements, including ozone and particulates, are available from stations at boston’s logan airport and nearby towns. for usage in models, the utility of these data for neighborhood-level modeling should be determined as a function of measurement type. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi satellite measurements of atmospheric and land use data satellite sensors measure land surface temperatures, rainfall, vegetation, and biomass burning. using the moderate resolution imaging spectrometer (modis) instrument, satellite measurements of daily daytime and nighttime temperature data with 0.05-degree resolution are available from the united states geological survey (usgs) land processes distributed active archive center using their website: us geological survey: land processes distributed active archive center (https://lpdaac.usgs.gov/data_access) rainfall data with 0.25-degree resolution was available from the nasa tropical rainfall measuring mission satellite measurements from 1997 until the end of its mission in 2014, when it was replaced by the current global precipitation measurement mission. data are available from: http://pmm.nasa.gov/data-access satellite modis measurements also included leaf area indices, including the normalized difference vegetation index (ndvi) and the enhanced vegetation index (evi). ndvi seasonal variations closely follow human-induced patterns, such as landscape disturbance and biomass burning. evi is calculated similarly to ndvi, but is considered to be more responsive than ndvi to canopy structural variations. together, ndvi and evi provide a surrogate assessment of green leaf biomass, photosynthetic activity, and the effects of seasonal rainfall. ndvi and evi data with 0.05-degree resolution available from the usgs land processes distributed active archive center: us geological survey: land processes distributed active archive center: https://lpdaac.usgs.gov/data_access numerical weather prediction (nwp) models the u.s. nws develops and runs several nwp models to provide short, medium, and long-term guidance over the u.s., north america, and the globe. nwp models are computer programs that use measured weather data, interpolate them to a regular grid, and then digitally solve equations of variables in space and time. the measured weather data include surface measurements (typically at airports) and vertical measurements (twice daily rawinsonde launches). these modeling systems can be classified as either deterministic (i.e., a single forecast is run) or probabilistic (in which an ensemble of many forecasts are run at the same time with slightly varying initial conditions and/or model configurations). the advantage of a deterministic forecast is that it is only one forecast model execution, so it can be run at the highest resolution possible, allowing for a better depiction of local weather features. the highest resolution deterministic forecast is 3 km for the continental us. at this resolution, forecasts can be made 15-48 hours in advance. an ensemble system consists of many forecasts run at the same time, so it will generally run at lower horizontal resolution, but will provide a range of possible model solutions which can be used to provide probabilistic forecasts (e.g., the percentage chance that measurable precipitation will occur at a given location). the maximum resolution is 16 km for forecasts made up to 87 hours in advance. in addition, short-term forecasting of thunderstorms can provide input to plant wetting and drying cycles that are precursors to pollen releases. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi operational air quality predictions based upon these nwp model results for weather variables, the nws also provides operational air quality prediction for the us [27]. operational predictions of the following air pollutant concentrations are issued for the contiguous 48 us states: ozone, wildfire smoke, and airborne dust. ozone and dust predictions are produced twice daily, once per day for smoke predictions, at hourly resolution for 48 hours in the future at 12 km spatial resolution. maps and tabulated values of these air pollutant predictions are available at http://airquality.weather.gov/, and binary files with predicted values in grib format are available at ftp://tgftp.nws.noaa.gov/sl.us008001/st.opnl/df.gr2/dc.ndgd/gt.aq/ historical predictions of ozone, smoke and dust are archived at the national oceanic and atmospheric administration's (noaa’s) national climatic data center and can be requested at: http://www.ncdc.noaa.gov/has/has.stationyearselect?datasetname=9950_01&subqueryby=st ation&applname=&outdest=file&dtypesort=dtypeord&stationsort=id. pm2.5 predictions are publicly available at http://nomads.ncep.noaa.gov/pub/data/nccf/com/aqm/ predictions of ozone and pm2.5 combine the noaa national centers for environmental prediction (ncep) operational north american mesoscale (nam) weather predictions with inventory based emission estimates from the epa and chemical processes within the community multi-scale air quality (cmaq) model [27, 28]. intermittent sources of windblown dust and of particulate emissions from wildfires based on u.s. forest service bluesky system are included as well. in order to reduce prediction bias, a post-processing algorithm is applied to pm2.5 predictions [29]. separate predictions of smoke from wildfires and dust from dust storms use the hybrid single particle lagrangian integrated trajectory (hysplit) model. verification of ozone and pm2.5 predictions relies on airnow compilation of observations from surface monitors. verification of smoke and dust predictions uses satellite retrievals of smoke and dust. see http://www.weather.gov/sti/stimodeling_airquality for more information about noaa nws air quality products. modeling approaches the previous sections provide public health, biological, and environmental context for the modelling capability sought for exacerbation risk prediction. figure 1 schematically summarizes the risk factors, data environment, and public health operations in an effect theory diagram. this summary view is followed by descriptions of several published model approaches, each addressing at least some of the risk factors listed. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi figure 1: effect theory diagram summarizing elements of the exacerbation risk prediction problem for enhanced public health response case-crossover logistic regression model basic concept: raun and ensor developed a model of asthma exacerbation risk in houston, texas from air quality measures [30] and have further extended this model to forecast days with increased risk of asthma exacerbation in houston. the forecast is used as the basis for preemptive messages sent to school nurses. providing this information allows school nurses to take action to avert exacerbations. the statistical model underlying the alerting system is drawn from the original study, and uses air quality measures, meteorological information, and lagged values of these measures in a conditional logistic regression framework. the pollutants examined were daily ozone, pm2.5, nitrogen dioxide (no2), sulfur dioxide, and carbon monoxide. quantity and quality of data on aeroallergen factors such as pollen and mold concentration were insufficient for inclusion. the authors developed single pollutant models employing a case-crossover design, so that individuals were treated as their own controls. for control exposure levels, raun and ensor used air quality data from days falling within the same month and on the same day of the week as exacerbations in order to limit bias. from these single-factor models, they derived multi-pollutant association models to account for interaction effects. they also segmented the overall model by time to examine trends and by demographics to examine effect modification. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi building from the multi-pollutant association models, the next objective was to identify as simplistic a model as possible to adequately forecast days with high risk of asthma exacerbation. a logistic regression model was used to classify days as high, medium, and low risk for asthma exacerbations. high-risk days are defined as days representing an increased relative risk of asthma exacerbation of ten percent or higher. alerts are issued at 6 a.m. on days predicted to be high risk. during model comparison, the pollutants found to best identify high-risk days for increased risk of asthma exacerbation were ozone and no2. figure 2 demonstrates the aggregate relationship between these key variables. summary pollutant measures important for predicting these days in houston were the level of the eight-hour maximum ozone the day before a high-risk day, and the night-time level of no2 leading up to the high risk day. wind speed the day of a high-risk day is also a key variable. essentially, on days of low wind speed in houston, the air is stagnant and pollution becomes a greater problem for the region. to incorporate wind speed, raun and ensor use forecasted wind speeds from weather underground. figure 2: temporal relationship of ozone and no2 measurements to volume of 911 calls for which asthma rescue medications were administered raun and ensor also found a seasonal relationship and account for this relationship by focusing on months of the year for which there is a high risk of an asthma exacerbation. a post-analysis of this seasonal relationship indicated that it might be influenced by the level of pollen present, as well as other aeroallergens. past applications: the model and alerting system has been applied in houston, texas. data were drawn from emergency calls for which an ambulance was dispatched and rescue medications were administered. data show decreased ambulance dispatch rates during june and july, and considerable spatial heterogeneity in dispatch rates within the city of houston. there is also a http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi differential impact, up to a factor of five, on children in schools across the city [31]. the training time period was from january 2004 through march 2013, which includes 14,300 emergency medical services (ems) ambulance-treated asthma cases in the study population (aged above 2 years). in this study period, 106 days were identified as high-risk days. the alerting system was piloted in 2015 through houston independent school district (hisd) [31] and will be deployed more fully through houston health department in the fall of 2016. during the pilot period, alerts were issued on two days, and appropriate action was taken by nurses within the houston independent school district to limit asthma exacerbations. benefits and drawbacks: a key benefit of the study is the direct feedback to health practitioners who could affect change in dispatch rate. it also provides these front-line health workers with situational awareness of the risk to children in houston. raun and ensor used ems call data for outcome measures. in boston, asthma-related ed visit records include persons transported by ems and those who arrive via other means (taxi, car) and are representative and useful as an outcome measure. furthermore, evaluation of the intervention on hospitalization rates or severe outcomes in houston would be beneficial in order to prove that the system is effective, and would secure buy-in by stakeholders. a potential drawback is that the significance and degree of influence of risk factors related to air quality in boston may differ from analogous findings in boston; the quantitative findings should not be assumed transferrable. this model did not explicitly account for respiratory viral transmission, whose influence could be considered by developers for the boston use case. joint modeling approach: population transmission and data-driven regression basic concept: eggo et al. developed a transmission model for common colds to assess the contribution of infection prevalence in asthma hospitalization patterns [32]. the model is a susceptible-infectious-recovered compartmental model, and thus represents transmission of infectious agents from person to person. the transmission model was used to simulate the prevalence of common cold infections in the population, where the population was stratified into adults and children. the behavioral parameters that governed mixing between adults and children were fitted, as well as biological parameters of viral transmission. the real school calendars for each city were inputted because children have lower contact rates when they are out of school. the model estimated a different rate of transmission of viruses on days when school was in session. these differences in contact rates impacted infection prevalence in the transmission model, and gave rise to waves of high and low infection prevalence through the year. in addition to generating and fitting the common cold model, eggo et al. fitted linear predictors of the asthma hospitalization rate to determine their impact on the target variable: daily asthma hospitalization rate. these variables included influenza prevalence, day of week, and some measures of air quality, although those variables were excluded by bayesian model comparison. by jointly fitting a model for infectious and non-infectious triggers of severe asthma exacerbations, as illustrated in figure 3, the model was able to quantify the effect of common cold infections in a novel way. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi figure 3: combination of population-based transmission and data-driven regression models, jointly fitted. past applications: eggo et al. applied this model to asthma hospitalizations in the eight largest metropolitan areas of texas (12.5 million population), using daily data from 2003-2010, where approximately 66,000 hospitalizations have a principal diagnosis code for asthma. these data came from the texas health care information collective hospital discharge database, and cover 96% of hospital discharges in the state of texas. because the data were hospital admissions, the asthma exacerbations under study were severe and required an inpatient stay in hospital. benefits and drawbacks: there are two major challenges to applying this method in boston: 1. bphc data are syndromic surveillance of ed visits, and thus are not of the same type used previously in this approach. it is critical to determine if ed visits, which may or may not result in an admission to hospital, follow the same temporal pattern as observed in admission data. 2. the geographic resolution of the study will be different: in the texas study the data were on a metropolitan level, whereas in boston, officials are interested in neighborhood level variation in ed utilization rates. this may provide challenges during the fitting procedure, and in interpreting which factors are key to the http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi observed hospitalization rate on that day. in addition, lower level data results in smaller numbers, and therefore a greater impact of stochastic effects. despite these challenges it is critical to apply a model including respiratory infection in boston because public health officials need to better understand the reasons for the patterns in their data. in addition, the benefit of including a variable of common cold prevalence is two-fold: in order to quantify the effect of interventions against other exacerbation triggers, the underlying baseline of risk and the changes to that baseline must be quantified, and respiratory viruses are a key part of that risk; and secondly we must determine whether common cold transmission or infection is a modifiable factor for asthma control, so that interventions can be developed to protect asthmatic patients from exacerbations that result from infection. population-level bayesian networks basic concept: the bayesian network (bn) is often represented as a directed acyclic graph, a diagram with nodes and directed edges. these nodes represent hypotheses that can be true or false based on conditional probability tables (cpts) and nodal inputs. the connected nodes are linked by conditional dependencies that can be based on expert reasoning and/or data-derived inference. bayesian networks thus incorporate data history and expert knowledge. they have been applied to use disparate types of evidence to determine likelihoods of significant target events. figure 4 from lin et al. [33] gives an example. figure 4: bayesian network structure for asthma detection through fusion of syndromic and environmental data. the network structure is determined by probabilistic relationships inferred among leaf nodes representing data inputs and higher level inference nodes. the figure’s structure was formed using guidance from the literature with partial data confirmation. the leaf node data were not raw data streams but were filtered by calibrated algorithms for scalability. for example, a leaf node for viral infections in school-age children might be implemented as algorithmic outputs for streams of counts of pediatric ed visits with chief complaints of wheezing or asthma diagnosis codes. combinations of leaf node levels are results weighted by cpts to derive an overall degree http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi of concern, based on the evidence sources that are available at the time; e.g. increased weighting for high pollen levels during high wind-speed measurements. past applications: these networks have been widely applied in physical science and individual patient applications, including the context of algorithm-aided asthma diagnosis [34]. scalability concerns have limited their application for population health surveillance, but researchers have used them to emulate human evidence-based decision-making by shifting the computational burden of filtering primary evidence to other analytic methods, by using simplified node structures guided by domain expertise, and by using generic probability tables [35]. they were applied to fuse human care-seeking data with drinking water quality data to monitor for waterborne disease outbreaks [36] and in related applications. benefits and drawbacks: bayesian networks can accommodate both continuous and discrete data, multiple data rates, and missing or sparse values. by virtue of their graphical nature, bns can provide the user with a transparent visualization of the basis for a derived anomalous output. they can also serve for high-level decision support, combining the results of component analytic methods such as control charts, regression models, or less transparent machine learning methods. nodal structure depends on dependencies among subnodes. examples are risk factor interactions such as the school calendar and low temperatures or combinations of wind speed and pollen levels. such dependencies may not be derivable from data alone and may depend on domain expertise that is difficult to validate. even contributions of single risk factors may be challenging to quantify without sufficient training data at useful spatial resolution. discrete event simulation modeling basic concept: simulating the joint influence of multiple stressors on health care utilization is challenging, especially for a complex multi-factorial disease like asthma. predicting the influence of interventions on health outcomes of interest is even more challenging, given that some interventions will reduce multiple exposures (e.g., availability of air conditioned spaces reducing exposure to heat and air pollution) and others will involve tradeoffs (e.g., decreased exposure to outdoor air pollution but increased exposure to indoor air pollution) or complex feedback loops. discrete event simulation modeling has been recommended [37] when simulating diseases where there are interactions between individuals and their environments, when individuals are influenced by multiple risk factors, when past history can influence future outcomes, when disease processes involve a series of associated events, and when it is of interest to evaluate multiple subpopulations. urban asthma meets all of these criteria, and discrete event simulation is therefore an appealing platform for testing candidate interventions and evaluating their consequences. past applications: discrete event simulation modeling has been used for an array of health policy analyses, with more recent application of the first discrete event simulation model of asthma and the physical environment [38–40]. briefly, this model simulated the joint influence of pm2.5, no2, indoor mold, and cockroach allergen on pediatric asthma exacerbation. the model was developed for a notional population representative of low-income residents of boston, and was used to evaluate the health implications of multiple interventions in the indoor environment (such as smoke-free housing, repairing broken exhaust fans, or integrated pest http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi management), although the model platform could easily accommodate other types of interventions and evaluate their implications. for each intervention, one million children were simulated for ten years, providing adequate sample size to evaluate changes in hospitalizations and other infrequent outcomes as a function of incremental changes in exposure. the final analyses allowed for comparisons across individual interventions as well as their combination, with quantification of the public health benefits of the measures. benefits and drawbacks: discrete event simulation provides a modeling platform that can synthesize available epidemiological evidence and syndromic surveillance information and help to prioritize among candidate interventions. if parameterized appropriately, it can incorporate the latest scientific understanding regarding exposures and health outcomes, and it can be adapted over time to accommodate new information. aside from aggregate benefits, the model could also help identify the characteristics of high-risk days or the days for which candidate interventions might yield the largest benefits, information of interest to bphc and other stakeholders. while the model is computationally intensive, it could provide key foundational information and could be run rapidly to evaluate the influence of forecasted stressor exposures on health outcomes, as a way to dynamically inform decisions about interventions. one major challenge relates to the accuracy of the model, which depends considerably on accurate characterization of the multivariable exposure patterns across individuals and subpopulations. moving from a stylized analysis to an analysis of the at-risk population of interest would require efforts such as constructing a synthetic population database of individuals within boston, as done previously by linking microdata from the american community survey with census tract population attributes and regression models to predict behaviors as a function of sociodemographic information [41]. the syndromic surveillance data and associated datasets could potentially be the foundation for such an analysis, in combination with publicly available population datasets. similarly, the model would need to be able to accurately characterize associations like behavioral responses to interventions or how medication adherence would be influenced by community-scale interventions. some of these associations could be empirically derived from information in the syndromic surveillance system or electronic health records, but some considerable upfront analyses would be required to incorporate all of the associations of interest. ultimately, if epidemiological analyses were conducted or exposure models were developed with a discrete event simulation application in mind, the information value of the analyses would be maximized and the simulation model would be most meaningful. artificial neural network approach basic concept: an artificial neural network (ann) is a tool meant to emulate the brain’s biological process for learning to combine multiple, weighted streaming inputs to obtain useful outputs. separate inputs are represented as interactive neuron-like nodes whose results are weighted, combined, and passed to other nodes for further processing. the perceptron, the simplest and most common form of ann, applies trial weights to the input nodes, sums their weighted values, and applies a transfer function to the weighted sum to obtain an output. in supervised training mode, the output is compared to observed or desired values, errors are calculated and fed back to the network, nodes are reweighted, and the process is repeated until the errors are acceptably small. after sufficient testing and cross-validation, the ann is applied to new data inputs to aid in classification or decision tasks. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi this structure cannot represent the range of complex learning problems, and in practice, designers generalize the perceptron with an input layer, one or more hidden layers, and an output layer working in parallel. weightings and appropriate transfer functions are applied in each layer for the feed-forward multi-layer perceptron model, one of the most common forms of ann. while the basic perceptron is mainly a static classifier, modelers have used several approaches to enable anns to learn from combinations of recent past values and outcomes. in particular, the time lagged recurrent networks (tlrn) extends the basic feed-forward model with short-term memory structures. thus, the ann is an approach, based on current knowledge of recursive human learning, to represent a system with complex, nonlinear behavior that is difficult to model explicitly. as such, anns have been used to aid in complex medical decision-making [42,43]. past applications: moustris et al. used the tlrn extensions of anns for prediction of pediatric hospital admissions for asthma exacerbations in athens, greece [44]. inputs included hourly weather data and pollution data obtained from seven metropolitan measurement sites. for training and evaluation, outputs were daily counts of pediatric asthma admissions collected from three main children’s hospitals. elemental weather variables were air temperature, wind speed, relative humidity, cloudiness, sunshine, and global radiation. from these variables, the authors derived the physiologically equivalent temperature (pet) and formed ann inputs from recent pet values. elemental pollutant elements were measurements of surface ozone, pm2.5, carbon monoxide, no2, and sulfur dioxide. from these variables, the authors derived the summary european regional pollutant index and formed ann inputs from recent values of this index. the tlrn structure adopted by moustris et al. was determined by trial and error in search of the best and simplest network for predicting pediatric asthma admissions, and the authors settled on a structure with one input layer, one hidden layer, and one output layer. they built anns with this structure to predict admissions for three age groups: 0-4 years, 5-14 years, and the combined 0-14 years. for ann development and learning, they used environmental and admissions data from 2001-2003 for training, and data from 2004 for testing and evaluation. comparison of predicted and actual pediatric admissions yielded “fairly good” agreement, with index of agreement above 0.83 and adjusted r2 values above 0.5 for both the 0-4-year and combined age groups, but with weaker agreement for ages 5-14. benefits and drawbacks: a principal benefit of anns is that they are appropriate to treat problems with variables that a) are difficult to express mathematically, b) have nonlinear relationships to the outcome of interest, and c) do not fit known theoretical distributions. the complexity of such problems is a challenge to overcoming the main drawback of anns, as stated in moustris et al.: “[anns] cannot explain in an intelligible form the relative importance of the various input variables used in the model and the procedure through which the answer to the problem was given”. this obstacle is common to many machine-learning approaches and must be overcome through a combination of validation, education, and historical experience before anns and related approaches can be adopted for routine prospective use. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 19 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi development and implementation of practical modeling capability distinguishing features of the boston asthma exacerbation risk use case the development and implementation of practical tools to improve the public health management of asthma exacerbations depend on routinely available data representing known risk factors. for this use case, known risk factors are upper respiratory virus transmission, particularly in school-age children, harsh or extreme weather conditions, and poor air quality. the practicality of a risk prediction tool depends on the available data and technological resources, but also on the operations and response mechanisms intended to benefit from the tool. for the asthma risk response in boston, response measures are multi-level communications with neighborhood and community programs, with high-risk patients and their families, and with care providers ranging from school nurses to hospital emergency staff. through these measures, improved and timely understanding of exacerbation risk can promote increased awareness without excessive caution, enabling continuity of school attendance and other daily activities, reduction of the number of resulting episodes, and clinical preparedness. a key feature of this use case is captured in the regression of the known dependence of both asthma prevalence and exacerbation risk on attributes such as race, age group, ethnicity, and neighborhood of residence. translation into modeling requirements the main requirement of the desired prediction tool is notification of enhanced exacerbation risk soon enough to improve the interventions described above. necessary improvement features are timeliness, population-at-risk indications, and reason(s) for enhanced risk. models should allow as much stratification as possible by risk group for targeted interventions. based on the public health resources and operations presented, these requirements broadly translate into: • risk predictions on a geographic scale finer than city level: a practical scale would be neighborhood level, with predictions specific to 15 regions characterized by distinct racial and ethnic concentrations. even finer resolution may be useful for some entities and outcomes. • predictions with a lead time of at least 48 hours: depending on the type of intervention, both staffing and response-time limitations limit the utility of shorter warning times. longer warning times are dubious because of the volatility of environmental risk factors and the nature of the interventions. • justification for increased risk: public health practitioners and clinical care stakeholders have made it clear that model outputs must include the risk factors or combinations responsible for heightened risk. • likelihood and severity details: for enhanced utility and credibility, the predicted risk results should explain the degrees of uncertainty and concern as clearly as possible, analogous to weather forecasts. the degree of concern could be expressed in terms of a locally relevant measure of illness burden, such as number of asthma-related ed visits by subpopulation. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 20 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi in initial implementation at least, the model or other prediction tool(s) are to be used at the bphc, not at multiple distributed locations, and results are then to be forwarded to community groups, schools, and care providers and other agencies and organizations determined by the needed action. whether the means of communication will be by instantaneous electronic message, by email, or by telephone will depend on the type of partner and intervention. analytic tools must be incorporated without impact on daily operations, into a working syndromic system implemented with no supercomputers or networked cluster processing, including daily patient records from ten emergency departments. any climatic or other environmental model inputs must be available as data streams for routine electronic ingestion, also without operational impact. information system requirements modeling requirements need to take into consideration the capabilities of the information systems the model is to be deployed on. during the import process all data feeds needed by the model must be made available electronically. climactic and environmental data can be large and system intensive. file sizes and whether the data is to be received in real time, near real time, or intermittently are all factors that will impact the abilities of the system to ingest information. network transmission requirements also play a role. workforce resources should also be considered in system design. properly trained staff must be available on a regular basis to respond when notifications (emails, text messages, etc.) indicating system issues are received. the data import system should send notices when there is a problem with the feed and no data arrives; when automatic sanity checks on the data fail; and when the database encounters an error. these staff must be ready and prepared to fix local errors and to liaise with the data providers when the issue is off site. pre-processing is a possible technique that can be used to ease the burden on the system as a whole. any processing that can be moved away from the main model’s processes and done during down times can be of great value in creating an efficient system; a staggered electronic ingestion of large data files and associated preparation for use in the model may prevent system overload, as illustrated in figure 5. circumstances where partial data analysis can be pre-formed, outside of the main model’s process and at more convenient times, should be sought out to alleviate the burden on the model. the model’s processing time and the availability of the results is critical. practical models may not have ideal results but are timely and support population health interventions. computing power and processing needs may determine internal vs. external (cloud) hosting. the error checking processes must also be implemented at this stage. offsite/cloud hosting has its own set of possible error conditions that must be accounted for with error checking and adequate staffing. the output of the model must be user friendly. it should include tables, maps, and other visualizations to make the results end-user friendly. results can range from static documents to dynamic web pages that update in near real time at the user’s request. as the model matures, the findings may be exposed to the other agencies or the public. processes for continuous training for the model will be needed to maintain timeliness and validity of the model. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 21 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi figure 5: technical schema to support analytic asthma exacerbation prediction tool the bphc technology workforce will be responsible for the system sanity check. this includes data and system quality metrics, system maintenance and repair, and liaison with data providers. privacy and security requirements will need to be vetted for each component information system along with cost considerations. modeling challenges: problem-driven, not method-driven multiple analytic methods were discussed in the modeling approaches section, and a primary decision is whether to apply a mechanistic model, a machine learning approach, or a hybrid or other modeling approach. one determinant of the analytic approach is the amount and quality of data support for potential modeling methods. sufficient historical data are required for model development and testing, and then ongoing data streams are required for updated implementation. eight years of historical ed patient records, including the data fields identified above, will be available to researchers who sign the bua. one attendee asserted that restrictions of these data might limit candidate data mining approaches. as explained in the risk factor evidence assessment section, some aeroallergen variables such as pollen and mold measurements are unavailable for boston, but historical weather data, ozone, pm2.5 and other air quality measures and predictions are freely available from noaa at 12-km resolution or better, and so will be available to bphc as inputs for prospective modeling. another challenge is determining the spatial data resolution of each data source, and determining what level is necessary to stratify model outputs by subdivision, such as neighborhood, and if possible, to apply appropriate interpolation methods to approximate the necessary resolution. an important modeling challenge is to provide predictive value beyond obvious triggers such as increased risk at the height of cold season or during extreme temperature days. such predictions require accurate weighting of risk factors and treatment of their interactions. regardless of http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 22 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi analytic approach, trust in predictions requires both validation using historical information and clear, prospective explanation of which factor(s) are responsible for alerts. a final consideration is the type and frequency of maintenance that a chosen model will require. translation challenges after a prediction tool is developed and tested, successful translation into the investigation and response process requires a) credible evaluation, b) relevant, concise, and user-friendly visualization, and c) adequate training in its use reinforced by prospective implementation experience. each of these components presents challenges. evaluation should demonstrate the ability to predict increased exacerbations resulting from multiple risk factors in all seasons. the evaluation of a prediction tool should not be confused with technology gaps in the chain of data acquisition to obtain model inputs or with gaps in steps to communicate model outputs. for example, in the end-to-end public health surveillance process, various agency links may be through automated electronic transmission, by email, or by telephone, reflecting infrastructure issues beyond the scope of analytic model development. visualizations should be developed in consideration of user workflow and several layers of response to exacerbation risk. technology translation may be no one’s formal task, and developers and users must allocate time and effort to determine and refine useful output presentations and/or screens, whether in terms of stoplight indicators, expected ed visit counts, hospitalization rates per 1,000, or otherwise. similarly up-to-date training in the use of prediction tools requires input from both modelers and system administrators. the bphc environmental surveillance and response staff anticipated helpful byproducts of successful translation. first, the understanding that timely and accurate information are available can strengthen the incentive to improve communication technology. second, timely and shareable risk predictions can inform internal and external operations policies. the effectiveness of technical solutions and modified interventions can be monitored at the health department in several ways: by tracking asthma-related ed visit trends, examining customary correlations between ed visits with respiratory infections and asthma exacerbations, and monitoring asthmarelated ed visits from ethnic, race, or neighborhood groups known for elevated risk. more detailed metrics such as asthma-attributable missed school days or non-transported ems calls are harder to track using available data but may be inferable from partner group communications. the health department model implementation must also be evaluated with metrics involving the quality and reliability of risk factor data sources, processing speed and other performance characteristics of the model, and its impact on the surveillance system. regarding enhanced interventions that could result from the model findings, measures to track and evaluate these enhancements will be needed. additionally, the health department must identify mechanisms to gather and incorporate feedback from relevant stakeholders. http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 23 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi discussion overview predictive modeling is underutilized for public health surveillance and response. this consultancy brought together a diverse group of subject matter experts, modelers, computer scientists and local decision makers to explore the development of a predictive modeling capability for asthma exacerbations in boston. exacerbations present a complex problem involving environmental factors, circulating viruses, and medical management. the process fostered information sharing across domains and provided insights into factors influencing models and useful outputs. the multidisciplinary consultancy was challenged to combine medical, epidemiological, environmental, computer science, and statistical expertise to promote development of models that can predict exacerbation increases in boston with 48-hour lead time. such a model must use routinely available electronic data feeds and conduct analysis within the computational capacity of bphc. presentations and discussion suggested that the acquisition rates of syndromic and environmental data are sufficient for the required analysis. to be determined is the spatial resolution of predictions afforded by the spatial granularity of the various data sources. model development and implementation experience will be required to determine both the effectiveness of developed models and their benefits for public health response and communication among bphc, its partner agencies, and the public. effectiveness of consultancy: benefits and lessons learned the bphc consultancy hosts felt that the event “surpassed expectations”, and multiple attendees cited the useful exchange of information on public health needs and practices, availability and quality of data sources, and modeling possibilities. beyond anecdotal reactions, an email survey on the effectiveness of the consultancy was administered. the 12 responses reflected the inperson discussion after the event—that useful information was exchanged on the public health burden of asthma in boston, local investigation and response mechanisms, risk factors and the available environmental and ed data for tracking those factors, and candidate analytic approaches for the required predictions. for both the description of the asthma burden and surveillance problem in boston and the discussion of risk factors and available environmental data sources, 75% of attendees said that objectives were “fully met,” the rest evaluated them as “partially met”. responses related to translating models for public health benefit were less clear. only 5 of 12 respondents felt that the objective of describing “strategies for translating a model for asthma” was fully met. the same limited number felt that the objective of describing “functional requirements for operationalizing a model in boston” was met. these response options depend on the results of the model, which were unknown at the time of the consultancy. these limited responses highlight the difficulty of working across disciplines to define analytic requirements and to clarify the public health benefits of prediction tools for a complex health issue with multiple seasonal risk factors, especially in the short time available for the consultancy. overcoming this difficulty requires individuals who: • grasp the public health business process and interagency relationships well enough to elicit relevant modeling requirements; http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 24 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi • have sufficient knowledge of the data and networking environments to know what streaming input processes are feasible; and • understand analytic tools and evaluation methodologies well enough to know what results are realistic. limitations we consider limitations of the consultancy process, the available data, practical models, and the resultant effects on interventions. the budget of the in-person consultancy was limited to 1.5 days and two-dozen participants, so activities were restricted to bphc model requirements analysis (data, informatics, and asthma response mechanisms/relationships), risk factor and data discussion, and presentation of model approaches. data available in near-real time do not represent all possible risk factors. sufficiency of the spatial resolution of these data for neighborhood-level modeling is unclear, especially for pollutant data. a developer of data mining models suggested that the available eight years of historical syndromic data from one city might be too short for training of some methods. however, modeling with lengthy data history may be problematic because the health care system is evolving. for example, patients who may have been admitted in prior years are now managed in observation units. such changes may compromise the value of training with years of patient data. another limitation is that the outcome data for prospective modeling are limited to free-text chief complaints and demographic fields in ed patient records. diagnosis codes are available in historic data for model validation, but they are not available in time for prospective use. other asthma outcomes, such as days of school or work missed specifically because of asthma, are difficult to quantify, and the corresponding data are not available in a timely way. the bphc staff noted that the effect of potential model results on response activities is uncertain because current and novel interventions at different levels depend on multiple city departments and agencies. these activities may require building new communication networks, leveraging community resources, and potential policy changes. this progress will require full engagement of decision makers from multiple sectors. the actionable information resulting from the models developed will provide a foundation for engagement of these decision makers. conclusions public health practice is evolving with the increasing availability of electronic data and technology systems capable of processing, analyzing, and visualizing data results. the models presented at the consultancy offer various approaches to the development of a prospective predictive asthma model for boston. some models may be better suited to retrospective data mining for risk factor characterization or evaluating new electronic data streams. these methods may inform the development of a practical predictive model for asthma. a practice-oriented model that operates within the technical requirements for end-user acceptance would be considered a success. this definition of success differs from an academic research perspective. the design of the consultancy and the subsequent process was end-user http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston 25 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e199, 2016 ojphi driven by both local decision makers who need to use the output and the workforce who support these systems. while working across disciplines can be a challenge, understanding the various system requirements (data sources, model, technology and end user needs) is essential to operationalize the methods. ongoing collaboration is a dynamic process as environmental data sources, clinical management of asthma, and the urban landscape change. system end-users also contribute to the continuous development of the technology. this approach may develop a road map for the expanded use of models for public health practice and response and ongoing evolution of the model and system with changing environments. acknowledgements we thank the boston public health commission and particularly executive director monica valdes lupi jd, mph for hosting and leading the consultancy. we also acknowledge support of dr. lauren ancel meyers of the university of texas at austin, and of the biosurveillance ecosystem group at the defense threat reduction agency and the advice of karen stark, technical bsve lead at digitalinfuzion, inc. financial disclosure the organization, preconference calls, and the consultancy itself were supported and funded by the defense threat reduction agency, and under grant nih k24 106822. contents of this report are solely the responsibility of the authors and do not necessarily represent the official view of the defense threat reduction agency. references 1. kiley c, hannan j. the 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community-wide health risk assessment using geographically resolved demographic data: a synthetic population approach. plos one. 9(1), e87144. pubmed http://dx.doi.org/10.1371/journal.pone.0087144 42. frize m, ennett cm, stevenson m, trigg hce. 2001. clinical decision support systems for intensive care units: using artificial neural networks. med eng phys. 23(3), 217-25. pubmed http://dx.doi.org/10.1016/s1350-4533(01)00041-8 43. leegon j, jones i, lanaghan k, aronsky d. predicting hospital admission in a pediatric emergency department using an artificial neural network. amia annu symp proc amia symp amia symp. 2006;1004. 44. moustris kp, douros k, nastos pt, larissi ik, anthracopoulos mb, et al. 2012. seven-daysahead forecasting of childhood asthma admissions using artificial neural networks in athens, greece. int j environ health res. 22(2), 93-104. pubmed http://dx.doi.org/10.1080/09603123.2011.605876 http://ojphi.org/ cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston introduction asthma burden current and potential public health responses health data sources: boston’s syndromic surveillance system asthma exacerbation risk factor evidence assessment available data sources for asthma exacerbation risk factor modeling respiratory virus infection data surface and rawinsonde measurements of atmospheric data satellite measurements of atmospheric and land use data numerical weather prediction (nwp) models operational air quality predictions modeling approaches case-crossover logistic regression model joint modeling approach: population transmission and data-driven regression population-level bayesian networks discrete event simulation modeling artificial neural network approach development and implementation of practical modeling capability distinguishing features of the boston asthma exacerbation risk use case translation into modeling requirements information system requirements modeling challenges: problem-driven, not method-driven translation challenges discussion overview effectiveness of consultancy: benefits and lessons learned limitations conclusions acknowledgements financial disclosure references isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts leveraging city data to understand the opioid epidemic in philadelphia lia n. pizzicato*2, 1, caroline c. johnson1 and kendra m. viner1 1philadelphia department of public health, philadelphia, pa, usa; 2cste applied epidemiology fellowship, atlanta, ga, usa objective to match fatal overdose information across city data sources to understand which systems overdose decedents may have interacted with prior to their death introduction philadelphia is in the midst of a drug epidemic that killed 702 philadelphians in 2015, 907 in 2016, and is on trajectory to kill 1,200 in 2017. opioids are involved in the majority of fatal overdoses, contributing to 80% of overdose deaths in 2016. in 2016, the ageadjusted death rate for opioid-involved overdoses was 40.4 deaths per 100,000 residents, up from 17.9 deaths per 100,000 residents in 2010. despite the epidemiologic work accomplished to date, gaps in knowledge still exist, especially for vulnerable populations such as those with serious mental illness or those who were ever incarcerated, homeless, or within the juvenile justice system. matching individuals who died of an overdose across city systems could provide insight into missed opportunities for interventions. findings will help inform policy for those systems that serve clients at highest risk for overdose. methods individuals who succumbed to fatal overdoses involving opioids between january 1, 2012 and june 30, 2016 were matched to other city data systems going back to january 1, 2000. descriptions of city systems that were matched to fatal overdose data is provided in table 1. frequencies were calculated to determine the number of individuals who received services or received services in the three years prior to death, as indicated by one of the city systems. results between january 1, 2012 and june 30, 2016, 2,163 individuals died from an opioid-involved overdose. overdose decedents were predominately male (69.1%), between the ages of 25-34 (28.0%), and white, non-hispanic (63.5%). heroin was the most common opioid detected in the system found in 67.1% of overdose decedents. in the years prior to death, 75.4% of individuals had received a service provided by a city agency and 61.6% had received a service within the three years immediately prior to death. overdose decedents utilized the most services from community behavioral health (cbh), a managed care organization providing behavioral health services for philadelphia’s medicaid population, both ever (59.5%) and in the three years prior to death (46.2%). many decedents were also incarcerated within the department of prisons with 50.4% ever incarcerated and 27.9% incarcerated in the three years prior to death (table 2). additionally, 20.9% and 17.5% of overdose decedents had a positive std or hepatitis c test, respectively, ever reported to the department of public health (table 3). conclusions this match of overdose decedents to other city systems highlights missed opportunities to help individuals who struggle with opioid dependence. historically, philadelphia has taken a recovery oriented approach to drug use, which focuses on drug treatment, and these data suggest that this approach is not sufficient for preventing subsequent fatal overdose. a harm reduction approach, which seeks to reduce the harms of drug use through interventions such as overdose reversal training and naloxone distribution, needle and syringe exchange, and education on safe injection practices, needs to be prioritized in this epidemic alongside recovery oriented practices. table 1: city systems matched to fatal overdoses table 2: systems providing services in years prior to death (n=2,163) table 3: infectious disease testing (n=2,163) keywords opioid; overdose; heroin acknowledgments we would like to thank james moore and zheng wen for integrating city data systems with fatal overdose data to produce the final dataset. this study was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded through the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-04 by the substance abuse and mental health services administration. *lia n. pizzicato e-mail: lia.pizzicato@phila.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e144, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts the prevalence of rabies cases in the territory of azerbaijan, january 2015-june 2016 nigar safi*, kliment asadov, shalala k. zeynalova, eldar gasanov and natig javadov state veterinary control service, baku, azerbaijan objective to show the instability of an epizootic situation on rabies cases of animals in the republic of azerbaijan, on the example of the cases analysis in electronic integrated disease surveillance system (eidss) electronic reporting system introduction rabies is an infectious disease which was and remains to be one of the most serious diseases of all species of hematothermal animals and humans, in many regions of the world. the epizootic situation on rabies in the republic of azerbaijan has been unfavorable for many years, which is confirmed by scientific data and the veterinary cases reporting in the eidss system. this system was introduced in the country in 2009 and is the electronic system of disease control. the program allows to provide monitoring and prevention of diseases within the concept “one world one health system” by integration of systems of observation of animal diseases, human diseases, and disease carriers. methods on the basis of the data on rabies cases entered in special forms and also aggregative data collected on anti-rabies vaccination, the analysis of information on quantity of cases and their prevalence on administrative and territorial units (rayons) of the country is carried out. the graphical analysis (charts and the map) on the basis of necessary criteria are constituted in the analyses module, visualization of the avr reporting and in the microsoft excel program. results the analysis of the rabies cases confirmed at the virology department of the republican veterinary laboratory shows that rabies has been identified in 36 cases in 2015, 25 cases in january june, 2016, in total 61 cases has been registered for the period of “january 2015 – june 2016”. an epizootologically unfavorable situation is revealed in 27 regions. the most unfavorable situation is the northwest regions of the country, the most part of which is covered with mountainy-forest area with domination of wild fauna. specific structure of animals: dogs – 31 cases in 19 areas (51%), cattle – 21 cases in 12 areas (34%), a small cattle-1 case (2%), wild animals (specify types) 8 cases in 8 areas (13%) that is visually shown on charts 1 and 2. the cattle were bitten by wolves and jackals. conclusions thus, prevalence of rabies cases of different species of animals in the country, once again proves natural and focal character of the disease: the reservoir of rabies is in the wild nature and geographical conditions impact the spread of rabies. cases of rabies in animals are registered annually. in 2015, vaccination captured about 250000 dogs, and 244400 dogs were vaccinated in the first 6 months of 2016. despite a huge group of vaccinations, restriction of rabies spread isn’t observed and the tendency is trending to the increase of rabies case indicators amongst the dogs. it is necessary to pay close attention to preventive vaccination of domestic (including non-productive) animals. if materiel resources are available, it is possible to carry out the vaccination of the cattle in the territories adjacent to the forests. in the threatened territories with woodlands, there is no alternative to oral vaccinations, which is confirmed by positive experience of many countries. there is an extreme need of carrying out of oral vaccination of wild carnivorous animals with obligatory control of the immune status. keywords rabies; detection; electronic reporting; vaccination *nigar safi e-mail: hasanovag@state.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e162, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts leveraging discussions on reddit for disease surveillance albert park* and mike conway biomedical informatics, university of utah, salt lake city, ut, usa objective we aim to explore how to effectively leverage social media for vaping electronic cigarette (e-cigarette) surveillance. this study examines how members of a social media platform called reddit utilize topically-oriented sub-communities for e-cigarette discussions. introduction in recent years, individuals have been using social network sites like facebook, twitter, and reddit to discuss health-related topics. these social media platforms consequently became new avenues for research and applications for researchers, for instance disease surveillance. reddit, in particular, can potentially provide more indepth contextual insights compared to twitter, and reddit members discuss potentially more diverse topics than facebook members. however, identifying relevant discussions remains a challenge in large datasets like reddit. thus, much previous research using reddit data focused on selected few topically-oriented sub-communities. although such approach allows for topically focused datasets, a large portion of related data can be missed. in this research, we examine all sub-communities in which members are discussing e-cigarettes in order to determine if investigating these other sub-communities could result in a better smoking surveillance system. methods in this study, we use an archived reddit dataset1 that had been used in previous studies2,3. we first preprocessed the dataset, which included converting text to lower case and removing punctuation. due to the size of the dataset (114,320,798 posts and 1,659,361,605 associated comments from 239,772 sub-communities), we identified 4 terms to extract posts or comments about e-cigarettes via a lexiconbased approach. the terms are ‘e cig’, ‘elec cig’, and ‘electronic cig’. we included any partial matches in this process to cover a variation of e-cigarette terms. for example, a partial match of ‘cig’ can cover ‘cig’, ‘cigs’, ‘cigarette’, and ‘cigarettes’. we presented the wordcloud of the names and frequencies of sub-communities, in which members discussed e-cigarettes. results we extracted 354,587 posts/comments that were made by 176,252 unique member ids from 6,039 unique sub-communities. there were 6 sub-communities with more than 8,000 e-cigarette posts. the subcommunities are ‘askreddit’ (59,939) ‘cigars’ (51,684) ‘electronic_ cigarette’ (24,393), ‘trees’ (17,752), ‘pics’ (8,792), ‘stopsmoking’ (8,589). other notable sub-communities are ‘news’ (5,010), ‘politics’ (4,662), ‘worldnews’ (3,785), ‘science’ (3,279), ‘drugs’ (2,967), ‘pipetobacco’ (2,099), ‘cigarettes’ (1,401), ‘teenagers’ (1,016), ‘askmen’ (918), ‘marijuana’ (826), ‘fitness’ (818), ‘askwomen’ (698), ‘cubancigars’ (695), and ‘vaporents’ (608). members were participating not only in sub-communities related to smoking and smoking cessation, but also in science, news, health, teenager, and q&a sub-communities. the overview of the sub-communities that members participated to discuss e-cigarette are summarized in figure 1. conclusions we present preliminary findings concerning the various subcommunities in which members had discussion on e-cigarettes in the popular social media platform reddit. our initial results suggest that reddit members openly discuss electronic cigarette-related issues in many sub-communities that are unrelated to smoking. for the purpose of e-cigarettes surveillance, understanding the discussions in unrelated sub-communities, for example the subreddit ‘teenagers’, can provide opportunities to gain an in-depth perspective on the increased use of e-cigarettes by youth or non-smoker4. moreover, high levels of activities in q&a sub-communities like ‘askreddit’, ‘askmen’, and ‘askwomen’ could indicate ineffective information dissemination regarding e-cigarettes5, warranting further investigation. for the purpose of disease surveillance, we conclude that understanding the discussion in unrelated sub-communities has the potential to improve the practice of public health surveillance. keywords data mining; surveillance system; social media; electronic cigarette; smoking acknowledgments university of utah’s institutional review board exempted the study procedure and data (irb 00076188). ap was funded by the national library of medicine of the national institutes of health under award number t15 lm007124. mc was funded by the national library of medicine of the national institutes of health under award numbers r00lm011393 & k99lm011393. references 1. reddit_member. i have every publicly available reddit comment for research. ~ 1.7 billion comments @ 250 gb compressed. any interest in this? 2015. archived at: http://www.webcitation.org/6kgaunxde isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts 2. park a, conway m. longitudinal changes in psychological states in online health community members: understanding the long-term effects of participating in an online depression community. j med internet res. 2017 mar 20;19(3):e71. 3. park a, conway m. towards tracking opium related discussions in social media. online j public health inform 4. mcmillen rc, gottlieb ma, shaefer rmw, winickoff jp, klein jd. trends in electronic cigarette use among u.s. adults: use is increasing in both smokers and nonsmokers. nicotine tob res. 2015 oct;17(10):1195–202. 5. park a, zhu s-h, conway m. the readability of electronic cigarette health information and advice: a quantitative analysis of webbased information. jmir public heal surveill. 2017 jan 6;3(1):e1. *albert park e-mail: alpark1216@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e52, 2018 modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms susannah paul, mph1*, osaro mgbere, phd, ms, mph2, 3, raouf arafat, md, mph2, biru yang, phd, mph2, eunice santos, mph2 1 rollins school of public health, emory university, atlanta, ga 2 houston health department, houston, tx 3 institute of community health, university of houston college of pharmacy, texas medical center, houston, tx *susannah paul, mph, rollins school of public health, emory university, atlanta, ga. susannahpaul@outlook.com abstract objective the objective was to forecast and validate prediction estimates of influenza activity in houston, tx using four years of historical influenza-like illness (ili) from three surveillance data capture mechanisms. background using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. anticipating surges gives public health professionals more time to prepare and increase prevention efforts. methods data was obtained from three surveillance systems, flu near you, ilinet, and hospital emergency center (ec) visits, with diverse data capture mechanisms. autoregressive integrated moving average (arima) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. estimates were then compared to actual ili percentages for the same period. results forecasted estimates had wide confidence intervals that crossed zero. the forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ilinet forecasted estimates and actual percentages had the least differences. ilinet performed best when forecasting influenza activity in houston, tx. mailto:susannahpaul@outlook.com modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi objective our objective was to forecast and validate prediction estimates of influenza-like illness (ili) activity for mmwr weeks 27 through 37 of 2016 using historical ili data from mmwr week 27 of 2012 through week 26 of 2016 from three surveillance sources (flu near you, ilinet, and the houston health department’s syndromic surveillance system based on hospital emergency center visits) that have diverse data capture mechanisms. introduction effective management of both seasonal and pandemic influenza requires early detection of the outbreak through timely and accurate surveillance linked with a rapid response to mitigate crowding [1]. several diverse data sources, including historical and real-time, have been used to forecast influenza activity based on predictive models that could facilitate key preparedness actions and improve understanding of the epidemiological dynamics or evaluate potential control strategies [2-6]. houston health department (hhd) conducts ili surveillance with data from five different systems. each system is used based on agreed upon terms for data use in public health surveillance. three of the data sources were selected for this study; however, all five systems are routinely used for ili surveillance. despite the variety of syndromic surveillance systems, hospital emergency center (ec) visits are utilized for both descriptive and inferential statistics. advanced analysis for ili activity are performed with hospital ec data. while ilinet and flu near you (fny) are used conclusion though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ili activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness. keywords: influenza-like illness, syndromic surveillance, forecast, houston, texas, flu near you, ilinet, hospital emergency center, arima, houston, texas correspondence: susannah paul, mph, rollins school of public health, emory university, atlanta, ga. susannahpaul@outlook.com doi: 10.5210/ojphi.v9i2.8004 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes mailto:susannahpaul@outlook.com modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi for descriptive analysis, historically hospital ec visit data are readily available and the mechanism for reporting from healthcare to public health lends itself to the timely situational awareness for seasonal epidemic and pandemic level ili activity. for this reason, the exploration of forecasting methods was applied and validated with a comparison of ilinet, fny, and hospital ec visits. ideally, the forecasting methods can become part of routine ili surveillance at hhd. electronic surveillance system for the early notification of community based epidemics (essence) includes statistical algorithms to detect aberrations in syndromic surveillance for various conditions including conditions with seasonal patterns. however, essence does not provide forecasting based on time series data. supplemental methods such as prediction or forecasting with autoregressive integrated moving average (arima) can complement the automated features in essence. public health jurisdictions that take part in national syndromic surveillance program (nssp) have access to rstudio pro on the biosense platform. the software version and methods in this study can be replicated in the biosense platform. therefore, users of the nssp biosense platform can complement essence automated statistical algorithms with arima methods. soon, houston will be participating in nssp at which time forecasting and validation can be applied as part of the biosense platform. ili surveillance is a cornerstone in the early detection of seasonal influenza epidemics and other pandemics such as the h1n1. because ili surveillance is of paramount importance for public health authorities and healthcare, a variety of syndromic surveillance systems have been explored in the scientific literature and in practice to inform public health and clinical surveillance of ili activity. the hhd uses several syndromic surveillance systems to monitor ili activity which include ilinet, hospital ec visits, and novel data sources such as fny. the surveillance activities for influenza in houston are adapted to meet the current public health needs. for example, since 2007 the hhd has used sentinel providers in outpatient clinics to obtain laboratory specimens for virologic surveillance of ili and to characterize flu activity in houston [7]. the sentinel provider network was instrumental in monitoring the novel 2009 h1n1 pandemic in houston [7]. more recently, the ili data has been used to explore the dynamics of patient health behavior associated with repeat episodes of ili [8]. however, no attempt has been made thus far to evaluate the forecasting capabilities of existing syndromic surveillance systems (ilinet, hospital ec visit data, and fny) using houston’s data. these diverse real-time data capture mechanisms may have the potential to go beyond early detection and forecast future ili or influenza outbreaks in the community. such estimates could help guide policy makers’ decisions, and key preparedness tasks such as public health surveillance, development and use of medical countermeasures, communication strategies, deployment of strategic national stockpile assets in anticipation of surge demands, and hospital resource management [2]. modern epidemiological forecasts of common illnesses, such as the flu, rely on both traditional surveillance sources as well as digital surveillance data such as social network activity and search queries [9]. reliable forecasts could aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. previous studies have applied statistical methods on fny and ilinet data to compare and validate their potentials to inform ili surveillance both individually and in combination with each other [10,11]. arima technique has modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi been used to forecast seasonal influenza surveillance, including ili at national, state, and local levels. ili has seasonality which can be corrected for with arima. ili, like other infectious conditions, does not assume independence and the measures of occurrence are expected to vary across time. statistical methods used on ili data must be robust enough to handle the violation of the independence assumption and to reduce the background noise. arima results are considered reliable despite the intercorrelation of data points. arima had been applied by other researchers on the fny, ilinet, and hospital ec visits with informative and accurate results [2-6]. results from forecasting methods applied to ili data have been reported for fny and ilinet at national levels [10, 11], and provided useful information for population level ili surveillance. prediction for ili occurrence is being explored for the first time on data specific to houston jurisdiction. there is a need to understand if arima with seasonal correction can be applied to various data from ili syndromic surveillance systems in different geographic settings. ilinet and fny dashboard can be accessed by any health department. since any given health department will differ in the information available for ili surveillance, it becomes important to know which data source for ili can provide the most accurate predictions for ili activity. the authors fill a gap in the scientific literature on the use of these promising data sources to model and predict influenza activity. the objective of this study therefore was to forecast and validate prediction estimates of influenza activity in the city of houston in texas using ili historical data from three surveillance systems, namely fny, ilinet, and hospital ec visits. methods study population and setting houston is the fourth most populous city in the u.s. with 2.3 million residents as of january 2017 [12]. only data collected within the jurisdiction of houston, texas were included in this analysis. the city of houston jurisdiction was determined using zip codes. data sources the three data sources used for this study include fny, hospital ec visits, and ilinet. each of the three data sources represents a different stage of disease manifestation of ili which is reflected in the magnitude, and possibly timing of ili activity. fny is a crowdsourced internet-based participatory syndromic surveillance program to track and monitor weekly ili occurrence based on self-reports from volunteers [13]. the self-reports represent individuals who may or may not have accessed the healthcare system so the severity of symptoms may vary from mild to severe. hospital ec visits from the hhd’s syndromic surveillance system are a good data source for ili surveillance and represent individuals whose symptoms are severe enough to seek medical care from an emergency center. ilinet is considered the gold standard for ili outpatient surveillance and may be the reference point for the timing and magnitude of ili activity for many health departments. characteristics that are inherent in ili data include the use of symptom data to measure the occurrence of ili versus the use of laboratory confirmed influenza positives. modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi data collection and measurements time series data were obtained from the three data sources and arranged in series of weekly occurrence of ili percent. like most methods for syndromic surveillance, the time series are not longitudinal and individuals are not followed across time. data used for our analysis comprised of weekly ili percentages from mmwr week 27 of 2012 through mmwr week 26 of 2016, which corresponds to about july 2012 through june 2016. forecast estimates were validated using ili percentages from mmwr weeks 27 through 37 of 2016. all data were obtained from public health surveillance systems. each of the data sources used captures data differently. fny is available as both a web-based platform and a phone application. users self-report various symptoms on a weekly basis. the number of responses with symptoms related to influenza-like illness are aggregated weekly over the total number of responses. ilinet is a collaborative effort between the centers for disease control and prevention (cdc), health departments, and health care providers. providers report weekly on the total number of patients seen and the proportion of patients with influenza-like illness. the hhd’s syndromic surveillance system receives health data via secure electronic transmission from hospital ec visits for public health surveillance. in houston, hospital ec visits data is considered more representative compared to the other two data sources because it has better “participation” due to the real-time automated transmission of health data. daily, the hhd receives 1,500 to 2,000 records for hospital registrations and discharges for city of houston jurisdiction. in houston, hospital ec visits are the preferred data source because ili activity based on hospital ec visits consistently matches national trends. this study received exempt status approval from the hhd investigative review committee because the data were deidentified and aggregated prior to analysis. data management all analysis was completed using rstudio 3.3.1 and microsoft excel. there were 21 (10%) missing observations from ilinet and one missing observation (0.5%) from the hospital ec visits data. the missing data were estimated using predictive mean matching. the ‘mice’ package in r imputes missing values with plausible values using the predictive mean matching method. this method uses an algorithm to pull information from other values in the specified variable to predict possible values. predictive mean matching estimates a linear regression for observed values and picks a value randomly from the posterior predictive distribution of the coefficients of the previous regression to produce a new set of coefficients. these coefficients predict values for all observations. for each missing observation, we picked a set of observations with predicted values close to predicted values of the missing observations [14]. data were divided into segments from mmwr week 27 of the current year through mmwr week 26 of the following year. this resulted in four time periods, each containing the typical start, peak, and end of the influenza season. modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi arima modeling and forecasting we used autoregressive integrated moving average (arima) models to describe autocorrelations in the data and forecast time series [15]. the arima model has several advantages for forecasting compared with other methods and is especially useful in modeling the temporal dependence structure of a time series [16,17]. stationarity since the arima model requires stationarity, we used augmented dickey-fuller (adf) tests and kwiatkowski-phillips-schmidt-shin (kpps) unit root tests to determine if each of the data source time series variables were stationary or would require differencing. each of the sources were also tested using seasonal root tests to determine the appropriate number of seasonal differences required [15]. fitting an arima model the auto.arima() function in the r ‘forecast’ package which estimates parameters and model orders using maximum likelihood estimation (mle) was used to fit the model. this method finds parameter values that maximize the probability of obtaining the observed data. after differencing d times, p and q are chosen by looking for the lowest akaike information criteria (aic) value. the autocorrelation function (acf) plots of the residuals and portmanteau ljung-box tests were used to determine if there is any autocorrelation in the residuals [15]. obtaining point forecasts from arima models once a model is fitted, the forecast () function can be used to predict future values. the forecasting equation (equation 1) is a univariate linear equation where the predictors are the lags of the variable and/or lags of the forecast errors and/or a possible constant: equation 1. ŷt = μ + ϕ1 yt-1 +…+ ϕp yt-p θ1et-1 -…θqet-q where, θ = moving average parameters of order q, ϕ = autoregressive parameters of order p, ŷt = prediction estimates at time t, yt-p = lagged values of y, and e = error term. modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi results table 1. peak weeks per year and ili percentages by data capture mechanism year data source peak week4 peak ili %4 mean4 standard deviation 4 mean (octmay) 20122013 flu near you 2012, 45th 28.57 3.29 6.13 3.841 hospital ec visits 2012, 51st 5.25 1.94 1.20 2.411 ilinet 2012, 51st 2.62 0.72 0.79 1.013 20132014 flu near you 2013, 51st 10.29 2.22 2.40 2.481 hospital ec visits 2013, 51st 6.59 1.87 1.38 2.351 ilinet 2013, 51st 3.73 0.67 0.94 0.961 20142015 flu near you 2014, 37th 9.43 2.77 2.96 3.562 hospital ec visits 2014, 51st 3.80 1.55 0.99 2.002 ilinet 2014, 46th 1.48 0.28 0.41 0.412 20152016 flu near you 2016, 25th 6.9 1.90 1.65 2.123 hospital ec visits 2016, 9th 2.76 1.24 0.61 1.493 ilinet 2015, 51st 0.96 0.18 0.23 0.235 1mean was calculated using weeks 40 through 22. 2mean was calculated using weeks 40 through 21. 3mean was calculated using weeks 39 through 21. 4the time interval was week 27 through week 26 of the following year. 5mean was calculated using weeks 38 through 20. preliminary analysis depicted similarities in peak times and overall trend of increases and decreases of ili activity (table 1). flu near you did have a much larger range of ili percentages and was heavily influenced by fluctuating and smaller sample sizes. the results also indicated different peak weeks during the 2015-2016 influenza season. our study noted that the 2016 influenza season was later, and the overall severity of seasonal influenza was also milder compared to the previous three seasons. modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi fny point estimates continually decreased from 3.26% (95% ci: -1.00 – 7.51) to 2.06% (95% ci: -2.59 – 6.72). the 95% confidence intervals for fny point estimates crossed zero and were wide with a mean confidence range of 9.18%. the ili percentage estimates for the hospital ec visits data continually increased from 0.72% (95% ci: 0.19 – 1.26) to 1.32% (95% ci: -0.35 – 3.00). all except the initial confidence interval for the 27th week crossed zero with a mean range of 2.43%. lastly, ilinet ili percentage estimates start at 0.01% (95% ci: -0.23 – 0.25), decreased to 0.09% (95% ci: -0.70 – 0.53), and increased slowly again to -0.05% (95% ci: -1.03 – 0.92). the mean range between upper and lower 95% limits was 1.18%. like the other data sources, the 95% confidence interval also crossed zero for ilinet estimates (table 2). fny had the highest average confidence interval difference (9.18%) between upper and lower 95% bounds (figure 2-a). all the 95% confidence intervals were relatively wide and lower bounds were negative percentages, which cannot be actualized because ili percentages can only be positive. the data sources did not agree on an increasing or decreasing predictive trend. ilinet forecasted estimates were closest to observed ili percentages for weeks 27-37 of 2016 (figure 2c). the hospital ec visits data forecasted estimates resulted in a substantial difference compared to actual ili percentages for the same period but did follow the same overall increasing trend (figure 2-b). table 2. comparison of ili forecast estimates and actual percentages by data source data source estimate week 271,2 actual week 271 estimate week 371,2 actual week 371 estimate direction actual direction flu near you 3.26 (-1.00 – 7.51) 2.10 2.06 (-2.59 – 6.72) 2.31 decrease increase hospital ec visits 0.72 (0.19 – 1.26) 0.56 1.32 (-0.35 – 3.00) 0.84 increase increase ilinet 0.01 (-0.23 – 0.25) 0.02 -0.05 (-1.03 – 0.92) 0.00 decrease 4 decrease 1 values were from mmwr week 27 and week 37 of 2016. 2 95% confidence intervals. 3 it is not possible to have ili percentages less than 0. 4 estimates did increase slightly after the initial decreasing trend. modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi figure 2-a. flu near you forecasted estimates for weeks 210-219 using ili percentages from weeks 1-209 figure 2-b hospital ec visits forecasted estimates for weeks 210-219 using ili percentages from weeks 1-209 modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi figure 2-c ilinet forecasted estimates for weeks 210-219 using ili percentages from weeks 1-209 week refers to the range from the 26th3 week of 2012 through the 37th week of 2016. each graph represents historical ili percentages from mmwr week 27 of 2012 through mmwr week 26 of 2016 in black, forecasted estimates for mmwr weeks 27-37 of 2016 in blue, and actual ili percentages for mmwr weeks 27-37 of 2016 in green. the dark blue shaded area represents the 80% confidence interval and the gray shaded area represents the 95% confidence interval. ilinet forecasted values (figure 2-c, table 2) are closer to actual ili percentages as evidenced by smaller differences between actual and estimated values when compared to hospital ec visits data (figure 2-b, table 2) and fny (figure 2-a, table 2) data. discussion though several surveillance systems have been designed to provide warning, few provide reliable data in near-real time, and fewer still have demonstrated the capability to provide advanced forecasting of impending influenza cases [1,18]. our study recorded varying peak week, peak ili %, and mean over the period from 2012-2016 with resultant effects on the ili forecast estimates by data capture mechanisms. this is consistent with previous findings where wide variability in local and regional level data for ili have been reported [19]. this may reflect the voluntary nature of influenza activity reporting by public health partners and health-care providers. we noted high inconsistency and variability in the number of users who self-reported using fny (mean (sd): 69 (41) weekly responses). this may be associated with the fact that there were fewer fny users in the houston area during the initial stages of the tool release. however, as awareness of participatory surveillance increased, the fny data decreased in variability as the number of users reporting increased. monitoring of online search trends and the use of online self-reporting illness tools such as fny appear to have potential for supporting and enhancing traditional syndromic surveillance systems. internet-based participatory syndromic surveillance programs modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi have been used to signify the start or end of transmission of seasonal pathogens [20], provide situational awareness, aid in the epidemiologic description of a disease, and to perform surveillance during a mass gathering [21]. we noted that ili showed consistent decreases in peak ili% and mean based on the hhd syndromic surveillance data during the study period. although the forecasted estimates indicated a substantial difference compared to actual ili percentages for the same period, both followed the same trend direction. ec chief complaint data have been used in the early detection of disease clusters or outbreaks [22, 23], and in the identification of the start or end of the season for pathogens such as influenza [24, 25]. for instance, the ec data had flagged the start of influenza season 2 to 3 weeks earlier than the cdc ilinet data in 2 of 3 eligible years examined [24]. none of the data sources accurately forecasted future ili in the population due mainly to the wide variability recorded except for ilinet which exhibited the least difference with forecasted estimate close to the actual ili report. our results were similar to that of the cdc 2013–2014 influenza season challenge where using a variety of digital data sources and methods nine of the competing teams failed to accurately forecast all influenza season milestones [26]. our predictions were based on historical data and may not be ideal to predict unusual events that have not occurred previously. forecasting also must consider the noise component which is difficult to estimate or predict. while the times series in our study represent occurrence of ili at the population level, it should be recognized that background noise is a common phenomenon that occurs in public health surveillance data. it is unclear in our study the extent to which noise affected the predicted models. in addition, forecasting time series data is difficult due to inherent uncertainties of trend and retention of historical properties. however, considering the degree of uncertainty, short-term estimates based on these data sources could be useful for preparation or to increase level of awareness in the houston community. similarly, the cdc noted during the influenza season challenge that is was possible to obtain reasonably accurate forecasts in the short term compared to long term [26]. further model fitting and forecasting should consider including specific seasonal components like seasonal arima models to capture specific seasonal patterns. this method can be replicated in other software that performs arima. as an example, for public health surveillance, the authors described the application of forecasting methods and results from data on ili. limitations there is need to highlight some limitations that may be associated with our study outcomes. first, we were not able to include influenza laboratory results in our analysis. this may have significant impact on the accuracy of using ili forecasted results to estimate influenza activity. second, even though hospital ec visits data does provide a true measure of ili cases as seen in the seasonal peaks every year which match national peaks and often coincide with the detection of influenza related outbreaks in group settings, the hospital ec visits may represent patients that access ec as their first step towards medical care or treatment, as their decisions are based in part on the severity of their symptoms. third, since all influenza activity reporting by public health partners and healthcare providers is voluntary, it is difficult to maintain a steady and consistent flow of data for ilinet and fny during each flu season. fourth, the meaningfulness and usefulness of predictions depend modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi heavily on the quality and consistency of the source of ili data. established surveillance data sources are more likely to produce reliable estimates due to consistency and large volume of historical data available compared to newer, untested data. finally, while information obtained from these diverse syndromic surveillance systems is routinely used to determine influenza activity’s magnitude and timing, the authors acknowledge that further work is needed before a prediction of ili activity can be made with absolute confidence. strengths despite these limitations, we believe that our current findings highlight the inherent potentials of the three diverse data capture mechanisms for monitoring of seasonal influenza which can inform activities for prevention and control and pandemic preparedness in the houston metropolitan area. our study shows promise that forecasting methods can be integrated into routine public health surveillance practice. the availability of multiple years of historical ili data helps identify overall trends and unusual activity with more reliability. the data sources provide proportion (percent) of ili per week which offers more information for population level surveillance compared to using only counts. also, the arima forecasting with ‘forecast’ package within rstudio pro makes it possible to conduct prediction of ili occurrence within the biosense platform. the standard features for essence do not include prediction modeling. the utility of automated real-time syndromic surveillance systems increases when forecasting component is combined with statistical detection algorithms based on change detection applied to times series. while the extent of the biases suggested by this analysis cannot be known precisely, combining these data sources may give a true picture of the influenza activity in houston. the authors believe that there is value in the availability of participatory surveillance such as fny not only because of the utility for tracking and monitoring ili trends with descriptive methods but also because of the platform provides flu news, vaccine availability, and real-time maps with ili activity [27]. these methods can be applied to ili data sources in various geographic areas. nonetheless, continuous improvement in the quantity and quality of data obtained from these data sources may help enhance the forecasting capabilities and early detection of potential surge in influenza activity. conclusions our study concludes that the variations in the predictive estimates were directly associated with the data sources. forecasted estimates rely heavily on sources consistently reporting enough responses to reduce variability. forecasts based on data from fny and other user-reported platforms will require enough users from the population to consistently report so that historical ili data can be a better representation of overall influenza-like illness activity. this could lead to better estimations of influenza activity. ilinet forecasted estimates and the actual ili reports exhibited the least difference though all sources generally had wide confidence intervals. similar surveillance systems have the potential to reasonably estimate overall trend and estimates of influenza activity. our results demonstrate that there are significant opportunities to improve the forecasting performance and that selective superiorities among the three data sources could be leveraged upon to improve ili surveillance in houston. for instance, combining the data sources with enough observations to forecast future trends may provide estimates that are closer to actual influenza activity in the houston community. these estimates could be used to empower decision modeling and forecasting influenza-like illness (ili) in houston, texas using three surveillance data capture mechanisms online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e187, 2017 ojphi makers to lead and manage more effectively by providing timely and useful evidence for the public health surveillance to prepare and respond. acknowledgements the hhd would like to acknowledge the flu near you team at healthmap for their work on the dashboard and marketing material for flu near you, for coordinating the updates on flu near you for end users, which include trends of ili across the country, and providing comments regarding the manuscript. the hhd would like to acknowledge the many groups and individuals that conduct the operational component for seasonal influenza surveillance at hhd. the ilinet data was obtained for houston jurisdiction with permission from texas dshs. financial disclosure the hhd receives funding for this project through a joint organizational partnership between the council of state and territorial epidemiologists (cste) and the skoll global threats fund (sgtf), provided by cste through sub-awards from the sgtf award #15-03126. the contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the sgtf, cste, or the hhd. competing interests the authors declared no potential conflicts of interest with 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health surveill 2017;3(2):e18. url: https://publichealth.jmir.org/2017/2/e18 doi: 10.2196/publichealth.7304 pmid: 28389417. pmcid: 5400887 https://publichealth.jmir.org/2017/2/e18 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts mers pui surveillance and restrospective identification in essence-fl, 2013-2015 julia g. munroe*, rachael straver, heather rubino, scott pritchard, david atrubin and janet j. hamilton florida department of health, tallahassee, fl, usa objective to retrospectively identify initial emergency department (ed) and urgent care center (ucc) visits for florida’s middle east respiratory syndrome coronavirus disease (mers-cov) patients under investigation (puis) in the florida department of health’s (doh) syndromic surveillance system, the electronic surveillance system for the early notification of community-based epidemics (essence-fl), using information gathered from pui case report forms and corresponding medical records for the purpose of improving syndromic surveillance for mers-cov. the results of this study may be further utilized in an effort to evaluate the current mers-cov surveillance query. introduction human mers-cov was first reported in september 2012. globally, all reported cases have been linked through travel to or residence in the arabian peninsula with the exception of cases associated with an outbreak involving multiple health care facilities in the republic of korea ending in july 2015. while the majority of mers-cov cases have been reported in the arabian peninsula, several cases have been reported outside of the region. most cases are believed to have been acquired in the middle east and then exported elsewhere, with no or rare instances of secondary transmission. two cases of mers-cov were exported to the united states and identified in may 2014. one of these cases traveled from saudi arabia to florida. doh conducts regular surveillance for mers-cov through the investigation of persons with known risk factors. puis have most often been identified by physicians reporting directly to local health departments and by doh staff regularly querying ed and ucc chief complaint data in essence-fl. essence-fl currently captures data from 265 eds and uccs statewide and has been useful in identifying cases associated with reportable disease and emerging pathogens. methods from 2013-2015 doh identified and investigated 62 suspected cases of mers-cov, including one confirmed case in may 2014. specimens were collected from all 62 patients under investigation (puis) and 61 were ruled out. of the 61 puis who were ruled out, ten were part of the contact investigation initiated following the identification of mers-cov in may 2014 and were not included in this analysis. doh utilizes a mers-cov pui case report form to collect data regarding demographics, clinical presentation, and risk factors. retrospectively, additional documents including medical records and discharge summaries were gathered and utilized to evaluate puis identified in essence-fl. name of the facility where puis presented, date and time of visit, age at event, and sex were identified using pui case report forms and corresponding medical records and discharge summaries. visit details for each of the identified facilities were queried in essence-fl and pulled for all visits with corresponding age at event and sex for the patient’s visit date. additional pui information including chief complaint, discharge diagnosis, zip code, race, and ethnicity were gathered for the purpose of matching corresponding essence-fl data fields. essence-fl visit details were narrowed by zip code (or lack of zip code for residents of other countries) and match details were recorded and evaluated. the fields examined were not always complete in essence-fl. visits were considered matches when all available data in the fields examined were consistent with information obtained in the pui case report form and available medical records and discharge summaries. results of the 52 puis included in this analysis, 39 sought treatment at facilities participating in essence-fl at their time of visit. comparing information obtained from pui documents with data provided in essence-fl, 30 ed visits were successfully matched to puis, including an initial ed visit for the patient with a confirmed case of mers-cov. conclusions following preliminary identification, all matches are to be confirmed with the appropriate hospitals. future work to examine the chief complaints associated with patients’ initial ed visits identified in essence-fl will serve as a way to validate and improve upon the query currently being used as a surveillance tool for mers-cov. detailing these methods also has value in the replication of this study for other diseases and in the development and validation of other disease-specific queries. summarizing the reasons why puis were unable to be matched to essence-fl visits is also useful in improving system robustness. keywords surveillance; syndromic; mers; investigation; essence *julia g. munroe e-mail: julia.munroe@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e113, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts selection of syndromes and algorithms for monitoring bovine laboratory test data howard burkom*1, leah estberg2, judy akkina2, karen meidenbauer1 and morgan hennessey2 1johns hopkins applied physics laboratory, laurel, md, usa; 2center for epidemiology and animal health, us dept of agriculture, ft. collins, co, usa objective standardize selection of indicator data streams and corresponding alerting algorithms for syndromic, reportable disease, and confirmed diagnostic categories derived from veterinary laboratory test order data for bovines. introduction the johns hopkins university applied physics laboratory is collaborating with epidemiologists of the us dept. of agriculture’s animal and plant health inspection service (aphis) center for epidemiology and animal health (ceah) to increase animal health surveillance capacity. ceah monitors selected syndromic animal health indicators for stakeholder reporting. this project’s goal was to extend this capacity to bovine veterinary laboratory test accession data. methods indicators for weekly monitoring were derived from bovine test records from the colorado state university veterinary diagnostic laboratory system from 27 jun 2010 29 may 2016. selected indicator types were syndromic test orders, disease-specific orders, and disease-specific positive results. indicators were adopted if aphis epidemiologists considered them worth monitoring and if they were represented by at least 100 lab accessions. ten syndromes were chosen for routine monitoring based on body systems, bovine-specific concerns (e.g. mastitis), and concepts to capture novel threats. reportable diseases were chosen from the list published by the colorado dept. of agriculture [1]. based on aphis concerns and test order frequencies, 4 diseases were chosen for weekly monitoring: bluetongue, brucellosis, epizootic hemorrhagic disease, and paratuberculosis. to monitor positives, we considered both the number and the ratio of herds with at least one positive result for each disease. for included tests (excluding results quantified with antibody levels), we counted an accession as “positive” if the result field contained strings “positive”, “suspect”, or “detect” without negation terms. for weekly counts, we added the number of herds with any positives after deduplication. diseases adopted for monitoring of positive results were bovine viral diarrhea, trichomoniasis, and paratuberculosis. from experience and literature, we compared variants of 4 algorithm types, including: the c2 method of the cdc early aberration reporting system, a cusum control chart with a sliding baseline, the temporal scan statistic gscan applied to hospital infection counts, and the cdc historical limits method. we adapted a semisynthetic simulation approach for algorithm comparison in which authentic disease count data are used as baseline, and simulated signals are added to the background as detection targets. in discussions about specific diseases and veterinary testing practice, ceah required sensitivity to one-week data spikes as well as effects of health threats with multi-week incubation periods and more gradual test ordering. for such gradual signals, we chose the lognormal signal model of sartwell applied to incidence data for many diseases. incubation periods vary widely by disease, and for this project, we chose lognormal parameters such that 90% of reported cases would occur within 6 weeks. we conducted separate algorithm detection trials for spike and gradual signals. calculations of sensitivity, alert rate, and timeliness were derived with sets of 1000 repeated trials for each combination of algorithm and syndrome or disease. we applied minimum performance requirements of 95% sensitivity, ≤1 alert per 8 weeks, and mean detection delays of <2 weeks. the rule adopted for recommending an alerting method was to seek the method with the lowest alert rate that satisfied the sensitivity, alert rate, and delay criteria. results the table below shows the syndromes with chosen algorithms and thresholds for detection of the gradual signals. the scan statistic gscan and the historical limits method histlim achieved consistently higher sensitivities with acceptable alert rates than the other methods applied. the presentation will extend the results to reportable disease and clinical positive indicators and to the spike signals for all indicators. conclusions among results for both signal types, the results yielded a few preferred methods covering all chosen indicator streams. monitored indicators with median weekly counts = 0 remain a challenge requiring more background data and veterinarian judgment. from analysis of orders from the few available laboratories, manual review will be required to achieve accurate syndromic categorization for each lab. monitoring of test positives will require combined analysis of positive herd counts and percentages (of all tested herds) due to routine variation in laboratory submissions. syndromes with algorithms chosen for gradual target signals keywords animal health; livestock surveillance; laboratory data; alerting algorithm; monte carlo trials acknowledgments the authors acknowledge the provision of laboratory test data and helpful advice from barbara powers and tracy baszler, colorado state university veterinary diagnostic laboratories, ft. collins, co, usa references [1] colorado department of agriculture, livestock health: reportable diseases in colorado, https://www.colorado.gov/pacific/aganimals/ livestock-health, last accessed aug. 23, 2017. *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e22, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts enhancing surveillance on the biosense platform through improved onboarding processes travis mayo*1, michael coletta2, sophia crossen3 and kirsten oliver4 1inductivehealth informatics, atlanta, ga, usa; 2centers for disease control and prevention, atlanta, ga, usa; 3kansas department of health and environment, topeka, ks, usa; 4west virginia department of health and human resources, charleston, wv, usa objective this session will present the impacts of enhancements made to national syndromic surveillance program (nssp) biosense platform onboarding in 2017 from the perspective of cdc and public health jurisdictions. introduction in 2017, the national syndromic surveillance program (nssp) continued to expand as a national scope data source with over 6,500 facilities registered on the biosense platform, including 4,000 active, 1,800 onboarding, and 700 planned or inactive facilities. 2,086 of the active facilities are emergency departments across 49 sites in 41 states. the growth of data available in nssp has been driven by continued enhancements to tools and processes used by the nssp onboarding team. these enhancements help to rapidly integrate new healthcare facilities and onboard new public health sites in support of american hospital association (aha) emergency department (ed) representativeness goals. furthermore, with these improvements to the onboarding process, including the master facility table update process and automated data validation reporting, nssp has broadened stakeholder participation in the onboarding process. keywords onboarding; quality; validation; automation; nssp *travis mayo e-mail: tmg4@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e39, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts an open source inventory to evaluate public health surveillance systems kc decker1, catherine ordun2 and dimitrious g. koutsonanos*1, 2 1rollins school of public health, emory university, atlanta, ga, usa; 2booz allen hamilton, mclean, va, usa objective the objective of this project is to advance the science of biosurveillance by providing a user curated cataloging system, to be used across health department and other users, that advances daily surveillance operations by better characterizing three key issues in available surveillance systems: duplication in biosurveillance activities; differing perspectives and analyses of the same data; and inadequate information sharing. introduction a variety of government reports have cited challenges in coordinating national biosurveillance efforts at strategic and tactical levels. the general accountability office (gao), an independent nonpartisan agency that investigates how the federal government funding and performs analysis at the request of congressional committees or by public mandate, has published 64 reports on biosurveillance since 2005. the aim of this project is to better characterize these issues by collecting and analyzing a sample of publicly documented biosurveillance systems, and making our data and results available for the public health community to review and evaluate. this study openly publishes the data files of information collected (i.e. csv, xls), the python nlp scripts, and a freely available web-based application developed in r shiny that filters against the 227 biosurveillance systems and activities to promote a more transparent understanding of how public health practitioners conduct surveillance activities. methods collected and reviewed data on 424 systems, of which 227 systems and activities met our criteria; implemented a new approach to develop a standard framework for data collection using natural language processing (nlp); openly published all data files publicly on github and developed an online analytics application; and convened a workshop of experts from across federal, state, not-for-profit, academic and commercial entities in november 2015 in washington, d.c., to review the methodology and results of this study. results the results of this project include a fully functional web application and code (available through github) for the continued expansion, categorization and analysis of surveillance systems. unique findings currently rendered through the 227 surveillance systems include: out of 227 systems, 20 were established in the year 2006, alone, with an increase in systems established following 1990; 68% of all systems catalogued are focused solely on human surveillance; 45% of all cataloged systems used statistical analysis and only 4% are using natural language processing; and 43% of all biosurveillance systems in our inventory reported using “health department” data as a data source. conclusions we believe this project is the first step for public health practitioners and researchers to contribute to a transparent inventory of systems and activities. results provide meaningful metadata on an over focus on human surveillance, over-reliance on a single data source (health departments) and a lack of advanced data science practices being applied to systems in the field. the value of this project 1) provides a starting point for the development of a standard framework of categories to use for cataloging biosurveillance systems, 2) offers openly available data and code on github [3] for others to integrate into their research, and 3) introduces a set of methodological issues to consider in a biosurveillance inventorying exercise. top ten data dissemination groups by number of systems top ten conditions groups by number of systems and activities keywords biosurveillance; data science; open source; analytics; webapplication acknowledgments we would like to express our gratitude to the association of state & territorial health officials (astho) for hosting the november 2015 workshop that convened biosurveillance experts. references 1. u.s. government accountability office. gao-15-664t, biosurveillance: additional planning, oversight, and coordination needed to enhance national capability [internet]. washington (dc): u.s. government accountability office; 2015 [cited 2016mar02]. retrieved from: http://gao.gov/products/gao-15-664t 2. national biodefense science board. recommendations for action: modernizing and enhancing our nation’s biosurveillance capabilities report from the national biodefence science board [document on the internet]. washington (dc): u.s. department of health and human services, assistant secretary for preparedness and response; 2015. [cited 2016mar02]. retrieved from: http://www.phe. gov/preparedness/legal/boards/nprsb/recommendations/documents/ biosrveillance-capabilities.pdf 3. phillips n. development of python scripts to classify surveillance systems; 2015 [repository on the internet]. retrieved from: https://github.com/nick-phillips/sos, web application at http://bit. ly/1hunket *dimitrious g. koutsonanos e-mail: dimkoy@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e2, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts a scoping review of enterovirus d-68 ashley weeks1, lisa waddell2, andrea nwosu*1, christina bancej1, shalini desai1, tim booth3 and amanda shane1 1centre for immunization and respiratory infectious diseases, public health agency of canada, ottawa, on, canada; 2national microbiology laboratory, public health agency of canada, guelph, on, canada; 3national microbiology laboratory, public health agency of canada, winnipeg, mb, canada objective to create a scoping review on enterovirus d-68 (ev-d68) that will serve as a useful tool to guide future research with the aim of filling critical information gaps and supporting the development of public health preparedness activities. introduction ev-d68 is a non-polio enterovirus, primarily resulting in respiratory illness, with clinical symptoms ranging from mild to severe. infection has also been associated with severe neurological conditions like acute flaccid myelitis (afm). ev-d68 was first discovered in 1962, with infrequent case reports until 2014 at which point a widespread multinational outbreak mostly affecting the pediatric population occurred across north america, europe, southeast asia and africa. this outbreak was associated with an increase in afm, with cases being reported in canada, the united states, norway, and france. with this new and emerging threat, public health and other organizations were called upon to implement response measures such as establishment of case definitions, surveillance mechanisms, and recommendations for clinical and public health management. the response to the 2014 outbreak in canada highlighted several important ev-d68 evidence gaps including a lack of risk factor and clinical information available for non-severe cases, and uncertainty around seasonal, cyclical and secular trends. given the increased reporting of ev-d68 cases associated with severe outcomes, it’s critical that public health establishes what is known about ev-d68 in order to support decisionmaking, education and other preparedness activities and to highlight priority areas for future research to fill critical knowledge gaps. scoping reviews provide a reproducible and updateable synthesis research methodology to identify and characterise all the literature on a broad topic as a means to highlight where evidence exists and where there are knowledge gaps. in order to systematically characterise the ev-d68 knowledge base, a scoping review was conducted to map the current body of evidence. methods a literature search of published and grey literature on ev-d68 was conducted on may 1, 2017. a standardized search algorithm was implemented in four bibliographic databases: medline, embase, global health and scopus. relevant grey literature was sought from a priori identified sources: the world health organization, united states centers for disease control and prevention, the public health agency of canada, the european centre for disease prevention and control, and thesis registries. two-level relevance screening (title/ abstract followed by full-text) was performed in duplicate by two independent reviewers using pretested screening forms. conflicts between the reviewers were reconciled following group discussion with the study team. english and french articles were included if they reported on ev-d68 as an outcome. there were no limitations by date, publication type, geography or study design. conference abstracts were excluded if they did not provide sufficient outcome information to characterize. the articles were then characterized by two independent reviewers using a pretested study characterization form. the descriptive characteristics of each article were extracted and categorized into one of the following broad topic categories: 1) epidemiology and public health, 2) clinical and infection prevention and control (ipc), 3) guidance products, 4) public health surveillance, 5) laboratory, and 6) impact. the epidemiology and public health category contained citations describing prevalence, epidemiological distribution, outbreak data and public health mitigation strategies. clinical and ipc citations included details regarding symptoms of ev-d68 infection, patient outcomes, clinical investigation processes, treatment options and infection prevention and control strategies. the guidance category included citations that assess risk, provide knowledge translation or provide practice guidelines. public health surveillance citations provided details on surveillance systems. citations in the laboratory category included studies that assessed the genetic characteristics of circulating evd68 (phylogeny, taxonomy) and viral characteristics (proteins, viral properties). lastly, the impact category contained citations describing the social, economic and resource burden of ev-d68 infection. each broad topic category was subsequently characterised further into subtopics. results the search yielded a total of 384 citations, of which 300 met the inclusion criteria. twenty-six of forty-three potentially relevant grey literature sources were also included. preliminary literature characterization suggests that the majority of the published literature fell under the topic categories of epidemiology, clinical, and laboratory. there were limited published articles on public health guidance, ipc, surveillance systems and the impact of ev-d68. the grey literature primarily consisted of webpages directed towards the public (what ev-d68 is, how to prevent it, what to do if ill, etc.). this scoping review work is presently underway and a summary of the full results will be presented at the 2018 annual conference. conclusions the body of literature on ev-d68 has increased since the 2014 outbreak, but overall remains small and contains knowledge gaps in some areas. to our knowledge, this scoping review is the first to classify the entirety of literature relating to ev-d68. it will serve as a useful tool to guide future research with the aim of filling critical information gaps, and supporting development of public health preparedness activities. keywords enterovirus d68; scoping review; ev-d68 *andrea nwosu e-mail: andrea.nwosu@canada.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e89, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts cause-specific school absenteeism monitoring identifies community influenza outbreaks jonathan temte*1, shari barlow1, yenlik zheteyeva2, maureen landsverk1, emily temte1, amber schemmel1, brad maerz1, ron gangon1, erik reisdorf1, pete shult1, mary wedig1, thomas haupt3, ashley fowlkes2 and amra uzicanin2 1family medicine and community health, university of wisconsin, madison, wi, usa; 2centers for disease control and prevention, atlanta, ga, usa; 3wisconsin department of public health, madison, wi, usa objective the oregon child absenteeism due to respiratory disease study (orchards) was implemented to assess the relationships between cause-specific absenteeism within a school district and medically attended influenza visits within the same community. introduction transmission and amplification of influenza within schools has been purported as a driving mechanism for subsequent outbreaks in surrounding communities. however, the number of studies assessing the utility of monitoring school absenteeism as an indicator of influenza in the community is limited. orchards was initiated to evaluate the relationships between all-cause (a-tot), illness-related (ai), and influenza-like illness (ili)-related absenteeism (a-ili) within a school district and medically attended influenza a or b visits within the same community. methods orchards was based at the oregon school district (osd), which enrolls 3,640 students at six schools in south-central wisconsin. parents reported influenza-like symptoms on an existing phone-based absenteeism reporting system. attendance staff identified ili using a simple case definition. absenteeism was logged into the osd’s existing electronic information system (infinite campus), and an automated process extracted counts of a-tot, a-i, and a-ili each school day from 9/02/14 through 6/08/17. parents of students with acute respiratory infections (ari) were invited to contact study staff who assessed the students’ eligibility for the study based on presence of ili symptoms. from 1/05/15 through 6/08/17, data and nasal swabs were collected from eligible osd students whose parents volunteered to have a study home visit within 7 days of ili onset. specimens were tested for influenza a and b at the wisconsin state laboratory of hygiene using the cdc human influenza virus real-time rt-pcr diagnostic panel. for community influenza, we used data from the wisconsin influenza incidence surveillance project (wiisp) that monitors medically attended influenza using rt-pcr at five primary care clinics surrounding the osd. data analysis: over-dispersed poisson generalized additive loglinear regression models were fit to the daily number of medically attended influenza cases and daily absenteeism counts from three sources (a-tot, a-i, and a-ili) with year and season (calendar day within year) as smooth functions (thin plate regression splines). two subgroups of a-ili representing kindergarten through 4th grade (k-4) and 5th-12th grade (5-12) were also evaluated. results during the study period, 168,859 total absentee days (8.57% of student days), 36,104 illness days (1.83%), and 4,232 ili days (0.21%) were recorded. home visits were completed on 700 children [mean age = 10.0 ± 3.5 (sd) years]. influenza rt-pcr results were available for 695 (99.3%) children: influenza a was identified in 54 (13.3%) and influenza b in 51 (12.6%) specimens. there were one large and early outbreak of influenza a (h3n2) followed by b in 2014/15, an extremely late combined outbreak of influenza a (h1n1) and b in 2015/16, and a combined outbreak of influenza a/(h3n2) and b in 2016/17. pcr detection of influenza a or b, as compared to no influenza, was strongly associated with a child with a-ili-positive status (or=4.74; 95% ci: 2.78-8.18; p<0.001). nearly 2,400 medically attended ari visits were reported during the study period. of these, 514 patients were positive for influenza (21.5%): 371 (15.5%) influenza a and 143 (6.0%) influenza b. the temporal patterns of medically attended influenza were very similar to influenza cases in osd students. comparisons of the regression models demonstrated the highest correlation between absenteeism and medically attended influenza for 5th-12th grade students absent with ili with a -1 day time lag and for all students with a-ili with a -1 day lag (table); a-i also had moderate correlation with a -15 day lag period. conclusions cause-specific absenteeism measures (a-i and a-ili) are moderately correlated with medically attended influenza in the community and are better predictors than all-cause absenteeism. in addition, a-i preceded community influenza cases by 15 days. the monitoring system was easily implemented: a-i surveillance was fully automated and a-ili required only minor review by attendance staff. the resulting correlations were likely lowered by the presence of other viruses that resulted in a-ili (e.g., adenovirus) and by breaks in the school year during which absenteeism data did not accrue. automated systems that report cause-specific absenteeism data may provide a reliable method for the early identification of influenza outbreaks in communities. from a preparedness perspective, 15-day advance warning is significant. the addition of a laboratory component could increase usefulness of the cause-specific student absenteeism monitoring as an early-warning system during influenza pandemics. correlation and lag time comparing absenteeism to medically attended influenza keywords school; absenteeism; influenza acknowledgments this study was possible thanks to a collaboration between the centers for disease control and prevention, wisconsin state lab of hygiene, wisconsin department of health services, university of wisconsin department of family medicine and community health, quidel corporation, and oregon school district staff, students, and families *jonathan temte e-mail: jon.temte@fammed.wisc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e103, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper university of pittsburgh, pittsburgh, pa, usa introduction we describe an automated system that can detect multiple outbreaks of infectious diseases from emergency department reports. a case detection system1 obtains data from electronic medical records, extracts features using natural language processing, then infers a probability distribution over the diseases each patient may have. then, a multiple outbreak detection system (mods) searches for models of multiple outbreaks to explain the data. mods detects outbreaks of influenza and non-influenza influenza-like illnesses (ni-ili). methods mods searches over models of multiple outbreaks to maximize the probability of the data2. we start with bayes’ theorem which states p(m|data)=(p(data|m)p(m))/p(data) where p(m) is the prior probability of an epidemiological model of zero, one, or more outbreaks, p(data|m) is the probability of the data given a model, and p(data) is the probability of the data. the method searches for the map (maximum a posteriori) model that maximizes the above numerator. a model consists of baseline levels of non-outbreak influenza and ni-ili, zero, one, or more influenza outbreaks, and zero, one, or more ni-ili outbreaks. searching for the map model requires mods to search over the set of basic parameters for multiple influenza and niili outbreaks. we search over combinations of multiple influenza and ni-ili outbreaks; our current implementation (running on a single 1.6ghz processor) takes about 24 hours to adequately search the space of models for a single dataset for one year. results we conducted a battery of experiments with simulated outbreaks consisting of zero, one or two influenza outbreaks and zero, one, or two ni-ili outbreaks. we start with records of actual influenza, niili, and other patients, construct a model of multiple outbreaks, then instantiate the model by: •randomly sampling patients without influenza or ni-ili according to a poisson distribution. •randomly sampling influenza and ni-ili patients according to the postulated outbreak model. we then search over models consisting of zero, one, or two influenza outbreaks and zero, one, or two ni-ili outbreaks and report the map model. in general, mods can accurately predict starting dates and peaks for multiple influenza outbreaks, as well as the start, duration, and level of co-occurring ni-ili outbreaks. for instance, we constructed a model with two influenza outbreaks with start/peak days 20/138 and 80/222 and a ni-ili outbreak with start/duration 70/25 (shown in diagram “simulated outbreaks”). on day 100, before either influenza peak, mods predicted one influenza outbreak with start/peak days of 20/138 and one ni-ili outbreak with start/duration days of 71/19. on day 150, after the first influenza peak but before the second, mods predicted two influenza outbreaks with start/peak days of 20/138 and 76/215 and one ni-ili outbreak with start/duration days of 71/19. we also ran our system on data from allegheny county for the 2009-2010 influenza season. given data starting on june 1, 2009, by september 1 the system predicted a peak at about october 15 with a small ni-ili outbreak in june. previous analysis using thermometer sales as a measure indicate the actual peak was around october 193. conclusions this work demonstrates that we can predict and characterize multiple, overlapping outbreaks from clinical data. in particular, it shows that the data have the required fidelity to detect and characterizate multiple, overlapping outbreaks. simulated outbreaks keywords outbreak detection; bayesian modeling; influenza acknowledgments this work was supported by nih grant r01 lm011370 on “probabilistic disease surveillance.” john aronis was supported by the national library of medicine training grant t15lm007059 to the university of pittsburgh. references 1. tsui f-c, wagner m, cooper g, que j, harkema h, dowling j, sriburadej t, espino j, vorhees r. probabilistic case detection for disease surveillance using data in electronic medical records. online journal of public health. 2011;3(3). 2. cooper g, villamarin r, tsui f-c, millett n, espino j, wagner m. a method for detecting and characterizing outbreaks of infectious disease from clinical reports. journal of biomedical informatics, 2015, volume 53, 15-26. 3. villamarin r, cooper g, tsui f-c, wagner m, espino j. estimating the incidence of influenza cases that present to emergency departments. proceedings of the international society for disease surveillance. 2010. *john m. aronis e-mail: jma18@pitt.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e5, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 1ishikawa associates, roslindale, ma, usa; 2international society for disease surveillance, brighton, ma, usa; 3association of state and territorial health officials, crystal city, va, usa objective promote inter jurisdictional syndromic surveillance (sys) data sharing practices with a training model that engages participants in collaborative learning. introduction sharing public health (ph) data and practices among ph authorities enhances epidemiological capacities and expands situational awareness at multiple levels. ease of data sharing through the biosense application, now part of the national syndromic surveillance program (nssp), and the increased use of sys nationwide have provided opportunities for region-level sharing of sys data. in addition, there is a need to build workforce competence in sys given powerful new information technology that can improve surveillance system capacities. peer-to-peer learning builds the relationships and trust among individuals and organizations that are required for inter jurisdictional data sharing. methods the sys data sharing workshops are based on a training model in which participants first share a limited amount of sys data, learn for one another, and then make plans to grow inter jurisdictional sys data sharing (figure 1). workshop objectives and agenda were developed by ishikawa associates, isds, astho, and cdc. hhs regions for workshops were selected based on sys data availability, biosense participation, and interest in sys data sharing. each data sharing workshop was tailored to participant needs for the knowledge and skills necessary for sys data sharing. using a nonformal education approach1, participants selected a syndrome(s) to share limited emergency department visit data before the workshop. the workshop agenda then includes sharing those data, exchanging best practices, documenting perceived benefits and barriers to sys data sharing, and brainstorming solutions and setting next steps. results regional sys data sharing workshops have been conducted in 8 of the 10 hhs regions, reaching 98 surveillance professionals from 63 state and 35 local ph agencies. across the workshops, influenzalike-illness (ili) was the most frequently selected syndrome of interest, primarily because many jurisdictions already used sys for ili surveillance and the data was available for analysis and sharing. additionally, the regular flu season experienced nationwide helped to choose a timeframe for analysis. other selected syndromes reflected the diversity of sys applications, including co poisoning, drug overdose, asthma, heat-related illness, and gastrointestinal illness. perceived benefits to sys data sharing included cross-border case-finding, identification of regional patterns and trends, enhanced national situational awareness, hypothesis generation and testing, and retrospective analyses to improve ph practice2. data quality, legal issues, lack of metadata, and the absence of specific functionalities in biosense were listed as barriers. action items have included work on a green paper on barriers to data sharing, presentations at national conferences, regular information exchanges, syndrome definitions, and increased data sharing with cdc and other stakeholders through the biosense application. conclusions the outcomes of these workshops include demonstration of an effective training format for engaging ph surveillance professionals through relationship building, trial data sharing, and collaborative priority setting and action planning as a necessary first step to identifying and addressing barriers to data sharing. ongoing training will be required as new jurisdictions use sys and experienced ones seek to improve their practice. figure 1: illustration of the syndromic surveillance data sharing workshop model. keywords data sharing; syndromic surveillance; non-formal eduction; training acknowledgments dhis/csels/cdc for sustained workshop support and engagement. the workshop participants and their agencies for in-kind contributions of work and enthusiasm. references 1. nonformal education manual, u.s. peace corps, pub. no. m0042, reprinted 2011. 2. isds. check! explore barriers and solutions to data sharing on biosense 2.0. hhs region 5 data sharing workshop participants. www.syndromic.org. *charlie ishikawa e-mail: charlie@ishikawa.associates online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e124, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd 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geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts syndromic surveillance and uefa euro 2016 in france – health impact assessment erica fougère, céline caserio-schönemann*, jamel daoudi, anne fouillet, marc ruello, isabelle pontais, manuel zurbaran, emmanuel lahaie and anne gallay santé publique france, saint-maurice, france objective to describe the surveillance indicators implemented for the health impact assessment of a potential health event occurring before, during or after the uefa euro 2016 football matches in order to timely implement control and prevention measures. introduction france hosted 2016 uefa european football championship between june 10 and july 10. in the particular context of several terrorist attacks occurring in france in 2015 [1], the french national public health agency « santé publique france » (formerly french institute for public health surveillance-invs) was mandated by the ministry of health to reinforce health population surveillance systems during the uefa 2016 period. six french regions and 10 main stadiums hosted 51 matches and several official and nonofficial dedicated fan zones were implemented in many cities across national territory. three types of hazard have been identified in this context: outbreak of contagious infectious disease, environmental exposure and terrorist attack. the objectives of health surveillance of this major sporting event were the same as for an exceptional event including mass gathering [2] : 1/ timely detection of a health event (infectious cluster, environmental pollution, collective foodborne disease…) to investigate and timely implement counter measures (control and prevention), 2/ health impact assessment of an unexpected event. the french national syndromic surveillance system sursaud® was one of the main tools for timely health impact assessment in the context of this event. methods french national syndromic sursaud® system has been set up in 2004 and supervised by santé publique france for 12 years. it allows the daily automatic collation of individual data from over 650 emergency departments (ed) involved in the oscour® network and 61 emergency general practitioners’ (gps) associations (sos médecins) [3]. about 60,000 attendances in ed (88% of the national attendances) and 8,000 visits in sos médecins associations (95% of the national visits) are daily recorded all over the territory and transmitted to santé publique france. medical information such as provisional medical diagnosis coded according to the international classification of diseases, 10th revision (icd-10) for eds and specific thesaurus for sos médecins is routinely monitored through different syndromic indicators (si). si are defined by medically relevant clusters of one or several diagnoses, serving as proxies for conditions of public health interest. from june 10 to july 10, 19 si were daily analyzed through automatic national and regional dashboards. si were divided into 3 groups of public health surveillance interest : 1/ description of population health: injuries, faintness, myocardial infarction, alcohol, asthma, heat-related symptoms, anxious troubles ; 2/ infectious diseases/symptoms with epidemic potential or diseases/symptoms linked with an environmental exposure: fever, fever associated with cutaneous rash, meningitis, pneumonia, gastroenteritis, collective foodborne disease ; 3/ symptoms potentially linked with a cbrn-e exposure: influenza-like illness, burns, conjunctivitis, dyspnea/ difficulty breathing, neurological troubles, acute respiratory failure. daily analysis were integrated into specific uefa 2016 surveillance bulletins and daily sent to the ministry of health including week-ends. results si followed during the uefa euro 2016 period were nonspecific and potentially affected or influenced by several events appart from the championship. between june 10 and july 10, two moderate heat-wave periods occurred on a large part of mainland france : the first one from june 22 to 25 (beginning in the west-south of france and then moving north and east of the country) and the second one from july 8 to 11 in the east-south. an increase in heat-related indicators (hyperthermia/heat stroke, dehydration, hyponatremia and burns) has been observed during both periods in five french regions including four hosting regions. only minor increases in the other si followed during the euro 2016 period were observed. conclusions health surveillance implemented during 2016 uefa european football championship through a daily analysis of non-specific si from the french syndromic surveillance system sursaud® did not show any major variation associated with the sporting event. the observed variations were related with specific environmental conditions (heat-waves). together with the health surveillance system, preventive plans were set up during the event essentially by offering flyers with information and useful tips on the main preventive attitudes and measures to adopt in a summer festive context (risks associated with alcohol and drug intake, injuries, heat and sun exposure, dehydration, unprotected sexual behaviour…). keywords uefa 2016; sursaud; health impact assessment; france; mass gathering acknowledgments to oscour® and sos médecins partners and to the regional sursaud® team references [1] vandentorren s, paty ac, baffert e, et al. syndromic surveillance during the paris terrorist attacks. 387, no. 10021, p846–847, 27 february 2016 [2] lombardo j, sniegoski c, loschen w, et al. public health surveillance for mass gatherings. johns hopkins apl tech dig 2008;27(4): 347-355 [3] caserio-schönemann c, bousquet v, fouillet a, henry v. the french syndromic surveillance system sursaud®. bull epidémiol hebd 2014;3-4:38-44 *céline caserio-schönemann e-mail: celine.caserio-schonemann@santepubliquefrance.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e171, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts syndromic surveillance of air pollution incidents across international borders helen hughes*1, 2, 3, alec dobney1, anne fouillet6, céline caserio-schönemann6, thomas hughes4, 5, gillian e. smith1 and alex j. elliot1 1public health england, birmingham, united kingdom; 2the farr institute @ herc, liverpool, united kingdom; 3nihr health protection research unit in gastrointestinal infections, liverpool, united kingdom; 4john radcliffe hospital, oxford, united kingdom; 5royal college of emergency medicine, london, united kingdom; 6santé publique france, paris, france objective to assess the impact on human health observed in association with periods of poor air quality which extended across international borders, affecting both london (uk) and paris (france). in particular to quantify increased levels of emergency department (ed) attendances for asthma and wheeze/ difficulty breathing, and how different age groups were affected. here, using ed syndromic surveillance from england and france, we aimed to identify and describe the acute impact of periods of particularly poor air quality during 2014 on human health in both london and paris. introduction the impact of poor air quality (aq) on human health is a global issue, with periods of poor aq known to occur in multiple locations, across different countries at, or around the same time. the public health england (phe) emergency department syndromic surveillance system (edsss) is a public health legacy of the london 2012 olympic and paralympic games, monitoring anonymised daily attendance data in near real-time from a sentinel network of up to 38 eds across england and northern ireland during 2014. the organisation de la surveillance coordonnée des urgences (oscour®) is a similar ed system coordinated by santé publique france and has been running in france since 2004, established following a major heatwave in 2003 to improve real-time public health surveillance capabilities. this truly national network included around 540 eds in 2014. methods periods of poor aq during 2014 in both london and paris, which were likely to have an acute impact on human health were identified from the daily particulate monitoring data made available by the monitoring authorities in each location.1,2 daily ed syndromic surveillance data for selected health indicators (asthma, difficulty breathing type attendances and myocardial ischaemia (mi)) were gathered from edsss and oscour® for london and paris respectively. the standard method used for the daily statistical analysis of edsss(rammie method),3 was also applied to oscour® and used to identify days where the numbers of attendances reported in both the edsss and oscour® systems were statistically significantly different to the historical data, based on the previous 2 years. results distinct differences were identified between the impact observed on different age groups, with increased asthma ed attendances for children during/ following some aq events, though a greater impact was observed in adults around other aq events. increases in ed attendances for asthma were identified at several points where no aq events were reported, both short lived spikes during the summer period in particular and a more sustained increase towards the start of autumn. conclusions despite edsss and oscour® having been developed in different countries, at different times and resulting from different drivers, both systems use very similar syndromic indicators to identify asthma, difficulty breathing and mi attendances. using these systems the short term impacts of multiple aq events which crossed international boundaries were successfully identified and investigated by english and french public health authorities. periods of poor aq are not the only events that can affect asthma type attendances as identified here, thunderstorm activity and the beginning of a new academic year also coincided with increased attendances in both london and paris. harmonisation of surveillance methods across different international jurisdictions is possible and there is the potential for future cross border surveillance and harmonisation of methods between countries to improve international health surveillance and early warning of potential public health threats affecting multiple countries. keywords international; syndromic surveillance; air quality acknowledgments london: we acknowledge the contribution and support from the ed clinicians and trust staff; the on-going support of the royal college of emergency medicine; the technical support provided by emis health and l2s2 ltd in edsss. this piece of work was supported by the farr institute for health informatics research (mrc grant: mr/m0501633/1). this work is part funded by the national institute for health research health protection research unit (nihr hpru) in gastrointestinal infections at university of liverpool in partnership with public health england (phe), in collaboration with university of east anglia, university of oxford and the institute of food research. helen hughes is based at university of liverpool. the views expressed are those of the author(s) and not necessarily those of the nhs, the nihr, the department of health or phe. paris: we acknowledge the contribution and support from the ed clinicians and hospital staff. references 1. airparif. association de surveillance de la qualité de l’air. la pollution de l’air en île-de-france. www.airparif.asso.fr/. 2. department for environment food & rural affairs. uk-air: air information resource. uk-air.defra.gov.uk/. 3. morbey ra, elliot aj, charlett a, verlander nq, andrews n, smith ge. the application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (rammie) method for syndromic surveillance in england. bioinformatics. 2015;31:3660-5. *helen hughes e-mail: helen.hughes@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e139, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 1health policy, planning, and statistics, illinois department of public health, chicago, il, usa; 2cdc epicenters program, chicago, il, usa objective to enhance cre surveillance and communication by incorporating social network measures to quantify patient sharing between facilities. introduction cre are multidrug-resistant bacteria associated with up to 50 percent mortality in infected persons (1). cre are increasingly problematic in illinois healthcare facilities, especially long-term acute care hospitals (ltachs); therefore, illinois implemented the extensively drug-resistant organism (xdro) registry (www.xdro. org). mathematical models have identified patient sharing between healthcare facilities as a mechanism for regional spread (1), and the importance of each facility within a network can be quantified using social network analysis (2). degree centrality is a measure representing the number of facilities with which a facility has shared at least one patient, and hence, a measure of “risk” of receiving a cre colonized patient. eigenvector centrality is more sophisticated in that it quantifies how well a given node is connected to other “wellconnected” nodes (3). we expect that facilities that have high degree and/or eigenvector centrality – and, thus, higher “risk” of encountering a cre colonized patient – will have higher incidence of cre, as will facilities that share patients with ltachs. understanding facilitylevel characteristics that predict higher cre rates will enhance the xdro registry’s usefulness as a surveillance tool. methods we obtained facility-level cre cases from the xdro registry and characteristics from the 2013 illinois department of public health (idph) annual hospital questionnaire. the network analysis was done using idph hospital discharge data, and centrality measures were generated using ucinet (harvard analytic technologies 2002). centrality, number of beds, number of patients shared with ltachs, and proximity to chicago were considered as predictors of higher cre rates. multivariable negative binomial regression was used to compare incidence rate ratios; we constructed separate models for the state, and stratified by chicago region. results higher centrality and sharing patients with an ltach was associated with higher cre rates. these associations hold true for the state and after restricting analysis to the chicago region (figure 1). after controlling for number of beds, city proximity, and ltach sharing, the highest quintile of eigenvector centrality was associated with an over two-fold higher cre incidence compared to the other quintiles combined (irr 2.3 95% ci: 1.2-4.7); the association remained when analyzing facilities near chicago (3.0, 1.6-5.4). degree centrality did not predict a higher cre rate for the state, but did for the chicago region (1.5, 1.2-2.1). sharing patients with ltachs was a significant predictor in the chicago eigenvector model (2.0, 1.03-4.0) and the statewide degree model (2.5, 1.1-5.8). conclusions there is strong evidence that a facility’s centrality and sharing patients with ltachs predicts higher cre rates. combining a surveillance tool such as the xdro registry with social network measures is a powerful way to understand the geographic spread of antibiotic resistant bacteria. quantifying patient sharing using the principles of social network analysis allows public health agencies to identify facilities likely to contribute to the spread of antibiotic resistant bacteria, and plan focused interventions. figure 1: 111 facilties in the chicago region keywords surveillance; healthcare; social network analysis; hai acknowledgments idph cre taskforce references (1) centers for disease control and prevention (cdc). (2012). cre toolkit: guidance for control of carbapenem-resistant enterobacteriaceae (cre). atlanta, georgia: cdc. (2) lee, b., mcglone, s., song, y., avery, t., eubank, s., chang, c., bailey, r., wagener, d., burke, d., platt, r., huang, s. (2011). social network analysis of patient sharing among hospitals in orange county, california. american journal of public health. vol. 101, no. 4. (3) borgatti, s. p., everett, m. g., & johnson, j. c. (2013). analyzing social networks. sage publications limited. *michael j. ray e-mail: michael.j.ray@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e32, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts travel and triage: pilot project to detect infections after medical tourism procedures pinar erdogdu*, barbara carothers, rebecca greeley and stella tsai new jersey department of health, trenton, nj, usa objective medical notes provide a rich source of information that can be used as additional supporting information for healthcare-associated infection (hai) investigations. the medical notes from 10 new jersey (nj) emergency departments (ed) were searched to identify cases of surgical-site infections (ssi). introduction epicenter, nj’s statewide syndromic surveillance system, collects ed registration data. the system uses chief complaint data to classify ed visits into syndrome categories and provides alerts to state and local health departments for surveillance anomalies. after the 2014 ebola outbreak in west africa, the new jersey department of health (njdoh) started collecting medical notes including triage notes, which contain more specific ed visit information than chief complaint, from 10 eds to strengthen hai syndromic surveillance efforts. in 2017, the njdoh was aware of one nj resident whose surgical site was infected following a cosmetic procedure outside of the us. this event triggered an intensive data mining using medical notes collected in epicenter. the njdoh staff searched one week of medical notes data in epicenter with a specific keyword to identify additional potential cases of surgical-site infections (ssi) that could be associated with medical tourism. methods the nj resident whose surgical site was infected following a cosmetic procedure outside of the us was interviewed by njdoh staff for details about their procedure. first, the patient’s interview results were reviewed to prepare a set of ssi and travel related keywords to be used in performing data mining in medical notes collected in epicenter. the interviewed patient had tummy tuck and liposuction surgeries; therefore, it was decided to search for “tummy tuck” as a keyword in epicenter. the medical notes from august 31, 2017 through september 8, 2017 were reviewed to identify patients who developed ssi following a cosmetic procedure outside of the us. results the search yielded 8 ed visits, one of which was identified as possible surgical site infection. the medical notes details indicated that the ed patient, a 21-year old female who had abdominoplasty (tummy tuck) and liposuction surgeries about a month prior, presented with post-surgical complaints such as pain, surgical dehiscence, and purulent drainage at the surgery site. chief complaint text for the same ed patient indicated the patient had headache and dizziness which were less specific than medical notes. the njdoh staff contacted the ed to obtain additional information regarding the infection. the lab results from the ed showed that the patient was identified as having a post-surgery infection, which prompted public health to follow-up whether it was an hai. conclusions the limitation for this project was that the keyword search was conducted only on one week of data. the timeframe was kept short to pilot testing the keyword identified. the centers for disease control and prevention suggests clinicians should consider nontuberculous mycobacteria (ntm) infections in the differential diagnosis for all people who have wound infections after surgery abroad, including surgery that has occurred weeks to months previously (1). future studies will explore larger data sets with additional keywords (e.g. country and organism) to see if potential cases can be identified as possible hai and/or outbreak that will lead to public health investigations. keywords syndromic surveillance; healthcare-associated infections; epicenter; new jersey acknowledgments kristen weiss (health monitoring) references 1. centers for disease control and prevention. nontuberculous mycobacteria in medical tourists to the dominican republic. https:// wwwnc.cdc.gov/travel/notices/alert/medical-tourism-dominicanrepublic active-countries.html. *pinar erdogdu e-mail: pinar.erdogdu@doh.nj.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e62, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts enhanced aedes spp. surveillance across jurisdictions in arizona’s border region mariana g. casal*1, nicolette dent1, jose arriola2, victor dominguez2, elizabeth lueck3, george gentzsch4, kathleen walker5, shelly jacobs2 and robert guerrero1 1office of border health, arizona department of health services, tucson, az, usa; 2santa cruz county health department, nogales, az, usa; 3cochise county health department, bisbee, az, usa; 4us costums and border protection, tucson, az, usa; 5university of arizona department of entomology, tucson, az, usa objective this surveillance project aims to increase and broaden coverage of aedes spp. ovitrap locations in arizona’s u.s.-mexico border region through interagency collaboration. introduction as part of a statewide effort to enhance surveillance for aedes spp. mosquitoes (1,2) the office of border health (obh) took the lead in providing technical assistance on surveillance in counties bordering mexico. in 2016, obh sought ways to enhance surveillance in a wider geographic area. trap locations closer to the border were established as a priority, given high amount of traffic across the international line, high border aedes mosquito activity, and native cases of dengue reported at the border in mexico. methods the arizona office of border health partnered with u.s. customs and border protection to select possible locations for ovitrapping near the border. border patrol health and safety tucson coordination accompanied obh and preparedness staff on three occasions to scout areas around pre-selected border patrol facilities. county, and border patrol staff contributed to trap maintenance. bids provided technical assistance to identify positive traps, collected data for reporting to the state, and collaborated with experts at the university of arizona entomology department to verify results and identify aedes spp. results out of 15 border patrol stations within border lands in santa cruz county, and cochise county, obh epidemiologist considered 10 viable trapping sites. two facilities were eventually eliminated because of logistical challenges. obh visited eight facilities and selected five locations within five miles of the u.s. –mexico border and two located less than 30 miles from the border. obh epidemiologists inspected sites for potential mosquito habitat and set ovitraps low to the ground in areas protected from rain. some facilities had areas of standing water discovered in unused tires, truck-washing stations, heavy-lifting equipment, and natural washes. border patrol staff complained of mosquito activity around some of the stations. after inspection obh set an average of three traps at each site. one site had evidence of mosquito larvae activity. conclusions border patrol facilities offer ideal trap locations given their proximity to the international line. secure facilities offer extra protection for traps against tampering. the partnership across local, state, tribal, and federal lines allowed arizona office of border health to expand surveillance locations, allowing two jurisdictions to set the first aedes-specific traps since arizona began the 2016 campaign, “fight the bite.” keywords aedes mosquito; ovitraps; inter-agency collaboration; mosquito surveillance references 1.fredericks, a. c., & fernandez-sesma, a. (2014). the burden of dengue and chikungunya worldwide: implications for the southern united states and california. annals of global health,80(6), 466–475. http://doi.org/10.1016/j. aogh.2015.02.006 2.rafael moreno-sanchez, mary hayden, craig janes, geoffrey anderson, a web-based multimedia spatial information system to document aedes aegypti breeding sites and dengue fever risk along the us–mexico border, health & place, volume 12, issue 4, december 2006, pages 715-727, issn 1353-8292, http://dx.doi.org/10.1016/j. healthplace.2005.10.001. *mariana g. casal e-mail: mariana.casal@azdhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e100, 2017 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts environmental health surveillance: a critical function of public health laboratories sylvia checkley*1, 2, sumana fathima2, norman neumann2, 3 and shamir mukhi4, 2 1ecosystem and public health, university of calgary, calgary, ab, canada; 2provincial laboratory for public health, calgary, ab, canada; 3school of public health, university of alberta, edmonton, ab, canada; 4canadian network for public health intelligence, national microbiology laboratory, winnipeg, mb, canada objective the objectives of this environmental health surveillance system were to provide a robust system for monitoring of water quality trends, and information to be used for mitigation of potential health problems, resource planning, risk analyses and decision making. introduction canada experienced 92 waterborne diseases outbreaks between 1975 and 20011. in addition, at any one time about 1500 communities in canada are unable to use their drinking water. the source of exposure in disease outbreaks is often not known, so the true disease burden attributable to water related exposure may be much higher. researchers have investigated risk factors for waterborne disease2,3. however, providing acces to surveiallance tools of use by frontline staff in the field as well as by surveillance professionals was key to making this type of system successful. methods this system involved drinking water and recreational water samples submitted to the provincial laboratory for public health in alberta, canada. a web-based system was developed for real-time integration of laboratory data for routine use through a secure data interface. data was classified by the tool to make interpretation explicit. this tool was available for use by environmental health officers, widespread in the field including trends over time within specific locations. ongoing support was available for training and specific questions. routine reporting was also developed for a broad range of stakeholders through active feedback. reporting was also responsive to user needs, for example, 2013 floods in southern alberta. results baselines have been created. weekly and seasonal trends are described. reporting was automated. information was packaged for stakeholders in the field and at the policy making level. conclusions routine surveillance can be useful at multiple levels from field application to regional emergencies. this was demonstrated through examples involving drinking water and recreational water, routinely and in emergency situations. keywords environmental public health surveillance; drinking water; waterborne disease acknowledgments thank you to the all staff at the alberta provincial laboratory for public health who make this work possible. references 1. eggerston l. investigative report: 1766 boil-water advisories now in place across canada cmaj 2008 178 (10): 1261. 2. jones aq, majowicz se, edge vl, thomas mk, macdougall l, fyfe m, atashband s, kovacs sj. drinking water consumption patterns in british columbia: an investigation of associations with demographic factors and acute gastrointestinal illness. sci tot environ 2007 388:54-65. 3. thomas mk, charron df, waltner-toews d, schuster c, maarouf ar, holt jd. a role of high-impact weather events in waterborne disease outbreaks in canada, 1975–2001. int j environ health res 2006 16(3):167–180. *sylvia checkley e-mail: slcheckl@ucalgary.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e187, 201 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts characterizing fentanyl-associated mortality using the literal causes of death brandon ramsey*, heather rubino, janet j. hamilton and david atrubin florida department of health, tallahassee, fl, usa objective to characterize fentanyl-associated mortality in florida using free text queries of the literal causes of death listed on death certificates. introduction in october 2015, the centers for disease control and prevention (cdc) released health advisory #384 to inform people about increases in fentanyl fatalities. florida’s statewide syndromic surveillance system, electronic surveillance system for the early notification of community-based epidemics (essence-fl), captures electronic death record data in near real time which allows for the monitoring of mortality trends across the state. one limitation of using death record data for fentanyl surveillance is the lack of a fentanyl-specific overdose icd-10 code; however, the literal cause of death fields (“literals”) provide a level of detail that is rich enough to capture mentions of fentanyl use. the “literals” are a free text field on the death certificate, recorded by a physician at the time of death and detail the factors that led to the death. essence-fl has the benefit of not only receiving death record data in near real-time, but also receiving the literal cause of death fields. this work analyzes trends in fentanyl-associated mortality in florida over time by using the literal cause of death fields within death records data obtained from essence-fl. methods the “literals” elements of florida vital statistics mortality data from 2010 through 2015 accessed via essence-fl were queried for the term ^fent^. no necessary negations or extra term inclusions were deemed necessary after looking at the records pulled with ̂ fent^ alone. deaths were analyzed by various demographic and geographic variables to characterize this population in order to assess which groups are most heavily burdened by fentanyl-associated mortality. population estimates by county for 2015 were obtained from the u.s. census bureau to calculate mortality rates. language processing in r studio was used to determine which other substances were commonly reported when fentanyl was listed on the death certificate, in order to assess polydrug use and its impact on increased mortality. results compared to the number of fentanyl-associated mortalities in 2010 (82), fentanyl-associated mortality in 2015 (599) was 6.5 times higher after controlling for the natural increase in total mortality between 2010 and 2015. almost three-fourths of the deaths in 2015 were male (73%), which is higher than the proportion of male deaths in 2010 (55%). the age group with the largest burden of fentanyl-associated mortality was the 30 – 39 age group, with almost one-third of the deaths in 2015 coming from this age group (31%) compared to only 10% in 2010, a roughly 200% increase. fentanyl-associated mortality was almost exclusive to people that are caucasian, with 94% of the fentanyl-associated mortalities in 2015 occurring among caucasians. multi-drug use was also identified for those with fentanyl-associated mortality. mentions of other drugs were present in at least 10% of the deaths. some of the other drugs mentioned in the “literals” included heroin, cocaine, and alprazolam. there was county variation in the number of fentanyl morality deaths ranging from 21.19 deaths per 100,000 to 0.29 deaths per 100,000 residents. two counties with the highest rates were located adjacent to one another. conclusions having death record data readily available within the state syndromic surveillance system is beneficial for rapid analysis of mortality trends and the analytic methods used for syndromic surveillance can be applied to mortality data. free text querying of the “literals” in the vital statistics death records data allowed for surveillance of fentanyl-associated mortality, similar to methods used for querying emergency department chief complaint data. although underlying icd-10 codes can lack detail about certain causes of death, the “literals” provide a clearer picture as to what caused the death. the “literals” also make it possible to look at potential drug combinations that may have increased risk of mortality, which will be explored more thoroughly. further work will explore other data sources for fentanyl usage and mortality trends, as well as examine potential risk factors and confounders. keywords fentanyl; mortality; text query *brandon ramsey e-mail: brandon.ramsey@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e127, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts assessing the distribution and drivers of vaccine hesitancy using medical claims data sandra goldlust*, elizabeth lee and shweta bansal georgetown university, washington, dc, usa objective the purpose of this study was to investigate the use of large-scale medical claims data for local surveillance of under-immunization for childhood infections in the united states, to develop a statistical framework for integrating disparate data sources on surveillance of vaccination behavior, and to identify the determinants of vaccine hesitancy behavior. introduction in the united states, surveillance of vaccine uptake for childhood infections is limited in scope and spatial resolution. the national immunization survey (nis) the gold standard tool for monitoring vaccine uptake among children aged 19-35 months is typically constrained to producing coarse state-level estimates.1 in recent years, vaccine hesitancy (i.e., a desire to delay or refuse vaccination, despite availability of vaccination services)2 has resurged in the united states, challenging the maintenance of herd immunity. in december 2014, foreign importation of the measles virus to disney theme parks in orange county, california resulted in an outbreak of 111 measles cases, 45% of which were among unvaccinated individuals.3 digital health data offer new opportunities to study the social determinants of vaccine hesitancy in the united states and identify finer spatial resolution clusters of under-immunization using data with greater clinical accuracy and rationale for hesitancy.4 methods our u.s. medical claims data comprised monthly reports of diagnosis codes for under-immunization and vaccine refusal (figure 1). these claims were aggregated to five-digit zip-codes by patient age-group from 2012 to 2015. spatial generalized linear mixed models were used to generate county-level maps for surveillance of under-immunization and to identify the determinants of vaccine hesitancy, such as income, education, household size, religious group representation, and healthcare access. we developed a bayesian modeling framework that separates the observation of vaccine hesitancy in our data from true underlying rates of vaccine hesitancy in the community. our model structure also enabled us to borrow information from neighboring counties, which improves prediction of vaccine hesitancy in areas with missing or minimal data. estimates of the posterior distributions of model parameters were generated via markov chain monte carlo (mcmc) methods. results our modeling framework enabled the production of county-level maps of under-immunization and vaccine refusal in the united states between 2012-2015, the identification of geographic clusters of under-immunization, and the quantification of the association between various epidemiological factors and vaccination status. in addition, we found that our model structure enabled us to account for spatial variation in reporting vaccine hesitancy, which improved our estimation. conclusions our work demonstrate the utility of using large-scale medical claims data to improve surveillance systems for vaccine uptake and to assess the social and ecological determinants of vaccine hesitancy. we describe a flexible, hierarchical modeling framework for integrating disparate data sources, particularly for data collected through different measurement processes or at different spatial scales. our findings will enhance our understanding of the causes of underimmunization, inform the design of vaccination policy, and aid in the development of targeted public health strategies for optimizing vaccine uptake. figure 1. instances of vaccine refusal (per 100,000 population) for united states counties in 2014 as observed in medical claims data. keywords medical claims; spatial epidemiology; surveillance; vaccine coverage; vaccine refusal references 1. salmon da, smith pj, navar am, pan wky, omer sb, singleton ja, et al. measuring immunization coverage among preschool children: past, present, and future opportunities. epidemiol rev. 2006;28: 27–40. 2. macdonald ne. vaccine hesitancy: definition, scope and determinants. vaccine. 2015 aug 14;33(34):4161–4. 3. zipprich j, winter k, hacker j, xia d, watt j, harriman k. measles outbreak-california, december 2014–february 2015. morb mortal wkly rep. 2015;64:153–154. 4. lee ec, asher jm, goldlust s, kraemer jd, lawson ab, bansal s. mind the scales: harnessing spatial big data for infectious disease surveillance and inference. j infect dis. in press. *sandra goldlust e-mail: sandra.goldlust@georgetown.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e12, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts maricopa county’s use of nssp essence to detect cases during a hepatitis a outbreak rasneet s. kumar*, james matthews, jessica r. white, alice carrigan, jennifer collins, nicole fowle and jigna narang maricopa county department of public health, office of epidemiology, phoenix, az, usa objective to demonstrate the utility of the national syndromic surveillance program’s (nssp) version of the electronic surveillance system for early notification of community-based epidemics (essence) for case detection during a 2017 outbreak of hepatitis a virus (hav) infection among persons experiencing homelessness in maricopa county, arizona. introduction on 3/29/2017, the maricopa county department of public health (mcdph) received three reports of confirmed hav infection from an onsite clinic at campus a that assists individuals experiencing homelessness, a population at risk for hav transmission. to identify the scope of the problem, the department initiated rapid hav infection case detection using nssp essence. methods mcdph created a myessence dashboard that searched for chief complaint keywords and discharge diagnosis codes from 15 maricopa county emergency department and inpatient hospital records using three separate queries: (1) hav infection; (2) hepatitis virus infection; and (3) hav infection symptoms combined with terms for homelessness. the dashboard was reviewed retrospectively for the 90 days prior to the initial report of the cluster (12/28/2016 – 3/29/2017). based on this review, mcdph epidemiologists decided to use the first query (hav infection-specific) only, because reviewing the line list for all three queries was resource-intensive and resulted in duplicate cases. the query was monitored every weekday morning from 3/30/2017 to the close of the outbreak on 7/22/2017. when a potential hav infection case was identified in essence, epidemiologists attempted to identify the patient in arizona’s medical electronic disease surveillance intelligence system (medsis) using date of birth. potential cases with an existing hav infection, medsis record, and a listed address matching campus a were interviewed by a disease investigator. when potential cases did not have a hav infection report in medsis, a review of the medical record or follow-up with the facility’s infection preventionist was conducted to confirm hav infection diagnosis and homelessness status before contacting the patient for interview. following patient interview, all cases with a positive hav immunoglobulin m antibody, hav infection symptomology, and association with campus a, were considered outbreak cases. all other patients were considered unrelated to the outbreak. mcdph evaluated the timeliness of reporting by comparing the date when outbreak cases were first available in essence to the date they were entered into medsis. results from 3/30/2017 – 7/22/2017, mcdph identified 37 potential hav infection cases in essence. eleven cases were classified as outbreak cases, while the other 26 patients lacked recent hav infection symptoms, laboratory confirmation, or association with campus a. all 11 outbreak cases’ records included the icd-cm-10 code b15.9 (hepatitis a without hepatic coma), and 3 records included the code z59 (problems related to housing and economic circumstances). the hav infection-specific query in essence identified 11 (73%) of the 15 total outbreak cases; however, all cases were reported in medsis prior to being flagged in essence. on average, cases were reported to medsis 9 days earlier than identified in essence (range, 0 21 days). of note, essence helped identify the outbreak index case, previously lost to follow-up, as an individual experiencing homelessness. this information helped mcdph perform additional follow-up, which revealed that the individual had arrived from san diego, ca, a city with an ongoing outbreak of hav infection in their homeless and drug-using population. this epidemiologic link was identified on 3/30/2017, over a month before the centers for disease control and prevention confirmed a match by genome sequencing between the index case and the neighboring outbreak on 5/5/2017. conclusions use of essence identified most of the hav infection cases from this outbreak among individuals experiencing homelessness but no sooner than traditional surveillance methods. however, use of essence allowed for identification of the outbreak index case, leading to epidemiological linkage to outbreak origin approximately one month prior to molecular laboratory confirmation. keywords hepatitis a virus; homeless; syndromic surveillance; essence *rasneet s. kumar e-mail: rasneetkumar@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e145, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts spatial temporal cluster analysis to enhance awareness of disease re-emergence on a global scale forest m. altherr*1, maneesha chitanvis1, ashlynn daughton1, 2, geoffrey fairchild1, william rosenberger1, nicholas generous1, nidhi parikh1, derek aberle1, nileena velappan1, emily alipio lyon1, attelia hollander1 and alina deshpande1 1bioscience division, los alamos national laboratory, los alamos, nm, usa; 2university of colorado, boulder, boulder, co, usa objective the application of spatial analysis to improve the awareness and use of surveillance data. introduction the re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. re-emerging infectious disease (red) alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past. geospatial methods assist researchers in making informed decisions by incorporating the power of place to better explain the relationships between variables. methods disease incidence and indicator data derived for the red alert project were analyzed for spatial associations. using aggregate country-level data, spatial and spatiotemporal clusters were identified in arcmap 10.5.1. the identified clusters were then used as the outcome for a series of binary logistic regression models to determine significant covariates that help explain global hotspots. these methods will continue to evolve and be incorporated into the red alert decision support ecosystem to provide analysts with a global perspective on potential re-emergence. results hotspots of high disease incidence in relation to neighboring countries were identified for measles, cholera, dengue, and yellow fever between 2000 and 2014. disease-specific predictors were identified using aggregate estimates from world bank indicator dataset. data was imputed where possible to enhance the validity of the gi * statistic for clustering. in the future, as data streams become more readily available, hotspot modeling at a finer resolution will help to improve the precision of spatial epidemiology. conclusions spatial methods enhance the capability of understanding complex population and disease relationships, which in turn improves surveillance and the ability to predict re-emergence. with tools like red alert, public health analysts can better prepare to respond rapidly to future re-emerging disease threats. keywords re-emergence; surveillance; spatial; hotspot *forest m. altherr e-mail: faltherr@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e23, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss disease control, nyc department of health and mental hygiene, queens, ny, usa objective to investigate a communicable disease syndromic surveillance signal using multiple data sources. introduction from june 4-8, 2015, the new york city (nyc) syndromic surveillance system detected five one-day citywide signals in sales of over-the-counter (otc) antidiarrheal medications using the cusum method with a 56-day moving baseline. the otc system monitors sales of two classes of antidiarrheal medications, products with loperamide or bismuth, from two nyc pharmacy chains. to determine if this increase reflected a concerning cluster of diarrheal illness, we examined multiple communicable disease surveillance data systems. methods we evaluated the otc sales data using emergency department chief complaint, school nurse visits, enteric reportable diseases, nosocomial reports, clinical laboratory, water quality, and social media (table) for 30 days prior to the first signal through one week following the last signal (may 12-june 15). results we compared weekly counts of antidiarrheal sales during this period for 2014 and 2015. bismuth sales were slightly higher in june 2015. loperamide sales were similar between the two years. promotional sales for both classes of medications were identified before and during the signals. we observed no temporal or spatial diarrheal signals in our ed system during this period. the ratio of diarrheal to all ed visits decreased, suggesting declining diarrheal illness. a diarrheal signal on june 1 was observed in the school nurse system, but there were no specific increases by grade or neighborhood. weekly trend analyses of giardiasis, cryptosporidiosis, salmonellosis, and shigellosis identified five spatial and one borough level signal for giardiasis. the magnitude and location of the signals did not correlate with the otc sales increase. campylobacteriosis diagnoses increased in mid-may and early june but this was consistent with seasonal trends. a signal in clinical laboratory submissions was identified on june 10 but volume was not unusual and the baseline was low. no gastrointestinal illness outbreaks were reported through the nora or sentinel nursing home systems. findings from dep identified one coliform positive, e. coli negative event at a station on june 8. routine pathogen monitoring for giardia and cryptosporidium showed no abnormal findings, and screening for viruses was negative. water quality complaints were within the expected range, and there was no increase in mentions of gastrointestinal illness on social media. google trends for gastrointestinal illness did not appear to increase compared with the previous year. conclusions there were five one-day signals indicative of possible diarrheal illness from our syndromic systems over a five day period in early june 2015. after further investigation of syndromic and other systems, findings possibly reflected sales promotions but did not suggest increased diarrheal illness in nyc. using multiple complementary systems can provide useful situational awareness when investigating disease signals. data sources keywords 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yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and 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hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini 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di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david 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characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts developing a prototype opioid surveillance system at a 2-day virginia hackathon catherine ordun, jessica bonnie, jung byun*, daewoo chong, richard latham and joshua wei data science & machine intelligence, booz allen hamilton, fairfax, va, usa objective a team of data scientists from booz allen competed in an opioid hackathon and developed a prototype opioid surveillance system using data science methods. this presentation intends to 1) describe the positives and negatives of our data science approach, 2) demo the prototype applications built, and 3) discuss next steps for local implementation of a similar capability. introduction at the governor’s opioid addiction crisis datathon in september 2017, a team of booz allen data scientists participated in a two-day hackathon to develop a prototype surveillance system for business users to locate areas of high risk across multiple indicators in the state of virginia. we addressed 1) how different geographic regions experience the opioid overdose epidemic differently by clustering similar counties by socieconomic indicators, and 2) facilitating better data sharing between health care providers and law enforcement. we believe this inexpensive, open source, surveillance approach could be applied for states across the nation, particularly those with high rates of death due to drug overdoses and those with significant increases in death. methods the datathon provided a combination of publicly available data and state of virginia datasets consisting of crime data, treatment center data, funding data, mortality and morbidity data for opioid, prescription drugs (i.e. oxycodone, fentanyl), and heroin cases, where dates started as early as 2010. the team focused on three data sources: u.s. census bureau (american community survey), state of virginia opioid mortality and overdose data, and state of virginia department of corrections data. all data was cleaned and mapped to county-levels using fips codes. the prototype system allowed users to cluster similar counties together based on socioeconomic indicators so that underlying demographic patterns like food stamp usage and poverty levels might be revealed as indicative of mortality and overdose rates. this was important because neighboring counties like goochland and henrico counties, while sharing a border, do not necessarily share similar behavioral and population characteristics. as a result, counties in close proximity may require different approaches for community messaging, law enforcement, and treatment infrastructure. the prototype also ingests crime and mortality data at the county-level for dynamic data exploration across multiple time and geographic parameters, a potential vehicle for data exchange in real-time. results the team wrote an agglomerative algorithm similar to k-means clustering in python, with a flask api back-end, and visualized using fips county codes in r shiny. users were allowed to select 2 to 5 clusters for visualization. the second part of the prototype featured two dashboards built in elasticsearch and kibana, open source software built on a nosql database designed for information retrieval. annual data on number of criminal commits and major offenses and mortality and overdose data on opioid usage were ingested and displayed using multiple descriptive charts and basic nlp. the clustering algorithm indicated that when using five clusters, counties in the east of virginia are more dissimilar to each other, than counties in the west. the farther west, the more socioeconomically homogenous counties become, which may explain why counties in the west have greater rates of opioid overdose than in the east which involve more recreational use of non-prescription drugs. the dashboards indicated that between 2011 and 2017, the majority of crimes associated with heavy-use of drugs included larceny/fraud, drug sales, assault, burglary, drug possession, and sexual assault. filtering by year, county, and offense, allowed for very focused analysis at the county level. conclusions data science methods using geospatial analytics, unsupervised machine learning, and leverage of nosql databases for unstructured data, offer powerful and inexpensive ways for local officials to develop their own opioid surveillance system. our approach of using clustering algorithms could be advanced by including several dozen socioeconomic features, tied to a potential risk score that the group was considering calculating. further, as the team became more familiar with the data, they considered building a supervised machine learning to not only predict overdoses in each county, but more so, to extract from the model which features would be most predictive county-to-county. next, because of the fast-paced nature of an overnight hackathon, a variety of open source applications were used to build solutions quickly. the team recommends generating a single architecture that would seamlessly tie together python, r shiny, and elasticsearch/kibana into one system. ultimately, the goal of the entire prototype is to ingest and update the models with real-time data dispatched by police, public health, emergency departments, and medical examiners. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords clustering; opioid; machine learning; elasticsearch; data science acknowledgments the booz allen opioid hackathon team was led by jessica bonnie, and consisted of richard latham, jung byun, daewoo chong, joshua wei, and catherine ordun. references https://data.virginia.gov/datathon-2017/ https://vimeo.com/236131006?ref=tw-share https://vimeo.com/236131182?ref=tw-share *jung byun e-mail: smiwjung@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e6, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts modeling spatial and temporal variability by bayesian multilevel model xiaoxiao song*1, 2, yan li1, 2, wei liu1, 2, le cai1, 2, wenlong cui1, 2 and mingyue wang1, 2 1school of public health, kunming medical university, kunming, china; 2yunnan provincial collaborative innovation center for public health and disease prevention and control, kunming, china objective the purpose of this article was to quantitative analyses the spatial variability and temporal variability of influenza like illness (ili) by a three-level poisson model, which means to explain the spatial and temporal level effects by introducing the random effects. introduction the early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. in order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety. one common question of big data analysis is most of the data have the multilevel or hierarchy structure, in other word the big data are non-independent. traditional multilevel or hierarchical model can only deal with 2 or 3 hierarchical data structure, which bound health big data further research for modeling, forecast and early-warning in the public health surveillance, in particular involving complex spatial and temporal variability of infectious diseases in the reality. methods all the data based the issc project from april 1 2012 through march 31 2014 in the china. we adopted markov chain monte carlo algorithm (mcmc) in bayesian hierarchical (multilevel) model, which means to explain the spatial and temporal level effects by introducing the random effects. in order to calculate the geographical variations and temporal variation of ili cases during two years surveillance, we constructed spatial and temporal model of three levels, which was day-in-months → months-in-two-year →monitoring units (fig-1). level one was repeated measures within every month, which was referred as day-in-months and the maximum value was 31 days. level two was the variation tendency of months which was 24 months. level three was the effect of spatial distribution of monitoring units, which took the spatial heterogeneity into account rather than dependence. this model was then adopted to evaluate and improve the early warning capacity of syndromic surveillance. results we adopted multilevel spatio-temporal model (day-in-months → months-in-two-year →monitoring units) to analyze the points data collected from 2 counties in china, including two hospitals at county level, 15 central hospital at township level and 152 health care units in the villages. the analysis of totally 108163 pieces of point data on ili case indicated there are significant spatial and temporal variation among these cases. among two thirds of the variation attributes to the difference of geographical locations of these monitoring sites. the remaining one third of the variation attributes to the time dimensions, such as seasonal effect. conclusions the variation of monitoring data collected from health care units mainly attributes to the difference of geographic locations for monitoring sites, yet only one third of the change attribute to the time change, such as seasons, holidays and festivals. therefore, it is critical to select the location of monitoring site, which is more rational to select the hotspots with representative characters rather than try to cover the whole monitoring area. keywords bayesian multilevel model; spatial variability; temporal variability; influenza like illness references 1. goldstein h. multilevel statistical model[m]. 922. john wiley & sons, 2011 2. leckie, g. and charlton, c. (2013). runmlwin a program to run the mlwin multilevel modelling software from within stata. journal of statistical software, 52 (11),1-40. *xiaoxiao song e-mail: chinasxx@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e114, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epi info cloud data analytics to improve quality of hiv surveillance in vietnam. diep t. vu*1, duc h. bui2, giang t. le1, hai k. nguyen3, duong c. thanh4, nghia v. khuu5, gerald jones6 and huong t. phan2 1centers for disease prevention and control, hanoi, viet nam; 2vietnam authority of hiv/aids control, hanoi, viet nam; 3hanoi medical university, hanoi, viet nam; 4national institute of hygiene and epidemiology, hanoi, viet nam; 5pasteur institute in ho chi minh city, ho chi minh, viet nam; 6centers for disease control and prevention, atlanta, ga, usa objective to use epi info cloud data analytics (ecda) to improve the management, quality and utilization of the vietnam national hiv surveillance data. introduction hiv surveillance in vietnam is comprised of different surveillance systems including the hiv sentinel surveillance (hss). the hss is an annual, multi-site survey to monitor hiv sero-prevalence and risk behaviors among key populations. in 2015, the vietnam administration on hiv/aids control (vaac) installed the epi info cloud data analytics (ecda), a free web-based analytical and visualization program developed by the centers for disease control and prevention (cdc)(1) to serve as an information management system for hiv surveillance. until 2016, provincial surveys, recorded on paper, were computerized and submitted to vaac, which was responsible for merging individual provincial datasets to form a national hss dataset. feedback on hss issues were provided to provinces 3 to 6 months after survey conclusion. with the use of tablets for field data collection in 2017, provincial survey data were recorded electronically and transferred to vaac at the end of each survey day, thus enabling instant updating of the national 2017 hss dataset on daily basis. upon availability of the national hss dataset on vaac’s server, ecda enhanced wider access and prompt analysis for staff at all levels (figure 1). this abstract describes the use of ecda, together with tablet-based data collection to improve management, quality and use of surveillance data. methods after the installation of the ecda on vaac’s server in 2015, investments were made at all levels of the surveillance systems to build the capacity to operate and maintain the ecda. these included trainings on programming, administration, and utilization of ecda at the central level; creating a centralized database through abstracting and linking different surveillance datasets; developing analysis templates to assist provincial-specific reports; and trainings on access and use of the ecda to provincial staff. one hundred and eighty five ecda analyst accounts, authorized for submission, viewing and analysis of data, were created for surveillance staff in 63 provinces and 7 agencies. six administrator accounts, created for users at central and regional level, were authorized for editing data and management of user accounts. in 2017, more ecda activities were conducted to: (i) develop analysis dashboards to track progress and data quality of hss provincial surveys; (ii) facilitate frequent data reviews at central and regional levels; (iii) provide feedback to provinces on survey issues including sample selection. results since 2015, separate national datasets including the hss, hiv case reports, hiv routine program reports were systematically cleaned and merged to form a centralized national database, which was then centrally stored and regularly backed up. access to the national database was granted to surveillance staff in all 63 provinces through 185 designated ecda accounts. during the 2017 hss surveys, 70 ecda users in 20 hss provinces were active to manage and use the hss data. twelve weekly reviews of hss provincial data were conducted at national level throughout the 2017 hss survey. ninety percent of provinces received feedback on their survey data as early as the first week of field data collection. the national 2017 hss dataset and its analysis were available immediately after the completion of the last provincial survey, which was about 3 to 6 months quicker than reports of previous years. more importantly, the fresh results of the 2017 hss survey were available and used for the 2018 vietnam hiv national planning circle (table 1). conclusions ecda is a quick, relevant, free program to improve the management and analysis of hiv surveillance data. using ecda, it is easy to generate and modify analysis dashboards that enhances utilization of surveillance data. successful administration and use of the ecda during the 2017 hss survey is positive evidence for ministry of health to consider institutionalization of the program in vietnam surveillance systems. table 1: hiv surveillance data before and after using ecda *after the completion of provincial surveys isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords health informatics; epi info; management of surveillance data; hiv/ aids; sentinel surveillance acknowledgments to surveillance staff at all level for their dedicated work on the hss data. the technical assistance to government of vietnam to deploy ecda and tablets has been supported by the united states president’s emergency plan for aids relief (pepfar) through the cdc. references 1. cdc. ecda software, https://www.cdc.gov/epiinfo/cloud.html. *diep t. vu e-mail: vubichdiep@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e40, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 1boston children’s hospital, boston, ma, usa; 2washington university in st. louis, st. louis, mo, usa; 3harvard medical school, boston, ma, usa; 4boston university, boston, ma, usa objective develop a platform to enable local surveillance of foodborne illness reported on social media and restaurant review sites for supplementing traditional foodborne disease surveillance programs. in this presentation, we will discuss our collaboration with local public health departments to develop a foodborne disease surveillance dashboard. introduction foodborne illness affects 1 in 4 americans, annually. however, only a fraction of affected individuals seek medical attention. to supplement traditional approaches to foodborne disease surveillance, researchers and public health departments are considering reports of foodborne illness on social media sites [1, 2]. in this project, we work with local public health departments to develop a platform that uses digital data sources such as, twitter and yelp, to supplement foodborne disease surveillance efforts. in addition to monitoring reports of illness, this platform can also be used to respond to suspected foodborne illness reports and spur restaurant inspections to ensure food safety. to this end, we have developed a dashboard that monitors social media chatter for reports of food poisoning in real-time. the dashboard facilitates responding to illness reports and contacting consumers to provide additional information through a reporting form. the dashboard is low cost, easy to use and designed to enable easy implementation for any region. methods our database currently consist of 1.5 million foodservice reviews and 680 million tweets. for the tweets, approximately 10% have a geo-coordinate provided by the users. we inferred the geocoordinates of another 46% of tweets using the ‘location’ field from the twitter user profile by querying the google maps api. for automated detection of foodborne illness reports, we first develop a list of keywords consisting of foodborne disease symptoms and disease names. next, we use text matching to filter the reports that contain at least one of the keywords. we then use a supervised machine learning classifier to extract the relevant reports. a report, for example, in which an individual mentions experiencing food poisoning after eating at a restaurant is considered relevant. however, a report is considered irrelevant when a keyword is used in another context (e.g. “this restaurant is sick!”). we developed a support vector machine classifier (svm) that aims to create maximum separation between the irrelevant and relevant reports by identifying the optimal hyperplane. the process of developing a reliable classifier is iterative and requires refinement over multiple rounds of feature selection and parameter configuration. results the svm classifier was evaluated using 6084 tweets. the classifier had an accuracy and precision of 85% and 82%, respectively. these performance results are promising, especially since the training set was unbalanced and relevant and irrelevant tweet classes were extremely similar. we next ran the classifier on a real-time twitter stream of tweets containing at least one foodborne illness keyword. over a four-month period in 2015, approximately 50% of the tweets were identified by the classifier as being true self-reported food poisoning incidents (figure 1). conclusions restaurants with lower food safety scores have been associated with higher outbreak reports [3]. real-time surveillance of foodborne illness reports can aid local public health departments to identify and limit the spread of foodborne disease outbreaks. keywords foodborne illness; surveillance; social media; outbreak references 1. nsoesie eo, et al. online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. prev med. 2014;67:264-9. 2. harris jk, et al. health department use of social media to identify foodborne illness chicago, illinois, 2013-2014. morbidity and mortality weekly report. 2014;63(32):681-5. 3. irwin k, et al. results of routine restaurant inspections can predict outbreaks of foodborne illness: the seattle-king county experience. am j public health. 1989;79(5):586-90. *jared b. hawkins e-mail: jared.hawkins@childrens.harvard.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e60, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts what do we know about the behavior of animal rabies in chile trough the last years? raul alegria-moran*1, 2, 3, daniela miranda1, alonso parra4, 3 and lisette lapierre1, 3 1department of preventive veterinary medicine, faculty of veterinary and animal science, universidad de chile, santiago, chile; 2phd program in agriculture, forestry and veterinary science, universidad de chile, santiago, chile; 3emerging and re-emerging zoonoses research network, santiago, chile; 4unit of zoonosis and vectors, department of environmental health, ministerio de salud, gobierno de chile, santiago, chile objective this study aims to analyze the evolution of the epidemiological behavior of rabies in chile during the period 2003 to 2013, through the epidemiological characterization of a number of variables and description of spatial and temporal patterns of animal cases. introduction rabies is a zoonotic disease caused by an rna virus from the family rhabdoviridae, genus lyssavirus. worldwide distributed, control of rabies has been considered to be particularly amenable to a “one health” strategy (1). in chile, rabies was considered endemic in domestic dog population until the late 1960s, when a surveillance program was established, decreasing the number of human cases related to canine variants until the year 1972 (2). rabies is recognized as a endemic infection in chiropterans of chile and prompted the surveillance of the agent in this and other species (3). methods an epidemiological characterization of the registered cases from the national program for prevention and control of rabies was carried. during the period 2003-2013, 927 cases were reported. descriptive statistics and descriptive mapping, recording origin of the sample, number of cases per region, animal reservoir implicated and viral variant were performed. a spatial autocorrelation analysis was carried using moran’s i indicator for the detection of spatial clusters (4), using the local indicators of spatial association (lisa) statistics (5), at national and regional level of aggrupation (north, central and south zone). temporal descriptive analysis was carried. results 927 positive cases were recorded. 920 (99.2%) cases came from passive surveillance, while 7 (0.8%) cases by active surveillance, total positivity was 77.02% and 1.37% respectively. positivity was reported mainly in the central zone (88.1%), mainly in valparaiso (19.1%), metropolitana (40.6%) (figure 1), maule (11.8%) regions concentrated in urban centers. main positive reservoirs were bats (99.8%), specifically tadarida brasiliensis and viral variant 4 was the most commonly diagnosed. lisa test gives a moran’s i indicator of 0.1537 (p-value = 0.02) for the central zone (table 1). rabies tend to decrease in fall and winter season (2.9 cases vs 13 cases during summer). conclusions wildlife rabies in bats remains endemic in chile, concentrated in urban areas. the main reservoirs are insectivorous bats. there is a significant spatial autocorrelation of animal rabies cases in the central zone of chile. results are relevant to the design of preventive and control measures. table 1. moran’s i index and p-value for the three macro-zones. figure 1. choropleth map of the distribution of animal rabies in metropolitana region, chile, between the years 2003 and 2013. keywords animal rabies; zoonotic diseases surveillance; one health; spatial autocorrelation acknowledgments the authors acknowledge staff from the rabies laboratory from instituto de salud pública (isp), chile. references 1. who 2016. rabies fact sheets, media centre. 2. favi, m. & durán, j. 1991. epidemiología de la rabia en chile (1929-1988) y perspectivas en mamíferos silvestres. avances en ciencias veterinarias, 6. 3. favi, m., bassaletti, á., lópez, j., rodríguez, l. & yung, v. 2011. descripción epidemiológica del reservorio de rabia en murciélagos de la región metropolitana: chile. 2000-2009. rev chil infectol, 28, 223-228. 4. pfeiffer, d. u. & stevens, k. b. 2015. spatial and temporal epidemiological analysis in the big data era. prev vet med, 122, 213-220. 5. pfeiffer, d., robinson, t., stevenson, m., stevens, k., rogers, d. & 365 clements, a. 2008. spatial analysis in epidemiology, oxford university press. *raul alegria-moran e-mail: ralegria@veterinaria.uchile.cl online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e163, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts analisis of leptospirosis lethal cases in lviv region olena zubach* and alexandr zinchuk department of infectious diseases, danylo halytsky lviv national medical university, lviv, ukraine objective study of the structure of lethal cases in patients diagnosed with leptospirosis in the lviv region. introduction mortality rate of leptospirosis in ukraine remains high year after year. the study of the peculiarities of lethal cases over a long period enables researchers to specify possible mechanisms of infection which cause the development of the severest cases of leptospirosis and to prevent disease emergence by applying adequate preventive measures. methods we have analyzed case reports of patients diagnosed with leptospirosis, who were treated and died in lviv regional clinical hospital of infectious diseases from 1987-2016. results over the last 30 years, 942 patients with leptospirosis were treated in lviv regional clinical hospital of infectious diseases, and 125 of them died. the mortality rate was 13.27%. men died twice as often as women – 83 (66.4%) and 42 (33.6%), respectively, p<0.001. the average age at death was 56.5±11.98 years old. women died at an older age (59.62±9.6) as compared to men (54.93±12.78), p<0.05. patients who died from leptospirosis were admitted into the hospital on the 5.81±2.31 day, which is considered to be a late admission. the average length of stay in the inpatient department was 6.21±6.54 days. rural residents died much more often, 78 cases (62.4%) compared with city residents, 47 cases (37.6%), p<0.01. the mortality from leptospirosis was lowest during the summer months (6.79%) compared to winter (19.85%, p <0.001), spring (23.81%, p <0.001) and autumn (13.78%, p < 0.01) months. it should be noted that the mortality during the autumn-summer period was significantly lower (10%) than during the spring-winter period (21.76%, p <0.001). the causative agent was verified by microscopic agglutination test and lysis in 66 (52.8%) of the deceased patients, and in 59 individuals (47.2%) the agent could not be identified due to early period of serological investigation, when no anti-leptospirosis antibodies had been produced so far. in these cases the diagnosis of leptospirosis was based on typical clinical signs and epidemiological anamnesis. the main serogroups of leptospirae, which caused lethal cases, were l. icterohaemorrhagiae – 51 (40.8%), l. grippotyphosa – 5 (4%), l. kabura – 3 (2.4%), mixed l. icterohaemorrhagiae plus l. grippotyphosa – 3 (2.4%), l. cynopteri and l. hebdomadis 2 (1.6%) patients each. epidemiological anamnesis could be determined in 84 (67.2%) patients. most often, patients associated the disease with the following factors: 45 (36%) – with the presence of rats or mice-like rodents in a house, yard or workplace, 26 (20.8%) – with agricultural work, where contact with rodents’ feces was also possible, 5 (4%) – with professional activity, 4 (3.2%) – with fishing, and 4 (3.2%) – with swimming in water pools. in 41 (32.8%) patients, leptospirosis could not be associated with any factor. conclusions l.icterohaemorrhagiae still remains the most common cause of mortality of leptospirosis – 51 (40.8%). in 56.8% of the cases, the disease was caused by contact with rodents. over the last 30 years, men died more often of leptospirosis in the lviv region than women (p<0.001), whereas, the deceased women were considerably older than men (p<0.05). rural residents died much more often than city residents (p<0.01). the highest mortality rate was recorded in the spring – 23.81%, the lowest was recorded in summer – 6.79% (p <0.001). active deratization measures help to reduce morbidity and mortality of leptospirosis. we believe that the question regarding the accuracy of the final diagnosis of “leptospirosis” in 59 (47.2%) dead patients is still under discussion, as the diagnosis was established based on clinical symptoms only, while pma result was negative. after all, the clinical picture of the severe forms of leptospirosis is similar to the typical symptoms of hemorrhagic fever with renal syndrome and crimean hemorrhagic fever. keywords leptospirosis; lethality; surveillance acknowledgments we would like to express our gratitude to the archive of lviv regional clinical hospital of infectious diseases and especially dangerous infections laboratory of lviv oblast laboratory center of the ministry of health of ukraine for assistance with research. references 1. tubiana s., mikulski m., becam j., lacassin f., lefèvre p., gourinat a-s., goarant c., d’ortenzio e. risk factors and predictors of severe leptospirosis in new caledonia. plos neglected tropical diseases.2013;7(1):e1991. 2. taylor aj., paris dh., newton pn. a systematic review of the mortality from untreated leptospirosis. plos neglected tropical diseases. 2015; 25;9(6):e0003866. *olena zubach e-mail: dr_zubach@i.ua online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e92, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts conducting operational research during outbreaks to improve preparedness and response jennifer nuzzo*, matthew p. shearer and diane meyer johns hopkins center for health security, baltimore, md, usa objective the outbreak observatory (oo) aims to: ●strengthen outbreak/epidemic preparedness and response activities through real-time, one-the-ground observations and analyses ●identify best practices based on operational experience that are broadly applicable across outbreak response agencies ●serve as an independent voice to advocate for policies that support preparedness and response activities based on expert assessment of the resources required to build and maintain necessary outbreak response capabilities ●support local practitioners’ efforts to publish their experiences sharing the firsthand experience of responders is critical for building outbreak preparedness and response capacity, and oo will serve as a dedicated mechanism to collect, analyze and disseminate this information introduction each significant outbreak and epidemic raises questions that must be answered in order to better inform future preparedness and response efforts, such as: ●what are the systems and resources needed to characterize an outbreak? ●what systems and resources are needed to bring an outbreak to a close? while we can anticipate these types of questions, the absence of dedicated mechanisms to record operational experiences and challenges can result in valuable, ephemeral data that are crucial for improving outbreak response not being consistently collected or analyzed. participation in outbreaks by external experts can be instrumental in ensuring that this important operational information is documented, analyzed and shared with the broader public health community. there is a particular need for observers external to the response who can capture and analyze applied data about the operational response to outbreaks—eg, the systems and strategies involved in responding to the such events—in order to improve our understanding of best practices for detecting and responding to these events. these can then be shared so that the entire public health community can access and incorporate lessons learned into their own preparedness and response plans. external observers can also help describe the important work performed by local responders during outbreaks and advocate for necessary preparedness and response program resources. the outbreak observatory is currently in a pilot phase and is looking for international and us partners who may be interested in collaborating with members of our team during their next outbreak response. methods when an outbreak occurs, oo will reach out to our partners to assess their interest in having project team member(s) travel to their location to observe the ongoing outbreak for the purpose of collaborating on a joint analysis of the response. the team member(s) would engage with local officials to identify operational challenges and best practices to better understand their perspectives and experiences. prior to the oo team’s arrival, they will provide local responders a list of sample questions that the team is interested in exploring for the purpose of potential future written analysis, with the goal of focusing on those questions that are most relevant to both the local and broader public health communities. once a preliminary list of study questions is developed, team members will engage with local responders to discuss their experiences. once on location, the oo team member(s) will regularly report their findings back to the project director. the oo team will work with the project director and local partners to compose and submit the findings to a peer-reviewed journal, ensuring that local practitioners receive appropriate authorship credit. results oo aims to fill gaps in existing health security literature by sharing the experiences of practitioners involved in outbreak responses and co-authoring peer-reviewed publications with those responders. we envision that these publications will be available more quickly than existing outbreak reports. we will disseminate our findings to pertinent policymakers, members of the broader biosecurity and public health communities and the public to ensure that important lessons reach all appropriate audiences, especially those responsible for planning and resource allocation decisions for outbreak and epidemic response. in support of this, we have created a communication platform (www. outbreakobservatory.org) to publish interim observations via rapid communication channels (eg, communications with policymakers, social media, blog posts, video logs). all publications will be developed in partnership with local practitioners. conclusions the lessons learned from previous epidemic and outbreak responses are critical to informing future response efforts. however, this data is often lost in the midst of an outbreak, when responders are too busy with the situation at hand to collect and analyze operational data. outbreak observatory endeavors to bridge this research gap, helping to capture and analyze this data and making it available to the broader public health community. keywords preparedness; outbreak; epidemic; response acknowledgments dr. carlos castillo-salgado, athalia christie and dr. noreen hynes for their help in developing oo. *jennifer nuzzo e-mail: jnuzzo1@jhu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e104, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts facilitating the use of epidemiological models for infectious disease surveillance alina deshpande* and kristen margevicius analytics intelligence and technology, los alamos national laboratory, los alamos, nm, usa objective 1. to develop a comprehensive model characterization framework to describe epidemiological models in an operational context. 2. to apply the framework to characterize “operational” models for specific infectious diseases and provide a web-based directory, the biosurveillance analytics resource directory (bard) to the global infectious disease surveillance community. introduction epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. a standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. los alamos national laboratory (lanl) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. we offer this framework and an associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models. such a framework could help the understanding of diverse models by various stakeholders with different preconceptions, backgrounds, expertise, and needs, and can foster greater use of epidemiological models as tools in infectious disease surveillance. methods we define, “operational” as the application of an epidemiological model to a real-world event for decision support and can be used by experts and non-experts alike. the term “model” covers three major types, risk mapping, disease dynamics and anomaly detection. to develop a framework for characterizing epidemiological models we collected information via a three-step process: a literature search of model characteristics, a review of current operational infectious disease epidemiological models, and subject matter expert (sme) panel consultation. we limited selection of operational models to five infectious diseases: influenza, malaria, dengue, cholera and foot-and-mouth disease (fmd). these diseases capture a variety of transmission modes, represent high or potentially high epidemic or endemic burden, and are well represented in the literature. we also developed working criteria for what attributes can be used to comprehensively describe an operational model including a model’s documentation, accessibility, and sustainability. to apply the model characterization framework, we built the bard, which is publicly available (http://brd.bsvgateway.org). a document was also developed to describe the usability requirements for the bard; potential users (and non-users) and use cases are formally described to explain the scope of use. results 1. framework for model characterization the framework is divided into six major components (figure 1): model purpose, model objective, model scope, biosurveillance (bsv) goals, conceptual model and model utility; each of which has several sub-categories for characterizing each aspect of a model. 2. application to model characterization models for five infectious diseases—cholera, malaria, influenza, fmd and dengue were characterized using the framework and are included in the bard database. our framework characterized disparate models in a streamlined fashion. model information could be binned into the same categories, allowing easy manual comparison and understanding of the models. 3. development of the bard our model characterization framework was implemented into an actionable tool which provides specific information about a model that has been systematically categorized. it allows manual categoryto-category comparison of multiple models for a single disease and while the tool does not rank models it provides model information in a format that allows a user to make a ranking or an assessment of the utility of the model. conclusions with the model characterization framework we hope to encourage model developers to start describing the many features of their models using a common format. we illustrate the application of the framework through the development of the bard which is a scientific and non-biased tool for selecting an appropriate epidemiological model for infectious disease surveillance. epidemiological models are not necessarily being developed with decision makers in mind. this gap between model developers and decision makers needs to be narrowed before modeling becomes routinely implemented in decision making. the characterization framework and the tool developed (bard) are a first step towards addressing this gap. keywords epidemiological models; database; decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e3, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts acknowledgments thanks to dr. madhav marathe, dr. bryan lewis, dr. steven eubank (virginia bioinformatics institute) and dr. reid priedhorsky (lanl) for their advice and providing information for the characterization of the epi-simdemics model. references 1. hoshen mb, morse ap. a weather-driven model of malaria transmission. malaria j. 2004; 3: 32. 2. kulldorrf m, nagarwalla n. spacial disease clusters: detection and inference. stat med. 1995; 14: 799–810. pmid: 7644860 3. barrett cl, bisset kr, eubank sg, feng x, marathe mv. episimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks. international conference for high performance computing, networking, storage and analysis. 2008. sc 2008; available: http://ieeexplore.ieee.org/ xpls/abs_all.jsp?arnumber=5214892&tag=1. *alina deshpande e-mail: deshpande_a@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e3, 2017 isds16_abstracts-final 55 isds16_abstracts-final 56 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 1analytics, intelligence, and technology division, los alamos national laboratory, los alamos, nm, usa; 2university of utah, salt lake city, ut, usa; 3university of iowa, iowa city, ia, usa objective to improve traditional outbreak surveillance systems by utilizing the content of wikipedia articles. introduction traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. recently, wikipedia access logs (e.g., mciver 20141, generous 20142) have been shown to be effective in this arena. much richer wikipedia data are available, though, including the entire wikipedia article content and edit histories. we study two different aspects of wikipedia content as it relates to unfolding disease events: 1) we demonstrate how to capture case, death, and hospitalization counts from the article text, and 2) we show there are valuable time series data present in the tables found in certain articles. we argue that wikipedia data cannot only be used for disease surveillance but also as a centralized repository system for collecting disease-related data in near real-time. methods most outbreak articles we surveyed contained a variety of useful information in the text (e.g., dates, locations, case and death counts, demographics). these data are generally swiftly updated as new information become available, and sources are often provided so that external review can occur. in order to recognize certain key phrases in the wikipedia article narrative, we trained a named-entity recognizer (ner). ners are sequence labelers (they label sequences of words). we trained stanford’s ner to automatically identify three entity types: 1) deaths, 2) infections, and 3) hospitalizations. we demonstrated the viability of tabular data using the ebola virus epidemic in west africa article. we elicited 39 unique tables from the 5,137 revisions made to the article from march 29, 2014 to october 14, 2014. for each affected country, each table contained case and death count time series. results to test the ner’s performance, we averaged precision, recall, and f1 score results from 10-fold cross-validation. our 14-article training set achieved precision of 0.812 and recall of 0.710, giving us an f1 score of 0.753. the classifier’s performance is respectable and will likely improve given a larger, more expansive training set. to determine the accuracy and timeliness of the wikipedia west african ebola epidemic time series, we used caitlin rivers’ crowdsourced ebola data as ground truth. we compared the 39 wikipedia epidemic time series to the ground truth data by computing the rootmean-square error (rmse). the average rmse values for each country’s time series are listed in table 1. the rmse values are low, indicating that the time series found on the wikipedia article are both timely and accurate. conclusions internet data are becoming increasingly important for disease surveillance because they address some of the existing challenges, such as the reporting lags inherent in traditional disease surveillance data, and they can also be used to detect and monitor emerging diseases. additionally, internet data can simplify global disease data collection. we envision this work being incorporated into a community-driven open-source emerging disease detection and monitoring system. a community-driven effort to improve global disease surveillance data is imminent, and wikipedia can play a crucial role in realizing this need. average cases and deaths rmse across all table revisions. keywords natural language processing; named-entity recognition; ebola; wikipedia; disease surveillance acknowledgments this work is supported in part by nih/nigms/midas under grant u01-gm097658-01 and the dtra joint science and technology office for chemical and biological defense under project numbers cb3656 and cb10007. lanl is operated by los alamos national security, llc for the department of energy under contract de-ac52-06na25396. references 1. mciver dj, brownstein js. wikipedia usage estimates prevalence of influenza-like illness in the united states in near real-time. plos computational biology. 2014 apr 17;10(4):e1003581. 2. generous n, fairchild g, deshpande a, del valle sy, priedhorsky r. global disease monitoring and forecasting with wikipedia. plos computational biology. 2014 nov 13;10(11):e1003892. *geoffrey fairchild e-mail: gfairchild@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e110, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts leveraging the nssp r studio server to automate qa monitoring and reporting peter j. rock* and michael d. singleton college of public health, university of kentucky, lexington, ky, usa objective the aim of this project was to develop a nimble system to both monitor and report on the quality of kentucky emergency department syndromic surveillance (sys) data at system-wide and facility levels. introduction in 2016, the cdc funded 12 states, under the enhanced state opioid overdose surveillance (esoos) program, to utilize sys to increase timeliness of state data on drug overdose events. in order to operationalize the objectives of the grant, there was a need to assess and monitor the quality of kentucky’s sys data, with limited resources. we leveraged the nssp’s r studio server to automate quality assurance (qa) monitoring and reporting to meet these objectives. methods using the r server, we pulled data from the process messages table, aggregating messages to single patient encounters. in addition to compiling the code on a powerful remote server, the server can access the process table messages relatively quickly. we developed an r markdown report to produce a report that includes a variety of systemand facility-level metrics that highlight key indicators of system performance and data flows. by using r, we were able to create an auto-generating qa report that runs weekly and e-mails for analyst review. quality metrics included: % completeness of chief complaint and discharge diagnosis codes (overall and by facility)[fig 1 & fig 2]; visit trend by day of visit (with interactive spark lines) [fig 2]; maximum date of message created, date message arrived at nssp server, date of visit, and total messages[fig 3]; message arrived trend (interactive sparklines)[fig 3]; volume and type of error messages failing to process[fig 4]; message volume by adt type[fig 5]; and volume of patient class by type by day[not shown]. our sys analyst reviews the report and delivers it to stakeholders with general comments about ongoing and newly emerging data quality concerns. results the report has proven to be beneficial in ongoing qa monitoring. the report is shared weekly with key stakeholders at the kentucky department for public health, kentucky health information exchange, nssp, and regional essence users. findings are reviewed at monthly sys stakeholder meetings. the report has identified numerous errors, dead feeds, and other systems changes in near real-time; leading to corrective action and general data quality enhancement. weekly monitoring of qa has improved data feed stability and communication of identified issue with key stakeholders. conclusions the r studio server provides a nimble platform to develop, refine, and automate a qa reporting system that can lead to improved sys data quality. in kentucky, in addition to improving overall data quality, these weekly reports and subsequent communication have help built relationships among key stakeholders and elevated the importance of syndromic surveillance data locally. continual monitoring of data is critical to ensure quality and therefor the validity of the data. figure 1 figure 2 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts figure 3 figure 4 figure 5 keywords quality assurance; r studio; nssp; r markdown acknowledgments we acknowledge and thank the following agencies for their support of this work: the kentucky department for public health, the kentucky health information exchange, and the national syndromic surveillance program. *peter j. rock e-mail: pjrock2@uky.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e53, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 1nigeria field epidemiology and laboratory training programme, asokoro, nigeria; 2rivers state ministry of health, portharcourt, nigeria; 3world health organisation, south south zone, nigeria objective this study describes the epidemiological characteristics and the transmission dynamics of the evd outbreak in a south-southern city of nigeria introduction ebola virus disease (evd) is a severe illness that spread in the human population through human-to-human transmission. in the past, evd outbreaks occurred in the rural communities of africa, near tropical rainforests, but the most recent outbreak in west africa has also involved major urban areas and big cities, with air travel playing an important role in its spread.on july 23, 2014, the evd outbreak was declared in nigeria following the confirmation of evd in a traveller, who arrived acutely ill at the international airport in lagos, south western nigeria from liberia .the outbreak subsequently filtered to a south southern nigeria city, by a symptomatic contact who escaped surveillance in lagos and flew to the south southern city. methods a detailed case investigation was initiated on the 27th, august, 2014 to confirm the alert of a possible outbreak in the south southern city of the death of a physician from illness with symptoms compatible of evd. the possible source of exposure in the deceased physician was explored by tracing his contacts retrospectively. stored sample of the physician’s blood taken while he was ill was also sent for laboratory analysis. standardised case and contact definitions were adopted. contact identification, listing and follow up was done till the 21st after the last possible exposure with immediate isolation of cases. analysis for confirmation of evd was also done using rt-pcr. results the stored sample of the physician’s blood was positive for evd on rt-pcr. the retrospective tracing of the deceased physician’s contact revealed he managed a patient, a contact of a confirmed case in another city of nigeria, who escaped surveillance, and flew into the south-southern city discreetly by plane while already very ill with symptoms of ebola in the first week of august. this patient was said to have been managed in a hotel room by the physician for 5 days. three secondary cases arose from this index case (the deceased physician). the deceased physician transmitted the virus to a patient he shared a hospital room with for 2 nights during the course of his illness. none of the health care workers that took care of him in the hospital was infected. he also generated 2 other cases within his household (his wife and sister ) who were both involved in caring for him at home while he was ill, giving a total of 4 cases with 527 contacts listed in all (attack rate = 7.6/1000). there were two deaths (case fatality ratio = 50%). the mean incubation period was 13 days± 4 days and average serial interval was 18 days± 2 days conclusions infection prevention and control measures in most health care facilities in nigeria are focused on protecting health care workers from infected patients with little consideration on preventing cross infections among patients. active surveillance and screening of passengers should not only be limited to international ports of entry but should be promptly and strictly enforced at domestic airports and inter-state borders as soon as an outbreak is declared to limit the spread of the outbreak locally. the evd transmission chain in portharcourt, south southern nigeria. keywords epidemiologic characteristics; transmission dynamics; evd outbreak; nigeria *olawunmi o. adeoye e-mail: wunmiolat@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e46, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts advancing ghsa: lessons learned about strengthening his and disease surveillance pia d. macdonald*, niamh darcy, rita sembajwe, eileen reynolds, henry chidawanyika, christopher kelley, michael mckay, adam preston and gordon cressman rti international, berkeley, ca, usa objective the objective is to discuss two decades of international experience in health information and disease surveillance systems strengthening and synthesize lessons learned as applicable to implementation of the global health security agenda (ghsa). introduction rti international has worked on enhancing health information and disease surveillance systems in many countries, including the democratic republic of the congo (drc), guinea, indonesia, kenya, nepal, philippines, tanzania, zambia, and zimbabwe. strengthening these systems is critical for all three of the prevent, detect and respond domains within the global health security agenda. we have deep experience in this area, ranging from implementing district health information software (dhis), electronic medical records, health facility registries, ehealth national strategies, electronic integrated disease surveillance and response system (eidsr), mobile real-time malaria surveillance and response, national weekly disease surveillance, patient referral system, and community based surveillance. these experiences and lessons learned can inform work being done to advance the ghsa. we will discuss several examples, including activities in zimbabwe and tanzania. rti has been working in zimbabwe for over six years to strengthen the national health information system. this work has included the configuration and roll-out of dhis 2, the national electronic health information system. in doing so, rti examined and revitalized the weekly disease surveillance system, improving disease reporting timeliness and completeness from 40% to 90%. additionally, rti has integrated mobile technology to help more rapidly communicate laboratory test results, a laboratory information management systems to manage and guide test sample processing, and various other patient level systems in support of health service delivery at the local level. this work has involved capacity building within the ministry of health to allow for sustainable support of health information systems practices and technology and improvements to data dissemination and use practices. similarly, rti has worked for more than five years to help strengthening the national his in tanzania. these activities have included stakeholder coordination, developing national ehealth strategy and enterprise architecture, harmonizing indicators, redesigning routine reporting instruments, national dhis 2 roll-out, information technology infrastructure management and user help desk support, reducing the number of parallel information systems, data dissemination and use, development of district health profiles, development of the national health facility registry, and supporting roll-out of the electronic integrated disease surveillance system. methods we will profile selected projects and synthesize critical lessons learned that pertain to implementation of the ghsa in resource constrained countries. results we will summarize our experience and lessons learned with health information and disease surveillance systems strengthening. topics such as those that relate to advancing the ghsa real time surveillance and reporting action package areas will be discussed, including: indicator and event based surveillance systems; interoperable, interconnected, electronic real-time reporting system; analysis of surveillance data; syndromic surveillance systems; systems for efficient reporting to who, fao and oie; and reporting network and protocols in country. conclusions our experience working over the past 14 years in 9 countries on different his and disease surveillance system strengthening projects has led to a deep understanding of the challenges around implementation of these systems in limited resource settings. these experiences and lessons learned can inform initiatives and programs to advance the ghsa. keywords global health security; surveillance; capacity building; informatics acknowledgments rti acknowledges the support of the us centers for disease control and prevention, president’s emergency plan for aids relief (pepfar), us agency for international development, bill &melinda gates foundation, and qualcomm inc. additionally, rti acknowledges the vast support and contributions of the many national and sub-national public health institutions and staff with whom we have collaborated. *pia d. macdonald e-mail: pmacdonald@rti.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e172, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts the canadian chronic disease surveillance system: a distributed surveillance model lisa lix*1, 2 and kim reimer3 1community health sciences, university of manitoba, winnipeg, mb, canada; 2george and fay yee centre for healthcare innovation, winnipeg, mb, canada; 3british columbia ministry of healthy living and sport, victoria, bc, canada objective to describe the process, benefits, and challenges of implementing a distributed model for chronic disease surveillance across thirteen canadian jurisdictions. introduction the public health agency of canada (phac) established the canadian chronic disease surveillance system (ccdss) in 2009 to facilitate national estimates of chronic disease prevalence, incidence, and health outcomes. the ccdss uses population-based linked health administrative databases from all provinces/territories (p/ts) and a distributed analytic protocol to produce standardized disease estimates. methods the ccdss is founded on deterministic linkage of three administrative health databases in each canadian p/t: health insurance registration files, physician billing claims, and hospital discharge abstracts. data on all residents who are eligible for provincial or territorial health insurance (about 97% of the canadian population) are captured in the health insurance registration files. thus, the ccdss coverage is near-universal. disease case definitions are developed by expert working groups after literature reviews are completed and validation studies are undertaken. feasibility studies are initiated in selected p/ts to identify challenges when implementing the disease case definitions. analytic code developed by phac is then distributed to all p/ts. data quality surveys are routinely conducted to identify database characteristics that may bias disease estimates over time or across p/ts or affect implementation of the analytic code. the summary data produced in each p/t are approved by scientific committee and technical committee members and then submitted to phac for further analysis and reporting. results national surveillance or feasibility studies are currently ongoing for diabetes, hypertension, selected mental illnesses, chronic respiratory diseases, heart disease, neurological conditions, musculoskeletal conditions, and stroke. the advantages of the distributed analytic protocol are (figure 1): (a) changes in methodology can be easily made, and (b) technical expertise to implement the methodology is not required in each p/t. challenges in the use of the distributed analytic protocol are: (a) heterogeneity in healthcare databases across p/ts and over time, (b) the requirement that each p/t use the minimum set of data elements common to all jurisdictions when producing disease estimates, and (c) balancing disclosure guidelines to ensure data confidentiality with comprehensive reporting. additional challenges, which include incomplete data capture for some databases and poor measurement validity of disease diagnosis codes for some chronic conditions, must be continually addressed to ensure the scientific rigor of the ccdss methodology. conclusions the ccdss distributed analytic protocol offers one model for national chronic disease surveillance that has been successfully implemented and sustained by phac and its p/t partners. many lessons have been learned about national chronic disease surveillance involving jurisdictions that are heterogeneous with respect to healthcare databases, expertise, and population characteristics. figure 1. features, benefits, and challenges of a distributed model for disease surveillance in canada keywords national surveillance; distributed analysis model; administrative data acknowledgments this research was made possible through collaborations between the public health agency of canada (phac) and the provincial/territorial governments of british columbia, manitoba, ontario, quebec, new brunswick, nova scotia, newfoundland and labrador, yukon, northwest territories, and nunavut. *lisa lix e-mail: lisa.lix@umanitoba.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e140, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 1viable knowledge masters, abuja, nigeria; 2university of the witwatersrand, johannesburg, south africa objective to investigate the compliance of private health facilities to the integrated disease surveillance and response (idsr) system in nigeria. introduction the outbreak of the ebola virus disease (evd) in africa in 2014 presented a major threat and concern across the world, spreading to two other continents (europe and north america). though the epidemic is on a downward trend, there is a need to evaluate the performance of the systems in place to detect and control such outbreaks and determine the need for improvement in countries affected. with its first traceable case reported to have been in guinea, the outbreak spread to nigeria through an air traveler from liberia which led to an outbreak in the country that luckily, was quickly contained (1). this imported case was initially managed at a private health facility (phf) eventually leading to 20 cases and eight deaths, four of which were health workers from the initial managing phf (1). despite effort to contact the authorities about the suspected imported case by the phf, it reportedly took some time before the health authorities could be reached and action at control instituted. this might suggest an inefficiency of the idsr system which was previously adopted by nigeria as a means of implementing the international health regulation (ihr) of 1969. the ihr is a set of regulations that the world health assembly uses to implement its constitutional responsibility to prevent the international spread of diseases. hemorrhagic fevers like evd ought to be reported immediately upon suspicion to the health authorities but the delay despite effort suggests this system is not efficient. this is important as phfs are noted to attend to over 60% of the nigerian population. thus, it is important to carry out an assessment of the idsr system in phfs to forestall a repeat episode and limit the impact of outbreak of infectious diseases in future. methods this study will be carried out in the south-west of nigeria where about 40% of phfsin the country are located (2). we intend to carry out a mixed-methods study which will include desk reviews, key informant interviews, focus group discussions, analysis of routine data, a cross sectional study of health workers and health facility assessments. desk review will be completed to understand the legislation and policies establishing the idsr in nigeria and opportunities for improvement. key informants at the federal, state and local government level will be interviewed to understand more about the regulation and implementation of the idsr across the different levels of governance in the country. routine health data will be pulled from the national health information system to assess reporting of phfs. in addition, health facility assessments will be completed along with assessment of the knowledge of health workers in phfs on the country system for notifiable diseases. results the study will critically assess the legislation that establishes the idsr as the means of implementing the ihr in nigeria. it will provide the status of implementation of the regulation for implementing the ihr. the study will further assess the knowledge of private healthcare providers on the idsr system in nigeria and the factors that affect their compliance with this regulation. furthermore, it will provide information for the readiness of phfs to report notifiable diseases and will also investigate the differences in reporting rate between public and private health facilities. conclusions the study will provide a snapshot of the status of phfs on participation in the idsr in nigeria and factors that may be affecting them. it will also highlight areas of inadequate legislation and system failures and will make proposals aimed at addressing these shortfalls. keywords surveillance; communicable diseases; research proposal; global; nigeria references 1. shuaib f, gunnala r, musa eo, mahoney fj, oguntimehin o, nguku pm, et al. ebola virus disease outbreak—nigeria, july–september 2014. mmwr morb mortal wkly rep [internet]. 2014 [cited 2015 aug 25];63(39):867–72. available from: http://www.cdc.gov/mmwr/ preview/mmwrhtml/mm63e0930a2.htm?lang=de 2. makinde oa, azeez a, bamidele s, oyemakinde a, oyediran ka, adebayo w, et al. development of a master health facility list in nigeria. online j public health inform [internet]. 2014 oct 16 [cited 2014 nov 27];6(2). available from: http://www.ncbi.nlm.nih.gov/ pmc/articles/pmc4235326/ *olusesan a. makinde e-mail: makindeo@viableknowledgemasters.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e138, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluating the burden of brucellosis in hospitalized patients in armenia, 2016 vigen asoyan*1, 2, alvard hovhannisyan1, 2, anahit mkrtchyan1, 2, mher davidyants1, 2, hripsime apresyan1, 2, lusine atoyan1, 2 and lyudmila niazyan3 1“nork” infection clinical hospital of moh, yerevan, armenia; 2yerevan state medical university, department of infectious diseases, yerevan, armenia; 3ch2m hill armenia branch office, yerevan, armenia objective to understand the disease burden, we studied the epidemiological and clinical characteristics and associated costs for brucellosis patients hospitalized in nork hospital in 2016. introduction brucellosis, endemic in armenia, is recognized as a significant public health challenge with a major economic burden. to address the regional threat of brucellosis for both animal health and public health, the “one health surveillance of brucellosis in armenia” was initiated in december 2016. the project aims to develop scientifically sound strategies and policies for sustainable control of the disease. methods in 2016, 265 patients diagnosed with brucellosis were hospitalized at “nork” hospital, of whom 16 were 0-14 years old and 94% were males. diagnosis was confirmed using agglutination test and elisa. the spss program was used to analyze the data. results distribution of the disease by marz revealed that the most cases came from ararat (53), followed by kotayk (49), armavir (38), aragatsotn (36), yerevan (28), gegharkunik (26), vayots dzor (24), syunik (8), and lori (3). clinical data indicated that 71% of patients had acute brucellosis with fever, arthralgia and night sweating while 29% suffered chronic brucellosis with damage of organ systems. the primary complaints included arthralgia (80%), sweating (60%) and fever (40%). joint pain was mainly located in knee, elbow, and sacroiliac regions. average grade of fever was 37,9±0,95oc. total days spent in hospital were 1798, economic losses for the hospital were estimated at amd 36 million per year. conclusions those at the highest risk for brucellosis were males living in ararat and kotayk marzes who work with livestock. keywords brucellosis; armenia; epidemiology *vigen asoyan e-mail: ziloras@battelle.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e118, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts user generated sql queries inform evaluation of nssp essence aaron kite-powell*, michael coletta and jamie smimble cdc, atlanta, ga, usa objective the objective of this work is to describe the use and performance of the nssp essence system by analyzing the structured query language (sql) logs generated by users of the national syndromic surveillance program’s (nssp) electronic surveillance system for the early notification of community-based epidemics (essence). introduction as system users develop queries within essence, they step through the user-interface to select data sources and parameters needed for their query. then they select from the available output options (e.g., time series, table builder, data details). these activities execute a sql query on the database, the majority of which are saved in a log so that system developers can troubleshoot problems. secondarily, these data can be used as a form of web analytics to describe user query choices, query volume, query execution time, and develop an understanding of essence query patterns. methods essence sql query logs were extracted from april 1, 2016 to august 23, 2017. overall query volume was assessed by summarizing volume of queries over time (e.g., by hour, day, and week), and by site. to better understand system performance the mean, median, and maximum query execution times were summarized over time and by site. sql query text was parsed so that we could isolate, 1) syndromes queried, 2) sub-syndromes queried, 3) keyword categories queried, and 4) free text query terms used. syndromes, sub-syndromes, and keyword categories were tabulated in total and by site. frequencies of free text query terms were analyzed using n-grams, wordclouds, and term co-occurrence relationships. term cooccurrence network graphs were used to visualize the structure and relationships among terms. results there were a total of 354,101 sql queries generated by users of essence between april 1, 2016 and august 23rd, 2017. over this entire time period there was a weekly mean of 4,785 sql queries performed by users. when looking at 2017 data through august 23 this figure increases to a mean of 7,618 sql queries per week for 2017, and since may 2017 the mean number of sql queries has increased to 10,485 per week. the maximum number of user generated sql queries in a week was 29,173. the mean, median, and maximum query execution times for all data was 0.61 minutes, 0 minutes, and 365 minutes, respectively. when looking at only queries with a free text component the mean query execution time increases slightly to 0.94 minutes, though the median is still 0 minutes. the peak usage period based on number of sql queries performed is between 12:00pm and 3:00pm est. conclusions the use of nssp essence has grown since implementation. this is the first time the essence system has been used at a national level with this volume of data, and number of users. our focus to date has been on successfully on-boarding new sites so that they can benefit from use of the available tools, providing trainings to new users, and optimizing essence performance. routine analysis of the essence sql logs can assist us in understanding how the system is being used, how well it is performing, and in evaluating our system optimization efforts. keywords evaluation; system usage; public health practice *aaron kite-powell e-mail: lyv8@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e64, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 1division of health informatics and surveillance, centers for disease control & prevention, atlanta, ga, usa; 2communicable disease epidemiology & immunizations section, public health – seattle & king county, seattle, wa, usa; 3division of integrated biosurveillance, armed forces health surveillance center, baltimore, md, usa; 4international society of disease surveillance, brighton, ma, usa; 5council of state and territorial epidemiologists, atlanta, ga, usa; 6infectious disease clinical research program and henry m jackson foundation, uniformed services university of the health sciences, bethesda, md, usa; 7new hampshire department of health & human services, concord, nh, usa objective this roundtable will provide a forum for the syndromic surveillance community of practice (cop) to discuss the public health impacts from the icd-10-cm conversion, and to support jurisdictional public health practices with this transition. it will be an opportunity to discuss key impacts on disease surveillance and implementation challenges; and identify solutions, best practices, and needs for technical assistance. introduction on october 1, 2015, the number of icd codes will expand from 14,000 in version 9 to 68,000 in version 10. the new code set will increase the specificity of reporting, allowing more information to be conveyed in a single code. it is anticipated that the conversion will have a significant impact on public health surveillance by enhancing the capture of reportable diseases, injuries, and conditions of public health importance that have traditionally been the target of syndromic surveillance monitoring. for public health departments, the upcoming conversion poses a number of challenges, including: 1) constraints in allocating resources to modify existing systems to accommodate the new code set, 2) lack of icd-10 expertise and training to identify which codes are most appropriate for surveillance, 3) mapping syndrome definitions across code sets, 4) limited understanding of the precise icd-10 cm codes that will be used in the us healthcare system, and 5) adjusting for changes in trends over time that are due to transitions in usage of codes by providers and billing systems. to accommodate the icd-9 to icd-10 transition, the centers of disease control and prevention (cdc) partnered with the international society of disease surveillance (isds) cop to form a workgroup to develop the master mapping reference table (mmrt). this tool maps over 130 syndromes across the two coding systems to assist agencies in modifying existing database structures, extraction rules, and messaging guides, as well as revising established syndromic surveillance definitions and underlying analytic and business rules. description representatives from the icd10 workgroup will lead a discussion of icd-10-cm coding impact since october 1, 2015 (including feedback and stories from local, state, and federal public health as well as from ehr vendors and other partners) to share experiences using the mmrt tool, and impacts, challenges, and best practices relating to the icd 9/10 transition. audience engagement a slide presentation will feature how jurisdictions have prepared for and integrated icd-10 coding and mapping into their surveillance systems, as well as share icd 9/10 conversion experiences. the cop audience will be queried on specific questions during the discussion such as: 1) how did the transition impact dataflow and content from hospital emergency departments?, 2) what was the conversion impact on surveillance systems (such as downtime, improvement in accuracy, etc.)?, 3) what methods did your health department use to try to analyze data before/after the conversion?, 4) how did you use the mmrt tool developed by the isds icd10 working group to accommodate changes in syndrome definitions?, and 5) did you attempt to develop more precise syndrome definitions, and how do you plan to validate syndromic categories? an audience response “clicker” system will be used to collect answers to these questions as a catalyst for further discussion. the answers will be compiled and presented to the audience, followed by discussion aimed at identifying conversion challenges faced by the audience, solutions, and best practices. keywords surveillance; methodologies; public health; analytics *david j. swenson e-mail: dswenson@dhhs.state.nh.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e180, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses 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health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, 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is properly cited. isds 2015 conference abstracts automating ambulatory practice surveillance for influenza-like illness andrew walsh* health monitoring, pittsburgh, pa, usa objective to investigate the viability of using prediagnostic syndromic surveillance data from ambulatory practices for influenza-like illness surveillance introduction data submitted to ilinet from ambulatory practices are a primary feature of influenza-like illness (ili) surveillance in the united states. practices count relevant patient records and submit this data manually to ilinet. the ongoing data collection is useful for surveillance, and a significant amount of historical data has accumulated which is useful for research purposes and comparisons of the present season to the past. however, the tabulation of this data is costly, and retention of sentinel practices can be challenging as there is no mandate to submit data. increasingly, the epicenter syndromic surveillance system is receiving data from ambulatory practices. syndromic surveillance data is sent automatically in near-realtime. meaningful use requirements incentivize practices to participate in ongoing data transmission. syndromic surveillance data from ambulatory practices is thus a possible substitute for the current, more labor-intensive surveillance of ambulatory practices. methods chief complaints, triage notes and patient temperature were collected from 141 hospitals and 710 ambulatory practices for patients from 5 philadelphia-area counties. an ilinet-compatible classification was defined using cough or sore throat in either the chief complaint or triage notes along with either mention of fever in one of those fields or a measured patient temperature over 100°f. the existing epicenter classification of ili specified, which looks for reference to influenza or ili in the text fields, was also used. registrations were classified using both criteria. results a total of 1,497,521 ed registrations and 836,431 ambulatory registrations were collected from june 30, 2014 to june 28, 2015. of these, 10,661 (0.71%) ed registrations and 2,359 (0.28%) ambulatory registrations were classified as ilinet compatible, while 18,363 (1.23%) ed registrations and 11,088 (1.33%) ambulatory registrations were classified as ili specified. figure 1 shows the weekly time series of the percentage of visits by classification. the registrations classified as ili specified more closely matched the expected values, with a baseline around 2% of visits and a peak of 4%. the temporal pattern of these registrations was also most closely matched the expected pattern for flu season. the percentage of ambulatory visits with an ili specified increased earlier in flu season than the ed visits. this pattern was not consistent across all counties. philadelphia county in particular exhibited minimal change in ili visits at any point during flu season. conclusions the current criteria used to define ili-related visits for ilinet may not be applicable to patient-provided data. although the ilinet criteria use symptoms that patients can identify and report, in practice those symptoms are not always individually recorded in chief complaints for ili patients. a registrar is more likely to summarize multiple symptoms as “flu symptoms,” “uri” (for upper respiratory infection), or similar. in essence, the registrar is performing a first level of classification into an ili category. if syndromic surveillance data is to be used for ili surveillance, the criteria for classifying visits will need to account for this summarizing of symptoms by the data providers. the ili time series suggest different usage patterns of ambulatory practices and eds at different points in the flu season. this may be indicative of access to ambulatory care; patients may seek treatment there earlier if they can, while those without ambulatory access will wait longer until their disease is severe enough to warrant visiting the ed. this could explain the geographic differences observed, but it cannot explain all of the temporal differences, as influenza does not typically span a month or more even without treatment. it may also be the case that other, milder respiratory illnesses circulate in the fall. figure 1: weekly time series of the percentage of ili-related healthcare visits to eds (red) and ambulatory practices (blue) based on either standard ilinet symptoms (solid) or mention of influenza or ili (dashed) in the prediagnostic text. keywords ilinet; ambulatory; syndromic surveillance; influenza acknowledgments we wish to thank the pennsylvania department of health for funding support and data for this work. *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e79, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa 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terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a 2014.isds.abstracts.final.pdf isds annual conference proceedings 2014. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2014 conference abstracts effect of the ukrainian crisis on the current measles situation and ways of improving surveillance tetyana chumachenko*1, dmytro chumachenko3 and tetyana karlova2 1epidemiology department, kharkiv national medical university, kharkiv, ukraine; 2kharkiv region laboratory center of the state sanitary epidemiological service of ukraine, kharkiv, ukraine; 3national aerospace university, kharkiv, ukraine objective to estimate the current measles situation in the kharkiv oblast (eastern region of ukraine) and to develop ways of improving the surveillance and control of measles in elimination phase during the crisis in ukraine. introduction deterioration of socio-economic conditions in ukraine created a threat of the spread of communicable diseases, including vaccine preventable diseases [1]. children in ukraine routinely receive two doses of the measles-mumps-rubella (mmr) vaccine according to the national immunization schedule. measles is targeted for elimination in ukraine [2]. but now ukraine crisis carries significant public health risk and requires changing tactics of surveillance and epidemiological control against measles. methods we used the data about reported cases of measles in the region and vaccination of sick people; the results of laboratory tests (elisa), which confirmed the cases of measles, data about the level of measles immunity among the kharkiv region population. results in ukraine, there is a mandatory registration of cases of measles. doctor who identified patients with measles or suspected it necessarily informs the surveillance authorities of suspected cases. in 2013 there were 18 cases of measles (incidence was 0.66 per 100000 population) in the kharkiv oblast. as of august 1, 2014 the incidence was 4.54 per 100000 people (124 cases). as of 01.09.2014, there were 163 probable cases and 128 confirmed cases of measles in the kharkiv oblast, the cases were distributed equally among the children and adults 64 cases of measles per each group. it should be noted that among these cases, 48 people were gypsies, who moved from the combat zone (slavyansk, donetsk oblast, lugansk oblast, ukraine), among them 38 (29.7%) were children, 10 (7.8%) – adults. gypsies often avoid preventive vaccinations. most cases have been reported in kharkiv region which was the main place of resettlement of refugees and in lozovaya region which was their entrepot (fig.1). the largest number of cases were reported in may and june, 2014, when the flow of refugees from the area of anti-terrorist operation (ato) increased. given the fact that at the beginning of the school year (september 1), the flow of refugees from the area of ato in kharkiv region grew by people with school-age children and students, a worsening of the measles situation in the area should be expected. in the period before the crisis in ukraine, an effective syndromic surveillance was implemented in the kharkiv oblast. suspected clinical cases of measles (people with maculapapular rash and temperature above 38°c) were laboratory confermed. this approach allowed to identify sick people with measles more fully and to distinguish measles cases from other infections with rash. in kharkiv oblast laboratory confirmation of cases was noted in 66.4 68.15% of cases. conclusions the crisis in ukraine led to a deterioration of the current measles situation due to refugees from the area of ato, especially gypsies. in host territories unvaccinated people need to be vaccinated and people with unknown vaccination status should be laboratory tested and if necessary vaccinated too. for that we should provide opportunities for vaccination of unaccounted population (allocation of vaccines, medical staff etc.) to achieve the goals of eliminating measles in spite of the crisis in ukraine implementation of the syndromic surveillance of measles should be continued in order to identify all the cases fully. laboratories for serological investigation and conformation of suspected cases in kharkiv should be empowered. fig.1. map of number of measles cases in kharkiv oblast from 01/01/2014 to 09/01/2014 keywords ukraine; vaccine preventable diseases; a treat of the spread of communicable diseases references 1. world health organization (who). ukraine crisis. donor alert. 15 august 2014. http://www.who.int/hac/donorinfo/donoralert_ukraine_15aug.pdf?ua=1 2. world health organization (who). surveillance guidelines for measles, rubella and congenital rubella syndrome in the who european regional office for europe: 2012. *tetyana chumachenko e-mail: tatalchum@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * (1):e188, 201 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts creation of a kansas spring extreme weather syndrome definition and unique records zachary m. stein* bureau of epidemiology and public health informatics, kansas department of health and environment, topeka, ks, usa objective to evaluate syndrome definitions capturing stormand extreme weather-related emergency department visits in kansas hospitals participating in the national syndromic surveillance program (nssp). introduction kansas storms can occur without warning and have potential to cause a multitude of health issues. extreme weather preparedness and event monitoring for public health effects is being developed as a function of syndromic surveillance at the kansas department of health and environment (kdhe). the syndromic surveillance program at kdhe utilized emergency department (ed) data to detect direct health effects of the weather events in the first 9 months of 2016. current results show injuries directly related to the storms and also some unexpected health effects that warrant further exploration. methods a basic syndrome definition was defined based on extreme spring and summer weather events experienced in kansas. this broad definition pulled records from kansas eds that included the following in the chief complaint or triage notes fields: ●storm ●rain ●torna(dos) ●wind ●flood this broad syndrome definition was performed on data submitted to the kansas’s production server through nssp between january 1st and august 30th, 2016. after the initial pull, duplicate records for the same patient and visit were removed. the remaining set was then searched by hand to identify terms caught by the syndrome definition that were not related to storm activity or extreme weather. record chief complaints were then scanned by hand to identify common words containing the search criteria and then removed. keywords not of interest to the syndrome definition that were caught were: migraine, window, drain, restrain, train, and many other proper nouns that contained one of the keywords. these remaining visits were then sorted by nature of visit and unexpected records were recorded for future direction of syndrome definition development. results the initial data pull under these conditions yielded 17,691 unique emergency department visits from january 1st to august 30th during the 2016 year. from this, records were classified based on key words resulting in the pull. the table below shows the initial pull results, the remaining records after errant results were expunged, the percentage of visits that were removed, and the most common reason for removal. of these records remaining after cleaning, 20 were related to storms, 62 were related to rain, 7 were related to tornado activity, 66 were related to wind, and 14 were related to flooding along with the mixed variable instances shown in the table. a majority of the wind-related ed visits were injuries and the majority of the tornado activity events were related to injuries sustained while taking shelter. many of the injuries mentioning storms were sustained in preparation for the storm, and a handful were due to mental stresses regarding storm activity. conclusions syndrome definition development is an iterative process that will vary by region. by manually looking at line-level data details, future searches can better accommodate these errant results and false positives. these studies will facilitate more rapid extreme weather response in kansas and allow better situational awareness. along with general storm-related injuries, knowledge of the unusual records caught by a syndrome definition can also help direct public education in preparation of future storms. with injuries sustained while taking shelter and injuries sustained in preparation for the storm, we can take these unique ed visits and work on interventions to prevent future occurrences. case counts captured by syndrome definitions and common false positives keywords syndromic surveillance; kansas; storm; weather; injuries acknowledgments data collection was supported by the grant or cooperative agreement number 1 u50 oe000069-01, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services. *zachary m. stein e-mail: zstein@kdheks.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e128, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts builiding methods for a proactive prescription drug surveillance system fan xiong*1, 2 1cdc / cste applied epidemiology fellowship program class of xiii, atlanta, ga, usa; 2kansas department of health and enviroment topeka, ks, topeka, ks, usa objective this study aims to show the application of longitudinal statistical and epidemiological methods for building a proactive prescription drug surveillance system for public health. introduction prescription drug monitoring programs (pdmps) are operating in 49 states and several u.s. territories. current methods for surveillance of prescription drug related behaviors, include the mean daily dosage of morphine milligram equivalent (mme) per patient, annual percentage of days with overlapping prescriptions per patient, and annual multiple provider episodes for multiple controlled substance prescription drugs per patient that are described elsewhere.1,2 this work builds on these efforts by extending longitudinal methods to prescription drug behavior surveillance in order to predict risks associated with prescription drug use. methods schedule ii prescription opioids from january 1, 2014 to february 29, 2016 from the kansas tracking and reporting of controlled substances (ktracs) was used for this analysis. prescription opioids were linked to the 2016 version of the morphine milligram equivalent conversion table from the national center for injury prevention and control.3 population estimates were based on the 2015 county vintage single-year of age bridged-race estimates from the national center for health statistics and used to calculate age-adjusted rates. a daily high dose opioid prescription was defined as having greater than or equal to 90 morphine milligram equivalent. since this is a unit-day measure with patients experiencing multiple daily high dose opioid days, the prentice, william, and peterson (pwp) recurrent event model was used to estimate the number of high-dose opioid days for kansas patients by gender and age groups.4,5 start time was the first prescription date with a high-dose opioid and stop time was the next high-dose opioid date during a study period from january 1, 2014 to feb 29, 2016. the pwp model is a statistical model that allows for the estimation of covariates on an event history (i.e. total time with prescription opioids, specifically high-dose opioids). analysis was completed with a stratified cox-proportional hazard model, sandwich covariance for dependent observations, and statistical significance was assessed with a wald chi-square. proc phreg in sas/stat(r) 14.1 was used since it has a new fast option for fitting large proportional counting process hazard model. results the age-adjusted rate of daily high-dose opioid patients was 3.2 patients per 100 kansas population-year (95% ci: 3.1 – 3.2). kansas patients aged 85 and older had the highest age-specific rate of 11.7 (95% ci: 11.5 –11.9). preliminary recurrent event analysis shows on average nearly a quarter of approximately 50 million schedule ii opioid patient days were high-dose opioid patient days among 785,514 kansan patients with any prescribed opioid history. in an initial result stratified by the number of high-dose opioid days and adjusting only for age, males on average had approximately 7% higher hazard of recurrent schedule ii high-dose opioid prescription days than females (β: 0.07, s.e: 0.002, p<0.0001). kansas patients aged 45 to 54 compared to kansas patients 85 and older on average had approximately 14% higher hazard of recurrent schedule ii highdose opioid prescription days (β: 0.14, s.e: 0.007, p<0.0001). conclusions this work demonstrates the application of survival analysis techniques to estimate the population at risk for high-dose opioids, which varies by the length of the total opioid prescription history. early results from the recurrent event analysis showed that kansas male and patients aged 45 to 54 years had the longest history of high-dose opioids. annual cross-sectional population estimates may incorrectly estimate the estimated risk of high-dose prescription opioids since it assumes all patients have the same prescription history. pdmps are longitudinal databases. survival analysis methods like recurrent event models can leverage the longitudinal structure to more precisely estimate risk statistics. future work includes incorporation of health outcomes data and further prescription covariates to assess the timing and intensity of opioid potency escalation. keywords prescription opioids; morphine milligram equivalent; prescription drugs acknowledgments this work was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-03. dr. belle federman at the kansas department of health and environment for best methods to communicate the findings of this study. references 1. paulozzi lj, strickler gk, kreiner pw, koris cm. controlled substance prescribing patterns—prescription behavior surveillance system, eight states. 2013. mmwr surveill summ. 2015 oct 16;64(9):1-4. 2. definitions of pbss measures. brandeis’ university center of excellence for prescription drug monitoring program’s prescription behavior surveillance system. available at http://www. pdmpexcellence.org/sites/all/pdfs/definitions%20of%20pbss%20 measures.pdf 3. national center for injury prevention and control. cdc compilation of opioid analgesic formulations with morphine milligram equivalent conversion factors, 2016 version. atlanta, ga: centers for disease control and prevention. 2016. available at http://www.pdmpassist. org/pdf/bja_performance_measure_aid_mme_conversion.pdf. 4. prentice, r. l., williams, b. j., and peterson, a. v. (1981), “on the regression analysis of multivariate failure time data,” biometrika, 68. 373–379. 5. amorim ld, cai j. modelling recurrent events: a tutorial for analysis in epidemiology. international journal of epidemiology. 2015 feb 1. 44(1):324-33. *fan xiong e-mail: fxiong@kdheks.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e13, 2017 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat houston health department, houston, tx, usa objective to describe the use of hepatitis c virus (hcv) viral load (vl) results and geospatial analysis to guide prevention efforts. introduction approximately 2.7 million americans live with chronic hcv, with roughly 30,000 new cases in 20131. fortunately, recent clinical trials have shown great advances using interferon-free, oral direct-acting antivirals, with cure rates over 95%2. but only a few people have been treated3, and most are unaware of the infection4. this presents an opportunity for public health to address unmet needs, but most jurisdictions have limited surveillance and prevention efforts. leveraging hcv surveillance, this analysis presents a cost-effective method to improve situational awareness and guide prevention efforts in houston. methods the houston health department (hhd) receives elr and other reports for chronic and acute hepatitis c, which include vl results. these are stored in consilience software’s maven 5.0. for this analysis, vl results were abstracted from maven and managed in sas v9.3. geospatial analysis was performed using esri’s arcgis v10.1. cdc hiv vl guidance5 was used and modified for hcv. average vl for each patient each year was calculated for trend and geospatial analyses. finally, principal component analysis and bidirectional stepwise linear regression were performed on average vl by zip, us census data, and american community survey data using sas v9.3. results since 2009, over 9,500 vl results were reported to hhd. this number increased annually, with over 2,700 reported from jan-may 2015. the average hcv log value by zip code in 2015 ranged from 4.58 to 6.95 (figure 1). the final linear regression model is average hcv log value (iu/ml) = 5.52577 + 0.00601*(% of families in poverty) + 0.0029*(% african american) from several social and demographic variable risk factors, the model found that percent of families in poverty and percent african american were positively correlated with the average log value (table 1). conclusions vl analyses are a viable method of identifying areas of decreased level of hcv suppression and increased potential for hcv transmission. this is crucial, as complications from unsuppressed hcv include cirrhosis and hepatocellular carcinoma6. additionally, medicaid programs have limited access to the most effective hcv therapies by various eligibility criteria, though most are not based on clinical evidence7. the utilization of these methods to significantly improve outcomes of prevention and intervention activities needs to be evaluated. table 1. stepwise coefficient for the linear model. figure 1. average hcv viral load by zip, 2015. keywords hepatitis c; linear regression; geospatial analysis references 1 centers for disease control and prevention [internet]. atlanta (ga): centers for disease control and prevention; 2015. hepatitis c faqs for the public; 2015 may 31 [cited 2015 august 26]. available from: http://www.cdc.gov/hepatitis/hcv/cfaq.htm#statistics 2 rice, cm, saeed, m. hepatitis c: treatment triumphs. nature. 2014 june 5; 510(7503): 43-44. 3 dore, gj, ward, j, thursz, mj. hepatitis c disease burden and strategies to manage the burden. j of viral hepatitis. 2014 may; 21(supp 1): 1-4. 4 denniston, mm, klevens, rm, mcquillan, gm, jiles, rb. awareness of infection, knowledge of hepatitis c, and medical follow-up among individuals testing positive for hepatitis c: national health and nutrition examination survey 2001-2008. hepatology. 2012 june; 55(6): 1652-1661. 5 centers for disease control and prevention. guidance on community viral load: a family of measures, definitions, and method for calculation. atlanta (ga): centers for disease control and prevention (us); 2011 aug. 42 p. 6 el-serag, hb. epidemiology of viral hepatitis and hepatocellular carcinoma. gastroenterology. 2012 may; 142(6): 1264-73. 7 center for health law and policy innovation. examining hepatitis c virus treatment access: a review of select state medicaid fee-forservice and managed care programs. boston (ma): harvard law school (us); 2015 april. 56 p. *ryan m. arnold e-mail: ryan.arnold@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e87, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts updating syndromic surveillnce baselines following public health interventions roger morbey, alex elliot, andre charlett, sally harcourt* and gillian smith public health england, birmingham, united kingdom objective to adjust modelled baselines used for syndromic surveillance to account for public health interventions. specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in england. introduction public health england’s syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. we use regression models to create baselines for expected activity and then identify any periods of signficant increases. the introduction of a rotavirus vaccine in england during july 2013 (bawa, z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines. methods we identified syndromes where rates had dropped significantly following the vaccine’s introduction. for these indicators, we introduced new variables into the regression models used to create baselines. specifically we tested for a ‘step-change’ drop in rates and a change in the seasonality of baselines. finally we checked the new models accuracy against actual syndromic data before and after the vaccine introduction. results we were able to improve model fit post-intervention, with the best-fitting models based on a change in seasonality. all postintervention regression models had reduced average residual square error. reductions in residual errors ranged from <1% to 60% when a ‘step-change’ variable was included and 4% to 75% when accounting for seasonality. furthermore, every syndrome showed a better model fit when a change in seasonality was included. conclusions prior to the vaccine’s introduction, rotavirus caused a spring-time peak in vomiting and diarrhoea recorded by syndromic surveillance systems. failure to account for the reduction in this peak post-vaccine would have made surveillance systems less effective. in particular, any increased activity during spring may have been undetected. moreover, models that did not account for changes in seasonality would increase the chances of false alarms during other seasons. by adjusting our baselines for the changes in seasonality due to the vaccine we were able to maintain effective surveillance systems. selected syndromic indicators with baselines before and after intervention. keywords intervention; aberration detection; syndromic; rotavirus acknowledgments rm, ae, and gs are supported by the national institute of health research’s (nihr) health protection research unit in emergency preparedness and response. the views expressed are those of the author(s) and not necessarily those of the nhs, the nihr, the department of health or public health england. references bawa, z., et al. assessing the likely impact of a rotavirus vaccination program in england: the contribution of syndromic surveillance. clinical infectious diseases : an official publication of the infectious diseases society of america 2015;61(1):77-85. *sally harcourt e-mail: sally.harcourt@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e25, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts disaster surveillance: perspectives from federal, state, and local levels eric v. bakota*1, david atrubin2, michael coletta3 and aaron kite-powell3 1health department, harris county public health, houston, tx, usa; 2florida department of health, tallahassee, fl, usa; 3cdc, atlanta, ga, usa objective in this panel, attendees will learn about how disaster surveillance was conducted in response to hurricanes irma and harvey, as well as the role of cdc at the federal level in supporting local response efforts. by hearing and discussing the challenges faced and solutions identified, attendees will be better able to respond in the event of a low-frequency/high-consequence disaster occurring within their jurisdiction. introduction in this panel, the presenters will discuss their perspective in responding to hurricanes harvey and irma. hurricane harvey made landfall on august 25th and over the course of 4 days dropped approximately 27 trillion gallons of water on texas and louisiana.1 the flooding that ensued was unprecedented and forced over 13,000 people into shelters.2 these individuals needed to have their basic needs -food, shelter, clothing, sanitationmet as well as their physical and mental health needs. the george r brown conference center (grb) and nrg stadium center were set up as mega-shelters to house shelterees. hurricane irma made landfall on september 10th in the florida keys as a category 4 hurricane. the hurricane caused 72 deaths3 and forced thousands of people into shelters.4 these weather events created novel challenges for local response efforts. decision makers needed timely and actionable data, including surveillance data. keywords disaster epidemiology; informatics; preparedness references 1. sanchez r, yan h, simon d. harvey aftermath: houston ‘open for business’; other cities suffering. cnn. 2017 sep 1. 2. sullivan k, hernandez a, fahrenthold d. harvey leaving record rainfall, at least 22 deaths behind in houston. chicago tribune. 2017 aug 29. 3. impact of hurricane irma. boston globe. accessed 2017 oct 10. https://www.bostonglobe.com/news/bigpicture/2017/09/11/impacthurricane-irma/w6wbn9k2lxd4gpmtu26aun/story.html 4. smith a. after hurricane irma, many ask: how safe are shelters? tampa bay times. 2017 september 21. *eric v. bakota e-mail: eric.bakota@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e108, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts medicaid claims data to supplement zika-related birth defects case identification carrie w. mills*, tenzin tseyang, katharine mcveigh and george askew nyc department of health and mental hygiene, long island city, ny, usa objective to assess the use of medicaid claims data to conduct surveillance for cases of zika-related birth defects identified after birth among infants born in new york city (nyc). introduction as a part of the zika birth defects surveillance, a national effort coordinated by the centers for disease control and prevention (cdc), nyc is conducting enhanced surveillance of all births with defects included in the congenital zika syndrome (czs) phenotype among infants born in nyc beginning in 2016. the intent of the project is to provide background on the prevalence of these conditions, regardless of cause. the surveillance project builds on the new york state (nys) congenital malformations registry, a passive, mandatory reporting system that relies on reporting from hospitals and providers. for the surveillance project, potential cases of zika-related birth defects (zbd) are identified by hospital and administrative data of birth records with one or more of the international classification of diseases, 10th revision (icd-10) diagnostic codes associated with czs.1 the list of included diagnostic codes was specified by the nys registry following guidance established by cdc. full medical record chart abstraction of the birth hospital visit of potential cases is then conducted applying further inclusion guidelines to identify zbd cases. recent reports of late presentation of birth defects consistent with czs suggest that some cases are being missed due to identification and diagnosis of the condition after birth.2 as one component of a broader strategy to obtain a more accurate surveillance count, we seek to identify potential zbd cases first diagnosed in the 6-month postpartum period using medicaid claims data. methods we will obtain medicaid records for all infants born in nyc in 2016 from jan 1, 2016 through june 30, 2017 using salient, a data mining system of medicaid data (salient interactive miner, version 5.70.079). the 85 icd-10 diagnostic codes currently being used to identify potential zbd cases will be applied to birth records and all outpatient and inpatient visits to a medical provider for the 6-month period after birth. all visits containing one or more of the codes from either primary or secondary diagnosis will be identified. a unique list of infants receiving one or more included diagnoses within the 6-month postpartum period will be obtained and cross-referenced with the current case list using a matching algorithm based on child’s name, date of birth, and other identifying variables. results preliminary results surveillance measures to-date have identified 380 cases of infants born in nyc in 2016 with birth defects that could be due to zika virus; it is anticipated that a majority have medicaid. (in 2015, 59% of all births in nyc were to mothers with medicaid.) analysis will determine (a) the extent of overlap of cases identified from surveillance activities and medicaid claims data, and (b) the extent of zbd potential cases missing from surveillance but found with medicaid data of inand out-patient visits. descriptive statistics will include age and class of earliest diagnosis of infants. those identified by medicaid analysis will be considered potential zbd cases pending full abstraction of record. full results pending. conclusions if results indicate missed potential zbd cases, medical chart abstraction of such cases will be warranted. further, as czs is a relatively new syndrome, findings may provide support in the determination of accurate follow-up time for future surveillance projects.3 full conclusion pending. keywords birth defects; zika; medicaid; surveillance acknowledgments congenital malformations registry, new york state department of health nyc zika pregnancy registry, nyc department of health and mental hygiene references 1. moore ca, staples je, dobyns wb, et al. characterizing the pattern of anomalies in congenital zika syndrome for pediatric clinicians. jama pediatrics. 2017;171(3):288-295. doi:10.1001/ jamapediatrics.2016.3982. 2. cragan jd, mai ct, petersen ee, et al. baseline prevalence of birth defects associated with congenital zika virus infection — massachusetts, north carolina, and atlanta, georgia, 2013–2014. mmwr morb mortal wkly rep. 2017;66:219–222. doi: http://dx.doi. org/10.15585/mmwr.mm6608a4. 3. shapiro-mendoza ck, rice me, galang rr, et al. pregnancy outcomes after maternal zika virus infection during pregnancy — u.s. territories, january 1, 2016–april 25, 2017. mmwr morb mortal wkly rep. 2017;66:615-621. doi: http://dx.doi.org/10.15585/ mmwr.mm6623e1. *carrie w. mills e-mail: cmills@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e146, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts ed visits for ami, stroke, acs & copd after the statewide smoking ban in cook co., il megan t. patel* and victoria w. persky epidemiology and biostatistics, university of illinois chicago, chicago, il, usa objective to utilize ed chief complaint data obtained from syndromic surveillance to quantify the effect of the illinois smoking ban on acute myocardial infarction (ami), acute coronary syndrome (acs), stroke, and chronic obstructive pulmonary disease (copd) related ed visits in adults in cook county, il. introduction tobacco use is the leading global cause of preventable death, killing more than five million people per year [1]. in addition, exposure to secondhand smoke is estimated to kill an additional 600,000 people globally each year [1]. in 1986, the us surgeon general’s report declared secondhand smoke to be a cause of lung cancer in healthy nonsmokers [2]. the first law restricting smoking in public places was enacted in 1973 in arizona that followed the 1972 surgeon general’s report providing awareness of the negative health effects associated with the exposure to air pollution from tobacco smoke [3]. smoke-free laws were slowly enacted after this time point with most occurring after the year 2000 [4]. in july 2007, the smoke free illinois act (sb0500, public act 095-0017) was passed in il [5]. the ban went into effect on jan 1, 2008 and illinois joined 22 other states in prohibiting smoking in virtually all public places and workplaces including offices, theaters, museums, libraries, schools, commercial establishments, retail stores, bars, private clubs, and gaming facilities [5-6]. while many studies have examined the effect of smoking bans on hospitalizations, this study would be the first to examine the effect of the comprehensive smoking ban in il on ed visits by utilizing chronic disease categories created with ed chief complaint data captured by syndromic surveillance [7]. the author hypothesizes that the comprehensive smoking ban in il significantly reduced the ed visits associated with ami, acs, stroke, and copd in adults in cook county, il. methods ed visits with chief complaints consistent with categories for ami, acs, stroke and copd captured by the cook co. dept. of public health local instance of essence from jan 1, 2006 – dec 31, 2013 were included in the analysis. proc genmod with a log link and negative binomial distribution was utilized for the analysis. all data was aggregated at the monthly level. the total number of ed visits of the health effect of interest was the dependent variable. the total ed visits during the same period of time, was used as the offset variable, sub-grouped by age and gender where appropriate. a binary variable was utilized to capture the effect of the time period after the implementation of the statewide smoking ban; 0 for before the ban and 1 for after the ban. when examining the effect of the statewide ban, cook co. as an entirety was examined as well as ed visits stratified by zip codes that already had a smoking ban in place at that time point and those that did not, and stratifying by urban (chicago) vs. suburban cook co. seasonality was addressed by including month, month squared and month cubed in the model. influenza was addressed by including a binary variable to indicate when influenza was occurring in the area based on percent influenza-like-illness ed visits that were occurring above the threshold for the area during that time period. age and gender were also evaluated as confounders and effect modifiers. sas 9.4 was utilized to perform the analyses. results results are presented in table 1. reductions of ed visits after the smoking ban implementation were seen in ami and acs disease categories for the overall adjusted model, at 3% and 3.5% respectively. stroke associated ed visits were not affected by the smoking ban. copd associated ed visits were not reduced immediately by the smoking ban, but did have a significant reduction 6 months after implementation of the ban at 3.6%. stronger effects were seen in individuals 70 years and older, females, the urban population, and zip codes without a prior ban for ami, acs, and copd. conclusions an immediate, significant reduction in ed visits associated with ami and acs was associated with the il statewide smoking ban in cook co., il. copd associated ed visits were significantly reduced 6 months after the ban implementation. the effect was greater in individuals 70 years and older, females, the urban population, and zip codes without a prior ban. keywords chronic disease; smoking ban; syndromic surveillance acknowledgments demian christiansen and kelley bemis references 1. who, who report on the global tobacco epidemic. implementing smoke-free environments. 2009, who: geneva, switzerland. 2. dc, the health consequences of involuntary exposure to tobacco smoke : a report of the surgeon general. 2006, u.s. dept. of health and human services, centers for disease control and prevention, coordinating center for health promotion, national center for chronic disease prevention and health promotion, office on smoking and health: atlanta, ga. 3. eriksen, m. and f. chaloupka, the economic impact of clean indoor air laws. ca cancer j clin, 2007. 57(6): p. 367-78. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts 4. foundation, a.n.r. overview list how many smokefree laws? 2015 10/2/2015 [cited 2015 10/5/2015]; available from: http://www.nosmoke.org/pdf/mediaordlist.pdf. 5. smoke free illinois act, in public act 095-0017. 2007. 6. goodman, p., et al., effects of the irish smoking ban on respiratory health of bar workers and air quality in dublin pubs. am j respir crit care med, 2007. 175(8): p. 840-5. 7. callinan, j.e., et al., legislative smoking bans for reducing secondhand smoke exposure, smoking prevalence and tobacco consumption. cochrane database syst rev, 2010(4): p. cd005992. *megan t. patel e-mail: mtoth2@uic.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e7, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 1graduate school public health, san diego state university, san diego, ca, usa; 2department of geography, san diego, ca, usa; 3center human dynamics in the mobile age, san diego, ca, usa; 4graduate school of public health, san diego, ca, usa objective create a flexible user-friendly geo-based social media analytic tool for local public health professionals. with the goal of increasing situational awareness, system has capability to process, sort and display tweets with text terms of potential public health interest. we continue to refine the social media and research testbed (smart) via feedback from surveillance professionals. introduction introduction: numerous methods using social media for syndromic surveillance and disease tracking have been developed. many websites use twitter and other social media to track specific diseases or syndromes.1 many are intended for public use and the extent of use by public health agencies is limited.2 our work builds on 4 years of experience by our multi-disciplinary team3 with a focus on local surveillance of influenza. 4,5 methods tweets with key words of interest are collected continuously using customized geo-targeted twitter apis. based on topic, different areas are monitored: influenza32 north american cities; ebola12 west african cities, u.s. 3 cities and 5 areas with airline hubs; hiv2 us states & worldwide; vaccine exemptionany location, california wildfires -4 metro areas. cities are described by a 17 m. radius from twitter account location or coordinates. collected tweets are processed using machine learning programs trained to filter and sort tweets. english tweets are coded for: geolocation; retweet; image or other media, hashtag(s), url(s). processed tweets are stored in a database and displayed as requested on the smart dashboard built with python®, javascript®, and node.js®. results the smart dashboard allows selection of geographic subsets (cities, areas, countries) for each topic area. figure 1. the top display provides a summary of all tweets in database (all points, daily, weekly, monthly). other panels include: scalable time trend; word cloud; top 10 for urls, hashtags, mentions, retweets, media displayed and geocoding status. each display panel allows sub-setting by (all, past 30 days, past week and yesterday). raw and/or filtered tweets are displayed. rates for selected tweets are calculated per appropriate denominator population, listed and displayed on selectable maps. for each display users can add a keyword for additional refinement and then display the raw tweets for their query. permitted users can download samples of tweets to excel for further examination. conclusions situational awareness can be enhanced by using geo-targeted social media analytics and gis methods displayed in a user-friendly manner. the system provides a feasible method to track and monitor issues of public health concern. in the long term, utility of social media tracking and improvement of the smart system depends on use by professionals and feedback to developers in this emerging field of syndromic surveillance. figure 1. screenshot of smart dashboard keywords social media; twitter; syndromic; dashboard; influenza acknowledgments this study is partially supported by the national science foundation grant #1416509, project titled “spatiotemporal modeling of human dynamics across social media and social networks”. we especially thank chris allen, jiue-an yang, su han, elias issa, and jessica dozier for data analysis. references 1. sample websites include: health map www.healthmap.org; sickweather www.sickweather.com; now trending — nowtrending. hhs.gov ; and crowdbreaks www.crowdbreaks.com. 2. velasco, e et al. social media and internet-based data in global systems for public health surveillance: a systematic review. milbank quarterly 2014 92(1): 7-33. 3. tsou, mh et al. mapping social activities and concepts with social media (twitter) and web search engines (yahoo and bing): a case study in 2012 us presidential election. cartography and geo info sci, 2013 40(4). 4. nagel, ac et al. the complex relationship of real space events and messages in cyberspace: case study of influenza and pertussis using tweets. j med internet res; 2013 15(10). 5. aslam aa et al. 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the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 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kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for 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essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of 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markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts application of gis for optimization of epidemiological monitoring volodymyr malakhov, volodymyr zavialkin*, oleksandra tarasyuk and valentyna smolnytska state institution lviv research institute of epidemiology and hygiene of the ministry of health of ukraine (si lrieh moh), lviv, ukraine introduction technology that combines traditional manipulations with databases and complete visualization of geographic (spatial) analysis employing maps has been developed in order to explore the possibilities for geographical information systems (gis) to be used in sanitary and epidemiological surveillance system based on the analysis of morbidity and identification of influence of hazardous chemical environmental factors on human health. methods graphical analytic method of information processing allowed visual establishing of mathematically determined cause-and-effect relationships between levels of air chemical pollution and morbidity levels for purulent bacterial meningitis. results calculated average annual contaminations of atmosphere of 20 administrative rayons and seven cities of lviv oblast with carbon oxide, lead, sulfur dioxide, and dust during the period 2006-2014 were the objects of the study. during a year, 1,920 air samples were collected per each ingredient for each rayon and city according to laboratory data of facilities of the state sanitary and epidemiological service in lviv oblast. average annual levels of the chemical substances were determined within the m.a.c. in all rayons and cities. however, 4-6% of individual samples in the rayons and 8-10% of individual samples in the cities exceeded the allowed concentrations, which imposed a real ecological danger. fig. 1. levels of carbon oxide air contamination within rayons of lviv oblast morbidity intensity rates for purulent bacterial meningitis were determined for the same period according to statistical reports on infectious disease morbidity in lviv oblast. in different years, human morbidity fluctuated from 0.7 to 2.3 per 100 thousand of population in the oblast. the study found the correlation between the concentrations of carbon monoxide, lead, sulfur dioxide, and dust in the air and levels of incidence of bacterial meningitis in people in the cities of lviv oblast with 1,092 thousand inhabitants, which compose 42.3% of all oblast population. correlation coefficients are r = 0.78 (p<0.001), r = 0.70 (p<0.001), r = 0.51 (p<0.005), and r =0.68 (p<0.02), respectively. fig. 2. correlation dependencies between air contamination and population morbidity rates for purulent bacterial meningitis within rayons of lviv oblast. the search for a correlation between chemical contamination of atmosphere and the morbidity level the rayon population of the oblast for purulent bacterial meningitis testified the existence of a statistically significant dependence between the level of morbidity for all population layers and atmosphere contamination with sulfur dioxide, lead, carbon monoxide, and dust. the correlation coefficients are r = 0.62 (p<0.002), r = 0.52 (p<0.005), r = 0.63 (p<0.005), r = 0.56 (p<0.05), correspondingly. the study found the correlation between the concentrations of sulfur dioxide, and lead in the air of lviv oblast and levels of incidence of purulent bacterial meningitis in children. correlation coefficients are r = 0.55 (p<0.05) and r = 0.57 (p<0.001), respectively. conclusions using gis approach, the study resulted in the development of medical-geographical maps of administrative rayons of lviv oblast. the maps include peculiarities for each year of surveillance. causeand-effect relationships between the levels of the anthropogenic pollution of the air basin of lviv oblast and morbidity levels for purulent bacterial meningitis for the oblast population have been spatially and temporally visualized as a study result. keywords meningitis; air chemical pollution; correlation *volodymyr zavialkin e-mail: vladyk@i.ua online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e8, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts active case-finding and enhanced data collection to identify neglected tropical diseases nhlanhla nhlabatsi*1, vusie lokotfwako1, dumsile mabundza1, fortunate bhembe1, phinda khumalo1, njabulo lukhele1, elizabeth mvila1, siphiwe m. shongwe-gama2, thulani maphosa2 and harriet nuwagaba-biribonwoha2 1ministry of health, mbabane, swaziland; 2icap columbia university, mbabane, swaziland objective to strengthen public health surveillance to monitor neglected tropical diseases (ntds) like leprosy as a control measure to avert disabilities in the kingdom of swaziland. introduction leprosy is a chronic infectious disease caused by mycobacterium leprae. it is a contagious disease that affects the skin, mucous membrane, and nerves causing discoloration and lumps on the skin and in severe cases disfigurement and deformities. the mode of transmission remains uncertain, but is believed that m.leprae is spread from person to person primarily as a nasal droplet infection. the incubation period for a bacterial disease generally is 5 – 7 years. progress in the fight against leprosy has been one of the greatest public health success and in the country, was eliminated in the mid1990s. however on the 22nd august 2017 a confirmed leprosy cases was reported by the national referral hospital. methods following the confirmed case an investigation was conducted to fully understand the possible source of the case and identify further cases. the assessment was done in three parts that is, hospital visit to follow up on the index case; conducting home visits to collect data for leprosy and their contact and a file review of all clients who were once diagnosed as having leprosy results the index case was identified and his condition is improving as he has been initiated on mdt which he will be taking for a minimum of six months. eleven clients were visited in their homes. their age range was 31 to 91 years but the majority were above 60 years with a median age of 70 years. there were 7 females and 4 males.. the clients presented with permanent nerve damage either from the face, upper or lower limbs. the common disabilities and deformities post treatment were sagging of face, nasal collapse, blindness and clawing of fingers and feet. other patients had plantar-palmar ulcers and abscesses from trauma, injuries or burns sustained due to nerve damage and inadequate protection. they reported to be experiencing stigma and are being discriminated. none of the clients presented with clinical signs and symptoms suggestive of leprosy. there were 18 files that were reviewed out of 58 who are known to exist. six of the 18 files belonged to clients who were seen during home visits. one of the clients was epidemiologically linked to the index case as they used to live together in 1994. conclusions the country seem to be experiencing the re-emerging of leprosy. since the index case is epidemiologically linked to one of the old cases this therefore confirms the incubation period of leprosy being from 15 to 20 years. there is need to strengthen leprosy prevention and control measures as well as strengthening of leprosy surveillance in the context of idsr. there is an urgent need to raise public awareness and provide clients with protective clothing. furthermore, there is need to strengthen the bilharzia and worms control program to incorporate leprosy as it is one of the ntds targeted for elimination in swaziland. keywords leprosy; case-finding; swaziland acknowledgments 1. ministry of health, mbabane, swaziland 2. icap, columbia university, mailman school of public health, mbabane, swaziland 3. columbia university, department of epidemiology, mailman school of public health, new york, united states of america references 1. heymann d.l 2008, control of communicable diseases manual, 18th edition, american public health association, geneva, switzerland. *nhlanhla nhlabatsi e-mail: ntini0064@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e91, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 86 (page number not for citation purposes) isds 2015 conference abstracts how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson public health services, county of san diego, san diego, ca, usa objective this presentation highlights the necessary steps to effectively validate a health level 7 (hl7) syndromic surveillance interface during the onboarding and implementation process. introduction current local, state, and national initiatives related to meaningful use and the modernization of electronic health records, and the growing availability of electronic information exchanges, have become important drivers to establishing syndromic surveillance systems. effective implementation of electronic syndromic surveillance interfaces requires approaches that ensure the receipt of quality, timely, and reliable information. while there are published specifications for the hl7 adt message and national institute of standards and technology (nist) validation tools, there has been little documentation about the necessary steps for a local public health department to validate and confirm that an interface that is producing consistent and quality information. the lack of effective validation efforts has led to incomplete or inconsistent data utilized by syndromic systems and their intended audiences. the county of san diego has developed and utilized a framework for validating new syndromic interfaces. this presentation will highlight several pragmatic methods to validate the hl7 message content, provide specific examples of validation, and describe the pitfalls that could result from a poorly validated syndromic interface. methods message content validation involves reviewing, configuring and achieving meaningful content within the hl7 syndromic surveillance messages. the content validation process is viewed across similarly linked messages tied to patient encounter workflows. the method of validation involves several validation domains that are applied to the syndromic messages on a per hospital basis. these validation methods are: • higher complexity syndromic use case development, • validation of selective priority constraining and encoding, • message timeliness and time sequence validation, • validity of trace back data elements, • completeness and validity of priority message segments, • validation of organization location information, • validation of content useful for syndromic category mapping, • internal consistency of the message, and • external parallel validation with hospital medical records or other external surveillance systems. when applied, these methods are often the catalyst for message reconfigurations. in turn, additional rounds of validation can be applied to ensure message changes meet criteria. a set of syndromic message content validation guidelines have been developed to guide the validation work. these tools describe the corresponding steps to validation with criteria established to determine success or indicate improvements needed. in addition, a message content worksheet has been developed as a tool to track and document each data provider’s validation activity and outcomes. results during onboarding for syndromic surveillance, the county of san diego actively engaged with hospitals, their electronic health record (ehr) vendors, and a local health information exchange (hie). numerous best practices have been identified. hospitals have different information workflows, usage of message types, and assignments of patient status. when assigning a patient treatment location, several different approaches are used across hospitals or by the ehr rules-based workflows. these differences also include differing classifications of inpatient and ambulatory related messages, message segment values, and the sequence of timeliness regarding the patient’s syndromic message versus the patient’s actual encounter experience. conclusions public health agencies across the united states vary in their capacity to onboard meaningful use syndromic data. public health agencies should not rely on ehr vendors alone or other system implementation surrogates to validate the quality of the syndromic information. the county of san diego has highlighted the importance and value in systematic validation of an hl7-based syndromic data source. following these steps has led to optimized technical approaches to onboarding future hospitals, as well as insights into how the data can be used in a meaningful way. preliminary outcomes have shown that it is valuable to have public health involved in the onboarding process and, more importantly, during the hl7 message content validation activities. keywords syndromic; surveillance; validation; interface; meaningful use *jeffrey johnson e-mail: jeffrey.johnson@sdcounty.ca.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e19, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts baseline assessment of public health surveillance in the kingdom of swaziland harriet nuwagaba-biribonwoha*2, nhlanhla nhlabatsi3, william macwright1, vusie lokotfwako3, tony a. trong4, paige ryland1, affan shaikh1, joy sylvester1 and scott j. mcnabb1 1public health practice, llc, belmont, ma, usa; 2icap at columbia university, new york, ny, usa; 3epidemiology and disease control unit, swaziland ministry of health, mbabane, swaziland; 4u.s. centers for disease control and prevention, atlanta, ga, usa objective to assess essential support functions for integrated disease surveillance and response(idsr) in the kingdom of swaziland and make recommendations for a national idsr roadmap. introduction implementation of the idsr framework for fulfillment of the international health regulations (2005) ([ihr 2005]) has been challenging in swaziland due to distribution of idsr functions across units within the strategic information department (sid) and other external departments within the ministry of health. we conducted a qualitative assessment and a strength, weaknesses, opportunities and threats (swot) analysis of current public health surveillance (phs) support structures to inform implementation of idsr. methods key informant interviews, focus group discussions, and a desk review were performed. participants were personnel at essential units, departments and programs at the national level as well as at health facilities and clinics at regional and local levels. transcripts were coded into swot matrices using maxqda for each building block of phs: structures, workforce, resources, processes (detect, report, assess/analyze, respond, feedback), and informatics. results selected strengths included existence of immediate notifiable disease reporting through the epidemic and pandemic response unit (epr) and reporting of summary health facility data to the health management information system (hmis) unit and laboratory network. weaknesses included lack of clear roles and responsibilities for idsr among sid units, limited coordination between sid units, lack of data sharing, lack of standard operating procedures (sops), uncoordinated case investigations and response, minimal analysis conducted for public health surveillance and limited feedback for reporters.. identified opportunities were political will for establishing of roles and responsibilities and mechanisms for coordination and data sharing. threats were limited data access, limited funding for feedback, lack of analysis for idsr and paper-based reporting conclusions currently there is limited use of surveillance data for decision making due to lack of coordination. findings were presented at a dissemination meeting to representatives of relevant departments, and there was consensus on the need to clearly define the role and responsibilities of different programs for idsr. in march 2016, a consensus meeting was held to designate roles and responsibilities for idsr, a direct result of this assessment. additional resources and funding is needed to support these highly important initiatives to ensure the safety and health security of the swazi nation. keywords africa; public health surveillance; idsr; public health surveillance; data sharing acknowledgments this work is supported by the president’s emergency plan for aids relief (pepfar) through the centers for disease control and prevention under the terms of cooperative agreement number 1u2ggh001271. its contents are solely the responsibility of the authors and do not necessarily represent the official views of pepfar or the centers for disease control and prevention. *harriet nuwagaba-biribonwoha e-mail: hn2158@cumc.columbia.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e173, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts spread of middle east respiratory coronavirus: genetic versus epidemiological data daniel a. janies*, colby ford, lambodhar damodaran and zachary witter bioinformatics and genomics, university of north carolina at charlotte, charlotte, nc, usa objective here we use novel methods of phylogenetic transmission graph analysis to reconstruct the geographic spread of mers-cov. we compare these results to those derived from text mining and visualization of the world health organization’s (who) disease outbreak news. introduction mers-cov was discovered in 2012 in the middle east and human cases around the world have been carefully reported by the who. mers-cov virus is a novel betacoronavirus closely related to a virus (neocov) hosted by a bat, neoromicia capensis. mers-cov infects humans and camels. in 2015, mers-cov spread from the middle east to south korea which sustained an outbreak. thus, it is clear that the virus can spread among humans in areas in which camels are not husbanded. methods phylogenetic analyses we calculated a phylogenetic tree from 100 genomic sequences of mers-cov hosted by humans and camels using neocov as the outgroup. in order to evaluate the relative order and significance of geographic places in spread of the virus, we generated a transmission graph (figure 1) based on methods described in 1. the graph indicates places as nodes and transmission events as edges. transmission direction and frequency are depicted with directed and weighted edges. betweenness centrality, represented by node size, measures the number of shortest paths from all nodes to others that pass through the corresponding node. places with high betweenness represent key hubs for the spread of the disease. in contrast, smaller nodes at the periphery of the network are less important for the spread of the disease. web scraping and mapping due to the journalistic style of the who data, it had to be structured such that mapping software can ingest the data. we used import.io to build the api. we provided the software a sample page, selected the data that is pertinent, then provided a list of all urls for the software. we used tableau to map the information both geographically and temporally. results geographic spread of mers-cov based on transmissions identified in phylogenetic data most important among the places in the mers-cov epidemic is saudi arabia as measured by the betweenness metric applied to a changes in place mapped to a phylogenetic tree. in figure 1, the circle representing saudi arabia is slightly larger compared to other location indicating its high importance in the epidemic. saudi arabia is the source of virus for jordan, england, qatar, south korea, uae, indiana, and egypt. the united arab emirates has a bidirectional connection with saudi arabia indicating the virus has spread between the two countries. the united arab emirates also has high betweenness. the united arab emirates is between saudi arabia and oman and between saudi arabia and france. south korea, and qatar have mild betweeness. south korea is between saudi arabia and china. qatar is between saudi arabia and florida. other locations (jordan, england, indiana, and egypt) have low betweenness as they have no outbound connections. visualization of geographical transmissions in who data certain articles include the infected individuals’ countries of origin. ln constrast, many reports are in a lean format that includes a single paragraph that only summarizes the total number of cases for that country. if we build the api in a manner that recognizes features in the detailed reports, we can generate a map that draws lines from origin to reporting country and create visualizations. however, since only some of the articles contain this extra information, mapping in this manner will miss many of the cases that are reported in the lean format. conclusions our goal is to develop methods for understanding syndromic and pathogen genetic data on the spread of diseases. drawing parallels between the transmissions events in the who data and the genetic data has shown to be challenging. analyses of the genetic information can be used to imply a transmission pathway but it is hard to find epidemiological data in the public domain to corroborate the transmission pathway. there are rare cases in the who data that include travel history (e.g. “the patient is from riyadh and flew to the uk”). we conclude that epidemiological data combined with genetic data and metadata have strong potential to understand the geographic progression of an infectious disease. however, reporting standards need to be improved where travel history does not impinge on privacy. a transmission graph for mers-cov based on viral genomes and place of isolation metadata. the direction of transmission is represented by the arrow. the frequency of transmission is indicated by the number. the size of the nodes indicates betweenness. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e4, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords phylogenetics; transmission graph; mers-cov; syndromic; genetic acknowledgments research reported in this publication was supported by a unc research opportunities initiative grant to unc charlotte, nc state university, and unc-chapel hill. the data used in this report are publically available in genbank and the who. we gratefully acknowledge all the researchers and institutions who submitted prepublication data to the public domain. references janies, d. a., pomeroy, l. w., krueger, c., zhang, y., senturk, i. f., kaya, k. and çatalyürek, ü. v., phylogenetic visualization of the spread of h7 influenza a viruses. cladistics. 2015. 31: 679–691. doi:10.1111/cla.12107 *daniel a. janies e-mail: djanies@uncc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e4, 2017 isds16_abstracts-final 92 isds16_abstracts-final 93 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts tracking health effects of wildfires: the oregon essence wildfire pilot project melissa powell* and laurel boyd oregon heatlh authority, portland, or, usa objective to build capacity to conduct syndromic surveillance at the local level by leveraging a health surveillance need. introduction wildfires occur annually in oregon, and the health risks of wildfire smoke are well documented1. before implementing syndromic surveillance through oregon essence, assessing the health effects of wildfires in real time was very challenging. summer 2015 marked the first wildfire season with 60 of 60 eligible oregon emergency departments (eds) reporting to essence. the oregon essence team developed a wildfire surveillance pilot project with two local public health authorities (lphas) to determine their surveillance needs and practices and developed a training program to increase capacity to conduct surveillance at the local level. following the training, one of the lphas integrated syndromic surveillance into its routine surveillance practices. oregon essence also integrated the evaluation findings into the summer 2016 statewide wildfire surveillance plan. methods oregon essence staff recruited two lpha preparedness coordinators whose jurisdictions are regularly affected by wildfire smoke to participate in the pilot project. a state public health emergency preparedness liaison served as facilitator in order to increase syndromic surveillance capacity among state preparedness staff. a pre-season interview assessed data and surveillance needs, risk communication practices, and typical response activities during wildfires. initial project calls focused on determining specific queries that would meet local needs. participants wanted total ed visit numbers and health outcomes including asthma, chest pain or heart problems. both lphas were interested in using the data to assess health effects on vulnerable populations, including elderly, children, and migrant workers. oregon essence staff also recommended queries that would be used if large numbers of people were displaced (e.g., medication refills, dialysis). before the onset of wildfire season, oregon essence epidemiologists created queries and a myessence page for each participant. lpha staff practiced running the queries, modifying them, and discussed interpretation and data-sharing best practices. during wildfire season, brief weekly webinars enabled participants to ask questions and learn additional techniques including displaying time series as proportions and adjusting geographic parameters to focus on areas with poor air quality. results 2015 was a severe wildfire season in oregon, with over 685,000 acres burned2. for the first time, local and state public health were able to monitor and share near real-time health information on interagency smoke calls. in the post project evaluation, participants reported increased knowledge of syndromic surveillance, interpretation, and risk communications. there were no marked increases in total emergency department visits, or visits for asthma, heart palpitations, or other heart complaints. the public may have adhered to warnings and effectively protected themselves against exposure to wildfire smoke, or health effects may have been less severe and not reflected in emergency department data. over the next several years, oregon essence will integrate select urgent care data, which may better capture morbidity due to wildfire smoke. conclusions framing syndromic surveillance training around a health surveillance need was effective because participants were engaged around a high-priority health hazard. in summer 2016, oregon essence integrated wildfire health surveillance into a biweekly essence seasonal hazard surveillance report and invited wildfire response partners to subscribe. local essence users can use or modify the queries. in 2017, oregon essence will incorporate air quality data from the environmental protection agency so partners can monitor air quality and health effects simultaneously. keywords wildfire; essence; capacity building; training acknowledgments thank you to the jackson and lane county health departments and oregon public health emergency preparedness staff who participated in this pilot project. this publication was supported by cooperative agreements, number nu90tp000544 and 5u50oe000068-02, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services. references 1. liu j, pereira g, uhl s, et al. a systematic review of the physical health impacts from non-occupational exposure to wildfire smoke. environ res. 2015 jan;0:120-132. 2. northwest annual fire report [internet]. northwest interagency coordination center. 2015. [cited 2016 august 17]. available from: http://gacc.nifc.gov/nwcc/content/pdfs/archives/2015_nwcc_ annual_fire_report.pdf *melissa powell e-mail: melissa.e.powell@state.or.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e141, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts princess marina hospital hiv rates:interrupted time series analysis for policy review mooketsi molefi* family medicine & public health, university of botswana, gaborone, botswana objective we aimed to assess the effect of the amended public health act of 2013 on facility-based hiv testing in princess marina hospital introduction hiv testing remains the mainstay of optimal hiv care and is pivotal to control and prevention of the disease, however efforts to attain optimal testing levels have been undermined by low hiv testing especially in developing countries. botswana in response, amended its public health act in september 2013 but the effect of this action on facility based hiv testing rates has not been evaluated methods we carried out an effect assessment using interrupted time-series analysis method, where we accessed electronic medical records of patients seen in princess marina hospital from june 2011 to may 2015. rates were developed from the proportion of patients that tested each month out of the number that registered, and that figure used that as our data point in the series. september 2013 served as our intervention period in the series. we ran the (i) crude and (ii) sexstratified model regression models in stata® yielding newey-west coefficients with their 95% confidence intervals. graphical display of the models were also produced to visual appreciation and inspection results two hundred and twenty-nine thousand six hundred and ninety two patients were registered between june 2011 and may 2015. of those tested the significant majority being females (65%). from the newey-regression output there was no significant change in the level of hiv testing immediately after the intervention however there was a change in trend(p=0.002) post the intervention. stratification by gender, revealed no statistically significant difference between males and females, either in the levels nor the trend post intervention compared to pre-intervention conclusions the amendment of the public health act of 2013, has brought about trend change in hiv testing however there has not been any apparent difference in the levels nor trends on hiv testing between males and females. nationwide health facility-based studies could assist assess the overall effect of the amended act on hiv testing rates keywords hiv testing rates; interrupted time series analysis; public health act; princess marina hospital acknowledgments i would like to express my sincere gratitude to the afya-bora consortium, primary mentors and site mentors, botswana-upenn partnership, university of botswana (faculty of medicine), ministry of health (leadership, hrdc data management unit), princess marina hospital and the gaborone district health management team references 1. provider initiated hiv testing and counseling: one day training programme, field test version. who guidelines approved by the guidelines review committee. geneva2011. 2. donnell d, baeten jm, kiarie j, thomas kk, stevens w, cohen cr, et al. heterosexual hiv-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. the lancet. 2010;375(9731):2092-8. 3. lawn sd, harries ad, anglaret x, myer l, wood r. early mortality among adults accessing antiretroviral treatment programmes in subsaharan africa. aids. 2008;22(15):1897-908. 4. mcmahon jm, pouget er, tortu s, volpe em, torres l, rodriguez w. couple-based hiv counseling and testing: a risk reduction intervention for us drug-involved women and their primary male partners. prevention science : the official journal of the society for prevention research. 2015;16(2):341-51. 5. shan d, duan s, gao j, yang y, ye r, hu y, et al. [analysis of early detection of hiv infections by provider initiated hiv testing and counselling in regions with high hiv/aids epidemic in china]. zhonghua yu fang yi xue za zhi [chinese journal of preventive medicine]. 2015;49(11):962-6. 6. hensen b, baggaley r, wong vj, grabbe kl, shaffer n, lo yrj, et al. universal voluntary hiv testing in antenatal care settings: a review of the contribution of provider initiated testing & counselling. tropical medicine & international health. 2012;17(1):59-70. 7. ijadunola k, abiona t, balogun j, aderounmu a. provider-initiated (opt-out) hiv testing and counselling in a group of university students in ile-ife, nigeria. the european journal of contraception & reproductive health care : the official journal of the european society of contraception. 2011;16(5):387-96. 8. baisley k, doyle am, changalucha j, maganja k, watson-jones d, hayes r, et al. uptake of voluntary counselling and testing among young people participating in an hiv prevention trial: comparison of opt-out and opt-in strategies. plos one. 2012;7(7):e42108. 9. topp sm, chipukuma jm, chiko mm, wamulume cs, bolton-moore c, reid se. opt-out provider-initiated hiv testing and counselling in primary care outpatient clinics in zambia. bulletin of the world health organization. 2011;89(5):328-35a. 10. tlhakanelo jt, mulumba-tshikuka jg, molefi m, magafu mg, matchaba-hove rb, masupe t. the burden of opportunisticinfections and associated exposure factors among hiv-patients admitted at a botswana hospital. 2015. 11. bernard ej. botswana’s draconian public health bill approved by parliament, bonela will challenge it as unconstitutional once president signs into law (update 3). hiv justice network. 2013. 12. biglan a, ary d, wagenaar ac. the value of interrupted timeseries experiments for community intervention research. prevention science : the official journal of the society for prevention research. 2000;1(1):31-49. *mooketsi molefi e-mail: mooketsimolefi@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e78, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts heroin overdose hospitalization risk due to prescription opioids using pdmp in wi. gayatri raol*1, 2 and dr. ousmane diallo1 1office of health informatics, wisconsin division of public health, madison, wi, usa; 2council of state and territorial epidemiologist, atlanta, ga, usa objective using the wisconsin enhanced opioid surveillance system, the present study evaluates the heroin hospitalization risk among the opioid recipients using the prescription drug monitoring data (pdmp) with following specific objectives: 1. evaluate the risk of heroin overdose hospitalization following the prescription of opioid. 2. assess the time elapsed between last prescribed opioid and first heroin overdose hospitalization. 3. identify the main predictors of heroin overdose hospitalization among prescribed opioid users. introduction nationally and in wisconsin, heroin is the leading cause of opioid related death and hospitalization. opioids are commonly prescribed for pain. every day, over 1,000 people are treated in emergency departments for misusing prescription opioids1. in 2015, more than 15,000 people died from overdoses involving prescription opioids1. approximately, three out of four heroin users report having abused prescription opioids prior to using heroin2. in wisconsin from 2010 to 2014 the number of deaths involving any opioid increased by 51% and for heroin increased by 192%. through the federal government funding and support wisconsin has established a statewide tool to help combat the ongoing prescription drug abuse epidemic by providing valuable information about controlled substance prescriptions that are dispensed in the state. pdmp is continue to be among the most promising state-level interventions to improve opioid prescribing, inform clinical practice, and protect patients at risk. methods this was a retrospective cohort study of pdmp patients who were prescribed an opioid and were subsequently hospitalized for heroin overdose between 2013 and 2015. our analysis used a combination of univariate and survival data analysis to estimate the risk of heroin overdose hospitalization from the time of the last prescribed opioid to the first day of hospitalization. the outcome was defined as heroin hospitalization with any code of 965.01 (icd9 2013 first quarter to third quarter of 2015), t40.1x1a, t40.1x4a (icd10 fourth quarter of 2015). the exposure was defined as prescription of opioid limited to dea class i, ii, iii, & iv. our analysis used a combination of univariate and survival data analysis to estimate the risk of heroin hospitalization from the time of the last prescribed opioid to first day of hospitalization due to heroin overdose. cox regression hazard modeling was used to analyze survival time data and to identify the main predictors of heroin hospitalization. data were analyzed using the sas 9.2 and the study was initiated with the data governance board approval. results from 2013 to 2015, a total of 1,397,493 unique patients that were hospitalized linked to 1,448,224 patients reported in the pdmp who received controlled substance. among those 699,014 (48%) had at least one hospitalization event and out of those 396 (6%) had at least one hospitalization episode due to heroin. annual ed visit rates due to heroin overdose have doubled from 179 in 2013 to 396 in 2015. on average, people who stopped receiving prescription drugs were at a 72% increased risk of being hospitalized for heroin overdose within five months with log-rank test significance (p=0.01). males, 90 morphine milligram equivalent recipients, and kidney disease morbidity were 3.63, 2.99, and 5.64 times higher risk to have heroin overdose hospitalization, respectively. conclusions patients with a history of stopping prescription drugs within the previous five months are at higher risk for subsequent hospitalization for heroin overdose. factors such as alcohol use, age, gender, and tapering of prescription influence the risk of heroin hospitalization. it may be prudent to transition patients to alternative treatments before they become addicted to the prescribed drugs. figure 1: risk of heroin overdose is higher between 0-35 months after the prescribed opioid ended keywords prescription drugs; heroin; overdose acknowledgments my sincere thanks goes to dr. ousmane diallo (co-author) and milda aksamitauskas for thier continuous support and guidance throughout this study. i would also like to extent my thanks to the office of health informatics for many useful comments. references 1. center of disease control and prevention (cdc). prescription opioid overdose data. https://www.cdc.gov/drugoverdose/data/overdose. html. accessed 5 may 2017 2. center of disease control and prevention, heroin overdose data; january 26 2017. https://www.cdc.gov/drugoverdose/data/ heroin. html. accessed 5 may 2017 *gayatri raol e-mail: gayatr.raol@dhs.wisconsin.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e41, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluating the sensitivity of a passive diarrheal disease surveillance system during a post-drought nhlanhla nhlabatsi*1, vusie lokotfwako1, phinda khumalo1, siphiwe m. shongwe-gama2, maria dlamini1, zanela simelane1, thulani maphosa2 and harriet nuwagababiribonwoha2 1ministry of health, mbabane, swaziland; 2icap columbia university, mbabane, swaziland objective to evaluate the difference in sensitivity between passive and active diarrheal and malnutrition disease surveillance system post-drought period in swaziland introduction over the past decade swaziland has experienced recurring drought episodes. in 2016 the country experienced challenges regarding water supplies in both urban and rural areas due to the drought impact. a rapid health and nutrition assessment was conducted in 2016 revealed an increase in number of cases of acute watery diarrhea of all age groups. while there is a high demand for epidemiological data in the country a passive system through health management information system (hmis) and immediate disease notification system (idns) has been used to monitor acute watery diarrhea and a set of priority notifiable diseases in the country. methods an active sentinel surveillance system was set up in four regional hospitals for monitoring of all diarrheal cases of the under-fives. a data abstraction form was developed and used to extract data from outpatient registers and inpatient mainly from the children’s ward over a period of 15 weeks. two surveillance officers trained on integrated disease surveillance and response (idsr) collected and analyzed on weekly basis and further compared with data from a passive surveillance system that included the hmis and idns. results while acute gastroenteritis was the most prevalent type of diarrheal disease (93%), about 35.5% (1788 in active surveillance vs 1147 passive surveillance) of the cases of diarrheal cases are being underreported in the passive surveillance. similar observation was made on malnutrition with more than 51% of the cases not reported in passive surveillance (186 cases vs 91 cases). conclusions the process exposed gaps in data collected for passive surveillance and also differing data standards indicating inconsistency and under reporting which may be misleading for public health purposes. low sensitivity in terms missing cases within the passive surveillance was observed when comparing within the active surveillance sentinel sites. it was also noted that having multiple data sources poses challenges in the country as they provide varying diseases trends and burden estimate. keywords sensitivity; surveillance; swaziland acknowledgments 1. ministry of health, mbabane, swaziland 2. icap, columbia university, mailman school of public health, mbabane, swaziland 3. columbia university, department of epidemiology, mailman school of public health, new york, united states of america references 1. who. integrated disease surveillance and response *nhlanhla nhlabatsi e-mail: ntini0064@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e119, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts response to ebola virus disease outbreak in nigeria, west africa: the zaria experience aisha abubakar*, kabir sabitu, mohammed nasir sambo, abdulrazaq gobir, sani ibrahim, sulaiman bashir and ahmad umar department of community medicine, ahmadu bello university zaria, zaria, nigeria objective to assess the formation and function of a joint committee of the ahmadu bello university (abu) and the ahmadu bello university teaching hospital (abuth) to prevent and control evd in zaria and the north west sub region of nigeria. introduction the ebola virus disease (evd) outbreak in west africa was unprecedented in spread and its attendant response. there were over 15,000 confirmed cases and over 9,000 suspected cases. the response to the outbreak was massive within africa and beyond. the outbreak in nigeria affected 19 people and led to 7 deaths (cfr 37%). there were more than 891 contacts of these cases under surveillance as of 23rd september 2014. nigeria was declared evd free by the world health organization in october 2014. nationwide there was targeted preparedness to prevent and control evd. in zaria, this led to the formation of a joint committee of the ahmadu bello university (abu) and the ahmadu bello university teaching hospital (abuth) to prevent and control evd in zaria and the sub region as a whole. methods a joint multidisciplinary committee was formed by abu and abuth with representatives from the department of community medicine, internal medicine, nursing sciences, veterinary public health, medical microbiology, mass communication, directorate of public affairs abu zaria, general administration and management services division abuth, the university health services and the centre for disease risk management under the department of geography. four subcommittees were created steered by the main committee. the subcommittees were surveillance; case management; infection control and social and mass mobilization subcommittees results the committee conducted seminars and trainings in case management, surveillance and infection control. mass media campaigns included radio jingles production and airing as well as production of flyers and posters on evd prevention and control. there was a phone-in live radio programme. a screening exercise for raised temperature was conducted using laser thermometers at main entry points. a case of suspected evd was managed who turned out to be a case of dengue haemorrhagic fever. conclusions the committee was enriched by its multidisciplinary nature and a blueprint for the control and prevention of evd was developed in line with national and global standards. the committee was hampered with lack of funds to implement fully the blueprint for the prevention and control of evd in zaria and its environs. the committee transformed into the abu/abuth epidemic preparedness and response committee after the outbreak was over to address other emerging epidemics. keywords ebola virus disease; prepareness; prevention acknowledgments the ahmadu bello university zaria, nigeria the ahmadu bello university teaching hospital zaria references abu/abuth joint committee for the prevention and control of ebola virus disease (abupace) blueprint for prevention and control of ebola virus disease in abu/abuth zaria 2014. pages 1-44 world health organization. who declares end of ebola outbreak in nigeria www.who.int/mediacentre/news/statements/2014/nigeriaends-ebola/en/ *aisha abubakar e-mail: draishau@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e166, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts hepatitis a surveillance using commercial laboratory data lauren canary*1, william thompson1, francisco averhoff1, harvey kaufman3, christos petropoulos2, noele nelson1 and claudia vellozzi1 1division of viral hepatitis, u.s. centers for disease control and prevention, atlanta, ga, usa; 2laboratory corporation of america, burlington, nc, usa; 3quest diagnostics, madison, nj, usa objective to evaluate the use of commercial laboratory data for monitoring trends in hav infections over time and identifying geographic and demographic characteristics of hav case clusters for the purpose of targeting interventions. introduction hepatitis a virus (hav) infections have persisted in the united states despite the availability of an effective vaccine. recent outbreaks of hav infections among unvaccinated adults attributed to consumption of hav-contaminated food, or person-to-person contact in certain populations (e.g., men who have sex with men) or settings (e.g., homeless shelters) have emphasized the importance of targeted vaccination of at-risk adults. methods we used commercial laboratory data from quest diagnostics (quest) and laboratory corporation of america (labcorp) to identify unique individuals within each database who tested positive for hav igm antibody (indicative of acute hav infection) from january 2011 through june 2017. though de-depulication across the two laboratories was not possible, comparison of case characteristics indicated limited possible overlap of cases (<0.5%) and thus data from the two laboratories were combined. demographic characteristics associated with the first positive test were used to classify cases by age, gender, state of residence, insurance type, and provider specialty. persons co-infected with hepatitis b and/or hepatitis c were identified based on positive test results for hepatitis b surface antigen and hepatitis c rna, respectively. results a total of 6,702,256 hav igm test results from quest and 7,043,555 hav igm test results from labcorp were processed. of those, 24,697 (0.4%) and 13,785 (0.2%) tests, respectively, had a ‘reactive’, or positive result, indicating acute hav infection. from these test results, we identified 15,415 unique individuals from quest and 10,622 unique individuals from labcorp with an acute hav infection between january 2011 and june 2017. among the 26,037 acute cases, the majority were female (14,056; 54.0%), were aged 50 or older (13,940; 53.5%), resided in large central or fringe metropolitan areas (17,842; 68.5%), and had tests ordered by family or internal medicine providers (12,358; 47.5%; table). we identified 330 cases (1.3%) among incarcerated persons. although data could not be de-duplicated across labs, we estimated a minimum of 630 persons (2.4%) were co-infected with hepatitis b and 852 persons (3.3%) were co-infected with hepatitis c. from 2011 to 2015, there were 7,370 cases of acute hav reported to cdc, whereas quest and labcorp test results indicated 19,822 cases over the same time period. trends in cases by month revealed seasonal increases in cases in late summer and early fall months (figure 1). mapping of acutely-infected individuals demonstrated a range of cases from 0 to 1,119 cases by county over the study period (figure 2). conclusions hav igm test results over a 6-year period from two commercial laboratories serving the united states suggest continuing hepatitis a transmission. most cases occur among older adults, and appear to cluster geographically in metropolitan areas. commercial laboratory data is a useful tool for supplementing case-based surveillance and informing prevention efforts. characteristics of acute hav cases figure 1. monthly total number of persons tested positive for acute hav infection figure 2. geographic distribution of persons who tested positive for acute hav infection keywords big data; viral hepatitis; surveillance *lauren canary e-mail: lcanary@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e132, 2018 safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi safe opioid prescription: a smart on fhir approach to clinical decision support shyamashree sinha1,2, mark jensen1,3, sarah mullin1, peter l elkin1 1. department of biomedical informatics, university at buffalo state university of new york 2. department of anesthesiology, university at buffalo state university of new york 3. department of philosophy, university at buffalo state university of new york abstract background prescription opioid pain medication overuse, misuse and abuse have been a significant contributing factor in the opioid epidemic. the rising death rates from opioid overdose have caused healthcare practitioners and researchers to work on optimizing pain therapy and limiting the prescriptions for pain medications. the state of new york has implemented a prescription drug monitoring program(pdmp), amended public health law to limit the prescription of opioids for acute pain and utilized the resources of the state and county health departments to help in curbing this epidemic. the recent publication of guidelines for prescription opioids from cdc [1] and asipp (american society of interventional pain practitioners) have independently reviewed literature and found good evidence of limiting opioid prescription for acute and chronic non cancer pain. [2] method over the last decade, advanced technology has increased the complexity of electronic health records systems leading to the development of clinical decision support systems (cdss) to aid the work flow of healthcare providers. there are several systematic reviews on the effectiveness and utility of cdsss. a common consensus is that commercially and locally developed cdss are effective in improving patient measures while actual workload improvement and efficient cost-cutting measure are not significantly improved by cdss. patient provider involvement in developing cdss is a determinant of its success and utilization rates. [7] therefore, a plug and play form of cdss which can be implemented from an external platform through secure channels would be more effective. design the health level seven’s (hl7) open licensed interoperability standard fast health interoperability resources (fhir) has a platform, substitutable medical applications and reusable technologies (smart) for cdss app development by a third party. [3] we adopted these open source standard to plan to develop an app for accessible and efficient implementation of the recently published guidelines for management of pain with prescription opioid medications. safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi introduction the mortality rate due to opioid overdose has been consistently rising over the last decade, reaching epidemic proportions. new york state, including western new york, has had a steady rise in opioid related deaths from 2014 to 2015: natural and semisynthetic opioid overdose death rose by 13.3%, methadone related deaths rose by 9.1%, and synthetic opioid other than methadone related deaths rose by 135.7% [1]. there is evidence that overuse, misuse. and abuse of prescription opioid pain medication is one of the major contributing factors in the rising death toll. this reasoning led to the publication of the recent cdc guidelines for prescription [2]. the population health problem due to prescription opioid use, abuse and dependence has been progressively worsening over the last two decades. clinicians are faced with the challenge of treating pain adequately to improve quality of life while trying to prevent the potential of overuse, abuse and dependence among patients who are being treated by prescription opioid medications. many studies have shown narcotic pain medications are known to decrease pain threshold and increase the need for pain medications [3]. the need for clinical decision support for pain control and monitoring of patients’ medication has led to many attempts at developing tools based on established guidelines. the assip recently published a two-part guideline on pain control which gives a stepwise approach to pain medication management and adequate pain control. this guideline states opioid pain medication therapy should only be limited to patients with intractable chronic pain who have shown demonstrable improvement with therapy [4]. recently, the centers for disease control and prevention published a guideline for chronic opioid prescription based on a systematic review of current literature in 2016 [2]. these practice guidelines provide clinicians with a useful tool for making decisions of optimizing the use of prescription pain medications. however, the attempts at implementation of these guidelines in the form of applications have had limited outcomes that have been discussed later. aim the goals for this cdss tool are to achieve proper monitoring of prescription drugs, patients’ medication list and potential interactive medications, surveillance for abuse/ misuse, patient involvement in alternative therapy, reporting problems and obtaining adequate pain control. the final step would be to implement the system in clinical practice and to monitor usage rates and measure the outcomes of successful or unsuccessful implementations. doi: 10.5210/ojphi.v9i2.8034 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi clinical decision support systems (cdss) based on electronic health records have been evolving for more than a decade. a paper published by the federal health it unit outlines the need, importance and available technology behind the evolving cdss tools [5]. based on their recommendations a stepwise process of designing a cdss would be to: 1. identify and quantify a ‘high-priority gap’ in clinical quality between current outcomes and a stated goal. ‘high priority’ is the need for quality of care improvement aligns with the strategic goals and treatment outcomes that the health care organization is aiming for. 2. map the workflow by which the clinical care pertaining to the target issue is delivered. 3. design a future state workflow that includes the best ideas for closing the gap and eliminates as much waste as possible. 4. identify the information necessary to support the future state workflow. then, design, test, and perfect cds interventions using the four rightsright info, right person, right medium and right format. our goal is the creation of an application (app) based decision support tool that is high priority in terms of population health needs, minimizes the consumption of time for the provider, and is more interactive for the patient. in addition, this app could be utilized as an aide in implementation of the goals of precision medicine, focusing on patient-centered care. therefore, in line with these goals, we propose the creation of the application opiacutepain that follows the opioid prescribing guidelines set forth by the cdc to aide in the efficient and accurate prescription of opioid medication for acute pain. 1. background 1.1 justification for implementation of a cdss for prescription of opioid pain medication the state of new york has recently implemented an amendment of the ny public health law (nyphl 3331) limiting the number of days of prescription for opioid pain medication to not more than seven days for any acute non-cancer/non-terminal illness pain [6]. due to the rising death rates from opioid overdose, the cdc's prescription opioid pain medication guidelines are aimed at curbing opioid dependence, abuse and misuse of pain medications. the patients on opioid pain medications for chronic pain are required to be monitored by prescription drug monitoring programs (pdmps) and regular urine screening. applying the proper cdss implementation to prescription opioid pain medications, the five steps are as follows [2]: · high priority gap in the clinicians’ prescription of pain medication for acute pain conditions. the amendment of state law and cdc guidelines all aim at cutting down on that acute pain prescriptions. · the work flow of prescription of pain medication starts as follows ➢ with the patient being aware of the other options for pain control safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi ➢ then, deciding upon a treatment goal and working towards fulfilment ➢ followed by an evidence or guideline based approach to prescribing pain medications · design a cdss based on the latest technology. in order to be effective, the cdss for opioids should be interactive and less invasive. which means it can be used by the providers on demand and would be able to pull the right information for the patient in order to aid the clinician with decision making · the smart on fihr app would be written based on the current guidelines from cdc and asipp. 1.2 review of current opioid-related apps and cds systems the number of attempts at developing cdss has resulted in an aversion to yet another system. the clinicians and healthcare providers look at it as more of a deterrent in their work flow than any help [7] [5]. however, different technological approaches may be able to help in finding an answer to the complexity of electronic health records. the need for development of newer cdss were initially based on national stimuli for ehr adoption, meaningful use requirements, medicare access and chip re authorization act(macra) and merit-based incentive payment system(mips). the rise of precision medicine, genomics and dynamic changes in the approach to medical care increased the complexity of care needed more emphasis on coordination of care. the increasing cost of medical care, patient empowerment/awareness and technological push for app culture are also drivers for development of newer cdss [7] [5]. there are many opioid conversion calculators and morphine equivalency calculators on the market. however, each one has issues as a point of care tool, whether a hard to use interface, requiring the internet to download guidelines, having no citations for evidence based assessment, or requiring many plug in values that could be automatically taken from the electronic health record (ehr) or electronic medical record (emr). especially when it comes to conversion, many of the apps are not reliable. a recent study assessed the output variability and professional medical involvement in the authorship of 23 different apps [8]. 52% of the apps had no stated medical professional involvement and 48% of the apps provided references for opioid conversion ratios [8]. the cdc recently released on app summarizing the 2016 guidelines. cdc opioid prescribing guideline 2016 contains a morphine equivalency calculator, brief synopses of recommendations from the guidelines, glossary and a section on how to perform motivational interviewing with pain patients. the app includes links to full sections of the cdc guideline and references, but requires an internet connection. the mme calculator, which allows for multiple drugs to be calculated for a total mme/day, is built in and does not require internet connection and provides an alert when mme/day≥90. the alert suggests referring to a specialist and scheduling reassessment at least every 3 months, as well as suggesting recommendations through proper links in the app [9]. the ph medical opioid converter app by philip eagan from the itunes app store, use 2016 cdc guideline for prescribing opioids for chronic non-cancerous pain patients. the app includes an easy to use morphine equivalence calculator and an opioid to opioid calculator, using generic or trade names. although the app has a simple to use interface and is based on current recommendations, it is the only one that comes at a cost ($1.99), it does not have an android safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi counterpart, and very little information can be found about the author. in addition, like the cdc app, the guidelines and links are not built into the app and therefore, an internet connection is required [10]. the safe opioids app was developed with support from the substance abuse and mental health services administration through the prescribers' clinical support system for opioid therapies. it includes four categories: evaluation, manage, discontinue, and other online resources. under evaluation, there is information on the assessment of pain, linking to an outside the app pdf, the opioid risk tool, and links to drug prescription monitoring programs. under manage, there is information on opioid management including links to medical guidelines on clinical use in treatment of chronic pain (e.g.: va/dod clinical practice guidelines) from 2010. there is also a list of common side effects and advise for talking to patients about opioid abuse. there are also links to a tool for assessing depression. the app provides citations, showing how they are using evidence based medicine for decisions, however, the app is outdated and the citations have not been updated since 2010 [11]. opioidcalc nyc, developed by the new york city department of health and mental hygiene (dohmh), calculates mme based on the guidelines developed with the help of the cdc that outline key principles of safe and judicious prescribing practices, including high dosage defined as mme/day ≥100. the app provides citations for evidence-based practice decisions. the app allows users to quickly and easily calculate the total daily mme a patient is taking, based on type of opioid analgesic, strength, and quantity. multiple types of opioid analgesics can be included in the total daily mme calculation. an alert is displayed for total daily mme greater than or equal to 100, indicating an increased risk for overdose. this alert also suggests reassessing the patient’s pain status and treatment plan and provides a link to the dohmh opioid-prescribing guidelines for additional information. it does not include i.v. formulations of medications and does not align with current cdc guidelines [12]. opioid pain medication documentation and surveillance system (pods) from electronic health records is one of the early attempts at medication management and patient education efforts. like many other apps included in the electronic health records it was time intensive and cumbersome due to the fact that it was not interactive with the rest of the athena ehr-opioid therapy for chronic non-cancer pain pop-up [13]. there were many good features in this system like patient safety features and some early warnings. the pop up in the ehr had a patient identifier on graphic user interphase, caution window where the patients risk factors against prescribing pain medication were mentioned, the treatment options, data tables, treatment checklist and information for researchers would appear in separate sections of the window. the dosage calculator and other options were present in a dropdown section. the usability testing of this cdss showed the lack of provider education, confusion in dosing calculations and titration schedules, access to relevant patient information, provider discontinuity, documentation, and access to validated assessment tools. clinicians reported the time constraints in completing each prescribing decision and effective pain management based on guidelines. the figure below shows the pop up window. safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi figure 1. gui source: https://www.ncbi.nlm.nih.gov/books/nbk43756/figure/advancesmichel_92.f1/?report=objectonly [int j med inform. 2015 apr;84(4):248-62. doi: 10.1016/j.ijmedinf.2015.01.004. epub 2015 jan 17.] 2. proposed solution the smart on fhir platform for interactive and integrated cdss development is an open source resource which will be used to implement cdc guidelines with an aim to cut down the rate of opioid prescription for acute pain. the third party app development process aids the wide scale utilization of the app in fhir compatible ehr environment. this is a process of using cell phone technology in building a more dynamic (can be used by healthcare providers and patients through different portals) cdss tool that is user friendly and does not have to be built in as a part of the ehr system. the smart app will be independently placed in an app library from where it can be accessed by the clinician while making the decision for treatment of pain. figure 2. explains the flow of the platform. safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi figure 2. flow diagram for opiacutepain a smart on fhir approach for prescription opioids for acute pain the cdc guidelines published in 2016 [2] were aimed at providing a decision making tool for clinicians, nurses and other healthcare providers to aid in the treatment of chronic and acute pain. the following chart (figure 3) shows the different categories of pain the patients may present with and how the clinician may make their decision. acute pain (first) acute pain (prior history) acute on chronic pain chronic pain on opioid inform physician of treatment goals for pain(recommendation 2) inform physician of treatment goals for pain(recommendation 2) inform physician of treatment goals for pain(recommendation 2) inform physician of treatment goals for pain(recommendation 2) non pharmacologic (rec 1) assess baseline pain and functionpeg assessment scale (see appendix a) assess baseline pain and functionpeg assessment scale (see appendix a) assess baseline pain and functionpeg assessment scale (see appendix a) safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi alerts: history of mental illness, alcohol or other substance abuse, smoking alerts: history of mental illness, alcohol or other substance abuse, smoking alerts: history of mental illness, alcohol or other substance abuse, smoking alerts: history of mental illness, alcohol or other substance abuse, smoking figure 3. categories of pain and recommendations from cdc guidelines the characteristics of the smart application that we propose are as follows: the application is aimed at providing interactive ehr based guidelines for acute pain patients where non pharmacologic, non-opioid pain medications are tried first followed by a short term opioid pain medication therapy. the potential for fatal overdose may be there if the patient is on benzodiazepines while being on opioid pain medication. concurrent benzodiazepine usage may lead to fatalities in 31%–61% of the total overdose deaths [1]. this could be prevented by the interactive application. in addition, the initial daily dose of opioids should not exceed 50 mme/day and could go up to a maximum of 90mme per day. for dosages from 50 to 100 mme/day, risk for overdose increased by a factor of 1.9 to 4.6 compared to dosages of 1 to 20 mme/day. greater than 100 mme/day increased by a factor of 2 to 8.9 compared to 1 to 20 mme/day [1]. risk for serious harm on higher dosage of opioid outweighs the treatment benefits. the app will include a medication list of all the opioids a patient is currently using, and if there are opioids present, the conversion chart to calculate total mme/day produced by the cdc will be used to automatically calculate the total for the clinician (figure 4). figure 4. cdc calculating mme conversion factor chart safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi finally, the app will check if a urine drug screen (uds) has been ordered. the cdc guidelines state, that a uds provides the ability to “identify patients who might be at higher risk for opioid overdose or opioid use disorder, and help determine which patients will benefit from greater caution and increased monitoring or interventions when risk factors are present [1]. depending on the outcome of medications and observations, recommendations will be provided that include counseling, doing the peg-assessment, ordering a uds, not prescribing opioid medications, tapering of opioid medications, and checking the state prescription drug monitoring program (pdmp) data. an outline of the workflow of the proposed app is included in figure 5. 3. methods 3.1 fhir fhir® (fast healthcare interoperability resources) is a next generation standards framework that builds on health level seven international (hl7), an ansi-accredited standard developing organization which provides standards for the exchange, integration, sharing, and retrieval of electronic health information supporting clinical practice and management. according to the fhir standards, it “leverages existing logical and theoretical models to provide a consistent, easy to implement, and rigorous mechanism for exchanging data between healthcare applications”. [14] in order to assure alignment with the current hl7 standards and interoperability, fhir has builtin mechanisms for traceability to the hl7 rim [14]. the basic building blocks of fhir are called resources which share a set of characteristics, including a definition, a common set of metadata, and a human readable part [14-16]. table 1 provides the list of resources and their elements that are of interest to the application. following meaningful use stage 2 criteria, resources rely on ontologies and terminologies, specifically snomed-ct, loinc, rxnorm [14]. therefore, our cdss is ontology and terminology driven. currently, fhir version 1.0.2 is supported by epic, cerner, and allscripts professional, three prominent electronic health record (ehr) systems [7]. for instance, according to the open epic website, epic’s integration works with fhir 1.0.2 (dstu2). with this specification, epic supports retrieving data for most of the top-level resources such as “patient”, “observation”, “procedure”, “medication.” since these are primarily the resources needed for our proposed app, this app could seemingly be used any system equipped with epic, cerner, or allscripts professional and can easily be accessed through their web services and other methods, such as relational databases. safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi table 1. fhir resources used for opiacutepain resource type: patient definition: the patient resource provides general demographic information about a person receiving health care services from a specific organization. elements definition use in app patient.name.given first name used to make sure you are querying correct patient patient.name.family last name used to make sure you are querying correct patient patient.birthdate birthdate used to make sure you are querying correct patient patient.gender gender used to make sure you are querying correct patient resource type: medicationorder definition: an order for both supply of the medication and the instructions for administration of the medication to a patient. elements definition use in app medicationorder.status lifecycle of prescription: want active not draft medicationorder.dateended date when prescription was stopped used to filter in only active/current prescriptions medicationorder.patient a link to a resource representing the person to whom the medication will be given. medication for specific patient safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi medicationorder.medicationcodeableconcept identifies the medication being administered. uses rxnorm codes to identify medications medicationorder.reasoncodeableconcept reason for writing prescription can check for opioids used for chronic pain (snomed-ct code) medicationorder.dosageinstruction.dose[x] gives dose and units (ie 5 ml) used for mme calculator medicationorder.dosageinstruction.timing gives how many times daily (timing can be coded by http://hl7.org/fhir/timingabbreviation) used for mme calculator resource type: diagnostic report definition: findings and interpretations of diagnostic tests performed on patients. the report includes clinical context such as requesting and provider information elements definition use in app diagnosticreport.code codes that describe diagnostic reports urinary drug screen (using loinc code) ordered diagnosticreport.result references observation resource type: observation definition: simple name/value pair assertions for laboratory data and other results such as vital signs, imaging results, and social history. elements definition use in app observation. patient a link to a resource representing the patient to whose lab values they are. observation. code codes identifying names of simple observations; use loinc or snomed codes use loinc codes for uds results safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi observation. date date range into which the observation falls recent urinary drug screen or screen from history observation. encounter identifies the medication being administered. uses rxnorm codes to identify medications observation. value[x] information determined as a result of making the observation .value quantity gives a value; .code able concept gives a code; .value string gives a string output like “positive” or “negative” can check for positive and negative results from uds safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi 3.2 smart on fhir smart on fhir is an open access standard based technology platform that creates apps that can run across different healthcare systems using the fhir standards. with this interoperable system, patients, clinicians and healthcare providers can easily and efficiently access a library of apps to improve care based on personalized and precision medicine standards [8]. unlike other cds systems, smart on fhir focuses on creating an open and interoperable based technology platform, using fhir standards, that allows vendor independent third-party apps to run securely using ehr data [8]. a publicly accessible app gallery links to dozens of clinical applications built on this platform that have been providing care at healthcare institutions such as boston children’s hospital and duke medicine. smart on fhir platform has even been expanded to include genomic data standards to unify how genomic variant data are accessed from multiple sequencing systems [8,13]. smart on fhir has multiple components which include adaption of fhir standards, authorization and authentication. authorization is done using oauth2, a web standard for authorization whose key function is to enable an end user to approve a smart app to access an ehr. authentication is accomplished through openid connect, a web standard for authentication. it defines an oauth2-based protocol allowing end users to sign into apps using external identity providers. the smart on fhir system, a health it system that has implemented all of these components, can then run against a smart on fhir app. smart on fhir profiles require data to be coded using meaningful use terminologies and express constraints such as terminology restrictions, element cardinality restrictions, data type choice restrictions, and hierarchical structuring of resources [8]. source code and examples of implementation are publicly available [3]. in addition, sandboxes, secure virtual testing environments that mimic a live ehr, are available through outlets such as cerner's open developer [7,16]. 3.3 ontologies and terminologies fhir is designed to work with over 40 standardized terminologies and ontologies widely used in the biomedical informatics community [17]. see table 2. this project uses snomed-ct, rxnorm and loinc. snomed ct is a standardized vocabulary for clinical terms used by physicians and other health care providers for the electronic exchange of medically-relevant health information [18]. in addition to providing thesaurus-capability for interlinking other coding systems and terminologies, snomed-ct provides a taxonomy, which is encoded in an ontological format. elkin et. al [19] used the mayo clinic vocabulary server (mcvs) to successfully map free text clinical concepts to snomed -ct codes with a positive predictive value of 99.8 percent. rxnorm provides a standardized coding system for drugs, linking rxnorm identifiers to multiple other drug vocabularies [20]. rxnorm also provides some semantic structure by separating drug formulations from ingredients and separating brand names from clinical names, and so on. however, there is substantial overlap amongst codes, with no taxonomic way of grouping together the functional characteristics of drug families, such as the benzodiazepines. loinc is a common reference terminology for clinical and laboratory measurements, assays, patient information, and so on [21]. however, unlike rxnorm, loinc does not have any taxonomic structure encoded in way that allows machine reasoning as is possible with an ontological implementation. neither terminology safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi links codes via formalized relations that support moving between levels of granularity in any systematic way. this is problematic for our project due to the large of number of potential codes we need to search on discover the prescription of opioids. over 13,000 codes for various forms of opioids alone, not including benzodiazepines, and related drugs like muscle relaxants. there exists no subset of codes that would map to the entire value set constructed of drugs with the general opioid characteristics for potential of abuse. enumerating all the potential codes is possible, even could be done automatically with the right scripting tools. however, it is not an extensible or easily updated methodology. if this application were to be effectively deployed in a clinical environment, a much more robust way of coding the resource requests would be required. at a minimum, the high-level logic of the decision tree would need to decouple form the particular value sets the enumerate all the possible drug and procedure codes table 2: list of standard terminologies available in fihr name of standard name of standard snomed ct ietf language rxnorm mime types multipurpose internet mail extensions loinc medical device codes defined in iso 11073-10101 ucum: unitsofmeasure dicom code definitions nci metathesaurus health canada drug identification number ama cpt codes nucc provider taxonomy ndf-rt national drug file – reference terminology hgnc: human gene nomenclature committee unique ingredient identifier (unii) ensembl reference sequence identifiers ndc/nhric codes refseq: national center for biotechnology information (ncbi) cvx (vaccine administered) clinvar iso 2 letter country codes sequence ontology nubc code system for patient discharge status hgvs: human genome variation society radlex dbsnp: single nucleotide polymorphism database icd-9 & icd-10 cosmic: catalogue of somatic mutations in cancer safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi icpc international classification of primary care lrg: locus reference genomic sequences icf classification of functioning, disability and health omim: online mendelian inheritance in man version 2 tables pubmed a hl7 v3 code system pharmgkb: pharmacogenomics knowledge base anatomical therapeutic chemical classification system clinicaltrials.gov anatomical therapeutic chemical classification system european bioinformatics institute fhir allows for the use of concept maps that allow for the linking of one or more codes (taken here to be concepts) to one or more codes in another system. the mappings use equivalence properties, similar to the kinds of synonymy relations found in thesauri, such as narrower, wider, specializes. in addition to mapping between concepts (codes), fihr allows for the mapping between one or more concepts and a value set. this would allow for the trigger for query on medication order to use a generic set of codes to map to a much broader set of enumerated rxnorm codes for all relevant opioids. however, it still doesn’t alleviate the problems surrounding the maintenance and generation of that list as the guidelines or coding systems change. 3.4 proposed development a hybrid html5 app is currently being built using the smart javascript client library, an open library designed to assist with calling the fhir api and handling the smart on fhir authorization and authentication workflow [4]. a html5 app was chosen for cross-platform use and to facilitate the app within the ehr environment by running it through a browser widget. native ios and android applications may be developed in the future. in order to access the fhir resources in the ehr through the ehr's web services, the app will use the following process. when a clinician wants to use opiacutepain to assess the risk of prescribing, the ehr redirects to the smart launch uri, implemented in the file launch.html, then redirects to the fhir authorization server, and then after a successful authentication, redirects to the file index.html. the fhir authorization server can then be accessed using the fhir-client.js file and calling fhir.oauth2.authorize in the launch.html file with the client id. the authorization code is then exchanged for an access token to the authorization server using the fhir client. when index.html is invoked, the smart application will have the ability to request fhir resources from the ehr for the patient to run the smart application. the fhir javascript client also facilitates calling and searching code able concepts through the function by codes to find our medication list through coded terms. finally, the visual form of the application can be called from draw visualization, a built in function that uses the identification safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi placeholders defined in index.html. this file contains the format for the output of the app. the displayed output for the clinician within the app will contain the mme/day risk calculator when it can be calculated based on listed medications, whether or not a previous uds was ordered and the result, a table of medications for benzodiazepines and opioid medications, and patient identification details, including gender, date of birth, and condition. finally, the appropriate patient specific recommendation based on the guidelines presented by the cdc and dependent on the outcomes from the medication lists, urine drug testing results, and mme/day calculator will be displayed for the clinician within the app. usability testing will be done following the creation of the app to ensure that it adds to the workflow process of the clinician and is designed efficiently and productively for clinicians who frequently prescribe opioid medications. 4. discussion and conclusion prescription opioid pain medications are a big contributor to the opioid epidemic. attempts have been made in the past to create cdss tools within the ehr to aid clinicians in prescribing and managing patients with chronic pain. the effectiveness of cdss depend on how well they are integrated and how easy it is to access them. the smart on fhir platform addresses both ease of access while maintaining safety and security of sensitive patient information through an authorization process. it also gives feedback based on personalized patient information. this type of third-party cdss may be able to improve quality of care through precision medicine. in addition, there is no user training required as the app is able to collect the back ground information automatically and provide it on demand through a secure authentication process. the fhir interoperability standards makes this usable by all ehrs that are compatible with hl7 fhir standards. 5. limitations this opipain smart on fhir app is an open source interoperability standard based third party app that has not been tested and is a work in progress. the template that it is built on is new and has not received much feedback on its usability and effectivity. the outcome measure of these apps would take longer. taking this background into consideration the following aspects would have to be taken into account in further developing and implementing this app. 1. the accuracy of the ehr-specific logic to transform the specific data structures to corresponding fhir resources and with smart specific profiles so that it is able to pull up the proper information for this on demand app. 2. the smart on fhir apps that have been used so far have not been able to validate the data that they have been pulling through the app. it is a work in progress and can only be evaluated on implementation. 3. semantic parsing of terminologies through fhir would be based on the concept mapping which relies on how good the terminologies and codes are. safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi the interventions for regulating and monitoring prescription pain medications and optimizing pain management cannot be delayed till there are better methods of concept mapping. hence it would only be as good as the current mapping standards. references 1. rudd ra. 2016. increases in drug and opioid-involved overdose deaths—united states, 2010– 2015. mmwr morbidity and mortality weekly report. 65. 2017 feb;20(2s):s3-s92. pubmed pmid: 28226332. 2. dowell d, haegerich tm, chou r. 2016. cdc guideline for prescribing opioids for chronic pain—united states, 2016. jama. 315(15), 1624-45. pubmed https://doi.org/10.1001/jama.2016.1464 3. mandel jc, kreda da, mandl kd, kohane is, ramoni rb. 2016. smart on fhir: a standards-based, interoperable apps platform for electronic health records. j am med inform assoc. 23(5), 899-908. epub feb 2016. doi:https://doi.org/10.1093/jamia/ocv189. pubmed 4. manchikanti l,et. al. 2017 responsible,safe, and effective prescription of opioids for chronic non-cancer 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https://itunes.apple.com/us/app/phmedical-opioid-converter/id1082147868?mt=8 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26977696&dopt=abstract https://doi.org/10.1001/jama.2016.1464 https://doi.org/10.1093/jamia/ocv189 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26911829&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23322549&dopt=abstract https://doi.org/10.1007/s40264-013-0015-0 https://doi.org/10.1007/s40264-013-0015-0 safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi 11. safe opioids app aims to prescribe narcotics more appropriately with patients; by douglas maurer, do/mph/faafp | february 1, 2016 https://www.imedicalapps.com/2016/02/safe-opioids-prescribing-narcoticspatients/ 12. opioidcalcnyc app. 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(2017) https://www.nlm.nih.gov/healthit/snomedct/index.html 19. elkin pl, brown sh, husser cs, bauer ba, wahner-roedler d, et al. 2006. evaluation of the content coverage of snomed ct: ability of snomed clinical terms to represent clinical problem lists. mayo clin proc. 81(6), 741-48. pubmed https://doi.org/10.4065/81.6.741 20. rxnorm nlm. umls (2017) https://www.nlm.nih.gov/research/umls/rxnorm/ 21. 1998. huff sm etal. (1998) development of the logical observation identifier names and codes (loinc) vocabulary. j am med inform assoc. 5(3), 276-92. pubmed https://doi.org/10.1136/jamia.1998.0050276 22. manchikanti l,et. al. 2017 responsible,safe, and effective prescription of opioids for chronic non-cancer pain: american society of interventional pain physicians (asipp) guidelines. pain physician. acknowledgements/grants this work has been supported in part by an nih ncats clinical and translational science award (ul1tr001412-01) and an nih t32grant (t32 gm099607) from the department of anesthesiology. https://doi.org/10.1111/j.1526-4637.2009.00652.x https://doi.org/10.1111/j.1526-4637.2009.00652.x https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19594846&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26198304&dopt=abstract https://doi.org/10.1093/jamia/ocv045 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16770974&dopt=abstract https://doi.org/10.4065/81.6.741 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9609498&dopt=abstract https://doi.org/10.1136/jamia.1998.0050276 safe opioid prescription: a smart on fhir approach to clinical decision support online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(2):e193, 2017 ojphi appendix 1: opiacutepain: flow diagram isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala hln consulting, llc, palm desert, ca, usa objective to leverage an existing open source quality assurance software tool created for the immunization domain and modify it to serve as a quality assurance tool for syndromic surveillance messages. introduction the cms ehr incentive programs include a measure for meaningful use of ehr systems for submitting syndromic surveillance messages to public health [1]. the stage 2 measure defines the standard for transmission to be hl7 v2.5.1 admit/discharge/transfer messages according to the phin messaging guide for syndromic surveillance and conformance clarification for ehr certification of electronic syndromic surveillance, addendum to phin messaging guide for syndrome surveillance [2]. the national institute of standards and technology (nist) provides an online testing tool for validating messages [3]. while some jurisdictions use the biosense platform for receiving, managing, and analyzing syndromic surveillance data, there is no consistent tool that is available to jurisdictions to assess the quality and conformance of data submissions both at the time of on-boarding a new reporting facility and on an ongoing basis during production operations [4]. the new york city citywide immunization registry (cir), the immunization information system for nyc that has been operational since 1997, has as part of its software suite an open source, webbased data quality assurance (qa) tool used by its research scientists to qualify new sites for reporting data electronically via hl7 v2 messages, and for monitoring the ongoing quality of data submissions over time [5]. a validation process evaluates incoming messages against the rules established by an implementation guide (ig) and stores the result of the evaluation in a cir database table that is accessible by the qa tool which displays the data to an administrative user. this project served as a proof-of-concept for implementing a similar process for syndromic surveillance. methods building upon existing open source software, the project developed a self-contained, end-to-end prototype of the quality assurance system for syndromic surveillance. the resulting product has two main components: 1. a data validation process, using a local instance of nist hl7 v2 validation tool and repackaged to accept a sample hl7 v2.5.1 adt message via file upload, evaluate it against the phin guide, and store both the raw message and any errors or warnings generated in a local cir compliant database. 2. an enhanced version of the cir qa tool that reads the raw messages, errors and warnings from the previous step and allows users to query and display messages for a specific reporting facility and date range. results the system was created and deployed using open source software. various test messages, based on nist test data set, were sent through the system and the results were examined against known outcomes. the user interface required only minor modification (figure) to accommodate the change in message type from vxu to adt. conclusions this project demonstrated the successful leverage of public health developed, open source software from one domain to another. it also established new capacity for public health agencies to monitor the quality of data submissions for syndromic surveillance. future enhancements to this product might include: • tighter integration with biosense through export of “passing” messages into formats that can be imported into the biosense platform. • enhancement of the summary statistics and reports available for message batches. • more complete role-based security to recognize differing staff responsibility for different parts of the public health agency workflow that this product supports. • user-configurable support for variations in the implementation guide specifications used to evaluate message and present errors and warnings. keywords hl7; adt; syndromic surveillance acknowledgments the authors are thankful both the centers for disease control and prevention, center for surveillance, epidemiology, and laboratory services (cdc/csels) for funding this work, and the association of state and territorial health officers (astho) under whose auspices this project was conducted. references [1] http://www.cms.gov/regulations-and-guidance/legislation/ ehrincentiveprograms/ [2] http://www.cdc.gov/phin/resources/phinguides.html#ss [3] http://hl7v2-ss-testing.nist.gov/mu-syndromic/ [4] http://www.cdc.gov/biosense/ [5] http://www.nyc.gov/html/doh/html/hcp/cir.shtml *noam h. arzt e-mail: arzt@hln.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e6, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa 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magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity 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disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next 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at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts correlation of tweets mentioning influenza illness and traditional surveillance data zachary heth*2, 1, kelley bemis1 and demian christiansen1 1communicable diseases, cook county department of public health, forest park, il, usa; 2cste applied epidemiology fellowship, atlanta, ga, usa objective to determine if social media data can be used as a surveillance tool for influenza at the local level. introduction the use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. in 2016, the cook county department of public health (ccdph) collected localized twitter information to evaluate its utility as a potential influenza sentinel data source. tweets from mmwr week 40 through mmwr week 20 indicating influenza-like illness (ili) in our jurisdiction were collected and analyzed for correlation with traditional surveillance indicators. social media has the potential to join other sentinels, such as emergency room and outpatient provider data, to create a more accurate and complete picture of influenza in cook county. methods we developed a java program which included a customized geofence around suburban cook county to collect tweets from twitter’s stream application programming interface (api) (available at https://github.com/foodsafecookco/twitterstreamprogram). the java program looked for tweets within the geofence or for tweets with a profile location naming a suburban cook county municipality and copied them to a text file if the tweet contained “flu” or “influenza”. captured data included the user’s twitter handle, tweet text, tweet time and date, x and y coordinates (if available), and profile location. tweets were then manually reviewed to determine if they met the following criteria: 1) language indicated the user was recently ill with influenza; 2) user appeared to reside in ccdph jurisdiction. tweets meeting these criteria were included in the analysis. tweets were aggregated by mmwr week and analyzed for correlation, using pearson methods (data were normal), with two traditional surveillance sources: 1) the percent of visits to all suburban cook county emergency departments for ili as reported to the cook county electronic surveillance system for the early notification of community-based epidemics (essence), and 2) the percent of laboratory specimens testing positive for influenza at seven local sentinel laboratories. analysis was performed in excel 2013 and sas 9.4. results from mmwr week 40-20, 113 tweets indicating influenzalike illness were collected within cook county’s jurisdiction. due to technical issues with the program, data were not collected for weeks 52, 2, and 17-19. correlations were compared for the percent of laboratory specimens testing positive for influenza (lsl) and percent of visits to emergency departments for ili (edili) to the total number of tweets per mmwr week. a strong correlation exists between lsl and edili r=0.92 (p-value<0.0001) indicating the traditional sentinels have a strong positive relationship. the correlation between number of tweets and lsl was 0.46 (p-value =0.0138), indicating a moderate positive relationship. correlation between number of tweets and edili was similarly moderate, r=0.52 (p-value=0.0049). correlations to edili stratified by age (0-4, 5-17, 18-64, 65+) also showed a moderate positive relationship (range 0.50 to 0.55, all p-values < 0.01). twitter use peaked one week before the recorded peak of other surveillance indicators. when twitter counts were shifted one week to align the peak in tweets with the peak of the influenza season, the correlations were 0.54 for lsl and 0.61 for edili (p-value=0.0034 and 0.0007, respectively). conclusions overall, the tweets collected had a moderately positive relationship with the severity of influenza activity in cook county. when the data were transitioned to match peaks, there was an increase in the correlations’ strength for both lsl and edili. this data indicates that publicly shared social media data may be an underutilized source of syndromic data at the local level, potentially capable of predicting seasonal influenza peaks before traditional data sources. other jurisdictions may consider using the open source program created by ccdph to determine the relationship of influenza related social media to their own local influenza surveillance data. for the 20172018 influenza season, we established a redundant system for tweet collection in case one of the systems goes down. exploring machine learning (in place of manual review) to detect tweets indicating illness is also a promising avenue to simplify data collection and cleaning. data will be collected using the same system for the 2017-2018 influenza season and correlations re-evaluated with more complete data. keywords twitter; influenza; syndromic surveillance; essence; social media acknowledgments this study/report was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-04. *zachary heth e-mail: zheth@cookcountyhhs.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e105, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts modelling the transmission and control strategies of varicella in shenzhen xiujuan tang* shenzhen center for disease control and prevention, shenzhen, china objective to model the transmission dynamics of varicella among school children in shenzhen, to determine the effect of the school-based vaccination intervention. introduction varicella (chickenpox) is a highly transmissible childhood disease. between 2010 and 2015, it displayed two epidemic waves annually among school populations in shenzhen, china. however, their transmission dynamics remain unclear and there is no school-based vaccination program in shenzhen to-date. in this study, we developed a mathematical model to compare a school-based vaccination intervention scenario with a baseline (i.e. no intervention)scenario. methods data on varicella reported cases were downloaded from the infectious disease reporting information management system. we obtained the population size, age structure of children aged 15 or under, the class and school distribution from shenzhen education bureau. we developed an agent-based susceptible-exposedinfectious-recovered (abm-seir) model that considered withinclass, class-to-class and out-of-school transmission modes. the intervention scenario was that school-wide vaccination intervention occurred when an outbreak threshold was reached within a school. we varied this threshold level from five to ten cases. we compared the reduction of disease outbreak size and estimated the key epidemiological parameters under the intervention strategy. results our abm-seir model provided a good model fit to the two annual varicella epidemic waves from 2013 to 2015. the transmission dynamics displayed strong seasonality. our results suggested that a school-based vaccination strategy could effectively prevent large outbreaks at different thresholds. conclusions there was a considerable increase in reported varicella cases from 2013 to 2015 in shenzhen. our modelling study provided important theoretical support for disease control decision making during school outbreaks and the development of a school-based vaccination programme. fig 1. the abm simulation results of varicella reported cases in shenzhen from 2013 to 2015. the simulation median is plotted in red, reported cases are in black dashed line, the fitted transmission rate, β(t), is the blue line at the bottom and the 95% confidence interval (c.i.) is in grey. school holidays are shaded in yellow. fig 2. simulation results with vaccination strategy from 2014-2015. the black dashed line is the confirmed cases which could be regarded as the baseline (i.e. no intervention) scenario. simulation median is plotted in blue with 90% c.i. in grey. panel (a), (b), (c), (d), (e) and (f) are simulation results with vaccination threshold set to be 5, 6, 7, 8, 9 and 10 (cases per week per school) respectively. the red dashed lines are the maximum weekly varicella cases during the simulation period (blue line), which represents the outbreak size under different outbreak thresholds. keywords varicella; transmission; modelling *xiujuan tang e-mail: 13742573@qq.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e147, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts morbidity surveillance and treatment of mdr-tb with the support of ngos in ukraine inga pozharova*4, galyna daragan1, dmytro stepanskiy2 and iryna kolesnikova3 1state establishment dnipropetrovsk medical academy of the ministry of health of ukraine, dnipro, ukraine; 2state establishment dnipropetrovsk medical academy of the ministry of health of ukraine, dnipro, ukraine; 3bogomolets national medical university, kyiv, ukraine; 4municipal healthcare institution kramatorsk city tuberculosis hospital, kramatorsk, ukraine objective describe the common work of donetsk state phthisiological service and non-governmental organizations that has been conducted since 2014 in order to achieve the global goal for tuberculosis elimination. introduction the global strategy for eliminating tuberculosis (tb) epidemic “end tb” has been implemented in the world since 2016. its main goal is to reduce the 2015 tb incidence rate by 90% and 2015 tb mortality rate by 95% by 2035. in ukraine, in 2016, the incidence rate of new cases of tuberculosis among the general population was 54.7 per 100 thousand of population (2015 55.9), the rate of decrease was 2.1 ± 0.1%. in donetsk oblast (that is under control of ukrainian authorities), the incidence rate increased by 2.4% and was 56.4 per 100 thousand of population. the mortality rates were 19 ± 0.6% in the country and 29 ± 2.5% in donetsk oblast. however, according to the world health organization (who) estimates, we need to enhance the annual incidence rate reduction by 10% by 2025, and tb mortality rate should be reduced to 6.5% in order to achieve the strategy-targeted values. in ukraine, as well as globally, there is a crisis of multidrugresistant tuberculosis (mdr-tb). according to who estimates, ukraine belongs to five european countries where 2/3 of mdr-tb cases were registered; the proportion of mdr-tb cases among newly diagnosed tb cases was 16%, and 48% of repeated cases. in ukraine, this rate is equal to 24.3% and 58.2% in donetsk oblast, respectively. such results in the donetsk region may be related to the beginning of hostilities in eastern ukraine in 2014, which lead to the active migration of population and breakdown of the supply of anti-tb drugs. according to monitoring data, 20% of with mdr-tb on the territory of the donetsk region controlled by ukraine were lost and did not seek medical assistance. methods this work describes a retrospective cohort study of mdr-tb patients’ treatment efficacy. the mdr-tb diagnosis was confirmed by the bsl3 laboratory by molecular genetic testing of sputum using gene expert and bacteriological methods to determine resistance to rifampicin and isoniazid. all mdr-tb patients were divided into 3 cohorts. the first cohort of 86 patients received outpatient treatment within the project “outpatient home treatment model using mobile response team” supported by the foundation for development of ukraine in kramatorsk, donetsk oblast, during one year (2014-2015). the response team consisting of a nurse, a driver and a doctor (if necessary) delivered drugs to patients with tb six times a week. the second cohort of 477 mdr-tb patients has received outpatient treatment via the red cross society since 2015. a nurse visited patients every day and controlled drugs administration. in addition to the treatment, patients were given food kits twice a month. the third cohort of 391 mdr-tb patients received outpatient treatment in healthcare institutions without any support of non-governmental organizations. before the beginning of the controlled treatment, psychologists worked with patients from risk groups. results surveillance data in donetsk oblast showed the increase of the mdr-tb morbidity rate from 15.0 to 20.1 per 100 thousand of population. according to the analysis results, mdr-tb is found in 31% of cases among all newly diagnosed tb cases. the successful treatment rate of all tb cases in donetsk oblast in 2015 was 61.9 ± 5.4%, which is lower than the average rate in ukraine, which is 11.1 ± 1.0% (who indicator is 75%). the number of mdr-tb cases with “treatment failure” (treatment was completed, but bacterial secretion continued) 9.3 ± 2.6% and “interrupted treatment” 13.3 ± 2, 6% is very high in donetsk oblast. results analysis of the controlled treatment showed that the treatment efficacy for mdr-tb patients in the first cohort was 75%. in the second cohort, 205 out of 477 patients completed their treatment. the treatment efficacy was 78.5%. in the third cohort 45.0% of patients were cured, which corresponds to general rates in this oblast. however, the treatment efficacy for mdr-tb patients, who received the support of non-governmental organizations, was 30,0-33.5% higher than among patients who did not receive such support (table 1). conclusions the cooperation of the state phthisiatrician service with nongovernmental organizations concerning patients at the outpatient stage of treatment and development of treatment adherence increased the treatment efficacy for mdr-tb patients by almost 30%, which is important to control the spread of dangerous sources of tuberculosis agents in order to improve the epidemic situation. cohort analysis of treatment results and clinical mdr-tb monitoring reflects the peculiarities of the epidemic situation and reveals errors in the work of the state phthisiatrician service in donetsk oblast. nevertheless, the results show that common work of healthcare institutions, non-governmental organizations and volunteers can bring significant results in strengthening tb patients care. table 1. treatment results of mdr-tb patients (2014-2016) keywords surveillance; tuberculosis; non-goverment organization *inga pozharova e-mail: ingapozharova@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e152, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using scan statistic to detect heroine overdose clusters with hospital emergency room visit data jianhua chen*, hwa-gan chang, mark hammer, nicole d’anna and kitty gelberg new york state department of health, albany, ny, usa objective to utilize syndromic surveillance data timely detecting herion overdose outbreaks in the community. introduction early detection of heroin overdose clusters is important in the current battle against the opioid crisis to effectively implement prevention and control measures. the new york state syndromic surveillance system collects hospital emergency department (ed) visit data, including visit time, chief complaint, and patient zip code. this data can be used to timely identify potential heroin overdose outbreaks by detecting spatial-temporal case clusters with scan statistic. methods heroin overdose cases (heroin_od) were identified from ed visits by searching heroin_od key terms in the chief complaints. then the space-time permutation model (using the satscan package) was applied to detect clusters of heroin_od. ed visit date served as the time variable and the case residential zip code was the spatial coordinate variable for the satscan analysis. a sas program was developed to carry out the prospective scan statistics analysis weekly and produces reports of detected clusters in table and map format. cluster detection parameters were set to detect heroin overdose aggregation in maximum geographic radium of 20 kilometer (km) and maximum time span up of 21 days at the p-value <= 0.05. chief complaints within the clusters are reviewed to ensure accuracy of detection. messages have been developed and are shared with community members including law enforcement and public health identifying the cluster and offering suggestions of activities that can occur at the local level to identify and address the cause of the cluster, as well as to reduce potential harm. this includes the 23 syringe exchange programs (seps) regulated by the new york state department of health. results using ed visit data from 138 ny upstate hospitals, a total of 12 heroin_od clusters were detected by the satscan analysis during the period of 9/1/2016 through 9/17/2017. there were 845 cases identified. the average age was 35 years and ranged from 7 to 95 years. 69% of the cases were on the 20 to 39 age group and 66% were among males. a cluster was identified earlier 2017 in suffolk county, and the local sep was alerted. this encouraged communication between partners within the alerted county which ultimately resulted in identifying the substance endangering people who used drugs in the area. it also helped public health to partner with public safety, ensuring that the availability of the substance was interrupted. conclusions as the space-time permutation scan statistic only requires disease counts, event date and disease location, the method can be easily implemented for detecting disease outbreaks using data routinely collected from disease surveillance systems. the current study showed that scan statistic is a useful tool for identifying clusters of non-fatal overdoses from specific drugs. this method also returns important information to assist outbreak investigations, such as geographic location and time-span of the potential outbreaks. keywords heroin overdose; cluster detection; scan statistics *jianhua chen e-mail: jianhua.chen@health.ny.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e26, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts creation of a technical tool to improve syndromic surveillance onboarding in tennessee jeffrey leegon*, caleb wiedeman, vamshi krishna nukala and paul petersen surveillance, tennessee department of health, nashville, tn, usa objective to show how the creation of a software tool and implementation of new processes improved the efficiency of syndromic surveillance onboarding at the tennessee department of health. introduction syndromic surveillance is commonly supported by information generated from electronic health record (ehr) systems and sent to public health via standardized messaging. before public health can receive syndromic surveillance information from an ehr, a healthcare provider must demonstrate reliable and timely generation of messages according to national standards. this process is known as onboarding. onboarding at the tennessee department of health (tdh) focused heavily on human review of hl7 messages. however, the visual inspection of messages was time-intensive and delayed efforts to provide constructive feedback to participating healthcare providers. to ease the quantity of manual review done during the onboarding process, tdh created an application to assist in the process of reviewing syndromic surveillance messages. methods the application for reviewing syndromic surveillance messages was developed in python 3.6, a general purpose programming language. python was selected because of the strong libraries already developed in the language for data analysis, database interaction, and interacting with healthcare related data. to support tdh onboarding efforts, the application performed three tasks: file handling, hl7 processing, and database loading. file handling was completed using python core libraries. healthcare facilities participating in onboarding regularly uploaded test hl7 batch files containing all emergency department (ed) visits to a secure file transfer protocol (sftp) server owned by the state of tennessee. files are then retrieved from the sftp server and delivered to the tdh integration engine, rhapsody. rhapsody processes the incoming files and makes a copy available to the python application. the copies are then loaded by the application into a database and backed up in an archive. after the application has finished handling the received files, the raw hl7 messages within the files are processed to extract relevant information needed to validate the message. the extraction was supported by the “python-hl7” library. the application referenced a csv file with the names and locations of all of the phin 2.0 guide hl7 data elements to guide data processing. processed hl7 fields and file metadata were then extracted into a relational database using the python library sql alchemy. results the resulting relational database contained two tables. the first table captured the file metadata: filename, date and time when the file is loaded, message count, age of the file, and if the data have been deleted. the second data table contained selected data elements from the raw hl7 messages. these data were kept in a singular table and database view for each healthcare provider participating in onboarding was created. maintaining a separate view for each healthcare provider allowed for specific time periods to be monitored and analyzed based on when the healthcare provider has implemented message changes. additionally, arrival times for message updates to data elements such as discharge diagnosis, race, and ethnicity were logged in the database tables. conclusions the creation of this application has greatly assisted in the process of reviewing syndromic surveillance messages at tdh by automating data extraction and organization for all received hl7 messages. the database tables of extracted hl7 data elements allowed for easy analysis in any tool that can connect to a database (e.g., sas, r, python, excel, tableau) and faster, more manageable message validation. tdh has been able to communicate better feedback to more healthcare providers because of the efficiencies gained after deploying the application. keywords syndromic; surveillance; programming; python; automation acknowledgments the authors would like to acknowledge john paulett, the creator of the easy-hl7 library in python, for use of the library and support during the development of this program. *jeffrey leegon e-mail: jeffrey.leegon@tn.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e33, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts detecting overlapping outbreaks of influenza john m. aronis*1, nicholas e. millett1, michael m. wagner1, fuchiang tsui1, ye ye1, jeffrey ferraro2, 3, peter j. haug2, 3 and gregory f. cooper1 1department of biomedical informatics, university of pittsburgh, pittsburgh, pa, usa; 2university of utah, salt lake city, ut, usa; 3intermountain healthcare, salt lake city, ut, usa introduction influenza is a contagious disease that causes epidemics in many parts of the world. the world health organization estimates that influenza causes three to five million severe illnesses each year and 250,000-500,000 deaths [1]. predicting and characterizing outbreaks of influenza is an important public health problem and significant progress has been made in predicting single outbreaks. however, multiple temporally overlapping outbreaks are also common. these may be caused by different subtypes or outbreaks in multiple demographic groups. we describe our multiple outbreak detection system (mods) and its performance on two actual outbreaks. this work extends previous work by our group [2,3,4] by using modelaveraging and a new method to estimate non-influenza influenza-like illness (ni-ili). we also apply mods to a real dataset with a double outbreak. methods mods is part of a framework for disease surveillance developed by our group. in this framework, a natural language processing system extracts symptoms from emergency department patient-care reports. these features are combined with laboratory results and passed to a case detection system that infers a probability distribution over the diseases each patient may have. these diseases include influenza, ni-ili, and other (appendicitis, trauma, etc.). this distribution is expressed in terms of the likelihoods of the patients’ data. these are given to mods which searches a space of multiple outbreak models, computes the likelihood of each model, and calculates the expected number of influenza cases day-by-day. this work differs from past work in three important ways. first, we address the problem of detecting and characterizing multiple, overlapping outbreaks. second, we do not rely on simple counts, but use likelihoods given evidence in the free-text portion of patient-care reports as well as laboratory findings. third, we explicitly account for non-influenza influenzalike illnesses. this is important because some forms of influenza-like illness (such as respiratory syncytial virus) are contagious and exhibit outbreak activity. this research was approved by the university of pittsburgh and intermountain healthcare irbs. results we conducted a set of experiments with simulated outbreaks. mods is able to detect a single outbreak six to eight weeks before the peak. it is also able to recognize a second outbreak approximately halfway between peaks for simulated double outbreaks. we conducted experiments using real outbreaks and compared our results to thermometer sales [5]. using data from allegheny county pennsylvania for the 2009-2010 influenza season, on september 1 mods predicted an outbreak with a peak on october 5. the thermometer peak was october 21. the figure “prediction on october 1 for allegheny county” compares mods’ prediction on october 1 to thermometer sales. using data from salt lake city utah for the 2010-2011 influenza season, on november 1 mods predicted an outbreak with peak on december 7. the first thermometer peak was december 29. on january 20 mods predicted a second outbreak with peak on february 9. the second thermometer peak was march 5. the figure “prediction on january 20 for salt lake city” compares mods’ prediction on january 20 to thermometer sales. conclusions we have built a multiple outbreak detection system that can detect and characterize overlapping outbreaks of influenza. although the system currently predicts outbreaks of influenza, it is built on a general bayesian framework that can be extended to other diseases. future work includes incorporating multiple forms of evidence, modeling other known contagious diseases, and detecting outbreaks of new previously unknown diseases. prediction on october 1 for allegheny county 2009-2010 prediction on january 20 for salt lake city 2010-2011 keywords influenza; outbreak; detection; bayesian acknowledgments this work was supported by nih grant r01 lm011370 on probabilistic disease surveillance. john aronis was supported by the national library of medicine training grant t15lm007059 to the university of pittsburgh. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e14, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts references 1. world health organization, influenza fact sheet, 2003 2. wagner m, tsui f-c, cooper g, espino j, levander j, villamarin r. probabilistic, decision-theoretic disease surveillance and control. online journal of public health informatics, 2011, volume 3, number 3. 3. cooper g, villamarin r, tsui f, millett n, espino j, wagner m. a method for detecting and characterizing outbreaks of infectious disease from clinical reports. journal of biomedical informatics, 2015, volume 53, 15-26. 4. aronis j, millett n, wagner m, tsui f, ye y, cooper g. a method for detecting and characterizing multiple outbreaks of infectious diseases. online journal of public health informatics, 2016, volume 8, number 1. 5. villamarin r, cooper g, wagner m, tsui f, espino j. a method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza. journal of biomedical informatics, 2013, volume 45, 444-457. *john m. aronis e-mail: jma18@pitt.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e14, 2017 isds16_abstracts-final 14 isds16_abstracts-final 15 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarakoon1, roshan hewapathirana2 and achini jayatilleke3 1postgraduate institute of medicine, colombo 7, sri lanka; 2health informatics society of sri lanka, colombo, sri lanka; 3university of tokyo, tokyo, japan objective to customize and pilot an open source public health information tool (dhis2) for injury surveillance in a resource constrained setting, sri lanka. introduction injuries are a major but neglected global public health problem. in the lowand middle-income countries (lmic), the problem is particularly acute due to disproportionately high incidence of injuries. most of these injuries are preventable with appropriate interventions. lack of complete, accurate and timely injury data is one of the main obstacle for injury prevention in lmics. in 2001, world health organization (who) published injury surveillance guidelines emphasizing the importance of injury surveillance at country levels to cope with this grave problem. although most of the developed countries have developed their own injury surveillance systems, there is no customizable generic injury surveillance system which can be used in lmics. however, district health information system 2 (dhis2) is a free and open source application used in many countries to collect aggregated public health data. although it is being used for aggregated public health data it has not being used for injury surveillance. methods dihs2 is mainly used for aggregated data and it has a tracker module still in the development. for injury surveillance the tracker module was used to create the data entry form in order to capture individual patient records. then these records were aggregated to generate custom reports. data elements were created according to who injury surveillance guidelines and categorized according to the recommended data-sets. data entry form was designed according to the end user requirements. javascript was used to customize the data entry form and enhance user friendly layout. some hard coding was done to further enhance the usability of the data entry form speeding up the data entry. in sri lanka, injury data are collected on a paper based form and are subsequently entered into computer systems. in march and april 2013, we collected data from 654 patients with injuries admitted to a base-hospital which has a group of nurses trained on (basic) injury surveillance. results we commenced the paper based data collection process at the time of admission and continued until the patient was disposed. subsequently, the data were entered into the customized dhis2 application. out of 654 patients, 27% were injured due to road traffic crashes, 13% due to violence, and the other 60% due to unintentional causes. customized dhis2 solution provided following features 1. comply with the changing data and process needs without a major retooling; flexible enough to capture new data items and reporting/care process as needed, 2. data validation, 3. handling missing information, 4. data backing-up, and 5. flexible report generation. by piloting in the tertiary care setup, it was noted that an injury surveillance system has to have an effective mechanism to identify duplicates during the transfer process. also, the time taken to reporting was an important consideration among the participating nurses. conclusions our experience reveals that the open source public health information tool, dhis2 has the potential to be customized for injury surveillance in resource constrained countries like sri lanka and it is a sustainable option for injury surveillance in such countries. this study shows that the dhis2 is a cost-effective solution for resource constrained contexts. being an open source framework, it has a potential to be customized to different requirements/scenarios. the flexibility of the dhis2 allowed system designers to accommodate any changes in the business process quickly in the system. human computer interaction should be a major concern in designing such a system since the information system (electronic reporting forms) may consume majority of the time taken in the reporting process. keywords injury surveilllance; dhis2; trauma references holder y, peden m, krug e, lund j, gururaj g, kobusingye o. injury surveillance guidelines. geneva: world health organization, 2001. store m. explore the challenges of providing documentation in open source projects. the university of oslo. 2007. available at: http:// urn.nb.no/urn:nbn:no-15782. *achala u. jayatilleke e-mail: achala@pgim.cmb.ac.lk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e125, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epi archive: automated synthesis of global notifiable disease data hari s. khalsa, sergio cordova*, nicholas generous, prabhu s. khalsa, byron tasseff and james arnold a-1, los alamos national laboratory, los alamos, nm, usa objective lanl has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the biosurveillance ecosystem (bsve) as a novel data stream. these data have many applications including improving the prediction and early warning of disease events. introduction most countries do not report national notifiable disease data in a machine-readable format. data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. this presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. while most nations likely store incident data in a machinereadable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1. a survey conducted by lanl of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. the majority (>70%) produce reports as pdf files on a regular basis. the bulk of the pdf reports present data in a structured tabular format, while some report in natural language. the structure and format of pdf reports change often; this adds to the complexity of identifying and parsing the desired data. not all websites publish in english, and it is common to find typos and clerical errors. lanl has developed a tool, epi archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible. methods we conducted a survey of the national notifiable disease reporting systems notating how the data are reported and in what formats. we determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found. the development of software to regularly ingest notifiable disease data and make it available involves three or four main steps: scraping, detecting, parsing and persisting. scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. we then designed and wrote code to automate the downloading of the data for each country. we store all artifacts presented as files (pdf, xlsx, etc.) in their original form, along with appropriate metadata for parsing and data provenance. detecting: this step is required when parsing structured nonmachine-readable data such as tabular data in pdf files. we combined the nurminen methodology of pdf table detection with in-house heuristics to find the desired data within pdf reports2. parsing: we determined what to extract from each dataset and parsed these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages. persisting: we store the data in the epi archive database and make it available on the internet and through the bsve. the data is persisted into a structured and normalized sql database. results the epi archive tool currently contains national and/or subnational notifiable disease data from twenty nations. when a user accesses the epi archive site, they are prompted with four fields: country, subregion, disease of interest, and date duration. upon form submission, a time series is generated from the users’ specifications. the generated graph can then be downloaded into a csv file if a user is interested in performing personal analysis. additionally, the data from epi archive can be reached through a rest api (representational state transfer application programming interface). conclusions lanl, as part of a currently funded dtra effort, is automatically and continually collecting global notifiable disease data. while 20 nations are in production, more are being brought online in the near future. these data are already being utilized and will have many applications including improving the prediction and early warning of disease events. keywords notifiable disease data; pdf tables; document extraction; data sharing; web scraping acknowledgments this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra). references [1] van panhuis wg, paul p, emerson c, et al. a systematic review of barriers to data sharing in public health. bmc public health. 2014. 14:1144. doi:10.1186/1471-2458-14-1144 [2] nurminen, anssi. “algorithmic extraction of data in tables in pdf documents.” (2013). *sergio cordova e-mail: sergioc@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e8, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 1public health ontario, toronto, on, canada; 2university of toronto, toronto, on, canada; 3alberta health services, edmonton, ab, canada; 4university of calgary, calgray, ab, canada; 5university of alberta, edmonton, ab, canada objective the objective of this study was to carry out a mixed-methods evaluation of the ability of standardized supports to improve the usefulness of school absenteeism syndromic surveillance for public health in alberta. introduction syndromic surveillance uses pre-diagnostic data to inform communicable disease prevention and control. among health zones in the province of alberta, canada, practices employed by public health when using elementary school illness-cause absenteeism data vary widely. methods the alberta real time surveillance system network (artssn) collects, analyzes and reports on school absenteeism data for all schools in participating health zones across alberta. the two largest health zones in alberta, edmonton zone and calgary zone, participate in artssn and further agreed to participate in a study where one health zone was randomly allocated (flip of a coin) were randomized to receive the standardized supports intervention, or to continue with their regular practice. the intervention consisted of (1) a cumulative sum aberration detection algorithm applied to the artssn data and (2) a protocol outlining how to investigate and follow up on aberrant events. the zone receiving the intervention additionally continued with their regular practices to ensure they could meet their required public health responsibilities. staff in both intervention and control zones used electronic data collection forms, known as “logbooks” to document and track alerts received, any subsequent investigations done, and any public health action as a result of the alert. at the end of the data collection period, semi-structured interviews were conducted to understand more fully their alert investigation method, rationale for public health action, and their uptake of the study intervention. frequencies of logbook elements were generated for numeric or categorical data to compare descriptive statistics between intervention and control groups, and logbook free-text elements and qualitative data collected through semi-structured interviews were analyzed thematically. the analysis was performed using excel, and sas statistical software. results the edmonton (intervention) zone relied heavily on the regular artssn alerts and study generated alerts. between february 2014 and february 2015, the intervention zone investigated 246 alerts. in comparison, the calgary (control) zone relied on public health nurses communicating regularly with school principals to detect outbreaks in the schools. during the same time period, the control zone investigated 20 alerts. the intervention health zone resulted in the detection of 19 outbreaks, while the control zone resulted in 16 outbreaks detected. in terms of public health actions, there were 39 instances where the intervention health unit provided advice on environmental cleaning as a method of infection control. these actions increased in the second year of the study after 4 hours per week of dedicated nursing time were assigned. while many of the outbreaks in the control health zone were monitored, follow-up actions such as advice on environmental cleaning were not reported. conclusions these study findings suggest that using automated processes to analyze school absenteeism data results in more frequent alerts than traditional systems relying on principal-nurse interactions. the response to the syndromic surveillance was augmented by the presence of standardized processes, particularly with respect to public health actions such as advice on environmental cleaning, and frequent hand washing. this increase in response only occurred once sufficient human resources were provided to investigate the alerts. keywords syndromic surveillance; public health; school absenteeism; evaluation *ian johnson e-mail: ian.johnson@oahpp.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e33, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne 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programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia 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emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 assessing quality of care and elder abuse in nursing homes via google reviews jared mowery1, amanda andrei1, elizabeth leeds hohman1, jing jian1, megan ward1 1. the mitre corporation abstract background: it is challenging to assess the quality of care and detect elder abuse in nursing homes, since patients may be incapable of reporting quality issues or abuse themselves, and resources for sending inspectors are limited. objective: this study correlates google reviews of nursing homes with centers for medicare and medicaid services (cms) inspection results in the nursing home compare (nhc) data set, to quantify the extent to which the reviews reflect the quality of care and the presence of elder abuse. methods: a total of 16,160 reviews were collected, spanning 7,170 nursing homes. two approaches were tested: using the average rating as an overall estimate of the quality of care at a nursing home, and using the average scores from a maximum entropy classifier trained to recognize indications of elder abuse. results: the classifier achieved an f-measure of 0.81, with precision 0.74 and recall 0.89. the correlation for the classifier is weak but statistically significant: 𝒓𝒓 = 0.13, p < .001, and 95% confidence interval (0.10, 0.16). the correlation for the ratings exhibits a slightly higher correlation: 𝒓𝒓 = 0.15, p < .001. both the classifier and rating correlations approach approximately 0.65 when the effective average number of reviews per provider is increased by aggregating similar providers. conclusions: these results indicate that an analysis of google reviews of nursing homes can be used to detect indications of elder abuse with high precision and to assess the quality of care, but only when a sufficient number of reviews are available. keywords: social media, geriatric nursing, patient safety, natural language processing, supervised machine learning correspondence: jmowery@mitre.org doi: 10.5210/ojphi.v8i3.6906 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 introduction studies of social media and healthcare phenomenon have explored a wide variety of applications, including a growing body of literature analyzing physician review websites (prws). many prws contain reviews of physicians and medical facilities written by patients or relatives of patients, which often include both a text component and one or more numeric ratings. the influence of prws is likely to grow over time. the number of prw reviews has been increasing rapidly, with one study finding that yelp reviews related to patient experiences grew at a rate of 1.5 times annually between 2007 and 2012 [1]. a survey of consumers found that 59% of respondents reported prws to be "somewhat important" or "very important", and that among consumers who sought online ratings, 35% reported selecting a physician based on good ratings and 37% reported avoiding a physician with bad ratings; meanwhile, 43% of respondents who did not seek online ratings reported a lack of trust for information on the websites [2]. a survey of 854 patients visiting a pre-operative evaluation clinic at mayo clinic in minnesota showed that 84% had not visited a prw, although 28% strongly agreed that a positive review alone would cause them to seek care from a provider, and 27% indicated a negative review would cause them to choose against using a provider [3]. the influence of prws on consumer decisions suggests that determining the aspects of care that are important to reviewers, and ascertaining the accuracy of information on prws regarding those aspects of care, would help consumers make more informed decisions. online reviews also create opportunities to improve the quality, safety, and efficiency of patient care, but only if accurate indicators can be extracted. compared to paper surveys of patients or inspections of facilities, online reviews offer more timely, cost-effective information. however, online reviews are largely unstructured and are not subject to the same quality control measures as survey instruments, inspections, or clinical studies. this presents a need to better understand how online reviews relate to existing quality measures, such as the degree to which the online reviews are accurate, the number of reviews needed to achieve useful correlations with other quality measures, the aspects of care or customer service reflected in online reviews compared to existing quality measure data, and the efficacy of methods for extracting useful information from the text of an online review. it is essential to validate the utility of online reviews as a measure of the technical quality of care. a survey of studies on prws observed that most information on prws is related to “structural quality and patient satisfaction” and is not risk-adjusted [4], which places doubt on whether prws accurately reflect a provider’s technical quality of care. multiple studies have examined the relationship between online and offline patient reviews and quality measure data [5] for a variety of provider types. multiple studies have examined hospitals based on a variety of prws and social media services, with many finding positive correlations or useful indicators, including the national health service (nhs) choices website [6,7], yelp [8-10], twitter [11], two korean web portals [12], and facebook [13]. studies focused on physicians have used nhs choices [14], ratemds.com [15,16], a set of nine prws [17], and two german prws [18]. the variety of study results suggests that finding sufficiently strong correlations between reviews and quality care measures is challenging, and that the degree of challenge varies as a function of the healthcare provider type, review type, prw, and quantity of data available. for example, a http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 comparison of two german physician prws found that different correlations existed for each between reviews and other quality care measures [18]. elder abuse in nursing homes is an area of particular concern, since it involves a vulnerable patient population, many of whom may be unable to report abuse. in addition, the technical quality of care may be difficult to assess from elderly patients’ review data. a survey of 236 vulnerable elderly patients in two managed care institutions found that global ratings of care correlated with the quality of communication, but not with the technical quality of care [19]. a survey of 3,487 elderly patients at 18 general practices in england for treatment involving hypertension and influenza vaccinations also found no statistically significant correlation between patients’ assessments and the technical quality of their primary care [20]. although these two surveys did not use social media data, they suggest potential difficulties in evaluating elder care quality from patient reviews. this paper examines nursing homes, using google review ratings as well as a maximum entropy classifier trained to recognize indications of elder abuse in text. in each case, this study calculates the correlation with centers for medicare and medicaid services (cms) inspection results from the nursing home compare (nhc) data set [21]. maximum entropy classifiers, a type of machine learning classifier, use hand-annotated data to learn how to classify their inputs as belonging to one of several output classes. in this case, the maximum entropy classifier learned to determine whether a google review’s text and rating were indicative of elder abuse, based on examples which a human being had labeled as either indicative or not indicative of elder abuse. machine learning classifiers produce their estimate based on the presence or absence of features, such as consecutive pairs of words. for example, a review containing “soiled sheets”, “call light”, and “ignored calls” would be likely to indicate elder abuse, while a review containing “always attentive”, “clean linens”, and “polite staff” would be unlikely to indicate elder abuse. this study uses the nhc data set, which contains cms inspection results for nursing homes. the deficiencies found during inspections are categorized as either fire and safety deficiencies or health deficiencies. this study uses the health deficiencies as ground truth data. therefore, for consistency, this study defines the “technical quality of care” as the nursing home’s adherence to cms’ standards for care as represented by the set of health deficiencies in the nhc data. the health deficiencies cover a wide variety of potential problems, which include failing to maintain accurate clinical records, failing to grant patients access to their medical records, prohibiting patients from having visitors, failing to use licensed or certified staff, failing to notify family members of changes in a patient’s condition, using unnecessary physical restraints, administering unnecessary medications, subjecting patients to abuse or physical punishment, failing to maintain a clean facility, giving incorrect medications to patients, failing to have a registered nurse on duty, and failing to ensure the call system is functional. to our knowledge, this is the first study to use an automated analysis of online reviews for assessing the technical quality of care and the presence of elder abuse in nursing homes. two recent studies are related to elder care providers. first, an exploratory study of dutch social media data used search queries to locate 116 long-term elder care reviews relating to four safety risks and found that 72 reviews provided added value, according to inspectors from the dutch http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 healthcare inspectorate [22]. second, an analysis of 146 patients found that nursing home compare (nhc) ratings of nursing homes do not include all aspects of care relevant to patients [23]. since this study includes google reviews obtainable for each provider without using keyword or phrase-based filtering, the reviews may also describe aspects of care not included in the nhc data set, which may limit the correlation achievable between the nhc data and online reviews. successfully extracting indications of the technical quality of care and the presence or absence of elder abuse from online review websites can benefit patients and the healthcare system in several respects, including (1) quantifying the utility of online reviews in measuring technical quality of care as opposed to other factors such as bedside manner, (2) aiding patients or family members of patients in choosing a facility, (3) helping cms prioritize inspections of facilities to maximize the likelihood of uncovering and preventing abuse, and (4) supporting further studies analyzing the correlates of elder abuse to guide policy-makers. methods overview this section describes the methods used to collect and analyze google reviews and to correlate them with health deficiency data. the data collection section describes the nhc data set, as well as the methods for collecting google reviews and splitting the review data into training and test sets. the maximum entropy classifier section describes the definition of elder abuse developed in this study and the maximum entropy classifier which was trained to recognize elder abuse. the ratings section discusses properties of the review ratings. finally, the correlations, aggregations, and analyses section discusses using correlation calculations to maximize statistical power, aggregating similar providers to extrapolate the correlation coefficients achievable with more reviews per provider, and performing several further analyses to understand factors which influence the results. data collection this study used the provider information and deficiencies spreadsheets from the nursing home compare data set, updated december 17, 2015, as ground truth data. the deficiencies spreadsheet lists deficiencies reported by inspectors visiting cms-certified nursing homes, including deficiencies that are likely to indicate elder abuse. the deficiencies file includes 479,167 deficiencies for 15,584 providers. the deficiencies are split into 323,994 health deficiencies and 155,173 fire safety deficiencies. the data also includes inspection dates, correction dates, and other metadata, but this study uses only the counts of health deficiencies for each facility. in addition to the facilities which received deficiencies, there are 77 facilities in the provider information spreadsheet which did not appear in the deficiencies data. these providers were assumed to have no deficiencies and are included in this study, resulting in a total of 15,661 providers. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 the google maps api [24] provides a capability to search for a business by name and location, and returns up to 5 google reviews, including the optional review text and a rating from 1 (worst) to 5 (best). when there are more than 5 reviews available for a nursing home, google returns the 5 most helpful reviews. querying the google maps api for each of the providers in the nhc data set yielded 16,160 reviews. of those, 4,631 reviews did not include text and were discarded since the maximum entropy classifier requires text. this left 11,529 reviews spanning 5,516 providers. since there were 15,661 total providers, 35.22% of providers in the nhc data set were included in this study. a set of 2,500 reviews were hand-annotated to train the classifier, pulled from facilities which had a total number of deficiencies between 20 and 39, inclusive. the total deficiencies included both health and fire safety related deficiencies. since reviews used for training were not included in testing and subsequent analysis, this choice preserved the providers with the greatest and least numbers of deficiencies for subsequent analysis. hand-annotation of the 2,500 reviews resulted in 714 being marked as indicative of elder abuse (28.56%). the distribution of providers, binned by the number of health deficiencies they received from cms inspectors, was affected by the filtering used to generate the test set of providers (figure 1). the first distribution shows all providers in the nhc data set, regardless of whether the providers had matching reviews. the second distribution includes only providers which had at least one review, even if that review lacked text. the third distribution counts providers only when they had at least one review which contained text, which is necessary for applying the maximum entropy classifier. this third distribution corresponds to the 35.22% of providers examined in this study. the training data for the classifier was selected from this distribution. the final distribution shows the providers after removing the providers which had at least one review included in the training data set: this final set of providers was used as test data for this study. note that the provider bins are affected unevenly, since reviews for training were selected from providers whose total deficiency counts (including both health and fire safety deficiencies) were between 20 and 39, inclusive. additionally, when a review was selected for training, any provider with a review having the same review text was removed—even if that specific review was not used for training—along with all reviews for that provider. this guaranteed that during testing, the classifier would never encounter a review whose text it had seen during training. although additional providers and reviews could have been retained for the testing data set by including providers which had at least one review not used in training, doing so would have potentially skewed the test results in two ways: • the classifier’s score could have been biased if a provider’s reviews had been split between the training and testing data, since reviews for a single provider could be conditionally dependent on one another due to shared features. • the review count per provider in the testing data would have been disproportionately lower for providers whose reviews were used in training. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 figure 1: number of providers binned by the number of health deficiencies each received. the sequence of plots shows the progression of changes in the provider distribution as providers were removed to form the final (bottom) plot of providers in the test set. the exclusion of providers with an annotated review excluded all providers which had the text of any of their reviews match the text of any review used in training. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 maximum entropy classifier the maximum entropy classifier was trained on 2,500 reviews that were hand-annotated for indications of elder abuse. to test inter-rater agreement, a subset of 100 reviews was labeled by two additional annotators, resulting in a krippendorff’s alpha coefficient [25] of 0.79. for this study, elder abuse was defined as including both intentional and unintentional abuse, as well as neglect. common examples included staff (1) failing to respond to call lights in a timely manner, (2) allowing patients to develop chronic bed sores, (3) leaving patients in soiled clothing or sheets for extended periods of time, and (4) demonstrating incompetence in recognizing or reporting residents’ medical problems. poor food quality was also a common complaint, but was only marked as abuse if either the review indicated the quality was so poor that it adversely impacted a resident’s health, or descriptions of the food clearly indicated negligence, such as serving food that was still frozen. rude, condescending, or dismissive behavior was only marked as abuse if it was directed toward a patient and seemed to be on-going, such that it could be regarded as psychological abuse. to focus the classifier on identifying reviews which could be the most helpful to inspectors, a review was only considered an indication of abuse if it provided reasonably specific information identifying an abuse. as a consequence, reviews describing a facility as “horrible”, or advising readers that loved ones sent to the facility would die, were not marked as indicative of abuse unless the review also included a more specific complaint of abuse. the maximum entropy classifier uses apache’s opennlp [26] implementation. for each review, uniform resource locators (urls) appearing in the review text were replaced with a url token, and then unigram and bigram features were extracted. the classifier also uses the google rating divided by 5.0 as a feature, as well as the review length in [0.0, 1.0], with 1.0 corresponding to a length of 2,000 characters (longer lengths are assigned a value of 1.0). the classifier uses gaussian regularization with σ = 1.0 and 10,000 iterations to ensure convergence. the classifier’s performance was tested using stratified 10-fold cross-validation. ratings while the classifier was designed to detect specific references to elder abuse, the google ratings were used as an indication of the technical quality of care. the distribution of google ratings as a function of providers binned by their number of health deficiencies reveals that reviews typically exhibit extreme polarity between 1 and 5 star reviews, with the ratio of 1 to 5 star reviews correlating with the number of deficiencies found by cms inspectors (figure 2). this distribution includes all providers which had at least one review, regardless of whether the review contained text or was used in the training data set. the distribution of provider counts as a function of the number of google reviews available for the provider remains relatively unchanged between the original data and the test set (figure 3). although providers with fewer reviews were more likely to be removed due to having only nontext reviews, the distribution remained approximately the same due to providers whose review counts decreased after removing non-text reviews. the test set exhibits a distribution similar to the original data. however, the predominance of providers with one or two reviews presents a challenge for assessing the quality of care in nursing homes. the average rating in the test set is 3.04. there are 8,787 ratings with 4,161 nursing homes, which yields an average number of reviews per provider of 2.11. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 figure 2: google rating distributions for providers in each health deficiency bin. provider bins corresponding to high health deficiency counts have a higher ratio of 1 star (bad) to 5 star (good) reviews than providers in bins corresponding to low health deficiency counts. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 figure 3: number of providers as a function of the number of google reviews each provider received. the text providers had at least one review which contained text. test set providers had at least one text review and also had no reviews with text matching any review text used to train the classifier. correlations, aggregations, and analyses this section discusses several methods to address the challenge of having few reviews per nursing home, including correlation calculations to maximize statistical power, and aggregating providers to simulate a larger number of reviews per provider without generating synthetic reviews. additional analyses are described to examine the extent to which google’s selection of the top five reviews influences the results, the correlations as a function of the number of deficiencies received by providers, and the relationships between deficiency categories. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 maximizing the statistical power of correlation calculations is valuable since limited numbers of reviews are available per nursing home and past studies have found varying correlation strengths between reviews and other quality measure data. this study uses a rank inverse normal transform-based pearson correlation coefficient to maximize statistical power for continuous variables, which bishara et al. [27] found yielded few type i and type ii errors while maximizing statistical power for testing the significance of bivariate correlations involving continuous variables with reasonable sample sizes (n ≥ 20). this study also uses the henzezirkler test for multivariate normality, which was recommended by a monte carlo study of 13 tests for multivariate normality [28]. the mvn package [29] for r [30] is used for most of the statistical calculations. both the per-provider average classifier scores versus health deficiency counts and the average ratings versus health deficiency counts fail the henze-zirkler test, indicating normalization or rank-based methods are required. furthermore, shapiro-wilk’s normality test shows that none of the deficiency count, average ratings, and average classifier score univariate distributions are normal. the rankit equation [31], a type of rank-based inverse normal transformation, was used to normalize each of the three univariate distributions since it was found to be an accurate normalization method [32]. after normalization, the classifier versus deficiency data passes the henze-zirkler test. however, the rating versus deficiency data fails the henze-zirkler test even after applying the rin transformation, so the spearman correlation coefficient may be preferable for correlations of ratings and deficiencies, especially since the ratings data is drawn from a discrete distribution. another method of overcoming the limited number of reviews per provider is to aggregate similar providers. aggregating similar providers simulates having a larger average number of reviews per provider without generating any synthetic reviews. first, providers were sorted in order by the number of cms deficiencies they received, so that similar providers were adjacent. next, given a stride value s, each non-overlapping, consecutive group of s providers was merged to produce an aggregated provider whose deficiencies were the union of the deficiencies for each of the individual providers, and whose ratings and classifier scores were the average of the average ratings and classifier scores for the individual providers. effectively, given the average number of reviews per provider n = 2.11, applying a stride s increases the average number of reviews per aggregated provider to s · n. however, this extrapolation has limitations. for example, adjacent providers may differ in the distribution of their deficiency codes, which would reduce the correlation between the aggregated provider’s reviews and the aggregated provider’s deficiency distribution, yielding lower correlation test results. fortunately, as will be discussed in the results section, categories of deficiencies have moderately strong correlations with one another. two additional analyses can help in interpreting the results. first, comparing correlation coefficients between the full test set and the set of providers which received four or fewer reviews can help measure any influence on the results due to google’s selection of the top five reviews. second, examining providers binned by their deficiency counts can reveal whether the correlations tested in this study vary as a function of the number of deficiencies received by providers. finally, examining correlations between the maximum entropy classifier, the ratings, and categories of health deficiencies—including categories of abuse-related deficiencies—can provide insights into their strengths and weaknesses. this study includes a correlogram of http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 spearman correlation coefficients between all health deficiencies, the google ratings, the classifier scores, a subset of deficiencies chosen to reflect severe quality of care issues, minor deficiencies (the health deficiencies remaining after excluding the severe deficiencies), three deficiencies which explicitly mention “abuse” in their descriptions, and the set of health deficiencies which do not include “abuse” in their descriptions. the severe deficiencies category is designed to capture the deficiencies which describe poor technical quality of care or indications of possible abuse, regardless of whether the deficiency description includes the word “abuse”. examples of severe deficiencies include using unnecessary physical restraints, using unnecessary drugs to restrain patients, not complying with legal requirements for providing care (such as having the necessary licenses), not allowing residents to accept visitors, not giving residents access to private phones, and not preventing dehydration. in addition, the three deficiencies which contain “abuse” in their description are also included in the severe deficiencies set. results overview to measure the usefulness of online reviews for assessing the quality of care, both the online ratings and the classifier scores were compared to the number of cms deficiencies per provider. to measure the usefulness of online reviews for detecting elder abuse, the maximum entropy classifier was tested using 10-fold cross-validation. the maximum entropy classifier and ratings sections present correlations with deficiency count data for the maximum entropy classifier and review ratings data, respectively. the maximum entropy classifier section also reports the 10-fold cross-validation results for the classifier. the correlations, aggregations, and analyses section presents results demonstrating each of the following: that the correlation coefficients increase significantly as the number of reviews per provider is increased by aggregating providers; that google’s selection of the top five reviews has little impact on the results of this study; that the average classifier score, average rating, and average deficiency counts for providers binned by their health deficiency counts are consistent across bins; and finally, that a correlogram analysis reveals strong correlations between deficiency categories, which results in the ratings correlating best with abuse related deficiency categories while the maximum entropy classifier’s high precision makes it best-suited to supporting investigators searching for indications of elder abuse. maximum entropy classifier the classifier achieved an f-measure of 0.81, with precision 0.74 and recall 0.89, based on 10fold cross validation with 2,500 hand-annotated reviews and a krippendorff’s alpha coefficient of 0.79. the rin pearson correlation coefficient for the deficiency versus classifier data set indicates a weak but statistically significant correlation: 𝑟𝑟𝑅𝑅𝑅𝑅𝑅𝑅 = 0.13, p < .001, and 95% confidence interval (0.10, 0.16). the spearman correlation coefficient for the classifier is similar: 𝑟𝑟𝑆𝑆 = 0.13, p < .001. finally, the regular pearson correlation coefficient is 𝑟𝑟𝑝𝑝 = 0.14, p < .001, and 95% confidence interval (0.11, 0.17). these results show that the elder abuse classifier successfully locates indications of elder abuse in review text with high precision, and that the correlation with the overall quality of care at a facility, as represented by the cms deficiency counts, is low. although the weak correlation with cms deficiency counts is expected since the http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 classifier is not intended to assess the overall quality of care, the correlations, aggregations, and analyses section will demonstrate that there are substantial correlations between abuse-related deficiencies and other deficiencies. ratings the ratings data achieves a slightly higher spearman correlation with the deficiency data: 𝑟𝑟𝑆𝑆 = 0.15, p < .001. for comparison purposes, the rin pearson correlation coefficient for the ratings is 𝑟𝑟𝑅𝑅𝑅𝑅𝑅𝑅 = 0.15, p < .001, and 95% confidence interval (0.12, 0.18) and the regular pearson correlation coefficient is 𝑟𝑟𝑝𝑝 = 0.15, p < .001, and 95% confidence interval (0.12, 0.18). however, the pearson correlation coefficients for the ratings should be treated with caution, since the ratings and deficiency data fail the henze-zirkler test for bivariate normality, which indicates the result may be imprecise. the correlation coefficients for the ratings are higher than the correlation coefficients for the classifier, which is expected since the deficiency counts represent an overall measure of a nursing home’s quality of care, whereas the classifier is designed to identify abuse. correlations, aggregations, and analyses the correlations for both the classifier scores and the ratings with deficiency counts are statistically significant but weak, due to the small number of reviews available per provider. however, the correlation strength steadily increases as similar providers are aggregated to effectively increase the number of reviews per provider, approaching a value of approximately 0.65 (figure 4). at strides of 5 and above, both the deficiency versus classifier and deficiency versus rating data sets pass the henze-zirkler test. therefore, the correlations shown are all rin transform-based pearson correlation coefficients. for each correlation with classifier scores at each stride, p < .001, and for each correlation with google ratings at each stride, p < .001. therefore, the set of statistical correlation tests remains valid under a bonferroni correction. since google selects the top five reviews to return when a provider has more than five reviews, the correlation results could be influenced by google’s selections. restricting the analysis to use only providers with 4 or fewer reviews results in a deficiency versus classifier rin pearson correlation coefficient of 𝑟𝑟𝑅𝑅𝑅𝑅𝑅𝑅 = 0.13 with (p < .001) and 95% confidence interval (0.10, 0.17), while the deficiency versus rating spearman correlation coefficient becomes 𝑟𝑟𝑆𝑆 = 0.16 with (p < .001). the similarity of these correlation results to results which include providers with five or more reviews suggests that the google selection does not have a significant effect. this may be due to the relatively small number of providers with five or more reviews in this study (figure 3). http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 figure 4: rin transform-based pearson correlation coefficients between google ratings or classifier scores and cms health deficiency counts as a function of stride. the stride is the number of providers with similar deficiency counts merged to produce an aggregated provider with an effectively larger number of reviews. aggregating providers enables extrapolating the potential correlation coefficients achievable with more reviews per provider without generating synthetic reviews. google ratings correlate better with health deficiency counts, while classifier scores correlate nearly as well, even though the classifier scores reflect references to elder abuse rather than overall quality of care. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 figure 5: illustration of correlations between average google ratings, average classifier scores, and average health deficiency counts for providers in each health deficiency bin. the relationships between the averages remains stable across deficiency bins except for the [100+] bin, which contains only six providers. examining average classifier scores and average ratings across provider deficiency bins indicates that the correlations are consistent even as the provider quality of care varies (figure 5). each series has been normalized to have a minimum value of 0.0 and a maximum of 1.0. the ratings have also been reversed, such that a rating of 1 equates to a value of 1.0, while a rating of 5 equates to 0.0. this makes visual comparison of the series data easier, since it means higher values indicate poorer quality of care or a greater degree of abuse for each series. although the deficiency bin range is shown on the x-axis, displaying the average number of deficiencies facilitates visual comparison of the ground truth data with the classifier and rating data, and it also shows that the average deficiency count for the [100+] bin is high. there are few providers in the bins on the right (figure 1), which means those values will not be statistically significant. http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 the ratings and the classifier scores both consistently reflect the average number of deficiencies across deficiency bins. figure 6: spearman correlations between google ratings, classifier scores, and categories of deficiencies. abuse deficiencies include only deficiencies whose cms descriptions included the word abuse, while the set of severe deficiencies include both abuse deficiencies and additional deficiencies chosen as indicators of very poor technical quality of care or possible elder abuse. the severe and minor deficiencies are disjoint sets, as are the abuse and non-abuse deficiencies. deficiency categories correlate strongly with one another, which contributes to classifier scores correlating well with ratings. the similarity between the rating and classifier results corroborates anecdotal experience from annotating the reviews: many negative reviews are either indicative of elder abuse or lack sufficient detail to determine whether abuse is taking place. there are moderate to strong spearman correlation coefficients between the maximum entropy classifier, ratings, and several deficiency categories (figure 6). for consistency, since high values for classifier scores and deficiency counts are both indicative of poor quality of care or elder abuse, the ratings were scaled to [0.0, 1.0], with 1.0 corresponding to a rating of 1 (worst) and 0.0 corresponding to a rating of 5 (best). the sum of p-values for all tests is < .001. notably, the google ratings correlate better than the classifier scores with both the severe deficiencies and the explicit abuse deficiencies, even though the classifier is trained to identify indications of abuse and achieved a precision of 0.74. the higher correlation coefficient for the http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 ratings may be explained by the significant correlations observed between each of the four deficiency categories: facilities which provide poor technical quality of care are more likely to receive deficiencies indicative of abuse, and vice versa. as a result, the google rating result benefits since it reflects reviewers’ overall impression of a facility, which leverages the dependencies between deficiencies. moreover, the annotation guidelines used to train the classifier are designed to identify references to abuse which contain sufficient detail to identify the type of abuse. this is useful for supporting inspectors (e.g. in a similar manner to [21]), but it also means the classifier will not benefit as much as the ratings from the dependencies between poor overall quality of care and deficiencies. discussion google review data exhibits a weak but statistically significant correlation with centers for medicare and medicaid services’ inspection results for nursing homes listed in the nursing home compare data set. the spearman correlation coefficient for the ratings, 𝑟𝑟𝑆𝑆 = 0.15 (p < .001), is slightly higher than the rin transform-based pearson correlation coefficient for the classifier scores 𝑟𝑟𝑅𝑅𝑅𝑅𝑅𝑅 = 0.13, p < .001, and 95% confidence interval (0.10, 0.16). the classifier achieved an f-measure of 0.81, with precision 0.74 and recall 0.89, based on 10-fold crossvalidation with 2,500 hand-annotated reviews. comparisons of correlations between ratings, classifier scores, and deficiency categories revealed that ratings exhibit higher correlations with deficiency counts, even for categories of deficiencies limited to severe or abuse-related deficiencies. this indicates ratings are a better overall measure of the technical quality of care at a facility, while the classifier’s high precision is better suited to supporting inspectors looking for reviews which contain sufficient detail to identify the type of elder abuse. aggregating providers with similar deficiency counts causes the rin transform-based pearson correlation coefficients, for both ratings and classifier scores with deficiency counts, to approach approximately 0.65 as the effective number of reviews per provider increases. this suggests that as the popularity of prws increases, the validity of online review data in assessing nursing homes will increase correspondingly. these correlations contribute to the body of literature which has already demonstrated correlations between online review data and quality of care for other types of healthcare providers, such as hospitals and physicians. this study aggregated providers with similar total deficiency counts to perform the extrapolation, although slightly better results may be achievable by clustering providers based on the distribution of their deficiencies. in addition, since chains of nursing homes are sometimes investigated for poor quality of care or fraud, aggregating nursing homes by ownership may be a useful investigative tool. the positive correlations found in this study also suggest that as the number of online reviews grows, the reviews and the nhc data could jointly enable consumers to make more informed decisions. the methods are complementary: cms inspections provide detailed information on specific aspects of care but have limited timeliness, while online reviews provide subjective information in a timely manner on aspects of care noted by the reviewers. a study on consumer use of the nhc data set found that consumers have limited awareness of it, and the study authors suggest that including measures of "consumer satisfaction" could increase its usefulness [33]. a study of nursing home information and search capabilities on state websites found that less than http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 a quarter were perceived as easy to find [34]. popular online review websites could incorporate the nhc data into their services, which would address both the limited awareness of the nhc data and the lack of consumer feedback in the nhc data. further research is needed to understand the factors that influence which studies find correlations between online review data and other quality care measures. the factors will naturally include the volume of data, prw, and the provider type, but comparisons with other studies suggest additional possibilities. two previous studies using non-social media surveys of elderly patients did not find a correlation between the survey data and the technical quality of care [19,20]. since those studies surveyed elderly patients while this study used online reviews in which reviewers were likely to be younger, and to be relatives of patients rather than patients themselves, age and reviewer perspective may both have been factors. in addition, the type of healthcare service provided may be a factor, since the high correlations between categories of deficiencies in the nhc data suggest that a variety of aspects of the overall quality of care, many of which are readily discernable by visiting relatives, will be indicative of the technical quality of care and the likelihood of elder abuse. this may not generalize to other healthcare provider types, in which there may be a wider range of services offered to patients and in which the technical competence of staff may be harder to discern without a medical background. future research could also address potential sources of bias in online ratings. in one consumer survey, when participants were asked about the implications of writing a negative review, 34% expressed concern over having their identity disclosed, while 26% expressed concern that the physician might take action against them [2]. although a survey of studies on prws found that approximately 90% of ratings were positive and that there was no evidence of “doctor-bashing” [4], the reviews of nursing homes in this study exhibited strong polarization. there could be many reasons for this difference, including a reviewer being more willing to choose a low rating for a facility than for a named physician, self-selection biases varying between prws, and whether a prw allows anonymous reviews. the increasing popularity of prws may also increase providers’ incentives to generate positive fake reviews of themselves or negative fake reviews of their competitors. examining factors taken into account by humans when using online review data, such as the number of reviews for a provider and the degree of emotion or factual information expressed in the reviews [35], may yield useful methods for automated assessments of the review data’s validity, in addition to direct methods for detecting fake reviews. geography is another possible factor, since a study of general practitioners found that the practitioner’s location influenced correlations with referral volume and peer-nominated awards [36]. there may also be a self-selection bias, since users of prws can be characterized by psychographic variables, information-seeking behavior, and health status [37]. finally, since consumers will vary in their prioritization of different aspects of nursing home care [22], efforts to isolate aspects of care through machine learning techniques such as clustering (e.g [9].) could provide consumers with information tailored to each consumer’s priorities. conclusion although the online review data has many potential sources of bias and there is ample room for further research to improve the accuracy of information extracted from online reviews, this study still found that both the maximum entropy classifier and the google ratings approach a rin pearson correlation coefficient of approximately 0.65 as the effective number of reviews http://ojphi.org/ ojphi assessing quality of care and elder abuse in nursing homes via google reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e201, 2016 increases. this correlation indicates that as more online reviews become available, they will become a valuable resource for assessing the technical quality of care and the prevalence of elder abuse in nursing homes. this could help patients choose nursing homes, help regulators protect patients from abuse, help inspectors work more efficiently, and help policy-makers make decisions by providing additional quantifiable data. moreover, if legal and financial restrictions on collecting reviews from multiple prws could be overcome, it is likely that some nursing homes would have a sufficient number of reviews to use the correlations found in this study, benefiting patients, regulators, inspectors, and policy-makers in the near-term. acknowledgements the authors would like to thank the mitre corporation for funding this research. conflicts of interest none declared. as a not-for-profit operator of federally funded research and development centers, the mitre corporation is not permitted to compete with industry. 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24686918&dopt=abstract http://dx.doi.org/10.2196/jmir.3145 assessing quality of care and elder abuse in nursing homes via google reviews introduction methods overview data collection maximum entropy classifier ratings correlations, aggregations, and analyses results overview maximum entropy classifier ratings correlations, aggregations, and analyses discussion conclusion acknowledgements conflicts of interest references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet french institute for public health surveillance, saint-maurice, france objective identification of the main factors influencing the stability and the quality of the french emergency departments (ed) syndromic surveillance system. introduction since 2004, the french syndromic surveillance system oscour® has been implemented by the national institute for public health surveillance (invs) and is daily used to detect and follow-up various public health events all over the territory [1]. beginning with 23 ed in 2004, the coverage and data quality have permanently been increasing until including about 650 ed in august 2015. initially based on a voluntary participation of ed, a mandatory transmission has been decided in july 2013, with major modification on the structural organization of the data transmission in some regions and on coding practices of the new ed. besides this juridical context, the system is based on automatically data collection by ed physicians without recording added information for public health surveillance. this represents the main theorical condition to ensure stability and quality, even in case of occurrence of major public health events susceptible to drastically increase the workload [2]. methods four evaluation criteria on essential characteristics for a syndromic system were daily supervised during 19 consecutive months (from may 2014 to november 2015) through a dashboard [3]: 1/ stability and 2/regularity of data transmission at d+1 (expected delay), evaluated by a prospective calculation of the proportion of ed having transmitted their data on time, 3/ the data transmission delay during a 7-day period, when data are not transmitted at d+1, 4/ the data quality of medical information (icd10 codes). three main factors influencing these criteria have been analyzed: 1/ temporal factors (day of week, day-off, vacations), 2/ health events occurring in 2015 (the exceptional influenza epidemic from january to march and a major heat wave in july), 3/ the influence to move from a voluntary to a mandatory system on data quality and transmission. results every day, about 40,000 attendances recorded in 550 ed are transmitted to invs, corresponding to 82% of the total number of attendances expected from the ed including in the network. from may 2014 to august 2015, this number increase of 27% (+12,000 attendances) due to the introduction of 150 new ed related to the move to a mandatory system (figure 1). from may to september 2014, major instability was observed in data transmission, correlated with these numerous new ed. since autumn 2014, the part of attendances transmitted on time presents daily variation from 80 to 90%. no difference is noted in the number of ed transmitting data between days of week and week-end. variations are essentially due to technical problem in the ed or in the regional platform centralizing the ed data for the whole region. in 2015, the two major health events occurring in france did not impact negatively the data transmission (figure 1). the impact of the move to a mandatory system is currently analyzing. conclusions the setting-up of the daily analysis of data transmission indicators allowed identifying frailty issues. efficient solutions with it unit and ed were implemented, enabling a better stability and regularity in a 3-months delay. finally the follow-up of such indicators in routine is an added-value for the reactivity of actions when technical difficulties occur. it is also crucial for supporting the epidemiological analysis and interpretation of data. such indicators are included in the daily bulletins and dashboards published weekly at national and regional levels. keywords evaluation analysis; syndromic system; oscour system; lesson learned acknowledgments to emergency department data providers and all invs regional unit for their substantial contribution to the system. references [1] josseran l, nicolau j, caillère n, astagneau p, brücker g. syndromic surveillance based on emergency department activity and crude mortality: two examples. euro surveill 2006;11:225-9. [2] caserio-schönemann c, bousquet v, fouillet a, henry v. le système de surveillance syndromique sursaud (r). bull epidémiol hebd 2014;3-4:38-44. 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simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya 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bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts cause of death in under 5 children in a demographic surveillance site in pakistan muhammad imran nisar*, muhammad ilyas, komal naeem, urooj fatima and fyezah jehan pediatrics, aga khan university, karachi, pakistan objective to identify cause of deaths among children below age of 5years from a prospective cohort of women in one urban and four peri-urban settings of karachi, pakistan introduction pakistan ranks 26th in childhood mortality rates, globally. pakistan, with other 4 countries is responsible for about half of the deaths of children age under 5. despite such burden vital registration system is not well established, health facilities are not easily accessible and mostly deaths occur at home, making identification of cause of death (cod) difficult methods from jan 2007-dec 2012 under-5 mortality was identified by chws during their 3-monthly visits. a research assistant conducted verbal autopsies (va). each va form was analyzed by 2 physicians, independently, and assigned a cause. va is analyzed by a third physician in case two physicians do not agree on a cause. cause specific mortality fractions (csmf) were calculated for each identified cod. results 833(58%) neonatal deaths and 591(42%) under-5 deaths (excluding neonates) were identified. among neonates most common cods were perinatal asphyxia(30.4%), neonatal sepsis/meningitis(28%), pre-term birth complication(11%) and neonatal pneumonia(6%). for post-neonatal deaths most common cods were sepsis (19%), diarrheal disease (17%), pneumonia (17%) and meningitis (8%). conclusions we describe the csmf for different cods among neonated and children under 5. strategies for prevention of most common causes and making health facilities easily accessible will decrease this burden. keywords mortality; children; neonates acknowledgments aga khan university *muhammad imran nisar e-mail: imran.nisar@aku.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e174, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts analysing trends of guillain-barre syndrome (gbs) and dengue cases in hong kong xin wang* shenzhen center for disease control and prevention, shenzhen, china objective to study the trends of gbs and dengue in hong kong, the ecological associations between gbs, dengue, and local meteorological factors. to examine the non-stationary oscillating association among these factors. introduction guillain-barre syndrome (gbs) is a severe paralytic neuropathy associated with virus infections such as zika virus and chikungunya virus. there were also case reports of dengue fever preceding gbs. with the aim to understand the mechanisms of gbs and dengue outbreaks, this ecological study investigates the relationships between gbs, dengue, meteorological factors in hong kong and global climatic factors from january 2000 to june 2016. methods the correlations between gbs, dengue, multivariate el nino southern oscillation index (mei) and local meteorological data were explored by spearman’s rank correlations and cross-correlations. three poisson regression models were fitted to identify non-linear associations among gbs, dengue and mei. cross wavelet analyses were applied to infer potential non-stationary oscillating associations among gbs, dengue and mei. results we found a substantial increasing of local gbs and dengue cases (mainly imported) in recent year in hong kong. the seasonalities of gbs and dengue are different, in particular, gbs is low while dengue is high in the summer. we observed weak but significant correlations between gbs and local meteorological factors. mei could explain over 17% of dengue’s variations based on poisson regression analyses. we report a possible non-stationary oscillating association between dengue fever and gbs cases in hong kong. conclusions we report increasing patterns of both local gbs cases and imported dengue cases in hong kong, and investigate the possible mechanism behind these patterns. this study has led to an improved understanding about the timing and ecological relationships between mei, gbs and dengue. fig 1. trends and seasonality of gbs and dengue cases (scaled by number of population in hong kong). panel (a), annual cases of gbs and dengue cases show a sudden increase in recent years. panel (b), monthly cases of gbs and dengue cases. the grey shaded area of panel (a,b) is mei. panel (c), boxplot of gbs cases per day. panel (d), boxplot of dengue cases per day. fig 2. poisson regression results among dengue, mei and gbs. panel (a) shows regression coefficients between dengue and mei, panel (b) shows regression coefficients between gbs and mei and panel (c) shows regression coefficients between gbs and dengue. in all three panels, we consider time lags from 0 to 11 months. the vertical black bars are 95% confidence intervals and the squares in the middle are the mean estimate of regression coefficients. the blue dotted line is p-value of each correlation coefficient. the horizontal dashed light blue lines on all panels indicate the 0.05 significance level. the red dotted line is r2 of each regression coefficient. the horizontal dashed pink lines represent the median level of all r2. fig 3. wavelet coherence and phase plots of dengue and gbs data from 2000-15 in hong kong. panel (a) is dengue time series with peaks shaded in grey. panel (b) are phase plots of dengue and gbs. data are shown in red and blue, and the black dashed line shows phase difference. panel (c) shows cross wavelet average power level and wavelet coherence plots of dengue and gbs. the horizontal axis labels of 5, 10 and 15 represent year 2005, 2010 and 2015. keywords guillain-barre syndrome; dengue; survaillance *xin wang e-mail: szwxin@163.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e93, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts post-vaccination rabies sero-survey in georgia, 2015 natia kartskhia*, lena ninidze, lasha avaliani and tengiz chaligava national food agency of the ministry of agriculture, tbilisi, georgia objective the objective of this survey was to study vaccination coverage and quality in dogs in georgia through the detection of post-vaccination antibodies. introduction rabies is endemic in georgia with up to 100 confirmed cases in animals per year. there is an estimated 350,000 domestic and stray dogs/cats in georgia. the prophylactic vaccination of domestic animals against rabies was reestablished in georgia in 2013. each year since 2013, coverage has increased aiming to cover approximately 70% of the total population of dogs/cats in georgia. methods only vaccinated dog populations were included in the serosurvey. using random selection, five locations were selected. the survey was conducted over a period of 4-8 weeks after vaccination. in order to study vaccination coverage, the total dog population was registered. samples were taken only from vaccinated dogs (confirmed by vaccination papers) and samples were sent to the laboratory of the ministry of agriculture where they were tested for the presence of antibodies using elisa. epidemiological information and gps coordinates were recorded in the electronic integrated disease surveillance system (eidss) and geographic information system (gis). results out of 572 dogs in sampled villages, 373 animal’s vaccination was confirmed leading to 65% vaccination coverage. out of 255 samples, 241 were suitable for testing; 237 samples (98.3%) were positive for the existence of antibodies. antibody titer was not measured. conclusions based on the results of the survey, it can be seen that vaccination coverage is generally not high (65%) and needs improvement. the vaccination quality (as determined through the existence of antibodies) is good (98.3%). in further surveys, antibody titers must be measured in order to extract more information regarding vaccination quality. keywords rabies; vaccination; sero-survey acknowledgments the research study described in this presentation was made possible by financial support provided by the us defense threat reduction agency. the findings, opinions and views expressed herein belong to the authors and do not reflect an official position of the department of the army, department of defense, or the us government, or any other organization listed *natia kartskhia e-mail: natiakartskhia@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e183, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts assessing local risk factors of beijing hand-footmouth disease in china jiaojiao wang*1, zhidong cao1, daniel d. zeng1, 2 and quanyi wang3 1the state key laboratory of management and control for complex systems, institute of automation, chinese academy of sciences, beijing, china; 2university of chinese academy of sciences, beijing, china; 3beijing center for disease prevention and control (cdc), beijing, china objective hfmd incidence varies between geographic regions at the township in beijing. the objective of this study was to examine spatial heterogeneity for the association between hfmd incidence and demographic and socioeconomic factors. introduction hand-foot-mouth disease (hfmd) is a common childhood illness and the drivers of hfmd incidence are still not clear [1]. in mainland china, continuing and increasing hfmd epidemics have been recorded since 2008, causing millions of infections and hundreds of deaths annually. in beijing, 28,667 cases were reported in 2015 and the incidence was 133.28/100,000. the variations in beijing hfmd epidemics over population, space, and time that have been revealed [2] emphasize the need for further research about risk factors of hfmd occurrence. this study aims to explore local effects on hfmd incidence led by potential factors. methods hfmd data. beijing hfmd data during 2008–2012 period were provided by the beijing center for disease prevention and control. hfmd incidence adopted in this study was the annual average value during the five years. predictor variables. potential risk factors obtained from the case records (demographic, occupation, health-seeking behavior) and spatial pois (points of interest) consisted of 22 variables involving residence, restaurant, education, medical facilities, business facilities, infrastructure. the scale of different kinds of pois (1/100,000) was noted by calculating the ratio of the number of pois to the population at certain township or street committee. model specification. some initial associations between hfmd incidence and 8 predictor variables (population density, shopping mall, supermarket, pharmacy, kindergarten, middle school, parking lot, health seeking behavior) were revealed using pearson correlation analysis and the exploratory regression. an ordinary least squares (ols) model was fitted to diagnose the residual normality and dependence. geographically weighted regression (gwr) was chosen to model the relationship, compare the difference from ols regression and measure how much improvement the local model gained. results gwr model with residual independence (moran’s i = 0.0214, p = 0.3405) and lower aicc, performing much better than ols model with residual dependence (moran’s i=0.1271, p = 0.0000) and higher aicc. prediction accuracy by gwr (local r2 ranging from 0.42 to 0.90, r2=0.88) was higher than that by ols (r2=0.57). the higher local r2 values clustered in the east of fangshan and urban-rural transition area. higher coefficient for intercept mainly occurred in north-western and south-eastern portion of beijing. the coefficients for predictors showed shifting patterns from positive to negative at different township. the local effects led by supermarket and shopping mall showed similar spatial pattern, as well as those led by kindergarten and middle school. the scale of pharmacy was positively related to hfmd incidence in the west of daxing and the junction part of chaoyang and tongzhou. conclusions this study quantitatively assessed local risk factors of beijing hfmd occurred in china using gwr model which outperformed ols regression. the findings could provide valuable information for adequate disease intervention measures and regional policy. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e1, 2017 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e9, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts keywords demographic and socioeconomic factors; spatial heterogeneity; geographically weighted regression acknowledgments supported by nsfc (71603253), the national major research program of china (2016yfc1200702) and the early career development award of sklmccs (y3s9021f37). references 1. lu j, sun l, zeng h, et al. enterovirus contamination in pediatric hospitals: a neglected part of the hand-foot-mouth disease transmission chain in china?. clinical infectious diseases, 2016, 62(4): 524-525. 2. wang j, cao z, zeng d d, et al. epidemiological analysis, detection, and comparison of space-time patterns of beijing hand-foot-mouth disease (2008–2012). plos one, 2014, 9(3): e92745. *jiaojiao wang e-mail: jiaojiao.wang@ia.ac.cn online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e9, 2017 isds16_abstracts-final 209 isds16_abstracts-final 210 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts public health impact of syndromic surveillance data—a literature survey stefanie p. albert* and rosa ergas bureau of infectious disease and laboratory sciences, massachusetts department of public health, boston, ma, usa objective to assess evidence for public health impact of syndromic surveillance. introduction systematic syndromic surveillance is undergoing a transition. building on traditional roots in bioterrorism and situational awareness, proponents are demonstrating the timeliness and informative power of syndromic surveillance data to supplement other surveillance data. methods we used pubmed and google scholar to identify articles published since 2007 using key words of interest (e.g., syndromic surveillance in combinations with emergency, evaluation, quality assurance, alerting). the following guiding questions were used to abstract impact measures of syndromic surveillance: 1) what was the public health impact; what decisions or actions occurred because of use of syndromic surveillance data?, 2) were there specific interventions or performance measures for this impact?, and 3) how, and by whom, was this information used? results thirty-five papers were included. almost all articles (n=33) remarked on the ability of syndromic surveillance to improve public health because of timeliness and/or accuracy of data. thirty-four articles mentioned that syndromic surveillance data was used or could be useful. however, evidence of health impact directly attributable to syndromic surveillance efforts were lacking. two articles described how syndromic data were used for decision-making. one article measured the effect of data utilization. conclusions within the syndromic surveillance literature instances of a conceptual shift from detection to practical response are plentiful. as the field of syndromic surveillance continues to evolve and is used by public health institutions, further evaluation of data utility and impact is needed. keywords impact; utility; evaluation; literature acknowledgments special thanks to— mark bova, kevin cranston, alfred demaria, rosa ergas, gillian haney, monina klevens, and sita smith. *stefanie p. albert e-mail: stefanie.albert@state.ma.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e79, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua nbic/dhs, bethesda, md, usa objective nbic analysts evaluated the options and effectiveness of airport symptom-based health screening programs available during emerging disease outbreaks occurring outside the u.s. introduction the national biosurveillance integration center (nbic) has the responsibility to integrate, analyze, and share the nation’s biosurveillance information provided from capabilities distributed across public and private sectors. the integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts. in addition to its interagency partners, nbic supports the office of health affairs and dhs components responsible for safeguarding u.s. ports of entry. more than 150 u.s. international airports process an estimated two billion passengers and 50 million metric tons of cargo arriving in the u.s. from more than 1,000 international airports located outside the u.s. entry and customs screening are points where travelers from international destinations pass; a logical location for assessing health of incoming travelers in order to identify and control import of diseases of emerging diseases. nbic examined peer-reviewed literature, region-specific disease spectrum/frequency, and air travel patterns to assess options for ports of entry health screening as well as the challenges and potential benefits for active screening programs. methods analysts reviewed information from peer-reviewed publications and open data/information sources to assess disease characteristics and spatial distribution. regional relative proportion of ebola virus, mers-cov, and other common regional infectious diseases was estimated using data from the global infectious diseases and epidemiology network (gideon). flight passenger volume information was obtained from customs and border protection (cbp). a crude estimate of the number of cases for a particular disease transiting a u.s. airport was obtained from disease frequency and total passenger volume data. in addition, analysts reviewed available peerreviewed literature to evaluate health screening programs at airports and the potential effectiveness for controlling import of emerging diseases. results he initial symptoms of mers-cov and ebola virus infections are common to many respiratory and enteric infectious illnesses. in saudi arabia, published literature indicates at least 50% of travelers experience an episode of influenza-like illness during travel and more than 80% of the infectious causes of febrile illness among travelers to saudi arabia and the united arab emirates are common agents also endemic in the u.s. peer-reviewed literature indicates that 11-47% of travelers to sub-saharan africa experience at least one episode of febrile illness during their travel, and more than 90% of the infectious causes of febrile illness among travelers to west africa (guinea, sierra leone, and liberia) are common agents also endemic in the u.s. based on historical travel trends, at least 150 passengers from west africa (guinea, liberia, and sierra leone) and 1,500 middle east passengers (saudi arabia and united arab emirates) arrive each day at u.s. airports. given the frequency of febrile illness among travelers to these regions as well as the relative proportion of infectious disease causes, symptom-based screening alone would be unlikely to identify targeted rare emerging pathogens and would be confounded by a large portion of non-infectious health conditions and common infections endemic to the u.s. conclusions in agreement with models and meta-analyses found in the peer-reviewed literature, symptom based health screening, in the absence of additional screening measures such as exposure history or physical examination, is an inefficient method for identifying rare targeted illnesses. furthermore, additional factors contribute to the effectiveness and practicality of symptom-based health screening programs: including impact to employee safety, impact to passenger safety and entry procedures, disease syndrome and ease of distinguishing, program coordination, and outcomes of introducing the screening program. additional studies are needed to determine the best practices and support policy development to guide the use of health screening options at airports. keywords airport; travel; biosurveillance; emerging infectious disease *andrew hickey e-mail: andrew.hickey@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e61, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts hl7 balloting process for the implementation guide for syndromic surveillance peter hicks*1, emilie lamb2, shandy dearth2 and dave trepanier2 1cdc, atlanta, ga, usa; 2isds, boston, ma, usa objective to provide a forum to engage key stakeholders to discuss the process for updating and revising the implementation guide (ig) for syndromic surveillance (formerly the phin message guide for syndromic surveillance) and underscore the critically of community and stakeholder involvement as the implementation guide is vetted through the formal health level seven (hl7) balloting process in 2018. introduction syndromic surveillance seeks to systematically leverage health-related data in near “real-time” to understand the health of communities at the local, state, and federal level. the product of this process provides statistical insight on disease trends and healthcare utilization behaviors at the community level which can be used to support essential surveillance functions in governmental public health authorities (phas). syndromic surveillance is particularly useful in supporting public health situational awareness, emergency response management, and outbreak recognition and characterization. patient encounter data from healthcare settings are a critical inputs for syndromic surveillance; such clinical data provided by hospitals and urgent care centers to phas are authorized applicable local and state laws. the capture, transformation, and messaging of these data in a standardized and systematic manner is critical to this entire enterprise. in august 2015, a collaborative effort was initiated between the cdc, isds, the syndromic surveillance community, onc and nist to update the national electronic messaging standard which enables disparate healthcare systems to capture, structure, and transmit administrative and clinical data for public health surveillance and response. the phin messaging guide for syndromic surveillance -release 2.0 (2015) provided an hl7 messaging and content reference standard for national, syndromic surveillance electronic health record technology certification as well as a basis for local and state syndromic surveillance messaging implementation guides. this standard was further amended with the release of the phin messaging guide for syndromic surveillance release 2.0, erratum (2015) and the hl7 version 2.5.1 phin messaging guide for syndromic surveillancerelease 2.0, nist clarifications and validation guidelines, version 1.5 (2016). isds is now engaged in a process, supported by a cdc cooperative agreement, to formally revise the existing guide and generate an hl7 v 2.5.1 implementation guide (ig) for syndromic surveillance v2.5 for hl7 balloting in 2018. this roundtable will provide a forum to present and discuss the hl7 balloting process and the outstanding activities in which the syndromic surveillance community must participate during the coming months for this activity to be successful. keywords messaging guide; syndromic surveillance; hl7 *peter hicks e-mail: phicks@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e42, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski emerging and zoonotic infectious diseases section, michigan department of health and human services, lansing, mi, usa introduction lyme disease is an emerging disease in michigan and is the most commonly reported vector-borne illness. the bacterium causing lyme disease, borrelia burgdorferi, is transmitted to humans or dogs through the bite of an infected tick. in the spring of 2015, a veterinarian from an island on lake michigan began to see locallyacquired lyme disease in pets. in previous years the vector of lyme disease, ixodes scapularis, had not been found on the island. the michigan department of health and human services (mdhhs) was invited to the island to conduct an environmental investigation and provide health education to local residents. methods to determine the tick population on the island, tick drags were conducted, which is a method for collecting ticks. the method involves dragging a 1-square-meter strip of white cloth mounted on a dowel tied to rope through terrain that may harbor ticks. tick drags were done in multiple locations on the island. results multiple life stages of ixodes scapularis were found on the island. in total, 24 i. scapularis ticks were collected on the day of the island visit and two were positive (8.3 percent) for b. burgdorferi. health education was presented at a town meeting and included information about lyme disease, the vector for the disease, and methods on how to prevent tick-borne disease in humans and pets. “ticks and your health” brochures and tick identification cards were provided to the residents. conclusions the intersection of people, animals, and the environment is well represented in this response to public concern of lyme disease. animal health surveillance for lyme disease prompted environmental surveillance for ticks and led to proactive health education. by collaborating effectively with individuals from many fields, the one health approach allowed for a comprehensive response of the emergence of lyme disease on the island. keywords one health; lyme disease; ticks acknowledgments dr. jeffrey powers, dvm dr. meghan weinberg, phd, mph; michigan department of health and human services dr. jennifer sidge, dvm, michigan state university dr. jean tsao, phd, michigan state university dr. graham hickling, phd, university of tennessee this study/report was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-02 *veronica a. fialkowski e-mail: fialkowskiv@michigan.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e111, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts the rhino community of practice: building a space for data users and enthusiasts in washington state amanda d. morse*, kevin wickersham, natasha close, elyse kadokura and tom e. hulse office of communicable disease epidemiology, washington state department of health, shoreline, wa, usa objective to grow and facilitate a community of syndromic surveillance data users in washington state, improving and expanding local syndromic practice. introduction prior to june 2016, there were 45 registered users of syndromic surveillance data in washington state, with 29 (64.4%) representing 5 of washington’s 35 local health jurisdictions and 16 (35.6%) at the state level. of those registered users, 9 (8.8%) had logged into essence at least once in the 6 months before october 2016. in june 2016, the washington state syndromic surveillance program began accepting meaningful use data and sought to increase its user base. to accomplish this, the washington state department of health (wa doh) designated a staff member to oversee outreach efforts to increase the visibility of syndromic data in the state, including the establishment of a community of practice. methods the washington state syndromic surveillance program—the rapid health information network (rhino)—began the process of stakeholder engagement by delivering a needs assessment to 15 current and potential users of the electronic surveillance system for the early notification of community-based epidemics (essence) platform. the survey assessed interest in participation in a community of practice for washington state syndromic data users, the timing and format for meetings, needs for technical assistance, and topic areas of interest. rhino then used the survey results to create a bank of topics for community of practice calls and develop a strategy for long-term outreach and engagement. in april of 2017, the rhino team developed a new strategic plan and outlined metrics for evaluating growth and challenges in the program’s outreach efforts, including plans for outreach to novel disciplines like emergency preparedness. these metrics included counts of invitations for speaking engagements, essence users, onsite essence trainings and attendees at those trainings, organizations and disciplines represented in the community of practice, community members, and webinars facilitated for the community. rhino staff compiled monthly tabulations of these metrics to track progress over the course of the year and aid in adjustments to outreach efforts as necessary. results rhino received 10 responses to the survey, with 9 respondents from local health jurisdictions and 1 from wa doh. respondents indicated particularly strong interest in regular webinars, a database of resources, and live trainings to support syndromic practice in their work. they also expressed concerns about the distance which would be required for in-person meetings. rhino established that meetings would occur via webinar every other month and held 6 webinars between october 2016 and october 2017 on a broad range of topics including developing syndrome definitions, basic essence functions, using essence’s report manager tool, monitoring influenza-like-illness in essence, and using syndromic data for situational surveillance. in addition to the community of practice webinars, rhino staff developed technical guides for both the washington and national surveillance program’s (nssp) essence platforms, a handbook for using syndromic surveillance data in washington state, and a curriculum for onsite essence training. between october 2016 and october 2017, rhino offered 8 onsite essence trainings for groups of users at the washington state department of health and local health jurisdictions, serving a total of 36 attendees. over the course of the year, essence users in washington state increased to 75, with 40 (53.3%) of them logging into the system at least once over the previous 6 months and 20 (26.7%) listed as “new users” who have not yet activated their accounts. the community of practice itself has 86 members representing 16 agencies and 19 disciplines. as rhino’s profile increased and more potential users became aware of the availability of syndromic data, rhino began receiving invitations to present for external partners. between march 2016 and october 2017, rhino received 8 invitations to present to audiences of potential syndromic data users. these audiences included leadership at the washington state department of health and emergency preparedness and response organizations. in the next year, the program will continue offering data trainings and partner meetings to better serve the needs of both current and potential data users in washington state. as more jurisdictions begin to have production-quality data, rhino will continue offering onsite training. rhino has also built a relationship with the northwest tribal epidemiology center in portland, oregon to begin the process of exploring data sharing with the tribal nations and organizations located within washington. conclusions through the development and implementation of a detailed outreach plan, rhino increased the user base and profile of syndromic surveillance data in washington state. this work was made possible through the careful construction of strong relationships with new and potential partners and the decision to diversify rhino’s staff to include members with backgrounds beyond epidemiology. keywords community of practice; essence training; data user outreach acknowledgments the authors would like to thank our partner organizations in washington state and oregon for their commitment to strengthening relationships and improving syndromic practice in our state and broader region. particular gratitude is due to the washington traffic safety commission for funding rhino’s outreach and legislative efforts. *amanda d. morse e-mail: amanda.morse@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e183, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluating twitter for foodborne illness outbreak detection in new york city katelynn devinney*1, adile bekbay1, thomas effland2, luis gravano2, david howell1, daniel hsu2, daniel o’hallorhan1, vasudha reddy1, faina stavinsky1, haena waechter1 and bruce gutelius1 1bureau of communicable disease, nyc department of health and mental hygiene, queens, ny, usa; 2columbia university, new york, ny, usa objective to incorporate data from twitter into the new york city department of health and mental hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection. introduction an estimated one in six americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. eating food prepared outside of the home is an established risk factor for foodborne illness2. new york city (nyc) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. in 2012, the nyc department of health and mental hygiene (dohmh) began collaborating with columbia university to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of yelp reviews to identify reports of foodborne illness4. annually, dohmh manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on yelp which lead to the detection of about 30 outbreaks associated with a restaurant in nyc. given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. dohmh sought to incorporate and evaluate an additional data source, twitter, to enhance foodborne illness complaint and outbreak detection efforts in nyc. methods dohmh epidemiologists continue to collaborate with computer scientists at columbia university who developed a text mining algorithm that identifies tweets indicating foodborne illness. twitter data are received via a targeted application program interface query that searches for foodborne illness key words and uses metadata to select for tweets with a possible nyc location. each tweet is assigned a sick score between 0–1; those meeting a threshold value of 0.5 are manually reviewed by an epidemiologist, and a survey link is tweeted to users who have tweeted about foodborne illness, requesting more information regarding the date and time of the foodborne illness event, restaurant details, and user contact information. survey data are used to validate complaints and are incorporated in a daily analysis using all sources of complaint data to identify restaurants with multiple foodborne illness complaints within a 30-day period. this system was launched on november 29, 2016. results during november 29, 2016–september 27, 2017, 12,015 tweets qualified for review (39/day on average); 2,288 (19.0%) indicated foodborne illness in nyc, and 1,778 (14.8%) were tweeted a survey link (510 foodborne illness tweets were either deleted by the twitter user or were tweets from a user who was already sent a survey for the same foodborne illness incident). the survey tweets resulted in 92 likes, 12 retweets, 65 replies, 232 profile views and 348 survey link clicks. of the 1,778 surveys sent, 27 were completed (response rate 1.5%), of which 20 (74.7%) confirmed foodborne illness associated with a nyc restaurant; none had been reported via 311/yelp. of those, 11 (55%) provided a phone number, of which 10 (90.9%) completed phone interviews. the completed surveys contributed to the identification of two restaurants with multiple foodborne illness complaints within a 30-day period. conclusions the utility of twitter for foodborne illness outbreak detection continues to be evaluated. while the survey response rate has been low, the identification of new complaints not otherwise reported to 311 and yelp suggests this will be a useful tool. future plans include using feedback data collected by dohmh epidemiologist review to increase the sensitivity and specificity of the text mining algorithm and improve the location detection for twitter users. in addition, we plan to implement enhancements to the survey and create a web page to promote survey responses. furthermore, we intend to share this system with other health departments so that they might incorporate twitter in their outbreak detection and public health surveillance activities. keywords social media; outbreak detection; text mining references 1. scallan e, griffin pm, angulo fj, tauxe rv, hoekstra rm. foodborne illness acquired in the united states--unspecified agents. emerg infect dis. 2011 jan;17(1):16-22. 2. jones tf, angulo fj. eating in restaurants: a risk factor for foodborne disease? clin infect dis. 2006 nov 15;43(10):1324-8. 3. new york city health and nutrition examination survey, 2013-2014 [internet]. new york: new york city department of health and mental hygiene and the city university of new york; 2017 [cited 2017 aug 28]. available from: http://nychanes.org/data/ 4. harrison c, jorder m, stern h, stavinsky f, reddy v, hanson h, waechter h, lowe l, gravano l, balter s; centers for disease control and prevention (cdc).. using online reviews by restaurant patrons to identify unreported cases of foodborne illness new york city, 20122013. mmwr morb mortal wkly rep. 2014 may 23;63(20):441-5. *katelynn devinney e-mail: kdevinney@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e120, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 1ahmadu bello university zaria, abuja, nigeria; 2nigerian field epidemiology and laboratory training program, abuja, nigeria; 3kaduna state primary health care agency, kaduna, nigeria objective to evaluate the case-based measles surveillance system in kaduna state of nigeria and identify gaps in its operations introduction in africa, approximately 13 million cases of measles and 650,000 deaths occur annually, with sub-saharan africa having the highest morbidity and mortality (1). measles infection is endemic in nigeria and has been documented to occur all year round despite high measles routine and supplemental immunisation coverage (2,3). the frequent outbreaks of measles in kaduna state prompted the need for the reevaluation of the measles case-based surveillance system methods we adapted the updated cdc guidelines on surveillance evaluation to assess the systems usefulness, representativeness, simplicity, timeliness, stability and acceptability. a retrospective record review of the measles case-based surveillance data from 2010– 2012 to assess data quality, and representativeness. we calculated the annualized detection rate of measles and non measles febrile rash, proportion of available results, proportion of lgas that investigated at least one case with blood, proportion of cases that are igm positive and the incidence of measles. we compared the results with who (2004) recommended performance indicators to determine the quality and effectiveness of measles surveillance system results according to the stake holders, the case-based surveillance system is still useful and acceptable.the proportion of focal sites reporting declined from 96% in 2010 to 88% in 2012. median interval between specimen collection and release of result improved from 31 days in 2011 to 16 days in 2012. however the best median turnaround time of 7days was recorded in 2010. the annualized detection rate of measles and non-measles febrile rash fell below the recommendated who standard in 2011 and 2012. case definitions are simple and understood by all the operators. we found a progressive decline in the timeliness and data quality in the years under review. conclusions this evaluation showed that the surveillance system was still useful. also, the efficiency and effectiveness of the laboratory component as captured by the “median interval between specimen collection and the release of results improved in 2012 compared to 2011 and 2010. however, there was a progressive decline in the timeliness and completeness of weekly reports in the years under review keywords measles; case-based; surveillance; evaluation; nigeria acknowledgments we wish to thank the mangement of kaduna state primary health care agency for providing the data needed for the evaluation and the nfeltp for providing the resources references 1. simons e, ferrari m, fricks j, wannemuehler k, anand a, burton a, et al. assessment of the 2010 global measles mortality reduction goal: results from a model of surveillance data. lancet: 2013, 379(9832):2173–8. 2. onoja ab, adeniji aj and faneye a. measles complications in a nigerian hospital setting. clin rev opin. 2013;5(2):18–23. 3. adu fd, akinwolere oao, uche ln. low seroconversion rates to measles vaccine among children in nigeria *. bull world health organ. 1992;70(4):457–60. *celestine a. ameh e-mail: cameh1085@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e47, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts data sharing among three states in the biosense platform during the 2017 us solar eclipse stacey hoferka*1, caleb wiedeman2, kristen heitzinger3 and mike schardein3 1illinois department of public health, chicago, il, usa; 2tennessee department of health, nashville, tn, usa; 3kentucky department of public health, frankfort, ky, usa objective describe cross-jurisdictional data sharing practices using essence and facilitated by the biosense platform for a national mass gathering event, and the dashboard views created to enhance local data for greater situational awareness. introduction in 2016, the biosense platform for national syndromic surveillance made substantial enhancements including data processing changes, a national essence instance, and management tools to support diverse data sharing needs. on august 21, 2017, a total solar eclipse occurred over much of the united states. the event resulted in large gatherings over multiple days to areas in the path of totality (pot). in the days leading up to the event, public health and emergency preparedness included syndromic surveillance in their monitoring plans. to support this effort, illinois (il), kentucky (ky), and tennessee (tn) established inter-jurisdictional aggregate data sharing to get a more inclusive view of cause-specific illness or injury in emergency department (ed) visits before, during, and after the eclipse. methods following best practices outlined by colleagues at oregon health authority, in their july 2017 guidelines “using essence for mass gathering surveillance”, the tristate collaboration between il, ky, and tn provided participating state-level epidemiologists access to aggregate data in all three states. dashboards for each state were created to include hospital ed visits in counties that fell in the pot and shared to view trends in syndromes such as gastrointestinal illness (gi), influenza-like illness, heat-related illness (hri), and substance abuse. counts from event-specific keyword queries related to the eclipse were also shared. results shared dashboards included data from 64 facilities (31 il, 10 ky, and 23 tn) and monitoring was performed from august 16 – august 23. during the monitoring period, 41,471 ed visits were reported from the shared facilities (10,610 il; 7,740 ky; 23,157 tn). out of state residents accounted between 3% to 8.6% of reported visits. there was a sharp increase in ed visits referencing the eclipse across all three states during the monitoring period. a total of 71 visits were identified as eclipse-related (19 il, 44 ky, and 8 tn). ky requested one hospital to identify patient encounters related to the eclipse by including the term “eclipse” in the patient chief complaint, il and tn did not. minor fluctuations in syndrome trends were observed across all three states. conclusions mass gatherings may cause a sudden increase of healthcare resource utilization in the municipalities where they occur. in il and ky, the pot occurred over a rural part of each state, whereas in tn the path went through a major metropolitan area. ed coverage and completeness varied across all three states. expanded data access and visualization of syndromes in nearby states allowed il, ky, and tn to enhance their surveillance efforts and verify observed syndromic trends across jurisdictional boundaries. the essence instance in the biosense platform fostered a collaborative environment that quickly enabled the sharing of limited data across multiple jurisdictions during the august 21, 2017 total solar eclipse. keywords data sharing; mass gathering; syndromic; eclipse *stacey hoferka e-mail: stacey.hoferka@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e106, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts visualization the dynamic interactive maps for results of spatio-temporal scanning xiaoxiao song*1, 2, yan li1, 2, xia xiao1, 2, le cai1, 2, wei liu1, 2, wenlong cui1, 2 and ling yue1 1school of public health, kunming medical university, kunming, china; 2yunnan provincial collaborative innovation center for public health and disease prevention and control, kunming, china objective the purpose of this article was to provide static and interact mapping for the results’ satscan with r package thereby reduce the gap between decision-makers and researchers. introduction scan statistics is one of the most widely used method for detecting and locating the clusters of disease or health-related events through the space-time dimension. although the specific software of satscan is available for free and easier to use graphical user interface (gui) interface, the click way and the resulting text format have became obstacles in biosurveillance since automated or reproduction operation and the fast communicate information tool appeared. with the power of r software and ‘rsatscan’ package, we extended the visualization results to become a faster, more effective communication and motivation tool. methods all the data are from a syndromic surveillance and real-time early warning system, which established 3 counties in the yunnan province in the china for improving the ability to handle public health emergencies events and reduce the potential risk of disease spread. to illustrate the purpose of visualization, we only use one county data from 2017/9/1 to 2017/9/30 which includes two data sources: primary schools’ absentees and health clinics’ records. based on the ‘rsatscan’ package which makes it easy to work within satscan from r, we developed three ways for the results of spatio-temporal scan: traditional tables, static maps and interactive visualize maps. especial the last interactive visualization benefits from dynamic queries which may be an incredible tool to explore potential “clusters” data. data are collected from web-based by smart-phone or internet including 83 health clinics and 118 primary schools for one month. all the programs are run on rstudio. the retrospective spatio-temporal scan parameters for two data sources as follow: analysis type=retrospective space-time, analysis type=space-time permutation, model type= high rates (poisson), time precision=day, time aggregation units=day, maxspatialsizeindistancefromcenter=10 km, maxtemporalsize=14 day. results 76211 records in the health clinics and 6066 absenteeism in the primary schools are collected. three ways for the spatio-temporal scan results are provided in table (tab-1), static maps (fig 1) and interactive visualize maps online, some of them are presented in html format. the table shows two data sources results by stack ways. the first column is the order of most likely to cluster. follow is the code for center of the circular scan. the remaining indicators include time interval, risk value, observed and expected value, p values from 999 montel carlo simulation. see the table in more details. the static maps have the advantages of vivid communicates information for where are the potential “cluster” both in two data sources over the space. what is more, one benefit of this way can provide the possible association between medical institution information and primary school absence information through the overlap circular. the most excited is the interactive visualization with html format. from the click the navigate widgets on the left top, you can choose different layers. if you want to know more cluster information by the different potential cluster, clicking the map or dots or circular, and the pop-up dialogue box will show with the related clusters results of scan statistics methods. see the detail in the website: http://rpubs.com/ynsxx/318257 conclusions these innovation ways can provide the ability to process information faster and to use that information to boost productivity and results. it is easy to help decision-makers to visualize communicates information faster than traditional reports. and the r code will more suitable for prospective analysis. keywords visualization; spatio temporal scan; package ‘rsatscan’ references kleinman k, r package ‘rsatscan’, 2015. *xiaoxiao song e-mail: chinasxx@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e68, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts mortality surveillance in the netherlands: severity of winter 2016/2017 liselotte van asten*1, marit de lange1, anne teirlinck1, lenny stoeldraijer2, carel harmsen2 and wim van der hoek1 1centre for infectious disease control netherlands, national institute for public health and the environment (rivm), utrecht, netherlands; 2statistics netherlands, the hague, netherlands objective weekly numbers of deaths are monitored to increase the capacity to deal with both expected and unusual (disease) events such as pandemic influenza, other infections and non-infectious incidents. the monitoring information can potentially be used to detect, track and estimate the impact of an outbreak or incident on all-cause mortality. introduction the mortality monitoring system (initiated in 2009 during the influenza a(h1n1) pandemic) is a collaboration between the centre for infectious disease control (cib) of national institute for public health and the environment (rivm) and statistics netherlands. the system monitors nation-wide reported number of deaths (population size 2017: 17 million) from all causes, as cause of death information is not available real-time. data is received from statistics netherlands by weekly emails. methods once a week the number of reported deaths is checked for excess above expected levels at 2 different time-lags: within 1 and 2 weeks after date of death (covering a median 43% and 96% of all deaths respectively). a weekly email bulletin reporting the findings is sent to the infectious disease early warning unit (at cib) and a summary of results is posted on the rivm website. any known concurrent and possibly related events are also reported. when excess deaths coincide with hot temperatures, the bulletin is sent to the heat plan team (also at rivm). data are also sent to euromomo which monitors excess mortality at a european level. for the dutch system baselines and prediction limits are calculated using a 5 year historical period (updated each july). a serfling-like algorithm based on regression analysis is used to produce baselines which includes cyclical seasonal trends (models based on historical data in which weeks with extreme underreporting have been removed. also periods with high excess mortality in winter and summer were removed so as not to influence the baseline with previous outbreaks). results increased mortality started two weeks after the influenza epidemic started and remained increased during the remainder of the influenza epidemic except for a drop in week 52 (coinciding with christmas holidays) (figure 1). excess mortality was primarily observed in persons 75 years or older. cumulative excess mortality was estimated at 7,503 deaths occurring during the 15 weeks of the 2016/2017 influenza epidemic (week 48 of 2016 through week 10 of 2017) and at 8,890 during the total winter season (44 weeks running from week 40 up to week 20 of the next year). conclusions in terms of number of deaths during the winter season (weeks 40-20 the next year) and during the influenza epidemic (weeks 4810), the 2016/2017 season in the netherlands was of high severity compared with the previous five seasons. mortality was only higher in the 2014/2015 season when the longest influenza epidemic was recorded of 21 weeks. keywords mortality; surveillance; influenza *liselotte van asten e-mail: liselotte.van.asten@rivm.nl online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e153, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 1french institute for public health surveillance, saint-denis cedex 9, réunion; 2epiconcept, paris, france objective to describe a new surveillance system based on an online selfreported symptoms and to present the first results. introduction during the past ten years, the syndromic surveillance has mainly developed thanks to clinical data sources (i.e. emergency department, emergency medical call system, etc.). however, in these systems, the population doesn’t play an active role. it is now important that the population becomes an actor of this surveillance; especially since several european experiences about influenza showed that the population could participate to an internet-based monitoring. in reunion island, the population is very sensitive to public health concerns. in this context, the health authorities implemented since april 2014 a web-based surveillance system, called “koman i lé”, that allows to follow the perceived health among people who don’t systematically see their general practitioner. methods the surveillance system is based on the use of the internet-based cohort. individual volunteers aged over 18 with internet access and living in reunion island are included. during the registration, sociodemographical data are collected. upon registration, each user is sent a weekly email, taking them to the “koman i lé” website. they fill in a short survey asking if they had any of the 17 symptoms during the previous week: fever, cough, headache, diarrhea, vomiting, nausea, stomac ache, sore throat, muscle pain, weakness, skin rash, red eyes, asthma attack, asthenia, rhinorrhea, dyspnoea, joint pain. indicators are constructed from syndromic groups. each week, epidemiologists analyse the symptoms and indicators by age group, gender, occupational category and zip code; and make a report as a feedback to the participants (figure1). moreover, in the website a custom feedback is given to each volunteer so that they can compare their health to that of the population. in addition, it is also possible to make ad hoc surveys on various public health subjects. results as of august 6, 2015, there are 359 participants from 22 of the 24 cities of the island. among them, 67% are women, and 46% are between 30 and 44 years old. thirty-three percent of the users live in saint-denis, the capital of reunion island. since the beginning of “koman i lé”, the three most frequently reported symptoms are tiredness, rhinorrhea, and headache. the weekly monitoring of the different indicators highlighted an increase in the percentage of participants who presented an influenza-like illness in weeks 24 and 25 in 2014. this increased coincided with the start of an ili outbreak detected by the other surveillance systems. moreover, “koman i lé” also allowed to observe an increase in participants who had red eyes during a major outbreak of conjunctivitis that occurred from january to april 2015. conclusions the surveillance system allows the setting up of a volunteers’ cohort in general population. the first results show that it possible from the data collected each week to monitor the health of the population and to detect unusual or expected health events and to follow an epidemic, despite the low number of participants. with a larger user base, systems like “koman i lé” will help to improve the health surveillance on reunion island. in conclusion, the sentinel population project is original because for the first time the general population participates to syndromic surveillance. information reported by individuals can increase traditional public health methods for more timely detection of disease outbreaks. finally, the sentinel population allows the population of reunion island to take an active part in the health regional policy. figure1. organization of the population sentinel project keywords sentinel population; web-based monitoring; syndromic surveillance acknowledgments all “koman i lé” users who have contributed information. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e139, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy 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morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung 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information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts what value can google search data add to existing syndromic surveillance systems? helen k. green*1, obaghe edeghere1, alex elliot1, ingemar cox2, 3, rachel mckendry2 and gillian smith1 1public health england, birmingham, united kingdom; 2university college london, london, united kingdom; 3university of copenhagen, copenhagen, denmark objective to carry out an observational study to explore what added value google search data can provide to existing routine syndromic surveillance systems in england for a range of conditions of public health importance and summarise lessons learned for other countries. introduction globally, there have been various studies assessing trends in google search terms in the context of public health surveillance1. however, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. a proposed advantage is improved timeliness relative to established surveillance systems. however, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services. methods we sought to determine the added public health utility of google search data alongside established syndromic surveillance systems in england2 for a range of conditions of public health importance, including allergic rhinitis, scarlet fever, bronchitis, pertussis, measles, rotavirus and the health impact of heatwaves. google search term selection was based on diagnostic and clinical codes underlying the syndromic indicators, with google trends3 used to identify additional related internet search terms. daily data was extracted from syndromic surveillance systems2 and from the google health trends application programming interface (api) from 2012 to 2017 and a retrospective daily analysis undertaken during pre-identified public health events to identify a) whether signals were detected during these events and b) assess the correlation with analogous syndromic surveillance indicators through calculation of spearman correlation coefficients and lag assessment to determine timeliness. results we detected increases in google search term frequency during public health events of interest. good correlation was seen with comparable syndromic surveillance indicators on a daily timescale for several health outcomes, including the search terms hayfever, scarlet fever, bronchiolitis and heatstroke. weaker correlation was seen for conditions which occur in small numbers and are vaccine preventable such as measles and pertussis. lag analysis showed similar timeliness between daily syndromic and google data, suggesting that, overall, google data did not provide an earlier or delayed signal compared to syndromic surveillance indicators in england. conclusions to the best of our knowledge this is the first time trends in google search data have been compared against syndromic data for a range of public health conditions in england. these findings demonstrate the potential utility of internet search query data in conjunction with existing systems in england, with syndromic surveillance data found to be as timely as google data. these findings also have important implications for countries where there are no such healthcarebased syndromic surveillance systems in place. factors to consider with analyses of google search trend data in the context of disease surveillance have been highlighted, including the choice of search terms and interpretation of the reasons behind searching the internet. keywords syndromic surveillance; google; public health acknowledgments we acknowledge support from: nhs 111 and nhs digital for their assistance and support in the nhs 111 system; ooh providers submitting data to the gpoohss and advanced heath & care; ed clinicians and nhs trust staff supporting edsss, the royal college of emergency medicine and technical support provided by emis health and l2s2 ltd; tpp and participating systmone practices and university of nottingham, clinrisk, emis and emis practices submitting data to the qsurveillance database and google for access to their google health trends api. the authors would like to thank mr paul loveridge of the phe realtime syndromic surveillance team (resst) and dr bill lampos of the department of computer science, ucl for their technical expertise. this work has been supported by the i-sense epsrc irc in early warning sensing systems for infectious diseases grant (ep/k031953/1) and a google research sponsorship. hkg received support from i-sense to attend isds. ram is supported by the royal society wolfson merit award. aje and ges are supported by the national institute for health protection research health protection unit (nihr hpru) in emergency preparedness and response at king’s college london in partnership with phe. the views expressed are those of the author(s) and not necessarily those of the nhs, the nihr, the department of health or public health england. references 1nuti sv, wayda b, ranasinghe i, wang s, dreyer rp, chen si, murugiah k. the use of google trends in health care research: a systematic review. plos one. 2014 oct 22;9(10):e109583. 2public health england. syndromic surveillance: systems and analyses. 2017. available online: https://www.gov.uk/government/collections/ syndromic-surveillance-systems-and-analyses 3google. 2017. google trends. available online: https://trends.google.com/trends/ *helen k. green e-mail: helen.k.green@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e27, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts molecular characterization of salmonella spp. from cattle and chicken farms in uganda takiyah a. ball, paula j. fedorka-cray*, joy horovitz and siddhartha thakur north carolina state university college of veterinary medicine, raleigh, nc, usa objective determine the amr phenotypes and genotypes of salmonella isolates recovered from cattle and poultry farms in the wakiso district of uganda. introduction antimicrobial resistance (amr) is a major concern in developing countries. uganda is one of many developing countries that are beginning to implement a surveillance program countrywide to monitor amr within the animal, environmental, and human sectors. not only is there a concern for amr, but the emergence of multidrug resistance (mdr) of salmonella is also becoming a major one health problem. few new drugs are being produced. when current treatments fail, new antimicrobials for treatment of these microorganisms are limited (5). in salmonella, amr genes are usually found on plasmids that are transferable. most plasmids that carry resistance are conjugative plasmids, promoting the transfer of dna from cell to cell (1). class i integrons are located on transposable plasmids and are known to transfer amr through an assortment of gene cassettes (3). extended-spectrum β-lactamases (esbls) are also known to encode genes located on integrons and transposons (2). esbls confer resistance to third generation cephalosporins, a drug of choice for treatment of salmonella infections. esbls are now reported in enterobacteriaceae all over the world. examples of common esbls include blactx-m, blaoxa, blatem, blacmy, and blashv (2). it has been reported that esbls evolved from the kluverya species chromosome by mutation and gene transposition (4). in our previous study, we phenotypically characterized salmonella from cattle and poultry farms within the wakiso district of uganda. based on the high prevalence of mdr in the isolates collected we continued investigating at the molecular level. for the salmonella isolates, we wanted to characterize genotypes by first analyzing the relatedness of the isolates with pulse field gel electrophoresis (pfge). next, we wanted to look to see which dna plasmids were present. we looked at 28 replicon plasmids and the class 1 integron, int1. the salmonella isolates were also screened for esbl genes based on their resistance profiles. methods fecal and environmental samples from cattle and poultry farms were cultured using standard laboratory methods. amr profiles were identified among all poultry and cattle salmonella using the sensitiretm system per manufacturer’s directions. fifty-six salmonella isolates were screened for 28 replicon type plasmids, esbl genes, and class i integrons by pcr. the 56 isolates were subjected to pfge to determine relatedness. results salmonella was recovered from 51/379 (13.5%) and 5/400 (1.3%) of poultry and cattle samples, respectively. salmonella enteritidis 16/51 (31.7%) and kentucky 11/51 (21.6%) were most often recovered on poultry farms. salmonella was most often resistant to tetracycline and sulfisoxazole. all salmonella kentucky isolates were resistant to ciprofloxacin. five replicon plasmids were identified among all poultry and cattle salmonella: incfiis 18/56 (32.1%), inci1α 12/56 (21.4%), incp 8/56 (14.3%), incx1 8/56 (14.3%), and incx2 1/56 (1.8%). the class i integron, int1, was positive in one poultry isolate presenting mdr. pfge cluster analysis of the 56 isolates showed 17 distinctive cluster types and displayed distinct clusters by replicon types incp, incx, incfiis, and inci1α. no isolates displayed the esbl genes that were screened. conclusions in conclusion, we observed some degree of association between the amr and plasmids. these plasmids also have an association with the pfge cluster types and the salmonella serotypes presented in this study. these salmonella serotypes may be harboring these particular plasmids which confer resistance to select antimicrobials. future work with these isolates will include whole genome sequence screening to detect differences between amr phenotypes and genotypes. keywords antimicrobial resistance; surveillance; salmonella spp; one health acknowledgments we would like to acknowledge funding from the north carolina state college of veterinary medicine and the who agisar secretariat. acknowledgement goes to our collaborators at the makerere college of veterinary medicine in uganda, dr. francis ejobi, samuel maling, david apollo munanura, allan odeke, disan muhangazi, sarah tegule, mark ogul, mutumba paul, elizabeth basemera, and dr. eddie wampande in the central diagnostic laboratory and his staff. acknowledgment also goes to dr. megan jacobs and her staff at the nc state college of veterinary medicine diagnostic laboratory. we would like to recognize our colleagues dr. glenn tillman, dr. mustafa simmons, and mary crews at the usda in food safety and inspection service and dr. kim cook, and jodie plumblee, in the usda bacterial epidemiology and antimicrobial resistance unit, in athens ga. references 1. bennett, p. m. (2008). plasmid-encoded antibiotic resistance: acquisition and transfer of antibiotic resistance genes in bacteria. br j pharmacol, 153 suppl 1, s347-357. doi:10.1038/sj.bjp.0707607 2. bradford, p. a. (2001). extended-spectrum beta-lactamases in the 21st century: characterization, epidemiology, and detection of this important resistance threat. clin microbiol rev, 14(4), 933-951, table of contents. doi:10.1128/cmr.14.4.933-951.2001 3. fluit, a. c., & schmitz, f. j. (2004). resistance integrons and superintegrons. clin microbiol infect, 10(4), 272-288. doi:10.1111/j.1198743x.2004.00858.x 4. humeniuk, c., arlet, g., gautier, v., grimont, p., labia, r., & philippon, a. (2002). beta-lactamases of kluyvera ascorbata, probable progenitors of some plasmid-encoded ctx-m types. antimicrob agents chemother, 46(9), 3045-3049. 5. ling, l. l., schneider, t., peoples, a. j., spoering, a. l., engels, i., conlon, b. p., lewis, k. (2015). a new antibiotic kills pathogens without detectable resistance. nature, 517(7535), 455-459. doi:10.1038/nature14098 *paula j. fedorka-cray e-mail: paula_cray@ncsu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e148, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts viral causes of influenza like illness in uganda, 2008 to 2017. derrick e. mimbe*1, denis k. byarugaba2, bernard erima1, edison mworozi3, monica millard1, titus tugume1, jocelyn kiconco1, paska lamunu1, hannah kibuuka1 and fred wabwire-mangen3 1makerere university walter reed project, kampala, uganda; 2makerere university college of veterinary medicine, animal resources and bio-security, kampala, uganda; 3makerere university college of health sciences, kampala, uganda objective to determine viral causes of influenza-like illness in uganda. introduction respiratory pathogens continue to present an ever increasing threat to public health (1,2). influenza, respiratory syncytial virus, human metapneumovirus and other respiratory viruses are major etiological agents for influenza like illnesses (ili) (3-5). establishment of viral causes of ili is critical for prevention and mitigation strategies to disease threats. makerere university walter reed project (muwrp) together with the ugandan ministry of health and partners undertook surveillance to determine viral causes of influenza-like illness in uganda. methods from 2008, muwrp established hospital-based sentinel sites for surveillance activities. a total of five hospital-based sites were established, where patients aged 6 months or older presenting with ili were enrolled. consents were obtained as required, and a throat and/ or nasopharyngeal swab collected. samples were screened by pcr for viral causes. results from october 2008 to march 2017 a total of 9,472 participants were enrolled in the study from five hospital-based surveillance sentinel sites. majority of participants were children under 5 years n= 8,169 (86.2%). 615 (6.5%) samples tested positive for influenza a, while 385 (4.1%) tested positive for influenza b viruses and 10 (0.1%) were co-infections between influenza a and b. of the 2,062 influenza negative samples, results indicated positivity for the following organisms; adenoviruses (9.4%), respiratory syncytial b (7.3%), parainfluenza-3 (4.5%), parainfluenza-1 (4.3%), respiratory syncytial a (3.5%), human bocavirus (1.7%), human metapneumovirus (1.7%), human coronavirus (1.5%), parainfluenza-4 (1.4%) and parainfluenza-2 (0.9%) by pcr. conclusions influenza viruses account for about 11% of the causes of influenza like illness, with influenza a being the dominant type. among the other viral causes of ili, adenoviruses were the most dominant. detection of other viral causes of ili is an indication of the public health threats posed by respiratory pathogens. keywords influenza; influenza like illness; surveillance acknowledgments the authors would want to acknowledge the project and sentinel surveillance site staff for their contribution and dedication. this study was supported by the us department of defence through the armed forces health surveillance branch global emerging infections surveillance and response and henry jackson foundation for advancement of medicine. references iom (institute of medicine), 2009. microbial evolution and coadaptation: a tribute to the life and scientific legacies of joshua lederberg. washington, dc: the national academies press. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts horton kc, dueger el, kandeel a, abdallat m, el-kholy a, al-awaidy s, et al., 2017. viral etiology, seasonality and severity of hospitalized patients with severe acute respiratory infections in the eastern mediterranean region, 2007–2014. plos one 2017; 12(7). kamigaki t, aldey pp, mercado es, tan ag, javier jb, lupisan sp, oshitani h, tallo vl., 2017. estimates of influenza and respiratory syncytial virus incidences with fraction modeling approach in baguio city, the philippines, 2012-2014. influenza other respir viruses. jul; 11(4):311-318. moe n, stenseng ih, krokstad s, christensen a, skanke lh, risnes kr, nordbo sa, dollner h., 2017. the burden of human metapneumovirus and respiratory syncytial virus infections in hospitalized norwegian children. j infect dis. jul 1; 216(1):110-116. shapiro d., bodinayake ck, nagahawatte a, devasiri v, kurukulasooriya r, hsiang j, nicholson b6, de silva ad7, ostbye t, reller me, woods cw, tillekeratne lg, 2017. burden and seasonality of viral acute respiratory tract infections among outpatients in southern sri lanka. am j trop med hyg. jul; 97(1):88-96. *derrick e. mimbe e-mail: dmimbe@muwrp.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e197, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluation of approaches that adjust for biases in participatory surveillance systems kristin baltrusaitis1, kathleen noddin2, colleen nguyen2, adam crawley*3, john s. brownstein2, 4 and laura f. white1 1department of biostatistics, boston university school of public health, boston university, boston, ma, usa; 2computational health informatics program, boston children’s hospital, boston, ma, usa; 3skoll global threats fund, san francisco, ca, usa; 4harvard medical school, boston, ma, usa objective to estimate and compare influenza attack rates (ar) in the united states (us) using different approaches to adjust for reporting biases in participatory syndromic surveillance data. introduction because the dynamics and severity of influenza in the us vary each season, yearly estimates of disease burden in the population are essential to evaluate interventions and allocate resources. the cdc uses data from a national health-care based surveillance system and mathematical models to estimate the overall burden of disease in the general population. over the past decade, crowd-sourced syndromic surveillance systems have emerged as a digital data source that collects health-related information in near real-time. these systems complement traditional surveillance systems by capturing individuals who do not seek medical care and allowing for a longitudinal view of illness burden. however, because not all participants report every week and participants are more likely to report when ill, the number of weekly reports is temporally and spatially inconsistent and the estimates of disease burden and incidence may be biased. in this study, we use data from flu near you (fny), a participatory surveillance system based in the us and canada1, to estimate and compare influenza-like illness (ili) ars using different approaches to adjust for reporting biases in participatory surveillance data. methods this analysis uses fny data from the 2015-16 influenza season. four different approaches of bias adjustment were assessed. the first approach includes all fny participants, defined as users and household members, who submitted at least one symptom report, whereas the second approach only includes fny participants who submitted at least 10 symptom reports. the third approach includes all fny participants who submitted at least one symptom report, but drops the first symptom report for all participants. for the first three approaches, all missing reports were assumed to be non-ili when estimating attack rates. finally, the fourth approach includes fny participants who submitted at least 10 symptom reports and uses multiple imputation to account for missing reports. age-stratified and overall estimates of ili ars were calculated for each of the four approaches to bias adjustment by dividing the sum of the weekly incident cases of ili, defined as the first report of fever with cough and/ or sore throat, by the population at risk at the beginning of the period. results during the 2016-2017 influenza season, fny received an average of 10,723 unique symptom reports per week from 46,390 registered users and their household members. for fny, the youngest age group assessed, 5-17, had the largest ili ar, and the ili ars decreased as the age group increased for all approaches. overall, the approach that drops all first reports had the smallest ars, whereas the approach that selects a cohort of users who submit at least 10 reports during the season and imputes the missing reports had the largest ars. although the influenza ars estimated by the cdc were less than the ili ars estimated using fny data for all age-groups, a similar pattern was observed across age groups, except for the 50-64 age group, which had the largest influenza ar. conclusions as expected, the ars estimated using fny data were greater than the cdc’s influenza ars because fny estimates ars of ili and does not adjust for the probability of reporting ili when experiencing non-flu illness. the approach of dropping the first report had the smallest ars because during the 2015-16 influenza season the weekly percent of ili cases that were first time reports ranged from 18-59%. this approach was developed to adjust for the potential correlation between symptom presence and willingness to join the platform. however, important information about the dynamics of disease may be lost when using this approach. the multiple imputation method was used only for individuals who submitted at least 10 reports to maintain a missing data rate below 30%. the imputation model also assumed that data were missing at random, which may not be appropriate in this case, because approximately 30% of fny users have reported that they are more likely to report when ill. as shown in table 1, the ar estimate depends on the bias adjustment approach. simulationbased studies should be performed to further evaluate these methods. estimated attack rates (%) with 95% ci by age group for the 2015-2016 influenza season keywords community-based participatory research; digital disease detection; influenza; public health surveillance acknowledgments we thank all of the participants who contributed their time and information to the fny system. references 1. smolinski ms, crawley aw, baltrusaitis k, chunara r, olsen jm, wójcik o, et al. flu near you: crowdsourced symptom reporting spanning 2 influenza seasons. am j public health. 2015 2. rolfes ma, foppa im, garg s, flannery b, brammer l, singleton ja, et al. estimated influenza illnesses, medical visits, hospitalizations, and deaths averted by vaccination in the united states. 2016 dec 9 [2017 sept 25]; https://www.cdc.gov/flu/about/disease/2015-16.htm *adam crawley e-mail: acrawley@skollglobalthreats.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e124, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze national food agency (nfa) of the ministry of agriculture, tbilisi, georgia introduction anthrax is a globally distributed zoonotic disease caused by bacillus anthracis, a soil-borne, gram-positive, spore forming bacteria. bacillus anthracis can infect people who slaughter or eat animals that are infected. recent reports indicate the incidence of human anthrax has increased steadily over the last several years in georgia (2007-2012). the georgian national animal health program has implemented an anthrax control program. the ministry of agriculture, the national food agency (nfa), and the laboratory of the ministry of agriculture (lma) are engaged in diagnosis and control of anthrax in animals. epidemiological investigation and surveillance are used to determine the origin of anthrax affected animals and their route of migration, however, for successful implementation, proper animal traceability is required. identification of cattle is one of the components of epidemiological investigation and has been ongoing in georgia since 2012. methods during 2012-2014, 1,292,754 cattle were identified with ear tags. in 2014, four fatal cases in cattle (with ear tags) were investigated to determine the origin of the animals. results the cattle were found in seasonal pastures or on animal migration routes. all animals were from different regions relative to the seasonal pastures they were moved to and died. conclusions implementation of this new approach to livestock monitoring within the anthrax control program is considered to be one of the main factors to improve epidemiological investigation and surveillance. in 2014, as a result of this program, georgian legislation was updated to require the tagging and identification of cattle, enabling traceability of individual animals. the resulting traceability due to tagging of cattle is known to have reduced illegal movement. as a result, control of the cattle vaccination program was improved. this minimized the migration of unvaccinated animals in seasonal pastures, which is a major risk factor for the spread of the disease. keywords bacillus anthracis; anthrax; cattle; tagging * e-mail: online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e88, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epi evident: biosurveillance to monitor, compare, and forecast disease case counts natalie tomaszewski*1, meeshu agnihotri3, huiwen cheng4, ashutosh bhadke5, michael henry2 and lauren e. charles2 1university of southern california, santa ana, ca, usa; 2pacific northwest national laboratory, richland, wa, usa; 3georgia tech university, atlanta, ga, usa; 4university of indiana, bloomington, in, usa; 5university of arizona, tucson, az, usa objective epi evident is a web based application built to empower public health analysts by providing a platform that improves monitoring, comparing, and forecasting case counts and period prevalence of notifiable diseases for any scale jurisdiction at regional, country, or global-level. this proof of concept application development addresses improving visualization, access, situational awareness, and prediction of disease behavior. introduction the epi evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. epi evident addresses the taxing analytical evaluation of how diseases behave differently across countries. this application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks. developed at the pacific northwest national laboratory (pnnl), epi evident utilizes time-series disease case count data from the biosurveillance ecosystem (bsve) application epi archive (1). this diverse data source is filtered through the flexible epi evident workflow for forecast model building designed to integrate any entering combination of country and disease. the application aims to quickly inform analysts of anomalies in disease & location specific behavior and aid in evidence based decision making to help control or prevent disease outbreaks. methods a workflow was constructed to define the best disease forecast model for each location based on an adjustable method approach. the differences in disease behavior across countries was achieved through a react/python application with a user-friendly output for monitoring and comparing different combinations. the forecast model building workflow consisted of three major steps to determine the best fit model for a given disease-country pair: data type, model type, and model comparison & selection. testing various disease-country combinations allowed for direct evaluation of the workflow efficiency, flexibility, and criteria for determining the best fit model. data type was characterized as either seasonal, cyclic, or sporadic. depending on data type, a specific time series forecasting model was applied. in general, seasonal or cyclic data required either an auto-regression integrated moving average (arima) model or a seasonal auto-regression integrated moving average (sarima) model while sporadic datasets employed a poisson model. several model candidates for a single country and disease combination were then compared to determine which was the best fit model. arima and sarima model selection criteria included their respective order significance, residual diagnostics, and lowest possible combination of akaike information criterion and root mean square error (rmse) values. poisson model selection criteria involved poisson or negative binomial distribution and event probability, lag dependency of immediate past events or seasonality, and lowest possible rmse. to enhance the user’s monitoring and comparisons across multiple countries and diseases, each forecasted case counts supplied a corresponding period prevalence. this period prevalence was calculated by dividing the case counts by the population in the selected country and timeframe. population records were obtained through the public world health organization database (2). results a variety of visualization tools on epi evident allows convenient interpretation on behaviors of diseases spanning multiple countries simultaneously (figure 1). countries, diseases, and timeframe are selected and displayed within a matrix alongside with their corresponding forecasts for case counts and period prevalence. by providing this full representation, users can easily interpret and anticipate disease behavior while monitoring, comparing, and forecasting case counts and period prevalence across multiple countries. for future work, the epi evident workflow can be scaled to accommodate any disease-country combination with automated model selection to allow easier and more efficient biosurveillance. conclusions epi evident empowers analysts to visualize, monitor, compare, and forecast disease case counts and period prevalence across countries. epi evident exemplifies how filtering diverse data through a flexible workflow can be scalable to output distinctive models for any given country and disease combination. thus, providing accurate forecasting and enhanced situational awareness throughout the globe. implementing this application’s methodology helps enhance and expand biosurveillance efficacy for multiple diseases across multiple countries simultaneously. figure 1. epi evident workbench with a toolbar (top) for country, disease, and timeframe selection, descriptive statistics, and matrix display of diseasecountry pairs (left) and corresponding forecasts (right). keywords biosurveillance; notifiable diseases; public health; forecast acknowledgments this work was funded by the defense threat reduction agency (project number cb10190) isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. generous nicholas, fairchild geoffrey, khalsa hari, tasseff byron, arnold james. epi archive: an automated data collection of notifiable disease data. online journal of public health informatics. 2017. 9(1):e37 2. http://apps.who.int/gho/data/view.main.pop2040?lang=en accessed: 6/20/2017 *natalie tomaszewski e-mail: ntomasze@usc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e9, 2018 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts detection of brucellosis through active surveillance, armenia, 2014 liana torosyan*, lilit avetisyan and artavazd vanyan national center for disease control and prevention, ministry of health, republic of armenia, yerevan, armenia objective in the spring of 2014, people from vulnerable households in all marzes of armenia were examined with the aim of active surveillance. introduction brucellosis is a serious disease caused by bacteria of the brucella genus. it principally affects ruminants but may be transmitted to humans. registration of cases in cattle farms causes considerable economic losses and creates favorable conditions for mass infection among humans. in armenia the expansion of animal industries and urbanization are the main reasons for occurrence and development of brucellosis. methods blood was sampled from people on farms reported as having infected animals. blood samples were tested by the wrighthuddleston method. the standard case definition of brucellosis was used for diagnosis. a questionnaire-based interview was carried out among the population to identify the form of contact with animals and to analyze epidemiological links. during the investigation provisions were followed in governmental decree ra 19.01.2006 n480-n and brucellosis prevention, epidemiology, diagnosis, treatment, preventive measures. results a total of 11160 people from 1054 households were enrolled in the study, of which 3625 (32.5%) underwent a laboratory examination. nearly 6% (641) refused to be tested. over 6% of those tested (226) were positive for antibodies to brucellae. of these, 129 (3.5%) had chronic brucellosis. those testing positive for brucellosis were treated appropriately. these included 203 (90%) adults and 23 (10%) below 14 years old; 147 (65%) were male and 79 (35%) were female. of those diagnosed with brucellosis, working in animal husbandry accounted for 46.6% (106), while those who harvested milk accounted for 37.6% (85) and those using raw milk made up 15.4% (35). conclusions cases were most frequently reported among people 20-55 years of age; the highest percentage of positives were among 41-45 year old males who had contact with infected animals. the main risk factor for acquiring brucellosis is animal husbandry. keywords brucellosis; surveillance; armenia *liana torosyan e-mail: liana_torosyan@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e175, 2017 isds annual conference proceedings 2017. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2016 conference abstracts rates and causes of stillbirth in a demographic surveillance site in karachi, pakistan muhammad imran nisar*, muhammad ilyas, komal naeem, urooj fatima, yasir shafiq and fyezah jehan pediatrics, aga khan university, karachi, pakistan objective to determine burden, timing and causes of stillbirths in a prospective cohort of pregnant from a low income community setting in peri urban karachi introduction stillbirth remained a neglected issue absent from mention in millennium development goals. an estimated 2.6 million babies are stillborn every year withhighest rate in pakistan, 43.1 stillbirths/1000 births. there is lack of good quality prospective population based data in pakistanregarding burden, timing and causes of stillbirths methods from jan – dec 2012, community helath workers (chws) identified pregnant women through 3 monthly household visits. pregnant women were then followed up till end of their pregnancy. in case of a stillbirth, a detailed verbal autopsy (va) interview was undertaken 2 weeks after the outcome by a research assistant. va forms were then reviewed by 2 independent physicians who assigned a cause for stillbirth. in case of disagreement, va form was reviewed by a third physician. a consensus between two physicians was required for a definitive cause. results there were a total of 273 stillbirths (3.04%) reported. stillbirth rate was 30.7/1000 births. distribution of antepartum and intrapartum stillbirths was 83% and 17%. three most common causes of stillbirths included pregnancy induced hypertension(37%), antepartum hemorrhage (10%) and obstructed labor(6%) (fig. 1). conclusions we have reported a high burden of stillbirths that take place during the intrapartum period. this reemphasizes need for good quality antenatal care in these settings. appropriate measure needs to be taken targeting most common causes of stillbirths, focusing on improved antepartum health care facilities keywords still birth; burden; low income countries *muhammad imran nisar e-mail: imran.nisar@aku.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(1):e184, 2017 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts risk of hiv among the seasonal labour migrants of nepal satya narayan yadav* communinty medicine, institute of medicine, ayurveda teaching hospital, kathmandu, kathmandu, nepal objective the objective was to assess the risk of hiv infection among the seasonal labour migrants of nepal. introduction hiv and aids is not a new problem to global community and human civilization. though much efforts had been taken yet its devastating effects can be seen in many areas like human productivity, public health, human rights etc. nepal is experiencing a concentrated epidemic of hiv with prevalence at, or over, 5 percent in certain high-risk groups, such as intravenous drug users (idus), msm, fsw, and migrant laborers in india who go to cities such as mumbai. the possibility of transmission of hiv infection from these highrisk groups to the general population is a serious health concern. nepal’s vulnerability to hiv has increased because of several factors including poverty coupled with the lack of employment opportunities, large-scale migration and ten years of conflict. [1] ibbs survey conducted in 2008 in mid-terai regions reported the prevalence of hiv among seasonal migrants who had sexual contact with female sex workers in india was 2.6% [1] which indicates unsafe sex being one of the major factors of hiv transmission among the seasonal migrants. similar study conducted among seasonal migrants reported that only 62% used condom during sex with sex worker and hiv infection was found only on those who visited mumbai (6.1%) and had sex with sex workers without using condom [2]. seasonal migration for income generation in mid-terai part of nepal is present since the time immemorial. people migrate to india generally to bihar, punjab, uttaranchal, maharashtra, uttar pradesh, delhi states. [2] risk of hiv transmission among the seasonal migrants is very high. separated from their spouses and adrift from social bindings, many to these migrants exercise unsafe sexual practices. regular monitoring and health assistance to this population is lacking, especially in the case of those who migrate to neighboring countries like india, compared to those who receive authorized permission to work in other countries. methods analytical cross-sectional study was conducted to assess the risk of hiv among seasonal labour migrants of three vdcs from three district of mid terai region of nepal which is the transition point for seasonal migrants going to india. the study population was the male migrants of mid-terai region visiting the study area who give oral consent and show interest to participate. 333 seasonal labor migrants’ men aged between 18 to 47 years who went to india for work for at least three months and have returned home within the last three years was selected purposively. results the results found that majority of migrants were 15-25 age group which accounts for the 69.4% of the total participants and most of the respondents were found disadvantaged dalit caste group which accounts for the 60.96 % of the total participants whereas 3.9% of participants were upper caste as well minority religious group. majority of the participants were hindu which accounts 84.7% and other were muslim, buddhist and christian (15.3%). about 42 percent of the participants had their sexual intercourse onset at the age less than 18 years of age. the majority of the migrants were found to be married, i.e. 86.49 percent. among those that had sex with women 42.68 percent ever had sex with female sex worker (fsw) and rest 57.33 percent had no sex with fsw in abroad it was reported that 61.25 percent ever had sex with fsw in nepal among them those who had sex with fsw 79.59 percent of the participants used condom during last sexual contact with fsws in nepal whereas 20.41 percent of the participants had not used condom. about 27.27 percent of the participants had sexual contact with the male partner whereas 72.73% had reported never had sexual contact with male partners. 53.22 % used condoms when having sexual contact with the male partners and 46.77 did not used condom. among total respondents, 23.7 percent migrants were at risk of hiv and 76.3 percent migrants did not at risk of hiv. the risk of hiv in age group distribution found that, age group of 26-35 years was found to be 3.40 times higher in risk than 36-45 years. similarly the risk of hiv was 4.643 times higher among age group 15-25 years as compared to 36-45 years. among them disadvantage dalit caste had more risk than upper caste. similarly illiterate had more risk than literate. in distribution of risk of hiv unmarried had high than ever married. conclusions the study showed that seasonal migrants of nepal have increased vulnerability to hiv. the unmarried labor worker and disadvantaged caste group were in the higher risk of facing hiv infection. it is necessary to design better service delivery focusing on these areas and need to explore the real situations of labor migrants. keywords risk of hiv; seasonal migrants; terai region acknowledgments i thank to office of family health international (fhi) and department of epidemioology and diseae control diviion of nepal that help to provide necessary information. references integrated biological and behavioural surveillance survey (ibbs) among seasonal migrants of western and mid to far western regions, 2008, fhi/new era. integrated biological and behavioural surveillance survey 2002, fhi/ new era, ibbs. national center for aids and std control 2007 national estimates of hiv. lowe d, francis c 2006 protecting people on the move :applying lessons learned in asia to improve hiv/aids interventions for mobile people. fhi poudel kc, masamine j, okumura j, joshi ab, wakai s. 2004. migrants’ risky sexual behaviours in india and at home in far western nepal. “tropical medicine and international health 9(8):897-903”. poudel kc, okumura j, sherchand jb, jimba m, murakami i, wakai s. 2003. mumbai disease in far western nepal: hiv infection and syphilis among male migrant-returnees and non-migrants. “tropical medicine and international health 8(10):933-9”. *satya narayan yadav e-mail: satya.yd@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e167, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts animals positive for yersinia pestis in armenia ruben danielyan* national center for disease control and prevention, shirak branch, yerevan, armenia objective the objective of this study was to determine the species composition of mammals and parasites involved in the epizootic process of plague in armenia and their geographic distribution. introduction plague was first identified in armenia in 1958 when y. pestis was isolated and cultured from the flea species ct. teres collected from the burrows of common voles in the northwestern part of the country. in the process of digitalizing archived data, a statistical and spatial analysis of the species composition of mammals and parasites involved in the epizootic process of plague between 1958 and 2016 was performed. methods the plague archives of the ncdcp were exploited. the geographic addresses from which strains of y. pestis were isolated from mammals and their parasite species were analyzed and grouped into 38 administrative regions (fig.1). for geostatistical analysis, databases were created using microsoft excel and converted into a esri geodatabase (fig.2). results data from the especially dangerous pathogen laboratories indicate that 9329 y. pestis strains were isolated in 27 of the 38 regions of the country with 7022 (75%) of the strains found in just four regions: abovyan 2597 (28%), sisian 1953 (21%), martuni 1416 (15%) and ashotsk 1056 (11%) (fig.3). during this period, plague bacteria were isolated from 17 mammal species including 15 rodents, mustela nivalis (weasel), and neomys fodiens (shrew) (fig.4). y. pestis was isolated from 22 species of fleas belonging to 11 genera along with two families of ticks. of the 9329 bacterial isolates, 6540 (70.2%) came from fleas, 2646 (28.3%) came from mammals and 143 (1.5%) were from ticks (fig.5). conclusions in armenia, the primary mammalian host for y. pestis is the common vole microtus arvalis from which 2600 isolates (27.9%) were taken. flea species from which large numbers of plague bacteria have been isolated include ct. teres-3758 (40.3%), ct. wladimiri-1262 (13.5%) and c. caspia-667 (7.1%). figure 1. the number of isolated strains of y.pestis by years. figure 2. the geostatistical analysis of isolated strains of y.pestis in armenia. figure 3. in four regions where was discovered 75% of y. pestis. figure 4. the 17 species mammals including 15 rodents, weasel and shrew. figure 5. the 11 genera fleas and two families of ticks. keywords yersinia pestis; geostatistical analysis; epidemiology *ruben danielyan e-mail: roubendanielyan@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e94, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 1emergency medicine, unc chapel hill, chapel hill, nc, usa; 2applied physics laboratory, johns hopkins university, laurel, md, usa; 3international society for disease surveillance, boston, ma, usa objective the purpose of the roundtable is to seek feedback from attendees on the components needed to improve syndromic surveillance practice through access to the shared knowledge, practices, and tools of the isds community of practice (cop). introduction knowledge management is defined as “the process of capturing, distributing, and effectively using knowledge.” [1] isds members have varying degrees of experience with public health surveillance and syndromic surveillance specifically, and will all benefit from more structured access to documentation on components related to syndromic surveillance, including but not limited to, the onboarding of facilities, data quality monitoring tools, case definitions, and data processing tools. to build a knowledge management capability, the first step is to gather initial requirements and priorities from the cop. description the roundtable will begin with a brief introduction to knowledge management and the requirements gathering goals of the roundtable. participants will be organized into small groups and tasked with identifying the types of information needed for a syndromic surveillance knowledge management repository for capturing current and future knowledge. some ideas that may be generated include syndrome definitions, data processing tools (etl, negation processing, etc.), onboarding best practices, data source pros and cons, visualization tools, data analysis tools, and data quality tools and metrics. audience engagement small groups will ensure participation among all participants. each group will be asked to brainstorm the components that should be included in a knowledge management product. groups will also discuss how content is contributed, vetted, maintained, and accessed by the cop. the larger group will discuss prioritization for the knowledge management system and next steps for community engagement in this endeavor. keywords knowledge management; community of practice; syndromic surveillance; best practices references [1] davenport, thomas h. (1994), saving it’s soul: human centered information management. harvard business review, march-april, 72 (2)pp. 119-131 *amy ising e-mail: ising@ad.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e181, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e273, 2019 isds 2019 conference abstracts automating antimicrobial usage reporting andrew walsh1, cindy hou2, nikunj vyas2 1 health monitoring, pittsburgh, pennsylvania, united states 2 jefferson health, cherry hill, new jersey, united states objective to leverage existing healthcare transaction messages to automate the aggregation of antimicrobial usage statistics in a method compatible with submission to the national healthcare safety network (nhsn) antimicrobial usage module. introduction antimicrobial stewardship is crucial to the ongoing viability of existing therapies. to facilitate this stewardship, nhsn allows hospitals to submit data on their antimicrobial usage and receive feedback on how their usage compares to other facilities [1]. this feedback can be used by hospital personnel to assess whether their antimicrobial policies are consistent with current best practices. participation in this program has so far been limited. there are several barriers to participating, including the challenge of mapping local medication information to the nhsn list of antimicrobials, the burden of tabulating the necessary statistics, and the technical requirements of generating appropriate cda documents for submission. an automated solution that obtained the necessary data from existing hl7 interfaces and generated cda documents in the correct format could significantly lower some of the barriers to submitting antimicrobial usage information to nhsn. methods a continuous feed of hl7 adt and ras messages was established between a multi-hospital health system and the epicenter syndromic surveillance system. medication administration data elements included time of administration, patient location, and a facility-specific medication identifier. patient data included time of admissions, transfers and discharges and all relevant patient locations. facility medication codes were reconciled to nhsn antimicrobial identifiers via a multi-step, semi-automated process. a medication formulary was provided by the health system mapping their codes to national drug code (ndc) identifiers. the rxnorm api [2] was queried to map ndc identifiers to rxnorm identifiers. a second round of rxnorm api queries linked the formulation-specific rxnorm identifiers to related parent rxnorm identifiers for antimicrobials provided by nhsn. the final mapping from facility medication codes to nhsn antimicrobial identifiers was manually reviewed and edited to remove duplicates and to add links by name that were not found automatically. nhsn requires reporting by administration route, which was provided in most cases by the formulary. in rare cases, that route was not sufficiently specific and had to be refined by looking at the site of administration for individual doses. therapy days were calculated as the total number of unique patient identifiers receiving at least one dose of a given antimicrobial; these were totaled per day, per route, and per inpatient location. days present were calculated as the total number of unique patient identifiers associated with a given inpatient location at any time during each day. facility-wide inpatient admissions were calculated as the total number of unique patient identifiers associated with an admission to any inpatient location during each day. results the rxnorm api yielded mapping between 15,472 ndc identifiers and 847 rxnorm codes, covering 86 (96%) nhsn antimicrobials. an initial merge using the ndc identifiers from the provided formulary yielded 252 matches to nhsn antimicrobials. manual reconciliation eliminated duplicates to leave 239 unique antimicrobials from the formulary. since not all ndc identifiers in the formulary could be associated with an rxnorm code, there was the potential for additional antimicrobials to be present but not matched to an nhsn code. the names of the nhsn antimicrobials were used to search the generic and brand names of medications in the formulary, yielding 6 additional antimicrobials with appropriate routes. after these steps of automated and manual reconciliation and excluding formulations administered via nonreportable routes, a total of 216 antimicrobial formulations were identified that can be reported to nhsn. these covered 67 (74%) nhsn antimicrobials. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e273, 2019 isds 2019 conference abstracts for july 2018, 206,921 medication administration messages were received, including 11,637 administrations of 48 nhsn antimicrobials at 14 nhsn inpatient locations were observed across all four nhsn routes. these represented 7% of completed administrations. they accounted for 6,909 days of therapy with all antimicrobials via all routes at all locations. figure 1 shows the time series of days of therapy by nhsn route of administration. a total of 950 (0.6%) administrations had medication code 99999 and could not be identified. an additional 189,717 adt messages for 5,420 distinct visits were received. these yielded 13,885 facility-wide days present and a 14,797 location-specific days present summed across all inpatient locations and facilities, from 4,054 facility-wide admissions. figure 2 shows the time series of facility-wide days present and summed location-specific days present for each facility. conclusions reconciling local facility formularies with a national standardized list of antimicrobials can be a complicated task requiring some amount of human intervention. once completed, however, hl7 messages from existing interface engines can supply sufficient information for calculating the necessary antimicrobial usage statistics to report to nhsn. acknowledgement health monitoring would like to thank the new jersey department of health for financial support of this work. references 1. centers for disease control and prevention [internet]. atlanta: national health safety network; 2017 dec 29. antimicrobial use and resistance (aur) module; 2018 jan [cited 2018 sep 10]. available from: https://www.cdc.gov/nhsn/pdfs/pscmanual/11pscaurcurrent.pdf 2. peters lb, bodenreider o. 2010. restful services for accessing rxnorm. amia annu symp proc. 2010, 983. figure 1: time series of antimicrobial days of therapy by route http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e273, 2019 isds 2019 conference abstracts figure 2: time series of days-present from adt data by facility and aggregation http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts hl7 terminology management for disease surveillance emily roberts*, theron jeppson, rachelle boulton and josh ridderhoff informatics program, utah department of health, salt lake city, ut, usa objective the objective of this abstract is to illustrate how the utah department of health processes a high volume of electronic data. we do this by translating what reporters send within an hl7 message into “epidemiologist” language for consumption into our disease surveillance system. introduction in 2013, the utah department of health (udoh) began working with hospital and reference laboratories to implement electronic laboratory reporting (elr) of reportable communicable disease data. laboratories utilize hl7 message structure and standard terminologies such as loinc and snomed to send data to udoh. these messages must be evaluated for validity, translated, and entered into utah’s communicable disease surveillance system (ut-nedss), where they can be accessed by local and state investigators and epidemiologists. despite the development and use of standardized terminologies, reporters may use different, outdated versions of these terminologies, may not use the appropriate codes, or may send local, home-grown terminologies. these variations cause problems when trying to interpret test results and automate data processing. udoh has developed a two-step translation process that allows us to first standardize and clean incoming messages, and then translate them for consumption by ut-nedss. these processes allow us to efficiently manage several different terminologies and helps to standardize incoming data, maintain data quality, and streamline the data entry process. methods udoh uses the electronic message staging area (emsa) to receive elr messages, manage terminologies such as loinc and snomed, translate messages, and automatically enter laboratory data into ut-nedss. loincs and other terms, such as facility name, sent by reporting facilities in an hl7 message are considered child terms. all child terms are mapped to a master loinc or term and each master loinc or term is mapped to a specific value within utnedss. in emsa, the rules engine used for automated processing of electronic data is set to run at the master level and these rules will determine how the message is processed. no rules are set up or run on child terms. results as of 09/20/2017, emsa contains 2,613 unique child loincs that are mapped to 906 master loincs. those 906 master loincs are mapped to 179 ut-nedss test types and 2003 child facility names are mapped to 1043 master facility names. conclusions mapping child terminologies from an hl7 message to a master vocabulary helps us to standardize incoming data, allows us to accept non-standard terminologies and correct reporting errors. translating this data into a format that is understandable to epidemiologists and investigators enables ut-nedss to work effectively in identifying outbreaks and improving health outcomes. this framework is working for elr and will continue to grow and accept more data and the different terminologies that come with that. keywords hl7; terminology; surveillance; translate *emily roberts e-mail: erroberts@utah.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e43, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin epidemiology, maricopa county department of public health, phoenix, az, usa objective to demonstrate how a local public health department used the centers for disease control and prevention (cdc) framework for program evaluation and a logic model to enhance its syndromic surveillance program. introduction the mission of the maricopa county department of public health (mcdph; arizona) is to protect and promote the health and well-being of its residents and visitors. surveillance efforts allow epidemiologists to quantify and characterize public health threats, but traditional methods take time. in an effort to enhance situational awareness, the office of epidemiology dedicated resources to begin developing a robust syndromic surveillance program. this abstract outlines steps for enhancing syndromic surveillance at a local public health department. methods the cdc framework for program evaluation was used to systematically improve mcdph’s existing syndromic surveillance program. [1] first, stakeholders from the state and county syndromic surveillance programs were engaged. the mcdph syndromic surveillance strategic planning and development workgroup was formed to identify existing resources, current challenges, and a unified mission. meetings were arranged with arizona department of health services (adhs) staff to exchange ideas for future projects. second, a logic model was created to describe mcdph’s existing and future syndromic surveillance efforts. the mcdph logic model was influenced by the national syndromic surveillance program’s logic model for enhancing syndromic surveillance capacity and practice. [2] third, the scope of the program was focused by identifying five priority initiatives for the year. the remaining steps are in progress. plans were established to measure outcomes of the program, evaluate progress for meeting goals, and share lessons learned. results the mcdph strategic planning workgroup has been meeting bi-weekly since june 2015. the workgroup identified goals and activities and organized them in a logic model (figure). using input from state and county public health officials, five priority syndromes were identified: heat-related illness, arboviral disease, drug overdose, influenza-like illness, and gastrointestinal illness. for each syndrome, workgroup members began (1) developing technical guides for accessing and analyzing data, and (2) seeking collaborations with external entities. mcdph is now actively involved with the following activities: cste heat syndrome workgroup, adhs arboviral syndromic surveillance use case project, and “flu near you” local use initiative. the workgroup plans to review its progress quarterly and adjust activities that are not adequately achieving goals. by sharing mcdph’s experience, the workgroup is achieving a goal to contribute knowledge to the nation-wide community of practice. conclusions in mcdph’s experience, the cdc framework for program evaluation was an effective tool for strategic planning, while the logic model helped focus efforts on the appropriate initiatives. in less than 3 months, the workgroup collaborated with local, state, and national stakeholders, identified challenges faced by the existing program, prioritized goals, and launched activities to enhance surveillance for five priority syndromes. the immediate next steps will be to finalize technical guides, validate syndromic surveillance queries, evaluate progress of the program, and continue to share lessons learned with the community of practice. the authors hope that by sharing this experience, other public health practitioners will be encouraged to enhance syndromic surveillance at their local health departments. logic model of the syndromic surveillance program at the maricopa county department of public health keywords strategic planning; evaluation; local level acknowledgments the authors thank the mcdph syndromic surveillance strategic planning workgroup for its contributions. references 1. centers for disease control and prevention. framework for program evaluation in public health. mmwr 1999;48(no. rr-11). 2. centers for disease control and prevention. national syndromic surveillance program funding opportunity announcement logic model [internet]. atlanta (ga): cdc division of health informatics and surveillance; 2015 mar 20 [cited 2015 aug 31]. available from: http://www.cdc.gov/nssp/foa-logic-model.html. *jessica r. white e-mail: jessicawhite@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e80, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts refocusing the vietnam hiv surveillance to the most burden areas for epidemic control diep t. vu*1, duc h. bui2, giang t. le1, duong c. thanh3, nghia v. khuu4, abu abdul-quader1 and huong t. phan2 1centers for disease prevention and control, hanoi, viet nam; 2vietnam authority of hiv/aids control, hanoi, viet nam; 3national institute of hygiene and epidemiology, hanoi, viet nam; 4pasteur institute in ho chi minh city, hanoi, viet nam objective to describe an exercise to identify priority provinces to be focused in the vietnam national hiv sentinel surveillance (hss). introduction the vietnam national hss was established in 1994. in the late 1990s and early 2000s, when the epidemic was increasing rapidly, the hss helped with the intensive close monitoring of the hiv epidemic. in its first 10 years, the hss was rapidly expanded from 6 to 40 provinces and in some years, it was conducted semi-annually. after two decades, the hiv epidemic situation has changed. in most provinces, hiv prevalence has reported to have declined. compared to the peak period, the hiv prevalence among key populations (kp) in the past decade decreased from 40-60% to 20% or lower. in many provinces, hiv prevalence was less than 10% among people who inject drugs (pwid) and less than 3% among female sex workers (fsw), and among men who have sex with men (msm) (table 1). at the same time, the hiv programme has since been scaled up widely with various interventions and expanded to most of the 63 provinces. in 2014, the government of vietnam and international stakeholders conducted a joint review of the health sector response to the hiv epidemic and concluded that for better monitoring of the epidemic, a more focused and higher quality surveillance system was needed(1). in 2015, surveillance stakeholders conducted a detailed review of the hss to discuss prioritization of the surveillance activities. methods the prioritization exercise followed a principle that the hss should be conducted in locations where there is a large population of kp with a high hiv prevalence and it is feasible to implement. criteria for prioritizing provinces for inclusion were: 1) a high estimated kp size; 2) high hiv prevalence, measured as a 5 year (2011-2015) average prevalence (p); 3) few years with low hiv prevalence, defined as p <5% among pwid, <3% among fsw and msm; 4) few years with insufficient hss sample size, defined as n<150 for pwid, n<250 for fsw and msm. steps to prioritize provinces were: reviewed provincial data on kp estimates; hiv prevalence and achieved hss sample sizes in 5 years, 2011-2015. developed a ranking algorithm taking into account kp size estimates, hiv prevalence and achieved sample sizes. for each survey on pwid, fsw, msm, took top ranked provinces for which sum of kp size estimates of these provinces exceeded 50% of the national kp size estimates. held a consultation workshop among domestic and international surveillance stakeholders to discuss the prioritization exercise. issues of regional representation of the hss in the north, south, central and highland regions was added as a criteria to adjust the priority list of hss provinces. the consensus reached in the workshop was the basis for proceeding a formal approval at ministry of health. results the data review and panel discussion suggested that the number of provinces to implement hss should be 20 for pwid, 13 for fsw, and 7 for msm surveys. while total number of provinces reduced from 40 to 20, all 4 geographical regions of the country were covered. even with the reduction of the geographical coverage of the hss, large proportions of the kps (63.9% of pwid, 58.9% of fsws and 36% of msm) were covered under the hss (table 2). in february 2017, the ministry of health officially approved the 20 priority provinces as a part of the new strategic direction of the vietnam national hss. conclusions adjusting the hss to better align it with the hiv epidemic and programmatic needs is necessary. refocusing the hss to high burden epidemic areas saves time and resources, thus enables more focus on data quality improvement. innovation to advance survey methods, adherence to survey protocol, and additional bio-markers to better monitor the epidemic will be the emphasis of the hss towards a more timely and robust surveillance system in vietnam. table 1: status of the hss before the prioritization exercise * five year (2011-2015) average hiv prevalence ** n <150 for pwid survey and n<250 for fsw or msm survey source: national hiv sentinel surveillance database, ministry of health. table 2: geographical and population coverage of the hss before and after the prioritization. * % compared to national kp size estimates keywords hiv/aids; sentinel surveillance; policy isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments vaac, regional institutes and provincial hiv/aids centers for their dedicate work on the hss. the technical assistance to strengthen the hss has been supported by the president’s emergency plan for aids relief (pepfar) through the cdc. references 1. world health organization. regional office for the western pacific, 2016, joint review of the health sector response to hiv in viet nam 2014. *diep t. vu e-mail: vubichdiep@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e80, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 1michigan department of health and human services, lansing, mi, usa; 2cdc/cste applied epidemiology fellowship, atlanta, ga, usa objective to describe the strengths and weaknesses of michigan’s legionellosis surveillance system and the influence of diagnostic methods on the temporal and geographic distribution of legionellosis cases in michigan. introduction in michigan, both presentations of legionellosis, pontiac fever (pf) and legionnaires’ disease (ld), are reportable through the michigan disease surveillance system (mdss), a web-based electronic database. legionella pneumophila serogroup 1 is responsible for 5090% of cases.1,2 several diagnostic tests are available with varying sensitivities and specificities. urinary antigen testing (uag) is the most commonly used test but only reliably detects l. pneumophila-1. culturing is the gold standard test but is limited by antibiotic interference, technical expertise, and time.3 the purpose of this study was to evaluate michigan’s legionellosis surveillance system and to determine if diagnostic methods influenced case distribution. methods descriptive and quantitative analyses were conducted using suspect and confirmed legionellosis cases reported to the mdss from 2004– 2013. michigan’s legionellosis surveillance system was evaluated according to the 2001 mmwr surveillance system evaluation guidelines focusing on data quality, timeliness, and sensitivity.4 a survey of local health departments (lhd) was used to assess data quality, acceptability, and usefulness. the median time between each step in the reporting system was calculated and compared with state reporting requirements. data from 2013 hospitalized ld cases reported through the mdss were compared with 2013 michigan hospital discharge data for ld cases to estimate the system’s sensitivity. clinical laboratories in michigan were surveyed for their diagnostic techniques and procedures. results from 2004–2013, 1756 cases of legionellosis were reported. average annual incidences of 1.44 and 0.07 per 100,000 were calculated for ld and pf, respectively. annual legionellosis incidence between 2004 and 2013 increased from 1.23 to 2.75 per 100,000. the median time from diagnosis to reporting was 2 days, with a median of 14 days between case entry and completion. among all reported cases, 13.8% of key variables had unknown or missing values; however, completeness improved by more than 20% over 10 years. hospital discharge data recorded 284 ld cases in 2013, while the mdss recorded 246 cases. of these, 189 were able to be matched on birthdate and zip code, for a calculated reporting sensitivity of 67.5%. overall, 87.8% of cases were confirmed via uag, of which 30.5% were confirmed by additional testing. cultures were performed on 10.5% of the cases. conclusions overall, the surveillance system showed moderate sensitivity and reporting times in excess of the 24-hour state requirements. however, key variable completeness increased more than 20% over 10 years. with few cases diagnosed by culture, linking cases to an environmental source, and therefore investigating outbreaks, is challenging. the high proportion of cases confirmed by uag alone could lead to 10-50% of cases going undiagnosed. further research is needed to develop standardized molecular diagnostic testing methods, e.g. pcr, that are rapid, noninvasive, and comprehensive to allow for linking cases with environmental sources. keywords surveillance evaluation; waterborne disease; legionellosis acknowledgments mdhhs bureau of disease control, prevention, and epidemiology staff: jevon mcfadden, md, mph; shannon johnson, mph, edward hartwick, ms, tiffany henderson, mph, joyce lai, mph, keira wickliffe berger, rn, msn, mph, timothy bolen. mdhhs bureau of laboratories staff: james rudrik, phd, william crafts, mt. this study/report was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-03. references 1. benin al, benson rf, besser re. trends in legionnaires’ disease, 1980-1998: declining mortality and new patterns of diagnosis. clin infect dis. 2002; 35: 1039-46. 2. fields bs, benson rf, besser re. legionella and legionnaires’ disease: 25 years of investigation. clin microbiol rev. 2002; 15(3): 506-526. 3. murdoch dr. diagnosis of legionella infection. clin infect dis. 2003; 36: 64-69. 4. german rr, lee lm, horan jm, et al. updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr recomm rep. 2001; 50 (rr-13):1-35. available from: http://www.cdc.gov/mmwr/preview/ mmwrhtml/rr5013a1.htm. *leigh m. tyndall snow e-mail: tyndallsnowl@michigan.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e167, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara 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guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluation activities from the national syndromic surveillance program sebastian romano*1, cassandra davis1, krystal collier2, sara johnston2, hana tesfamichael1 and hussain yusuf1 1centers for disease control and prevention, atlanta, ga, usa; 2arizona department of health services, phoenix, az, usa objective the objective of this session is to discuss syndromic surveillance evaluation activities. panel participants will describe contexts and importance of selected evaluation and performance measurement activities in nssp. discussions will explore ways to strengthen evaluation in syndromic surveillance activities in the future. introduction syndromic surveillance uses near-real-time emergency department healthcare and other data to improve situational awareness and inform activities implemented in response to public health concerns. the national syndromic surveillance program (nssp) is a collaboration among state and local health departments, the centers for disease control and prevention (cdc), other federal organizations, and other entities, to strengthen the means for and the practice of syndromic surveillance. nssp thus strives to strengthen syndromic surveillance at the national and the state, and local levels through the coordinated activities of the involved partners and the development and use of advanced technologies, such as the biosense platform. evaluation and performance measurement are crucial to ensure that the various strategies and activities implemented to strengthen syndromic surveillance capacity and practice are effective. evaluation activities will be discussed at this session and feedback from audience will be sought with the goal to further strengthen evaluation activities in the future. keywords syndromic surveillance; public health practice; evaluation; performance measurement *sebastian romano e-mail: wwj5@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e122, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts tourism and health information system (this) in the caribbean, june-september 2017 jonathan edwin*, lisa indar and virginia asin-oostburg surveillance, disease prevention and control division, caribbean public health agency, port of spain, trinidad and tobago objective the new tourism and health information system (this) was implemented for syndromic surveillance in visitor accommodations in the caribbean region. the objective was to monitor for illnesses and potential outbreaks in visitor accommodations (hotels/guest houses) in the caribbean in real-time using the web-based application. introduction travel and tourism pose global health security risks via the introduction and spread of disease, as demonstrated by the h1n1 pandemic (2009), chikungunya (2013), and recent zika virus outbreak. in 2016, nearly 60 million persons visited the caribbean. historically no regional surveillance systems for illnesses in visitor populations existed. the tourism and health information system (this), designed by the caribbean public health agency (carpha) from 2016-2017, is a new web-based application for syndromic surveillance in caribbean accommodation settings, with real-time data analytics and aberration detection built in. once an accommodation registers as part of the surveillance system, guests and staff can report their illness to front desk administration who then complete an online case questionnaire. alternatively guests and staff from both registered and unregistered accommodations can self-report their illness using the online questionnaire in the this web application. reported symptoms are applied against case definitions in real-time to generate the following syndromes: gastroenteritis, fever & respiratory symptoms, fever & haemorrhagic symptoms, fever & neurologic symptoms, undifferentiated fever, and fever & rash. reported data is analyzed in real-time and displayed in a data analytic dashboard that is accessible to hotel/guest house management and surveillance officers at the ministry of health. data analytics include syndrome trends over time, gender and age breakdown, and illness attack rates. methods visitor accommodations from the following countries participated: bahamas, barbados, belize, bermuda, guyana, jamaica, trinidad & tobago, and turks & caicos islands. national staff from the ministry of health, ministry of tourism, and/or tourism authority/ board engaged accommodations to participate. participating accommodations were provided with training by national staff on how to report cases and use data analytic functions. they were asked to provide registration information to carpha, such as contact information to create login credentials, and data on occupancy rates for low/high seasons, number of staff, and number of lodging rooms to calculate illness attack rates. weekly email reminders to accommodations to report cases of illness in the this web application, or to confirm ‘nil’ cases by email were sent by carpha staff. results of the 105 accommodations engaged by national staff, 39.1% (n=41) registered to participate, accounting for 3738 lodging rooms. from epidemiological week 24-39, five cases of syndromes from three accommodations in two countries were reported in the this web application (table). a case of gastroenteritis and fever & respiratory symptoms were self-reported from an unregistered accommodation. three cases of gastroenteritis were reported by hotel administration from two registered accommodations. the average response rate to weekly emails confirming ‘nil’ cases was 32.1% (range: 10.5-83.3%). one accommodation reported by email a cluster of 7 cases with possible conjuctivitis. no outbreaks or aberrations were detected in the this web application. conclusions engagement of caribbean visitor accommodations in public health surveillance is a novel but critical undertaking for promoting health, safety, and security for both visitors and locals in the tourism dependent caribbean region, but it will take time to establish. confirming the absence of illness is an important public health endeavor for visitor accommodations. preliminary results have demonstrated that it is possible for public health to work in a voluntary basis with the private accommodation sector. to establish more consistent and reliable reporting public health legislation and policies will need to be explored. as more data is gathered, assessments of the validity and sensitivity of the system will need to be conducted. syndromes reported in the tourism and health information system, juneseptember 2017 notes: 1. the threshold for gastroenteritis is an attack rate ≥2%; for fever & hemorrhagic, fever & neurologic, and fever & rash, the alert threshold is a single case. 2. attack rate % calculation = number of cases / [(number of rooms x occupancy rate x average 2 persons per room) + number of staff] keywords public health surveillance; health information systems; disease outbreaks; disease notification; travel *jonathan edwin e-mail: edwinjon@carpha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e184, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 1boston children’s hospital, boston, ma, usa; 2harvard medical school, boston, ma, usa; 3harvard extension school, cambridge, ma, usa objective • describe injury-related surveillance using clinical narratives within electronic health records • present a user friendly, physician transferrable operated natural language processing (nlp) module, which can identify injury related events from electronic health record narratives • present a variety of use cases and results introduction when hazardous materials or products emerge in the market, injury prevention researchers take action to promote awareness and legislation with the goal to prevent further injuries. this cannot be achieved without reliable data on trends and outcomes identifying large cohorts with the injury of interest. lags in providing such data will delay knowledge sharing to prevent avoidable and potentially fatal injuries. glass tables and earth magnets are two examples of consumer products with potential for significant injuries, particularly to children. magnet toys caused a large number of injuries with associated morbidity and mortality. for months there were no available data to support policy or prevention initiatives. similarly, certain disease and injury mechanisms such as penetrating oral trauma are not included as structured data and cannot be collected using icd-9/icd-10 codes. data on these types of injury mechanisms exist exclusively within the clinical narrative. methods central to our methodological approach is the belief that those people creating the data (i.e. physicians) are the best people to guide and direct surveillance from clinical narrative. we created a case identification software module which we named “dr. t” (document reviewing tool). the module uses a combination of two nlp methods: regular expressions1,2 (regex) and bag of words classifier3. the module uses a regex wizard accessible to researchers through innovative user interface elements, to generate strings to match in the emr text. cases identified using regex do not suffer the usual shortcomings associated with icd-9 code based systems. we use these cases to form powerful training and validation sets for a bag of words classifier. we train the classifier and assess its performance on the validation set. finally, the classifier is applied to unclassified data, which then presents the results to the user/reviewer. results physicians of different levels and computer user skills have used the system. training time on the module has ranged from 1-4 hours with residents, fellows, and young faculty trained within less than an hour. administrator support (mini help-desk) ranges from 1-5 hours per project. table 1 presents selected projects and their impact. conclusions although only used at a single center thus far, we have demonstrated feasibility of nlp based surveillance used by clinicians for injury prevention, research, and advocacy. our findings have been well-received by the medical literature and have made an impact on pediatric safety. nlp-based modules can make surveillance applications from the narrative form available to clinicians who otherwise would not use nlp. our methods are open source and scalable, and dissemination of this concept answers the call for timely data in the field of injury prevention. table 1: publications; time to complete keywords injury surveillance; natural language processing; case identification; software; pediatric injury prevention references 1. friedl jef. mastering regular expressions. 3rd ed. sebastapol, ca: o’reilly; 2006 2. goyvaerts j, levithan s, safari tech books online. regular expressions cookbook. 1st ed. beijing ; cambridge: oreilly; 2009 3. hutton jj. pediatric biomedical informatics computer applications in pediatric research. translational bioinformatics,. dordrecht ; new york: springer; 2012 4. agbo c, lee l, chiang v, et al. magnet-related injury rates in children: a single hospital experience. j pediatr gastroenterol nutr. jul 2013;57(1):14-17 5. kimia aa, waltzman ml, shannon mw, et al. glass table-related injuries in children. pediatr emerg care. mar 2009;25(3):145-149 6. kimia a, lee l, shannon m, et al. holiday ornament-related injuries in children. pediatr emerg care. dec 2009;25(12):819-822 7. aprahamian n, lee l, shannon m, hummel d, johnston p, kimia a. glass thermometer injuries: it is not just about the mercury. pediatr emerg care. oct 2009;25(10):645-647 8. hennelly k, kimia a, lee l, jones d, porter sc. incidence of morbidity from penetrating palate trauma. pediatrics. dec 2010;126(6):e15781584 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e20, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts hospitalization of mental disorders in saint louis county: “where you live matters” echo wang* and lara d. dame st. louis department of public health, saint louis, mo, usa objective we used hospitalization rates for mental disorders to determine utilization patterns and the need for community-based mental health services. introduction hospitalization rates for mental health disorders provide important information to help us prioritize community needs for mental health and urgent care plantation. in saint louis county, there were over 13,000 hospitalizations for mental disorders between 2010 and 2014. for all age groups, depressive disorders, including major depression and mood disorder not-otherwise-specified, were the most common primary diagnostic grouping for hospitalizations among mental disorders, followed by bipolar disorder. in 2012, the saint louis county department of planning defined 5 geographic areas (inner north, outer north, south, west and central) within and crossing saint louis county’s borders. among them, the inner north has the greatest poverty, as opposed to the west which has the least. these geographic areas, along with neighborhood poverty level, were analyzed to better understand the demographics of saint louis county residents experiencing mental disorders. methods hospitalization for a mental disorder—that is, a principal diagnosis of international classification of disease, ninth revision, code 290 – 319—among saint louis county residents from 2010 to 2014 were obtained from the missouri department of health and senior services (dhss), bureau of health care analysis and data dissemination. hospitalization rate was calculated by age, gender/sex, race/ethnicity, neighborhood poverty and geographic area using sas 9.4. the five geographic areas were created by the saint louis county department of planning for the 2012 citizen survey, which were defined based on the 49 zip codes within and crossing saint louis county’s borders. esri arcgis was used to assign each census tract to one of the five survey areas based on having greater than 50 percent of its area falling within a particular survey area. the maps were created using esri arcmap version 10.3. the maps compare geographic and social economic patterns of rates of hospitalization of a mental disorder in saint louis county by census tract. results the greatest burden of mental health-related hospitalizations was among children ages 15-17, black/african americans, and neighborhoods with “high” poverty. hospitalization rates of mental disorders among children (age 0 17) increased from 67.3 to 81.1 per 10,000 from 2010 to 2014; among adults (18+), rates increased from 124.7 to 134.8 per 10,000 from 2010 to 2014. from 2010 to 2014, children living in the inner north area were more than twice as likely to be hospitalized for a mental disorder as children who were living in the west. similarly, hospitalization rates for mental disorders among adults living in the inner north area were nearly three times greater than adults living in the west. conclusions as illustrated by the maps, there is an obvious, positive association between poverty level and mental health-related hospitalizations among residents of saint louis county. thus, although a likely overlooked policy concern, heightened focus on community-based mental health care facilities in certain areas, specifically the outer and inner north regions, may be both ethical and cost-effective. furthermore, early prevention should be developed and introduced to children at the transition age period (15-17). isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords mental health; disparity; health equity; poverty; at risk acknowledgments to saint louis department of public health chronic disease team references wojas e, meausoone v, norman c. adult psychiatric hospitalizations in new york city. department of health and mental hygiene: epi data brief (71); june 2016. *echo wang e-mail: ewang@stlouisco.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e133, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts developing and deploying universal diagnostic platforms for one-health biosurveillance harshini mukundan* chemistry, los alamos national laboratory, los alamos, nm, usa objective our goal is to develop deployable strategies for infectious disease diagnosis at the point-of-care that are applicable to multiple hosts of infection conforming to the global one health strategy for diagnostics. we aim to develop methods that do not require prior knowledge of the pathogen in question, and can facilitate rapid and effective decision-making and situational awareness. introduction there is an urgent need for diagnostic strategies for infections which are host-independent, so as to effectively track zoonotic spread, monitor animal carriers of pathogens, and evaluate transmission dynamics. infection of a host pathogen or humanby an animal results in recognition by the immune response, which consequently causes release of inflammatory mediators. many scientists have explored the use of cytokines as diagnostic indicators of disease, but the conserved nature of the immune response in humans and animals results in cross-reactivity among many pathogens, making evaluation of the results difficult, especially in high disease burden populations. measuring the pathogen-specific signature, however, is advantageous as it offers discrete identification of active infection, and discrimination from exposure. it also offers a universal strategy that can be applied to human and animal hosts of infection allowing for one health biosurveillance. achieving this, however, requires the development of a) tailored strategies for the measurement of biochemically disparate pathogen signatures in clinical samples and b) ultra-sensitive detection of such signatures in the host. the sensor team at los alamos national laboratory is working on both of these aspects, and the development of one health diagnostic platforms, the focus of the work presented here. methods many of the biomarkers secreted by bacterial pathogens and recognized by innate immune receptors elicit host cytokine responses that are amphiphilic (largely glycolipids, lipoproteins or lipoglycans). based on such, our team has developed tailored methods – membrane insertion and lipoprotein capture for the capture and detection of these greasy and stealthy molecules in infected blood. sensitivity and specificity, assay optimization, alinearity associated with lipidic molecules and assay parameter development for biomarkers associated with bacterial pathogens will be presented. considerations for clinical sampling for amphiphile detection in blood, and clinical study design will be demonstrated. our team has developed an ultra-sensitive biosensor for detection of biomarkers in complex matrices based on the evanescent field properties of single mode planar optical waveguides. detection of amphiphilic biomarkers in clinical samples, using the waveguide-based biosensor and tailored methods for the detection of such molecules has allowed for the diagnosis of infection in both human and animals hosts. herein, we present two examples of the same; 1) diagnosis of tuberculosis in humans, cattle and badgers using pathogen-based diagnostic strategies; and 2) detection of gram-negative lipopolysacharides in beef and in patients with salmonella-induced sepsis. results we have developed tailored ultra-sensitive waveguide-biosensor assays for the detection of lipoarabinomannan (and other biomarkers) from mycobacteria and demonstrated feasibility in blinded clinical studies in humans and cattle, demonstrating one -health compatibility. we have also demonstrated detection of lipopolysacharides from eight different serotypes of shiga-toxin carrying e. coli in beef, and salmonella typhimurium in pediatric patients using the same approach. conclusions we have demonstrated clinical feasibility of a one health strategy for point-of-care diagnostics of bacterial infections in blood for tuberculosis and gram-negative pathogens in clinical samples. the results will be demonstrated with several discussion points on the consideration of unconventional biochemistry of pathogens for diagnostics, factors influencing point-of-care deployment and integration of diagnostic platforms for biosurveillance. one health considerations and challenges therein will also merit discussion keywords universal diagnostics; one health; deployable sensors acknowledgments ubs concept and the gram-positive and gram-negative assay development was supported by los alamos national laboratory directed research award to mcmahon and mukundan. the lipopolysacharide assay for cattle was supported by usda (mukundan). the tuberculosis diagnostics work was supported by nih r21 (dorman, jhu with mukundan, lanl lead). we thank several people at lanl, unm, jhu for technical and administrative support. we thank the patients who generously contributed samples to our effort from uganda, kenya and south korea. references sakamuri, r; et al. accurate tracking of bovine tuberculosis biomarkers in infected cattle using a novel biomarker capture strategy, analytical sciences, 33(4): 457-460, 2017. noormohammed a et al. detection of salmonella lps in patient blood by lipoprotein capture. proc. spie 10072, optical diagnostics and sensing xvii: toward point-of-care diagnostics, 100720a, 2017. doi:10.1117/12.2253506 stromberg l et al. membrane insertion for the detection of lipopolysaccharides: exploring the dynamics of amphiphile-inlipid assays, plos one, april 2016. sakamuri rm et al. association of lipoarabinomannan with high density lipoprotein in blood: implications for diagnostics. tuberculosis (edinb). 2013 may;93(3):301-7. doi: 10.1016/j.tube.2013.02.015. epub 2013 mar 16 mukundan h et al. understanding the interaction of lipoarabinomannan with membrane mimetic architectures. tuberculosis, 92(1) 32-47, 2012. mukundan h et al., waveguide-based sensors for pathogen detection, invited review, sensors, 9(7), 5783-5809. *harshini mukundan e-mail: harshini@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e107, 2018 estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 awatef ahmed ben ramadan, md, mph, phd1,2,3*, jeannette jackson-thompson, msph, phd1,2,3, chester lee schmaltz, phd1,2 1. missouri cancer registry and research center, university of missouri-columbia 2. school of medicine department of health management and informatics, university of missouricolumbia 3. mu informatics institute, university of missouri-columbia *awatef ahmed ben ramadan, university missouri informatics institute, missouri cancer registry (mcr-arc) , university of missouri, columbia, mo usa aab365@mail.missouri.edu abstract objectives: to measure and interactively visualize female breast cancer (fbc) incidence rates in missouri by age, race, stage and grade, and senate district of residence at diagnosis from 2008 to 2012. methods: an observational epidemiological study. the fbc cases in counties split by senate districts were geocoded. population database was created. a database was created within seer*stat. the incidence rates and the 95% confidence interval (ci) were age standardized using us 2000 standard population. the census bureau’s cartographic boundary files were used to create maps showing missouri senate districts. incidence results were loaded along with the maps into instantatlas™ software to produce interactive reports. results: cancer profiles were created for all 34 missouri senate districts. an area profile and a double map that included interactive maps, graphs, and tables for the 34 missouri senate districts were built. conclusion: the results may provide an estimation of social inequality within the state and could provide clues about the impact of level of coverage and accessibility to screening and health care services on disease prevention and early diagnosis. correspondence: awatef ahmed ben ramadan, university missouri informatics institute, missouri cancer registry (mcr-arc), 401 clark hall, columbia, mo 65211-4380, (573) 882-7775, university of missouri, columbia, aab365@mail.missouri.edu doi: 10.5210/ojphi.v9i3.8084 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:aab365@mail.missouri.edu mailto:aab365@mail.missouri.edu estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi introduction breast cancer incidence rates could be increased by increasing the intensity of breast cancer screening measures and interventions. these rates might be decreased by increasing prevention measures for cancer risk factors [1]. the central cancer registry database is considered to be a high-quality source to estimate the epidemiological rates because it follows very strict regularly updated measures and standards [2]. many studies have shown that there are inequalities between cancer cases according to age, race, and stage and grade at diagnosis [3-9]. numerous evidence-based studies have concluded that the use of interactive geographic mapping software could allow users to interact easily with the datasets and help in publishing high-quality interactive reports. distribution of geospatial health data could help public health leaders and decision makers in designing, developing, and adopting effective and efficient strategies and programs to improve public health outcomes targeting the heavily affected geographical areas with the visualized health event [10-12]. the aims of the current study were to: 1) measure female breast cancer (fbc) incidence rates in missouri from 2008 to 2012 according to the fbc cases’ age at diagnosis, race, and the senate district of residence at diagnosis; 2) visualize the measured incidence rates in instantatlas™ interactive mapping reports; and 3) compare spatial variances and potential disparities in incidence data between some senate districts and the state of missouri. methods the study’s design was an observational epidemiological study. the investigators did secondary analysis of all fbc cases in the missouri cancer registry (mcr) database from january 1, 2008 through december 31, 2012. the calculated incidence rates were age standardized using us 2000 standard population for comparability across regions with differing age structures. we calculated the 95% confidence interval (ci) for these rates using seer*stat statistical package [13]. the investigators compared the calculated missouri demographic and geographical incidence rates using the same statistical package. the fbc cases in counties split by senate districts were geocoded to determine their district of residence; obtaining the denominator for districts with split counties presented a challenge. the investigators used tiger/line® shapefiles and esri arcmap™ to assign these fbc cases to senate districts [14]. population data at the district, age, race, and year level for these cases was created by combining census american community survey (acs) and population estimation program (pep) data. a database was created in (seer*stat), a statistical software package for analyzing cancer data; variables were created and imported to aid analyzing mcr’s fbc in seer*stat. the census bureau’s cartographic boundary files were used to create maps showing missouri counties and state senate districts [15]. incidence results were loaded along with the cartographic boundary estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi files into the instantatlas™ software to produce interactive mapping reports that display our study’s results. we will attach our interactive mapping reports to the mcr-arc website. the interactive reports include maps, graphs, and tables for missouri’s 34 senate districts. the senate district assignment process included all of mcr’s fbc cases diagnosed from 1996 through 2012 who were residents of missouri at diagnosis; the final analysis and maps only include those diagnosed from 2008 through 2012 with a known county of residence. to keep the registered fbc cases’ confidentiality, we suppressed cells with small counts, using a commonly-used threshold of five or fewer fbc cases [16]. race was assessed due to persistent disparities between african-american and white fbc patients, these data are collected by reporting facilities and may be a mixture of self-reported and assigned values. for the years included in this study, fairly detailed racial categories could be specified (e.g., vietnamese) and up to five races could be recorded. results the senate districts’ incidence rates of fbc were classified, as shown in tables 1-3, along with the following variables: all malignant fbc cases, age at diagnosis (<50, 50-64, and 65+), race (african-american or white), late stage (regional + distant), and high grade (iii + iv). the tables contain the incidence rates for all 34 senate districts and missouri and the 95% confidence intervals of the measured incidence data for all the above-mentioned variables. table 1. female breast cancer (fbc) incidence rates across different age groups of females in missouri (2008-2012). senate district <50 years old 50-64 years old 65+ years old rate ll ul rate ll ul rate ll ul 1 46.2 38.8 54.6 284.9 252.8 320.1 509.4 461.1 561.4 2 45.4 38.5 53.1 304.5 267.8 344.9 483.3 425.4 546.8 3 36.4 29.6 44.4 276.5 242.3 314.2 410.4 363.6 461.6 4 44.1 36.7 52.7 283.6 249.5 321.1 508.7 457.5 563.9 5 39.5 32.3 47.8 278.7 242.4 319.0 475.0 413.6 543.0 6 42.4 35.0 51.0 232.6 202.0 266.5 403.0 357.9 452.1 7 42.2 34.8 50.6 270.3 236.0 308.1 464.4 409.2 524.9 8 40.5 33.7 48.3 273.0 239.5 309.8 392.1 340.8 449.0 9 41.1 33.7 49.6 305.4 268.4 346.1 468.3 415.0 526.4 10 41.7 34.6 49.9 268.4 235.1 305.1 412.7 364.1 465.8 11 35.5 28.6 43.7 256.8 222.8 294.5 417.8 371.4 468.2 12 43.0 35.6 51.5 252.0 220.3 287.0 392.7 349.5 439.7 13 46.5 39.2 54.7 314.6 278.5 354.0 436.0 387.9 488.3 14 54.9 46.5 64.4 330.0 293.0 370.3 437.9 387.9 492.5 15 51.1 43.5 59.8 326.0 292.8 361.9 544.7 494.5 598.6 estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi 16 38.0 30.7 46.3 236.5 205.5 270.8 419.0 372.4 469.8 17 42.1 35.1 50.1 296.3 260.5 335.7 469.1 414.5 528.8 18 42.2 34.5 51.0 263.1 230.4 299.1 407.0 364.0 453.6 19 41.9 34.4 50.4 281.6 245.5 321.5 492.3 432.4 558.1 20 42.5 35.4 50.5 276.1 242.8 312.7 395.5 348.8 446.8 21 36.7 29.9 44.7 254.9 222.2 291.1 385.6 341.5 433.8 22 44.4 37.2 52.6 237.0 205.8 271.5 373.3 321.6 431.0 23 47.4 39.8 56.0 299.2 265.8 335.6 430.1 379.4 485.7 24 47.1 39.4 55.9 295.3 263.3 330.2 542.6 494.0 594.6 25 39.8 32.5 48.2 225.3 195.4 258.6 363.2 322.7 407.3 26 44.2 37.0 52.3 323.9 289.9 360.7 461.3 413.1 513.5 27 44.5 36.9 53.3 239.5 208.1 274.4 371.8 329.2 418.4 28 33.9 27.2 41.7 264.8 232.8 299.9 391.0 350.6 434.7 29 32.7 26.3 40.1 193.3 166.6 223.0 408.3 366.5 453.7 30 41.2 33.5 50.2 292.8 256.1 333.3 435.9 388.5 487.4 31 43.2 35.8 51.7 256.3 223.9 292.1 409.6 364.4 458.7 32 33.2 26.8 40.7 205.2 176.4 237.3 339.3 298.4 384.2 33 31.2 24.7 38.8 194.3 166.6 225.4 346.6 306.9 390.0 34 41.4 34.5 49.4 260.1 227.8 295.7 404.2 355.7 457.4 missouri 41.9 40.6 43.2 268.9 263.1 274.8 428.0 419.6 436.5 table 2. female breast cancer (fbc) incidence rates by race (african-american and white) in missouri (2008-2012). senate district white race african-american race rate ll ul rate ll ul 1 141.5 131.6 152.0 176.8 121.7 248.1 2 139.1 128.4 150.6 152.9 86.8 247.0 3 119.7 110.3 129.9 ^ ^ ^ 4 139.6 126.9 153.2 135.4 119.0 153.5 5 134.9 116.9 154.9 131.1 116.8 146.5 6 116.6 107.2 126.6 106.3 50.1 193.2 7 129.5 117.8 142.2 131.9 108.5 158.9 8 120.1 110.1 130.9 132.3 81.6 201.1 9 120.0 103.9 138.1 146.1 132.3 160.9 10 121.4 111.7 131.8 142.2 86.3 219.7 11 117.5 107.6 128.1 116.9 79.8 164.6 12 118.0 108.8 127.8 ^ ^ ^ estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi 13 133.1 119.0 148.6 139.8 125.0 156.0 14 145.2 128.6 163.5 144.3 130.2 159.5 15 155.6 145.5 166.4 100.8 42.3 201.8 16 117.9 108.3 128.1 66.1 26.3 138.9 17 136.2 125.6 147.6 99.8 52.2 171.0 18 120.6 111.1 130.6 186.0 114.3 284.1 19 132.0 120.6 144.2 175.5 126.6 235.7 20 123.5 114.0 133.7 ^ ^ ^ 21 114.6 105.3 124.6 87.9 46.5 151.0 22 115.3 105.5 125.8 ^ ^ ^ 23 134.4 124.1 145.4 147.8 90.3 228.5 24 153.8 142.7 165.5 132.0 102.4 167.5 25 108.7 99.6 118.6 101.2 72.7 136.9 26 140.2 130.3 150.7 84.3 32.8 198.6 27 115.2 105.8 125.3 128.4 82.2 190.3 28 111.8 103.1 121.0 ^ ^ ^ 29 104.9 96.6 113.8 ^ ^ ^ 30 130.4 119.9 141.5 78.4 33.4 152.5 31 121.1 111.6 131.2 117.3 53.4 220.5 32 99.4 90.8 108.6 ^ ^ ^ 33 96.1 87.8 104.9 ^ ^ ^ 34 120.3 110.6 130.6 105.8 60.4 170.4 missouri 123.8 122.1 125.6 134.8 129.4 140.5 ^: incidence statistics based on small number of new cases are suppressed to help protect confidentiality. as commonly used by mcr-arc and other central cancer registries, the threshold of five (5) was utilized. table 3. incidence rates of all malignant, high grade, and late stage of female breast cancer (fbc) cases in missouri (2008-2012). senate district all malignant high grade (iii + iv) late stage (r+d) rate ll ul rate ll ul rate ll ul 1 43.7 39.9 47.8 28.0 25.1 31.2 29.3 26.6 32.1 2 34.7 30.8 39.0 27.8 24.4 31.4 28.9 25.9 32.1 3 50.8 46.4 55.5 37.5 33.6 41.6 33.4 30.0 36.9 4 50.3 45.9 55.0 34.3 31.0 37.8 29.6 26.8 32.6 5 54.1 48.8 59.8 46.9 42.5 51.3 38.6 34.9 42.3 6 42.1 38.3 46.3 29.2 25.6 33.1 24.4 21.4 27.5 7 38.0 34.0 42.4 32.9 29.2 36.8 30.0 26.9 33.3 8 38.0 33.9 42.5 33.2 29.3 37.2 29.1 25.9 32.5 estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi 9 51.7 46.9 56.8 38.0 34.1 41.9 36.1 32.7 39.5 10 44.5 40.3 48.9 24.2 20.8 27.7 29.5 26.2 32.8 11 44.4 40.3 48.9 29.1 25.4 32.9 33.8 30.4 37.4 12 45.5 41.6 49.7 33.7 30.0 37.5 31.1 27.9 34.5 13 48.6 44.2 53.3 36.6 33.0 40.2 32.5 29.5 35.6 14 50.6 46.0 55.6 42.4 38.7 46.1 32.2 29.2 35.2 15 46.2 42.3 50.4 29.9 27.0 32.9 26.3 23.9 28.8 16 45.6 41.4 50.0 35.5 31.5 39.5 33.5 30.1 37.1 17 42.7 38.3 47.3 31.2 27.6 34.9 30.0 26.8 33.3 18 52.4 48.2 56.9 29.6 26.1 33.3 31.0 27.9 34.3 19 40.9 36.4 45.7 31.9 28.1 35.9 25.5 22.3 28.8 20 36.4 32.6 40.5 37.4 33.7 41.3 27.4 24.4 30.6 21 46.1 41.9 50.5 32.4 28.6 36.4 34.1 30.7 37.7 22 44.9 40.2 50.1 35.0 30.9 39.2 29.6 26.2 33.2 23 40.5 36.4 44.9 30.5 27.1 34.0 31.2 28.2 34.4 24 40.3 36.7 44.1 30.7 27.7 33.8 26.2 23.7 28.8 25 52.8 48.5 57.4 35.1 31.2 39.2 34.5 31.0 38.2 26 39.7 36.0 43.8 32.0 28.8 35.3 25.5 22.9 28.3 27 43.1 39.2 47.3 36.2 32.3 40.2 30.7 27.3 34.2 28 42.7 39.1 46.7 41.7 37.9 45.6 33.1 29.9 36.4 29 39.8 36.2 43.6 34.8 31.1 38.7 33.9 30.5 37.4 30 42.8 38.7 47.3 37.6 33.9 41.5 27.7 24.7 30.9 31 46.2 42.1 50.6 36.3 32.6 40.2 32.5 29.3 35.9 32 46.1 42.1 50.5 44.7 40.4 49.1 30.5 27.0 34.2 33 45.5 41.5 49.8 38.1 33.9 42.4 26.2 22.9 29.7 34 44.3 40.1 48.9 30.7 27.0 34.5 26.4 23.3 29.7 missouri 44.5 43.8 45.2 34.0 33.4 34.6 30.2 29.7 30.8 from the current study’s results, as shown in tables # 1, 2, & 3, we can build fbc incidence profiles for the 34 missouri senate districts. these profiles enable us to compare individual district’s results to the state of missouri and to other districts’ incidence data. the current study investigators produced senate districts interactive reporting maps using the census bureau’s cartographic boundary shapefiles. our senior statistician uploaded these mapping data along with our fbc incidence results obtained by analyzing mcr data using seer*stat to instantatlas™ data visualization software to generate interactive maps [17,18]. these interactive maps, in addition to the fbc incidence rates, also display mortality for fbc and other cancers and survival data on county, senate district, and senate district grouped to county boundaries (sdgc). the maps visualize fbc incidence data and the mapping reports are in two layouts: area profile and double map formats. these maps consist of joint spatial and statistical estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi data. the following figures show the final layouts of the instantatlas™ mapping reports we constructed at the missouri cancer registry and research center (mcr-arc) to present missouri fbc incidence data. figure 1. area profile interactive instant atlas™ report displaying female breast cancer (fbc) incidence data by senate district (age 65+ years old) 2008-2012 [17]. both mapping reports displayed our results in different formats (example: charts, tables, maps) [17,18]. the area profile report shows a single map and focuses on displaying many indicators for every senate district and compares the districts’ findings to each other and to missouri. the double map focuses on exploring the inferential statistical relationships between the selected indicators [17,18]. estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi figure 2. double map interactive instantatlas™ report displaying female breast cancer (fbc) incidence rates and percent late stage and high grade by senate district (age <50 years old) compared to the female breast 5-year cause-specific survival by senate district (age <50 years old) 2008-2012 [18]. discussion the central cancer registry database is created from different data sources, including hospitals (inpatient and outpatient settings), pathology labs, ambulatory surgical centers, long-term care facilities, physician offices and free-standing cancer clinics and treatment centers (192.650192.657 rsmo) and data exchange with other states’ central registry. the mcr data undergo strict quality control measures and the data have been assessed continually according to nationwide standards [1,2]. for all these reasons, central cancer registry databases are considered the best population-based sources to estimate the distribution of cancer incidence within the studied states. calculation and visualization of the fbc age-standardized incidence rates could help public health officials and policy makers to be informed about fbc distribution by age, race, grade and stage at diagnosis, and senate district. this might effectively impact fbc policy and research, determine estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi female at-risk groups, support targeting fbc geographical foci, and evaluate and compare diagnostic and treatment strategies all over missouri. potential problems in high population density areas — kansas city metropolitan area, saint louis metropolitan area and the city of springfield — district limits do not follow county boundaries [19]. in these areas, the census bureau’s tiger/line shapefiles software was used to determine the district containing the latitude and longitude of the address at diagnosis [14]. a problem encountered is that we did not have successfully geocoded street addresses of all fbc cases due to missing or inaccurate addresses. in these situations, we categorized them as residents of the most likely senate district by matching to cases that were successfully assigned into their senate district with the same county (if known), race (if known and categorized as white, africanamerican, and other), year of diagnosis (categorized into two time periods), and the nine-digit postal zip code. when multiple senate districts matched, then the most common one was selected; when none matched, then the process was iteratively repeated by removing the least significant digit of the postal zip code until a senate district was imputed for every case. unlike with county-level data, a detailed population file by age (in 19 groups of mostly 5-years except with <1 year-olds and 85+), race (bridged single-race), year, and sex was not found at the senate district level and had to be constructed. the limitations of this population file are that for senate districts that do not follow county boundaries, there is a mismatch between the office of management and budget (omb) 1977 and 1997 race classifications; granular age-groupings were approximated; and there is an increasing inaccuracy as one moves away from 2009-2013. conclusion measurement of incidence rates by race, age, stage and grade at diagnosis and district of residence may provide an estimation of social inequality within the state and could provide clues about the impact of level of coverage and accessibility to screening and health care services on disease prevention and early diagnosis [1]. the detailed and visually presented fbc age-adjusted incidence profiles by senate district might lead researchers and policy makers to understand effectiveness of current breast cancer initiatives and interventions and give clues about possible environmental and socioeconomic risk factors on breast cancer. according to the study results and by future research based on these results, policy makers might embrace new effective breast cancer screening and prevention initiatives and interventions in missouri, nationally, and internationally. acknowledgements core activities of the missouri cancer registry are supported in part by a cooperative agreement between the centers for disease control and prevention (cdc) and the missouri department of health and senior services (dhss) (5nu58dp003924-04/05) and a surveillance contract estimated and interactively visualized female breast cancer incidence rates in missouri senate districts: 2008-2012 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e197, 2017 ojphi between dhss and the university of missouri. the publication’s content is exclusively the responsibility of the authors and does not necessarily reflect the views of the funders. conflicts of interest none declared references 1. ellis l, woods lm, estève j, eloranta s, coleman mp, et al. 2014. cancer incidence, survival and mortality: explaining the concepts. int j cancer. 135(8), 1774-82. http://onlinelibrary.wiley.com/doi/10.1002/ijc.28990/epdf. pubmed https://doi.org/10.1002/ijc.28990 2. curado mp, edwards b, shin hr, storm h, ferlay j, et al. cancer incidence in five continents, volume ix. iarc press, international agency for research on cancer; 2007. available at 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of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts nbic biofeeds: deploying a new, digital tool for open source biosurveillance across federal agencies heather baker*1, asher grady1, collin schwantes1, emily iarocci1, rachel campbell1, gus calapristi2, scott dowson2, michelle hart2, lauren e. charles2 and teresa quitugua1 1national biosurveillance integration center, washington, dc, usa; 2pacific northwest national laboratory, richland, wa, usa objective the national biosurveillance integration center (nbic) is deploying a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the u.s. department of homeland security (dhs) and its federal interagency partners. introduction nbic integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. to meet its mission objectives, nbic utilizes a variety of data sets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. nbic biofeeds is a digital tool designed to improve the efficiency of analyzing large volumes of open source reporting and increase the number of relevant insights gleaned from this dataset. moreover, the tool provides a mechanism to disseminate tailored, electronic message notifications in near-real time so that nbic can share specific information of interest to its interagency partners in a timely manner. nbic is deploying the tool for operational use by the center and eventual use by federal partners with biosurveillance mission objectives. core functionality for data collection, curation, and dissemination useful to other federal agencies was implemented, and nbic is incorporating custom taxonomies for capturing metadata specific to the unique missions of nbic partners. methods nbic intends to implement operational use of the capability in fy 2018. the core components of the system are data collection, curation, and dissemination of information deemed important by nbic subject matter experts. nbic biofeeds has captured information from more than 70,000 unique sources published from around the globe and presents, on average, 9,000 new biosurveillance-relevant articles to users each day. nbic leverages a variety of data feeds, including third party aggregators like google and subscription-based feeds such as healthmap, as well as really simple syndication (rss) feeds and web-scraping of highly relevant sources. the nbic biosurveillance taxonomy embedded in the tool consists of more than 600 metadata targets that cover key information for understanding the significance of an active biological event, including etiologic agents, impact to humans and animals (e.g., infection severity, healthcare workers involved, type of host), social disruption, infrastructure strain, countermeasures engaged, and ‘red flag’ characteristics (e.g., pathogen appearance in a new geographic area, unusual clinical signs). this taxonomy serves as a foundation for data curation and can be tailored by nbic partners to more directly meet their own mission objectives. at this time, metadata is predominately captured by nbic analysts, who manually tag information, which triggers the population of three automatically-disseminated products from the tool: 1) the nbic daily biosurveillance review, 2) immediate and daily summary email notifications, and 3) custom-designed rss feeds. these products are meant for individual recipients in the federal interagency and for consumption by other biosurveillance information technology systems, such as the department of defense, defense threat reduction agency (dtra) biosurveillance ecosystem (bsve). nbic is working in partnership with dtra to integrate nbic biofeeds as an application directly into the bsve and further develop the bsve as an all-in-one platform for biosurveillance data analytics. to improve the efficiency and effectiveness of gaining insights using nbic biofeeds, developers of the tool at the pacific northwest national laboratory (pnnl) are researching and testing a variety of advanced analytics techniques focused on: 1) article relevancy ratings to improve the review of queried data, 2) significance ratings to elucidate the perceived severity of an event based on reported characteristics, 3) full-text article retrieval and storage for improved machine-tagging, and 4) anomaly detection for emerging threats. testing and implementation of new analytic capabilities in nbic biofeeds is planned for this fiscal year. results nbic biofeeds was developed to serve as a sophisticated and powerful open source biosurveillance technology of value to the federal government by providing information to stakeholders conducting open source biosurveillance as well as those consuming biosurveillance information. in fy 2018, nbic biofeeds will begin operational use by nbic and an initial set of users in various federal agencies. user accounts for testing purposes will be available to other federal partners, and a broad scope of federal stakeholders can receive products directly from nbic biofeeds based on their interests. conclusions nbic biofeeds is expected to enable more rapid recognition and enhanced analysis of emerging biological events by nbic analysts. nbic anticipates other federal agencies with biosurveillance missions will find this technology of value and intends to offer use of the platform to those federal partners that can benefit from access to the tool and information generated from nbic biofeeds. keywords nbic; biofeeds; biosurveillance; digital *heather baker e-mail: heather.baker@associates.hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e154, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts advanced visualization and analysis of data quality for syndromic surveillance systems brian e. dixon*1, 2, 3, chen wen2, tony french2, jennifer williams2 and shaun j. grannis4, 2 1epidemiology, indiana university fairbanks school of public health, indianapolis, in, usa; 2regenstrief insitute, indianapolis, in, usa; 3roudebush va medical center, health services research & development service, indianapolis, in, usa; 4indiana university school of medicine, indianapolis, in, usa objective to extend an open source analytics and visualization platform for measuring the quality of electronic health data transmitted to syndromic surveillance systems. introduction effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems. however, the quality of data in these systems can be poor or “unfit for use.” therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers1. current methods for examining data quality largely rely on manual queries and processes conducted by epidemiologists. better, automated tools for examining data quality are desired by the surveillance community. methods using the existing, open-source platform atlas developed by the observational health data sciences and informatics collaborative (ohdsi; www.ohdsi.org), we added new functionality to measure and visualize the quality of data electronically reported from disparate information systems. our extensions focused on analysis of data reported electronically to public health agencies for disease surveillance. specifically, we created methods for examining the completeness and timeliness of data reported as well as the information entropy of the data within syndromic surveillance messages sent from emergency department information systems. results to date we transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients into the ohdsi common data model. we further measured completeness, timeliness and entropy of the syndromic surveillance data. in figure-1, the ohdsi tool atlas summarizes the analysis of data completeness for key fields in over one million syndromic surveillance messages sent to indiana’s health department in 2014. completeness is reported by age category (e.g., 0-10, 20-30, 60+). gender is generally complete, but both race and ethnicity fields are often complete for less than half of the patients in the cohort. these results suggest areas for improvement with respect to data quality that could be actionable by the syndromic surveillance coordinator at the state health department. conclusions our project remains a work-in-progress. while functions that assess completeness, timeliness and entropy are complete, there may be other functions important to public health that need to be developed. we are currently soliciting feedback from syndromic surveillance stakeholders to gather ideas for what other functions would be useful to epidemiologists. suggestions could be developed into functions over the next year. we are further working with the ohdsi collaborative to distribute the atlas enhancements to other platforms, including the national syndromic surveillance platform (nssp). our goal is to enable epidemiologists to quickly analyze data quality at scale. data completeness for key syndromic surveillance fields summarized across 1 million emergency department messages transmitted in 2014. keywords data quality; syndromic surveillance; health information exchange acknowledgments research reported in this abstract was supported by the national library of medicine of the national institutes of health under award number r21lm012219. the content is solely the responsibility of the authors and does not necessarily represent the official views of the national institutes of health. references 1. dixon be, rosenman m, xia y, grannis sj. a vision for the systematic monitoring and improvement of the quality of electronic health data. studies in health technology and informatics. 2013;192:884-8. *brian e. dixon e-mail: bedixon@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e28, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts implemention of a laboratory information system in zimbabwe rita sembajwe1, tendai shamu*2, fortunate machingura1 and henry chidawanyika1 1rti international, decatur, ga, usa; 2zimbabwe ministry of health and child care, harare, zimbabwe objective understand the challenges that exist in the zimbabwe health systems, that could be addressed through the integration of a laboratory information management system (lims). understand key aspects for consideration when selecting and adapting a lims in a resource limited setting. showcase improvements in laboratory information management processes following adoption of a lims. introduction zimbabwe’s national health laboratory services faces multiple challenges related to inadequate financial support and skilled human resources, insufficient infrastructure, and inefficient tracking of clinical samples collected by health facilities. the slow turnaround time and poor management of the sample testing process, as well as delivery of results remain critical challenges. compounding these problems further is a manual system for tracking large volumes of samples. this laborious and time-consuming process is inefficient for management of high amounts of incoming medical samples, frequently resulting in incomplete and inaccurate data. additionally, health facilities are unable to monitor clinical samples and results in transit, leading to misplaced samples and missing results. furthermore, although the laboratory service runs on a tiered network system with lower level laboratories referring surveillance samples to higher level laboratories, processing of samples is not fulfilled promptly. the solutions to these challenges are divergent sometimes even pointing in different directions. to this end, the zimbabwe ministry of health and child care (mohcc) has identified and integrated a lims to improve tracking of samples from the time of collection through results delivery. methods our methods included an environmental needs assessment, user requirement analysis, followed by a lims customization and integration. the overarching aim has been to integrate the electronic open source bika lims into zimbabwe’s national health information systems (his), to improve laboratory information management. the user requirements gathering exercise, included focus group discussion meetings with potential lims users, and direct observations, to guide the establishment of lims specifications. the needs assessment focused on the system functionality. specifically, it investigated those aspects that would improve the ability: to track clinical samples such as creating and activating an ‘alerting’ capability when results are not reported within the set turnaround time; for users to see lists and counts of clinical samples at various testing levels; to uniquely identify samples received in the laboratories. guided by these requirements, an environmental scan of off-the-shelf and open source lims platforms was conducted to identify a few options for the zimbabwe context. primary factors for shortlisting included: an existing community of practice for support; interoperability; customizability and configurability; and local awareness of the platform. in a lims national user’s meeting, involving relevant levels of the health system (laboratories, central, provincial and district hospitals), a review of lims platform options was performed to narrow down selections. it evaluated the extent to which the user requirements (workflow, equipment interface, result management, inter-operable, quality control, and stock management) were being met. based on the evaluation, a single system (lims) was selected, adopted and adapted for use at six representative laboratories, including zimbabwe’s national microbiology reference laboratory. on-site classroom and desk-side training, for knowledge transfer to local lims users, characterised the implementation phase. local champions were identified from laboratory technicians and equipped to offer first line support. both on-site and remote support was provided to lims users. the monitoring phase is ongoing, using interview guides and lims user meetings to understand challenges and ways to improve the system. results a lims was successfully customized and integrated into zimbabwe’s national health information system infrastracture in six regional laboratories, to improve overall laboratory information management, timeliness of reporting and quality control. since its full implementation between 2013 and 2017, average turnaround time for results improved significantly from 10 to 21 days in 2013 to only 3 days in 2017. data quality improved; the number of untested clinical samples reduced from an average of 6 in 100 in 2013, to average of less or equal to 1 in 100, in 2017. also, there have been observed improvements in zimbabwe’s laboratory information management workflow and results reporting. high user satisfaction and increased lims use have led to the demand for lims expansion to additional laboratories. the lims has also managed to reduce the time required to produce disease notification reports. conclusions lims are proving to be an effective method for tracking samples and laboratory results in low resource settings like zimbabwe. lims has provided an efficient way for record, store, and track timely reporting of laboratory data, allowing for improved quality of data. overall, lims has increased efficiency in laboratory workflow and introduced the ability to adequately track samples from time of collection. keywords laboratory information systems; health information systems; health informatics; laboratory information management systems acknowledgments this work falls under a project that has been supported by the u.s. president’s emergency plan for aids relief, through the u.s. centers for disease control and prevention. references southern africa development community (sadc). 2009. assessment report on reference laboratories in the sadc region. gaborone: directorate of social and human development and special programs, sadc secretariat *tendai shamu e-mail: shamut@nmrl.org.zw online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e46, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluation of syndrome algorithms for detecting pneumonia emergency department visits priscilla wong* and hilary parton new york city department of health and mental hygiene, long island city, ny, usa objective to validate and improve the syndromic algorithm used to describe pneumonia emergency department (ed) visit trends in new york city (nyc). introduction the nyc department of health and mental hygiene (dohmh) uses ed syndromic surveillance to monitor near real-time trends in pneumonia visits. the original pneumonia algorithm was developed based on ed chief complaints, and more recently was modified following a legionella outbreak in nyc. in 2016, syndromic data was matched to new york state all payer database (sparcs) for 2010 through 2015. we leveraged this matched dataset to validate ed visits identified by our pneumonia algorithm and suggest improvements. an effective algorithm for tracking trends in pneumonia could provide critical information to inform and facilitate public health decision-making. methods the dohmh syndromic surveillance system includes daily ed data from 53 nyc hospitals. most syndrome algorithms rely solely on chief complaint, which has historically been reported more consistently than discharge diagnosis. for this analysis, the validation dataset was restricted to matched visits with consistent age (plus or minus two years) and sex between the syndromic and sparcs datasets. the original pneumonia algorithm used a basic text search function to identify any mention of icd-9-cm and icd-10-cm diagnosis codes indicating pneumonia or key words “pneumon” or “monia” within the chief complaint only. the updated algorithm additionally searches the chief complaint for any mention of key words specific to legionella (“legiona”, “legionn”, “legione”) and also searches for pneumonia icd codes in the discharge diagnosis field. syndrome sensitivity and positive predictive value (ppv) were evaluated by comparing visits identified by each algorithm to visits identified by billing diagnosis codes. a true sparcs pneumonia ed visit was defined to contain an admitting or principal diagnosis billing code indicating pneumonia. alternate algorithms were created using regular expressions, which allowed for more flexible and accurate pattern matching. the algorithms were further revised based on additional inclusion and exclusion key words identified using the validation dataset. results between 2010 and 2015, there were a total of 204,101 true pneumonia visits based on the sparcs billing records. evaluation of the original algorithm found a sensitivity of 15.6% (31,771/204,101) and a ppv of 55.6% (31,771/57,180). over the same time period, syndromic surveillance identified a total of 127,560 pneumonia visits using the updated algorithm; 86,590 of the 127,560 syndromic cases identified were determined to be a true visit based on the billing diagnosis codes, resulting in an algorithm sensitivity of 42.4% and ppv of 67.9%. of the 127,560 cases identified by the updated algorithm, 19 cases were classified as a pneumonia visit solely due to the presence of legionella key words in the chief complaint. regular expression usage as opposed to the basic text search on the updated algorithm found similar sensitivity (42.4%, 86,561/204,101) and ppv (68.0%, 86,561/127,238). among all true pneumonia visits with a non-blank discharge diagnosis field, 65.3% (68,001/104,223) had mention of a pneumonia diagnosis code. use of the discharge diagnosis code field in addition to the chief complaint found the algorithm to be almost three times more sensitive and 1.2 times greater in ppv than an algorithm without use of discharge diagnosis. seasonal trends captured with and without use of discharge diagnosis were both similar to the true pneumonia trend indicated by sparcs. among the 117,540 pneumonia cases missed by the updated algorithm, 58.6% had fewer than three words in the chief complaint. with the most popular key words among the missed cases being highly non-specific (i.e., “fever”, “cough”, “pain”), inclusion of these key words in addition to regular expression and discharge diagnosis field usage elevated algorithm sensitivity at a severe cost to the ppv. including “fever” in the list of pneumonia key words resulted in a sensitivity of 56.5% (115,280/204,101) and a ppv of 9.0% (115,280/1,282,342), while addition of the key word combination “fever” and “cough” led to a sensitivity of 46.7% (95,264/204,101) and a ppv of 29.8% (95,264/319,876). as we were unable to identify novel key word indicators that were good markers for pneumonia events, regular expression search functionality was the best improvement to the pneumonia syndrome algorithm. this revised, new algorithm maintained sensitivity (42.4%, 86,561/204,101) and provided slight improvements to ppv (68.0%, 86,561/127,219). however, performance of the updated algorithm varied across age groups. the algorithm was most effective in identifying younger cases (43.9% sensitivity, 80.4% ppv for those 17 years and younger), while it performed the worst among those 65 years and older (39.6% sensitivity, 58.7% ppv). conclusions based on our evaluation of the pneumonia syndromic surveillance algorithm, we found that search of the discharge diagnosis field greatly improved algorithm sensitivity and ppv and usage of regular expressions increased ppv slightly. including additional words possibly indicating pneumonia did not substantially improve sensitivity or ppv. however, integration of the ed chief complaint triage notes which are not currently utilized could further enhance the effectiveness of the pneumonia syndrome algorithm and better characterize daily pneumonia trends in nyc. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords pneumonia; syndromic surveillance; validation *priscilla wong e-mail: pwong4@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e10, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e287, 2019 isds 2019 conference abstracts linking emergency medical service data to death records for opioid mortality surveillance joseph r. tatar, jennifer broad wisconsin department of health services, madison, w isconsin, united states objective to identify the correlates of opioids as an underlying cause of death by linking coroner/medical examiner vital death records with emergency medical service (ems) ambulance run data. by combining death data to ems ambulance runs, the goal was to determine characteristics of the emergency response— particularly for opioid overdose events—that may connect to increased mortality. introduction opioid abuse has increased exponentially in recent years throughout the united states, leading to an increase in the incidenc e of emergency response activities, hospitalization, and mortality related to opioid overdose. as a result, states that have been hit particularly hard during this period—such as wisconsin— have allocated considerable resources to addressing this crisis via enhanced public health surveillance and outreach, procurement and administration of medical countermeasures, prescription drug monitoring programs, targeted preventive and acute treatment, first responder and hospital staff training, cross-agency collaboration, and incident management system activities. central to these efforts is the identification of the primary drivers of opioid overdose and death to improve the precision and efficacy of targeted public health interventions to address the opioid crisis. the present study sought to accomplish this end by syncing rich data sources at the point of emergency response (ems ambulance runs) to ultimate mortality outcomes (vital death records). methods in the state of wisconsin, data systems supporting the surveillance of ems ambulance runs and coroner/medical examiner death records are both maintained under the department of health services, enhancing the ability of public health researchers to connect these records using matched identifiers. two years of ems ambulance run data (2016-2017) were matched to three years of vital death records (2016-2018) that listed opioids as a contributing cause of death. ambulance runs and death records for individuals aged 10 years or younger were removed from the data prior to matching and were not included in the final analytic set. records between these two systems were matched using patient first and last name, gender, date of birth, and zip code. ambulance runs for a suspected opioid overdose were identified by mining text fields from ems primary and secondary impressions as well as incident narrative details that identified an opioid as a likely cause of the event. ambulance runs resulting in narcan/naloxone administration were also identified as opioid-related overdose. coroner/medical examiner death records that identified opioids as a contributing cause were classified as an opioid-related death. analyses examining correlates of deaths with opioids as a contributing cause focused on patient demographics, narcan/naloxone administration rates and dosage, date and time of the ambulance run, lag between ems response and time of opioid-related death, physical location and urbanicity of the incident, and the type of response by ems personnel (i.e. treated and transported, treated and released, no treatment, patient refusal, doa). results from 2016-2017, there were over 800,000 emergency ambulance runs among those aged 11 years and older. opioid overdose ambulance runs accounted for 1.1% (9,761) of those runs. there were over 100,000 deaths in wisconsin and 1.7% (1,797) were related to opioids (i.e. opioids were a contributing cause). linking resulted in 268 people with opioid overdose ambulance runs who had an opioid-related death. of these, 34% died at the scene of the ambulance run, 12% died later that day, 16% died within a week of the ambulance run, and 37% died after a week. while all of these deaths had a contributing cause of opioids, 97% also had an underlying cause of death of drug overdose. comparing those who died to those who didn’t die, those who died were more likely to be male, younger, and had the event occur on a saturday. additionally, while there were no differences in the likelihood of narcan/naloxone receipt by opioid-related death, individuals who died were more likely to have received multiple narcan/naloxone doses during the ambulance run than those who did not. of those who died at the scene, the majority (32%) were aged 30 to 39 years. of those who died later, the majority (32%) were aged 20 to 29 years. also, for those who died at the sc ene, http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e287, 2019 isds 2019 conference abstracts the majority of the events occurred from eight pm to midnight while for those who died later, the majority of events occurred from four to eight pm. conclusions the majority of linked deaths to opioid ambulance runs were due to an underlying cause of drug overdose with opioids as a contributing cause. this demonstrates that the impressions of the ems personnel were correct. the fact that so many of those who died did so at the scene highlights the continued need for community naloxone distribution. additionally, there appear to be characteristic differences between those who died, those who died at the scene, and those who didn’t die. the results from this study highlight the benefits of connecting multiple sources of data to facilitate the identification of emergency health care drivers of opioid-related death, but there is still work to be done. future analyses from this project will seek to link the existing data to hospitalization and post-discharge care records to capture a more complete picture of opioid-related deaths across the entire patient lifecycle. this future work will serve to fill key gaps in the surveillance process, particularly for instances opioid overdose and death where ems was not called into service. acknowledgement we would like to thank the office of health informatics and the office of preparedness and emergency health care, especially the ems section for their support of this project. funding for this study comes from the enhanced state opioid overdose surveillance grant, generously provided by the centers for disease control and prevention. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts application of pcr for surveillance of natural foci of especial dangerous pathogens oksana semenyshyn*, oksana velychko, liliya vasiunets, lesya hatsiy, iryna kulish and oleh kogut laboratury of edp, state institution lviv oblast laboratory center of ukraine of the ministry of health of ukraine, lviv, ukraine objective study the advantages of using the pcr method for monitoring of natural foci within the edp surveillance system. introduction natural foci of especially dangerous pathogens (edp) (tularemia, leptospirosis, anthrax, tick-borne infections) are known in lviv oblast for more than 50 years. an integral part of the epidemiological surveillance of these infections is the monitoring of environmental objects that can detect the circulation of pathogens in natural biotopes. identification and studying of the activity of natural foci of edp in the territory of lviv oblast in previous years was conducted using classical laboratory and epidemiological methods methods since 2015 in the edp laboratory are conducted investigations of field materials (mouse-like rodents, ixodic ticks, gray rats, surface water bodies, wild birds dungs, soil) during the monitoring process of the edp natural foci. from 2015 to 2017 samples of field materials were investigated in order to identify specific dna regions of causative agents of tularemia, anthrax, leptospirosis, q-fever, lyme borreliosis, etc., using the «primer designtm genesig», «genekam biotechnology ag», «mo bio laboratories, inc», «analytic jena ag» kits, which were provided within the cooperative biological engagement program (cbep), and «amplisens» kits. during this period samples were analyzed from 299 mouse-like rodents, 7325 ixodic ticks, 128 open water reservoirs, 51 wild birds dung, 92 soil samples and 17 gray rats. in addition, enzyme-linked immunosorbent assay (elisa) was used. for immunoserological monitoring selected serum samples of 80 people living in enzootic territories were used. currently, 40 human samples were investigated for the presence of igm and igg antibodies using the “serion elisa classic francisella tularensis igg/igm”. results according to the results of the research that was conducted in previous years it was revealed that in the territory of lviv oblast there are stable natural foci of tularemia, leptospirosis, tick-borne infections (lyme borreliosis, tick-borne encephalitis) present. the main reservoirs of pathogens are 5-6 species of small mouse-like rodents and gray rats who are carriers of two dominant species of ixodic ticks. live cultures of tularemia and leptospirosis were isolated from these types of reservoirs and carriers and positive results of immunoserological analysis were found. tularemia antigen was detected in 29.2% of ticks samples (d. reticulatus mainly), causative agents of lyme borreliosis d. reticulatus was detected in 17.3% of ticks and i.ricinusin was present in 8.0% of ticks. during the serological investigation of mouse-like rodents antibodies against f.tularensis were detected in 6.5% of samples, antibodies against leptospira in 6.7%, and antibodies against borrelia from 14.6% to 27.7%. during the monitoring period of burial places of animals killed from anthrax on the territory of the oblast where classical laboratory methods were used, b.anthracis was not detected. however, due to the ability of the pathogen to maintain its pathogenic properties for a long time, monitoring of such areas will be relevant in the future. according to analysis of mouse-like rodents, ticks, samples of surface water bodies, wild birds dung, soil and gray rats were received 3.18% positive samples that confirmed the presence of f.tularensis, 9.74% positive samples of pathogenic leptospira spp, 20.75% positive samples of b. burgdorferi s.l.. results of dna detection of tularemia, leptospirosis and lyme borreliosis were obtained on already known enzootic territories as well as on new ones. during the investigation of soil samples collected from burial places of animals, dna of b.anthracis were not detected. also, results of pcr-tested samples of ticks and rodents were negative and did not confirm the presence of c. burnetii. based on data received from pcr analysis, anti-epidemic measures were carried out in places where positive results were found: additional acaricidal treatment of areas of ticks breeding, deratization measures and educational work with the population. the investigation of 40 serum samples collected from people living in the enzootic territory gave negative results, but the work continues. conclusions pcr methodology provides a possibility to monitor the natural foci of edp at a fundamentally new level. its requires less time for getting results if compared to classical methods, the level of biorisks during the work is lower, and it is possible to conduct samples at the same time to detect several pathogens. keywords epidemiological surveillance; natural foci; tularemia; leptospirosis; lyme borreliosis acknowledgments authors would like to express their gratitude to the biological threat reduction program, cooperative biological engagement program (defense treat reduction agency, usa) for their support in conducting this research. references 1. practical guide for laboratory diagnosis of infectious diseases, moscow, meditsina shiko publishing house, 2009. 2. ucdcm information sheet as of 07/21/2010 no. 04.4-31/40/868 on epidemic and epizootic situation with zoonotic infections common for humans and animals (tularemia, anthrax, brucellosis, ornithosis, listeriosis) and methods of their prevention in ukraine. 3. methodical recommendations “application of polymerase chain reaction for the detection of pathogens of human infectious diseases” kyiv, 2003 (approved by the ministry of health of ukraine). *oksana semenyshyn e-mail: lab.oni.lviv@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e95, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts self-reported selected zoonotic diseases among animal handlers in ahmedabad city krupali b. patel*1 and dr. deepak b. saxena2 1center for development research (zef c), bonn university, bonn53113, germany; 2indian institute of public health gandhinagar, gandhinagar, india objective the present study aims to document the burden of self-reported selected zoonotic diseases (z/d/s) among animal handlers in urban areas of ahmedabad introduction the usual mechanism of disease or infection transmission from vertebrate animals to humans and vice-versa is classified as zoonosis [1]. globally out of all microbial pathogenic disease, 61% are zoonotic with 13% species are regarded as emerging or reemerging [2]. studies suggest the prevalence of innumerable known and important z/d/s such as leptospirosis, rabies, avian influenza but the extent of burden of zoonotic diseases amongst high-risk cohorts such as animal handlers within urban geography not adequately documented methods a cross-sectional study conducted amongst animal handlers residing in the urban/peri-urban areas of ahmedabad. a purposive sample of 170 animal handlers was included in this study. the sample size estimated based on operational feasibility and response saturation for 10%. all individuals engaged in handling animals (such as cattle, buffalos, cows, goat, dog, hen, sheep etc.) recruited from three different zones (south, east, and new west zone) randomly out of six zones of ahmedabad city, gujarat, india. data collected in vernacular language by using pretested questionnaire during the month of march to may 2017. data entered into excel and analyzed by using spss v.18. the burden was estimated in form of proportion of self-reported disease. the ethical permission was sought from the ethical review board of indian institute of public health gandhinagar results total 170 animal handlers participated in this study and majority of them were females. around 76% participants belonged to 26 to 60 years of age group with the mean age of 42±15years. there were 44% of respondents illiterate however out of total literate, 50% studied up to primary or more. around one-third, respondents belonged to below poverty line status. the cumulative prevalence of self-reported z/d/s was found 23% among respondents however amongst their family members was found 17%. the point prevalence of self-reported z/d/s during the study was found to be 17% and 18% amongst their family members. self-reported z/d/s includes vector born, animal bite and respiratory diseases. average experience and hours/day spent on handling animal was reported respectively 22±15yrs (median age of 20yrs) and 5±2 hrs. it was observed that median value for types of breeds of animals was five (iqr= 3 to 8) which ranged from 1 to 70 animals. different breeds of animals reported by the livestock keepers, which included buffalo 64%, cow 38%, goat 20%, dog 5% and sheep & bulls 4%. on inquiring about their perception on acquiring by virtue of engagement in animal handling can be a cause of the disease was reported by only one-third of participants. knowledge about common zoonotic reported for rabies (11%), respiratory disease (10%) followed by vector born disease (7%) and skin disease (1.2%). average knowledge on the mode of transmission of z/d/s was reported only 4.1%. the study also documented the commonly used methods for prevention of zoonotic disease, most common practice was found hand washing practice (83%) followed by avoiding contact to animal placenta with naked hands (68%). conclusions the prevalence of self-reported z/d/s was underestimated when compared to other studies within india. one of the common reasons could be poor awareness of z/d/s amongst high-risk groups. results suggest that it is important to initiate screening and improve the awareness of z/d/s amongst animal handlers to improve the reporting of z/d/s. table 1: disease burden of last five years possibly linked to animal handling knowledge & practices of animal handlers in urban ahmedabad keywords self reported; zoonotic disease; animal handlers; urban area; knowledge & practices acknowledgments to all participants, cattle nuisance control department of ahmedabad municipal corporation and indian institute of public health gandhinagar for cooperation and support. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references [1] a tripartite concept note. sharing responsibilities and coordinating global activities to address health risks at the animal-humanecosystems interfaces, 2010. [2] dubal, z.-b., barbuddhe, s.b. and singh, n.p. important zoonotic diseases: prevention and control. technical bulletin no. 39. icar research complex for goa (indian council of agricultural research), old goa403 402, goa, india, 2014. *krupali b. patel e-mail: pkrups78@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e168, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller university of north carolina at chapel hill, chapel hill, nc, usa objective identify and describe how the case definition used to identify mvc patients can impact results when conducting mvc surveillance using ed data. we compare mvc patients identified using external cause of injury codes (e-codes), text searches of triage notes and chief complaint, or both criteria together. introduction in 2012, an estimated 2.5 million people presented to the ed for a mvc injury in the u.s.1 national injury surveillance is commonly captured using e-codes.2 however, use of e-codes alone to capture mvc-related ed visits may result in a different picture of mvc injuries compared to using text searches of triage or chief compliant notes. methods mvc-related ed visit data were obtained from the north carolina disease event tracking and epidemiologic collection tool (nc detect) from one central county in nc for the year 2013. data were categorized based on case definition used to identify the record as pertaining to an mvc. the three case definitions were: 1) mvc injuries identified using e-codes (e810-e825), 2) text searches for mvc-related key words in either the triage note or chief complaint field, and 3) mvc injuries identified using both text searches and e-codes. demographic and descriptive characteristics included: sex, age, disposition, transport mode, payor source, visit time, and injury diagnosis (based on the barell injury matrix3). descriptive statistics were used to describe differences in patient characteristics based on the case definition used to identify mvc injury. analyses were conducted using sas version 9.2 (cary, nc) and microsoft excel 2007. results most ed visits contained both mvc-text and mvc-related e-codes (n=13422, 76%). another 4265 visits were identified by including the additional case definitions of text only (n=2139, 12%) or e-code only (n=2101, 12%). patients identified using e-codes only were more likely to be male, arrive by ambulance, and admitted to the hospital compared to patients identified by text searches or both text and e-codes. review of triage notes for those patients without e-codes suggests that patients identified with text searches are more likely to be presenting to the ed for late effects or chronic injuries from mvcs in the past. conclusions the choice of case definition used for mvc surveillance appears to impact the picture of mvc injury severity. when developing a research question or surveillance project, it is important that public health researchers are aware of the impact case definition has on their results. keywords mvc injury; surveillance; ed visit data online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e126, 2016 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts acknowledgments nc detect is funded with federal funds by north carolina division of public health (nc dph), public health emergency preparedness grant (phep), and managed through a collaboration between nc dph and the unc department of emergency medicine’s carolina center for health informatics (unc cchi). the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. the nc detect data oversight committee (doc) includes representatives from the nc dph, unc nc detect team and nc hospital association. references 1. centers for disease and control vital signs, morbidity and mortality weekly report (mmwr) oct. 2014. 2. centers for disease control and prevention. web-based injury statistics query and reporting system (wisqars) [online]. (2003). national center for injury prevention and control, centers for disease control and prevention. www.cdc.gov/ncipc/wisqars. 3. barell, vita, et al. “an introduction to the barell body region by nature of injury diagnosis matrix.” injury prevention 8.2 (2002): 91-96. *jennifer l. jones e-mail: jjones86@live.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e126, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton florida department of health, tampa, fl, usa objective objective: to characterize flakka usage in florida using multiple data sources within the electronic surveillance system for the early notification of community-based epidemics (essence-fl) introduction syndromic surveillance has historically been used to track infectious disease, but in recent years, many jurisdictions have utilized the systems to conduct all hazards surveillance and provide situational awareness with respect to previously identified issues. flakka is a synthetic drug (class: cathinones) that recently has been featured in the media. flakka is a stimulant that causes delusions, aggression, erratic behavior, a racing heart and sometimes death. two specific counties (one in florida and one in kentucky) have been at the center of this emerging epidemic. in august 2015, florida department of health (fdoh) partner agencies requested flakka-related health data in an effort to better understand the epidemiology and context of this problem. essence-fl is a large syndromic surveillance system, with four main data sources, that captures 87% of all emergency department (ed) visits statewide. methods an iterative process was used to design a query to identify flakkarelated visits in the essence-fl ed data. a concatenated chief complaint discharge diagnosis field was queried using the following string: ^flakka^,or,^flaka^,or,^flacka^,or,^flaca^,or,^flacca^. an analysis of the data was conducted to describe flakka-related emergency department visits with respect to person, place, and time. calls to the florida poison information center network (fpicn), one of the four main data sources within essence-fl, were queried to provide additional situational awareness. results of the analysis were shared with the florida fusion center and subsequently with law enforcement partners. results from september 1, 2014 – august 31, 2015, a total of 917 flakkarelated ed visits were identified using the specified query. figure 1 demonstrates sharply increasing ed visits related to flakka in 2015. in florida, broward county was demonstrated to be at the epicenter of this emerging problem. a total of 85% of all flakka-related visits occurred at broward county eds, followed by 10% at palm beach county eds. additionally, the analysis showed that males presented to the ed for flakka-related visits nearly four times as frequently as females. of the 917 flakka-related visits, 80% of them were in 20-50 year olds. ninety-one flakka exposure calls were received by the fpicn between june 2015 (when flakka received its own poison index code) and the end of august 2015. these calls demonstrated a nearly identical demographic (66% broward county residents, 74% male, and 72% of the calls were from the 20-50 year old age category) as the essence-fl ed data. conclusions having a comprehensive syndromic surveillance system in place that captures nearly all of the ed visits in florida greatly facilitated this analysis and proved valuable to fdoh’s partner agencies. flakka is an emerging public health and safety issue, which while currently focused in one geographic area of the state, is likely to spread elsewhere. essence-fl proved useful in rapidly assessing the geographic spread, age, and gender impacted. while syndromic surveillance systems were not initially designed to conduct surveillance for emergent drug usage, having near real-time surveillance systems capable of providing timely, relevant data is critical in quickly characterizing emerging public health issues and helping to prioritize resource utilization. implementing algorithms that search for previously unused words (e.g., flakka) or words or phrases that are being used in excess of their expected values would be beneficial in detecting these emerging threats more rapidly. keywords flakka; situational awareness; syndromic surveillace; drug use *david atrubin e-mail: david.atrubin@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e20, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless 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andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor 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progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e301, 2019 isds 2019 conference abstracts zoophy: a bioinformatics pipeline for virus phylogeography and surveillance matthew scotch, arjun magge, matteo vaiente arizona state university, tempe, arizona, united states objective we will describe the zoophy system for virus phylogeography and public health surveillance [1]. zoophy is designed for public health personnel that do not have expertise in bioinformatics or phylogeography. we will show its functionality by performing case studies of different viruses of public health concern including influenza and rabies virus. we will also provide its url for user feedback by isds delegates. introduction sequence-informed surveillance is now recognized as an important extension to the monitoring of rapidly evolving pathogens [2]. this includes phylogeography, a field that studies the geographical lineages of species including viruses [3] by using sequence data (and relevant metadata such as sampling location). this work relies on bioinformatics knowledge. for example, the user first needs to find a relevant sequence database, navigate through it, and use proper search parameters to obtain the desired data. they also must ensure that there is sufficient metadata such as collection date and sampling location. they then need to align the sequences and integrate everything into specific software for phylogeography. for example, beast [4] is a popular tool for discrete phylogeography. for proper use, the software requires knowledge of phylogenetics and utilization of beauti, its xml processing software. the user then needs to use other software, like treeannotator [4], to produce a single (“representative”) maximum clade credibility (mcc) tree. even then, the evolutionary spread of the virus can be difficult to interpret via a simple tree viewe r. there is software (such as spread3 [5]) for visualizing a tree within a geographic context, yet for novice users, it might not be easy to use. currently, there are only a few systems designed to automate these types of tasks for virus surveillance and phylogeogra phy. methods we have developed zoophy, a pipeline for sequence-informed surveillance and phylogeography [1]. it is designed for health agency personnel that do not have expertise in bioinformatics or phylogeography. we created a large database of all virus sequences and metadata from genbank [6] as well as a smaller database for selected viruses perceived to be of great interest for health agencies including: influenza (a, b, and c), ebola, rabies, west nile virus, and zika virus. in figure 1a, we show our front-end architecture, created in the style of the influenza research database [7], that enables the user to search by: virus, gene name, host, time-frame, and geography. we also allow users to upload their own list of genbank accessions or unpublished sequences. hitting “search” produces a results tab which includes the metadata of the sequences. we provide a feature to randomly down-sample by a specified percentage or number. we also allow the user to download the metadata in csv format or the unaligned sequences in fasta format. the final tab, "run", includes a text box for specifying an email in order to send job updates and final results on virus spread. we also enable for the user to study the influence of predictors on virus spread (via a generalized linear model). currently, we have predictors such as temperature, great circle distance, population, and sample size for selected countries. we also offer experts the ability to specify advanced modeling parameters including the molecular clock type (strict vs. relaxed), coalescent tree prio r, and chain length and sampling frequency for the markov-chain monte carlo. when the user selects “start zoophy”, a pre-processor eliminates incomplete or non-disjoint record locations and sends the rest for analysis. results when initiated, the zoophy pipeline includes sequence alignment via mafft [8] and creation of an xml template via beastgen for input into beast for discrete phylogeography. it then uses treeannotator [3] to create an mcc tree from the posterior distribution of sampled trees. zoophy uses the mcc as input into spread3 for a recreation of the time-estimated migration via a map. if the user selects the glm option, the system runs an r script to calculate the bayes factor of the inclusion probabili ty for http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e301, 2019 isds 2019 conference abstracts each predictor and draws a plot including the regression coefficient and its 95% bayesian credible interval. we are currently working on new visualization techniques such as those demonstrated by dudas et al. that combine time-oriented spread via a map and evolution on a phylogenetic tree annotated by discrete locations [9]. conclusions recent advances in phylodynamics, bioinformatics, and visualization have demonstrated the potential of pipelines to support surveillance. one example is nextstrain which can perform real-time virus phylodynamics [10]. the system has recently been added as an app to the global initiative on sharing avian influenza data (gisaid) database for influenza tracking using dna sequences [11]. this presentation will highlight a pipeline for virus phylogeography designed for epidemiologists who are not experts in bioinformatics but wish to leverage virus sequence data as part of routine surveillance. we will describe the development and implementation of our system, zoophy, and use real-world case studies to demonstrate its functionality. we invite isds delegates to use the system via our web portal, https://zodo.asu.edu/zoophy/ and provide feedback on system utilization. acknowledgement this work was supported by the national library of medicine of the nih under award r01lm012080 (to ms). references 1. scotch m, mei c, brandt c, sarkar in, cheung k. 2010. at the intersection of public-health informatics and bioinformatics: using advanced web technologies for phylogeography. epidemiology. 21(6), 764-68. pubmed https://doi.org/10.1097/ede.0b013e3181f534dd 2. gardy jl, loman nj. 2018. towards a genomics-informed, real-time, global pathogen surveillance system. nat rev genet. 19, 9-20. pubmed https://doi.org/10.1038/nrg.2017.88 3. avise jc. phylogeography: the history and formation of species. 2000, cambridge, mass.: harvard university press. 4. suchard ma, lemey p, baele g, et al. 2018. bayesian phylogenetic and phylodynamic data integration using beast 1.10. virus evol. 4(1), vey016. pubmed https://doi.org/10.1093/ve/vey016 5. bielejec f, baele g, vrancken b, et al. 2016. spread3: interactive visualization of spatiotemporal history and trait evolutionary processes. mol biol evol. 33(8), 2167-69. pubmed https://doi.org/10.1093/molbev/msw082 6. benson da, cavanaugh m, clark k, et al. 2018. genbank. nucleic acids res. 46, d41-47. pubmed https://doi.org/10.1093/nar/gkx1094 7. zhang y, et al. 2017. influenza research database: an integrated bioinformatics resource for influenza virus research. nucleic acids res. 45, d466-74. pubmed https://doi.org/10.1093/nar/gkw857 8. katoh k, standley dm. 2014. mafft: iterative refinement and additional methods. methods mol biol. 1079, 131-46. pubmed https://doi.org/10.1007/978-1-62703-646-7_8 9. dudas g, et al. 2017. virus genomes reveal factors that spread and sustained the ebola epidemic. nature. 544(7650), 309-15. pubmed https://doi.org/10.1038/nature22040 10. hadfield j, megill c, bell sm, huddleston j, et al. 2018. nextstrain: real-time tracking of pathogen evolution. bioinformatics. 34(23), 4121-23. pubmed https://doi.org/10.1093/bioinformatics/bty407 11. nextflu. 2018; available from: https://www.gisaid.org/epiflu-applications/nextflu-app/. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20924230&dopt=abstract https://doi.org/10.1097/ede.0b013e3181f534dd https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29129921&dopt=abstract https://doi.org/10.1038/nrg.2017.88 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29942656&dopt=abstract https://doi.org/10.1093/ve/vey016 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27189542&dopt=abstract https://doi.org/10.1093/molbev/msw082 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29140468&dopt=abstract https://doi.org/10.1093/nar/gkx1094 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27679478&dopt=abstract https://doi.org/10.1093/nar/gkw857 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24170399&dopt=abstract https://doi.org/10.1007/978-1-62703-646-7_8 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28405027&dopt=abstract https://doi.org/10.1038/nature22040 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29790939&dopt=abstract https://doi.org/10.1093/bioinformatics/bty407 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e301, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e301, 2019 isds 2019 conference abstracts figure1. zoophy search portal. a) a search for pdm09 h1n1 hemagglutinin (ha) sequences for 2017 in the u.s. b) the returned search result, including a u.s. heatmap of 361 pdm09 sequences. before running the analytic pipeline, we show the geographic distribution of samples and enable the user to download the metadata and unaligned (fasta) sequence file. c) we demonstrate geospatial results pertaining to spread and evolution that we will soon implement into zoophy. http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 1public health ontario, toronto, on, canada; 2university of toronto, toronto, on, canada objective to describe results of a prospective study to assess the impact of using a standard process by which public health units (phus) investigate syndromic surveillance alerts for respiratory illness. introduction public health in ontario, canada has no standardized system for carrying out syndromic surveillance. previous research had demonstrated wide variation in the implementation of syndromic surveillance. methods we recruited 15 phus that routinely accessed syndromic data (9 intervention, 6 control). many already received alerts of aberrant events directly from their system. both intervention and control groups were encouraged to continue standard practices to receiving and responding to these alerts, but in addition, intervention phus received alerts from a standard statistical algorithm designed by the study team to maximize specificity, and were asked to implement an evidence-based protocol for investigating all alerts. data collection forms (“logbooks”) collected qualitative and quantitative information about the alerts and follow-up. logbook data were grouped into themes, and tabulated to determine how frequently they occurred. results between october 2013 to february 2015, 15 phus received 1,969 alerts for respiratory and influenza-like illness syndromes from emergency department visit data. of these alerts, 942 alerts were for the intervention units and 1,027 were for the control health units. two hundred and twelve (24%)) of the intervention alerts were generated by the study. phus in the intervention group checked alerts three times more frequently than control health units for alternate explanations as specified in the protocol. control health units performed 20% more epidemiological investigations of aberrant events they received. figure 1 illustrates the types of actions taken. for control health units, 549 (53%) of the alerts were deemed to warrant a response but 341 (33% of all alerts) of these responses were described as “ watchful waiting”. in contrast, for intervention health units the numbers were 165 (18%) and 119 (13%) respectively. overall, less than 10% of the alerts led to internal and external notifications. the high percentage of alerts warranting a response in the control group is partially due to one control health unit having a low threshold for designated that action was warranted. next steps include adjusting for repeated measures by health units in calculating statistical significance. conclusions the use of a standard protocol appears to have altered the approach to verification and validation of alerts. however, the large number of alerts translated into few tangible public health actions. syndromic surveillance of emergency department visits appeared mainly to be used for “watchful waiting” and situational awareness. keywords syndromic surveillance; evaluation; response; public health acknowledgments we wish to acknowledge the participating health units and epidemiologists: algoma public health, brant public health, halton public health, hamilton public health, kfla public health, lambton public health, leeds-grenville lanark public health, niagara public health, north bay public health, ottawa public health, peterborough public health, peel public health, simcoe-muskoka public health, sudbury district health unit, and toronto public health references references chu a, savage r, whelan m, rosella lc, crowcroft ns, willison d,winter al, davies r, gemmill i, mucchal pk and johnson i. chu a, savage r, willison d, crowcroft ns, rosella lc, sider d, garay j, gemmill i, winter al, davies r and johnson i. the use of syndromic surveillance for decision-making during the h1n1 pandemic: a qualitative study. bmc public health 2012, 12:929 (30 october 2012) http://www.biomedcentral.com/1471-2458/12/929 *ian johnson e-mail: ian.johnson@oahpp.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e34, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma 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participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts monitoring of the epidemic situation with q fever in the regions of ukraine olha zarichna* rickettsial infections laboratory, si “lviv research institute of epidemiology and hygiene of the ministry of health of ukraine”, lviv, ukraine objective to investigate q fever pathogen distribution among ixodic ticks, myomorphic rodents, febrile patients, residents of enzootic areas with q fever and persons in contact with q fever, specifically infected persons in the southern and western regions of ukraine. introduction improvement of the q fever epizootic and epidemiological surveillance system remains an urgent veterinary service and healthcare problem in ukraine. the grounds for this should be laid by the results of monitoring studies of persons with a professional infection risk (livestock farms, animal processing enterprises, veterinary specialists, etc.) and living in enzootic territories, as well as research of q fever pathogen possible sources reservoirs. methods real-time pcr detection of specific dna segments of coxiella burnetii with application of commercial reagent kits. immunofluorescence microscopy detection of antigens/antibodies of studied rickettsia in biological substrates using luminescent immune sera labeled with fluorescein-5-isothiocyanate. epidemiological methods analysis of infectious diseases foci epidemiological maps. statistical methods data analysis using such software as excel and quantum gis (1.6.0). results primarily, q fever endemic areas are formed because of the circulation of coxiella burnetii in warm-blooded animal populations and their blood-sucking ectoparasites, which are the main source of the infection in humans. based on the aggregated data received from multi-year research projects in ukraine, q fever enzootic territories were found in 18 administrative regions, crimea and the city of sevastopol. currently we know of 257 areas where the pathogen was detected. the epidemic process in these territories is manifested by sporadic human diseases and the detection of the pathogen in natural carriers. the possibility of the natural foci epidemic potential increase in these territories is confirmed by the higher titers of q fever pathogen specific antibodies detected in the local population. the results of the research of the infected material that was collected in southern ukraine during 2014-2016, showed the preservation of the q fever causative agent in natural foci both in danube-dniester interfluve area of odesa region and in trans-dnistrer areas, and its significantly less prevalent in the area adjacent to odessa. in addition, the signs of natural foci formation have been revealed in other areas, which is indicative of current epidemic activity of natural foci of the infection. the results of serological studies and clinical and epidemiological surveys indicate that in the immunological structure of the population of the danube-dniester interfluve areas of odessa region, q fever is most common in rural population of working age, especially those constantly contact with farm animals. in the ivanofrankivsk region, serological studies in 2014 -2016, detected no q fever seropositive people, indicating the pathogen being in the reserve stage, which corresponds to the inter-epidemic period. at the same time, the detection of c. burnetii in ticks in the enzootic territories indicates the possibility of the pre-epidemic process being formed. since by pathogen range and transmission mechanisms q fever in ukraine is associated with many natural-focal zoonotic infections, it is advisable to monitor endemic areas using a modern observation algorithm using the introduction of geoinformation systems and the molecular genetic characteristics of circulating strains. this will increase the effectiveness of the detection of current natural and anthropurgic foci of such infections, will contribute to their detailed characterization and systematization, improve epidemiological surveillance and prevent the emergence of epidemic outbreaks among the population. the results of the research will contribute to the improvement of differential diagnosis of febrile states with an unclear etiologic agent. conclusions the results of the q fever pathogen detection in the material collected in southern and western regions of ukraine showed that the area of prevalence of this agent has been expanded to the areas and settlements that are not included in the list of enzootic territories. involvement in the ecological cycles of ixodic ticks and mouse-like rodents was observed. the presence of polyvectoral and polyhostal natural foci of this infection was found. the circulation of the causative agent of q fever in the territories of odesa and ivanofrankivsk regions where epidemic outbreaks and sporadic disease in people were also observed. keywords coxiella burnetii; q fever; natural foci; ixodic ticks; myomorphic rodents acknowledgments authors would like to express their gratitude to the state institution odesa regional laboratory center of the ministry of health of ukraine and state institution ivano-frankivsk regional laboratory center of the ministry of health of ukraine. references 1. surveillance atlas of infectious diseases // http://atlas.ecdc.europa.eu/ public/index.aspx. 2. ucdcm information sheet as of 07/21/2010 no. 04.4-31/40/868 on epidemic and epizootic situation with zoonotic infections common for humans and animals and methods of their prevention in ukraine. *olha zarichna e-mail: olha_zar@i.ua online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e149, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts impact of the nssp’s transition to essence on chief complaint field-based syndromes rasneet s. kumar* and jessica r. white maricopa county department of public health, office of epidemiology, phoenix, az, usa objective to evaluate the effect and implications of changing the chief complaint field during the national syndromic surveillance program (nssp) transition from biosense 2.0 analytical tools to biosense platform – essence introduction in january 2017, the nssp transitioned their biosense analytical tools to electronic surveillance system for early notification of community-based epidemics (essence). the chief complaint field in biosense 2.0 was a concatenation of the record’s chief complaint, admission reason, triage notes, and diagnostic impression. following the transition to essence, the chief complaint field was comprised of the first chief complaint entered or the first admission reason, if the chief complaint was blank. furthermore, the essence chief complaint field was electronically parsed (i.e., the original chief complaint text was altered to translate abbreviations and remove punctuation). this abstract highlights key findings from maricopa county department of public health’s evaluation of the new chief complaint field, its impact on heat-related illness syndromic surveillance, and implications for ongoing surveillance efforts. methods for this evaluation, we used the heat-related illness query recommended in council of state and territorial epidemiologists’ (cste) 2016 guidance document for implementing heat-related illness syndromic surveillance. before the transition, we used biosense 2.0’s, phpmyadmin analytical tool to generate a list of patients who visited maricopa county emergency departments or inpatient hospitals between 5/1/2016 – 9/30/2016 due to heat-related illness. after the transition, we used the cc and dd category “heatrelated illness, v1” in essence, which was based on the cste heat-related illness query, to generate a list of patients for the same time period. we compared the line-lists and time-series trends from phpmyadmin and essence. results the phpmyadmin analytical tool identified 785 heat-related illness records with the query (figure). 642 (82%) of these heatrelated illness records were also captured by essence. reasons for 143 (18%) records not being identified by essence included: the patient’s admission reason field contained keywords that were not available in the essence chief complaint field (n=94, 66%); data access changed, which disabled access to patients who resided in zip codes that crossed a county border (30, 21%); discrepancies between essence parsing and text in the original chief complaint (11, 8%); heat-related illness discharge diagnoses were removed by the facility after the phpmyadmin line-list for heat-related illness was extracted (7, 5%); and one record was undetermined. conversely, essence captured 36 additional heat-related illness records, not previously captured by phpmyadmin. reasons included: a query exclusion term was located in the patient’s admission reason but not the essence chief complaint field (16, 44%); a heat-related illness discharge diagnosis code was added by the facility after the data were extracted by phpmyadmin (4, 11%); and 16 (44%) were undetermined. time-series trend evaluation revealed a significant correlation between the two surveillance tools (pearson coefficient = 0.97, p < 0.01). conclusions though the data trends over time were not significantly affected by changes in the chief complaint field, differences in the field’s composition have important implications for syndromic surveillance practitioners. free-text queries designed to search the chief complaint field in essence may not retrieve records previously identified with biosense 2.0 analytical tools, which may limit individual casefinding capacity. the elimination of admission reason from the chief complaint field in essence has the greatest effect on case-finding capacity. furthermore, surveillance reports produced by essence cannot be directly compared to reports that were previously published with data from biosense 2.0. these limitations may be addressed if essence creates a feature that allows users to easily query fields (e.g., admission reason) in addition to the chief compliant field. keywords essence; chief complaint; transition; evaluation; heat-related illness acknowledgments the authors thank the arizona department of health services for contributions *rasneet s. kumar e-mail: rasneetkumar@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e44, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e274, 2019 isds 2019 conference abstracts cross disciplinary consultancy: negation detection use case mike conway1, howard burkom2, amy ising3 1 university of utah, salt lake city, utah, united states, 2 johns hopkins applied physics laboratory, baltimore, maryland, united states, 3 unc chapel hill, chapel hill, north carolina, united states objective this abstract describes an isds initiative to bring together public health practitioners and analytics solution developers from both academia and industry to define a roadmap for the development of algorithms, tools, and datasets to improve the capabilities of current text processing algorithms to identify negated terms (i.e. negation detection). introduction despite considerable effort since the turn of the century to develop natural language processing (nlp) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. those methods that have emerged (e.g. the negex algorithm [1]) are confined to local implementations with customised solutions. important reasons for this lack of progress include (a) limited shareable datasets for developing and testing methods (b) jurisdictional data silos, and (c) the gap between resource-constrained public health practitioners and technical solution developers, typically university researchers and industry developers. to address these three problems isds, funded by a grant from the defense threat reduction agency, organized a consulta ncy meeting at the university of utah designed to bring together (a) representatives from public health departments, (b) universi ty researchers focused on the development of computational methods for public health surveillance, (c) members of public health oriented non-governmental organisations, and (d) industry representatives, with the goal of developing a roadmap for the development of validated, standardised and portable resources (methods and data sets) for negation detection in clinical text used for public health surveillance. methods free-text chief complaints remain a vital resource for syndromic surveillance. however, the widespread adoption of electronic health records (and federal meaningful use requirements) has brought changes to the syndromic surveillance practice ecosystem. these changes have included the widespread use of ehr-generated chief complaint “pick lists” (i.e. pre-defined chief complaints that are selected by the user, rather than text strings input by the user at a keyboard), triage note templated text, and triage note freetext (typically much more comprehensive than traditional chief complaints). a key requirement for a negation detection algori thm is the ability to successfully and accurately process these new and challenging data streams. preparations for the consultancy included an email thread and a shared website for published articles and data samples leadin g to a structured pre-consultancy call designed to inform participants regarding the purpose of the consultancy and to align expectations. then, health department users were requested to provide data samples exemplifying negation issues in the classification proce ss. presenting developers were asked to explain their underlying ideas, details of method implementation, size and composition of corpora used for evaluation, and classification performance results. results the consultancy was held on january 19th & 20th 2017 at the university of utah’s department of biomedical informatics, and consisted of 25 participants. participants were drawn from various different sectors, with representation from isds (2), the defense threat reduction agency (1), universities and research institutes (10), public health departments (5), the department of veterans affairs (4), non-profit organisations (2), and technology firms (1). participants were drawn from a variety of different professional backgrounds, including research scientists, software developers, public health executives, epidemiologists, and analysts. day 1 of the consultancy was devoted to providing an overview of nlp and current trends in negation detection, including a detailed description of widely used algorithms and tools for the negation detection task. key questions included: should our focus http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e274, 2019 isds 2019 conference abstracts be chief complaints only, or should we widen our scope to emergency department triage notes?, how many other nlp tasks (e.g. reliable concept recognition) is it necessary to address on the road to improved negation detection? with this background established, day 2 centered on presentations from five different united states local and regional health departments (king county wa, boston ma, north carolina, georgia, and tennessee) on the various approaches to text processing and negation detection utilized across several jurisdictions. several key areas of focus emerged as a result of the consultancy discussion. first, there is a clear need for a large, easil y accessible corpus of free-text chief complaints that can form a standardised testbed for negation detection algorithm development and evaluation. annotated data, in this context, consists of chief complaints annotated for concepts (e.g. vomiting, pain in ches t) and the negation status of those concepts. it is important that the annotation include both annotated clinical concepts and negation status to allow for the uniform evaluation and performance comparison of candidate negation detection algorithms. further, the annotated corpus should consist of several thousand (as opposed to several hundred) distinct and representative chief complaints in order to compare algorithms against a sufficient variety and volume of negation patterns. conclusions the consultancy was stimulating and eye-opening for both public health practitioner and technology developer attendees. developers unfamiliar with the everyday health-monitoring context gained an appreciation of the difficulty of deriving useful indicators from chief complaints. also highlighted was the challenge of processing triage notes and other free-text fields that are often unused for surveillance purposes. practitioners were provided with concise explanations and evaluations of recent nlp approaches applicable to negation processing. the event afforded direct dialogue important for communication across professional cultures. please note that a journal paper describing the consultancy has recently been published in the online journal of public health informatics [2]. acknowledgement the consultancy was funded by a grant from the defense threat reduction agency. references 1. chapman ww, bridewell w, henbury p, cooper g, buchanan b. 2001. a simple algorithm for identifying negated findings and diseases in discharge summaries. j biomed inform. 34(5), 301-10. pubmed https://doi.org/10.1006/jbin.2001.1029 2. conway m, mowery d, ising a, velupillai s, doan s, et al. 2018. cross-disciplinary consultance to breige public health technical needs and analytic developers: negation detection use case. online j public health inform. 10(2), e209. pubmed https://doi.org/10.5210/ojphi.v10i2.8944 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12123149&dopt=abstract https://doi.org/10.1006/jbin.2001.1029 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=30349627&dopt=abstract https://doi.org/10.5210/ojphi.v10i2.8944 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts seroprevalence and molecular epidemiosurveillance of brucellosis in pakistan usman waheed*1, zargham nawaz butt2, waqas o. ashraf3 and qaiser mahmood khan2 1microbiology, pathobiology, university of veterinary & animal sciences, lahore, pakistan, lahore, pakistan; 2national institute for biotechnology & genetic engineering, faisalabad, faisalabad, pakistan; 3islamia university, bahawalpur, bahawalpur, pakistan objective to detect the presence of brucella in serum samples of occupationally exposed human and animals by conventional screening methods. to perform epidemiosurvelliance of brucella molecular based tests including genus and species specific pcr. to check the brucella prevalence in occupationally exposed human. introduction livestock sector contributes more than 58% to agriculture-based economy of pakistan. diseases of socio-economic importance are posing an enormous pressure to the growth of this sector. zoonotic diseases are generally neglected in wake of epizootics having epidemic potential. one health is a multi-sectoral approach to control zoonotic diseases at animal level to mitigate risk of transfer to the humans and environment. despite various control programs, zoonosis is known to cause public health emergencies at various regional and national levels. oie declared brucellosis as a model bacterial disease to control zoonosis in developing countries. genus brucella is expanding with its discovery in various amphibian species and marine mammals and demands control efforts at various levels. reporting of zoonosis is less than actual prevalence in third world countries like pakistan where disease is considered endemic but no official data is available. in this study, brucellosis was used as a model disease to emphasize the significance of one health. methods in total, 183 occupationally exposed human and 324 animal blood samples were collected from five different geographical areas of punjab and one region from kp. for detection of brucells, rose bangal plate test (rbpt) and celisa were carried out on serum samples. for molecular epidemiosurveillance genus specific pcr bcsp31 and specie specific pcr is711 were conducted. fifty-seven milk samples as environmental samples were aslo collected. for the testing of milk for the detection of brucella, milk ring test (mrt) was applied. results serologically in animals 26(8%) samples were found positive by rbpt & 31(9%) by celisa. disease was detected in 42(13%) & 59(18%) samples by applying molecular methods using genus specific pcr bcsp31 & specie specific pcr is711. disease was recorded in humans as 16(8%), 24(13%), 33(18%), 56(30%) by rbpt, celisa, pcr bcsp31 & pcr is711, respectively. out of 57 milk samples collected from different areas were tested by milk ring test (mrt) & 12(21%) samples were found positive. conclusions it is a significant finding that raw milk is a constant source of disease exposure to farmers, milking men and general users. disease prevalence was more in people associated with milking activities possibly due to use of raw milk. this study validate the prevalence of brucellosis in pakistan with significant presence of disease in occupationally exposed individuals emphasizing the close collaboration between veterinary and human health sectors. this study will broaden our knowledge of disease prevalence and epidemiology in pakistan. the data produced from this study will help in future control and eradication of this important zoonosis using one health approach. keywords brucellosis; occupationally exposed; pakistan; animals *usman waheed e-mail: dr.usman.waheed@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e81, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat houston health department, houston, tx, usa objective our objective is to report the antimicrobial susceptibilities of streptococcus pneumoniae received from a local safety net hospital via electronic laboratory reporting (elr), and compare susceptibility percentages with those of the centers for disease control and prevention’s active bacterial core surveillance (abcs) program. introduction since november 2014, the houston health department has been receiving antimicrobial resistance information for streptococcus pneumoniae from a safety net hospital via electronic laboratory reporting (elr). antimicrobial characteristics and vaccination rates of pneumococcal disease are of public health interest due to potential implications in treatment and prevention. ten states participate in the cdc’s active bacterial core surveillance (abcs) program. texas, which represents a different and diverse demographic compared to other states, is not an abcs participating state. no studies have compared local antimicrobial susceptibility percentages to those of the abcs. the aim of this study is to 1) report the antimicrobial susceptibility of s. pneumoniae in a local cohort, 2) characterize the demographics of the cohort including the use of pneumococcal vaccine, and 3) compare antimicrobial susceptibility percentages of the local cohort to the 2013 abcs program. methods selected local antimicrobial susceptibility results received between november 2014 and july 2015 were compared to the abcs susceptibilities using binomial methods. the data source for the local information is susceptibility elrs submitted from the local safety net hospital lab, and the data source for the abcs information is the 2013 abcs report.1 the local study cohort consisted of individuals who had positive blood cultures and reported susceptibilities for s. pneumoniae at a safety net hospital in houston (n=27). the susceptibility characterization of s. pneumoniae from elr includes percentages susceptible, intermediate, and resistant by antimicrobial drug. results from november 2014 to july 2015, there were 27 individuals with pneumococcal disease with reported susceptibilities. of the 27 patients, 9 (33%) had been vaccinated. the racial breakdown is 44% african american, 41% hispanic or latino, and 15% caucasian. about 59% were aged 50-64 years, while 26% were aged 25-49 years at the time of disease onset. we found that erythromycin shares a similar percentage susceptible as its 2013 abcs counterpart (p=0.206; 95% confidence interval 0.335 to 0.797), while penicillin has a significantly different percentage susceptible than the 2013 abcs penicillin (p < 0.01; 95% confidence interval 0.224 to 0.612). the susceptibility profile of s. pneumoniae in the local hospital cohort is displayed in table 1 below. conclusions statistical comparison of pneumococcal disease in a local safety net hospital cohort to abcs statistics can identify differences in the susceptibility profile and use of pneumococcal vaccine. erythromycin has a similar percentage susceptible in both cohorts, while penicillin has a significantly different percentage susceptible. the limitations of the study include the small sample size and potential biases due to multiple comparisons. the automated retrieval of susceptibility information via elr is an important tool for public health surveillance. these findings, which are not population-based, can inform the design and development of enhanced population-based surveillance of pneumococcal disease based on elr. table 1. susceptibility profile of s. pneumoniae in a local hospital cohort, houston, tx, november 2014 july 2015 keywords electronic lab reporting; antimicrobial resistance; pneumococcal; streptococcus pneumoniae; vaccination acknowledgments the authors would like to thank ryan arnold and nancy vuong for their assistance in reviewing this manuscript. references 1. centers for disease control and prevention. active bacterial core surveillance (abcs) report, emerging infections program network, streptococcus pneumoniae, 2013 [internet]. centers for disease control and prevention; 2015. available from: http://www.cdc.gov/ abcs/reports-findings/survreports/spneu13.pdf *avi raju e-mail: avi.raju@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e154, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence 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for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts viral load testing to monitor the hiv epidemic among pwid in vietnam nghia v. khuu*1, thuong v. nguyen1, hau p. tran1, phuc d. nguyen1, thinh x. vu1, ton tran1, diep t. vu2, giang t. le2, duc h. bui3, duong c. thanh4, van t. tieu5, linh n. nguyen6, huong t. phan3 and abu abdul-quader2 1disease control and prevention, pasteur in ho chi minh city, ho chi minh city, viet nam; 2centers for disease control and prevention, vietnam office, hanoi, viet nam; 3vietnam authority of hiv/aids control, ministry of health, hanoi, viet nam; 4national institute of hygiene and epidemiology, hanoi, viet nam; 5hcmc provincial aids center, ho chi minh city, viet nam; 6long an provincial aids center, longan, viet nam objective to share vietnam’s experiences piloting the integration of viral load (vl) testing into the national hiv sentinel surveillance (hss) system to better understand the level of hiv viral transmission among people who inject drugs (pwid). introduction vietnam initiated the hss system in 1994 in selected provinces with high hiv burden. the surveillance has two components: monitor hiv sero-prevalence and risk behaviors among key population including pwid. however, no vl data were collected among hiv infected people. in 2016, vietnam piloted an added component of vl testing to the existing hss system. the purpose was to test the feasibility of adding vl testing to the hss so that vl data among pwid would be available. the pilot was conducted in two provinces in southern vietnam-ho chi minh city and long an. it was expected that adding the vl testing to the existing hss would also save resources and help monitor hiv viral transmission among pwid in the community regardless if they are currently on anti-retroviral therapy (art). methods male pwids were enrolled into 2016 hss+ following the standard operating procedure (sop)[1]. community-based sampling was based on random selection of wards/communes listed in the sampling frame. in each selected ward/commune, all eligible pwid were invited to voluntarily participate in the survey. eligibility criteria were males 16 years of age or older, reporting injecting drug in the past month, and residing in the selected area.. the survey included an interview using a standardized questionnaire and 7ml blood drawn for hiv testing. blood specimens were transferred from districts to provincial labs for plasma separation in the same day. each plasma specimen was divided into three aliquots of 1ml each. one aliquot was used to test for hiv diagnosis at provincial labs, using the national hiv testing strategy iii[2]. the remaining 2 aliquots were stored at provincial labs at 2-80c and within 5 days, were shipped to pasteur institute in ho chi minh city (pihcm) where the plasma specimens were stored at -800c. processing of samples for vl testing was conducted at the end of the survey where all plasma specimen were transferred to pihcm lab, which was 2 months since the collection of the first blood specimen. vl was undertaken on cobas amplyprep/ cobas taqman 48, with identification threshold 20 cps/ml and specificity of 100% using kit cap-g/ctm hiv-1 v 2.0. the vl testing results were sent back to relevant provicial aids centers to return to respective participants, within 3 months. results five hundred male pwid (hcmc: 300; la: 200) were enrolled into 2016 hss/hss+ and agreed to provide blood specimen without any refusal. 84 tested positive for hiv (16.8%. hcmc: 15.0%; la: 19.5%), 43 (51.2%) specimens had unsuppressed vl (>1000 copies/ml) (hcmc: 66.7%; la: 33.3%), 35 (41.7%) specimens had undetected level (<50 copies/ml or undetected) (hcmc: 31.1%; la: 53.9%), and 7.1% had vl that ranged from 50-1000 copies/ ml (hcmc: 2.2%; la: 12.8%). among those who had vl < 1000 copies/ml, 22 (53.7%) had ever been on art. conclusions the pilot survey has measured vl among male pwid, including those who were aware of their hiv status and those who did not know their status before. findings indicate that a significant proportion of pwid do not have their vl suppressed leading to high-risk of hiv transmission from pwid to their sexual partners[3] in the community although level of unsuppressed viral load is not a direct measure of hiv viral transmission in itself. this pilot indicated that it was feasible to add vl testing into hss and vietnam government can add it as a routine practice in hss and can be expanded in the coming years. table 1. results of hiv serological testing and vl testing among hss+ specimens in ho chi minh city and long an, 2016 keywords viral load testing; hiv sentinel surveillance; vietnam; pwid acknowledgments this study has been supported by the president’s emergency plan for aids relief (pepfar) through the centers for disease control and prevention (cdc) under the terms of 5u2ggh001628-02. references 1. vietnam authority of hiv/aids control. standar operasional prosedur for hiv/sti sentinel surveillance integrated with behaviors surveys and test return. may 2016 2. moh, national guideline on hiv testing, in 1098/qd-byt, moh, editor. 2013, moh: hanoi. 3. escudero, d. j., lurie, m. n., mayer, k. h., king, m., galea, s., friedman, s. r., & marshall, b. d. l. (2017). the risk of hiv transmission at each step of the hiv care continuum among people who inject drugs: a modeling study. bmc public health, 17, 614. http://doi.org/10.1186/s12889-017-4528-9 *nghia v. khuu e-mail: khuu.nghia@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e198, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluation of an arboviral syndrome query used in maricopa county, arizona kaitlyn sykes*1, 2, rasneet s. kumar1, melissa kretschmer1 and jessica r. white1 1maricopa county department of public health, phoenix, az, usa; 2cste applied epidemiology fellowship, atlanta, ga, usa objective to evaluate arizona’s arboviral syndromic surveillance protocol in maricopa county. introduction timely identification of arboviral disease is key to prevent transmission in the community, but traditional surveillance may take up to 14 days between specimen collection and health department notification. arizona state and county health agencies began monitoring national syndromic surveillance program biosense 2.0 data for patients infected with west nile virus (wnv), st. louis encephalitis virus (slev), chikungunya, or dengue virus in august 2015. zika virus was added in april 2016. our novel methods were presented at the international society for disease surveillance 2015 annual conference. [1] twice per week, we queried patient records from 15 maricopa county biosense-enrolled emergency department and inpatient hospitals for chief complaint keywords and discharge diagnosis codes. our “case investigation decision tree” helped us determine whether records had a high or low degree of evidence for arboviral disease and necessitated further investigation. this study evaluated how arizona’s protocol for conducting syndromic surveillance compared to traditional arboviral surveillance in terms of accuracy and timeliness in maricopa county from august 2015 through december 2016. methods we followed guidelines from the centers for disease control and prevention (cdc) to evaluate two major attributes of the protocol: accuracy [measured as positive predictive value (ppv) and sensitivity] and timeliness. [2] arizona’s medical electronic disease surveillance intelligence system (medsis) was considered the “gold standard” system and biosense was the test system. ppv was calculated as the proportion of records identified by biosense that were reported to medsis, regardless of final case classification. sensitivity was the proportion of confirmed or probable cases in medsis identified by biosense. though not all medsis cases were seen at biosensereporting facilities, the sensitivity demonstrates how each query contributed to arboviral surveillance overall. we assessed timeliness in two ways: (1) the difference between the date when keywords or diagnosis codes were first identified by biosense and the date the same patient was first reported to medsis; and (2) the difference between the date the biosense record was first reviewed by the maricopa county department of public health (mcdph) syndromic surveillance team and the date the same patient was first investigated through medsis by the mcdph disease investigators. we assessed whether timeliness was affected by the method in which a record was identified in biosense (i.e., chief complaint keyword or discharge diagnosis code). results the arboviral syndromic surveillance queries identified 62 records during the evaluation period (table). for each arboviral query, the proportion of biosense records that were also reported through medsis ranged from 25.0% to 32.4%, except chikungunya, which had a ppv of 0%. biosense records that had a high degree of evidence for arboviral disease tended to have a higher ppv compared to those with low evidence. biosense records that were not already reported to medsis met neither clinical nor exposure criteria for the arboviral diseases and were not deemed a public health risk. the sensitivities of the wnv and zika queries to detect confirmed or probable cases in medsis were 8.2% and 5.6%, respectively, while slev, chikungunya, and dengue queries were 0%. on average, patients were reported to medsis 7 days prior to biosense identifying keywords or diagnosis codes. in addition, medsis cases were investigated by mcdph disease investigators 10 days prior to mcdph syndromic surveillance team review of biosense records, on average. the average time between medsis report date and biosense identification date was shorter for biosense records identified by chief complaint keywords than by diagnosis codes (4 and 8 days after medsis, respectively). conclusions arizona’s arboviral syndromic surveillance protocol provided mcdph with situational awareness, but biosense data were not available more quickly than traditional mandated reporting. through this process, we reviewed patient records that mentioned arboviral diseases and confirmed that these reportable conditions were captured in our traditional surveillance system. the decision tree was effective at prioritizing records for further investigation. timeliness may be improved by updating the queries to include more chief complaint keywords and reviewing biosense more than twice per week. mcdph plans to evaluate arizona’s updated arboviral syndromic surveillance protocol, which was adapted for biosense platform’s electronic surveillance system for early notification of communitybased epidemics (essence). keywords arboviral; evaluation; surveillance acknowledgments this study was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists and funded by cdc cooperative agreement number 1u38ot000143-04. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. white, j. r., imholte, s., & collier, k. (2016). using syndromic surveillance to enhance arboviral surveillance in arizona. online j public health inform, 8(1), e81. 2. german, r. r., et al. (2001). updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr recomm rep, 50(rr-13), 1-35. *kaitlyn sykes e-mail: kaitlynsykes@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e123, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts how missing discharge diagnosis data in syndromic surveillance leads to coverage gaps kayley dotson* and mandy billman epidemiology resource center, indiana state department of health, indianapolis, in, usa objective to identify surveillance coverage gaps in emergency department (ed) and urgent care facility data due to missing discharge diagnoses. introduction indiana utilizes the electronic surveillance system for the early notification of community-based epidemics (essence) to collect and analyze data from participating hospital emergency departments. this real-time collection of health related data is used to identify disease clusters and unusual disease occurrences. by administrative code, the indiana state department of health (isdh) requires electronic submission of chief complaints from patient visits to eds. submission of discharge diagnosis is not required by indiana administrative code, leaving coverage gaps. our goal was to identify which areas in the state may see under reporting or incomplete surveillance due to the lack of the discharge diagnosis field. methods emergency department data were collected from indiana hospitals and urgent care clinics via essence. discharge diagnosis was analyzed by submitting facility to determine percent completeness of visits. a descriptive analysis was conducted to identify the distribution of facilities that provide discharge diagnosis. a random sample of 20 days of data were extracted from visits that occurred between january 1, 2017 and september 6, 2017. results a random sample of 179,039 (8%) ed entries from a total of 2,220,021 were analyzed from 121 reporting facilities. of the sample entries, 102,483 (57.24%) were missing the discharge diagnosis field. over 40 (36%) facilities were missing more than 90% of discharge diagnosis data. facilities are more likely to be missing >90% or <19% of discharge diagnoses, rather than between those points. comparing the percent of syndromic surveillance entries missing discharge diagnosis across facilities reveals large variability. for example, some facilities provide no discharge diagnoses while other facilities provide 100%. the number of facilities missing 100% of discharge diagnoses (n = 19) is 6.3 times that of the facilities that are missing 0% (n = 3). the largest coverage gap was identified in public health preparedness district (phpd)1 three (93.16%), with districts five (64.97%), seven (61.94%), and four (61.34%) making up the lowest reporting districts. see table 2 and figure 12 for percent missing by district and geographic distribution. phpd three and five contain a large proportion (38%) of the sample population ed visits which results in a coverage gap in the most populated areas of the state. conclusions querying essence via chief complaint data is useful for realtime surveillance, but is more informative when discharge diagnoses are available. indiana does not require facilities to report discharge diagnosis, but regulatory changes are being proposed that would require submission of discharge diagnosis data to isdh. the addition of discharge diagnose is aimed to improve the completeness of disease clusters and unusual disease occurrence surveillance data. table 1. emergency department facilities by percent (%) missing discharge diagnosis. table 2. percent (%) missing discharge diagnosis by public health preparedness district (phpd). figure 1. indiana public health preparedness districts isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords syndromic surveillance; discharge diagnosis; outbreak coverage acknowledgments the authors gratefully acknowledge ann kayser and noah zarr for their support and contributions to the project. references 1. preparedness districts [internet]. indianapolis (in): indiana state department of health, public health preparedness; 2017 [cited 2017 sept 20]. available from: https://www.in.gov/isdh/17944.htm. *kayley dotson e-mail: kdotson0914@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e134, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts tracking drug-related overdoses at the local level: using syndromic surveillance in the co-ncr emery shekiro*1, lily sussman2 and talia brown2 1denver public health, denver, co, usa; 2boulder county public health, boulder, co, usa objective in order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the colorado north central region (co-ncr). secondarily, we used free text fields to understand the use of unspecified diagnosis fields. introduction the united states is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. in colorado the rate of fatal drug overdose increased 68% from 20022014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. the cdc’s national syndromic surveillance program (nssp) provides nearreal time monitoring of emergency department (ed) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. colorado north central region (co-ncr) receives data for 4 local public health agencies from 25 hospitals across adams, arapahoe, boulder, denver, douglas, and jefferson counties. access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. however, use of these data also comes with many challenges. for example, we learned from key informant interviews with ed staff in boulder and denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses. methods boulder county public health (bcph) and denver public health (dph) developed a query in early notification of community based epidemics (essence) using icd-10-cm codes to identify cases of drug-related overdose [t36-t51] from october 2016 to september 2017. the case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. the first diagnosis code in the file was considered the primary diagnosis. chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the concr. we also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county. results the query identified 2,366 drug-related overdoses in the concr. the prevalence of drug overdoses differed across age groups. the detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). females comprised 56.1% of probable general drug overdoses. the majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (t50), narcotics (t40) (12.6%), or non-opioid analgesics (t39) (7.8%). for some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields. conclusions syndromic surveillance offers the opportunity to capture drugrelated overdose data in near-real time. we found variation in drugrelated overdose by demographic groups. unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. chart reviews and access to more fields from the ed electronic health record could improve local drug surveillance. general drug overdose ed visits in the colorado north central region from october 2016 to september 2017 by week keywords syndromic surveillance; drug overdose; essence acknowledgments we would like to acknowledge the contributions of yushiuan chen, michele askenazi, kathryn deyoung, art davidson, and christine billings to the co-ncr syndromic surveillance system. references 1. colorado vital statistics. death data. colorado department of public health and environment. 2015. 2. colorado health and hospital association. hospital discharge dataset. 2015. *emery shekiro e-mail: emery.shekiro@dhha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e185, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts ed and poison center surveillance for the great american solar eclipse in oregon laurel boyd*1, sandy giffin2, meredith jagger1 and melissa powell1 1oregon public health authority, portland, or, usa; 2oregon poison center, portland, or, usa objective identify surveillance priorities for emergency department (ed) and oregon poison center (opc) data ahead of the 2017 great american solar eclipse gatherings in oregon and create a suite of queries for use in the health intelligence section of the oregon public health division (ophd) incident management team (imt). introduction oregon’s statewide syndromic surveillance system (oregon essence) has been operational since 2012. non-federal emergency department data (and several of their associated urgent care centers) are the primary source for the system, although other data sources have been added, including de-identified call data from opc in 2016 (1). ophd epidemiologists have experience monitoring mass gatherings (2) and have a strong relationship with opc, collaborating on a regular basis for routine and heightened public health surveillance. nevertheless, surveillance for the great american solar eclipse (august 2017) presented a challenge due to the 107 reported simultaneous statewide eclipse-watching events planned for the day of the eclipse (some with estimated attendance of greater than 30,000 people and most in rural or frontier regions of the state). scientific literature is limited on mass gathering surveillance in the developed world (3), particularly in rural settings (4), so opc and ophd worked together to develop a list of health conditions of interest, including some that would warrant both an ed visit and a call to opc (e.g., snake bites). monitoring visits in both data sources in would allow for assessment of total burden on the healthcare system, especially in the case of snake bites where only specific bites require administration of anti-venom. methods ahead of the planned mass gatherings, ophd health intelligence and opc compiled a list of expected risks from the literature (4,5) and input from members of the imt including the public information officer, who monitored media for stories about health. priority health conditions presented a clear risk to public health (e.g., limited supply of snake anti-venom warranted surveillance of snake bites) or were the subject of substantial media coverage. query development focused on risks that had specific, well-defined health effects and that would be captured by syndromic ed and opc data. during an enhanced surveillance period (8/18-8/24), ophd health intelligence reviewed and interpreted trends in common queries with opc and disseminated a daily statewide surveillance report. results ophd and opc created four new queries for both ed and opc data streams: snake bites, psychedelic mushrooms, 2nd and 3rd degree body burns and eye-related calls and visits. ed queries used chief complaint, discharge diagnosis, or triage note. opc queries used generic code, therapy and clinical effect. from 8/18-8/22, ophd health intelligence distributed daily surveillance reports to the ophd imt and external partners. an increased in eye-related injuries was identified on the day after the eclipse, prompting ophd health intelligence to consult with opc. ed surveillance data indicated that the increase in eye-related visits was likely a seasonal trend. opc staff reviewed the charts of patient calls captured by the query and concluded the calls were not related to retinal issues from looking at the sun. no other trends were noted in the joint ophd/opc queries. conclusions ophd health intelligence piloted four new queries for surveillance during this mass gathering event and exercised the process for disseminating trend information from opc and ed data. the eclipse event was fairly quiet and very few trends of note were captured by surveillance. prior to this event, opc data had not been a part of the health intelligence surveillance plan. however, assessing trends in opc data provides an opportunity to better understand trends seen in ed data (e.g., whether or not a surge in ed visits for snake bites is accompanied by a surge in opc calls for anti-venom is meaningful). by building a process to review disparate data in tandem, ophd and opc strengthened regional surveillance for this event. applicable queries will continue to be used for planned event surveillance and several additional queries are currently under development. keywords poison center; mass gathering; eclipse; syndromic surveillance; query creation acknowledgments jamie bash, richard leman, emilio debess, david lehrfeld, eric gebbie, dewayne hatcher, paul cieslak references 1. laing r, powell m. integrating poison center data into oregon essence using a low-cost solution. 2017;9(1):2579. 2. jagger ma, jaramillo s, boyd l, johnson b, reed kr, powell m. mass gathering surveillance : new essence report and collaboration win gold in or. 2017;9(1):2579. 3. steffen r, bouchama a, johansson a, dvorak j, isla n, smallwood c, et al. non-communicable health risks during mass gatherings. lancet infect dis. 2012;12(2):142–9. 4. polkinghorne bg, massey pd, durrheim dn, byrnes t, macintyre cr. prevention and surveillance of public health risks during extended mass gatherings in rural areas: the experience of the tamworth country music festival, australia. public health [internet]. 2013;127(1):32–8. available from: http://dx.doi.org/10.1016/j.puhe.2012.09.014 5. lombardo js, sniegoski ca, loschen wa, westercamp m, wade m, dearth s, et al. public health surveillance for mass gatherings. johns hopkins apl tech dig (applied phys lab. 2008;27(4):347–55. *laurel boyd e-mail: laurelhifi@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e109, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* johns hopkins university applied physics laboratory, laurel, md, usa objective the objective of this project is to give users the ability to run custom r scripts from within the essence system. this capability would allow for custom analytics and visualizations to be baked into the system for daily use. it would also provide a sandbox area for new ideas and features to be tested before being developed more fully into the essence codebase for a more seamless use in the future. the project must do this while maintaining a secure environment for public health data to reside. introduction the use of r is increasing in the public health disease surveillance community. the isds pre-conference workshops and newly formed r group for surveillance have been well attended and continue to grow in popularity. the use of r in the national syndromic surveillance program (nssp) has also been of value to many users who wish to analyze and visualize public health data using custom r scripts. this interest in r, combined with a desire from many essence users to create custom analytics and visualizations, led to a summer internship project to look into the feasibility and ways r could be integrated into essence. methods the project aimed to perform three tasks: gather potential use cases and design potential user interfaces and interactions, determine the security requirements to accomplish the project, and prototype some portion of the project to determine feasibility. initial use cases were gathered by speaking with existing essence super users with r familiarity. these included allowing users to use the essence system to perform queries and then applying r scripts to generate new graphs and text-based output. initial user interface mock ups and r workflows were then created and reviewed. the vast majority of the project was spent determining the security requirements and ways to complete the task in a safe manner. existing free and commercial r platforms were investigated and ways of embedding r into existing java-based web applications were discovered. finally, one of the workflows was prototyped to show the feasibility of embedding r into essence. results eight major use cases were discovered, including short and long running scripts using single and multiple query inputs of both time series and data details types. an “apply r script” button was added to the query portal to allow short running, single query input scripts to be ran as a prototype of one of these use cases. the query results will be then sent to an rserve process to run the script. each script must be approved by a local administrator prior to use to alleviate many security concerns dealing with on-the-fly script deployment. the presentation will also discuss many other security aspects that were discovered dealing with potential sandboxing and jailing of r processes. conclusions integrating r into essence is feasible and could provide users with the ability to perform custom data analysis and visualization. this would allow the community to build and share visualizations and analytics within essence that have not been developed yet. keywords r; essence; visualization; analysis *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e48, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. 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nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts near real-time surveillance of disease during 2016-17 influenza season in the u.s. sushruth k. reddy, jhobe steadman and john tamerius* quidel corporation, san diego, ca, usa objective demonstrate performance of the virena global wireless surveillance system, an automated platform utilized in conjunction with the sofia fia analyzer, for near real-time transmission of infectious disease test results to public health and other healthcare organizations. introduction public health agencies worldwide all enjoy the same mission— providing healthcare warnings, guidance, and support to the public and healthcare professionals they represent. a critical element in achieving this mission is accessing timely and comprehensive surveillance information about disease in their regions of responsibility. advances in diagnostic technologies for infectious disease and in the wireless conveyance of information hold great promise for advancing the quality of surveillance information and in facilitating the delivery of timely, accurate, and impactful public health information. quidel corporation has developed a cloud–based, wireless communications system that is fully integrated with its sofia fluorescence immunoassay (fia) platform for rapid, point-of-care diagnosis of infectious disease. the system, called the virena global wireless surveillance system (hereinafter, virena) provides test results to public health organizations and other appropriate entities in near-real time. currently, more than 4,000 sofia instruments are transmitting results automatically by virena. this presentation describes the use of virena in surveilling influenza in the u.s. in the 2016-2017 influenza season, when over 700,000 influenza-likeillness (ili) patient results were transmitted. the methods employed, results, and the promise of this innovative system will be discussed. methods the sofia fluorescent immunoassay analyzer (fia) is a small fda-cleared, clia-waived bench top device that uses immunofluorescence-based, lateral-flow technology for rapid analyte detection within 15 minutes for influenza. with sofia2, a recent upgrade, positive influenza test results can be obtained in as few as 3 minutes, depending on virus levels. the results are encrypted, and automatically transmitted by virena--often within 5 seconds-to a dual cloud system for further encryption and formatting. the test results (also including location, date, and patient age) are subsequently pushed to participating public health and healthcare organizations for daily collection and analysis. healthcare providers utilizing the virena system may also access their own data and facility-de-identified regional and national data, using a passwordenabled internet application called myvirena.com. results between august 1, 2016 and october 6, 2017, 706,654 ili patient results were transmitted by virena from over 3,000 clinical sites in the united states. the influenza positivity rate (influenza a and b combined) peaked on february 9th at 33% and maintained this level for two weeks (figure 1). during this period, as many as 7,048 results were transmitted by virena per day. influenza a activity peaked on the same day at 26%, and influenza b peaked at 18% nearly 6.5 weeks later. in the six months from december 15th to june 15th, the influenza positivity rate for patients with ili was 10% or greater in the united states. data analysis for individual states revealed significant differences in time of onset of influenza and in the peak positivity rates. for example, the state of arizona experienced peak positivity rates for influenza activity (42%) as late as mid-may, driven largely by influenza b. in california, influenza a peaked at 43% on january 16th and maintained a positivity rate greater than 15% for nearly three months, while influenza b averaged below 4% for the entire period. age-specific analysis showed that children in the 2 to 18 year old group had the highest positivity rate (44%, n=251,756) and the longest incidence period. virena data demonstrated similar influenza activity trends on national and regional levels as that depicted by the clinical laboratory and nrevss data collected by the cdc; however, the virena data were collected and reported sooner (figure 2). conclusions the virena system represents a major stride for disease surveillance, providing clinical testing data in near real-time time, with local, national, and global scope. this first substantial evaluation of the virena system, with over 4,000 transmitting sofia analyzers, demonstrates capabilities for near real-time assessment of disease onset, regionally varying positivity rates, durations of outbreaks, differential assessment of influenza a and b prevalence, and dynamic mapping throughout the year. with expanding regional and metropolitan coverage, the virena system holds promise as both a powerful surveillance tool, and as a valuable resource for healthcare quality initiatives, epidemiological research, and the development of new mathematical models for influenza forecasting. figure 1: influenza status in u.s.a. by virena 9-1-16 to 10-6-17 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts figure 2: comparison of virena and cdc ili and nrevss results 9-1-16 to 10-6-17 keywords surveillance; influenza; infectious disease; pandemic *john tamerius e-mail: johntamerius@quidel.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e155, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts identifying sociomarkers of pediatric asthma patients at risk of hospital revisiting eun kyong shin*, ruhi mahajan, oguz akbilgic and arash shaban-nejad uthsc oak ridge national laboratory(uthsc-ornl) center for biomedical informatics, memphis, tn, usa objective asthma is one of the most common chronic childhood diseases in the united states [2, 3]. in addition to its pervasiveness, pediatric asthma shows high sensitivity to the environment. combining medical-social dataset with machine learning methods we demonstrate how socio-markers play an important role in identifying patients at risk of hospital revisits due to pediatric asthma within a year. introduction a socio-marker is a measurable indicator of social conditions where a patient is embedded in and exposed to, being analogous with a biomarker indicating the severity or presence of some disease state. social factors are one of the most clinical health determinants [1], which play a critical role in explaining health outcomes. sociomarkers can help medical practitioners and researchers to reliably identify high-risk individuals in a timely manner. methods we collected data from three different sources: pediatric asthma encounter records from jan 1st, 2016 to dec 31st, 2016 at a children’s hospital, the 2010 u.s census data and neighborhood quality survey data by memphis property hub. after merging these datasets we examine the effect of social features in identifying the patients who visited the hospital more than once during the observation period. we only use the first time visit (3,678 cases) to avoid over-counting of the same patients. in addition to demographic features (age, gender, insurance type, and race (african american and white)), we incorporate the social features such as the proportion of individuals living below the federal poverty level, blight prevalence, neighborhood quality, neighborhood quality inequality, trash dumping presence, the broken window pervasiveness within the zip code area of patients’ residence are included. we then implemented a support vector machine (svm) based classification model using abovementioned 11 social features. the classification outcome is either patient visits the hospital only onetime (class 0) or revisits the hospital within a year (class 1). among 3,678 unique patients in the dataset, only 823 pediatric patients revisited hospital with asthma. so, to overcome the class imbalance issue, we have used 823 patients’ data (randomly selected in 1,000 iterations) from each class. further, to avoid overfitting and ensure generalizability, we divided the dataset as training, test, and validation with a proportion of 60%, 20%, and 20%, respectively. the reported test (5-folds cross-validation using training and testing data) and validation accuracy of the svm method are averaged over 1,000 iterations to avoid sampling error and bias. results the proposed socio-marker model resulted in an average classification accuracy of 63.70% for the test set and 63.67 % for the validation set. further, the average specificity (the total true negative cases divided by the sum of true negative and false positive) and sensitivity (the total number of true positive cases divided by the sum of positive predicted cases) is found to be 62.79% and 64.77%, respectively for the test set and 62.79% and 64.83%, respectively for the validation set. results of this study suggest that socio-marker features that are not directly related to a patient’s medical conditions can still predict whether the patient will come back to the hospital within a year or not with approximately 64% accuracy. conclusions bringing the socio-marker features in the surveillance system may ease the burden of detecting the patients at risk of revisiting the hospital. the results should be interpreted with caution because we only used 12-month period of observation and the visit beyond the observation window is not considered. also the patients may have visited different hospitals which are not captured in the data. keywords sociomarkers; asthma patients at risk; machine learning; health surveillance; social components of health references 1. booske bc, athens jk, kindig da, park h, remington pl: different perspectives for assigning weights to determinants of health. university of wisconsin: population health institute 2010. 2. subbarao p, mandhane pj, sears mr: asthma: epidemiology, etiology and risk factors. canadian medical association journal 2009, 181(9):e181-e190. 3. gold dr, wright r: population disparities in asthma. annu rev public health 2005, 26:89-113. *eun kyong shin e-mail: eshin3@uthsc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e135, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron louisiana office of public health, infectious disease epidemiology, new orleans, la, usa objective to validate arboviral syndromes and evaluate the utility and practicality of detecting and monitoring arboviral disease using ed chief complaint, admit reason and diagnosis text data. introduction the louisiana office of public health (oph) infectious disease epidemiology section conducts emergency department (ed) syndromic surveillance using the louisiana early event detection system (leeds). leeds automatically processes electronic chief complaint, admit reason and diagnosis data to identify ed visits indicative of specific syndromes. in response to local transmission of chikungunya virus in the caribbean and the first travel-associated case in louisiana in may of 2014, oph conducted an arboviral syndromic surveillance study to validate arboviral syndromes and evaluate the utility and practicality of detecting and monitoring arboviral disease using ed chief complaint, admit reason and diagnosis text data. methods oph developed four syndromes that were applied to statewide ed chief complaint, admit reason and diagnosis text data from april 19, 2014 through august 8, 2015 to monitor arboviral febrile illness: a travel syndrome to capture mentions of international travel, particularly travel to the caribbean; a chikungunya syndrome to capture specific mentions of chikungunya; an arboviral syndrome to capture other arboviral diseases or insect bites and fever; and a fever and joint pain syndrome. oph conducted chart reviews of a sample of the records captured by the four syndromes to evaluate if they may have been arboviral cases. each case reviewed was classified as a confirmed arboviral case, possible arboviral, or non-arboviral based on clinical presentation and any lab work done. oph also reconciled reported cases of chikungunya and dengue over the period with the syndromic surveillance data to identify which, if any, were captured by the four syndromes. results the four syndromes captured a total of 165 patients during the study period. the majority of the patients (129) were captured by the arboviral syndrome, followed by 33 fever and joint paint (one patient fell under both arboviral and fever and joint pain syndromes), 2 chikungunya and 2 travel. of the 165 patients, oph conducted chart reviews of 67 patients: 5 were classified as confirmed arboviral cases, 3 as possible arboviral, and 59 as non-arboviral. 48 of the 129 patients captured by the arboviral syndrome were reviewed: 2 were confirmed arboviral, 3 were possible arboviral, and 43 were non-arboviral. 16 of the 33 patients captured by the fever and joint pain syndrome were reviewed: 1 was possible arboviral and 15 were non-arboviral. 2 patients were captured by the chikungunya syndrome, both of which were confirmed arboviral, and 2 by the travel syndrome, 1 of which was confirmed arboviral and 1 non-arboviral. the confirmed arboviral cases consisted of 3 chikungunya cases and 2 west nile cases. reconciliation of reported chikungunya and dengue cases against the syndromic surveillance data revealed that most were not captured because of lack of specificity in the chief complaint or admit reason, for example “fever,” “generalized weakness,” or “viral illness.” conclusions this study demonstrated that using ed chief complaint, admit reason and diagnosis text data to monitor and detect arboviral disease is a difficult task. arboviral illness usually begins with common symptoms that could be indicative of many different diseases, and a review of reported chikungunya and dengue cases confirmed that chief complaint and admit reason are often non-specific and therefore difficult to capture with text syndromes. the arboviral and fever and joint pain syndromes were too sensitive, often picking up insect bites resulting in abscesses or allergic reactions (arboviral) or chronic conditions (fever and joint pain). alternatively, the travel and chikungunya syndromes were too specific, since chief complaint and admit reason data rarely include specific arboviral disease names or travel history information. while specific arboviral disease can be detected in diagnosis information, diagnosis is not always included in syndromic surveillance data. due to these constraints, in this study only 7% of reviewed cases were confirmed arboviral cases, 4% were possible arboviral, and 88% were non-arboviral. therefore, while a small number of confirmed arboviral cases were detected, ed syndromic surveillance based on chief complaint, admit reason and diagnosis text data is not a practical method for detecting arboviral disease and does not provide an accurate indicator to monitor arboviral disease. keywords arboviral; syndromic surveillance; chikungunya *jenna iberg johnson e-mail: jenna.ibergjohnson@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e62, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts exploring the value of learned representations for automated syndromic definitions scott lee*1, drew levin2, jason thomas1, patrick finley2 and charles heilig1 1centers for disease control and prevention, decatur, ga, usa; 2sandia national laboratories, albuquerque, nm, usa objective to better define and automate biosurveillance syndrome categorization using modern unsupervised vector embedding techniques. introduction comprehensive medical syndrome definitions are critical for outbreak investigation, disease trend monitoring, and public health surveillance. however, because current definitions are based on keyword string-matching, they may miss important distributional information in free text and medical codes that could be used to build a more general classifier. here, we explore the idea that individual icd codes can be categorized by examining their contextual relationships across all other icd codes. we extend previous work in representation learning with medical data [1] by generating dense vector embeddings of these icd codes found in emergency department (ed) visit records. the resulting representations capture information about disease co-occurrence that would typically require sme involvement and support the development of more robust syndrome definitions. methods we evaluate our method on anonymized ed visit records obtained from the new york city department of health and mental hygiene. the data set consists of approximately 3 million records spanning january 2016 to december 2016, each containing from one to ten icd-9 or icd-10 codes. we use these data to embed each icd code into a high-dimensional vector space following techniques described in mikolov, et al. [2], colloquially known as word2vec. we define an individual code’s context window as the entirety of its current health record. final vector embeddings are generated using the gensim machine learning library in python. we generate 300-dimensional embeddings using a skip-gram network for qualitative evaluation. we use the tensorflow embedding projector to visualize the resulting embedding space. we generate a three-dimensional t-sne visualization with a perplexity of 32 and a learning rate of 10, run for 1,000 iterations (figure 1). finally, we use cosine distance to measure the nearest neighbors of common icd-10 codes to evaluate the consistency of the generated vector embeddings (table 1). results t-sne visualization of the generated vector embeddings confirms our hypothesis that icd codes can be contextually grouped into distinct syndrome clusters (figure 1). manual examination of the resulting embeddings confirms consistency across codes from the same top-level category but also reveals cross-category relationships that would be missed from a strictly hierarchical analysis (table 1). for example, not only does the method appropriately discover the close relationship between influenza codes j10.1 and a49.2, it also reveals a link between asthma code j45.20 and obesity code e66.09. we believe these learned relationships will be useful both for refining existing syndrome categories and developing new ones. conclusions the embedding structure supports the hypothesis of distinct syndrome clusters, and nearest-neighbor results expose relationships between categorically unrelated codes (appropriate upon examination). the method works automatically without the need for sme analysis and it provides an objective, data-driven baseline for the development of syndrome definitions and their refinement. table 1 figure 1: t-sne visualization of [300 dimensional skip-gram] embedded icd code vectors. the heterogeneous structure suggests distinct syndrome definitions. image generated using google’s online tensorflow projector. keywords word embeddings; deep learning; syndrome definitions; icd codes isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments this work was supported by laboratory directed research and development funding from sandia national laboratories. sandia national laboratories is a multimission laboratory managed and operated by national technology and engineering solutions of sandia llc, a wholly owned subsidiary of honeywell international inc. for the u.s. department of energy’s national nuclear security administration under contract dena0003525. references [1] choi y, chiu cy-i, sontag d. learning low-dimensional representations of medical concepts. amia summits on translational science proceedings. 2016;2016:41-50. [2] mikolov t, sutskever i, chen k, corrado gs, dean j. distributed representations of words and phrases and their compositionality. inadvances in neural information processing systems 2013 (pp. 31113119). *scott lee e-mail: yle4@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e11, 2018 ojphi kiwi: a technology for public health event monitoring and early warning signal detection 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 kiwi: a technology for public health event monitoring and early warning signal detection shamir mukhi 1 , ellie andres 1 , bryan demianyk 1 , ben gammon 1 , harold kloeze 2 1. canadian network for public health intelligence, national microbiology laboratory, winnipeg, manitoba 2. canadian food inspection agency, owen sound, ontario abstract objectives: to introduce the canadian network for public health intelligence’s new knowledge integration using web-based intelligence (kiwi) technology, and to pefrom preliminary evaluation of the kiwi technology using a case study. the purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats. methods: a prototype of the kiwi technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. the technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. special attention was given to the evaluation of the system’s automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper. results: kiwi was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. the pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (iip) and 2 community reported events (cre) for processing. ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (ais). kiwi’s automated sense-making algorithm (sma) had sensitivity = 63.9% (95% ci: 60.2-67.5%), specificity = 88.6% (95% ci: 87.3-89.8%), positive predictive value = 59.8% (95% ci: 56.1-63.4%), and negative predictive value = 90.3% (95% ci: 89.0-91.4%). discussion: literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. results show that the kiwi technology is well posied to address some of the suggested challenges. a limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating kiwi into regular surveillance activities. conclusions: kiwi is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 background internet biosurveillance internet biosurveillance emerged in the mid-1990s and has matured into a globally recognized technique for providing early warning of, and situational awareness for, public health threats [12]. this web-based approach utilizes unstructured real-time or near real-time data to support and complement traditional indicator-based surveillance. many internet biosurveillance systems have been developed with examples of well-established and analyzed systems including argus, biocaster, epispider, gphin, healthmap, medisys, and promed-mail [3-8]. since internet biosurveillance, or non-traditional event-based surveillance, is distinct from traditional indicator-based surveillance, it is recognized that applying the centers for disease control and prevention’s “updated guidelines for evaluating public health surveillance systems” [9] to the evaluation of internet biosurveillance systems is not suitable [10]. therefore, criteria specific to event-based surveillance systems were developed during a workshop in 2010, which was held at the pacific northwest national laboratory in richland, washington. these criteria include the following attribute families: event (i.e. description of event including source, causative agent, and detection mode), readiness (i.e. system validation and stakeholder willingness to use the system), operational aspects (e.g. administration/maintenance requirements, and system redundancy and ability to accommodate various levels of data), geographic coverage, population coverage, input data (e.g. accessibility, quality, quantity, and utility of input data), output (e.g. accessibility, quality, quantity, and utility of output data), and cost (i.e. funding sustainability, and research, evaluation, and operational expenses). six of these eight attribute families were used to guide categorization of kiwi evaluation results based on a specific case study. cost and operational aspects were not direct results of this evaluation; however, operational aspects may be identified throughout the introduction of kiwi. internet biosurveillance systems have been compared to one another and evaluated both qualitatively and quantitatively throughout literature and system challenges are readily discussed [3-8]. in reference to event-based biosurveillance systems, keller and colleagues suggested in 2009 that the future development of event-based systems should focus on establishing keywords: digital disease detection, event-based surveillance, internet-based surveillance, epidemiology, text mining, biosurveillance. correspondence: shamir.nizar.mukhi@phac-aspc.gc.ca doi: 10.5210/ojphi.v8i3.6937 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:shamir.nizar.mukhi@phac-aspc.gc.ca ojphi kiwi: a technology for public health event monitoring and early warning signal detection 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 collaborative networks of public health practitioners for the verification and dissemination of early warning signals [3]. in 2010, hartley and colleagues suggested that “prominent challenges [with event-based systems] include interoperability, interface customizability, scalability, and event traceability” [4]. in addition, it was suggested that biosurveillance capability can be expanded with the use of emerging media such as social networking sites and that the similarities and differences between event-based systems indicate that a more powerful resource can be created by combining them. the idea of combining existing internet biosurveillance systems to create a stronger platform is echoed throughout the literature [4, 6-7]. in 2013, hartley and colleagues suggested the use of interactive functions for users such as scoring options and comment fields [1]. in 2014, a systematic review by velasco and colleagues assessed 13 eventbased surveillance systems from canada, the european union, japan, and the united states and identified that no system had been incorporated into a national surveillance program [8]. in summary, literature suggests the need for:  enhancing technologies including the ability to customize the system’s user interface, trace events from beginning to end, adjust to various volumes of data input and coverage (scalability), and be functional despite jurisdictional boundaries (interoperability);  establishing collaborative networks of public health professionals for the verification and dissemination of early warning signals and addition of interactive functions;  combining existing event-based systems; and  integrating event-based systems into national surveillance initiatives. kiwi is uniquely designed within an existing national surveillance platform and is well posied to address the additional challenges proposed in literature regarding internet based monitoring and surveillance systems. canadian network for public health intelligence the canadian network for public health intelligence (cnphi) is a public health agency of canada (phac) initiative developed and managed by the national microbiology laboratory (nml) [11]. cnphi is a secure and comprehensive framework of applications and resources designed to enable multi-jurisdictional surveillance, response, and collaboration in the field of public health; cnphi supports initiatives ranging from zoonotic disease detection to drinking water advisories, nosocomial infections, food borne illnesses and many others. the platform was established in 2003 and is built upon six focus areas: knowledge management, surveillance, alerting, collaboration, event management, and laboratory systems. to complement and support surveillance activities currently performed using the cnphi platform, an event-based monitoring application called “knowledge integration using webbased intelligence” (kiwi) has been developed within the knowledge management focus area. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 the knowledge integration using web-based intelligence technology the purpose of cnphi’s kiwi technology is to support surveillance activities by monitoring events using unstructured data sources for the early detection and awareness of potential public health threats. kiwi is designed to collect information from various internet sources and process intelligence using an automated sense-making algorithm (sma). individual information pieces (iips) are raw individual information items from rss feeds. once processed by kiwi’s automated sma, iips either remain iips or become potential early warning signals, also referred to as anticipatory intelligence signals (ais). aiss are presented to a community of cnphi users for manual relevancy rating. highly rated aiss become early warning signals (ews), which are then disseminated to the user community. users are also encouraged to record community reported events (cre), which are entered into the kiwi system as additional unprocessed aiss for community rating. figure 1 is a schematic of the information flow that underpins kiwi. figure 1. kiwi information flow. kiwi takes advantage of both automated and manual processing to reduce the amount of time and resources required for reviewing iips and to ensure ews relevance through manual validation. the kiwi interface presents iips and aiss/ewss, or signals, in both map and listing formats with many search and filtering options. the map format allows users to view signals by geography, while the listing format allows users to view details including, but not limited to, title, description, source, and full-text link. each signal is accompanied by supporting information via health condition specific links to tools such as google trends and the global infectious diseases and epidemiology network (gideon). each signal is equipped with forums for community interaction and tools for following related signals over time. in addition, if users ojphi kiwi: a technology for public health event monitoring and early warning signal detection 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 want to remain updated on specific signals, they can “watch” the signal and receive email notifications. since cnphi users are multi-disciplinary and multi-jurisdictional, kiwi can be customized to meet the requirements of various organizations and collaborations. figure 2 displays an overview of kiwi’s high-level components including data collection, processing, analysis, and dissemination. details such as specific data sources and dictionaries (keywords and weights) vary by kiwi program (ex: zoonotic). figure 2. an overview of kiwi’s technology collection and storage rss feeds are used to collect information in the form of iips from internet-based sources such as eurosurveillance, medisys, promed-mail, and others. iips are indexed and made available directly on the kiwi platform by use of customized searches. data processing each iip is fed through text mining software (alchemy) to extract keywords, entities (e.g. geography), and keyword characteristics such as relevance and sentiment. entities are used to map iips when geography is provided and gives details for tabulation. identified keywords are accompanied by values indicating percent relevancy (r) and sentiment score (s). each keyword is then searched in pre-assembled kiwi dictionaries where matched keywords are given additional weight (w), which can be either a positive or negative value. the following formula is used to calculate total intelligence relevance (tir): ∑ (r s)*w, where tir is the sum of ojphi kiwi: a technology for public health event monitoring and early warning signal detection 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 keyword relevance minus sentiment multiplied by weight for each keyword. tir is used to determine whether an iip becomes an ais or ews. within the kiwi technology, duplicate iips may occur from the same source or from different sources. the current implementation does not automatically remove duplicates, however, the system flags similar iips that have occurred within the last 4 weeks. further work in this area could be beneficial. data analysis aiss are manually rated by users on a scale of one to five, with one being not-relevant and five being extremely relevant (figure 3). iips rated higher than not-relevant are considered valid aiss, and those rated greater or equal to relevant are considered valid ewss. note that valid aiss become ewss automatically if 60% of users rate greater than or equal to relevant with a minimum of 10 raters, otherwise manual validation takes place by analysts. community rating may be used as a gold standard for calculating sensitivity, specificity and predictive values for the sense-making algorithm. dissemination kiwi includes an interactive interface to view signals and the technology is capable of creating auto-generated reports summarizing aiss and validated ewss. generated reports may be disseminated to appropriate communities via associated cnphi collaboration centre, which include options for managing documents and notifying select workgroups via email. autogenerated reports may include the following signal-related information: title, description, date posted, program, source, signal type (ais/ews/cre), detection method, and average rating. case study though kiwi is designed to accommodate various programs, or topics/disciplines, the described study focuses solely on the zoonotic program. the zoonotic program was customized in collaboration with the centre for emerging and zoonotic disease integrated intelligence and response (cezd-iir). for the purpose of this paper, this customization of kiwi will be referred to as kiwi-zoonotic. kiwi-zoonotic was piloted in two phases between june and november of 2015, with each phase lasting approximately one month in duration. the purpose of the first phase was to familiarize pilot participants with the technology, and verify its functionality and usability, while the second phase aimed to measure kiwi performance. pilot participants (phase i: n = 20, phase ii: n = 37) were real end-users of zoonotic/emerging disease intelligence including veterinarians, epidemiologists, scientists, analysts, and others from local, provincial and federal institutions and agencies across canada. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 preparation in preparation of the kiwi-zoonotic pilot, data sources, dictionaries, and relevancy rating decision making criteria were configured to meet cezd-iir requirements. data sources were identified and a standardized tool for prioritizing information sources was applied to refine selected sources[12]. kiwi-zoonotic utilizes the following internet-based information sources: eurekalert, eurosurveillance, ibis, medisys, outbreak news, pig process, promed-mail, science daily, and the poultry site. the kiwi technology requires three dictionaries: health conditions, relevant terms, and significant terms.  health conditions is a list of known health conditions and diseases of interest with assigned weights reflecting relevance. for the zoonotic program, this dictionary contains known zoonotic and known emerging diseases. keyword weights were determined by sorting diseases by their presence or absence on various notifiable disease lists (animal/human and provincial/national/international). these weights were then adjusted based on relevance to the community of users via feedback from program team leads.  relevant terms is a list of terms used as a proxy for unknown health conditions to identify potential signals of interest with emerging capacity. for the zoonotic program, this dictionary contains disease agents such as viruses, bacteria and others. keywords were assigned neutral weight as there is no hierarchy in relevance of disease agents.  significant terms is a list of terms used to define signal importance, such as, outbreaks, unknown diseases, new diseases, et cetera. for the zoonotic program, keywords were grouped by the following categories: exclusion terms, epidemiological terms, and novel terms. exclusion terms were given negative weight; epidemiological terms were given positive weight based on levels of keyword severity (for example, case versus outbreak versus pandemic); and novel terms were assigned positive weight without hierarchical variation. for the purposes of the pilot, these dictionaries were configured for the detection of known zoonotic and animal diseases as well as significant and relevant terms utilized for the detection of emerging diseases. a decision making tool was developed to aid kiwi-zoonotic users in rating the relevancy of aiss. the tool was based on the international health regulations of 2005 [13] and adjusted for the purpose of zoonotic and emerging diseases (figure 3). ojphi kiwi: a technology for public health event monitoring and early warning signal detection 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 figure 3. relevancy assessment tool for zoonotic and emerging diseases evaluation methods six of eight attribute families described earlier were used to categorize variables assessed during the kiwi-zoonotic evaluation: events, geographic coverage, population coverage, readiness, data input, and data output. events were represented by the number of potential early warning signals detected during the two pilot phases. the number of iips indicates the pool of possible events while the number of potential early warning signals indicates the number of potentially relevant events. these variables were measured over both pilot periods. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 geographic coverage was calculated by identifying the number of countries referred to in detected aiss and dividing that number by the total number of countries. the numerator was determined by viewing kiwi-zoonotic’s map of potential early warning signals, and the denominator of 193 countries was based on members of the united nations. this variable was measured over a one year time period (april 2015-2016). population coverage is a qualitative variable describing the population of interest including two population types: health conditions/diseases and species affected. this variable is not time dependent. readiness was represented by user participation in the kiwi-zoonotic pilot. descriptive statistics were used to identify the proportion of participants who accessed kiwi-zoonotic, rated signals, commented on aiss, conducted searches for iips, and entered cres. the proportion of participants who rated signals was measured over both pilot periods, while remaining variables were measured during phase i because the purpose of phase i and ii differed. data input was represented by source performance, which was assessed by calculating the number of aiss produced per source and plotting it against the proportion of aiss identified as relevant per source. source performance was measured during phase ii of the pilot because sources monitored were modified based on outcomes of phase i. phase ii data provided the most recent information. data output was represented by assessing the automated sma. kiwi’s automated sma for the zoonotic pilot was analyzed by calculating its sensitivity, specificity, and predictive values. average community relevancy rating was used as the gold standard in these calculations.  iips detected by the automated sma (automatic) that were rated “not relevant” were treated as false positives, and those rated higher than “not relevant” were treated as true positives.  iips not detected by the automated sma (manual) that were rated “not relevant” were treated as true negatives, and those rated higher than “not relevant” were treated as false negatives. analysts reviewed iips for missed potential early warning signals on a daily basis and entered them manually for community rating. community reported events are directly input into the system as aiss without being processed by the automated sma. since cres are unprocessed, they were excluded from this portion of the analysis. variables were measured over both pilot periods. results iips – events, geographic coverage, and population coverage the kiwi-zoonotic pilot lasted a combined total of 65 days (phase i = 36 days; phase ii = 29 days) and resulted in the collection of 3243 iips (phase i = 1618 iips; phase ii = 1625) and 2 cres (phase i = 1; phase ii = 1). ojphi kiwi: a technology for public health event monitoring and early warning signal detection 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 the kiwi-zoonotic system detected events on a global scale with total geographic coverage, within monitored sources, at 83.9% of countries. since kiwi-zoonotic focuses on animal and zoonotic disease, both animal (wild, agricultural, and domestic) and human events were captured by the system. the most frequently occurring iips were those referring to dengue fever, avian influenza, ebola, chikungunya virus, and zika virus events. readiness the rate of user participation during phase i of the kiwi-zoonotic pilot was 85.0% (17/20). 76.5% (13/17) of participants who accessed the system rated aiss, 47.1% (8/17) commented on aiss, 35.3% (6/17) conducted customized searches for iips, and 5.9% (1/17) entered cres. during phase ii, 77.8% (28/36) of participants rated aiss. during the pilot period, the average number of iips collected on a daily basis was 50, and the average number of potential early warning signals identified on a daily basis was 16. with the use of kiwi’s automated sma and analysts, there is a 68% reduction in the number of signals that users would be required to view and rate on a daily basis. sources – data input kiwi-zoonotic configured ten sources for data input including the following: promed-mail, outbreak news, medisys, science daily, ibis, eurekalert, the poultry site, eurosurveillance, pig progress, and cres. phase i of the pilot used six of these ten sources including promedmail, medisys, eurekalert, eurosurveillance, science daily, and cres, while phase ii used all ten sources. during phase ii of the pilot, 545 aiss were collected with the highest proportions of aiss input from promed-mail (34.3%; n = 187), outbreak news (26.6%; n = 145), and medisys (13.8%; n = 75). promed-mail and outbreak news also produced the highest proportions of relevant aiss with 44.0% (142/323) and 30.0% (97/323) respectively. the denominator of 323 represents the number of aiss rated greater than “not relevant” by users. figure 4 displays each source with its corresponding ais frequency and proportion of relevant aiss produced. sma – data output during the kiwi-zoonotic pilot (phases i & ii), a total of 3243 iips were processed by the automated sma. 1025 processed iips became aiss (phase i = 481; phase ii = 544) and 2218 iips remained iips. of the 1025 processed aiss, 70.8% (726) were detected through kiwi’s automated sma and 29.2% (299) were identified manually by analysts, see figure 5. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 figure 4. proportion of relevant anticipatory intelligence signals by source and ais frequency figure 5. breakdown of signals entering the kiwi during the pilot period of the 726 iips detected as aiss by the automated sma, 434 were true positives and 292 were false positives (true positive rate = 59.8%; false positive rate = 40.2%). of the 2517 iips not 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 0 20 40 60 80 100 120 140 160 180 200 p ro p o rt io n o f a is s id e n ti fi e d a s r e le v a n t number of aiss produced figure 5. proportion of relevant anticipatory intelligence signals by source and ais frequency promed-mail outbreak news ibis the poultry site eurosurveillance science daily medisys eurekalert cres pig progress (1619 + 1626) = 3243 iips (raw un-processed) (481 + 544) = 1025 total processed aiss (1286 + 1231) = 2517 non-aiss (not sma detected) (149 + 150) = 299 manual aiss (not sma detected) (241 + 193) = 434 true positives (rated > "not relevant") (116 + 129) = 245 false negatives (rated > "not relevant") (91 + 201) = 292 false positives (rated "not relevant") (332 + 394) = 726 automatic aiss (sma detected) (33 + 21) = 54 true negatives (rated "not relevant") (1137 + 1081) = 2218 true negatives (not sma detected) (1 + 1) = 2 cres, (non-processed aiss) (1170 + 1102) = 2272 total true negatives (phase i + phase ii) = total pilot period += (482 + 545) = 1027 total aiss sma (processed) non-aisais non-ais ais manual review by analysts + + = ojphi kiwi: a technology for public health event monitoring and early warning signal detection 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 detected as aiss by the automated sma, 2272 were true negatives and 245 were false negatives (true negative rate = 90.3%; false negative rate = 9.7%), see table 1. table 1. kiwi's sense-making algorithm zoonotic pilot phases i, ii, & total ais nonais total detected phase i 241 91 332 phase ii 193 201 394 total 434 292 726 not detected phase i 116 1170 1286 phase ii 129 1102 1231 total 245 2272 2517 total phase i 357 1261 1618 phase ii 322 1303 1625 total 679 2564 3243 the prevalence of aiss was 20.9% (679/3243). the probability that an ais will be detected by the automated sma is 63.9% (sensitivity; 434/679), and that a non-ais will not be detected by the automated sma is 88.6% (specificity; 2272/2564). the probability that a detected ais will be a true positive is 59.8% (positive predictive value; 434/726), and that a non-detected iip will be a true negative is 90.3% (negative predictive value; 2272/2517), see table 2. table 2. kiwi's sense-making algorithm zoonotic pilot total diagnostics percent 95% confidence interval prevalence 20.9% 19.6-22.4% sensitivity 63.9% 60.2-67.5% specificity 88.6% 87.3-89.8% + predictive value 59.8% 56.1-63.4% predictive value 90.3% 89.0-91.4% of 323 true aiss (detected manually, automatically, or via cre) during phase ii of the pilot (1 cre plus 322 total ais phase ii; table 1), 32 (9.9%) met the threshold for automatically becoming an ews and an additional 37 (11.5%) met the criteria for manual ews validation. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 discussion kiwi-zoonotic has broad geographic coverage, and processes individual information pieces, or iips, on a daily basis. broad geographic coverage as a proportion of countries is important for the monitoring of diseases on a global scale despite population size or land mass. increasing kiwi’s geographic coverage is limited by the geographic reach of sources being monitored and event occurrence. the system’s automated sma increases system usability by significantly decreasing the number of iips rated manually by users, which has been shown to increase user willingness to participate in the rating process. since approximately one quarter of users did not contribute towards rating of the kiwi signals, assessment of factors contributing to user willingness to participate in the rating process and the further refinement of kiwi’s sma to reduce the number of false positives deserves future attention. all kiwi-zoonotic sources provided relevant information with varying proportions of relevant ais production (figure 4). promed-mail and outbreak news ranked highly in both the proportion of relevant aiss and the number of aiss produced. though cres, pig progress, the poultry site, and ibis each produced a small number of signals, their proportions of relevant signals were high. the remaining sources, eurekalert, medisys, science daily, and eurosurveillance, each provided high proportions of non-relevant signals, or false positives, per source. false positive signals are useful for refining keyword dictionaries and the sma’s overall ability to distinguish relevant signals. a study by barboza and colleagues evaluated seven event-based systems (argus, biocaster, gphin, healthmap, medisys, promed, and puls) on the following characteristics: usefulness, simplicity, flexibility, representativity, completeness, sensibility, and timeliness [6]. researchers concluded that no system ranked highly on every characteristic and thus systems with different strengths should be combined to make a stronger system. kiwi-zoonotic takes advantage of this by including medisys and promed-mail as sources which, in combination, ranked highly during the barboza study in numerous evaluation characteristics. the goal of the kiwi-zoonotic automated sma is to reduce the number of iips being rated manually by users. with this in mind, it is more important for the automated sma to maintain a low false negative rate rather than a low false positive rate. false positives are simply rated “not relevant” by the user community and do not become ewss, while false negatives require more resources to locate and manually enter into the system. since positive predictive value can be calculated as 1-(false positive rate) and negative predictive value can be calculated as 1-(false negative rate), we can alternatively say that it is more important for the automated sma to maintain a high negative predictive value rather than a high positive predictive value. kiwi-zoonotic’s automated sma performed highly in specificity and negative predictive value, which is of value for our purposes. the automated sma performed moderately in sensitivity and positive predictive value based on the expected range of 38-72% sensitivity [6]. further ojphi kiwi: a technology for public health event monitoring and early warning signal detection 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 efforts to maximize sma sensitivity, while maintaining high specificity, will benefit the kiwi system as a whole. conclusion kiwi is well poised to uniquely address the challenges proposed in literature regarding eventbased surveillance in the following ways: (a) kiwi allows for unique user interfaces by discipline/collaboration, the ability to “watch” individual information pieces (iips) and view trends of iips by health condition (thus allowing users to follow events from start to finish), data volume is not limited, geographic/population coverage is high/broad, and the cnphi platform is specifically designed for multi-jurisdictional data sharing, support, and collaboration, (b) kiwi has been integrated into an already thriving community of public health professionals who discuss, comment on, and rate ais relevancy for the verification of early warning signals, (c) kiwi utilizes a variety of sources including numerous existing event-based systems, and (d) the goal of kiwi is to support cnphi’s existing activities in public health surveillance and response. the automated sense-making algorithm for kiwi’s zoonotic program is useful for the detection of iips related to zoonotic and emerging diseases, and it seems to perform well in maintaining a low rate of false negatives. further evaluation would be useful in validating this over a longer duration. the purpose of the kiwi technology is to provide situational awareness and early warning signal detection in support of surveillance activities. resulting signals have the potential to influence public health action and complement traditional surveillance methods by providing timely information. limitations the purpose of this paper was to introduce the kiwi technology, evaluate it briefly based on the kiwi-zoonotic pilot, and show how kiwi is uniquely designed within the context of national surveillance and collaboration. event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper. the kiwi technology is limited by its dependence on online sources with available rss feeds. the timeliness of early warning signal development is limited by the timeliness of manual detection of missed signals and of user rating. there is no factor in the rating process that accounts for rater expertise per program, and average relevancy rating is currently used as a threshold for signal relevance. a limitation of this current method is that an early warning signal may be rated high by an expert and low by the majority of users and not become an early warning signal. future work should be done to identify whether an adaptive weighted approach may correct for this current limitation. sample size for pilot participation in kiwi-zoonotic was small for capturing the overall readiness of public health professionals to use such a system on a regular basis and with full integration into surveillance activities, and pilot duration was not long enough to capture seasonal patterns of disease. ojphi kiwi: a technology for public health event monitoring and early warning signal detection 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e208, 2016 acknowledgements we would like to acknowledge that the pilot phases were enabled through the cedz-iir project which was supported by funding from the canadian safety and security program (cssp), managed through defence research and development canada (drdc) centre for security science (css) and hosted by the canadian food inspection agency (cfia) providing logistics and management for the project. authors would like to acknowledge the cnphi team and the cezd-iir project team. references 1. hartley dm, nelson np, arthur r, barboza p, collier n, lightfoot n, linge j, goot e, mawudeku a, madoff l. an overview of internet biosurveillance. clin microbiol infect [internet]. 2013 [cited 2016 apr];19(11):1006-13. available from: http://onlinelibrary.wiley.com/doi/10.1111/1469-0691.12273/epdf 2. dion m, abdelmalik p, mawudeku a. big data and the global public health intelligence network (gphin). can commun dis rep [internet]. 2015 [cited 2016 apr];41(9):209. available from: http://search.proquest.com/docview/1713962713?pq-origsite=gscholar 3. 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apr]. 74 p. available from: http://www.who.int/ihr/publications/9789241596664/en/ http://journals.plos.org/plosone/article/asset?id=10.1371%2fjournal.pone.0090536.pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.436.2208&rep=rep1&type=pdf http://www.columbia.edu/itc/hs/pubhealth/p8475/readings/cdc-updated-guidelines.pdf http://online.liebertpub.com/doi/pdf/10.1089/bsp.2011.0096 http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2533573/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski emerging and zoonotic infectious diseases section, michigan department of health and human services, lansing, mi, usa introduction histoplasmosis is an infectious disease caused by a fungus called histoplasma capsulatum. fungal spores are found in the soil, mostly associated with bird and bat droppings, and if inhaled can cause lung infection. histoplasmosis is a reportable disease in michigan and a case definition was implemented in 2007. cases are reported into the michigan disease surveillance system (mdss), a web-based electronic database, and investigated by local health departments (lhd). an evaluation of the histoplasmosis surveillance system was conducted. methods the histoplasmosis surveillance system was evaluated using the 2001 centers for disease control and prevention updated guidelines for evaluating public health surveillance systems. acceptability was assessed by matching a comprehensive list of hospitals in michigan to hospitals that directly report into mdss. to evaluate timeliness, average times between diagnosis date, date reported to lhd, and investigation completion date were calculated. completed cases from 2014 were reviewed to determine if cases met the case definition criteria. positive predictive value (ppv) was then calculated for cases classified as a case in mdss, but that did not meet the case definition upon review. results from 2004 to 2014, a total of 1,608 confirmed or probable cases were reported into mdss, with a slight increasing trend in case numbers over time. overall, mdss is simple to use and fairly flexible, allowing for changes and adaptations to the case report form. overtime the proportion of cases classified as not a case to cases classified as confirmed or probable has increased. in 2014, 72% of histoplasmosis cases reported into mdss did not meet the case definition. between 50 and 70% of hospitals in each region of michigan use mdss to report, which shows a reasonable acceptance of the michigan reporting system. cases were reported to mdss or a lhd a mean of 14 days after diagnosis (n=729). on average, case investigations took 35 days to complete (n=1,145). prior to 2007, case investigation time averaged 48 days, and decreased to a mean of 31 days after the implementation of the case definition in 2007. in 2014, 124 cases were reported as confirmed or probable, and were marked completed at the end of the respective year. after the state’s review of cases, 50 cases (40 percent) were classified incorrectly and needed follow-up. the ppv was 79.84% (95% ci: 71.7%-86.5%). conclusions michigan’s histoplasmosis surveillance system is relatively simple, but the misclassification of cases is troublesome. development of tools for lhds to aid in classification of cases may improve the ppv and decrease case investigation time. increasing the number of hospitals that report directly to mdss would indicate more acceptability, and increase sensitivity. there are advantages of increased use of electronic laboratory and the shift toward electronic medical records, such as an increase in number of cases reported to public health, however, the number of cases classified as ‘not a case’ increases as well, which my increase lhd workload. keywords surveillance; evaluation; fungal disease acknowledgments this study/report was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-02 mdhhs bureau of disease control, prevention, & epidemiology staff: edward hartwick, ms, tiffany henderson, mph references 1. german rr, lee lm, horan jm, et al. updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr recomm. rep. 2001; 50 (rr13):1-35. 2. lenhart sw, schafer mp, singal m, et al. histoplasmosis: protecting workers at risk. dhhs (niosh). 2004; 2005-109: 1-26 3. wheat lj, freifeld ag, kleiman mb, et al. clinical practice guidelines for the management of patients with histoplasmosis: 2007 update by the infectious diseases society of america. clin. infect. dis. 2007; 45: 807-25. 4. whitfield k, kelly h. using the two-source capture-recapture method to estimate the incidence of acute flaccid paralysis in victoria, australia. world health organization. 2002; 80: 846-851. *veronica a. fialkowski e-mail: fialkowskiv@michigan.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e112, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of 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surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts social network analysis across healthcare entities, orange county, fl, 2016 danielle rankin* epidemiology, florida department of health in orange county, orlando, fl, usa objective to create a baseline social network analysis to assess connectivity of healthcare entities through patient movement in orange county, florida. introduction in the realm of public health, there has been an increasing trend in exploration of social network analyses (snas). snas are methodological and theoretical tools that describe the connections of people, partnerships, disease transmission, the interorganizational structure of health systems, the role of social support, and social capital1. the florida department of health in orange county (dohorange) developed a reproducible baseline social network analysis of patient movement across healthcare entities to gain a county-wide perspective of all actors and influences in our healthcare system. the recognition of the role each healthcare entity contributes to orange county, florida can assist doh-orange in developing facility-specific implementations such as increased usage of personal protective equipment, environmental assessments, and enhanced surveillance. methods doh-orange received centers for medicare and medicaid services data from the centers for disease control and prevention division of health care quality promotion. the dataset contains the frequency of patients transferred across medicare accepting healthcare entities during 2016. we constructed a directional sociogram using r package statnet version 2016.9, built under r version 3.3.3. node colors are categorized by the type of healthcare entity represented (e.g., longterm care facilities, acute care hospitals, post-acute care hospitals, and other) and depict the frequency of patients transferred with weighted edges. node sizes are proportional to the log reduction of the total degree of patients transferred, and are arranged with the fruchtermanreingold layout. we calculated standard network indices to assess the magnitude of connectedness across healthcare entities in orange county, florida. additionally, we calculated node-level indices to gain a perspective of the strength of each individual entity. results a total of 48 healthcare entities were included in the sociogram, with 44% representing orange county, florida. although the majority of the healthcare entities are located in nearby counties, 90% of patient movement occurred across orange county entities. the range of patient movement was 1 to 5196 with a median of 15 patients transferred in 2016. the network in orange county is sparse with a density of 0.05, but the movement of patients across the healthcare entities is predominately symmetric (reciprocity=97%). the sociogram is centralized (degree centrality= 0.70) and contains a vast amount of entities that serve as connectors (betweenness centrality=0.53). the node-level indices identified our acute care hospitals and long term acute care hospitals are the connectors of our county health system. conclusions the sna of patient movement across healthcare entities in orange county, florida provides public health with knowledge of the influences entities contribute to the county healthcare system. this will contribute to identifying changes in the network in future research on the transmission risks of specific diseases/conditions, which will enhance prioritization of targeted interventions within healthcare entities. in addition, snas can assist in targeting disease control efforts during outbreak investigations and support health communication. a sna toolkit will be distributed to other local county health departments for reproduction to determine baseline data and integrate county-specific snas. keywords sna; social network analysis; patient movement acknowledgments the florida department of health in orange county would like to thank sarah matthews for assistance with obtaining the data to conduct the social network analysis. additonally, we would like to express our gratitude to the centers for disease control and prevention (cdc) division of healthcare quality promotion for providing the dataset used in the analysis of this study. i would like to express my gratitude to dr. german gonzalez, alvina chu, and karen elliott from the florida department of health for mentoring and guiding me through my fellowship. references 1. luke da, harris jk. network analysis in public health: history, methods, and applications. annual review of public health. 2007;28(1):69-93. doi:10.1146/annurev. publhealth.28.021406.144132. *danielle rankin e-mail: danielle.rankin@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e170, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 1ibm, langata, kenya; 2carnegie mellon university, pittsburgh, pa, usa objective large scale surveys has been used extensively to monitor childhood immunization rates. the purpose of this research is to find measurable features that informs the state of immunization in kenya. introduction despite the steady increase in immunization coverage in kenya, the most recent kenya demographic and health survey (kdhs) shows that there is still immunization inequality across the country. nationally, 2 out of every 3 (66.67%) children has been fully immunized but only 2 out of every 5 (40%) children in the north eastern region were fully vaccinated1. there is a need to identify the characteristics of the households with children who are not fully immunized for effective intervention. methods kenya ministry of health2 provides information on required childhood immunization. bcg, dpt 1, 2 & 3, polio 1, 2 & 3, measles and vitamin a are required to be given to children across the country at different ages. in this research, a child is considered fully vaccinated if he/she was up to the age of receiving a particular vaccination and had received the vaccination. 26 variables on wealth assets, media exposure and the demographic data of a mother were used as independent variables for building a generalized boosted regression model. the dependent variable was a child being fully vaccinated or not. the receiver operator characteristics (roc) of the model was examined and the predictor importance of the variables were extracted. surveyed households were further clustered into groups using these most important measurable features. results initially, 26 variables used was able to classify full immunization at an roc measure of 64.24%. the predictor importance of these variables can be seen in figure 1. the region a mother lives in, her highest educational level, the wall material of the house she lives and her frequency of listening to radio were four features with high predictor importance which can be measured without survey data. with these four variables, the model was able to classify at a rate of 63.56% which is not a significant drop from the initial classification rate. the 6079 households analyzed were divided into three clusters using these four variables based on akaike information criterion. information on these clusters can be seen below: • low record of being fully up-to-date immunized: the 1325 households in this cluster do not listen to radio at all, have no education, live in houses with natural materials (grass/thatch) and can be found mostly in the north eastern region of the country. 37.8% of these households have been fully immunized. • intermediate record of being fully up-to-date immunized: the 2817 households in this cluster listen to radio almost every day, have either primary or secondary school education, build their houses with natural wall materials and can be found in nyanza, rift valley and western part of the country. 52.2% of these households have been fully immunized. • high record of being fully up-to-date immunized: the 1933 households in this cluster listen to radio almost every day, have higher, secondary or primary educational levels, build their houses with finished (cement, bricks) materials and can be found in nairobi, central and eastern regions of the country. 55.8% of these households have been fully immunized. conclusions the region a mother lives, her level of education, her frequency of listening to the radio and house wall materials are informative in predicting whether her child is fully immunized or not. while this gives us some insight on the wealth and demographic characteristics of the mothers whose children are not immunized, we are missing some important information such as health seeking behavior and proximity to healthcare facilities which could influence a mother’s decision to immunize her child. figure 1 keywords kenya; immunization; clustering acknowledgments thanks to united states agency for international development (usaid) for providing the dataset used for this analysis. references 1. centers for disease and control and prevention. kenya joins the world in ‘closing the immunization gap’. global health – kenya [internet]. 2014 april [cited 2015 august 26]; available from: http://www.cdc. gov/globalhealth/countries/kenya/blog/closing_immunization-_gap. htm 2. division of vaccines and immunization. multi year plan. republic of kenya: ministry of health; 2010. *toluwani e. awoyele e-mail: tawoyele@andrew.cmu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e89, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e288, 2019 isds 2019 conference abstracts monitoring and improving syndromic surveillance data quality mischele a. vickers infectious diseases and outbreaks division, alabama department of public health, montgomery, alabama, united states objective to monitor and improve the data quality captured in syndromic surveillance for alabama department of public health syndromic surveillance (alasys). introduction the public health problem identified by alabama department of public health syndromic surveillance (alasys) was that the data reflected in the user application of essence (electronic surveillance system for the early notification of community-based epidemics) was underestimating occurrences of syndromic alerts preventing alabama department of public health (adph) from timely recognition of potential public health threats. syndromic surveillance (sys) data in essence were not reliable for up to a week after the visit date due to slow processing, server downtime, and untimely data submission from the facilities. for alasys, 95 percent of data should be submitted within 24 hours from time of visit, for near real time results. the slow data processing caused latency in the data deeming it less useful for surveillance purposes, consequently the data was not meaningful for daily alerts. for example, if a user ran a report to assess the number of emergency department (ed) visits that mentioned heroin in the chief complaint (cc), depending on the status of the data coming from the facility (processing, sending, or offline), the number of visits visible to the user could vary from one to several days. with the opioid epidemic alabama is currently facing, this de lay poses a major public health problem. methods during the data quality improvement review, alasys phra addresses all three data quality metrics: (1) completeness – any data element of interest that is less than 95 percent complete, that field is reported to the facility along with some guidance to reference the public health information network (phin) messaging guide for assistance on how to correct; (2) validity – any data element of interest that is less than 95 percent conforming is flagged for corrections (this is not a very big data quality issue for production or onboarding); and (3) timeliness – data are requested to be sent in a timely manner (i.e., at least once every 24 hours); anything sent more than 24 hours after the visit is highlighted and sent to the facility in a report. now that quality data is coming into essence, alasys staff had to address the issue of latency to improve representativeness. alasys has approximately 82 faciliti es sending data to production. when updates occur from the nssp or a facility was not sending data in a timely manner, facilities in essence would appear to be offline. this bottleneck of data being processed caused a backlog of data sometimes in excess of 3 days. for example, the data coming to the essence platform would, in some cases, appear 7 days after the patient visit. these occurrences led to the development of the alasys “current production” spreadsheet. this allows the alasys team to record the status of each facility in the event data is not current, e.g., a facility temporarily drops from production due to a vendor change or upgrade. at any given moment, alasys staff know the count of facilities in production, regardless of the overall general stat us. alasys phra has developed queries in r studio to help monitor the data flow status. if the data drops, this is noted on the current production spread sheet and alasys staff is aware, even before the disruption of the data flow is reflected in essence. the query returns the name of facilities that are sending data on a particular date. this has allowed alasys staff to identify data drops earlier. results after the implementation of the current production spread sheet, monitoring of the timeliness metric in syndromic surveillance data has improved. by analyzing the nssp data validation reports for completeness and validity, and providing feed back to the vendors and facilities, the data quality of what is captured in essence has also improved. the data quality reports that target the onboarding facilities were used to transition seven facilities (six hospital s and one urgent care) from onboarding to production during the period. the completeness data quality reports were used to validate the completeness metric in order to support the transition to production. the data quality reports that targeted the product ion data http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e288, 2019 isds 2019 conference abstracts generated conversations between the alasys team and the facilities regarding barriers that impeded their improvement. the timeliness metric for example, some facilities are set up to send data once every 24 hours. this results in a lag time to ess ence of up to almost 48 hours. facilities may not be able to improve their timeliness measure without incurring a cost from the ve ndor for an upgrade. in other instances, facilities are able to send in real time. however, at the time of this document, the biosense platform is only capable of accepting data in 15minute increments for essence. alasys staff were able to improve representativeness using the current production status spreadsheet. this communication tool allows users more reliability of the data by knowing the status of the facilities in the catchment area on any given day. for example, the user is able to know if a fe ed is down on a day they want to run a report. that same report, run again later, (when the feed is active) may show up as a fluctuation in the data. understanding the nature of the data in this scope will help improve and support reliability. alasys phra will incorporate the spreadsheet into an access database to be displayed on the alasys data management internal website. the curre nt production status spreadsheet as a validation tool, also supports the monitoring of the timeliness metric. an internal website for syndromic surveillance data management has been developed so users can be informed of the facility st atus and the latest data quality reports of each facility. the development of the “alasys data intranet” web site will be available for registered adph staff who use essence. the alasys data analysis and reporting (a&r) will be available for users to check the status of the data feed for facilities before making a report. this intranet will provide back door information so the essence user can have a window of confidence with the data prior to creating a report. conclusions by engaging the facilities with the data quality reports, alasys staff was able to find out some of the barriers such as the facility not having funds to upgrade to a more timely system. also uncovered was acknowledgment the capability (including limitations) of how quickly the nssp server can process data. for example, while a facility may be able to submit in real time, (as opposed to near-real time) the ability to process data in real time is not option at the time of this document. alasys staff also learned when data is not appearing in essence, this absence does not necessarily mean that the data is not being sent from the facility. point to consider also, is the understanding, nature and behavior of the data helps to improve reliability. when reporting using alasys data, it is important to be mindful of the limitations. developing a check and balance system for data validation to find root cause for proper evaluation and resolve will support improvement to data quality. utilizing properly calibrated measuring tools helps to ensure that data quality metrics are effective and measuring as intended. acknowledgement alabama department of public health centers for disease control and prevention national syndromic surveillance program http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts impact of users’ experience with a web-based reporting portal on timeliness and quality of reporting lixuan wang*, nina rothschild and david lee new york city department of health and mental hygiene, queens, ny, usa objective as part of new york city department of health and mental hygiene’s (nyc dohmh) efforts to improve provider reporting, the division of disease control surveyed and conducted focus groups with users of a web-based reporting portal called reporting central (rc) to learn about their experience with submitting provider reports through rc and the impact of their experience on data submission. introduction the new york city department of health and mental hygiene’s (nyc dohmh) division of disease control (ddc) conducts surveillance of more than 90 specific diseases and conditions and relies on both provider reports and electronic laboratory reports for data. while laboratory reports provide vital laboratory data and represent the majority of the surveillance data that dohmh receives, they are not always timely or sufficient to confirm a case. provider reports, in contrast, contain data often not available in laboratory reports and can be more prompt than laboratory reports. health care providers submit provider reports through multiple channels, including through mailing or faxing paper forms, phone calls, and reporting central (rc). in 2016, providers used rc to submit ~51,000 provider reports. methods in june 2017, we used phone calls and messages posted on rc’s homepage to recruit a convenience sample of ~50 rc users who agreed to participate in a survey and focus group. participants were assigned to one of five focus groups based on the type and size of the facility where they worked at the time of recruitment: large outpatient facilities (n=2), small outpatient facilities (n=1) and inpatient facilities (n=2). participants were asked to complete a 14-question paper survey before the focus group. using a discussion guide informed by dohmh surveillance subject matter experts, a moderator facilitated discussions on 1) facilitators of/barriers to using rc and 2) recommendations for improving rc. each focus group lasted ~90 minutes. the discussions were audioand video-recorded and transcribed. survey results were descriptively summarized with excel. focus group data were thematically analyzed with nvivo by two coders. results forty-seven participants responded to the survey, and 45 individuals from a total of 37 health care facilities joined the focus groups. about 70% of survey participants rated the difficulty level of rc as 3 or less on a 1-7 scale (with 7 being the most difficult), and 30% of participants rated the difficulty level as 1. participants from inpatient facilities rated rc as more difficult to use (mean rating=3.2) compared with participants from large outpatient facilities (mean rating=2.5) and participants from small outpatient facilities (mean rating=1.8). survey respondents from inpatient facilities reported taking 3-30 minutes (mean=11.4) to submit one report using rc, compared with 3-15 minutes (mean=7.8) for survey respondents from large outpatient facilities and 3-10 minutes (mean=4.4) for survey respondents from small outpatient facilities. in subsequent focus group discussions, the majority of participants said that rc is intuitive, the section flow is easy to follow, and training new users requires little effort. participants with experience using paper forms stated that reporting through rc is quicker and easier than reporting via paper forms. two themes emerged from the analysis of focus group data, revealing the impact of participants’ experiences with submitting provider reports on timeliness and quality of data reporting. timeliness of data submission participants noted that flawed functionalities (e.g., lack of auto-save functionality and insufficient time before automatically getting logged out of rc) lead to delayed data submission. participants from inpatient facilities demonstrated more familiarity with time requirements for reporting and acknowledged the priority of submitting reports in their daily work routine. participants from outpatient facilities, by contrast, did not acknowledge this priority when describing their reporting workflow and showed less understanding of the importance of timely reporting. participants from small outpatient facilities questioned the necessity of requiring providers to report because dohmh is also receiving data from laboratories. quality of data submission participants noted the complexity of selecting the correct data from a long drop-down menu that populates from previous saved entries as a possible contributor to erroneous data entry. lack of access to some required data and the omission of fields in rc for capturing some relevant data such as patient’s gender, housing status, etc. also compromise quality. conclusions the majority of participants stated that rc is intuitive and easy to use compared to paper forms. this finding encourages us to promote rc adoption among health care providers who currently use paper reports or do not report. focus group participants’ proposed enhancements to rc to facilitate timeliness, and quality of data submission include 1) enabling auto-save or save function to reduce data loss in case of crash and automatic log-out, and 2) increasing the amount of time for completing the report, including the amount of time during which the computer is inactive, before automatic log-out. this second enhancement might be particularly helpful for inpatient facilities that frequently report complex cases. the findings also suggest the potential value of educating health care providers, especially at small outpatient clinics, about the importance and necessity of timely data submission. keywords provider reporting; web-based reporting portal; user's experience; timeliness and quality of disease reporting *lixuan wang e-mail: lwang4@health.nyc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e45, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts monitoring out-of-state patients during a 2017 hurricane response using essence caleb wiedeman*1, julie shaffner2, kelly squires1, jeffrey leegon1, rendi murphree2 and paul e. petersen1 1tennessee department of health, nashville, tn, usa; 2centers for disease control and prevention, atlanta, ga, usa objective to demonstrate the use of essence in the biosense platform to monitor out-of-state patients seeking emergency healthcare in tennessee during hurricanes harvey and irma. introduction syndromic surveillance is the monitoring of symptom combinations (i.e., syndromes) or other indicators within a population to inform public health actions. the tennessee department of health (tdh) collects emergency department (ed) data from more than 70 hospitals across tennessee to support statewide syndromic surveillance activities. hospitals in tennessee typically provide data within 48 hours of a patient encounter. while syndromic surveillance often supplements diseaseor condition-specific surveillance, it can also provide general situational awareness about emergency department patients during an event or response. during hurricanes harvey (continental us landfall on august 25, 2017) and irma (continental us landfall on september 10, 2017), tdh supported all hazards situational awareness using the electronic surveillance system for the early notification of community-based epidemics (essence) in the biosense platform supported by the national syndromic surveillance program (nssp). the volume of out-of-state patients in tennessee was monitored to assess the impact on the healthcare system and any geographicor hospital-specific clustering of out-of-state patients within tennessee. results were included in daily state health operations center (shoc) situation reports and shared with agency response partners such as the tennessee emergency management agency (tema). methods data were monitored from august 18, 2017 through september 24, 2017. a simple query was established in essence using the patient location (full details) dataset. data were limited to hospital ed visits reported by tennessee (site = “tennessee”). to monitor ed visits among residents of texas before, during, and after major hurricane harvey, data were queried for a patient zip code within texas (state = “texas”). ed visits among florida residents were monitored similarly (state = “florida”) before, during, and after major hurricane irma. additionally, a free text chief complaint search was implemented for the terms “harvey”, “irma, “hurricane”, “evacuee”, “evacuate”, “florida”, and “texas”. chief complaint search results were then filtered to remove encounters with patient zip codes within tennessee. results from august 18, 2017 through september 24, 2017, tennessee hospital eds reported 277 patient encounters among texas residents and 1,041 patient encounters among florida residents. the number of encounters among patients from texas remained stable throughout the monitoring period. in contrast, the number of encounters among patients from florida exceeded the expected value on september 7, peaked september 10 at 116 patient encounters, and returned to expected levels on september 16 (figure 1). the increase in patients from florida was evenly distributed across most of tennessee, with some clustering around a popular tourism area in east tennessee. no concerning trends in reported syndromes or chief complaints were identified among texas or florida patients. the free text chief complaint query first exceeded the expected value on september 9, peaked on september 11 with 5 patient encounters, and returned to expected levels on september 14. from august 18 through september 24, 21 of 30 visits captured by the query were among florida residents. one tennessee hospital appeared to be intentionally using the term “irma” in their chief complaint field to indicate patients from florida impacted by the hurricane. conclusions the essence instance in the biosense platform provided tdh the opportunity to easily locate and monitor out-of-state patients seen in tennessee hospital eds. while tdh was unable to validate whether all patients identified as residents of florida were displaced because of major hurricane irma, the timing of the rise and fall of patient encounters was highly suggestive. likewise, seeing no substantial increase ed patients with residence in texas reassured tdh that the effects of hurricane harvey were not impacting hospital emergency departments in tennessee. tdh used information and charts from essence to support situational awareness in our shoc and at tema. use of patient zip code to identify out-of-state residents was more sensitive than chief complaint searches by keyword during this event. essence allowed tdh to see where out-of-state patients appeared to be concentrating in tennessee and monitor the need for targeting messaging and resources to heavily affected areas. additionally, close surveillance of chief complaints among out-of-state patients provided assurance that no unusual patterns in illness or injury were occurring. essence is the only tdh information source capable of rapidly collecting health information on out-of-state patients. essence allowed tdh to quickly identify a change within the patient population seen at tennessee emergency departments and monitor the situation until the patient population returned to baseline levels. figure 1: emergency department (ed) encounters reported among patients with residence in texas and florida, august 18, 2017 – september 24, 2017. keywords essence; biosense; emergency preparedness; syndromic surveillance *caleb wiedeman e-mail: caleb.wiedeman@tn.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e150, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts sero-prevalence of foot and mouth disease in cattle in borena zone, oromia regional state, ethiopia asamenew tesfaye melkamsew* virology, national animal health diagnostic and investigation center (nahdic), sebeta, ethiopia objective to determine the sero-prevalnce of fmd and indicate patterns of animal movement in borena zone, ethiopia introduction the foot and mouth disease (fmd) virus is a highly contagious and economically devastating trans boundery disease of clovenhooved domestic and wild animals1. methods a cross-sectional study was carried out between april and november 2015 to investigate the sero-prevalence of foot-and-mouth disease (fmd) in cattle using serology and questionnaire servey in borena zone. results a total of 363 sera samples were collected from nine peasant associations found in three different districts. an overall seroprevalence of 42.7% (95%: ci= 37.7-47.84) was found during the study. there was statistically significant difference among the districts (χ2 = 10.43, p=0.005) and the highest prevalence was found in dire district which accounted for 52.8% (95%: ci, 44.061.4). soda peasant association of dire district and surupa peasant association of yabello district accounted for highest sero-prevalence 65.5% (95%: ci, 49.4-78.5) and 65.0% (95%: ci= 40.4-78.5), respectively. statistical significant difference in footand-mouth disease seroprevalence (χ2 =31.1, p=0.000) was found among the peasant associations. similarly, there was significance difference (χ2 =17.4, p=0.000) in the prevalence of foot-and-mouth disease between age groups. though the seroprevalence foot-and-mouth disease was higher in females than in males, there was no significant difference (χ2=1.63, p=0.202) between sex. the different risk factors analyzed during this study indicated that, peasant associations (pas), district and age were seen to be significantly associated (p<0.05) with the seroprevalence of foot-and-mouth disease. the questionnaire survey revealed that foot-and-mouth disease outbreak was commonly seen during june to august (short rainy season) and december to february (long dry season), locally called adolessa and bona, respectively. younger (1-3 years) animals were most susceptible than calf and adults (>3years). moreover, an extrinsic factor like dry season enforces pastoralist to travel a longer distance to look for grazing lands and water sources that creates suitable conditions for foot-andmouth disease transmission between infected and susceptible animals. conclusions fmd is an important transboundery animal disease that affects the livelihood of farmers and economy of the country. in pastoral areas like borena where livestock movement is common during dry season, the disease is devastating and spreading from one area to the other. therefore, an extensive regular serological survey, virus isolation, and characterizations of fmd virus need to be conducted for a possible development of poly-valent vaccines that contains commonly circulating serotypes of fmd virus in ethiopia. table 2: sero-prevalence of fmd in peasant associations of different districts found in borena zone of oromia χ2=31.1, p=0.000 *= pa’s(peasant association) table 3: seroprevalence of fmd in different age groups table 4: seroprevalence of fmd between the two sexes isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords borena; fmd; sero-prevalence; 3abc-elisa acknowledgments acknowledgements we are grateful to nahdic in supporting this serosurveillance study and dr. getachew gari for his constructive comments and suggestions during prepartion of the manuscript. references 1. gelagay ayelet, mana mahapatra, esayas gelaye, berhe g. egziabher, tesfaye rufeal, mesfin sahle, nigel p. ferris, jemma wadsworth, geoffrey h. hutchings, and nick j. knowles. genetic characterization of foot-and-mouth disease viruses, ethiopia, 1981–2007. emerg infect dis. 15(9): 1409–1417.2009 *asamenew tesfaye melkamsew e-mail: asefiker@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e82, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger kfl&a public health, kingston, on, canada objective this roundtable will provide a hands-on workshop to learn about three surveillance systems developed and used by the emergency department syndromic surveillance team at kfl&a public health. it will be an opportunity to address issues relevant to syndromic surveillance including: equity, emergency response, health preparedness, and health systems management. additionally, participants will be able to apply new knowledge on improving health equity, and its relationship to social determinants of health, in their own jurisdictions. introduction in 2012, canada and other world health organization member states endorsed the rio political declaration on social determinants of health, a global commitment to address health inequities by acting on the social, economic, environmental, and other factors that shape health. the public health informatics team at kfl&a public health works on various surveillance projects to better support vulnerable populations, and prepare for emergency situations. description the facilitators will present three of the surveillance tools used at kfl&a public health: public health information management system (phims), social determinants of health (sdoh) mapper, and south eastern health integrated information portal (shiip). the goals of the facilitators are: to teach participants to use these tools and apply them to their own jurisdictions, and to achieve highquality outcomes for syndromic surveillance and emergency response systems so as to improve health equity. phims aims to enable the visualization and spatial analysis of environmental data with underlying population based indicators. phims consists of layers of environmental information across ontario and allows users to view maps demonstrating environmental or demographic data as they apply to specific geographic areas. this is useful for observing where environmental events are occurring, detecting potential emergency situations, and identifying areas with vulnerable populations. sdoh mapper allows users to customize maps demonstrating social determinants of health as they apply to specific geographic areas within the province of ontario, and visualize graphs with access to seven layers related to the marginalization and deprivation levels for specified populations. this is useful for observing trends in marginalization and deprivation across dissemination areas in ontario, and for examining health inequities in an area over time. shiip is a portal-based technology solution that enhances individual patient care while providing real-time feedback and summarized data to help plan care. the primary objective of shiip is to develop an integrated portal with core functionalities that will facilitate the sharing of information and enable person-centred care coordination. shiip is designed to identify and assist in the delivery of care for complex/high needs patients, and will facilitate reporting, performance monitoring and quality improvement efforts. audience engagement the facilitators will provide demonstrations of the three surveillance tools (phims, shiip, and the sdoh mapper) and will teach participants how to use the tools and apply them to their own jurisdictions. participants will be given access to the phims and the sdoh mapper tools at the workshop so that they may take part in interactive demonstrations. each tool will be presented for 20 minutes, after which participants will be asked to take part in an open discussion and to comment on benefits and potential improvements for each tool. participants may also ask questions regarding the use and/or application of each tool at this time. participants will be required to bring a laptop with a wireless connection and google chrome so that they can access the web-based tools. the end result of the roundtable will be a list of functionality and enhancements requirements to be considered by kfl&a’s syndromic surveillance team, as well as having promoted surveillance and response systems that include the use of deprivation indices and social determinants of health to stakeholders, in hopes of improving health equity. keywords equity; gis; maps; deprivation; marginalization online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e182, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts application of tablet for data collection in hiv sentinel surveillance in vietnam duong c. thanh*1, ha t. nguyen1, giang t. le2, duc h. bui3, lo t. dang3, diep t. vu2, nghia v. khuu4, tuan a. nguyen1 and huong t. phan3 1hiv/aids, national institute of hygiene and epidemiology, ha noi, viet nam; 2centers for disease control and prevention, hanoi, viet nam; 3vietnam authority of hiv/aid control, hanoi, viet nam; 4pasteur institute in ho chi minh city, ho chi minh, viet nam objective to describe the implementation process, successes, challenges, and lessons learned of the application of tablet for data collection and data system in hiv sentinel surveillance in vietnam introduction vietnam has routinely monitored hiv sero-prevalence among key populations through its hiv sentinel surveillance system (hss). in 2010, this system was updated to include a behavioral component (hss+) among people who inject drugs, female sex workers, and men who have sex with men. hss+ has historically used a paperbased questionnaire for data collection(1). at the end of the survey, provincial data were manually entered into computers using epidata entry forms (http://www.epidata.dk/) and submitted to the vietnam authority of hiv/aids control (vaac). as a result, feedback to provinces on data issues was not provided until after fieldwork completion. one recent survey used tablets for data collection and found that it saved time, required fewer staff, and reduced costs compared to paper-based data collection(2). in 2017, vietnam introduced tablet for behavioral data collection in hss+ to improve data quality, resource saving, and to provide more timely access to data. methods development of data entry forms and data system survey data entry forms were designed using free epi info™ software for mobile devices(3) and installed on tablets. a sql database was established via sfpt data transfer to the current database in vaac’s server. field data were instantly synced to the national database when the internet signal was available (picture 1). real-time data analysis was granted to surveillance staff at all levels using authorized access to the database via epi info™ cloud data analytics (ecda), dashboards were used to track progress and data quality (figure 1). hss+ data were frequently reviewed by the national surveillance technical working group (nstwg) and timely feedback was provided. deployment manuals and e-leaning materials were developed. the nstwg conducted a pilot to test the forms and data flow from field to the national database before installed into all tablets. four to seven tablets were distributed to each province depending on number of hss+ sites and populations. surveillance staff at provincial aids centers (pacs) were trained by the nstwg on how to use the tablet to interview, check, update, save data, and sync data to cloud and to the national database, and to backup the provincial dataset. they then provided trainings to their local field staff. the nstwg provided technical assistance and troubleshooting through field visits and online support to help local staff address issues regarding tablet use in addition to other hss/hss+ issues. results currently, 18 hss+ provinces have implemented the 2017 hss+. of these, nine provinces applied tablets exclusively. two provinces used tablets, but also used paper-based questionnaires when not enough tablets were available. seven global fund supported provinces used the paper-based questionnaires and entered data into tablets after interview completion due to copies of completed paper-based questionnaires are required by these provincial project management units (pmu) for fund re-imbursement. additional updates were required after the first few days, which created issues around updating forms once revised forms were sent out by nstwg. another challenge was that local staff were not familiar with using tablets at the beginning. also frequent complaints were mainly on data entry and synchronization regarding participant identity code or a record could not be synced. the nstwg and pac staff were able to monitor the hss+ progress and provided feedback daily. most commonly, feedbacks were provided on participant codings and site names. using the tablet did not require staff, time or money for data entry and eliminated data entry errors. in general, staff prefered to use this data collection mode. conclusions this mobile device application for data collection in routine hss+ in vietnam is feasible and accepted. however, harmonization and coordination from the central global fund pmu and provincial pmu will be required to successfully roll-out this system in all hss+ provinces. this application in addition to ecda help to improve data quality, due to timeliness of the data, is cost saving and reduces workload. most importantly, better quality and timely data will facilitate preparation for timely local planning and response. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords hss; hss+; tablet; mobile device; data collection acknowledgments to the survey participants, surveillance staff at all level for their dedicated work on the hss/hss+, and the us cdc for ecda deployment and tablets through pepfar program. references 1. thanh dc et al. brief behavioural surveys in routine hiv sentinel surveillance: a new tool for monitoring the hiv epidemic in vietnam. western pacific surveillance and response journal. vol 6, no. 1/2015 2. national institute of hygiene and epidemiology. hiv/sti integrated biological and behavioural surveillance in vietnam. hanoi, 2014. 3. https://www.cdc.gov/epiinfo/mobile.html *duong c. thanh e-mail: congthanhnihe@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e30, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 1iowa state university statistics department, ames, ia, usa; 2armed forces health surveillance center, silver spring, md, usa objective to demonstrate the current features and functionality of the cdcplot application, and to introduce potential new features of the application. introduction the cdc provides data on incidences of diseases on its website (https://data.cdc.gov/). data is available at national, regional, and state levels, and is uploaded to the cdc’s website on a weekly basis. the cdcplot web application (available at https://michaud.shinyapps.io/ cdcplot/), built using the shiny package in r, provides a quick and user-friendly method of visualizing this data. users are able to the select timeframes, locations, and diseases which they wish to view, and plots are produced. there is an optional alert threshold, which will alert users when a disease increases significantly from one week to the next. in addition, cdcplot provides visualizations of cdc data on pneumonia and influenza mortality. methods an integral feature of cdcplot is automated weekly updating. every thursday, a script is run which scrapes data for individual diseases from the https://data.cdc.gov/ website. this individual disease data is reformatted and combined into a single data file which contains counts for all diseases, across all locations. for each disease and location, we compute alert thresholds, and set alerts when the weekly disease counts surpass these thresholds. alert thresholds are calculated using a moving average technique. the shiny r package lets us create menus which allow users to specify diseases, locations, and time ranges to plot. data can be plotted as weekly counts or cumulative counts, with the option to overlay multiple year’s worth of data for comparison. plots are made using the ggplot2 r package. source code for the app is available at https://github.com/nlmichaud/weeklycdcplot. the app can also be run locally from within r by a call to: rungithub(‘nlmichaud/weeklycdcplot’) results cdcplot can be used to quickly compare disease counts across locations and time periods, simplifying the process of obtaining and viewing data. the different geographical scales that the app provides can also be used to easily identify specific locations that are giving rise to high disease counts. for example, looking at legionellosis data for the whole country shows a spike in cases in mid-august of 2015. refining our plots to look at individual regions shows that most of these cases came from the mid-atlantic region. refining further still to look at all states and locations within the mid-atlantic region reveals a large spike in cases in new york city, which was experiencing an outbreak of legionellosis at that time. conclusions potential future additions to the app include: • tables displaying data counts for user-selected diseases and time ranges. • additional methods for calculating alert thresholds. • maps displaying geographical distributions of disease counts. • an automated news filtering option, which would search through recent news stories to see if disease incidence spikes for user selected diseases can be tied to news reports. keywords cdc; disease; visualization *nicholas l. michaud e-mail: michaud@iastate.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e140, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, 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heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e275, 2019 isds 2019 conference abstracts dashboard prototype for improved hiv monitoring and reporting for indiana yvette temate-tiagueu1, kamran ahmed1, joseph amlung2, dennis stover2, philip j. peters1, john t. brooks1, sridhar papagari sangareddy1, jina j. dcruz1 1 cdc, atlanta, georgia, united states, 2 indiana state department of health, indianapolis, indiana, united states objective the objective was to design and develop a dashboard prototype (dp) that integrates hiv data from disparate sources to improve monitoring and reporting of hiv care continuum metrics in indiana. the tool aimed to support indiana state department of health (isdh) to monitor key hiv performance indicators, more fully understand populations served, more quickly identify and respond to crucial needs, and assist in planning and decision-making. introduction in 2015, isdh responded to an hiv outbreak among persons using injection drugs in scott county [1]. information to manage the public health response to this event and aftermath included data from multiple sources (e.g., hiv testin g, surveillance, contact tracing, medical care, and hiv prevention activities). during the outbreak, access to timely and accurate data for program monitoring and reporting was difficult for health department staff. each dataset was managed separately and tailored to the relevant hiv program area’s needs. our challenge was to create a platform that allowed separate systems to communicate with each other and design a dp that offered a consolidated view of data. isdh initiated efforts to integrate these hiv data sources to better track hiv prevention, diagnosis, and care metrics statewide, support decision-making and policies, and facilitate a more rapid response to future hiv-related investigations. the centers for disease control and prevention (cdc) through its info-aid program provided technical assistance to support isdh’s data integration process and develop a dp that could aggregate these data and improve reporting of crucial statewide metrics. after an initial assessment phase, an in-depth analysis of requirements resulted in several design principles and lessons learned that later translated into standardization of data formats and design of the data integration process [2]. methods specific design principles and prototyping methods were applied during the 9 months that lasted the dp design and development process starting from june 2017. requirements elicitation, analysis, and validation the elicitation and analysis of the requirements were done using a dashboard content inventory tool to gather and ana lyze hiv reporting needs and dashboard requirements from stakeholders. results of this analysis allowed us to validate project goals, list required functionalities, prioritize features, and design the initial dashboard architecture. the initial scope was s cott county. design mapping the design mapping exercise reviewed different scenarios involving data visualization using dp, clarified associations among data from different programs and determined how best to capture and present them in the dp. for example, we linked data in separate datasets using unique identifier or county name. this step’s output was to refine dp architecture. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e275, 2019 isds 2019 conference abstracts parallel design in a parallel design session, we drew dashboard mockups on paper with end users. these mockups helped illustrate how information captured during design mapping would be translated into visual design before prototype implementation. drawings were converted to powerpoint mockups for validation and modifications. the mockup helped testers and future users, interact and rapidly understand the dp architecture. the model can be used for designing other dp. integration data integration was conducted in sas by merging datasets from different program areas iteratively. next, we cleaned (e.g., deleted records missing crucial information) and validated data. the integration step solved certain challenges with isdh data (e.g. linking data across systems while automating data cleaning was planned for later), increased data consistency and reduced redundancy, and resulted in a consolidated view of the data. prototyping after data integration, we extracted a reduced dataset to implement and test different dp features. the first prototype was i n excel. we applied a modular design that allowed frequent feedback and input from isdh program managers. developers of the first prototype were in two locations, but team members kept in close contact and further refined the dp through weekly communicati ons. we expanded the dp scope from scott county to include all counties in indiana. beta version to enable advanced analysis and ease collaboration of the final tool across users, we moved to tableau desktop professional version 10. all excel screens were redeveloped and integrated into a unique dashboard for a consolidated view of isdh programs. after beta version completion, usability tests were conducted to guide the dp production version. technical requirements all users were provided tableau reader to interact with the tool. dp is not online, but shared by isdh through a protected sh ared drive. provisions are made for the dp to use a relational database that will provide greater data storage flexibility, management, and retrieval. dp benefits from the existing security infrastructure at isdh that allows for safeguarding personal identifiable information, secured access, backup and restoration. results system content isdh’s data generated at the county and state level were used to assess the following domains: hiv testing, hiv surveillance, contact tracing, hiv care coordination, and syringe exchange. the dp was populated through an offline extract of the integrated datasets. this approach sped up the tableau workbook and allowed monthly update to the uploaded datasets. the system also included reporting features to display aggregate information for multiple population groups. stakeholders’ feedback to improve users’ experience, the development team trained and offered stakeholders multiple opportunities to provide feedback, which was collected informally from isdh program directors to guide dp enhancements. the initial feedback was collected through demonstration to cdc domain experts and isdh staff. they were led through different scenarios and provided comments on overall design and suggestions for improvement. the goal of the demos was to assess ease of use and benefits and determine how it could be used to engage with stakeholders inside and outside of isdh. dp action reporting the dp reporting function will allow users to download spreadsheets and graphs. some reports will be automatically generated and some will be ad-hoc. all users, including the isdh quality manager and grant writers, can use the tool to guide program evaluations and justifications for funding. the tool will provide a way for isdh staff to stay current about work of grantees , document key interactions with each community, and track related next steps. in addition, through an extract of the integrated dataset (e.g., out-of-care hiv positives), dp could support another isdh program area, linkage to care. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e275, 2019 isds 2019 conference abstracts conclusions we describe the process to design and develop a dp to improve monitoring and reporting of statewide hiv-related data. the solution from this technical assistance project was a useful and innovative tool that allows for capture of time -crucial information about populations at high risk. the system is expected to help isdh improves hiv surveillance and prevention in indiana. our approach could be adapted to similar public health areas in indiana. references 1. peters pj, et al. 2016. hiv infection linked to injection use of oxymorphone in indiana, 2014–2015. n engl j med. 375(3), 229-39. pubmed https://doi.org/10.1056/nejmoa1515195 2. ahmed k, et al. 2018. integrating data from disparate data systems for improved hiv reporting: lessons learned. online j public health inform. 10(1), e49. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27468059&dopt=abstract https://doi.org/10.1056/nejmoa1515195 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts arizona sonora binational cases: five years of surveillance mariana g. casal*1, 2, raquel m. bravo-clouzet1, 2, irene ruberto1, 3, teresa jue1, 3, robert guerrero1, 2 and kenneth komatsu1, 3 1office of border health, arizona department of health services, tucson, az, usa; 2office of border health, tucson, az, usa; 3bureau of epidemiology and disease control, phoenix, az, usa objective to describe 5 years of binational infectious disease surveillance using the binational variable in the medical electronic surveillance system in arizona introduction infectious diseases can spread across borders. (1) the arizona department of health services (adhs) collects information on binational infectious disease cases and shares it with mexico. infectious disease investigation in arizona is enhanced by using an electronic surveillance platform known as the medical electronic disease surveillance intelligence system (medsis), and in 2010 a specific variable for binational cases with mexico was added to the platform. adhs also maintains a binational case definition in the state reportable communicable morbidities manual. arizona partners with the us centers for disease control and prevention (cdc)’s division of global migration and quarantine (dgmq), us mexico unit (usmu), in a monthly binational case reporting project, and shares information with the ministry of health of the state of sonora, mexico, (son moh) to reinforce ongoing communication, to establish baseline disease patterns, and to help detect binational outbreaks. in 2007, the ministry of health of the state of sonora began to use the medsis system for real-time secure case notification, and secure file sharing, using the arizona’s health services portal and secure e-mail accounts for confidential communication between both states. methods from 2011 to 2015, the adhs binational border infectious disease surveillance (bids) program maintained a database to collect information on binational cases with mexico, and coordinated regularly with son moh to investigate and respond to binational cases and outbreaks. in addition, a sas program was created to search for possible binational cases not designated as binational using variables such as an address in mexico and mexican citizenship. the adhs bids program investigated all suspected binational cases with mexico and classified them as binational according to the case definition established by the council of state and territorial epidemiologists (cste). the adhs bids program also shared binational cases from mexico with cdc dgmq usmu through an epi-x forum and with son moh through medsis or secure e-mail. results between 2011 and 2015, the adhs bids program investigated 2,158 possible binational cases with mexico. from those, 70.44% (n=1520) were classified as binational with mexico according with the cste case definition. the majority of cases were classified as binational because of travel to mexico (n =1089, 71.6%), with 59% traveling to sonora (n =641). the majority of cases during those 5 years were enteric diseases (n=1086, 71.4%), followed by vector borne diseases (n =131, 8.6%). most of the binational cases reported had symptom onset between june and august, following the seasonality of both southbound travel and enteric diseases. regular communication with sonora facilitated detection of an average of three binational outbreaks per year. all confirmed binational cases were reported to cdc dgmq usmu and son moh. conclusions continuous sharing of infectious disease surveillance information between both states is essential in understanding the magnitude and types of reportable diseases in the arizona/sonora border region. proper use of the binational medsis variable enables a quicker identification of binational status, allowing for the prompt investigation of possible binational cases and detection of binational outbreaks. binational outbreaks led to collaborative arizona/sonora investigations on several occasions, strengthening our relationship, coordination, collaborations, and understanding of the surveillance system used by son moh. real-time exchange of information using the same secure surveillance system enables better situational awareness, more timely and accurate binational communication, and binational collaborations. arizona sonora binational cases keywords binational variable; reportable; infectious diseases; border region acknowledgments bids program at us mexico unit san diego and arizona department of health services. references 1. barnett ed, walker pf. role of immigrants and migrants in emerging infectious diseases. med clin north am. 2008 nov; 92(6):1447-58, xi-xii. doi: 10.1016/j.mcna.2008.07.001 *mariana g. casal e-mail: mariana.casal@azdhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e96, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using electronic health records for public health hypertension surveillance timothy d. mcfarlane*1, brian e. dixon1, 2, 3 and p. joseph gibson4 1epidemiology, indiana university richard m fairbanks school of public health, indianapolis, in, usa; 2center for biomedical informatics, regenstrief institute, indianapolis, in, usa; 3center for health information and communication, department of veterans affairs (va), health services research and development (hsr&d) service, roudebush va medical center, indianapolis, in, usa; 4marion county public health department, indianapolis, in, usa objective to assess the equivalence of hypertension prevalence estimates between longitudinal electronic health record (ehr) data from a community-based health information exchange (hie) and the behavioral risk factor surveillance system (brfss). introduction hypertension (htn) is a highly prevalent chronic condition and strongly associated with morbidity and mortality. htn is amenable to prevention and control through public and population health programs and policies. therefore, public and population health programs require accurate, stable estimates of disease prevalence, and estimating htn prevalence at the community-level is acutely important for timely detection, intervention, and effective evaluation. current surveillance methods for htn rely upon community-based surveys, such as the brfss. while brfss is the standard at the stateand nationallevel, they are expensive to collect, released once per year, and their confidence intervals are too wide for precise estimates at the local level. more timely, frequently updated, and locally precise prevalence estimates could greatly improve the timeliness and precision of public health interventions. the current study evaluated ehr data from a large, mature hie as an alternative to community-based surveys for timely, accurate, and precise htn prevalence estimation. methods two years (2014-2015) of ehr data were obtained from the indiana network for patient care for two major health systems in marion county, indiana, representing approximately 75% of the total county population (n=530,244). these data were linked and evaluated for prevalent htn. six htn phenotypes were defined using structured data variables including clinical diagnoses (icd9/10 codes), blood pressure (bp) measurements (htn = ≥140mmhg systolic or ≥90mmhg diastolic), and dispensed htn medications (table 1). phenotypes were validated using a random sample of 600 records, comparing ehr phenotype htn to htn as determined through manual chart review by a registered nurse. each phenotype was further evaluated against brfss estimates for marion county, and stratified by sex, race, and age to compare ehr-generated htn prevalence measures to those known and in current use for chronic disease surveillance. comparisons were made using the two one-sided statistical test (tost) of equivalence, wherein the null hypothesis is the brfss and ehr prevalence estimates are different by +/-5% and the alternative is estimates differ by less than +/-5%. rejection of the null resulted in the conclusion of equivalence of the estimates for use in population/public health. results in general, the performance of the ehr phenotypes was characterized by high specificity (>87%) and low to moderate sensitivity (range 25.4%-95.3%). the false positive rate was lowest among the phenotype defining htn by both clinical diagnosis and bp measurements (0.3%), and sensitivity was greatest for the phenotype combining all three structured data elements (95.2%). the prevalence of htn in marion county, indiana (2014-2015) for the ehr sample (n=530,244) ranged between 13.7% and 36.2%, compared to 28.4% in the brfss sample (table 1). only one ehr phenotype (≥1 htn bp measurement) demonstrated equivalence with brfss prevalence at the county level (difference 0.9%, 90% ci for difference -2.3%4.0%). htn prevalence by sex, race, age, sex and age, and sex and race (n=120 comparisons) failed to demonstrate equivalence between ehr and brfss measures in all but two comparisons, both among females aged 18-39 years. differences between ehr and brfss htn prevalence at the subgroup level varied but were particularly pronounced among older adults. as suspected, htn prevalence precision was improved in the ehr sample with the largest subgroup 95% ci width of 0.7% for male african americans compared to the brfss sample 95% ci width of 29.6%. conclusions the applicability of the tested htn phenotypes will vary based upon which ehr structured data elements are available to public health (i.e., icd10, vitals, medications). we found that htn surveillance using a community-based hie was not a valid replacement for the brfss, although the hie-based estimates could be readily generated and had much narrower confidence intervals. table 1. hypertension prevalence for ehr and brfss samples in marion county, in 2014-2015 δ=difference between brfss and ehr htn prevalence; *difference statistically equivalent by tost (p<0.05); † n = 934; ‡ n = 530,244; keywords hypertension surveillance; electronic health record; health information exchange; community survey; public health informatics acknowledgments this project was supported by the u.s. centers for disease control and prevention and the global task force for health via a contract to indiana university (entitled “enhancing doh capacity for using ehr data for cardiovascular disease surveillance”). isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references mozaffarian d, et al. heart disease and stroke statistics — 2016 update. circulation. 2016; 133: e38-e360. yoon s, fryar c, carroll m. htn prevalence and control among adults: united states, 2011–2014. nchs data brief no. 220. 2015; hyattsville, md: national center for health statistics, centers for disease control and prevention, us dept of health and human services. *timothy d. mcfarlane e-mail: timmcfar@iu.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e193, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts implementation of a facility based county surveillance system using epi info samantha spoto*1, michael wiese1 and michelle lyman2, 1 1epidemiology, florida department of health in hillsborough county, tampa, fl, usa; 2university of south florida college of public health, tampa, fl, usa objective the florida department of health in hillsborough county (dohhillsborough) routinely reviews the essence-fl system to assess syndromic trends in emergency department (ed) and urgent care data (ucc). collection of this type of symptom data from long term care facilities (ltcfs) and child care centers is of interest in order to better understand how these illness patterns present in vulnerable populations outside of the eds. introduction surveillance in nursing homes (enserink et al., 2011) and day care facilities (enserink et al., 2012) has been conducted in the netherlands, but is not commonly practiced in the united states (buehler et al., 2008). outbreaks of illnesses within these facilities are required to be reported to the epidemiology program, however a small fraction of outbreaks reported come from ltcfs. without regular communication between ltcfs and the epidemiology program, it is likely that many outbreaks are going unreported due to lack of awareness of the reporting requirements by facility staff. to better understand the prevalence of illness in ltcfs and improve communication between ltcfs and doh-hillsborough a weekly surveillance survey was created using epi info web survey. methods the online facility search tool from the agency for healthcare administration (ahca) was used to query assisted living facilities and nursing homes in hillsborough county in july 2017. the information provided included the number of beds a facility is licensed to have. interest in participation was solicited from larger ltcfs within the county in august 2017 and 23 facilities volunteered to receive weekly surveys, with a total volume of 3,276 beds. a form was created in epi info to capture weekly information per facility of the number of residents and staff with new onset of various symptoms. symptom groups include gi, rash, respiratory, and those with respiratory symptoms who also have a fever (to assess influenza-like illness); number of positive flu tests for the week is also asked. starting with week 38, an email has been sent once a week to participating facilities with a link to the epi info web survey (figure 1) and instructions to fill out the information for the previous week. results to date, 12 weeks of information has successfully been captured in epi info and transferred to microsoft excel for graphical visualization of percentage of residents/staff reported each week in the county with new onset of the above symptoms. low levels of illness (<6%/ week of total reported residents/staff) have been reported for various syndromes each week. over the 12 week period an average of 3.9 facilities submit data per week, with a total of 10 of 23 facilities participating at least once. in week 42 phone calls were made to faclities that had not submitted any responses in an attempt to elicit more participation and troubleshoot any problems faclities may have encountered. prior to week 42, an average of 3.2 facilties reported per week. after reminder phone calls were conducted, the average number of responses for weeks 42-48 was 4.4 with the highest in week 42 (6 responses). starting in week 42 the survey has also been implemented for 15 child care facilities, with four participating over the seven weeks with an average of 2.1 responses per week. conclusions since implementation, the main limitation with the data collection is lack of regular participation from facilities. the current goal of the project is to increase the number of regular responses from both ltcfs and child care facilities. the phone calls made in week 42 increased the response rate for ltcfs, particularly for that week. preliminary results from the first 12 weeks of data indicate that using epi info web survey as a syndromic surveillance tool for local facilities has potential if regular participation can be acheived. figure 1. screenshot of the epi info web survey for long term care facilties, to be completed on a weekly basis. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords syndromic surveillance; long term care facility; epi info references buehler, j., sonricker, a., paladini, m., soper, p., mostashari, f. (2008), syndromic surveillance practice in the united states: findings from a survey of state, territorial, and selected local health departments. advances in disease surveillance, 6:3. enserink, r., meijer, a., dijkstra, f., van benthem, b., van der steen, j. t., haenen, a., van delden, h., cools, h., van der sande, m., veldman-ariesen, m.-j. and on behalf of the sentinel surveillance network on infectious diseases in nursing homes study group (2011), absence of influenza a(h1n1) during seasonal and pandemic seasons in a sentinel nursing home surveillance network in the netherlands. j am geriatr soc, 59: 2301–2305. doi:10.1111/ j.1532-5415.2011.03715.x enserink, r., noel, h., friesema, i., de jager, c., kooistra-smid, a., kortbeek, l., duizer, e., van der sande, m., smit, h., van pelt, w. (2012), the kizss network, a sentinel surveillance system for infectious diseases in day care centers: study protocol. bmc infectious diseases, 12:259. doi:10.1186/1471-2334-12-259 *samantha spoto e-mail: samantha.spoto@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e136, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts effect of the work week on demographics of heatrelated illness patients in syndromic surveillance em stephens* virginia department of health, richmond, va, usa objective to describe the differences in patient populations between those who seek care for heat exposure during the work week and those who seek care during the weekend. introduction as global temperatures increase, so too does interest in the effect of climate change on the population’s health. 2016 represented the hottest year on record globally and well above the 20th century average in virginia.1,2 with large-scale climate change comes an increase in severe weather patterns, including heat waves.3 heat waves can have immense health impacts on a community, including heat stroke, heat exhaustion, and dehydration. previous analyses of emergency department (ed) data indicate that certain populations – specifically males and rural residents – are more at risk for heat-related illness.4,5 none of these studies, however, looked for temporal relationships between the population seeking care and the day of the week. syndromic surveillance data can be used to further describe those communities affected by heat exposure as well as identify any temporal patterns in visits. methods the virginia department of health (vdh) receives data from 148 eds and urgent care centers (uccs) as part of its syndromic surveillance program. during regular surveillance of a heat wave, it was observed that males made up a larger proportion of heat-related visits during the week than they did over the weekend. data received on visits between january 1, 2015 and july 31, 2017 were used for a retrospective, cross-sectional analysis of demographic risk factors for heat-related illness. during this time frame, 6,739 visits were identified using the september 2016 council for state and territorial epidemiologists (cste) syndrome definition for heat-related illness.6 the effect of various demographics and visit factors on weekday heat exposure was measured using chi-squared tests. the variables in question included sex, race, ethnicity, rural vs. urban residence, and age group. odds ratios, 95% confidence intervals, and p-values were reported for these analyses. analyses were conducted using sas 9.3 with a significance level of 0.05. results of the total 6,739 visits identified for heat-related illness, 4,782 (71.0%) occurred during the work week and 1,957 (29.0%) occurred on the weekend. the odds of seeking care for heat-related illness on a weekday were 1.84 times higher for males than for females, p < 0.001, 95% ci [1.65, 2.06]. blacks or african americans were more likely to seek care than whites during the work week with an odds ratio of 1.38, p < 0.001. 95% ci [1.20, 1.57]. adults aged 18-64 years were more likely to seek care during the work week than both children aged 0-17 years (or = 1.61, p < 0.001, 95% ci [1.37, 1.89]) and adults aged 65 years or older (or = 1.36, p < 0.001, 95% ci [1.17, 1.58]). no significant relationship between ethnicity or rural vs. urban residence and work week visits for heat-related illness was observed. conclusions the patient population that seeks care for heat-related illness differs between the work week and the weekend. these data suggest the presence of potential mediators or confounders that make males, blacks or african americans, and adults aged 18-64 more likely to suffer from heat-related illness during the week. collecting data on patients’ health behaviors, risk factors, and occupation could further elucidate this relationship. syndromic surveillance, however, does not include the level of detail needed to investigate anything beyond basic demographics. with an increase in the intensity and frequency of heat waves on the horizon, the issue of heat-related illness is one of growing public health concern. syndromic surveillance data can be used to describe patterns in the patient population most at risk. public health action is then needed to protect these communities while further research explores the relationships in greater depth. keywords heat-related illness; syndromic surveillance; temporal; work week acknowledgments many thanks to jonathan falk, mph and diane woolard, phd for the advice and review. references 1 nuccitelli, d. (2017, july 31). 2017 is so far the second-hottest year on record thanks to global warming. the guardian. retrieved from http:// bit.ly/2vkpzpg 2 boyer, j. (2017, january 18). 2016 was the planet’s warmest year in modern records, but it wasn’t for richmond or even va. richmond times-dispatch. retrieved from http://bit.ly/2jptckg 3 duffy, p. b. (2012, january 21). increasing prevalence of extreme summer temperatures in the u.s. climate change, 111(2), 487-495. https://doi.org/10.1007/s10584-012-0396-6 4 hess, j. j., saha, s., & luber, g. (2014 november). summertime acute heat illness in u.s. emergency departments from 2006 through 2010: analysis of a nationally representatitve sample. environmental health perspectives 122(11), 1209. http://dx.doi.org.proxy.library. vcu.edu/10.1289/ehp.1306796 5 sanchez, c. a., thomas, k. e., malilay, j., & annest, j. l. (2010, january). nonfatal natural and environmental injuries treated in emergency departments, united states, 2001-2004. family & community health 33(1), 3-10. doi:10.1097/fch.0b013e3181c4e2fa 6 berisha, v., braun, c. r., cameron, l., hoppe, b., lane, k., mamou, f., menager, h., roach, m., white, j. r., wurster, j. (2016, september). heat-related illness syndrome query: a guidance document for implementing heat-related illness syndromic surveillance in public health practice. retrieved from http://bit.ly/2w884aj *em stephens e-mail: emily.stephens@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e110, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 1epidemiology, maricopa county department of public health, phoenix, az, usa; 2arizona department of health services, phoenix, az, usa objective to develop a protocol for enhancing traditional arboviral surveillance with syndromic surveillance and to evaluate the protocol for accuracy, effectiveness, and timeliness. introduction arizona is facing multiple public health threats from arboviral diseases. state and local public health departments are monitoring two mosquito-borne outbreaks within its borders and two in adjacent territories. to prevent transmission, viremic patients must be identified in a timely manner and encouraged to avoid additional mosquito exposure and vector control actions must be implemented. using traditional surveillance, however, health departments may not be alerted until the laboratory confirms and reports a positive result, which may take up to 14 days after specimen collection. the arizona department of health services (adhs) partnered with local public health jurisdictions to enhance traditional arboviral surveillance by incorporating syndromic surveillance. methods in collaboration, adhs and county epidemiologists developed a protocol for identifying and responding to possible arboviral cases using chief complaint and diagnosis data from the national syndromic surveillance platform. arizona’s arboviral syndromic surveillance protocol outlines steps for both state and county public health staff. in phase 1, adhs uses phpmyadmin two times per week to extract emergency department and inpatient visit data. the query retrieves records that include variant spellings for chikungunya, dengue, west nile virus, or st. louis encephalitis. these records are posted to county-specific spreadsheets on a secure server. in phase 2, counties use a decision tree to determine whether patient visits warrant further investigation (figure). for visits of interest, staff note whether the patient was previously reported via traditional surveillance and whether the medical record provides additional information to change the level of suspicion. counties decide whether to contact the patient to gather more information, provide education, or alert their vector control department. results this abstract was submitted 1.5 weeks after arizona officially began using syndromic surveillance for enhanced arboviral surveillance (on 8/24/15). after 3 data extractions by adhs, records for 12 visits from 11 unique patients were retrieved by the arboviral query. the patient records, from 2 arizona counties, mentioned west nile virus (10) and st. louis encephalitis (1). eight of the 11 patients were previously identified via traditional surveillance. medical records from the remaining 3 patients were reviewed. arboviral diseases were not confirmed by the medical records of these 3 patients. conclusions in collaboration with county health departments, adhs developed a protocol for identifying and responding to possible arboviral cases using syndromic surveillance. initially, the query, which searches for records that specifically mention an arboviral disease, retrieved 11 unique patients from 2 counties. dividing responsibilities between adhs and county staff has been helpful for managing time and resources for this enhanced surveillance effort. adhs plans to continue using syndromic surveillance for arboviral surveillance through january 2016. as data are collected, state and county epidemiologists will monitor the protocol’s effectiveness and timeliness and implement modifications if necessary. in january, a full evaluation will assess the query’s ability to identify true positive cases, the speed at which possible cases are identified compared to traditional surveillance, and the time dedicated by state and county staff for enhanced surveillance. as the evaluation continues, the authors will continue to share the findings. figure. decision tree for triaging arboviral records identified through syndromic surveillance keywords arboviral; syndromic; use case; collaborative acknowledgments the authors thank the arboviral and syndromic teams at both adhs and maricopa county department of public health for their contributions. *jessica r. white e-mail: jessicawhite@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e81, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e225, 2019 isds 2019 conference abstracts a strategy of analysis of free-text e-death certificates using machine learning yasmine baghdadi, anne gallay, céline caserio-schönemann, marie-michèle thiam, anne fouillet santé publique france, saint-maurice, france objective the aim of this study is to present the syndromic groups that will be routinely monitored for the reactive mortality surveillance based on free-text medical causes of death. introduction in 2004, santé publique france, the french public health agency set up a reactive all-cause mortality surveillance based on the administrative part of the death certificate, in the final objectives 1/ to detect unexpected or usual variations in mortality and 2/ to provide a first evaluation of mortality impact of events. in 2007, an electronic death registration system (edrs) was implemented, enabling electronic transmission of the medical causes of death to the agency in realtime. to date, 12% of the mortality is registered electronically. a pilot study demonstrated that these data were valuable for a reactive mortality surveillance system based on causes of death [1]. a strategy has thus been developed for the analysis in routine of the medical causes of death with the objectives of early detection of expected and unexpected outbreaks and reactive evaluation of their impact. this system will allow approaching the cause accountability when an excess death will be observed. methods mortality syndromic groups (msg) were defined as clusters of medical causes of death (pathologies, syndromes or symptoms) that meet the objectives of the surveillance system. the causes of death are available reactively in free-text (words, terms, expressions) and with a delay of 6 to 24 months in icd10 codes format. we explored multiple biomedical classifications such as the mesh, snomed, umls or icd10 to learn from their various ways to classify diseases. based on icd10, we defined msgs by a list of icd10 codes, each codes belonging to a unique msgs. each msg definition was then discussed in working group including medical and epidemiological experts. additionally, we used a dictionary (provided by the epidemiology center on causes of death (inserm-cépidc)) of each term/expression found in the death certificates since the early 2000 to enrich variety of expression of each msg. we classified causes of death into msgs from e-death certificates from 2012 to 2016: 1/ using the icd10 codes assigned by inserm-cépidc based on rules defined by who in order to produce the national mortality statistics and 2/ using a linear support vector machine (svm) method to classify free-text causes of death. then we compared the fluctuations of the weekly numbers of each msg built by using both classification methods (icd10 codes and the svm classification) [2]. results a list of a hundred msgs was defined, divided into 20 topics (respiratory conditions, digestive conditions, infectious conditions, cardio and cerebrovascular conditions, general symptoms…). 60 msgs were dedicated to alert and detection of both expected seasonal epidemics (12 msgs) and unexpected events (42 msgs). they contain unspecific or acute pathologies and symptoms. 40 msg included medical causes of death related to chronic diseases and medical history. the list of established msgs was composed of: msgs for detection of expected seasonal events such as: “influenza”, “low acute respiratory infection”, “gastroenteritis”, “chikungunya”, “heat related death”, “dehydration”… http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e225, 2019 isds 2019 conference abstracts msgs for detection of the impact of unexpected events such as: “epilepsy”, “choc”, “coma”, “unspecified fever”, “headache”, “suicide”, “drugs/opioids poisoning”… msgs for chronic diseases and medical history: “chronic digestive diseases”, “chronic endocrine diseases”, “genitourinary chronic diseases”, “history of diseases”… the weekly number of msgs built using svm classification was close and highly correlated to the weekly number of msgs built using icd10 codes (figure 1). seasonality and peaks were visible using both classifications. for instance, the increase of the msg “influenza” occurred during winter months which are known to be the circulating months of the influenza virus (figure 1, left) [3]. for unusual and rare events such as death due to burns, we observed that the weekly numbers of msg “burns” were also similar using both methods. we observed (figure 1, right) that the outbreak that occurred in september 2016 related to a major bus accident was found using icd10 codes or svm classification. conclusions the use of free-text causes of death for reactive mortality surveillance requires the development of a strategy for the analysis of these data. defining msgs was essential for the implementation of automatic classification methods of the death certificates in routine. the dynamic of msgs using icd10 codes or svm classification were comparable. however, the use of icd10 codes for reactive mortality surveillance is not an option due to the delay of availability of the codes. the uses of machine learning methods, thus, enable to harness free-text causes of death for the reactive mortality surveillance with an objective of detection and early impact assessment. acknowledgement the authors thank the medical and epidemiological expert for the discussion and validation of definitions of mortality syndromic groups references 1. lassalle m, caserio-schönemann c, gallay a, rey g, fouillet a. 2017. pertinence of electronic death certificates for real-time surveillance and alert, france, 2012–2014 [1]. public health. 143, 85-93. pubmed https://doi.org/10.1016/j.puhe.2016.10.029 2. baghdadi y, bourrée a, robert a, rey g, gallay a, et al. 2018. automatic classification of medical causes from free-text death certificates for reactive mortality surveillance in france [2]. int j med inform. (under review). 3. bedford t, riley s, barr ig, broor s, chadha m, et al. 2015. global circulation patterns of seasonal influenza viruses vary with antigenic drift [3]. nature. 523(7559), 217-20. pubmed https://doi.org/10.1038/nature14460 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28159032&dopt=abstract https://doi.org/10.1016/j.puhe.2016.10.029 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26053121&dopt=abstract https://doi.org/10.1038/nature14460 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e225, 2019 isds 2019 conference abstracts figure 1: weekly numbers of msgs “influenza” and “burns” using icd10 codes (black) and svm classification (red) from 2012 to 2016 in france http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts executing a one health approach during a zoonotic outbreak response peter woodward*1, melissa kretschmer1, hayley yaglom2, craig levy1, peter peter mundschenk3, anne justice-allen4, ronald klein1, tammy sylvester1 and jigna narang1 1epidemiology, maricopa county department of public health, phoenix, az, usa; 2arizona department of health services, phoenix, az, usa; 3arizona department of agriculture, phoenix, az, usa; 4arizona game and fish department, phoenix, az, usa objective demonstrate the utility of a one health collaboration during a leptospirosis outbreak to expand outreach in human, environmental and animal health arenas. introduction the one health paradigm emphasizes cooperation and interdisciplinary collaboration to promote health and well-being among people, animals and the environment. though the concept of one health has been around since the 1800’s, the phrase “one health” was more recently coined, and projects are being developed globally under its sponsorship. maricopa county department of public health (mcdph) has been working at a local level to enhance its one health surveillance efforts and partnerships. this one health partnership, comprised of representatives from the arizona department of agriculture (ada), arizona department of health services (adhs), arizona game and fish department (azgfd), arizona veterinary medicine association (azvma), centers for disease control & prevention (cdc), mcdph, midwestern university (mwu) veterinary school, and local veterinarians, was employed during a response to the recent emergence of leptospirosis in maricopa county, arizona. leptospirosis is a zoonotic bacterial disease typically prevalent in tropical regions, especially island countries or low-lying areas that flood. in the united states, cdc reports 100-200 human cases annually. within the last five years, there have been two confirmed travel-associated human cases reported in maricopa county. however, no locally acquired human or canine leptospirosis cases were reported. two separate clusters of canine leptospirosis were reported in maricopa county in 2016; the first was detected in february among canines within a household. to increase awareness in the veterinary community, the azvma published an article summarizing the cluster. this article might have aided in the identification of a second larger cluster in november that involved multiple veterinary and boarding facilities throughout maricopa county. following both clusters, capacity increased for canine and human surveillance, laboratory testing, and environmental remediation, and discussions were initiated regarding wildlife testing through the efforts of the one health team. methods a coordinated review of canine medical records verified suspicion of leptospirosis within the exposed canine population. a cdc questionnaire was modified by the one health team and facilities were visited to identify possible sources of canine infection. a knowledge, attitudes and practice (kap) survey was distributed through the azvma to guide veterinary education efforts. lecture series, educational materials, and health alerts were created with input from one health agencies for physicians, veterinarians, dog boarding facilities, and owners. cdc subject matter experts assisted in the implementation of a serosurvey of both dog owners and veterinary staff to determine if zoonotic transmission had occurred at the home, veterinary clinic or boarding facility. cdc laboratory testing provided leptospirosis speciation of canine urine specimens. results medical records were abstracted for 79 suspect leptospirosis canine cases and 48 owners were interviewed to assess their risk and exposure factors for their dogs. prior to the visit, some facilities had already implemented self-directed infection control activities. no procedural gaps were identified at the four canine boarding facilities and veterinary clinics visited. the kap survey was completed by 216 arizona veterinarians and technicians. educational outreach included three azvma newsletter articles distributed to approximately 1,100 registered veterinarians, one fact sheet regarding the leptospirosis vaccine, and three factsheets targeting prevention and infection control messages for boarding facilities, veterinary clinics and the home. a three-part lecture series presented jointly by adhs, ada, mcdph, and mwu was attended by approximately 150 veterinarians. a health alert about the possibility of leptospirosis human cases was distributed by mcdph to healthcare providers. eighty-five dogs with either compatible symptoms or exposure were tested through the cdc laboratory, 68 (80%) were positive. canine testing revealed different leptospirosis species between the two clusters, suggesting it was unlikely that they had a common source of exposure. no zoonotic transmission was identified among the 118 people tested in the serosurvey. conclusions pre-existing connections between public health and animal health partners helped facilitate and expand laboratory testing, diagnosis, reporting, outbreak tracking and prevention. the serosurvey provided a novel opportunity to identify cases amongst exposed people and provided insight into zoonotic transmission. information gained from the kap survey provided a gap analysis in veterinary services and guided education efforts. since july 2017, no new canine cases have been reported to public health. however, further studies to identify sources of transmission in wildlife are being developed. the collaborative efforts of multiple agencies culminated in a robust outbreak response and the strengthened processes and relationships can be leveraged for future emerging diseases. keywords one health; outbreak; leptospirosis *peter woodward e-mail: peterwoodward@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e125, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts temporal patterns in chlamydia repeat testing and positivity rates in massachusetts elizabeth c. dee*1, 2, katherine k. hsu2, benjamin a. kruskal3, john t. menchaca1, bob zambarano4, noelle cocoros1, brian herrick5, michelle d. weiss5, ellen hafer6, diana erani6, mark josephson6, jessica young1 and michael klompas1, 7 1department of population medicine, harvard medical school and harvard pilgrim health care institute, boston, ma, usa; 2bureau of infectious disease and laboratory sciences, massachusetts department of public health, boston, ma, usa; 3atrius health, boston, ma, usa; 4commonwealth informatics, waltham, ma, usa; 5cambridge health alliance, cambridge, ma, usa; 6massachusetts league of community health centers, boston, ma, usa; 7department of medicine, brigham and women’s hospital, boston, ma, usa objective to evaluate current rates and temporal trends in adherence with national guidelines recommending chlamydia test-of-cure for pregnant females and test-of-reinfection for all patients. introduction sexually transmitted disease treatment guidelines have incrementally added repeat testing recommendations for chlamydia trachomatis infections over time, including test-of-cure 3 to 4 weeks following completion of treatment for pregnant women and test-of-reinfection for all patients approximately 3 months after infection. however, few studies have investigated adherence to these recommendations and whether the evolution of guidelines have led to changes in repeat testing patterns over time. methods the electronic medical record support for public health surveillance network (espnet) was leveraged to analyze electronic health record data for three independent practice groups serving approximately 20% of the massachusetts population. we identified all cases with laboratory-confirmed chlamydia trachomatis infections between 2010 and 2015 and evaluated the frequency, timing, and results of subsequent chlamydia tests in the following year. results between 2010 and 2015, 972 pregnant female cases, 10,309 non-pregnant female cases, and 4,973 male cases had a positive c. trachomatis laboratory result. test-of-cure within 3-5 weeks following an index positive test occurred in 36.8% of pregnant females. testof-reinfection within 2-4 months of an index test occurred in 39.2% of pregnant females, 17.9% of non-pregnant females, and 9.0% of males. there were no significant increases in test-of-cure or test-ofreinfection rates over the study period for any groups. among cases with repeat tests, 15.9% of pregnant females, 14.6% of non-pregnant females, and 16.3% of males had at least one repeat positive result within one year of the index positive result. conclusions chlamydia test-of-cure and test-of-reinfection rates are low, with no evidence of improvement over time. there are substantial opportunities to improve adherence to chlamydia repeat testing recommendations. keywords communicable disease; electronic medical record; std treatment guidelines; chlamydia *elizabeth c. dee e-mail: elizabeth_dee@harvardpilgrim.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e186, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts forecasting emergency department admissions for pneumonia in tropical singapore cindy lim* and mark chen saw swee hock school of public health, national university of singapore, singapore, singapore objective to develop a forecasting model for weekly emergency department admissions due to pneumonia using information from hospital-based, community-based and laboratory-based surveillance systems introduction pneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. in the past few decades, the threat of emerging pathogens presenting as pneumonia, such as severe acute respiratory syndrome, avian influenza a(h5n1) and a(h7n9), and middle east respiratory syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections. an unexpected increase in the number of hospital admissions for pneumonia or severe respiratory infections could be a signal of a change in the virulence of the influenza viruses or other respiratory pathogens circulating in the community, or an alert of an emerging pathogen which warrants further public health investigation. the purpose of this study was to develop a forecasting model to prospectively forecast the number of emergency department (ed) admissions due to pneumonia in singapore, a tropical country. we hypothesise that there is complementary information between hospital-based and community-based surveillance systems. the clinical spectrum of many respiratory pathogens causing pneumonia ranges from asymptomatic or subclinical infection to severe or fatal pneumonia, and it is usually difficult to distinguish between the different pathogens in the absence of a laboratory test. infected persons could present with varying degrees of severity of the infection, and seek treatment at different healthcare facilities. hospital-based surveillance captures the more severe manifestation of the infection while community-based surveillance captures the less severe manifestation of the infection and enables earlier detection of the infection. thus, the integration of information from the two surveillance systems should improve the prospective forecasting of ed admissions due to pneumonia. we also investigate if the inclusion of influenza data from the laboratory surveillance system would improve the forecasting model, since influenza circulates all-year round in singapore and is a common aetiology for pneumonia. methods this was a retrospective study using aggregated national surveillance data and meteorological data during the period 3 january 2011 to 1 january 2017. we compared the performance of autoregressive integrated moving average model (arima) with multiple linear regression models with arima errors, with and without the inclusion of influenza predictors at forecast horizons of 2, 4, 6 and 8 weeks in advance. weekly data between the study period of 3 january 2011 and 1 january 2017 were split into training and validation sets, with the first three years of data used as the base training set. time series cross validation was used to estimate the models’ accuracy and out-of-sample forecast accuracy was based on the calculation of the mean absolute error (mae) and mean absolute percent forecast error (mape). results the multiple linear regression model with arima errors that included influenza predictors was the best performing model while the basic arima model was the worst performing model for all forecast horizons. the two multiple linear regression models with arima errors had a mape of less than 10% for all forecast horizons. conclusions data from different multiple surveillance systems and the inclusion of influenza trends can be used to improve the forecast of ed admissions due to pneumonia in a tropical setting, despite the absence of large differences between seasons. accurate forecasting at the national level can prepare healthcare facilities for an impending surge. keywords pnuemonia; emergency department; surveillance; influenza *cindy lim e-mail: limcindy2000@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e12, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 98 (page number not for citation purposes) isds 2015 conference abstracts impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 1school of public health, university of hong kong, hong kong, china; 2guangzhou center for disease control and prevention, guangzhou, china; 3laboratory animal unit, university of hong kong, hong kong, china; 4the eight people’s hospital of guangzhou, guangzhou, china objective this study assessed the effect of disinfection and closing live poultry markets in china on avian influenza a(h7n9) virus detection and viability in a natural setting. we characterized virus detection at different sampling sites to assess exposure risk to the general public and live poultry traders. introduction h7n9 virus emerged in eastern china in march 2013, which led to >550 human cases and >200 deaths in 2 years. live poultry markets (lpms) are considered as a major source of human h7n9 infections. in late 2013, the virus had spread to the southern provinces including guangdong. its provincial capital guangzhou, detected its first local h7n9 human case in mid-january 2014 and reaching 10 cases in a month. as a response, guangzhou government announced a two-week city-wide market closure, banning trading and storing of live poultry. guangzhou center for disease control and prevention took this opportunity to establish enhanced surveillance on top of the existing routine lpm surveillance, to assess the impact of such on h7n9 viral isolation and survival. methods we analyzed environmental samples in routine surveillance from 4 retail lpms collected on the same day immediately before and after disinfection. to further assess the impact of market closure and disinfection on virus activity, enhanced surveillance with 12 rounds of intensive sampling were carried out before, during and after the 2-week city-wide lpm closure, in 3 retail and 1 wholesale lpms. samples were collected from different sites, including poultry cages, drinking water for chicken, inner surface of defeathering machines, barrels holding poultry meat, chopping boards, surfaces of processing tables and wastewater. samples were tested for aivs by rrt-pcr and also h7n9 by culture. results based on the 214 environmental samples collected from the routine surveillance, the pooled estimated reduction ratios were 58.0% (95% ci 8.9% – 80.6%) and 64.2% (95% ci 30.6% – 81.5%) for h7n9 and aiv respectively after disinfection. a total of 1466 environmental samples were collected from the targeted enhanced lpm sites. the figure shows the aiv and h7n9 detection and isolation rates before, during and after the market closure (period shaded in gray). we also found a higher h7n9 virus detection in chopping boards in retail lpms and wastewater in wholesale lpms. during the market closure, h7n9 viral rna detection and isolation rates in retail markets decreased by 79% (95% ci, 64% – 88%) and 92% (95% ci, 58% – 98%), respectively. viable h7n9 virus could be cultured from wastewater samples collected up to 2 days after market closure. our findings indicate that poultry workers and the general population are constantly exposed to h7n9 virus at these markets and that market closure and disinfection rapidly reduces the amount of viable virus. conclusions market closure and disinfection reduced h7n9 viral rna contamination in the lpm environment by >70% and infectious virus by >90%. however, live virus could be detected for around 2 days after the intervention, especially in wastewater sources. to strike the balance between minimizing human infection risk, demands of live poultry from the general public and interest of the poultry industry, coordination between the public health and veterinary sector should be strengthened under a “one-health” approach. keywords avian influenza; influenza a(h7n9); live poultry markets; surveillance; disinfection acknowledgments this work is supported by the national science and technology major projects of china, science and technology planning project of guangdong province, china, science and technology program of guangzhou, china, national natural science foundation of china, the harvard center for communicable disease dynamics from the national institute of general medical sciences, the area of excellence scheme of the hong kong university grants committee, and the health and medical research fund of the food and health bureau, government of the hong kong special administrative region. *eric h.y. lau e-mail: ehylau@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e21, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena piramal swasthya management and research institute, hyderabad, india objective the objectives of this paper are 1. to describe the functioning of a highway emergency response and accident mitigation service and 2. to characterize the profile of the accidents and the victims served by this project introduction the increasing frequency and severity of road traffic accidents (rta) in india have caused grave concern for road safety, posing serious challenge to transport policy makers, planners, regulators, police, engineers and civil society alike. with just 1% of world’s vehicles, india leads with 10% of world’s total road traffic fatalities, resulting in untold misery to lakhs of people and costing about 3% of the gdp1. due to the impact of the rta, the united nations declared the current decade “the decade of action for road safety with a goal to save five million lives”2. post-crash response is very critical to reduce the mortality and morbidity due to accidents. piramal swasthya in collaboration with the national highways authority of india and general insurance company set up the highway emergency response and accident mitigation service between hyderabad and vijayawada, as a pilot project to address the post-crash response. methods we report a descriptive cross-sectional analysis of data pertaining to accidents that occurred on the stretch mentioned above, over a period of one year from june 1st 2014 to 30th may 2015. any rta on this stretch could be reported by calling 1033. and all such calls were attended by a trained individual at a 24x7 central 4-seater call center. a fully equipped trauma ambulance was despatched to the site immediately. the victim was attended to by well-trained paramedical workers stationed in the ambulance and then transported to the nearest health care facility. information regarding the accident was also transmitted to the nearest police official. thus the entire service was integrated with the police at the ground level, thus leading to a very robust network of emergency response. data were entered on an android based hand held device by a trained paramedic and analysed using descriptive statistics. results a total of 1379 calls were received reporting a total of 1311 accidents took place in the study period leading to injuries to 1489 people. of the 1311 accidents, 774 (59.04%) were attended to by 1033 ambulance. an incoming call was attended within an average of four seconds. on an average, an ambulance was dispatched to the site of accident by one min & 19 sec. more than half the accidents were of the collision variety (55.42%) while the non-collision variety contributed about one-fifth of the accidents. most commonly, accidents took place in the evenings between 4 pm and 8 pm (24.03%) followed by those in the afternoon (20.15%). the age of the victims ranged from 1 yr to 93 yrs with a mean age of 36.4 (±14.6) yrs. a majority of the accident victims were males (83.73%). more than half the accident victims were between 21 and 40 yrs of age (56.64%). conclusions highway emergency response and accident mitigation service with a dedicated call centre, ambulance and integration with the police force is an effective service to provide post-crash response to accident victims on the national highway. this service attended to 60% of the accidents in the service area. incidence of fatalities was 5.63%. we recommend this service at national level to provide emergency ambulance care service to accident victims on national highways. time taken for specific critical activities after receiving the distress call keywords emergency response; accident mitigation; ambulance; call centre; road traffic injury acknowledgments we are grateful to all the call centre officials who have been handling all the calls since the inception of the programme. we acknowledge the contributions of the staff members of piramal swasthya management & research institute in helping us complete this paper. references [1] world health organization. global status report on road safety 2013 – supporting a decade of action. geneva switzerland. [cited 22nd july 2015] available from: http://www.who.int/violence_injury_ prevention/road_safety_status/2013/en/ [2] united nations road safety collaboration. 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locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts wearable sensor application for integrated early warning and health surveillance lauren e. charles*, devin p. wright, zhuanyi huang, cree white, fnu anubhav, yuanqing jin and michael henry pacific northwest national laboratory, richland, wa, usa objective the wearable sensor application developed by pacific northwest national laboratory (pnnl) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. the application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. the information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. this application is the first of its kind being developed for integration into the defense threat reduction agency’s biosurveillance ecosystem (bsve). introduction wearable devices are a low cost, minimally invasive way to monitor health. sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. in addition, this data can be used to monitor individuals that are in high risk of health-related problems. however, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. in addition, the environment surrounding the individual needs to be considered when interpreting the data. to address these issues, pnnl has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals. methods the prototype application described here was a product of pnnl’s bsve application development competition. the final product that will be deployed in the bsve is currently under development by pnnl and will vary slightly in the exact design and architecture described. data. wearable sensor data was collected from the rim2rim (r2r) watch study of individuals hiking the grand canyon in arizona [1]. weather information was obtained from nearby weather stations and mapping features were derived from google maps. calculations. a physiological strain index (psi) was calculated using core temperature estimates derived through a kalman filter approach and heart rate [2,3]. application. the prototype backend application development was based in python with a mongodb. the front-end development was built using a scalable architecture and modular approach with components in react and d3. results a prototype application was developed this past summer through the pnnl bsve app competition (fig 1). the application was aimed at visualizing wearable sensor data from the grand canyon r2r hike dataset. simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data. many features described in the prototype application will be incorporated into the final bsve application. the key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from bsve internal data sources, and 3) the alerts will provide more information and have their own page for reviewing. conclusions the wearable sensor application developed by pnnl for integration into the bsve provides an early warning system for individual and group health using physiologic and environmental parameters. the application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. with this tool, an analyst can easily monitor the health of individuals and groups with the aid of realtime alerting tool for early detection of abnormal health parameters. figure 1. wearable sensor application prototype group page (left) and individual page (right). group views include box & whiskers and parallel coordinate plots of sensor readings, map view of individuals with alert status, and alert counter. individual views include alerts, time series of sensors, weather, map view, and elevation overtime. keywords wearable sensors; health surveillance; early warning; real-time data; physiologic strain index acknowledgments this work was funded by the defense threat reduction agency (project number cb10190). isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references [1] aviña ge, abbott r, anderson-bergman c, et al. 2017. rim-to-rim wearables at the canyon for health (r2r watch): experimental design and methodology. hci international 2017 conference proceedings. [2] buller mj, tharion wt, cheuvront sn, et al. 2013. estimation of human core temperature from sequential heart rate observations. institude of physics and engineering in medicine physiological measurement 34(7). [3] moran ds, shitzer a, & pandolf kb. 1998. a physiological strain index to evaluate heat stress. american journal of physiology regulatory, integrative and comparative physiology 275(1): r129-r134. *lauren e. charles e-mail: lauren.charles@pnnl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e69, 2018 a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget elham hatef1, hadi kharrazi1, ed vanbaak2, marc falcone2, lindsey ferris2, kory mertz2, chad perman3, alice bauman3, elyse c lasser1, jonathan p. weiner1 1. center for population health it, department of health policy and management, johns hopkins bloomberg school of public health, baltimore, md 2. chesapeake regional information system for our patients (crisp), columbia, maryland 3. office of population health improvement, maryland department of health, baltimore, md abstract maryland department of health (mdh) has been preparing for alignment of its population health initiatives with maryland’s unique all-payer hospital global budget program. in order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. the broad adoption of electronic health records (ehrs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (hie) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. mdh’s ehr-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. this article explores the potential expansion of health it capacity as a method to improve population health across maryland. first, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. we then present an assessment of the current and near real-time availability of digital data in maryland including the geographic granularity on which each measure can be assessed statewide. finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the maryland hie. this paper is intended to serve as a high level guiding framework for communities across the us that are undergoing healthcare transformation toward integrated models of care using universal interoperable ehrs. keywords: population health measures, maryland all-payer model, health information exchange, electronic health record abbreviations: admission-discharge-transfer (adt) systems, alternative payment models (apm), behavioral risk factor surveillance system (brfss), blood pressure (bp), body mass index (bmi), centers for medicare and medicaid services (cms), chesapeake regional information system for our patients (crisp), clinical quality measure (cqm) aligned population health reporting tool (caliphr tool), consolidated clinical document architecture (c-cda), electronic health records (ehrs), emergency department (ed), fast healthcare interoperability resources (fhir), follow up (f/u), health information exchange (hie), health services cost review commission (hscrc), maryland department of health (mdh), maryland medical care data base (mcdb), maryland state health improvement process (ship), merit-based incentive program (mips), national quality forum (nqf), office of the national coordinator for health information technology (onc), quality reporting document architecture (qrda). correspondence: elham hatef, md, mph; center for population health it, department of health policy and management, johns hopkins bloomberg school of public health, 624 n. broadway, room 501, baltimore, a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi maryland 21205, 443-2872284, ehatef1@jhu.edu doi: 10.5210/ojphi.v9i3.8129 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction over the recent period, the maryland department of health (mdh) has been preparing for alignment of its population health initiatives with the next phase of the maryland hospital allpayer model [1,2]. maryland’s federally-supported all-payer model [2] is a unique demonstration project that allocates hospitals with a fixed global budget from all payers, both public and private, to provide care for every state resident. the state considers the alignment of all stakeholders critical to improving the health of marylanders especially in communities with restricted resources and poor health outcomes. essential to this process is the development and implementation of indicators for monitoring the health of the residents and holding the hospital systems, now under the centers for medicare and medicaid services (cms) demonstration project “microscope”, accountable for achieving those end-points. to move towards this goal, we identified and performed initial preparatory work on a starter set of measures that address broad indicators of community level health such as chronic disease, social risk factors associated with illness, and potentially inappropriate hospital utilization. similar to a handful of other leading states, maryland’s development and wide scale implementation of electronic health records (ehrs) [3] and a universal state-wide health information exchange (hie) which is moving towards health data interoperability [4], provides an unprecedented opportunity to assess the community-level population health on an automated basis. this growing community-wide real-time digital health information infrastructure complements the assessments generated from ad-hoc surveys, vital records, and health plan claims data. a proposed population health framework and set of measures in maryland aligned with the allpayer model is described in a separate paper [5]. this article explores the potential role of ehrs and other health it resources as the basis of these comprehensive statewide population health measures at the neighborhood level and also offers a technical assessment of the maryland health it platform’s readiness. we believe it will serve as a model for other us states or regions that are, or soon will be, in similar situations. following, we describe a digital measurement progression plan and data assessment for four selected population health measures at various geographic levels. we then provide recommendations for improving digital data collection and management in support of those metrics as part of the universal monitoring system over time. in addition, we briefly describe the current status of ehr data collected by the statewide maryland hie (a.k.a., chesapeake regional information system for our patients, or crisp) [6] and provide recommendations for increasing the population level coverage of the reported metrics. we offer this description and assessment as a potential guide to health care and public health agencies across the nation and globe. mailto:ehatef1@jhu.edu a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi maryland’s population health progression plan and data assessment the progression plan maryland’s proposed population health measurement plan over a ten-year window includes four initial community-level measurement domains: body mass index (bmi) screening and follow up (f/u), screening for high blood pressure (bp) and f/u, smoking status and cessation assistance, and monitoring of falls-related injuries. the proposed population health measures are considered in reference to maryland state health improvement process (ship) [7], which is aligned with the healthy people 2020 objectives established by the us department of health and human services [7]. while the maryland ship defines the population health focus areas for the state, it is limited to long term multiple-year goals for population health and is mainly based on periodic survey data at the state, county or other large geographic levels. the proposed digital population health measurement progression plan (table 1) considers the underlying ship limitations and the availability, accessibility, and quality of the new statewide electronic data. the novel digital data sources rely less on survey and billing data, and instead, attempt to utilize ehr data that are individual-level, geographically granular, and real-time. for instance, collecting data on bmi screening based on clinician-patient interaction (or lack thereof), helps to address the long-term state goal of detecting “adults who are a healthy weight” and “children and adolescents who are obese”. it also supports the longer-term idea of developing a state-wide surveillance system to assess obesity across the state with individual level data. this ehr-derived measure is based on the national quality forum (nqf) measure # 0421 [8] and cms measure # 69 [9]. the nqf measure is defined for those 18 years and older. we expanded the measure to include those younger than 18. the combination of this measure with the existing survey-based bmi measure captured through maryland’s ship provides the potential to extract bmi metrics across all patient age groups and locations on a much shorter interval. assessment of the current and near time available non-clinical data for population health measurements table 1 outlines existing and near time available data sources for each of the proposed measures, changes in data availability and completeness over time, and the geographic/ individual granularity on which each measure can be assessed in maryland. a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi table 1. proposed maryland digital measurement progression plan and data assessment for four selected population health measures over time and at various geographic levels measurement type based on available data → process and output outcome/ impact time frame for collecting proposed measures → short term (current) near term (2 years) mid to long term (3 to 5 years) longer term (8 to 10 years) available geographic level over time (based on available data source) → zip code/ track county state individual zip code/ track available data sources over time → hscrc brfss∗∗ medicaid ehr (documented measures by providers) crisp (transmitted and stored data from providers to crisp) mcdb measurement domains ↓ and change in measurement domains over time → ship categories bmi screening and follow-up for community/ population (nqf measure # 0421 and cms measure # 69) bmi score based on self-reported weight and height of a representative sample (12,369 people) for the state of maryland bmi score based on measured height and weight by providers in the clinic bmi score based on data found in a c-cda. intervention and procedure orders within a c-cda are not available, which is necessary to calculate follow-up visits healthy living adults who are a healthy weight obesity surveillance in a specific catchment area using ehr data children and adolescents who are obese screening for high bp and follow-up for community/population (cms measure # 22v5) screening for high bp and follow-up for a community/popula tion (with specific bp) bp measure based on data found in c-cda. there is partial coverage for data needed to calculate followup visits claims data on screening for high bp and follow-up visit quality preventive care emergency department visit rate due to hypertension bp surveillance in a specific catchment area with application of bp measurements through ehr a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi * note that the full measurement plan also includes cost and patient experience measures. ** currently in-use data. other data sources are available in different time frames which potentially could provide population health assessment. bmi: body mass index, bp: blood pressure, brfss: behavioral risk factor surveillance system, c-cda: consolidated-clinical document architecture, cms: center for medicare and medicaid services, crisp: chesapeake regional information system for our patients, ed: emergency department, ehr: electronic health record, hscrc: health services cost review commission, mcdb: maryland medical care data base, nqf: national quality forum, ship: state health improvement process. current adult smoking within population based on the brfss questionnaire asking current smoking habits among adults of a representative sample (12,369 people) for the state of maryland claims data on smoking medical assistance individual data on smoking/tobacco use cessation, and medical assistance most data elements needed to calculate smoking cessation will be found in a c-cda claims data on smoking medical assistance healthy living adults who currently smoke application of smoking status measurement through ehr for surveillance of smoking trends in a specific catchment area falls; fall-related injury rate number of falls resulted in an ed visit or hospitalization in a zip code including physician services categorized as an outpatient data or emergency room visit history of falls; a representative sample (12,369 people) for the state of maryland claims data on falls related ed visit and hospitalization individual data on falls related visit in ed or inpatient data on falls related visit in ed or inpatient claims data on ed visit and hospitalization healthy communities fall-related death rate falls surveillance including repeated falls among individuals in a specific catchment area using ehr data a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi currently in-use data sources: self-reported bmis, smoking habits, and history of falls are collected through the cdcdesigned behavioral risk factor surveillance system (brfss) survey of 12,369 maryland residents on a county level. indeed, in maryland (and most other states), the brfss is the major source of data for the ship and currently reported population health measures [7]. current and near-time available data sources: there are other sources of available data which could address population health measurements of interest. for instance, maryland medical care data base (mcdb) includes enrollment, provider, and claims data for maryland residents enrolled in private insurance. this database provides claims on screening for high bp and f/u visits, smoking cessation assistance, and falls related emergency department (ed) visit and hospitalization [10]. in addition, maryland’s health services cost review commission (hscrc) database [11] provides administrative and billing information for all regulated hospital care under the allpayer model. the hscrc’s inpatient dataset contains discharge medical record abstracts including all diagnoses and billable services provided for each inpatient admission. the hscrc’s outpatient data contains medical abstracts and billing data on all emergency department and other outpatient surgeries, clinic visits, and referred outpatient ancillary utilization occurred in a hospital setting. though not currently applied for the purpose of population health measurement, these datasets could provide the number of falls resulted in an ed visit or hospitalization in a zip code or at the census block level, or even individual level. maryland has not yet required the facilitation of the technical infrastructure and legal issues related to accessing these data sources for the purpose of the population health assessment and improvement. assessment of the current and near time available clinical data (ehr) for population health measurements currently available data sources: maryland has a comprehensive health-related digital data infrastructure [12]. presently, crisp, the state hie, is able to partially calculate the population health measures for all hospitals using the clinical quality measure (cqm) [13] aligned population health reporting tool (caliphr tool), “a tool designed to give eligible providers the ability to meet the clinical quality measurement requirements of federal and state incentive, as well as value-based programs”. [14] most health delivery systems in maryland have been compliant with cms meaningful use [15] (now part of the medicare access and chip reauthorization act (macra); the ehr incentive payment program) [16] and are recording vitals data including bmi, bp, and smoking status for most visits (more than 75% of their visits) and falls-related injuries for many visits (more than 50% of their visits); however, as of 2016 crisp receives ehr data from the health delivery systems on bmi, bp, and smoking status for only about 25% of patients and on falls related injuries for <10% of patients. this substantial drop between what is collected at the health delivery system and what is actually being transmitted to crisp is a result of the vital data not being always captured in the “consolidated clinical document architecture” (c-cda) documents [17] that are sent to crisp from the health delivery systems. as of 2017 the c-cda a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi [17] serves as the base standard for generating electronic clinical documents and is used by crisp for receiving and storing ehr data from eligible providers across the health delivery systems in maryland. maryland is still required to address the legal issues to accessing the currently available and transmitted ehr data to crisp for the purpose of population health assessment and improvement. near-time available data sources: the 2015 edition health it certification criteria (2015 edition) from the office of the national coordinator for health information technology (onc) aims “to facilitate greater interoperability for several clinical health information purposes and enables health information exchange through new and enhanced certification criteria, standards, and implementation specifications”. [18] the roll out of the 2015 edition health it certification criteria [18] by different ehr vendors should potentially result in an increased number of providers and practice sites sending their ehr data in c-cda documents to hies across the country. in maryland, an anticipated 2,000 providers send their ehr data in c-cda’s documents to crisp. approximately 80 of these practice sites integrated with the caliphr tool [14], will have the ability to meet the clinical quality measurement requirements of federal and state incentive, and could partially calculate each proposed population health measure by the end of 2017. these upgraded ehrs will be capable of exporting quality reporting document architecture (qrda) category 1 files [19] which are necessary to calculate the ehr-based proposed measures. the qrda category 1 files are the onc’s recommended standard document format for the exchange of electronic clinical quality measures (ecqms) data which are reinforced by cms [19]. by 2018 all providers participating in the ehr incentive program or the quality payment program will be required to have adopted 2015 edition certification [18]. crisp anticipates having 3,500 providers sending their ehr data in c-cda documents and approximately 130 of these practice sites will be integrated with the caliphr tool. this advancement provides an opportunity for the state to move toward the application of ehr data as the main data source for population health assessment and improvement. maryland will need to address the legal issues related to this new application of the available data. conclusions and potential strategy to improve electronic data quality for population health measurements optimal interoperability and adequate data exchange infrastructure are vital to operationalizing population health interventions and measurements [20]. capturing and collecting data for the population health measures require special efforts from multiple stakeholders to expand the data exchange infrastructure and data interoperability among health providers [21]. as the market continues to mature and new interoperability standards become available, there may be alternative data collection methods employed for each population health measure, on a case by case basis. a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi in this report, we described maryland’s incremental approach by selecting a number of initial population health measures and focusing on available data from providers and practices while planning for an expansion of new data sources. this allows public health agencies to extract useful population health data from day one, while gradually making progress towards the overall goal of conducting more comprehensive and outcome-based measurements. this strategy will be impacted by market/industry factors, data availability, funding, and measure authoring cycles. as a result, the timelines and milestones will be subject to necessary change and must remain flexible. future steps for statewide and/or local health departments interested in utilizing health it for population health measurement, may include various challenges and opportunities with different timelines. following, we offer some recommendations to help guide this process: • in the next 3-5 years, states interested in developing a population health measurement approach will need to continue to monitor cms programs that require cqm reporting such as merit-based incentive payment systems (mips), alternative payment models (apm), and the hospital inpatient quality reporting program [16] and align state required measures as much as possible. this will be particularly important with ecqms [22] as it may be difficult to require ehr vendors to calculate state-specific ecqms [23]. • states should consider additional steps to support data collection at the state level and continue to support data gathering from their hie. states may consider (1) mandating their providers to submit qrda data on the proposed measures to a state organization; (2) incentivize voluntary submission of other key population health measures; and/or, (3) contract with an organization to provide practice level support to providers to collect and report population health measures via qrda. • states may want to consider incorporating and aggregating multi payer claims data (commercial, medicaid and medicare) to selected population health measures such as bmi and bp f/u intervention (not the actual bmi or bp score) and to measure other population health measures such as falls risk intervention activities namely education and awareness. • in the longer term (8 to 10 years), states should take additional steps to support coalescing interoperable data at the state level by continuously gathering data from their hies. • states should work with stakeholders to monitor the evaluation of emerging standards, such as “fast healthcare interoperability resources” (fhir); a standard for exchanging healthcare information electronically) [24], and to determine if additional data collection/submission mechanisms should be incorporated in their population health measurement approach. they should continue to support their ongoing cms-motivated apm transformation efforts. • states should consider incorporation of home health, self-monitoring, and/or data gathering on population health measures such as self-reported bmi and bp information by smart watch, personal home-based tablets, and telehealth activities. in addition, they should consider recording of tobacco use status outside the standard clinical settings. they may also consider measurements of a state-wide health it infrastructure for population health: building a community-wide electronic platform for maryland’s all-payer global budget online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e195, 2017 ojphi patient engagement strategies, incentives and deterrents, not just status reporting of tobacco. • states should address the various privacy regulations and data governance provisions with regard to access to individual level data. in this paper, we presented a population health assessment model for communities across the us on the road to interoperable ehrs that one day will offer real-time data collection on almost all residents. we introduced the potential expansion of health it capacity as a key tool for improving population health across the country which for the first time in the history of us health system will soon be feasible on a wide scale. but for this to happen a special coordinated effort from multiple stakeholders will be required to expand the interoperable digital infrastructure along the lines we lay out in this article at the community level. financial disclosure this project was supported through a state innovation model round two design grant from the centers for medicare and medicaid services’ (cms) innovation center awarded to the maryland department of health (mdh). maryland’s health information exchange (a.k.a., chesapeake regional information system for our patients, or crisp) and the center for population health it at the johns hopkins bloomberg school of public health have provided technical expertise as subcontractors. any recommendations put forth in this article are the views of the authors and do not necessarily represent the views of mdh or cms. competing interests no competing interests. references 1. maryland population health improvement plan. planning for population health improvement. available at: https://mmcp.dhmh.maryland.gov/documents/sim%20round%20two/appendix%20c_ma ryland%20population%20health%20improvement%20plan_for%20website.pdf 2. maryland all-payer model. available at: 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all-payer global budget maryland’s population health progression plan and data assessment the progression plan assessment of the current and near time available non-clinical data for population health measurements assessment of the current and near time available clinical data (ehr) for population health measurements conclusions and potential strategy to improve electronic data quality for population health measurements financial disclosure competing interests references isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts suitability of data for the surveillance of toxicological events in companion animals alexandra swirski*, david pearl, olaf berke, terri o’sullivan and deborah stacey population medicine, university of guelph, n1h7j6, on, canada objective our objective was to assess the suitability of the data collected by the animal poison control center, run by the american society for the prevention of cruelty to animals, for the surveillance of toxicological exposures in companion animals in the united states. introduction there have been a number of non-infectious intoxication outbreaks reported in north american companion animal populations over the last decade1. the most devastating outbreak to date was the 2007 melamine pet food contamination incident which affected thousands of pet dogs and cats across north america1. despite these events, there have been limited efforts to conduct real-time surveillance of toxicological exposures in companion animals nationally, and there is no central registry for the reporting of toxicological events in companion animals in the united states. however, there are a number of poison control centers in the us that collect extensive data on toxicological exposures in companion animals, one of which is the animal poison control center (apcc) operated by the american society for the prevention of cruelty to animals (aspca). each year the apcc receives thousands of reports of suspected animal poisonings and collects extensive information from each case, including location of caller, exposure history, diagnostic findings, and outcome. the records from each case are subsequently entered and stored in the antox database, an electronic medical record database maintained by the apcc. therefore, the antox database represents a novel source of data for real-time surveillance of toxicological events in companion animals, and may be used for surveillance of pet food and environmental contamination events that may negatively impact both veterinary and human health. methods recorded data from calls to the appc were collected from the antox database from january 1, 2005 to december 31, 2014, inclusive. sociodemographic data were extracted from the american 2010 decennial census and the american community surveys. choropleth maps were used for preliminary analyses to examine the distribution of reporting to the hotline at the county-level and identify any “holes” in surveillance. to further identify if gaps in reporting were randomly distributed or tended to occur in clusters, as well as to look for any predictable spatial clusters of high rates of reporting, spatial scan statistics, based on a poisson model, were employed. we fitted multilevel logistic regression models, to account for clustering within county and state, to identify factors (e.g., season, human demographic factors) that are related to predictable changes in call volume or reporting, which may bias the results of quantitative methods for aberration/outbreak detection. results throughout the study period, over 40% of counties reported at least one call to the hotline each year, with the majority of calls coming from the northeast. conversely, there was a large “hole” in coverage in midwestern and southeastern states. the location of the most likely high and low call rate clusters were relatively stable throughout the study period and were associated with socioeconomic status (ses), as the most likely high risk clusters were identified in areas of high ses. similar results were identified using multivariable analysis as indicators of high ses were found to be positively associated with rates of calls to the hotline at the county-level. conclusions socioeconomic status is a major factor impacting the reporting of toxicological events to the apcc, and needs to be accounted for when applying cluster detection methods to identify outbreaks of mass poisoning events. large spatial gaps in the network of potential callers to the center also need to be recognized when interpreting the spatiotemporal results of analyses involving these data, particularly when statistical methods that are highly influenced by edge effects are used. keywords toxicology; companion animals; multilevel models; geospatial analysis acknowledgments the authors would like to thank the aspca for providing the data for the project. references 1. brown ca, jeong k-s, poppenga rh, et al. outbreaks of renal failure associated with melamine and cyanuric acid in dogs and cats in 2004 and 2007. j vet diagnostic investig 2007;19:525–531. *alexandra swirski e-mail: aswirski@uoguelph.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e172, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts one health approach through interactive urban health governance framework in a smart city, india yasobant sandul*1, walter bruchhausen1 and dr. deepak b. saxena2 1center for development research (zef), university of bonn, bonn, germany; 2indian institute of public health gandhinagar, gandhinagar, india objective the present study aims to operationalize one health approach through local urban governance system in a rapidly urbanized indian city, ahmedabad, india. in ahmedabad (proposed smart city), gujarat, india: 1. to understand the pattern of zoonotic diseases in reference to urban governance system 2. to develop a conceptual one health governance framework with reference to zoonotic diseases 3. to assess the key indicators for convergence for inter-sectorial professional collaborations in one health introduction smart governance refers to the emergence of joint action by the health and non-health sectors, public and private actors and citizens. although, there are growing literature on governance and its potential impact on health, major challenges on collective action across sectors have been witnessed in developing countries like india. in the same line, the current forms of global health governance façades operational issues and does not sufficiently meet the needs at local levels. in light of these perceived shortcomings, the local governance becomes subject of interest and should be debated especially with reference to global urbanization. rapid and unplanned urbanization followed by the combination of high population density, poverty and lack of infrastructure have more side effects and fostering conditions for communicable diseases to flourish. evidence suggests that new megacities could be incubators for new epidemic and zoonotic diseases, which can spread more rapidly and become worldwide threats. in india, ministry of urban development initiated the concept of converting few major cities into “smart city” in 2015-16. however, one of the major critiques of available smart city guideline is that it has no such focus on prevention of emerging and/or re-emerging zoonotic diseases. the emergence and/or re-emergence of zoonotic diseases should be considered as potential threats for these upcoming smart cities and hence, should be addressed by one health approach (health and non-health sectors, public and private actors) through an appropriate local governance strategy. with rapid urbanization and healthcare transformation in india, the operationalization of one health approach might become a major challenge, because of, the absence of the systematic effect at the national level and urban cities are riven between central, state and municipal authorities in terms of health policy, planning, health needs etc. there is also lack of information sharing or collaborations between the health and non-health sectors, public and private actors at the city level. understanding these challenges can offer important lessons for strengthening both local urban governance and one health. methods for objective-1: to understand the pattern of zoonotic diseases in reference to urban governance system 1. is there existing literature indicates the importance of governance system in prevention of zoonotic diseases in urban settings urban governance system & zoonotic diseases (systematic review) 2. is prevalence of zoonotic disease vary in accordance with change of local urban governance (outcome: prevalence of zoonotic diseases & exposure: governance index for last 10 years) for objective-2: to develop a conceptual one health governance framework with reference to zoonotic diseases 1. is there evidence of existing one health governance framework exists one health governance framework (systematic review & swot analysis) 2. to map the urban agencies working for zoonotic diseases institutions for zoonotic diseases (mapping) 3. is convergence possible for one health in prevention of zoonotic diseases (policy maker, system-level professionals qualitative key informant interviews) for objective-3: to assess the key indicators for convergence for inter-sectorial professional collaborations in one health 1. is developed governance framework operational at field levelkap among healthcare providers, veterinarians, environmental specialists 2. is there possibilities of convergence at field level for one health in prevention of zoonotic diseases (qualitative key informant interviews) results this is first of kind unique study to come up with a local urban governance convergence approach for “one health” for the upcoming smart city ahmedabad, which may further be scaled up to other smart cities of india. conclusions urban health governance framework for a smart city to develop an one health approach. keywords health governance; one health; governance framework; urban health; india acknowledgments ahmedabad municipal corporation, ahmedabad, india indian institute of public health gandhinagar, india references [1] world health organization. governance for health in 21st century. available from: http://www.euro.who.int/__data/assets/pdf_ file/0019/171334/rc62bd01-governance-for-health-web.pdf [last accessed on december 2016] [2] dodgson r, lee k, drager n. global health governance: a conceptual review. london: london school of hygiene and tropical medicine; 2002. [3] burris s. governance, microgovernance, and health. temple law rev. 2004;77:334–362. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts [4] hein w. global health governance and national health policies in developing countries: conflicts and cooperation at the interfaces. in: hein w, kohlmorgan l, eds. globalization, global health governance and national health policies in developing countries: an exploration into the dynamics of interfaces. hamburg: deutschen uebersee-instituts; 2003:33–71. [5] navarro v, muntaner c, borrell c, et al. politics and health outcomes. lancet. 2006; 368(9540):1033-7. *yasobant sandul e-mail: dryasobant@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e156, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts status of legislation and factors affecting disease surveillance in nigeria: a qualitative inquiry olusesan a. makinde*1, 2 and clifford o. odimegwu1 1university of the witwatersrand, abuja, nigeria; 2viable knowledge masters, abuja, nigeria objective assess the legal framework establishing disease surveillance in nigeria and identify major factors affecting the performance of the surveillance system. introduction the outbreak of infectious diseases with a propensity to spread across international boundaries is on an upward rise. such outbreaks can be devastating with significant associated morbidity and mortality. the recent ebola virus disease outbreak in west africa which spread to nigeria is an example(1). nigeria like several other african countries implements the integrated disease surveillance and response (idsr) system as its method for achieving the international health regulations (ihr). yet, compliance to the idsr is questioned. this study seeks to investigate the legal instruments in place and the factors affecting performance of the disease surveillance in the country. methods the study reports the first objective of a larger study to investigate compliance to disease surveillance by private health providers(2). an investigative search of the literature for legal instruments on disease surveillance in nigeria was carried out. in addition, key informants were identified and interviewed at the national level and in selected states. the six states in the south-west were identified for an in-depth study. the ihr focal person and the national health management information system officer were interviewed at the national level. the state epidemiologists and the state health management information system (hmis) officers across the six states were interviewed. each state has only one state epidemiologist and one hmis officer as such it was a total sample. in all, 14 key informants were interviewed. results six legal instruments were identified as seen in table 1. the most recent comprehensive legal instrument on infectious disease control in nigeria is a 2005 policy on idsr. this is further supported by the national health act of 2014. however, the national health act is not detailed for infectious disease control. the substantive law which governs infectious diseases in nigeria, the quarantine act was enacted almost a century ago during the colonial era in 1926. none of the states studied has an active law on infectious disease surveillance as noted by key informants. while all states refer to the idsr policy, none has formally ratified the document. there are two independent overlapping data collection systems on infectious diseases: the idsr and the national health management information system (nhmis). data on malaria, hiv and tuberculosis are among data collected across the two systems. this was identified by key informants as a problem since the data collection forms differed across systems and almost always result in differing statistics. in addition, this duplication causes overburdening of frontline workers expected to fill the parallel data collection tools and results in inefficiency of the system. funding of the surveillance system was identified to be inadequate with significant reliance on international partners. conclusions a review of the national law on disease surveillance to address emerging global health security challenges is necessary. state legislators need to enact or ratify national laws on infectious disease monitoring and control in their states. the duplication across the nhmis and the idsr surveillance system requires harmonization to improve efficiency. government needs to invest more resources in disease surveillance. legal instruments for disease surveillance in nigeria keywords legislation; outbreak; nigeria; policy; surveillance references 1. makinde oa. as ebola winds down, lassa fever reemerges yet again in west africa. j infect dev ctries [internet]. 2016 feb 28;10(02):199– 200. available from: http://www.jidc.org/index.php/journal/article/ view/8148 2. makinde oa, odimegwu co. disease surveillance by private health providers in nigeria: a research proposal. online j public health inform [internet]. 2016 mar 24;8(1). available from: http://ojphi.org/ ojs/index.php/ojphi/article/view/6554 *olusesan a. makinde e-mail: sesmak@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e83, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts morbidity patterns associated with seasonal influenza a/h1n1in swaziland vusie lokotfwako*1, nhlanhla nhlabatsi1, phinda khumalo1, siphiwe shongwe2, bongani tsabedze1, njabulo lukhele1, gugu maphalala1, nomcebo phungwayo1, xolisiwe dlamini1, thulani maphosa2 and harriet nuwagaba-biribonwoha2 1epidemiology and disease control unit, ministry of health, mbabane, swaziland; 2icap columbia university, mbabane, swaziland objective to establish morbidity patterns of influenza a/h1n1 in swaziland from 10th july to 15th august 2017. introduction influenza infection is caused by the influenza virus, a singlestranded rna virus belonging to the orthomyxoviridae family. influenza viruses are classified as types a, b and c. influenza a and b viruses can cause epidemic disease in humans and type c viruses usually cause a mild, cold-like illness. the influenza virus spreads rapidly around the world in seasonal epidemics, resulting in significant morbidity and mortality. on the 10th of july 2017, a case of confirmed influenza a/h1n1 was reported through the immediate disease notification system from a private hospital in the hhohho region. a 49 year old female was diagnosed of influenza a/h1n1 after presenting with flu-like symptoms. contacts of the index case were followed and further positive cases were identified. methods upon identification of the index case, the rapid response teams conducted further investigations. two nasal swaps from each sample were taken and sent to a private laboratory in south africa for the detection of the virus rna using rt-pcr to assess for the presence influenza a, b and influenza a/h1n1. further laboratory results were sourced from a private laboratory to monitor trends of influenza. data was captured and analyzed in stata version 12 from stata cooperation. descriptive statistics were carried out using means and standard deviations. the pearson chi square test and student t test were used to test for any possible association between influenza a/ h1n1 and the explanatory variables (age and sex). results surveillance data captured between 10th july 2017 and 15th august 2017 indicated that a total of 87 patients had their samples taken for laboratory confirmation. there were 45 females and 42 males and the mean age was 27 years (sd= 17). at least 25 of the 87 patients tested positive for influenza a while only 1 tested positive for influenza b. the prevalence of influenza a/h1n1 was 16%. the prevalence of influenza a/h1n1 among males was 19% compared to 13% in females; however the difference was not statistically significant (p=0.469). there was no association noted between age and influenza a/h1n1 (p=0.427). upon further sub-typing results indicated that the circulating strain was influenza a/h1n1 pdm 09 strain which is a seasonal influenza. the epidemic task forces held weekly and ad-hoc meetings to provide feedback to principals and health messaging to the general population to allay anxiety. conclusions though who has classified the influenza a/h1n1 strain pdm 2009 as a seasonal influenza, surveillance remains important for early detection and management. there is therefore an urgent need to set up sentinel sites to monitor and understand the circulating influenza strains. health promotion remains crucial to dispel anxiety as the general public still link any influenza to the 2009 pandemic influenza. finally the ministry of health should consider introducing influenza vaccines into the routine immunization schedule especially for children. keywords morbidity; influenza; swaziland acknowledgments 1. ministry of health, mbabane, swaziland 2. icap, columbia university, mailman school of public health, mbabane, swaziland 3. columbia university, department of epidemiology, mailman school of public health, new york, united states of america references 1. global epidemiological surveillance standards for influenza. 2014 [cited 2015 15 april]; available from: http://www.who.int/influenza/ resources/documents/influenza_surveillance_manual/en/. 2. human cases of influenza at the human-animal interface, 2013. wkly epidemiol rec, 2014. 89(28): p. 309-20. 3. who global influenza surveillance network. manual for the laboratory diagnosis and virological surveillance of influenza. 2011 [cited 2015 april27]; available from: http://www.who.int/influenza/ gisrs_laboratory/manual_diagnosis_surveillance_influenza/en/. *vusie lokotfwako e-mail: vusielokza@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e151, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts improving syndromic data quality through implementation of error capture module robert laing* and laurel boyd public health division, oregon health authority, portland, or, usa objective to streamline emergency department data processing in oregon essence (oregon’s statewide syndromic surveillance) by systematically and efficiently addressing data quality issues among submitting hospital systems. introduction oregon public health division (ophd), in collaboration with the johns hopkins university applied physics laboratory, implemented oregon essence in 2011. essence is an automated, electronic syndromic surveillance system that captures emergency department data from hospitals across oregon. while each hospital system sends hl7 2.5.1-formatted messages, each uses a uniquely configured interface to capture, extract, and send data. consequently, essence receives messages that vary greatly in content and structure. emergency department data are ingested using the rhapsody integration engine 6.2.1 (orion health, auckland, nz), which standardizes messages before entering essence. mechanisms in the ingestion route (error-handling filters) identify messages that do not completely match accepted standards for submission. a sub-set of these previously-identified messages with errors are corrected within the route as they emerge. existence of errors does not preclude a message’s insertion into essence. however, the quality and quantity of errors determine the quality of the data that essence uses. unchecked, error accumulation also can cause strain to the integration engine. despite ad-hoc processes to address errors, backlogs accrue. with no metadata to assess the importance and source of backlogged errors, the essence team had no guide with which to mitigate errors. the essence team needed a way to determine which errors could be fixed by updating the rhapsody integration engine and which required consultation with partner health systems and their data vendors. to formally address these issues, the essence team developed an error-capture module within rhapsody to identify and quantify all errors identified in syndromic messages and to use as a guide to prioritize fixing new errors. methods members of ophd’s informatics team and the oregon essence team met to brainstorm solutions to error accumulation and messageprocessing inefficiencies. the team agreed that existing infrastructure and resources were sufficient to accomplish this project. using rhapsody, the team created filters that generated error messages each time an hl7 message failed to validate pre-determined message parameters (a standard hl7 2.5.1 syndromic message definition). in order to capture information about errors that were currently being fixed by the ingestion route, two filters were inserted into the processing route: one before and one after previously-existing errorfixing message-modifiers. the team created a filemaker database to collect information about each error identified, including submitter, location of error (segment, field, component), type of error (too long for field, not in value set, doesn’t adhere to correct message structure). the team enabled error-capture for 81 days (june 16 – september 5, 2017) at which point they evaluated error data so as to guide repair of message modifying filters within the integration engine. results the module captured 16,273,963 error messages over 81 days. the two error capture filters (before and after existing modifiers) each generated 50% of the error messages. the module identified errors across seven hl7 message segments (dg1, evn, obx, pid, pr1, pv1, pv2). one submitter produced 87% of the error messages. of those, 93% were errors in two fields in the pid segment. based on the results, essence team members contacted this submitter and resolved this error, greatly reducing the ingestion and error assessment burden of rhapsody and the development team. currently the essence team is still analyzing the rest of the errors, applying fixes and contacting submitting facilities as needed. once completed, the essence team will enable the error capture module bi-annually to continue refinement of its system strategically guide data quality work. conclusions oregon essence developed a tool to evaluate errors in emergency department hl7 messages it receives for syndromic surveillance. its quick development and reusability make it a costeffective and sustainable data quality solution for focusing effort in regional installations of syndromic surveillance. keywords essence; data quality; interoperability; syndromic surveillance; oregon acknowledgments thank you to melissa powell, mph; meredith jagger, mph; and michelle barber, ms, all of whom advised the development of ophd’s data quality assessment tool. *robert laing e-mail: laing.robert@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e47, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts why you should participate in hhs (health and human services) regional epi groups michael coletta* csels/dhis, centers for disease control and prevention, atlanta, ga, usa objective within the biosense platform, users have the ability to view hhs region level data that can provide insight into what may be happening around the country. epidemiologists can examine this information for changes in trends of subsyndromes or other potential issues of public health concern and compare it to their local data. however, the insight that regional data can provide is limited without better understanding of what is happening in the jurisdictions that make up the region. this round table will discuss the benefits of engaging with other jurisdictions within regions and attempt to define rules of engagement that can be used to facilitate interactions. introduction one of the early successes for the national syndromic surveillance program’s (nssp’s) biosense platform was community agreement on what should make up national and regional picture of the data. for nssp to meet program objectives, national level surveillance and situational awareness had to be made available – not just to cdc, but to the entire community. to make this possible, the community had to agree on a limited dataset that would be sufficient to produce national and regional picture. currently when nssp staff at cdc or a particular program review hhs regional data, they can only see trends at high levels. although, this information is proving useful, when very unusual data spikes occur there is insufficient information to determine its public health significance. cdc would like to set up hhs regional epi groups made up of syndromic surveillance practitioners within regions in order to communicate about potentially unusual findings and discuss implications for local jurisdictions. keywords nssp; hhs regional epidemiology groups; national surveillance; regional surveillance *michael coletta e-mail: mac0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e199, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat health department, city of houston, houston, tx, usa objective our objective is to provide evidence for the data quality of flu near you (fny) by evaluating the national and houston datasets against cdc ili data. introduction flu near you allows individuals to volunteer to be a sentinel node of the syndromic surveillance (sys) network. the platform has the potential to provide insight into the spread of influenza-like illness (ili). cdc’s ilinet is the gold standard for tracking ili at the national level, but does not track into the local level. local health departments (lhd) frequently express a need for granular data specific to their jurisdictions. fny attempts to meet this need by collecting and sharing data at the zip code level. knowing how well fny data correlates to ilinet data will give local health departments an important tool to communicate the arrival of influenza to their jurisdiction. however, there is significant skepticism at the quality of fny data as compared to validated datasets. methods fny pushes out a weekly survey to each user. the survey tracks if and when a user (and his or her family) has received a flu shot and experienced ili. the data were deidentified and provided by the skoll global threats fund to the houston health department (hhd). the fny data were compared to ilinet’s national summary of influenza-like illness and influenza positive tests by estimating the correlation coefficient for the 2014-2015 influenza season. fny total ili counts were correlated to total positive influenza tests and fny percent ili was compared to ilinet’s unweighted percent ili. the mean correlation coefficients were estimated by bootstrapping fny data (n = 1000 at each stratum). mean correlation coefficients over 1000 bootstraps were estimated for a sequence of weekly participation rates from n=10 to n= 10435 in increments of 10. bootstrapped samples were stratified by zip code to account for fluctuations in weekly participation for both fny and ilinet, as both datasets see an increase in user participation during influenza season. r version 3.2 was used for all analyses; hhd received the line-list dataset from fny that contained nearly 400,000 entries. each entry corresponds to a single person (either the user or the user’s family member) and his or her symptoms for the preceding week. fny is voluntary and not all users contribute each week; with the data being deidentified there is no way to connect entries to user profiles or user families. as such, each entry is treated as independent. ilinet data came from cdc. results key finding: • correlation of the full fny dataset against ili is very high (r2 = .94). • correlation of the full fny dataset against positive influenza tests is also high (r2 = .92). • weekly reports from < 200 weekly users have high variance in their correlation to ilinet and a moderate correlation coefficient (r2: 0.3 – 0.7). • reports from >= 200 weekly users seems to be the inflection point for diminishing returns with respect to improving mean correlational coefficient of the pseudo-replicated data. • fny correlates well with both ili visits as well as positive influenza tests. • at low participation counts, (< 400 per week) fny correlates better with positive influenza tests than percentage with ili. • overall, fny data correlates well with national ilinet data, even at limited participation levels. conclusions approximately two-thirds of the counties within the united states have a population of < 50,000. as such, fny provides a simple, lowcost opportunity for public health officials within those jurisdictions to obtain data that reasonably mirrors ilinet. for larger jurisdictions, fny is another tool available to track and identify seasonal influenza and engage the public on prevention. this comparison supports the idea that fny will give local officials in smaller jurisdictions more confidence to guide public health action in their community. keywords influenza; flu near you; local data acknowledgments i would like to thank skoll global threats fund for sharing the flu near you data. *eric v. bakota e-mail: eric.bakota@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e8, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha 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allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris 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surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson university of liverpool, neston, united kingdom objective savsnet—the small animal veterinary surveillance network—collects and collates real-time data from veterinary diagnostic laboratories and veterinary practices across the uk to support research and disease surveillance in companion animals. introduction statutory veterinary disease surveillance generally focuses on food animals with only minimal resources committed to companion animals. however, the close contact between owners and pets suggests that disease surveillance in these species could benefit both animal and human health. following a successful pilot, savsnet ltd. was set up as a joint venture between the university of liverpool (uol) and the british small animal veterinary association (bsava) to deliver companion animal health data for research and surveillance. savsnet consists of two projects: the first collates results from commercial diagnostic laboratories whilst the second collects data from enrolled veterinary practices for consultations where owners have provided consent by opt-out. both projects have been approved by the uol’s research ethics committee and the aims are supported by the royal college of veterinary surgeons (rcvs), the uk’s regulatory body for the veterinary profession. applications to use the data are encouraged and are assessed by a panel consisting of bsava, uol and independent members. data access attracts a nominal fee that is used for long-term sustainability. currently, savsnet data is being used for a wide range of projects by academic collaborators, phd researchers, undergraduate students and commercial companies. methods the data collected from laboratories is provided in a variety of formats using different protocols. the supplied data is parsed using bespoke algorithms and stored in a common database. the data includes available animal signalment, postcode area of the submitting veterinary practice (121 areas in the uk), tests performed, results and the interpretation of those results. the data from veterinary practices is collected in real-time directly from the consultation room. savsnet works closely with practice management software (pms) providers so that, at the end of a consultation, the system displays a modal window that displays webcontent retrieved from savsnet servers. the window requires that the veterinarian characterises the reason for the animal’s presentation using a panel of simple buttons. in a small random sample of consultations (5–10%), additional questions are asked to further characterise the consultation. the data submitted to the database includes the syndrome code and questionnaire data, animal and practice identifiers, signalment, full owner’s postcode (street level) and the free text entered by the veterinarian. in theory, this mechanism allows two-way communication directly into the consultation room although the full potential of this has not yet been explored. in order to maintain interest in the project and ensure data quality, data providers—both laboratories and veterinary practices—receive summaries of the data they have provided through interactive, realtime web-based portals that display a range of statistics, tables, graphs and other graphics, relating to the data they have supplied. for veterinary practices, the portal displays such data in comparison to other anonymous practices. results the experiences of savsnet to-date indicate that commercial laboratories and veterinary practices are prepared to provide large volumes of data. such collaboration is largely altruistic but is likely to be enhanced by savsnet’s independence, the minimal effort required to contribute data and the benefits provided by the data portals. in addition, our experiences show that pms providers are prepared to co-operate to modify their software to present a modal window at the end of a consultation to enable data to be collected in real-time. conclusions collecting data from commercial laboratories and veterinary practices is feasible and provides an important data resource for research and surveillance. the model used by savsnet is welltolerated by data-providers, is scalable and provides the potential for two-way communication into the consultation room. many of the techniques and methods being used and developed could easily be cascaded to other veterinary fields. keywords companion animals; electronic health records; surveillance; informatics; database acknowledgments the authors would like to thank: the bsava and the university of liverpool for their continued support; all our data providers (laboratories and veterinary practices) for contributing to the project ; and the pms providers with whom we work to deliver savsnet complience in their software. *philip h. jones e-mail: p.h.jones@liverpool.ac.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e127, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who 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atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 1rush university, chicago, il, usa; 2pangaea information technologies, ltd, chicago, il, usa; 3h-core, llc, chicago, il, usa objective to compare the influenza-like illness (ili) rates in the emergency departments (ed) of a community hospital versus a large academic medical center (amc). introduction in recent years, the threat of pandemic influenza has drawn extensive attention to the development and implementation of syndromic surveillance systems for early detection of ili. emergency department (ed) data are key components for syndromic surveillance systems. however, the lack of standardization for the content in chief complaint (cc) free-text fields may make it challenging to use these elements in syndromic surveillance systems. furthermore, little is known regarding how ed data sources should be structured or combined to increase sensitivity without elevating false positives. in this study, we constructed two different models of ed data sources and evaluated the resulting ili rates obtained in two different institutions. methods geographic utilization of artificial intelligence in real-time for disease identification and alert notification (guardian) – a syndromic surveillance program – was used to automate ili detection using chief complaints with free text and vital signs (i.e., iliv1), and the entire electronic medical record (emr) (i.e., iliv2) for a large amc and a community hospital during the 2014-2015 influenza season. the guardian system defined ili as fever (temperature ≥ 100°f) and cough and/or sore throat. the first step in our data analysis was to compute the daily ili rates (i.e., the total number of ili patients divided by the total ed census per day) and the 7-day moving average ili rates (hence forth referred as the ili rate). thereafter, we performed graphical and statistical (i.e., descriptive and anova with post hoc bonferroni test) analyses and compared the results generated from the data obtained through the two ili detection approaches. results compared to the amc ili rate for iliv1, the community hospital had significantly higher ili rates for both iliv1 and iliv2 (p<0.01), as shown in table 1. for iliv1, the community hospital had, on average, 3.84% higher ili rates as compared to the amc. in addition, iliv2 yielded higher mean ili rates than iliv1 in both institutions, with a difference of 1.89% for the community hospital and 7.55% for the amc. figure 1 indicates that the ili rates derived from iliv1 and iliv2 for the community hospital and the ili rates derived from iliv2 for the amc are typically above 5%, while the ili rates derived from iliv1 for the amc is below 5%. all pairs of ili models vs. settings were significantly different from each other (f= 217.84, df = 949, p<0.01). conclusions the community hospital had higher iliv1 rates as compared to the amc mainly due to frequent use of cc free-text fields by community hospital triage nurses. there are clinical, operational, and cultural differences between amc and community hospital eds. with detailed clinical documentation at triage, surveillance systems can generate reliable probabilities/weights for early ili detection. since cc free-text fields are so data rich, surveillance systems that utilize natural language processors can improve ili and other disease detection. in addition, depending on the setting, institutions similar to the amc in this study should consider using the entire emr for ili surveillance. table 1: comparison of ili models between amc and community hospital settings figure 1. comparison of ili models between amc and community hospital settings keywords guardian; influenza-like illness; emergency department acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. *gillian s. gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e35, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control 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nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts assessment of a surveillance case definition for heroin overdose in emergency medical services data michael d. singleton*1, 2 and peter j. rock2 1university of kentucky, lexington, ky, usa; 2kentucky injury prevention and research center, lexington, ky, usa objective the aims of this project were 1) to assess the validity of a surveillance case definition for identifying heroin overdoses (hod) in a nemsis 3–compliant, state ambulance reporting system; and 2) to develop an approach that can be applied to assess the validity of case definitions for other types of drug overdose events in similar data state data systems. introduction in 2016, the centers for disease control and prevention funded 12 states, under the enhanced state opioid overdose surveillance (esoos) program, to utilize state emergency medical services (ems) and emergency department (ed) syndromic surveillance (sys) data systems to increase timeliness of state data on drug overdoses. a key aspect of the esoos program is the development and validation of case definitions for drug overdoses for ems and ed sys data systems. kentucky’s esoos team conducted a pilot validation study of a candidate ems case definition for hod, using data from the kentucky state ambulance reporting system (kstars). we examined internal, face validity of the ems hod case definition by reviewing pertinent information captured in kstars data elements; and we examined external agreement with hod cases identified kentucky’s statewide hospital billing database. methods from kstars, we extracted ems emergent transports by any ambulance service to hospitals in a single, large health care system in kentucky. we included responses with dispatch dates between january 1, 2017 and march 31, 2017. from kentucky’s statewide hospital claims data system, we extracted inpatient discharges, ed visits and observational stays at the destination hospitals, with admit dates in the same range. we classified ems cases as hod based on specific combinations of the following criteria for ems data elements: primary or secondary provider impression of heroin poisoning (t40.1x4), heroin-related keywords in the patient care narrative or chief complaint, and patient’s response to naloxone as indicated in the medications list1. we used standard drug overdose case definitions for icd-10-cm-coded hospital billing data2 to classify hospital records from the destination facilities to the same categories. we produced descriptive analyses of the heroin overdose cases detected in both data sources, ems and hospital. to assess the degree of overlap in the hod cases identified by the two data systems, we matched the identified ems hod cases against the entire set of ukhc hospital cases. finally, we assessed the validity of the classification of ems cases as heroin overdoses by reviewing the ems patient care narratives and related ems data elements, as well as the icd-10-cm hospital diagnostic codes for cases that matched to a hospital record. results we identified 5,517 emergent ems transports to the destination hospitals in the first quarter of 2017. of these, 94 (17/1,000) were identified by our case definition as a hod. we identified 29,631 unduplicated, emergent encounters at the destination hospitals (including inpatient discharges, ed visits, and observational stays; and excluding elective and newborn encounters). of these, 105 (3.5/1,000) included a diagnostic code for hod. linkage of ems and hospital cases indicated that 141 unique hod cases were identified in the two files combined. of these, 58 (41%) were identified as hod in both systems. 23 hod cases identified in ems were matched to a hospital record that had no mention of a hod; and 13 could not be matched to a hospital record. additionally, 47 hod cases identified in the destination hospitals were not matched to an ems transport to those destination facilities. overall, 76 out of the 94 (81%) ems cases identified as heroin overdoses were judged likely to be true heroin overdoses, as indicated by either 1) positive response to naloxone and patient admission of recent heroin use, or 2) hospital diagnosis of heroin overdose, or both. for 2% of identified cases, there was evidence of a false positive finding. the remaining 17% of identified heroin cases were inconclusive: there was information suggestive of opioid overdose, but no clear evidence to suggest, nor to rule out, that the opioid was heroin. generally, inconclusive cases were identified as heroin overdoses due to positive response to naloxone, combined with mention of the word “heroin” in the narrative that did not indicate an hod. examples of the latter include negations (patient denies heroin use) or a bystander who stated that the patient had a history of heroin use. conclusions we assessed the performance of a straightforward case definition for heroin overdose for ems data. face validity of 81% of identified heroin overdoses was supported by clerical review of ems records and/or hospital icd-10-cm diagnostic codes. some proportion of the other 19% of cases that were identified as heroin overdoses may have been overdoses involving opioids other than heroin, but we could not quantify that proportion based on the available information. future work will consider sensitivity (true heroin overdoses that may fail to be captured by this case definition) and refinements to the basic definition that may yield improved results. lessons learned from this pilot project will inform subsequent, larger-scale validation studies for ems drug overdose case definitions. keywords drug overdose; case definition; heroin; emergency medical services; surveillance acknowledgments we acknowledge and thank the following agencies for their support of this work: the kentucky department for public health, kentucky board of emergency medical services, and kentucky office of health policy. references 1. rhode island enhanced state opioid overdose surveillance (esoos). case definition for emergency medical services. aug 2017. 2. injury surveillance workgroup 7. consensus recommendations for national and state poisoning surveillance. the safe states alliance. atlanta, ga. april 2012. *michael d. singleton e-mail: msingle@email.uky.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e31, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts assessing definitions of heroin overdose in ed & ems data using hospital billing data peter j. rock* and michael d. singleton college of public health, university of kentucky, lexington, ky, usa objective the aim of this project was to assess the face validity of surveillance case definitions for heroin overdose in emergency medical services (ems) and emergency department syndromic surveillance (sys) data systems by comparing case counts to those found in a statewide emergency department (ed) hospital administrative billing data system. introduction in 2016, the centers for disease control and prevention funded 12 states, under the enhanced state opioid overdose surveillance (esoos) program, to utilize state emergency medical services (ems) and emergency department syndromic surveillance (sys) data systems to increase timeliness of state data on drug overdose events. an important component of the esoos program is the development and validation of case definitions for drug overdoses for ems and ed sys data systems with a focus on small area anomaly detection. in fiscal year one of the grant kentucky collaborated with cdc to develop case definitions for heroin and opioid overdoses for both sys and ems data. these drug overdose case definitions are compared between these two rapid surveillance systems, and further compared to emergency department (ed) hospital administrative claims billing data, to assess their face validity. methods the most recent available data were pulled from multiple hospitals in a large healthcare system serving an urban region of kentucky. definitions for acute heroin overdose were applied to all three sources. for sys and ed data, definitions were queried against the same hospitals within this geographic region and aggregated to week-level totals. sys and ed data are similar with the exception of additional textual information available in sys (such as chief complaint). our ems definition of heroin overdose was loosely based on a draft definition that was produced by the massachusetts department of public health, and relies more on textual analysis versus icd10 codes used in sys and ed data systems. while sys and ed used the same hospitals as the frame of selection, ems used incidents that occurred in the approximate catchment area served by those hospitals. weekly totals from all three data sources were plotted in r studio with loess-smoothed trend lines. unsmoothed times series plots also demonstrate highly correlated trends, but the smoothed trend lines are less cluttered and easier to interpret. results visual interpretation of the loess-smoothed trend lines shows very similar trajectories among all three sources [fig 1]. the resultant graph demonstrates that individually, the time courses described by sys and ems data track closely with the one observed in ed data. the absolute counts between the three sources showed some differences, as expected. the ems system captures a slightly different cohort that may include people that do not go to the ed (observation patients, refused transport, etc.) and sys/ed have slightly different definitions (as ed does not include a free-text chief complaint. these types of limitations are better explored through data linkage that may or may not include medical record review to establish ground truth. conclusions public health surveillance of drug overdoses has traditionally relied on ed billing data. in most states, however, there is a lag of at least several months before this data becomes available for analysis. in some jurisdictions the delay may be considerably longer. rapid surveillance data sources may allow for more timely identification of changes in overdose patterns at the local level. in addition, sys/ems can be used together to confirm that a spike seen in one rapid system is confirmed within the other, with relative ease. though the comparison is a rather simple or crude visual analysis of three data systems at a common geographic level, there is still appears to be a common pattern among the three systems. while this does not carry the validity of cross-data matched analysis, it does provide some of the utility of looking at these system collective without match; and therefore may be of use to surveillance users that may be limited by de-identified data. keywords drug overdose; syndromic; ems; administrative billing data; heroin acknowledgments we acknowledge and thank the following agencies for their support of this work: the kentucky department for public health, the kentucky health information exchange, kentucky board of emergency medical services, the kentucky office of health policy, the national syndromic surveillance program, and the centers for disease control and prevention. *peter j. rock e-mail: pjrock2@uky.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e97, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts implementation of integrated disease surveillance and response (idsr) in swaziland siphiwe m. shongwe-gama*2, thulani maphosa2, phinda khumalo1, vusie lokotfwako1, nhlanhla nhlabatsi1, ruben sahabo2 and harriet nuwagaba-biribonwoha2, 3 1epidemiology and disease control unit, ministry of health, mbabane, swaziland; 2icap at columbia university, swaziland, mbabane, swaziland; 3department of epidemiology, columbia university, mailman school of public health, columbia, dc, usa objective to strengthen public health surveillance and monitor implementation of integrated disease surveillance and response in the kingdom of swaziland. introduction swaziland adopted the integrated disease surveillance and response (idsr) strategy in 2010 to strengthen public health surveillance (phs) that fulfills international health regulations (2005) and the global health security agenda (ghsa). this strategy allows the ministry of health (moh), epidemiology and disease control unit (edcu) to monitor, prevent and control priority diseases in the country. we used a health systems strengthening approach to pilot an intervention model for idsr implementation at five hospitals in swaziland over a pilot phase of three months. methods our intervention included cross-country idsr trainings, sensitizations and onsite trainings targeting national and regional health teams for over 250 health workers. the edcu developed and disseminated standardized case definitions for health facilities (hfs) to detect, confirm and report priority conditions. trained health care workers were tasked to cascade knowledge sharing and sensitization about idsr with their hfs during in-service trainings. the facilities were to use idsr standard case definition as guidelines for diagnosing and reporting cases; submit monthly reports on all priority conditions to health management information system (hmis) and intensify reporting through immediate disease notification system (idns) for all notifiable conditions. indicators and monitoring tools for disease surveillance and response as recommended by the technical guidelines for idsr in the african region were developed. the intervention was evaluated at five purposively selected high-volume referral hospitals (attending to ≥1500 to 15000 outpatient visits per month), which also have maternity services. structured questionnaires in the form of a monitoring tool, checklists and observations were used to collect data. quantitatively, monthly reports submitted by the five facilities to hmis were reviewed and analyzed for completeness and timeliness. clinic supervisors were identified from outpatient, inpatient, maternity and laboratory departments as key informants to explore successes and challenges of idsr implementation. additionally, idsr officers visited health facilities and observed the registers and reporting forms used to report idsr priority conditions and the availability of idsr guidelines. results the five hfs submitted monthly reports from june to august 2017 with a calculated completeness of 80% in june 2017, 60% in july and 40% in august. timeliness was calculated was at 20% in june, 20% in july and 40% in august. idsr officers observed that all five hfs document cases of priority diseases in registers during consultations and use daily tally sheets. however, it was observed that diseases reported through the immediate diseases notification system were not all documented in the morbidity registers and vice versa. health workers reported to be unaware about all diseases that require immediate notification to trigger investigation, hence some disease like perinatal deaths were never notified through the idns system during the period of evaluation. all five hospitals reported not utilizing the standard cases definitions provided to identify and report idsr priority diseases. conclusions the proportion of completeness and timeliness from the five hfs during the evaluation period was low compared to who recommended standards of >= 80% from all hfs. this therefore, poses challenges in monitoring and responding to the priority conditions as per idsr standards and recommendations. all five hospitals reported not utilizing the standard cases definitions to identify and report idsr priority diseases and this poses challenges in comparison of data across sites, monitoring priority diseases, conditions and events and also identifying the alert or epidemic thresholds. there is need to capacitate more health workers on idsr for swaziland to strengthen phs and be able to prevent and control public health threats timely. keywords integrated disease surveillance and response (idsr); timeliness and completeness; immediate diseases notification system (idns); reporting; health facilities acknowledgments this work is supported by the president’s emergency plan for aids relief (pepfar) through the centers for disease control and prevention under the terms of cooperative agreement number 1u2ggh001271. its contents are solely the responsibility of the authors and do not necessarily represent the official views of pepfar or the centers for disease control and prevention. *siphiwe m. shongwe-gama e-mail: mabakass@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e137, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e276, 2019 isds 2019 conference abstracts dashboards as strategy to integrate multiple data streams for real time surveillance fabian eckelmann, stephane ghozzi, alexander ullrich robert koch institute, berlin, germany objective providing an integrative tool for public health experts to rapidly assess the epidemiological situation based on data streams from different surveillance systems and relevant external factors, e.g. weather or socio-economic conditions. the efficient implementation in a modular architecture of diseaseor task-specific visualisations and interactions, their combination in dashboards and integration in a consistent, general web application. the user-oriented development through an iterative process in close collaboration with epidemiologists. introduction the mission of the infectious-disease-epidemiology department at the robert koch institute is the prevention, detection and control of infections in the german population. for this purpose it has a set of surveillance and outbreak-detection systems in place. some of these cover a wide range of diseases, e.g. the traditional surveillance of about 80 notifiable diseases, while others are specialised for the timely assessment of only one or a few diseases, e.g. participatory syndromic surveillance of acute respiratory infections. many different such data sources have to be combined to allow a holistic view of the epidemiological situation. t he continuous integration of many heterogeneous data streams into a readily available and accessible product remains a big challenge in infectiousdisease epidemiology. methods the first step in the development of visualisation and analysis dashboards was the identification of relevant epidemiological questions. this was done through the review and analysis of existing epidemiological tools and workflows, among others through surveys and interviews. with the help of domain experts we identified the relevant data sources for specific tasks. we then chose data visualisations that are common in the field of infectious-disease epidemiology, e.g. disease maps, epicurves and age pyramids, as well as visualisations that were suggested by experts, e.g. time-series graph with severity thresholds. in an iterative process of propositions and expert feedback, we refined the user experience, adjusting variables, control parameters and the layout. we have used two different technologies for the dashboard development. for tasks that needed extensive data integration and statistical computing we used the shiny web-framework of the statistical programming language r, which allows for a seamless integration of data-wrangling, statistical methods and web design with interactive visualizations. for tasks where a more flexible and fluid user experience is desired and for the integration in a general web application, we used the more versatile single -page application (spa) framework angularjs in combination with asp.net. in both approaches we used standard open-source visualisation libraries such as leaflet or plotly. the dashboards were designed in a modular way, abstracting data sources and visualisations in order to reuse them and adapt them easily to other data sources. where applicable, interfa ces to live data bases and olap cubes where developed and implemented. results we have developed a set of dashboards that allow the exploration of infectious-disease data, each designed for a specific epidemiological task. while still under active development, the dashboards are accepted and routinely used by epidemiologists of the robert koch institute. the expansion to other user groups (e.g. local health agencies) is planned for the near future. fu rther dashboards will be developed as new epidemiological tasks are identified. a general dashboard ("signals dashboard", see figure 1 a) is displaying laboratory confirmed cases and their distribution across time, space, age and sex in linked widgets. additionally it highlights anomalous clusters of cases in al l widgets and lists the anomalies in an interactive table. the dashboard is available for all (approx. 80) notifiable diseases. the "severity dashboa rd" (figure 1 b) integrates influenza-related syndromic data, virological information and laboratory confirmed cases. the indicators http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e276, 2019 isds 2019 conference abstracts transmissibility, seriousness and impact, as defined by the pisa guidelines of the world health organization, are displayed i n time-series charts (absolute and cumulative) and tables; parameter-adjustable severity assessments are computed on the fly. this dashboard has then been adapted to monitor in real time the severity of rotavirus infections. one further dashboard focusses on vaccine-preventable diseases and allows the simultaneous exploration of incidences and vaccination rat es through synchronized maps and histograms. lastly, a "context dashboard" enables the exploration of possible connections between tick-related diseases such as tbe and lyme disease on the one hand, and weather and environment as external factors on the ot her. it provides visual comparisons through maps and time-series charts, correlation analysis and statistical modeling. the user can choose a set of (lagged) variables to be included in a linear statistical model, which is immediately trained. the contributions and significance of the chosen factors, as well as the fit and prediction accuracies, are displayed in tables, scatter plots and time series. both "signals" and "severity" dashboards serve the rapid assessment of the epidemiological situation and as such display live data as read from internal databases and cubes. the others are at present rather meant for retrospective analyses but will be connected to live data str eams in the future. conclusions dashboards can provide a way to integrate different epidemiological data streams and statistical methods, offering experts a useful tool to assess the epidemiological situation. close collaboration between epidemiologists and data scientists in the design a nd development is beneficial to the relevance and sustainability of such a tool. acknowledgement the authors would like to thank their colleagues with whom these tools have been developed and acknowledge their valuable contributions: silke buda, wiebke hellenbrand, teresa kreusch, thorsten rieck, anika schielke, anette siedler, kristin tolksdorf. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e276, 2019 isds 2019 conference abstracts figure 1. a) the "signals dashboard" giving an overview of the distribution of lab reported infectious disease cases in germany as well as highlighting anomolous clusters of cases; b) the "severity dashboard" showing influenza indicators in comparison t o severity thresholds computed from historical data. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts enhanced surveillance of heat-related illness in pinal county dylan c. kent*1, rachel z. garcia2, samuel packard2, graham briggs2, clancey hill2, stephanie griffin1, edward bedrick1, krystal collier3 and matthew roach3 1public health, university of arizona, tucson, az, usa; 2pinal county public health services district, florence, az, usa; 3arizona department of health services, phoenix, az, usa objective using a syndromic surveillance system to understand the magnitude and risk factors related to heat-related illness (hri) in pinal county, az. introduction extreme heat is a major cause of weather-related morbidity and mortality in the united states (us).1 hri is the most frequent cause of environmental exposure-related injury treated in us emergency departments.2 more than 65,000 emergency room visits occur for acute hri each summer nationwide.3 in arizona, hri accounts for an estimated 2,000 emergency room patients and 118 deaths each year.4 as heat-related illness becomes increasingly recognized as a public health issue, local health departments are tasked with building capacity to conduct enhanced surveillance of hri in order to inform public health preparedness and response efforts. in pinal county, understanding the magnitude and risk factors of hri is important for informing prevention efforts as well as developing strategies to respond to extreme heat. methods to gain a better understanding of the magnitude of hri in pinal county, historical cases were reviewed from hospital discharge data (hdd) from 2010-2016. cases were included if the discharge record included any icd codes consistent with hri (icd-9 codes 992 or icd-10 codes t67 or x30) and if the patient’s county of residence was pinal county. recent hri cases during the summer of 2017 were identified using the national syndromic surveillance program biosense platform. the essence syndromic surveillance tool within the biosense platform includes data reported by local hospitals. this data can be used to detect abnormal activity for public health investigation. hri cases were identified in essence based on icd-10 codes and chief complaint terms according to a standardized algorithm developed by the council of state and territorial epidemiologists.1 both emergency department and admitted patients with a hri were abstracted from hdd and essence. to assess hri risk factors for the summer of 2017, a survey instrument was developed. survey questions included the nature and location of the hri incident, potential risk factors, and knowledge and awareness of hri. cases were identified in essense on a weekly basis from may 1, 2017-september 12, 2017, and follow up phone interviews were conducted with eligible cases. for hri cases eligible for interview, three attempts were made to contact the patient by phone. cases were excluded if the patient was incarcerated, deceased, or did not have a hri upon medical record review. an exploratory analysis was performed for the data from hdd, essence, and interviews. results pinal county public health services district identified 1,321 hri cases from 2010-2016, an average of 189 per year. hospital discharge data suggest hri cases are more likely to occur in males between the ages of 20-44 years old (27%). it is also notable that a sharp increase in hri cases is observed each year in mid-to-late june, with an estimated 14% of annual cases occurring during the third week of june. further analysis of hdd showed 31% of cases received medical treatment in casa grande in central pinal county. between may 1st and september 12th of 2017, 161 hri cases were detected using essence. of which 149 cases were determined to be hri; 22 cases did not have contact information, and 4 cases were ineligible due to incarceration or death. a total of 31 hri cases were interviewed out of the eligible 123 essense cases (25% response rate). interview data indicated occupational exposure to extreme heat as a major risk factor for hri. additional risk factors reported during interviews included exposure to extreme heat while at home or traveling, although interview results are not representative due to a small sample size (n=31). conclusions syndromic surveillance combined with interviews and a review of hdd provides an informative approach for monitoring and responding to hri. data suggest pinal county should expect an increase in hri cases by mid-june each year, typically coinciding with the first national weather service extreme heat warning of the season. preliminary results suggest that cases occur more frequently in working males ages 20-44 years old in occupations that expose workers to extreme heat conditions. additional information is needed to assess risk factors for hri among vulnerable populations in pinal county who were not represented in this study, including individuals who are homeless, undocumented, elderly, or in correctional facilities. future areas for improvement include improving the phone interview script to include english and spanish language versions and performing medical record abstractions on all hri cases. enhanced syndromic surveillance is recommended to provide information on risk factors for hri to inform prevention efforts in pinal county. keywords heat-related illness; hospital discharge data; essence acknowledgments thank you to our colleagues at the arizona department of health services, arizona state univeristy, and the university of arizona. references 1. heat-related illness syndrome query: a guidance document for implementing heat-related illness syndromic surveillance in public health practice. in: epidemiologists cosat, ed. vol 1.02016:1-12. 2. pillai sk, noe rs, murphy mw, et al. heat illness: predictors of hospital admissions among emergency department visits-georgia, 2002-2008. j community health. 2014;39(1):90-98. 3. centers for disease control and prevention. climate change and extreme heat: what you can do to prepare. 2016; available from https:// www.cdc.gov/climateandhealth/pubs/extreme-heat-guidebook.pdf 4. trends in morbidity and mortality from exposure to excessive natural heat in arizona, 2012 report. in: services adoh, ed2012. *dylan c. kent e-mail: dkent@email.arizona.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e111, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e289, 2019 isds 2019 conference abstracts progress in the development of the standardized public health emergency preparedness terminology nikolay lipskiy1, james tyson1, 2, shauna mettee zarecki1, jacqueline burkholder1 1 ophpr, deo, cdc, atlanta, georgia, united states, 2 united states public health service commissioned corps, atlanta, georgia, united states objective the purpose of this project is to demonstrate the progress in development of a standardized public he alth (ph) emergency preparedness and response data ontology (terminology) through collaboration between the centers for disease control and prevention (cdc), division of emergency operations (deo), and the logical observation identifiers names and codes (loinc) system. introduction the u.s. department of homeland security national incident management system (nims) establishes a common framework and common terminology that allows diverse incident management and support organizations to work together across a wide variety of functions and hazard scenarios [1]. using common terminology helps avoid confusion and enhances interoperability, particularly in fast-moving public health (ph) emergency responses. in addition, common terminology allows diverse incident management and support organizations to work together across a wide variety of functions and scenarios [1]. loinc is one of a suite of designated standards for the electronic exchange of public health and clinical information. implementation of loinc facili tates improvement of semantic interoperability, including unified terminology [2]. more than 68,100 registered users from 172 countries use loinc to move interoperable data seamlessly between systems [3]. the cdc division of emergency operations (deo) leads development of standardized ph emergency preparedness and response terminology to improve effective and interoperable communications between national and international partners. realizing the scale of loinc support and implementation across the global public health arena, cdc deo collaborates with loinc to further enhance and harmonize the current ph emergency response terminology and to attain critical ph emergency management and preparedness and response requirements. methods deo analyzed 87,863 loinc terms that were included in loinc version 2.64, released on 06/15/2018 [3]. access to this loinc version was obtained through the regenstrief loinc mapping assistant (relma). relma is a windows-based loinc utility developed by the regenstrief institute (indiapolis, usa) for searching the loinc database and mapping local codes to loinc codes [4]. the relevance of loinc terminology to ph emergency preparedness and response was assessed through evaluating existing loinc terminology against terminology specified by the world health organization ph emergency operation centers (eoc). the following functions were evaluated: 1) managing and commanding; 2) operating; 3) planning/intelligence; 4) logistics and 5) finance/administration [5]. loinc terminology was also evaluated against the cdc ph eoc minimum data set (mds) [6] that contains 315 standardized terms. analysis of fully specified loinc terms was conducted through assessment of such loinc term parts (attributes) as the code, name (component), system, method and class. recommendations of gaps and enhancements were coordinated with loinc management for inclusion of the new terminology in the release of version 2.65 . results a new loinc method, “cdc.eoc”, is under development. currently, the “emergency management incident” terminology presented by loinc is limited by such characteristics as event type, event location and event name and requires amplification regarding to ph operations (i.e., communication, logistics etc.). as a result of this investigation, emergency management terms are now being classified according to the type of incident or event (i.e., hurricane, outbreak, etc.) under loinc code 80394-0. similarly laboratory and clinical terms are being classified under a provisional loinc code (89724-9). two panels were created: 1) the emergency medical systems from the national emergency http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e289, 2019 isds 2019 conference abstracts medical services information system (nemsis) was added under the nemsis.panel (n= 177 terms) and 2) the data elements for emergency departments systems (deeds) panel (n = 152 terms) was added with two subpanels: attach.ed and panel.ed. assessing existing loinc taxonomy and codification, deo is working with the loinc management team on evaluating additional options for reconciliation the ph emergency preparedness and response common information exchange reference model and loinc standard. this process aims to further improve semantic interoperability of ph emergency preparedness and response information. conclusions the loinc terminology standardization is essential for improving ph preparedness and response data exchange and semantic interoperability. collaboration with the regenstrief institute (loinc) allows cdc to meet the terminology needs of ph emergency management and defines new opportunities for reconciliation data exchange between nims partners. this collaborative effort incorporates critically needed ph emergency and preparedness terminology and hierarchical structure in the loinc standard. references 1. fema national incident management system. third edition, october 2017. at: https://www.fema.gov/medialibrary-data/1508151197225ced8c60378c3936adb92c1a3ee6f6564/final_nims_2017.pdf 2. us national library of medicine. logical observation identifiers names and codes (loinc). at: https://www.nlm.nih.gov/research/umls/loinc_main.html 3. loinc. the international standard for identifying health measurements, observations, and documents. at: https://loinc.org/ 4. relma-the reginstrief institute loinc mapping assistant. at: https://loinc.org/relma/ 5. who. framework for a public health emergency operations centre. interim document. november, 2015. at: http://www.who.int/ihr/publications/9789241565134_eng/en/ 6. cdc. public health information network vocabulary access and distribution system (phin vads). minimum data set for ph emergency operations center. at: https://phinvads.cdc.gov/vads/searchvocab.action http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts experience of gis technology application in the surveillance of tick-borne infections iryna ben*1, igor lozynskyi1 and oksana semenyshyn2 1laboratory of transmissible virus infections, state institution lviv research institute of epidemiology and hygiene of the ministry of health of ukraine, lviv, ukraine; 2si “lviv regional laboratory center of the ministry of healthcare of ukraine”, lviv, ukraine objective the main aim of this work is to estimate the projected risks based on the incidence rate of natural foci infections and to expand the list of criteria for the characterization of natural foci of tick-borne infections. introduction the epidemiological situation of natural foci of tick-borne infections (tbi) in ukraine, as well as globally, is characterized by significant activation of processes due to global climate change, growing human-induced factors and shortcomings in the organization and running of epidemiological surveillance [1]. for the western region of ukraine, among all tick-borne zoonoses the most important are tick-borne viral encephalitis (tbve), lyme disease (ld), human granulocytic anaplasmosis (hga) among others [2-4]. taking into account the increased incidence rate for these infections, we have developed baseline criteria (indicators of natural contamination of the main carriers and levels of the serum layer among the population in relation to the tbi pathogens in the endemic areas) to identify areas with different risk of contamination through gis-technologies [5]. methods epi info 7.1.1.14 software was used to analyze patient questionnaires with tick-borne infections (tbi) for 2010-2015. prevalence maps of vector-borne infections were created by means of gis technology using the qgis 2.0.1. software to assess the risks of infection. maps demonstrating the distribution of tbve, ld and hga were also developed based on contamination risk assessment criteria. results retrospective epidemiological analysis of incidence rates for tbve, ld and hga was conducted based on laboratory tests that were performed in the laboratory of vector-borne viral infections of the state institution lviv research institute of epidemiology and hygiene of the ministry of health of ukraine. a direct correlation between the infection of i. ricinus, b. burgdorferi and lb (p <0.05) and infections of i. ricinus ticks, anaplasma and incidence of hga (p <0.05) was established. however, this connection has not been confirmed for indicators with tbe. data was obtained during the assessment of possible risks of tickborne infections. for tbve, the indicator of predicted risks based on the basic criteria was 60.3%, taking into account the cases of the disease. this was based on indicators of natural infection of the main carriers and the level of the serum layer among the population on the tbi activators in the endemic areas. the data obtained can be explained by the low level of morbidity and the detection of tbve cases. the predicted risk for ld according to these criteria is 88.9%, due to the high level of clinical and laboratory diagnosis. as for the hga, the predicted risk indicator reaches 66.7% due to the fact that the study of human anaplasmosis in ukraine is at the initial level (the incidence rate and incidence are not included in the official reporting system). taking into account the results obtained, it is advisable to supplement the list of criteria for determining the degree of activity of natural foci of tick-borne infections and the identification of areas with high risk of morbidity. these calculations were made by grouping statistical data (indicators) [5]. the reliability of the difference between the same indicators for individual zones was 95% (table 1). conclusions tick-borne zoonoses are a serious problem for the public health system of the western region of ukraine. extending the list of criteria for the characterization of natural foci of tick-borne infections will improve epidemiological surveillance and focus on key measures in high and medium-risk areas for the rational use of funds. parameters and criterias for epidemic risk areas (p<0,05) keywords tick-borne viral encephalitis; lyme borreliosis; human granulocytic anaplasmosis; risk zone; western region of ukraine references 1. nordberg m. tick-borne infections in humans. aspects of immunopathogenesis, diagnosis and co-infections with borrelia burgdorferi and anaplasma phagocytophilum. linköping university medical dissertations no.1315. linköping, sweden 2012. 2. morochkovsky r. clinical characteristic of tick-borne encephalitis in volhynia and optimization of treatment. ternopil state medical academy i. gorbachevsky dissertations. ternopil, ukraine 2003. 3. zinchuk o. lime borreliosis: clinical and immunopathogenetic features and emergency preventive treatment. lviv national medical university d. galitsky dissertations. lviv, ukraine 2010. 4. ben i., biletska h. epidemiologic aspects of human granulocytic anaplasmosis in the western region of ukraine. lik sprava. 2015 oct-dec;(7-8). 5. ben i., lozynsky i. application of gis-technologies for risk assessment of areas with tick-borne infections. materials of the regional scientific symposium within the framework of the concept of “unified health” and a review and selection of scientific works with the support of ccdd in ukraine. 2017 april 24-28, kyiv. *iryna ben e-mail: iryna_ben@ukr.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e126, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts title: sero-prevalence of bovine and human brucellosis on selected farms in south-western uganda mary l. nanfuka* animal health, ministry of agriculture animal industry and fisheries, entebbe, uganda objective to determine the seroprevalence of brucellosis antibodies in cattle in 3 districts in south-western uganda (mbarara, kiruhura and bushenyi) and to determine the seroprevalence of brucellosis among the cattle keepers on farms with cattle detected with brucellosis antibodies in the same districts and also to determine the level of awareness of brucellosis disease among individuals that get in contact with livestock in the same districts introduction brucellosis is among the zoonotic diseases that continue to afflict man and animals in uganda. the increase in the number of disease outbreaks in animals from 1990 to 2013 and the number of human patients diagnosed with brucellosis in private clinics and hospitals has placed the infection to be among the top re-emerging diseases in the country. brucellosis infection in humans is non-specific and caused by direct or indirect contact with infected animals or their products. brucellosis manifests as intermittent fever, headache, weakness, profuse sweating, chills, weight loss, generalized aching that may involve multiple organ systems in the body. in animals, brucella organisms localize in the reproductive organs, causing abortions, decreased milk yields and temporary sterility. its effects impact negatively to the sale value of the affected animals causing financial losses to the animal owners. methods purposive surveys were conducted in selected farms that were in an area with reported human cases. ethical clearances were sought. screening for brucellosis was done using sat. all positive samples were subjected to i-elisa that detects igm immunoglobulins. a total of 1503 cattle from 113 farms were tested for brucellosis results brucellosis test results from a total of 1503 cattle showed a seroprevalence of 14% in kiruhura, 18% bushenyi and 23% mbarara districts respectively. elisa-positive brucella cases from farm attendants on the sampled farms in the same districts had a prevalence of 4% in kiruhura, 9% in bushenyi and 12% in mbarara conclusions our findings underscore brucella exposure as one of the major reemerging diseases that should be treated with great concern by both ministry of health and maaif. we therefore recommend that the ministries of health and agriculture should increase on community sensitization on the risk of brucella infections in humans from cattle, and promote measures that can protect high risk families from getting infected. this survey indicated that brucellosis infections are still prevalent in uganda and continue to occur in the local communities. the public health and animal health service providers need to work together in compiling the disease epidemiological data for a concerted disease intervention measures. keywords brucellosis; sero prevalence; south western uganda acknowledgments i am very grateful to the united states department of agriculture biosecurity enhancement programme (usda/bep) for funding my msc programme. dr robert tweyongyere who was a co author of this work references 1. al dahouk s, tomaso h, nöckler k, neubauer h, frangoulidis d. 2003 laboratory-based diagnosis ofbrucellosis--a review of the literature. part ii: serological tests for brucellosis. clin lab. 49(11):577-89. 2.al sekait ma. seroepidemiological survey of brucellosis antibodies in saudi arabia. ann saudi med 1999; 19:219–222. 3.baba mm, sarkindared se, brisibe f (2001). serological evidence of brucellosis among predisposed patients with pyrexia of unknown origin in the north eastern nigeria. cent. eur. j. public health. 9: 158161.161 4. kungu, j. m., okwee-acai, j., ayebazibwe, c., okech, s. g., & erume, j. (2010). sero-prevalence and risk factors for brucellosis in cattle in gulu and amuru districts, northern uganda. africa journal of animal and biomedical sciences, 5 (3), 36-42. 5. krause, d.o. & hendrick, s. (eds), 2010, zoonotic pathogens in the food chain, cabi, wallingford. http://dx.doi. org/10.1079/978145936815.0000 *mary l. nanfuka e-mail: nanfukamry@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e171, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper informatics, association of state and territorial health officials, arlington, va, usa objective to inform the community on the progress of electronic case reporting (ecr) utilizing the shared infrastructure and applications of the public health community platform (phcp). introduction the phcp is a community-led initiative to provide shared infrastructure, services, and applications to the public health community as solutions for complex public health informatics problems. the project has progressed by establishing a governance structure led by an executive committee representative of the public health practice community. the executive committee has established the strategic path for the continued development of the phcp and prioritized ecr as the initial use case for implementation. case reporting from clinical providers still requires forms to be manually filled out and sent via mail or fax to the public health agency (pha). reporting requirements vary among phas and providers may be unaware of reporting requirements and mechanisms in their practicing jurisdiction. the complexities of case reporting from clinical providers have resulted in inconsistent, incomplete, inaccurate, and delayed reporting of persons that are of interest to public health. this impacts the overall ability of phas to interpret surveillance data and take informed action to minimize the population burden of these conditions. the ecr solution coincides with expected meaningful use stage 3 rules that will include case reporting as a public health option. methods ecr includes the design, development, and implementation of a common electronic infrastructure (i.e., content, platform, and tools) to leverage ehrs so clinical care can more efficiently send standardsbased, secure, and confidential case reports for reportable conditions to state, territorial, local, and tribal phas. there are several components needed to achieve ecr, including: • standard data extractions from ehrs • data transaction and transportation protocols • trigger codes to filter clinical encounters and identify those most likely to include a reportable condition • decision support rules and logic defined by each jurisdiction to determine if a filtered patient encounter is reportable in the relevant jurisdiction • web forms and forms managers to collect additional data elements for public health the phcp is currently coordinating the development, integration, and deployment of these components. this includes the successful completion of an hl7® standard for an initial case message and implementation and integration of a production version reportable conditions knowledge management system (rckms) under development by cste1 (integral to providing jurisdictionally defined rules for decision support logic). the information flow is illustrated in figure 1. the process is designed for pha customization due to the variability in state and local reporting requirements. centrally locating customization on the phcp relieves the burden on ehr systems to account for all permutations of pha differences, allowing phas to modify their data collection based on emerging needs. furthermore, phcp-located resources decreases the need for phas to locally create duplicative services and applications to support ecr. results pilot teams consiting of phas, ehr vendors, and clinical providers are being formed to test this information flow and integration with the phcp and rckms. conclusions as data collection becomes automated with ecr, it is expected that the pha workforce will be redirected toward data analysis, interpretation, response, and prevention (rather than focusing on the completion of case report forms). as that data is transformed into knowledge, public health will be able to provide greater input into the learning health system and improve health outcomes in the population. figure 1. transactional information flow for ecr. keywords ecr; case reporting; public health community platform; interoperability acknowledgments the phcp executive committee: art davidson (naccho co-chair), j.t. lane (astho co-chair), bill brand, jim collins, mark conde, rebecca coyle, & bryant karras. cdc/csels references 1. council for state and territorial epidemiologists. surveillance / informatics: reportable condition knowledge management system [internet]. atlanta (ga): cste; no date [cited 2015 aug 31]. available from: http://www.cste.org/group/rckms. *marcus rennick e-mail: mrennick@astho.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e155, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts transforming public health surveillance through open public health information janelle kibler2, dr. scott mcnabb*1, james lavery1, ziad memish3, affan shaikh4 and ngozi erondu5 1rollins school of public health, emory university, atlanta, ga, usa; 2emory university, atlanta, ga, usa; 3who, riyadh, saudi arabia; 4public health practice, grass valley, ca, usa; 5london school of hygiene and tropical medicine, london, united kingdom objective the goal of this editorial is to shed light on the lack of transparency that exists in the sharing of public health data and to reverse this presumption in favour of open public health information properly vetted and openly accessible. open public health information is a critical step to revitalize public health practice and is a human right. introduction public health practice that prevents, detects, and responds to communicable and noncommunicable disease threats is hindered by poor access to public health data and information. this includes timely sharing of case-based information, respecting patent and publication rights, and the ethical sharing of specimens. disagreements about information shared and under what circumstances plus who has right to the data, clinical specimens, and their derivative products impede research and countermeasures. delayed or inaction by public health authorities undermines trust and exacerbates the crisis. evident in 2014 by the delayed public health emergency of international concern declaration of the ebola virus outbreak in west africa by the world health organization, the governing presumption is that access to public health information should be restricted, constrained, or even hoarded; this is a failed approach. this lack of transparency prevents information availability when and where it is needed and obstructs public health efforts to efficiently and ethically prevent, detect, and respond to emerging threats. a better way forward is to reverse this presumption in favour of open public health information properly vetted and openly accessible. open public health information is a critical step to revitalize public health practice and is a human right. while there is limited global consensus among scientists and public health practitioners on best practices to guide national health authorities, researchers, ngos, and industry as they navigate the ethical, political, technical, and economic challenges associated with the sharing of essential public health information (e.g., pathogen isolates, clinical specimens, and patient-related data), grounding this discussion on the guiding principles of open public health information can help navigate the complex privacy, security, communication, and access needs, and ensure that collaboration and sharing occur in a manner that is ethically and socially just, efficient, and equitable. built on existing governance frameworks such as the international health regulations (ihrs) and the pandemic influenza preparedness framework (pip), open public health can transform public health surveillance, allowing for the rapid sharing of data and products during outbreaks for mutual benefit and enhanced global health security. methods this abstract represents a larger editorial style manuscript, thus no methods were developed in the abstract. results this editorial style manuscript aims to reverse the presumption that public health data is damaging to one in favour of open public health information properly vetted and openly accessible. conclusions similar to other open movements (i.e., open data, open government, open development, and open science) that seek to address the world’s greatest challenges through transparency, collaboration, reuse of and free access to ideas, open public health offers an ideal solution to overcome the challenges in the 21st century. keywords public health data; data transparency; open data *dr. scott mcnabb e-mail: scottjnmcnabb@emory.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e84, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts outbreak of ed visits related to the use of synthetic cannabinoids, mayotte island pascal vilain*1, salamata bah-assoumani2, ali-mohamed youssouf2 and laurent filleul1 1french national public health agency, regional unit indian ocean, sain-denis, réunion; 2hospital center of mayotte, mamoudzou, mayotte objective to confirm and to characterize the increase in emergency department (ed) visits related to the use of synthetic cannabinoids (sc) introduction on october 2016, the indian ocean regional health agency was alerted about an increase in ed visits related to adverse reactions associated with use of sc on mayotte island. in this context, an investigation based on a syndromic surveillance system was implemented by the regional unit of the french national public health agency. methods an extraction of anonymized records routinely collected by the syndromic surveillance system (1) was carried out from january 1st, 2012 to october 30, 2016. ed visits related to the consumption of sc were identified from icd-10 codes of the principal diagnostic according to two levels of confidence: a probable case was defined as ed visit coded x69 (intentional self-poisoning by and exposure to other and unspecified chemicals and noxious substances). this code has been implemented specifically by ed physicians since august 2015; a suspect case was defined as ed visit coded: f11 (mental and behavioral disorders due to use of opioids), f12 (mental and behavioral disorders due to use of cannabinoids), f16 (mental and behavioral disorders due to use of hallucinogens), f18 (mental and behavioral disorders due to use of volatile solvents), f19 (mental and behavioral disorders due to multiple drug use and use of other psychoactive substances). based on these data, an epidemic curve and a descriptive analysis of ed visits were carried out. results in total, 146 ed visits related to adverse events associated with use of sc were registered from january 1st, 2012 to october 30, 2016. the epidemic curve shows two waves between 2015 and 2016 with a particularly high peak in august 2015 (figure 1). in total, 49% (n=72/146) of these ed visits were probably related to adverse reactions associated to use sc and 51% (n=74/146) meet to the suspect case definition. on the surveillance period, men represented 84% of the patients (n=122) and median age (min – max) was 23 (862) years old. when the severity score variable was filled (n = 138), a vital emergency was reported for 4% (n = 5) of patients and 19% of patients were hospitalized. conclusions data from syndromic surveillance system allowed to confirm an increase in ed visits related to adverse reactions associated with use of sc in mayotte island. to our knowledge, it’s the first time that an outbreak related to use sc is described in the ocean indian areathis phenomenon was particularly marked in 2015 with a peak of ed visits on august 2016. after this outbreak, the regional unit of the french national public health agency recommended the pursuit of the coding x69 in principal diagnosis with the following case definition: any patient with an adverse reaction attributed to synthetic cannabinoid use whether suspected by the medical team or declared by the patient himself or if the patient is in possession of the substance; and to raise awareness ed physicians to the notification of these poisonings to the regional addictive surveillance center. in conclusion, the young population, weakened by a precarious socio-economic situation, is a target for new synthetic drugs and a threat to public health. this emerging risk in mayotte must be taken into account and must be actively monitored. in this context, collaborative work with the emergency services must continue in parallel with targeted prevention measures. epidemic curve of ed visits related to adverse reactions associated to use of synthetic cannabinoids, mayotte island, january 1st 2012 to october 30, 2016 keywords syndromic surveillance; synthetic cannabinoid; outbreak acknowledgments we thank all healthcare workers of emergency departments of mayotte references 1. vilain p, maillard o, raslan-loubatie j, abdou ma, lernout t, filleul l. usefulness of syndromic surveillance for early outbreak detection in small islands: the case of mayotte. online journal of public health informatics. 2013;5(1):e149. *pascal vilain e-mail: pascal.vilain@ars.sante.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e157, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts surveillance for mass gatherings: ncaa final four 2017 in maricopa county, arizona william e. smith*1, kate goodin1, rasneet s. kumar1, vjollca berisha1, craig levy1, siru prasai1 and kristen pogreba-brown2 1office of epidemiology, maricopa county department of public health, phoenix, az, usa; 2university of arizona, school of public health, tucson, az, usa objective to describe and present results for the enhanced epidemiologic surveillance system established during the 2017 national collegiate athletic association division i men’s college basketball championship (final four) events. introduction final four-associated events culminated in four days of intense activity from march 31st through april 3rd, and added an estimated 400,000 visitors to maricopa county’s 4.2 million residents. methods preparation included: refinements in enhanced surveillance for previous events (including super bowl xlii); a rehearsal on information sharing for team leads; just in time training for field team members; a tabletop exercise on 2/22; and solicitation of lessons learned from jurisdictions recently hosting the final four. enhanced surveillance began on 3/24 and continued through 4/10 (one week before the first major event until one week after the championship game) with intensified surveillance from 3/31-4/3. subject matter experts for each enhanced surveillance component functioned as team leads. a surveillance coordinator was assigned to review data and prepare reports. team members were sent a plan of the day detailing daily surveillance activities. an enhanced surveillance (surv) alert requesting an increased index of suspicion for events of public health significance was sent to pre-established lists of healthcare providers. urgent care clinics within five miles of venues were asked to report influenza-like, gastrointestinal, rash, and neurological illness visits daily. emergency department records in the national syndromic surveillance program, electronic surveillance system for early notification of communitybases epidemics (essence) were monitored daily for influenza-like illness, gastrointestinal illness, injury, records of interest, heat-related illness and event-specific terms. mumps and meningitis were added after outbreak reports were received from home jurisdictions of final four teams. death certificate data, office of the medical examiner line lists and preliminary reports of death were reviewed daily for reportable diseases or circumstances of public health significance. communicable disease data was reviewed daily for notifiable disease cases of concern, aberration detection as compared to the previous four years, outbreak review, and influenza-like-illness. field teams of staff and volunteers were deployed to three days of music fest, four days of fan fest, and three final four games. attendees presenting to first aid stations were requested to complete an electronic questionnaire capturing illness and injury syndromes. these were submitted and epidemiologically assessed in near-realtime. syndrome-specific data were geo-located on venue maps during events to identify spatial clustering. patient presentation rates (ppr) and transport to hospital rates (tthr) per 10,000 attendees were calculated. to enhance animal health system surveillance, veterinarians and agencies that work with animals were notified to increase the index of suspicion for unusual animal disease, keep alert for outbreaks with zoonotic potential, and update 24/7 emergency contact lists. health-related media reports, final-four-specific reports, healthaggregated twitter reports, and breaking news alert subscriptions were monitored. poison control center (pcc) reports were assessed by conducting regular queries of the national poison data system (npds). reports from the 24/7 disease reporting line were monitored. a one page enhanced surveillance report was developed for daily distribution to inter-disciplinary partners; a more detailed report was distributed to health and medical partners. physicians overseeing the health/medical care of teams were included in information sharing. public health intelligence information was exchanged with epidemiologists from home jurisdictions of final four teams. results 301 field questionnaires were completed, including 146 from final four games, 127 from the music fest, and 28 from the fan fest. final four games experienced a ppr of 9.5, and a tthr of 0.52. music fest results were a ppr of 9.4, and a tthr of 0.15. for the fan fest, there was a ppr of 5.5, and a tthr of 0. pcc data review resulted in investigation of four cases for potential ricin exposure. these reports were determined to be exposure to castor beans and the castor bean plant (ricinus communis) only. one report indicating potential phosgene occupational exposure to an air conditioning system worker was reviewed, and judged unlikely to cause noted symptoms. outbreak information from home jurisdictions of final four teams resulted in increased index of suspicion for mumps, additional surveillance and mentions in media surveillance reports. review of communicable disease, mortality, and essense data resulted only in routine investigations. conclusions surveillance information from disparate surveillance systems was synthesized into reports which enhanced health and medical situational awareness and information sharing; interdisciplinary partners highlighted the utility of the one-page report. enhanced surveillance allowed the rapid identification and characterization of potential threats, and provided an evidence base for public health decisions. establishment of field teams allowed for near-real-time tracking of patient presentations and transports and rapid identification and characterization of syndromes of concern and potential threats. public health intelligence information exchange with home jurisdictions of final four teams resulted in targeted surveillance for mumps and meningitis. keywords mass gathering; syndromic surveillance; public health surveillance; epidemiological surveillance; information sharing acknowledgments thank you to the 2017 ncaa final four enhanced surveillance team. *william e. smith e-mail: williamsmith@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e173, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts improving the quality of data exchange formats in the u.s. national tuberculosis surveillance system wilfred bonney*1, 2, sandy f. price1 and roque miramontes1 1data management, statistics and evaluation branch, division of tuberculosis elimination, national center for hiv/aids, viral hepatitis, std, and tb prevention, office of infectious diseases, centers for disease control and prevention, atlanta, ga, usa; 2public health informatics fellowship program, division of scientific education and professional development, center for surveillance, epidemiology, and laboratory services, centers for disease control and prevention, atlanta, ga, usa objective the objective of this presentation is to use a congruence of standardization protocols to effectively ensure that the quality of the data elements and exchange formats within the ntss are optimal for users of the system. introduction disease surveillance systems remain the best quality systems to rely on when standardized surveillance systems provide the best data to understand disease occurrence and trends. the united states national tuberculosis surveillance system (ntss) contains reported tuberculosis (tb) cases provided by all 50 states, the district of columbia (dc), new york city, puerto rico, and other u.s.-affiliated jurisdictions in the pacific ocean and caribbean sea [1]. however, the ntss currently captures phenotypic drug susceptibility testing (dst) data and does not have the ability to collect the rapid molecular dst data generated by platforms such as cepheid genexpert mtb/ rif, hain mtbdrplus and mtbdrsl, pyrosequencing, and whole genome sequencing [2-6]. moreover, the information exchanges within the ntss (represented in hl7 v2.5.1 [7]) are missing critical segments for appropriately representing laboratory test results and data on microbiological specimens. methods the application of the standardization protocols involves: (a) the revision of the current report of verified case of tuberculosis (rcvt) form to include the collection of molecular dst data; (b) the enhancement of the tb case notification message mapping guide (mmg) v2.03 [8] to include segments for appropriately reporting laboratory test results (i.e., using logical observation identifiers names and codes (loinc) as a recommended vocabulary) and microbiology related test results (i.e., using systematized nomenclature of medicine -clinical terms (snomed ct) as a recommended vocabulary); and (c) the standardization of the laboratory testing results generated by the variety of molecular dst platforms, reported to tb health departments through electronic laboratory results (elr), using those same standardized loinc and snomed ct vocabularies in hl7 v2.5.1 [7]. results the application of the standardization protocols would optimize early detection and reporting of rifampin-resistant tb cases; provide a high-quality data-driven decision-making process by public health administrators on tb cases; and generate high-quality datasets to enhance reporting or analyses of tb surveillance data and drug resistance. conclusions this study demonstrates that it is possible to apply standardized protocols to improve the quality of data, specifications and exchange formats within the ntss, thereby streamlining the seamless exchange of tb incident cases in an integrated public health environment supporting tb surveillance, informatics, and translational research. keywords tuberculosis; exchange formats; surveillance system; loinc; snomed ct acknowledgments the authors acknowledge the support of centers for disease control and prevention (cdc), northrop grumman, tb controllers and laboratories throughout the usa who report cases to the cdc. references [1] centers for disease control and prevention (us), division of tuberculosis elimination. reported tuberculosis in the united states, 2015 [internet]. 2015 [cited 2017 sept 19]. available from https:// www.cdc.gov/tb/statistics/reports/2015/default.htm [2] davis jl, kawamura lm, chaisson lh, et al. impact of genexpert mtb/rif on patients and tuberculosis programs in a low-burden setting. a hypothetical trial. am j respir crit care med. 2014 jun 15;189(12):1551-9 [3] helb d, jones m, story e, et al. rapid detection of mycobacterium tuberculosis and rifampin resistance by use of on-demand, near-patient technology. j clin microbiol. 2010 jan;48(1):229-37 [4] hillemann d, rüsch-gerdes s, boehme c, et al. rapid molecular detection of extrapulmonary tuberculosis by the automated genexpert mtb/rif system. j clin microbiol. 2011 apr;49(4):1202-5. [5] lin syg, desmond ep. molecular diagnosis of tuberculosis and drug resistance. clin lab med. 2014 jun;34(2):297-314 [6] lin syg, rodwell tc, victor, t. c., et al. pyrosequencing for rapid detection of extensively drug-resistant mycobacterium tuberculosis in clinical isolates and clinical specimens. j clin microbiol. 2014 feb;52(2):475-82 [7] hl7 international. hl7 version 2.5.1 implementation guide: electronic laboratory reporting to public health, release 2 (us realm) [internet]. 2014 [cited 2017 sept 19]. available from https:// www.hl7.org/implement/standards/product_brief.cfm?product_ id=329 [8] centers for disease control and prevention (us), division of tuberculosis elimination. tuberculosis case notification mmg v2.03 [internet]. 2010 [cited 2017 sept 18]. available from https:// wwwn.cdc.gov/nndss/case-notification/historical-documentation.html *wilfred bonney e-mail: nto5@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e48, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 1centre for food-borne, environmental and zoonotic infectious diseases, public health agency of canada, saint-hyacinthe, qc, canada; 2national microbiology laboratory, public health agency of canada, saint-hyacinthe, qc, canada; 3national microbiology laboratory, public health agency of canada, winnipeg, mb, canada objective to summarize the first 4 years (2009-2012) of national surveillance for ld in canada and to conduct a preliminary comparison of presenting clinical manifestations in canada and the united-states. introduction ld, caused by borrelia burgdorferi in north america is transmitted to humans from wild animal reservoir hosts by ixodes spp. ticks1 in their woodland habitats2. ld risk in canada occurs where tick vectors are established in southern british columbia (i. pacificus) and in southern parts of central and eastern canada where i. scapularis is spreading from the united states (us)3. ld became nationally notifiable in canada in 2009 and demographic data on human cases is submitted by provinces to canadian notifiable disease surveillance system of the public health agency of canada (phac). a lyme disease enhanced surveillance system was initiated by phac in 2010 to obtain more detailed data on ld cases. these surveillance systems aim to identify changing trends in ld incidence, the population at risk and the types of clinical disease in canada. surveillance data for 2009-2012 are analyzed to describe the early patterns of ld emergence in canada. patterns of ld cases (age, season of acquisition and presenting manifestations) were compared against those reported in the us. methods the numbers and incidence of reported cases by province, month, year and sex were calculated. logistic regression was used to examine trends over time. acquisition locations were mapped and presenting clinical manifestations reported for jurisdictions where data was available. variations by province, year, age and sex as well as presenting clinical symptoms were explored by logistic regression. an initial comparative analysis was made of presenting symptoms in canada and the us. results the numbers of reported cases rose significantly from 144 in 2009 to 338 in 2012 (coefficient = 0.34, standard error = 0.07, p <0.05), mostly due to an increased incidence of infections acquired in canada. most cases occurred in locations where vector tick populations were known to be present. incidence was highest in adults aged 55 to 74 years and in children aged 5 to 14 years. most cases (95%) were acquired from april to november. of cases acquired in endemic areas, 39.7% had manifestations of early ld, while 60.3% had manifestations of disseminated ld. the proportion of cases acquired in endemic areas presenting with early ld was lower than that reported in the us. conclusions ld incidence is increasing in canada. most cases are acquired where vector tick populations are spreading and this varies geographically within and among provinces. the lower proportion of cases presenting with early ld in canada compared with the us suggests lower awareness of early ld in canada, but this requires further study. figure: the incidence of reported ld cases per 100,000 population during 2009-2012 by age and sex. . figure: the reported location of acquisition of ld cases acquired in canada from 2009-2012 keywords lyme disease; surveillance; tick-borne disease references 1. ogden nh, lindsay lr, morshed m, sockett pn, artsob h. the emergence of lyme disease in canada. can.med.assoc.j. 2009 jun 9;180(12):1221-4. 2. kurtenbach k, hanincova k, tsao ji, margos g, fish d, ogden nh. fundamental processes in the evolutionary ecology of lyme borreliosis. nat.rev.microbiol 2006 sep;4(9):660-9. 3. ogden nh, koffi jk, pelcat y, lindsay lr. environmental risk from lyme disease in central and eastern canada: a summary of recent surveillance information. can.commun.dis.rep. 2014 jun 3;40(5):74-82. *jules koffi e-mail: jules.konan.koffi@phac-aspc.gc.ca online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e63, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 1department of parasitology, faculty of medicine, university of peradeniya, kandy, sri lanka; 2department of community medicine, faculty of medicine, university of peradeniya, kandy, sri lanka; 3institute for health metrics and evaluation, university of washington, washington, wa, usa objective to develop a food hygiene surveillance system to improve food safety measures within food establishments in the plantation sector of sri lanka. introduction wholesome food in adequate quantities is essential to human beings for their existence (1). however, diseases spread due to contaminated food are a common problem throughout the world and an important cause of reduced economic productivity (2,3). food borne illness can, therefore, be considered a major international health problem and a significant cause of economic loss (4,5). approximately 10 to 20% of food-borne disease outbreaks cause due to contamination by the food handlers. in sri lanka, information about food hygiene practices in plantation sector is scarce. therefore, this study was designed as a preliminary study to identify hygiene practices in food processing in the plantation sector for the establishment of a s urveillance system in sri lanka. methods this cross-sectional study was carried out in tea plantation sector in kandy, sri lanka from july to september 2013. information regarding sanitary conditions, hygiene behavior, education status and de-worming date was obtained from food handlers using structured and pre-tested questionnaire. the data was analyzed with the spss version 17 statistical software. results 375 food handlers from 18 to 53 years were enrolled. 59.6% of them had primary education or below whereas others had secondary education. majority (91.3%) wash their hands with soaps after the use of toilets while only 16% wash with soap before meals. when food handing, 58% wash their hands always with soaps while others wash rarely. 52% of them wash vegetables with water and 48% used salt water to wash vegetables prior to preparing the meals. 66% of them had a habit of eating raw vegetables and only 32% trim their nails in regular pattern. more than half of food handlers (54%) wash equipments and tools with soap rarely before and after food handling while others use soaps every time. only 6% of them undergo regular deworming treatment. conclusions this study revealed that knowledge of food hygiene practices among food handlers is poor. community health education programs, promoting better food hygiene and improved sanitation should be considered, when planning a food hygiene surveillance system. keywords food hygiene surveillance; sri lanka; food borne illness acknowledgments we would like to express thanks to university of peradeniya for material and financial support to conduct this study. our sincere thanks also goes to food handlers who have voluntarily participated in this study. references 1. who. establishing or strengthening national food contamination monitoring programmes: guidelines, geneva. world health organization; 1979. 2. käferstein fk. actions to reverse the upward curve of food-borne illness. food control. 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oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts free-text mining to improve syndrome definition matching across emergency departments kristin arkin*1, 2 1centers for disease control and prevention, atlanta, ga, usa; 2idaho division of public health, boise, id, usa objective we sought to use free text mining tools to improve emergency department (ed) chief complaint and discharge diagnosis data syndrome definition matching across facilities with differing robustness of data in the electronic surveillance system for the early notification of community-based epidemics (essence) application in idaho’s syndromic surveillance system. introduction standard syndrome definitions for ed visits in essence rely on chief complaints. visits with more words in the chief complaint field are more likely to match syndrome definitions. while using essence, we observed geographic differences in chief complaint length, apparently related to differences in electronic health record (ehr) systems, which resulted in disparate syndrome matching across idaho regions. we hypothesized that chief complaint and diagnosis code co-occurrence among ed visits to facilities with long chief complaints could help identify terms that would improve syndrome match among facilities with short chief complaints. methods the essence-defined influenza-like illness (ili) chief complaint syndrome was used as the base syndrome for this analysis. syndromematched visits were defined as visits that match the syndrome definition. we assessed chief complaints and diagnosis code co-occurrence of syndrome-matched visits using the rcran tidytext package and developed a bigram network from normalized, concatenated chief complaint and diagnosis code (ccdd) fields and normalized diagnosis code (dd) fields per previously described methodologies.1 common connections were defined by a natural break in frequency of pair occurrence for ccdd pairs (30 occurrences) and dd pairs (5 occurrences). the essence syndrome was revised by adding relevant bigram network clusters and logic operators. we compared time series of the percent of ed visits matched to the essence syndrome with those matched to the revised syndrome. we stratified the time series by facilities grouped by short (average < 4 words, “group a”) and long (average ≥ 4 words, “group b”) chief complaint fields (figure 1). influenza season start was defined as two consecutive weeks above baseline, or the 95% upper confidence limit of percent syndromematched visits outside of the cdc ili surveillance season. season trends and influenza-related deaths in idaho residents were compared. results during august 1, 2016 through july 31, 2017, 1,587 (1.17%) of 135,789 ed visits matched the essence syndrome. bigram networks of ccdd fields produced clusters already included by the essence syndrome. the bigram network of dd fields (figure 2) produced six clusters. the revised syndrome definition included the essence syndrome, 3 single dd terms, and 3 two dd terms combined. the start of influenza season was identified as the same week for both ili syndrome definitions (essence baseline 0.70%; revised baseline 2.21%). the essence syndrome indicated the season peaked during morbidity and mortality weekly report (mmwr) week 2017-05 with the season ending mmwr week 2017-14. the revised syndrome indicated 2017-20 as the season end. multiple peaks seen with the revised syndrome during mmwr weeks 2017-02, 2017-05, and 2017-10 mirrored peaks in influenza-related deaths during mmwr weeks 2017-03, 2017-06, and 2017-11. ili season onset was five weeks earlier with the revised syndrome compared with the essence syndrome in group a facilities, but remained the same in group b. the annual percentage of ed visits related to ili was more uniform between facility groups under the revised syndrome than the essence syndrome. unlike the trend seen with the essence syndrome, the revised syndrome shows lowlevel ili activity in both groups year-round. conclusions in idaho, dramatic differences in ed visit chief complaint word counts were seen between facilities; bigram networks were found to be an important tool to identify diagnosis codes and logical operators that built more inclusive syndrome definitions when added to an existing chief complaint syndrome. bigram networks may aid understanding the relationship between chief complaints and diagnosis codes in syndrome-matched visits. use of trade names and commercial sources is for identification only and does not imply endorsement by the centers for disease control and prevention, the public health service, or the u.s. department of health and human services. figure 1. percent of influenza-like illness-related emergency department visits by mmwr week for the original essence syndrome (grey) and revised syndrome (blue) grouped by facilities with short (top) and long (bottom) chief complaint fields. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts figure 2. a bigram network displaying common diagnosis code pairs for emergency department visits matched to the essence influenza-like illness syndrome. keywords syndromic; syndrome definition; free-text mining; essence; ili references 1. silge, j., robinson, d. (2017). “text mining with r”. o’reilly. *kristin arkin e-mail: kristinaarkin@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e13, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts are the french samu data relevant for health surveillance? isabelle pontais*1, florian franke1, barbara philippot2, françois valli3, gilles viudes2 and céline caserio-schönemann1 1santé publique france, saint-maurice cédex, france; 2paca emergency network observatory (orupaca), marseille, france; 3samu 06 / national emergency medical service, nice, france objective to evaluate whether samu data could be relevant for health surveillance and proposed to be integrated into the french national syndromic surveillance sursaud® system. introduction the syndromic surveillance sursaud® system developed by santé publique france, the french national public health agency collects daily data from 4 data sources: emergency departments (oscour® ed network) [1], emergency general practioners (sos médecins network), crude mortality (civil status data) and electronic death certification including causes of death [2]. the system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity and mortality. however some information could be missed by the system especially for non-severe (absence of ed consultation) or, in contrast, highly severe purposes (direct access to intensive care units). the french pre-hospital emergency medical service (samu) [3] represents a potential valuable data source to complete the sursaud® surveillance system, thanks to reactive pre-hospital data collection and a large geographical coverage on the whole territory. data are still not completely standardized and computerized but a governmental project to develop a national common it system involving all french samu is in progress and will be experimented in the following years. methods a pilot study was performed in the south of france paca region, where data from the six local samu structures are centralized into an interconnected database. a minimal set of variables required for health monitoring (administrative and medical items) and modalities for data extraction and transmission to santé publique france were defined. samu data were transmitted daily to santé publique france and the paca regional team developed a microsoft access® application to import decrypted data, request database and analyze indicators. retrospective part of the study was performed over a 2-year period (2013-2014) and the prospective part during 2015 was based on daily data collection. completeness and quality of variables were analyzed. samu indicators including several level of specificity were built and compared to existing sursaud® indicators in different situations (for detection, seasonal follow-up and health impact assessment) using spearman coefficient correlation. results during the pilot study, data from five of the six samu structures of paca region were structured enough to be analyzed. on the study period, almost 2,400,000 files were recorded and 89% contain medical information. data completeness was high (87%) and stable during the whole period. the annual rate of samu solicitation was 16 for 100 inhabitants at the regional scale. 15% of the records were opened only for medical advice. in contrast, patients were evacuated directly in intensive care unit in 9.5% of cases without ed admission. coding quality depended on the existence and the use of official thesauri and varied widely among samu structures. despite coding variations, samu indicators for winter epidemics were significantly correlated with ed and sos médecins indicators. respectively with ed flu, bronchiolitis and gastroenteritis indicators, the strongest correlations were found for samu lower respiratory infection (0.74), samu bronchiolitis (0.72) and samu gastroenteritis / diarrhea / vomiting (0.81). conclusions this pilot study demonstrated the feasibility to collect daily samu activity data. the key strengths of samu data were a large geographic coverage, the subsidiarity with sursaud® system data sources, the follow-up of prehospital activity and for patients directly admitted into an intensive care unit. some limitations were highlighted related to differences in coding practices especially for medical diagnosis. the generalization of this study will require the standardization of coding practices and homogenization of thesaurus. the implementation of the national samu information system should allow in a very next future to widely progressing on these topics. keywords emergency medical service; pilot study; evaluation; emergency department; france acknowledgments to the 6 french samu and to oru paca for providing data, to paca regional unit for analysis and to all emergency department data providers and sos médecins associations for their substantial contribution to the system. references [1] fouillet a, bousquet v, pontais i, gallay a and caserio-schönemann c. the french emergency department oscour network:evaluation after a 10-year existence. online journal of public health informatics issn 1947-2579-7(1):e74, 2015 [2] caserio-schönemann c, bousquet v, fouillet a, henry v. le système de surveillance syndromique sursaud (r). bull epidémiol hebd 2014;3-4:38-44. [3] baker, d.j.. the french prehospital emergency medicine system (samu): an introduction (2005) cpd anaesthesia, 7 (1), pp. 20-25. *isabelle pontais e-mail: isabelle.pontais@laposte.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e70, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua national biosurelliance integration center, department of homeland security, washington dc, dc, usa objective the national biosurveillance integration center (nbic) coordinated information sharing with the u.s. department of agriculture (usda/aphis) and the department of interior (doi/ nwhc) to integrate information and provide shared situational awareness of the 2014-2015 highly pathogenic avian influenza (hpai) outbreak in the u.s. across all levels of government. introduction nbic integrates, analyzes, and shares national biosurveillance information provided from capabilities distributed across public and private sectors. the integration of information enables early warning and shared situational awareness of nationally significant biological events to inform critical decisions directing response and recovery efforts. the 2014-2015 hpai h5 outbreak in the u.s. was the largest hpai outbreak in the country’s history and resulted in the culling of millions of domestic birds and significant economic losses through loss wages, direct production losses, cost of recovery, consumer price increases, and trade restrictions. nbic worked closely with liaisons from usda/aphis and doi/ nwhc over the course of the outbreak to integrate information from both agencies and open source reporting into reports and data sets providing early and sustained shared situational awareness to over 1400 federal, state, and local authorities. methods nbic collects data from numerous sources by automated and manual methods. most information gathered is unstructured data, such as media reports and is collected, analyzed, and visualized using government funded and publically available information technology tools. whenever possible, open source reports are verified, validated, and contextualized through consultation of reports from interagency partners, communication with interagency liaisons and other subject matter experts, or through formal interagency requests for information. results of integrated, collaborative analyses of the aggregated data are communicated in reports disseminated to all levels of government through secure automated feeds to government systems, posts on government information sharing portals, or direct email. results in mid-december 2014, doi/nwhc reported the first confirmation of hpai h5 in a captive gyrfalcon in whatcom county, washington at a site near an area of british columbia, canada where an outbreak of hpai h5n2 occurred earlier that month. nbic began reporting on the outbreak through the national biosurveillance integration system (nbis) monitoring list, a daily summary of high-priority events distributed to federal, state, and local partners. the next day, nbic issued its first biosurveillance event report, a more extensive report that includes assessment of potential impact and comprehensive background information. over the course of the outbreak, nbic analysts communicated closely with doi/nwhc and usda/aphis liaisons and continuously monitored open-source media; including state and federal government, industry, and academic reports, and updates to provide timely and accurate information to a broad range of stakeholders. nbic integrated data made available by individual federal authorities into a single map that was updated regularly to reflect the most current information available on the locations of hpai detections in wild, backyard, captive, and commercial birds. nbic provided structured datasets to the department of health and human services office of the assistant secretary for preparedness and response, which were used to generate maps for sharing within the geohealth platform. nbic also worked with the federal emergency management administration to communicate situational updates to regional planning and response personnel. by june 2015, hpai h5 was identified in wild, captive, backyard, or commercial birds in 21 u.s. states. detections in backyard and commercial poultry were confirmed in 15 u.s. states with over 48 million domestic birds affected. conclusions nbic’s ongoing efforts to cultivate and maintain interagency relationships across the federal government to enhance biosurveillance capabilities facilitated situational awareness. nbic provided integrated maps not available elsewhere and coordinated timely reporting shortly after initial notification by the responsible authorities. nbic continues to coordinate with doi/nwhc and usda/aphis through agency liaisons and provide situational awareness to its government partners and department leadership. keywords hpai; outbreak; situational awareness; government *yandace k. brown e-mail: yandace.brown@hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e49, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e238, 2019 isds 2019 conference abstracts development of text-based algorithm for opioid overdose identification in ems data andrew patton1, 2, rochelle ereman2, matt willis2, haylea a. hannah2, karina arambula2 1 johns hopkins bloomberg school of public health, baltimore, maryland, united states 2 marin county health and human services, san rafael, california, united states objective to develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (ems) records. introduction opioid overdoses have emerged within the last five to ten years to be a major public health concern. the high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. however, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. in addition, opioid overdoses are a non-reportable condition. as a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near realtime surveillance and treatment/outreach system for opioid overdoses using an already existing ems data collection framework. methods marin county department of health and human services ems data (2015-2017) was used for development of the system. the pool of data for model development and evaluation consisted of 15,000 ems records randomly selected from 2015, 2016, and 2017. each record was manually classified in a binary manner with the criteria of “more likely than not opioid related”, using only selected text fields. the event did not need to be exclusively opioid related, nor did opioids have to be the primary cause for the ems call. 2,000 records were selected for review by the medical director for marin county ems, with a cohen’s kappa coefficient of approximately 0.94. overall, the proportion of opioid overdoses was less than 0.01 amongst the 15,000 records. an enriched data set of 80 randomly selected overdoses and 320 randomly selected non-overdoses was created for the purposes of feature engineering. these 400 records were excluded for further use in model training and testing. within the enriched set, the descriptive text fields were tokenized based on the hypothesis that opioid overdoses and non-overdoses are separable based on the content of the descriptive fields. each field was tokenized as words, bigrams (pairs of consecutive words), and trigrams (triplets of consecutive words). the frequencies of each token as a percentage of overall words were calculated separately for opioid overdoses and nonoverdoses. structured fields used in the analysis were not tokenized prior to frequency calculations. the frequencies for each token/phrase were then compared across opioid overdoses status with a proportion test for equality at an alpha of 0.05 with a bonferroni correction for multiple comparisons. the tokens/phrases that were statistically significantly more likely to be present in opioid overdoses were assigned to a quintile based on their p-value, with smallest p-values assigned five, and largest pvalues assigned one. tokens/phrases statistically significantly more likely to be present in non-overdoses were scored in the same manner, with the smallest p-value assigned negative five, and the largest p-value negative one. the tokens/phrases that were statistically different across opioid overdose status were stored along with their quintile scores in dictionaries that were kept for future modeling use. from the initial 15,000 classified records, excluding the 400 used for the enriched data set, 10,000 records were randomly selected for model training and development. each record had their text fields tokenized into words, bigrams, and trigrams, and each was compared with the corresponding dictionary. if a token was present in the entry and also in the dictionary, that token’s quintile score was assigned to the record, with multiple tokens being summed to produce a score for each field-token option. the final created feature was the count of opioid specific terms such as “heroin”, “fentanyl”, “narcan”, etc. within the main narrative field. the intent was to create a variety of numerical features that were indicative of presence of tokens/phrases that were positively associated with opioid overdoses such that higher scores were more associated. several models including support vector machines, neural nets, gradient boosted machines, and logistic regression were tested via 10-fold cross validation, with logistic regression yielding the best error rates and lowest computational costs. although all models resulted in a sensitivity greater than 85 percent, http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e238, 2019 isds 2019 conference abstracts logistic regression was by far the best in terms of false positive rate. the coefficients for the logistic regression model were selected from the eight created features along with patient sex and patient age by best subsets selection via akaike information criterion (aic), and the probability threshold for classification was selected via optimizing the receiver operating curve (roc). results following the variable selection and threshold optimization for logistic regression, the sensitivity and specificity of the model were between 90 percent and 95 percent. however, given the large number of records fed through the algorithm either each week for 'real-time' surveillance and treatment/outreach, or for larger retrospective data sets, improving specificity is crucial to reduce the number of false positives. additionally, given that a public health treatment/outreach staff has a finite amount of time and resources, limiting false positives will allow them to focus on the true cases. further model improvements were made with a series of binary filters that allowed for overall sensitivity/specificity improvements as well as ensuring that the records sent for outreach are appropriate for outreach. the application of the filters pushed the classification sensitivity and specificity to greater than 99 percent. further, the filters removed cases inappropriate for outreach at greater than 90 percent efficiency. conclusions the algorithm was able to classify opioid overdoses in ems data with a sensitivity and specificity greater than 99 percent. it was implemented into a viable public health treatment/outreach system through the marin county department of health and human services in may 2018, and has identified approximately 50 overdoses for outreach as of september, 2018. it is possible, using minimal computational power and infrastructure to develop a fully realized surveillance system through ems data for nearly any size public health entity. additionally, the framework allows for flexibility such that the system can be tailored for specific clinical or surveillance needs there is no 'black box' component. lastly, the application of this methodology to other diseases/conditions is possible and has already been done using the same data for both sepsis and falls in older adults. acknowledgement this project was supported by a grant from california department of public health. references 1. r core team. 2018. r: a language and environment for statistical computing. available: https://www.r-project.org/. 2. rstudio team. 2018. rstudio: integrated development environment for r. available: http://www.rstudio.com/. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts assessment of the use of ed chief complaint data for monitoring chronic diseases megan t. patel* epidemiology and biostatistics, university of illinois chicago, chicago, il, usa objective to create chronic disease categories for emergency department (ed) chief complaint data and evaluate the categories for validity against ed data with discharge diagnoses and hospital discharge data. introduction syndromic surveillance (ss), traditionally applied to infectious diseases, is more recently being adapted to chronic disease prevention. its usefulness rests on the large number of diverse individuals visiting emergency rooms with the possibility of real-time monitoring of acute health effects, including effects from environmental events and its potential ability to examine more long-term health effects and trends of chronic diseases on a local level [1-3]. methods emergency department chief complaint (cc) data captured by the cook county department of public health local instance of essence from jan 1, 2006 – dec 31, 2013 was utilized to generate chronic disease categories for: cvd, ami, acs, angina, stroke, diabetes, hypertension, asthma, and copd based on disease symptoms, natural language processing for free text chief complaints, and associated terms present in emr system menus. a standard category was created for each chronic disease category based on discharge diagnoses (icd-9 code), and their associated terms. the icd-9 based categories were applied to the discharge diagnosis field within the ed data. the chief complaint based chronic disease category definitions were compared to the standard classification by determining the sensitivity, specificity, positive predictive value, and negative predictive value. the standard chronic disease categories created with icd-9 codes for the chronic disease category validation were also applied to illinois hospital discharge data for cook county from jan 1, 2006 – dec 31, 2013. this data was compared to the chief complaint categories from the ed data for the same time period by visual analysis through time series and strength of correlation by pearson correlation coefficient analysis. essence version 1.17 was utilized for the free-text query development and sas 9.4 was utilized to perform the analyses. results for the validation analysis, 1,366,525 (24.76%) ed visits of individuals 40 years and older and 867,509 (15.72%) ed visits of individuals less than 18 years of age with a valid chief complaint and discharge diagnosis were included. validation results are presented in table 1. specificity was generally high for most of the categories, with the narrow definitions having a higher specificity (narrow ami = 0.9996, broad ami = 0.9119). however, the loss in sensitivity is substantial in moving from the broader definition to the narrow definition (broad ami = 0.5444, narrow ami = 0.1040). the positive predictive values had a wide range from 0.0128 for the broad acs category to 0.7199 for the narrow asthma definition. the negative predictive values were high for all chronic disease categories ranging from 0.9501 for the narrow cvd category to 0.9996 for angina. the pearson correlation coefficients are presented in table 2. graphs showing the comparisons of the chief complaint based ed data to the hospitalization data by chronic disease category definition are presented in figure 1. pearson correlations ranged from 0.9323 for narrow asthma to 0.1992 for hypertension. conclusions based on the high specificity and correlation coefficients in comparison to hospital discharge data, emergency department chief complaint data captured with syndromic surveillance could be utilized to examine chronic disease categories: asthma, copd, cvd, ami, acs, stroke, and diabetes at a local, state or national level. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords chronic disease; evaluation; ed data; syndromic surveillance acknowledgments demian christiansen, kelley bemis, and victoria persky references 1. bassil, k.l., et al., temporal and spatial variation of heat-related illness using 911 medical dispatch data. environ res, 2009. 109(5): p. 600-6. 2. mathes, r.w., k. ito, and t. matte, assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in new york city. plos one, 2011. 6(2): p. e14677. 3. zanobetti, a. and j. schwartz, air pollution and emergency admissions in boston, ma. j epidemiol community health, 2006. 60(10): p. 890-5. *megan t. patel e-mail: mtoth2@uic.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e32, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e290, 2019 isds 2019 conference abstracts seeing data in a new light: data visualization techniques to improve understanding shannon dewitt centers for disease control and prevention, atlanta, georgia, united states objective the presenter will demonstrate complex health and environment surveillance data visualization techniques within the cdc’s environmental public health tracking network. introduction in 2002, the united states (us) centers for disease control and prevention (cdc) launched the national environmental public health tracking program (tracking program) to address the challenges and gaps in the nation’s environmental health surveillan ce infrastructure. the tracking program’s mission is to provide information from a nationwide network of int egrated health and environmental data that drives actions to improve the health of communities. as a primary objective of the tracking program, the environmental public health tracking network (tracking network) was developed as an online surveillance syst em with data available for 23 topics and over 450 different health, environmental, and population measures. the integration and display of such disparate data can be challenging. for data consumers without scientific training, or even scientists and public health professionals with limited time, it can be difficult to examine and explore the data in an online surveillance system. additionally, casual data consumers may not require complex data details; a big picture perspective may be appropriate to their needs. the tracking network – which applies standardized data, a modern user interface, techniques catering to a variety of data consumers, and best practi ces in data visualization – provides a dynamic data query system that allows users to visualize different types of environmental health data in numerous ways including a variety of charting, mapping, and graphing options. methods after the tracking program identifies important environmental health topics, data standards are developed to aid in data validation and to ensure consistency and comparability of the data. the data are aggregated into standardized stratifications, summarize d, and used to calculate environmental health measures. visualization requirements for each measure are determined and developed on the tracking network. in addition, public health content are developed to provide important context for the user. the final step is the release of the environmental health measures onto the national tracking network, where they can be queried, visuali zed, compared, and analyzed with all of the other environmental health measures on the tracking network. results launched in 2009, the tracking network developed at cdc is home to over 450 standardized environmental health measures spanning 23 topics and multiple years, and can be displayed at a geographic level of state, county, and census tract. the tracking network allows data consumers to interpret this data visually using tools including dynamic timeline maps, infographics, advanced charting and a streamlined user interface designed to be simple to use. with varying user levels in mind, this collection of tools provides a variety of avenues for the users to explore the data. visualization results can be exported and embedded into other websites with data interpretation statements, benchmarks, and other visual cues that allow a broad audience to be able to access to environmental public health surveillance data. conclusions while the internet contains a wealth of health and environmental datasets, the tracking network provides a centralized location to access over 450 environmental health measures and provides a variety of visualization tools to translate the data into useful information. the speaker will present a range of display options available on the tracking network, highlight ways to present data for easy understanding and consumption, and provide a brief look into the future of data visualization. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts assessing prior pain visits and medical history risk factors for opioid overdose andrew walsh* health monitoring, pittsburgh, pa, usa objective identifying text features of emergency department visits associated with risk of future drug overdose. introduction opioid overdoses are a growing cause of mortality in the united states.1 medical prescriptions for opioids are a risk factor for overdose2. this observation raises concerns that patients may seek multiple opioid prescriptions, possibly increasing their overdose risk. one route for obtaining those prescriptions is visiting the emergency department (ed) for pain-related complaints. here, two hypotheses related to prescription seeking and overdoses are tested. (1) overdose patients have a larger number of prior ed visits than matched controls. (2) overdose patients have distinct patterns of pain-related complaints compared to matched controls. methods ed registrations were collected via the epicenter syndromic surveillance system. regular expression searches on chief complaints identified overdose visits. overdose visits were matched with control visits from the same facility with maximal similarity of gender, age, home location and arrival time. a year of prior ed visits for cases and controls were matched using facility-specific patient identifiers or birthdate, gender and home location. patient history chief complaints were sanitized to standardize spelling, expand abbreviations and consolidate phrases. word frequency comparisons between groups identified candidate terms for modeling. odds ratios of patient history terms were calculated with univariate logistic regression. multivariate lasso logistic regression selected covariates for prediction. these models were fit to data from one quarter and cutoffs for covariate inclusion were validated on the following quarter’s data. model predictions were validated on a 1% sample of ed registrations from the next quarter. results quarter three of 2016 yielded 23,769 overdose ed visits and matching controls; quarter four yielded 21,957 pairs; and 15,824 ed visits were sampled from the first quarter of 2017 including 130 overdose visits. contrary to expectations, patients in the control group averaged 0.7 additional ed visits in the prior year relative to controls; this pattern was consistent across quarters and regardless of how prior visits were matched (fig 1). prior visits for various pain categories were also more common among control patients than overdose patients (e.g. odds ratio for “back pain”: 0.78). terms associated with drug use (e.g. “detox” odds ratio: 2.66) and mental health concerns (e.g. “psychological” odds ratio: 4.28) were most consistently overrepresented in the history of overdose patients (table 1). terms associated with chronic disease were most overrepresented in the history of control patients (table 2). the best predictive model achieved a sensitivity of 57% and a specificity of 86% on test data (fig 2). conclusions while a history of more overall ed visits and more ed visits related to pain were not associated with overdose ed visits, vocabulary of prior ed visits did predict future overdose ed visits. performance of predictive models exceeded expectations, given the relative scarcity of overdoses among ed visits and the simplicity of chief complaints used for prediction. the correlation between past and future overdose visits highlights the need for targeted intervention to break addiction cycles. table 1: most overrepresented terms in case histories table 2: most overrepresented terms in control histories fig 1: distribution of prior visits by case/control status isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts fig 2: roc performance comparisons among 4 models based on current chief complaint vocabulary for predicting future case status keywords opioid; overdose; heroin; syndromic surveillance; prescription drugs acknowledgments we wish to thank our public health customers for funding support and data for this work. references 1. case a, deaton a. rising morbidity and mortality in midlife among white non-hispanic americans in the 21st century. pnas. 2015 december; 112(49). 2. larochelle mr, et al. opioid prescribing after nonfatal overdose and association with repeated overdose: a cohort study. ann intern med. 2016 january; 164(1). *andrew walsh e-mail: andy.walsh@hmsinc.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e98, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts enhanced surveillance to monitor response to a provincial overdose emergency, canada tianxin chu*1, sara forsting1, jat sandhu1, 2, geoff ramler1, shannon riley1, miranda compton1, mark lysyshyn1, 2 and patricia daly1, 2 1vancouver coastal health, vancouver, bc, canada; 2the university of british columbia, vancouver, bc, canada objective to describe the use of multiple data sources to monitor overdoses in near real-time in order to evaluate response to the provincial overdose emergency introduction on april 14, 2016, british columbia (bc)’s provincial health officer declared a public health emergency due to a significant increase in drug-related overdoses and deaths in the province. despite the declaration, 161 suspected drug overdose deaths were reported across the province in december 2016, a 137% increase over the number of deaths occurring in the same month of 2015 [1]. in response to the surge overdoses, vancouver coastal health authority (vch), one of 5 health regions within bc, rapidly implemented a number of novel harm reduction initiatives. overdose prevention sites (ops) were opened on december 8, 2016. at these sites, people using illicit drugs are supervised by peers who can provide rapid intervention if an overdose occurs. the mobile medical unit (mmu), a temporary state-of-art medical facility, was deployed on december 13, 2016 to reduce the congestion for the bc ambulance service (bcas) and a major urban emergency department (ed) [2]. following deployment of the mmu, services were transitioned to a permanent program at the downtown eastside connections clinic (dtes connections) in the spring of 2017. dtes connections was created to provide rapid access to addiction treatment [3]. in order to keep pace with the rapidly increasing number of novel harm reduction initiatives, enhanced surveillance programs were implemented at vch to monitor and evaluate these innovative harm reduction activities, including development of new surveillance programs for the mmu, ops and dtes connections, along with existing routine surveillance system from eds and a supervised injection site (insite). methods since 2011, after a spike of heroin-related deaths was reported in the vancouver region, vch started weekly monitoring of overdoses at nine eds and insite. daily data extracts from eds are automatically transferred to a secure driver by secure file transfer protocols. groups of icd 9/10 codes and keywords were refined to identify overdoses from eds. a formal epidemiological evaluation was conducted to measure the algorithm’s accuracy in 2013. a live connection with insite database was set up in 2011. overdose events at insite are clinically determined by clinical staff. substance injected, characteristics of overdose event and emergency interventions are entered in the database. with the implementation of mmu, ops and dtes connections, a series of protocols were developed to monitor visitors’ information and overdose events from each site. demographic information, visit information, clinical presentations and substance used are collected from mmu and dtes connections. a subset of data fields, including client handle, visit information, substance involved, overdose occurrence, naloxone intervention and ed transfer, are collected from ops to minimize impact on peers and community partners who run the sites. results between november 2016 and january 2017, a sharp increase in overdoses was identified from eds and insite. opioids, especially fentanyl and analogs, most likely contributed to the sudden increase. weeks with government income assistance payment showed an even greater increase in overdoses. since december 2016, six ops opened in vancouver. four of them are still operating and one received federal approval to become a supervised consumption site. by september 2017, there were 184,760 visits to the ops. 1,017 overdoses were reversed. a total of 2,798 patients visited the mmu during the whole operation period. 589 (21%) presented from treatment of overdose. the highest number of overdose visits occurred on december 21, 2016 after that month’s income assistance payment. since then, the number of visits fluctuated with most visits driven by non-overdose related reasons. 89% of overdose visits arrived by bcas and 79% of overdoses needed emergent and urgent care. 108 patients were transferred to dtes connections by bcas for treatment of overdoses by september 2017. all patients presented with opioid addiction issues. as of the end of september 2017, no deaths were reported from ops, mmu and dtes connections since operations. conclusions as vch continues responding to the drug overdose emergency in face of increasing drug overdoses, enhanced surveillance data have been widely used by the vch emergency overdose response committee for decision making on harm reduction activities, such as expanding operation hours at ops and insite on income assistance payment days; examining the impact on eds of opening the mmu; encouraging users to avoid using alone; opening new supervised injection service and women’s only ops; and referring ed patients with non-fatal overdose to rapid access opioid agonist treatment and outreach follow-up. keywords overdose; opioid; drug overdose surveillance; enhanced surveillance; syndromic surveilance acknowledgments we would like to acknowledge the support from all of the partner organizations that contribute to the new initiatives, including provincial health services authority and community partners, and the support from site managers, nurses, outreach workers and peers to assist with the data collection. references 1. b.c. coroners service monthly report. available from http://www2. gov.bc.ca/assets/gov/public-safety-and-emergency-services/deathinvestigation/statistical/illicit-drug.pdf 2. mobile medical unit opens in downtown eastside to treat overdose victims. available from http://www.vancourier.com/news/mobilemedical-unit-opens-in-downtown-eastside-to-treat-overdosevictims-1.4364941 3. first-of-its-kind drug treatment centre opening in downtown eastside. available from http://vancouversun.com/news/local-news/first-of-itskind-drug-treatment-centre-opening-in-downtown-eastside *tianxin chu e-mail: chu_career@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e112, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre the university of new south wales, sydney, nsw, australia objective to demonstrate use of routine laboratory-confirmed influenza surveillance data to forecast predicted influenza-attributable deaths during the current influenza season. we also assessed whether including information on influenza type produced better surveillance forecasts. introduction several countries prospectively monitor influenza-attributable mortality using a variation of the serfling seasonal time series model that uses sinusoidal terms for seasonality.1-4 typically, a seasonal model from previous years is used to forecast current expected mortality. using laboratory surveillance time series data in the model may enhance interpretation of the surveillance information. methods we fit a serfling-type, robust linear regression, time series model5 to weekly, all-age counts of influenza and pneumonia deaths for australia, 2007-2011. weekly laboratory-confirmed influenza counts were included as covariates; one model using total influenza was compared with a model including influenza type a and b. the two model forecasts of weekly deaths during 2012 were compared against observed deaths using root mean squared error (rmse). an indicator variable was used to adjust for inflated testing during the 2009 pandemic year and laboratory data was lagged by two weeks. results both models provided a reasonable forecast for 2012 (figures 1 and 2). rmse for the 2012 forecasts were 12.08 and 9.37, for the total influenza and type a and b models, respectively; the influenza type a and b model had a better fit. the total influenza model predicted that an increase of 100 total influenza notifications in a week was associated with an increase of 0.4 (95%ci: 0.02-0.9) deaths two weeks later. the influenza type a and b model predicted that an increase of 100 type a notifications in a week was associated with an increase of 1.2 (95%ci: 0.7-1.8) deaths two weeks later. however, parameter estimate for influenza type b was negative. conclusions we demonstrated a laboratory data-based time series model that may improve prospective mortality surveillance to assess the current season’s influenza impact. the model would allow week to week forecast of expected deaths as new laboratory data is received. also, the observed deaths could be compared with the forecast influenzaattributable deaths and if the observed deaths were higher than the forecast by a threshold amount, then we could signal a more virulent influenza strain or a more susceptible population than expected. some statistical challenges remain. keywords influenza; surveillance; statistical models; mortality acknowledgments data were provided by the australian institute of health and welfare and the australian department of health. references 1. the australian department of health. australian influenza surveillance report and activity updates, 2015. available from http://www.health. gov.au/flureport#current. accessed 26 august 2015. 2. centers for disease control and prevention. weekly u.s. influenza surveillance report, 2015. available from http://www.cdc.gov/flu/ weekly/. accessed 26 august 2015. 3. mortality monitoring in europe. european mortality bulletin week 33, 2015. available from http://www.euromomo.eu/. accessed 26 august 2015. 4. serfling r. methods for current statistical analysis of excess pneumonia-influenza deaths. public health reports 1963; 78(6): 494-506. 5. muscatello dj, morton pm, evans i, gilmour r. prospective surveillance of excess mortality due to influenza in new south wales: feasibility and statistical approach. communicable diseases intelligence quarterly report 2008; 32(4): 435-42. *aye m. moa e-mail: a.moa@unsw.edu.au online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e141, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts the myths and truths about comparing syndromic data across sites michael coletta* and aaron kite-powell csels/dhis, centers for disease control and prevention, atlanta, ga, usa objective as the biosense platform matures and more sites submit surveillance data, many in the community have voiced concerns about comparing data across sites. recently, a number of jurisdictions from across the country were asked to provide opioid overdose data to a news agency highlighting the epidemic. many jurisdictions requested information on how to present syndromic surveillance data from across sites and shared concern about how the data would be interpreted. this round table will address those concerns and explore options for comparing data across sites. introduction one of the more recent successes of nssp has been the introduction of more robust data quality monitoring and reporting. however, despite the increased insight into data quality, there are still concerns about data sharing and comparisons across sites. for nssp to be most effective, users need to feel confident in sharing data and making comparisons across sites. keywords nssp; sharing data; comparing data across sites; national surveillance; regional surveillance *michael coletta e-mail: mac0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e187, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts factors associated with hospitalization due to streptococcal infection in houston, texas 2015-2016 razina khayat*, sudipa biswas, najmus abdullah, hafeez rehman, kirstin short and salma khuwaja epidemiology, city of houston public health, houston, tx, usa objective to study the factors associated with streptococcal infection that led to hospitalization in houston, texas for years 2015-2016 introduction different studies have shown that streptococcal infections in adults are more common among older age, blacks, and underlying chronic medical conditions like diabetes, cardiovascular and kidney diseases. in specific, other studies have demonstrated that streptococcal pyogenes can cause severe illnesses and dramatic hospital outbreaks. furthermore, community-acquired pneumonia studies had also suggested that cardiovascular disease, severe renal disease, chronic lung disease and diabetes were associated with increased odds of hospitalization. methods data were extracted from houston electronic disease surveillance system (hedss) beginning january 1, 2015 to december 31, 2016. a total of 512 confirmed cases were investigated and analyzed during the study period. frequencies and percentages were calculated and chi square test was used to examine the association between hospitalization and other risk factors. odds ratio was calculated using unconditional logistic regression to determine the association of risk factors with hospitalization in streptococcal patients. results a total of 414 patients (81 %) of the confirmed cases were hospitalized. age, race, fever, sepsis, diabetes, cardiovascular and kidney diseases were significantly associated with hospitalization in the bivariate analysis. logistic regression analysis adjusted for confounding factors demonstrated that among clinical characteristics, fever (or 2.9; 95% ci 1.66-5.38) was three times more prevalent among hospitalized patients with streptococcal infection. patients with diabetes (or 7.92; 95% ci 3.08-20.36) were almost eight times more likely to be hospitalized than patients without diabetes among streptococcal patients, followed by cardiovascular disease (or 2.84; ci 1.32-6.10) which was three times more likely to be present. conclusions common clinical sign like fever was associated with hospitalization among streptococcal patient. similarly, risk factors like diabetes and cardiovascular diseases were significantly associated with hospitalization in streptococcal patients. prevention strategies need to be focused on streptococcal patients with chronic risk factors like diabetes, and cardiovascular disease. keywords streptococcal infection; hospitalization; chronic diseases acknowledgments thanks to the city of houston health department references parks t, barret l, jones n. invasive streptococcal disease: a review for clinicians. british med bulletin, 2015; 115 (7): 77-89. skoff th, farley mm, petit s, et al. increasing burden of invasive group b streptococcal disease in nonpregnant adults, 1990-2007. cid 2009; 49 (7): 85-92. *razina khayat e-mail: razina.khayat@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e127, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin public health, maricopa county, phoenix, az, usa objective to describe the enhanced epidemiologic surveillance efforts in place during super bowl xlix and related events, review epidemiologic surveillance results, discuss novel approaches for near real-time surveillance for situational awareness and early event detection and examine lessons learned for surveillance strategies during mass gatherings. introduction super bowl xlix took place on february 1st, 2015 in glendale, arizona. in preparation for this large scale public event and related activities, the maricopa county department of public health (mcdph) developed methods for enhanced surveillance, situational awareness and early detection of public health emergencies. methods from july 2014 to january 2015, an epidemiology work group and a public health and medical resource work group met on a monthly basis to develop and coordinate epidemiologic surveillance strategies with local, state and federal partners. surveillance strategies were developed and coordinated to monitor levels of disease activity and provide situational awareness during pro bowl (january 25th, 2015), nfl experience and super bowl central (january 24th, 2015 through february 1st, 2015) and super bowl. fiesta bowl (december 31st, 2015) was selected to pilot test surveillance strategies. strategies included enhanced surveillance alerts, enhanced animal surveillance, field syndromic surveillance at first aid stations, syndromic surveillance for emergency room visits, hotel surveillance, urgent care surveillance, mortality surveillance, emergency medical services (ems) surveillance, media surveillance and aberration detection algorithms for notifiable diseases. results surveillance strategies were successfully tested during fiesta bowl. during super bowl and associated events, field surveillance collected information on 4 distinct syndromes (gastrointestinal, respiratory, dermatological and neurological) as well as injuries. real-time mapping of field surveillance data was also in place and aided in the evaluation of syndromes reported. aberration detection algorithms were run daily to detect illness reported to arizona’s notifiable disease surveillance system. enhanced surveillance alerts were sent to healthcare providers asking them to report any increases in illness or unusual illness presentation. alerts were implemented from january 22th, 2015 to february 6, 2015. enhanced animal surveillance was in place during all events. the arizona prehospital information and ems registry system was utilized to monitor for gastrointestinal and respiratory syndromes. influenza-like illness surveillance for outpatient sentinel sites was also in place. poison control center data and newly developed algorithms for mortality surveillance within an all hazards approach were analyzed daily. mcdph monitored foodborne outbreaks and produced a daily outbreak report. syndromic surveillance in hotels and urgent cares located within a 5 mile radius of the events was conducted. in addition, the nfl clinic provided daily reports to mcdph. the mcdph public health incident command center was activated and the intelligence section, responsible for epidemiologic surveillance, produced daily intelligence section report summarizing results from all surveillance efforts. surveillance result highlights included influenza widespread activity, increased influenza activity reported from urgent cares and several influenza cases reported within the nfl clinic. in addition, an investigation into a cyanide single event exposure was investigated and determined not to be a public health threat. field surveillance efforts documented minor injuries at all events and sporadic cases of gastrointestinal and neurological (mostly headaches) disease. enhanced animal surveillance reports included a cat suspected for plague and tularemia and an investigation of highly pathogenic avian influenza in a backyard chicken flock. laboratory results in both instances were negative. aberration detection algorithms detected an increase in measles reports associated with disneyland exposure and syndromic surveillance systems were used during this investigation successfully. conclusions coordinated enhanced epidemiologic surveillance during super bowl xlix increased the response capacity and preparedness of the public health department to make informed decisions and take public health actions in a timely manner during this mass gathering event. mcdph plans to continue development of novel tools and protocols for epidemiologic surveillance during mass gatherings to increase capacity for near real-time surveillance for situational awareness and early event detection. keywords syndromic surveillance; mass gatherings; situational awareness *aurimar ayala e-mail: aurimarayala@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e90, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 1emergency preparedness and response, tri-county health department, greenwood village, co, usa; 2denver public health, denver, co, usa objective adverse health effects related to marijuana (mj) use may disproportionately impact populations based on age or gender. to explore whether disparities exist among persons seeking emergency department (ed) care related to mj use, tri-county health department (tchd) and denver public health (dph) developed mj use case definitions, described patient demographics, mapped patients’ geographic distribution relative to marijuana dispensary locations, evaluated access to healthcare, and investigated the potential impact of mj on pediatric health. introduction assessing health disparities and access to healthcare has been an important issue for emergency preparedness and response efforts in the denver metropolitan area. there have been several high profile mj-related illness outbreaks in the us over the past 2 years. the legalization and retail sale of recreational mj in colorado necessitates enhanced surveillance for adverse effects from mj use. tchd and dph coordinated to use syndromic surveillance data to provide situational awareness and timely outbreak detection related to mj, including health disparities and overall impacts on population health. methods we used syndromic surveillance data from ed visits to 15 hospitals in adams, arapahoe, denver, and douglas counties; census poverty data; education and school data from colorado; and locations of medical and recreational mj dispensaries. an mj-related case (mjc) was defined as a case where text in the chief complaint, provider’s diagnosis, and/or diagnosis icd9 code contained terms including “marijuana,” “mj,” “cannabis,” “edible,” “e854.1,” “304.3,” “969.6,” and “305.2.” we used the electronic surveillance system for the early notification of community based epidemic (essence) to identify and evaluate mj cases and created maps in arcgis to illustrate the distribution of pediatric and adult mjc relative to mj dispensaries and healthcare facilities. the patient identifier number was used for deduplication and provider’s diagnosis, chief complaint, and diagnosis code were verified accordingly. access to healthcare facilities was assessed by comparing aggregated patient zip code and county code information to ed locations. all mjcs were included and the demographic attributes of mjcs were analyzed after a manual review and de-duplication process. after october 2015, icd-10 codes will be employed and adjustments will be made to examine the mj search terms. results after two additional hospitals began reporting data in august 2015, the total number of monthly ed visits increased 23.9% for a total of 85,357 visits. there were 279 (3.3%) mjcs identified. by age group, persons 18-44 years represented 54% of mjcs as compared to persons aged 5-17 years (22.3%), 45-64 years (21.7%), and 65 years or older (1.9%). a higher percentage of mjcs were males (66.2%). mjcs were residents in denver county (43%), adams (15%), arapahoe (15%) and douglas (3%) counties, and denver and arapahoe counties had a higher percentage of pediatric mjcs (7.29% and 4.58%, respectively) than the other two counties. ongoing mjc analyses, including patients’ proximity to healthcare facilities and mj dispensaries and impacts on school-aged patients, will be presented in december. conclusions preliminary results provide an overview of the prevalence of ed visits related to mj use and identify differences in mjcs by age, gender, and geographic location. males and persons aged 18 to 44 years constituted a higher proportion of mjcs, which may be related to differences in usage patterns as identified by population health surveys such as the behavioral risk factor surveillance system (brfss). there were also geographic disparities, with denver and arapahoe counties having a higher percentage of pediatric mj cases. additionally, denver county had the most recreational marijuana dispensaries. more advanced spatial analysis will describe any association between distribution of mj dispensaries and pediatric cases. the study identified limitations in using syndromic surveillance data for this purpose, including the lack of a standardized case definition or icd-9 code to identify for mjcs. additionally, mjrelated health outcomes might be underreported in icd-9 codes. tchd and dph will continue monitoring syndromic surveillance data quality for a 180-day baseline period and add icd-10 codes to the search criteria to create more specific case definitions. keywords syndromic surveillance; marijuana; essence; cannabis *yushiuan chen e-mail: ychen@tchd.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e99, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin 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surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a medical allocations to persons with special needs during a bioterrorism event 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi medical allocations to persons with special needs during a bioterrorism event donald e brannen1, melissa branum1, sejal pawani1, sandy miller2, jeanne bowman3, and tracy clare4 1. greene county public health, greene county, ohio 2. clark county combined health district 3. champaign county health department 4. dayton-montgomery county public health abstract after the bioterrorism-anthrax attacks of 2001, public health officials were tasked with planning population-wide medicine dispensing. this planning started with assumptions and then evaluations of seasonal immunization clinics. research on the 2009 h1n1 pandemic-vaccination campaign showed that an adequately prepared public health system could have prevented over 16% of fluassociated hospitalizations. the 2011 ice storms revealed difficulties with sheltering medically fragile persons with disabilities. later research showed that training and preparedness levels increased responders’ willingness to serve. when triaging disaster survivors to community-mass-care-services of general shelters, medical shelters, or mental health services; sorting improved up to 15% when past traumatic effects, personal care assistance, or service methodology were accounted for. the number of persons who are disabled and dependent on electric medical equipment are increasing. this current study compared the time it takes to dispense medication to two different cohorts: a general-population cohort (n=31) and a special-needs cohort (n=30). the cohort comprised entirely of persons with special needs took 4.1 compared to 2.48 minutes per person in a general population cohort (p=.057). a person with any special needs took 3.73 versus 2.43 minutes for a person with no special needs (p=.082). modeling of service times per station and cohort type found significant delays at the medical station among persons in the general population who are pregnant (14 minutes or 840 seconds, p=.002) and persons in the special needs cohort with a language barrier (12.5 minutes or 750 seconds, p=.001). recommendations include planning for closed points of dispensing sites (pods) to those with special needs, ensuring a sufficient number of medical dispenser in open pods, and assigning extra capacity at the medical station area for special needs involving children, language, or pregnancy issues. correspondence: dr. donald e brannen, greene county public health, greene county, ohio, xenia, 45385 oh, usa+1 937 374-5600. dbrannen@gcph.info doi: 10.5210/ojphi.v8i3.6977 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. http://ojphi.org/ mailto:dbrannen@gcph.info medical allocations to persons with special needs during a bioterrorism event 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi introduction even before the bioterrorism attacks of 2001, there had been concern about the capacity of local public health to protect the public from an anthrax attack. strategies to get lifesaving medicine to those exposed were among the critical-bioterrorism-preparedness issues identified after the post 9/11 anthrax attacks in 2001 [1]. in ohio, like many other states, there are standing medical orders for the prophylactic use of antibiotics and vaccination in response to a release of a bioterrorism agent like smallpox or anthrax. these orders do not cover persons with known disease rather only those persons with suspected exposures. if the anthrax vaccine was made available under an emergency use authorization and the centers for disease control and prevention released the vaccine to the area with suspected exposure to the bioterrorism agent bacillus anthracis, public health staff, augmented with medical reserve corps (mrc) volunteers would dispense the vaccine, along with antibiotics. dispensing post-exposure prophylaxis medications to a large number of people at a well-designed clinic after a release of anthrax spores, results in more medicine dispensed per unit time, allowing for fewer deaths and faster epidemic eradication [2-4]. if local public health is unable to adequately respond to an anthrax attack, anthrax can cause illness or death 1 to 6 days after exposure. after the 2001 anthrax attacks, concurrent with preparation for a potential smallpox attack, individual public health officials had to make broadly applicable assumptions about staffing a mass clinic [5]. in the following years, local public health agencies started to evaluate their ability to conduct mass immunization clinics. these evaluations included studies of the total number of persons served within a specific time and the amount of resources required. nine county health departments in the west central region of ohio conducted studies of the region’s seasonal immunization clinics during the 2005-2006 flu season; with over 7,000 vaccinated and 997 randomly selected observations. the effectiveness at providing the required medical coverage to protect the population was associated with how the dispensing site was setup [6]. the clinic designs were one of three styles: traditional, mass, or queue designs. the traditional setup comprised serialaligned single stations, one person at a time per station. the queue setup comprised each station having a single waiting line. the mass setup had clients arrive at multiple stations in bulk with the first open station providing service. the mass style of clinic was most effective, but the least efficient in the short term; with the number of persons served per unit time dependent on the number of vaccinators. the number of lives saved by mitigating disease spread would allow the cost efficiency of the queue and mass styles to exceed the traditional clinic design with increasing virulence of the bioagent. however, independent of available resources, communities can have a variable response to mitigating disease spread, just by the type of culture that is prevalent within the responding organizations [7]. the united states government has stockpiled equipment and medicine in the sns to respond to disasters. these evaluations evolve around the strategic national stockpile (sns), a cache of medicine and supplies that would be used to mitigate the health effects from a terrorist attack with a chemical, biological or nuclear weapon [8]. there is concern on how the supplies would be deployed in response to a disaster when the number of patients exceeds the ability of the health care system to respond [9]. for example, anthrax is a bacterium that can cause illness after 1 to 6 days of exposure. one source of variability in the response is having too many people show up at any particular time. too many people increase the time spent in waiting lines. any delay in service http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi times can start an exponential increase of wait times for people who are just arriving for service. having persons wait at a mass dispensing clinic increases the chance for sequela or unrelated consequences to occur like a heart attack. in a disease outbreak this can also increase the inadvertent transmission of the disease causing agent. these inadvertent and unrelated events to the primary mission of dispensing the correct medicine in the right dosage can draw off essential staff from their mission. other issues that can arise from having too many people include increased stress and anxiety, which in turn may lead to further disruptions in the clinical dispensing process. a bad scenario is that so many people arrive that the crowd-surge itself causes injury and even worse the crowd-surge turns into a riot. the sns, maintained by the centers for disease control and prevention (cdc) was started in 1999 with a budget of about $50 million [8]. the sns inventory includes a variety of medicines and equipment to deal with public health emergencies including all sorts of natural disasters and bioterrorism events. included in the inventory are medicines to prevent anthrax before the onset of symptoms. immunoglobulin can treat current infection, but broad based antibiotics are the first line of defense in a population based exposure. if anthrax is suspected, there are standing orders to dispense prophylactic antibiotics and provide anthrax vaccine to persons with known or suspected exposure to bacillus anthracis. either doxycycline 100 mg by mouth for 60 days or ciprofloxacin 500 mg twice per day for 60 days is recommend in adults, with other specific recommendations for children and pregnant women (ohio department of health, standing medical order, protocol for local health departments: prophylactic use of antibiotics and vaccination, april 21, 2014). some regimens could be adjusted in conjunction with anthrax immunoglobulin and vaccine with advice from cdc. photographs: strategic national stockpile supplies packaged to fit into a plane or truck for quick delivery to affected areas. (pictures from the centers for disease control and prevention, www.cdc.gov. downloaded 9/13/2016). in an interview by national public radio’s reporter, nell greenfieldboyce, on june 27, 2016, it was noted that: “if there's a major anthrax attack, and there's just 48 hours to get prophylactic antibiotics to more than a million people how is it accomplished? while the federal government looks at a range of scenarios based on intelligence information and across all threats to stockpile medicine a or b, at the local level, the goal is how to get the medicines to those persons who are affected. the stockpile has 12-hour push packs with 50 tons of material to be delivered to a local http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi city or town. state and local public health workers would have to figure out how to get the medicine to those who need it. local public health officials would rely on mrc volunteers to deliver the medicines to the population. volunteers are needed because over the last decade we have lost 50,000 state and local health officials.” [8] strategic national stockpile: cache of medicine that would be used to mitigate health effects from a terrorist attack with anthrax and other agents. during the 2009 h1n1 pandemic, local public health was charged with providing immunizations to the population to limit illness and deaths from a new strain of influenza. to increase the effectiveness of the immunization campaign, partly due to the limited supply and capacity, persons most at risk were targeted to receive the vaccine. once cdc delivers the stockpile, it becomes a state asset. there have been concerns that local public health was outdated, inefficient, unresponsive, and unprepared for health hazards [10]. these gaps in the capacity of public health ability to respond to a bioterrorist threat, outbreaks, and weather events have persisted [11]. even without considering gaps in public health’s infrastructure, material, or personnel; modeling of managerial inefficiencies during the 2010-2011 flu season in the united states showed that one out of every six hospitalized cases could have been prevented by better management within the overall public health system [7]. even if local public health agencies had resources, staff, and volunteers, there is another component willingness-to-respond to an anthrax event. there are similarities among radioactive contamination and the persistence of anthrax spores, as both are persistent contaminants in the environment. the ability of communities to mobilize local mrc volunteers or citizen’s corps volunteers in a timely manner is crucial to respond to any form of disaster including anthrax or radiation related events. the concept of a community reception center is an idea where disaster evacuees are funneled through a single center and the ability of the community is matched with their needs. in the case of a persistent environmental contaminant, the effectiveness of volunteers to route survivors to the most appropriate resource is dependent on their willingness to serve. the willing volunteer’s training, medical knowledge, and preparedness significantly increased the odds (>18:1) of correctly routing survivors to the right resource [12]. using the concept of survivors arriving at a community reception center, the next question was to determine how best to triage survivors to mental health services. public health workers and mrc volunteers were 15% more effective at correctly triaging survivors to mental health services using an algorithm that incorporated evaluation of past trauma into the triage system [13]. the focus on improving the initial sorting of survivors to needed services was driven by a need to identify those most at risk. there is a legal mandate requiring equal access under the americans with disabilities act (ada) that persons who require assistance or accommodation for functional needs in daily life, but who do not require acute medical care for stabilization, be admitted and served in general populations shelters. in january 2011, there was an ice storm that affected the nation from new mexico to new england. the greene county mrc unit helped staff one of six general shelters in or near their county [14]. the mrc volunteers quickly ascertained that their shelter population was mostly comprised of persons with medical needs including insulin, oxygen, adult diapering, wheel chair assistance, and intellectual developmental issues. the shelter residents were in a tenuous state of health, initially medically stable but physically fragile; many were without their http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi medicines or medical equipment. there were issues of inadequate equipment for client transfers/transport, direct medical care, and outdated medicines. the ice storm sheltering illustrates an example of the issues that may arise when a residential shelter cohort turns out to have unexpected medical needs. this event highlighted a practical separation between compliant service under the ada and persons who require medical services during a public health emergency. chronic care triage to a general shelter, medical shelter, or medical care was evaluated accounting for functional needs support services, the presence of a personal care assistant, or the personal preference for service methodology [15]. triage process flow that considered service methodology, the presence of personal care assistance, and those that required ongoing medical services of medically stable disasters survivors before community-mass-care translated into proportional efficiency gains up to 8% in meeting chronic care health service needs in an effective, efficient, humane, and ada-compliant service in general emergency shelters. definitions any special needs: includes persons who are disabled and persons with special needs. closed pod (points of dispensing): these are non-medical dispensing sites, such as university, colleges, schools, hospitals, nursing homes, long term care or assisted living facilities, that provide medication to people who work or reside in that site. they are basically alternate dispensing modalities. disability: person with physical or mental impairment that limits one or more activities of daily living and that may require special accommodations. functional needs: has unfulfilled survival needs or requires assistance related to activities of daily living (adl), communication, and mobility, especially to maintain degrees of independence (examples include toileting, transferring, hygiene, food preparation and consumption, temperature maintenance, and obtaining safe shelter). medical needs: requires skilled nursing or medical care to maintain physical or mental health and stability as compromised by medical conditions (may be chronic, acute, or exacerbated by the disaster). open pod: dispensing sites that provide medication to the general public. special needs: particular things needed (regardless of where the needs derived from, whether they are social influences or factors including limited language proficiency, breastfeeding, pet-owners, elderly, children, families, religion, race/ethnicity, cultural, or geographic influence) by or provided to help people who have a condition that makes it difficult for them to do the things that other people do. vulnerable: has additional needs or influences outside of conventional expectations that impact the ability to protect or serve the self and often experiences disparity. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi background a release of a bacillus anthracis spores will require provision of prophylaxis to all those exposed to the agent. the main goal of mass prophylaxis is to bring people into a location where they can be given medication, such as antibiotics and vaccination. such locations are known as point of dispensing or pod sites. there can be open or closed pod sites. an open pod is a dispensing site which provides medication to the general public. a closed pod is not open to the public and is conducted within a defined area or facility such as a university, college, school, hospital, nursing home, long term care or assisted living facility; that provide medication to people who work or reside at that site. closed pods expedite dispensing of medications and have the advantage of reducing the population to be treated at an open pod. also, such sites help in reaching out to disabled and other at risk population by bringing the medicine to the agencies that specialize in servicing that population segment. as of 2010-2014, the u.s. the disabled population was 8.5% or 27,320,599. the number of disabled in the dayton metropolitan area (montgomery, greene and miami counties in ohio) is shown in table 1. table 1. the 2010-2014 disability population in dayton metropolitan area, ohio, usa. county name population (2010 census) disability % actual number of disabled people champaign 39,145 14.40 5,637 montgomery county 535,141 11.3% 60,471 greene county 161,569 8.1% 13,087 miami county 102,506 9.2% 9,431 total 799,216 10.3% 82,989 number of disabled people in dayton city, which is part of montgomery county, is 15.4% of 141,761 total population (2010 census) 21,831. persons with disabilities need to be considered while planning for providing medical countermeasures to public health emergencies. health department officials should keep the following statistics in consideration while planning.  there were 378,760 ohioans ages 5 and above (or 3.8% of the civilian noninstitutionalized population over age 5) experiencing blindness, deafness, or a severe vision or hearing impairment.  a higher percentage— 8.5%—of the population indicated a physical disability that substantially limits one or more basic activities such as walking, climbing, etc.  additional results from the census indicate that over 516,000 ohioans (or five percent) have a mental, physical or emotional condition that makes it difficult to concentrate, remember or learn;  while just over 268,000 (2.6%) have a condition that makes it difficult to dress, bathe, or get around inside the home.  eight percent (or 653,517 individuals) of the noninstitutionalized population ages 16 and older have a mental, physical, or emotional condition that limits their ability to go outside the home alone [16]. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi for such a population, we need to consider following alternate dispensing modalities.  closed pod sites with healthcare entities such as nursing homes, long term care facilities, and retirement homes can be an excellent option for institutionalized population. closed pod staff should be trained beforehand in order to handle such situations.  mobile medical vans: there are people who are unable to reach the pod sites because of various reasons such as unavailability of transportation, or they are physically disabled, homebound, or institutionalized. for such people, use of mobile vans in order to reach them and provide them medications is an option. at-risk individuals include people with access and functional needs that may interfere with their ability to access or receive medical care before, during, or after a disastrous emergency. these include children, pregnant women, people with pre-existing medical conditions, advanced age, limited english skills, homelessness, physical or mental disabilities, or other physical, mental, communication, or transportation challenges. planned federal updates to the 2017 health care preparedness and response capabilities are focusing on how to assess community planning for children, pregnant women, seniors, and individuals with access and functional needs, including people with disabilities and others with unique needs. planning to provide assistance before, during, and after an emergency to these individuals is needed. specifically, public health planners should be aware of how to provide assistance to people with disabilities and to develop or augment existing response plans for these populations. this support of the health care system to disabled persons would prevent stress on hospitals during an emergency and would allow disabled persons to remain in their residences. further assessment in and around specific scenarios are required to assess transport needs to prophylaxis sites, designation of medical care sites for those needing further evaluation, and assessment of specific treatment and access to care needs (e.g., partnering with regional dialysis networks) to ensure integration with prophylaxis of disabled persons potentially exposed to anthrax. for symptomatic individuals: a triage area will be set up directly at the entrance to the pod and will serve to immediately screen symptomatic, sick or known exposed patients before entering the registration station. those patients exhibiting symptoms and patients, who were known to be exposed but are asymptomatic, are further screened and will be transported to a hospital. transportation of patient(s) would be provided based on need. buses, ambulances, or other forms of transportation may be necessary [17]. in 2014, the center for medicaid and medicare (cms) mandated that all county boards of developmental disabilities cease adult day services by 2019 [18]. this directive puts a burden on community agencies that are providing adult daycare, as they will now have to increase the number of people they serve. the mandate in 2014 affects all the county boards of developmental disability and all the states of the nation. since the boards of developmental disability would have a conflict of interest as both the referral source and auditor/regulator while also providing competing services, cms mandated that the boards could only be a coordinator or facilitator of services and can no longer provide those services themselves and to get out of the business by 2019. the change in service providers could be traumatic to the lives of those adults with developmental disabilities http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi who are being served by the county boards right now. the challenge for the boards is to get out of the service by 2019 and to make it a seamless transition to their clients. this means hundreds of families in each city will be impacted, as this is a traumatic life-changing event for someone who has put their most intimate needs in others’ caring hands. these daily service disruptions would be greatly magnified during a disaster or terrorism event. this mandate also impacts how dispensing would occur to this vulnerable population, as locations and providers will change over the next several years as boards transition out of service provision to coordination and facilitator of services. specifically, agreements entered into for a closed (not open to the public) point of dispensing site that would be dedicated to serving just those residents within a specific facility would no longer be valid. local public health agencies have had agreements with county boards for closed pods for those agencies serving persons with developmental disabilities. the challenge now is to create relationships with those new or expanded providers’ of adult day services. to train public health staff and mrc volunteers, the west central ohio mrc units comprised of darke, shelby, miami, champaign, clark, preble, montgomery, and greene counties conducted a point of distribution (pod) exercise at the national center for medical readiness on november 7, 2015 using a comparative study design of persons with disabilities and persons without disabilities. methods the scenario consisted of two cohorts potentially exposed to terrorism related inhalation anthrax exposure. disaster preparedness, pods, sns, and bioterrorism training was followed by a fullscale exercise with volunteers from mrc, citizen corps, and ohio military reserve. volunteers were assigned as pod staff or recipients. staff triaged recipients for medical prophylaxis (dispensing of ciprofloxacin or doxycycline props) for inhalation anthrax. time metrics were measured to calculate throughput and service times. the first cohort was all special needs (sn) and the second cohort represented the general population (gp). this study was not submitted to an institutional review board, as it was a public health agency training event to prepare for a public health emergency and to educate disaster responders. this training was consistent with the core competencies and sub competencies for disaster medicine and public health [19]. specifically, the ability to describe the potential impact of a mass casualty incident on access to and availability of clinical and public health resources in a disaster or public health emergency. a traditional medical dispensing clinic was set up to provide medicine to persons potentially exposed to terrorism related inhalation anthrax. two cohorts (special needs versus general population) were separately evaluated and treated. volunteers were assigned as pod staff or recipients. staff triaged recipients for medical prophylaxis (dosing with ciprofloxacin or doxycycline) for inhalation anthrax. time metrics were measured to calculate throughput and service times. statistical analysis included: • critical path method—detecting the stations where a delay could most likely occur. • general linear modeling of services times. • univariate analysis of variance using factors for clinic stations and special needs. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi figure: the dashed line is the critical path. the critical station was dispensing in both cohorts. g=greeter, r=registration, s=screening, m=medical, d=dispensing, e=exit results the total time to complete the sn cohort (n=30) was 38 minutes and was 36 minutes for the gp cohort (n=31). net total service time was nearly double in the sn versus the gp cohort (123 minutes versus 69 minutes). express dispensing was less than half the time than for sn versus gp due to less demand on the station (11:22). in a traditional pod setup, screening, and medical station resources commensurate with the prevalence of sn are required to meet the needs during community mass care events. the service time for those with special needs was 3.73 minutes versus 2.43 minutes for those without (p=.082). in the general population, service time was 2.48 minutes versus over 4.1 minutes for special needs (p=.057). critical path analysis showed that the dispensing station was key in either cohorts of special needs or general population. modeling of wait times showed that children increased wait times by 4.03 minutes (p=.007). the critical station was dispensing in both cohorts. special needs cohort could take up to 9 times longer than the general population cohort. every station could be impacted. having children increased the wait times by over 4.03 minutes. best practices •standardized format. •well marked stations. •pre-briefing of staff/volunteers. •detailed job descriptions. •set objectives. •common language. •runners at stations. •surge capacity to relieve bottlenecks. •flexibility—change as needed. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi figure. comparison of average service times to dispense medicine to persons potentially exposed to anthrax spores by special needs status. figure. comparison of average net service times (excludes in line waiting) at the registration station of a medical dispensing clinic for persons potentially exposed to anthrax spores. 2.43 3.73* 2.48 4.1ǂ 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 no special needs (n=21) any special needs (n=40) general population cohort (n=31) special needs cohort (n=30) av er ag e m in ut es service time for medical dispensing to persons potentially exposed to anthrax ǂ p=.057 special needs versus general cohort. * p=.082 any special needs versus none. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi station greeter registration screener medical express exit allergies 60 740 140 0 260 40 blind 60 360 60 0 0 60 drug addiction language barrier minor 60 360 240 0 300 60 no special needs 60 480 112 79 237 60 pregnant 60 420 0 840 180 60 wheel chair 60 510 120 0 240 90 http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi station greeter registration screener medical express exit allergies 60 620 60 87 40 60 blind 150 210 300 240 180 60 drug addiction 60 600 30 0 30 0 language barrier 60 150 90 750 90 30 minor 195 360 0 0 90 45 no special needs 60 800 260 0 100 60 pregnant 60 990 60 90 60 30 wheel chair 90 570 40 80 60 60 figure and data table: average time per station by cohort type. the subgroup within the general population cohort that had minor children had significantly longer average registration times than the subgroups that had persons with other special needs (p = .006 versus other special needs) or no special needs (p = .008 versus no special needs). modeling of service times per station and cohort type found significant delays at the medical station among persons in the general population who are pregnant (840 seconds or 14 minutes, p=.002) and persons in the special needs cohort with a language barrier (750 seconds or 12.5 minutes, p=.001). table 2. regression model of waiting line study of medical dispensing to two cohorts (general population and special needs). unstandardized coefficients standardized coefficients t sig. model b std. error beta (constant) 660.42 63.3 10.43 0.000 screener ilw 106.01 31.6 0.343 3.36 0.001 medical ilw 58.72 11.1 0.516 5.3 0.000 express dispensing ilw 47.30 21.1 0.234 2.25 0.029 any other minors (if pickup total > 1) 242.65 86.8 0.275 2.8 0.007 note: ilw = in line waiting. take home messages • persons with greater medical needs will take longer to process. expect and plan for more resources to quickly and easily deal with these greater demands. • attack bottlenecks early. formalize adaptability: set aside a strike team to relieve bottlenecks early. • more medical staff members are needed to deal with persons with disability. • having persons pre-registered would bypass holdups at registration. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi • having rules to handle the more common medical situations could allow screeners to route persons directly to dispensing. • the net service times were the same for special needs including wheel chair bound, elderly, and blind persons as for non-special needs. the wait times were longer if children were with the client. key findings • persons with special needs required 3.73 to service compared to 2.43 minutes for persons without any special needs (p=.082). • the net total service time was 123 minutes (n=30) for the special needs cohort compared to 69 minutes for the general population cohort. • critical path analysis showed that the dispensing station was key in either cohorts of special needs or general population. • modeling of wait times showed that children increased wait times by 4.03 minutes (p=.007). • in line waiting was increased at the screening (1.76 minutes, p .001), medical (0.98 minutes, p<.001), and dispensing (0.78 minutes, p .029) stations. • modeling of service times per station and cohort type found significant delays at the medical station among 1) pregnancy (14 minutes or 840 seconds, p=.002) in the general population and 2) language (12.5 minutes or 750 seconds, p=.001) in the special needs cohort. service tips • use standardized formats such as well-marked stations, common terms, pre-briefing of staff and volunteers, detailed job descriptions, and set objectives. • attack bottlenecks early. formalize adaptability: set aside a strike team to relieve bottlenecks early; runners at stations; surge capacity to relieve bottlenecks; be flexible. • persons with greater medical needs will take longer to process. expect and plan for more resources to quickly and easily deal with these greater demands. having rules to handle the more common medical situations could allow screeners to route persons directly to overflow stations. for example, pregnancy and language caused significant delays at the stations. set aside extra staff at overflow stations to handle children, pregnant, and language barriers. • the wait times were longer at the registration line if children were present. having persons pre-registered, would bypass holdups at registration. • the net service times were the same for special needs including wheel chair bound, elderly, and blind persons as for non-special needs, but the in-line waiting (getting to stations) were longer. • the dispensing station is a critical node in the clinic pathway. have more dispensing staff. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi strengths the volunteers were trained in the morning and they could set up and run the clinic effectively afterwards. the estimates of effect are conservative as the median values of the processing times were used rather than mean for the comparison of service times for modeling except the univariate modeling which used the actual measures. discussion how does this study relate to public health informatics? scientists from midas (models of infectious disease agent study) funded by the national institutes of health model how infectious diseases may emerge and spread, using computers, in order to help public health officials prepare for actual outbreaks [20]. modeling the response to infectious diseases, especially with the rise of antimicrobial resistance and the potential for bioterrorism, is as important as modeling the infectious diseases. improving community mass care including mass public health clinics, open pods to dispense therapies and specific outreach activities to targeted, closed populations with the use of points of dispensing sites (closed pods) works even in the face of antimicrobial resistance, if the intervention includes triage to isolation and/or quarantine. likewise, the results of this study processed public health data from the closed pod on two groups of individuals and applied the results to improve the public health response at distributing antibiotics, referring ill or untreatable persons to medical care, isolation or quarantine. planned federal updates to the 2017 health care preparedness and response capabilities are focusing on how to assess community planning for children, pregnant women, seniors, and individuals with access and functional needs, including people with disabilities and others with unique needs. planning to provide assistance for those individuals who may require additional help before, during, and after an emergency is needed. specifically, public health planners should be aware of how to provide assistance to people with disabilities and to develop or augment existing response plans for these populations. this support of the health care system to disabled persons would prevent stress on hospitals during an emergency and would allow disabled persons to remain in their residences. further assessment in and around specific scenarios would be required to assess transport needs to prophylaxis sites, designation of medical care sites for those needing further evaluation, and assessment of specific treatment and access to care needs (e.g., partnering with regional dialysis networks to ensure integration with prophylaxis of disabled persons potentially exposed to anthrax. the results of the medical dispensing to persons with disabilities were surprising. the service times at the dispensing station were not statistically different between the general population cohort and those with any special needs. however, the time in between stations was increased resulting in total service time for the sn cohort taking longer overall. it is of interest that the presence of children increased the service times. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi table 3. numbers of medicare beneficiaries by the west central ohio counties and the number that are electricity dependent. (sources: u.s. dhhs, august 2016; u.s. census, july 2012). county medicare beneficiaries electricitydependent electricity dependent % population champaign 7,143 451 1.14 39565 clark 27,154 1,877 1.37 137206 clinton 8,501 549 1.31 41886 darke 9,929 669 1.27 52507 greene 27,633 1,571 0.96 163587 miami 20,070 1,170 1.13 103060 montgomery 97,830 5,285 0.99 534325 preble 7,994 483 1.15 41886 shelby 7,949 487 0.99 49167 note: the medicare data is from the u.s. department of health & human services and shows the number of medicare (over 2.4 million in the u.s.) beneficiaries rely upon electricity-dependent medical and assistive equipment, such as ventilators and wheel chairs and is updated monthly [21]. given that just in time training was provided, concerns about volunteers and local public health staff having the capability to conduct a mass-dispensing clinic are unfounded, as the volunteers and limited staff were effective in providing medicine to persons exposed to inhalation anthrax regardless of their special needs status. additional resources should be put in place for preregistration or additional onsite registration, especially for those with children. mass prophylaxis dispensing clinics should anticipate significant delays in service times for persons with special needs including pregnant, language barriers, and children. clinics dispensing to cohorts composed entirely of persons with special needs should reorder their traditional single waiting line per station to having an ‘inline’ medical exam station prior to the dispensing station (and an overflow medical exam station for pregnant and persons with language barriers). recommendations ensure memorandums of understanding are in place for select agencies involved in providing services to those with special needs to allow for public health to provide for closed pod dispensing to their clients. the critical node in a traditional clinic pathway is the dispensing station, therefore it is imperative to have a sufficient number of medicine dispensers to avoid a delay in service times. if a closed pod is not an alternative strategy to a traditional dispensing clinic, service times should be expected to be twice as long. assign extra capacity at the medical station area for special needs involving children, language, or pregnancy issues. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi limitations real world events may differ significantly. there are the typical concerns about security and logistics that vary with each clinic location. the two critical differences that will inevitability vary regardless of the clinic location, is the amount of exposure and the susceptibility of those exposed. the incubation period can be quite different depending on the amount of spores inhaled with the median incubation period being 9 days (95% confidence interval 8 to 10) and possibly less than 2.5 days with higher doses [22]. this is important because with shorter incubation, prophylaxis to be implemented very quickly if lives were to be saved. those with special needs will on average have more vulnerabilities and will require prophylaxis to be dispensed in less time after exposure to increase the rate of survival. when modeling using station and cohort type as factors small numbers caused great variability in standard errors possibly missing important causes of delays. more research is needed to explore dispensing to persons with drug addiction, blind persons, and other segments where this evaluation had great delays but non-significant results. acknowledgements the authors would like to thank the public health, medical reserve corps and ohio military reserve volunteers who participated on november 7, 2015 in staffing the stations and the staff at the wright state university’s national center for medical readiness. references 1. brannen de, stanley sa. 2004. critical issues in bioterrorism preparedness: before and after september 2001. j public health manag pract. 10(4), 290-98. 2. kaplan eh, craft dl, wein lm. 2002. emergency response to a smallpox attack: the case for mass vaccination. proc natl acad sci usa. 99(16), 10935-40. epub jul 2002. 3. davis tc, fredrickson dd, kennen em, arnold c, shoup e, et al. 2004. childhood vaccine risk/benefit communication among public health clinics: a time-motion study. public health nurs. 21(3), 228-36. 4. kaplan eh, craft dl, wein lm. 2003. analyzing bioterror response logistics: the case of smallpox. math biosci. 185(1), 33-72. 5. washington ml, mason j, meltzer mi. 2005. maxi-vac: planning mass smallpox vaccination clinics. j public health manag pract. 11(6), 542-49. 6. brannen de, mcdonnell m, howell m, sesler s, jez s, et al. (2006). cost analysis and critical path method of multiple mass dispensation clinics in greene, preble, montgomery, clark, and miami counties. ohio public health epidemiology conference. august 1, 2006. 7. brannen de, mcdonnell ma, & schmitt, a. (2013). organizational culture on community health outcomes after the 2009 h1n1 pandemic. journal of organizational culture communications & conflict, (17)1. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 17 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi 8. greenfieldboyce, nell. inside a secret government warehouse prepped for health catastrophes. morning edition. june 27, 2016 4:56 am et 9. bower wa, hendricks k, pillai s, guarnizo j, meaney-delman d, & centers for disease control and prevention (cdc). 2015. clinical framework and medical countermeasure use during an anthrax mass-casualty incident. mmwr recomm rep. 64(4), 1-22. 10. hearne sa, davis m, segal lm, unruh pj, earls mj, et al. 2004. trust for america’s health. ready or not? protecting the public’s health in the age of bioterrorism. biosecur bioterror. 2(1), 47-50. 11. levi j, segal lm, liebermann da, may k, lang a, et al. (2012 december). ready or not? protecting the public from diseases, disasters, and bioterrorism. december 2012. issue report. trust for america’s health. www.healthyamericans.org. 12. ludtke jr, narayan r, matariyeh a, brannen d, caudill k, et al. 2014. willingness to respond for radiologic incidents: a hands-on approach. am j disaster med. 9(4), 259-72. doi:10.5055/ajdm.2014.0178. 13. brannen de, barcus r, mcdonnell ma, price a, alsept c, et al. 2013. mental health triage tools for medically cleared disaster survivors: an evaluation by mrc volunteers and public health workers. disaster med public health prep. 7(1), 20-28. 14. brannen de, schmitt a, mcdonnell m. 2011. critical issues faced by mrc in a special needs shelter. domestic preparedness journal. 7(5), 17-21. 15. fannin a, brannen de, howell m, martin s. 2015. using functional needs and personal care assistance rather than disability status during chronic care triage in community mass care. disaster med public health prep. 9(3), 265-74. epub mar 2015. doi:http://dx.doi.org/10.1017/dmp.2015.21. 16. waldrop j, stern sm. disability status 2000: census 2000 brief. march 2003. http://www.census.gov/prod/2003pubs/c2kbr-17. 17. public health – dayton and montgomery county. mass dispensing and vaccination plan. 2011 may 14: 6-7. 18. kenny j. center for medicaid and medicare (cms) mandated that all county boards of developmental disabilities cease adult day services by 2019. wyso weekend, jun 27, 2016. wyso public radio. 19. walsh l1. 2012. subbarao i, gebbie k, schor kw, lyznicki j, strauss-riggs k, cooper a, hsu eb, king rv, mitas ja 2nd, hick j, zukowski r, altman ba, steinbrecher ra, james jj. core competencies for disaster medicine and public health. disaster med public health prep. 6(1), 44-52. doi:10.1001/dmp.2012.4. 20. modeling infectious diseases fact sheet national institute of general medical sciences [internet]. u.s national library of medicine. u.s. national library of medicine; [cited http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event 18 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e200, 2016 ojphi 2016oct3]. available from: https://www.nigms.nih.gov/research/specificareas/midas/background/pages/factsheet.aspx 21. pages default [internet]. pages default. [cited 2016oct3]. available from: http://www.phe.gov/empowermap/pages/default.aspx 22. wilkening da. 2008. modeling the incubation period of inhalational anthrax. med decis making. 28(4), 593-605. epub jun 2008. doi:http://dx.doi.org/10.1177/0272989x08315245. http://ojphi.org/ medical allocations to persons with special needs during a bioterrorism event introduction definitions background methods strengths discussion recommendations limitations acknowledgements references isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e277, 2019 isds 2019 conference abstracts data science, analytics and collaboration for a biosurveillance ecosystem karen a. stark, amol shah, jacob borgman, miko somborac, jeremy carson, loren hauser, krishna kola, hemant virkar digital infuzion, gaithersburg, maryland, united states objective while there is a growing torrent of data that disease surveillance could leverage, few effective tools exist to help public h ealth professionals make sense of this data or that provide secure work-sharing and communication. meanwhile, our ever more-connected world provides an increasingly receptive environment for diseases to emerge and spread rapidly making early warning and collaborative decision-making essential to saving lives and reducing the impact of outbreaks. digital infuzion's previous work on the defense threat reduction agency (dtra)'s biosurveillance ecosystem (bsve) built a cloud-based platform to ingest big data with analytics to provide users a robust surveillance environment. we next enhanced the bsve data sources and analytics to support an integrated one health paradigm. the resulting bsve and digital infuzion's harbinger platform include: 1) identifying and ingesting data sources that span global human, animal and crop health; 2) inclusion of non-health data such as travel, weather, and infrastructure; 3) the data science tools, analytics and visualizations to make these data useful and 4) a fullyfeatured collaboration center for secure work-sharing and communication across agencies. introduction after the 2009 h1n1 pandemic, the assistant secretary of defense for nuclear, chemical and biological defense indicated “biodefense” would include emerging infectious disease. in response, dtra launched an initiative for an innovative, rapidly emerging capability to enable real-time biosurveillance for early warning and course of action analysis. through competitive prototyping, dtra selected digital infuzion to develop the platform and next generation analytics. this work was extended to enhance collaboration capabilities and to harness data science and advanced analytics for multi-disciplinary surveillance including climate, crop, and animal as well as human data. new analysis tools ensure the bsve supports a one health paradigm to best inform public health action. digital infuzion and dtra first introduced the bsve to the isds community at the 2013 annual conference swap meet. digital infuzion is pleased to present the mature platform to this community again as it is now a fully developed capability undergoing fedramp certification with the department of homeland security’s national biosurveillance integration center and is the basis for digital infuzion's harbinger ecosystem for biosurveillance. methods we integrated over 170 global one health data sources using cloud-based automated data ingestion workflows that provide unified access with data provenance. we used modular automated workflows to implement data science including natural language processing (nlp), machine learning, anomaly detection, and expert systems for extraction of concepts from unstructured text. a first of its kind ontology for biosurveillance permits linking of data across sources. this ontology allows users to rapidly find all relevant data by looking at semantic relationships within and across data sets having varying quality, types, and usages to understand the best, most complete indicators of impending threats. we applied the following principles to the development of data science tools: 1) mathematics should be fully automated and operate 'under the hood' without need for user intervention; 2) 'at-a-glance' visualizations should summarize information, draw attention to key aspects and permit drill down into underlying data; 3) data science analytics and tools need to be valid ated with real-world data and by disease surveillance experts and 4) secure collaboration capabilities are essential to biosurveillance activities. this was a highly complex effort. we worked closely with surveillance analysts from multiple agencies and or ganizations to continuously guide the development of capabilities. we drew upon subject matter expertise in public health, machine learning, social media, nlp, semantics, big data integration, computational science, and visualization. a high level of automation, security and immediacy of data was applied to support rapid identification and investigation of potential outbreaks. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e277, 2019 isds 2019 conference abstracts results the platform now provisions integrated one health information. data sources were harmonized and expanded, along with historical information, to better predict and understand biothreats. these include global social media, human, plant, animal, and weather data. an analyst workbench delivers logical, intuitive and interactive visualizations enabling disease surveillance professionals t o identify critical, predictive information without extensive manual research. over 700 approved users currently have access t o the prototype. biosurveillance activities can be performed collaboratively among governmental agencies, public health officials, and the general public using the collaboration center and its sharing and messaging systems. data sharing is hipaa compliant and distinguishes public from private data using carefully controlled and approved roleand attribute-based access for security. to speed disease surveillance workflows, the workbench generates suggestions to the user on their current work. anomaly detection to alert to potential developing disease events employs fully automated analytics to conduct over 43 million calculations dai ly for more than 500 diseases in over 170 data sources, distilling this into a table that ranks the most significant anomalous increases that may indicate an outbreak and warrant investigation. a predictive disease modeling tool based on current and historical data u ses fuzzy logic to identify the likeliest outcome, even early in an outbreak when there is much uncertainty about th e disease and its characteristics. a complex automated workflow identifies health-related topics that are trending in twitter and evaluates their severity using novel lexicons and new reactive sentiment analysis. searches use the ontology to gather all rel evant information and are supported by the most advanced nlp with custom surveillance rules to provide succinctly extracted information. this alleviates the need for extensive reading by identifying exactly which data is needed and extracting key concepts from it. intuitive methods of visual representation, interactive displays, and drill-down capabilities were leveraged in all analytics for rapid understanding of results. finally, we added a software development kit to enable third party developers to continuously enhance the platform capabilities by adding new data sources and new analytic apps. this allows the platform to be adapted for specific needs and to keep pace with new scientific and technical discoveries and has resulted in over 50 analytic apps. conclusions the addition of one health data and analytics, and the integration of health data with unconventional data sources and modern approaches to data science and complex workflows, resulted in enhanced situational awareness and decision -making capabilities for users. the expanded collaboration center within the workbench, enables users to partner and collaborate with other agenci es and biosurveillance professionals both nationally and internationally to maximize the rapidity of responses to serious disease outbreaks. acknowledgement this project was supported by the defense threat reduction agency (dtra) and the department of homeland security (dhs) national biosurveillance integration center (nbic) via contracts to digital infuzion, inc. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using real-time syndromic surveillance to monitor the health effects of air pollution sally harcourt*1, lydia izon-cooper2, felipe d. colón-gonzález3, roger morbey1, 4, gillian smith1, 4, naima bradley5, karen exley5, alec dobney2, iain lake3 and alex elliot1, 4 1real-time syndromic surveillance team, public health england, birmingham, united kingdom; 2public health england environmental hazards and emergencies department, birmingham, united kingdom; 3university of east anglia, norwich, united kingdom; 4national institute for health research, health protection research unit in emergency preparedness and response, london, united kingdom; 5public health england environmental hazards and emergencies department, chilton, united kingdom objective to explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in england between 2012 and 2017. introduction the negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. [1] currently, during air pollution episodes public health england (phe) syndromic surveillance systems [2] provide a near real-time analysis of the health impact of poor air quality. in england, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached ‘high’ or ‘very high’ levels on the uk daily air quality index (daqi). [3-5] we now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. this would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base. methods the systematic approach will involve accessing historical data for air pollution incidents and syndromic surveillance data over the period 2012-17 across england. we will use pm10, pm2.5, ozone, no2, so2 and daqi data to define air pollution periods, and historical syndromic surveillance system data for respiratory syndromes (asthma, difficulty breathing, wheeze, cough, bronchitis, sore throat and allergic rhinitis), cardiac (all cardiovascular and myocardial infarction) and eye irritation/conjunctivitis syndromes. we will use regression modelling and cross-correlation analyses to determine the effects of air pollution, weather and pollen upon these syndromes and thus provide evidence of the sensitivity of these systems. historical data on additional environmental variables including temperature and precipitation, humidity and thunderstorm activity, pollen and fungal spores will be accounted for in the regression models, as well as data on influenza and respiratory syncytial virus (rsv) laboratory reports. we will include sub-national geographies and age/gender analyses in the study depending on the data availability and suitability. results initial results presented will include the preliminary descriptive epidemiology with a focus on asthma and the impact of air pollution incidents on health-care seeking behaviour using data from the phe national syndromic surveillance systems. conclusions we aim to demonstrate an innovative use of syndromic surveillance data to explore the impact of air pollution incidents on health-care seeking behaviour in england, in turn improving our understanding of the sensitivity and specificity of these systems for detecting the impact of air pollution incidents and to contribute to the knowledge base. this understanding will improve the public health response to future incidents. keywords syndromic surveillance; air pollution; time series; asthma references 1. world health organization (who). preventing disease through healthy environments. exposure to air pollution: a major public health concern. (http://www.who.int/ipcs/features/air_pollution.pdf). accessed 28/09/2017 2. public health england. syndromic surveillance: systems and analyses. (https://www.gov.uk/government/collections/syndromic-surveillancesystems-and-analyses). accessed 20/09/2017 3. department for environment food and rural affairs (defra). daily air quality index (daqi). (https://uk-air.defra.gov.uk/airpollution/daqi). accessed 28/06/2017 4. smith ge, et al. using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of march/ april 2014 in england. environ res 2015; 136: 500-504. 5. elliot aj, et al. monitoring the effect of air pollution episodes on health care consultations and ambulance call-outs in england during march/ april 2014: a retrospective observational analysis. environ pollut 2016; 214: 903-911. *sally harcourt e-mail: sally.harcourt@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e85, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts implementation of real-time laboratory-based influenza surveillance system, thailand phunlerd piyaraj*1, 2, nira pet-hoi3, chaiyos kunanusont2, supanee sangiamsak2, somsak wankijcharoen3, jarupa kanjanakornhirun3 and paithoon boonma2 1phramongkutklao college of medicine, bangkok, thailand; 2bangkok health research center, bangkok dusit medical services public company limited (bdms), bangkok hospital group, bangkok, thailand; 3chief information office, bangkok hospital head quarter, bangkok, thailand objective we describe the bangkok dusit medical services surveillance system (bdms-ss) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the bangkok dusit medical services group (bdms). introduction influenza is one of the significant causes of morbidity and mortality globally. previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. as part of strengthening influenza virus surveillance in response to the 2009 influenza a (h1n1) pandemic, the real-time laboratorybased influenza surveillance system, the bangkok dusit medical services surveillance system (bdms-ss), was developed in 2010 by the bangkok health research center (bhrc). the primary objective of the bdms-ss is to alert relevant stakeholders on the incidence trends of the influenza virus. type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. this system advances early warning and supports better decision making during infectious disease events.2 the bdms-ss operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in thailand. methods the bdms has a comprehensive network of laboratory, epidemiologic, and early warning surveillance systems which represents the largest body of information from private hospitals across thailand. hospitals and clinical laboratories have deployed automatic reporting mechanisms since 2010 and have effectively improved timeliness of laboratory data reporting. in april 2017, the capacity of near real-time influenza surveillance in bdms was found to have a demonstrated and sustainable capability. results from october 2010 to april 2017, a total of 482,789 subjects were tested and 86,110 (17.8%) cases of influenza were identified. of those who tested positive for influenza they were aged <2 years old (4.6%), 2-4 year old (10.9%), 5-14 years old (29.8%), 15-49 years old (41.9%), 50-64 years old (8.3%) and >65 years old (3.7%). approximately 50% of subjects were male and female. of these, 40,552 (47.0%) were influenza type b, 31,412 (36.4%) were influenza a unspecified subtype, 6,181 (7.2%) were influenza a h1n1, 4,001 (4.6%) were influenza a h3n2, 3,835 (4.4%) were influenza a seasonal and 196 (0.4%) were respiratory syncytial virus (rsv). the number of influenza-positive specimens reported by the realtime influenza surveillance system were from week 40, 2015 to week 39, 2016. a total of 117,867 subjects were tested and 17,572 (14.91%) cases tested positive for the influenza virus (figure 1). based on the long-term monitoring of collected information, this system can delineate the epidemiologic pattern of circulating viruses in near realtime manner, which clearly shows annual peaks in winter dominated by influenza subtype b in 2015-1016 season. this surveillance system helps to provide near real-time reporting, enabling rapid implementation of control measures for influenza outbreaks. conclusions this surveillance system was the first real-time, daily reporting surveillance system to report on the largest data base of private hospitals in thailand and provides timely reports and feedback to all stakeholders. it provides an important supplement to the routine influenza surveillance system in thailand. this illustrates a high level of awareness and willingness among the bdms hospital network to report emerging infectious diseases, and highlights the robust and sensitive nature of bdms’s surveillance system. this system demonstrates the flexibility of the surveillance systems in bdms to evaluate to emerging infectious disease and major communicable diseases. through participation in the thailand influenza surveillance network, bdms can more actively collaborate with national counterparts and use its expertise to strengthen global and regional surveillance capacity in southeast asia, in order to secure advances for a world safe and secure from infectious disease. furthermore, this system can be quickly adapted and used to monitor future influenzas pandemics and other major outbreaks of respiratory infectious disease, including novel pathogens. weekly distribution of number of collected samples with influenza viruses, bangkok hospital group, september 2015 to october 2016 keywords real-time surveillance system; influenza a h1n1; influenza a h3n2; influenza type b isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references ■ ghosh ts, vogt rl. active influenza surveillance at the local level: a model for local health agencies. am j public health 2008; 98(2):213-5. ■ baumbach j, mueller m, smelser c, et al. enhancement of influenza surveillance with aggregate rapid influenza test results: new mexico, 2003-2007. am j public health 2009;99 suppl 2:s372-7. ■ dalton cb, carlson sj, butler mt, et al. building influenza surveillance pyramids in near real time, australia. emerg infect dis 2013;19(11):1863-5. *phunlerd piyaraj e-mail: p_phunlerd@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e138, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts update on the cdc national syndromic surveillance program paula yoon and michael coletta* centers for disease control and prevention, atlanta, ga, usa objective inform conference attendees about the cdc national syndromic surveillance program (nssp), various program-related projects and who is working on them, what was accomplished during the past year, and nssp-development plans for the future. introduction the public health security and bioterrorism preparedness and response act of 2002 mandated establishing an integrated national public health surveillance system for early detection and rapid assessment of potential bioterrorism-related illness. in 2003, cdc created and launched the biosense software program. at that time, cdc’s focus was on rapidly developing and implementing web-based software to collect hospital emergency department data for analysis to detect and monitor syndromes of public health importance. during the ensuing decade, biosense evolved and now is part of cdc’s renamed national syndromic surveillance program (nssp). the broader vision of nssp aims to achieve two key goals: significantly improve technical capabilities for collecting and analyzing syndromic surveillance data, and to create and facilitate opportunities for collaboration among local, state, and national public health programs. through nssp, the syndromic surveillance community can be strengthened by access to improved technical capacity and to best-practices knowledge sharing among syndromic surveillance professionals. these nssp initiatives can help the nation-wide public health community strengthen situational awareness and enhance response capability to hazardous events. nssp encompasses people, partners, policies, information systems, standards, and resources. session attendees will learn more about nssp, its growing group of partners, what the program is doing now, and its future. description the 2014 cdc surveillance strategy calls for modernizing its health surveillance systems. through a host of improvements, cdc; its state, tribal, local, and territory public health partners; and the public health community-at-large will have better data and information to help inform decisions. the panel will discuss the biosense platform, formerly known simply as biosense, which is one of the four national surveillance programs identified for transformation in the cdc surveillance strategy. the biosense platform is now a component of nssp, which was launched last year by cdc’s division of health informatics and surveillance. the panel will discuss how the nssp staff is upgrading the biosense platform in three main areas: 1) data quality: improving data management, quality, and representativeness 2) technology: upgrading biosense platform technological capacity with better tools for data collection, processing, and analyses 3) partner engagement: strengthening the syndromic surveillance community of practice to promote data sharing that will further the science and practice of syndromic surveillance the panel will consist of cdc staff members who are leading these efforts and representatives from the biosense governance group (bgg), an organization representing public health jurisdictions participating in nssp. the bgg collaborates with cdc to develop ways to improve biosense platform performance including the tools and services it hosts. the panel will provide updates on current and near-term nssp activities, describe recent enhancements to the program, and engage the audience in discussions about ways to improve the utility of syndromic surveillance at all levels of the public health enterprise. discussion topics might include the anticipated deployment of new tools on the biosense platform such as essence, sas, and r studio professional; identifying technical assistance, training, and support needs of local and state programs; data work flow and ways to improve data quality; strengthening the nssp community of practice initiative by increasing local-and statelevel engagement; and ideas and topics for collaborations among jurisdictions and with cdc. audience engagement director of cdc’s division of health informatics and surveillance paula yoon, scd, will introduce the nssp team (program manager, biosense platform lead, data quality lead, partner engagement lead, and biosense governance group (bgg) partners) and provide historical perspective on biosense from inception in 2003 to its current role in nssp. then nssp team members will describe work underway and goal accomplishments in the three modernization focus areas identified earlier. the nssp program manager will offer a vision for the future. the bgg partners will describe their roles in representing the public health syndromic surveillance community. they also will facilitate an audience discussion intended to discover others’ viewpoints and recommendations. keywords nssp; biosense platform; cdc *michael coletta e-mail: mac0@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e183, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts prevalence of cchf virus in ticks and people and public awareness in zhambyl region, kazakhstan jennifer r. head*2, 4, yekatarina bumburidi1, stephanie j. salyer2, barbara knust2, mirzabekova g. kuralbekovna3 and daphne b. moffett1 1cdc-central asia office, almaty, kazakhstan; 2centers for disease control and prevention, atlanta, ga, usa; 3zhambyl oblast department of public health, taraz, kazakhstan; 4public health institute, san francisco, ca, usa objective as part of cdc’s global disease detection work, in conjunction with zhambyl region department of health, we conducted a tick survey and human seroprevalence knowledge, attitudes, and practices (kap) survey of livestock-owning households in zhambyl to assess cchf seroprevalence and risk factors. introduction crimean congo hemorrhagic fever (cchf) virus is a tick-borne pathogen that causes severe disease in people, with a distribution that extends from central asia to southern africa. in addition to tick bites, contact with bodily fluids from viremic livestock or from symptomatic humans are risk factors for infection. from 2000 to 2013, 73 cases of cchf were reported in zhambyl region, kazakhstan. cchf virus is categorized as an “especially dangerous pathogen” in kazakhstan and cchf is prioritized for surveillance and treatment. little is known about the seroprevalence of infection by cchf virus in zhambyl in ticks or people, and knowledge of risk factors for transmission of cchf virus among at-risk populations is believed to be low. methods rural villages were classified as “endemic” or “non-endemic”, where endemic areas reported ≥1 cchf case or a cchf viruspositive tick in the past 5 years. in each group, 15 villages were chosen by population proportional to livestock population size. livestock-owning households (n=969) were selected randomly from veterinary registries. one adult was randomly selected per house and ticks were collected from one randomly selected sheep or cow over 1 year of age per house. data were weighted accounting for sampling design and analyzed in r. results kap surveys were completed for 950 people (98%); of those, 923 (97%) submitted blood for elisa testing using vector-best kits. median age of human respondents was 46 years (range: 19 – 90); 54% were male. three individuals were anti-cchf igm positive, 12 anti-cchf igg positive and two positive for both. weighted seroprevalence of cchf in zhambyl was 1.6% (95% ci: 0.9, 3.0). in endemic villages, seroprevalence was 1.8% (95% ci: 1.0, 3.0), compared to 1.2% (95% ci: 0.4, 4.0) in non-endemic villages. of the 17 seropositive for cchf, median age was 54 years; 58% were male. none reported previous cchf diagnosis or illness with fever and hemorrhaging in the past five years. none reported high-risk tick exposure in the past four months. controlling for age and sex, milking animals, an activity in which 40.3% of the population had engaged, was associated with infection in poisson regression (or: 2.53, 95% ci: 1.27, 4.81). of respondents who had heard of cchf (n=791), 99.8% knew transmission was caused by a tick bite; few identified contact with animal blood (8.2%) or tick crushing (20.8%) as potential causes. of the five seropositive by igm, four participated in at least one of the following activities in the last four months: milking (n=3), birthing (n=2), shearing and slaughtering (n=1). one reported experiencing an illness with joint pain within the past four months. three were from non-endemic villages. entomologists inspected 465 cows and 528 sheep for ticks. ticks were found on 61.5% (95% ci: 48.1, 73.2) of cows (n=254) and 46.3% (95% ci: 24.3, 69.8) of sheep (n=264). ticks were grouped into pools by animal source and species. over ninety-seven percent of the tick pools were from the family ixoidadae, with the remaining from family argasidae. the genus hyalomma accounted for 65.8% of tick pools, rhipicephalus for 31.8%, ornithodoros for 2.4%, and argas for 0.5%. pools contained an average of 4.5 ticks (range: 1 – 26). ticks were stored live at 4°c for up to 24 hours before being crushed and extracts tested for cchf virus by pcr and antigen testing. of the 155 pools tested, seven (2.4%, 95% ci: 1.1, 5.0) were positive for cchf virus by either pcr (n=5) and/or antigen testing (n=4). a cchf virus-positive tick was found on 1.4% (95% ci: 0.4, 4.8) of all sheep and 4.8% (95% ci: 2.3, 10.0) of all cows. all cchf virus-positive ticks were hard ticks of family ixodidae, belonging to either genus hyalomma (n=5) or rhipicephalus (n=2). two pools were from non-endemic villages. conclusions presence of cchf virus-positive ticks and cchf-seropositive humans in non-endemic areas may suggest a wider range of virus circulation. these findings will be used to inform and target public health messaging. keywords cchf; serosurvey; kazakhstan; ticks; livestock *jennifer r. head e-mail: jrhead6@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e158, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts surveillance of a synthetic cannabinoid overdose outbreak using syndromic & ems data peter j. rock* and michael d. singleton college of public health, university of kentucky, lexington, ky, usa objective the aim of this project was to investigate anecdotal reports of an increase in synthetic cannabinoid (syncan) overdoses in lexingtonfayette county area of kentucky, using rapid surveillance systems including emergency department (ed) syndromic surveillance (sys) and emergency medical services (ems) data. introduction in mid-2017, the kentucky injury prevention and research center (a bonafide agent of kentucky department for public health-kdph) was alerted by members of kdph to anecdotal evidence of a possible increase of syncan (primarily “serenity”) overdoses. the situation presented an opportunity to demonstrate the capabilities of syndromic surveillance and emergency medical services (ems) data systems to provide rapid situational awareness about syncan overdoses. methods syncan cases were identified based on ems emergency runs with narratives including mentions of ‘serenity|k2|spice’ and occurred in the lexington-fayette county. in ed sys, syncan cases were identified for lexington-fayette county area ed visits with a chief complaints of ‘serenity|k2|spice’ or diagnosis code of t40.7x[1-4] a. the icd code was included after analysis of diagnosis codes in positive cases from a chief complaint only query revealed t40.7x[1-4]a as a primary code assigned in these cases. trends for lexington-fayette county area were compared to state-wide total to determine if the trend is unique or related to system-wide pattern changes. ems and ed sys trend results were compared for internal validity. ems incident addresses were geocoded to point-level to enable more granular analysis of geospatial patterns over time for identification of hotspots/clustering. results ed sys and ems results demonstrated a clear temporal increase in syncan overdoses beginning around march of 2017[fig 1]. further analysis indicated that this increase was most dramatically centered in the lexington-fayette county area [fig 2]. the vast majority of those overdosing were males (sys: 88.1%) with average age 37 compared to 11.9% and 36.0 for females, respectively. these demographics are similar to those reported by the new york city department of health and mental hygiene for a k2 outbreak in new york city in 20141. kernel density mapping demonstrated a strong clustering in a specific area of downtown lexington. additionally, analysis of ems data revealed that a large portion of these overdoses were being admitted for observational care and thus not being captured in sys data (based on the primary hospital’s submission types). from a practical standpoint, the rapid surveillance results only took 1-2 days to complete and highlight the utility of these data systems in preparing rapid data products. the results of the analysis were shared with local and state health department authorities, including the local emergency medical advisory board. the geospatial analysis provided local authorities with information to enable precise targeting of public health and public safety messaging. conclusions by analyzing data from these systems, we were able to quickly identify the geographic areas and demographic groups that were most affected, and to describe trends in syncan overdoses over time. as a result, we were able to provide highly-detailed data to local public health and public safety authorities to inform their response. figure 1. figure 2. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords synthetic marijuana; ems; geocoding; surveillance; serenity acknowledgments we acknowledge and thank the following agencies for their support of this work: the kentucky department for public health, the kentucky health information exchange, lexington-fayette county health department, and the kentucky board of emergency medical services. references 1. nolan, m. l., allen, b., kunins, h. v., & paone, d. (2016). a public health approach to increased synthetic cannabinoid-related morbidity among new york city residents, 2014-2015. international journal of drug policy, 34, 101–103. https://doi.org/10.1016/j. drugpo.2016.05.014 *peter j. rock e-mail: pjrock2@uky.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e174, 2018 microsoft word ojphi-11-e7 piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi piloting electronic case reporting for improved surveillance of  sexually transmitted diseases in utah  amanda whipple1, joseph jackson1,*, joshua ridderhoff 2, allyn k. nakashima1 1. utah department of health, salt lake city, ut, usa  2. utah department of technology services, salt lake city, ut, usa  abstract  objectives:  the  utah  department  of  health  (udoh)  developed  an  electronic  case  reporting  (ecr)  process to automatically transfer clinical data from a provider to the state health department, with  aims of improving sexually transmitted disease (std) surveillance data quality, decreasing the time  spent on std case investigations, and expanding the process to other diseases and larger healthcare  systems.  methods: reportable conditions trigger codes (rctc) were placed into the electronic health record  (ehr) system at planned parenthood association of utah (ppau) to trigger the automatic transfer of  clinical data to utah’s public health surveillance system. received data were de‐duplicated, processed,  and assigned directly to the public health surveillance system, with minimal manual intervention.  results: eighteen new data elements, important for std case investigations, were transferred to cases  with ecr. additionally, the clinical time spent transmitting data was vastly reduced. with the new ecr  process more complete and timely data is received by public health. providers, as well as public health,  now spend less time manually transmitting clinical data by fax and/or phone.  discussion:  automated  processes  are  challenging  but  can  be  achieved  with  a  robust  disease  surveillance  system,  flexible  rules  engine,  skillful  programming,  on‐going  analysis,  and  successful  partnerships. the ecr process created for this project can potentially be useful for other conditions  outside of stds.  conclusion: results of this demonstration project offer an opportunity for readers to learn about ecr  and apply lessons learned to improve their existing ecr systems, or future public health informatics  initiatives, at any state‐level jurisdiction.  key words: electronic case reporting, ecr, electronic health record, ehr, std surveillance  abbreviations:  application  programming  interface  (api),  chlamydia  trachomatis  (ct),  clinical  document architecture (cda), consolidated clinical document architecture (c‐cda), continuity of care  document  (ccd),  electronic  case  reporting  (ecr),  electronic  health  record  (ehr),  electronic  laboratory  reporting  (elr),  electronic  message  staging  area  (emsa),  extensible  markup  language  (xml),  human  immunodeficiency  virus  (hiv),  international  classification  of  diseases  (icd),  logical  observation identifiers names and codes (loinc), neisseria gonorrhoeoa (gc), planned parenthood  piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi introduction case reports of reportable sexually transmitted diseases (stds) and additional clinical information needed to conduct and close case investigations have historically been manually recorded on paper and sent to the utah department of health (udoh) through fax or email. this process can yield incomplete or inaccurate reports and often requires extensive and time-consuming follow-up by local health department (lhd) investigators to obtain all the information necessary to sufficiently complete case investigations [1,2]. with the introduction of electronic laboratory reporting (elr) in utah in 2013, laboratory reports of disease were available to udoh more quickly than with a paper-based and manual data entry system. elr is now implemented in many laboratories and state health departments nationwide, and although the elr process greatly reduces the burden of disease reporting for laboratories and providers, information needed for public health investigations to complete case reports is often still incomplete or missing altogether; therefore, follow-up with providers, as well as patients, may be necessary. in order to adequately close std cases in utah, further investigation must often be performed to collect complete information on patient demographics, facilities, providers, treatments, pregnancy status, and other relevant variables. utah is a low-morbidity state for chlamydia trachomatis (ct), neisseria gonorhoeae (gc), human immunodeficiency virus (hiv), and syphilis [3]; therefore, at the time of this pilot project, case investigations were initiated for all laboratory confirmed cases. the information required by udoh to adequately close a case investigation includes data not transmitted from the main laboratory that processes the majority of planned parenthood association of utah’s (ppau’s) specimens in utah. thus, recording the necessary case information required phone calls and faxes between utah providers and lhd investigators for obtaining clinical data. many clinical providers, including ppau, now employ electronic health record (ehr) systems in their practice. ehr data contain a wealth of patient information, including the data needed to close std case investigations in utah. as ehrs become more commonly used, electronic case reporting association of utah (ppau), reportable conditions trigger codes (rctc), secure file transfer protocol  (sftp), sexually transmitted disease (std), systemized  nomenclature of medicine (snomed), std  surveillance network (ssun), time‐motion (t‐m), utah department of health (udoh).  *correspondence: joejackson@utah.gov  doi: 10.5210/ojphi.v11i2.9733  copyright ©2019 the author(s)  this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics.  readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the  copy and the copy is used for educational, not‐for‐profit purposes.  piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi (ecr) as demonstrated in this report could provide an opportunity to obtain important clinical data electronically with the same automation benefits seen through elr [4,5]. background of ecr ecr is the automated process of generating and transmitting case report data from ehrs to public health. ecr affords the opportunity to enhance data obtained through elr while eliminating the need to manually acquire clinical data for epidemiologic investigations. ecr shows promise for capturing more complete and accurate clinical data as well as timelier detection and reporting of cases [6]. additionally, ecr can facilitate the formation of a robust and adaptable infrastructure to support rapid reporting of emerging conditions [2]. one common way ecr data are transmitted is through a continuity of care document (ccd). ccd is one of the document types defined by consolidated clinical data architecture (c-cda), a standardized method of implementing the clinical data architecture (cda) standard [7]. by using standard extensible markup language (xml) data elements defined by cda, a ccd records a patient’s history of care including procedures performed, clinical diagnoses, and prescribed medications in a standardized format. reporting facilities using a meaningful use stage 2compliant ehr system can automatically transmit these data to public health via a ccd where they can be consumed by, and imported into, a systematized electronic messaging interface to extract clinical data important for case reports, case investigations, and case closures. goals of ecr implementation in utah through a competitive process, utah secured funding through the std surveillance network (ssun) supported by the centers for disease control and prevention (cdc) to demonstrate the feasibility of implementing an ecr process with ppau, a high-volume reporter of ct and gc cases in utah. the goals of the ssun demonstration project were to route ehr data from ppau directly to udoh’s public health surveillance system (epitrax ®), with aims of improving std surveillance data timeliness and completeness, decreasing the time spent on std case investigations, and expanding the ecr infrastructure to other diseases and larger healthcare systems in the future. methods as the ccd is a natively exportable document from meaningful use stage 2-compliant ehr systems, the most functional way to obtain the necessary clinical data was to have ppau’s ehr vendor configure a daily export of ccds based on implemented reportable conditions trigger codes (rctcs). once an rctc was detected, the ehr system at ppau (nextgen ®) was triggered to automatically send the relevant ccd to udoh through a secure file transfer protocol (sftp). once received at udoh, the ccds were parsed into individual patient encounters, and then parsed again within the public health surveillance system by international classification of diseases (icd-10), logical observation identifiers names and codes (loinc), and systemized nomenclature of medicine (snomed) codes, which are diagnostic, laboratory testing, and clinical codes applied to all patient encounters. any records found with icd-10, loinc, and/or piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi snomed codes specific to ct or gc were then processed through the electronic message staging area (emsa). emsa is a sophisticated rules engine that de-duplicates cases, determines diseases and conditions, completes person-matching functions, and links laboratory results to disease cases. a detailed explanation of how the actual transport process was achieved is described below. message processing loinc, icd-10, and snomed triggers based on the rctc were implemented in ppau’s ehr. when a patient’s encounter included an identified trigger, a ccd for that patient was sent to udoh’s sftp server. ppau sent daily batches of all cases that contained the implemented triggers specific to ct and gc cases (hiv and syphilis cases were implemented after ct and gc, since historically ppau has a lower volume of those diseases). once received, a health data interchange application (nextgen connect integration engine) was used for initial message filtering, pre-processing, parsing, and routing. ccd documents, by design, contain the patient’s entire clinical history within the reporting facility. this historical clinical data may be re-sent repeatedly over an extended period of time; thus it was vital to limit which data were routed for importation from each received ccd to reduce duplication of data in epitrax. individual patient encounters were first identified in the ccd and split into an intermediate custom xml document, known as a “simplified ccda encounter,” with all relevant data from the original ccd linked to that encounter included. any encounters that were deemed to be irrelevant due to the age of the encounter in relation to the encounter of interest were filtered out. nextgen connect was also used to translate icd codes representing clinical diagnoses within each encounter during this processing. icd codes indicating pregnancy were used to populate pregnancy status in the “simplified ccda encounter” document. icd codes indicating a condition of interest (ct, gc, hiv, or syphilis) were mapped to a disease area of the “simplified ccda encounter.” any remaining icd codes that did not indicate a relevant concept were discarded. the mapping of icd codes was configured in emsa, and any unidentified icd codes were flagged in the emsa interface for resolution by udoh staff (figure 1). next, each “simplified ccda encounter” was routed to emsa. emsa is a complex web application that allows informaticists and epidemiologists to define criteria based on case definition rules to determine how electronic health messages should be used to affect case records in epitrax (figure 2). emsa uses these rules in coordination with an application programming interface (api) provided by epitrax to perform automated person matching and determine when to create or update cases in epitrax for investigation or surveillance. using these rules, emsa organizes data associated with each condition identified via triggered loinc, snomed, or icd codes. once emsa has determined that it has data relevant to a condition of interest, it first attempts to locate a matching case in epitrax. if ambiguous search results are encountered, the message can be diverted for review by udoh staff. if an existing case is identified, the new clinical data is compared against existing values known, and an evaluation is made as to whether the new data should update existing values or if duplicate information can be discarded. if no existing case is identified, emsa can trigger the creation of a new case in epitrax. piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi finally, emsa uses the epitrax api to append clinical data elements of interest to cases in epitrax, either via applicable defined data fields, or as case notes where necessary (figure 3). the epitrax api further works to prevent duplication of data in cardinal data elements, such as phone number, address, and treatment entries. treatment data not related to the case were also filtered out through the api. emsa has the ability to complete the above processes automatically and continually for nearly all of the large volume of data received by ecr. in addition to determining conditions and linking laboratory information to cases, one of the most important functions of emsa was the ability to de-duplicate cases. on average, udoh received 70 ccd messages daily, which were then parsed into a daily average of 235 individual sets of clinical data associated with a triggered condition. this highlights the need for a robust, automated rules engine to eliminate the need for manual review of all messages. figure 1: icd code management interface   piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi figure 2: condition rules management interface   piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi figure 3: udoh electronic message processing workflow   results starting in october 2016, clinical data were automatically electronically transferred from ppau’s ehr to udoh’s public health surveillance system. below are descriptions of the impact of the successful implementation of ecr on the quality of std case reports at udoh, including impacts on completeness, timeliness, and volume of reporting, as well as a description of the results of time-motion studies. completeness eighteen data elements important for std case investigations were provided by ecr and imported into epitrax when information was new or updated; none of this data was previously transmitted through elr from the laboratory that processes the majority of ppau’s std specimens (table 1). among the most important elements transmitted for local health investigators were patient demographics, treatment information, pregnancy status, and facility/clinician data. emsa xml (c) nextgen  connect split on  triggers rules holding (d) discard (e) epitrax xml (f) pre‐processed  message (b) epitrax api epitrax (g) original  message (a) emsa piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi table 1: new ecr data elements patient street pregnancy status patient unit number treatment name patient city treatment dosage patient zip code treatment quantity patient area code and phone number treatment start date patient ethnicity treatment stop date patient race clinician name patient birth sex clinician area code and phone number medical record number clinical diagnosis/condition from icd code timeliness more timely information was also available through ecr. when relying solely on elr, the average time for udoh to obtain all the relevant clinical information necessary to close out a std case could take over a week; however, after implementing ecr, the average time for udoh to receive all relevant clinical information was 4 hours and 34 minutes, followed by an average time to transfer those clinical data elements into epitrax of 3 hours and 24 minutes. all messages were received from ppau at udoh within 18 hours of generation (figure 4), with the majority of messages processed within one hour of receipt (figure 5). message receipt was calculated from the time the message was generated at ppau to receipt by the udoh integration engine. messages taking longer than five hours to receive were batches of files delayed in transfer by system issues between ppau and udoh’s sftp server or the transfer from the sftp server to the integration engine. manual intervention was required for some messages due to receiving novel coded vocabulary terms (requiring udoh staff to define the terminology), unexpected changes to message structure by vendors, equivocal person matching results, or incomplete data necessary to complete automated rule evaluation (in some cases requiring follow-up with providers). messages requiring manual intervention for any of the above issues took longer to resolve and complete processing than those processed automatically; however, only 194 system messages took more than 12 hours to process. piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi figure 4: time for udoh to receive ccds from ppau   figure 5: time from receipt at udoh until case is updated 0 5000 10000 15000 20000 25000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 n u m b e r  o f  c c d s time (hours) 0 1000 2000 3000 4000 5000 6000 7000 1 2 3 4 5 6 7 8 9 10 11 12 >12 n u m b e r  o f  m e ss a g e s time (hours) piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi time-motion studies for this project, time-motion (t-m) studies were conducted before and after the implementation of ecr to assess time saved by automating data transfer. ppau clinical time spent on faxes, emails, phone calls, paperwork, and ehr usage necessary to send clinical data to local health investigators was collected in 2014 prior to ecr implementation. results of the initial t-m study showed that prior to ecr implementation, ppau staff were conservatively spending 4.22 minutes per case of ct and/or gc to manually send clinical data to udoh (hiv and syphilis were not included in the study). as ppau diagnoses approximately 2,300 ct and gc cases yearly, this equates to nearly 162 hours being spent annually on this manual process. results of the follow-up t-m study conducted in 2018, after the implementation of ecr, showed that the time spent by ppau staff to transmit data to udoh per case of ct and/or gc decreased to 0.04 minutes (2.4 seconds) per case. this equates to roughly 1.5 hours annually to transmit std clinical data to udoh, a time savings of approximately 161 hours per year across the eight ppau clinics. volume as of august 31, 2018, the total number of std cases that have been updated with clinical information since the process became functional is 2,735 and increases daily. the volume for the below brief analysis is only for eight clinics (all ppau clinics in utah) and only four conditions (ct, gc, syphilis, and hiv). it should be noted that utah has a non-positive laboratory reporting rule for ct, gc, syphilis, and hiv confirmatory tests, meaning that positive as well as negative, indeterminate, and inconclusive laboratory reports of the aforementioned conditions are reported to udoh. this helps account for the large volume of ccd encounters that are moved to a holding area where the information is held for 18 months for future case matching. after 18 months the information is stripped of identifiable data but aggregate non-positive data is retained. the brief analysis of cases updated automatically with clinical data from october 1, 2017 to august 31, 2018 is as follows (table 2): table 2: ccd disposition breakdown, 10/01/2017 08/31/2018 ccds received by udoh (figure 3 (a)) 23,508 ccds containing at least one useable trigger code (icd, loinc, or snomed) (figure 3 (b)) 19,696 ccds processed and discarded due to unusable trigger codes (i.e. relevant but generic trigger codes that could not be mapped to a condition in epitrax) (figure 3 (e)) 3,812 system messages created from useable trigger codes; messages were split into patient encounters and split again by icd/loinc/snomed trigger codes as there could be multiple trigger codes in one encounter (figure 3 (c)) 79,567 piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi messages sent to the holding area; mostly non-positive test results (99.9% negative) (figure 3 (d)) 69,793 messages assigned to epitrax for investigation (figure 3 (f)) 8,033 messages deleted due to irrelevant treatment, cases older than 30 days, etc. (figure 3 (e)) 1,741 total std cases updated with clinical information (figure 3 (g)) 2,735 discussion despite successfully demonstrating that clinical data from ehrs can be automatically electronically transmitted directly to a public health surveillance system with little manual intervention, many challenges still exist in correctly and efficiently implementing ecr. the most common issues encountered were related to implementing correct trigger codes, defining when to trigger ccds for multiple patient encounters, linking treatment to encounters, and understanding the structure of the ehr as it related to the public health surveillance system. though some of what is described below is specific to this study, the lessons learned throughout the implementation of ecr in utah can be translated to other settings and are provided as guidance for readers to note when starting to implement ecr in their own jurisdictions. implementing trigger codes the transition of icd codes from icd-9 to icd-10 presents mapping issues, as both new and existing codes need to be added to rules engines to ensure all possible triggers are included. additionally, the use of icd-9 or icd-10 codes for the same disease may vary by provider; thus, an extensive list must be programmed within the ehr and the public health surveillance system, inclusive of all possible diagnostic codes that could be used. challenges can also be presented when providers do not use a code considered useful by public health (e.g., a generic or local code that cannot be mapped to a specific disease in the public health surveillance system) or if a provider misses adding a code altogether to a patient’s ehr encounter. in some limited situations, emsa was able to leverage the ability of the rules engine to update multiple cases in the public health surveillance system simultaneously and append clinical data from trigger codes that identify a family of conditions to all cases under that family. for example, clinical data from a ccd triggered by an icd-10 code of “z20.2” (contact with and (suspected) exposure to infections with a predominantly sexual mode of transmission) could be applied to both ct and gc cases under the same patient, if found. piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi defining when to trigger challenges also arise when trying to determine trigger timing. for this project, these challenges were overcome by working closely with ppau and their ehr vendor to determine at what point during a patient encounter (or a patient with multiple encounters) a provider adds a code to a patient chart and when that information should be triggered to public health. analysis of ppau staff workflow and usage of the ehr system led to determination of trigger timing that allows complete capture of the necessary data. this resulted in multiple ways that the trigger codes need to be checked for validity. message generation was triggered when a relevant code was used in a new or updated encounter or when a laboratory result was entered for the person. clinical practice also had to be modified in that all ppau users of the ehr were trained to record icd codes deemed usable by public health. linking treatment to encounters the general layout of ppau’s ehr contains both a patient management system as well as a financial management system, both of which contain information specific to std cases reported to public health. in addition, treatment information was especially problematic, as treatment administration is directly associated with a specific encounter, and situations commonly arose where multiple encounters were recorded for a single patient visit. especially in cases where treatment was administered for prophylaxis, encounters for treatment were often distinct from encounters with confirmatory diagnoses and did not contain trigger codes that could be used to link the treatment to a specific condition. further, because “medications prescribed” and “medications administered” were captured in multiple areas of the ehr, there was a lengthy process to correctly identify, map, and capture treatments for patients. issues also arose with unrelated treatments being administered in the same encounter as treatments for stds (e.g., azithromycin and ibuprofen for ct). udoh was able to overcome this challenge through a combination of flexible vocabulary mapping in the rules engine and by implementing restrictions within the public health surveillance system. treatment codes reported via ecr could be mapped to treatment values that were on an approved list of treatments for specific std conditions; those treatments that did not map to one of these treatments would then be excluded and not added to the case. understanding the ehr structure for this project, it was noted that the laboratory data transmitted from the ehr was insufficient in the ccd, as there are missing fields such as “specimen source”, “collection date”, and “accession number”. while there was sufficient information to determine condition and test result status from laboratory data, there was not sufficient data present to reliably de-duplicate laboratory results against results that may have already been entered. additionally, laboratory results sent from ppau via ccd were not linked to encounters via a unique identifier specific to the encounter, and could only be loosely associated to an encounter via date. as there were often multiple encounters for the same date, this would have resulted in an excess of duplicate laboratory data for the rules piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi engine to filter out. thus, ecr in this case serves as a supplement to elr, and was not used to directly add laboratory results to cases. another issue that was discovered was the use of custom templates. since the design of an ehr system is meant to be used across many healthcare settings, capturing detailed social and sexual history at ppau required the creation of custom templates. those pieces of data, however, could not be exported in the ccd. pregnancy status presented unique challenges as the only way to link pregnancy and a positive case of disease was if the patient received a pregnancy test on the same encounter that also had a trigger code that clearly identified a ct/gc diagnosis, or with an encounter on the same date as a positive ct/gc laboratory test. further, because pregnancy is a condition that can change over time, and investigators only need to know if a patient was pregnant at the same time as the ct and/or gc diagnosis, it was impossible to use the ehr data to set or change the pregnancy status of a case at a single point in time. rather, a cardinal “electronic pregnancy report” data element was added to relevant patients any time a relevant diagnosis code was received that would record a point-in-time pregnancy status. the list of “electronic pregnancy reports” could be viewed in its entirety for all events associated with that person to allow investigators to determine pregnancy status at a single point in time relevant to the specific case in question. limitations limitations were identified during this demonstration project, several of which were previously discussed in detail. many, although not all, of these limitations should decrease over time as ecr is more broadly implemented. the most notable limitations for the described ecr approach includes: public health being constrained by what providers send, ccd and ehr structure, dependency on correct and complete trigger code usage, and message creation timing. public health can only use and process what a provider can and is willing to send. the ccd was the most comprehensive and efficient standard available for this project. the ehr system was not capable of generating a message specific to public health. the structure of the ehr and ccd limited udoh’s ability to capture all the desired information. custom items, such as, social and sexual history, were not in a structured area of the ehr and were not included in the ccd standard and therefore could not be exported. lab test date (and other important laboratory information) was not included in the generated ccd for this project. several assumptions had to be made regarding the relationships of data due to the way the data were structured in both the ehr and ccd. exported information from an ehr is only as good as what the provider inputs. if a provider entered an incorrect code, a code that was not useful, or forgot to use a code, that information could not be exported or linked to other relevant information. the timing of when to trigger information for an event presented many issues. if a patient had multiple encounters for a single issue, defining when trigger codes must be used and messages generated could be extremely complicated. piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi future work and opportunities although challenges existed, many positive outcomes resulted from this project. in addition to realizing the initial goals of decreasing investigation time, improving the completeness, timeliness, and volume of std data, and receiving data not provided by elr, a better understanding and more efficient way of working was realized at udoh and within ppau clinics. the partnership between public health and clinical practice was strengthened, as well as a better understanding of what each group requires to work efficiently and how to work together to achieve something beneficial for all parties. this was a lasting outcome not originally included in the project design. the ecr process created and demonstrated in this project, though limited in scope by disease, will prove to be useful for other conditions that require provider diagnosis and/or clinical symptom observation for case classification (e.g., pertussis or zika virus). some examples of how this ecr process can be expanded beyond communicable disease include silicosis monitoring for occupational health, opioid overdose tracking for injury prevention, and blood pressure, bmi, and blood glucose supervision for chronic disease surveillance. this project demonstrated the feasibility of ecr from a single provider with eight clinics. ecr infrastructure was developed with the intent to enable expansion beyond small providers into larger healthcare systems. the automation capabilities demonstrated will be crucial for healthcare systems reporting more diseases and in higher volumes. conclusion when this project commenced udoh was in the distinctive position of figuring out the unknown. though the feasibility of harnessing clinical data from ehrs and transmitting that data automatically to a public health surveillance system was demonstrated, it was also discovered that ecr is much more sophisticated and technically challenging than merely pressing a button to export information. infrastructure needs to be created and refined (in terms of a dynamic public health surveillance database with a complex rules engine); programming needs to be analytical and on-going; clinical usage of the ehr may require provider retraining; timely communication between public health, the ehr vendor, and the clinical partner is crucial for success; and all these processes must be evaluated continuously. as a result of all these lessons learned, this demonstration project proved successful in creating a sustainable process that clinical practice can utilize to improve std case report data in utah. the successes achieved in this project have established a strong foundation for udoh to receive and process clinical data via ehrs which will serve as a very important knowledge base for future work with ecr initiatives, both internally and externally. this pilot project should be seen as a model for other agencies to use when initiating ecr activities in their jurisdictions. acknowledgements penny davies, lesley bailey, and fred pennington, planned parenthood association of utah kristen kreisel and mark stenger, centers for disease control and prevention piloting electronic case reporting for improved surveillance of sexually transmitted diseases  in utah    online journal of public health informatics * issn 1947‐2579 * http://ojphi.org * 11(2):e7, 2019  ojphi utah department of health, disease control and prevention informatics program utah department of technology services financial disclosure funding provided through a cooperative agreement with the u.s. centers for disease control and prevention, ps13-1306 std: surveillance network. competing interests no competing interests. references 1. kirbiyik u, dixon d, grannis s. effect of electronic health record systems access on communicable disease report completeness. online journal of public health informatics. issn 1947-2579. http://ojphi.org. 2014; 6(1). 2. mac kenzie w, et al. 2016. the promise of electronic case reporting. public health rep. 131(6), 742-46. pubmed https://doi.org/10.1177/0033354916670871 3. centers for disease control and prevention. sexually transmitted disease surveillance 2017. atlanta: u.s. department of health and human services; 2018. 4. klompas m, et al. harnessing electronic health records for public health surveillance. online journal of public health informatics. issn 1947-2579. http://ojphi.org. 2011; 3(3). 5. roberts e, et al. automated processing of electronic data for disease surveillance. online journal of public health informatics. issn 1947-2579. http://ojphi.org. 2018; 10(1). 6. birkhead g, klompas m, shah n. 2015. public health surveillance using electronic health records: rising potential to advance public health. front public health serv syst res. 4(5), 25-32. doi:10.13023/fphssr.0405.05. 7. health level seven international. 2018. hl7 cda r2 implementation guide: consolidated cda (c-cda) templates for clinical notes (us realm) dstu release 2.1 (with errata). available at: https://www.hl7.org/implement/standards/product_brief.cfm?product_id=408. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts integrating data from disparate data systems for improved hiv reporting: lessons learned kamran ahmed*1, yvette temate−tiagueu1, joseph amlung2, dennis l stover2, philip j peters1, john t brooks1, sridhar papagari sangareddy1 and jina j dcruz1 1centers for disease control and prevention, atlanta, ga, usa; 2indiana state department of health, indianapolis, in, usa objective to assess the integration process of hiv data from disparate sources for reporting hiv prevention metrics in scott county, indiana introduction in 2015, the indiana state department of health (isdh) responded to a large hiv outbreak among persons who inject drugs (pwid) in scott county1. information to manage the public health response to this event and its aftermath included data from multiple sources such as surveillance, hiv testing, contact tracing, medical care, and hiv prevention activities. each dataset was managed separately and had been tailored to the relevant hiv program area’s needs, which is a typical practice for health departments. currently, integrating these disparate data sources is managed manually, which makes this dataset susceptible to inconsistent and redundant data. during the outbreak investigation, access to data to monitor and report progress was difficult to obtain in a timely and accurate manner for local and state health department staff. isdh initiated efforts to integrate these disparate hiv data sources to better track hiv prevention metrics statewide, to support decision making and policies, and to facilitate a more rapid response to future hiv-related investigations. the centers for disease control and prevention (cdc) through its info-aid mechanism is providing technical assistance to support assessment of the isdh data integration process. the project is expected to lead to the development of a dashboard prototype that will aggregate and improve critical data reporting to monitor the status of hiv prevention in scott county. methods we assessed six different hiv-related datasets in addition to the state-level integrated hiv dataset developed to report hiv monitoring and prevention metrics. we conducted site visits to the isdh and scott county to assess the integration process. we also conducted key informant interviews and focus group discussions with data managers, analysts, program managers, and epidemiologists using hiv data systems at isdh, scott county and cdc. we also conducted a documentation review of summary reports of the hiv outbreak, workflow, a business process analysis, and information gathered during the site visit on operations, processes and attributes of hiv data sources. we, then, summarized the information flow, including the data collection process, reporting, and analysis at federal, state and county levels. results we have developed a list of lessons learned that can be translated for use in any state-level jurisdiction engaged in hiv prevention monitoring and reporting: standardization of data formats: the disparate data sources storing hiv-related information were developed independently on different platforms using different architectures; they were not necessarily designed to link and exchange data. hence, these systems could not seamlessly interact with each other, posing challenges when rapid data linkage was needed. to better manage unstructured data coming from disparate data sources and improve data integration efforts, we recommend standardization of data formats, unique identifiers for registered individuals, and coding across data systems. use of standard operating procedures can streamline data flow and facilitate automated creation of integrated datasets. this approach may be helpful for future integration efforts in other healthcare domains. data integration process: manually integrating data is time intensive, increases workload, and poses significant risk of human error in data compilation. hence, it may compromise data quality and the accuracy of hiv prevention metrics used by decision-makers. we propose an automated integration process using an extract, transform and load (etl) method to extract hiv-related data from disparate data sources, transforming it to fit the prevention metrics reporting needs and loading it into a state-level integrated hiv dataset or database. this approach can drastically decrease dependency on manual methods and help avoid data compilation errors. dashboard development: major challenges in the process of integrating hiv-related data included disparate data sources, compromised data quality, and the lack of standard metrics for some of the hiv-related metrics of interest. despite these challenges to data integration, creation of a dashboard to track hiv prevention metrics is feasible. integrating data is a critical part of developing an hiv dashboard that can generate real-time metrics without creating additional burden for the health department staff, if manual integration is no longer needed. stakeholder participation: due to the immediate need for outbreak response, involvement of stakeholders at all levels was limited. active stakeholder engagement in this process is essential. the stakeholders’ interest and participation can be improved by helping them understand the value of each other’s data, and providing regular feedback about their data and its best use in public health interventions. conclusions this assessment highlighted the importance of standardizing data formats, coding across systems for hiv data, and the use of unique identifiers to store individuals’ information across data systems. promoting stakeholder understanding of the value and best use of their data is also essential in improving data integration efforts. the results of this assessment offer an opportunity to learn and apply these lessons to improve future public health informatics initiatives, including hiv (but not limited to hiv), at any state-level jurisdiction. keywords integration; hiv; dashboard; data-systems references 1.peters pj, pontones p, hoover kw, patel mr, galang rr, shields j, et al. hiv infection linked to injection use of oxymorphone in indiana, 2014-2015. n engl j med. 2016;375(3):229-39. *kamran ahmed e-mail: drkamranrajput@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e49, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis johns hopkins university applied physics laboratory, laurel, md, usa objective the objective of this presentation is to describe the new federated query capability in essence and describe how this could affect public health practice in the future. specifically, this presentation will describe how a federated set of disease surveillance systems across the country could help improve national disease surveillance situational awareness along with its potential to connect non-essence systems in the future for even more complete coverage. it will also describe how this capability is different than other data sharing projects that attempt to centralize data, but how there is room for both to benefit from each other. introduction there are currently over 25 installations of essence across the us. among these, there are 3 instances of multi-jurisdictional implementations. these include a centralized regional system in the national capital region for md, dc, and va, a missouri system that includes hospitals and users from the st. louis area in illinois, and soon the national syndrome surveillance program (nssp) version of essence which will centralize data from many jurisdictions. while each of these systems provides valid ways to share data across jurisdictions, they require data to be sent to another jurisdiction. there are some jurisdictions which have legal or philosophical or technical issues with these types of data sharing arrangements. programs like distribute attempted to solve this by only sharing pre-aggregated data. this caused issues though for surveillance of new and emerging issues that requires a more ad-hoc query capability. this gap can be filled with a locally-ran system that has the ability to perform queries into remote systems and perform a federated query across other jurisdictions. methods a new capability has been developed for essence systems that allow local administrators to publish their data sources for use by another jurisdictions essence users. when this occurs, all data remains in the local essence system, but other essence users can send requests to that system. these requests, such as time series or data details requests, can be accepted or denied based on access control permissions on an individual user or jurisdiction basis. using this new technology that can connect essence systems to other essence systems, it will become possible technically to query for new and emerging disease trends by performing ad-hoc queries across many different jurisdictions. additionally, any current multijurisdictional system, such as the future nssp essence system, would also be able to technically participate in this new federate query framework. this provides local systems, which may want a customized essence system, the ability to share with multijurisdictional systems, which may provide essence to jurisdictions that can’t support a local system. results while the technology to perform federated queries across essence systems has now been developed, it will not be deployed into an active system until late 2015. early results on how the system has performed can be presented then. however, the potential ramifications of having this technology will be discussed along with potential future enhancements to support non-essence disease surveillance system federated query support. conclusions data and information sharing is not simple. many jurisdictions desire it and many projects have succeeded in many aspects of it. this is another piece of the data and information sharing puzzle that will allow jurisdictions that prefer not to centralize their data to still participate in a data and information sharing collaboration. keywords data sharing; information sharing; essence; federated queries *wayne loschen e-mail: wayne.loschen@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e22, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 1office of public health, department of veterans affairs, palo alto, ca, usa; 2bitscopic, inc., redwood city, ca, usa objective to compare syndromic surveillance alerting in va using praedico™ and essence. introduction many methods to detect outbreaks currently exist, although most are ineffective in the face of real data, resulting in high false positivity. more complicated methods have better precision, but can be difficult to interpret and justify. praedico™ is a next generation biosurveillance application built on top of a hadoop high performance cluster that incorporates multiple syndromic surveillance methods of alerting, and a machine-learning (ml) model using a decision tree classifier [1] evaluating over 100 different signals simultaneously, within a user friendly interface. methods 513 million va patient records, incorporating over 5 years of syndromic surveillance, were analyzed for identical syndromic surveillance groupings from outpatient icd-9 diagnosis codes. the praedico™ ml layer was trained by utilizing hundreds of false positive and true positive syndromic alerts. to guarantee high detection recall, the praedico™ algorithm leverages many known detection algorithms, including versions of cdc, cusum, ewma, and regression models [2]. the ml model combines these models and uses additional time series features to detect anomalies and user feedback received on previous alerts (high confidence alerts). low confidence alerts, which many not trigger an alert but show a natural increase or normal divergence from the mean in data distribution are presented for users review. to facilitate alert interpretation, a natural language interface provides human interpretable messages relaying event significance by comparing historical values collected from the same facilities. praedico™ syndromic alerts were compared to those generated by va essence for the period of june 2014 thru may 2015. results praedico™ alerts were significantly lower compared to essence generated alerts (table 1). this was expected as praedico™ leveraged user feedback to enhance anomaly detection and improve precision of outbreak detection. both praedico™ and essence categorized alerts as high and low confidence groups. in both systems, higher deviation levels from expected values resulted in high confidence alerts. 62% of praedico™ alerts directly correlated with essence alerts, suggesting that although the total number of praedico alerts was smaller, they were not simply a subset of essence alerts. praedico™ demonstrated higher seasonal sensitivity, adjusting for seasonality using historical and seasonal information, while essence alerts were more uniformly distributed over the year (figure 1). the increased december and january alerts were due to ili syndrome alerts, likely due to elevated influenza activity. respiratory, fever, and ili syndrome groups had the highest number of alerts, and were significantly higher with essence (figure 2). conclusions praedico™ demonstrated improved precision of surveillance syndrome clusters compared to va essence by reducing the number of alerts. by reducing alerting fatigue, users’ sensitivity to computergenerated alerts remain high, which in return results in further usage, feedback, and more gradual improvement in the algorithm’s output (specificity and sensitivity), adapting to the interest of users. table 1. total number of va syndromic alerts generated by praedico biosurveillance and essence figure 1. percentage of va total alerts by month for praedico and essence figure 2. syndromic distribution of praedico and essence alerts keywords essence; biosurveillance; veterans; big data references 1. classification and regression trees. l. breiman, et al., wadsworth, belmont, ca, 1984. 2. algorithms for rapid outbreak detection: a research synthesis. buckeridge, david l. et al., journal of biomedical informatics, volume 38, issue 2, 99 – 113. *mark holodniy e-mail: holodniy@stanford.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e169, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed 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programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts how can we assess the effects of urban environment on obesity using aggregated data? seon-ju yi1 and changwoo shon*2 1seoul national university, seoul, korea (the republic of); 2seoul institute, seoul, korea (the democratic people’s republic of) objective this study aimed to assess the effects of urban physical environment on individual obesity using geographically aggregated health behavior surveillance data applying a geo-imputation method. introduction ‘where we live’ affects ‘how we live’. information about ‘how one lives’ collected from the public health surveillance data such as the behavioral risk factor surveillance system (brfss). neighborhood environment surrounding individuals affects their health behavior or health status are influenced as well as their own traits. meanwhile, geographical information of subjects recruited in the health behavior surveillance data is usually aggregated at administrative levels such as a county. even if we do not know accurate addresses of individuals, we can allocate them to the random locations where is analogous to their real home within a locality using a geo-imputation method. in this study, we assess the association between obesity and built environment by applying random property allocation (1). methods data from the korean community health survey (kchs), which is the nationwide community-based cross-sectional survey conducted by 253 community health centers in south korea, were used (2). more than 90000 subjects recruited in the capital city seoul from 2011 to 2014. they were selected by two-step stratified random sampling (424 administrative communities with an average area of 1.16km2 and two house types) in each 25 counties. we re-allocated them randomly on the nested locality based on their community (administrative boundaries) and hose type (land-use) using gis program (figure 1). surrounding built environment elements such as fast-food markets, driving roads, public transit and road-crosse were measured within 500m buffer from randomly allocated locations as density or distance. variables associating obesity are measured by : 1) self-reported obesity (self-reported body mass index(bmi) ≥ 25) (figure 2), 2) perceived obesity, 3) intention to weight control. we implemented logistic regression models to estimate the effect of physical environmental factors on obesity. results the person who lives in a detached house, nearer fast food markets or with higher driving road density was more likely to be obese. those who live in a detached house was less perceived their obesity. those who live in a detached house, nearer fast food markets or with higher driving road density was less likely to intend to control their body weights. an association between intention to weight control and accessibility to subway station showed marginal effect. conclusions urban environments influenced individual’s obesity, perception, and intention to weight loss. since we used cross-sectional survey data, we do not account cumulative environmental influence. moreover, individuals’ self-selection of more healthier places were not accounted. even though we did not measure the environment at individuals’ real address, we can measure the effects of neighborhood environment more efficiently by using random property allocation. figure 1. land use map (up) and result of random allocation (down) in daehakcong, gwanakgu, seoul.png distribution of obese population in seoul, south korea keywords random property allocation; brfss; obesity; built environment; south korea acknowledgments this study was supported by the seoul institute and the seoul metropolitan government. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. walter sr, rose n. random property allocation: a novel geographic imputation procedure based on a complete geocoded address file. spatial and spatio-temporal epidemiology. 2013;6:7-16. epub 2013/08/27. doi: 10.1016/j.sste.2013.04.005. pubmed pmid: 23973177. 2. kim yt, choi by, lee ko, kim h, chun jh, kim sy, et al. overview of korean community health survey. j korean med assoc. 2012;55(1):74-83. (in korean) *changwoo shon e-mail: cwshon21@si.re.kr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e14, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts comparison of national and local syndromic surveillance data cook county, il, 2017 zachary heth*2, 1, kelley bemis1 and demian christiansen1 1communicable diseases, cook county department of public health, forest park, il, usa; 2cste applied epidemiology fellowship, atlanta, ga, usa objective this analysis was undertaken to determine how the data completeness, consistency, and other attributes of our local syndromic surveillance program compared to the national syndromic surveillance platform. introduction in 2005, the cook county department of public health (ccdph) began using the electronic surveillance system for the early notification of community-based epidemics (essence) as an emergency department (ed)-based local syndromic surveillance program (lssp); 23 (100%) of 23 hospitals in suburban cook county report to the lssp. data are transmitted in delimited ascii text files (i.e., flat files) and contain a unique patient identifier, visit date and time, zip code, age, sex, and chief complaint. discharge diagnosis and disposition are optional data elements. prior to 2017, the illinois department of public health placed facilities participating in the cook lssp in a holding queue to transform their flat file submissions into a hl7 compliant message; however as of 2017, eligible hospitals must submit hl7 formatted production data to idph to fulfill meaningful use. the primary syndromic surveillance system for illinois is the national syndromic surveillance program (nssp), which transitioned to an essence interface in 2016. as of december 2016, 20 (87%) of 23 hospitals reporting to the lssp also reported to idph and the nssp. as both syndromic surveillance systems aim to collect the same data, and now can be analyzed with the same interface, ccdph sought to compare the lssp and nssp for data completeness, consistency, and other attributes. methods our comparison of nssp to the lssp focused on data completeness for key demographic and medical variables and consistency in total visit counts. analysis of completeness utilized data from december 2016 for 20 hospitals contributing hl7 production data to idph at that time. total visit counts in both systems were compared for the same 20 hospitals from february 5th-11th 2017, a randomly chosen time period. a target threshold of less than 3% difference in total visit counts was set by the ccdph system users. analysis was completed in microsoft excel 2010. other attributes of the surveillance systems were qualitatively assessed by the primary system users at ccdph. results all variables required by the lssp had 98-100% completeness in both the lssp and nssp (unique patient identifier, age, sex, zip code, visit time and date, and chief complaint). however, the lssp optional data elements, discharge diagnosis and discharge disposition, were less complete, compared to the nssp (diagnosis: 56% versus 83%, disposition: 66% versus 80%). among variables required for nssp reporting but not reported to the lssp, completeness ranged from 100% (race, ethnicity) to 82% (county). optional data elements within nssp ranged in completeness from 73% (initial pulse oximetry) to 0% (initial blood pressure, insurance coverage). of the 20 hospitals evaluated for visit counts, only one hospital had <3% difference in visit counts in the lssp and nssp for all 7 days assessed. ten hospitals had >3% difference in visit counts on all seven days. average seven day differences for hospitals ranged from 0% to 54%. eighteen (90%) of 20 hospitals were reporting larger numbers of visits to nssp than to the lssp. conclusions overall completeness of data was similar between the national and our local essence systems with most required variables having over 98% completeness. nssp had higher completeness over the lssp for discharge diagnosis and disposition. additional data elements required by nssp, but unavailable in the lssp, had similarly high completeness but optional nssp variables of interest showed greater variability in reporting. differences in visit counts were higher than expected. an ongoing exploration of these differences has shown they are multifaceted and require hospital-specific interventions. there are strengths and limitations to both the nssp and lssp. ccdph has direct control over data sharing between jurisdictions in the lssp and there has historically been less system “down time” in the lssp compared to the nssp; however, the use of flat files instead of hl7, as well as having fewer incentives for hospital participation (e.g. meaningful use) after 2016, results in limited data collection and stagnant growth compared to the nssp. jurisdictions using their own lssps should consider analyzing their data completeness, consistency, and quality compared to the nssp. keywords nssp; syndromic surveillance; meaningful use; evaluation; hospital acknowledgments we would like to acknowledge the illinois department of public health as well as the national syndromic surveillance program. this study/report was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-04. *zachary heth e-mail: zheth@cookcountyhhs.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e71, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e291, 2019 isds 2019 conference abstracts strategies for clinical decision support for electronic case reporting noam h. arzt1, maiko minami1, daryl chertcoff1, janet hui2 1 hln consulting, llc, palm desert, california, united states, 2 council of state and territorial epidemiologists, atlanta, georgia, united states objective to discuss how clinical decision support (cds) for electronic case reporting (ecr) will evolve over time to provide multiple deployment models introduction as the knowledge required to support case reporting evolves from unstructured to more structured and standardized formats, it becomes suitable for electronic clinical decision support (cds). cds for case reporting confronts two challenges: a) while ehrs are moving toward local cds capabilities, it will take several years for ehr systems to consistently support this capability; and b) public health-related cds knowledge, such as zika infection detection and reporting rules, may differ from jurisdiction to jurisdiction. therefore, there is an ongoing need to manage reporting rules in a distributed manner. similarly, there is a need for more decentralized models of cds execution to overcome some of the disadvantages of centralized deployment and to leverage local cds capabilities as they become available over the next several years. methods the reportable condition knowledge management system (rckms) is a project funded by the cdc, through the council of state and territorial epidemiologists (cste), to develop a tool that allows jurisdictions to author rules that define whether a patient is reportable for certain conditions. rckms includes a decision support service (dss) that runs the jurisdictions’ rules and determines if a patient is reportable, for which condition(s) and to which jurisdiction(s). rckms currently plays a significa nt role in the broader digital bridge project that has been working to provide structure and governance around the national planning and implementation effort of ecr. rckms is currently a centralized cds service that can be accessed by ehrs until they all have local cds capabilities; and a knowledge authoring environment that allows ongoing distributed rule authoring. rckms supports the strategy for public health knowledge management, and it will evolve over time to provide the systems and services to sati sfy short-, mid-, and long-term public health cds requirements. in addition, rckms will comply with emerging technical standards that support this work. results rckms is currently being deployed as a single, central, national service on the aphl informatics messaging services (aims) platform, which is operated and maintained by association of public health laboratories (aphl). the aims platform connects directly with reporters and provides a routing and validation service for incoming and outgoing messages. two distributed cds scenarios for decentralized ecr models have been identified. in the first scenario, the decision support service component of the rckms software is installed within a clinical organization (as it would be in a centralized service) and executed locally. the second distributed cds scenario for ecr involves the distribution of the reporting specification rules without the software. in this scenario, local electronic health record (ehr) implementations would be required to consume the reporting specifications and utilize them in a cds capability within their ehr. conclusions it is expected that given the diversity of organizations, systems, and architectures in the united states that multiple deployment scenarios for cds for ecr will be simultaneously deployed for the foreseeable future. it cannot be stressed enough, however, that in all scenarios – centralized and distributed – there must be a centralized and uniform authoring of the reporting specification rules, since the specifications themselves originate from public health through a centralized process and must be administere d nationally through a well-established process. it is also essential that all sites have all the rules available to them, since there may http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e291, 2019 isds 2019 conference abstracts be multiple jurisdictions whose rules and reporting is required, with the determination of jurisdiction(s) based on where a patient lives and where they receive care. acknowledgement this project is being conducted with support from the centers for disease control and prevention (cdc) through the council of state and territorial epidemiologists (cste) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e239, 2019 isds 2019 conference abstracts early evaluation of impacts of cold waves and floods during winter 2018 in france anne fouillet, cécile forgeot, marie-michèle thiam, céline caserio-schönemann division of data science, santé publique france, saint-maurice, france objective the presentation describes the results of the daily monitoring of health indicators conducted by the french public health age ncy during the major floods and the cold wave that occurred in january 2018 in france, in order to early identify potential impact of those climatic events on the population. introduction the seine river rises at the north-east of france and flows through paris before emptying into the english channel. on january 2018 (from 22th january to 11th february, weeks 4 to 6), major floods occurred in the basin of seine river, after an important rainy period. this period was also marked by the occurrence on the same area of a first cold wave on week 6 (from 5th to 7th february), including heavy snowfall and ice conditions from 9th to 10th february. a second similar cold wave occured from 28th february and 1st march. floods of all magnitude are known to have potential health impacts on population [1], both at short, medium and long term both on physical (injuries, diarrhoeal disease, carbon monoxyde poisoning, vector-borne disease) and mental health. extreme cold weather have also the potential to further impact on human health through direct exposure to lower temperatures, and associat ed adverse conditions, such as snow and ice [2]. such situations may be particularly associated to direct impact like hypothermia, frostbite and selected bone/joint injuries). methods since 2004, the french public health agency (santé publique france) set up a national syndromic surveillance system sursaud, enabling to ensure morbidity and mortality surveillance [3]. in 2018, morbidity data were daily collected from a network involving about 700 emergency departments (ed) and 58 emergency general practitioners’ associations sos médecins. 92% of the national ed attendances and 95% of national sos médecins visits are caught by the system. both demographic (age and gender), administrative (date and location of consultation, transport) and medical information (chief complaint, medical diagnosis using icd10 codes in ed and specific thesauri in sos médecins associations, severity, hospitalization after discharge) are recorded for each patient. the daily and weekly evolution of the number of all-cause ed attendances and sos médecins consultations during the flooding period were compared to the evolution on the two previous years. the number of hospitalisations after ed discharge was also monitored. the immediate health impact of floods and cold waves was assessed by monitoring eight syndromic indicators: gastroenteritis, carbon monoxide poisoning, burnt, stress, faintness, drowning, injuries and hypothermia. analyses were performed by age group (<15 years, 15-64 years, more than 65 years) and at different geographical levels (national, paris region and districts located in the basin of seine river). results in 2018, syndromic surveillance did not show any major impact on all-cause ed attendances and sos médecins consultations from week 4 to week 6, neither in paris area nor in other areas along the seine river. the recorded numbers were comparable to the two precedent years in all age groups. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e239, 2019 isds 2019 conference abstracts a decrease of the all-cause ed attendances was observed during the 1st day with ice conditions in normandy and paris, mainly in children and adults aged 15-64 years. during week 6 in paris area, an increase of ed attendances was observed for injuries (+4% compared to the past weeks – figure 1) and to a lesser extent for hypothermia and frostbite (16 attendances compared to less than 9 for the past week s). similar increase in injuries were observed in normandy during the second cold wave (figure 1). conclusions during the flood episode, the rising water level was slow with foreseeable evolution, compared to other sudden flood events occurring in south of france in 2010 due to violent thunderstorms. this progressive evolution allows french authority to deploy wide specific organization in order to mitigate impact on concerned populations. that may explain the absence impact observed in ed at regional and national levels during the flood disaster. the evolution of injuries during 2018 episode is attributable to the cold wave that occurred simultaneously. as the french syndromic surveillance system is implemented on the whole territory and collects emergency data routinely since several years, it constitutes a reactive tool to assess the potential public health impact of both sudden and predictable dis asters. it can either contribute to adapt management action or reassure decision makers if no major impact is observed. acknowledgement to all emergency departments of the oscour network, sos médecins associations and to the regional teams in charge of syndromic surveillance. references 1. ahern m, kovats s. the health impacts of floods. in: few r, matthies f, eds. flood hazards and health: responding to present and future risks. london, earthscan, 2006:28–53. 2. hughes he, morbey r, hughes t, et al. 2014. using an emergency department syndromic surveillance system to investigate the impact of extreme cold weather events. public health. 128(7), 628-35. pubmed https://doi.org/10.1016/j.puhe.2014.05.007 3. caserio-schönemann c, bousquet v, fouillet a, henry v. 2014. the french syndromic surveillance system sursaud (r). bull epidemiol hebd (paris). 3-4, 38-44. figure 1: daily proportion of ed attendances for injuries among the whole attendances in paris area and normandy in adults aged 15-64 years – from november to march 2015-2018 – flood period in blue zone http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25065517&dopt=abstract https://doi.org/10.1016/j.puhe.2014.05.007 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts assessing scarlet fever re-emergence from notifiable disease surveillance in hong kong chun fan lee, benjamin j. cowling and eric h. lau* school of public health, the university of hong kong, hong kong, china objective this study examined the epidemiology of scarlet fever in hong kong based on notifiable disease surveillance data, in a period where a 10-fold upsurge in scarlet fever incidence occurred. high risk groups and important factors associated with scarlet fever transmission were identified. introduction scarlet fever is a notifiable disease in hong kong for over 40 years. there was relatively low activity of scarlet fever until an outbreak in mid-2011 which resulted in two deaths and more than 1,500 cases. scarlet fever incidence remained elevated since then with >10-fold increase comparing to that before the upsurge (1, 2). reemergence of scarlet fever was also reported in china in 2011 and the united kingdom in 2014 (3). we analyzed the patterns in scarlet fever incidence in hong kong using the notifiable disease surveillance data from 2005–2015. methods we analyzed 7,266 scarlet fever cases aged 14y or younger from 2005-2015, who were notified to the department of health. hierarchical multivariable negative binomial models were fitted to the data to study the effects of age, sex, school holidays, and other meteorological parameters, accounting for autocorrelation, seasonal and long-term trend. separate models were fitted to the data before and after the upsurge in 2011, excluding data in 2011 to allow for a 1-year window period. results we observed seasonal pattern throughout the study period (figure). among children aged ≤5y, the average scarlet fever incidence was 3.3 per 10,000 children in 2005-2010, which increased substantially to 18.1 per 10,000 children in 2012-2015. the final model included age, sex, school holidays in the preceding week, temperature, relative humidity, rainfall, long-term and bimodal seasonal trend. based on the model, we identified no significant longterm trend before the upsurge in 2011, but there was a mild decreasing trend of about 8% (95% ci=6-11%) per year after the upsurge. a major peak was identified in december to january, with a milder peak in may to june. we found that the most affected groups were kindergarten students (3-5y), followed by primary school students (6-11y). comparing to girls aged 0-2y, boys had significantly higher risk than girls except for the 0-2y age group, and boys aged 3-5y had the highest risk (adjusted incidence rate ratio (irr)=1.47, 95% ci=1.32-1.65). school holidays were significantly associated with lower incidence of scarlet fever, with an adjusted irr of 0.58 (95% ci=0.51–0.65) after the upsurge in 2011. temperature was found to be negatively associated with scarlet fever incidence (adjusted irr=0.963, 95% ci=0.940-0.987) after the upsurge. conclusions our study showed that elevated activity of scarlet fever was sustained for more than 5 years after the upsurge in 2011. we found that younger children who started schools, especially for boys aged 3-5 years, had a higher risk of scarlet fever, and there was significant effect of school holidays in reducing scarlet fever incidence. combining these findings, school-based control strategy is likely to be effective. sustained and consistent surveillance of scarlet fever allows continued monitoring of potential change in high risk group to drive updated and effective control strategy. weekly number of notified scarlet fever cases, hong kong, 2005–2015. gray bars indicate periods of school holidays (top). weekly average of temperature, relative humidity, and rainfall (bottom). keywords scarlet fever; re-emergence; children; school holiday acknowledgments we thank the centre for health protection, department of health, in hong kong for their kind support and assistance in collating the notification data. this work is supported by the health and medical research fund of the food and health bureau (grant no. hks-16-e09) and the harvard center for communicable disease dynamics from the national institute (grant no. u54 gm088558). the content is solely the responsibility of the authors and does not necessarily represent the official views of the national institute of general medical sciences or the national institutes of health. references 1. lau eh, nishiura h, cowling bj, ip dkm, wu jt. scarlet fever outbreak, hong kong, 2011. emerging infectious diseases. 2012 oct;18(10):1700-2. 2. centre for health protection. number of notifiable infectious diseases by month in 2011. http://www.chp.gov.hk/en/data/1/10/26/43/455. html 3. lee cf, cowling bj, lau ehy. epidemiology of reemerging scarlet fever, hong kong, 2005-2015. emerg infect dis. 2017 oct;23(10):1707-1710. *eric h. lau e-mail: ehylau@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e100, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts comparing and contrasting two essence syndrome definition query methods zachary m. stein* and sophia crossen bureau of epidemiology and public health informatics, kansas department of health and environment, topeka, ks, usa objective to compare and contrast two essence syndrome definition query methods and establish best practices for syndrome definition creation. introduction the kansas syndromic surveillance program (kssp) utilizes the essence v.1.20 program provided by the national syndromic surveillance program to view and analyze kansas emergency department (ed) data. methods that allow an essence user to query both the discharge diagnosis (dd) and chief complaint (cc) fields simultaneously allow for more specific and accurate syndromic surveillance definitions. as essence use increases, two common methodologies have been developed for querying the data in this way. the first is a query of the field named “cc and dd.” the cc and dd field contains a concatenation of the parsed patient chief complaint and the discharge diagnosis. the discharge diagnosis consists of the last non-null value for that patient visit id and the chief complaint parsed is the first non-null chief complaint value for that patient visit id that is parsed by the essence platform. for this comparison, this method shall be called the ccdd method. the second method involves a query of the fields named, “chief complaint history” and “discharge diagnosis history.” while the first requires only one field be queried, this method queries the cc history and dd history fields, combines the resulting data and deduplicates this final data set by the c_biosense_id. chief complaint history is a list of all chief complaint values related to a singular ed visit, and discharge diagnosis history is the same concept, except involving all discharge diagnosis values. for this comparison, this method shall be called the ccddhx method. while both methods are based on the same query concept, each method can yield different results. methods a program was created in r studio to analyze a user-provided query. simple queries were randomly generated. twenty randomly generated queries were run through the r studio program and disparities between data sets were recorded. all kssp production facility ed visits during the month of august 2017 were analyzed. secondly, three queries actively utilized in kssp practice were run through the program. these queries were firework-related injuries, frostbite and cold exposure, and rabies exposure. the queries were run on all kssp production facility ed visits, and coincided with the timeline of relevant exposures. results in the random query trials, an average of 5.4% of the cases captured using the ccdd field method were unique and not captured by the same query in the ccddhx method. using the ccddhx method, an average of 6.1% of the cases captured were unique and not captured by the ccdd method. when using the program to compare syndromes from actively utilized kssp practice, the disparity between the two methods was much lower. firework-related injuries during the time period queried, the ccdd method returned 171 cases and the ccddhx method returned 169 cases. all ccddhx method cases were captured by the ccdd method. the ccdd method returned 2 cases not captured by the ccddhx method. these two cases were confirmed as true positive firework-related injury cases. frostbite and cold exposure during the time period queried, ccdd method returned 328 cases and the ccddhx method returned 344 cases. the ccddhx method captured 16 cases that the ccdd method did not. the ccdd method did not capture any additional cases when compared to the ccddhx method. after review, 10 (62.5%) of these 16 cases not captured by the ccdd method were true positive cases. rabies exposure during the time period queried, the ccdd method returned 474 cases and the ccddhx method returned 473 cases. the ccddhx method captured 7 cases that the ccdd method did not. the ccdd method returned 8 cases not captured by the ccddhx method. after review, the 7 unique cases captured in the ccddhx method contained 3 (42.9%) true positive cases and 3 (37.5%) of the 8 cases not captured by the ccddhx method were true positives. conclusions the twenty random queries showed a disparity between methods. when utilizing the same program to analyze three actively utilized kssp definitions, both methods yielded similar results with a much smaller disparity. the ccddhx method inherently requires more steps and requires more queries to be run through essence, making the method less timely and more difficult to share. despite these downsides, ccddhx will capture cases that appear throughout the history of field updates. further variance between methods is likely due to the ccdd field utilizing the essence-processed cc while the ccddhx field utilizes the cc verbatim as produced by the ed facility. this allows the ccdd method to tap into the powerful spelling correction and abbreviation-parsing steps that essence employs, but incorrect machine corrections and replacements, while rare, can negatively affect syndrome definition performance. the greater disparity in methods for the random queries may be due to the short (3 letter) text portion of the queries. short segments are more likely to be found in multiple words than text of actual queries. utilizing larger randomly generated text segments may resolve this and is a planned next step for this research. our next step is to share the r studio program to allow further replication. the kansas syndromic surveillance program is also continuing similar research to ensure that best practices are being met. keywords essence; syndromic; surveillance; kansas; comparison isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments data collection was supported by the grant or cooperative agreement number 1 u50 oe000069-01, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official views of the centers for disease control and prevention or the department of health and human services *zachary m. stein e-mail: zstein@kdheks.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e34, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts enhancing epidemic detection using syndromic surveillance and early notification methods tippa wongstitwilairoong*1, saranath lawpoolsri niyom1, ngamphol soonthornworasiri1, jariyanart gaywee2 and jaranit kaewkungwal1 1faculty of tropical medicine, mahidol university, bangkok, thailand; 2royal thai army-armed forces research institute of medical sciences, bangkok, thailand objective this paper presents an investigation using early notification methods to enhancing epidemic detection in syndromic surveillance data from royal thai army in thailand. introduction early notification detection systems have taken a critical role in providing early notice of disease outbreaks. to improve the detection methods for disease outbreaks, many detection methods have been created and implemented. however, there is limited information on the effectively of syndromic surveillance in thailand. knowing the performance, strengths and weakness of these surveillance systems in providing early warning for outbreaks will increase disease outbreak detection capacity in thailand. methods this study describes and compares the capabilities of various outbreak detection algorithms using 37,043 unique syndromic daily reports based on medical information from both civilian and military personnel from the unit base surveillance of royal thai army (rta) along the thai-myanmar and thai-cambodia boarder areas. traditional epidemic detection method: mean plus two sd were compared with algorithms for early notification methods and which included regression, regression/ewma/poisson, cdc-c1, cdc-c2 and cdc-c3. early notification and epidemic detection methods were compared according to their ability to generate alert notifications. sensitivity, specificity, positive predictive value (ppv), negative predictive value and overall accuracy to detect or predict disease outbreaks were estimated. results this study shows that the preliminary results are promising for epidemic detection by early notification methods in syndromic surveillance in thailand. the majority of syndromic records were categorized into 12 symptoms. the three most common symptoms were respiratory, fever and gastrointestinal illness (11,501; 9,549 and 4,498 respectively). the results from the early notification systems were analyzed and their performances were compared with traditional epidemic detection method according to their ability to generate early warning alerts for the 3 symptoms. in our study regression/ewma/ poisson method had higher specificity across the 3 symptoms (94.5%, 94.7% and 95.9% respectively), but generated lower sensitivity (22.6%, 40.4% and 23.1%). cdc-c1, cdc-c2 and cdc-c3 algorithms are easy to understand and are widely used. cdc-c3 had higher sensitivity to detect gradual disease outbreak effects (64.2%, 70.2% and 57.7%), but it is known to produce higher alarm rates/false positive signals. conclusions within the syndromic surveillance data of rta, the cdc algorithm is the best chosen to use in the syndromic system due to being easy to understand and implement in a system with high sensitivity. cdc-c2 is the best early notification detection method due to its high sensitivity and ppv. however, cdc-c3 is shows the highest sensitivity, but exhibits the lowest specificity and ppv for all symptoms including a high alarm rates. to be useful, early notification detection methods must have acceptable operating characteristics. consequently, we should select the most appropriate algorithm method to explain the data well and in order to improve detection of outbreaks. the comparison methods used in this study may be useful for testing other proposed alert threshold methods and may have further applications for other populations and other diseases. keywords early notification; epidemic detection; syndromic acknowledgments great appreciation to all study site health workers, who coordinated, and collected data. references 1. chretien jp, burkom hs, sedyaningsih er, larasati rp, et al. syndromic surveillance: adapting innovations to developing settings. plos medicine 2008; vol 5: page 1-6. 2. burkom hs, elbert y, magruder sf, najmi ah, peter w, thompson mw. developments in the roles, features, and evaluation of alerting algorithms for disease outbreak monitoring. johns hopkins apl technical digest 2008; vol 27: page 313. *tippa wongstitwilairoong e-mail: tippaw@afrims.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e113, 2018 pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi pediatricians’ understanding and experiences of an electronic clinical-decision-support-system per nydert1,2, anikó vég3,4, pia bastholm-rahmner3,5, synnöve lindemalm1,2 1. astrid lindgren childrens hospital at karolinska university hospital stockholm, sweden. 2. karolinska institutet, department of clinical science, intervention and technology (clintec), stockholm, sweden. 3. public health care services committee administration, stockholm county council, stockholm, sweden. 4. uppsala university, department of public health and caring sciences, health services research, uppsala, sweden. 5. karolinska institutet, department of learning informatics, management and ethics. medical management centre (mmc), stockholm, sweden. abstract objectives: subsequent dosing errors after implementing an electronic medical record (emr) at a pediatric hospital in sweden led to the development, in close collaboration with the clinical profession, of a clinical decision support system (cdss) with dose range check and weight based dose calculation integrated directly in the emr. the aim of this study was to explore the understanding and experiences of the cdss among swedish pediatricians after one year of practice. methods: semi-structured interviews with physicians at different levels of the health care system were performed with seventeen pediatricians working at three different pediatrics wards in stockholm county council. the interviews were analysed with a thematic analysis without predetermined categories. results: six categories and fourteen subcategories emerged from the analysis. the categories included the use, the benefit, the confidence, the situations of disregards, the misgivings/risks and finally the development potential of the implemented cdss with weight based dose calculation and dose range check. conclusions: a need for cdss in the prescribing for children is evident to support the prevention of medication errors. after implementing a cdss, organized efforts are crucial to understand the need for further development based on the practical knowledge of the clinical profession. different contextual settings of health care organisations do affect the way how physicians think and act in work. when implementing a cdss in practice we need to describe and analyse the context where the cdss should be used as well as the prescribers’ needs in work. key words: electronic medical record, child, patient safety, qualitative abbreviations: clinical decision support system (cdss), dose calculating weight (dcw), dose range check (drc), electronic medical record (emr), weight based dose calculation (wbdc) pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi introduction the most frequent adverse drug events in the pediatric clinical setting is related to erroneous drug orders [1]. the reason for this type of medication errors can be found on a psychological and organizationally level as well as in a poor technical system [2-4]. the latter was a major concern when the mandatory electronic medical record (emr) of take care [5] was introduced in 2008 at the children’s hospital of karolinska university hospital, stockholm sweden with an intent to meet national regulations [6]. unfortunately, these regulations are written for paper based orders and do not take into considerations the need for added emr requirement of specific clinical situations. for example, the regulations state that the dose preferably should be written in milliliters. so, when prescribing a drug with the concentration of 10 mg/ml, in mg instead of ml, the risk of a 10-potency error is possible. since a vial intended for an adult can keep a 10-fold erroneous dose for an infant the risk is increased at pediatric wards. similar concerns had previously been stated by the american association of pediatrics, including the need for clinical decision support systems (cdss) such as dosing by weight and dose range checking [7]. articles have also shown the added benefit of weight based prescribing in pediatrics [8-10], the need to aggressively fix contra productive technology in cdss [11], the possibility to improve individualized pediatric pharmacotherapy by cdss [12] and to add manual services for identifying prescribing errors [13]. at our hospital, subsequent dosing errors related to the implemented emr triggered the initial demands of a pediatric cdss. recourses from the stockholm county and a call for priority changes on the emr provider made the development of an emr integrated cdss possible. in addition, the development of a mandatory dose calculation weight (dcw) was necessary alongside rules for an ageand weight specific dose range check (drc) provided by the pediatric drug group at the hospital. the cdss was developed by participatory design with the aid of a 20 pediatrician reference group and released with 1) a dose by weight function (gram/kg, milligram/kg, microgram/kg and units/kg), 2) a soft stop drc and 3) a mandatory dcw which needs to be updated regularly, based on the age of the child. at the end of the project a list of future development issues regarding the cdss was handed over to the emr provider. in this study, we have used semi-structured interviews with swedish pediatricians regarding their use of, frustration with, dependence on and risks of a highly anticipated cdss one year after the development through participatory design. it is a qualitative approach with the overall aim to explore and describe pediatricians’ understanding and experiences of the weight based dose calculation (wbdc) and drc while prescribing medicines to children. correspondence: per nydert, astrid lindgren children’s hospital at karolinska university hospital stockholm se-171 76 sweden, per.nydert@ki.se doi: 10.5210/ojphi.v9i3.8149 copyright ©2017 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi methods research design a qualitative approach was chosen since this is a fruitful way to explore people’s experiences, attitudes, thoughts and perceptions of different phenomena [14]. with the aim of getting deeper knowledge of the use and usefulness of the decision support system semi-structured interviews with physicians at different levels of the health care system were performed. setting and informants seventeen pediatricians (4.9% of the total number of pediatricians) working at three different pediatrics wards in stockholm county council were recruited to the interviews (table 1). the first two informants were selected from a personal register provided by the health authorities. during the interviews it turned up that these informants have used the emr and the cdss fairly minimal. for this reason the snow ball sampling strategy were used [14], which means that the participating informants were asked to mention one colleague with experience of the cdss. when additional data reached saturation, i. e. no more new information was achieved; the recruitment of new informants was stopped. all informants were contacted by the researchers (av or pbr) explaining the aim and procedure of the study. at the beginning of each interview the informant was informed about the purpose of the study, the voluntarily participation, and the confidential data procedure. all informants also received a consent form. the interviews lasted from 25 to 40 minutes. data was collected in 2012 at the physicians’ workplace. table 1 participants’ background variables number (n=17) sex female male 9 8 education fellow resident consultant 1 5 11 care units gastroenterology surgery neonatology emergency oncology 6 4 4 2 1 data collection we started each interview broadly with questions related to the difficulties in the drug prescribing process. we continued asking more specific questions about the physicians’ needs for drug information and their experiences about the cdss. examples of questions asked were: pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi to what extent do you currently use computerized decision support systems at point of care? do you consider the present system to be useful? what additional help should the support system give to you? data analysis the interviews were recorded as audio files, and then transcribed verbatim and processed as texts. a qualitative thematic analysis was performed [14]. the analysis was conducted by two authors experienced in qualitative analysis (av and pbr) in a stepwise manner as follows: 1) reading through each transcript individually to get a good grasp of all that had been said. 2) sections of text and key words in the transcripts, focusing on the research question, were marked and systematically coded. marked sections with related topics were subsequently grouped into sub-categories. 3) associations between sub-categories were identified. when opinions differed between research members, such as about meaning or origin, we returned to the transcripts and sought evidence to establish consensus. this iterative process was used throughout the whole analysis, i.e. moving from the whole transcripts to the condensed description and back again. 4) sub-categories were merged and named from an overall perspective. quotes were selected to illustrate each category. 5) quotes were chosen to illustrate and validate the categories. the software program nvivo 8 was used for sorting the data into different categories [15]. the focus during the analysis was to capture as many different views as possible, not to investigate how many informants described a certain view about the support system. ethical considerations swedish law [16] did not require approval of this study from the research ethics committee on the basis that our study posed no physical or psychological risk to the participants. however, all participants gave their informed consent prior to involvement in the study. results the results describe the experiences of the informants based on the problems and difficulties they experience when prescribing medicines for children with the cdss (table 2). table 2 six categories and fourteen subcategories emerged from the analysis of the interviews 1. use • use is influenced by clinical experience • habit leads to increased use • good that the cdss is not compulsory 2. benefit • prompts consideration • help with calculations reduces errors pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi • greatest benefit in emergency care 3. confidence in the wbdc and drc • use of a manual drc • double-checking the dosage 4. situations in which the doctor disregards the wbdc and/or the drc • when it is easy to work out the dosage using mental arithmetic • in case of special indications 5. misgivings/risks • false security and non-disease specific warnings • human error is unavoidable • wrong dcw 6. development potential • optional or compulsory – registering and signing for weight 1. use use is influenced by clinical experience some informants frequently use the support function as soon as they prescribe a medicine, while others do not prescribe medicines in such large quantities, as a result of which use is sporadic. according to specialists and consultants, it is doctors undergoing specialist training that make more use of and derive the most benefit from the wbdc. specialists prescribe a limited number of medicines with which they are familiar, while the specialists in training, who as a rule have less experience, go around different departments where they encounter different patient groups, and so they have a greater need for the aid. according to the informants, use of the wbdc increases whenever prescribing something with which they are not familiar. many of them report finding the wbdc and drc easy to use and to understand. a lot of them describe it as being good as, "it's straightforward. there's nothing difficult about it.", "the module works well." habit leads to increased use the informants reported that the more they use the wbdc and drc the more familiar they become with the aid, the more they rely on the information generated by the aid. one informant also says that the wbdc can be made a routine as it is an important safety factor. "there is always a false sense of security. of course there is. obviously the more you use it, the more you come to rely on it. however i do not believe there is an alternative to not having the drc, simply because there is a false sense of security." pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi good that the cdss is not compulsory the informants like the possibility to override the drc. if it were compulsory, it would lose its role as an aid as ultimately it is always the doctor who has to make the medical decision, and sometimes, due to the patient's status, they have to deviate from the recommendations. "it has nothing to say about what it thinks i should give instead. it doesn't do that, but just indicates… dose too low or do you really want to prescribe this dose. so it leaves the decision up to me. it doesn't forbid me from using that dose." 2. benefit the benefit for the informants from using the wbdc and drc is dependent on various factors. in part it depends on the type and number of medicines doctors prescribe and within which department, but also on how much experience they have. some think they benefit most from the drc, while others value the wbdc more. those doctors whom are familiar with the dose levels valued the additional check provided by the drc. they see this support as an assurance. the informants say that the wbdc helps them to set doses and the drc functions as a check on the wbdc, warning and alerting the doctor of underor over-prescribed doses. according to the informants the drc is noticed most when it changes color. "but the drc is of course one additional safety check that prevents you from making disastrous mistakes. what i mean is that often errors in pediatric care, historically, are that… a prescription can often be made out for ten times the potency or there may be a decimal error. now of course there is a drc which ensures that this sort of thing couldn't happen." prompts consideration those who have received a warning in the drc think it is a good thing and that it draws attention to the error, causing the doctor to stop and think. a number of informants also say that the doctor needs to consider when a warning has been received about too high or too low a dose, for example when escalating the dose level of prescribed antibiotics or pain medication. " if i give him one milliliter it is an under-prescribed dose. exactly, that happened to me the other day when i… instead of prescribing ten milliliters, it ended up as one milliliter, and it (the drc) said that it was an under-prescribed dose." help with calculations reduces errors all the informants said that the best support was that it prevented mistakes and helped avoid human error in calculations in terms of incorrect decimal places, which have historically occurred in pediatric healthcare. "but for example, pain medication and antibiotics, where it is known there are a lot of errors. one such case is intravenous paracetamol. as i understand it, it has been given at doses that were ten times too high and things like that. so it's good that an pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi alert is given in that case. or you could be unsure as to whether it should be fifteen milliliters, one hundred and fifty milliliters or 1.5 milliliters. so it's really good then." greatest benefit in emergency care although a number of specialists have considerable experience and are familiar with the drugs they prescribe, they say that it is still easy to make a miscalculation, especially in situations in which the prescriber is stressed, tired, on-call or prescribing medication to children outside of his or her own area of specialization. some informants think that the wbdc and drc are of greatest benefit within emergency care, where a lot of prescriptions are issued in a short time in high-pressure situations. "of course you still have to calculate doses sometimes, when there aren't any wbdc and you have to work it out yourself, and you're tired and stressed or … i usually use drugs that i frequently use. that's when you have it at your fingertips, i'm aware of the dose ranges and i can tell for myself. but sometimes, mainly when you're on-call, there might be a drug that you don't prescribe so often, and that's when it is particularly important of course." 3. confidence in the wbdc and drc use of a manual dose range check most doctors rely on the information from the drc, but in the end they perform their own reasonableness assessment of the dosage depending on the medicine in question. confidence however can be affected by contradictory information, i.e. when the information from the aid does not agree with information from other knowledge bases. one informant reported that in some cases inconsistent recommendations are given in national dosing guidelines and in the drc and as a result the informant is not sure which source to use. "i don't know if it's always right, because the national dosing guidelines can give a dose, only for it to come up as an excessive dose. i don't really know how it works… it calculates, not in accordance with the national dosing guidelines then, or i don't know…" double-checking the dosage a number of informants report that when they receive a warning from the drc or they have the dose level calculated in the wbdc, they still double-check it in the national dosing guidelines or local drug or disease specific instructions and re-calculate it. they might also ask a colleague to check and re-calculate the dose level. doctors report that they often ask nurses to calculate dose levels and to check that they are reasonable, especially when prescribing infusions where information may be flawed. doctors find this "double check" reassuring. "but when i double-check, i repeat the calculation several times, calculating backwards and forwards. i work out how much i want and then i calculate in the opposite direction so that… so i will pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi probably still have found a lot … so i don't just write it out, but i almost always check and double-check." 4. situations in which the doctor disregards the weight based dose calculation and/or the dose range check when it is easy to work out the dosage using mental arithmetic the informants say that they automatically work out the dose quickly in their heads when prescribing drugs that are easy to calculate. in these cases they forget about the wbdc or consciously decide not to use it. some informants describe the routine for working out the dosage as a habit, especially amongst those who prescribe between 10 and 15 drugs once they have learnt the dosages for these. "it depends on what kind of drug it is. sometimes i don't use it, if it is something that is easy to work out per kilogram. then i might issue a prescription directly in takecare…yes, without using the wbdc."’ in case of special indications informants say that there are drugs that are dosed above or below the reasonable dose level on special indications. the recommendations are also disregarded when prescribing certain infusions or when escalating antibiotics. "so sometimes we do prescribe dose levels other than what is normal. definitely, there may well be an indication for which some other dose has to be given. but then you probably know about that yourself. because…there are of course a few drugs and…then you have to understand why it is flashing yellow." "there are of course some drugs that are used at above the reasonable dose … yes, i do check whenever it [the drc] turns yellow, … and in this case there was an escalation so i knew why it was too much. it feels very reassuring." 5. misgivings/risks false security and non-disease specific warnings a theoretical risk seen by a few of the informants with the wbdc is potential over-reliance and not questioning the number of milliliters that the dose rage check recommends. one informant described this as the risk of being too much of a "checklist person" and no longer thinking independently and making one´s own decisions. another prescriber however thinks that the cdss: "only says whether the dose is too low or too high but leaves the decision up to the prescriber. but of course i do have to think about it." human error is unavoidable some doctors pointed out that there is always a risk of human error when dosing medicines. when a doctor makes a mistake in a prescription, this increases the risk of the nurse giving the patient an incorrect dose. using the cdss reduces the risk of errors of this kind. one doctor pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi reports that prior to the implementation of the cdss it was easy in stressful situations to confuse milliliters with milligrams. "….and the dose was right, but i didn't see that it said milliliters instead of milligrams… but they managed to give him two incorrect doses…. the child was fine, but of course it could have turned out badly." wrong dose calculating weight one risk pointed out by the informants is entering the wrong dcw in the medicines module from the very beginning. this can happen as a result of the aid allowing doctors to give the dcw in grams or kilograms. in order to avoid errors, some informants suggest inclusion of a function which checks the weight for reasonableness in the same way as the dose. " …the risk is that you might enter the wrong dosing weight. in takecare the weight is in kilograms, while in a lot of other systems it is in grams for neonates … so there ought to be a reasonableness check on dosing weight as well. it ought to do that." 6. development potential in general the informants think that the cdss is needed, and that it would be a shame if it were lost. one informant says that it is good that it is there, "it makes you wonder why there hasn't been something like this before." many of the informants however point out that it requires further development and adaptation to meet the needs of the user when setting a dose. below are a number of suggested developmental measures in which the child's weight is a central parameter for the way in which the system is used. optional or compulsory – registering and signing for weight in order to be able to use the cdss, the child's dcw must have been entered into the cdss. all the informants agree that weight is an important parameter when prescribing medicines for children, though there is some disagreement as to whether entering the dcw in the cdss should be compulsory. the informants say that use of the weight parameter is of decisive importance when prescribing medicines for neonates, but that this becomes less important the older the child is. some informants believe it is essential to give the current weight when prescribing drugs in pediatric medicine and that it is reasonable to require this. some find the cdss insistence on this irritating however. they refer to cases in which weight is of no particular significance or when prescribing drugs in some other context, for example when renewing a prescription or prescribing based on the child's body surface area. in such situations it is difficult to complete a prescription using the current solution. these prescribers want the indication of weight either to be optional or to apply only for infants. one suggestion is that a block is put in place based either on the patient's age or weight, or else the weight parameter can be inactivated. "weight might not play any role in that case, but instead they have to carry on with the same dose as they have had before because they're looking at the concentration in the blood for pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi example, that's when you don't have to worry so much about the child's weight." in order to save time and make prescription easier, the informants want the weight used by an administrating nurse in the medicines module to be automatically transferred from the medical records system to the dcw. "having to sign for the weight when the nurse has already taken a weight is an unnecessary step. because that's something that i can't check… if you want to sign something as a doctor, then it's something you have some control over… something you've done yourself… but if a nurse has weighed a child, then it's that nurse who signs for the weight in her notes, and then it should be there already so that it's just a matter of prescribing and not have to sign for that weight." discussion this study adds the views of pediatricians on swedish emr development. the qualitative approach is preferable as it explores the discourse of the users and made it possible in this study to feed back the unfinished parts to the developers. the need for constant development of drug related it-systems is known [2]. despite this, the suggested changes are not jet been taken into consideration by the emr provider. our belief is that the possibility to be prioritized in the developmental process is higher when actual dosing errors occurs. it’s harder to find recourses to optimize a cdss with a good reputation and functionality but with an apparent need for further risk minimization and better man machine interface, as shown in this article. a similar method to acquire information from iranian physicians’ on an emr was used which found a willingness to use an emr if these systems provide significant benefits over known problems [17]. and the addition of a cdss has shown to reduce the number of adverse drug events within pediatric and neonatal settings [18,19]. early in the process of interviewing we found it necessary for the informants to talk about other issues regarding the emr and not only the cdss as many informants experienced both emr and cdss as "a unit in the computer". this was allowed and the additional data adds to the list of developmental issues. to further elaborate on the acceptance, perception and use of the system we continued with additional interviews in a student project using an extended technology acceptance model [20] one interesting part in the current study is the lack of reported warning fatigue by the drc among the informants. warning fatigue and non compliance is otherwise a common issue [2123]. this could be an effect of a list restricted to high risk drugs and a build in real time warning system instead of a warning after completing the whole drug ordering process. the high override rates is otherwise a cause for concern which needs further analysis of the usability of the emr and cdss [23]. in addition to the overrides a recent review published the greatest challenges in cdss [24]. they found primarily a need to improve the human-computer interface which is in line with our findings. the systems need to excel, not letting the health staff just try harder [25]. likewise it is important to understand the psychological and organizational factors contributing to medications errors. if not engaging into a high safety organization serious events will continue to appear. the technical solutions are part of the advancement but can’t replace e.g. poor education, lack of sleep, work interruptions, intentional pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi deviations from routines and the possibility to question authorities. the informants vocalize a healthy skepticism to the emr and the cdss and some of their concern could be addressed by better functionality of the cdss. a list of suggested future challenges for our emr and cdss are published as a supplement (supplementary table 1) and the method of using a post development qualitative approach for tracking the need for additional changes has been one of few ways to scientifically communicate between developers and clinicians after the release and after the planned participatory development process. we encourage local development and follow up studies regarding cdss for wbdc and drc for emr in pediatrics. we also see a need to communicate these findings to both cdss developers and the national board of health and welfare to state requirements of certain tools for the pediatric population when entering an emr. conclusion a need for cdss in the prescribing for children is evident to support the prevention of medication errors. after implementing a cdss, organized efforts are crucial to understand the need for further development based on the practical knowledge of the clinical profession. different contextual settings of health care organisations do affect the way how physicians think and act in work. when implementing a cdss in practice we need to describe and analyse the context where the cdss should be used as well as the prescribers’ needs in work. a cdss adapted to physicians’ specific needs could then be a working tool for taking care of potential medication errors. financial disclosure this work was supported by a stockholm county council as financial support for behavior scientists (p.b-r and a.v.); regional agreement on medical training and clinical research between the stockholm county council and karolinska institutet (s.l. and p.n., project 20150224). conflict of interest no competing interests. references 1. wong ic, ghaleb ma, franklin bd, barber n. 2004. incidence and nature of dosing errors in paediatric medications: a systematic review. drug safety : an international journal of medical toxicology and drug experience. 27(9), 661-70. 2. beuscart rg, hackl w, nøhr c. detection and prevention of adverse drug events : information technologies and human factors. amsterdam; washington, dc: ios press; 2009. 3. cohen mr, & american pharmacists association. medication errors. 2nd ed. washington, dc: american pharmacists association; 2007. 4. kohn lt, corrigan j, donaldson ms. to err is human : building a safer health system. washington, d.c.: national academy press; 2000. 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antibiotic for example; even though it's the same antibiotic, it can be prescribed at different doses depending on what disease the child has. and that's when you don't know what the dose range is based on for the condition the child has." 2. prescribing also by body surface area "sometimes you would rather prescribe by body surface area, and that's when you need more parameters entered in the system. you should be able to set it so that you get body surface area there and not just weight. children change, so there is a lot from childhood – from a large head and small body to fairly big children with a big body but where the head doesn’t grow that much bigger." 3. import weight automatically from the medical records system to the cdss "sometimes we have big patients for whom weight plays less of a role, so sometimes you want to temporarily remove or inactivate the dose calculation weight." 4. a flexible system with the option of inactivating the weight parameter "and sometimes weight doesn't play any role, but i still have to enter it, so i do it just because the software requires it." 5. also check range for weight "there is a risk of entering the wrong dosing weight. in takecare the weight is in kilograms, while in a lot of other systems it is in grams for neonates … so there ought to be a reasonableness check on dosing weight as well. 6. more dose recommendations attached to the weight based dose calculation "if there were text in a panel in the weight based dose calculation, this would allow the user to see straight away that the recommended dose is 20 mg/kg x 3 for example, and since it's easier to enter them directly in the weight based dose calculation, the dose will come out right. to make it easier to see the instructions." 7. the weight based dose calculation is used also with infusions, sodium chloride and 'as needed' medication "we think in milliliters per kilogram and there's no weight based dose calculation for sodium chloride, so you can't write millimoles per kilo you have to work it out yourself, and there are only solutions available as so many millimoles per milliliter, so you have to work it out yourself and there is a risk of error." pediatricians’ understanding and experiences of an electronic clinical-decision-support-system online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 9(3):e200, 2017 ojphi 8. dose range check for bolus doses or continuous infusions "some drugs can also be used as a continuous infusion, so you want to give the full daily dose administered. that's when you get into really large amounts and then… if the weight based dose range check could be arranged so that it works for a bolus, or a check for continuous infusion. one example is midazolam, or lidocaine. there are typical drugs like this, and morphine." 9. selecting prescription in milligrams or micrograms "for some drugs we don't talk in milligrams when prescribing them, but in micrograms per kilogram… and then you could in some way select the prescription in milligrams or micrograms. there are those situations when you have to sit down and calculate backwards and forward yourself to check that you do in fact get the dose right." 10. improve userfriendliness "sometimes you want to be able to write times four or something like that. now i have to hit tab and sort of transfer the number, twenty milligrams per kilo, then i have to move twenty, tab, write in twenty again, tab, twenty again. if i could write twenty times four in the weight based dose calculation, for example, then it would be a bit quicker." 11. nurses should also have access to the cdss "nurses who issue drugs really should be able to use it (the weight based dose calculation with dose range check) as well. because they also have to consider whether they are giving a reasonable dose." pediatricians’ understanding and experiences of an electronic clinical-decision-support-system introduction methods research design setting and informants data collection data analysis ethical considerations results 1. use use is influenced by clinical experience habit leads to increased use good that the cdss is not compulsory 2. benefit prompts consideration help with calculations reduces errors greatest benefit in emergency care 3. confidence in the wbdc and drc use of a manual dose range check double-checking the dosage 4. situations in which the doctor disregards the weight based dose calculation and/or the dose range check when it is easy to work out the dosage using mental arithmetic in case of special indications 5. misgivings/risks false security and non-disease specific warnings human error is unavoidable wrong dose calculating weight 6. development potential optional or compulsory – registering and signing for weight discussion conclusion financial disclosure conflict of interest references supplementary material isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts field team syndromic surveillance for mass gatherings: ncaa final four 2017 william e. smith*1, rebecca scranton2, melissa kretschmer1, karen zabel1 and kristen pogreba-brown3 1office of epidemiology, maricopa county department of public health, phoenix, az, usa; 2arizona department of health services, phoenix, az, usa; 3university of arizona, college of public health, phoenix, az, usa objective to describe and present results of field-based near-real time syndromic surveillance conducted at first aid stations during the 2017 national collegiate athletic association division i men’s college basketball championship (final four) events, and the use of field team data to improve situational awareness for mass gathering events. introduction final four-associated events culminated in four days of intense activity from 3/31/17-4/3/17, which attracted an estimated 400,000 visitors to maricopa county (population 4.2 million). field teams of staff and volunteers were deployed to three days of music fest, four days of fan fest, and three final four games (games) as part of an enhanced epidemiologic surveillance system. methods attendees presenting to first aid stations were requested to complete an electronic questionnaire which captured illness and injury syndromes (after needed care was given). emergency medical services technicians and nurses (ems) conducted patient care. these were submitted and epidemiologically assessed in near-real-time to rapidly identify threats. syndrome-specific data were mapped during events to identify spatial clustering. field teams were provided with case contact log sheets, suspicious substance investigation and exposure registry forms to allow rapid investigation of significant public health events. patient presentation rates (ppr) and transport to hospital rates (tthr) were calculated per 10,000 attendees. patients presenting per hour of event and transports per hour of event were calculated. field reports were included in daily reports to inter-disciplinary partners, and shared during regular multi-agency coordination center briefings. results 301 field questionnaires were completed, including 146 from final four games (games), 127 from the music fest, and 28 from the fan fest (see figure). among the 153,780 attendees of the three games, there were 146 cases who presented to one of five first aid stations (over 12 hours). there were 27 illness cases who sought care (18.5% of games cases), among whom 21 (78%) were assessed by ems. illness cases not assessed by ems (n=6) included mostly allergy symptoms/medication needs. there were 50 injury cases who sought care (34.2% of games cases), among whom 10 (20%) were assessed by ems. sixty (41.1%) persons presented seeking a pain reliever, and 9 (6.2%) presented seeking an antacid. games experienced a ppr of 9.5, and a tthr of 0.52. patients presented at 12.2 per hour on average and there were eight transports to medical facilities (0.66 per hour). there were 127 cases among an estimated 135,000 music fest attendees who presented to one of two first aid stations (and at times 2 roving teams) over 3 days (22.5 hours) from 3/31 to 4/2. illnesses accounted for 29 cases (22.8% of music fest cases) and 28 of 29 were assessed by ems. there were 68 injury cases who sought care (53.5% of music fest cases), among whom 22 (32.4%) were assessed by ems. twenty-seven persons (21.3%) presented seeking a pain reliever and 2 (1.6%) sought an antacid. music fest results included a ppr of 9.4, and a tthr of 0.15. there were 5.6 patients presenting per hour on average, and there were two transports to the hospital (0.09 per hour). at the fan fest there were 28 cases among an estimated 50,803 attendees presenting to the first aid station (or roving teams) from 3/31-4/3 (over 37 hours). most cases sought care for an injury (n=22, 78.6% of cases). four persons sought care for an illness (14.3%), all with relatively minor complaints. for the fan fest, there was a ppr of 5.5, and a tthr of 0 (there were no transports to the hospital). there were 0.76 patient presentations per hour on average. no geographic clustering or public health threats requiring investigation were identified at any of the three sites. interdisciplinary partners requested additional field data during the response. conclusions injuries were more common than illnesses at all three sites. visits requiring pain relievers only were more common at games (41.1%) than at music fest (21.3%) or fan fest (3.6%). a greater percentage of visits requiring ems assessment were seen at the fan fest (78.6%) than at the music fest (40.2%) or the games (21.2%). the pprs per 10,000 attendees were highest at the games (9.5) and music fest (9.4), compared to the fan fest (5.5). the tthr per 10,000 attendees was highest at the games (0.52), compared to the music fest (0.15) and fan fest (0.0). the music fest field team reported greater effectiveness at fixed first aid stations compared to traveling with roving ems teams. field reports enhanced health and medical situational awareness and information sharing as evidenced by requests from interdisciplinary partners for additional field data. figure: syndromes and patient presentation metrics at games, music fest and fan fest. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords mass gathering; syndromic surveillance; epidemiological surveillance; public health surveillance; final four acknowledgments thank you to the 2017 final four enhanced surveillance field teams *william e. smith e-mail: williamsmith@mail.maricopa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e129, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts tracking suspected heroin overdoses in cdc’s national syndromic surveillance program alana m. vivolo-kantor*1, r. matthew gladden1, aaron kite-powell2, michael coletta2 and grant baldwin1 1division of unintentional injury prevention, centers for disease control and prevention, atlanta, ga, usa; 2division of health informatics and surveillance, centers for disease control and prevention, atlanta, ga, usa objective this paper analyzes emergency department syndromic data in the centers for disease control and prevention’s (cdc) national syndromic surveillance program’s (nssp) biosense platform to understand trends in suspected heroin overdose. introduction overdose deaths involving opioids (i.e., opioid pain relievers and illicit opioids such as heroin) accounted for at least 63% (n = 33,091) of overdose deaths in 2015. overdose deaths related to illicit opioids, heroin and illicitly-manufactured fentanyl, have rapidly increased since 2010. for instance, heroin overdose deaths quadrupled from 3,036 in 2010 to 12,989 in 2015. unfortunately, timely response to emerging trends is inhibited by time lags for national data on both overdose mortality via vital statistics (8-12 months) and morbidity via hospital discharge data (over 2 years). emergency department (ed) syndromic data can be leveraged to respond more quickly to emerging drug overdose trends as well as identify drug overdose outbreaks. cdc’s nssp biosense platform collects near real-time ed data on approximately two-thirds of ed visits in the us. nssp’s data analysis and visualization tool, electronic surveillance system for the notification of community-based epidemics (essence), allows for tailored syndrome queries and can monitor ed visits related to heroin overdose at the local, state, regional, and national levels quicker than hospital discharge data. methods we analyzed ed syndromic data using essence to detect monthly and annual trends in suspected unintentional or undetermined heroin overdose by sex and region for those 11 years and older. an ed visit was categorized as a suspected heroin overdose if it met several criteria, including heroin overdose icd-9-cm and icd-10-cm codes (i.e., 965.01 and e850.0; t40.1x1a, t40.1x4a) and chief complaint text associated with a heroin overdose (e.g., “heroin overdose”). using computer code developed specifically for essence based on our case definition, we queried data from 9 of the 10 hhs regions from july 2016-july 2017. one region was excluded due to large changes in data submitted during the time period. we conducted trend analyses using the proportion of suspected heroin overdoses by total ed visits for a given month with all sexes and regions combined and then stratified by sex and region. to determine significant linear changes in monthly and annual trends, we used the national cancer institute’s joinpoint regression program. results from july 2016-july 2017, over 72 million total ed visits were captured from all sites and jurisdictions submitting data to nssp. after applying our case definition to these records, 53,786 visits were from a suspected heroin overdose, which accounted for approximately 7.5 heroin overdose visits per 10,000 total ed visits during that timeframe. the rate of suspected heroin overdose visits to total ed visits was highest in june 2017 (8.7 per 10,000) and lowest in august 2016 (6.6 per 10,000 visits). males accounted for a larger rates of visits over all months (range = 10.7 to 14.2 per 10,000 visits) than females (range = 3.8 to 4.7 per 10,000 visits). overall, compared to july 2016, suspected heroin overdose ed visits from july 2016 were significantly higher for all sexes and us regions combined (β = .010, p = .036). significant increases were also demonstrated over time for males (β = .009, p = .044) and the northeast (β = .012, p = .025). no other significant increases or decreases were detected by demographics or on a monthly basis. conclusions emergency department visits related to heroin overdose increased significantly from july 2016 to july 2017, with significant increases in the northeast and among males. urgent public health action is needed reduce heroin overdoses including increasing the availability of naloxone (an antidote for opioid overdose), linking people at high risk for heroin overdose to medication-assisted treatment, and reducing misuse of opioids by implementing safer opioid prescribing practices. despite these findings, there are several limitations of these data: not all states sharing data have full participation thus limiting the representativeness of the data; not all ed visits are shared with nssp; and our case definition may under-identify (e.g., visits missing discharge diagnosis codes and lacking specificity in chief complaint text) or over-identify (e.g., reliance on hospital staff impression and not drug test results) heroin overdose visits. nonetheless, ed syndromic surveillance data can provide timely insight into emerging regional and national heroin overdose trends. keywords heroin; essence; syndromic surveillance references warner m, chen lh, makuc dm, anderson rn, minino am. drug poisoning deaths in the united states, 1980-2008. nchs data brief 2011(81):1-8. rudd ra, seth p, david f, scholl l. increases in drug and opioidinvolved overdose deaths united states, 2010-2015. mmwr morb mortal wkly rep 2016;65(5051):1445-1452. spencer mra, f. timeliness of death certificate data for mortality surveillance and provisional estimates. national center for health statistics 2017. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts richards cl, iademarco mf, atkinson d, pinner rw, yoon p, mac kenzie wr, et al. advances in public health surveillance and information dissemination at the centers for disease control and prevention. public health rep 2017;132(4):403-410. *alana m. vivolo-kantor e-mail: avivolo@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e189, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e278, 2019 isds 2019 conference abstracts electronic processing of antimicrobial susceptibilities to enhance communicable disease surveillance emily roberts informatics program, utah department of health, salt lake city, utah, united states objective illustrate how the utah department of health automatically processes antimicrobial susceptibility results that are received electronically introduction the emerging threat of antimicrobial resistant organisms is a pressing public health concern. surveillance for antimicrobial resistance can prevent infections, protect patients in the healthcare setting and improve antimicrobial use. in 2018, the utah department of health mandated the reporting of antimicrobial susceptibility panels performed on selected organisms. utah util izes the electronic message staging area (emsa), a home-grown application to translate, process, and enter electronic laboratory results into ut-nedss, utah’s integrated disease surveillance system. processing these results electronically is challenging due to the need to interpret results based on the antimicrobial agent combined with the organism it was performed on. the receipt of antimicrobial susceptibility panels has required enhancements to emsa for these results to be automatically processed. methods stand-alone antimicrobial susceptibility loincs are configured within emsa to concatenate during the preprocessing stage. this tells emsa that when this loinc is sent within an hl7 message to find the organism name in the corresponding obr 26.3 (the parent result field). emsa then creates a new fabricated code that combines the antimicrobial agent with the organism identified from the culture (example: ‘18906-8 pseudomonas aeruginosa’ is the fabricated code for ciprofloxacin susceptibility to pseudomonas aeruginosa). once these new fabricated antimicrobial susceptibility codes are created, interpretation rules are programmed using current clinical and laboratory standards institute (clsi) breakpoints for each unique organism/antimicrobial combination to determine if the result is susceptible/intermediate/resistant. the interpreted test is then run through a set of condition-specific rules to determine how it should be included into ut-nedss. results antimicrobial susceptibility panels performed on acinetobacter species, escherichia coli, klebsiella species, pseudomon as aeruginosa, enterobacter species, candida auris/haemulonii, mycobacterium tuberculosis, neisseria gonorrhoeae, salmonella species, shigella species, streptococcus pneumoniae and invasive staphylococcus aureus are now included in utah’s communicable disease reporting rule. currently, there are 36 antimicrobial agents programmed into emsa and there are a total of 217 antimicrobial susceptibility codes programmed into the system. conclusions processing electronic antimicrobial susceptibility results presents unique challenges for processing. interpretation of results can vary based on test method, performing laboratory, and organism. enhancing functionality within emsa was necessary for combining the antimicrobial agent and organism it was performed on. implementing systems capable of automatically processing complicated antimicrobial susceptibility results should be a priority for any health department interested in expanding their communicable disease rule to include antimicrobial susceptibility testing. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts farm biosecurity at backyard poultry of bangladesh and its role in spread of hpai muhammad asaduzzaman* international centre for diarrheal disease research, bangladesh, dhaka, bangladesh objective we have conducted this study to characterise the movement and contact patterns of poultry in bangladesh that could be associated with transmission of newly-introduced subtypes of avian influenza virus in two districts of bangladesh as well as to summarise the patterns arising from the network analysis in a way that can inform the parameterisation of spatially explicit stochastic models of transmission of newly-introduced subtypes of avian influenza virus in the two types of areas. introduction bangladesh is a south asian country with large human and poultry populations which is highly affected with frequent outbreaks of both high and low pathogenic avian influenza since 2007. very few studies have been carried out to reveal the farm biosecurity at backyard poultry that might have contributed to the spread of avian influenza in bangladesh, specially rural areas. therefore, we aimed to characterize biosecurity practices of poultry farm including the movement of live birds which is a well-known risk factor for the geographic dissemination of the virus among poultry flocks and personnel hygiene of poultry workers for rapid detection and effective risk management of incursion of hpai and lpai viruses. methods this cross sectional survey was carried out using a pretested questionnaire in backyard poultry holdings of kalkini upazila of madaripur district in dhaka division which has a relatively low proportion of commercial poultry farms and high proportion of backyard poultry holdings. 1-mode and 2-mode social network analysis was also carried out to show the farm to farm movements. from each primarily selected farm, details of the last 2 movements of live poultry along with source/destination details was collected with pre-tested questionnaire. later, data was stored in epi-info, analysed with stata 14 and ucinet. 315 backyard hh from 2 villages of kalkini upazila, madaripur district were randomly selected. results the study revealed that majority backyard farm owners do not maintain the standard biosecurity measures whereas a significant amount of the study included farms rear multiple poultry species. no poultry workers were found to use any personal protective equipment (ppes) while cleaning the litter/mats (figure 2). the farms with multiple poultry species feed them in same container and keep them in same shed which is a major risk factor for disease transmission. movement patterns differed in a number of aspects (table 1) and this information is useful for the establishment of the movement parameter settings in a simulation model of avian influenza incursion. conclusions the findings on farm biosecurity practices and movement pattern from this study will support to develop risk-based surveillance and contingency policies as well as to minimize the spread between poultry units and also from poultry to people for novel ai viruses in bangladesh. farm to farm movement of live birds (1-mode network analysis) figure 1. study area isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts figure 2. biosecurity practices in backyard farms of madaripur district keywords farm biosecurity; hpai; backyard poultry acknowledgments we thank european commission for funding and implementation of this work. *muhammad asaduzzaman e-mail: asaduzzaman@icddrb.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e128, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts which sections of electronic medical records are most relevant for real-time surveillance of influenzalike illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 1rush university, chicago, il, usa; 2pangaea information technologies, ltd, chicago, il, usa; 3h-core, llc, chicago, il, usa objective to investigate which section(s) of a patient’s electronic medical record (emr) contains the most relevant information for timely detection of influenza-like illness (ili) in the emergency department (ed). introduction effective real-time surveillance of infectious diseases must strike a balance between reliability and timeliness for early detection. traditional syndromic surveillance utilizes limited sections of the emr, such as chief complaints and/or diagnosis. however, other sections of the emr may contain more pertinent information than what is captured in a brief chief complaint. these other emr sections may provide relevant information earlier in the patient encounter than at the diagnosis or disposition stage, which can appear in the emr up to 24 hours after the patient’s discharge. comprehensive analysis may identify the most relevant section of emrs for surveillance of all major infectious diseases, including ili. methods this was a retrospective, cross-sectional study. the sample consisted of 100 randomly selected ed ili-positive patients at an academic medical center. these patients came to the emergency department during the 2014-2015 ili season (september 1, 2014 to april 30, 2015). geographic utilization of artificial intelligence in real-time for disease identification and alert notification (guardian) – a syndromic surveillance program – was used to identify the positive ili patients by applying the centers for disease control and prevention case definition of ili (i.e., fever with cough and/or sore throat) to the entire emr. for each patient, the presence or absence of each ili symptom was documented by a board-certified emergency physician for each section of the emr, specifically: registration/arrival complaints, triage chief complaints, flow sheet/ vital signs data, history of present illness (hpi), review of systems (ros), physical exam, assessment plan, diagnosis, free-text clinical notes, and discharge instructions, among others. in addition, efficacy of each emr section in detecting ili was documented. results the ili symptoms documented in the hpi section of the emr captured 80% of ili cases (table 1). thirty-nine percent of ili cases had ili symptoms documented in registration arrival complaints/ screening questions/triage chief complaints, while 91% of ili cases had symptoms listed in the free-text sections of ros and/or hpi plus flowsheet vital signs. in addition, only 46% of ili cases had ili symptoms documented in the discrete data fields of the emr. conclusions the hpi, ros, and nursing notes sections of the emr were information rich and the most relevant sections for ili surveillance. since 61% of cases reported ili symptoms in areas of the emr other than the commonly-used triage and registration sections, it is warranted that expanding ed syndromic surveillance to other areas of the emr may increase sensitivity. thus, reliable real-time syndromic surveillance systems need to be capable of processing both discrete and free-text data from various sections of the emr. table 1: percent of each section of emr that meets the definition of ili keywords influenza-like illness; guardian; electronic medical records acknowledgments guardian is funded by the us department of defense, telemedicine and advanced technology research center, award numbers w81xwh-09-1-0662 and w81xwh-11-1-0711. *gillian s. gibbs e-mail: gillian_gibbs@rush.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e36, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher 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craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts in-country acute flaccid paralysis surveillance review, nasarawa state, nigeria, 2017 maureen o. anyanwu*1, ndubuisi akpuh2 and adewole adefisoye3 1nigeria field epidemiology and laboratory training programme/ oyo state ministry of health, ibadan, nigeria; 2department of public health, ministry of health, rivers state, portharcourt, nigeria; 3african field epidemiology network, asokoro, nigeria objective in august, 2017, we conducted a peer review evaluation of the reported high stool adequacy and non-polio acute flaccid paralysis (afp) rates of the world health organisation (who) verified afp cases, in order to estimate and establish concordance for both surveillance core indicators in lafia and nasarawa egon lgas in nasarawa state. introduction nigeria is the only polio endemic country in africa. four (4) wpv1 cases were confirmed in 2013 after two years of silence. nigeria has a strong polio programme characterized by innovative and forward driven strategies, despite several challenges of which surveillance is one of the driving forces. near perfect surveillance core indicators reported over the past twelve (12) months across certain states and local government areas (lgas) were issues of concern, given security challenges among others. in august, 2017, we conducted a peer review evaluation of the reported high stool adequacy and non-polio acute flaccid paralysis (afp) rates of the world health organisation (who) verified afp cases, in order to estimate and establish concordance for both surveillance core indicators in lafia and nasarawa egon lgas in nasarawa state. methods the lgas to be visited and afp cases reported within ninety (90) days and verified to be true and adequate prior to peer review were selected. any person with strong surveillance knowledge and skill, working in nigeria with the government or partner agencies and involved in surveillance was identified as a peer reviewer, trained and deployed to the lgas. reviewers were not deployed to their geo-political zones where they work under routine conditions. data was collected by visiting the residence of the respective afp cases and eliciting responses, using a structured interviewer -administered peer review checklist. data was collated, analysed using microsoft excel 2010 and interpreted accordingly. the causes of incoherence were identified and presented to the lga disease surveillance and notification officers (dsnos) and state authority. an improvement plan which would be monitored and evaluated was elaborated. the afp surveillance data base for discordant afp cases was updated with the data generated from the peer review. results of the nineteen (19) afp cases reviewed, 63.2% (12/19) were females. the mean age of the total afp case patients was 3 years (sd 3.4). in lafia lga, eight (8) afp cases were verified and all were true afp cases and adequate. in nasarawa egon lga, eleven (11) cases were verified, 54.5% (6/11) were true afp cases and 90.9% (10/11) were found to be adequate. the major causes of the gaps identified include mothers/caregivers dividing collected stool specimen sample to make for two (2) stool samples meant to be collected 24 hours apart for case investigation. this was due to failure on the part of the lga dsnos to either inform the mothers/caregivers or underscore the importance of appropriate stool collection. the inability of the surveillance focal officers to adequately identify/differentiate other disease conditions that mimic afp and persistence of residual paralysis (in non-polio afp cases) in 5 (45.5%) cases were also identified in nasarawa egon lga. this was as a result of the lack of referral to the next level for physiotherapy care. conclusions in nasarawa egon lga, there were discordances in the reported afp performance core indicators. they include inadequate stool sample, wrong classification of afp cases and persistence of residual paralysis in non-polio afp cases. we therefore, recommend that the who state team should re-orient the lga dsnos on proper stool specimen collection for case investigation. also, the lga dsnos should sensitize parents/caregivers on appropriate protocol of stool specimen collection and advise them on referral to the next level of care. keywords afp; peer review; nasarawa state *maureen o. anyanwu e-mail: maureenanyanwu23@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e139, 2018 duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi duplication of effort across development projects in nigeria: an example using the master health facility list olusesan ayodeji makinde1,2,3, emmanuel c. meribole4, kolawole azeez oyediran5, fadeke a. fadeyibi4, marc cunningham5, yetunde hussein-fajugbagbe4, femi toye4, akin oyemakinde4, stephanie mullen5 1. measure evaluation, jsi, abuja, nigeria. 2. viable knowledge masters, abuja, nigeria. 3. demography and population studies program, schools of public health and social sciences, university of the witwatersrand, johannesburg, south africa. 4. department of health planning, research and statistics, federal ministry of health, abuja, nigeria. 5. measure evaluation, jsi, arlington, va, usa 22209. abstract objective: duplication of effort across development projects is often the resultant effect of poor donor coordination in lowand middle-income countries which receive development assistance. this paper examines the persistence of duplication through a case study of health facility listing exercises in nigeria. methods: document reviews, key informant interviews, and a stakeholder’s meeting were undertaken to identify similar health facility listing exercises between 2010 and 2016. results: as an outcome of this process, ten different health facility listing efforts were identified. discussions: proper coordination and collaboration could have resulted in a single list grown over time, ensuring return on investments. this study provides evidence of the persistence of duplication, years after global commitment to harmonization, better coordination and efficient use of development assistance were agreed to. conclusions: the paper concludes by making a proposal for strategic leadership in the health sector and the need to leverage information and communications technology through the development of an electronic health facility registry that can archive the data on health facilities, create opportunity for continuous updates of the list, and provide for easy sharing of the data across different country stakeholders thereby eliminating duplication. keywords: aid effectiveness, donor coordination; health facilities; health information system; health systems; international cooperation; master facility list correspondence: sesmak@gmail.com mailto:sesmak@gmail.com duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi doi: 10.5210/ojphi.v10i2.9104 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction donors commit significant resources to supporting the health systems of several lowand middle-income countries (lmics). between 1992 and 2006, donor funding for health and population nearly quadrupled, reaching us$13.6 billion annually [1]. relative annual contribution of external aid as a share of total health spending rose from 15.3% to 29.4% between 2001 and 2011 in low-income countries. also, external aid per capita increased by 25% per year from us$1.8 to us$6.1 for the same period [2]. development projects are often poorly coordinated across different donors resulting in duplication of effort and a waste of resources [3]. duplication of effort occurs when more than one project or intervention is needlessly implementing similar activities within a geographic location or country which arises often because of poor knowledge management and inadequate coordination of projects, thereby resulting in aid inefficiency [4]. while duplication of effort has been recognized as a challenge to international cooperation projects with strategies aimed at addressing them such as through direct budgetary support to governments (sector-wide approach) rather than financing through projects which often gave room for the duplication, several development interventions are still structured in the project model [5,6]. corruption, lack of transparency, and donor resistance to change are important factors that have limited the adoption of the sector-wide approach by donors [6]. in rwanda, improved donor coordination resulted in wide ranging successes which include: less duplication of effort, reduction in the number of parallel systems of accounting, procurement, and management, and reduction in the number of inappropriately designed and coordinated projects among other benefits [7]. however, this was achieved through the sector-wide approach and after gaining the confidence of donors through transparent processes [7]. realizing the wastage as a result of weak coordination, collaboration and partnerships, several global initiatives have been launched to foster harmonization, enhance coordination, promote leveraging of resources and forge the development of a joint agenda to address these problems. some of these global initiatives include: the high level forum on harmonization in rome (2003), the marrakech roundtable on managing for results (2004), the paris declaration on aid effectiveness (2005), the accra agenda for action (2008) and the busan partnership for effective development cooperation (2011) [8,9]. the paris declaration on aid effectiveness was developed on five mutually reinforcing principles: ownership, alignment, harmonization, managing for results and mutual accountability [8]. the importance of donor and partner coordination was less talked about as a means of achieving better development goals and the reduction of wastage of resources in lmics. however, recent observations suggest that donor coordination is as important as the amount of resources that are duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi being committed to development. increased commitment of donor resources has not been shown to have a similar effect on the level of development in recipient countries [10-12]. this is a challenge for both donors and recipient countries as investments are not yielding the desired returns and development outcomes. while donor coordination is an important factor, it requires a government institutional framework through a national coordinating agency with technical capacity to be in the driver’s seat and to liaise with donors for the effort to be meaningful. though the paris declaration gave responsibility for coordination to both the donor and recipient countries, the largest responsibility rests on recipient countries to make the best use of aid [8]. notwithstanding, there are documented instances of donors withholding fund availability information from recipient country institutions thereby challenging an appropriate planning mechanism [6,9,13]. in nigeria, the responsibility for donor coordination lies with the ministry of budget and national planning, which was until recently known as the national planning commission. for the health sector, donor funded projects are expected to coordinate with and support the federal ministry of health (fmoh), which is responsible for implementing the national government’s plan on health. however, coordination by and between these in-country institutions is still suboptimal. as part of strategies aimed at addressing donor coordination in the nigerian health sector, different interventions have been put in place such as the development of the national strategic health development plan (nshdp): 2010-2015 and a rationalization exercise to allocate donors to specific geographic areas [14]. the nshdp proposed the establishment of a health development partners forum which provides an opportunity for donors and the government to coordinate interventions. the nshdp further identified the need for the establishment of better funding mechanisms using the sector wide approach. this paper investigates the persistence of duplication on development projects in nigeria through a case study focusing on the creation of health facility lists. health facility lists are important planning tools for determining the capacity of services available in a country and the equity in distribution of the services among the population [15,16]. according to the world health organization, a master health facility list (mfl) “is a complete listing of health facilities in a country (both public and private) and is comprised of a set of identification items for each facility (signature domain) and basic information on the service capacity of each facility (service domain)” [17]. a mfl is also an important source for delineating registered and/or licensed from unregistered/ unlicensed health facilities in the country. information on the location and services provided by health facilities is important as nigeria begins to implement its plan for achieving universal health coverage (uhc) by 2030 [18]. one important objective of uhc is equity in access to services by the population which can be determined through the information available in the mfl [19]. an up-to-date mfl will also serve as the source of the statistics of the expected routine encounter reports from active health facilities in a country. one major challenge that has been reported in the management of the national health management information system is the difficulty with determining the statuses of the health facilities over time as some close out and new health facilities emerge [20]. this affects the ease of computation of the number of active health facilities that serve as the denominator statistic when calculating completion and reporting rates for the routine health information system. thus, the unavailability of an up-todate mfl in a country has several direct and indirect consequences on the health system. duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi the list of health facilities in a country infrequently changes thereby making the study of parallel efforts to develop health facility lists across the country a good case study to highlight duplication of effort. methods this study was carried out as part of a large project intervention to strengthen the national health information system in nigeria. a mixed-methods approach was adopted to collect the data to address the objectives of the study. a review of available government reports and strategic documents, key informant interviews (kiis) and a stakeholder’s workshop was carried out to [1] identify the health facility lists that were created since 2010 [2], understand the rationale for the creation of the various lists [3], learn about the breadth of the data collection that was done to create the lists [4] determine the availability of the health facility lists and the data they contain and [5] learn how the data was used since the creation of the lists. table 1 describes the research methods used and information sources consulted. document review the documents reviewed were identified through prior knowledge of the health facility listing projects by the researchers, searching the internet for any documents on health facility listing in nigeria using terms such as “health facility lists” and “nigeria” in google search and from information retrieved from key informants. openly accessible documents were downloaded from the websites of the organizations that carried out the exercises and additional documents were retrieved through formal request for the reports from the projects. key informant interviews key informants were interviewed to help identify additional health facility listing exercises that had taken place in the country, the rationale for the exercises, use of the information collected and the availability of the data. first, the government institutions that had carried out the health facility listing exercises or collaborated with a development partner were identified and approached. then, key informants who had played a major role during the health facility listing exercise carried out were identified and interviewed. during the interview, we investigated about the health facility listing exercise wanting to know the rationale, the number of health facilities covered, which donor was involved, and how the data had been used since the creation of the list. we also retrieved any reports where available. table 1: sources of information research method information source document review • directory of health facilities in nigeria [21], • publication describing the master health facility list in nigeria [22], • report and conference presentation of the strengthening health outcomes through the private sector (shops) project private health facility census [26,27], duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi • website reporting the project implemented by the office of the senior special assistant to the president of nigeria on the millennium development goals in collaboration with columbia university [23] • presentation on service availability mapping for hiv in nigeria at nigeria health and mapping summit and a poster at the 19th international aids society conference [28,29] • conference presentation on malaria diagnostic service availability‐mapping of private sector service delivery outlets in 7 pmi/maps supported states, nigeria [30] • reports of the gis mapping of primary healthcare facilities in nigeria [31] • report of the mapping exercise of reproductive, maternal, newborn, child and adolescent health in nigeria [32] key informant interview agencies/organizations • department of health planning, research and statistics • national agency for the control of aids • national aids and stis control programme • national primary healthcare development agency stakeholder’s workshop • government officers across different departments and agencies including all those that had done some health facility listing exercise, donors (including those that have funded the activities identified), implementing partners, bilateral and multilateral agencies, health facility regulatory authorities. stakeholder’s workshop the stakeholder’s workshop was convened by the department of health planning research and statistics of the fmoh and was held august 30–31, 2016 in abuja, nigeria. the meeting was convened with the funding support of the united states agency for international development (usaid) and technical assistance from measure evaluation. there were over 50 participants at the workshop and they cut across government departments and agencies, donor organizations, bilateral and multilateral agencies and local and international implementing partners. representatives of each government organization/ department that had carried out a health facility listing exercise were invited to make presentations on their projects. a generic microsoft powerpoint template of the expectations of each effort was developed to reinforce the information needed and in addition aid uniformity of presentations at the workshop. it included (i) the name of the organization/ project, (ii) year of the health facility list data collection, (iii) partner/ donor that supported the effort, (iv) purpose/ objective of the exercise, (v) types of health facilities covered, (vi) states covered, (vii) availability of the data, (viii) use of the data since creation and (ix) the total number of health facilities listed. duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi the presentations were delivered by government personnel from the various units that had carried out the exercises. members of the audience were then permitted to field questions on the presentations made. notes were taken by the lead author and the presentations delivered by each participant was also retrieved and analyzed. in addition, the group broke out into sessions to discuss the presentations and to jointly identify issues with the different efforts and the most suitable way forward in addressing the duplication. at plenary, the outcomes of the discussions were made by the team members. as an outcome of the stakeholder’s workshop, a technical working group (twg) was set up to lead the agreed interventions. members of the twg cut across the different technical departments and agencies of the federal ministry of health, one state level ministry of health, some donors, and bilateral and multilateral agencies. the goal of the twg was to help determine the minimum dataset on health facilities that would satisfy all the stakeholders and to identify best practices for managing health facility lists in order to avoid duplication. while this twg was given the immediate assignment to help in determining a minimum dataset for the mfl, the twg subsequently agreed on the need for the development of an electronic health facility registry (hfr) as the best means for managing the mfl data. results six independent government departments/ agencies were found to have partnered with different development organizations between 2010 and 2016 to create a total of 10 health facility and service availability lists across nigeria (table 2). some of these efforts covered a few states or specific diseases. it is noteworthy that government institutions led each of the exercises except two (the strengthening health outcomes through the private sector (shops) project – census of private health facilities which was conducted as research to determine the level of correctness of the government lists on private health facilities in the six states where the censuses were carried out and the malaria consortium/ dfid risk mapping exercise). the health facilities covered by the lists were hospitals and clinics predominantly. only the service availability mapping exercise carried out by the national aids and stis control programme in 2011/2012 included one other class of health facility beside hospitals and clinics. the directory of the health facilities published by the federal ministry of health in 2013 was identified as the federal government’s approved list of health facilities [21]. however, it had several shortcomings. only hospitals and clinics had been listed during its development, few parameters mainly on each health facility excluding global positioning system (gps) coordinates was collected and it lacked processes and an information system to manage the list and keep it updated [20]. the data collected during the exercise include: the name of the facility, state, local government area, ward of location, the ownership (private or public), level of care (primary, secondary or tertiary) and a unique identifier which was allotted to each facility through this effort. the process for completing this directory has been documented by the federal ministry of health [22]. the effort led by the office of the senior special assistant to the president on the millennium development goals was also reviewed [23]. the information collected by the project included geographic coordinates of health facilities and services available at the facilities. other data collected by the project were for other social infrastructure such as schools and water facilities duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi within each community. however, the website (http://nmis.mdgs.gov.ng/) which once displayed interactive maps of the health facilities and other infrastructure is no longer available. an effort to locate the office for formal interviews did not yield results, as the project was no longer active. for six of the 10 efforts identified, the service information collected on the health facilities was specific to disease programs such as: malaria, hiv/aids and reproductive, maternal, newborn, child, adolescent health and nutrition programs. based on information provided by key informants, it was discovered that gps devices were procured more than once for the specific purpose of collection of gps coordinate data by the different projects which was wasteful as gps coordinates do not change. if properly archived and shared after collection, the gps coordinates for health facilities could have served more than one project’s goal. furthermore, the cost of logistics towards the completion of the individual data collection efforts across the large geographic area which nigeria covers would have been quite significant based on projections from other efforts supported by measure evaluation. figure 1 shows the number of events that have taken place in each state across the country while figure 2 shows the states involved in each of the 10 exercises. discussion in the absence of an up-to-date mfl in nigeria, several projects embarked on the establishment of project-specific health facility lists in order to satisfy their project goals. these projects were occasionally across a few states and narrowly focused. yet, some were across the entire country. though the identified projects in table 2 were carried out for specific purposes, our study reveals that improper coordination of the activities of the different government departments and agencies led to a waste of resources and brewed inefficiency. using the available resources to strengthen the national mfl could have helped to enrich the effort previously completed in the country by the department of health planning, research and statistics of the fmoh (the custodians of health data for the country) further while also meeting the individual project goals. such efforts would have helped in growing the mfl and to expand the data elements as well as to establish processes and procedures for the continuous updating of the health facility list. in the next sections of the paper, we discuss our observed points of duplication and its attendant consequences. we further present strategies for addressing duplication including strategic leadership and the deployment of an electronic health facility registry (hfr) for managing the mfl as a potential intervention for addressing the duplication and improving the efficiency of the health facility listing efforts in the country. points of duplication in health facility listing efforts though it is well documented that aid fragmentation and poor coordination deters aid effectiveness in recipient countries, evidence continues to show that donor commitment to better coordination are yet to be achieved [3,9,24]. our analysis of the health facility listing projects revealed that duplication of effort persists in nigeria. duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi table 2: findings from the health facility lists assessment exercise nigerian government agency/ department/ project year of activity number of health facilities listed coverage rationale gps coordin ates collecte d data use funding agency department of health planning, research and statistics, federal ministry of health (dhprs) 2011 34,423 nationwide a national master health facility list that allocates unique identifiers to all health facilities in the country no for planning. recognized as the national health facility list. federal government of nigeria and usaid (measure evaluation) national aids and stis control programme (hiv/ aids division), department of public health, federal ministry of health/ national agency for the control of aids (nascp) 2011/ 2012 24,4731 32 states a hiv service availability mapping across 32 states in the country. yes two conference presentations. usaid (health systems 20/20)/ global fund national primary healthcare development agency (nphcda) 2011/ 2012 7,889 9 states mapping of all public primary healthcare facilities across priority states. yes planning. federal government of nigeria office of the senior special assistant to the president of nigeria on the millennium development goals (ossa mdg) 20112014 34,116 nationwide service availability mapping across the country. yes interactive website that is no longer available. conditional grants scheme program, federal government of nigeria shops project – census of private health facilities across six states2 (shops) 2012/20 13 5,086 6 states research project to validate government lists of private health facilities and establish baseline for engagement on a yes research on assessing completeness of state health facility lists. report and usaid (shops) duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi usaid project. conference presentation malaria consortium/kemri/ university of oxford 2014 20,817 nationwide malaria risk mapping in public primary and secondary health facilities listed in the national district health information system platform yes project planning dfid national malaria elimination program, department of public health, federal ministry of health (nmep) 2014 900 12 states health facility assessment to determine availability and quality of services offered for malaria. yes project planning, conference presentation global fund national malaria elimination program, department of public health, federal ministry of health (nmep) 2015 1,430 9 states health facility assessment to determine availability and quality of services offered for malaria. yes research, conference presentation world bank national primary healthcare development agency/ unfpa 2015 6,063 9 states mapping of reproductive, maternal, newborn, child and adolescent health in public and private health facilities across nine states. yes project planning unfpa national agency for the control of aids (naca) 2016 5,351 6 states service availability mapping across the country (suspended by donor midway) yes project planning global fund duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi figure 1: number of times health facility lists were generated by states figure 2: states covered by each of the identified exercises duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi several health facility listing efforts were clustered between 2010 and 2012, often funded by international aid with the aim of collecting health facility data that often overlapped with prior completed efforts. this was also about the time that the fmoh had updated its national mfl and had highlighted the need for expansion and strengthening of this list through the development of an information system to manage the records [21,22]. the different projects identified often collected data on the name of the facility, address, level of care, ownership, contact information, the gps coordinates and other specific parameters for that health facility most of which could easily have been obtained from prior completed efforts. the “bandwagon effect” cannot be ruled out as six different government departments within a relatively short time engaged in parallel health facility listing and mapping exercises across the country. the selling points to the projects were the visualizations and maps that could be generated from these relatively expensive data collection efforts. though the projects often had disease specific objectives which justified the different interventions, one comprehensive data collection effort with an expanded scope for the different program areas could have sufficed. several man-hours were spent collecting data that could have been more impactful if used in strengthening a single properly planned and coordinated effort. while the exact amount spent on each project was not available to the researchers, using estimates from the measure evaluation-supported effort to project costs reveal tens of millions of naira have been expended in the parallel efforts. consequences of duplication of health facility lists duplication of effort results in a waste of limited resources. despite significant investments, the health system in nigeria is still challenged. health indicators are poor with maternal mortality and infant mortality rates amongst the highest in the world [25]. though the responsibility for maintaining such national data resource in nigeria resides with the department of health planning, research and statistics within the fmoh, different donor agencies funded alternate government institutions/ departments to carry out responsibilities that were not characteristically theirs. this was possible because the departments/ projects were operating in vertical systems and coordination within the nigerian government was poor. multiple health facility lists lead to confusion as every party acclaims that their controlled list is the most comprehensive and thus should be used for national decisions. also, donor support towards multiple institutions in countries for similar work might empower the wrong department further disenabling the health system. though often to obtain quick results, this eventually results in in-fighting and a long term fractured health system. furthermore, the unattained health system goals could hamper future support as a result of donor fatigue. consequently, the desired objectives of the health system are not met and the overall return on the investments is not achieved. also, the sustainability of narrowly focused parallel health facility listing efforts which may provide immediate results will eventually fail should the project funds dry up. this has been observed in the effort led by the office of the senior special assistant to the president on the millennium development goals in partnership with columbia university. the interactive website developed by the project that the authors once visited is no longer available online. though a report on the project provided on the website of columbia university stated that the duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi project had been handed over to the team of the president’s aide [23], this unit did not have the statutory responsibility to maintain such system. funds to sustain the project beyond the grant would not have been allocated to that office thereby compromising ongoing maintenance of the website. as previously noted, the office has since been disbanded and there was no one to provide us official information on the project. addressing duplication in health facility listing in nigeria addressing duplication in the health system as a whole will be a joint responsibility of the donors and the country governments. this should include better coordination of funding and decision making between both parties. our two main suggestions for addressing the challenge of duplication with health facility listing are strategic leadership for the health system and by leveraging information and communications technology for sharing the mfl in an electronic hfr. the goal of the effort will be to eliminate duplication, improve efficiency in the use of aid while also helping the different stakeholders to achieve their individual project targets. strategic leadership duplication of effort is a manifestation of a failure in the coordination of the health system. this is often possible because of poor responsibility assignment across the different health related agencies that exist at the national level in the country. rather than work together, the institutions/ departments preferred to work in vertical projects and silos, which was inefficient and wasted resources. despite the plan to move towards a sector-wide funding approach for development assistance as outlined by the nshdp, this is still yet to be achieved as donor support is still being implemented in the vertical project pattern. the resulting manifestation is the persistence of duplication of projects. identifying the reasons for the non-implementation of such strategies planned in the nshdp: 2010-2015 and taking steps towards addressing them will be an important direction for the leadership of the health sector. the health facility registry model leveraging information and communications technology in addressing the issue of health facility lists is another approach that has been proposed. a hfr can be deployed to archive the information on health facilities in nigeria and thereby be used to share the data with interested parties based on appropriate governance principles. the hfr can also permit systems to pull data from it thereby allowing the government to maintain a single list of health facilities for the country and permitting other stakeholders to pull data from the system. a model for the hfr and how the data could be shared across different stakeholders is presented in figure 3. figure 3: health facility registry use demonstration duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi study limitations the study is mainly qualitative and exploratory. thus, many of the findings cannot be quantified. for instance, we could not obtain information on the cost of the different health facility listing efforts to determine the level of waste of resources by the parallel efforts. in addition, there may have been more health facility lists in circulation than reported, as we found additional projects on the internet after obtaining information from the stakeholders. conclusion our study provides evidence of how poor donor coordination and unclear national institution responsibilities result in aid inefficiency. our proposed strategies for solving the identified problems are both internal (within the nigerian government) and external (with the donors). the federal government needs to improve its in-house coordination, including streamlining of organizations with conflicting responsibilities to foster activities that will have the largest impact. it also needs to implement its plans such as the proposed sector wide approach of funding which was planned in the nshdp: 2010-2015. in the interim, donors need to use discretion to identify the government departments with specific responsibilities for activities that they would like to support thereby fostering institutionalization. supporting small components of national long-term goals rather than looking for quick wins will no doubt help recipient countries such as nigeria achieve development targets better. an information and communications technology-driven hfr as the harbor of the national mfl should be deployed for ongoing management of the records. the data on each health facility can be grown over time and thus respond to various vertical program interests. nodes to access this robust database can then be shared with different government agencies and development partners duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi that require such information for their various projects. this will help to eliminate the waste of resources by alternate service availability mapping exercises that are undertaken by different programs in nigeria. acknowledgments financial support for this project was provided by the u.s. agency for international development (usaid) under the terms of measure evaluation cooperative agreement aidoaa-l-14-00004. views expressed are not necessarily those of usaid or the united states government. we acknowledge the contributions of mr. oladipo adegbotolu and mr. olutobi adeogo during the course of project implementation. references 1. farag m, nandakumar ak, wallack ss, gaumer g, hodgkin d. 2009. does funding from donors displace government spending for health in developing countries? health aff (millwood). 28(4), 1045-55. pubmed https://doi.org/10.1377/hlthaff.28.4.1045 2. world health organization. who global health expenditure atlas [internet]. switzerland; 2014. 228 p. available from: http://www.who.int/health-accounts/atlas2014.pdf 3. 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nigeria’s health system. in new orleans, usa; 2013. available from: http://paa2013.princeton.edu/papers/132264 27. woodman b, baruwa s, toriola m, chatterji m, kinnan c, et al. a census of private health facilities in six states of nigeria. 2014 [cited 2015 jul 12]; available from: http://africacapacityalliance.org/n4a/wp-content/uploads/2014/06/shops-nigeria-privatesector-health-census_6.15.2014-final.pdf 28. azeez a. mapping hiv/ aids services in nigeria. in abuja, nigeria; 2011 [cited 2016 oct 21]. available from: https://www.measureevaluation.org/our-work/gis/nigeria-health-andmapping-summit-2011-resources/presentation-4 29. katz i, fadairo af, omeogu c, azeez a. geospatial mapping of quality measures of hiv services in nigeria: variations between states and services. in: proceedings of the 19th international aids society conference [internet]. washington dc, usa; 2012 [cited 2016 oct 21]. available from: http://www.abtassociates.com/abtassociates/files/9a/9a88b717e639-4ae8-9f9a-745ccefa1e19.pdf 30. ojo a, aiyenigba b, johnbull s, onu a, ipadeola o, et al. malaria diagnostic service availability‐mapping of private sector service delivery outlets in 7 pmi/maps supported states, nigeria. in new orleans, usa; 2014. 31. national primary healthcare development agency. geographical information system (gis) mapping of primary healthcare facilities in nigeria. abuja, nigeria; 2012. https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25422720&dopt=abstract https://doi.org/10.5210/ojphi.v6i2.5287 https://doi.org/10.1007/s10290-013-0157-2 duplication of effort across development projects in nigeria: an example using the master health facility list online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e208, 2018 ojphi 32. adedeji o, ojo w, akinyemi a, agunbiade o, olaniran o. mapping of reproductive, maternal, newborn, child and adolescent health in nigeria. abuja, nigeria; 2015 sep. 1 including few laboratories 2 identified during literature review and was not presented at the workshop duplication of effort across development projects in nigeria: an example using the master health facility list abstract doi: 10.5210/ojphi.v10i2.9104 introduction methods document review key informant interviews stakeholder’s workshop results discussion points of duplication in health facility listing efforts consequences of duplication of health facility lists addressing duplication in health facility listing in nigeria strategic leadership the health facility registry model study limitations conclusion acknowledgments references isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e227, 2019 isds 2019 conference abstracts anthrax laboratory diagnostic methods at the laboratory of the ministry of agriculture (lma) irma beradze animal disease diagnostics, lma, tbilisi, georgia objective laboratory of the ministry of agriculture (lma) conducts anthrax diagnostics using bacteriology and molecular biology methods: isolated cultures through the classical bacteriology methods are always confirmed by molecular biology assay (pcr). in the study the samples were screened for the presence of b. anthracis via two concurrent approaches to compare classical methods and a novel pcr method. before the tap-7 project, pcr was only used to confirm the identity of cultures isolated by the bacteriology. new sops and algorythm was created for better laboratory diagnostic. introduction bacillus anthracis, the etiologic agent of anthrax, is a member of a highly diverse group of endospore-forming bacteria. bacillus anthracis spores are typically found in soil, from which they may spread via contaminated dust, water, and materials of plant and animal origin. although anthrax is primarily a disease of herbivores, humans may contract anthrax directly or indirectly from animals. laboratory of the ministry of agriculture (lma) conducts anthrax diagnostics using bacteriology and molecular biology methods: isolated cultures through the classical bacteriology methods are always confirmed by molecular biology assay (pcr). in 2014, within tap7 project, identification and mapping of anthrax foci in georgia’’ anthrax suspected soil samples were tested using two lab diagnostic methods and they were compared to each other. methods anthrax suspected samples were tested by two methods classical method and new method. classical method included isolation of bacterium from soil samples using standard bacteriology tests and then pcr confirmed its identity. new method was initial pcr testing of soil samples 302 soil samples were tested by classical method. at the same time, approximately 10% (32 samples) of the already mentioned 302 soil samples were also tested by initial pcr. results 24 cultures isolated through bacteriology tests (gram staining; lysis by gamma phage; motility testing; detection of polydglutamic acid capsule by direct fluorescent antibody (dfa) were confirmed by pcr. out of the above mentioned 32-suspected samples, 11 were confirmed positive using the classical methods, versus 9 confirmed positive using the direct pcr approach. two bacteriologically positive samples appeared negative by the direct pcr method, i.e. only two samples did not match. conclusions the samples were screened for the presence of b. anthracis via two concurrent approaches to compare classical methods and a novel pcr method. before the tap-7 project, pcr was only used to confirm the identity of cultures isolated by the bacteriology. the purpose of the investigation of the new method was to identify if a less labor-intensive process with fewer points of operator manipulation was as efficacious as the classical method of bacteriology followed by pcr analysis of suspected samples. despise the limited sampling and the little difference in the efficacy of the two methods, classical method stays prior to new one. http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka illinois department of public health, chicago, il, usa objective to describe an r script developed to assess and produce reports on data quality in the biosense locker database. introduction syndromic surveillance requires reliable, accurate, and complete healthcare encounter data to assess patterns of illness and respond to public health events. illinois implemented syndromic surveillance statewide in response to meaningful use reporting objectives. to address the need for continuous, automated assessment following initial on-boarding of facility emergency department data, we developed an r script to assess the quality of data in the private biosense locker database. this script builds upon and adapts from scripts previously developed for syndromic surveillance1, 2 and data quality assessment3. methods this r script examines identifying variables in the hl7 messages from the locker, aggregates messages into ed visits based on these identifiers, processes the aggregated data to calculate metadata for each visit, and computes various data quality metrics. results are displayed in the console and written to an html file. given a user-specified time period and list of contributing facilities, this r script assembles a mysql query and executes it to retrieve messages from the database meeting the specified criteria. this script first examines the identifiers unique_visiting_id, patient_visit_id, unique_patient_id, and facilityid_uuid. it calculates the number of distinct values of uvid, pvid, and (fid, pvid) which appear, then calculates the number of uvid corresponding to each (fid, pvid) pair and vice versa. finally, it finds instances of more than one pvid matching a single (fid, pid) pair on a single date and tallies the number of such occurrences at each facility. this script aggregates messages into patient visits based on the ordered pair (facilityid_uuid, patient_visit_id); this pair of hl7 data elements as defined in the phin messaging guide uniquely identifies patient visits using a facility-generated identifier. any distinct values present in a single field in different messages from the same visit are concatenated to ensure capture of new or revised information submitted throughout the patient visit. in addition, several metadata variables are calculated: the first and last values of key date fields are determined, and the longest and latest entries in reasonfor-visit data elements (chief_complaint, triage_notes, diagnosis variables) and the date of the first non-null diagnosis and disposition are identified. indicator variables reflecting multiple values (within a single visit) for age, pid, mrn, uvid, and admit date are also created. a new data table of these aggregated visits and visit-level metadata variables is saved as an r object and exported as a pipe-delimited text file. finally, the r script produces an html file which displays message-level and visit-level data quality metrics. at the message level, results on the identifier variables are provided. several tables displaying visit-level data quality results by facility are provided, including a table of percent completeness of several key demographic and clinical variables, a table displaying percent of visits having multiple values of demographic and clinical variables that should be single-valued, and frequency tables of visits by age group and patient class. the script can also be easily extended to calculate and display other results or additional data quality metrics that may be developed in the future. this script was developed with input from the biosense data quality workgroup and was demonstrated on conference calls that led to the sharing among jurisdictions of results regarding identifiers. results since january 2013, illinois has performed on-boarding and validation to incorporate 156 hospitals into the biosense platform. this data quality assessment script, scheduled to run quarterly, produces a facility-level report that is shared with hospital and vendor contacts to support re-engagement and improvements following initial onboarding validation. conclusions moving forward, idph will utilize this script in its local hl7 integration project to improve data quality and facility monitoring on a continuous basis. keywords r programming; data quality; syndromic surveillance acknowledgments we would like to thank rosa ergas, harold gil, farah naz, and hailin yu. references 1. cdc nssp, shared_biosense_functions.r [computer software]. 2. cdc nssp, ili.r [computer software]. 3. gil h, rennick m, wiedeman c. ucep-biosense_dq-5_12_14.r [computer software]. *serena rezny e-mail: ‘serena.rezny@illinois.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e156, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household 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county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to 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syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e252, 2019 isds 2019 conference abstracts measuring trends in hepatitis c testing with commercial laboratory data lauren canary1, william w. thompson1, harvey w. kaufman2, noele p. nelson1 1 division of viral hepatitis, u.s. centers for disease control and prevention, altanta, georgia, united states, 2 quest diagnostics, secaucus, new jersey, united states objective using the two largest commercial laboratory data sources nationally, we estimated the annual rates of hepatitis c testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the cdc and united states preventive services task force. this panel will discuss strengths and weaknesses for monitoring hepatitis c testing using alternative data sources including self-reported data, insurance claims data, and laboratory testing data. introduction hepatitis c virus (hcv) infection is a leading cause of liver disease-related morbidity and mortality in the united states. approximately 75% of people infected with chronic hcv were born between 1945 and 1965. since 2012, the cdc has recommended one-time screening for chronic hcv infection for all persons in this birth cohort (baby boomers). the united states preventive services task force (uspstf) subsequently made the same recommendation in june 2013. we estimated the rate of hcv testing between 2011 and 2017 among persons with commercial health insurance coverage and compared rates by birth cohort. methods hepatitis c virus testing data were obtained from quest diagnostics (quest) and laboratory corporation of america (labcorp), two large u.s. commercial laboratories serving clinicians and hospitals in all 50 u.s states and the district of columbia. analysis was based on de-identified person-level data from hcv antibody immunoassay tests ordered by clinicians in the u.s. between 2011 and in 2017 (with labcorp data in 2017 limited to january through october). hcv antibody testing rates were calculated and defined as: the number of unique individuals who received their first hcv antibody test during a particular month per 100 uni que individuals who had any laboratory test performed by the commercial laboratory during the same month, presented as an annual average (mean) testing rate. persons born between 1945 and 1965 were classified as baby boomers and compared to persons born in all other years. results in 2011, prior to the cdc recommendation change, rates of hcv antibody testing relative to overall testing with each cohort were higher for the non-baby boomer cohort served by both quest and labcorp. in contrast, from 2012 thorugh2017, testing was more frequent among baby boomers than among non-baby boomers as a proportion of overall testing in each cohort. the rate of testing among baby boomers served by quest rose from 1.7 per 100 test requests in 2011 to 3.8 per 100, an increase of 131%, while the rate of testing among non-baby boomers rose from 2.3 per 100 to 3.1 per 100, a 35% increase. changes among patients served by labcorp were nearly identical; a 132% increase among baby boomers (1.7 per 100 in 2011 to 4.0 per 100 in 2017) and a 31% increase among non-baby boomers (1.7 per 100 in 2011 to 3.2 per 100 in 2017). conclusions this study demonstrates the utility of commercial laboratory data for assessing changes in hcv testing, as well as the potential impact of national recommendations supporting hcv testing of baby boomers. the study also highlights a prominent, the increase in hcv antibody testing in 2017 relative to 2011, prior to the recommendation change. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e252, 2019 isds 2019 conference abstracts acknowledgement we would like to acknowledge our colleagues at the laboratory corporation of america and quest diagnostics for providing the data. table 1. hcv antibody testing relative to overall testing in baby boomers and non-baby boomers from 2011 to 2017 by two commercial laboratories http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts creating a universal data release policy across programs in a state health department ekaette joseph-isang* kentucky department for public health, frankfort, ky, usa objective to describe the process of producing a universal data release policy for use by different programs in a state health department introduction the introduction of electronic health systems has led to easier collation, compilation, and analysis of data as well as easier access. for data to be put to be impactful use, it must be shared both for research and decision making purposes. data sharing and release should neither compromise privacy nor lead to wrong conclusions. the need to share information that guides policies and decision making should be balanced with the need for the data to be reliable. the aim was to produce a data release policy to be used as a baseline tool to guide the practice of data release and sharing across programs and with outside requesters. methods data release polices take into consideration different parameters as determined by the sponsoring organization. data release policies were collected from different programs within and outside the state department of health. spreadsheets and cohort brainstorming session were employed. parameters, suppression rules and cautionary release rules were tabulated and analyzed on spreadsheets to compare similarities and differences. a data release work group was constituted. preliminary findings showed that most data release policies were based on a variety of parameters. most used a combination of parameters parameters included the total population, number of count, relative standard errors, confidence intervals and in some cases geographical areas. program areas were consulted with regard their preferred parameters. program policies were also compared with those available on national data sites e.g. nchs and cdc wonder. survey data and data from counts were treated using different parameters with the aim of preserving confidentiality and endure reliability. based on the most frequent data requests, parameters that guaranteed confidentiality and reliability were agreed upon by the work group. this report outlines the process for creating a universally acceptable data release policy for the state health department and the advantages and disadvantages of using certain parameters. results a universal data release policy acceptable to different programs was produced with allowances for tightening the protocol where specific programs required more stringent data release policies. conclusions data release policies make for ease of handling data requests. there is no one size fits all. having a data release policy allows program managers to have a reference point for evaluating data requests. a data release algorithm provides an easily comprehensible visual tool. programs can be more stringent as their program peculiarities dictate but the data release policy serves as the minimum necessary to guide the process. keywords data release; data sharing; state health department; data policy; data sharing agreement *ekaette joseph-isang e-mail: ejosephisang@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e128, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur 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sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health 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impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts investigating other syndrome in essence from a data quality perspective dayaamayi kurimella* and jenna i. johnson infectious disease epidemiology, la office of public health, new orleans, la, usa objective this investigation takes a closer look at other syndrome in essence and null syndrome in leeds to understand what types of records are not tagged to a syndrome to elucidate data quality issues. introduction the louisiana office of public health (oph) infectious disease epidemiology section (idepi) conducts syndromic surveillance of emergency department (ed) visits through the louisiana early event detection system (leeds) and submits the collected data to essence. there are currently 86 syndromes defined in leeds including infectious disease, injury and environmental exposure syndromes, among others. leeds uses chief complaint, admit reason, and/or diagnosis fields to tag visits to relevant syndromes. visits that do not have information in any of these fields, or do not fit any syndrome definition are tagged to null syndrome. essence uses a different algorithm from leeds and only looks in chief complaint for symptom information to bin visits to syndromes defined in essence. visits that do not fit the defined syndromes or do not contain any symptom information are tagged to other syndrome. since the transition from biosense to essence, idepi has identified various data quality issues and has been working to address them. the nssp team recently notified idepi that a large number of records are binning to other syndrome, which led to the investigation of the possible underlying data quality issues captured in other syndrome. methods daily submissions of electronic data are imported to and processed by leeds and essence for syndrome classification. leeds and essence were queried to first pull total visits and the percent of those visits tagged to other syndrome in essence and null syndrome in leeds between the dates of 1/01/2017 and 10/02/2017. the counts and percentages from both systems were compared. the percentage of total visits tagged to other syndrome was stratified by facility to determine if there were significant differences between facilities. a line level review of visits tagged to null syndrome in leeds and other syndrome in essence was also conducted. this review showed that many records were pain related and many records were missing chief complaint. both systems were then queried for the percent of visits in other and null syndrome that did not have symptom information and the percent of visits in other and null syndrome that mentioned “pain” in chief complaint. results the average daily total visits in essence was 3279 visits per day compared to 5959 average visits per day in leeds, with counts in essence significantly dropping between 6/1/2017 and 7/1/2017. the average percentage of visits tagged to other syndrome in essence was 63.16% while the percent of visits tagged to null syndrome in leeds was 34.46%. in essence, 24.22% of all visits tagged to other syndrome were pain related and 23.98% of all visits tagged to other syndrome did not have any symptom information in chief complaint. in leeds, 43.03% of all visits tagged to null syndrome were pain related and 3.6% of all visits tagged to null syndrome had no symptom information. finally, the percentage of total visits tagged to other syndrome, stratified by facility, showed some facilities were disproportionately contributing to other syndrome and that some facilities had major lapses in data in essence. conclusions the dramatic difference in total visits between essence and leeds can be attributed to multiple reasons, most of which are likely related to the transition from biosense to essence. this difference makes it difficult to compare data between the two systems, and idepi is continuing to work on understanding and resolving why these counts are so different. one of the reasons for the higher percentage of total visits binned to other syndrome in essence compared to null syndrome in leeds is related to the different processing methods of the two systems. leeds uses chief complaint, admit reason and diagnosis fields for symptom information, while essence only uses chief complaint. this allows leeds to tag more visits to syndromes other than null syndrome. leeds also has more defined syndromes, which also contributes to the lower percentages of null syndrome. the higher percentage of other syndrome with no chief complaint in essence can partially be attributed to hl7 formatting issues. essence is not able to read chief complaint when it is populated if some hl7 formatting issues are present, while leeds is still able to read chief complaint when the same hl7 formatting issues exist. finally, the percentage of total visits tagged to other syndrome, stratified by facility has provided the facility level information necessary to address some of these data quality issues. some of the facilities with lapses in data can be traced back to issues in the master facility table (mft), while other facilities have hl7 formatting issues that need to be addressed directly with the facility. in conclusion, exploring other syndrome in essence can provide an interesting perspective into data quality. idepi’s ability to compare other syndrome in essence to null syndrome in leeds has helped to further identify the data quality issues. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords syndromic surveillance; other syndrome; data quality acknowledgments michael coletta, aaron kite powell and lakshmi radhakrishnan *dayaamayi kurimella e-mail: dayaamayi.kurimella@la.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e50, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts surveillance of stillbirth and syphilis screening using electronic health records brian e. dixon*1, 2, jane wang2, timothy e. o’connor3 and janet n. arno3, 2 1epidemiology, indiana university fairbanks school of public health, indianapolis, in, usa; 2regenstrief insitute, indianapolis, in, usa; 3indiana university school of medicine, indianapolis, in, usa objective to measure stillbirth delivery rates and syphilis screening rates among women with a stillbirth delivery using electronic health record data available in a health information exchange. introduction reports of infants born with congenital syphilis have increased in the united states every year since 2012. prevention depends on high performing surveillance systems and compliance with the u.s. centers for disease control and prevention (cdc) recommendations to perform syphilis testing early in pregnancy, in the third trimester and at delivery if a woman is at high risk, and following a stillbirth delivery. these guidelines exist, because untreated syphilis is associated with adverse fetal outcomes including central nervous system infection and death. surveillance of congenital syphilis and stillbirth is challenging because available data sources are limited. assessment of compliance with testing guidelines is particularly challenging, since public health agencies often lack access to comprehensive cohorts of tested individuals as most public health laws only require reporting of positive disease case information. methods using integrated electronic health records available in a community-based health information exchange, we examined syphilis testing patterns for women with a stillbirth delivery in indiana between 2010-2016. the cohort was examined to determine whether the women received syphilis testing in accordance with the cdc recommendations. during this time period, indiana recorded around 84,000 live births per year. data were extracted from electronic health records, including encounter data, laboratory test results and procedure data, captured by the indiana network for patient care (inpc), one of the largest community-based hie networks in the united states. the inpc connects over 90 health care facilities, including hospitals, physicians’ practices, pharmacy networks, long-term post-acute care facilities, laboratories, and radiology centers. in addition to clinical care, the inpc supports surveillance of stis1. women with a stillbirth delivery were identified using international classification of disease (icd) clinical modification (cm) codes from the 9th and 10th editions (icd-cm-9 and icd-cm-10). inclusion codes: icd-cm-9 codes 656.4, 779.9, v27.1, v27.3, v27.4, v27.6, v27.7, v32.01, v32.1, v32.2, v36.1; and icd-cm-10 codes p95, p96.9, o36.4, z37.1, z37.3, z37.4, z37.9. using the master person index for the inpc, we linked stillbirth deliveries with pregnancy encounters and laboratory testing data. we analyzed documentation of syphilis testing during the pregnancy (up to 270 days prior to the stillbirth delivery) as well as after the stillbirth delivery (up to 30 days). broad time ranges were utilized to account for potential delays in reporting of either the stillbirth delivery or the syphilis test results. documentation could include either presence of a result from a laboratory test for syphilis or a cpt code (80055, 86780, 86781, 86592, 86593) indicating performance of a syphilis test. results a total of 4,361 stillbirth deliveries attributable to 4,265 unique women were identified in the inpc between 2010-2016; representing a rate of 7.44 stillbirths per 1,000 live births during the same time period. of the stillbirth deliveries, syphilis testing occurred within 270 days prior to or 30 days after delivery for 2,763 (63.4%) cases. figure 1 displays the number of stillbirth cases observed each year and the number of cases in which syphilis testing occurred during the pregnancy or after delivery. conclusions using integrated electronic health records data, we discovered that fetal deaths occurred more frequently (7.44 versus 4.09 per 1,000) than previously estimated2 through fetal death reporting mechanisms in indiana. furthermore, we observed increasing rates of stillbirth within indiana in recent years. integrated data further enabled measurement of syphilis testing rates for stillbirth cases, which were similar to those reported by patel et al.3 using a large, national administrative data set. testing rates in indiana are well below the targets set by national and international public health organizations. accessing more complete data on populations using a health information exchange is valuable, although doing so may uncover a more negative picture of health in one’s community. deeper analysis of these trends is warranted to explore factors related to increasing rates as well as limited testing in this population. stillbirth cases and syphilis testing case counts between 2010-2016 in indiana. keywords syphilis; stillbirth; health information exchange; electronic health records acknowledgments research reported in this abstract was supported by the u.s. centers for disease control and prevention (cdc) under contract number 200-2016m-92342. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. dixon be, tao g, wang j, tu w, hoover s, zhang z, batteiger ta, arno jn. an integrated surveillance system to examine testing, services, and outcomes for sexually transmitted diseases. the 16th world congress on medical and health informatics (medinfo). 2017. in press. 2. macdorman mf, gregory ecw. fetal and perinatal mortality: united states, 2013. national vital statistics reports. 2015; 64(8). hyattsville, md: national center for health statistics. 3. patel cg, huppert js, tao g. provider adherence to syphilis testing recommendations for women delivering a stillbirth. sexually transmitted diseases. 2017;44(11):685-690. *brian e. dixon e-mail: bedixon@regenstrief.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e175, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts problems of epidemiological surveillance of west nile fever in ukraine iryna demchyshyna*, yuryi novohatniy and igor nebogatkin ukrainian center for diseases control and monitoring of the ministry of health of ukraine, kyiv, ukraine objective to define the problems of epidemiological surveillance of west nile fever (wnf) in ukraine. introduction flaviviridae are one of the most widespread arboviruses in ukraine. mosquitoes are vectors of wnf in a majority of cases due to bites during swimming, fishing, work in suburban areas and outdoor recreation without use of individual protection from mosquitoes. a study of the species composition of bloodsucking mosquitoes is conducted in ukraine. existence of natural foci of wnf viruses has been well-proven all over the territory of ukraine by testing igg antibodies in different groups of population, including children [1]. also, infection of mosquitoes (rna found in culex pipiens (including culex pipiens f. molestus, culiseta annulata)) was registered. infection of i. ricinus and d. reticulates was also determined, and it acts as a factor for circulation of virus in the wild too [2]. methods statistical, serological and epidemiological methods were used during the study. serological tests included reactions with igm and igg antibody in human serum performed using immunofluorescent and elisa methods. results in ukraine, the causative agent of wnf is detected in all landscapes. it is the main arboviral infection in the forest-steppe zone (53.1 % among all arboviral infections). enzootic territories are located in 18 regions, 47 administrative districts, and 63 settlements. the majority of natural foci of wnf is located in the dnieper leftbank steppes, and also in north-western and western forest-steppes. the enzootic territories are located on the east of steppe zone and on the east of forest-steppes. the smallest number of natural foci is registered in the dnieper right-bank part of the steppes. enzootic territories are absent in chernivtsi, chernihiv, sumy, ternopil, luhansk, kirovohrad oblasts and kyiv. most of them are located in zaporizhzhia with 9 administrative districts and 16 settlements; in rivno oblast 7 and 9; in kherson 5 and 4, and in poltava oblasts 2 and 4 respectively [3]. during the period from 2007 to 2016, 86 cases of wnf were registered. wnf was registered in 7 oblasts (zaporizhzhya 40 cases, poltava 24, donetsk 16, mykolaiv3, kherson, kharkiv, zhytomyr oblasts one case in each) [4]. registration of wnf cases separately from other viral hemorrhagic fevers has been conducted in the country since 2010 (official registration of total amount of viral hemorrhagic fevers has been performed since 2005). in enzootic territories, 2 cases of the diseases were registered and were associated with ticks bites. the strains of wnv were detected in bloodsucking mosquitoes in rivne and zaporizhzhia oblasts and in tick samples of ixodes genus collected in lviv oblast (probably may be found in other species of tick (argasidae and gamazoidea) where the causative agent is kept in natural foci under unfavorable conditions). laboratory diagnostics was conducted (mainly retrospectively) in zaporizhzhia, poltava, donetsk oblastss. all diagnoses (exception mykolaiv oblast in 2011, data is absent) were laboratory confirmed, including 10 cases confirmed in the state institution lviv research institute of epidemiology and hygiene of the ministry of health of ukraine, and 3 more cases were confirmed by a private laboratory [2]. in total, 129 samples of blood sera collected from patients with clinical manifestations of a fever of unknown origin were delivered to the laboratory of virology of ukrainian center for diseases control and monitoring during 2016-2017. samples were investigated using the immunofluorescent and enzyme immunoassay methods including immunoblot. west nile virus markers such as igm/igg antibodies have been detected in 4 cases (poltava oblast) [4]. conclusions mainly, single cases were registered. it is caused by insufficient level of diagnostics in most of the regions, as a result, diseases pass under other diagnoses. migratory birds (3 flyways of migratory birds pass through ukraine) and local animals (crows, jackdaws, doves and other) may be the possible reservoirs of causative agent of wnf. laboratory diagnostics need to be improved and more attention should be paid to testing of samples of blood serum from patients with suspected wnf. keywords fever; enzootic territories; surveillance; mosquitoes; laboratory testing acknowledgments authors express their sincere gratitude for diagnostic kits to dr. p. emmerich, german reference center for imported parasitic and viral infections, bernhard-nocht institute, department of virology. references [1] rusev i.t., zakusilo v.m., vinnuk v.d. bloodsucking mosquitoes of urbanized biocenosis and their role are in circulation of viruses of west nile fever. series are “biology, chemistry”. issue 24 (63). 2011. no. 2. p. 240-248. [2] lozinskyi i.m., beletska g.v., drul o.s., fedoruck v.i., kozlovskyi m.m., rogochiy e.g., sholomey m.v., ben i.i., shulgan a.m./ epidemic situation of western nile fever in ukraine. magazine of infectology, issue 6, no. 2, 2014 appendix 66-65. [3] official data of state statistic form of the ministry of health. [4] data of the state institution ukrainian center for diseases control and monitoring of the ministry of health of ukraine. *iryna demchyshyna e-mail: irad@i.ua online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e159, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts how do we present messy syndromic surveillance data to public health’s partners? david atrubin*1, rosa ergas2 and aaron kite-powell3 1florida department of health, tampa, fl, usa; 2massachusetts department of public health, boston, ma, usa; 3centers for disease control and prevention, atlanta, ga, usa objective to discuss data disclaimers and caveats that are fundamental to sharing syndromic surveillance (sys) data introduction with increasing awareness of sys systems, there has been a concurrent increase in demand for data from these systems – both from researchers and from the media. the opioid epidemic occurring in the united states has forced the sys community to determine the best way to present these data in a way that makes sense while acknowledging the incompleteness and variability in how the data are collected at the hospital level and queried at the user level. while significant time and effort are spent discussing optimal queries, responsible presentation of the data including data disclaimers is rarely discussed within the sys community. keywords data disclaimers; data sharing; variability within the data; syndromic surveillance; emergency department data *david atrubin e-mail: david.atrubin@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e73, 2018 cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case mike conway1, danielle mowery1,2, amy ising3, sumithra velupillai4,5, son doan6, julia gunn7, michael donovan7, caleb wiedeman8, lance ballester9, karl soetebier9, catherine tong10, howard burkom11 1. department of biomedical informatics, university of utah, salt lake city, utah, united states 2. informatics, decision-enhancement, and analytical sciences center (ideas 2.0), veterans affairs, salt lake city health care system, salt lake city, utah, united states 3. department of emergency medicine, university of north carolina chapel hill, north carolina, united states 4. institute of psychiatry, psychology and neuroscience, king’s college london, united kingdom 5. school of electrical engineering & computer science, kth royal institute of technology, stockholm, sweden 6. medical informatics, kaiser permanente southern california, san diego, california, united states 7. boston public health commission, boston, massachusetts, united states 8. tennessee department of health, nashville, tennessee, united states 9. georgia department of public health, atlanta, georgia, united states 10. international society for disease surveillance, brighton, massachusetts, united states 11. applied physics laboratory, johns hopkins university, baltimore, maryland, united states abstract this paper describes a continuing initiative of the international society for disease surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. funded by the defense threat reduction agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). the topic of this final consultancy, conducted at the university of utah in january 2017, is focused on defining a cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints and triage reports. keywords: negation detection, natural language processing, syndromic surveillance, chief complaints correspondence: mike.conway@utah.edu doi: 10.5210/ojphi.v10i2.8944 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. 1. introduction despite considerable effort since the turn of the century to develop natural language processing (nlp) methods and tools for public health surveillance, few standardised methods have emerged. those methods that have emerged (e.g. the negex algorithm [1]) are confined to local imple mentations with customised solutions. furthermore, agreement on international standards — even between countries as geographically proximal and culturally similar as the united states and canada — has proved elusive. important reasons for this lack of progress include (a) limited shareable datasets for developing and testing methods (b) jurisdictional data silos, and (c) the gap between resource-constrained public health practitioners and technical solution developers, typically university researchers and industry developers. to address these three problems, the international society for disease surveillance (isds) launched a technical conventions committee in 2013, tasked with collecting and curating surveillance-related use cases from public health stakeholders at the local, state, and federal levels in the united states, and generating detailed requirement templates and datasets to support these use cases, with the goal of developing and disseminating best practice to the public health community. in 2014, isds was awarded a three-year contract by the defense threat reduction agency (dtra) (analytic solutions for real-time biosurveillance project) with the aim of bringing together public health practitioners and solution developers with the overarching goal of supporting dtra’s biosurveillance ecosystem (bsve) [2,3]. bsve is a web-based, open-source, cloud-hosted dashboard for managing, visualising, and analysing disparate health and non-health related data to support public health surveillance and situational awareness. in this paper, we describe the process and results of a consultancy involving four types of public health stakeholders, including (1) representatives from public health departments (local, state, federal), (2) university researchers focused on computational methods for public health surveil lance, (3) members of public health oriented non-governmental organisations, and (4) industry representatives, all interested in developing validated, standardised and portable resources (meth mailto:mike.conway@utah.edu cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi ods and data sets) for negation detection in clinical text used for public health surveillance. as will be described more fully below, accurate negation detection is a vital step required for increasing the reliability of text-based syndromic surveillance methods. 2. materials and methods: consultancy under the isds dtra grant, five use case consultancies were conducted, as summarised in table 1. hosts of the first four consultancies found these events highly successful for communicating crossdisciplinary needs and analysing technological requirements [2,3]. the main objective of this grant was to develop an understanding of needs and research capabilities to support accurate negation detection for public health. the adaptation and implementation of existing methods in the host surveillance systems were beyond the scope of the grant and remain a challenge. interpreting “methods” as the elicitation and dialogic processes of the consultancy and “materials” as the composition of participants, this negation consultancy differed qualitatively from the prior consultancies under the isds dtra grant. for example, prior events were driven by a single health department (except for the second consultancy hosted by the united states department of defense) seeking practical tools to meet specific, routine surveillance needs. in contrast, this final use casebased consultancy was hosted, not by a health-monitoring organisation, but by a large, prolific, and well-established informatics department — the department of biomedical informatics at the university of utah – with connections to similar academic, governmental, and industrial research groups. knowing the interest of many health departments in improving and broadening their use of nlp for medical data, the grant’s advisory group chose an academic hosting approach to attract a variety of related expertise. moreover, instead of motivation from a single health department, needs were presented by three state (tennessee, georgia, north carolina) and two local (king county wa, boston ma) health departments with a range of surveillance capabilities and experience in usage of free-text data. table 1: dates, hosts, and subject matter of the five dtra-funded consultancies conducted by isds date host subject jun 9-10, 2015 north carolina division of public health asyndromic cluster detection in ed cc text oct 29-30, 2015 u.s. department of defense predictive models for infectious disease mar 30-31, 2016 boston public health commission estimating risk of asthma exacerbations jun 14-15, 2016 arizona department health risk mapping for incidence of arboviral disease jan 19-20, 2017 university of utah negation processing in free-text analysis cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi the primary need for text processing is classification of records — for most users so far, emergency department chief complaint text — into indicator bins in order that the size of each bin (i.e. the volume of classified chief complaints) may be monitored on a weekly, daily, or more frequent basis. most of the free-text data already used by public health departments are chief complaint or reasonfor-visit fields. entries in these fields are often fewer than six words and may lack verbs or punctuation (see table 2 for example chief complaints). health department users employ a limited range of standardised tools for classification and these tools are often neither well understood by inhouse staff (i.e. they are “black box” algorithms) nor optimised for local application. key application questions underlying the consultancy were: table 2: example emergency department chief complaints cva difgf breathing chest pain since last night, weakness, abdominal pain emesis infection on the power port crisis evaluation pt came for wt and bp ck to start phentermine follow up patient is here for cold symptoms, hx of chf fever fever,abd pain/from uc facial pain annual exam; right leg concernswelling and pain headache alcohol intoxication cough spasms psychosis neck pain fall • can/should classification tools already available be used more effectively, updated, or replaced to improve sensitivity? • how effective are more advanced tools for classifying various text-based data sources? (e.g. triage notes, chief complaints, and/or text fields from emergency department patient records) • what additional tools can be implemented and maintained given the scarce resources and limited in-house expertise of health departments? • while none of the health departments had the goal of eliminating human-in-the-loop routine surveillance activities, trained human expertise is both expensive and limited in supply. therefore, understanding the costs and benefits involved in interpreting output from the desired tool is essential for effective surveillance system planning. in a recent isds survey and subsequent publication on surveillance research priorities of public health practitioners, one of the top recommendations was the need to develop new “methods to process, categorize, and code unstructured data in electronic health records” [4]. preparations for the consultancy included an email thread and a shared website for published articles and data samples leading to a structured pre-consultancy call designed to inform participants regarding the purpose of cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi the consultancy and to align expectations. then, health department users were requested to provide data samples exemplifying negation issues in the classification process. presenting developers were asked to explain their underlying ideas, details of method implementation, size and composition of corpora used for evaluation, and classification performance results. as in the prior consultancies, data architecture, networking processes, and multipurpose automated systems were not the focus of the event, but they provided context and constraints for the discussion. finally, each attending health department was asked for an overview of the data acquisition, preprocessing, analysis, and interpretation steps used in its automated surveillance. 3. results: consultancy the consultancy was held on january 19th & 20th 2017 at the university of utah’s department of biomedical informatics, and consisted of 25 participants. participants were drawn from various different sectors, with representation from isds [2], the defense threat reduction agency [1], universities and research institutes [5], public health departments [6], the department of veterans affairs [4], non-profit organisations [2], and technology firms [1]. participants were drawn from a variety of different professional backgrounds, including research scientists, software developers, public health officials, epidemiologists, and analysts. further, participants were drawn from all major regions of the united states (e.g. south, east coast, west coast, intermountain west) and internationally (united kingdom & sweden). day 1 of the consultancy was devoted to providing an overview of nlp and current trends in negation detection, including a detailed description of widely used algorithms and tools for the negation detection task. key questions included: should our focus be chief complaints only, or should we widen our scope to emergency room triage notes?, how many other nlp tasks (e.g. reliable concept recognition) are necessary to address on the road to improved negation detection? with this background established, day 2 centered on presentations from five different united states local and state health departments (viz: king county wa, boston ma, north carolina, georgia, and tennessee) on the various approaches to text processing and negation detection across several jurisdictions. we then pivoted and discussed negex implementations, discussed the potential utility of social media — particularly twitter — for syndromic surveillance applications, and watched several demonstrations of syndromic surveillance systems that utilise text processing, including a demonstration of ecohealth alliance’s surveillance applications, a demonstration of geoviz and argus [6], and a demonstration of topic modelling approaches in the context of the bsve geospatial syndromic surveillance system. a post-consultancy survey of participants yielded the following reactions and suggestions: • respondents indicated that a greater focus on machine learning approaches (including inviting more machine learning researchers) would have been beneficial. • future consultancies could focus explicitly on connecting the public health and computer science communities. cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi • all participants judged the consultancy to be effective. suggested topics for future follow-up meetings included: • a session on big data text processing for syndromic surveillance in general (i.e. not restricted to the topic of negation detection). • a session on developing nlp resources for specific problems in syndromic surveillance (e.g. opioid abuse). 4. materials and methods: use case 4.1. why free text processing is important for public health since the early years of the 21st century and the widespread adoption of information technology systems in public health departments, free-text chief complaints — short texts that describe a patient’s symptoms before a preliminary diagnosis can be made — have come to be a vital resource for syndromic surveillance in the united states context [7]. traditionally, public health surveillance has relied on the routine filing of reportable disease diagnoses by laboratories or clinicians, sometimes involving considerable delays. in contrast, syndromic surveillance uses data sources that already exist but have not been designed with public health goals in mind. many systems use emergency room-generated triage chief complaints, nearly ubiquitous in the us context and available electronically during, or shortly after a patient encounter. chief complaint based systems (e.g. nc detect [8], ears [9]) are widely used for customary syndromic surveillance (e.g. annual flu monitoring [10]). free-text chief complaints remain a vital resource for syndromic surveillance. however, the widespread adoption of electronic health records (and federal meaningful use requirements [5]) has brought changes to the syndromic surveillance practice ecosystem. these changes have included the widespread use of ehr-generated chief complaint “pick lists” (i.e. pre-defined chief complaints that are selected by the user, rather than text strings input by the user at a keyboard), triage note templated text, and triage note free-text (typically much more comprehensive than traditional chief complaints). as will be explained below, a key requirement for a negation detection algorithm is the ability to successfully and accurately process these new and challenging data streams. see table 2 for examples of the heterogeneity of chief complaint data. figure 1 provides practical examples of negation detection and concept recognition. 4.2. impact of negation in this section, we first describe current negation detection practices at two public health de partments (state of georgia and city of boston, ma), before going on to describe important technical and human resource constraints frequently experienced by health departments. finally, we describe some current approaches to negation detection in the clinical nlp literature. cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi figure 1: chief complaint annotation 4.2.1. georgia department of health based on estimates from georgia data, negation occurs in about 3.5% of incoming non-missing chief complaints, and occurs disproportionally in certain types of syndrome classifications, such as influenza-like illness (ili), for which negation occurs in up to 6.8% of total visit records. if negation is not handled correctly then syndrome counts can be incorrectly tallied, and because negation occurs disproportionally among syndrome groups, select syndromes will be unequally affected. not handling negation can then result in incorrect signaling and/or lead to misidentification or misinterpretation of the significance of a syndrome analysis. there are a variety of reasons that offthe-shelf nlp techniques are likely to experience difficulties successfully processing negation in chief complaints. first, the text fields are often short, with a median of 3 words (19 characters), rarely consist of complete sentences, and do not abide by grammatical convention (i.e. they are typically “telegraphic” in character). second, there are frequent misspellings and many, sometimes seemingly arbitrary, abbreviations. the combination of the two — i.e. misspellings and idiosyncratic abbreviations — in conjunction with little context, makes it difficult to discern the explicit intent of a given chief complaint. third, there is variability in how the messages are written. one message may be written in well-formed, grammatically correct sentences that directly quotes from patients (example 1 below), while another may be written as a list of symptoms and non-symptoms (example 2 below). cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi example 1: “vomitingcough,cold,congestioncough & congestion, vomiting post tussis x 1 week. hed a fever 2 days ago of 102.3. denies fever since. not sleeping well” this message has concatenation, poor sentence structure, informative negation on sleeping, and context around the word fever. example 2: “llq abd pain, cough denies n/v/d, fever” there are a number of different abbreviations in this message, and little context affecting the words. in both examples there is a question as to whether fever should actually be counted as a symptom or not. 4.2.2. boston public health commission in boston’s experience the chief complaint is the most problematic of all the data elements sent though the syndromic surveillance system. the challenges include the aforementioned spelling errors, the use of jargon and abbreviations, and inconsistent documentation by health care workers with various degrees of clinical experience. boston has developed numerous data management processes to improve the utility and validity of chief complaint processing, including fixing simple spelling errors, removing punctuation and extra spaces, and the use of emt-p (i.e. emergency medical text processor) [11] and its attendant software components, the national library of medicine’s universal medical language system and its lexical variant generator [11,12]. how ever, one of the main issues with classifying the free-text chief complaint has consistently been negation. although the size of the chief complaint fields limits the number of negation terms in comparison to those found in longer text fields, ignored negation can result in frequent chief com plaint misclassification. the problem very likely worsens for triage notes and for recently adopted “expect” notes, used by boston hospitals to document patients’ clinical issues prior to arrival. another free-text information source are nursing note fields from electronic disease surveillance systems where negation terms are more frequently found than in chief complaint data. correctly categorising nursing notes is important for the development of metrics for situational awareness, response management and quality management. a further issue associated with free-text data is the accurate identification of terms expressing uncertainty. this issue is semantically related to negation and involves uncertainty regarding the truth of a particular assertion. for example, the chief complaint “patient thinks he is having a heart attack” is — in the absence of negation and uncertainty detection — interpreted without negation or context analysis as a myocardial infarction. although not as common as negated terms, uncertainty terms like these pose classification issues. boston analysed 3.1 million chief complaints for negation terms (“no”, “not”, or “denies”) and uncertainty terms (“thinks”, “seems”, or “might”) from 2013-2016. negation terms were identified in 8,348 cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi (0.2%) chief complaints and uncertainty terms in 140 chief complaints. in 2016, 1,782 expect notes were received with 542 (30%) containing at least one negation term and 22 (1%) contained at least one uncertainty terms. the changing structure and documentation processes in ehrs are likely to increase the need for data management processes to address linguistic issues. the need for accurate and efficient algorithms for negation detection is growing increasingly urgent. 4.3. resource constraints in public health departments of the health departments participating in the consultancy, only one was in the process of actively implementing an internal negation solution. barriers to implementation included limited and siloed information technology resources, differing organisational structures, and inconsistencies in the accepted software standards across health departments. for instance, some health departments have the ability and infrastructure to locally store and analyse incoming information, whereas others pass their data onto outside organisations with little or no manipulation, checking, or tracking done internally. additionally, there is hesitancy in public health information technology departments to adopt open source solutions without enterprise support. the general consensus that emerged from consultancy participants was that, for sustainable impact, any analytic solution from the group should be able to run successfully in a corporate windows environment using commodity hardware. 4.4. current approaches to negation detection the negex algorithm — originally developed in 2001 and the most widely used negation algorithm for clinical text processing [1] — relies on predefined target concepts (e.g. “headache”) and a list of negation terms that either (a) truly negates the target term (e.g. “no headache”) or (b) appear to indicate negation but do not (called pseudo-negations), as in “not ruled out”. furthermore, patterns are included to determine whether the negation term precedes (e.g. “no headache”) or follows (“headache never developed”) the target concept. if the algorithm finds a target concept, the algorithm then goes on to search for negation terms within a predefined number of surrounding words (typically 5-6). when evaluated on 1,235 target concepts (from 1000 discharge summary sentences), this approach resulted in 84.5% precision/positive predictive value and 77.8% recall/sensitivity [1]. see figure 2 for an explanation of these metrics. negex has been extended to handle negation scope by defining conjunction terms (e.g. “but”, “except”) and other types of modifiers (e.g. experiencer and historicity) in context [13], and to add further flexibility in user-defined modifiers, target concepts, and support for document-level assertions in pycontext [14]. negex and its extensions have also been used and developed for other use-cases and languages, e.g. swedish [15], dutch [16], french [17] and spanish [18]. a similar, but not identical, lexical approach is presented in garcelon et al. [19], where regular expressions are defined for french negation expressions along with exclusion rules to address double negation. approaches that rely on lexical surface features do not explicitly take linguistic relations between words into account, such as the relation between a negation term and the term(s) it syntactically governs (scope). to capture such relations, syntactic parse information can be useful. sohn et al. cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi developed an approach which relies on dependency paths between target concepts and negation terms, which reduced type i errors (false positives) in their evaluation data, reporting very high precision (96.6%) and moderately high recall (73.9%) [20]. gkotsis et al. used syntactic information from constituency trees (a formalism for representing the grammatical structure of a text) and defines a number of tree traversal operations to determine whether or not a target concept is negated by a negation term (89.4% precision, 94.6% recall) [21]. lexical approaches have also been combined with syntactic approaches. deepen [22] uses dependency parse tree information to define post-processing rules after first running the negex algorithm, an enhancement which results in fewer false positives (89.2-96.6% precision and 73.8 96.3% recall). negfinder [23] also adds syntactic analysis (context-free grammar) for cases where lexical approaches might fail, such as when the target term and negation term are far from each other (91.8% precision, 95.7% recall). similarly, huang et al. combine regular expression matching with grammatical parsing to improve negation scope detection [24]. tanushi et al. also employed an approach relying on dependency tree information and compared results with negex and pycontext on swedish clinical notes, concluding that results were similar but that the advantage of using syntactic information could be more generalisable [25]. in general, the main difference between lexical, “surface”-based approaches and syntactic ap proaches is how the scope of negation is detected. both these approaches rely, however, on rules that are developed manually. with the increased availability of labeled data, such as provided by the 2010 i2b2 challenge [26] for clinical text, approaches that automatically learn patterns (machine learning) have also been applied to this problem with promising results using support vector machines and a variety of features including surrounding contextual, grammatical and syntactic information [27-29]. common to all these approaches is that the target concept (i.e. the concept that is negated) is defined (more or less explicitly) in advance, and that the negation terms are separate terms or phrases. recent work includes analysis also of morphological negation (e.g. im in impossible) and double negations [30]. however, as is observed by wu et al. [27], although it is fairly straightforward to optimise a negation detection approach for a new use case and corpus, developing a generalisable approach remains challenging. cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi figure 2: nlp performance metrics 5. results: use case several key areas of focus emerged as a result of the consultancy discussion. first, there is a clear need for a large, easily accessible corpus of free-text chief complaints that can form a standardised testbed for negation detection algorithm development and evaluation. annotated data, in this context, consists of chief complaints annotated for concepts (e.g. vomiting, pain in chest) and the negation status of those concepts. see figure 1 for a sample of chief complaints and their associated negated concepts (note that (-) indicates a negated term, (+) indicates a term that is not negated, and (?) indicates an uncertain term). it is important that the annotation include both annotated clinical concepts and negation status to allow for the uniform evaluation and performance comparison of candidate negation detection cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi algorithms. further, the annotated corpus should consist of several thousand (as opposed to several hundred) distinct and representative chief complaints in order to compare algorithms on a sufficient variety and volume of negation patterns. corpus volume and variety are important due to the “long tail” problem. that is, most negation patterns are of a predictable format (e.g. “denies abdominal pain”, “no evidence of fever”) yet there is a long tail of more unusual negation styles (e.g. “bowel obstruction resolved”). while a corpus including all common and uncommon negation types is not possible, making inroads into the long tail is likely to improve performance for both rule-based and machine learning-based classifiers. unlike the general domain nlp field, which is increasingly focused on machine learning-based methods for language understanding, clinical nlp has relied on rule-based systems (e.g. negex & context). this is for two reasons. first, the annotation process is typically very expensive, especially for clinical data where medical expertise is required for annotation tasks, limiting the volume of annotated data generated. machine learning approaches require a considerable volume of annotated data to work well, with the additional requirement that a substantial proportion of annotated data be excluded from training and “held out” for evaluation purposes. for situations in which annotated data are limited, rule-based systems can achieve better results. second, machine learning algorithms are typically “black boxes” (i.e. the reasoning behind the classification decision is not interpretable by humans). in the medical and public health context, retaining interpretability is extremely important, particularly for decisions related to life-or-death and to resource allocation (money, time, and effort from trained personnel). in addition to the need for annotated data-sets and appropriate nlp algorithms, a framework for evaluation is required. typically, nlp algorithms are evaluated using precision (also known as positive predictive value), recall (also known as sensitivity), and f-score the harmonic mean of precision and recall. see figure 2 for an explanation of these metrics. evidence currently suggests that shallow rule-based methods (i.e. methods that do not utilise deep syntactic and semantic parsing) in the vein of negex remain competitive for clinical negation detection [27]. 5.1. shallow supervised machine learning method for negation detection unlike rule-based approaches that encode knowledge with specific observations such as if ... then rules, the supervised machine learning approaches depends heavily on the existence of training data. training data is required in order that the algorithm can “learn” from provided examples to build classifiers that predict unseen inputs. machine learning approaches consist of three major components: (1) training dataset, (2) at least one supervised machine learning algorithm, e.g., support vector machine, conditional random field, and (3) feature sets which serve as basic variables with which an algorithm builds a classifier. each component contributes substantially to the quality of classifiers. negation detection in clinical text using machine learning has been studied and reported promising results for some data sets [31]. cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi the performance of machine learning approaches is dependent on the data sets used for training and evaluation. it has been shown that a classifier can perform well when trained and tested in the same environments or hospital, but perform poorly when applied to different hospitals [32]. this poor generalisability of algorithm performance is due to variability in practices across institutions and geographical areas. compared to rule-based systems for negation detection, the machine learning approaches have limitations in both generalisability and customisability, though machine learning approaches have outperformed rule-based systems when training data are sufficient and features are appropriate [33]. with the emergence of deep learning (i.e. neural network-based machine learning algorithms with multiple layers), there are now several studies that have focused on using this approach for negation detection in general english [31], although studies in the clinical domain are less straightforward due to this methodology’s need for substantial amounts of training data and computing resources. 5.2. ensemble methods for negation detection, coupling machine learning with lexical and syntactic feature sets in an ensemble manner has proven at least as accurate as lexically-based approaches alone. this finding has been demonstrated by several machine learning-based nlp systems as part of the 2010 i2b2/va challenge on identifying concepts, assertions, and relations in clinical text for accurately identifying negated concepts [34]. the 2010 i2b2/va challenge dataset includes two corpora: the beth israel deaconess medical center (bidmc) corpus of discharge summaries and the partners healthcare (ph) challenge corpus of discharge summaries, for which several ensemble classifiers have been benchmarked. for example, the statistical assertion classifier (stac) is a support vector machine trained with lexical and syntactic features to classify entities for both negation and uncertainty for two clinical corpora: the aforementioned 2010 i2b2/va challenge corpora and the computational medicine center (cmc) corpus of radiology notes [35]. when compared to an extended version of negex called enegex, uzuner et al. [35] observed that stac outperformed enegex using a ± 4 word window and section headings. from their experiments, they also concluded that stac can be applied without modification to new corpora, achieving similar performance to enegex. in a complementary study of mitre’s carafe [36], an ensemble approach of conditional random fields, maximum entropy, and manually crafted rules was trained and applied to the 2010 i2b2/va challenge dataset. clark et al. [36] observed that a baseline approach of word features derived from the concept to be classified with word features within a ± 3 token window of the concept of interest accounted for most of the accuracy achieved (91% f1-measure). however, they improved carafe’s accuracy (93% f-measure, 93% precision, 93% recall) leveraging additional rich linguistic features: document structure, sentential structure, and semantic attributes of words in the sentence similar to those implemented by negex. performance improvement from the ensemble approach has been observed in other studies. for instance, negex features kernel (nf) is an ensemble approach that leverages feature outputs and rules from the negex algorithm to inform a linear kernel function using libsvm (an implementation cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi of the support vector machine algorithm) to support negation detection. tested on the 2010 i2b2/va challenge dataset, shivade et al. [28] observed that nf performed with improved recall (90% precision; 90% recall) over negex (90% precision; 80% recall), with additional improvements when nf is coupled with a bag-of-words kernel and semi-supervised models. 5.3. hybrid methods given the heterogeneity of chief complaint data (see figure 3) it is likely that different approaches will be required for different chief complaint types. for example, standard chief complaints (e.g. vomiting, denies headache), list-templated chief complaints (e.g. nausea:n;vomiting;n), and question-based chief complaints will require different approaches. we suggest the use of an initial filtering machine learning classifier to identify chief complaint type (e.g. the tagline system [37]), followed by routing to the appropriate nlp algorithm. figure 3: general purpose ml classifier 6. discussion participants in the consultancy reported that the consultancy was effective in communicating the growing need for automated negation detection for syndromic surveillance with free-text chief complaints and other, more complex text fields. conversely, public health practitioners appreciated summary explanations of deep and shallow, pure and hybrid algorithmic approaches potentially cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi applicable to their datasets. the opportunity for these practitioners and for solution developers from both academia — primarily computer scientists and biomedical informatics researchers — and industry was judged valuable by both participant types. inclusion of representatives from several local and state health departments helped participants develop a sense of the various different negation detection and text processing challenges and practical approaches utilised across jurisdictions. in fact, some departments had avoided processing triage notes and other more complex fields for lack of trusted algorithms. the consultancy had a number of limitations. first, a relatively short duration (1.5 days), in con junction with an ambitious agenda, led to a sense among participants that some topics remained under-explored. for example, some participants suggested that machine learning approaches to negation detection could have been examined in more depth during the meeting. second, a major barrier to developing text processing methods is the limited availability of annotated (coded) datasets for training and evaluating algorithms. a limited amount of utah department of health free-text chief complaint data was obtained for sharing with consultancy participants, but this dataset was not annotated due to issues related to cost and time, and hence the utility of the dataset for developing and testing solutions was limited. data provided by the other health departments consisted of freetext sample sets that were illustrative but not sufficient for calculation of statistical performance measures. 7. conclusion the consultancy was stimulating and eye opening for both technology developer and public health practitioner attendees. developers unfamiliar with the everyday health-monitoring context gained an appreciation of the difficulty of deriving useful indicators from chief complaints. also highlighted was the challenge of processing triage notes and other free-text fields that are often unused for surveillance purposes. practitioners were provided with concise explanations and evaluations of recent nlp approaches applicable to negation processing. the event afforded direct dialogue important for communication across professional cultures. key challenges to achieving enhanced processing of free-text data fields for routine population health monitoring were presented and explored. most prominent among these challenges was the lack of labelled, authentic surveillance datasets; validation and user acceptance of opaque machine-learning algorithms that may be required for high-accuracy classification of triage note and similar text fields; and inter-regional and even inter-facility differences in free-text conventions that may confound the portability of successful classifiers. the intent of the consultancy planners was that dialogue would promote greater understanding and direct further efforts towards the goal of improving syndromic surveillance systems. acknowledgements the organisation, preconference calls, and the consultancy itself were supported and funded by the defense threat reduction agency. contents of this report are solely the responsibility of the authors and do not necessarily represent the official view of the defense threat reduction agency. cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e209, 2018 ojphi sv’s contribution was supported by the swedish research council (2015-00359) and the marie sklodowska curie actions, cofund, project inca 600398. references 1. chapman ww, bridewell w, hanbury p, cooper gf, buchanan bg. 2001. a simple algorithm for identifying negated findings and diseases in discharge summaries. j biomed inform. 34(5), 301-10. pubmed https://doi.org/10.1006/jbin.2001.1029 2. faigen z, deyneka l, ising a, neill d, conway m, et al. 2015. cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use case. online j public health inform. 7(3), e228. pubmed https://doi.org/10.5210/ojphi.v7i3.6354 3. reid m, gunn j, shah s, donovan m, eggo r, et al. 2016. cross-disciplinary consultancy to enhance predictions of asthma exacerbation risk in boston. online j public health inform. 8(3), e199. pubmed https://doi.org/10.5210/ojphi.v8i3.6902 4. burkom hs. 2017. evolution of public health surveillance: status and recommendations. am j public health. 107(6), 848-50. pubmed https://doi.org/10.2105/ajph.2017.303801 5. purtle j, field ri, hipper t, nash-arott j, chernak e, et al. 2017. the impact of law on syndromic disease surveillance implementation. j public health manag pract. pubmed 6. choi j, cho y, shim e, woo h. 2016. web-based infectious disease surveillance systems and public health perspectives: a systematic review. bmc public health. 16(1), 1238. pubmed https://doi.org/10.1186/s12889-016-3893-0 7. conway m, dowling jn, chapman ww. 2013. using chief complaints for syndromic surveillance: a review of chief complaint based classifiers in north america. j biomed inform. 46(4), 734-43. pubmed https://doi.org/10.1016/j.jbi.2013.04.003 8. scholer mj, ghneim gs, wu s, westlake m, travers da, et al. 2007. defining and applying a method for improving the sensitivity and specificity of an emergency department early event detection system. amia annu symp proc. 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https://doi.org/10.1016/j.artmed.2015.03.001 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20962133&dopt=abstract https://doi.org/10.1136/jamia.2010.003228 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21685143&dopt=abstract https://doi.org/10.1136/amiajnl-2011-000203 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18952931&dopt=abstract https://doi.org/10.1197/jamia.m2950 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21515542&dopt=abstract https://doi.org/10.1136/amiajnl-2011-000164 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25954358&dopt=abstract cross disciplinary consultancy to bridge public health technical needs and analytic developers: negation detection use case 1. introduction 2. materials and methods: consultancy 3. results: consultancy 4. materials and methods: use case 4.1. why free text processing is important for public health 4.2. impact of negation 4.2.1. georgia department of health 4.2.2. boston public health commission 4.3. resource constraints in public health departments 4.4. current approaches to negation detection 5. results: use case 5.1. shallow supervised machine learning method for negation detection 5.2. ensemble methods 5.3. hybrid methods 6. discussion 7. conclusion references isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e240, 2019 isds 2019 conference abstracts enhancing syndromic surveillance with procedure data: a 2017-8 influenza case study andrew walsh health monitoring, pittsburgh, pennsylvania, united states objective to identify additional data elements in existing syndromic surveillance message feeds that can provide additional insight int o public health concerns such as the influenza season. introduction syndromic surveillance achieves timeliness by collecting prediagnostic data, such as emergency department chief complaints, from the start of healthcare interactions. the tradeoff is less precision than from diagnosis data, which takes longer to generate . as the use and sophistication of electronic health information systems increases, additional data that provide an intermediate balan ce of timeliness and precision are becoming available. information about the procedures and treatments ordered for a patient can indicate what diagnoses are being considered. procedure records can also be used to track the use of preventive measures such as vaccines that are also relevant to public health surveillance but not readily captured by typical syndromic data elements. some procedures such as laboratory tests also provide results which can provide additional specificity about which diagnoses w ill be considered. if procedure and treatment orders and test results are included in existing syndromic surveillance feeds, additional specificity can be achieved with timeliness comparable to prediagnostic assessments. methods hl7 messages were collected for syndromic surveillance using epicenter software. they were retroactively scanned for pr1 procedure segments; procedure codes and descriptions were extracted when available. influenza-related procedures were identified and classified as either a test for the virus or an administration of a vaccine. classification was based on the procedure code when a standard code set was used and could be identified, otherwise it was based on the text description of the procedure. messages were also scanned for the presence of ‘influenza’ in text fields. influenza test results were identified first by se lecting messages with ‘influenza’ in an obx segment and then further refining based on the test code and description. results a total of 443,074,748 messages from 2,577 healthcare facilities received between july 1, 2017 and august 31, 2018 were scanned for procedure information. procedure codes were present in 39,142,670 messages from 287 facilities. the most common procedures included blood glucose measurements and other diabetes maintenance activities, incentive spirometry, blood count and metabolic panels, safety observation, and vital signs. of those, 995,754 messages from 142 facilities contained influenza-related procedure codes for 106,610 visits. 14,672 visits from 62 facilities had one of 48 vaccine procedure codes, and 91, 948 visits from 127 facilities had one of 66 test codes. time series of both types of procedures showed a seasonal trend consistent with the influenza season. figure 1 shows the daily counts of influenza test orders and vaccine administrations. figure 2 breaks out the test orders by test type (antibody assay, antigen assay, pcr, or unspecified). seven facilities sent a total of 58,182 messages containing influenza test results. these included both positive and negative results. these results distinguished between influenza a and influenza b. figure 3 shows the daily counts of both positive and negative results by virus type; this also follows the expected seasonal pattern. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e240, 2019 isds 2019 conference abstracts conclusions since procedure information was not specifically requested from healthcare facilities, the overall representation of procedure data elements was low. these initial results indicate that such data would be useful both as a supplement to syndromic surveillance activities and as a new data source for other surveillance activities such as vaccine uptake tracking. given the frequency of procedures and treatments for chronic diseases such as diabetes and heart disease, these data may be relevant for understanding the prevalence of those conditions as well. tests and treatments relevant to other public health concerns like opioid use disorder were also present, suggesting a wide range of potential applications. it is also possible to obtain and extract influenza test results from these syndromic surveillance messages. both positive and negative results were present, providing information not just on the number of positive cases but also the rate of testing and rate of positive results. the pattern of testing and results also indicates that at least some facilities test for influenza throughout the season, contrary to some conventional wisdom about testing patterns. acknowledgement health monitoring would like to thank our customers for financial support of this work. figure 1: time series of influenza-related procedure orders by procedure type figure 2: time series of influenza-related test orders by test type http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e240, 2019 isds 2019 conference abstracts figure 3: time series of influenza test results by result and influenza type http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts evaluation of syndromic surveillance in detecting hepatitis a in los angeles county michael lim*, emily kajita and bessie hwang public health, los angeles county, los angeles, ca, usa objective to create a hepatitis a virus (hav) syndrome category with which to monitor emergency department (ed) visits for situational awareness during a currently emerging hepatitis a community outbreak in los angeles county (lac), and to evaluate its usefulness. introduction in early 2017, hav outbreaks were identified in san diego county (490 cases)1 and santa cruz county (73 cases)2 in california, affecting primarily the homeless and/or illicit drug users. as of october 10, 2017, lac had identified 12 outbreak-related hav cases. due to lac’s proximity to san diego county, and its own large homeless population, the syndromic surveillance team of the lac department of public health created a syndrome category and began querying its ed data to monitor for any increase in hav-related visits. methods ed data from 1/1/2017 to 10/10/2017 (cdc weeks 1-41) from all participating syndromic eds in lac were queried for patients who reported symptoms and signs of hav. for comparison, ed data from calendar year 2016 was also queried. the query consisted of key word searches primarily within the chief complaint field, and, if available, the diagnosis and triage note fields. based on the centers for disease control and prevention (cdc) clinical description of hepatitis a3, the hav syndrome category was defined as: jaundice (or elevated liver function tests) and nausea or vomiting. any ed visit that mentioned a diagnosis of hepatitis a also met the syndrome criteria. the resulting line lists were reviewed, and the query was periodically refined to exclude visits unrelated to hepatitis a (e.g., previous history of or vaccination for hepatitis a, other forms of hepatitis, and neonatal jaundice). the syndromic system was also queried for records that matched the 12 lac cases by hospital and admission date. in addition, the chief complaint, diagnosis, and triage note fields were reviewed for any mention of homelessness or intravenous drug use. results the syndromic system detected 158 ed patients meeting the hav syndrome category criteria. of these, 12.7% had a diagnosis of hav, 53.8% had jaundice, 36.7% had elevated liver enzymes, 65.2% had nausea, and 65.8% had vomiting. in 2016, 170 ed patients who met the syndrome criteria were detected: 8.2% had a diagnosis of hav, 64.1% had jaundice, 32.4% had elevated liver enzymes, 63.5% had nausea, and 71.2% had vomiting. in both years, no indications of homelessness or idu were found. of the 12 lac confirmed hav outbreak-related cases, three did not go to a hospital, and thus did not have any syndromic data. two went to non-participating syndromic eds, but medical chart review showed that they would not have met the syndrome criteria. of the remaining seven, all went to a participating syndromic ed, and three met the syndrome criteria, but none had any mention of homelessness or idu. conclusions no major change was seen in the trend of hav syndrome visits in 2017 compared to 2016 (figure 1). one of the challenges in monitoring hav incidence is that the clinical signs and symptoms are very general, and may be shared with many other conditions. an emerging outbreak may not be detected above background levels unless the increase in ed patients with hav is large, or consolidated over time. variability in data quality in the free text fields such as chief complaint and triage notes may be problematic. cases will be missed if data fields are not fully and accurately documented, and also if patients didn’t go to a participating syndromic hospital, or to a hospital at all. though many syndromic hospitals now report diagnosis information, such information may be delayed, waiting for lab results. using a stricter syndrome definition, such as requiring a specific diagnosis of hav, may result in underreporting, but may provide a more accurate baseline for detecting increases and monitoring trends. while the query relied primarily on ed chief complaint, diagnosis and triage notes also proved useful in detecting hav syndrome visits. none of the confirmed hav cases that were known to be homeless had any mention of homelessness. the lack of any records indicating homelessness or idu may indicate that these conditions are not currently reliably captured in the syndromic extract of ed patient records. lac will continue to monitor for increases in hav syndrome ed visits as a surrogate measure of hav spread in the community. syndromic surveillance, despite its limitations, remains a valuable complement to electronic lab reporting and other traditional reporting mechanisms. figure 1. proportion trend graph of hav syndrome ed visits. keywords syndromic surveillance; hepatitis a; homeless isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. san diego hepatitis a outbreak, 2017 [internet]. san diego, ca: san diego county health & human services agency [cited 2017 sep 21]. available from: http://www.sandiegocounty.gov/content/sdc/hhsa/ programs/phs/community_epidemiology/dc/hepatitis_a.html 2. santa cruz hepatitis a, 2017 [internet]. santa cruz, ca: county of santa cruz health services agency [cited 2017 sep 21]. available from: http://www.santacruzhealth.org/hsahome/hsadivisions/ publichealth/communicablediseasecontrol/hepatitisa.aspx 3. hepatitis a, acute 2012 case definition [internet]. atlanta, ga: centers for disease control and prevention national notifiable diseases surveillance system [cited 2017 sep 21]. available from:https://wwwn.cdc.gov/nndss/conditions/hepatitis-a-acute/casedefinition/2012/ *michael lim e-mail: milim@ph.lacounty.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e121, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epidemiological distribution of reported cryptosporidiosis cases in houston, texas, 2013-2016 najmus abdullah, razina khayat*, sudipa biswas, hafeez rehman and kirstin short epidemiology, city of houston public health, houston, tx, usa objective to demonstrate the demographic and clinical distribution of reported cryptosporidiosis cases in houston, texas, from 2013-2016 introduction cryptosporidiosis is a diarrheal disease caused by microscopic parasite cryptosporidium. modes of transmission include eating undercooked food contaminated with the parasite, swallowing something that has come into contact with human or animal feces, or swallowing pool water contaminated with the parasite. the disease is clinically manifested usually with chronic diarrhea and abdominal cramps. it is found to be more prevalent in immunocompromised patients like hiv and aids. cryptosporidiosis usually causes potentially life-threatening disease in people with aids. methods data were extracted from the houston electronic disease surveillance system (hedss) from january 1, 2013 to december 31, 2016. a total of 170 confirmed cases received during the study period were analyzed and crossed checked against the national enhanced hiv/aids reporting system (ehars) database to examine epidemiological distribution. sas 9.4 was used to analyze demographics, clinical characteristics as well as transmission factors. results approximately, 72% of the cases were males and 28% were females. the 35-44 year old age group (37%) had the highest prevalence. african americans (49%) and hispanics (30%) had the highest number of confirmed cryptosporidiosis cases. 133 of the 170 cases, 78% were previously reported to the ehars national database as hiv/aids cases. among the cases reported to ehars, 90% had aids. 10% of the reported cases were found to be deceased in ehars database. among the 170 reported cases, 30% were hospitalized. clinical presentations were diarrhea (44%), followed by abdominal cramps (23%), and nausea and vomiting (18%). most common transmission factors among cryptosporidiosis cases were found to be men who have sex with men (msm) (34%), followed by heterosexual contact with hiv/aids patients (14%), and msm with intravenous/injection drug user (idu) (5%). among the reported cases, 70% were receiving ongoing medical services for their hiv/aids status. conclusions cryptosporidiosis in patients with hiv/aids diagnosis is mostly prevalent in males, african american adults and those between 35-44 years of age, with common clinical presentations of diarrhea and abdominal cramps. the prevalence of cryptosporidiosis is found to be more common in aids patients. prevention strategies should be focused on raising awareness among immunocompromised patients with hiv and symptoms of cryptosporidiosis so they get evaluated and treated quickly to prevent conversion to aids disease. keywords cryptosporidiosis; hiv; disease surveillance acknowledgments thanks to the city of houston health department. references 1. caccio sm, pozio e. advances in the epidemiology, diagnosis and treatment of cryptosptidiosis. expert review of anti-infective therapy. 2006; 4(3): 429-443. 2. hunter pr, nichols g. epidemiology ad clinical features of cryptosporidium infection in immunocompromised patients. clinical microbiology reviews. 2002 jan; 15(1): 145-154. 3. http://www.cdc.gov/parasites/crypyo/gen_info/infect.html *razina khayat e-mail: razina.khayat@houstontx.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e114, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts barriers and facilitators of reporting foodborne illness jonathan l. chua* and mark chen national university of singapore, singapore, singapore objective to better understand the barriers and facilitators to reporting and assessing what improvements would increase participation. introduction traditional surveillance methods have a major challenge to estimating the burden of disease due to underreporting [1]. participatory surveillance techniques can help supplement to monitor and detect foodborne outbreaks while reducing the impact of underreporting [2]. as there is a low participation rate in singapore, this study aims to better understand the barriers and facilitators to reporting and assesses what improvements can increase participation. methods a total of 14 individuals participated in the study; 8 had informed health authorities of a possible foodborne outbreak while 6 patients were diagnosed with gastroenteritis at general practitioner (gp) clinics but did not report their illness to health authorities. we examined the barriers and facilitators to reporting foodborne illnesses to health authorities through semi-structured in-depth interviews and thematic analysis. results the median age of participants was 28 (interquartile range = 23-37). the majority were singaporeans and had or were pursuing university qualifications. the combination of perceived severity of illness and degree of certainty of the cause of illness are key reasons that are both barrier and facilitator to reporting foodborne illness to the authorities. the informants expected government intervention and hoped that their actions would prevent others from being affected as well. however, reporting to health authorities was usually delayed by the participants’ severity of illness. those who did not report were unaware of reporting channels and were concerned their actions would negatively affect food establishments. participants also shared what they would like to see in a reporting system. firstly, contact information should be easily accessible with a user-friendly system. secondly, a human touch and live acknowledgement was desirable when reporting the issue instead of being met with recorded voice machine messages. this would also reduce the number of subsequent follow up calls from the authorities to gather information. conclusions it is crucial for the public to be informed of easily accessible and user-friendly avenues to report foodborne incidences to the authorities. being able to communicate directly to relevant authorities immediately would help relay issues with the assurance that the matter would be looked into and acted upon. in trying to understand the barriers and facilitators to reporting, the study hopes to see a higher motivation of the public to report, so that necessary actions can be taken to reduce foodborne incidences. keywords participatory surveillance; underreporting; public health surveillance; barriers and facilitators acknowledgments we would like to acknowledge the foodborne outbreak team within the communicable disease division of the ministry of health, singapore for helping to recruit participants. also we would like to thank frontier medical and university health centre for allowing us to recruit participants at their clinics. lastly, we acknowledge the funding from national medical research council, singapore. references 1. fletcher sm, lewis-fuller e, williams h, et al. magnitude, distribution, and estimated level of underreporting of acute gastroenteritis in jamaica. j heal popul nutr. 2013;31(4 suppl.1):69-80. http://www. ncbi.nlm.nih.gov/pubmed/24992813. accessed october 4, 2017. 2. quade p, nsoesie eo. a platform for crowdsourced foodborne illness surveillance: description of users and reports. jmir public heal surveill. 2017;3(3):e42. doi:10.2196/publichealth.7076. *jonathan l. chua e-mail: jonathanlumenchua@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e101, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling school of public health, university of hong kong, hong kong, china objective this study described health-seeking behavior of the general population specific to different symptoms, at different times of the year. this information allows the estimation of population disease burden over the year using sentinel surveillance data. we will use influenza-like illness (ili) as an example. introduction the general health-seeking behavior has been well described in different populations. however, how different symptoms have driven health-seeking behavior was less explored. from the patient’s perspective, health-seeking behavior tends to be responsive to discomfort or symptoms rather than the type of diseases which is unknown before medical consultation, hence symptom-specific behavior may more realistically reflect responses from the public which is subsequently captured by syndromic surveillance. in hong kong, sentinel surveillance of common diseases, such as ili and acute diarrhoeal diseases, consists of general practitioners (gp), general outpatient clinics (gopc) and chinese medicine practitioners (cmp). these existing sources of syndromic surveillance data are affected by the choice of health services and health seeking behavior and hence may overor under-represent actual disease burden. by understanding health-seeking behavior at different times of the year, we could estimate the disease burden in the population, and population subgroup from multiple surveillance data. methods we conducted 4 rounds of longitudinal randomized telephone surveys over 1.5 year, to describe symptom-specific health-seeking behavior at times with different level of disease activity (figure 1). we collected information if healthcare service was being sought for and types of medical consultation after onset of different symptoms. the information were further utilized to estimate the ili burden in specific age groups, by the following relation: k1s1 = c11n1 + c12n2 + c13n3 k2s2 = c21n1 + c22n2 + c23n3 k3s3 = c31n1 + c32n2 + c33n3 where s i , k i , c ij and n i represent ili consultation rates from sentinel surveillance of health service type i, scaling factor from surveillance data to the population with ili seeking health service type i, likelihood of consultation to health service type i for age group j with ili, and the population size of age group j with ili. from the longitudinal survey, we obtained estimates for c ij and k i during the survey period. using the surveillance data from gp, gopc and cmp, we obtained estimates n i by solving the above relations. results fever is the symptom most likely to prompt people to seek medical attention. we further focused on ili, defined as fever ≥37.8 plus either cough or sore throat. there were 111 episodes of ili in the study period. of which, 85.6%, 6.3% and 18.9% visits gp, gopc and cmp respectively (some have sought for multiple health service types), while 9% did not seek for any medical attention. based on the relation in the methods, we estimated the weekly age-specific ili burden over time from gp, gopc and cmp surveillance (figure 2). the estimated ili burden for the population aged ≤15 and ≥55 years captured the peak in february-march in 2015 from surveillance of ili institutional outbreaks (e.g. schools, elderly care centers). however, no such increased ili burden was observed in the 16-54 years age group in the period. conclusions syndromic surveillance data from different sources (e.g. medical consultation or google flu trends) were affected by different health seeking or reporting behavior. by understanding and quantifying these behaviors at different times, it is possible to estimate disease burden in the population. timing covered by the 4 rounds of survey estimated weekly age-specific burden of ili keywords health seeking behaviour; symptoms; influenza like illness; sentinel surveillance acknowledgments this work is supported by the area of excellence scheme of the hong kong university grants committee, and the health and medical research fund of the food and health bureau, government of the hong kong special administrative region. *eric h.y. lau e-mail: ehylau@hku.hk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e64, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating 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infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts data model for initiatives to monitor exposure to antimicrobials (datamime) megan t. patel*1, carlos santos2, ron price3, george nelson4, lauren hall5, richard platt6 and william trick7 1mraia, chicago, il, usa; 2rumc, chicago, il, usa; 3lumc, maywood, il, usa; 4vumc, nashville, tn, usa; 5bs&w, dallas, tx, usa; 6harvard pilgrim, boston, il, usa; 7cchhs, chicago, il, usa objective plan, develop, and pilot an open source system that could be integrated into the pcornet (pcori) and sentinel (fda) national common data models (cdms) to generate antimicrobial use (au) reports submittable to cdc’s national healthcare safety network (nhsn). the system included ancillary tables, and data quality and report generation queries. the datamime system will allow hospitals to generate comparable au reports for hospital inpatients. introduction despite decades of attempts to promote judicious au, the us has high rates of per-person antimicrobial consumption, and extremely high rates of carbapenem use1. such profligate use is a key factor in the high rate of antimicrobial-resistant infections seen in us healthcare facilities2. antimicrobial stewardship (as) programs have been identified as a critical component of intervention strategies3. a core component of as programs is tracking au, which is needed to monitor trends in use, focus interventions on aberrant behaviors, promote judicious use, and evaluate the effectiveness of interventions. a system designed to extend two national data models would provide a scalable platform for rapid adoption of au reporting. methods virtual meetings were held with all participating sites (five hospitals in il, la, and tn) to develop the ancillary tables to capture intrahospital patient movement, and administration of antimicrobial agents. ancillary tables were designed & sites populated the tables with calendar year 2016 data. data characterization was performed to assess overall table statistics, and verify mappings of facility unit locations to nhsn location codes, medications to rxnorm, and routes of administration to one of four snomed categories. additional characterization focused on cdc’s nhsn validation protocol for the au module. analytical queries were developed to produce the output metrics required for submission to the nhsn au module. results two ancillary tables and two look-up tables were developed: a bed information table (table 1) to capture local location codes with a datetime stamp for precise tracking of patient location; a separate location look-up table allows mapping to other terminologies (table 2); and, an inpatient drug administration table (table 3) to capture data from the electronic medication administration record (emar) or bar coding medication administration (bcma) system, utilizing a route of administration look-up table (table 4). the data model was structured to accommodate use cases with alternative mapping terminologies for local location code, local term for route of administration, local codes for medication, and the option of including the ndc code. all sites populated the ancillary tables. for the bed information table, all sites utilized their adt table information for patient movement. for the medication administration table, most sites limited the inclusion criteria to the 89 antimicrobial agents required for reporting to the nhsn au module. aggregate results from participating sites for facility-wide measures and select antimicrobial agents are presented in table 5. conclusions the data model developed was able to produce the metrics required for reporting to cdc’s nhsn au module. the data dictionary language, implementation guidance, mappings, and queries will be distributed as a tool-kit for other pcornet and sentinel sites for reporting to the au module. in addition, this cdm could allow for the development of additional metrics including excessive use of antibiotic combinations of redundant spectra, syndrome specific antibiotic use, or increased use of excessively broad spectrum antibiotic classes. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords data model; antimicrobial use; nhsn; pcornet; sentinel acknowledgments helen zhang, alex patino, sue zelisko, ekta kishen, taoran qiu, julie lange, shazia sathar, laura shockro references 1. van boeckel tp, brower c, gilbert m, grenfell bt, levin sa, robinson tp, teillant a, laxminarayan r. global trends in antimicrobial use in food animals. proc natl acad sci usa 2015; 112(18):5649-54. 2. cdc. antibiotic resistance threats in the united states, 2013. atlanta, ga: us dept. of hhs, cdc; 2013. 3. dellit th, owens rc, mcgowan je, gerding dn, weinstein ra, burke jp, et al. idsa and the shea guidelines for developing an institutional program to enhance antimicrobial stewardship. clin infect dis 2007; 44(2):159-77. *megan t. patel e-mail: mtoth2@uic.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e35, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky los alamos national laboratory, los alamos, nm, usa objective to explore the use of wikipedia as a data source for disease surveillance. introduction infectious disease remains costly in human and economic terms. effective and timely disease surveillance is a critical component of prevention and mitigation strategies. the limitations of traditional disease surveillance systems have motivated new techniques based upon internet data sources such as search queries and social media. however, 4 challenges remain before internet-based disease surveillance models can be reliably integrated into an operational system: openness, breadth, transferability, and forecasting. we evaluated a new data source, wikipedia access logs, in these 4 challenges for global disease surveillance and forecasting. methods we used wikipedia article access logs and disease incidence reports to build linear models to analyze 3 years of data for 14 disease-location contexts. access logs for all wikipedia articles are freely available online [1]. we used official epidemiological reports available from government health agencies and the world health organization. as wikipedia does not provide article access counts for specific countries, we used language as a proxy. we selected articles by examining the english wikipedia article for the disease, enumerated relevant linked articles and identified corresponding articles in each language by following the inter-language wiki link. to nowcast, we aligned the article access counts with the incidence data in order to yield time series with the same frequency. for each article we computed pearson’s correlation r against the disease time series and selected the 10 highest correlated articles. we then built a linear multiple regression model. we assessed forecasting potential by repeating the process with the article time series shifted 28 days forward and backward in 1 day increments. to evaluate whether model transferability is possible, we computed a metric rt, the pearson’s r computed between the correlation scores r of each article found in both languages, for each pair of locations tested on the same disease. results among the 14 contexts we analyzed, 8 of the models succeeded for nowcasting and forecasting, 3 cases failed because patterns in the official data were too subtle to capture and 3 failed because the signal-to-noise ratio in the wikipedia data was too subtle to capture. performance fell along disease lines: all influenza and dengue models were successful, 2 of the 3 tuberculosis models were, and cholera, ebola, hiv/aids, and plague were unsuccessful. table 1 summarizes the nowcasting and forecasting performance of the models. table 2 lists the transferability scores rt for each pair of countries tested on the same disease. in the case of influenza, both japan/thailand and thailand/usa show promising preliminary results. conclusions human activity on the internet leaves traces that contain real and useful evidence of disease dynamics. wikipedia data are one of the few internet data sources that can meet all 4 challenges. wikipedia data are freely available to anyone (openness), they work in multiple locations for multiple diseases around the world with model success of r2 up to 0.92 (breadth), wikipedia based models can possibly be transferable to different locations with similarity of up to 0.81 (transferability), and they have forecasting value through a horizon of 28 days (forecasting). this preliminary study has several limitations. the methods need to be tested in more contexts, a better article selection procedure is needed, and better geo-location is needed.despite these limitations, wikipedia access logs is a useful data source for global disease monitoring and forecasting. table 1: summary of model performance table 2: summary of transferability scores keywords wikipedia; disease surveillance; internet data; search queries; global acknowledgments this work is supported in part by nih/nigms/midas under grant u01-gm097658-01 and the defense threat reduction agency (dtra), joint science and technology office for chemical and biological defense under project numbers cb3656 and cb10007. mac brown’s participation in discussions improved this work significantly. la-ur~14-22535 references [1] 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alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e292, 2019 isds 2019 conference abstracts streamlined development of analytic fusion capability for health surveillance susama agarwala, howard burkom, daniel wernig redd, johns hopkins applied physics lab, washington, district of columbia, united states objective our project goal is to enhance the capability of automating health surveillance[mou1] by us department of defense (dod) epidemiologists. we employ software tools that build and train bayesian networks (bns) to facilitate the development of analytic fusion of multiple, disparate data sources comprising both syndromic and diagnostic data streams for rapid estimation of overall levels of concern for potential disease outbreaks. working with previously developed heuristic bns, we evaluate the ability of machine learning algorithms to detect outbreaks with greater accuracy. we use historical training data on the ability to detect outbreaks of influenza-like illness (ili). introduction the motivation for this project is to provide greater situational awareness to dod epidemiologists monitoring the health of military personnel and their dependents. an increasing number of data sources of varying clinical specificity and timeliness are available to the staff. the challenge is to integrate all the information for a coherent, up-to-date view of population health. developers at the johns hopkins applied physics laboratory, in collaboration with medical epidemiologists at the armed forces health surveillance branch, previously designed a multivariate decision support tool to add to the dod implementation of the electronic surveillance system for early notification of community-based epidemics (essence). data sources included clinical encounter records including free-text chief complaints, filled prescription records, and laboratory test orders and results. filtered data streams were derived from these sources for daily monitoring, and alerting algorithms were customized and applied to the resulting time series. we built bns to derive overall levels of concern from the collection of data streams and algorithm outputs to derive, in the form of daily fusion alerts, the overall level of various outbreak concerns. visualizations made apparent which data features accounted for these concerns, including drill-down to the level of patient record details. advantages of the bn approach are this transparency and the capacity for assessments using incomplete data and incorporating novel and report-based data streams. the need for such fusion was nearly unanimous in a global survey of public health epidemiologists [1]. our proof-of-concept system based on commercial bn software was well received by a cross-section of dod health monitors. the new software tools we apply in this project use freely available r packages which provide more comprehensive tools for bn training and development. these results will allow us to improve the analytic fusion abilities of dod essence, as well as in civilian surveillance systems our testing procedures and results are presented below. methods we employ a 3.75-year dataset (2006-2010) with information from 502 us medical treatment facilities including 289 hospitals. our data include time series of daily counts and alerting algorithm outputs from each facility for syndrome groups based on a) chief complaints and diagnosis codes from clinic visits, b) groups of laboratory test orders and influenza test results, and c) selected groups of filled prescriptions. for each facility group, the challenge is to combine these data streams into a daily assessment of levels of concern for an ili outbreak. the software developed in this project facilitates the formation, training, and testing of bns for outbreak alerting based on the datasets above. underlying each bn is a directed acyclic graph whose leaf nodes represent discrete states of concern for each data stream, ranging from general streams such as ili-related chief complaints to specific ones such as positive flu test results. the states are derived from both daily stream counts and alerting algorithm outputs. internal nodes represent mid-level combinations of indicators, such http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e292, 2019 isds 2019 conference abstracts as ili concern based only on clinic data, and parent nodes which represent the calculated level of concern based on all data sources. the connectivity between the nodes, and the orientation of the edges of the graph are determined by the heuristic relationships adopted in previous projects [2]. each node is associated with a conditional probability table (cpt). training a bn requires state assignments for every node in the underlying graph, which must be obtained from a trusted source. these assignments can generate cpts at each node of the bn such that, when given only a set of evidence nodes, the levels of concern can be propagated up the network to provide the desired levels of concern at the decision nodes. truth data for ili outbreaks comes from two sources: 33 documented outbreaks with dates supplied by dod surveillance reports or media articles and, because many modest-sized outbreaks are unreported, 81 unconfirmed data-derived events with algorithmic alerts across multiple data sources. we use data from the more numerous unconfirmed events to train the bns. to avoid commercial software constraints, we use the free r package grain [3] to create, train, and test the bns. we test multiple bns for multivariate ili outbreak detection, all based on the same nodal structure with 18 parent, intermediate, and leaf nodes. candidate bns are created and trained using either a) cpts determined with a multivariate stochastic search in the previous project, augmented with ground truth data or b) a heuristic lookup table of state combinations. results table 1 shows the high odds ratios calculated for candidate bns. these statistics are calculated for decision node outputs for event vs non-event dates in the truth data with the constraint that every event is detected for at least one date. the machine learning advantage from the training data is evident from comparing the two rows. to show the advantage of fusing data sources, table 2 gives analogous odds ratios based on single-stream alerting algorithms. aside from the lower detection statistics, single streams offer no corroboration of statistical alerts. conclusions analytic fusion is essential for the efficient, timely use of a growing collection of complex, streaming information by a limited workforce of human health monitors. this project builds upon previous fusion capability for automated health surveillance by expediting new development and facilitating software implementation through open source tools. the detection results indicate a significant advantage in both sensitivity and alert rates of automated systems achievable with machine learning. moreover, if basic challenges of multivariate data acquisition and determination of truth datasets for supervised learning can be met, further improvements are likely using bn structure discovery methods as well as other machine learning approaches. references 1. hopkins rs, tong cc, burkom hs, et al. 2017. a practitioner-driven research agenda for syndromic surveillance. public health rep. 132(1_suppl), 116s-126s. pubmed https://doi.org/10.1177/0033354917709784 2. burkom hs, elbert y, ramac-thomas l, cuellar c, hung v. 2013. refinement of a population-based bayesian network for fusion of health surveillance data. online j public health inform. 5(1), e6. https://doi.org/10.5210/ojphi.v5i1.4413 3. højsgaard s. 2012. graphical independence networks with the grain package for r. j stat softw. 46(10). doi:10.18637/jss.v046.i10. table 1. fusion odds ratios intermediate/parent nodes ili outbreak influenza outbreak severe ili outbreak odds ratio (cpts made by hand) 75.90951 17.00181 61.52413 odds ratio (cpts modified with training) 107.2841 61.42208 63.97279 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28692395&dopt=abstract https://doi.org/10.1177/0033354917709784 https://doi.org/10.5210/ojphi.v5i1.4413 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e292, 2019 isds 2019 conference abstracts table 2. single stream odds ratios caper acd-9 syndrome caper chief complaint influenza antivirals influenza lab test median odds ratio 3.9 7.5 4.7 2.1 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts fleas as vectors of especially dangerous infections in jermuk region, 2010-2016 lusine markosyan* and vahe kulakhszyan laboratory of epizootology, ectoparasitology and entomology, ra moh national center for disease control and prevention snco, reference laboratory center branch, yerevan, armenia objective the goal was to determine the impact of flea number variation on the epizootic situation in the jermuk region. introduction the jermuk region of the zangezur mezofocus is part of the transcaucasian highland focus of plague. this enzootic area is polyvectorial. the mezofocus has rich fauna with approximately eight species of fleas: callopsylla caspia, ctenophthalmus wladimiri, frontopsylla elata, amphipsylla rossica, leptopsylla taschenbergi, nosopsyllus consimilis, palaeopsylla vartanovi, and doratopsylla dampfi. ct. wladimiri is the most abundant. however, special attention should be paid to c. caspia and n. consimilis as they are the only vectors specific for yersinia pestis. in these fleas, the bacteria form a plug that blocks digestion and induces starvation. afflicted fleas bite frenziedly in an effort to feed and the pressure that results releases bacteria from the plug, infecting a new host. fleas infected with plague during an epizootic are a serious threat to humans, especially when in contact with synanthropic rodents. a survey was conducted to catalog fleas in the foci. methods from 2010-2016 different species of fleas were collected in jermuk region of vayots dzor by combing the hair of captured rodents and processing their nests with heat and light. flea species, and their egglaying status, were identified by microscopic examination. results from 2010-2015 the density of c. caspia averaged 23 per hectare. fleas that were laying eggs were quantified via microscopy. in 2016 a drastic increase in c. caspia density was recorded; flea numbers averaged 225 per hectare. half of these were actively reproducing (table 1). these changes in flea numbers occurred with a stable rodent population of 60 per hectare. conclusions because of the drastic increase in flea density of 2016 compared to the period of 2010-2015, it is likely that diseases that depend on fleas to spread will increase in the near future in jermuk. so, it is necessary to monitor the epizootic situation of jermuk, as it is a popular resort in armenia. c. caspia numbers and fertility in jermuk keywords callopsylla caspia; fleas; population survey *lusine markosyan e-mail: lusine.markosyan.1976@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e130, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 1johns hopkins applied physics laboratory, laurel, md, usa; 2vet teaching hospital, colorado state university, fort collins, co, usa; 3center for epidemiological and animal health, us dept. of agriculture, fort collins, co, usa objective establish a laboratory-based syndromic surveillance system for horses in colorado. introduction the risk identification unit (riu) of the us dept. of agriculture’s center for epidemiology and animal health (ceah) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. in an initiative to increase the monitored species, health issues, and data sources, ceah epidemiologists are building a surveillance system based on weekly counts of laboratory test orders along with colorado state univ. laboratorians and statistical analysts from the johns hopkins univ. applied physics lab. initial efforts used 12 years of equine test records from 3 state labs covering most colorado horse testing. trial syndrome groups were formed based on riu experience and published articles1. data analysis, stakeholder input, and discovery of laboratory workflow details were needed to modify these groups and filter test records to eliminate alerting bias. customized statistical monitoring methods were sought based on specialized lab information characteristics and on likely presentation and health significance of syndrome-associated diseases. methods data transformation and syndrome formation focused on test names, order completion status, submitting organization, specimen type, analyte measured, animal owner zip code, and lab unit that received the order. we analyzed time series of weekly counts of tests included in candidate syndrome groups and conducted an iterative process of data analysis and veterinary consultation for syndrome refinement and record filters. this process produced a rule set in which records were .classified into syndromes using only test name when possible and otherwise, the specimen type or related body system was used with test name to determine the syndrome. test orders associated with government regulatory programs, veterinary teaching hospital inpatient testing protocols, or research projects, rather than clinical concerns, were excluded. we constructed a testbed for sets of 1000 statistical trials and applied a stochastic injection process assuming lognormally distributed incubation periods to choose an alerting algorithm with the syndrome-required sensitivity and an alert rate within the specified acceptable range for each resulting syndrome. alerting performance of the ears c3 algorithm traditionally used by ceah was compared to modified c2, cusum, and ewma methods, with and without outlier removal and adjustments for the total weekly number of nonmandatory tests. results the equine syndrome groups adopted for monitoring were abortion/ reproductive, diarrhea/gi, necropsy, neurological, respiratory, systemic fungal, and tickborne. data scales and seasonality differed widely among syndrome series. removal of mandatory tests reduced weekly observed counts by up to 80%. the riu group studied outcomes associated with each syndrome and called for detection of single-week signals for most syndromes with expected false-alert intervals >8 and <52 weeks, 8-week signals for neurological and tickborne monitoring (requiring enhanced sensitivity), 6-week signals for respiratory, and 4-week signals for systemic fungal. from the test-bed trials, recommended methods, settings and thresholds were derived. figure 1 shows a performance comparison based on 8-week signals for the neurological time series. conclusions understanding of laboratory submission sources, laboratory workflow, and of syndrome-related outcomes are crucial to form syndrome groups for routine monitoring without artifactual alerting. choices of methods, parameters, and thresholds varied by syndrome and depended strongly on veterinary epidemiologist-specified performance requirements. algorithm performance comparison for neurological syndrome keywords livestock surveillance; veterinary syndrome; alerting algorithm; animal health; detection performance references dorea fc, muckle ca, kelton d, et al. exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine. plos one. vol 8. united states2013:e57334. *howard burkom e-mail: howard.burkom@jhuapl.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e50, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective 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vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. 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zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colóngonzález2 1public health england, birmingham, united kingdom; 2university of east anglia, norwich, united kingdom objective to devise a methodology for validating the effectiveness of syndromic surveillance systems across a range of public health scenarios, even in the absence of historical example datasets. introduction whilst the sensitivity and specificity of traditional laboratory-based surveillance can be readily estimated, the situation is less clear cut for syndromic surveillance. syndromic surveillance indicators based upon presenting symptoms, chief complaints or preliminary diagnoses are designed to provide public health systems with support to detect multiple potential threats to public health. there is however, no gold standard list of all the possible ‘events’ that should have been detected. this is especially true in emergency response where systems are designed to detect possible events for which there is no directly comparable historical precedent. methods a scenario template specification was created to identify the information needed to validate syndromic systems. in order to estimate the number of extra cases presenting to syndromic systems two types of information were required; a model for the numbers of people affected each day, and a series of parameter estimates to determine if those affected would be captured by the surveillance system. results scenario templates enabled the collection of the relevant information and estimates for each scenario, using previous research, historical examples and public health expert knowledge. a number of parameters were identified as being required, including: the number of people who become symptomatic, the proportion of these who would seek health care, the population coverage of syndromic systems and the proportion of patients with a diagnosis linked to a syndromic indicator (see figure). conclusions the scenario approach has been combined with simulations to evaluate existing detection algorithms. the approach of identifying key parameters for estimation enables uncertainty to be quantified and combined to give a joint inference for the probability of detection based on both random noise and uncertainty due to modelling and parameter estimation. scenarios can be easily modified to identify how changes in any aspect of the scenario or the syndromic system would affect detection rates. patient presentation pyramid illustrating: parameters that need to be estimated in order to calculate the number of extra cases recorded by syndromic surveillance systems. keywords simulation; syndromic surveillance; scenarios acknowledgments we acknowledge support from: royal college of emergency medicine, eds participating in the emergency department system (edsss), ascribe ltd and l2s2 ltd; ooh providers submitting data to the gpoohss and advanced heath & care; tpp and participating systmone practices and university of nottingham, clinrisk, emis and emis practices submitting data to the qsurveillance database; and nhs 111 and hscic for assistance and support in providing the anonymised call data that underpin the remote health advice syndromic surveillance system. we thank the phe real-time syndromic surveillance team for technical expertise. the authors received support from the national institute for health research health protection research unit in emergency preparedness and response. the views expressed are those of the authors and not necessarily those of the nhs, the nihr, the department of health or public health england. *roger morbey e-mail: roger.morbey@phe.gov.uk online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e142, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic 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to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e267, 2019 isds 2019 conference abstracts using syndromic surveillance to classify and capture nonfatal occupational injuries and illnesses marija borjan, margaret lumia new jersey department of health, trenton, new jersey, united states objective to evaluate the use of a real-time surveillance tool to track a variety of occupationally-related emergency room visits through the state based syndromic surveillance system, epicenter. introduction this study uses data from the new jersey syndromic surveillance system (epicenter) as a data source to enhance surveillance of current non-fatal occupational injuries, illnesses, and poisonings. epicenter was originally developed for early detection and monitoring of the health of communities using chief complaints from people seeking acute care in hospital emergency rooms to identify health trends. currently, syndromic surveillance has not been widely applied to identify occupational injuries and il lnesses. incorporating syndromic surveillance data from epicenter, along with hospital discharge data, will enhance the classificat ion and capture of work-related non-fatal injuries with possible improved efforts at prevention. methods epicenter emergency department data from january to december 2014 was evaluated, using work-related keywords and icd-9 codes, to determine its ability to capture non-fatal work-related injuries. a collection of keywords and phrases specific to workrelated injuries was developed by manually assessing the free text chief complaint data field’s. sensitivity, specificity, an d positive predictive value (ppv), along with descriptive statistics was used to evaluate and summarize the occupational injuries identified in epicenter. results overall, 11,919 (0.3%) possible work-related injuries were identified via epicenter. of these visits 956 (8%) indicated workman’s compensation as payer. events that resulted in the greatest number of ed visits were falls, slips, trips (1,679, 14%). nature of injury included cuts, lacerations (1,041, 9%), burns (255, 2%), and sprains, strains, tears (185, 2). the part of the body most affected were the back (1,414, 12%). this work-related classifier achieved a sensitivity of 5.4%, a specificity of 99.8%, and a ppv of 2.8%. conclusions evaluating the ability and performance of a new and existing surveillance data source to capture work-related injuries can lead to enhancements in current data collection methods. this evaluation successfully demonstrated that the chief complaint reporting system can yield real-time knowledge of incidents and local conditions for use in identifying opportunities for prevention of workrelated injuries. acknowledgement this project was funded by niosh cooperative grant #5u60oh008485-12. the authors would like to thank stella tsai and teresa hamby, njdoh communicable disease service, infectious, and zoonotic disease program staff, and kristen weiss, health monitoring. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e279, 2019 isds 2019 conference abstracts enhancing provider reporting of notifiable diseases using hieenabled decision support brian e. dixon1, 2, shaun j. grannis4, 2, joe gibson3 1 epidemiology, indiana university fairbanks school of public health, indianapolis, indiana, united states, 2 regenstrief institute, indianapolis, indiana, united states, 3 marion county public health department, indianapolis, indiana, united states, 4 indiana university school of medicine, indianapolis, indiana, united states objective to enhance the process by which outpatient providers report surveillance case information to public health authorities following a laboratory-confirmed diagnosis of a reportable disease. introduction traditionally, public health agencies (phas) wait for hospital, laboratory or clinic staff to initiate case reports. however, this passive approach is burdensome for reporters and produces incomplete and delayed reports, which can hinder assessment of disease in the community and potentially delay recognition of patterns and outbreaks [1]. modern surveillance practice is shifting toward greater use of electronically transmitted disease information. the adoption of electronic health record (ehr) systems and health information exchange (hie) among clinical organizations and systems, driven by policies such as the ‘meaningful use’ program, is creating an information infrastructure that public health organizations can take advantage of to improve surveillance practice [2]. methods using an existing hie infrastructure, we developed, tested and implemented an intervention that pre-populates an electronic version of the official indiana state department of health communicable disease reporting form following detection of a notifiable disease in an electronic laboratory message. pre-populated form fields included patient demographics, notifiable disease confirmatory test results, and provider information. the intervention was delivered electronically to the provider using the hie network. seven representative, high priority diseases were included: chlamydia, gonorrhea, syphilis, salmonella, hepatitis b, hepatitis c, and histoplasmosis. the intervention was implemented in seven diverse primary care clinics in central indiana. control clinics were all other out patient settings connected to the hie that reported at least one communicable disease case to the county pha during the same time period. the primary outcome measure was reporting rate (provider reports received as a percent of all case reports received by the ph a for individuals with a notifiable disease). clinic-submitted case reports were collected between 9/9/2013 – 3/15/2014 and 9/15/2014 – 6/12/2016 from all clinics (intervention and non-intervention) that reported to the county pha before and during the intervention period. we grouped case reports into non-intervention and intervention groups based on whether or not the laboratory test occurred at an intervention clinic during the clinic's intervention period. reports received from intervention clinics outside of the clinics’ intervention period were classified as non-intervention reports. to evaluate the impact of the intervention on the outcome measures, we employed a difference-in-difference approach in which the change in reporting rates among intervention clinics before and after implementation was compared to the change in reporting rates among non-intervention clinics during the same timeframes. the analysis utilized generalized linear models with logistic regression using the logit link function for reporting rate. all statistical tests were performed using the nlestimate macro within enterprise sas version 9.4. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e279, 2019 isds 2019 conference abstracts results of 16,172 unique cases observed across all time periods, 1,453 (9%) were reported by intervention clinics. during intervention time periods, provider reporting rates significantly increased from 20% to 50% in intervention clinics while falling from 12% to 10% in the control clinics (p<0.001). the most prevalent conditions observed overall were hepatitis b (5,362; 33.5%), chlamydia (5,157; 32.2%), and hepatitis c (3,236; 20.2%). only chlamydia, gonorrhea, and hepatitis c possessed enough observations during the intervention periods for robust comparisons. with respect to the change in reporting rates for specific diseases, rates increased for chlamydia (56.9% vs. 66.4%), gonorr hea (55.6% vs. 58.3%), and hepatitis c (6.5% vs. 7.3%) in the intervention clinics. however, only the increase for chlamydia was statistically significant (p<0.001). rates for all three diseases decreased in control clinics during the same time period. conclusions the results of the study indicate that electronic, pre-populated case reporting forms integrated into providers’ ehr systems and workflow, enabled by an interoperable hie network, can be effective at increasing clinic reporting rates. these result s are encouraging and offer hope for initiatives that aim to help phas leverage the expanding health it infrastructure created thro ugh policies like ‘meaningful use.’ acknowledgement this project was supported by grant number r01hs020909 from the agency for healthcare research and quality. the content is solely the responsibility of the authors and does not necessarily represent the official views of the agency for healthcare research and quality. references 1. revere d, hills rh, dixon be, gibson pj, grannis sj. 2017. notifiable condition reporting practices: implications for public health agency participation in a health information exchange. bmc public health. 17(1), 247. pubmed https://doi.org/10.1186/s12889-017-4156-4 2. dixon b, grannis s. public health informatics infrastructure. in: magnuson ja, fu jpc, editors. public health informatics and information systems. health informatics: springer london; 2014. p. 69-88. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28284190&dopt=abstract https://doi.org/10.1186/s12889-017-4156-4 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts use of diagnosis code in mental health syndrome definition achintya n. dey*, deborah gould, nelson adekoya, peter hicks, girum s. ejigu, roseanne english, jenny couse and hong zhou ccsels/dhis, cdc, atlanta, ga, usa objective the objectives of this study are to (1) create a mental health syndrome definition for syndromic surveillance to monitor mental health-related ed visits in near real time; (2) examine whether cc data alone can accurately detect mental health related ed visits; and (3) assess the added value of using dx data to detect mental healthrelated ed visits. introduction between 2006 and 2013, the rate of emergency department (ed) visits related to mental and substance use disorders increased substantially. this increase was higher for mental disorders visits (55 percent for depression, anxiety or stress reactions and 52 percent for psychoses or bipolar disorders) than for substance use disorders (37 percent) visits [1]. this increasing number of ed visits by patients with mental disorders indicates a growing burden on the health-care delivery system. new methods of surveillance are needed to identify and understand these changing trends in ed utilization and affected underlying populations. syndromic surveillance can be leveraged to monitor mental health-related ed visits in near real-time. ed syndromic surveillance systems primarily rely on patient chief complaints (cc) to monitor and detect health events. some studies suggest that the use of ed discharge diagnoses data (dx), in addition to or instead of cc, may improve sensitivity and specificity of case identification [2]. methods we extracted a de-identified random sample of 50,000 ed visits with cc from the national syndromic surveillance program (nssp) for the period january 1—june 30, 2017. nssp’s biosense platform receives ed data from >4000 hospitals, representing about 55 percent of all ed visits in the country [3]. from this sample we extracted 22868 ed visits that included both cc and dx data. we then applied our mental health syndrome case definition which comprised mental health-related keywords and icd-9-cm and icd10-cm codes. we queried cc text for the words “stress,” “ptsd,” “anxiety,” “depression,” “clinical depression,” “manic depression,” “unipolar depression,” “agitated,” “nervousness,” “mental health,” “mental disorder,” “affective disorder,” “schizoaffective disorder,” “psycoaffective disorder,” “obsessive-compulsive disorder,” “mood disorder,” “bipolar disorder,” “schizotypal personality disorder,” “panic disorder,” “psychosis,” “paranoia,” “psych,” “manic,” “mania,” “hallucinating,” “hallucination,” “mental episode,” and “mental illness.” we queried dx fields either for icd-9cm codes 295-296; 300, 311 or for icd-10-cm codes f20-f48. the icd-9cm and icd-10-cm codes used to identify mental health-related ed visits are based on the mental health disorders most frequently seen in eds. alcohol and substance use, suicide ideation, and suicide attempt were excluded from this study because they are included in alternate syndromes [2]. we manually reviewed the cc text to validate the search terms. sensitivity, specificity, and positive predictive value will be calculated based on agreement of coding mental health against the human review of mental health visits. based on our case definition, the sample of 22868 ed visits with cc and dx data was further stratified into two groups: (1) mental health identified in either cc or dx, and (2) no mental health identified in cc and dx. group 1 was further stratified into three groups: (a) mental health identified only in cc, (b) mental health identified in both cc and dx, and (c) mental health identified only in dx. the sample of 27132 ed visits with cc and no dx data was further stratified into two groups: (1) mental health identified in cc, and (2) no mental health identified in cc (figure). results of the 50,000 sample of ed visits with cc data, 22868 visits had both cc and dx data. of the 22868 visits, we identified 1560 mental health-related ed visits using the mental health syndrome case definition. of those visits, 241 were identified by a cc only, 226 were identified by both cc and dx, and 1093 by a mental health-related dx. of the 27132 ed visits without dx data, 421 had mental health identified in cc. conclusions based on our preliminary analysis these findings suggest potential benefits of including dx data in syndrome binning for mental health. mental health terms are more likely to be found in dx data than in the cc (1093 vs. 662). using cc alone may underestimate the number of mental health-related ed visits. this study had several limitations. not all facilities reporting to nssp provide chief complaint data in the same manner, some provide cc as a drop down menu with predefined terms while others include the full text of cc. not all records contained a dx code which limited our ability to examine the added value of dx code for that subset. keywords emergency department; mental health; syndromic surveillance isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references [1] weiss aj, barrett ml, heslin kc, stocks c. trends in emergency department visits involving mental and substance use disorders, 2006–2013. hcup statistical brief #216. december 2016. agency for healthcare research and quality, rockville, md. http://www. hcup-us.ahrq.gov/reports/statbriefs/sb216-mental-substance-usedisorder-ed-visit-trends.pdf. [2] liljeqvist hennign et al. bmc medical informatics and decision making 2014, 14:84 http://www.biomedcentral.com/1472-6947/14/84. [3] gould dw, walker d, and yoon pw. the evolution of biosense: lessons learned and future directions. public health reports 2017, vol. 132(supplement i) *achintya n. dey e-mail: aad2@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e190, 2018 isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 1french armed forces center for epidemiology and public health, marseille, france; 2umr 912 sesstim, aix marseille university, marseille, france; 3french institute for public health surveillance, saint maurice, france objective evaluate the performance of 8 statistical methods for outbreak detection in health surveillance with historical data. introduction early detection of outbreaks is crucial in public health surveillance in order to enable rapid control measures. statistical methods are widely used for outbreak detection (1) but no study has proposed to evaluate and compare thoroughly the performance of these methods. methods we tested 23 outbreak detection algorithms from a thorough simulation study. time series were generated using a negative binomial model under 42 scenarios depending on different trends, baseline frequencies of reports, seasonalities (annual or biannual) and dispersion (2). the simulated dataset included 231,000 624-week time series (ts): 4,200 ts without outbreak, 16,800 ts with 3 outbreaks in the past only, 42,000 ts with current outbreak only and 168,000 ts with 3 past and 1 current outbreak. a current outbreak means that it occurred in the last 50 weeks. each simulated outbreak varied in duration and amplitude with tuning coefficients: k1 = 0, 2, 5 or 10 for past outbreaks and k2 = 1 to 10 for current outbreaks. we focused on 8 detection methods, some methods being only variants: cdc algorithm, rki3, bayes3, cusum glm rossi, outbreakp, and glr poisson, which are implemented in the r surveillance package (3), and a periodic poisson glm based algorithm. for each algorithm, we used the same tuning parameters for all ts. each method was evaluated through its false positive rate (fpr) and its probability of detection (pod: at least one alarm during the outbreak period), for the different scenarios and outbreak sizes. the nominal fprs were 0.005 for all the analyses. results for each of the 42 scenarios, figure 1 represents the mean of fprs with k1 = 5 and figure 2 represents the pod with k2 varying from 1 to 10. only the farrington, periodic poisson glm, and rki3 algorithms presented a pod> 80% for the largest outbreaks (k2 > 8) combined with a fpr< 20%, for each scenario. bayes3 method presented high pod with a fpr>20% for some scenarios. cdc algorithm presented heterogeneous pod depending on the scenario with a fpr<20%. for the other 3 methods, fpr and pod greatly depend on the scenario. conclusions we presented a systematic assessment of performance of 8 outbreak detection algorithms using a simulated dataset, large enough to include time series observed in the real surveillance systems. we also simulated a high diversity of outbreaks in terms of amplitude and duration. since no single algorithm has presented sufficient performance for all scenarios, combinations of methods must be investigated to achieve predefined minimum performance. other criteria of performance should be proposed in order to improve the choice of algorithms to be implemented in the surveillance systems. false positive rate for 8 outbreak detection algorithms (42 simulated scenarios – past outbreak amplitude k1 = 5) probability of detection of 8 outbreak detection algorithms according to current outbreak amplitude (k2) (past outbreak amplitude k1 = 5) (each curve corresponds to a simulated scenario) keywords health surveillance; outbreak detection algorithm; statistical method; performance evaluation; simulation study references 1. unkel s, farrington cp, garthwaite ph, robertson c, andrews n. statistical methods for the prospective detection of infectious disease outbreaks: a review. j r stat soc ser a stat soc. 2012;175(1):49–82. 2. noufaily a, enki dg, farrington p, garthwaite p, andrews n, charlett a. an improved algorithm for outbreak detection in multiple surveillance systems. stat med. 2013 mar 30;32(7):1206–22. 3. höhle m. surveillance: an r package for the monitoring of infectious diseases. comput stat. 2007;22(4):571–82. *gabriel bedubourg e-mail: gabrielbedubourg@hotmail.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e91, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra 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accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse 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jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e228, 2019 isds 2019 conference abstracts approach to onboarding emergency medical services (ems) data into a syndromic surveillance system samuel p. prahlow1, david atrubin1, allison culpepper1, janet j. hamilton2, joshua sturms1, karen card1 1 florida department of health, tallahassee, florida, united states, 2 council of state and territorial epidemiologists, atlanta, georgia, united states objective to describe the strategy and process used by the florida department of health (fdoh) bureau of epidemiology to onboard emergency medical services (ems) data into fdoh’s syndromic surveillance system, the electronic surveillance system for the early notification of community-based epidemics (essence-fl). introduction syndromic surveillance has become an integral component of public health surveillance efforts within the state of florida. the near real-time nature of these data are critical during events such as the zika virus outbreak in florida in 2016 and in the aftermath of hurricane irma in 2017. additionally, syndromic surveillance data are utilized to support daily reportable disease detection and other surveillance efforts. although syndromic systems typically utilize emergency department (ed) visit data, essence-fl also includes data from non-traditional sources: urgent care center visit data, mortality data, reportable disease data, and florida poison information center network (fpicn) data. inclusion of these data sources within the same system enables the broad accessibili ty of the data to more than 400 users statewide, and allows for rapid visualization of multiple data sources in order to address public health needs. currently, the essence-fl team is actively working to incorporate ems data into essence-fl to further increase public health surveillance capacity and data visualization. methods the essence-fl team worked collaboratively with various public health program stakeholders to bring ems data, aggregated by the fdoh bureau of emergency medical oversight emergency medical services tracking and reporting system (emstars) team, into essence-fl. the essence-fl team met with the emstars team to discuss use cases, demonstrate both systems, and to obtain project buy-in and support. initial project meetings included review of essence-fl system support, user types (roles and access), as well as data security and compliance. an overall project timeline was established, and deliverables we re added into system support contracts. multiple stakeholders, across disciplines representing each key use case, reviewed the florida version of the national emergency medical services information system (nemsis) version 3.4 data dictionary to identify program-specific data element needs. an element scoring spreadsheet was returned to the essence-fl team. these scores were aggregated and discordant scores were reviewed by the essence-fl team. a one-month extract of ems data was reviewed to assess variable completeness and relevance. monthly team meetings facilitated the final decisions on the data elements by leveraging lessons learned through onboarding other data sources, findings from the analysis of the one-month extract, stakeholder comments, and advice from other states known to be leveraging ems data for public health surveillance. results through a collaborative and broad approach with partners, the essence-fl team attained stakeholder buy-in and identified 81 data elements to be included in the ems feed to essence-fl. the final list of data elements was determined to best support health surveillance of this population prior to presenting to the ed. the inclusion of the ems data in essence-fl will increase the epidemiologic characterization and analysis of the opioid epidemic in florida. additional key use cases identified during this project included enhanced injury surveillance, enhanced occupational health surveillance, and characterization of potential differences between ems and ed visits. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e228, 2019 isds 2019 conference abstracts conclusions this comprehensive approach can be used by other jurisdictions considering adding ems data to their syndromic surveillance systems. when considering onboarding a new data source into a surveillance system, it is important to work closely with stakeholders from disciplines representing each of the key use cases to broaden buy-in and support for the project. through employing this comprehensive approach, syndromic surveillance systems can be better developed to include data that are widely utilizable to many different stakeholders in the public health community. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e332, 2019 isds 2019 conference abstracts an assessment of birth registration system and factors affecting in india and its states nutal kumari international institute for population sciences, india objective to see the level and trend of the vital registration system in india and its states form 2005-2015. to identify factors influencing civil registration in india introduction civil registration system (crs) in india has been in vogue for more than 100 years now. the registration of birth and deaths act, 1969 came into force in 1970. even after 4 decades of the enactment of the act, there are wide inter-state and intra-state variations. india is the second largest populated country in the world after china. whereas as the level of registration of birth and deaths varies from (84.4 and 69.3) registrations. in india, more than 250 thousand of registration centers are involved in central registration system and estimated births per years are 26 million, and the corresponding figure for death is 8 million (sethi 2016). methods the present study uses national family health survey (nfhs) 3 and 4 used for the analysis to see an assessment of birth registration system and the factors affecting in india and its states. multivariate analysis used to see the effect of a socio-demography factor that affects the registration of birth. results figure 1 presents information on prevalence birth registration under age five years with civil authority in india from nfhs3 and nfhs-4; this includes 27 percent (nfhs -3) 62 percent of children with birth certificates and 14 percent (nfhs3) 18 percent are children which has registered the birth but don’t have certificate. table 1 demonstrates prevalence of children under age five whose birth is registered with the civil authorities in india and its states from nfhs-3 and nfhs-4. birth registration among children under age five years double between nfhs-3 and nfhs -4 (from 41% to 81%). however, there is also an increase in all the states of india from 2005 to 2016. the percentage of births that were registered increase by more than 50 percentage points between 2005-06 and 2015-16 in jharkhand, bihar, uttar pradesh, madhya pradesh, and rajasthan table 2 present the percentage of children under age five years whose birth was registered with civil authorities accounting to background characteristics. the registration of birth was high in age group less than 2 in nfhs -3 (79%) but in nfhs -4 high in 2-4 age group (53%). the registration of male is high in nfhsand nfhs-4 (52%) as compared to a female child (48%). in the religion, the registration is high in hindu in both the survey. however, in caste, the low registration are found in scheduled caste (nfhs3 (19.2%) & nfhs4 (21.4%)) and scheduled tribal. with the increase in wealth index, there is an increase in the birth registration system. conclusions birth registration and subsequent issuance of a certificate do not only promote human rights to citizenship but it also facilitates human rights to good health, education, social security, and overall human development. therefore, timely registration of children should be pursued as a right issue. this study found that high levels of birth registration were related to a high level of awareness among the urban population regarding birth registration. however, findings of this study seem to suggest that it is more of a privilege for children whose parents are educated, wealthy and live in urban areas. references 1. sethi rc. civil registration system, sample registration system & annual health survey: issues and policy uses. the global summit on crvs. http://www.slideshare.net/kdcgroups/ session-5a-rcsethi accessed on 10/10/2016. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e332, 2019 isds 2019 conference abstracts table 1 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e332, 2019 isds 2019 conference abstracts table 2 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e332, 2019 isds 2019 conference abstracts figure 1: prevalence of birth registration under age five years with civil authority in india from nfhs-3 and nfhs-4 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts syndromic surveillance – reports of successes from the field antheny wilson*1, teresa hamby2, wei hou3, david j. swenson4, krystal collier5 and michele hoover1 1division of health informatics and surveillance, centers for disease control, atlanta, ga, usa; 2new jersey department of health, trenton, nj, usa; 3utah department of health, salt lake city, ut, usa; 4nh department of health, concord, nh, usa; 5arizona department of health services, phoenix, az, usa objective this panel will: ● discuss the importance of identifying and developing success stories ● highlight successes from state and local health departments to show how syndromic surveillance activities enhance situational awareness and address public health concerns ● encourage discussion on how to further efforts for developing and disseminating success stories introduction syndromic surveillance uses near-real-time emergency department and other health care data for enhancing public health situational awareness and informing public health activities. in recent years, continued progress has been made in developing and strengthening syndromic surveillance activities. at the national level, syndromic surveillance activities are facilitated by the national syndromic surveillance program (nssp), a collaboration among state and local health departments, the cdc, other federal organizations, and other organizations that enabled collection of syndromic surveillance data in a timely manner, application of advanced data monitoring and analysis techniques, and sharing of best practices. this panel will highlight the importance of success stories. examples of successes from state and local health departments will be presented and the audience will be encouraged to provide feedback. keywords success stories; syndromic surveillance; state health departments acknowledgments centers for disease control, new hampshire department of health, new jersey department of health, arizona department of health services *antheny wilson e-mail: nnp9@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e176, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e378, 2019 isds 2019 conference abstracts contact tracing in lassa fever outbreak response, an effective strategy for control? joan ejembi1, 2, uloaku emma-ukaegbu2, itopa garba2, anthony omale2, bala dogo2, lydia taiwo2 1 medical microbiology, ahmadu bello university, zaria, benue, nigeria, 2 nigeria field epidemiology and laboratory training programme, abuja, federal capital territory, nigeria objective to identify risk factors among contacts of lassa fever cases which can predispose to disease spread and institute control measures. introduction contact tracing is an important strategy employed in surveillance to aid prompt detection of infectious disease and control outbreaks. it involves the identification of those who have come in contact with an infectious person and following them up for the duration of the incubation period of the disease to promptly detect symptoms and signs and institute treatment thus reducing chances of disease spread to other susceptible individuals. it is a primary means of disease prevention. the importance of cooperation from contacts being traced cannot be overemphasized as they are required to promptly report symptoms, avoid gatherings and travell ing until they are cleared by the surveillance team. the follow-up should also link contacts who become symptomatic to designated care centers. in 2014, during the ebola outbreak in nigeria, the disease spread from lagos to another state in the country by a contact who travelled out of lagos to rivers state. to identify risk factors among contacts of lassa fever cases which can predispose to disease spread and institute control measures, we interviewed primary contacts of lassa fever cases during an outbreak response in kogi state nigeria, 2016 methods we identified contacts of lassa fever cases (confirmed/suspected/probable) among family, friends, community members, coworkers and health care workers, followed up for 21 days during a lassa fever outbreak which started in february 2016 at kogi state nigeria. contacts were interviewed using a structured interviewer administered questionnaire with sections on demography, risk factors for infection and spread of lassa fever, symptoms developed during the follow-up period and adherence to prorocol. control measures were instituted to address identified gaps. we defined a contact as anyone irrespective of age, occupation or sex who came in contact with any of the cases of lassa fever classified as either confirmed/suspected/probable and used standard idsr case definitions for suspected, confirmed and probable cases of lassa fever. data was analysed with epi info version 7 results overall 149 contacts were interviewed, 79 (53.0%) were female, the mean age of respondents was 33.2 +-10.1 and many were health care workers 61(40.9%). of the respondents, 18 (12.0%) live or work in areas infested with rodents, 21 (14.1%) ate bush meat, 2 (2.5%) of the females were pregnant and 20 (13.4%) of respondents travelled out of station with 1 (5%) contact crossi ng international borders. during the follow-up period, 14 (9.4%) developed symptoms suggestive of lassa fever. of these 12 (85.7%) sought treatment and the options were self-medication 3 (25.0%) and presenting at a health facility 9 (77.8%). the health facilities visited were mainly privately owned 7(77.9%) and only 1 (11.1) was a tertiary health care facility. we instituted the following interventions; health education of contacts and linkage of symptomatic contacts to the designated treatment center where treatment commenced and samples were collected and sent to reference lab for diagnosis. all samples 14(100%) came back negative for lassa fever. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e378, 2019 isds 2019 conference abstracts conclusions contact tracing is an important strategy in controlling outbreaks of infectious diseases. however, risk factors identified among contacts can hinder the effectiveness of this strategy and facilitate spread of the disease. we recommend training on health education and lassa fever transmission for contacts of cases and the need to adhere to protocol so that the ultimate ai m of interrupting transmission can be achieved. acknowledgement nigerian field epidemiology and laboratory training programme state ministry of health kogi state,nigeria nigeria center for disease control references 1. nigeria center for disease control. viral haemorrhagic fevers, preparedness and response plan. 2017 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e363, 2019 isds 2019 conference abstracts implementation of an electronic disease surveillance system in guinea, 2016-2018 eileen reynolds, boubacar diallo, pia macdonald social and statistical sciences, rti international, durham, north carolina, united states objective the objective is to share the progress and challenges in the implementation of the district health information software versi on 2 (dhis 2) as an electronic disease surveillance system platform in guinea, west africa, to inform global h ealth security agenda efforts to strengthen real-time surveillance in lowresource settings. introduction the west africa ebola outbreak of 2014-2016 demonstrated the importance of strong disease surveillance systems and the severe consequences of weak capacity to detect and respond to cases quickly. challenges in the transmission and management of surveillance data were one factor that contributed to the delay in detecting and confirming the ebola outbreak [1]. to help address this challenge, we have collaborated with the u.s. centers for disease control and prevention (cdc), the ministry of health (moh) in guinea, the world health organization and various partners to strengthen the disease surveillance system through the implementation of an electronic reporting system using an open source software tool, the district health information software version 2 (dhis 2). these efforts are part of the global health security agenda objective to strengthen real-time surveillance [2]. this online system enables prefecture health offices to enter aggregate weekly disease reports from health facilities and for that information to be immediately accessible to designated staff at prefecture, regional and national levels. incorporating dhis 2 includes several advantages for the surveillance system. for one, the data is available in real time and can be analyzed quickly using built-in data analysis tools within dhis 2 or exported to other analysis tools. in contrast, the existing system of reporting using excel spreadsheets requires the moh to manually compile spreadsheets from all the 38 prefectures to have case counts for the national level. for the individual case notification system, dhis 2 enables a similar accessibility of information that does not exist with t he current paper-based reporting system. once a case notification form is completed in dhis 2, the case-patient information is immediately accessible to the laboratories receiving specimens and conducting testing for case confirmation. the system is designed so that laboratories enter the date and time that a specimen is received, and any test results. the results are then immediately acce ssible to the reporting district and to the stakeholders involved including the national health security agency and the expanded program on vaccination. in addition, dhis 2 can generate email and short message service (sms) messages to notify concerned parties a t critical junctures in the process, for example, when a laboratory result is available for a given case. methods this presentation is based on review of project experience and documentation for a global health security project in guinea from 2015-2018. in addition, this includes a 2017 evaluation of the dhis 2 pilot phase in two regions each having five prefectures. results the use of dhis 2 for aggregate and individual case reports for disease surveillance was piloted in two regions in guinea in 2 017 for a period of six months. an evaluation of the pilot phase indicated strong capacity at the prefecture level to use the sys tem for weekly aggregate disease reporting as evidenced by the high weekly reporting rates as well as an assessment of users’ capacit ies. challenges observed during the pilot phase included weak follow-up and ownership by the national level moh, weak adherence by the laboratories to enter data on the receipt and test results of laboratory samples, and individual case reports not file d in all cases. in addition, the lack of uniformity of common data elements on the forms across different diseases made analysis and data quality more challenging. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e363, 2019 isds 2019 conference abstracts following the evaluation of the pilot phase the moh directed that the system should be used for aggregate weekly reporting, however that the individual case reporting in dhis 2 should wait until improvements could be made in the case report forms. prefectures have used dhis 2 for weekly aggregate disease reporting starting in january 2018. in addition, the moh plans to implement electronic individual case reporting in dhis 2 starting in october 2018. conclusions progress to date includes nationwide use of dhis 2 by all prefectures for the submission of weekly aggregate case reports. in addition, the new case report forms have been configured in dhis 2 and a training of trainers has been conducted at the national level to begin the process of implementing the electronic case reporting nationwide. challenges include the continuation of parallel weekly disease reporting in excel for an extended period after adoption of dhis 2 resulting in lower timeliness of weekly reports in dhis 2 in some prefectures, weak use of the system for data analysis, building capacity within the ministry of health to maintain the system without outside assistance, sufficient resources to pay for int ernet access and power back-up (such as solar power) to enable the health offices to effectively use the system, weak data privacy and security procedures, and the need to strengthen management of the national dhis 2 server. acknowledgement this work is supported by funding provided by the centers for disease control and prevention cooperative agreement 1u19gh001591-01. we acknowledge the support of the u.s. centers for disease control and prevention and the ministry of health of guinea for their technical support and collaboration of in all aspects of the work. in addition, we thank all the health workers and partners that have contributed to the implementation of dhis 2 in guinea and to strengthening the disease surveillance system. references 1. ministère de la santé-république de guinée, direction nationale de la prevention et santé communautaire, division prevention et lutte contre la maladie. plan de renforcement de la surveillance des maladies à potentiel epidémique en guinée (2015-2017), august 2015. 2. global hsa. real-time surveillance action package: ghsa action package detect 2 & 3. [cited 2018 oct 3]. available from: https://www.ghsagenda.org/packages/d2-3-real-time-surveillance http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts profile: karachi health and demographic surveillance system of pakistan (khdss) komal naeem*, muhammad ilyas, urooj fatima, momin kazi, fyezah jehan, yasir shafiq, murtaza taiyab, usma mehmood, rashid ali, anita k. zaidi and muhammad i. nisar pediatrics and child health, aga khan university, karachi, pakistan objective the mandate of establishing this dss is to provide a research platform for both observational and interventional studies, with focus on maternal and child health, which could influence decision-making and planning for health strategies at local, national and international levels. introduction the karachi health and demographic surveillance system was set up in year 2003 by the department of pediatrics and child health of the aga khan university, karachi, pakistan, in four peri-urban low socioeconomic communities of karachi and covers an area of 17.6 square kilometers.(figure 1). methods total population currently under surveillance is 299,009 for which a record of births, deaths, pregnancies and migration events is maintained by two monthly household visits. at each re-enumeration, community health workers move through the area using gis-derived maps and collect the information from households and conduct verbal autopsies for stillbirths and deaths of children under the age of five and adult female. primary health care centre at each site provide free care to children under 5. results the demographic characteristics for the year 2016 are summarized in table 1. the main demographic indicators for a period of five years enable us to study the trends of population dynamics and reasons for the change in the rates of stillbirth, under 5 children mortality and maternal mortality (table 2). under 5 mortality rates peaked in 2013 and 2016 due to measles epidemic. within the time period of five years, a reduction in neonatal mortality rates is observed (table 2). for over a decade, the khdss has been a platform for a variety of studies. at the outset, various epidemiological studies were conducted in the area of infectious diseases of children, identifying signs and symptoms in young infant requiring urgent referral, vaccine coverage and the impact of multiple interventions. the focus was on measuring burden of relevant and common childhood illnesses. some of these projects include: calculation of the incidence of various infectious diseases like typhoid bacteremia, pneumonia and diarrhea, evaluation of effectiveness of various treatment regimens for neonatal sepsis, assessment of the acceptance of hospitalized care, determining etiology of moderate to severe diarrhea, assessment of burden and etiology of neonatal sepsis and a multi-center cohort measuring the burden of stillbirths, neonatal and maternal deaths. (1-5) conclusions all the studies aim for improvement of public health policies and informed decision making at local and national levels. we have also established a bio-repository of a well-defined maternal and newborn cohort. demographic surveillance system profile 2016 demographic surveillance indicators and trends by year (2012-2016) figure 1: map showing location of the dss sites in karachi, pakistan. keywords karachi health and demographic surveillance system; low socioeconomic communities; child and maternal health; research platform; etiological studies and controlled trials references 1. group yicss. clinical signs that predict severe illness in children under age 2 months: a multicentre study. the lancet. 2008; 371(9607):135-42. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts 2. kotloff kl, nataro jp, blackwelder wc, nasrin d, farag th, panchalingam s, et al. burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the global enteric multicenter study, gems): a prospective, case-control study. the lancet. 2013;382(9888):209-22. 3. mir f, nisar i, tikmani ss, baloch b, shakoor s, jehan f, et al. simplified antibiotic regimens for treatment of clinical severe infection in the outpatient setting when referral is not possible for young infants in pakistan (simplified antibiotic therapy trial [satt]): a randomised, open-label, equivalence trial. the lancet global health. 2016. 4. shafiq y, nisar mi, kazi am, ali m, jamal s, ilyas m, et al. implementation of the anisa study in karachi, pakistan: challenges and solutions. the pediatric infectious disease journal. 2016;35(5):s60-s4. 5. group as. burden, timing and causes of maternal and neonatal deaths and stillbirths in sub–saharan africa and south asia: protocol for a prospective cohort study. journal of global health. 2016;6(2). *komal naeem e-mail: komal.naeem@aku.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e160, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e346, 2019 isds 2019 conference abstracts identifying children with special health care needs in alexandria, egypt iman wahdan, nessrin a. el-nimr epidemiology, high institute of public health, alexandria university, alexandria, egypt objective to test the feasibility of using an arabic version of cshcn screener in identifying cshcn in the egyptian setup and to estimate the prevalence of cshcn among children aged 6-14 years in alexandria, egypt using the arabic version of the cshcn screener. introduction children with special health care needs (cshcn) are defined as: “those who have or are at increased risk for a chronic physical, developmental, behavioural, or emotional condition and who also require health and related services of a type or amount beyon d that required by children generally.” [1] the care of cshcn is a significant public health issue. these children are medically complex, require services and supports well beyond those that typically developing children require, and command a considerab le proportion of the pediatric health care budget [2]. different tools were used to identify cshcn [3,4]. one of them is the cshcn screener [5] which uses a non-condition specific approach that identifies children across a range and diversity of childhood chronic conditions and special needs [6]. it identifies children with elevated or unusual needs for health care or educational services due to a chronic health condition. it focuses on health consequences a child experiences as a result of having an ongoing health con dition rather than on the presence of a specific diagnosis or type of disability. it allows a more comprehensive assessment of the performance of the health care system than is attainable by focusing on a single diagnosis [7]. the cshcn screener is only available in english and spanish [8]. in developing countries, obtaining reliable prevalence rates for cshcn is challenging. sophisticated datasets associated with governmental services and high quality research studies are less common due to fewer resources. egypt has no screening or surveillance systems for identifying cshcn [9]. methods a community based survey was conducted among a representative sample of children aged 6-14 years from the 8 health districts of alexandria, egypt using a multistage cluster sampling technique. the final sample amounted to 501 children from 405 families. data about the children and their families were collected by interviewing the mothers of the selected children using a pre -designed interviewing questionnaire. the questionnaire included their personal and family characteristics in addition to the arabic translation of cshcn screener. permission to translate the questionnaire into the arabic language was obtained from the child and adolescent health measurement initiative. validation and cultural adaptation of the translated cshcn screener were done. the survey questions were generally understandable by arabic speakers. as for the screener questions, the arabic translation was straightforward and clear. the difference between the arabic translation for the words “health conditions” and “medical conditions” in the 1st follow up questions was not clear for the respondents and the interviewers had to give an explanation for the two terms to help the respondents. so, it was easier for the respondents to answer the screener questions than the follow up questions. results out of the 501 children included in the study, 61 were identified by the screener to be cshcn, making a prevalence of cshcn of 12.2%. the prevalence of children with dependency on prescription medicine was 11.8%, while the prevalence of children with service use above that considered usual or routine was 11.8%. the prevalence of children with functional limitations was 12%. among these domains, in almost all children, the reason was a medical, behavioral or health condition (98.3%) and the conditi on has continued or is expected to continue for at least 12 months in all children. among cshcn, the majority (91.8%) had these three domains combined. sensory impairments ranked first among the most prevalent conditions requiring special health care with a prevalence of 2.8% which represented 23% of the conditions, followed by cognitive impairments with a prevalence of 2% representing 16.4% of all http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e346, 2019 isds 2019 conference abstracts conditions requiring special health care. impaired mobility was the third most common condition requiring special care with a prevalence of 1.8%. the table shows that cshcn were more likely to be in the younger age group (6-<10 years), to be males, to be the first in order among their siblings and to have an illiterate or just read and write father. on the other hand, cshcn were less likely to ha ve a university educated mother, to be living with both parents and to be from a family without an enough income. the only significant factor was the type of family (cor=0.88, 95% ci = 0.85-0.91). conclusions the study showed the feasibility to use the cshcn screener in the egyptian national health care services to easily identify the majority of children that need to be the focus of the national health care services. it could also be an easy tool to assess the quality of the ongoing school health programs in responding to the overall needs of school children. with the present egyptian policy of reform giving special attention to people in need particularly sensitive groups such as s chool children, it is therefore recommended that the school health services, in addition to the ongoing diagnostic, preventive and curative services add an additional measure, namely the screener for cshcn, which is a simple easily administered screening tool which will also assist to depict existing gaps in the health care system to ensure being comprehensive. acknowledgement the authors thank all the mothers and guardians in alexandria who participated in the study for their efforts, cooperation and patience. references 1. mcpherson m, arango p, fox c, et al. 1998. a new definition of children with special health care needs. pediatrics. 102, 137-40. pubmed https://doi.org/10.1542/peds.102.1.137 2. goldson e, louch g, washington k, scheu h. 2006. guidelines for the care of the child with special health care needs. adv pediatr. 53, 165-82. pubmed https://doi.org/10.1016/j.yapd.2006.04.012 3. newacheck pw, strickland b, shonkoff jp, et al. 1998. an epidemiologic profile of children with special health care needs. pediatrics. 102, 117-23. pubmed https://doi.org/10.1542/peds.102.1.117 4. stein rek, silver ej. 1999. operationalizing a conceptually based noncategorical definition. a first look at u.s. children with chronic conditions. arch pediatr adolesc med. 153, 68-74. pubmed https://doi.org/10.1001/archpedi.153.1.68 5. child and adolescent health measurement initiative. the children with special health care needs (cshcn) screener. baltimore: cahmi; 1998. 10p. 6. child and adolescent initiative. who are children with special health care needs (cshcn). baltimore: cahmi; 2012. 2p. 7. bethell cd, read d, neff j, et al. 2002. comparison of the children with special health care needs screener to the questionnaire for identifying children with chronic conditions–revised. ambul pediatr. 2, 49-57. pubmed https://doi.org/10.1367/1539-4409(2002)002<0049:cotcws>2.0.co;2 8. read d, bethell c, blumberg sj, abreu m, molina c. 2007. an evaluation of the linguistic and cultural validity of the spanish language version of the children with special health care needs screener. matern child health j. 11(6), 568-85. pubmed https://doi.org/10.1007/s10995-007-0207-2 9. kennedy p, ed. the oxford handbook of rehabilitation psychology. oxford, new york: oxford university press; 2012. table 1. crude odds ratio of special health care needs among children aged 6-14 years and their personal and family characteristics (alexandria, 2017) http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9714637&dopt=abstract https://doi.org/10.1542/peds.102.1.137 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17089866&dopt=abstract https://doi.org/10.1016/j.yapd.2006.04.012 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9651423&dopt=abstract https://doi.org/10.1542/peds.102.1.117 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9895002&dopt=abstract https://doi.org/10.1001/archpedi.153.1.68 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11888438&dopt=abstract https://doi.org/10.1367/1539-4409(2002)002%3c0049:cotcws%3e2.0.co;2 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17562154&dopt=abstract https://doi.org/10.1007/s10995-007-0207-2 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e346, 2019 isds 2019 conference abstracts personal and family characteristics cor 95% ci child’s age (from 6-<10 vs 10+) 1.21 0.71-2.08 sex (males vs females) 1.49 0.86-2.60 order among siblings (1st vs others) 1.11 0.65-1.92 father’s education (illiterate or read and write vs others) 1.12 0.64-1.91 mother’s education (university vs others) 0.93 0.31-2.76 type of family (living with both parents vs others) 0.88* 0.85-0.91 family income (not enough vs others) 0.76 0.44-1.29 * significant (p<0.05) http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts niche modeling of dengue fever using remotely sensed environmental factors and brt jeffrey l. ashby* and max j. moreno-madriñán fairbanks school of public health, indiana university, greencastle, in, usa objective in this paper we used boosted regression tree analysis coupled with environmental factors gathered from satellite data, such as temperature, elevation, and precipitation, to model the niche of dengue fever (df) in colombia. introduction dengue fever (df) is a vector-borne disease of the flavivirus family carried by the aedes aegypti mosquito, and one of the leading causes of illness and death in tropical regions of the world. nearly 400 million people become infected each year, while roughly onethird of the world’s population live in areas of risk. dengue fever has been endemic to colombia since the late 1970s and is a serious health problem for the country with over 36 million people at risk. we used the magdalena watershed of central colombia as the site for this study due to its natural separation from other geographical regions in the country, its wide range of climatic conditions, the fact that it includes the main urban centers in colombia, and houses 80% of the country’s population. advances in the quality and types of remote sensing (rs) satellite imagery has made it possible to enhance or replace the field collection of environmental data such as precipitation, temperature, and land use, especially in remote areas of the world such as the mountainous areas of colombia. we modeled the cases of df by municipality with the environmental factors derived from the satellite data using boosted regression tree analysis. boosted regression tree analysis (brt), has proven useful in a wide range of studies, from predicting forest productivity to other vector-borne diseases such as leishmaniosis, and crimean-congo hemorrhagic fever. using this framework, we set out to determine what are the differences between using presence/absence and case counts of df in this type of analysis? methods we combined data on dengue fever cases downloaded from the instituto nacional de salud (ins) programa sivigila ins site with population data downloaded from the 2005 general census administered by the national administrative department of statistics (departamento administrativo nacional de estadística, dane) and projected to 2012–2014 levels. we acquired remote sensing data from the national aeronautics and space administration (nasa) data servers for each day of the study period. imagery for each environmental variable was composited to reduce the effects of cloud cover and to match the iso week date format reporting of the case data. we aggregated these weekly composite images for each variable using gis to create annual minimum, maximum, and mean for a raster cell. these data were further aggregated to the municipality level using the gis, again for minimum, maximum, and mean. land use and elevation were only downloaded for one period given they change very little over time. the brt analysis was conducted twice: once using the bernoulli family of presence/absence and again using the poisson family of actual case counts. in the first analysis (bernoulli), any municipality reporting one or more cases of df in the year was coded as having disease “presence”, while all others were coded as not having disease “absence”. the brt model was run, using a twentyfive percent hold out of the data as a testing set, for each year. in the second analysis (poisson), the only change to the models consisted of replacing the presence/absence data with the actual cases of reported df within the municipality. the poisson family was chosen in the model since the count data were highly skewed. results we calculated rmse and pearson r values for each of the three years. the poisson model out-performed the bernoulli model across all years. the rmse values were considerably lower for the poisson model compared to the bernoulli model, reflecting a better model fit. the pearson r values were higher for the poisson model compared to the bernoulli model, again across all three years. we created maps to compare cases with the poisson and the bernoulli results. the maps shown in the figure reflect the results for 2012. the left panel represents the cases per 10,000 population per square kilometer for each municipality. the dark green color represents very low ratios of df, while the red color reflects a higher incidence of df. all maps used the same classification as the reported cases map for comparison, with an additional symbol (black) used for values outside the reported cases range. conclusions using actual reported case data and the poisson function within the brt functions created by elith et al. and the gbm package in r, we show that the differences between using presence/absence and case counts of df in a brt analysis gives a clearer picture of the spatial distribution of df. by using readily available and freely accessible data, we have shown that practitioners both within and outside of colombia can quickly create accurate maps of annual df incidence. the methods described here could also be extended to other regions and diseases, making it useful to a wide range of end users. maps showing the results for 2012 keywords dengue; boosted regression tree; aedes aegypti; remote sensing; gis *jeffrey l. ashby e-mail: jlashby@iupui.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e18, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts a pilot project to identify individuals who died from suicide and visited an ed before death jenny robertson* epidemiology, salt lake county health department, salt lake city, ut, usa objective to explore the use of ed syndromic surveillance data to retrospectively identify individuals who died from suicide and visited an ed before death in order to improve suicide surveillance and inform planning and prevention efforts in salt lake county, utah. introduction in 2015, suicide was the 8th leading cause of death in salt lake county, utah, and has recently been identified as a priority public health issue. for suicide, suicide ideation and suicide attempts surveillance, salt lake county health department staff use national violent death reporting system (nvdrs) mortality data to monitor historical trends and vital records mortality data and essence ed encounter morbidity data to monitor trends and populations in real time. to improve surveillance and better identify populations at higher risk of suicide, we tested whether we could retrospectively identify residents who died from suicide and visited an ed in the year before death. methods data for all essence ed encounters from january 1, 2016, through june 30, 2017, were downloaded from the national syndromic surveillance program biosense platform. salt lake county residents who died from suicide from january 1, 2017, through june 30, 2017, were linked to this essence dataset using date of birth and zip code. we performed chart reviews of the matched patients’ ed encounters and collected sociodemographic (name, residence, race, ethnicity, marital status, military service, sexual orientation), socioeconomic status (education, occupation) and suicide risk factor data (social isolation, addiction, physical health, relationship, financial, job, school, criminal, civil legal problems, eviction or housing problem, recent suicide or other death of family/friend, current depressed mood, current or recent mental health diagnosis and/or treatment, current alcohol or other substance use disorder, perpetrator or victim of interpersonal violence, history of abuse, and history of suicide ideation, plan and attempt). we used descriptive epidemiology to describe risk factors and circumstances. results fifteen salt lake county residents who died from suicide from january 1, 2017, through june 30, 2017, matched individuals in the essence ed dataset by date of birth and zip code. upon chart review, 14/15 matched by medical record number; the remaining patient was excluded due to medical record number mismatch. ultimately, 13% (14/105) of salt lake county residents who died from suicide from january 1, 2017, through june 30, 2017, were identified in essence as having visited an ed in the year before death. among them, they visited an ed a total of 30 times. based on chart review of 13/14 of these individuals, the most common suicide risk factors or circumstances were physical health problem (62%), current mental health diagnosis (62%), history of suicidal thoughts (54%) and current depressed mood (54%). the correlation between risk factors identified from essence and those identified from nvdrs was moderate (r = 0.57). conclusions it is possible to identify individuals who died from suicide and visited an ed before death. we are encouraged by the result that common risk factors found via chart review are similar to those we have found in our historical analyses of nvdrs suicide data. this risk factor information adds valuable context to real-time surveillance of suicide, suicide ideation and suicide attempts. next steps in this pilot are to complete the final chart review and develop and test triage note search queries to monitor suicide and suicidal thoughts and behavior and identify populations who have these common risk factors and may be at higher risk for suicide. it should be noted that during this work, several facilities’ data feeds dropped and the quantity of data decreased dramatically. that we were still able to identify 13% of our residents who died from suicide in essence despite the large loss of data suggests the true percentage is likely to be much higher once facilities are re-onboarded. this gives us confidence that we will be able to develop a reliable essence query for suicide risk factors specific to our residents. keywords suicide; essence; syndromic surveillance; real-time *jenny robertson e-mail: jrobertson@slco.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e86, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e253, 2019 isds 2019 conference abstracts methods for combining data from multi-jurisdiction sentinel surveillance projects elizabeth torrone1, eloisa llata1, jaeyoung hong1, preeti pathela2 1 dstdp, cdc, atlanta, georgia, united states, 2 new york city department of health and mental hygiene, new york city, new york, united states objective to identify best practices for combining public health data for multi-jurisdiction surveillance projects. introduction sentinel surveillance, where selected jurisdictions follow standardized protocols to collect and report enhanced public healt h data not available through other routine surveillance efforts, is a key part of national surveillance of sexually transmitted diseases (stds). although four stds are nationally notifiable conditions (chlamydia, gonorrhea, syphilis and chancroid), the burden of these conditions (over 2.3 million cases were reported in 2017) limits the amount of detailed clinical and demographic data available for all cases. sentinel surveillance in clinical settings serving at-risk populations, such as std clinics, provides an opportunity to collect enhanced data elements on persons seeking std-related services, such as sex of sex partners and anatomic site of infection. however, there are challenges in combining data across jurisdictions as estimated effect measures may vary by jurisdiction (e .g., some may have higher observed burden of disease among certain populations) and the amount of data contributed by jurisdiction may vary; combined this could lead to biased estimates if heterogeneity is not taken into account. methods using data from the std surveillance network (ssun), a sentinel surveillance project implemented in 10 jurisdictions, we investigated the effect of using different statistical methods to combine data across jurisdictions. we evaluated 5 methodologies: • “fully stratified” where estimates were provided separately for each jurisdiction; • “aggregated” where numerators and denominators were summed across jurisdictions without weighting; • “mean estimate” where the mean of the jurisdiction-specific estimates was estimated; • “random effects” where jurisdiction-specific estimates were combined using an inverse variance weighted random effects model to adjust for heterogeneity between jurisdictions; and • “stratified random effects” where a possible effect modifier was identified and used to group jurisdictions prior to calculating the estimate from the random effects model. through ssun, jurisdictions collect visit-level data on patients attending selected std clinics and report clinical and demographic data. as an illustrative example, we estimated rectal gonorrhea positivity among gay, bisexual, and other men who have sex wi th men (msm) attending participating clinics. jurisdiction-specific positivity was estimated as the # of unique msm testing positive at least once for rectal gonorrhea divided by all msm tested 1 or more times for rectal gonorrhea in all of the clinics in the jurisdiction. the stratifying variable for the stratified random effects method was the percent of msm screened in the jurisdiction’s clinics, as low screening coverage may reflect targeted testing of msm likely to be infected which may inflate observed posit ivity. for each of the five methods, we estimated rectal gonorrhea positivity and the corresponding 95% confidence interval (ci). results in 2017, 123,210 patients attended 30 std clinics participating in the 10 ssun jurisdictions, of which 31,052 (25.2%) were identified as msm (jurisdiction-specific range: 8.8% to 70.0%). (table 1) one jurisdiction (i) accounted for 39% of all msm http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e253, 2019 isds 2019 conference abstracts included in the analysis while one jurisdiction (j) accounted for only 1.6% of msm included. the proportion of msm tested for rectal gonorrhea at least once varied by jurisdiction, ranging from 44.3% to 76.9%. the fully stratified method identified differences in rectal gonorrhea positivity across jurisdictions, with jurisdiction-specific positivity ranging from 9.9% to 24.1%. aggregating across jurisdictions masked this heterogeneity and provided a single summary estimate of 15.2% (95% ci: 14.7, 15.7). taking the mean across the jurisdiction-specific estimates also provided a summary estimate; however, the uncertainty of the estimate increased (15.8%, 95% ci: 13.3, 18.7). accounting for the heterogeneity by using a random effects model resulted in an estimate of 15.5% (95% ci: 13.9, 17.2). after stratifying by a likely confounder (% of msm screened); the random effects estimate amon g 3 jurisdictions with lower screening coverage (<60%) was 19.7% (95% ci: 14.6, 24.8) and among 7 jurisdictions with higher screening coverage (≥60%) was 14.3% (95% ci: 12.9, 15.7). conclusions in a sentinel surveillance project implemented in 10 jurisdictions, there was substantial heterogeneity in the observed proportion of msm testing positive for rectal gonorrhea in selected std clinics. although a stratified analysis captured the heterogeneity across jurisdictions, it may not be feasible to present fully stratified estimates for all analyses (e.g., surveillance reports like ly provide metrics for multiple diseases). additionally, it limits the ability to succinctly communicate key findings. aggregating numer ators and denominators across jurisdictions to calculate a single summary estimate masks this heterogeneity and biases esti mates toward high volume jurisdictions. taking the mean across jurisdictions ensures that high-volume jurisdictions do not bias the overall estimate; however, the mean may be biased by very high or very low positivity estimates in a few jurisdictions. usin g a random effects model accounted for both varying sample sizes and differences in observed heterogeneity; although the summary estimat e was similar to the aggregate in this example, the wider 95% ci more accurately reflects the uncertainty in the estimat e. finally, stratifying by a likely effect measure modifier (% of msm screened) prior to estimating the measure from the random effects model captured key differences in jurisdictions while still providing a limited number of summary estimates. analysts using data from multi-jurisdiction surveillance projects should fully investigate possible biases when combining estimates across jurisdictions. if there is observed heterogeneity across jurisdictions and it is not feasible to provide fully stratified estimates, analysts could consider using methods to account for heterogeneity and minimize bias due to differing sample sizes, such as stratified random effects models. acknowledgement the authors thank the members of the ssun working group: bob kohn, robbie madera, and roxanne kerani, as well as the ssun jurisdictions that contributed data to this analysis. table 1. trends in rectal gonorrhea among msm tested in std clinics participating in the std surveillance network (ssun), 2017 jurisdictio n # of patient s % of patients identified as msm % of msm patients screened for rectal gonorrhea % msm testing positive for rectal gonorrhea fully stratifie d aggregate d across jurisdiction s mean of jurisdictio n estimates random effects model stratified random effects model a 6,262 14.3% 44.3% 14.4% n/a n/a n/a 19.7% (95% ci: 14.6, 24.8) b 8,160 8.8% 46.4% 24.1% n/a n/a n/a c 13,519 15.7% 58.5% 20.9% n/a n/a n/a d 6,017 44.6% 65.6% 18.2% n/a n/a n/a 14.3% (95% ci: e 15,083 16.8% 65.8% 14.7% n/a n/a n/a f 9,188 48.4% 67.8% 15.8% n/a n/a n/a http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e253, 2019 isds 2019 conference abstracts g 10,657 30.1% 68.1% 13.5% n/a n/a n/a 12.9, 15.7) h 3,665 46.2% 70.5% 12.3% n/a n/a n/a i 49,792 24.4% 71.7% 14.3% n/a n/a n/a j 867 70.0% 76.9% 9.9% n/a n/a n/a total 123,21 0 25.2% 67.5% n/a 15.2% (95% ci: 14.7, 15.7) 15.8% (95% ci: 13.3, 18.7) 15.5% (95% ci: 13.9, 17.2) n/a msm: gay, bisexual and other men who have sex with men; ci: confidence interval http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts developing policy to support novel caribbean tourism and health surveillance program lisa indar*, carl j. hospedales and virginia asin-oostburg carpha, port of spain, trinidad and tobago objective the regional tourism and health program (thp) is a novel program, comprising of a tourism surveillance system, training, standards and multisectoral partnerships. the objective was to develop regional mandate and policy to support this new program and its non-traditional surveillance system. introduction in january 2016, the caribbean public health agency (carpha), serving 24 member states(ms), began executing a regional tourism and health program (thp), recognizing that the health of caribbean economies is closely related to the health of its tourism industry since the caribbean is most tourism-dependent region in the world; that tourism is vulnerable to health, safety and environmental (hse) threats; and that travel and tourism impacted on global health security. high and increasing visitors to the caribbean can increase the health, safety and security risks by the introduction and spread of diseases, by both residents and visitors. this was exemplified by the h1n1 pandemic (2009), chikungunya (2013), and the recent zika epidemic. however, even though more people visit the caribbean than reside, there is no regional visitor/tourism surveillance system. there is also no regional mandate and policy for the reporting of visitor/tourism illnesses. this coupled with inadequate training, lack of standards and collaboration between tourism health stakeholders have contributed to disease spread. the thp is an innovative, multifaceted, integrated, regional program with components of a web based real time tourism and health information surveillance and response system (this), food safety and environmental sanitation training, standards and multisectoral health and tourism partnerships. it aims to promote the health, safety and security of caribbean visitors and residents. the thp is novel in that it involves the implementation of a non traditional, health information and surveillance system (visitor based illnesses), new data users (private sector, hotels, passenger ships, visitors), new partners (tourism sector) and at regional level. given the novelty and the multisectoral nature of the thp, a critical factor to support its implementation and sustainability was the development of regional mandate and policy to facilitate real time surveillance and response to detect and reduce the spread of illness. methods a multiprong approach was used to develop regional mandate and policy for the unique multisectoral thp program, from january 2016 to october 2017. this consisted of (i) weekly advocacy meetings with national and regional tourism and health public and private stakeholders to gain buy-in, recognition and support (ii) requesting letters of commitment from ms (iii) seeking support from the caribbean chief medical officers of health (cmos), who advises the ministers of health, at their annual meeting and convening a special cmo meeting on the thp (iv) seeking ministers of tourism support through the caribbean tourism organization(cto) forum (v) inclusion of tourism and health as a priority in the caribbean cooperation in health (cch4) strategy (which sets health priorities for the caribbean region) (vi) presenting the thp to the council for human and social development (cohsod), consisting of caribbean ministers of health requesting approval to develop a regional thp policy (september 2017) and (viii) convening of a regional thp stakeholders meeting (october 2017) with high level decision makers from national, regional and international health and tourism sectors. results letter of commitment to implement the thp were received from 10 ms to date. the report of the special cmos meeting on the thp on august 2017, stated that the cmos supported the implementation of the thp, to enhance their core surveillance and response capacity and recommended that thp be included as a cch4 priority, and the development of a regional policy to support implementation. the cohsod, approved the inclusion of tourism and health as a priority in the cch4 on september 2017. the 33rd meeting of the cohsod on september 23, 2017 took a decision to mandate carpha to prepare a regional policy for the thp; having noted the efforts of carpha to mitigate public challenges in the tourism, recognized the uniqueness of the engagement between public health and the private sector; understanding the need for an adequate regulatory environment to support the public health efforts. a meeting statement was issued on october 5th 2017 by representatives of the caribbean community’s (caricom) ms, carpha, caricom, cto, caribbean hotel and tourism association, pan american health origination and international health and tourism agencies supporting the implementation of the thp to strengthen their capacity to address tourism hse public health threats and the development of a regional thp policy that include the following: awareness among policy makers in public and private sectors; use of the this for real-time, early warning and response to travel-related public health threats; regional guidelines for managing travel-related public health issues; training /capacity building; standards and certification; multisectoral public private partnerships and networks and mandating the reporting of illness by the hospitality sector to national authorities. conclusions developing regional mandate and policy is a complex and long, but critical necessity for the implementation and the sustainability of this novel, multisectoral, non-traditional, multi-country tourism and health surveillance and response program. while the regional policy will take time to finalize, carpha and ms now have regional mandate to support the implementation of the thp, to strengthen capacity to prepare for, mitigate and respond to public health threats, which can transcend national boundaries. keywords policy; tourism and health; caribbean; survillance systems *lisa indar e-mail: indarlis@carpha.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e72, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts impact assessment of mass gatherings using labelling procedure in ed, nouvelle-aquitaine, 2016 laure meurice*1, anne bernadou1, antoine tignon2, patricia siguret2, stéphanie vandentorren1, céline caserio-schönemann3, laurent maillard2 and caroline ligier2 1french institute for public health surveillance, nouvelle-aquitaine regional office, bordeaux, france, bordeaux, france; 2regional emergency observatory nouvelle-aquitaine, bordeaux, france; 3french institute for public health surveillance, saint maurice, france objective to access the potential health impact on the population during mass gathering over time using labelling procedure in emergency department (ed). introduction the massive flow of people to mass gathering events, such as festivals or sports events like euro 2016, may increase public health risks. in the particular context of several terrorist attacks that took place in france in 2015, the french national public health agency has decided to strengthen the population health surveillance systems using the mandatory notification disease system and the french national syndromic surveillance sursaud®. the objectives in terms of health surveillance of mass gathering are: 1/ the timely detection of a health event (infectious cluster, environmental exposure, collective foodborne disease…) 2/ the health impact assessment of an unexpected event such as a terrorist attack. in collaboration with the regional emergency observatory (oru), a procedure for the labeling of emergencies has been tested to identify the ed records that could be considered as linked to the event. methods during summer 2016, the procedure was tested on seven major festive events throughout the region. in addition to the main medical diagnosis, a specific icd-10 code “y3388” was chosen to be used in associated diagnosis for records that were supposed to linked to the event. information on the labeling procedure was insured by the oru to the emergency departments. all records with medical diagnoses or medical pattern beginning by y33 have been analyzed. results no significant increase in the global indicator was observed in the ed impacted by mass gathering. the ed labelling procedure identified 260 records: two thirds corresponded to young men and 17% came from abroad. among the 250 records labeled in associated diagnosis, 39% were associated to traumatisms and 31% corresponded to alcohol intake. conclusions this study shows that a labelling procedure allows the identification, quantification and characterization of the population ed records associated with mass gathering. additionally, a labelling procedure to assess a potential impact of an event as mass gathering can be implemented fairly rapidly. table 1 main diagnoses identified among the labelled passages y33 (88) in associated diagnosis (n=250) keywords impact assessment; mass gathering; emergency data; labelling procedure; labelling procedure acknowledgments to the emergency departments and the emergency observatory regional network. *laure meurice e-mail: laure.meurice@santepubliquefrance.fr online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e15, 2018 untitled development of a standardized tool for prioritization of information sources development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi development and validation of a standardized tool for prioritization of information sources holy akwar1, harold kloeze2, and shamir mukhi1 1. canadian network for public health intelligence, national microbiology laboratory, winnipeg, manitoba 2. canadian food inspection agency, owen sound, ontario abstract purpose: to validate the utility and effectiveness of a standardized tool for prioritization of information sources for early detection of diseases. methods: the tool was developed with input from diverse public health experts garnered through survey. ten raters used the tool to evaluate ten information sources and reliability among raters was computed. the proc mixed procedure with random effect statement and sas macros were used to compute multiple raters’ fleiss kappa agreement and kendall's coefficient of concordance. results: ten disparate information sources evaluated obtained the following composite scores: promed 91%; wahid 90%; eurosurv 87%; medisys 85%; scidaily 84%; eurekal 83%; cshb 78%; germtrax 75%; google 74%; and cbc 70%. a fleiss kappa agreement of 50.7% was obtained for ten information sources and 72.5% for a sub-set of five sources rated, which is substantial agreement validating the utility and effectiveness of the tool. conclusion: this study validated the utility and effectiveness of a standardized criteria tool developed to prioritize information sources. the new tool was used to identify five information sources suited for use by the kiwi system in the cezd-iir project to improve surveillance of infectious diseases. the tool can be generalized to situations when prioritization of numerous information sources is necessary. key words: emerging pathogens, zoonotic diseases, early warning, intelligence, response correspondence: holy.akwar@phac-aspc.gc.ca doi: 10.5210/ojphi.v8i2.6720 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes introduction emerging and re-emerging infectious diseases of humans are caused by pathogens, 75% of which originate from animals or their products [1]. recent epidemics of these diseases (e.g., ebola, novel influenzas) have served as a reminder of the capacity of these diseases to occur unexpectedly in new locations and to affect new species [2]. surveillance of these pathogens is http://ojphi.org/ mailto:holy.akwar@phac-aspc.gc.ca development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi essential to protect human and animal health and to avoid potential economic disruption due to trade barriers or restrictions [3]. unfortunately, current disease surveillance has been ineffective and/or untimely in alerting officials to emerging zoonotic diseases [4]. the capacity to detect these diseases in a timely manner will improve potential interventions and effective management of health and socio-economic risks posed by them, and improve research capacity to investigate the biologic, socio-economic, ecologic and anthropogenic factors responsible for emergence and re-emergence of these diseases [5]. early warning systems that harness media sources leveraging low-cost technologies and engaging public health professionals are rare but critical to underpin the ability to respond to threats posed by these emerging and zoonotic pathogens [6]. the centre for emerging and zoonotic disease integrated intelligence and response (cezd-iir) is a canadian safety and security program (cssp) [7] funded technology demonstration project. it is designed to leverage the knowledge integration using web-based intelligence (kiwi) and collaboration centre technologies within the secured canadian network for public health intelligence (cnphi) [8] informatics platform to detect aberrations and increase early warnings signal detection in response to emerging and zoonotic disease threats of public health significance in canada. to detect these threats in a timely manner, real time monitoring of numerous information sources is required. however, it is impractical to stream all the media sources in the world to identify relevant and significant alerts. hence, a standardized prioritization tool was designed to screen the plethora of information sources available globally to identify the most relevant sources to serve as feeds to the kiwi system. the evaluation tool consists of independent criteria developed to assess an information sources’ ability to contribute to early detection of aberrations and/or public health threats of emerging nature. the evaluation tool is designed to remain “evergreen” in order to cope with the evolving nature of emerging diseases and their multiple facets (animal, human, and environmental interconnections with multiple stakeholders). epidemiological techniques are well documented for use to evaluate and validate new tools by measuring agreement beyond chance among two or more raters (evaluators). the kappa statistic is considered the method of choice to compute inter-rater agreement using nominal and categorical data [9,10]. the fleiss kappa is used to assess agreement among more than two raters [11,12]. many methods exist to compute various measures of fleiss kappa depending on the nature of the data. some of these include: a sas macros called %magree and %intracc; proc freq, means, print; proc mixed procedures etc [11,13]. this study evaluated ten disparate information sources using a newly developed standardized criteria tool to demonstrate the validity and effectiveness of the tool to select emerging and zoonotic disease information sources. the specific objectives were to validate the standardized criteria tool used for prioritizing information sources that could serve as feeds to the kiwi system for zoonotic and emerging diseases and then to identify five information sources to be used for the pilot phase of cezd-iir project. methods steps to develop standardized criteria were based on guidelines for the development of assessment tools produced by the australian national quality council and their ranking http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi methodology [14]. the criteria developed for evaluating and prioritizing information sources was based on the following assumptions: the system was being built mainly for emerging and zoonotic diseases and as such all the criteria were evaluated against this group of diseases; the system was being built to meet the needs of a broad-based stakeholder that are multi-disciplinary in nature. these stakeholders included canada’s health portfolio (health canada, public health agency of canada, and canadian food inspection agency), agriculture and agri-foods canada, provincial and territorial governments (public health), livestock producers in canada, and frontline clinicians (physicians and veterinarians). data collection and manipulation standardized criteria were identified and developed through expert consultations and online survey of 59 multi-disciplinary experts drawn from academia, government, and industry organizations within canada as follows: provincial government (35.6%); federal government (25.4%); industry (18.6%); academic institutions (13.6%); independent consultants (1.7%); and ”other” (3.4%). the survey allowed for a comprehensive assessment of stakeholder views and opinions regarding specific evaluation criteria. from the responses received and analyzed, ten criteria were identified as follows: 1. timeliness: the frequency and lag (the delay between the event occurrence and reporting) with which the information is produced and disseminated to those who need to know. 2. usefulness: the purpose of the information to the stakeholders. that is, how well the information supports the work of the stakeholders. 3. authoritativeness (reliability/accuracy): the extent to which a piece of information represents the true value of the event being described or measured. 4. relevance: alignment of the information content with the scope of the system. areas covered by information are well within the scope of the system. 5. representativeness: information represents the target population under investigation or consideration, enabling generalizability of inference from the information. (includes both demographic and geographic representativeness) 6. credibility: perceived or real credibility of the institution or source of the information. 7. validity: there is evidence that the information has been processed and authenticated. 8. quality: information from the source is consistent and complete every time. 9. linguistic style: the clarity and complexity of the language by which the information source presents its materials to its audience. 10. uniqueness (rare and specific): pertinent information that is highly specialized to a specific pathogen/disease or population or area of health and cannot be obtained elsewhere. this criterion was not evaluated using the tool. it has been included but as a binary function only in order to allow for the inclusion of those sources that present information that is valuable and unique but not available in other ways. each criterion was then given a relative weight (table 1) representing its relative importance to enabling early detection of emerging and zoonotic diseases that were elicited from the evaluators http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi through the survey and consultation processes. these criteria were utilized in a second survey which was administered to the internal project technical team and dedicated advisors. a total of 11 evaluators (raters) were each asked to evaluate the same ten information sources by utilization of the standardized criteria tool and guidelines were provided to navigate the web-links to access the content of each information source. raters could complete the exercise in one sitting or save their answers and resume the evaluation at another time. for each of the evaluation criteria as described above the raters were asked to select either the option that best described the source being evaluated or to select all appropriate choices as per the survey instructions for the specific criterion being evaluated. each information source was evaluated against each of the criteria; a total score for the source was computed by adding the points, adjusted by the weighting factor obtained for each criterion. the total score for an information source was equal to the sum of scores from criteria 1 to 9. if a rater determined that a source did not possess a criterion being assessed, the score for that criterion was entered as 0. once all the information sources had been evaluated using the proposed tool, the sources were ranked by total score from highest to lowest and the top five selected for potential use for the pilot phase of the project. statistical analysis all data were coded and analyzed in sas version 9.2 (sas institute inc., cary, nc, usa) analytical package. the independence between raters was ensured through the survey administration. one of the 11 raters evaluated only three information sources and was not included in this analysis, therefore descriptive statistics were performed on the ten raters and as well as the ten information sources. statistical inter-rater agreement on results of the evaluation of information sources was computed using kappa analysis. the traditional kappa or cohen kappa is designed to measure agreement only between two raters [12]. in order to measure agreement among more than two raters as was the case for this study, the fleiss kappa and kendall’s coefficient of concordance were used [11,15]. all scores given by the raters were on a continuous scale and for the purpose of performing kappa analysis, these continuous scale scores were transformed into an ordinal categorical scale. it is not uncommon to compute kappa, weighted kappa (using ordinal data) and intraclass-correlation (using continuous data) in the same study when the data are collected using a continuous scale [14]. hence, the rater’s scores were grouped into ordinal categories reflecting the total score they gave to each information source. the maximum points that any information source could obtain was 1438 due to the various weighting factors for the criteria (table 1) and this number was divided equally to create five score categories as follows: category 1 = 0 299 points (lowest points category) category 2 = 300 599 points category 3 = 600 899 points category 4 = 900 1199 points category 5 = 1200 1438 points (highest points category) different statistical methods were used to calculate multiple raters’ agreement (fleiss kappa values). the ordinal generalized linear mixed model approach [16] using the proc mixed http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi procedure with restricted maximum likelihood option and random effect statement for both raters and sources were used in computing the fleiss kappa values. in order to optimize the fleiss kappa value, a backwards elimination strategy was used. this involved starting with all the information sources, computing fleiss kappa agreement, then deleting one at a time, the information source that improved the agreement the most by being deleted, and repeating this process until no further improvement in agreement was possible (referred to as backward elimination strategy). furthermore, the sas macro called “%magree” was invoked to compute multiple raters’ agreement for ordinal categorical data (kendall's coefficient of concordance) and the “%intracc” macro invoked for continuous data. the kendall's coefficient of concordance was computed because it takes into consideration the extent of disagreement among raters. results a total of 59 experts from a wide range of organizations/institutions participated as described to develop the standardized weighted criteria (table 1). the weighting reflects the relative importance of each of the criteria to evaluate information sources that enable early detection, prevention and control of emerging and zoonotic diseases. table 1: a standardized weighted criteria evaluation tool developed through a survey of nation-wide stakeholders criteria relative weight* usefulness 19.6 timeliness 19.5 reliability 18.7 relevance 15.6 credibility 15.3 validity 14.5 representativeness 14.0 quality 13.9 linguistics 12.7 uniqueness exempted^ total score 143.8** *weights reflect the average score given by evaluators to each criterion ^ an information source will be exempted from evaluation and directly used as a feed to the system if it is unique and highly specialized in the type of information it produces relevant to this system that cannot be found elsewhere. ** this is the maximum scores that anyone information source can obtain when assessed using this tool. http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi ten raters then used the criteria identified in table 1 to evaluate ten information sources. scoring varied amongst the raters and the overall maximum score (averaged over all 10 information sources) given by a rater was 1274.6 out of possible 1438, with a minimum of 1122.8, mean of 1174.4 (std. deviation of 55.4) and median of 1154.6. the information sources were ranked from the most relevant (highest score) to the least relevant (table 2). promed had the highest score (91%) and the canadian broadcasting corporation (cbc, a publically supported radio and television broadcaster) had the lowest (70%). the mean score for all the sources was 1174.4 (std. deviation of 103.7) and the median score 1198.3. table 2: information sources evaluated by the project technical team and technical advisory group using the criteria tool information source score (max. possible by 10 raters =1438) % score rank name abbreviation promed promed 1313.28 91.33 1 world animal health information system wahid 1289.20 89.65 2 eurosurveillance eurosurv 1253.78 87.19 3 medisys medisys 1224.82 85.18 4 science daily scidaily 1208.04 84.01 5 eurek alert eurekal 1188.48 82.65 6 canadian swine health board cshb 1120.06 77.89 7 germtrax germtrax 1082.70 75.29 8 google google 1064.40 74.02 9 canadian broadcasting corporation cbc 999.20 69.49 10 statistical agreement analysis there was a noticeable difference in the variability of the ratings depending on the information sources, as an example, all raters (100%) scored promed very highly such that all scores from each rater entered category 5 (table 3) whereas raters were widely dispersed or split regarding scores they gave cshb. the proc mixed procedure with random effect statement estimation method restricted maximum likelihood (reml) produced an overall fleiss kappa of 0.41554 or 42.0% agreement for all 10 raters. http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi table 3: the distribution of raters by categories by information sources (%) information sources *score categories name abbreviation three four five promed promed 0 0 100 world animal health information system wahid 0 10 90 canadian swine health board cshb 10 50 40 eurosurveillance eurosurv 0 10 90 eurek alert eurekal 0 30 70 canadian broadcasting corporation cbc 20 80 0 germtrax germtrax 0 70 30 medisys medisys 0 30 70 science daily scidaily 0 40 60 google google 10 80 10 * no raters gave scores low enough to enter categories 1 and 2, these categories are excluded from the table above. fleiss kappa analysis number of information sources evaluated = 10 number of evaluators (multiple raters) = 10 null hypothesis: kappa = 0 (indicates agreement equal to chance) the fleiss kappa value was then optimized by systematically reducing the number of information sources. utilizing the backwards elimination strategy to eliminate information sources (without replacement) with the most variation in scores (table 3) and re-computing fleiss kappa produced the following results: removing cshb from information sources: overall fleiss kappa = 0.481 = 48.1% agreement removing scidaily from information sources: overall fleiss kappa = 0.534 = 53.4% agreement removing germtrax from information sources: overall fleiss kappa = 0.581 = 58.1% agreement removing medisys from information sources: overall fleiss kappa = 0.647 = 64.7% agreement removing eurekal from information sources: overall fleiss kappa = 0.725 = 72.5% agreement http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi the sas macro %magree produced a kendall's coefficient of concordance of 0.504 or 50.4% for the categorized ordinal data and the %intracc macro produced an interrater reliability of 0.507 or 50.7% for the continuous data, for all 10 raters. these results are essentially equal; the minor difference could be the result of loss of information when data are categorized. discussion the final fleiss kappa analysis performed using results from the ten evaluators (raters) was on the following five information sources after excluding the rest: 1. promed; 2. world animal health information system; 3. eurosurveillance; 4. canadian broadcasting corporation (cbc news); and 5. google table 4: guidance for interpretation of fleiss kappa agreement adapted from landis and koch [17] kappa value % agreement degree of agreement <=0 0 poor 0 0.2 0 20 slight 0.2 0.4 20 40 fair 0.4 0.6 40 60 moderate 0.6 0.8 60 80 substantial 0.8 1 80 100 almost perfect the five information sources mentioned above were evaluated by 10 professionally diverse raters using this new tool and achieved a fleiss kappa agreement of 72.5%. a fleiss kappa agreement of 72.5%, constitutes substantial agreement based on the reference table 4 for kappa agreement interpretation [17]. this result indicates that this tool can be used in screening future information sources to be used by the kiwi system. kappa is designed to estimate agreement between two raters and to have ten raters of different expertise use this standardized criteria tool to assess these information sources and still obtain a substantial agreement indicates the effectiveness of the tool to prioritize information sources. as the number of information sources evaluated increases, the value of kappa decreases as there is more room for disagreement with more information sources [18]. the results of this study are consistent with another study that evaluated the scoring accuracy and rater reliability of a new tool (work-ability support scale) and observed a fleiss kappa of 79% agreement [19]. it should be underscored that achieving a substantial agreement assessing the above five information sources doesn’t indicate how relevant these sources are to zoonotic and emerging diseases but rather how effective the criteria tool is for assessment of information sources. however, three valuable information sources made the top five based on both relevance to zoonotic and emerging diseases and their fleiss kappa http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi agreement and these were promed, world animal health information system and eurosurveillance. overall, this study obtained a fleiss kappa value of 42% for all ten information sources assessed, which is moderate agreement and consistent with, but higher than another study that validated a grading system for lateral nasal wall insufficiency that obtained a fleiss kappa of 17%, in which they recommended training on how scores are applied [20]. training of raters on how to use the assessment tool improves the agreement generally and is recommended for this new tool as well. the fleiss kappa of 42% and kendall’s value of 50% obtained in this study are consistent with findings of other studies: that assessed agreement between thoracic surgeons rating frail behavior of patient and obtained a fleiss kappa of 47% and kendall’s value of 85% [21]; and another that assessed the reliability of an injury scoring system for horses and obtained a fleiss kappa of 66% and kendall’s value of 88% [22]. the kendall’s value represents a robust measurement of agreement compared to the value generated by traditional kappa. this is because the kappa method does not adequately represent the agreement level and does not take into consideration the extent of disagreement. that is, kappa does not differentiate a disagreement of 1 and 5 versus a disagreement of 4 and 5, whereas kendall’s approach considers the magnitude of each disagreement (the disagreement between 1 and 5 is greater than the disagreement between 4 and 5). with respect to the actual scores of how pertinent the source was for zoonotic and emerging disease detection there was a reasonable diversity of ratings. of the ten information sources assessed, some scored quite high, for example promed, while other scored much lower. an example of the latter was the canadian swine health board (cshb). this might be related to the raters’ lack of in-depth knowledge about cshb which was a relatively new system being implemented in canada. for a vast majority of the information sources, the scores were mainly in categories 4 and 5. in all, five of the ten information sources (cshb, scidaily, germtrax, medisys, and eurekal) initially included in the overall fleiss kappa agreement, had scores that varied widely from raters. these five sources were either new and/or uncommon to most raters. computing a fleiss kappa for these relatively new and/or unknown sources produced an agreement of 4.8%, which is slight agreement and significantly lower compared to the 72.5% substantial agreement obtained from the other five sources that were well known to the raters regardless of their relevance to the zoonotic and emerging diseases. even though this study did not focus on the competence of the raters in assessing information sources, it is important to note that the rater’s knowledge of the information source being assessed is critical to an effective utilization of the tool. other scientific studies indicate that it is not uncommon to observe rater variation. for example, a study that used orthopedic specialists to classify ankle fractures observed kappa values varying from 20 to 64% [23]. poor multiple raters’ agreement (fleiss kappa 21%) was also reported among generalist physicians assessing the clinical significance of drug-drug interactions [24] and a study that assessed multiple raters’ variation in scoring radiological discrepancies observed a fleiss kappa of 17% [20]. as there was significant variation observed when raters used the tool to evaluate information sources that they were not familiar with, it is recommended that future raters be trained to use the tool as well as to improve their knowledge of the various information sources under evaluation. this is particularly important given the plethora of information sources available globally and the need to streamlined these sources to optimize the performance of early warning surveillance systems. http://ojphi.org/ development and validation of a standardized tool for prioritization of information sources online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(2):e187, 2016 ojphi limitations there were some limitations in this study. firstly, the raters were not randomly selected from a pool of expert evaluators and therefore may not properly reflect the performance of future raters if new information sources are evaluated with the same tool. secondly, the diverse professional background (federal & provincial/territorial governments, academia, and industry) of these raters might have contributed to some of the variation seen in the agreement. however, different experts were used to reflect and account for the joint multi-stakeholder nature of this project and tool being developed. despite the limitations mentioned above, the level of agreement obtained indicates that an effective standardized criteria tool for prioritizing information sources has been successfully developed, engaging broad-based canadian stakeholders. conclusion an effective standardized criteria tool for prioritizing information sources has been successfully developed, engaging broad-based canadian stakeholders. five information sources evaluated by 10 professionally diverse raters using this tool obtained a fleiss kappa agreement of 72.5%, which is substantial agreement. this study validates the utility and effectiveness of the standardized criteria tool for prioritizing information sources. the new tool was used to select five information sources suited for use by the kiwi system in the cezd-iir project to improve surveillance of infectious diseases. the use of the tool can be generalized to situations where prioritization of numerous information sources is needed to allow selection or rationalization of these sources. overall, results of this study indicate that this tool can be used in screening future information sources. this is particularly important given the plethora of information sources available globally and the need to streamline these to optimize the performance of surveillance systems. acknowledgements we thank members of the technical advisory group of the cedz-iir project for their participation in the evaluation of the information sources using the newly developed tool. we also thank two master of public health candidates from university of guelph, zana dukadzinac and shahnam sharifzadeh for their contributions with survey preparations and data collation. we would like to acknowledge that the cedz-iir project is supported by funding from the canadian safety and security program (cssp), managed through defence research and development canada (drdc) centre for security science (css) and hosted by the canadian food inspection agency (cfia) providing logistics and management for the project and thank the project manager, mr. harry gardiner for his support. references 1. merianos a. 2007. surveillance and response to disease emergence. curr top microbiol immunol. 315, 477-509. pubmed http://dx.doi.org/10.1007/978-3-540-70962-6_19 2. meslin fx, stohr k, heymann d. 2000. public health implications of emerging zoonoses. rev sci tech. 19, 310-17. pubmed http://dx.doi.org/10.20506/rst.19.1.1214 http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17848076&dopt=abstract 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http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23833035&dopt=abstract http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23833035&dopt=abstract http://dx.doi.org/10.1371/journal.pone.0098654 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24892734&dopt=abstract http://dx.doi.org/10.1007/s007760200028 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11956974&dopt=abstract development and validation of a standardized tool for prioritization of information sources introduction methods statistical analysis statistical agreement analysis discussion limitations conclusion acknowledgements references isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* state sanitary and epidemiological service of ukraine, kyiv, ukraine introduction according to the world health organization (who), the epidemic situation on poliomyelitis in the world is not good. ukraine is on the list of countries certified by who as the territory free of polio, and previously, population coverage with scheduled vaccination against polio was 95%. methods annual official reports on morbidity and reports on prophylactic vaccination and medical cards of the patients with acute flaccid paralysis over a period of 10 years in the carpathian region were used to perform analysis in our study. the carpathian region belongs to risk group as it is the area of intensive migration and a low rate of immunization. our research was focused on the following: to study current surveillance over poliomyelitis and to analyze the implementation of actions regarding the status of ukraine to be free of polio. results in the past, from 2 to 305 (in 1957) cases of polio were registered in zakarpattye region. from 19992014, sixty-one cases of acute flaccid paralysis (afp) were registered there. the number of registered afp cases varies from 3 to 7 in different years. we conducted analysis on vaccination coverage against polio among children of one year-old (polio-4) and obtained the following results: in 2005 97,4% of children were vaccinated, in 200699,6%, in 2007 98,6%, in 2008 96,2%, in 2009 88,4%,in 2010 65,5%, in 201161,6%, in 2012 48,0%, in 2013 82,2% and in 2014 the percentage of vaccinated children made up only 42,1%. in 2014, the total scope of vaccination of the ukrainian population was less than 60 %. conclusions as a result of our study, we found there were a large number of unvaccinated children who should be vaccinated. according to the who, ukraine is one of the countries with high risk of spreading “wild” polio virus if it is imported. a national strategy designed to support the polio-free status of ukraine should include high vaccination coverage using opv (oral polio vaccine), developing additional mass vaccination actions or introducing national days of immunization, and developing effective surveillance and immunization among risk groups. keywords poliomyelitis; vaccination; surveillence *oksana cyganchuk e-mail: ocsana.4@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e100, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a deployment of analytics into the healthcare safety net: lessons learned 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi deployment of analytics into the healthcare safety net: lessons learned david hartzband1 and feygele jacobs2 1. director of technology research, rchn community health foundation 2. president and ceo, rchn community health foundation abstract background: as payment reforms shift healthcare reimbursement toward value-based payment programs, providers need the capability to work with data of greater complexity, scope and scale. this will in many instances necessitate a change in understanding of the value of data, and the types of data needed for analysis to support operations and clinical practice. it will also require the deployment of different infrastructure and analytic tools. community health centers, which serve more than 25 million people and together form the nation’s largest single source of primary care for medically underserved communities and populations, are expanding and will need to optimize their capacity to leverage data as new payer and organizational models emerge. methods: to better understand existing capacity and help organizations plan for the strategic and expanded uses of data, a project was initiated that deployed contemporary, hadoop-based, analytic technology into several multi-site community health centers (chcs) and a primary care association (pca) with an affiliated data warehouse supporting health centers across the state. an initial data quality exercise was carried out after deployment, in which a number of analytic queries were executed using both the existing electronic health record (ehr) applications and in parallel, the analytic stack. each organization carried out the ehr analysis using the definitions typically applied for routine reporting. the analysis deploying the analytic stack was carried out using those common definitions established for the uniform data system (uds) by the health resources and service administration.1 in addition, interviews with health center leadership and staff were completed to understand the context for the findings. results: the analysis uncovered many challenges and inconsistencies with respect to the definition of core terms (patient, encounter, etc.), data formatting, and missing, incorrect and unavailable data. at a population level, apparent underreporting of a number of diagnoses, specifically obesity and heart disease, was also evident in the results of the data quality exercise, for both the ehr-derived and stack analytic results. conclusion: data awareness, that is, an appreciation of the importance of data integrity, data hygiene2 and the potential uses of data, needs to be prioritized and developed by health centers and other healthcare organizations if analytics are to be used in an effective manner to support strategic objectives. while this analysis was conducted exclusively with community health center organizations, its conclusions and recommendations may be more broadly applicable. http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi keywords: community health centers, analytics, decision-making, data correspondence: dhartzband@rchnfoundation.org doi: 10.5210/ojphi.v5i3.4933 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction community health centers are the backbone of the health care safety net, providing comprehensive primary care for the nation’s medically underserved communities and populations. in 2015, 1,429 community health centers operated in nearly 10,000 urban and rural sites across the country, serving over 25 million people. buoyed by hrsa’s long-standing focus on quality improvement and substantial investments in health center hit systems, health center organizations have implemented electronic health record applications in record numbers. ninety two percent (92%) of all federally qualified community health centers, and 85% of health center “look-alikes” those entities that meet all requirements of the health center program but are supported by state and local funds rather than federal grants report that an ehr was in use for all sites and all providers in 2015; only 2.4% have no ehr installed at any site and virtually all expect to adopt an ehr. in addition, 95.5% report using clinical decision support applications, and 64.1% exchange clinical information electronically with other key providers, health care settings or subspecialty clinicians.3 in addition, 88.9% participate in the centers for medicare and medicaid services (cms) ehr incentive program commonly known as "meaningful use." these statistics reflect a commitment to the adoption of new technologies to support the provision of high-quality clinical care and streamline operations. yet as the movement to value-based payment accelerates and strategic planning becomes more complex, community health center organizations, along with all other providers, must be prepared for new and increasingly sophisticated analytics to support clinical care and operations. as analytics are applied to ever-larger amounts of data and become both more important and more necessary, questions about their use become inevitable. how is data quality influenced by the use of health information technology (hit) such as electronic health records (ehr), or acquisition through other means? on an operational level, how can analytic results best be understood and used to address and improve healthcare practice? patient outcomes? cost reduction? what are the implications of problematic data quality on operational capacity?4 to address these questions and help community health center organizations plan for future use and integration of contemporary analytics, several health center organizations were recruited to engage in a project to evaluate:  health center data accuracy: do health center data systems ensure correct values and consistent formats for data? http://ojphi.org/ mailto:dhartzband@rchnfoundation.org deployment of analytics into the healthcare safety net: lessons learned 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi  health center data reliability: do health center data systems collect and report results that are consistent and correspond to results from cdc data sources?  health center data completeness: do health center data meet the criteria for all mandatory data items? at each participating organization, which included several community health centers and one state primary care association, a hadoop-based analytic stack was deployed alongside the organization’s other data systems. population-level statistics were compared for specific diagnoses and comorbidities calculated through the organization’s normal means and through the analytic stack for comparability and utility. background and literature documentation, reporting accuracy and data quality have been the focus of numerous studies. yang and colditz [4] recently undertook a review of nhanes survey data in an effort to benchmark the prevalence of obesity nationally. al kazzi et al. [5] examined the prevalence of obesity, overweight, tobacco and alcohol use comparing the data in a direct survey (the behavioral risk factor surveillance system brfss) with that in the nationwide inpatient sample administrative data base and found substantial differences between the two. o’malley et al.[6] examined the icd diagnostic coding process and potential sources of error in code accuracy. they found the principal sources of error to be related to both communication and documentation, citing lack of baseline information, communication errors, physician familiarity and experience with the presenting condition and insufficient attention to detail as well as training and experience of coders and discrepancies between electronic and paper record systems. their prescription for improvement was the specification of clear coding processes and a focus on heightening the awareness of all staff engaged in documentation with respect to data quality. devoe, et al.[7] compared the entries in ehrs with the same data in the medicaid claims data set for a group of 50 community health centers in oregon. they found gaps in data congruence across the study group, with some services documented in the medicaid data set but not the ehrs, and others documented in the ehrs but not in the medicaid data set. for the latter group, nearly 50% of services documented in the ehr were not found in the medicaid claims for hba1c, cholesterol screening, retinopathy screening and influenza vaccination. they also evaluated demographic characteristics and found that spanish speaking patients, as well as those who had gaps in insurance coverage, were more likely to have services documented in the ehr but not in the medicaid claims data, a finding especially relevant to community health centers, which disproportionately serve poor, uninsured individuals and those best served in languages other than english.5 outside of health care, other industries including discrete manufacturing and financial services have struggled with the overall issue of data quality [o’connor, 8]. over time, both of these industries have, for the most part, achieved very high levels of data quality and high levels of user confidence in their data, and the experience in these industries might provide some insight into data quality improvement in healthcare.6 two projects are especially instructive in this area. the c4 project at general motors, which began in 1986, [bliss 9] was an attempt to develop an entirely paperless design and manufacturing specification system for automotive manufacturing. the data http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi quality effort associated with this project was immense. a staff of close to 50 people was assigned to the various parts of data acquisition, normalization, maintenance and life cycle management. the project emphasized the design of processes to ensure data quality and integrity. in particular, data governance was monitored at least as much as data entry, storage and usage. this went a long way toward ensuring a high level of data quality. the same was true of a project at goldman sachs to develop an integrated trading system. the primary effort was the development of a set of data governance and data life cycle processes that focused on the awareness of data quality. 7 the studies in other industries point to the processes deployed and potential for change relevant to the health care industry, while the studies of health services data frame some of the known challenges and complexities that remain in the health care industry. for many community health centers, which serve especially vulnerable populations, and tend to be less well-resourced than hospital providers, understanding the complexities related to data collection and use, and developing appropriate strategies to improve data collection and use information more effectively, remain challenging. methods two urban and one rural multi-site community health center, operating in three different states, were recruited along with one state primary care association to participate in the study in 20142015. the three health centers varied in size and in the aggregate, served approximately 124,000 patients. the participation of the primary care association, which administers a data warehouse for member health center organizations, resulted in a total data set representing 50 chcs, operating more than 400 practice sites and serving approximately 1.3 million patients. the overall distribution of urban and rural sites was approximately equal. each organization made available data for a period of either two years (2012-2014) or three years (2011-2014). it should be noted that the deployment was undertaken specific to each site; that is, each location was treated as a unique project site or case. at each project site, a dual-path deployment and analytic process was used, wherein researchers worked with the it group local to the organization to install and integrate a new software application to provide analytic capacity, alongside the centers’ main systems.8 the purpose of this was to help each health enter assess the reliability of its existing systems in deriving results consistent with the new application. this software, an open-source (oss) hadoop-based analytic stack, consisted of the cloudera express hadoop distribution that includes the hadoop distributed file system (hdfs), yarn (mapreduce2), hbase (non-relational data management) and impala (sql-based query). hadoop was selected as the analytic system for several reasons: it is a welldefined and well-understood technology; it is in current use in many sectors; it is relatively easy to install and test; it provides the opportunity to manage and analyze data from very heterogeneous sources; and it is easy to use because analytic queries may be produced in sql. most significant is the use of hadoop for managing big data, and for predictive analytics. these are important considerations for health centers as they expand in capacity and require increasingly complex tools http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi for clinical and operational management. the local deployments provided an opportunity to test their use in the health center environment. the oss installation took between 1 week and 3 months, with the duration depending largely on the familiarity of the organization’s it staff with deploying open-source software. after deployment of this software, a read-only connection was made either to the database underlying the organization’s electronic health record (ehr) system, or to a data extract or warehouse maintained by the primary care association and one of the urban community health centers. the dual-path process provided for the comparison and separate evaluation of results and data quality for two different data collection and analysis approaches. the first method accessed and analyzed data through the normal processes in use at the health center. these processes included direct access to the ehr or ehr-based data warehouse and analysis through either the ehr’s query facility or the business intelligence (bi) tool in use at the health center. the second method consisted of extracting data from the ehr (or warehouse), normalizing the data according to standard (uds) definitions and conducting analysis using the hadoop-based stack. this method allowed for greater transparency and permitted data quality issues such as differences in definitions or ambiguity due to ehr complexity to be identified and addressed. after data were imported into the analytic stack and deployment was completed, an initial “levelup” exercise was performed. this exercise served both to test the analytic system and to facilitate the normalization of all core terms and data definitions between the chc’s operational systems and the analytic stack. the “level-up” exercise consisted of a number of defined queries performed through the organization’s regular systems (ehr, sql, bi tools) and compared with the same queries performed on the analytic stack with the data in the hdfs/hbase information store. the following queries were performed9:  number of patients served, per year number of patients served presenting with specific diagnoses including hypertension, diabetes, obesity, heart disease, and behavioral health conditions  rank order of prevalent10comorbidities  cost11 per patient, per year  cost, per comorbidity, per year. this exercise, undertaken by the organizations in concert with the researchers, took between 2 weeks and 6 months to perform, and was largely dependent upon the organization’s prior work on data normalization. in parallel with this technical deployment, training was provided to each organization’s it personnel and other staff, including in all cases the ceo or executive director. the training focused on the uses of the stack-driven analytics, an exploration of its advantages and disadvantages, and addressed how it differed from extant business intelligence and other reporting. lastly, informal interviews were conducted with the chief medical officer or ceo at each organization to review results, discuss important findings, and consider potential challenges and approaches to continued analysis. http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi study limitations the analysis was confined to community health center organizations, and included organizations operating in just three states. it may not be representative of a broader group of health centers nationally or of chcs in different states. the data focused on specific years, and included centers that had undergone an ehr migration prior to the analysis period, which is an experience that may not be shared by health centers generally. despite these limitations the data quality issues identified among the participants provide evidence of common data concerns and challenges. results the exercise revealed common data quality issues across each of the organizations. these include missing or unusable data, as well as differences between the definition of core terms, such as patient or encounter, both within and across organizations even though these terms have standard definitions as required by hrsa. finally, underreporting of certain diagnoses in comparison to the general population raises questions about the reliability of the data. while interesting in and of themselves, these initial results are important for what they may indicate about the bigger picture of data acquisition and use. table 1. below illustrates the values reported by the participating organizations, presented as ranges, for key diagnoses. as noted above, these results represent data from approximately 50 chcs comprising over 400 clinical sites and a total of 1.3 million patients, for a period of two to three years. reported population percentages (cdc faststats) for the u.s. population as a whole are presented for comparison table 1. population percent range values for selected diagnoses diagnosis range from ehr values range from analytic values u.s. population percentage (cdc)12 hypertension 17%-23% 4%*-22% 33.5% diabetes 6%-8% 2%*-8% 9.6% obesity 3%*-12% 3%*-12% 37.9% heart disease 1%-4% 1%-3% 11.5% several issues are evident in the results reported above. the first is that the hypertension, diabetes and obesity results include a number of outliers (marked *). in each case, the outlier data are attributable to one (out of >40) health center organization. if these data are discarded, the comparative ranges for the results derived from ehr data are similar to the results derived from the data imported into the analytic stack. the table below shows the effect of removing the outlier organization from the analytic stack results. with the outlier removed, the ehr and stack-driven results are closer, but there is still some variation illustrated by the data ranges, reflecting inconsistencies in the data. table 2. adjusted population percent range values for selected diagnoses http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi diagnosis range from ehr values range from analytic values u.s. population percentage (cdc) hypertension 17%-23% 17%-22% 33.5% diabetes 6%-8% 5%-8% 9.6% obesity 9%-10% 7%-12% 37.9% heart disease 1%-4% 1%-3% 11.5% in addition, the percentage of the population with obesity and heart disease diagnoses, in both the full data table and second, adjusted, table are notably low in comparison with the cdc’s reported figures for the u.s. population as a whole. we might expect the population percentages for these diagnoses in the community health center patient population to be at least the same as, if not higher than, those in the general population, given the documented level of disparities and characteristics of the population served. possible reasons for these discrepancies will be discussed in the conclusions section of this paper. more generally, the analysis revealed several types of potential data quality issues. although the nature and extent of the problems varied across sites, the problems including definitional conflicts, conversion issues and structural challenges were not unique to any site and to some extent were evident at all sites. these include: o errors resulting from deviation from standard definitions (for example, for patient, encounter, provider) even when guidelines for such definitions exist and are required for standard reporting (uds, in the case of chcs); o errors caused by omission, that is data simply not recorded; o errors resulting from incorrect entry ▪ values that are out-of-range & not caught by the ehr system, e.g. bmis of >1000, bp values of 320/250, hba1c values of >50; ▪ incorrect text entered for names, addresses, previous providers; ▪ values not entered into searchable fields; ▪ data recorded as text in clinical notes, but not into searchable fields; ▪ data imported from external sources (labs, registries, etc.) as text, but not into searchable fields; o errors resulting from the structure and complexity of ehr systems ▪ several systems were found to be sensitive to the form that data was entered, specifically icd-9 codes of 250., 250.0, 250.00 resulted in different query results as did 250.5, 250.50 etc. ▪ complexity of navigation and misalignment with provider workflows also appeared to be responsible for several types of errors. ▪ concentration on treatment of a single condition during an encounter, leading to low numbers of encounters with multiple diagnoses recorded. o data corruption and/ or loss of data resulting from migration to a new ehr platform. http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi conclusions and recommendations the length of time required to successfully complete the “level-up” exercise was substantially shorter in those organizations that had done extensive data normalization work prior to beginning the study. the organizations (pca and one large, urban chc) that took the least time (< 1 month) to deploy the analytic stack and perform the data quality exercise had previously undertaken substantial work to standardize definitions (semantic normalization) and to do format matching and format transformations (syntactic normalizations). this effort was not related to the study, and was in all cases done in conjunction with the creation and population of a data warehouse. in addition, these organizations had already begun an exploration of analytics that enabled them to quickly align with the deployment requirements and to think in terms of strategic analysis. conversely, the organization requiring the longest period of time to complete the level-up exercise had the most widespread use of idiosyncratic, non-standard (i.e. non-uds) definitions for core terms such as patient, encounter, and provider, as well as definitional mismatches between different clinical departments or between clinical & administrative departments within the organization. the potential under-reporting of key diagnoses, as evidenced in the data for hypertension, diabetes, obesity and heart disease falling well below nationally reported figures, is of a different nature. the patient populations of community health centers are not generally thought to be healthier than the general u.s. population. health centers patients are disproportionately poor, uninsured, and publicly insured, and disproportionately members of minority groups.13 in addition, health centers are more likely to treat patients with chronic illnesses compared to other primary care physicians.14 yet in all cases, the reported percentages for key diagnoses were below the values reported for the population as a whole, and they are especially conspicuous for obesity and heart disease. al kazzi et all [5] recently compared hospital discharge data reported in the u.s. inpatient reporting sample (nis, ahrq) to interview data reported in the behavioral risk factor surveillance system (brfss, cdc) for 2011 data. results for obesity showed a 9.6% population percentage in the nis and 27.4% population percentage in the brfss. the population percentages reported in the brfss figures, which are based on direct participant surveys, are thus almost 3 times greater than the results from hospital discharge records 15, and more aligned with other recent results.16 this suggests that the chc-reported data are consistent with other provider-reported data, as demonstrated by the nis results, but understated relative to other sources. to better understand the anomaly with respect to obesity in the health center data sets, these results were reviewed with the chief medical officers and other clinical staff at participating chcs. those interviewed estimated the obesity rate for the patients they served at forty percent. a recent paper in the journal of the american medical association, internal medicine [4] estimated that in the united states, forty percent of adult men and thirty percent of adult women are overweight, while thirty-five percent of men and thirty-seven percent women are obese. the estimate provided by the participant cmos is thus consistent with this data, and substantially higher than the data derived from the analysis. cmos interviewed cited two possible explanations for this. first, it was noted that providers did not often diagnose obesity, and when they did, they did not use the full range of icd-9 codes, which include three specific codes (278, unspecified obesity; 278.01, http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi morbid obesity, bmi>30; & 278.02, overweight, bmi>25). further, while the uds guidelines specify the use of the 22 v-codes for obesity, with a highly specific breakdown of bmi measurements, these apparently are also underutilized. it was conjectured that the data might reflect sensitivity to different cultural norms for defining obesity and overweight in the communities served.17 while more investigation needs to be done to understand the data anomaly, the range of 3%-12% reported by the health center organizations in this study seems unlikely and could reflect both reporting and recording bias, as well as data quality issues. obesity might be subjective (although bmi values are a typically-used standard), but heart disease is a specific diagnosable occurrence. the apparent underreporting of heart disease in the study group – approximately seven to eight percent, as compared with eleven percent nationally per the cdc is therefore harder to explain. most cmos thought that 20%-30% of their patients experienced some form of heart disease. possible causes of underreporting are still under investigation, although it should be said that our analysis was not age-adjusted. it was also not adjusted for the fact that, particularly in 2014, many patients not previously known to the health centers were seen for the first time as coverage expanded, and the addition of new patients, may affect the distribution of diagnoses in ways that we do not yet understand. comparing the body of data quality work in aerospace and financial services industries with that in healthcare can be instructive. each of the gm and goldman sachs projects referenced had several similarities besides emphasizing data governance. these included: 1) high level executive sponsorship – evp and/or ceo who actually participated in introducing and reviewing the projects; 2) a long period of pre-work during which core terms were defined, data was normalized, and workflows and work processes were redesigned or newly created in order to provide an environment that promoted data quality; 3) broad participation from across the organization, not just it; and 4) emphasis on standards where necessary or productive, but not as the primary or sole focus of effort. the most important characteristics in these industries’ efforts were term definition, data normalization and process redesign as well as broad participation in the entire effort across the phases of planning, initiation, deployment and ongoing improvement. these are industries that spent decades reengineering their workflows and processes for operational and informational efficiency and effectiveness. [13,14]. in contrast, our experience suggests that: 1) governance and information life cycle are not at the core of how the healthcare industry approaches such projects; 2) while executive sponsorship is the norm, executive participation is rare; 3) many projects are designed, led and carried out by the it group; and 4) standards are seen as a major part of the solution, including by federal agencies and regulators (i.e. onc, cms, hrsa). these issues remain to be tackled in healthcare organizations. this leaves us with the larger questions that were mentioned earlier. our current findings provide some indications of the influences on data quality that might explain, at least in part, the variations and unexpected results. these influences include: quality degradation from system migration; inadequate or inappropriate data entry causing missing or incorrect data; inaccessible data in text entries from provider notes, text lists and external text imports; inadequate definition and format normalization resulting in unusable data; systemic errors due to current practice norms; http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi idiosyncrasies in how different ehrs process diagnosis codes; and complexity of navigation in ehrs. many of these issues can at least be addressed by greater attention to detail at the data entry stage. improving data quality directly in the ehr is more effective than trying to address it after the data are entered. the achievement of the quadruple aim which encompasses improving the work life of health care providers, clinicians and staff as well as enhancing patient experience, improving population health, and reducing costs clearly necessitates early preparation and consistent attention to data quality. several recommendations are suggested by these results:  definitions of core terms should be reviewed and consensus reached on their application and use. moreover, data definitions and workflows should be aligned with standard practices.  workflows and other processes should be reviewed and redesigned as necessary to emphasize and promote data quality.  organizations should familiarize themselves with how their ehr processes data as it is entered (for example, how diagnosis codes are treated), and ensure that entered data is treated consistently by the ehr.  text data should be entered in a consistent manner that is retrievable for analysis as well as for use in diagnosis and patient care.  before migrating from one ehr platform to another, data should be cleaned and checked. extensive data checking should also be done after ehr system migration. care must be taken that the data from the retired system is backed up, potentially in a data extract, so that is available if any conversion loss occurs, and to vet the integrity of the migration process. these recommendations are aimed at helping the health centers to answer two strategic questions. first, how good are your data? clinical data from the ehr, data imported from labs and other providers, and financial data need to be carefully reviewed and vetted for accuracy, reliability and completeness. second, how good are your systems? this includes infrastructure (servers, storage, network) and software systems and applications as well as processes and workflows. finally, it includes staffing and staff training and engagement. supporting health centers with the expertise and funds to undertake this work should be prioritized. to date, the participating organizations have not moved substantially beyond the initial data quality exercise. the issue remains of how to integrate analytic results into the strategic and operational practice of a community health center, or a healthcare organization in general. the experience from this study indicates that while community health centers generally attain the highest standards of care and achieve good outcomes with respect to both quality and cost, considerable work may be needed to help all centers strengthen their awareness of data and information quality and move toward better integration of analytic results in practice. this awareness includes understanding how to perform complex analytic queries, and what applications might best be suited to particular types of analysis, but these are just two components of a forward– thinking data strategy. all staff at healthcare organizations, but particularly those engaged in using data for decision-making, must develop an awareness and appreciation of what data are available, http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi how it can best be analyzed and how these results relate to the clinical, operational and strategic needs of the organization. acknowledgements the authors would like to acknowledge the assistance of srini rao, ph.d., ceo of datycs for deployment and analytic support. conflicts of interest the authors have no conflicts of interest to report. references 1. nambier r, bhadwaj r, sethi a, vargheese r. 2013. a look at opportunities and challenges of big data analytics in healthcare. 2013 ieee international conference on big data. 10/2013. 2. raghupathi, w., v. raghupathi/ 2013. an overview of health analytics. j. health med. informat. 4:132. dpi:10.4172/2157-7420.1000132. 3. ward mj, marsolo ka, froehle cm. 2014. application of business analytics in healthcare. bus horiz. 57(5), 571-82. pubmed http://dx.doi.org/10.1016/j.bushor.2014.06.003 4. yang, l. & g.a. colditz. 2015. prevalence of overweight & obese in the u.s., 2007-2012. jama int. med. published online 22 june 2015. 5. al kazzi e.s., b. lau, t. li, e.b.schneider, m.a. makary, s. hutfless (2015) differences in the prevalence of obesity, smoking and alcohol in the united states nationwide inpatient sample and the behavioral risk factor surveillance system. plos one 10(11): e0140165. doi:10.1371/journal. pone.0140165 6. o’malley kj, cook kf, price md, wildes kr, hurdle jf, et al. 2005. measuring diagnoses: icd code accuracy. health serv res. 40(5 pt2), 1620-39. pubmed http://dx.doi.org/10.1111/j.1475-6773.2005.00444.x 7. devoe je, gold r, mcintyre p, puro j, chauvie s, et al. 2011. electronic health records vs medicaid claims: completeness of diabetes preventive care data in community health centers. ann fam med. 9(4), 351-58. pubmed http://dx.doi.org/10.1370/afm.1279 8. o’connor l. 2007. data quality management and financial services. proceedings of the 2007 mit information quality industry symposium. 5/2007. 9. bliss fw. 1996. the c4 program at general motors. the cad/cam handbook. association for computing machinery. mcgraw-hill, nyc, ny. pp. 309-320. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25429161&dopt=abstract http://dx.doi.org/10.1016/j.bushor.2014.06.003 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16178999&dopt=abstract http://dx.doi.org/10.1111/j.1475-6773.2005.00444.x http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21747107&dopt=abstract http://dx.doi.org/10.1370/afm.1279 deployment of analytics into the healthcare safety net: lessons learned 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi 10. kahn mg, raebel ma, glanz jm, riedlinger k, steiner jf. 2012. a progmatic framework for single-site and multi-site data quality assessment in electronic health record-based clinical research. med care. 50(0). doi:10.1097/mlr.0b013e318257dd67. 11. wieskopf ng, weng c. 2013. methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. j am med inform assoc. 20(1), 14451. pubmed http://dx.doi.org/10.1136/amiajnl-2011-000681 12. cai l, zhu y. 2015. the challenges of data quality and data quality assessment in the big data era. data sci j. 14(2). doi:10.5334/dsj-2015-002. 13. hammer m. 1990. reengineering work: don’t automate, obliterate. harv bus rev. (jul/aug), 104-12. 14. hammer m, champy ja. 1993. reengineering the corporation: a manifesto for business revolution. harper business books. nyc. isbn 0-06-662112-7 footnotes 1 as defined in health resources and services administration bureau of primary health care, uds reporting instructions for health centers: 2014 edition (http://bphc.hrsa.gov/datareporting/reporting/2014udsmanual.pdf) 2 http://whatis.techtarget.com/definition/data-hygiene. data hygiene is the collective process conducted to ensure the cleanliness of data. data is considered clean if it is relatively error-free 3 hrsa 2015 health center data. table 5. staffing & utilization. http://bphc.hrsa.gov/uds/datacenter.aspx?q=tall&year=2015&state= 4 c.f. [1] nambier, et al. 2013; [2] raghupathi, et al. 2013 and [3] ward, et al. 2014. 5 national association of community health centers a sketch of community health centers [august 2016] 6 study author dr. hartzband has had extensive experience in these industries including as the external architect for the general motors c4 project – an effort to develop a paperless design process for car manufacturing, and as a principal consultant to ernst & young for the goldman sachs integrated trading system effort 7 personal correspondence between dr. hartzband and goldman sachs team. 8 designed in accordance with various reviews of healthcare data quality assessment, especially: [10] kahn, et al. 2012; [11] weiskopf and weng, 2013 and [12] cai and zhu, 2015. 9 uds definitions are used for all terms including: visits, patients & conditions, http://www.bphcdata.net/docs/uds_rep_instr.pdf[removed hyperlink field] 10 data quality and access issues prevented the accurate calculation of comorbidities 11 actual cost (expenditure), not billed cost (revenue) – it is important to note that actual cost was not able to be calculated at any of the health centers and so these queries were not run. http://ojphi.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22733976&dopt=abstract http://dx.doi.org/10.1136/amiajnl-2011-000681 deployment of analytics into the healthcare safety net: lessons learned 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 5(3):e203, 2016 ojphi 12 figures from updated national center for health statistics, cdc fast stats (2013-2014) for adults over 40 years of age. cdc definitions for diagnosis are identical to those used by hrsa for uds except for heart disease, where the uds definitions encompass more codes, & therefore more conditions. 13 hrsa 2015 health center data. table 4. selected patient characteristics. http://bphc.hrsa.gov/uds/datacenter.aspx?q=tall&year=2015&state 14 nachc chartbook 2014. figure 1.9. http://nachc.org/wpcontent/uploads/2015/11/chartbook_december_2014.pdf 15 nis: discharge level data from approximately 8 million hospital stays (2011); brfss: 506,467 adult participants (2011) 16 nis: discharge level data from approximately 8 million hospital stays (2011); brfss: 506,467 adult participants (2011) 17 several chief medical officers suggested that cultural norms for what is considered obesity very greatly among communities, and that providers might be unwilling to make a diagnosis not in line with such norms. http://ojphi.org/ deployment of analytics into the healthcare safety net: lessons learned introduction background and literature methods results conclusions and recommendations acknowledgements conflicts of interest references isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts beyond mortality: violent injury surveillance using nc detect ed visit data katherine j. harmon*1, amy ising1, scott proescholdbell2 and anna waller1 1university of north carolina at chapel hill, chapel hill, nc, usa; 2injury and violence prevention branch, nc division of public health, nc department of health and human services, raleigh, nc, usa objective to describe violent injuries treated in north carolina (nc) emergency departments (eds) and compare to deaths reported by the nc violent death reporting system (nc-vdrs). introduction violence-related injuries are a major source of morbidity and mortality in nc. from 2005-2014, suicide and homicide ranked as nc’s 11th and 16th causes of death, respectively. in 2014, there were 1,932 total violent deaths, of which 1,303 were due to suicide (67%), 536 due to homicide (28%), and 93 due to another mechanism of violent injury (5%). these deaths represent a fraction of the total number of violence-related injuries in nc.1 this study examined ed visit data captured by nc detect to identify and describe violent injuries treated in nc eds and compare/contrast with fatalities reported by nc-vdrs. methods this descriptive epidemiologic study included all nc ed visits made to a 24/7, acute-care, civilian, hospital-affiliated ed from january 1, 2012 – september 30, 2015 reported to nc detect with an injury mechanism code indicating a violent injury due to one of the following injury types: self-harm, assault, legal intervention, or unintentional firearm. in addition, ed visits of an undetermined intent were separately examined. violence-related nc ed visits were classified according to definitions developed by the national center for injury prevention and control for wisqars™.2 descriptive analyses consisted of counts, percentages, and incidence rates. results from january 1, 2012-september 30, 2015, there were 182,385 violence-related nc ed visits captured by nc detect (492.1/100,000 person-years). the most common type of violent injury treated in nc eds was assault, with 132,550 visits (357.6/100,000 person-years), followed by self-inflicted injury (41,455 visits; 111.8/100,000 person-years), unintentional firearmrelated injury (5,940 visits; 15.9/100,000 person-years), and legal intervention (2,440 visits; 6.6/100,000 person-years). twelve percent of all violence-related nc ed visits were made by children 0-17 years of age (21,876 ed visits). there were an additional 20,867 nc ed visits for injuries of an undetermined intent. males visited a nc ed for treatment of violent injuries more often than females (550.3 versus 436.2 visits/100,000 person-years, respectively). young adults 20-24 years of age had the highest rate of violence-related nc ed visits (1,242.9), followed by individuals aged 25-34 (997.4), 15-19 (935.3), 35-44 (635.6) and 45-54 (461.3) (visits/100,000 person-years in parentheses). among violence-related nc ed visits, the most common mechanism of injury was struck by/against an object or person 35.0%), the most common mode of transport to the ed was private transportation (37.7%), the most common discharge disposition was discharged home from the ed (77.7%), the most common expected source of payment was self-pay (37.5%), and the most common time of visit was the evening hours of 6-11 pm (33.6%). violence-related ed visits differed from violence-related deaths reported by the nc-vdrs. on average, there were 25 times more annual violence-related ed visits than deaths. table 1 displays the average annual number of violence-related ed visits and deaths stratified by type of violent injury. the proportion of ed visits due to assault was greater than the proportion of deaths due to homicide, while the proportion of injuries/deaths attributable to self-inflicted injury/ suicide was higher among fatalities. a comparison of the self-inflicted injury and suicide data shows that women have a higher rate of ed visits for self-harm while men have a higher rate of suicide (data not shown). conclusions violence-related injuries are a common source of morbidity and mortality in nc. the annual number of violence-related ed visits exceeds the number of violence-related deaths, 25 to 1. because there are important differences between violent injury leading to ed visits and fatalities, comparing nc detect surveillance with nc-vdrs fatality data expands our understanding of violent injury in nc and better informs prevention efforts. table 1. violence-related north carolina emergency department visits and deaths average annual estimates were calculated from nc detect ed visit data from jan. 1, 2012 sept. 30, 2015. keywords violence; injury; surveillance; morbidity; emergency department acknowledgments nc detect is a statewide public health syndromic surveillance system, funded by the nc division of public health (nc dph) federal public health emergency preparedness grant and managed through collaboration between nc dph and unc-ch department of emergency medicine’s carolina center for health informatics. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. references 1. injury and violence prevention branch (ivpb). north carolina violent death reporting system annual report 2014. raleigh: nc. ivpb, nc dph, nc dhhs, 2017. available at: www.injuryfreenc.ncdhhs.gov/ datasurveillance/vdrs/2014-nc-vdrs-annualreport-final.pdf. 2. national center for injury prevention and control (ncipc). definitions of web-based injury statistics query and reporting system (wisqars™) nonfatal website. ncipc, cdc. www.cdc.gov/ ncipc/wisqars/nonfatal/definitions.htm. last updated march 21, 2007. accessed june 21, 2017. *katherine j. harmon e-mail: kjharmon@email.unc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e99, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epidemiology of suspected pesticide poisoning in livestock judy akkina* and leah estberg united states department of agriculture (usda), animal and plant health inspection service (aphis), veterinary services, fort collins, co, usa objective this study characterizes the epidemiology of suspected pesticide poisoning in livestock in the united states (u.s.) and canada using data from calls to the american society for the prevention of cruelty to animals (aspca) animal poison control center (apcc). introduction pesticides are used in agriculture and in the home to control pests such as insects, weeds, fungi and rodents. pesticide poisoning in animals is usually due to misuse or accidental exposure1. information on poisonings in livestock in north america is largely lacking2. examples of hotlines in the u.s. for animal poisoning consultations include the apcc ($65.00 fee) and the pet poison helpline (pph) ($59.00 fee). the apcc fields calls 24 hours/day, 7 days/week about animal poisonings from the u.s., its territories and canada. using data from almost 4 years of apcc calls we describe the occurrence, category and class of pesticides involved, and outcomes of suspected pesticide exposures in livestock. this information is useful to raise awareness, encourage the proper use of pesticides and identify specific pesticides with negative impact on livestock health. methods aphis contracts with the apcc to receive de-identified data weekly on livestock calls for the purpose of conducting surveillance. this retrospective study used data from all calls concerning bovine, camelid, caprine, equine, ovine, porcine and poultry species from 10/1/2013 to 9/2/2017, where the caller reported suspected pesticide exposure. there were 1,025 calls regarding 3,028 animals meeting this criteria, representing 52% of all livestock calls with any type of toxic exposure. caller type was 80% animal owners, 10% veterinarian or veterinary staff, and 10% other types. most callers (92%) provided their zip code, with 96% of calls from the u.s. and 4% from canada. variables used for descriptive analysis were: species; apcc staff assessment that illness was due to pesticide exposure; severity of illness; clinical signs; first, second and third ingredients of the pesticide, and pesticide ingredient class (e.g. pyrethrin). pesticides were grouped based on the first active ingredient into fungicide, herbicide, insecticide, and rodenticide categories. results the proportion of calls by species was equine (33%), poultry (26%), bovine (25%), caprine (8%), porcine (6%), ovine (2%), and camelid (0.5%). some animals were exposed to >1 pesticide product and some pesticide products had >1 ingredient class. the pesticide category with the highest number of exposed animals was insecticides (2,151), followed by herbicides (839), rodenticides (765) and fungicides (286). the treemap below illustrates the number and proportions of animals exposed to the 4 pesticide categories and the top 3 pesticide classes within each category based on the first active ingredient. for all pesticide exposures in all species, no illness was reported in 68% of animals. according to assessment by apcc staff, only 35% (333) of animals showing clinical signs were considered with confidence (medium or high likelihood) to be due to pesticide exposure. for these 333 animals, severity of illness was mild for 80% (266 animals), moderate for 18% (61 animals), major for 1% (3 animals) and caused death in 1% (3 animals). among animals with confidence that clinical signs were due to pesticide exposure the most frequent syndrome was dermatologic. conclusions suspected pesticide exposure was the most frequent reason a call concerning livestock was made to the apcc. callers reported that most animals showed no illness, and major illness or death was rare. livestock were most frequently exposed to the insecticide category, and 46% of the animals with exposure to insecticides were exposed to the pyrethrin class. this is consistent with the phasing out of organophosphate insecticides for residential use since 2000 and the increasing use of pyrethrin insecticides3, which are considered less toxic. limitations of this study include: 1) data from only one major animal poison control hotline was available for analysis and people may call their veterinarian directly or use the internet 2) calls regarding specific ingredients may be over represented due to corporate client relationships with the apcc 3) illness may have occurred after the call was made, therefore the proportion of animals with illness following suspected exposure may be an underestimate. keywords pesticide poisoning; livestock; poison control center; animal health acknowledgments copyright © 2017 the american society for the prevention of cruelty to animals (aspca). all rights reserved. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. wang y, kruzik p, helsber a, helsberg i, rausch w. (2006) pesticide poisoning in domestic animals and livestock in austria: a 6 year retrospective study. forensic science international 169:157-160. 2. gwaltney-brant sm. (2012). epidemiology of animal poisonings in the united states. in: gupta rc (ed.), veterinary toxicology: basic and clinical principles. elsevier, second ed: 80-87. 3. power le, sudakin dl. pyrethrin and pyrethroid exposures in the united states: a longitudinal analysis of incidents reported to poison centers. (2007) j of medical toxicology. 3(3):94-99. *judy akkina e-mail: judy.e.akkina@aphis.usda.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e115, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts genotyping of pathogenic leptospira by multiple locus variable-number tandem repeat analysis (mlva) vitalii ukhovskyi*1, taras tsarenko3, nataliia vydayko2, leonid korniienko3 and igor nebogatkin2 1leptospirosis laboratory with museum of microorganisms, institute of veterinary medicine of the national academy of agrarian sciences of ukraine, kiev, ukraine; 2state institution ukrainian center for diseases control and monitoring of the ministry of health of ukraine, kiev, ukraine; 3bila tserkva national agrarian university, bila tserkva, ukraine objective to introduce the method of molecular genotyping (mlva) to determine the genotype of field isolates of leptospira. introduction leptospirosis (ictherohemoglobinuria, leptospirosis biliousness) is a natural focal and zoonotic infectious disease dangerous for humans and farm animals. it is important to identify specific leptospira strains isolated from rodents or sick and suspicious animals by the serotype or genotype. in comparison with serotyping using micro agglutination test (mat), molecular genotyping makes it possible to accurately identify a specific pathogen strain. the genetic classification now becomes more significant than the phenotypic classification. methods specific oligonucleotide primers, which flank fragments of the genome locus of pathogenic leptospira varies in terms of the number of tandem repeats vntr-4, -7, -10 specific for l.interrogans, l.kirschneri, and l.borgpetersenii were used. the amplification products were detected using agar gel electrophoresis with the following identification of the fragment length with a molecular weight marker and comparison with the collection of vntr profiles of the strains described in the literature. results it was established that the method of leptospira molecular genotyping by determining the number of variable tandem repeats of a locus (vntr-variable number tandem repeats analysis) is suitable for molecular epizootology studies in ukraine. the advantages of the method are the simplicity of performance and availability for diagnostic and research laboratories in ukraine compared to other pathogen genome sequencing based genotyping methods, in particular multilocus sequence typing (mlst) or multispacer sequence typing (mst), which require complex equipment and operating conditions. the reference strain of leptospira m20 serotype copengageni serogroup icterohaemorrhagiae from the naas ivm collection of was studied and its vntr profile was identified with the genotype of the strain fiocruz l1-130 that is described in the literature as a serotype of copengageni serogroup icterohaemorrhagiae. the genotype of the leptospira field isolate obtained from a rat in lviv oblast of ukraine was specified and its identity was established in the aforementioned genotype. the obtained data support the prospects of using mlva genotyping method to study the distribution of different genotypes of leptospira. the research will continue to study the specificities of molecular epizootology of leptospirosis in ukraine. conclusions the method of leptospira molecular genotyping by multilocus analysis of the number of variable tandem repeats has been tested in the leptospirosis research laboratory in collaboration with the museum of microorganisms at the national academy of sciences, the ukraine institute of veterinary medicine, the elisa and pcr research laboratory, and the bila tserkva national agrarian university. the genotype of the reference strain has been correlated with its serological profile; identification of the genotype of the field isolate pathogenic leptospira has been completed. the tested method is planned to be implemented in surveillance and control over leptospirosis spreading in ukraine, and aimed to help in development and improvement of leptospirosis vaccine formulations. additionally, method of multiple-locus variable number tandem repeat analysis will be used for molecular epidemiology research in ukraine. keywords leptospira; genotyping; mlva; vntr acknowledgments the authors would like to acknowledge the united states department of defense, defense threat reduction agency (dtra), and cooperative biological engagement program (cbep) for their support in the development and presentation of this abstract. references salaün l, mérien f, gurianova s, baranton g, picardeau m. application of multilocus variable-number tandem-repeat analysis for molecular typing of the agent of leptospirosis. j clin microbiol. 2006;44(11):3954-3962. doi:10.1128/jcm.00336-06. caimi k, repetto sa, varni v, ruybal p. infection, genetics and evolution leptospira species molecular epidemiology in the genomic era. infect genet evol. 2017;54(july):478-485. doi:10.1016/j. meegid.2017.08.013. ayral f, zilber al, bicout dj, kodjo a, artois m, djelouadji z. distribution of leptospira interrogans by multispacer sequence typing in urban norway rats (rattus norvegicus): a survey in france in 2011-2013. plos one. 2015;10(10):1-14. doi:10.1371/journal. pone.0139604. *vitalii ukhovskyi e-mail: uhovskiy@ukr.net online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e131, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e420, 2019 isds 2019 conference abstracts wastewater as an indicator of virus circulation among population of dnipropetrovsk oblast, ukraine maryna bredykhina, oleksandr shtepa, valentyna rezvykh, olena paliychuk, oleksandr yurchenko, svetlana kovalenko, inga hernets state institution dnipropetrovsk oblast laboratory center of the ministry of health of ukraine, dnipro, ukraine objective the purpose of the study was to confirm the hypothesis of possible intestinal viruses circulation in wastewater in dnipropetrovsk oblast, ukraine. introduction the main reservoir of intestinal viruses in the environment is human feces and contaminated wastewater. sewage contamination preconditions further contamination of surface water serving as a source of water supply [1-3]. high resistance to physical and biological exposures ensures long-term survival of the viruses in water with various type and level of contaminants, especially in sewage. detection of enteroviruses of a specific serotype in sewage indicates a significant number of people releasing the virus with feces [1,4]. there are two peaks of enteroviruses concentration in sewage: in january-april, and in june-september [3]. sewage testing for enteroviruses is one of effective methods for their detection and risk assessment [5]. european region, including ukraine, is recognized as free from of wild polioviruses, and a systematic study of sewage samples is important for identifying the possibilities of their "silent" circulation [6]. methods wastewater samples from large sewerage collectors, sewage wells of infectious departments, city hospitals and district sewerage networks of dnipropetrovsk oblast were tested in 2007-2017 (39-64 samples monthly in the points are determined by the national regulations [2]). gauze tampons (moore's method) were used to collect wastewater [3]. in addition, samples were collected from wastewater flow into 1-liter sterile bottle with a sampler. concentration was carried out using enterosgel (hydroxyl methyl silicic acid) with high adsorption capacity [2]. the supernatant after all the concentration steps was used for culture on cell cultures rd, hep-2, l20b [2,3,7]. in the presence of cytopathic action in rd cells, culture liquid was inoculated into l20b cells to detect clear cytopathic activity. culture liquids were investigated to identify enteroviruses in neutralization reaction. in hep-2, cytopathic effects were observed in the form of clusters of different sizes cells, "grape clusters", which indicated the presence of adenoviruses. adenoviruses were confirmed by immunochromatographic tests for adenovirus antigens "cito test adeno" pharmasko, ukraine). results during 10 years, 150 viruses were isolated, 2 of them were a mixture of polioviruses. the frequency of detection of enteroviruses (including polioviruses) and adenoviruses was 2.5% (tab 1). the isolated strains of enteroviruses, including polioviruses, were sent for confirmation the public health center of the ministry of health of ukraine and regional who polio reference laboratories (moscow and helsinki). all polio strains were attributed to the vaccine strain sabin. also, the result coxsackie viruses b typing was confirmed. conclusions the data testify to presence of picornaviridae (polioviruses, coxsackie b, non-polio enteroviruses (npevs), and adenoviridae in the wastewater in dnipropetrovsk oblast, ukraine. the typical composition of viruses was not constant. types 1, 2 polioviruses (sabin) were occasionally isolated from wastewater. type 2 polioviruses (sabin) were isolated only in 2015. in 2009, 2012-2014, 2017, polioviruses did not stand out. polioviruses isolation is associated with mass immunization of children against polioviruses carried out to maintain polio-free status of the country. in average, 150,000 children are vaccinated annually. oral poliomyelitis vaccine (opv) produced in russia, france, belgium was used in 2007-2017 (attenuated sabin strains, 1,2,3 types). from april 2016, ukraine refused to use trivalent opv and switched to bivalent vaccine (sabin strains, types 1 and 2). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e420, 2019 isds 2019 conference abstracts sewage testing for polioviruses and their differentiation at who national and regional centers for polio diagnosis ensures a system for monitoring of possible "silent" circulation [6]. sewage testing using cell cultures is one of the most affordable, effective and reliable methods for controlling the presence of viruses in the environment [2,3,7]. rd and l20b cell lines are useful for poliovirus isolation from sewage [7]. in addition to polioviruses, 1,2,3 types coxsackie viruses b were isolated from wastewater samples. however, starting from 2013, coxsackie viruses were isolated only in sporadic cases (cox.vir.b5). in 2007-2011, npevs were isolated in some cases. because polio is on the verge of eradication, more attention should be paid to study of npevs [8]. for 10 years, adenoviruses were isolated, which are well preserved in wastewater [4]. the maximum number of adenoviruses was isolated in 2014. acknowledgement authors are thankful to the state institution public health center of the ministry of health of ukraine for confirmatory studies and dispatch of viral isolates to regional who polio reference laboratory. authors also express their gratitude to specialists of regional reference laboratories in moscow and helsinki for studying viral isolates. references 1. mas lago p, gary he, jr, perez ls, caceres v, olivera jb, et al. 2003. poliovirus detection in wastewater and stools following an immunization campaign in havana.vinternational. int j epidemiol. 32(5), 772-77. pubmed https://doi.org/10.1093/ije/dyg185 2. sanitary-virological control of water bodies / methodological recommendations approved by the order of the ministry of health of ukraine, 30.05.2007, 284: 8. 3. doan s., zadorozhna v., bondarenko v., zubkova n., burat t. comparative characteristics of the isolation of enterovirus from water of various types in ukraine. 2002,38-41. 4.bofill-mas s., lbinana-gimenez n., clemente-casares p., hundesa a., rodriguez-manzano j., allard a., calvo m., and girones r. quantification and stability of human adenoviruses and polyomavirus jcpyv in wastewater matrices. 2006. appl. environ. microbiol. 72:7894-7896. pubmed https://doi.org/10.1128/aem.00965-06 5. hovi t, shulman lm, van der avoort h, deshpande j, roivainen m, et al. 2012. role of environmental poliovirus surveillance in global polio eradication and beyond. epidemiol infect. 140, 1-13. pubmed https://doi.org/10.1017/s095026881000316x 6. 2007.world health organization regional office for europe. wild poliovirus isolated in switzerland’s sewer system, insignificant risk of outbreak. monthly afp surveillance bulletin. 7. figas a, wieczorek m, litwinska b. 2017. w.gu. detection of polioviruses in sewage using cell culture and molecular methods. pol j microbiol. 65(4), 479-83. pubmed https://doi.org/10.5604/17331331.1227676 8. fernandez-garcia md, kebe o, fall ad, ndiaye k. 2017. identification and molecular characterization of non-polio enteroviruses from children with acute flaccid paralysis in west africa, 2013–2014. sci rep. 7, 3808world health organization (who). http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14559748&dopt=abstract https://doi.org/10.1093/ije/dyg185 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17028225&dopt=abstract https://doi.org/10.1128/aem.00965-06 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21849095&dopt=abstract https://doi.org/10.1017/s095026881000316x https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28735334&dopt=abstract https://doi.org/10.5604/17331331.1227676 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e420, 2019 isds 2019 conference abstracts table 1. sewage testing for enteroviruses (including polioviruses) and adenoviruses, 2007-2017, dnipropetrovsk oblast, ukraine years 200 7 200 8 200 9 201 0 201 1 201 2 201 3 201 4 201 5 201 6 201 7 total number of samples 466 382 381 630 768 584 287 491 597 72 8 727 6041 % of positive samples 3.0 1.8 1.8 3.0 3.4 3.4 2.7 3.4 2.4 0.9 1.4 2.5 number of virus isolates, including: 14 7 7 19 26 20 8 17 14 7 11 150 poliovirus, type 1 1 2 3/2.0% poliovirus, type 2 poliovirus, type 3 1 1 2 1 2 7/4.7% mixture of polioviruses 1+3 1 1/0.7% mixture of polioviruses 2+3 1 1/0.7% coxsacki virus b1 3 1 1 4 8/5.3% coxsacki virus b3 1 1 7 2 13/8.7% coxsacki virus b5 2 1 1 2 1 1 3 10/6.6% non-polio enterovirus 2 1 1 1 5/3.3% echo virus type 1-6 1 1/0.7% adenovirus 5 3 4 14 14 13 8 16 11 5 8 101/67.3% http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e406, 2019 isds 2019 conference abstracts retrospective surveillance of perinatal hepatitis c virus exposure – tennessee, 2013-2017 heather e. wingate, lindsey sizemore, jennifer black, zachary heth, carolyn wester tennessee department of health, nashville, tennessee, united states objective 1. to quantify the burden of perinatal hepatitis c (hcv) exposure and examine the geographic variation in tennessee (tn). 2. develop new surveillance strategies for retrospective tracking of perinatal hcv exposures. introduction hepatitis c virus (hcv) infections are increasing nationwide and are of particular concern in tennessee, especially among individuals of reproductive age [1,2]. maternal hcv status reported on the birth certificate reveals that the rate of hcv among women giving birth in tn increased 163% from 2009-2014 [3]. further, a 2017 tn department of health (tdh) study found that 30% of reproductive aged women with newly reported chronic hcv in tn were determined to be pregnant. while current treatment options are not recommended for children under 12, it is critical to identify an infant’s hcv status in order for him/her to receive proper care. given the high rates of pregnancy reported among women with newly diagnosed hcv, we sought to expand viral hepatitis surveillance efforts to quantify the extent of the burden of hcv among women giving birth in tn, utilizing surveillance data in lieu of standalone birth certificate data. methods birth certificate data, denoting all live births in tn from 2013 to 2017, were obtained from the tdh birth statistical file (n=404,694). maternal hcv infection laboratory data were obtained from the tdh national electronic surveillance system (nedss) based system (nbs). maternal birth certificate and maternal hcv data were matched using a step-wise matching algorithm; records were required to match on one of the following criteria: (1) first name, last name, and date of birth (dob); (2) first name, maiden name, and dob; (3) phonetic first name, phonetic last name, dob; (4) phonetic first name, phonetic maiden name, and dob; or (5) social security number. for geographical variations, maternal county of residence was extracted from birth certificate data. as there is currently no case definition pertaining to hcvpositive pregnant women, laboratory data was used to determine perinatal exposure case status for each live birth as follows: (1) confirmed exposure, if a mother had at least 1 hcv rna-positive lab during pregnancy, or in the absence of a pregnancy lab, at least one hcv rna was conducted prior to pregnancy and the last hcv rna prior to pregnancy was positive; (2) probable exposure, if a mother did not have an hcv rna test, but had an hcv ab-positive lab preceding or during pregnancy; or (3) no exposure, if a mother had a history of hcv, but only hcv rna-negative labs during pregnancy, or in the absence of a pregnancy lab, at least one hcv rna was conducted prior to pregnancy and the last rna prior to pregnancy was negative. hcv infant exposure rates were calculated using the number of probable or confirmed hcv perinatal exposures divided by the total number of live births*1,000. results from 2013 to 2017, there were 4,909 perinatal hcv exposures, with an average exposure rate of 12.1 per 1,000 live births. the exposure rate increased by 93.7%, from 7.9 in 2013 to 15.3 in 2017 (table 1). using an estimated 5.8% transmission rate, 285 infants acquired hcv infection perinatally over the past 5 years in tn [4]. figure 1 depicts the rates of perinatal exposure per 1,000 live births in 2017, by county, and illustrates the large geographical variability of the perinatal hcv exposure rates. while the statewide average was 1.5%, this varied from 0% to 14.1% across tn. eastern tn counties had higher rates; some signifying 5% to 14.1% of all infants born were vertically exposed to hcv. limitations of our study included incomplete chronic hcv surveillance data, reporting bias, and external validity. chronic hcv surveillance in tn was not routine until july 2015, and chronic hcv was not reportable until january 1, 2017. with respect to data included in our study prior to july 2015, only electronic laboratory reports were used, which could have resulted in under-reporting. additionally, as pregnancy is not currently reportable in the context of hcv, we relied solely on birth certificate and nbs record http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e406, 2019 isds 2019 conference abstracts matching to identify exposure. lastly, our findings may not be generalizable to the rest of the us, as we only studied women of reproductive age in tn. strengths to our study included the utilization of two reliable data sources, nbs and birth certificate data to determine perinatal hcv exposure. analyzing data over a 5-year period allowed for a large sample size. additionally, unlike previous studies, we analyzed laboratory data versus birth certificate data which is physician-reported and has been shown to underestimate the prevalence of maternal hcv infection [5]. conclusions high numbers of reported hcv cases among reproductive aged women translates into high rates of perinatal exposure to hcv among live born infants. as compared to maternal hcv status reported on birth certificates, matching birth records with hcv surveillance databases provides advantages to perinatal surveillance by: 1) detecting more cases, and 2) providing the ability to tease out current versus prior infection in mother and, therefore, actual exposure. this type of maternal surveillance provides unique opportunities to reach out and ensure that hcv infected mothers receive important information regarding appropriate infant testing, as indicated by the 2018 case definition, as well as disease prevention [6]. beginning in 2018, tdh has started to conduct surveillance on hcv exposed infants using these methods to track potential transmission in real-time, allowing us to evaluate testing outcomes among these exposed infants and determine if the infants are in appropriate care. acknowledgement tennesse department of health, bureau of policy, planning and assessment, division of health statistics; stephen patrick, md, mph, ms, vanderbilt university; susan lopata, md, vanderbilt university. references 1. zibbell je, asher ak, patel rc, kupronis b, iqbal k, et al. 2018. increases in acute hepatitis c virus infection related to a growing opioid epidemic and associated injection drug use, united states, 2004 to 2014. am j public health. 108(2), 175-81. pubmed https://doi.org/10.2105/ajph.2017.304132 2. surveillance for viral hepatitis – united states. 2015. cdc. 3. patrick sw, et al. 2017. hepatitis c virus infection among women giving birth — tennessee and united states, 2009– 2014 [pmc.]. mmwr morb mortal wkly rep. 66(18), 470-73. pubmed https://doi.org/10.15585/mmwr.mm6618a3 4. benova l, mohamoud ya, calvert c, abu-raddad lj. 2014. vertical transmission of hepatitis c virus: systematic review and meta-analysis. clin infect dis. 59(6), 765-73. pubmed https://doi.org/10.1093/cid/ciu447 5. snodgrass sd, poissant tm, thomas ar. 2018. notes from the field: underreporting of maternal hepatitis c virus infection status and the need for infant testing — oregon, 2015 [pmc.]. morbidity and mortality weekly report. 67(6), 201-02. pubmed https://doi.org/10.15585/mmwr.mm6706a6 6. hepatitis c. perinatal infection 2018 case definition. cdc figure 1. rates of perinatal hcv exposure per 1,000 live births in tn, 2017 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29267061&dopt=abstract https://doi.org/10.2105/ajph.2017.304132 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28493860&dopt=abstract https://doi.org/10.15585/mmwr.mm6618a3 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24928290&dopt=abstract https://doi.org/10.1093/cid/ciu447 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29447143&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29447143&dopt=abstract https://doi.org/10.15585/mmwr.mm6706a6 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e406, 2019 isds 2019 conference abstracts table 1. hcv perinatal exposure per live birth in tn: 2013 to 2017 year probable exposure confirmed exposure no exposure total exposed* total live births hcv exposed per 1,000 live births 2013 304 327 38 631 79,954 7.9 2014 311 501 38 812 81,609 9.9 2015 350 631 87 981 81,374 12.1 2016 477 770 162 1,247 80,755 15.4 2017 430 808 191 1,238 81,002 15.3 total 1,872 3,037 516 4,909 404,694 12.1 *total exposed was calculated using the sum of probable and confirmed exposures. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e293, 2019 isds 2019 conference abstracts the power of consumer activism and the value of immunization registries in a pandemic michael popovich, todd watkins, ousswa kudia scientific technologies corportation, phoenix, arizona, united states objective if public health agencies used direct communication channels to individuals by building on existing immunization networks, the public would receive correct information quicker during a pandemic. furthermore, there is value that can be leveraged from social networks to advance public health efforts to manage disease events and encourage consumers being more proactive in managing their own health care. introduction epidemiologists and public health professionals work to ensure the risk and impact of existing and emerging diseases are minimized and do not turn into a pandemic. timely and accurate information has become imperative as the world has become more interconnected through travel and technology. recently, new information systems have played a key role in improving public health’s early warning and case management for disease outbreaks. improved analytics to predict risk in populations have helped researchers open new doors to disease cures and medicines. the role of technology and public health to support these efforts has become more valuable. health information systems are traditionally used for electronic medical records or payer billing systems and are not paired with technology advancements. efforts today to link information and technology to engage consumers are cha mpioned by health plans and healthcare providers. empowering individuals to be proactive when presented with their medical records is not a simple problem to solve. information must be actionable but it still may not achieve the desired success. what if the health community engaged consumers with a social mission to help them fight disease outbreaks by becoming frontline activists to report occurrences and outcomes, and become “intelligent connections” to extend the right information to their s ocial networks? this would encourage consumer technology to be better integrated with health information technology with continued investment in sustaining critical public health ecosystems. a large part of health information systems are immunization information systems (iis) where administered vaccines are documented in a confidential computer based system in a specific geographic area [1]. the iis can be used for disease surveillance purposes and provide valuable information to public health authorities [2]. recently, myir was created where any iis, pharmacy or provider can provide patients direct access to family immunization records. providers can communicate to patients using myir to increase engagement and send vaccine reminders. methods a public-health engagement approach to empower consumers begins by offering individuals a mission they care about that will contribute to the social good and make them more attentive to their own healthcare. our approach was to place a mission for e very cell phone owner by using a commonly understood health event. the most significant public health event in the 20th century was the power of vaccines and the most significant action an individual can take to reduce their risk of a vaccine-preventable disease is to stay up-to-date on their immunizations [3]. however, there is a gap between believing in the value of immunizations and ensuring one’s immunizations are current. the challenge is to engage individuals, empower them to be advocates of their own health and in an outbreak, become sources of trusted public health messages as they communicate in their social network. a few experiments were conducted using myir. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e293, 2019 isds 2019 conference abstracts increase sustainment users who had not used myir in over 30 days were contacted. the baseline looked at users that were logging into myir more than once a month. the target was non-engaged users which were about 8,000 accounts. customer engagement i in november 2017, an email was sent to 7,772 users that asked them, "did you get your flu shot?" if they clicked yes, they received positive affirmation. if they clicked no, the message was an encouragement to get their flu shot before thanksgiving 2017. customer engagement ii in january 2017, a healthy lifestyle page was created within myir. it featured a food blogger who offers readers nutritious easy meal ideas. outreach efforts myir louisiana users were targeted who had failed to complete the two step enrollment process to access their immunization histories. efficacy of flu in april 2018, 212 myir users in washington and louisiana were asked: did you get a flu shot this year? do you feel like you got the flu this year? results increase sustainment 27% of people opened the email and 3.6% of these individuals used myir within 30 days to access their records. customer engagement i 9% answered the question with 80% saying “yes” they got their flu shot. as a result, 25 new immunizations were administered t o these individuals within 60 days. customer engagement ii a 7.1% increase in returning users were tracked and a 9.1% increase in engaged users. there was a 6.7% increase in average session duration. outreach effort 556 emails were sent which contained instructions to finalize enrollment for myir. there was a 30% open rate and 50 individuals completed the process. efficacy of flu 78% responded they did receive the flu shot this year. of these, 61.5% felt they got the flu this year which equates to a 38. 5% efficacy rate. in february, cdc had determined the interim estimates for the effectiveness of the influenza were 36% [4]. conclusions our aim was to show examples where public health agencies using direct communication channels to individuals could increase the efficacy of reaching the public with correct information. it was not designed to prove to be statistically effective but to show the potential of engaging individuals that have access to their immunization records. these early experiments and the growing data http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e293, 2019 isds 2019 conference abstracts assets in iis's help create a framework and technical platform to accelerate the potential value of en gaging individuals in response plans for pandemic preparedness. immunization information systems and technology have reached a point where information is available across wide networks of stakeholders. while health plans, providers and pharmacists struggle to engage their networks, by encouraging patients to be proactive in their healthcare, public health immunization assets may be the tipping point to accelerate this movement. continued investment of immunization programs, private sector innovation, and consumer empowerment are essential to evolve and sustain data assets. as these assets create added value to each stakeholder, the investment will create a positive return . the value of this virtual ecosystem is untapped and opportunities to use it to drive down healthcare costs and improve patient outcomes are unlimited. acknowledgement we would like to thank theresa munanga for her editorial assistance and all stc employees, especially those who assisted with the myir studies. references 1. centers for disease control prevention (cdc). about immunization information systems. atlanta: cdc; 2012 may 15 [cited 06 jun 2018]. available from: http://www.cdc.gov/vaccines/programs/iis/about.html 2. derrough t, olsson k, gianfredi v, et al. 2017. immunisation information systems – useful tools for monitoring vaccination programmes in eu/eea countries, 2016. euro surveill. 22(17), 30519. pubmed https://doi.org/10.2807/1560-7917.es.2017.22.17.30519 3. centers for disease control and prevention (cdc). 1999. ten great public health achievements--united states, 1900-1999. mmwr morb mortal wkly rep. 48(12), 241-43. https://www.cdc.gov/mmwr/preview/mmwrhtml/00056796.htm. pubmed 4. flannery b, chung jr, belongia ea, et al. 2018. interim estimates of 2017-18 seasonal influenza vaccine effectiveness – united states. february. mmwr morb mortal wkly rep. 67(6), 180-85. https://www.cdc.gov/mmwr/volumes/67/wr/mm6706a2.htm. pubmed https://doi.org/10.15585/mmwr.mm6706a2 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28488999&dopt=abstract https://doi.org/10.2807/1560-7917.es.2017.22.17.30519 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10220250&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29447141&dopt=abstract https://doi.org/10.15585/mmwr.mm6706a2 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e434, 2019 isds 2019 conference abstracts evaluation of pedestrian/bicycle crash injury case definitions for use with nc detect katherine j. harmon1, 2, 3, amy ising3, laura sandt1, 2, anna e. waller3 1 highway safety research center, chapel hill, north carolina, united states, 2 collaborative sciences center for road safety, chapel hill, north carolina, united states, 3 carolina center for health informatics, unc school of medicine, chapel hill, north carolina, united states objective to evaluate four icd-10-cm based case definitions designed to capture pedestrian and bicycle crash-related emergency department (ed) visits in north carolina’s statewide syndromic surveillance system, nc detect. introduction over the last few decades, the united states has made considerable progress in decreasing the incidence of motor vehicle occupants injured and killed in traffic collisions [1]. however, there is still a need for continued motor vehicle crash (mvc) injury surveillance, particularly for vulnerable road users, such as pedestrians and bicyclists. in nc, the average annual number of pedestrian-motor vehicle crashes increased by 13.5 percent during the period 2011-2015, as compared to 2006-2010 [2]. therefore, the carolina center for health informatics (cchi), as part of a nc governor’s highway safety program-funded project to improve statewide mvc injury surveillance, developed and evaluated four icd-10-cm based case definitions for use with nc detect, nc’s statewide syndromic surveillance system. methods we created four pedestrian/bicycle crash injury case definitions based on icd-10-cm transportation codes (“v-codes”): trafficrelated pedestrian crashes, traffic/non-traffic-related pedestrian crashes, traffic-related bicycle crashes, and traffic/nontraffic-related bicycle crashes. these definitions were based on the centers for disease control and prevention (cdc) “icd10-cm external cause of injury codes”. [3] we then applied these pedestrian/bicycle crash case definitions to 2016-2017 nc detect ed visit data and data obtained from a single nc level i trauma center. next, we linked the two data sources using the variables date of visit, time of visit, and medical record number. since trauma center data are collected and verified by a designated trauma registrar, we considered the data obtained from the level i trauma center to be the “gold standard”. results the linkage between the two data sources was successful, with 99.5% of all level i trauma center records linking to ed visits in nc detect. however, we found discrepancies in the assignment of codes between the ed visit and trauma center data. for example, 47.5% of nc detect ed visits that linked to a pedestrian/bicycle crash record in the trauma center data, were missing an icd-10-cm injury mechanism code of any category. historically, the proportion of injury-related ed visits that were missing corresponding injury mechanism codes was low (<15%). however, the transition from icd-9-cm to icd-10-cm increased the proportion of injury-related visits missing injury mechanism codes [4]. among the 92 nc detect ed visits missing injury mechanism codes, 35.9% contained a pedestrian/bicycle crash-related keyword in the chief complaint or triage note. among the 100 linked records with valid icd-10-cm injury mechanism codes, the percent agreement between the two data sources on whether the ed visit was a “pedestrian” or “bicycle” crash was 54.4% and 71.9%, respectively. percent agreement decreased for “traffic” and “non-traffic” designations, however. the most common v-code assigned to misclassified pedestrian/bicycle crashes in the nc detect ed visit data was “v87.7xxa-person injured in a collision between other specified motor vehicles (traffic)”. although the linkage study used data obtained from only a single level i trauma center and primarily a single facility in nc detect, we felt that the results of this limited linkage study were generalizable to statewide nc detect ed visit data. for example, many facilities in nc detect underreport injury mechanism codes. therefore, we added pedestrian/bicycle crash injury-related keywords to the traffic/non-traffic pedestrian/bicycle crash injury case definitions (table 1). after inclusion of these keywords, the number of identified pedestrian and bicycle crash injury-related ed visits identified in nc detect increased by 16.9% and 57.9% from january-june 2018, respectively (figure 1). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e434, 2019 isds 2019 conference abstracts conclusions pedestrian and bicycle crashes represent a major cause of mvc injury morbidity and mortality. therefore, the development and evaluation of case definitions is key for the successful surveillance of these types of injuries. the inclusion of keywords can help account for some of the injury mechanism data missingness common to ed surveillance systems. acknowledgement this study was supported by project number m3da-18-14-03 from the nc ghsp. data attribution & disclaimer: nc detect is a statewide public health syndromic surveillance system, funded by the nc dph federal public health emergency preparedness grant and managed through collaboration between nc dph and unc-ch department of emergency medicine’s cchi. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. references 1. nhtsa. traffic safety facts 2015. dot hs 812 384. washington, dc: us department of transportation; 2017. https://crashstats.nhtsa.dot.gov/api/public/viewpublication/812384. accessed sept 12, 2018. 2. thomas l, vann, m, levitt d. north carolina pedestrian crash trends and facts 2011-2015. rp 2017-42. chapel hill, nc: university of north carolina highway safety research center; 2018. http://www.pedbikeinfo.org/pbcat_nc/pdf/summary_ped_facts11-15.pdf. accessed sept 12, 2018. 3. ncipc. help and tools for injury data; atlanta, ga: cdc 2018. https://www.cdc.gov/injury/wisqars/dataandstats.html. accessed sept 12, 2018. 4. harmon k, barnett c, marshall s, waller a. implementing the external cause matrix for injury morbidity – north carolina emergency department data – january 2015 – may 2015. chapel hill, nc: carolina center for health informatics and the injury prevention research center; 2016. https://ncdetect.org/files/2017/03/icd10ccmexternalcausematriximplementation_ncsqi_201607.pdf. accessed sept 12, 2018. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e434, 2019 isds 2019 conference abstracts figure 1. comparison of pedestrian/bicycle crash injury-related ed visits with and without keywords: nc detect, january 1 – june 30, 2018 table 1. list of pedestrian and bicycle crash injury-related keywords used in nc detect case definitions crash type pedestrian bicycle inclusions 'pedestrian', 'ped struck', 'peds struck', 'ped vs mvc', ' peds vs mvc', 'ped vs car', or 'peds vs car' 'bicycle', 'bike', ' pedal', or 'bicyclist' exclusions 'moped', 'scooter’, ’pedal', 'bicycle', or 'bike' motorcyclist', 'motor cyclist', 'scooter', 'motorcycle','pedal pulse','pedal edema', 'pedal pulses', 'moped','dirt bike', 'dirtbike','motor bike', 'motorbike', 'car or bike', or 'pedestrian' http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e280, 2019 isds 2019 conference abstracts epi archive: automated synthesis of global notifiable disease data hari s. khalsa, sergio r. cordova, nicholas generous, prabhu s. khalsa a-1, los alamos national laboratory, los alamos, new mexico, united states objective automatically collect and synthesize global notifiable disease data and make it available to humans and computers. provide th e data on the web and within the biosurveillance ecosystem (bsve) as a novel data stream. these data have many applications including improving the prediction and early warning of disease events. introduction government reporting of notifiable disease data is common and widespread, though most countries do not report in a machine readable format. this is despite the who international health regulations stating that “[e]ach state party shall notify who, by the most efficient means of communication available.” [1] data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. this presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. while most nations likely store incident data in a machinereadable format, governments can be hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational [2]. a survey conducted by lanl of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. the majority (>70%) produce reports as pdf files on a regular basis. the bulk of the pdf reports present data in a structured tabular format, while some report in natural language or graphical charts. the structure and format of pdf reports change often; this adds to the complexity of identifying and parsing the desired data. not all websites publish in english, and it is common to find typos and clerical errors. lanl has developed a tool, epi archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible. methods a survey of the national notifiable disease reporting systems is periodically conducted notating how the data are reported and in what formats. we determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found. the development of software to regularly ingest notifiable disease data and make it available involves three to four main steps: scraping, detecting, parsing and persisting. scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies acros s the reporting mechanisms. we then design and write code to automate the downloading of data for each country. we store all artifacts presented as files (pdf, xlsx, etc.) in their original form, along with appropriate metadata for parsing and data provenance. detecting: this step is required when parsing structured non-machine-readable data, such as tabular data in pdf files. we combine the nurminen methodology of pdf table detection with in-house heuristics to find the desired data within pdf reports [3]. parsing: we determine what to extract from each dataset and parse these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e280, 2019 isds 2019 conference abstracts persisting: we store the data in the epi archive database and make it available on the internet and through the bsve. the data is persisted into a structured and normalized sql database. results epi archive currently contains national and/or subnational notifiable disease data from thirty-nine nations. when a user accesses the epi archive site, they are able to peruse, chart and download data by country, subregion, disease and time interval. acce ss to a cached version of the original artifacts (e.g. pdf files), a link to the source and additional metadata is also available through the user interface. finally, to ensure machine-readability, the data from epi archive can be reached through a rest api. http://epiarchive.bsvgateway.org/ conclusions lanl, as part of a currently funded dtra effort, is automatically and continually collecting global notifiable disease data. while thirty-nine nations are in production, more are being brought online in the near future. these data are already being utilized and have many applications, including improving the prediction and early warning of disease events. acknowledgement this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra). references 1. who international health regulations, edition 3. http://apps.who.int/iris/bitstream/10665/246107/1/9789241580496-eng.pdf 2. van panhuis wg, paul p, emerson c, et al. 2014. a systematic review of barriers to data sharing in public health. bmc public health. 14, 1144. doi:https://doi.org/10.1186/1471-2458-14-1144. pubmed 3. nurminen a. 2013. algorithmic extraction of data in tables in pdf documents (master’s thesis). retrieved from https://dspace.cc.tut.fi/dpub/bitstream/handle/123456789/21520/nurminen.pdf. http://ojphi.org/ https://doi.org/10.1186/1471-2458-14-1144 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25377061&dopt=abstract https://dspace.cc.tut.fi/dpub/bitstream/handle/123456789/21520/nurminen.pdf isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e448, 2019 isds 2019 conference abstracts using syndromic surveillance to quantify ed visits for coagulopathy cases kelly walblay, megan patel, stacey hoferka communicable diseases, illinois department of public health, chicago, illinois, united states objective to determine whether emergency department (ed) visits were captured in syndromic surveillance for coagulopathy cases associated with an outbreak linked to synthetic cannabinoid (sc) use and to quantify the number of ed visits and reasons for repeat visits. introduction in march 2018, the illinois department of public health (idph) was informed of a cluster of coagulopathy cases linked to sc use. by june 30, 2018, 172 cases were reported, including five deaths, where 74% were male and the mean age was 35.3 years (range: 18–65 years). all cases presented to an emergency department (ed) at least once for this illness. ninety-four cases provided clinical specimens and all tested positive for brodifacoum, a long-acting anticoagulant used in rodenticide. cases were reported to the health department by the illinois poison control center and direct reporting from hospitals. redcap was the primary database for tracking cases and collecting demographic information, risk factor data and healthcare facility utilization, including number of ed visits. syndromic surveillance was utilized to monitor ed visits related to the cluster, assist with case finding and provide situational awareness of the burden on the eds and geographic spread. in this study, we retrospectively used syndromic surveillance along with the data in redcap to quantify the number of ed visits per coagulopathy case, understand the reasons for repeat visits, and determine whether visits were captured in syndromic surveillance. methods illinois hospital ed data submitted to the national syndromic surveillance platform instance of essence (essence), was compared to data present in our primary redcap database. a subset of the cases, males 18-44 years of age (n=105; 61% of cases), were included in this analysis. illinois essence data in males aged 18-44 years from march 10, 2018–june 30, 2018 were matched to cases in the redcap database by age, zip code, initial visit date, facility, and reason for visit including: chief complaint, discharge diagnosis, and triage note. if the initial visit was found, the matching criteria and medical record number were used to search for additional related visits. the number of visits in essence and reasons for visits were totaled for each patient. reasons for repeat visits were categorized into four categories: continued gross bleeding or symptoms associated with coagulopathy, medical evaluation or follow-up, laboratory work and prescription refill. repeat visits may fall into more than one category. the number and dates of ed visits captured in essence per case were compared to that reported in redcap. an epidemic curve was constructed to display the number of ed visits and type (i.e. primary visit or repeat visit) captured by redcap only, essence only or both by visit date. results of the 105 cases in redcap, 89 (85%) were matched to at least one ed v isit in essence from march 10, 2018–june 30, 2018. the mean number of essence ed visits per case was 1.9 visits and the median was one visit (range: 1–11 visits). the main chief complaints for the primary visit included hematuria (n=31), abdominal pain (n=20), back pain/flank pain (n=13), k2 (n=11), bleeding from multiple sites (n=8), vomiting blood (n=7), and urinary tract infection or kidney stones (n=7). of the 89 cases matched to a visit in essence, 84 (94%) cases, representing 142 (79%) of ed visits, were captured by syndrome definitions that were being utilized to monitor the cluster. forty-three cases (48%) had at least two visits in essence. the reasons for return visits captured in essence (n=84) were continued gross bleeding or symptoms as sociated with coagulopathy (n=53), medical evaluation or follow-up (n=14), laboratory work (n=13), prescription refill (n=7) or unknown (n=2). of the 105 cases, the number of ed visits reported in redcap matched the number of visits found in essence for 49 cases (47%). for 24 cases (23%), essence identified more visits than redcap and for 16 cases (15%), redcap had more ed visits reported than captured in essence. sixteen cases (15%) in redcap were not found in essence. all of the unmatched visits were due to essence data quality, including a lack of reporting hospital admissions, lack of submitting data to essence, and missing data including: date of birth, medical record number, and triage notes. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e448, 2019 isds 2019 conference abstracts conclusions syndromic surveillance was a useful tool in describing the burden of ed visits for patients in the illinois coagulopathy outbreak linked to sc use. essence data helped to quantify the number of ed visits per patient and identify patients that re-presented for the same illness. the most common reason for repeat ed visits was continued symptoms, which may be attributed to misdiagnosis at the initial healthcare visit. ed visits that were not picked up by essence were a result of data quality issues from select facilities that were not reporting hospitalizations or key information such as date of birth, medical record number or triage notes. engagement with healthcare facilities to provide this information will improve the data quality of syndromic surveillance. acknowledgement this study was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-05. figure 1. emergency department (ed) visits for coagulopathy cases by surveillance system and type of visit, illinois, march 10-june 30, 2018 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using discharge diagnoses for early notification of reportable diseases in georgia rene borroto*1, jessica grippo1, karl soetebier1, wendy smith2, bill williamson1, patrick pitcher1, lance ballester1 and cherie drenzek1 1georgia department of public health, atlanta, ga, usa; 2fulton county board of health, atlanta, ga, usa objective to describe how the georgia department of public health (dph) uses icd-9 and icd-10-based discharge diagnoses (ddx) codes assigned to emergency department (ed) patients to support the early detection and investigation of outbreaks, clusters, and individual cases of reportable diseases. introduction the georgia dph has used its state electronic notifiable disease surveillance system (sendss) syndromic surveillance (ss) module to collect, analyze and display analyses of ed patient visits, including ddx data from hospitals throughout georgia for early detection and investigation of cases of reportable diseases before laboratory test results are available. evidence on the value of syndromic surveillance approaches for outbreak or event detection is limited (1, 2). use of the ddx field within datasets, specifically as it might be used for investigation of outbreaks, clusters, and/or individual cases of reportable diseases, has not been widely discussed. methods the ddx field of the ed data set sent to dph by 120 facilities was queried for diseases that are immediately-reportable, as well as those reportable within 7 days of diagnosis. the query was performed twice a day using a combination of sas 9.4 and the internal query capabilities of sendss. district epidemiologists (de) were notified by email, with an excel file attached that includes the details of the patient’s visit. des contacted infection control practitioners (icps) of the facilities where the patients had received a discharge diagnosis of a reportable disease. true or false positives were determined after the outcome of the follow-up with the icp had been known and after the patient had been entered as a case of reportable disease in sendss by the de. hence, if the patient was a confirmed or probable case of a reportable disease, it was recorded as a true positive, and true negative otherwise. this led to the calculation of predictive value positive (pvp) by reportable disease. results table 1 shows the number of notifications sent to des that were later demonstrated to be true positives and false positives. it also shows the pvp by diseases being reported, for the period spanning from 05/01/2016 to 08/31/2017. use of these notifications has allowed early investigation and identification of 158 cases of notifiable diseases by des. this includes 25 epi-linked cases (varicella=12, pertussis=4, cryptosporidiosis=3, shigellosis=2, malaria=2, and viral meningitis=2), as well as two clusters of varicella, one cluster of pertussis, and one outbreak of varicella in an elementary school that had not been reported to the local health department. a notable limitation of this study is that no systematic and exhaustive tracking is done of all notifications, as des have latitude to decide whether to follow up on a specific notification, depending on other local data that could be related to the event. therefore, the ppvs may be biased due to over-/under-estimation of unknown size and direction. one exception to this is varicella notifications, whose outcomes have been exhaustively tracked by the dph surveillance coordinator of this disease. conclusions the use of ed discharge diagnoses to examine potential cases of reportable diseases can help improve detection and early response by local health departments to outbreaks, clusters, and individual cases of reportable diseases. exhaustive tracking of all the notifications by specific diseases may improve the estimation of the ppvs of the notifications sent to des. table 1. true positives, false positives, and predictive value positive, by reportable disease, state of georgia. source: discharge diagnosis field from emergency departments and urgent care centers sending data to the ss module of sendss keywords discharge diagnosis; reportable disease; surveillance; emergency department acknowledgments we are grateful to the epidemiologists of the 18 public health districts of georgia for their hard work. we want to acknowledge the excellent work done by carolyn adam, the varicella surveillance coordinator at the georgia dph. references 1.j buehler, a sonricker, m paladini, p soper, f mostashari. syndromic surveillance practice in the united states: findings from a survey of state, territorial, and selected local health departments. advances in disease surveillance. 2008; 6(3): 1-20. 2.r hopkins, c tong, h burkom, et al. a practitioner-driven research agenda for syndromic surveillance. public health reports 2017; 132(supplement1): 116s-126s. *rene borroto e-mail: rene.borroto@dph.ga.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e191, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e229, 2019 isds 2019 conference abstracts biosurveillance study of schmallenberg disease in azerbaijan in 2012-2017 shalala zeynalova biosafety level-3 laboratory, ministry of agriculture, azerbaijan objective schmallenberg virus (sbv) is an orthobunyavirus that primarily infects domestic and wild ruminants and causes symptoms such as transient fever, diarrhea, reduced milk production, congenital malformations and abortion. the first virus was identified in 2011 at the onset of a major outbreak in europe (germany, hungary, and france). introduction in 2012 2017 in azerbaijan there was an unexpected increase of abortions in cattle and sheep that was unrelated to brucellosis or chlamydia infection. the first confirmed case of schmallenberg disease was received from beylagan district of azerbaijan in october 2012. the import of cattle from europe to azerbaijan has commenced in 2012. therefore, the surveillance study was launched to determine spread of infection among cattle and sheep and to monitor the situation in the country. methods state veterinary control service notified 42 regional veterinary offices of azerbaijan to commence the monitoring of schmallenberg disease. blood samples were collected from sheep, and cattle and biopsies of heads or necks from aborted fetuses were sampled too.. the collected samples were tested in the republican veterinary laboratory. elisa was used to investigate the presence of specific antibodies against schmallenberg virus in the blood samples using idexx schmallenberg ab test kit. the commercially available real-time pcr kits (vetmax™ schmallenberg virus kit) were applied to test the biopsy samples. both tests were recommended by the world organization for animal health. results total, 40,257 blood samples were collected from suspicious cattle and sheep. 671 biopsies samples were taken from fetuses. 4,281 cattle and 999 sheep with antibodies against sbvwere detected. the pcr results showed that the 77 biopsies samples were positive for sbv. the highest numbers of seropositive animals were found in ganja, aghdash, barda, and baku. conclusions this biosurveillance study determined sbv in the samples of cattle and sheep in azerbaijan, therefore, it is important to carry out annual seromonitoring and start the vaccination program. it is essential to check the passport of imported cattle, which has the disease history and seroprevalence of sbv. references 1. laloy e, breard e, sailleau c, viarouge c, desprat a, et al. 2014. schmallenberg virus infection among red deer, france, 2010-2012. emerg infect dis. 20, 131-34. doi:https://doi.org/10.3201/eid2001.130411. pubmed 2. larska m, krzysiak mk, kesik-maliszewska j, rola j. 2014. cross-sectional study of schmallenberg virus seroprevalence in wild ruminants in poland at the end of the vector season of 2013. bmc vet res. 10, 967. doi:https://doi.org/10.1186/s12917-014-0307-3. pubmed http://ojphi.org/ https://doi.org/10.3201/eid2001.130411 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24377838&dopt=abstract https://doi.org/10.1186/s12917-014-0307-3 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25528665&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e241, 2019 isds 2019 conference abstracts epidemiological and space-time analysis of beijing intestinal infectious diseases jiaojiao wang1, zhidong cao1, daniel dajun zeng1, quanyi wang2 1institute of automation, chinese academy of sciences, 2beijing center for disease prevention objective to investigate epidemiological features and identify high relative risk space-time intestinal infectious diseases clusters at the township level in beijing city in order to provide the scientific evidence for making prevention and control measures. introduction intestinal infectious diseases (iid) is a common cause of illness in the community and results in a high burden of consultations to general practice, mostly affecting the health of infants, preschool children, young adults and elderly people, especially those living in low income countries. according to the published study on the global burden of disease, intestinal infectious diseases were responsible for 221,300 deaths worldwide in 2013. the chinese ministry of health has listed bacillary dysentery, amebic dysentery, typhoid fever and paratyphoid fever as notifiable class-b communicable diseases and other infectious diarrhea as notifiable classc communicable diseases to be included in the surveillance system and reporting network since 2004. many studies of iid in different regions have been published. however, the epidemiological characteristics and space-time patterns of individual-level iid cases in a major city such as beijing are still unknown. we aim to analyze the epidemiology features and identify space-time clusters of beijing iid at a fine spatial scale in this study. methods data collection. data on iid cases in the 2008-2010 period were provided by beijing center for disease prevention and control, china, including basic social-demographic information and clinical diagnosis (mainly including upper respiratory tract infection, indigestion, gastrointestinal disorders, bacillary dysentery, amebic dysentery, typhoid fever, paratyphoid fever and other infectious diarrhea). the demographic data for each township was calculated based on 2010 census data and the data published in the beijing statistical yearbook. epidemiological analysis. the home addresses from iid case records were matched to the geographic coordinates of the township level divisions. age-gender incidence of iid (1/100,000) was defined as the number of iid cases in each age-gender group divided by the population size of that age-gender group. total incidence was defined as the total number of iid cases divided by the average population size during the study period. space-time analysis. local spatial autocorrelation analysis based on indicators of spatial association (lisa) was used to measure the spatial autocorrelation of iid incidence. the high-high and low-low townships suggested the clustering of similar values for iid incidence, whereas the low-high and high-low townships indicated spatial outliers. the spatial and space-time scan statistics combined the covariates (gender and age) method were used to reveal the space-time clusters of beijing iid. results epidemiological features. a total of 561,199 individual-level iid cases were reported in beijing in the period, in which 95 cases without the township information. 22.1% (124,025) of the cases were in the 0 to 4-year age group. secondly 21.8% (122,345) were in the 50+-year age group. next 13.17% were in the 25 to 29-year age group (73,931) and 11.9% were in the 20 to 24-year age group (66,787). among the total iid cases, 307,920 were male, and 253,278 were female. the average male-to-female sex ratio was 1.22. total iid incidence was 1003.54 /100,000 (1035.16 in 2008, 992.67 in 2009 and 985.30 in 2010). total iid age-specific incidence in the 0 to 4-year age group (19,004.95) was the highest, followed by 3267.40 in the 25 to 29-year age group. the sex ratio of iid cases varied among the different age-gender groups. for the 50+-year age group, the incidence in female was higher than that in male. however, for the other age groups, the incidence in female was usually lower. the monthly distribution of iid cases exhibited significant seasonality and periodicity. the annual peaks in incidence mostly occurred between may and july. the annual number of iid cases was the lowest (183,326) in 2008 and the greatest (193,237) in 2010. space-time patterns. lisa analysis found that the borders between old city (xicheng and dongcheng) and urban districts (haidian, chaoyang, shijingshan and fengtai) showed the clear high-high positive spatial association for iid incidence. rural http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e241, 2019 isds 2019 conference abstracts areas (yanqing, huairou, miyun and pinggu) and outlying districts (the west of mentougou and fangshan, the southeast of daxing and tongzhou) showed the stable low-low positive spatial association for iid incidence. the townships showing low-low negative spatial association were mainly distributed in the urban-rural transition zones around the old city, while the high-low spatial outliers mainly scattered in xinggu county of pinggu and shahe town of changping. detected spatial scan clusters varied from year to year. the most likely clusters occurred in 15 townships around chongwenmenwai of dongcheng district (2008, relative risk (rr) = 9.39, log likelihood ratio (llr) = 53927.93, p-value (p) < 0.001), donghuamen and qianmen of dongcheng district (2009, rr = 35.01, llr = 53286.52, p < 0.001), donghuamen of dongcheng district (2010, rr= 43.83, llr = 62674.76, p < 0.001). the most likely space-time cluster (rr = 41.3, p < 0.001) was located in donghuamen and qianmen of dongcheng district during the period from 2009/5/1 to 2010/10/31. the secondary space-time clusters (rr = 2.02, p < 0.001) were mainly scattered in the west part of beijing including 133 townships during the period from 2010/6/1 to 2010/9/30. conclusions the detected locations and space-time patterns of beijing iid clusters are important for the local health officials to determine the source of the cluster to design effective prevention strategies and interventions against beijing iid. the variations in beijing iid epidemics over population, space, and time that were revealed by this study emphasize the need for more thorough research about the driving forces and risk factors (climate, geography, environment, and social-economic) that contribute to prevent and control beijing iid outbreaks. references 1. gbd 2013 mortality and causes of death collaborators. 2015. global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the global burden of disease study 2013. lancet. 385(9963), 117-71. pubmed https://doi.org/10.1016/s0140-6736(14)61682-2 2. ghoshal uc, et al. 2018. the role of the microbiome and the use of probiotics in gastrointestinal disorders in adults in the asia-pacific region background and recommendations of a regional consensus meeting. j gastroenterol hepatol. 33(1), 5769. pubmed https://doi.org/10.1111/jgh.13840 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25530442&dopt=abstract https://doi.org/10.1016/s0140-6736(14)61682-2 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28589613&dopt=abstract https://doi.org/10.1111/jgh.13840 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e268, 2019 isds 2019 conference abstracts validating syndromic data for opioid overdose surveillance in florida randolph e. schilke, karen card, junwei jiang, joshua sturms, steve mccoy, leah colston florida department of health, tallahassee, florida, united states objective assess the validity of florida (fl) enhanced state opioid overdose surveillance (esoos) non-fatal syndromic case definitions. introduction in 2017, fl department of health (doh) became one of thirty-two states plus washington, d.c funded by the center for disease control and prevention (cdc) under the esoos program. one of the objectives of this funding was to increase the timeliness of reporting on non-fatal opioid overdoses through syndromic surveillance utilizing either the emergency department (ed) or emergency medical services (ems) data systems. syndromic case validation is an essential requirement unde r esoos for nonfatal opioid-involved overdose (oiod). fl’s esoos program conducted oiod validation and quality monitoring of ems case definitions, using data from fl’s emergency medical services tracking and reporting system (emstars). we examined measurement validity with oiod cases identified from fl’s statewide hospital billing database, fl agency for health care administration (ahca). methods from fl-emstars, we extracted ems data where the type of service requested was a 911 response, the patient was treated then transported by ems to a hospital facility in florida and was 11 years of age or older. additionally, all incident -patient encounters excluded those who were dead at the scene. we included all responses with dispatch dates between january 1, 2016, and december 31, 2016. from fl-ahca, we extracted ed and inpatient discharge information with admission dates and patient age covering the same ranges as our ems encounters. we classified fl-emstars cases based on combinations, like that of rhode island [1], using providers primary impression (ppi), providers secondary impression (psi) and response to the administration of naloxone . fl-ahca cases were defined by the following t and f codes from the international classification of diseases 10: t40.0 -t40.4, t40.60, t40.69, f11.12, f11.120, f11.121, f11.122, f11.129, f11.22, f11.220, f11.221, f11.222, f11.229, f11.92, f11.920, f11.921, f11.922, f11.929. for all “t” codes, the 6th character was either a “1” or “4,” because esoos is focused on unintentional and undetermined drug overdoses, ergo we excluded ed visits that are related to intentional self-harm (i.e., “2”) or assault (i.e., “3”). lastly, for all “t” codes, the 7th character we included was the initial ed encounter (i.e., “a”) because the purpose of the system is to capture increases or decreases in acute overdoses. to improve our match rate, account for typographical errors, and account for the discriminatory power some values may contain, we employed probabilistic linkage using link plus software developed by the cdc cancer division. blocking occurred among social security number (ssn), event date, patient age in years, and date of birth (dob). next, we matched both datasets on ten variables: event date, age, sex, dob, ethnicity, facility code , hospital zip code, race, ssn, and patient’s residence zip code. further pruning was performed to ensure all matches were within a 24-hour time interval. data management and statistical analyses were performed using sas® statistical software, version 9.4 (sas institute inc., cary, nc, usa). we assessed ems measurement validity by sensitivity, specificity, and positive predictive val ue (ppv). next, risk factors were identified by stepwise multivariable logistic regression to improve the accuracy of the fl-esoos definition. significant risk factors from the parsimonious multivariable model were used to simulate unique combinations to estimate the maximum sensitivity and ppv for oiod. results prior to merging, fl-emstars contained 1,308,825 unique incident-patient records, where fl-ahca contained 8,862,566 unique incident-patient records. of these, we conservatively linked 892,593 (68.2%) of the fl-emstars dataset with fl-ahca. our probabilistic linkage represents an 18.2% linkage improvement over previous fl-doh deterministic strategies (j jiang, unpublished cste presentation, 2018). among the matched pairs we estimated 8,526 oiod, 0.96% prevalence, using the flahca case definition. whereas the fl-esoos syndromic case definition estimated 6,188 oiod, 0.69% prevalence. the fl http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e268, 2019 isds 2019 conference abstracts esoos oiod syndromic case definition demonstrated 31.64% sensitivity, 99.61% specificity, and 43.60% ppv. among false negatives, the response to administrated naloxone among oiod was 39.37% “not known,” 37.95% “unchanged,” and 0.28% “worse.” we altered the fl-esoos emstars case definition for oiod to include those who were administered naloxone regardless of their response to the medication. we observed 12.37% sensitivity increase to 44.01%, 0.56% specificity decrease to 99.05%, and 12.78% ppv decrease to 30.82%. are final multivariable model is as follows: lnodds(opioid overdose)= 12.66 – 0.5459(med albuterol) – 0.9568(med aspirin) – 0.5765(med midazolam hydrochloride) – 0.8690(med morphine sulfate) + 1.4103 (med naloxone) – 0.7694(med nitroglycerine) + 0.3622(med oxygen) – 0.3702(med phenergan) – 0.8820(med epinephrine 1:10000) – 0.7397(med fentanyl) – 0.6376(med sodium bicarbonate) – 0.2725(med normal saline) + 0.3935(med other-not listed) + 0.6300(ppi general malaise) + 0.8476(ppi other, non-traumatic pain) + 0.8725(ppi airway obstruction) + 0.4808(ppi allergic reaction) + 1.4948(ppi altered level of consciousness) + 1.5481(ppi behavioral/psychiatric disorder) + 1.3843(ppi cardiac arrest) + 2.3913(ppi poisoning/drug ingestion) + 2.2418(ppi intentional drug use; related problems) + 0.2783(ppi respiratory distress) + 2.0305(ppi respiratory arrest) + 0.4292(ppi stroke/cva) + 0.5402(ppi syncope/fainting) + 0.5219(psi other, non-traumatic pain) + 0.9355(psi allergic reaction) + 0.3521(psi altered level of consciousness) + 0.9036(psi poisoning/drug ingestion) + 0.9661(psi intentional drug use; related problems) + 0.3766(psi respiratory distress) + 1.1802(psi respiratory arrest). we plotted the multivariable sensitivity and ppv by probaiblity cutoff value to determine which would produce the best discrimination (see figure 1). by incorporating a probability cutoff value ≥ 0.22, we can inprove both sensitivity and ppv. specifically, we can achieve 45.48% sensitivity, 99.32% specificity, and 45.48% ppv. conclusions the sensitivity of the fl-esoos surveillance system is not generally high but could still be useful if subsequent validation shows sensitivity stability. regarding maximizing fl-esoos sensitivity and ppv, we deomonstrated that our mulitvariable model with an appropriate probability cutoff value performes better than the current case definition. this study contributes to the limi ted literature on florida non-fatal opioid overdoses with a specific emphasis on validating ems records. new unique indicator combinations are possible to increase sensitivity and ppv but should be thoroughly investigated to balance the tradeoffs to optimize the system’s ability to detect non-fatal overdoses and to discriminate true cases. references 1. rhode island department of health. rhode island enhanced state opioid overdose surveillance (esoos) case definition for emergency medical services (ems).; 2017. 2. jiang j, mai a, card k, sturms j, mccoy s. ems naloxone administration for implication of opioid overdose. presentation presented at the: 2018; cste annual conference. figure 1. sensitivity ppv curve by probability cutoff http://ojphi.org/ isds annual conference proceedings 2015. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2015 conference abstracts situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande los alamos national laboratory, los alamos, nm, usa objective the objectives of this project are to identify properties that influence the progression of an outbreak, evaluate the ability of a property-based algorithm to differentiate between military and civilian outbreaks and different pathogens, and develop a decision support tool to enhance situational awareness during an unfolding outbreak. introduction the cdc defines a foodborne outbreak as two or more people getting the same illness from the same contaminated food or drink. these illnesses are often characterized as gastroenteritis until the causative agent is identified (bacterial or viral). due to the globally interconnected food distribution system, local foodborne disease outbreaks often have global impacts. therefore, the rapid detection of a gastroenteritis outbreak is of utmost importance for effective control. situational awareness is important for early warning or detection of a disease outbreak, and tools that provide such information facilitate mitigation actions by civil/military health professionals. we have developed the surveillance window app (swap), a web based tool that can be used to help understand an unfolding outbreak. the app matches user input information to a library of historical outbreak information and provides context. this presentation will describe our analysis of global civilian and military gastrointestinal outbreaks and the adaptation of the swap to enhance situational awareness in the event of such outbreaks. methods we collected data on about 100 civilian and military outbreaks caused by five pathogens (e. coli, salmonella spp., campylobacter jejuni, shigella spp. and norovirus). outbreak history (causative agent, location, time of year, environmental conditions, population at risk), time series, and methods of detection were compiled. by comparing civilian and military outbreaks and performing cross pathogen analysis, we identified properties that would distinguish between different types of outbreaks that could be used in an algorithm applied in the swap. analyses were performed to understand the influence of identified properties on cumulative case numbers and duration of an outbreak. results we identified five properties with a potential to distinguish between the different outbreak types. these properties are cumulative case count, time information, product or site/event (e.g. salad or wedding), source of contamination (e.g. cooked food, uncooked food, live animals), and season. using these properties, we compared outbreaks occurring in civilian and military populations (table 1). similarly, these properties were used to differentiate between outbreaks caused by different pathogens. figure 1 shows the outbreak trends for cumulative case count, duration, and time to peak for different pathogens. the epidemiological and biological reasons for these differences are discussed in the presentation. the swap based evaluation of outbreaks is ongoing. initial analyses showed that when data for military outbreaks were used as input, more than 50% of the top matches were other military outbreaks. we are currently working on swap based analyses for identification of the pathogen. conclusions the swap is a free web based tool that facilitates understanding of an unfolding outbreak in the context of a similar historical epidemic. our research indicates that this tool may be used in identifying probable causes and pathogens associated with a gastrointestinal outbreak. an early and accurate identification of cause will aid public health officials during the surveillance of the outbreak and in developing appropriate control measures. table 1: a comparison of military and civilian outbreak properties figure1: cross pathogen comparison for selected gastrointestinal outbreak pathogens. the figure shows cumulative case counts, duration and time to peak for outbreaks differ based on the pathogen. keywords surveillance window app (swap); food borne outbreaks; decision support tools; situational awareness and surveillance; gastroenteritis acknowledgments this project was finded by defence threat reduction agency. references http://swap.lanl.gov *nileena velappan e-mail: nileena@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(1):e170, 2016 assessing the usage of dating sites and social networking sites in newly diagnosed hiv positive men who have sex with men (msm) in harris county, texas, 2014 najmus abdullah*, sudipa biswas, weilin zhou, hafeez rehman, salma khuwaja and raouf r. arafat the epidemiologic charateristics, healthcare associated and household transmission dynamics of evd outbreak in a south-southern city of nigeria olawunmi o. adeoye*1, endie waziri1, uchenna anebonam1, ifeoma nwaduito2, pauline green2, william komakech3, nnanna onyekwere2, gabriele poggensee1 and patrick m. nguku1 ebola virus disease surveillance and response preparedness in northern ghana martin n. adokiya*1 and john koku awoonor-williams2, 3 somebody’s poisoned the waterhole: aspca poison control center data to identify animal health risks kristen alldredge*1, 3, leah estberg1, cynthia johnson1, howard burkom2 and judy akkina1 minnesota e-health data repositories: assessing the status, readiness & opportunities to support population health bree allen*1, 2, karen soderberg1 and martin laventure1 evaluation of the measles case-based surveillance system in kaduna state (2010-2012) celestine a. ameh*2, muawiyyah b. sufiyan1, matthew jacob3, endie waziri2 and adebola t. olayinka1 integrating r into essence to enable custom data analysis and visualization jonathan arbaugh and wayne loschen* refocusing hepatitis c prevention through geographic viral load analyses ryan m. arnold*, biru yang, qi yu and raouf r. arafat a method for detecting and characterizing multiple outbreaks of infectious diseases john m. aronis*, nicholas e. millett, michael m. wagner, fuchiang tsui, ye ye and gregory f. cooper an open source quality assurance tool for hl7 v2 syndromic surveillance messages noam h. arzt* and srinath remala role of animal identification and registration in anthrax surveillance zviad asanishvili, tsira napetvaridze, otar parkadze, lasha avaliani, ioseb menteshashvili and jambul maglakelidze using syndromic surveillance to rapidly describe the early epidemiology of flakka use in florida, june 2014 – august 2015 david atrubin*, scott bowden and janet j. hamilton situational awareness of childhood immunization in kenya toluwani e. awoyele*2, 1, meenal pore1 and skyler speakman1 surveillance for mass gatherings: super bowl xlix in maricopa county, arizona, 2015 aurimar ayala*, vjollca berisha and kate goodin estimating flunearyou correlation to ilinet at different levels of participation eric v. bakota*, eunice r. santos and raouf r. arafat performance of early outbreak detection algorithms in public health surveillance from a simulation study gabriel bedubourg*1, 2 and yann le strat3 modernization of epi surveillance in kazakhstan: transition to risk assessment and real-time monitoring based on situational center zhandarbek bekshin2, aizhan esmagambetova2, stanislav kazakov3, alexry burdakov*1, damir kobzhasarov2 and andrey ukharov1 analysis of alternatives for combined and/or collaborative syndromic surveillance within dod and va robert e. bell*1, mark holodniy2 and julie a. pavlin3 utility of syndromic surveillance in detecting potential human exposures to rabies kelley bemis*, megan t. patel, mabel frias and demian christiansen disproportionate emergency room use as an indicator of community health kelley bemis*, samantha gray, megan t. patel and demian christiansen is there a need for one health surveillance (ohs)? john berezowski*1, judy akkina2, victor d. vilas3, katrina devore4, fernanda c. dórea5, céline dupuy6, melody j. maxwell3, vivek singh7, flavie vial1 and laura streichert4 classification and capture of work-related non-fatal injuries through a real-time syndromic surveillance system marija borjan* and margaret lumia role of influenza in ed visits and hospitalizations of adults over 65 years in france vanina bousquet*1, larissa vernier1, yann le strat1, isabelle bonmarin1, christophe leroy2, maurice raphaël3, gilles viudes5, andré de caffarelli4 and céline caserio-schönemann1 openness, transparency and equity in public health surveillance data sharing matthew brack*, michael edelstein, asha herten-crabb and david r. harper an ehars dashboard for state hiv surveillance elliott s. brannon*1, tera reynolds2, 3, sunyang fu2, 3, kristen harden3, dolorence okullo2, 3 and shreyas ramani2, 3 sharing situational awareness of the 2014-2015 highly-pathogenic avian influenza outbreak across government yandace k. brown* and teresa quitugua developing the scalable data integration for disease surveillance (sdids) platform david buckeridge*1, maxime lavigne1, kate zinszer2, anya okhmatovskaia1, samson tu3, csongor nyalus3, mark musen3, wilson lau4, lauren carroll4 and neil abernethy4 equine syndromic surveillance in colorado using veterinary laboratory test order data howard burkom*1, yevgeniy elbert1, jerrold dietz1, barbara powers2, cynthia johnson3, leah estberg3 and judy akkina3 enhanced mosquito surveillance for aedes spp. in santa cruz county, arizona mariana g. casal*1, 2, jose arriola3, steven erly1, 2, 4, nicolette dent1, 5, shelly jacobs3, kacey ernst4, kathleen walker4 and mary hayden6 impact assessment of a terrorist attack using syndromic surveillance, france, 2015 céline caserio-schönemann1, marc ruello1, delphine casamatta2, guillaume debaty3, pascal chansard4, magali bischoff5, carlos el khoury5, philippe pirard1, anne fouillet1 and hervé le perff2 impact of the 2015 july heat waves in france on heat-related causes céline caserio-schönemann*, anne fouillet, aymeric ung, manuel zurbaran, vanina bousquet, karine laaidi, mathilde pascal, sébastien denys, thierry cardoso and anne gallay leptospirosis in bangladesh: an urgent need for coordinated surveillance system shovon chakma1, 2 and sultan mahmood*1, 2 data exchange between immunization registry and disease surveillance system hwa-gan chang*1, griffin jacqueline2, hans proske2, megan meldrum1 and elizabeth rausch-phung1 social media analytics for post-disasters disease detection in the philippines lauren charles-smith*3, corinne ringholz1, benjamin brintz3, 2 and courtney d. corley3 towards influenza surveillance in military populations using novel and traditional sources lauren charles-smith*1, alexander rittel1, 2, umashanthi pavalanathan1, 3 and courtney d. corley1 ed visits related to marijuana exposures in the denver metropolitan area of colorado yushiuan chen*1, michele askenazi1, kathryn h. deyoung2, bernadette albanese1, lourdes w. yun2 and todd hockenberry1 washington state one health initiative: a model framework to put one health in action wayne clifford* using bayesian networks to assist decision-making in syndromic surveillance felipe j. colón-gonzález*1, iain lake1, gary barker2, gillian e. smith3, alex j. elliot3 and roger morbey3 surveillance strategies during low ebola transmission in a district in sierra leone allison m. connolly*, alyssa j. young, brooke mancuso, mary-anne hartley, adam hoar, guddu kaur, john mark esplana, laura fisher and anh-minh a tran day of week analysis of myocardial infarctions using essence-fl emergency department data allison b. culpepper*, david atrubin, janet j. hamilton and dongming cui key challenges for eradication of poliomyelitis in ukraine oksana cyganchuk* an improved ewma-based method for outbreak detection in multiple regions sesha k. dassanayaka* and joshua french evaluation of point of need diagnostic tests for use in california influenza outbreaks ashlynn daughton* and alina deshpande military and civilian disease outbreaks: a comparative analysis ashlynn daughton*2, nileena velappan2, esteban abeyta2, 1 and alina deshpande2 enhancing the biosense platform: findings from an essence and sas pilot project cassandra n. davis* french national health insurance information system for malaria surveillance francois delon*1, marc c. thellier2, aurélie mayet1, eric kendjo2, aïssata dia1, rémy michel1, gilles chatellier3 and guillaume desjeux4 evaluation of case detection of marijuana-related emergency department visits kathryn h. deyoung*1, robert beum1, yushiuan chen2, moises maravi1, lourdes w. yun1, michele askenazi2, judith shlay1 and arthur davidson1 hospital readmissions among the homeless population in albuquerque, new mexico victoria f. dirmyer* better, stronger, faster: why add fields to syndromic surveillance? new jersey, 2015 pinar erdogdu*, teresa hamby and stella tsai new master mapping reference table (mmrt) to assist icd-10 transition for syndromic surveillance brooke evans*1, peter hicks3, julie a. pavlin4, aaron kite-powell7, atar baer5, david j. swenson6, rebecca lampkins2, achala u. jayatilleke3 and laura streichert1 secondary data analysis of hiv/aids control programme data, enugu state, (2010-2013) chinyere c. ezeudu*, patrick m. nguku and abisola oladimeji evaluation of hiv/aids surveillance system (2010-2013) in enugu state april 2014 chinyere c. ezeudu*1, patrick m. nguku1, abisola oladimeji1 and olufunmilayo fawole2 triage notes in syndromic surveillance – a double edged sword zachary faigen*1, amy ising2, lana deyneka1 and anna e. waller2 eliciting disease data from wikipedia articles geoffrey fairchild*1, 3, lalindra de silva2, sara y. del valle1 and alberto m. segre3 one health in action: lyme disease veronica a. fialkowski*, erik foster, kim signs and mary grace stobierski evaluation of the michigan disease surveillance system for histoplasmosis reporting veronica a. fialkowski*, leigh m. tyndall snow, kimerbly signs and mary grace stobierski development of food hygiene surveillance system in plantation sector, sri lanka lahiru s. galgamuwa*1, devika iddawela1 and samath d. dharmaratne2, 3 from ebola to heroin; the use of ems data for near real time alerting and surviellance alexander garza* global disease monitoring and forecasting with wikipedia nicholas generous*, geoffrey fairchild, alina deshpande, sara y. del valle and reid priedhorsky alert-enabled application integrating data quality monitoring for multiple sources harold gil* and nicholas l. michaud evaluation of vaccine preventable diseases (measles and diphtheria) surveillance system delhi, india, 2013 kapil goel* validation of sys data to inform surveillance of health disparities in nebraska sandra gonzalez*1, 2, ashley newmyer1 and ming qu1 early estimation of the basic reproduction number using minimal outbreak data carl grafe* literature review of mental health and psychosocial aspects of ebola virus disease anna grigoryan*1, rebecca bitsko2, ha young lee3, barbara lopes-cardozo3 and ruth perou2 weather outlook: cloudy with a chance of…—classification of storm-related ed visits teresa hamby*, stella tsai, hui gu, gabrielle goodrow, jessie gleason and jerald fagliano a digital platform for local foodborne illness and outbreak surveillance jared b. hawkins*1, gaurav tuli1, sheryl kluberg1, 4, jenine harris2, john s. brownstein1, 3 and elaine nsoesie2 tick-borne encephalitis virus, coxiella burnetii & brucella spp. in milk, kazakhstan john hay1, christina farris2, phil elzer3, alexei andrushchenko4, sue hagius3, allen richards2 and timur ayazbayev4 emerging infectious diseases and health surveillance at u.s. air travel ports of entry: perspective from within the department of homeland security andrew hickey*, diana y. wong, tyann blessington, asher grady, chandra lesniak, sarah cheeseman barthel, scott teper, william bilado, jayme henenfent, tiana garrett, neil bonzagni, mark freese and teresa quitugua preliminary look into the icd9/10 transition impact on public health surveillance peter hicks1, julie a. pavlin6, atar baer2, david j. swenson*7, rebecca lampkins5, achala u. jayatilleke1, aaron kite-powell3, brooke evans4 and laura streichert4 an exploration of public events and alcohol related incidents briana a. holliday* mantle: an open source platform for one health biosurveillance and research andrew g. huff*1, 2 and toph allen1 augmenting surveillance to minimize the burden of norovirus-like illness in ontario: using telehealth ontario data to detect the onset of community activity stephanie l. hughes* and andrew papadopoulos product landscape of rapid diagnostic tests for viral hemorrhagic fever pathogens noah hull*2, andrew hickey1 and teresa quitugua1 what’s the buzz about arboviral disease syndromic surveillance? jenna iberg johnson* and christine scott-waldron regional syndromic surveillance data sharing workshops: process and early outcomes charlie ishikawa*1, katrina devore2, scott gordon3, mark sum3 and laura streichert2 knowledge management tools for the isds community of practice amy ising*1, wayne loschen2 and laura streichert3 injury surveillance with district health information system 2 (dhis2) achala u. jayatilleke*1, megha ganewatta1, pamod amarkoon1, roshan hewapathirana2 and achini jayatilleke3 how to effectively validate an hl7 syndromic surveillance interface jeffrey johnson*, brit colanter and marjorie richardson savsnet: collating veterinary electronic health records for research and surveillance philip h. jones*, alan d radford, peter-john m noble, fernando sánchez-vizcaíno, tarek menacere, bethaney heayns, susan bolan, maya wardeh, rosalind m gaskell and susan dawson motor vehicle crash (mvc) case definitions and how they impact mvc surveillance jennifer l. jones*, dennis m. falls, clifton a. barnett, amy ising and anna e. waller creating a universal data release policy across programs in a state health department ekaette joseph-isang* syndromic surveillance of emergency department visits for the 2015 special olympics emily kajita*, monica z. luarca, choiyuk chiang, han wu and bessie hwang washington’s methods for analytics interoperability and metrics (aim), approaches to data integration and dissemination in population health bryant thomas karras*1, ali h. mokdad2, adam aaseby3 and william lober2 electronic surveillance for injury prevention using a physician-operated system amir kimia1, 2, assaf landschaft3, maria jorina1, lois lee1, 2 and al ozonoff1, 2 surveillance for lyme disease in canada, 2009-2012 jules koffi*1, robbin lindsay3 and nicholas ogden2 resolving disconnected patient records to support patient care and population health jacob krive2, 3, 4, annamarie hendrickx*1 and terri godar1 evaluating syndromic surveillance systems iain lake1, felipe j. colón-gonzález*1, roger morbey2, alex j. elliot2, gillian e. smith2 and richard pebody3 three years of population-based cancer registration in kumasi: providing evidence for population-based cancer surveillance in ghana dennis o. laryea*1, 2, fred k. awittor2, cobbold sonia2 and kwame o. boadu3 impact of interventions on influenza a(h7n9) virus activity in live poultry markets eric h.y. lau*1, jun yuan2, kuibiao li2, connie leung3, zhicong yang2, caojun xie2, yufei liu2, biao di2, benjamin cowling1, xiaoping tang4, gabriel leung1, malik peiris1 and ming wang2 using health-seeking pattern to estimate disease burden from sentinel surveillance eric h.y. lau*, qiqi zhang, kin on kwok, irene o. wong, dennis k. ip and benjamin j. cowling increase in adverse health effects related to synthetic cannabinoid use royal k. law*1, josh schier1, colleen martin1, arthur chang1, amy wolkin1 and jay schauben2 using national health insurance claims data to supplement notifiable infectious disease surveillance system heeyoung lee1, kwan lee2, seon-ju yi*1, gichan park1, hwami kim1, soyoon min1, jee soo suh1, young-man kim1, soojung jo1 and daeun jeong1 whispers, the usgs-nwhc wildlife health information sharing partnership event reporting system julianna b. lenoch* evaluation of hepatitis c surveillance in washington state natalie linton* experience report: association between flow chart, electronic patient record and telephone monitoring in the success of fighting dengue feaver in the hospital and emergency services in são bernardo do campo, brazil andrea m. losacco*, eliana v. miranda, renata martello, karla possendoro, meire a. pinheiro and gabriela falchi data sharing across jurisdictions using essence federated queries wayne loschen*, rekha holtry and sheri lewis improving the value proposition of surveillance tools: innovative uses for va essence cynthia a. lucero-obusan*1, patricia schirmer1, gina oda1 and mark holodniy1, 2 interplay of socio-cultural and environmental factors on microbial contamination of food in samaru, kaduna state, nigeria beatty v. maikai* and mujtaba a. salman disease surveillance by private health providers in nigeria: a research proposal olusesan a. makinde*1 and clifford o. odimegwu2 towards automated risk-factor surveillance: using digital grocery purchasing data to measure socioeconomic inequalities in the impact of in-store price discounts on dietary choice hiroshi mamiya*, erica moodie, deepa jahagirdar and david buckeridge “koman i lé” : an online self-reported symptoms surveillance system in reunion island nadège marguerite1, pascal vilain*1, etienne sévin2, farid sahridji2 and laurent filleul1 building the road to a regional zoonoses strategy: a survey of zoonoses programs in the americas melody j. maxwell*1, 2, mary hofmeister freire de carvalho2 and victor del rio vilas2 towards self validation: progress and roadmap for automating the validation of biosense partner facilities travis mayo1, matthew dollacker1, corey cooper1 and sara imholte2 using health helpline mediated self-swabbing as a surveillance tool for influenza danielle mcgolrick*1, paul belanger2, allison maier2, harriet richardson1, kieran moore2, nino lombardi3 and anna majury1, 3 a bayesian hierarchical model for estimating influenza epidemic severity nicholas l. michaud* and jarad niemi cdcplot: an application for viewing weekly cdc mmwr disease count data nicholas l. michaud*1, aaron kite-powell2 and jarad niemi1 using laboratory data to aid early warning in prospective influenza mortality surveillance aye m. moa*, david j. muscatello, robin turner and c raina macintyre surveillance systems that include deprivation indices & social determinants of health kieran moore and paul belanger tracking trends in marginalization and deprivation across ontario with sdoh mapper kieran moore and paul belanger real-time surveillance of environmental and demographic data in ontario with phims kieran moore and paul belanger facilitating the sharing of patient information between health care providers kieran moore and paul belanger ensuring the week goes smoothly improving daily surveillance visualization roger morbey*1, alex j. elliot1, elizabeth buckingham-jeffery2 and gillian e. smith1 using scenarios and simulations to validate syndromic surveillance systems roger morbey*1, alex j. elliot1, gillian e. smith1, iain lake2 and felipe j. colón-gonzález2 the burden of seasonal respiratory pathogens on a new national telehealth system roger morbey*1, sally harcourt1, alex j. elliot1, richard pebody1, maria zambon1, john hutchison3, judith rutter2 and gillian e. smith1 identifying depression-related tweets from twitter for public health monitoring danielle mowery*1, hilary a. smith2, tyler cheney2, craig bryan2 and michael conway1 socio-demographic inequalities in hiv testing and prevalence among older adults in rural tanzania, 2013 angelina c. mtowa*1, annette a. gerritsen2, sally mtenga1, mary mwangome1 and eveline geubbels1 a timeliness study of disease surveillance data post elr implementation in houston kasimu muhetaer*, eunice r. santos, avi raju, kiley allred, biru yang and raouf r. arafat correlation between influenza-like illness reported by ilinet and nssp, kansas, 2014-2015 daniel j. neises* and farah naz surveillance of the naural foci of especially dangerous infections in southern ukraine zoya nekhoroshykh*, g.m. dzhurtubayeva, n.m. protsyshyna, n.v. pilipenko, s.v. pozdnyakov, n.a. popova and e.a. egorova surveillance of the naural foci of especially dangerous infections in sourthern ukraine zoya nekhoroshykh*, galina dzhurtubayeva, n.m. protsyshyna, s.v. pozdnyakov, n.v. pilipenko, n.a. popova and e.a. egorova a tool to improve communicable disease surveillance data candace m. noonan-toly*1, charles didonato2 and hwa-gan chang1 flea-borne rickettsiae in almaty oblast, kazakhstan talgat nurmakhanov1, yerlan sansyzbayev1, heidi st. john2, christina farris2 and allen richards2 denver county clostridium difficile trends and associated risk factors 2011-2013 anna d. oberste*1, kathryn h. deyoung1, helen johnston2, stephanie gravitz1, emily mccormick1 and arthur davidson1 evaluation of national influenza sentinel surveillance system in nigeria, jan-dec 2014 amaka p. onyiah*1, 2, muhammad s. balogun1, adebayo a. adedeji2 and patrick m. nguku1 ebola virus disease outbreak in lagos, nigeria; 2014: an epidemiological investigation folasade f. osundina*, abisola oladimeji, olufemi ajumobi, saheed gidado, adebola t. olayinka and patrick m. nguku cancer health disparities in southeastern wisconsin yi ou* methods to measure socio-economic inequalities in health for indian adolescents priyanka parmar*1, manu r. mathur1, georgios tsakos2 and richard g. watt2 investigating a syndromic surveillance signal with complimentary data systems hilary b. parton*, robert mathes, jasmine abdelnabi, lisa alleyne, andrea econome, robert fitzhenry, kristen forney, megan halbrook, stephanie ngai and don weiss use of electronic health records to determine the impact of ebola screening julie a. pavlin*1, gosia nowak2, aaron kite-powell3, lindsey beaman1 and timothy whitman4 prospective spatio-temporal and temporal cluster detection by salmonella serotype eric r. peterson*, vasudha reddy, haena waechter, lan li, kristen forney and sharon k. greene factors influencing the stability and quality of the french ed surveillance system isabelle pontais*, vanina bousquet, marc ruello, céline caserio-schönemann and anne fouillet monitoring media content about vaccines in the united states: data from the vaccine sentimeter guido a. powell*1, kate zinszer1, aman d. verma1, lawrence c. madoff3, chi bahk2, john s. brownstein4 and david buckeridge1 law, policy, and syndromic disease surveillance: a multi-site case study jonathan purtle*2, robert field1, esther chernak1, tom hipper1 and jillian nash1 susceptibility profile of drug-resistant streptococcus pneumoniae based on elr avi raju*, eunice r. santos, eric v. bakota, biru yang and raouf r. arafat predicting facility-level carbapenem-resistant enterobacteriaceae (cre) incidence based on social network measures michael j. ray*1, michael y. lin2 and william trick2 the public health community platform: implementing electronic case reporting marcus rennick*, scott gordon, monica huang, mark sum and paula soper an r script for assessment of data quality in the biosense locker database serena rezny* and stacey hoferka characterizing public health actions in response to syndromic surveillance alerts laura rivera1, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, li ye1, 2, shelly bolotin1, 2, wendy lou1 and ian johnson*1, 2 the impact of standardized decision support on syndromic surveillance in alberta laura rivera1, faiza habib3, ye li1, 2, rita k. biel3, rachel savage2, natasha crowcroft1, 2, laura rosella2, 1, shelly bolotin1, 2, david strong3, 4, christopher sikora3, 5 and ian johnson*1, 2 syndromic surveillance evaluation of influenza activity in at-risk sub-populations heather rubino*, janet j. hamilton, allison b. culpepper, hunter davis, david atrubin and melissa murray jordan epidemic situation in ukraine related to the quality of drinking water iryna rudenko* acute gastroenteritis: contribution of sos médecins network marc ruello*1, benjamin larras1, noémie fortin1, nathalie jourdan da-silva1, pascal chansard2, céline caserio-schönemann1, vanina bousquet1, anne fouillet1 and isabelle pontais1 which sections of electronic medical records are most relevant for real-time surveillance of influenza-like illness? dino p. rumoro1, shital c. shah1, marilyn m. hallock1, gillian s. gibbs*1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 the impact of documentation style on influenza-like illness rates in the emergency department dino p. rumoro1, shital c. shah1, gillian s. gibbs*1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 natural language processing and technical challenges of influenza-like illness surveillance dino p. rumoro1, gillian s. gibbs*1, shital c. shah1, marilyn m. hallock1, gordon m. trenholme1, michael j. waddell2 and joseph p. bernstein3 one health e-surveillance for early detection of gastrointestinal disease outbreaks fernando sánchez-vizcaíno*1, barry rowlingson2, alan d radford1, alison hale2, emanuele giorgi2, sarah j o’brien3, susan dawson4, rosalind m gaskell1, philip h jones1, tarek menacere1, peter-john m noble4, maya wardeh1 and peter diggle2 data blindspots: high-tech disease surveillance misses the poor samuel v. scarpino*1, james g. scott2, rosalind eggo2, nedialko b. dimitrov2 and lauren a. meyers2, 1 a comparison of clinical surveillance systems in new york city lauren schreibstein*1, remle newton-dame1, katharine h. mcveigh1, sharon e. perlman1, lorna e. thorpe2, hannah mandel1 and michael buck1 monitoring and evaluation mechanism for multi-center capacity building gestational diabetes program for physicians in india megha sharma* avian flu, ebola, mers, and other emerging challenges for influenza surveillance practitioners alan siniscalchi2 and brooke evans*1 hit conformance testing: advancing syndromic surveillance system interoperability robert snelick and sheryl l. taylor* visualizing the local experience: hiv data to care tool lauren e. snyder*1, dean mcewen1, mark thrun2, 1 and arthur davidson1 extending an uncertainty taxonomy for suspected pneumonia case review brett r. south*1, 2, heidi s. kramer2, melissa castine1, danielle mowery1, 2, barbara jones2 and wendy chapman1, 2 comparison of exposure to risk factors for giardiasis among endemic and travel cases alexandra swirski*1, david pearl1, andrew peregrine1 and katarina pintar2 african one health e-surveillance initiative joy sylvester*1, herbert kazoora2, meeyoung park1, sheba gitta2, betiel h. haile1 and scott j. mcnabb1 strengthening community surveillance of ebola virus disease in sierra leone anhminh a. tran*, adam hoar, alyssa j. young, allison connolly, mary-anne hartley, samuel boland, brooke mancuso, guddu kaur, john esplana, erin polich and laura fisher surveillance of anthrax foci across pipeline constructions in georgia, 2003-2014 nikoloz tsertsvadze, lile malania, nato abazashvili, julieta manvelian, mariam broladze and paata imnadze situational awareness of health events using social media and the smart dashboard ming-hsiang tsou2, 3, 1, chin-te jung2, 3, 1 and michael peddecord*4, 3, 1 evaluation of legionellosis surveillance in michigan focusing on diagnostic testing leigh m. tyndall snow*1, 2, veronica a. fialkowski1, 2 and mary grace stobierski1 highway emergency response and accident mitigation service (heram) – a field report balaji utla*, dr. shailendra kumar b. hegde, dr. sri ranga prasad saride, dr. ramanuja chary kandaala, mr. sridhar upadhya and anukrati saxena enhancing biosurveillance specificity using praedico™, a next generation application alireza vahdatpour2, cynthia a. lucero-obusan1, chris lee2, gina oda1, patricia schirmer1, anosh mostaghimi1, farshid sedghi2, payam etminani2 and mark holodniy*1 biosense and violence: progress toward violence prevention using syndromic surveillance jennifer vahora* and stacey hoferka situational awareness for unfolding gastrointestinal outbreaks using historical data nileena velappan*, ashlynn daughton, esteban abeyta, geoffrey fairchild, william rosenberger and alina deshpande interest of prospective spatio-temporal analysis from ed data to detect unusual health events pascal vilain*, sébastien cossin and laurent filleul using syndromic surveillance to identify synthetic cannabinoids or marijuana adverse health events in virginia amanda wahnich* lessons learned from the transition to icd-10-cm: redefining syndromic surveillance case definitions for nc detect anna e. waller, katherine j. harmon* and amy ising automating ambulatory practice surveillance for influenza-like illness andrew walsh* enhancing epicenter data quality analytics with r andrew walsh* alcohol-related ed visits and ohio state football: putting the o-h in etoh kristen a. weiss* and andrew walsh enhancing syndromic surveillance at a local public health department jessica r. white* and kate goodin using syndromic surveillance to enhance arboviral surveillance in arizona jessica r. white*1, sara imholte2 and krystal collier2 evaluating the biosense syndrome for heat-related illness in maricopa county, arizona jessica r. white*, kate goodin and vjollca berisha sensitivity and specificity of the fever syndromes in biosense and essence caleb wiedeman* analysis of ed and ucc visits related to synthetic marijuana in essence-fl, 2010-2015 michael wiese*1 and charles r. clark2 comparison of air passenger travel volume data sources for biosurveillance diana y. wong*, teresa quitugua and julie waters a suggestion to improve timely feedback of infectious disease surveillance data at a provincial level in south korea seon-ju yi*, gichan park, hwami kim, soyoon min, jee soo suh, soojung jo, daeun jeong, young-man kim and heeyoung lee update on the cdc national syndromic surveillance program paula yoon and michael coletta* use of peripheral health units in low-transmission ebola virus disease surveillance alyssa j. young*, allison connolly, adam hoar, brooke mancuso, john mark esplana, guddu kaur, laura fisher, mary-anne hartley and anh-minh a tran place matters: revealing infectious disease disparities using area-based poverty kimberly yousey-hindes*1, sharon k. greene2, kelley bemis3 and kristen soto4 addressing health equity through data collection and linked disease surveillance iris zachary*1, jeannette jackson-thompson1, 2, 3, emily leary1 and eduardo simoes1, 2, 3 epizootology and molecular diagnosis of lumpy skin diesease among livestock in azerbaijan shalala k. zeynalova* exploring usability of school closure data for influenza-like illness surveillance yenlik zheteyeva*, hongjiang gao, jianrong shi and amra uzicanin efficient surveillance of childhood diabetes using electronic health record data victor w. zhong*1, jihad s. obeid2, jean b. craig2, emily r. pfaff1, joan thomas1, lindsay m. jaacks3, daniel p. beavers4, timothy s. carey1, jean m. lawrence5, dana dabelea6, richard f. hamman6, deborah a. bowlby2, catherine pihoker7, sharon h. saydah8 a isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e392, 2019 isds 2019 conference abstracts integrated west nile virus surveillance in harris county, texas, 2003 to 2018 leann liu, mustapha debboun, kasimu muhetaer, eric v. bakota, martin reyna, dana beckham, les becker, umair shah dccp/dcme, harris county public health, houston, texas, united states objective this abstract aims to: 1) describe human wnv infections in harris county excluding the city of houston, texas, 2003 to 2018; 2) explore geographical distributions of wnv positive mosquito pools in relation to human cases; 3) provide a brief overview of the county’s rigorous multidisciplinary wnv surveillance and control in mosquitoes and humans. introduction west nile virus (wnv) is the leading cause of autochthonous arboviral disease in the united states [1]. the virus is primarily spread to people through the bite of infected culex species of mosquitos. wnv was first identified in harris county, texas, in 2002 [2]. since then, the mosquito-borne virus has become endemic in the region, with surges in 2012 and 2014. although majority of individuals infected are asymptomatic, wnv induced neuroinvasive infections often result in hospitalizations and adverse outcomes [3-7], thus may pose a significant concern in public health and healthcare. the harris county public health (hcph) surveillance and epidemiology unit (seu) conducts surveillance of wnv in humans, in collaboration with mosquito and vector control division (mvcd) that monitors the virus in mosquito populations and birds. mosquito abatement activities are implemented in areas where positive mosquitoes and human cases are identified. in this integrated model, clusters of wnv positive mosquito pools in relation with human cases provide comprehensive surveillance data to guide targeted efforts of mosquito control, disease prevention, and community education. methods surveillance data of human wnv cases and wnv tested mosquito pools 2003 to 2018 in harris county excluding the city of houston were used for the analysis. human cases included were confirmed and probable cases. frequencies, percentages, ageadjusted annual average rates were used to describe the data. geographical locations of wnv positive mosquito pools and human cases were mapped and analyzed using esri arcgis to determine the spatial relationship between the positive mosquito pools and human cases. space-time analysis was performed on 16 years of human disease data using scan statistics in satscan™ to test the effect of time and identify significant geographical clusters of wnv cases over time, which revealed a statistically significant cluster in 2012 to 2014 in northwest of harris county. subsequently, human cases and wnv positive mosquito pools of 2012 to 2014 were selected for hotspot analysis to verify the results from satscan analysis and visualize the geographical cluster. human cases were aggregated into census tracts and analyzed by optimized hotspot method; the positive mosquito pools were geocoded using their intersection locations and analyzed by esri getis-ord gi hotspot method. results from 2003 to 2018, a total of 295 confirmed and probable human wnv cases were identified, including 217 neuroinvasive and 78 fever cases. the median age of patients was 58 years old; 64.8% were male. the onset of majority of the cases (80.7%) concentrated in july to september. among case-patients, 72.9% were hospitalized, and 3.4% died. the annual average age-adjusted incidence rate of wnv was 0.7 per 100,000 during 2003 to 2018. however, the rate was 1.8 and 2.7 per 100,000 in 2012 and 2014, respectively. over 68% of the cases clustered in west and northwest of the county. the annual wnv positive mosquito pools ranged from 0.2% to 10.2% amongst the years. the number of wnv positive mosquito pools in 2014 was more than 4 times higher than the average number during those years, the highest record in the county. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e392, 2019 isds 2019 conference abstracts hot spots of human cases and the positive mosquito pools were both identified in northwest harris county. there was a significant geographical overlap between human cases and the positive mosquito pools. the space-time analysis for the 16 years detected a significant high-risk cluster in 2012 to 2014 in northwest harris county. findings from optimized hotspot analysis on human cases are consistent with the results from satscan analysis. statistically significant hot spots of positive mosquito pools identified by getis-ord gi hotspot analysis displayed highly overlay with the statistically significant cluster of human cases detected by satscan space-time analysis. conclusions wnv infection is underestimated. studies suggest that every one case identified represent five infections [8,9]. therefore, the actual number of cases is likely substantially higher. clinicians should be alerted in wnv season and consider testing and reporting as early as possible, especially in neuroinvasive patients. the public should be encouraged to utilize personal protection, particularly during peak seasons. this analysis shows that gis mapping and optimized hotspot analysis of wnv human cases in relation to positive mosquito pools can provide statistical evidences of areas most affected, thus inform targeted mosquito control, prevention and education strategies in people living in areas with high virus activities. acknowledgement we would like to acknowledge the colleagues in hcph seu and mvcd for their contributions to wnv surveillance. references 1. reimann ca, hayes eb, diguiseppi c, et al. 2008. epidemiology of neuroinvasive arbovrial disease in the united states, 1999-2007. am j trop med hyg. 79, 974-79. pubmed https://doi.org/10.4269/ajtmh.2008.79.974 2. kristy m. lillibridge, ray parsons, yvonne randle, et al. the 2002 introduction of west nile virus into harris county, texas, an area historically endemic for st. louis encephalitis 3. mostashari f, bunning ml, kitsutani pt, et al. 2001. epidemic west nile encephalitis, new york, 1999: results of a household-based seroepidemiological survey. lancet. 358, 261-64. pubmed https://doi.org/10.1016/s0140-6736(01)054800 4. busch mp, wright dj, custer b, et al. 2006. west nile virus infections projected from blood donor screening data, united states, 2003. emerg infect dis. 12, 395-402. pubmed https://doi.org/10.3201/eid1205.051287 5. carson pj, borchardt sm, custer b, et al. 2012. neuroinvasive disease and west nile virus infection, north dakota, usa, 1999–2008. emerg infect dis. 18, 684-86. pubmed https://doi.org/10.3201/eid1804.111313 6. sejvar jj, haddad mb, tierney bc, et al. 2003. neurologic manifestations and outcome of west nile virus infection. jama. 290(4), 511-15. pubmed https://doi.org/10.1001/jama.290.4.511 7. the outbreak of west nile virus infection in the new york city area in 1999 8. surveillance for human west nile virus disease --united states. 2010 / 59. 1999—2008. mmwr. ss02, 1-17. pubmed 9. west nile virus and other arboviral diseases — united states. 2013 / 62. 2012. mmwr. (25), 513-17. pubmed http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19052314&dopt=abstract https://doi.org/10.4269/ajtmh.2008.79.974 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11498211&dopt=abstract https://doi.org/10.1016/s0140-6736(01)05480-0 https://doi.org/10.1016/s0140-6736(01)05480-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16704775&dopt=abstract https://doi.org/10.3201/eid1205.051287 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22469465&dopt=abstract https://doi.org/10.3201/eid1804.111313 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12876094&dopt=abstract https://doi.org/10.1001/jama.290.4.511 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20360671&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23803959&dopt=abstract isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts surveillance for prevention & identification of gi illness outbreaks associated with swimming pools sheharyar minhas*1, 2 1rollins school of public health, emory university, bensalem, pa, usa; 2nazareth hospital, philadelphia, pa, usa objective to prevent and identify gastrointestinal outbreaks due to swimming pools using a two-part surveillance system i) model aquatic health code (mahc) guideline survey and ii) syndromic surveillance introduction swimming in contaminated pools can cause gastroenteritis from water contaminated by viruses, bacteria, or parasites. germs that cause gastroenteritis are shed in feces of infected persons, and easily spread to uninfected persons swimming in pools. symptoms of gastrointestinal illness can include nausea, vomiting, watery or bloody diarrhea, and weight loss. common causes of swimming-related gastroenteritis included viruses (norovirus), parasites (giardia, cryptosporidium), and bacteria (escherichia coli, shigella). cryptosporidium is most common agent associated with swimming pool outbreaks. in 2011-2012, public health officials from 32 states reported 90 swimming-pool associated outbreaks to cdc’s waterborne disease and outbreak surveillance system (wbdoss). these 90 outbreaks resulted in 1,788 cases, 95 hospitalizations, 1 death. 52% of these outbreaks were caused by cryptosporidium. methods literature search was conducted using published peer-reviewed articles via pubmed and internet websites including, cdc and u.s. consumer product safety commission, agency for toxic substance and disease registry. statistical data on gi illness outbreaks associated with swimming pools prevalence and outcomes were also reviewed. current surveillance methods used for detecting prevalence of waterborne disease outbreaks are based on examples from ohio and nebraska to determine approaches and effectiveness of the systems. results survey and education packet distribute a survey with questions about current mahc guideline adherence and mahc educational packets that include the incident response guidelines and the water contamination response log strengths: low cost, simple, and acceptable limitations: not timely event reporting event reporting develop a website for reporting contamination events based on the water contamination response log strengths: timely reporting limitations: complex to setup and maintain, moderate cost, and may not be acceptable pool inspections require pools to undergo periodic inspections to monitor adherence to mahc guidelines strengths: complete and representative limitations: complex, expensive, not timely event reporting the current system is based on state reporting to the cdc through the paper-based reporting waterborne disease outbreaks surveillance system (wbdoss), and the national outbreak reporting system (nors), an electronic reporting system in place since 2009 cdc uses waterborne disease outbreak surveillance data to ○ identify the types of etiologic agents, and settings associated with outbreaks ○ evaluate the adequacy of regulations to promote healthy and safe swimming ○ establish priorities to improve prevention, guidelines, and regulations at the local, state, and federal levels the wbdoss is not sufficient to capture early detection and reporting of agi outbreaks. we recommend the these surveillance approaches: syndromic surveillance of wbd outbreaks to capture early outbreaks of diarrheal, and as many suspected cases as possible in a timely manner sentinel surveillance at specific healthcare facilities in the proximity of swimming pools where outbreaks can occur active lab-based surveillance would offer more robust and complete analysis of the prevalence and incidence of acute gi illness outbreaks in the state conclusions our study concluded that state health department should begin a two-part surveillance system: i) distributing mahc guideline surveys & education packet; ii) syndromic surveillance system for outbreaks. mahc guideline survey and education packet would be cost effective to educate pool operators on current mahc guidelines and gather baseline data on adherence to mahc guidelines for responding to contamination events. afterwards, once baseline data is gathered and awareness of the mahc guidelines is established, the state health department can determine if event reporting or pool inspections are necessary to increase either the timeliness or representativeness of the surveillance system. syndromic surveillance would be the most timely and sensitive surveillance system. this is important to achieve health department’s goal of early outbreak detection. both predictive value and data quality are limitations of syndromic surveillance system. acute gastrointestinal illness is also caused by sources other than pool contamination which can cause false positives. keywords gastrointestinal outbreaks; swimming pools; model aquatic health code (mahc); syndromic surveillance acknowledgments to rollins school of public health at emory university in atlanta and department of medicine at nazareth hospital in philadelphia. references 1-cdc. protracted outbreaks of cryptosporidiosis associated with swimming pool use --ohio and nebraska, 2000 mmwr 2001; 50(20); 406-410. http://www.cdc.gov/mmwr/preview/mmwrhtml/ mm5020a3.htm 2-cdc. outbreaks of illness associated with 2-recreational water — united states, 2011–2012 mmwr. 64(24); 668-672. http://www.cdc. gov/mmwr/preview/mmwrhtml/mm6424a4.htm?s_cid=mm6424a4_w isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts 3-cdc. the model aquatic health code. august 2015. http://www.cdc. gov/mahc/index.htm 4-cdc. (n.d.) decoding the mahc: the model aquatic health code. retrieved from https://www.cdc.gov/healthywater/pdf/swimming/ pools/mahc/decoding-the-mahc.pdf 5-cdc. (2016). fecal incident response recommendations for aquatic staff. retrieved from https://www.cdc.gov/healthywater/swimming/ pdf/fecal-incident-response-guidelines.pdf 6-cdc. (n.d.) water contamination response log. retrieved from https://www.cdc.gov/healthywater/pdf/swimming/pools/watercontamination-response-log.pdf 7-cdc. (2016). model aquatic health code aquatic facility inspection report. retrieved from https://www.cdc.gov/mahc/pdf/mahc-aquaticfacility-inspection-report.pdf *sheharyar minhas e-mail: sminhas7@yahoo.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e177, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e254, 2019 isds 2019 conference abstracts monitoring of anthrax prevalence in georgia 2009-2017 lela kerdzevadze administrative, laboratory of the ministry of agriculture, tbilisi, georgia objective one of the main objectives of these studies was to improve anthrax laboratory diagnostics in order to properly monitor the prevalence and distribution of the disease in georgia. for this geographic information system (gis) was implemented and used as the additional tool to the laboratory tests for better visualization, summary results and risk assessment. introduction anthrax is an acute infectious disease of historical importance caused by bacillus anthracis (b. anthracis), a spore -forming, soilborne bacterium with a remarkable ability to persist in the environment. anthrax is endemic in many countries, including georgia. laboratory of the ministry of agriculture (lma) has been actively working on the disease science 1907 and constantly improvin g diagnostics. in 2009-2017 the laboratory participated in cooperative biological studies. one of the main objectives of these studies was to improve anthrax laboratory diagnostics in order to properly monitor the prevalence and distribution of the disease in georgia. methods in 2009 -2011, within gg18, lma tested 130(animal and environmental) anthrax suspected samples collected from different regions of georgia. later, in 2014 – 2017, studies (tap7; gg27) were focused on soil sample collection and 2825 specimens were collected from the entire country. samples were tested according to treat agent detection and response (tadr) algorithm following standard operation procedures (sops). cultures were isolated through bacteriology tests gram strain, lysis by gamma phage, motility test, dfa and confirmed by molecular biology (pcr). in 2009, within the studies, geographic information system (gis) was implemented and used as the additional tool to the laboratory tests for better visualization, summary results and risk assessment. results totally, 2955 collected samples were tested. 86 cultures were isolated and confirmed. the results anthrax cases were mapped by regions, rayons and villages, also positive cases were mapped by sample type and course, majority of positive cases were in kvemo kartli (53%), 19% were from kakheti, 19% from imereti and less distributed in other regions. applying modern gis the final map of anthrax foci in georgia was created including both old (historical data) and new (recent data) foci. conclusions the studies aimed to improve anthrax laboratory diagnostic in georgia. better laboratory diagnostic with modern gis analysis supports the monitoring of the disease prevalence in georgia and significantly improves public health system in the country. http://ojphi.org/ an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi an innovative mobile data collection technology for public health in a field setting shamir mukhi1*, karna dhiravani1, brad micholson1, lin yan1, jenna hatchard1, samira mubareka2, christopher bergeron3, tim beattie4 1. canadian network for public health intelligence, national microbiology laboratory, winnipeg, mb 2. sunnybrook research institute, university of toronto, toronto, on 3. first nations and inuit health branch manitoba region, winnipeg, mb 4. water and air quality bureau, health canada, ottawa, on abstract objectives: the canadian network for public health intelligence (cnphi) is a secure, web-based scientific informatics and biosurveillance platform that leverages disparate public health information resources and expertise for the direct benefit of local, regional and national decision makers. cnphi fosters collaboration and consultation through innovation in disease surveillance, intelligence exchange, research and response to protect, promote and support public health. the objective of this article is to present the cnphi ‘on the go’ mobile application, and to discuss preliminary evaluation of the technology. the mobile application is intended to enable rapid mobile data collection using both online and offline modes supporting various stages of surveillance and response through the extension of data collection and analysis to the mobile environment. methods: two needs assessment meetings were held with stakeholders representing individuals from government, academia and research institutions, to inform the development of the cnphi “on the go” mobile application. an initial version of the mobile technology (an “app”) was developed and piloted by end-users with expertise in the field. two focused pilots were conducted to test the technology: pilot 1: 17-7-2017 to 21-11-2017 (6 participants); pilot 2: 25-7-2017 to 15-9-2017 (2 participants). an initial consultation was held with the project leads to identify data elements for mobile data collection. a custom data collection form was designed using cnphi’s web data technology for each pilot, which was then made available through the mobile app. the technology was assessed using feedback received during each pilot as well as through a survey that was conducted at the conclusion of pilots. results: pilot participants reported that the mobile technology allowed seamless data collection, data management and rapid information sharing. participants also reported that the entire process was seamless, simple, efficient, and that fewer steps were required for data collection and management. further, significant efficiencies were gained by directly entering information using the mobile app without having to transfer handwritten information into an electronic database. an overall positive experience was reported by participants from both pilots. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi introduction communication, collaboration and information sharing are critical for effective and efficient public health surveillance systems. enhancements of the traditional approaches to data collection and reporting to support surveillance and response activities would allow public health professionals from different jurisdictions to effectively collaborate, communicate, and collect critical situational intelligence through a common platform at short notice. this requires secure and familiar tools, allowing flexible and rapid interventions through the generation and dissemination of public health intelligence. mobile phone and pad related technology has been or is being fully adopted in most communities in canada and globally, for personal and professional communications. in canada, 87% of the population owned a mobile phone in 2016 [1]. in addition to that, 77% of the population had a smart phone in 2016, which is 26% higher than in 2012, demonstrating a rapidly increasing trend [1]. advances in mobile technology have provided an opportunity to use it to inform surveillance activities related to the transmission of pathogenic microorganisms or other threats to public health and safety. the use of mobile technology for the purpose of data collection, information transmission and reporting provides a potential for real-time monitoring and early warning intelligence, alleviating the resource intensity traditionally required for indepth field investigations and paper based data collection methods [2]. lack of rapid access to data poses challenges for effective and timely response during public health emergencies. despite its significance, seamless data collection and management during discussion: literature suggests that traditional methods of surveillance and data collection using a paper based methodology pose many challenges such as data loss and duplication, difficulty in managing the database, and lack of timely access to the data. accurate and rapid access is critical for public health professionals in order to effectively make decisions and respond to public health emergencies. results show that the cnphi “on the go” app is well poised to address some of the suggested challenges. a limitation of this study was that sample size for pilot participation was small for capturing overall feedback on the readiness of the technology for integration into regular surveillance activities and response procedures. conclusions: cnphi “on the go” is a customizable technology developed within an already thriving collaborative cnphi platform used by public health professionals, and performs well as a tool for rapid data collection and secure information sharing. keywords: data collection, mobile, epidemiology, informatics, biosurveillance correspondence: shamir.mukhi@canada.ca doi: 10.5210/ojphi.v10i2.9114 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi public health emergencies has been problematic in the past [3]. the current method of using paper to collect the data is cumbersome and may lead to delays in making crucial decisions related to public health. the traditional surveillance and data collection method is also prone to other issues such as data duplication, data loss and errors during data entry into an electronic database. timely and accurate data is important for public health personnel to effectively make decisions and to provide services. world health organization (who) has reported that despite all the benefits and high penetration rate, mobile technology is still not commonly used for the purpose of data collection, data transmission and reporting in public health [2]. the canadian network for public health intelligence (cnphi) was developed by public health agency of canada in 2004 to improve pan-canadian public health surveillance, communication and response through a comprehensive framework of applications and resources [4]. cnphi is currently used by a large number of federal, provincial and territorial public health professionals, including epidemiologists, lab scientists, physicians, nurses, animal health professionals, drinking water regulators, policy groups and provides an infrastructure for public health surveillance in canada. cnphi has continued to make significant progress in filling gaps in data collection, collation, analysis and reporting by leveraging and building upon its current infrastructure. in 2014, cnphi initiated a canadian safety and security program (cssp) funded project ‘cnphi on the go’ – intended to provide infrastructure to support the usage of mobile technology to foster bi-directional communications, support investigations, and generate geoaware biosurveillance intelligence related to human, animal, and environmental public health issues of concern. the project, since it is built on the existing cnphi platform, benefits every stage of surveillance and response through the extension of data collection and analytical capacities through inter-jurisdictional and interdisciplinary collaboration. the cnphi on the go project focused on extending the cnphi platform for rapid data collection and support for response related activities. this article focuses on the rapid data collection component, which is based on the extension of cnphi’s web data technology [5] on the mobile app and how it was used for the purpose of the mobile data collection in the field environment. web data is an innovative technology developed by cnphi, which allows users to rapidly deploy secure and adaptable forms for collecting data. web data is an important technology in the context of response because it provides a platform for response teams to collect and analyze data in a systematic way. the web data technology has been tested during the 2009-2010 h1n1 influenza pandemic and has been in use since then by public health personnel in canada [6]. web data technology extended on the mobile app allows users to create user-defined databases and surveys that can be used to collect data through openand closed-ended questions, whether quantitative or qualitative in nature. some of the key features of this technology include: a dynamic form builder, interactive queries/reports, a customizable data uploader and advanced analysis. it provides users with seamless access to data via a secure, web-based model with multiple levels of access, as well as jurisdictional and field level controls. the technology provides users with an ability to perform integrated search and interrogation and also an ability to produce instant reports [5]. we partnered with sunnybrook research institute (sri) and health canada, first nations inuit health branch – manitoba region (fnihb-mb) to pilot the an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi web data technology on the cnphi on the go app. the pilots were carried out in field environments, which included remote areas where internet connectivity can be problematic. method, design and development prior to development of the mobile app, needs assessment meetings were held with a group of stakeholders to inform the development of cnphi on the go. the meetings were attended by a multi-disciplinary team representing individuals from government, academia and research institutions. these stakeholders comprised an array of disciplines, including; epidemiologists, drinking water regulators, animal health veterinarians, public health inspectors, laboratory specialists and medical personnel. the meetings focused on identifying opportunities and challenges to support project implementation, and identifying gaps and additional capabilities that are required to make it operational. some of the specific topics discussed during the meetings included brainstorming ideas for mobile data collection and offline data collection. an initial meeting was held in 2015 followed by a second meeting in 2016. in summary, the discussions from these meetings identified the following key areas: • simplicity: participants mentioned that the app should be simple to use. it is important that the users feel comfortable with the system and are able to easily enter the data and navigate the application; • bi-directional: the participants also mentioned that there needs to be a function that allows for a two-way communication between the field workers and administrators in close to real-time. team members will therefore have an ability to exchange important information and disclose their location in a timely manner if required and appropriate; • offline: the participants also mentioned that the issues related to mobile phone coverage must be taken into account; • security: issues related to security and user authentication need to be considered. the cnphi on the go comprises two main components: • mobile infrastructure: this includes a dedicated mobile server that enables secure connectivity for mobile devices and integration with the existing cnphi web platform through an interface with a user access management system and a business logic integration layer. • mobile devices: this includes the mobile “app” specifically designed for mobile devices providing required functionality. the cnphi on the go app includes a multitude of features that extend specific cnphi functionalities and facilitate seamless access for the users. specifically: • mobile web data: the web data technology, which allows users to create the dynamic forms and surveys on cnphi has now been extended to the mobile platform, allowing users to perform data collection from their mobile device. once the data is submitted, the responses can be viewed under the web data interface on the web version. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi • offline data submission: the forms are accessible once downloaded and would allow users to perform data collection and submission in the absence of connectivity. once the data is saved within the app, records are queued for submission and automatically submitted to the web platform once stable connectivity is established. • access: the mobile platform uses the common cnphi user access management system and can only be used by individuals who are registered users of the cnphi platform. • pin code: the app allows users to set a 4 digit pin. the pin enables access to the app functionality regardless of the internet connectivity and also allows users to conveniently and quickly log-in instead of using their username and password. the pin is saved as long as the user does not sign out from the app. • geo-location: the app includes built-in automated capability to record latitude and longitude, if enabled by the user, before sending data to the web platform. • shared device: the app is designed to allow possible sharing of a device by multiple personnel. this is achieved by the “remove stored data” functionality which erases all stored data including a pin, if enabled. figure 1: a high level view of the cnphi on the go as an extension of the existing cnphi platform. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi pilots: deployment and testing the initial version of the app (“alpha version”) was developed on the android platform and was piloted in partnership with two organizations in the summer of 2017 (fnihb-mb: 17-7-2017 to 21-11-2017; sri: 25-7-2017 to 15-9-2017). fnihb – mb led the first pilot. the group is responsible for monitoring drinking water systems in first nations communities in manitoba. while collecting samples for bacteriological testing, they also collect metadata including sampling times and locations, residual chlorine values, turbidity readings, and other parameters from drinking water systems. a custom form was designed for this pilot and made available via the app, which was used in the field by environmental health officers (ehos). the mobile app was piloted in remote areas and inside structures with limited internet connectivity in order to test data collection capabilities under true field conditions. the second pilot was with sri, who led a project to demonstrate the feasibility of environmental sampling to detect the influenza virus in swine barns. it is believed that environmental sampling techniques that prove to be practical and effective at detecting the influenza virus would momentously benefit public health efforts in disease surveillance. similarly, this capability would also support the early identification of emerging zoonotic infectious diseases of public health significance. as part of the project, data was collected using environmental samplers in a swine barn. a custom data collection form was designed for this application using the web data technology, which was then made available through the mobile app. sample data was entered on the android device and laboratory testing results were superimposed once they were available. the objective of the pilots was first to familiarize users and to introduce them to cnphi mobile technology and enable collection of sampling data from the field using the cnphi on the go app. figure 2: data flow during the pilot phase. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi during the planning stages of the pilot, an initial consultation was held with the project leads to identify data elements for mobile data collection. once the data elements were finalized, two forms were developed using the web data technology on the cnphi web platform. the forms consisted of numerous fields including checkboxes, radio buttons and drop-down lists, among other selection and entry strategies. figure 3: data collection form that was developed for a) sri and b) fnihb-mb pilot the next step was to identify participants for both pilots who were provided appropriate access to the cnphi platform in order to gain access to the forms. the participants were provided with a user guide to download and use the app. instructions were included on accessing the form on the app and downloading it for offline use. information was also provided on special features, such as setting-up a pin for offline data collection, and the limitations of the app during the pilot phase. a total of 146 records were collected using the mobile platform from both pilots. during the course of the pilots, feedback was collected as per cnphi’s agile philosophy for application development. thus, any issues or bugs were addressed and rectified on an ongoing basis. moreover, participants were also informed when a new version of the app was released or when any modifications were made to any app features. the submitted records were instantly available through the web platform for interactive interrogation and further analysis. instant capabilities included, data queries and analysis, gis mapping and cluster analysis. evaluation of the cnphi on the go app as per cnphi’s agile approach to application development, user feedback and comments were submitted by participants to cnphi during the entire pilot phase on a routine basis. some of the an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi issues encountered during the pilot included: issues with log-in (a longer than usual time to login); incorrect gps coordinates in the records; issues with offline data collection; and incorrect dates in some records. using cnphi’s philosophy to development provided an ability to implement fixes and changes on an ongoing basis in partnership with users. a questionnaire was designed and sent to pilot leads at the conclusion of each pilot to explore the strengths and limitations of the application, to capture user experience and to compare and contrast their prior data management approaches with cnphi on the go. results pilot 1: fnihb – mb the fnihb-mb drinking water safety program uses sensors and test units to monitor water quality and collect relevant information across first nations communities in manitoba. as part of this program, field personnel, such as community based water monitors, collect and analyze water sample results locally in a laboratory that is set up in the community. after this step, results are reported to the eho. in addition, ehos will also take water samples for submission to accredited labs and collect additional information from the field during site visits or follow-up inspections. current practice involves recording of sample information and associated results using a paper form, which is faxed to the central office for review by eho’s and then manually entered into a database. this places a significant administrative burden on personnel and introduces delays in the process. it was mentioned that there can be significant lags between collection and receipt of water sampling and other monitoring data from the utilities. importantly, it was recognized that the flow of key information that can provide early warning of changing conditions, a decline in water quality or a loss of control in a drinking water system is subject to a significant lag. the pilot helped to highlight the need for improvements to the flexibility and timeliness of field data collection and the need for more agile generation of intelligence in support of risk management decisions and public health protection. through stakeholder discussions, other drinking water agencies also identified similar challenges related to a lack of timely collection and interrogation of field data. due to this, there is a clear need to modernize the overall data collection and reporting process in regards to drinking water safety programs. having an app to record and submit sample results would accelerate data submission and the availability of results, alleviating the workload and time required for manual entry. in addition, concurrent readings from the field related to key operational controls, such as chlorine residuals or turbidity fluctuations, better equip decision-makers to implement more timely health protection measures whenever the safety of drinking water is compromised or threatened. pilot 2: sunnybrook research institute the traditional process for data collection employed by sri involves manually writing sample information on a paper form, then transferring the information into an excel document. as additional data becomes available (including laboratory results), it is also entered manually into the excel document. information sharing is achieved through the use of encrypted excel documents sent via e-mail. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi benefits of using cnphi on the go in the field included efficiency, enhanced biosafety through the reduction in fomites though the elimination of notepaper and writing implements, and realtime access to data with analytics. minor issues were resolved within very short timeframes, thus enabling ongoing data collection and analysis without delay. both sri and fnihb-mb pilots reported an overall positive experience of using the app. some of the key findings include: cnphi on the go allowed seamless data collection while at the sampling site, followed by seamless data management on the web platform, and rapid information sharing for anyone with appropriate access. it made the entire process much simpler, less time-consuming, and overall fewer steps were required for data collection and management. it also allowed for quick data extraction when required. a major benefit is the ability to collect data without an active internet connection. further, significant efficiencies were gained by directly entering information on the app without having to write it down or subsequently transfer it to an electronic system. integration of the web and mobile platforms for secure information sharing between multiple involved parties provides a key benefit for rapid data collection and sharing. both programs see the application being used on a regular basis for the collection of monitoring and surveillance data where smartphones are used. discussion based on the results of the two pilots, the cnphi on the go app provides a significant opportunity for rapid data collection from the field. further, through its integration with the existing cnphi web-based platform, the technologies are positioned to enhance existing initiatives across various health protection disciplines, already well evolved on cnphi. specifically, the technology can provide the following key benefits: • remote data collection: the app allows users to collect public health data in remote locations without wi-fi or cell reception. • turnaround time: the turnaround time of the data collection is near-real time, allowing decision-makers to access the data instantly and use tools that allow for querying, analysis, visualization and intelligence generation. • precision: there is a reduction in transcription errors given that the data does not need to be manually entered from paper into a database. • secure data: access to the app requires cnphi credentials and follows the same security standards including built-in user audit functions. • efficiency: the data collection process requires less human resource commitment resulting in administrative cost savings. • easy and simple to use: the app is designed to be easy and simple to use. moreover, additional features such as the 4 digit pin allow users to quickly log-in without needing to enter the username and password. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi • flexibility: the web data technology provides an ability to interactively develop forms, providing flexibility to non-technical users for rapidly developing secure forms for data collection. it also allows administrators to modify the forms once deployed or create new forms as needed. • multiple types of data: data fields include; text, pick lists, checkboxes, radio buttons and numbers, allowing various types of information to be collected using the mobile web data technology. • early warning: faster access to data improves not only analysis and program responsiveness, but improves the ability to react to unexpected changes. moreover, since the data collection forms can be customized, further questions can be added to investigate interesting or critical findings in the field. • customizable: in contrast to a purpose-built approach, the cnphi on the go platform is customizable (flexible by design) allowing for dynamic and rapid deployment based on the need. • analysis: web data provides administrators with integrated search and interrogation capabilities. it also provides the ability for multiple users to access records at the same time while allowing for the production of instant data reports. it is important to note that although the platform enables a completely dynamic capability for data collection, avoidance of collection of personal or sensitive information is respected across the platform. conclusion the cnphi on the go is well poised to uniquely address the challenges related to rapid mobile data collection by enabling rapid form development and distribution, online and offline data collection, secure access to the data, and interactive data analysis. some of the tools on the cnphi platform, such as, the response centre technology have also been extended to the mobile app. this provides a capability for a two-way exchange of information between field personnel and administrators, allowing tasks and information to be managed in an efficient manner. moreover, a potential addition of other tools within the cnphi web platform would provide an infrastructure to support rapid data collection, bi-directional communication, investigations, outbreak management and the generation of geo-aware bio-surveillance intelligence related to public health issues of concern including human, animal, and environmental health. it will provide tools allowing close to real-time addition of intelligence related to the entire spectrum of surveillance, prevention, preparedness and response to events potentially affecting the health of the canadian public. some of the potential applications of the mobile technology include: • support for outbreak investigations: data for outbreak investigations could be collected in the field using mobile technology and submissions can be done in near-real time, which would provide the outbreak response team with quick access to data prompting quick analysis and response. • an inventory tool for key resources: the technology could also be used as an inventory management tool for validating and managing the working conditions of emergency kits. an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi • support for active or passive surveillance (e.g., collecting data on cases with influenza-like illness at monitoring clinics; a screening tool to monitor travel related illness by quarantine officers at ports of entry). • emergency management: during emergencies (e.g., wild fires, floods or extreme weather events) field activities and information can be managed under rapidly changing conditions. field personnel can be tasked and can respond with information while their safety and status can be monitored. limitations one of the limitations is that the current version of the app is only designed for android operating systems. however, there are plans to expand to other operating systems in the future. additionally, sample size for pilot participation was small for capturing the overall readiness of public health professionals to use such a platform on a regular basis and with full integration into surveillance activities. further pilots and testing have been planned to support informed ongoing evolution of the technology. acknowledgements we would like to acknowledge that the pilots were supported by funding from the canadian safety and security program (cssp), managed through defence research and development canada (drdc) centre for security science (css). authors would like to thank the contributions of the cnphi team, karren prost and eho’s from fnihb – mb for their support and input throughout the pilots. references 1. radio-television, c., & telecommunications commission. (2017). communications monitoring report 2017. canadian radio-television and telecommunications commission. 2. world health organization. (2011). mhealth: new horizons for health through mobile technologies. mhealth: new horizons for health through mobile technologies. 3. mahundi m, kaasbøll j, twaakyondo h. (2011). health information systems integration in tanzania: tapping the contextual advantages. in ist-africa conference proceedings, 2011 (pp. 1-11). ieee. 4. mukhi s, aramini j, kabani a. 2007. contributing to communicable diseases intelligence management in canada: cacmid meeting, march 2007, halifax, nova scotia. can j infect dis med microbiol. 18(6), 353-56. pubmed https://doi.org/10.1155/2007/386481 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18978985&dopt=abstract https://doi.org/10.1155/2007/386481 an innovative mobile data collection technology for public health in a field setting online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e202, 2018 ojphi 5. mukhi sn, chester tls, klaver-kibria jd, nowicki dl, whitlock ml, et al. 2011. innovative technology for web-based data management during an outbreak. online j public health inform. 3(1). pubmed https://doi.org/10.5210/ojphi.v3i1.3514 6. kloeze h, mukhi sn, alexandersen s. 2013. swine influenza test results from animal health laboratories in canada. can vet j. 54(5), 501. pubmed https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23569597&dopt=abstract https://doi.org/10.5210/ojphi.v3i1.3514 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24155436&dopt=abstract an innovative mobile data collection technology for public health in a field setting abstract introduction method, design and development pilots: deployment and testing evaluation of the cnphi on the go app results pilot 1: fnihb – mb pilot 2: sunnybrook research institute discussion conclusion limitations acknowledgements references isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e347, 2019 isds 2019 conference abstracts identifying persons who inject drugs in medical examiner data in maricopa county, az rasneet s. kumar1, kaitlyn sykes1, 3, kate goodin1, brian hanratty1, 2, jessica r. white1 1 office of epidemiology, maricopa county department of public health, phoenix, arizona, united states, 2 bioinformatics core facility, biodesign institute, arizona state university, tempe, arizona, united states, 3 cste applied epidemiology fellowship, atlanta, georgia, united states objective to determine whether data from the office of the medical examiner are useful for conducting injection drug use surveillance i n maricopa county, arizona, and to describe the characteristics of decedents who died from a drug overdose, were investigated by the county’s medical examiner, and had an indication of injection drug use. introduction the rate of drug overdose deaths in the united states has increased steadily since 2000. injection drug use, a practice assoc iated with infectious disease transmission, has likely increased along with this upward trend in drug overdoses. injection drug use surveillance is difficult to conduct at a public health department because there are no specific internal classification of diseases codes to identify this risk behavior in hospital discharge or vital registration data. maricopa county department of public health office of epidemiology aimed to identify indications of injection drug use within data from the office of the medical examiner methods the office of epidemiology receives toxicology results for deaths which were ruled by the maricopa county’s medi cal examiner as substance use related, also referred to as overdose deaths. we explored data from decedents who were investigated by the maricopa county’s medical examiner between 7/1/20166/30/2017, had a cause of death from a drug overdose, and screened positive for at least one commonly injected drug (i.e., opioids general, heroin, methamphetamine, or cocaine). to narrow our search for indications of injection drug use, we requested preliminary investigation reports and medical examiner reports for a random sample of reports (10% from each drug class) from the office of the medical examiner. preliminary investigation report s, produced by a scene investigator, included the decedent’s medical history, risk factors, circumstances of death, circumstance s of death discovery, and scene characteristics. medical examiner reports included toxicology screen results, autopsy findings, an d the cause of death. the office of the medical examiner provided these reports to the office of epidemiology in portable documen t format (pdf) for this analysis. we built a query to identify keywords related to injection drug use (e.g. inject, syringe, ne edle) and injection injuries (e.g. cellulitis, abscess). we used pdf xchangeviewer’s optical character recognition (ocr) function to convert the pdf reports to text data and used python’s string and collections modules to parse text data for occurrences of keywords within the reports. reports that included at least one keyword were manually reviewed and classified as probable or ruled out for injection drug use, and characteristics of those with a probable indication of injection drug use were described. results during the period of interest, 1,127 deaths were caused by drug overdose, of which 930 decedents screened positive for opioids (n=673, 59.7% of drug overdose deaths), heroin (358, 38.5%), methamphetamine (445, 39.5%), and/or cocaine (100, 11%). indications of injection drug use were identified within 48 (32.7%) of the 147 preliminary investigation and medical examiner reports that were reviewed (table). common indicators of injection drug use included: history of drug use as a reported risk factor; presence of prescription drugs, illicit drugs, or drug paraphernalia at the scene; body position at the scene; and injuries associated with needle use identified during the autopsy. the most common terms that indicated injection drug use were “syringe”, “intravenous”, “needle”, and “inject”. among persons who had an indication of injection drug use, 85.4% were male, 52.1% were between the ages of 20 and 39 years, and 85.4% were white. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e347, 2019 isds 2019 conference abstracts conclusions data from the office of the medical examiner’s preliminary investigation and medical examiner reports provided rich context for understating the underlying risk factors and circumstances that contributed to drug overdose deaths. injection drug use paraphernalia found at the scene of death and injuries found during autopsy were well-documented in these reports, which helped us quantify the proportion of decedents who died from overdose that may have injected drugs, by drug class. we were able to describe basic characteristics of this sample, which were consistent with previously published reports describing people who inject drugs. this surveillance method has limitations, however. decedents reviewed by a medical examiner represent a subset of the drug using population, and findings may not be generalized to the full population. other data sources and analytical methods must be employed to accurately estimate the number of people who inject drugs in maricopa county and to describe their characteristics and experiences. acknowledgement this report was supported in part by an appointment to the applied epidemiology fellowship program administered by the council of state and territorial epidemiologists (cste) and funded by the centers for disease control and prevention (cdc) cooperative agreement number 1u38ot000143-04 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts public health surveillance for the great american solar eclipse in oregon laurel boyd*1, meredith jagger1, kathryn kuspis1, melissa powell1 and sandy giffin2 1oregon public health authority, portland, or, usa; 2oregon poison center, portland, or, usa objective develop a public health surveillance plan for the oregon public health division (ophd) in anticipation of the expected influx of visitors for the 2017 great american solar eclipse. introduction the great american solar eclipse of 2017 provided a rare opportunity to view a complete solar eclipse on the american mainland. much of oregon was in the path of totality and forecasted to have clear skies. ahead of the event, ophd aggregated a list of 107 known gatherings in mostly rural areas across the state, some with estimated attendance of up to 30,000 attendees. temporary food vendors and a range of sanitation solutions (including open latrines) were planned. international travelers were expected, along with large numbers of visitors traveling by car on the day of the eclipse. the potential for multiple simultaneous mass gatherings across the state prompted ophd to activate an incident management team (imt) and to create a health intelligence section to design a mass gathering surveillance strategy. statewide syndromic surveillance (oregon essence) has been used to monitor previous mass gatherings (1) and captures statewide emergency department (ed), urgent care, oregon poison center, and reportable disease data. methods the ophd health intelligence section identified five categories of public health effects associated with large outdoor gatherings based on literature review (2–5) and an internal risk assessment. these included health system status (total visit or call counts), communicable disease (fever, bloody diarrhea and reportable disease counts), injuries and substance abuse (including motor vehicle accidents), and outdoor exposure (heat-related illness, snake bites and asthma-related visits). an event-related category monitored trends in eclipse-related visits or eye-related injuries (looking directly at the sun). where possible, syndromic trends were assessed in multiple data sources. these categories were used to create dashboards within oregon essence and shared in a guidance document for local health departments and hospitals. health intelligence monitored syndromes of interest during a period of enhanced surveillance (9/18-9/22), and met daily with members of the ophd imt to share surveillance summaries, which were also sent to ophd leadership and external partners. results during the enhance surveillance period, the ophd health intelligence section did not identify statewide increases in healthcare utilization (total ed visits and calls to the oregon poison center), but did observe increases in visits at select emergency departments in the state. visits by out-of-state residents (as determined by patient zip code at time of registration) increased during the surveillance period. fever-related visits increased as well but were not accompanied by reports of illnesses clusters. increases were noted for motor vehicle accidents, eye-related injuries, and “eclipse”-related visits. increases in eye-related injuries appeared to be an annual seasonal trend and not related to the eclipse. there were no increases of note in the other queries monitored. development of new queries (west nile virus) was begun based upon mosquito pool surveillance findings. surveillance highlights were posted publicly in a special edition of the biweekly oregon essence hazard report (see image 1). conclusions statewide public health surveillance during the 2017 great american solar eclipse in oregon did not identify clusters of infectious disease or other opportunities for real-time public health intervention. nevertheless, surveillance identified increases in motor vehicle accidents, especially among out-of-state residents, due perhaps to increased road travel for the event. preparations for this event increased capacity of state health department staff to conduct this type of surveillance in the future. tools created for the eclipse have been used in several imt activations since the eclipse. portion of the 8/30/17 oregon essence hazard report published online with surveillance findings from for the great american solar eclipse keywords mass gathering; syndromic surveillance; eclipse; incident command system acknowledgments tasha poissant, jamie bash, lisa takeuchi, alexia zhang, magdalena scott, roza tammer, emilio debess, david lehrfeld, eric gebbie, dewayne hatcher isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1. jagger ma, jaramillo s, boyd l, johnson b, reed kr, powell m. mass gathering surveillance : new essence report and collaboration win gold in or. 2017;9(1):2579. 2. who. public health for mass gatherings: key considerations. world health organization. 2015. 3. lombardo js, sniegoski ca, loschen wa, westercamp m, wade m, dearth s, et al. public health surveillance for mass gatherings. johns hopkins apl tech dig (applied phys lab. 2008;27(4):347–55. 4. polkinghorne bg, massey pd, durrheim dn, byrnes t, macintyre cr. prevention and surveillance of public health risks during extended mass gatherings in rural areas: the experience of the tamworth country music festival, australia. public health. 2013;127(1):32–8. 5. burdick te. wilderness event medicine: planning for mass gatherings in remote areas. vol. 3, travel medicine and infectious disease. 2005. p. 249–58. *laurel boyd e-mail: laurelhifi@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e161, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e364, 2019 isds 2019 conference abstracts improving integrated disease surveillance and response capacity in guinea, 2015-2018 jennifer hemingway-foday, ousmane souare, eileen reynolds, boubacar dialio, marlyatou bah, almamy karamokoba kaba, moussa kone, telly dialio, nouhan camara, mohammed kante, siba guliavogul, massaran kante, pia macdonald rti international, united states objecive the objective is to discuss capacity building for integrated disease surveillance and response in guinea and synthesize lessons learned for implementing the global health security agenda in similar settings. introduction the 2014-2016 ebola outbreak in guinea revealed systematic weaknesses in the existing disease surveillance system. the lack of public health workers adequately trained in integrated disease surveillance and response (idsr) contributed to underreporting of cases and problems with data completeness, accuracy, and reliability. these data quality issues resulted in difficulty assessing the epidemic's scale and distribution and hindered the control effort [1,2]. in 2015, the guinean ministry of health (moh) recognized the importance of the idsr framework as a tool for improving disease surveillance and emphasized idsr strengthening as a priority activity in the post-ebola transition [3]. to support this strategic objective, we engaged with the moh, cdc, and key surveillance partners to strengthen surveillance capacity through a national initiative to improve idsr tools, including assistance with developing guinea-specific idsr technical guidelines, simplified and standardized case notification forms, and supportive job aids to facilitate appropriate idsr implementation by health workers at all levels of the system. methods the ebola outbreak highlighted the need for streamlined and standardized case reporting tools that promote accurate application of standard case definitions, adherence to idsr technical guidelines, and integration of data from clinical and laboratory sources [1]. we partnered with the moh and cdc to update case notification forms and create job aids for improved idsr implementation at all health levels. using a one health approach, we helped organize and facilitate a series of workshops between the moh, ministries of agriculture and environment, cdc, national laboratory, and other surveillance partners to review and update the guinea-specific idsr priority diseases. this resulted in the identification of 14 priority diseases and events, which are the focus of weekly epidemiological surveillance. by bringing together the ministries of health, agriculture, and environment, the workshops resulted in improved tools for zoonotic disease detection, reporting, and responses. this included agreement on 3 new zoonotic diseases (anthrax, brucellosis, and rabies) for weekly reporting, as well as recommendations for enhancing surveillance of zoonotic diseases already included in weekly surveillance, such as influenza and ebola. to further promote collaboration, we helped establish a technical working group and implemented a series of workshops for the ministries and surveillance partners to review and revise case notification forms for the 14 priority diseases and events. within the moh, we also solicited feedback from health workers at the national, regional, and district levels to identify needs throughout the health system. as a result, each form now has an agreed-upon data collection structure that is consistent with idsr guidelines. standardized sections were applied across forms for case identification, notification, hospitalization, actions taken, and feedback tracking. the standardization improves data consistency across forms and establishes familiarity with common data elements, which leads to more complete data capture. additionally, each form promotes accurate case classification by collecting diseasespecific information on risk factors, signs and symptoms, and laboratory analysis and results. the revised forms also use a logical data collection flow that follows the patient’s information from the site of identification, to higher levels of care (if required), laboratory, and the national level, thus improving data integration and completeness. the forms have been incorporated into the national dhis2 electronic surveillance system, which allows data entry at the district, regional, national, and laboratory level and supports rapid and complete reporting. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e364, 2019 isds 2019 conference abstracts results the development of revised case notification forms demonstrates an effective, collaborative, one health approach to idsr. all three ministries participated in the development and revision of the forms and subsequently, approved and adopted the forms for surveillance of priority diseases. this one health approach has provided the government of guinea with a framework for identifying and strengthening surveillance of its five zoonotic diseases of greatest public health concern, which enables measurement of progress towards achieving the objectives of the ghsa zoonotic disease action package. we collaborated with the moh to launch nationwide training of trainers for the new case notification forms, including the use of dhis2 to manage, report, analyze, and present data. the training of trainers produced a cadre of 55 trainers, representing the participating ministries, national laboratory, and key surveillance partners such as who. by the end of 2018, idsr training will cover health workers at all levels of the system in all 38 of guinea’s health districts. incorporating dhis2 as a platform for managing case data further demonstrates guinea’s capacity to conduct event-based surveillance and track the 14 priority diseases and events in real-time, an essential indicator of the ghsa real-time surveillance action package. conclusions guinea’s idsr strengthening activities are an important step towards achieving the ghsa objective of establishing a functional public health surveillance system capable of detecting events of significance for public health, animal health, and health security. the updated case notification forms, coupled with the use of dhis2 for real-time reporting, provide critical tools to promote more complete, accurate, and timely data; however, successful implementation will rely on effectively training health workers throughout the system and providing on-going supportive supervision. the multi-sectoral approach to developing idsr tools helped establish a foundation for future collaboration across ministries using a one health approach to strengthen guinea’s national health surveillance system. while the idsr activities have focused heavily on building capacity for human disease surveillance, it is critical that similar attention is given to animal health. the moh and surveillance partners should continue to work with the ministries of agriculture and environment to build surveillance capacity for detecting and controlling zoonotic threats while they are still in animal populations and to develop compatible human and animal surveillance data fields for more efficient, integrated data systems. acknowledgements this work is supported by funding provided by the centers for disease control and prevention cooperative agreement 1u19gh001591-01. references 1. mcnamara la, schafer ij, nolen ld, gorina y, redd jt, et al. 2016. ebola surveillance – guinea, liberia, and sierra leone. mmwr suppl. 65(3), 35-43. doi:https://doi.org/10.15585/mmwr.su6503a6. pubmed 2. bell bp, damon ik, jernigan db, et al. 2016. overview, control strategies, and lessons learned in the cdc response to the 2014–2016 ebola epidemic. mmwr suppl. 65(suppl-3), 4-11. doi:https://doi.org/10.15585/mmwr.su6503a2. pubmed 3. ministere de la santé-république de guinée, direction nationale de la prevention et santé communautaire, division prevention et lutte contre la maladie. plan de renforcement de la surveillance des maladies à potentiel epidémique en guinée (2015-2017), august 2015. http://ojphi.org/ https://doi.org/10.15585/mmwr.su6503a6 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27389614&dopt=abstract https://doi.org/10.15585/mmwr.su6503a2 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27389903&dopt=abstract isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts a proposed sys case definition for opioid overdose related ed visitsan evaluation in three regions yushiuan chen*2, sandra gonzalez3, 4, harold gil5, michele askenazi1, felicia quintana-zinn3, arthur davidson6, christine billings7 and ming qu3 1tri-county health department, greenwood village, co, usa; 2tri-county health department, greenwood village, co, usa; 3nebraska department of health and human services, lincoln, ne, usa; 4university of nebraska-lincoln, lincoln, ne, usa; 5marion county public health department, marion county, co, usa; 6denver public health, denver, co, usa; 7jefferson county public health, golden, co, usa objective the objective is to develop a standard opioid overdose case definition that could be generalized nationally. introduction opioid ods have been rising globally and nationally. the death rate from ods in the united states has increased 137% since 2000, including a 200% increase of od deaths involving opioids1. the pilot project, a collaboration across 3 states, allowed information sharing with syndromic surveillance (sys) partners across jurisdictions, such as sharing a standard sys case definition and verifying its applicability in each jurisdiction. this is a continuation of the work from an initial pilot project presented during the isds opioid od webinar series. methods three regions (colorado north central region [co-ncr]), state of nebraska [ne], and marion county, indiana) participated in the development and evaluation of the opioid od case definition. data sources included essence and 2015 hospital discharge data (hdd) for the first two jurisdictions. work was conducted in 3 stages. stage i and ii consisted of the development and validation of an opioid misuse definition. in stage i, the percent of completeness of admission date, chief complaint (cc), and discharge diagnosis (dd) was assessed from january 2015 to august 2016 sys emergency department (ed) data from each of the 3 participating jurisdictions. data selected for the time period with the best completeness among all jurisdicions was utilized to develop a case definition. completeness of essence data submission was assessed at all jurisdicions. the threshold for best data quality was 80% of completeness. sys ed data was analyzed for the selection of cc search terms and icd9/icd102 dd codes, and the reported chief complaint-discharge diagnosis (ccdd) were validated by analyzing consistency between cc and dd. in stage ii, the consistency of dd reporting corresponding to the opioid case definition was assessed for co-ncr and ne data by performing pearson correlation analysis to compare the weekly counts of opioid misuse cases in 2015 sys ed data to those obtained in hdd. stage iii consisted of the development of an opioid od case definition that meets the dd code reporting requirements of the centers for disease control and prevention (cdc), prescription drug overdose prevention for states awardees. this definition consisted of an essence query containing cc, and ccdd components. for stage iii, sys ed data was analyzed for the august 2016 to august 2017 time period. the case definition was evaluated by assessing the consistency between the cc and dd reported for each identified opioid od possible case. triage notes were used for case validation. results stage i: mean percent of completeness of dd codes for co-ncr, ne and marion county, in, 2015 ed sys data was ≥ 85%. in the co-ncr, of 963 cases detected by the cc definition, 99.4% had an opioid misuse diagnostic code in the dd, while of 1,445 cases detected by the dd, 66.2 % had an associated opioid misuse in the cc search terms. in ne, of 6 cases detected by the cc definition, 33% identified opioid misuse dd. however, of 42 cases detected by the dd definition, only 5% identified opioid misuse cc search terms. in marion county, in, of 95 cases detected by the cc definition, 70% identified opioid related diagnosis codes. of 191 cases detected by the dd definition, only 20% identified opioid-related cc search terms. stage ii: results of the pearson correlation analysis indicate statistically significant correlations between 2015 sys and hdd data for the dd code based opioid definition for both co (r = 0.92, p < 0.0001), and ne (r = 0.63, p < 0.0001). stage iii: in ne, 56% of the cases detected by the cc component, identified opioid od dd codes, and only 8% of the cases detected by the dd component identified opioid od search terms in the cc. triage notes were consistent with opioid od in 55% of the cases detected by the dd component. however, for co-ncr, of 235 cases detected by the cc component, 215 identified opioid od dd codes. of 465 cases detected by the dd component, 46% identified opioid od search terms in the cc field. triage notes values were consistent with opioid od reported dd codes in 80% of the cases. conclusions results suggest that dd codes reported in sys ed data correlated with hdd. indicators of opioid od signs and symptoms were observed in ccdd. therefore, the sys case definition proposed through this pilot project may be applied by other states to support real-time monitoring of opioid od related hospital ed visits, and consequences of opioid od. further study includes exploring how triage notes search terms may improve the identification of opioid od related ed visits. keywords opioid overdose; emergency department; drug/substance misuse; syndromic surveillance; essence acknowledgments boulder county public health (tailia brown), denver public health (cali zimmerman), jefferson county public health (angel anderson and kate watkins), hospitals, cdc nssp, johns hopkins university essence references 1. rudd, r. a., aleshire, n., zibbell, j. e., & gladden, r. m. (2016). increases in drug and opioid overdose deaths —united states, 2000– 2014. mmwr. morbidity and mortality weekly report, 64(50-51), 1378–1382. 2. ising, a., proescholdbell, s., harmon, k. j., sachdeva, n., marshall, s. w., & waller, a. e. (2016). use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies. injury prevention, 22(suppl 1), i43–i49. *yushiuan chen e-mail: ychen@tchd.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e87, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts improving timeliness of georgia emergency room data lance s. ballester*, karl soetebier, bill williamson, rene borroto, jessica grippo, patrick pitcher and cherie drenzek georgia department of public health, atlanta, ga, usa objective to explore the timeliness of emergency room surveillance data after the advent of federal meaningful use initiatives and determine potential areas for improvement. introduction timeliness of emergency room (er) data is arguably its strongest attribute in terms of its contribution to disease surveillance. timely data analyses may improve the efficacy of prevention and control measures. there are a number of studies that have looked at timeliness prior to the advent of meaningful use, and these studies note that er data were not fast enough for them to be useful in real time2,3. however, the change in messaging practices in the meaningful use era potentially changes this. other studies have shown that changes in processes and protocol can dramatically improve timeliness1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness. methods er data were collected from march 2017 through september 2017 from both the georgia department of public health’s (gdph) state electronic notifiable disease surveillance system (sendss) syndromic surveillance module and the centers for disease control and prevention (cdc) national syndromic surveillance program’s (nssp) essence systems. patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after august 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals. the timeliness of individual records was determined in a number of different ways. all timeliness measurements were determined by subtracting the earlier time event from the later time of the event. the overall measure of timeliness is the time between the patient’s triage time and the data being present in the essence data system. in between, georgia’s sendss system receives and processes the data. this is illustrated in figure 1. due to the skewed nature of these measures, they were analyzed using medians and gaussian kernel density plots. results the study in total included records from 118 georgia hospitals, 14,203 data files and 1,897,501 patient records. overall median timeliness of data from triage time to being available in sendss for analyses was 33.62 hours (iqr=28.5), and in essence was 45.08 hours (iqr=37.05). the distributions of triage time of day, time available in sendss staging, and time available in essence analysis can be seen in figure 2. additionally, lines were added for when sendss makes data available for its own analyses and when it sends data to essence. these latter lines represent places where the sendss system itself could improve, and potential improved times were noted based on the kernel densities. peak triage times for georgia hospitals were between 10 am to 11 pm, shown in black. this represents the ideal timeliness if hospitals sent their data immediately. however, data was all batched by georgia hospitals and sent at different times of the day. the distribution of the time patient records arrived at sendss staging was indicated in blue. during the period of this study, georgia processed data into its sendss system at 6:30am and 11:30am every day and sent data to the essence system at 1pm each day. these times are highlighted on the plot in green, and red respectively. new potential improved times, based on the kernel density of data being available in sendss staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to essence at 9am and 12:30pm to ensure time for data to be properly processed. these were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis. the purple line on the plot represents the times that data were available in essence’s system for analysis. this was notably delayed by a median 4.15 hours after the data was sent to essence on a typical day. conclusions a data driven approach to choosing processing times could improve timeliness of data analyses in the sendss and essence systems. by conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords timeliness; meaningful use; essence references 1. centers for disease control. progress in improving state and local disease surveillance--united states, 2000-2005. mmwr morbidity and mortality weekly report. 2005;54(33):822-825. 2. jajosky r, groseclose s. evaluation of reporting timeliness of public health surveillance systems for infectious diseases. bmc public health. 2004;4(1). 3. travers d, barnett c, ising a, waller a. timeliness of emergency department diagnoses for syndromic surveillance. amia annual symposium proceedings. 2006;vol. 2006:769. 4. ward m, brandsema p, van straten e, bosman a. electronic reporting improves timeliness and completeness of infectious disease notification, the netherlands, 2003. eurosurveillance. 2005;10(1):7-8. *lance s. ballester e-mail: lanceballester@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e74, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts burden and trend of measles in nigeria: five-year review case-base surveillance data baffa s. ibrahim*1, 2, yahaya mohammed2, rabi usman2, aisha abubakar3 and patrick nguku2 1center for international health, education and biosecurity, university of maryland global initiative, abuja, nigeria; 2nigeria field epidemiology and laboratory training program, abuja, nigeria; 3ahmadu bello university, zaria, nigeria objective we reviewed measles specific integretaged disease surveillance and response (idsr) data from nigeria over a five-year period to highlights its burden and trends, and make recommendations for improvements. introduction measles is a vaccine preventable, highly transmissible viral infection that affects mostly under-five year children. the disease is caused by a morbillivirus; member of the paramyxovirus family. methods we conducted a secondary data analysis of measles specific idsr records of all states in nigeria from january 2012 to september 2016. the record had reported measles cases with laboratory outcomes from all the states. idsr weekly epidemiological data were obtained from surveillance unit, nigerian centre for disease control (ncdc). results a total of 131,732 cases were recorded within the period. highest number of cases 57,892(43.95%) were recorded in 2013 while the least number of cases 11,061(8.4%) were recorded in 2012. a total of 817 deaths were recorded, giving a case fatality rate (cfr) of 0.62%. the cfr showed a decreasing trend over the years with the highest cfr (1.43%) recorded in 2012 and the least cfr (0.44%) recorded in 2016. only 8,916 (6.7%) cases were confirmed by laboratory investigation. the northwest region recorded the highest attack rate (ar) of 149.7 cases per 100,000 population, followed by the northeast region with 140.2 cases per 100,000 population, while the south-south region recorded the least ar of 15.8 cases per 100,000 population. cfr per region followed similar pattern, with the northcentral region having the highest cfr of 4.38%. the trend of measles cases followed the same pattern. cases peaked at march, then gradually reduced to lowest level in june. conclusions measles infection remains a burden especially in the northern region of nigeria. though measles fatalities were on decline over the years, laboratory diagnosis of cases has been suboptimal. we recommended improvement on routine immunization and measles case management, and strengthening of regional laboratories capacity for measles diagnosis. regional and yearly distribution of number of cases, number of deaths, attack rates and case fatality rates for measles cases in nigeria from january 2012 to september 2016. distribution of number of cases, deaths, and case fatality rates for measles cases in nigeria from january 2012 to september 2016. map of nigeria showing measles attack rate per 100,000 population across the states, 2012 2016 map of nigeria showing measles case fatality rates (cfr) across the states, 2012 – 2016 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts comparative yearly trend of measles cases in nigeria from 2012 to 2016. keywords disease outbreak; measles; nigeria; vaccination acknowledgments authors gratefully acknowledge nigeria field epidemiology and laboratory training program and all staff of the nigeria center for disease control (ncdc) for their support. references 1. who | measles. who [internet]. world health organization; 2017 [cited 2017 apr 10]; available from: http://www.who.int/mediacentre/ factsheets/fs286/en/ 2. akande tm. a review of measles vaccine failure in developing countries. niger. med. pract. same ventures; 2007;52:112–6. 3. ibrahim bs, gana gj, mohammed y, bajoga ua, olufemi aa, umar as, et al. outbreak of measles in sokoto state north-western nigeria, three months after a supplementary immunization campaign: an investigation report 2016. australas. med. j. 2016;9:324–35. *baffa s. ibrahim e-mail: baffasule@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e102, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e379, 2019 isds 2019 conference abstracts detection of a salmonellosis outbreak using syndromic surveillance in georgia rene borroto, jessica pavlick, karl soetebier, bill williamson, patrick pitcher, cherie drenzek georgia department of public health, atlanta, georgia, united states objective describe how the georgia department of public health (dph) used data from its state electronic notifiable disease surveillance system (sendss) syndromic surveillance (ss) module for early detection of an outbreak of salmonellosis in camden county, georgia. introduction evidence about the value of syndromic surveillance data for outbreak detection is limited [1]. in july 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in camden county, georgia, where one meal was served on 7/27/2018 and on 7/28/2018. methods sendss-ss and sas were used for cluster detection of emergency department (ed) patients with similar chief complaint (cc), triage notes (tn), or discharge diagnoses (ddx) by facility, time of ed visit, and zip code / county of residence. a sas -based free-text query related to food poisoning in the cc and ddx fields was also performed on a daily basis. countyand hospitalspecific charting of the diarrhea syndrome was also conducted in sendss-ss, whereas countyand zip code-specific charting of the same syndrome were done in both sendss-ss and sas [2]. results on sunday july 29th, 2018, three children and three adults were seen within 18 hours at the ed of hospital a in camden county, georgia. all patients complained of diarrhea, vomiting, and food poisoning, after a large family reunion that had been held t he day before. this early cluster was detected by the sasbased free-text query of ‘food poisoning’ and the sas-based cluster detection tool for patients with diarrhea syndrome. the district epidemiologists (de) in the coastal health district were notified on monday, july 30th, 2018. one-year high daily spikes of the diarrhea syndrome occurred from july 29th to july 31st, 2018 in a local hospital ed (fig 1), camden county, and zip code 31548. two hipaa-compliant line lists with a total of 27 patients seen at eds were emailed to the des to support active case finding. no further spikes of the diarrhea syndrome were detected in camden county during the 2-week period after the 3-day spike. conclusions syndromic surveillance was a useful surveillance tool for early detection of a salmonellosis outbreak, helping with the active search for outbreak cases, tracking the peak of the outbreak, and assuring that no further spikes were occurring. acknowledgement we are grateful to the epidemiologists of the coastal district, robert thornton, meredith avery, and elizabeth goff, for their hard work during this outbreak. references 1. hopkins r, tong c, burkom h, et al. 2017. a practitioner-driven research agenda for syndromic surveillance. public health rep. 132(1_suppl), 116s-26s. pubmed https://doi.org/10.1177/0033354917709784 2. zhang g, llau a, suarez j, o’connell e, rico e, et al. 2008. using essence to track a gastrointestinal outbreak in a homeless shelter in miamidade county, 2008. advances in disease surveillance. 5, 139. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28692395&dopt=abstract https://doi.org/10.1177/0033354917709784 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e379, 2019 isds 2019 conference abstracts figure 1. emergency department patients with syndrome of diarrhea, camden county, 8/16/2017 – 8/15/2018 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e333, 2019 isds 2019 conference abstracts asthma vs. pm2.5: a bridge between health and environmental surveillance. victoria f. dirmyer presbyterian healthcare services, albuquerque, new mexico, united states objective to inform asthmatic, health plan patients of air quality conditions in their specific geographic location and to assess if th e communication is successful in reducing the number of emergency department visits for asthmatic/respiratory flare ups. introduction southwest states are prone to wildfires, dust storms, and high winds especially during the monsoon season (june – september). wildfire smoke is a complex mixture of carbon monoxide, carbon dioxide, water vapor, hydrocarbons, nitrogen, oxides, metals, and particulate matter (pm). dust storms are made up of aerosols and dust particles varying in size; particles bigger than 10 µm are not breathable, but can damage external organs such as causing skin and eye irritations. particles smaller than 10 µm are inhalable and often are trapped in the nose, mouth, and upper respiratory tracts, and can cause respiratory disorders such as asthma and pneumonia. numerous studies have characterized the epidemiological and toxicological impact of exposure to pm in dust or smoke form on human health [1]. all of these environmental conditions can have impacts on cardiovascular conditions such as hypertension and cause respirator y flare ups, especially asthma. previous studies have shown a relationship between pm exposure and increases in respiratory-related hospital admissions [1-4]. in an analysis of the health effects of a large wildfire in california in 2008, reid, et. al, observed a linear increase in risk for asthma hospitalizations (rr=1.07, 95% ci= (1.05, 1.10) per 5 µg/m3 increase) and asthma emergency department visits (rr=1.06, 95% ci=(1.05, 1.07) per 5 µg/m3 increase) with increasing pm2.5 during wildfires [5]. in a study specific to new mexico, resnick, et. al, found that smoke from the wallow fire in arizona in 2011 impacted the health of new mexicans, observing increases in emergency department visits for asthma flare-ups in santa fe, espanola, and albuquerque residents [6]. this current study will evaluate the effectiveness of outreach to asthmatic members during times of poor air quality; informing them of the air quality, instructing them to limit their outdoor activity, and to remind them to carry or access their inhalers or other medical necessities if/when needed. methods a recent 12-month eligible member list was generated including member id, street address, zip code, and a count of the number of emergency department (ed) visits for the specified time period. the member list was then geocoded using the tool quest. any records that did not map to a latitude and longitude within the state boundary of new mexico were excluded. the geocoded list was then joined to a list of members who had an indicator for asthma (a hospital admission or ed visit with a primary diagnosis for asthma). this list of asthmatic, eligible members was then mapped using qgis 3.2. the new mexico environment department’s (nmed) air quality bureau operates a network of ambient air monitors across the state. monitors range in size from neighborhood level to regional and pollutants measured include ozone, pm2.5, pm10, nitrogen dioxide, and sulfur dioxide. each individual air monitor was mapped to a point location with individual buffer zones (depende nt on the monitor’s collection size). asthmatic members were mapped to air monitor buffers using a spatial overlap program in qgis. each air monitor then had a list of asthmatic members who were tied to the air monitor and would be contacted if the air quality index (aqi) value for that air monitor was less than good (>50). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e333, 2019 isds 2019 conference abstracts results in a given 12-month period, there were 38,364 asthmatic members mapped to a geographic point within the state boundary of new mexico. of the 14 air monitors across the state, 9,965 (26%) asthmatic members mapped to an air monitor. nmed posts air monitor readings on their website daily. during the upcoming 2019 monsoon season, air monitors with a daily aqi reading of >50 will trigger the emailing of a scripted letter to asthmatic members (connected to that specific monitor) informing them of poor air quality in their area and alerting them to limit their outdoor time and to ensure that their medications are up -to-date and easily available. in order to construct this letter in a non-intrusive, succinct manner, collaboration with business partners (who have experience with working with members on a 1:1 basis) within the organization will assist with ensuring a targeted message. after the 2019 monsoon season, this project will be evaluated to determine if the intervention was beneficial in reducing the number of ed visits for the members who were contacted. ed visit rates specific to asthma (inclusion of asthma specific diagnosis code) will be compared for the pre and post intervention monsoon seasons. conclusions combining external, state-level data with internal member-level data can have powerful results. due to protected health information (phi), state level data sometimes is unavailable at a person-level basis, and thus pointed, individual interventions are not possible. by combining internal and external data sources on different health related topics, it is possible to create a more cohesive, personlevel, health-impactful view of a person and their environment. acknowledgement this project did not receive any external funding for completion. references 1. fann n, alman b, broome ra, et al. 2018. the health impacts and economic value of wildland fire episodes in the u.s.: 2008-2012. sci total environ. 610-611, 802-09. pubmed https://doi.org/10.1016/j.scitotenv.2017.08.024 2. delfino rj, brummel s, wu j, et al. 2009. the relationship of respiratory and cardiovascular hospital admissions to the southern california wildfires of 2003. occup environ med. 66(3), 189-97. pubmed https://doi.org/10.1136/oem.2008.041376 3. gan rw, ford b, lassman w, et al. 2017. comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions. geohealth. 1(3), 122-36. pubmed https://doi.org/10.1002/2017gh000073 4. kanatani kt, ito i, al-delaimy wk, et al. 2010. desert dust exposure is associated with increased risk of asthma hospitalization in children. am j respir crit care med. 182(12), 1475-81. pubmed https://doi.org/10.1164/rccm.201002-0296oc 5. reid ce, jerrett m, tager ib, petersen ml, mann jk, et al. 2016. differential respiratory health effects from the 2008 northern california wildfires: a spatiotemporal approach. environ res. 150, 227-35. pubmed https://doi.org/10.1016/j.envres.2016.06.012 6. resnick a, woods b, krapfl h, toth b. 2015. health outcomes associated with smoke exposure in albuquerque, new mexico, during the 2011 wallow fire. j public health manag pract. 21(suppl 2), s55-61. pubmed https://doi.org/10.1097/phh.0000000000000160 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28826118&dopt=abstract https://doi.org/10.1016/j.scitotenv.2017.08.024 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19017694&dopt=abstract https://doi.org/10.1136/oem.2008.041376 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28868515&dopt=abstract https://doi.org/10.1002/2017gh000073 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20656941&dopt=abstract https://doi.org/10.1164/rccm.201002-0296oc https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27318255&dopt=abstract https://doi.org/10.1016/j.envres.2016.06.012 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25621447&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25621447&dopt=abstract https://doi.org/10.1097/phh.0000000000000160 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e421, 2019 isds 2019 conference abstracts 2017 – 2018 winter weather surveillance in louisiana dayaamayi kurimella, gillian richardson louisiana department of health, new orleans, louisiana, united states objective the louisiana early event detection system (leeds), louisiana’s syndromic surveillance system, was used to monitor uncharacteristically low temperatures during the 2017-2018 winter season and determine the impact of these temperatures on the number of weather related personal injuries in emergency departments throughout louisiana. introduction the infectious disease epidemiology section (idepi) in the louisiana office of public health (oph) has several applications for syndromic surveillance including situational awareness during unusual and/or high profile events, such as the uncharacteristic winter weather louisiana experienced during the 2017-2018 winter season. december 8th, 2017 saw freezing temperatures with some parts of the state experiencing snow, and january 16 – 18, 2018 saw record breaking freezing temperatures throughout the state. both weather events led to many state office, school and business closures. the freezing temperatures from january 16th – 18th led to office closures that lasted longer than the freezing temperatures due to the infrastructure damage the freezing temperatures caused. for example, orleans parish experienced low water pressure throughout its water systems due to broken pipes following the freeze, leading to boil water advisories. many people throughout louisiana had broken pipes at their homes, resulting in flooding and further contributing to low water pressure in some areas. idepi used syndromic surveillance to monitor the impacts of the severe winter weather and its aftermath on weather related personal injuries throughout the state. methods leeds was queried to obtain the number of cold weather related emergency department visits. this was done by querying for records that mentioned “cold exposure,” “fell on ice,” “freeze,” “hypothermia,” “ice,” “slipped on ice,” or “snow” in the chief complaint or admit reason. records were excluded if they mentioned “antifreeze,” “device,” “jaundice,” “lice,” “notice, ” “office,” “orifice,” “police,” “practice,” “septicemia,” “twice,” or “voice” in the chief complaint or admit reason. a record review was done to determine if the returned visits were in fact cold weather related, and the number of visits were grouped by triage date. the daily cold weather related ed visit counts were plotted against weather data taken from essence, the national syndromic surveillance program’s syndromic surveillance application. the data obtained from essence was used to calculate the daily average minimum temperature throughout louisiana from december 1, 2017 through february 29, 2018. this was done by selecting all of the fourteen louisiana weather stations in essence and calculating the daily average minimum temperature across all stations. the number of cold weather related ed visits were then plotted against the daily minimum temperature in louisiana. the initial active surveillance took place during the extreme cold weather that occurred from january 15, 2018 – january 20, 2018. data starting on december 1, 2017 was pulled to provide a baseline. additional data through february 28, 2018 was pulled retrospectively to analyze the overall trend of cold weather related ed visits throughout winter season. results cold weather related ed visits and daily average minimum temperatures were analyzed for the time period of december 1, 2017 to february 28, 2018. the average number of cold weather ed visits for this time period was 1.8 visits with a standard deviation of +/4.4 visits, and the average minimum temperature was 40.9 °f. the number of cold weather related ed visits went above 6.1 visits on the following occasions: december 9 th had 7 visits and an average minimum temperature of 25.9 °f and january 17th-18th had 37 and 18 visits, respectively, with respective average minimum temperatures of 12.5 °f and 16.1 °f. the 2017 2018 winter weather surveillance in louisiana graph also reflects these results. of the cold weather related ed visits that took place on either december 9 th or january 17th-18th, 81% indicated an ice or snow related personal injury, 14% indicated hypothermia, and 5% indicated cold exposure. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e421, 2019 isds 2019 conference abstracts conclusions the three coldest days, january 16th -18th, corresponded with the largest spikes in cold weather related ed visits. the spike in visits on december 9, 2017 did not correspond to very low temperatures, but areas of louisiana did experience snowfall on december 8th, which led to ice formation. a record review of all visits that fit the inclusion criteria of a cold weather related ed visit showed that many of the visits that took place from january 19th-21st were also personal injuries that had either just taken place from slipping on ice or had occurred several days prior and remained unresolved. ultimately, syndromic surveillance was instrumental in maintaining situational awareness during the uncharacteristic winter weather experi enced in louisiana. the results of this winter weather surveillance were published in the louisiana morbidity report. idepi will continue to use syndromic surveillance during periods of uncharacteristic winter weather to maintain situational awareness, which may be used in public safety messaging to reduce the number of cold weather related personal injuries in the population that is not accustomed to such conditions. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e282, 2019 isds 2019 conference abstracts essence features for 2019 wayne loschen johns hopkins university applied physics laboratory, laurel, maryland, united states objective the objective of this presentation is to discuss the new features that are under development for essence in 2019. this is a chance to describe the features, the use cases for the features, and open a dialogue with the community on potential new enhancements that are available. introduction the essence system is a community-driven disease surveillance system. installed in over 25 jurisdictions across the us, the system is built on a single codebase that is shared across all instances. while each individual location can customize many of the settings, data sources, and configurations, the underlying code and functionality is shared. this means that when one jurisdi ction works with the johns hopkins university applied physics laboratory (jhu/apl) to create a new feature, it is available to all sites. methods the roadmap for essence in 2019 is based upon ongoing and future projects between jurisdictions and jhu/apl. while specifics can change before features are deployed, the following list of new features have exceptional capabilities. these include: social elements to the user experience: ability for users to share what they are doing within essence with their peers. abili ty for users to see what others are querying and find interesting. support sharing both within a system and across jurisdictions. text analysis: text analysis and visualizations to help support the user in building new free-text queries.provide correlation, trend, and association analytics for words and phrases to help the user determine what should or should not be included in their queries. site monitoring: back-end tools and checks to better monitor an essence system for issues and data irregularities. this administrative tool will help the system maintain its day-to-day availability and improve visibility of errors and issues that may develop over time. longitudinal surveillance: visualizations and cohort clustering analytics to determine the types of patients who are usi ng the healthcare systems that provide data to essence. these tools can show patient uses over time with trends to better inform utilization of healthcare resources in a community. opioid overdose surveillance: visualizations and analytics to better support the surveillance activities related to the opioid overdose crisis. work with additional data sources (ems, poison control, death records, etc.) to determine the benefit of fus ing multiple pieces of information into a common picture for improved opioid surveillance. and other new features… results the presentation will describe the current roadmap, demonstrate features that are mature enough in the software development process, share mock-ups for features still in the early stages of development and provide use cases for each of the new features discussed. conclusions no organization can build a successful system without the participation and buy-in from its stakeholder community. essence is an excellent example of a tool built on collective input whose ongoing enhancements benefit all its users. by seeing the roadmap http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e282, 2019 isds 2019 conference abstracts and understanding the new features for 2019, the community can prepare for upcoming enhancements and begin discussions about other needs and use cases that will drive the development of the next round of essence features. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e230, 2019 isds 2019 conference abstracts c. burnetii shedding study in domestic animals in georgia marine nikolaishvili animal disease diagnostics department, laboratory of the ministry of agriculture, tbilisi, georgia objective q fever is poorly understood in georgia and its prevalence is largely underestimated in both humans and animals.one of the main goal of the project was shedding study in domestic animals – isolation of c. burnetii from suspected seropositive animal blood, milk samples. introduction q fever is a zoonotic bacterial disease resulting from infection by coxiella burnetii. domestic ruminants (cattle, sheep, and goats) are considered the main reservoir for the pathogen, which can also infect humans. q fever is poorly understood in georgia and its prevalence is largely underestimated in both humans and animals. in georgia q fever laboratory diagnostic was started and implemented at the laboratory of the ministry of georgia (lma) withi n gg20, ,prevalence, epidemiological surveillance, and laboratory analysis of coxiella burnetii in georgia’’. methods lma conducted coxiella burnetii shedding evaluation in three specific farms from kvemo kartli (tsalka, dmanisi) and mtskhetamtianeti (dusheti). seropositive cattle and small ruminants were sampled per week. sampling lasted 7 weeks and t otally 581 samples samples (blood, milk and swab) were tested. testing were conducted in a bsl3 laboratory under bsl3 working conditions. accm medium was used (2xaccm-2 acidified citrate cysteine medium ph4.75g n naoh). the samples were incubated at 37°c using co2. results as a result of the study, one culture was bacteriologically isolated from seropositive cattle milk sample (the sample was tak en on the third week of the study in beshtasheni farm, tslka, kvemo kartli) and confirmed by molecular biology (pcr). conclusions the study confirmed q fever existence in georgia. traditionally considered an obligate intracellular agent, the requirement t o be grown in tissue culture cells, embryonated eggs, or animal hosts has made it difficult to isolate c. burnetii strains. within the study one culture was isolated from the seropositive animal milk sample that was collected in the third week of the study. shedding of coxiella burnetii in milk by infected cows appeared to be the most frequent positive sample for the bacterium. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using drug overdose syndromic surveillance data to impact local public health action mandy billman* and kayley dotson indiana state department of health, indianapolis, in, usa objective the overall objective of this session is to discuss opportunities to use drug overdose syndromic surveillance (sys) data to encourage action among local public health partners. after this roundtable discussion, participants will be able to: -identify opportunities to promote use of drug overdose sys data to their health partners. -plan for potential drug overdose public health interventions. -develop relationships with roundtable attendees to continue the conversation and sharing of ideas about use of drug overdose sys data. introduction since 2008, drug overdose deaths exceeded the number of motor vehicle traffic-related deaths in indiana and the gap continues to widen1. as the opioid crisis rages on in the united states the federal government is providing funding opportunities to states, but it often takes years for best practices to be developed, shared, and published. indiana state department of health (isdh) has developed a standard process for monitoring and alerting local health partners of increases in drug overdoses captured in indiana’s syndromic surveillance system (essence). isdh is launching a pilot project to encourage local partners to start a conversation about overdose response capabilities and planning efforts in their community. other states have published articles about drug overdose syndromic surveillance (sys) data being used to inform local public health action, however, the local overdose response activity details were vague 2,3. with the opioid crisis continuing to spiral out of control in the united states, it is imperative to work together as local, state, and national partners to find potential solutions to this crisis. keywords opioid; drug overdose; syndromic surveillance; local public health response; ed data references 1 overdose prevention [internet]. indianapolis: indiana state department of health; 2017. indiana special emphasis report: drug overdose deaths 1999-2015; august 2017. [cited 2017 sept 25]. available from: http://www.in.gov/isdh/files/2017_ser_drug_deaths_indiana. pdf 2 daly e, dufault k, swenson d, lakevicius p, metcalf e, chan b. use of emergency department data to monitor and respond to an increase in opioid overdoses in new hampshire, 2011-2015. public health rep. 2017; 132(suppl): 73s-79s. available from: https://www.ncbi.nlm. nih.gov/pubmed/?term=28692390 3 ising a, proescholdbell s, harmon k, sachdeva n, marshall s, waller a. use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies. inj prev. 2016 apr; 22 suppl 1: i43-9. available from: https://www.ncbi.nlm.nih. gov/pubmed/?term=27044495 *mandy billman e-mail: abillman@isdh.in.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e192, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts automated processing of electronic data for disease surveillance emily roberts*, rachelle boulton, josh ridderhoff and theron jeppson informatics program, utah department of health, salt lake city, ut, usa objective the objective of this abstract is to illustrate how the utah department of health processes a high volume of electronic data in an automated way. we do this by a series of rules engines that does not require human intervention. introduction national initiatives, such as meaningful use, are automating the detection and reporting of reportable disease events to public health, which has led to more complete, timely, and accurate public health surveillance data. however, electronic reporting has also lead to significant increases in the number of cases reported to public health. in order for this data to be useful to public health, it must be processed and made available to epidemiologists and investigators in a timely fashion for intervention and monitoring. to meet this challenge, the utah department of health (udoh)’s disease control and prevention informatics program (dcpip) has developed the electronic message staging area (emsa). emsa is a system capable of automatically filtering, processing, and evaluating incoming electronic laboratory reporting (elr) messages for relevance to public health, and entering those laboratory results into utah’s integrated disease surveillance system (ut-nedss) without impacting the overall efficiency of utnedss or increasing the workload of epidemiologists. methods after parsing and translating messages, emsa runs the messages through a series of rules to determine if a test result should update an existing ut-nedss event, create a new ut-nedss event, be archived for possible use in future cases (e.g. to help identify seroconversion) or if the test result should be discarded. all of these rules can be configured specifically for each reportable condition. first, emsa runs age-based rules. if the incoming message is too old for the indicated condition, emsa does not continue processing and the message is discarded. emsa then attempts to person match to determine if the person reported in the elr message matches a known person in ut-nedss. if the person matches, emsa will then evaluate whether the laboratory result should append to any events associated with the person, create a new event under that person, or create a new person and event. this process occurs through two different rule sets: whitelist rules, and test specific rules. whitelist rules are condition-specific and, when available, based on cdc’s case definition guidelines to determine when a new lab test result should be considered part of an existing case or a catalyst to trigger a new event. whitelist rules run against all existing events found for the person matched, and once a single event is matched, then the more-specific test result-based rules come into play. within an event matched by the whitelist rules, we have another set of rules based on the test result, collection date, accession number, and test status, to determine whether to add the laboratory report to the event, update an existing laboratory report, or if the laboratory report is a duplicate to be discarded. the message also runs through rules based on test and test result, and sometimes off organism, that determine whether that result can even be used to update the case or not. whitelist rules also determine if too much time has passed since the matching event occurred for the incoming laboratory result to be appended to the matching event. whitelist rules exist for both morbidity and contact events, and are based on timeframes such as onset date and treatment dates. if a particular incoming laboratory test result matches a known person in ut-nedss, and the whitelist rules determine that the laboratory result matches that person’s disease condition and can “update an existing event”, the laboratory result is run through another set of rules, called “test specific rules”. test specific rules match incoming laboratory tests results to a ut-nedss disease condition, and determine whether each unique test type and test result combination can “create a new event” and/or “update an existing event”. all tests that do not meet the criteria for inclusion into ut-nedss, either by updating an event or creating a new event, are held in emsa, in what is termed the “graylist” for a period of 18 months. when emsa creates a new event, it queries the graylist to determine if a previous reported lab should be pulled and added to the new event. graylist rules determine how far back emsa is allowed to search for previous test results. results from 10/10/2016 to 9/30/2017, the utah department of health has received a total of 995,486 electronic messages that required processing. of those 995,486 messages, 23,787 (2.4%) were deleted, 17,839 (1.8%) were identified as duplicates and subsequently deleted, 853,853 (85.8%) were sent to graylist, and 99,657 (10%) were added to ut-nedss. of the 99,657 messages, 85,705 (86%) were processed from raw electronic messages to assignment into ut-nedss without any human intervention. conclusions elr improves the timeliness, completeness, and accuracy of laboratory reporting to public health, but often results in a significant increase in laboratory reporting to public health agencies. this increase in volume can overwhelm epidemiologists and investigators if manual processes for reviewing all incoming elr messages are needed for processing laboratory results and entering data into surveillance systems. in order to fully leverage the benefits of elr for public health surveillance, we knew we needed a highly automated process for receiving, parsing, translating, and entering data into ut-nedss that would mitigate the challenges associated with the increased volume. we developed emsa and its series of rule sets to meet this challenge. keywords automated; surveillance; electronic; data; processing *emily roberts e-mail: erroberts@utah.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e2, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e407, 2019 isds 2019 conference abstracts root-cause analysis of bacteraemia increase and surveillance data in hemodialysis caroline pohl ep oudin, patricia sermande, evelyne lenormand, johan bardil, ingrid marianne east reunion hospital group objective to investigate the bacteraemia increase in haemodialysis sector based on data from specific dialysis nosocomial infections national network surveillance (dialin) and through an association of litigation and risk management protocol (alarm). introduction in 2017, the dialysis centre of east reunion hospital group (erhg) based in saint-benoit highlighted an increase in bacteraemia’s rates. it was a significant rising compared to previous years. indeed, erhg is participating since 2013 to the france haemodialysis infections network surveillance (dialin) [1], created in 2005 and that is allowing assessing bacteraemia. dialin is a multicentre prospective permanent survey that has followed six voluntary centres in 2005 and forty-two in 2016. objectives of this network are firstly to produce data about acquired infections in haemodialysis sector such as infection incidence rate and standardized ratios allowing centres to compare themselves and, secondly, to improve the quality of care .the current study describe how a root cause analysis has been conducted through the alarm risk assessment methodology to set up action plans and to reduce the phenomenon [2] [3] methods five years (2013-2017) of erhg haemodialysis data were obtained from the haemodialysis infections national network surveillance (dialin). to investigate and to analyse clinical incidents, the french national authority for health (has) [2] recommends the use of an association of litigation and risk management (alarm) protocol. it is a powerful method for the investigation and analysis of serious incidents by risks managers [4]. well established in industries sectors, the alarm method of investigation is well introduced in french healthcare system since the last ten years. it was used to provide root cause analysis of this phenomenon. individual’s risk factors of each patients (endogenous factors) have been analysed but these risks were identical every year. thus, we focused on elements different in 2017 from previous years (exogenous factors). we practised audits about hand hygiene, standard precautions, catheter connection and disconnection practices. our investigations covered several domains of risks or contributary factors such as patient, professional worke rs, teams, clinical practices protocols, technical and organisational context, care management and hospital regional health policy. results data from dialin pointed out that the erhg bacteraemia’s rate was similar or lower to the national network until 2016 (n= 0 in 2016 or 1 in 2015 bacteraemia per year only in catheter's access vascular). no infections nor bacteraemia on fistula were not ed as showned on figure 1 and figure 2. in 2016, there were 68 haemodialysis chronic patients, 8996 dialysis sessions and incidence of all infections was 0.11 over 1000 sessions. in 2017, there were 84 haemodialysis chronic patients,10377 dialysis sessions and incidence of all infections is 0.77 over 1000 sessions. bacteraemia’s rate was higher than national network and erhg previous years. the analysis of potential causes by alarm method gave us different explanations. first of all, an increase of dialysis sessions and patients number could explain the increase. then, this method allowed us to highlight a lower hand hygiene indicator for the service and an equipment issue. a batch of extra-corporal-circuit line was defective and a national withdrawal of any batch was initiated thanks to the erhg. secondary, the human factors like recruitment of new members with non-compliance of internal http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e407, 2019 isds 2019 conference abstracts processes, management and human resources issues, under stress work conditions, bad working atmosphere, communication issues between haemodialysis professional workers, contributed to the bacteraemia increase. the investigations had also highlighted a misuse of antiseptic serving to catheter 's connection and disconnection process. some nurses did not respect the activity time of antiseptic and others nurses splashed the antiseptic instead of cleaned with a sterile wipe. responses have been taken to stop this issue including the cooperation of healthcare team with the support of hygiene expert team. nevertheless, because of the multiplicity of risk factors and identified roots causes, the phenomenon has not been stop promptly. despite a slowdown, the phenomenon persists in 2018. actions have been decided to standardize practices, to work in pairs, and to improve hand hygiene. news equipments and an other antiseptic following national guidelines (alcoholic chlorhexidin 2%) were chosen by a multidisciplinary team. conclusions bacteraemia for dialysis patients might evolve towards serious complications as endocarditis or death in worth cases. during this period, no deaths nor endocarditis linked to bacteraemia have been revealed. the use of a risk management protocol derived from the industry allowed finding roots causes and set up actions plans to solve the phenomenon. erhg participation to the dialin surveillance is continuing. references 1. cpias, auvergne rhône alpes. annual report dialin; 2016. 2. has, gestion des risques, grille alarm. jam, n°14 août/septembre/octobre; 2010. 3. reason jt. human error.new york:cambridge university press;1990. 4. vincent c, taylor-adams s, chapman ej, hewett d, prior s, et al. 2000. how to investigate and analyse clinical incidents: clinical risk unit and association of litigation and risk management protocol. bmj. 320(7237), 777-81. pubmed https://doi.org/10.1136/bmj.320.7237.777 figure 1. evolution of bacteriemia incidence hemodialysis vascular acess site in erhg http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10720366&dopt=abstract https://doi.org/10.1136/bmj.320.7237.777 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e407, 2019 isds 2019 conference abstracts figure 2. incidence rates of acess site bacteriemia for 1000 days of use erhg vs network dialin http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e435, 2019 isds 2019 conference abstracts finding chances to intervene before the fatal overdose: linking ed and mortality data evan mobley, chelsea fischer, andrew hunter bureau of health care analysis and data dissemination, missouri department of health and senior services, jefferson city, missouri, united states objective link emergency department (ed) with death certificate mortality data in order to examine the prior medical history of opioid overdose victims leading up to their death. introduction in 2017, 951 missouri residents died from an opioid overdose—a record number for the state [1]. this continues the trend from 2016, which saw an increase of over 30% in opioid overdose deaths compared to 2015. the missouri department of health and senior services (mdhss) manages several public health surveillance data sources that can be used to inform about the opioid epidemic. opioid overdose deaths are identified through death certificates which are collected through the vital records system. mdhss also manages the patient abstract system (pas), which contains ed and inpatient hospitalization data from approximately 132 non-federal missouri hospitals. pas contains about 130 variables, which include demographic data, diagnoses codes, procedures codes, and other visit information. records can have up to 23 diagnosis fields, which are coded using icd-10-cm (international classification of diseases, clinically modified). the first diagnosis field is the primary reason for a visit. methods linkage and analysis of the data was performed using sas enterprise guide 6.1. opioid overdose deaths were identified through icd-10 analysis looking for drug poisoning underlying cause of death codes and opioid-specific codes found in the multiple cause (contributing cause) of death fields. table 1, below, summarizes the icd-10 codes used. mortality data from the 951 decedents were linked to ed data from 2016 and 2017. records were linked using multiple passes over the ed records. records were first linked on social security number. following this linkage, ed records with no initial match went through a second pass and linked on name and date of birth. finally, a third pass for records still without a match was conducted using date of birth, census tract, and sex. after these passes, the linkages were reviewed to identify any false positives. the 23 diagnosis fields contained in pas were analyzed to look for patterns in diagnosis coding. icd-10cm codes were too broad so ccs (clinical classifications software) categories were utilized. results in total, 3,500 ed records were linked to the 951 decedents. after removing false positives, the total number of ed records was 3,357. approximately 70% (687) of decedents were linked to at least one ed record. one hundred and eighty-eight visits were due to drug overdose (153 opioid overdoses). the most common primary diagnosis ccs categories (category numbers in parentheses) were: substance-related disorders (661), spondylosis; intervertebral disc disorders; other back problems (205), abdominal pain (251), and other nervous system disorders (95). collectively, these four categories represented over 20% of all primary diagnoses. across all 23 diagnosis fields there were similar results. the most common ccs categories were as follows: substance-related disorders (661), other aftercare (257), essential hypertension (98), and mood disorders (657). pie charts (fig. 1 and 2) below show proportions of ccs categories across all diagnoses fields and primary diagnosis broken into three major categories: pain/injury, substance abuse/mental health, and other. in order to reduce the impact of ccs categories with small numbers, these graphics represent only ccs categories that made up 1% or more of the total collection of diagnoses codes. of the 687 decedents that were matched successfully to ed records, 96% had at least one pain/injury or one substance abuse/mental health icd-cm code in at least one record, and 68% had both. conclusions these findings suggest that many overdose decedents visited the ed in the years prior to death. many of these visits were not due to an overdose; however, they could be indicative of a problem with opioids (i.e. pain, drug-seeking, substance use-related). ed staff and public health professionals could utilize these opportunities to refer patients to recovery services and recommend they heed caution when using opioids. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e435, 2019 isds 2019 conference abstracts references 1. missouri department of health and senior services. (2018). missouri resident overdose deaths by opioid type. retrieved september 27, 2018 from https://health.mo.gov/data/opioids/pdf/opioid-dashboard-slide-9.pdf. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e435, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e242, 2019 isds 2019 conference abstracts examining and improving reproducible research practices in public health kimberly j. johnson, bobbi j. carothers, xiaoyan wang, todd combs, douglas a. luke, jenine k. harris public health, w ashington university in st. louis, st. louis, missouri, united states objective our presentation will explain current use, and barriers to use, of reproducible research practices in public health. we will also introduce a set of modules for researchers wishing to increase their use of reproducible research practices. introduction an important goal of surveillance is to inform public health interventions that aim to reduce the burden of disease in the population. ensuring accuracy of results is paramount to achieving this goal. however, science is currently facin g a “reproducibility crisis” where researchers have found it difficult or impossible to reproduce study results. organized and well-documented statistical source code that is publicly available could increase research reproducibility, especially for resear ch relying on publicly available surveillance data like the brfss, nhanes, gss, seer, and others. as part of our overall goal to improve training around reproducible research practices, we surveyed public health data analysts to determine current practices and barriers to code sharing. methods we conducted a cross-sectional web-based survey about code organization, documenting, storage, and sharing. we surveyed public health scientists who reported recently conducting statistical analyses for a report or manuscript. a total of 247 of 278 screened eligible to filled out the survey, and 209 answered every applicable question. we used traditional descriptive statistics and graphs to examine the survey data. results most participants reported using some promising coding practices, with 67% including a prolog to introduce the code and 85% including comments in statistical code to explain operations and analyses. of 10 common code organization strategies (e.g., naming variables logically, using white space), most (82%) respondents reported employing at least three of the strategies and just under half (47%) reported using five or more. over half of participants (59%) reported code was developed or checked by two or more people. many participants also reported promising file management habits for data and code used in publications. threequarters (75%) had a variable dictionary to accompany the dataset used, 48% created clean versions of code files, and 64% created clean versions of data files at the time of publication. forty three percent of participants reported that if they suddenly left their current position, it would not be easy for others to find their statistical code files. public code sharing was much less common amon g participants with just 9% reporting sharing code publicly from a recent publication and 20% of those surveyed reported ever having shared code publicly. the top two barriers to using reproducible research practices were lack of training in reproducible research (n=108) and data privacy issues (n=105). journals and funders not requiring reproducible practices were barriers selected by 94 and 84 participants, respectively. few participants identified fear of errors being discovered (n=26) or a lack of workplace incentives (n=32) as barriers. conclusions most participants were using some promising practices for organizing and formatting statistical code but few were sharing statistical code publicly. the second most frequently identified barrier to using reproduciible practices was data privacy, which could prohibit easily sharing a data source. with surveillance data often being publicly available, researchers working with surveillance data have overcome this top barrier without any change to current research practices. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e242, 2019 isds 2019 conference abstracts researchers using surveillance data could greatly increase research reproduciblity by adopting promising practices for code formatting, like using logical variable names and limiting line length, and posting code in a public repository like github. to overcome the top barrier to use of reproducible research practices, lack of training, we developed brief training modules on formatting, documenting, and sharing statistical code and data. as part of our presentation we will introduce and provide access to these online modules. the introduction will focus on the relevant modules for surveillance data users, which include statistical code formatting and statistical code sharing via github. with fewer barriers to practicing reproducible research, public health researchers using surveillance data have the opportunity to be leaders in improving the adoption of reproducible research practices and subsequently improving the quality of research we rely on to improve public health. acknowledgement this project was supported by the robert wood johnson foundation (rwjf) increasing openness and transparency in research program. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e294, 2019 isds 2019 conference abstracts towards obesity surveillance using multifaceted online social relational factors in reddit albert park, yaorong ge software and information systems, university of north carolina at charlotte, charlotte, north carolina, united states objective we aim to better understand online social interactions and environments of individuals interested in weight management from a social media platform called reddit. introduction overweight and obesity are recognized as one of the greatest modern public health problems [1], yet worldwide prevalence of obesity has nearly doubled over the past 30 years [2]. as part of a strategy to control the obesity pandemic, the who recommends an obesity surveillance at the population level [3]. empirical studies have shown the importance of social networks in obesity [4] and new strategies focusing on social interactions and environments have been proposed [5] to prevent the further increase in obesity prevalence. with the increasing use of the internet, online social networks, interactions, and environments (i.e., online social relational factors) deserve more attention. nearly threequarters of americans go online daily [6], for functions like connecting with individuals via social network sites [7]. like face to face interactions, studies have suggested that social interactions and networks on the internet can influence behavior changes [8]. previous studies examining social networking sites typically examine a few selected social networking sites (example studies [9,10]), although individuals could be members of multiple social networking sites. to better leverage online social relational factors for the purpose of characterizing and monitoring population obesity trends, we investigate weight manageme nt community members’ other communities and their level of participation, a first step toward utilizing online multifactorial social interactions and environments. methods in this study, we studied reddit (http://www.reddit.com), a popular social interaction site, because reddit hosts many subreddits (i.e., sub-communities), including weight management communities called r/loseit. first, we use a dataset [11] — made available on reddit — that had been used in many informatics studies [12-14]. for this study, we used a portion of the dataset from jan 2015 to may 2015. in the first five months of 2015, 5,006,186 members were active in 96,462 subreddits, while submitting 17,851,56 1 posts and 266,268,920 associated comments. second, we identified members with more than 3 posts on r/loseit in that period and removed ‘bot’ accounts by manually examining the top 20 frequent posters and their account ids. third, we extracted these members’ entire discussions made on reddit, regardless of the subreddits. fourth, we identified these members’ overall activities on reddit and visualized in a network [15]. results after removing bot accounts, we identified 7,734 members who had more than 3 posts in r/loseit from jan 2015 to may 2015. on average, these members participated in 78.5 subreddits (standard error: 0.1; median: 49.0), while participating in 13,649 unique subreddits as a whole. members’ participated subreddits are summarized in figure 1. the size of the nodes represents the number of participating members and the thickness of edges represents the number of members who participated in both subreddits. conclusions we present preliminary findings towards better understanding the online multifactorial social interactions and environments on a social networking site called reddit. we provide evidence that members encounter many social interactions that occur outside of the community of our interest, the weight management community. however, what members discuss outside of the weight management community as well as the interactions’ influence on weight managements and changes remain unanswered. for example, many members also participate in a subreddit called r/fitness, a community that could share many similar interests with http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e294, 2019 isds 2019 conference abstracts r/loseit. however, the purpose for participating in both communities is unknown. on the basis of our initial analysis, we suggest leveraging online multifaceted social relational factors for the purpose of characterizing and monitoring population obesity trends. acknowledgement we restricted our analysis to publicly available discussion content. the study was determined to be not human subjects by the university of north carolina-charlotte's institutional review board. ap’s contribution was supported by ap’s startup funding provided by the department of software and information systems, university of north carolina-charlotte. references 1. jeffery rw, utter j. 2003. the changing environment and population obesity in the united states. obes res. 11(suppl), 12s-22s. pubmed https://doi.org/10.1038/oby.2003.221 2. world health organization. global health observatory (gho) data: obesity. (2009). available at: http://www.who.int/gho/ncd/risk_factors/obesity_text/en/. archived at: http://www.webcitation.org/6rqich7oq. 3. world health organization. 2000. obesity: preventing and managing the global epidemic. report of a who consultation. world health organ tech rep ser. 894, i-xii, 1-253. pubmed 4. christakis na, fowler jh. 2007. the spread of obesity in a large social network over 32 years. n engl j med. 357, 370-79. pubmed https://doi.org/10.1056/nejmsa066082 5. leroux js, moore s, dubé l. 2013. beyond the ‘i’ in the obesity epidemic: a review of social relational and network interventions on obesity. j obes. 2013, 348249. pubmed https://doi.org/10.1155/2013/348249 6. perrin a. one fifth of americans report going online ‘almost constantly’. pew research center (2015). available at: http://www.pewresearch.org/fact-tank/2015/12/08/one-fifth-of-americans-report-going-onlinealmost-constantly/ archived at. http://www.webcitation.org/6s9ztxrdl. 7. greenwood s, perrin a, duggan m. 2016. social media update 2016. pew research center internet. 1-9. http://www.pewinternet.org/2016/11/11/social-media-update-2016/. archived at. http://www.webcitation.org/6q0fdwnri. 8. laranjo l, et al. 2015. the influence of social networking sites on health behavior change: a systematic review and meta-analysis. j am med inform assoc. 22, 243-56. pubmed https://doi.org/10.1136/amiajnl-2014-002841 9. park a, et al. 2016. ‘how did we get here?’: topic drift in online health discussions. j med internet res. 18, e284. pubmed https://doi.org/10.2196/jmir.6297 10. park a, conway m, chen at. 2018. examining thematic similarity, difference, and membership in three online mental health communities from reddit: a text mining and visualization approach. comput human behav. 78, 98-112. pubmed https://doi.org/10.1016/j.chb.2017.09.001 11. reddit_member. i have every publicly available reddit comment for research. ~ 1.7 billion comments @ 250 gb compressed. any interest in this? (2015). available at: https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_available_reddit_comment/ archived at. http://www.webcitation.org/6kgaunxde. 12. park a, conway m. tracking health related discussions on reddit for public health applications. annu. symp. proceedings. amia symp. 2017, 1362– 1371 (2017). 13. park a, conway m. 2018. harnessing reddit to understand the written-communication challenges experienced by individuals with mental health disorders: analysis of texts from mental health communities. j med internet res. 20, e121. pubmed https://doi.org/10.2196/jmir.8219 14. park a, conway m. 2017. towards tracking opium related discussions in social media. online j public health inform. 9(1), e073. https://doi.org/10.5210/ojphi.v9i1.7652 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14569035&dopt=abstract https://doi.org/10.1038/oby.2003.221 http://www.who.int/gho/ncd/risk_factors/obesity_text/en/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11234459&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17652652&dopt=abstract https://doi.org/10.1056/nejmsa066082 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24062945&dopt=abstract https://doi.org/10.1155/2013/348249 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25005606&dopt=abstract https://doi.org/10.1136/amiajnl-2014-002841 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27806924&dopt=abstract https://doi.org/10.2196/jmir.6297 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29456286&dopt=abstract https://doi.org/10.1016/j.chb.2017.09.001 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29636316&dopt=abstract https://doi.org/10.2196/jmir.8219 https://doi.org/10.5210/ojphi.v9i1.7652 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e294, 2019 isds 2019 conference abstracts 15. bastian, m., heymann, s. & jacomy, m. gephi: an open source software for exploring and manipulating networks. (2009). figure 1. an overview of r/loseit members’ participation in other subreddits. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epizootic monitoring of erysipeloid foci in the republic of armenia, 2007-2016 laura mkrtchyan*, armine ghazazyan and ruben danielyan ra ncdcp reference laboratory center branch, laboratory of especially dangerous and natural foci infections, yerevan, armenia objective the goal of this study was to characterize the epidmiological, geographic, and historical characteristics of erysipeloid outbreaks in the republic of armenia. introduction erysipeloid is a zoonotic bacterial infection transmitted to humans from animals. symptoms include inflamed joints and skin; there is also a generalized type of the infection in which bacteria spread through the lymphatic and blood vessels, leading to the emergence of widespread skin lesions and the formation of secondary foci of infection in internal organs. morbidity has no age or gender specifics; there is summer and autumn seasonality. the agent of the infection erysipelothrix rhusiopathiae can be found in many domestic and wild animals. wild rodents and ectoparasites play an essential role in spreading the disease and serve as a source of infection contaminating the environment. methods tests are conducted on both national and marz levels in reference laboratory center of ncdc snco and marz branch laboratories of especially dangerous, zoonotic and natural foci infections respectively. tests for detection of e. rhusiopathiae and confirmation of epizootics are conducted on rodents and ectoparasites collected from their hair and nests from 373 sectors of armenia. tests include smear microscopy and a bioassay in which cultures from a suspension of rodent organs or an emulsion of ticks and fleas are injected into white mice to assess the presence of agent in the organs or parasites. results ten years of monitoring indicates that erysipeloid epizootics have been recorded annually in armenia with a total number of 119 cases. the most outbreaks were recorded in 2011 when 26 cases were recorded while in 2009 there were 20. the lowest number of cases recorded was five in 2008. kotayk, aragatsotn and lori marzes have the least number of cases with only 1-3 recorded epizootics, while vayk, gegharkunik and shirak marzes are considered active foci with 5-7 cases reported. microbiological analyses indicates that 80% of cultures were isolated from field mice, 13.3% from gamasid ticks, 4.2% from fleas and 2.5% from ixodid ticks. conclusions the presence of e. rhusiopathiae is stable in armenia. it is found among rodents, where the epidemiological situation remains unfavorable. constant regular tests/analyses are required to prevent human and animal infection. there is a need to enhance the area of test sites and apply most up-to-date methods of analysis i.e. elisa, pcr so that the live bioassays in mice can be halted. keywords erysipeloid; armenia; epidemiology; disease monitoring *laura mkrtchyan e-mail: lara.mkrtchyan@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e116, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e393, 2019 isds 2019 conference abstracts knowledge of malaria and antimalarial drug dispensing practices in buea community marcelus u. ajonina1, 2 1 health sciences, meridian global university, buea, central region, cameroon, 2 meridian global education and research foundation, buea, southwest region, cameroon objective this study was aimed at assessing the knowledge of malaria as well as perception and dispensing practices of antimalarials among vendors in buea community introduction lack of knowledge of rational use of antimalarial drugs among dispensers is a serious problem, especially in areas of intense transmission thus increasing the risk of resistance and adverse drug reactions. methods a community-based cross-sectional survey of a random sample of 140 drug vendors living within the buea community was conducted between march and june 2017. questionnaire was designed to obtain information from drug vendors on the general knowledge of malaria as well as dispensing practices. data were analyzed using spss statistics 20.0 and were considered significant at p ≤ 0.05 results knowledge of malaria symptoms, transmission, and prevention was reasonable among 55.8% (77) of the respondents. only 33.6% (47) of the respondents could attribute the cause of malaria to protozoan of genus plasmodium species. of the 140 vendors, 115(82.7%) prescribe antimalarial drugs. the knowledge of the national protocol was malaria case management among dispensers was 35.0%. vendors in hospital/community pharmacies were 2.4 times (or = 3.14, 95% ci: 4.14 8.74, p < 0.001). more knowledgeable about malaria treatment protocol than those of in drugstores. the prevalence of selfprescription of antimalarials was 39.3%. self-prescription was significantly higher in drugstores than hospital/community pharmacies (p=0.004). in all, 56(40.6%) of vendors showed good practices regarding antimalarial drug dispensing with majority (51.7%) from community pharmacies (or=2.27,95% ci: 1.13-4.56). conclusions findings reveal moderate knowledge of malaria but poor prescription and dispensing practices of antimalarial drugs among vendors, thus indicating a need for routine monitoring and evaluation to prevent emergence of resistant strains to current efficacious antimalarials acknowledgement we thank the all participants from various drug retail outlets from the buea community who made this study possible by giving their consent. we equally thank meridian global education and research foundation (mgerf), cameroon for financial support. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e449, 2019 isds 2019 conference abstracts using the ca opioid overdose surveillance dashboard to track opioid overdose deaths jaynia a. anderson, natalie demeter, mar-y-sol pasquiers, stephen wirtz safe and active communities branch, california department of public health, sacramento, california, united states objective demonstrate the use of timely, actionable data from a data visualization tool, the california opioid overdose surveillance dashboard, which integrates statewide, geographicand demographic-specific data, by describing the changes in opioid overdose deaths in california. introduction california continues to face a serious public health crisis with the opioid epidemic having substantial health and economic impacts. the epidemic is dynamic and rapidly changing, involving both prescription opioids influenced by prescribing and dispensing patterns as well as illicit opioids influenced by the availability of heroin and recently, the increased availability of fentanyl. the complexity of the issue necessitates data-informed actions through multi-sector, strategic collaboration at both the state and local levels to address the problem comprehensively. with nearly 2,000 opioid overdose deaths per year and wide variation of overdose rates across counties and demographic groups, there is a need for integrated, timely, actionable data for use by state policy makers, local opioid safety coalitions, media, community stakeholders, and the public to monitor and combat this dynamic epidemic at the state and local level. using fatality data from the california opioid overdose surveillance dashboard [1], the opioid overdose epidemic is described along with the differential geographic and demographic impacts. methods as part of california department of public health’s prevention for states grant funded by the centers for disease control and prevention, the california opioid overdose surveillance dashboard was developed as a data tool to provide enhanced visualization and integration of non-fatal and fatal opioid-involved overdose data and opioid prescription data. the dashboard was built on an open source rstudio server using shiny, an r package that provides a framework for building web applications. data incorporated on the dashboard include emergency department visits, hospitalizations, fatalities, and prescriptions related to opioid overd oses among california residents, presented in raw counts, crude rates, and age-adjusted rates at the state, county, and zip code levels, as well as by sex, age, and race/ethnicity. overdose deaths are identified using icd-10 (international classification of diseases, 10th revision) codes x40-x44, x60-x64, x85, y10-y14, and t40.0-t40.6, recorded in the underlying cause of death and multiple cause of death fields on death certificates. fentanyl overdose deaths are identified using a text search on contributing cause of death fields on death certificates. using data from the california opioid overdose surveillance dashboard, we present one perspective of the epidemic by using 2017 death data to describe the changing trend and geographic and demographic variation of prescription drug, heroin, and fentanyl overdose deaths. results overall trends from 2011-2017 show that deaths due to opioid overdoses have increased. prescription drug overdose death rates have slightly decreased by 6% from 3.93/100,000 in 2011 to 3.7/100,000 in 2017. heroin overdose death rates have increased by 89% from 0.90/100,000 in 2011 to 1.70/100,000 in 2017. fentanyl overdose death rates have increased by 320% from 0.25/100,000 in 2011 to 1.05/100,000 in 2017. the highest rates of prescription opioid overdose deaths are primarily concentrated in northern rural counties, while the highest rates of heroin and fentanyl overdose deaths are more dispersed throughout the state with many coastal counties showing higher rates of overdose deaths (figure 1). prescription opioid overdose deaths are concentrated among older ages showing highest rates among 55 to 59 year olds (8.27/100,000). in contrast, heroin and fentanyl overdose death rates are concentrated among younger ages with the highest rates seen among 25 to 29 year olds, 4.54/100,000 and 2.78/100,000, respectively (figure 2). males died from prescription opioid, heroin, and fentanyl overdoses at significantly higher rates than females. prescription opioid and fentanyl overdose death http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e449, 2019 isds 2019 conference abstracts rates (11.5/100,000 and 4.80/100,000, respectively) are significantly higher among native americans compared to other races/ethnicities (table 1). non-hispanic whites had significantly higher prescription opioid and heroin overdose death rates (6.90/100,000 and 2.96/100,000, respectively) compared to non-hispanic black, hispanic, and asian residents of california. conclusions fatality data from 2017 show the characteristics of the opioid overdose epidemic in california are changing. while still high, overdose deaths from prescription opioids, seen primarily in older age groups and northern rural california, are slightly declining. concurrently, we are seeing sharp rises in heroin and fentanyl overdose death rates among younger adults throughout the state. regardless of any change in trend, there remain clear disparities in overdose death rates by race/ethnicity; with native amer icans having the highest rates for both prescription and illicit opioids, and non-hispanic whites have higher rates of prescription opioid and heroin overdose deaths. given the varying demographic and geographic impacts based on the type of opioid, as demonstrated with the use of death data, there needs to be targeted datainformed interventions to address and prevent prescription and illicit opioid overdoses. death data is just one perspective on the epidemic, other data sources (emergency department visits, hospitalizations, and prescriptions) are needed complete the picture to truly provide a robust data-informed approach. the california opioid overdose surveillance dashboard integrates these multiple data sources and serves as a valuable tool in providing specific and timely data to infor m approaches and interventions at the state and local level in continuing to fight california’s opioid overdose epidemic. the enhanced visualization, geographicand demographic-specific data, and increasingly timely data allow for state and local policy makers, local opioid safety coalitions, and community stakeholders to track the dynamics and impact of the epidemic and to identify those who are most vulnerable and differentially impacted. acknowledgement the california opioid overdose surveillance dashboard is funded by the centers for disease control and prevention’s prevention for states grant. references 1. california opioid overdose surveillance dashboard https://discovery.dev.cdph.ca.gov/cdic/oddash/ figure 1. overdose deaths from prescription opioids, heroin, and fentanyl in california, 2017 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e449, 2019 isds 2019 conference abstracts figure 2. overdose death rates from prescription opioids, heroin, and fentanyl by 5-year age groups in california, 2017 table 1. overdose death rates from prescription opioids, heroin, and fentanyl by sex and race/ethnicity per 100,000 residents, california, 2017 prescription opioids heroin fentanyl crude rate (95% ci) crude rate (95% ci) crude rate (95% ci) sex male 5.22 (4.90, 5.55) 2.96 (2.72, 3.21) 1.75 (1.57, 1.95) female 2.67 (2.45, 2.91) 0.65 (0.55, 0.78) 0.42 (0.34, 0.52) race/ethnicity white 6.90 (6.59, 7.33) 2.96 (2.69, 3.25) 1.67 (1.47, 1.89) black 4.46 (3.67, 5.38) 1.93 (1.42, 2.56) 1.43 (1.00, 1.99) hispanic 2.10 (1.89, 2.35) 1.17 (1.01, 1.35) 0.75 (0.62, 0.90) native american 11.52 (7.46, 17.07) 5.76 (3.00, 10.04) 4.80 (2.34, 8.79) asian 0.74 (0.55, 0.99) 0.32 (0.20, 0.50) 0.23 (0.13, 0.38) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e269, 2019 isds 2019 conference abstracts what can you really do with 35,000 statistical alerts a week anyways? michael coletta, hong zhou csels/dhis, centers for disease control and prevention, atlanta, georgia, united states objective find practical ways to sort through statistical noise in syndromic data and make use of alerts most likely to have public health importance. introduction the national syndromic surveillance program’s (nssp) instance of essence* in the biosense platform generates about 35,000 statistical alerts each week. local essence instances can generate as many as 5,000 statistical alerts each week. while some states have well-coordinated processes for delegating data and statistical alerts to local public health jurisdictions for review, many do not have adequate resources. by design, statistical alerts should indicate potential clusters that warrant a syndromic sur veillance practitioner‘s time and focus. however, practitioners frequently ignore statistical alerts altogether because of the overwhelming volume of data and alerts. in 2008, staff in the virginia department of health experimented with rules that could be used to rank the statistical output generated in essence alert lists. results were shared with johns hopkins university applied physics lab (jhu/apl), the developer of essence, and were early inputs into what is now known as “myalerts,” an essence function that syndromic surveillance practitioners can use to customize alerting and sort through statistical noise. nssp–essence produces a shared alert list by syndrome, county, and age-group strata, which generates an unwieldy but rich data set that can be studied to learn more about the importance of these statistical alerts. ultimately, guidance can be developed to help syndromic surveillance practitioners set up meaningful essence myalerts effective in identifying clusters with public health importance. methods the region/syndrome alert list generated from nssp’s instance of essence on the biosense platform was downloaded and ranked based on five criteria: 1. observed count causing the alert 2. expected count generated by essence 3. total number of alerts for that syndrome in that county and number of prior alerts during that week for the same syndrome, county, and age group 4. density of alerts during the prior week 5. recency of the latest alert alerts were then ranked based on: 1. higher absolute counts (regardless of expected value) 2. higher partial chi-square, (obs-exp)2 / exp 3. higher total alerts for a given county/syndrome 4. higher number of earlier alerts for same county/syndrome/age group 5. multiple alerts same day > alerts on consecutive days > alerts separated by days without alerts 6. alerts present on more recent days http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e269, 2019 isds 2019 conference abstracts the top 20 alerts with the highest scores were then reviewed and if anything unusual was noticed (i.e. problems unrelated to recent data quality problems or onboardings, seasonal trends, etc.) then there was follow-up with the site. the alert list rankings were then evaluated for differences among factors available in the essence myalert function. we compared the top 5% of ranked alerts to the remaining 95% to determine if there were significant differences in the following factors: 1. total number of alerts across six age groups (including all ages) within 8 days of each syndrome and county stratum; 2. average alert frequency across six age groups (including all ages) within 8 days for each stratum; 3. average count across the strata; 4. average expected value across the strata; 5. average of the difference between the count and expected values for each stratum; and 6. average level across the strata. results preliminary interactions with sites revealed important clusters – some already known and some not. for example, a cluster of healthcare workers exposed to neisseria meningitides, and kids exposed to a bat at summer camp and presenting for prophylaxis were among the clusters identified. additionally there were differences seen in the adjustable myalert parameters when comparing the top 5% to the lower 95% of ranked alerts. conclusions the differences seen and preliminary feedback suggests that this ranking method may be effective in identifying alerts representing true clusters of public health importance. testing designed to evaluate myalert parameters based on the differences seen in the top 5% of ranked alerts is underway in sites where more detailed data access is available. more study is needed; however, there are indications that cutoff values for these parameters may be a valuable way for syndromic surveillance practitioners to reduce the review burden and focus on the most important statistical clusters identified by essence statistical algorithms. acknowledgement jeremy smith formerly of the virginia department of health *essence stands for the electronic surveillance system for the early notification of community-based epidemics and is designed by johns hopkins university applied physics laboratory. table 1 category total alerts daily average count daily average expected daily average difference (countexpected) daily average level average alerts across 8 days bottom 95% 31265 5 3 3 0.0207 5 top 5% 1684 27 17 10 0.0036 8 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts syndromic surveillance for situation awareness: understanding syndrome performance kristin arkin*1, 2 1centers for disease control and prevention, atlanta, ga, usa; 2idaho division of public health, boise, id, usa objective in august 2017, a large influx of visitors was expected to view the total solar eclipse in idaho. the idaho syndromic surveillance program planned to enhance situation awareness during the event. in preparation, we sought to examine syndrome performance of several newly developed chief complaint and combination chief complaint and diagnosis code syndrome definitions to aid in interpretation of syndromic surveillance data during the event. introduction the august 21, 2017 total solar eclipse in idaho was anticipated to lead to a large influx of visitors in many communities, prompting a widespread effort to assure idaho was prepared. to support these efforts, the idaho syndromic surveillance program (issp) developed a plan to enhance situation awareness during the event by conducting syndromic surveillance using emergency department (ed) visit data contributed to the national syndromic surveillance program’s biosense platform by idaho hospitals. issp sought input on anticipated threats from state and local emergency management and public health partners, and selected 8 syndromes for surveillance. ideally, the first electronic message containing information on an emergency department visit is sent to issp within 24 hours of the visit and includes the chief complaint for the visit. data on other variables, such as diagnosis codes, are updated by subsequent messages for several days after the visit. chief complaint (cc) text and discharge diagnosis (dd) codes are the primary variables used for syndrome match; delay in reporting these variables adversely affects timely syndrome match of visits. because our plan included development of new syndrome definitions and querying data within 24 hours of visits, earlier than issp had done previously for trend analysis, we sought to better understand syndrome performance. methods we defined messages with completed cc and dd as the last message regarding a visit where term count increased from previous messages regarding that visit, indicating new information was added to the field. we retrospectively assessed the total number of ed visits and calculated the daily frequency of completed cc and dd by days since visit date for visits during june 1–july 31, 2017. additionally, we calculated facility mean word count in cc fields by averaging the word count of parsed, complete cc fields for visits occurring june 1–july 31, 2017 for each facility. during july 10–24, 2017, we calculated the daily frequency of visits occurring in the previous 90 days for total ed visits and syndrome-matched visits for 8 selected syndromes (heat-related illness; cold exposure; influenza-like-illness; nausea, vomiting, and diarrhea; animal/bug bites and stings; drowning/submersion; alcohol/ drug intoxication; and medication replacement). syndrome-matched visits were defined as visits with cc or dd that match the syndrome definition. we calculated the percent of syndrome-matched visits by syndromes defined with cc or cc and dd combined (ccdd) over time. syndromes with fewer than 5 matched visits were excluded from analysis. results complete ccs were received for 99.1% of visits and complete dds were received for 89.8% of visits. complete ccs were submitted for 58.2% of visits within 1 day of the visit, 88.9% of visits within 3 days, and 98.9% of visits within 7 days. in contrast, complete dds were submitted for 24.3% of visits within 1 day, 38.7% of visits within 3 days, and 53.7% of visits within 7 days (table 1). during the observation period, data submission from facilities representing approximately 33% of visits was interrupted for 5 (36%) of 14 days. heat-related illness, cold exposure, and drowning/ submersion, were excluded from syndrome-match analysis. during the 9 days of uninterrupted data submission, 100% syndrome-matched visits for syndromes defined by cc alone and 69.1% syndromematched visits for syndromes defined by ccdd were identified within 6–7 days of initial visit. facilities with interrupted data submission contributed 75% of cc syndrome-matched visits and 33% of ccdd syndrome-matched visits. the facility mean word count in cc fields from these facilities was >15 compared with 2–4 from other facilities. conclusions examination of syndrome performance prior to a known event quantitated differences in timeliness of cc and dd completeness and syndrome match. ccs and dds in visit messages were not complete within 24 hours of initial visit. cc completion was nearly 34 percentage points greater than dd completeness 1 day after initial visit and did not converge until ≥15 days after initial visit. higher percentages of syndrome match within 6–7 days of initial visit were seen by cc alone than ccdd defined syndromes. facilities using longer ccs contributed disproportionately to syndrome matching using cc, but not ccdd syndrome definitions. syndromic surveillance system characteristics, including timeliness of ccs and dds, length of ccs, and characteristics of facilities from which data transmission is interrupted should be considered when building syndrome definitions that will be used for surveillance within 7 days of emergency department visits and when interpreting syndromic surveillance findings. table 1. frequency of complete chief complaint (cc) and diagnosis code (dd) by number of days since visit date. keywords event surveillance; syndromic surveillance; situational awareness; situation awareness *kristin arkin e-mail: kristinaarkin@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e179, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts public health surveillance in a large evacuation shelter post hurricane harvey aisha haynie, sherry jin, leann liu*, sherrill pirsamadi, benjamin hornstein, april beeks, sarah milligan, erika olsen, elya franciscus, natasha wahab, ana zangene, delisabel lopez, lyndsey hassmann, deborah bujnowski, martina salgado, norma arcos, amanda nguyen, vishaldeep sekhon, richard williams, valeria y. brannon, jennifer kiger, brian reed, mac mcclendon, les becke and umair shah harris county public health, houston, tx, usa objective 1) describe hcph’s disease surveillance and prevention activities within the nrg center mega-shelter; 2) present surveillance findings with an emphasis on sharing tools that were developed and may be utilized for future disaster response efforts; 3) discuss successes achieved, challenges encountered, and lessons learned from this emergency response. introduction hurricane harvey made landfall along the texas coast on august 25th, 2017 as a category 4 storm. it is estimated that the ensuing rainfall caused record flooding of at least 18 inches in 70% of harris county. over 30,000 residents were displaced and 50 deaths occurred due to the devastation. at least 53 temporary refuge shelters opened in various parts of harris county to accommodate displaced residents. on the evening of august 29th, harris county and community partners set up a 10,000 bed mega-shelter at nrg center, in efforts to centralize refuge efforts. harris county public health (hcph) was responsible for round-the-clock surveillance to monitor resident health status and prevent communicable disease outbreaks within the mega-shelter. this was accomplished through direct and indirect resident health assessments, along with coordinated prevention and disease control efforts. despite hcph’s 20-day active response, and identification of two relatively small but potentially worrisome communicable disease outbreaks, no large-scale disease outbreaks occurred within the nrg center mega-shelter. methods active surveillance was conducted in the nrg shelter to rapidly detect communicable and high-consequence illness and to prevent disease transmission. an online survey tool and novel epidemiology consulting method were developed to aid in this surveillance. surveillance included daily review of onsite medical, mental health, pharmacy, and vaccination activities, as well as nightly cot-to-cot resident health surveys. symptoms of infectious disease, exacerbation of chronic disease, and mental health issues among evacuees were closely monitored. rapid epidemiology consultations were performed for shelter residents displaying symptoms consistent with communicable illness or other signs of distress during nightly cot surveys. onsite rapid assay tests and public health laboratory testing were used to confirm disease diagnoses. when indicated, disease control measures were implemented and residents referred for further evaluation. frequencies and percentages were used in the descriptive analysis. results harris county’s nrg center mega-shelter housed 3,365 evacuees at its peak. 3,606 household health surveys were completed during 20 days of active surveillance, representing 7,152 individual resident evaluations, and 395 epidemiology consultations. multifaceted surveillance uncovered influenza-like illness and gastrointestinal (gi) complaints, revealing an influenza a outbreak of 20 cases, 3 isolated cases of strep throat, and a norovirus cluster of 5 cases. disease control activities included creation of respiratory and gi isolation rooms, provision of over 771 influenza vaccinations, generous distribution of hand sanitizer throughout the shelter, placement of hygiene signage, and frequent bilingual public health public service announcements in the dormitory areas. no widespread outbreaks of communicable disease occurred. additionally, a number of shelter residents were referred to the clinic after reporting exacerbation of chronical conditions or mental health concerns, including one individual with suicidal ideations. conclusions effective public health surveillance and implementation of disease control measures in disaster shelters are critical to detecting and preventing communicable illness. hcph’s rigorous surveillance and response system in the nrg center mega-shelter, including online survey tool and novel consultation method, resulted in timely identification and isolation of patients with gastrointestinal and influenza-like illness. these were likely key factors in the successful prevention of widespread disease transmission. additional success factors included successful partnerships with onsite clinical and pharmacy teams, cooperative and engaged shelter leadership, synergistic internal surveillance team dynamics, availability of student volunteers, sufficient quantities of influenza vaccine, and access to mobile survey technology. challenges, mostly related to scope and magnitude of response, included lack of pre-designed survey tools, relatively new staff without significant disaster experience, and simultaneous management of multiple surveillance activities within the community. personal hurricane-related losses experienced by hcph staff also impacted response efforts. hcph’s rich disaster response experiences at the nrg mega-shelter and developed surveillance tools can serve as a planning guide for future public health emergencies in harris county and other jurisdictions. keywords public health surveillance; emergency response; shelter surveillance; hurricane harvey; epidemiology *leann liu e-mail: lliu@hcphes.org online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e162, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e365, 2019 isds 2019 conference abstracts improving measles syndromic surveillance via dried blood spot testing in laos 2016-17 andrew d. nguyen, andrew dang khai nguyen, chanthavy soulaphy, michael marks, jennie musto queensland health, australia objective to evaluate whether dried blood spot (dbs) testing improves diagnostic uptake in vientiane capital city province, lao people's democratic republic (pdr) compared to conventional diagnostic techniques (venous blood by venepuncture) during syndromic surveillance from 2016-17. to also explore reasons for low blood sampling uptake via quantitative results and qualitative responses from health care workers; in addition to the perceived acceptance of dbs compared to venepuncture. introduction the lao pdr is aiming for measles elimination despite ongoing outbreaks of the disease. outbreak detection in the country relies on recognising cases meeting a set “fever and rash” case definition incorporated into the syndromic surveillance system run by the national center for laboratory and epidemiology (ncle). suspected cases are passively identified by presentations at health care facilities, with information forwarded to the ncle's early alert and response network (ewarn) along with event-based reported data [1]. world health organization (who) measles surveillance guidelines require ≥80% of “fever and rash” cases be sampled for testing; currently only 20% sampling occurs in laos [2,3]. sampling using dbs has been proposed as an alternative to conventional venepuncture in facilitating suspected measles case detection. in this study, dbs was proposed to improve blood uptake of syndromic cases, by evaluating whether it increased ascertainment compared to conventional venepuncture. it also analysed reasons for poor diagnostic uptake among healthcare personnel involved in syndromic surveillance. methods a mixed methods study involving a cross-sectional study and a qualitative survey was undertaken in vientiane capital city province. nine district and central hospitals were included to represent the general provincial population composition. surveillance data were provided through routine surveillance staff review of hospital logbooks and community health centres together with case investigation forms forwarded to the ncle's ewarn. a sample size of 166 was calculated with 80% power to detect a 20% difference in uptake in syndromic surveillance between dbs and venepuncture. a 1:1 matching of venepuncture and dbs notifications was set. a randomly selected sample of 105 from a total of 204 notifications of “fever and rash” from june-september 2016 during routine venepuncture-based surveillance was compared with a preliminary 13 collected notifications from a proposed 100 in june-september 2017 following introduction of dbs to routine use. resource limitations in 2017 restricted the dbs samples (n=13) analysed at this preliminary stage. reasons for baseline poor sampling uptake using 2016 venepuncture data (n=204) were separately explored according to categories including demographics, hospital, provisional diagnosis and measles immunisation. microsoft excel 2007 and stata v14.0 were used for descriptive, univariate and multivariate analyses of explanatory variables. qualitative questionnaires were physically administered to personnel at each hospital according to their involvement in syndromic surveillance in july-september 2016-17. given time constraints, a limited sample of surveillance personnel involved in the study (n=7) completed qualitative questionnaires. questionnaires explored reasons for poor uptake using a framework analysis of five themes focused on demographics, aetiology of reasoning, venous and dbs acceptance, and sampling preference. patterns were correlated with quantitative data. results baseline characteristics were similar across both study periods. a high frequency of "fever and rash" cases was detected among those 0-9 years (71.19%) in the study periods analysed. blood samples were obtained from 25.77% of "fever and rash" notifications using conventional venepuncture, reflecting current poor diagnostic uptake. direct comparison of 2016 and 2017 periods was underpowered at the time of analysis (n=105 vs n=13). but preliminary results indicated dbs had no difference in improving diagnostic uptake (23.07% vs 25.77%; or 0.83; ci 0.14-3.41) compared to baseline venepuncture. exploration of baseline 2016 venepuncture data (n=204) revealed only three "fever and rash" notifications were forwarded to the ewarn from hospitals involved in this study period. hospitals also varied in blood sampling. presenting at nasaithong district hospital was less associated http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e365, 2019 isds 2019 conference abstracts with uptake than not presenting there (or 0.15; ci 0.003-0.99). those presenting at xaythany district (or 4.53; ci 1.66-12.56) and settathirath central (or 3.09; ci 1.39-6.77) hospitals had greater odds of blood sampling than those who did not. logistic regression indicated a borderline increased odds of 1.02 (ci 1.00-1.05) for having bloods taken with each year of increased age. with provisional diagnoses, there were six suspected measles cases, with only three suspected cases being tested. measles diagnosis was not associated with blood uptake (or 2.35; ci 0.69-7.55). varicella diagnosis was less associated with uptake than not having varicella (or 0.06; ci 0.001-0.39), even after multivariable analysis. qualitative results described staff concerned with competing demands from clinical workloads and fulfilling syndromic surveillance reporting requirements. a common theme was in neglecting the syndromic case definition in lieu of the clinical case definition, fueling misunderstanding of reporting requirements. patient cultural beliefs were identified as being associated with altered blood sampling rates. respondents were equally split on patient preference between dbs and venepuncture techniques. conclusions results for dbs and venepuncture analysis were limited by data collection. however, this is one of the first studies to analyse the use of dbs in syndromic surveillance. preliminary results revealed no difference in diagnostic uptake between dbs and venepuncture, indicating poor blood ascertainment regardless of technique. collected data reflected current diagnostic uptake trends in the lao pdr and was representative of vientiane capital city province. quantitative and qualitative analyses of uptake indicate weaknesses in syndromic surveillance, varying by institution, cultural beliefs and understanding of case definitions. completion of dbs data collection will be expected to corroborate current findings. further studies exploring diagnostic uptake limitations and dbs viability in low resource settings may build on this data and inform syndromic surveillance opinion on using dbs. acknowledgement this project was supported by the ncle (lao pdr), the chadwick trust (uk) and the london school of hygiene & tropical medicine. supervision was assisted by who lao pdr country office personnel. references 1. sengkeopraseuth b. et al. 2016. hidden varicella outbreak, luang prabang province, the lao people’s democratic republic, december 2014 to january 2015. western pac surveill response j. 7(1), 1-5. pubmed https://doi.org/10.5365/wpsar.2015.6.2.010 2. world health organization. who-recommended standards for surveillance of selected vaccine-preventable diseases. in: department of vaccines and biologicals. geneva, switzerland: world health organization. 2003; 1-59. 3. musto j. measles epidemiology in lao people's democratic republic. (unpublished data). 2017. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27757246&dopt=abstract https://doi.org/10.5365/wpsar.2015.6.2.010 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e348, 2019 isds 2019 conference abstracts improving public health surveillance methods via smart home technologies kirti s. sahu1, arlene oetomo1, plinio p. morita1, 2, 3 1 university of w aterloo, waterloo, ontario, canada, 2 university of toronto, toronto, ontario, canada, 3 techna institute, toronto, ontario, canada objective the objective of this study is to explore individual, household and population-level health indicators collected in the home via smart thermostats. the study’s approach is to (a) identify if it is possible to isolate specific user behaviours using the motion and thermostat sensor data, and (b) develop remote monitoring of healthy behaviours at population level. furthermore, this study is interested in identifying if observed patterns will suffer variations. as a result, it will be possible to understand human behaviours and consequently understand lifestyle habits of a person or a group of people. introduction public health surveillance relies on surveys and/or self-reported data collection, both of which require manpower, time commitment, and financial resources from public health agencies and participants. the survey results can quickly become outdated due to f astpaced changes in our society. the health habits of canadians have rapidly evolved with technology and research indicates we are becoming a sedentary society, thus the levels of physical activity (pa) are very important population level health indicators. we will present a novel method to gather data at a granular level in near real-time, with minimal effort from participants. simple thermostats are found in nearly every house in canada, and smart thermostats enable efficient temperature adjustment, saving energy costs by adjusting according to human activity. thermostats are ubiquitous in canadian homes and the current expansion of smart thermostats make them an ideal data source over traditional methods. utilizing technology that can be deployed at a population level will enable vast granular data collection beyond capabilities of traditional surveys. in this project ubilab [1] is exploring the use of the zero-effort technology using sensor data collected by smart thermostats and other associated sensors to develop an innovative health surveillance platform and monitor an individual’s health at the household level as well as health indicators at population level. utilizing the smart wi-fi thermostat, we able to report on pa, sedentary behaviour, and sleep patterns at the household level. the thermostat and remote sensors (rs) contain temperature and motion sensors, which can be used to monitor activity in the home (i.e. lack of travel indicates sedentary behaviour), as well as sleep characteristics. this is beneficial as no action is required from participants, allowing individuals to go about their lives unperturbed. this powerful system will be able to deliver real-time health insights to public health professionals. methods zero-effort-technologies [2] represent the future of ambient assisted living (aal), in which sensors gather data generated by the person without conscious effort by the user. such data could be integrated with other technologies to give the system the ability to tackle unsolved remote monitoring issues challenged the traditional data collection method barriers. for example, when the rs is placed in the bedroom, they can provide insights on sleep duration and quality. this addresses the challenges of declining participant engagement, low response rates in surveys and focus groups, and technical barriers to wearable technology. this eliminates recall bias, common when asking participants to quantify the amount of pa and types of behaviours they engaged in. using the motion data, we can quantify the amount of pa in the home to determine individual levels of pa. the ubilab partnered wi th ecobee [3], a canadian smart wi-fi thermostat company, leveraging data from over 10,000 households in north-america collected through the donate your data (dyd) [4] program. a small pilot study (n = 8) was done to validate the use of motion sensor readi ngs of movement between rooms through a cross comparison with fitbit [5] step data. and the dyd dataset was analyzed for patterns using python [6], pandas [7], elasticsearch [8], and kibana [8]. this method will enable the delivery of personalized insights to monitor individualand populationlevel health behaviours. results physical activity, sedentary behaviour and sleep (pass) indicators [9] are measured through surveys (i.e. canadian health measures survey and canadian community housing survey) administered by statistics canada. using this technology public http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e348, 2019 isds 2019 conference abstracts health agencies will enable to collect novel health indicators, monitor health in real -time and deliver health insights to canadians to increase health literacy. a positive association between fitbit and ecobee data was found (spearman’s correlation coefficient = 0.7, p > 0.001) from 380 person hours from the pilot study. indicators (sleep, interrupted sleep, daily indoor activity, sede ntary) based on the pass indicators framework from the public health agency of canada (phac) [2] were measured using dyd data. single occupant ecobee households in canada averaged 7.2 hours of sleep in 24-hours, 2.1 hours of interrupted sleep, were active for 85 minutes daily, and spent 4.44 hours being sedentary. recently, we have improved data collection adding fitbit charge 2 hrs, to capture sleep and heart rate not previously possible with the fitbit zip. adding more sensors functionality is crucia l for algorithm modifications, this includes collecting additional data via the samsung smartthings hub [10]; presence, light usage, and luminance. ecobee is sharing participants and data from their own study, increasing variability within data. we have improved our data storage and analysis process, moving the big data architecture from python to elasticsearch for real-time data streaming and analysis. we are also actively collaborating with phac and improving our algorithm and analysis process using their feedback. conclusions this is a key opportunity to innovate traditional data collection methods, empowering patients through education and leveraging technology infrastructures to enable healthcare and policy decisions to be made with relevant and real -time data. lessons learned at the individual and community health levels will be shared with community members and researchers. implications include understanding short-term impacts with minimal effort and new health policies at the community level. increased awareness and improvement can help to better physical activity, sleep and sedentary behaviour which may lead to improvements in overall health and wellbeing. acknowledgement the ubilab team would like to thank ecobee and their staff for their assistance throughout this project. thank you to dr. plinio morita for his guidance and leadership. references 1. waterloo u of. ubilab. https://uwaterloo.ca/ubiquitous-health-technology-lab/. 2. public health agency of canada canada. ca. https://www.canada.ca/en/public-health.html. accessed october 26, 2018. 3. ecobee | smart home technology |. https://www.ecobee.com/. accessed october 26, 2018. 4. donate your data | smart wifi thermostats by ecobee. https://www.ecobee.com/donateyourdata/. accessed september 21, 2017. 5. fitbit official site for activity trackers & more. https://www.fitbit.com/en-ca/home. accessed september 21, 2017. 6. welcome to python.org. https://www.python.org/. accessed november 22, 2017. 7. python data analysis library — pandas: python data analysis library. https://pandas.pydata.org/. accessed january 14, 2018. 8. elasticsearch. https://www.elastic.co/. accessed october 26, 2018. 9. physical activity, sedentary behaviour and sleep (pass) indicator framework for surveillance canada.ca. https://www.canada.ca/en/services/health/monitoring-surveillance/physical-activity-sedentary-behavioursleep.html. accessed january 14, 2018. 10. samsung. samsung smart thing hub. 2018. https://www.smartthings.com/products/smartthings-hub. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts mandating syndromic surveillance reporting from emergency departments: the washington experience amanda d. morse, natasha close*, cynthia harry and kevin wickersham office of communicable disease epidemiology, washington state department of health, shoreline, wa, usa objective to protect syndromic surveillance data reporting from emergency departments in washington state beyond the cessation of meaningful use incentive funding in 2021. introduction as syndromic surveillance reporting became an optional activity under meaningful use stage 3 and incentive funds are slated to end completely in 2021, washington state sought to protect syndromic reporting from emergency departments. as of december 2016, washington state emergency departments had received $765,335,529.40 in incentive funding, with facilities receiving an average of three payments of $479,974.04 each.1 considering the public health importance of syndromic surveillance reporting and the fiscal impact of mandatory reporting, the washington state department of health (wa doh) sought a new statute to require reporting from all emergency departments within the state. methods stakeholder negotiations occurred in four distinct phases: initial outreach to gauge support for the proposed legislation and solicit comments on items for inclusion in the bill language (april to october 2016), negotiations on the proposed text (october 2016 to january 2017), sustained contact with key groups during the legislative session (january to may 2017), and targeted messaging and comment solicitation to inform the development of the administrative codes to accompany the statute (may 2017 to march 2018) (figure 1). wa doh secured funding from the washington traffic safety commission (wtsc) to hire a designated staff member to coordinate and lead the syndromic surveillance program’s legislative agenda. results during the first phase, wa doh contacted a diverse group of stakeholders, including 7 state agencies, 5 data provider groups, 3 professional associations, 3 public health non-profits, 35 local health jurisdictions, and 29 tribal organizations to provide information on syndromic surveillance and gauge interest in the proposed legislation. key partners included the wtsc, the washington state hospital administration (wsha), the washington poison center (wapc), and several large to medium-sized local health jurisdictions (lhjs). early stakeholder negotiations indicated broad support among local health jurisdictions, state agencies, programs handling violence and injury topics, communicable disease specialists, the department of labor and industries, and the office of financial management; however, support from data providers was more cautious. early relationship-building and maintaining frequent contact was key to securing support from data providers and allowed wa doh to draft a mid-session amendment to the bill granting greater data access for providers and clarifying the foundational fee structure for data distribution. during the second phase of outreach and communication, areas of more intense negotiation included data access levels for providers, timeliness of data availability, and the proposed inclusion of a “sundown clause” which would end the effective period of the statute when federal incentive spending expired. wa doh secured bipartisan co-sponsorship for the bill in the washington state senate and stakeholder negotiations continued throughout the legislative session (january-may 2017). common concerns from stakeholders and legislators included the maintenance of patient privacy, the costs associated with participation for emergency departments, and data sharing agreements with facilities providing syndromic data. the bill passed unanimously out of the washington state senate and with broad bipartisan support out of the washington state house. governor jay inslee signed the bill into law on may 5, 2017 and wa doh has continued negotiations as they develop the administrative codes to accompany the statute. conclusions broad and sustained interactions with a diverse group of stakeholders, as well as willingness to compromise on data sharing and cost issues with providers, was key to washington state’s successful effort to mandate syndromic surveillance reporting from emergency departments. keywords policy; emergency department; community collaboration; meaningful use; meaningful use isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments the authors would like to thank our bill co-sponsors; the washington traffic safety commission; the washington state hospital association; washington poison center; the whatcom county department of health, public health seattle-king county, tacoma-pierce county public health, the benton-franklin health district, drs. cathy wasserman and scott lindquist, and our other legislative partners. particular gratitude is due to the washington traffic safety commission for funding rhino’s outreach and legislative efforts. references 1. medicare and medicaid incentive provider payments by state, program type and provider type january 2011 to december 2016. centers for medicare and medicaid. https://www.cms.gov/regulationsand-guidance/legislation/ehrincentiveprograms/downloads/ december2016_paymentsbystateprogramandprovider.pdf last accessed: 11 september 2017. *natasha close e-mail: natasha.close@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e75, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e283, 2019 isds 2019 conference abstracts evaluation of first electronic case reports received in illinois stacey hoferka illinois department of public health, chicago, illinois, united states objective comparison of content in ecr and elr cases reporting review technical challenges and strategies for data management introduction communicable disease reporting from providers can be a time-consuming process that results in delayed or incomplete reporting of infectious diseases, limiting public health's ability to respond quickly to prevent or control disease. the recent develop ment of an hl7 standard for automated electronic initial case reports (eicr) represents an important advancement for public health surveillance. the illinois department of public health (idph) participated in a pilot with the public health informatics inst itute and an illinois-based provider group to accept eicr reports for gonorrhea and chlamydia. methods the provider group working with their ehr vendor submitted a batch of ct and gc reports directly to idph in september 2017 according to the published eicr standard. a summary of the provider and phii work has been presented previously in the sti ecr learning community. the eicr reports received from the provider were compared to case report data in the communicable disease surveillance system, i-nedss. data was extracted from i-nedss that included race and ethnicity, timing of specimen collection, result, elr submission surveillance action and treatment. results idph received a batch of 89 files containing 77 unique persons, with 54 chlamydia (ct), 13 gonorrhea (gc) an d 10 co-infected case reports. the communicable disease surveillance system had captured 76 (98.7%) of the persons reported in the pilot. amon g those, an electronic laboratory report (elr) was received for 72 (95%) cases, on average within 1 day of the lab report date. data in i-nedss had a completion of 45% for race and ethnicity compared to 99% for race and 92% for ethnicity in the eicr files. information on treatment in the surveillance system was reported for 18 (24%) cases compared to 67 (87%) cases. conclusions this pilot was the first submission of real patient data submitted using the eicr standard to idph. data was more complete fr om provider eicr reports for key demographic of race and ethnicity and treatment. a comparison with the current surveill ance system showed near complete and timely case capture from elr data. integrated reporting of both elr and eicr can produce a more complete case report through automated submissions and potentially reduce burden of data collection on health department communicable disease investigators. as public health reporting moves in this direction, public health agencies will have some substantial tasks to correctly ingest, map and interpret the increased amounts of information that are contained in the eicr. further, the advantages of case reporting will be dependent on automated processes within the communicable disease system to merge data and apply business rules to automatically process completed case reports for high volume diseases, such as stis. this work wi ll continue as providers are ready to submit reports from different vendor products from a near real-time production environment. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts analytics for investigation of disease outbreaks (aido) ashlynn daughton*1, 2, maneesha chitanvis1, nileena velappan1, forest m. altherr1, geoffrey fairchild1, william rosenberger1, attelia hollander1 and alina deshpande1 1biosecurity and public health, los alamos national laboratory, los alamos, nm, usa; 2university of colorado, boulder, boulder, co, usa objective analytics for the investigation of disease outbreaks (aido) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. a representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. these historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks. introduction situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. aido is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes. methods public health analysts of the biology division at los alamos national laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. properties vary by disease, as they are related to diseasespecific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. these properties are then incorporated into a similarity algorithm (s in figure 1) to identify outbreaks that are similar to user inputs. results aido currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., dengue, malaria), foodborne (e.g., salmonella, campylobacteriosis), waterborne (e.g., cholera), and person-to-person transmitted (e.g., measles)). in addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak. conclusions the analytics provided by aido allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. the tool is continually updated with new functionalities and additional data. figure 1 illustrates the algorithm used in the tool to calculate similarity scores that reflect the extent of the match between users’ inputs and outbreaks in our disease libraries. keywords unfolding disease outbreak; outbreak investigation; infectious disease; decision-support acknowledgments this project is supported by the chemical and biological technologies directorate joint science and technology office (jsto), defense threat reduction agency (dtra), and the national biosurveillance integration center (nbic) of the department of homeland security (dhs). *ashlynn daughton e-mail: adaughton@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e29, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using essence to detect bomb-making activity: what’s appropriate? andrew torgerson* saint louis county department of public health, berkeley, mo, usa objective to describe a novel application of essence by the saint louis county department of public health (dph) in preparation for a mass gathering and to encourage discussion about the appropriateness of sharing syndromic surveillance data with law enforcement partners. introduction in preparation for mass gathering events, dph conducts enhanced syndromic surveillance activities to detect potential cases of anthrax, tularemia, plague, and other potentially bioterrorism-related communicable diseases. while preparing for saint louis to host a presidential debate on october 9, 2016, dph was asked by a partner organization whether we could also detect emergency department (ed) visits for injuries (e.g., burns to the hands or forearms) that could possibly indicate bomb-making activities. methods using the electronic surveillance system for the notification of community-based epidemics (essence), version 1.9, dph developed a simple query to detect visits to eds in saint louis city or saint louis county with chief complaints including the word “burn” and either “hand” or “arm.” a dph epidemiologist reviewed the results of the query daily for two weeks before and after the debate (i.e., from september 25, 2016 to october 23, 2016). if any single ed visit was thought to be “suspicious” – if, for example, the chief complaint mentioned an explosive or chemical mechanism of injury – then dph would contact the ed for details and relay the resulting information to the county’s emergency operations center. results during the 29 day surveillance period, essence detected 27 ed visits related to arm or hand burns. the essence query returned a median of 1 ed visit per day (iqr 0 to 2 visits). of these, one was deemed to merit further investigation – two days before the debate, a patient presented to an ed in saint louis county complaining of a burned hand. the patient’s chief complaint data also mentioned “explosion of unspecified explosive materials.” upon investigation, dph learned that the patient had been injured by a homemade sparkler bomb. subsequently, law enforcement determined that the sparkler bomb had been made without any malicious intent. conclusions dph succeeded in using essence to detect injuries related to bomb-making. however, this application of essence differs in at least two ways from more traditional uses of syndromic surveillance. first, conventional syndromic surveillance is designed to detect trends in ed visits resulting from an outbreak already in progress or a bioterrorist attack already carried out. in this case, syndromic surveillance was used to detect a single event that could be a prelude to an attack. the potential to prevent widespread injury or illness is a strength of this approach. second, conventional syndromic surveillance identifies potential outbreak cases or, in the case of a bioterrorist attack, potential victims. in this case, syndromic surveillance was used to identify a potential perpetrator of an attack. while public health and law enforcement agencies would ideally coordinate their investigative efforts in the wake of an attack, this practice has led to conversations within dph about the appropriateness of routinely sharing public health surveillance data with law enforcement. keywords mass gathering; essence; injury; preparedness *andrew torgerson e-mail: atorgerson@stlouisco.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e194, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e408, 2019 isds 2019 conference abstracts seroprevalence and factors associated with seropositivity to rift valley fever virus in livestock yusuf b. ngoshe1, lida avenant1, mk rostal2, karesh b. william2, janus t. paweska3, jansen van vuren3, claudia cordel4, veerle msimang3, 1, peter n. thompson1 1 epidemiology section, production animal studiesstudy, university of pretoria, pretoria, gauteng, south africa, 2 ecohealth alliance, nyc, new york, united states, 3 national institute for communicable diseases, pretoria, gauteng, south africa, 4 execuvet, bloemfontein, free state, south africa objective the objective of this study was to estimate the prevalence of antibodies to rvfv in domestic cattle, sheep, and goats in a study area in the central interior of south africa, and to identify factors associated with seropositivity. introduction rift valley fever (rvf) is a mosquito-borne viral zoonosis. this study aimed to estimate the prevalence of antibodies to rvf virus (rvfv) in cattle, sheep, and goats in south africa, near the 2010-2011 outbreak epicenter and identify factors associated with seropositivity. methods a cross-sectional study was conducted during 2015-2016 within a ~40,000 km2 region between bloemfontein and kimberley. farms were selected using random geographic points with probability proportional to the density of livestock-owning households. livestock were randomly sampled from the farm closest to each selected point. a questionnaire was used to collect information concerning animal, management, and environmental factors. sera samples were screened for rvfv antibodies using igg inhibition elisa. data were analyzed using multilevel logistic regression models. results on 234 farms, 3,049 animals (977 cattle, 1,549 sheep and 523 goats) were sampled. estimated rvf seroprevalence, adjusted for clustering and sampling weights, was 42.9% (95% ci: 35.7-50.4%) in cattle, 28.0% (95% ci: 21.3-35.4%) in sheep and 9.3% (95% ci: 5.8-13.9%) in goats. compared to animals <2y of age, seroprevalence was higher in animals 2-4y (or=2.8, p<0.001) and >4y old (or=17.0, p<0.001). seroprevalence was also higher on private vs. communal land (or=4.3, p=0.001) and was positively associated with the presence of perennial rivers (or=1.6, p=0.03) and seasonal pans (or=1.8, p=0.005) on the farm. the odds of seropositivity was higher in domestic ruminants recently vaccinated between 2014-2015 (or=2.1, p=0.007) compared to those never vaccinated. conclusions the presence of igg antibody against rvfv among domestic ruminants, born after the most recent outbreak (<4y category), and association with known rvf risk factors, indicates the possibility that viral circulation has occurred during the interepidemic period. acknowledgement the authors acknowledge and are extremely grateful to all the farmers that participated in this study and to the state veterinarians and animal health technicians of free state and northern cape provinces for facilitating identification of farms and contacts with the farmers. we also thank karissa whiting for the development of the electronic mobile application for the survey data collection. the field and laboratory work for this project was funded by the understanding rift valley fever in south africa project, sponsored by the u.s. department of defense, defense threat reduction agency. the content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred. the ecohealth alliance, university of pretoria and national research foundation are acknowledged for financial support. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts epizootologic potential of certain natural foci infections in northeastern armenia arsen manucharyan* laboratory of epizootology, ectoparasitology and entomology, ra moh, ncdcp snco, reference laboratory center branch, yerevan, armenia objective the objective of this study was to analyze the epizootic potential of four areas of tavush marz. introduction tavush marz, in northeastern armenia, occupies 9,1% of its territory. in recent years in this area either no surveys were conducted or they were incomplete. tavush marz is a tourism center as well as a border marz with strategic importance. the presence of tularemia was first confirmed in 1949 in noyemberyan. natural foci of tularemia are located in forest zones where sylvemus uralensis and its flea vectors are the source of infection. methods tests were conducted in four areas of tavush marz. materials for laboratory analyses were collected via sampling from animal populations and their nests; insect traps, nets, aspirators were used as well. collected rodents, fleas and mosquitoes were tested on the presence of natural foci especially dangerous infections. the acquired data with coordinates of collection sites were included in the geographic database. results the first detection of the aedes albopictus species of culicoidea subfamily in armenia was on the border between noyemberyan and georgia in 2016; this is a vector of especially dangerous infections and arboviruses. the presence of the mosquito was confirmed in 2017 and it makes up 13.5% of the mosquitoes collected in the northeast. it is capable of transmitting the chikungunya, dengue, and west nile viruses. since the end of 2015, we have recorded a significant increase in carriers and vectors, mainly s. uralensis, microtus socialis, and ectoparasites specific to them. in october 2016 six cases of infection with tularemia were recorded in the village of tsakhkavan in tavush region and in bagratashen village of noyemberyan region in 2017. conclusions analysis of the density of rodents and vectors, as well as their typical ectoparasites leads us to conclude that there are favorable conditions for the spread of not only tularemia but also other natural foci infections and that a comprehensive and regular epizootological survey is required to control this situation. average density of sylvemus uralensis per hectare average density of m. socialis per hectare fleas characteristic to s. uralensis and m. socialis keywords epizootologic potential; natural foci; tularemia *arsen manucharyan e-mail: arsen.manucharyan.1976@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e117, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e334, 2019 isds 2019 conference abstracts disease surveillance system of bangladesh: combating public health emergencies mohammed husain, mahmudur rahman, asm alamgir, m. salim uzzaman, meerjady sabrina flora directorate general of health services, bangladesh objective a) to observe trends and patterns of diseases of public health importance and response b) to predict, prevent, detect, control and minimize the harm caused by public health emergencies c) to develop evidence for managing any future outbreaks, epidemic and pandemic introduction disease surveillance is an integral part of public health system. it is an epidemiological method for monitoring disease patterns and trends. international health regulation (ihr) 2005 obligates who member countries to develop an effective disease surveillance system. bangladesh is a signatory to ihr 2005. institute of epidemiology, disease control and research (iedcr <www.iedcr.gov.bd>) is the mandated institute for surveillance and outbreak response on behalf of government of the people’s republic of bangladesh. the iedcr has a good surveillance system including event-based surveillance system, which proved effective to manage public health emergencies. routine disease profile is collected by management information system (mis) of directorate general of health services (dghs). expanded program of immunization (epi) of dghs collect surveillance data on epi-related diseases. disease control unit, dghs is responsible for implementing operational plan of disease surveillance system of iedcr. the surveillance system maintain strategic collaboration with icddrr,b. methods the iedcr is conducting disease surveillance in several methods and following several systems. surveillance data of priority communicable disease are collected by web based integrated disease surveillance. it is based on weekly data received from upazilla (sub-district) health complex on communicable disease marked as priority. they are: acute watery diarrhea, bloody dysentery, malaria, kala-azar, tuberculosis, leprosy, encephalitis, any unknown disease. government health facilities at upazilla (sub-district) send the data using dhis2. during outbreak, daily, even hourly reporting is sought from the concerned unit. moreover, iedcr conducts disease specific specialized surveillance systems. data from community as well as from health facilities are collected for influenza, nipah, dengue, hiv, cholera, cutaneous anthrax, non-communicable diseases, food borne illness. data from health facilities are collected for antimicrobial resistance, rotavirus and intussusception, reproductive health, child health and mortality, post mda-surveillance for lymphatic filariasis transmission, molecular xenomonitoring for detection of residual wucheria bancrofti, dengue (virological), emerging zoonotic disease threats in high-risk interfaces, leptospirosis, acute meningo-encephalitis syndrome (ames) focused on japanese encephalitis and nipah, unintentional acute pesticide poisoning among young children. data for event based surveillance are collected from usual surveillance system as well as from dedicated hotlines (24/7) of iedcr, media monitoring, and any informal reporting. case detection is done by syndromic surveillance, laboratory diagnosed surveillance, media surveillance, hotline, cell phone-based surveillance. dissemination of surveillance is done by website of iedcr, periodic bulletins, seminar, conference etc. line listing are done by rapid response teams working in the surveillance sites. demographic information and short address are listed in the list along with clinical and epidemiological information. initial cases are confirmed by laboratory test, if required from collaborative laboratory at us cdc (atlanta). when the epidemiological trend is clear, then subsequent cases are detected by symptoms and rapid tests locally available. results in 2017, 26 incidents of disease outbreak were investigated by national rapid response team (nrrt) of iedcr. in the same year, 12 cases of outbreak of unknown disease was investigated by nrrt of iedcr at different health facilities. joint surveillance with animal health is being planned for detection and managing zoonotic disease outbreaks, following one health principles. department of livestock, ministry of environment and icddrb are partners of the joint surveillance based on one health principles. disease control unit of dghs, district and upazilla health managers utilizes the disease surveillance data for public health management. they analyze also the surveillance data at their respective level to serve their purpose. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e334, 2019 isds 2019 conference abstracts conclusions a robust surveillance is necessary for assessing the public health situation and prompt notification of public health emergency. the system was introduced at iedcr mainly for malaria and diarrhea control during establishment of this institute. eventually the system was developed for communicable disease, and recently for non-communicable diseases. it is effectively used for managing public health emergencies. notification and detection of public health emergency is mostly possible due to media surveillance. data for syndromic surveillance for priority communicable diseases is often not sent timely and data quality is often compromised. tertiary hospitals are yet to participate in the web based integrated disease surveillance system for priority communicable diseases. but they are part of specialized disease surveillances. data from specialized surveillance with laboratory support is of high quality. evaluation of the system by conducting research is recommended to improve the system. specificity and sensitivity of case detection system should also be tested periodically. references 1. cash, richard a, halder, shantana r, husain, mushtuq, islam, md sirajul, mallick, fuad h, may, maria a, rahman, mahmudur, rahman, m aminur. reducing the health effect of natural hazards in bangladesh. lancet, the, 2013, volume 382, issue 9910 2. iedcr. at the frontline of public health. updated 2013. www.iedcr.gov.bd 3. ao tt, rahman m, et al. 2016. low-cost national media-based surveillance system for public health events, bangladesh. emerg infect dis. 22(4). www.iedcr.gov.bd. accessed oct 1, 2018. pubmed https://doi.org/10.3201/eid2204.150330 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26981877&dopt=abstract https://doi.org/10.3201/eid2204.150330 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e394, 2019 isds 2019 conference abstracts leaving a mobile footprint: utilizing data to combat the 2017 2018 influenza season jill d. tenhaken dsrip, harris county public health, houston, texas, united states objective during this session, participants will be able to understand how harris county public health utilized data to make informed decisions on how to combat the influenza season. introduction the 2017 – 2018 influenza season was classified by the centers for disease control and prevention (cdc) as ‘high severity’ across all age groups. furthermore, cdc noted that this was the first year to be categorized as such, with the highest peak percentage of influenza-like-illnesses (ili), since 2009. in harris county alone, there were 2,665 positive flu tests reported in comparison to the previous season at 1,395 positive tests. in response to the severity of this year’s flu season, harris county public health (hcph) collaborated across the department to deploy five pop up influenza vaccination events utilizing our mobile fleets open to the general public. hcph epidemiologists are able to collect influenza data from multiple systems and compile it into useful reports/tools. these data include latitudinal and longitudinal data, allowing us to create highly localized maps of where influenza has had impacted communities the hardest. this granular data allowed hcph to target 5 areas with our mobile fleet that had a) high levels of influenza and b) generally limited healthcare/public health infrastructure. our mobile fleet is made up of 8 different recreational vehicles that have been retrofitted to offer various public health services including: immunizations, medical visits, dental visits, pet adoptions, mosquito and vector control education, and a fresh food market. the fleet allows hcph to offer a full menu of public health services anywhere within the county. while our efforts for this abstract were focused on controlling the influenza outbreak, we leveraged the opportunity to engage with the public on multiple issues such as environmental, veterinary, mosquito control, dental health, and accessible healthy food options. methods as positive flu reports mounted, our epidemiology program provided surveillance data of influenza and ili in harris county. data was obtained through multiple sources including: national electronic disease surveillance system (nedss), which includes electronic laboratory reporting; national respiratory enteric virus surveillance system (nrevss), which includes all flu tests done in laboratories in houston; and last, the flu portal, which school nurses in harris county upload school absenteeism rates due to ili. once collected and compiled, our geographic information system (gis) team used the data to generate spatial maps of harris county illustrating the disproportionally high rates. specifically, our gis team was able to utilize arcgis, and cross layer them with the flu data provided from the epidemiologists. utilizing these maps, hcph leadership mobilized the preparedness team to lead a data driven response in five different zip codes throughout the county to hold the influenza vaccination events. results the mobile fleet was operational on five separate dates in five separate zip codes during february and march of 2018. overall, 477 individuals were provided the influenza vaccine. of those 477, 304 were 18 years or older, with 173 being under 18 years of age. conclusions having timely and actionable data is an essential first step to understand and stop an outbreak of any size. however, surveillance data alone won't prevent an outbreak from spreading. that data must be married to effective public health action. our mobile fleet is able to deliver precision public health services by targeting communities most affected and vulnerable to the spread of disease. as surveillance geospatial data becomes more granular so too must our public health service delivery modes become more precise and targeted. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e255, 2019 isds 2019 conference abstracts multidimensional semantic scan for pre-syndromic disease surveillance mallory nobles1, ramona lall2, robert mathes2, daniel b. neill3 1 carnegie mellon university, pittsburgh, pennsylvania, united states, 2 nyc dept. of health and mental hygiene, new york, new york, united states, 3 center for urban science and progress, new york university, new york, new york, united states objective we present a new approach for pre-syndromic disease surveillance from free-text emergency department (ed) chief complaints, and evaluate the method using historical ed data from new york city’s department of health and mental hygiene (nyc dohmh). introduction an interdisciplinary team convened by isds to translate public health use-case needs into well-defined technical problems recently identified the need for new “pre-syndromic” surveillance methods that do not rely on existing syndromes or pre-defined illness categories [1]. our group has recently developed multidimensional semantic scan (muses), a pre-syndromic surveillance approach that (1) uses topic modeling to identify newly emerging syndromes that correspond to rare or novel diseases; and (2) uses multidimensional scan statistics to identify emerging outbreaks that correspond to these syndromes and are localized to a par ticular geography and/or subpopulation [2,3]. through a blinded evaluation on retrospective free-text ed chief complaint data from nyc dohmh, we demonstrate that muses has great potential to serve as a “safety net” for public health surveillance, facilitating a rapid, targeted, and effective response to emerging novel disease outbreaks and other events of relevance to public health that do not fit existing syndromes and might otherwise go undetected. methods multidimensional semantic scan uses topic modeling to learn illness categories directly from the data, eliminating the need for predefined syndromes. topic models are a set of algorithms that automatically summarize the content of large collections of documents by learning the main themes, or topics, contained in the documents [4]. our method learns two sets of topics: a set of topics over the historical data designed to capture common illnesses, and a set of emerging topics over only the most recent data that are optimized to capture any new illnesses not captured by the historical topics. we then use multidimensional scan statistics to identify clusters of cases isolated to a certain topic, hospital, and/or demographic group of patients [5]. to evaluate the ability of muses to detect a diverse set of emerging patterns relevant to public health in large and complex data, we apply our algorithm to historical chief complaint data from nyc. this dataset has over 28 million ed cases from 53 nyc hospitals during 2010-2016. for each hospital we have data on the patients' free-text chief complaint, date and time of arrival, age group, gender and discharge icd-9 diagnosis code. public health practitioners at nyc dohmh performed a blinded evaluation of the top 500 highest-scoring clusters detected by our method and by a competing state of the art keyword-based approach [6-8]. for each of these clusters, the evaluators indicated if the cluster (1) represents a meaningful collection of cases and (2) i s, in their judgement, of significant interest to public health. results the blinded evaluation by nyc dohmh demonstrated that our method correctly identifies a larger number of events of interest to public health than the baseline keyword-based scan method. 320 (64%) of the top 500 results from muses corresponded to meaningful health events, while the keyword-based method only detected 246 such events (49.2%). muses also identified 6 more highly relevant events and 74 less meaningless clusters than the keyword-based method. figure 1 shows that for any fixed number of clusters that public health officials choose to examine, muses identifies more meaningful events than keyword -based scan. alternatively, for any desired number of true clusters detected, muses exhibits substantially higher precision: for example, in order to identify 100 true clusters, it had to report 159 total clusters (precision = 63%) as compared to 225 total clusters (precision = 44%) for the keyword-based scan. this corresponds to a 53% reduction in the number of false positive clusters. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e255, 2019 isds 2019 conference abstracts additionally, to determine how our approach might provide situational awareness of emerging health concerns following a natural disaster, we examined the clusters identified by our approach in the week following october 29, 2012, when hurricane sandy struck new york city and caused a historic level of damage. these results show a progression of clusters from acute cases rel ated to falls and shortness of breath, to mental health issues like depression and anxiety, to chronic health issues that require maintenance procedures, like dialysis and methadone distribution. it is of note that public health officials manually inspected emergency room data immediately following hurricane sandy and noticed an increase in the words “methadone”, “dialysis” and “oxygen” [7]. the ability of muses to automatically identify similar symptoms as human experts highlights its ability to learn meaningful but n ovel combinations of symptoms. conclusions our muses system offers a novel method for pre-syndromic surveillance that achieves the goals set forth by public health practitioners during the isds consultancy. when evaluated against a state of the art baseline, muses identifies a larger number of events of interest, has a lower false positive rate, and produces more coherent results. this ability to report newly emerging case clusters of high relevance to public health, without overwhelming the user with a large number of false positives, suggest hi gh potential utility of the approach for day-to-day operational use as a “safety net” for public health surveillance, complementing existing syndromic surveillance approaches. we are currently building a pre-syndromic surveillance system based on the muses approach and plan to make this software widely available to public health partners in the near future. acknowledgement we wish to thank the bcd syndromic surveillance unit at nyc department of health and mental hygiene for providing retrospective data for this study and for participating in the blinded evaluation, and the department of homeland security hidden signals challenge for providing funding support for system development. references 1. faigen z, deyneka l, ising a, et al. 2015. cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use case. online j public health inform. 7(3), e228. pubmed https://doi.org/10.5210/ojphi.v7i3.6354 2. maurya a, murray k, liu y, dyer c, cohen ww, et al. semantic scan: detecting subtle, spatially localized events in text streams. 2016. arxiv preprint arxiv:1602.04393. 3. nobles m, deyneka l, ising a, neill db. 2015. identifying emerging novel outbreaks in textual emergency department data. online j public health inform. 7(1), e45. https://doi.org/10.5210/ojphi.v7i1.5710 4. blei d, ng a, jordan m. 2003. latent dirichlet allocation. j mach learn res. 3, 993-1022. 5. neill db. 2012. fast subset scan for spatial pattern detection. j r stat soc b. 74(2), 337-60. https://doi.org/10.1111/j.1467-9868.2011.01014.x 6. burkom h, elbert y, piatko c, fink c. 2015. a term-based approach to asyndromic determination of significant case clusters. online j public health inform. 7(1), e11. https://doi.org/10.5210/ojphi.v7i1.5675 7. lall r, levin-rector a, mathes r, weiss d. 2014. detecting unanticipated increases in emergency department chief complaint keywords. online j public health inform. 6(1), e93. https://doi.org/10.5210/ojphi.v6i1.5069 8. walsh a, hamby t, st john tl. 2013. identifying clusters of rare and novel words in emergency department chief complaints. online j public health inform. 6(1), e146. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26834939&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26834939&dopt=abstract https://doi.org/10.5210/ojphi.v7i3.6354 https://doi.org/10.5210/ojphi.v7i1.5710 https://doi.org/10.1111/j.1467-9868.2011.01014.x https://doi.org/10.5210/ojphi.v7i1.5675 https://doi.org/10.5210/ojphi.v6i1.5069 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e255, 2019 isds 2019 conference abstracts figure 1: performance comparison http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e243, 2019 isds 2019 conference abstracts exploring missouri's new, innovative, and multilayered health data suppression rules whitney b. coffey bureau of health care analysis and data dissemination, missouri department of health and senior services, jefferson city, missouri, united states objective by the end of this session, users will be able to describe the innovative and multilayer ed suppression rules that are applied to missouri's homegrown health data web query system. they will also be able to use the lessons learned and user feedback descri bed in the session to facilitate discussions surrounding the application of suppression to their specific data systems. introduction in spring 2017, the missouri department of health and senior services (modhss) launched the missouri public health information management system (mophims) web-based health data platform. missouri has supported a similar data system since the 1990s, allowing the public, local public health departments, and other stakeholders access to community level birth, death, and hospitalization data (among other datasets). the mophims system is composed of two separate pieces. community data profiles are topic-, disease-, or demographic-specific reports that contain 15-10 indicators relevant to the report. because these static reports are developed in-house a multilayered suppression rule is not required. the second piece of mophims, the data micas, or missouri information for community assessent, can be used to create customized datasets that slice and dice up to a dozen demographic and system-specific variables to answer complex research questions. the mophims interface features, among other things, a new and innovative method for addressing confidentiality concerns through the suppression of health data. this pioneering approach integrates multi-level logic that uses inner and outer cell analytics, the use of exempt and conditionally exempt variables, and multiple levels of user access. moving beyond a simple model of suppressing any values below a certain threshold, mophims takes a bold step in providing users exceptionally granular data while still protecting citizen privacy. methods in order to implement this new suppression methodology, modhss worked with both internal information technology resources (oa-itsd) and outside contractors to develop the suppression rules utilized in the data micas. before these meetings began, modhss analysts met weekly to determine the overall goals and frames for the rule, knowing that writing the code to implement the complicated and comprehensive vision would be a collaborative and iterative process. because the mophims system is homegrown and this specific confidentiality process is not currently utilized (to our knowledge) elsewhere, all of those at the discussion table were required to be innovative, open to criticism, and willing to engage in e xtremely detailed explanations. a team of users from missouri’s local public health departments provided feedback throughout this process. a basic description of the process flow that occurs before suppression is applied in mophims follows. to begin, de-identified record-level data are loaded into online analytical processing (olap) cubes and relational databases. no suppression is applied to these back end databases. the information is then aggregated for display on the front end screens of the data micas based on customized user selections. depending upon which level of access a user has logged in, suppression is then applied to the data output generated using these customized selections. not only are the rules applied to data tables but also to the mophims dat a visualization tools, which include multiple types of charts and maps. results table cells are suppressed in the data micas if a frequency less than 5 (but greater than 0) is the result of a custom query using three or more conditionally exempt variables, which are described below. additional rows/columns with cell frequencies above this threshold may be suppressed to ensure a total of at least 3 rows/columns are suppressed. suppression is applied to either rows http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e243, 2019 isds 2019 conference abstracts or columns, depending on which is more numerous in a given table. the suppression rules are applied separately to both the inner and outer rows/columns of a table. this layering of suppression across individual pieces of customized datasets negates the possibility of users algebraically determining the hidden values. a small set of variables were classified as exempt (unlikely to identify individuals or break modhss privacy standards) and alone, or in combination, would not trigger suppression. these include state totals, multi-year ranges, gender, and basic age groups. some variables, however, could be potentially identifying, either individually or used in tandem. these are considered conditi onally exempt and include things like: county/zip geographies, single years of data, race, ethnicity, expanded age groups, and speci fic causes/diagnoses. if three or more conditionally exempt variables are used in a query then suppression is invoked. in addition to the rules themselves, mophims contains a mechanism that allows users to log in at different levels of access. public and registered user levels are free and available to all operators with a valid e-mail address. partner level access is reserved for epidemiologists at the state and local level who are using the data micas for program planning, evaluation, and grant writing. because these individuals are required to adhere to the same data dissemination policies as those who create the mophims syst em, partner level access turns off suppression in the mophims system. values that would be suppressed at the public or registered user levels are shown in italicized, red font. a multi-level approval process is required for individuals to obtain partner level access to mophims. conclusions modhss created an innovative suppression system that allows public health planners to access granular data through customizable queries without risking a confidentiality breach. users have indicated this is highly prefera ble to a blanket suppression rule that hides any value under a certain threshold. additionally, approved mophims users can view specially formatted values that would otherwise have been suppressed. the flexibility associated with creating a homegrown web query system has allowed the formation and implementation of this multilayered rule, which likely would not have been possible if using an offthe-shelf product. data disseminators are encouraged to review current confidentiality and suppression rules to determine whether they might be modified to provide more granular data users while still protecting the privacy of citizens. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e380, 2019 isds 2019 conference abstracts development of a recurrent neural network model for prediction of dengue importation sun-young kim, kyung-duk min, seohyun lee, sarang park seoul national university, seoul, korea (the republic of) objective we aim to develop a prediction model for the number of imported cases of infectious disease by using the recurrent neural net work (rnn) with the elman algorithm [1], a type of artificial neural network (ann) algorithm. we have targeted to predict the number of imported dengue cases in south korea as the number of dengue cases is greater than other mosquito-borne diseases [2]. introduction in recent years, mosquito-borne diseases such as zika, chikungunya, and dengue have become particularly problematic due to global climate change. rising temperatures and changes in precipitation are considered to be associated with habitat suitability of mosquito vectors [3] and viruses [4]. to address such crossborder infectious diseases, countries have come up with various strategies to control and manage mosquito-borne diseases. in line with this, international efforts have been made to minimize the burden of global infectious diseases. in 2014, global health security agenda (ghsa) [5] has been launched in collaboration with the international organizations, member countries of ghsa, and non-governmental organizations in order to improve national and global capacities against global public health threat. in addition, various quarantine programs have been operated in and b etween countries borderlines and airports with cutting edge ict technologies. these efforts could be made more effective when the authorities have reliable predicted future trends or events [6], utilize their capacities more efficiently and provide timely alerts to the public. however, very few studies have been conducted to deal with imported disease, while much attention has been paid to the endemic diseases. in this study, we aim to develop a prediction model for imported infectious disease by using the approach of ann. we have chosen to model the imported cases of dengue in korea, as the number of imported dengue cases is larger than other mosquito-borne diseases. additionally, japan, one of south korea’s neighboring countries, has recently experienced a utochthonous dengue virus transmission, which has raised concerns about localization in korea as well as in japan [7]. methods as our prediction target was the monthly number of imported dengue cases, among the alternative types of ann, our study used recurrent neural network (rnn) models, which has been developed to model the temporal sequenced data. specifically, elman algorithm [1] was used to develop an rnn and the model was implemented by an r package “rsnns”. a conventional autoregressive integrated moving average (arima) model was also developed to compare and verify the predictabilities between the rnn and conventional arima modeling approach. the arima model predicted the number of dengue cases that are likely to be imported from indonesia in 2016, based on the reported number of imported dengue cases from the country between the year 2011-2015. the analysis was conducted by an r package “forecast.” to develop an rnn, the number of hidden layers and the number of nodes for each hidden layer need to b e determined. the grid searching method was employed for the determination based on rooted mean squared error (rmse), a measurement of the model performance under which a lower value indicates a better model fit. for the grid search, we chose a range of 1-3 for the number of hidden layers, and a range of 10-40 for the number of nodes for each hidden layer (29,791 combinations in total for the rnn model). to this end, we have divided the dengue importation data into two sets, i.e., the training set versus validation set. the training data set included data which had a time period of 48 months from 2012 to 2015, and the validation set had data for over 12 months in 2016, which was the latest data set available during the time of our study. as the sequential external validation approach was http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e380, 2019 isds 2019 conference abstracts adopted, we used 12 rnns for each data point in the validation set. in other words, we predicted the number of imported dengu e cases from the given target country at the point in time in january 2016, using the rnn developed based on the data from january 2012 to december 2015. we then subsequently predicted the cases in february 2016 by using the data from 2012 to january 2016 and iterated the process similarly for predictions at other time points in 2016. through the process, we have obtained the predicted number of imported dengue cases in 2016, and computed an rmse by comparing the value to those in the observed data. via the grid searching method used, a total of 29,791 rmse values were calculated for models of each target country, and a model with the least rmse was selected as the best-fitting model. results the rmse for the best-fitting rnn model was 14.152. in comparison to the arima model, of which rmse was 16.466, the rnn model showed improved predictability. the rnn-based prediction model can be utilized to improve the effectiveness of both national and individual level interventions for preventing the imported cases of dengue and its subsequent localization in korea. conclusions since dengue’s enlistment in 2000 as nationally notifiable infectious disease, it has been reported to be one of the most common infectious disease imported into south korea [8]. concerning the rapid change in climate and disease patterns, the south korean government should be prepared to be more responsive towards any potential imported cases of infectious disease (especially dengue) by developing an active surveillance system and specific countermeasures. in this context, our prediction model can be utilized to enhance the response system whi ch is designed to reduce the number of imported cases in the future and prevent any possible localization of the disease. our analysis also suggests areas for futur e research to further advance the prediction models for infectious disease importation in general. since the current rnn-based prediction model still has its limitations, it would be crucial to put in more efforts in improving the model performance and applicabil ity. the ann algorithms need further development in order for it to effectively manage the availability of relevant big data in the near future. acknowledgement this work was supported by seoul national university big data institute through the data science research project 2017. references 1. elman jl. 1990. finding structure in time. cogn sci. 14(2), 179-211. https://doi.org/10.1207/s15516709cog1402_1 2. choe yj, choe sa, cho si. 2017. importation of travel-related infectious diseases is increasing in south korea: an analysis of salmonellosis, shigellosis, malaria, and dengue surveillance data. travel med infect dis. 19, 22-27. pubmed https://doi.org/10.1016/j.tmaid.2017.09.003 3. kraemer mu, sinka me, duda ka, mylne aq, shearer fm, et al. 2015. the global distribution of the arbovirus vectors aedes aegypti and ae. albopictus. elife. 4, e08347. pubmed https://doi.org/10.7554/elife.08347 4. alto bw, bettinardi d. 2013. temperature and dengue virus infection in mosquitoes: independent effects on the immature and adult stages. am j trop med hyg. 88(3), 497-505. pubmed https://doi.org/10.4269/ajtmh.120421 5. arthur gf, michael m, leah fm, maureen b, mitsuaki h, et al. 2017. contributions of the us centers for disease control and prevention in implementing the global health security agenda in 17 partner countries. infect dis j. 23(13). doi:10.3201/eid2313.170898. 6. eisen l, eisen rj. 2011. using geographic information systems and decision support systems for the prediction, prevention, and control of vector-borne diseases. annu rev entomol. 56, 41-61. pubmed https://doi.org/10.1146/annurev-ento-120709-144847 http://ojphi.org/ https://doi.org/10.1207/s15516709cog1402_1 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28919170&dopt=abstract https://doi.org/10.1016/j.tmaid.2017.09.003 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26126267&dopt=abstract https://doi.org/10.7554/elife.08347 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23382163&dopt=abstract https://doi.org/10.4269/ajtmh.12-0421 https://doi.org/10.4269/ajtmh.12-0421 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20868280&dopt=abstract https://doi.org/10.1146/annurev-ento-120709-144847 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e380, 2019 isds 2019 conference abstracts 7. yoshimura y, sakamoto y, amano y, nakaharai k, yaita k, et al. 2015. four cases of autochthonous dengue infection in japan and 46 imported cases: characteristics of japanese dengue. intern med. 54(23), 3005-08. pubmed https://doi.org/10.2169/internalmedicine.54.4475 8. yeom j-s. 2017. current status and outlook of mosquito-borne diseases in korea. j korean med assoc. 60(6), 468-74. https://doi.org/10.5124/jkma.2017.60.6.468 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26631883&dopt=abstract https://doi.org/10.2169/internalmedicine.54.4475 https://doi.org/10.5124/jkma.2017.60.6.468 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e322, 2019 isds 2019 conference abstracts a tool for promoting responsible antibiotic prescribing across settings and sectors anette hulth1, sonja löfmark1, jeff andre2, rachel chorney2, emily cohn2, moriah ellen3, nadav davidovitch3, jacob moran-gilad3, amy greer4, david fisman5, john brownstein2, 6, derek macfadden2, 7 1 public health agency of sweden, solna, sweden, 2 healthmap, boston children’s hospital, boston, massachusetts, united states, 3 ben gurion university, beersheva, israel, 4 university of guelph, guelph, ontario, canada, 5 dalla lana school of public health, toronto, ontario, canada, 6 harvard medical school, boston, massachusetts, united states, 7 department of medicine, university of toronto, toronto, ontario, canada objective to develop, evaluate, and implement a universal online platform termed open stewardship to promote responsible antimicrobial prescribing (antimicrobial stewardship). introduction antibiotic resistance is a mounting public health threat calling for action on global, national and local levels. antibiotic use has been a major driver of increasing rates of antibiotic resistance. this has given rise to the practice of antibiotic stewardship, which seeks to reduce unnecessary antibiotic use across different care settings. antibiotic stewardship has been increasingly applied in hospital settings, but adoption has been slow in many ambulatory care settings including primary care of humans. uptake of antibiotic stewardship in veterinary care has been similarly limited. audit and feedback systems of antibiotic use coupled with patterns of antibiotic use and best practice guidelines have proven useful in outpatient settings, but scaleup is limited by heterogeneous systems of care and limited resources. methods a multi-sectoral team with partners from canada, israel and sweden is developing a web-based platform for administering antibiotic stewardship across multiple care settings and sectors, for human and animal prescribers. there are several interventions which support behaviour change and can be applied to antibiotic stewardship programs. systematic reviews have found beneficial effects of numerous behaviour change interventions for optimizing clinical practice such as computerized reminders [1], opinion leaders as champions for change [2], and audit and feedback [3]. a recent cochrane review [4] found that interventions to enable correct use of antibiotics improved policy compliance, and that enabling interventions that included feedback were more likely to be effective. we will use antibiotic prescribing benchmarking, focused guidelines, and local patterns of antibiotic resistance as key components that can be deployed as feedback through this antibiotic stewardship platform. the open stewardship platform will be hosted on an aws cloud-based server using industry standard encryption. the platform will function with a central administrator who will enroll and deliver feedback to participating prescribers. this platform will be evaluated prospectively in two countries (canada and israel) to evaluate user experience of the feedback as well as impact on antimicrobial prescribing. the evaluation will include prescribers from both human and animal health. after the prospective evaluation, the platform will be made available online for broad multi-sectoral use. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e322, 2019 isds 2019 conference abstracts results we have designed the interface for a web-based platform for antibiotic stewardship which will be used in a multinational prospective primary care stewardship intervention in 2019 and 2020 and subsequently rolled out for broad public use (www.openasp.org). the platform layout can be seen in figure 1. data capture for aggregate prescriber level antibiotic use and local guidelines will be possible through both a manual graphical user interface and a dataset template upload. antibiotic resistance data will be pulled from a companion database (www.resistanceopen.org). administrators will be able to generate unique feedback forms containing visualizations and snapshots from antibiotic use, guidelines, and antibiotic resistance data (figure 2). these can then be delivered by email on an individual or scheduled basis for one or multiple prescribers simultaneously. participating prescribers will also have the option to login to view their own profile and browse antibiotic use, resistance and guidelines. conclusions antibiotic stewardship needs to be adopted in a fashion that is country and context specific and not administered from the top down. with our approach we seek to empower groups from any country or care setting to provide regional and tailored stewardship feedback through an open interface. we have here demonstrated the design of an web-based antibiotic stewardship platform which will be evaluated prospectively and subsequently made available for open and broad multi-sectoral use in keeping with a one health approach. acknowledgement we would like to thank funding sources including: the joint programming initiative in antimicrobial resistance, the canadian institutes for health research, the swedish research council, and the israel ministry of health. references 1. shojania kg, jennings a, mayhew a, ramsay cr, eccles mp, et al. 2009. the effects of on-screen, point of care computer reminders on processes and outcomes of care. cochrane database syst rev. (3), cd001096. pubmed 2. flodgren g, eccles mp, shepperd s, scott a, parmelli e, et al. 2011. an overview of reviews evaluating the effectiveness of financial incentives in changing healthcare professional behaviours and patient outcomes. cochrane database syst rev. (7), cd009255. pubmed 3. ivers n, jamtvedt g, flottorp s, young jm, odgaard-jensen j, et al. 2012. audit and feedback: effects on professional practice and healthcare outcomes. cochrane database syst rev. (6), cd000259. pubmed 4. davey p, brown e, charani e, fenelon l, gould im, et al. 2013. interventions to improve antibiotic prescribing practices for hospital inpatients. cochrane database syst rev. (4), cd003543. pubmed http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19588323&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21735443&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22696318&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22696318&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=23633313&dopt=abstract online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e322, 2019 isds 2019 conference abstracts figure 1: the platform interface. figure 2: generation of the feedback form/e-mail. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e231, 2019 isds 2019 conference abstracts calendar effects to forecast influenza seasonality: a case study in milwaukee, wi r. b. simpson1, aishwarya venkat1, tania alarcon1, ken chui1, yuri naumov2, jack gorski2, sanjib bhattacharyya3, elena naumova1 1 friedman school of nutrition science and policy, tufts university, malden, massachusetts, united states, 2 blood research center, milwaukee, wisconsin, united states, 3 mhdl, milwaukee, wisconsin, united states objective in the presented study, we examined the impact of school holidays (autumn, winter, summer, and spring breaks) and social events (super bowl, nba finals, world series, and black friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza a, and b) in milwaukee, wi, in 2004-2009 using routine surveillance. introduction influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. at some extend school holidays and travel restrictions serve as a socially accepted control measure [1,2]. a study of a spatiotemporal spread of influenza among school-aged children in belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality [3]. stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. while christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections [4]. a study on county-level vital statistics of the us from 1974-2009 showed that super bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in bowl-participating counties. the effect is most pronounced for highly virulent influenza strains and when the super bowl occurs closer to the influenza seasonal peak. simulation studies exploring how social mixing affects influenza spread [5] demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. while the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete. methods the city of milwaukee health department laboratory (mhdl), wisconsin routinely collect tests from residents of metropolitan areas and vicinities of the marquette university (mu). we obtained weekly counts of total tested, all influenza positives, positives for influenza a and b, from mhdl between 5/16/043/7/09 (before the surge of tests associated with “swine flu”). cases for <1 and 1-4 age groups were combined. meteorological data are routinely collected by a monitoring station at the general mitchell international airport located 7.5 miles from milwaukee. daily dewpoint values representing the perceived ambient temperature corrected for the air moisture content were downloaded from the open source website [6] and aggregated to weekly averages with sunday designating the beginning of each week. school holidays were obtained from academic calendars on the mu website with holiday weeks defined as having one or more school holiday observed [7]. selected social events were retrieved from a public website [8]. as part of exploratory analysis, average cases per week (c/w) for each outcome for school holiday and non-holiday weeks were compared using a non-parametric the mann–whitney u-test. we analyzed the association between weekly cases and holiday effects using negative binomial regression with sets of indicator variables for non-overlapping school holidays and social events and with adjustments for weather fluctuations with harmonic terms (model 1). results are presented as relative risk (rr) estimates along with their confidence intervals (95%ci). further analyses examined seasonal signatures (lead-lag structures) using a segmented regression approach for weekly counts and rates 5 academic weeks (aw) before, 2-6 weeks during, and 5 weeks after select holidays (model 2). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e231, 2019 isds 2019 conference abstracts results over 251 study weeks, 2282 tests were submitted, out of which 1098 cases were from 5-24 y.o. age group. 477 (21%) tests we positive, with 399 (84%) cases of influenza a (73 tests were not subtyped) and 78 (16%) cases of influenza b. figure 1 shows the time series of weekly counts of influenza tests and percent positives with superimposed information on school holiday occurrences. overall, during 135 weeks of the school period the average number of tests was two times higher as compared to those during 116 holiday weeks (11.9±10.3 vs 5.8±6.5 c/w, p<0.001). similarly, the average weekly number of positive tests was higher in nonholiday than during holiday periods (2.9±5.7 vs 0.7±2.6 c/w, p<0.001). the reduction in tests during holidays was confirmed by the regression model (rr=0.71; 95%ci=[0.60-0.86]). the reduction in weekly tests was most pronounced during the winter break (15-19 aw) for all age groups (4.8±3.0 c/w, p<0.001; rr=0.3; 95%ci=[0.23-0.41]) and especially for school-aged children, young adults and adults (rr=0.14; 95%ci=[0.09-0.22] and rr=0.32; 95%ci=[0.16-0.62] for 5-24 and 25-44 age groups, respectively). in contrast, during the spring break (27-30 aw) the number of tests has almost doubled (20.4±10.4 c/w; p<0.001) as compared to the school period, with the most noticeable increase in 5-24 and 25-44 age groups. spring break differential effects were primarily due to later peaks in influenza b shown by segmented regression results in figure 2. the seasonal increase in weekly rates is the steepest after the winter holidays. the effects of the selected sporting and social events were inconclusive. conclusions the differential effects of calendar events on seasonal influenza can be detected by routine surveillance and further explored with respect to lead-lag structures. we recommend incorporating location-specific calendar effects in influenza near-term forecasting models tailored to susceptible age groups to better predict and assess targeted intervention measures. acknowledgement nih grants: u19ai062627, no1a150032. references 1. jackson c, vynnycky e, mangtani p. 2016. the relationship between school holidays and transmission of influenza in england and wales. am j epidemiol. 184(9), 644-51. pubmed https://doi.org/10.1093/aje/kww083 2. chu y, et al. 2017. effects of school breaks on influenza-like illness incidence in a temperate chinese region: an ecological study from 2008 to 2015. bmj open. 7(3), e013159. pubmed https://doi.org/10.1136/bmjopen2016-013159 3. luca g, et al. 2018. the impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for belgium. bmc infect dis. 18(1), 29. pubmed https://doi.org/10.1186/s12879017-2934-3 4. stoecker c, sanders n, barreca a. 2016. success is something to sneeze at: influenza mortality in cities that participate in the super bowl. am j health econ. 2(1), 125-43. https://doi.org/10.1162/ajhe_a_00036 5. shi p, et al. 2010. the impact of mass gatherings and holiday traveling on the course of an influenza pandemic: a computational model. bmc public health. 10, 778. pubmed https://doi.org/10.1186/1471-2458-10-778 6. www.wunderground.com. 7. www.marquette.edu/mucentral/registrar/archivedacademiccalendars.shtml. 8. www.timeanddate.com. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27744384&dopt=abstract https://doi.org/10.1093/aje/kww083 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28264827&dopt=abstract https://doi.org/10.1136/bmjopen-2016-013159 https://doi.org/10.1136/bmjopen-2016-013159 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29321005&dopt=abstract https://doi.org/10.1186/s12879-017-2934-3 https://doi.org/10.1186/s12879-017-2934-3 https://doi.org/10.1162/ajhe_a_00036 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21176155&dopt=abstract https://doi.org/10.1186/1471-2458-10-778 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e231, 2019 isds 2019 conference abstracts figure 1. weekly number tests and % of influenza positives tests and percent positives with superimposed school holiday occurrences in milwaukee, wi (2004-2009) figure 2. weekly number of tests, positive tests, influenza a and b (panels a-d, respectively) for each study year with superimposed winter and spring school holiday occurrences in milwaukee, wi. average and standard deviation values for four outcomes and their weekly rates (in italic) for five time periods: before (5 weeks), during (2-6 weeks), and after (5 weeks) the winter and spring breaks (time is shown in weeks starting september 1st); superscripts b and c indicate t-test significance for during-after and before-after comparisons, respectively. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e436, 2019 isds 2019 conference abstracts identifying emergency department care in the year prior to suicide death dylan delisle1, 2, anna e. waller1, 2, katherine wolff1, 2, katherine j. harmon1, 2, 3, amy ising1, 2 1 carolina center for health informatics, dept. of emergency medicine, unc-chapel hill, chapel hill, north carolina, united states, 2 carolina center for health informatics, chapel hill, north carolina, united states, 3 highway safety research center, chapel hill, north carolina, united states objective to identify potential emergency department (ed) visits prior to suicide deaths in north carolina (nc) and describe pre-suicide care-seeking in eds. introduction suicide is a leading cause of mortality in the united states, causing about 45,000 deaths annually [1]. research suggests that universal screening in health care settings may be beneficial for prevention, but few studies have combined detailed suicide circumstances with ed encounter data to better understand care-seeking behavior prior to death. methods this project used data from the nc violent death reporting system (nc-vdrs), a repository of all violent deaths in north carolina, and the nc disease event tracking and epidemiologic collection tool (nc detect), which includes all visits to 24/7, acute-care, civilian, hospital-affiliated eds in nc. we identified all suicide deaths recorded in the nc-vdrs between 1/1/2014 – 9/30/2015, and all nc detect ed visits between 1/1/2013 – 9/30/2015. descriptive analyses were conducted for each data source, separately. for all nc-vdrs suicides, we identified ed visits likely made by the same individual within the 48 hours prior to death. we identified these ed visits based on the variables arrival date, sex, date of birth (dob), county of residence, and a chief complaint consistent with self-harm/suicide; we refer to these as suicide-related ed visits. for the subset of nc-vdrs suicides with a suicide-related ed visit, made within 48 hours of death, we identified all ed visits associated with the decedent made to the same facility or healthcare system in the year prior to death. we then categorized the pre-suicide ed visits according to the primary reason healthcare was sought by the patient (e.g. mental health problem, substance abuse/overdose, pain, etc.). results from 1/1/2014-9/30/2015, there were 2,953 suicide deaths captured in nc-vdrs data; 2,435 (82%) of these included dob. between 1/1/2013 – 9/30/2015, there were 13,463,345 ed visits captured by nc detect; 12,884,596 (96%) included dob. for 961 suicides (32.5%), no ed visit was found with the same dob, sex and county of residence. for the remaining 1,474 suicides, at least one ed visit was found for a patient with the same dob, sex and county of residence and occurring on or before the date of death. for 406 suicides, a suicide-related ed visit was identified; 122 of these patients had at least one additional ed visit in the year prior to death. a total of 516 ed visits were identified for these 122 suicides, including the suicide-related ed visit, with an average of 3.2 (range: 1-25) visits. gender, race and mechanism of death were similar between those with an ed visit prior to death and the total suicide population (table 1). the chief complaint and diagnosis codes for the 394 prior ed visits varied. the most frequent diagnoses/chief complaints were long term medication use (35.3%), hypertension (29.9%), pain (20.8%), and diabetes mellitus (18.8%). depression was coded for 62 visits (15.7%); only 16 visits (4.1%) received a code for suicide ideation. of the 122 patients with both a suicide-related ed visit and at least one prior ed visit, nc-vdrs data indicated that 28 individuals (23.0%) were perceived as depressed at the time of death and 46 individuals (37.7%) had a history of suicidal thoughts. at the time of death, 32 individuals (26.4%) had alcohol in their system. in the nc-vdrs data, mental health problems were documented for a higher percentage of males with a suiciderelated ed visit than the total male suicide population (56.9% versus 47.0%), and a higher percentage were in treatment for mental health issues at the time of death (50.0% versus 40.0%). conversely, females with a suicide-related ed visit had a lower percentage of documented mental health problems and treatment (66.7% versus 71.0%, and 55.6% versus 64.0%, respectively) than the total female suicide population in nc-vdrs. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e436, 2019 isds 2019 conference abstracts conclusions for nearly a third of nc-vdrs suicides, no indication of any ed visit by a patient with the same dob, sex, and county of residence was found. while it is likely we were unable to identify all ed visits prior to suicide, the findings from this pilot study suggest many suicide victims did not seek nc ed care in the year prior to death. overall, a suicide-related ed visit was found for only 13.7% of nc suicides in the study period, indicating that most people who selfinflict fatal injury do not make it to an ed for care prior to death. ed visits in the year prior to death by suicide indicated a variety of diagnoses, but rarely depression or suicidality; this suggests that universal screening at ed visits would have been necessary to identify any suicide risk present. limitations of this study include that we were unable to directly link suicide deaths and ed visits using patient identifiers. additionally, we relied solely on secondary data used for public health surveillance and, therefore, had no access to medical record information that may have documented depression or suicidal ideation that was not coded as such. findings from this pilot study can inform future work to identify ed visits prior to suicide. acknowledgement nc detect is a statewide public health syndromic surveillance system, funded by the nc division of public health (nc dph) federal public health emergency preparedness grant and managed through collaboration between nc dph and unc-ch department of emergency medicine’s carolina center for health informatics. the nc detect data oversight committee does not take responsibility for the scientific validity or accuracy of methodology, results, statistical analyses, or conclusions presented. the north carolina violent death reporting system (nc-vdrs) is a public health, population-based surveillance system that contains detailed information on deaths that result from violence. the nc-vdrs is an incident-based, relational database that combines information from multiple sources, such as death certificates, medical examiner reports, and incident reports from law enforcement agencies. it is operated by the north carolina division of public health’s injury and violence prevention branch to provide injury and violence prevention specialists and policy-makers with timely information on the victims, suspects, relationships, circumstances, and weapons that are associated with every incident of violence that results in a fatality in north carolina. references 1. national center for injury prevention and control. suicide rising across the us. vital signs, june 2018; atlanta, ga: centers for disease control and prevention 2018. https://www.cdc.gov/vitalsigns/suicide/. accessed sept 25, 2018. table 1. demographic information for total suicide population, suicides with ed visit within 48 hours of death, and suicides with ed visits in year prior to death, north carolina, 1/1/2014-9/30/2015 total suicide population suicides with ed visit within 48 hours of death (a) suicides with additional ed visits in year prior to death (b) population n = 2953 406 122 age mean 47.8 47.5 50.9 sex (%) male 74.3 74.8 72.1 female 25.7 25.2 27.9 race (%) white 86.4 84.2 87.7 black 8.9 7.9 4.9 mechanism (%) firearm 56.2 61.2 52.5 suffocation 20.7 19.3 18.9 poisoning 18.3 15.8 27.1 (a) this column represents the subset of the total suicide population for whom an ed visit could be identified in the 48 hours prior to death. (b) this column represents the subset of the suicides with ed visit within 48 hours of death for whom additional ed visits in the year prior to death could be identified. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e295, 2019 isds 2019 conference abstracts trends in injection opioid use and bloodborne pathogen related diseases in new jersey jenna lifshitz2, 1, pinar erdogdu2, stella tsai2 1 brandeis university, w altham, massachusetts, united states, 2 new jersey department of health, trenton, new jersey, united states objective to utilize new jersey’s syndromic surveillance data in the study and comparison of trends in injection opioid use and infection with selected bloodborne pathogens (bbps) over the years 2013-2017. introduction when the opioid epidemic began in the early 1990s, pills such as oxycodone were the primary means of abuse. beginning in 2010, injection use of, first, heroin and then synthetic opioids dramatically increased, which led the number of overdose deaths involving opioids to increase fivefold between 1999 and 2016 [1]. it would be expected that bbp rates would rise with this increase in injection use, and, nationally, there has been a rise in acute hepatitis c (hcv) rates, although the other two main bbps, acu te hepatitis b (hbv) and acute human immunodeficiency virus (hiv) have been flat and declining, respectively [2,3]. in this study, we compared new jersey’s reported incidence of these three bbps (acute hbv, acute hcv, and hiv) over five years (2013-2017) with syndromic surveillance data for opioid use over the same time period in order to test the hypothesis that emergency department (ed) visits for opioid use could be used as a predictor of bbp infection. methods to indirectly track the number of injection opioid users, we wrote a custom classifier for epicenter, new jersey's statewide syndromic surveillance system, to search ed chief complaints for the number of visits related to injection opioid usage. our custom classifier creation started with the cdc’s national syndromic surveillance program (nssp) essence chief complaint category classifier for opioid overdose [4]. as we were looking to count not just overdoses but all visits likely to be associated with injection drug use, we chose not to omit the keyword “withdrawal,” differing from cdc’s classifier in which it is a negative indicator. positive indicator keywords included “opioid,” “heroin,” “narcot,” “opiate,” “fentanyl, ” “naloxo,” “narcan,” "ivdu," and the icd9 and icd-10 codes e850.[0-2], 304, 305.5, f11, t40.[0-6], and 965. these keywords were used to target the chief complaints of people using injection opioids. negative indicators included “patch,” “allerg,” and “med” to eliminate medical opioid use. negative indicators also included “vicodin,” “tramadol,” “percocet,” “oral,” and t40.5 to filter out opioids most commonly used in pill form, as well as other drugs. cases of acute hcv and acute hbv were totaled using cdrss, new jersey's communicable disease reporting and surveillance system. in order to maintain consistency, we used the respective 2012 case definition for each disease. numbers of new hiv infections were accessed from nj’s reportable disease list [5]. all of the data sets followed the epidemiologic years 2013–2017 (based upon mmwr weeks). results hiv diagnosis rates slightly decreased over time. hbv rates trend upwards, similar to the rates of injection drug use (idu) for the first three years but start to drop after 2015. aside from an unexplained dip in 2016, the hcv rates generally track the epicenter data for idu (figure 1). on a regional scale, nj’s northwest region had the highest rates per capita of the five nj regions and the most similar trending between the hcv and epicenter data sets (figure 2). this result follows the nationwide trend of the opioid epidemic occurring more widely in rural areas, as this region is the most rural region in new jersey [6]. in figures 1 & 2, idu (epicenter) and hiv are plotted on the primary (left) axis and hcv and hbv are plotted on the secondary (right) axis. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e295, 2019 isds 2019 conference abstracts conclusions both idu related visits and cases of acute hcv show an ongoing upward trend. this result agrees with the initial hypothesis. however, the association between acute hbv cases and idu wasn’t as strong. this finding can be attributed to the fact that while hbv is a bbp, it is most commonly transmitted vertically from an infected mother to her child at birth, whereas hcv is primarily transmitted through the sharing of needles or syringes [7,8]. there is no apparent relationship between hiv rates and injection drug rates, likely because hiv has a 0.3% infection risk rate from a single infected needlestick versus the 1.8% risk of acquiring hcv and 22-31% risk of acquiring hbv [9]. acknowledgement we would like to thank bernice carr, ms, mph, maryellen wiggins, msn, rn, teresa hamby, msph, and abdel r. ibrahim, ph.d. for their contributions. references 1. centers for disease control and prevention (cdc). understanding the epidemic; august 30, 2017. https://www.cdc.gov/drugoverdose/epidemic/index.html. accessed 13 july 2018. 2. cdc. viral hepatitis; may 19, 2016. https://www.cdc.gov/hepatitis/hbv/statisticshbv.htm. accessed 24 july 2018. 3. cdc. hiv in the united states: at a glance; june 26, 2018. https://www.cdc.gov/hiv/statistics/overview/ataglance.html. accessed 24 july 2018. 4. cdc. nssp update; may, 2017. https://www.cdc.gov/nssp/documents/nssp-update-2017-05.pdf. accessed 23 july 2018. 5. njdoh. hiv, std, and tb services; december, 2016. https://www.cdc.gov/hiv/statistics/overview/ataglance.html accessed 27 july 2018 6. schranz aj, barrett j, hurt cb, malvestutto c, miller wc. 2018. challenges facing a rural opioid epidemic: treatment and prevention of hiv and hepatitis c. curr hiv/aids rep. 15(3), 245-54. pubmed https://doi.org/10.1007/s11904-018-0393-0 7. rolls da, et al. 2013. hepatitis c transmission and treatment in contact networks of people who inject drugs. plos one. 8(11), e78286. pubmed https://doi.org/10.1371/journal.pone.0078286 8. perrillo rp. 1993. hepatitis b: transmission and natural history. gut. 34(2 suppl), s48-49. pubmed https://doi.org/10.1136/gut.34.2_suppl.s48 9. berry aj. 2004. needle stick and other safety issues. anesthesiol clin north america. 22(3), 493-508. pubmed https://doi.org/10.1016/j.atc.2004.04.003 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29796965&dopt=abstract https://doi.org/10.1007/s11904-018-0393-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24223787&dopt=abstract https://doi.org/10.1371/journal.pone.0078286 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=8314492&dopt=abstract https://doi.org/10.1136/gut.34.2_suppl.s48 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15325715&dopt=abstract https://doi.org/10.1016/j.atc.2004.04.003 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e295, 2019 isds 2019 conference abstracts figure 1: comparison of hcv, hbv, hiv, and idu (measured by epicenter in ed visits related to injection drug use) data for all of nj in cases per million for 2013-2017 figure 2: comparison of hcv and idu (measured by epicenter in ed visits related to injection drug use) data for the northwest nj region (morris, passaic, sussex, and warren counties) in cases per million for 2013-2017 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts qualitative and quantitative predictions of infectious diseases in shirak marz armine andryan* ncdcp snco of the ra ministry of health, yerevan, armenia objective the goal of this study was to identify the periodicity of seven zooanthroponoses in humans, and set epidemic thresholds for future occurrences. introduction the frequency of disease outbreaks varies as a result of complex biological processes. analysis of these frequencies can reveal patterns that can serve as a basis for predictions. methods a 40 year regression analysis of the following infectious diseases was performed using arc-gis10.1—anthrax, brucellosis, erysipeloid, leptospirosis, plague, tularemia and yersiniosis. results the analyses covered many years and revealed the dynamics of epidemics for infections. yearly periodicities of (3.1 + 2.8) (3.8 + 2.2) 5 4.6 2.2 5.1 2.1 2.6 were determined for theoretically calculated zooanthroponoses. these coincide with the recorded activity of (6 6 5 5 2 5 3 2) that correspond to (1977-19831989-1994-1999-2001-2006-2009-2011 (2017-2023-2028). these years had more cases of disease than other years. the predicted years (2017, 2023, 2028) are those of potential risk, when 0.7-0.9% of the total disease burden will consist of epidemiologically associated cases. disease severity was correlated with natural factors including air temperature, humidity, number of annual heat days, geographical factors, type of landscape, number of carriers, and the contact intensity between disease carrier and transmitter. partial control indicators (pcis) were determined to characterize the epidemic situation. these are determined from the perennial average characteristic of the given area from which the mean square deviation is removed. the detection indicator is the normal size of a given disease, with minimal and maximal deviation of the range. it can be compared to the epidemic threshold and helps yield shortand long-term quantitative predictions with high reliability indicators (96.5% p <0.035). conclusions a 3-5 year periodicity for zooanthroponoses was identified. conditions contributing to the occurrence of these epidemics differ by region. in shirak marz, the pcis for the different diseases are: brucellosis-47, anthrax-12, plague-8, tularemia-6, leptospirosis-175, erysipeloid-12, yersiniosis-18. these numbers represent years of positive points as a maximum threshold. the stability index was identified, for instance, for brucellosis s = 1.2, amplitude 5.2, perennial average 28.8, orientation month january, seasonal morbidity ratio 18-42 cases. our predictions indicate that 2017 will be a peak year with 95% probability; intensive index: 16.8 (per 100,000 population), seasonal illness cases: 42 ± 3.5 between march and november. the application of numerical thresholds in predictive epidemiological surveillance provide clear triggers that make public health responses more targeted and rational. keywords regression analysis; periodicity; predictive epidemiological surveillance *armine andryan e-mail: anarmine@mail.ru online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e163, 2018 ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates jared mowery the mitre corporation abstract background: influenza (flu) surveillance using twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness. however, research is needed to determine the impact of twitter users’ misdiagnoses on surveillance estimates. objective: this study establishes the importance of twitter users’ misdiagnoses by showing that twitter flu surveillance in the united states failed during the 2011-2012 flu season, estimates the extent of misdiagnoses, and tests several methods for reducing the adverse effects of misdiagnoses. methods: metrics representing flu prevalence, seasonal misdiagnosis patterns, diagnosis uncertainty, flu symptoms, and noise were produced using twitter data in conjunction with opensextant for geo-inferencing, and a maximum entropy classifier for identifying tweets related to illness. these metrics were tested for correlations with world health organization (who) positive specimen counts of flu from 2011 to 2014. results: twitter flu surveillance erroneously indicated a typical flu season during 2011-2012, even though the flu season peaked three months late, and erroneously indicated plateaus of flu tweets before the 2012-2013 and 2013-2014 flu seasons. enhancements based on estimates of misdiagnoses removed the erroneous plateaus and increased the pearson correlation coefficients by .04 and .23, but failed to correct the 2011-2012 flu season estimate. a rough estimate indicates that approximately 40% of flu tweets reflected misdiagnoses. conclusions: further research into factors affecting twitter users’ misdiagnoses, in conjunction with data from additional atypical flu seasons, is needed to enable twitter flu surveillance systems to produce reliable estimates during atypical flu seasons. keywords: biosurveillance, social media, natural language processing, supervised machine learning http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 correspondence: jmowery@mitre.org doi: 10.5210/ojphi.v8i3.7011 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction many studies have investigated using social media data or online data to perform biosurveillance [1, 2]. eysenbach [3] was the first to use trends in internet searches as a means of estimating flu prevalence, and ritterman et al. [4] subsequently became the first to use twitter data for flu surveillance. twitter flu surveillance systems generally rely on keyword filters and classifiers to produce weekly counts of tweets indicative of flu prevalence. lamb et al. [5] developed a classifier which distinguishes between tweets reflecting an awareness of the flu and tweets describing an infection with the flu, which tightens the causal relationship between weekly counts of flu tweets and centers for disease control (cdc) or who measurements. smith et al. [6] demonstrated that tweets related to general awareness of the flu yield substantially different trends than tweets related to infections, and nagar et al. [7] reported that a classifier incorporating an annotator’s estimate of the likelihood that a tweet indicated illness was important for their analysis of flu prevalence in new york city. zuccon et al. [8] tested a wide variety of classifier types, with results indicating the choice of classifier has a limited effect on accuracy. recent studies have expanded the twitter flu surveillance systems in a variety of ways, including encompassing multiple countries [9, 10], combining multiple indicators [10, 11], increasing geospatial resolution [7, 12–14], handling additional languages [15, 16], and estimating the secondary attack rate and serial interval [17]. however, twitter flu surveillance relies on twitter users’ diagnoses of the flu. there are many potential causes of misdiagnoses. nsoesie and brownstein [1] observe that many existing systems likely measure influenza-like illness (ili), which can be caused by a variety of non-flu pathogens. chew and eysenbach’s twitter content analysis during the 2009 pandemic [18] contains a rich set of metrics reflecting emotion levels, misinformation, and news or blog links that could all influence twitter authors in choosing whether to tweet about an infection, and whether to diagnose that infection as the flu. since twitter is not a representative sample of the united states’ population [19-21], twitter flu surveillance estimates will be biased. studies have investigated potential variations in the peak time, morbidity, and rate of flu transmission as a function of age http://ojphi.org/ mailto:jmowery@mitre.org ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 group and social networks [22-25]. region and humidity may also influence flu mortality rates and spread [26-27]. finally, although positive specimen counts for the cdc or who are used as ground truth data, variations in the collection and testing of specimens, participation levels of laboratories, and other factors may introduce sampling biases. detecting atypical flu seasons reliably is important, since they may require atypical responses from governments and healthcare organizations to save lives and increase efficiency. this study focuses on flu seasons with atypical onset times, such as the 20112012 flu season, since these yield the most direct evidence for misdiagnoses. since this study is intended to quantify twitter users’ misdiagnoses rather than maximize the correlation between flu estimates and who counts, it does not incorporate additional data sources which could obscure misdiagnosis patterns in twitter, such as search query volumes or time-lagged positive specimen count data. many of the algorithms were implemented using the r project for statistical computing [28]. methods data collection and classification this study used gnip decahose [29] data, which is a 10% pseudo-random sample of publicly available tweets. the tweet volumes collected each week between the weeks starting on 2011-08-01 and 2014-09-15 exhibit several gaps due to internet connectivity issues and hardware failures. these gaps were corrected by extrapolating from nearby data using a two pass process. the first pass applied a sliding median filter of width 15 to approximate the expected counts for each week. any range of weeks with week indices [a, b] in which zero tweets were collected was replaced by the estimated values from a linear interpolation between the values at indices a − 2 and b + 2. the second pass applied a sliding median filter of width 7 to the results of the first pass. the following equation was used to produce a corrected count �̂�𝑡𝑖𝑖 for each week i: �̂�𝑡𝑖𝑖 = � 𝑠𝑠𝑖𝑖 if 𝑡𝑡𝑖𝑖 < 0.9𝑠𝑠𝑖𝑖, 𝑡𝑡𝑖𝑖 otherwise. (1) where 𝑠𝑠𝑖𝑖 is the output of the second sliding median filter and 𝑡𝑡𝑖𝑖 is the tweet count after zeroes were replaced by the first pass. the constant 0.9 was chosen to apply the correction only when the weekly count was at least 10% less than the expected count, which served as a rough method for identifying weeks during which data loss occurred. applying equation 1 compensated for the gaps in data collection (figure 1). http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 figure 1: tweets collected per week. the original series shows the number of tweets collected from the decahose feed. the first pass series depicts the result of using linear interpolation to replace the counts for weeks in which zero tweets were collected. the corrected series shows the estimated number of tweets which would have been collected for each week if there had not been data collection gaps. the metrics based on twitter data must also be adjusted to compensate for the data losses. the following equation produced adjusted counts for each week i: c�𝑖𝑖 = ⎩ ⎨ ⎧ �̂�𝑐𝑘𝑘 if (𝑡𝑡𝑖𝑖 or 𝑡𝑡𝑖𝑖−1 or 𝑡𝑡𝑖𝑖+1 = 0), with the maximum 𝑘𝑘 s. t. 𝑘𝑘 < 𝑖𝑖 and 𝑡𝑡𝑘𝑘 > 0, 𝑐𝑐𝑖𝑖 �̂�𝑡𝑖𝑖 𝑡𝑡𝑖𝑖 if 𝑡𝑡𝑖𝑖 ≠ �̂�𝑡𝑖𝑖and 𝑡𝑡𝑖𝑖 > 0, 𝑐𝑐𝑖𝑖 otherwise. (2) where 𝑐𝑐𝑖𝑖 is the count produced by a metric and �̂�𝑐𝑖𝑖 is the count adjusted for potential data loss. this equation assumes the fraction of tweets which match the criteria for a metric is consistent, so the value of the metric during a week which experienced data loss can be approximated by applying the same fraction to the number of tweets expected during that week. for weeks in which no tweets were collected, the adjusted metric value for the most recent week in which tweets were collected was used. although a better estimate could have been obtained through linear interpolation, this approach uses only data which would have been available at the time. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 this study used the who’s weekly positive counts of flu virus specimens in the united states, including types a and b [30], as ground truth data. the 2011-2012 flu season peaked approximately three months late compared to the 2012-2013 and 2013-2014 flu seasons. this is valuable for quantifying the extent to which twitter users’ misdiagnoses adversely affect the correlation strength between twitter flu surveillance estimates and who positive specimen counts, since tweets in late 2011 most likely reflect misdiagnoses. the maximum entropy classifier was trained on 1,274 english language tweets containing illness or symptom related terms collected between december 31, 2011 and january 31, 2012. each tweet was hand-annotated by a single annotator for indications that the author, or someone the author knew, was ill. examples of illness included flu, common colds, allergies, and symptoms such as nausea, sore throat, and nasal congestion. instances of symptoms not due to illness, such as nausea due to overeating, stomach pain due to consuming spicy foods, and muscle aches due to exercise, were not counted as illness. the tweets which were related to illness according to the classifier are referred to as “sick tweets” in this paper. due to the expense of developing classifiers for multiple languages, non-english tweets were not considered in this study. the maximum entropy classifier used apache’s opennlp [31] implementation. retweets and tweets containing urls were excluded to help reduce the number of tweets related to news stories or memes. unigrams, bigrams, and the tweet length in [0.0, 1.0], with 1.0 corresponding to a length of 200 characters, were used as features since they are commonly used and computationally inexpensive. the classifier used gaussian regularization with σ = 1.0 and 10,000 iterations to ensure convergence. the classifier’s performance was tested using stratified 10-fold cross-validation. to bias the classifier in favor of precision over recall, only tweets whose classifier score exceeded 0.75 were designated as sick tweets. the constant 0.75 was chosen since it yielded weekly counts typically over 100 for sick tweets which contained the word “flu”. the lowest non-zero weekly count was 97, and the average count was 696. metrics collection this study collected several metrics from the sick tweets. tweets were filtered using illness and symptom related keywords, restricted to the united states by applying opensextant [32] to the user-provided location fields, and then limited to the english language using the cybozu labs language detection library for java [33]. out of the 13,273,284 tweets containing illness or symptom related terms, opensextant provided estimated locations for 3,667,309 of them, or 27.6%. retweets and tweets containing urls were excluded to match the classifier training data. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 table 1: case-insensitive queries used to define each metric. each metric is restricted to english tweets classified as sick tweets from the united states. query example flu flu feeling miserable. go away flu! uncertainty might or maybe or hope i might be coming down with a fever uncertaintyf (might or maybe or hope) and flu sore throat… nose like a tap… might be flu symptom sore throat or fever had a sore throat for days now symptomf (sore throat or fever) and flu fever all day, hope it’s not flu most of the metrics were simply defined as the fraction of tweets each week which matched a case insensitive query (table 1). the flu metric contained only sick tweets with the word “flu”, which are referred to as “flu tweets” in this paper. the uncertainty metric is intended to measure twitter authors’ uncertainty in their diagnoses, such as “i might be getting sick”, “maybe this is just an allergy”, or “i hope this is not the flu”. the symptom metric measures tweets containing two common symptoms of influenza-like illness: fevers and sore throats. finally, metrics with the suffix “f” have been restricted to flu tweets. since the weekly counts of flu tweets were generally over 100, this study did not examine misspellings of query terms or the use of slang. the noise metric is an estimate of the expected fraction of flu tweets during periods in which the flu is not prevalent. the thirteen weeks occurring in the middle of each year were used to estimate the noise level, which corresponds to an estimate that approximately one quarter of weeks during the year are not substantially affected by the flu season. the mean count for each of these midyear periods was used as a noise estimate. due to the difficulty of distinguishing flu tweets arising from flu infections from tweets arising from misdiagnoses, noise cannot effectively be measured during periods in which the flu is prevalent. therefore, each consecutive pair of midyear noise estimates was linearly interpolated to generate the complete noise estimate. the noise level gradually decreased during the period tweets were collected, which may be a consequence of the atypical 2011-2012 flu season (figure 2). http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 figure 2: noise estimate based on linearly interpolating noise estimates from each midyear period. the midyear series shows the weeks which were used to estimate the noise for each midyear period. each series has been divided by the corrected total number of tweets collected each week. misdiagnosis measurement since who positive specimen counts show the flu was not prevalent from august 2011 through december 2011, despite an increase in flu tweets, the flu tweets from that time period largely represent misdiagnoses. measuring the number of misdiagnosis tweets over time for a typical flu season is potentially valuable for counteracting their effects on twitter flu surveillance, but there are two major challenges: 1) separating the misdiagnosis tweets from the small number of correct diagnoses of the flu, classifier false positives, and other sources of noise from august 2011 to december 2011, and 2) estimating misdiagnosis tweets for january 2012 through may 2012, since direct measurement is complicated by the genuine prevalence of the flu. to address the first challenge, this study subtracts the noise metric from the flu metric. the noise metric is an estimate of the fraction of flu tweets expected during periods in which the flu is not prevalent. since the flu was not prevalent in late 2011, the flu metric should have equaled the noise metric during that time period. therefore, subtracting the noise metric leaves the flu tweets which contributed to the unexpected rise in flu tweets during late 2011. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 to address the second challenge, this study estimates misdiagnosis tweets from late 2011 and extrapolates them to early 2012. the weekly fractions of misdiagnosis tweets from august to december 2011 were estimated by smoothing the flu tweets, subtracting the noise metric, and normalizing by the noise metric: 𝑚𝑚𝑖𝑖 = 𝑚𝑚𝑚𝑚𝑚𝑚(𝑓𝑓𝑖𝑖) − 𝑛𝑛𝑖𝑖 𝑛𝑛𝑖𝑖 (3) where i is the week (limited to august through december 2011), 𝑚𝑚𝑖𝑖 is a unitless factor which estimates the fraction of misdiagnosis tweets when multiplied by the noise metric, med is a sliding median filter of width 5, 𝑓𝑓𝑖𝑖 is the flu metric, and 𝑛𝑛𝑖𝑖 is the noise metric. both 𝑓𝑓𝑖𝑖 and 𝑛𝑛𝑖𝑖 are expressed as fractions of the corrected total tweet count for week i. the smoothing is intended to reduce the effects of noise, and the normalization by 𝑛𝑛𝑖𝑖 helps account for factors which may change from season to season by assuming the misdiagnosis estimate is proportional to the noise estimate. this study hypothesized two extrapolations based on m: tapered and symmetric. the tapered extrapolation assumes misdiagnosis tweets taper off as the flu season progresses, which continues the downward trend seen in misdiagnosis tweets at the end of 2011. the tapering was implemented with a linear interpolation between the misdiagnosis fraction at the end of 2011 (week starting 2012-01-02) and the estimate of the noise baseline at the end of the flu season (week starting 2012-06-04). tapering could be caused by psychosocial factors, such as decreasing anxiety due to news media coverage reporting that the flu season was mild or late. the symmetric extrapolation assumes the misdiagnosis tweet pattern is symmetric around the end of 2011, and the symmetry was implemented by concatenating the weekly counts in the weeks [2011-08-01, 2012-01-02] with the reversed weekly counts in weeks [2011-08-01, 2011-12-26]. the symmetric extrapolation assumes misdiagnosis tweets do not taper off as the flu season progresses, and that twitter authors’ misdiagnoses are symmetric around the typical peak of a flu season. this could correspond to twitter users’ misdiagnoses reflecting their expectations of flu prevalence during a typical flu season. both estimates of the misdiagnosis errors cover the same range of weeks. copying the unitless estimates 𝑚𝑚𝑖𝑖 and the extrapolated values (weeks 2011-08-01 to 2012-06-04) to the corresponding weeks centered on january 1st of the 2012-2013 (weeks 2012-07-30 to 2013-06-03) and 2013-2014 (weeks 2013-07-29 to 2014-06-02) flu seasons, and then multiplying by the noise metric, yielded the final estimate of the fraction of misdiagnosis tweets for 2011-2014 (figure 3). since the misdiagnosis estimate was constructed to be proportional to the noise estimates from the midyear periods, and since those midyear periods were likely to have few tweets correctly diagnosing the flu, the midyear periods were excluded from the misdiagnosis estimates. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 figure 3: estimated weekly fraction of misdiagnosis tweets. finally, the two misdiagnosis based estimates of flu prevalence were produced by subtracting the weekly estimates of the fraction of misdiagnosis tweets from the weekly fraction of flu tweets for each of the two extrapolations. misdiagnosis cross-validation the previous section used the prior knowledge that who positive specimen counts for late 2011 are approximately equal to the positive specimen counts when flu is not prevalent. however, this means its results can only be tested against data from early 2012 onward, or that it must rely on comparisons with recent who positive specimen counts. therefore, this study also uses a form of 3-fold cross-validation, in which an estimate is produced for a “test” flu season by using misdiagnosis tweet rates estimated by taking the difference between the who positive specimen counts and fractions of flu tweets for the remaining two “training” flu seasons. for each flu season, the same range of weeks was used as in the previous section. however, this approach requires comparing positive specimen counts and fractions of flu tweets. this paper used a simple linear regression, p ~ cf, between the who positive specimen counts (p) and the fraction of flu tweets for the non-test weeks (f) to obtain a constant (c) representing a best estimate of the unit conversion factor. the linear regression did not include a constant term, so the linear regression only estimated the single coefficient c. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 𝑚𝑚𝑖𝑖 = � 𝑐𝑐𝑓𝑓𝑖𝑖 − 𝑝𝑝𝑖𝑖 𝑐𝑐 − 𝑛𝑛𝑖𝑖� × 𝑛𝑛𝑖𝑖−1 (4) equation 4 details obtaining the unitless misdiagnosis estimate 𝑚𝑚𝑖𝑖 for a flu season, where i is the week, c is the coefficient for unit conversion obtained via linear regression, 𝑓𝑓𝑖𝑖 is the flu metric, 𝑝𝑝𝑖𝑖 is the positive specimen count from the who, and 𝑛𝑛𝑖𝑖 is the noise metric. the final misdiagnosis tweet fraction estimate for the test flu season was obtained by averaging the unitless misdiagnosis estimates for the two training flu seasons and multiplying by the noise metric for the test flu season. the misdiagnosis tweet fraction estimate was subtracted from the test flu season’s weekly fractions of flu tweets to yield the final estimate of flu prevalence. results data collection and classification the maximum entropy classifier achieved an f-measure of .76, with .73 precision and .79 recall. there were 354 true positives compared to 129 false positives, and 697 true negatives compared to 94 false negatives. to produce the actual counts of sick tweets, the classifier’s threshold was increased to .75 to favor precision over recall, since precision is more important for this study. the .75 threshold achieved an f-measure of .72, with .86 precision and 0.61 recall. the pearson correlation coefficient between the sick tweets and the who’s positive specimen counts is r = .66 (p < .001), which demonstrates that there is a significant degree of correlation even before filtering the sick tweets to examine only flu tweets. metrics the flu metric achieved a pearson correlation with the who positive specimen counts of r = .72 (p < .001), which is an improvement over the correlation for sick tweets of r = .66. however, the flu metric erroneously reports a typical flu season occurring in late 2011 and early 2012, as well as plateaus of flu tweets occurring prior to the start of the next two flu seasons (figure 4). the 2011-2012 flu season is erroneous in the sense that there is a substantial rise in flu tweets in late 2011 despite the lack of a corresponding increase in who positive specimen counts, resulting in the flu tweets exhibiting a pattern of elevated counts roughly centered on december even though the actual flu season peak occurred months later, according to the who positive specimen counts. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 figure 4: flu prevalence estimates versus who positive specimen count data (who) for the linear combination of the flu, noise, and uncertain metrics (lin), and the flu metric alone (flu). although the uncertain metric improves the correlation, both the flu and linear combination results erroneously estimated a 2011-2012 flu season occurring at the typical time, and produced plateaus of misdiagnosis tweets before each subsequent flu season. to measure the relative efficacy of the remaining metrics, the pearson correlation coefficients between linear regressions of the metrics and the who positive specimen count data were calculated (table 2). in each case, the linear regression included a constant term. to reduce over-fitting, each calculation used 10-fold cross-validation, in which the folds were obtained by partitioning the date range into 10 approximately equallength time periods. the combination of using 10-fold cross-validation and linear regression increased the difficulty of obtaining high correlation coefficients, which reduced the correlation for the flu metric from r = .72 to r = .54. introducing the noise metric substantially improved the correlation result, while adding the sick tweets metric yielded no additional benefit. holding the number of regressors constant by substituting the other metrics for the sick metric revealed that only the uncertain metric provided a substantial benefit. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 table 2: pearson correlation coefficients for multiple variable linear regressions using 10-fold cross-validation. the uncertain metric substantially increases the correlation with the who’s positive specimen count. note: the correlation for the flu metric is 0.72 when not using 10-fold cross-validation and multiple variable linear regression. r flu .54 flu + noise .73 flu + sick + noise .73 flu + uncertain + noise .77 flu + uncertainf + noise .73 flu + symptom + noise .72 flu + symptomf + noise .72 while the uncertain metric improved the correlation coefficient, the regressions failed to remove the misdiagnosis tweets, which erroneously indicated a typical 2011-2012 flu season and erroneously showed plateaus of flu activity occurring before each of the next two flu seasons (figure 4). misdiagnosis measurement the flu, symmetric, and tapering metrics all correlate with the who’s ili positive specimen counts (table 3). the sum of p values for each correlation in the table was p < .001, indicating that the set of correlations passes the bonferroni correction. however, the metrics vary in correlation strength: the flu metric suffers from significant plateaus of misdiagnosis tweets preceding each flu season, the symmetric metric can be rejected since it produces flu estimates below the noise baseline during each of the three flu seasons, and the tapering metric successfully removes the false positive plateaus preceding each flu season but shows the flu seasons starting late (figure 5). the tapering metric achieved slightly higher correlations than the other two metrics in all three test conditions, and the tapering metric gains the most benefit when more of the atypical 2011-2012 flu season is included in the test. however, the test which excludes none of the data from the 2011-2012 season is only included for reference; since the late 2011 tweets were used to construct the misdiagnosis tweets estimate, using that data comingles tuning and testing data. http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 13 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 table 3: pearson correlation coefficients for the flu metric as well as the flu metric after subtracting the symmetric and tapering estimates of misdiagnosis tweets. the rows present the correlations when excluding none of the data, the first half of the typical 2011-2012 flu season, or the entire 2011-2012 flu season. flu tweets from late 2011 were used to measure the misdiagnosis tweets, and are included in the row for excluding none of the data. exclusion flu symmetric tapering none .72 .73 .81 half .82 .77 .83 2011-2012 .84 .83 .85 figure 5: estimated flu prevalence before and after subtracting estimated misdiagnosis tweets for each of the tapering and symmetric extrapolation methods. the symmetric method can be rejected since it produces flu estimates below the noise level for all three flu seasons. the tapering method successfully removes the plateaus of misdiagnosis tweets which precede each of the three flu seasons, but shows the 2012-2013 and 20132014 flu seasons starting late. the tapering and symmetric methods frequently overlap in the plot, due to sharing the same weekly misdiagnosis estimates for late 2011. the tapering metric indicates that approximately 47,907 tweets were misdiagnoses, although this may be an overestimate since the 2012-2013 and 2013-2014 flu seasons http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 14 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 start late according to the tapering metric. there were 121,234 flu tweets total, which suggests that roughly 39.52% of the flu tweets reflected misdiagnoses. misdiagnosis cross-validation removing estimated misdiagnosis tweets based on 3-fold cross-validation for the three flu seasons successfully removes the plateaus of misdiagnosis tweets occurring before the 2012-2013 and 2013-2014 flu seasons, while accurately reflecting the correct start dates for the 2012-2013 and 2013-2014 flu seasons (figure 6). however, the erroneous estimate for the 2011-2012 flu season remains. the pearson correlation coefficient was r = .76 (p < .001), compared to r = .72 for the flu metric. figure 6: comparison of the flu metric, after subtracting the 3-fold misdiagnosis estimate, to who positive specimen counts. the 3-fold estimate successfully removes the plateaus of flu tweets occurring prior to the starts of the 2012-2013 and 2013-2014 flu seasons, and accurately reflects the start dates of the 2012-2013 and 2013-2014 flu seasons, but it is unable to remove sufficient misdiagnosis tweets from the 2011-2012 flu season to reveal the season’s atypical timing. discussion this study establishes the importance of misdiagnoses by showing that the pattern of flu tweets during the 2011-2012 flu season fails to approximate the who positive specimen counts, and that the flu tweets exhibit plateaus of misdiagnosis tweets preceding each of the next two flu seasons. this study quantifies the importance of misdiagnosis tweets by showing that the tapering metric increases the correlation coefficient from r = .72 for the http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 flu metric alone to r = .81, removes the plateaus of misdiagnosis tweets prior to the 20122013 and 2013-2014 flu seasons, and yields an estimate that 39.52% of flu tweets (47,907 / 121,234) reflect misdiagnoses. finally, this study demonstrates that misdiagnoses can be counteracted via the uncertain and noise metrics (r = .54 increased to r = .77) and by applying 3-fold cross-validation to produce an estimate of seasonal misdiagnosis patterns (r = .76). however, each approach has limitations. only the tapering metric enabled detection of the 2011-2012 flu season, and it was developed with the prior knowledge that who positive specimen counts in late 2011 were low. this is useful for quantifying the impact of misdiagnoses, but presents a challenge for non-retrospective flu surveillance. while an implementation could use time-lagged who counts and apply the tapering metric only once the flu season began, this may not be robust and it would sacrifice the ability to detect the start of the flu season via twitter data. non-retrospective flu surveillance can be enhanced by using either the uncertain and noise metrics or the 3-fold crossvalidation estimate of seasonal misdiagnosis patterns. however, only the latter successfully removed misdiagnosis tweet plateaus before the 2012-2013 and 2013-2014 flu seasons, which is necessary to accurately detect the beginnings of the 2012-2013 and 2013-2014 flu seasons. the limited availability of twitter data in atypical flu seasons is a significant challenge for further analysis of misdiagnosis tweets. analyzing multiple countries during an atypical flu season may be beneficial, but evidence that flu is spread by air travel [34] means that results for each country could not be treated as statistically independent. further research could address improvements to data collection and classification, such as developing classifiers for multiple languages, experimenting with more complex classifiers and feature extraction, examining the effects of different annotation guidelines, using larger volumes of annotated tweets, and using expanded queries including misspellings and references to taking medications. in addition, demographic differences between twitter users and who sampling may introduce additional inaccuracies. finally, the data losses experienced during certain weeks of data collection may have produced inaccurate estimates despite the corrections described in the methods section. this study focused on quantifying seasonal misdiagnosis errors specifically in twitter data, rather than incorporating multiple exogenous data sources or statistical techniques to obtain the best possible estimate of flu prevalence. many studies have shown that using multiple data sources and applying a variety of models can improve flu estimates. as a recent example, santillana et al. demonstrated that using a combination of timelagged cdc data and a new, timely source of electronic health records, which are not available to the public, can improve the accuracy of flu surveillance systems [35]. twitter flu surveillance research is promising, but identifying misdiagnosis tweets remains a challenge. although this paper presents methods of enhancing twitter flu surveillance for flu seasons by using estimates of seasonal misdiagnosis tweeting patterns, these same seasonal misdiagnosis patterns also indicate a risk that there is only a weak causal connection between individuals infected with the flu and twitter authors reporting http://ojphi.org/ ojphi twitter influenza surveillance: quantifying seasonal misdiagnosis patterns and their impact on surveillance estimates 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e198, 2016 flu infections. the weak causal connection is illustrated by the lack of correlation between flu tweets and who positive specimen counts during the 2011-2012 flu season, even after applying corrections for seasonal misdiagnosis patterns. further research, in conjunction with data from additional atypical flu seasons, is needed to enable twitter flu surveillance systems to produce reliable estimates of flu, rather than ili, during atypical flu seasons. acknowledgements the author would like to thank the mitre corporation for funding this research. conflicts of interest none declared. as a not-for-profit operator of federally funded research and development centers, the mitre corporation is not permitted to compete with industry. references 1. nsoesie eo, brownstein js. 2015. computational approaches to influenza surveillance: beyond timeliness. cell host microbe. 17(3), 275-78. pubmed http://dx.doi.org/10.1016/j.chom.2015.02.004 2. paul mj, sarker a, brownstein js, nikfarjam a, scotch m, et al. social media mining for public health monitoring and surveillance. pacific symposium on biocomputing (psb); 2016; kohala coast, hawaii. 2016. pp. 468-79. 3. eysenbach g. 2006. infodemiology: 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misdiagnosis patterns and their impact on surveillance estimates introduction methods data collection and classification metrics collection misdiagnosis measurement misdiagnosis cross-validation results data collection and classification metrics misdiagnosis measurement misdiagnosis cross-validation discussion acknowledgements conflicts of interest references isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e422, 2019 isds 2019 conference abstracts adolescent prescription opioid misuse, illicit opioid use and overdose michele k. bohm, heather clayton national center for injury prevention and control, centers for disease control and prevention, atlanta, georgia, united state s objective given the evolving opioid overdose epidemic, we examined the interrelationships between nonmedical use of prescription opioids and illicit opioid use in adolescents. introduction the number of overdose deaths involving illicit opioids such as heroin and illicitly-manufactured fentanyl (imf) is now higher than deaths involving prescription opioids. adolescents misusing prescription opioids are more likely to use heroin. although nonmedical use of prescription opioids (nupo) among adolescents is decreasing, there is still relatively high prevalence of this behavior. such high prevalence, along with the evolving epidemiology of the drug overdose epidemic as well as the association between nupo and heroin use, signal that nupo in adolescents is still an important issue. understanding the interrelationships between nupo and illicit opioid use in adolescents can inform prevention efforts. the purpose of this study is to: 1) present the magnitude of the drug overdose problem in adolescents, 2) compare the prevalence of heroin use and injection drug use (idu) between students reporting nupo and those not reporting nupo, and 3) determine whether a dose-response relationship exists between these behaviors among adolescents. this information will be beneficial when focusing on adolescents at risk for heroin use by helping to determine whether any nupo is associated with heroin use or if such risk is only noted at a higher frequency of nupo behavior. methods we analyzed data from two surveillance sources to capture adolescent overdose mortality and behavioral risk factors. overdose death data for decedents aged 15 to 19 years were obtained for 2010 and 2016 from cdc wonder, an online database with national mortality data based on death certificates for u.s. residents. we identified deaths involving prescription and illicit opioids using international classification of disease, 10th revision (icd-10) codes for drug overdose deaths. each death is assigned one underlying cause of death code and the following identified overdoses: x40-44 (unintentional), x60-64 (intentional), x85 (homicide), or y10-14 (undetermined intent). additionally, for overdose deaths attributed to specific drugs or drug categories, icd-10 multiple cause of death codes were used to determine the number of deaths involving any opioid, either prescription or illicit (t40.1-t40.4 and t40.6), prescription opioids (t40.2 or t40.3), heroin (t40.1), and heroin and/or synthetic opioids (e.g., fentanyl) excluding methadone (t40.1 or t40.4). we compared the proportion of overdose deaths involving prescription opioids that also involved heroin or synthetic opioids in 2010 and 2016. the second data source, the 2017 national youth risk behavior survey (yrbs), a nationally representative cross-sectional survey of high school students, was analyzed to look at behavioral risk factors. we assessed lifetime nupo (lnupo) and calculated frequency of lnupo by heroin use, injection drug use (idu), and heroin/idu using logistic regression models to g enerate adjusted prevalence ratios (apr) and corresponding 95% confidence intervals (ci). we used linear contrast analysis to determine dose-response relationships between frequency of lnupo and heroin use, idu and heroin/idu. results the number of adolescents aged 15 to 19 years who died of drug overdose increased from 831 in 2010 (3.8 per 100,000) to 873 in 2016 (4.1 per 100,000). while the proportion of overdose deaths involving prescription opioids declined during this time period, the proportion involving heroin and/or synthetic opioids, such as fentanyl increased. in 2016, two-thirds of overdose deaths among decedents aged 15 to 19 years involved either a prescription or illicit opioid. the percent of deaths involving prescription opioids that also involved heroin and/or synthetic opioids, such as fentanyl increased from 5% in 2010 to 25% in 2016. using the 2017 yrbs sample, we estimate that 14% of high school students nationwide have ever used prescription opioids nonmedically in their lifetime. compared to students reporting no lnupo, students reporting lnupo were more likely to report heroin use (9.2% vs. 0.4%), idu (7.8% vs. 0.4%), and heroin/ idu (10.1% vs. 0.7%). we observed http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e422, 2019 isds 2019 conference abstracts a positive dose-response relationship with frequency of lnupo. adjusted prevalence ratios for heroin, idu and heroin/idu increased with increasing frequency of lnupo and were even significantly higher among those reporting just one or two occasions of lnupo than among those reporting no lnupo. conclusions our findings on opioid-involved drug overdose mortality and opioid use patterns confirm nupo is still a concern for adolescents. we report a five-fold increase, from 2010 to 2016, in the percent of adolescent overdose deaths involving prescription opioids that also involved illicit opioids such as heroin and/or imf. this may reflect deliberate polysubstance use among adolescents using prescription opioids nonmedically, but should also be considered in the context of stable prevalence of reported heroin use in yrbs and the national survey on drug use and health. in addition to issues with selfreport bias, adolescents may not selfidentify as a person who uses heroin, for example, if they unknowingly use counterfeit prescription pills that contain heroin or imf. health risk behaviors established in adolescence often continue into young adulthood and understanding associations between opioid initiation, misuse, and overdose is critical for prevention efforts. although we found a dose-response relationship between the frequency of lnupo and the prevalence of heroin and idu, we also report significantly higher heroin use and idu among students reporting just one or two occasions of lnupo compared to students reporting no lnupo. this underscores the importance of prevention efforts aimed at all adolescents who use prescription opioids nonmedically, with particular emphasis on those frequently misusing them. clinical, community, and school-based efforts can address nupo, noting these associations. http://ojphi.org/ a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi lessons learned from introducing a village health registry in malawi prestor j kubalalika centre for community health promotion, chichiri, blantyre3, malawi abstract the village health registry (vhr) was a community health data collection tool introduced in 1998. it was first introduced in mwanza district of malawi with the objectives of collecting community-based data, analyzing and taking action in a local setting. the tool was collecting and updating data such as demography, immunization status for children under one year, growth monitoring for children under five, monitoring of all pregnant women, incidence of malaria, acute respiratory infections, diarrhea cases, water and sanitation and deaths, by visiting households in every village every month. the tool was able to collect all targeted information as required. the data collected by the tool appeared to be more reliable than that obtained through a national information system used by the ministry of health (moh) for the same district and the same year. it was easy for health centers to accurately order supplies based on actual requirements, to follow-up cases during disease outbreaks and to identify deficiencies in immunization coverage rates. despite promising results, the vhr registry fell into disuse following the establishment of a national register. the moh’s health information system (his) data used projections which normally did not represent the actual situation on the ground while the vhr registry gave real physical data which was representative and verifiable. the potential of the vhr outweighed that of the his. although the his had been rolled out nationally, there were shortfalls which moh could consider rectifying to reach its full potential. in conclusion, the vhr was worth adopting as it would give moh realistic statistics to be effectively used at all levels. keywords: village health registry, mwanza district, ministry of health, community health workers, health information system. correspondence: prestor j kubalalika, director, centre for community health promotion, p.o. box 31247, chichiri, blantyre3, malawi. prestorkubalalika@gmail.com doi: 10.5210/ojphi.v10i2.9117 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:prestorkubalalika@gmail.com a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi introduction the village health registry (vhr) was a community health data collection tool which was introduced in mwanza district of malawi in january 1998. the tool was established after the district health management (dhm) had realized that there was no reliable community data collection tool for different programs. the district had no accurate data collection methods therefore the information used was mostly based on projections from figures generated by either the national statistical office (nso) or from previous year’s data files. such figures were seen to be inaccurate in that they were not representing the actual physical situations on the ground. to make matters worse, even health centers (hcs) did not know their actual catchment areas’ populations as well as targets for specific services. the notable anomalous situation was discovered in the expanded program on immunization (epi). during the program’s monthly routines and or mass immunization campaigns, the program was registering coverage as high as more than 100% for the presumed under one year target population for services such as polio, dpt3, measles and vitamin a supplementation. due to such unreliable demographic data, it was later discovered that there were some sporadic occurrences of measles outbreaks in areas where coverage for both routine and mass campaigns was registering above 100%. these observations and others therefore, made the dhm decide to create a communitybased demographic data collection tool. the tool was at that time intended for epi services only and it was provided for every village in the district hence the name village health registry (vhr). in this registry, the sections include; (a) household (hh) where every member was recorded, (b) immunization for children aged under one year, where every child under one year in the village was entered and followed on its immunization status. (c) growth monitoring (gm) and vitamin “a” supplementation for children aged under five, where all children from birth to five years were registered. (d) antenatal care, for every pregnant woman who was monitored until delivery. (e) death for every death from these villages together with the indicated cause, and finally (f) summary where total figures of each section were collated at the end of every year. literature review health management information system (hmis) are one of the six building blocks essential for health system strengthening. according to measure evaluation (2016), hmis is a system where data are collected basically for supporting health facilities and organizations in their planning, management, and decision making. health information systems (his) provide a broad overview of healthcare information systems with emphasis on historical foundations and current issues. major issues include healthcare overview through use of information technology, medical and public health informatics, information technology infrastructure, computing ethics, computer security, consumer informatics, clinical software, clinical education computing, research computing, health information exchange, and the future of healthcare computing. the world health organization (2014) observed that over the years, most developed health systems are either technology-driven or adopted with no consideration to local settings which a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi prompted it (who) to publish the “developing health management information system”. this is a guide for developing countries aiming at providing health managers and designers of health information with a set of practical guidelines for a logical approach to develop his taking into consideration a country-specific setting. in malawi, the existing his are unnecessarily fragmented and incapable of generating quality information at the needed time. it is for this reason that concerted efforts have been underway, in collaboration with partners, to harmonize and synthesize various data-management systems in the entire health sector (moh 2015). the ministry of health (moh) further noted that despite those concerted efforts, they still face challenges in the areas of data collection, analysis, dissemination, and use. these are further exacerbated by continued existence of problems of data accuracy, timelines of reporting, analysis, and completeness (moh 2015). village health registry this paper reports the experience and outcome of a village health registry (vhr) as a community health data collection tool which was introduced in the mwanza district of malawi in january 1998. the vhr was established after the district health management (dhm) had realized that there was no reliable community data collection tool for different programs. the district had no accurate data collection methods therefore the information used was mostly based on projections from figures generated by either the national statistical office (nso) or from previous year’s data files. we compared the information obtained by the registry in its first year of operation, 1998, with data of the same year within the same area as published by the national health information system. methods population studied: mwanza is one of the districts situated in the southern-eastern region of malawi. it lies between 15˚ and 16˚ south and 34˚ and 35˚ east. the district borders with ntcheu district to the north, blantyre district to the east, chikwawa district to the south and mozambique to the west. the district covers an area of 2,259 km2 with various topography ranging from valleys to plains and hills which influence climatic conditions. it comprises a population of 166,443 in 239 villages and 14 health centers. development of the registry the registry was paper based and was developed by mwanza district health management in 1998 and comprised of sections including; (a) household (hh); where every hh and its members were recorded, (b) immunization for children aged under one; where every child under one year in the village was entered from their hhs and followed on their immunization status. (c) growth monitoring (gm) and vitamin “a” supplementation for under five children; where all children from birth to five years were registered and monitored for five years. (d) antenatal care; for every pregnant woman monitored until delivery. (e) death; every death from these villages was a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi registered with their causes indicated, and finally (f) summary; where total figures of each section were collated at the end of every year. collection of information community health workers (chws) were initially trained in the use of the tool by the district maternal and child health (mch) coordinator who was one of the architects of the tool for three days. every village in all the 14 health facilities was assigned a registry book which had all the components and these registry books were given to all the chws known as; “health surveillance assistants (hsas)”. each hsa was given the number of registry books according to villages he/she was working in. these chws/hsas visited every household in these villages once every month to either record any new information occurring during the month or to update the already registered information. utilization of the vhr information –at the village and health centre level: every month chws would describe coverage for any of the registered indicators for each village purely based on the vhr data. these data were then shared with community-based health volunteers such as village health committees (vhcs). these volunteers together with their chws after analyzing each village’s monthly data would then seek audience with local leaders i.e. village heads to give them monthly updates about the collected data. the village heads would later convene special meetings for everyone for feedback. this was mostly so if and when a certain indicator wasn’t performing well and needed people to promptly act upon either an individual or as a group. otherwise these meetings were routinely scheduled after every three months (quarterly). chws together with vhcs would thereafter plot and or update performance graphs for each indicator per village. this action helped chws at village level to; i. closely monitor all performance indicators ii. identify health problems and take immediate action iii. make proper and realistic monthly work plans iv. consult relevant community leaders for action (if any) on specific issues v. know populations for every age groups in each village vi. give logical and realistic information to their hcs on village health performance indicators vii. know all the new born babies for every village every month viii. make consistent follow ups of children under one year old on immunization and gm for under-fives. ix. identify pregnant women and advise them to start attending ancs x. be well conversant with particular hh’s health needs and offered on the-spot advice. xi. strengthen relationships and understanding between them and individual hhs plus local leaders. xii. have at hand all village/community health related data at all times a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi this village data would thereafter be taken to the health centre level where all chws under the hc summed up monthly indicator performance data for all villages under the hc. they would later together plot and/or update monthly performances graphs from the collated data. before sending the data to the district level however, they would analyze the data and the graphs by identifying underperforming indicators and the corresponding villages. solutions were discussed and possible actions decided upon at that level. this monitoring system, allowed the hcs to; a) become aware of any looming dangers which would make them take immediate actions (as hfs) before it was too late. b) consult with responsible underperforming villages as early as possible to seek their prompt action or response. c) know exactly what their target populations for various services were which enabled them realistically order supplies for their facilities. d) make follow-ups on defaulters of various monitored indicators e) monitor performance coverage for various vaccine antigen and other services f) know actual villages with low immunization coverage within their hc catchment areas g) come up with exact physical target and total populations for each hc h) have readily available community health related data at hand for each village at all times i) know numbers of deaths with causes utilization of the vhr information – at district level: upon compilation and/or collation of hc data, every hc sent the data to the district for an overall aggregation. each hc data were accompanied by a write up attachment explaining any weak areas and actions decided or taken to abate them or sought help and or advices from the district health office. the district would thereafter be able to; a) collate data from all hcs and come up with realistic physical total population and its break down by age groups. b) know the actual number of children from the district alone to those from outside of it c) come up with meaningful target populations for specific services d) identify actual underperforming hcs and villages with low immunization, antenatal attendances and watsan coverage, high malaria, diarrhea and ari incidences. e) identify straggling hcs and offer specific targeted assistance such as supportive supervision and/or supplies. f) know the most prevalent communicable diseases and strategically apply targeted intervention measures. g) come up with performance and determinant graphs for each indicator h) order and distribute supplies based on realistic figures and requirements i) easily compile districts’ deaths figures/rates with precision and accuracy a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi results data obtained from the vhr and routine registries tables 1 and 2 show the demographic data obtained by the vhr and the national his. we noted small differences in numbers between the two. tables 3 and 4 show the data on immunization coverage for both systems. the data from the national his appear to indicate some immunization rates of more than 100%. the rankings of immunization rates between different health centers differ in different tables according to the source of the data. table 1 shows the vhr demographic data from 1998, the initial year of vhr data collection, for each of the 14 health centers. table 1: vhr demographic data for 1998 health centre total pop under1 pop under5 pop wcba # of vges neno-parish 9,985 500 1,701 2,490 14 kunenekude 9,854 494 174 2,475 20 tulonkhondo 8,854 445 1,499 2,224 13 mdh 30,865 1,541 5,256 7,712 56 chifunga 6,654 335 1,135 1,652 07 magareta 7,653 342 1,309 1,921 10 neno-rural 25,439 1,278 4,329 6,368 14 matandani 8,795 446 1,505 2,191 10 thambani 10,009 506 1,711 2,502 26 lisungwe 16,123 798 2,756 4,021 26 luwani 5,543 285 956 1,396 03 matope 7,761 394 1,328 1,942 18 nkula 5,643 285 964 1,444 5 nsambe 13,265 669 2,261 3,328 17 totals 166,443 8,318 26,884 41,666 239 a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi table 2: national (his) demographic data health centre total pop under1 pop under5 pop wcba neno-parish 10,354 518 1,760 2,589 kunenekude 10,223 511 1,738 2,556 tulonkhondo 9,223 461 1,568 2,306 mdh 31,246 1,562 5,312 7,812 chifunga 7,023 351 1,194 1,756 magareta 8,022 401 1,364 2,006 neno-rural 25,808 1,290 4,387 6,452 matandani 9,164 458 1,558 2,291 thambani 10,378 519 1,764 2,595 lisungwe 16,492 825 2,804 4,123 luwani 5,912 296 1,005 1,478 matope 8,130 407 1,382 2,033 nkula 6,012 301 1,022 1,503 nsambe 13,634 682 2,318 3,409 totals 171,621 8,581 29,176 42,905 table 3 below shows the vhr data on immunization aggregated for each of the health centers, as well as the numbers and causes of deaths recorded by the registry in that year. we see that immunization coverage was generally high, ranging from 82% for measles to 90% for bcg. one health center had a measles immunization rate of only 71% and another dpt3 rate of only 75%. table 4 shows the immunization coverage based on routine data. most of the health centers’ coverage is above the target population. these data were the ones being used in his which did not represent the actual population on the ground. although the data were considered as for mwanza, some of the children were not from the district. some belonged to neighboring districts while others from across the border with malawi. a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi table 3: vhr immunization data for 1998 1998 vhr vaccine coverage deaths health centre total pop target pop bcg % dpt3 % opv3 % measles % under one deaths no. common cause neno-parish 9,985 500 451 90 412 82 399 80 353 71 4 malaria kunenekude 9,854 494 426 86 419 85 421 85 402 81 6 pneumonia tulonkhondo 8,854 445 441 99 438 98 437 98 420 94 4 malaria mdh 30,865 1,541 1312 85 1,302 84 1,302 84 1,293 84 28 malnutrition & malaria chifunga 6,654 335 329 98 318 95 312 93 301 90 2 malaria magareta 7,653 342 302 88 289 85 283 83 251 73 5 measles & malnutrition neno-rural 25,439 1,278 1192 93 1,072 84 1,083 85 1,001 78 7 malaria, pneumonia & diarrhea matandani 8,795 446 442 99 440 99 438 98 422 95 3 diarrhea thambani 10,009 506 466 92 422 83 417 82 369 73 6 measles, cholera & malaria lisungwe 16,123 798 621 78 601 75 603 76 589 74 8 malnutrition, malaria & fever luwani 5,543 285 280 98 278 98 274 96 269 94 2 cholera matope 7,761 394 361 92 349 89 341 87 333 85 6 malaria, malnutrition & diarrhea nkula 5,643 285 239 84 221 78 216 76 209 73 3 pneumonia & fever nsambe 13,265 669 661 99 650 97 652 97 643 96 5 malnutrition & diarrhea totals 166,443 8,318 7523 90 7,211 87 7,178 86 6,855 82 98 a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi table 4: routine immunization coverage data for 1998 1998 routine immunization coverage health centre tota l pop target pop bcg % dpt 3 % opv 3 % measl es % neno-parish 10,354 518 522 100.8 517 99.8 521 100.6 536 103.5 kunenekude 10,223 511 513 100.4 526 102.9 518 101.4 511 100.0 tulonkhondo 9,223 461 471 102.2 470 102.0 469 101.7 473 102.6 mdh 31,246 1,562 1,601 102.5 1,573 100.7 1,582 101.3 1,599 102.4 chifunga 7,023 351 350 99.7 349 99.4 355 101.1 361 102.8 magareta 8,022 401 398 99.3 412 102.7 408 101.7 411 102.5 neno-rural 25,808 1,290 1,306 101.2 1,296 100.5 1,289 99.9 1,293 100.2 matandani 9,164 458 462 100.9 462 100.9 458 100.0 449 98.0 thambani 10,378 519 604 116.4 582 112.1 591 113.9 624 120.2 lisungwe 16,492 825 951 115.3 851 103.2 841 101.9 853 103.4 luwani 5,912 296 294 99.3 296 100.0 298 100.7 300 101.4 matope 8,130 407 505 124.1 438 107.6 451 110.8 466 114.5 nkula 6,012 301 420 139.5 306 101.7 312 103.7 320 106.3 nsambe 13,634 682 753 110.4 691 101.3 689 101.0 690 101.2 totals 171,621 8,581 9,150 106.6 8,769 102.2 8,782 102.3 8,886 103.6 a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi limitations there are, however, a number of limitations of the vhr. although the vhr had shown enough potential to collect important and accurate data, there were some notable limitations which still made it unable to effectively achieve its intended purpose. one clearly observed limitation was impact limitation. this had been due to limited knowledge of chws on how to effectively use the tool. most of them were not trained therefore made them unable to understand how they could collect data using this registry. this again was compounded by limited or insignificant supportive supervision by moh to districts and within districts on the tool. there was little consultation to relevant experienced stakeholders before nationally rolling out this tool. this made the moh miss some important and critical operational information which could make the registry as reliable as expected. this was why there was need for thorough and comprehensive consultation and adequate training to all chws and supervisors. the other limitation was data-based. for this register to show real impact there was need for enough data to be collected. at that point, there was little vhr data available at all levels of moh system which could be aggregated to make significant impact. this was the case because chws did not use the tool to collect the needed data anymore as most of them were not knowledgeable of it. there was need therefore to equip all chws with knowledge and maximum supportive supervision at all levels. the moh had weak data management systems in place at all levels which made it difficult for the vhr data to stand out as expected. districts did not have enough resources and knowledgeable personnel to effectively manage and later on supervise the flow of vhr data from community to hc and district levels as expected. the moh system did not have vhr data storage at any of its hmis levels. finally, there was no reliable and sustainable funding for the implementation of the register, or important operations such as trainings, supervisions and procurement of supplies. discussion the results of immunization rates obtained from the vhr and the national his are quite different. the data from the national his appears to indicate immunization rates of more than 100%, suggesting a problem in either the numerators of number of children immunized, or denominators of the population, or both. the rankings of immunization rates between different health centers differ in different tables according to the source of the data. this has serious implications as some of the data obtained by the vhr indicate problems with low immunization rates – if these are missed by an inaccurate his, remedial measures to improve rates will not be undertaken which will result into potential implications for population health. this vhr approach of data collection appears to be more reliable as the information is based on physical count rather than extrapolation or intuition. data from this registry were more genuine as the registry books were conveniently village based. the information from this registry was more accurate and readily available as it could easily be accessed and verified at health centre, village and household levels at any time. every individual was well accounted for and could easily be physically identified using unique code numbers. both total and target populations were a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi established through physical counting rather than estimations as is the case with the national system. the data were updated at the household level where every individual was entered. any upward or downward variations to the data due to migration, death or births were spontaneously noticed and captured within a month by responsible chws. the data excluded all those from outside villages and districts hence no hc had its total and target population exceeding the actual as was the case with the national routine system. follow-up of the data were so simple at hc, village and household levels when needed. the moh has weak data management systems in place at all levels which makes it difficult for the vhr data to stand out as expected. districts do not have enough resources and knowledgeable personnel to effectively manage and later on supervise the flow of vhr data from community to hc and district levels as expected. the moh system does not have vhr data storage at any of its hmis levels. finally, there is no reliable and sustainable funding for the implementation of the registry, or important operations such as training, supervisions and procurement of supplies. this registry could be a vital community-based health data collection tool if it were used appropriately. it has the potential of being a reliable data collection instrument with accurate and verifiable community level health statistics. despite this, however, more needs to be done on its development and implementation to ensure the realization of its maximum intended potential. it is a shame that the registry has fallen into disuse, and lessons need to be learned from this experience. there was need to fully consult relevant stakeholders who had experience, as in any information system, before nationally rolling it out. thorough and conclusive training needed to be done to all chws before giving them the task of using the tool. there was also need to officially pilot it in a few districts with maximum support which would be taken as reference points when rolling it out. for constructive supervision at all levels, deliberate training of trainers (tots) sessions need to be done. this will equip such trainers with knowledge and ability to understand the registry and be able to also train others in their respective districts. the trained dhmt members and program managers would in turn be able to effectively supervise chws at health center level. strong and coherent data management flow systems from village to health center and district levels which can easily be followed and verified should be put in place. for security and safety of this data, every health center needs to have a special computer and trained personnel specifically for managing this information. conclusion based on experience with vhr, it is indeed worthwhile adopting it at a national level. however, it needs to be implemented step by step rather than in a hurry as was the case. wide and extensive consultations with relevant stakeholders were required. before rolling it out, there was need to pilot it in a few districts which would be used as reference points. enough training and refreshers could also be conducted to all chws and their supervisors. finally the moh had to incorporate other interested partners to help it with some other aspects of the tool. a lessons learned from introducing a village health registry in malawi online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e217, 2018 ojphi reference 1. chaulagai cm. moyo & r pendame (2015). health management information system in malawi: issues and innovations. https://www.researchgate.net/publication/265048139_health_management_information_sy stem_in_malawi_issues_and_innovations 2. johns hopkins school of public health. (2014). 312.633.81 health management information systems. http://www.jhsph.edu/courses/course/20055/2014/312.633.81/healthmanagement-information-systems 3. measure evaluation. (2016). health management information systems (hmis) https://www.measureevaluation.org/resources/training/materials/health-managementinformation-systems-hmis-1 4. ministry of health. (2015). malawi national health information system policy. http://int.search.myway.com/search/ggmain.jhtml?searchfor=malawi+health+management +information+system&n=783999a2&p2=%5e9n%5exdm007%5ettab02%5emw&ptb= 5990ffea-a16a-4265-b23cf9e7e765d8ed&qs=&si=cm6ese3zlnmcfq3jgwodgokhg&ss=sub&st=hp&trs=wtt&tpr=sbt&ts=1497084894108 5. singogo e, kanike e, van lettow m, cataldo f, zachariah r, et al. (2013). village registers for vital registration in rural malawi. https://www.ncbi.nlm.nih.gov/pubmed/23718633 6. statistics norway. (2017). the health management information system in malawi. assessment of data quality and methods for improvement. https://www.ssb.no/en/helse/artikler-og-publikasjoner/the-health-management-informationsystem-in-malawi 7. world health organization. (2004) developing health management information systems: a practical guide for developing countries. http://www.wpro.who.int/publications/pub_9290611650/en/ lessons learned from introducing a village health registry in malawi introduction literature review village health registry methods population studied: development of the registry collection of information utilization of the vhr information –at the village and health centre level: utilization of the vhr information – at district level: results data obtained from the vhr and routine registries limitations discussion conclusion reference isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e349, 2019 isds 2019 conference abstracts increased seizure activity in florida associated with hurricane irma, september 2017 david atrubin1, janet j. hamilton2 1 bureau of epidemiology, florida department of health, tallahassee, florida, united states 2 council of state and territorial epidemiologists, atlanta, georgia, united states objective using florida’s syndromic surveillance data, to describe the increase in seizure activity in the days after hurricane irma made landfall in 2017 introduction on september 10, 2017, irma made landfall in the florida keys as a category 4 hurricane and subsequently tracked up the west side of the state. due to the size of the storm, it impacted nearly all of florida. the electronic surveillance system for the early notification of community-based epidemics (essence-fl), the state’s syndromic surveillance system, captures 98% of the emergency department (ed) visits statewide and has historically served a vital function in providing near real-time ed data that are used to track post-disaster morbidity and mortality. after previous hurricanes and tropical storms, increases in carbon monoxide poisonings, animal bites, and injuries have been documented. during post-irma surveillance, an additional increase in seizurerelated ed visits was observed. methods twice-daily hurricane irma surveillance reports were produced from sept 10-22, 2017. in addition to specialized queries specific to storm surveillance, analysis was conducted using essence-fl’s syndrome and subsyndrome categories. the subsyndrome category of seizure captures ed visits which list the words seizure or convulsion in the patient chief complaint. daily number of seizure visits were compared against a 28-day baseline using an exponentially weighted moving average algorithm. additionally, daily seizure visits as a percentage of total ed visits were calculated and plotted. results on september 11, 12, and 13, ed visits for seizures were increased above the expected levels. on these dates respectively, 336 visits (270 expected, p < 0.01), 349 visits (278 expected, p < 0.01), and 306 visits (267 expected, p < 0.01) seizure visits occurred statewide. september 10 showed the largest increase in seizure visits as a percent of all visits. conclusions routine post-storm surveillance was able to identify an increase in seizure visits at eds in florida. this hurricane-related increase, while not detected using our syndromic surveillance system during previous storms, supports findings of increased emergency medical service calls for convulsions and seizures after hurricanes katrina and rita (both in 2005) found by other researchers [1]. due to the size, strength, and projected path of hurricane irma, stress (a known seizure trigger) is a possible biological explanation for the increase that was observed. a greater understanding of storm-related public health threats allows the florida department of health to better plan for these events and communicate this information to the public and our partners. post-storm analysis was complicated by large changes in overall ed volumes during and immediately following the hurricane, and further exploration of the association found in this study is encouraged. references 1. davis js, allan bj, pearlman am, carvajal dp, schulman ci. 2012. optimal emergency personnel allocation after a natural disaster. am j disaster med. 7(1), 31-36. pubmed https://doi.org/10.5055/ajdm.2012.0078 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22649866&dopt=abstract https://doi.org/10.5055/ajdm.2012.0078 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e349, 2019 isds 2019 conference abstracts figure 1 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e366, 2019 isds 2019 conference abstracts informatics & surveillance in global health: informatics capacity for zika outbreak wycliffe odongo, ray ransom, olga henao centers for disease control and prevention, atlanta, georgia, united states objective to assess challenges of establishing surveillance and research study systems and present strategies for rapid deployment in global health for the outbreak response. introduction in response to the february 2016 zika virus (zikv) outbreak, an inter-agency agreement between the u.s. centers for disease control and prevention (cdc) and u.s. agency for international development (usaid) commissioned further research on the epidemiology, transmission, diagnosis, and birth defects associated with zikv. the surveillance and research activities condu cted included ecology studies focusing on the transmission dynamics; pregnancy and infant cohort studies to look for birth defects, developmental outcomes and risk factors associated with zikv infection; and laboratory studies evaluating the usefulness of multiple zika diagnostic platforms. these studies were established by either setting up new systems, or leveraging on existing surveillance systems to include zikv research specific data elements. conducted using country-specific protocols, these research systems included key data elements for cross-site analysis. challenges faced included collection of non-standardized data, differing functional requirements, varying security and confidentiality protocols and limitations of informatics infrastructure. these challenges highlight an opportunity to evaluate and present the informatics-based components necessary to rapidly deploy surveillance and research study activities during a global health emergency situation. we highlight the key challenges and pr esents strategies for setting up rapid surveillance and research study activities. additional areas of focus also include system architecture, global partnerships, and workforce development. methods information systems used in the zikv ecology, pregnancy and diagnostic studies were evaluated in 12 countries in as ia, africa and the americas. the research data collection and enrolment for the studies started at different time points (between feb 2017 and aug 2017). a baseline survey (structured questionnaire) was administered to the 12 data points of contacts (pocs) in each country to identify existing or selected information systems for use, functional requirements (for data collection, hosting, analytics and integration), existing informatics and infrastructural capacity. recommendations were made on the selection and configuration of information technology (it) systems gaps identified in the baseline; with follow up visits to 5 selected sites for interventi on implementation as part of cdc’s technical assistance. 6 key informant interviews were conducted with subject matter experts on the 6 proprietary(commercial/custom) and 12 semi-structured follow up interviews with data pocs to assess the implementation of the recommendations and interventions. technical assistance impact was measured by averaging the number of i nformatics technical assistance requests monthly from the countries over approximately a one year period (mar 2017apr-2018). the delone and mclean information system success model was used to measure success . information quality was scored using complet eness, format and timeliness; system quality was scored using availability, adaptability, integration and ease of use; and service q uality was measured using reliability and user satisfaction ratings. results 13 (5 open source, 8 proprietary or custom systems) health information systems were identified; 9 exclusively for data collection while 4 had extended functionalities to include extract transform and load (etl); and, analytics. selection of these systems was based on awareness and popularity of information technologies in country. open source systems included redcap, epiinfo, dhis2, kobo toolbox, commcare; while proprietary include university of virginia’s multi-schema information capture (music), ms access, sams (cdc’s secure access management system) and 3 custom in-house systems. two (2) pregnancy study sites (kenya and guatemala) used redcap to enroll and follow up over 1700, and 436 pregnant women respectively while 1 site(thailand) used a custom web-based visual basic system for collecting data on 1000 pregnant women; ecology studies in 3 sites(brazil, colombia and peru) used smartphones installed with commcare to collect data on 560 non-human subjects; diagnostic http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e366, 2019 isds 2019 conference abstracts studies in 10 sites used existing acute febrile illness (afi) platforms running custom software, dhis2, kobo toolbox, epi info, redcap and ms access systems data. technical assistance (ta) requests were grouped into eight (8) core functional areas with systems design (21.9%), data transmission and synchronization (18.5%) and network configuration and diagnostics (13.2%) identified as key the top 3 areas of ta (n=820). ta requests to cdc ranged between an average of 4(mean=4, s.d =0.23) currently and 11(mean=11.25, s.d=0.16) requests per country per month at the beginning of the pregnancy coh ort studies (n=3) in kenya, guatemala and thailand. diagnostic studies (n=10) ranged from 26.8 (s.d=14.6) to 7.5(s.d=4.3) while ecology averaged at 1.7 (n=3, s.d=1.5) requests per country per month. mean scores of information quality, system quality and service quality were significantly different between sites, as well as between types of information systems (p<0.05). total mean scores of informa tion quality, system quality and service quality for were 68.6, 59.8 and 61.4, respectively. conclusions robust open source systems exist but their functioalities are not fully exploited. with rapidly changing contexts and outbreak type scenarios, surveillance and research systems must be flexible to rapidly adapt their functional requirements. with appropriat e information systems selection guidelines and deliberate informatics technical capacity building we could greatly improve the ability to rapidly deploy systems for outbreak response and global health surveillance and research. informatics capacity to incorpor ate design thinking and standardization in surveillance system design and implementation could help realize their potential to pr ovide fast and accurate data for action especially in multi-site contexts. data exchange and security policies across disparate systems in global health,need to be re-aligned with disease surveillance systems’ functional requirements. acknowledgement ray ransom, terrence lo, olga henao and rama sima for their help during the data collection and analysis. all the collaborating partners in the zika research studies. disclaimer: the views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the centers for disease control. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts beginner r methods for syndromic surveillance data validation elyse kadokura* wa state doh, shoreline, wa, usa objective to share practical, user-friendly data validation methods in r that result in shorter validation time and simpler code. introduction there are currently 123 healthcare facilities sending data to the washington (wa) state syndromic surveillance program. of these facilities, 30 are sending to the national syndromic surveillance program’s (nssp) production environment. the remainder are undergoing validation or in queue for validation. given the large number of wa healthcare facilities awaiting validation, staff within the state syndromic surveillance program developed methods in r to reduce the amount of time required to validate data from an individual facility. methods the dplyr package and r markdown file format were used to more rapidly conduct syndromic data validation. dplyr, written by hadley wickham, was created for easy data manipulation.1 the syntax of this package is user-friendly, providing a function for almost every common data manipulation task and utilizing the piping operator from the magrittr package. data fields of interest for syndromic surveillance are classified as required (r), required but may be empty (re), or optional (o). for r or re data fields, dplyr can be used to check for patterns of missingness as well as verify that the correct value sets are being used for code fields. for character fields, dplyr can be used to pull samples of free-text, calculate word or character counts, or search for string patterns of interest. r markdown makes it easy for users to create reproducible reports in many different document types including html, pdf, and word.2 r markdown files combine r code chunks and plain text to create easy-to-read, professional data validation reports that can be used internally or shared with data submitters for their review. results the amount of time spent validating any single facility has decreased significantly. this has allowed the number of facilities undergoing data validation at one time to increase from 12 to 22. however, the length of time between beginning and completing data validation per facility has not decreased. while reporting data issues to facilities takes less time, the lag in the validation process still occurs while waiting for facilities to correct these issues at the feed origination. conclusions in order to increase the number of healthcare facilities that are sending production quality data more quickly, more resources need to be directed at providing facilities with support on how to correct data issues rather than solely reporting the problems. keywords validation; surveillance; syndromic references 1. anderson, s. dplyr and pipes: the basics [internet]. 2014 [cited 2017 oct 10]. available from: http://seananderson.ca/2014/09/13/dplyrintro.html 2. broman, k. knitr with r markdown [internet]. [cited 2017 oct 10]. available from: http://kbroman.org/knitr_knutshell/pages/ rmarkdown.html. *elyse kadokura e-mail: elyse.kadokura@doh.wa.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e3, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts nbic collaboration at multiple jurisdictional levels during the zika epidemic emily iarocci*, collin schwantes, anne folley, chandra lesniak, tiana garrett-cherry and teresa quitugua department of homeland security, national biosurveillance integration center, washington, dc, usa objective an important part of the national biosurveillance integration center’s (nbic) mission is collaboration with federal, state, local, tribal, and territorial governments for the purpose of enhancing early warning, shared situational awareness, and related decision support for infectious disease events. several such collaborations occurred at multiple jurisdictional levels during the recent zika epidemic in the americas and the associated microcephaly and other neurological disorders public health emergency of international concern (pheic). the collaborations and their known outcomes from this major infectious disease event are described below, and nbic stands ready to support similar efforts for future events. introduction nbic is charged with enhancing the capability of the federal government to enable early warning and shared situational awareness of acute biological events to support better decisions through rapid identification, characterization, localization, and tracking. a key aspect of this mission is the requirement to integrate and collaborate with federal and, state, local, tribal, and territorial (sltt) government agencies. nbic develops and disseminates a variety of products to its stakeholders, including daily reports, ad-hoc reports, analytic collaborations, and leadership briefings upon request. stakeholders interact with and utilize nbic’s products in different ways, depending on the mission and jurisdiction involved. specific collaborations with individual stakeholders are most frequent and evident during major infectious disease events, such as the recent zika epidemic in the americas and the associated microcephaly and other neurological disorders pheic. collaborative efforts and known outcomes among varying levels of government are described in detail below in order to highlight nbic’s integration focus and capabilities in this role. methods nbic conducted a thorough review of data gathered, reports written, briefings delivered, and both direct and indirect collaborations completed during the zika epidemic period from late april 2015 – march 2017. this review was completed with the intent to document collaborative efforts, feedback, and outcomes with multiple jurisdictions. results between april 2015 and march 2017, nbic worked both directly and indirectly with several of its stakeholders to describe and clarify the zika event as it unfolded in the americas. within the department of homeland security (dhs), nbic provided briefings for department leadership, including the dhs secretary and assistant secretary of the office of health affairs), and communicated with other dhs components, such as the federal emergency management agency (fema) and the u.s. coast guard, to ensure the dhs workforce received effective messaging about zika infection risks and protections. for its federal partners, nbic coordinated and responded to requests for information about zika across several departments, including the departments of defense, state, and health and human services (hhs). in addition, nbic analysts collected, structured, and provided information about imported and locally-acquired cases described in open source reporting to the hhs assistant secretary for preparedness and response (aspr) for inclusion as a layer on the geohealth platform interactive map before zika reporting was added to the national notifiable disease system. inclusion of this map layer on aspr’s website was nbic’s first public facing collaboration effort. finally, nbic coordinated the incorporation of maps and diagrams from aspr and dod’s armed forces health surveillance branch in nbic products to broaden the distribution of key information. outside of the federal government, nbic received feedback from the texas department of state health services that a september 2016 nbic daily monitoring list with a map depicting zika cases near the border of mexico and texas, as well as the locations of border crossings, filled a gap in its understanding of the number and distribution of cases in mexico. state health officials used this information to help target public and clinician outreach activities in texas. in addition to the regular reports disseminated to the sltt community, nbic supported monthly calls for the community to provide infectious disease event updates, including zika updates, and responded to questions from the community regarding the event and federal government response efforts. conclusions major global or regional infectious disease events that have a direct or potential impact on the health of u.s. citizens require substantial collaboration efforts across multiple jurisdictions. these events foster communication and coordination between organizations toward the common goal of serving the american people and keeping them safe and healthy. the zika epidemic in the americas and the associated microcephaly/neurological disorders pheic is an example of such an event, and nbic supported its partners and the multiple jurisdictions they serve, as evidenced by the results presented. nbic continues to expand its network and support capabilities, and is available to serve as integrators for both major and lesser infectious disease events of concern to their stakeholders. keywords biosurveillance; national biosurveillance integration center; geohealth platform; interagency; zika *emily iarocci e-mail: emily.iarocci@associates.hq.dhs.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e76, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e335, 2019 isds 2019 conference abstracts jurisdictional etiquette workgroup: an offshoot of a syndromic surveillance consortium kike oduba1, tolulope olumuyiwa1, biru yang1, joanne schulte1, kaye reynold2, eric bakota3 1 houston health department, houston, texas, united states, 2 fortbend county health and human services, richmond, texas, united states, 3 harris county public health, houston, texas, united states objective • to demonstrate the importance of a cross-jurisdictional etiquette workgroup in the texas southeast region that leverages on the syndromic surveillance consortium • to promote data sharing and communicate the findings of disease to assist rapid investigation and data sharing key words: essence (electronic surveillance system for early notification of community-based epidemics) introduction syndromic data is shifting the way surveillance has been done traditionally. most recently, surveillance has gone beyond city limits and county boundary lines. in southeast texas, a regional consortium of public health agencies and stakeholders in the 13 -county area governs the local essence system. the houston health department, (hhd) is responsible for deploying essence to the entire region. to effectively monitor the health of the region’s population, a need arose to establish clear guidelines for disease investigation and data sharing triggered by syndromic surveillance across the area. since houston’s instance of essence serves all 13 counties, the consortium instituted a crossjurisdictional etiquette group. the purpose of the group is to determine the standard protocol for responding to essence alerts and best practices for data sharing and use among consortium members. methods to achieve these goals, it was determined that a smaller group of stakeholders besides governing officials is needed to provi de guidance for regional data sharing and use. the etiquette group was established in the first quarter of 2018 and it included four consortium representatives from the 6/5 south region of texas. their first meeting tackled issues relating to data sharing. results the following products emerged from the activities of the etiquette group within 3 months of its existence: • publication/presentation guidance/policy to avoid duplication of efforts and misrepresentation of jurisdiction. • procedure for alert responses • instructions for within-systems management of alerts; • instructions for events/times of interest (e.g., political convention, olympics); • instructions of syndromes of interest/syndrome-specific policies; • instructions for changing the syndrome definition; http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e335, 2019 isds 2019 conference abstracts • notification procedures for identification of a single case of reportable disease/important free text element within data. conclusions cross jurisdictional workgroups can influence rapid investigations of disease, protect patient health information and promote privacy and data security and confidentiality by establishing set rules/guidelines for data exchange. all 13 -counties in the region rely on these guidelines as a standard for responsibly accessing, using and sharing data in the texas southeast essence syste m. lessons learned: • as the etiquette group continues to evolve, there is need for more resources to help foster data use and sharing among jurisdictional partners. • partner engagement is limited due to ongoing process of configuring the new system essence. • since disease has no boundaries, allocation of jurisdictional responsibilities for responding to alerts should be operationalized • continuous training is essential to ensure all system users adhere to the protocols in place for meaningful data use and data sharing http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e450, 2019 isds 2019 conference abstracts utilization history of emergency medical services among west virginia drug overdose decedents ada okorie office of maternal, child, and family health, w est virginia department of health and human resources, charleston, w est virginia, united states objective opioid and illicit substance abuse continues to have major public health implications in the state of west virginia. by analyzing the emergency medical service (ems) utilization history of drug overdose decedents, opportunities to improve surveillance of fatal and non-fatal drug overdoses can be identified which can help lead prevention efforts of fatal drug overdoses in the state. introduction west virginia continues to lead the nation in drug overdose deaths per capita. in 2016, the age -adjusted rate of drug overdose deaths was 52 per 100,000 [1]. in the same year, there were roughly 64,000 overdose deaths in the united states, a 21.5% rate increase from 2015 [1]. the drug overdose epidemic in west virginia has taken a significant toll on individuals, families, communities, and resources. as part of a rapid response plan to help reduce the burden of overdose deaths, the west virginia department of health and human resources conducted an investigative report to study 830 overdose related deaths that occurred in 2016 and identify opportunities for intervention in the 12 months prior to death. utilization of ems among decedents was analyzed to determine demographic differences between decedents at different time points of ems contact: ems contact at death only; ems contact 12 months prior to death only; and both ems contact at death and 12 months prior to death. methods a list of decedents that had died in 2016 from a drug overdose was obtained from the west virginia vital registration office and then matched to ems ambulance run data. the inclusion criteria for this decedent sample were: state residency, drug overdose as the primary cause of death, and a history of ems utilization. overall, 588 west virginia overdose decedents were identified for analysis. drug classes, identified by forensic toxicology reports, and demographic information including gender, age, race, marital status, education level, and occupation of each decedent were analyzed to identify trends related to overdose deaths. a ‘death run’ was defined as an ems run that occurred within 48 hours of death. a ‘prior ems run’ was defined as an ems run that occurred within 12 months prior to death. results among decedents with an ems contact, 50% (n=295) of decedents’ only contact was at death. of the remaining half of decedents with an ems contact: one-third (n=195) had both a previous ems run in the 12 months prior to death and at death; and 17% (n=98) of decedents only ems contact was in the year prior to death that was not a fatal run (table 1). there were gender differences in ems utilization among male and female decedents at death run only, 12 months prior to death only, and at both time points. when comparing time points, the largest percentage of ems contact among males and females occurred at death run only; although males (53% n=206) had more contact with ems at death run only compared to females (45%, n=89). however among those that had utilized ems at both time points, females had more encounters with ems (38.3%, n=75) than male decedents (30.61%, n=120) (table 1). decedents aged 15-24 years (64.5%, n=20) had the largest percentage of ems utilization at death run only compared to the other age groups. decedents aged 65 years and older of prior ems runs (50.0%), compared to other age groups (table 1). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e450, 2019 isds 2019 conference abstracts of the decedents that received at least one naloxone administration in their ems history (n=178), decedents that utilized ems at both time points received the largest administration at 44% (n=80). this was followed by 41% (n=73) of decedents that had ems contact at death only. conclusions for half of the decedents analyzed, their only encounter with ems was associated with their death. this could be explained by the type of drugs that contributed to their deaths, as stronger illicit and/or pharmaceutical drugs such as fentanyl, contributed to more overdose deaths in this population than other drug types [2]. although decedents aged 15-24 years had highest ems contact at death run only, illicit drugs were more commonly found in this particular group than other age groups [2]. evidence has shown that a prior non-fatal overdose in the past, increases the risk of a fatal overdose in the future [3]. one-third (n=195) of decedents in this analysis had both a prior contact with ems in the year before death and within 48 hours of death. however, it is unknown whether their previous contacts with ems was associated with an overdose. further investigation into chief complaints of ems runs would need to be done to assess the association between prior ems contact due to a non-fatal overdose and risk of a subsequent fatal overdose. in this analysis, women had a larger percentage of ems contact at both time points than men. studies have indicated that women are more at risk than men for having a fatal overdose [4]. one possibility is that the concurrent use of opioid prescription and illicit drugs, occurs more often among women than in men elevating their risk of having non-fatal and fatal overdoses. identifying high-risk individuals with previous overdoses can help to minimize the gap between overdose and accessibility to treatment services. as part of the rapid response plan, the west virginia drug control policy act was passed to improve dru g overdose surveillance and help strengthen response [5]. the policy enacted the creation of a central repository that will store drug overdose information, making drug overdoses a notifiable condition. acknowledgement this abstract was prepared in collaboration with the west virginia violence and injury prevention program of the west virginia department of health and human resources with support from the centers for disease control and prevention grant: prescription drug overdose for states program (nu17ce002735-02-03). its contents are solely the responsibility of the authors and does not represent the official views of the cdc. references 1. seth p, scholl l, rudd ra, bacon s. 2018. overdose deaths involving opioids, cocaine, and psychostimulants — united states, 2015–2016. mmwr morb mortal wkly rep. 67, 349-58. pubmed https://doi.org/10.15585/mmwr.mm6712a1 2. west virginia violence and injury prevention center. 2016 wv overdose fatality analysis: healthcare systems utilization, risk factors, and opportunities for intervention. 2017 dec 20. 3. stoové ma, dietze pm, jolley d. 2009. overdose deaths following previous non-fatal heroin overdose: record linkage of ambulance attendance and death registry data. drug alcohol rev. 28(4), 347-52. pubmed https://doi.org/10.1111/j.1465-3362.2009.00057.x 4. evans e, kelleghan a, li l, min j, huang d, et al. 2015. gender differences in mortality among treated opioid dependent patients. drug alcohol depend. 155, 228-35. pubmed https://doi.org/10.1016/j.drugalcdep.2015.07.010 5. west virginia legislature. west virginia drug control policy act [internet]. 2017. available from: http://www.wvlegislature.gov/bill_status/bills_text.cfm? billdoc=hb2620 sub enr.htm&yr=2017&sesstype=rs&billtype=b&houseorig=h&i=2620. table 1. ems history of 2016 overdose decedent population http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29596405&dopt=abstract https://doi.org/10.15585/mmwr.mm6712a1 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19594787&dopt=abstract https://doi.org/10.1111/j.1465-3362.2009.00057.x https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26282107&dopt=abstract https://doi.org/10.1016/j.drugalcdep.2015.07.010 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e450, 2019 isds 2019 conference abstracts all drug overdose decedents (n=830) west virginia population (ages 15+) ems run – any history (n=588) ems run – within 12 months prior to death (n=98) ems run – within 48 hours of death (n=295) ems run – within 12 months prior and within 48 hours of death (n=195) sex males 67.11% (557) 51.00% (912,270) 70.40% (392) 16.84% (66) 52.55% (206) 30.61% (120) females 32.90% (273) 49.00% (933,822) 71.80% (196) 16.33% (32) 45.41% (89) 38.27% (75) total 100.00% (830) 100.00% (1,846,092) 70.80% (588) 16.67% (98) 50.17% (295) 33.16% (195) age (years) 15-24 6.62% (55) 13.00% (235,043) 56.36% (31) 6.45% (*) 64.52% (20) 29.03% (*) 25-34 22.65% (188) 12.00% (218,633) 67.02% (126) 18.25% (23) 43.65% (55) 38.10% (48) 35-44 28.67% (238) 12.00% (228,148) 72.27% (172) 13.37% (23) 54.07% (93) 32.56% (56) 45-54 25.42% (211) 14.00% (252,328) 79.15% (167) 16.17% (27) 52.10% (87) 31.74% (53) 55-64 14.22% (118) 15.00% (269,885) 71.19% (84) 22.62% (19) 45.24% (38) 32.14% (27) 65 + 2.41% (20) 18.00% (328,124) 44.40% (*) 50.00% (*) 10.00% (*) 25.00% (*) *data suppressed due to low count http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e452, 2019 isds 2019 conference abstracts evaluation of syndromic surveillance for opioid overdose reporting in illinois frances rose lendacki1, 2, stacey hoferka1 1illinois department of public health, chicago, illinois, united states, 2division of epidemiology & biostatistics, university of illinois at chicago, school of public health, chicago, illinois, united states objective to evaluate capacity of the biosense essence platform and pre-defined overdose queries to identify emergency department admissions related to opioid overdose, in compliance with 2018 mandatory overdose reporting laws in illinois introduction accuracy in identifying drug-related emergency department admissions is critical to understanding local burden of disease and assessing effectiveness of drug abuse prevention and overdose-reduction initiatives. in 2018 the illinois department of public health (idph) began implementation of a mandatory opioid overdose reporting law, applicable to all hospital emergency departments (ed). the mandate requires reporting of patient demographics, causal substance and antagonist ed administration within 48 hours of presentation. this reporting is not name-based. idph currently utilizes a near real-time syndromic surveillance (sys) reporting system for all hospital ed, capturing most of the mandated criteria. leveraging this existing system facilitates adherence to the mandate while imposing minimal additional burden of reporting on local hospitals. the division of patient safety and quality at idph has thus chosen to evaluate the completeness of overdose reporting and compliance with the opioid overdose mandate that have resulted from use of the current syndromic surveillance system. methods multi-level internal and external validation methods are being employed to evaluate the accuracy of opioid overdose reporting through syndromic surveillance. an initial internal evaluation compared overdoses captured using hospital discharge data (hdd) and sys data. this analysis compared daily overdose counts in the two datasets from 166 illinois facilities, from admissions from april 1 through june 30 2017, inclusive. the opioid overdose query from hdd referenced icd-10 poisoning codes; sys utilized the preset opioid overdose version 1 (v1.0) query in the essence tool from the cdc’s national syndromic surveillance program’s biosense platform. daily and quarterly overdose counts by surveillance method were compared and visualized by facility. three facilities were chosen for a secondary, case-level data comparison based on: magnitude of overdose discrepancies, overall overdose burden, and availability of linked data elements. individual overdose visits were matched across sys and hdd datasets based on: date of birth, sex and approximate date of admit. cases identified in sys but missing from (1) discharge diagnosis query and (2) discharge database overall were quantified. cases identified by hdd that were (3) not identified in the sys overdose query or (4) missing from the sys database entirely were also counted. results from april 1 to june 30 2017, among the 166 providers analyzed, the hdd query identified 2,998 opioid overdose-related visits; sys identified 3,266 (268 cases or 8.9% difference) (figure 1, r=0.724). a total of 25 (15%) of facilities had equivalent overdose visit counts between datasets: all were among those with low case burden (13 or fewer overdose visits per facility over the quarter). among facilities with a higher number of overdose presentations, differences in quarterly case counts (sys minus hdd) ranged from –56 to 120. discrepant counts were found in 85% of centers (figure 2). hdd captured a larger number of overdoses in 93 facilities (56%). sys captured a larger number of overdoses in 48 facilities (29%). the ten facilities with highest syndromic caseload accounted for 33% of overall case burden (1069); the ten with the highest discharge counts accounted for 29% (897 cases). however, the top ten facilities by surveillance type were notably different: the 2nd and 3rd highest using syndromic surveillance ranked 30th and 41st using discharge surveillance over the same period. the center with 5th highest caseload by discharge criteria ranked 38th using syndromic surveillance. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e452, 2019 isds 2019 conference abstracts in secondary case-level analyses: across datasets from three facilities, both hdd and sys captured 43.5% of overdoses, while 56% were only in sys data and 0.5% were only in hdd. discrepancies in the all-visit (“denominator”) datasets were found, requiring follow-up with facilities directly. conclusions next steps in these evaluations include further characterization of cases missed differentially by syndromic and discharge surveillance. an external validation phase will engage facility staff to query the electronic medical record directly. hospital personnel will review and confirm opioid overdose events captured by sys and hospital facilities will investigate and resolve discrepancies in data quality. these analyses have the potential to inform more accurate definitions for opioid-related overdose seen in emergency departments. such improved surveillance can aid allocation of medication (naloxone and naltrexone), promotion of intervention (i.e. methadoneassisted treatment programs), and drug abuse prevention. engagement of facility staff in public health surveillance has resulted in 187 hospital-registered users for the biosense platform to date, demonstrating the ability of surveillance improvement efforts to foster public health partnership. finally, optimizations of automated hospital surveillance systems can help reduce the burden of reporting overdoses and ed morbidity in general, to encourage time spent on monitoring and response. figure 1. correlation of opioid overdose caseloads derived from syndromic surveillance and discharge criteria in illinois emergency departments, april june 2017. n=166 facilities; pearson correlation of quarterly caseloads by facility = 0.724. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e452, 2019 isds 2019 conference abstracts figure 2. facility-level discrepancies in overall opioid overdose counts using syndromic vs. discharge criteria in illinois emergency departments, april june 2017. shown: of 166 facilities analyzed, 50 with greatest differences in aggregate quarterly counts of ed admissions related to opioid overdose, as identified using each surveillance method. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts a semantic framework to improve interoperability of malaria surveillance systems jon hael simon brenas*1, mohammad s. al-manir2, kate zinszer3, christopher j. baker2 and arash shaban-nejad1 1uthsc/ornl, memphis, tn, usa; 2university of new brunswick, saint john, nb, canada; 3université de montréal, montréal, qc, canada objective malaria is one of the top causes of death in africa and some other regions in the world. data driven surveillance activities are essential for enabling the timely interventions to alleviate the impact of the disease and eventually eliminate malaria. improving the interoperability of data sources through the use of shared semantics is a key consideration when designing surveillance systems, which must be robust in the face of dynamic changes to one or more components of a distributed infrastructure. here we introduce a semantic framework to improve interoperability of malaria surveillance systems (siema). introduction in 2015, there were 212 million new cases of malaria, and about 429,000 malaria death, worldwide. african countries accounted for almost 90% of global cases of malaria and 92% of malaria deaths. currently, malaria data are scattered across different countries, laboratories, and organizations in different heterogeneous data formats and repositories. the diversity of access methodologies makes it difficult to retrieve relevant data in a timely manner. moreover, lack of rich metadata limits the reusability of data and its integration. the current process of discovering, accessing and reusing the data is inefficient and error-prone profoundly hindering surveillance efforts. as our knowledge about malaria and appropriate preventive measures becomes more comprehensive malaria data management systems, data collection standards, and data stewardship are certain to change regularly. collectively these changes will make it more difficult to perform accurate data analytics or achieve reliable estimates of important metrics, such as infection rates. consequently, there is a critical need to rapidly re-assess the integrity of data and knowledge infrastructures that experts depend on to support their surveillance tasks. methods in order to address the challenge of heterogeneity of malaria data sources we recruit domain specific ontologies in the field (e.g. idomal (1)) that define a shared lexicon of concepts and relations. these ontologies are expressed in the standard web ontology language (owl). to over come challenges in accessing distributed data resources we have adopted the semantic automatic discovery & integration framework (sadi) (2) to ensure interoperability. sadi provides a way to describe services that provide access to data, detailing inputs and outputs of services and a functional description. existing ontology terms are used when building sadi service descriptions. the services can be discovered by querying a registry and combined into complex workflows. users can issue sparql syntax to a query engine which can plan complex workflows to fetch actual data, without having to know how target data is structured or where it is located. in order to tackle changes in target data sources, the ontologies or the service definitions, we create a dashboard (3) that can report any changes. the dashboard reuses some existing tools to perform a series of checks. these tools compare versions of ontologies and databases allowing the dashboard to report these changes. once a change has been identified, as series of recommendations can be made, e.g. services can be retired or updated so that data access can continue. results we used the mosquito insecticide resistance ontology (miro) (5) to define the common lexicon for our data sources and queries. the sources we created are csv files that use the irbase (4) schema. with the data defined using we specified several sparql queries and the sadi services needed to answer them. these services were designed to enabled access to the data separated in different files using different formats. in order to showcase the capabilities of our dashboard, we also modified parts of the service definitions, of the ontology and of the data sources. this allowed us to test our change detection capabilities. once changes where detected, we manually updated the services to comply with a revised ontology and data sources and checked that the changes we proposed where yielding services that gave the right answers. in the future, we plan to make the updating of the services automatic. conclusions being able to make the relevant information accessible to a surveillance expert in a seamless way is critical in tackling and ultimately curing malaria. in order to achieve this, we used existing ontologies and semantic web services to increase the interoperability of the various sources. the data as well as the ontologies being likely to change frequently, we also designed a tool allowing us to detect and identify the changes and to update the services so that the whole surveillance systems becomes more resilient. keywords semantic; interoperability; malaria surveillance acknowledgments this work is supported by the bill and melinda gates foundation. references 1. p. topalis, e. mitraka, v dritsou, e. dialynas and c. louis, “idomal: the malaria ontology revisited” in journal of biomedical semantics, vol. 4, no. 1, p. 16, sep 2013. 2. m. d. wilkinson, b. vandervalk and l. mccarthy, “the semantic automated discovery and integration (sadi) web service designpattern, api and reference implementation” in journal of biomedical semantics, vol. 2, no. 1, p. 8, 2011. 3. j.h. brenas, m.s. al-manir, c.j.o. baker and a. shaban-nejad, “change management dashboard for the siema global surveillance infrastructure”, in international semantic web conference, 2017 4. e. dialynas, p. topalis, j. vontas and c. louis, “miro and irbase: it tools for the epidemiological monitoring of insecticide resistance in mosquito disease vectors”, in plos neglected tropical diseases 2009 *jon hael simon brenas e-mail: jhael@uthsc.edu online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e90, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts syndromic surveillance in religiious festival involving circumambulation in india vishal diwan*1, 3, ponnaiah manickam2, viduthalai virumbi balagurusamy2, priyank soni1, ashish pathak1, 3, jeromie wesley thangaraj2, vivek parashar1, p ganeshkumar2, chandrasekar ravichandran2, ankit garg1, madhusudhana rao2, sendhil kumar2, v vettrichelvan2, manoj murhekar2 and vijay k. mahadik1 1public health and environment, r.d. gardi medical college, ujjain, india; 2national institute of epidemiology, chennai, india; 3karolinska institutet, stockholm, sweden objective to study operation feasibility and prepadness of a a tablet-based participatory syndromic surveillance among pilgrims during annual ritual circumbulation (panchkroshi yatra) coveirng 15 miles daily in ujjain, madhya pradesh india introduction panchkroshi yatra is an annual ritual of circumambulation (yatra) of temples (mahadevs) and 100,000 devotees walk for around 15 miles per day for six days and cover a total of 73 miles to worship important mahadevs. the festival is held every year at the city of ujjain, madhya pradesh, central india. the yatra attracts large number of pilgrims especially from rural areas and usually women outnumber men. during the yatra, the pilgrims halt at several places and prepare their food in outdoors. we described the public health preparedness, implemented a tablet-based participatory syndromic surveillance among pilgrims of the yatra and reviewed satisfaction of the pilgrims regarding implementation of public health measures, ujjain during 21-26, april, 2017. methods we described preparedness and arrangements done for the yatra. we designed tablet-based android to collect information from pilgrims on socio-demographic-economic details, location and self-reported health problems (syndromes). trained investigators collected data from consenting pilgrims at strategically located halting places. we interviewed a convenient sample of consenting participants to assess satisfaction regarding the public health measures such as sanitation, water, safety, food and cleanliness. results the district team organized round-the-clock medical camps in strategic locations (mainly at temple or halting place) of the route of the yatra with few camps having admission facility for emergency conditions. there were no mobile medical units. ambulance services were on standby at all medical camps. our satisfactory survey of 360 participants indicated that 79% were satisfied with these medical facilities (79%). district administration alongwith local village administration (panchayat) had set up outlets selling provisions necessary meeting cooking needs. eighty percent pilgrims were satisfied with food and refreshment arrangements. permanent and temporary toilets were set-up at the halt-locations but not on the route. snitation measures such as chlorination and solid waste management were in place. pilgrims’ satisfaction for urinals (53%) and toilets (60%) was less as compared to cleanliness (74%). electrical supply and lighting were arranged properly. volunteers were available to provide assistance to pilgrims. provision of safe drinking water and potable water were arranged by the authorities and the villageresidents made water available through well, pots etc. the survey suggested that only 5% of them were not satisfied with water related arrangements. security arrangements such as deployment of police, crowd management, and traffic control and fire safety were wellarranged by the authorities and majority of the respondents expressed satisfaction on these arrangements (79-84%). we interviewed 6435 pilgrims for any self-reported symptoms. more than half (56%) of the responders were female and majority (64%) aged 1559 years. around 44% were from ujjain district. every second person (around 47%) reported illness with one or other symptoms. most of them complained of injury with blister (11%). other common complaints include stomach ache (8%), redness in eyes (7%), fever (7%), cough (6%), vomiting (4%), diarrhea (4%) and throat pain (3%) (figure) conclusions the participants’ response indicates that all the public health and safety measures were satisfactory except the need for setting up urinals along the fixed route of circumambulation. table-based surveillance during the yatra indicated that injury was the most commonly selfreported health problem. implementation of such surveillance helps in tracking health events and therefore, may facilitate preparedness and response. we recommend implementation of such tablet-based surveillance during such mass gathering events. keywords participatory; mass gathering; syndromic surveillance; india isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments we are grateful for the department of health and family welfare, government of madhya pradesh for study permission, national institute of epidemiology, chennai and r.d. gardi medical college, ujjain for financial support. we are also thankul to vesper infotech pvt ltd, study participants and project team references 1. qanta a ahmed, yaseen m arabi, ziad a memish, health risks at the hajj, lancet 2006; 367: 1008–15 2. tam js, barbeschi m, shapovalova n, briand s, memish za & kieny mp. research agenda for mass gatherings: a call to action. the lancet infectious diseases, 2012;12,3, 231-239 3. henning kj, overview of syndromic surveillance. what is syndromic surveillance? mmwr morb mortal wkly rep 53 (suppl): 5-11 (2004). 4. chandrasekhar, cp, ghosh j information and communication technologies and health in low income countries: the potential and the constraints. bulletin of the world health organization, 2001, 79: 850–855 *vishal diwan e-mail: vishaldiwan@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e180, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e409, 2019 isds 2019 conference abstracts surveillance for lyme disease in canada: 2009-2015 jules koffi, salima gasmi public health agency of canada, saint-hyacinthe, quebec, canada objective this study aims to describe incidence over time, geographic and seasonal distribution, demographic and clinical characteristics of lyme disease cases in canada. introduction lyme disease (ld), a multisystem infection that is manifested by progressive stages [1], is emerging in central and eastern provinces of canada due to northward expansion of the geographic range of ixodes scapularis, the main vector in these regions [2]. in 2004, approximately 40 human cases of ld were reported in canada. in 2009, ld disease became nationally notifiable, with provincial and territorial health departments reporting clinician-diagnosed cases to the public health agency of canada (phac). this study summarizes seven years (2009-2015) of national surveillance data for ld in canada. methods national lyme disease surveillance data is collected through two surveillance systems, the canadian national disease surveillance system (cndss) and the lyme disease enhanced surveillance system (ldes). the cndss collects only demographic data (age and sex), and information on episode date and case classification. the ldes system captures additional data, including: possible geographic location of infection (for both locally acquired and travel-related cases); clinical manifestations; and results of laboratory testing. nine provinces out of ten participate to ldes that means they provide a part of or all the data elements of this surveillance system. the 2009 national lyme disease case definition [3] that distinguishes confirmed and probable cases (table 1) is used to classify and report cases diagnosed by clinicians.this study describes the incidence over time, seasonal and geographic distribution, demographic and clinical characteristics of reported ld cases. logistic regression was used to explore variations among age groups, sex and year of reporting clinical manifestations to better understand potential demographic risk factors for the occurrence of ld. different models were used with as outcomes absence or presence of: erythema migrans (early lyme disease), neurologic and cardiac symptoms and multiple erythema, migrans (early disseminated lyme disease); and arthritis (late disseminated lyme disease). the most parsimonious multivariate models were sought by backward elimination of nonsignificant variables until all factors in the model were significant (p<0.05). results the number of reported ld cases increased more than six-fold, from 144 in 2009 to 917 in 2015, mainly due to an increase in infections acquired in canada. for the provinces participating into the ldes system, the month of illness onset for lyme disease cases acquired in canada was available for 2010 cases. most cases were reported during the summer months of june (20.7%), july (35.4%) and august (17.3%) (figure 1). an increase in incidence of ld was observed in provinces from manitoba eastwards (figure 2). this is consistent with our knowledge of range expansion of the tick vectors in this region. in the western provinces the incidence has remained low and stable. all cases reported by alberta, saskatchewan and newfoundland and labrador were acquired outside of the province, either elsewhere in canada or abroad. there was a bimodal distribution for ld by age with peaks at 5–9 and 45–74 years of age (figure 3). the most common presenting symptoms were a single erythema migrans rash (74.2%) and arthritis (35.7%) (figure 4). in the multivariate analysis for clinical manifestations, children aged 0–9 years had a greater number of cases reported as early ld (erythema migrans only) than patients aged 10–19 and 30–39 years (p<0.05). for early disseminated manifestations, young adults 20–29 years of age reported more neurologic manifestations, cardiac manifestations or multiple erythema migrans than the reference age group of 0–9 years (p<0.05). for late disseminated manifestations, children under 15 years of age were more frequently reported as having arthritis than other age groups. conclusions lyme disease incidence continues to increase in canada as does the geographic range of ticks that carry the ld bacteria. this increasing of ld incidence might also be due to changing in knowledge, attitudes, and practices of clinicians who diagnose the disease and or of the public health workers who collect and report the data. ongoing surveillance, preventive strategies as well as early disease recognition and treatment will continue to minimize the impact of ld in canada. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e409, 2019 isds 2019 conference abstracts acknowledgement the authors thank all the provincial and regional public health workers who collect and report data to the public health agency of canada. references 1. aguero-rosenfeld me, wang g, schwartz i, wormser gp. 2005. diagnosis of lyme borreliosis. clin microbiol rev. 18, 484-509. pubmed https://doi.org/10.1128/cmr.18.3.484-509.2005 2. ogden nh, koffi kj, pelcat y, lindsay lr. 2014. environmental risk from lyme disease in central and eastern canada: a summary of recent surveillance information. can commun dis rep. 40(5), 74-82. http://www.phacaspc.gc.ca/publicat/ccdr-rmtc/14vol40/dr-rm40-05/ assets/pdf/14vol40_05-eng.pdf. pubmed https://doi.org/10.14745/ccdr.v40i05a01 3. public health agency of canada. case definition for communicable diseases under national surveillance. ottawa: public health agency of canada; 2017. https://www.canada.ca/en/public-health/services/ reports-publications/canadacommunicable-disease-report-ccdr/ monthly-issue/2009-35/definitionscommunicable-diseases-nationalsurveillance/lyme-disease.html [accessed 2017 aug 17]. figure 1: month of lyme disease illness onset for locally-acquired infection: canada, 2009-2015 (n=2,010) http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16020686&dopt=abstract https://doi.org/10.1128/cmr.18.3.484-509.2005 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29769885&dopt=abstract https://doi.org/10.14745/ccdr.v40i05a01 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e409, 2019 isds 2019 conference abstracts figure 2: reported locations of lyme disease acquisition, canada, 2009–2015 figure 3: incidence of lyme disease by age group and sex, canada 2009-2015 (n=3,004) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e409, 2019 isds 2019 conference abstracts figure 4: percentage of clinical manifestations for lyme disease infections acquired in canada, 2009 -2015 (n=1,657) table 1: 2009 national lyme disease case definition confirmed case probable case clinical evidence of illness with laboratory confirmation: clinical evidence of illness without a history of residence in or visit to an endemic area but with laboratory evidence of infection: isolation of borrelia burgdorferi from an appropriate clinical specimen positive serologic test using the two-tier elisa and western blot criteria or detection of b. burgdorferi dna by pcr or clinician-observed erythema migrans without laboratory evidence but with history of residence in or visit to an endemic area or clinical evidence of illness with a history of residence in, or visit to, an endemic area and with laboratory evidence of infection, i.e., positive serologic test using the two-tier elisa and western blot criteria http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e323, 2019 isds 2019 conference abstracts active surveillance in the investigation and analysis of rvf in western and central uganda mary l. nanfuka1, 2, milton bahati1, 2, eugene arinaitwe1, 2 1 animal health, ministry of agriculture animal industry and fisheries, entebbe, entebbe, uganda, 2 national animal disease diagnostics and epidemiology center (naddec), entebbe, entebbe, uganda objective to detect presence of circulating rift valley fever virus (rvfv) in animals of western and central uganda following its confirmation in humans. to establish and communicate reliable information using the one health plat form significance: although in e. africa rvf was initially detected and known to be a disease endemic in kenya, the people in uganda were still hesitating whether the disease is already in existence. following its first detection in 2016 in humans there was need to carry out an investigation in the hot spot areas of the human infection to get the real picture and to inform the policy makers for informed decisions. introduction rift valley fiver is viral zoonotic disease which was investigated and reported in uganda in 2010 [1]. for some time now people are not aware whether the disease was still circulating or emerged in animals reared as a result of the inter country trade by the community of the cattle corridor in uganda, since the last reports in 1968 [2]. the increase in the number of disease outbreaks in some parts of central and western uganda from 2016 to date and the number of human patients investigated, diagnosed and confirmed with rvf by ministry of health (moh) under the one health program, has placed the disease to be among the top re emerging diseases in the country[3,4] and number 5 of the multisectoral prioritization of zoonotic diseases in uganda, 2017 under one health perspective [5]. methods rift valley fever was investigated in cattle, goats and sheep of gomba,mityana, kiboga and kiruhura in central and western uganda. this followed 2 people that had been confirmed with rvf in 2016 [1] samples were aseptically collected from hot places from 543 victim’s animals including those of the neighbouring areas covering the victims routes of movement plus those areas where people were still sick and where death had reportedly occurred. samples were then delivered to naddec laboratory from where tests were conducted. results samples were screened using a competition igg elisa, then igm elisa to capture the recently infected animals. the positive samples from the igm elisa were then confirmed using rt-pcr; 169/543 (31%) tested positive to igg screening elisa indicating exposure to rvf. the actual infection was found to be 13% (22/169) with igm elisa and 3/22 (13.6%) with rt-pcr. conclusions zoonotic diseases continue to be a public health burden to the people of uganda. considering some people’s behavior of eating the sick and dead animals, has posed a difficult situation to combat the ailment which has resulted in negative socioeconomic impacts, affecting the national policies that range from health security to control of diseases. uganda has however developed capacity to investigate, test and confirm rvf disease. since exposure was found in all animal species, detailed active surveillance plan and procedures have been set up to investigate any additional cases in animals to reduce chances of spread to humans and to cub international spread and also to determine the magnitude of exposure. acknowledgement i acknowledge my coauthors for the knowledge and skills added to this manuscript and the entire staff of the national animal disease diagnostics and epidemiology center for all the support granted to me in this work http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e323, 2019 isds 2019 conference abstracts references 1. nabukenya i, et al. 2016. investigation and response to rift valley fever and yellow fever outbreaks in humans in uganda. int j infect dis. 53, 129. 2. nyakarahuka l, et al. 2018. prevalence and risk factors of rift valley in humans and animals from kabale, 2016. plos negl trop dis. 12(5). doi:10.1371/journal.pntd.0006412. pubmed 3. wang lf, crameri g. emerging zoonotic viral diseases.rev sci tech int epiz.2014;33 4. institute of medicine (u.s.), committee on achieving sustainable global capacity for surveillance and response to emerging diseases of zoonotic origin, keusch g. sustaining global surveillance and response to emerging zoonotic diseases, 2009 5. munyua p, bitek a, osoro e, pieracci eg, muema j, et al. 2016. prioritization of zoonotic diseases in kenya,2015 [pubmed]. plos one. 11, e0161576. doi:10.1371/journal.pone.0161576. pubmed 6. sekamatte m, et al. 2018. multisectoral prioritization of zoonotic diseases in uganda, 2017: a one health perspective. plos one. 13(5), e0196799. pubmed table showing rvf positivity in the different animal species using elisa and rt-pcr district goats cattle sheep +igg elisa +igm elisa rt-pcr negative gomba 0 114 0 27 7 0 80 mityana 46 76 18 45 6 0 89 kiboga 20 80 51 40 4 2 105 kiruhura 55 63 20 57 5 1 75 121 goats, 333 cattle and 89 sheep, 169 tested positive, 22 tested positive and 3 were positive with rt-pcr. 349 tested negative. a total of 543 samples were collected for analysis http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29723189&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27557120&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29715287&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e244, 2019 isds 2019 conference abstracts forecasting hospital pneumonia admissions using influenza surveillance, climate and community data joshua wong1, hanley ho1, angela chow1, 2 1 department of clinical epidemiology,tan tock seng hospital, singapore, singapore, 2 saw swee hock school of public health, national university of singapore, singapore, singapore objective using the information that we have available, our primary objective is to explore if there was any cross-correlation between pneumonia admissions and hospital influenza positivity. we then aim to develop a data driven approach to forecast pneumonia admissions using data from our hospital’s weekly surveillance. we also attempted using external sources of information such as national infectious diseases notifications and climate data to see if they were useful for our model. introduction influenza peaks around june and december in singapore every year. facing an ageing population, hospitals in singapore have been constantly reaching maximum bed occupancy. the ability to be able to make early decisions during peak periods is important. tan tock seng hospital is the second largest adult acute care general hospital in singapore. pneumonia-related emergency department (ed) admissions are a huge burden to the hospital's resources. the number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population’s immunity to the circulating strain. while many pneumonia cases are of unknown origin, they tend to mirror the influenza seasons very closely. methods we used data from epidemiological week (e-week) 1 of 2013 to e-week 34 of august 2017 to train our model, with the next 52 weeks (e-week of 35 of 2017 to e-week 34 of 2018) being used as validation cohort. pneumonia and influenza data were obtained from our hospital’s weekly surveillance. national level acute upper respiratory illness (auri) was obtained from ministry of health’s (moh) weekly infectious diseases bulletin. climate data were obtained from the national environment agency’s website. daily rainfall, temperature and wind data from the s20 satellite station were used. automatic autoregressive (a-arima), nonseasonal and seasonal vector autoregressive models (var) were used to either analyse the univariate pneumonia trends or simultaneously model pneumonia, influenza, auri notification and climatic data. granger-causality tests were performed to check if these variables were causal of pneumonia admissions. as most of the seasonal variation are seen in older patients, stratified analysis were performed on those that were below and above 65 years old. forecasts were calculated up to 3 weeks in advance. mean absolute error (mae), mean absolute percentage error (mape), and root mean squared error (rmse) were used to validate the model performance. these performance metrics were applied on 3-week ahead forecasts comparing a-arima, var, and seasonal-adjusted var. results figure 1 shows that both influenza and pneumonia admissions follow similar trends. we see that the number of influenza cases have reduced as compared to the previous years. the number hospital influenza cases and the number of auri cases nationwide are strongly cross-correlated with pneumonia admissions. granger-causality tests confirmed the directionality of the relationships (p <0.01). climate factors do not strongly affect the number of pneumonia admissions. (fig 2) unsurprisingly, the a-arima model showed that the 1-day forecasts were most accurate (mae: 7.0; mape: 12.7; rmse: 8.7 for elderly subgroup). however, the 3-day ahead forecasts were only slightly less precise (mae: 7.2 ; mape: 13.2; rmse: 9 for elderly subgroup). testing for significant lags using the various information criteria suggested that a lag3 model should be used. the non-seasonal and seasonal var models showed that historical pneumonia admissions and influenza positivity was the best model. the mape for all 3 models hovered between 12-13%, with the a-arima model performing slightly better. this is not surprising as the a-arima takes the latest information at hand to derive the best model. accounting for seasonality allowed better precision as compared to the nonseasonal var but was not better as compared to the a-arima model. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e244, 2019 isds 2019 conference abstracts conclusions hospital surveillance data are the most useful for developing forecast models for hospital pneumonia admissions. climate data were likely not to be useful as singapore does not experience much variaton in weather throughout the year. pneumonia peaks do not follow necessarily fall on the same week every season. therefore, both the autoregressive and seasonal-adjusted vector autoregressive models can be useful complements to each other for forecasting pneumonia admissions. fig 1: weekly trend for hospital influenza and pneumonia admissions fig 2: cross-correlations between pneumonia admissions and various factors http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e244, 2019 isds 2019 conference abstracts fig 3: forecasted values for the a-arima model table 1: stratified a-arima lag1 to lag4 models. prefix 'e' represent elderly; prefix 'a' represent adults. table 2: comparison of performance of the 3 models e_t-1 e_t-2 e_t-3 e_t-4 a_t-1 a_t-2 a_t-3 a_t-4 mae 7 7.2 7.2 7.7 3.4 3.4 3.7 3.7 mape 12.7 13.2 13.2 14.2 19.6 20 21.3 21.3 rmse 8.7 9.1 9 9.6 4.4 4.4 4.7 4.7 a-arima var seasonal var mae 9.2 9.8 9.8 mape 12.8 12.8 12.8 rmse 11.1 12.1 11.9 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e256, 2019 isds 2019 conference abstracts optimization of linkage between north carolina ems and ed data: ems naloxone cases jonathan fix1, dennis falls2, scott proescholdbell3, amy ising2, tony fernandez4, 2, anna e. waller2 1 epidemiology, unc chapel hill, carrboro, north carolina, united states, 2 emergency medicine, unc-ch, chapel hill, north carolina, united states, 3 north carolina division of public health, raleigh, north carolina, united states, 4 ems performance improvement center, chapel hill, north carolina, united states objective to improve linkage between north carolina’s emergency medical services (ems) and emergency department (ed) data using an iterative, deterministic approach. introduction the opioid overdose crisis has rapidly expanded in north carolina (nc), paralleling the epidemic across the united states. the number of opioid overdose deaths in nc has increased by nearly 40% each year since 2015 [1]. critical to preventing overdose deaths is increasing access to the life-saving drug naloxone, which can reverse overdose symptoms and progression. over 700 ems agencies across nc respond to over 1,000,000 calls each year; naloxone administration was documented in over 15,000 calls in 2017 [2]. linking ems encounters with naloxone administration to the corresponding ed visit assists in understanding the health outcomes of these patients. however, less than 66% of nc ems records with naloxone administration in 2017 were successfully linked to an ed visit record. this study explored methods to improve ems and ed data linkage, using a multistage process to maximize the number of correctly linked records while avoiding false linkages. methods ems data were provided by the ems performance improvement center [2] (emspic); ed data were provided by nc detect [3]. optimization of current ems/ed linkage methods began by extracting a non-random subset of ems encounters with naloxone administration between january 1, 2017 and november 30, 2017 from 12 nc counties, representing easte rn, central and western regions and the overall linkage performance of the larger dataset. records were eligible for linkage if ems recorded that the patient was “treated and transported” to the ed. all records in the subset were manually reviewed in nc detect to identify corresponding ed visit records. this produced a “gold standard” dataset of linked ems/ed records. to evaluate linkage performance, we first identified all records eligible for linkage. any ems transport to either a hospital outside of nc or an nc ed not included in nc detect (e.g., military, va and tribal hospitals) was excluded. since existing linkage is performed daily and both ems and ed records are updated over time to correct errors and missing data, existing linkage method s were re-run on updated data to evaluate the improvement provided solely by linking the most upto-date data. unlinked ems records for which the encounter was an inter-facility transfer, transfer to helicopter transport, or the patient died during transfer were deemed ineligible for linkage, as these patients likely either bypassed or never made it to the ed. to initially improve linkage quality, we updated the mapping file of ems/ed destinations. an exact destination match was required for linkage and the ems destination variable is recorded as free-text; thus, all variations of a destination name and spelling were identified and mapped to a standardized name. the maximum time difference between ems drop-off and ed intake was then allowed to exceed 60 minutes, in iterations of 90, 120, 240, and 360 minutes. with each iteration, we compared the linked ids with the gold standard dataset to identify false links. finally, a multistage linkage process was applied. first, deterministic linkage was run requiring exact matche s for date of birth (dob), sex/gender, and destination, and up to 360-minute difference between ems/ed times. the unlinked records were then http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e256, 2019 isds 2019 conference abstracts processed a second time, requiring exact matches for sex/gender and destination, dob to be within +/ 10 days or +/1 year, and up to 60-minute difference between ems/ed times. this multistage process was then run for all 2017 ems encounters with naloxone administration to ensure that the new method was not over fit to the data subset. potential bias in the linkage was assessed by comparing the distributions of age (mean and median) and gender (% male) among the linked and unlinked records in each dataset.vstatistical analyses were completed using sas 9.4 (cary, nc). linkage was executed using sql server. results between 1/1/2017 and 11/30/2017, there were 14,793 ems encounters with documented naloxone administration. of these, 12,089 (81.7%) were recorded as “treated and transported”; 1,906 ems encounters were included in the 12-county subset. the average age of patients was 45.1 years among all naloxone encounters and 45.2 years in the subset. 57.5% of all encounters were male; 58.1% were male in the subset. after removing ems transports to non-nc or non-nc detect hospitals, the existing subset linkage was 61.8% (1,154/1,866). this included 38 (2.0%) false positives, apparently caused by ed records purged since this linkage was conducted. when the existing methods were run against the most current data, linkage improved to 72.2% (1,389/1,866), reflecting an absolute improvement of 10.4% by simply using updated data. only 1 (0.05%) false positive was identified in this process. following removal of unlinked inter-facility transfers, deaths during ems transport, and transfers to helicopters, the records eligible for linkage dropped to 1,781. linkage improved to 79.5% (1,417/1,781) when hospital names were standardized. linkage using standardized hospital names and relaxing the ems/ed time difference performed at the following levels: 82.3% at 90 minutes, 83.3% at 120 minutes, 87.9% at 240 minutes, and 89.4% at 360 minutes. even when using the most relaxed time difference (+/ 360 minutes), only one false positive was identified, the same produced during initial linkage at +/60 minutes. the final multistage method produced linkage of 91.0% (1,620/1,781), with no additional false positives. applying the initial methods to the statewide ems dataset produced linkage of 64.8%. the multistage linkage process performed nearly identically on statewide data as observed for the subset, at 91.1%. for statewide data, the age of linked patients was younger (mean = 44.7 years [sd = 18.4], median = 41.0 years) than that of unlinked patients (mean = 48.0 years [sd = 19.3], median = 47.0 years). additionally, linked patients were more likely to be male (58.1%) when compared to unlinked patients (54.2%). conclusions high quality linkage between ems and ed records is essential for research and public health surveillance examining health outcomes. using a multistage process, we improved the linkage of ems encounters with documented naloxone administration to ed visits in north carolina in 2017 from 64.8% to 91.1%, with less than 0.05% false positive rate. this improved linkage will facilitate future analyses of relationships between exposures during ems encounters and outcomes experienced in hospitals. future research should evaluate the generalizability of this linkage methodology to all ems records, not just those with naloxone administration, as well as to pre-2017 data. implementation of probabilistic linkage or machine learning as a final stage in a multistage process may further improve linkage outcomes, overcoming missing data or unpredictable errors in the data. acknowledgement funding provided by the cdc national center for injury prevention and control enhanced state opioid overdose surveillance (esoos) grant to the nc division of public health (grant 5nu17ce924902). references 1. kansagra sm, cohen mk. 2018. the opioid epidemic in nc: progress, challenges, and opportunities. n c med j. 79(3), 157-62. pubmed https://doi.org/10.18043/ncm.79.3.157 2. ems performance improvement center. about emspic. https://www.emspic.org/about. 3. nc detect. background. http://ncdetect.org/background/ http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29735617&dopt=abstract https://doi.org/10.18043/ncm.79.3.157 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e232, 2019 isds 2019 conference abstracts comparing cerebral palsy surveillance definition to icd codes and written diagnoses sarabeth mathis, matthew maenner, lucinda england, fatuma abdirizak, kim van naarden braun, deborah christensen, nicole dowling, maureen durkin4, robert fitzgerald5, russell kirby6, laura schieve1, marshalyn yeargin-allsopp1, patricia dietz1 1 national center on birth defects & developmental disabilities, centers for disease control and prevention, atlanta, georgia, united states, 2 oak ridge institute for science and education, oak ridge, tennessee, united states, 3 hackensack meridian health, edison, georgia, united states, 4 university of w isconsinmadison, madison, wisconsin, united states, 5 w ashington university in st louis, st louis, missouri, united states, 6 university of south florida, tampa, florida, united states objective to compare prevalence estimates obtained by the addm cerebral palsy surveillance method to other administrative or diagnostic indications of cerebral palsy. introduction cerebral palsy (cp) is the most common cause of motor disability in children. cp registries often rely on administrative data such as cp diagnoses or international classification of diseases (icd) codes indicative of cp. however, little is known about the validity of these indicators. we calculated sensitivity, specificity, positive and negative predictive values of cp icd-9 codes and cp diagnoses compared to a “gold standard” cp classification based on detailed medical and education record review. methods this sample includes 50,332 8-year-olds living in four us sites (32 counties in alabama, 5 counties in georgia, 10 counties in wisconsin, and 5 counties in missouri) in 2006, 2008, and 2010. the autism and developmental disabilities monitoring (addm) network reviewed medical and education records for these children as part of the us centers for disease control and prevention population-based surveillance of developmental disabilities. all of these children received special education services or were assigned one or more icd-9 codes associated with a variety of developmental disabilities by community medical providers. medical and education records were reviewed by trained staff; if the records contained cp diagnoses or motor findings indicative of cp, detailed clinical information was abstracted for additional review by trained clinicians who determined whether the child met the cp case definition based on all information available. abstracted records were also reviewed for evidence of known motor disorders or genetic conditions that disqualified a child from being a cp case, such as inborn error of metabolism or muscular dystrophy. trained clinicians reviewed and excluded children with confirmed disqualifying conditions. we calculated cp prevalence, sensitivity, specificity, and positive and negative predictive values for three different method s used to identify cases, using the addm surveillance case identification as the gold standard. these methods include: 1) icd-9 codes for cp (342–344); 2) a cp diagnosis written in the medical or education records, excluding children with disqualifying conditions, and 3) both icd-9 codes (342–344) and a cp diagnosis written in the medical or education records, excluding children with disqualifying conditions. in an attempt to avoid requiring record review for method 1, we considered using icd-9 codes for disqualifying conditions. however, we found that icd codes for these conditions did not correlate well with disqualifying conditions identified in medical record reviews; therefore disqualifying conditions were not considered for method 1. methods 2 and 3 did require review of medical records for disqualifying conditions and for a written cp diagnosis, but overall were less extensive than traditional addm surveillance methods. in order to determine the impact of different classification criteria on how and which children are captured by surveillance methods, we compared demographic and other characteristics of all children who met the addm surveillance case definition. we compared children who would and would not be classified as cp cases using method 3. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e232, 2019 isds 2019 conference abstracts results out of the total 50,332 children, 1294 met the addm surveillance case definition, 2201 had cp icd codes (method 1), 1502 had a written cp diagnosis and no disqualifying conditions (method 2), and 1345 had both cp icd codes and a written diagnosis and no disqualifying conditions (method 3). each study year, between 32—48% of abstracted children were excluded due to disqualifying conditions found in medical records. the addm network gold standard cp prevalence was 3.3 per 1000 in 2006, 3.1 per 1000 in 2008, and 2.9 per 1000 in 2010. for method 1, sensitivity was 90.0%, specificity was 97.4%, positive predictive value was 51.6% and negative predictive value was 99.7%. method 1 prevalence estimates were 5.3 per 1000 in 2006, 4.6 per 1000 in 2008, and 4.6 per 1000 in 2010. for method 2, sensitivity was 98.1%, specificity was 88.4%, ppv was 84.5% and npv was 98.4% compared to the addm network definition. method 2 estimated prevalence was 3.9 per 1000 for 2006, 3.6 per 1000 for 2008, and 3.2 per 1000 for 2010. for method 3, sensitivity was 89.6%, specificity was 99.5%, ppv was 84.3% and npv was 99.7%. method 3 estimated prevalence was 3.5 per 1000 for 2006, 3.2 per 1000 for 2008, and 2.8 per 1000 for 2010. using pearson’s chi-square tests, we compared demographic and other characteristics of addm network cp case children who also met method 3 case definition (n = 1134) and children who met the addm network cp definition but not method 3 case definition (n = 160). demographic information was not different between these children. addm network cp case children who did not meet method 3 criteria were significantly less likely to require a wheelchair for mobility than children who met method 3 criteria (4.4% versus 27.4%, p < .05). conclusions relying on icd-9 codes without excluding disqualifying conditions to identify cp cases (method 1) resulted in high sensitivity (90%), but low positive predictive value as well as an overestimated cp prevalence when compared with the addm network method. use of a written diagnosis and excluding disqualifying conditions (method 2) resulted in very high sensitivity (98%), with fewer false positives but overestimated cp prevalence compared to the addm estimate. in contrast, using both cp icd codes and a written cp diagnosis and excluding disqualifying conditions (method 3) yielded prevalence estimates similar to addm network cp estimates; this approach also had high sensitivity, specificity, and ppv. methods 2 and 3 still require manual record revi ew, unlike method 1. for method 2, reviewers would need to review all records for cp and disqualifying conditions. method 3 only requires review of records with cp icd codes, comprising 4% of all records currently reviewed. method 3 would fail to capture children without both a written diagnosis and icd codes; and this approach may be less sensitive for detecti ng cp among children with less severe motor impairment than using the gold standard. using icd codes and written cp diagnoses contained in medical and education records combined with a limited medical record review to identify disqualifying conditions could lower operational costs of cp surveillance while preserving accurate prevalence estimates compared with the more labor-intensive processes currently used. further evaluation is needed to determine if improvements in efficiency are worth potential trade-offs in the data collected by the system. of particular importance is whether the approach could capture all the necessary indicators that are important to stakeholders. additional analyses would also need to evaluate whether the surveillance methods affect other findings, such as previously observed disparities, co-occurring conditions, or cp severity. acknowledgement this project was supported in part by an appointment to the research participation program at the centers for disease control and prevention administered by the oak ridge institute for science and education through an interagency agreement between the u.s. department of energy and the centers for disease control and prevention http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e381, 2019 isds 2019 conference abstracts effect of climate on enteric fever incidence in ahmedabad, india veena iyer1, ayushi sharma1, susanna cottagiri abraham1, divya nair h1, bhavin solanki2, dileep mavalankar1 1 indian institute of public health, gandhinagar, gandhinagar, india, 2 ahmedabad municipal corporation, ahmedabad, india objective this study is an attempt to explore the relationship of ef incidence with climate variables and enso events in the seventh most populous city in india. introduction enteric fever (ef) is a grave systemic infection, which has been controlled quite effectively in developed countries, but con tinues to be a grave public health concern for india. environmental drivers such as rainfall, temperature, relative humidity and el ni ñosouthern oscillations (enso) are known to influence the transmission of salmonella typhi and paratyphi. india possesses the largest population burden of ef, yet very few studies have explored its climatic associations. methods we analyzed address-confirmed widal positive, monthly ef cases reported by ahmedabad municipal corporation and local climate data recorded by the meteorology office from 1986-2017. ef incidence trend in the city was cross validated using ef monthly reports from one large public hospital and from private reports. we also collected data for temperature, humidity and rainfall from meteorological centre of ahmedabad, population data from census department, and identified iod and enso events from national oceanic and atmospheric administration (noaa) for the same period. results our study recorded 29,808 widal positive cases for 32 years. ef incidence trend over last 32 years showed a decadal pattern. initial study period (19861995) showed higher and erratic case rates, while cases were more restrained during the last decade ( 19952005), although a steady rise is persisting. we also observed a consistent rise in ef cases in the last 8 years ( fig 1). analysis of annual pattern of monthly-normalized ef cases revealed a bimodal distribution of peaks, in the month of june and september. peaks of ef cases showed a lag and lead of one month with tmax and tmin. the first ef peak in june lagged the tmax peak in may by a month and the second ef peak in september led the tmax peak in october by a month. the second peak of ef cases in september coincided with the peak humidity in the same month. the dip between the two ef peaks coincided with maximum rainfall peak in july (fig 2 a,b,c). spearman’s rank correlation showed a small positive but significant correlation between monthly ef case rates and climate variables (tab 1). a poisson model showed significant but weak association between ef incidence and all climate variables tmin, rh and rainfall. in our study t max had the strongest association with ef cases, wherein an increase of one case was accompanied by a 0.1°c increase of the tmax (tab 2). over the 32 years, there were 4 strong and 4 moderate el nino years, 5 strong and 2 moderate la nina years and 17 neutral yea rs. figure 3 shows that except for the two el nino years which coincided with positive iod events, the remaining six el nino years experienced a subdued rainfall. six out of seven la nina years experienced high rainfall. the early el nino events of 1986, 1 987, 1991 and the most recent one of 2015 exhibit a trend of low rainfall and high cases. this trend is diluted in the middle el nino years, 1994, 1997, 2002 and 2009 showing high and low rainfall and relatively lesser annual case rates. although the highest case rate was recorded in a la nina year 59/100,000 in 1988, average case rates were highest for el nino years (25.5), lower for la nina (20.5) and lowest for neutral years (17.6). however, we were unable to establish any statistical significance between average ef case rates during each of these periods. a spearman correlation between ef cases and rainfall was small but significant for el nino (rs= 0.35, p=0.001) and for neutral years (rs= 0.20, p= 0.004), but not for la nina years. a repeated measures anova http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e381, 2019 isds 2019 conference abstracts analysis showed no significant difference in average ef cases during the three enso categories, however visual profile plot (fig 4) of estimated marginal monthly means over the year showed distinct differences – early rise and peaking of cases in the el nino and la nina years, and a much more restrained rise without conspicuous peaks in neutral years. the 2 positive iod events that occurred along with the strong el nino events in 1994 and 1997 may have led to lowering of case rates during el nino years, and thus the lack of a significant increase in ef incidence rates . but this could also be due to the fact that our analysis, unlike a time series analysis, has used an el nino year as a variable, which does not accommodate the fact that el nino does not run by a calendar year. we were unable to conduct a geospatial analysis which may have better correlated our data with temperature and rainfall intensity during the three enso phases in our region. uneven development of urban infrastructure would also influence rates of illness. furthermore, the cases reported to the epidemic cell were based on slide and/or tube widal positive tests which is considered a poor diagnostic test. despite these numerous and at times opposing factors influencing trends of ef, the upswing in case incidence rate early in the el nino and la nina years, when the weather is still balmy and water shortages haven’t yet begun in the city, merits deeper investigation. conclusions future control strategies for ef need to consider the influence of local environment, geographical climate variation and seas onal patterns. this relationship between enso events and ef cases needs to be investigated with larger and longer data sets from different cities and towns in the sub-continent. one of the limitation of our study is we need longer and larger, spatially distributed dataset of ef incidences to associate it better with climate phenomena. figure 1. inter-annual variability of ef cases normalized by per 100,000 population in the city of ahmedabad from 1986-2017. figure 2. monthly variability of ef case rates with (a) maximum (tmax) and minimum temperatures (tmin), (b) relative humidity (rh) at 8.30 and 17.30 hrs, and (c) accumulated rainfall, in ahmedabad http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e381, 2019 isds 2019 conference abstracts figure 3. trend of enteric fever incidence by el nino, neutral and la nina years with accumulated annual rainfall over the study period (1986-2017) figure 4. profile plot of estimated marginal means of (a) monthly ef incidence (b) ef incidence in jjas window (june, july, august and september) across el nino, la nina and neutral years table 1. spearman rank correlation (rs) between monthly average ef normalized case rates and monthly averages of weather variables 1986-2017 weather variables rs pvalue (0.01 level) tmax .279 .000 tmin .341 .000 rh at 8:30 hrs .186 .000 rh at 17:30 hrs .134 .000 accumulated rainfall .188 .000 table 2. association of climate variables and ef cases in a association of climate variables and ef cases in a poisson model dependent variableef cases irr std error p-value 95% c.i. tmax 1.1061 .00637 0.000 [1.09 – 1.11] tmin 0.9693 .00459 0.000 [0.96 -0.98] http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e381, 2019 isds 2019 conference abstracts rh at 08:30 hours 1.0089 .00139 0.000 [1.01 -1.01] rh at 17:30 hours 1.0087 .00110 0.000 [1.01 -1.01] av. accumulated rainfall 1.0002 .00006 0.013 [1.00 -1.00] lr chi2(5) = 2086.49; prob > chi2 = 0.0000; log likelihood = -8937.7701; pseudo r2 = 0.1045 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e395, 2019 isds 2019 conference abstracts legislation and policy changes for tuberculosis surveillance in mongolia: a qualitative analysis oyunchimeg erdenee, hiroshi koyama public health, graduate school of medicine, gunma university, maebashi, gunma, japan objective in order to track progress towards tb goals, we investigated the legal framework for disease surveillance, specifically policy changes for tb surveillance in mongolia during the mdgs and the sdgs era. introduction mongolia is one of countries in the who western pacific region with a high tb burden. the national stop tb strategy 20102015 implemented and developed strong surveillance and response system in the country. however, new tb incidence and deaths have not decreased significantly. political commitment is critical for effective tb surveillance and that commitment can be demonstrated by a country’s legal framework, which governs the practice of prevention and control. therefore, this study is aimed at investigating the legal underpinnings for disease surveillance to help identify what policy changes have occurred in tuberculosis surveillance. methods we conducted a literature review that included government strategy, programme guidelines and procedures, to examine the overall disease surveillance system in mongolia, and used a framework analysis to investigate operation of the tb surveillance system (cdc 2001 guideline). first, nine of core functions and six of support functions for the tb surveillance system were placed on the y axis, and the national tb strategies, programme, guidelines and procedures were placed on the x axis. next, the strategies, programme, guidelines and procedures were unpacked and allocated to cells based on whether they were consistent with the essential functions of the surveillance system. these data points were then used to develop a matrix to enable understanding of correspondence and changes between the legal documents during mdgs to sdgs. results result 1. mongolia has an emerging disease surveillance and response unit and a national centre for communicable disease responsible for implementing the international health regulations in the country. the legal framework for the surveillance system was updated regularly and overall, 11 legal instruments were identified. result 2. however, currently there is no specific national tb strategy since 2015. recently, national programme of prevention and control on communicable disease 2017-2020 and guidelines for tb care 2017 were approved. the result of framework analysis shows that during mdgs era, the legal documents had weaknesses that were related to “feedback” from the core and “training and resources” from the support functions. on other hand, the weaknesses of the legal documents for sdgs were related to “outbreak preparedness and response” from the core and “training and supervision” from the support functions. conclusions there is an urgency to update the legal framework to enable a comprehensive strategy specifically for tb surveillance nationwide. also, additional studies should be done continuously and should incorporate other parts of the assessment, including co-ordination, laboratories,to help determine the factors that influence the overall structure of tuberculosis surveillance in the country. legal instruments for disease surveillance in mongolia. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e395, 2019 isds 2019 conference abstracts number focus legal instrument type of document year of document purpose 1 cd strengthening prevention and control of hospital acquired infections ministerial order #336 1997 first complete legal document to control and reduce hospital acquired infection in mongolia 2 cd national programme for communicable diseases government resolution #129 2002 mid-term directions for hospital acquired infection prevention and control with estblishment of sentinel surveillance system with improved laboratory-based monitoring. 3 cd national programme on prevention and control for communicable diseases (2017-2021) government resolution #11 2017 (updated version of 2002) reduce the spread of infectious diseases by strengthening the capacity of multisectors to strengthen surveillance, prevention and mitigation of epidemics, and to provide flexible, quality, accessible and prompt response to infectious diseases. 4 cd strengthening prevention and control of hospital acquired infections ministerial order #85 2008 ministry of health replaced the previous infection control guidelines with intention of moving closer towards international standards 5 cd emerging diseases and public health emergencies (2012 2016) ministerial order # 2012 aim is designed to strenghten preparedness planning, prevention, early detection and rapid response to emerging diseases and other public health emergencies 6 ncd national programme for non-communicable diseases government resolution # 2005 strengthen ncd prevention, screening and risk management by population-based prevention systems and participation and cooperation of organizations, communities, the international community 7 ncd national programme for non-communicable diseases government resolution # 2013 (updated version of 2005) strengthen ncd prevention, screening and risk management by population-based prevention systems and participation and cooperation of organizations, communities, the international community, 8 ncd national programme for non-communicable diseases government resolution #289 2017 (updated version of 2013) strengthen ncd prevention, screening and risk management by population-based prevention systems and participation and cooperation of organizations, communities, the international community, 9 ncd cancer registration and surveillance ministerial order #431 2014 national programme for cancer prevention, control, and intervention through gathering analyzing, sharing, informing about new incidence or deaths of cancer 10 gener al state policy on public health parliament statement #81 2001 the state policy on public health is to protect and promote the health of the population by ensuring the harmony of nature, human beings and society, to create favorable conditions for living and working in healthy and safe environment. 11 gener al health law law 2011 the purpose of this law is to define the state http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e395, 2019 isds 2019 conference abstracts framework analysis for tb surveillance surveillance system functions (cdc 2001) indicator of the government documents mdg era reflectio n of the functions for mdgs sdg era reflection of functions for sdgs 6. combat hiv/aids, malaria and other diseaseshalt and begin to reverse the incidence of malaria and other major diseases. goal 3.3 by 2030, end the epidemics of aids, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases (2000-2015) (2015-2030) i.core functions national programm e for combating communi cable disease national strategy on tb (2010-2015) criteria results national programme of prevention and control on communicable disease guideline for tb care / minister order a/306 criteria results (20022010) (2017-2020) (2017)) 1.case detection purpose to reduce the prevalence and mortality of tuberculosi s by 2010 by introducing a direct short-term treatment at all levels of health services eliminate tb in mongolia defined as an incidence rate of fewer than 1 case per 1,000,000 population yes combat the spread of communicable disease prevention and response, to strengthen intersectoral cooperation and cooperation, and to establish a healthy behavior for infectious diseases. 1.management and organization of tuberculosis care 2.detection, diagnosis and treatment for drug sensitive tb 3.detection, diagnosis and treatment for drug resistant tb 4.detection, diagnosis and treatment for coinfection of tb and hiv 5.routine detection and control of tb contacts 6.guidelines for tb control and prevention 7.management guideline for supply of essential products for tb care yes 2.case confirmation yes 8.procedures for registration and reporting of tuberculosis yes 3.case registration yes yes 4.case reporting yes yes http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e395, 2019 isds 2019 conference abstracts 5.data management goal to increase the level of verification of diagnosis of pulmonary tb up to 75 percent; by 2015, reduce tb prevalence to 154 per 100,000 by 2015, reduce tb mortality to 15 per 100,000 yes by 2020, reduce tb prevalence to 158.9 per 100,000 by 2020, reduce tb mortality to 6.5 per 100,000 yes 6.data analysis to increase level of recovery for pulmonary tb incidence up to 90 percent yes yes 7.outbreak preparedness yes no 8.outbreak response yes no 9.feedback no yes ii.support functions 1.guidelines objectives subprogram on tuberculos is control and prevention 1.strengthen human resources, organization, and management of the mongolia tb programme 2.early detection and improve quality of dots services. 3.early detection and timely, appropriate treatment of multidrug resistant tb (mdr-tb). 4.overcome stigma and discrimination 5.ensure equitable access to quality tb services for all people yes 1.stabilize active screening of tuberculosis in the community; 2.introduce new techniques and technologies for tb diagnosis and treatment; 3.provide diagnosis and treatment of tuberculosis with continuous medicines, reagents and test kits; 4.increase the capacity of doctors, specialists, and human resources to provide health care services in yes 2.laboratory capacity yes yes http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e395, 2019 isds 2019 conference abstracts 3.supervision strategies and activities 1.1 advocacy to improve political commitment and development of supportive policy environment, 1.2 strengthening information, monitoring and evaluation system, 1.3 strengthening human capacity, yes tuberculosis, and create conditions for them to maintain their employment; 5.strengthen tb surveillance system and expand research and research. 6.organize advocacy work for policy makers and decision makers at the national and local levels to reduce tuberculosis;involve governmental and non-governmental organizations in social and psychological support for clients; 7.increase access to tb services through expanding community-based activities; improving the prevention and control of infectious diseases of the health organization and creating a client-friendly environment; 8.collaborate with the media to intensify the activities of providing health education to citizens and strengthening the right knowledge and attitudes. no 4.training 2.1 early detection and treatment through strengthening laboratory services and other means 2.2 support patients through treatment 2.3 strengthening tb drug management 2.4 engagement of nonntp providers in tb control (publicprivate mix dots) 2.5 improving coordination of tb/hiv collaborative activities 3.1 expand programmatic management of mdr-tb 4.1 behavior change communication 5.1 improve access to dots in peripheral areas 5.2 expansion of services for vulnerable populations no no 5.resources no yes http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e350, 2019 isds 2019 conference abstracts indigenous epidemiology: identifying health disparities and health priorities adrian dominguez, abigail echo-hawk, kelsey liu urban indian health institute, seattle, w ashington, united states objective to create an interactive, user friendly platform for partners and data users to increase awareness around relevant health disparities and strengths within the urban ai/an community. introduction historically, there has been a lack of data available to assess the health and well -being of urban american indian/alaska natives (ai/ans) in the united states. furthermore, there has been limited data showing the increasing disparities that exists between urban ai/ans and non-hispanic whites (nhw). organizations looking to address these disparities have limited resources and mechanisms to address this rising problem [1]. in 2017, urban indian health institute (uhi) released a series of community health profiles (chps) detailing the health status of urban ai/an communities to provide detailed information to assist in program planning, grant writing and advocacy through increased documentation of current health disparities faced by urban ai/an populations. methods data from 2010-2014 from several national data sources was analyzed using statase version 13 or sas version 9.4. using tableau 10.1 this data was translated into an interactive dashboard to provide data on demographics, social determinants of health, mortality, sexually transmitted infections, maternal and child health, substance use, and mental health. these indicators were selected based off of healthy people 2020 goals and examined disparities that exist between urban native populations compared to nhw populations in the same areas. by creating an online data tool, the dashboard is able to provide information and data regarding descriptive statistics on social and health disparities, and can be used to assist public health professionals, community mem bers, and ai/an organizations with program planning and interventions. results key findings from the results showed that urban ai/an people continue to face significant socio-economic disparities when compared to non-hispanic whites, for example more than twice as many urban ai/ans over 16 were unemployed between 20102014 when compared to their nhw counterparts (15.8% vs. 7.4%). however, urban ai/ans experience significantly lower rates of suicide when compared to nhw communities. additionally, although urban ai/an women gave birth at younger ages on average when compared to nhw women of reproductive age, they were significantly less likely to deliver by cesarean section. lastly, significantly fewer urban ai/ans reported using alcohol in the past month when compared to nhws (44% vs. 60%). conclusions this analysis of national surveillance data highlights the strengths of the urban ai/an community around suicide and alcohol use in the past month and point to areas for improvement. by translating these results into a tableau dashboard, this data is mor e user friendly and can be used to support programs in identifying health priorities. acknowledgement this project was supported by the indian health service via funding through grants (entitled “epidemiology program for american indian/ alaska native tribes and urban indian communities”). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e350, 2019 isds 2019 conference abstracts references 1. ncai policy research center. retrieved july 23, 2018, from http://www.ncai.org/policy-researchcenter/research-data/data http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e423, 2019 isds 2019 conference abstracts an assessment of overdose surveillance at a local public health department samir t. parmar, brittany k. yarnell epidemiology, marion county public health department, indianapolis, indiana, united states objective to assess the data sources used to monitor overdose events in marion county and improve community overdose surveillance. introduction mirroring public health response to infectious disease outbreaks, many public health departments are taking an outbreak management approach to respond to drug overdose surges [1-3]. the marion county public health department (mcphd) has developed an overdose response plan (orp) integrating drug overdose surveillance and community stakeholder response strategies. effective drug overdose surveillance requires accurate and reliable data streams. this work assessed data sources utilized for county overdose surveillance and provided recommendations to improve overdose surveillance. methods data sources utilized as of september 2018 for opioid overdose surveillance in marion county were assessed on utilization history by epidemiologists. general recommendations to improve overdose surveillance were created based on the findings. the three primary sources were emergency department data, ambulance run data, and death certificate data. secondary sources included indiana poison center (ipc) and toxicology data. general recommendations were generated based on challenges/solutions encountered and good practices observed from other health departments [4,5]. results the assessment of data sources and utilization showed variation of data entry at the hospital level, limited identifiers in some cases, and varying timeliness ranges which may limit combined use of many data sources. the emergency department data source showed particular variation in data entry, limited unique identifier information), and no incident location information which impedes geographical surveillance. periodic data checks by the ambulance service data holder appears to drastically increase data quality. intermittent data feed drops from specific emergency departments also interfered with effective surveillance. recommendations were generated based on lessons learned during successful partnerships with indianapolis emergency medical services, ipc, and emergency departments and challenges encountered during overdose surveillance work (figure 1). in application of the strategy, the mcphd is interested in linking data and looking for other ways to improve our overdose response to get a fuller picture of what is happening with overdoses, so we applied the steps in figure one to find areas of improvement. we found that limited identifiers and incomplete fields are our biggest challenge to linking datasets, so to combat these gaps we identified sources that have the necessary fields of interest and have been working with others to improve the data quality. additionally, data sources will be evaluated on experiences with three categories: completeness in data fields, timeliness of data delivery, and consistency of data feed. data quality measures were developed for completeness by fields present per record, timeliness by lag time from time added to time of event, and consistency by record counts per facility over time. we also recognized that meeting with partners is necessary to share how we are using the data and additional datasets that we might use in the future. additionally we have been meeting with academic researchers so that we can expand our analyses to identify other issues related to overdoses. finally, in order to make a difference in marion county we are applying our findings to our outreach and interventions to hopefully prevent more overdoses and deaths. future plans include data partnerships include police drug arrest data, fire department naloxone administration data, prescription drug monitoring data, medicaid claims data, and health information exchange overdose data. future research partnerships will consider a solutions based framework [6]. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e423, 2019 isds 2019 conference abstracts conclusions the results of our work demonstrate the value in surveillance assessment to summarize limitations of the many data sources utilized at a local level to conduct overdose surveillance. our evaluation approach provides a path to improve and fill in surveillance gaps with new processes. other health departments interested in optimizing overdose surveillance may seek a similar evaluation approach. periodic data linkages have not been implemented which presents an opportunity to glean valuable insights on longitudinal patterns of drug use in the population. future collaboration with researchers presents an opportunity to improve mcphd orp, safe syringe access and support program, and substance use outreach services interventions. acknowledgement thanks to tammie nelson and joe gibson of the marion county public health department for supporting this endeavor and assisting with writing. references 1. moore k, boulet m, lew j, papadomanolakis-pakis n. 2017. a public health outbreak management framework applied to surges in opioid overdoses. j opioid manag. 13(5), 273-81. pubmed https://doi.org/10.5055/jom.2017.0396 2. rudd ra. 2016. increases in drug and opioid-involved overdose deaths—united states, 2010–2015. mmwr morb mortal wkly rep. 65. pubmed https://doi.org/10.15585/mmwr.mm655051e1 3. rowe c, wheeler e, jones ts, yeh c, coffin po. 2019. community-based response to fentanyl overdose outbreak, san francisco, 2015. j urban health. 96(1), 6-11. pubmed https://doi.org/10.1007/s11524-018-0250-x 4. chen h, hailey d, wang n, yu p. 2014. a review of data quality assessment methods for public health information systems. int j environ res public health. 11(5), 5170-207. pubmed https://doi.org/10.3390/ijerph110505170 5. massachusetts. department of public health. an assessment of opioid-related deaths in massachusetts (2013-2014). massachusetts department of public health; 2016. 6. wiehe se, rosenman mb, chartash d, lipscomb er, nelson tl, et al. 2018. a solutions-based approach to building datasharing partnerships. egems (wash dc). 6(1). pubmed https://doi.org/10.5334/egems.236 figure 1 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29199393&dopt=abstract https://doi.org/10.5055/jom.2017.0396 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28033313&dopt=abstract https://doi.org/10.15585/mmwr.mm655051e1 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29725887&dopt=abstract https://doi.org/10.1007/s11524-018-0250-x https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24830450&dopt=abstract https://doi.org/10.3390/ijerph110505170 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=30155508&dopt=abstract https://doi.org/10.5334/egems.236 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e296, 2019 isds 2019 conference abstracts twitter: a complementary tool to monitor seasonal influenza epidemic in france ? pascal vilain1, luce menudier2, laurent filleul2 1 regional office of french national public health agency, saint-denis, réunion, 2 french national public health agency, saint-maurice, france objective to investigate whether twitter data can be used as a proxy for the surveillance of the seasonal influenza epidemic in france and at the regional level. introduction social media as twitter are used today by people to disseminate health information but also to share or exchange on their hea lth. based on this observation, recent studies showed that twitter data can be used to monitor trends of infectious diseases such as influenza. these studies were mainly carried out in united states where twitter is very popular [1-4]. in our knowledge, no research has been implemented in france to know whether twitter data can be a complementary data source to monitor seasonal influenza epidemic. methods for this exploratory study, an r program allowing to the collection, pre-processing (geolocation and classification) and analysis of tweets related to influenza-like illness was developed. collection stream api was used to collect tweets in french language that contained terms “grippe”,”grippal”, “grippaux” without to specify geolocation coordinates. pre-process in order to identify tweets localized in france, a combination of automated filters has been implemented. at the end, were retained: • tweets with geolocation coordinates in france (gps coordinates, country code, country, place name) • tweets whose place indicated in user’s profile matched with a city, department or region of france • tweets included fr-related time zone but excluding all tweets reporting a fr time zone but a nonfr place-code. in the second time, a support vector machine (svm) classifier was used to filter out noise from the database. to train the classifier, 1500 tweets were randomly sampled. each of these 1500 training tweets was manually inspected and tagged as valid or invalid according to the likelihood that they indicated influenza-like illness. this hand-tagged training set was converted to vector representation using their term-frequency-inverse document frequency (tf-idf) scores. these tfidf vectors were then input to the svm for training. to evaluate performances of the classifier: accurency, recall and fmeasure were calculated from a 1000 randomly sampled tweets manually tagged. analysis data collected over the period from august 8, 2016 to march 26, 2017 were compared to those of the french syndromic surveillance system sursaud® (oscour® and sos médecins network) [5] by spearman's rank correlation coefficient. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e296, 2019 isds 2019 conference abstracts ethical in accordance to the national commission on informatics and liberty, information about user account were removed in database except location variables. usernames contained in the text of the tweet have also been deleted. results over the study period, the system collected 238,244 influenza-related tweets of which 130,559 were located in france. after a cleaning step, 22,939 tweets were classified by the algorithm as an influenza-like illness (ili). the performances of the classifier were 0.739 for accuracy, 0.725 for recall and 0.732 for f-measure. figure 1 shows that the weekly number of ili tweets follows the same trend as the weekly number of ed visits and physicians consultations for ili. regardless of data source, spearman's correlation coefficients were positive and statistically significant at the national level and for each region of france ( table 1). conclusions this exploratory study allowed to show that twitter data can be used to monitor the epidemic of seasonal influenza in france and at regional level, in complementarity with existing systems. the system needs to be improved to confirm the trends observed during the next influenza epidemic. acknowledgement we thank philippe oesterle and jean-bernard candapanaiken from regional health agency in indian ocean, céline caserio-schönemann from french national public health agency and luc vitrant. we also thank all physicians of oscour® and sos médecins networks. references 1. broniatowski da, paul mj, dredze m. 2013. national and local influenza surveillance through twitter: an analysis of the 2012-2013 influenza epidemic. plos one. 8(12), e83672. pubmed https://doi.org/10.1371/journal.pone.0083672 2. gesualdo f, stilo g, agricola e, gonfiantini mv, pandolfi e, et al. 2013. influenza-like illness surveillance on twitter through automated learning of naïve language. plos one. 8(12), e82489. pubmed https://doi.org/10.1371/journal.pone.0082489 3. paul mj, dredze m, broniatowski d. 2014. twitter improves influenza forecasting. plos curr. 6. pubmed 4. allen c, tsou mh, aslam a, nagel a, gawron jm. 2016. applying gis and machine learning methods to twitter data for multiscale surveillance of influenza. plos one. 11(7), e0157734. pubmed https://doi.org/10.1371/journal.pone.0157734 5. ruello m, pelat c, caserio-schönemann c, et al. 2017. a regional approach for the influenza surveillance in france. online j public health inform. 9(1), e089. https://doi.org/10.5210/ojphi.v9i1.7671 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24349542&dopt=abstract https://doi.org/10.1371/journal.pone.0083672 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24324799&dopt=abstract https://doi.org/10.1371/journal.pone.0082489 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25642377&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27455108&dopt=abstract https://doi.org/10.1371/journal.pone.0157734 https://doi.org/10.5210/ojphi.v9i1.7671 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e296, 2019 isds 2019 conference abstracts figure 1. epidemic curves of weekly number of ili tweets and weekly number of visits (oscour®) or consultations (sos médecins) for ili by region of france, w36-2016 to w12-2017 table1. spearman's rank correlation coefficient between ili tweets visits (oscour®) or consultations (sos médecins) for ili by region of france, w36-2016 to w12-2017. region of france oscour® sos médecins rs p rs p auvergne-rhône-alpes 0.88 <0.001 0.86 <0.001 bourgogne-franche-comté 0.85 <0.001 0.85 <0.001 bretagne 0.83 <0.001 0.88 <0.001 centre-val de loire 0.85 <0.001 0.90 <0.001 corse 0.67 <0.001 0.70 <0.001 grand-est 0.87 <0.001 0.84 <0.001 hauts-de-france 0.85 <0.001 0.88 <0.001 île-de-france 0.81 <0.001 0.85 <0.001 normandie 0.83 <0.001 0.87 <0.001 nouvelle-aquitaine 0.81 <0.001 0.89 <0.001 occitanie 0.80 <0.001 0.87 <0.001 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e296, 2019 isds 2019 conference abstracts pays de la loire 0.90 <0.001 0.86 <0.001 provence-alpes-côte d 0.87 <0.001 0.86 <0.001 metropolitan france 0.89 <0.001 0.90 <0.001 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e336, 2019 isds 2019 conference abstracts large scale mobile medical service programme: data insights for strengthening local surveillance vishal dogra, shailendra hegde, nitin rathnam, sridhar emmadi, vishal phanse piramal swasthya, india objective we report the findings of andhra pradesh state’s mobile medical service programme and how it is currently used to strengthen the disease surveillance mechanisms at the village level. introduction india has an integrated disease surveillance project that reports key communicable and infectious diseases at the district and subdistrict level. however, recent reviews suggest structural and functional deficiencies resulting in poor data quality [1]. hence evidence-based actions are often delayed. piramal swasthya in collaboration with government of andhra pradesh launched a mobile medical unit (mmu) programme in 2016. this mobile medical service delivers primary care services to rural population besides reporting and alerting unusual health events to district and state health authorities for timely and appropriate action. the mmu service in the indian state of andhra pradesh is one of the oldest and largest public-private initiatives in india. two hundred and ninety-two mmus provide fixed-day services to nearly 20,000 patients a day across 14,000 villages in rural andhra pradesh. every day an mmu equipped with medical (a doctor) and non-medical (1 nurse, 1 registration officer, 1 driver, 1 pharmacist, 1 lab technician, 1 driver) staff visit 2 service points (villages) as per prefixed route map. each mmu also has its own mobile tablet operated by registration officer for capturing patient details. the core services delivered through mmus are the diagnosis, treatment, counseling, and free drug distribution to the beneficiaries suffering from common ailments ranging from seasonal diseases to acute communicable and common chronic non-communicable diseases. the routinely collected patient data is daily synchronized on a centrally managed data servers. methods for this analysis, we used aggregated and pooled data that were routinely collected from august 2016-march 2018. patient details such as socio-demographic variables (age, sex etc.) medical history and key vitals (random blood sugar, blood pressure, pulse rate etc.) and disease diagnosis variables were analyzed. besides, communication and action taken reports shared with government of andhra pradesh were also analyzed. we report the findings of the programme with reference to strengthing the village level communicable disease surveillance. unusual health events were defined as more than 3 patients reporting the epidemiologically linked and similar conditions clustered in the same village. results we observed 4,352,859 unique beneficiaries registrations and 9,122,349 patient visits. of all unique beneficiaries, 79.3% had complete diagnosis details (53% non-communicable disease, 39% communicable and 8% others conditions). a total of 7 unusual health events related to specific and suspected conditions (3 vector-borne diseases related, 4 diarrhea-related) were reported to district health authorities, of which 3 were confirmed outbreaks (1 dengue, 1 malaria, and 1 typhoid) as investigated by local health authorities. conclusions mobile medical services are useful to detect unusual health events in areas with limited resources. it increases accountability and response from the government authorities if the timely information is shared with competent health authorities. careful evaluation of the mobile health interventions is needed before scaling-up such services in other remote rural areas. acknowledgements we thank government of andhra pradesh for their support in technical design, implementation and running of this programme. devesh varma and wipro team for the smooth operation of information and technology platform. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e336, 2019 isds 2019 conference abstracts references 1. kumar a, goel mk, jain rb, khanna p. 2014. tracking the implementation to identify gaps in integrated disease surveillance program in a block of district jhajjar (haryana). j family med prim care. 3(3), 213-15. pubmed https://doi.org/10.4103/2249-4863.141612 2. raut d, bhola a. 2014. integrated disease surveillance in india: way forward. global journal of medicine and public health. 3(4), 1-10. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25374856&dopt=abstract https://doi.org/10.4103/2249-4863.141612 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e367, 2019 isds 2019 conference abstracts selection of a geographic area of interest for syndromic surveillance erica fougere, delphine casamatta, guillaume spaccaferri santé publique france, lyon, auvergne-rhône-alpes, france objective define analytic areas at a sub-regional level to better meet the needs of local decision-makers. introduction since 1 january 2016, the auvergne and rhône-alpes regions have merged as part of the territorial reform. the new region is composed of 12 departments and accounts for more than 8 million inhabitants. its territory is heterogeneous in population density with very urban areas (clermont-ferrand, grenoble, lyon and saint-etienne) and important mountainous areas (arc alpin, massif central). in france since 2004, the syndromic surveillance system sursaud® [1] coordinated by the french public health agency (santé publique france) collects morbidity data on a daily basis from two data sources: the emergency departments (ed) network oscour® and the emergency general practitioners sos médecins associations. in auvergne-rhône-alpes, the number of structures participating in the scheme has gradually increased from 2006 to today; as of 1 september 2018, all emergency services (n = 84) and all sos médecins associations (n = 7) transmit their data on a daily basis. both data sources collect medical diagnoses, using icd10 codes in the ed network and specific medical thesaurus in sos médecins associations. these data are routinely analyzed to detect and follow-up various expected or unusual public health events all over the territory [2]. a reflection on the analysis of monitoring data at the sub-regional level was conducted in the region in order to refine the analyses carried out and better meet the expectations of local partners. method the sursaud® system has been progressively upgraded in recent years reaching its regional completeness in 2018. at the same time, the quality of the data also improved, making it possible to work on finer spatial levels. three infra-regional partitioning scenarios were studied: the territorial hospital groups (ght) created in 2016. although they represents 15 groupings in our region, the 12 departments and the 3 emergency physicians networks, we wondered about the possibility of carrying out analyzes on this scale to answer to local needs. the former rhône-alpes region had the particularity of being endowed with three networks of emergency physicians rooted in the region for more than 10 years. these networks are: the renau network (north alpine emergency network) which covers the savoie, haute-savoie, and part of isère regions (including grenoble university hospital); the resuval network (emergency network of the rhone valley) which covers the ain, the rhône (including the university hospital of lyon), the drôme, the ardèche (except the northern part) and the rest of the isère regions; the reulian network (loire and northern ardèche emergency network) which covers the loire (including the university hospital of saint-etienne) and the northern part of ardèche (annonay). these networks were created to structure the sectors and the organization of health care provision in emergency medicine. results a breakdown into departments seemed of little relevance due to the large number of departments within the region and the significant heterogeneity in terms of activity, data transmission and quality. at this departmental scale, the volume of activity in the number of emergency reports transmitted varies from 51,300 to 608,400 annual visits depending on the department. similarl y, the coding of diagnoses varies between 19% and 81%, depending on the department. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e367, 2019 isds 2019 conference abstracts for the 15 ghts, very recent in the region, the organization is not yet homogeneous in terms of healthcare provision and business software. indeed, ghts represent between 53,000 and 514,000 annual emergency visits. in addition, the quality of the data provided varies from 10 to 94% of coded diagnoses. this breakdown was thus not finally retained. in order to have coherent territories of relatively homogeneous weight, three sectors were defined: the first two sectors relied on the networks of emergency physicians in the east and center of the region (renau and résuval). for the west, the territory of the reulian network was merged with the former auvergne region, which was also consistent with medical practices. consequently, these three networks make it possible to have a logic of organization of health care and present relatively balanced sectors. these sectors comprise between 21 and 34 emergency services, and account for one to three sos médecins associations. this represents between 545,000 and 1,028,000 annual visits to emergency services, and between 93,000 and 109,000 annual consultations with sos médecins. the transmission quality of emergency visits data at d + 1 varies between 51% and 73%. a breakdown into 3 sectors (west, center, and east) was finally retained. to illustrate the situation, a study on the characteristics and sub-regional spatio-temporal diffusion of respiratory syncytial virus (rsv) bronchiolitis epidemics in children under 2 years in the auvergnerhône-alpes region did not show any major differences between sectors to identify the start of the outbreak (+/1 or 2 weeks). however, the identification of the start of the outbreak in 1 or 2 sectors only did not seem to trigger outbreak onset at regional level. there is therefore a delay in informing the start of the outbreak for the sector (and the facilities) concerned, which may also delay the possibility to adapt health care provision with respect to the epidemic peak. another use of these sectors was performed during the surveillance of allergies in the region. in the spring, the pollens are different from one week to another and depending on the territory. also, thanks to these three sectors, it is possible to refine the observations to allow a prevention closer to the field. in the spring of 2018, in the east sector, a peak higher than in previous years was observed, while in the other two sectors, this dynamics was relatively similar to the one observed over the last two years. conclusions the 3 networks of emergency physicians in the former rhône-alpes region, which have been active for more than 10 years, show that there is a real logic of health care provision between facilities belonging to the same network (common health problems, geographical characteristics, etc.). it was therefore relevant to rely on these networks to propose a sub-regional breakdown. in order to balance the sectors (in terms of the number of reports of emergency visits, number of facilities, population), it was decided to group within the western sector the reulian territory and the territory of the former auvergne the results of the study on bronchiolitis show that the analysis of the spread of rsv-related bronchiolitis outbreaks at the subregional level could allow a better anticipation of outbreak onset, and therefore of the epidemic peak which constitutes the main stake for the organization of health care provision. the interest of this surveillance in sectors will also have to be evaluated for other seasonal disease. acknowledgement to oscour® and sos médecins regional partners and to the intervention unit of santé publique france in auvergne-rhônealpes. references 1. caserio-schönemann c, bousquet v, fouillet a, henry v. 2014. le système de surveillance syndromique sursaud (r). bull epidemiol hebd (paris). (3-4), 38-44. 2. josseran l, nicolau j, caillère n, astagneau p, brücker g. 2006. syndromic surveillance based on emergency department activity and crude mortality: two examples. euro surveill. 11(12), 225-29. pubmed https://doi.org/10.2807/esm.11.12.00668-en http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17370967&dopt=abstract https://doi.org/10.2807/esm.11.12.00668-en isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e367, 2019 isds 2019 conference abstracts figure 1. regional map of metropolitan france (left) and geographic area of interest for syndromic surveillance in ara (right) http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts using syndromic data for opioid overdose surveillance in utah wei hou, elizabeth brutsch, angela c. dunn*, cindy l. burnett, melissa p. dimond and allyn k. nakashima utah department of health, salt lake city, ut, usa objective to monitor opioid-related overdose in real-time using emergency department visit data and to develop an opioid overdose surveillance report for utah department of health (udoh) and its public health partners. introduction the current surveillance system for opioid-related overdoses at udoh has been limited to mortality data provided by the office of the medical examiner (ome). timeliness is a major concern with ome data due to the considerable lag in its availability, often up to six months or more. to enhance opioid overdose surveillance, udoh has implemented additional surveillance using timely syndromic data to monitor fatal and nonfatal opioid-related overdoses in utah. methods as one of the agencies participating in the national syndromic surveillance program (nssp), udoh submits de-identified data on emergency department visit from utah’s hospitals and urgent care facilities in close to real-time to the nssp platform. emergency department visit data are available for analysis using the electronic surveillance system for the early notification of community-based epidemics (essence) system provided by nssp. essence provides udoh with patient-level syndromic data for analysis and early detection of abnormal patterns in emergency visits. a total of 38 out of 48 acute care hospitals and multiple urgent care facilities are enrolled in the system in utah. more than 90% of these hospitals report chief complaint data, and discharge data are available from about 15% of the facilities. data were analyzed by querying key terms in the chief complaint field including: any entry of: ‘overdose’, drug and brand names for opioids, street names, ‘naloxone’, and missspellings. exclusion terms included any mention of: ‘denies’, ‘quit’, ‘refill’, ‘withdraw’, ‘dependence’, etc. data containing any icd entry of: t40.0-t40.4, t40.60, and t40.69 were included in the analysis. results between september 1, 2016 and august 31, 2017, utah department of health identified 4,063 opioid-related overdose emergency department (ed) visits through the essence system using both chief complaint and discharge diagnosis queries. of these visits, 3,865 (95%) were identified using chief complaints alone and 198 (5%) visits were added by searching the discharge diagnosis field. opioidrelated visits comprised approximately 0.3% of the total ed visits (1,267,244) reported during this time (graph 1). more than half of the opioid-related emergency visits were reported from just five facilities. rate of opioid-related visits ranging from 0 to 292 visits per 100,000 population per year (median: 108 visits per 100,000 population per year), with an overall rate for the state of 129 visits per100, 000 population per year. the highest rate of opioid-related visits occurred among patients aged 18 to 24 (219 visits per 100,000 population per year), and 59% of all opioid-related patients in utah were female. conclusions the results presented are estimates of opioid-related overdoses reported using close to real-time data. these results would not include visits with incomplete or incorrectly coded chief complaints or discharge codes, or cases of opioid overdose who do not present to an emergency department or urgent care facility. the results from using syndromic data are consistent with existing surveillance findings using mortality data in utah. this suggests that syndromic surveillance data are useful for rapidly capturing opioid events, which may allow for a timelier public health response. udoh is currently evaluating syndromic surveillance data versus hospital discharge data for opioid-related emergency department visits, which may further optimize queries in essence, in order to provide improved opioid surveillance data to local public health partners. this analysis demonstrates that using syndromic surveillance data provides a more time-efficient alternative, enabling more rapid public health interventions, which improved opportunities to reduce opioid-related morbidity and mortality in utah. monthly ed visits for opioid-related overdose as percent of total visits in utah keywords syndromic surveillance; opioid overdose; utah; real-time surveillance *angela c. dunn e-mail: angeladunn@utah.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e195, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e437, 2019 isds 2019 conference abstracts improved identification and assessment of heat-related illnesses in pinal county, az dametreea carr infectious disease and epidemiology section, pinal county public health services district, florence, arizona, united states objective determine risk factors and risk populations for heat-related illnesses in pinal county, az by improving hri case identification and assessment. introduction extreme heat and related illnesses are a critical concern in arizona from may to september each year. from 2008 to 2016, arizona medical facilities had an average of 1,790 emergency visits and 442 hospital admissions for heat-related illnesses (hri) during the summer months. in 2016 alone, arizona emergency departments (eds) received a total of 2,484 visits for hri and 527 of these cases were admitted as inpatients [1]. pinal county, which has an estimated population of 430,237 individuals [2], contributes to the number of hri visits to arizona emergency departments that occur each year. in order to determine the burden of hris within the county, pinal county public health services district (pcphsd) began to conduct heat-related illness surveillance in 2017 and found that 149 hri cases had been reported that year[3]. pcphsd continued to conduct hri surveillance through summer 2018 to build on surveillance activities from 2017 and meet the following goals: • improve hri case identification and assessment through enhanced hri surveillance and interview processes, • determine risk factors and risk populations for hris in pinal county, and • recommend and implement practical interventions to prevent hris among pinal residents. methods the process to improve identification and assessment of hri cases among pinal residents included the use of the national syndromic surveillance program’s (nssp) essence for case identification, and qualtrics survey software to collect additional information from confirmed cases regarding their heat exposure. first, cases were identified using essence syndromic surveillance system, which identifies cases based on specific queries and definitions. pcphsd used the definition for heat illness version 1 to identify cases who’s chief complaint (cc) and/or hospital discharge diagnosis (dd) included key hri terms. next, pcphsd utilized two essence queries to search cases by “patient location” and “facility location.” these two queries were used to ensure that all hri cases among pinal county residents were identified regardless of case address or the facility visited. the second step in the process included de-duplication of cases identified in each query and adding them to a line list for further assessment. pcphsd then conducted a thorough medical chart review for each case to determine if they met the hri case definition. confirmed cases met the following criteria: • identification in essence as an hri, • medical records compatible with hri clinical symptoms, • symptom onset within 24 hours of heat exposure, • hri visit to an ed between may 1st and september 30th, 2018, and • resident of pinal county. • exclusion: heat exposure not related to environmental/natural heat. step three of the process included the conduction of hri case interviews using qualtrics survey software. interview questions were designed to obtain information not included in case medical records, and to collect information regarding risk factors, risk populations, and potential areas of intervention. interviews were completed within 7-10 minutes, included a maximum of three call attempts to each case, and included a follow-up text message. cases were sent an information packet if they expressed interest in receiving additional information at the conclusion of their interview. the information packet included a thank you letter, heat safety tips, local resources for homelessness and utility assistance, and heat relief options available through the pinal county heat relief network. the final step of the process included data analysis to determine areas and modes of intervention to prevent hris among pinal residents. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e437, 2019 isds 2019 conference abstracts results essence identified 168 hri cases from may 1st to august 31st, 2018. of these cases, 103 were confirmed. (note that 48 cases from august and two from july are still under chart review). preliminary findings for confirmed cases from may 1st to july 31st show that the majority of hri cases in pinal county are nonhispanic white (61.2%), male (75.7%), and range in age from 2059 years (71.8%). nearly one-third of reported hri cases occurred among casa grande residents (33 cases). this was followed by san tan valley with 20 cases, and apache junction with 16 cases. with regard to hospital visits, 86.4% of cases visited an ed within pinal county. other county ed visits were to maricopa (10.7%), pima (1.9%), and coconino (1%). the most frequently visited hospitals within pinal county were banner casa grande medical center (50.5%) and banner goldfield medical center (12.6%). assessment of cases by month shows that july had the greatest number of cases overall (51 cases) and the greatest number of cases in one day (5 cases). this was followed by the month of june with 41 cases total and a maximum of 3 cases in one day. maximum temperatures in june and july reached 111of and 116of, respectively. with regard to response rate, 53.2% of confirmed cases completed an interview. interview data are currently being assessed to determine risk factors and risk populations for hri. final results will include may to september hri data, an analysis of risk factors (e.g. occupation, recreation, homelessness, access to heatsafety resources), an updated response rate, and the percentage of cases who requested an information packet. final data analyses will be completed by december 2018. conclusions improvements to the process for hri surveillance and case interviews has helped pcphsd to more effectively identify and assess hri cases. utilizing two essence queries assisted with hri case identification, and improvements to the case contact protocol and interview questionnaire increased response rate from 25% in 2017 [3] to 53.2% in 2018. other improvements included the use of follow-up text messaging and spanish text/interview options. preliminary results indicate that non-hispanic white males between the ages of 20-59 years have a greater risk of getting an hri than other demographic groups. interventions should therefore be geared toward this population. also, with hris occurring in multiple cities throughout pinal county, it is recommended that the pinal county heat relief network expand outreach activities to recruit additional organizations to participate as heat relief stations. final analysis of interview data will take place to determine if hris among pinal residents are related to occupation, recreation, socioeconomic status, or a combination of these. pcphsd will use final results to provide targeted heat-safety information and resources to the most appropriate groups. it is the hope of pcphsd that this project will benefit at-risk pinal county residents and reduce the burden of hris during future summers. acknowledgement special thanks to clancey hill and rachel zenuk from pinal county public health services district; and krystal collier, matthew roach, sara chronister, sara johnston, melissa kretchmer, and laura erhart from arizona department of health services. references 1. centers for disease control and prevention. environmental public health tracking network, state emergency department visits data. [internet]. 2018. available from: www.cdc.gov/ephtracking. accessed on 10/12/2018. 2. united states census bureau. quick facts: pinal county, az. [internet]. 2017 jul 1. available from: https://www.census.gov/quickfacts/fact/table/pinalcountyarizona/pst045217#viewtop. 3. kent dc, garcia rz, packard s, briggs g, hill c, et al. 2018. enhanced surveillance of heat-related illness in pinal county. online j public health inform. 10(1), e111. doi: 10.5210/ojphi.v10i1.8829 http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts recognizing recreational water exposure and habituating hab surveillance in essence kathryn kuspis*, meredith jagger, melissa powell and rebecca hillwig public health division, oregon health athority, portland, or, usa objective use essence to create a sustainable process for identifying ed and urgent care visits that may be related to harmful algal bloom exposure in oregon. introduction harmful algal blooms (habs) consist of colonies of prokaryotic photosynthetic bacteria algae that can produce harmful toxins. the toxins produced by habs are considered a one health issue. habs can occur in all types of water (fresh, brackish, and salt water) and are composed of cyanobacteria or microalgae. as the climate changes, so do many of the factors that contribute to the growth of habs, which in turn, can increase the incidence of hab-related illness in humans. there are three main pathways that hab toxins can affect human health: dermal, gastrointestinal (gi), and neurological. swimming in or consuming contaminated water and eating contaminated shellfish are ways to develop hab-related illnesses. contact with cells from a bloom while recreating can cause a rash on the body. most commonly, hab-related illnesses present with gi symptoms that resemble food poisoning and can affect the liver. rarely, habs that produce cyanotoxins can present with neurological symptoms. issuing and lifting freshwater hab advisories is within the preview of the environmental public health section at the oregon public health division. however, most water bodies in the state are not monitored. because of this, syndromic surveillance was considered as a potentially useful source of hab exposure information, and the oregon essence team was asked to develop a query to help monitor hab-related complaints. methods preliminary research was done on habs and the associated health issues, and past advisories were examined to identify locations of interest. next, keywords and symptoms were evaluated. initially, the objective was to create a single query for hab syndromic surveillance, but it became evident that multiple queries would have to be developed to fully encompass the various types of hab-related illnesses: gi, neurological, and rash. most commonly oregon essence uses chief complaint and discharge diagnosis (ccdd) queries. however, the icd-10 codes relating to habs are not widely used, with only two occurrences since june 2015. it was determined that using the already established essence syndromes of neuro, gi, and rash would be most useful. to make the queries hab-specific, an additional exposure element needed to be added. exposures to habs that are of interest occur in recreational freshwater sources. after running this query in the ccdd field, it was determined that the triage note field would yield better results. this is because this field often includes the patient’s verbatim complaints. this produced higher quality results, and a seasonal curve of cases could be seen in the historic data. since the microcystin threshold for illness is significantly lower for pets; and a permanent hab alert in southern oregon was established after several dogs died from drinking contaminated water, tracking neurological cases that followed canine illness was investigated. a free-text triage note query was developed for patients mentioning dogs, and it was combined with the essence neuro syndrome. after several attempts, it was clear that this would not be helpful for surveillance of hab-related illnesses. ultimately, four query configurations were developed to monitor hab-related illness. most importantly, a free-text recreational water query was developed to stand alone and then be paired with three distinct essence syndromes. recreational water query text: (, (, ^ lake^, andnot, (, ^road^, or, ^rd^, or, ^sky^, or, ^oswego^, or, ^view^,),), or, ^swim^, or, (, ^ river ^, andnot, (, ^driver^, or, ^hood^, or, ^rd^, or, ^road^, or, ^three^,),), or, ^ boat^,), andnot, ^feels like^ all queries were compiled into a myessence page that could be shared for easy monitoring by all members of the team (figure 1). results the essence team monitored the hab myessence page. the monitoring period for this project stretched from may to early august (mmwr weeks 19-31). motoring was often informed by hab alerts and required looking closely at individual visits. over this time, the number of recreational water related visits varied, but the average was approximately 110 visits a week. this techniques also helped identify cases possibly related to unreported blooms. the months of june and july saw 15 specific cases that were potentially due to hab exposure. these cases were highlighted and forwarded to environmental public health for investigation. conclusions this process helped refine the use of the triage note field when constructing keyword queries. while not all oregon facilities provide triage notes, using specific terms allows essence users to search for words that may not be included in chief complaints. this is most useful when searching for specific places or events. with further analysis, users can see what chief complaints are most likely to occur in conjunction with specific exposures. moving forward, the development of a recreational water query has proven to be useful beyond the scope of this hab project. alternative versions of this query have been used in other contexts. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts keywords hab; harmful agal bloom; essence; surveillance; recreational water references harmful algal bloom (hab)-associated illness. (2017, june 01). retrieved august 01, 2017, from https://www.cdc.gov/habs/index. html *kathryn kuspis e-mail: kathryn.a.kuspis@state.or.us online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e164, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e451, 2019 isds 2019 conference abstracts utilizing syndromic surveillance for hurricane irma-related co poisonings in florida prakash r. mulay, kirtana ramadugu, david atrubin, heather rubino, carina blackmore florida department of health, tallahassee, florida, united states objective this study describes how florida poison information center network (fpicn) and emergency department (ed) data accessed through florida’s syndromic surveillance system were used to conduct near real-time carbon monoxide (co) poisoning surveillance and active case finding in response to hurricane irma in florida. introduction on september 10, 2017, hurricane irma made landfall in florida. over 90% of florida counties reported power outages as of september 11. during power outages, co poisonings often occur due to indoor use of fuel combustion sources (e.g., cooking, heating) or generators for electricity. co poisoning is a reportable condition in florida; health care providers and laboratories are required to report suspected cases to the florida department of health (fdoh). in florida, approximately 202 cases of co poisoning are reported each year (three-year average from 2014 to 2016). in addition to passive surveillance, fdoh uses the electronic surveillance system for the early notification of community-based epidemics (essence-fl) to find cases of co poisoning. essence-fl provides access to ed data from 98% (255 out of 260) of eds in florida and all statewide fpicn call data (includes three poison control centers). essence-fl provides near real-time access to these data sets, as ed data are uploaded every 2 hours or once a day (depending on the hospital system) and fpicn data are uploaded every 10 minutes. the statewide fpicn database includes information about substance, signs and symptoms, exposure scenario, and patient identification information provided by the individual caller or clinician from a health care facility. methods in addition to receipt of health care provider reports through traditional disease reporting, active case finding was conducted using essence-fl during hurricane irma. exposure calls to the fpicn indicating co exposure were extracted from the statewide database. calls coded with the following medical outcomes were excluded: no health effect, not followed – judged as nontoxic exposure, not followed – minimal clinical effects possible, unrelated effect – the exposure was probably not responsible for the effect(s), and confirmed non-exposure. to query essence-fl ed data, a free-text query was created and executed against the concatenated chief complaint and discharge diagnosis (ccdd) field: (^carbon^,andnot,(,^retention^, or,^narcosis^,),),or,^monox^,or,(,^generator^,and, (,^fumes^,or,^expos^,or,^nausea^, or,^headach^,or,^exhaust^,or,^garage^,or,^inhale^,),) . results of these queries were analyzed and sent to county and regional epidemiologists daily for investigation. reports of co poisoning exposures were investigated by collecting medical records and conducting interviews using an expanded risk factor questionnaire [1]. cases were classified using florida’s reportable disease case definition [2] and documented in the electronic reportable disease surveillance system, merlin (see process flow chart). descriptive analysis of hurricane irma-related co poisoning cases reported in merlin was conducted to characterize morbidity, mortality, and exposure scenarios. results in september 2017, fdoh investigated 666 reports of co poisoning and identified 529 people (79.4%) who met the case definition for co poisoning. among 529 cases, 56.3% were reported by ed data, 5.7% by fpicn data, 29.1% from both data sets, and the remaining 8.9% by other sources (e.g., self-report, media). about 60.1% of cases were only reported by fpicn and ed data, 33.1% by health care providers and laboratories, and 6.8% by other sources. among 15 deaths, 20% were identified through active cas e http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e451, 2019 isds 2019 conference abstracts finding using ed and fpicn data. co poisoning cases peaked on september 12 (within two days of hurricane landfall) and decreased by september 16, as power was restored. about 95% of cases reported co exposures within the first week of hurricane landfall. merlin data analysis of 529 cases identified some notable findings related to hurricane irma. co poisoning rates were highest among those aged 5–14 years (4.8 per 100,000 population), and the mean age was 33.2 years (median: 31 years, range: 3 months – 89 years). most cases were in females (55.6%), non-hispanics (58.3%), and whites (73%). co exposures were predominantly caused by generator use (97.5%). among 516 generator-related exposures, 15.7% of people had a co detector, 62.8% did not have co detector, and it was unknown for 21.5%. among 516 residential exposures due to generator use, 31.3% of people reported generator use inside the home, attached garage, or other attached structures, and 66% reported generator use outside the home , including covered decks and carports. among 340 people who reported generator use outside the home, 63.5% reported having a generator within 20 feet of windows, doors, air conditioners, or air intake vents. conclusions even though co poisoning is a reportable condition in florida, use of active surveillance was key in the public health response to hurricane irmarelated co poisonings. fdoh would not have identified 60% of these hurricane-related co poisoning cases without access to fpicn and ed data. during hurricane irma, active case finding complemented routine disease surveillance not only in early detection of co poisonings but also in guiding rapid public health response. similarly, in the 2005 hurricane season, fdoh monitored fpicn data and identified an increase in co poisonings [3]. based on near-real-time co poisoning surveillance, fdoh produced daily situation reports, sent out a press release about the dangers of co poisoning from generator use, prepared a youtube video, and conducted educational outreach through social media and text alert. other jurisdictions may benefit from use of near real-time ed and poison control center data to better understand the magnitude and characteristics of co poisonings during power outages in their areas. public education messages need to emphasize outdoor use of generators (at least 20 feet away from doors, windows, and air conditioners) and use of co detectors. acknowledgement bureau of epidemiology staff, florida department of health. references 1. florida department of health. carbon monoxide poisoning enhanced case report form; october 2017. available at: www.floridahealth.gov/diseasesand-conditions/disease-reporting-and-management/diseasereporting-and-surveillance/_documents/crf-co-hurricane-irma-enhanced-surveillance.pdf 2. florida department of health. carbon monoxide poisoning case definition; 2018. available at: www.floridahealth.gov/diseases-and-conditions/diseasereporting-and-management/disease-reporting-andsurveillance/_documents/cd-carbon-monoxide.pdf 3. monitoring poison control center data to detect health hazards during hurricane season—florida, 20032005. 2006. jama. 295(21), 2469-70. https://doi.org/10.1001/jama.295.21.2469 http://ojphi.org/ https://doi.org/10.1001/jama.295.21.2469 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e451, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e410, 2019 isds 2019 conference abstracts surveillance of respiratory viruses in long term care facilities mary m. checovich1, shari barlow1, peter shult2, erik reisdorf2, jonathan l. temte1 1 family medicine & community health, university of wisconsin-madison, madison, wisconsin, united states, 2 wisconsin state laboratory of hygiene, madison, wisconsin, united states objective to assess the feasibility of conducting respiratory virus surveillance for residents of long term care facilities (ltcf) using simple nasal swab specimens and to describe the virology of acute respiratory infections (ari) in lctfs. introduction although residents of ltcfs have high morbidity and mortality associated with aris, there is very limited information on the virology of ari in ltcfs [1,2]. moreover, most virological testing of lctf residents is reactive and is triggered by a resident meeting selected surveillance criteria. we report on incidental findings from a prospective trial of introducing rapid influenza diagnostic testing (ridt) in ten wisconsin ltcfs over a two-year period with an approach of testing any resident with ari. methods any resident with new onset of respiratory symptoms consistent with ari had a nasal swab specimen collected for ridt by nursing staff. following processing for ridt (quidel sofia influenza a+b fia), the residual swab was placed into viral transport medium and forwarded to the wisconsin state laboratory of hygiene and tested for influenza using rt-pcr (ivd cdc human influenza virus real-time rt-pcr diagnostic panel), and for 17 viruses (luminex nxtag respiratory pathogen panel [rpp]). the numbers of viruses in each of 7 categories [influenza a (flua), influenza b (flub), coronaviruses (cor), human metapneumovirus (hmpv), parainfluenza (para), respiratory syncytial virus (rsv) and rhinovirus/enterovirus (r/e)], across the two years were compared using chi-square. results totals of 164 and 190 specimens were submitted during 2016-2017 and 2017-2018, respectively. rpp identified viruses in 56.2% of specimens, with no difference in capture rate between years (55.5% vs. 56.8%). influenza a (21.5%), influenza b (16.5%), rsv (19.0%) and hmpv (16.5%) accounted for 73.5% of all detections, while coronaviruses (15.5%), rhino/enteroviruses (8.5%) and parainfluenza (2.5%) were less common. specific distribution of viruses varied significantly across the two years (table: x2=48.1, df=6; p<0.001). conclusions surveillance in ltcfs using nasal swabs collected for ridt is highly feasible and yields virus identification rates similar to those obtained in clinical surveillance of ari with collection of nasopharyngeal specimens by clinicians and those obtained in a schoolbased surveillance project of ari with collection of combined nasal and oropharyngeal specimens collected by trained research assistants. significant differences in virus composition occurred across the two study years. rsv varied little between years while hmpv demonstrated wide variation. simple approaches to surveillance may provide a more comprehensive assessment of respiratory viruses in ltcf settings. acknowledgement funding provided by the wisconsin partnership program / partnership education and research committee. in-kind support was provided by quidel corporation. we appreciate contributions from our ltcf study sites. references 1. uršič t, gorišek miksić n, lusa l, strle f, petrovec m. 2016. viral respiratory infections in a nursing home: a six-month prospective study. bmc infect dis. 16, 637. published online nov 2016. doi:https://doi.org/10.1186/s12879-016-1962-8. pubmed http://ojphi.org/ https://doi.org/10.1186/s12879-016-1962-8 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27814689&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27814689&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e410, 2019 isds 2019 conference abstracts 2. masse s, capai l, falchi a. 2017. epidemiology of respiratory pathogens among elderly nursing home residents with acute respiratory infections in corsica, france, 2013–2017. biomed res int. 2017, 1. published online dec 2017. doi:https://doi.org/10.1155/2017/1423718. pubmed distribution of respiratory viruses in ltcfs over two sequential seasons. respiratory virus season virus category (percent of specimens) flua flub cor hmp v par a rsv r/e 2017/2018 17.6 24.2 20.9 0 5.5 18.7 13.2 2017/2018 24.8 10.1 11.0 30.3 0 19.3 4.6 combined 21.5 16.5 15.5 16.5 2.5 19.0 8.5 http://ojphi.org/ https://doi.org/10.1155/2017/1423718 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29392127&dopt=abstract isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts tablet-based participatory syndromic surveillance at simhashta festival in india vishal diwan*1, 2, anette hulth2, ponnaiah manickam3, viduthalai virumbi balagurusamy3, deepak agnihotri1, vivek parashar1, ashish pathak1, 2, kantilal sahu4 and vijay k. mahadik1 1public health and environment, r.d. gardi medical college, ujjain, india; 2karolinska institutet, stockholm, sweden; 3national institute of epidemiology, chennai, india; 4department of health and family welfare, bhopal, india objective to develop, test and study tablet-based participatory syndromic surveillance system for common infectious disease conditions at community level in simhashta religious mass gathering in ujjain, india, 2016. introduction infectious disease surveillance for generating early warnings to enable a prompt response during mass gatherings has long been a challenge in india 1,2 as well as in other parts of the world 3,4,5. ujjain, madhya pradesh in central india hosted one of the largest religious festival in the world called ‘simhasth kumbh mela’ on the banks of river kshipra, where more than 50 million attendees came for holy dip during april 22 to may 21, 2016. the attendees included pilgrims (residents and visitors), observers, officials and volunteers. we developed an android application with automated summary reports and an interactive dashboard for syndromic surveillance during the gathering. methods we established the participatory surveillance at all 22 sectors of the festival area, and at 20 out-patient hospitals and 12 pharmacies. we trained 55 nursing and social work graduate trainees to collect data from all these settings. the data collectors visited designated spots daily during a fixed time and collected age, gender, residence and self-reported symptoms from consenting attendees during the festival period. the application automatically added date, time and location of interview to each record and data was transmitted to a web server. we monitored the data in the interactive dashboard and prepared summary report on a periodic basis. daily summary report of self-reported symptoms by time, place and person was shared daily evening with the festival surveillance authority. results of the total 93,020 invited pilgrims, 91% participated in the surveillance. almost 90% of those were from outside the festival city, 60% were men and 57% were aged 15 to 44 years. almost 50% of them self-reported presence of at least one symptom. most frequently reported symptoms were dehydration due to heat (13%), cold (13%), fever (7%) and loose stool (5%). during the festival period of over one month, surveillance data indicated increasing trends of self-reported cough and fever and declining trends of self-reported dehydration (figure-1). the designated public health authorities for the festival did make use of the information for appropriate action. this tabletbased application was able to collect, process and visualise around 2500 records per day from the community without any data loss. conclusions to our knowledge, this is the first report from india documenting real-time surveillance of the community using hand-held devices during a mass gathering. despite some implementation issues and limitations in the approach and data collected, the use of digital technology provided well-timed information avoiding tedious manual work and reduced a good amount of human resources and logistics involved in reporting symptoms with a traditional paperbased method in such a large population. in retrospect, the main utility of the surveillance output was that of giving reassurance to the officials, as no major outbreaks occurred during the event. we believe that this experience and further analyses will provide input for the establishment and use of such a surveillance system during mass gatherings. the team of investigators propose improving the methods and tools for future use. keywords mass gatherings; participatory; syndromic surveillance; india; tablet based acknowledgments we are grateful for the department of health and family welfare, government of madhya pradesh for study permission, r.d. gardi medical college, ujjain for financial support. we are also thankul to study participants and project team references 1. david s, roy n. public health perspectives from the biggest human mass gathering on earth: kumbh mela, india. international journal of infectious diseases. 2016 jan 28. 2. sridhar s, gautret p, brouqui p. a comprehensive review of the kumbh mela: identifying risks for spread of infectious diseases. clinical microbiology and infection. 2015;21(2):128-33. 3. tam js, barbeschi m, shapovalova n, briand s, memish za, kieny mp. research agenda for mass gatherings: a call to action. the lancet infectious diseases. 2012;12(3):231-9. 4. nsoesie eo, kluberg sa, mekaru sr, majumder ms, khan k, hay si, brownstein js. new digital technologies for the surveillance of infectious diseases at mass gathering events. clinical microbiology and infection. 2015;21(2):134-40. 5. world health organization. communicable disease alert and response for mass gatherings. intechnical workshop. geneva, switzerland 2008 apr (pp. 29-30). 6. cariappa mp, singh bp, mahen a, bansal as. kumbh mela 2013: healthcare for the millions. medical journal armed force india. 2015; 71 278e81 *vishal diwan e-mail: vishaldiwan@hotmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e182, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e245, 2019 isds 2019 conference abstracts identification of clinical indicators of opioid overdose using innovative ems software analytics. silvia r. verdugo1, pam farber1, john selters1, todd stout1, karla d. wagner2 1 firstwatch solutions inc., carlsbad, california, united states, 2 university of nevada, reno, reno, nevada, united states objective to develop a set of clinical indicators of opioid overdose using emergency medical services (ems) records that included data from computer aided dispatch (cad), proqa systems, electronic patient care reporting (epcr) and hospital medical records. introduction in north america we experience the highest rate of drug related mortality in the world. in the us, overdose is now the leading cause of death among adults under 50. each day more than 115 people in the united states die due to an opioid overdose. the opioid overdose national crisis is rapidly evolving due to changes in drug availability and the presence of adulterated fentanyl in some areas leading to a critical need for innovative methods to identify opioid overdoses for both surveillance and intervention purposes. as an effort to strengthen our understanding of the epidemic through surveillance of emergency medical services (ems) we have developed a set of clinical indicators that identify opioid overdose within the information provided by an electronic patient care reporting (epcr), computer aided dispatch (cad), proqa systems and hospital medical records. methods we initially created a set of ems agency specific opioid overdose filters using firstwatch® software as part of a public health research study. following that initial development, we have built a generic set of opioid overdose identifiers. in the initial approach we used a zoll data system software for epcr and tritech inform cad to define 3 set of identifiers: (t1) captured calls in which naloxone was administered and a positive clinical response was documented, (t2) had the same criteria as t1 except there was no positive response to the administration of naloxone, and (t3) consisted of calls in which one or more drug-related keywords were present within the narrative of the epcr. because the initial analysis was conducted in the context of a single research study, we aimed to create a more generalizable set of identifiers of opioid overdose that would function across different ems agencies, software, and data sources. in addition, we included variables provided by hospital medical records to our filtering criteria to provide a more robust and complete set of opioid overdose clinical indicators. results utilizing the ems data sources cad, proqa and epcr as well as hospital medical records we have developed a set of identifiers of opioid overdose. utilizing firstwatch® software analytics the following variables where coded into the software: 1. cad data. chief complaint and opioid overdose keyword search; 2. proqa.protocols 6, 9, 23, 31 and 32; 3. epcr.primary and secondary impressions, chief complaint, intervention of narcan (naloxone) administration, vital signs and opioid overdose keyword search; 4. medical records.patient's admission and discharge diagnosis (diagram 1). the clinical indicators obtained from this analysis where created to be utilized across different ems specific software vendors for cad, proqa and epcr systems. for the medical records variables a single software vendor was available to be integrated into the analysis. nonetheless, as we used the international statistical classification of diseases and related health problems codes on their 10th revision (icd-10) our determining variable codes could be generalized to other hospital record system if they would become available. conclusions correctly identifying an opioid overdose can a be a challenge. its clinical features are non-specific and bystanders fear repercussions of disclosing the nature of the 911 call. determining the correct number of opioid overdoses requires a tailored identification process. a combination of clinical determinants and incorporation of multiple ems data sources appears to be feasible in determining opioid overdose related 911 calls. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e245, 2019 isds 2019 conference abstracts acknowledgement the initial public health research study conducted to develop clinical indicators of overdose described as t1, t2 and t3 was supported by grants from the national institute of general medical sciences (gm103440) and the national institute on drug abuse (r01da040648) and was approved by the unr institutional review board. references 1. the united nations office on drugs and crime (unodc) '2017 world drug report'. 2. hedegaard h, warner m, miniño am. drug overdose deaths in the united states, 1999–2016. nchs data brief, no 294. hyattsville, md: national center for health statistics. 2017. 3. multiple cause of death 1999–2016 on cdc wide-ranging online data for epidemiologic research (cdc wonder). atlanta, ga: cdc, national center for health statistics. 2017. 4. cdc/nchs, national vital statistics system, mortality. cdc wonder, atlanta, ga: us department of health and human services, cdc; 2017. diagram 1: data sources and fields used to create clinical indicators of opioid overdose. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts approaching evaluations of surveillance system pilots through an ownership perspective carrie eggers* center for global health, cdc, atlanta, ga, usa objective we used experiences in multiple countries to determine that owner engagement is critical for successful evaluations of surveillance system viability. introduction pilot projects help determine utility and feasibility of a system, but even if considered successful, cost could prevent further scale-up. when evaluating a surveillance system pilot, cost and benefits are key factors to examine. in cote d’ivoire and tanzania, ministry of health (moh) and non-governmental partners receive funding under the global health security agenda to strengthen disease surveillance for earlier detection and improved response to potential infectious disease outbreaks. to this end, community based surveillance (cbs) projects were implemented in 2016 as a means for early warning of potential events to facilitate a more rapid response. currently, these cbs projects are being evaluated collaboratively with the primary stakeholder, the host country government (hcg), as lead, and partners such as cdc providing technical assistance. in other instances, partners may conduct an evaluation and share the results and recommendations with the hcg; however, if the hcg is not actively engaged as the primary executor, outcomes may not be endorsed or implemented. therefore, these evaluations were approached from an owner’s (hcg) perspective. in this way, the governmental agencies develop capabilities to conduct similar activities in other areas, reduce dependencies on outside entities, and promote enactment of resulting recommendations. methods once the determination was made that an evaluation was necessary to decide the usefulness of the projects for future planning, key stakeholders worked together to design and execute the evaluation. for cote d’ivoire, the evaluation team consisted of representatives from the moh’s national institute of public health, directorate of informatics and health information, and directorate for the coordination of the expanded immunization program, along with delegates from cdc and implementing partners. in tanzania, evaluation team members came from the moh, the ministry of agriculture, forestries and livestock, who, cdc and implementing partners. team members participated in either planning, conducting or analyzing the evaluation, while some contributed to a combination or to all aspects. moh members led the effort with cdc and other partners providing technical assistance, while implementing partners contributed only to planning and logistics to reduce the potential for bias. for the initial step, representatives came together to fully document the system to be evaluated. this system description details the purpose, relevant stakeholders and current operation of the pilot system. as the evaluation question should remain within the scope of the system’s purpose, it was necessary to definitively understand and confirm the goal and objectives set out for the system. next, the sites, participants and roles, and data flows were described, noting that verification of the actual processes would occur during the site visit portion of the evaluation. total cost of ownership was calculated by considering solution costs, implementation costs and ongoing support, and then broken down by district. the cbs pilots implemented early warning notification systems in two districts in cote d’ivoire and in five districts in tanzania using a combination of paper-based and electronic reporting formats. evaluation teams visited pilot sites and routine surveillance sites for comparison and conducted in-person interviews using questionnaires specific to the individual’s role. data were either collected in the field on paper forms or electronically on tablets for subsequent upload to a centralized database for later analysis. data from project and routine reporting databases were comparatively analyzed to calculate timeliness, validity, usefulness, acceptability and value of the early warning system pilots. results although final interpretations of the evaluation results are pending, the evaluations were successfully led by the hcg and jointly conducted with other stakeholder engagement. leadership by the owners of the systems has already resulted in the recognition that certain aspects of the pilot surveillance systems demonstrate a successful and affordable approach, while others will need to consider more cost-effective strategies. though further analysis will likely continue to show the utility of cbs strategies, the ownership approach is resulting in an outcome of broad stakeholder input with approval from the host country government. conclusions community based surveillance can help to detect events of public health importance and effect earlier introduction into the health system for more timely situational awareness and response. however, it is difficult to determine the costs associated with different strategies of implementation and operation in order to ascertain the value for public health action. additionally, pilot implementations of these systems are often funded at a level that cannot be replicated nationally and not for a prolonged period of time. while it is believed that cbs can be a cost effective early notification system, continual monitoring and routine evaluation is required. by routinely monitoring cost and quality, sustainability of the system can be continually assessed and system adaptations made accordingly. key to remember is that evaluation must occur from an owner’s perspective and must engage the people who are going to govern, operate and provide the ongoing resources for system operation. in this way, effectiveness and efficiency can be continually monitored within the parameter of cost so that viability of the system can be ascertained. keywords community based surveillance; evaluation; stakeholder engagement; global health *carrie eggers e-mail: ceggers@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e169, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts poison center data for public health surveillance: poison center and public health perspectives emily v. glidden1, jay schauben2, prakash r. mulay3 and royal law*1 1hsb/ett, cdc/ondieh/nceh, atlanta, ga, usa; 2florida/usvi poison information center, jacksonville, fl, usa; 3florida department of health, tallahassee, fl, usa objective to discuss the use of poison center (pc) data for public health (ph) surveillance at the local, state, and federal levels. to generate meaningful discussion on how to facilitate greater pc and ph collaboration. introduction since 2008, poisoning is the leading cause of injury-related death in the united states; since 1980, the poisoning-related fatality rate in the united states (u.s.) has almost tripled1. many poison-related injuries and deaths are reported to regional pcs which receive about 2.4 million reports of human chemical and poison exposures annually2. federal, state, and local ph agencies often collaborate with pcs and use pc data for ph surveillance to identify poisoning-related health issues. many state and local ph agencies have partnerships with regional pcs for direct access to local pc data which help them perform this function. at the national level, the national center for environmental health (nceh) of the centers for disease control and prevention (cdc) conducts ph surveillance for exposures and illnesses of ph significance using the national poison data system (npds), the national pc reporting database and real-time surveillance system. though most pc and ph officials agree that pc data play an important role in ph practice and surveillance, collaboration between ph agencies and pcs can be hindered by numerous challenges. to address these challenges and bolster collaboration, the pc and ph collaborations community of practice (cop) has collaborated with members to provide educational webinars; newsletters highlighting the intersection of ph and pc work; and in-person meetings at relevant national and international conferences. the cop includes over 200 members from state and local ph departments, regional pcs, cdc, the american association of poison control centers (aapcc), and the u.s. environmental protection agency (epa). keywords poison control center; department of public health; center for disease control and prevention; collaboration references 1warner m, chen lh, makuc dm, anderson rn, and minino am. drug poisoning deaths in the united states, 1980–2008. national center for health statistics data brief, december 2011. accessed 8/29/2012. 2mowry jb, spyker da, brooks de, zimmerman a, schauben jl (2016) 2015 annual report of the american association of poison control centers’ national poison data systems (npds): 33rd annual report, clinical toxicology, 54:10, 924-1109. *royal law e-mail: hua1@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e77, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e324, 2019 isds 2019 conference abstracts cost-effectiveness analysis of one health surveillance strategies for rabies control kristyna rysava, michael tildesley university of warwick objective to evaluate public health and economic impact of intersectoral one health enhanced surveillance strategies of canine rabies in the philippines to inform vaccine procurement and provision plans. introduction rabies control programmes are being implemented across the philippines, with a number of islands and provinces on track for the elimination of both human and dog rabies [1,2]. in spite of considerable progress in control programmes, costs of post exposure prophylaxis (pep) remain high with bite incidence rapidly increasing every year. indiscriminate pep administration can strain healthcare budgets, and eventually redirect focus from essential mass dog vaccination campaigns. it is an ethical imperative to improve access to pep for those at risk; however, under the current framework operating in the philippines, indiscriminate pep administration results in excessive expenditure on non-case patients and subsequent financial strains and vaccine shortages, whilst vulnerable communities remain undetected. at the same time, incursions represent an obstacle to achieving and maintaining rabies freedom [3,4]and have been shown to pose a threat to elimination goals [5]. the lack of formal surveillance is the primary cause, leading to late detection of disease at which point substantial secondary transmission within the dog population already occurs. there is, therefore, an urgent need to enhance and streamline surveillance to maximize detection potential for rapid outbreak response and to guide policy decisions regarding public health practice. workable surveillance criteria are needed for more judicious and effectiveuse of pep to identify high risk exposures and thus prevent unnecessary risk and further transmission when vaccine stocks are limited. methods integrated models that capture epidemiological and health dynamics are essential to evaluate cost-effectiveness of control strategies and have the potential to directly inform rabies control programmes. here we sought to develop an epidemiological model for rabies transmission within the dog population and from dogs to humans, incorporating information on health-seeking behaviour collected through a longitudinal enhanced surveillance study of dog bite-injury patientsongoing atanti-rabies clinics in albay province, philippines.through computational simulations, we investigated changes in rabies dynamics and economic benefits of three potential surveillance scenarios: (1) current practice of indiscriminate pep administration with no investigation of biteincident histories, (2) quarantine of suspect dogs identified through bite-histories of patients presenting at clinics and (3) quarantine of suspect dogs with detailed triage of patients and follow up outbreak investigations. results utilizing data collected at anti-rabies clinics, we found that bite incidence in albay is high (monthly mean=796, sd=337) with pep administered unsystematically. all patients presented at clinics received at least 1 dose of pep, 95% of patients received 2 doses and 89% of patients received 3 doses. only 3% of patients received the fourth dose, likely owing to the cost patients are charged for the last dose (first three doses are provided free of charge). additionally, 17% received a dose of costly rig. this is consistent with previous reports of generous use of pep and rig in the philippines [6]. we found that in comparison to the current practices (scenario1) the integrated bite-case management strategies – quarantine of suspect dogs (scenario 2) and quarantine of suspect dogs with detailed triage of patients and outbreak investigations (scenario 3) – demonstrated a substantial reduction in costs through savings on expensive pep and rig despite additional expenditures on surveillance. the total costs for rabies prevention in humans would be reduced by 47% and 57% deploying scenarios 2 and 3 respectively. however, an ongoing risk of human deaths persists for as long as rabies continues to circulate in domestic dog populations. we have, therefore, investigated the impact of dog quarantine on rabies dynamics in the context of disease elimination and persistence. scenarios 2 and 3 resulted in a clear decline in incidence of both dog and human cases. moreover, under increased detection of infected dogs through field investigations (scenario 3) rabies appears to persist solely through repeat exogenous incursions. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e324, 2019 isds 2019 conference abstracts conclusions coalescing our understanding of health landscapes with that of transmission dynamics enables us to evaluate the demand of pep provisioning, how this demand will evolve across the elimination timeframe, and the effectiveness of individual intervention strategies in preventing human deaths. joint investigations foster intersectoral relationships and collaborative investments between public health and veterinary services. triage of patients and investigations of suspect dogs allows for improved pep recommendations and reduction of the current unnecessary expenditures whilst active field investigations lead to increased and early detection of rabies in dogs and identification of rabies exposed persons who would otherwise not seek care. quarantine of rabies suspect dogs appears powerful in curtailing transmission, but large-scale vaccination of dogs is necessary for complete interruption of transmission of the virus and sustained elimination of rabies, given the enduring risk of re-introductions from neighbouring populations [7-9]. however, early detection of incursions is critical and can preclude an undesired outbreak. integrated one health approaches of rabies surveillance have the potential to substantially increase case detection [10, 11] and ultimately generate vital evidence for verifying freedom from disease [12]. references [1] miranda lm et al., 2017, transbound emerg dis.; [2] barroga trm et al., 2018, trop med and inf dis.; [3] zinsstag j et al., 2017, science translational medicine.; [4] bourhy h et al., 2016, plos pathogens.; [5] tohma k et al., 2016, genetics and evolution.; [6] hampson ket al., 2015, plosneg trop dis.; [7] putra aag et al., 2013, emerg inf dis.; [8] bamaiyi ph et al., 2015, j vet advanc.; [9] windiyaningsih c et al., 2004, j med assoc thai.; [10] rajeev m et al., in print, vaccine.; [11] rysava k et al., in print, vaccine.; [12] hampson k et al., 2016, biorxiv. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e382, 2019 isds 2019 conference abstracts epidemiological distribution of reported west nile cases in houston, texas, 2014-2017 razina khayat, alex nguyen, sudipa biswas, hafeez rehman, kirstin short, najmus abdullah informatics, city of houston, houston, texas, united states objective to demonstrate an overview of the epidemiological and clinical distribution of reportable west nile cases in houston, texas, from 2015-2017. introduction west nile virus (wnv) is considered the leading cause of domestically acquired arboviral disease and is spread through mosquitoes. in general, the majority of the cases are asymptomatic. one in five people infected will display mild symptoms like fever, he adache, body ache, nausea, and vomiting. only about 1 in 150 people infected will develop serious neurologic complications such as encephalitis and meningitis. according to cdc, in 2017, there were 133 confirmed cases including 5 deaths and 14 presumptive blood donors reported in the state of texas. out of the confirmed cases, there were 85 neuroinvasive and 48 nonneuroinvasive disease cases. methods data were extracted from houston’s electronic disease surveillance system (hedss) from january 1, 2014, to december 31, 2017. a total of 45 confirmed cases are included in this analysis to examine the epidemiologic characteristics of the wnv cases. a confirmed case is an illness with onset of acute focal limb weakness and an mri showing a spinal cord lesion largely restri cted to gray matter and spanning one or more spinal segments. results among the confirmed cases, 67% of were males. age group 60 and above (47%) had the highest proportion of wnv cases. whites (26%) represented the highest number of confirmed cases followed by hispanics (24%). seventy six percent of the cases were hospitalized. non-neuroinvasive clinical presentations found among confirmed wnv cases were fever (94%), headache (76%) followed by chills and rigors (68%). among the neuroinvasive presentations, altered mental status had the highest proportion (24%), followed by stiff necks (18%), ataxia (12%), and seizure (9%). conclusions wnv is mostly prevalent in white male adults over 60 years of age, with majority of cases have common neuroinvasive symptoms like altered mental status, stiff necks, and ataxia. for non-neuroinvasive cases clinical symptoms were fever, headache, chills and rigors. wnv infection is a markedly underreported disease as most of the infected people do not seek medical care due to mild or no symptoms. currently there are no specific treatments available. thus, continued monitoring and surveillance activities are warranted for prevention and control of wnv complications as well as decreasing the risk of infection. acknowledgement we thank the flollowing agencies for providing data for this study: texas department of state health services. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e382, 2019 isds 2019 conference abstracts houston health department, diivision of disease prevention and control. houston electronic disease surveillance system. references 1. cdc. west nile virus [internet]. atlanta: center for disease control and prevention (cdc), national center for emerging and zoonotic infectious diseases (ncezid), and division of vector-borne diseases (dvbd); last reviewed: september 19, 2018. available from: https://www.cdc.gov/westnile/index.html http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e284, 2019 isds 2019 conference abstracts forming collaborations through the data quality committee to address urgent incidents sophia crossen2, krystal s. collier1, courtney fitzgerald3, kaitlyn ciampaglio4, lakshmi radhakrishnan5, jill baber6 1 public health statistics, arizona department of health services, phoenix, arizona, united states, 2 kansas department of environment and health, topeka, kansas, united states, 3 cerner corporation, kansas city, missouri, united states, 4 division of health informatics and surveillance, centers for disease control and prevention, atlanta, georgia, united states, 5 mcking consulting corporation, atlanta, georgia, united states, 6 north dakota department of health, bismark, north dakota, united states objective the national syndromic surveillance program (nssp) community of practice (cop) works to support syndromic surveillance by providing guidance and assistance to help resolve data issues and foster relationships between jurisdictions, stakeholders, and vendors. during this presentation, we will highlight the value of collaboration through the international society for disease surveillance (isds) data quality committee (dqc) between jurisdictional sites conducting syndromic surveillance, the centers for disease control and prevention’s (cdc) nssp, and electronic health record (ehr) vendors when vendor-specific errors are identified, using a recent incident to illustrate and discuss how this collaboration can work to address suspected data anoma lies. introduction on november 20, 2017, several sites participating in the nssp reported anomalies in their syndromic data. upon review, it was found that between november 17-18, an ehr vendor’s syndromic product experienced an outage and errors in processing data. the isds dqc, nssp, a large ehr vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire nssp cop. description syndromic surveillance site administrators in several jurisdictions noticed data anomalies after running regular daily and weekend trend reports on november 20, 2017, including large amounts of either missing or extra data. after investigating the data fur ther, they were able to narrow their focus to locations using one common ehr vendor. some sites also noticed similar trends in electronic laboratory reporting data. while the anomalies were not consistent across or even within sites, these issues were reported to the nssp, which supported early awareness and response efforts. the ehr vendor resources became aware early on november 18th when internal data quality reports contained irregularly large amounts of data. beginning on monday november 20th the ehr vendor team followed procedures for notifying clients and public health programs of the issue via emails and posting to shared community forums. the routine dqc meeting on december 8, 2017 included a brief discussion of the incident, bringing this to the attention of community members not yet aware of the issue or not sure how to proceed in correcting errors in their data. the nssp engaged its own cdc-based site inspectors to review data trends for all facilities and work with sites to identify facilities using the ehr vendor product so they could better target their investigations. the inspectors also sent a notice to all site administrators informing the community of potential anomalies in their data. this prompted site administrators nationwide to evaluate data for facilities using the vendor product, and review communications sent by vendor representatives to some of the affected sites. the nssp then provided information to sites regarding the process for deleting data from the nssp servers and essence. to address ongoing questions and confusion that persisted throughout the community, the dqc held another meeting on january 12, 2018 to bring sites, the nssp, and ehr vendor representatives together to discuss how to correct data anomalies. solution s were proposed and potential drawbacks for each were discussed. because the impact to each site was somewhat unique, multiple solutions were employed based on site specific concerns, capabilities, and requirements. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e284, 2019 isds 2019 conference abstracts acknowledgement centers for disease control, cerner corporation, data quality committee http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e257, 2019 isds 2019 conference abstracts partnership in surveillance: a kerala model to emerging public health threats sukumaran ariyari directorate of health services, india objective to prove the role of partnerships in disease surveillance and response to emerging public health threats in kerala state, india. introduction kerala is a small state in india, having a population of only 34 million (2011 census) but with excellent health indices, human development index and a worthy model of decentralised governance. integrated disease surveillance program, a centrally supported surveillance program, in place since 2006 and have carved its own niche among the best performing states, in india. laboratory confirmation of health related events/disease outbreaks is the key to successful and timely containment of such events, which need support from a wide range of laboratories-from primary care centers to advanced research laboratories, including private sector. in a resource constraint setting, an effective model of partnership have helped this state in achieving great heights. networking with laboratories of medical education department, and premier private sector laboratories, financing equipment and reagents through decentralised governance program, resource sharing with other national programs, laboratories of food safety, fisheries and water authorities have resulted in laboratory confirmation of public health events to the extend of 75-80% in the past 5 years in the state. etiological confirmation accelerated response measures, often multidisciplinary, involving human health sector, animal health, agriculture, wild life and even environmental sectors, all relevant in one health context. methods during 2013-14, state launched a laboratory networking initiative, with aid and guidance from central government through a mutually beneficial mou, linking all the 5 govt medical college microbiology laboratories with the state health surveillance system. a state laboratory coordinator was designated, and these teaching hospital were requested to assist the state in testing of outbreak samples from adjoining 3-4 districts. additional funds were provided for these institutions after a team assessment and periodic monitoring. all the 14 districts of state gained remarkably in laboratory confirmation of various outbreaks. during 2013, when one of the remote districts in the state detected an unusual fever cluster among the indigenous community, investigation by a multidisciplinary team, supported by a reputed private sector virology laboratory of an academic institution of the neighbouring state, confirmed lyme disease, first time in the state. in 2014 and 2015, the same laboratory confirmed another hitherto unreported disease, kyasanur forest disease, in the same district. these two events lead to the establishing of a private public partnership model in disease surveillance in the state. this model shared physical infrastructure in the govt hospital premises with technological support from the virology center. since then, this laboratory has contributed to >90% of laboratory confirmation of health events in the district. eventually, the same laboratory became the pioneer in confirmation of the first nipah virus outbreak in the state in 2018. this laboratory is also the reference laboratory for h1n1 and avian influenza for whole of south india. this surveillance network, has since then, established additional units in other parts of the state through special government order. from the response perspective also, the state adopted similar partnership approach. the strategy for control of kyasanur forest disease(kfd) is a classical example. monkey deaths were autopsied by wildlife experts, domestic animals were treated for tick infestation by the veterinary officers, research work done at veterinary university, human cases treated and vulnerable population vaccinated by human health officers, tribal and revenue department addressed the welfare aspects of the affected indigenous communities, and the district collector coordinated all related activities. it was a pathbreaking experience, and since 2015, till date, no new case is reported from the district, unlike hotspots in other parts of india. in 2014, the state gained from fisheries department laboratory, by confirmation of a fish toxin from an event of food borne infection outbreak. in the same year, veterinary university laboratory isolated vibrio cholera from water samples from a cholera outbreak. in 2018, the state surveillance unit, engaged with veterinary university of the state to undertake mat testing of human leptospirosis cases for facilitating the identification of serovars, another landmark effort, approved by govt of india. the state surveillance system also receives tremendous support from laboratories of research centers like rajeev gandhi center for biotechnology and vector control research center of icmr (indian council of medical research center). the state is now, http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e257, 2019 isds 2019 conference abstracts preparing a draft action plan for constituting a one health governance secretariate in kerala, to bring together all the stakeholders in disease surveillance, for optimizing their contribution. results state health surveillance system detected 135,130,140,130,disease outbreaks during the years 2014,15,16,17,and 93, till date in 2018. the laboratory confirmation of 65%,75%,80%,82.5% and 65.5% in respective years facilitated prompt response by the state. this was made possible with an extensive laboratory collaboration with partners ranging from institutional labs of state government as well as decentralised local self governments,(12.3%) regional public health labs(13.8%), referral network labs of govt medical college hospitals (16.2%), manipal center for viral research lab(11.5%) kerala water authority labs (6.2%), food security and safety department (2.3%) and a small contribution by private laboratories (1.5%) during 2017. in 2018, 324 human samples were tested and 16 samples confirmed for nipah virus disease, from mcvr manipal. the same laboratory confirmed lyme disease (2013) and kyasanur forest disease (2014 and 2015) from human samples. 3 environmental samples were tested positive for legionnaires bacteria from cooling system of 2 tourist hotels, following notification of legionnaires pneumonia among 2 foreign tourists (2016 and 17). fish toxin "ciguaterin" was confirmed from an incident of food borne outbreak by a laboratory attached to fisheries department (2015) a unique example of one health application in disease surveillance and outbreak response. laboratories attached to kerala water authority supports testing of water samples during water borne infections and food safety department facilitates analysis of food items during food borne infections. 7 water samples tested positive for vibrio cholerae during a cholera outbreak, done through research wing of veterinary university microbiology lab in 2016. an instance of primary amoebic meningoencephalitis was confirmed through a premier private tertiary center laboratory. leptospira serovars are being identified through a collaborative project with a veterinary university(2018). conclusions kerala state in india has shown many successful models in development sector. partnership in laboratory surveillance is the most recent one in the segment. besides interdepartmental collaboration, a unique model of private public partnership is also tried by this state, resulting in historic achievements like high eteological confirmation of outbreaks including the most recent and first ever nipah virus disease ample evidence for state's commitment to ihr compliance as well. this model, i feel is replicable in similar situations in resource poor countries across the globe. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e396, 2019 isds 2019 conference abstracts leptospirosis, climate and satellite-based environmental factors: a temporal modeling pandji w. dhewantara, wenbiao hu, wenyi zhang, wenwu yin, fan ding, abdullah mamun, ricardo j. soares magalhaes university of queensland, australia objective to quantify the effects of climate variability, selected remotely-sensed environmental factors on human leptospirosis in the highrisk counties in china. introduction leptospirosis is a zoonotic disease caused by the pathogenic leptospira bacteria and is ubiquitously distributed in tropical and subtropical regions. leptospirosis transmission driven by complex factors include climatic, environmental and local social conditions [1]. each year, there are about 1 million cases of human leptospirosis reported globally and it causes approximately 60,000 people lost their lives due to infection [2]. yunnan province and sichuan province are two of highly endemic areas in the southwest china that had contributed for 47% of the total national reported cases during 2005-2015 [3]. factors underlying local leptospirosis transmission in these two areas is far from clear and thus hinder the efficacy of control strategies. hence, it is essential to assess and identify local key drivers associated with persistent leptospirosi s transmission in that areas to lay foundation for the development of early-warning systems. currently, remote sensing technology provides broad range of physical environment data at various spatial and temporal scales [4], which can be used to understand the leptospirosis epidemiology. utilizing satellite-based environmental data combined with locally-acquired weather data may potentially enhance existing surveillance programs in china so that the burden of leptospirosis could be reduced. methods this study was carried out in two counties situated in different climatic zone in the southwestern china, mengla and yilong county (fig 1). total of 543 confirmed leptospirosis cases reported during 2006-2016 from both counties were used in this analysis. time series decomposition was used to explore the long-term seasonality of leptospirosis incidence in two counties during the period studied. monthly remotely-sensed environmental data such as normalized difference vegetation index (ndvi), modified normalized water difference index (mndwi) and land surface temperature (lst) were collected from satellite databases. climate data include monthly precipitation and relative humidity (rh) data were obtained from local weather stations. lagged effects of rainfall, humidity, normalized difference vegetation index (ndvi), modified normalized difference water index (mndwi) and land surface temperature (lst) on leptospirosis was examined. generalized linear model with negative binomial link was used to assess the relationships of climatic and physical environment factors with leptospirosis. best-fitted model was determined based on the lowest information criterion and deviance. results leptospirosis incidence in both counties showed strong and unique annual seasonality. bi-modal temporal pattern was exhibited in mengla county while single epidemic curve was persistently demonstrated in yilong county (fig 2). total of 10 and 20 models were generated for mengla and yilong county, respectively. after adjusting for seasonality, final best-fitted models indicated that rainfall at lag of 6-month (incidence rate ratio (irr)= 0.989; 95% confidence interval (ci) 0.985-0.993, p<0.001) and current lst (irr=0.857, 95%ci:0.729-0.929, p<0.001) significantly associated with leptospirosis in mengla county (table 1). while in yilong, rainfall at 1-month lag, mndwi (5-months lag) and lst (3-months lag) were associated with an increased incidence of leptospirosis with a risk ratio of 1.013 (95%ci: 1.003-1.023), 7.960 (95%ci: 1.241-47.66) and 1.193 (95%ci:1.0951.301), respectively. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e396, 2019 isds 2019 conference abstracts conclusions our study identified lagged effect and relationships of weather and remotely-sensed environmental factors with leptospirosis in two endemic counties in china. rainfall in combination with satellite derived physical environment factors such as flood/water indicator (mndwi) and temperature (lst) could help explain the local epidemiology as well as good predictors for leptospirosi s outbreak in both counties. this would also be an avenue for the development of leptospirosis early warning system in to support leptospirosis control in china. references 1. haake da, levett pn. 2015. leptospirosis in humans. curr top microbiol immunol. 387, 65-97. pubmed https://doi.org/10.1007/978-3-662-45059-8_5 2. costa f, et al. 2015. global morbidity and mortality of leptospirosis: a systematic review. plos negl trop dis. 9, e0003898. pubmed https://doi.org/10.1371/journal.pntd.0003898 3. dhewantara pw, et al. 2018. epidemiological shift and geographical heterogeneity in the burden of leptospirosis in china. infect dis poverty. 7, 57. pubmed https://doi.org/10.1186/s40249-018-0435-2 4. herbreteau v, salem g, souris m, hugot jp, gonzalez jp. 2007. thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. health place. 13, 400-03. pubmed https://doi.org/10.1016/j.healthplace.2006.03.003 table 1. parameter estimates of the best-fitted model of the association of climatic and remotely-sensed variables with leptospirosis incidence in mengla county and yilong county, china http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25388133&dopt=abstract https://doi.org/10.1007/978-3-662-45059-8_5 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26379143&dopt=abstract https://doi.org/10.1371/journal.pntd.0003898 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29866175&dopt=abstract https://doi.org/10.1186/s40249-018-0435-2 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16735137&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16735137&dopt=abstract https://doi.org/10.1016/j.healthplace.2006.03.003 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e351, 2019 isds 2019 conference abstracts mental health and opioid addiction comorbidities among chronic pain patients bill saunders1, 3, kevin cevasco2, 1 1 w atson health, ibm, cambridge, massachusetts, united states, 2 george mason university, fairfax station, virginia, united states, 3 unc charlotte, charlotte, north carolina, united states objective assessing mental health and opioid addiction comorbidities among chronic pain patients using a large longitudinal clinical, operational, and laboratory data set. introduction the national institute for drug abuse report, common comorbidities with substance use disorders, states there are “many individuals who develop substance use disorders (sud) are also diagnosed with mental disorders, and vice versa.” [1] prescription opioids are amongst the most commonly used drugs that lead to illicit drug use [2]. much of the data about the starting point of the prescription opioid addiction is in the patient health history and is recorded within the provider electronic health record and administrative systems. description there are a variety of addiction and misuse risk screening tools available and selecting appropriate tools screening can be confusing for providers. examples of common screening tools: opioid abuse risk screener (oars), opioid risk tool (ort), screener and opioid assessment for patients with pain (soapp), current opioid misuse measure (comm), diagnosis, intractability, risk, and efficacy (dire). these opioid risk screening tools are interview based and vary in how they survey for psychosocial factors. the screening tools are useful, but are meant only to alert the provider to conduct further investigation [3]. understanding how the comorbidities recorded in the patient’s clinical interactions may help improve risk assessment investigations and ongoing monitoring programs. studying the chronic pain patients’ longitudinal clinical, operational, and laboratory records provides the basis for better study controls than those using population based on emergency department admission and mortality events. methods the analysis leverages ibm's explorys electronic health record (ehr) data, a large integrated source of real world clinical, operational and lab data across 39 large integrated delivery networks that span the continuum of care. in addition to demographic characteristics of drug abusers, we will describe common comorbidities of selected mental health diagnoses, examine codingrelated issues, distinguish chronic and episodic addiction and look for regional differences due to state/local level prescribing training and provider addiction awareness. references 1. abuse ni on d. part 1: the connection between substance use disorders and mental illness [internet]. [cited 2018 sep 29]. available from: https://www.drugabuse.gov/publications/research-reports/commoncomorbidities-substance-use-disorders/part-1-connection-between-substance-use-disordersmental-illness 2. lankenau se, teti m, silva k, bloom jj, harocopos a, et al. 2012. initiation into prescription opioid misuse amongst young injection drug users. int j drug policy. 23(1), 37-44. pubmed https://doi.org/10.1016/j.drugpo.2011.05.014 3. hudspeth rs. 2016. safe opioid prescribing for adults by nurse practitioners: part 1. patient history and assessment standards and techniques. j nurse pract. 12(3), 141-48. https://doi.org/10.1016/j.nurpra.2015.10.012 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21689917&dopt=abstract https://doi.org/10.1016/j.drugpo.2011.05.014 https://doi.org/10.1016/j.nurpra.2015.10.012 effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi effective uses of social media in public health and medicine: a systematic review of systematic reviews dean giustini1, syed mustafa ali2, matthew fraser3, maged n. kamel boulos3 1. university of british columbia, biomedical branch library, vancouver canada 2. mercy corps, islamabad, pakistan 3. alexander graham bell centre for digital health, university of the highlands and islands, elgin, united kingdom abstract introduction: research examining the effective uses of social media (sm) in public health and medicine, especially in the form of systematic reviews (srs), has grown considerably in the past decade. to our knowledge, no comprehensive synthesis of this literature has been conducted to date. aims and methods: to conduct a systematic review of systematic reviews of the benefits and harms (“effects”) of sm tools and platforms (such as twitter and facebook) in public health and medicine. to perform a synthesis of this literature and create a ‘living systematic review’. results: forty-two (42) high-quality srs were examined. overall, evidence of sm’s effectiveness in public health and medicine was judged to be minimal. however, qualitative benefits for patients are seen in improved psychosocial support and psychological functioning. health professionals benefited from better peer-to-peer communication and lifelong learning. harms on all groups include the impact of sm on mental health, privacy, confidentiality and information reliability. conclusions: a range of negatives and positives of sm in public health and medicine are seen in the sr literature but definitive conclusions cannot be made at this time. clearly better research designs are needed to measure the effectiveness of social technologies. for ongoing updates, see the wiki “effective uses of social media in health: a living systematic review of systematic reviews”. http://hlwiki.slais.ubc.ca/index.php/effective_uses_of_social_media_in_healthcare:_a_living_system atic_review_of_reviews doi: 10.5210/ojphi.v10i2.8270 copyright ©2018 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the emergence of social media (sm) and social networking services to communicate in real-time and on-the-go by patients and health professionals was recognized as an important public health development more than a decade ago [1-3]. http://hlwiki.slais.ubc.ca/index.php/effective_uses_of_social_media_in_healthcare:_a_living_systematic_review_of_reviews http://hlwiki.slais.ubc.ca/index.php/effective_uses_of_social_media_in_healthcare:_a_living_systematic_review_of_reviews effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi in 2007, kamel boulos described social networking services (snss) as collaborative, mediated environments [4], where personal computers and mobile devices can be used to foster stronger connections, and new forms of information can be shared. some examples of sm types are wikis (e.g., wikipedia), social networking sites (e.g., facebook, linkedin), media-sharing sites (e.g., youtube, slideshare), blogs and micro-blogs (e.g., blogger, twitter), immersive worlds (e.g., second life), and 3-d virtual globes (e.g., google earth) [4]. according to the pew research centre, the popularity of snss is linked to the adoption of healthier lifestyles and coping skills for day-to-day health concerns [5]. pew reports that patients enjoy helping each other and sharing their healthcare experiences [5]. patients say that the internet is a helpful way to find answers to health problems; in fact, the research shows that frequent use of social networks is associated with increased patient awareness and empowerment [6]. health professionals have increased their social networking via twitter, facebook, blogs, vlogs (video blogs, e.g., on youtube), infotainment, games and infographics [7]. background in a 2013 systematic review, moorhead et al. identified seven key ways that sm are being used in healthcare: i. to provide information on a range of issues; ii. to provide answers to medical questions; iii. to facilitate dialogue between patients and health professionals; iv. to collect data on patient experiences and opinions; v. to use sm as a health intervention, for health promotion and health education; vi. to reduce illness stigma; and vii. to provide a mechanism for online consultations [8]. social media research has improved considerably in the past decade. in fact, better empirical research is now conducted, and many higher quality studies are being published [8-10]. the aim in this sr is to conduct a qualitative synthesis of systematic reviews of the effective uses of sm in public health and medicine, both for patients and health professional groups. methods this paper is a systematic review and qualitative synthesis of papers published between 2003 to 2017. the authors perform a systematic review of srs as the volume of primary papers would have made meaningful synthesis of that literature impossible. we selected the sr methodology as it provided a more complete view of the literature and varying interventions, populations and settings. in addition, different types of papers can be compared and contrasted [11]. in exploring the relationships between studies, this sr aims to answer two questions. 1. what are the most effective uses, benefits and harms of sm usage in health and medicine? effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi 2. what tools are more effective (e.g., for information-sharing, communication, education, mobile and e-learning) as interventions in health programmes and services? in what ways are they beneficial or effective (or harmful), and for whom? at the start, we registered our protocol at prospero, the prospective register of systematic reviews [12]. here, we follow the entreq guidelines to enhance the transparency of our methods and to improve the reporting of the qualitative evidence [13]. information sources: bibliographic databases and search engines comprehensive searches were performed in fifteen bibliographic databases and search engines from 1995 to 2017 (table i). table i. bibliographic databases searched 1. pubmed.gov 2. medline (ovid) 3. cinahl (ebsco) 4. embase (ovid) 5. psycinfo (ebsco) 6. lisa: library and information science abstracts 7. library, information science & technology abstracts (lista) 8. education resource information center (eric) (ebsco) 9. academic search complete (ebsco) 10. alt health watch 11. health source 12. communication and mass media complete (3 citations) 13. proquest dissertations 14. google scholar 15. web of science reference harvesting and citation searches were conducted in google scholar and in the web of science. to locate recent papers, the authors also created current awareness alerts in databases such as medline, embase, the web of science and google scholar. key journals and prominent e-health journals were identified using the web of science and searched iteratively to increase sensitivity and to overcome any indexing deficiencies (table ii). as the top impact factor journals in e-health were available online, manual searching was conducted by scanning online tables of contents and by using the journals’ search engines. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi table ii. top e-health and informatics journals (manually searched table of contents) applied clinical informatics bmc medical informatics and decision making • bmc medical research methodology • cin: computers informatics nursing • digital health • health informatics journal • informatics for health & social care • international journal of medical informatics • journal of the american medical informatics association (jamia) • journal of biomedical informatics • journal of medical internet research • journal of telemedicine and telecare • methods of information in medicine • plos one • telemedicine and e-health search terms – effective uses of social media in healthcare the search strategies for medline and embase were developed based on the strategies reported by moorhead [8] and hamm [9], and adapted accordingly. to achieve optimal sensitivity, we created strategies that combined keywords with controlled or index terms (table iii). table iii. keywords and their associated controlled terms for searching a. "social media" or "social web" or "social software" or "social network*" or "web2" or "web 2.0" or "health 2.0" or "medicine 2.0" or "nursing 2.0" or "pharmacy 2.0" or “telemedicine 2.0”; b. blog* or facebook or flickr or googl* or “instant messaging” or instagram or microblog* or myspace or “online forum*” or patientslikeme or pinterest or podcast* or second life or snapchat or twitter or tweet* or tumblr or “user generated content” or “video sharing” or "virtual world*" or webcast* or “web log” or whatsapp or wiki* or youtube or zotero; c. patient* or health consumer*; d. “health provider*” or “health professional*” or “physician*” or “doctor*” or “hospital*” or med* student* or pharm* student* or nurs* student*. the following terms were added at various points in combination to refine and filter results: effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi • best practice*, benefit*, barrier*, risk*, compar*, concern*, cost*, cost-effect*, effectiv*, effectiveness, evaluat*; • harm*, impact, improve*, limitation*, negativ*, positiv*, private, privacy, problem*, safety, trend*, trolling, use*; • ehealth, e-health, e*learn*, healthcare, “health care”, medicine, public health, telehealth, mhealth • systematic review* or meta-analy* or systematic. the full search strategy for ovid medline is listed in appendix a. data extraction and analysis our searches yielded 8521 papers from 15 bibliographic databases, search engines and manual searches (reference harvesting and citation searching). our results were imported into refworks for title and abstract screening and results were loaded into ms excel. the casp (critical appraisal skills programme) tool for systematic reviews was used to assess the quality of papers [14]. we performed a thematic analysis using methods as described by braun and clarke [15]. titles and abstracts of retrieved citations were independently coded and assessed by two reviewers (dg, sma). papers viewed as eligible were obtained in full-text and assessed further using predefined inclusion and exclusion criteria. any disagreement between reviewers was resolved by a third (mnkb). reasons for exclusion were recorded using preferred reporting items for systematic reviews and meta-analyses (prisma). our aim was to describe and synthesize papers but we did not attempt to determine effect sizes in the studies. in excel, we created columns and rows to describe the papers and their features, and piloted our spreadsheet for data extraction. variables included the type of systematic searches performed, timeframe of searches, number of studies included, types of studies, sm tools and sns platforms examined, study and population characteristics, focus on the interventions, outcomes measured and results, author conclusions, and broader themes and categories. table iv. inclusion and exclusion criteria developed using pico we developed our inclusion and exclusion criteria by using our pico and research questions: participants/ population • adults. seniors. young adults, late adolescents and teens. health professionals, students and/or patients. • where mixed patient or health professional populations are examined, it will be important to determine the population and intervention being evaluated. intervention(s), exposure(s) • systematic reviews discussing sm as an intervention will be considered relevant. • web or internet-based interventions featuring sm and information-sharing technologies are considered relevant. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi • sm applications and social networking sites (snss) or platforms which create a virtual network of users in a publicly-accessible environment will be considered relevant. • the social media application must be used as part of a programme in health or medical education, public health, health promotion, health communication and/or informationsharing. • some online tools, such as blogs, wikis, podcasts and webcasts, and applications, such as whatsapp and wechat, will not necessarily be included if they do not offer the same level of dynamic interaction and real-time engagement afforded by snss. comparator(s)/ control • some studies will have no comparison or comparator intervention. • others will examine one tool against another; facebook versus twitter, for example. outcome(s) primary & secondary outcomes • effective uses of social media, tools and applications in public health and medicine. • barriers, limitations and facilitators to implementing and using social media. setting or context(s) • the healthcare setting will not always be specified though many of the papers with health topics will imply healthcare settings. • the contexts may be online, "virtual" and electronic, or other social media spaces on the web. study type or methodology • the study type or methodology will be systematic in nature, with systematic searches of the literature, including a qualitative or quantitative analysis. • studies that are not full systematic reviews will be considered if they are deemed to have utilized systematic searches. table v. inclusion and exclusion criteria inclusion criteria the following criteria helped us to make final judgements with respect to inclusion: i. papers were published as systematic reviews in peer-reviewed journals, or as unpublished dissertations and theses, and other grey reports; ii. papers published in english; iii. full text was available; iv. study populations were more than 18 years of age and either adults, health professionals, patients, non-professional caregivers or a combination; v. papers analysing more than one intervention with at least one among them being social media will be included; the effects of social media should be clearly outlined; effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi vi. papers that examine online social networking sites and learning management systems, tools or platforms with a high degree of social interaction or sociality will be considered; vii. papers that examine effective uses of social media, including benefits and limitations will be included; viii. papers scored high (>9) using the casp checklist for systematic reviews instrument. exclusion criteria the following criteria helped us to make a final assessment with respect to exclusion: • papers considered not systematic or whose focus was not primarily on the use of social media as the major intervention studied were excluded; • papers that focussed primarily on paediatric or early teenage populations were not considered relevant for the review, and excluded; • other exclusions: studies on interventions that are considered not relevant to our review, such as studies of the internet, icts (information and communication technologies), websites, most tele-health and e-health related studies will be excluded unless there was a significant social media aspect; • papers written in languages other than english; • full-text of papers were not available; • papers without relevant outcomes on effectiveness, benefits, limitations, harms; • papers that scored at 8 or below using the casp instrument. results in our searches, we identified a total of 8375 papers: 2051 in medline, 4075 in embase, 1062 in cinahl, 562 in psycinfo, and a total of 625 from the remaining databases. reference harvesting, search engine and manual searching added 146 papers for a total of 8521 papers (table vi). deduplication and screening reduced the number of papers to 232 which were assessed for eligibility using full-text. this reduced yield to 102 papers which were then critically appraised using casp. two reviewers independently provided scores out of 10 for each paper by indicating “yes” to each of ten items on the casp checklist. we had strong inter-rater agreement of the 102 papers (an overall agreement of 92%). discrepancies were adjudicated by a third reviewer. in the analysis, 42 papers scored high (9 out of 10) or a perfect 10 out of 10 score. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi table vi. prisma flow diagram illustrating study selection study validity a summary of the quality assessment of the 42 papers is contained in appendix b. study populations the study populations in the 42 papers dealt mostly with patients (n=30), health professionals or health students (n=8), or both patients and health professionals (n=4). ages ranged from young adults averaging 18 years of age up to adults in their senior years. the maximum age in the three studies that examined the effects of sm on older (senior) people was up to 80 years of age. publication years of papers in our inclusion set, 34 papers were published between 2014 and 2016. in 2014, there were 12 papers published, with 13 papers in 2015 and 9 papers in 2016. six papers were published between 2010 and 2013, and two papers were published in an early period from 2004 to 2009. the authors effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi observed a marked increase in the number of papers published starting in 2010. (since our searches were conducted to december 2016, we have seen a sharp increase in the numbers of papers published in 2017 and 2018 – a major reason why we have made this a living systematic review but more about that later.) description of the included reviews the summary of the 42 systematic reviews in this paper are listed in appendix c. the papers were published from 2004 to 2016. all but two (eysenbach et al., 2004; griffiths et al., 2009) were published between 2010 and 2016. the papers reveal a range of quantitative, qualitative and mixed method designs. a wide variance in the search strategies show a low of one reported database in one study (househ et al., 2014) to more than 11 reported in several papers (eysenbach et al., 2004; williams et al., 2014; dyson et al., 2016). the number of included papers ranged from a low of 3 (shaw et al., 2016) to a high of 170 (sawesi et al., 2016). the average number of included papers was 36.571 in each of the 42 included srs. breakdown of the # of srs by country of the corresponding authors (n=42 papers) ten papers were conducted in australia [16-25], 9 in the united states [26-34], 8 in the united kingdom [8,35-41], 3 in canada [42-44] and hong kong [45-47], and one each in chile [48], italy [49], korea [50], mexico [51], netherlands [52], new zealand [53], portugal [54], saudi arabia [55], and singapore [56]. all 42 papers were written in english and available in full-text. the included papers were published in a range of journals in public health and medicine such as informatics, general medicine and medical education. twenty papers (47%) were published in informatics journals; 12 of those (29%) were published by the journal of medical internet research. ten papers (24%) were published in general medicine or medical education journals. the remaining papers appeared in specific biomedical journals where the focus of the systematic review seemed appropriate for the journal in question: infectious diseases (n=3), pharmacy (n=2), public health (n=2), and one paper each in journals specializing in nutrition, paediatrics, rehabilitation and microbiology and immunology. table vii. journal titles & subject domain of included srs • journal of medical internet research (12 papers) • jmir medical informatics (1 paper) • cognitive computation (1 paper) • computers in human behavior (1 paper) • digital health (1 paper) • games for health journal (1 paper) • health informatics journal (1 paper) • jamia (1 paper) • journal of telemedicine and telecare (1 paper) effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi o total: 20 papers under e-health & informatics • medical teacher (2 papers) • academic medicine (1 paper) • health education (1 paper) o total: 4 papers under medical education • plos one (2 papers) • bmj (1 paper) • bmj open (1 paper) • bmc health serv res (1 paper) • diabetes research and clinical practice (1 paper) o total: 6 papers under general medicine • sexually transmitted diseases (1 paper) • sexually transmitted infections (1 paper) • travel medicine and infectious disease (1 paper) o total: 3 papers under infectious diseases • human vaccines & immunotherapeutics o total: 1 paper under microbiology & immunology • nutrition review (1 paper) o total: 1 paper under nutrition • children and youth services review (1 paper) o total: 1 paper under paediatric health (but covering populations >18 years old) • research in social and administrative pharmacy (1 papers) • british journal of clinical pharmacology (1 paper) o total: 2 papers in pharmacy • bmc public health (1 paper) • american journal of public health (1 paper) o total: 2 papers in public health • disability and rehabilitation (1 paper) o total: 1 paper in rehabilitation • hku theses online (1 paper) o total: 1 paper in hkuto effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi thematic analysis and categorization the 42 papers in this review were analyzed using a method as described by braun and clarke [14]. the work of thomas and harden [57] helped to inform our thematic and narrative synthesis. thematic analysis is a flexible means of identifying, analyzing, and reporting patterns within data [14]. data analysis was completed by note-taking on first impressions of reading the papers. each paper was then analyzed, and themes or categories were noted in relation to study participants, technologies or health conditions. following the analysis of papers, a hierarchical content analysis was conducted. themes from the reviews were coded and placed into categories. these were placed into higher order or smaller sub-themes. according to thomas and harden (2008), thematic synthesis consists of three stages: coding of text 'line-by-line', development of 'descriptive themes' and a generation of 'analytical themes' [57]. while the development of descriptive themes is closely linked to the primary studies, the analytical themes represent a stage of interpretation where the reviewers 'go beyond' the primary studies and generate new interpretive constructs, explanations and hypotheses. findings from the thematic analysis the thematic analysis is presented as a conceptual map in appendix d. the conceptual map reveals the multi-dimensional nature of sm, its most common uses and tools, potential benefits and challenges. all 42 papers were classified into 9 major themes or categories: 1) uses of social media 2) social media formats 3) population groups using sm 4) technology and healthcare 5) benefits of sm 6) limitations (including harms) of sm 7) disease types and prevention 8) outcome measures 9) psychology and emotions. while the nine themes provide insight into the 42 papers, and aid in our categorization, we developed 97 subthemes and categories for further classificatory detail. our thematic analysis shows that the general public, patients and health professionals use sm for multiple reasons. in alphabetical order, the following themes emerged: behavioural changemanagement [21,24,25,31,47,49,53,54], disease prevention and management [39,47,49,53], disease surveillance [27], health education and communication [8,11,22,39,49], online learning [24,28,32,33,37,40,41], online reporting and symptom reporting [25,38,41], outbreak management [27], pharmacy practice and education [16], and professional development [22]. the most common sm types were blogs [28,39,43,52], bulletin boards [20,42,53], discussion boards & forums [20,42,53], facebook [16,21,22,24,25,27,28,38-40,43,50,52-54], internet chatrooms & support groups [20], learning management systems (lms) [46], listservs, mobile sharing apps [25], skype [43], text-messaging applications, twitter [21,25,28,38-40,52], effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi videogames and virtual worlds [18,42,56], wikis (including wikipedia) [16] and youtube [38,39,43]. key populations in the thematic analysis were adolescents and teens (young people/youth >18 years old) [8,19,24,29,35,36,43,47,48,54], older (senior) adults (up to 80 years old) [32,45,51], health professionals [18,28,33,37,40] pharmacists [16,37], physicians [28,37,40], students in healthcare [28,37,46] and vulnerable populations [17,48,51]. several important information technology themes emerged such as the use of mobile apps [25], digital technology [47], information and communication technologies [31,45], mhealth (mobile health) [25], mobile phones [25,41,45,49], social media technologies and social networking [3538,47,48,51,54], tele-monitoring, video gaming [31], virtual and 3d (three-dimensional) learning environments [18,22,31,37], and web 2.0 [22]. recurring themes of benefits were related to increased community support [42,43], greater social connectedness [43,52], health management [8,21,25,27,31,32,41,49,53], health promotion [24,25,39,47,49], internet support groups [20,32,42], knowledge acquisition [22,40], learning opportunities [28,40] and social interaction [20,51]. social media were used by patients across health conditions and disease types to manage depression [15,31,41], diabetes [37,52], mental health [15,31,41] and sexual health [14,19,43,50]. other themes were increased awareness of aids and hiv prevention [28,37,43], medications and prescription adherence [51], immunizations and vaccinations [35,45], and obesity and weight management [16,20,21,50]. other themes included cancer [30], cardiovascular disease [25], influenza [49], travel medicine [39], traumatic brain injury [17], infectious diseases [27], and noncommunicable diseases [53]. in papers that examined sm usage by health professionals, the following main themes emerged: medical and pharmacy education [11,23,32,34,45], e-professionalism [11,17,36], and professional development and training [11,51]. the theme of limitations (barriers and/or harms of sm) for health professionals and health students emerged in e-professionalism [16,40], media richness of tools (e.g., the degree to which they provide affordances for sharing, greater online presence and self-disclosure potential) [8,20,53], and problems related to trolling and flaming [43]. the use of sm by patients was connected to a range of negative and positive psychological themes such as depression [20,36,45], emotions [52], risk behaviours [21,27,47,54], loneliness [45,50], mental health [20,36,45], self-harm and self-inflicted injury [43], self-management [25,32], social cognitive [54], social isolation [36,45,51,53] and suicidality [43]. positive and negative effects of sm in health populations positive effects and benefits sm have been used across a range of populations with both positive and negative effects. one paper in our inclusion set is eysenbach et al.’s highly-cited paper from 2004, which examined 45 papers from the early period of sm use from 1995 to 2003 [42]. the authors found that the benefits of peer-to-peer (p2p) communities and electronic self-support were difficult to assess with most effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi studies showing ‘no effects’. some authors acknowledged anecdotal evidence of electronic selfhelp groups but recommended better evaluation to determine the precise impact [8,36,54]. one large sr in our review synthesized 170 papers; a total of 112 randomized controlled trials, 7 case studies, 19 cohort studies, 15 cross-sectional analyses, and 17 quasi-experimental trials up to 2016 [31]. the authors found that 88.8% (151/170) showed some positive impact of sm on patient behaviours and health outcomes, and 82.9% reported major improvements in patient engagement by using sm platforms [31]. for example, facebook provided a forum for reporting personal experiences, asking questions, and receiving direct feedback for people living with diabetes [31]. text messaging enhanced successful engagement and hiv patients used internet-based interventions to access information and manage health problems [31]. some patients, in using telemonitoring, video and game-based interventions, found it useful to communicate with healthcare providers via information technologies [31]. in two well-performed meta-analyses, published in 2014 and 2015 respectively, the use of sm by patients in different age groups was found to result in some self-reported and measurable changes in behaviour [21,54]. some social networking services (sns) showed statistically-measurable effects in promoting healthy behaviours [21,54]. in 2015, laranjo et al. called for better research to be done in health behaviour change theory pointing out the phenomenon of social 'network alteration' where close ties and ‘homophily’ encourage health-behaviour-change diffusion in social networks [54]. maher et al. reported that 90% of the papers they examined revealed significant improvements in health behaviour change. however, it was unclear whether sns-based interventions were equally useful for all health behaviours longer term [21]. several recent srs published from 2014 to 2016 showed that increased interactions on sm [34,35,41,53] triggered positive changes in managing health problems [8,34,35,42,52,55]. although the effects were modest, the act of iterative information-sharing (a key activity on sm) provided benefits for patients in improved care [25,32], self-efficacy [20,21,55] and adoption of healthier lifestyles [8,26,35,55]. in public health studies from 2011 to 2015, sm were increasingly used in educating at risk and vulnerable populations [8,19,27,48]. facebook and twitter were, from 2011 to 2016, the most preferred sm tools (n=15 papers), followed by blogs (n=4), youtube (n=3), virtual worlds (n=3), and electronic bulletin boards (n=3). facebook and twitter were often chosen by users in meeting others and exchanging information about health concerns [8,20,35], and repeatedly used to access information and expertise [17,24,26,35,36]. there was little evidence statistically that they actually promoted health [35]. adults aged 18 to 49 make up the largest group using facebook and twitter, and officials applied them to target demographic groups for projects in sexual health, health promotion, and in disease screening [21,29,30]. the affordances of facebook and twitter (i.e., openness or “publicness”, high potential for or degree of sociality, ease of use) were well-suited to the social networking needs of most users [8]. every one of the 42 included papers demonstrated some benefit, even the review focussed on deliberate acts of self-harm in sm [43]. several reviews (n=18) looked at effectiveness or aimed to identify the benefits or “effects” of sm in some way [9,21,24-26,33,35,36,42,45-49,52-54,58]. in three reviews, information-sharing conferred benefits, such as closer social network ties, increased emotional support, and reduced anxieties and stigma [35,36,42]. the sustained effects effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi of sm were not proven; sm were often characterized as having potential rather than proven to be effective [25,35,47,53]. older (senior) patients were the main study populations in two papers from 2016 [45,51]. from 2013 to 2016, older adults were mentioned as the study population in several papers [8,30,44,45,55]. to assist older patients in coping with social isolation, sm tools were shown to be effective for some seniors. in most cases, sm should be selected with the population in mind where the best platforms have been studied empirically and deployed accordingly [45]. research on other types of icts (e.g., mobile phone–based instant messaging apps) should be conducted to promote understanding of ict-based social-isolation interventions for older people [45]. in reviews from 2011 to 2016, various diseases and health concerns were seen as the focus of interventions for depression, hiv, diabetes, cancer, heart disease, fitness, weight management and sexual health across all age ranges [19,20,26,30,33,34]. repeatedly, papers revealed that patients and health consumers were using sm to engage in socially-supportive activity and conversations [20,42]. selected e-health online social networks, such as patientslikeme and daily strength, were mentioned in four papers (8, 48, 52, 54), but their precise benefits and effectiveness were not clearly measured. in public health, there was growing usage of sm to educate the public about avoiding infectious agents [19,24,49] and to monitor emerging health threats [27]. at least two reviews characterized sm as having benefits in public health and developing policy [8,48]. positive feedback (if not clear evidence) was provided in some studies where sm was used in online and mobile learning [28,37,40]. identified benefits of using mobile-enabled sm in health education were related to the acquisition of new skills and knowledge for users on-the-go [28,40]. health professionals used sm to engage in mobile and socially-distributed learning and peer-topeer interaction [28,40,46]. use of facebook and twitter was viewed favourably and rarely associated with harmful effects in e-professionalism or social relationships [16,28,52]. benetoli et al. showed that pharmacists accessed facebook many times a day, using mobile devices (e.g., smartphones, tablets), but their use was restricted during work time in community pharmacies [16]. networking with colleagues on facebook seemed to break feelings of isolation experienced by some pharmacists, especially those in rural areas [16]. participants reported that some dispensaries blocked facebook [16]. the use of facebook and twitter in academic health was more open and focused on the lack of evidence of sm’s positive or negative effects; in fact, they were shown to be equally or more effective than other platforms [37]. commonly-cited challenges for healthcare workers using their mobiles were technical glitches (43%), variable learner participation (43%), and privacy/security concerns (29%) [28]. overall, the use of sm by health professionals was very positive with good levels of learner satisfaction [22,40]. sm resulted in increases in learner satisfaction and positive experiences for students, especially in problem-based learning [46]. one review found positive effects from the adoption of educational technologies, and listed 8 roles that tools such as digital learning objects, interactive whiteboards, plasma screens and learning management systems play in developing skills [46]. more training in using educational technologies was also mentioned [16,28,40,46]. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi problematic or negative effects of sm some problematic or harmful effects of sm were seen. as social networks take larger roles in our personal and professional lives, problematic effects are increasingly likely to emerge. some papers showed that sm has a role in increasing risky behaviours (displayed online which could be taken offline) as well as a diminished sense of well-being [8,35,45,50,52]. balatasoukas et al. said that negative emotions expressed on sm are contagious, thereby networking the problematic behaviours [35]. the idea of contagion was why some health information shared in social networks seems to be of poor quality, a phenomenon that affects the reliability of information as it is shared ‘virally’. in some cases, sm were responsible for spreading incorrect medical advice, false claims, misinformation and even fake news [8,9,30]. much debate and polarization exist about the impact of sm on the health and mental well-being of patients [36,52], especially teenagers. harmful effects include increased exposure to social isolation, depression and cyber-bullying [33]. other harmful effects in younger people include the triggering of addictive behaviours, with losses in self-efficacy and confidence [50]. further, some young adults participating in discussion forums and bulletin boards described feeling harassed and aggressively targeted (and “tracked down”) [20,42,53]. the benefits of sm for adolescents include better self-esteem and social capital, safe identity experimentation, and more opportunity for selfdisclosure with peers [33]. facebook provided efficient ways to contact adolescents but significant positive (or negative) effects were not seen from this tracking [23]. one paper stated that adolescents progress to new media quickly, enjoy health anonymity online [33] and create hidden “secret” worlds [23]. some users seek privacy in matters of sexual health [19,27,48], hiv prevention and testing [47], and in managing their sexual behaviour [24,29]. the barriers and limitations of sm, especially on facebook and twitter, may be related to ‘context collapse’, where personal and professional boundaries can overlap undesirably [59]. when sm were used in medical education, the benefits of private social networks and learning management systems were not fully measured [46]. one problem is the failure to identify whether facebook or twitter are more or less effective than private platforms (or as supplements in ‘blended’ models). broad use of sm is inhibited by the tension between openness on the web and upholding medical privacy and confidentiality norms. some adverse events were seen but they occurred infrequently confirming for some that sm can be used safely by medical educators (notwithstanding their benefits are hard to prove) [11,17,36]. twelve limitations of using sm in communication was drawn to help health professionals fully appreciate all the challenges [8]. a few studies published in 2014 and 2015 raised the concern about professional misconduct for health workers and distractions related to sm use [11,17,36]. these concerns inhibit the use of sm generally. more robust research is needed in medical education to understand how tools and platforms can be used in mobile learning especially at a distance. the effects of sm on medical education, recruitment, and professionalism should also be studied further [28,37]. sm may lead to better social interactions for some, but recurring concerns were expressed about harms. for example, concerns about cyberbullying and its effects on the well-being of young adults were commonly expressed [36]. sm had positive effects on health provider-patient relationships, but some users express discomfort with communicating via social networks [52]. in one paper, extreme uses of sm resulted in trolling (deliberate provocation of others) and flaming (mocking effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi or encouraging deliberate self-harm). in the study of detrimental effects of sm by young people, self-harm extended to increased suicidality [42]. the authors, however, pointed to a lack of evidence linking sm use and harmful behaviours, and reminded health providers to promote general internet safety practices to avoid adverse events [42]. limitations evidence is inconclusive in this review, the evidence was revealed to be inconclusive with respect to benefits or harms. not surprisingly, the quality of the primary research is weak. further, the tools and platforms revealed only moderate positive and negative effects. from the evidence, little is known about the sustainability or long-term effects of these technologies. the speed at which sm tools, platforms, mobile devices and user practices change, for different populations, present difficulties for e-health researchers. constant variation and change make it difficult to measure effects, what works and how, and this complicates any effort to compare findings in the research. there were methodological flaws in the primary research cited in the papers we examined. for example, many papers did not pool their findings by study type or time period. generally, there was confusion about sm terminologies, categories and definitions. researchers using terms such as ‘information and communication technologies’, ‘digital tools’ and ‘online learning platforms’ are encouraged to define them and use them with more precision. some papers did not understand the defining characteristics of sm technologies and platforms and confused them with interactive websites. in a majority of papers (n=20), conflicting results and conclusions meant that the findings were not generalizable. findings that examined sm use in resource-rich countries, such as the united states, united kingdom and australia, could not be compared to poorer countries; the reverse was also true. for example, some developing countries periodically block their citizens from using facebook and twitter making them unreliable platforms to use in public health. further, sm must be examined in the ecological contexts of health promotion and evidence-based decision-making; some interventions thought to be portable from one socio-economic or cultural setting or context to another were clearly not. in many srs, sm were used as a subset of other e-health interventions making it difficult to assess specific tools and platforms. reflections on the study methodology there are inherent weaknesses in the methodology we used. for our own results, we had to rely on the critical appraisal of individual studies, as well as on the interpretation of results, in each paper we examined. further, the srs we examined differed considerably in their study populations, interventions, comparisons and outcomes, sometimes with contradictory findings and results. some srs did not do a good job of specifying inclusion criteria, and literature searches were not routinely performed to high standards. as a whole, the reviews were heterogeneous; thus sensitivity, subgroup, or meta-analyses could not be performed. due to poor quality, it was difficult to extrapolate overall themes and conclusions from dissimilar studies with any confidence. pooling older studies (n=2 from 2004 and 2009) with newer ones (n=40 from 2010 to 2016) presented difficulties in longitudinal cross-comparisons as settings and contexts changed. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi future research social technologies and the mobile devices used to access them (including device penetration, capabilities, user communities, digital skills of user groups) are rapidly changing. e-health researchers need more responsive approaches to evaluation that address these rapid changes. by the time an evaluation is published, the results may not apply to the current version of the tool under investigation (the latest version can often be different from the one being evaluated). in 2015, vandelanotte et al. said that most research examining the value of social networks has been disappointing [58]. to demonstrate the effective uses of sm in healthcare, more creative and experimental study designs will be required. future research will have to account for the speed of change as many studies revealed that some sm tools were no longer available. repeatedly, we felt that the primary research was hampered by heterogeneity of interventions, and other methodological limitations. moreover, there seems to be a need to conduct better studies, with appropriate controls, where the focus is on patients. an important emerging issue is the lack of evidence and understanding of the exponential costs of using sm. while many tools are free to use, their implementation in healthcare is not cost-neutral. cost-effectiveness and return-oninvestment studies are needed to demonstrate value in the long term. demonstrated economic benefits could guide future practices in the field. to inform future research, studies should adopt better and more consistent definitions of social media. definitions should account for more recent tools (e.g., instagram, snapchat, whatsapp, wechat, etc.), and the emergence of fraudulent research, misinformation and fake news. snss such as whatsapp and facebook have been implicated in identity theft, spreading spam, and creating trust in false information and fake treatments [60,61]. our paper is already out of date. many new srs have been published recently. however, these papers will now be part of a living systematic review [62]. see our wiki http://hlwiki.slais.ubc.ca/index.php/effective_uses_of_social_media_in_healthcare:_a_living_sy stematic_review_of_reviews where updates will be posted and papers that satisfy our inclusion criteria will be included with a brief thematic categorization. conclusions sm have been widely-studied in health and medicine from 2003 to 2017 but evidence of their effectiveness is inconclusive. the positive, measurable effect of sm in the delivery of health services and programmes is lacking and the quality of papers is modest. however, our sr provides a starting point for future research and in identifying effective uses of social media. future investigations of sm effects should focus on best practices, patient-oriented research, and the costs-benefit of using certain tools or platforms in varying healthcare settings. our paper suggests that sm research has entered a mobile-intensive period where patients and health professionals seek better ways to conduct their online activities and lifelong learning. future research should identify not just how patients use sm in their daily lives but seek to understand their positive and negative effects. researchers must examine the circumstances that lead to adoption of sm in specific ecologies and populations and undertake cost-effectiveness and returneffective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi on-investment studies. better research designs in e-health are strongly needed given the increased prominence 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https://doi.org/10.4258/hir.2015.21.2.67 61. viviani m, pasi g. 2017. credibility in social media: opinions, news, and health information—a survey. wiley interdiscip rev data min knowl discov. https://doi.org/10.1002/widm.1209 62. elliott jh, turner t, clavisi o, thomas j, higgins jp, et al. 2014. living systematic reviews: an emerging opportunity to narrow the evidence-practice gap. plos med. 11(2), e1001603. pubmed https://doi.org/10.1371/journal.pmed.1001603 https://doi.org/10.1016/j.chb.2014.04.011 https://doi.org/10.1007/s12559-016-9382-z https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27562728&dopt=abstract https://doi.org/10.1186/s12913-016-1691-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26946250&dopt=abstract https://doi.org/10.1093/nutrit/nuv106 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25005606&dopt=abstract https://doi.org/10.1136/amiajnl-2014-002841 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medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi 63. sandelowski m, leeman j. 2012. writing usable qualitative health research findings. qual health res. 22(10), 1404-13. pubmed https://doi.org/10.1177/1049732312450368 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22745362&dopt=abstract https://doi.org/10.1177/1049732312450368 effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi appendix a. sample search strategy (medline) database: medline via ovid <1946 to present file> search title: effective uses of social media in public health and medicine search dates: 25 june 2016, updated 5 december 2016 results: 2051 internet and social media related mesh: 1. blogging/ 2. computer-assisted instruction/ 3. computer communication networks/ 4. computers/td, ut 5. electronic mail/ 6. exp internet/ 7. mass media/td, ut 8. medical informatics/ 9. online systems/td, ut 10. search engine/ 11. social media/ 12. user-computer interface/ internet and social medial related keywords: 13. blog*.mp. 14. facebook*.mp. 15. (forum* adj3 (internet or web* or chat*)).mp. 16. googl*.mp. 17. "health 2.0".mp.or "medicine 2.0".mp. 18. microblog*.mp. 19. myspace.mp. 20. (online or on-line).mp. 21. patientslikeme.mp. 22. podcast*.mp. 23. second life.mp. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi 24. (social adj3 media*).mp. 25. (social adj3 network*).mp. 26. (twitter or tweet*).mp. 27. user generated content.mp. 28. (virtual adj3 (world* or communit*)).mp. 29. ("web 2.0" or "web 2").mp. 30. web-based.mp. 31. webmd.mp. 32. (website* or web site* or webpage* or web page*).mp. 33. wiki*.mp. 34. world wide web.mp. 35. youtube.mp. 36. or/1-35 [internet/social media mesh and keywords] health care, patient care, self-care, information-sharing terms 37. exp attitude to health/ 38. exp health education/ 39. exp health promotion/ 40. exp health/ 41. exp self care/ 42. exp self-help groups/ 43. communication/ 44. "delivery of health care"/ 45. health behavior/ 46. health communication/ 47. information dissemination/ 48. information seeking behavior/ 49. information services/ 50. "information storage and retrieval"/ 51. patient care/ 52. social support/ 53. (health adj3 (behavio?r* or care or communicat* or educat* or promot* or service*)).mp. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi 54. (inform* adj3 (disseminat* or retriev* or seek* or service*)).mp. 55. (self adj3 (care or help or support*)).tw. 56. or/36-54 [mesh words for health promotion/information seeking] 57. 36 and 55 [combination of social media + health information terms] search filters to identify effective uses, best practices and harms 58. best practice*.mp. 59. benefit*.mp. 60. concern*.mp. 61. confidential*.mp. 62. cost*.mp. or cost-effect* or effective* 63. impact*.mp. or limitation**.mp. or negativ*.mp. or positive*.mp. or problem*.mp. 64. risk*.mp. or harm*.mp. or safety.mp. 65. troll*.mp. or trend*.mp. 66. or/ 58-65 sr filter (to filter out non-systematic review research) 67. meta analysis.mp,pt. 68. systematic review.mp,pt. 69. search*.tw. 70. or/67-69 [hiru sr filter to balance sensitivity and specificity] limits: publication years 2003-2016 effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi appendix b. quality assessment of the 42 papers papers included in this review quality assessment indicators casp tool for systematic reviews i ii focused question right type of papers relevant studies included quality assessment combined results overall results precision of results applicable to local population important outcome considered benefits worth harm and cost balatsoukas et al, 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 benetoli et al, 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 best et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 brunner et al, 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 campos et al, 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 capurro et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 cartledge et al, 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 chang et al, 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 charles-smith et al, 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 chen et al, 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 cheston et al, 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 dyson et al, 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 eysenbach et al, 2004 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 ghanbarzadeh et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 gold et al, 2011 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 golder et al, 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 griffiths et al, 2009 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 househ et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi jin et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 jones et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 koskan et al, 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 laranjo et al, 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 luo 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 maher et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 mcalpine et al 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 merolli et al 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 mita et al. 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 moorhead et al. 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 10 newbold and campos 2011 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 ngwenya and mills 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 odone et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 pander et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 patel et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 rice et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 robinson et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 rolls et al. 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 sarker et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 sawesi et al. 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 schnall et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 8 shaw et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 smailhodzic et al, 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi smith and lambert 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 song et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 stellesfson et al. 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 10 swanton et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 taggart et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 theng et al. 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 toma et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 whitehead and seaton 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9 9 williams et al. 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10 9 willis et al. 2016 ✓ ✓ ✓ ✓ ✓ ✓ n/a ✓ ✓ ✓ 9 9 effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi appendix c. summary of the 42 systematic reviews study main aims type of study, methods, topic, population included papers & main findings balatsoukas et al, 2015 [35] to review research on integration of expert-led health promotion interventions with online social networking sites (snss). • scoping search and systematic narrative synthesis. • the effectiveness of sns for health promotion. • various (adults, families, students, general web users, office workers, patients, children and parents, youth, teenage girls, adolescents). • 42 papers included. • 26 papers evaluated effectiveness; 6 rcts, 4 observational, 14 pilot studies, other (2). • rcts showed no clear effect of snss; more positive effects on both self-reported and objectively measured behaviour change were reported in pilot studies. • there are positive effects on emotional health if positive experiences are shared. however, this can also be negative, due to contagion of negative emotions. benetoli et al, 2015 (australia) [16] to investigate the use of sm in professional pharmacy practice and pharmacy education, and include evaluation of research designs. • systematic review and narrative synthesis. • use of sm to enhance e-professionalism and pharmacy education. • pharmacists (educators, preceptors, interns and students). • 24 papers included. • survey methods were used in 17 studies; focus groups in two; interviews in one; and direct observation in seven. • the use of sm in pharmacy is increasing but findings are not generalizable to other countries. • sm in general and snss were used mainly for personal reasons. wikis, facebook, and twitter were used in pharmacy education with positive feedback from students. best et al, 2014 (uk) [36] to examine the influence of sm on adolescent well-being. • systematic narrative review and theoretical framework. • the influence of sm on adolescent well-being. • adolescents (over 19 y/o excluded; mean age of 19). • 43 papers included. • survey research (55%), followed by qualitative (12%), longitudinal (12%), content analysis (11%), experimental (4%), case control (3%) and mixed method studies (3%). • snss can be beneficial and harmful for mental wellbeing. • benefits include increased self-esteem, perceived social support, increased social capital, safe identity experimentation and increased opportunity for selfeffective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi disclosure. harmful effects were increased exposure to harm, social isolation, depression and cyberbullying. brunner et al, 2015 (australia) [17] to examine the use of sm on patients with traumatic brain injury (tbi). • systematic review with qualitative synthesis. • the benefits, harms; barriers, facilitators of sm use in patient populations who have had a tbi. • patients with tbi. • 16 papers included. • conference abstracts/proceedings (43%), qualitative studies (19%), qualitative descriptive studies (13%), systematic review (6%), case series (6%), thesis (6%), narrative literature review (6%). • sm use in tbi rehabilitation can increase social support; further investigation is needed into the benefits of sm for social support. there is little information on the use of sm in tbi patients after injury. online safety is a risk and cognitive and behavioural disabilities are barriers. campos et al, 2016 (mexico) [51] to examine use of sm by the elderly to promote independent living, social integration, to improve health, reduce cognitive decline, and prevent early death. • systematic review. • use of ambient and snss in social integration of the elderly / older adults. • elderly populations. • 53 papers included. • controlled trial (24), non-controlled (5), case series, 2), controlled case series (4), other (18). • there was an increase in participation of older adults in snss who can benefit from the use of ambient and social technologies. different technologies have been suggested to socially integrate the elderly, but they can be expensive. • snss are a way to promote socialization of elderly adults. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi capurro et al, 2014 (chile) [48] to identify the best uses of snss for public health research and practice and to identify gaps. • systematic review. • use of snss for public health research and practice. • hard-to-reach populations (adolescents, patients with chronic non-communicable diseases and individuals at risk for stds and hiv). • 73 papers included. • cross-sectional observational (63), rcts (4), systematic review (1), other (5). • use of sm is increasing but difficult to measure its effects. issue is mostly studied in high-income countries. • sm may be effective in studying diverse populations, in sexual health and alcohol, tobacco or substance abuse. cartledge et al, 2013 (uk) [37] to examine implementation of snss as interventions in healthcare education; use of snss by students for educational purposes. • systematic review. • use of snss in medical education. • medical, pharmacy and nursing students. undergraduates and post-grads in health. • 9 papers included studies. • this review examined 9 case studies where sm was used in medical education. • medical educators can use sm to benefit learning. there were no problems with professionalism and positive feedback was received from learners. • however, there was no solid evidence that sm is equally or more effective than other media in medical education. chang et al, 2013 (usa) [26] to systematically describe the use and impact of social media in online weight management interventions. • systematic review. • effects of sm in online weight management. • adults • 20 papers included. • rcts (20). • few studies measure the effects of sm in online weight management interventions; its impact is still unknown. • findings are consistent with previous systematic reviews on internet-based behavioral interventions and electronic peer-to-peer support group interventions, which have found that the effect of the technology being studied was not isolated; thus, their effectiveness is not known. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi charles-smith et al, 2015 (usa) [27] to identify and target specific sm tools to use in public health interventions. • systematic review. • the use of sm in disease surveillance practice and outbreak management to support public health. • general population. • 60 papers included. • case studies, cohort studies, retrospective studies. • sm is shown to be effective in improving public health, and may be effective at disease surveillance, and at identifying adolescent populations displaying substance abuse, especially alcohol, sexual behaviour. sm can improve community health outcomes for atrisk adolescents. • public health should integrate sm analytics into disease surveillance and outbreak management practice. chen et al, 2016 (hong kong) [45] to examine the effects of ict interventions on reducing social isolation of the elderly. • systematic review using narrative synthesis. • the effects of sm on one or more attributes of social isolation among elderly. • elderly populations. • 25 papers included. • rcts (6), cohort studies (6), surveys (4), other (9). • sm tools may be effective in tackling social isolation of the elderly, but not for every senior. usage should be tailored and other platforms should be studied empirically. • research on other types of icts (eg, mobile phone– based instant messaging apps) should be conducted to promote understanding of ict-based social-isolation interventions for the elderly. cheston et al, 2013 (usa) [28] to examine the use of sm in medical education to determine outcomes, challenges and opportunities. • systematic review. • the use of sm interventions in medical education. • medical students in all years, physicians, specialists, residents, fellows. • 14 papers included. • nine studies (64%) used a single-group cross-sectional or posttest design, whereas four studies (29%) employed a two-group nonrandomized design. one rct (7%). • sm is associated with improved exam scores, attitudes (e.g., empathy), and skills (e.g., reflective writing). • opportunities related to incorporating sm were promoting learner engagement (71% of studies), feedback (57%), and collaboration and professional development (both 36%). effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi • commonly cited challenges were technical issues (43%), variable learner participation (43%), and privacy/security concerns (29%). dyson et al, 2016 (canada) [43] to examine the use of sm in deliberate selfharming behaviours. • systematic review. • the use of sm related to self-harm or suicidality in children and / or adolescents • teens and adolescents aged 12 to 21. • 26 papers included. • cross-sectional observational (19%), descriptive (35%), qualitative (42%), mixed (4%). • sm can create a sense of community and platforms used by those who ‘self harm’ are described as supportive. • support included suggestions for formal treatment, advice on stopping self-harming behaviour, and encouragement. harms included normalizing and accepting self-harming; discussion of motivation or triggers, concealment, suicidal ideation or plans; and live depictions of self-harm acts. eysenbach et al, 2004 (canada) [42] to compile and evaluate the evidence on the effects on health and social outcomes of computer-based peerto-peer (p2p) communities and electronic self-support groups. • systematic review. • the effect on health & social outcomes of p2p online support and electronic self-support groups. • adults and patients. • 45 papers included. • 20 rcts, 3 meta-analyses, 2 non rcts, one cohort study, and 11 before and after studies, other. • the outcomes measured most often were depression and social support; but most studies showed no effect. there is no evidence to support concerns over sm harming people. • "effect" of p2p communities is unclear. studies are confounded by effects of co-interventions. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi ghanbarzadeh et al, 2014 (australia) [18] to examine various 3d virtual worlds in health and medical contexts and categorize them into meaningful categories. • systematic review. • the application of 3dvw in healthcare. • healthcare communities and researchers. • 62 papers included. • study types of the papers were not mentioned. • 3dvws offer innovative ways to perform health activities within six categories: academic education, professional education, treatment, modeling, lifestyle, and evaluation. • most research focused on education in health care, and most studies were undertaken in just two countries, the united states and the united kingdom. gold et al, 2011 (australia) [19] to examine the extent to which snss are used for sexual health promotion and describe the breadth of these activities. • systematic searches and content analysis of sm sites. • the use of snss for sexual health promotion. • young people (no age ranges given). • 178 activities included. • study types are not applicable. • snss are being used to deliver health promotion, although the activities have not been described or evaluated for their effectiveness in improving health outcomes. • recommends cost-effectiveness studies in the future. golder et al, 2015 (uk) [38] to summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media. • systematic review using narrative synthesis. • the number and frequency of sharing adverse events on sm and user comments. • population not mentioned. • 51 papers included. • studies assessed over 174 social media sites with discussion forums (71%) being the most popular. adverse events in sm varied from 0.2% to 8% of posts. • there are more adverse events in sm, particularly in sharing ‘symptom’ related and ‘mild’ adverse events. • cost-effectiveness analysis of all pharmacovigilance systems, including social media is urgently required. griffiths et al, 2009 (australia) [20] to review the available evidence concerning the effect of internet support groups (isgs) on depressive symptoms. • systematic review. • the use of isgs by patients with depression. • patients with a depression diagnosis. • 31 papers included. • more than half of the studies reported a positive effect of isgs on depressive symptoms. however, only two (20%) of these studies employed a control group. • studies with lower design quality tended to be associated with more positive outcomes (p = .07). overall, studies of breast cancer isgs were more likely to effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi report a reduction in depressive symptoms than studies of other isg types. househ et al, 2014 (saudi arabia) [55] to explore the range of sm platforms used by patients and examine the benefits and challenges of using these tools from a patient perspective. • systematic review. • the use of sm platforms as used by patients. • patients and health consumers. • 12 papers included. • four studied programs or interventions that use sm; three focused on literature reviews, three were observational, one was a survey, and one was conceptual in nature. • sm can have a positive effect in community engagement, information sharing, data collection, appointment setting, prescription notifications, providing health information, engagement of the elderly, improved participation, autonomy, motivation, trust, and perceived self-efficacy. • concerns are privacy, security, the usability of social media programs, manipulation of identity, and misinformation. jin et al, 2014 (hong kong) 46] to examine the effects of educational technologies on student learning and staff engagement in problem-based learning. • systematic review. • the use of educational tech in problem-based learning. • postsecondary students and tutors in medicine, dentistry, speech and hearing sciences. • 28 papers included. • the review demonstrates the generally positive effect of educational technologies in pbl. positive outcomes for learning include providing rich, authentic problems and/or case contexts for learning; student development of medical expertise; making disciplinary thinking explicit; providing a platform to elicit articulation, collaboration, and reflection; reducing perceived cognitive load. • limitations included cumbersome scenarios, infrastructure requirements, and the need for staff and student support in light of the technological demands of new affordances. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi jones et al, 2014 (usa) [29] to examine the effectiveness of sm and text messaging interventions designed to increase sexually transmitted disease (std) knowledge. • systematic review. • the use of sm to increase std knowledge and reduced risky behaviours. • young adults aged 15 through 24 years. • 11 papers included. • rcts (6), feasibility study (2), pre/post-test design (3). • there is some evidence indicating that sm and text messaging increases knowledge regarding the prevention of stds. the interventions may also affect behaviour, such as screening/testing for stds, sexual risk behaviors, and std acquisition, but the evidence for effect is weak. koskan et al, 2014 (usa) [30] the use and taxonomy of sm in cancer-related studies. • systematic review. • the use of sm by cancer patients, and its impact on the digital divide and health literacy; cancer disparities. • cancer care communities • 69 papers included. • use of sm in cancer from 1996 to 2007 focuses on discussion forums, message boards and support group websites. by 2008, researchers began to view the benefits of blogging during cancer treatment and survivorship. intervention studies were not reported until 2010. • most research analyses the content of sm forums where users asynchronously post or respond, share resources, reliable cancer information or emotional support. • adults aged 18 to 49 make up large group using facebook and twitter which might be useful for cancer screening. laranjo et al, 2015 (portugal) [54] to examine the use and effectiveness of interventions using social networking sites (snss) to change health behaviours. • systematic review and meta-analysis. • effectiveness of snss in changing health behavior-related outcomes. • participants were diverse in age; three studies recruited students, and two studies involved young adults. • 12 papers included. • rcts (9), quasi-experimental (3) studies. • overall, sns interventions appear to show statistically significant effect in promoting health-related behaviours. • most studies evaluated multi-component interventions, posing problems in isolating specific effects of snss. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi • health behavior change theories were seldom mentioned but two innovative studies used 'network alteration', showing a positive effect. luo et al, 2015 (hong kong) [47] to examine the effectiveness of social networking interventions (snis) in hiv prevention. • systematic review. • effectiveness of snis as an intervention in hiv prevention. • sexually active young adults, teens. • 11 papers included. • rcts (11). • the strength of using sn in hiv prevention is moderate. some studies show snis help high-risk populations modify their behaviours. there is insufficient evidence overall. maher et al, 2014 (australia) [21] to systematically review the current level of evidence regarding the effectiveness of online social network (osn) health behaviour interventions. • systematic review and qualitative synthesis. • uses of osns to deliver health behaviour change interventions. • adults or children were included, regardless of health status (healthy, or participants with specific health conditions or diseases). • 10 papers included. • rcts (6), pre-post studies (4) using a mix of health social networks (n=2), research osn websites (n=3), and multi-component delivered via pre-existing osns (facebook n=4 and twitter n=1). nine studies reported significant improvements in aspects of health behaviour change. • effect sizes were small and statistically non-significant. engagement in studies was relatively low, 5-15% fidelity. • it is unclear whether osn-based interventions are equally useful for all health behaviours, for the long term, or whether they may be more effective for some than others. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi mita et al, 2016 (new zealand) [53] to synthesize evidence of the effect of sm use compared with no use as part of interventions to reduce risk factors for non-communicable diseases. • systematic review. • use of sm in reduction of non-communicable disease risk factors. • varied. no population restrictions. • 16 papers included. • sm is used with low levels of media richness and presence (e.g., discussion boards, bulletin boards). trials assessing sm interventions aimed at modifying risk factors for non-communicable diseases showed sm use improved primary outcomes, but poor study quality limits generalizability. • further trials should isolate effects of sm and effects of media richness of platforms. studies that integrate sm into interventions had a greater effect for primary outcomes (ie., for weight loss, physical activity, healthy eating). moorhead et al, 2013 (uk) [8] to identify the uses and benefits, limitations of social media for health communication. • systematic review. • uses and benefits of sm in health communication. • general public, patients, health professionals (children, teens, patients, seniors, and/or health providers). • 98 papers included. • there are some benefits to using sm in health communication such as increased interactions, greater access to tailored information, peer support, public health surveillance, and potential to influence public policy. • health information on sm needs to be monitored for quality and reliability; users’ confidentiality and privacy need to be maintained. eight gaps in the literature and key recommendations for future research were provided. odone et al, 2015 (italy) [49] to summarize the evidence on the effectiveness of sm interventions to promote vaccination uptake and coverage. • systematic review. • use of information technologies to promote vaccination and immunisation. • parents/children eligible for immunization. • 19 papers included. • rcts (7), non-rcts (5), cross-sectional (3), operational research (3), case-control study (1). • text messaging, patient portals and computer reminders may increase rates of vaccine-immunization. youth are willing to use facebook for health-related reminders. • data is insufficient overall on the effects of sm, email and smartphone applications. however, it is estimated that youth in 18-29 y/o group receive 87.7 messages a day. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi patel et al, 2015 (uk) [39] to evaluate clinical outcomes from applications of contemporary sm in chronic disease; develop a conceptual taxonomy of sm uses. • systematic review. • uses (and categorisation) of sm in chronic disease. • adults (more than 18 years of age). • 42 papers included. • quantitative(27), qualitative(12), mixed method studies (3) • overall impact of social media on chronic disease was variable, with 48% of studies indicating benefit, 45% neutral or undefined, and 7% suggesting harm. among studies showing benefit, 85% used facebook or blogs, and 40% were based within the domain of support. using social media to provide social, emotional, or experiential support in chronic disease, especially with facebook and blogs, appears most likely to improve patient care. rolls et al, 2016 (australia) [22] to review use of sm by health professionals in developing virtual communities that facilitates professional networking, knowledge sharing, and evidenceinformed practice. • systematic searches with integrative review synthesis. • the use of sm by health professionals for a variety of purposes such as informationsharing and networking. • health care professionals (physicians, nurses, midwives, pharmacist, social worker, allied health personnel). • 72 papers included. • 44 qualitative, 20 mixed methods, and 8 literature reviews. • there is emerging evidence that health professionals use sm to develop virtual communities and to share domain knowledge. these virtual communities, however, currently reflect tribal behaviors of clinicians that may continue to limit knowledge sharing. sawesi et al, 2016 (usa) [31] to examine use of it platforms and sm to engage patients in healthcare and change in health behaviours. • systematic review. • education of young people and their health. • adolescents and health professionals. • 170 papers included. • rcts (112), case study (7), cohort study (19), crosssectional analysis (15), quasi-experimental trials (17). • it platforms can enhance patient engagement and improve health outcomes. 88.8% (151/170) of studies showed positive impact on patient behaviour and 82.9% reported high levels of improvement in patient engagement. only 47.1% referenced specific behaviour theories and 33.5% assessed usability of it platforms. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi shaw et al, 2016 (australia) [23] to determine the use of sm as a health intervention in addressing the health of adolescents. • systematic review. • the uses, benefits and dangers of sm in adolescents. • adolescents and health professionals. • 3 papers included. • facebook may provide the most effective access to adolescents but are moving to twitter and instagram. • the reviewed studies did not show significant positive or negative results from using facebook interventions. • adolescents have a tendency to progress to newer media, and often create a hidden “secret” world in using sm. smailhodzic et al, 2016 (netherlands) [52] to provide an oveview of the effects of sm use for health-related reasons on patients and their relationship with healthcare professionals. • systematic review. • the effects on patients (both positive and negative) of using sm. • patients. • 22 papers were included. • quantitative (9), qualitative (7), mixed method (6) studies. • uses of sm were for social support, emotional and esteem support, expression, network and information support. effects of sm were enhanced psychological, enhanced subjective well-being (and diminished), addiction to social sm, loss of privacy, being targeted for promotion. • sm use by patients was found to affect the healthcare professional and patient relationship by leading to more equal communication between patient and provider, but increased switching of doctors, and suboptimal interaction. smith et al, 2014 (uk) [11] to evaluate the use, attitudes and perceptions of both teachers and students towards sm platforms (facebook and twitter) in healthcare higher education practice. • systematic review. • uses, attitudes and perceptions of sm use in healthcare. • medical students in all years, physicians, specialists, residents, fellows, pharmacy, allied health. • 16 papers included. • this review provides some qualified support for use of facebook and twitter in healthcare higher education as part of a “blended” approach to classroom teaching. • sm is used to enhance communication and increase accessibility, exposure and interactivity of students to real-world practices and expertise. • students perceive sm to be of value, but the role of faculty members in a predominantly “social” community has been acknowledged as a potential conflict. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi song et al, 2014 (korea) [50] to explore the relationship between facebook use and loneliness. • systematic review and meta-analysis. • the relationship between using facebook and loneliness. • general facebook users. • 8 papers included. • researchers observed a significant overall effect in the positive relationship between facebook use and loneliness. • people who are lonely may use facebook to enhance social resources they lack offline. lack of social support may lead to loneliness, which in turn, leads to facebook use. lonely individuals may benefit from facebook but more research is needed to examine its effects. • excessive problematic use of sm is an important issue for future research because unhealthy, compulsive use is likely an important factor in using facebook and feeling lonely. stellefson et al, 2013 (usa) [32] to review effectiveness of web 2.0 selfmanagement interventions for older adults (mean age ≥ 50) with one or more chronic disease(s). • systematic review. • the use of sm as interventions for selfmanagement in older adults. • broad population of chronically ill older patients. • 15 papers included. • rcts (11), randomized cluster, quasi-experimental, cross-sectional, qualitative designs • self-managed patients felt greater self-efficacy in talking to health providers and receiving feedback and support. asynchronous tools (eg, email, discussion boards) and progress tracking were useful for selfmanagement. • sm engagement may be associated with improvements in health behaviours (eg, physical activity) and health status. • factors influencing long term use of sm are not yet understood. dropouts may have led to distorted effects. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi swanton et al, 2015 (australia) [24] to examine effects of new media interventions on sexual health behaviours and factors moderating the effect on those behaviours. • systematic review and meta-analysis. • the effects of new media on changing sexual health behaviours. • sexually active young adults, teens. • 15 papers included. • twelve studies examined the effects of new-media on condom use; nine looked at effect on std testing. • sm interventions lead to increased condom use and std testing but the effects were not homogeneous. using sm to encourage testing is more effective with women than with men, and generally more effective in younger adults. • interactivity, target population and study design influenced efficacy of the interventions. taggart et al, 2015 (usa) [33] to examine the use of sm to communicate about hiv prevention and treatment. • systematic review. • using social media to communicate about hiv prevention and treatment. • social media users: health professionals, clinicians, general users having hiv-related interests. • 35 papers included. • qualitative (9), quantitative (11) and mixed method (15) studies. • sm is used among diverse users and the frequency of use, satisfaction and effects of sm varied across studies. • access to information, communicability, anonymity, sense of social and emotional support are key reported benefits. • technology barriers, lack of privacy, cost and lack of physical interaction are the main disadvantages. theng et al, 2015 (singapore) [56] to examine the use of video games, gamification and virtual environments in diabetes management. • systematic review. • the self-management of diabetes using video games and virtual media. • patients living with diabetes. • 10 papers included. • rcts (3), quasi-experimental (5), focus group discussion (1) experimental (1). • four studies employed video games as intervention, three utilized virtual reality environments and three studies adopted principles from gamification and relevant theory. • overall, video games were effective in diabetes but drawing strong conclusions is a challenge. • gamification and virtual environments increase patients’ intrinsic motivation and positive reinforcement. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi toma et al, 2014 (uk) [41] to summarise the evidence surrounding the role of online social networking services in diabetes care. • systematic review and meta-analysis. • the use of snss to assess hba1c as a measure of glycaemic control with type 1 or type 2 diabetes. • patients with diabetes. • 34 papers included. • snss can improve hba1c control in diabetics. sns offers a novel approach to improving glycaemic control compared with standard management especially in type 2 diabetes. • snss may be more efficient for patients with type 2 rather than type 1 disease. larger randomised controlled trials in addition to cost-effectiveness studies are needed to understand the use of snss in diabetes care. whitehead et al, 2016 (australia) [25] to assess the effectiveness of mobile phone and tablet apps in self-management of key symptoms of long-term conditions. • systematic review. • use of mobile apps to improve disease-specific clinical outcomes. • adult patients with long-term conditions. • 9 papers included. • apps were shown to be somewhat effective in improving outcomes for patients managing their chronic diseases, especially those with diabetes and chronic lung problems. • barriers are language and literacy, cost, availability and connectivity; cost-effectiveness studies are needed to demonstrate the impact and value of apps. • without good patient motivation and adherence, mhealth interventions such as apps are likely to be ineffective. williams et al, 2014 (canada) [44] to examine the use of sm to promote healthy diet and exercise in the general population. • systematic review. • the use of sm interventions to promote healthy behaviours. • adult populations (mostly middle-aged caucasian women of mid-to-high socioeconomic status). • 22 papers included. rcts. • sm is commonly used as an intervention but there is little evidence that sm interventions demonstrate a significant benefit for improving healthy diet and exercise. • most research is from the us affecting generalizability. no significant differences between sm interventions and alternate or no intervention controls in promoting healthy behaviours. this may be due to low levels of participation and the difficulty in affecting behavioural changes as seen across different interventions. while initial positive changes may be seen, these are often not sustained. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi willis et al, 2016 (usa) [34] to examine weight management interventions delivered by online social networks (osns) to improve body weight, body composition, and chronic disease risk factors. • systematic review. • using osns in managing body weight and chronic disease risk factors. • adult populations. • 5 papers included. • in all 5 studies, weight loss, though modest, was statistically significant in osn groups independent of intervention length. three studies (60%) reported significant decreases in body weight when osns were paired with health educator guidance and support. • one study reported significant weight loss of ≥5%. there is great potential for weight management delivered through osns. interventions supported by professional guidance generate a more positive effect than self-guided osns. • to date, cost-effectiveness of osn interventions in weight management has not been evaluated. effective uses of social media in public health and medicine: a systematic review of systematic reviews online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(2):e215, 2018 ojphi appendix d. a thematic analysis of the 42 papers presented as a conceptual map conceptual map effective uses of social media in public health and medicine: a systematic review of systematic reviews abstract introduction background methods information sources: bibliographic databases and search engines search terms – effective uses of social media in healthcare data extraction and analysis results study validity study populations publication years of papers description of the included reviews breakdown of the # of srs by country of the corresponding authors (n=42 papers) thematic analysis and categorization findings from the thematic analysis positive and negative effects of sm in health populations positive effects and benefits problematic or negative effects of sm limitations evidence is inconclusive reflections on the study methodology future research conclusions references appendix a. sample search strategy (medline) database: medline via ovid <1946 to present file> search title: effective uses of social media in public health and medicine appendix b. quality assessment of the 42 papers appendix c. summary of the 42 systematic reviews appendix d. a thematic analysis of the 42 papers presented as a conceptual map isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e337, 2019 isds 2019 conference abstracts lessons learned from a dod and virginia data sharing pilot using nssp essence erin e. austin1, paul e. lewis2, arden norfleet1, jamaal russell2 1 virginia department of health, richmond, virginia, united states, 2 armed forces health surveillance branch, silver spring, maryland, united states objective this panel will focus on the experiences from the department of defense (dod) and virginia department of health (vdh) data sharing project using the national syndromic surveillance program (nssp) essence and will discuss lessons learned, challenges, and recommendations within the following areas: 1) data sharing authority, 2) coordination and implementation of data sharing with a focus on personnel, training, and managing access and 3) communication between local, state, and federal agencies. introduction the dod and vdh both maintain local essence installations to monitor the health status of their military and civilian populations, respectively, and submit syndromic surveillance data to the nssp essence to foster data sharing and collaborative initiatives among public health entities. military treatment facilities (mtfs), housed on dod installations, provide healthcare to all se rvice members and their beneficiaries stationed in the area. service members and their beneficiaries represent a substantial portion of the local community and interact with the civilian population throughout daily activities. sharing syndromic surveillance data between dod and public health jurisdictions can provide public health situational awareness among both civilian and military populations to support disease surveillance. dod and vdh engaged in a pilot project to develop processes and procedures for data sharing, data access, and communication with the aim they can serve as best practices for other jurisdictions seeking to share syndromic surveillance data with dod. description the pilot project began in june 2018 with the centers for disease control and prevention (cdc) nssp team providing technical support. nssp essence users from the vdh state and local health departments across nine virginia city/counties participated i n the project. vdh shared syndromic surveillance data from 34 healthcare facilities (17 urgent cares, 3 emergency care ce nters, and 14 hospitals) with dod, which shared syndromic surveillance data from 18 mtfs (16 clinics and 2 hospitals) in virginia. to standardize the analysis of syndromic surveillance data and use of nssp essence across project participants, myessence tabs were created and shared by between vdh and dod. the goal was to facilitate and enhance communication between local public health departments and their dod counterparts through the sharing of syndromic surveillance data. acknowledgement thank you to mike coletta and the nssp team at cdc for their support. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e232, 2019 isds 2019 conference abstracts comparing spatio-temporal methods of non-communicable disease surveillance. mico hamlyn, frederic b. piel sahsu, imperial college london, london, united kingdom objective to determine the merits of different surveillance methods for cluster detection, in particular when used in conjuction with s mall area data. this will be investigated using a simulated framework. this is with a view to support further surviellance work using real small area data. introduction health surveillance is well established for infectious diseases, but less so for non-communicable diseases. when spatio-temporal methods are used, selection often appears to be driven by arbitrary criteria, rather than optimal detection capabilities. our aim is to use a theoretical simulation framework with known spatio-temporal clusters to investigate the sensitivity and specificity of several traditional (e.g. satscan and cusum) and bayesian (incl. baystdetect and dcluster) statistical methods for spatio-temporal cluster detection of non-communicable disease. methods count data were generated using various random effects (re). a subset of areas was randomly given an increased relative risk (rr) to simulate disease clusters. simulations were conducted in r using a grid of 625 areas. we used 12 times= nteps within a hierarchical poisson model. multiple values of model parameters, including res and the rr within clusters, were then tested. the range of re (values) was derived from real-world data from england on common and rare diseases. rr ranging between 1.2 and 1.8 were tested to reflect both low and high exposures to pollutants and other risk factors. roc analysis, based on 50 simulations, was used to assess the performance of each statistical method for each combination of parameter values. results our roc analysis suggested that satscan usually had the highest specificity at low sensitivities (<0.5), although its maximum sensitivity was often lower than when using the bayesian methods. in scenarios where the rr within clusters was lower, all methods had less sensitivity at a given specificity. cusum usually performed quite similarly to satscan, while the two bayesian methods considered often misidentified a high proportion of disease clusters. p-values generated by satscan need to be considered with caution as they did not relate closely with the sensitivity or specificity of the roc curves from our simulations. conclusions real-world investigations of spatio-temporal signals (e.g. disease clusters) are often complex and time consuming. identifying the best method to reduce the risks of identifying false positives and of missing real clusters is therefore essential. despite the inherent constraints of theoretical simulations, such a framework allows to objectively assess the performance of different methods. overall, our simulation framework suggested that satscan would usually be the easiest, most user-friendly and best performing space-time methods for non-communicable disease surveillance. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e424, 2019 isds 2019 conference abstracts an assessment of the ems drug overdose to death pathway from 2011 to 2017 samir t. parmar1, timothy d. mcfarlane2 1 epidemiology, marion county public health department, indianapolis, indiana, united states, 2 indiana university richard m fairbanks school of public health, indianapolis, indiana, united states objective to characterize the appropriateness of naloxone administration, causes of death, and history of indianapolis emergency medical services (i-ems) service utilization among the drug overdose population in marion county, indiana between 2011 to 2017. introduction drug overdoses are now the leading cause of accidental death in the united states, with an estimated 60,000 deaths in 2016 [1]. nationally, ems overdose responses with naloxone administration have nearly doubled from 2012 to 2016 from 573.6 to 1004.4 per 100,000 ems events [2]. resuscitation using the opioid antagonist, naloxone is recommended in cases of suspected opioid ods, and has been increasingly used by ems agencies, law enforcement, healthcare providers, and good samaritans [3]. while naloxone can save lives, it is not clear how often its use is appropriate; delivering the right care to the right patient at the right time. it has been suggested that community paramedic programs teamed with recovery services may help link od patients to recovery and rehabilitation services and establish mechanisms for follow-up care [4]. prior to implementing community ems programs, it is important to understand the ems utilization patterns of the od population. i-ems interactions may present an opportunity for behavioral intervention and linkage to services to prevent future od and death in the opioid-using population. accurately documenting substances involved in drug overdose deaths has been of increasing interest to marion county and indiana with a recent law requiring toxicology testing [5,6]. this project linked individual-level data across public health information systems to assess the appropriateness of naloxone administration, the frequency of i-ems service utilization until final death outcome among the i-ems od deceased cohort, and underlying causes of death among the cohort. methods the study setting is marion county, indiana, in which the state capital, indianapolis is located. the population size at the 2010 census was 903,393. we performed individual-level data linkage between i-ems overdose run data from january 1, 2011 to december 17, 2017, marion county coroner toxicology data from 2011 to 2017, and marion county death certificate data from 2011 to 2017. observations were linked according to first name, last name, and date of birth. the appropriateness of naloxone administration was assessed by quantifying the following: the probability of naloxone administration given opioid positive toxicology (sensitivity); and probability of opioid toxicology given naloxone administration (positive predictive value). primary exposure of history of od (via i-ems) and the outcomes of all-cause mortality, non-od, and od mortality were assessed. the following icd10 drug overdose coding was utilized: x40-x44, x60-x64, x85, y10-y14 to identify mortality type. standardized mortality ratios (smr) were calculated for the cohort based on marion county census population-level estimates and marion county death data with sex and age adjustment on exposure. additionally, we investigated repeat od frequencies and time to death among the cohort. results of 8,384 individuals who utilized i-ems for drug overdose, 6,590 (78.6%) individuals were administered naloxone on least one iems run, 850 (10.1%) died, and 260 (3.1%) of the deceased had a toxicology report conducted after death from 2011 to 2017. among the 260 individuals who had a toxicology report, 92% were administered naloxone given they tested positive for opioid and 87.7% tested positive for opioids given they were administered naloxone (table 1). additionally, 82.8% of individuals were administered naloxone given they tested negative for opioids. thus, up to 8% of opioid ods patients were not provided naloxone when potentially necessary and 17% were provided naloxone when potentially unnecessary. one-hundred-fifteen (13.5%) died on the same day of their last ems overdose run, yet only 61 (53%) of these individuals had a toxicology report, with a 90.7% administered naloxone given they tested positive for opioids and 87.5% tested positive for opioids given they were administered naloxone (table 1). of 850 individuals that died, 13.5% died on the same day of their last ems run, 5.6% died next day, 12.4% died within 2 to 7 days, 9.8% within 8 to 30 days, 18.5% within 31 to 181 days, and http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e424, 2019 isds 2019 conference abstracts 10.8% within 181 to 365 days, 29.4% greater than 365 days. based on icd10 drug overdose coding 293 (34.5%) individuals had drug overdose deaths, with 161 (18.9%) having x44 (accidental poisoning by other and unspecified drugs) and 112 (13.2%) having x42 (accidental poisoning by narcotics and psychodysleptics). five-hundred-fifty-seven (65.5%) individuals had non-drug overdose deaths. after accidental poisoning due to drugs, heart, lung, and brain complications related to drug use appeared as common underlying causes of death. of 850 deceased individuals, 86.4% only had one overdose ems run, 13.7% had two or more ems runs, and 759 (89.3%) had at least one ems run where naloxone was administered. the smr for drug-related utilizers of ems was 4.04 compared to the general marion county population, after adjustment for sex and age. conclusions this work looked at deceased individuals with i-ems overdose interactions during the period 2011 through 2017. our results described two important features of ems naloxone administration, providing the right care at the right time and maximizing available resources. for deceased individuals with known toxicology and same-day i-ems interaction, the sensitivity of i-ems naloxone administration was 90.7%. of the deceased i-ems overdose cohort, 70.6% died within one year of their last i-ems overdose interaction and 13.7% had two or more ambulance runs for drug overdose. although the most common cause of death in the i-ems overdose cohort was related to drug poisoning, we observed over 65% died from other causes. acknowledgement the authors would like to thank brad ray of indiana university purdue university-indianapolis and joe gibson of the marion county public health department for their support. references 1. opioid overdose [internet]. centers for disease control and prevention. centers for disease control and prevention; 2017 [cited 2018 oct 8]. available from: https://www.cdc.gov/drugoverdose/data/index.html 2. cash re, kinsman j, crowe rp, rivard mk, faul m, et al. 2018. naloxone administration frequency during emergency medical service events—united states, 2012–2016. morbidity and mortality weekly report. 67(31), 850. 3. adams j. surgeon general's advisory on naloxone and opioid overdose [internet]. surgeongeneral.gov. [cited 2018 oct 8]. available from: https://www.surgeongeneral.gov/priorities/opioid-overdose-prevention/naloxone-advisory.html 4. 5 things community paramedics need to know about the opioid epidemic [internet]. 5 things community paramedics need to know about the opioid epidemic. ems1; 2018 [cited 2018 oct 8]. available from: https://www.ems1.com/opioids/articles/383830048-5-things-community-paramedics-need-to-know-about-theopioidepidemic 5. ray b, quinet k, dickinson t, watson dp, ballew a. 2017. examining fatal opioid overdoses in marion county, indiana. j urban health. 94(2), 301-10. 6. indiana general assembly. senate bill 139 investigation of overdose deaths [internet]. indiana code 2017 indiana general assembly, 2018 session. indiana general assembly; 2018 [cited 2018 oct 8]. available from: http://iga.in.gov/legislative/2018/bills/senate/139#digest-heading table 1: sensitivity and specificity of naloxone administration by time to death time to death sensitivity of naloxone administration positive predictive value of naloxone administration same day 90.7% 87.5% within 1 day 90.2% 87.3% ever 92.0% 87.7% http://ojphi.org/ http://www.cdc.gov/drugoverdose/data/index.html http://www.surgeongeneral.gov/priorities/opioid-overdose-prevention/naloxone-advisory.html http://www.ems1.com/opioids/articles/383830048-5-things-community-paramedics-need-to-know-about-thehttp://iga.in.gov/legislative/2018/bills/senate/139#digest-heading isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts utilization of syndromic surveillance to identify naled-related illness in florida prakash r. mulay* florida department of health, tallahassee, fl, usa objective to describe the use of florida poison information center network (fpicn) and electronic surveillance system for the early notification of community-based epidemics (essence-fl) emergency department (ed) chief complaints data to identify acute naled-related illness following aerial spraying in miami-dade county, florida in response to the zika outbreak. introduction pesticide-related illness and injury is a reportable condition in florida. in august and september 2016, aerial spraying for mosquito control was conducted in an effort to reduce the population of aedes aegypti mosquitoes in miami-dade county.1 two areas wynwood (in august) and miami beach (in september) were sprayed with naled. naled is an organophosphate insecticide registered with the u.s. environmental protection agency (epa) which is applied via aerial ultra-low volume (ulv) spraying. in addition to routine surveillance using fpicn and reportable disease surveillance data to identify acute naled-related illness, the florida department of health (doh) also monitored ed chief complaints data to identify any associated increase in ed visits. methods in 2016, doh used three datasets to monitor illness related to naled exposure: fpicn call data, reportable disease (merlin) data, and ed chief complaints. product code 2327991 was used to search fpicn data for naled-related exposure calls. fpicn calls with the following medical outcomes were excluded: no health effect, not followed judged as nontoxic exposure, unrelated effect, and confirmed nonexposure. individuals who met the reportable disease surveillance case definition for pesticide-related illness and injury2 were entered into merlin. essence-fl was monitored to evaluate ed visits in miami-dade county for the syndrome categories and free text chief complaints involving eyes (free text queries for eye irritation, eye burning, eye redness, and conjunctivitis), skin (syndrome category for rash), and respiratory (sub-syndrome categories for shortness of breath, wheezing, acute bronchitis, sore throat, and asthma) illnesses. results twenty-two exposure calls were identified through fpicn data in 2016. seven calls were excluded after doh review determined that these individuals were not exposed to naled. fifteen exposure calls were investigated and eight individuals met the surveillance case definition for pesticide-related illness and injury. among the eight doh cases, one individual was exposed in august (12.5%), and seven in september (87.5%). everyone had low severity illness, five (62.5%) were female, and the mean age was 39.6 years (range: 27 to 46 years). two cases (25%) were work-related. review of essence ed chief complaints data for eye, skin, and respiratory illnesses showed a few statistically significant increases in daily patient visits. however, these increases were not attributed to aerial spraying. conclusions fpicn data are useful in identifying cases of naled-related illness. near real-time access to ed chief complaints data along with fpicn and merlin data has enhanced surveillance capability for doh and helped address public health concerns related to naled-related illness following aerial spraying in miami-dade county. keywords pesticide; naled; poison center; essence; surveillance references 1. likos a, griffin i, bingham am, et al. local mosquito-borne transmission of zika virus—miami-dade and broward counties, florida, june–august 2016. mmwr morb mortal wkly rep 2016;64:1032–8. 2. cdc. case definition for acute pesticide-related illness and injury cases reportable to the national public health surveillance system. cincinnati, oh: us department of health and human services, cdc; 2012. available at http://www.cdc.gov/niosh/topics/pesticides/pdfs/ casedef.pdf. *prakash r. mulay e-mail: prakash.mulay@flhealth.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e196, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e297, 2019 isds 2019 conference abstracts understanding socio-cultural factors related to obesity: sentiment analysis on related tweets albert park1, delia s. west2 1 university of north carolina at charlotte, charlotte, north carolina, united states, 2 university of south carolina, columbia, south carolina, united states objective we aim to better understand socio-cultural factors (i.e., attitudes or perceptions of cultural groups) associated with food consumption and weight loss via sentiment analysis on tweets, short messages from twitter. introduction obesity can lead to the death of at least 2.8 million people each year [1], yet the rate of obesity around the world has continuously increased over the past 30 years [1]. societal changes, including increased food consumption and decreased physical activity, have been determined as two of the main drivers behind the current obesity pandemic [2]. examining socio-cultural factors (i.e., attitudes or perceptions of cultural groups) [3] associated with food consumption and weight loss can provide important insights to guide effective interventions and a novel surveillance approach to characterize population obesity trends from sociological perspectives. the primary goal of this study is to examine socio-cultural factors associated with food consumption and weight loss by conducting sentiment analysis on related online chatters. the secondary goal is to discuss the potential implications of being exposed to these different chatters in the online environment. scientific evidence in support of using social media to understand socio-cultural factors and its potential implications can be illustrated in two concise assertions. first, online chatters, including discussions on social media, have been shown to be an effective data source for understanding public interests [4,5]. second, prolonged participation in social media has been suggested to have impacts on users [6-8]. methods in this study, we examined twitter (www.twitter.com), a highly popular, free-to-use, micro-blogging social media platform that can instantly broadcast short messages to the world. these short messages are called tweets and we collected weight loss rela ted and food consumption related tweets using python library called tweepy [9]. we used hashtags from a previous study [10], including #weightloss, #diet, #fitness, and #health for collecting weight loss related tweets. similarly, we used #food, #foodporn, and #foodie to collect food consumption related tweets. we then used a rule-based model called vader [11], a sentiment analysis tool (i.e., computational process of categorizing sentiment) developed for social media text, to measure tweets’ sentiment. we used the compound score, which is a normalized and weighted composite score that ranges from -1.0 (most negative) to 1.0 (most positive). lastly, we conducted independent sample t-test to compare the sentiments of two types of tweets. results we collected 81,535 (from 41,436 unique user id) weight loss related tweets from august 30th to september 2nd of 2018 and 86,277 (from 36,977 unique user id) food consumption related tweets from august 28th to september 2nd of 2018. the mean sentiment score for weight loss related tweets was 0.17 (sample standard deviation: 0.39), whereas the mean sentiment for food consumption related tweets was more positive, scoring 0.26 with sample standard deviation of 0.34. the independent sample ttest suggests that the sentiment difference between the two types of tweets is statistically significant (t=52.10, p < .001). however, it is important note that the mean sentiment for both types of tweets was in the positive range. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e297, 2019 isds 2019 conference abstracts conclusions we present preliminary findings concerning socio-cultural factors associated with food consumption and weight loss within twitter chatters. our initial results suggest that individuals expressed more positive sentiment when tweeting about food consumption than when tweeting about weight loss. the results not only reflect the social norms of social media, twitter in this particular study, but also suggest how social media can indirectly promote more food consumption over weight loss via social norms theory [12] and how online social norms can reach individual members. this is especially important for young adults, the main demographic user group for social media [13], as they develop lasting health related habits and behaviors. although in its infancy, our research suggests that online sociocultural environment could be a potential socio-environmental risk factor for obesity. the next step is to utilize the findings to create online sociocultural environment that can promote the healthy choices. acknowledgement we restricted our analysis to publicly available discussion content. the study was determined to be not human subjects by the university of north carolina-charlotte's institutional review board (ethics committee). author ap’s contribution to this research was partially supported by ap’s startup research funding provided by the department of software and information systems, university of north carolina-charlotte. the content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. references 1. world health organization. global health observatory (gho) data: obesity [internet]. 2009. available from: http://www.who.int/gho/ncd/risk_factors/obesity_text/en/ archived at. http://www.webcitation.org/6rqich7oq 2. hill jo. 1998. environmental contributions to the obesity epidemic. science. 280(5368), 1371-74. pubmed https://doi.org/10.1126/science.280.5368.1371 3. swinburn b, egger g, raza f. 1999. dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. prev med. 29(6), 563-70. pubmed https://doi.org/10.1006/pmed.1999.0585 4. park a, conway m. 2017. towards tracking opium related discussions in social media. online j public health inform. 9(1), e073. https://doi.org/10.5210/ojphi.v9i1.7652 5. park a, conway m. tracking health related discussions on reddit for public health applications. annu symp proceedings amia symp. 2017;2017:1362–71. 6. park a, conway m. 2018. harnessing reddit to understand the written-communication challenges experienced by individuals with mental health disorders: analysis of texts from mental health communities. j med internet res. 20(4), e121. pubmed https://doi.org/10.2196/jmir.8219 7. park a, conway m. 2017. longitudinal changes in psychological states in online health community members: understanding the long-term effects of participating in an online depression community. j med internet res. 19(3), e71. pubmed https://doi.org/10.2196/jmir.6826 8. park a, hartzler al, huh j, mcdonald dw, pratt w. homophily of vocabulary usage: beneficial effects of vocabulary similarity on online health communities participation. annu symp proceedings amia symp. 2015;2015:1024–33. 9. roesslein j. tweepy [internet]. 2009. available from: http://www.tweepy.org 10. turner-mcgrievy gm, beets mw. 2015. tweet for health: using an online social network to examine temporal trends in weight loss-related posts. transl behav med. 5(2), 160-66. pubmed https://doi.org/10.1007/s13142-015-0308-1 11. hutto cj, gilbert e. vader: a parsimonious rule-based model for sentiment analysis of social media text. eighth int conf weblogs soc media (icwsm-14). 2014;216–25. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9603719&dopt=abstract https://doi.org/10.1126/science.280.5368.1371 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=10600438&dopt=abstract https://doi.org/10.1006/pmed.1999.0585 https://doi.org/10.5210/ojphi.v9i1.7652 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29636316&dopt=abstract https://doi.org/10.2196/jmir.8219 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28320692&dopt=abstract https://doi.org/10.2196/jmir.6826 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26029278&dopt=abstract https://doi.org/10.1007/s13142-015-0308-1 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e297, 2019 isds 2019 conference abstracts 12. perkins hw, berkowitz ad. 1986. perceiving the community norms of alcohol use among students: some research implications for campus alcohol education programming. int j addict. 21(9–10), 961-76. pubmed https://doi.org/10.3109/10826088609077249 13. smith a, anderson m. social media use in 2018 [internet]. pew research center internet, science & tech. 2018. available from: http://www.pewinternet.org/2018/03/01/social-media-use-in-2018/ archived at. http://www.webcitation.org/70nid9vv2 table 1. characteristics of the analyzed datasets weight loss tweets food consumption tweets dates august 30, 2018 september 2, 2018 august 28, 2018 september 2, 2018 number of tweets 81,535 86,277 number of unique user id 41,436 36,977 mean sentiment score (sample standard deviation) 0.17(0.39) 0.26(0.34) the independent sample t-test suggests that the sentiment difference between the two types of tweets is statistically signi ficant (t=52.10, p < .001) http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=3793315&dopt=abstract https://doi.org/10.3109/10826088609077249 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts responder safety, tracking, and resilience — georgia, 2016 –2017 jessica grippo*, laura edison, karl soetebier and cherie drenzek acute disease epidemiology, georgia department of public health, atlanta, ga, usa objective to better understand the importance of monitoring responders during public health emergencies and to learn how the georgia department of public health (dph) developed and deployed an electronic responder monitoring tool. introduction during an emergency, the state of georgia depends on public health staff and volunteers to respond. it is imperative that staff are safe before, during and after deployment. emergency response workers must be protected from the hazardous conditions that disasters and other emergencies create1. in october 2016 and september 2017, hurricanes matthew and irma caused widespread evacuation of georgia residents, initiating a tremendous sheltering effort. hundreds of public health responders were deployed to assist with sheltering and other aspects of the response. dph rapidly developed a novel electronic responder safety, tracking and resilience module, which was used to track public health responders and monitor their health and safety while deployed. methods dph rapidly developed a novel electronic responder safety, tracking, and resilience module (r-star), within the existing state electronic notifiable disease surveillance system to monitor the health and safety of responders. r-star was originally used during hurricane matthew, where it was launched the day of the storm, and was launched again four days before hurricane irma made landfall. responders were emailed a web-based link to register, indicating demographic information, contact information, work location, subject area, vaccination status, and whether they considered themselves mentally and physically fit to deploy. responders then received a daily email with a link to document their daily deployment location, duties, and whether they had any hazardous exposures, illness, or injuries while deployed. a post-deployment survey was sent to responders after hurricane matthew to solicit feedback about the responder safety module. results during hurricane matthew, 128 responders representing 11 georgia public health districts registered in r-star. seven responders reported illness or injury and were contacted to determine if medical services were needed; all remained healthy post-deployment. during hurricane irma, 1240 responders representing dph and 16 public health districts, and other employers, including law enforcement, fire, and education, registered in r-star. of 472 responders completing daily health checks during their irma deployment, 48 reported an injury, illness, or exposure, and were contacted to determine if services were needed. the daily health checks led to the identification of an outbreak of influenza in one of the shelters and resulted in vaccination or antiviral prophylaxis administration to 76 responders. fifty responders to hurricane matthew completed the post-deployment survey; 95% found r-star easy to use, and 92% indicated that they liked being monitored. supervisors indicated that the module could be used to: 1) roster and credential responders prior to an event; 2) track where responders are, monitor their health and safety during an event, and quantify the human resources deployed during a declared emergency; and, 3) to distribute post-response responder resources, monitor responder health, and gather information for after-action reports. conclusions r-star was widely used and well received despite being implemented with no prior training, with a dramatic increase in the number of responders registering from the first implementation in 2016 to the second implementation in september 2017. monitoring responder health and safety is crucial to responding to and preventing outbreaks during a response, and ensuring responders get appropriate mental and physical support after a deployment. lessons learned from both events will be used to create a just-in-time training curriculum, and develop a more robust r-star, which will enable responder rostering, credentialing, tracking and monitoring before, during, and after an event to ensure the health and safety of our responders as well as for future planning. keywords responder; hurricane; health monitoring references 1. centers for disease control and prevention (2017). emergency responder health monitoring and surveillance (erhms). retrieved from centers for disease control and prevention: https://www.cdc.gov/niosh/erhms/default.html. *jessica grippo e-mail: jessica.grippo@dph.ga.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e165, 2018 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1):e226, 2019 isds 2019 conference abstracts an algorithm for early outbreak detection in multiple data streams sesha k. dassanayake1, joshua french2 1 mathematics and computer science, rhodes college, memphis, colorado, united states, 2 university of colorado denver, denver, colorado, united states objective to propose a computationally simple, fast, and reliable temporal method for early event detection in multiple data streams introduction current biosurveillance systems run multiple univariate statistical process control (spc) charts to detect increases in multiple data streams [1]. the method of using multiple univariate spc charts is easy to implement and easy to interpret. by examining alarms from each control chart, it is easy to identify which data stream is causing the alarm. however, testing multiple data streams simultaneously can lead to multiple testing problems that inflate the combined false alarm probability. although methods such as the bonferroni correction can be applied to address the multiple testing problem by lowering the false alarm probability in each control chart, these approaches can be extremely conservative. biosurveillance systems often make use of variations of popular univariate spc charts such as the shewart chart, the cumulative sum chart (cusum), and the exponentially weighted moving average chart (ewma). in these control charts an alarm is signaled when the charting statistic exceeds a pre-defined control limit. with the standard spc charts, the false alarm rate is specified using the in-control average run length (arl0). if multiple charts are used, the resulting multiple testing problem is often addressed using family-wise error rate (fwer) based methods – that are known to be conservative for error control. a new temporal method is proposed for early event detection in multiple data streams. the proposed method uses p-values instead of the control limits that are commonly used with standard spc charts. in addition, the proposed method uses false discovery rate (fdr) for error control over the standard arl0 used with conventional spc charts. with the use of fdr for error control, the proposed method makes use of more powerful and up-to-date procedures for handling the multiple testing problem than fwer-based methods. methods the proposed method can be applied to multiple univariate cusum or ewma control charts. it can also be applied to a variation of the hotelling t2 chart which is a common multivariate process monitoring method. the hotelling t2 chart is analogous to the shewart chart. montgomery et. al [2] proposed a variation of the hotelling t2 chart where the t2 statistic is decomposed into components that reflect the contribution of each data stream. first, a tolerable fdr level specified. then, at each new time step disease counts from each of the m geographic regions y1t, y2t, …, ymt are collected. these disease counts are used to calculate the charting statistics s1t, s2t, …, smt for each region. meanwhile by inspecting historical data from each region, a non-outbreak period is identified. using data from the non-outbreak period, bootstrap samples are drawn with replacement from each region and charting statistics are calculated. using the charting statistics, empirical non-outbreak distributions are generated for each region. with the empirical non-outbreak distributions and the current charting statistic for each region s1t, s2t, …, smt , corresponding p-values p1t, p2t, …, pmt are calculated. the multiple testing problem that occurs in comparing multiple p-values simultaneously is handled using the storey -tibshirani multiple comparison procedure [3] to signal alarms. results as an illustration, all three methods – ewma, cusum, and hotelling t2 (components) were applied to a data set consisting of weekly disease count data from 16 german federal sates gathered over a 11 year period from 2004-2014. the first two years of data from 2004-2005 were used to calibrate the model. figure 1 shows the results for the state of rhineland palatinate. the three plots in figure 1 show (a) the weekly disease counts for rhineland palatinate (b) the ewma statistic (shown in red), the cusum statistic (shown in dark green) and (c) the component of the hotelling t2 statistic corresponding to the illustrated state (shown in blue). the actual outbreak occurred on week 306 (shown by the orange line). notice the two false alarms – alarms that occur before week 306 with the hotelling t2 statistic (dark green) on weeks 34 and 292; similarly, the cusum statistic signals a false alarm on week 57. however, the ewma statistic does not signal any false alarms before the outbreak (red). figure 2 zooms on the alarm statistics for the time period from weeks 280 – 330. the hotelling t2 statistic misses the onset of actual outbreak on week 306. http://ojphi.org/ online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1):e226, 2019 isds 2019 conference abstracts the cusum statistic detects the outbreak on week 307 – one week later. however, the ewma statistic detects the outbreak right at the onset on week 306. conclusions extensive simulation studies were conducted to compare the performance of the three control charts. performance was compared in terms of (i) speed of detection and (ii) false alarm rates. simulation results provide convincing evidence that the ewma and the cusum are considerably speedier in detecting outbreaks compared to hotelling t2 statistic: compared to the cusum, the ewma is relatively faster. similarly, the false alarm rates are larger for hotelling t2 statistic compared to the ewma and the cusum: false alarms are rare with both the ewma and the cusum statistics with ewma statistic having a slight edge. overall, ewma has the best performance out of the three methods with the new algorithm. thus, the new algorithm applied to the ewma statistic provides a simple, fast, and a reliable method for early event detection in multiple data streams. references 1. fricker rd. introduction to statistical methods for biosurveillance. new york, ny: cambridge university press; 2013. 399p. 2. runger gc, alt fb, montgomery dc. 1996. contributors to multivariate statistical process control signal. commun stat theory methods. 25(10), 2203-13. https://doi.org/10.1080/03610929608831832 3. storey jd, tibshirani r. 2003. statistical significance for genomewide studies. proc natl acad sci usa. 100, 9440-45. pubmed https://doi.org/10.1073/pnas.1530509100 figure 1 figure 2 figure 1 http://ojphi.org/ https://doi.org/10.1080/03610929608831832 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12883005&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12883005&dopt=abstract https://doi.org/10.1073/pnas.1530509100 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts data quality improvements in national syndromic surveillance program (nssp) data girum s. ejigu*, lakshmi radhakrishnan, paul mcmurray and roseanne english division of health informatics and surveillance, center for surveillance, epidemiology, and laboratory services, centers for disease control and prevention (cdc), atlanta, ga, usa objective review the impact of applying regular data quality checks to assess completeness of core data elements that support syndromic surveillance. introduction the national syndromic surveillance program (nssp) is a community focused collaboration among federal, state, and local public health agencies and partners for timely exchange of syndromic data. these data, captured in nearly real time, are intended to improve the nation’s situational awareness and responsiveness to hazardous events and disease outbreaks. during cdc’s previous implementation of a syndromic surveillance system (biosense 2), there was a reported lack of transparency and sharing of information on the data processing applied to data feeds, encumbering the identification and resolution of data quality issues. the biosense governance group data quality workgroup paved the way to rethink surveillance data flow and quality. their work and collaboration with state and local partners led to nssp redesigning the program’s data flow. the new data flow provided a ripe opportunity for nssp analysts to study the data landscape (e.g., capturing of hl7 messages and core data elements), assess end-to-end data flow, and make adjustments to ensure all data being reported were processed, stored, and made accessible to the user community. in addition, nssp extensively documented the new data flow, providing the transparency the community needed to better understand the disposition of facility data. even with a new and improved data flow, data quality issues that were issues in the past, but went unreported, remained issues in the new data. however, these issues were now identified. the newly designed data flow provided opportunities to report and act on issues found in the data unlike previous versions. therefore, an important component of the nssp data flow was the implementation of regularly scheduled standard data quality checks, and release of standard data quality reports summarizing data quality findings. methods nssp data was assessed for the national-level completeness of chief complaint and discharge diagnosis data. completeness is the rate of nonnull values (batini et al., 2009). it was defined as the percent of visits (e.g., emergency department, urgent care center) with a non-null value found among the one or more records associated with the visit. national completeness rates for visits in 2016 were compared with completeness rates of visits in 2017 (a partial year including visits through august 2017). in addition, facility-level progress was quantified after scoring each facility based on the percent completeness change between 2016 and 2017. legacy data processed prior to introducing the new nssp data flow were not included in this assessment. results nationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (n=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (n=80,603,991). of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. as for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (n=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (n=54,776,310). of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016. conclusions the newly designed nssp data flow provided more opportunity to identify data quality issues. by applying data quality checks within the newly designed nssp data flow, data quality issues related to hl7 messages and processed data could be identified early. improvements in data quality were demonstrated by measuring percent completeness of chief complaint and discharge diagnosis data in 2017 and comparing with data from 2016. overall, several factors helped improve data quality: implementation of routine and targeted data quality checks; investigation of the root cause of data quality issues; and communication of such findings by engaging the nssp team, sites, and vendors. keywords nssp; data quality; completeness; chief complaint; discharge diagnosis acknowledgments we thank paula yoon, david walker, michael coletta, alan davis, niketta womack, nssp partners and biosense governance group. references batini, c., cappiello. c., francalanci, c. and maurino, a. (2009) methodologies for data quality assessment and improvement. acm comput. surv., 41(3). 1-52. *girum s. ejigu e-mail: kwa7@cdc.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e201, 2018 isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts syndromic surveillance on the mental health impact of political rallies in charlottesville, virginia em stephens* virginia department of health, richmond, va, usa objective to describe the impact of civil unrest on the mental health of a community in near real-time using syndromic surveillance. introduction as part of a wide-spread community discussion on the presence of monuments to confederate civil war figures, the charlottesville city council voted to remove a statue of general robert e. lee.1 multiple rallies were then held to protest the statue’s removal. a ku klux klan (kkk) rally on july 8, 2017 (mmwr week 27) and a unite the right rally on august 12, 2017 (mmwr week 32) held in charlottesville both resulted in violence and media attention.2,3 the violence associated with the unite the right rally included fatalities connected to motor vehicle and helicopter crashes. syndromic surveillance has been used to study the impact of terrorism on a community’s mental health4 while more traditional data sources have looked at the impact of racially-charged civil unrest.5 syndromic surveillance, however, has not previously been used to document the effect of racially-charged violence on the health of a community. methods the virginia department of health (vdh) analyzed syndromic surveillance data from three emergency departments (eds) in the charlottesville area (defined to include charlottesville city and albemarle county), regardless of patient residence following the unite the right rally. visits to these eds between january 1 and september 2, 2017 were analyzed using the enhanced surveillance system for the early notification of community-based epidemics (essence) and microsoft sql 2012. encounters were identified as acute anxiety-related visits based on an international classification of diseases, tenth revision (icd-10) discharge diagnosis beginning with ’f41’. analyses were conducted using the essence algorithm ewma 1.2 and sas 9.3. results the greatest number of visits with a primary diagnosis of anxiety in 2017 (n=20) was observed in mmwr week 34 (august 20-26). this represented a statistically significant increase over baseline with a p-value of 0.01. by race, a significant increase over baseline in visits with a primary diagnosis of anxiety was observed among blacks or african americans. the largest volume of visits was observed in mmwr week 33 with a total of 8 identified visits or 1.8% of total ed visit volume. the increase in visits for anxiety observed in weeks 33-35 was 2.2 times greater among blacks or african americans than it was among whites, p = 0.016, 95% ci [1.14, 4.16]. conclusions previous work done in virginia to identify ed visits related to anxiety included only chief complaint criteria in the syndrome definition. due to a change in how one ed in the charlottesville area reported data during the study period, this syndrome definition could not be applied. in order to remove any potential data artifacts, only those visits with an initial diagnosis of anxiety were included in the analysis. the resulting syndrome definition likely underestimated the occurrence of anxiety in the charlottesville area, both because it lacked chief complaint information and because syndromic surveillance does not include data on visits to mental health providers outside of eds. this analysis presents a trend over time rather than a true measure of the prevalence of anxiety. this analysis, while conservative in its inclusion criteria, still identified an increase in visits for anxiety, particularly among blacks or african americans. in today’s political environment of race-related civil unrest, a way to measure the burden of mental illness occurring in the community can be invaluable for public health response. in charlottesville, the identification of a community-wide need for mental health support prompted many local providers to offer their services to those in need pro-bono.6 keywords anxiety; civil unrest; syndromic surveillance acknowledgments many thanks to erin austin, mph, jonathan falk, mph, and diane woolard, phd for the advice and review. isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts references 1 suarez, c. (2017, february 6). charlottesville city council votes to remove statue from lee park. the daily progress. retrieved from http://bit.ly/2wyohhv 2 spencer, h., & stevens, m. (2017, july 8). 23 arrested and tear gas deployed after a k.k.k. rally in virginia. the new york times. retrieved from http://nyti.ms/2tcibgu 3 hanna, j., hartung, k., sayers, d., & almasy, s. (2017, august 13). virginia governor to white nationalists: ‘go home … shame on you’. cnn. retrieved from http://cnn.it/2vvaght 4 vandentorren, s., paty, a. c., baffert, e., chansard, p., caserioschönemann, c. (2016, february). syndromic surveillance during the paris terrorist attacks. the lancet (387(10021), 846-847. doi:10.1016/ s0140-6736(16)00507-9 5 yimgang, d. p., wang, y., paik, g., hager, e. r., & black, m. m. civil unrest in the context of chronic community violence: impact on maternal depressive symptoms. american journal of public health 107(9), 1455-1462. doi:10.2105/ajph.2017.303876 6 deluca, p. (2017, august 19). downtown charlottesville library offers free counseling. nbc29.com. retrieved from http://bit.ly/2yizhbl *em stephens e-mail: emily.stephens@vdh.virginia.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e181, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e397, 2019 isds 2019 conference abstracts malaria burden through routing reporting: relationship between incidence estimates simon p. kigozi1, 2 1 disease control, london school of hygiene & tropical medicine, kampala, uganda, 2 infectious disease research collaboration, kampala, uganda objective to evaluate the relationship between test positivity rate and test-confirmed malaria case rate both in time and space, to provide better understanding of the utility and representativeness of hmis data for changing malaria burden in endemic settings. introduction routine surveillance is an important global strategy for malaria control. however, there have been few studies comparing routine indicators of burden, including test positivity rate (tpr) and test-confirmed malaria case rates (cmcr), over spatial and temporal scales. methods we studied the relationship between these indicators in children under 11 years, presenting with suspected malaria at outpatient department clinics in three health facilities in locations of varying transmission across uganda. we evaluated trends in each indicator over time (month) and space (by village) and explored the relationship between them using multivariable regression models. results overall, 65,710 participants visited the three clinics during the study period. pairwise relationships between tpr and cmcr showed good agreement over time, particularly for tpr’s below 50% and during low transmission seasons, but the relationship was complex at the village level. village mean annual tpr remained constant while cmcr was drastically reduced with increased distance from health facility, highlighting health care access’ importance. the forth quartile of distance from the health facility, relative to the first, was associated with reduction in cmcr with irr of 0.40 (95% ci: 0.23 -0.63; p=0.001), 0.55 (0.40-0.75; p<0.001), and 0.25 (0.12-0.51; p<0.001) for nagongera, kihihi, and walukuba respectively. regression analysis results emphasized a non-linear (cubic) relationship between tpr and cmcr, after accounting for month, village, season and demographic factors. conclusions altogether, these results suggested a strong non-linear relationship between the two indicators regardless of transmission setting. however, the current malaria surveillance system in uganda may under-represent burden from patients living furthest from sentinel health facilities. acknowledgement we would like to thank all the: staff at the three health centre iv’s of nagongera, walukuba, and kihihi; the uganda malaria surveillance project data officers at these sites; and, the wider umsp/idrc team that have been instrumental to the smooth running of this surveillance work over the years. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e438, 2019 isds 2019 conference abstracts improving risk factor identification for opioid overdose deaths in tennessee sarah j. nechuta, jenna moses, molly golladay, adele lewis, julia goodin, melissa mcpheeters tennessee department of health, nashville, tennessee, united states objective to examine specific drugs present based on postmortem toxicology for prescription opioid, heroin, and fentanyl overdoses classified based on icd-10 coding. to compare drugs identified from postmortem toxicology with those listed on the death certificate for opioid overdoses. introduction using death certificates alone to identify contributing substances in drug overdose deaths may result in misclassification and underestimation of the burden of illicit and prescription opioids and other drugs in drug-related deaths. to enable timely and targeted prevention in tennessee (tn), the identification and monitoring of new drugs and trends in use should utilize toxicology and medicolegal death investigation data directly, as recommended by others [1-3]. these data can inform mortality outcome definitions for improved surveillance and risk factor identification [4-7]. to our knowledge, this is the first analysis to use statewide linked toxicology and death certificate data in tn. methods we identified 615 opioid involved overdose deaths in tn of unintentional (underlying icd-10 codes: x40-x44) or undetermined (underlying icd-10 codes: y10-y14) intent during june 1st to december 31st 2017. utilizing the interim medical examiner database (i-med), we identified postmortem toxicology reports for 454 cases, which were from one of three national laboratories used by a state regional forensic center. toxicology data were abstracted and independently verified by two co-authors and linked to the tn death statistical file that included cause of death information (literal text and icd-10 codes) and demographics. the analysis focuses on cases with an available toxicology report. results we identified 171 prescription opioid overdoses, 221 fentanyl overdoses, and 113 heroin overdoses. table 1 displays postmortem toxicology profiles for major drugs/classes. for prescription opioid deaths (excluding fentanyl and heroin), positive toxicology results for prescription opioids were as follows: methadone (11%), buprenorphine (14%), hydrocodone (14%), oxycodone (36%) and oxymorphone (also a metabolite, 47%). benzodiazepines were present in close to 58% of prescription opioid overdoses; stimulants (cocaine, amphetamines, methamphetamines) in about 25%. for fentanyl and heroin deaths, prescription opioids were detected in about 26% and 34%, respectively; stimulants in about 57.9% and 52.2%, respectively, and benzodiazepines 36-37%. fentanyl was present on toxicology in about half of heroin overdoses, and 6–monoacetylmorphine in 72.6%.table 2 displays a comparison between death certificate (dc) listed drugs and drugs identified via toxicology. close to all fentanyl deaths identified from the dc were identified via toxicology (98.7%). benzodiazepines were involved in 34% of deaths based on dc, and 46% based on toxicology. stimulants were involved in about in 39% of deaths based on dc, and 45% based on toxicology. based on toxicology, about 20% of decedents were using antihistamines at overdose and 10% were using antidepressants. conclusions using medical examiners’ data, including toxicology data, improves estimation of contributing drugs involved in opioid deaths. this analysis provides jurisdiction-specific data on drugs that can help with monitoring trends and informs risk factor identification. future work includes adding information on prescribed opioid and benzodiazepines using tn’s prescription drug monitoring database and evaluating demographic variation in contributing drugs between toxicology and dc data to identify susceptible populations.acknowledgement this work was supported by funding from the centers for disease control & prevention (nu17ce924899-02-00) and (5 nu17ce002731-0200). the funder had no role in study design, data collection and analysis, or decision to publish. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e438, 2019 isds 2019 conference abstracts references 1. slavova s, o'brien db, creppage k, dao d, fondario a, haile e, hume b, largo tw, nguyen c, sabel jc, wright d, council of s, territorial epidemiologists overdose s. drug overdose deaths: let's get specific. public health rep. 2. horon il, singal p, fowler dr, sharfstein jm. 2018. standard death certificates versus enhanced surveillance to identify heroin overdose-related deaths. am j public health. 108(6), 777-81. pubmed https://doi.org/10.2105/ajph.2018.304385 3. mertz kj, janssen jk, williams ke. 2014. underrepresentation of heroin involvement in unintentional drug overdose deaths in allegheny county, pa. j forensic sci. 59(6), 1583-85. pubmed https://doi.org/10.1111/1556-4029.12541 4. landen mg, castle s, nolte kb, gonzales m, escobedo lg, et al. 2003. methodological issues in the surveillance of poisoning, illicit drug overdose, and heroin overdose deaths in new mexico. am j epidemiol. 157(3), 273-78. pubmed https://doi.org/10.1093/aje/kwf196 5. davis gg. 2014. national association of medical e, american college of medical toxicology expert panel on e, reporting opioid d. complete republication: national association of medical examiners position paper: recommendations for the investigation, diagnosis, and certification of deaths related to opioid drugs. j med toxicol. 10(1), 100-06. pubmed https://doi.org/10.1007/s13181-013-0323-x 6. slavova s, bunn tl, hargrove sl, corey t. 2017. linking death certificates, postmortem toxicology, and prescription history data for better identification of populations at increased risk for drug intoxication deaths. pharmaceut med. 31(3), 155-65. https://doi.org/10.1007/s40290-017-0185-7 7. hurstak e, rowe c, turner c, behar e, cabugao r, et al. 2018. using medical examiner case narratives to improve opioid overdose surveillance. int j drug policy. 54, 35-42. pubmed https://doi.org/10.1016/j.drugpo.2017.12.017 table 1. postmortem toxicology results among prescription opioid, fentanyl, and heroin overdose deaths in tennessee, n (%) prescription opioid* (n = 171) fentanyl * (n = 221) heroin* (n = 113) positive toxicology positive toxicology positive toxicology yes no yes no yes no fentanyl 3 (1.8) 168 (98.2) 217 (98.2) 4 (1.8) 58 (51.3) 55 (48.7) 6–monoacetylmorphine 1 (0.6) 170 (99.4) 37 (16.7) 184 (83.3) 82 (72.6) 31 (27.4) morphine alone 32 (18.7) 139 (81.3) 89 (40.3) 132 (59.7) 112 (99.1) 1 (0.9) morphine and codeine 3 (1.8) 168 (98.2) 9 (4.1) 212 (95.9) 51 (45.1) 62 (54.9) codeine 5 (2.9) 166 (97.1) 9 (4.1) 212 (95.9) 52 (46.0) 61 (54.0) oxycodone 62 (36.3) 109 (63.7) 20 (9.1) 201 (90.9) 15 (13.3) 98 (86.7) hydrocodone 24 (14.0) 147 (86.0) 9 (4.1) 212 (95.9) 11 (9.7) 102 (90.3) oxymorphone** 81 (47.4) 90 (52.6) 17 (7.7) 204 (92.3) 5 (4.4) 108 (95.6) methadone 19 (11.1) 152 (88.9) 6 (2.7) 215 (97.3) 3 (2.6) 110 (97.4) buprenorphine 24 (14.0) 147 (86.0) 6 (2.7) 215 (97.3) 4 (3.5) 109 (96.5) benzodiazepines 99 (57.9) 72 (42.1) 80 (36.2) 141 (63.8) 42 (37.2) 71 (62.8) cocaine 18 (10.5) 153 (89.5) 74 (33.5) 147 (66.5) 34 (30.1) 79 (69.9) other stimulants 25 (14.6) 146 (85.4) 54 (24.4) 167 (75.6) 25 (22.1) 88 (77.9) *defined using death certificate data. **also a pharmacologically active metabolite of oxycodone. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29672148&dopt=abstract https://doi.org/10.2105/ajph.2018.304385 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25041514&dopt=abstract https://doi.org/10.1111/1556-4029.12541 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12543628&dopt=abstract https://doi.org/10.1093/aje/kwf196 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24132519&dopt=abstract https://doi.org/10.1007/s13181-013-0323-x https://doi.org/10.1007/s40290-017-0185-7 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29353022&dopt=abstract https://doi.org/10.1016/j.drugpo.2017.12.017 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e438, 2019 isds 2019 conference abstracts table 2. comparing postmortem toxicology results with death certificate listed drugs for opioid-involved overdose deaths in tennessee, n death certificate positive toxicology fentanyl 220 223 heroin* 114 87 morphine alone 56 178 morphine and codeine 1 8 codeine 6 59 oxycodone 83 93 hydrocodone 41 42 methadone 23 26 buprenorphine 28 32 oxymorphone** 70 101 tramadol 10 10 benzodiazepines 155 201 cocaine 91 108 amphetamines/methamphetamine 87 93 antihistamines 14 90 antidepressants 27 45 *heroin or 6-monoacetylmorphine. **also a pharmacologically active metabolite of oxycodone. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e246, 2019 isds 2019 conference abstracts identifying high-risk areas for dengue infection using mobility patterns on twitter roberto c. souza1, daniel b. neill2, renato m. assunção1, wagner meira1 1 computer science, universidade federal de minas gerais, belo horizonte, mg, brazil, 2 new york university, new york, new york, united states objective we develop new spatial scan models that use individuals' movement data, rather than a single location per individual, in orde r to identify areas with a high relative risk of infection by dengue disease. introduction traditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. however, human mobility plays a key r ole in dengue transmission, especially due to the mosquito day-biting habit [1], and relying solely on individuals’ residential address as a proxy for dengue infection ignores a multitude of exposures that individuals are subjected to during their daily routine s. residence locations may be a poor indicator of the actual regions where humans and infected vectors tend to interact more, and hence, provide little information for dengue prevention. the increasing availability of geolocated data in online platforms such as twitter offers a unique opportunity: in addition to identifying diseased individuals based on the textual content, we can also follow them in time and space as they move on the map and model their movement patterns. comparing the observed mobility patterns for case and control individuals can provide relevant information to detect localized regions with higher risk of dengue infection. incorporating the mobility of individuals into risk modeling requires the development of new spatial models that can cope wit h this type of data in a principled way and efficient algorithms to deal with the ever-growing amount of data. we propose new spatial scan models and exploit geo-located data from twitter to detect geographic clusters of dengue infection risk. methods as the spatial tracking of a large sample of infected and non-infected individuals is expensive and raises serious privacy issues, we instead analyze geolocated twitter data (tweets), which is readily and publicly available. we identify “infected” individuals (cases) as those individuals who have at least one tweet classified as a current, personal experience with dengue. we note that, because of the incubation period and recovery time, infected twitter users are likely to mention dengue in their tweets days after they are infected, and usually not at the location where the exposure (mosquito bite) occurred. once we have identified cases and controls based on the textual content of the messages, we then compare the mobility patterns of the two groups. the key aspect of our method is that the input is a series of locations rather than a single location, such as the residence address, for each individual. the number of positions ni composing each mobility pattern can vary substantially between individuals i, and thus simple approaches like counting the total numbers of case and control tweets per location would be biased and inaccurate; moreover, individuals with larger numbers of tweets may be more likely to be identified as a case. nevertheless, our assumption is that the entire mobility patterns will be informative of the riskier areas if we compare the spatial patterns from infected and non-infected individuals. we have developed two new spatial scan methods (unconditional and conditional spatial logistic models) which correctly accoun t for the multiple, varying number of spatial locations per individual. both models use the proportion of an individual’s tweet s in each location as an estimate of the proportion of time spent in that location; the estimate is biased by individuals’ propensity to tweet in different locations, but is expected to capture the large amounts of time spent at frequently visited locations. our unconditional model controls the variable contribution of each individual through a non-parametric estimation of the odds of being a case and has a semi-parametric logistic specification. when estimating the previous offset becomes a complex task, we propose a case-control matching strategy in the conditional model to control for the number of tweets ni. based on the subset scan approach [2], we search for localized regions where the infection risk is substantially higher than in the rest of the map by maximizing a loglikelihood ratio statistic over subsets of the data. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e246, 2019 isds 2019 conference abstracts results we demonstrate the detection of high-risk clusters for dengue infection using twitter data we collected in brazil during the year of 2015, when a strong surge of dengue hit several cities. we apply our method to the cities with highest number of case individuals. there are many points of interest, such as hospitals and parks, inside the detected regions. as those places are non-residential, standard approaches would fail to consider them as potential infection places in the event of a spike in the number of cases. figure 1 shows the detected regions in the city of campinas, brazil. synthetic and real-world evaluation results demonstrate that our methods work better than either just mapping each individual to their most frequent location (which is a proxy for home addre ss) and running a traditional spatial scan, or scanning using tweet volume as an input. conclusions identifying places where people have higher risk of being infected, rather than focusing on residential address locations, ma y be key to surveillance for vector-borne diseases such as malaria and dengue, allowing public health officials to focus mitigation actions. the stochasticity of location data is not appropriate for typical spatial cluster detection tools such as the traditional spatial scan statistic [3]. each user is represented by a different number of geographic points and the variability of these numbers is large; traditional approaches can be easily misled if not extended to account for this special structure. dengue is just one of many infectious diseases with a well-known etiology but a huge number of uncertain and difficult to obtain parameters that quantify factors such as infected mosquito population, likelihood of being bitten by an infected mosquito, and human movement in the mosquito-infested areas. our methods add to the set of tools that spatial epidemiologists have available to search for spatially localized risk clusters using readily available twitter data. we expect that our method will also be useful to other public h ealth surveillance problems where movement data can bring relevant information. acknowledgement this work was partially funded by fapemig, cnpq and capes and also by the projects inweb, masweb, eubra-bigsea, inct-cyber, atmosphere and by the google research awards for latin america program. references 1. stoddard st, et al. 2009. the role of human movement in the transmission of vector-borne pathogens. plos negl trop dis. 3(7), e481. pubmed https://doi.org/10.1371/journal.pntd.0000481 2. neill db. 2012. fast subset scan for spatial pattern detection. j r stat soc b. 74(2), 337-60. https://doi.org/10.1111/j.1467-9868.2011.01014.x 3. kulldorff m. 1997. a spatial scan statistic. commun stat theory methods. 26(6), 1481-96. https://doi.org/10.1080/03610929708831995 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19621090&dopt=abstract https://doi.org/10.1371/journal.pntd.0000481 https://doi.org/10.1111/j.1467-9868.2011.01014.x https://doi.org/10.1080/03610929708831995 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e246, 2019 isds 2019 conference abstracts figure 1: detected regions in the city of campinas, brazil. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e383, 2019 isds 2019 conference abstracts epidemiological trends of reported legionnaires’ disease in houston, texas, 2014-2017 razina khayat, najmus abdullah, sudipa biswas, hafeez rehman, kirstin short informatics, city of houston, houston, texas, united states objective to study trends and patterns in legionnaires’ disease cases in houston, texas, from 2014-2017. introduction legionellosis is a respiratory illness that is mostly (80-90%) caused by the bacterium legionella pneumophila. it is associated with a mild febrile illness, pontiac fever, or legionnaires’ disease [1], a source of severe, community-acquired pneumonia. legionella bacteria mostly affect elderly persons specifically those with underlying debilitating illnesses and with lowered immune systems. water is the major natural reservoir for legionella, and the pathogen is found in many different natural and artificial aquatic environments such as cooling towers or water systems in buildings, including hospitals. an abrupt increase in the incidence of legionnaires’ has been noted since 2003 throughout the nation. according to cdc, about 6,000 cases of legionnaires’ disease were reported in the united state in 2015 [1]. incidence rates of legionnaires for the year 2015 were 1.06 and 1.90 (ref) for texas and the united states respectively [2]. increased number of reported cases might be due to the fact of an older population, more at risk individuals, aging plumbing infrastructure, and increased testing for legionnaires’ disease by various hospitals and laboratories. methods data were extracted from houston’s electronic disease surveillance system (hedss) from january 1, 2014, to december 0 7, 2018. confirmed cases were analyzed to examine the epidemiologic trends across years 2014 to 2018. demographic characteristics such as age, race, and gender were also analyzed. incidence rates, case fatality and time lapse from date of diagnosis to dat e of reporting to the health department were also studied. data were analyzed using sas statistical software, version 9.4. only houston residents were included in the analysis. to be considered confirmed, a case must be clinically compatible and fulfill at least one of the confirmatory laboratory criteria. results there were 218 cases of ld reported to the city of huston from 2014 to 2018. only 116 cases (53%) were classified as confirmed. reported cases may have been not confirmed due to the lack of fulfilling the case criteria for the case. providers may have ordered a non-confirmative test, or the case may not have satisfied the clinical compatibility due to loss to follow-up or for other reasons. most of the confirmed cases were reported from larger for-profit hospitals (500+beds) in the area. the majority of cases were diagnosed by urinary antigen test (95, 82%). there were four deaths due to legionnaires disease during this period giving a case fatality rate of 3.4%. death rates were inaccurate, though, and could be higher than reported since cases were not followed up after being reported to the state. from 2014 to 2018, legionnaires’ disease incidence rates increased from 0.71 to 1.36 per 100,000 , an average annual increase of 17%. in 2014–2018, the incidence of ld was higher among men compared with women. 67 cases (58%) were male, and 49 (42%) were female. female cases remained stable throughout the years while male cases increased from 6 to 23, an increase of approximately four folds. the median age was 60 years with a range of 21 to 96 years. ld incidence increased with age; it was highest among residents 65 years and older (42,36%). african americans had the highest incidence of ld (40, 35%) followed by hispanics (29, 25%). african americans cases had more than doubled through years 2014-2018 from 6 to 13. cases were higher in warmer months specifically in july (14) an august (13). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e383, 2019 isds 2019 conference abstracts conclusions cases were higher in the warmer months and the highest among the elderly, men, and those of african american r ace. elr was the prime source of initial case reporting to the health department. the number of legionnaire’s cases observed were increasi ng with each passing year. the ratio of confirmed cases to those reported were only 53% thus raising awareness and app ropriate education to the investigators and providers are highly advised. it is critical to the control of ld that enhanced surveillan ce is maintained at a high level. consequently, more consideration should be given for the more widespread use of legionna ires confirming test when a patient presents with pneumonia. hospitals and other healthcare facilities often have large, complex water systems, making them potentially high -risk settings for transmission of legionellosis to vulnerable patients or residents. we recommend all healthcare facilities have a water management program to control legionella. acknowledgement we thank the flollowing agencies for providing data for this study: 1. texas department of state health services. 2. houston health department, diivision of disease prevention and control. 3. houston electronic disease surveillance system. references 1. centers for disease control and prevention. (2018). infection control assessment tools. retrieved october 5, 2018, from https://www.cdc.gov/legionella/ 2. texas health and human services. (2018). legionellosis. retrieved october,5, 2018, from https://www.dshs.texas.gov/idcu/disease/legionnaires/ http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e285, 2019 isds 2019 conference abstracts harnessing data science through healthcare it interoperability sheryl l. taylor national institute of standards and technology, gaithersburg, maryland, united states objective to provide tools to generate national and local syndromic surveillance electronic messaging specifications and to test implementations in which the set of requirements have been implemented in order to confirm or refute the conformance to those requirements, thereby promoting healthcare information technology (hit) interoperability in the public health sector. introduction the ability to harness data science for use in improving population health and public health surveillance begins with the application of interoperability standards to electronic messaging for data exchange between hit used by public health authorities (phas) and the providers who submit patient data to them. when electronic transmissions between these entities are not based on interoperability standards, the patient data that are exchanged may be incomplete, inaccurate, invalid, and/or untimely. as a result, local phas and the centers for disease control and prevention (cdc) may be unable to fulfill their goals of monitoring public health trends and improving population health. methods as part of the effort to meet the need for the application of interoperability standards to electronic messaging for data exchange between hit modules that submit and collect syndromic surveillance data for public health, the national institute of standards and technology (nist), in collaboration with the cdc and the international society for disease surveillance (is ds), developed and maintains a set of validation tools. these tools are focused on standardized syndromic surveillance messaging and are used for hit certification testing by the office of the national coordinator (onc) and for on-boarding by various public health jurisdictions in the us. in addition, isds informatics personnel are using the nist implementation guide authoring and management tool (igamt) for creating the first hl7-ballotted version of a guide for syndromic messaging, the hl7 2.5.1 implementation guide for syndromic surveillance release 1. this guide is a messaging specification that defines how disparate healthcare applicati ons are to codify and transmit administrative and clinical data for public health surveillance and response. igamt is part of an integrated platform that also includes the nist test case authoring and management tool (tcamt), a testing infrastructure and framework, and the nist general validation tool (gvt). this web-based platform enables domain experts, such as the isds informatics experts, to control the automatic process for generating computable standards and associated testing tools. results developed through collaboration between nist, the cdc, and isds, the 2015 edition syndromic surveillance test suite has been used in the onc hit certification program for validating over one hundred hit modules against the syndromic messaging guide developed by the cdc and the public health information network, the phin guide for syndromic surveillance messaging release 2.0 and the associated erratum. during the collaborative process, nist contributed expertise based on many years of co-authoring and using hit interoperability specifications, and the cdc and isds contributed expertise pertaining to the syndromic surveillance domain. outcomes of this process included increased awareness by all involved parties regarding the challenges of writing computable standards and the challenges associated with testing hit under constrained circumstances, such as with the onc hit certification program. the recognition of the need for well-defined standards, as well as testing using real-world scenarios and clinical data, led to the development of igamt and tcamt for automating the production of these artifacts; and with these tools came the ability to automate generation of testing resources, such as syndromic surveillance validation tools that are customized to national-level specifications as well as to state/local-level specifications for use in on-boarding procedures. as of early 2017, states with jurisdictions requiring providers to validate the ability of their hit modules to generate syndromic messages using the nist national-level syndromic surveillance test suite in their on-boarding process included arkansas, florida, indiana, kansas, maryland, south carolina, and washington. now that a national-level hl7balloted syndromic surveillance implementation guide has been generated using igamt, representatives of several additional phas have expressed interest in using the components of http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e285, 2019 isds 2019 conference abstracts the nist integrated platform for generating local-level specifications and testing tools. state and local jurisdictions often require certain data to be submitted in addition to the data required by the national-level specification. local-level testing tools used during the on-boarding process would enable jurisdictions to validate syndromic messages created by submitters in order to confirm or refute the conformance to the local-level requirements. conclusions improving population health and public health surveillance by utilizing the power of data science requires the ubiquitous deployment of standardsbased data exchange, that is, interoperability, between the numerous disparate hit modules in use by providers and phas today. nist has created a development platform that enables the domain experts at the cdc and isds to use automated tools to generate nationaland local-level syndromic surveillance electronic messaging specifications and the associated testing tools that confirm or refute conformance to the requirements in these specifications. these tools promote interoperability as the foundation for harnessing data science for the benefit of the public and the public health entities that serve them. acknowledgement this effort is accomplished by the national institute of standards and technology in collaboration with the centers for disease control and prevention and the international society for disease surveillance. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e258, 2019 isds 2019 conference abstracts performance of machine learning method to classify free-text medical causes of death yasmine baghdadi1, alix bourrée1, 2, aude robert3, grégoire rey3, anne gallay1, pierre zweigenbaum2, cyril grouin2, anne fouillet1 1 santé publique france, saint-maurice, france, 2 cnrs-limsi, orsay, france, 3 inserm-cépidc, le kremlin-bicêtre, france objective this study aims to implement and evaluate two automatic classification methods of free-text medical causes of death into mortality syndromic groups (msgs) in order to be used for reactive mortality surveillance. introduction mortality is an indicator of the severity of the impact of an event on the population. in france mortality surveillance is pa rt of the syndromic surveillance system sursaud and is carried out by santé publique france, the french public health agency. the set-up of an electronic death registration system (edrs) in 2007 enabled to receive in real-time medical causes of death in free-text format. this data source was considered as reactive and valuable to implement a reactive mortality surveillance system using medical causes of death [1]. the reactive mortality surveillance system is based on the monitoring of mortality syndromic groups (msgs). an msg is defined as a cluster of medical causes of death (pathologies, syndromes, symptoms) that meet the objectives of early detection and impact assessment of events [2]. since causes of death are entered in free-text format, their automatic classifications into msgs require the use of natural language processing methods. we observe a constant increase in the use of these methods to classify medical information and for health surveillance over the last two decades [3]. methods data consisted of the medical part of electronic death certificates received in routine by santé publique france from 2012 to 2016. we split the dataset into training and test sets. among each set, a subset of certificates was selected by a random sampling without replacement. two annotators manually assigned msgs to each death certificates in all subsets. discordances were discussed and corrected if necessary. the agreement rate between the two annotators was 0.90 on the test set. final annotated subsets repre sent the ground truth against which the methods tested were evaluated. the final evaluation was performed on the test set of 1,000 death certificates while the classifiers were trained on 3500 death certificates. two classification methods were implemented: a rule-based method and a supervised machine learning method. the rule-based method was based on four processing steps: applying standardization rules, splitting of medical expression using delimiters, spelling correction and dictionary projection. the supervised machine learning method was set up using a linear support vector machine (svm) classifier. we trained a multi-label classifier using the oneversus-all strategy. we implemented two models: one based on surface features (svm model) and the other, a hybrid model, combining surface features and features obtained by the rule-based method. surface features were bags-of-word unigrams and bigrams and of character trigrams. the rule-based method and the two supervised machine learning models were evaluated using the three evaluation measures: precision (positive predictive value), recall (sensitivity) and f-measure (p/r/fm). the study focused on the classification performance of msgs defined for the reactive detection of outbreaks and are composed of unspecific or acute pathologies, or general symptoms (related to pain, fever, cognitive disorder…). only the 40 msgs mentioned at least 3 times in the test set were considered in this study, they belonged to 13 topics (respiratory conditions, cardio and cerebrovascular conditions, infectious diseases, digestive conditions…). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e258, 2019 isds 2019 conference abstracts results with the rule-based method, among the 40 msgs, 24 obtained a p/r/fm over 0.90. they belonged mainly to the topics cardio and cerebrovascular conditions (5 msgs), respiratory conditions (6), and general symptoms (5). four msgs obtained p/r/fm below 0.85 belonging to the topics infectious conditions (2), blood condition (1) and unspecified causes of death (1). the hybrid model obtained p/r/fm over 0.90 for 25 msgs. among them, 21 were the same as the rule-base method. performance of the rule-based method and the hybrid model were over 0.95 for the same 13 msgs. the hybrid model obtained p/r/fm below 0.85 for 4 msgs also belonging to the same topics as those of the rule-based method. the svm model had lower classification performance than the two other models. conclusions for syndromic mortality surveillance both precision and recall are important for all msgs. indeed to meet the objective of a reactive detection of events, high precision is needed to limit false alarms. to measure the impact of an event, the surveillance system should have high recall, to avoid an underestimation of this impact. this is especially true for rarer diseases. the results showed that the rule-based method and the hybrid model are the most effective to classify causes of death into msgs. for some msgs with less than 5 mentions in the test set (7%), these results must be qualified. also, to improve classification performance for m sgs with performance below 0.90, and to confirm these results further analysis must be conducted. the results suggest the relevance of these methods to set up a reactive mortality surveillance system for detection and alert based on free -text causes of death. such a system will provide useful information to health authorities regarding the causes of death during an event, helping them to adapt counter and prevention measures. acknowledgement the authors thank thomas lavergne for his valuable advice during this study. the authors also thank the it department of limsi for computer support and help in computer setup. references 1. lassalle m, caserio-schönemann c, gallay a, rey g, fouillet a. 2017. pertinence of electronic death certificates for real-time surveillance and alert, france, 2012–2014. public health. 143, 85-93. pubmed https://doi.org/10.1016/j.puhe.2016.10.029 2. baghdadi y, gallay a, caserio-schönemann c, thiam m-m, fouillet a. 2018. towards real-time mortality surveillance by medical causes of death: a strategy of analysis for alert. rev epidemiol sante publique. 66, s402. https://doi.org/10.1016/j.respe.2018.05.453 3. wang y, wang l, rastegar-mojarad m, moon s, shen f, et al. 2018. clinical information extraction applications: a literature review. j biomed inform. 77, 34-49. pubmed https://doi.org/10.1016/j.jbi.2017.11.011 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28159032&dopt=abstract https://doi.org/10.1016/j.puhe.2016.10.029 https://doi.org/10.1016/j.respe.2018.05.453 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29162496&dopt=abstract https://doi.org/10.1016/j.jbi.2017.11.011 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e352, 2019 isds 2019 conference abstracts mental health outcomes for individuals with chronic hepatitis c infection. william w. thompson1, mohammed a. khan1, jay soh1, lauren canary1, michael blank2, noele p. nelson1 1 national center for hiv/aids, viral hepatitis, std, and tb prevention, us centers for disease control & prevention, atlanta, georgia, united states, 2 university of pennsylvania perelman school of medicine, philadelphia, pennsylvania, united states objective using data from the 2011–2015 ibm marketscan® commercial claims and encounters, we sought to assess the relationship between mental health outcomes and chronic hepatitis c infection after adjusting for important confounders. persons with hcv antibody and rna test results between 2011 and 2015 and continuous enrollment in fee-for-service plans were included in the analysis introduction hepatitis c virus (hcv) infection is a leading cause of liver disease-related morbidity and mortality in the united states and hcv incidence has been increasing. mental illness may impact the likelihood of initial hcv infection, progress and adherence to treatment along the hepatitis c care cascade, and risk of subsequent reinfection for those cured of hepatitis c. the relation ship between hcv infection and mental illness is not well understood and many studies have lacked sufficient sample size to adjust for important confounders. we sought to explore the association between chronic hcv infection and mental illness after adjusting for important confounders. methods we obtained data from the 2011–2015 ibm marketscan® commercial claims and encounters and medicare supplemental and coordination of benefits databases. these data consist of inpatient and outpatient service claims for persons with employer sponsored health insurance coverage and their dependents. persons with hcv antibody and rna test results between 2011 and 2015 and continuous enrollment in a fee-for-service plan were included in the analysis. chronic hcv infection was defined by a positive hcv rna test result. controls without chronic hcv infection had a negative hcv antibody test result and no positive hcv antibody or rna test result in the preceding or followi ng year. the index date was defined by the date of the earliest positive hcv rna or negative hcv antibody test. demographic characteristics were obtained from the marketscan® enrollment tables. all enrollees in the study population were at least 18 years old during the year of the index date. the analysis sample was restricted to persons who were identified as recei ving outpatient prescription drug claims data feeds. we estimated adjusted odds ratios (or) for the association between mental illness (icd -9 code 295 or 296) and hcv rna status. multivariate models included age (18-44, 45-64, 65+ years), sex, region, and an adjusted charlson comorbidity index which excluded liver disease and hepatocellular carcinoma. results we identified 2,847 individuals with chronic hcv infection (hcv rna+) and 57,418 controls who were hcv antibody negative. with respect to age, 83% of hcv rna+ individuals were aged 45-64 years while only 43% of the hcv antibody negative individuals were in the same age range. similarly, for sex, 62% and 40% of hcv rna+ individuals and controls, respectively, were male. for unadjusted analyses, age, sex, region, comorbid conditions, and mental illness (or= 2.25 [95% ci; 1.52 3.34]) were all statistically associated with hcv rna+. for the multivariate adjusted models, these same variables remained statisti cally significant. for the multivariate model, individuals with a mental illness were more likely to be hcv rna+ relative to hcv antibody negative controls. (or= 1.95 [95% ci; 1.30 2.93]). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e352, 2019 isds 2019 conference abstracts conclusions this study demonstrated a strong association between mental illness and hcv chronic infection after adjusting for important confounders including other comorbid conditions. a growing body of research suggests that persons with mental illness are at increased risk for contracting and transmitting hcv due to high rates of substance use and high-risk sexual behavior among infected persons as well as high rates of sexual victimization. hcv prevention efforts should be directed toward individuals with mental illness or seeking treatment for mental illness. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts a suite of mechanistic epidemiological decision support tools paul fenimore*, benjamin mcmahon, nicolas hengartner, timothy germann and judith mourant los alamos national laboratory, los alamos, nm, usa objective we will demonstrate tools that allow mechanistic contraints on disease progression and epidemic spread to play off against interventions, mitigation, and control measures. the fundamental mechanisms of disease progression and epidemic spread provide important constraints on interpreting changing epidemic cases counts with time and geography in the context of on-going interventions, mitigations, and controls. models such as these that account for the effects of human actions can also allow evaluation of the importance of categories of epidemic and disease controls. introduction we present the epiearly, epigrid, and epicast tools for mechanistically-based biological decision support. the range of tools covers coarse-, medium-, and fine-grained models. the coarsegrained, aggregated time-series only data tool (epiearly) provides a statistic quantifying epidemic growth potential and associated uncertainties. the medium grained, geographically-resolved model (epigrid) is based on differential equation type simulations of disease and epidemic progression in the presence of various human interventions geared toward understanding the role of infection control, early vs. late diagnosis, vaccination, etc. in outbreak control. a fine-grained hybrid-agent epidemic model (epicast) with diurnal agent travel and contagion allows the analysis of the importance of contact-networks, travel, and detailed intervention strategies for the control of outbreaks and epidemics. methods we use three types of methods for simulation and analysis. they are: (1) bayesian and regression methods allowing estimation of the basic reproductive number from case-count data; (2) ordinarydifferential equation integration with modifications to account for discreteness of disease spread when case counts are small (we include spaceand time-dependent effects); and (3) methods that hybridize agent-based travel, mixing, and disease progression with nested-mass action contagion (i.e. not fully agent-based). from the perspective of decision support, the crucial feature of mechanistic infectious epidemiological models is a way to capture the human interventions that determine epidemic outcome. categorizing types of mitigation into those that change the force of infection, and those that branch disease progression allows a common framework that can be extended from medium-grained models through fine-grained. our canonical example is our epigrid tool which allows for the modulation of the force of infection (i.e. contagion) with time (and potentially space), the vaccination of a susceptible population in a geographicallytargeted manner, movement controls, and branching our disease progression model to account for earlyvs. late-intervention during host disease progression. results we will present analysis of diseases that exemplify the various aspects of analysis in support of outbreak and epidemic control. human and animal diseases relevant to this demonstration include rinderpest, avian influenza, and measles. we will begin with epiearly’s estimate of epidemic potential using aggregated timedependent case-count data. the key observation for epiearly is that under a wide range of situations a disease’s reproductive number should be generalized to a distribution of possibilities to account for inherent randomness and other factors (including the variability of a disease contact network). we will then continue with a demonstration of epigrid’s capabilities for understanding and modelling the role of interventions including contagion control (the force of infection), treatment (changing disease progression and infectiousness depending on treatment), vaccination, culling, and movement controls. we will briefly touch on the capabilities of epicast for more detailed analysis of specific intervention strategies. conclusions we will demonstrate examples where modeling either contributed or plausibly would contribute to informing epidemic and outbreak control constrained by the possibilities of the underlying epidemic and disease dynamics. keywords intervention; geospatial; epidemic; disease progression acknowledgments we thank the defense threat reduction agency for their support. *paul fenimore e-mail: paulf@lanl.gov online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e1, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e368, 2019 isds 2019 conference abstracts streamlining disease surveillance system implementation in tanzania: lessons learnt green m. sadru immunization, john snow inc. (jsi), dar es salaam, dar es salaam, tanzania, united republic of objective to support streamlining of vpd surveillance into integrated diseases surveillance and response (idsr) system in tanzania introduction tanzania adopted idsr as the platform for all disease surveillance activities. today, tanzania’s idsr guidelines include surveillance and response protocols for 34 diseases and conditions of public health importance, outlining in detail necessary recording and reporting procedures and activities to be taken at all levels. a total of 15 disease-specific programs/sections in the ministry of health, community development, gender, elderly and children (mohcdgec) are linked to the idsr, though the extent to which each program uses idsr data varies. over the years, idsr procedures and the structures that support them have received significant government and external resources to maintain and strengthen detection, notification, reporting and analysis of surveillance information. however, with the imminent phasing out of programs (such as the polio eradication program) that have supported idsr strengthening and maintenance in the past, resources for surveillance will become more limited and the government will need to identify additional resources to sustain the country’s essential surveillance functions. maternal and child survival program (mcsp), a usaid funded program supported mohcdgec managing active and passive surveillance systems in improving coordination and strengthen the system taking into consideration declining resources as well as transitioning to polio end game where most of the financial resources were derived from to support vaccine preventable diseases surveillance. the support complements other global health security agenda (ghsa) on the key thematic areas (prevent, detect and report) support to the mohcdgec and working with the newly formed emergency operations center (eoc) to improve response. methods between february and november 2018, the mohcdgec and mcsp undertook activities to generate information for future plans to strengthen tanzania’s disease surveillance system to address the global health security agenda (ghsa): 1) desk review of country’s disease surveillance 2) meetings with stakeholders involved in surveillance; 3) workshop where stakeholders discussed and developed strategies for streamlining disease surveillance; 4) asset mapping to identify assets (human, financial, physical 5) stakeholders meeting to further discuss and agree on future strategies, activities. results the disease surveillance system review found the functions for surveillance being implemented at different levels (figure 1). these include identifying cases; reporting suspected cases, conditions, or events; investigating and confirming suspected cases, outbreaks and events. to facilitate decision making at different levels, it was found that analysing and response are done at all levels. a total of 15 disease-specific programs/sections in the mohcdgec are linked to the idsr, though the extent to which each program uses idsr data varies. key strengths and opportunities the government’s adoption of the idsr platform and the fact that the mohcdgec has a dedicated department to monitor idsr performance has been a great achievement of the program. the system is fully adaptable to support all disease surveillance with clear supervisory structures in place at regional and council levels. at the operational level there is presence of full-time, competent and dedicated government employees and exhibiting awareness of their responsibilities, and resourcefulness. the entire surveillance program benefits from government and external funding for disease-specific surveillance-related programs (e.g. funds for polio eradication and malaria program). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e368, 2019 isds 2019 conference abstracts despite the achievements, there are notable challenges faced by the program including disease-specific programs often requiring additional information and opting to set up parallel surveillance systems rather than integrating with the idsr; surveillance activities often not being considered high priority at council level relative to curative service and/or surveillance not being a line item in budgets; electronic data transmission platforms not being able to support transmission of all e-idsr data with the result that health facility data (including diseases for immediate notification) may not get reported in weekly transmissions; high turnover of surveillance staff and unsystematic orientation of newly-deployed staff; discrepancies in reported hmis, idsr, and diseasespecific program data indicating data quality issues. asset mapping: at the time of the review, the number of staff available varied widely between programs, with the national laboratory and the national aids control program (nacp) reporting the highest number at council level and immunization and vaccine development (ivd) having significant number of persons supporting vaccine preventable disease surveillance. at the time of the review, most of the funds were allocated in capacity building through training and supportive supervision compared to core surveillance function. key inteventions to streamlining and harmonizing of surveillance supported the roll out of electronic idsr to ensure real time surveillance through dhis2 supported proceedures to establishement of surveillance expert working group (ewg); development of term of reference for ewg to guide implementation of idsr activities development of transition plan highlighting key stakeholders and the support they provide to strengthening surveillance in the country; development of workplan to guide implementation of agreed recommendations which includes; 1. coordinating activities of all stakeholders involved in surveillance, 2. developing or advocating for an interoperable and harmonized reporting system through dhis2 that will accommodate the needs of the various diseaseand event-surveillance programs, 3. promoting synergies at national level so that active surveillance is expanded as appropriate to other diseases and supports case based surveillance, 4. building capacity of rhmts/chmts in leadership and management to manage human and financial resources and prioritize surveillance; 5. coordinating and strengthening disease and event-surveillance at community level by having at least one trained focal person at the community for all disease surveillance.trained focal person at the community for all disease surveillance. conclusions streamlining and strengthening of the surveillance system could be achieved by existing coordination structures within mohcdgec. strengthening idsr by implementing an interoperable of reporting systems including integration of laboratory data will achieve harmonization, consistency in data and appropriate response. at the regional and council level, priority activities identified include strengthening coordination, orientation and training for financial and human resources management for surveillance aimed at strengthening surveillance and response teams. the idsr should strengthen active surveillance to adopt case based surveillance as deemed appropriate for more diseases. a proposed plan for implementing key activities to achieve integration and streamlining of disease surveillance has been developed and it is hoped that resources will be made available for immediate implementation. acknowledgement mcsp disease surveillance, is a ghsa -funded activity which is co-funded with usaid maternal and child health funding. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e368, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e411, 2019 isds 2019 conference abstracts survey of tick-borne-disease from ornithodoros spp. in uninhabited islands of korea. sun-woo han1, yong-sun jo1, jeong byoung chae1, yoon kyoung cho1, nam-shik shin1, hee-jeong youn1, hwa-young youn1, hyang-mi nam2, hyun-joo kim2, hae-eun kang2, joon-seok chae1 1 seoul national university , seoul, korea (the republic of), 2 animal and plant quarantine agency, gimcheon, korea (the republic of) objective the aim of this study is to survey data of ticks distribution of korean islands and to investigate pathogens in argasid ticks. introduction ticks and tick-borne diseases have been thought global important issues, because it's affect to animal and human health and are the cause of significant economic losses. the genus ornithodoros spp., which is included in family argasidae, is usually associated with wild animals including seabirds and it was difficult to investigate because seabirds’ nests are found in inaccessible uninhabited islands. however, ornithodoros spp. has been known for the vector of many diseases including african swine fever. method in this study, nest with soil and litter of seabirds were collected, to investigate ornithodoros species from 9 uninhabited islands, nan-do, chilsan-do, chilbal-do, sogukhol-do, googul-do, gaerin-do, sasu-do, hong-do (hallyeohaesang) and dokdo located western and southern part of the korea from july, 2017 to september in 2018. the islands are known for breeding places of migratory and resident birds. maximum ten nests with soil and litter of seabirds were collected from one uninhabited island for the conservation of the islands environment. ticks were collected from nest with soil and litter of seabirds using tullgren funnel and were assayed for tick identification by pcr using 16s rrna gene and tick-borne pathogens including rickettia spp., borrelia spp., bartonella spp., ehrlichia chaffensis, ehrlichia canis, anaplasma phagocytophilum and anaplasma bovis by nested pcr. results total 65 ornithodoros species ticks from 338 seabird's (black-tailed gull, larus crassirotris; streaked shearwater, calonectris leucomelas and swinhoe's storm petrel, oceanodroma monorhis) nesting soil with litter in 9 uninhabited islands. in the sequence identification of 16s rrna gene fragment of ornithodoros species, o. capensis and o. sawaii were 37 and 28, respectively. in the analyses of tick-borne pathogens, borrelia spp. (n=5) was detected in o. sawaii from gaerin-do and googul-do. the total detection rate of borrelia sp. from ornithodoros spp. was 7.69% (5/65). conclusions in this research, we discovered that o. sawaii are habitat in west and southern part of uninhabited islands to breeding place of black-tailed gulls and streaked shearwater and o. capensis are habitat in uninhabited islands related to breeding place of streaked shearwater. this is first report of the borrelia spp. from ornithodoros sawaii in korea. acknowledgement this research was supported by a fund (no. z-1543085-2017-18-01) from research of animal and plant quarantine agency, the republic of korea. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e425, 2019 isds 2019 conference abstracts analysis of emergency department visits for motor vehicle injuries in utah, 2016 akanksha acharya bureau of epidemiology, utah department of health, salt lake city, utah, united states objective to describe the characteristics of emergency department (ed) visits for motor vehicle injuries in utah using 2016 syndromic surveillance data. introduction motor vehicle injury is the leading cause of death in injury category in the united states. in 2016, motor vehicle crashes were one of the main causes of death resulting from injury (8.8 per 100,000 population) in utah. motor vehicle crashes can lead to physical and economic consequences that impact the lives of individuals and their families. in addition, the treatment of injuries places an enormous burden on hospital emergency departments (eds). currently; there are no data sources other than syndromic data in the utah department of health to monitor ed visits due to motor vehicle injuries in real time. methods utah participates in the national syndromic surveillance program (nssp) to which all hospitals in the state submit ed visit data via the electronic surveillance system for the early notification of community-based epidemics (essence). essence was used to analyze 2016 ed visit data. total population data were obtained from utah population estimates. data from 2017 was not included due to major system changes at a major healthcare system that interrupted data feeds resulting in lower than expected data volume. motor vehicle injury is defined by existing subsyndrome definition in the centers for disease control and prevention essence system. all ed visit data were analyzed by querying key terms in the chief complaint field including any mention of: vehicle, wheeler, motorcycle, motor scooter, motor cycle, motor cross, truck, motorbike etc. exclusion terms included any mention of: car dealership, hit head and car door. ages were divided into seven groups for data distribution and comparison: 0–17, 18–24, 25–34, 35–44, 45–54, 55–64 and ≥ 65 years. results in 2016, a total of 28,472 ed visits (2% of total visits) were identified using the motor vehicle injury query. the ed visit rate for motor vehicle injuries was highest among persons aged 18–24 years (1,682 per 100,000 population). rates continued to decline with increasing age after 18–24 years. the rate of females visiting the ed was higher than males (1,040 versus 826 per 100,000 population respectively; p < 0.01) (figure 1). the majority of injuries (11722(52%)) were reported between 10:00 a.m. and 5:59 p.m. injuries were highest august-september (5913(22%)). conclusions syndromic data is a robust source of data for analyzing ed visits due to motor vehicle injuries in real time, and providing information to injury prevention programs for targeting interventions. our data suggest an increased risk of visiting an ed due to motor vehicle injuries by age group (18-24 year olds), sex (females), month (august-september), and time (10:00 a.m. to 5:59 p.m.). these results do not include visits with incomplete or incorrectly coded chief complaints or discharge codes, patients of motor vehicle injuries who do not present to the ed, or not classified as ‘emergency’ patient class. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e425, 2019 isds 2019 conference abstracts figure 1: emergency department visit rate for motor vehicle injuries per 100,000 population by age and sex, utah 2016 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e234, 2019 isds 2019 conference abstracts comparing syndromic data to discharge data to measure opioid overdose emergency department visits emilia s. pasalic, alana m. vivolo-kantor, pedro martinez cdc national center for injury prevention and control (ncipc), atlanta, georgia, united states objective epidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic. introduction timely and accurate measurement of overdose morbidity using emergency department (ed) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the united states. however, from jurisdiction to jurisdiction, differing sources and types of ed data vary in their quality and comprehensiveness. many jurisdictions collect timely emergency department data through syndromic surveillance (sys) systems, while others may have access to more complete, but slower emergency department discharge datasets. state and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ed data. these choices may affect the number, timeliness, and accuracy of the cases identified. methods cdc partnered with 45 states and the district of columbia to combat the worsening opioid overdose epidemic through three cooperative agreements: prevention for states (pfs), data driven prevention initiative (ddpi), and enhanced state opioid overdose surveillance (esoos). to support funded jurisdictions in monitoring non-fatal opioid overdoses, cdc developed two different sets of indicator guidance for measuring non-fatal opioid overdoses using ed data, with each focusing on different ed data sources (sys and discharge). we report on the following attributes for each type of ed data source [1,2]: 1) timeliness; 2) data quality (e.g., percent completeness by field); 3) validity; and 4) representativeness (e.g., percent of facilities included). results when comparing timeliness across data sources, sys data has clear advantages, with many jurisdictions receiving data within 24 hours of an event. for discharge data, timeliness is more variable with some jurisdictions receiving data within weeks while others wait over 1.5 years before receiving a complete discharge dataset. data quality and completeness tends to be stronger in discharge datasets as facilities are required to submit complete discharge records with valid icd-10-cm codes in order to be reimbursed by payers. by contrast, for sys data systems, participating facilities may not consistently submit data for all possible fields, including diagnosis. validity is dependent on the data source as well as the case definition or syndrome definition used; with this in mind, sys data overdose indicators are designed to have high sensitivity, with less attention to specificity. discharge data overdose indicators are designed to have a high positive predictive value, while sensitivity and specificity are both important considerations. discharge datasets often include records for 100% of ed visits from all nonfederal, acute care-affiliated facilities in a state included. by contrast, representativeness of facilities in sys data systems varies widely across states with some states having less than 50% of facilities reporting. conclusions cdc funded partners share overdose morbidity data with cdc using either ed sys data, ed discharge data, or both. cdc indicator guidance for ed discharge data is designed for states to track changes in health outcomes over time for descriptive, performance monitoring, and evaluation purposes and to create rates that are more comparable across injury category, time, and place. considering these objectives, cdc placed a higher priority on data quality, validity (i.e., positive predictive value), and representativeness, all of which are stronger attributes of discharge data. cdc’s indicator guidance for ed sys data is designed for states to rapidly identify changes in nonfatal overdoses and to identify areas within a particular state that are experiencing rapid change in the frequency or types of overdose events. when considering these needs, cdc prioritized timeliness and validity in terms of sensitivity, both of which are stronger attributes of sys data. sys and discharge ed data each lend themselves to different informational applications and interpretations based on the strengths and limitations of each dataset. an effective, informed public http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e234, 2019 isds 2019 conference abstracts health response to the opioid overdose epidemic requires continued investment in public health surveillance infrastructure, careful consideration of the needs of the data user, and transparency regarding the unique strengths and limitations of each dataset. acknowledgement the authors would like to acknowledge cdc's state and jurisdictional partners funded through the esoos, pfs, and ddpi cooperative agreements; cdc staff from csels, nchs, and ncipc; and the cste icd-10-cm drug poisoning indicators workgroup for their engagement, comments, and brilliant epidemiological insight during the indicator development and testing processes.the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the centers for disease control and prevention. references 1. pencheon d. (2006). oxford handbook of public health practice. 2nd ed. oxford: oxford university press. 2. centers for disease control and prevention (cdc) evaluation working group on public health surveillance systems for early detection of outbreaks. (may 7, 2004). framework for evaluating public health surveillance systems for early detection of outbreaks. mmwr. morbidity and mortality weekly reports. retrieved from: https://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm figure 1. comparing syndromic data to discharge data to measure opioid overdose emergency department visits http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e338, 2019 isds 2019 conference abstracts monitoring heat-related illness through syndromic surveillance in los angeles county jimmy duong, michael lim, emily kajita, bessie hwang public health, los angeles county, los angeles, california, united states objective to analyze los angeles county’s (lac) extreme heat season in 2018 and evaluate the council of state and territorial epidemiologists’ (cste) syndrome query for heat-related-illness (hri) in los angeles county (lac) introduction lac experienced several days of record-breaking temperatures during the summer of 2018. downtown los angeles temperatures soared to 108°f in july with an average daily maximum of 92°f. extreme heat events such as these can pose major risks to human health. syndromic surveillance can be a useful tool in providing near real-time surveillance of hri. in 2014, a working group was formed within the cste climate change subcommittee to define and analyze hri. the workgroup’s goal was to provide guidance to public health professionals in adapting and implementing an hri syndrome surveillance query. the acute communicable disease control program’s (acdc) syndromic surveillance unit utilized cste’s hri query to provide surveillance during the extreme heat season in 2018 in lac. additional modifications to the cste query were evaluated for potential improvements towards characterizing hri trends. methods from may 1 to september 30, 2018, emergency department (ed) data were queried for cases using the cstes definition for hri. the queries consisted of key word searches within the chief complaint (cc) data field, and, if available, the diagnosis data fields. the query was derived from the cste hri query published in 2016 [1]. in addition, acdc explored the utility of expanding the cste syndrome definition to include additional chief complaints commonly associated with hri such as dehydration and syncope. both queries were applied on all participating syndromic eds in lac alongside daily high temperature data trends. local temperature data for downtown los angeles weather station kcqt were taken from the weather underground website. spearman correlation coefficients were calculated for each query during the heat season. similarly, both queries were also applied during colder months from october 1, 2017 to april 30, 2018 for comparison. lastly, results for dehydration and syncope were independently assessed apart from other hri query terms during both heat seasons and colder months. results the cste hri query and the query with the added terms yielded 1,258 and 63,332 ed visits, respectively, during the heat season. on july 6, the maximum daily temperature peaked at 108 °f; the hri and the query with the added terms yielded 136 and 618 ed visits, respectively. the hri query and the hri query with the added terms had a correlation coefficient of 0.714 (p <0.0001) and 0.427 (p <0.0001), respectively. during colder months, the cste hri query and the query with the added terms yielded 377 and 86,008, respectively, with correlation coefficients of 0.342 (p < 0.0001) and 0.133 (p < 0.052). the syncope-only query saw no variation in hri classified encounters throughout the heat season (mean: 328; min: 228; max: 404) or colder months (mean: 328; min: 261; max: 404) with correlation coefficients of 0.238 (p = 0.003) and 0.155 (p = 0.024), respectively. similarly, the dehydration-only query saw no variation in hri classified encounters throughout the heat season (mean: 96; min: 58; max: 258) or colder months (mean: 94; min: 60; max: 160) with correlation coefficients of 0.596 (p < 0.0001) and -0.016 (p = 0.822). conclusions the cste hri query proved to be a strong indicator for hri, and the addition of terms associated with dehydration and syncope to the cste hri query weakened the correlation with temperature. compared to the original cste hri query, the added terms yielded a 4934% increase in hri classified encounters during the heat season; however, these were likely due to causes other than hri -adding the extra terms resulted in a weaker correlation with temperature. additionally, the comparative analysis showed that, with the added terms, the volume of hri encounters was larger during colder months than hotter months suggesting misclassification of non-hri illnesses. surveillance of hri has proven to be difficult because many of the hri symptoms are too commonly associated with non-hri conditions which would explain the weaker correlations when adding additional chief http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e338, 2019 isds 2019 conference abstracts complaints associated with hri. in conclusion, the cste syndrome definition for hri proved to be the most robust query for hri during the heat season. case counts of hri are difficult due to symptom overlap with many other medical conditions. however, syndromic surveillance using the cste hri query is useful for trend analysis in near real-time during heat events. references 1. council of state and territorial epidemiologists. heat-related illness syndrome query: a guidance document for implementing heat-related illness syndromic surveillance in public health practice. version 1.0. 2016 sep. 12 p. figure 1. heat-related ed visits, defined by cste’s hri syndrome definition, per day in los angeles county during the heat season from 5/1/2018 to 9/30/2018 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e338, 2019 isds 2019 conference abstracts figure 2. dehydration-related ed visits per day in los angeles county during the heat season from 5/1/2018 to 9/30/2018 figure 3. syncope-related ed visits per day in los angeles county during the heat season from 5/1/2018 to 9/30/2018 http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 1 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 china’s national health policies: an ontological analysis guobin dai1, fang deng1, arkalgud ramaprasad2*, thant syn3 1. central south university, changsha, hunan, people republic of china 2. university of illinois at chicago, chicago, illinois, united states of america 3. texas a&m international university, laredo, texas, united states of america abstract the health care system in china is facing a multitude of challenges owing to the changing demographics of the country, the evolving economics of health care, and the emerging epidemiology of health as well as diseases. china’s many national health care policies are documented in chinese text documents. it is necessary to map the policies synoptically, systemically, and systematically to discover their emphases and biases, assess them, and modify them in the future. using a logically constructed ontology of health care policies based on the common bodies of knowledge as a lens, we map the current policies to reveal their ‘bright’, ‘light’, and ‘blind/blank’ spots. the ontological map will help (a) develop a roadmap for future health care policies in china, and (b) compare and contrast china’s health care policies with other countries’. keywords: national health care policy, ontology, text mapping, assessment, roadmap. correspondence: prasad@uic.edu* doi: 10.5210/ojphi.v5i3.4933 copyright ©2016 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. introduction the health care system in china is facing a multitude of challenges owing to the changing demographics and politics of the country, the evolving economics of health care, and the emerging epidemiology of health as well as diseases (1-4). for example, ‘[p]aralleled with the rapid socio-economic development and demographic transition, an epidemic of noncommunicable chronic diseases (ncds) has emerged in china over the past three decades, resulting in increased disease and economic burdens… [and encouragement] to use communitybased health services.’ (3) between 1950-2009 it ‘shifted public financing to private sources; it turned public hospitals and clinics into commercial, for-profit enterprises; it decentralized china’s health system and it altered the price structure for public facilities, thereby enabling them to earn profits.’ (1) in 2009, the ‘chinese government committed usd 124 billion of additional public spending for the first three years of health care reform.’ (1) these three years had five specific targets. they were: ‘(1) expanding insurance coverage; (2) making public health service available and equal for all; (3) improving the primary care delivery system to http://ojphi.org/ mailto:prasad@uic.edu* ojphi china’s national health policies: an ontological analysis 2 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 provide basic health care; (4) establishing a national essential drug system; and (5) piloting public hospital reform.’ (1) following the 2009 reform, five sets of comprehensive policies were introduced (5). they are summarized and quoted below as follows: • medical security: to achieve universal coverage through insurance systems for urban residents (urban employees’ and residents’ basic medical insurance), farmers (new rural cooperative medical service), and those living in poverty in both urban and rural areas (medical assistance). • public health services: full government funding for a minimum package of nine essential services (including maternal and child health, immunization, mental health, aged care, chronic disease management, and health education) with a view to equal access to public health services for all. • primary care: establishing a comprehensive network of basic health services through a three-tier rural network and urban community health services, with chinese medicine integrated into these service delivery networks rather than as a freestanding system. • pharmaceutical supply system reform: setting an essential drug list that is linked to basic medical insurance and cooperative medical service, with open tender based on government reference price. • pilot hospital reforms: trialing strategies for ownership and government, quality, regulation, training, and revenue oversight. these new policies did not completely eliminate the earlier polices. thus, the post-2009 policies coexist with many pre-2009 policies. given the number and complexity of these policies it would be difficult to obtain their ‘big picture’ from the corresponding text documents. despite the best intentions of the policy makers, the complexity of the formulation process and the associated negotiations are likely to introduce differential emphases and biases (5-7). it is necessary to map the text of the policies systemically and systematically to discover their emphases and biases. such an analysis will help the health policy-advisors and -makers formulate better policies. it can provide feedback on the current policies and feed-forward to future ones (8). ontology of health care policies the ontology of health care policy represents our conceptualization of the health care policy domain (9). it is an ‘explicit specification of [our] conceptualization,’ (10) and can be used to systematize the description of a complex system such as national health care policies (11). the ontology organizes the terminologies and taxonomies that constitute health care policies. ‘our acceptance of [the] ontology is… similar in principle to our acceptance of a scientific theory, say a system of physics; we adopt, at least insofar as we are reasonable, the simplest conceptual scheme into which the disordered fragments of raw experience can be fitted and arranged.’ (12) the method of constructing and presenting an ontology for a domain is an iterative process (13, 14) that systemically and systematically deconstructs health care policy into its dimensions and associated elements. it is a new method of analyzing and synthesizing knowledge in a domain http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 3 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 (15, 16). it has been applied to analyzing and synthesizing chile’s and india’s national health care policies (17, 18). here we apply it to china’s health care policies. we deconstruct health care policy into (a) the scope of the policy, (b) the focus of the policy, (c) the desired outcomes of the policy, (d) the type of care which is the objects of the policy, and (e) the population for which it is intended. thus, a health care policy is ideally seen as having a defined scope, being focused on an aspect of health care, with desired outcomes, for particular types of care, and targeted at a given population. thus, the underlying argument is: health care policy = f (scope, focus, outcome, care, population) these five dimensions define the domain of health care policies. in practice, all health care policies may not cover all the dimensions. for example, some may not specify the outcome, some the focus, and some a combination of these. we will refer to policies in which some dimensions are excluded from the definition as fragments of policies rather than full policies. each dimension of the ontology is expressed by a taxonomy of its constituent elements (figure 1). the dimensions and the elements are expressed both in english and chinese. the elements are defined in the glossary in the appendix. the taxonomies are derived from the common terminology in the body of knowledge on each dimension, especially in the health care policy domain. thus, the scope of a policy may be global, national, regional, local-urban, localrural, or restricted to the provider. the focus of the policy may be drugs, food, financial, legal, insurance, technology, information, treatment, personnel, or administration. the personnel focus may be on the physician, nurses, or staff; the physicians may be general or specialist. the outcome of the policy may be accessibility, cost, quality, satisfaction, safety, parity, or timeliness of health care. further, the care could be preventive, for illness (mental, physiological, episodic, chronic, or occupational), palliative, or emergency. last, the population care for may be the individual, family, or community. the individuals may be children, adolescents, adults (female, pregnant woman, worker, disabled, others), and aged. (note: we will capitalize the words which refer to the dimensions and elements in the ontology, except in describing full or partial components.) symbolically: scope ⊂ [global, national, regional, local (urban, rural), provider] focus ⊂ [drugs, food, financial, legal, insurance, technology, information, treatment, personnel (physician (general, specialist), nurses, staff), administration] outcomes ⊂ [accessibility, cost, quality, satisfaction, safety, parity, timeliness] care ⊂ [preventive, illness (mental, physiological, episodic, chronic, occupational), palliative, emergency] population ⊂ [individual (children, adolescents, adults (female, pregnant woman, worker, disabled, others), aged), family, community] the ontology in figure 1 helps visualize the dimensions, elements, and components of health care policy in natural english and chinese. each dimension is represented by a column. the taxonomy of elements constituting each dimension is listed in the respective column. all the http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 4 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 elements together constitute the elements of the system of policies. the columns are connected by adjacent words/phrases—they help concatenate the elements from different columns into sentences which describe the components of the health care system. figure 1: ontology of china’s health care policy and illustrative components http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 5 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 three illustrative components derived from the ontology are shown in figure 1. they are: • national financial policies on accessibility of preventive care for family—these may include programs/policies to provide financial incentives for families to travel to obtain preventive care. • local urban regulatory policies on cost of palliative care for individual aged—these may include programs/policies to limit the cost of palliative care of senior citizens. • provider administration programs/policies on cost of illness episodic care of individual adolescents—these may include providers’ policies on cost of care of ill teens. these three and 43,677 others encapsulated in the ontology are logically the potential components of health care policies. the ontology presents the combinatorial complexity of health care policies concisely and thus helps us take a systemic view of the problems they address. an element in the ontology may or may not be instantiated in a particular policy. studying across policies, some of them may be instantiated frequently, some infrequently, and others not at all. we will label the frequently instantiated portions the ‘bright’ spots, the infrequent ones the ‘light’ spots, and the overlooked ones the ‘blind/blank’ spots. the luminosity of each spot is a product of two opposing dynamics. a ‘bright’ spot may be so because it is effective and important; it may also be a consequence of habit and herd effect (‘more of the same’), irrespective of whether it is effective or important. a ‘light’ spot may be so because it is ineffective and unimportant; it may also be a consequence of difficulty of implementing it (‘path of least resistance’), irrespective of its potential effectiveness or importance, or its recent emergence in importance. a ‘blind/blank’ spot may have been simply overlooked by design or by accident; or, it may be infeasible. knowing the ‘bright’, ‘light’, and ‘blind/blank’ spots in the policies and their antecedent reasons will help develop more systemic and systematic approaches to the challenge of health care policies. in the following, we present an ontological map of china’s health care policies, highlight the ‘bright’, ‘light’, and ‘blind/blank’ spots, and discuss possible reasons for the same. before presenting the results, we will first describe the method we used for mapping. in the conclusion, we will present the potential implications of this program of research and the planned extensions to what is presented here. the distinguishing feature of the proposed ontology is that it articulates the logic of the system in structured natural english and chinese sentences. it is neither an abstract, philosophical discussion for meta-cognition (imagination) of the system, nor a concrete, syntactically precise formalization of the system for machine cognition. it is between the two extremes—a pragmatic, semantically rich articulation of the system for human cognition. the ontology itself can be refined by adding subcategories of elements, coarsened by aggregating categories of elements, extended by adding additional dimensions, and reduced by eliminating current dimensions. thus, it can be adapted to design health care policy at different levels of complexity and granularity (13, 15, 19). http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 6 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 validity of the ontology the validity of the ontology will determine how well the ontology captures and represents china’s health care policy. the common methods of validation focus on induced ontologies at a finer level of detail, greater formalism, and machine readability, but not on deduced ontologies at a higher-level of abstraction such as ours. hence, we draw on the traditional constructs of validity and assert the ontology’s face validity, content validity, semantic validity, systemic validity, and external validity, which are commonly used in social science research (20, 21). the ontology is a complete and closed description of china’s national health care policy. it is logically derived as described earlier. it is a novel reorganization and representation of traditional bodies of knowledge from multiple disciplines such as public policy, health care, and social science. it derives part of its validity from that of the knowledge in the underlying disciplines. it comprehensively describes a country’s health care policy using structured natural english and chinese understandable to novices and experts alike. it deconstructs the combinatorial complexity of a construct and presents it visually as an easily readable, parsimonious text-table (22). the ontology encapsulates all 43,680 possible components of health care policy in a readable form on a single sheet of paper. thus, its face validity is high. each logical component of national health care policy derived from the ontology is semantically meaningful in english and chinese, as illustrated above—thus, its semantic validity is high. it is easy to ascertain whether a component is instantiated or not. obversely, it is also easy to ascertain the component that matches an instantiation. its dimensions are based on the common bodies of knowledge from underlying disciplines. the taxonomies include the basic categories of knowledge from these disciplines. hence, the content validity of the dimensions, the taxonomies, and the large number of consequent components is high. further, the ontology encapsulates all the possible components of china’s national health care policy. it has high systemic validity. lastly, the ontology is also validated through application to the data (policies). the application will highlight errors of omission of significant elements and of commission of irrelevant elements in the ontology. these errors are easily corrected—the ontology is easily extensible and reducible—and the validity of the ontology enhanced. in the same vein, most of the ontology has been validated through application to chile’s and india’s health care policies and learning from the same. methods two of the authors (who are chinese) downloaded all the 289 health care policies in china from 2000-2016 which are currently in effect. these represent the population of policies post-2009 and currently operational policies pre-2009. the year 2009 represents a key turning point in the evolution of china’s health care policies to achieve comprehensive universal health coverage by 2020 (2). the policies were downloaded from the website of the national health and family planning commission of the people’s republic of china http://ojphi.org/ ojphi china’s national health policies: an ontological analysis (http://www.nhfpc.gov.cn/zhuzhan/index.shtml). each policy has a separate address from which the current file was downloaded. the distribution of policies by year is shown in figure 2. one of the two authors is trained as a physician and is a faculty member; the other is a doctoral student. the former is very familiar with china’s health care system. they downloaded all the policies as ms word files (in chinese) for coding. the two then coded half the policies (from the chinese text) each onto the ontology using an excel tool developed by another author. subsequently, each of them verified the other’s coding. differences in interpretation were resolved through discussion. thus, the final coding is the consensus of the two. figure 2: distribution of china’s health care policies (coded) by year a policy is coded on an item only if that item is explicitly part of it, not implied or inferred from it. for example, it may be argued that all national policies should be coded for local-urban and local-rural. but they were coded only for national unless the latter two are explicitly part of the policy. we sought to balance the errors of commission (over-coding) with the errors of omission (under-coding). the coders tried to balance over-reading the policy and thus over-coding, and under-reading the same and thus under-coding. the coders’ knowledge of the domain—both chinese, one a physician and faculty member, and the other a doctoral student—helped minimize the errors in coding. we note that a program or policy may instantiate multiple components, a component, parts of multiple components, or part of a component of the ontology. thus, there was no restriction on how many elements of the ontology could be encoded with reference to a policy, or a requirement that a policy should be encoded with reference to all the dimensions of the ontology. thus, a policy could be encoded to:(a) an element from each dimension, (b) multiple elements from each dimension, (c) an element from some dimensions, or (d) multiple elements from some dimensions. we also note that the coding was binary—whether the element (or its synonym) was 7 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 8 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 present or not in the program or policy. the coding was not weighted; each policy and each element was assigned equal weight. results we present the results of the analysis as an ontological map (figure 3). it highlights the emphasis on the elements of the ontology in the 289 policies. the number in parentheses adjacent to an element indicates its frequency of occurrence in the 289 policies. the data bar below the element is a visual indicator of the same scaled to the maximum frequency of occurrences of any one element, national (231) in this case. a policy may be coded on multiple elements on a dimension (column) or no items. hence the sum of the numbers in each column may not equal 289—it may be more or less. the visualization in figure 3 clearly highlights the areas of emphases, limited emphases, and no-emphasis. we will discuss the map in detail below from right to left. figure 3: ontological map of monads of china’s health care policies population most of the policies focus on one or two segments of the population. the dominant focus of the policies is the individual-children (102), community/group (83), and family (71). the policies are focused on children, communities, and families. the secondary focus of the policies is on individual-adults-pregnant woman (52), individual-adults-female (40), and individual-aged (37). the tertiary focus is on individual-adults-worker (19), individual-adults-other (17), and individual-adolescents (10). it is noted, however, that there are 10 or more programs that address the needs of each segment of the population identified in the ontology. there are no ‘blind/blank’ spots in the population dimension. http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 9 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 care on average, each policy is focused on two types of care. the dominant focus of care is on preventive (175) followed by illness-episodic (115). the secondary focus is on emergency (62), illness-mental (54), illness-chronic (51), and illness-physiological (41) care. the tertiary focus is on illness-occupational (31) and palliative (16) care. there is a minimum of 16 policies on each type of care; there are no ‘blind/blank’ spots in the care dimension. outcomes on average, each policy is focused on two or more types of outcome. the dominant outcomes sought are safety (174) and quality (169) of care. the secondary outcomes are cost (105) and timeliness (93) of care. parity (41), satisfaction (41), and accessibility (18) are tertiary outcomes. focus the policies, in the aggregate, are multi-focused—on average, more than four focuses per policy. the dominant focus is on administration (196), technology (169), legal (132), drugs (112), and information (107). the secondary focus is on personnel-staff (87), personnel-physiciangeneral (85), financial (84), and personnel-physician-specialist (83). the tertiary focus is on food (67), personnel-nurses (54), and insurance (26). scope the focus of the policies is predominantly national (231) and on the provider (236). the former is to be expected since the study is based on national policies. there is however, a secondary emphasis of these policies which is global (47), local-rural (46), local-urban (32), and regional (23). ontological map summary in summary, the dominant focus of the policies based on the ontological map may be summarized as follows: provider/national administration/technology/legal/drugs/information policies on safety/quality of preventive/illness-episodic care of individual-children/ communitygroup/family. discussion the five focus of china’s health care policy reform are: (a) medical security through insurance, (b) comprehensive public health services, (c) basic primary care, (d) supply system for essential drugs, and (e) hospital reforms (1, 5). we will discuss our results from the ontological map with reference to these focuses. the relatively low emphasis on insurance (26) among the 289 policies runs contrary to the first focus. it may be partly because china has already achieved universal insurance coverage (2). http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 10 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 despite the coverage there are many significant issues in translating it into significant health outcomes—for example, in the managing non-communicable chronic diseases (3) and access to rural health care (23). the results above suggest the need for a greater focus on insurance in conjunction with different combinations of outcomes and types of care. the emphasis on public health is evident in the dominance of community/group (83) and family (71). however, relatively less emphasis on illness-chronic (51) appears to limit the comprehensiveness of the care, especially in light of the growing importance of chronic disease management in china (3, 24). the heavy emphasis on individual-children (102), community/group (83) and family (71) combined with that on preventive (175) and illness-episodic (115) can be said to indicate the focus on basic primary care. however, the limited emphasis on accessibility (18) and illnesschronic may significantly weaken the delivery of such care. the dominance of policies on drugs (112) is likely a manifestation of the emphasis on the supply system for essential drugs. it appears to be closely associated with quality (169) and safety (174) of illness-episodic (115) and preventive (175) care. it also appears to be closely associated with treatment (169), legal (132), administration (196), and provider (236) policies. it is an important but complex issue (25-28) and the policies seek to address the same. there is probably no better indicator of the emphasis on hospital reforms than the priority given to providers (236) and administration (196). the two appear to be dominant in conjunction with technology (178), information (107), and other policies. together, they can be seen to be part of a recipe for transforming the hospitals. in summary, the results from the ontological map of monads highlight the alignment, mis-alignment, and non-alignment of the 289 policies with the five focuses of the 2009 health care reforms in china. the alignments have to be reinforced, the mis-alignments redirected, and non-alignments rectified in the future for the transformation to be effective. conclusions the challenge of reflecting upon china’s national health care policies and reorienting them is one of synthesizing a large body of text-based knowledge in chinese systemically and systematically. it is a knowledge management problem and a ‘big-text-data’ analysis problem. the ontology of china’s health care policies is comprehensive lens for the analysis and synthesis of this corpus in the original language. it can be used to develop a roadmap for future research on china’s health care policies. the paper makes three basic contributions to understanding, comprehensive assessment, and direction of china’s health care policies. they are: • an approach using an ontology based on national health care policy. the approach can be extended, refined, revised, and adapted to other contexts (provinces, cities, etc.); http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 11 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 • a method of mapping the national health care policies onto the ontology based on the original text of the policies (in this case chinese); and • a method of analyzing the dominant, less dominant and non-dominant focus of a nation’s policies, and the consequent gaps in them. china’s health care policies have undergone waves of reform (4). in making these reforms there is a need for policy makers to understand the implications of the above findings for future planning, and prioritizing for formulation (and reformulation) of policy. our research will allow the policy makers to ask, as discussed above, for example: are the dominant foci e.g. preventative care, illness-episodic care, quality, and safety balanced against the low focus elements such as illness-chronic care, palliative care, and accessibility? do they need to be? ultimately ontological analysis such as this can provide a pragmatic basis for deliberations (7) by policy makers and interest groups for policy formulation. the framework, the method, and the results can be useful to advance health policy making in general and china’s health policies in particular. it can be used to assess the strengths and weaknesses of a country’s health policy, and to compare and contrast policies of different countries based on a common framework. the present study is a continuation of our study of chile, india, and australia’s healthcare policies and programs. understanding the antecedents and consequences of the emphases in other countries can provide insights into the policies of the parent country. in ending the discussion, we should also highlight some of the limitations of the research. the ontology may be incomplete or over-specified. in the future, should it be necessary, the ontology can be extended, reduced, refined, or coarsened as appropriate. considerable effort has been expended in the translation of the ontology, construction of the glossary, and the monitoring of coding to minimize errors. while the coders tried to stay true to the text of the policies without imputing their own expectations, one cannot exclude the possibility of over-coding and under-coding. given the large population of programs (289) and the significant variation in the frequency of the elements, despite the potential errors, the results are likely to be robust. given the data, errors in the ontological map are unlikely. however, there is room for variation in the interpretation of the luminosity of the different elements. since the method of construction of the map is completely transparent, it would be easy to compare and contrast different interpretations of the same. in summary, despite the limitations, the insights are strong. their explanation as to why the corpus is as described may vary, but there is little room for variation in the description of the policies corpus. financial disclosure this study is partially supported by the chinese national social science fund 15bgl104 and chinese ministry of education of humanities and social science youth project 12yjc630028. the views represented in this article are those of the individual authors only. http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 12 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 references 1. hsiao wc, li m, zhang s. universal health coverage: the case of china. 2013. 2. yip wc, hsiao wc, chen w, hu s, ma j, et al. 2012. early appraisal of china’s huge and complex health-care reforms. lancet. 379(9818), 833-42. pubmed 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driven by financial incentives, results in very high use of antibiotics, injections, and corticosteroids. health aff. 31(5), 1075-82. doi:http://dx.doi.org/10.1377/hlthaff.2010.0965. pubmed appendix: glossary policy: health care policy. scope: reach of the health care policy. global: policy applicable in all countries of the world. national: policy applicable everywhere in china. regional: policy applicable to a region of china. local: policy applicable within a defined part of china. urban: policy applicable within local urban areas. rural: policy applicable within local rural areas. provider: policy applicable to a health care providing institution. focus: focus of the health care policy. drugs: policies regarding drugs used in health care. food: policies regarding food and nutrition in health care. financial: policies regarding health care finance. legal: policies regarding legal issues in health care. insurance: policies regarding health care insurance. technology: policies regarding health care technology. information: policies regarding health care information. treatment: policies regarding treatment. personnel: policies regarding health care personnel. physician: policies regarding physicians. general: policies regarding general physicians. specialist: policies regarding specialist physicians. http://ojphi.org/ http://dx.doi.org/10.1377/hlthaff.2010.0965 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22566449&dopt=abstract ojphi china’s national health policies: an ontological analysis 15 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 nurses: policies regarding health care nurses. staff: policies regarding health care staff. administration: policies regarding health care administration. outcomes: the intended outcomes of health care policy. accessibility: the accessibility of health care to the population. cost: the cost of health care to the population. quality: the quality of health care delivered to the population. satisfaction: the population's satisfaction with health care. safety: the safety of health care delivered to the population. parity: the parity of health care delivered to the population segments. timeliness: the timeliness of health care delivery to the population. care: the different types of health care. preventive: care to prevent illnesses and diseases in the population. illness: care of illnesses when they occur. mental: care of mental illness. physiological: treatment of physiological illness episodic: care during illness episodes -time bound. chronic: care of chronic illnesses -continuing. occupational: treatment of occupational illness. palliative: care to alleviate pain and suffering. emergency: care of emergency illness population: the population targeted by the policy. individual: individual recipients of health care. children: children who are recipients of health care. adolescents: adolescents receiving health care. http://ojphi.org/ ojphi china’s national health policies: an ontological analysis 16 online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 8(3):e196, 2016 adults: adults receiving health care. female: women receiving health care. pregnant women: pregnant women receiving maternal health care. workers: workers receiving occupational/work related health care. disabled: disabled adults receiving health care. other: adults other than mothers and workers. aged: older people receiving elderly health care. family: family, as an entity, receiving health care. community/group: community/group as an entity receiving health care. http://ojphi.org/ china’s national health policies: an ontological analysis introduction ontology of health care policies validity of the ontology methods results population care outcomes focus scope ontological map summary discussion conclusions financial disclosure references appendix: glossary isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e326, 2019 isds 2019 conference abstracts evaluation of animal rabies surveillance system, ekiti state, nigeria, 2012-2017 joyce o. adebayo1, 2, victor a. ojo2, gabriel ogundipe3, patrick m. nguku1 1 nigeria field epidemiology and laboratory training program (nfeltp), abuja, nigeria, 2 ministry of agriculture and rural development, ado ekiti, ekiti state, nigeria, 3 university of ibadan, ibadan, oyo state, nigeria objective the objectives of this study are to evaluate the current animal rabies surveillance system in the state and suggest r ecommendations. introduction rabies is a zoonotic, neglected viral disease. every 10 minutes, the world loses a life, especially children, to dog-mediated rabies. yet it is 100% preventable. africa, including nigeria, has major share of the disease. eradication of human rabies relies majorly on control of rabies in animals and this cannot be achieved without good surveillance system of the disease in animal, especi ally dogs. there is little or no information as to whether the surveillance system in nigeria is effective. methods we reviewed the medical records of all rabies cases reported in the 10 government and 5 registered private veterinary health facilities in the 16 lgas of the state. we extracted 44 cases of rabies in all, between review period of 2012 -2017. we also interviewed 25 key stakeholders in the system using key informant interview (kii) and questionnaires. we followed the steps stated in cdc guideline for evaluation of public health surveillance system to assess the key attributes and componen ts of the system, and analysed the data using microsoft excel. results two (20%) of the government and only one in five private veterinary health facilities had records on rabies cases. all reported cases of suspected rabies involved dog bites. the confirmatory status of 32 (72.7%) of the suspected cases were unknown. six (37.5%) lgas did not have access to any veterinary health facility. average of 1 technical staff per veterinary facility was seen. overall, the system was useful and flexible. it was fairly simple, acceptable and representative. both sensitivity and predictive value positive (pvp) were less than 1% while the timeliness, data quality and stability were poor. conclusions the surveillance system was performing below optimal level. there is need for improvement in the animal rabies surveillance system to achieve elimination of human rabies in nigeria. acknowledgement 1. nigeria field epidemiology and laboratory training program, abuja, nigeria 2. ministry of agriculture and rural development, ado-ekiti, ekiti state, nigeria references adedeji ao, okonko io, eyarefe od, adedeji ob, babalola et, et al. 2010. an overview of rabies history, epidemiology, control and possible elimination. afr j microbiol res. 4(22), 2327-38. aliyu t. 2010. prevalence of rabies virus antigens in apparently healthy dogs in yola, nigeria. researcher. 2(2), 1-14. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e326, 2019 isds 2019 conference abstracts ameh vo, dzikwi aa, umoh ju. 2014. assessment of knowledge, attitude and practice of dog owners to canine rabies in wukari metropolis, taraba state nigeria. glob j health sci. 6(5), 226-40. pubmed burgos-cáceres, s. 2011. canine rabies: a looming threat to public health. animals (basel). 1(4), 326-42. pubmed dutta jk, dutta tk. 1994. rabies in endemic countries. bmj. 308(6927), 488-89. pubmed ehimiyein am, ehimiyein io. 2014. rabies– its previous and current trend as an endemic disease of humans and mammals in nigeria. j exp biol agric sci. 2(2320), 137-49. el-moamly a. 2014. immunochromatographic techniques: benefits for the diagnosis of parasitic infections. austin chromatography, 1(4), 1–8. fekadu, m. (1993). canine rabies. j vet res (pulawy). 60, 421-27. kasempimolporn s, saengseesom w, huadsakul s, boonchang s, sitprija v. 2011. evaluation of a rapid immunochromatographic test strip for detection of rabies virus in dog saliva samples. j vet diagn invest. 23(6), 1197-201. pubmed muriuki j, thaiyah a, mbugua s, kitaa j, kirui g. 2016. knowledge,attitude and practices on rabies and socioeconomic value of dog keeping in isumu and siaya countries, kenya. int j vet sci. 5(1), 29-33. national population commission. 2009. 2006 population and housing census of the federal republic of nigeria. official gazette of the federal republic of nigeria. 96(2), 1. ogunkoya a, aina o, adebayo o, oluwagbenga a, tirmidhi a, et al. 2012. rabies antigen spread amongst apparently healthy dogs in nigeria: a review. rita brazil. 8(october), 74. oie. world organization for animal health. 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(2014). dog vaccination: the key to end dog-transmitted human rabies: oie world organisation for animal health. otolorin gr, umoh ju, dzikwi aa, anglais ae. 2014. prevalence of rabies antigen in brain tissue of dogs slaughtered for human consumption and evaluation of vaccination of dogs against rabies in aba, abia state nigeria. world j public health sciences. 3(1), 5-10. panda s, mitra j, chowdhury s, sarkar sn. 2016. detection of rabies viral antigen in cattle by rapid immunochromtographic diagnostic test. explor anim med res. 6(1), 119-22. sharma p, singh ck, narang d. 2015. comparison of immunochromatographic diagnostic test with heminested r everse transcriptase polymerase chain reaction for detection of rabies virus from brain samples of various species. vet world. 8(2), 135-38. pubmed singh ck, kaw a, bansal k, dandale m, pranoti s. 2012. approaches for antemortem diagnosis of rabies 1. cibtech journal of biotechnology. 1(1), 1-16. takayama n. 2008. rabies: a preventable but incurable disease. j infect chemother. 14(1), 8-14. pubmed wang h, feng n, yang s, wang c, wang t, et al. 2010. a rapid immunochromatographic test strip for detecting rabies virus antibody. j virol methods. 170(1-2), 80-85. pubmed who. (2016). who | rabies. who. wu x, hu r, zhang y, dong g, rupprecht ce. 2009. reemerging rabies and lack of systemic surveillance in people’s republic of china. emerg infect dis. 15(8), 1159-64. pubmed http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25168987&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26486619&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26486619&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=8136662&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22362801&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27047061&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18297443&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20837065&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19751575&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e298, 2019 isds 2019 conference abstracts updates to the hl7 2.5.1 implementation guide for syndromic surveillance peter hicks1, emilie lamb2, dave trepanier2, shandy dearth2 1 cdc, atlanta, georgia, united states 2 isds, braintree, ma 02184, massachusetts, united states objective to describe the latest revisions and modifications to the “hl7 2.5.1 implementation guide for syndromic surveillance” (formerly the phin message guide for syndromic surveillance) that were made based on community commentary and resolution of feedback from the hl7 balloting process. in addition, the next steps and future activities as the ig becomes an “hl7 standard for trial use” will be highlighted. introduction in 2011, the centers for disease control and prevention (cdc) released the phin messaging guide for syndromic surveillance v. 1. in the intervening years, new technological advancements including electronic health record capabilities, as well as new epidemiological and meaningful use requirements have led to the periodic updating and revision of the message guide. these updates occurred through informal and semi-structured solicitation and in response to comments from across public health, governmental, academic, and ehr vendor stakeholders. following the message guide v.2.0 release in 2015, cdc initiated a multiyear endeavor to update the message guide in a more systematic manner and released further updates via an erratum and a technical document developed with the national institute of standards and technology (nist) to clarify validation policies and certification parameters. this trio of documents were consolidated into the message guide v.2.1 release and used to inform the development of the nist syndromic surveillance test suite (http://hl7v2-ss-r2testing.nist.gov/ss-r2/#/home), validate test cases, and develop a new rules-based ig built using nist’s implementation guide authoring and management tool (igamt). as part of a cooperative agreement (coag) initiated in 2017, cdc partnered with isds to build upon prior activities and renew efforts in engaging the syndromic surveillance community of practice for comment on the message guide. the goal of this coag is have the final product become an “hl7 standard for trial use” following the second phase of formal hl7 balloting p in fall 2018. methods isds coordinated a multi-stakeholder working group to revisit the consolidated message guide, v.2.1 and collect structured comments via an online portal, which facilitated the documentation, tracking, and prioritization of comments for developing consensus and reconciliation and resolution when there were errors, conflicts, or differing perspectives for select specifications. over 220 comments were received during the most recent review period via the hl& balloting process (april – june 2018) with sixteen elements captured for each comment, which included: subject, request type, clinical venue application, submitter name, ig section #, priority, working and final resolution (figure 1). the online portal was used to communicate with members of the message guide workgroup to provide feedback directly to one another through a ‘conversation tab’. this became an important feature in teasing out underlying concerns and issues with a given comment across different local, state, and private sector partners (figure 2). some comments were able to be fully described and resolved using this feature. following the hl7 balloting period, isds continued the weekly webinar-based review process to delve into specific issues in detail. each week isds staff would lead the webinars structured around similar comment types (e.g. values sets, dg1 segments, in1 segments, conformance statements, etc.). this leveraged the expertise of individuals and institutions with concerns revolving around a specific domain, messages segment, or specification described within the message guide. comments for which consensus and resolution was achieved were “closed-out’ on the portal inventory and new assignments for review would be disseminated across the message guide workgroup for consideration and discussion during the subsequent webinar. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e298, 2019 isds 2019 conference abstracts results to date this review process has identified and updated a wide-range of specification and requirements described within the message guide v.2.0. these include: specifications for persistent patient id across venues of service, inclusion of the icd-10-cm value set for diagnosis, removal of the icd-9-cm requirement for testing and messages, modification of values such as pregnancy status, travel history, and medication list from “o” to “re”, and the update of value sets and phin vads references for fips, snomed, icd-10-cm, acuity, patient class, and discharge disposition.. conclusions the results of this multi-agency comment and review process will be synthesized and compiled by isds. the updated version of the message guide (re-branded to the hl7 v 2.5.1 implementation guide for syndromic surveillance) will go through a second round of review and commentary thru hl7 in fall 2018. this systematic and structured review and documentation process has allowed for the synthetization and reconciliation of a wide range of disparate specifications, historical hold-overs, and requirements via the perspectives of a diverse range of public health partners. as this review process continues it is anticipated that the final hl7 balloted “standard for trial use” ig 2.5 will represent a more refined and extensible product that can support syndromic surveillance activities across a wider and more diverse range of clinical venues, ehr implementations, and public health authorities. isds and cdc have recommended that future modifications to the promoting interoperability (pi) programs (formerly meaningful use) reference and require the utilization of the revised implication guide for certification. the hl7 2.5.1 implementation guide can be found: https://cdn.ymaws.com/www.healthsurveillance.org/resource/resmgr/docs/group_files/message_guide/ig_sys_release_1.pdf acknowledgement we thank the surveillance professionals that assisted with the implementation guide. this work is supported cooperative agreement with the cdc. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e384, 2019 isds 2019 conference abstracts epidemiology of sftsv in ticks collected from national park in the rok, 2015-2018 yoon kyoung cho2, jun gu kang2, young sun jo2, sun-woo han2, weon-hwa jheong1, hyesung jeong1, joon-seok chae2 1 environmental health research department, national institute of environmental research, hwangyeong-ro 42, seo-gu, incheon, 22689, republic of korea., incheon, korea (the republic of), 2 laboratoy of veterinary internal medicine, bk21 plus program for creati ve veterinary science research, research institute for veterinary science and college of veterinary medicine, seoul national university, seoul 08826, the republic of korea;, seoul, korea (the republic of) objective to survey the distribution of ixodid tick and infection of severe fever with thrombocytopenia syndrome virus (sftsv) in natural environments from deogyusan national parks in korea. introduction severe fever with thrombocytopenia syndrome (sfts) is an emerging viral disease in east-asian countries, including china, japan, and the republic of korea (rok). the causative agent of sfts is the sfts virus (sftsv) transmitted by hematophagous ticks. methods to investigate the prevalence of sftsv in the rok, a total of 4,223 ticks were collected by flagging from deogyusan national park from 2015 to 2018. one-step reverse transcription-polymerase chain reaction (rt-pcr) and nested pcr were used to detect sftsv-specific gene fragment from each ticks. the sequence data were analyzed using chromas and aligned using clustal x. the phylogenetic analysis was constructed using the neighbor-joining method in mega7. results of the collected adult and nymph ticks, haemaphysalis longicornis (3611, 85.5%) were the most abundant, followed by h. flava (502, 11.88%), ixodes nipponensis (109, 2.5%), and ixodes ovatus (1, 0.02%). the infection rate of sftsv in total ticks was 5.8% (245/4,223), and the infection rate by year was 3.69% in 2015, 7.97% in 2016, 5.08% in 2017 and 4.68% in 2018. the infection rates of sftsv were getting decreased each year in deogyusan national park. in addition, infection rate was higher in spring and summer of each season. phylogenetic analysis was performed and sftsv sequences obtained in this study were included in korean/japanese sftsv clade. conclusions in conclusion, we confirmed the sequence of two clades, and it is thought that the epidemiological investigation of sftsv is necessary through further studies. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts return of test results in vietnam hiv sentinel surveillance: implementation and preliminary results giang t. le*1, duc h. bui2, diep t. vu1, duong c. thanh4, nghia v. khuu3, huong t. phan2, sheryl lyss1 and abu abdul-quader1 1centers for disease control and prevention, hanoi, viet nam; 2vietnam authority of hiv/aids control, ministry of health, hanoi, viet nam; 3pasteur institute, ho chi minh, viet nam; 4national institute of hygiene and epidemiology, hanoi, viet nam objective to describe the implementation and preliminary results of returning hiv test results to participants in vietnam hiv sentinel surveillance. introduction knowledge of one’s hiv serostatus helps improve quality of life for those who test positive and decreases the risk of hiv transmission. who recommends that all participants in hiv prevalence surveys be provided access to their test results, especially those who test hiv positive [1]. anonymous vietnam hiv sentinel surveillance (hss), implemented since 1994, focuses on people who inject drugs (pwid), female sex workers (fsw), and men who have sex with men (msm) [2]. according to national guidelines, the hiv testing algorithm for surveillance purposes was based on two tests whereas the diagnostic algorithm for individuals was based on three tests. thus, surveillance test results could not be returned to participants [3] who were instead encouraged to learn their hiv serostatus by testing at public confirmatory testing sites. in 2015, a three-test strategy was applied as part of hss so that test results could be returned to participants. methods in 2015, return of hiv test results was implemented as a pilot in 16 hss provinces. hss participants were asked to identify which of the designated hiv testing and counselling centers (htc) in the province was most convenient for them. participants were then given appointment cards with an assigned survey id to receive their test results at the chosen venue at a specific date and time. specimens, with assigned survey ids, were transferred to the respective hiv laboratory at the province aids center (pac) for confirmatory testing. the same three-test algorithm was used for surveillance purposes as well as to return confirmatory test results to participants [3]. final test results were classified as “positive”, “negative” or “indeterminate”. hiv confirmatory test results were made available at all designated htc in the provinces within 10 days after blood collection; thus, if a participant presented at a location, date or time that differed from the appointment card, s/he could still receive the test result. in some settings in which provinces integrated hss with either static or mobile htc, three rapid tests were used at point-of-care so that same-day test results were available. in this case, participants received test results at the end of the specified time regardless of their infection status. at the htc, individuals showed their appointment cards. the ids were used to identify the correct test results which were then given verbally to participants by htc counsellors. test results were not returned by phone or email. individuals who tested positive were immediately referred to hiv treatment and other available health/ social services in the province. the proportion of participants who received their test results was calculated for each survey group and province. results the number of provinces that reported returning of hiv test results in 2015 and 2016 were 14 and 15, respectively. overall, among 15,530 persons tested through hss in 2015 and 2016, 7,354 persons returned to receive their test results. the proportion of participants who returned for test results varied by province and survey population (table 1). in some provinces where hss was integrated with htc, such as hai phong and dong thap, 100% of participants received their test results within a day [4]. conclusions returning hiv test results to hiv surveillance participants is feasible and beneficial in low-income countries like vietnam. this enhancement facilitates participants learning their serostatus and contributes toward vietnam’s achievement of hiv control [4]. based on the pilot experiences, vietnam ministry of health decided to extend test result notifications to all 20 hss provinces in 2017. key factors that contributed to the success of the activity were fast turnaround time, roles and level of commitment of pac, and coordination between the survey and htc. the returning rate in hss 2015 and 2016 are promising but these could be improved further. better coordination and commitment between the survey and hiv testing service are needed to further increase return rates so that hivpositive individuals can learn their serostatus and be better linked to care and treatment services. table 1: median and range of proportions, by province, of survey participants who received test results, by survey groups in hss 2015-2016 figure 1: process of laboratory testing, returning of hiv test results and referral of participants in hss. keywords hiv; test return; sentinel surveillance; vietnam isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts acknowledgments the technical assistance provided to vietnam government to deploy returning of hiv test results in hss has been supported by the president’s emergency plan for aids relief (pepfar) thru the cdc. references 1. who, guidelines for second generation hiv surveillance: an update: know your epidemic, 2013. 2. vaac, guidance for epidemiological surveillance of hiv/aids & sexually transmitted infections, 2012. 3. moh, national guideline on hiv serology testing, in decision 1098/ qd-byt, 2013. 4. vaac, primarily results of hss, 2016. *giang t. le e-mail: letonggiang@gmail.com online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e178, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e266, 2019 isds 2019 conference abstracts using syndromic surveillance and climatic data to detect high intensity hfmd seasons arden norfleet office of epidemiology, virginia department of health, richmond, virginia, united states objective to assess the relationship between seasonal increases in emergency department (ed) and urgent care center (ucc) visits for ha nd, foot, and mouth disease (hfmd) among children 0-4 years old and average dew point temperatures in virginia. to determine if this relationship can be used to develop an early warning tool for high intensity seasons of hfmd, allowing for earlier targe ted public health action and communication to the community and local childcare centers during these high intensity seasons. introduction hand, foot, and mouth disease is a highly infectious disease common among early childhood populations caused by human enteroviruses (enterovirus genus) [1]. the enteroviruses responsible for hfmd generally cause mild illness among children in the united states with symptoms of fever and rash/blisters, but have also been linked to small outbreaks of severe neurological disease such as meningitis, encephalitis, and acute flaccid myelitis [2]. enteroviruses circulate year-round but increase in the summer-fall months across much of the united states [3]. the drivers of this seasonality are not fully understood, but research indicates climatic factors, rather than demographic ones, are most likely to drive the amplitude and timing of the seasonal peaks [3]. a recent cdc study on nonpolio enteroviruses identified dew point temperature as a strong predictor of local enterovirus seasonality, explaining around 30% of the variation in intensity of transmission across the united states [3]. methods syndromic surveillance data on ed and ucc visits among 0-4 year olds in virginia were analyzed from january 1, 2012 to august 31, 2018. visits for hfmd were identified using the following chief complaint and discharge diagnosis terms: hand, foot, and mouth; hfm; fever with rash, lesions, or blisters; icd10 code: b08.4; or snomed ct code: 266108008. visits for hfmd among 0-4 year olds were aggregated by week and calculated as a proportion of all ed and ucc visits among this age group during the study period. hourly dew point readings from the richmond international airport from january 1, 2012 to august 31, 2018 were obtained from the national oceanic and atmospheric administration (noaa), national climatic data center (ncdc). noaa readings were averaged by week to establish a mean dew point for each week during the study period. correlation analyses were performed on weekly dew point temperatures and weekly percent of hfmd visits. weekly dew point averages were used to determine lowactivity weeks at which to measure baseline percentages of hfmd visits. a low-activity week was defined as periods of two or more consecutive weeks in which each week had an average dew point temperature of less than 55.4 degrees fahrenheit [3]. to assess if hfmd seasons varied in intensity from year to year, a kruskal-wallis test was used to assess significant differences by year among the mean weekly percent of hfmd visits during highactivity weeks. an early warning threshold for a high intensity season was developed by calculating the mean percent of hfmd visits during lowactivity weeks for the previous three years and adding two standard deviations. threshold rates were calculated for years 201 5 through 2018 and compared to the percentage of 0-4 year old hfmd visits during high-activity weeks. the week where percent of hfmd visits crossed the early warning threshold in 2018 was assessed to determine when public health notifications could have been made to alert the community about a high intensity (above threshold) hfmd season if this early warning tool had been utilized. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e266, 2019 isds 2019 conference abstracts results between january 1, 2012 and august 31, 2018, there were 27,181 visits for hfmd among children aged 0-4 years. mean and median weekly percent of hfmd visits were 1.33% and 1.01% of total 0-4 year old visits, respectively, with a range from 0.18% to 5.32%. these visits were most prominent during the summer or fall each year, with annual peaks occurring between weeks 22 46. weekly percent of hfmd visits and average weekly dew point temperatures were significantly correlated (r=0.562, p<.0001). the mean weekly dew point temperature for high-activity weeks was 67.2 degrees fahrenheit, with a range between 49.3 and 73.5 degrees. a kruskal-wallis test showed a significant difference in the mean weekly percent of visits by year for high-activity weeks (p<.0001). over the 4 years of data to which the threshold was applied, percent of hfmd visits crossed the threshold in 2016 and 2018, indicating both years experienced high intensity hfmd seasons (fig. 1). percent of hfmd visits never crossed the early warning threshold in 2015 nor 2017. in 2018, the threshold was met on week 21 (week ending june 2, 2018) which was more than 3 weeks prior to when public health notifications were made using routine surveillance methods through essence. conclusions visits for hfmd among the young childhood population (0-4 year olds) in virginia exhibit annual summer-fall seasonality with significant differences between the percent of visits from year to year. seasons exhibiting a significantly higher percent of hfmd visits during high-activity weeks warrant a greater level of public health communication and outreach to educate parents, physicians and childcare centers about the disease and prevention measures. it can be difficult to differentiate high intensity seasons from low intensity seasons in the early weeks of increasing disease activity. traditional syndromic surveillance methods using essence identify significant increases in hfmd visits from the previous 90 days, but do not readily alert on differences in seasonali ty from year to year. these results support the use of dew point temperature data to develop an early warning tool for high intensity seasons of hfmd. this early warning tool will allow for more efficient use of resources and targeted outreach during years with particularly high hfmd activity within the young childhood population. this early warning tool will be implemented by the virginia department of health in 2019 to evaluate its effectiveness at identifying high hfmd activity in real -time. acknowledgement i would like to thank erin austin, jonathan falk, and tim powell for their guidance and review. references 1. khetsuriani n, lamonte-fowlkes a, oberst s, pallansch ma. 2006. enterovirus surveillance—united states, 1970–2005. mmwr surveill summ. 55(no. ss-8), 1-20. https://www.ncbi.nlm.nih.gov/pubmed/16971890. pubmed 2. centers for disease control and prevention. (2018). hand, foot, and mouth disease (hfmd). retrieved sept 25, 2018, from https://www.cdc.gov/hand-footmouth/about/complications.html. 3. pons-salort m, oberste ms, pallansch ma, et al. 2018. the seasonality of nonpolio enteroviruses in the united states: patterns and drivers. proc natl acad sci usa. 115, 3078-83. doi:https://doi.org/10.1073/pnas.1721159115. pubmed http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16971890&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16971890&dopt=abstract https://doi.org/10.1073/pnas.1721159115 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29507246&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e266, 2019 isds 2019 conference abstracts figure 1 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e286, 2019 isds 2019 conference abstracts improving varicella investigation completeness in pennsylvania jonah long, wayne fleming, janine strick pennsylvania department of health, jackson center, pennsylvania, united states objective the objective of this study was to evaluate the impact of efforts made to improve the completeness of select varicella (chickenpox) case investigation variables. introduction routine childhood administration of varicella-containing vaccine has resulted in the number of varicella (chickenpox) cases in pennsylvania falling from nearly 3,000 cases in 2007 to less than 400 cases in 2017. prior to 2018, the completeness of varicella case investigation data documented in pennsylvania’s electronic disease surveillance system (pa-nedss) was not routinely monitored by department of health (doh) staff. a pilot project was initiated in april 2018 to monitor and improve completeness of select varicella case investigation variables. methods varicella cases reported to pa-nedss during mmwr year 2018 (mmwr weeks 1 – 26) in pennsylvania (excluding philadelphia county) with a classification status of probable or confirmed were included in the pilot project (n=223). doh epidemiology staff prioritized 11 key varicella investigation variables and developed a sas program to identify cases with missing data, which w ere summarized in weekly reports and provided to doh immunization staff for followup. doh immunization staff reviewed missing data reports and communicated with case investigators to reconcile missing data. varicella case data from the project period were compared with a 10-year baseline to evaluate the 11 targeted variables for change in percent completion. results percent completion of all 11 variables improved during the intervention period, with a median relative increase of 10.2% (ran ge: 4.2% — 25.5%) compared to baseline. all but two variables (pregnancy status and number of days hospitalized) exhibited a statistically significant (p<0.05) improvement in percent completion. in addition, among eight variables that include an unkn own response option, only one variable (number of varicella vaccine doses received) measured an increase in the percentage of unknown responses during the project period compared with baseline; however, this increase was not statistically significant (p=0.180 ). conclusions prioritization of key varicella investigation variables for improved completion was successful and did not result in significant increases of unknown responses. as varicella cases become less common, varicella case investigation data become increasingly important. increased completeness of these data will enhance doh communication of varicella surveillance findings, particularly for severe cases. based on the success of this interagency collaboration, similar efforts are being developed for additional reportable conditions. table 1. varicella variable completeness during 2008-2017 and 2018. variable 2008-2017 (n=8,895) 2018 (n=223) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e286, 2019 isds 2019 conference abstracts mean percent complete (standard deviation) percent complete relative percent change χ2 p valu e onset date 92.4 (2.9) 99.6 +7.8 <0.001 hospitalization 90.8 (5.0) 100.0 +10.2 <0.001 days hospitalized1 72.4 (11.7) 90.9 +25.5 0.1614 pregnant2 89.8 (3.2) 97.7 +8.8 0.05904 rash onset date 94.2 (1.3) 99.6 +5.7 <0.001 lesion severity 89.9 (1.7) 99.6 +10.7 <0.001 immunocompromised 81.5 (4.9) 99.6 +22.1 <0.001 complications 81.2 (7.4) 99.6 +22.6 <0.001 transmission setting known 82.9 (4.5) 99.6 +20.1 <0.001 received varicella vaccine 90.9 (1.8) 99.6 +9.5 <0.001 varicella vaccine doses received3 96.0 (1.7) 100.0 +4.2 0.02044 1denominator: hospitalized cases; n=167 (2008-2017), n=11 (2018); 2denominator: female cases, >12 years; n=1,001 (20082017), n=44 (2018); 3denominator: vaccinated cases; n=5,037 (2008-2017), n=79 (2018); 4fisher exact (right-sided) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e439, 2019 isds 2019 conference abstracts monitoring sexual violence visits in emergency department data to improve public health amanda d. morse, kirstin mcfarland, natasha close office of communicable disease epidemiology, washington state department of health, shoreline, washington, united states objective to describe characteristics of sexual violence emergency department visits in washington state. introduction although sexual violence is a pressing public health and safety issue, it has historically been challenging to monitor population trends with precision. approximately 31% of incidents of sexual violence are reported to law enforcement and only 5% lead to an arrest [1], making the use of law enforcement data challenging. syndromic surveillance data from emergency departments provides an opportunity to use care-seeking to more accurately surveil sexual violence without introducing additional burdens on either patients or healthcare providers. methods using the national syndromic surveillance program (nssp) electronic surveillance system for early notification of communitybased epidemics (essence) platform, staff from the rapid health information network (rhino) program at the washington state department of health created a syndrome definition for sexual violence in emergency department data using a combination of icd-10 diagnostic codes and chief complaint terms likely to be used for sexual violence visits. staff executed the query using both the chief complaint query validation and facility location (full details) data sources in the nssp essence platform. validation occurred by examining visits from 2017 using the original chief complaint, diagnosis combo, and original triage notes fields in the data details output to determine if a visit was a true positive for sexual violence. staff then used the r studio platform to create n-grams to analyze commonly occurring terms in the data. rhino staff collaborated with colleagues in the agency's injury and violence prevention section to better understand the trends observed in the data and the utility of using syndromic surveillance to inform public health practice. results the query identified 1,550 visits for sexual violence in 2017. female patients were disproportionately represented (87.16%), with female patients aged 1029 years making up 47.03% of captured visits. overall, patients 10-29 years of age represented 52.90% of all identified visits. older patients, particularly older men were less represented. among the captured visits, staff analyzed a sample of 347 visits and found that 88.76% were correctly identified as being related to sexual violence. although triage notes are an optional field for washington state syndromic reporting and only present in approximately 40% of records, analysis of the triage notes also provided contextual details on the time (36.89%) and place (18.44%) of the incident, and the identity of the assailant (17.29%). among patients 10-29 years old, several increases in the percentage of emergency department visits for sexual violence were observed in conjunction with secondary and post-secondary school term breaks, as well as at the beginning of autumn and end of spring terms at most washington state universities. the trend was present in both patients 10-19 years and 20-29 years, with a stronger signal in the 10-19 year age group. the pattern was not present in either older or younger patients. conclusions the seasonal trend associated with the academic calendar in patients 10-29 years of age is consistent with other data on teen and campus dating violence [2,3] and provides another piece of information to validate and inform the work of social service groups serving adolescents and young adults.syndromic data is particularly well suited to translating surveillance into actionable public health—having additional data to support the hypotheses of state rape prevention and education programs has the potential to http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e439, 2019 isds 2019 conference abstracts encourage greater participation from universities and other academic institutions to improve consent and sexual violence response programs. regardless of whether patients are affiliated with a specific institution, the overall safety of the communities where students live is of importance to academic institutions. similarly, yearly increases in visits during annual mass gatherings may be of use in communicating with event organizers strategies for reducing incidents of violence during that period. indexing the query for sexual violence within the nssp essence platform allows surveillance practitioners to quickly and easily monitor emergency department visits for sexual violence using a standardized methodology. as a national platform, nssp essence facilitates collaboration across borders between local, state, tribal and urban indian, and national public health agencies. this improved availability and performance of the query. additionally, the point-and-click nature of the essence platform makes using syndromic data more accessible for local health and social service staff who are not trained in epidemiology. the ease of collaboration between partners using the platform also makes it well suited to work which span state, local, and tribal, and urban indian health needs. data and query sharing increases the likelihood that the data will be actionable and therefore positively influence public health. acknowledgement the authors are greatful for the guidance received from partners at the centers for disease control and prevention. references 1. the criminal justice system. statistics | rainn. https://www.rainn.org/statistics/criminal-justice-system. accessed september 13, 2018. 2. big problem on campus. rainn | the nation's largest anti-sexual violence organization. https://www.rainn.org/news/bigproblem-campus. accessed september 13, 2018. 3. violence cs. statistics. rainn | the nation's largest anti-sexual violence organization. https://www.rainn.org/statistics/campus-sexual-violence. accessed september 13, 2018. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e439, 2019 isds 2019 conference abstracts emergency department visits for sexual violence by patient sex and age group in washington state among reporting facilities, 2017 (n=1550) patient ten year age group patient sex female n (%) male n (%) total n (%) 00 09 145 (9.35%) 44 (2.84%) 189 (12.19%) 10 19 315 (20.32%) 26 (1.68%) 341 (22.00%) 20 29 414 (26.71%) 65 (4.19%) 479 (30.90%) 30 39 243 (15.68%) 24 (1.55%) 267 (17.23%) 40 49 133 (8.58%) 19 (1.23%) 152 (9.81%) 50+ 101 (6.52%) 21 (1.35%) 122 (7.87%) total 1351 (87.16%) 199 (12.84%) 1550 (100.00%) query criteria chief complaint terms icd-10 diagnosis codes inclusion exclusion inclusio n exclusion sexual assault grape t74.2 z04.41 sexual assualt scrape t76.2 rape disability z56.81 sane exam forensic nurse sab sample of emergency department visits for sexual violence in washington state among reporting facilities, 2017 (n=347) match validity n (%) true positive 308 (88.76%) false positive 39 (11.24%) contextual information in triage notes n (%) time of incident 128 (36.89%) place of incident 64 (18.44%) assailant 60 (17.29%) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e398, 2019 isds 2019 conference abstracts national surveillance for health-related workplace absenteeism, united states 2017-18 matthew groenewold1, sherry burrer2, faruque ahmed3, amra uzicanin3 1 national institute for occupational safety and health, cdc, cincinnati, ohio, united states, 2 national institute for occupational safety and health, cdc, atlanta, georgia, united states, 3 national center for emerging and zoonotic infectious diseases, cdc, atlanta, georgia, united states objective to describe the methodology of the national institute for occupational safety and health (niosh) system for national surveillance of health-related workplace absenteeism among full-time workers in the united states and to present initial findings from october through july of the 2017–2018 influenza season. introduction during an influenza pandemic, when hospitals and doctors’ offices are—or are perceived to be—overwhelmed, many ill people may not seek medical care. people may also avoid medical facilities due to fear of contracting influenza or transmitting it to others. therefore, syndromic methods for monitoring illness outside of health care settings are important adjuncts to traditional disease reporting. monitoring absenteeism trends in schools and workplaces provide the archetypal examples for such approaches. niosh’s early experience with workplace absenteeism surveillance during the 2009–2010 h1n1 pandemic established that workplace absenteeism correlates well with the occurrence of influenza-like illness (ili) and significant increases in absenteeism can signal concomitant peaks in disease activity. it also demonstrated that, while population-based absenteeism surveillance using nationally representative survey data is not as timely, it is more valid and reliable than surveillance based on data from sentinel worksites [1]. in 2017, niosh implemented population-based, monthly surveillance of health-related workplace absenteeism among full-time workers. methods each month, niosh updates an influenza season-based time series of health-related workplace absenteeism prevalence among full-time workers with the previous month’s estimate (i.e., with a 1-month lag). data for this surveillance system come from the current population survey (cps), a monthly national survey of approximately 60,000 households administered by the bureau of labor statistics. the cps collects information on employment, demographics and other characteristics of the noninstitutionalized population aged 16 years or older. a full-time worker is defined as an employed person who reports that they usually work at least 35 hours per week. health-related workplace absenteeism is defined as working fewer than 35 hours during the reference week due to the worker’s own illness, injury, or other medical issue. because the cps questions refer to one week of each month, absenteeism during the other weeks is not measured. these one-week measures are intended to be representative of all weeks of the month in which they occur. monthly absenteeism prevalence estimates for the current influenza season are compared to an epidemic threshold defined as the 95% upper confidence limit of a baseline established using data from the previous five seasons aggregated by month. point estimates that exceed the epidemic threshold signal surveillance warnings; estimates whose lower 95% confidence limits exceed the epidemic threshold generate surveillance alerts. estimates of total absenteeism are calculated as are estimates stratified by sex, age group, geographic region (hhs service regions), and occupation. all analyses are weighted using the cps composite weight and estimates of all standard errors are adjusted to account for the complex design of the cps sample. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e398, 2019 isds 2019 conference abstracts results during the period october 2017 through july 2018, the prevalence of health-related workplace absenteeism among full-time workers began at 1.7% (95% ci 1.6–1.8%) in october, increased sharply beginning in november, peaked in january at 3.0% (95% ci 2.8–3.2%), and declined steadily thereafter to end at a low of 1.4% (95% ci 1.3–1.5%) in july. the january absenteeism peak significantly exceeded the epidemic threshold, signaling a surveillance alert. absenteeism remained elevated in february, but not significantly, signaling a surveillance warning. (figure 1) peak absenteeism in the 2017-2018 influenza season exceeded that of all of the five previous seasons except the 2012-2013 season. (figure 2) analyses stratified by sex generated surveillance alerts for male workers in january and february. surveillance alerts were also signaled for the following strata: workers aged 45–64 years in january and february; workers in hhs region 6 in january and february and region 9 in december and march; and workers in management, business, and financial occupations and installation, maintenance, and repair occupations in january and in production and related occupations in february. unlike surveillance alerts, the numerous surveillance warnings generated in stratified analyses are not reported due to small sample sizes in several strata. conclusions results of initial analyses for the 2017–2018 influenza season indicate that, among full-time workers in the united states, the prevalence of healthrelated workplace absenteeism began to increase in november, peaked in january and was significantly higher than the average of the previous five seasons. these findings are consistent with official characterizations of 2017–2018, based on traditional ili, hospitalization, and virologic surveillance data, as a high severity season that accelerated in november and peaked in january and february [2,3]. analyses further suggest that male workers; workers aged 45–64 years; workers living in hhs regions 6 and 9; and those working in management, business, and financial; installation, maintenance, and repair; and production and related occupations may have been especially impacted. while not timely enough to serve as an early warning system, population-based workplace absenteeism is, nevertheless, a useful syndromic measure of a pandemic’s impact on the working population. it also provides information that can be used to maintain health situational awareness during the inter-pandemic period, to evaluate the impact of pandemic control measures, and to inform future pandemic preparedness and response planning. absenteeism surveillance can provide an important supplementary measure of a pandemic’s overall impact because morbidity and mortality statistics may not fully reflect the disruption caused to the social and economic life of the community. this is especially true when disease makes people too sick to work but not sick enough to seek medical care. references 1. groenewold mr, konicki dl, luckhaupt se, gomaa a, koonin lm. 2013. exploring national surveillance for healthrelated workplace absenteeism: lessons learned from the 2009 influenza pandemic. disaster med public health prep. 7, 160-66. pubmed https://doi.org/10.1017/dmp.2013.8 2. garten r, blanton l, elal ai, et al. 2018. update: influenza activity in the united states during the 2017–18 season and composition of the 2018–19 influenza vaccine. mmwr morb mortal wkly rep. 67, 634-42. pubmed https://doi.org/10.15585/mmwr.mm6722a4 3. world health organization. 2018. review of the 2017–2018 influenza season in the northern hemisphere. wkly epidemiol rec. 34(93), 429-44. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24618167&dopt=abstract https://doi.org/10.1017/dmp.2013.8 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29879098&dopt=abstract https://doi.org/10.15585/mmwr.mm6722a4 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e398, 2019 isds 2019 conference abstracts figure 1 figure 2 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e353, 2019 isds 2019 conference abstracts multimorbidity network surveillance: chronic disease clusters and social disparities eun kyong shin1, youngsang kwon2, arash shaban-nejad1 1 pediatrics, uthsc, memphis, tennessee, united states, 2 university of memphis, memphis, tennessee, united states objective we study how multimorbidity prevalence is related to socio-economic conditions in memphis, tn. in addition, we demonstrate that the accumulation of chronic conditions, which is measured by affinity in multimorbidity, is unevenly distributed through out thecity. our research shows that not only are socioeconomic disadvantages linked to a higher prevalence in each major chronic condition, but also major chronic conditions are heavily clustered in socially disadvantaged neighborhoods. introduction chronic diseases impose heavy burdens onhealth systems, economies, andsocieties [1]. half of all americans live with at least one of the chronic conditions and more than 75% of health care cost is associated with people with chronic diseases [2]. multimorbidity, the coexistence of two ormore chronic conditions in an individual or a population, often require complex and ongoing care and a deep understanding of different risk factors, and their indicators.multimorbidity has been increased over the past years and the trend is expected to continue across the u.s. knowing how different chronic conditions are related to one another andwhat are the underlying socioeconomic factorsis crucial to design and implement effective health interventions. we introduce “multimorbidity network affinity”, which measures the degree of how multiple chronic conditions are clustered within a geographic unit. accura te estimations of how chronic conditions are spatially clustered and linked to other sociomarkers [3] and socio-economic disadvantages facilitate designing effective interventions. methods multiple datasets including major chronic condition data from the center for disease control and prevention (cdc) 500 cities, and socio-demographic data from the u.s. census bureau and the environmental systems research institute (esri) demographics data have been consistently integrated. then, network analytics have been performed to examine the inter -relations among a selected number of major chronic conditions and their manifestations in memphis. to checkwhether a distinctive geographic pattern in multimorbidity is present, we carried out a test using global moran’s i and getis-ord gi*statistics. if apattern is detected, we use robust regression to explore how affinity isassociatedwiththe socio-economic disadvantages of the area. results the network analysis confirms the existence of close relationships between various chronic conditions. ourspatial analysisshowthat the geo-distinctive patterns of clustered comorbidities are associated with socio-economic deprivation. statistical results suggest that neighborhoodswith higherrates of crime, poverty, and unemployment are associated with an increased likelihood of having dense clusters of chronic conditions. conclusions this study shows the importance of geospatialfactors in multimorbidity network surveillance. moreover, it demonstrates how socioeconomic disadvantages and multimorbidity network are connected. the healthdisadvantages are disproportionately accumulated in socially disadvantaged areas. network analysis enables us to discover the links between commonly co-observed chronic diseases and explore the complexity of their interactions. this will improve the surveillance practice and facilitate timely response as well as public health planning and decision making. references 1. wu s-y, green a. the growing crisis of chronic disease in the united states. rand corporation. 2000. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e353, 2019 isds 2019 conference abstracts 2. anderson g, horvath j. 2004. the growing burden of chronic disease in america. public health rep. 119(3), 263-70. pubmed https://doi.org/10.1016/j.phr.2004.04.005 3. shin ek, mahajan rm, akbilgic oa, shaban-nejad a. sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits. npj digital medicine (2018) 1:50; doi:10.1038/s41746-018-0056-y. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15158105&dopt=abstract https://doi.org/10.1016/j.phr.2004.04.005 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e247, 2019 isds 2019 conference abstracts impact of a new diagnoses thesaurus on the french ed syndromic surveillance system cécile forgeot1, isabelle pontais1, emmanuel dos ramos4, gilles viudes3, christophe vincent-cassy2, françois dubos3, anne fouillet1, céline caserioschönemann1 1 data, sante publique france, saint-maurice, france, 2 sfmu, french society for emergency healthcare, paris, france, 3 fedoru, emergency regional observatory federation, paris, france, 4 orupaca, paca emergency network observatory, hyères, france objective the study aims to evaluate the potential impact of the revision of the thesaurus used by ed physicians to code medical diagnoses, on the syndromic indicators used daily to achieve the detection objective of the french syndromic surveillance system. introduction as part of the french syndromic surveillance system sursaud®, the french public health agency (santé publique france) collects daily data from the emergency department (ed) network oscour® [1]. the system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity. individual ed data contain demographic (age, gender, residence zip code), administrative (dates of attendances and discharge, ed, etc.) and medical information (chief complaint, main and associated medical diagnoses, severity). medical diagnoses are encoded using the icd10 classification. then syndromic groups are built based on these icd10 codes for ensuring syndromic surveillance in routine. even if icd10 is recommended on the national guidelines for coding ed attendances, this thesaurus offers a too large variety of codes. particularly, it includes lots of diseases that may never be observed or confirmed in ed. this variety let selection of the appropriate codes difficult for physicians in a reactive use and could discourage them to code diagnoses. in order to encourage appropriate and reactive coding practice, we decided in 2017 to produce a new diagnoses thesaurus with a limited list of icd10 codes. then a committee of medical and epidemiological experts was created by the federation of regional emergency observatories (fedoru), to propose an operational thesaurus that includes relevant codes for both ed in a daily routine practice and syndromic surveillance. methods the committee has met 10 times since 2017. since it would have been hard to work on the complete icd10 list, the work was based on a more limited thesaurus already used by part of french ed. only codes, which were pertinent regarding ed activity a nd interest for public health alert, have been considered. the main principles that have guided the selection were to 1) keep codes related to diagnoses that physicians are able to diagnose on a clinical basis or with rapid diagnostic tests, 2) remove diagn oses providing redundant information regarding other variables (such as circumstantial information) and 3) ensure that a substitution code was kept when a removed code was frequently used or was of interest for syndromic surveillance. among the 86 syndromic groups defined on the basis of a list of icd10 codes selected in the complete thesaurus, 34 are daily analyzed by santé publique france for outbreak detection and early assessment of public health events. those 34 syndromic groups have been recalculated by considering the revised thesaurus on a three-year period (from 2015 to 2017) at national level. in order to measure the potential impact of the revised thesaurus on the syndromic groups, we have considered three evaluation measures: http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e247, 2019 isds 2019 conference abstracts 1. the proportion of icd10 codes deleted (removal rate) from the initial definition of each syndromic group, due to the limitation of the thesaurus (calculated for the 86 syndromic groups); 2. the mean difference in the daily number of attendances between the initial and the new versions of each syndromic group (calculated for the 34 syndromic groups); 3. the linear correlation coefficient between the daily numbers of attendances of the initial and the new version of each syndromic group, in order to assess if the daily fluctuations of the new syndromic group are similar to those of the initial syndromic group (calculated for the 34 syndromic groups). results among the 86 syndromic groups, 75 (85%) have been impacted by the revised thesaurus, which implied codes removal. among those 75 syndromic groups, the number of icd10 codes included in their definition has been reduced by 71% on average. this removal rate varied between 17% and 100%. syndromic groups including initially more than 100 codes have been the most concerned by a limitation of the number of icd10 codes. among the 34 syndromic groups daily analyzed for outbreak detection, 32 have been impacted by code removal with a mean removal rate of 68% (0%-97%). on average, 77% of daily attendances have been retained by the new version of syndromic groups, varying from 15% to 100%. only 3 syndromic groups have kept less than 60% of attendances: decrease of well -being (36%), conjunctivitis (32%) and hypothermia (15%). on average, the correlation coefficient has been of 0.96, varying from 0.57 to 1. the lowest values have been observed for the same three syndromic groups listed above: decrease of well-being (0.57), conjunctivitis (0.91) and hypothermia (0.59). 18 among the 34 syndromic groups had a correlation coefficient higher than 0.99. conclusions the study showed that most of the syndromic groups were impacted by the revised thesaurus, which resulted in a removal of about two thirds of the icd10 codes usually considered in daily surveillance. however, more than three quarters of attendances were still retained in the new syndromic groups. this new thesaurus was conceived to rationalize the number of diagnoses codes but a substitution code was systematically proposed to replace removed codes. those results highlighted that a large number of codes included in the complete icd10 thesaurus were rarely used and that the most frequent codes were kept in the revised thesaurus version. however, this study showed that a few syndromic gr oups were strongly impacted by the revised thesaurus and can suffer of reduced performances to detect unusual variations. based on thos e results, a second round of exploration of specific parts of the complete icd10 thesaurus will be necessary to adapt either syndromic groups or the revised thesaurus. even if the number of attendances may be reduced due to the removal of icd10 codes, temporal variations remain similar for the majority of syndromic groups. syndromic surveillance system does not aim to provide exhaustive quantification of attendances for a pathology, but aims to be able to detect expected or unusual public health variations. th ese evaluation results correspond to the worst-case scenario assuming that ed physicians will not modify their encoding habits by using the substitution codes but keep using their current thesaurus. however, we expect that this new and simplified version will facilitate diagnosis encoding task and lead toward a better diagnosis encoding rate. once this new thesaurus will be widely used, we can expect a substantial improvement of the quality of ed medical data and then of syndromic surveillance results. finally, this study enhances the importance that both data providers and epidemiologists in charge of syndromic surveillan ce work closely, in order to improve system in shared objectives. references 1. fouillet a, bousquet v, pontais i, gallay a, caserio-schönemann c. 2015. the french emergency department oscour network: evaluation after a 10-year existence. online j public health inform. 7(1), e74. https://doi.org/10.5210/ojphi.v7i1.5740 http://ojphi.org/ https://doi.org/10.5210/ojphi.v7i1.5740 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e369, 2019 isds 2019 conference abstracts tracking harmful chemicals and pathogens using the human health observatory at asu rolf u. halden, elizabeth terlinden, simona kraberger, matthew scotch, joshua steele, arvind varsani biodesign center for environmental health engineering, arizona state university, tempe, arizona, united states objective to highlight the new science of population and urban metabolism metrology, for characterizing human exposures to biological agents, narcotics, antimicrobials and other contaminants of emerging concern using community wastewater as a diagnostic matrix. introduction sewerage systems of villages, townships, cities and megacities convey the urine, stool, blood, and sputum of community member s, enabling through analysis of community wastewater, a near real-time assessment of population health status and of emerging public health threats. signature compounds and biomarkers targeted analytically for surveillance may include chemical and biologica l threat agents, transformation products of the same, human metabolites, biomarkers of exposure and other markers of interest. additional information can be gleaned by analyzing, in a similar fashion, municipal sewage sludge resulting from wastewater treatment, a material that during treatment becomes enriched in persistent, hydrophobic and potentially bioaccumulative pollutants, while more biodegradable compounds are converted to methane mostly. when taken together, these multi-dimensional data sources promise to yield critical information on the health status, sustainability and resilience of rural and urban human populations in a new scientific approach termed population metabolism metrology, or for city environments, urban metabolism metrology. methods starting in 2001, samples of wastewater and municipal sludge were collected from cities across the united states and from arou nd the world. obtained samples were logged, archived and split samples analyzed for chemical and biological markers of human health concern. sampling and analyses are ongoing. the sample archive is a shared resource available to the international research community. results over the course of 15+ years, wastewater and municipal sludges from over 300 cities around the world have been collected (see figure 1 below). the resultant expansive specimen archive today is known as the human health observatory (hho) at arizona state university. it constitutes the largest repository of wastewater process flow samples in the world. the municipal s ludge samples contained in the human health observatory constitute the u.s. national sewage sludge repository (nssr) and international sewage sludge repository (issr). archived samples have been analyzed for over 300 chemical and biological threat agents, resulting in u.s. national inventories of dozens of harmful chemicals, timeand space-resolved insights into human exposure to pesticides and substances of abuse, the identification of chemical threats fostering drug resistance and antibiot ic crossresistance, and the discovery of hundreds of novel viruses of potential human health importance. public health and policy events informed by the hho and nssr include the u.s. federal ban of antimicrobials in personal care products regulated by the u.s food and drug administration, and the florence statement of triclosan and triclocarban, a global call for elimination of unnecessary and ineffective antimicrobials from consumer products. conclusions the analysis of wastewater and municipal sewage sludges collected at centralized wastewater treatment facilities around the world represents an effective, inexpensive and rapid approach for public health assessment and threat detection. among the notable success stories thus far are the 2017 us fda ban of multiple antimicrobials whose overuse has been linked to the emergence of drug resistance and cross-resistance of human pathogens to antibiotics used in human medicine. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e369, 2019 isds 2019 conference abstracts acknowledgement this project was supported in part by grant numbers 1r01es015445, r01es020889, their multiple supplements from the national institute of environmental health sciences (niehs) and by grant number ltr 05/01/12 from the virginia g. piper charitable trust. references 1. magee hy, maurer mm, cobos a, pycke bfg, venkatesan ak, et al. 2018. us nationwide reconnaissance of ten infrequentlymonitored antibiotics in municipal biosolids. sci total environ. 643, 460-67. pubmed https://doi.org/10.1016/j.scitotenv.2018.06.206 2. halden ru. invited plenary talk: urban metabolism metrology: a new discipline elucidating the human condition in cities around the world, 252nd american chemical society’s national meeting, philadelphia, pa, august 21-25, 2016. 3. venkatesan ak, done hy, halden ru. 2015. united states national sewage sludge repository at arizona state university-a new resource and research tool for environmental scientists, engineers, and epidemiologists. environ sci pollut res int. 22(3), 1577-86. pubmed https://doi.org/10.1007/s11356-014-2961-1 4. venkatesan ak, halden ru. wastewater treatment plants as chemical observatories to forecast ecological and human health risks of manmade chemicals. sci rep-uk 2014, 4. 5. muhire bm, varsani a, martin dp. 2014. sdt: a virus classification tool based on pairwise sequence alignment and identity calculation. plos one. 9(9), e108277. pubmed https://doi.org/10.1371/journal.pone.0108277 6. cheung kh, yip ky, townsend jp, scotch m. 2008. hcls 2.0/3.0: health care and life sciences data mashup using web 2.0/3.0. j biomed inform. 41(5), 694-705. pubmed https://doi.org/10.1016/j.jbi.2008.04.001 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29945081&dopt=abstract https://doi.org/10.1016/j.scitotenv.2018.06.206 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24824503&dopt=abstract https://doi.org/10.1007/s11356-014-2961-1 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25259891&dopt=abstract https://doi.org/10.1371/journal.pone.0108277 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=18487092&dopt=abstract https://doi.org/10.1016/j.jbi.2008.04.001 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e369, 2019 isds 2019 conference abstracts figure 1. u.s. national (a) and international sampling locations (b) represented in the human health observatory (hho) at arizona state university. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e259, 2019 isds 2019 conference abstracts phylogenetic analysis of ukrainian bacillus anthracis strains from various sources oleksandr v. biloivan1, angela duerr2, julia schwarz3, vasiliy arefev1, oleksii solodiankin1, borys stegniy1, anton gerilovych1 1 laboratory of molecular diagnostics, national scientific center "institute of experimental and clinical veterinary medicine", kharkiv, kharkivska oblast, ukraine, 2 bundeswehr institute of microbiology, munich, bavaria, germany, 3 bundeswehr institute of microbiology, munich, bavaria, germany objective due to the lack of information about the phylogenetic origins of ukrainian bacillus anthracis strains, the goal of this work was to make phylogenetic analysis of ukrainian isolates obtained from various sources (soil, clinical material from infected humans and animal products) for better understanding of phylogenetic origins of this pathogen in ukraine and eastern europe. introduction anthrax is a widely spread zoonotic disease with natural transmissive cycle involving wildlife, livestock and humans [1]. it is caused by bacillus anthracis, a highly pathogenic gram-positive, spore-producing bacterium, which poses a serious threat to public and animal health due to its mortality both for animals and for humans [2-4]. the ability of b. anthracis spores to remain viable in soils for decades enables their isolation from freely accessible environment [5]. this unique feature to form highly resistant spores in the environment plays a major role in the ecology and evolution of this pathogen [6]. during the spore phase, evolution is greatly reduced in rate, which limits the amount of genetic diversity found among isolates of this species [1]. all these factors demonstrate the need for reliable anthrax diagnosis and trace-back methods. this comprises bio forensic capabilities including state-of-the-art methods for accurate genotyping of b. anthracis strains. methods 23 thermolysates of b. anthracis broth cultures isolated from various sources (vesicles from eleven different people infected with cutaneous anthrax when disease’s sporadic outbreaks were detected in ukraine in 1963-2002, as well as two samples from sheep wool, and eight soil samples) were obtained from the central epidemiological station (kyiv, ukraine), as well as from i.i. mechnikov ukrainian scientific and research anti-plaque institute (odessa, ukraine). these anthrax cultures were confirmed with classical microbiological methods (microscopy, cultivation on solid and liquid media), “string of pearls” reaction, an d using bioassay on living white mice (the mortality was observed two days after subcutaneous injection of 0,2-0,5 ml of cells’ suspension). all these tests were carried out at the institutions where samples were obtained. besides, one b. anthracis isolate was cultivated from soil sample of an animal grave site nearby koviagy village, valky district, kharkiv region. all samples were analyzed at the bundeswehr institute of microbiology (munich, germany). to confirm the presence of the anthrax genome and plas mids, we isolated genomic dna (gdna) from thermolysates and studied the presence of the genomic marker dhp61 as well as the plasmid specific marker paga (pxo1) and capc (pxo2) using qpcr. quality of the isolated gdna was tested using the agilent bioanalyzer. to characterize regional and global phylogeographic patterns of these strains, canonical single nucleotide polymorphisms anal ysis (cansnp) was conducted using high resolution melt (hrm). three thermolysates of broth cultures isolated and soil sample isolated from animal grave site in kharkiv region were analyzed using newseq full genome sequencing. results b. anthracis chromosomal dna-marker dhp61 as well as pxo1 marker paga and pxo2 plasmid marker capc could be detected in all thermolysates. however, the soil isolate from the koviagy grave site was positive for dhp61 but contained only the pxo1 plasmid. the bioanalyzer assay revealed that only 6 out of the 23 thermolysates had good enough dna quality to be sequenced. so far only genomes of thermolysates of soil samples from mykolaiv and sumy regions, the thermolysate of sick patient's vesicle from kherson region as well as the soil sample from the animal grave site in kharkiv region have been sequenced. for the resi dual 3 thermolysates the full genome analysis is still in progress. the sequencing results showed that the b. anthracis strain isolated from mykolaiv soil sample belongs to the vollum linage group and other thermolysates from sumy and kherson regions are closel y http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e259, 2019 isds 2019 conference abstracts clustering with isolates from japan. thus, human isolate from kherson region is clustering with the japanese isolate ba104 which was obtained from pig during sporadic anthrax incident in 1982 and soil isolate from sumy region is clustering with the ba 10 3 isolate which was obtained from beef cattle in japan in 1991. in contrast, we analyzed the genomic sequence of the pxo2-negative isolate from grave site in kharkiv region using bionumerics software and found that it has high similarity to sti strain. conclusions the infrequent sporadic occurrence of anthrax in the country of ukraine is likely caused by a heterogeneous population of b. anthracis. the found sti strain in the grave site of kharkiv region is probably an environmental recovery of the russian anthrax live vaccine which was commonly used for vaccination of animals in the former soviet union the sequencing result of the soil isolate from mykolaiv region indicates the occurrence of another cansnp group, the vollum group, which is quite untypical for ukraine. the latter is mainly prevalent in the asian regions (namely pakistan) and therefore might have been introduced to ukraine over the silk road. other two thermolysates from sumy and kherson regions also showed unexpected results clustering with japanese isolates. the further research of ukrainian b. anthracis isolates will allow us to expand our knowledge about the population structure and evolution of anthrax in ukraine. acknowledgement special thanks to: 1. my scientific supervisor, director of nsc “iecvm”, doctor of veterinary sciences, professor, naas academician, borys tymofiyovych stegniy; 2. colleagues from bundeswehr institute of microbiology for strong material and methodological support in this research; 3. partners from deutsche gesellschaft für internationale zusammenarbeit (giz) company for organization of this research project in frames of german biosecurity program; 4. colleagues from the central epidemiological station (kyiv, ukraine) and i.i. mechnikov ukrainian scientific and research anti-plaque institute (odessa, ukraine) for providing of samples for research. references 1. van ert mn, easterday wr, huynh ly, okinaka rt, hugh-jones me, et al. 2007. global genetic population structure of bacillus anthracis. plos one. 2(5), e461. pubmed https://doi.org/10.1371/journal.pone.0000461 2. freidlander am. 1997. anthrax, p. 467–478. in f. r. sidell, e. t. takafuji, and d. r. franz (ed.), medical aspects of chemical and biological warfare. office of the surgeon general, washington, d.c. 3. hoffmaster ar, fitzgerald cc, ribot e, mayer lw, popovic t. 2002. molecular subtyping of bacillus anthracis and the 2001 bioterrorism-associated anthrax outbreak, united states. emerg infect dis. 8, 1111-16. pubmed https://doi.org/10.3201/eid0810.020394 4. keim p, van ert mn, pearson t, vogler aj, huynh ly, et al. 2004. anthrax molecular epidemiology and forensics: using the appropriate marker for different evolutionary scales. infect genet evol. 4, 205-13. pubmed https://doi.org/10.1016/j.meegid.2004.02.005 5. eitzen em. 1997. use of biological weapons, p. 437–450. in f. r. sidell, e. t. takafuji, and d. r. franz (ed.), medical aspects of chemical and biological warfare. office of the surgeon general, washington, d.c. 6. biloivan o, duerr a, schwarz j, grass g, arefiev v, et al. (2018) phylogenetic analysis of ukrainian bacillus anthracis strains. third annual btrp ukraine regional one health research symposium, abstract directory: 122. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17520020&dopt=abstract https://doi.org/10.1371/journal.pone.0000461 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12396925&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=12396925&dopt=abstract https://doi.org/10.3201/eid0810.020394 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15450200&dopt=abstract https://doi.org/10.1016/j.meegid.2004.02.005 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e235, 2019 isds 2019 conference abstracts cost-effective surveillance for infectious diseases through specimen pooling and multiplex assays christopher bilder1, joshua tebbs2, christopher mcmahan3 1 statistics, university of nebraska-lincoln, lincoln, nebraska, united states, 2 university of south carolina, columbia, south carolina, united states, 3 clemson university, clemson, south carolina, united states objective to develop specimen pooling algorithms that reduce the number of tests needed to test individuals for infectious diseases wit h multiplex assays. introduction an essential tool for infectious disease surveillance is to have a timely and cost -effective testing method. for this purpose, laboratories frequently use specimen pooling to assay high volumes of clinical specimens. the simplest pooling algorithm empl oys a two-stage process. in the first stage, a set number of specimens are amalgamated to form a “group” that is tested as if it were one specimen. if this group tests negatively, all individuals within the group are declared disease free. if this group tes ts positively, a second stage is implemented with retests performed on each individual. this testing algorithm is repeated across all individu als that need to be tested. in comparison to testing each individual specimen, large reductions in the number of tests occur when overall disease prevalence is small because most groups will test negatively. most pooling algorithms have been developed in the context of single-disease assays. new pooling algorithms are developed in the context of multiplex (multipledisease) assays applied over two or three hierarchical stages. individual risk information can be employed by these algorithms to increase testing efficiency. methods monte carlo simulations are used to emulate pooling and testing processes. these simulations are based on retrospective chlamydia and gonorrhea testing data collected over a two-year period in idaho, iowa, and oregon. for each simulation, the number of tests and measures of accuracy are recorded. all tests were originally performed by the aptima combo 2 assay. sensitivities and specificities for this assay are included in the simulation process. the r statistical software package is used to perform al l simulations. for reproducibility of the research, programs are made available at www.chrisbilder.com/grouptesting to implement the simulations. results reductions in the number of tests were obtained for all states when compared to individual specimen testing. for example, the pooling of idaho female specimens without taking into account individual risk information resulted in a 47% and a 51% reduction in tests when using two and three stages, respectively. with the addition of individual risk information, further reductions in tests occurred. for example, the pooling of idaho female specimens resulted in an additional 5% reduction of tests when compared directly to not using individual risk information. these reductions in tests were found to be related to the type of risk information available and the variability in risk levels. for example, males were found to have much more variability than females. for idaho, this resulted in a 15% further reduction in tests than when not using the risk information. conclusions significant reductions in the number of tests occur through pooling. these reductions are the most significant when individual risk information is taken into account by the pooling algorithm. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e235, 2019 isds 2019 conference abstracts acknowledgement this research is supported by grant r01 ai121351 from the national institutes of health. the authors thank cardea services and the state public health laboratories in idaho, iowa, and oregon for providing access to their testing data. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e412, 2019 isds 2019 conference abstracts syndrome development to assess idu, hiv, and homelessness in ma emergency departments stefanie p. albert, rosa ergas, sita smith, gillian haney, monina klevens bureau of infectious disease and laboratory sciences, massachusetts department of public health, boston, massachusetts, united states objective we sought to measure the burden of emergency department (ed) visits associated with injection drug use (idu), hiv infection, and homelessness; and the intersection of homelessness with idu and hiv infection in massachusetts via syndromic surveillance data. introduction in massachusetts, syndromic surveillance (sys) data have been used to monitor injection drug use and acute opioid overdoses within eds. currently, massachusetts department of public health (mdph) sys captures over 90% of ed visits statewide. these real-time data contain rich free-text and coded clinical and demographic information used to categorize visits for population level public health surveillance. other surveillance data have shown elevated rates of opioid overdose related ed visits, emergency medical service incidents, and fatalities in massachusetts from 2014-2017 [1-3]. injection of illicitly consumed opioids is associated with an increased risk of infectious diseases, including hiv infection. an investigation of an hiv outbreak among persons reporting idu identified homelessness as a social determinant for increased risk for hiv infection. methods to accomplish our objectives staff used an existing mdph sys idu syndrome definition [4], developed a novel syndrome definition for hiv-related visits, and adapted maricopa county's homelessness syndrome definition. syndromes were applied to massachusetts ed data through the cdc’s biosense platform. visits meeting the hiv and homelessness syndromes were randomly selected and reviewed to assess accuracy; inclusion and exclusion criteria were then revised to increase specificity. the final versions of all three syndrome definitions incorporate freetext elements from the chief complaint and triage notes, as well as international statistical classification of diseases and related health problems, 9th (icd-9) and 10th revision (icd-10) diagnostic codes. syndrome categories were not mutually exclusive, and all reported visits occurring at massachusetts eds were included in the analysis. syndromes created for the hiv infection syndrome definition, we incorporated the free-text term “hiv” in both the chief complaint and triage notes. visit level review demonstrated that the following exclusions were needed to reduce misspellings, inclusion of partial words, and documentation of hiv testing results: “negative for hiv”, “hiv neg”, “negative test for hiv”, “hive”, “hivies”, and “vehivcle”. additionally, the following diagnostic codes were incorporated: v65.44 (human immunodeficiency virus [hiv] counseling), v08 (asymptomatic hiv infection status), v01.79 (contact with or exposure to other viral diseases), 795.71 (nonspecific serologic evidence of hiv), v73.89 (special screening examination for other specified viral diseases), 079.53 (hiv, type 2 [hiv-2]), z20.6 (contact with and (suspected) exposure to hiv), z71.7 (hiv counseling), b20 (hiv disease), z21 (asymptomatic hiv infection status), r75 (inconclusive laboratory evidence of hiv), z11.4 (encounter for screening for hiv), and b97.35 (hiv-2 as the cause of diseases classified elsewhere). building on the maricopa county homeless syndrome definition, we incorporated a variety of free-text inclusion and exclusion terms. to meet this definition visits had to mention: “homeless”, or “no housing”, or, “lack of housing”, or “without housing”, or “shelter” but not animal and domestic violence shelters. we also selected the following icd-10 codes for homelessness and inadequate housing respectively, z59.0 and z59.1. we analyzed mdph sys data for visits occurring from january 1, 2016 through june 30, 2018. rates per 10,000 ed visits categorized as idu, hiv, or homeless were calculated. subsequently, visits categorized as idu, hiv, and meeting both idu and hiv syndrome definitions (idu+hiv) were stratified by homelessness. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e412, 2019 isds 2019 conference abstracts results syndrome burden on ed the mdph sys dataset contains 6,767,137 ed visits occurring during the study period. of these, 82,819 (1.2%) were idu-related, 13,017 (0.2%) were hivrelated, 580 (<0.01%) were related to idu + hiv, and 42,255 visits (0.6%) were associated with homelessness. the annual rate of idu-related visits increased 15% from 2016 through june of 2018 (from 113.63 to 130.57 per 10,000 visits); while rates of hiv-related and idu + hiv-related visits remained relatively stable. the overall rate of visits associated with homelessness increased 47% (from 49.99 to 73.26 per 10,000 visits). rates of idu, hiv, and idu + hiv were significantly higher among visits associated with homelessness. among visits that met the homeless syndrome definition compared to those that did not: the rate of idu-related visits was 816.0 versus 118.03 per 10,000 ed visits (x [2]= 547.12, p<0. 0001); the rate of visits matching the hiv syndrome definition was 145.54 versus 18.44 per 10,000 ed visits (x [2]= 99.33, p<0.0001); and the rate of visits meeting the idu+hiv syndrome definition was 15.86 versus 0.76 per 10,000 visits (x [2]= 13.72, p= 0.0002). conclusions massachusetts is experiencing an increasing burden of ed visits associated with both idu and homelessness that parallels increases in opioid overdoses. higher rates of both idu and hiv-related visits were associated with homelessness. an understanding of the intersection between opioid overdoses, idu, hiv, and homelessness can inform expanded prevention efforts, introduction of alternatives to ed care, and increase consideration of housing status during ed care. continued surveillance for these syndromes, including collection and analysis of demographic and clinical characteristics, and geographic variations, is warranted. these data can be useful to providers and public health authorities for planning healthcare services. acknowledgement special thanks to the maricopa county department of public health, and the following massachusetts department of public health bureau of infectious disease and laboratory sciences staff: mark bova, katherine brown, dan church, kevin cranston, alfred demaria, and shauna onofrey. references 1. vivolo-kantor am, seth p, gladden rm, et al. 2018. vital signs: trends in emergency department visits for suspected opioid overdoses — united states, july 2016–september 2017. mmwr morb mortal wkly rep. 67(9), 279-85. doi:https://doi.org/10.15585/mmwr.mm6709e1. pubmed 2. massachusetts department of public health. chapter 55 data brief: an assessment of opioid-related deaths in massachusetts, 2011-15. 2017 august. available from: https://www.mass.gov/files/documents/2017/08/31/data-briefchapter-55-aug-2017.pdf 3. massachusetts department of public health. ma opioid-related ems incidents 2013-september 2017. 2018 feb. available from: https://www.mass.gov/files/documents/2018/02/14/emergency-medical-services-data-february-2018.pdf 4. bova m. using emergency department (ed) syndromic surveillance to measure injection-drug use as an indicator for hepatitis c risk. powerpoint presented at: 2017 northeast epidemiology conference. 2017 oct 18 – 20; northampton, massachusetts, usa. http://ojphi.org/ https://doi.org/10.15585/mmwr.mm6709e1 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29518069&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e412, 2019 isds 2019 conference abstracts rate of idu, hiv, and idu+hiv related visits captured by mdph sys per 10,000 ed visits by year (2016 through 2018*) year idu-related visits hiv-related visits meets idu and hiv definitions (idu+hiv) homeless visits all mdph sys ed visits (n) 2016 113.63 16.89 0.59 49.99 2,367,004 2017 125.19 20.56 0.95 66.88 2,842,048 2018 janjune* 130.57 20.39 1.08 73.26 1,558,085 total 122.38 19.24 0.86 62.44 6,767,137 * data are current as of 8/19/2018 and may be subject to change. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e340, 2019 isds 2019 conference abstracts potential applications of emerging technologies in disease surveillance jay huang, wayne loschen johns hopkins university applied physics laboratory, laurel, maryland, united states objective the objective of this presentation is to explore emerging technologies and how they will impact the public health field. new technologies such as blockchain, artificial intelligence (ai), and the internet of things (iot) will likely be incorporated into epidemiological methods and processes. this presentation will provide an overview of these technologies and focus on how they may impact public health surveillance in the future. introduction with the increase in the amount of public health data along with the growth of public health informatics, it is important for epidemiologists to understand the current trends in technology and the impact they may have in the fi eld. because it is unfeasible for public health professionals to be an expert in every emerging technology, this presentation seeks to provide them with a better understanding of how emerging technologies may impact the field and the level of expertise required to realize benefits from the new technologies. furthermore, understanding the capabilities provided by emerging technologies may guide future training and continuing education for public health professionals. methods analysis of current capabilities and potential advances in emerging technologies such as blockchain, ai, and iot were performed by reviewing articles and whitepapers. in addition to a literature review, interviews will be performed with public health ex perts to determine how the emerging technologies align with current practices and the extent to which they may solve existing public health surveillance challenges. results the literature review revealed many emerging technologies and potential applications in the public health field, includ ing: blockchain blockchains can serve as electronic health information exchanges that hold the metadata and access information for patient electronic health records (ehrs) [1]. these systems can ensure data privacy protections while also facilitate relevant data sharing from ehrs to disease surveillance systems. furthermore, blockchain technology can be used in food supply chain management systems. during food contamination events, epidemiologists can trace through the blockchain to identify possible source s of the contamination [2]. ai ai can be used to improve the prediction and detection capabilities of disease surveillance systems. machine learning algorit hms can reveal patterns in the data and enable faster anomaly detection. furthermore, machine learni ng models can be trained on data to create predictive models. iot urban iot systems can monitor environmental indices including water and air quality, energy consumption, waste management, and traffic congestion in smart cities [3]. the data collected from such systems can be incorporated into more comprehensive disease surveillance systems and assist epidemiologists in better understanding populations and environmental risk factors. we will http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e340, 2019 isds 2019 conference abstracts analyze and discuss such prospective applications with public health professionals to determine their potential impact on public health processes and practices in the next one, five, and ten years. conclusions blockchain, ai, iot and other emerging technologies have applications in public health surveillance and impact the field to varying degrees. in addition to technological advances, there will be barriers to adoption that must be overcome before the value provided by the technologies can be realized. many new technologies will require significant collaboration between public health departments, healthcare providers, and other partners to successfully incorporate the technologies into epidemiological proce sses. these collaborations include forming consortiums to exchange data in a blockchain and working with iot providers for data access. some technologies will require public health professionals to obtain additional training before they can take full advantage of the capabilities provided, while other technologies may be implemented by external partners allowing epidemi ologists to utilize the new capabilities without the need to completely understand the underlying concepts. as emerging technologies are introduced i nto the public health field, a strong understanding of their capabilities and suitable applications will allow public health professionals to fully capture the benefits provided by the new technologies. references 1. ekblaw a, azaria a, halamka jd, lippman a. a case study for blockchain in healthcare:“medrec” prototype for electronic health records and medical research data. inproceedings of ieee open & big data conference 2016 aug 22 (vol. 13, p. 13). 2. yiannas f. 2018. a new era of food transparency powered by blockchain. innov (camb, mass). 12(1-2), 4656. https://doi.org/10.1162/inov_a_00266 3. zanella a, bui n, castellani a, vangelista l, zorzi m. 2014. internet of things for smart cities. ieee internet of things journal. 1(1), 22-32. https://doi.org/10.1109/jiot.2014.2306328 http://ojphi.org/ https://doi.org/10.1162/inov_a_00266 https://doi.org/10.1109/jiot.2014.2306328 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e426, 2019 isds 2019 conference abstracts analytic fusion for essential indicators of the opioid epidemic howard burkom1, joseph downs1, raghav ramachandran1, wayne loschen1, laurel boyd2, matthew laidler2, joshua van otterloo2 1research and exploratory development, johns hopkins applied physics laboratory, baltimore, maryland, united states, 2oregon health authority, public health division, salem, oregon, united states objective in a partnership between the public health division of the oregon health authority (oha) and the johns hopkins applied physics laboratory (apl), our objective was develop an analytic fusion tool using streaming data and report-based evidence to improve the targeting and timing of evidence-based interventions in the ongoing opioid overdose epidemic. the tool is intended to enable practical situational awareness in the essence biosurveillance system to target response programs at the county and state levels. threats to be monitored include emerging events and gradual trends of overdoses in three categories: all prescription and illicit opioids, heroin, and especially high-mortality synthetic drugs such as fentanyl and its analogues. traditional sources included emergency department (ed) visits and emergency management services (ems) call records. novel sources included poison center calls, death records, and report-based information such as bad batch warnings on social media. using available data and requirements analyses thus far, we applied and compared bayesian networks, decision trees, and other machine learning approaches to derive robust tools to reveal emerging overdose threats and identify at-risk subpopulations. introduction unlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. while the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis. recent efforts have produced key terms and phrases in available data sources and numerous userfriendly dashboards allowing inspection of hundreds of plots. the current effort seeks to distill and present combined fusion alerts of greatest concern from numerous stratified data outputs. near-term plans are to implement best-performing fusion methods as an essence module for the benefit of oha staff and other user groups. methods by analyzing historical oha data, we formed features to monitor in each data source to adapt diagnosis codes and text strings suggested by cdc’s injury prevention division, published ems criteria [reference 1], and generic product codes from cdc toxicologists, with guidance from oha emergency services director david lehrfeld and from oregon poison center director sandy giffen. these features included general and specific opioid abuse indicators such as daily counts of records labelled with the “poisoning” subcategory and containing “fentanyl” or other keywords in the free -text. matrices of corresponding time series were formed for each of 36 counties and the entire state as inputs to region-specific fusion algorithms. to obtain truth data for detection, the oha staff provided guidance and design help to generate plausible overdose threat scenarios that were quantified as realistic data distributions of monitored features accounting for time delays and historical distributions of counts in each data source. we sampled these distributions to create 1000 target sets for detection based on the event duration and affected counties for each event scenario. we used these target datasets to compare the detection performance of fusion detection algorithms. tested algorithms included bayesian networks formed with the r package grain, and also random forest, logistic regression, and support vector machine models implemented with the python scikit-learn package using default settings. the first 800 days of the data were used for model training, and the last 400 days for testing. model results were evaluated with the metrics: sensitivity = (number of target event days signaled) / (all event days) and positive predictive value (ppv) = (number of target event days signaled) / (all days signaled). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e426, 2019 isds 2019 conference abstracts these metrics were combined with specificity regarded as the expected fusion alert rate calculated from the historical dataset with no simulated cases injected. results the left half of figure 1 illustrates a threat scenario along oregon’s i5 corridor in which string of fentanyl overdoses with a few fatalities affects the monitored data streams in three counties over a seven-day period. the right half of the figure charts the performance metrics for random forest and bayesian network machine learning methods applied to both training and test datasets assuming total case counts of 50, 20, and 10 overdoses. sensitivity values were encouraging, especially for the bayesian networks and even for the 10-case scenario. computed ppv levels suggested a manageable public health investigation burden. conclusions the detection results were promising for a threat scenario of particular concern to oha based on a data scenario deemed plausible and realistic based on historical data. trust and acceptance from public health surveillance of outputs from supervised machine learning methods beyond traditional statistical methods will require user experience and similar evaluation with additional threat scenarios and authentic event data. credible truth data can be generated for testing and evaluation of analytic fusion methods with the advantages of several years of historical data from multiple sources and the expertise of experienced monitors. the collaborative generation process may be standardized and extended to other threat types and data environments. next steps include the addition to the analytic fusion capability of report-based data that can influence data interpretation, including mainstream and social media reports, events in neighboring regions, and law enforcement data. acknowledgement we would like to acknowledge the hopkins apl internal research and development program and substantial support from the following programs within the oregon public health division: emergency medical services and trauma systems program, center for health statistics, acute and communicable disease prevention program, and injury and violence prevention program along with support from the oregon poison center, the national poison data system and the injury prevention & control center at the centers for disease prevention and control. references 1. rhode island enhanced state opioid overdose surveillance (esoos) case definition for emergency medical services (ems), http://www.health.ri.gov/publications/guidelines/esooscasedefinitionforems.pdf, last accessed: sept. 9, 2018. figure 1: schematic depicting overdose scenario randomized for monte carlo trials, with detection performance metrics tabulated for trials of 50, 20, and 10 cases over a 7-day period. http://ojphi.org/ http://www.health.ri.gov/publications/guidelines/esooscasedefinitionforems.pdf isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e327, 2019 isds 2019 conference abstracts spatio-temporal analysis of highly pathogenic avian influenza outbreaks in ghana. esther n. dsani1, 2 1 veterinary services, ministry of food and agriculture, ghana, accra, greater accra, ghana, 2 school of public health, university of ghana, accra, ghana objective the purpose of the study was to characterize the spatial distribution and temporal patterns of laboratory confirmed h5n1 outbreaks from january 2007 to december 2017 in ghana. introduction highly pathogenic avian influenza (hpai) subtype h5n1 virus causes a highly contagious disease in poultry with up to 100% mortality and occasionally causes sporadic human infection. the first outbreak of hpai h5n1 in africa was reported in nigeria in 2006 and has since been reported in seven other african countries with confirmed human cases and outbreaks in poultry. since the emergence of highly pathogenic avian influenza (hpai), virus subtype h5n1 in ghana in 2007, outbreaks in poultry have led to dire economic consequences for the poultry sector, resulting from mass destruction of affected flocks. an economy heavily dependent on agriculture, the persistence of outbreaks threaten the livelihood of farmers who depend on poultry production for survival. despite significant efforts made in hpai-h5n1 control and prevention in ghana, outbreaks persist and continue to spread to new areas. it is uncertain to what extent different pathways contribute to the introduction and the dissemination of the virus in ghana. there is a need to understand the complex nature of the interactions between local and migratory fowl, the risk of transmissi on due to human endeavor and trade mechanisms that increase the likelihood of hpai-h5n1 outbreaks in ghana. methods data for the study was sourced from national outbreak records at the veterinary services directorate. the study analyzed outbreak data for the years 2007-2017. data retrieved from outbreak reports included the date of onset of outbreak, location and geographic coordinates, type and number of poultry species affected, natural deaths of birds and type of farming system on outbreak farms. we calculated frequency distributions for the types of poultry species affected, the type of farming system and mortality rates on affected premises. we described the distribution of hpai-h5n1 outbreaks using coordinate maps in arcgis and displayed relevant sites of waterfowl and wild bird habitation. to describe the temporal pattern of hpai-h5n1 outbreaks in ghana for the period, we created an epidemic curve by plotting the monthly number of outbreaks for the period january 2007 to december 2017 in excel. we used space -time scan statistics to determine significant local clusters. results a total of sixty-six (66) outbreaks of hpai-h5n1 occurred in ghana from january 2007 to december 2017. the outbreak sites were distributed in seven (7) out of ten (10) regions in ghana. the affected regions are located in the southern and middle belt of ghana. most of the outbreaks (74.2%) occurred in densely populated areas of the greater accra region. overall, layer flocks were mostly affected with 56% of affected premises constituting layer farms. commercial farms and backyard farms made up the majority of affected farms (50% and 42.4%). free ranging birds were the least affected farm type (7.6%). two epidemic waves were identified for h5n1 in ghana; the first wave with 6 outbreaks, lasted a period of four (4) months from april to july 2007, and the second with 60 outbreaks, spanned a period of 2 years from april 2015 to november 2016. temporal distribution of the outbreaks showed that the outbreak peaked in may 2007 for the first wave and in july 2017 for the second wave with minor peak s observed in april and july 2016. the decrease in the number of the outbreaks after july in both waves is attributed to the onset of http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e327, 2019 isds 2019 conference abstracts slaughter and trade restrictions for poultry in affected areas. space-time scan statistics identified significant primary clusters of h5n1 outbreaks in the coastal belt of the greater accra region, characterized by major commercial activities and the presence of wetlands of relevance to wild birds and migratory waterfowl. conclusions two (2) major waves of h5n1 outbreaks occurred in ghana between 2007 and 2017. the distribution of outbreaks and poultry species in both waves, show that the epidemiology of h5n1 virus in ghana is changing. the findings highlight the importance of reviewing existing control and preventive measures as well as strengthening avian influenza surveillance in proposed high risk areas. acknowledgement we thank dr. helena acquah and dr. fenteng danso, veterinary epidemiologists of the epidemiology unit at the veterinary services directorate of ghana for their support. references foreign animal diseases. revised 2008 seventh edition. committee on foreign and emerging diseases of the united states animal health association. avian influenza, oie terrestrial manual 2015 to kkw, et al. 2012. avian influenza a h5n1 virus: a continuous threat to humans. emerg microbes infect. 1(9), e25. pubmed https://doi.org/10.1038/emi.2012.24 watanabe y, ibrahim ms, suzuki y, ikuta k. 2012. the changing nature of avian influenza a virus (h5n1). trends microbiol. 20(1), 11-20. pubmed https://doi.org/10.1016/j.tim.2011.10.003 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26038430&dopt=abstract https://doi.org/10.1038/emi.2012.24 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22153752&dopt=abstract https://doi.org/10.1016/j.tim.2011.10.003 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e299, 2019 isds 2019 conference abstracts use of essence apis to support flexible analysis and reporting aaron kite-powell1, wayne loschen2 1 nssp, cdc, atlanta, georgia, united states, 2 johns hopkins university applied physics laboratory, laurel, maryland, united states objective to describe and provide examples of the electronic surveillance system for the early notification of community-based epidemics (essence) application programming interface (api) as a part of disease surveillance workflows. introduction the essence application supports users’ interactive analysis of data by clicking through menus in a user interface (ui), and provides multiple types of user defined data visualization options, including various charts and graphs, tables of statistical alerts, table builder functionality, spatial mapping, and report generation. however, no ui supports all potential analysis and visualization requirements. rapidly accessing data processed through essence using existing access control mechanisms, but de-coupled from the ui, supports innovative analyses, visualizations and reporting of these data using other tools. methods the essence api gives users the ability to query essence data and functionality via a representational state transfer (rest) web api designed to use https protocol. as with logging into the essence application normally, use of the api also requires users to authenticate with their username and password by including it in the code. this makes programmatic interfaces with the application possible whereby a tool or program makes a request to the api endpoint and the api returns the result of the quer y in a structured form. the essence api is a collection of endpoints that return different sets of data, including essence time series graphs, time series data, data details data, aggregated data created using the table builder functionality, number of unique facilities or regions (i.e. counties) reporting for a query, and results from the detector algorithms and alert list. all of the query p arameter information is stored in the api url, which the user can create programmatically or by first creating their query from within essence, and then clicking the “api url” to generate the necessary url. api results are generally available in both json and csv formats. results epidemiologists in the cdc nssp have developed r code that uses these apis to create customized rmarkdown reports and visualizations not possible within the essence application, as well as to automate extraction of data from essence to support routine reporting for other cdc program areas (e.g., influenza-like illness, and suspected opioid encounters). anecdotally, some sites utilize the api to populate publically facing dashboards with aggregated data from essence. programmatic access to processed essence data via the apis also supports easily sharable exploratory analysis and visualization that can serve as a sandbox for testing new methods for future inclusion within essence. conclusions the development and use of the essence apis in public health surveillance will support more efficient and timely access to machine-readable data de-coupled from point and click user interfaces, and has the potential to spur new and innovative ways of using data that has traditionally been less programmatically accessible to users. new tools and programs can leverage the dat a in web or mobile applications, traditional reports, and more easily integrate disparate data sources for comprehensive surveillance. http://ojphi.org/ isds annual conference proceedings 2018. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. isds 2018 conference abstracts study of the mortality of vaccine-preventable infections in ukraine (1965– 2015) hennadii mokhort*, alina kovalchuk and roman rodyna epidemiology, bogomolets national medical university, kyiv, ukraine objective the aim of this work was to determine the impact of vaccination on the dynamics of mortality and the contribution of vaccine preventable infections to the structure of total infectious mortality of the population of ukraine over the past 50 years to develop a more effective system of surveillance for this group of infections. introduction infectious diseases are still the cause of a large number of deaths in ukraine. analysis of infectious mortality allows the study of the dynamics of diseases that pose the greatest danger. in particular, those that are vaccine-preventable and suggest more effective methods for organizing an epidemic surveillance system. methods this work describes a retrospective population epidemiological study. the material for the statistical analysis was taken from the statistical form c-8 “distribution of deceased by sex, age groups and causes of death” of the ukrainian center for disease control and monitoring of the moh of ukraine for the period 1965-2015. this work analyzed the mortality dynamics of 1965, 1991 and 2015, which correspond to the firstly achieved 90-95% vaccination coverage against diphtheria, whooping cough, tetanus and poliomyelitis (1965), the first year of ukraine’s independence (1991), after its separation from the soviet union and the end of the study period (2015). results our data shows the difference in the number of deaths from all vaccine-preventable and non-vaccine-preventable infections in 1965, 1991 and 2015 among the total population, children aged 0-14 years and in the age group 15 years and older. we also have data on the proportion of some infections in the nosological structure and total infectious mortality. the proportion of all infectious diseases decreased in the total number of deaths from 7.47% in 1965 to 1.53% in 1991 and 2.51% in 2015. the proportion of deaths from all infections was significantly higher in the overall structure of child mortality. it was 39.4% in 1965 and 7.25% in 2015. almost the same decrease of proportion is demonstrated by all non-vaccine-preventable infections. the proportion of all vaccine-preventable infections (diphtheria, tetanus, whooping cough, poliomyelitis, hepatitis b, tuberculosis) in the total number of deaths decreased from 3.77% in 1965 to 0.85% in 2015. the decrease in the proportion of children deaths from vaccinepreventable infections was from 2.12% in 1965 to 0.35% in 2015. there is a 2.6-fold decrease in the total number of deaths from all vaccine-preventable infections among the general population, but for the children’s population the reduction rate in 2015 compared to 1965 was 31.2 times. in the context of infant infectious mortality, vaccine-preventable infections (inclusive of tuberculosis) were 5.39% in 1965 and 4.8% in 2015. potentially vaccine-preventable infections (pneumonia, meningococcal infection, influenza and other acute upper respiratory infections) demonstrated a child mortality rate of 80.52% in 1965 and 60.17% in 2015, and the number of deaths from these infections among children in 2015 was 37.3 times less than in 1965. conclusions collection of data on infectious diseases mortality should be included into the epidemiological surveillance system. decrease in mortality from non-vaccine-preventable infections may indicate a significant impact of natural, demographic and economic factors that can influence the decrease in mortality from vaccine-preventable diseases too. in ukraine, vaccination of certain infections certainly had and in the future will also have an important value for controlling infectious incidence and mortality. moreover, vaccination continues to be the most accessible and effective intervention for achieving global or regional eradication of infections. keywords vaccine-preventable infections; mortality; vaccination *hennadii mokhort e-mail: hennadii.mokhort@nmu.ua online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 10(1):e188, 2018 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e385, 2019 isds 2019 conference abstracts evaluation of a acute febrile illness surveillance network (gafinet), south korea. heeyoung lee1, yeon-hee sung2, kyoung-ho song1, yang lee kim3, jeong yeon kim4, jieun kim5, won suk choi6 1 center for preventive medicine and public health, seoul national bundang hospital, seongnam, korea (the republic of), 2 gidcc, seongnam, korea (the republic of), 3 3the catholic univ. of korea uijeongbu mary’s hospital, uijeongbu-si, korea (the republic of), 4 4gpmc(gyeonggi provincial medical center,, suwon, korea (the republic of), 5 hanyang university college of medicine, seoul, korea (the republic of), 6 korea university college of medicine, ansan, korea (the republic of) objective the purpose of this study is to describe and evaluate the results of the gafinet(gyeonggi acute febrile illness surveillance network) operated for one year. introduction after mers outbreak in 2015, the provincial government and infectious disease control center (gidcc) initiated an emergency department (ed) based gyeonggi-do provincial acute febrile illness (afi) surveillance network (gafinet) to monitor for a subsequent outbreak of emerging or imported infectious diseases since september 2016. following pilot operation from september to december 2016, the operation was run for one year from june 2017 to may 2018. gafinet initiative involves ten hospitals, consisted of four university-affiliated hospitals and six provincial medical centers in gyeonggi-do province. these hospitals participated in this network voluntarily. methods periodic surveillance for finding afi patients in ed of participating hospitals was performed prospectively (figure 2). afi was defined as 1) fever: body temperature ≥38 °c at admission, or 2) chief complaint of febrile or chilling sensation. demography of patients and chief complaints were investigated in this first step. cases were classified into six categories based on their clinical diagnoses: 1) respiratory afi [afri], 2) gastroenteric afi [afgi], 3) exanthematic afi [afei], 4) other infectious afi, 5) noninfectious afi, and 6) unclassified afi. participating infectious diseases specialists regularly reviewed and reformed this classification via web based system. nosocomial afi cases or the patients transferred from another hospital were excluded. when a patient had a history of international travel or he/she were undiagnosed in three days after ed admission, more comprehensive information including history and final diagnosis were obtained. for a baseline data, ageand sex-stratified ed visits were also gathered weekly. the proportion of afi cases per 1000 visits was determined for one week period and analyzed by febrile diseases categories with age-stratification. characteristics of cases with international travel histories or undiagnosed cases were also described separately. the results were presented to participating researchers as visualized dashboards in web-based systems. also we compared the trend and peak time of gafinet data with the surveillance data of kcdc for influenza, hand foot and mouth disease, and tsutsugamushi disease. results the total number of patients in the emergency room in 10 hospitals was 366,695 for one year. among them, 40,897 patients were diagnosed with acute febrile illness (11.2%). 47.8% were under 10 years of age, and 508 1,769 patients were fever related patients per weak. the number of patients with foreign travel was 0 11 in each week, and the number of patients with unknown fever in the week was 0 6. 1 to 9 years of age accounted for the largest proportion (27.6% 48.3%) of acute febrile patients. the most common infectious diseases were acute febrile respiratory disease, and other acute febrile infections and acute febrile gastrointestinal disease are http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e385, 2019 isds 2019 conference abstracts followed. in acute febrile respiratory disease and acute febrile illness, seasonal pattern was seen. a total of 157 patients with acute febrile illnesses with overseas travel ability, 0 11 persons per week, consistently occurred. acute gastroenteritis (age) was the most frequent diagnosis in 28 patients (17.8%), fever of unkonwn origin(fuo) was 25(15.9%), acute pharyngotonsilitis 17(10.8%), viral infection 17 (10.8%), influenza 7 (4.5%) and colitis 5 (3.2%). the cause of fever was not found in 30 of the acute febrile patients with overseas travel ability. a total of 77 patients with acute febrile disease were admitted to the hospital. in comparison with national surveillance data, the trend of occurrence of influenza in gafinet and national data was similar. both data peaked in the first week of 2018. also trend of hand-foot and mouth disease(hfmd) was similar in two data. but peak weak was little different in both data(cdc at 30 weeks, gafinett at 29 weeks in 2017). in gafinet, the final diagnosis was confirmed as tsutsugamushi disease in total 43 cases. the number of reported cases was small, and the epidemic peaked at the same time as the cdc surveillance data, but the outbreak occurred in the same period (october to november). conclusions gafinet has both the characteristics of indicator based surveillance and event based surveillance. data were collected over a period of one year to examine the feasibility and applicability of the indicator based surveillance. in comparison with national surveillance data, some feasibility was verified by similar trends, but the necessity of operating regional surveillance data still needs to be discussed. it should also be noted that some diseases have different peak times of one week. the role of event based surveillance is mainly aimed at surveillance of fever patients with international travel history. the advantage of the gafinet's classification system in the emergency room of participating hospitals is that the patient's cases of fever were constantly monitored. however, there is still a limit to the lack of budget and manpower in the samping and labaratory test. real time visual feedback of surveillance data helped to increase participation and discuss the results. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e399, 2019 isds 2019 conference abstracts neonatal tetanus surveillance in bayelsa state of nigeria: a five-year review abisoye s. oyeyemi, hilda c. afakwu, esievoadje akpofure, luke e. izibekien community medicine, niger delta university, yenagoa, bayelsa, nigeria objective to assess the performance of neonatal tetanus surveillance in bayelsa state of nigeria. introduction neonatal tetanus (nt) though a preventable disease, remains a disturbing cause of neonatal morbidity and mortality particularly in low income countries where maternal and child care are substandard and antitetanus immunization coverage is still poor. the disease, which is mostly fatal, is particularly common in hard to reach and rural areas where deliveries take place at home or with untrained attendants without adequate sterile procedures and in unclean environment. since eliminating nt became a global tar get, significant reductions in nt deaths have been reported. the most recent estimates by who (2015) put death of newborns due to nt at 34,019, a 96% reduction from the situation in the late 1980s. all countries are committed to “elimination” of maternal and neonatal tetanus (mnt), i.e., a reduction of nt incidence to bel ow one case per 1000 live births per year in every district. a strong neonatal tetanus surveillance (nts) is however required to achi eve this. as of march 2018, only 14 countries were yet to eliminate mnt and this includes nigeria. the different types of nts recommended are conducted to varying degrees of efficiency and effectiveness in nigeria under the major surveillance strategy – the integrated disease surveillance and response (idsr). these include routine monthly surveillance, zero reporting, active surveillance and retrospective record review. nigeria comprises six geopolitical zones, 36 states and a federal capital territory (fct), and is made up of 774 local government areas (lga) (districts) – an lga being the lowest administrative level. this study was conducted in bayelsa state – one of the six states in the south zone. it is made up of eight lgas, more than half of which are riverine and consists of many hard-to-reach communities, where formal functional health facilities are few and far between. health workers are in short supply and fundin g of health care delivery is poor in the state. methods this was a retrospective review of all confirmed cases of neonatal tetanus that were managed at the two tertiary hospitals in the state niger delta university teaching hospital okolobiri (nduth), and federal medical centre yenagoa (fmc) between january 2009 and december 2013. these were the only two public facilities that had the capacity to manage nt cases in the state. relevant data including sociodemographics, pregnancy and birth history of patients, cord care and tetanus toxoid immunization of mothers were abstracted from the case files. the cases were traced to the office of the state epidemiologist, where all cases were expected to be documented and investigated in line with the existing neonatal tetanus surveillance. ethical approval was obtained from the research and ethics committee of nduth for the research and permission was given to access case files. results a total of 48 cases were managed in both facilities (36/75.0% in nduth and 12/25.0% in fmc) in the period under review but only 13 cases (27.1%) were reported to the office of the state epidemiologist. figure 1 shows the number of cases per year of review. the cases were resident in seven out of the eight lgas. the mean age of cases was 8.98 (sd = 5.14) days and 29 (60.4%) were male while 19 (39.6%) were female. available evidence showed that only 2.1% of the cases were protected at birth (mothers had tt2+); 91.7% of mothers did not have antenatal care and all the mothers were delivered by traditional birth attendants; 7 0.8% had their umbilical cord cut with new (?sterile) blade; and 43.8% had their cord treated with methylated spirit, others were treated with just water or some herbal preparation. educational attainment of mothers of cases was primary (54.2%) and secondary (45. 8%). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e399, 2019 isds 2019 conference abstracts conclusions there were gaps in neonatal tetanus surveillance in bayelsa state as only 27.1% of cases were captured at the state level. many mothers and their newborns were still not protected against tetanus, and delivery and cord care were done in unhygienic conditions. there is an urgent need to strengthen nt surveillance, improve vaccination against tetanus, and encourage skilled birth attenda nce in the state. acknowledgement we thank the staff of the hospitals and that of the office of the state epidemiologist who facilitated data collection. references 1. who. immunization, vaccines and biologicals: tetanus. http://www.who.int/immunization/diseases/tetanus/en/. accessed on 23 jul 2018 2. who. immunization, vaccines and biologicals: maternal and neonatal tetanus elimination (mnte): the initiative and challenges.http://www.who.int/immunization/diseases/mnte_initiative/en/ accessed on 23 jul 2018. 3. who. who-recommended standards for surveillance of selected vaccine-preventable disease. who. 2003 4. bayelsa state ministry of health. health facilities and their distribution across the local government areas of bayelsa state. 2010. figure 1. number of cases seen at the facilities and reported to the office of the state epidemiologist http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e248, 2019 isds 2019 conference abstracts influenza laboratory testing and its application in timely department of defense biosurveillance jessica f. deerin, paul e. lewis armed forces health surveillance branch, alexandria, virginia, united states objective to describe influenza laboratory testing and results in the military health system and how influenza laboratory results may b e used in dod electronic surveillance system for early notification of community-based epidemics (essence) introduction timely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. dod essence has the unique advantage of ingesting multiple data sources from the military health system (mhs), including outpatient, inpatient, and emergency department (ed) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. the influenza-like illness (ili) syndrome definition includes icd-9 or icd-10 codes that may increase the number of false positive alerts. laboratory-confirmed influenza data provides an increased positive predictive value (ppv). the gold standard for influenza testing is molecular assays or viral culture. however, the tests may take 3-10 days to result. rapid influenza diagnostic tests (ridts) have a lower sensitivity, but the timeliness of receiving a result improves to within <15 minutes. we evaluate the utility of ridts for routine ili surveillance. methods administrative medical encounters for ili and influenza laboratory-confirmed data were analyzed from the mhs from june 2013 – september 2017 (figure 1). the medical encounters and laboratory data include outpatient, inpatient, and ed data. the ili syndrome case definition is a medical encounter during the study period with an icd-9 or icd-10 codes in any diagnostic position (icd-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; icd-10 codes = b97.89, h66.9, j00, j01.9, j06.9, j09, j09.x, j10, j10.0, j10.1, j10.2, j10.8, j11, j11.0, j11.1, j11.2, j11.8, j12.89, j12.9, j18, j20.9, j40, r05, r50.9). the ili dataset was limited to care provided in the mhs as laboratory data is only available for dir ect care. we describe influenza laboratory testing practices in the mhs. we aggregated the ili encounters and ridt positive results into daily counts and generated a weekly pearson’s correlation. results influenza tests are ordered throughout the year; the mean weekly percentage of ili encounters in which an influenza laborator y test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. the mean weekly percentage of positive influenza laboratory results among all ili encounters is 0.82%, with a range from 0.01% to 5.73% ( figure 2). the percent of ili encounters in which a test is ordered increases as the influenza season progresses. influenza laborator y tests conducted in the mhs include ridts, pcr, culture, and dfa. among all influenza tests ordered in the mhs, 66.0% were ridts, 22.7% were pcr, and 11.3% were viral culture. often, a confirmatory test is ordered following a ridt; 20% of ridts have follow-up tests. the mean timeliness of influenza test result data in the mhs was 11.26 days for viral culture, 2.94 days for pcr, and 0.11 days for ridts. the ridt results were moderately correlated with ili encounters for the entire year (mean weekly pearson correlation coefficient rho=0.60, 95% ci: 0.55, 0.66, figure 3). during the influenza season, the mean weekly pearson correlation coefficient increases to rho=0.75, 95% ci: 0.70, 0.79. conclusions the dod has the unique advantage of access to the electronic health record and laboratory tests and results of all mhs beneficiaries. this analysis provides evidence for increased utilization of positive ridts in essence. the moderate correlation between the ili syndrome and positive ridts may be associated with icd-10 codes included in the ili syndrome definition that contribute to false positive influenza cases. ongoing research is focused on improving this ili syndrome definition using icd -10 codes. rapid influenza diagnostic tests provide more timely results than other influenza test types. in conjunction with ili medical encounter http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e248, 2019 isds 2019 conference abstracts data, positive ridt data provides a more complete and timely picture of the true burden of influenza on the mhs population for early warning of influenza outbreaks. acknowledgement the views expressed in this article are those of the author and do not reflect the official policy or position of the department of defense or the u.s. government. support was provided by the armed forces health surveillance branch of the public health division at the defense health agency. figure 1. figure 2. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e248, 2019 isds 2019 conference abstracts figure 3. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e325, 2019 isds 2019 conference abstracts environmental surveillance and vaccine derived polio virus type 2 isolation, gombe state, nigeria. raymond s. dankoli world health organization (w.h.o), gombe, gombe, nigeria objective to evaluate vaccine derived polio virus 2 isolation rate from environmental surveillance and its contribution to polio eradication initiative (pei) introduction nigeria is the only country in africa yet to be certified free of wild polio virus (wpv). the country consists of 36 states a nd a federal capital territory. gombe is one of the 19 polio high risk states in the north-eastern geo-political zone of the country. the last case of wpv isolated in gombe state was in 2013. one of the strategies for polio eradication is a sensitive acute flaccid paralysis (afp) surveillance system in which any afp is promptly detected and timely investigated. the focus of the investigation is to analyze two faecal samples of the patient, and/or sometimes those from contacts for any possible isolation of polio virus [1] (pv). afp surveillance is meant to be applicable to any human population at any time; however, there are situations in which there are good reasons to suspect that negative results of afp surveillance are not reliable. supplementary information is required in such situations and one approach for that is environmental surveillance (es), in which a search for pv is made in environmental specimens contaminated by human feaces [2] es in the african region started in nigeria in july 2011 [3,4]. since the introduction of this strategy, it has achieved its objective of complimenting the afp surveillance system. there has been a gradual increase in the number of es sites in nigeria from 2011 to date4. the increase is largely due to the successes recorded in terms of the pv isolation from the sites, pv epidemiology, th e large population size and mobility [4,5]. the last cases of wpv1 and wpv3 from environmental samples had dates of collection in may 2014 (kaduna) and july 2012 (kano) respectively [4]. es was initiated in gombe state in december 2016. four es sites were identified and sample collection began soon afte r training of personnel responsible for collection of the sewage sample. the four identified es sites are baba roba valley, unguwauku railway bridge, gadan bayan moonshine and dan gusau bridge. since inception of es in gombe state, 2 ambiguous vaccine derived poliovirus type 2 (avdpv2) were confirmed from sewage samples collected from baba roba valley site on the 30th january 2017 and from dan gusau bridge site on the 6th march 2017. in 2018, a circulating vaccine derived poliovirus type 2 (cvdpv2) was also detected from sewage samples collected on the 9th april 2018 from baba roba valley site. we reviewed the laboratory results from the 2 surveillance methods so as to evaluate the vdpv2 isolation rate. methods es involves collection of one litre of environmental sample (sewage water) via grab sampling method in accordance with world health organization’s (who) guidelines for environmental surveillance for polioviruses [2]. all es sewage samples were transported in a 1 litre container appropriately packaged in a giostyle with 8 frozen icepacks to maintain reverse cold chain to a polio laboratory where the samples are analyzed as per who es testing standard operating procedures. poliovirus type 2 isolat es are sent to the reference laboratory at the us centre for disease control for sequencing for pv isolation. we reviewed all the results of the environmental samples (es) and stool samples from patients with acute flaccid paralysis (afp) from january 2017 to june 2018. the environmental samples were from five pre-selected sites that was based on the perceived risks for polio circulation that included poor sanitation, overcrowding, extend of drainage population, availability of sewage system and absence of discharge into the sites. the stool samples were from patients detected with afp in gombe local government area. the results from the two methods of surveillance for pv were evaluated and compared based on yields and isolates (negative results, vdpv2, non-polio enterovirus (npent). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e325, 2019 isds 2019 conference abstracts results a total of 309 sewage samples from five [5] sites and 142 afp stool samples from gombe lga were collected from january 2017 to june 2018. three 3(0.97%) of the sewage samples yielded vdpv2, 102(33.01%) had non-polio enteroviruses (npent) and 41 (13.27%) negative samples. on the other hand, no vdpv was isolated from the afp stool samples, the npent detection rate was 13(9.16%) and 121(85.21%) samples were negative. the non-polio afp (npafp) and stool adequacy rates for gombe lga during the reporting period were calculated to be 17.2 and 100% respectively. conclusions the polio virus (vdpv) isolation from es in this review is higher than in afp surveillance. this has demonstrated amongst others benefit of es its ability to detect polio virus even in the absence of the virus among afp cases. es can thus detect virus that are probably missed by afp surveillance and hence allow for early response so as to curtail further transmission. the high npafp and stool adequacy rates are indication of a sensitive surveillance system nonetheless, the virus isolation from the afp surveillance was very low. it is important to mention here that other laboratory indicators were not factored into this review. we recomme nd therefore that both es and afp surveillance be done together where facility, resources and personnel are available to implement acknowledgement we sincerely acknowledge the gombe state ministry of health, gombe state primary health care development agency, all sample collectors and state data assistance w.h.o gombe office for their support and information sharing during this review. references 1. who. field guide for supplementary activities aimed at achieving polio eradication, publication no. who/ epi/gen/95.1. geneva: world health organization, 1995. 2. who. guidelines for environmental surveillance of poliovirus circulation. world health organization 2003, department of vaccines and biologicals, 2003. (http://www.who.int/vaccines-documents/doxgen/h5-surv. htm). accessed 6 october 2010. 3. gumede n, et al. 2015. status of environmental surveillance in the african regional. afr health monit. (19), 38-41. 4. muluh tj, et al. 2016. contribution of environmental surveillance toward interruption of poliovirus transmission in nigeria, 2012–2015. j infect dis. 213(suppl 3), s131-35. https://doi.org/10.1093/infdis/jiv767 pubmed 5. asghar h, et al. 2014. environmental surveillance for polioviruses in the global polio eradication initiative. j infect dis. 210(suppl 1), s294-303. doi:https://doi.org/10.1093/infdis/jiu384. pubmed table 1. summary of result from five (5) es sites and afp stool samples in gombe lga january 2017 – june 2018 name of es site no. of afp cases no. of sewage/ stool samples collected vdpv2 sabin npent negative sabin + npent sabin 2 npafp rate % stool adequacy baba roba valley 66 2 14 18 3 16 13 dan gusau bridge 65 1 12 21 10 9 12 unguwauku railway bridge 64 0 4 27 12 10 11 gadan bayan moonshine 65 0 18 17 5 14 11 unguwan bari bari 49 0 7 19 11 7 5 http://ojphi.org/ https://doi.org/10.1093/infdis/jiv767 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26908747&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26908747&dopt=abstract https://doi.org/10.1093/infdis/jiu384 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25316848&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e325, 2019 isds 2019 conference abstracts es total (%) 309 3 (1.0) 55(17.8) 102(33.0) 41(13.3) 56(18.1) 52(16.8) afp stool samples (%) 71 142 0 (0) 8 (5.6) 13(9.2) 121 (85.2) 0 (0) 0 (0) 17.2 100 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e370, 2019 isds 2019 conference abstracts a novel method for rapid mapping of the spatial intensity of influenza epidemics david j. muscatello1, robert n. leong1, robin m. turner2, anthony t. newall1 1 school of public health and community medicine, unsw sydney, unsw sydney, new south w ales, australia, 2 university of otago, dunedin, new zealand objective using the epidemic of influenza type a in 2016 in australia, we demonstrated a simple but statistically sound adaptive method of automatically representing the spatial intensity and evolution of an influenza epidemic that could be applied to a laboratory surveillance count data stream that does not have a denominator. introduction surveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness. mapping epidemics can be challenging because influenza infections are incompletely ascertained, ascertainment can vary spatially, and often a denominator is not available. rapid, more refined geographic or spatial intelligence could facilitate better preparedness and response. methods weekly counts of persons with laboratory confirmed influenza type a infections in australia in 2016 were analysed by 86 substate geographical areas. weekly standardised epidemic intensity was represented by a z-score calculated using the standard deviation of below-median counts in the previous 52 weeks. a geographic information system was used to present the epidemic progression. results there were 79,628 notifications of influenza a infections included. of these, 79,218 (99.5%) were allocated to a geographical area. the maps indicated areas of elevated epidemic intensity across australia by week and area, that were consistent with the observed start, peak and decline of the epidemic when compared with weekly counts aggregated at the state and territory level. an exam ple is shown in figure 1. conclusions the methods could be automated to rapidly generate spatially varying epidemic intensity maps using a surveillance data stream. this could improve local level epidemic intelligence in a variety of settings and for other diseases. it may also increase our understanding of geographic epidemic dynamics. acknowledgement we thank the australian department of health for providing the influenza laboratory data used in the study. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e370, 2019 isds 2019 conference abstracts figure 1. example map as at 9 september 2016, showing intensity by area during the peak week of the season. insets show capital cities http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e260, 2019 isds 2019 conference abstracts real-time monitoring of a mass k2-related overdose outbreak – connecticut, 2018 kristen soto, erin grogan, alexander senetcky, susan logan epidemiology, connecticut department of public health, hartford, connecticut, united states objective to describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance k2. introduction on august 15, 2018, the connecticut department of public health (dph) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid k2. abuse of k2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. this investigation aimed to characterize the use of syndromic surveillance da ta to monitor a cluster of suspected overdoses in real time. methods the epicenter syndromic surveillance system collects data on all emergency department (ed) visits at connecticut hospitals. e d visits associated with the event were identified using ad hoc keyword analyses. the number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. gender, age, and repeated ed visits were assessed. after the event, surveillance findings were summarized for partners results during the period of august 15–16, 2018 the number of ed visits with a mention of k2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of march–july, 2018. an additional 25 ed visits were identified using other related keywords (e.g., weed). after the event, 72 ed visits were identified with k2 and location keywords in the chief complaint or triage notes. these 72 visits comprised 53 unique patients, with 12 patients returning to the ed 2–5 times over the two day period. of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). surveillance findings were shared with partners in real time for situational awareness, and in a summary report on august 21. conclusions data from the epicenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. next steps related to this event involve: monitoring data for reference to areas of concentra ted substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real -time communication with stakeholderswithin dph and with external partners during events. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e440, 2019 isds 2019 conference abstracts monitoring suicide-related events using national syndromic surveillance program data marissa l. zwald, kristin m. holland, francis annor, aaron kite-powell, steven a. sumner, daniel bowen, alana m. vivolo-kantor, deborah m. stone, alex e. crosby cdc, atlanta, georgia, united states objective to describe epidemiological characteristics of emergency department (ed) visits related to suicidal ideation (si) or suicidal attempt (sa) using syndromic surveillance data. introduction suicide is a growing public health problem in the united states [1]. from 2001 to 2016, ed visit rates for nonfatal self-harm, a common risk factor for suicide, increased 42% [2-4]. to improve public health surveillance of suicide-related problems, including si and sa, the data and surveillance task force within the national action alliance for suicide prevention recommended the use of real-time data from hospital ed visits [5]. the collection and use of real-time ed visit data on si and sa could support a more targeted and timely public health response to prevent suicide [5]. therefore, this investigation aimed to monitor ed visits for si or sa and to identify temporal, demographic, and geographic patterns using data from cdc’s national syndromic surveillance program (nssp). methods cdc’s nssp data were used to monitor ed visits related to si or sa among individuals aged 10 years and older from january 1, 2016 through july 31, 2018. a syndrome definition for si or sa, developed by the international society for disease surveillance’s syndrome definition committee in collaboration with cdc, was used to assess si or sa-related ed visits. the syndrome definition was based on querying the chief complaint history, discharge diagnosis, and admission reason code and description fields for a combination of symptoms and boolean operators (for example, hang, laceration, or overdose), as well as icd-9cm, icd-10-cm, and snomed diagnostic codes associated with si or sa. the definition was also developed to include common misspellings of self-harmrelated terms and to exclude ed visits in which a patient “denied si or sa.” the percentage of ed visits involving si or sa were analyzed by month and stratified by sex, age group, and u.s. region. this was calculated by dividing the number of si or sa-related ed visits by the total number of ed visits in each month. the average monthly percentage change of si or sa overall and for each u.s. region was also calculated using the joinpoint regression software (surveillance research program, national cancer institute) [6]. results among approximately 259 million ed visits assessed in nssp from january 2016 to july 2018, a total of 2,301,215 si or sarelated visits were identified. over this period, males accounted for 51.2% of ed visits related to si or sa, and approximately 42.1% of si or sa-related visits were comprised of patients who were 20-39 years, followed by 40-59 years (29.7%), 10-19 years (20.5%), and ≥60 years (7.7%). during this period, the average monthly percentage of ed visits involving si or sa significantly increased 1.1%. as shown in figure 1, all u.s. regions, except for the southwest region, experienced significant increases in si or sa ed visits from jan uary 2016 to july 2018. the average monthly increase of si or sa-related ed visits was 1.9% for the midwest, 1.5% for the west (1.5%), 1.1% for the northeast, 0.9% for the southeast, and 0.5% for the southwest. conclusions ed visits for si or sa increased from january 2016 to june 2018 and varied by u.s. region. in contrast to previous findings reporting data from the national electronic injury surveillance program – all-injury program, we observed different trends in si or sa by sex, where more ed visits were comprised of patients who were male in our investigation [2]. syndromic surveillance data can fill an existing gap in the national surveillance of suicide-related problems by providing close to real-time information http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e440, 2019 isds 2019 conference abstracts on si or sa-related ed visits [5]. however, our investigation is subject to some limitations. nssp data is not nationally representative and therefore, these findings are not generalizable to areas not participating in nssp. the syndrome definition may under-or over-estimate si or sa based on coding differences and differences in chief complaint or discharge diagnosis data between jurisdictions. finally, hospital participation in nssp can vary across months, which could potentially contribute to trends observed in nssp data. despite these limitations, states and communities could use this type of surveillance data to detect abnormal patterns at more detailed geographic levels and facilitate rapid response efforts. states and communities can also use resources such as cdc’s preventing suicide: a technical package of policy, programs, and practices to guide prevention decision-making and implement comprehensive suicide prevention approaches based on the best available evidence [7]. acknowledgement rasneet kumar, zachary stein, and members of the isds syndrome definition committee references 1. stone dm, simon tr, fowler ka, et al. 2018. vital signs: trends in state suicide rates — united states, 1999–2016 and circumstances contributing to suicide — 27 states, 2015. mmwr morb mortal wkly rep. 67(22), 617-24. pubmed https://doi.org/10.15585/mmwr.mm6722a1 2. cdcs national center for injury prevention and control. web-based injury statistics query and reporting system (wisqars). https://www.cdc.gov/injury/wisqars/index.html. published 2018. accessed september 1, 2018. 3. mercado mc, holland k, leemis r, stone d, wang j. 2017. trends in emergency department visits for nonfatal self-inflicted injuries among youth aged 10 to 24 years in the united states, 2005-2015. jama. 318(19), 1931-33. doi:https://doi.org/10.1001/jama.2017.13317. pubmed 4. olfson m, blanco c, wall m, et al. 2017. national trends in suicide attempts among adults in the united states. jama psychiatry. 74(11), 1095-103. doi:https://doi.org/10.1001/jamapsychiatry.2017.2582. pubmed 5. ikeda r, hedegaard h, bossarte r, et al. 2014. improving national data systems for surveillance of suicide-related events. am j prev med. 47(3) (suppl. 2), s122-29. doi:https://doi.org/10.1016/j.amepre.2014.05.026. pubmed 6. national cancer institute. joinpoint regression software. https://surveillance.cancer.gov/joinpoint/. published 2018. accessed september 1, 2018. 7. centers for disease control and prevention. preventing suicide: a technical package of policy, programs, and practices. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29879094&dopt=abstract https://doi.org/10.15585/mmwr.mm6722a1 https://doi.org/10.1001/jama.2017.13317 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29164246&dopt=abstract https://doi.org/10.1001/jamapsychiatry.2017.2582 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28903161&dopt=abstract https://doi.org/10.1016/j.amepre.2014.05.026 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25145729&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e236, 2019 isds 2019 conference abstracts developing phenotypes from electronic health records for chronic disease surveillance sarah conderino1, justin feldman1, tom carton2, lorna thorpe1 1 population health, new york university langone medical center, new york, new york, united states, 2 louisiana public health institute, new orleans, louisiana, united states objective to utilize clinical data in electronic health records (ehrs) to develop chronic disease phenotypes appropriate for conducting population health surveillance. introduction chronic diseases, including hypertension, type 2 diabetes mellitus (diabetes), obesity, and hyperlipidemia, are some of the leading causes of morbidity and mortality in the united states. monitoring disease prevalence guides public health programs and policies that help prevent this burden. ehrs can supplement traditional sources of chronic disease surveillance, such as health surveys and administrative claims datasets, by offering near real-time data, large sample sizes, and a rich source of clinical data. however, few studies have provided clear, consistent ehr phenotypes that were developed to inform population health surveillance. methods retrospective ehr data were obtained for patients seen at new york university langone health in 2017 (n=1,397,446). to better estimate chronic disease burden among new york city (nyc) adults, the patient population was limited to nyc residents aged 20 or older, who were seen in the ambulatory primary care setting (n=153,653). rule-based algorithms for identifying patients with hypertension, statin-eligibility, diabetes, and obesity were developed based on a combination of diagnostic codes, lab results or vitals, and relevant prescriptions. we compared the performance of our metric definitions to selected phenotypes from the literature using percent agreement and cohen’s kappa. patients with discordant disease classifications between the two sets of definitions were analyzed through natural language processing (nlp) on the patients’ 2017 medical notes using a support vector machine model. statin-eligibility is a novel phenotype and therefore did not have a comparable definition in the literature. sensitivity analyses were conducted to determine how disease burden changed under alternative rules for each metric. results of 153,653 adult ambulatory care patients in 2017, an estimated 53.7% had hypertension, 12.4% had diabetes, 27.8% were obese, and 30.0% were statineligible under our proposed definitions. the estimated prevalence of hypertension increased from 28.1% to 53.7% when diagnostic codes were supplemented with blood pressure measurements and anti-hypertensive medications, while the estimated prevalence of diabetes increased less than one percentage point with inclusion of diabetes-related medications and elevated a1c measurements. there was high agreement between our obesity (94.5% agreement, k=0.86) and diabetes (96.2% agreement, k=0.81) definitions and selected definitions from the literature and moderate agreement between the hypertension definitions (74.8% agreement, k=0.41). nlp classification of discordant cases had greater alignment with the classification results of our definitions for both hypertension (78.0% agreement) and diabetes (71.2% agreement) but did not show strong agreement with either obesity algorithm. sensitivity analyses did not have large impacts on prevalence estimates for any of the indicators, with all estimates within two percentage points of the final algorithms. conclusions our proposed rule-based phenotypes using prescriptions, labs, and vitals improved ascertainment of conditions beyond diagnostic codes and were robust to modifications per sensitivity analyses. results from our algorithms were highly consistent with standard phenotypes from the literature and may improve case capture for surveillance purposes. these algorithms can be replicated acr oss diverse ehr networks and can be weighted to generate population prevalence estimates. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e328, 2019 isds 2019 conference abstracts surveillance on arboviral infections in georgia by using one health approach giorgi chakhunashvili, david tsereteli communicable diseases, national center for disease control and public health, tbilisi, georgia objective identify cases of west nile virus in black sea region of georgia through active surveillance. introduction arbovirus infections are causing enormous global burden, while their geographic distribution expands and affects new regions and areas. west nile virus (wnv), one of the most important pathogens among arboviruses, was historically associated with causing mild febrile illness, however, after the outbreak occurred in the north america, which caused more severe illness, it has received wider recognition. it is believed that the disease can reemerge after a hiatus of several years, and affect new territories, which has happened in 2018 in greece, with 31 dead among 271 infections by the end of september. in georgia, there is a lack of clinical suspicion on wnv because of the low awareness among medical society, and the existent passive surveillance system seems to be improved. methods in order to assess the situation in georgia, medical histories and electronic integrated disease surveillance system (eidss) database was studied, and active surveillance has been conducted with the following case selection criteria: residence black sea region; diagnosis fever of unknown origin (fuo). enzyme-linked immunosorbent assay (elisa) was performed. mosquitoes were obtained by using light traps and aspirators, and are now being studied. medical personnel was trained on using wnv case definition. results three laboratory positive cases were identified from 36 suspected cases. two of them were males (66%). age distribution – 28-35 y.o. all three cases resided in the city of batumi in the adjara region. a total of 572 mosquitoes were obtained. according t o preliminary analysis, the species include: culex pipiens, aedes albopictus, a. aegypti, a. caspius, a. geniculatus, anopheles claviger. conclusions the preliminary data suggests that the burden of wnv in georgia should be studied with more in-depth approaches and with just passive surveillance activities. it is very important to establish coordinated rapid efforts for disease identification by ph ysicians and veterinarians; and to provide better harmonization of diagnostic tools and integrated national surveillance syst em. disease transmission risk needs to be assessed for adequate planning of preventive measures. at this stage, no animals were studied, however, in order to fulfill the one health approach, we are planning to study horses in the near future. acknowledgement the activities were supported by centers for disease control and prevention (cdc). we thank cdc for providing technical and financial assistance. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e355, 2019 isds 2019 conference abstracts pulmonary function should be the surveillance tool in obese children to prevent asthma yungling l. lee institute of epidemiology and preventive medicine, national taiwan university, taipei, taiwan objective to investigate the important mediator linking from obesity to childhood asthma introduction the mediating pathways in the obesity-asthma link are largely unknown. we aimed to investigate the mediating pathways and to search for the most prominent pathological mechanism between central obesity and childhood asthma. methods in taiwan children health study, we collected data on an open cohort of children ranged from 9 to 13 years old. children's respiratory outcomes, atopic conditions, obesity measures, and pulmonary function were surveyed annually between 2010 and 2012. fractional exhaled nitric oxide concentrations were recorded in 2012. generalized estimating equations and general linear models were used to examine the associations among central obesity, possible mediators, and asthma. structural equation model s were applied to investigate the pathways that mediate the link between central obesity and asthma. results central obesity (waist-to-hip ratio) most accurately predicted childhood asthma. in the model of active asthma, the percentage of mediation was 28.6% for pulmonary function, 18.1% for atopy, and 5.7% for airway inflammation. the percentage of mediation for pulmonary function was 40.2% in the model of lifetime wheeze. pulmonary function was responsible for the greatest percentage of mediation among the three mediators in both models. conclusions decline in pulmonary function is the most important pathway in central obesity–related asthma. pulmonary function surveillance should be applied to obese children for asthma risk prediction. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e413, 2019 isds 2019 conference abstracts synthetic cannabinoid-related ed visit trends in maryland using essence, 2013-2018 adejare atanda, jessica c. goodell, sherry adams, veronica black maryland deartment of health, united states objective develop a free text query to track synthetic cannabinoid-related ed visits. assess trends in synthetic cannabinoid use from 2013-2018 using spatial and time-series analysis. introduction maryland utilizes essence for identification of emerging public health threats, including non-fatal overdoses. synthetic cannabinoids are heterogeneous psychoactive compounds identified as substances of abuse [1]. in march 2018, the illinois department of public health received reports of unexplained bleeding in patients who reported using these products [2]. as a result, cdc initiated coordination of national surveillance activities for possible cases of coagulopathy associated with synthetic cannabinoids use. by may 2018, state health departments reported 202 cases, including five deaths [3]. on april 3, 2018, maryland reported its index case a female in her 20’s who presented to an ed with nausea, blood in her stool, vaginal bleeding, bruising, an elevated internal normalized ratio (> 12.2), and bleeding oral ulcers after quitting use of a synthetic cannabinoid. she was successfully treated with vitamin k. the first reported mortality in a maryland resident was a male in his 30’s who called ems for fever and blood in his urine but subsequently went into cardiac arrest and was unable to be resuscita ted. the patient was known to use synthetic cannabinoids. brodifacoum exposure was confirmed by laboratory testing. as of september 2018, the maryland poison control center had received reports of 43 cases, and 3 deaths linked to the outbreak. methods to support surveillance and timeliness of synthetic cannabinoids reporting, we developed a case definition by conducting key word searches to identify terms/phrases used by providers in maryland ed’s to document synthetic cannabinoid visits. this process yielded the following terms: “synthetic marijuana”, “spice”, and “k2”. subsequently, we created a free text query based on the case definition and variations of the terms/phrases. this query allowed us to capture data on ed visits for synthetic cannabinoid use in the chief complaint (cc), discharge diagnosis (dd), and clinical impression (ci) fields of essence data. finally, descriptive and geographic spatial analyses were conducted of synthetic cannabinoid-related morbidity (ed visits) for 2013-2017 (data for 2018 is incomplete); and time trends analyzed for 2013-2018. results from 2013 to 2017, a total of 1,097 ed visits across maryland were synthetic cannabinoid-related (table 1). the overall crude synthetic cannabinoid-related ed visit rate was 20 per 100,000 population. the number of synthetic cannabinoid-related ed visits increased 8-fold, from 40 in 2013 to 353 in 2017. females made the most synthetic cannabinoid-related ed visits (n = 861, 78%). adults aged 15-24 and 25-34 made 349 (32%) and 367 (33%) visits respectively to an ed for a synthetic cannabinoidrelated event. whites and blacks made 466 (42%) and 498 (45%) visits respectively to an ed for a synthetic cannabinoid-related event. people who were non-hispanic (n= 988, 90%), black (n = 498, 45%), female (n = 861, 78%), and aged 25-34 (367, 33%) visited an ed for a synthetic cannabinoid-related event more than any other demographic group. time trend analysis shows an increase from baseline in synthetic cannabinoid-related ed visits starting from july 2014 (figure 1). three spikes are noted thereafter in april, july, and september 2015 respectively. consequently, ed visits for synthetic http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e413, 2019 isds 2019 conference abstracts cannabinoid-related events dropped to a new baseline value in december 2015. two spikes are also noted for synthetic cannabinoid-related ed visits in may and september 2017 respectively with a new baseline established starting january 2 018. spatial analysis shows geographic clustering of synthetic cannabinoid-related morbidity in three maryland jurisdictions; baltimore city, fredrick county, and washington county (figure 2). the top five maryland counties with crude synthetic cannabinoid-related ed visit rates included allegany, baltimore city, frederick, st. mary’s and washington; ranging from 87 in washington county to 38 in st. mary’s county. the top ten crude synthetic cannabinoid-related ed visit rates per 100,000 population from 2013 to 2017 among all maryland zip codes ranged from 87 in washington county to 38 in st. mary’s county. spatial analysis also shows that hospitals with the greatest burden of synthetic cannabinoid-related ed visits were close to zip codes of communities with high crude synthetic cannabinoid-related ed visit rates (figure 3). conclusions data from the essence program can be considered acceptable for monitoring synthetic cannabinoid-related ed visits in maryland. it is useful for obtaining near real-time data about synthetic cannabinoid-related events, and as we have shown in our analysis, for the identification of key groups and geographic locations most in need of targeted i nterventions to reduce morbidity and mortality. finally, it also provides us with the ability to retrospectively identify outbreaks, and to link data trends t o ongoing interventions. references 1. riederer am, et al. 2016. acute poisonings from synthetic cannabinoids — 50 u.s. toxicology investigators consortium registry sites, 2010–2015. mmwr morb mortal wkly rep. 65, 692-95. pubmed https://doi.org/10.15585/mmwr.mm6527a2 2. horth, roberta. notes from the field: outbreak of severe illness linked to the vitamin k antagonist brodifacoum and use of synthetic cannabinoids — illinois, march–april 2018. 3. centers for disease control and prevention. outbreak of life-threatening coagulopathy associated with synthetic cannabinoids use. may 2018. retrieved from: https://emergency.cdc.gov/han/han00410.asp table 1. synthetic cannabinoid-related morbidity (ed visits), by year and demographic characteristics, 2013-2017 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27413997&dopt=abstract https://doi.org/10.15585/mmwr.mm6527a2 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e413, 2019 isds 2019 conference abstracts figure 1. monthly time trends of synthetic cannabinoid-related ed visits, maryland, 2013-2018 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e413, 2019 isds 2019 conference abstracts figure 2. geographic spatial analysis showing clustering of synthetic cannabinoid-related ed visits by patient residence, maryland, 2013-2017. figure 3. geographic spatial analysis by zip code (patients) and total number of synthetic cannabinoid-related ed visits by hospital, maryland, 2013-2017. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e341, 2019 isds 2019 conference abstracts short-term health impact assessment after irma in french islands elise daudens-vaysse1, marie barrau1, lydéric aubert1, patrick portecop2, eric fontanille3, cécile forgeot1, pierre-marie linet4, lazare noubou5, didier roux6, céline gentil1, philippe malfait1, manuel zurbaran1, quiterie mano1, frédérique dorleans1, isabelle pontais1, harris gladone7, anthony forbin7, tiphanie succo1, céline caserioschönemann1, caroline six1 1 santé publique france, fort-de-france, martinique, 2 samu 971, pointe-à-pitre, guadeloupe, 3 agence régionale de santé de martinique, fort-defrance, martinique, 4 centre hospitalier fleming, marigot, saint martin (french part), 5 centre hospitalier de bruyn, gustavia, saint barthélemy, 6 agence régional de santé de guadeloupe, saint-martin et saint-barthélemy, pointe-à-pitre, guadeloupe, 7 gcs e-santé archipel 97-1, baiemahault, guadeloupe objective describe short-term health effects of the hurricane using the syndromic surveillance system based on emergency departments, general practitioners and dispensaries in saint-martin and saint-barthélemy islands from september 11, 2017 to october 29, 2017. introduction in saint-martin (31 949 inhabitants) and saint-barthélemy (9 625 inhabitants) islands in the french west indies, the surveillance system is based on several data sources: (1) a syndromic surveillance system based on two emergency departments (ed) of saintbarthélemy (hl de bruyn) and saint-martin (ch fleming) and on mortality (sursaud® network [1])); (2) a network of sentinel general practitioners (gp’s) based on the voluntary participation of 10 gps in saintmartin and 5 in saint-barthélemy; (3) the notifiable diseases surveillance system (31 notifiable diseases to individual case-specific form); (4) the regional surveillance systems of leptospirosis and arboviruses based on the biological cases reported by physician s and laboratories of two islands. on september 6, 2017, hurricane irma struck saint-martin and saint-barthélemy islands. both islands were massively destroyed. this storm led to major material damages, such as power outages, disturbance of drinking water systems, road closures, destruction of medical structures and evacuation or relocation of residents. in this context, the usual monitoring system did not work and life conditions were difficult. the regional unit of french national public health agency set up an epidemiological surveillance by sending epidemiologists in the field in order to collect data directly from ed physicians, gp’s and in dispensaries. those data allowed to describe short-term health effects and to detect potential disease outbreaks in the aftermath of hurricane irma. this paper presents results of the specific syndromic surveillance. methods before irma, ed data were collected daily directly from patients’ computerized medical files that were filled in during medical consultations at ed. among the collected variables, the diagnosis was categorized according to the 10th revision of the international classification of diseases (icd-10). this surveillance system was completed by aggregated data of emergency medical services (ems), also including medical diagnosis coded using the icd10. because of the sudden disruption in hospital departments due to hurricane, electronic transmission was stopped. to replace it , ed data collection turned temporary into paper-forms and several epidemiologists were sent in saint-martin and saint-barthélemy to collect data directly from the ed physicians. this system remained until the end of october when connections and data transmission were restored. because of destruction of medical structures, dispensaries were opened in different strategic areas of the island, 3 in saint-martin and none in saint-barthélemy. general practitioners have progressively reopened their practice (8 gp’s in saint martin and 5 in saint-barthélemy) and patient's data were collected and integrated into the surveillance system. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e341, 2019 isds 2019 conference abstracts based on a literature review and former experience, the main pathologies identified for the health risk assessment were: (1) somatic pathologies directly or indirectly related to the hurricane (trauma, wounds, cuts, burns, secondary infection); (2) infectious diseases related to the lack of hygiene partly due to damaged water and electricity networks and unavailable health care structures (gastroenteritis, food infections, respiratory diseases, skin infections, tetanus and other pathologies that may occur in the longer term linked to the incubation period especially leptospirosis and hepatitis a); (3) chronic pathologies by discontinuity of care (renal insufficiency, diabetic, cardio-respiratory decompensation, etc.); (4) pathologies related to animal bites and mosquito bites (vectorborne diseases); (5) psychological and / or psychiatry disorders. then in the french west indies, from september 11 to october 29,2017, data were routinely analyzed to detect and follow -up various expected or unusual variations of one or more pathology of the above list. results the following week after irma (2017-37), the weekly number of ed visits compared to the mean activity observed in normal situation has increased: 1225 ed visits vs. 313 in 2017-35 in saint-martin and 227 ed visits vs. 94 ed visits in 2017-35 in saintbarthélemy. ed activity has gradually decreased to finally return to a based-activity as observed before the hurricane at the end of october. from september 11 to october 29, 25% of recorded emergency consultations in saint-martin island were trauma, wounds, burns and cuts. as in saint-martin, 42% of emergency visits in saint-barthélemy were pathologies directly or indirectly related to the passage of irma (trauma, wounds, etc). others major causes of ed visits were for treatment renewal (diabetes, renal insufficiency, etc.) and gyneco-obstetric activity because general practitioners had stopped their activity. in dispensaries and general practitioners, the most common pathology was gastroenteritis (11% in saint-martin) over the entire period of surveillance. at the beginning of the surveillance, skin infections were the most frequently found (20%) in saint-martin and psychological disorders (3%) in saint-bartélemy, while at the end respiratory infections were the most frequent (6%) in both islands. no increase in visits for chronic diseases, food-borne diseases, acute respiratory or diarrhea illness were detected. no autochthonous confirmed cases of cholera, leptospirosis, vector-bone disease, hepatitis a or typhoid fever had been reported, due to the destruction of the laboratory. conclusions syndromic surveillance in the french west indies allowed the epidemiologists to assess rapidly the health impact of hurricane in saint-martin and saint-barthélemy. the well-established relations between french national public health agency and local professionals of both affected islands allowed to temporary switch from an electronic into a paper-based data transmission without any interruption of data analysis. although several cluster suspicions have been investigated (especially of gastroenteritis, scabies, etc), no massive outbreak was detected. then even with a degraded system, syndromic surveillance allowed to reinsure authority of the absence of major health impact due to irma. acknowledgement the authors thank the epidemiology team: christine castor, françois clinard, pauline delezire, erica fougere, jean-rodrigue ndong, valérie ponties, hélène prouvost, cyril rousseau and aymeric ung. the authors thank the emergency departments ch fleming and hl de bruyn and general practitioners for providing data and their contribution to the surveillance. the authors thank the french health regional agency (ars) of martinique and guadeloupe, saint-martin and saint-barthélemy. references 1. caserio-schönemann c, bousquet v, fouillet a, henry v. 2014. le système de surveillance syndromique sursaud ®. bull epidemiol hebd (paris). 3-4, 38-44. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e427, 2019 isds 2019 conference abstracts beyond overdose: surveillance of recreational drug use and corresponding toxicology testing terra wiens, nate wright, stefan saravia, matt wogen, jon roesler, ruth lynfield health promotion and chronic disease division, minnesota department of health, st. paul, minnesota, united states objective implement a novel surveillance system for recreational substance use, including toxicology testing, to enable situational awareness and more accurately assess the health care burden related to recreational substance use. introduction drug overdose deaths are increasing nationally and in minnesota (mn). this is only a fraction of the overall burden that recreational drug use exacts on emergency departments (ed) and hospitals. in addition to opioids and other drugs, three outbreaks of synthetic cannabinoids and cathinones have occurred in mn recently. icd codes do not adequately identify patients treated for drug use. also, toxicology data for these patients are limited: routine toxicology testing is not performed at hospitals as results are not timely enough to be useful for clinical care. even when such testing is performed, hospital laboratories are unable to detect newer synthetic drugs. in order to more quickly respond to clusters of substance use, identify substances causing atypical symptoms or severe illness, and understand the burden of overdoses and substance use in mn, the mn department of health (mdh) developed the mn drug overdose and substance abuse pilot surveillance system (mndosa). mndosa data collection began in november 2017 and includes two pilot sites in northeastern mn, and one in the twin cities metropolitan area. methods all patients who present to a participating ed where the principal diagnosis is attributed to the recreational use of drugs or other substances (excluding alcohol alone and suicide attempts) are included. reports are sent to mdh daily with a few key data variables. specimens for a subset of “patients of special interest” (psi) are sent to the mdh public health laboratory to be tested for a wide range of substances. psi include patients who die in the ed, are hospitalized, have an unusual clinical presentation, and/or are part of a cluster. medical records of the psi are reviewed, and a standardized data abstraction form is completed. results through august 24, 2018, 963 ed visits were reported to mndosa. the median age was 34 years for males, 33 for females. the majority of cases were male (68%) (table 1). among all patients reported to mndosa through august 24, 2018, 23% were hospitalized. a slightly higher percentage of females were hospitalized compared to males (27% vs. 22%; p=0.054). opioids were one of the substances most frequently suspected by clinicians to be related to the health care encounter (28% of all reports for males and 37% for females). heroin was more frequently suspected for females than males (27% for females, 19% males, p=0.012). methamphetamine (27% of all reports for males and 28% for females) and synthetic cannabinoids and cathinones (24% for males and 6% females, p < 0.001) were also commonly suspected. female patients were significantly more likely to have non-benzodiazepine prescription medication suspected (10% for females, 4% males, p < 0.001). forty-one urine specimens from mndosa cases have been analyzed thus far (table 2). one of the most frequently detected substances was methamphetamine, which was found in 26 samples (63%); however, only 20 (49%) were suspected by clinicians to have methamphetamine on board. specimens of seven patients suspected to have been exposed to heroin were tested, yet only two tested positive for the major metabolite of heroin, while six were positive for fentanyl and two for acetyl fentanyl. with the exception of synthetic cannabinoids and cathinones, all substances were detected more frequently in toxicology testing than were suspected by the healthcare providers who made the mndosa report. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e427, 2019 isds 2019 conference abstracts conclusions mndosa is unique as it collects real-time data rather than relying on data sources with long delays in reporting. this allows for a near real-time response and notification of key stakeholders, such as the poison center, clinicians, local public health, and the public, when a new or concerning substance or cluster is identified. this innovative surveillance system has the potential to improve population health through describing patterns of drug overdose and substance use in mn communities, identifying clusters of drug overdoses in near-real time, identifying the specific substances causing severe illness and/or death, and describing at-risk populations to guide prevention efforts. most importantly, mndosa can better estimate the overall health care burden related to recreational substance use, beyond the typical enumeration of overdose deaths. toxicology lab results indicate that patients have substances such as methamphetamine, opioids, marijuana and cocaine on board more frequently than the attending provider suspects. additionally, the number of substances detected in these specimens indicates that polysubstance use is highly prevalent among these cases. having a better understanding of the substances that may be involved in a patient’s ed visit or hospitalization can help improve patient care. improved toxicology testing of non-fatal cases would allow us to better describe the current landscape of substances used in our communities and provide situational awareness to public health professionals. this pilot is identifying surveillance challenges, determining feasibility, and establishing best methods for expansion to other sites. lessons learned thus far include that active identification and reporting of mndosa patients are burdensome to ed staff; thus, an informatics-based approach to passively identify and report mndosa patients is vital to continued surveillance. laboratory methods must be robust enough to resolve an evergrowing number of drugs and metabolites over a wide range of potential blood and urine concentrations. as the number of drugs, metabolites, and adulterants continues to grow, the toxicology panels used for testing need to continue expanding. mndosa next steps include incorporating an informatics-based approach to surveillance, expanding mndosa to other hospitals, evaluating the surveillance system against other data sources, and incorporating non-targeted toxicology testing to improve the ability to detect emerging and novel substances. acknowledgement cste/samhsa funding, alejandro azofeifa, joanne bartkus, paul moyer, jason peterson, cori dahle, richard danila, mark kinde, roon makhtal, deborah anderson, jon cole, travis olives, elisabeth bilden, nicholas van deelen table 1. mndosa reports, november 2017 – august 24, 2018 number of ed visits reported 963 deceased <1% hospitalized 23% atypical clinical presentation 6% male female number of mndosa reports, % of all reports 68% 32%* age median age 34 33 age range 14-70 13-80 race, % by gender, non-exclusive categories black 39% 19%* white 37% 38% american indian/alaska native 11% 25%* asian, native hawaiian/pacific islander 1% 5%* other race 4% 2% unknown or missing race 9% 12% hospitalized, % 22% 27% * chi-square p-value < 0.001 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e427, 2019 isds 2019 conference abstracts table 2. substances suspected vs. detected in mndosa lab specimens (n=41) analyte substances suspected (from mndosa reports) substances detected (from mdh lab testing) methamphetamine 49% (20) 63% (26) opioids heroin or 6-mam 17% (7) 5% (2) specimens with at least one commonly prescribed opioid 10% (4) 46% (19) specimens with at least one fentanyl or fentanyl analogue 2% (1) 29% (12) specimens with at least one benzodiazepine** 15% (6) 73% (30) thc 12% (5) 44% (18) cocaine 7% (3) 10% (4) synthetic cannabinoids or cathinones 12% (5) 0% **may include substances given as clinical care http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e300, 2019 isds 2019 conference abstracts use of n-grams and term relationship graphs in the syndrome definition development process nimi idaikkadar, nelson adekoya, aaron kite-powell, achintya n dey centers for disease control objective to describe the use of uni-grams, bi-grams, and tri-grams relationships in the development of syndromic categories. introduction the use of syndromic surveillance systems has evolved over the last decade, and increasingly includes both infectious and noninfectious topic areas. public health agencies at the national, state, and local levels often need to rapidly develop new syndromic categories, or improve upon existing categories, to enhance their public health surveillance efforts. documenting this development process can help support increased understanding and user acceptance of syndromic surveillance. this presentation will highlight the visualization process being used by cdc’s national syndromic surveillance program (nssp) program to develop and refine definitions for syndromes of interest to public health programs. methods development of a syndromic definition is an iterative process that starts with an analyst testing how different terms, which are assumed to be associated with the topic of interest, and diagnostic codes are noted in the chief complaint and discharge diagnosis code fields. the analyst then manually scans through the resulting line list of patient chief complaint text and diagnostic codes to determine whether the query terms match the intended syndromic concept. typically, more terms and diagnostic codes are then added to the query using boolean operators, and other terms are negated and removed. to facilitate summarization of the resulting terms and diagnostic codes cdc’s nssp program developed programs with r that extracted data using the essence application programming interface (api), and the chief complaint query validation data source (ccqv). we use n-gram analysis, which is extensively used in text mining, to show co-occurrences of words in a consecutive order. the co-occurrences of words can be a uni-gram which represents a single word, bi-gram for two words, and tri-grams for three words. the process tokenizes the chief complaint text and diagnosis code fields, with some pre-processing of the text and removal of stopwords. uni-grams, bi-grams, and tri-grams are then calculated for the top 200 combinations along with term and diagnostic code co-occurrence. other visualizations that can be used are network graphs, which show the connections between different chief complaints terms and also between discharge diagnosis codes and chief complaint terms. the use of these graphs provides an insight into the frequency and relationship between terms and codes. results to support the development of new syndrome definitions we used the r program to produce two time series graphs. the first time series graph is used to show the volume of visits over the user’s indicated time period and the second shows the median chief complaint compared over the user’s indicated time period. a series of histograms showing frequency of the uni-gram, bi-grams, and tri-grams are also used during the development process. lastly, two network diagrams are used to show the co-occurrence between term and diagnostic codes. the use of this range of graphs during the syndrome definition development process provides multiple ways to view the characteristics of the chief complaint and discharge diagnosis fields. the sample graphs below can be used by the analyst to illustrate key information. conclusions through this development process and the use of graphs the relationship between the syndrome definition and search terms can be visualized. in addition when using this process, the analyst could be specific as to the syndrome of interest or be broad, allowing a generic trend series monitoring of the syndrome. the search words can also be based on specific local or regional terms and the relationship terms set to include or exclude certain terms. use of this process for the development of syndrome definitions can support the use of syndromic surveillance and offer the opportunity to further refine the process. after the syndrome has been developed, the analyst can consider spatial or temporal analysis. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e300, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e300, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e300, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e270, 2019 isds 2019 conference abstracts a semantic platform for surveillance of adverse childhood experiences jon hael simon brenas, eun kyong shin, arash shaban-nejad uthsc/ornl, memphis, tennessee, united states objective we introduce the semantic platform for adverse childhood experiences (aces) surveillance (spaces). it facilitates the access to the relevant integrated information, enables discovering the causality pathways and assists researchers, clinicians, publi c health practitioners, social workers, and health organization in studying the aces, identifying the trends, as well as planning and implementing preventive and therapeutic strategies. introduction adverse childhood experiences (aces) have been linked to a variety of detrimental health and social outcomes. in the last 20 years, the association between aces with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse [1], mental health disturbances and impaired memory [2], nervous, endocrine and immune systems impairments [3], and criminal activities [4] have been studied. one of the challenges in studying and timely diagnosis of aces is that the links between specific childhood experiences and their health outcomes are not totally clear. similarly, an integrated dataset buil tfrom multiple sources is often required for effective aces surveillance. the spaces project aims at providing a semantic infrastructure to facilitate data sharing and integration and answer causal queries [5] to improve aces surveillance. methods we create the aces ontology to facilitate the integration of data coming from various distributed sources (e.g. ontologies, databases, surveys, interviews, and literature) and maintain interoperability between the data sources. we re -used some of the existing bioontologies in the domain, although they captured the domain’s knowledge in different levels of granularity, e.g., homeless is defined in snomed ct and medical dictionary for regulatory activities (meddra) with different hierarchies, or some related concepts may be disconnected, e.g., snomed ct defines abuse but not verbal abuse while meddra defines verbal abuse but not as an abuse. in order to improve reasoning with the aggregated data, we perform two different kinds of inference. semanti c inference uses the aces ontology to creates new conclusions by connecting similar concepts. on the other hand, statistical inference is used to formulate rules that link co-occurring concepts. these two kinds of inference, statistical and semantic, work in tandem and the conclusions from one method can then be used as the basis for the other kind of inference. results the aces ontology is a unique resource for capturing knowledge in the domain of adverse childhood experiences. the ontology will be openly and freely available via the relevant online community’s portals (e.g. the ncbo bioportal). the logical validation of the ontology is performed using description logic reasoners. a set of use-case scenarios are designed to demonstrate the feasibility and usability of the ontology for data integration and intelligent query answering. conclusions in this paper, we present the space project that aims to develop a platform to improve adverse childhood experiences surveillance. the tool uses semantic and statistical methods to improve data access, integration, and reasoning. acknowledgement this work was funded by memphis research consortium (mrc). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e270, 2019 isds 2019 conference abstracts references 1. felitti vj, anda rf, nordenberg d, williamson df, spitz am, et al. 1998. relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: the adverse childhood experiences (ace) study. am j prev med. 14(4), 245-58. pubmed https://doi.org/10.1016/s07493797(98)00017-8 2. anda rf, felitti vj, bremner jd, et al. 2006. the enduring effects of abuse and related adverse experiences in childhood: a convergence of evidence from neurobiology and epidemiology. eur arch psychiatry clin neurosci. 256(3), 174-86. pubmed https://doi.org/10.1007/s00406-005-0624-4 3. danese a, mcewen bs. 2012. adverse childhood experiences, allostasis, allostatic load, and age-related disease [allostasis and allostatic load.]. physiol behav. 106(1), 29-39. pubmed https://doi.org/10.1016/j.physbeh.2011.08.019 4. garbarino j. 2017. aces in the criminal justice system [child well-being and adverse childhood experiences in the us.]. acad pediatr. 17(7) (supplement), s32-33. pubmed https://doi.org/10.1016/j.acap.2016.09.003 5. okhmatovskaia a, shaban-nejad a, lavigne m, buckeridge dl. 2014. addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective. stud health technol inform. 205, 1125-29. pubmed http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9635069&dopt=abstract https://doi.org/10.1016/s0749-3797(98)00017-8 https://doi.org/10.1016/s0749-3797(98)00017-8 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16311898&dopt=abstract https://doi.org/10.1007/s00406-005-0624-4 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21888923&dopt=abstract https://doi.org/10.1016/j.physbeh.2011.08.019 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28865658&dopt=abstract https://doi.org/10.1016/j.acap.2016.09.003 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25160364&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e386, 2019 isds 2019 conference abstracts evaluation of redcap to supplement foodborne disease surveillance systems mugdha golwalkar1, kailey lewis1, marcy mcmillian1, heather mendez1, 2, katie garman1, john dunn1, steffany j. cavallo1 1 tennessee department of health, nashville, tennessee, united states, 2 centers for disease control and prevention public health associate program, atlanta, georgia, united states objective the objective of this study is to evaluate the use of a supplementary data management application to meet surveillance demands for foodborne disease in tennessee and to highlight successes, challenges, and opportunities identified through this process. introduction the tennessee department of health (tdh) foodborne disease program conducts routine surveillance for foodborne illnesses and enteric disease outbreaks and participates in statewide enhanced surveillance as part of the foodborne disease center for outbreak response enhancement (foodcore) and the foodborne diseases active surveillance network (foodnet) supported by the centers for disease control and prevention (cdc). tdh uses the cdc nedss base system (nbs) application for routine disease surveillance. however, nbs serves multiple disease programs within tdh and modifications to the system for the rapidly changing data demands, grant requirements, and outbreak needs of the foodborne program, may not be a priority for the system as a whole. in 2014, the tdh foodborne disease program began using the research electronic data capture (redcap) application as a solution to changing surveillance needs. foodcore, foodnet, and routine surveillance data elements are entered into redcap to supplement nbs, depending on program specific needs and system capability. methods redcap was queried for foodcore, foodnet, and routine surveillance projects. projects were categorized by surveillance activity type. epidemiologists provided qualitative feedback on successes and challenges in using redcap to supplement nbs, which were then categorized into attributes according to existing frameworks for evaluating public health surveillance systems [1,2]. results as of august 2018, the tdh foodborne program housed 45 individual redcap databases dedicated to surveillance. four primary database categories were identified: routine case-based surveillance (8), enhanced/active surveillance (6), aggregate outbreak/cluster surveillance tracking (6), and outbreak-specific databases (25). the redcap application programming interface (api) and an open database connection to nbs within sas 9.4 (cary, nc) were used to create unilateral data flow from nbs to redcap, where possible. successes and challenges in using redcap fell into six main surveillance system attributes: flexibility, ease of data management, stability, simplicity, efficiency, and acceptability. successes included the high level of control over data and databases offered by redcap, the flexibility to rapidly implement program-specific changes, and the accessibility and reliability of redcap as a de facto back-up of nbs data. challenges included lack of interoperability between redcap databases and with nbs, leading to dual data entry, overuse of redcap resulting in unnecessarily complex and decentralized data storage (figure 1), and increased personnel time on data management and extraction for metrics and reports. conclusions using redcap in tennessee to supplement an existing disease surveillance application increased flexibility and functionality of the foodborne disease surveillance system, but also added complexity and time involved in data management. the nationally notifiable diseases surveillance system modernization initiative (nmi) is developing a standardized message mapping guide (mmg) in collaboration with states and cdc, which incorporates foodnet data elements and would transition data collection tools http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e386, 2019 isds 2019 conference abstracts in nbs for foodborne diseases to a more portable and flexible format. implementation of this mmg could minimize case-based data entry into redcap. tools that offer increased interoperability between nbs and redcap and between redcap databases could also improve the efficiency of using complementary applications for rapidly changing foodborne disease surveillance needs. acknowledgement authors would like to acknowledge corinne davis, redcap administrator, for her assistance in creating and organizing databases for the foodborne diseases program. references 1. german rr, lee lm, horan jm, milstein rl, pertowski ca, waller mn. updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mmwr recomm rep. 2001;50(rr-13):1–35. 2. calba c, goutard fl, hoinville l, et al. 2015. surveillance systems evaluation: a systematic review of the existing approaches. bmc public health. 15, 448. doi:https://doi.org/10.1186/s12889-015-1791-5. pubmed figure 1: data and surveillance process flow for the tennessee department of health foodborne program. each redcap database box reflects a category containing the number of databases specified in parentheses. http://ojphi.org/ https://doi.org/10.1186/s12889-015-1791-5 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25928645&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e400, 2019 isds 2019 conference abstracts on estimation the relative risk of small area and visualization spatio-temporal map xiaoxiao song1, 2, yan li3, 2, le cai4, 2, wei liu4, 2, wenlong cui4, 2 1 department of epidemiology and statistics school of public health, kunming medical university, kunming, yunnan province, china, 2 yunnan provincial collaborative innovation center for public health and disease prevention and control, kunming, china, 3 kunming medical university, kunming, china, 4 school of public health, kunming medical university, kunming, china objective the purpose is to propose a serial of approach for estimation for disease risk for ili in "small area" and present the risk values by spatio-temporal disease mapping or an interactive visualization with html format. introduction disease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. possibly the most famous uses of disease mapping in epidemiology were the studies by john snow of the cholera epidemics in london. accurate estimation relative risk of small areas such as mortali ty and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden. recently, as the innovative algorithms and the available software, more and more disease risk index has been pouring out. this abstract will provide several estimation risk index, from raw incidence to model-based relative risks, and use visual approach to display them. methods all the data are from a syndromic surveillance and real-time early warning system in the yunnan province in the china. for brief introduction aim, we are using the ili (influenza-like illness) data in december 2017 in one county. the relative risks of disease in small area are including: raw incidence, a standardized morbidity ratio (smr), empirical bayes smoothin g estimation relative risk (eb-rr) and the besag-york-mollio model (bym). the incidence in each small area is common used for descriptive the risk but fail to comparable directly since the different population at risk in each area. smr is a good way to deal with this incomparability. but smr can give rise to imprecisely estimate in areas with small populations. empirical bayes estimation approach has been used for smoothing purpose and can be seen as a compromise between relative risks and p-values. however, all above approaches are inept to have spatial or spatio-temporal structure in mind. bym based the bayesian inference can handle both the area-specific spatial structured component (such as intrinsic conditional autoregressive component) and the exchangeable random effect (unstructured component). all the analyses are implemented in the r software with inla package (http://www.r inla.org). the outcome of relative risk estimation with visual way and interactive maps showing are using ggplot2 and leaflet packages. results 1. the spatio-temporal raw cases of ili from 2017/12/01 to 2017/12/31 is fig.1 2. the smr and eb-rr estimation rr of ili are in fig.2 and fig.3 3. the most excited is the interactive visualization with html format for all the risk indexes is visited http://rpubs.com/ynsxx/424814 in detail. and the screenshot is fig.4 conclusions small area disease risk estimation is important for disease prevention and control. the faster function of computer with powe r r software can lead to advance in disease mapping, allowing for complex spatio-temporal models and communicate the results with visualization way. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e400, 2019 isds 2019 conference abstracts figure 1. the spatio-temporal raw cases of ili from 2017/12/01 to 2017/12/31 figure 2. the smr estimation risk of ili http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e400, 2019 isds 2019 conference abstracts figure 3. the eb-rr estimation risk of ili figure 4. the screenshot of interactive visualization all the risk indexes http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e354, 2019 isds 2019 conference abstracts prevalence of hepatitis c testing among non-institutionalized individuals in the us, nhis 2013-2017 jae eui soh3, 1, mohammed a. khan3, 2, william w. thompson3, lauren canary3, claudia j. vellozzi4, noele p. nelson3 1 department of biostatistics and bioinformatics, emory university, atlanta, georgia, united states, 2 department of epidemiology, emory university, atlanta, georgia, united states, 3 division of viral hepatitis, centers for disease control and prevention, atlanta, georgia, united states, 4 grady memorial hospital, atlanta, georgia, united states objective using a large nationally representative dataset, we estimated the prevalence of self-reported hepatitis c testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the cdc and united states preventive services task force. introduction hepatitis c virus (hcv) infection is the most common blood-borne disease in the us and the leading cause of liver-related morbidity and mortality. approximately 3.5 million individuals in the us were estimated to have been living with hepatitis c in 2010 and approximately half of them were unaware that they were infected. among hcv infected individuals, those born between 1945 and 1965 (usually referred to as the baby boomer cohort) represents approximately 75% of current cases. because of the substantial burden of disease among this age group, cdc expanded its existing hepatitis c risk-based testing recommendations to include a one-time hcv antibody test for all persons born between 1945 and 1965. the united states preventive services task force (uspstf) subsequently made the same recommendation in june 2013. methods the following question "have you ever had a blood test for hepatitis c?" has been administered annually from 2013 through 2017 on the national health interview survey (nhis). the nhis is a nationally representative cross -sectional face-to-face household interview of civilian noninstitutionalized individuals in the u.s. the nhis survey uses a complex multistage probability design that includes stratification, clustering, and oversampling. we estimated the prevalence of hepatitis c testing for adults in the us during the study period from 2013 to 2017. in addition, we carried out stratified analyses comparing those with private insurance to those who did not have private insurance. we reported weighted estimates taking into account the nhis survey design. the r statistical software (r core team, 2018) was used to estimate weighted prevalence estimates for hepatitis c testing. results during the study period from 2013-2017, there were 148,674 adults who responded to the ever tested for hepatitis c question. in addition, 33.56% of these individuals were born between 1945 and 1965; among all adults, the weighted percentage of individuals that responded yes they had received a hepatitis c screening test was 12.82% (95% ci: 12.54-13.10%) while for baby boomers the estimate was 13.93% (95% ci: 13.51-14.35%). figure 1 presents the annual trend in the hepatitis c test prevalence over the study period by birth cohorts. for both cohorts, there were significant increases over time in hepatitis c testing prevalence. the two trend lines began to diverge in 2015 with the baby boomer cohort reporting higher rates of hepatitis c testing. for the baby boomer cohort, there was also a substantial increase in reported hepatitis c testing in 2017 relative to 2016. similar trends were found for the samples when we restricted the sampl e to only those with private insurance. compared to the people with private insurance, the baby boomers with 'non -private’ insurance, including medicaid, medicare, or militarygovernment sponsored insurances, reported higher rates of testing. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e354, 2019 isds 2019 conference abstracts conclusion across the five-year period from 2013 through 2017, we found increasing rates of self-reported hepatitis c testing among noninstitutionalized u.s. adults. for the baby boomer cohort, we saw a substantial increase in testing in 2017, which was likely due in part, to increased awareness among both physicians and patients of the cdc and uspstf recommendation to have all baby boomers tested. efforts to increase the awareness of these recommendations should continue. additional targeted promotions among hard to reach populations should also be considered. figure 1. annual hepatitis c virus (hcv) test prevalence by birth cohorts, nhis, 2013-2017 table 1. annual hepatitis c virus (hcv) test prevalence by birth cohort; total sample and with private insurance holders, nhis, 2013-2017 total sample with private insurance year 1945-1965 birth cohort other birth cohorts 1945-1965 birth cohort other birth cohorts 2013 12.27 (11.47-13.07) 11.23 (10.59-11.88) 11.50 (10.49-12.51) 11.52 (10.69-12.35) 2014 12.41 (11.60-13.21) 11.54 (10.84-12.24) 11.43 (10.44-12.42) 11.71 (10.80-12.62) 2015 13.40 (12.47-14.33) 11.70 (11.06-12.34) 12.30 (11.21-13.39) 11.58 (10.81-12.36) 2016 14.38 (13.52-15.23) 12.81 (12.14-13.49) 13.39 (12.41-14.36) 12.43 (11.63-13.22) 2017 17.33 (16.34-18.33) 13.91 (13.15-14.67) 16.87 (15.71-18.02) 14.30 (13.35-15.26) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e261, 2019 isds 2019 conference abstracts supervised learning for automated infectious-diseaseoutbreak detection benedikt zacher, alexander ullrich, stephane ghozzi infectious-disease epidemiology, robert koch institute, berlin, germany objective by systematically scoring algorithms and integrating outbreak data through statistical learning, evaluate and improve the performance of automated infectious-disease-outbreak detection. the improvements should be directly relevant to the epidemiological practice. a broader objective is to explore the usefulness of machine-learning approaches in epidemiology. introduction within the traditional surveillance of notifiable infectious diseases in germany, not only are individual cases reported to the robert koch institute, but also outbreaks themselves are recorded: a label is assigned by epidemiologists to each case, indicating whether it is part of an outbreak and of which. this expert knowledge represents, in the language of machine leaning, a "ground truth" for the algorithmic task of detecting outbreaks from a stream of surveillance data. the integration of this kind of information in the design and evaluation of algorithms is called supervised learning. methods reported cases were aggregated weekly and divided into two count time series, one for endemic (not part of an outbreak) and one for epidemic cases. two new algorithms were developed for the analysis of such time series: farringtonoutbreak is an adaptation of the standard method farringtonflexible as implemented in the surveillance r package: it trains on endemic case counts but detects anomalies on total case counts. the second algorithm is hmmoutbreak, which is based on a hidden markov model (hmm): a binary hidden state indicates whether an outbreak was reported in a given week, the transition matrix for this state is learned from the outbreak data and this state is integrated as factor in a generalised linear model of the total case count. an explicit probability of being in a state of outbreak is then computed for each week (one-week ahead) and a signal is generated if it is higher than a user-defined threshold. to evaluate performance, we framed outbreak detection as a simple binary classification problem: is there an outbreak in a given week, yes or no? was a signal generated for this week, yes or no? one can thus count, for each time series, the true positive s (outbreak data and signals agree), false positives, true negatives and false negatives. from those, classical performance scores can be computed, such as sensitivity, specificity, precision, f-score or area under the roc curve (auc). for the evaluation with real-word data we used time series of reported cases of salmonellosis and campylobacteriosis for each of the 412 german counties over 9 years. we also ran simple simulations with different parameter sets, generating count time ser ies and outbreaks with the sim.pointsource function of the surveillance r package. results we have developed a supervised-learning framework for outbreak detection based on reported infections and outbreaks, proposing two algorithms and an evaluation method. hmmoutbreak performs overall much better than the standard farringtonflexible, with e.g. a 60% improvement in sensitivity (0.5 compared to 0.3) at a fixed specificity of 0.9. the results were confirmed by simulations. furthermore, the computation of explicit outbreak probabilities allows a better and clearer interpretation of detection results than the usual testing of the null hypothesis "is endemic". conclusions methods of machine learning can be usefully applied in the context of infectious-disease surveillance. already a simple hmm shows large improvements and better interpretability: more refined methods, in particular semi-supervised approaches, look thus http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e261, 2019 isds 2019 conference abstracts very promising. the systematic integration of available expert knowledge, in this case the recording of outbreaks, allows an evaluation of algorithmic performance that is of direct relevance for the epidemiological practice, in contrast to the usual intrinsic statistical metrics. beyond that, this knowledge can be readily used to improve that performance and, in the future, gain insights in outbreak dynamics. moreover, other types of labels will be similarly integrated in automated surveillance analyses, e.g. user feedback on whether a signal was relevant (reinforcement learning) or messages on specialised internet platforms that were found to be useful warnings of international epidemic events. acknowledgement the authors would like to thank bettina rosner for helpful explanations and comments on outbreak data. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e249, 2019 isds 2019 conference abstracts influenza surveillance using wearable mobile health devices benjamin bradshaw, kevin j. konty, ernesto ramirez, wei-nchih lee, alessio signorini, luca foschini data science, evidation health, brooklyn, new york, united states objective to describe population-level response to influenza-like illness (ili) as measured by wearable mobile health (mhealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mhealth devices for influenza surveillance. introduction influenza surveillance has been a major focus of data science efforts to use novel data sources in population and public health [1]. this interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. such information can inform mitigation efforts including the targeting of interventions and public health messaging. the key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns [2]. we assess the potential utility of wearable mhealth devices by establishing the aggregate responses to ili along three dimensions: steps, sleep, and heart rate. surveillance based on mhealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near realtime, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. these features suggest the potential of mhealth data to deliver “faster, more locally relevant” surveillance systems [3]. methods during the 2017/2018 influenza season, surveys were conducted within the achievement platform, a health app that integrates with a variety of wearable trackers and consumer health applications [4]. the achievement population has given consent agreeing to participation in studies like the one presented here and permitting access to their data. surveys queried users as to whether they had experienced flu-like (ili) symptoms in the preceding 14 days. respondents who had experienced symptoms were then asked to identify symptom days. those who had not experienced symptoms were queried again two weeks later. positive responses were re-indexed to align by date of symptom onset. individual respondent’s measures were standardized on a per -individual level in the 6 week period centered on the index date. population-level mean signals were directly computed across several dimensions including steps, sleep, and heart rate. uncertainty was quantified using resampling. results beginning february 17th, 2018, surveys were distributed to achievement users. within the first week 31,934 users had responded to the survey. over a 12week period, 124,892 individuals completed the survey with 25,512 reporting flu-like symptoms in a two week period prior to the survey. of these, 9,495 had wearable device data in the 90-day window surrounding their symptom dates and 3,362 respondents had “dense” data defined as no more than 4 consecutive missing days in the 6-week period surrounding the index date. population-level signals to ili were clearly evident for five measures across the three dimensions. ste p count [fig. 1] and time spent active [fig. 2] decreased 1 day prior to reported symptom onset date (index date), with a minimum at day 2 of -.24 std. dev. for step count and -.25 std. dev for time spent active, and a return to baseline at day 8. sleeplessness [fig.3] and time spent in bed [fig. 4] increased one day prior to index, peaking 4 days after index at a mean increase of .16 std. dev. for sleeplessness and .13 std. dev. for time spent in bed, and returning to baseline at 7 days. heart rate was elevated from 1 day before index to day 6 with a peak increase of .18 std. dev. on days 2 and 3 after index. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e249, 2019 isds 2019 conference abstracts conclusions the potential of mhealth devices to register illness has been recognized [5]. this study is the first to present population-level influenza signals in a large network of mhealth users. mobile health device data linked to ili-specific survey responses taken during the 2017/18 flu season demonstrate clear aggregate patterns across several dimensions including sleep, steps, and heart rate. these signals suggest the potential for systems to rapidly process individual-level responses to classify ili and to use such classifiers for ili surveillance. the data described here, high resolution individual-level behavioral and physiological data linked to timely survey responses, suggests the potential to further enhance outbreak detection and improve characterization of ili patterns. the setting of our study, a very large network of mobile health device users who have consented to the prospective use of their data and to being queried about their health status, could provide a framework for automated prospective influenza surveillance us ing “real world evidence” [6]. employed over a population-representative sample, this approach could provide adjunct to standard clinically-based sentinel systems. references 1. althouse bm, scarpino sv, meyers la, et al. 2015. enhancing disease surveillance with novel data streams: challenges and opportunities. epj data sci. 4, 17. pubmed https://doi.org/10.1140/epjds/s13688-015-0054-0 2. henning kj. 2004. what is syndromic surveillance? mmwr suppl. 53, 5-11. pubmed 3. simonsen l, gog jr, olson d, viboud c. 2016. infectious disease surveillance in the big data era: towards faster and locally relevant systems. j infect dis. 214, s380-85. pubmed https://doi.org/10.1093/infdis/jiw376 4. https://www.myachievement.com/ 5. li x, dunn j, salins d, zhou g, zhou w, et al. 2017. digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. plos biol. 15(1), e2001402. pubmed https://doi.org/10.1371/journal.pbio.2001402 6. https://www.fda.gov/scienceresearch/specialtopics/realworldevidence/default.htm figure 1: mean standardized total steps around reported ili symptom onset date http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27990325&dopt=abstract https://doi.org/10.1140/epjds/s13688-015-0054-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15714620&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28830112&dopt=abstract https://doi.org/10.1093/infdis/jiw376 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28081144&dopt=abstract https://doi.org/10.1371/journal.pbio.2001402 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e249, 2019 isds 2019 conference abstracts figure 2: mean standardized active minutes around reported ili symptom onset date figure 3: mean standardized minutes awake around reported ili symptom onset date http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e249, 2019 isds 2019 conference abstracts figure 4: mean standardized time in bed around reported ili symptom onset date http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e441, 2019 isds 2019 conference abstracts opioid seizures by law enforcement in relation to emergency room visits tanner turley, evan mobley, andrew hunter health & senior services, state of missouri, jefferson city, missouri, united states objective to evaluate the relationship between heroin and non-heroin opioid seizures reported by law enforcement and the number of er visits due to heroin and non-heroin opioid poisoning in selected counties in missouri. introduction in 2016, there were approximately 63,000 deaths nationally due to drug overdose. this trend continues to increase with the provisional number of us deaths for 2017 being approximately 72,000 [1]. this increase in overdose deaths is fueled largely by the opioid class of drugs. the opioid epidemic began in the 1990s with a steady rise in prescription opioid overdoses. however, after 2010 a rise in heroin overdose deaths also began to occur. in addition to the heroin deaths increasing, there was a sharp rise in overdose deaths due to synthetic opioids including illicitly manufactured fentanyl beginning in 2013 [2]. in missouri, er visits follow similar trends with heroin overdose visits greatly increasing after 2011. while pdmps help function as data sources that provide information on the licit drug supply, they cannot give much knowledge on the illicit supply. because of this, drug seizure data from law enforcement can provide a much-needed tool in understanding the supply of illicit substances and their impact on a county’s morbidity. methods data sources used in this analysis include the el paso intelligence center (epic) drug seizure database thanks to cooperation by the midwest hidta (high intensity drug trafficking area) office and missouri highway patrol. er visit data was retrieved from the missouri patient abstract system, which includes er visits for non-federal hospitals. data was aggregated on a quarterly basis from 2014-2016 resulting in 12 observations (n) for every county observed. a subset of counties were selected and reviewed based on both high counts and high rates of ed visits for opioid overdoses [3]. the counties reviewed were franklin, greene, jefferson, st. francois, st. louis city and st. louis county. the majority of these counties were located in the greater st. louis are with greene and st. francois counties being notable exceptions. greene county contains the city of springfield and is located in southwest missouri. st. francois is the most rural county in our subset and is located south of the st. louis area. for each county, the number of er visits were compared to the number of drug seizures reported by law enforcement facilities in epic. numbers were compared for both heroin and non-heroin opioids. records were identified as a heroin overdose or non-heroin opioid overdose based on cdc drug poisoning guidance [4]. if an er discharge record contained codes for both heroin and a non-heroin opioid, the record was counted in the heroin column only. this method avoided counting records twice. the spearman correlation coefficient was calculated in sas to determine if there was a possible relationship between seizures and ed visits at the county level due to the relatively few data points, the presence of outlier observations in the seizure numb ers, as well as violations of statistical normality among the county seizure data. the spearman correlation coefficient is a better alternative in this case to the commonly used pearson correlation coefficient due to its ability to handle skewed data and outliers [5]. as with the pearson correlation coefficient, a score of 0 is read as the variables have no discernable relationships, and scores of 1 or -1 denote a perfect linear relationship between the observed variables (positive and negative respectively). results initial results showed correlational effects between ed visits and seizures to be generally moderate or weak on the county le vel. the strongest relationships observed were found in st. louis city for both heroin (r=-0.455) and non-heroin opioids (r=-0.51) as well as jefferson county for both heroin (r=0.536) and nonheroin (r=0.50). st. louis county also had a notable relationship for heroin seizures and heroin ed visits with r=-0.55. p values were also calculated to test if correlation values differed significantly from the null hypothesis of r=0 (i.e. no correlation). in all examined cases, there was no p value that was less than the sta ndard cutoff of 0.05 which indicates none of the results are markedly different given the null hypothesis of r=0 is true [6]. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e441, 2019 isds 2019 conference abstracts of particular interest is the contrast in results between st. louis city and jefferson county. st. louis city had a moderate negative relationship with seizures and ed visits with ed visits tending to decrease as drug seizures increased. whereas, jefferson county had a moderate positive relationship with ed visits increasing alongside drug seizures. due to their close geographic proximi ty, it is likely that both counties influence one another. further evaluation is required to gauge regional effects. conclusions due to the complexity of the opioid epidemic, the value of having varied data sources cannot be understated. while the correlational effects observed here are not indicative of a strong relationship between ed visits and drug seizures, further evaluation and research of both data sources is highly recommended. as additional data is gathered in the future, stronger analyses than the spearman correlation coefficient may be used to further explore the relationship between drug overdose morbidity and law enforcement seizure data. other relationships may also be explored such as drug seizures in relation to drug overdose mortality. references 1. national center for health statistics. (2018, september 12). retrieved september 19, 2018, from https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm 2. overdose o. (2017, august 30). retrieved september 19, 2018, from https://www.cdc.gov/drugoverdose/epidemic/index.html 3. bureau of health care analysis and data dissemination, missouri department of health and senior services. (2018, june 27). er visits due to opioid misuse. retrieved september 19, 2018 from https://health.mo.gov/data/opioids/pdf/opioid-dashboard-slide-16.pdf 4. cdc prescription drug overdose team. (2013, august 12). guide to icd-9-cm and icd-10 codes related to poisoning and pain. retrieved september 2018 from https://www.cdc.gov/drugoverdose/pdf/pdo_guide_to_icd-9-cm_and_icd-10_codes-a.pdf 5. mukaka m. (2012, september). a guide to appropriate use of correlation coefficient in medical research. retrieved september 20, 2018, from https://www.ncbi.nlm.nih.gov/pmc/articles/pmc3576830/ 6. wasserstein rl, lazar na. 2016. the asa’s statement on p-values: context, process, and purpose. am stat. 70(2), 129-33. https://doi.org/10.1080/00031305.2016.1154108 http://ojphi.org/ https://doi.org/10.1080/00031305.2016.1154108 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e371, 2019 isds 2019 conference abstracts acute hepatitis a infection and vaccination in the veterans health administration gina oda1, cynthia a. lucero-obusan1, patricia schirmer1, mark holodniy1, 2 1 public health surveillance and research, department of veteran affairs, palo alto, california, united states, 2 stanford university division of infectious diseases and geographic medicine, stanford, california, united states objective to describe the epidemiology of hepatitis a virus (hav) within the veterans health administration (vha). introduction since hepatitis a vaccination became widely recommended in the us in the mid-1990’s, rates of acute hepatitis a virus (hav) infection have steadily declined, however, since 2011, incidence of new cases of hav appears to be increasing [1], often linked with foodborne outbreaks and socio-economic trends such as homelessness and substance abuse [2]. in 2016, the cdc reported vaccination coverage among adults aged > 19 was 9.5%, 19-49 was 13.4%, and > 50 was 5.4% [3]. cdc issued a health alert network advisory in june 2018 with additional guidance on identification and prevention of hav and updates on outbreaks in multiple states [4] which prompted our program to conduct a more formal review of hav infections in vha. herein we describe recent trends in hav infection, vaccination and associated risk factors among veterans. methods we queried va data sources from october 1, 2016 – june 30, 2018 for hav igm laboratory tests, hav-coded outpatient encounters and hospitalizations (icd-10-cm: b15), and pharmacy data for hepatitis a vaccinations administered in vha outpatient and inpatient settings. patients with coded hav encounter or hospitalization were compared to individuals with hav igm positive results to determine positive predictive value (ppv) of hav outpatient and inpatient diagnostic codes. a total of 30 (20 outpatient and 10 inpatient hav encounters across both fiscal years) were randomly selected for detailed chart review to determine if patients were properly coded. additionally, patients with positive hav igm results were analyzed for icd -10-cm coded outpatient and inpatient encounters indicative of homelessness (icd-10-cm: z59.0) and/or substance abuse (icd-10-cm: f1x, excluding nicotine and cannabis). rates were calculated using total unique users of vha care for matching fiscal year ti me frames and geographic area as denominators. we reviewed a sample of 10 electronic medical records (emr) of patients from hawaii to determine vaccine indications in the setting of a state-wide outbreak. results a total of 136,970 hav igm tests were performed between october 1, 2016 – june 30, 2018. we identified 247 unique patients with positive hav igm. the overall incidence during the study time period was 2.05 per 100,000 population of unique users of vha care. the state with the highest incidence was west virginia (9.49 per 100,000) (figure 1). the overall percent positivity of patients tested for hav igm was 0.18% (highest of 1.16% for kentucky). there were 1,085 hav-coded outpatient encounters (680 unique patients) but only 58 patients had a positive hav igm result (ppv= 8.5%). there were 371 hav-coded hospitalizations (335 unique patients) but only 39 patients had a positive hav igm result (ppv=11.6%). among these encounters, 270 outpatients had hav documented as the principal diagnosis for the visit (40 of these were hav igm+) and 38 hospitalized patients had hav as the principal discharge diagnosis code (25 of these were hav igm+). therefore, the ppv when hav was the principal diagnosis code improved to 14.8% for outpatient encounters and 65.8% for inpatients. chart review of 30 randoml y selected outpatient and inpatient hav-coded emr found that only 3 (10%) were correctly coded. of the remaining 27, 14 (47%) had a positive hav igg or hav total test result, but negative or no hav igm testing, 3 (10%) had a remote history of hav, 3 (10%) were rule-out hav but testing was negative, 2 (7%) were miscodes of hepatitis b (hbv) or hepatitis c virus (hcv) infections, and 5 (17%) were other miscodes. the median vaccination rate during the study time frame was 0.31% [range: 0.11% (puerto rico) to 3.48% (hawaii)]. additional states with vaccination rates above the median included kentucky, michigan, west virginia, and california (1.05%, 1.02%, 0.93%, 0.67%, respectively). review of 10 sample emr of patients from hawaii, the state with highest vaccination rate, indicated that during their vaccination peak, patients were receiving the 2 nd in their 2-dose hav http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e371, 2019 isds 2019 conference abstracts series, the first having been given in august 2016 at the time of a state-wide outbreak associated with raw scallops. of 247 patients with positive hav igm, 91 (37%) had presence of icd-10cm encounter codes for one or more of the following risk factors associated with hav outbreaks (in order of frequency): substance abuse (63/247; 26%), homelessness (36/247; 15%), hcv (30/247; 12%), and hbv (2/247; 0.8%). wayne county, mi, jefferson county, ky, and san diego county, ca all had clustering of 4 or more cases of acute hav with risk factors of homelessness, substance abuse, and hcv. conclusions acute hav was identified in the vha patient population in states associated with recognized outbreaks during the study time frame. associated risk factors of substance abuse, homelessness, and hcv found in the veteran population also matched national hav outbreak data, including clustering in specific counties where outbreaks occurred. overall, ppv for hav-coded encounters was low for both inpatients and outpatients due to frequent miscoding. ppv was improved among inpatients with a principal discharge diagnosis of acute hav. vaccination rates were likely underestimated as data prior to the study time period was not evaluated and patients may have received vaccine outside of va, however rates tended to be above the median in states with known outbreaks, possibly indicating ongoing response. in the case of hawaii, emr review indicated that a strong public health resp onse demonstrated by a high post-outbreak vaccination rate with veterans being monitored and brought back for their 2nd of 2 vaccine series occurred following the august 2016 hav outbreak associated with raw scallops.2 additional education of va providers is warranted regarding the timely recognition of, proper testing for, and coding of acute hav infections and improving vaccination rates, particularly among individuals who are at increased risk for infection or complications from hav. acknowledgement the views expressed are those of the authors and do not necessarily reflect the position or policy of the department of veterans affairs or the united states government. references 1. cdc. 2016. progress toward eliminating hepatitis a disease in the united states. mmwr suppl. 65(1), 2941. pubmed https://doi.org/10.15585/mmwr.su6501a6 2. cdc. hepatitis a outbreaks. https://www.cdc.gov/hepatitis/outbreaks/hepatitisaoutbreaks.htm. accessed september 6, 2018. 3. cdc. national health interview survey, atlanta, ga: us department of health and human services, cdc; 2016. https://www.cdc.gov/vaccines/imzmanagers/coverage/adultvaxview/pubs-resources/nhis2016.html#hepa. accessed september 12, 2018. 4. cdc. health alert network advisory: outbreak of hepatitis a virus (hav) infections among persons who use drugs and persons experiencing homelessness. june 11, 2018. https://emergency.cdc.gov/han/han00412.asp. accessed september 6, 2018. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26916458&dopt=abstract https://doi.org/10.15585/mmwr.su6501a6 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e371, 2019 isds 2019 conference abstracts figure 1. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e237, 2019 isds 2019 conference abstracts development of a custom spell-checker for emergency department data sophie rand, ramona lall bureau of communicable diseases, nyc dohmh, long island city, new york, united states objective to share progress on a custom spell-checker for emergency department chief complaint free-text data and demonstrate a spellchecker validation shiny application. introduction emergency department (ed) syndromic surveillance relies on a chief complaint, which is often a free -text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. we are building a spell -checker, customized with language found in ed corpora, as our first step in cleaning our chief complaint field. this exercise would el ucidate the value of pre-processing text and would lend itself to future work using natural language processing (nlp) techniques, such as topic modeling. such a tool could be extensible to other datasets that contain free-text fields, including electronic reportable disease lab and case reporting. methods chief complaints may contain words that are incorrect if they are misspelled (e. g.,“patient has herpertension”), or, if the word yields a syntactically incorrect phrase (e.g., the word “huts” in the phrase: “my toe huts”). we are developing a spell-checker tool for chief complaint text using the r and python programming languages. the first stage in the development of the spellchecker is the identifying and handling of misspellings; future work will address syntactic errors. known abbreviations are identified using regular expressions, and unknown abbreviations are addressed by the spell-checker. the spell checker performs 4 steps on chief complaint data: identification of misspellings, generation of a substitute candidate word list, word sense disambiguation to identify replacement word, and replacement of the misspelled word, based on methods found in the literature [1]. as the spell-checker requires a dictionary of correctly spelled, healthcare-specific terms including all terms that would appear in an ed corpus, we used vocabularies from the unified medical language system, ed-specific terminology, and domain expert user input. dictionary construction, misspelling identification algorithms, and word list generation algorithms are in the development stage. simultaneously, we are building an r shiny interactive web application for syndromic surveillance analysts to manually correct a subset of misspelled words, which we will use to validate and evaluate the performance of the spell -checker tool. references 1. tolentino hd, matters md, walop w, et al. 2007. a umls-based spell checker for natural language processing in vaccine safety [pubmed]. bmc med inform decis mak. 7(1). doi:https://doi.org/10.1186/14726947-7-3. pubmed results project still in development phase. http://ojphi.org/ https://doi.org/10.1186/1472-6947-7-3 https://doi.org/10.1186/1472-6947-7-3 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17295907&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e237, 2019 isds 2019 conference abstracts conclusions the audience will learn about important considerations for developing a spell-checker, including those for data structure of a dictionary and algorithms for identification of misplaced words and identification of candidate replacement words. we will demonstrate our word list generation algorithm and the shiny application which uses these words for spell -checker validation. we will share relevant code; after our presentation, audience members should able to apply code and lessons to their own projects and/or to collaborate with the nyc department of health and mental hygiene. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e428, 2019 isds 2019 conference abstracts childhood home based unintentional injuries surveillance in punjab india ravinder k. soni dayanand medical college and hospital, ludhiana, punjab, india objective 1. to determine the prevalence and pattern of unintentional injuries among children 2. to study the physical environment of house for various risk factors leading to unintentional injuries. introduction injury and violence are public health problems now-a-days all over the world. over 950000 children less than 18 years of age die as a result of injuries, 95% of which occur in low and middle income countries (lmic) including india. unintentional injuries account for 90% of these cases. the death rate due to unintentional injuries is almost double in lmic as compared to developed countries. it is seen that most of childhood unintentional injuries occur in and around the home of children. india, with a population of app 1.25 billion, had about 40% children. india is passing through a major socio-economic, epidemiological and technological transition. migration and rapid urbanization is contributing towards the development and growth. mechanization is changing the traditional lifestyle and thereby resulting an increase in injuries in india. despite efforts to understand the burden of injuries, the magnitude in terms of morbidity and mortality is still not clear as injury information did not receive much importance in the health sector. few small studies have reported the prevalence and causes of childhood unintentional injuries. however, there is lack of proper surveillance data on burden of unintentional injuries among children methods we conducted a surveillance study in 30 villages of ludhiana district of punjab, india. a total of 900 houses having at least one child below the age of 19 years formed the sample of study. the data pertaining to socio demographic profile, physical environment of house and injury details (in last 5 years) were recorded on the pre designed performa. the data were statistically analyzed using spss version 20.0. results in the 900 houses, there were 1910 children below the age of 19 years. of these houses, unintentional injuries to their children was observed in 386(42.9%) houses during the last 5 years. the prevalence of unintentional injuries among children was 20.6%. there were 60.1% children below the age of 5 years who suffered injuries. 67% of injuries occurred among male children. majority of injuries (63.5%) occurred during evening time, of which 87.2% occurre d while the child was playing in and around the home. fall was the most common (64.5%) mode of injury followed by 12.3% cut injuries due to chopper/fodder cutter or old instruments/machinery lying the courtyard. there were 16 injuries due electric current, 6 cases of dog bite, 4 cases of drowning and 4 cases of unintentional ingestion of poisonous substance. 48.3% of injuries were severe, 35.9% minor, 9.4% trivial and 5.9% very severe. there were 2 fatal death also. conclusions the burden of unintentional childhood injuries in india is substantial but it a neglected health problem. there is a strong need for continuous surveillance of childhood injuries in india using systematic techniques so as to plan for timely intervention. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e342, 2019 isds 2019 conference abstracts strengthening health surveillance through the development of interagency relationships emily v. glidden, royal k. law hsb/ett, cdc/ondieh/nceh, atlanta, georgia, united states objective to discuss the development of a set of tools for interagency collaborations on health surveillance to determine the core contents of the tools based on known gaps in health surveillance to determine collaborators in development and timelines for completion introduction in 2010, the council of state and territorial epidemiologists (cste) identified data collected by poison centers (pcs) as an important tool for allhazards exposure and illness surveillance. in response to this, the centers for disease control and prevention (cdc), cste, and the american association of poison control centers (aapcc) members created the poison center public health community of practice (cop). the cop acts as a platform, to facilitate sharing experiences, identify best practices, and develop relationships among federal agencies, state and local health departments (hd), and pcs. since its inception, the c op garnered over 250 members, hosted more than 25 webinars regarding pc-hd collaborations, and produced five newsletters highlighting subjects pertinent to pc and hd personnel. to date, the cop’s primary focus has been to strengthen pc-hd partnerships; however, recent events highlight opportunities to expand the public health impact of the cop. in this roundtable, we will discuss how the cop was leveraged by federal and state health agencies to build new multidisciplinary and inter-agency relationships and how these experiences have led to the proposed guidance. some examples are summarized below. in march 2018, the illinois department of public health (idph) responded to a cluster of synthetic cannabinoid exposures presenting to healthcare facilities with unexplained coagulopathy. the cdc’s national center for environmental health (nceh) also identified the outbreak via routine surveillance activities using the national poison data system (npds). in response, nceh enhanced their npds surveillance activities, to monitor for related clusters elsewhere, and facilitated information sharing between pcs, hds and federal agency partners. nceh requested that the cop provide a national platform for nceh and idph to disseminate information on the outbreak in a timely manner. representatives of the drug enforcement agency (dea) and the federal bureau of investigation (fbi) participated in the webinar and informed stakeholders about associated regulations and law enforcement efforts. nceh was able to disseminate case definitions for public health surveillance as well as relevant points of contact for pc and hd staff to report suspected outbreak-related cases. following the webinar, nceh collected previously unreported, potentially outbreak-related cases. the webinar hosted 130 attendees (many reporting multiple participants per attendee record), largely due to the cop’s expansive network and helped enable information sharing, communication, and collaboration among national and state stakeholders. also in march 2018, the cop hosted a webinar to inform members of the ongoing inter-agency public health efforts on hab surveillance, toxicology, and regional protection efforts. harmful algal blooms (habs) are a growing public health problem an d pc data collection is an invaluable health surveillance resource. in the webinar members from nceh, the united states environmental protection agency (epa), national center for emerging zoonotic infectious disease (ncezid), and the florida department of health all presented information about on their individual activities with habs. the webinar discussions identified 1) shortfalls in current national poison data system (npds) exposure monitoring efforts for habs and 2) a lack of familiarity with existing public health resources for state and local agencies to report exposures and access hab subject matter experts (smes). as a result, nceh habs smes and aapcc personnel have begun to help modify current npds habs coding and develop new codes to bolster ongoing health surveillance efforts to improve hab exposure monitoring. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e342, 2019 isds 2019 conference abstracts description findings and lessons learned from activities outlined in the introduction include the need for: 1) standardized inter -agency communication, 2) increased knowledge and utilization of state reporting and monitoring systems, and 3) inter-agency collaborations to prevent the duplication of efforts. in this roundtable, we will: 1) discuss how to develop information and tools for inter-agency public health communication and messaging, 2) identify key stakeholders including potential national, state, and local agencies who can help bolster communication messaging, and 3) develop appropriate points of contact within these agencies. potential components of the guidance may include: 1) a comprehensive list of state resources available to pc and ph personnel, 2) recommended interagency points of contact, 3) lessons learned from collaborative projects, and 4) pc abilities to share and analyze data for public health practice and health surveillance. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e329, 2019 isds 2019 conference abstracts use of slaughter condemnation data to detect cattle health events in near real-time judy akkina, leah estberg usda, united states objective use united states cattle slaughter condemnation data as an animal health indicator for early detection of health events and to characterize trends in condemnation reasons. introduction data collected at livestock slaughter can be a useful source of non-specific health indicators including clinical signs, symptoms and proxy measures [1]. when monitored in near real-time, this data can enable the detection of both livestock and human health threats [1]. in the united states (us), the federal meat inspection act requires ante-mortem inspection of animals and post-mortem inspection of carcasses by veterinarians to ensure the meat product will be fit for human consumption [2]. inspections are carried out by the united states department of agriculture (usda) food safety inspection service (fsis) and results are recorded in the public health information system (phis), including the reason for condemnation if the animal or carcass is deemed unfit. since april 2016 the usda, animal and plant health inspection service (aphis), veterinary services (vs) has used data from the phis to monitor changes in the weekly count/rate of cattle condemnations for three cattle types, beef cows, dairy cows, and fed cattle (steers and heifers), and for selected condemnation reasons (central nervous system (cns), dead, emaciation, mastitis, moribund, pneumonia, pyrexia, and septicemia). these eight reasons were chosen from 45 potential reasons recorded at condemnation as likely to identify clinical signs associated with foreign animal diseases of interest and to monitor trends in important animal health issues such as pneumonia and mastitis. methods phis slaughter and condemnation data is downloaded weekly and stored and analyzed in an access database. tableau visualization software is used for mapping and time series signaling charts (example basin chart figure 1). only establishments slaughtering more than 600 cattle/week are included in analysis because smaller establishments may not operate weekly and many have very low slaughter volumes. with the small establishments excluded, our monitoring covers 93%, 90%, and 85% of slaughtered beef, dairy, and fed cattle, respectively. weekly analysis is conducted by cattle type for each included establishment and for establishments grouped into geographic based catchment basins. the basins were created to identify signals occurring over a region, even if at the establishment level the increase is not large enough to produce a signal. another purpose for basins is to allow sharing of results at a summary level that does not identify an individual establishment. weekly normalized condemnation counts are created by dividing the actual condemn counts by the total number destined for slaughter that week, and then multiplying by the average number destined for slaughter per week for the past year. the early aberration reporting system (ears c3) time series alerting algorithm is used to identify unusual increases (signals) in these weekly normalized condemnation counts. an analyst characterizes establishment and basin signals as unremarkable, notable or needing follow up with fsis. an unremarkable signal is defined as at least one previous signal or normalized count of similar magnitude in the past 12 months and no signal for that establishment the previous week. a notable signal is defined as no previous signal or normalized count of similar magnitude in the past 12 months or two or more signals occurring over sequential weeks. basin signals are researched to determine which establishments are responsible for the signal. an analyst determines which highly notable basin and establishment signals need follow up with fsis. results are summarized in a weekly report for vs cattle commodity staff about any noteworthy increases in condemnations which could indicate the emergence of disease and may warrant further investigation. in addition a 2017 annual report was completed to describe, visualize, summarize and compare condemnations from 2015-17 by cattle type and condemnation reason. results in 2017 the percentage of beef, dairy and fed cattle condemned for the eight monitored reasons out of all cattle presented for slaughter at the monitored establishments was 0.375%, 1.651%, and 0.022%, respectively. for beef cows the three most frequent reasons were pneumonia, dead and septicemia, accounting for 84.3% of monitored condemnations. cows that either arrive at the establishment dead or die at the establishment prior to slaughter are included in the dead condemnation count. for dairy cows the three most frequent reasons were dead, septicemia and pneumonia, accounting for 93.6% of condemnations. for fed cattle the three http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e329, 2019 isds 2019 conference abstracts most frequent reasons were pneumonia, dead, and septicemia, accounting for 98.2% of condemnations. beef cow condemnations exhibited consistent seasonal trends for dead, emaciation, mastitis and pneumonia. dairy cow condemnations exhibited consistent seasonal trends for dead, emaciation and pyrexia. fed cattle condemnations were less influenced by seasonal trends with only dead and septicemia showing some consistent seasonality. during 2017 three notable establishment signals required follow up with fsis: cns in dairy cows, septicemia in fed cattle, and dead in beef cows. for 2018 (through august) 5 notable establishment signals required follow up: septicemia in both beef and dairy cows, emaciation and cns in beef cows, and dead in dairy cows. signals were attributed to various reasons including: changes in establishment protocol (cns), relief inspectors classifying condemns differently and animals not responding to treatment on the farm (septicemia), a holiday effect with sick animals held longer in holding pens before slaughter (dead), a decrease in the quality of cattle purchased by the establishment (emaciation), and extremely hot weather (dead). conclusions a higher percentage of dairy cattle were condemned overall and were more likely to be condemned for being dead on arrival or just prior to slaughter compared to beef or fed cattle. culled dairy cows tend to be older than beef or fed cattle and are maintained in an intense production system, therefore they are more likely to develop age related problems or chronic diseases [3]. in addition to timely identification of potential animal health issues, cattle producers could use this information to help focus on management practices which may decrease condemnations and result in improved animal health and revenue for producers [2]. some limitations to this work are that trends described only apply to the cattle population slaughtered at fsis inspected facilities and included in our monitoring. condemnations are determined by fsis inspectors at each individual slaughter establishment resulting in the potential for inspector bias to affect the data. references 1.dupuy c, morignat e, dorea f, cucrot c, calavas d, et al. 2015. pilot simulation study using meat inspection data for syndromic surveillance: use of whole carcass condemnation of adult cattle to assess the performance of several algorithms for outbreak detection. epidemiol infect. 143(12), 2559-69. pubmed https://doi.org/10.1017/s0950268814003495 2. white t, moore d. (2009) reasons for whole carcass condemnations of cattle in the united states and implications for producer education and veterinary intervention. javma oct;235(8). 3. haredasht s, vidal g, edmondson a, moore d, silva-del-rio n, martinez-lopez b. (2018) characterization of the temporal trends in the rate of cattle carcass condemnation in the us and dynamic modeling of the condemnation reasons in california with a seasonal component. front vet sci jun;5(87). figure 1 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25566974&dopt=abstract https://doi.org/10.1017/s0950268814003495 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e356, 2019 isds 2019 conference abstracts towards estimating childhood obesity prevalence using electronic health records timothy d. mcfarlane epidemiology, indiana university richard m fairbanks school of public health, indianapolis, indiana, united states objective to discuss the use of electronic health records (ehrs) for estimation of overweight and obesity prevalence in children aged 2 to 19 years and to compare prevalence between the convenience sample obtained from ehrs to prevalence adjusted for potential selection bias. introduction although recent data suggests childhood obesity prevalence has stabilized, an estimated 1 in 3 u.s. childre n are overweight or obese [1]. further, there is variation by racial and ethnic groups, location, age, and poverty [2], resulting in a need for local data to support public health planning and evaluation efforts. current methods for surveillance of childhood weight status rely on self-report from community-based surveys. however, surveys have long time intervals between data collection periods, are expensive, and are not often able to produce precise small -area estimates. ehrs have been increasingly proposed as an alternative or supplement to community surveys. childhood weight and height is collected as a part of routine care, and leveraging these data from ehrs may provide rapid and locally precise esti mates of childhood weight status. a concern for the use of ehrs is the potential for selection bias. ehrs represent only those seeking healthcare and may not generalize to the population. additionally, the type of clinical visit (e.g., wellness vs. acute) may affect the prevalence estimates and the likelihood of collecting height and weight data in the ehr. thus, in addition to ehrs being a convenience sample, there may be additional selection biases based on the type of visit and whether height and weight was measured and recorded. the current work sought to quantify the effect of visit type on childhood overweight and obesity prevalence and generate weights to adjust prevalence for potential ehr-related selection bias. methods two years (2014-2015) of ehr data were obtained from the indiana network for patient care, a community health information exchange. data included clinical encounters of patients living in the eight-county metropolitan area of indianapolis, indiana. bmi was calculated using recorded height and weight from the most recent encounter. encounters were screened for valid bmi entries by examining records in the 0-5th and 95-100th percentiles. bmi results were validated using the following procedure: censoring records with one encounter; removing encounters with implausible values (5 < bmi < 100); calculating the mean bmi across remaining encounters; calculating the percent difference from the mean bmi for each encounter; and removing encounters with bmi results greater or less than 10% from the mean bmi. records which could not be validated were censored and treated as missing height and weight. using the ageand sexspecific centers for disease control and prevention growth charts, patients were classified as underweight (0-5th percentiles), normal weight (5-85th percentiles), overweight (85-95th percentiles), and obese (>95th percentile). wellness visits were identified using the following icd-9-cm or icd-10-cm diagnosis codes: v20.2, v70.0, v70.9; and z00.121, z00.129, z00.00, z00.01. to adjust for potential selection bias, two stabilized inverse probability weights (sipw) were constructed. first, to account for potential selection bias induced by visit type and, second, to account for potential selection bias due to censoring (i.e., missing height and weight data). the sipw were generated using logistic regression models to calculate the predicted probabilities for visit type and uncensored observations as a function of the covariates race, ethnicity, age, gender, and insurance. the sipw weights were specified as depicted below, where w=1 is a wellness visit, l=observed covariates, and c=0 is uncensored for each child, i. [figure 1] the final weight (swfinal) was applied to the sample to create a pseudo-population in which there is no association between covariates, l and visit type and which has the same distribution of covariates, l, as the censored individuals not included in the http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e356, 2019 isds 2019 conference abstracts 1 pseudo-population, thus making censoring occur at random, given the observed covariates. under the assumption of exchangeability and no unmeasured or residual confounding, the pseudo-population will no longer have selection bias due to differences in visit type and missing data. results the sample consisted of 130,626 unique individuals between the ages of 2 and 19 years, of which 92,755 (71%) had at least one recorded height and weight result. of the 10,184 records screened for bmi results, 5,242 (51%) were validated using measurements from previous encounters. the final sample consisted of 87,804 records with a valid bmi result (67%) and 42,822 records censored due to missing data (33%). compared to the u.s. census, the ehr sample overrepresented older girls (e.g., 31.2% vs. 41.2% 1519 year-old girls) and under-represented younger girls (e.g., 34.3% vs. 29.5% for 5-9 year-old girls). wellness visits were associated with censoring due to missing data; only 3% of censored encounters were wellness visits compared to 33% of uncensored encounters [p(χ21>14437 =< 0.0001)]. in the unweighted sample, the overall prevalence of overweight or obesity was 36.5%. the overweight or obesity prevalence was lower among wellness visits2 (33.9%) than other visits (37.8%; p(χ 1>124.2=< 0.0001). similarly, wellness visits had lower prevalence estimates when stratified by sex, race, age, ethnicity, and insurance (table 1). after weighting the sample by swfinal, the overall prevalence of overweight or obesity was 36.2% and the difference between wellness (35.1%) and other visits (36.7%) was attenuated, though statistically significant [p(χ2 >22.2 =<0.001). likewise, the differences between wellness and other visits in the weighted pseudo-population were attenuated when stratified by covariates, compared to unweighted analyses (table 1). while the sipw method demonstrated some adjustment for selection bias due to visit type and censoring due to missing data, the adjustment was incomplete, likely as a result of unmeasured and imperfectly measured covariates. conclusions wellness visits were associated with lower childhood overweight and obesity prevalence and were more likely to have weight and height measurements recorded in the ehr than other visit types. adjusting prevalence for ehr-related selection bias using stabilized inverse probability weights may produce more valid estimates but the lack of social determinant data in ehrs results in imperfect adjustment. future work should integrate individualor community-level social determinants of health data into the weighting models. references 1. skinner ac, skelton ja. 2014. prevalence and trends in obesity and severe obesity among children in the united states, 1999-2012. jama pediatr. 168(6), 561-66. pubmed https://doi.org/10.1001/jamapediatrics.2014.21 2. ogden cl, et al. 2018. differences in obesity prevalence by demographics and urbanization in us children and adolescents, 2013-2016. jama. 319(23), 2410-18. pubmed https://doi.org/10.1001/jama.2018.5158 figure 1. table 1. overweight and obesity prevalence differences between wellness and other clinical visit types. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24710576&dopt=abstract https://doi.org/10.1001/jamapediatrics.2014.21 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29922826&dopt=abstract https://doi.org/10.1001/jama.2018.5158 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e356, 2019 isds 2019 conference abstracts unweighted %△ * weighted %△ * overall -3.9 -1.6 sex female male -4.6 -3.0 -1.2 -2.1 race white black/african american asian other unknown -5.3 -1.7 0.0 -7.1 -3.7 -2.3 -0.6 0.5 -6.1 -0.2 ethnicity hispanic or latino non-hispanic of latino unknown -0.5 -3.8 -4.2 0.5 -1.6 -3.5 age 2-5 years 6-11 years 12-19 years -2.8 -2.7 -3.2 -2.2 -1.4 -1.6 care setting emergency inpatient outpatient other 4.6 13.2 -3.0 -52.1 6.6 17.3 -1.2 -48.7 insurance commercial medicaid/medicare other unknown -2.9 -3.4 -2.7 10.5 -2.4 -1.7 -2.1 5.6 *%△ = (prevalence % among wellness visits) (prevalence % among other visits) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e414, 2019 isds 2019 conference abstracts systematic review: national notifiable infectious disease surveillance system in china liping wang1, xiang ren1, 2, benjamin j. cowling2, lingjia zeng1, mengjie geng1, peng wu2, zhongjie li1, hongjie yu3, george gao1 1 chinese center for disease control and prevention, beijing, beijing, china, 2 the university of hong kong, hong kong, hong kong, china, 3 fudan university, shanghai, china objective we aimed to review the development and changes of national notifiable disease surveillance system (nndss) from 1950 to 2013, and to analyze and summarize the changes in regulations and public health surveillance practices in china. introduction infectious disease was the second most common cause of death in 1949, and the epidemic situation of infectious diseases was so severe that the chinese government made major investments to the control and prevention of infectious diseases. during the past 60 years the development of the notifiable disease surveillance system in china has experienced 3 phases, including germination stage, development stage, improvement and consolidation stage [1]. as the quality of infectious diseases surveillance has been improved stepwisely, the national morbidity of class a and b notifiable disease decreased from 7157.5 per 100,000 in 1970 to 225.8 per 100,000 in 2013, and the mortality decreased from 56.0 per 100,000 in 1959 to 1.2 per 100,000 in 2013 [ 2]. methods research articles, online reports and grey literature from january 1950 to february 2013 relevant to disease surveillance in china were searched in databases including pubmed, china national knowledge infrastructure (cnki), and wanfang data. retrieved articles were screened by inclusion criteria of containing the infectious diseases prevention and control, related laws and regulations, and development of surveillance system. results in the systematic review, 20 articles were retrieved from pubmed, 1129 articles from cnki, 480 articles from wanfang database, after abstract screening and eliminating overlaps, 73 articles were included, including 10 english articles and 63 chinese articles. laws and regulations on notifiable diseases in china administrative measures for infectious diseases control was issued in 1955 to deal with 18 diseases (classes a and b) for their notification, monitoring, reporting and treatment. in 1956, 7 more infectious diseases were added into class b infectious diseases. regulation on the administration of acute infectious diseases was issued in 1978, infectious diseases in class a and b including suspected cases must be reported within specific time respectively. the law of the people's republic of china on the prevention and treatment of infectious diseases, the first infectious disease law in china, was issued in 1989 and revised in 2004. the number of notifiable infectious diseases was increased to 35, including 2 class a, 21 class b and 12 class c notifiable diseases in 1989. the 2004 revised version contained total 37 notifiable diseases and clarified infectious disease prevention, epidemic situation report, notification and release, epidemic control, medical treatment, supervision and management, logistic measures, legal responsibility and supplementary provisions. the organization of notifiable disease surveillance and management in 1950s, the government administration council approved the bill of the establishment of health epidemic prevention stations (heps) nationwide. chinese academy of medical sciences (cams) was established in 1956, and the chinese academy of preventive medicine (capm) was established in 1986, which was in charge of the national infectious disease surveillance data collection, management, analysis and feedback. in 2002, the capm officially changed its name to the chinese center for disease control and prevention (cdc), so did all levels of health epidemic prevention station. as mentioned in the law of the people's republic of china on the prevention and treatment of infectious diseases, cdcs at all levels are responsible for infectious disease http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e414, 2019 isds 2019 conference abstracts surveillance, prediction, epidemiological investigation, epidemic reporting and other prevention and control. in addition, the law clarified the establishment of infectious disease surveillance system, the specific duties and tasks of the administrative department of public health and healthcare technology institutions in the infectious disease surveillance (figure 1). notifiable diseases diagnostic criteria notifiable diseases diagnostic criteria (trial edition) was issued in 1990 and revised in 2004. diagnostic criteria defined suspected case, probable case and confirmed case. suspected case mainly based on clinical symptoms and signs; probable case was the suspected cases with hemogram blood test. confirmed case was based on blood test results and pathogen specific antigen or antibody test results, e.g. ig g, ig m or virus tested positive among suspected or probable cases. reporting method of notifiable diseases surveillance system during 1950 to 1985, monthly collection of reports was delivered by post mail level by level hierarchically (from county hepss to prefectural or city hepss, then to provincial hepss and eventually to capm). the notifiable infectious diseases reporting network covered the whole country firmly launched at the mid-1960s. in 1986, the prototype of electronic reporting was sprout. over 200 network nodes achieved electronic submission of the national notifiable infectious diseases monthly report by post-delivery, some provinces even had adopted more efficient reporting means by e-mail autonomously. during 1986 to 2003, different reporting cards are used for collecting class a, b, and c infectious diseases respectively. at the beginning of each year, the provincial hepss updated the population census data and the administrative changes. at the end of each year, the provincial hepss reported detailed age-gender and occupation specific diseases morbidity and mortality data, as well as amended monthly reports for delay or missing, to capm. the internet-based timely reporting system was officially launched in 2004. china cdc built the notifiable infectious diseases and emergent public health event reporting system that covered all hospitals and medical institutes nationwide, which collected individual case data with unified reporting card. by 2013 the system with over 70,000 reporting units covered 100% county and above level cdcs, 98% of county and above level medical institutions, and 94% of township level healthcare units. conclusions monthly reporting was replaced by real-time reporting, and the weekly, monthly and yearly cumulative incidence and death was replaced by individual case reporting. the hierarchical reporting structure, were changed to reporting directly to national data center. the notifiable disease surveillance system network has been expanded, the diagnosis capacity and criteria, surveillance data report methods and sensitivity have been improved gradually. the notifiable disease surveillance system optimized step by step with internet-based timely reporting technology and direct filling infectious disease case information from healthcare facilities. acknowledgement the views expressed are those of the authors and do not necessarily represent the policy of the chinese center for disease control and prevention. references 1. cheng m. 2005. etc. the history and development trend of disease surveillance in china. dis surveill. 20(3), 113-14. 2. the national health and family planning commission of the people's republic of china. the national epidemic situation of notifiable diseases in 2012. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e414, 2019 isds 2019 conference abstracts figure 1 organization chart of notifiable diseases surveillance system http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e262, 2019 isds 2019 conference abstracts syndrome definitions for drug overdose: how far down the rabbit hole do we go? alana m. vivolo-kantor, brooke hoots division of unintentional injury prevention, centers for disease control and prevention, atlanta, georgia, united states objective to discuss the process for developing and revising suspected drug overdose queries in syndromic surveillance (sys) systems. introduction state and local jurisdictions have been exploring the use of sys data to monitor suspected drug overdose outbreaks in their communities. with the increasing awareness and use of sys systems, staff from the centers for disease control and prevention (cdc) worked to develop several queries that jurisdictions could use to better capture suspected drug overdose visits. in 2017, cdc released their first two queries on heroin overdose and opioid overdose, followed in 2018 by stimulant and all drug overd ose queries. over time, and with the assistance from the sys community and the cdc-funded enhanced state opioid overdose surveillance (esoos) state health departments, cdc has revised the queries to address suggestions from jurisdictions. however, it’s not clear how often and in what way the syndrome definitions are updated over time. this is particularly true as new drugs emerge and the names of those drugs are integrated into syndrome definitions (e.g., recent “spice” and “k2” synthetic cannabi noid outbreaks). description this roundtable will provide a forum for national, state, and local users of sys and drug overdose syndrome queries to discuss the process of query development, with an eye towards determining when a definition is “good enough.” cdc staff will facilitate t he discussion and present the current portfolio of drug-related overdose queries. participants will be encouraged to provide feedback on the queries, share what has been/has not been working in their jurisdiction with regard to syndrome query development, and discuss the process for revising queries as the epidemic evolves. the focus of this roundtable will be on suspected drug overdose query development and revision with emergency department sys data. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e373, 2019 isds 2019 conference abstracts analysis of three crispr loci among yersinia pestis stains from georgia and neighboring countries ekaterine zhghenti, gvantsa chanturia molecular biology and genomic research, national center for disease control and public healt h, tbilisi, georgia objective the purpose of our study was crispr-based analysis of y. pestis isolates from georgia and neighboring countries. introduction particular family of tandem repeats, such as clustered regularly interspaced short palindromic repeats (crispr) found in a wide range of prokaryotic genomes. crisprs consist of highly conserved repeats interspaced with non-repetitive elements or "spacers" usually of viral origin. in the yersinia pestis genome, three crispr elements ypa, ypb and ypc are found. the distribution of spacers and their arrays in y. pestis strains is region and focus specific and can provide important information for genotypi ng and evolutionary research of bacteria. methods whole-genome sequencing (wgs) of 14 y. pestis strains from georgia, armenia, azerbaijan, russian federation (rf) and kirgizstan were performed using a combination of different next generation sequencing (ngs) platforms: illumina, pacbio and 454 technologies. identification of spacer sequences from crispr loci was conducted to evaluate the genetic diversity among the isolates. the spacers arrays were acquired and analyzed. drs and spacers were identified and extracted. the selected spacer sequences were compared against the microbial genome database in genbank. leader sequences were obtained for each crispr locus and aligned using mafft software. results in all 14 sequenced genomes, three crispr elements were found. all alleles show the same organization, conserved 28 bp repeated sequence is interspaced with spacers, 31 or 34bp in length. a total of 18 spacers were found: seven for the yp1, six for the yp2 and five foryp3. according to the spacer arrays sequence, biovar microtus strains from georgia and armenia had the same allel e sizes with the presence of seven motifs, for yp1 (a1b1c1d1e1m1n1), six motifs for yp2 (a2j2b2k2l2m2) and five for yp3 loci (a3b3c3d3e3) identical to pestoidesg (genotype 1). spacers/spacer arrays of biovar medievalis strains from georgia, azerbaije n and kabardino balkaria (rf) were the same as kim (genotype 59). conclusions crispr analysis of georgian strains identified two independent phylogenetic groups that is in agreement with previous study data (snp typing, is100 fingerprinting). biovar microrus strains from transcaucasian highland placed within one of the most ancient branches of the evolutionary model of y. pestis. this study enhances our understanding and fulfills the existing data of genetic characteristics of y. pestis strains circulating in the region. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e271, 2019 isds 2019 conference abstracts accuracy of icd codes for identification: review of chlamydia, gonorrhea and syphilis yenling a. ho1, saurabh rahurkar2, janet arno3, 4, brian e. dixon1, 2, 5 1 indiana university fairbanks school of public health, indianapolis, indiana, united states, 2 regenstrief institute, indianapolis, indiana, united states, 3 indiana university school of medicine, indianapolis, indiana, united states, 4 marion county public health department, indianapolis, indiana, united states, 5 roudebush va medical center, indianapolis, indiana, united states objective the purpose of this study is to review the extant literature for evidence on the validity of icd-9-cm and -10-cm codes for the purpose of identifying cases of chlamydia, gonorrhea, and syphilis. introduction administrative data refers to data generated during the processes of health care. these data are a rich source of patient health information, including diagnoses and problem lists, laboratory and diagnostic tests, and medications. established standards are used to code each data into the appropriate coding systems. the international classification of diseases, ninth and tenth revisions, clinical modification (icd-9-cm and icd-10-cm) codes are the coding standard for diagnoses and have been frequently used to identify cases for the creation of cohorts in examining care delivery, screening, prevalence, and risk factors [1,2]. however, while some studies have assessed the validity and reliability of icd-cm codes to identify various conditions such as cerebral palsy and rheumatoid arthritis [3,4], the evidence for using icd codes to accurately identify sexually transmitted infection (sti) cases is largely unexamined. the purpose of this study is to review the extant literature for evidence on the validity of icd codes for identifying cases of chlamydia, gonorrhea, and syphilis. our findings will inform efforts to improve the use of administrative data for sti-related health service and surveillance researches. methods our systematic review followed a protocol consistent with the preferred reporting items for systematic reviews and meta analysis (prisma). we comprehensively searched pubmed and scopus databases for peer-reviewed articles published before february 2018. articles were identified with search terms related to our stis of interest (chlamydia, syphilis or gonorrhea), pelvic inflammatory disease (pid), administrative codes, and validation studies. pid was included as 33% -50% of pid cases are due to chlamydia or gonorrhea [5]. only empirical publications appearing in peer-reviewed english language journals were included. further, we excluded articles classified as letters to the editor, policy briefs, perspectives, commentaries, summaries of fu ture research plans, and grey literature. additionally, articles without abstracts were also excluded. the screening process used by our review is outlined in figure 1. briefly, all articles were subjected to a two-step screening process. first, we reviewed articles based on title and abstract. we eliminated studies that did not focus on stis or on validation in the context of stis. articles were included if they focused on any combination of the stis of interest, or on pid, and were validation studies on diagnostic testing or administrative codes. second, selected articles were then reviewed in full to identify studies which included the stis of int erest, assessed and listed icd-9-cm or -10-cm codes, and measured validity. the snowball technique was used on included articles, whereby we reviewed all references found in the references of the included articles. results our search strategy identified 1,754 articles to be screened by title and abstract. of these, only five (0.29%) articles met the initial inclusion criteria. after full text review, only two articles [6,7] met the final inclusion criteria to be included in the systematic review. both articles focused on pid with no assessment of syphilis. they utilized icd-9-cm codes to identify cases with pid and performed chart reviews to determine true pid status. results of both articles found positive predictive value (ppv) of pid to be between 18%–79%. only one article [7] examined the ppv of chlamydia (56%; 5/9 cases) and gonorrhea (100%; 4/4 cases) separately. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e271, 2019 isds 2019 conference abstracts conclusions we identified just two studies that evaluated the validity of icd codes in identifying the stis of interest. both studies focused on pid cases in which chlamydia and gonorrhea diagnoses and tests might be documented. additionally, since both studies were published before 2015, neither evaluates the use of administrative data following the u.s. transition to icd-10 codes. given these findings, further studies are required to examine the predictive value of icd-9 and -10 codes for all three diseases in the general population. acknowledgement the work described is supported by the national library of medicine (nlm) training grant under grand number t15lm012502. the content is solely the responsibility of the authors and does not necessarily represent the official views of the nlm. references 1. tao g, zhang cx. 2008. hiv testing of commercially insured patients diagnosed with sexually transmitted diseases. sex transm dis. 35(1), 43-46. pubmed https://doi.org/10.1097/olq.0b013e318148c35a 2. evans he, mercer ch, rait g, et al. 2009. trends in hiv testing and recording of hiv status in the uk primary care setting: a retrospective cohort study 1995-2005. sex transm infect. 85, 520-26. pubmed https://doi.org/10.1136/sti.2008.034801 3. oskoui m, ng p, dorais m, et al. 2017. accuracy of administrative claims data for cerebral palsy diagnosis: a retrospective cohort study. cmaj open. 5(3), e570-75. pubmed https://doi.org/10.9778/cmajo.20170013 4. sauer bc, teng c-c, accortt na, et al. 2017. models solely using claims-based administrative data are poor predictors of rheumatoid arthritis disease activity. arthritis res ther. 19(1), 86. pubmed https://doi.org/10.1186/s13075-017-1294-0 5. haggerty cl, ness rb. 2006. epidemiology, pathogenesis and treatment of pelvic inflammatory disease. expert rev anti infect ther. 4(2), 235-47. pubmed https://doi.org/10.1586/14787210.4.2.235 6. satterwhite cl, yu o, raebel ma, et al. 2011. detection of pelvic inflammatory disease: development of an automated case-finding algorithm using administrative data. infect dis obstet gynecol. 2011, 428351. https://doi.org/10.1155/2011/428351. 7. ratelle s, yokoe d, blejan c, et al. 2003. predictive value of clinical diagnostic codes for the cdc case definition of pelvic inflammatory disease (pid): implications for surveillance. sex transm dis. 30(11), 866-70. pubmed https://doi.org/10.1097/01.olq.0000087945.08303.38 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=17724427&dopt=abstract https://doi.org/10.1097/olq.0b013e318148c35a https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19564649&dopt=abstract https://doi.org/10.1136/sti.2008.034801 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28720597&dopt=abstract https://doi.org/10.9778/cmajo.20170013 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28482933&dopt=abstract https://doi.org/10.1186/s13075-017-1294-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16597205&dopt=abstract https://doi.org/10.1586/14787210.4.2.235 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14603097&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=14603097&dopt=abstract https://doi.org/10.1097/01.olq.0000087945.08303.38 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e271, 2019 isds 2019 conference abstracts figure 1. flowchart of the article selection process for the systematic review. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e339, 2019 isds 2019 conference abstracts new technologies to treatment of spotted fever, gve vii santo andre, sp, brazil. andrea m. losacco1, angela maria m. moriwake2, simone c. caravaggi2, milena camaral3 1 epidemiology, infectology institute "emílio ribas", sao paulo, sao paulo, brazil, 2 gve vii, santo andre, sp, brazil, 3 department of health surveillance, diadema, sp, brazil objective to perform the spatial distribution of spotted fever in the metropolitan area of sao paulo municipality (mrsp), coverage area of epidemiological surveillance group vii – of santo andre (gve7), to determine clusters of disease incidence, and through qr code to be able to access data from any smartphone as an aid to the early treatment of new suspected cases. introduction the use of new technologies such as online maps and the qr code facilitates the knowledge dissemination in the health science , aiding in diagnostic elucidation and intelligent decisions making, thus offering an improvement in the quality of care provided to patients. cases with suspected spotted fever should be approached as potentially serious, which may develop with shock within a few hours and, if not addressed can progress to death. in the case of spotted fever, early onset determines the cure of these cases. methods the spatial distribution of confirmed spotted fever cases was performed in the region of the seven municipalities inserted into gve7, using the information system of notifiable diseases (sinan) database and google maps online tool, and determining clusters of disease incidence. the qr code was generated through the qr code maker online tool to access map and to verify if the displacement of each new suspect case coincides with the incidence clusters of the disease, and to determine early treatment of these patients. results during the study period, 496 suspected cases of spotted fever were reported, of which 64 cases were laboratory test confirmed with a lethality rate of 65%. most of the probable infection sites are located near the regions close to forest remnants and near the dams. the main concentration of cases is in recreio da borda do campodistrict in santo andre, 27 cases (43% of total). the other priority areas for spotted fever occurrence in the mrsp in the period were the districts of alvarenga, cooperativa (border of municipality of diadema), and montanhao (in the municipality of sao bernardo do campo). figure 1. qr code and google maps spotted fever incidence clusters, gve vii santo andre. conclusions in order to validate the use of these technologies as positive, it will be necessary to analyze the closure of the new suspected spotted fever cases treated in the region studied. positive spatial correlation between neighboring areas may result from the disease having an occurrence characteristic in endemic areas and spreading to the nearest areas. we can conclude that the use of new technol ogies to determine the early onset of treatment for spotted fever suspected cases based on the origin of the patients treated in the region of gve7 can determine the success in the evolution of these cases. acknowledgement milena camarai – health department municipality of diadema. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e339, 2019 isds 2019 conference abstracts references barros-silva pm, pereira sv, fonseca lx, maniglia fv, de oliveira sv, de caldas ep. febre maculosa: uma análise epidemiológica dos registros do sistema de vigilância do brasil. scientia plena. 2014 apr 25;10(4 (a)). biggs hm. 2016. diagnosis and management of tickborne rickettsial diseases: rocky mountain spotted fever and other spotted fever group rickettsioses, ehrlichioses, and anaplasmosis—united states. mmwr recomm rep. 65, 1-44. pubmed https://doi.org/10.15585/mmwr.rr6502a1 czuszynski k, ruminski j. interaction with medical data using qr-codes. inhuman system interactions (hsi), 2014 7th international conference on 2014 jun 16 (pp. 182-187). ieee. dube s, ndlovu s, nyathi t, sibanda kqr. code based patient medical health records transmission: zimbabwean case. inproceedings of informing science & it education conference (insite) 2015 (pp. 521-520). kanzaki a, natsuaki m, matsutani s, mase k, nakajima e, et al. 2018. two cases of japanese spotted fever infected in rokko mountain near urban area of southern hyogo prefecture. j dermatol. 45(6), e146-47. doi:https://doi.org/10.1111/1346-8138.14208. pubmed nasser jt, lana rc, silva cm, lourenço rw, silva dc, et al. 2015. urbanização da febre maculosa brasileira em município da região sudeste: epidemiologia e distribuição espacial. rev bras epidemiol. 18, 299-312. pubmed https://doi.org/10.1590/1980-5497201500020002 parra f. 2016. reflexões sobre as relações entre usuário-interator e tecnologias emergentes a partir do qr code. temática. 2018 may 23;14(5). pinter a et al.; a febre maculosa brasileira na região metropolitana de são paulo. boletim epidemiológico paulista. 13(151), 3-47. raghavan rk, goodin dg, neises d, anderson ga, ganta rr. 2016. hierarchical bayesian spatio–temporal analysis of climatic and socio–economic determinants of rocky mountain spotted fever. plos one. 11(3), e0150180. pubmed https://doi.org/10.1371/journal.pone.0150180 figure 1. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27172113&dopt=abstract https://doi.org/10.15585/mmwr.rr6502a1 https://doi.org/10.1111/1346-8138.14208 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29315729&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26083504&dopt=abstract https://doi.org/10.1590/1980-5497201500020002 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26942604&dopt=abstract https://doi.org/10.1371/journal.pone.0150180 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e387, 2019 isds 2019 conference abstracts factors predicting retention in care and health outcomes among hiv patients merlene ramnon florida department of health, state, wellington, florida, united states objective to provide knowledge on the factors that predict retention in care and health outcomes among hiv patients and be able to understand viral load and its relation to retention in care. introduction the prevalence of persons living and diagnosed with hiv infection in the united states in 2010 to 2014 increased in number and rate (center for disease control & prevention (cdc), 2016). in 2015, persons aged 25–29 years had the highest rate (33.4), followed by persons aged 20–24 years (31.2) (cdc, 2016). consistent reduced viral load is associated with reduced morbidity and mortality and a lower likelihood of transmitting hiv to sex partners (cdc, 2011). retention into hiv care promotes life and decreases the risk for hiv transmission (yehia et. al. 2015). preventing hiv transmission, prevention intervention strategy is critical and should be ongoing to all hiv patients consistently. new cases of hiv in the united states are increasing by approximately 30, 000 per year and with this increase, more providers are needed (weiser et al.2016). methods quantitative cross sectional study: 2017 palm beach county needs assessment survey was used, the data used was secondarydeidentified data. the sample size consisted of 357 survey participants.the surveys were collected from september 2016 to january 2017. the florida department of health (fdoh) institutional review board (irb) approval was granted before data collection.. the participants were not at risk due to de-indentifieddata. the demographic and clinical data was reviewed. ethical practices were followed by securing data and only the data needed to conduct study were utilized. the independent variables were: age, educational level, race, gender, condom use, unprotected sex, sexual orientation, blood tests-viral load, medical care type facility. the dependent variables were: medical care/in care, miss hiv meds and hospitalization. four research questions are posed in this study, the results section list the research questions. statistical test were computed with the use of spss with anova and linear regression results rq:is there a statistical significant association between age of hiv patients, retention in care and health outcomes, in palm beach county? analysis of variance (anova) was conducted to investigate if there was a statistical significant association between age of hiv patients and retention in care . analysis result: anova, f (9, 0.393.) =2.181, p<0.05 (p=0.023). there was statistically significant association between age and retention in care between groups. post hoc (dunnett test revealed differences between the 50-54 p =0.006, between 55-59, p=0.009 and 60 ≥ p=0.010 rq2: is there a statistically significant association between hiv patients at risk for sexually transmitted diseases and retention in care as evidenced by unprotected sex? analysis of variance (anova) was conducted to investigate if there was a statistical significant association between at risk for std of hiv patients and retention in care as evidenced by unprotected sex. analysis result: anova, f (3, 4.531) =15.975, p<0.001 (p=0.000). there was statistically significant association between at risk for std and retention in care as evidenced by unprotected sex . http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e387, 2019 isds 2019 conference abstracts post hoc (dunnett) test revealed differences between retention in care and risk for sexually transmitted diseases as evidenced by unprotected sex, p=0 rq3: are msm hiv patients who attend health department clinics and or other health care facilities, more likely to retain in care than other groups of hiv patients? analysis of variance (anova) was conducted to investigate if msn patients who attend health department clinics and other health care facilities, more likely to retain in care than other groups of hiv patients? anova, f (4, 0.280) = 1.516, p > 0.05 (p= 0.197). there was no statistically significant association between msn hiv patients who attend health department clinics and other health care facilities than other groups of hiv patients more likely to remain in care? rq4: do patients knowledge of viral load test predict retention in care? logistic regression was conducted to investigate knowledge of viral load and retention in care. retention in care and viral load tests regression model was statistically significant the regression model showed p < 0.01, p=0.000 viral load test significantly predicted retention in care. coefficients of viral load greater than 1000 and less than 200 were statistically significant: viral load >1000 p = 0.010;viral load < 200 p = 0.004 conclusions limitations to the study included the time frame to complete the study and the use of secondary data which was available to conduct the study. low viral load is indicative of better health outcomes. many studies have attempted to address barriers to retain in care and more work is needed to address the factors that impact retention in care. findings are consistent with other research that retention in care are due to social, behavioral and system factors. some of the reasons the patients gave for their not in care are treatment of staff in clinic and or doctors office, long wait times, transportation, language barrier, child care and the clinic hours. the three most frequent answers were treatment of staff in clinic, long wait times and transportation. the burden o fnew hiv infection transmitting hiv if patients do not remain in care. findings are consistent with other research that retention in care are due to social, behavioral and system factors. three most frequent answers were treatment by staff, long wait times and transportation. acknowledgement 1. florida department of healthresearch excellence initiative 2. ryan white-palm beach county 3. palm beach county health department: medical director-dr. alina alonso, center administrator-ms. lawanta stewart and hiv provider-dr. samuel frimpong. references center for disease control & prevention. diagnoses of hiv infectionin the united states and dependent areas, 2015 hiv surveillance report, 2016; 27. drachler ml, drachler cw, teixeira lb, leite jcd. 2016. the scale of self-efficacy expectations of adherence to antiretroviral treatment: a tool for dentifying risk for non-adherence to treatment for hiv. plos one. 11(2), e0147443. pubmed https://doi.org/10.1371/journal.pone.0147443 kambugu, a., zhang, y., braitstein, p., christopoulos, k.a…martin, j.n. (2010). retention in care among hiv infected patients in resourcelimited settings: emerging insights and new directions. current hiv/aids report, 2010.; 7(4), 234-244. roscoe c, hachey dm. topic 8: retention in hiv care. national hiv curriculum, 2017. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26895270&dopt=abstract https://doi.org/10.1371/journal.pone.0147443 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e387, 2019 isds 2019 conference abstracts thompson ma, mugavero mj, amico kr, cargill va, chang lw, et al. 2012. …nachega, j.b. guidelines for improving entry into and retention in care and antiretroviral adherence for persons with hiv: evidencebased recommendations from an international association of physicians in aids care panel. ann intern med. 156(11), 817-33. pubmed https://doi.org/10.7326/0003-4819-156-11-201206050-00419 weiser j, beer l, west bt, duke cc, gremel gw, et al. 2016. qualifications, demographics, satisfaction and future capacity of the hivcare provider workforce in the united states, 20132014. clin infect dis. 63(7), 966-75. pubmed https://doi.org/10.1093/cid/ciw442 yehia br, stewart l, momplaisir f, mody a, holtzman cw. 2015. shea, j.a. barriers and facilitators to patient retention in hiv care. bmc infect dis. 15, 246. pubmed https://doi.org/10.1186/s12879-015-0990-0 logistic regression result model df mean square f sig regression 2 3.005 17.663 0.000b residual 353 .170 total 355 results of logistic regression linear regression model r rsquare adjusted r square standard error of estimates durbin watson 1 .032bb .091 .086 .41244 1.803 a. predictor constant blood test, viral load < 200 blood test viral load > 1000 b. dependent variable: medical care results of linear regression http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22393036&dopt=abstract https://doi.org/10.7326/0003-4819-156-11-201206050-00419 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27358352&dopt=abstract https://doi.org/10.1093/cid/ciw442 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26123158&dopt=abstract https://doi.org/10.1186/s12879-015-0990-0 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e442, 2019 isds 2019 conference abstracts risk assessment tool for religious mass gathering events of india upasana sharma, sankara sarma sctimst, india objective to develop a risk assessment tool to assess the public health and environmental risks associated with religious mass gathering events of tamil nadu, a state in the southern part of india introduction in spite of the fact that mass gatherings are an undeniably regular element of our society attended by huge crowds yet such occasions are not very well understood. even though such gatherings are accumulations of "well people", vast number of people associated with mass gatherings can put a serious strain on the entire health care system [1].the public health implications of mass gathering events include a potential increased risk for disease transmission because of the variability and mobility of those attending the event and increased media attention. risk assessment for mass gathering events is crucial to identify the poten tial health hazards which aids in planning and response activities specific to the event [2]. preparing for mass gatherings offer an opportunity to improve health service delivery, enhance health promotion and strengthen public health systems [3]. in india, many of the religious festivals are observed with mass gatherings and prayers. large crowd participate in such festivals as participants to observe the unique rituals and also as spectators. literature indicates that in india, we might be well eq uipped for response activities but the scientific concept of risk assessment i.e., to understand the existing risks, identify the risks, characterize the risks and plan for risk reduction strategies accordingly are at an infant stage .the little that has been done in the field of mass gatherings has generally focused on description of preparedness activities of single event, crowd control, prevention of stampedes with little attention to public health preparedness. the present project is an attempt to systemize the process of risk assessment by developing a risk assessment tool consisting of characteristics peculiar to planned religious mass gatherings of indian context. methods qualitative approach was followed to identify the risks associated with mass gathering events and to identify the domains and items to be included in the risk assessment tool. firstly, an extensive review of literature about the risks associated with the mass gathering events was done. secondly, key informants (n=20) involved in planning and management of religious mass gathering events in the state of tamil nadu, india were purposively identified and interviewed using a semi structured interview guide. principle of redundancy was followed. content/thematic analysis was done using atlas.ti software. currently, the project is i n the phase of obtaining content validity of the developed tool. followed by this, a mobile application based upon the validated tool will be developed which will be further field tested for feasibility in a selected mass gathering event in tamil nadu. using a self administered content validity questionnaire, the experts will be asked to assess the relevance of the items of the tool. agreement proportions between the experts will be calculated. s-cvi (scale content validity index), index for inter-rater agreement (agreement proportion) and kappa agreement coefficient will be calculated. results a sum total of 48 unique health risks have been identified. stampedes, fire accidents, structural collapse, drowning, outbrea k of communicable diseases, exacerbation of existing medical illnesses (like cardiac diseases, asthma etc) etc are the some of the health risks identified. six domains (characteristics related to event, participant, environment, disaster preparedness, medi cal service preparedness and pre event planning activities) and 21 items have been generated from the content analysis of key informant interviews and literature review. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e442, 2019 isds 2019 conference abstracts conclusions some special events and unforeseen events occur in places of mass gatherings besides fixed places of worshipping .such events cause more damage to human beings and property. special events like idol procession, chariot pulling, fire walking, animal sacrificing happen pulling larger crowds within the mass gatherings. in order to inform all planning and delivery activities it is essential to understand the mass gathering context and risk assessments. this tool can be used by public health managers to identify key public health and environmental risks at the planning stage before the event takes off. at the planning stage, u se of this tool will help in putting the required measures in place in order to address the potential risks identified. the tool can be used as a guiding instrument during and after the event as well. the investigators further plan to develop a mobile based app from this risk assessment tool and test it out in a selected mass gathering event of the state of tamil nadu located in southern part of india. feedback from public health managers about the mobile based risk assessment tool can be instrumental in further modifying the tool. by contributing to public health preparedness activities during mass gathering events in a country with poor resources like india, this research activity is an initiative that is expected to lead to health systems strengthening. references 1. arbon p. 2004. the development of conceptual models for mass-gathering health. prehosp disaster med. 19(3), 208-12. pubmed https://doi.org/10.1017/s1049023x00001795 2. world health organization. public health for mass gatherings: key considerations. geneva: who; 2015. 3. tam js, barbeschi m, shapovalova n, briand s, memish za, et al. 2012. research agenda for mass gatherings: a call to action. lancet infect dis. 12(3), 231-39. pubmed https://doi.org/10.1016/s14733099(11)70353-x http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15571196&dopt=abstract https://doi.org/10.1017/s1049023x00001795 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22252148&dopt=abstract https://doi.org/10.1016/s1473-3099(11)70353-x https://doi.org/10.1016/s1473-3099(11)70353-x isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e401, 2019 isds 2019 conference abstracts organisation of local actors and data reporting in veterinary public health sofia mlala1, françois dedieu2, viviane hénaux1 1 university of lyon, french agency for food, environmental and occupational health & safety (anses), laboratory of lyon, lyon, france, 2 interdisciplinary laboratory for science, innovations and societies (lisis), university of paris, champs-sur-marne, france objective the objectives were to understand the functioning of the local network of actors involved in the french bovine infectious diseases surveillance system and the influence of their organisation on data reporting from the field. introduction disease surveillance systems can be based on two components of surveillance: active surveillance in which the diseases are looked for on a regular basis in a defined population, and passive surveillance where the diseases are looked for whenever specific sanitary events are notified. the first type of surveillance is fundamental to detect clinically unexpressed infections and to estima te the prevalence of the disease in the global population. the second type of surveillance is essential to detect new disease cases as early as possible after their appearance, and necessitates a clinical expression of infections. active surveillance is more complete but also takes more resources to be implemented. as for passive surveillance, although it has the advantage of sparing resources, it is subject to variability in data reporting according to the reporters. a recent study was conducted in france on a specific data reporting in veterinary public health: the declaration of bovine abortions [1]. it is the main clinical sign for some diseases that can have a serious economic impact on the production and that can be transmittable to humans. this study has highlighted individual obstacles to abortion declaration by farmers and veterinarians, but it has also shown that in different departments of france with the same bovine farming characteristics (similar types of production, mean sizes of the farms, density of farms, etc.), large differences could be observed regarding their abortion declaration rate (a department is a french administrative and territorial division cover ing a mean surface area of 5,800 km2). this result suggests that there is another level of factors influencing data reporting, different from the individual factors related to the characteristics of the farms. we formulated the hypothesis that these other factors wer e related to the local governance of animal health surveillance data collection. our study was thus developed in the continuity of this previous research to explore the variation of data reporting in relati on with the organisation of animal health surveillance actors of bovine production at the local level. in france, an official organisation chart sets how actors should act and interact with one another at the national, regional and departmental scales, and yet some differences can be observed at the departmental level, mostly regarding the relations between actors, due to a difference in the resources available for each actor according to the local context. methods we used the methodology and tools of sociology of organisations [2]. a series of 34 semi-structured interviews were conducted in spring and summer 2018 with the animal health surveillance actors of two french departments (respectively, 16 and 18 interviews). the two departments were chosen with similar bovine farming characteristics (both mixed production types, similar mean size a nd number of herds) in order to reduce the influence of this factor on data reporting. we also looked for different levels of data reporting, through their respective abortion declaration rates, based on the data available in a national database. in both departments, the interviewees were bovine farmers, rural veterinarians, representatives of farmers’ and veterinarians’ organisations and of departmental veterinary laboratories, departmental veterinary services and departmental councils ( figure 1). the material collected was analyzed by creating sociograms that characterized roles and interdependences between actors. then, the underlying mechanisms were identified and related to the level of data reporting. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e401, 2019 isds 2019 conference abstracts results our results showed evidence of the central position of veterinarians in the network of actors, as all major surveillance actors needed either their expertise of veterinary medicine or their proximity to farmers as an important resource for their action. there was complementarity and good collaboration between veterinarians and the farmer health-support association in both departments. nevertheless, veterinarians wanted to remain the reference actor in all farms regarding sanitary issues and they had an advan tage over the farmer health-support association in one of the two departments studied, where the membership rate to the health-support association was less high. veterinarians also had to face another form of competition, which was more obvious, from some farmer organisations and cooperatives that have an official delegation for the sale of veterinary medicine. in the two departments of the study, the place taken by these organisations on the medicine market was very different and so was the pressure exerted by th is competition on veterinarians’ activities. in one of the two departments studied, there was a specific form of organisation between veterinary clinics and farmers that materialised by an association between them. in this setting, they agreed on an annual package that covered the most frequent veterinary acts for a fixed price. the veterinarians of these associations also organised regular trainings for farmers, to enable them to dispense primary care to their animals. in these specific forms of organisation, there were a lot of exchanges and trust b etween farmers and their veterinarians, which diminished their asymmetry of competence (as typically observed in a patient -to-doctor relation), to approach a more collaborative relation. conclusions these first results show interesting discrepancies at the local level between the for ms of organisation and the nature and force of the relations between veterinary public health actors. in the following of the analysis, we will concentrate on the impact of these identified differences on the quality of data reporting. we aim to identify conditions that foster a fluent and sustainable communication of surveillance information between actors. these conditions would thus facilitate data reporting from the field, which is a key component of the surveillance systems, enabling a precocious detection of new disease infections. in public health as well as veterinary public health, a better understanding of the local forms of organisation and the way t hey influence data circulation between actors helps creating and improving surveillance systems in a way that is more adapted to the field situations, sometimes different from what theory foresees. this is made possible by an interdisciplinary approach between sociology and epidemiology in disease surveillance. acknowledgement i am grateful to all the people who generously gave their time to contribute to the sociological survey. references 1. bronner a, hénaux v, fortané n, hendrikx p, calavas d. 2014. why farmers and veterinarians do not report all bovine abortions, as requested by the clinical brucellosis surveillance system in france? bmc vet res. 10, 93-104. pubmed https://doi.org/10.1186/1746-6148-10-93 2. friedberg e. le pouvoir et la règle. dynamiques de l’action organisée. ed. paris: le seuil; 1993. french. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24762103&dopt=abstract https://doi.org/10.1186/1746-6148-10-93 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e401, 2019 isds 2019 conference abstracts figure 1. schema of the relations among the main actors involved in animal health surveillance in france. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e429, 2019 isds 2019 conference abstracts drug overdose trends among black indiana residents: 2013-2017 raven helmick trauma and injury prevention, indiana state department of health, indianapolis, indiana, united states objective to understand trends in race-specific mortality rates between blacks and whites to discover any racial inequalities that might exist for drug overdose deaths. to delve into the types of drugs that are prominently involved in black drug overdose deaths from 2013-2017 in the state of indiana. introduction black hoosiers, the largest minority population in indiana, make up almost 10% of the state’s population, and accounted for 8% of the total resident drug overdose deaths from 2013-2017 compared to whites at 91%. however, a closer look at racespecific mortality rates might reveal racial inequalities. therefore, the purpose of this project was to analyze drug overdose morality rates among white and black hoosiers to discover possible racial inequalities and to discover trends in drug involvement in overdose deaths among blacks. methods drug overdose deaths that occurred in indiana between 2013 and 2017 were identified using the underlying and contributing cause of death icd-10 codes and abstracted from the indiana state department of health’s annual finalized mortality dataset. race-specific drug overdose death rates were calculated and compared among racial groups. drug overdose deaths in blacks were examined for trends over time and by the types of drugs involved. results between 2013 and 2017, drug overdose mortality rates for whites increased from 17.05 to 27.28 per 100,000. blacks saw a higher rate increase during this same time frame: from 10.74 to 30.62 per 100,000, surpassing the mortality rate of whites by the end of 2017. drug overdose deaths in blacks increased 197% from 2013-2017 and drug specific mortality rate increases were seen across all drug category’s. opioids, which were involved in 61% of the 2017 drug overdose deaths among blacks, had a rate increase from 3.05 to 18.62 per 100,000 between 2013 and 2017. drug specific overdose mortality rate increases were also seen for overdoses involving cocaine (1.76 to 10.62 per 100,000), benzodiazepines (0.32 to 3.08 per 100,000), and psychostimulants other than cocaine (0.16 to 1.69 per 100,000) such as amphetamines. conclusions while white hoosiers had higher drug overdose mortality rates between 2013 and 2016, black hoosiers had a greater mortality rate increase and surpassed the mortality rate in whites in 2017. opioids, the most frequently involved substance in overdose deaths among blacks from 2013-2017, showed increasing rates during this time period. however, increases in drug specific overdose mortality rates for cocaine, benzodiazepines, and psychostimulants other than cocaine also call for public health attention. these results promote the inclusion of minority health experts in drug overdose prevention efforts and issue a call for future prevention efforts to be targeted toward the state’s largest minority population. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e429, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e250, 2019 isds 2019 conference abstracts integrating phylodynamic techniques for next-generation hiv surveillance in florida shannan n. rich2, carla mavian1, veronica richards2, robert cook2, mattia prosperi2, marco salemi1 1 emerging pathogens institute, department of pathology, immunology, and laboratory medicine, university of florida, gainesville, florida, united states, 2 department of epidemiology, college of public health and health professions and college of medicine, university of florida, gainesville, florida, united states objective we aim to 1) develop and implement a novel theoretical and technical framework able to dynamically model hiv transmission clusters in near-real time; 2) validate the model with real data; and 3) host focus groups with governmental stakeholders to identify optimal strategies for precision public health interventions. introduction reducing hiv incidence requires a ‘precision public health’ approach encompassing prevention campaigns, targeted interventions, and ‘nextgeneration’ surveillance through multimodal instruments, including sequencing. molecular epidemiology methods (phylogenetics and phylodynamics) have recently gained traction for use in identifying and tracking epidemic transmission clusters, as well as reconstructing the demographic history of viral pathogen populations. however, such methods are not equipped to identify both transmission clusters and their corresponding dynamics in real time, and transmission clusters are assumed to be unrealistically static over the course of the epidemic. we will focus on the ongoing hiv epidemic in florida, which has one of the highest hiv incidence rates in the united states. although key hiv transmission risk groups have been identified in florida through classical epidemiology surveillance methods, there remains a critical need for detection and tracking of expanding transmissi on clusters in near-real time. methods we propose to develop and test a new phylodynamic method, hiv dynamic identification of transmission epicenters (hiv dynamite), that will support existing hiv surveillance efforts. in collaboration with the florida department of health (fdoh), we will leverage an existing dataset, which contains over 44,300 sequences, and apply hiv-dynamite to identify transmission clusters and infer growth trends of these clusters within epidemics. hiv-dynamite will also be used to identify and predict infection trends and virus spread by conferring with demographic data. the system will be validated using newly obtained longitudinal data. focus group discussions with the fdoh, the centers for disease control and prevention (cdc), and other stakeholders will be conducted to confer how to employ hiv-dynamite into statewide informatics systems and to design future intervention strategies. results these methods are still under development. conclusions in conclusion, this study aims to both complement and enhance existing efforts, such as the cdc’s hiv-trace, which is currently based on sequence data alone and lacks dynamic or geographic spread components. this approach has the potential to be incorporated into other settings within the us with comparable statewide surveillance and virus sequencing coverage through national reference centers. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e250, 2019 isds 2019 conference abstracts acknowledgement this project was supported by the florida department of health and the national institute of allergy and infectious diseases. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e302, 2019 isds 2019 conference abstracts using evaluation to inform the biosense platform: results from a 2018 survey cassandra n. davis division of health informatics and surveillance, centers for control and disease prevention, atlanta, georgia, united states objective to assess the present status of utility, functionality, usability and user satisfaction of the biosense platform. introduction since 2015, cdc’s division of health informatics and surveillance staff have conducted evaluations to provide information on the utility, functionality, usability and user satisfaction associated with the national syndromic surveillance program’s biosense platform tools. the biosense platform tools include: 1) access and management center (amc), a tool that enables site administrators to manage users and data permissions; 2) electronic surveillance system for early notification of communitybased epidemics (essence), a software application that enables syndromic surveillance related data visualization and analysis; 3) adminer, a tool that allows users to access site data on the datamart; and 4) rstudio, an application that can be used for data analysis and visualization. the evaluation findings have informed activities that led to improvements in functionality, development or procurement of platform associated tools, and development of resource materials. in may 2018, nssp conducted an evaluation with eight jurisdictions that participated in the first user acceptance testing (uat) evaluations in 2015. the purpose of the evaluation was to assess the present status of utility, functionality, usability and user satisfaction of the tools on the biosense platform, and delineate progress since 2015. methods cdc’s evaluation framework and utilization-focused evaluation were used to inform and engage stakeholders, develop the evaluation questions, metrics, and methodology. eight selected jurisdictions participated in an online, epi-info survey that captured quantitative and qualitative information. prior to the survey, participants received a presentation about the evolution of the biosense platform since 2015, and were provided an overview of components to evaluate. the participants were asked to assess the following key areas based on use of the biosense platform within the past 30 days: 1) the utility, functionality and usability of the amc, essence, adminer and rstudio; 2) how well the enhanced data flow has enabled them to conduct syndromic surveillance activities; 3) usefulness of the quick start guides. additionally, participants were asked to provide suggestions for other improvements to the biosense platform, and to indicate their overall satisfaction. descriptive statistics were generated and thematic analysis was conducted to identify themes from qualitative responses. results overall, participant’s responses remained positive about the utility, functionality, usability and overall satisfaction of the biosense platform. participants indicated using the biosense platform regularly (e.g. daily, weekly and/or monthly) within those 30 days. certain functions have been used more than others across the various tools to conduct syndromic surveillance, with at least 50% of participants reporting use. these included creating data access rules, viewing and verifying raw and processed data, running time series, conducting free-text queries, and assessing data details and total er visit counts by hospital, county/region, or state. the challenges ranged from tool performance to user interpretation of the function. participants reported that the enhanced data flow improved their data quality and helped identify issues. although participants scored essence to have average usability per the system usability scale (sus score=63.5 in 2018), the biosense platform and its tools were reported as useful by 88% of participants. further, participants continue to be comfortable using the amc, however creating data access rules that are outside of simple use cases continue to be a challenge. participants comfort level with adminer improved from 2016 to 2018 with all participants reporting comfortable in using the tool. the use of each tool’s quick start guides varied. of those who used the guides, all of the participants agreed that the adminer and data dictionary guides were useful. there was a smaller number of participants agreeing that the other guides were useful. lastly, participants provided recommendations to improving the biosense platform. the most frequent recommendations were improving the data access control architecture, and sharing aggregate data with hospitals in their state. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e302, 2019 isds 2019 conference abstracts conclusions the development and operationalization of the biosense platform and associated tools has been in an environment of continuing advancements in technology and changing public health needs and priorities. up-to-date evaluation activities have helped to ensure that biosense is best suited to address these challenges and meet the syndromic surveillance needs of users. overall, the findings outlined above indicate that the functionality and utility of biosense are well suited to meet user needs. acknowledgement harold gil, martha sanchez, michele vickers, caleb wiedeman, erin austin, yushuian chen, natasha close, katie arends, jennifer broad, amanda thomas, michael coletta, alan davis, robert brown, david walker, hussain yusuf, violanda grigorescu, roseanne english references 1. bangor a, kortm p, miller j. 2009. determining what individual sus scores mean: adding an adjective rating scale. j usability stud. 4, 114-23. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e359, 2019 isds 2019 conference abstracts anti-microbial resistance surveillance in typhoidal salmonella in ahmedabad veena iyer, anal ravalia, kankshi bhavsar, susanna cottagiri abraham, dileep mavalankar indian institute of public health, gandhinagar, gandhinagar, india objective to report on (i) the health care eco-system that produces data on amr, and (ii) pattern of resistance in typhoidal salmonellae isolates in the city of ahmedabad in western india. introduction india carries the highest burden of enteric fever in the world. this is further aggravated by the high prevalence of antimicr obial resistance (amr) in typhoidal salmonellae. the strategy to combat resistance has been to combine and cycle anti-microbials based on the regional amr pattern of the organism. but this requires that resistance patterns and genetic mechanisms are mapped at a regional level and regularly recorded and disseminated by a national surveillance system. methods through municipality records and internet searches we identified 1696 private and 83 public labs. our screening of these yielded 4 public medical colleges, 4 private healthcare-institution-attached labs, and 4 corporate labs which were probably performing culture and antibiotic sensitivity testing (ast). only 2 public medical colleges and 1 corporate lab shared their data with us ( fig 1). there was considerable variation in culturing and sensitivity testing methodology across labs. results out of 51,260 blood cultures, salmonellae isolates were detected in only 146 (0.28%); 67 (54%) of these were resistant. multidrug resistance was absent. the extremely low isolation rates in our three facilities may be indicative of lower referral rat es of suspected patients for blood culture or, possibly, lower incidence of salmonella infection in ahmedabad. anti-microbial susceptibility testing (ast) was conducted on 124 isolates, of which 67 (54%) were found resistant. multi drug resistance was absent, but ciprofloxacin resistance varied widely between the private and public sector labs. the minimal resistance to 3rd generation cephalosporins probably indicates initiation of resistance to this important group of antibiotics in the city's typhoidal salmonella. notably, isolates from the private sector lab showed complete resistance to azithromycin. concurrent resistance to more than 1 antibiotic was very high, 88%, amongst the 67 resistant isolates. although we were unable to estimate the true size of salmonella positivity against total blood cultures in our city, the difference in proportion of amr isolates reported in our public and private samples, 30% vs 100%, is important because it may be indicative of high levels of amr in the private. notably, isolates from the public sector showed higher resistance to ciprofloxacin and from private sector showed complete resistance to azithromycin. the higher ciprofloxacin resistance in the public sector may be indicative of more usage of the relatively cheaper ciprofloxacin among public hospital clientele. the 100% resistance to azithromycin seen in our private sample is a significant finding, and has also been reported in another recent study from ahmedabad [1]. out of approximately 1779 big and small facilities in ahmedabad, we identified 12 (4 public and 8 private) laboratories which had the ability to report amr in typhoidal salmonella. 2 public and 4 private refused to share data with us. based on data shared by 3 medium-sized private facilities, we believe that salmonella isolation and testing in private health-institution-attached laboratories is negligible. our data collection efforts over one year led to reasonable volume of data from only 2 publicly funded teachin g hospitals and 1 private standalone lab. although all facilities claimed to follow clsi guidelines, the total number of antibiotics tested at each facility varied. minimum inhibitory concentration to assess extent of resistance was not reported by any of the labs. the publicly-funded teaching hospitals http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e359, 2019 isds 2019 conference abstracts in the city have the largest concentration of microbiologists and the motivation to test for amr in indoor patients. but they did not consistently test all isolates against all antibiotics in their list. the proportion of private hospitals and laboratories th at conduct asts in ahmedabad is relatively small. for individual labs, both private and public, there is no inherent incentive to detect citylevel amr patterns or subsequent molecular level mechanisms of transmission of resistance. this lack of enthusiasm among microbiologists to further process their samples through more specialized lab testing and analysis is an issue in other parts of the world too [2]. thus patchy performance of ast and incomparability of sensitivity across labs results in poor surveillance [3]. the antibiotic regimen currently recommended by icmr for treatment of enteric fever in the entire country is based on 209 salmonella isolates from only four public institutes [4]. across india’s cities and towns, there are several hundreds of public and private hospitals and laboratories undertaking asts, just like the ones in ahmedabad presented in this study [5]. unless practitioners are guided by regional data on resistance in endemic organisms, uninformed prescription practices will worsen existing microbial resistance. drawing these varied facilities, or at least a representative sample of them into a cohesive network is essential for surveillance of antimicrobial resistance in all major bacterial pathogens; particularly so for typhoidal salmonella which are endemic in our part of the world and are primarily exposed to antibiotics consumed by humans since they are obligate human parasites. only a representative network of labs will provide the contextualized and stratified data necessary for development of the most accurate strategy to formulate regional prescription guidelines. however, this is an enormous challenge in our setting. conclusions high resistance to ciprofloxacin and azithromycin in ahmedabad may be due to increased use of these two antibiotics in the public and private sectors respectively. but they are in need of further molecular characterization. clinical microbiological methods lack uniformity and laboratory referral networks are not developed even in large cities of india. although some useful data is produced by a few individual labs, the crucial exercise of meaningful networking for effective surveillance remains. as we enter an era of internationally linked anti-microbial resistance surveillance systems, the biggest challenge lies in selecting performing laboratories and inducing them to integrate with it. references 1. jeeyani hn, mod hk, tolani jn. 2017. current perspectives of enteric fever: a hospital based study of 185 culture positive cases from ahmedabad, india. international journal of contemporary pediatrics. 4(3), 81621. https://doi.org/10.18203/2349-3291.ijcp20171492 2. petti ca, polage cr, quinn tc, ronald ar, sande ma. 2006. laboratory medicine in africa: a barrier to effective health care. clin infect dis. 42, 377-82. pubmed https://doi.org/10.1086/499363 3. masterton rg. 2000. surveillance studies: how can they help the management of infection? j antimicrob chemother. 46, 53-58. doi:https://doi.org/10.1093/jac/46.suppl_2.53. 4. icmr. treatment guidelines for antimicrobial use in common syndromes indian council of medical research department of health research new delhi, india. 2017 5. gandra s, merchant at, laxminarayan r. 2016. a role for private sector laboratories in public health surveillance of antimicrobial resistance. future microbiol. 11, 709-12. doi:https://doi.org/10.2217/fmb.16.17. pubmed http://ojphi.org/ https://doi.org/10.18203/2349-3291.ijcp20171492 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16392084&dopt=abstract https://doi.org/10.1086/499363 https://doi.org/10.1093/jac/46.suppl_2.53 https://doi.org/10.2217/fmb.16.17 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27192102&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27192102&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e359, 2019 isds 2019 conference abstracts figure 1. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e357, 2019 isds 2019 conference abstracts trend analysis in hepatitis c testing, optumlabs® data warehouse, 2011–2017 jane sullivan1, jae eui soh2, 3, mohammed a. khan2, 3, william w. thompson3, noele p. nelson3 1 optumlabs, eden prairie, minnesota, united states, 2 emory university, atlanta, georgia, united states, 3 division of viral hepatitis, centers for disease control and prevention, atlanta, georgia, united states objective using administrative claims for privately insured and medicare advantage enrollees from a large, private, u. s. health plan, we estimated the prevalence of hepatitis c testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the cdc and united states preventive services task force. this panel will discuss strengths and weaknesses for monitoring hepatitis c testing using alternative data sources including selfreported data, insurance claims data, and laboratory testing data. introduction hepatitis c virus (hcv) infection is the most common blood-borne disease in the us and the leading cause of liver-related morbidity and mortality. approximately 3.5 million individuals in the us were estimated to be living with hcv in 2010 and approximately half of them were unaware that they were currently infected. among hcv infected individuals, those born between 1945 and 1965 (usually referred to as the baby boomer cohort) represents approximately 75% of current cases. because of the substantial burden of disease among this age group, cdc expanded its existing hcv risk-based testing recommendations to include a one-time hcv antibody test for all persons born between 1945-1965. the united states preventive services task force (uspstf) subsequently made the same recommendation in june 2013. methods we obtained health plan enrollment information and claims data from the 2011 2017 optumlabs® data warehouse, and utilized data from patients enrolled in either commercially insured programs or medicare advantage. we examined trends in hcv testing for the birth cohort born between 1945 and 1965 and compared their trend in testing to individuals who were not in the birth cohort. we developed two different estimates for hcv testing incidence in order to make comparisons to other commercial claims datasets. the denominator for both estimates was the number of adults continuously enrolled in one or more health plan(s) in a given calendar year (allowing up to a 45-day gap in coverage). the numerator for the first estimate was the number of people receiving any hcv related test in the current calendar year who had not received any hcv related test including hcv antibody test, hcv rna test or hcv genotype test in the previous calendar years. the numerator for the second estimate was the number of people who were given an hcv antibody test (cpt: 86803 and 80074) in a given calendar year, irrespective of previous testing history. results during the study period 2011 2017, there were 20,332,848 unique adults who met the inclusion criteria in the optumlabs® data. approximately 7.1 million (35.0%) of these individuals were born between 1945 and 1965. on average, there were approximately 2.8 million birth cohort enrollees for any given calendar year. for the birth cohort, the annual incidence of hcv testing was about 2% per year during the time period between 2008 and 2011 (data not shown). in general, between 2011 and 2017, the trends in testing rates were consistent across both estimation methods. specifically for the birth cohort, the hcv testing rate increased substantially between 2012 and 2017, peaking in 2017 at 8.56% [95% ci: 8.53-8.59%] and 10.24% [95% ci: 10.21-10.27%]. the greatest increase occurred between 2016 and 2017 when the testing rate almost doubled. in contrast, for the non -birth cohort, the hcv testing rate started in 2012 at a rate similar to the birth cohort but did not increase in a similar fashion and did not see a substantial increase in hcv testing in 2016 or 2017. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e357, 2019 isds 2019 conference abstracts conclusions since cdc and uspstf recommended universal testing for the birth cohort in 2012 and 2013, respectively, hepatitis c testing rates have been increasing across all age groups. the rate of increase for the birth cohort was substantially greater than th at for the non-birth cohort. cdc and uspstf recommendations are likely a strong contributing factor impacting hepatitis c testing rates in the us. efforts to promote hepatitis c testing should continue. figure 1. annual hepatitis c virus (hcv) antibody test rate by birth cohort, optumlabs®, 2011-2017 table 1. annual hepatitis c virus (hcv) antibody test rate by birth cohort, optum, 2011-2017 year 1945-1965 birth cohort # cases (%; 95% ci), measure 1 # cases (%; 95% ci), measure 2 other birth cohorts # cases (%; 95% ci), measure 1 # cases (%; 95% ci), measure 2 2011 2,821,115 48,290 (1.71; 1.70 1.73) 67,787 (2.40; 2.39 2.42) 4,106,323 94,924 (2.31; 2.30 2.33) 127,347 (3.10; 3.08 3.12) 2012 2,818,707 59,734 (2.12; 2.10 2.14) 81,084 (2.88; 2.86 2.90) 4,792,744 115,334 (2.41; 2.39 2.42) 156,156 (3.26; 3.24 3.27) 2013 2,831,810 76,904 (2.72; 2.70 2.73) 101,366 (3.58; 3.56 3.60) 4,906,456 121,066 (2.47; 2.45 2.48) 167,680 (3.42; 3.40 3.43) 2014 2,536,225 75,782 (2.99; 2.97 3.01) 100,983 (3.98; 3.96 4.01) 4,651,235 115,690 (2.49; 2.47 2.50) 162,187 (3.49; 3.47 3.50) 2015 2,703,076 98,599 (3.65; 3.63 3.67) 129,394 (4.79; 4.76 4.81) 5,066,428 134,287 (2.65; 2.64 2.66) 187,532 (3.70; 3.69 3.72) 2016 3,128,298 145,562 (4.65; 4.63 4.68) 183,002 (5.85; 5.82 5.88) 5,719,535 161,164 (2.82; 2.80 2.83) 223,024 (3.90; 3.88 3.92) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e357, 2019 isds 2019 conference abstracts 2017 3,290,050 281,595 (8.56; 8.53 8.59) 336,838 (10.24; 10.21-10.27) 6,061,195 187,659 (3.10; 3.08 3.11) 260,427 (4.30; 4.28 4.31) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e415, 2019 isds 2019 conference abstracts time series analysis of infectious disease mortality in ukraine (1965-2015) hennadii mokhort, olga sokolovska epidemiology, bogomolets national medical university, kyiv, ukraine objective the aim of our work is to determine the main trends and structure in infectious disease mortality in ukraine over the last 50 years. introduction monitoring of long-term infectious disease mortality trends is of great value to national public health systems both in estimation of the efficacy of preventive programs, and in development of the new strategies of preventive measures. in the developed countries, there are a number of studies with long-term time series of infectious disease mortality analysis in epidemiological and historical aspects. our research was based on the work by armstrong gl, conn la and pinner rw, 1999. literature review revealed that such analysis has been never carried out in ukraine up to now. methods our study is designed as a descriptive retrospective epidemiological analysis. we constructed time series of infectious mortality in all oblasts of ukraine during the period of 1965-2015 years. we used annual statistical forms c-8 “distribution of deceased by sex, age and death cause” provided by ministry of health. the cause of death was accounted in accordance with international statistical classification of diseases, injuries, and causes of death: based on the recommendations of the ninth revision conference, 1975. we analyzed infectious diseases belonging to the class of infectious and parasitic diseases (45 nosology and nosology groups – codes 001 – 139). we also included into our analysis some other infectious diseases belonging to other classes: neoplasm (cervix carcinoma – code 180); heart diseases (rheumatic fever, rheumatic heart disease – codes 390-398); diseases of the respiratory system (acute respiratory infection, influenza, viral pneumonia, pneumococcal pneumonia, other acute forms of pneumonia – codes 460-466, 487, 480, 481, 482, 483, 485, 486); diseases of nervous system (non-infectious and non-parasitic meningitis, codes 320-322). therefore, all infections that are reported in ukraine were included to this research. nosologies were grouped using several disease classifications: in accordance with international classification (belonging or not to infectious disease class); by transmission method or localization of an infectious agent (respiratory, intestinal or alimentary, blood borne, contact and other infections); by ecological principle (anthroponosis, zoonosis, sapronosis and other). all time series were divided onto two periods: 1965-1991 (soviet period) and 1992-2015 (period of independent ukraine). average mortality (mortality coefficient) of these periods was compared to each other for calculation of percentage decrease/increase of each disease’s mortality rate. additionally, we determined the proportion (%) of infectious mortality compare to the total mortality of population of ukraine. limited scope of this study does not allow us to present data regarding the age distribution, thus we focus on general characteristics. although the practice of presenting data was changed over the course of 50 years covered by this abstract, the data are comparable and can be used for analysis. results total number of fatal cases caused by infectious diseases in ukraine during 1965-2015 years is 1,268,560 or 4.05% of all deaths caused by different reasons. 550,329 deaths or 43.38% of all infectious deaths belong to class of infectious and parasitic diseases, other 718,231 or 56.62% belong to infectious diseases of other classes. percentage of respiratory infections is 80.28%, intestinal infections – 1.72%, blood infections – 16.94% and other infections – 1.05%. additionally, proportion of anthroponosis is 98.31%, proportion of zoonosis – 0.42%, sapronosis – 0.22%, other – 1.05%. during 1965-2015, percentage of infectious diseases in overall structure was within the range from 10.53% (1965) to 2.99% (2015). overall mortality rate of infectious diseases decreased from 80.49 per 100,000 population (1965) to 41.77 per 100,000 (2015). this finding demonstrates to reduction of overall infectious mortality in ukraine. it is important to mention that decrease of overall infectious mortality happened simultaneously with an increase of mortality caused by non-infectious diseases. non-infectious mortality increased from 683.92 per 100 000 population (1965) to 1354.77 per 100 000 population (2015). http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e415, 2019 isds 2019 conference abstracts the first 10 causes of death from infectious diseases in ukraine in 1965-2015 included the following nosological units and infectious groups: 1. respiratory tuberculosis and other forms of tuberculosis (30.3%); 2. acute respiratory infections + influenza + viral pneumonia + pneumococcal pneumonia and other acute pneumonias (28.36%); 3. acute rheumatic fever + chronic rheumatic heart disease (15.93%); 4. malignant neoplasm of cervix uteri (10.42%); 5. aids (4.8%); 6. septicemia (2.86%); 7. meningococcal infection + meningitis, excluding infectious and parasitic meningitis (2.64%); 8. other infectious and parasitic diseases and long-term effects of other infectious and parasitic diseases (1.05%); 9. acute intestinal infections due to unspecified micro-organisms and ill-defined, including toxical dyspepsia (0.93%) and 10. viral hepatitis (0.79%). the average mortality rate declined for the most infectious diseases during 1992-2015 comparing to 1965-1991. for certain diseases or their groups, the range varied from 12.71% (pneumonia) to 92.69% (influenza). the biggest decrease was observed in intestinal infections group (up to 73.99%). all respiratory infections demonstrated a decrease to 18.14%; other infections with unknown path of transmission resulted in 4.6% decrease, but blood borne infections demonstrated an increase of 59.72% (mostly caused by aids). some other infections also demonstrated increase in mortality rate: foodborne botulism – up to 29.94%, tuberculosis – up to 26.52%, diphtheria – up to 376,53%, erysipelas – to 371,26%, aids – to 100%. conclusions infectious diseases are not the main mortality cause among the population of ukraine during the past 50 years. over the last halfcentury, the proportion of infectious diseases in the mortality structure of the population of ukraine demonstrated a decreasing tendency, while non-infectious disease mortality had an opposite trend, which can be explained by epidemiological transition (omran ar, 1971). however, there is always a possibility of rapid spreading of infectious diseases and increasing their proportion in the structure of total mortality. possible growth of mortality rate caused by aids, tuberculosis and diphtheria is an issue of concern. international experience demonstrated that these three infections could be successfully controlled. the long-term trends of aids, tuberculosis and diphtheria mortality rates in ukraine require regulatory interventions and show the need for emergency measures by the state services to these and some other infections, including vaccine-controlled. thus, our study of the long-term trends of infectious mortality can be used to make decisions of public health in ukraine on the control of infectious morbidity and mortality. acknowledgement the author is thankful to the ministry of health of ukraine for giving access and sharing their invaluable data set on the disease mortality. references armstrong gl, conn la, pinner rw. 1999. trends in infectious disease mortality in the united states during the 20th century. jama. 281(1), 61-66. pubmed https://doi.org/10.1001/jama.281.1.61 omran ar. 1971. the epidemiologic transition: a theory of the epidemiology of population change. milbank fund q. 49, 509-38. pubmed https://doi.org/10.2307/3349375 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9892452&dopt=abstract https://doi.org/10.1001/jama.281.1.61 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=5155251&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=5155251&dopt=abstract https://doi.org/10.2307/3349375 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e263, 2019 isds 2019 conference abstracts tracking environmental hazards and health outcomes to inform decision-making in the united states heather strosnider, patrick wall, holly wilson, joseph ralph, fuyuen yip tracking program, us cdc, atlanta, georgia, united states objective to increase the availability and accessibility of standardized environmental health data for public health surveillance and d ecisionmaking. introduction in 2002, the united states (us) centers for disease control and prevention (cdc) launched the national environmental public health tracking program (tracking program) to address the challenges in environmental health surveillance described by the pe w environmental commission [1]. the report cited gaps in our understanding of how the environment affects our health and attributed these gaps to a dearth of surveillance data for environmental hazards, human exposures, and health effects. the tracking program’s mission is to provide information from a nationwide network of integrated health and environmental dat a that drives actions to improve the health of communities. accomplishing this mission requires a range of expertise from environmental health scienti sts to programmers to communicators employing the best practices and latest technical advances of their di sciplines. critical to this mission, the tracking program must identify and prioritize what data are needed, address any gaps found, and integrate the da ta into the network for ongoing surveillance. methods the tracking program identifies important environmental health topics with data challenges based on the recommendations in the pew commission report as well as input from federal, state, territorial, tribal, and local partners. for each topic, the firs t step is to formulate the key surveillance question, which includes identifying the decision-maker or end user. next, available data are evaluated to determine if the data can answer the question and, if not, what enhancements or new data are needed. standards a re developed to establish data requirements and to ensure consistency and comparability. standardized data are then integrated into the network at national, state, and local levels. standardized measures are calculated to translate the data into the informa tion needed. these measures are then publically disseminated via national, state, and local web-based portals. data are updated annually or as they are available and new data are added regularly. all data undergo a multi-step validation process that is semi-automated, routinized, and reproducible. results the first set of nationally consistent data and measures (ncdm) was released in 2008 and covered 8 environmental health topics. since then the ncdm have grown to cover 14 topics. additional standardized data and measures are integrated into the national network resulting in 23 topics with standardized 450 measures (figure 1). on the national network, measures can be queried via the data explorer, viewed in the info-by-location application, or connected to via the network’s application program interface (api). on average, 15,000 and 3300 queries are run every month on the data explorer and the api respectfully. additional locally relevant data are available on state and local tracking networks. gaps in data have been addressed through standards for new data collections, models to extend available data, new methodologies for using existing data, and expansion of the utility of non-traditional public health data. for example, the program has collaborated with the environmental protection agency to develop daily estimates of fine particulate matter and ozone for every county in the conterminous us and to develop the first national database of standardized radon testing data. the program also collaborated with the national aeronautics and space administration and its academic partners to transform satellite data into data products for public health. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e263, 2019 isds 2019 conference abstracts the tracking program has analyzed the data to address important gaps in our understanding of the relationship between negative health outcomes and environmental hazards. data have been used in epidemiologic studies to better quantify the association between fine particulate matter, ozone, wildfire smoke, and extreme heat on emergency department visits and hospitalizations. results are translated into measures of health burden for public dissemination and can be used to inform regulatory standards and public health interventions. conclusions the scope of the tracking program’s mission and the volume of data within the network requires the program to merge tradition al public health expertise and practices with current technical and scientific advances. data integrated into the network can be used to (1) describe temporal and spatial trends in health outcomes and potential environmental exposures, (2) identify populations most affected, (3) generate hypotheses about associations between health and environmental exposures, and (4) develop, guide, and assess the environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors. the program continues to expand the data within the network and the applications deployed for others t o access the data. current data challenges include the need for more temporally and spatially resolved data to better unders tand the complex relationships between environmental hazards, health outcomes, and risk factors at a local level. national standards are in development for systematically generating, analyzing, and disseminating small area data and real -time data that will allow for comparisons between different datasets over geography and time. acknowledgement the authors wish to acknowledge the hardwork and contributions towards the tracking program made by the staff, contractors, and fellows at cdc and at the state and local health departments as well as the partners in other federal programs and national organizations. references 1. pew environmental health tracking project team. america’s environmental health gap: why the country needs a nationwide health tracking network. johns hopkins school of hygiene and public health, department of health policy and management; 2000. figure 1 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e443, 2019 isds 2019 conference abstracts surveilling non-opioid substance use: utilizing multiple data sources in marion county, indiana brittany k. yarnell, james m. groh epidemiology, marion county public health department, indianapolis, indiana, united states objective to assess the prevalence of non-opioid substance use—including cocaine, methamphetamine and “spice”—within marion county, indiana and propose response recommendations utilizing a current opioid response plan. introduction cocaine, methamphetamine, and “spice” are addictive, non-opioid substances that negatively impact a person’s health through direct and indirect means. direct health concerns of non-opioid substance use include anxiety, paranoia, seizure, heart attack, stroke, and potentially death while indirect health concerns include the acquisition of disease and infections, particularly sexually transmitted infections (stis). substance users experience an increased risk of acquiring stis since the y may exchange sex for substances, use substances within a social setting that may lead to sexual activity, or engage in risky sexual behavior as a result of impaired judgement associated with substance use. the current study evaluated the use of multiple data sources to monitor changes in the rate of cocaine, methamphetamine, and “spice” related emergency department visits as well as cocaineand methamphetamine-related death rates, within marion county, indiana between 2013 and 2017. methods two data sources were used in this study. first, prevalence rates of non-opioid substance related emergency department (ed) visits were calculated using marion county (in) ed data from electronic surveillance system for the early notification of community-based epidemics (essence) between 2013 and 2017. second, cocaine and methamphetamine death rates were calculated using coroner toxicology data related to marion county deaths between 2013 and 2017. cocaine and methamphetamine deaths were defined as any death in which cocaine and methamphetamine was found in the toxicology results, respectively. all rates were calculated per 100,000 and age-adjusted to the 2000 u.s. census using sas enterprise guide v7.1. results non-opioid substance related ed visits have persistently risen between 2013 and 2017 (figure 1). metha mphetamine and “spice” related ed visits exhibited similar prevalence patterns, increasing from 0.99 (0.72, 1.58) to 5.32 (4.67, 6.21) and 0.46 (0.28, 1.00) to 4.13 (3.57, 4.94) per 100,000, respectively, between 2013 and 2016. cocaine-related ed visits consistently exhibited the highest prevalence rates, ranging from 3.72 (3.17, 4.44) to 23.56 (22.16, 25.11) per 100,000 in 2013 and 2016, respectively. in 2017, all non-opioid substance related ed visits drastically increased to 47.78 (45.79, 49.91), 48.48 ( 46.48, 50.67), and 42.08 (40.23, 44.13) per 100,000 for cocaine, methamphetamine, and “spice,” respectively. further, we looked at cocaineand methamphetamine-related death rates using coroner toxicology results. we found that between 2013 and 2017, the cocaine-related death rate nearly tripled, from 4.82 (4.20, 5.64) per 100,000 in 2013 to 13.01 (11.97, 14.23) per 100,000 in 2017 (figure 2). similarly, methamphetamine-related death rates increased from 1.31 (0.99, 1.92) per 100,000 in 2013 to 10.15 (9.25, 11.28) per 100,000 in 2017 (figure 2). we did not calculate death rates of those who were found to have “spice” in their system at the time of death due to low prevalence. conclusions the increase of non-opioid substance related ed visits in marion county may indicate that non-opioid substance use— particularly cocaine, methamphetamine, and “spice”—may be an emerging public health issue in marion county. this growing concern is further supported by the consistent increase in cocaineand methamphetamine-related death rates. a limitation to our study is the inconsistent reporting of the substance in ed chief complaints and missing fields for discharge diagnoses and triage notes. as such, this inconsistency may have led to an underestimation of the prevalence rates of nonopioid substance related ed visits. the addition of triage notes and more reliable discharge diagnoses in 2017 ultimately culminated in a sharp increase in non-opioid substance related ed visits in 2017. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e443, 2019 isds 2019 conference abstracts certain aspects of marion county public health department’s established opioid response plan may be used to address the growing concern of non-opioid substance use. these aspects include, but are not limited to, engaging community partners, creating a task force, establishing focus groups, and providing resources. while these aspects are critical to effectively respond to non-opioid substance use epidemics, establishing the various components prior to an outbreak enable communities to reduce the impact of such epidemics, if not prevent them from occurring. additionally, it is important to incorporate participatory aspects into a non-opioid substance response plan such that community members are the driving force to provide context for the impact that non-opioid substance use is having on the community while also offering insight into which interventions would be most effective. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e272, 2019 isds 2019 conference abstracts application of intelligent multiagent approach to lyme disease simulation dmytro chumachenko1, tetyana chumachenko2 1 informatics department, national aerospace university "kharkiv aviation institute", kharkiv, ukraine, 2 kharkiv national medical university, kharkiv, ukraine objective the objective of this research is to develop the model for calculating the forecast of the lyme disease dynamics what will help to take effective preventive and control measures using the intelligent multi-agent approach. introduction climate warming, globalization, social and economic crises lead to the activation of natural foci of vector-borne infections, among which a special place belongs to lyme disease (ixodic tick borreliosis – itb), the vectors of which are the ixodes ticks. more than 5,000 cases are registered in the united states every year [1]. in european countries, the number of cases may reach up to 8,00010,000 per year. incidence rate for itb in france is 39.4 per 100,000 population, in bulgaria – 36.6 [2]. in ukraine, among all ticks, 10-70% are infected with borrelia; from 10% to 42.2% of ukrainian population had contact with the causative agent of itb. mathematical modeling as an element of monitoring of natural focal infections makes it possible to assess the epidemiological potential of foci in the region and in individual territories, to forecast the trends of the epidemic process and to determine the main priorities and directions in the prevention of itb. the most modern and effective method of simulation is multi-agent simulation, which is associated with the concept of an intelligent agent, as some robot, purposefully interacting with other similar elements and the external environment under given conditions. an intelligent agent is an imitation model of an active element, the state and behavior of which in various situations of achieving the goal vary depending on the state and behavior of other agents and the environment, in analogy with the intellectual behavior of a live organism (including a human) under similar conditions. as the epidemic process of lyme disease is characterized by vector transmission, heterogeneous tick population, variable pathogen infectivity, heterogeneous environment, and seasonal changes in tick activity, the use of classical statistical methods for predicting the dynamics of morbidity cannot show high accuracy. the multiagent approach to simulation of the epidemic process of lyme disease allows considering all of the above features, and since the dynamics of the modeled system is formed from the behavior of local objects (humans and ticks), we expect that a model constructed using a multiagent approach will yield a higher accuracy of prognosis morbidity. the multiagent model will allow not only to calculate the forecast, but also to reveal the factors influencing increase of the incidence of lyme disease the most. methods the research is based on official reports of new lyme disease cases registered during 2006-2017. the data were collected by the state institution kharkiv laboratory center of the ministry of health of ukraine from the healthcare facilities of kharkiv oblast of ukraine as a part of passive routine disease surveillance. in total, 1016 cases of lyme disease registered during the study period were included in this research. the multi-agent approach to simulation of epidemic process has been used for the model development. the epidemiological model of lyme disease is based on the gromashevsky’s concept of the epidemic process, according to which the epidemic process exists with the continuous interaction of the three main components source of infection, mechanism of transmission and susceptible organism. the most profitable type of agent in the study of epidemic process is an emotionally-motivated intellectual agent for the most complete and accurate model of human behavior. let’s consider the agent as a set of properties: a=<s,st,c,ta,l>, a∈a, s∈s, c∈c, ta∈ta, (1) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e272, 2019 isds 2019 conference abstracts where st is time in state s, a is set of all agents, s is set of different agent’s states, c is set of working area’s cells, ta is set of possible agent’s types, l is length of life. the set of agent states is predefined and is constant. depending on the process being studied, the set can be supplemented by different states, the initial set is: s={susceptible, exposed, infected, dead}. (2) the composition of the workspace leads to the appearance of a set of cells, as conditional abstract objects. it is assumed that one cell can simultaneously include a number of agents as well as one object-vector of infection (tick). it is assumed that depending on the type of working area where the agent is located the specific nature of the epidemic process is changed. the vector transmission of the disease from tick to agent is realized as follows. to begin with, the possibility of contact with tick is checked. it is believed that this is possible if other agents are also processed in the same cell along with the tick processed as well. each pair agent-tick is compared. if tick is infected, it is believed that with a certain probability healthy agent can become infected. to automate the prediction of the incidence of lyme disease, a software package has been developed using c# programing language that allows to calculate prognosis morbidity based on existing statistical data in real time. in the developed model, the configuration of the software package includes data for the period 2006-2017. this data include incidence rate of itb per 100,000 population, the population's seeking healthcare providers for tick bites, the results of studies of ticks removed from humans for the presence of borrelia, the number and proportion of ticks infected by borrelia collected by the flagging method. results the calculated prognosis shows the expected increase in the number of cases with a certain three-year cycle: an increase in the number of cases of ixodic tick borreliosis within 2 years with a slight decrease for the next year. a comparative analysis of the accuracy of forecasting by moving average method to 3 years [3] and multiagent simulation showed that the latter describes the existing data better, therefore, the forecast will be more accurate. the accuracy rates using t he moving average method is 85.4%, with the use of multiagent simulation 96.6%. conclusions a comparative assessment of the accuracy of predicting the dynamics of the epidemic process using the moving average method [3] and the multiagent approach was carried out. a higher accuracy is noted with the use of multi-agent simulation (96.6% against 85.4% using moving average method), based on which a software package has been developed, which makes it possible to calculate the expected morbidity rate of lyme disease. that means that the hypothesis made of the research was confirmed. th e adequacy of the tested prognosis was verified on the real statistical data collected by the state institution kharkiv laboratory cente r of the ministry of health of ukraine on the incidence of lyme disease in kharkiv oblast (ukraine) from 2006 to 2017. the obtained forecast revealed the expected persistence of the unstable epidemic situation with respect to lyme disease, which dictates the need to develop a set of preventive measures aimed at reducing the morbidity of people by itb. virtual verification of the effectiveness of such events will be the next step in our study. references 1. gulia-nuss m, nuss ab, et al. 2016. genomic insights into the ixodes scapularis tick vector of lyme disease. nat commun. 7, 10507. pubmed https://doi.org/10.1038/ncomms10507 2. lindgren e, jaenson tgt. lyme borreliosis in europe: influences of climate and climate change, epidemiology, ecology and adaptation measures. world health organization, 2016. 35 p. 3. chumachenko t, chumachenko d, sukhorukova m. simulation of the epidemic process of ixodes tick borreliosis. cbep ukraine regional one health research symposium and peer review session. kyiv: cooperative biological engagement program (cbep), 2017. pp. 168. http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26856261&dopt=abstract https://doi.org/10.1038/ncomms10507 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e372, 2019 isds 2019 conference abstracts acute hepatitis a infections among veterans in outbreak states, 2016-2018 cynthia a. lucero-obusan1, gina oda1, patricia schirmer1, mark holodniy1, 2 1 department of veterans affairs, public health surveillance and research, palo alto, california, united states, 2 stanford university, department of infectious diseases and geographic medicine, stanford, california, united states objective to conduct surveillance for acute hepatitis a virus (hav) infections in veterans from states reporting outbreaks among high-risk individuals beginning in fiscal year (fy) 2017. introduction although cases of acute hav have declined in recent years, elevated numbers of hav infections began to be reported by california and michigan in the fall of 2016 [1,2]. since this time, associated outbreaks have been reported in 9 additional states (arizona, utah, kentucky, missouri, tennessee, indiana, ohio, arkansas, and west virginia) [3]. no common source of food, beverages or drugs have been identified and transmission appears to be primarily person-to-person with high-risk individuals including people experiencing homelessness, those who use illicit drugs and their close direct contacts. in june 2018, cdc issued a health ale rt network advisory providing additional guidance on identification and prevention of hav and updates on the outbreaks [4]. this prompted our office to more closely review our hav surveillance, to identify veterans who may be part of these outbreaks, and assess risk factors and outcomes of hav infection. methods we queried va data sources starting in fy 2017 (october 1, 2016 – june 30, 2018) for hav igm laboratory tests and hav-coded outpatient encounters and hospitalizations (icd-10-cm: b15) to identify potential case patients. we performed a detailed chart review on all hav igm positive veterans residing in or treated in an outbreak state during the identified outbreak time frame as reported by each state health department. data elements collected included: (1) demographics; (2) risk factors, exposures and hepatitis a vaccination status; (3) treatment locations (i.e. outpatient, emergency department, inpatient, intensive care uni t); (4) presenting signs and symptoms; (5) laboratory data (including liver function tests (lfts) and hepatitis testing); and (6) outcomes (i.e. deaths). county-level rates for positive hav igm test results were calculated using total unique users of vha care for matching fiscal year time frames in each county as denominators. results a total of 247 hav igm positive individuals were identified among 136,970 hav igm tests performed during the study period. among these, 67 individuals resided in an outbreak state and were identified for further chart review. additional laboratory review revealed that 5 of the 67 were positive for hav total ab with no hav igm performed (all five patients came from a single facility and were asymptomatic at the time of testing). based on review of clinical data for the remaining 62 hav igm positive patient s, 22 (35%) did not meet the cste clinical case definition criteria [5] of having signs or symptoms consistent with acute viral hepatitis plus either jaundice or elevated alt/ast levels. these patients were either asymptomatic or had relevant symptoms that could be explained by other diagnoses. none had documented jaundice and only 4 had any lft elevation, which was mild (alt: 60-83 iu/l, ast: 36-103 iu/l). there was often no mention of the positive hav igm test result in the patient visit records. in the cases where the results were documented, it was thought to be a false positive or cross reactivity, related to recent receipt of hav vaccination, or prolonged persistence of hav igm from a prior infection. patient characteristics of the 40 patients meeting the case definition are summarized in table 1. none of confirmed cases had documentation of hav vaccination prior to their acute infection. the top 5 counties of residence among confirmed cases were jefferson, ky (7, 18%), san diego, ca (6, 15%), wayne, mo (4, 10%), butler, mo (3, 8%) and macomb, mi (3, 8%). additionally, the top three counties (jefferson, san diego and wayne) were each noted to have clustering of cases of acute hav with risk factors of homelessness, substance abuse and/or needle exposure. incidence rates for hav igm+ test results were calculated for all reported outbreak counties and the 25 counties with the highest rates are shown in figure 1. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e372, 2019 isds 2019 conference abstracts conclusions occurrence of acute hav infections among veterans during october 2016 – june 2018 followed patterns reported by states with outbreaks during the same time frame, including high hospitalization rates. risk factors of homelessness, substance abuse and/or needle exposures were noted in the veteran population, similar to national hav outbreak data. county-level clustering of cases in states with outbreaks was also observed among veterans, with incidence rates of hav igm+ as high as 13 per 10,000 veterans. additional education of va providers is needed regarding recognition of and appropriate testing for acute hav infections. hav igm should not be ordered in asymptomatic patients with normal lfts as the pretest probability of hav infection is low, leading to false positives and confusion in interpreting test results. improving hepatitis a vaccination rates among veterans is important, particularly among individuals who are at increased risk for infection or complications from hav and in outbreak states to limi t further spread of this outbreak. references 1. hepatitis a outbreak in california. available at: <a href="https://www.cdph.ca.gov/programs/cid/dcdc/pages/immunization/hepatitis-a outbreak.aspx">https://www.cdph.ca.gov/programs/cid/dcdc/pages/immunization/hepatitis-aoutbreak.aspx</a>. accessed september 18, 2018. 2. michigan hepatitis a. outbreak. available at: <a href="https://www.michigan.gov/mdhhs/0,5885,7-33971550_2955_2976_82305_82310-447907--, 00.html">https://www.michigan.gov/mdhhs/0,5885,7-33971550_2955_2976_82305_82310-447907--,00.html</a>. accessed september 18, 2018. 3. cdc. 2017 – outbreaks of hepatitis a in multiple states among people who use drugs and/or people who are homeless. available at: <a href="https://www.cdc.gov/hepatitis/outbreaks/2017marchhepatitisa.htm">https://www.cdc.gov/hepatitis/outbreaks/2017march-hepatitisa.htm</a>. accessed september 18, 2018. 4. cdc. health alert network advisory: outbreak of hepatitis a virus (hav) infections among persons who use drugs and persons experiencing homelessness. june 11, 2018. available at: <a href="https://emergency.cdc.gov/han/han00412.asp">https://emergency.cdc.gov/han/han00412.asp</a>. accessed september 18, 2018. 5. position statement cste. hepatitis a, acute 2012 case definition. available at: <a href="https://wwwn.cdc.gov/nndss/conditions/hepatitis-a-acute/case definition/2012/">https://wwwn.cdc.gov/nndss/conditions/hepatitis-a-acute/case-definition/2012/</a>. accessed september 18, 2018. table 1. characteristics of veterans with acute hav infection, 2016-2018 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e372, 2019 isds 2019 conference abstracts figure 1. reported outbreak counties with the highest hav igm+ incidence rates among veterans, fy 2017 – fy 2018 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e374, 2019 isds 2019 conference abstracts assessing the burden of arboviral diseases using a multiplexed serological survey in french guiana claude flamand1, camille fritzell1, lena berthelot1, jessica vanhomwegen2, sarah bailly1, nathanael hoze2, severine matheus1, antoine enfissi1, henrik salje2, felix djossou3, sandrine fernandes-pellerin2, mirdad kazanji1, simon cauchemez2, dominique rousset1 1 epidemiology unit, institut pasteur in french guiana, cayenne, french guiana, 2 institut pasteur of paris, paris, france, 3 cayenne hospital center, cayenne, french guiana objective to assess the level of circulation of denv, chikv, zikv, mayv in french guiana. introduction arboviral infections have become a significant public health problem with the emergence and re-emergence of arboviral diseases worldwide in recent decades [1-6]. given the increasing number of cases, geographic spread, but also health, social and economic impact of arboviral outbreaks, estimating their true burden represents a crucial issue but remains a difficult task [7-10]. in french guiana, the epidemiology of arboviral diseases has been marked by the occurrence several major dengue fever (denv) outbreaks over the past few decades, recent emergences of chikungunya (chkv) and zika virus (zikv) and the circulation of mayaro virus (mayv) [11-14]. methods to assess antibody seroprevalence against denv, chikv, zikv, mayv a random 2-stage household cross-sectional survey was conducted among the general population. we enrolled 2,697 individuals aged 1-87 years from june 1 to 12 october 2017. we performed detection of denv, chikv, zikv, mayv igg antibodies on collected blood samples using a microsphere immunoassay (mia). socio-economic data, environmental variables and exposure to mosquitoes, perceptions of the illness and risk of contracting arboviral infections were collected using a standardized questionnaire administrated to all individuals included in the survey. cross-reactivity between same families of viruses was taking into account using seroneutralisation and modeling approaches. results overall seroprevalence rates for antibodes against denv were 69.5% [66.4%-72.5%] and differed significantly according to age and geographical area. seroprevalence rates of chikv, zikv and mayv antibodies were respectively 19.3% [16.5% -22.5%], 23.1% [19.5%-27.2%] and 9.6% [8.1%-11.3%] and did not differed significantly according to gender or age. the distribution of seroprevalence rates for chikv, zikv antibodies differed from extrapolations obtained from routine surveillance systems and brings valuable information to assess the epidemic risk of future outbreaks. mayv has been circulating in the southern part of fg, at levels that appear to be substantially higher than those estimated from epidemiological and virological surveillance. conclusions serological surveys provide the most direct measurement for defining the immunity landscape for infectious diseases, but the methodology remains difficult to implement particularly in the context of high cross-reactivity between flaviviruses or alphaviruses [15]. the development of reliable, rapid and affordable diagnosis tools and the use of innovative modeling approaches represent a significant issue concerning the ability of seroprevalence surveys to differentiate infections when multiple viruses co-circulate. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e374, 2019 isds 2019 conference abstracts acknowledgement this study was supported by the 2014–2020 european regional development fund under epi-arbo grant agreement (gy0008695), by the centre national d'etudes spatiales and by the regional helath agency of french guiana. we also acknowledges funding from calmette and yersin allocated by the pasteur institut department of international affairs. references 1. bhatt s, gething pw, brady oj, messina jp, farlow aw, et al. 2013. the global distribution and burden of dengue. nature. 496(7446), 504-07. pubmed https://doi.org/10.1038/nature12060 2. stanaway jd, shepard ds, undurraga ea, halasa ya, coffeng le, et al. 2016. the global burden of dengue: an analysis from the global burden of disease study 2013. lancet infect dis. 16(6), 712-23. pubmed https://doi.org/10.1016/s1473-3099(16)00026-8 3. brathwaite dick o, san martín jl, montoya rh, del diego j, zambrano b, et al. 2012. the history of dengue outbreaks in the americas. am j trop med hyg. 87(4), 584-93. pubmed https://doi.org/10.4269/ajtmh.2012.11-0770 4. petersen lr, jamieson dj, powers am, honein ma. 2016. zika virus. n engl j med. 374(16), 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spectrum of disease among pregnant women infected by zika virus: systematic monitoring in french guiana, 2016. euro surveill. 22(44), 17-00102. pubmed https://doi.org/10.2807/15607917.es.2017.22.44.17-00102 15. fritzell c, rousset d, adde a, kazanji m, van kerkhove md, et al. 2018. current challenges and implications for dengue, chikungunya and zika seroprevalence studies worldwide: a scoping review. plos negl trop dis. 12(7), e0006533. pubmed https://doi.org/10.1371/journal.pntd.0006533 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29113627&dopt=abstract https://doi.org/10.2807/1560-7917.es.2017.22.44.17-00102 https://doi.org/10.2807/1560-7917.es.2017.22.44.17-00102 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=30011271&dopt=abstract https://doi.org/10.1371/journal.pntd.0006533 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e430, 2019 isds 2019 conference abstracts ems heroin overdoses with refusal to transport & impacts on ed overdose surveillance peter rock, michael singleton college of public health, university of kentucky, lexington, kentucky, united states objective the aim of this project was to explore changing patterns in patient refusal to transport by emergency medical services for classified heroin overdoses and possible implications on heroin overdose surveillance in kentucky. introduction as a centers for disease control and prevention enhanced state opioid overdose surveillance (esoos) funded state, kentucky started utilizing emergency medical services (ems) data to increase timeliness of state data on drug overdose events in late 2016. using developed definitions of heroin overdose for ems emergency runs, kentucky analyzed the patterns of refused/transported ems runs for both statewide and local jurisdictions. changes in ems transportation patterns of heroin overdoses can have a dramatic impact on other surveillance systems, such as emergency department (ed) claims data or syndromic surveillance (sys) data. methods as part of the esoos grant, kentucky receives all emergency-only ems runs monthly from kentucky board for emergency medical services, kentucky state ambulance reporting system data. heroin cases were classified based on text and medications (narcan) administered, with comparisons to historic data discussed elsewhere (rock & singleton, 2018). transportation classifications are based on ems standard elements defining treatment with transportation vs refusal to transport to hospital and canceled runs were excluded. initial analysis included trend analysis at state and local levels, as well as demographic comparisons of refusal vs transported heroin overdose encounters. results statewide trends in ems heroin overdoses with refusal transport significantly increased from 5% (n=42) in 2016 quarter three to 22% (n=290) in 2018 quarter two (fig 1). initial demographic analysis does not show any significant difference between refusals/transported for age, gender, or race. however, there are significant differences among geographic regions in kentucky with heroin encounter refusal proportion ranging from 3%-48% in 2018 quarter two. specifically, one urban area (fig 2) shows the change in proportion of refusal increasing from 15% (n=23) in 2016 quarter three to 47% (n=110) in 2018 quarter two. in this geographic area, combined refused/transported ems heroin overdoses compared to traditional ed data demonstrates opposing heroin overdose patterns for the same local with ems showing and increasing trend overtime and ed showing a decreasing trend (fig 3). conclusions traditional public health surveillance for heroin overdose has historically relied on ed billing data, though agencies are starting to use syndromic surveillance, too (vivolo-kantor et al., 2016). these systems share similar underlying ed data, albeit with different components, quality, and limitations. however, in terms of the overdose epidemic, both are limited to only heroin overdoses that result in ed hospital encounters. the recent drastic increase in refused transport can have significant impacts on heroin surveillance. jurisdictions relying on sys or ed data for monitoring overdose patterns and/or evaluating interventions may be significantly underestimating acute overdose occurrence in the population. this analysis highlights the importance of this preclinical data source in surveillance of the heroin epidemic. acknowledgement we acknowledge and thank the following agencies for their support of this work: the kentucky department for public health, kentucky board of emergency medical services, the kentucky office of health policy, the national syndromic surveillance program, and the centers for disease control and prevention. supported by cooperative agreement number 5nu17ce924880http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e430, 2019 isds 2019 conference abstracts 03, funded by the centers for disease control and prevention. its contents are solely the responsibility of the authors and do not necessarily represent the official view of the centers for disease control and prevention. references rock pj, singleton md. 2018. assessing definitions of heroin overdose in ed & ems data using hospital billing data. online j public health inform. 10(1), e97. doi:10.5210/ojphi.v10i1.8695. vivolo-kantor am, seth p, gladden rm, mattson cl, baldwin gt, et al. (2016). morbidity and mortality weekly report vital signs: trends in emergency department visits for suspected opioid overdoses — united states, 67(9), 279–285. retrieved from https://www.cdc.gov/mmwr/volumes/67/wr/pdfs/mm6709e1-h.pdf figure 1 http://ojphi.org/ https://www.cdc.gov/mmwr/volumes/67/wr/pdfs/mm6709e1-h.pdf isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e430, 2019 isds 2019 conference abstracts figure 2 figure 3 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e388, 2019 isds 2019 conference abstracts fatal overdose surveillance in the commonwealth of pennsylvania craig costigan, carrie thomas goetz, meghna patel pennsyvlania department of health, harrisburg, pennsylvania, united states objective review strategies and policies employed to get independent c/mes from a state with a de-centralized c/me system to start reporting overdose death data to the department of health. summarize flexibility needed to collect overdose death data from c/mes with a variety of case management systems/methods. preview how overdose death data is displayed on the prescription drug monitoring programs’ interactive data report. introduction the severity of the nationwide opioid epidemic necessitates a fully-informed and evidenced-based response on the part of public health organizations. to support that aim, pennsylvania applied for and received the center for disease control and prevention's enhanced state opioid overdose surveillance (esoos) grant. methods today’s poster presentation will outline issues with recruiting coroners/medical examiners (c/mes) for participation who are unique to jurisdictions that utilize a decentralized c/me system, and how those issues were addressed. those issues include tension between state and county governments, time and staff concerns on the part of c/mes, and the variety of case management systems that the c/mes use. results at the beginning of this project, two out of 67 counties were submitting comprehensive toxicology and risk factor data to the pennsylvania department of health. as of september 2018, thirty-eight out of 67 counties are submitting death data. the presentation will also discuss what data is collected and how it is reported. conclusions the outreach strategy successfully increased the number of coroners and medical examiners that submit death data. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e251, 2019 isds 2019 conference abstracts laboratory based of surveillance for leptospirosis in lviv oblast, ukraine liliia vasiunets, oksana semenyshyn, oksana velychko, lesja hatsiy, iryna kulish edp laboratory, state institution lviv oblast laboratory center of the ministry of health of ukraine, lviv, lviv, ukraine objective to estimate effectiveness of pcr method for epidemiology surveillance for leptospirosis in lviv oblast and compare it with microscopic agglutination test (mat). introduction leptospirosis is one of the most important zoonotic diseases based on the severity of the clinical course, frequency of fatal outcome and long-term clinical consequences. in ukraine, leptospirosis is one of the most widespread natural-focal infectious diseases. based on data of the public health center of the ministry of health of ukraine in 2017, the incidence rate was 0.77 per 100,000 population (330 cases), mortality rate was 0,08 per 100 000 population (case fatality rate was 10,9%). in lviv oblast, the disease was registered as sporadic cases that were not related to each other (in 2017, the incidence rate was 0.72 per 100,000 population [1]. laboratory testing of samples collected from patients and environmental objects that may be the source of the pathogen is an integral part of the epidemiological surveillance of leptospirosis. modern laboratory diagnostics of leptospirosis is based on microbiological, immunological and molecular-biological methods used in various combinations [2,3]. molecular genetic diagnostic methods that allow detection of the leptospira spp. rna/dna are the most promising for diagnosis of leptospirosis in the early stages of the disease. investigation of environmental objects allows timely detection of the pathogen in natural foci and conducting a set of anti-epidemic necessary measures. methods we used the following pcr kits “leptospira pathogenic-real time (fr001) genecam biotechnology ag” and “lps pcr kit variant frt-50f “amplisens” for leptospira dna detection. “ultra clean blood spin dna isolation kit mo bio laboratories, inc.” and a set of reagents from the clinical materials “ribo-prep” for the isolation of rna / dna loci of leptospira spp. were used. in parallel, 37 human and 27 rodent serum samples were studied using mat. pcr and mat positive gray rats samples were additionally studied using the bacteriology method (adrenal cortex seeded on the liquid media). epidemiological investigation (namely, patient interviewing, investigation of places where the infection was acquired, exploring the living conditions) and outbreak investigation report writing were conducted for all recorded cases (41). results results of the human samples investigation. during 2016-2017 and 7 months of 2018, 41 cases of leptospirosis were registered in lviv oblast. all these cases were confirmed with laboratory methods, including pcr; dna of leptospira spp. was detected in 15 patients (36,6%), and mat was positive in 26 cases (63,4%). in 8 patients (19,5%) both pcr and mat testing gave positive results. over the past three years, 5 fatal cases of leptospirosis (12.1%) have been registered, including two patients who died during the first week of the disease. for those two patients, the diagnosis was confirmed by pcr and mat (leptospira lysis in mat was noticed in the titre of 1:100-1:200); for other two patients, the diagnosis was confirmed using mat only (1:800); and in the last patient from this group, leptospira lysis was noticed in low titres in mat. results of epidemiological investigation revealed that the most patients were infected through contact way of transmission (78.1%), including contact with objects and food contaminated with rodent excrement, and water-borne transmission (19.5%) during bathing, fishing, hunting, field work; in other 2.4% of cases the way of transmission was not identified. epidemiological history showed that the main source of infection for humans in natural and urban foci were grey rats and rodents that could adapt to transforming ecosystems conditions. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e251, 2019 isds 2019 conference abstracts results of animal samples investigation. among 27 samples of gray rats, caught in places where patients probably got infected, in 11 samples (40.7%) a specific 16s rrna of leptospira spp. was detected and also mat was positive; 1 samples (3,7%) from this group was seropositive in mat only. l. icterohaemorrhagiae live culture was isolated from 3 samples of grey rats that were positive in pcr and mat. results of environmental samples investigation showed the following: among 89 of water samples collected from recreation areas (lakes), 4 samples (4.5%) were positive (16s rrna of leptospira spp.). pcr of 8 samples of drinking water collected from leptospirosis foci gave negative results. conclusions in lviv oblast, ukraine, the potential of laboratory diagnostics of leptospirosis has increased due to introduction of pcr method in diagnostic algorithm. results of clinical materials investigations revealed that with pcr it is became possible to confirm the diagnosis within the first several days from the onset of the disease (in 15 patients). diagnosis was confirmed using mat in 26 patients starting from the second week of the disease. at the same time, mat is crucial, since it enables to identify the etiological structure of the disease and monitor the dynamics of the immune response. investigation of animal and environmental samples with mat and pcr methods allowed to establish causal relationships of patients with possible sources of infection. pcr method allowed to conduct epidemiological surveillance for leptospirosis at a new level, as the time for receiving results compare to the classical methods as well as biological risks during work with biomaterials have decreased. currently, the combination of pcr and mat methods for laboratory research in the surveillance of leptospirosis is optimal. understanding environmental and epidemiological determinants allows for the identification of appropriate public health approaches to improve the situation with leptospirosis, such as reducing populations of pathogen reservoirs (rats) by conducting deratization measures, vaccinations of dogs and livestock, and regulatory compliance. acknowledgement authors would like to express their gratitude to defense threat reduction agency and ukraine biological threat reduction program for the providing diagnostic pcr kits. references 1. about the epidemiological situation with leptospirosis in ukraine in 2017 and measures to prevent it / information letter of phc of the ministry of health of ukraine, 20.07.2018 # 2651. – kyiv. – 2018. – 26 p. 2. budihal sv, perwez k. 2014. leptospirosis diagnosis: competancy of various laboratory tests. j clin diagn res. 8(1), 199–202. 3. world health organization. (2003). human leptospirosis: guidance for diagnosis, surveillance and control. world health organization. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e360, 2019 isds 2019 conference abstracts building electronic disease surveillance capacity in the peruvian navy with sages shraddha patel, miles stewart, martina siwek johns hopkins applied physics laboratory, laurel, maryland, united states objective to introduce sms-based data collection into the peruvian navy’s public health surveillance system for increased reporting rates and timeliness, particularly from remote areas, as well as improve capabilities for analysis of surveillance data by decision makers. introduction in the past 15 years, public health surveillance has undergone a revolution driven by advances in information technology (it) with vast improvements in the collection, analysis, visualization, and reporting of health data. mobile technologies and open sour ce software have played a key role in advancing surveillance techniques, particularly in resource-limited settings. johns hopkins university applied physics laboratory (jhu/apl) is an internationally recognized leader in the area of electronic disease surveillance. in addition to the electronic surveillance system for the early notification of community-based epidemics (essence) used by several state and local jurisdictions and the cdc in the u.s., jhu/apl has also developed the suite for automated global electronic biosurveillance (sages). sages is a collection of modular, open-source software tools designed to meet the challenges of electronic disease surveillance in resource-limited settings. jhu/apl is working with the peruvian navy health system to improve their electronic disease surveillance capabilities. the peruvian navy currently uses a sages-based system called alerta disamar that was implemented several years ago in an effort supported by the armed forces health surveillance branch, and in collaboration with the naval medical research unit no. 6 (namru-6). the system uses both webbased and ivr-based (interactive voice response) data collection from several navy health facilities in peru. for the present effort, jhu/apl is implementing a new sms-based data collection capability for the peruvian navy. methods jhu/apl is engaged with the peruvian navy health system to upgrade the existing sages-based alerta disamar surveillance system which relies on remote data collection using ivr (interactive voice recording) technology, with a sages -based system that uses sms (short message service) text messages for remote data collection. based on peruvian navy requirements, jhu/apl created mobile data entry forms for android smartphones using the sages mcollect application. sages mcollect is built using open data kit open source tools along with added features such as 128-bit encryption and quality checks. the jhu/apl team engages closely with end users and other stakeholders to determine system requirements and to deploy the system, as well as t o train end users and the system administrators who will need to maintain the system once it is deployed. the jhu/apl team, consisting of both information technology and public health expertise, conduct a country-level capabilities and needs assessment to address design considerations and operational end user requirements. this assessment takes into account the requirements and objectives of the peruvian navy, while keeping in mind infrastructure, cost, and personnel constraints. a pilot test of sms based data collection is currently underway with 10 health clinics within the navy. results many challenges exist when implementing electronic disease surveillance tools in resource-limited settings, but using a tailored approach to implementation in which specific needs, constraints, and expectations are identified with stakeholders helps increase the overall adoption and sustainment of the system. jhu/apl believes sms-based data collection will be more sustainable than ivr-based data collection for the peruvian navy. conclusions jhu/apl is deploying a sages-based electronic disease surveillance system for the peruvian navy that has great potential to increase reporting rates from its health facilities as well as improve data quality and timeliness, thus resulting in greater awareness and enhanced public health decision making. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e360, 2019 isds 2019 conference abstracts acknowledgement this project is supported by the uniformed services university, center for global health engagement and is conducted in close coordination with united states southern command surgeon’s office. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e343, 2019 isds 2019 conference abstracts customizing essence queries for select mental health subindicators achintya n. dey, michael coletta, hong zhou, nelson adekoya, deborah gould ccsels/dhis, cdc, atlanta, georgia, united states objective emergency department (ed) visits related to mental health (mh) disorders have increased since 2006 [1], indicating a potential burden on the healthcare delivery system. surveillance systems has been developed to identify and understand th ese changing trends in how eds are used and to characterize populations seeking care. many state and local health departments are using syndromic surveillance to monitor mh-related ed visits in near real-time. this presentation describes how queries can be created and customized to identify select mh sub-indicators (for adults) by using chief complaint text terms and diagnoses codes. the mh sub-indicators examined are mood and depressive disorders, schizophrenic disorders, and anxiety disorders. wider adoption of syndromic surveillance for characterizing mh disorders can support long-term planning for healthcare resources and service delivery. introduction syndromic surveillance systems, although initially developed in response to bioterrorist threats, are i ncreasingly being used at the local, state, and national level to support early identification of infectious disease and other emerging threats to public h ealth. to facilitate detection, one of the goals of cdc’s national syndromic surveillance program (nssp) is to develop and share new sets of syndrome codes with the syndromic surveillance community of practice. before analysts, epidemiologists, and other practitioners begin customizing queries to meet local needs, especially monitoring ed visits in near -real time during public health emergencies, they need to understand how syndromes are developed. more than 4,000 hospital routinely send data to nssp’s biosense platform, representing about 55 percent of ed visits in the united states [2]. the platform’s surveillance component, essence,* is a web-based application for analyzing and visualizing prediagnostic hospital ed data. essence’s chief complaint query validation (ccqv) data source, which is a national -level data source with access to chief complaint (cc) and discharge diagnoses (dd) from reporting sites, was designed for testing new queries. methods we used essence ccqv to query weekly data for the nine week period from the first quarter of 2018 and looked at three common mh sub-indicators: mood and depressive disorders, schizophrenic disorders, and anxiety disorders. we developed four query types for each mh sub-indicator. query-1 focused on dd codes; query-2 focused on cc text terms; query-3 focused on a combination of cc, dd, and no exclusion for mental health co-morbidity; and query-4 focused on a combination of cc and dd and excluded mental health co-morbidity. we also examined the summary distribution of cc texts to identify keywords related to mh sub-indicators. for mood and depressive disorders, we queried icd-9 codes 296, 311; icd-10 codes f30–f39; cc text terms for words “depressive disorder,” bipolar disorder,” “mood disorder,” “depression,” “manic episodes,” and “psychotic.” for schizophrenic disorders, we queried icd-9 codes 295; icd-10 codes f20–f29; cc text terms for words “psychosis,” “psychotic,” “schizo,” “delusional,” “paranoid,” “auditory,” “hallucinations,” and “hearing voices.” for anxiety disorders, we queried icd-9 codes 300, 306, 307, 308, 309; icd-10 codes f40–f48; cc text terms for words “anxiety,” “anexiy,” “aniety,” “aniexty,” “ansiety,” “anxety,” “anxity,” “anxiety,” “phobia,” and “panic attack.” results we identified 2.3 million average weekly ed visits for the 9-week period queried. table 1 shows average weekly ed visits of select mh sub-indicators from the four query types. because query 4 focused on specific mh outcomes and excluded mh comorbidities, the average weekly ed visit for all three subindicators was almost half that of query 3, which focused on broader http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e343, 2019 isds 2019 conference abstracts concepts by including mh co-morbidities. among mood and depressive disorders, query 4 identified on average 23,352 ed visits per week versus 45,504 visits per week for query 3. similarly, for schizophrenic disorders and anxiety disorders, query 4 identified on average 4,988 and 32,790 visits per week compared with 9,816 and 53,868 visits, respectively, for query 3. further, more mhrelated visits were identified using the dd-coded query (query 1) than cc-based text terms (query 2). conclusions analysts can benefit from having queries on select sub-indicators readily available and can use these to facilitate routine mhrelated monitoring of ed visits, or customize the queries by including local text terms. consistent with our previous work [3], this analysis demonstrated that mh-related ed visits are more likely to be found in dd codes than in cc alone. * electronic surveillance for the early notification of community-based epidemics references 1. weiss aj, barrett ml, heslin kc, stocks c. trends in emergency department visits involving mental and substance use disorders, 2006–2013. hcup statistical brief #216 [internet]. rockville (md): agency for healthcare research and quality; 2016 dec [cited 2018 aug 14]. available from: http://www.hcupus.ahrq.gov/reports/statbriefs/sb216-mental-substance-use-disorder-ed-visit-trends.pdf. 2. gould dw, walker d, yoon pw. 2017. the evolution of biosense: lessons learned and future directions. public health rep. 132(suppl 1), 7s-11s. pubmed https://doi.org/10.1177/0033354917706954 3. dey an, gould d, adekoya n, hicks p, ejigu gs, et al. 2018. use of diagnosis code in mental health syndrome definition. online j public health inform. 10(1), e190. table 1. average weekly emergency department visits for 9-week period during first quarter of 2018 for select mental health sub-indicators mental health sub-indicators emergency department visits mean (sd) query 1 query 2 query 3 query 4 mood and depressive disorders 42,533 (1,982) 6,867 (387) 45,504 (2,098) 23,762 (521) schizophrenic disorders 7,436 (305) 2,303 (88) 9,816 (353) 4,988 (164) anxiety disorders 48,050 (2,445) 11,710 (650) 53,868 (2,745) 32,790 (1,925) http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28692386&dopt=abstract https://doi.org/10.1177/0033354917706954 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e416, 2019 isds 2019 conference abstracts use of ambulance dispatch calls for surveillance of severe acute respiratory infections? susana monge1, 2, janneke duijster1, geert jan kommer1, jan van de kassteele1, gé donker3, thomas krafft4, paul engelen5, jens p. valk6, 7, jan de waard8, jan de nooij8, wim van der hoek1, liselotte van asten1 1 centre for infectious disease control netherlands, national institute for public health and the environment (rivm), utrecht, bilthoven, netherlands, 2 european centre for disease prevention and control, (ecdc), stockholm, sweden, 3 nivel primary care database – sentinel practices, utrecht, netherlands, 4 maastricht centre for global health, maastricht university, maastricht, netherlands, 5 meldkamersupport, hellevoetsluis, netherlands, 6 dispatch center regional ambulance services noord nederland, drachten, netherlands, 7 university medical center groningen, groningen, netherlands, 8 regional ambulance service hollands midden, den haag, netherlands objective we aim to assess whether influenza circulation, as measured through influenza-like-illness (ili) in primary care, is reflected in ambulance dispatch (ad) calls. introduction surveillance of severe influenza infections is lacking in the netherlands. ambulance dispatch (ad) data may provide information about severity of the influenza epidemic and its burden on emergency services. the current gold standard, primary care-based surveillance of influenza-like-illness (ili), mainly captures mild to moderate influenza cases, and does not provide adequate information on severe disease. monitoring the severity of the annual epidemic, particularly among groups most at risk of complications, is of importance for the planning of health services and the public health response. methods we analysed all calls from four ambulance dispatch centers serving 4.3 million people in the netherlands, between january 2014 and december 2016. the main complaint and urgency level is recorded during triage; those possibly caused by respiratory infections were grouped as respiratory syndrome calls (rsc). we modelled the proportion of all rsc calls against the weekly ili incidence (we allowed up to 4-week lags and leads), from sentinel primary-care surveillance. we used binomial regression with identity link to obtain differences in proportions. we built separate models by age group, urgency level and time of day. we tested heterogeneity of effects by season. results we included 289,307 calls; 6.7% were rsc. overall, proportion of rsc increased by 0.114 percentage points for each increase of 1/10,000 population in ili incidence. in our study population, this translated into 550 ambulance calls attributable to influenza (as measured by ili) per year. association was stronger in the models including only out-of-office hours, children (<15 years) and highest urgency level calls. in the latter two, the effect varied by season. rsc was best associated with ili from the previous 1-3 weeks in all models, except in children where rsc preceded ili by 1 week. conclusions our results demonstrate the potential usefulness of ambulance dispatch data to complement existing influenza surveillance by providing information on the volume and timing of severe cases attributable to influenza within the yearly epidemics. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e358, 2019 isds 2019 conference abstracts use of the oscour network data to describe low back pain attendances in french ed cécile forgeot1, gilles viudes2, guilhem noel3, anne fouillet1, céline caserio-schönemann1 1 data, sante publique france, saint-maurice, france, 2 fedoru, emergency regional observatory federation, paris, france, 3 paca emergency network observatory (orupaca), marseille, france objective the study describes the characteristics of attendances for low back pain (lbp) in the french emergency departments (ed) network oscour®, in order to give an overview of this disease before launching a prevention campaign. introduction lbp is one of the leading contributors to disease burden worldwide [1]. in france, lbp is a frequent reason of general practice consultations. according to a study published in 2017 and based on 2014 data issued of the national health insurance cross schemes information system (sniiram) [2], this pathology stands for 30% of thickness leave and 4 of 5 people will suffer of low back pain during their own life. most often, lbp is a chronic pathology with acute episodes which most often require emergency care. in order to prevent chronicity, french health care insurance launched into a mainstream national prevention campaign during spring 2018. this campaign was also targeted for health professional to inform them of the best recommendations to provide to their patients. then the french society of emergency medicine (sfmu) has been asked to relay this campaign to emergency departments (ed) where lbp is a frequent reason of attendance. since 2004, the french syndromic surveillance system sursaud® [3] coordinated by the french public health agency (santé publique france) daily collects morbidity data from the emergency departments (ed) network oscour®. almost 92% of the french ed attendances were recorded by the system in 2017. the availability of this large ed dataset on the whole territory since several years gives the opportunity to describe lbp attend ances before the potential fallout of the national prevention campaign. methods for each ed attendance, the sursaud® system daily collects individual data containing demographic (age, gender, zip code), administrative (ed unit, date of attendance, transport…) and medical information (medical diagnosis (icd10), chief complaint, severity, hospit.). these data are routinely analyzed to detect and follow-up various expected or unusual public health events all over the territory [3] and also constitute a large database to perform in-depth studies on specific public health issues. ed attendances with a medical diagnosis of lbp have been identified using at least one of the following icd10 codes “m545”, “m5450”, “m5456”, “m5457”, “m5458”, “m5459”. those data have been analyzed from 01/01/2014 to 31/12/2017 (504 ed) for the following age groups; less than 18 years old (yo), 18 to 34 yo, 35 to 49 yo, 50 to 64 yo, 65 to 84 yo and 85 yo and over, at national and r egional levels. ed attendances have been also described by month, day of week and hour of day. hospitalizations after discharge, stay duration in ed services, transport and associated diagnoses were also analyzed. results from 2014 to 2017, 481,291 ed attendances for lbp were recorded corresponding to 1.12% of the total number of ed attendances with a coded diagnosis. 60% of annual ed attendances for lbp concern 18 to 50 years old adults. the proportion of lbp attendances among the all-cause activity remains stable between 2014 and 2017. at the regional level, lbp proportion among the all-cause activity is similar to the national value in metropolitan regions (0.8% in brittany to 1.6% in corsica) and is lower than the national value in overseas regions (0.4% in mayotte to 0.8% in guyane) except for saint-barthélémy (1.8%). at the national level, almost 10% of ed attendances for lbp are hospitalized after discharge. this proportion increases with age to reach 43% for the 85 years old and more. proportion of hospitalization ranges between 5.6% (in paris area) and 17.1% (in brittany) in metropolitan regions and between 2.8% (guyane) and 9.3% (reunion island) in overseas regions. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e358, 2019 isds 2019 conference abstracts from 2014 to 2017, ed attendances for lbp remain stable by month. however, we observed a slight decrease along the week with more attendances on monday (17.8% of lbp attendances) than the other days. the attendances are more frequent in the morning (between 6 and 12 am). at the national level, mean stay duration for lbp attendances in ed is almost 5 hours whereas median stay duration is 2 hours and 45 minutes. stay duration is longer for patient arrived during night hours (from midnight to 6 am) and for those hospitalized after discharge. stay duration is also increasing with age. at the regional level, mean stay duration varies from 3 to more than 6 hours. conclusions the broad coverage of the french ed network on the whole territory since several years enables to give an overview of ed attendances for acute lbp and their characteristics. one strength of the system is its strong partnership between epidemiologists and the ed physicians. it enables to verify that the results of the study are consistent with their perception on the field. the results of this study will be used as reference to evaluate potential benefits of this campaign. finally, this study is a good illustration of how the syndromic surveillance system in collaboration with ed physicians, can quickly provide valuable data to support political strategies. references 1. maher c, underwood m, buchbinder r. 2017. non-specific low back pain. lancet. 389(10070), 736-47. doi:https://doi.org/10.1016/s0140-6736(16)30970-9. pubmed 2. maladie a. le patient adulte atteint de lombalgie commune; livret d’information octobre 2017 données sniraam 2014, https://www.ameli.fr/sites/default/files/documents/346618/document/lombalgieprofessionnels-de-sante_assurance-maladie.pdf 3. caserio-schönemann c, bousquet v, fouillet a, henry v, pour l’équipe projet sursaud®. 2014. le système de surveillance syndromique sursaud®. bull epidemiol hebd (paris). (3-4), 38-44. http://ojphi.org/ https://doi.org/10.1016/s0140-6736(16)30970-9 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27745712&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e402, 2019 isds 2019 conference abstracts power, potential, and pitfalls of surveillance using clinical ancillary services data beth t. poitras1, 2, rebecca s. payne3, 2, nicholas d. seliga4, 2 1 defense health agency, falls church, virginia, united states, 2 navy and marine corps public health center, portsmouth, virginia, united states, 3 battelle memorial institute, hampton, virginia, united states, 4 oak ridge institute for science and education, oak ridge, tennessee, united states objective discuss the power of utilizing dod clinical ancillary services data for infectious disease surveillance, the steps used to mitigate pitfalls which may occur during the surveillance process, and the potential of adapting this data for surveillance of emerging infectious diseases. introduction military service members and their families work and live around the world where both endemic and emerging infectious diseases are common. timely infectious disease surveillance helps to inform medical and policy decisions which ensure mission readiness and beneficiary health. the epidata center (edc) at the navy and marine corps public health center has performed public health surveillance, including routine infectious disease monitoring among service members, their families, and others eligible for military medical benefits for the department of the navy (don) and department of defense (dod) since 2005. the edc stores and maintains 15 databases totaling over 20 terabytes of health and administrative data. these include administrative data from outpatient encounters and inpatient admissions, health level-7 (hl7) formatted ancillary services data, and medical event reports. these data provide the potential for robust surveillance methodologies to monitor diseases of interest and identify trends and outbreaks. the primary intent and design of these data sources is not for disease surveillance, but rather for administrative and billing purposes. however, due to the availability of this data, it is routinely used by academic organizations, private industry, health systems, and government organizations to conduct health surveillance and research. ancillary services data in particular can be very powerful for near-real time infectious disease surveillance in the dod as the aggregated data is available within 1 to 2 days after processing. the edc has demonstrated the value of using laboratory data for surveillance through outbreak detection and longitudinal health trends for specific diseases among select populations. the fact that this data is not designed for surveillance does present several pitfalls in regards to analysis, from issues ranging from free text interpretation to changing testing practices. these pitfalls can be mitigated through standardized processes and detailed quality assurance testing. the edc has harnessed the power of available administrative health data to improve health outcomes and influence policy among military beneficiaries. methods the edc has established and validated methods for using and interpreting ancillary services data. key steps involved in the process for infectious disease surveillance include: • reviewing diagnostic criteria; • defining relevant search terms and test types; • consulting clinicians for technical input when needed; • developing algorithms using retrospective data; • developing quality checks; • automating the process to reduce daily workload; • documenting processes and methods. • variables essential to interpretation within ancillary services records are not standardi zed across the dod. several pitfalls can occur during the surveillance process due to complexities related to free text, layout of the full results, and differences between laboratory practices. typically, these pitfalls can be grouped into one of the following categories: • data irregularities that include unexpected abbreviations and numerous misspellings; this may result in misclassification or missed cases. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e402, 2019 isds 2019 conference abstracts • data changes resulting from shifts in testing practices due to new or discontinued laboratory tests, or differing data entry methods. • classification challenges for diseases that require sequential testing or clinical compatibility information, which limits the ability to positively identify cases. however, records can be identified as ‘suspect cases’ (i.e., syphilis, lyme disease, varicella, yellow fever and others). • technical issues, at the medical facility, server, or edc level, often causes lapses in data, which results in a delay in case reporting. despite these pitfalls, their impact can be mitigated by routinely reviewing algorithms, employing data analytic techniques that account for likely misspellings and abbreviations, and incorporating data quality checks that flag unexpected or unclassifiable results. outside of automated processes, human interaction is important; edc analysts must remain astute and vigilant to investigate unusual or unexpected occurrences, shifts in the volume of cases or data. results due to the pitfalls outlined, the edc has developed powerful and robust methods to circumvent the issues of using administrative health data for near realtime clinical ancillary services based disease surveillance. the methods developed to address the pitfalls of working with administrative health data have been used in the daily active surveillance of over fifty reportable infectious diseases, weekly surveillance of influenza, and monthly surveillance of malaria and tuberculosis. in addition to using these methods for routine surveillance, the edc adapts this methodology for new reports for specific concerns. further, the edc continues to develop and adapt these methodologies to quickly address emerging infectious threats and the pitfalls associated with the data. pharmacy transactions and administrative data from outpatient encounters and inpatient discharges supplement and enhance laboratory-based surveillance, particularly when only a diagnosis or presumptive treatment occurs (such as with influenza). while this method provid es timely information, built in quality assurance checks and routine reviews of algorithms must occur to address changes in testing practices, the use of new tests, variation in laboratory technician entry of results, and to ensure data integrity. conclusions the edcs comprehensive surveillance provides the don and dod leadership and preventive medicine community with the ability to monitor and respond to ongoing and emerging infectious disease threats. while the primary purpose of administrative health data is not for health surveillance, the edc has recognized the rich source of health information which may be extracted from this data. processes have been developed to mitigate the pitfalls that may occur when administrative data is adapted for health surveillance. this data provides a real-time snapshot of the health of military beneficiaries and provides awareness of possible outbreaks, health trends, and geographic hotspots. beyond routine surveillance this data has the potential to be used to rapidly create new methodologies to detect emerging infections which can be combined with other data sources, such as pharmacy transactions and medical encounters, to provide a more robust picture of cases by accounting for variance in clinical practice. this data often guides military health policy and procedures and is essential for a medically ready force. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e444, 2019 isds 2019 conference abstracts sustainable development goal 3.6 and road traffic injury surveillance, nigeria west africa, 2013–2016 obafemi j. babalola, patrick nguku, merissa a. yellman africa epidemiology network, nigeria objective this study aimed to describe rtc characteristics and trends in nigeria and determine progress towards halving rtc-related deaths/injuries by 2020 [i.e., sustainable development goal (sdg) target 3.6]. introduction globally, road traffic crashes (rtcs) annually kill 1.3 million people and injure 20-50 million others. nigeria accounts for an estimated 15% of rtc deaths in the who african region. methods we abstracted, cleaned, and analyzed rtc surveillance data routinely collected from crash scenes from 2013 -2016. federal road safety corps (frsc) is the lead agency for rtc surveillance and regularly collates data from the 6 geopolitical zones (which contain the 36 nigerian states and the federal capital territory). we defined road traffic injury as a fatal or non-fatal injury incurred from collision on a public road involving ≥1 moving vehicle(s). we calculated descriptive statistics, frequencies, and proportions to describe rtc characteristics and trends. results from 2013-2016, 283,949 persons were involved in 42,813 rtcs, resulting in 23,412 deaths and 127,264 injuries. twenty-eight percent of rtcs involved ≥1 fatality. ninety percent of persons involved in rtcs were ≥18 years old, with a male -female ratio of 3:1. the same proportion was also found for rtc deaths and for injuries. despite containing only 15% of the country’s population, the north-central geopolitical zone accounted for 37% of rtcs. the most common cause of rtcs was speed violations (26%). from 2013-2016, there were reductions of 30% for rtcs, 22% for number of fatalities, and 25% for number of injuries. conclusions nigeria reduced rtc deaths/injuries and achieved modest progress toward sdg target 3.6. to further progress, frsc can help by enhancing enforcement of speed violations and by educating road users about road safety practices. also, they could investigate why certain geographical areas had disproportionate amounts of rtcs, deaths, and injuries. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e264, 2019 isds 2019 conference abstracts using essence to meet local needs for mental health data: query & results lily sussman, talia brown boulder county public health objective in order to meet local mental health surveillance needs, we created multiple mental health-related indicators using emergency department data from the colorado north central region (co-ncr) early notification of community based epidemics (essence), a syndromic surveillance (sys) platform. introduction mental health is a common and costly concern; it is estimated that nearly 20 percent of adults in the united states live with a mental illness [1] and that more money is spent on mental illness than any other medical condition [2]. one spillover effect of unmet mental health needs may be increasing emergency department utilization. national analysis by healthcare cost and utilization project (h-cup) found a 55% increase in emergency department visits for depression, anxiety, and stress reactions between 20062013 [3]. local public health agencies (lphas) can play an important role in reducing costs and burden associated with mental illness. there is opportunity to use emergency department data at a local level to monitor trends and evaluate the effectiveness of local strategies. essence, available in 31 states, provides near-real time observation-level emergency department data, which can be analyzed and disseminated according to local needs. using essence data from 6 local counties in colorado, we developed methods to estimate the overall burden of mental health and specific mental health disorders seen in the emergency department. methods boulder county public health expanded on existing methods to develop multiple mental health queries in essence using data from the six colorado counties that currently participate in the colorado north central region (co-ncr) sys (i.e., adams, arapahoe, boulder, denver, douglas, and jefferson counties). our query was based solely off relevant international classification of disease version 10 clinical modification (icd-10-cm) mental health codes: f20-f48, f99, r45.851, x71–x83, t14.91, and r45.851. we also included t36-t65 and t71 where intentional self-harm was specified. in addition to an overall mental health query we created 11 sub-queries for: anxiety disorder, conversion disorder, intentional self-harm/suicide attempt, mood disorder, obsessive compulsive disorder (ocd), dissociative disorder, schizophrenia, somatoform disorders, stress adjustment disorder, suicide ideation, and other mental health disorder). one observation could fall into multiple subcategories through inclusion of multiple discharge diagnosis (dd). one challenge of using the dd field in essence is that in colorado, similar to other states, there can be excess of 40 unique icd10-cm codes listed in the dd field, and queries identify cases by searching all listed codes. for this project, that is problematic as codes may refer to historic and underlying health conditions, rather than acute cause of the ed visit. to handle this, we performed a secondary analysis to determine whether observations were “true mental health cases” based on order of codes listed in dd field, triage notes and chief complaint. we then calculated sensitivity, specificity, positive predictive value (ppv) and negative predictive value(npv) of including observations where mental health was listed as the first (or primary) code, first or second, or first second or third code. our analysis revealed that observations where mental health codes are listed later were less likely to be identifiable as true mental health cases, and led to our decision to only include observations with qualifying codes listed first or second. to assess the mental health burden, we developed code in sas 9.4 that parsed essence output by discharge diagnosis, create aforementioned sub-queries, and calculated counts and age-adjusted rates (based on 2000 us population) to summarize demographic and geographic trends. results there were 22,451 observations with mental health discharge diagnosis codes for the six colorado counties between january and june 2018. of these codes, 13,331 had a mental health code as the first and/or second listed dd and were counted as true mental health visits. the age-adjusted rates of any mental health visit ranged from approximately 425 per 100,000 in douglas county to 1,026 per 100,000 in denver county. the most common reasons for mental health visits across the region were anxiety, mood disorder, and suicide ideation (figure 1). there was a significant spike in mental health ed visits among the 15-24 age group, http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e264, 2019 isds 2019 conference abstracts followed by decreasing rates in older age groups (figure 2). younger age groups most commonly had ed visits for mood disorder (all age groups under 24), while in the age groups 25-34, 35-44, 65-74 and 75+ the most common reason for ed visit was anxiety. also of note, ed visits for suicide ideation and selfharm were highest for the 15-24 age group. males and females had similar rates of ed visits for most diagnoses, which is notable given males generally utilize healthcare services at lower rates than females. conclusions syndromic surveillance is a valuable addition to available mental health surveillance. our methods and results demonstrate the feasibility of tracking overall and specific mental health trends using the essence platform. unlike other available mental health data, essence provides data that is local, observation level, and near-real time. through continued collaboration with public health, medical and other stakeholders we hope this data can be pivotal in gauging disparities in mental health burden, monitoring trends, and prioritizing solutions. references 1. mental illness. national institute of mental health. https://www.nimh.nih.gov/health/statistics/mental-illness.shtml 2. roehrig c. 2016. mental disorders top the list of the most costly conditions in the united states: $201 billion. health aff (millwood). 35(6), 1130-35. https://www-healthaffairs-org.ezp.welch.jhmi.edu/doi/pdf/10.1377/hlthaff.2015.1659. pubmed https://doi.org/10.1377/hlthaff.2015.1659 3. weiss aj, barrett ml, heslin kc, stocks c. trends in emergency department visits involving mental and substance use disorders, 2006-2013. hcup statistical brief #216. agency for healthcare research and quality. http://www.hcupus.ahrq.gov/reports/statbriefs/sb216-mental-substance-use-disorder-ed-visit-trends.pdf. december 2016. figures 1-2 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27193027&dopt=abstract https://doi.org/10.1377/hlthaff.2015.1659 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e375, 2019 isds 2019 conference abstracts automated immunization surveillance: using business intelligence to improve up-to-date rates judy shlay1, emily m. kraus1, nicole steffens1, noam h. arzt2, arthur davidson1 1 denver public health department, denver, colorado, united states, 2 hln consulting, llc, palm desert, california, united states objective to describe a business intelligence system designed to reprocess and utilize an immunization information system’s (iis) data to visualize, and track population trends in immunization coverage in an urban population. introduction iis have effectively increased vaccination rates through targeted engagement and outreach, often with clinicians and patients. little has been published around iis use for generating meaningful population health measures. to leverage iis data for sub-county population health measures, new tools are required to make iis data easily accessed for this distinct use case. human papillomavirus (hpv), the most common sexually transmitted infection in the united states, has a highly effective (97%) vaccine to prevent infection when administered to individuals 9-26 years old. according to the national immunization survey, only 47% of colorado females 13-17 years had completed the hpv vaccine series in 2011. in 2012, denver metropolitan health departments were awarded a three year grant to support the alliance for hpv free colorado, where media and clinic coaching were used to improve hpv vaccination coverage among adolescents (11-17 years) in adams, arapahoe, denver, douglas, and jefferson counties. recent hpv vaccination schedule changes from three to two required doses highlighted further challenges i n monitoring vaccination utd rates. description we describe a denver metropolitan area hpv case study where iis data were used to inform and evaluate the impact of alliance for hpv free colorado activities. iis data were processed through the immunization calculation engine (ice)tm, a state-of-theart open-source web application that provides immunization evaluation and forecasting, typically for patients and providers at the point of care. with the iis data, the goal of ice processing was to identify communities of low adolescent hpv coverage (11 -17 years) for targeted media placement and track hpv trends over time at the clinic and population level. the immunization business intelligence system (ibis), processed iis data from the colorado department of public health and environment; using ice, the validity of each vaccine was evaluated. each hpv vaccine was evaluated for validity and an assessment made for each individual regarding hpv series initiation and completion (i.e., percent of individuals receiving 1, 2 or 3 valid hpv doses) depending on interval between vaccine and age at first dose. ibis components and functionality were developed through collaborative design with a goal of developing lessons relevant for future surveillance efforts. tableau dashboards were constructed to calculate rates of hpv initiation and completion for each participating county and healthcare practice. ibis contained data on 33 million vaccines administered to 2.5 million adults and children residing in metro counties. in 2017, ibis received approximately 2 million vaccines administered to 959,000 adults and children, representing roughly 35% of the 2.7 million metro residents estimated by the american community survey (2016). specific to hpv vaccines, ibis received over 900,000 hpv vaccines administered to roughly 400,000 individuals by over 1100 clinics; 2017 data included 91,951 hpv vaccines administered to 81,795 patients. between 2015 and 2017, 186,489 hpv vaccines were administered to 116,901 adolescents 11 to 17 years residing in the denver metro area. using ice, 85% of hpv vaccines were valid, 10% were accepted as extra doses not needed to complete the hpv series, 4% were invalid because the dose was given too soon after the previous dose, and less than 1% as invalid because the dose was administered too early (under nine years). as of 12/31/2017, 65,447 or 56% of adolescents 11 to 17 years had completed the hpv vaccine series, among those receiving any hpv vaccines. county specific completion rates varied from 53% to 60%, among adolescents receiving any hpv vaccines. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e375, 2019 isds 2019 conference abstracts completion increased with age from 7% at 11 years, 34% at 12 years, 70% at 14 years, 76% at 15 years and then declined to 68% at 17 years of age. among adolescents receiving any vaccines in the past decade, hpv completion rates were lower but increase d with age from 2% at 11 years to 39% at 14 years and down to 22% at 17 years. tableau reports displayed monthly age and county specific hpv completion rates, tracking trends over time. as ice implemented modifications aligned with 2016 hpv schedule changes (from 3 doses to 2), ibis was updated and trend data were reprocessed to accurately reflect current acip rules. ibis was indexed to optimize direct query using tableau for stratified dashboard repor ting by demographic and/or geographic populations. iis-based vaccination surveillance and reporting provided important guidance for public health program direction. ibis repurposed a knowledge management system for a population-focused hpv surveillance use case applies across the metro area of colorado. ibis was built on a scalable platform, allowing for expansion of data capture and reporting across broader geographies and demographic groups, as well as different vaccines, vaccine groups and vaccine schedules. collaboration across public health entities was important to construct appropriate infrastructure to build and maintain ibis for broader public health use. future development of ibis includes expanding reporting to 10 additional colorado counties and vaccines in 2018. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e431, 2019 isds 2019 conference abstracts enhanced surveillance of nonfatal emergency department opioid overdoses in california natalie demeter, jaynia a. anderson, mar-y-sol pasquires, stephen wirtz california department of public health, sacramento, california, united states objective to track and monitor nonfatal emergency department opioid overdoses in california for use in the statewide response in the opioid epidemic. introduction the opioid epidemic is a multifaceted public health issue that requires a coordinated and dynamic response to address the ongoing changes in the trends of opioid overdoses. access to timely and accurate data allows more targeted and effective programs and policies to prevent and reduce fatal and nonfatal drug overdoses in california. as a part of a centers for disease control and prevention enhanced state opioid overdose surveillance grant, the goals of this surveillance are to more rapidly identify changes in trends of nonfatal drug overdose, opioid overdose, and heroin overdose emergency department visits; identify demographic groups or areas within california that are experiencing these changes; and to provide these data and trends to state and local partners addressing the opioid crisis throughout california. emergency department (ed) visit data are analyzed on an ongoing quarterly basis to monitor the proportion of all ed visits that are attributed to nonfatal drug, opioid, and heroin overdoses as a portion of the statewide opioid overdose surveillance. methods california emergency department data were obtained from the california office of statewide health planning and development. data were (and continue to be) analyzed by quarter as the data become available, starting in quarter 1 (q1) 2016 through q1 2018. quarters were defined as standard calendar quarters; january-march (q1), april-june (q2), july-september (q3), and octoberdecember (q4). counts of nonfatal ed visits for all drug overdoses, all opioid overdoses, and heroin overdoses were defined by the following icd-10 codes in the principle diagnosis or external cause of injury fields respectively; t36x-t50x (all drug), t40.0x-t40.4x t40.6 and t40.69 (all opioid), and t40.1x (heroin). eligible ed visits were limited to ca residents, patients greater than 10 years of age, initial encounters, and were classified as unintentional overdoses or overdoses of undetermined intent. overdose ed visits are described by quarter, drug, sex, and age for q1 2016 – q1 2018. results on average, 6,450 emergency department visits in california are attributed to drug overdose every quarter. between q1 2016 and q1 2018, on average 1,785 (range: 1,559-2,011 ed visits) of those visits were due to opioid overdoses and a further 924 (52%) of those ed visits were due to heroin overdoses. about 26-30% of all drug overdose ed visits were for opioid overdoses in california during q1 2016 – q1 2018. quarterly, that is around 6.00-7.64 opioid overdose ed visits for every 10,000 ed visits (table 1), with about half those (3.09-4.30 ed visits) being heroin overdose ed visits. males accounted for approximately 52% of all drug overdose ed visits, 65% of all opioid overdose ed visits, and 76% of all heroin overdose ed visits per quarter. across all quarters, 25-34 year olds had the highest proportion of emergency department visits attributed to opioid and heroin overdose compared to all other age groups. however, 11-24 year olds had the highest proportion of emergency department visits attributed to all drug overdoses compared to all other age groups for all quarters except one. between q1 2016 and q1 2018, the proportion of emergency department visits attributed to all drug overdoses increased by 1.8%, all opioid overdoses increased 3.1%, and heroin overdoses increased by 13.5%. conclusions overall trends for the proportion of all emergency department visits for all drug overdoses and all opioid overdoses are relatively stable over this time period, however the proportion of heroin overdose ed visits shows a more substantial increase between q1 2016 and q1 2018. in addition, heroin overdose ed visits account for over half of all opioid overdose ed visits during this time in california. ongoing surveillance of drug, opioid, and heroin overdose ed visits is a crucial component of assessing and responding to the opioid overdose crisis in california and helps to better understand the demographics of those who could be at risk of a future http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e431, 2019 isds 2019 conference abstracts fatal opioid overdose. timely data such as these (in addition to prescribing, hospitalization, and death data) can inform local and statewide efforts to reduce opioid overdoses and deaths. acknowledgement this project was supported by the u.s. centers for disease control and prevention, nu17ce924903-02-00. table 1. proportion of all ca emergency department visits attributed to drug overdose (per 10,000 visits) quarter and year all drug all opioid heroin q1 2016 24.05 6.57 3.09 q2 2016 26.19 7.05 3.34 q3 2016 24.69 6.77 3.49 q4 2016 24.55 6.82 3.56 q1 2017 23.45 6.00 3.19 q2 2017 25.73 7.44 4.11 q3 2017 25.81 7.64 4.30 q4 2017 24.38 6.73 3.39 q1 2018 24.49 6.77 3.51 http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e389, 2019 isds 2019 conference abstracts flexibility of ed surveillance system to monitor dengue outbreak in reunion island pascal vilain1, muriel vincent1, anne fouillet2, katia mougin-damour3, xavier combes4, adrien vague5, fabien vaniet5, laurent filleul2, luce menudier1 1 regional office of french national public health agency in indian ocean, saint-denis, réunion, 2 french national public health agency, saintmaurice, france, 3 hospital centre, saint-paul, réunion, 4 university hospital centre, saint-denis, réunion, 5 hospital centre, saint-benoît, réunion objective to describe the characteristics of ed vitis related to dengue fever and to show how the syndromic surveillance system can be flexible for the monitoring of this outbreak. introduction in reunion island, a french overseas territory located in the southwestern of indian ocean, the dengue virus circulation is sporadic. since 2004, between 10 and 221 probable and confirmed autochthonous dengue fever cases have been reported annually. since january 2018, the island has experienced a large epidemic of denv serotype 2. as of 4 september 2018, 6,538 confirmed and probable autochthonous cases have been notified[1]. from the beginning of the epidemic, the regional office of national public health agency (ansp) in indian ocean enhanced the syndromic surveillance system in order to monitor the outbreak and to provide hospital morbidity data to public health authorities. methods in reunion island, the syndromic surveillance system called oscour® network (organisation de la surveillance coordonnée des urgences) is based on all emergency departments (ed) [2]. anonymous data are collected daily directly from the patients’ computerized medical files completed during medical consultations. every day, data files are sent to the ansp via a regional server over the internet using a file transfer protocol. each file transmitted to ansp includes all patient visits to the ed logged during the previous 24 hours (midnight to midnight). finally, data are integrated in a national database (including control of data quality regarding authorized thesauri) and are made available to the regional office through an online application [3]. following the start of dengue outbreak in week 4 of 2018, the regional office organized meetings with physicians in each ed to present the dengue epidemiological update and to recommend the coding of ed visit related to dengue for any suspect case (acute fever disease and two or more of the following signs or symptoms: nausea, vomiting, rash, headache, retro-orbital pain, myalgia). during these meetings, it was found that the version of icd-10 (international classification of diseases) was different from one ed to another. indeed, some ed used a90, a91 (icd-10 version: 2015) for visit related to dengue and others used a97 and subdivisions (icd-10 version: 2016). as the icd-10 version: 2015 was implemented at the national server, some passages could be excluded. in this context, the thesaurus of medical diagnosis implemented in the national database has been updated so that all codes can be accepted. ed visits related to dengue fever has been then described according to age group, gender and hospitalization. results from week 9 of 2018, the syndromic surveillance system was operational to monitor dengue outbreak. the regional office has provided each week, an epidemic curve of ed visits for dengue and a dashboard on descriptive characteristic of these visits. in total, 441 ed visits for dengue were identified from week 9 to week 34 of 2018 (figure 1). on this period, the weekly number of ed visits for dengue was correlated with the weekly number of probable and confirmed autochthonous cases (rho=0.86, p<0.001). among these visits, the male/female ratio was 0.92 and median (min-max) age was 44 (2-98) years. the distribution by age group showed that 15-64 year-old (72.1%, n=127) were most affected. age groups 65 years and more and 0-14 year-old represented respectively 21.8% (n=96) and 6.1% (n=27) of dengue visits. about 30% of dengue visits were hospitalized. conclusions according buehler et al., “the flexibility of a surveillance system refers to the system's ability to change as needs change. the adaptation to changing detection needs or operating conditions should occur with minimal additional time, personnel, or other http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e389, 2019 isds 2019 conference abstracts resources. flexibility generally improves the more data processing is handled centrally rather than distributed to individual dataproviding facilities because fewer system and operator behavior changes are needed...” [4]. during this dengue outbreak, the syndromic surveillance system seems to have met this purpose. in four weeks (from week 5 to week 9 of 2018), the system was able to adapt to the epidemiological situation with minimal additional resources and personnel. indeed, updates were not made in the it systems of each eds’ but at the level of the national ansp server (by one person). this surveillance system was also flexible thank to the reactivity of ed physicians who timely implemented coding of visits related to dengue fever. in conclusion, ed surveillance system constitutes an added-value for the dengue outbreak monitoring in reunion island. the automated collection and analysis data allowed to provide hospital morbidity (severe dengue) data to public health authorities. although the epidemic has decreased, this system also allows to continue a routine active surveillance in order to quickly identify a new increase. acknowledgement all emergency departments of the reunion island. references 1. santé publique france. surveillance de la dengue à la réunion. point épidémiologique au 4 septembre 2018. http://invs.santepubliquefrance.fr/fr/publications-et-outils/points-epidemiologiques/tous-les-numeros/oceanindien/2018/surveillance-de-la-dengue-a-la-reunion.point-epidemiologique-au-4-septembre-2018. [accessed september 8, 2018]. 2. vilain p, filleul f. la surveillance syndromique à la réunion: un système de surveillance intégré. [syndromic surveillance in reunion island: integrated surveillance system]. bulletin de veille sanitaire. 2013;(21):9-12. http://invs.santepubliquefrance.fr/fr/publications-et-outils/bulletin-de-veille-sanitaire/tous-lesnumeros/ocean-indienreunion-mayotte/bulletin-de-veille-sanitaire-ocean-indien.-n-21-septembre-2013. [accessed september 4, 2018]. 3. fouillet a, fournet n, caillère n, et al. 2013. sursaud® software: a tool to support the data management, the analysis and the dissemination of results from the french syndromic surveillance system. ojphi. 5(1), e118. https://doi.org/10.5210/ojphi.v5i1.4426 4. buehler jw, hopkins rs, overhage jm, sosin dm, tong v, & cdc working group. 2004. framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the cdc working group. mmwr recomm rep. 53(rr-5), 1-11. pubmed http://ojphi.org/ https://doi.org/10.5210/ojphi.v5i1.4426 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15129191&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e389, 2019 isds 2019 conference abstracts figure 1. weekly number of ed visits related to dengue and weekly number of probable and confirmed autochthonous dengue fever cases, from week 1 to week 34, 2018, reunion island. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e344, 2019 isds 2019 conference abstracts exploring drug overdose mortality data in harris county, texas eric v. bakota, deborah bujnowski, larissa singletary, sherri onyiego, nadia hakim, dana beckham health department, harris county public health, houston, texas, united states objective in this session, we will explore the results of a descriptive analysis of all drug overdose mortality data collected by the h arris county medical examiner's office and how that data can be used to inform public health action. introduction drug overdose mortality is a growing problem in the united states. in 2017 alone over 72,000 deaths were attributed to drug overdose, most of which were caused by fentanyl and fentanyl analogs (synthetic opioids) [1]. while nearly every community has seen an increase in drug overdose, there is considerable variation in the degree of increase in specific communities. the harris county community, which includes the city of houston, has not seen the massive spikes observed in some communities, such as west virginia, kentucky, and ohio. however, the situation in harris county is complicated in mortality and drug use. from 2010 2016 harris county has seen a fairly stable overdose-related mortality count, ranging from 450 618 deaths per year. of concern, the last two years, 2015-2016, suggest a sharp increase has occurred. another complexity is that harris county drug related deaths seem to be largely from polysubstance abuse. deaths attributed to cocaine, methamphetamine, and benzodiazipine all have risen in the past few years. deaths associated wit h methamphetamine have risen from approximately 20 per year in 2010 2012 to 119 in 2016. this 6-fold increase is alarming and suggests a large-scale public health response is needed. methods data were collected by the harris county institute of forensic sciences (ifs), which is part of the harris county medical examiner's office. ifs is the agency responsible for collecting and analyzing human tissue of the deceased for toxicological information about the manner and cause of death. ifs is able to test for the presence of multiple substances, including opioids, benzodiazepines, methamphetamines, cocaine, ethanol, and many others. these data were cleaned and labeled for the presence of opioids, cocaine, benzodiazepine, z-drug (novel drug), amphetamines, ethanol, and carisoprodol. explorative descriptive analyses were then completed in r (version 3.4) to identify trends. an rshiny app was created to further explore the data by allowing for rapid filtering and/or subsetting based on various demographic characteristics (e.g., age, sex, race). results we found that harris county is experiencing a modest upward trend of drug related overdoses, with 529 observed in 2010 and 618 in 2016. we also found that the increase was not uniform across all classified drugs: amphetamines, cocaine, and ethanol all saw increases. deaths involving amphetamine increased substantially from 21 in 2010 to 119 in 2016 (figure 1). deaths involving cocaine saw the next sharpest increase with 144 in 2010 and 237 in 2016. deaths associated with opioids remained fairly const ant, with 291 deaths in 2010 and 271 deaths in 2016. differences in mortality across race and sex groups were also observed. the proportion of amphetamine deaths among whites jumped sharply, while the proportion of opioid and benzodiazepine deaths among whites decreased in recent years. the proporti on of amphetamine and cocaine deaths among men rose more sharply than with women in the past three years, whereas for opioids, the proportion of women dying has dropped. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e344, 2019 isds 2019 conference abstracts conclusions it is undeniable that the opioid epidemic is a true public health emergency for the nation. new surveillance tools are needed to better understand the impact and nature of this threat. additionally, as we have found in harris county, the threat may be polysubstance in nature. our report offers two important insights: 1) that mortality data is a useful and actionable surveillance resource in understanding the problem of substance abuse; and 2) public health needs to look at substance abuse from a holistic and comprehensive perspecti ve. keeping the purview limited to opioids alone may create significant blind spots to the public healt h threat facing us. acknowledgement we would like to acknowledge emily dean and rhett lacey for their significant work at hcph to understand the threat of substance abuse, and jason wiersema and the team at ifs for help in obtaining and contexualizing the data. references 1. national institute of health. (2018) overdose death rates. retreived from https://www.drugabuse.gov/relatedtopics/trends-statistics/overdosedeath-rates figure 1. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e330, 2019 isds 2019 conference abstracts 21st century approach: using data and novel technologies to address the opioid crisis eric yazel2, jessica b. dennison2, crystal l. henderson1 1 iu richard m. fairbanks school of public health, indianapolis, indiana, united states, 2 clark memorial hospital, jeffersonville, indiana, united states objective to use novel technologies to develop a rapid response framework to reach opioid overdose patients in an area which is challenging from both a geography and population distribution standpoint. introduction clark county, indiana is geographically located in between the urban area of louisville, kentucky and scott county, indiana. scott county is the site for the largest hiv outbreak in the history of the united states, directly related to high rates of iv dru g abuse. the unique geographic location of clark county in combination with the recent hiv and hepatitis c outbreaks in clark and neighboring counties has greatly informed the development of an effective response to overdoses and the opioid epidemic in general. furthermore, clark county has a unique population distribution, with a population of over 125,000 and a land area of over 300 square miles. despite this large area, over 80% of the population lives within 9 miles of the southern border of the county. this leads to a mix of both urban and rural challenges. there are several areas of the county that have greater than 15 minute emergency response times, which is often the difference between life and death in an overdose situation. these factors led to the devel opment of the clark county rapid response project. the rapid response project is a community-based, multidisciplinary framework to address the opioid addicted patient, from initial use to successful recovery. the project uses data driven technology to init iate the care of opiate overdose patients and administer lifesaving interventions. methods clark county has partnered with the indiana state department of health utilizing the early notification system that monitors statewide overdose activity. once an alert is sent out, the response involves the use of two early notification systems. everbridge is a one touch notification system that allows rapid dissemination of information to various community partners to allow them to initiate the appropriate response. pulse point is a smart phone application that allows cpr and trained community laypeople to respond to a cardiac arrest or overdose patient in a public place. it provides directions to the patient as well as to the nearest aed. clark county has also simultaneously instituted a county-wide cpr training initiative and offered narcan training as well. this is a major paradigm shift, as prior methods of deployment of trained laypeople essentially relied on the chance that an overdose will be reached by a first responder. results everbridge has allowed for the rapid notification of county entities and deployment of resources to overdose ‘hot spot’ areas. the pulse point initiative has dramatically increased the number of cpr and narcan trained responders and provided means of delivering them to the appropriate patient population in a timely manner. both these technologies have dramatically increased the delivery of resources to the overdose patient and decreased response times to the delivery of care. conclusions using data driven technology to inform how clark county health department and first responders collectively address the opioid crisis is a novel approach. since january 2018, clark county health department has used essence (electronic surveillance system for the early notification of communitybased epidemics) to determine where and when an increase of drug overdose activity is occurring throughout the county. this affords county health officials the ability to inform in “near real-time” first responders, the emergency department and other community stakeholders, relevant information thus allowing for the rapid deployment of county resources to the areas most affected. our collective efforts to save lives is further enhanced by the county using of novel technologies like pulse point which is used to deploy both cpr and narcan trained laypersons directly to sites in the community where overdoses are occurring. in a community, which is in large part considered rural and, in many places, has a greater than 15 minute emergenc y response time, using pulse point and everbridge technologies has uniquely positioned clark county to be on the cutting edge of http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e330, 2019 isds 2019 conference abstracts saving lives as we leverage data and technology to address the opioid epidemic in our communities. this has markedly improved access to treatment and response times to overdose patients in clark county, indiana. acknowledgement the authors of this abstract would like to acknowledge the indiana state department of health, lifespring health services, clark memorial hospital, and the community foundation of southern indiana. each institution is a stakeholder and representatives from each are working alongside us as we strive to disrupt and address the opioid crisis in meaningful and tangible ways. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e417, 2019 isds 2019 conference abstracts using syndromic surveillance data to aid public health actions in tennessee caleb wiedeman1, kevin morris1, cassandra jones1, marisa hopper hopper2, paul e. petersen1 1 tennessee department of health, nashville, tennessee, united states, 2 shelby county health department, memphis, tennessee, united states objective to demonstrate the utility of syndromic surveillance data in aiding public health actions and response acros s multiple investigations in tennessee. introduction syndromic surveillance data is typically used for the monitoring of symptom combinations in patient chief complaints (i.e. syndromes) or health indicators within a population to inform public health actions. the tennessee department of health collects emergency department (ed) data from more than 80 hospitals across tennessee to support statewide situational awareness. most hospitals in tennessee provide data within 48 hours of the patient being seen in the emergency department. the timeliness of syndromic surveillance data allow for rapid estimates of impact in emergency department populations. tennessee has successfully used these data to monitor influenza, heat related illnesses, and emergency department impacts from disaster evacuations. in addition to assessing impact and trends, syndromic surveillance can also provide early warnings for conditions of public health concern and increase the lead time public health has to initiate a response. in tennessee, routine syndromic surveillance for mumps, hepatitis a, and other conditions has been successfully conducted statewide. three successes from these surveillance efforts include detecting a clinically diagnosed but unreported case of mumps, early identification of hepatitis a cases during tennessee’s ongoing 2018 hepatitis a outbreak, and the detection of an epidemiologically unlikely clinical diagnosis of mumps associated with an exposure at a recreational center. methods syndromic surveillance data in tennessee are monitored daily for chief complaints and discharge diagnosis codes that could require immediate public health actions. chief complaints are monitored using essence’s built in record of interest (roi) syndrome while icd10 and snomed codes are monitored for specific conditions of interest. examples include mumps, measles, rubella, hepatitis a, and other immediately notifiable conditions that have time sensitive public health interventions associated with them. results in 2017, tennessee was investigating a large increase in mumps cases and outbreaks and exposure responses were occurring across the state. in early june, a patient was seen at a hospital emergency department with a chief complaint of right sided testicular swelling, right sided jaw swelling, and a measured temperature of 99.0 at the time of the visit. based on presentation alone, the patient was diagnosed by the physician with uncomplicated mumps and discharged. two days, but less than 48 hours after patient was seen at the emergency department, the discharge diagnosis of mumps was received in tennessee’s syndromic surveillance system and detected during routine review of incoming discharge diagnoses. upon detection in tennessee’s syndromic surveillance data, both the regional health department where the patient listed his residence and the regional health department where the hospital was located were notified of the ed visit. the hospital was immediately contacted that morning and further investigation revealed that the physician who diagnosed the patient with mumps never ordered laboratory testing to support the diagnosis and that the patient had remained in the jurisdictional area surrounding the hospital. because no laboratory testing was ordered, the infection preventionist at the hospital was not made aware of the patient’s diagnosis and no notification to public health had been made. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e417, 2019 isds 2019 conference abstracts rapid contact and coordination with the hospital, regional health departments, and county heal th department allowed for the patient to be seen at a county health department for specimen collection on the same day the visit was detected. the patient was confirmed pcr positive for mumps the following week. epidemiologic follow up with the patient revealed that the patient was not linked to any of the ongoing outbreaks in tennessee, but had likely been exposed and exposed others at multiple out of state events. tennessee was able to follow up with the affected states and notify them of the potential exposures. in early 2018, tennessee’s ongoing hepatitis a outbreak was detected. to supplement traditional investigative efforts, monitoring for chief complaints generally indicative of hepatitis was initiated across the state, along with di agnosis code monitoring for hepatitis a and unspecified hepatitis. although all ages were monitored, follow up was focused on visits occurring in the most prevalent age group (18 – 44), chief complaints including substance abuse indicators, and icd10 codes indicative of hepatitis a. routine syndromic surveillance for hepatitis a identified visits meeting the outbreak characteristics which were referred to local and regional health departments for follow up. although many visits were confirmed to have been already reported or ruled out (particularly in non-outbreak counties), there were numerous times when laboratory tested hepatitis a cases where detected in syndromic surveillance data prior to them being reported. early detection provided by syndromic surveillance increased the lead time for public health to identify potential high risk contacts and initiate post-exposure prophylaxis. in the summer of 2018, a young child with chief complaint of “diagnosed monday with mumps” was identified during a regular local review of syndromic surveillance data. local public health follow up with the infection preventionist revealed that the child was fully vaccinated and had no known exposure to mumps. the patient was brought to the emergency department due to concerns that he was misdiagnosed at the clinic where he was seen previously. unfortunately, the patient was seen outside of the optimal window for testing, but the evaluation at the emergency department and additional follow up by public health made the concern for mumps very low. soon after that follow up was initiated, the local health department received a call from the general public about a sign put up at local recreational center stating that children attending the center had been exposed to mumps. there were no known cases of mumps in tennessee at that time and the only active mumps investigation in the local jurisdiction was the follow up on the emergency department visit. rapid follow up with the parent of the patient confirmed that they were the “mumps” exposure being referred to at the recreational center and public health was able to work with the recreational center to remove the signage, as the risk of the patient actually having mumps was low. information from the investigation initiated by the syndromic surveillance data allowed for public health to rapidly connect the dots between the exposure notice and the patient. conclusions regular monitoring of syndromic surveillance data provides important opportunities for public health intervention that would not be possible otherwise. in all of the instances mentioned, timely syndromic surveillance data monitoring and follow up benefited public health responses by filling information gaps, helping initiate conversations with hospitals, and serving as another safety net for unreported illnesses. conditions with post-exposure prophylactic interventions that can benefit from increased lead time are valuable targets for routine syndromic surveillance. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e403, 2019 isds 2019 conference abstracts practical story: real people, real urine, unreal uti charlene offiong1, barbara trautner2, ed septimus4, kristi kuper3, tolulope olumuyiwa1 1 houston health department, houston, texas, united states, 2 baylor college of medicine, houston, texas, united states, 3 vizient inc, houston, texas, united states, 4 harvard medical school , boston, massachusetts, united states objective • to describe findings from the joint collaborative between the houston health department and houston -based hospitals • to promote cross sectional partnerships and collaborations across health agencies introduction asymptomatic bacteriuria (asb) is defined as the presence of bacteria in the urine of a patient without signs or symptoms of a urinary tract infection (uti). it is one of the most common reasons for inappropriate antibiotic use in hospitalized patients. without efforts to check inappropriate use, our communities could see increased numbers of highly resistant bacterial pathogens contributing to the public health threat of antimicrobial resistance. treatment itself may be associated with subsequent antimicrobial resistance, adverse drug effects, and cost. the houston health department (hhd) has made it a priority to address antibiotic resistance and stewardship by working collaboratively with members of the healthcare community to address this patient safety issue. as such hhd, in conjunction with infectious diseases experts from the hhd antimicrobial stewardship executive committee formed a joint learning collaborative to work on an asymptomatic bacteriuria stewardship project. the goal of the project was to engage with healthcare professionals across facilities within the houston area to work collaboratively to help reduce unnecessary testing and treatment of asb. methods the project is a joint learning collaborative between hhd and selected acute care facilities within the city of houston. space was limited to no more than 8 hospitals and enrollment occurred on a first come, first serve basis. activities conducted as part of the project included a project launch meeting held at hhd that as attended by participants, education by project subject matter experts (smes), monthly calls with smes to provide case-based feedback and intervention tools. the project launch meeting included a brief overview of the project, review of an asymptomatic bacteruria algorithm (referred to as “kicking uti” algorithm), instructions on how to classify cases, project timeline and plan implementation. the project timeline was 8 months (this included the kick off meeting in month 1, data collection in months 2-4, intervention period during months 5-7, preliminary report in month 4 and final report at month 8. participants were encouraged to do the interventions in one area (e.g. emergency room or a single ward) vs. institution wide. intervention tools provided included a case classification form with instructions, an electronic form that was pre-formatted for local data collection (using microsoft access), and project launch worksheet. the project launch worksheet asked participants about their goals for the project, areas of desired improvements, units/wards to be targeted and key members of the project (e.g. executive champion, project champion, and active participants) at their facility. the agenda for the monthly calls included discussing data collection (i.e. number of cases classified), sme review of challenging cases, and utilization of education and project tools. finally, onsite visits by the smes and hhd representatives were offered to participants to increase local site engagement. results seven acute care hospitals and 1 rehabilitation facility were enrolled in the collaborative. participants from the institutions included 11 clinical pharmacists and one nurse. half of the participants originally targeted emergency departments (ed). the remaining participants conducted interventions on the medical/surgical wards and one facility conducted interventions on the brain injury floor. additional activities were adapted and added throughout the program period. these included: 1) choice of ward versus ed 2) targeted providers (working with mid-level providers to discourage standard urine testing in the emergency department) and 3) strategies for education. strategies for education included utilizing nurse practitioners to educate nurses, designing project marketing tools (flyers, posters, and pocket cards), pharmacy rounds, resident orientation and one-to-one education. site visits were conducted at 3 facilities and included a range of interventions from 1:1 peer to peer discussions to large presentations to medical staff. outcomes for 3 sites included pre-project asb treatment rates of 61% http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e403, 2019 isds 2019 conference abstracts and post project asb treatment rates of 24%, representing a 37% decrease in asb treatment for these sites. in addition, two health systems that participated in the study utilized the information obtained from the project to work with their laboratory departments to change testing practices by increasing the threshold of urine white blood cells required in the sample before reflex to testing for the presence of bacteria. conclusions this project showed that collaboration between a city health department and local institutions can be successful in reducing the overtreatment of asb. hhd facilitated collaboration, assisted with eliminating barriers to knowledge sharing and served as a partner in setting transparent goals. a cross disciplinary approach to promoting patient safety indirectly lead to gai ns in public health. in person interaction between the health department, smes, and representatives from local facilities helped to increase engagement throughout the project. the results of this project will be shared on the health department website as a way of forging community practices and stretching the role of the health department to serve as an advocate for public health and patient safety. future projects would benefit from having increased participation from facillity stakeholders to promote institutional sustainability. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e361, 2019 isds 2019 conference abstracts effects of the el niño southern oscillation on influenza peak activity timing byung-chul chun, kwan hong, hari hwang, sangho sohn preventive medicine, korea university medical college, seoul, korea (the republic of) objective this study aimed to explore the effects of el niño and la niña events on the timing of influenza a peak activity in european countries. introduction influenza causes a significant burden to the world every year. in the temperate zone, influenza usually prevalent in the wint er season, however, it is hardly predictable when the influenza epidemic will begin and when the peak activity will come. influenza has a peak in early winter sometimes and a peak in late winter in another year. however, it is not well known what determines these epidemics timing, and the global climate change is expected to influence the timing of influenza epidemics. methods the weekly influenza surveillance data of 5 european countries (uk, norway, germany, greece, and italy) from january 2005 to july 2018 were retrieved from who flunet database. uk and norway are considered the northern part of europe, otherwise germany, greece, and italy are considered western southern part. the el niño southern oscillation (enso) were retrieved from korean meteorological administration. we used the definition of el niño as the positive sea surface temperature anomalies (≥0.5 degree in celcius), while la niña events are negative anomalies (≤-0.5 degree) of 3 months moving average. the weeks with the highest activities of influenza a and b in each season were identified and coded as 1, 2, 3 if the peak appeared the 1st 2nd and 3rd week from the beginning of the year respectively. the influenza data of 2008/2009 and 2009/2010 were excluded from the analysis to eliminate the bias due to a pandemic influenza outbreak. we compared the means of these peak weeks according to the presence of the anomalies using the general linear model with scheffe multiple comparison and wilcoxon signed rank sum test. results from january 2005 to july 2018, there were 3 el niño and 5 la niña events by the enso excluding 2009 el niño. the influenza a peak activity was observed at 9th week (mean±sd, 8.7±4.8) from the beginning of the year in no anomaly event, but the peak appearance timing was significantly shortened to 6th week (6.2±2.7) and 5 week (5.1±3.9) when el niño and la niña events occurred, respectively (both p<0.05). influenza a made the peak at usually 10 week (9.9±5.0) in northern 2 countries in no anomalies, but at 6th (6.4±3.9) week in any events of an anomaly in the surface sea temperature (p=0.072). in the southern 3 countries, influenza peaks were observed at 8th (7.9±4.8) week in usual without anomalies, but at 5th (5.0±3.3) week in el niño or la niña events (p=0.049). conclusions both el niño and la niña affect the timing of influenza a peak activity; the enso associated the early emergency of peak influenza activities in european countries. references 1. fisman dn, tuite ar, brown ka. 2016. impact of el niño southern oscillation on infectious disease hospitalization risk in the united states. proc natl acad sci usa. 113(51), 14589-94. pubmed https://doi.org/10.1073/pnas.1604980113 2. oluwole osa. 2015. seasonal influenza epidemics and el niños. front public health. 3, 250. pubmed https://doi.org/10.3389/fpubh.2015.00250 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27791069&dopt=abstract https://doi.org/10.1073/pnas.1604980113 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26618150&dopt=abstract https://doi.org/10.3389/fpubh.2015.00250 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e361, 2019 isds 2019 conference abstracts 3. zaraket h, saito r, tanabe n, taniguchi k, suzuki h. 2008. association of early annual peak influenza activity with el niño southern oscillation in japan. influenza other respir viruses. 2(4), 127-30. pubmed https://doi.org/10.1111/j.1750-2659.2008.00047.x http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=19453463&dopt=abstract https://doi.org/10.1111/j.1750-2659.2008.00047.x isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e445, 2019 isds 2019 conference abstracts tracking community naloxone dispensing: part of a strategy to reduce overdose deaths jennifer dolatshahi, lara maldjian, alice welch, casey fulmer, emily winkelstein bureau of alcohol and drug use prevention, care & treatment, new york city department of health and mental hygiene, long island city, new york, united states objective describe the development of an individual-level tracking system for community-based naloxone dispensing as part of new york city’s (nyc) comprehensive plan to reduce overdose deaths. we present data from the first year of the initiative to illustrate results of the tracking system and describe the potential impact on naloxone dispensing program. introduction the number of unintentional overdose deaths in new york city (nyc) has increased for seven consecutive years. in 2017, there were 1,487 unintentional drug overdose deaths in nyc. over 80% of these deaths involved an opioid, including heroi n, fentanyl, and prescription pain relievers [1]. as part of a comprehensive strategy to reduce overdose mortality in nyc, the nyc department of health and mental hygiene’s (dohmh) overdose education and naloxone distribution (oend) program makes naloxone kits available to laypeople free-of-charge through registered opioid overdose prevention programs (oopps). naloxone kits contain two doses of naloxone and educational materials. the oend program distributes kits to registered oopps, which then dispense kits to individuals via community-based trainings. in this context, distribution refers to kits shipped to programs, whereas dispensing refers to kits given to individuals. increased nyc funding has enabled recruitment of more oopps— including syringe exchange programs, public safety agencies, shelters, drug treatment programs, health care facilities, and other community-based programs—and greater dispensing of naloxone kits to laypeople. naloxone distribution has undergone a dramatic expansion, from 2,500 kits in 2009 to 61,706 kits in 2017 [2]. in 2018, dohmh aims to distribute more than 100,000 kits to oopps. in order to target naloxone dispensing to neighborhoods in nyc with the highest overdose burden, we developed a tracking system able to capture individual-level geographic data about naloxone kit recipients. prior to the development of the tracking system, dohmh collected quarterly, aggregate-level naloxone dispensing data from oopps. these data included only the oopps’ zip codes but not recipient residence. oopp zip code was used as a proxy for kits dispensed to individuals. without individual-level geographic information, however, we could not determine whether naloxone kit dispensing reached people in neighborhoods with high overdose mortality rates. to overcome these barriers, dohmh developed a comprehensive but flexible individual-level data collection method. methods to both capture individual-level data from each naloxone recipient in nyc and meet the needs of oopps’ varying capacities, dispensing settings, and any existing organizational data requirements, dohmh devised a two-pronged data collection system. the naloxone recipient form (nrf) system, launched january 1, 2018, primarily employs a short paper form (or nrf) to collect dispensing data. the nrf is a one-page document designed with the opentext™ teleform processing application. it captures individual data and oopp information. individual data include: reason for obtaining a kit, whether first -time receipt of a kit, age, and zip code of residence. oopp information includes: program name and zip code of dispensing location. forms are completed by oopps and recipients at oend trainings, compiled by the oopp, then scanned back to dohmh. we then import forms into teleform, which reads the nrf data directly into a database without need for manual data entry and only moderate need for data verification. the second component of the nrf system allows larger organizations and dispensers in clinical settings with electronic health records to submit data extracts to dohmh that are pulled directly from organizations’ data systems. together with these organizations, we customized these data extracts for direct importation into the master nrf database. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e445, 2019 isds 2019 conference abstracts to demonstrate improvements in our tracking of naloxone dispensing after the development of the nrf system, we mapped the geographic spread of naloxone kits in nyc during the first three months of 2018 (q1 2018) by recipients’ zip code of residence and oopps’ zip codes. results a total of 138 oopps [2] reported any dispensing from january to june, 2018, of which 107 reported individual-level data using the nrf system, accounting for 27,899 kits dispensed to 23,610 individual recipients [3]. logistical barriers to implementing the nrf system varied among oopps, thus the data underestimate citywide dispensing during this time period. some oopps experienced delays in reporting recipient-level data until a more-tailored data collection strategy was devised. visual inspection of oopp-level distribution and individual-level dispensing maps using q1 2018 data (see figure 1 and figure 2) demonstrate the difference between oopp-level distribution data and individual-level dispensing data. mapping data indicate that the largest numbers of naloxone kits were dispensed to people in neighborhoods with the highest burden of overdose in nyc. conclusions the nrf system provides dohmh, as well as oopps in nyc, with individual-level data to more accurately track naloxone dispensing citywide. the simplicity and flexibility of the nrf system allows for timely and geographically precise data collection from dozens of organizations across nyc with little or no additional cost to oopps. as new organizations have registered as oopps, particularly large health care or human services systems, dohmh has developed new methods for incorporating dispensing data into the nrf system. ongoing communication with oopps of all types and an iterative data collection improvement process have ensured that the system remains comprehensive while also being re sponsive to individual program’s capacities and data needs. our analysis comparing oopp-level and individual-level dispensing confirmed higher numbers of naloxone kit dispensing in neighborhoods with larger numbers of overdoses, a program goal. the nrf system also provides an opportunity to target increases in naloxone dispensing to improve the effectiveness of our overdose prevention efforts. references 1. nolan ml, tuazon e, blachman-forshay j, paone d. unintentional drug poisoning (overdose) deaths in new york city, 2000-2017. new york city department of health and mental hygiene: epi data brief (104); september 2018. 2. nyc dohmh opioid overdose prevention program (oopp) database. all data is provisional. 3. nyc dohmh naloxone recipient form (nrf) database. all data is provisional. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e445, 2019 isds 2019 conference abstracts figure 1. new york city kit distribution data (fig. 1) did not reflect where kits were going http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e445, 2019 isds 2019 conference abstracts figure 2. a new system was needed to understand naloxone dispensing. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e432, 2019 isds 2019 conference abstracts enhancing tx sys by integrating ems and poison data for opioid overdose surveillance jyllisa mabion department of state health services, united states objective to improve texas syndromic surveillance by integrating data from the texas poison center and emergency medical services for opioid overdose surveillance. introduction in recent years, the number of deaths from illicit and prescription opioids has increased significantly resulting in a nation al and local public health crisis. according to the texas center for health statistics, there were 1340 opioid related deaths in 2015 [1]. in 2005, by comparison, there were 913 opioid related deaths. syndromic surveillance can be used to monitor overdose trends i n near real-time and provide much needed information to public health officials. texas syndromic surveillance (txs2) is the statewide syndromic surveillance system hosted by the texas department of state health services (dshs). to enhance the capabilities of txs2 and to better understand the opioid epidemic, dshs is integrating both texas poison center (tpc) data and emergency medical services (ems) data into the system. much of the data collected at public health organizations can be several years old by the time it is released for public use. as a result, there have been major efforts to integrate more real-time data sources for a variety of surveillance needs and during emergency response activities. methods guided by the oregon public health division’s successful integration of poison data into oregon essence, dshs has followed a similar path [2]. dshs already receives tpc data from the commission on state emergency communication (csec), hence copying and routing that data into txs2 requires a memorandum of understanding (mou) with csec, which is charged with administering the implementation of the texas poison control network. ems records are currently received by the dshs office of injury prevention (oip) via file upload and extracted from web services as an xml file. regional and local health operations, the division where the syndromic surveillance program is located, and oip, are both sections within dshs. therefore, it is not necessary to have a formal mou in place. both parties would operate under the rules and regulations that are established for data under the community health improvement division. csec and ems will push data extracts to a dshs sftp folder location for polling by rhapsody in amazon web services. the message data will be extracted and transformed into the essence database format. data are received at least once every 24 hours. results txs2 will now include tpc and ems data, giving system users the ability to analyze and overlay real -time data for opioid overdose surveillance in one application. the integration of these data sources in txs2 can be used for both routine surveillance and for unexpected public health events. this effort has led to discussions on how different sections within dshs can collaborate by using syndromic surveillance data, and has generated interest in incorporating additional data streams into txs2 in the future. conclusions while this venture is still a work in progress, it is anticipated that adding tpc and ems data to txs2 will be beneficial in surveilling not just opioid overdoses but other conditions and illnesses, as well as capturing disaster related injuries. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e432, 2019 isds 2019 conference abstracts references 1. texas health data, center for health statistics [internet]. austin (tx): department of state health services. available from: http://healthdata.dshs.texas.gov/opioids/deaths 2. laing r, powell m. 2017. integrating poison center data into oregon essence using a low-cost solution. online j public health inform. 9(1), e042. https://doi.org/10.5210/ojphi.v9i1.7620 http://ojphi.org/ https://doi.org/10.5210/ojphi.v9i1.7620 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e376, 2019 isds 2019 conference abstracts camera-based, mobile disease surveillance using convolutional neural networks olatunde o. madandola, altansuren tumurbaatar, saiteja abbu, liangyu tan, lauren e. charles pacific northwest national laboratory, richland, w ashington, united states objective automated syndromic surveillance using mobile devices is an emerging public health focus that has a high potential for enhanced disease tracking and prevention in areas with poor infrastructure. pacific northwest national laboratory sought to develop an android mobile application for syndromic biosurveillance that would i) use the phone camera to take images of human faces to detect individuals that are sick through a machine learning (ml) model and ii) collect image data to increase training data available for ml models. the initial prototype use case is for screening and tracking the health of soldiers for use by the department of defense’s disease threat reduction agency. introduction infectious diseases present with multifarious factors requiring several efforts to detect, prevent, and break the chain of transmission. recently, machine learning has shown to be promising for automated surveillance leading to rapid and early interventions, and extraction of phenotypic features of human faces [1,2]. in addition, mobile devices have become a promising tool to provide onthe-ground surveillance, especially in remote areas and geolocation mapping [3]. pacific northwest national laboratory (pnnl) combines machine learning with mobile technology to provide a groundbreaking prototype of disease surveillance without the need for internet, just a camera. in this android application, visiondx, a machine learning algorithm analyses human face images and within milliseconds notifies the user with confidence level whe ther or not the person is sick. visiondx comes with two modes, photo and video, and additional features of history, map, and statistics. this application is the first of its kind and provides a new way to think about the future of syndromic surveillance. methods data. human healthy (n = 1096) and non-healthy (n = 1269) facial images met the criteria for training the machine learning model after preprocessing them. the healthy images were obtained from the chicago face database [4] and california institute of technology [5]. there are no known collections of disease facial images. using open source image collection/curation services, images were identified by a variety of keywords, including specific infectious diseases. the criteria for image inclusion was 1. a frontal face was identified using opencv library [6], and 2. the image contained signs of disease through visual inspection (e.g., abnormal color, texture, swelling). model. to identify a sick face from a healthy one, we used transfer machine learning and experimented with various pretrained convolutional neural networks (cnn) from google for mobile and embedded vision applications. using mobilenet, we trained the final model with our data and deployed it to our prototype mobile app. google mobile vision api and tensorflow mobile were used to detect human faces and run predictions in the mobile app. mobile application. the android app was built using android studio to provide an easily navigable interface that connects every action between tabbed features. the app features (i.e., map, camera, history, and statistics) are in tab view format. the custommade camera is the main feature of the app, and it contains face detection capability. a real -time health status detection function gives a level of confidence based the algorithm results found on detected faces in the camera image. results pnnl's prototype android application, visiondx, was built with user-friendly tab views and functions to take camera images of human faces and classify them as sick or healthy through an inbuilt ml model. the major functions of the app are the camera, map, http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e376, 2019 isds 2019 conference abstracts history, and statistics pages. the camera tab has a custom-made camera with face detection algorithm and classification model of sick or healthy. the camera has image or video mode and results of the algorithm are updated in milliseconds. the statistics view provides a simple pie chart on sick/healthy images based on user selected time and location. the map shows pins representing all labeled images stored, and the history displays all the labeled images. clicking on an image in either view shows the image with metadata, i.e., model confidence levels, geolocation, and datetime. the cnn model prediction accuracy has ~98% validation accuracy and ~96% test accuracy. high model performance shows the possibility that deep learning could be a powerful tool to detect sickness. however, given the limited dataset, this high accuracy also means the model is most likely overfit to the data. the training set is limited: a. the number of training images is small compared to the variability in facial expressions and skin coloring, and b. the sick images only contained overt clinical signs. if tr ained on a larger, diverse set of data, this prototype app could prove extremely useful in surveillance efforts of individual to large groups of people in remote areas, e.g., to identify individuals in need of medical attention or get an overview of population health. in effort to improve the model, visiondx was developed as a data collection tool to build a more comprehensive dataset. within the tool, users can override the model prediction, i.e., false positive or false negative, with a simple toggle button. lastly, the app was built to protect privacy so that other phone aps can't access the images unless shared by a user. conclusions developed at pnnl for the defense threat reduction agency, visiondx is a novel, camera-based mobile application for real-time biosurveillance and early warning in the field without internet dependency. the prototype mobile app takes pictures of faces and analyzes them using a state-of-the-art machine learning model to give two confidence levels of likelihood of being sick and healthy. with further development of a labeled dataset, such as by using the app as a data collection too, the results of the algorithm will quickly improve leading to a ground-breaking approach to public health surveillance. acknowledgement this work was funded by the defense threat reduction agency (project # cb10190) references 1. ferry q, steinberg j, webber c, et al. 2014. diagnostically relevant facial gestalt information from ordinary photos. elife. 3, e02020. pubmed https://doi.org/10.7554/elife.02020 2. lopez dm, de mello fg, dias c, et al. 2017. evaluating the surveillance system for spotted fever in brazil using machine-learning techniques. front public health. 5, 323. doi:https://doi.org/10.3389/fpubh.2017.00323. pubmed 3. fornace km, surendra h, abidin t, et al. 2018. use of mobile technology-based participatory mapping approaches to geolocate health facility attendees for disease surveillance in low resource settings. int j health geogr. 17(1), 21. doi:https://doi.org/10.1186/s12942-018-0141-0. pubmed 4. ma ds, correll j, wittenbrink b. 2015. the chicago face database: a free stimulus set of faces and norming data. behav res methods. 47(4), 1122-35. doi:https://doi.org/10.3758/s13428-014-0532-5. pubmed 5. computational vision. archive. (1999). sept 22, 2018 at http://www.vision.caltech.edu/html-files/archive.html 6. bradski g. (n.d.) the opencv library. sept 30, 2018 at http://www.drdobbs.com/open-source/the-opencvlibrary/184404319 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24963138&dopt=abstract https://doi.org/10.7554/elife.02020 https://doi.org/10.3389/fpubh.2017.00323 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29250519&dopt=abstract https://doi.org/10.1186/s12942-018-0141-0 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29914506&dopt=abstract https://doi.org/10.3758/s13428-014-0532-5 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25582810&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e376, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e390, 2019 isds 2019 conference abstracts icu respiratory admissions data for influenza severity surveillance? liselotte van asten1, angie luna pinzon1, dylan w. de lange2, 3, evert de jonge2, 4, frederika dijkstra1, sierk marbus1, gé donker5, wim van der hoek1, nicolette f. de keizer2,6 1 centre for infectious disease control netherlands, national institute for public health and the environment (rivm), utrecht, bilthoven, netherlands, 2 national intensive care evaluation, amsterdam, netherlands, 3 department of intensive care medicine, university medical center, university utrecht, utrecht, netherlands, 4 department of intensive care, leiden university medical center, leiden, netherlands, 5 nivel primary care database – sentinel practices, utrecht, netherlands, 6 department of medical informatics, amsterdam umc, amsterdam public health research institute, amsterdam, netherlands objective intensive care unit (icu) data are registered for quality monitoring in the netherlands with near 100% coverage. they are a ‘big data’ type source that may be useful for infectious disease surveillance. we explored their potential to enhance the surveillance of influenza which is currently based on the milder end of the disease spectrum. we ultimately aim to set up a real-surveillance system of severe acute respiratory infections. introduction while influenza-like-illness (ili) surveillance is well-organized at primary care level in europe, little data is available on more severe cases. with retrospective data from icu’s we aim to fill this current knowledge gap and to explore its worth for prospective surveillance. using multiple parameters proposed by the world health organization we estimated the burden of severe acute respiratory infections (sari) to icu and how this varies between influenza epidemics. methods we analyzed weekly icu admissions of adults in the netherlands (2007-2016) from the national intensive care evaluation (nice) quality registry (100% coverage of adult icu in 2016; population size 14 million adults. a sari syndrome was defined as admission diagnosis being any of 6 pneumonia or pulmonary sepsis codes in the acute physiology and chronic health evaluation iv (apache iv) prognostic model. influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. in recent years nice has explored and promoted increased timeliness and automation of data transfer. results annually, 11-14% of medical admissions to adult icus were for a sari (5-25% weekly). admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to icu-sari. between influenza epidemics, severity indicators varied: icusari incidence (between 558-2,400 cumulated admissions nation-wide, rate: 0.40-1.71/10,000 inhabitants), average apache score (between 71-78), icu-sari mortality (between 13-20%), icu-sari/ili ratio (between 8-17 sari icu cases per 1,000 expected medically attended influenza-like-illness in primary care), peak incidence (between 101188 icu-sari admissions nationally in the highest week, rate: between 0.07-0.13/10,000 population). icus use different types of electronic health records (ehrs). data submitted to the nice registry is mainly based on routinely collected data extracted from these ehrs. the timeliness of data submission varies between a few weeks and three months. together with icus, the nice registry has recently undertaken actions to increase timeliness of icu data submission. conclusions in icu data, great variation can be seen between the yearly influenza epidemic periods in terms of different influenza severity parameters. the parameters also complement each other by reflecting different aspects of severity. prospective syndromic icusari surveillance, as proposed by the world health organization would provide insight into severity of ongoing influenza epidemics which differ from season to season. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e390, 2019 isds 2019 conference abstracts currently a subset of hospitals provide data with a 6-week delay. this can be a worthwhile addition to current influenza surveillance, which, while timelier, is based on milder cases seen by general practitioners (primary care). future increases in data timeliness will remain an aim. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e345, 2019 isds 2019 conference abstracts hepatitis c testing trends among large commercially insured populations, 2011–2017 mohammed a. khan2, 1, jae eui soh2, 1, william w. thompson2, noele p. nelson2 1 emory university, atlanta, georgia, united states, 2 centers for disease control and prevention, atlanta, georgia, united states objective we estimated the rate of hepatitis c testing between 2011 and 2017 among persons with commercial health insurance coverage and compared rates by birth cohort. introduction hepatitis c virus (hcv) infection is the most common blood-borne infection in the us, and a leading cause of liver-related morbidity and mortality. approximately 3.5 million individuals in the us were estimated to have been living with hepatitis c in 2010, and approximately half of them were unaware that they were infected. among hcv infected individuals, those born between 1945 and 1965 (usually referred to as the baby boomer cohort) represent approximately 75% of current cases. because of the substantial burden of disease among this age group, cdc expanded its existing hepatitis c risk-based testing recommendations to include a one-time hcv antibody test for all persons born between 1945 and 1965. the united states preventive services task force (uspstf) subsequently made the same recommendation in june 2013. description we obtained data from the 2011–2017 ibm marketscan® commercial claims and encounters and medicare supplemental and coordination of benefits databases. these data consist of inpatient and outpatient service claims for persons with employer sponsored health insurance coverage and their dependents. this analysis was restricted to adults 18 years of age and older with continuous enrollment in a commercial or medicare supplemental plan for at least one calendar year during the study period (a 45day gap in coverage was allowed) who received outpatient prescription drug claims data feeds. claims for hepatitis c antibody testing were identified using current procedural terminology (cpt) codes (80074, 86803). we defined the annual hepatitis c testing rate as the number of patients with an hcv antibody test claim divided by the total number of study-eligible enrollees in a given calendar year. testing rates were calculated for persons born between 1945 and 1965 and all other adults. there were 54,298,561 unique adults who were continuously enrolled for at least one calendar year during the study period. among these, 4,629,040 (9%) had one or more inpatient or outpatient service claim with a cpt code for hepatitis c antibody testing during the study period. the overall estimated annual testing rate increased from 2.2% in 2011 to 5.3% in 2017. the testing rate increased from 1.7% to 7.8% among the 1945–1965 birth cohort and 2.5% to 4.0% in other birth cohorts. the average annual percent change in testing was 30.1% among the 1945–1965 birth cohort and 8.2% among other birth cohorts. testing rate increased markedly (64.1%) between 2016 and 2017 in the 1945–1965 birth cohort, but not in other birth cohorts (7.7%). in this sample of individuals covered by commercial insurance, hepatitis c testing rates have increased slowly between 2011 and 2016. in 2017, there was a substantial increase in testing rates among the baby boomer cohort due most likely to an increase in awareness of cdc and uspstf recommendations by both providers and individual patients associated with cdc health promotion efforts and increased marketing efforts by drug manufacturers. efforts should continue to promote and increase the awareness of these recommendations and have people tested and treated for hcv. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e331, 2019 isds 2019 conference abstracts a fairer mirror: equity-limited healthcare system rankings samuel reisman1, 2, begum ahmed1, mostafa balboul1, zev blumenkranz3 1 college of medicine, suny downstate college of medicine, brooklyn, new york, united states, 2 suny downstate school of public health, brooklyn, new york, united states, 3 new york medical college, valhalla, new york, united states objective ● describe the diverse determinants of national health and how they are compositely graded in health care system rankings. ● articulate intrinsic reasons why equity should not be subsumed within other evaluative categories. ● design an equity-limited ratings framework for limiting maximum ratings of inequitible healthcare systems. introduction healthcare systems are often evaluated using comparative health care rankings. simulations have shown that maximally inequitable health care systems can perform well in published, influential health care system rankings by excelling in non -equity categories [1], resulting in highly ranked yet grossly inequitable healthcare systems. recently, despite below average equity rankings, the healthcare systems of australia and new zealand ranked among the top four in the commonwealth fund’s international comparative study mirror, mirror 2017 [2]. equity rankings should logically limit non-equity rankings given the insignificance of healthcare system improvements to those lacking adequate healthcare coverage. we analyzed whether an equity-limited ranking methodology would limit overall rankings for significantly inequitable healthcare systems while maintaining the general findings of the commonwealth fund study. methods we reanalyzed the commonwealth fund’s 2017 international health care system comparison using a modified, equit y-limited methodology. for each country, maximum non-equity domain summary scores were limited to the equity domain summary score. countries were ranked using the mean of the five domain-specific performance scores. overall rankings were compared to the original rankings. results seven of eleven countries had an overall rank change in the equity-limited model. countries with above average overall ratings but poor equity ratings had the greatest changes in overall rank. australia’s overall ranking decreased from second to seventh, thereby matching its equity ranking of seventh. new zealand changed from fourth to eighth overall, matching its equity ranking as wel l. other changes were less significant, with changes of only one overall rank position. notably, the bottom three countries and the top country were unchanged. conclusions equity-limited ranking methodologies can prevent inequitable health care systems from attaining high overall ratings. such equitylimited rankings are logical considering the diminished significance of health care system improvements to those lacking adeq uate health coverage. methodologies that incorporate equity limits should be used to produce fairer rankings that respect the dignity and rights of all individuals. references 1. reisman s, blumenkranz z. comparative health care system rankings can obscure maximal inequities: a simulation study. society for public health education (sophe) 69th annual conference. 2018, june. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e331, 2019 isds 2019 conference abstracts 2. schneider ec, sarnak do, squires d, shah a, doty mm. mirror, mirror 2017: international comparison reflects flaws and opportunities for better u.s. health care. the commonwealth fund. 2017, july. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e418, 2019 isds 2019 conference abstracts using syndromic surveillance to monitor response to cyanotoxin contamination event kelly e. cogswell acute and communicable disease prevention, oregon health authority, portland, oregon, united states objective examine healthcare seeking behavior in a population exposed to low levels of cyanotoxins in the public drinking water supply and quantify how publicity of the event may have affected perceptions of risk in the affected population. introduction cyanotoxins are unregulated, emerging contaminants that have been associated with adverse health effects, including gastroenteritis, when consumed at high levels [1,2]. in may and june of 2018 cyanotoxins were detected in the public drinking water system for salem, or at levels above environmental protection agency (epa) health advisory levels for sensitive groups [3]. sensitive groups were defined as children under 6, elderly adults, pregnant women, nursing mothers, people with compromised immune systems, people receiving dialysis, people with pre-existing liver conditions, and pets. several health advisories were issued, and there was substantial media coverage of the event. the oregon health authority (oha) organized an incident management team (imt), which coordinated activities with other state and local agencies. oregon essence staff used syndromic surveillance to monitor the population for health effects and healthcare seeking behavior. methods oregon essence staff developed syndromic surveillance queries to monitor visits made to local emergency departments (i.e., visits by hospital location), as well as visits made by residents of the affected area (i.e., visits by patient location). specifically, oregon essence staff monitored total visits, gastroenteritis syndrome, visits by age group, and mentions of the word ‘water’ daily during the relevant time period. oha communications staff tracked media coverage of the event. after the event, oregon essence staff reconciled syndromic surveillance visit data with water test data, health advisory status, and media coverage to characterize how messaging may have affected healthcare seeking behavior. results cyanotoxins were detected at levels above epa guidelines for sensitive groups on 9 days between may 23, 2018 and june 19, 2019. oha identified 67 news articles related to the event published in may and 179 published in june. additionally, there was an unquantified amount of activity on social media, and a mass text alert that was sent out by the oregon office of emergency management. visits for gastroenteritis were highest on the days immediately following the issuance of the first drinking water advisory. the first drinking water advisory was issued three days after the first results that contained cyanotoxins at levels exceeding the epa guidelines for sensitive groups were received. visits where the word ‘water’ was mentioned were similarly elevated immediately after the first drinking water advisory was issued. however, visits for gastroenteritis were also above expected levels on one day that had a water sample above epa guidelines for sensitive groups, but before the first drinking water advisory was issued. conclusions because cyanotoxins are unregulated, limited federal guidance was available and it took several days for the oregon health authority to develop state guidance and educational materials. this delay contributed to public confusion about the level of risk associated with drinking the water, as well as confusion about which groups of people should avoid drinking the water. our data suggest that emergency department visit behavior was largely driven by publicity of the event. visits to the emergency department for gastroenteritis and mentions of the word ‘water’ decreased as more public information and guidance became available. however, we cannot rule out a real health effect related to cyanotoxins in the drinking water for area residents. one lesson learned from this type of high profile event relates to tracking of media coverage; it is difficult to measure how many people media coverage actually reaches, and attempting to characterize media coverage becomes more difficult after the event. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e418, 2019 isds 2019 conference abstracts references 1. u.s. epa (united states environmental protection agency). 2015. drinking water health advisory for the cyanobacterial toxin cylindrospermopsin. epa 820r15101, washington, dc; june, 2015. available from: http://water.epa.gov/drink/standards/hascience.cfm 2. u.s. epa (united states environmental protection agency). 2015. drinking water health advisory for the cyanobacterial toxin microcystin. epa 820r15100, washington, dc; june, 2015. available from: http://water.epa.gov/drink/standards/hascience.cfm 3. u.s. epa (united states environmental protection agency). 2015. 2015 drinking water health advisories for two cyanobacterial toxins. epa 820f15003, washington, dc; june, 2015. available from: https://www.epa.gov/sites/production/files/2017-06/documents/cyanotoxins-fact_sheet-2015.pdf http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e404, 2019 isds 2019 conference abstracts precision public health through clinic-based syndromic surveillance in communities ta-chien chan, yung-chu teng, yen-hua chu, tzu-yu lin academia sinica, taipei city, taiwan objective sentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. the traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ili), and hand, foot, and mouth disease (hfmd) to health authorities on a weekly basis. however, this is labor-intensive and time-consuming work. in this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites. introduction in december 2009, taiwan’s cdc stopped its sentinel physician surveillance system. currently, infectious disease surveillance systems in taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (rods) from emergency rooms, and the outpatient and hospitalization surveillance system from national health insurance data. however, the timeliness of data exchange and the number of monitored syndromic groups are limited. the spatial resolution of monitoring units is also too coarse, at the city level. those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. based on past epidemic experience, daily and small area surveillance can detect early aberrations. in addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. traditional disease-based reporting systems cannot capture this kind of signal. therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases. methods we collected data on 23 syndromic groups from participating clinics in taipei city (in northern taiwan) and kaohsiung city (in southern taiwan). the definitions of 21 of those syndromic groups with icd-10 diagnoses were adopted from the international society for disease surveillance (https://www.surveillancerepository.org/icd-10-cm-master-mapping-reference-table). the definitions of the other two syndromic groups, including dengue-like illness and enterovirus-like illness, were suggested by infectious disease and emergency medicine specialists. an enhanced sentinel surveillance system named “sentinel plus” was designed for sentinel clinics and community hospitals. the system was designed with an interactive interface and statistical models for aberration detection. the data will be computed for different combinations of syndromic groups, age groups and gender groups. every day, each participating clinic will automatically upload the data to the provider of the health information system (his) and then the data will be transferred to the research team. this study was approved by the committee of the institutional review board (irb) at academia sinica (as-irb02-106262, and as-irb02-107139). the databases we used were all stripped of identifying information and thus informed consent of participants was not required. results this system started to recruit the clinics in may 2018. as of august 2018, there are 89 clinics in kaohsiung city and 33 clinics and seven community hospitals in taipei city participating in sentinel plus. the recruiting process is still ongoing. on average, the monitored volumes of outpatient visits in kaohsiung city and taipei city are 5,000 and 14,000 per day. each clinic is provided one list informing them of the relative importance of syndromic groups, the age distribution of each syndromic group and a timeseries chart of outpatient rates at their own clinic. in addition, they can also view the village-level risk map, with different alert colors. in this way, medical practitioners can know what’s going on, not only in their own clinics and communities but also in the surrounding communities. the department of health (figure 1) can know the current increasing and decreasing trends of 23 syndromic groups by red and blue color, respectively. the spatial resolution has four levels including city, township, village and clinic. the map and bar chart represent the difference in outpatient rate between yesterday and the average for the past week. the line chart represents the daily outpatient rates for one selected syndromic group in the past seven days. the age distribution of each syndromic group and age-specific outpatient rates in different syndromic groups can be examined. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e404, 2019 isds 2019 conference abstracts conclusions sentinel plus is still at the early stage of development. the timeliness and the accuracy of the system will be evaluated by comparing with some syndromic groups in emergency rooms and the national notifiable disease surveillance system. the system is designed to assist with surveillance of not only infectious diseases but also some chronic diseases such as asthma. integrating with external environmental data, sentinel plus can alert public health workers to implement better intervention for the right population. acknowledgement this research was supported by a grant titled “multidisciplinary health cloud research program: technology development and application of big health data” from academia sinica and a grant titled “implementing integrated surveillance network of dengue fever” from the national health research institute, taiwan and a grant from the department of health, taipei city government. we would like to thank the departments of health in kaohsiung and taipei, as well as taipei medical association for help in recruiting the participating clinics and hospitals. references 1. james w. buehler as, marc paladini, paula soper, farzad mostashari: syndromic surveillance practice in the united states: findings from a survey of state, territorial, and selected local health departments. advances in disease surveillance 2008, 6(3). 2. ding y, fei y, xu b, yang j, yan w, et al. 2015. measuring costs of data collection at village clinics by village doctors for a syndromic surveillance system — a cross sectional survey from china. bmc health serv res. 15, 287. pubmed https://doi.org/10.1186/s12913-015-0965-2 3. kao jh, chen cd, tiger li zr, chan tc, tung th, et al. 2016. the critical role of early dengue surveillance and limitations of clinical reporting -implications for non-endemic countries. plos one. 11(8), e0160230. pubmed https://doi.org/10.1371/journal.pone.0160230 4. chan tc, hu th, hwang js. 2015. daily forecast of dengue fever incidents for urban villages in a city. int j health geogr. 14, 9. pubmed https://doi.org/10.1186/1476-072x-14-9 5. chan tc, teng yc, hwang js. 2015. detection of influenza-like illness aberrations by directly monitoring pearson residuals of fitted negative binomial regression models. bmc public health. 15, 168. pubmed https://doi.org/10.1186/s12889-0151500-4 6. ma ht. syndromic surveillance system for detecting enterovirus outbreaks evaluation and applications in public health. taipei, taiwan: national taiwan university; 2007. figure 1. the department of health dashboard in sentinel plus http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26208506&dopt=abstract https://doi.org/10.1186/s12913-015-0965-2 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27501302&dopt=abstract https://doi.org/10.1371/journal.pone.0160230 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25636965&dopt=abstract https://doi.org/10.1186/1476-072x-14-9 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25886316&dopt=abstract https://doi.org/10.1186/s12889-015-1500-4 https://doi.org/10.1186/s12889-015-1500-4 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e265, 2019 isds 2019 conference abstracts using probabilistic matching to improve opioid drug overdose surveillance, new jersey bretta j. jacquemin, teresa hamby, stella tsai center for health statistics, state of new jersey, department of health, trenton, new jersey, united states objective link syndromic surveillance data for potential opioid-involved overdoses with hospital discharge data to assess positive predictive value of cdc opioid classifiers for conducting surveillance on acute drug overdoses. introduction the opioid drug overdose crisis presents serious challenges to state-based public health surveillance programs, not the least of which is uncertainty in the detection of cases in existing data systems. new jersey historically had slightly higher unintent ional drug overdose death rates than the national average, but by 2001 dramatic increases in drug overdose deaths in states like west virginia began to drive up the national rate (figure 1). although the rise in new jersey’s fatal overdose rates has mirrored the national rate since 1999, the rate has dramatically increased since 2011from 9.7 per 100,000 (868 deaths) to 21.9 per 100,000 in 2016 (1,931 deaths), an increase of 125% in five years [1]. the new jersey department of health has been funded by the centers for disease control and prevention (cdc) to conduct surveillance of opioid-involved overdoses through the enhanced surveillance of opioid-involved overdose in states (esoos) program, and to conduct syndromic surveillance through the national syndromic surveillance program (nssp); this has presented a collaboration opportunity for the department’s surveillance grantee programs to use existing resources to evaluate and refi ne new jersey’s drug overdose case definitions and develop new indicators to measure the burden of overdose throughout the state and to facilitate effective responses. methods this work examined using probabilistic matching strategies to assess how accurately syndromic surveillance data identifies potential opioid-involved overdose patients by linking to hospital discharge records after subsequent treatment in an emergency department or inpatient setting for either a confirmed opioidinvolved overdose or another condition(s). new jersey syndromic surveillance data from nssp’s essence system from december 2016 with either cdc’s ccdd classifiers “cdc opioid overdose v1” or “cdc heroin overdose v3” were selected for inclusion (“nj essence data”). nj essence data were restructured to produce one record per patient visit, with each record assigned one or more overdose classifiers; these records were then matched to the universe of acute care hospital discharge billing records from the new jersey hospital discharge data system (“ub data”) from the same time period. confirmed drug overdoses were flagged in the ub data by using the cdc’s baseline esoos case definition, which searches all diagnosis fields for icd-10-cm codes indicating an unintentional or undetermined intent drug overdose, an opioid overdose, or a heroin overdose. optionally, there are suggested codes for mental and behavioral health conditions that indicate opioid abuse or dependence with intoxication (table 1). using sas® software and proc sql, data were matched using a three-round “blocking” strategy based on facility identifier and admission date, and combinations of date of birth, sex, patient zip code, and age. concordance of essence opioid overdose classifiers with indicator categories used by cdc’s esoos was evaluated. suspected opioid overdoses from nj essence that matched to ub records for mental health conditions that were not also acute overdoses were reviewed. results there were 253 records in nj essence data with either “cdc opioid overdose v1” or “cdc heroin overdose v3” ccdd classifiers; restructuring the data resulted in 149 unique records of potential opioid overdoses. of these, 106 (71%) records from nj essence were successfully matched to emergency department or inpatient records. eighty (80) records (54%), were matched in the first round using facility identifier and date of admission, date of birth, sex, and patient’s home zip code. of the 43 unmatched nj essence records, 33 (77%) were patients missing age and date of birth. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e265, 2019 isds 2019 conference abstracts of the 106 matched records (table 2): • 74 opioid-involved overdoses in nj essence matched to any drug overdose records in the ub data, for an overall ppv of 70%. • 69 opioid-involved overdoses in nj essence matched to opioid-involved overdose records, for an opioid-involved ppv of 65%. • 54 heroin-involved overdoses in nj essence matched to heroin-involved overdose records, for a heroin-involved ppv of 92%. • 32 matched records were nj essence positive for opioids and ub negative, and 24 (75%) were classified as potential heroin overdoses. • 18 records had at least one mental and behavioral health condition code as part of the final discharge record. • 3 were flagged with the mental and behavioral health conditions with opioid intoxication indicator. only one record appeared to be a possible false positive, with an nj essence record indicating a “suspected heroin overdose or an overdose by unspecified drugs and of undetermined intent”, but a discharge record indicated a primary diagnosis code of i4 6.9 (sudden cardiac arrest) and other systemic diagnoses but no poisoning or mental or behavioral health codes reported. conclusions nj essence data with cdc opioid or heroin overdose classifiers was able to correctly identify opioid -involved overdoses in matched records for patients experiencing an acute overdose better than 2 out of 3 times. for patients experiencing an acute heroin overdose the ppv was over 90%. cases with discordance in classification matched to records that may have been possible undetected drug intoxications or other mental and behavioral health conditions. this work does not confirm that the cdc opioid or heroin overdose classifiers accurately capture all or even most drug overdoses treated in new jersey hospitals reported to nssp essence as of december 2016. a total of 1,461 discharges for acute drug overdoses were identified in ub data using the esoos case definition; 1,069 were treated and released from the emergency department, and 392 were admitted for further inpatient care. the 106 matched records only represent 7% of total overdose records identified in the ub data. further suggested work includes follow-up on possible data quality issues, pursuing a comprehensive project using all ubidentified overdoses matched to a broader selection of nj essence data to examine what may be missed by the cdc’s nssp overdose classifiers, and using more recent data to test improvements made to the system since the original data pull. acknowledgement nj esoos is funded by cdc grant number 5 nu17ce924890; nj nssp is funded by cdc grant number 6 nu50oe000083. references 1. centers for disease control and prevention, national center for injury prevention and control. web-based injury statistics query and reporting system (wisqars) [online]. (2005) [2018 oct 1]. available from url: www.cdc.gov/injury/wisqars http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e265, 2019 isds 2019 conference abstracts figure 1. unintentional drug overdose deaths, 1999-2016 source: cdc wisqars, unintentional drug overdose deaths 1999-2016 (x40-x44). note: nj 2009 drug overdose death rate from new jersey department of health njshad; west virginia and nebraska were selected as comparison states because in 2016 they had the highest and lowest rates of drug overdose deaths, respectively. table 1. case definitions for esoos and nssp essence surveillance definition codes, classifiers esoos unintentional or undetermined intent, any drug overdose t36-t50 esoos unintentional or undetermined intent, any opioid overdose t40.0-t40.4, t40.60, t40.69 esoos unintentional or undetermined intent, heroin overdose t40.1 esoos optional mental and behavioral health conditions, with intoxication f11.12, f11.22, f11.92 nssp "cdc opioid overdose v1" "cdc heroin overdose v3" table 2. results ub (esoos) http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e265, 2019 isds 2019 conference abstracts essence count no match any drug overdose opioid overdose heroin overdose mental & behavioral health only no drug or mbh codes cdc opioid overdose v1 45 19 18 13 5 3 5 cdc heroin overdose v3 104 24 56 56 54 3 21 any cdc classifier 149 43 74 69 59 6 26 http://ojphi.org/ the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza nicholas e. millett1, john m. aronis1,*, michael m. wagner1, fuchiang tsui1, ye ye1, jeffrey p. ferraro2, peter j. haug2, per h. gesteland2, gregory f. cooper1 1. real-time outbreak and disease surveillance (rods) laboratory, department of biomedical informatics, university of pittsburgh, pittsburgh, pennsylvania 2. department of biomedical informatics, university of utah, salt lake city, utah abstract the prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. this paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. we model outbreaks with compartment models and explicitly model noninfluenza influenza-like illnesses. *corresponding author. email: jma18@pitt.edu. current address: real-time outbreak and disease surveillance laboratory, department of biomedical informatics, 5607 baum boulevard, university of pittsburgh, pittsburgh, pennsylvania 15206-3701 doi: .10.5210/ojphi.v11i2.9952 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. 1 introduction the prediction and characterization of outbreaks of infectious diseases remains an open and important problem [1]. influenza, with nearly annual outbreaks in temperate regions of the world, provides an ideal test domain [2]. this paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten real outbreaks collected from two locations over five years. like several other systems, we model outbreaks with compartment models [3-5]. we differ from this past work in that we use the full text of patient care reports, rather than just chief complaints [6], counts of syndromes from sentinel physicians [5], counts of internet queries [7], etc. doing so provides a rich source of evidence that may provide an early signal of an outbreak. we use the evidence to reason about likelihoods, such as p(findings|influenza) or p(findings|rsv), rather than just simple counts. the approach is quite general, since the findings can include any the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi evidence about a patient's disease status, including history, symptoms, signs, labs, and other information. this paper extends our previous work [8] by using a more sophisticated model of non-influenza influenza-like illness (niili), modeling a probability distribution over influenza outbreak start dates, and testing on a set of real outbreak data collected over five years at two locations widely separated in the united states. 2 system architecture we have developed an end-to-end framework for outbreak detection and [9]. it starts with patient care reports, extracts findings with natural language processing (nlp), assigns likelihoods to each patient case with a case-detection system (cds), and constructs a model with an outbreakdetection system (ods) that can be used for prediction and characterization. a patient's care report contains the most detailed and complete record of their present illness available. much of the information in it (including chief complaint, history of present illness, a detailed patient assessment, treatment, and response to treatment) is in free-text. other information, such as laboratory findings, is codified. in our system, such data, including symptoms and signs, are extracted using natural language processing software [10]. some patient care reports include a laboratory test for influenza which can provide a definitive diagnosis of influenza. the findings (free-text derived and coded) for each patient are passed to cds which derives the probability of those findings given each of influenza, niili, and other. niili implicitly includes several diseases, such as respiratory syncytial virus (rsv) and parainfluenza, and other includes everything else such as trauma, appendicitis, etc. cds uses a bayesian network that represents the joint probability distribution of each patient's findings (including laboratory results) and the three disease categories just mentioned [11]. as mentioned, it provides the likelihoods p(e(p,d)|influenza), p(e(p,d)|niili), and p(e(p,d)|other), where e(p,d) is the set of findings for patient p on day d. that is, the probability of the patient's findings given they have each of influenza, niili, or other. ods takes all of the evidence from the first day of the monitored period through the present, evaluates thousands of possible outbreak models against the data, and makes projections about the future. let m1,…,mn be a representative set of models and e(1:c) be all of the data available through the current day c. ods computes the expected number of influenza cases on each day d, with: expected number of influenza cases on day d = ∑ mi n i=1 (d)p(mi|e(1: c)) (1) where p(mi|e(1:c)) is the probability of model mi given the data up to the current day, and mi(d) is the number of influenza cases predicted by model mi on day d. typically, d>c since we want to predict the future, but we can assess the past with d<c. ods is based on an earlier system described in [8]. the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi most patients spend only a few hours in the ed and their report is completed and available shortly after they leave. however, a significant number of reports take several days to complete (sometimes spread over multiple follow-up reports) and are not immediately available to a realtime system. we avoid data leakage (when the system is tested using data that are theoretically— but not actually—available each day [12]) by using only the records that are available at the time of evaluation, then retroactively augmenting previous evidence as records become available, and recomputing previous likelihoods. 3 modeling influenza we model immunizing infectious diseases like influenza with seir models [13] that divide the population into four compartments: • susceptible individuals who could become infected. • exposed individuals who have the disease but cannot yet transmit it. • infectious individuals who have the disease and can transmit it. • recovered individuals who are immune to contracting the disease. typically, a large part of the population is initially susceptible, a significant number may be recovered, and the infection is introduced by a small number of infectious individuals. after the entire population has been partitioned into these four compartments, they move through the compartments according to several parameters: • r0 is the expected number of individuals that an infectious person would infect if he were introduced into an entirely susceptible population. • latent period is the average amount of time before an exposed individual becomes infectious. • infectious period is the average amount of time an individual is infectious. when modeling an outbreak of influenza, ods generates thousands of possible seir models and uses each one according to equation 1. these models are generated by randomly and uniformly drawing values from table 1. these ranges have been determined by expert opinion and past outbreaks of as reported in the literature [2,13,14]. the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi table 1: seir parameter ranges parameter minimum value maximum value initial s 0.0*population 1.0*population initial e 0 0 initial i 1 100 r0 1.1 1.9 latent period 1 3 infectious period 1 8 although ods models influenza in the general population, its evidence comes from hospital ed's, which represent only a fraction of the actual number of influenza cases. to adjust for that difference, we estimate that 1% of people infected with influenza in the general population actually go to an ed based on estimates reported in [15]. 4 outbreak detection and characterization equation 1 relies on the value p(mi|e(1:c)) for each mi. we can find this probability using bayes’ rule: 𝑃(𝑀𝑖 |𝐸(1: 𝑐)) = 𝑃(𝐸(1: 𝑐)|𝑀𝑖 )𝑃(𝑀𝑖) 𝑃(𝐸(1:𝑐)) (2) we can interpret p(e(1:c)|mi) as how well mi explains the data. note that p(e(1:c)) is the same for every model and p(mi|e(1:c)) is proportional to the numerator. 4.1 the prior probability of a model in temperate climates, such as the united states, outbreaks of influenza occur nearly every year. if an outbreak does occur, it can peak as early as october 1 or as late as april 1, but typically influenza outbreaks peak during the winter months. peak dates depend on start dates. we model the distribution of start dates with the following procedure: 1. create 100,000 models with start dates between june 1 and the following march 1. 2. save the set s of models with peak infectious counts that occur between october 1 and april 1 of the following year. 3. create a probability distribution over the start dates in s by setting p(d) to the fraction of models in s that start on day d. we assume that the probability an influenza outbreak will occur in any given year is 0.9. we create a special non-outbreak model, m0 with m0(d)=0 for any day d, and set p(m0=0.1). let mi be an the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi outbreak model that starts on day d. for each possible start day d, ods samples and evaluates n equiprobable outbreak models that start on that day, so: 𝑃(𝑀𝑖 ) = 𝑃(𝑜𝑢𝑡𝑏𝑟𝑒𝑎𝑘)×𝑃(𝑑) 𝑛 (3) where p(outbreak)=0.9. 4.2 the posterior probability of a model in equation 2 we need p(e(1:c)|mi) for each model. assuming that the evidence on each day e(d) is independent of any other day given a model of influenza, we have: 𝑃(𝐸(1: 𝑐)|𝑀𝑖 ) = ∏ ∑ [𝑃(𝐸(𝑑)|𝑟)𝑏𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑟|𝑀𝑖 (𝑑), 𝜃)] (4) 𝑝𝑡𝑠(𝑑) 𝑟=0 𝑐 𝑑=1 here, pts(d) is the total number of patients in the ed on day d, binomial(r|mi(d), 𝜃 ) is the probability that exactly r people with influenza go to the ed given there are mi(d) people in the population who independently choose to go the ed with probability 𝜃 . we sum over all the possibilities. if we assume that on each day the patients in the ed present independently of each other, we have: 𝑃(𝐸(𝑑)|𝑟) = ∏ 𝑃(𝐸(𝑝, 𝑑)|𝑟) 𝑝𝑡𝑠(𝑑) 𝑝=1 (5) that is, the probability of all of the evidence e(d) on day d is the product of the probability of the evidence of each patient e(p,d) on that day. finally, we need the probability of each patient's evidence given there are r influenza patients in the ed. if we assume that influenza, niili, and other are mutually exclusive and exhaustive for a single patient, then: 𝑃(𝐸(𝑝, 𝑑)|𝑟) = 𝑃(𝐸(𝑝, 𝑑)|𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎)𝑃(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎|𝑟) + 𝑃(𝐸(𝑝, 𝑑)|𝑁𝐼𝐼𝐿𝐼)𝑃(𝑁𝐼𝐼𝐿𝐼|𝑟) + 𝑃(𝐸(𝑝, 𝑑)|𝑜𝑡ℎ𝑒𝑟)𝑃(𝑜𝑡ℎ𝑒𝑟|𝑟) (6) we estimate p(influenza|r) as r/pts(d). we describe our method to estimate p(niili|r) later. finally, p(other|r)=1-(p(influenza|r)+p(niili|r)). 4.3 the probability that an outbreak is occurring the probability that an outbreak has started by day c is simply 1-p(m0|e(1:c)) where m0 is the non-outbreak model. (recall that m0 is the non-outbreak model with m0(d)=0 for each day d.) but the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi even if an outbreak has started by day c, that does not mean that it is occurring on day c, since it may have already ended. we want the probability that some outbreak is occurring on day c. the probability that an arbitrary individual in the population has influenza on day c given model mi is: 𝑃(𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎 𝑜𝑛 𝐷𝑎𝑦 𝑐|𝑀𝑖 ) = 𝐸𝑀𝑖 (𝑐)+𝐼𝑀𝑖 (𝑐) 𝑁 (7) where 𝐸𝑀𝑖 (𝑐) is the number of exposed people and 𝐼𝑀𝑖 (𝑐) is the number of infectious people on day c for model mi, and n is the total population in the region being monitored. we can now compute the probability that an outbreak is occurring on day c for model mi as: 𝑃(𝑂𝑢𝑡𝑏𝑟𝑒𝑎𝑘 𝐼𝑠 𝑂𝑐𝑐𝑢𝑟𝑟𝑖𝑛𝑔 𝑜𝑛 𝐷𝑎𝑦 𝑐|𝑀𝑖 ) = ∏ [1 − (1 − 𝑃(𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎 𝑜𝑛 𝐷𝑎𝑦 𝑑|𝑀𝑖 )) 𝑁 ]𝑐𝑑=𝑠𝑡𝑎𝑟𝑡(𝑀𝑖) (8) the term 1 − 𝑃(𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎 𝑜𝑛 𝐷𝑎𝑦 𝑑|𝑀𝑖 ) is the probability that an arbitrary individual in the population is not infected, (1 − 𝑃(𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎 𝑜𝑛 𝐷𝑎𝑦 𝑑|𝑀𝑖 )) 𝑁 is the probability that nobody is infected, and 1 − (1 − 𝑃(𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎 𝑜𝑛 𝐷𝑎𝑦 𝑑|𝑀𝑖 )) 𝑁 is the probability that at least one individual is infected, given model mi. the product is the probability that at least one individual in the population is infected on each day from the start of mi to the current day c. finally, the probability there is an ongoing outbreak on day c is: 𝑃(𝑂𝑢𝑡𝑏𝑟𝑒𝑎𝑘 𝐼𝑠 𝑂𝑐𝑐𝑢𝑟𝑟𝑖𝑛𝑔 𝑜𝑛 𝐷𝑎𝑦 𝑐) = ∑ [𝑃(𝑂𝑢𝑡𝑏𝑟𝑒𝑎𝑘 𝐼𝑠 𝑂𝑐𝑐𝑢𝑟𝑟𝑖𝑛𝑔 𝑜𝑛 𝐷𝑎𝑦 𝑐|𝑀𝑖 )𝑃(𝑀𝑖 |𝐸)]𝑀𝑖 (9) where mi ranges over the entire set of models. 4.4 estimating niili by definition, niili—non-influenza influenza-like illness—presents like influenza. furthermore, some forms of niili, such as rsv, display outbreak activity. properly recognizing outbreaks of influenza requires us to account for niili. (equation (6 specifies how we incorporate an estimate of niili to score models.) appendix a describes a technical approach to estimating the level of niili during the summer and throughout the influenza season. 5 experimental results our datasets consisted of 5 years of data from 2010-2015 for both allegheny county (ac), pennsylvania and salt lake county (slc), utah. we assume that a new influenza season starts on june 1 of a given year. thus, the first season is defined to span from june 1, 2010 through may 31, 2011, the second season spans from june 1, 2011 through may 31, 2012, and so on through may 31, 2015. the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi table 2: ods peak date errors date of prediction relative to actual peak -6 weeks -4 weeks -2 weeks 0 weeks 2010-2011 ac 7 (1.00) 13 (1.00) 7 (1.00) 5 (1.00) 2011-2012 ac 18 (0.15) 25 (0.08) 33 (0.16) 43 (0.96) 2012-2013 ac 53 (0.10) 61 (0.10) 23 (1.00) 28 (1.00) 2013-2014 ac 19 (0.29) 14 (1.00) 19 (1.00) 17 (1.00) 2014-2015 ac 8 (0.60) 9 (1.00) 13 (1.00) 8 (1.00) ac average absolute error 7.5 12.0 15.5 20.2 2010-2011 slc 14 (0.28) 2 (1.00) 12 (1.00) 7 (1.00) 2011-2012 slc -4 (0.79) 2 (1.00) 1 (1.00) 6 (1.00) 2012-2013 slc 41 (0.12) -7 (0.74) 3 (1.00) 7 (1.00) 2013-2014 slc 48 (0.21) 3 (1.00) 5 (1.00) 1 (1.00) 2014-2015 slc 55 (0.12) 56 (0.20) 5 (1.00) -1 (1.00) slc average absolute error 4.0 3.5 5.2 4.4 overall average absolute error 6.33 7.14 9.78 12.3 our gold standard for determining the actual peak of an outbreak in the population is laboratoryconfirmed cases of influenza associated with emergency department visits. according to this standard, the peak of an outbreak is the maximum of a seven day central moving average of the daily positive lab test counts. we refer to this as the laboratory-peak. appendix b shows the counts of positive influenza lab tests, along with the cds expected number of ed influenza cases, for each influenza season from 2010-2015 for both regions. these results give an indication of the relative size and shape of each outbreak. note that these include graphs that appear symmetric and look like seir curves (as in figure 1), graphs that include two distinct peaks (as in figure 2), graphs that are asymmetric (as in figure 6), and graphs that display little or no outbreak activity (as in figure 3). we ran ods on each day from august 2 through the following june 1 for each influenza season in each region. on a given day, ods uses cds data through the previous day, so running ods on june 1 uses data through may 31, consistent with our definition of the end of an influenza season. we started running ods on august 2 (using data through august 1) because the data from june 1 through july 31 is used to compute the initial influenza and niili priors used by ods. (see appendix a.) the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi our metric for quantifying ods performance is the absolute error between the ods-expected peak date and the actual peak date determined from the gold standard (positive lab tests). table 2 shows the peak date error for each influenza season and region at different time points relative to the actual peak date. in parentheses next to each peak date error is the probability of outbreak computed by ods. the error value is the difference between the ods-computed expected peak date and the actual peak date. thus, a positive error means that the ods-predicted peak is later than the actual peak. predictions made with greater then 50% probability are shown in bold. the average absolute error values are for predictions with greater than 50% probability. in general, we would only trust a peak prediction by ods when its probability of an outbreak is high at that point. for example, for the 2013-2014 season for ac, at a point 6 weeks before the actual peak, the ods predicted peak was 19 days after the actual peak. at this point we can see the probability of outbreak was computed to be only 0.29. ods sampled 100 models per day on each of the 274 possible outbreak start days for a total of 27,400 outbreak models and one non-outbreak model. ods required approximately 45 minutes of runtime to analyze all the data for a given year (corresponding to a single row in table 2 when using a macbook pro with a single intel 2.2ghz i7 processor. the research protocol was approved by both institutional irbs (university of pittsburgh: pro08030129; intermountain healthcare: 1024664). all patient data were de-identified and analyzed anonymously. no consent was given. 6 limitations the work reported here has some limitations. the data is limited to hospital emergency departments (eds) and does not include clinics or private practices. furthermore, these eds are urban and suburban, and do not include rural areas. ods relies on natural language processing software to extract findings from patient care reports and a case-detection system to produce likelihoods, so we rely on the accuracy of those systems. we used the number of laboratory-confirmed influenza cases as a gold standard because we want to recognize and characterize influenza, as opposed to influenza-like illnesses in general. however, the total number of laboratory-confirmed cases of influenza is relatively small. furthermore, we assumed that the rate of testing is constant throughout an outbreak, but it is possible that testing policies change as an outbreak develops. we also assume that an outbreak can be modeled with a single seir model with constant parameters. we will address this assumption below. 7 discussion table 2 shows that ods's peak date predictions are almost always late. an examination of the graphs of counts of positive influenza lab tests in appendix b provides a clue why: the outbreaks are not symmetric. (allegheny county 2012-2013 in figure 5 is a good example.) to explain this the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi asymmetry, we need to reexamine our basic assumption that there is a single outbreak of influenza each year that can be modeled by a seir model. table 3: influenza a and b peaks year region influenza a influenza b b/a offset (weeks) 2010-2011 3 (includes ac) 815 148 0.18 1 2011-2012 3 (includes ac) 183 42 0.23 5 2012-2013 3 (includes ac) 1533 274 0.18 11 2013-2014 3 (includes ac) 566 119 0.21 11 2014-2015 3 (includes ac) 1757 113 0.06 13 2010-2011 8 (includes slc) 391 278 0.71 0 2011-2012 8 (includes slc) 419 13 0.03 4 2012-2013 8 (includes slc) 780 254 0.33 0 2013-2014 8 (includes slc) 976 39 0.04 20 2014-2015 8 (includes slc) 1429 177 0.12 14 there is rarely a single outbreak of influenza each year. typically, there is an outbreak of influenza a, followed by a smaller outbreak of influenza b. table 3 compares the relative sizes of influenza a and b outbreaks for hhs region 3 (which includes allegheny county) and region 8 (which includes salt lake county), according to data from the cdc's fluview [16]. for instance, the first row states that for the influenza year 2010-2011 in region 3, there were 815 laboratory-confirmed cases of influenza a at its peak, and 148 laboratory-confirmed cases of influenza b at its peak. also, the influenza b peak was 18% as large as the influenza a peak and occurred one week later. notice that in table 2, ods's worst results were for allegheny county 2011-2012, 2012-2013, 2013-2014. also, in table 3 there was a substantial outbreak of influenza b that peaked several weeks after influenza a in exactly those years. we believe that the presence of a second, smaller outbreak of influenza b explains most of the deviation of ods's predictions from the actual peak dates. however, there are other factors that can affect the prediction of the peak of an outbreak. the basic parameters of the model—especially r0—can change based on social or environmental factors [17]. this could happen, for instance, if individuals in the population self-quarantine or increase hand washing as an outbreak progresses. immunization programs and individual decisions about whether and when to be vaccinated could also change the course of the outbreak [18]. these changes would have the effect of reducing the number of individuals in the susceptible compartment of the seir model. note that in both of these examples, the original predicted peak is later than the peak when the changes are taken into consideration. to help address this problem, we could learn the relationships between social and environmental conditions and the parameters of a seir model. for instance, we could learn the relationship between r0 and school closings by comparing deviations of the number of infectious the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi from standard seir models with known patterns of holidays, school closings, etc. a simple way to learn such a relationship could start with the assumption that the value of r0 changes daily according to the number of people affected by school closings. that is, r0(d)=f(c(d)), where c(d) is the number of people affected by school closings on day d. it would be reasonable to assume that f is a linear function since 𝑅0 = 𝛽𝐷𝑁 where 𝛽 is the rate at which any two individuals come into effective contact, d is the duration of infectiousness, and n is the population. we can then set the parameters of f to the values that maximize p(e(d)|r0(d)=f(c(d))) over all of the available training data. of course, r0 depends on more than just social mixing, and f may be a nonlinear, multi-parameter function; in that case, we could investigate ways to approximate it. properly modeling outbreaks of influenza will require models that incorporate multiple outbreaks as well as parameters that vary according to day-to-day information. the current work provides a baseline from which to proceed, and insights into which directions to take. 8 conclusions we have described a system—ods—that can predict and characterize outbreaks of influenza, and the results of testing it on ten datasets collected over five years at two locations. our analysis showed that ods can reliably detect the presence of an outbreak, but systematically predicts the peak later than it actually occurred. we believe this is primarily due to the inability of seir models to correctly model the influenza a and b outbreaks that occur most years. further work with ods, or similar systems, needs to be based on outbreak models that can describe multiple, overlapping outbreaks of influenza. funding this work was supported by nih grant r01 lm011370 on probabilistic disease surveillance. john aronis was supported by the national library of medicine training grant t15lm007059 to the university of pittsburgh. part of this work was done on the olympus high performance compute cluster located at the pittsburgh supercomputing center at carnegie mellon university, which was supported by national institute of general medical sciences modeling infectious disease agent study (midas) informatics services group grant 1u24gm110707. references 1. lipsitch m, riley s, cauchemez s, ghani ac, ferguson nm. 2009. managing and reducing uncertainty in an emerging influenza pandemic. n engl j med. 361(2), 112-15. pubmed https://doi.org/10.1056/nejmp0904380 2. world health organization. influenza fact sheet. 2003. 3. shaman j, karspeck a. 2012. forecasting seasonal outbreaks of influenza. proc natl acad sci usa. 109(50), 20425-30. pubmed https://doi.org/10.1073/pnas.1208772109 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https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=15714638&dopt=abstract the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi 16. centers for disease control and prevention. flu activity and surveillance; 2016. available from: www.cdc.gov/flu/weekly/fluactivitysurv.htm 17. shaman j, pitzer ve, viboud c, grenfell bt, lipsitch m. 2010. absolute humidity and the seasonal onset of influenza in the continental united states. plos biol. 8(2). pubmed https://doi.org/10.1371/journal.pbio.1000316 18. gani r, hughes h, fleming d, griffin t, medlock j, et al. 2005. potential impact of antiviral drug use during influenza pandemic. emerg infect dis. 11(9), 1355-62. pubmed https://doi.org/10.3201/eid1209.041344 appendix a modeling niili we compute the level of niili in three stages. first, we compute summer baseline levels of niili. then, we compute the level of niili during influenza season using a bayesian time-series. finally, we adjust the level of niili to account for the increased level of apparent niili during an outbreak of influenza. we compute baseline levels of influenza and niili in the summer when it is highly unlikely there is an outbreak. because they present similarly, and one can be explained by the possible presence of the other, we need to compute them in tandem. let s be the set of all possible pairs of influenza and niili prior probabilities. let si be a specific pair of priors {pi(influenza),pi(niili)}. we have: 𝑃𝑖 (𝑜𝑡ℎ𝑒𝑟) = 1 − (𝑃𝑖 (𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎) + 𝑃𝑖 (𝑁𝐼𝐼𝐿𝐼)) (10) since the si are mutually exclusive and exhaustive, we can compute the probability of the data on day d with: 𝑃(𝐸(𝑑)) = ∑ [𝑃(𝐸(𝑑)|𝑠𝑖 )𝑖 𝑃(𝑠𝑖 )] (11) assuming there are n p(si) and they are uniformly equal: 𝑃(𝐸(𝑑)) = 1 𝑛 ∑ 𝑃(𝐸(𝑑)|𝑠𝑖 ) 𝑖 (12) we can now compute the expected probability of influenza on day d as: 𝑃(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎|𝐸(𝑑)) = 𝑃(𝐸(𝑑)|𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎)𝑃(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎) 𝑃(𝐸(𝑑)) (13) again assuming the p(si) and are equal: 𝑃(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎|𝐸(𝑑)) = ∑ 𝑃(𝐸(𝑑)|𝑠𝑖)𝑃𝑖(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎) 𝑃(𝐸(𝑑)|𝑠𝑖) 𝑖 (14) https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=20186267&dopt=abstract https://doi.org/10.1371/journal.pbio.1000316 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16229762&dopt=abstract https://doi.org/10.3201/eid1209.041344 the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi we can compute p(niili|(e(d)) and p(other|e(d)) similarly. finally, let summer be all the days of summer. then: 𝑃𝑠𝑢𝑚𝑚𝑒𝑟 (𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎) = 1 |𝑠𝑢𝑚𝑚𝑒𝑟| ∑ 𝑃(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑧𝑎|𝐸(𝑑)) 𝑑∈𝑠𝑢𝑚𝑚𝑒𝑟 (15) and: 𝑃𝑠𝑢𝑚𝑚𝑒𝑟 (𝑁𝐼𝐼𝐿𝐼) = 1 |𝑠𝑢𝑚𝑚𝑒𝑟| ∑ 𝑃(𝑁𝐼𝐼𝐿𝐼|𝐸(𝑑))𝑑∈𝑠𝑢𝑚𝑚𝑒𝑟 (16) that is, we estimate the background probability of influenza and niili as their mean daily probabilities during a summer period. given the summer baseline levels of niili and influenza, we now compute the probability of niili during the remainder of the year with a bayesian time-series. let e(p,d) be the evidence for patient p on day d, e(d) be all of the evidence on day d, and nd be the number of patients on day d. also, let pd(influenza), pd(niili), and pd(other) denote the prior probabilities for niili, influenza, and other for day d. let us first assume that we have prior probabilities pd(niili), pd(influenza), and pd(other) for day d. we can then derive the posterior probability of niili for patient p on day d as: 𝑃(𝑁𝐼𝐼𝐿𝐼|𝐸(𝑝, 𝑑)) = 𝑃(𝐸(𝑝, 𝑑)|𝑁𝐼𝐼𝐿𝐼)𝑃𝑑(𝑁𝐼𝐼𝐿𝐼) ∑ 𝑃(𝐸(𝑝,𝑑)|𝑑𝑥)𝑃𝑑(𝑑𝑥)𝑑𝑥 (17) where dx ranges over influenza, niili, and other. now we derive the posterior probability of niili on day d as the expected fraction of patients who have niili on that day: 𝑃(𝑁𝐼𝐼𝐿𝐼|𝐸(𝑑)) = 1 𝑁 ∑ 𝑃(𝑁𝐼𝐼𝐿𝐼|𝐸(𝑝, 𝑑)) 𝑁𝑑 𝑝=1 (18) we derive pd(influenza|e(d)) and pd(other|e(d)) similarly. finally, we assume that the prior probability of niili on day d+1 is the posterior probability of niili on day d: 𝑃𝑑+1(𝑁𝐼𝐼𝐿𝐼) = 𝑃𝑑 (𝑁𝐼𝐼𝐿𝐼|𝐸(𝑑)) (19) we can derive pd+1(influenza) and pd+1(other) similarly. thus, starting with the baseline probabilities for influenza, niili, and other computed in the summer, we update them each day with the available evidence. the probability of niili rises and falls with the probability of influenza. this is an artifact of the data: since niili and influenza have similar symptoms, p(e(p,d)|niili) will increase for patients with influenza, causing the likelihoods for niili to shadow the likelihoods for influenza. we will modify the computation of niili above with the goal of keeping the niili priors lower during an outbreak of influenza since we do not expect a simultaneous niili outbreak. given the niili priors pd(niili) computed above, we will derive a new sequence of niili priors p’d(niili). set p’1(niili)=p1(niili) and assume c>1. we run ods for day c and derive p(mi|e(1:c)) for each model mi using the niili priors p’1(niili),…,p’c(niili). let pc(outbreak|e(1:c)) be the the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi probability that an outbreak is occurring on day c given e(1:c), and pc(outbreak|mi) be the probability that an outbreak is occurring on day c given model mi (as defined by equation 8). we compute p’c+1(niili) with: 𝑃′𝑐+1(𝑁𝐼𝐼𝐿𝐼) = (1 − 𝑃𝑐 (𝑜𝑢𝑡𝑏𝑟𝑒𝑎𝑘|𝐸(1: 𝑐))𝑃𝑐+1(𝑁𝐼𝐼𝐿𝐼) + ∑ 𝑃𝑐 (𝑜𝑢𝑡𝑏𝑟𝑒𝑎𝑘|𝑀𝑖 )𝑃(𝑀𝑖 |𝐸(1: 𝑐))𝑃𝑠𝑡𝑎𝑟𝑡(𝑀𝑖)(𝑁𝐼𝐼𝐿𝐼)𝑖 (20) this equation says that we use the original niili prior on day c, pc+1(niili), weighted by the probability of no outbreak on day c, plus the sum over all models of the original niili prior on the start day of a model, 𝑃𝑠𝑡𝑎𝑟𝑡(𝑀𝑖)(𝑁𝐼𝐼𝐿𝐼), weighted by the probability an outbreak is occurring on day c given model mi, pc(outbreak|mi) and the posterior of model mi given the evidence through day c, p(mi|e(1:c)). the result of using equation 20 is that when an influenza outbreak is occurring, the niili priors are weighted toward the values at the start of the most likely ongoing outbreak models, and do not have the behavior of increasing due to influence of the influenza outbreak. the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi appendix b counts of positive influenza lab tests figure 1: allegheny county 2010-2011 figure 2: salt lake county 2010-201 the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi figure 3: allegheny county 2011-2012 figure 4: salt lake county 2011-2012 the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi figure 5: allegheny county 2012-2013 figure 6: salt lake county 2012-2013 the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi figure 7: allegheny county 2013-2014 figure 8: salt lake county 2013-2014 the design and evaluation of a bayesian system for detecting and characterizing outbreaks of influenza online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e6, 2019 ojphi figure 9: allegheny county 2014-2015 figure 10: salt lake county 2014-2015 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e362, 2019 isds 2019 conference abstracts field-based evaluation of malaria outbreak detection & response, mudzi and goromonzi haylea a. hannah1, audrey brezak1, audrey hu1, simbarashe chiwanda2, maayan s. simckes1, debra revere1, gerald shambira2, mufuta tshimanga2, joseph mberikunashe3, tsitsi juru2, notion gombe2, danuta kasprzyk1, daniel montano1, janet baseman1 1 university of w ashington, seattle, w ashington, united states, 2 university of zimbabwe, harare, zimbabwe, 3 ministry of health and child care, harare, zimbabwe objective to conduct a field-based assessment of the malaria outbreak surveillance system in mashonaland east, zimbabwe. introduction infectious disease outbreaks, such as the ebola outbreak in west africa, highlight the need for surveillance systems to quickly detect outbreaks and provide data to prevent future pandemics [1-3]. the world health organization (who) developed the joint external evaluation (jee) tool to conduct country-level assessments of surveillance capacity [4]. however, considering that outbreaks begin and are first detected at the local level, national-level evaluations may fail to identify capacity improvements for outbreak detection. the gaps in local surveillance system processes illuminate a need for investment in on-the-ground surveillance improvements that may be lower cost than traditional surveillance improvement initiatives, such as enhanced training or strengthening data transfer mechanisms before building new laboratory facilities [5]. to explore this premise, we developed a methodology for assessing surveillance systems with special attention to the local level and applied this methodology to the malaria outbreak surveillance system in mashonaland east, zimbabwe. methods in a collaboration between the zimbabwe field epidemiology training program and the university of washington, an interview guide was developed based on the centers for disease control and prevention’s (cdc) updated guidelines for surveillance evaluations and who’s jee tool [4,6]. the guide was tailored in country with input from key stakeholders from the ministry of health and child care and national malaria control program. interview guides included questions focused on outbreak detection , response, and control procedures, and surveillance system attributes (preparedness, data quality, timeliness, stability) and functionality (usefulness). the team utilized the tool to evaluate surveillance capacity in eleven clinics across two malaria -burdened districts of mashonaland east, mudzi and goromonzi. twenty-one interviews were conducted with key informants from the provincial (n=2), district (n=7), and clinic (n=12) levels. main themes present in interviews were captured using standard qualitative data analysis methods. results the majority of key informants interviewed were nurses, nurse aids, or nurse officers (57%, 12/21). this evaluation identifie d clinic-level surveillance system barriers that may be driving malaria outbreak detection and response challenges. cl inics reported little opportunity for cross-training of staff, with 81% (17/21) mentioning that additional staff training support was needed. only one clinic (10%, 1/11) had malaria emergency preparedness and response guidelines present, a resource recomme nded by the national malaria control program for all clinics encountering malaria cases. a third of interviewees (33%, 7/21) reported having a standard protocol for validating malaria case data and 29% (6/21) reported challenges with data quality and validation, such as a duplication of case counts. while the surveillance system at all levels detects malaria outbreaks, clinics experience barrier s to timely and reliable reporting of cases and outbreaks to the district level. stability of resources, including transportation and staff capacity, presented barriers, with half (48%, 10/21) of interviewees reporting that their clinics were under -staffed. additionally, the assessment revealed that the electronic case reporting system (a who-developed sms application, frontline) that is used to report malaria cases to the district was not functioning in either district, which was unknown at the provincial and national levels. to detect malaria outbreaks, clinics and districts use graphs showing weekly malaria case counts against threshold limit values http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e362, 2019 isds 2019 conference abstracts (tlvs) based on historic five-year malaria case count averages; however, because tlvs are based on 5-year historic data, they are only relevant for clinics that have been in existence for at least five years. only 30% (3/ 10) of interviewees asked about outbreak detection graphs reported that tlv graphs were up-to-date. conclusions this surveillance assessment revealed several barriers to system performance at the clinic-level, including challenges with staff cross-training, data quality of malaria case counts, timeliness of updating outbreak detection graphs, stability of transportation, prevention, treatment, and human resources, and usefulness of tlvs for outbreak detection among new clinics. strengthening these system barriers may improve staff readiness to detect and respond to malaria outbreaks, resulting in timelier outbreak response and decreased malaria mortality. this evaluation has some limitations. we interviewed key informants from a non -random sample covering 30% of all clinics in mudzi and goromonzi districts; thus, barriers identified may not be representative of all clinics in these districts. secondly, evaluators did not interview individuals who may have been involved in outbreak detection and resp onse but were not present at the clinic when interviews were conducted. lastly, many of the evaluation indicators were based on self reported information from key informants. despite these limitations, convenience sampling is common to public health practice , and we reached a saturation of key informant themes with the 21 key informants included in this evaluation [7]. by designing evaluation tools that focus on local-level knowledge and priorities, our assessment approach provides a framework for identifying and addressing gaps that may be overlooked when utilizing multi-national tools that evaluate surveillance capacity and improvement priorities at the national level. acknowledgement the authors would like to thank zimbabwe ministry of health and child care and zimbabwe field epidemiology training program staff for their contribution to this work, key informants in mashonaland east for their participation in this assessment, and the university of washington, department of epidemiology and seal team stakeholders for their support. references 1. organzation wh. international health regulations third edition. vol third. geneva, switzerland; 2005. doi:10.1017/cbo9781107415324.004. 2. global hsa. implementing the global health security agenda: progress and impact from u.s. government investments.; 2018. https://www.ghsagenda.org/docs/default-source/default-document-library/global-healthsecurity-agenda-2017-progress-and-impact-from-u-s-investments.pdf? sfvrsn=4. 3. mcnamara la, schafer ij, nolen ld, et al. 2016. ebola surveillance — guinea, liberia, and sierra leone. mmwr suppl. 65(3), 35-43. doi:https://doi.org/10.15585/mmwr.su6503a6. pubmed 4. world health organization (who). joint external evaluation tool: international health regulations (2005). geneva; 2016. http://apps.who.int/iris/bitstream/10665/204368/1/9789241510172_eng.pdf. 5. groseclose sl, buckeridge dl. 2017. public health surveillance systems: recent advances in their use and evaluation. annu rev public health. 38(1), 57-79. doi:https://doi.org/10.1146/annurev-publhealth-031816044348. pubmed 6. centers for disease control and prevention. updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. mwwr. 2001;50(no. rr-13). 7. dworkin sl. 2012. sample size policy for qualitative studies using in-depth interviews. arch sex behav. 41(6), 1319-20. doi:https://doi.org/10.1007/s10508-012-0016-6. pubmed http://ojphi.org/ https://doi.org/10.15585/mmwr.su6503a6 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27389614&dopt=abstract https://doi.org/10.1146/annurev-publhealth-031816-044348 https://doi.org/10.1146/annurev-publhealth-031816-044348 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=27992726&dopt=abstract https://doi.org/10.1007/s10508-012-0016-6 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22968493&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e433, 2019 isds 2019 conference abstracts evaluation of essence syndromic definitions for ed visits related to falls in icy weather jessica hensley1, 3, 4, sandra gonzalez1, 2, derry stover1, thomas safranek1, ming qu1 1 nebraska department of health and human services, lincoln, nebraska, united states, 2 university of nebraska-lincoln, lincoln, nebraska, united states, 3 usphs, omaha, nebraska, united states, 4 american military university, charlestown, west virginia, united states objective this project evaluated and compared two essence syndromic surveillance definitions for emergency department (ed) visits related to injuries associated with falls in icy weather using 2016-2017 data from two hospitals in douglas county, nebraska. the project determined the validity of the syndromic surveillance definition as applied to chief complaint and triage notes and compared the chief complaint data alone to chief complaint plus triage notes definitions to find the most reliable definition for ed visits resulting from fall-related injuries. introduction icy weather events increase the risk for injury from falls on untreated or inadequately treated surfaces. these events often result in ed visits, which represents a significant public health and economic impact [1]. the goal of this project was to start the process toward an evaluation of the public health impact and the economic impact of falls associated to icy weather in douglas county, ne for the ultimate purpose of designing and implementing injury prevention related public health protection measures. additionally, the validated definition will be used by ne dhhs occupational health surveillance program to identify work related ice-related fall injuries that were covered by workers compensation. to achieve the goal, the first step was to identify a valid and reliable syndromic surveillance. specifically, this project looked at the applicability of the essence syndromic surveillance definitions related to injuries associated with falls. two syndromic surveillance definitions were compared, one that includes triage note and chief complaint search terms, and another that only includes chief complaint. the hypothesis was that the essence syndromic surveillance definition that includes triage note and chief complaint search terms, rather than the syndromic surveillance definition that only includes chief complaint, would be more effective at identifying ed visits resulting from fall-related injuries. methods this project included 751 eds visits from two hospitals located in douglas county nebraska, during ice events on december 1618, 2016, january 10-12, 2017, and january 15-18, 2017. two essence syndromic surveillance definitions, “chief complaint or triage note” and “chief complaint only,” were used to identify fall-related ed visits from two participating eds in douglas county, ne. in the chief complaint and the triage note fields, the keywords selected were: fall, fell, or slip. in that the essence time series analysis indicated the increase in the number of falls were associated with ice events from baseline, an assumption was made that the increase was a result of the weather. then, the syndromic surveillance event detection of nebraska database was used to find the patient and visit identification numbers. these two identification numbers were used to identify the ehrs needed for a gold standard review. chart data was used to evaluate the reliability and validity of the two syndromic surveillance definitions for the detection of falls on the study dates. this analysis was used to find the sensitivity, specificity and predictive value. results the sensitivity, specificity and positive predictive value for the “chief complaint only” definition yielded 71.7%, 100%, and 100% respectively. the “chief complaint or triage note” definition results were 90.9%, 98.8%, and 95.5% for these analyses. negative predictive value for both definitions was 97.5%. conclusions the sensitivity indicates both definitions are unlikely to give false positives, and the positive predictive value indicates both definitions successfully identify most of the true positives found in the visits. however, the “chief complaint only” definition resulted in a minimally higher specificity and positive predictive value. therefore, the results indicate that although both definitions have similar specificity and positive predictive value, the “chief complaint or triage note” definition is more likely than the “chief complaint only” definition to correctly identify ed visits related to falls in icy weather. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e433, 2019 isds 2019 conference abstracts acknowledgement brian buss, dvm, mph, dacvpm, cdc career epidemiology field officer references 1. beynon c, wyke s, jarman i, robinson m, mason j, et al. 2011. the cost of emergency hospital admissions for falls on snow and ice in england during winter 2009/10: a cross sectional analysis. environ health. 10(60). pubmed number of falls detected by the essence “chief complaint only” falls definition and the gold standard chart review detected by essence fall related visits non-fall related visits tot al yes 38 0 38 no 15 579 594 total 53 579 632 number of falls detected by the essence “chief complaint or triage note” falls definition and the gold standard chart review detected by essence fall related visits non-fall related visits tot al yes 150 7 157 no 15 579 594 total 165 586 751 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=21682855&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e446, 2019 isds 2019 conference abstracts trends in suspected opioid overdoses from emergency departments in 11 states and dc stephen liu, matthew gladden, alana m. vivolo-kantor, puja seth centers for disease control and prevention (cdc), atlanta, georgia, united states objective this presentation will provide insight into how the extensive spread of illicitly-manufactured fentanyl impacted opioid overdose rates throughout the midwest and neighboring states. introduction recent reporting using data from cdc’s national syndromic surveillance program indicates that rates of emergency department (ed) visits involving suspected opioid overdoses increased by 70% in the midwest from the third quarter (q3) 2016 (july– september) to the q3 2017. large increases in the use and distribution of illicitly-manufactured fentanyl (imf) and fentanyl analogs, are a key factor driving increased opioid overdose rates in the midwest and east of the mississippi river. fentanyl is a synthetic opioid 50–100 times more potent than morphine. a better understanding of the distribution of changes in opioid overdose rate from q3 2016 to q3 2017 within states needed to inform response and prevention efforts. methods the cdc’s enhanced state opioid overdose surveillance program currently funds 32 states and washington dc to increase timeliness of opioid overdose reporting and detect rapid changes in trends. data from nine states (il, md, mo, nc, oh, pa, va, wi, wv) were analyzed. midwest states sharing subregional data with cdc were selected to better understand geographic and temporal patterns driving previously reported increases in ed visits involving suspected opioid over doses from q3 2016 through q3 2017. bordering states (md, nc, pa, va, wv) sharing subregional data with cdc were also included to determine trends in states contiguous to the midwest. state subregions were defined using publicly available state government sources in consultation with state public health departments and were mainly divided by public health districts. . fifty of 56 possible state subregions across 9 states met two inclusion criteria: 1) reported 25 opioid overdose ed visits per quarter and 2) did not report a change of 50% between any two quarters. opioid overdoses were defined according to jurisdictional and national definitions that included searches of chief complaint text (e.g., searching for words “opioid” and “overdose”) and icd-10-cm diagnostic/billing codes. state subregional rates were defined as number of opioid overdoses divided by the total number of ed visits in the state subregion, multiplied by 10,000. quarterly and yearly percent change in opioid overdose ed visits from q3 2016 to q3 2017 were described with a focus on high burden subregions reporting large yearly increases of 25% from q3 2016 to q3 2017. we categorized a subregion as having an opioid overdose outbreak when at least one quarterly rate increase in opioid overdoses of 50% occurred. results over 7 million ed visits were reported each quarter. average subregional opioid overdose rates increased at a consistent quarterly rate of between 16% 19% during q3 2016 to q2 2017. from q2 2017 to q3 2017, opioid overdose rates only increased 1%. overall, subregions reported a mean yearly increase in opioid overdose rates of 51% from q3 2016 to q3 2017. this yearly increase in opioid overdose from q3 2016 to q3 2017 was unequally distributed across subregions with 9 (18%) of subregions reporting an increase in opioid overdose rates of 100%, 13 (26%) reporting an increase of 50% <100%, 7 (14%) reporting an increase of 25% <50%, 13 (26%) reporting an increases of 0% to <25%, and 8 (16%) reporting a decrease. analyses of the 29 high burden subregions found that 16 (55%) reported at least one opioid overdose outbreak compared to 3 of 21 other subregions (14%) (table 1). the 16 high burden subregions that reported any outbreak had a mean yearly increase in opioid overdose rates from q3 2016 to q3 2017 of 107%, range 33% to 266%. eight of these 16 high burden subregions either reported two opioid overdose outbreaks or an opioid overdose outbreak and a quarterly increase of between 25% <50%. all states had at least one subregion report an opioid overdose outbreak between q3 2016 to q2 2017. also, within oh, pa, wi, and wv, half or more of their subregions reported an outbreak between q3 2016 and q3 2017 (table 1). fifteen of the 22 (68%) quarterly opioid overdose outbreaks occurred either during q4 2016 or q1 2017 (table 1). across all outbreaks, state subregions reported a mean quarterly increase of 83% with a range from 50 156%. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e446, 2019 isds 2019 conference abstracts conclusions opioid overdose outbreaks in a subset of 16 high burden subregions across all 9 states were a key factor driving increases in the states’ opioid overdose rates. half of these 16 subregions experienced a single opioid overdose outbreak while the other half experienced two or more sharp increases. the majority of opioid overdose outbreaks occurred during october 2016 to march 2017 were concentrated in ohio, a state reporting extremely large increase in imf supply and overdose deaths involving fentanyl, a nd states contiguous with ohio. although most subregions reported outbreaks at the beginning of the study period, higher opioid overdose rates persisted in the vast majority of subregions reporting opioid overdose outbreaks. this outbreak pattern is con sistent with previous findings showing large increases in the supply of imf and fentanyl analogs, i ncluding carfentanil, in midwestern states during 2016 and in early 2017. although opioid overdose outbreaks were concentrated in subregions in oh, pa, and wv, all 9 states included in the analysis were impacted by at least one outbreak during the study period. findings highlight the need for targeting of hotspots within states and implementation of public health interventions to reduce harm and surge resources duri ng an outbreak. these short-term efforts, however, must be complemented by a sustained response to reduce increased drug overdose rates that persist after an initial outbreak. a key limitation of this study was that it only included data from a single yea r and as a result may underestimate the length and severity of outbreaks. as changes in the illicit opioid market continue, surveillance of local outbreaks must be supplemented by broader surveillance designed to detect both localized introduction of new novel psychoacti ve substances as well as large scale changes in the illicit opioid market. references 1. vivolo-kantor am, seth p, gladden rm, et al. 2018. vital signs: trends in emergency department visits for suspected opioid overdoses— united states, july 2016–september 2017. mmwr morb mortal wkly rep. 67, 279-85. pubmed https://doi.org/10.15585/mmwr.mm6709e1 2. o’donnell j, gladden rm, mattson cl, kariisa m. 2018. notes from the field: overdose deaths with carfentanil and other fentanyl analogs detected — 10 states, july 2016–june 2017. mmwr morb mortal wkly rep. 67, 767-68. pubmed https://doi.org/10.15585/mmwr.mm6727a4 3. o’donnell jk, halpin j, mattson cl, goldberger ba, gladden rm. 2017. deaths involving fentanyl, fentanyl analogs, and u-47700 — 10 states, july–december 2016. mmwr morb mortal wkly rep. 66, 1197202. pubmed https://doi.org/10.15585/mmwr.mm6643e1 table 1. number and percent of opioid overdose outbreaks by quarter, high burden subregions, and state: 9 states july 2016 to september 2017 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29518069&dopt=abstract https://doi.org/10.15585/mmwr.mm6709e1 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=30001560&dopt=abstract https://doi.org/10.15585/mmwr.mm6727a4 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=29095804&dopt=abstract https://doi.org/10.15585/mmwr.mm6643e1 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e377, 2019 isds 2019 conference abstracts comparison and diagnosis of entamoeba in stool sample from rural community of nepal sandeep thapa microbial genetics, kathmandu center for genomics and research laboratory, gwarko, state 2: lalitpur, nepal objective to find out the prevalence of entamoeba species in rural community of nepal. the purpose of the study is to evaluate nested pcr, microscopic examination and elisa assay for detection and differentiation of entamoeba species. introduction nepal being a developing country has many health problems, which escalate in intensity at different times of the year or in epidemic form. amebiasis is one of the infectious diseases that is highly seen in rural area of nepal caused by entamoeba species [1,2]. recent reports show that open defecation, drinking untreated water, unsanitary habits and lack of basic health knowledge cause higher mortality and morbidity in our country. e. histolytica is an anaerobic pathogenic parasitic. however, e. dispar and e.moshkovskii exits as non-pathogenic. likewise, e. histolytica, e. dispar and e. moshkovskii are morphologically identical but genetically distinct species [3]. methods a total of 270 faecal sample were collected from south eastern terai region of nepal after the informed consent form. the samples were processed by direct wet smear and formalin ethyl acetate concentration technique [4]. eventually, microscopic examination were performed for the detection of entamoeba species along with other intestinal parasites. furthermore, enzyme immunoassay were executed to detect antigens of e. histolytica through elisa. additionally, microscopically positive samples for entamoeba species cysts were further characterized using a nestedpcr targeting 16s-like ribosomal rna gene [5]. the pcr generate amplicons which was subjected to 2% agarose gels electrophoresis and visualized under uv transilluminator. results 8.52% of the total collected samples were microscopically positive for entamoeba cysts either singly or in combination with other intestinal parasites. likewise, among 270 stool sample, viral diarrheal was most significant form of diarrhoea found in 76.67% of patients. among different organisms, as. lumbricoids and e. histolytica, g. lambia and h. nana were identified in most of the patients accounting for 11.11%, 8.52%, 2.59% and 1.11% respectively. however, lumbricoids, g. lambia, tenia solium and e. histolytica were present in an individual patient while two patient was found with both as. lumbricoids and g. lambia. among several symptoms, diarrhoea seems to be the common symptoms infecting all of the patients which is followed by fever and vomiting which accounts for 55.1% and 46.2% correspondingly. whereas, nausea appears to be the least common symptoms infecting only 14.4% of patients. subsequently, 56 cases were pcr positive, 51 cases were elisa positive whereas 47 were found to be positive by microscopy. conclusions molecular techniques are indeed promising tools for epidemiological studies, particularly in discriminating the pathogenic from the non-pathogenic species of the entamoeba species. this study reports a new nested multiplex pcr strategy for detection and differentiation of e. histolytica, e. dispar and e. moshkovskii which is highly rapid, specific and sensitive which is useful for proper diagnosis, immunological assay and drug testing. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e377, 2019 isds 2019 conference abstracts acknowledgement this work was supported by the wellcome tdr-who. we would like to thank all the member of kcgrl and global hospital. references 1. chaudhary m, maharjan m. 2014. association of anaemia with parasitic infection in pregnant women attending antenatal clinic at koshi zonal hospital. neplalese journal of zoology. 2(1), 1-7. 2. tandukar s, ansari s, adhikari n, shrestha a, gautam j, et al. 2013. intestinal parasitosis in school children of lalitpur district of nepal. bmc res notes. 6(1), 449. pubmed https://doi.org/10.1186/1756-0500-6-449 3. clark c, espinosa cantellano m, bhattacharya a. entamoeba histolytica: an overview of the biology of the organism (2013). amebiasis: world scientific, 1-45. 4. dhanabal j, selvadoss pp, muthuswamy k. 2014. comparative study of the prevalence of intestinal parasites in low socioeconomic areas from south chennai, india. j parasitol res. 2014, 630968. pubmed https://doi.org/10.1155/2014/630968 5. van den bossche d, cnops l, verschueren j, van esbroeck m. 2015. comparison of four rapid diagnostic tests, elisa, microscopy and pcr for the detection of giardia lamblia, cryptosporidium spp. and entamoeba histolytica in feces. j microbiol methods. 110, 78-84. pubmed https://doi.org/10.1016/j.mimet.2015.01.016 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24207086&dopt=abstract https://doi.org/10.1186/1756-0500-6-449 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24587897&dopt=abstract https://doi.org/10.1155/2014/630968 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=25615719&dopt=abstract https://doi.org/10.1016/j.mimet.2015.01.016 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e391, 2019 isds 2019 conference abstracts identification and assessment of repeat drug overdose visits at eds in virginia inderbir sohi, erin e. austin, jonathan falk virginia department of health, richmond, virginia, united states objective to identify and assess the characteristics of individuals with repeated emergency department (ed) visits for unintentional opioid overdose, including heroin, and how they differ from individuals with a single overdose ed visit. introduction the virginia department of health (vdh) utilizes syndromic surveillance ed data to measure morbidity associated with opioid and heroin overdoses among virginia residents. understanding which individuals within a population use ed services for repeated drug overdose events may help guide the use of limited resources towards the most effective treatment and prevention efforts. methods vdh classified syndromic surveillance visits received from 98 eds (82 hospitals and 16 emergency care centers) between january 2015 and july 2018. an unintentional opioid overdose, which included heroin, was classified based on the chief complaint and/or discharge diagnosis (icd-9 and icd-10) using microsoft sql server management studio. ed visits were categorized as either a single or a repeat visit, where a repeat visit was defined as two or more separate ed visit records from the same individual. ed visit records were matched to individuals using medical record number. each match represented a repeat visit for one person. rstudio was used to conduct pearson’s chi-square tests for sex, race, and 10-year age groups among both visit groups and to assess visit frequency among the repeat visit group. results between january 2015 and july 2018, 9,869 ed visits for opioid overdose were identified, of which 734 (7.4%) were repeat visits among 597 individuals occurring among 57 eds. the proportion of individuals with repeated opioid overdose visits was significantly different compared to the proportion of individuals with a single opioid overdose visit by sex (male 66% vs. 61%) and age group (20-29 years 34% vs 30%) (p < 0.05). no significant difference was found by race. eds had an average of 10 individuals who had repeated opioid overdose visits, with a range from 1 to 62 individuals. individuals with repeated opioid overdose visits made on average 2.2 visits to eds, with a range of 2 to 6 visits. the overdose visit rate among eds ranged from 0.6 to 51.3 opioid overdoses per 100,000 ed visits, with four eds having a rate greater than 40 opioid overdose visits per 100,000 ed visits. conclusions approximately 7% of ed visits during the study period for opioid overdose were identified as repeat visits using the medical record number. individuals with repeated opioid overdose visits differed from those with a single opioid overdose visit with respect to sex and age. repeated opioid overdose visits were disproportionately higher for males and individuals aged 20-29. hospital utilization by individuals with repeated opioid overdose visits can provide information on which eds or communities that may require further attention. some limitations of this study are that the method utilized to identify individuals may result in an underestimation of repeat visits because limited personally identifying information was used to match visit records, and repeat visits that occurred before and after the study period would not be captured. acknowledgement i would like to acknowledge kelly shaw and tony jing for their assistance with the analysis. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e419, 2019 isds 2019 conference abstracts validation of a surveillance-based definition for hepatitis b treatment eligibility. kevin guerra, regan deming, angelica bocour, ann winters disease control / communicable disease, new york city department of health, long island city, new york, united states objective to assess the accuracy of a surveillance-based definition for hepatitis b treatment eligibility among new york city residents with chronic hepatitis b infection. introduction approximately 100,000 new york city (nyc) residents are currently diagnosed with chronic hepatitis b virus (hbv) infection [1]. routine monitoring and treatment, where indicated, are necessary to reduce hbv disease progression. using the 2017 european association for the study of the liver (easl) [2] guidelines on hbv infection management, we developed a surveillance-based definition for treatment eligibility. validation of this definition will support the creation of a population-level hbv care continuum, which will allow us to monitor gaps from hbv diagnosis to viral suppression and to develop public health interventions to address these gaps. methods laboratories everywhere are required to electronically report the following hbv tests to the nyc department of health and mental hygiene (dohmh) for all nyc residents: positive and negative (as of april 2018) dna, positive surface antigen, positive e antigen, positive core igm, and alanine aminotransferase (alt) (when ordered at the same time as another reportable hbv test) . using reportable hbv tests, treatment eligibility was defined as ever having an hbv dna result >2000 iu/ml and alt>40 u/l. we assessed the accuracy of the surveillance-based definition by calculating sensitivity, specificity, positive predictive value (ppv), and negative predictive value (npv) by applying the definition to the test data of people participating in two dohmh programs that included clinical information on treatment eligibility: the enhanced surveillance project (provider interviews conducted for 300 randomly selected patients with chronic hbv) and the check hep b patient navigation program (program providing hbvrelated patient navigation at community organizations, health centers, and hospitals). everyone meeting inclusion criteria in the enhanced surveillance project who were also i dentified as being in care and being monitored (two or more hbv dna results reported at any time) were included in our analysis. for check hep b, we included everyone enrolled prior december 31, 2017 who also met our criteria of being in care and being monitored. to determine treatment eligibility using surveillance data, we used all hbv dna and alt results reported prior to january 31st, 2016 for the enhanced surveillance project and prior to december 31st, 2017 for check hep b. results treatment eligibility was 62.0% (145/234) among people from the enhanced surveillance project (table 1a) and 40.0% (161/402) among people enrolled in check hep b (table 1b). sensitivity of the surveillance-based definition was low using both data sources (enhanced surveillance project: 26.2%; check hep b: 24.2%) and specificity high (enhanced surveillance project: 92.1%; check hep b: 94.2%). ppv was 84.4% and 73.6% for the enhanced surveillance project and check hep b, respectively, while npv was 43.4% and 65.0% for the enhanced surveillance project and check hep b respectively. conclusions our surveillance-based definition had high specificity, indicating that the great majority of patients who were truly not treatment eligible were correctly classified. however, sensitivity was low, indicating that the surveillance-based definition was unable to accurately identify those considered treatmenteligible from either data source. low sensitivity suggests that clinicians are likely using other clinical factors not included in laboratory-based reporting to assess a patient’s eligibility for treatment, such as fibrosis and cirrhosis, and that clinicians might be using guidelines other than easl (e.g., american association for the study of liver diseases (aasld) [3]) to determine treatment eligibility. we will conduct chart reviews to better understand the variability in criteria being used. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e419, 2019 isds 2019 conference abstracts these chart reviews will allow us to further refine our surveillance-based definition (e.g., by incorporating different hbv tests or for clinical criteria that are not laboratory-based, including information from external sources such as regional health information organizations (rhios)), eventually supporting the creation of an hbv care continuum for nyc. references 1. france am, bornschlegel k, lazaroff j, kennedy j, balter s. 2012. estimating the prevalence of chronic hepatitis b virus infection--new york city, 2008. j urban health. 89(2), 373-83. pubmed https://doi.org/10.1007/s11524-011-9653-7 2. european association for the study of the liver. 2017. electronic address eee, european association for the study of the l. easl 2017 clinical practice guidelines on the management of hepatitis b virus infection. j hepatol. 67(2), 370-98. pubmed https://doi.org/10.1016/j.jhep.2017.03.021 3. terrault na, bzowej nh, chang km, hwang jp, jonas mm, et al. 2016. american association for the study of liver diseases. aasld guidelines for treatment of chronic hepatitis b. hepatology. 63(1), 261-83. doi:https://doi.org/10.1002/hep.28156. pubmed table 1a and table 1b http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=22246675&dopt=abstract https://doi.org/10.1007/s11524-011-9653-7 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28427875&dopt=abstract https://doi.org/10.1016/j.jhep.2017.03.021 https://doi.org/10.1002/hep.28156 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26566064&dopt=abstract isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e405, 2019 isds 2019 conference abstracts progress towards companion animal zoonotic disease surveillance in the u.s. army sheldon g. waugh, sara b. mullaney army public health center, united states objective we assesed the feasibility of a zoonotic disease surveillance system through the current ehr (rovr) for all poas and goas. additionally, we conducted a retrospective observational study querying and collecting reported zoonoses of interest, for 201 7. introduction dogs, cats and other companion animals have played an integral role in many aspects of human life. human and companion animal (cas) interactions have a wide range of benefits to human health [1-3]. the threat of zoonotic transmission between cas and humans is exacerbated by proximity (56% of dog owners and 62% of cat owners sleep with their animal next to them [4]) and the number of diseases cas share with humans. many of these highlighted zoonoses are spread by direct contact, and others are vector-transmitted (e.g., fleas, ticks, flies, and mosquitos). within the realm of the one-health concept, cas can serve multiple roles in zoonotic transmission chains between humans and animals. they can serve as intermediate hosts between wildlife reservoirs and humans, or as possible sentinel or proxy species for emerging diseases [5]. given the large number of cas within the united states (estimated 72 million pet dogs, 81 million pet cats), understanding and preventing the diseases prevalent in ca populations is of utmost importance. biosurveillance is a critical component of one health initiatives including zoonotic disease mitigation and control. as lead service for veterinary animal and public health services, the army has a responsibility to champion biosurveillan ce efforts to support one health initiatives, improving servicemember, family, and retiree health across the joint force. additionally, wit h military personnel experiencing apparent increased rates of job-reducing ailments such as diarrheal, bacterial and viral disease [68], it is essential that the army focus on maximizing their operational potential by minimizing the amount of time personnel are sick from these transmissible diseases and observing potential sources of infection. by observing the zoonotic disease burden in privately owned (poas) and government-owned (goas) animals, public health investigators can increase focus on what transmittable diseases are at greatest risk of being spread from companion animals to military personnel. to address this potential source of infection, the department of defense (dod) sought and continues to seek to establish a centralized and integrated veterinary zoonotic surveillance system to provide commanders with a clear picture of disease burd en [9]. with this assigned responsibility, the army veterinary service (vs) seeks to centralize and enhance surveillance efforts through the remote online veterinary record (rovr) electronic health record (ehr), an enterprise web-based application to support the army vs, accurately establishing a zoonotic epidemiological baseline and sustaining consistent future reporting. methods through a requested effort and proof of concept, the army public health center’s (aphc) one health division tested the feasibility of a zoonotic disease surveillance system through the current ehr (rovr) for all poas and goas. we obtained one year (2017) worth of zoonotic encounters of interest through rovr, querying a population of roughly 202,000 animals (n=202,217). we conducted a retrospective observational study comparing reported zoonoses of interest between ca populations. maximum likelihood estimations of frequency detailed comparisons of frequency and prevalence between goas and poas, within the rovr ehr. additionally, we evaluated the accuracy of surveillance data queried, proposed potential metrics and dashboards for commanders and stakeholders to easily observe zoonotic burden of companion animals and developed potential courses of action for future tools, collaborations, and educational interventions. results of the 512 collected zoonotic encounters, giardia and hookworm were the two most prevalent zoonoses overall, with 4.23 and 5.43 cases per 10,000 outpatient visits (opvs), respectively. we observed a significant differential frequency of gi ardia and http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e405, 2019 isds 2019 conference abstracts hookworm between goas and poas (63% (ci: 54.6-71.4) vs 12.7% (ci: 9.7-16.1) and 2.5% (ci: 0.1-5.9) vs 41.9% (ci: 37.146.8) of all queried zoonotic diseases of interest, respectively). in addition to back-end database and querying improvements, we suggested the development of an educational intervention based at army first-year graduate veterinary education program (fygve) locations to emphasize the important benefits of capturing zoonotic diseases of interest correctly, early stages in t he clinical experience. the intervention would focus on increasing accurate data capture with the ultimate goal of a phased regional rollout through education and collaboartive buy-in. conclusions from these results and recent cdc guidance of data-driven surveillance, we’ve proposed a phased surveillance development plan focused on systematic data collection, collaboration, and evaluation. our idenfitied overexpressed zoonoses will focus our efforts on tracking giardia and hookworm through multi-year trends. this assessment and proof of concept allows for illumination of gaps and limitations within the army vs to effectively track the zoonotic burden of goa and poa populations. our current and future work will look to close surveillance gaps and help identify potential routes of transmission from companion animals to humans. references 1. edney at. 1995. companion animals and human health: an overview. j r soc med. 88(12), 704p-08p. pubmed 2. wells dl. 2009. the effects of animals on human health and well-being. j soc issues. 65(3), 523-43. https://doi.org/10.1111/j.1540-4560.2009.01612.x 3. o’haire m. 2010. companion animals and human health: benefits, challenges, and the road ahead. journal of veterinary behavior: clinical applications and research. 5(5), 226-34. https://doi.org/10.1016/j.jveb.2010.02.002 4. krahn le, tovar md, miller b. 2015. are pets in the bedroom a problem? mayo clin proc. 90(12), 1663-65. pubmed https://doi.org/10.1016/j.mayocp.2015.08.012 5. day mj, breitschwerdt e, cleaveland s, karkare u, khanna c, et al. 2012. surveillance of zoonotic infectious disease transmitted by small companion animals. emerg infect dis. 18(12). https://doi.org/10.3201/eid1812.120664 6. cook gc. 2001. influence of diarrhoeal disease on military and naval campaigns. j r soc med. 94(2), 95-97. pubmed https://doi.org/10.1177/014107680109400217 7. sanchez jl, gelnett j, petruccelli bp, defraites rf, taylor dn. 1998. diarrheal disease incidence and morbidity among united states military personnel during short-term missions overseas. am j trop med hyg. 58(3), 299-304. pubmed https://doi.org/10.4269/ajtmh.1998.58.299 8. russell kl, hawksworth aw, ryan mak, strickler j, irvine m, et al. 2006. vaccine-preventable adenoviral respiratory illness in us military recruits, 1999–2004. vaccine. 24(15), 2835-42. pubmed https://doi.org/10.1016/j.vaccine.2005.12.062 9. richardson tr. dod directive 6400.04e: dod veterinary public and animal health services [internet]. monterey, california. naval postgraduate school; 2000 [cited 2017 jul 26]. available from: http://calhoun.nps.edu/handle/10945/9216 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=8786595&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=8786595&dopt=abstract https://doi.org/10.1111/j.1540-4560.2009.01612.x https://doi.org/10.1016/j.jveb.2010.02.002 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26478564&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=26478564&dopt=abstract https://doi.org/10.1016/j.mayocp.2015.08.012 https://doi.org/10.3201/eid1812.120664 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11234214&dopt=abstract https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=11234214&dopt=abstract https://doi.org/10.1177/014107680109400217 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=9546406&dopt=abstract https://doi.org/10.4269/ajtmh.1998.58.299 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=16480793&dopt=abstract https://doi.org/10.1016/j.vaccine.2005.12.062 isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e405, 2019 isds 2019 conference abstracts http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e447, 2019 isds 2019 conference abstracts using syndromic surveillance data to study the impact of media content on self-harm kristin m. holland, francis annor, marissa l. zwald, jing wang, michael coletta, aaron kite powell, deborah m. stone, steven a. sumner, daniel bowen, alana m. vivolo-kantor, alex e. crosby division of violence prevention, centers for disease control and prevention, atlanta, georgia, united states objective to describe national-level trends in nonfatal self-harm and suicidal ideation among 10-19 year old youth from january 2016 through december 2017 and examine the impact of popular entertainment on suicidal behavior. introduction in 2016, a half million people were treated in u.s. emergency departments (eds) as a result of self-harm [1]. not only is self-harm a major cause of morbidity in the u.s., but it is also one of the best predictors of suicide. given that approximately 40% of suicide decedents visited an ed in the year prior to their death and that the majority of medically-serious self-harm patients are treated in eds [2], eds serve as a critical setting in which to monitor rates and trends of suicidal behavior. to date, the majority of ed data for self-harm are generally two to three years old and thereby can only be used to describe historical patterns in suicidal behavior. thus, in 2018, a syndrome definition for suicide attempts and suicidal ideation (sa/si) was developed by the international society for disease surveillance (isds) syndrome definition committee in conjunction with centers for disease control and prevention (cdc) staff, allowing researchers to better monitor recent trends in medically treated suicidal behavior using data from the cdc’s national syndromic surveillance program (nssp). these data serve as a valuable resource to help detect deviations from typical patterns of sa/si and can help drive public health response if atypical activity, such as geospatial or temporal clusters of sa/si, is observed. such patterns may be indicative of suicide contagion (i.e., exposure to the suicide or suicidal behavior of a friend or loved one, or through media content, that may put individuals at increased risk of suicidal behavior). research has demonstrated that suicide contagion is a real phenomenon [3]. 13 reasons why is a netflix series focused on social, school, and family-related challenges experienced by a high school sophomore; each episode in the 13-episode series describes a problem faced by the main character, which she indicates contributed to her decision to die by suicide. the series premiered march 31, 2017 and is rated tv-ma by tv parental guidelines [4] (may be unsuitable for those under age 18 years due to graphic content). nevertheless, the series has become popular among youth under 18 years of age. of note, in the final episode, the main character’s suicide by wrist laceration is graphically depicted. following the premiere of the series, researchers and psychologists acro ss the u.s. expressed concern that this graphic depiction of suicide could result in a contagion effect, potentially exacerbating suicidal thoughts and behavior among vulnerable youth viewers. to date, the only empirical data demonstrating the potential iatrogenic effects of this graphic portrayal of suicide comes from a study of google trends data demonstrating an increase in online suicide queries in the weeks following the show, with most of the queries focusing on suicidal ideation (e.g., “how to commit suicide ,” “how to kill yourself”) [5]. however, there has been no study to examine changes in nonfatal self-harm trends following the series debut. methods nssp data were aggregated at the national level from january 2016 through december 2017 to examine weekly trends in the percentage of ed visits that involved sa/si among all ed visits for youth aged 10-19. google trends data were also used to examine suicide-related online searches conducted during this period. additional sensitivity analyses to explore these findings will be conducted by hhs region using nssp data. http://ojphi.org/ isds annual conference proceedings 2019. this is an open access article distributed under the terms of the creative commons attributionnoncommercial 4.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(1): e447, 2019 isds 2019 conference abstracts results preliminary results suggest an increase in ed visits due to sa/si among 10-19 year old youth across the u.s. beginning about two weeks after the premiere of 13 reasons why (april 16, 2017) and lasting a total of six weeks before weekly percentages of sa/si ed visits returned to their endemic levels during the week of may 28-june 3, 2017. the peak of the increase represented a 26% increase in sa/si compared to the highest weekly percentage of these visits in the previous 15 weeks in 2017. additionally, t his peak coincided with marked peaks in online searches for phrases including “13 reasons why” from march 26-june 3, 2017, “how to kill yourself” from april 16-june 3, 2017, and “how to slit wrists” from april 2-june 3, 2017 as demonstrated by google trends data. conclusions this study demonstrates the utility of syndromic surveillance data for tracking sa/si at the national level and for detecting atypical fluctuations in trends over time. using syndromic surveillance data for this purpose could help spark public health response to emerging health threats. however, it is important to note that nssp data are subject to some limitations; for instance, although about 60% of ed visits in the u.s. are captured in nssp, the system is not nationally representative and thus, these findings are not generalizable to areas not participating in nssp. additionally, our definition may underor over-estimate sa/si based on differences in chief complaints or discharge diagnosis data between jurisdictions. further, hospital part icipation in nssp can vary across months–a factor that could contribute to trends observed in nssp data. finally, these analyses explored the concurrent trends in sa/si among youth and the popularity of only one television series. although these analyses point to an association between the increases in sa/si and the time period in which the series reached its peak popularity as evidenced by google trends, there m ay have been other sociocultural factors that impacted sa/si trends during the study period. still, preliminary findings suggest that media content containing graphic depictions of suicide viewed by youth audiences may contribute to increases in ed visits for selfharm and suicidal ideation, as well as greater interest in searching for information about suicidal behavior online. while it is impossible to assess causation, these results are consistent with the phenomenon of suicide contagion. it is also possible that the series or related media coverage during this time increased help-seeking among some youth or their families that contributed to the increases observed. regardless of the underlying mechanism, entertainment content creators may consider referring to the recommendations for reporting on suicide (www.reportingonsuicide.org), which can help reduce the risk of suicide among vulnerable individuals and avoid contributing to suicide contagion while promoting suicide prevention messages. finally, ongoing surveillance of suicidal behavior using nssp data could help reduce the burden of nonfatal self-harm by catalyzing the implementation of prevention efforts. references 1. center for disease control and prevention, national center for injury prevention and control. (2018). webbased injury statistics query and reporting system (wisqars). available from www.cdc.gov/ncipc/wisqars. accessed 10-3-2018. 2. ahmedani bk, simon ge, stewart c, et al. 2014. health care contacts in the year before suicide death. j gen intern med. 29, 870-77. pubmed https://doi.org/10.1007/s11606-014-2767-3 3. gould m, jamieson p, romer d. 2003. media contagion and suicide among the young. am behav sci. 46(9), 1269-84. https://doi.org/10.1177/0002764202250670 4. the tv parental guidelines. (2018). available from http://tvguidelines.org/. accessed 10-3-2018. 5. ayers jw, althouse bm, leas ec, dredze m, allem jp. 2017. internet searches for suicide following the release of 13 reasons why. jama intern med. 177(10), 1527-29. pubmed https://doi.org/10.1001/jamainternmed.2017.3333 http://ojphi.org/ https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=24567199&dopt=abstract https://doi.org/10.1007/s11606-014-2767-3 https://doi.org/10.1177/0002764202250670 https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=28759671&dopt=abstract https://doi.org/10.1001/jamainternmed.2017.3333 malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. prestor j kubalalika1, mph 1. director, rep-public health consultants (reppheco), malawi. abstract background: intermittent preventive treatment with sulfadoxine-pyrimethamine in infants (sp-ipti) is a malaria control strategy which, together with the delivery of routine childhood immunizations, as recommended by the world health organization (who) was implemented in lilongwe district of malawi from september 2008 to november in 2009. a study was performed by lilongwe district health office (dho) in collaboration with funding from unicef to evaluate the safety of sp-ipti and identify potential new adverse events (aes) spontaneously identified, reported, monitored and evaluated. methods: a cohort event monitoring study was conducted on 15, 000 infants in 4 health facilities (hfs) after administration of sp-ipti to infants during routine immunizations. a total of about 50 community health workers (chws) and volunteers were trained in pharmacovigilance and were supervised by senior personnel in all the five hfs. infants received half tablets of sp immediately after receiving dpt-hepb+hib (pentavalent) 2 vaccine / (ipti 1), pentavalent 3 / (ipti 2) at 10 and 14 weeks respectively and measles vaccines/(ipti 3) at 9 months. these children were recorded and their mothers were given diary cards with pictures of possible aes. community health workers (chws) and volunteers followed up every child after 10 days of administration/registration to collect the diary cards where parents indicated types of aes observed on their children as well as starting and end dates of such possible aes. the indicated aes were entered into a computer database from all the collected diary cards according to hfs. possible side effects/aes that were looked for were; persistent crying, fever, vomiting, diarrhoea, skin rashes, abdominal pains, insomnia, nausea, mouth sores, and itching among other related possible side effects. results: a total of 15,105 children received the ipti and were followed in all four health facilities. out of this, 50.3% (7,594) were male while 49.7% (7, 511) were females. of these, 19.2% [1247], 95% ci (276-304) developed aes as follows; 42% persistent crying, 28% fever, 18% vomiting, 5.2% skin rashes and 6.8% presented with other minor symptoms while 80.8% (13,858) did not develop any side effect. 43.2% (1254) of those who showed symptoms were ipti1 recipients, 35.3% (1022) received ipti2 while 21.5% (624) were from those who received ipti3. conclusions: this study showed that simultaneous administration of sp-ipti together with immunizations was a safe strategy for implementation with very minimal serious aes to infants. in this case therefore, strategies towards strengthening such spontaneous reporting in malawi should not only be left to service providers but also to beneficiaries or their caregivers. malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi literature review sp-ipti is the administration of a full therapeutic course of sp delivered through the expanded programme on immunization (epi) at intervals corresponding to routine vaccination schedule for the second and third dose of dpt/penta and measles vaccination-usually at 8-10 weeks and ̴̴ 9months of age-to infants at risk of malaria (who 2011). pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. the aims of pv are to enhance patient care and patient safety in relation to the use of medicines; and to support public health programmes by providing reliable, balanced information for the effective assessment of the risk-benefit profile of medicines (who 2018). jeetu g et al (2010) described pharmacovigilance as a sunshade to describe the processes for monitoring and evaluating adverse drug resistances (adr) and it is a key component of effective drug regulation systems, clinical practice and public health programmes. this would consider litigious and important drug safety issues that have the potential to affect public health adversely beyond national boundaries. in addition, european medicines agency (2016) stated that the process of pharmacovigilance involves several distinct steps such as collection of information on potential side effects, detecting if any new or changing side effects have arisen, deciding if action is needed to optimize the safe and effective use of the medicine and then communicating this to the users of the medicine. coomarasamy a et al (2016) said that any untold medical occurrence in a participant, which does not necessarily have a causal relationship with the trial event is called an adverse event and that any untold and unintended responses to the trial intervention, at any dose administered, including all aes judged by either the reporting investigator or sponsor as having a reasonable causal relationship to the trial intervention is called adverse reaction. walshe k (2000) stated that adverse events are instances which indicate or may indicate that a patient has received a poorquality care. methods the pharmacovigilance study was performed on all under one-year-old children from the four health facilities of kawale, mitundu, chiwamba and likuni in lilongwe district of malawi. infants were given half tablets of sp immediately after receiving pentavalent 2, 3 and measles vaccines at correspondence: prestorkubalalika@gmail.com doi: 10.5210/ojphi.v11i2.9956 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. mailto:prestorkubalalika@gmail.com malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi 6, 14 weeks and 9 months respectively. chws and volunteers who were trained on follow up and collection of diary cards were recording every child based on the location each child was coming from. mothers/caregivers of these children were given diary cards with pictures of possible side effects on them. they were asked to indicate/check alongside any side effect they observed on their children and to also indicate the starting and finishing dates of observed side effects. these cards were issued immediately after children received immunization and sp right at the clinic site. then responsible chws and/or volunteers from where these children were coming, took note of their location details for easy follow-up. the possible side effects indicated on the diary cards were; persistent crying, fever, vomiting, skin rashes, diarrhoea, abdominal pains, nausea, itching, insomnia and any other they would observe. after ten days, chws and/or volunteers were following up every child at their homes to collect the diary cards from their mothers or caregivers. the collected diary cards were then taken to the program coordinator who later entered all aes indicated on these cards into the database. in the event of a suspected serious adverse event (sae), immediate reports were sent to relevant supervisors who in turn made quick follow ups where thorough diagnosis was made and/or samples taken. these were then sent to paediatric experts at kamuzu central hospital (kch) for examination and responsible suspects (children) were assessed before making a confirmation or otherwise if the reaction was indeed due to sp or not. the diary card the card was where information on symptoms was entered by parents/caregivers and it contained pictures of possible aes. the left part of the card was where detailed information about the child was indicated for easy and proper follow up. the right part of it was where parents/caregivers were indicating any of the suspected possible symptoms they observed on their children. this was the most important part of the diary card. parents/caregivers were supposed to check/tick in the boxes next to pictures which corresponded to symptoms children showed and then they were indicating the starting and finishing dates of such symptoms. then there was a table on the far right of the card which contained other possible symptoms to look for and indicated accordingly. at the immediate bottom of that table was the provision for outcomes which were supposed to check if any of it happened. this card was later followed-up after ten days from administration of ipti and collected by chws and/or responsible volunteers. the card was then handed over to the study coordinator who entered the information in the computer database. these cards were finally filed together per health facility. if by any chance the coordinator suspected a serious adverse event (sae), he would use the information from the this card to make further follow ups to the children beginning with the health facility it came from where confirmations were made that either the children still had the symptoms or had since recovered before deciding to forward/transport the case to experts at the referral kamuzu central hospital for further analysis and/or action otherwise they would rule the case out of possible sae right there at hf level. before parents/caregivers were given this card at the immunisation clinic site, chws and volunteers were explaining to them on how to use it. they also advised them on the importance of the card and that they should properly keep it. they were also told that after ten days, either the chw or the volunteer of their area would come to collect the card. they were further advised that malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi in case they noticed and suspected any sae within the ten days before the arrival of the chw/volunteer, ̴̴ they ̴̴ shouldn’t ̴̴ wait ̴̴ but ̴̴ rush ̴̴ with ̴̴ the ̴̴ child ̴̴ to ̴̴ their ̴̴ nearest ̴̴ hf ̴̴ for ̴̴ medical ̴̴ attention (see appendix 1). data entry information from the diary card was entered into a computer database where all doses received were indicated plus statuses of symptoms (if the cards showed any). if, however the child did not present with any symptom, the diary card was still collected from the parent/caregiver and information to the same was entered into the database. this means that on this database, only the top ̴̴part ̴̴(which ̴̴gave ̴̴details ̴̴of ̴̴the ̴̴child’s ̴̴name ̴̴and ̴̴location ̴̴of ̴̴its ̴̴residence) ̴̴was ̴̴entered ̴̴leaving ̴̴ the ̴̴symptom ̴̴part ̴̴since ̴̴the ̴̴child ̴̴did ̴̴not ̴̴show ̴̴any ̴̴symptoms. ̴̴every ̴̴child’s ̴̴record was dully kept by the coordinator such that after ipti1/pentavalent 2, the child was expected to come for the second time for ipti 2/pentavalent 3 and another card was given which was later handed over to the coordinator for entry until the third dose at 9 months when the child got measles vaccine together with sp-ipti3 dosage. this database contained space for all the three ipti doses and after the child had finished receiving the three doses and his/her data entered, then he/she was regarded as fully covered (see appendix 2). results out of 15,105 total enrolments, 50% (7,555) received ipti-sp while the other 50% (7,550) did not receive. among those who did not receive, were those who were not yet due for ipti (those who got penta 1 at 6 weeks) and for those who were enrolled, but for some other reasons, their mothers/caregivers refused to continue taking part plus those that were untraceable/unaccounted for due to unidentified relocations and/or those who deliberately gave false addresses. however out of 7,555 who received ipti, 2,900 (38.4%) reacted while 4,655 (61.6%) did not. therefore, in this case, out of the total 15,105 who enrolled, 19.2% (2,900) reacted and 80.8% (12,205) did not. in all, there were 7,594 (50.3%) male and 7,511 (49.7%) female infants. of the total 2,900 (19.2%) children with symptoms, 1,219 (42%) showed persistent crying which was due to (among other reasons) the vaccine injection they were given during immunisation. 812 (28%) had fever which would also be as a result of the vaccine, 524 (18%) vomited, 160 (5.5%) developed mild skin rashes which lasted for a few days, 82 (2.8%) had diarrhoea, 42 (1.4%) presented with insomnia, 18 (0.6%) mild abdominal pains, 12 (0.4%) had itching, 10 (0.3%) dark cloured urine while 21 (0.7%) had shown other minor symptoms. out of all those who had the above symptoms, no one had shown serious adverse event due to sp within the 10 days monitoring period. most of these children had mild reactions which took not more than 2 days and no one was admitted for the whole monitoring period of the study. crying and fever were more prevalent (98.8%) than the rest which were attributed to vaccines and some mild sores due to injections which were normal to mothers/caregivers that evidently lasted for a few days and were treated with some pain killers and tapped sponging on injected areas/sites. to those who showed diarrhoea, insomnia, abdominal pains, itching, dark cloured urine, the reactions took not more than two days such that most of them disappeared/ceased on their own without medical treatment. malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi as shown in figure 2, of the three ipti doses, ipti 1 presented with more symptoms (43.2%) followed by ipti 2 (35.2%) and ipti 3 (21.6%). as already explained above, the more the number of children per ipti dose, the higher the symptom coverage. this means that ipt1 had more children who received the ipti-sp than the other two. table 1: vaccine/sp enrollment and ipti symptoms h/facility sex number with symptoms number without symptoms total male female total vacc sp vacc sp vacc sp chiwamba 1,480 740 1511 756 2,991 1,496 521 2470 2,991 kawale 2,345 1173 2,173 1087 4,518 2,260 839 3679 4,518 likuni 1,232 616 1371 686 2,603 1,302 553 2050 2,603 mitundu 2537 1269 2456 1228 4,993 2,497 987 4006 4,993 total 7,594 3,798 7,511 3757 15,105 7,555 2,900 12,205 15,105 this table shows the enrollment of all 15,105 children. the sex section is split into male/female and ̴̴total ̴̴columns. ̴̴each ̴̴column ̴̴i.e. ̴̴male, ̴̴is ̴̴again ̴̴split ̴̴into ̴̴two ̴̴i.e. ̴̴“total ̴̴of ̴̴those ̴̴who ̴̴received ̴̴ vaccines” ̴̴(sum ̴̴of ̴̴penta ̴̴1,2 ̴̴& ̴̴measles) ̴̴which ̴̴is ̴̴labeled ̴̴“vacc”. ̴̴then ̴̴the ̴̴other ̴̴column ̴̴“sp” ̴̴ means that out of those who got vaccinated, how many received ipti-sp.below is the enrollment coverage chart for children who received sp-ipti per dosage; fig 1: coverage of sp-ipti per dose the above chart shows coverage for each ipti dosage given (1-3) which shows that there were more children that received ipti1 at 43% i.e. (6,495, of the total 15,105 children who were enrolled) that was concurrently given with pentavalent 2 vaccine (at 10 weeks) than the other two ipti doses. the graph shows that utilization was dropping (from 43% in ipti1 to 22% ipti3) after every dose given such that by the time these children were receiving ipti3 at 9 months during measles vaccine, 21% of those who got first dose had dropped out as has always been the case with childhood immunisation. some of the reasons for this drop out was that there was no further malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi follow-up to those who dropped-out and/or some children had received ipt1 with third pentavalent dosage (at 14 weeks) ̴̴and/or ̴̴measles ̴̴hence, ̴̴they ̴̴couldn’t ̴̴have ̴̴come ̴̴back ̴̴again ̴̴for ̴̴the ̴̴remaining ̴̴ ipti dosage since they would have already finished their corresponding vaccination dosage. the graph below shows number of children who received ipti as above that had developed symptoms per dosages; fig 2: ipti symptoms per dosage the above graph shows children who developed symptoms after taking sp-ipti (1-3). there were overall, more children who received ipti1 that reacted and presented with symptoms i.e. (1,254 out of the total 2,900 who reacted) than the other two which was also the case as the trend kept declining with number of doses given. this was so because obviously there were already more children who received ipti1 than the other two as shown in the previous chart. this scenario therefore explains that; the more the children who received a particular ipti dose, the more symptoms were shown and vice versa. although some of those who reacted previously returned for later doses, the number of returnees was still lower than of those who came before and this was the case in all the three doses. below however is a graph showing children who presented with various specific symptoms; 0 200 400 600 800 1000 1200 1400 ipti1 ipti2 ipti3 1254 1022 624 c h i l d r e n doses number of children with symptoms against doses malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi fig 3: specific ipti symptoms the graph shows that out of 2,900 children who were followed and reacted after 10 days of administration, most of them presented with persistent crying (1,219) then fever (812) and the least was dark cloured urine as seen in the above graph. interpretation/discussion the fact that 19% of 15,105 children reacted and showed symptoms after taking ipti-sp, this coverage however, did not present any serious adverse event following sp administration as would be anticipated. most of the symptoms were mild which took a few days to subside, be healed and/or treated. there were no any hospital admissions made although some of them had been referred to experts after showing some symptoms like skin rashes but these were eventually ruled out of any danger of saes for they subsequently subsided within 10 days of active surveillance period. in this case therefore, it was clear that using sp as a preventive treatment to infants against malaria together with immunisation was safe and that most of the symptoms that manifested were due to some mild reactions which were not as serious to children that were promptly treated and/or managed. the study therefore showed that sp-ipti could effectively be used to children together with immunisation in preventing malaria in infants as was the case with iptp in pregnant women (according to world health organization). the study also proved that ipti could be rolled out to other districts of the country taking into account its safety on the health of infants during this period. on the other hand, in all four health facilities, clinicians indicated (though anecdotally) that number of infants presenting with malaria was drastically reduced as compared to previous months. as a result of this, they were not seeing as many children as in previous months before the study and as a result, these health facilities were not running out of anti-malaria drugs as was previously the case when they were seeing and treating a lot of children against malaria (this assertion could not be scientifically proven with data evidence). 1219 812 524 160 82 42 18 12 10 21 0 200 400 600 800 1000 1200 1400c h i l d r e n symptoms number of children against specific symptoms malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi mothers/caregivers on the other hand, commended the initiative as their children were protected from malaria such that they were able to have more time doing household chores than frequenting health facilities to have their children treated against the disease which was so prevalent among infants. they as a result, also had enough time to do some other things in their homes for their families such as business without being interrupted by going to health facilities to have their children treated for malaria. this also brought financial stability in some homes who more often were used to frequently buying anti-malaria treatment from shops and/or private clinics whenever their children were sick from the disease. due to this, they would save the money they could use to buy the drug to buying some basic household needs. to families who could not afford buying itns, for their children this was a huge relief as although they were exposed to mosquito bites, their children were on the other hand assured of not catching malaria due to ipti protection. some mothers/caregivers who were staying far away from health facilities regarded this initiative as worthwhile for they were able to save some money they would use on transport to and from health facilities for buying food and other items in their homes. in addition to this, the study found out that it also helped increase immunisation attendances since mothers/caregivers brought their children for ipti which in the process, helped raise the immunisation coverage of these health facilities that enabled them reach their immunisation target populations as expected. mothers/caregivers frequented immunisation clinics to have their children administered with sp-ipti at the same time receive vaccinations. this also helped reduce the epi immunisation drop-out rates in these facilities hence it was seen as a service which made them kill two birds with one stone i.e. protecting their children against childhood immunisable diseases by vaccines as well as malaria by ipti. the ipti pharmacovigilance therefore showed that it was possible and safe to combine the two (immunisation and sp) on infants without registering any serious adverse event. it therefore proved that doing this would effectively address two problems at once which would help save children’s ̴̴lives ̴̴against corresponding diseases. it also proved that it would reduce the burden on clinicians of seeing many children with malaria which would eventually enable them serve more other patients with different diseases hence improve the health of both infants and other patients. limitations despite the fact that the study was successfully done and that its results were encouraging, not all children were adequately followed during the study period from ipti1 to ipti 3. there was 21% drop-out rate which was due to several factors and misconceptions/myths as well as suspicions against the study being one of them. some women/caregivers dropped out due to poverty which made them fail to finish attending clinics for lack of transport money to those living far away from immunisation ̴̴clinic ̴̴sites ̴̴as ̴̴well ̴̴as ̴̴good ̴̴clothes ̴̴to ̴̴put ̴̴on ̴̴during ̴̴such ̴̴clinics ̴̴(although ̴̴it ̴̴wasn’t ̴̴ a prohibition). the other limitation was inadequate supervision on the part of chws and volunteers by their immediate supervisors in specific health facilities. this was affecting some follow-ups to these children after 10 days which in turn, also affected the study. some chws were unable to manage malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi their time properly between their day to day work and ipti follow-up exercise (which was an addition) such that they were failing to visit and collect the diary cards from mothers/caregivers on time. there was also a limitation of failure to track some registered children due to unidentified relocations especially at kawale health centre which is in lilongwe city. this was very prevalent during month-ends where some women were relocating to other parts of the city without informing chws or volunteers. this made it difficult for chws to know where such children had relocated to. misconceptions and myths were some other limitations which made some women stop participating in the study. it was difficult for chws and volunteers to convince such parents/caregivers to remain in the study. there was an incorrect/inaccurate registration of children to be followed at immunisation sites immediately after these children got ipti. some chws and/or volunteers were unable to correctly list down details ̴̴of ̴̴these ̴̴children’s ̴̴locations ̴̴which ̴̴resulted ̴̴in ̴̴going ̴̴to ̴̴wrong ̴̴directions ̴̴during ̴̴ follow-ups in the process missing the right children that needed follow-ups at such particular times. although this initiative showed success and that it was recommended to be rolled out, this was eventually not the case. this was so because at that time, sp was the first-line anti-malaria treatment drug which was later changed to lumefantrine-artemether (la) in malawi before this initiative was implemented. this made it impossible for the initiative to be rolled out as a result; it wasn’t ̴̴implemented ̴̴at ̴̴national ̴̴level. conclusion this study proved to be a success in the sense that it managed to show that sp-ipti could be combined with immunisation at the same time during childhood epi vaccinations. it also proved that active surveillance was more effective than passive when following the outcome of a specific drug and/or treatment. the study further showed that there was no serious adverse event following sp-ipti against malaria in infants. it also showed that it was possible to do this pharmacovigilance at national level and that this approach was a more viable way of assessing if the drug or treatment is compatible with the prevailing conditions in specific targeted groups of people. the study results showed that indeed there was need to combine the anti-malarial drug with childhood immunisation if we were to address the issue of malaria in infants. this approach addressed the accessibility gap parents/caregivers had to health facilities where many children were also protected against malaria besides immunisable diseases. references alexandra de sousa leon paul rabarijaona ofori tenkorang ebenezer inkoom hantamalala v. ravelomanantena sabrina njarasoa jeremiah nee whang jean louis ndiaye youssoupha ndiaye mouhamed ndiaye (2012). pharmacovigilance of malaria intermittent preventive treatment in infants coupled with routine immunizations in 6 african countries https://academic.oup.com/jid/article/205/suppl_1/s82/869855 malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi coomarasamy a, williams h, trucanowicz e. (2016) definitions of adverse events, seriousness and causality. https:www.ccbi.nlm.nih.gov/books/nbk362736/ european medicines agency. (2016). presentation pharmacovigilance at european medicines agency http://www.ema.europa.eu/docs/en_gb/document_library/presentation/2016/02/wc500201 046.pdf jeetu g, anusha g. (2010). pharmacovigilance: a worldwide master key for drug safety monitoring. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc2964775/ kieran walshe (2000). adverse events in health care: issues in measurement. qualitysafety.bmj.com/9/1/47 who. (2018) pharmacovigilance. http://www.who.int/medicines/areas/quality_safety/safety_efficacy/pharmvigi/en/ who. (2011) intermittent preventive treatment for infants using sulfadoxine-pyremethamine (ipti-sp) for malaria control in africa: implementation field guide. www.who.int/malaria/publications/atoz/whoivb11_07/en/ http://www.who.int/malaria/publications/atoz/whoivb11_07/en/ malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi appendices appendix 1: the diary card malaria intermittent preventive treatment (ipti) pharmacovigilance in malawi: a case of lilongwe district. online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e9, 2019 ojphi appendix 2: database on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi on the potential, feasibility, and effectiveness of chat bots in public health research going forward stanley mierzwa1*, samir souidi2*, terry conroy3, mohammad abusyed4, hiroki watarai3, tammy allen3 1. vennue foundation, connecticut; john jay college of criminal justice, new york, usa 2. information technology, population council, new york; indiana university, indiana, usa 3. vennue foundation, connecticut, usa 4. vennue foundation, dhaka, bangladesh abstract this paper will discuss whether bots, particularly chat bots, can be useful in public health research and health or pharmacy systems operations. bots have been discussed for many years; particularly when coupled with artificial intelligence, they offer the opportunity of automating mundane or error-ridden processes and tasks by replacing human involvement. this paper will discuss areas where there are greater advances in the use of bots, as well as areas that may benefit from the use of bots, and will offer practical ways to get started with bot technology. several popular bot applications and bot development tools along with practical security considerations will be discussed, and a toolbox that one can begin to use to implement bots will be presented. keywords: artificial intelligence; ai; bots; chat bots; bot framework; public health technology; pharmacy technology *correspondence: smierzwa@vennue.org; ssouidi@popcouncil.org doi: 10.5210/ojphi.v11i2.9998 copyright ©2019 the author(s) this is an open access article. authors own copyright of their articles appearing in the online journal of public health informatics. readers may copy articles without permission of the copyright owner(s), as long as the author and ojphi are acknowledged in t he copy and the copy is used for educational, not-for-profit purposes. 1. introduction an important concept in public health research is that from time to time a new technology or approach to obtain the best possible data, or the application of a new service or tool, will make such an impact that it is then used in education and routinely used in practice for decades. humans have for many centuries sought ways to automate with machines: in fact, the first humanoid-robot was invented by arab inventor al-jazari, of the turkish dynasty of artukids, in 1206 ad. [1] for long-lasting impact, one can look at the use of the body temperature thermometer, which was initially invented for medical use in the late 1800’s; this enormously successful device is still in on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi use today. simply put, it made a positive change on health care that has been sustained over the century since its introduction. technology is still a catalyst that can be impactful to the level or degree of the body temperature thermometer, and it is up to researchers, practitioners and engineers to continue to experiment and try new ideas and approaches. this paper will focus on whether the use of a bot (a software application that uses steps or scripts to automate a task and is also known as a web robot or internet bot), can be beneficial to public health research efforts. through a variety of toolkits available, chatbots utilize natural language understandings (nlu) services. with nlu, chatbots focus on the use of a conversational interface, one that permits a user to interact using their natural form of speaking. natural languages (such as english, hindi, or portuguese) are used for everyday communication. in contrast to artificial languages, such as programming languages and mathematical notations, natural languages evolve as they pass from generation to generation, and are hard to pin down with explicit rules. [2] an informal literature review of journal articles found in pubmed central indicates a lack of practical discussions or thoughts on the topic of bots specifically in use in public health research. 2. research project background a goal for many in the public health sphere is to improve or ensure healthy lives and promote wellbeing for people of all ages. this project explores the use of a simple-to-use chatbot, thus contributing to goal 3 of the united nations 2030 sustainable development goals (sdgs). the good health and well-being sdg is broad, but includes targets aiming at increasing development, training, and retention of health workforces in developing countries. another goal includes reducing risk associated with the management of health care. exploring the use of chatbot technology aligns with the sdgs and it is one the authors would explore otherwise, given the increase of its use in the business sector, but it also demonstrates that the united nations is keeping a keen eye on positive technology trends in industry. a literature review was performed to determine the popularity of the use of chatbots in public health using key search terms. tables 1 and 2 outline the results of our search term results focused on chatbots. there are many sources to look for research publications these days, from open-source journals, to google scholar and private journals owned by organizations and universities. for the purpose of this paper, we decided to review pertinent articles in the pubmed central (pmc) housed within the us national library of medicine of the national institutes of health. the reason for the decision to focus on the pmc was that the authors have published other research papers in journals that would ultimately be linked and housed in the pmc. at present the pmc, which began in the year 2000, now includes over five million full-text records, from thousands of journals. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi table 1. pmc general search term results. search term (general) number of found articles databases found “chatbots” 39 pubmed; pubmed central “chat bots” 40 pubmed; pubmed central; pubmed health “virtual assistant” 409 bookshelf; pubmed; pubmed central; pubmed health table 2. pmc search term specific to title or abstract results. search term (title or abstract) number of found articles databases found “chatbots” 15 pubmed; pubmed central “chat bots” 2 pubmed “virtual assistant” 15 pubmed as with any new technology solution or technological approach, certain industries will engage with chatbots sooner than others. much of the initial use or engagement can be due to qualities such as competition, cost savings, or strategic advantage. the case for early adopters of chatbots has been evident in several industries that have much to gain from their use. these include: hospitality, banking/financial, retail service business, and publishing. [4] a generation of users is eager to use technology in business-related sectors and has moved ahead; however, there is the potential for greater emphasis on the health-related or public health sector. companies look for self-service ability to make the customer experience more convenient and familiar to consumers whose primary communication platforms have become messaging apps and social networks. more than one in three brands or companies say customers and prospects prefer to complete a transaction or resolve service issues without speaking to a human associate, if possible. when asked which technologies would most improve the customer experience, 40% of sales and marketing leaders cited virtual reality (vr). thirty-four percent (34%) believe artificial intelligence (ai) will be the biggest game-changer. [7] according to a study by juniper research, the health care and banking industries are primed to see the next largest benefit from the use of chatbots because they handle such large volumes of human interaction. juniper’s research predicts that by 2022, chatbots will save organizations more than $8 billion annually worldwide by significantly reducing the amount of time it takes to resolve inquiries. [7] 3. methods (tools) as with any software product or software development tools, there are many avenues one can take to solve a problem or implement a solution. in this section we review several of the larger segment bot tools available that have been in use, but it should be noted that the bot development market is on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi still developing and different toolsets can emerge. during the time of writing of this paper, several new chatbot developer tools began have begun to gain traction, including oracle, rulai and nuance communications. the authors utilized this analysis to decide on which chatbot development environment and toolset to create a sample chatbot. 3.1 bot tools available from ibm watson ibm watson assistant (formerly watson conversation) offers a comprehensive and developer friendly portfolio. it marries a technically robust conversational platform to strong developer tools and enables the creation of a wide range of solutions. [8] forrester research reports that the ibm watson assistant is a leader in their wave position and that it may be less applicable to device and consumer applications, but more geared toward widespread enterprise device deployments. links for getting started with building a chatbot with ibm can be found at: https://www.ibm.com/watson/how-to-build-a-chatbot/ 3.2 bot tools available from google google entered the chatbot market via their ai and machine learning products, dialogflow (formerly api.ai). forrester reports it as a strong performer via their wave position. the tool has the reputation of being able to provide a simple user experience in development; it also allows for the rapid creation of a conversational interface. google has an advantage with their solution because of their vast experience in ai. forrester says that a pilot-to-production solution can be achieved in a month and a half. 3.2 microsoft bot framework microsoft has developed and made available a host of tools, starter example packages, and documentation that can be quickly utilized to create chatbots, or integrate bot qualities into existing applications. these toolsets include the ability to create locally available chatbots via visual studio, a bot builder software development kit (sdk), and an emulator and with c# support. one can build a bot facility offline, with use of the sdk and test it locally with the available emulator. in addition, microsoft has launched the azure bot service. the azure service permits one to create a bot, publish it to azure or to one’s own local web server and, via the public web, allow it to go live and interact with end-users. a user can create his or her own azure bot and begin traveling through the creation of the hosted box by logging into the azure portal, selecting ai + machine learning from the azure marketplace, then selecting web app bot. 3.3 facebook bot toolsets facebook has strong communications in its services and dominates the field because of the many users it has connected. facebook recently opened up its messenger service to developers, thus creating the opportunity for programmers to utilize it to create bots. the facebook messenger via bot permits multiple people to communicate in a single conversation, adding complexity and advantages. facebook m is the service the company plans to push since it includes their artificial intelligence and could perhaps provide extremely intelligent and more accurate bots. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi 3.4 amazon bot tool – amazon lex amazon lex is a tool made available by amazon and is based on the same technology behind their popular alexa product. the toolset allows developers to create text or voice bots. integration with such services as twilio allows users to set up amazon lex to respond to sms messages. developers can use the amazon web service (aws) sdks to build ios, android, java, python and a host of other programming languages to utilize lex. table 3. relevant chatbot tools comparison for selection by authors – as of april 2019. ibm watson dialogic flow microsoft bot framework facebook bot (messenger) amazon lex languages supported over 13 over 20 over 10 (via luis) many english only year developed 2007 2010 (purchased by google in 2016) 2016 2016 2010 difficulty level to entry of use medium (quick to get started with tutorials and watson assistant) medium (quick to get started with available conversational cards) medium low low require internet for operation yes – must use ibm cloud account yes – google cloud platform (gcp) yes – microsoft azure (can deploy on private servers) yes yes platform integrations available watson discovery; assistant; watson api’s google assistant; alexa; cortana; facebook messenger azure; azure cognitive services; storage; facial recognition; text analysis chatfuel; plugins for google and bing search; video; audio aws lambda; cognito; polly sdk programming node.js; node.js; python; java; c#; node.js javascript; java; js; python; .net; on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi language supported go; ruby; c#; php ruby; php; cli; go; c++ example of external entities bot tool can communicate with facebook; slack; intercom; alexa; sms vehicles; mobile devices; speakers, phones; facebook messenger; amazon lex skype; slack; qna maker; kik; facebook messenger; office 365 customer chat plugin for website; travel assistant; digital marketing; ecommerce salesforce; ms dynamics; zendesk; quickbooks available sdks for ios and android yes yes; and many more yes yes. messenger platform apis yes intended or focused use cases media; advertising; finance; health; education; iot; customer engagement customer service; commerce; enterprise productivity; iot virtual assistant; customer care; enterprise ecommerce; shopping list; account linking insurance; education; field service; tight integration with alexa; high quality speech recognition; cost free to begin and test. free to begin and test. free to begin and test. free to begin and test. free to begin and test. 3.5 prototype health research bot the nonprofit vennue foundation envisions a prototype chatbot to enhance the quality of care delivered to patients in low-income countries. vennue’s mission is to improve patient health outcomes in the developing world through a unique strategy of pharmacy workforce training. in countries where vennue has a presence, the local pharmacy is often a patient’s only point of access to health care. the vennue chatbot will enhance the quality of health care services delivered at the point of dispensing. the envisioned chatbot, planned for development after the writing of this paper, will aid pharmacy personnel by allowing them to use natural language input to ask questions related to pharmacy knowledge they may lack or do not feel confident about. given the growing burden of diabetes in developing countries, and in view of the frequent visits that diabetes patients make to their pharmacies, vennue’s chatbot will focus on answering diabetes-related queries. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi worldwide, diabetes is one of the fastest growing chronic diseases, impacting more than 425 million people and directly causing an estimated 1.6 million deaths. [16] unmanaged diabetes significantly increases the risk for blindness, kidney failure, heart attacks, stroke and lower limb amputation. in low-income countries, the risk for developing these complications is elevated because the prevalence of diabetes is rising more rapidly than in developed countries, and the ratio of physicians to patients is severely inadequate. [14][15] current practice standards encourage diabetes self-management education and support (dsmes). to help alleviate the uneven physician-to-patient ratios in the developing world, chatbots have an opportunity to be part of the dsmes by personalizing care and addressing the needs of patients through cost-effective, evidence-based responses and behavioral change interventions. diabetes can be treated and its consequences avoided or delayed with diet, physical activity, medication, and regular screening and treatment for complications. a chatbot can empower pharmacy workers to harness these intervention tactics for diabetes patients. the vennue chatbot will be designed to enable pharmacy personnel to easily screen patients for diabetes and pre-diabetes, and then make efficient referrals to higher levels of care for follow-up care including clinical diagnosis and treatment. it also may be programmed to answer diabetesrelated questions most frequently asked by pharmacy clients in low-resource settings. in addition, the chatbot may recommend lifestyle modifications and other diabetes consultation points that pharmacy staff can then convey verbally to their patients, immediately at the point of care. vennue will ensure the chatbot is fit-for-purpose with locally-relevant information and usability. the creation and results of the vennue pharmacy chatbot will be discussed in a future research pilot results peer-reviewed paper. the pilot will include data obtained from actual implementation of the chatbot in community-based and hospital pharmacies located in bangladesh and nepal, where vennue’s pharmacy workforce training program has demonstrated success. serving every day on the frontlines of patient care, pharmacy personnel in the developing world hold remarkable potential to improve health and promote well-being. if equipped with practical training and a locally-relevant chatbot, the pharmacy workforce can fundamentally transform the quality of care and consultation offered to diabetes patients. after reviewing the different development frameworks available to build a chatbot, the authors decided on the amazon lex platform. although any of the tools discussed in section 3 above would have met the need for this prototype, the authors selected lex because it was thought to have the lowest barrier to software development and a short development time. in addition, the lex platform is considered a strong performer via the forrester wave position and can be seen as third among the solutions analyzed by forrester. 3.6 bot general workflow design an example of a bot conversation-like flow can be seen in figure 1. it is important to understand some of the key terms used in bot development: intent, utterances, slots, prompts, and fulfillment. figure 1 below demonstrates the use of these key terms in the context of a sample engagement and on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi vision of a chatbot being used. in this sample chatbot, english audio would be used, but with corresponding english text to complement the chatbot conversation or transaction. figure 1. example of bot development terms. aws lex provides an easy-to-use amazon console where the user would define bots, intents, and slot types. the bot performs the automated tasks, such as guiding a customer through ordering a pizza. it is powered by the automatic speech recognition (asr) and natural language understanding (nlu) capabilities, the same technology powering the amazon alexa. the intent represents an action a user would like to perform. a bot can support one or more related intents. in our pizza example, a developer can create a bot that also orders drinks. intents can utilize zero or more slots or parameters. adding slots is part of the intent configuration. during runtime of the chatbot application, amazon lex prompts the user for specific slot values. the user must provide values for all minimally required slots before amazon lex can fulfill the intent. each slot will include a type, and for this the user creates his or her custom slot types or can use built-in slot types available with amazon. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi 3.7 amazon lex development environment aws lex does not require deep learning or coding experience. aws lex could be developed in the aws console utilizing any web browser or using the aws software development kit (sdk) combined with the aws command line interface (cli) and can be integrated with many programming languages. in our sample development of the pizza ordering chatbot, we used the aws console to build an aws lex. aws offers thorough documentation on its website with many how-to examples to develop aws lex. the authors followed a step-by-step guide for creating such a chatbot provided by amazon. [5] the amazon console allows the user to define and create these elements in a short time without any coding. the next step is to link the aws lex to aws lambda where the back-end code handles the ai logic and will interact with the user’s input and aws lex. aws lambda function allows the solution to operate the back-end logic code without provisioning or managing any servers. in our sample application to provide feasibility, the python language was used in the aws lambda function. the front-end facing application in our feasibility test required coding in order for the mobile device to interact with the aws lex. in our example, we used a custom android java mobile application for the front-end user interface for the “orderingpizza” chatbot. 3.8 development back-end a hosted server with amazon web service (aws) houses the amazon lex developed logic that captures, processes, and provides feedback to the end users who initiated the natural language query. in figure 2 below the overall component architecture is represented in a simple block diagram. the blocks in figure 2 cover the front-end android application presented to the users, the use of an api gateway, and the aws hosted components. the authors created a simple lex app to prove feasibility; this mimicked the ability to use the service to order a pizza. [5] the amazon lex creation interface service makes it easy to get started with adding intents and methods to interact with the chatbot. a critical component discovered during this feasibility assessment is that the solution designer needs to give much attention to the data design of the back-end of the chatbot. it is not approached the same way one would approach the development of a relational database – which the authors feel is a much more straightforward mechanism. the chatbot backend data requires much more design thought, on the outcomes of the queries and then to back into the outcomes with the relevant potential queries from an end user. in addition, the technology engineers need to collaborate very closely with the subject matter experts in designing the data to be used – this was clearly apparent during design sessions with the vennue expert pharmacy education staff. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi figure 2. overall architecture of feasibility chatbot built using the amazon lex back-end. while utilizing the amazon lex platform for creation of a chatbot, the user has the ability to create the logic for the application. in our simple example demonstrated in figure 3, we provide a glimpse into the initiation of engaging with the chatbot via utterances. [5] with the logic builder, the user can continue to add and grow the number of conversational inputs and expand the learning of the application. figure 3. sample amazon lex logic builder. in order for a chatbot intent to be fulfilled, slots need to be completed. the number of slots can vary, and there generally will be required a certain amount of fulfilled prior to yielding an effect. in figure 4 we demonstrate the amazon lex slots screen example, where the sample elements mentioned that need to be fulfilled can be seen. [5] on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi figure 4: slot definitions. as part of the process to create the chatbot, there needs to exist a “hook” or connection to the lex back-end in order to fulfill the request; a sample screen is shown in figure 5. [5] lambda functions can be created, or built-in functions may already exist, which will minimize the need to write code. figure 5: aws lambda function. in a final step, a connection or link is created between the chatbot and the lambda function. by using the lambda function, the code or logic lives in the aws cloud; it is not engaged on the client side of the application. in our example, the connection created to the lambda function will perform the actual ordering of the pizza, requiring the minimum number of slots fulfilled. figure 6: linking aws lex with aws lambda function. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi 3.9 development front-end to complement the amazon lex back-end component developed during the feasibility effort, we created a front-end application to provide the interface for the end user. the front-end application was developed for use with android devices; this was decided because such smartphone or tablet devices are readily available in bangladesh and nepal (the locations of the end users who would ultimately utilize the system). android devices continue to be the dominant mobile device operating system in use in the developing world. [6] the front-end android application was built using the java programming language, using the open-source eclipse integrated development environment (ide). in testing feasibility, a private web link was provided to end users in bangladesh and nepal so that they could install the application, which was not published or made available in the google play store. interaction or queries sent from the front-end application were submitted to the amazon lex back-end chatbot application using the internet. the access to the back-end was also restricted for use only by this specific feasibility chatbot application. 4. cyber and other security threat concerns as with any process or system automation, researchers and information technology engineers must always consider the risks associated with their technology implementation. given the hypersensitivity and attention given to cyber security in this current day and climate, it would be negligent to not provide such a focus. in performing an initial cyber security vulnerability assessment, the authors have considered the threats that may come from both remote and local attack surfaces. the remote attack surface, which would include systems that are connected to the internet and possibly accessible via the web, is a possibility with the introduction of chatbots. the authors used amazon lex and amazon web services in the feasibility test application they developed. by their very nature, these web tools and qualities create the potential for cyber threats. the local attack surface would include the android device that would host the java-based application that utilizes the amazon lex back-end. these android devices have the potential to be attacked if connected to a mobile or broadband internet connection and have the ability and potential to download other applications that could introduce vulnerabilities and affect the integrity of the hosted chatbot application. there are many methods for performing a cyber security vulnerability assessment of a system or application. in tables 4 and 5 below we demonstrate an initial approach one can take to begin a cyber security vulnerability assessment. 4.1 cyber security threat mitigations the top three cyber threat concerns related to the remote attack surface of the built chatbot feasibility application are: a. amazon lex service hosting chatbot logic availability. b. created chatbot system exposed and modified. c. access control to aws chatbot and breach of access. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi table 4: remote attack surface (aws) top threat concerns. likelihood consequence high medium low high a. availability medium b. exposed low c. breach the following are the top three cyber threat concerns related to the local attack surface of the android application built for chatbot feasibility: a. elevation of account privileges (rooting). b. inter-process communication – ability to monitor local activity. c. lack of availability cause of compromised device. table 5: local attack surface (android app) top threat concerns. likelihood consequence high medium low high medium c. compromised low a. elevation b. inter-process 5. discussion the term bot has been used frequently in a negative manner. one can often find news in the trade press and on security blogs discussing how bots are affecting elections, stealing information from users unaware that their systems are infected, stealing concert tickets, and so forth. however, it would be beneficial to perhaps change the name of the bots that are doing well. perhaps we should call them “gbots”, “good bots”, “botters”, or rbots” something to highlight this new class of positive communication and web robots. the bots discussed in this paper are of the “good” type: those that can be beneficial to companies, researchers, users, and students. in the case of communications, chat bots can be an effective way to increase more intelligent dialog and engagement. in 2017, 42% of all internet traffic was not human, and there were significant year-over-year increases in both bad bot (+9.5%) and good bot (+8.8) traffic. [10] the result is that 57.8% of internet traffic is human, but more important is the worrisome fact that bad bots take up the greater percentage of traffic. initial adoption of chatbots has been rapid among certain business-related industries such as hospitality, banking, and services; the public health field, however, seems to be a bit hesitant in on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi moving quickly into tools using artificial intelligence. further evidence-based research into the uptake of using chatbots or artificial intelligence in public health would benefit the field. in this feasibility paper, the authors would like to introduce the idea of a consistent cyber security section in future research papers that include a technology component. the benefits of using technology and web-connected technology are well known in research areas. however, it would be prudent to assess the risks associated with using the technology from a cyber threat perspective. introducing at a minimum a cyber-risk likelihood and consequence analysis would bring confidence to those facilities that utilize the proposed technology solution. there are areas in the public health sphere that have built chatbots and have had positive experiences. in a study of patients with breast cancer, a chatbot was used as a way to help them by providing support and answers to their concerns during the course of their disease. it was found that using chatbot reminders improved proper use of medications.[18] researchers also determined that patients appreciated a question of the day provided via the chatbot, and that patients shared information with the chat related to personal and intimate topics, such as sexuality and hair loss. in another research study, which included a self-report survey of physicians on their perceptions of using chatbots in health care, there were positive impressions with the use of introducing chatbots. more than half of physicians surveyed agreed that health care chatbots could help patients better manage their own health (54%), improve access and timeliness to care (53%), or reduce travel time to their health care provider (52%). in addition, 65% of physicians believed that health care chatbots could improve nutrition or diet, enhance medication or treatment adherence, increase activity or exercise, or reduce stress. [19] this aligns well with the vision of creating the pharmacy chatbot proposed by vennue foundation, particularly because of the importance of medication adherence in diabetes care. in the same physician study, challenges were perceived related to the fact that chatbots cannot understand or display human emotion. a majority of the physicians (70%) expressed their concern of risks associated with health care chatbots for patients. [19] 6. conclusion health-related industries must be sure that automation does not create new unintended issues associated with the introduction of new technology. health care as a whole is in fourth place for the creation of bad bots. [10] 57.58% of the bots used in health care are good bots, but 24.37% are bad. this indicates that the health sector is a targeted area for bad bot actors but does not necessarily indicate that the health industry is not doing enough; the health care sector is just a large area that unfortunately produces targeted content. as with any technology integration or implementation effort, a new bot’s purpose must be to make improvements. when introducing bots into public health research, developers will need to ensure they are embarking on the effort to: 1) solve an existing problem, by minimizing the number of steps normally taken; 2) make the end user experience better, faster, or more exact than alternate means in process or that already exist; 3) permit the bot to run on technology easily available to the end user community; and 4) make the interaction with the bot as natural in conversation as possible. on the potential, feasibility, and effectiveness of chat bots in public health research going forward online journal of public health informatics * issn 1947-2579 * http://ojphi.org * 11(2):e4, 2019 ojphi in focusing on the development of a chatbot for use in public health or pharmacy education, the authors found that building such a solution is feasible given the many options that are available for use by a variety of technology vendors. although expert programming may not be required for developing the back-end chatbot logic, solution developers will still require technological expertise and comfort with creating solutions in the cloud. in addition, a key aspect in creation of a chatbot will be the data science and design. this was an area that proved more difficult than anticipated when the authors tried to translate the end user requirements into practical use. it is critical that developers work closely with subject matter domain experts when building the database that includes the fulfillment, advice, suggestion, or response for the user of the chatbot. 7. limitations many software applications and software development tools are available for bot use; however, we did not do an exhaustive review of every option that could be approached. we rather selected top tier tools that the authors felt were more mature and have a track record of use in various industries. the next versions of chatbots are evolving and will hopefully include the introduction of multilingual products. at this time, chatbots are mostly focused on or work using the english language; this needs to change. the authors did not perform a full pilot test with an applied chatbot application as part of this research paper. it is envisioned that a successive actual chatbot pilot will be performed with the vennue foundation in dhaka, bangladesh and kathmandu, nepal. as part of this upcoming pilot, the chatbot would provide a tool for pharmacy staff to utilize the built-in intelligence in the application to obtain diabetes screening information. acknowledgments we thank tammy allen and the team at vennue foundation for their excellent collaboration in envisioning whether the use of a chatbot would be appropriate in the pharmacy education environment in the developing world. the vennue team’s insights were invaluable in considering the steps necessary to set up and pilot this first generation public health pharmacy worker prototype chatbot system. we are grateful to mandip pokharel of vennue nepal and mirajul islam samrat of vennue bangladesh for their collaborations at the local pharmacies in-country and their insights on the needs of the pharmacies on the ground. a special hearty thank you to netania budofsky for her thorough edit of the research paper, which included questioning certain content to better understand it from a non-expert reader’s perspective. references 1. delamater, natalie. 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